diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzadmy b/data_all_eng_slimpj/shuffled/split2/finalzzadmy new file mode 100644 index 0000000000000000000000000000000000000000..7f14f1f1bceb262dbe02c623f91b34b11cc352e8 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzadmy @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\\label{S1}\n\nIn this paper we study the singular points of suitable weak solutions \nto the three dimensional incompressible Navier--Stokes equations.\nAlthough general boundary and geometric conditions are important, \nwe consider merely an initial value problem of the Navier-Stokes equations in the whole space $\\Omega_T=\\mathbb{R}^3 \\times (0,T)$:\n\\begin{equation}\n\\label{E11}\n\\begin{split}\n(\\partial_t - \\nu\\Delta) v + \\divg (v\\otimes v) +\\nabla p &= 0 \\\\\n\\divg v &= 0\n\\end{split}\n\\end{equation}\nwith the initial data $v(x,0)= v_0(x)$, \nwhere $v$ and $v_0$ are three dimensional solenoidal vector fields \nand the pressure $p$ is a scalar field. \nIn this paper, we let the viscosity $\\nu=1$ since it is not important in our regularity analysis. \nWe denote by $z=(x,t)$ space-time points, space balls by $B(x,r)=\\set{y\\in\\mathbb{R}^3 : |y-x| h}.\n\\end{equation}\nThe Lebesgue integral can be expressed by the Riemann integral of such level sets.\nIn particular, for $00} \\big[h \\boldsymbol m(E(h))^{1\/q}\\big]\n\\end{equation}\nis finite.\n\\end{defn}\n\nWe recall the definition of suitable weak solutions (see also \\cite{CKN} and \\cite{MR1488514}).\n\n\\begin{defn}[Suitable weak solutions]\nLet $v_0 \\in L^2(\\mathbb{R}^3)$ denote a given initial data, which is weakly divergence free vector field.\nWe say that $(v,p)\\in V(\\Omega_T) \\times L^{3\/2}(\\Omega_T)$ is a suitable weak solution to the initial value problem if for all $\\phi\\in C^\\infty_0(\\Omega_T)$\n\\begin{equation}\n\\int \\left(v\\cdot \\partial_t\\phi - \\nabla v:\\nabla\\phi + v\\otimes v : \\nabla \\phi + p \\nabla\\cdot\\phi\\right) dz = 0\n\\end{equation}\nwhere $dz=dxdt$.\nThe vector field $v$ is weakly divergence free for almost all time and satisfies\nthe localized energy inequality \n\\begin{equation}\n\\label{E24}\n\\begin{split}\n&\\int |v(x,t)|^2 \\phi dx + 2\\int_0^t \\int |\\nabla v|^2\\phi dz \\\\\n&\\le \\int_0^t \\int |v|^2 (\\partial_t\\phi+\\Delta\\phi) dz\n+ \\int_0^t \\int (|v|^2+2p) v \\cdot \\nabla \\phi dz\n\\end{split}\n\\end{equation}\nfor almost all $0 < t \\le T$ and for all non-negative test functions $\\phi \\in C_0^{\\infty}(\\Omega_T)$ and\n\\[\\int |v(x,t)-v_0(x)|^2 dx \\to 0 \\quad \\text{as} \\quad t \\to 0.\\]\n\\end{defn}\n\n\\begin{defn}[The parabolic Hausdorff dimension]\nFor fixed $\\rho>0$ and $S\\subset\\mathbb{R}^3\\times\\mathbb{R}$, let $\\mathcal{C}(S,\\rho)$ be the family of all coverings of parabolic cylinders $\\set{Q(z_j,r_j)}$ that covers $S$ with $0t\\}\\cap Q_2} \\dist(z,\\partial 3\/2Q)^{9\/2}|\\nabla v|^{9\/5}dz\\]\nfor the modified distance function\n\\[\\dist(z,\\partial 3\/2Q) = \\inf_{w\\in\\partial 3\/2Q} \\set{|z-w|,1\/4},\\]\nwith \\eqref{E42} and $q=10\/9$ in Proposition 5.1 of\nGiaquinta--Modica\\cite{GM}, we obtain\n\\[\\norm{\\nabla v}_{L^{2+\\delta}(Q)}\n\\leq \\overline{C} \\norm{\\nabla v}_{L^{2}(3\/2Q)} +\n\\overline{C}\\left(\\int_{3\/2Q} |\\nabla p_3\\cdot v|^{(2+\\delta)\/2}\ndz\\right)^{1\/(2+\\delta)}.\\] Since $p_3$ is harmonic in $3\/2B$, the mean value property gives\n\\[\\sup_{x\\in 3\/2B} |\\nabla p_3| \\lesssim \\fint_{2B} |p_3| dx.\\]\nBy Jensen's inequality and Young's inequality we obtain that\n\\begin{align*}\n\\int_{3\/2Q} |\\nabla p_3\\cdot v|^{(2+\\delta)\/2} dz\n&\\le \\int_{-9\/4}^{9\/4} \\sup_{x\\in 3\/2B} |\\nabla p_3|^{(2+\\delta)\/2}\n\\int_{3\/2B} |v|^{(2+\\delta)\/2} dx dt \\\\\n&\\lesssim \\int_{-4}^4 \\left(\\fint_{2B} |p_3| dx\\right)^{(2+\\delta)\/2} \\left(\\fint_{2B} |v|^{(2+\\delta)\/2} dx\\right) dt \\\\\n&\\lesssim \\int_{-4}^4 \\left(\\fint_{2B} |p_3|^{3\/2} dx\\right)^{(2+\\delta)\/3} \\left(\\fint_{2B} |v|^{3(2+\\delta)\/2(1-\\delta)} dx\\right)^{(1-\\delta)\/3} dt \\\\\n&\\lesssim \\norm{p}_{L^{3\/2}(2Q)}^{1+\\delta\/2}\n\\norm{v}_{L^{3(2+\\delta)\/2(1-\\delta)}(2Q)}^{1+\\delta\/2}.\n\\end{align*}\n\nRecall that $p\\in L^{3\/2}$ and $v\\in L^{10\/3}$. If\n$\\delta<\\frac{2}{29}$, then we have\n\\[\\frac{3(2+\\delta)}{2(1-\\delta)} < \\frac{10}{3}\\]\nand the righthand side is bounded. \n\\end{enumerate}\n\nThis completes the proof of Theorem \\ref{T11}.\n\n\\begin{rem}\nSeregin \\cite{SER} obtained a different version of reverse H\\\"older inequalities under the assumption $v = \\divg b$ and $b \\in L^{\\infty}(0,T; \\BMO)$. \n\\end{rem}\n\n\n\n\n\\section{Proof of Theorem \\ref{T12}}\n\\label{S5}\n\nWe begin by recalling a very well-known lemma about the Hausdorff measure of an upper density of a locally integrable function.\n\n\\begin{lem}\n\\label{T51}\nLet $f \\in L_{\\loc}^1(\\mathbb{R}^d)$ and $0 < \\alpha < d$.\nDenote \n\\[E_\\alpha(x,r) = r^{-\\alpha} \\int_{B(x,r)} |f| dy,\\]\nThen \n\\[\\mathcal{H}^\\alpha\\set{\\limsup_{r\\to0} E_\\alpha(x,r) > 0}=0.\\]\n\\end{lem}\n\n\\begin{proof}\nFor reader's convenience, we give a sketch of the proof.\nFix a compact set $K$ in an open unit cube $Q$ and $n \\in \\mathbb{N}$.\nSet\n\\[F_n = K \\cap \\set{\\limsup_{r\\to0} E_\\alpha(x,r) > 1\/n}.\\]\nFix $\\delta<\\dist(K,Q^c)$.\nFor each $x \\in F_n$ there exists $r<\\delta\/5$ such that \n\\[E_\\alpha(x,r) > 1\/(2n).\\]\nBy Vitali's covering lemma there exists countable disjoint balls $B(x_j,r_j)$ such that $F_n \\subset \\bigcup B(x_j,5r_j)$. \nSince\n\\[\\sum r_j^\\alpha \\lesssim \\int_{\\bigcup B(x_j,r_j)} |u| dy\\]\nand \n\\[\\sum r_j^d \\lesssim \\delta^{d-\\alpha} \\sum r_j^\\alpha \\lesssim \\delta^{d-\\alpha} \\int_Q |u| dy,\\]\nwe have $\\mathcal{H}^\\alpha(F_n) = 0$.\nSince $K$ and $n$ are arbitrary, we get the result.\n\\end{proof}\n\nWe need one more elementary lemma.\n\n\\begin{lem}\n\\label{T52}\nThe condition \n\\begin{equation}\n\\label{E51}\nN=\\norm{v}_{L^\\infty(0,T; L^{3,w}(\\Omega))}<\\infty,\n\\end{equation}\nimplies that for almost all $0 \\le t \\le T$ and for all $0 < q < 3$, $x \\in \\mathbb{R}^3$, and $00$.\nBy H\\\"older's inequality we have \n\\[r^{-1} \\int_{Q(z,r)} |\\nabla v|^2 dz \\lesssim \\left(r^{-1+2\\delta} \\int_{Q(z,r)} |\\nabla v|^{2+\\delta} dz\\right)^{2\/(2+\\delta)}.\\]\nThe Caffarelli--Kohn--Nirenberg regularity theorem \\cite{CKN} implies that \n\\[\\mathcal{S} \\subset \\set{z : \\limsup_{r\\to0} r^{-1+2\\delta} \\int_{Q(z,r)} |\\nabla v|^{2+\\delta} dz > 0}.\\]\nTherefore, $\\mathcal{H}^{1-2\\delta}(\\mathcal{S})=0$ by Lemma \\ref{T51}.\nThis completes the proof of Theorem \\ref{T12}.\n\n\n\\section{Proof of Theorem \\ref{T13}}\n\\label{S6}\n\nWe divide the proof several steps.\n\n\\begin{enumerate}[\\bf{Step} 1)]\n\\item\nWe first claim that for any $\\gamma \\in (0,\\delta\/10)$ and $z=(x,t) \\in \\Omega_T$\n\\begin{equation}\n\\label{E61}\nv \\in L^{4+2\\delta-\\gamma}(Q(z,R)).\n\\end{equation}\nMore precisely, we shall show that \n\\begin{equation}\n\\label{E62}\n\\int_{Q(z,R)} |v|^{4+2\\delta-\\gamma} dz \\lesssim_{\\delta,\\gamma} R^{\\gamma} N^{2+\\delta-\\gamma} \\int_{Q(z,R)} |\\nabla v|^{2+\\delta} dz + R^{1-2\\delta+\\gamma} N^{4+2\\delta-\\gamma}\n\\end{equation}\nwhere the implied constant can be found explicitly.\nIn order to see this, we use H\\\"older's inequality to write \n\\begin{align*}\n&\\int_{B(x,R)} |v|^{4+2\\delta-\\gamma} dy \n=\\int_{B(x,R)} |v|^{2+\\delta-\\gamma} |v|^{2+\\delta} dy \\\\\n&\\le \\left(\\int_{B(x,R)} |v|^{3-3\\gamma\/(2+\\delta)} dy\\right)^{(2+\\delta)\/3}\n\\left(\\int_{B(x,R)} |v|^{3(2+\\delta)\/(1-\\delta)} dy\\right)^{(1-\\delta)\/3}.\n\\end{align*}\nUsing \\eqref{E52} with $q=3-3\\gamma\/(2+\\delta)$, we estimate the first integral on the right as \n\\[\\left(\\int_{B(x,R)} |v|^{3-3\\gamma\/(2+\\delta)} dy\\right)^{(2+\\delta)\/3} \\lesssim \\left(\\frac{2+\\delta}{\\gamma}+\\frac{\\gamma}{2+\\delta}-2\\right)^{(2+\\delta)\/3} R^{\\gamma} N^{2+\\delta-\\gamma}.\\]\nThus, we have \n\\[\\int_{B(x,R)} |v|^{4+2\\delta-\\gamma} dy \\lesssim R^{\\gamma} N^{2+\\delta-\\gamma} \\left(\\int_{B(x,R)} |v|^{3(2+\\delta)\/(1-\\delta)} dy\\right)^{(1-\\delta)\/3}.\\]\nSince $3(2+\\delta)\/(1-\\delta)$ is the Sobolev exponent of $2+\\delta$, we apply the Sobolev--Poincar\\'e inequality to get \n\\begin{align*}\n&\\left(\\int_{B(x,R)} |v|^{3(2+\\delta)\/(1-\\delta)} dy\\right)^{(1-\\delta)\/3} \\\\\n&\\lesssim \\left(\\int_{B(x,R)} |v-(v)_R|^{3(2+\\delta)\/(1-\\delta)} dy\\right)^{(1-\\delta)\/3} + R^{1-\\delta} |(v)_R|^{2+\\delta} \\\\\n&\\lesssim \\int_{B(x,R)} |\\nabla v|^{2+\\delta} dy + R^{1-\\delta} |(v)_R|^{2+\\delta}\n\\end{align*}\nwhere $(v)_R = \\fint_{B(x,R)} v(y,t) dy$.\nFinally, using the Jensen inequality and \\eqref{E52} with $q=2+\\delta$, we obtain \n\\[R^{1-\\delta} |(v)_R|^{2+\\delta} \\lesssim \\left(\\frac{2+\\delta}{1-\\delta}\\right)^{(2+\\delta)\/3} N^{2+\\delta} R^{-1-2\\delta}.\\]\nIntegrating in time yields the estimate \\eqref{E62}.\n\\item\nWe fix $\\psi$ satisfying $\\psi(y) = 1$ for $y \\in B(x,R\/2)$ and $\\psi(y) = 0$ for $y \\notin B(x,R)$.\nWe now use the decomposition of a localized pressure \\eqref{E31} and \\eqref{E32} with this $\\psi$, that is,\n\\[p\\psi = p_1 + p_2 + p_3\\]\nwhere \n\\begin{align*}\np_1(x,t)\n&=|\\overline{v}|^2\\psi(x,t)+ \\frac{3}{4\\pi} \\int \\partial_{y_i} \\partial_{y_j} \\left(\\frac{1}{|x-y|}\\right)(\\overline{v}_i \\overline{v}_j\\psi)(y,t) dy, \\\\\np_2(x,t) &= \\frac{3}{2\\pi} \\int \\frac{x_i-y_i}{|x-y|^3}\n(\\overline{v}_i\\overline{v}_j\\partial_j\\psi)(y,t) dy\n+ \\frac{3}{4\\pi} \\int \\frac{1}{|x-y|} (\\overline{v}_i\\overline{v}_j\\partial_i\\partial_j\\psi)(y,t) dy, \\\\\np_3(x,t) &= \\frac{3}{4\\pi} \\int \\frac{1}{|x-y|} (p\\Delta \\psi)(y,t) dy \n+ \\frac{3}{2\\pi} \\int \\frac{x_i-y_i}{|x-y|^3} (p\\partial_i\\psi)(y,t) dy.\n\\end{align*}\nWe notice that $p_1$ and $p_2$ involve $v$ only.\nSince we have $v \\in L^{4+2\\delta-\\gamma}(Q(z,R))$, \\eqref{E61} in the previous step, we see that\n\\begin{equation}\n\\label{E63}\np_1 + p_2 \\in L^{2+\\delta-\\gamma\/2}(Q(z,R\/5))\n\\end{equation}\nby a direct consequence of $L^q$-continuity of singular integral operators and potential estimates. \nOn the contrary, the representation of $p_3$ strongly depends on the outward data of $p$, so we don't expect that $p_3$ gain such a higher integrability in time.\nBut, since $p_3$ is harmonic in $B(x,R\/4)$, we have \n\\begin{equation}\n\\label{E64}\np_3 \\in L^{5\/3}(t-R^2\/25,t+R^2\/25 : L^\\infty(B(x,R\/5))).\n\\end{equation}\n\\item\nIn the previous steps we showed that the weak solution is locally higher integrable.\nConsidering scaled functional with higher exponent, we can get a better bound for the size of the singular set.\nIndeed, it is a consequence of scaling structure of weak solutions.\nWe have for all $0 < r < R\/5$, by H\\\"older's inequality, \n\\begin{align*}\nr^{-2} \\int_{Q(z,r)} |v|^3 dz \n&\\lesssim \\left(r^{-1+2\\delta-\\gamma} \\int_{Q(z,r)} |v|^{4+2\\delta-\\gamma} dz\\right)^{3\/(4+2\\delta-\\gamma)}, \\\\\nr^{-2} \\int_{Q(z,r)} |p_1+p_2|^{3\/2} dz \n&\\lesssim \\left(r^{-1+2\\delta-\\gamma} \\int_{Q(z,r)} |p_1+p_2|^{2+\\delta-\\gamma\/2} dz\\right)^{3\/(4+2\\delta-\\gamma)}, \n\\end{align*}\nand, by Jensen's inequality, \n\\begin{align*}\nr^3 \\fint_{Q(z,r)} |p_3|^{3\/2} dz \n&\\lesssim r^3 \\fint_{t-r^2}^{t+r^2} \\left(\\fint_{B(x,r)} |p_3|^{15\/(7-6\\delta+3\\gamma)} dx\\right)^{(7-6\\delta+3\\gamma)\/10} dt \\\\\n&\\lesssim r^3 \\left(\\fint_{t-r^2}^{t+r^2} \\left(\\fint_{B(x,r)} |p_3|^{15\/(7-6\\delta+3\\gamma)} dx\\right)^{(7-6\\delta+3\\gamma)\/9} dt\\right)^{9\/10} \\\\\n&= \\left(r^{-1+2\\delta-\\gamma} \\int_{t-r^2}^{t+r^2} \\left(\\int_{B(x,r)} |p_3|^{15\/(7-6\\delta+3\\gamma)} dx\\right)^{(7-6\\delta+3\\gamma)\/9} dt\\right)^{9\/10}.\n\\end{align*}\nCombining those estimates, we obtain \n\\begin{align*}\n&r^{-2} \\int_{Q(z,r)} |v|^3 +|p|^{3\/2} dz \\\\\n&\\lesssim \\left(r^{-1+2\\delta-\\gamma} \\int_{Q(z,r)} |v|^{4+2\\delta-\\gamma} + |p_1+p_2|^{2+\\delta-\\gamma\/2} dz\\right)^{3\/(4+2\\delta-\\gamma)} \\\\\n&\\quad + \\left(r^{-1+2\\delta-\\gamma} \\int_{t-r^2}^{t+r^2} \\left(\\int_{B(x,r)} |p_3|^{15\/(7-6\\delta+3\\gamma)} dx\\right)^{(7-6\\delta+3\\gamma)\/9} dt\\right)^{9\/10}.\n\\end{align*}\nThere exists a positive number $\\varepsilon$ such that if \n\\[\\left(\\int_{Q(z,r)} |v|^{4+2\\delta-\\gamma} + |p|^{2+\\delta-\\gamma\/2} dz + \\int_{t-r^2}^{t+r^2} \\left(\\int_{B(x,r)} |p_3|^{15\/(7-6\\delta+3\\gamma)} dx\\right)^{(7-6\\delta+3\\gamma)\/9} dt\\right) < \\varepsilon r^{1-2\\delta+\\gamma}\\]\nfor some $r < R\/5$, then $z$ is a regular point by the well-known $L^3$-regularity criterion.\n\\item\nIn the previous setp, we obtain that for each $z=(x,t) \\in \\mathcal{S} \\cap Q(z_0,R\/10)$ we should have for all $0 < r < R\/50$ \n\\begin{equation}\n\\label{E65}\n\\begin{split}\n\\varepsilon r^{1-2\\delta+\\gamma} \n&\\le \\int_{Q(z,r)} |v|^{4+2\\delta-\\gamma} + |p|^{2+\\delta-\\gamma\/2} dz \\\\\n&\\quad + \\int_{t-r^2}^{t+r^2} \\left(\\int_{B(x,r)} |p_3|^{15\/(7-6\\delta+3\\gamma)} dx\\right)^{(7-6\\delta+3\\gamma)\/9} dt.\n\\end{split}\n\\end{equation}\nFix $r < R\/50$ and consider the covering $\\set{Q(z,r) : z \\in \\mathcal{S} \\cap Q(z_0,R\/10)}$.\nAdapting the argument in \\cite{MR2864801}, we can choose a finite disjoint sub-family \n\\[\\set{Q(z_j,r) : j=1,2,\\dots,J(r)}\\]\nsuch that $\\mathcal{S} \\cap Q(z_0,R\/10) \\subset \\bigcup Q(z_j,5r)$ by the Vitali covering lemma.\nWe need to pay more attention in order to use the disjointness of the subcovering.\nSumming the inequality \\eqref{E65} at $z_j$ for $j=1,2,\\dots,J$ yields\n\\begin{align*}\nJ \\varepsilon r^{1-2\\delta+\\gamma}\n&\\le \\sum_{j=1}^{J} \\int_{Q(z_j,r)} |v|^{4+2\\delta-\\gamma} + |p|^{2+\\delta-\\gamma\/2} dz \\\\\n&\\quad + \\sum_{j=1}^{J} \\int_{t_0-R^2\/25}^{t_0+R^2\/25} \\left(\\int_{B(x_j,r)} |p_3|^{15\/(7-6\\delta+3\\gamma)} dx\\right)^{(7-6\\delta+3\\gamma)\/9} dt \\\\\n&\\le \\int_{Q(z_0,R\/5)} |v|^{4+2\\delta-\\gamma} + |p|^{2+\\delta-\\gamma\/2} dz \\\\\n&\\quad + \\int_{t_0-R^2\/25}^{t_0+R^2\/25} \\sum_{j=1}^{J} \\left(\\int_{B(x,r)} |p_3|^{15\/(7-6\\delta+3\\gamma)} dx\\right)^{(7-6\\delta+3\\gamma)\/9} dt \n\\end{align*}\nFor the last sum, we use Young's inequality, that is, for $\\theta \\in [0,1]$ \n\\[\\sum_{j=1}^{J} a_j^\\theta \\le J^{1-\\theta} \\left(\\sum_{j=1}^{J} a_j\\right)^\\theta.\\]\nDue to the fact that $p_3$ is harmonic in $B(z_0,R\/5)$ and the estimates \\eqref{E63} and \\eqref{E64}, there are positive numbers $A_1, A_2 \\in \\mathbb{R}_+$ such that \n\\begin{equation}\n\\label{E66}\n\\begin{split}\nJ \\varepsilon r^{1-2\\delta+\\gamma}\n&\\le \\int_{Q(z_0,R\/5)} |v|^{4+2\\delta-\\gamma} + |p|^{2+\\delta-\\gamma\/2} dz \\\\\n&\\quad + J^{(2+6\\delta-3\\gamma)\/9} \\int_{t_0-R^2\/25}^{t_0+R^2\/25} \\left(\\int_{B(x_0,R\/5)} |p_3|^{15\/(7-6\\delta+3\\gamma)} dx\\right)^{(7-6\\delta+3\\gamma)\/9} dt \\\\\n&=: A_1+A_2 J^{(2+6\\delta-3\\gamma)\/9}.\n\\end{split}\n\\end{equation}\n\\item\nThe minimum number of parabolic cylinders $Q(z,r)$ required to cover the set $\\mathcal{S} \\cap Q(z,R\/5)$ is less than or equal to $J$.\nWe rewrite \\eqref{E66} as \n\\[J \\varepsilon r^{C_1} \\le A_1 + A_2 J^{C_2}\\]\nwhere $C_1=1-2\\delta+\\gamma$ and $C_2=(2+6\\delta-3\\gamma)\/9$.\nApplying Young's ineaulity to $A_2 J^{C_2}$, we get \n\\[J \\varepsilon r^{C_1} \\le A_1 + (1-C_2) [A_2 (\\varepsilon r^{C_1})^{-C_2}]^{1\/(1-C_2)} + C_2 J \\varepsilon r^{C_1}\\]\nand so \n\\[J(r) \\lesssim A_1 \\varepsilon^{-1} r^{-C_1} + A_2^{1\/(1-C_2)} \\varepsilon^{-1\/(1-C_2)} r^{-C_1\/(1-C_2)}.\\]\nTherefore, by an elementary calculation we get \n\\[\\limsup_{r\\to0} \\frac{\\log J(r)}{-\\log r} = \\frac{C_1}{1-C_2} = \\frac{9-18\\delta+9\\gamma}{7-6\\delta+3\\gamma}.\\]\nSince $\\delta$ and $\\gamma$ can be arbitrarily close to $\\delta_0$ and $0$ respectively, we conclude the assertion \\eqref{E15}.\n\\end{enumerate}\nThis completes the proof of Theorem \\ref{T13}.\n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Introduction}\n\\label{sec:intro}\n\nObservations at astronomical and cosmological scales indicate that a majority of the matter content of our Universe is in the form of non-relativistic, long-lived, and non-luminous dark matter~\\cite{PDC, GalaxyFormation, LargeScaleStructure, Clowe}. Extensions of the Standard Model favour a candidate for dark matter in the form of a Weakly Interacting Massive Particle (WIMP)~\\cite{Steigman, Jungman}. Its interaction with normal matter can be probed directly via elastic scattering off target nuclei, thus motivating searches through direct detection \\cite{Goodman:1984dc}. The XENON collaboration has constructed and commissioned the first ton-scale liquid-xenon dark matter detector, aiming to observe primarily low-energy nuclear recoils of WIMPs with unprecedented sensitivity.\n\nXENON1T, a dual-phase time projection chamber (TPC)~\\cite{Bolozdynya, Xe1T_TPC}, was designed to improve the sensitivity of its predecessor, XENON100~\\cite{xenon_xe100-SI, xenon_xe100-SD}, by two orders of magnitude for the spin-independent WIMP-nucleon interaction cross section. The increased sensitivity is achieved through reducing the background and increasing the target mass, i.e. the amount of liquid xenon (LXe) enclosed by the TPC, by a factor of 32, allowing for a sensitive volume, after fiducialization, of $\\sim$1 ton. In rare-event searches, understanding and minimizing background events that occur within the sensitive volume of the detector is of utmost importance. This necessitates the use of construction materials with low intrinsic radioactivity, passive and active detector shielding, and sophisticated analysis techniques in order to prevent background events within the parameter space where a WIMP signal is expected. \n \nThe XENON1T radioassay program addresses backgrounds that come from radioactive impurities within detector construction materials. Radioassay of candidate materials provides information about the type and amount of expected emissions, thus allowing for selection and strategic placement of the most radiopure materials within the detector. The measured results provide the material-induced radiogenic component to the overall background model of XENON1T. Through Monte Carlo simulations using the activities from each component, accurate predictions of the detector sensitivity were performed~\\cite{xenon_xe1t-sensitivity}.\n\nHere we present an overview of the screening and material selection process for XENON1T. Section~\\ref{sec:bkgrd} describes expected background sources and reduction methods. The techniques and instruments used to identify and estimate radioimpurities of each sample are detailed in Section~\\ref{sec:techniques}. Section~\\ref{sec:results} describes the various materials and components that were screened with respect to the decay chains and isotopes that are of greatest concern. For each relevant decay chain and single-line emission, the radioassay results are presented in Table~\\ref{table:Samples}. We summarize the results in Section~\\ref{sec:impact} with a discussion of the impact on the XENON1T sensitivity with respect to the materials measured in this study.\n\n\n\n\\section{Background expectation and minimization}\n\\label{sec:bkgrd}\n\n\nParticle interactions with either atomic electrons or nuclei of the xenon target result in electronic recoil (ER) events or nuclear recoil (NR) events, respectively. The nuclear recoil background, predominantly from neutrons, is the most dangerous, as the signature of a WIMP is a single-scatter, NR event. Background discrimination and rejection techniques include removing multiple-scatter events, fiducializing the target volume through event vertex reconstruction, and exploiting the difference in energy loss per unit track length between ER and NR events~\\cite{xe100analysis}. However, ER events that occur within the fiducial volume can leak into the NR region of the WIMP discrimination parameter space as well as obscure other rare event searches that are otherwise possible in the ER channel. The aim, therefore, is to mitigate sources of both types of backgrounds and to accurately estimate the number of expected background events within the WIMP search region. \n\nExternal background from cosmic rays, i.e.~hadronic components and muon-induced neutrons, is suppressed by operating the detector at an average depth of 3600 meters water equivalent in the Laboratori Nazionali del Gran Sasso (LNGS), thus reducing the muon flux by a factor of 10$^{6}$ relative to a flat overburden~\\cite{LNGS_muonflux}. A water shield instrumented with veto PMTs surrounds the detector by at least 4 meters on all sides to provide passive shielding and to reject coincident events detected via Cherenkov radiation~\\cite{xenon_xe1t-MuonVeto}. Solar neutrinos are another potential source of external background, both ER and NR, the latter from coherent neutrino-nucleus scattering. \n\nSources of ER background that are intrinsic to the xenon target, e.g. the beta emitter $^{85}$Kr, and the double-beta emitter $^{136}$Xe, are expected to be uniformly distributed throughout the xenon, thus cannot be reduced through fiducialization. However $^{85}$Kr can be significantly reduced through distillation from $^{\\mathrm{nat}}$Kr to $<0.2$ ppt~\\cite{Kr_column}, and $^{136}$Xe, comprising 8.9$\\%$ of natural xenon, has a subdominant contribution of $<2\\%$ to the total ER background~\\cite{xenon_xe1t-sensitivity}. Although not natively intrinsic to the scintillator, the noble gas $^{222}$Rn (T$_{1\/2}=$3.8 d), originating from the long-lived $^{226}$Ra (T$_{1\/2}=$1600 yr), mixes with the xenon and becomes homogeneously distributed within the target. Beta decays of its daughter isotopes are the dominant source of ER background. Moreover, $^{214}$Pb and the daughter isotopes from its decay to ground state adhere to material surfaces (plate-out) and can lead to \\mbox{($\\alpha$, n)} reactions within the target volume. Because of plate-out effects from both parent and daughter isotopes of $^{222}$Rn, the level of contamination for this isotope is determined by directly measuring its emanation from construction materials. This technique will be described in a separate publication~\\cite{RnEmanation}. Additionally, a significant reduction in radon by online purification has recently been demonstrated by the XENON collaboration through the use of a cryogenic distillation technique~\\cite{RnDistillation}.\n\nThe radioassay program described in this paper targets the background from radionuclei present as residual traces in the detector components. The most common radioactive contaminants are long-lived (T$_{1\/2} >$ 1 yr) primordial radionuclei within the $^{238}$U and $^{232}$Th decay chains and the single isotope $^{40}$K. The latter isotope as well as several isotopes within the primordial chains decay via high-energy gamma emissions that cannot be completely removed through fiducialization. Additionally, several isotopes belonging to these chains release neutrons either directly through spontaneous fission of heavy nuclei or indirectly via \\mbox{($\\alpha$, n)} reactions following alpha decays within the chains. \n\nIn addition to primordial radioisotopes, anthropogenic radioisotopes, such as $^{137}$Cs and $^{110\\mathrm{m}}$Ag, can be found in some detector materials. These isotopes are either manufactured for medical or industrial use or are generated from nuclear power plant waste, nuclear accidents, or military testing. Cosmogenic isotopes, such as $^{54}$Mn, $^{46}$Sc, and $^{56-58}$Co, can be found mainly in metal components as a result of activation from exposure to cosmic rays~\\cite{cosmogenic}. An additional common radionuclide is $^{60}$Co, which is primarily anthropogenic in origin in stainless steel and cosmogenically induced in copper. Most of the listed radionuclei, including many of the isotopes within the primordial decay chains, can be detected with high sensitivity by the XENON1T radioassay techniques described in Section~\\ref{sec:techniques}.\n\n\n\\section{Techniques and measurements}\n\\label{sec:techniques}\n\nTo determine the amount and isotopic composition of radionuclides present in the XENON1T materials, gamma-ray spectroscopy and mass spectrometry methods were used. The former provides a non-destructive technique sensitive to almost every relevant gamma emitter and allows to detect a break of secular equilibrium within the primordial decay chains. To reach the detection sensitivity required by current dark matter search experiments, e.g. at or below the $\\mu$Bq\/kg level for some materials, large sample masses ($\\sim10$$-$$20\\,$kg) and long counting times ($\\sim15$$-$$20\\,$d) are usually necessary. This is particularly the case for low-energy gamma emitters due to self-absorption within the sample volume.\n\nMass spectrometry, in particular, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Glow Discharge Mass Spectrometry (GDMS) were used to assess the composition of a given sample through separation and measurement of individual isotopes. This is particularly useful in determining the amount of $^{238}$U and $^{232}$Th within materials. Because ICP-MS and GDMS require just a few grams of sample material and short measurement times, they are also used when the mass of the sample is too small or the available measurement time too short to achieve the desired sensitivity in an HPGe spectrometer.\n\n \n\\subsection{Germanium spectrometers}\n\\label{sec:Ge}\n\nThe XENON collaboration utilizes several of the world's most sensitive germanium spectrometers, the Gator~\\cite{gator} detector and the four GeMPI detectors~\\cite{gempi}, that are located in ultra-low background facilities at LNGS at the same depth as the XENON1T detector. These spectrometers have an excellent energy resolution over the energy range of interest (\\mbox{$\\sim50 - 2650\\,$ keV} with, e.~g. \\mbox{$<3$ keV} FWHM at \\mbox{1332 keV}) and can reach sensitivities down to the $\\mu$Bq\/kg level. All detectors are p-type, intrinsically pure germanium crystals in a coaxial configuration, with masses between \\mbox{2.2$-$2.3 kg} and enclosed in a low-background cryostat housing. The sensitive region of the cryostat protrudes into an inner chamber made of electro-refined, Oxygen-Free High-Conductivity (OFHC) copper, with a material sample capacity of several liters in volume. The inner chamber is constantly purged with pure nitrogen to suppress the influx of radon. The copper is surrounded by a \\mbox{15$-$25} cm thick lead shield, where the innermost layer of \\mbox{2$-$5 cm} has a low level of $^{210}$Pb contamination. Radon-free nitrogen-flushed glove boxes are located on top of each detector.\n\nThe radioassay program used three additional spectrometers, Corrado, Bruno, and GIOVE~\\cite{giove}, that are operated underground in the Low-Level Laboratory at Max Planck Institut f{\\\"u}r Kernphysik (MPIK) in Heidelberg. The laboratory has an overburden of 15 meters water equivalent that reduces the muon flux by a factor of 2$-$3 and the hadronic background component by a factor of 1000 as compared to sea level. The spectrometers are surrounded by copper and lead shielding. Additionally, an active muon veto and polyethylene to moderate neutrons surround the Giove detector. These three detectors are p-type germanium crystals with masses between 0.9$-$1.8 kg that can reach sensitivities between 0.1$-$1 mBq\/kg. \n\nGiven the higher background and lower detection sensitivity with respect to the spectrometers operated at LNGS, the MPIK detectors were mostly employed for radioassay of components that are far from the sensitive volume of the XENON1T TPC, such as the tank for the water shield as well as the support structures and calibration systems within the shield. Most of the materials from components closest to the active volume of the TPC were screened with the GeMPI or Gator detectors at LNGS. For several smaller samples, additional detectors at the LNGS underground low-background facility STELLA (SubTerranean Low Level Assay) were used~\\cite{GePaolo}.\n\n\nSamples were cleaned using the same techniques as in the final detector construction when possible. Otherwise, the standard procedure was to clean each sample with a mild acid soap (Elma EC70), followed by rinsing with deionized water and immersion in high-purity ethanol \\mbox{($>95$\\%)}. Each step utilized an ultrasonic bath for 20 minutes. Samples where acid soaps or immersion in liquids should be avoided, such as photomultiplier tubes and cables, were cleaned by wiping the surface with ethanol. During transport of the samples to the detector glovebox, they were either enclosed in clean plastic bags or wrapped in plastic foils in order to prevent the plate-out of $^{222}$Rn daughters from the environment. The samples were then stored in an outer glovebox of the detector prior to their measurement in order to let the radon and its daughters decay.\n\n\nFor every measured sample, a Monte Carlo simulation based on the GEANT4 toolkit~\\cite{Agostinelli} was used to calculate the detection efficiencies for each emitted gamma line. The efficiencies were used in combination with the sample mass, measurement time, and branching ratio of each characteristic \\mbox{gamma-ray} line to determine the specific activities or detection upper limits of each radioisotope. Further details on analysis procedures for the HPGe detectors can be found in~\\cite{gempi, gator}.\n\n\n\\subsection{Inductively Coupled Plasma Mass Spectrometry}\n\nInductively Coupled Plasma Mass Spectrometry is one of the most sensitive analytical techniques for trace element analysis. The intrinsic radioactivity of materials can be deduced by measuring the concentration of long-lived radionuclides. The sample (fractions of a gram are enough for a measurement) is introduced as an aqueous solution through a peristaltic pump, nebulized in a spray chamber, then atomized and ionized in a plasma. The ions are extracted into an ultra-high vacuum system and separated according to their mass-to-charge ratio by the mass analyzer. The concentration of the ions is calculated based on the response of reference standard solutions. Depending on the nature of the sample, sensitivities on the order of 10$^{-11}$ to 10$^{-13}$ g\/g for $^{238}$U and $^{232}$Th and 10$^{-7}$ to 10$^{-8}$ g\/g for $^{39}$K can be reached. This corresponds to activities of 1$-$100 $\\mu$Bq\/kg and a few mBq\/kg, respectively. The uncertainty of measurement is between 20$-30\\%$ and accounts for several factors, such as the instrumental precision, the single replicate measurement and the single-point calibration curve.\n\nFor this radioassay campaign, measurements were performed with a 7500a ICP-MS from Agilent Technologies and an Element II HR-ICP-MS from ThermoFisher Scientific. Both instruments are located in a ISO6 clean room at the Chemistry Laboratory of LNGS.\n\n \n\\subsection{Glow-discharge mass spectrometry}\n\nThe Glow Discharge Mass Spectrometry measurements for XENON1T were performed at EAG Laboratories~\\cite{EAG}. Rather than being introduced as an aerosol as in ICP-MS, a negative bias is applied to the solid sample material while exposed to an argon-based plasma in order to induce sputtering via ion-target collisions. Once the material is sputtered into the plasma and subsequently ionized, an ion beam is extracted and focused through a high resolution mass spectrometer. Ions are separated according to their \\mbox{mass-to-charge} ratio. Sensitivity of \\mbox{sub-ppb} level or 10$^{-10}$ g\/g ($\\sim$1 mBq\/kg) can be reached with an uncertainty between 20$-30\\%$. Electrical conductivity of the sample is needed for stable and reproducible glow formation, thus the reliability and sensitivity of GDMS may vary depending upon properties of the target material. As with ICP-MS, GDMS is particularly useful in determining the $^{238}$U and $^{232}$Th concentrations. Although ICP-MS provides a better sensitivity than GDMS, the choice to use GDMS was primarily due to the availability and location of the measurement facilities.\\\\\n\n\n\n\n\\section{Radioassay results}\n\\label{sec:results}\n\nResults obtained through the radioassay program are shown in Table~\\ref{table:Samples}. Throughout the text, the samples are identified by their unique item numbers (``$\\#$\"). The detector is shown in Figs. \\ref{fig:TPC_section} and \\ref{fig:TPC_exploded} to introduce the most relevant subgroups and components. These are given in the ``XENON1T Use\" column of Table~\\ref{table:Samples} in the case where the material or component was chosen for detector construction. \n\nSupplier information is provided where applicable. For measurements conducted with the HPGe spectrometers, the sample mass and measurement duration are noted. Uncertainties, including both statistical and systematic, the latter primarily from efficiency simulations, are given in parentheses as $\\pm$ 1$\\sigma$ of detected activities or at 95$\\%$ confidence level for upper limits. Unless otherwise specified, the uncertainties of ICP-MS and GDMS measurements are 30$\\%$ and are primarily systematic, as described in Section 3.2.\n\nThe upper part of the $^{232}$Th decay chain is measured directly by mass spectrometry methods but is only detectable from gamma-ray spectroscopy following the $^{228}$Ra decay \\mbox{(T$_{1\/2}= 5.7$ yr)}. Rather than assuming secular equilibrium in the upper part of this chain, the two results are presented together with an indication of which part of the chain was measured. It is worth noting that, with one exception (copper $\\#$4), all samples for which both $^{232}$Th with ICP$-$MS and $^{228}$Ra with HPGe spectroscopy were measured show consistent results, thus indicating no break in secular equilibrium at $^{228}$Ra for these samples.\n\nIn addition to the decay chain and radioisotope activities listed in Table~\\ref{table:Samples}, Table~\\ref{table:metalother} shows results from cosmogenic radionuclei with \\mbox{short-to-moderate} \\mbox{half-lives} (T$_{1\/2} <$ 1 yr), as detected with HPGe spectrometers. \n\nAll results reported here will be made available via the radioassay community database at http:\/\/www.radiopurity.org.~\\cite{persephone}. Further details on many of the XENON1T samples and their measurements can be found in~\\cite{Piastra}.\n\n\\begin{figure}[]\n\\begin{center}\n\\includegraphics*[width=0.9\\linewidth]{figures\/scaledsectionview.png}\n\\end{center}\n\\caption\n{\\label{fig:TPC_section}The XENON1T TPC with cryostat, section view, subgroups are indicated with reference to the ``XENON1T Use\" column of Table~\\ref{table:Samples}.}\n\\setlength{\\belowcaptionskip}{0pt}\n\\end{figure}\n\n\n\\begin{figure}[]\n\\begin{center}\n\\includegraphics*[width=.7\\linewidth]{figures\/explodedviewall.png}\n\\end{center}\n\\caption\n{\\label{fig:TPC_exploded}The XENON1T TPC with cryostat, subgroups are indicated with reference to the ``XENON1T Use\" column of Table~\\ref{table:Samples}.}\n\\setlength{\\belowcaptionskip}{0pt}\n\\end{figure}\n\n\n\n\\subsection{Metal samples}\n\\label{sec:metal_samples}\n\nCommercially available Oxygen-Free High Conductivity or Oxygen-Free Electrolytic copper (OFHC or OFE copper) from primarily two different distributors was used for several major components of the TPC: the 74 field-shaping rings that surround the TPC ($\\#$1), the top and bottom PMT array support structures ($\\#$2, $\\#$3), and the bottom structural ring of the field cage ($\\#$4), comprising $\\sim$190 kg of the detector mass. Copper is intrinsically radiopure, with detected activities of the natural decay chains at the ppt level (see Table~\\ref{table:Samples}). One can see that the $^{60}$Co activity from cosmogenic activation varies from batch to batch, depending on the storage and shipment of the material~\\cite{cosmometal}. Because of its relative purity, copper was used as a substitute for stainless steel wherever possible. A sample of copper plated with 2 $\\mu$m thick gold ($\\#$6) was considered for the field-shaping rings to minimize radon emanation, however the samples showed significantly higher $^{40}$K activity that was most likely introduced as part of the electrochemical plating process. \n\nThe radiopurity of stainless steel can vary between batches, depending upon the source of the raw material, the method of heating and forming the material, as well as the location and duration of storage of the metal (cosmogenic activation). In order to minimize emissions from stainless steel components near the sensitive volume, the cleanest batches of available material were required. \nTherefore many batches of 304 and 316 stainless steel from six different manufacturers (17 samples in total) were screened for radiopurity. The samples originated from different melts and consisted of varying thicknesses. The NIRONIT Edelstahlhandel GmbH $\\&$ Co. samples ($\\#$8$-$10) that were particularly low in $^{226}$Ra, $^{232}$Th, and $^{60}$Co were selected for production of various TPC components and the cryostat vessels. Materials for components that are in direct contact with the liquid xenon, such as the cryostat pipe ($\\#$11), were selected for low $^{226}$Ra contamination in order to minimize emanation of $^{222}$Ra that can mix with the xenon and end up in the fiducial volume. \n\nIn many of the stainless steel batches, a depletion in $^{226}$Ra with respect to the upper half of the $^{238}$U chain is observed, thus indicating a clear break in secular equilibrium that most likely occurred during processing of the raw materials. In particular, a disequilibrium of more than a factor of 10 can be seen for items $\\#$17, $\\#$23, and $\\#$30. Additionally, a break between the upper and lower parts of the $^{232}$Th chain is observed in some of the HPGe measurements ($\\#$12$-$14, $\\#$17, $\\#$25, $\\#$30). Because these results were obtained through HPGe spectroscopy, the $^{232}$Th activity was not measured directly, thus the break in this chain is a possible indication of depletion in $^{228}$Ra. \n\nThe induced background from the XENON1T structural components, such as the water tank and outer support structures ($\\#$24$-$27, in addition to many screened samples not listed), was shown to be negligible in the Monte Carlo simulations due to their distance from the sensitive volume~\\cite{xenon_xe1t-sensitivity}. The screened stainless steel hardware ($\\#$28, $\\#$29) used for critical internal components, such as for the resistor chain and electrode fasteners, also had a negligible background contribution as the total mass used in the final construction was less than 1 kg. \n\nTitanium was considered as a potential cryostat material because of its high tensile strength as compared to copper and potentially lower radioactivity as compared to stainless steel. It has previously been used in the LUX experiment~\\cite{LUX_titanium} and investigated for use in the upcoming LZ experiment~\\cite{LZ_titanium}. Three different grades of titanium from four different suppliers were measured. The measured contamination of the titanium samples ($\\#$32$-$40) showed roughly a factor of 10 higher activity in the uranium chain as compared to the stainless steel used for the cryostat ($\\#$9). The other difference in contamination between the two material types was with respect to $^{60}$Co, which is subdominant in titanium but of concern in stainless steel, and $^{46}$Sc, a prominent cosmogenic isotope in titanium, as shown in Tables~\\ref{table:Samples} and~\\ref{table:metalother}, respectively. Additionally, the lower mechanical strength of titanium as compared to stainless steel would have required a thicker cryostat. When taking this into account in the Monte Carlo simulations, the neutron background from a titanium cryostat was considerably higher than for its stainless steel counterpart, therefore the latter material was chosen to construct the XENON1T cryostat.\n\n\n\\subsection{Plastic samples}\n\\label{sec:plastic_samples}\n\nDue to its good VUV reflectivity ($>$95$\\%$), a dielectric constant similar to liquid xenon, low-outgassing properties as compared to other plastics, and machinability, polytetrafluoroethylene (PTFE) is the material of choice for reflective surfaces within the field cage. Because it directly encloses the LXe sensitive volume, its radioactive content must be sufficiently low and also precisely measured to achieve an accurate background estimate. All of the PTFE samples were measured using ICP-MS for better quantification of the primordial chain progenitor isotopes and to complement the HPGe measurements where available, showing levels typically at the tens of ppt or $\\mu$Bq\/kg level ($\\#$46$-$50). PTFE doped with 15$\\%$ quartz to increase the reflectivity ($\\#$51) was also measured, however showed gross contamination in all of the natural chains as seen in Table~\\ref{table:Samples}. The primordial chains, due to alpha decays, are of particular concern for PTFE, as neutrons can be generated in the material via $^{19}$F($\\alpha$, n) reactions~\\cite{Norman}. Thus efforts were also made to minimize the total amount of this material used in construction. \n\nPolyamide-imide (PAI, in this case Torlon 4203L) was investigated for use as an insulating, structural material as it has a high dielectric constant, good mechanical strength, low thermal contraction, and allows for high-precision machining. Radioassay results ($\\#$53, $\\#$54) showed activities from the primordial chains to be a factor of 10 to 100 higher than its structural counterpart, PTFE. However, due to the absence of fluorine, neutron emission via ($\\alpha$, n) is not an issue with PAI. It was used for small but critical components, e.g. as insulating spacers. \n\nCommercially available PEEK (polyether ether ketone) screws were used at locations inside the TPC that required a high dielectric constant but with limited load-bearing requirements. Only one PEEK sample was measured ($\\#$52), yielding results on the order of 1$-$10 mBq\/kg, comparable to that of PAI.\n\nFor all of the plastic samples, no clear break in secular equilibrium is observed in the primordial decay chains. However the case of equilibrium is inconclusive, as only upper limits were measured for most samples. One exception is the PTFE doped with quartz ($\\#$51), that shows a clear break in the $^{232}$Th chain.\n\n\n\n\\subsection{Photomultiplier tubes and related components}\n\\label{sec:pmts}\n \nThe radioactive budget of the Hamamatsu R11410 3-inch diameter photomultiplier tubes was initially estimated through screening of the raw materials used in fabrication. Subsequently, several versions of PMTs were produced and screened with the goal of minimizing the total radioactivity of the tube to arrive at the final version, R11410-21. Of this version, the averaged activities of 320 PMTs measured with Gator and 40 PMTs measured with GeMPI I are reported in Table~\\ref{table:Samples} ($\\#$69, $\\#$70). Where only an upper limit was found, no entry is provided. Further details on the specific material contributions and the development of these low-background photomultipliers are given in~\\cite{LowRadPMTs}.\n\nSeveral samples of cables for the PMTs were screened to find clean batches. The detected activities for the signal and high-voltage cabling ($\\#$55$-$56, and $\\#$57$-$58, respectively) that were selected for final construction were typically at the tens or lower mBq\/kg level, with the exception of the considerably higher presence of $^{40}$K, particularly in the high-voltage (kapton) cables. The remaining PTFE ($\\#$59$-$61) and kapton coaxial and flat cables ($\\#$62$-$64) were not used due to higher levels from the primordial decay chains. \n\nThe connectors for the PMT signal and high-voltage cables, respectively, consisted of male\/female pairs of micro-miniature coaxial (MMCX) connectors made from a copper-zinc alloy ($\\#$66) and of subminiature-D (D-sub) pins made from either a copper-beryllium alloy ($\\#$65, $\\#$68) or a gold-plated copper alloy ($\\#$67). Due to the minimal total mass and the locations of the connector assemblies relative to the sensitive volume (in the cryostat pipe and on top of the diving bell), their radioimpurities are considered to have a negligible contribution to the overall background budget. Therefore all of the screened batches were used in the final construction. Additionally, a measurement of a representative sample of the high-voltage connectors mounted in custom-made PTFE holders, as produced for the final assembly, was performed with a new HPGe spectrometer, GeMSE, and showed consistent results~\\cite{GeMSE}.\n\nConnected directly to the base of each PMT is a voltage divider network that consists of a Printed Circuit Board (PCB, $\\#$94) with sockets ($\\#$93), solder ($\\#$99), resistors ($\\#81-84$), and capacitors ($\\#$86, $\\#$88). Several batches of the same types of components were screened, as there was some variation among batches and with respect to different PCB materials. The final PCBs assembled with components (referred to as the PMT base in Table~\\ref{table:Samples}) used the cleanest components where possible and then screened with an HPGe spectrometer ($\\#$100). The activity per assembled base was measured to be about a factor of 10 lower than the activity from the PMT itself.\n\nSeveral of the components for the PMTs show a clear break in secular equilibrium in the $^{238}$U chain indicating a depletion of $^{226}$Ra, particularly the connectors ($\\#$66), the sockets ($\\#$93), many of the resistors, and, consequently, the assembled bases ($\\#$100).\n\n\n\\subsection{Other samples}\n\\label{sec:other_samples}\n\n\nSeveral components that were composites of different materials, such as insulated conductors for electrode high voltage (a copper rod inserted into a PTFE insulator, $\\#$103) and capacitive sensors to measure the LXe level (``levelmeters\", $\\#$101$-$102) were screened post-fabrication and showed acceptable activities. The remaining components listed under ``Miscellaneous'' showed high levels in the primordial decay chains. However, these components are used in the calibration or leveling systems that are located within or outside of the Cherenkov detector and quite far from the TPC sensitive volume, therefore have negligible contributions to the background. \n\n\n\n\n\n\n\n\\subsection{Summary of material placement}\n\\label{sec:instrument}\n\n\\begin{figure}[]\n\\begin{center}\n\\includegraphics*[width=0.7\\linewidth]{figures\/FC_template.png}\n\\end{center}\n\\caption\n{\\label{fig:TPC_items}The XENON1T TPC with material item numbers as given in Table~\\ref{table:Samples}.}\n\\setlength{\\belowcaptionskip}{0pt}\n\\end{figure}\n\n\nThe contribution from each material to the background depends upon its total mass and proximity to the sensitive volume as well as its type and energy of emission. The locations of screened materials used for the major components of the XENON1T TPC are indicated in Fig~\\ref{fig:TPC_items} by item number. The radioassay results from Table~\\ref{table:Samples} in combination with the material distribution within the instrument informed the XENON1T background predictions, as described in~\\cite{xenon_xe1t-sensitivity}. \n\nThe field cage of the XENON1T TPC consists of PTFE reflector panels and support pillars ($\\#$50), the latter hold and maintain separation between the 74 high-purity copper field-shaping rings ($\\#$1). The bottom ends of the PTFE pillars are mounted to a copper ring ($\\#$4) and are supported on the top by a stainless steel ring ($\\#$10). Bottom and top arrays of photomultiplier tubes ($\\#$69, $\\#$70, $\\#$100) face the target liquid-xenon volume enclosed by the field cage. The bottom array consists of a copper support plate ($\\#$2, $\\#$3) with a PTFE layer underneath ($\\#$49) for electrical insulation and a polished PTFE surface ($\\#$49) at a stand-off distance above the Cu plate in order to reflect the VUV light from the surfaces surrounding the PMT photocathodes. The top array consists of the same layers as the bottom array, mounted upside-down inside of the stainless steel diving bell that controls the LXe level ($\\#$10, shown in Fig~\\ref{fig:TPC_exploded}). In front of the photocathode surfaces of each PMT array are stainless steel screening electrodes ($\\#$10, not indicated in Fig~\\ref{fig:TPC_items}) to protect the PMTs from the field cage high voltage, small PTFE reflectors ($\\#$48), and the three electrodes ($\\#$10) that provide the electric field across the TPC (cathode below the target, gate and anode electrodes above the target). \n\nComponents not shown in Fig~\\ref{fig:TPC_items} include small parts such as the 5 G$\\mathrm{\\Omega}$ resistors ($\\#$71) that connect neighbouring copper field-shaping rings, PMT cabling and connectors ($\\#$55$-$58, $\\#$65$-$68), and small copper ($\\#$1), PEEK ($\\#$52), and stainless steel screws ($\\#$8, $\\#$28, $\\#$29) that were used throughout the TPC. Also not shown are components mounted onto or near the top stainless steel ring such as PAI ($\\#$53, $\\#$54) and PTFE ($\\#$50, $\\#$103) insulating spacers, and the levelmeters ($\\#$102) which are used to precisely measure the vertical position of the xenon liquid\/gas interface. \n\nOther TPC components (not shown in Fig~\\ref{fig:TPC_items}) are two long levelmeters ($\\#$101) which are used during LXe filling and a stainless steel with polyethylene high-voltage feedthrough (made from $\\#$8, $\\#$108) inside of a PTFE insulator ($\\#$47) that span the length of the field cage. The PMT signal and high-voltage cables ($\\#$55, $\\#$57) extend from the bottom PMT array along the length of the field cage and from the top PMT array inside the diving bell. The cables are then routed over the diving bell and connect to the cables ($\\#$56, $\\#$58, connected by $\\#$65$-$68) that arrive from the data acquisition room via the cryostat pipe ($\\#$11, partially shown in Figs~\\ref{fig:TPC_section} and~\\ref{fig:TPC_exploded}). \n\nThe cryostat ($\\#$9), shown in Figs~\\ref{fig:TPC_section} and~\\ref{fig:TPC_exploded}, consists of an inner stainless steel vessel that encloses the TPC and liquid xenon, nested inside an outer vessel that is evacuated for thermal insulation. The cryostat vessels and their domes ($\\#$8, $\\#$9) are covered by mylar insulation ($\\#$41) to reduce heat losses. Not shown are components outside of the cryostat, such as the calibration systems ($\\#$104, $\\#$105, $\\#$107) and stainless steel support structures ($\\#$25$-$27) within the water shield, and the 10 meter high, 9.6 meter diameter stainless steel tank that contains the water shield ($\\#$24).\n\n\n\n\n\n\\section{Discussion and impact on the XENON1T background}\\label{sec:impact}\n\nThe results from the radioassay campaign were used as source terms in the detector Monte Carlo simulations. The detected radioactive isotopes and decay chains were uniformly distributed within each component of the mass model according to their measured radioactivities. Each background source, before ER\/NR descrimination, is given in terms of an event rate over a \\mbox{1 ton} super-ellipsoid fiducial volume with respect to the energy region of interest (ROI). As electronic recoils and nuclear recoils induce a different response in liquid xenon, nuclear recoils in the (4, 50) keV$_{\\mathrm{nr}}$ interval yield the same signal intensities from scintillation as ER events in the (1, 12) keV$_{\\mathrm{ee}}$ (electron equivalent) energy ROI. The simulation and analysis details are given in~\\cite{xenon_xe1t-sensitivity}. \n\nFigure \\ref{fig:counts_All}, top, shows the relative expected contributions to the total ER background events for external background sources (i.e.~solar neutrinos), sources of intrinsic backgrounds ($^{136}$Xe, $^{85}$Kr, and $^{222}$Rn), and for each of the main XENON1T components. Thanks to the material selection campaign described in this work, the material-induced gamma-ray background is negligible within the (1, 12) keV$_{\\mathrm{ee}}$ WIMP search region compared to the contribution from $^{222}$Rn emanation. The dominant intrinsic $^{222}$Rn contamination was estimated to be 10 $\\mu$Bq\/kg in the liquid xenon target, however this can be further reduced through online purification~\\cite{RnDistillation}. A more detailed comparison with respect to the energy and select fiducial volumes can be found in~\\cite{xenon_xe1t-sensitivity}.\n\n\n\n\\begin{figure}[h]\n\n\\begin{center}\n\\includegraphics*[width=1.0\\linewidth]{figures\/ER_all_counts_break.pdf}\n\\includegraphics*[width=1.0\\linewidth]{figures\/NR_all_counts_v4.pdf}\n\\end{center}\n\n\\caption\n{\\label{fig:counts_All}Electronic recoil (top) and nuclear recoil (bottom) background contributions from materials (red) and from intrinsic and external sources (blue). The number of events per year in a 1$-$ton fiducial target is shown in the electron equivalent (1, 12) keV$_{\\mathrm{ee}}$ region of interest for electronic recoil events, corresponding to a nuclear recoil energy interval of (4, 50) keV$_{\\mathrm{nr}}$.}\n\\end{figure}\n\n\nThe expected contributions to the nuclear recoil background in XENON1T are shown in Fig. \\ref{fig:counts_All}, bottom. Most of the NR background comes from materials, as there are no significant intrinsic sources. Considering materials only, the stainless steel components (cryostat, TPC) are the dominant source, in total contributing 40$\\%$. The PMTs contribute 28$\\%$, primarily due to the high concentration of $^{238}$U and $^{232}$Th and their daughter isotopes in the ceramic stem of each PMT. Because of the proximity of the PTFE reflectors to the sensitive volume, the presence of heavier nuclei and their daughters contribute 22$\\%$ by the mechanisms described in Section~\\ref{sec:plastic_samples}. Coherent neutrino-nucleus scattering (CNNS) is subdominant, with a contribution similar to the TPC copper ($\\sim3\\%$ of total). The muon-induced nuclear recoil background is also subdominant due to effective coincidence-tagging with the Cherenkov muon-veto detector~\\cite{xenon_xe1t-MuonVeto}. \n\nAfter conversion into observable signals, ER\/NR discrimination was applied to all background events. Assuming an ER rejection efficiency of \\mbox{99.75$\\%$} at an NR acceptance of 40$\\%$, the total expected NR background in XENON1T for a 1 ton~$\\times$~2 year exposure is expected to be \\textless1 event in the (4, 50)\\,keV$_{\\mathrm{nr}}$ energy range. This corresponds to a best sensitivity to the spin-independent WIMP-nucleon cross section of \\mbox{$\\sigma_\\mathrm{SI} \\lesssim 10^{-47}~\\mathrm{cm}^{2}$} at a WIMP mass of \\mbox{m$_{\\chi}$= 50 GeV\/c $^{2}$}~\\cite{xenon_xe1t-sensitivity}.\n\nIn the planned upgrade of XENON1T to XENONnT, the LXe target mass will increase to a total of $\\sim$6 tons. This will require a \\mbox{$\\sim$40$\\%$} increase in the linear dimensions of the TPC and nearly double the number of PMTs. The larger detector will improve the sensitivity by another order of magnitude, reaching \\mbox{$\\sigma_\\mathrm{SI} \\lesssim 10^{-48}~\\mathrm{cm}^{2}$} at \\mbox{m$_{\\chi}$= 50 GeV\/c $^{2}$}~\\cite{xenon_xe1t-sensitivity}, assuming a negligible contribution from materials and a total exposure of 20 ton$\\cdot$years.\n\nMost of the existing sub-systems for XENON1T were designed to be reused for XENONnT, however the upgrade requires the construction of a new TPC and inner cryostat. As material-induced ER backgrounds are expected to be even lower than in XENON1T, the screening effort and material selection is focused on reducing the nuclear recoil background. This is being addressed particularly through continued efforts to identify low-activity stainless steel and by pursuing viable alternatives to PTFE, where possible.\n \n \n\\section{Acknowledgments}\n\nWe gratefully acknowledge support from the National Science Foundation, Swiss National Science Foundation, Deutsche Forschungsgemeinschaft, Max Planck Gesellschaft, German Ministry for Education and Research, Netherlands Organisation for Scientific Research (NWO), Weizmann Institute of Science, I-CORE, Initial Training Network Invisibles (Marie Curie Actions, PITNGA-2011-289442), Fundacao para a Ciencia e a Tecnologia, Region des Pays de la Loire, Knut and Alice Wallenberg Foundation, Kavli Foundation, and Istituto Nazionale di Fisica Nucleare. We are grateful to Laboratori Nazionali del Gran Sasso for hosting and supporting the XENON project.\n\n\n\n\n\\clearpag\n\\thispagestyle{empty\n\\newgeometry{margin=3cm}\n\n\\begin{sidewaystable*}[tp]\n\\resizebox{\\textwidth}{!}{\n\\tabcolsep=4pt\n\\centering\n\\begin{tabular}{ cllllccccccccccccc}\n\n\\toprule\n\\\\\n\\textbf{Item} & \\textbf{Sample} & \\textbf{Supplier} & \\textbf{XENON1T Use} & \\textbf{Facility} & \\textbf{Mass [kg]} & \\textbf{Time [d]} & \\textbf{Units} & \\textbf{$^{235}$U} & \\textbf{$^{238}$U} & \\textbf{$^{226}$Ra} & \\textbf{$^{228}$Ra ($^{232}$Th)$^{\\dagger}$} & \\textbf{$^{228}$Th} & \\textbf{$^{40}$K} & \\textbf{$^{60}$Co} & \\textbf{$^{137}$Cs} \\\\\n\\hline\n\\hline\n\\\\\n\\multicolumn{16}{l}{\\textbf{Copper}} \\\\\n\\hline\n\\input{tables\/Cu.tex}\n\\\\\n\\\\\n\\multicolumn{16}{l}{\\textbf{Stainless steel}} \\\\\n\\hline\n\\input{tables\/SS.tex}\n\\input{tables\/SS2.tex}\n\\input{tables\/structureSS.tex}\n\\\\\n\\\\\n\\multicolumn{16}{l}{\\textbf{Titanium}} \\\\\n\\hline\n\\input{tables\/titanium.tex}\n\\\\\n\\hline\n\\\\\n& & & & & & & & & & & & & & \\textbf{(Continued)} \\\\\n\n\\end{tabular}}\n\\end{sidewaystable*}\n\n\n\n\\clearpage\n\\thispagestyle{empty}\n\\newgeometry{margin=3cm}\n\n\n\\begin{sidewaystable*}[tp]\n\\resizebox{\\textwidth}{!}{\n\\tabcolsep=4pt\n\\centering\n\\begin{tabular}{ cllllccccccccccccc}\n\n\\multicolumn{16}{l}{\\textbf{(Continuation)}} \\\\\n\\toprule\n\\\\\n\\textbf{Item} & \\textbf{Sample} & \\textbf{Supplier} & \\textbf{XENON1T Use} & \\textbf{Facility} & \\textbf{Mass [kg]} & \\textbf{Time [d]} & \\textbf{Units} & \\textbf{$^{235}$U} & \\textbf{$^{238}$U} & \\textbf{$^{226}$Ra} & \\textbf{$^{228}$Ra ($^{232}$Th)$^{\\dagger}$} & \\textbf{$^{228}$Th} & \\textbf{$^{40}$K} & \\textbf{$^{60}$Co} & \\textbf{$^{137}$Cs} \\\\\n\\hline\n\\hline\n\\\\\n\\multicolumn{16}{l}{\\textbf{Cryostat insulation}} \\\\\n\\hline\n\\input{tables\/Superinsulation.tex}\n\\\\\n\\\\\n\\multicolumn{16}{l}{\\textbf{Plastics}} \\\\\n\\hline\n\\input{tables\/Plastics.tex}\n\\\\\n\\\\\n\\multicolumn{16}{l}{\\textbf{Cables}} \\\\\n\\hline\n\\input{tables\/Cables.tex}\\\\\n\\\\\n\\\\\n\\multicolumn{16}{l}{\\textbf{Connectors}} \\\\\n\\hline\n\\input{tables\/Connectors.tex}\\\\\n\\hline\n\\\\\n& & & & & & & & & & & & & & \\textbf{(Continued)} \\\\\n\n\n\\end{tabular}}\n\\end{sidewaystable*}\n\n\n\n\n\\clearpage\n\\thispagestyle{empty}\n\\newgeometry{margin=3cm}\n\n\\begin{sidewaystable*}[tp]\n\\resizebox{\\textwidth}{!}{\n\\tabcolsep=4pt\n\\centering\n\\begin{tabular}{ cllllccccccccccccc}\n\n\n\\multicolumn{16}{l}{\\textbf{(Continuation)}} \\\\\n\\toprule\n\\\\\n\\textbf{Item} & \\textbf{Sample} & \\textbf{Supplier} & \\textbf{XENON1T Use} & \\textbf{Facility} & \\textbf{Mass [kg]} & \\textbf{Time [d]} & \\textbf{Units} & \\textbf{$^{235}$U} & \\textbf{$^{238}$U} & \\textbf{$^{226}$Ra} & \\textbf{$^{228}$Ra ($^{232}$Th)$^{\\dagger}$} & \\textbf{$^{228}$Th} & \\textbf{$^{40}$K} & \\textbf{$^{60}$Co} & \\textbf{$^{137}$Cs} \\\\\n\\hline\n\\hline\n\\\\\n\\multicolumn{16}{l}{\\textbf{Photosensors (weighted average)}} \\\\\n\\hline\n\\input{tables\/PMTs.tex}\n\\\\\n\\\\\n\\multicolumn{16}{l}{\\textbf{Electronics}} \\\\\n\\hline\n\\input{tables\/electronics.tex}\n\\\\\n\\\\\n\\multicolumn{16}{l}{\\textbf{Miscellaneous}} \\\\\n\\hline\n\\input{tables\/misc_tpc.tex}\n\\\\\n\\bottomrule\n\n\n\\end{tabular}}\n\\caption{\\small{Measured activities of material samples for the XENON1T radioassay program. For HPGe spectrometer measurements, uncertainties are $\\pm1\\sigma$ for detected lines (in parentheses) and 95$\\%$ C.L. for upper limits. Mass spectrometry measurement uncertainty is 30$\\%$ unless otherwise noted. Listed activities for $^{238}$U that are denoted with ``*\" were inferred assuming natural abundance~\\cite{UraniumRatio}. Direct mass spectrometry measurements of $^{232}$Th are denoted with ``$^{\\dagger}$\". The ``XENON1T Use\" column refers to materials that were used in the final assembly, with reference to the subgroups labeled in Figs \\ref{fig:TPC_section} and \\ref{fig:TPC_exploded}. Dash marks in the table indicate that the sample was not used in construction (``XENON1T Use\"), or that an attribute (``Mass\", ``Time\", or a particular isotope) was either unknown or not applicable to the measurement.}}\n\\label{table:Samples}\n\n\n\n\\end{sidewaystable*}\n\n\\restoregeometry\n\n\n\n\n\\clearpage\n\\thispagestyle{empty}\n\n\\begin{table*}[ht]\n\\centering\n\\begin{tabular}{clccccccc}\n\n\\toprule\n\\textbf{Item} & \\textbf{Sample} & \\textbf{Units} & \\textbf{$^{56}$Co} & \\textbf{$^{57}$Co} & \\textbf{$^{58}$Co} & \\textbf{$^{54}$Mn} & \\textbf{$^{46}$Sc} \\\\ \n\\midrule[0.06em]\n1 & Copper, CW009A & mBq\/kg & 0.06(2) & 0.2(1) & 0.36(4) & $<$ 0.027 & $-$ & \\\\\n2 & Copper, C10100 & mBq\/kg & 0.31(3) & 0.4(1) & 1.8(2) & 0.22(3) & 0.08(2) & \\\\\n3 & Copper, C10100 & mBq\/kg & $-$ & 0.40(1) & 0.35(4) & 0.15(2) & $-$ & \\\\\n4 & Copper, C10100 & mBq\/kg & 0.15(2) & 0.7(2) & 1.1(1) & 0.35(4) & $-$ & \\\\\n8 & Stainless steel, AISI 316Ti & mBq\/kg & $<$ 0.8 & $<$ 7.4 & $<$ 1.5 & 1.2(5) & $-$ & \\\\\n9 & Stainless steel, AISI 304L & mBq\/kg & $-$ & $-$ & $<$ 0.6 & 0.5(2) & $-$ & \\\\\n10 & Stainless steel, AISI 304 & mBq\/kg & $-$ & $-$ & $-$ & 1.1(3) & $-$ & \\\\\n11 & Stainless steel, AISI 316L & mBq\/kg & $-$ & $-$ & $-$ & 1.4(3) & $-$ &\\\\\n32 & Titanium, grade 1 & mBq\/kg & $-$ & $-$ & $-$ & $-$ & 2.15(3) &\\\\\n33 & Titanium, grade 1 & mBq\/kg & $-$ & $-$ & $-$ & $-$ & 1.9(2) &\\\\\n34 & Titanium, grade 4 & mBq\/kg & $-$ & $-$ & $-$ & $-$ & 1.9(2) &\\\\\n35 & Titanium, grade 2 & mBq\/kg & $-$ & $-$ & $-$ & $-$ & 2.7(3) &\\\\\n38 & Titanium, grade 1 & mBq\/kg & $-$ & $-$ & $-$ & $-$ & 1.8(2) &\\\\\n39 & Titanium, grade 1 ($\\#$38, welded) & mBq\/kg & $-$ & $-$ & $-$ & $-$ & 1.0(1) &\\\\\n40 & Titanium, grade 1 & mBq\/kg & $-$ & $-$ & $-$ & $-$ & 2.2(3) &\\\\\n \\bottomrule\n \n\\end{tabular}\n\\caption{Cosmogenic radioisotopes detected in metal samples. The ``Item\" numbers are cross-referenced with those in Table \\ref{table:Samples}. Uncertainties are $\\pm1\\sigma$ for detected lines (in parentheses) and 95$\\%$ C.L. for upper limits.}\n\\label{table:metalother}\n\\end{table*}\n\n\n\n\n\n\\clearpage\n\\restoregeometry\n\\twocolumn\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{Introduction}\n\n\nThe arithmetic intersection theory of toric varieties with respect to toric line bundles equipped with their canonical metric was first studied by Maillot in \\cite{Mail}. Later, the systematic extension of the toric dictionary to Arakelov geometry was carried out by Burgos Gil, Philippon and Sombra in \\cite{BPS}. It turns out from their study that suitably metrized toric line bundles can be expressed in terms of families of concave functions on convex polytopes and that the height of the toric variety with respect to this choice is related to the integral of such functions. Their theory allows to treat a large spectrum of height functions, namely the ones arising from toric line bundles equipped with toric metrics; this includes the canonical heights studied by Maillot and the Fubini-Study height. On the other hand, the techniques developed in \\cite{BPS} only apply to the computation of the height of toric subvarieties and do not solve, for instance, the problem of determining the height of a general cycle of codimension $1$. This question was answered in a very special case by \\cite{Mail}, where a relation between the canonical height of a hypersurface in a smooth projective toric variety and the Mahler measure of the corresponding polynomial was given. Other computations have been performed by Cassaigne and Maillot in \\cite{CM} for the Fubini-Study height of hypersurfaces in projective spaces. Extending the techniques of \\cite{BPS}, we give here a combinatorial formula for the height of a $1$-codimensional cycle in a toric variety for a much more general choice of metrics. \n\\vspace{\\baselineskip}\n\\\\For the sake of simplicity, we restrict for the moment to the case of an ambient proper toric variety $X_\\Sigma$ of dimension $n$ over $\\mathbb{Q}$, leaving the treatment of the case of an arbitrary base adelic field to the body of the paper. Let $\\mathfrak{M}$ stand for the set of places of $\\mathbb{Q}$. As usual in toric geometry, we denote by $M$ the lattice of characters of the torus of $X_\\Sigma$, by $N$ its dual lattice, and by $M_\\mathbb{R}$ and $N_\\mathbb{R}$ the corresponding real vector spaces. We are interested in a combinatorial expression for the height of a cycle of codimension $1$ in $X_\\Sigma$ with respect to a suitable choice of a metrized (Cartier) divisor. By the linearity of height functions, we can restrict to the case of an irreducible hypersurface $Y$. Moreover, since irreducible hypersurfaces in $X_\\Sigma$ not intersecting its dense open torus have to coincide with $1$-codimensional toric orbits, whose height has already been calculated in \\cite[Proposition 5.1.11]{BPS}, we can assume that the generic point of $Y$ lies in the dense open orbit of $X_\\Sigma$. Under this assumption, $Y$ is described by an irreducible Laurent polynomial $f$ with rational coefficients. Its Newton polytope $\\NP(f)$ is a nonempty subset of $M_\\mathbb{R}$ capturing enough information for the intersection theoretical properties of $Y$. For instance, \\hyperref[degree of an hypersurface]{Proposition \\ref*{degree of an hypersurface}} implies that the degree of $Y$ with respect to a toric divisor $D$ on $X_\\Sigma$ generated by its global sections is given by \\[\\deg_D(Y)=\\MV_M(\\Delta,\\dots,\\Delta,\\NP(f)),\\]where $\\Delta$ is the polytope in $M_\\mathbb{R}$ associated to $D$ and $\\MV_M$ denotes the mixed volume of convex bodies in $M_\\mathbb{R}$ with respect to a suitably normalized Haar measure.\n\\\\The height of a cycle in $X$ is the arithmetic counterpart of its degree with respect to a divisor $D$. Its definition requires as an extra datum the choice of an adelic semipositive metric on $D$, see \\hyperref[section about Arakelov on toric varieties]{section \\ref*{section about Arakelov on toric varieties}} for a precise definition. To have a combinatorial description of heights in toric varieties, it is necessary to ask $D$ to be a toric divisor (with associated polytope $\\Delta$) and the metric on it to be ``toric invariant'' in some sense. In such a situation, Burgos Gil, Philippon and Sombra have shown that a combinatorial description is possible, translating the additional information of the metric into an extra dimension on the convex geometrical side: an adelic semipositive toric metric on $D$ is associated to a family $(\\vartheta_v)_{v\\in\\mathfrak{M}}$ of continuous concave functions on $\\Delta$, called the \\emph{roof functions} of the metric, such that $\\vartheta_v=0$ for all but finitely many $v$. We show how the height of $Y$ with respect to the adelic semipositive toric metrized divisor $\\overline{D}$ can be expressed using such an extra dimensional representation, in a spirit analogous to the formula for its degree mentioned above. The key idea consists in associating to the polynomial $f$ defining $Y$, for every place $v$ of $\\mathbb{Q}$, a suitable function which we call the \\emph{$v$-adic Ronkin function} of $f$ and denote by $\\rho_{f,v}$. It is a concave function on $N_\\mathbb{R}$ whose value at $u$ can be interpreted as an average of $-\\log|f|$ on the fiber of the tropicalization map over $u$. When $v$ is archimedean, it is the Ronkin function studied by Passare and Rullg{\\aa}rd among others, while for non-archimedean places it coincides with the $v$-adic tropicalization of the polynomial $f$. Its Legendre-Fenchel dual $\\rho_{f,v}^\\vee$ is a concave function on $M_\\mathbb{R}$ which is supported on the Newton polytope of $f$. Recall now that Philippon and Sombra have introduced in \\cite{PS1} a polarized version of the integration of a concave function with bounded support, called the \\emph{mixed integral}. For the choice of a suitably normalized Haar measure on the vector space $M_\\mathbb{R}$, it is a multilinear symmetric real valued function $\\MI_M$ taking as entries $n+1$ concave functions supported on convex bodies in $M_\\mathbb{R}$.\n\n\\begin{introtheorem}\\label{main theorem introduction}\nThe height of $Y$ with respect to $\\overline{D}$ is given by \\[h_{\\overline{D}}(Y)=\\sum_{v\\in\\mathfrak{M}}\\MI_M\\big(\\vartheta_v,\\dots,\\vartheta_v,\\rho_{f,v}^\\vee\\big).\\]\n\\end{introtheorem}\n\nDespite the complexity of the computation of the archimedean Ronkin function, the formula in the previous theorem clarifies the relation between the defining polynomial of an irreducible hypersurface and its height with respect to an adelic semipositive toric metrized divisor. \nIt is easy to specialize it to the case of the canonical metric on $D$, where it reduces to the equality proved in \\cite{Mail}, or of the Fubini-Study metric in the projective setting. We hope that a better understanding of the properties of mixed integrals and archimedean Ronkin functions could be used to deduce both lower and upper bounds for the height of $Y$. More importantly, our result asserts that the collection of the $v$-adic Ronkin functions of a hypersurface contains enough information to determine its height; we wonder whether other arithmetical properties of $Y$ might be read in terms of such functions.\n\\vspace{\\baselineskip}\n\\\\To show the stated result, we prove more precise formulas for the local height and the toric local height of a $1$-codimensional cycle. We also show some new properties of mixed integrals and we propose a more uniform definition and study of $v$-adic Ronkin functions which is independent on whether the place $v$ is archimedean or not. The obtained formulas for the height extend to the case of admissible adelic toric metrized divisors as alternated sums of mixed integrals, as in \\cite[Remark 5.1.10]{BPS}.\n\\\\For an arbitrary adelic base field $K$, we remark that one needs to prove that the global height of $Y$ with respect to an adelic semipositive toric metrized divisor is a finite sum and hence well-defined. This is automatic if $K$ is a global field, because of \\cite[Proposition 1.5.14 and Theorem 4.9.3]{BPS}. We show it here for an arbitrary adelic field $K$ with product formula, in which case the formulas for the height stay true. In the more general setting of an adelic field $K$ not satisfying the product formula, it is easy to verify that the same equality for the global height holds up to the sum by the \\textit{defect} of $K$, see \\cite[Definition 1.5.9]{BPS}. Finally, the recent work \\cite{GH} suggests that similar statements might hold for a base $M$-field.\n\\vspace{\\baselineskip}\n\\\\We now briefly summarize the content of each section. \n\\\\In \\hyperref[section about convex geometry]{section \\ref*{section about convex geometry}}, we recall the tools from convex geometry which are needed throughout all the paper: Legendre-Fenchel duality of concave functions, real Monge-Amp\\`ere measures and mixed integrals. In particular, we re-interpret the recursive formula for mixed integrals proved by Philippon and Sombra in terms of mixed real Monge-Amp\\`ere measures. We then make use of it to deduce two elementary, though useful, properties of such operators. Finally, we describe what happens when one of the functions appearing in the mixed integral is the indicator function of a line segment.\n\\\\\\hyperref[section about Ronkin functions]{Section \\ref*{section about Ronkin functions}} deals with the key object of our work: $v$-adic Ronkin functions of Laurent polynomials, which are introduced and described after recalling the needed preliminaries in tropical and non-archimedean geometry. In this context, the discussion of a notion of minimal boundaries allows to treat the archimedean and non-archimedean cases homogeneously. \n\\\\In \\hyperref[section about Arakelov on toric varieties]{section \\ref*{section about Arakelov on toric varieties}} we briefly recall the general adelic Arakelov framework and focus then on the results obtained by Burgos Gil, Philippon and Sombra in the toric setting. To keep the treatment of archimedean and non-archimedean places on equal footing, we rephrase their description of the Chambert-Loir measure of semipositive toric metrized divisors in terms of minimal boundaries of tropical fibers.\n\\\\As a needed step for the main proof, we combinatorially describe the Weil divisor of the rational function defined by a Laurent polynomial on a toric variety. This result can be of independent interest and has then been set aside in \\hyperref[section divisor of rational functions]{section \\ref*{section divisor of rational functions}}.\n\\\\\\hyperref[section hypersurface]{Section \\ref*{section hypersurface}} is dedicated to the proofs of our main results \\hyperref[toric local height of hypersurfaces]{Theorem \\ref*{toric local height of hypersurfaces}} and \\hyperref[hypersurfaces are integrable and their global height]{Theorem \\ref*{hypersurfaces are integrable and their global height}}, which are formulas for the local height and the toric local height of cycles of codimension $1$ in toric varieties. We then make use of them to prove the integrability statement and a formula for their global heights, with respect to the choice of adelic semipositive toric metrized divisors.\n\\\\For binomial hypersurfaces, such a formula is compatible with the one deduced from \\cite{BPS}. This is shown in \\hyperref[section about examples]{section \\ref*{section about examples}}, where we also apply our results to some other particular cases. We provide convex geometrical formulas for the canonical height of $1$-codimensional cycles, obtaining the quoted result by Maillot, and for the Fubini-Study height of a projective hypersurface. We also propose a new height function, the $\\rho$-height, for which we give a compact formula. We do not know any application of such a height, which could be anyway worth studying.\n\\vspace{\\baselineskip}\n\\\\\\textbf{Terminology and notations.} A \\emph{variety} $X$ is assumed to be a reduced and irreducible separated scheme of finite type over a field. By an \\emph{irreducible hypersurface} in it we mean a closed integral subscheme of codimension $1$ in $X$. A \\emph{divisor} on $X$ is a Cartier divisor, unless otherwise stated. Toric varieties are assumed to be normal; whenever the choice of the base field $K$ is clear from the context, the notation $X_\\Sigma$ will refer to the toric variety over $K$ associated to the fan $\\Sigma$.\n\\\\The term \\emph{measure} on a topological space stands for a signed Borel measure on it; in particular, measures admit a well-defined push-forward via continuous mappings. A measure which only takes non-negative real values on Borel subsets is called a \\emph{positive measure}.\n\\vspace{\\baselineskip}\n\\\\\\textbf{Acknowledgments.} The author wishes to thank his PhD supervisors Mart\\'{\\i}n Sombra and Alain Yger for their guidance and many fruitful discussions, as well as C\\'esar Mart\\'{\\i}nez for the interest shown in a preliminary version of this text. Also, he is grateful to the two anonymous referees for the careful reading and the valuable remarks, which highly contributed to improve the quality of the text. The work has been prepared at the Universit\\'e de Bordeaux and at the Universitat de Barcelona as part of the author's PhD project, and partially supported by the MINECO research project MTM2015-65361-P and the CNRS project PICS 6381 ``G\\'eom\\'etrie diophantienne et calcul formel''.\n\n\n\\section{Preliminaries in convex geometry}\\label{section about convex geometry}\n\nThis section is devoted to recalling notions from convex geometry that will be useful in the sequel. We follow the conventions and notations of \\cite[Chapter 2]{BPS}, referring to \\cite[\\S12]{R} for a more complete treatment of the subject. We refer to these two sources for the proofs of the statements we make here.\n\\\\For the whole section, let $N$ be a lattice of rank $n$ and $M:=\\hom(N,\\mathbb{Z})$ its dual lattice. Denote by $N_\\mathbb{R}=N\\otimes_\\mathbb{Z}\\mathbb{R}$ and by $M_\\mathbb{R}=M\\otimes_\\mathbb{Z}\\mathbb{R}$ the corresponding $n$-dimensional real vector spaces.\n\\\\By a \\emph{polyhedron} in $N_\\mathbb{R}$ we mean a convex subset of $N_\\mathbb{R}$ obtained as the intersection of finitely many closed halfspaces $\\{u\\in N_\\mathbb{R}:\\langle x,u\\rangle+c\\geq0\\}$, with $x\\in M_\\mathbb{R}$ and $c\\in\\mathbb{R}$. If all the slopes $x$ can be chosen in $M$, the polyhedron is said to be \\emph{rational}. A \\emph{polytope} is a bounded polyhedron. A polytope in $N_\\mathbb{R}$ whose vertices all lie in $N$ is called a \\emph{lattice polytope}; it is in particular a rational polytope. A compact convex subset of $N_\\mathbb{R}$ is called a \\emph{convex body}.\n\n\n\\subsection{Legendre-Fenchel duality}\n\nA function $f:N_\\mathbb{R}\\to\\mathbb{R}\\cup\\{-\\infty\\}$ is said to be \\emph{concave} if it is not identically $-\\infty$ and for every $u_1,u_2\\in N_\\mathbb{R}$ and for every $t\\in\\left[0,1\\right]$, one has the inequality \\[f(tu_1+(1-t)u_2)\\geq tf(u_1)+(1-t)f(u_2).\\] The \\emph{effective domain} of a concave function $f$ is the set on which the function takes values different from $-\\infty$ and it is denoted by $\\dom(f)$: it is a convex subset of $N_\\mathbb{R}$. A concave function is said to be \\emph{closed} if it is upper semicontinuous. Every concave function with closed effective domain and continuous on it is closed. The \\emph{recession function} of a closed concave function $f$ is the concave conical function which takes on $u\\in N_\\mathbb{R}$ the value \\[\\rec(f)(u):=\\lim_{\\lambda\\to\\infty}\\frac{f(v_0+\\lambda u)}{\\lambda}\\in\\mathbb{R}\\cup\\{-\\infty\\},\\] for any $v_0\\in\\dom(f)$, see \\cite[Theorem 8.5]{R}. Finally, a concave function $f$ with effective domain a polyhedron in $N_\\mathbb{R}$ is \\emph{piecewise affine} if \\[f(u)=\\min_{\\alpha\\in S}(\\langle\\alpha,u\\rangle+c_\\alpha)\\] for every $u\\in\\dom(f)$, with $S$ a finite subset of $M_\\mathbb{R}$ and $c_\\alpha\\in\\mathbb{R}$ for every $\\alpha\\in S$. \n\\\\To each concave function $f$ on $N_\\mathbb{R}$, one can associate its \\emph{Legendre-Fenchel dual}, which is the closed concave function $f^\\vee$ on $M_\\mathbb{R}$ defined as \\[f^\\vee(x):=\\inf_{u\\in N_\\mathbb{R}}(\\langle x,u\\rangle-f(u)),\\]for every $x\\in M_\\mathbb{R}$, see \\cite[\\S 2.2]{BPS}. If $f$ is closed, $(f^\\vee)^\\vee=f$. The effective domain of $f^\\vee$ is a convex subset of $M_\\mathbb{R}$, which one calls the \\emph{stability set of $f$} and denotes by $\\stab(f)$.\n\\\\The following example is classical and will play a role later on.\n\n\\begin{ex}\\label{indicator and support function}\nAny nonempty convex body $B$ in $M_\\mathbb{R}$ induces a concave function $\\Psi_B$ on $N_\\mathbb{R}$, called the \\emph{support function} of $B$ and defined as \\[\\Psi_B(u):=\\min_{x\\in B}\\langle x,u\\rangle\\]for every $u\\in N_\\mathbb{R}$. Its Legendre-Fenchel dual is the \\emph{indicator function $\\iota_B$ of $B$}, which is the function taking the value $0$ on $B$ and $-\\infty$ elsewhere. Hence, $\\dom(\\Psi_B)=N_\\mathbb{R}$ and $\\stab(\\Psi_B)=B$. Notice that, whenever $B$ is a polytope, $\\Psi_B$ is a conic piecewise affine concave function.\n\\end{ex}\n\nWe also recall that there exists a number of operations that one can define on concave functions, in addition to the usual pointwise sum and scalar multiplication. Among these, the \\emph{sup-convolution} of two concave functions $f$ and $g$ on $N_\\mathbb{R}$ with non-disjoint stability sets is defined as\n\\[(f\\boxplus g)(v):=\\sup_{u_1+u_2=v}(f(u_1)+g(u_2))\\]\nand the \\emph{right scalar multiplication} of $f$ by $\\lambda\\in\\mathbb{R}_{\\geq0}$ as\n\\[(f\\lambda)(u):=\\lambda f(u\/\\lambda).\\]\nAlso, the \\emph{translate} of a concave function $f$ on $N_\\mathbb{R}$ by a point $u_0\\in N_\\mathbb{R}$ is set to be \\[(\\tau_{u_0}f)(u):=f(u-u_0).\\]These operations are dual, via Legendre-Fenchel duality, to the usual pointwise addition, scalar multiplication and sum by a linear function, respectively, see \\cite[Proposition 2.3.1 and Proposition 2.3.3]{BPS}.\n\n\n\n\\subsection{Real Monge-Amp\\`ere measures}\\label{subsection about real Monge-Ampere measures}\n\nFor any closed concave function $f$ on $N_\\mathbb{R}$ with $\\dom(f)=N_\\mathbb{R}$ and for any Haar measure $\\mu$ on $M_\\mathbb{R}$, one can define a corresponding \\emph{real Monge-Amp\\`ere measure} $\\mathcal{M}_\\mu(f)$, as in \\cite[\\S2.7]{BPS}. It is a measure on $N_\\mathbb{R}$, of total mass $\\mu(\\stab(f))$, being supported on finitely many points if $f$ is piecewise affine. The Monge-Amp\\`ere operator, associating to each closed concave function $f$ with $\\dom(f)=N_\\mathbb{R}$ the corresponding measure $\\mathcal{M}_\\mu(f)$ on $N_\\mathbb{R}$ is homogeneous of degree $n$ with respect to pointwise scalar multiplication. It was shown in \\cite{PR} that such an operator admits a polarization: for $f_1,\\dots,f_n$ closed concave functions with effective domain $N_\\mathbb{R}$, their \\emph{mixed real Monge-Amp\\`ere measure} is defined as the measure\n\\begin{equation}\\label{mixed Monge-Ampere measure}\n\\MM_\\mu(f_1,\\dots,f_n):=\\sum_{k=1}^n(-1)^{n-k}\\sum_{1\\leq i_1<\\dots0$.\n\\\\The mixed real Monge-Amp\\`ere measure of piecewise affine concave functions can be made explicit in terms of the hypographs of their Legendre-Fenchel duals. We denote by $\\delta_v$ the Dirac measure supported on $v$.\n\n\\begin{proposition}\\label{real Monge-Ampere measure of piecewise affine functions}\nFor $i=1,\\dots,n$, let $g_i$ be a piecewise affine concave function with effective domain a polytope $Q_i\\subset M_\\mathbb{R}$, $\\Gamma_i$ the hypograph of $g_i$. Denote by $\\pi:M_\\mathbb{R}\\times\\mathbb{R}\\to M_\\mathbb{R}$ the projection onto the first factor. For any choice of a Haar measure $\\mu$ on $M_\\mathbb{R}$ one has \\[\\MM_\\mu\\big(g_1^\\vee,\\dots,g_n^\\vee\\big)=\\sum_{v\\in N_\\mathbb{R}}\\MV_\\mu\\bigg(\\pi\\Big(\\Gamma_1^{(v,-1)}\\Big),\\dots,\\pi\\Big(\\Gamma_n^{(v,-1)}\\Big)\\bigg)\\ \\delta_v,\\] and the sum is finite.\n\\end{proposition}\n\\begin{proof}\nFor a piecewise affine concave function $g$ with bounded domain in $M_\\mathbb{R}$, its Legendre-Fenchel dual is a piecewise affine concave function with domain $N_\\mathbb{R}$ and \\cite[Proposition 2.7.4]{BPS} affirms that \\[\\mathcal{M}_\\mu\\big(g^\\vee\\big)=\\sum_{v\\in N_\\mathbb{R}}\\mu(v^*)\\delta_v,\\]with $v^*=\\{x\\in M_\\mathbb{R}: g^\\vee(v)=\\langle x,v\\rangle-g(x)\\}$. From the definition of the Legendre-Fenchel duality, one has hence that\n\\begin{equation*}\n\\begin{split}\nv^*&=\\bigg\\{x\\in M_\\mathbb{R}:\\langle x,v\\rangle-g(x)=\\min_{y\\in M_\\mathbb{R}}(\\langle y,v\\rangle-g(y))\\bigg\\}\\\\&=\\Big\\{x\\in M_\\mathbb{R}:(x,g(x))\\in\\Gamma(g)^{(v,-1)}\\Big\\},\n\\end{split}\n\\end{equation*}\nand so \\[\\mathcal{M}_\\mu\\big(g^\\vee\\big)=\\sum_{v\\in N_\\mathbb{R}}\\mu\\Big(\\pi\\Big(\\Gamma(g)^{(v,-1)}\\Big)\\Big)\\delta_v.\\]\nThe sum is moreover supported on finitely many $v\\in N_\\mathbb{R}$, corresponding to the directions of the finitely many exposed faces of $\\Gamma(g)$.\n\\\\By \\cite[\\S2]{AW}, the relation \\[\\Gamma(g_i\\boxplus g_j)=\\Gamma(g_i)+\\Gamma(g_j)\\] on the hypographs of $g_i$ and $g_j$ holds for any $i,j\\in\\{1,\\dots,n\\}$. As a consequence, for every subset $\\{i_1,\\dots,i_k\\}\\subseteq\\{1,\\dots,n\\}$, \\cite[Proposition 2.3.1]{BPS} and the linearity of $\\pi$ yield\n\\begin{equation*}\n\\begin{split}\n\\mathcal{M}_\\mu\\big(g_{i_1}^\\vee+\\dots+g_{i_k}^\\vee\\big)&=\\mathcal{M}_\\mu\\big((g_{i_1}\\boxplus\\dots\\boxplus g_{i_k})^\\vee\\big)\\\\&=\\sum_{v\\in N_\\mathbb{R}}\\mu\\Big(\\pi\\Big(\\Gamma_{i_1}^{(v,-1)}+\\dots+\\Gamma_{i_k}^{(v,-1)}\\Big)\\Big)\\delta_v\\\\&=\\sum_{v\\in N_\\mathbb{R}}\\mu\\Big(\\pi\\Big(\\Gamma_{i_1}^{(v,-1)}\\Big)+\\dots+\\pi\\Big(\\Gamma_{i_k}^{(v,-1)}\\Big)\\Big)\\delta_v,\n\\end{split}\n\\end{equation*}\nand the sum is finite. The statement follows then from the definition of the mixed real Monge-Amp\\`ere measure, rearranging the terms.\n\\end{proof}\n\nWe can now prove a recursive formula relating the notions of the mixed real Monge-Amp\\`ere measure and the mixed integral of concave functions, via Legendre-Fenchel duality. A vector $u\\in N$ is said to be \\emph{primitive} if it is nonzero and there is no other element $u^\\prime\\in N$ such that $ku^\\prime=u$ for some positive integer $k$.\n\n\\begin{theorem}\\label{recursive formula with mixed integral and mixed real Monge-Ampere measure}\nFor $i=0,\\dots,n$, let $g_i$ be a continuous concave function on a rational polytope $Q_i$ in $M_\\mathbb{R}$. Then \n\\begin{multline*}\n\\MI_M(g_0,\\dots,g_n)=-\\sum_{\\substack{u\\in N\\\\\\primitive}}\\Psi_{Q_0}(u)\\MI_{M(u)}\\big(g_1|_{Q_1^{u}},\\dots,g_n|_{Q_n^{u}}\\big)\\\\-\\int_{N_\\mathbb{R}}g_0^\\vee\\ d\\MM_{M}\\big(g_1^\\vee,\\dots,g_n^\\vee\\big),\n\\end{multline*}\nthe first sum being finite.\n\\\\In particular, if $g_i$ is a piecewise affine concave function on $Q_i$ with hypograph $\\Gamma_i$ for any $i=0,\\dots,n$, denoting by $\\pi:M_\\mathbb{R}\\times\\mathbb{R}\\to M_\\mathbb{R}$ the projection onto the first factor, one has\n\\begin{multline*}\n\\MI_M(g_0,\\dots,g_n)=-\\sum_{\\substack{u\\in N\\\\\\primitive}}\\Psi_{Q_0}(u)\\MI_{M(u)}\\big(g_1|_{Q_1^{u}},\\dots,g_n|_{Q_n^{u}}\\big)\\\\-\\sum_{v\\in N_\\mathbb{R}}g_0^\\vee(v)\\MV_{M}\\bigg(\\pi\\Big(\\Gamma_1^{(v,-1)}\\Big),\\dots,\\pi\\Big(\\Gamma_n^{(v,-1)}\\Big)\\bigg).\n\\end{multline*}\n\\end{theorem}\n\\begin{proof}\nBy \\cite[Proposition 2.5.23 (1)]{BPS}, any continuous concave function on a polytope can be approximated, with respect to uniform convergence, by a sequence of piecewise affine concave functions on the polytope itself. On the other hand, the Legendre-Fenchel duality and the real Monge-Amp\\`ere operator are continuous with respect to uniform limits of concave functions, see \\cite[Proposition 2.2.3]{BPS} and \\cite[\\S3]{RT}, respectively. It is not difficult to show that the same holds for mixed integrals. Thanks to \\hyperref[real Monge-Ampere measure of piecewise affine functions]{Proposition \\ref*{real Monge-Ampere measure of piecewise affine functions}}, it is hence enough to prove the formula in the particular case of $g_0,\\dots,g_n$ being piecewise affine concave functions.\n\\\\Let hence $g_i$ be a concave piecewise affine function on the rational polytope $Q_i$ in $M_\\mathbb{R}$, $\\Gamma_i$ its hypograph, for $i=0,\\dots,n$. The choice of a basis of $N$ (and of the dual basis of $M$) endows $N_\\mathbb{R}$ and $M_\\mathbb{R}$ with an euclidean structure, allowing to consider the sets \\[\\mathbb{S}^{n-1}:=\\{w\\in N_\\mathbb{R}:\\|w\\|=1\\}\\subseteq N_\\mathbb{R}\\]and\\[\\mathbb{S}^n_-:=\\{(v,t)\\in N_\\mathbb{R}\\times\\mathbb{R}:\\|(v,t)\\|=1,t<0\\}\\subseteq N_\\mathbb{R}\\times\\mathbb{R}.\\] After a change of sign due to the use of a different notation, \\cite[Proposition 8.5]{PS2} affirms that\n\\begin{multline}\\label{equation in the proof of the recursive formula for mixed integrals}\n\\MI_M(g_0,\\dots,g_n)=-\\sum_{w\\in\\mathbb{S}^{n-1}}\\Psi_{Q_0}(w)\\MI_{n-1}\\big(g_1|_{Q_1^w},\\dots,g_n|_{Q_n^w}\\big)\\\\-\\sum_{r\\in\\mathbb{S}^n_-}\\Psi_{\\Gamma_0}(r)\\ \\MV_n(\\Gamma^r_1,\\dots,\\Gamma^r_n),\n\\end{multline}\nwhere, on the right hand side, one refers to the mixed integral with respect to the measure obtained restricting $\\vol_M$ to $w^\\perp$ and to the mixed volume with respect to the restriction of $\\vol_{M\\oplus\\mathbb{Z}}$ to $r^\\perp$.\n\\\\Concerning the first sum on the right hand side of \\eqref{equation in the proof of the recursive formula for mixed integrals}, if a term in the sum is different from zero, then there exists a subset $I\\subset\\{1,\\dots,n\\}$ such that the Minkowski sum of $Q_i^w$, with $i\\in I$, is of dimension $n-1$; in particular, denoting $Q:=Q_1+\\dots+Q_n$, $Q^w=Q_1^w+\\dots+Q_n^w$ needs to be of dimension $n-1$. As a consequence, one can restrict the sum to the set of vectors $w\\in\\mathbb{S}^{n-1}$ for which $Q^w$ is a $(n-1)$-dimensional face of $Q$. This set is included in the set of vectors of unitary length which are perpendicular to a $(n-1)$-dimensional face of $Q$, hence it is finite since $Q$ is a polytope. Moreover, since $Q$ is rational, the ray spanned by such a vector $w$ contains a unique primitive vector $u\\in N$. The linearity of $\\Psi_{Q_0}$ yields hence the equality\n\\[\\sum_{w\\in\\mathbb{S}^{n-1}}\\Psi_{Q_0}(w)\\MI_{n-1}\\big(g_1|_{Q_1^w},\\dots,g_n|_{Q_n^w}\\big)=\\sum_{\\substack{u\\in N\\\\\\primitive}}\\frac{\\Psi_{Q_0}(u)}{\\|u\\|}\\MI_{n-1}\\big(g_1|_{Q_1^u},\\dots,g_n|_{Q_n^u}\\big).\n\\]\nThe fact that the restriction of $\\vol_M$ to $u^\\perp$ is equal to the measure $\\vol_{M(u)}$ multiplied by $\\|u\\|$, see \\cite[proof of Corollary 2.7.10]{BPS}, allows to conclude that the first sum in \\eqref{equation in the proof of the recursive formula for mixed integrals} coincides with the desired one.\n\\\\Regarding the second sum in \\eqref{equation in the proof of the recursive formula for mixed integrals}, there exists an obvious bijection between $\\mathbb{S}^n_-$ and $N_\\mathbb{R}$ given by associating to each $r\\in\\mathbb{S}^n_-$ the only vector $v\\in N_\\mathbb{R}$ such that $(v,-1)$ lies on the line spanned by $r$. Hence,\n\\[\\sum_{r\\in\\mathbb{S}^n_-}\\Psi_{\\Gamma_0}(r)\\ \\MV_n(\\Gamma^r_1,\\dots,\\Gamma^r_n)=\\sum_{v\\in N_\\mathbb{R}}\\frac{\\Psi_{\\Gamma_0}(v,-1)}{\\|(v,-1)\\|}\\ \\MV_n\\Big(\\Gamma^{(v,-1)}_1,\\dots,\\Gamma^{(v,-1)}_n\\Big).\\]Directly by the definition of the Legendre-Fenchel duality, one has that $\\Psi_{\\Gamma_0}(v,-1)=g_0^\\vee(v)$. The statement follows then from the fact that for every Borel set $E$ in $(v,-1)^\\perp$, the measure of $E$ with respect to the restriction of $\\vol_{M\\oplus\\mathbb{Z}}$ to $(v,-1)^\\perp$ equals $\\|(v,-1)\\|\\cdot\\vol_M(\\pi(E))$, again by \\cite[proof of Corollary 2.7.10]{BPS}.\n\\end{proof}\n\n\\begin{rem}\\label{recursive formula with sum over the faces}\nFor a rational polytope $P$ of full dimension $n$ in $M_\\mathbb{R}$, every facet $F$ of $P$, that is a face of dimension $n-1$, admits a distinguished orthogonal vector: it is the unique primitive vector $v_F\\in N$ which satisfies $P^{v_F}=F$. Under the additional assumption that the Minkowski sum $Q:=Q_1+\\dots+Q_n$ is of dimension $n$ in $M_\\mathbb{R}$, the formula in \\hyperref[recursive formula with mixed integral and mixed real Monge-Ampere measure]{Theorem \\ref*{recursive formula with mixed integral and mixed real Monge-Ampere measure}} can be written as\n\\begin{multline*}\n\\MI_M(g_0,\\dots,g_n)=-\\sum_{F}\\Psi_{Q_0}(v_F)\\MI_{M(v_F)}\\big(g_1|_{Q_1^{v_F}},\\dots,g_n|_{Q_n^{v_F}}\\big)\\\\-\\int_{N_\\mathbb{R}}g_0^\\vee\\ d\\MM_{M}\\big(g_1^\\vee,\\dots,g_n^\\vee\\big),\n\\end{multline*}\nthe first sum being over the finite set of facets of the polytope $Q$. Indeed, in such a situation the application $F\\mapsto v_F$ realizes a bijection between the set of facets of $Q$ and the set of primitive vectors $u\\in N$ for which $Q^u$ is a $(n-1)$-dimensional face of $Q$, which are the only vectors for which the term of the sum in the statement of the theorem does not vanish.\n\\end{rem}\n\n\\begin{rem}\\label{remark for the Legendre-Fenchel dual version of the recursive formula for mixed integrals}\nThe statement of \\hyperref[recursive formula with mixed integral and mixed real Monge-Ampere measure]{Theorem \\ref*{recursive formula with mixed integral and mixed real Monge-Ampere measure}} can be reformulated in terms of Legendre-Fenchel duality. For $i=0,\\dots,n$, let $f_i$ be a concave function on $N_\\mathbb{R}$ with stability set a rational polytope $Q_i$ in $M_\\mathbb{R}$. Under the assumption that $Q_1+\\dots+Q_n$ is of dimension $n$ in $M_\\mathbb{R}$, \\hyperref[recursive formula with sum over the faces]{Remark \\ref*{recursive formula with sum over the faces}} yields\n\\begin{multline}\\label{version of recursive formula in terms of Legendre-Fenchel duals}\n\\MI_M(f^\\vee_0,\\dots,f^\\vee_n)=-\\sum_{F}\\Psi_{Q_0}(v_F)\\MI_{M(v_F)}\\big(f^\\vee_1|_{Q_1^{v_F}},\\dots,f^\\vee_n|_{Q_n^{v_F}}\\big)\\\\-\\int_{N_\\mathbb{R}}f_0\\ d\\MM_{M}\\big(f_1,\\dots,f_n\\big).\n\\end{multline}\nIndeed, it is sufficient to readily apply the previous theorem to the functions $f_0^\\vee,\\dots,f_n^\\vee$, which are continuous on their domain and satisfy the equality $(f_i^\\vee)^\\vee=f_i$ for each $i=0,\\dots,n$ by concavity and closedness. It is easy to verify that the choice $f_0=\\dots=f_n=f$ in \\eqref{version of recursive formula in terms of Legendre-Fenchel duals} yields the formula in \\cite[Corollary 2.7.10]{BPS}.\n\\end{rem}\n\nWe present now two applications of the recursive formula proved above. The first one concerns the computation of the mixed integral when all except one entry are indicator functions in the sense of \\hyperref[indicator and support function]{Example \\ref*{indicator and support function}}.\n\n\\begin{corollary}\\label{mixed integral with indicator functions}\nLet $Q_1,\\dots,Q_n$ be rational polytopes in $M_\\mathbb{R}$ and $f$ a concave function on $N_\\mathbb{R}$ with stability set a rational polytope. Then\n\\[\\MI_M\\big(\\iota_{Q_1},\\dots,\\iota_{Q_n},f^\\vee\\big)=-\\MV_M(Q_1,\\dots,Q_n)\\cdot f(0).\\]\n\\end{corollary}\n\\begin{proof}\nBy symmetry, one can develop the recursive formula in \\hyperref[remark for the Legendre-Fenchel dual version of the recursive formula for mixed integrals]{Remark \\ref*{remark for the Legendre-Fenchel dual version of the recursive formula for mixed integrals}} with respect to $f^\\vee$ to obtain\n\\[\\MI_M\\big(\\iota_{Q_1},\\dots,\\iota_{Q_n},f^\\vee\\big)=-\\int_{N_\\mathbb{R}}f\\ d\\MM_M\\Big(\\iota_{Q_1}^\\vee,\\dots,\\iota_{Q_n}^\\vee\\Big),\\]\nthe indicator functions $\\iota_{Q_1},\\dots,\\iota_{Q_n}$ being zero where defined. The duality in \\hyperref[indicator and support function]{Example \\ref*{indicator and support function}} and the fact that\n\\[\\MM_M\\big(\\Psi_{Q_1},\\dots,\\Psi_{Q_n}\\big)=\\MV_M(Q_1,\\dots,Q_n)\\delta_0\\]\nbecause of \\hyperref[real Monge-Ampere measure of piecewise affine functions]{Proposition \\ref*{real Monge-Ampere measure of piecewise affine functions}} conclude the proof.\n\\end{proof}\n\nThe second application explains how the mixed integral behaves with respect to pointwise sum by a constant in one entry.\n\n\\begin{corollary}\\label{mixed integral with pointwise sum by a constant}\nLet $g_i$ be a concave function defined on a rational polytope $Q_i\\subseteq M_\\mathbb{R}$, for $i=0,\\dots,n$, and $c\\in\\mathbb{R}$. Then\n\\[\\MI_M(g_0,\\dots,g_{n-1},g_n+c)=\\MI_M(g_0,\\dots,g_n)+c\\cdot\\MV_M(Q_0,\\dots,Q_{n-1}).\\]\n\\end{corollary}\n\\begin{proof}\nDenoting by $c\\delta_0$ the concave function which has value $c$ at $0$ and $-\\infty$ otherwise, it follows from the definitions that $g_n+c=g_n\\boxplus c\\delta_0$. The multilinearity of mixed integrals implies then that \\[\\MI_M(g_0,\\dots,g_{n-1},g_n+c)=\\MI_M(g_0,\\dots,g_n)+\\MI_M(g_0,\\dots,g_{n-1},c\\delta_0).\\]\nUsing the fact that $(c\\delta_0)^\\vee=-c$, the recursive formula in \\hyperref[recursive formula with mixed integral and mixed real Monge-Ampere measure]{Theorem \\ref*{recursive formula with mixed integral and mixed real Monge-Ampere measure}}, developed with respect to $c\\delta_0$, yields\n\\[\\MI_M(g_0,\\dots,g_{n-1},c\\delta_0)=\\int_{N_\\mathbb{R}}c\\ d\\MM_M(g_0^\\vee,\\dots,g_{n-1}^\\vee)=c\\cdot\\MM_M(g_0^\\vee,\\dots,g_{n-1}^\\vee)(N_\\mathbb{R}).\\]\nThe statement follows then from the fact that the total volume of the mixed Monge-Amp\\`ere measure of $g_0^\\vee,\\dots,g_{n-1}^\\vee$ is equal to $\\MV_M(\\dom(g_0),\\dots,\\dom(g_{n-1}))$ by \\cite[Proposition 3(iv)]{PR}.\n\\end{proof}\n\nWe conclude the section by proving a formula expressing the mixed integral of a $(n+1)$-tuple of concave functions on $M_\\mathbb{R}$ where one of them is the indicator function of a line segment. \\\\Let $m$ be a primitive vector of $M$ and consider the quotient $P:=M\/\\mathbb{Z}m$. Since $m$ is primitive, $P$ is a lattice of rank $n-1$. By abuse of notation, let $\\pi$ denote both the projection from $M$ to $P$ and the induced linear map from $M_\\mathbb{R}$ to $P_\\mathbb{R}$. For each closed concave function $g$ defined on a compact subset $B$ of $M_\\mathbb{R}$, let\n\\begin{equation}\\label{definition of direct image of concave functions}\n\\pi_*g:\\pi(B)\\to\\mathbb{R},\\quad x\\mapsto\\max_{y\\in \\pi^{-1}(x)}g(y)\n\\end{equation}\nbe the \\emph{direct image} of $g$ by $\\pi$. It is a well defined closed concave function with domain a bounded subset of $P_\\mathbb{R}$, see \\cite[Theorem 5.7 and Theorem 9.2]{R}. Finally, for $x_1,x_2\\in M_\\mathbb{R}$, denote by $\\overline{x_1x_2}$ the line segment in $M_\\mathbb{R}$ with extremal points $x_1$ and $x_2$. The following lemma is a generalization of \\cite[exercise 3 at page 128]{Ewald} and seems to be well-known to experts, though we could not find an adequate reference in the literature for its proof.\n\n\\begin{lemma}\\label{lemma for mixed volumes and projections}\nIn the above hypotheses and notations and for $n\\geq2$, let $Q_1,\\dots,Q_{n-1}$ be polytopes in $M_\\mathbb{R}$. Then,\n\\[\\MV_M\\big(\\overline{0m},Q_1,\\dots,Q_{n-1}\\big)=\\MV_P\\big(\\pi(Q_1),\\dots,\\pi(Q_{n-1})\\big).\\]\n\\end{lemma}\n\\begin{proof}\nThe vector $m$ being primitive, it can be extended to a basis of the lattice $M$, see for instance \\cite[Theorem 5 at page 21]{Lekk}. We suppose fixed throughout the proof such a basis $(m_1,\\dots,m_{n-1},m)$ of $M$ and the induced isomorphism $M_\\mathbb{R}\\simeq\\mathbb{R}^n$; under this identification, the normalized volume $\\vol_M$ corresponds to the Lebesgue measure $ \\vol_n$ on $\\mathbb{R}^n$. Since $(\\pi(m_1),\\dots,\\pi(m_{n-1}))$ is a basis of $P$, such a lattice is isomorphic to the span of $m_1,\\dots,m_{n-1}$ in $M$ and hence it is identified with the linear subspace $\\mathbb{R}^{n-1}\\times\\{0\\}$ of $\\mathbb{R}^n$. Moreover, $\\vol_P$ corresponds to the $(n-1)$-dimensional Lebesgue measure $\\vol_{n-1}$ on $\\mathbb{R}^{n-1}\\times\\{0\\}$ and the map $\\pi$ to the vertical projection.\n\\\\The claim reduces then to the particular case of a family of polytopes $Q_1,\\dots,Q_{n-1}$ in $\\mathbb{R}^n$, $m=(0,\\dots,0,1)$ and $\\pi$ the vertical projection. Denoting by $S$ the vertical segment of unitary length and rearranging the terms in the definition of the mixed volume given for instance in \\cite[Definition 2.7.14]{BPS} one obtains\n\\begin{multline*}\n\\MV_n(S,Q_1,\\dots,Q_{n-1})=\\\\\\sum_{k=1}^{n-1}(-1)^{n-1-k}\\sum_{1\\leq i_1<\\dots0$, which is always possible since $m\\neq0$, Jensen's formula yields, for every $\\theta_2,\\dots,\\theta_n$,\n\\begin{equation}\\label{equality in the proof of the Ronkin function of a binomial}\n\\int_{\\theta_1\\in[0,2\\pi]}\\log\\big|e^{-m_1u_1+im_1\\theta_1}\\cdot\\dots\\cdot e^{-m_nu_n+im_n\\theta_n}-1\\big|\\ d\\theta_1=-2\\pi\\sum_{j=1}^k\\log\\frac{|\\alpha_j|}{e^{-m_1u_1}}\n\\end{equation}\nwith $\\alpha_1,\\dots,\\alpha_k$ being the zeros of the univariate polynomial \\[\\big(e^{-m_2u_2+im_2\\theta_2}\\cdot\\dots\\cdot e^{-m_nu_n+im_n\\theta_n}\\big)T-1\\] lying inside the closed disk of radius $e^{-m_1u_1}$, repeated according to multiplicity. The only complex zero of the above polynomial has modulus $e^{m_2u_2+\\dots+m_nu_n}$; the integral in \\eqref{equality in the proof of the Ronkin function of a binomial} is then zero if $m_1u_1+\\dots+m_nu_n>0$, otherwise it equals $-2\\pi(m_1u_1+\\dots+m_nu_n)$. It follows that\n\\[\\rho_f(u)=\\min(m_1u_1+\\dots+m_nu_n,0),\\]\nhence the claim.\n\\end{ex}\n\n\n\\section{Heights of toric varieties}\\label{section about Arakelov on toric varieties}\n\nA well-suited framework to develop Arakelov geometry is provided by the study of varieties over adelic fields. In this setting, local and global heights of cycles of arbitrary dimension can be defined following \\cite{Z}, \\cite{G0} and \\cite{C-L}. A more general approach involving $M$-fields has been suggested in \\cite{G1}. Even if the theory is often phrased in terms of line bundles, we adopt here the equivalent point of view of divisors, which turns out to be more convenient in the toric case, see for instance \\cite{BPRS}.\n\n\n\\subsection{Adelic fields}\n\nBy a \\textit{place} on a field $K$, we mean an equivalence class of absolute values on $K$, that could be either archimedean or non-archimedean. Whenever $\\mathfrak{M}$ is a collection of places on $K$, the subset of archimedean places in $\\mathfrak{M}$ is denoted by $\\mathfrak{M}_\\infty$.\n\n\\begin{defn}\\label{adelic family}\nLet $K$ be a field. A family of places $\\mathfrak{M}$ on $K$ is said to be \\emph{adelic} if it satisfies the following properties:\n\\begin{enumerate}\n\\item for every $v\\in\\mathfrak{M}\\setminus\\mathfrak{M}_\\infty$, one (and hence all) absolute value in the class of $v$ is associated to a nontrivial discrete valuation\n\\item for each $\\alpha\\in K^*$, the set of places $v$ for which $|\\alpha|_v\\neq1$ for any $|\\cdot|_v\\in v$ is finite.\n\\end{enumerate}\n\\end{defn}\n\nIt is clear that the two conditions of the previous definition do not depend on the choice of the representant of the class $v$.\n\n\\begin{defn}\\label{adelic field}\nAn \\emph{adelic field} is a field $K$ together with an adelic family of places $\\mathfrak{M}$ on $K$ and a choice of an absolute value $|\\cdot|_v$ and of a real positive number $n_v$ for each place $v\\in\\mathfrak{M}$. An adelic field $\\left(K,(|\\cdot|_v,n_v)_{v\\in\\mathfrak{M}}\\right)$ is said to satisfy the \\emph{product formula} if for every $\\alpha\\in K^*$ \\[\\sum_{v\\in\\mathfrak{M}}n_v\\log|\\alpha|_v=0.\\]\n\\end{defn}\n\nWhenever there is no ambiguity on its adelic structure, an adelic field will be simply denoted by $K$.\n\\\\The following property is an easy, though fundamental, consequence of the definition.\n\n\\begin{lemma}\\label{adelic fields have finitely many archimedean places}\nAny adelic field $K$ only admits finitely many archimedean places.\n\\end{lemma}\n\\begin{proof}\nSince an absolute value on $K$ is non-archimedean if and only if it is bounded on the image of $\\mathbb{Z}$ in $K$, a field with positive characteristic has no archimedean absolute values. Suppose hence that $K$ has characteristic zero. In this case it contains a copy of $\\mathbb{Q}$ and any archimedean absolute value $|\\cdot|_v$ on $K$ restricts to an archimedean absolute value on $\\mathbb{Q}$. By Ostrowski's theorem, one has $|2|_v>1$. The second axiom in \\hyperref[adelic field]{Definition \\ref*{adelic family}} allows hence to conclude the claim.\n\\end{proof}\n\nFor an adelic field $K$ and a finite field extension $F$ of $K$, there exists a canonical way of endowing $F$ with the structure of an adelic field, see \\cite[Remark 2.5]{G1} and \\cite[\\S3]{MS} for the detailed construction. With this induced adelic structure, $F$ satisfies the product formula whenever $K$ does.\n\n\\begin{ex}\\label{examples of adelic fields}\nThe archetypical example of an adelic field satisfying the product formula is given by the field $\\mathbb{Q}$, together with the collection of all its nontrivial places, the standard normalized absolute value for each of them and weights equal to $1$.\n\\end{ex}\n\nMore generally, any \\emph{global field}, that is a number field or the function field of a smooth projective curve over a field $k$ with the structure described in \\cite[Example 1.5.4]{BPS}, is an adelic field satisfying the product formula.\n\n\n\\subsection{Local and global heights}\n\nLet $K$ be an adelic field satisfying the product formula and $X$ a proper variety of dimension $n$ over $K$. For every place $v\\in\\mathfrak{M}$, denote by $K_v$ the completion of $K$ with respect to $|\\cdot|_v$ and by $\\mathbb{C}_v$ the completion of an algebraic closure of $K_v$ with respect to the unique extension of the absolute value. It is a well-known fact that $\\mathbb{C}_v$ is algebraically closed; moreover, $\\mathbb{C}_v$ comes with an absolute value that one denotes, with abuse of notation, by $|\\cdot|_v$. The pair $(\\mathbb{C}_v,|\\cdot|_v)$ is hence an algebraically closed complete field as in \\hyperref[section about Ronkin functions]{section \\ref*{section about Ronkin functions}}. \n\\\\The base change $X_{\\mathbb{C}_v}$ is a scheme of finite type over $\\spec \\mathbb{C}_v$ to which one associates its Berkovich analytification $(X_v^{\\an},\\mathscr{O}_{X_v^{\\an}})$, whose underlying topological space is compact because of the properness of $X$. To stress its dependence on the choice of the place $v$, $X_v^{\\an}$ is called the \\emph{$v$-adic analytification} of $X$. Similarly, one can consider the base change $X_{K_v}$ of $X$ over $\\spec K_v$ and consider its Berkovich analytification $X_{K_v}^{\\an}$. The two spaces are related by the isomorphism\\[X_{K_v}^{\\an}\\simeq X_v^{\\an}\/\\Gal(\\overline{K}_v^{\\sep}\/K_v),\\] as shown in \\cite[Proposition 1.3.5]{Ber}. Moreover, there exists a surjective morphism of locally ringed space $\\pi_v:X_v^{\\an}\\to X_{\\mathbb{C}_v}$.\n\n\\begin{rem}\nBy Ostrowski's and Gelfand-Mazur theorems, if $v$ is an archimedean absolute value on $K$, $\\mathbb{C}_v$ is isometric to the field $\\mathbb{C}$ endowed with a power of the usual absolute value. In this case, the Berkovich space $(X_v^{\\an},\\mathscr{O}_{X_v^{\\an}})$ is isomorphic to the usual complex analytification of $X_\\mathbb{C}$.\n\\end{rem}\n\nFor any line bundle $L$ on $X$, its $v$-adic analytification is the analytic line bundle \\[L_v^{\\an}:=\\pi_v^*L_{\\mathbb{C}_v}\\] on $X_v^{\\an}$. Continuous metrics on $L_v^{\\an}$ are defined as in \\cite[\\S1.1.1]{C-L1}, unregardingly on the nature of the place $v$. Relevant classes of metrics on $L_v^{\\an}$ are \\emph{smooth} metrics in the archimedean case and \\emph{algebraic} (or, equivalently, formal, see \\cite[Proposition 8.13]{GK}) metrics when $v$ is non-archimedean, see for example \\cite[\\S1]{C-L1} and \\cite[8.8 and 8.12]{GK} for the precise definitions. A divisor $D$ on $X$ together with a continuous $\\Gal(\\overline{K}_v^{\\sep}\/K_v)$-invariant metric $\\|\\cdot\\|_v$ on the analytic line bundle $\\mathscr{O}(D)_v^{\\an}$ is called a \\emph{$v$-adic metrized divisor} and it is denoted by $\\overline{D}_v$ or also by $(\\mathscr{O}(D),\\|\\cdot\\|_v)$. Sums and pull-backs of $v$-adic metrized divisors can be defined as in \\cite[\\S1.2]{C-L1}.\n\\\\A $v$-adic metrized divisor $\\overline{D}_v$ is said to be \\emph{semipositive} if the corresponding metric can be approximated by semipositive smooth (when $v$ is archimedean) or algebraic (when $v$ is non-archimedean) semipositive metrics in the sense of \\cite[\\S1.4]{BPS}. For any $d$-dimensional subvariety $Y$ of $X$ and for any $d$-tuple of $v$-adic semipositive metrized divisors $\\overline{D}_{0,v},\\dots,\\overline{D}_{d-1,v}$ on $X$, there exists a positive measure \n\\begin{equation}\\label{measure for semipositive metrized line bundles}\nc_1(\\overline{D}_{0,v})\\wedge\\dots\\wedge c_1(\\overline{D}_{d-1,v})\\wedge\\delta_{Y}\n\\end{equation}\non $X_v^{\\an}$, which was first introduced in \\cite[D\\'efinition 2.4 and Proposition 2.7 b)]{C-L} in the non-archimedean setting and extended in \\cite[\\S3.8]{G2} under weaker assumptions. The suggestive notation for the measure in \\hyperref[measure for semipositive metrized line bundles]{(\\ref*{measure for semipositive metrized line bundles})} is compatible with the wedge product of first Chern forms in the smooth archimedean case, while it is justified by the recent advances in the theory of forms and currents on Berkovich spaces otherwise, as shown in \\cite[\\S6.9]{CLD} and \\cite[Theorem 10.5]{GK}.\n\\\\Recall also that for a $d$-dimensional cycle $Z$ in $X$ and a family $(D_0,s_0),\\dots,(D_d,s_d)$ of divisors on $X$ with rational sections of the associated line bundles, one says that $s_0,\\dots,s_d$ \\emph{meet $Z$ properly} if for every $J\\subseteq\\{0,\\dots,d\\}$, each irreducible component of $|Z|\\cap\\bigcap_{i\\in J}|\\divisor(s_i)|$ has dimension $d-\\#J$, $|\\cdot|$ denoting here the support of a cycle.\n\n\\begin{defn}\\label{local height definition}\nLet $Z$ be a $d$-dimensional cycle in $X$ and $\\big(\\overline{D}_{0,v},s_0\\big),\\dots,\\big(\\overline{D}_{d,v},s_d\\big)$ a collection of $v$-adic semipositive metrized divisors on $X$ with rational sections of the corresponding line bundles, with $s_0,\\dots,s_d$ meeting $Z$ properly. The \\emph{$v$-adic local height of $Z$ in $X$ with respect to $\\big(\\overline{D}_{i,v},s_i\\big)$} for $i=0,\\dots,d$ is defined, linearly in its irreducible components, by the recursive formula \n\\begin{multline*}\nh_{\\overline{D}_{0,v},\\dots,\\overline{D}_{d,v}}(Z;s_0,\\dots,s_d):=h_{\\overline{D}_{0,v},\\dots,\\overline{D}_{d-1,v}}(Z\\cdot\\divisor(s_d);s_0,\\dots,s_{d-1})\\\\-\\int_{X_v^{\\an}}\\log\\|s_d\\|_{d,v}\\ c_1(\\overline{D}_{0,v})\\wedge\\dots\\wedge c_1(\\overline{D}_{d-1,v})\\wedge\\delta_{Z},\n\\end{multline*}\nwhere $\\|\\cdot\\|_{d,v}$ denotes the metric of $\\overline{D}_{d,v}$ and one sets the height of the zero cycle to be zero.\n\\end{defn}\n\nThe integrals appearing in the previous definition are well-defined, as shown in \\cite[Th\\'eor\\`eme 4.1]{CLT} in both the archimedean and non-archimedean setting, and \\cite[Theorem 1.4.3]{GH} for the case of non-archimedean valuations which are not necessarily discrete. The $v$-adic local height function is moreover symmetric and multilinear with respect to sums of metrized divisors with rational sections of the associated line bundles, see \\cite[Proposition 3.4 and Remark 9.3]{G}.\n\\vspace{\\baselineskip}\n\\\\The adelic structure on the field $K$ allows to define a \\emph{semipositive metrized divisor} $\\overline{D}$ on $X$ by the choice, for every place $v\\in\\mathfrak{M}$, of a continuous semipositive metric on $\\mathscr{O}(D)_v^{\\an}$. This global definition induces a notion of a $v$-adic local height function at each place of $K$. Some care has to be taken when defining global heights as sums of such $v$-adic local heights, since they do not need to be well-defined in general. \n\n\\begin{defn}\\label{global heights of integrable cycles}\nA $d$-dimensional irreducible subvariety $Y$ of $X$ is said to be \\emph{integrable} with respect to the choice of $d+1$ semipositive metrized divisors $\\overline{D}_0,\\dots,\\overline{D}_d$ if there exists a birational proper map $\\varphi:Y^\\prime\\to Y$, with $Y^\\prime$ projective, and sections $s_i$ of $\\varphi^*\\mathscr{O}(D_i)$ for each $i=0,\\dots,d$, meeting $Y^\\prime$ properly, such that the $v$-adic local height \\[h_{\\varphi^*\\overline{D}_{0,v},\\dots,\\varphi^*\\overline{D}_{d,v}}(Y^\\prime;s_0,\\dots,s_d)\\] is zero for all but finitely many places $v\\in\\mathfrak{M}$. A $d$-dimensional cycle is said to be \\emph{integrable} if each of its irreducible components is. If $Y$ is an integrable $d$-dimensional irreducible subvariety, the \\emph{global height of $Y$ in $X$ with respect to $\\overline{D}_0,\\dots,\\overline{D}_d$} is defined as \\[h_{\\overline{D}_0,\\dots,\\overline{D}_d}(Y):=\\sum_{v\\in\\mathfrak{M}}n_v\\ h_{\\varphi^*\\overline{D}_{0,v},\\dots,\\varphi^*\\overline{D}_{d,v}}(Y^\\prime;s_0,\\dots,s_d).\\]\nThe \\emph{global height} of integrable cycles is defined by linearity.\n\\end{defn} \n\nThe previous definition does not depend on the choice of the projective resolution $Y^\\prime$ of $Y$ nor of the sections $s_0,\\dots,s_d$, as a consequence of \\cite[Proposition 3.6 and Remark 9.3]{G}, \\cite[Theorem 1.4.17 (3)]{BPS} and the product formula on $K$. As its local counterparts, the global height is symmetric and multilinear with respect to sums of metrized divisors. Moreover, it is well-behaved under proper transformations, in the sense of the next proposition.\n\n\\begin{proposition}\\label{heights and proper morphisms}\nLet $\\varphi:X^\\prime\\to X$ be a dominant morphism of proper varieties over $K$, $\\overline{D}_0,\\dots,\\overline{D}_d$ semipositive metrized divisors over $X$ and $Z^\\prime$ a $d$-dimensional cycle in $X^\\prime$. The cycle $\\varphi_*Z^\\prime$ is integrable with respect to $\\overline{D}_0,\\dots,\\overline{D}_d$ if and only if $Z^\\prime$ is integrable with respect to $\\varphi^*\\overline{D}_0,\\dots,\\varphi^*\\overline{D}_d$ and in this case \\[h_{\\overline{D}_0,\\dots,\\overline{D}_d}(\\varphi_*Z^\\prime)=h_{\\varphi^*\\overline{D}_0,\\dots,\\varphi^*\\overline{D}_d}(Z^\\prime).\\]\n\\end{proposition}\n\\begin{proof}\nThe statement about integrability is \\cite[Proposition 1.5.8 (2)]{BPS}, while the equality of the global heights follows from the same property on local heights, as proved in \\cite[Proposition 3.6 and Remark 9.3]{G} in the more general context of pseudo-divisors.\n\\end{proof}\n\n\n\\subsection{Heights on toric varieties}\\label{section height on toric varieties}\n\nIn this section the basic constructions and results of the arithmetic geometry of toric varieties are recalled, following the treatment of \\cite{BPS}. Let hence $X_\\Sigma$ be a proper toric variety of dimension $n$ over an adelic field $K$, with torus $\\mathbb{T}$ and dense open orbit $X_0$. Denote by $N$ and $M$ the character and cocharacter groups of $\\mathbb{T}$ and by $N_\\mathbb{R}$ and $M_\\mathbb{R}$ the associated (reciprocally dual) real vector spaces. The toric variety $X_\\Sigma$ is associated with a complete fan $\\Sigma$ in $N_\\mathbb{R}$, whose collection of $k$-dimensional cones is written $\\Sigma^{(k)}$. For any $\\tau\\in\\Sigma^{(1)}$, denote by $v_\\tau$ its minimal nonzero integral vector and by $V(\\tau)$ its associated orbit closure, consistently with \\cite[\\S3.1]{Ful}.\n\\\\Divisors on $X_\\Sigma$ which are invariant under the torus action are called \\emph{toric divisors} and admit a nice combinatorial description, as follows. By \\cite[\\S I.2, Theorem 9]{KKMS}, there exists a bijection between the set of toric Cartier divisors on $X_\\Sigma$ and the set of \\emph{virtual support functions} on $\\Sigma$, that is piecewise linear real-valued functions on the support of $\\Sigma$, with integral slope on each cone of $\\Sigma$. The toric Cartier divisor constructed from the virtual support function $\\Psi$ is denoted by $D_\\Psi$ and it defines a distinguished rational section $s_\\Psi$ of $\\mathscr{O}(D_\\Psi)$ satisfying $\\divisor(s_\\Psi)=D_\\Psi$. The corresponding Weil divisor is given by\n\\begin{equation}\\label{divisor of toric section}\n[D_\\Psi]=\\sum_{\\tau\\in\\Sigma^{(1)}}-\\Psi(v_\\tau)V(\\tau).\n\\end{equation}\nIn particular, the rational section $s_\\Psi$ is regular and nowhere vanishing on $X_0$. A toric divisor $D_\\Psi$ also determines a polyhedron \\[\\Delta_\\Psi:=\\{x\\in M_\\mathbb{R}:x-\\Psi\\geq0\\}\\] in $M_\\mathbb{R}$, which is bounded because of the properness of $X_\\Sigma$, see \\cite[Proposition at page 67]{Ful}, and coincides with the stability set of $\\Psi$ if $\\Psi$ is concave. Many algebro-geometric properties of a toric divisor are read from its associated virtual support function: for instance, $D_\\Psi$ is generated by global sections if and only if $\\Psi$ is concave, and it is ample if and only if $\\Psi$ is concave and restricts to different linear functions on different maximal cones of $\\Sigma$.\n\\\\Regarding the arithmetic part of the toric dictionary, let $D_\\Psi$ be a toric divisor on $X_\\Sigma$, with associated virtual support function $\\Psi$. The continuous metrics on $\\mathscr{O}(D_\\Psi)$ admitting a combinatorial description are the ones which are invariant under the action of a certain compact torus, see \\cite[\\S4.2]{BPS} for more details about this notion. In concrete terms, a continuous metric $\\|\\cdot\\|_v$ on $\\mathscr{O}(D_\\Psi)_v^{\\an}$ is called a \\emph{$v$-adic toric metric} if the map\n\\[(X_0)_v^{\\an}\\to\\mathbb{R},\\quad p\\mapsto\\|s_\\Psi(p)\\|_v\\]\nis constant along the fibers of the $v$-adic tropicalization map $\\trop_v:(X_0)_v^{\\an}\\to N_\\mathbb{R}$ introduced in \\hyperref[definition of tropicalization]{Definition \\ref*{definition of tropicalization}}. A toric divisor $D$ together with a $v$-adic toric metric on $\\mathscr{O}(D)$ is called a \\emph{$v$-adic toric metrized divisor}. To a $v$-adic toric metrized divisor $\\overline{D}_v$ one can associate the real-valued map $\\psi_{\\overline{D}_v}$ on $N_\\mathbb{R}$ satisfying the equality\n\\begin{equation}\\label{definition of the metric function}\n\\psi_{\\overline{D}_v}\\circ\\trop_v=\\log\\|s_D\\|_v\n\\end{equation}\non the analytic torus $(X_0)_v^{\\an}$, $s_D$ being the distinguished rational section of $\\mathscr{O}(D)$.\n\\\\The map $\\psi_{\\overline{D}_v}$, which will be referred to as the \\emph{metric function} of $\\overline{D}_v$, has been introduced by Burgos Gil, Philippon and Sombra in their study of Arakelov geometry of toric varieties to encode many arithmetic properties of $\\overline{D}_v$, see \\cite[Chapter 4]{BPS}. For instance, it is smooth in the archimedean case if the metric is smooth, while in the non-archimedean setting it is rational piecewise affine if the metric is algebraic, see \\cite[Theorem 4.5.10 (1)]{BPS} and \\cite[Proposition 2.5.5]{GH}. Also, the semipositivity of $\\overline{D}_v$ is translated into the concavity of its corresponding metric function.\n\n\\begin{theorem}\\label{characterization of toric metrics}\nLet $D$ be the toric divisor associated to the virtual support function $\\Psi$. The assignment $\\|\\cdot\\|_v\\mapsto\\psi_{\\overline{D}_v}$ is a bijection between the space of $v$-adic semipositive toric metrics on $\\mathscr{O}(D)_v^{\\an}$ and the space of concave functions $\\psi$ on $N_\\mathbb{R}$ such that $|\\psi-\\Psi|$ is bounded.\n\\end{theorem}\n\\begin{proof}\nThis is \\cite[Theorem 4.8.1 (1)]{BPS}. The extension to the general non-archime\\-dean case is \\cite[Theorem 2.5.8]{GH}.\n\\end{proof}\n\nIf $\\overline{D}_v$ is a $v$-adic semipositive toric metrized divisor, the Legendre-Fenchel dual of the metric function of $\\overline{D}_v$ is called the \\emph{roof function} of $\\overline{D}_v$ and denoted by $\\vartheta_{\\overline{D}_v}$: it is a concave function on $M_\\mathbb{R}$ with effective domain the polytope $\\Delta_\\Psi$.\n\\\\A toric divisor admits a $v$-adic semipositive metric if and only if it is generated by global sections, as proved in \\cite[Corollary 4.8.5]{BPS}. For such divisors, moreover, there exists a distinguished choice of a $v$-adic semipositive metric.\n\n\\begin{defn}\\label{canonical metric definition}\nLet $D$ be a toric divisor generated by global sections, $\\Psi$ its associated virtual support function. The \\emph{$v$-adic canonical metric} on $D$ is the semipositive toric metric on $\\mathscr{O}(D)_v^{\\an}$ corresponding to $\\Psi$ in the bijection of \\hyperref[characterization of toric metrics]{Theorem \\ref*{characterization of toric metrics}}.\n\\end{defn}\n\nIn the non-archimedean case, the canonical metric on $D$ coincides with the algebraic metric induced by the canonical model of $X_\\Sigma$ and $D$, see \\cite[Example 4.5.4]{BPS}.\n\\\\For semipositive $v$-adic toric metrized divisors, the measure in \\eqref{measure for semipositive metrized line bundles} can be expressed in terms of the associated metric functions. Indeed, recall from \\hyperref[section on boundaries]{subsection \\ref*{section on boundaries}} that there exists an embedding \\[\\iota_v:N_\\mathbb{R}\\times\\mathcal{B}_{\\mathbb{C}_v}\\to X_{0,v}^{\\an}\\]which fits into the \\hyperref[diagram]{commutative diagram (\\ref*{diagram})}, and denote by $\\Haar_{\\mathcal{B}_{\\mathbb{C}_v}}$ the Haar measure on $\\mathcal{B}_{\\mathbb{C}_v}$ normalized to have total mass $1$.\n\n\\begin{theorem}\\label{Chambert-Loir measure in the toric case}\nFor $i=0,\\dots,n-1$, let $\\overline{D}_{i,v}$ be a semipositive $v$-adic toric metrized divisor on $X_\\Sigma$, $\\Psi_i$ the virtual support function associated to $D_i$ and $\\psi_{i,v}$ the metric function of $\\overline{D}_{i,v}$. Then, the positive measure \\[c_1(\\overline{D}_{0,v})\\wedge\\dots\\wedge c_1(\\overline{D}_{n-1,v})\\wedge\\delta_{X_\\Sigma}\\]is zero outside $X_{0,v}^{\\an}$ and \\[c_1(\\overline{D}_{0,v})\\wedge\\dots\\wedge c_1(\\overline{D}_{n-1,v})\\wedge\\delta_{X_\\Sigma}|_{X_{0,v}^{\\an}}=(\\iota_v)_*\\big(\\MM_M(\\psi_{0,v},\\dots,\\psi_{n-1,v})\\times\\Haar_{\\mathcal{B}_{\\mathbb{C}_v}}\\big).\\]\nIn particular, \\[(\\trop_v)_*\\big(c_1(\\overline{D}_{0,v})\\wedge\\dots\\wedge c_1(\\overline{D}_{n-1,v})\\wedge\\delta_{X_\\Sigma}|_{X_{0,v}^{\\an}}\\big)=\\MM_M(\\psi_{0,v},\\dots,\\psi_{n-1,v})\\]as measures on $N_\\mathbb{R}$. \n\\end{theorem}\n\\begin{proof}\nThe first statement follows from \\cite[Theorem 1.4.10 (1)]{BPS} and \\cite[Corollary 1.4.5]{GH}. The expression for the measure in the archimedean and the discrete non-archimedean case is obtained from \\cite[Theorem 4.8.11]{BPS} and multilinearity; the general non-archimedean case is deduced from \\cite[Theorem 2.5.10]{GH}. The last assertion is an easy consequence of the commutativity of the \\hyperref[diagram]{diagram (\\ref*{diagram})}.\n\\end{proof}\n\nMoving to the global case, a (\\emph{semipositive}) \\emph{toric metric} on a toric divisor $D$ is a choice, for each place $v\\in\\mathfrak{M}$, of a (semipositive) $v$-adic toric metric on the line bundle $\\mathscr{O}(D)$. The toric divisor $D$ together with a (semipositive) toric metric is called a (\\emph{semipositive}) \\emph{toric metrized divisor} and it is denoted by $\\overline{D}$. From the point of view of convex geometry, the semipositive toric metrized divisor $\\overline{D}$ is completely described by the collection $(\\psi_v)_{v\\in\\mathfrak{M}}$ of its metric functions or, equivalently, by the collection $(\\vartheta_v)_{v\\in\\mathfrak{M}}$ of its roof functions.\n\\\\A notion of well-behaving toric metrics was defined in \\cite[Definition 4.9.1]{BPS}.\n\n\\begin{defn}\\label{definition of adelic toric metric}\nA toric metric $(\\|\\cdot\\|_v)_{v\\in\\mathfrak{M}}$ on a toric divisor is said to be \\emph{adelic} if for all but finitely many $v\\in\\mathfrak{M}$ the $v$-adic toric metric $\\|\\cdot\\|_v$ is the canonical one, in the sense of \\hyperref[canonical metric definition]{Definition \\ref*{canonical metric definition}}.\n\\end{defn}\n\nIn convex terms, a toric metric on the toric divisor $D$ associated to the virtual support function $\\Psi$ is adelic if and only if the family $(\\psi_v)_v$ of its metric functions satisfies $\\psi_v=\\Psi$ for all but finitely many $v\\in\\mathfrak{M}$.\n\\\\It follows from \\cite[Theorem 5.2.4]{BPS} that any toric subvariety of $X_\\Sigma$ is integrable with respect to the choice of adelic semipositive toric metrized divisors. In particular, one can compute the global height of the $n$-dimensional cycle $X_\\Sigma$ with respect to such choices.\n\n\\begin{theorem}\\label{height of a toric variety}\nLet $\\overline{D}_0,\\dots,\\overline{D}_n$ be toric divisors over $X_\\Sigma$, equipped with adelic semipositive toric metrics. Then\\[h_{\\overline{D}_0,\\dots,\\overline{D}_n}(X_\\Sigma)=\\sum_{v\\in\\mathfrak{M}}n_v\\MI_M(\\vartheta_{0,v},\\dots,\\vartheta_{n,v}),\\] where $\\vartheta_{i,v}$ is the roof function of $\\overline{D}_{i,v}$, for every $i=0,\\dots,n$ and $v\\in\\mathfrak{M}$.\n\\end{theorem}\n\\begin{proof}\nThis is \\cite[Theorem 5.2.5]{BPS}.\n\\end{proof}\n\n\n\\section{Divisors of rational functions}\\label{section divisor of rational functions}\n\nThe present section focuses on the combinatorial description of the Weil divisor on a toric variety of the rational function coming from a Laurent polynomial. This result will be used in the proof of the main theorems in the next section.\n\\\\To fix notation, let $X_\\Sigma$ be a proper smooth toric variety of dimension $n$ over a field $K$, $M$ the character lattice of its torus $\\mathbb{T}$ and $N_\\mathbb{R}$ the associated dual vector space. The toric variety $X_\\Sigma$ has a dense open orbit $X_0$ isomorphic to $\\mathbb{T}$ and hence the function field of $X_\\Sigma$ coincides with $K(M)$. In particular, any Laurent polynomial $f=\\sum c_m\\chi^m$ gives rise to a rational function on $X_\\Sigma$, which is regular and coincides with $f$ on $X_0$. To avoid confusion, one will denote by $\\tilde{f}$ such a rational function.\n\\\\Recall from \\cite[Theorem 3.1.19 (a)]{CLS} that the toric variety $X_\\Sigma$ is smooth if and only if each cone of $\\Sigma$ is a smooth cone, that is it is generated by a part of a basis of the lattice $N$. For each cone $\\tau$ in $\\Sigma$ of dimension $1$, denote by $v_\\tau$ its minimal nonzero integral vector, which generates $\\tau\\cap N$ as a monoid. If $\\sigma$ is a smooth cone of dimension $n$ in $N_\\mathbb{R}$, the collection $(v_\\tau)_\\tau$, with $\\tau$ ranging in the set of one dimensional faces of $\\sigma$, is a basis of $N$ and hence gives a dual basis $(v_\\tau^\\vee)_\\tau$ of the lattice $M$.\n\n\\begin{lemma}\\label{lemma prime ideal of the orbit}\nLet $\\sigma$ be a strongly convex polyhedral rational cone in $N_\\mathbb{R}$. For every face $\\tau$ of $\\sigma$ of dimension $1$, the orbit closure $V(\\tau)$ in the affine toric variety $X_\\sigma$ is the subvariety corresponding to the prime ideal \\[\\mathfrak{p}=\\big(\\chi^m:m\\in\\sigma^\\vee\\cap M,m\\notin\\tau^\\perp\\big)\\]of $\\mathscr{O}(X_\\sigma)=K[\\sigma^\\vee\\cap M]$. Moreover, if $\\sigma$ is smooth and of maximal dimension in $N_\\mathbb{R}$, $\\mathfrak{p}$ is principal and generated by $\\chi^{v_\\tau^\\vee}$.\n\\end{lemma}\n\\begin{proof}\nRecall for example from \\cite[\\S 3.1]{Ful} that the orbit closure $V(\\tau)$ is the toric variety $\\spec K[\\sigma^\\vee\\cap\\tau^\\perp\\cap M]$ and can be embedded in $X_\\sigma=\\spec K[\\sigma^\\vee\\cap M]$ via the surjection of rings sending $\\chi^m$ to itself if $m\\in\\tau^\\perp$, and to $0$ otherwise. Then $V(\\tau)$ is seen as the subvariety of $X_\\sigma$ corresponding to the kernel of such homomorphism, that is\n\\[\\mathfrak{p}=\\bigoplus_{\\substack{m\\in\\sigma^\\vee\\cap M\\\\m\\notin\\tau^\\perp}} K\\chi^m=\\big(\\chi^m:m\\in \\sigma^\\vee\\cap M,m\\notin\\tau^\\perp\\big),\\]\nproving the first statement.\n\\\\Suppose now that $\\sigma$ is smooth and of dimension $n$ in $N_\\mathbb{R}$; denote by $v_1,v_2,\\dots,v_n$ the basis of $N$ given by the minimal integral vectors of the rays of $\\sigma$, with the assumption that $v_1=v_\\tau$. By definition, \\[\\sigma=\\mathbb{R}_{\\geq0}v_1+\\dots+\\mathbb{R}_{\\geq0}v_n.\\]As a result, denoting by $(v_i^\\vee)_{i=1,\\dots,n}$ the basis of $M$ dual to $(v_i)_{i=1,\\dots,n}$, one has that \\[\\langle v_i^\\vee,u\\rangle=\\lambda_i\\geq0\\]for every $i\\in\\{1,\\dots,n\\}$ and for every $u=\\sum_i\\lambda_iv_i\\in\\sigma$. In particular, $v_\\tau^\\vee\\in\\sigma^\\vee$. It is easy to check that $v_\\tau^\\vee$ is integrally valued on each element of $N$ and hence it belongs to $M$. It follows then from $\\langle v_\\tau^\\vee,v_\\tau\\rangle=1$ that \\[\\Big(\\chi^{v_\\tau^\\vee}\\Big)\\subseteq\\mathfrak{p}.\\]\nFor the reverse inclusion, consider $m\\in \\sigma^\\vee\\cap M$ with $m\\notin\\tau^\\perp$. By assumption, $\\langle m,v_\\tau\\rangle\\in\\mathbb{Z}$ and $\\langle m,v_\\tau\\rangle\\geq0$; moreover, since $m\\notin\\tau^\\perp$, one has $\\langle m,v_\\tau\\rangle\\geq1$. For each $u=\\sum_i\\lambda_iv_i\\in\\sigma$ one has \\[\\langle m-v_\\tau^\\vee,u\\rangle=\\lambda_1\\langle m-v_\\tau^\\vee,v_\\tau\\rangle+\\sum_{i\\geq2}\\lambda_i\\langle m,v_i\\rangle\\geq\\lambda_1\\left(\\langle m,v_\\tau\\rangle-1\\right)\\geq0.\\]As a result, $m-v_\\tau^\\vee\\in\\sigma^\\vee\\cap M$ and hence $\\chi^m=\\chi^{v_\\tau^\\vee}\\cdot\\chi^{m-v_\\tau^\\vee}\\in\\big(\\chi^{v_\\tau^\\vee}\\big)$, completing the proof.\n\\end{proof}\n\n\\begin{rem}\nThe last statement of the previous lemma is not true for a general strongly convex polyhedral rational cone $\\sigma$ of maximal dimension in $N$. For example, if $\\sigma$ has more than $n$ faces of dimension $1$, the divisor class group of $X_\\sigma$, which is generated by the classes of the orbit closures associated to the rays, turns out to be nontrivial, as a consequence of \\cite[Proposition at page 63]{Ful}. \n\\end{rem}\n\nFor a nonzero Laurent polynomial $f\\in K[M]$, the subset $V(f)$ of zeros of $f$ in $X_0$ is a closed subscheme of the dense open orbit. Its closure in $X_\\Sigma$ is a closed subscheme of $X_\\Sigma$, denoted by $\\overline{V(f)}$. Taking into account multiplicities, one can consider the associated Weil divisor $\\Big[\\overline{V(f)}\\Big]$. It is the zero cycle when $f$ is a monomial.\n\n\\begin{theorem}\\label{divisor of Laurent polynomial}\nLet $f$ be a nonzero Laurent polynomial, $\\tilde{f}$ the rational function on $X_\\Sigma$ arising from $f$. Then, \\[\\divisor(\\tilde{f})=\\Big[\\overline{V(f)}\\Big]+\\sum_{\\tau\\in\\Sigma^{(1)}}\\Psi_{\\NP(f)}(v_\\tau)V(\\tau),\\]\nwhere $\\Psi_{\\NP(f)}$ denotes the support function of the Newton polytope of $f$. In particular, $\\Big[\\overline{V(f)}\\Big]$ is rationally equivalent to the cycle $-\\sum_{\\tau\\in\\Sigma^{(1)}}\\Psi_{\\NP(f)}(v_\\tau)V(\\tau)$ on $X_\\Sigma$.\n\\end{theorem}\n\\begin{proof}\nBy \\cite[formula at page 55]{Ful}, the irreducible components of $X_\\Sigma\\setminus X_0$ are exactly the orbit closures $V(\\tau)$, with $\\tau$ ranging in the set of $1$ dimensional cones of $\\Sigma$. Since moreover the restriction of $\\tilde{f}$ to $X_0$ is the regular function $f$, it follows from the classical theory of divisors that \\[\\divisor(\\tilde{f})=\\Big[\\overline{V(f)}\\Big]+\\sum_\\tau \\nu_\\tau(\\tilde{f})V(\\tau),\\] where $\\nu_\\tau(\\tilde{f})\\in\\mathbb{Z}$ is the order of vanishing of $\\tilde{f}$ along $V(\\tau)$. The statement of the theorem then follows from the fact that, for every $\\tau\\in\\Sigma^{(1)}$, such an order equals $\\Psi_{\\NP(f)}(v_\\tau)$.\n\\\\This claim can be proved locally; fix a ray $\\tau\\in\\Sigma^{(1)}$ and let $\\sigma$ be any maximal dimensional cone of $\\Sigma$ containing $\\tau$. The fan being complete and consisting of smooth cones, such a $\\sigma$ exists and the minimal integral vectors $v_1,\\dots,v_n$ of its rays are a basis of $N$. Assume moreover that $v_1=v_\\tau$ and, for simplicity, denote by $R:=K[\\sigma^\\vee\\cap M]$ the ring of regular functions over $X_\\sigma$. The order of vanishing of $\\tilde{f}$ along $V(\\tau)$ is computed as the valuation of $\\tilde{f}$ determined by the valuation ring $R_{\\mathfrak{p}}$, the localization of $R$ at the prime ideal $\\mathfrak{p}$ corresponding to the subvariety $V(\\tau)$ in $X_\\sigma$. By \\hyperref[lemma prime ideal of the orbit]{Lemma \\ref*{lemma prime ideal of the orbit}}, the cone $\\sigma$ being smooth and maximal dimensional, one has that $\\mathfrak{p}=\\big(\\chi^{v_\\tau^\\vee}\\big)$. The maximal ideal $\\mathfrak{p}R_\\mathfrak{p}$ of $R_\\mathfrak{p}$ is hence the principal ideal generated by $\\chi^{v_\\tau^\\vee}$.\n\\\\Suppose first that $\\tilde{f}=\\sum_m c_m\\chi^m$ lies in $R$, that is every $m$ appearing in $\\tilde{f}$ belongs to $\\sigma^\\vee\\cap M$. By definition of the valuation in $R_\\mathfrak{p}$,\n\\begin{equation*}\n\\begin{split}\n\\nu_\\tau(\\tilde{f})&=\\max\\ \\left\\{l\\in\\mathbb{N}:\\tilde{f}\\in(\\mathfrak{p}R_\\mathfrak{p})^l\\right\\}=\\max\\ \\left\\{l\\in\\mathbb{N}:\\tilde{f}\\in\\big(\\chi^{lv_\\tau^\\vee}\\big)\\right\\}\\\\&=\\max\\ \\left\\{l\\in\\mathbb{N}:\\chi^{m-lv_\\tau^\\vee}\\in R_\\mathfrak{p}\\text{ for all }m\\text{ with } c_m\\neq0\\right\\}.\n\\end{split}\n\\end{equation*}\nThe condition $\\chi^{m-lv_\\tau^\\vee}\\in R_\\mathfrak{p}$ is equivalent to the fact that $\\langle m,v_\\tau\\rangle\\geq l$. Indeed, if the first is true, then \\[\\langle m,v_\\tau\\rangle-l=\\langle m-lv_\\tau^\\vee,v_\\tau\\rangle\\geq0.\\] Conversely, for each $u=\\sum_i\\lambda_iv_i\\in\\sigma$ one has\\[\\langle m-lv_\\tau^\\vee,u\\rangle=\\lambda_1(\\langle m,v_\\tau\\rangle-l)+\\sum_{i\\geq2}\\lambda_i\\langle m,v_i\\rangle\\geq0,\\]and so $m-lv_\\tau^\\vee\\in\\sigma^\\vee\\cap M$. As a consequence,\n\\begin{equation*}\n\\begin{split}\n\\nu_\\tau(\\tilde{f})&=\\max\\ \\left\\{l\\in\\mathbb{N}:\\langle m,v_\\tau\\rangle\\geq l\\text{ for all }m\\text{ with } c_m\\neq0\\right\\}\\\\&=\\min\\ \\{\\langle m,v_\\tau\\rangle: m\\text{ with }c_m\\neq0\\}=\\Psi_{\\NP(f)}(v_\\tau).\n\\end{split}\n\\end{equation*}\nFor a general $\\tilde{f}=\\sum_m c_m\\chi^m$, the fact that $\\sigma^\\vee$ has dimension $n$ in $M_\\mathbb{R}$ ($\\sigma$ is indeed strongly convex) assures that there exists a big enough vector $m_0\\in\\sigma^\\vee\\cap M$ for which $m+m_0\\in\\sigma^\\vee\\cap M$ for each $m$ such that $c_m\\neq0$. Hence\\[\\tilde{f}=\\frac{\\sum_mc_m\\chi^{m+m_0}}{\\chi^{m_0}},\\]with both the numerator and the denominator belonging to $R$. Applying the result for such elements one deduces\\[\\nu_\\tau(\\tilde{f})=\\nu_\\tau\\left(\\sum_mc_m\\chi^{m+m_0}\\right)-\\nu_\\tau(\\chi^{m_0})=\\Psi_{\\NP(f)+m_0}(v_\\tau)-\\langle m_0,v_\\tau\\rangle=\\Psi_{\\NP(f)}(v_\\tau),\\]concluding the proof.\n\\end{proof}\n\n\n\\section{Local and global heights of hypersurfaces}\\label{section hypersurface}\n\nFix for the whole section an adelic field $\\left(K,(|\\cdot|_v,n_v)_{v\\in\\mathfrak{M}}\\right)$ satisfying the product formula. Let $X_\\Sigma$ be a proper toric variety over $K$, of dimension $n$, with torus $\\mathbb{T}=\\spec K[M]$ and dense open orbit $X_0$. For an effective cycle $Z$ on $X_\\Sigma$ of pure codimension $1$, whose prime components intersect $X_0$, we present a series of results concerning its integrability and its local and global height with respect to suitable choices of metrized divisors on $X_\\Sigma$.\n\n\n\\subsection{Degrees}\n\nWith the notations and assumptions given above, the effective cycle $Z$ can be written as \\[Z=\\sum_{i=1}^r\\ell_i Y_i\\] for prime divisors $Y_1,\\dots,Y_r$ intersecting $X_0$. For every $i=1,\\dots,r$, the closed irreducible subvariety of $X_0$ obtained as the intersection between $Y_i$ and $X_0$ is associated to a prime ideal of height one in $K[M]$, which is principal since $K[M]$ is a unique factorization domain; denote by $f_i$ an irreducible Laurent polynomial generating such an ideal. The Laurent polynomial $f=f_1^{\\ell_1}\\cdot\\dots\\cdot f_r^{\\ell_r}$ is called a \\emph{defining polynomial} for the cycle $Z$ and is uniquely defined up to multiplication by an invertible element of $K[M]$, that means by a monomial. Moreover,\n\\begin{equation}\\label{cycle of defining polynomial}\n\\Big[\\overline{V(f)}\\Big]=Z,\n\\end{equation}\nthat is the cycle associated to the closure of the subscheme $V(f)$ in $X_\\Sigma$ agrees with $Z$. Let \\[\\Psi_f:=\\Psi_{\\NP(f)}\\] be the support function, in the sense of \\hyperref[indicator and support function]{Example \\ref*{indicator and support function}}, of the Newton polytope $\\NP(f)$ of $f$; it is a piecewise linear function on $N_\\mathbb{R}$. It is not necessarily a virtual support function on the fan $\\Sigma$, but it can always be made such after a suitable refinement of the fan.\n\n\\begin{lemma}\\label{a suitable resolution exists}\nFor any proper toric variety $X_\\Sigma$ there exist a smooth projective toric variety $X_{\\Sigma^\\prime}$ with fan $\\Sigma^\\prime$ in $N_\\mathbb{R}$ and a proper toric morphism $\\pi:X_{\\Sigma^\\prime}\\to X_\\Sigma$ satisfying:\n\\begin{enumerate}\n\\item $\\pi$ restricts to the identity on the dense open orbit of $X_{\\Sigma^\\prime}$ and $X_\\Sigma$\n\\item $\\Psi_f$ is a virtual support function on $\\Sigma^\\prime$.\n\\end{enumerate}\n\\end{lemma}\n\\begin{proof}\nOne can always refine the complete fan $\\Sigma$ to a fan $\\Sigma^\\prime$ in such a way that $\\Psi_f$ is a virtual support function on $\\Sigma^\\prime$. After possibly refining again, one can suppose that $\\Sigma^\\prime$ is the fan of a projective toric variety (because of the toric Chow lemma, see \\cite[Theorem 6.1.18]{CLS}) and that each of its cones is generated by a part of a basis of $N$ (see \\cite[\\S2.6]{Ful}). The associated toric variety $X_{\\Sigma^\\prime}$ is smooth, projective and it satisfies (2). Finally, since $\\Sigma^\\prime$ is a refinement of $\\Sigma$, the toric morphism $\\pi$ given by \\cite[Theorem 3.3.4]{CLS} is proper and restricts to the identity on the dense open orbit of $X_{\\Sigma^\\prime}$.\n\\end{proof}\n\nThe previous lemma, together with the fact that intersection theoretical properties are stable under birational transformations, allows to compute the degree of a cycle of codimension $1$ in a toric variety with respect to a family of toric divisors generated by global sections.\n\n\\begin{proposition}\\label{degree of an hypersurface}\nLet $D_{\\Psi_1},\\dots,D_{\\Psi_{n-1}}$ be toric divisors on $X_\\Sigma$ generated by global sections, $Z$ an effective cycle on $X_\\Sigma$ of pure codimension $1$ and prime components intersecting $X_0$, with defining polynomial $f$. Then \\[\\deg_{D_{\\Psi_1},\\dots,D_{\\Psi_{n-1}}}(Z)=\\MV_M(\\Delta_{\\Psi_1},\\dots,\\Delta_{\\Psi_{n-1}},\\NP(f)),\\]where $\\MV_M$ denotes the mixed volume function associated to the measure $\\vol_M$ (see \\hyperref[lattice volume]{Remark \\ref*{lattice volume}}) and $\\Delta_{\\Psi_i}$ the polytope associated to the toric divisor $D_{\\Psi_i}$, for each $i=1,\\dots,n-1$.\n\\end{proposition}\n\\begin{proof}\nConsider the smooth projective toric variety $X_{\\Sigma^\\prime}$ and the proper toric morphism $\\pi:X_{\\Sigma^\\prime}\\to X_\\Sigma$ given by \\hyperref[a suitable resolution exists]{Lemma \\ref*{a suitable resolution exists}}. Since the support function $\\Psi_f$ is a virtual support function on $\\Sigma^\\prime$, one can consider the corresponding toric divisor $D_f$ on $X_{\\Sigma^\\prime}$ and the associated distinguished rational section $s_f$ of $\\mathscr{O}(D_f)$. The product $\\tilde{f}s_f$ is a rational section of $\\mathscr{O}(D_f)$, with associated Weil divisor satisfying\n\\begin{equation*}\n\\pi_*\\divisor\\big(\\tilde{f} s_f\\big)=\\pi_*(\\divisor(\\tilde{f})+\\divisor(s_f))=Z\n\\end{equation*}\nby \\hyperref[divisor of Laurent polynomial]{Theorem \\ref*{divisor of Laurent polynomial}}, \\eqref{divisor of toric section}, \\eqref{cycle of defining polynomial} and the definition of $\\pi$. The projection formula in \\cite[Proposition 2.3 (c)]{Ful2} and B\\'ezout's theorem yield\n\\[\\deg_{D_{\\Psi_1},\\dots,D_{\\Psi_{n-1}}}(Z)=\\deg_{\\pi^*D_{\\Psi_1},\\dots,\\pi^*D_{\\Psi_n-1},D_f}(X_{\\Sigma^\\prime}).\\]\nThe function $\\Psi_f$ being concave, $D_f$ is generated by global sections. Moreover, the virtual support functions associated to the toric divisor $\\pi^*D_{\\Psi_i}$ on $X_{\\Sigma^\\prime}$ agrees with $\\Psi_i$, for every $i=1,\\dots,n-1$. The combinatorial description in \\cite[Proposition 2.10]{O} of the degree of a toric variety with respect to toric divisor generated by global sections concludes then the proof.\n\\end{proof}\n\n\\begin{rem}\\label{remark for degree of toric divisor}\nBy \\cite[formula at page 55]{Ful}, the irreducible components of $X_\\Sigma\\setminus X_0$ are the orbit closures $V(\\tau)$, with $\\tau$ ranging in the set of $1$ dimensional cones of $\\Sigma$. It follows that if $Z$ is a prime divisor of $X_\\Sigma$ not intersecting $X_0$, it coincides with $V(\\tau)$ for some $\\tau\\in\\Sigma^{(1)}$. In such a case, the degree of $Z$ with respect to a collection $D_{\\Psi_1},\\dots,D_{\\Psi_{n-1}}$ of toric divisors on $X_\\Sigma$ generated by global sections is given by\n\\[\\deg_{D_{\\Psi_1},\\dots,D_{\\Psi_{n-1}}}(V(\\tau))=\\MV_{M(v_\\tau)}\\big(\\Delta_{\\Psi_1}^{v_\\tau},\\dots,\\Delta_{\\Psi_{n-1}}^{v_\\tau}\\big),\\]\nwhere $v_\\tau$ is the minimal nonzero integral vector of $\\tau$, see \\cite[formul{\\ae} (3.4.1) and (3.4.4)]{BPS}.\n\\end{rem}\n\n\\begin{rem}\\label{can reduce to the case of a toric variety compatible with the polynomial}\nThe reduction to the case of a smooth projective toric variety employed in the proof of \\hyperref[degree of an hypersurface]{Proposition \\ref*{degree of an hypersurface}} equally works when computing the local height of the cycle $Z$ with respect to a family of $v$-adic semipositive toric metrized divisors $\\overline{D}_{0,v},\\dots,\\overline{D}_{n-1,v}$. Indeed, let $f$ be a defining polynomial for $Z$, $X_{\\Sigma^\\prime}$ and $\\pi$ as in the statement of \\hyperref[a suitable resolution exists]{Lemma \\ref*{a suitable resolution exists}}. For every family of rational sections $s_0,\\dots,s_{n-1}$ of $\\mathscr{O}(D_0),\\dots,\\mathscr{O}(D_{n-1})$ respectively for which the following local heights are well-defined, the local arithmetic projection formula in \\cite[Theorem 1.4.17 (2)]{BPS} asserts that\\[h_{\\overline{D}_{0,v},\\dots,\\overline{D}_{n-1,v}}(Z;s_0,\\dots,s_{n-1})=h_{\\pi^*\\overline{D}_{0,v},\\dots,\\pi^*\\overline{D}_{n-1,v}}(Z^\\prime;\\pi^*s_0,\\dots,\\pi^*s_{n-1}),\\] where $Z^\\prime$ is the cycle in $X_{\\Sigma^\\prime}$ associated to the subscheme obtained as the closure of $V(f)$ and has hence $f$ as a defining polynomial. Because of \\cite[Proposition 4.3.19]{BPS}, the pull-back of $\\overline{D}_{i,v}$ via $\\pi$ is a $v$-adic semipositive toric metrized divisor on $X_{\\Sigma^\\prime}$ whose metric function coincides with the one of $\\overline{D}_{i,v}$, for every $i=0,\\dots,n-1$ and $v\\in\\mathfrak{M}$. It follows that any combinatorial formula for the local height of $Z^\\prime$ in $X_{\\Sigma^\\prime}$ with respect to $\\pi^*\\overline{D}_{0,v},\\dots,\\pi^*\\overline{D}_{n-1,v}$ only involving the defining polynomial of $Z$ and the metric functions of the metrized divisors equally holds for the local height of $Z$ in $X_\\Sigma$ with respect to $\\overline{D}_{0,v},\\dots,\\overline{D}_{n-1,v}$. Similarly, the reduction step can be adopted when dealing with the integrability and the global height of $Z$, because of \\hyperref[heights and proper morphisms]{Proposition \\ref*{heights and proper morphisms}}.\n\\end{rem}\n\n\n\\subsection{Local heights}\\label{section about local heights}\n\nLet $f$ be a defining polynomial for the cycle $Z$ and, as in the previous subsection, denote by $\\Psi_f$ the support function of its Newton polytope. Under the assumption that $\\Psi_f$ is a virtual support function on the fan of $X_\\Sigma$, it determines a toric divisor $D_f$ on $X_\\Sigma$ together with a distinguished rational section $s_f$ of $\\mathscr{O}(D_f)$, as in \\hyperref[section height on toric varieties]{subsection \\ref*{section height on toric varieties}}.\n\n\\begin{defn}\\label{v-adic Ronkin metric}\nIn the notation above, and for a place $v\\in\\mathfrak{M}$, the \\emph{$v$-adic Ronkin metric} on $D_f$ is the $v$-adic semipositive toric metric on $\\mathscr{O}(D_f)_v^{\\an}$ corresponding to the $v$-adic Ronkin function $\\rho_{f,v}$ via \\hyperref[characterization of toric metrics]{Theorem \\ref*{characterization of toric metrics}}.\n\\end{defn}\n\nThe previous definition makes sense since, for every $v\\in\\mathfrak{M}$, the $v$-adic Ronkin function of $f$ is concave on $N_\\mathbb{R}$ and has bounded difference from $\\Psi_f$ because of \\hyperref[properties of Ronkin functions]{Proposition \\ref*{properties of Ronkin functions}}. If not otherwise specified, $\\overline{D}_{f,v}$ will denote the divisor $D_f$ equipped with the $v$-adic Ronkin metric $\\|\\cdot\\|_{f,v}$ defined above. By definition,\n\\begin{equation}\\label{Ronkin metric definition}\n\\log\\|s_f\\|_{f,v}=\\rho_{f,v}\\circ\\trop_v\n\\end{equation}\non $X_{0,v}^{\\an}$. To lighten the notation, we will drop the subscript $v$ whenever the choice of the place is clear from the context.\n\n\\begin{proposition}\nLet $f$ and $g$ be two nonzero Laurent polynomials and assume that $\\Psi_f$ and $\\Psi_g$ are virtual support functions on the fan of $X_\\Sigma$. Then, $\\overline{D}_f+\\overline{D}_g=\\overline{D}_{f\\cdot g}$.\n\\end{proposition}\n\\begin{proof}\nThe equality $\\NP(f\\cdot g)=\\NP(f)+\\NP(g)$ implies that $\\Psi_{f\\cdot g}=\\Psi_f+\\Psi_g$. In particular, $\\Psi_{f\\cdot g}$ is a virtual support function on the fan $\\Sigma$ and then defines a toric divisor $D_{f\\cdot g}$ on $X_\\Sigma$ which satisfies $D_{f\\cdot g}=D_f+D_g$ because of \\cite[\\S3.4]{Ful}. The statement follows now from \\cite[Proposition 4.3.14 (1)]{BPS} and \\hyperref[Ronkin function of a product]{Proposition \\ref*{Ronkin function of a product}}.\n\\end{proof}\n\nThe key property of the Ronkin metric is given in the following proposition.\n\n\\begin{proposition}\\label{local height of hypersurfaces}\nLet $X_\\Sigma$ be a smooth projective toric variety, $Z$ an effective cycle on $X_\\Sigma$ of pure codimension $1$ and prime components intersecting $X_0$. Let $f$ be a defining polynomial for $Z$ and $\\tilde{f}$ the associated rational function on $X_\\Sigma$. Assume moreover that $\\Psi_f$ is a virtual support function on the fan $\\Sigma$. For a fixed place $v$ of $K$, let $\\overline{D}_0,\\dots,\\overline{D}_{n-1}$ be toric divisors on $X_\\Sigma$, equipped with $v$-adic semipositive toric metrics. Then\n\\begin{equation}\\label{local height of hypersurfaces equation}\nh_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z;s_0,\\dots,s_{n-1})=h_{\\overline{D}_0,\\dots,\\overline{D}_{n-1},\\overline{D}_f}\\big(X_\\Sigma;s_0,\\dots,s_{n-1},\\tilde{f}s_f\\big),\n\\end{equation}\nfor every choice of rational sections $s_0,\\dots,s_{n-1}$ of $\\mathscr{O}(D_0),\\dots,\\mathscr{O}(D_{n-1})$ respectively with $\\divisor(s_0),\\dots,\\divisor(s_{n-1}),Z$ intersecting properly. \n\\end{proposition}\n\\begin{proof}\nThe product $\\tilde{f}s_f$ is a rational section of $\\mathscr{O}(D_f)$ on $X_\\Sigma$ with associated Weil divisor\n\\begin{equation*}\n\\divisor\\big(\\tilde{f} s_f\\big)=\\divisor(\\tilde{f})+\\divisor(s_f)=Z\n\\end{equation*}\nby \\hyperref[divisor of Laurent polynomial]{Theorem \\ref*{divisor of Laurent polynomial}}, \\eqref{divisor of toric section} and \\eqref{cycle of defining polynomial}. Hence, the sections $s_0,\\dots,s_{n-1},\\tilde{f}s_f$ meet $X_\\Sigma$ properly and the right hand side term in \\eqref{local height of hypersurfaces equation} is well defined.\n\\\\\\hyperref[local height definition]{Definition \\ref*{local height definition}} stating that\n\\begin{multline*}\nh_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z;s_0,\\dots,s_{n-1})=h_{\\overline{D}_0,\\dots,\\overline{D}_{n-1},\\overline{D}_f}\\big(X_\\Sigma;s_0,\\dots,s_{n-1},\\tilde{f}s_f\\big)\\\\+\\int_{X_\\Sigma^{\\an}}\\log\\|\\tilde{f}s_f\\|_f\\ c_1(\\overline{D}_0)\\wedge\\dots\\wedge c_1(\\overline{D}_{n-1}),\n\\end{multline*}\nthe proposition follows from the vanishing of the integral on the right hand side. Indeed, thanks to \\hyperref[Chambert-Loir measure in the toric case]{Theorem \\ref*{Chambert-Loir measure in the toric case}}, such an integral is supported on the analytification of the dense open orbit of $X_\\Sigma$, where the rational function $\\tilde{f}$ coincides with the regular function $f$. Together with the definition of the Ronkin metric in \\eqref{Ronkin metric definition}, this yields\n\\begin{multline*}\n\\int_{X_\\Sigma^{\\an}}\\log\\|\\tilde{f}s_f\\|_f\\ c_1(\\overline{D}_0)\\wedge\\dots\\wedge c_1(\\overline{D}_{n-1})=\\int_{X_0^{\\an}}\\log|f|\\ c_1(\\overline{D}_0)\\wedge\\dots\\wedge c_1(\\overline{D}_{n-1})\\\\+\\int_{X_0^{\\an}}(\\rho_{f,v}\\circ\\trop_v)\\ c_1(\\overline{D}_0)\\wedge\\dots\\wedge c_1(\\overline{D}_{n-1}).\n\\end{multline*}\nFor every $i=0,\\dots,n-1$, denote by $\\psi_i$ the metric function of $\\overline{D}_i$. The tropicalization map being continuous, the change of variables formula and \\hyperref[Chambert-Loir measure in the toric case]{Theorem \\ref*{Chambert-Loir measure in the toric case}} imply on the one hand that\n\\begin{equation*}\n\\int_{X_0^{\\an}}(\\rho_{f,v}\\circ\\trop_v)\\ c_1(\\overline{D}_0)\\wedge\\dots\\wedge c_1(\\overline{D}_{n-1})=\\int_{N_\\mathbb{R}}\\rho_{f,v}\\ d\\MM_M(\\psi_0,\\dots,\\psi_{n-1}).\n\\end{equation*}\nOn the other hand, \\hyperref[Chambert-Loir measure in the toric case]{Theorem \\ref*{Chambert-Loir measure in the toric case}}, together with the change of variables formula and Fubini's theorem, gives\n\\begin{multline*}\n\\int_{X_0^{\\an}}\\log|f|\\ c_1(\\overline{D}_0)\\wedge\\dots\\wedge c_1(\\overline{D}_{n-1})=\\\\\\int_{N_\\mathbb{R}}\\bigg(\\int_{\\mathcal{B}_{\\mathbb{C}_v}}(\\log|f|\\circ\\iota_v)\\ d\\Haar_{\\mathcal{B}_{\\mathbb{C}_v}}\\bigg)\\ d\\MM_M(\\psi_0,\\dots,\\psi_{n-1}).\n\\end{multline*}\nThe definition of the maps $\\iota_v$ and $\\rho_{f,v}$ assures that the inner integral coincides with the opposite of the $v$-adic Ronkin function of $f$, concluding the proof.\n\\end{proof}\n\n\n\\subsection{Toric local heights}\n\nRecall from \\hyperref[canonical metric definition]{Definition \\ref*{canonical metric definition}} that any toric divisor generated by global sections admits a distinguished $v$-adic semipositive toric metric, the canonical metric. This allows to define a local height with respect to toric divisors that is independent of the choice of the sections. Such a notion was introduced in \\cite[\\S5.1]{BPS} as a key step in the proof of the formula for the global height of a toric variety. \n\n\\begin{defn}\nFor a place $v$ of $K$, let $\\overline{D}_0,\\dots,\\overline{D}_d$ be toric divisors on $X_\\Sigma$, endowed with $v$-adic semipositive toric metrics. Denote by $\\overline{D}_0^{\\can},\\dots,\\overline{D}_d^{\\can}$ the same divisors equipped with their $v$-adic canonical metric. Let $Y$ be an irreducible $d$-dimensional subvariety of $X_\\Sigma$ and $\\varphi:Y^\\prime\\to Y$ a birational morphism, with $Y^\\prime$ projective. The \\emph{$v$-adic toric local height} of $Y$ with respect to $\\overline{D}_0,\\dots,\\overline{D}_{d}$ is defined as\n\\[h^{\\tor}_{\\overline{D}_0,\\dots,\\overline{D}_d}(Y):=h_{\\varphi^*\\overline{D}_0,\\dots,\\varphi^*\\overline{D}_d}(Y^\\prime;s_0,\\dots,s_d)-h_{\\varphi^*\\overline{D}_0^{\\can},\\dots,\\varphi^*\\overline{D}_d^{\\can}}(Y^\\prime;s_0,\\dots,s_d),\\]\nwhere $s_i$ is a rational section of $\\varphi^*\\mathscr{O}(D_i)$, for every $i=0,\\dots,d$ and $s_0,\\dots,s_d$ meet $Y^\\prime$ properly. The definition extends by linearity to any cycle of dimension $d$.\n\\end{defn}\n\nThe toric local height of a cycle does neither depend on the choice of the sections $s_0,\\dots,s_d$, nor on the birational model $Y^\\prime$ of $Y$ because of \\cite[Theorem 1.4.17 (2) and (3)]{BPS}. Moreover, the definition is nonempty: Chow's lemma provides $Y$ with a projective birational model, while the moving lemma assures the existence of rational sections meeting $Y^\\prime$ properly.\n\\\\We prove here a formula for the toric local height of an effective cycle $Z$ on $X_\\Sigma$ of pure codimension $1$ and prime components intersecting $X_0$.\n\n\\begin{theorem}\\label{toric local height of hypersurfaces}\nLet $X_\\Sigma$ be a proper toric variety, $Z$ an effective cycle on $X_\\Sigma$ of pure codimension $1$ and prime components intersecting $X_0$. For a place $v$ of $K$, let $\\overline{D}_0,\\dots,\\overline{D}_{n-1}$ be toric divisors on $X_\\Sigma$, equipped with $v$-adic semipositive toric metrics. Then\n\\[h^{\\tor}_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z)=\\MI_M\\big(\\vartheta_0,\\dots,\\vartheta_{n-1},\\rho_{f,v}^\\vee\\big)+\\deg_{D_0,\\dots,D_{n-1}}(X_\\Sigma)\\cdot\\rho_{f,v}(0),\\]\nwhere $f$ is a defining polynomial for $Z$ and $\\vartheta_i$ is the roof function of $\\overline{D}_i$, for $i=0,\\dots,n-1$.\n\\end{theorem}\n\\begin{proof}\nBecause of \\hyperref[can reduce to the case of a toric variety compatible with the polynomial]{Remark \\ref*{can reduce to the case of a toric variety compatible with the polynomial}}, one can assume that $X_\\Sigma$ is a smooth projective toric variety on whose fan $\\Psi_f$ is a virtual support function. Thanks to the moving lemma, one can choose rational sections $s_0,\\dots,s_{n-1}$ of $\\mathscr{O}(D_0),\\dots,\\mathscr{O}(D_{n-1})$ respectively such that $\\divisor(s_0),\\dots,\\divisor(s_{n-1}),Z$ intersect properly. \\hyperref[local height of hypersurfaces]{Proposition \\ref*{local height of hypersurfaces}} implies then that\n\\begin{multline*}\nh^{\\tor}_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z)=h_{\\overline{D}_0,\\dots,\\overline{D}_{n-1},\\overline{D}_f}\\big(X_\\Sigma;s_0,\\dots,s_{n-1},\\tilde{f}s_f\\big)\\\\-h_{\\overline{D}_0^{\\can},\\dots,\\overline{D}_{n-1}^{\\can},\\overline{D}_f}\\big(X_\\Sigma;s_0,\\dots,s_{n-1},\\tilde{f}s_f\\big).\n\\end{multline*}\nBy adding and subtracting the quantity\n\\[h_{\\overline{D}_0^{\\can},\\dots,\\overline{D}_{n-1}^{\\can},\\overline{D}_f^{\\can}}\\big(X_\\Sigma;s_0,\\dots,s_{n-1},\\tilde{f}s_f\\big)\\]\non the right hand side, one obtains that\n\\[h^{\\tor}_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z)=h^{\\tor}_{\\overline{D}_0,\\dots,\\overline{D}_{n-1},\\overline{D}_f}(X_\\Sigma)-h^{\\tor}_{\\overline{D}_0^{\\can},\\dots,\\overline{D}_{n-1}^{\\can},\\overline{D}_f}(X_\\Sigma).\\]\nDenote by $\\Psi_i$ the virtual support function on $\\Sigma$ associated to the toric divisor $D_i$, for every $i=0,\\dots,n-1$. Thanks to \\cite[Corollary 5.1.9]{BPS}, the previous equality yields\n\\[h^{\\tor}_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z)=\\MI_M\\big(\\vartheta_0,\\dots,\\vartheta_{n-1},\\rho_{f,v}^\\vee\\big)-\\MI_M\\big(\\Psi_0^\\vee,\\dots,\\Psi_{n-1}^\\vee,\\rho_{f,v}^\\vee\\big).\\]\nSince they admit by hypothesis a semipositive toric metric, the toric divisors $D_0,\\dots,\\allowbreak D_{n-1}$ are generated by global sections. For every $i=0,\\dots,n-1$, the function $\\Psi_i$ is hence concave and conic and so it is the support function of the polytope $\\Delta_i:=\\stab(\\Psi_i)\\subseteq M_\\mathbb{R}$. The statement of the theorem follows from a combination of \\hyperref[indicator and support function]{Example \\ref*{indicator and support function}}, \\hyperref[mixed integral with indicator functions]{Corollary \\ref*{mixed integral with indicator functions}} and the combinatorial expression for the degree of a toric variety with respect to toric divisors generated by their global sections, see for example \\cite[Proposition 2.10]{O}.\n\\end{proof}\n\n\n\\subsection{Global heights}\n\nTo state the result concerning the global case, recall that for a defining polynomial $f$ for $Z$, the support function of its Newton polytope is denoted by $\\Psi_f$; whenever it is linear on each cone of a complete fan $\\Sigma$, it defines a toric divisor $D_f$ on the toric variety $X_\\Sigma$, together with a distinguished rational section $s_f$ of $\\mathscr{O}(D_f)$.\n\n\\begin{defn}\\label{definition of Ronkin metric}\nIn the above assumptions, the \\emph{Ronkin metric} on $D_f$ is the choice, for every place $v\\in\\mathfrak{M}$, of the $v$-adic Ronkin metric on $D_f$ defined in \\hyperref[v-adic Ronkin metric]{Definition \\ref*{v-adic Ronkin metric}}.\n\\end{defn}\n\nUnless otherwise stated, $\\overline{D}_f$ will denote the toric divisor $D_f$ equipped with its Ronkin metric. By definition, it is a semipositive toric metrized divisor.\n\n\\begin{lemma}\\label{Ronkin metric is adelic}\nThe Ronkin metric on $D_f$ is adelic.\n\\end{lemma}\n\\begin{proof}\nFor a non-archimedean place $v\\in\\mathfrak{M}$, the function $\\rho_{f,v}$ coincides with the tropicalization of the Laurent polynomial $f$, as claimed in \\hyperref[remark about known version of Ronkin functions]{Remark \\ref*{remark about known version of Ronkin functions}}. The fact that $f$ has finitely many nonzero coefficients and the second axiom in \\hyperref[adelic family]{Definition \\ref*{adelic family}} imply that $\\rho_{f,v}=\\Psi_f$ for all but finitely many non-archimedean places. The statement follows then from \\hyperref[adelic fields have finitely many archimedean places]{Lemma \\ref*{adelic fields have finitely many archimedean places}}.\n\\end{proof}\n\nThe definition of such a toric metrized divisor and the study of the local height of $Z$ in \\hyperref[section about local heights]{section \\ref*{section about local heights}} allow to answer the question of the integrability of $Z$ and to give a formula for its global height, implying \\hyperref[main theorem introduction]{Theorem \\ref*{main theorem introduction}} in the introduction.\n\n\\begin{theorem}\\label{hypersurfaces are integrable and their global height}\nLet $X_\\Sigma$ be a proper toric variety, $Z$ an effective cycle on $X_\\Sigma$ of pure codimension $1$ and prime components intersecting $X_0$. Let $\\overline{D}_0,\\dots,\\overline{D}_{n-1}$ be toric divisors on $X_\\Sigma$, equipped with adelic semipositive toric metrics. Then, $Z$ is integrable with respect to $\\overline{D}_0,\\dots,\\overline{D}_{n-1}$ and its global height is given by\n\\[h_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z)=\\sum_{v\\in\\mathfrak{M}}n_v\\MI_M\\big(\\vartheta_{0,v},\\dots,\\vartheta_{n-1,v},\\rho_{f,v}^\\vee\\big),\\]\nwhere $f$ is a defining polynomial for $Z$ and $\\vartheta_{i,v}$ is the roof function of $\\overline{D}_{i,v}$, for every $i=0,\\dots,n-1$ and $v\\in\\mathfrak{M}$.\n\\end{theorem}\n\\begin{proof}\nBecause of \\hyperref[can reduce to the case of a toric variety compatible with the polynomial]{Remark \\ref*{can reduce to the case of a toric variety compatible with the polynomial}}, one can assume that $X_\\Sigma$ is a smooth projective toric variety on whose fan $\\Psi_f$ is a virtual support function. Let hence $s_0,\\dots,s_{n-1}$ be rational sections of $\\mathscr{O}(D_0),\\dots,\\mathscr{O}(D_{n-1})$ respectively such that $\\divisor(s_0),\\dots,\\divisor(s_{n-1}),Z$ intersect properly. Because of \\hyperref[local height of hypersurfaces]{Proposition \\ref*{local height of hypersurfaces}}, the $v$-adic local height of $Z$ with respect to the above choice of sections is given by\n\\begin{equation}\\label{key equality in the proof of global heights}\nh_{\\overline{D}_{0,v},\\dots,\\overline{D}_{n-1,v}}(Z;s_0,\\dots,s_{n-1})=h_{\\overline{D}_{0,v},\\dots,\\overline{D}_{n-1,v},\\overline{D}_{f,v}}\\big(X_\\Sigma;s_0,\\dots,s_{n-1},\\tilde{f}s_f\\big).\n\\end{equation}\nBecause of \\hyperref[Ronkin metric is adelic]{Lemma \\ref*{Ronkin metric is adelic}}, each member of the family $\\overline{D}_0,\\dots,\\overline{D}_{n-1},\\overline{D}_f$ is an adelic semipositive toric metrized divisor on $X_\\Sigma$. As a consequence of the first assertion in \\cite[Proposition 5.2.4]{BPS}, $X_\\Sigma$ is integrable with respect to such a choice of metrized divisors and hence \\cite[Proposition 1.5.8 (1)]{BPS} allows to conclude that \\[h_{\\overline{D}_{0,v},\\dots,\\overline{D}_{n-1,v},\\overline{D}_{f,v}}\\big(X_\\Sigma;s_0,\\dots,s_{n-1},\\tilde{f}s_f\\big)=0\\] for all but finitely many places $v\\in\\mathfrak{M}$. Comparing with \\eqref{key equality in the proof of global heights}, one deduces that $Z$ is integrable with respect to $\\overline{D}_0,\\dots,\\overline{D}_{n-1}$.\n\\\\From the same equality, the global height of $Z$ is seen to satisfy\n\\[h_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z)=h_{\\overline{D}_0,\\dots,\\overline{D}_{n-1},\\overline{D}_f}(X_\\Sigma).\\]The formula for the global height of $Z$ follows then from \\hyperref[height of a toric variety]{Theorem \\ref*{height of a toric variety}}.\n\\end{proof}\n\n\\begin{rem}\nAs in \\hyperref[remark for degree of toric divisor]{Remark \\ref*{remark for degree of toric divisor}}, if $Z$ is an irreducible hypersurface on $X_\\Sigma$ not intersecting $X_0$ it coincides with $V(\\tau)$ for a $1$ dimensional cone $\\tau$ of the fan $\\Sigma$. In such a case, $Z$ is integrable with respect to a family of adelic semipositive toric metrized divisors $\\overline{D}_0,\\dots,\\overline{D}_{n-1}$ on $X_\\Sigma$ and its global height is given by\n\\[h_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(V(\\tau))=\\sum_{v\\in\\mathfrak{M}}n_v\\MI_{M(v_\\tau)}\\big(\\vartheta_{0,v}|_{\\Delta_0^{v_\\tau}},\\dots,\\vartheta_{n-1,v}|_{\\Delta_{n-1}^{v_\\tau}}\\big),\\]\nsee \\cite[Proposition 5.1.11 and Proposition 5.2.4]{BPS}. In the previous formula, $\\Delta_i$ is the polytope associated to the divisor $D_i$ and $\\vartheta_{i,v}$ is the roof function of $\\overline{D}_{i,v}$ for every $i=0,\\dots,n-1$ and $v\\in\\mathfrak{M}$, while $v_\\tau$ is the minimal nonzero integral vector of $\\tau$.\n\\end{rem}\n\n\\begin{rem}\nLocal and global heights of cycles are symmetric and multilinear with respect to sums of semipositive metrized divisors, provided that all terms are defined. The formulas obtained for $1$-codimensional cycles in toric varieties are consistent with these properties, the sum of semipositive toric metrized divisors corresponding to the sup-convolution of the associated roof functions, as shown in \\cite[4.3.14 (1)]{BPS}.\n\\end{rem}\n\n\n\\section{Examples}\\label{section about examples}\n\nFor a fixed adelic field $\\left(K,(|\\cdot|_v,n_v)_{v\\in\\mathfrak{M}}\\right)$ satisfying the product formula and a proper toric variety $X_\\Sigma$ over $K$, of dimension $n$, with torus $\\mathbb{T}=\\spec K[M]$ and dense open orbit $X_0$, we apply in this section the formula in \\hyperref[hypersurfaces are integrable and their global height]{Theorem \\ref*{hypersurfaces are integrable and their global height}} to four particular cases. In the first one, we focus on specific hypersurfaces of $X_\\Sigma$, while in the following three we made relevant choices of the metrized divisors.\n\n\n\\subsection{Binomial hypersurfaces}\n\n\nFor a primitive vector $m$ in $M$ one can consider the Laurent binomial $f=\\chi^m-1$; it is irreducible in $K[M]$ as can be verified by considering its Newton polytope. Hence the closure $Z$ in $X_\\Sigma$ of the subvariety $V(f)$ of the torus $\\spec K[M]$ is an irreducible hypersurface of $X_\\Sigma$ with defining polynomial $f$.\n\\\\Let $\\overline{D}_0,\\dots,\\overline{D}_{n-1}$ be toric divisors on $X_\\Sigma$, equipped with adelic semipositive toric metrics, with $\\vartheta_{i,v}$ the roof function of $\\overline{D}_{i,v}$, for every $i=0,\\dots,n-1$ and $v\\in\\mathfrak{M}$. By \\hyperref[example Ronkin of a binomial]{Example \\ref*{example Ronkin of a binomial}}, $\\rho_{f,v}$ coincides for every $v\\in\\mathfrak{M}$ with the support function of the segment $\\overline{0m}$ in $M_\\mathbb{R}$. The formula in \\hyperref[hypersurfaces are integrable and their global height]{Theorem \\ref*{hypersurfaces are integrable and their global height}} implies then that $Z$ is integrable with respect to $\\overline{D}_0,\\dots,\\overline{D}_{n-1}$ and that its global height is given by\n\\[h_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z)=\\sum_{v\\in\\mathfrak{M}}n_v\\MI_M\\big(\\vartheta_{0,v},\\dots,\\vartheta_{n-1,v},\\iota_{\\overline{0m}}\\big),\\] because of \\hyperref[indicator and support function]{Example \\ref*{indicator and support function}}. Considering, as at the end of \\hyperref[subsection about mixed integrals]{subsection \\ref*{subsection about mixed integrals}}, the quotient lattice $P:=M\/\\mathbb{Z}m$ and the associated projection $\\pi:M\\to P$, \\hyperref[mixed integral with indicator function of a segment]{Proposition \\ref*{mixed integral with indicator function of a segment}} allows to deduce\n\\begin{equation}\\label{height of toric subvariety}\nh_{\\overline{D}_0,\\dots,\\overline{D}_{n-1}}(Z)=\\sum_{v\\in\\mathfrak{M}}n_v\\MI_P\\big(\\pi_*\\vartheta_{0,v},\\dots,\\pi_*\\vartheta_{n-1,v}\\big),\n\\end{equation}\nwith $\\pi_*\\vartheta_{i,v}$ denoting the direct image of $\\vartheta_{i,v}$ by $\\pi$ for every $i=0,\\dots,n-1$ and $v\\in\\mathfrak{M}$, see \\eqref{definition of direct image of concave functions}.\n\n\\begin{rem}\nLet $Q$ be the dual lattice of $P=M\/\\mathbb{Z}m$. The projection $\\pi:M\\to P$ induces an injective dual map $Q\\to N$, with image $m^\\perp\\cap N$. By identifying $Q$ with such an image, which is a saturated sublattice of $N$, one can consider the restriction of the fan $\\Sigma$ to $Q_\\mathbb{R}$; its corresponding toric variety $X_{\\Sigma_Q}$ is proper and has torus $\\spec K[P]$. It also comes with a toric morphism $\\varphi:X_{\\Sigma_Q}\\to X_\\Sigma$, whose restriction to the dense open orbit coincides with the closed immersion of split tori $\\spec K[P]\\to\\spec K[M]$ given by the surjection $\\pi:M\\to P$, see \\cite[pages 81-83]{BPS}. Finally, the push-forward of the cycle $X_{\\Sigma_Q}$ by $\\varphi$ is the cycle $Z$ associated to the hypersurface defined by $\\chi^m-1$. Indeed, the image of $\\varphi$ coincides by properness with the closure in $X_\\Sigma$ of the image of $\\spec K[P]\\to\\spec K[M]$, which is an irreducible $(n-1)$-dimensional subscheme of $\\spec K[M]$ contained in $V(\\chi^m-1)$ as $\\chi^{\\pi(m)}-1=0$.\n\\\\Hence, equality \\eqref{height of toric subvariety} can also be obtained from \\hyperref[height of a toric variety]{Theorem \\ref*{height of a toric variety}}, the arithmetic projection formula and the fact that $\\pi_*\\vartheta_{i,v}$ is the roof function of the pull-back of $\\overline{D}_{i,v}$ via $\\pi$, for every $i=0,\\dots,n-1$ and $v\\in\\mathfrak{M}$, because of \\cite[Proposition 4.3.19 and Proposition 2.3.8 (3)]{BPS}.\n\\end{rem}\n\n\n\n\\subsection{The canonical height}\n\nA toric divisor $D$ on $X_\\Sigma$ generated by global sections admits by \\hyperref[canonical metric definition]{Definition \\ref*{canonical metric definition}} a distinguished semipositive toric metric at any place. The metrized divisor obtained by the choice of such a family of $v$-adic canonical metrics is denoted by $\\overline{D}^{\\can}$; it is an adelic semipositive toric metrized divisor.\n\\\\For a cycle $Z$ of dimension $d$ in $X_\\Sigma$, the \\emph{canonical global height} of $Z$ with respect to a family $D_0,\\dots,D_d$ of toric divisors on $X_\\Sigma$ generated by global sections is defined to be its global height with respect to $\\overline{D}^{\\can}_0,\\dots,\\overline{D}^{\\can}_d$ and it is also denoted by $h^{\\can}_{D_0,\\dots,D_d}(Z)$. The machinery developed in the previous sections allows to express the canonical global height of an effective cycle on $X_\\Sigma$ of pure codimension $1$ via convex geometry.\n\n\\begin{proposition}\\label{canonical height of hypersurfaces}\nLet $Z$ be an effective cycle on $X_\\Sigma$ of pure codimension $1$ and prime components intersecting $X_0$ and $D_0,\\dots,D_{n-1}$ a family of toric divisors on $X_\\Sigma$ generated by global sections. The canonical global height of $Z$ with respect to $D_0,\\dots,D_{n-1}$ is given by \\[h^{\\can}_{D_0,\\dots,D_{n-1}}(Z)=-\\deg_{D_0,\\dots,D_{n-1}}(X_\\Sigma)\\cdot\\sum_{v\\in\\mathfrak{M}}n_v\\rho_{f,v}(0)\\] for any choice of a defining polynomial $f$ for $Z$.\n\\end{proposition}\n\\begin{proof}\nDenoting for any $i=0,\\dots,n-1$ by $\\Psi_i$ the function associated to $D_i$, the property of being globally generated implies that $\\Psi_i$ is the support function of the lattice polytope $\\Delta_i:=\\stab(\\Psi_i)\\subseteq M_\\mathbb{R}$. The roof function of $\\overline{D}^{\\can}_{i,v}$ is hence $\\iota_{\\Delta_i}$ for every $i=0,\\dots,n-1$ and for every $v\\in\\mathfrak{M}$, because of \\hyperref[indicator and support function]{Example \\ref*{indicator and support function}}. It follows from \\hyperref[hypersurfaces are integrable and their global height]{Theorem \\ref*{hypersurfaces are integrable and their global height}} and \\hyperref[mixed integral with indicator functions]{Corollary \\ref*{mixed integral with indicator functions}} that\n\\[h^{\\can}_{D_0,\\dots,D_{n-1}}(Z)=-\\MV_M(\\Delta_0,\\dots,\\Delta_{n-1})\\cdot\\sum_{v\\in\\mathfrak{M}} n_v\\rho_{f,v}(0),\\]\nwith $f$ any defining polynomial for $Z$. To conclude, recall that the degree of $X_\\Sigma$ with respect to $D_0,\\dots,D_{n-1}$ is given by the mixed volume of the associated polytopes, as proved in \\cite[Proposition 2.10]{O}.\n\\end{proof}\n\nThe case of the base field $\\mathbb{Q}$ with the adelic structure described in \\hyperref[examples of adelic fields]{Example \\ref*{examples of adelic fields}} is particularly interesting for arithmetic purposes. For a Laurent polynomial $f$ in $n$ variables and complex coefficients, one defines its (\\emph{logarithmic}) \\emph{Mahler measure} to be \\[m(f):=\\frac{1}{(2\\pi)^n}\\int_{\\theta_1,\\dots,\\theta_n\\in[0,2\\pi]}\\log\\left|f\\big(e^{i\\theta_1},\\dots,e^{i\\theta_n}\\big)\\right|\\ d\\theta_1\\dots d\\theta_n.\\]Such a quantity is notoriously difficult to compute and is sometimes related to special values of $L$-functions, see \\cite{Smyth}, \\cite{Dening}, \\cite{Boyd} and \\cite{Lalin}.\n\\\\In \\cite[Proposition 7.2.1]{Mail}, Maillot expressed the canonical height of a hypersurface in a toric variety over $\\mathbb{Q}$ in terms of the Mahler measure of the associated section. While its proof relies on the study of the arithmetic Chow ring of the ambient toric variety, we here deduce his result from \\hyperref[canonical height of hypersurfaces]{Proposition \\ref*{canonical height of hypersurfaces}}.\n\n\\begin{corollary}[Maillot]\\label{canonical height over the rationals}\nIn the hypotheses and notations of \\hyperref[canonical height of hypersurfaces]{Proposition \\ref*{canonical height of hypersurfaces}}, assume moreover that the base adelic field is $\\mathbb{Q}$ with its usual adelic structure. Let $f$ be a defining polynomial for $Z$ having as coefficients integer numbers with greatest common divisor $1$. Then,\\[h^{\\can}_{D_0,\\dots,D_{n-1}}(Z)=\\deg_{D_0,\\dots,D_{n-1}}(X_\\Sigma)\\cdot m(f).\\]\n\\end{corollary}\n\\begin{proof}\nLet $f$ be a defining polynomial for $Z$ satisfying the assumptions. Because of \\hyperref[remark about known version of Ronkin functions]{Remark \\ref*{remark about known version of Ronkin functions}}, for every non-archimedean place $v$ of $\\mathbb{Q}$\\[\\rho_{f,v}(0)=f^{\\trop}(0)=0.\\] At the unique archimedean place $v$ of $\\mathbb{Q}$ one has by definition that $\\rho_{f,v}(0)=-m(f)$. The statement follows then directly from \\hyperref[canonical height of hypersurfaces]{Proposition \\ref*{canonical height of hypersurfaces}}.\n\\end{proof}\n\n\n\\subsection{The $\\rho$-height}\n\nThe strategy adopted in the \\hyperref[section hypersurface]{previous section} to prove the main results of the paper suggests the introduction of a distinguished height function. Let $Z$ be an effective cycle on $X_\\Sigma$ of pure codimension $1$ and prime components intersecting $X_0$ and assume that the support function of the Newton polytope of a defining polynomial for $Z$ is a virtual support function on the fan $\\Sigma$. By \\hyperref[a suitable resolution exists]{Lemma \\ref*{a suitable resolution exists}}, this is always the case up to a birational toric transformation. In this setting, the choice of a defining polynomial $f$ for $Z$ determines a toric divisor $D_f$ on $X_\\Sigma$ and a distinguished toric metric on it, the Ronkin metric, as introduced in \\hyperref[definition of Ronkin metric]{Definition \\ref*{definition of Ronkin metric}}. The so-obtained metrized divisor, which is denoted by $\\overline{D}_f$, is an adelic semipositive toric metrized divisor by \\hyperref[Ronkin metric is adelic]{Lemma \\ref*{Ronkin metric is adelic}}.\n\n\\begin{defn}\nIn the above hypotheses and notations, the \\emph{$\\rho$-height} of $Z$, denoted by $h_{\\rho}(Z)$, is defined as its global height with respect to $\\overline{D}_f,\\dots,\\overline{D}_f$, for a choice of a defining polynomial $f$ for $Z$.\n\\end{defn}\n\nAs shown below, the $\\rho$-height of $Z$ is independent of the choice of the defining polynomial $f$. Even if it is not clear whether such a height has a significant geometrical interpretation or arithmetical application, it is straightforward to give a combinatorial formula for it.\n\n\\begin{proposition}\\label{formula for the rho height}\nIn the above hypotheses and notations, the $\\rho$-height of $Z$ is given by \\[h_\\rho(Z)=(n+1)!\\sum_{v\\in\\mathfrak{M}}n_v\\int_{\\NP(f)}\\rho_{f,v}^\\vee\\ d\\vol_M,\\]where $f$ is a defining polynomial for $Z$ and $\\NP(f)$ is its Newton polytope.\n\\end{proposition}\n\\begin{proof}\nThe statement follows trivially from \\hyperref[hypersurfaces are integrable and their global height]{Theorem \\ref*{hypersurfaces are integrable and their global height}}, \\hyperref[properties of Ronkin functions]{Proposition \\ref*{properties of Ronkin functions}} (3) and the properties of mixed integrals.\n\\end{proof}\n\n\\begin{rem}\nThe equality in \\hyperref[formula for the rho height]{Proposition \\ref*{formula for the rho height}} shows that the $\\rho$-height of $Z$ does not depend on the choice of a defining polynomial for it. Indeed, if $f^\\prime$ is another such polynomial, it must satisfy $f^\\prime=c\\cdot \\chi^m\\cdot f$ for some nonzero monomial $c\\cdot \\chi^m\\in K[M]$. For every $v\\in\\mathfrak{M}$, one has then that \\[\\rho_{f^\\prime,v}=-\\log|c|_v+m+\\rho_{f,v}\\] by \\hyperref[Ronkin function of a product]{Proposition \\ref*{Ronkin function of a product}} and \\hyperref[example Ronkin of a monomial]{Example \\ref*{example Ronkin of a monomial}}. The stated independence follows hence from the relation \\[\\rho_{f^\\prime,v}^\\vee=\\tau_m\\rho_{f,v}^\\vee+\\log|c|_v\\] obtained using \\cite[Proposition 2.3.3]{BPS} and from the product formula on $K$.\n\\end{rem}\n\nIt is significant to stress that the formula in \\hyperref[formula for the rho height]{Proposition \\ref*{formula for the rho height}}, though compact, is difficult to evaluate because of the complexity of the archimedean Ronkin function.\n\n\n\\subsection{The Fubini-Study height}\n\nAs a last example, consider the ambient toric variety $X_\\Sigma$ to be the $n$-dimensional projective space over $K$. Denote by $D_\\infty$ the toric divisor on $\\mathbb{P}^n_K$ whose associated Weil divisor is the hyperplane at infinity; the corresponding sheaf is the universal line bundle $\\mathscr{O}(1)$ on $\\mathbb{P}^n_K$. If not otherwise specified, the notation $\\overline{D}_\\infty$ will refer to $D_\\infty$ equipped with the Fubini-Study metric at archimedean places, see \\cite[Example 1.1.2]{BPS}, and the canonical one at non-archimedean places, in the sense of \\hyperref[canonical metric definition]{Definition \\ref*{canonical metric definition}}. It turns out that $\\overline{D}_\\infty$ is an adelic semipositive toric metrized divisor. Thanks to \\hyperref[hypersurfaces are integrable and their global height]{Theorem \\ref*{hypersurfaces are integrable and their global height}}, any effective cycle $Z$ on $\\mathbb{P}^n_K$ of pure codimension $1$ is then integrable with respect to $\\overline{D}_\\infty,\\dots,\\overline{D}_\\infty$ and the corresponding global height \\[h_{\\FS}(Z):=h_{\\overline{D}_\\infty,\\dots,\\overline{D}_\\infty}(Z)\\]is called the \\emph{Fubini-Study height} of $Z$.\n\n\\begin{rem}\nThe Fubini-Study height defined here coincides with the one introduced in \\cite{Fal2} and studied in \\cite{PhilIII}. Examples of the computation of such height for projective hypersurfaces can be found in \\cite{CM}.\n\\end{rem}\n\nSpecializing \\hyperref[hypersurfaces are integrable and their global height]{Theorem \\ref*{hypersurfaces are integrable and their global height}}, one can write the Fubini-Study height of a projective hypersurface in terms of convex geometry. To do so, denote by $\\mathfrak{M}_\\infty$ the collection of archimedean places of $K$, which is a finite set by \\hyperref[adelic fields have finitely many archimedean places]{Lemma \\ref*{adelic fields have finitely many archimedean places}}. After fixing an isomorphism $M\\simeq\\mathbb{Z}^n$, consider the standard simplex \\[\\Delta^n:=\\big\\{(x_1,\\dots,x_n): x_1+\\dots+x_n\\leq1,x_i\\geq0\\text{ for all }i=1,\\dots,n\\big\\}\\] in $M_\\mathbb{R}\\simeq\\mathbb{R}^n$ and, agreeing that $x_0:=1-\\sum_{i=1}^nx_i$, set the function $\\vartheta_{\\FS}:\\Delta^n\\to\\mathbb{R}$ to be \\[\\vartheta_{\\FS}(x):=-\\frac{1}{2}\\sum_{i=0}^nx_i\\log x_i,\\]which is defined on the boundary of $\\Delta^n$ by continuity.\n\n\\begin{proposition}\\label{Fubini-Study height of a hypersurface}\nLet $Z$ be an effective cycle on $\\mathbb{P}^n_K$ of pure codimension $1$ and prime components intersecting $X_0$. The Fubini-Study height of $Z$ is given by\\[h_{\\FS}(Z)=\\sum_{v\\in\\mathfrak{M}_\\infty}n_v\\MI_M\\big(\\vartheta_{\\FS},\\dots,\\vartheta_{\\FS},\\rho_{f,v}^\\vee\\big)-\\sum_{v\\in\\mathfrak{M}\\setminus\\mathfrak{M}_\\infty}n_v\\rho_{f,v}(0),\\]where $f$ is a defining polynomial for $Z$.\n\\end{proposition}\n\\begin{proof}\nThe roof functions of the metrized divisor $\\overline{D}_\\infty$ are given by the function $\\vartheta_{\\FS}$ at archimedean places, as remarked in \\cite[Example 2.4.3 and Example 4.3.9 (2)]{BPS} and by the indicator function of $\\Delta^n$ at non-archimedean places, by \\cite[Example 4.3.9 (1)]{BPS} and \\hyperref[indicator and support function]{Example \\ref*{indicator and support function}}. The statement follows then from \\hyperref[hypersurfaces are integrable and their global height]{Theorem \\ref*{hypersurfaces are integrable and their global height}} and \\hyperref[mixed integral with indicator functions]{Corollary \\ref*{mixed integral with indicator functions}}, together with the fact that $\\MV_{M}(\\Delta^n,\\dots,\\Delta^n)=1$ because of the conventions introduced in \\hyperref[subsection about real Monge-Ampere measures]{subsection \\ref*{subsection about real Monge-Ampere measures}} and \\hyperref[lattice volume]{Remark \\ref*{lattice volume}}.\n\\end{proof}\n\nThe non-archimedean contributions to the Fubini-Study height are easily computable, since for every Laurent polynomial $f$ with set of coefficients $\\Gamma(f)$,\n\\[-\\rho_{f,v}(0)=\\log\\max_{c\\in\\Gamma(f)}|c|_v\\]\nif $v\\in\\mathfrak{M}\\setminus\\mathfrak{M}_\\infty$. From this equality one easily obtains the following special case.\n\n\\begin{corollary}\\label{FS height over the rational}\nAssume the base adelic field to be $\\mathbb{Q}$ with its usual adelic structure. The Fubini-Study height of an effective cycle $Z$ on $\\mathbb{P}^n_\\mathbb{Q}$ of pure codimension $1$ and prime components intersecting $X_0$ is given by \\[h_{\\FS}(Z)=\\MI_M\\big(\\vartheta_{\\FS},\\dots,\\vartheta_{\\FS},\\rho_{f,\\infty}^\\vee\\big),\\]where $f$ is a defining polynomial for $Z$ whose coefficients are integer numbers with greatest common divisor $1$.\n\\end{corollary}\n\nBecause of the presence of an archimedean Ronkin function, the formula in \\hyperref[FS height over the rational]{Corollary \\ref*{FS height over the rational}} appears arduous to evaluate. It would anyway be interesting to use it to study arithmetical properties of projective hypersurfaces or recover similar results to the ones obtained in \\cite{CM}.\n\n\n\\bibliographystyle{amsalpha}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\nThere exists a very old problem constantly met in various aspects of applied\nmathematics, which can be formulated as follows. Very often realistic problems \nare so complicated that they do not allow for exact solutions. It is standard\nfor such problems to use some kind of perturbation theory\n\\cite{Bog1961, Gia1972, Nay1973}. Then one gets answers in terms of expansions\nin powers of a small parameter, or a small variable, say for $x \\ra 0$. However,\noften the problem of interest corresponds not to a small variable, but, rather \nthe opposite, to large values of this variable; very often it is the infinite \nlimit $x \\ra \\infty$ that is of the most interest \\cite{Kle2006}. One could find \nthis limit, provided the general formula of expansion terms would be given and \nthe derived expansion would produce convergent series. None of these conditions \nis usually valid. As a rule, only a few expansion terms can be derived. \nAdditionally, the resulting series are divergent, being only asymptotic \n\\cite{Erd1955, Har1949}. Then the question arises: how, from the knowledge of \nseveral terms of an asymptotic expansion at a variable $x \\ra 0$ could one find \nthe limit corresponding to $x \\ra \\infty$?\n\nOne often extrapolates small-variable expansions by means of Pad\\'{e} \napproximants \\cite{Bak1996}. However, the straightforward use of these \napproximants yields\n$$\nP_{M\/N}(x) \\sim x^{M-N} \\qquad (x\\ra\\infty) \\; ,\n$$\nwhich, depending on the relation between $M$ and $N$, can tend to:\n\n\\begin{itemize} \n\n\\item \ninfinity (when $M > N$), \n\n\\item\nzero (when $M < N$), \n\n\\item\na constant (if $M = N$).\n\n\\end{itemize}\n\nIn that sense, the limit $x \\ra \\infty$ is not defined. \n\nWhen the character of the large-variable limit is known, one can invoke the \ntwo-point Pad\\'{e} approximants \\cite{Bak1996}. However the accuracy of the \nlatter is not high and one confronts several difficulties: \n\n\\begin{enumerate}\n\n\\item\nFirst of all, when constructing these approximants, one often obtains spurious \npoles yielding unphysical singularities \\cite{Bak1996}, sometimes a large \nnumber of poles \\cite{Saf1976}. \n\n\\item\nSecond, there are the cases when Pad\\'{e} approximants are not able to sum \nperturbation series even for small values of an expansion parameter \\cite{Sim1991}. \n\n\\item\nThird, in the majority of cases, to reach a reasonable accuracy, one needs \nto have tens of terms in perturbative expansions \\cite{Bak1996}, while often \ninteresting problems provide only a few terms. \n\n\\item\nFourth, defining the two-point Pad\\'{e} approximants, one always meets an \nambiguity in distributing the coefficients for deciding which of these must\nreproduce the left-side expansion and which the right-side series. This \nambiguity worsens with the increase of the approximants' orders, making it \ndifficult to compose two-point Pad\\'{e} tables. For the case of a few terms, \nthis ambiguity makes the two-point Pad\\'{e} approximants practically \ninapplicable. For example, it has been shown \\cite{Sel1989} that, for the \nsame problem, one may construct different two-point Pad\\'{e} approximants, \nall having correct left and right-side limits, but differing from each other \nin the intermediate region by a factor of 40, which gives 1000$\\%$ uncertainty. \nThis demonstrates that in the case of short series the two-point Pad\\'{e} \napproximants do not allow one to get a reliable description. \n\n\\item\nFifth, the two-point Pad\\'{e} approximants cannot always be used for \ninterpolating between two different expansions, but only when these two \nexpansions have compatible variables \\cite{Bak1996}. When these expansions\nhave incompatible variables, the two-point Pad\\'{e} approximants cannot be \ndefined in principle.\n\n\\item\nFinally, interpolating between two points, one of which is finite and another\nis at infinity, one is able to characterize the large-variable limit of only \nrational powers \\cite{Bak1996}. \n\n\\end{enumerate}\n\nAnother method that allows for the extrapolation of divergent series is \noptimized perturbation theory, based on the introduction of control functions\ndefined by an optimization condition and guaranteeing the transformation of \ndivergent series into convergent series \\cite{Yuk1976a, Yuk1976b, Yuk2002}. \nSince 1976, when optimized perturbation theory was introduced \n\\cite{Yuk1976a, Yuk1976b}, a number of variants of different control functions \n(see discussion in (\\cite{Yuk1999, Yuk2002}) have been put forward. \nKleinert \\cite{Kle1993, Kle2006} variational perturbation theory, where control \nfunctions are introduced through a variable transformation and variational \noptimization conditions, is particularly worth mentioning. This method provides \ngood accuracy for the extrapolation of weak-coupling expansions to the \nstrong-coupling limit, especially when a number of terms in the weak-coupling \nperturbation theory are available \\cite{Jan1995}. \n\nIn the present paper, we address the problem of extrapolating small-variable \nasymptotic expansions to their effective strong-coupling limits by employing\nanother approach, based on self-similar approximation theory \n\\cite{Yuk1990a, Yuk1990b, Yuk1991, Yuk1992, Yuk1993, Yuk1994, Yuk1996}.\nThe main difference of this approach from optimized perturbation theory\nis that we possess the approximation methods without introducing control functions, \nwhich makes calculations essentially simpler. Self-similar approximation theory \ncan be combined with Kleinert variational perturbation theory \\cite{Kle2005}.\nThis, however, also requires the introduction of variational control functions. \nIn the present paper, however, we pay most attention to considering simpler ways \nnot involving control functions. \n\nThere exists a principal problem, when one accomplishes an extrapolation in the \ncase for which the exact solution is not known and only a few terms of \nweak-coupling perturbation theory are available. This is the problem of the \nreliability of the obtained extrapolation. In such a case, it is important to \nbe able to do the extrapolation by several methods, comparing their \nresults. If these results yield close values, this suggests that the \nextrapolation is reliable. \n\nIn line with this idea, we aim at employing different variants of self-similar \napproximations, applying them to the same problems and comparing the results. \nIf the approximants for a problem, obtained by different methods, are close to \neach other, this would suggest that the derived values are reliable. \n\nWe consider several variants of self-similar approximants for each problem and \nshow that they really are close to each other, hence they can successfully \nextrapolate asymptotic expansions, valid at $x \\ra 0$, to their effective limits \nof $x \\ra \\infty$. We especially concentrate on the strong-coupling limit, where\napproximate methods usually are the least accurate, leading to the maximal errors.\nWe show that, even in this least favourable situation, with just a few perturbative \nterms available, the self-similar extrapolation methods provide reasonable accuracy.\nFor completeness, we also show that the self-similar methods allow\nus to construct the approximants displaying good accuracy in the whole region of\nthe studied variable. For instance, effective equations of state can be derived,\nthese being in good agreement with experimental data. \n\nThe difference of the present paper from our previous publications is in the \nfollowing.\n\n(i) We study several types of self-similar approximants and compare their accuracy,\nwhich allows us to draw conclusions on the reliability of the method.\n\n(ii) A large set of examples of different natures is analyzed, demonstrating\nthe generality of the method of self-similar approximants and their \neffectiveness for extrapolating different functions met in various problems\nof applied mathematics.\n\n(iii) We consider a new type of approximants resulting from a double\nself-similar renormalization and show how they improve the accuracy as compared\nwith exact results, when these are available.\n\n(iv) We show an effective way for calculating the large-variable critical \nexponents. \n\n(v) The method is shown to provide good accuracy for the whole range of the \nvariable. This is demonstrated by constructing the equation of state that exactly\nreproduces a phenomenological equation for quantum hard spheres. \n \n\n\\section{Formulation of extrapolation problem}\n\nSuppose we are interested in the behaviour of a real function $f(x)$ of a \nreal variable $x \\in [0,\\infty)$. Also let this function be defined by a \ncomplicated problem that does not allow for an explicit derivation of the\nform of $f(x)$. What can only be done is use some kind of perturbation theory \nyielding asymptotic expansions representing the function\n\\be\n\\label{2.1}\nf(x) \\simeq f_k(x) \\qquad (x \\ra 0)\n\\ee\nat small values of the variable $x \\ra 0$, with $k=0,1,\\ldots$ being the \nperturbation order. The perturbative series of $k$-th order can be written as \nan expansion in powers of $x$ as\n\\be\n\\label{2.2}\n f_k(x) = f_0(x) \\left ( 1 + \\sum_{n=1}^k a_n x^n \\right ) \\; ,\n\\ee\nwhere $f_0(x)$ is chosen so that the series in the brackets would start \nwith the term one. It is convenient to define the reduced expression\n\\be\n\\label{2.3}\n \\overline f_k(x) \\equiv \\frac{f_k(x)}{f_0(x)} = 1 + \n\\sum_{n=1}^k a_n x^n \\; ,\n\\ee\nwhich will be subject to self-similar renormalization. \n\nNote that practically any perturbative series can be represented in \nform (2.2). For instance, if we have a Laurent-type series\n$$\n f_{m+k}(x) = \\sum_{n=-m}^k c_n x^n \\; ,\n$$\nit can be transformed to (2.2) by rewriting it as \n$$\nf_{m+k}(x) = \\frac{c_{-m}}{x^m} \\left (\n 1 + \\sum_{n=1}^{m+k} a_n x^n \\right ) \\; .\n$$ \n\nHere we consider the series in integer powers, or those that can be \nreduced to such, since this is the most frequent type of perturbation-theory\nexpansions. Thus, the Puiseaux expansion \\cite{Pui1850} of the type\n$$\nf_k(t) = \\sum_{n=n_0}^k c_n t^{n\/m} \\; ,\n$$ \nwhere $n_0$ is an integer and $m$ is a nonzero natural number, can be \nreduced to form (1.2) by the change of the variable $ t = x^m$. It is\npossible to generalize the approach to the series of the type\n$$\n f_k(x) = \\sum_{n}^k c_n x^{\\al_n} \\qquad ( \\al_n < \\al_{n+1} ) \\; ,\n$$\nwith arbitrary real powers $\\alpha_n$ arranged in an ascending order.\nWhen $\\alpha_n$ pertains to an ordered group, the latter expression \ncorresponds to the Hahn series \\cite{Ked2001, Mac1939}.\n\nAs is known, the most difficult region for approximating is that of the \nlarge variable, where approximants are usually the least accurate. This \nis why our main interest here will be the large-variable behaviour of the \nfunction, where its asymptotic form is\n\\be\n\\label{2.4}\n f(x) \\simeq Bx^\\bt \\qquad (x\\ra\\infty) \\; .\n\\ee\nThe constant $B$ is called the critical amplitude and the power $\\beta$\nis the critical exponent.\n\nAfter employing the self-similar renormalization for the reduced function\n(2.3), we get a self-similar approximant $\\overline f_k^*(x)$, which gives a\nself-similar approximant\n\\be\n\\label{2.5}\nf_k^*(x) = f_0(x) \\overline f_k^*(x)\n\\ee\nfor the sought function $f(x)$. Considering for the latter the limit \n$x \\ra \\infty$, we find the related approximation for the critical \namplitude and critical exponent. In many cases the exponent is known from \nother arguments. Then, we need to find only the critical amplitude. \n\n\n\\section{Variants of self-similar approximants}\n\nIn the cases, when one can compare the derived approximants with known \nexpressions, one can easily evaluate the accuracy of the approximants. \nBut how could we trust the approximants, when no exact expression for the \nsought function is available? In that case, it would be desirable to have\nto hand several variants of approximants in order to compare them with \neach other. If all of them give close results, this would suggest that \nthe method is reliable. \n \nSeveral types of approximants, based on self-similar approximation theory, \nhave been derived. We shall not repeat their derivation here. This can \nbe found, along with all the details, in our previous publications. We \nshall just present the corresponding expressions and explain how they \nwill be used for the problem of extrapolation to infinity.\n\n\n\\subsection{Self-similar factor approximants}\n\nSelf-similar factor approximants have been introduced in Refs. \n\\cite{Glu2003, Yuk2003}. For the reduced expansion (2.3), the $k$th order\nself-similar factor approximant reads as\n\\be\n\\label{3.1}\n \\overline f_k^*(x) = \\prod_{i=1}^{N_k} ( 1 + A_i x)^{n_i} \\; ,\n\\ee\nwhere\n\\begin{eqnarray}\n\\label{3.2}\nN_k = \\left\\{ \\begin{array}{ll}\nk\/2 , & ~ k = 2,4,\\ldots \\\\\n(k+1)\/2 , & ~ k =3,5,\\ldots\n\\end{array} \\right. \n\\end{eqnarray}\nand the parameters $A_i$ and $n_i$ are defined from the accuracy-through-order\nprocedure, by expanding expression (3.1) in powers of $x$, comparing the \nlatter expansion with the given sum (2.3), and equating the like terms in \nthese expansions. When the approximation order $k=2p$ is even, the above \nprocedure uniquely defines all $2p$ parameters. When the approximation order \n$k=2p+1$ is odd, the number of equations in the accuracy-through-order \nprocedure is $2p$ which is by one smaller than the number of parameters. Then, \nusing the scale invariance arguments \\cite{Yuk2007}, one sets $A_1=1$, \nthus, uniquely defining all parameters. Another way is to find one of the \ncoefficients $A_i$ from the variational optimization of the approximant \n\\cite{Yuk2009b}. Both these approaches give close results, though the scaling \nprocedure of setting $A_1$ to one is simpler. \n\nWith approximant (3.1), the self-similar approximant for the sought \nfunction (2.5) becomes\n\\be\n\\label{3.3}\n f^*_k(x) = f_0(x) \\prod_{i=1}^{N_k} ( 1 + A_i x)^{n_i} \\; .\n\\ee\nIf the zero-order factor has the large-variable form\n\\be\n\\label{3.4}\n f_0(x) \\simeq A x^\\al \\qquad (x\\ra\\infty) \\; ,\n\\ee\nthen approximant (3.3) behaves as \n\\be\n\\label{3.5} \n f_k^*(x) \\simeq B_k x^\\bt \\qquad (x\\ra\\infty) \\; .\n\\ee\nUnder a given exponent $\\beta$, the powers $n_i$ must satisfy the equality\n\\be\n\\label{3.6}\n \\bt = \\al + \\sum_{i=1}^{N_k} n_i \\; ,\n\\ee\nwhile the critical amplitude $B$ is approximated by\n\\be\n\\label{3.7}\n B_k = A \\prod_{i=1}^{N_k} A_i^{n_i} \\; .\n\\ee\n \nIt is worth stressing that the factor $f_0(x)$ in Eq. (3.3) is explicitly defined\nby the perturbative expansion (2.2), so it is known. The factor approximants (3.3)\nmay have singularities when some $A_i$ and $n_i$ are negative. This makes it \npossible to associate such singularities with critical points and phase transitions.\nInvestigation of the critical points and the related critical exponents, by means \nof the factor approximants, has been done in our previous publications \n\\cite{Glu2003, Yuk2003, Yuk2004, Yuk2007, Yuk2009b}. \n\n\n\\subsection{Self-similar root approximants}\n\nThe derivation of the self-similar root approximants can be found in\nRefs. \\cite{Glu1998, Yuk1998, Yuk2002}. The self-similar renormalization\nof the reduced expansion (2.3) yields\n\\be\n\\label{3.8}\n R_k(x) = \\left ( \\left ( \\left ( \\ldots ( 1 + A_1 x )^{n_1} +\nA_2x^2 \\right )^{n_2} + A_3x^3 \\right )^{n_3} + \\ldots +\nA_k x^k \\right )^{n_k} \\; .\n\\ee\nThe $k$th order approximant for the sought function then becomes\n\\be\n\\label{3.9}\n f_k^*(x) = f_0(x) R_k(x) \\; .\n\\ee\nIn Ref. \\cite{Yuk2002}, it has been rigorously proved that the parameters \n$A_i$ and $n_i$ are uniquely defined, provided that $k$ terms of the \nlarge-variable expansion at $x \\ra \\infty$ are known, and the condition\n$pn_p - p + 1 = const$ holds for $p = 1,2,\\ldots, k-1$. Then expression (3.8) \nleads to\n\\be\n\\label{3.10}\n R_k(x) \\simeq A_k^{n_k} x^{kn_k} \\qquad (x\\ra\\infty ) \\; .\n\\ee\nWith the given exponent $\\beta$, the power $n_k$ satisfies the relation\n\\be\n\\label{3.11}\n\\bt = \\al + kn_k \n\\ee\nand the $k$th order approximation for the critical amplitude is\n\\be\n\\label{3.12}\n B_k = A A_k^{n_k} \\; .\n\\ee\n \n\n\\subsection{Iterated root approximants}\n\nSelf-similar root approximants are uniquely defined when their parameters \nare prescribed by the large-variable behaviour of the sought function. However, \nif we try to find these parameters from the small-variable expansion (2.2), \nthen we meet the problem of multiple solutions \\cite{Yuk2004}. To avoid this \nproblem, one has to impose additional conditions on the parameters. Such a \nstraightforward condition would be the requirement that all $k$ terms in root\n(3.8) would contribute to the large-variable amplitude \\cite{Glu2010}. For \nthis, it is necessary and sufficient that the internal powers $n_j$ be \ndefined as\n\\be\n\\label{3.13}\n n_j = \\frac{j+1}{j} \\qquad ( 1 \\leq j \\leq k-1 ) \\; ,\n\\ee\nwith the external power related to the exponent $\\beta$ as\n\\be\n\\label{3.14}\n n_k = \\frac{\\gm}{k} \\qquad (\\gm = \\bt - \\al ) \\; .\n\\ee\nThen expression (3.8) becomes the iterated root approximant\n\\be\n\\label{3.15}\n R_k(x) = \\left ( \\left ( \\left ( \\ldots ( 1 + A_1 x )^2 +\nA_2x^2 \\right )^{3\/2} + A_3x^3 \\right )^{4\/3} + \\ldots +\nA_k x^k \\right )^{\\gm\/k} \\; ,\n\\ee\nwhere all parameters $A_j$ are uniquely defined by the accuracy-through-order \nprocedure. \n\nIn the large-variable limit, Eq. (3.15) yields\n\\be\n\\label{3.16}\n R_k \\simeq \\frac{B_k}{A} \\; x^\\gm \\qquad (x\\ra\\infty) \\; ,\n\\ee\nwith the critical amplitude\n\\be\n\\label{3.17}\nB_k = A \\left ( \\left ( \\ldots \\left ( A_1^2 + A_2 \\right )^{3\/2}\n+ A_3 \\right )^{4\/3} + \\ldots + A_k \\right )^{\\gm\/k} \\; .\n\\ee\n\nIt may happen that the iterated root approximants are well defined up to an \norder $k$, after which they do not exist because some of the parameters $A_p$ \nare negative. At the same time, the higher-order terms of perturbation-theory\nexpansion can be available up to an order $k+p$. How then could we use these\nadditional terms for constructing the higher-order approximants? \n\n\n\\subsection{Corrected root approximants}\n\nCorrections to the iterated root approximants (3.15), employing the higher-order \nterms, can be constructed \\cite{Glu2010} by defining the corrected root \napproximants\n\\be\n\\label{3.18}\n \\widetilde R_{k\/p}(x) = R_k(x) C_{k\/p}(x) \\; ,\n\\ee\nwith the correction function \n\\be\n\\label{3.19}\n C_{k\/p}(x) = 1 + d_{k+1} x^{k+1} \\left ( \\left ( \\left (\n\\dots ( 1 + b_1 x )^2 + b_2 x^2 \\right )^{3\/2} + \nb_3 x^3 \\right )^{4\/3} + \\ldots + b_{p-1} x^{p-1} \n\\right )^{-(k+1)\/(p-1)} \\; ,\n\\ee\nwhere $p>2$ and all parameters are defined from the accuracy-through-order \nprocedure, when the terms of the expansion of form (3.18) are equated with\nthe corresponding terms of the perturbation theory expansion. Here, the critical \nexponent is defined by the iterated root approximant (3.16), so that the \nlimit $x \\ra \\infty$ of the correction function is finite:\n\\be\n\\label{3.20}\nC_{k\/p}(\\infty) = 1 + d_{k+1} \\left ( \\left (\n\\dots \\left ( b_1^2 + b_2 \\right )^{3\/2} + b_3 \\right )^{4\/3} + \n \\ldots + b_{p-1} \\right )^{-(k+1)\/(p-1)} \\; .\n\\ee\n\nThe corresponding approximation for the sought function takes the form\n\\be\n\\label{3.21}\n f_{k\/p}^*(x) = f_0(x) \\widetilde R_{k\/p}(x) \\; .\n\\ee\nIts large-variable behaviour is\n\\be\n\\label{3.22}\n f_{k\/p}^*(x) \\simeq B_{k\/p} x^\\bt \\qquad (x\\ra\\infty) \\; ,\n\\ee\nwith the corrected critical amplitude\n\\be\n\\label{3.23}\nB_{k\/p} = A B_k C_{k\/p}(\\infty) \\; .\n\\ee\n\n\n\\subsection{Self-similar power transforms}\n\nIt is possible to get improvement of approximants by employing power \ntransforms \\cite{Glu2006}. For this purpose, we define the power transform \nof the reduced expansion (2.3) as\n\\be\n\\label{3.24}\nP_k(x,m) \\equiv \\overline f_k^m(x) \\; ,\n\\ee\nwhich is expanded in powers of $x$ giving\n\\be\n\\label{3.25}\n P_k(x,m) \\cong \\sum_{n=0}^k b_n(m) x^n \\; .\n\\ee\nAfter the self-similar renormalization of expansion (3.25), we get a \nself-similar approximant $P_k^*(x,m)$. We then accomplish the inverse\ntransformation \n\\be\n\\label{3.26}\n \\overline F_k(x,m) = \\left [ P_k^*(x,m) \\right ]^{1\/m} \\; .\n\\ee\nThe powers $m_k =m_k(x)$ are defined by the variational condition\n\\be\n\\label{3.27}\n \\frac{\\prt\\overline F_k(x,m)}{\\prt m} = 0 \\; .\n\\ee\nFinally, the corresponding approximation for the sought function is given by\n\\be\n\\label{3.28}\n f_k^*(x) = f_0(x) \\overline F_k(x,m_k) \\; .\n\\ee\nWhen we are interested in the large-variable limit, condition (3.27) reduces \nto the differentiation of only critical amplitude. \n\n\n\\subsection{Double self-similar approximants}\n\nAnother way of improving the accuracy is by employing the procedure of \nself-similar renormalization twice. The fact that the accuracy does improve can \nbe illustrated by those examples for which exact solutions are known. \n\nThe double renormalization is accomplished as follows. First, renormalizing \nthe reduced expansion (2.3), we construct the self-similar approximants (2.5). \nThe approximants $\\overline f_k^*(x)$ form the approximation sequence \n$\\{\\overline f_k^*(x)\\}$. Introducing the expansion function $x(\\vp)$ by the equation\n\\be\n\\label{3.29} \n \\overline f_1^*(x) = \\vp \\; , \\qquad x = x(\\vp) \\; ,\n\\ee\nwe define\n\\be\n\\label{3.30}\n y_k(\\vp) = \\overline f_k^*( x(\\vp) ) \\; .\n\\ee\nBy this definition, the sequence $\\{y_k(\\vp)\\}$ is bijective to the sequence \n$\\{\\overline f_k^*(x)\\}$. In view of Eq. (3.29), we have\n\\be\n\\label{3.31}\n y_1(\\vp) = \\vp \\; .\n\\ee\n\nConsider the sequence $\\{y_k(\\vp)\\}$ as the trajectory of a dynamical system\nin discrete time, that is, of a cascade, with the initial condition (3.31). \nEmbed this approximation cascade into an approximation flow:\n\\be\n\\label{3.32}\n \\{ y_k(\\vp) : \\; k \\in \\mathbb{Z}_+ \\} \\subset\n\\{ y(t,\\vp) : \\; t \\in \\mathbb{R}_+ \\} \\; ,\n\\ee\nwhere\n$$\n \\mathbb{Z}_+ \\equiv \\{ 0, 1, 2, \\ldots \\} \\; , \\qquad\n\\mathbb{R}_+ \\equiv [0, \\infty) \\; ,\n$$\nso that the flow trajectory passes through all points of the cascade trajectory,\n\\be\n\\label{3.33}\n y(t,\\vp) = y_k(\\vp) \\qquad ( t = k) \\; .\n\\ee\nThe evolution equation for the flow reads as\n\\be\n\\label{3.34}\n\\frac{\\prt}{\\prt t} \\; y(t,\\vp) = v(y) \\; ,\n\\ee\nwith $v(y)$ being the flow velocity.\n\nIntegrating the evolution equation (3.34) gives\n\\be\n\\label{3.35}\n \\int_{y_k}^{y_k^*} \\frac{dy}{v(y)} = \\tau_k \\; ,\n\\ee\nwhere $y_k = y_k(\\vp)$ and $\\tau_k$ is the minimal effective time necessary \nfor reaching the approximate fixed point $y_k^*(\\vp)$. The latter, \naccording to definition (3.30), is a twice renormalized self-similar \napproximant\n\\be\n\\label{3.36}\n y_k^*(\\vp) = \\overline f_k^{**}(x(\\vp) ) \\; .\n\\ee\nKeeping in mind definition (3.30) also allows us to rewrite integral (3.35) \nas\n\\be\n\\label{3.37}\n \\int_{\\overline f_k^*}^{\\overline f_k^{**}} \\frac{d\\vp}{v_k(\\vp)} = \n\\tau_k \\; ,\n\\ee\nwhere\n$$\n \\overline f_k^{*} = \\overline f_k^{*}(x) \\; , \\qquad\n\\overline f_k^{**} = \\overline f_k^{**}(x) \\; .\n$$\nAssuming that we reach the quasi-fixed point in one step, we may set \n$\\tau_k =1$. \n\nEmploying in the evolution integral (3.37) the Euler discretization for \nthe velocity\n\\be\n\\label{3.38}\n v_k(\\vp) = y_k(\\vp) - \\vp = \\overline f_k^{*}(x(\\vp)) -\n\\overline f_1^{*}(x(\\vp)) \n\\ee\nand calculating this integral gives the twice renormalized approximant \nfor the sought function\n\\be\n\\label{3.39}\n \\overline f_k^{**}(x) = f_0(x) \\overline f_k^{**}(x) \\; .\n\\ee\nThe large-variable limit of the latter \n\\be\n\\label{3.40}\n\\overline f_k^{**}(x) \\simeq B_k^* x^\\bt \\qquad \n(x \\ra \\infty)\n\\ee\ndefines the approximate expression for the critical amplitude $B_k^*$. \nUsually, integral (3.37) can be calculated only numerically. \n\nIn the following sections, the above methods of extrapolation will be\nillustrated by a number of examples of different nature, with the emphasis\non the large-variable limit $x \\ra \\infty$. Analyzing these examples, \nwe shall pay most attention to the possibility of obtaining accurate\napproximate expressions by taking just a few terms in the small-variable \nexpansions, bearing in mind that complicated realistic problems usually \nprovide us with only a small number of terms of perturbation theory. \n\n\n\\section{Explicitly defined functions}\n\nIn order to clearly demonstrate how the method works and to show that it\nreally provides good accuracy, it is illustrative to start with functions \nwhose explicit form is given. This will allow us to easily evaluate the \naccuracy of approximants. The consideration of such simpler cases is \nnecessary before considering the complicated problems whose exact solutions \nare not known, since only then it is possible to explicitly demonstrate \nthe efficiency of the method and to evaluate what accuracy of the used \napproximants should be expected.\n\nThe variable $x$ will be varying in the range \n$[0, \\infty)$.\n\n\\subsection{Function-1}\n\nConsider a function\n\\be\n\\label{4.1}\nf(x) = \\frac{1}{2} \\left ( \\sqrt{4+x} \\; - \\; 1 \\right ) \\; ,\n\\ee\nwhich is of importance because of giving the golden ratio\n$$\n\\frac{1}{f(1)} = 1 + f(1) = 1.618034 \\; .\n$$\nIn its small-variable expansion \n\\be\n\\label{4.2}\nf_k(x) = \\sum_{n=0}^k c_n x^n\n\\ee\nthe first five coefficients are\n$$\n c_0 = \\frac{1}{2} \\; , \\qquad c_1 = \\frac{1}{8} \\; , \\qquad\nc_2 = -\\; \\frac{1}{128} \\; , \\qquad c_3 = \\frac{1}{1024} \\; , \n\\qquad c_4 = -\\;\\frac{5}{32768} \\; .\n$$\nHere $f_0 = c_0$.\n\nDespite its simplicity, this function expansion is not trivial, since the \nfirst two coefficients are positive, after which they start alternating.\n\nThe large-variable behaviour \n\\be\n\\label{4.3}\nf(x) \\simeq B x^\\bt = 0.5 \\sqrt{x}\n\\ee\nshows that \n$$\n B = 0.5 \\; , \\qquad \\bt = 0.5 \\; .\n$$\nUsing the approximants, described above, we fix the exponent $\\beta$,\nconcentrating on the accuracy of calculating the critical amplitude.\n\nThe method of factor approximants of Sec. 3.1 yields $B_4 = 0.440$.\nPower transforms of Sec. 3.5, with the factor approximants, do not \nprovide essential improvement. The optimization condition (3.27) \nresults in two solutions for $m$, which yields for the amplitudes\nthe values $0.416$ and $0.455$. The iterated root approximants of \nSec. 3.3 give $B_2 = 0.374, B_3 = 0.385, B_4 = 0.393$. The corrected \niterated roots of Sec. 3.4 give $B_{2\/2} = 0.422$. Power transforms, \nwith iterated roots again yield two solutions for $B_2$, with the values\n$0.404$ and $0.433$. All these results are close to the Pad\\'{e} \napproximant $P_{2\/2} = 0.433$. Essential improvement of accuracy is \nachieved by the double approximants of Sec. 3.6 on the basis of the \niterated roots, giving $B_4^* = 0.476$. \n\n\n\\subsection{Function-2}\n\nLet us take a more complicated function\n\\be\n\\label{4.4} \n f(x) = \\frac{2}{\\pi} \\; {\\rm arccot}(-x) \\exp \\left ( 1\n- \\; \\frac{1}{1+x} \\right ) \\; .\n\\ee\nIn expansion (4.2), using the value ${\\rm arccot}(0) = \\pi\/2$, we have \n$$\nc_0 = 1 \\; , \\qquad c_1 = 1.637 \\; , \\qquad\nc_2 = 0.137 \\; , \\qquad c_3 = -0.364 \\; , \n\\qquad c_4 = -0.064 \\; .\n$$\nAgain $f_0 = c_0$. Here the first three coefficients are positive, while the \nnext two are negative. The limit at infinity is\n\\be\n\\label{4.5}\n f(\\infty) = 2e = 5.437 \\; ,\n\\ee\nwhere the equality ${\\rm arccot}(-\\infty) = \\pi$ is used. \n\nThe irregularity in the coefficient signs makes the extrapolation more \ndifficult. The factor approximants give $f_4^*(\\infty) = 9.049$. Power\ntransforms, with the factor approximants, improve the result yielding\nthe limit $5.192$. Iterated roots give $R_3(\\infty) = 3.399$, \n$R_4(\\infty) = 3.547$. Corrected iterated roots are close to the latter \nvalues: $R_{2\/2}(\\infty) = 3.424$. Power transforms, with iterated roots, \ngive two values: $3.547$ and $4.535$. As we see, the power-transformed factor \napproximants are the most accurate. \n\n\n\\subsection{Function-3}\n\nExpanding the function\n\\be\n\\label{4.6}\n f(x) = \\frac{{\\rm arccot}(-x)}{1+ e^{-x} } \\; ,\n\\ee\nwe get the coefficients\n$$\n c_0 = \\frac{\\pi}{4} \\; , \\qquad \nc_1 = \\frac{1}{2}\\left ( 1 + \\frac{\\pi}{4} \\right ) \\; , \n\\qquad\nc_2 = \\frac{1}{4} \\; , \\qquad \nc_3 = -\\; \\frac{1}{6 } \\left ( 1 + \\frac{\\pi}{16} \\right )\\; , \n\\qquad c_4 = -\\;\\frac{5}{48} \\; .\n$$\nHere $f_0 = c_0$. Again, the first three coefficients are positive, while the \nnext two are negative. The limit at infinity is\n\\be\n\\label{4.7}\n f(\\infty) = \\pi \\; .\n\\ee\n\nAs in the previous case, the irregularity in the coefficient signs makes\nextrapolation difficult. For instance, Pad\\'{e} approximants fail, the best\nof them giving $1.414$, which is rather far from limit (4.7). The factor\napproximants give $f_4^*(\\infty) = 4.759$. Power-transformed factor \napproximants are more accurate, yielding the limit $3.142$. Iterated roots\nare not good, with the limit $1.698$. Power-transformed iterated roots give\ntwo solutions: $3.742$ and $2.267$. Thus, the power-transformed factor \napproximant, with the value $3.142$, is the best.\n\n\n\\subsection{Debye-H\\\"{u}kel function}\n\n\nThe Debye-H\\\"{u}kel function\n\\be\n\\label{4.8}\n D(x) = \\frac{2}{x} - \n\\frac{2}{x^2} \\left ( 1 - e^{-x} \\right ) \n\\ee\nappears in the theory of strong electrolytes \\cite{Lan2000}. Its expansion\ngives the sign-alternating coefficients\n$$\nc_0 = 1 \\; , \\qquad c_1 = - \\; \\frac{1}{3} \\; , \n\\qquad c_2 = \\frac{1}{12} \\; \\qquad c_3 = - \\; \\frac{1}{60} \\; ,\n$$\n$$\nc_4 = \\frac{1}{360} \\; , \n\\qquad c_5 = -\\; \\frac{1}{2520} \\; \\qquad c_6 = \\frac{1}{20160} \\; .\n$$\nHere $f_0 = c_0$.\n\nThe large-variable behaviour is\n\\be\n\\label{4.9}\n D(x) \\simeq \\frac{2}{x} \\qquad ( x \\ra \\infty) \\; .\n\\ee\n\nFactor approximants give $B_4 = 1.640$. Power-transformed factor approximants\nresult in $B_5 = 1.779$. Corrected factor approximants yield $B_{2\/2} = 1.642$. \nIterated roots result in $B_2 = 2.449$, $B_3 = 2.229$, $B_4 = 2.127$. For \ncorrected iterated roots, we have $B_{1\/2} = 1.611$, $B_{1\/3} = 1.841$, \n$B_{1\/4} = 1.934$, $B_{2\/2} = 1.130$, $B_{2\/3} = 1.712$, $B_{2\/4} = 1.811$. \nPower-transformed iterated roots in the fourth order give two solutions: \n$1.993$ and $2.049$. The best two-point Pad\\'{e} approximant $P_{2\/2}$ \ngives the critical amplitude $1.333$, which is much worse than the self-similar\napproximants of the same fourth order.\n\n\n\\subsection{Stirling function}\n\nThe Stirling series expansion for the function\n\\be\n\\label{4.10}\n f(x) = \\frac{1}{\\sqrt{2\\pi}} \\; e^{1\/x} x^{1\/x} \\Gm\\left ( 1 +\n\\frac{1}{x} \\right ) \n\\ee\ncan be written as\n\\be\n\\label{4.11}\nf_k(x) = \\frac{1}{\\sqrt{x}} \\left ( 1 + \n\\sum_{n=1}^k a_n x^n \\right ) \\; ,\n\\ee\nwith the coefficients\n$$\n a_1 = \\frac{1}{12} \\; , \\qquad a_2 = \\frac{1}{288} \\; \n\\qquad a_3 = -\\; \\frac{139}{51840} \\; ,\n$$\n$$\n a_4 = -\\;\\frac{571}{2488320} \\; , \\qquad \na_5 = \\frac{163879}{209018880} \\; \\qquad \na_6 = \\frac{5246819}{75246796800} \\; ,\n$$\n$$\n a_7 = -\\;\\frac{534703531}{902961561600} \\; , \\qquad \na_8 = -\\; \\frac{4483131259}{86684309913600} \\; .\n$$\nHere $f_0 = 1\/ \\sqrt{x}$.\n\nThe limit at infinity is\n\\be\n\\label{4.12}\nf(\\infty) = \\frac{1}{\\sqrt{2\\pi}} = 0.398942 \\; .\n\\ee\n\nFactor approximants yield the limit $f_6^*(\\infty) = 0.454$. \nPower-transformed factor approximants improve the accuracy, giving \n$f_5^*(\\infty) = 0.406$. Iterated roots result in $B_2 = 0.485$, $B_3 = 0.422$, \nbut the fourth-order approximant is complex. Corrected iterated roots give \nthe limit $B_{2\/1} = 0.408$, $B_{2\/2} = 0.312$, $B_{2\/3} = 0.405$. Pad\\'{e}\napproximants are essentially worse. \n\n\n\\section{Functions defined through integrals}\n\nMany functions are defined by means of integral representations. Expansions\nof such functions often result in strongly divergent series. However, \nself-similar approximants provide rather accurate extrapolation from the zero\nvariable to its infinite limit. \n\n\n\\subsection{Integral-1}\n\nConsider the integral\n\\be\n\\label{5.1}\nf(x) = ( 1 + 2x) \\int_0^\\infty \\frac{e^{-t}}{1+x^2t^2} \\; dt \\; .\n\\ee\nIts expansion in powers of $x$ contains the coefficients\n$$\nc_0 = 1 \\; , \\qquad c_1 = 2 \\; , \\qquad\nc_2 = -2 \\; , \\qquad c_3 = -4 \\; , \\qquad c_4 = 24 \\; ,\n$$\n$$\nc_5 = 48 \\; , \\qquad c_6 = -720 \\; , \\qquad\nc_7 = -1440 \\; , \\qquad c_8 = 40320 \\; , \\qquad c_9 = 80640 \\; .\n$$\nThe general expressions for the latter are\n$$\n c_{2n} = (-1)^n (2n)! \\; , \\qquad c_{2n+1} = (-1)^n 2 (2n)! \\; .\n$$\nThe limit of Eq. (5.1) at infinity is\n\\be\n\\label{5.2}\n f(\\infty) = \\pi \\; .\n\\ee\n\nFactor approximants yield $f_4^*(\\infty) = 1.965$, $f_5^*(\\infty) = 2.015$,\ndemonstrating good numerical convergence, e.g., giving in the ninth order\nthe limit $3.113$. Iterated roots lead to $R_2(\\infty) = 1.754$, \n$R_3(\\infty) = 2.071$, but the higher-order approximants are complex. \nPower-transformed iterated roots in fourth order give two solutions, \n$1.971$ and $2.071$. Corrected iterated roots in the fourth order\ngive $2.582$ and display good numerical convergence in higher orders. \nPad\\'{e} approximants of the same order are less accurate, for instance, \n$P_{2\/2} = 1.875$.\n \n\n\\subsection{Complimentary error function}\n\nThe complimentary error function\n\\be\n\\label{5.3}\nf(x) = {\\rm erfc}(-x)\n\\ee\nis expressed through the error function as\n$$\n {\\rm erfc}(x) \\equiv 1 - {\\rm erf}(x) \\; ,\n$$\nthe error function being\n$$\n {\\rm erf}(x) \\equiv \\frac{2}{\\sqrt{\\pi}} \\int_0^x e^{-t^2} dt \\; .\n$$\nHence, function (5.3) is defined by means of the integral\n$$\n {\\rm erfc}(x) \\equiv \n\\frac{2}{\\sqrt{\\pi}} \\int_x^\\infty e^{-t^2} dt \\; .\n$$\nExpanding Eq. (5.3), we get the coefficients\n$$\nc_0 = 1 \\; , \\qquad c_1 = 1.12838 \\; , \\qquad\nc_2 = 0 \\; , \\qquad c_3 = -0.37613 \\; , \\qquad c_4 = 0 \\; .\n$$\nThe limit at infinity is\n\\be\n\\label{5.4}\nf(\\infty) = 2 \\; .\n\\ee\n\nAll self-similar approximants give close results. Thus, factor \napproximants yield $f_4^*(\\infty) = 3.772$. Iterated roots give \n$R_3(\\infty) = 2.382$. Power-transformed iterated roots of fourth order \nhave two solutions $2.305$ and $3.739$. Taking into account more \nexpansion terms results in better accuracy. Thus, $f_5^*(\\infty)=2.629$.\n\n\n\\subsection{Integral-2}\n\nThe function\n\\be\n\\label{5.5}\nf(x) = \\frac{{\\rm erfc}(-x)}{1+e^{-x}}\n\\ee\nis defined through the integral representation for the complimentary \nerror function considered in the previous subsection. The coefficients \nof the corresponding expansion are\n$$\n c_0 = \\frac{1}{2} \\; , \\qquad c_1 = 1 \\; , \\qquad\nc_2 = 1.62838 \\; , \\qquad c_3 = -0.41779 \\; , \\qquad \nc_4 = -0.23508 \\; .\n$$\nThe limit at infinity is\n\\be\n\\label{5.6}\n f(\\infty) = 2\\; .\n\\ee\n\nFactor approximants overestimate the limit, yielding \n$f_3^*(\\infty) = 5.052$, $f_5^*(\\infty) = 3.286$. Power-transformed factor \napproximants, on the other hand, underestimate it, giving to fourth order \n$1.392$. Iterated root approximants lead to $B_3 = 1.371$, $B_4 = 1.893$. \nPower-transformed iterated roots to fourth order give the limit $1.684$. \nIn the same order, Pad\\'{e} approximants give $1.027$. Iterated root \napproximants here are the most accurate. \n\nThe large-variable behaviour of functions (5.3) and (5.5) involves \nexponentials. Therefore the accuracy of approximations can be essentially \nimproved by employing exponential self-similar approximants \\cite{YukGlu1998}. \nHowever, here we limit ourselves by the analysis of approximants described \nin Sec. 3.\n\n\\subsection{Mittag-Leffler function}\n\nA particular case of the Mittag-Leffler function\n\\be\n\\label{5.7}\n E(x) = e^{x^2} {\\rm erfc}(x) \\; ,\n\\ee\nwhich is expressed through the complimentary error function, appears in\nthe model of anomalous diffusion \\cite{Pir2005}. The small-variable \nexpansion yields the coefficients\n$$\n c_0 = 1 \\; , \\qquad c_1 = -\\; \\frac{2}{\\sqrt{\\pi}} \\; , \\qquad\nc_2 = 1 \\; , \\qquad c_3 = -\\; \\frac{4}{3\\sqrt{\\pi}} \\; , \\qquad \nc_4 = \\frac{1}{2} \\; .\n$$\nIn the large-variable limit, one has\n\\be\n\\label{5.8}\n E(x) \\simeq \\frac{B}{x} \\qquad (x \\ra \\infty) \\; ,\n\\ee\nwith the critical amplitude \n\\be\n\\label{5.9}\n B = \\frac{1}{\\sqrt{\\pi}} = 0.56419 \\; .\n\\ee\n\nFactor approximants give in fourth order $B_4 = 0.511$. The same result \nholds for the corrected factor approximants $B_{2\/2} = 0.511$. \nPower-transformed factors yield, in fourth order, the amplitude $0.541$. \nIterated roots lead to $B_1 = 0.886$, $B_2 = 0.741$, $B_3 = 0.680$, \n$B_4 = 0.650$. Corrected iterated roots give in fourth order $0.403$. \nPower-transformed iterated roots yield three solutions, all being close \nto $0.641$. The accuracy improves, when more terms in the expansion are \ntaken into account. For instance, the factor approximants in sixth order \ngive $B_6 = 0.532$. \n\n\n\\section{Anharmonic and nonlinear models}\n\n\nDivergent series often appear in applying perturbation theory to \nanharmonic and nonlinear models that are typical for many problems in \nphysics and chemistry. In these problems, perturbation theory is usually \ndone with respect to a parameter called the {\\it coupling parameter} which \ncharacterizes the strength of interactions or anharmonicity of an external\nfield.\n\n\n\\subsection{Zero-dimensional anharmonic model}\n\n\nThis is one of the simplest models that, at the same time, demonstrates \nmathematical features typical of many problems in chemistry and physics.\nThe partition function of this model reads as\n\\be\n\\label{6.1} \n Z(g) = \\frac{1}{\\sqrt{\\pi}} \\int_{-\\infty}^\\infty\n\\exp\\left ( - x^2 - gx^4 \\right ) \\; dx \\; ,\n\\ee\nwhere $g \\in [0, \\infty)$ is a dimensionless coupling parameter. Weak-coupling\nperturbation theory yields the series \n\\be\n\\label{6.2}\n Z_k(g) = \\sum_{n=0}^k c_n g^n \\; ,\n\\ee\nwith the coefficients\n$$\n c_n = \\frac{(-1)^n}{\\sqrt{\\pi}\\; n!} \\; \\Gm\\left ( 2n +\n\\frac{1}{2} \\right ) \\; .\n$$\nExplicitly, the first few coefficients are\n$$\n c_0 = 1 \\; , \\qquad c_1 = -\\; \\frac{3}{4} \\; , \\qquad\nc_2 = \\frac{105}{32} \\; , \n$$\n$$\nc_3 = -\\; \\frac{3465}{128} \\; , \\qquad\nc_4 = \\frac{675675}{2048} \\; .\n$$\nIn the strong-coupling limit,\n\\be\n\\label{6.3}\n Z(g) = \\simeq B g^{-1\/4} \\qquad (g\\ra\\infty ) \\; ,\n\\ee\nwith \n\\be\n\\label{6.4}\n B = 1.022765 \\; .\n\\ee\n\nFixing the exponent $\\beta$, we calculate the critical amplitude $B_k$, \ncomparing it with the known exact value from Eq. (6.4). Factor approximants\ngive to fourth order $B_4 = 0.838$. Corrected factor approximants, to the \nsame order, yield $B_{2\/2} = 1.131$. Iterated root approximants give \n$B_2 = 0.760$, but the higher-order approximants are complex. Corrected \niterated roots result in $B_{2\/2} = 0.678$. Power-transformed iterated \nroots of fourth order produce two solutions, $0.879$ and $0.971$. As we see,\nthe best accuracy is provided by the corrected factor approximants and \npower-transformed iterated roots. \n\n\n\\subsection{One-dimensional anharmonic oscillator}\n\n\nThe anharmonic oscillator is described by the Hamiltonian\n\\be\n\\label{6.5}\n \\hat H = - \\; \\frac{1}{2} \\; \\frac{d^2}{dx^2} +\n\\frac{1}{2} \\; x^2 + gx^4 \\; ,\n\\ee\nin which $x \\in (-\\infty, +\\infty)$ and $g$ is a positive anharmonicity \nparameter. Perturbation theory for the ground-state energy yields \n\\cite{Hio1978} the series\n\\be\n\\label{6.6}\n E_k(g) = \\sum_{n=0}^k c_n g^n \\; ,\n\\ee\nwith the coefficients\n$$\nc_0 = \\frac{1}{2} \\; , \\qquad c_1 = \\frac{3}{4} \\; , \\qquad\nc_2 = -\\; \\frac{21}{8} \\; \\qquad c_3 = \\frac{333}{16} \\; , \\qquad\nc_4 = -\\; \\frac{30885}{128} \\; .\n$$\nThe strong-coupling limit is\n\\be\n\\label{6.7}\n E(g) \\simeq 0.667986 g^{1\/3} \\qquad\n(g \\ra \\infty ) \\; .\n\\ee\n\nFactor approximants give $B_3 = 0.750$, $B_5 = 0.725$, $B_7 = 0.712$.\nCorrected factor approximants yield $B_{3\/4} = 0.728$. The power-transformed\nfactor approximant of fourth order gives $0.681$. Iterated root \napproximants result in $B_2 = 0.572$, $B_3 = 0.855$, but the fourth-order\napproximant is complex. Corrected iterated roots give $B_4 = 0.587$, and\npower-transformed iterated roots, $0.665$. The latter value is the closest\nto the exact amplitude in Eq. (6.7).\n\nComparing these results with those obtained by means of the Kleinert variational\nperturbation theory \\cite{Jan1995}, we see that the latter provides better \naccuracy. However, we would like to recall that our main aim in the present \npaper is to test the methods of self-similar approximation theory, without \ninvolving the introduction of variational or other control functions, and based \non just a few initial terms of perturbation theory. Although, in our case, the \naccuracy is lower than in the Kleinert method, the calculations are much \nsimpler. \n\n\n\\subsection{Scalar field theory}\n\n\nConsider the so-called $m \\phi^2$ quantum field theory on a $d$-dimensional\ncubic lattice with lattice spacing $a$. The free energy of the system can be\nexpressed \\cite{Ben1994} as the integral\n\\be\n\\label{6.8}\nf(x) = x \\exp \\left \\{ 2 \\int_0^\\infty e^{-t} \\ln \\left [\ne^{-xt} I_0(xt) \\right ] dt \\right \\} \\; ,\n\\ee\nwhere $I_0(\\cdot)$ is a modified Bessel function of zero order and \n$x = 1\/ m a^2$. Expanding the integral in powers of the variable $x$ yields \nthe series \n\\be\n\\label{6.9}\n f_k(x) = x \\left ( 1 + \\sum_{n=1}^k a_n x^n \\right ) \\; ,\n\\ee\nwith the coefficients\n$$\na_1 = -2 \\; , \\qquad a_2 = 3 \\; , \\qquad a_3 = - \\; \\frac{10}{3} \\; ,\n\\qquad a_4 = \\frac{29}{12} \\; ,\n$$\n$$\na_5 = - \\; \\frac{11}{10} \\; ,\\qquad a_6 = \\frac{391}{180} \\; \\qquad\na_7 = - \\; \\frac{2389}{630} \\; .\n$$\nWhen passing to continuous space, one takes the limit $a \\ra 0$, which means\nthat $x \\ra \\infty$. The sought continuous-space limit is\n\\be\n\\label{6.10}\n f(\\infty) = \\frac{e^\\gm}{2\\pi} = 0.28347 \\; .\n\\ee\n\nFactor approximants of fourth order give the limit $0.322$ and \npower-transformed factor approximants, $0.333$. Iterated root approximants \nyield $f^*_2(\\infty) = 0.408$, $f^*_3(\\infty) = 0.377$, $f^*_4(\\infty) = 0.365$. \nTheir accuracy can be improved by taking more terms in expansion (6.9), e.g., \n$f^*_{12}(\\infty) = 0.280$. Corrected iterated roots give $f^*_{2\/2}(\\infty) = 0.266$, \nand power-transformed iterated roots of fourth order lead to $0.356$ and $0.347$. \nThe best Pad\\'{e} approximant, up to fifth order, gives $P_{2\/3} = 0.326$. \nFor these low orders, the most accurate is the corrected root approximant \n$f^*_{2\/2}(\\infty) = 0.266$. \n\n\n\\subsection{Nonlinear Schr\\\"{o}dinger equation}\n\n\nThe nonlinear Schr\\\"{o}dinger equation serves as a basic tool for modelling \nseveral different problems, such as those of waves on the surface of a deep \nfluid \\cite{Zak1968}, electromagnetic waves in fibre optics \\cite{Kar2011}, \nand Bose-Einstein condensates \\cite{Pet2008, Yuk2009a, Yuk2011}. For the last \ncase, it is often called the Gross-Pitaevskii equation, although Bogolubov was\nthe first to write down this equation for Bose systems in his famous Lectures \non Quantum Statistics published in 1949 \\cite{Bog1949} and wrote on it many \ntimes since (see, e.g., Refs. \\cite{Bog1967, Bog1970}). This equation for \nnonequilibrium superfluids was also studied in \\cite{Bog1963}. The one-dimensional \nstationary nonlinear Schr\\\"{o}dinger equation for Bose condensed atoms in a \nharmonic trap reads \n\\be\n\\label{6.11}\n\\hat H_{NLS} \\psi = E\\psi \\; ,\n\\ee\nwith the nonlinear Hamiltonian\n\\be\n\\label{6.12}\n \\hat H_{NLS} = - \\; \\frac{1}{2} \\; \\frac{d^2}{dx^2} +\n\\frac{1}{2} \\; x^2 + g| \\psi|^2 \\; .\n\\ee\nHere $g$ is a dimensionless coupling parameter. The energy levels can be \nrepresented in the form\n\\be\n\\label{6.13}\n E(g) = \\left ( n + \\frac{1}{2} \\right ) f(g) \\; ,\n\\ee\nwhere $n = 0,1,2,\\ldots$ is a quantum index labelling the eigenvalues. \nEmploying the optimized perturbation theory for the function $f(g)$,\nas in \\cite{Yuk1998}, gives the expansion\n\\be\n\\label{6.14}\n f_k(g) = 1 + \\sum_{n=1}^k a_n z^n \n\\ee\nin powers of the effective coupling \n$$\nz \\equiv \\frac{J_n}{n+1\/2} \\; g \\; ,\n$$\nin which\n$$\n J_n \\equiv \\frac{1}{2^n\\pi n!} \\int_{-\\infty}^\\infty\n\\exp \\left ( -2x^2 \\right ) H_n^4(x) \\; dx \\; ,\n$$\nwith $H_n(\\cdot)$ being a Hermite polynomial. The coefficients in \nexpansion (6.14) are\n$$\n a_1 = 1 \\; , \\qquad a_2 = - \\; \\frac{1}{8} \\; , \\qquad \na_3 = \\frac{1}{32} \\; , \\qquad a_4 = - \\; \\frac{1}{128} \\; .\n$$\nThen for the strong-coupling limit we have\n\\be\n\\label{6.15}\n f(g) \\simeq \\frac{3}{2} \\; z^{2\/3} \\qquad ( z\\ra \\infty) \\; .\n\\ee\nHence the critical amplitude is $B = 3\/2$.\n\nFactor approximants give $B_4 = 1.496$, which is very close to $1.5$.\nCorrected factor approximants, to fourth order, yield $1.451$ and \npower-transformed factor approximants, $1.477$. Iterated roots result \nin $B_2 = 1.379$, $B_3 = 1.415$, $B_4 = 1.435$. Corrected iterated roots\ngive $B_{2\/2} = 1.492$ and power-transformed iterated roots, $1.426$. \nFor the double self-similar approximant, based on iterated roots, we \nget $B_4^* = 1.498$. The latter is slightly better than the \nvalue $B_4 = 1.496$, given by the factor approximant, but calculating \nthe doubly renormalized approximants is essentially more complicated. \nOf course, calculations, employing any of the self-similar approximants, \nare much less time consuming than the direct solution of the nonlinear \ndifferential equation (6.11). \n\n\\section{Problems in many-body theory}\n\nPerturbation theory in many-body problems is usually accomplished with \nrespect to the coupling parameter characterizing the interaction \nstrength. However, this coupling parameter is often rather large. \nMoreover, perturbative expansions practically always yield divergent \nseries for any finite value of the coupling parameter. Another difficulty\nis that the many-body problems, as a rule, are so much complicated that\nthey allow one to calculate only a few low-order terms of perturbation \ntheory. We show here that self-similar approximants allow for an \neffective extrapolation of such short series, giving good accuracy even \nin the extreme case of infinitely strong coupling. \n\n\n\\subsection{Lieb-Liniger Bose gas}\n\n\nLieb and Liniger \\cite{Lie1963} have considered a one-dimensional Bose \ngas with contact interactions. The ground-state energy of the gas can be \nwritten as an expansion with respect to the coupling parameter as\n\\be\n\\label{7.1}\n E(g) \\simeq g - \\; \\frac{4}{3\\pi} \\; g^{3\/2} +\n\\frac{1.29}{2\\pi^2} \\; g^2 - 0.017201 g^{5\/2} \\; .\n\\ee \nIn the strong-coupling limit, we have the Tonks-Girardeau expression\n\\be\n\\label{7.2}\n E(\\infty) = \\frac{\\pi^2}{3} = 3.289868 \\; .\n\\ee\nBy the change of the variables\n\\be\n\\label{7.3}\ne(x) \\equiv E\\left ( x^2 \\right ) \\; , \\qquad\ng \\equiv x^2 \\;\n\\ee\nexpansion (7.1) reduces to the form\n\\be\n\\label{7.4}\n e(x) \\simeq x^2 \\left ( 1 + a_1 x + a_2 x^2\n+ a_3 x^3 \\right ) \\; ,\n\\ee\nin which\n$$\na_1 = - \\; \\frac{4}{3\\pi}=- 0.424413 \\; , \\qquad\na_2 = \\frac{1.29}{2\\pi^2} = 0.065352 \\; , \\qquad\na_3=-0.017201 \\; .\n$$\nThe fourth-order term can be set as having $a_4 = 0$.\n\nDifferent self-similar approximants yield close results. The most accurate\namong them correspond to iterated root approximants displaying fast \nnumerical convergence: $E^*_2(\\infty) = 8.713$, $E^*_3(\\infty) = 4.765$,\n$E^*_4(\\infty) = 3.2924$. The last expression provides very good accuracy,\nwhen compared with the exact value (7.2). \n\n\n\\subsection{Bose-Einstein condensation temperature}\n\n\nThe Bose-Einstein condensation temperature of ideal uniform Bose gas in\nthree-dimensional space is known to be\n\\be\n\\label{7.5}\n T_0 = \\frac{2\\pi\\hbar^2}{mk_B} \\left [\n\\frac{\\rho}{\\zeta(3\/2)} \\right ]^{2\/3} \\; ,\n\\ee\nwhere $m$ is atomic mass and $\\rho$, gas density. The ideal gas is, however, \nunstable below the condensation temperature \\cite{Yuk2011}. Atomic \ninteractions stabilize the system and shift the transition temperature\nby the amount\n\\be\n\\label{7.6}\n \\Dlt T_c \\equiv T_c - T_0 \\; .\n\\ee\nThis shift, at asymptotically small gas parameter\n\\be\n\\label{7.7}\n \\gm \\equiv \\rho^{1\/3} a_s \\; ,\n\\ee\nin which $a_s$ is atomic scattering length, behaves as\n\\be\n\\label{7.8}\n \\frac{\\Dlt T_c}{T_0} \\simeq c_1 \\gm \\qquad ( \\gm \\ra 0 ) \\; .\n\\ee\nMonte Carlo simulations \\cite{Arn2001a, Arn2001b, Kas2001, Pro2001, Nho2004} \ngive \n\\be\n\\label{7.9}\n c_1 = 1.3. \\pm 0.05 \\; .\n\\ee\n\nAt the same time, the coefficient $c_1$ can be defined \n\\cite{Kas2004a, Kas2004b, Kas2004c} as the strong-coupling limit \n\\be\n\\label{7.10}\nc_1 = \\lim_{g\\ra\\infty} c_1(g) \\equiv B \n\\ee\nof a function $c_1(g)$ that is available only as an expansion in an \neffective coupling parameter,\n\\be\n\\label{7.11}\n c_1(g) \\simeq b_1 g + b_2 g^2 + b_3 g^3 + b_4 g^4 + b_5 g^5 \\; ,\n\\ee\nwhere \n$$\n b_1=0.223286\\; , \\qquad b_2=-0.0661032 \\; , \\qquad\nb_3=0.026446 \\; , $$\n$$\nb_4=-0.0129177 \\; , \\qquad b_5=0.00729073 \\; .\n$$\nExpansion (7.11) can be represented as\n\\be\n\\label{7.12}\nc_1(g) \\simeq b_1 g \\left ( 1 + a_1 g + a_2 g^2 + \na_3 g^3 + a_4 g^4 \\right ) \\; ,\n\\ee\nwith the coefficients\n$$\n a_n \\equiv \\frac{b_{n+1}}{b_1} \\qquad ( n = 1,2,3,4 ) \\; .\n$$\n\nPad\\'{e} approximants do not provide good accuracy, the best of them \ngives $c_1(\\infty) = 0.985$. Factor approximants, to third order, yield \n$B_3 = 1.025$. At fourth order, factor approximants give $B_4 = 1.096$, \nif one of the parameters $A_i$ is set to one, and $1.446$, if it is \ndefined by the variational procedure. On average, the latter values \ngive $B_4 = 1.271$. Iterated roots result in $B_2 = 1.383$ to second order \nand $B_3 = 0.854$ to third order; the fourth-order approximant is\ncomplex. Corrected iterated roots give \n$B_{1\/2} = 0.924$, $B_{1\/3} = 1.289$, $B_{2\/2} = 1.309$. Power-transformed \niterated roots give two solutions, $1.227$ and $1.388$, which on average\nmakes $1.308$. The corrected iterated root $B_{2\/2} = 1.309$ produces the \nmost accurate result, practically coinciding with that found by the\nMonte Carlo simulations \\cite{Arn2001a, Arn2001b, Kas2001, Pro2001, Nho2004}.\nKastening \\cite{Kas2004a, Kas2004b, Kas2004c}, using the Kleinert variational \nperturbation theory involving seven loops, found the value $1.27 \\pm 0.11$, \nwhich is close to our results. \n \n\n\\subsection{Unitary Fermi gas}\n\nThe ground-state energy of a dilute Fermi gas can be obtained by means \nof perturbation theory \\cite{Bak1999, Ket2008} with respect to the effective \ncoupling parameter\n\\be\n\\label{7.13}\ng \\equiv | k_F a_s | \\; ,\n\\ee\nwhere $k_F$ is a Fermi wave number, and $a_s$, atomic scattering length.\nThis perturbation theory yields the expansion\n\\be\n\\label{7.14}\n E(g) \\simeq c_0 + c_1 g + c_2 g^2 + c_3 g^3 + c_4 g^4 \\; ,\n\\ee\nwith the coefficients\n$$\n c_0 = \\frac{3}{10} \\; , \\qquad c_1 = - \\; \\frac{1}{3\\pi} \\; , \\qquad\nc_2 = 0.055661 \\; ,\n$$\n$$\nc_3 = -0.00914 \\; , \\qquad c_4 = -0.018604 \\; .\n$$\n \nThe scattering length, and, respectively, the effective coupling parameter\n(7.13), can be varied by means of Feshbach resonance techniques in a rather \nwide range, including $g \\ra \\infty$. The latter limit corresponds to the\nsystem called a unitary Fermi gas. Numerical calculations \\cite{Car2003, Ast2004}\nyield\n\\be\n\\label{7.15}\n E(\\infty) = 0.132 \\; .\n\\ee\n\nExpansion (7.14) can be rewritten in the form\n\\be\n\\label{7.16}\n E(g) \\simeq c_0 \\left ( 1 + a_1 g + a_2 g^2 + a_3 g^3 + a_4 g^4 \n\\right ) \\; ,\n\\ee\nin which\n$$\na_n \\equiv \\frac{c_n}{c_0} \\qquad ( n = 1,2,3,4 ) \\; .\n$$\n \nFactor approximants give $E^*_4(\\infty) = 0.174$ and corrected factor \napproximants, $0.143$. Power-transformed factor approximants yield $0.162$.\nIterated roots give $E^*_3(\\infty) = 0.169$, $E^*_4(\\infty) = 0.163$. Corrected\niterated roots result in $E^*_{1\/2}(\\infty) = 0.103$ and power-transformed\niterated roots, in $0.163$. Doubly renormalized iterated roots improve the\nlimit to $0.146$. Pad\\'{e} approximants are not accurate, the best of them \ngiving $P_{2\/2} = 0.170$.\n\n\n\\subsection{One-dimensional Heisenberg antiferromagnet}\n\n\nThe ground-state energy of an equilibrium one-dimensional Heisenberg \nantiferromagnet can be represented \\cite{Hor1984} as the infinite time \nlimit for the energy $E(t)$ of a nonequilibrium antiferromagnet. At small \ntime $t \\ra 0$, one has an expansion\n\\be\n\\label{7.17}\n E(g) \\simeq -\\; \\frac{1}{4} \\left ( 1 + \\sum_{n=1}^4 a_n t^n \n\\right ) \\; ,\n\\ee\nwith the coefficients\n$$\n a_1 = 4 \\; , \\qquad a_2 = -8 \\; , \\qquad \na_3 = -\\; \\frac{16}{3} \\; , \\qquad a_4 = 64 \\; .\n$$\nIn the other limit, this ground-state energy was calculated by Hulthen \n\\cite{Hul1938} exactly:\n\\be\n\\label{7.18}\nE = E(\\infty) = - 0.4431 \\; .\n\\ee\nWe apply the self-similar approximations to extrapolate the small-time\nexpansion (7.17) to the infinite time limit $t \\ra \\infty$ determining\n$E(\\infty)$.\n\nFactor approximants yield $E^*_4(\\infty) = - 0.570$, with power-transformed \nfactor approximants resulting in practically the same value. Corrected \nfactor approximants give $E^*_{2\/2}(\\infty) = - 0.211$. Corrected iterated \nroots also underestimate the limit, giving $- 0.254$. Iterated roots give\n$E^*_3(\\infty) = - 0.511$, $E^*_4(\\infty) = - 0.482$. Power-transformed \niterated roots yield $- 0.475$. The best Pad\\'{e} approximant is\n$P_{2\/2} = - 0.329$. The most accurate here is the power-transformed \niterated root approximant $E^*_4(\\infty) = - 0.475$.\n \n\n\n\\subsection{Fr\\\"{o}hlich optical polaron}\n\n\nThe ground-state energy of the Fr\\\"{o}hlich optical polaron, in the \nweak-coupling perturbation theory \\cite{Koc1982, Sel1989} reads as\n\\be\n\\label{7.19}\n E(g) \\simeq - g \\left ( 1 + a_1 g + a_2 g^2 \\right ) \\; ,\n\\ee\nwith the coefficients\n$$\n a_1 = 1.591962 \\times 10^{-2} \\; , \\qquad\na_2 = 0.806070 \\times 10^{-3} \\; .\n$$\nIn the strong-coupling limit, the asymptotic behaviour of the \nground-state energy has been found by Miyake \\cite{Miy1975, Miy1976}\nin the form\n\\be\n\\label{7.20}\n E(g) \\simeq Bg^2 \\qquad ( g\\ra \\infty) \\; ,\n\\ee\nwith the amplitude\n\\be\n\\label{7.21}\n B = -0.108513 \\; .\n\\ee\n\nSince just a few terms in the perturbative expansion are available, the\nPad\\'{e} approximants are not applicable at all, yielding unreasonable \nvalues for the amplitude, by many orders differing from Eq. (7.21). \nSelf-similar approximants give more realistic values. Thus, factor \napproximants give for the amplitude $B$ the value $0.061$ and iterated \nroots, $0.049$. The doubly renormalized iterated roots improve the \naccuracy, giving the value $0.1287$ for the amplitude. \n\n\n\\section{Characteristics of polymer systems}\n\n\nPolymers are rather complicated molecules and are highly important in many\nbranches of physics and chemistry. As a rule, their characteristics \nare calculated by means of perturbation theory with respect to a small \nparameter, although in reality this parameter can be quite large. Self-similar\napproximants can successfully extrapolate these characteristics to arbitrary\nvalues of the parameters, including asymptotically large values. \n\n\n\\subsection{Randomly branched polymers}\n\n\nMany characteristics of polymers are expressed through their structure \nfactors. The structure factor of three-dimensional branched polymers is \ngiven \\cite{Lam1990, Mil1991} by the confluent hypergeometric function\n\\be\n\\label{8.1}\n S(x) = F_1 \\left ( 1\\; ; \\frac{3}{2}\\; ; \\frac{3}{2}\\; x \n\\right ) \\; ,\n\\ee\nin which $x$ is a dimensionless wave-vector modulus. The long-wave \nexpansion \n\\be\n\\label{8.2}\nS(x) \\simeq c_0 + c_1 x + c_2 x^2 + c_3 x^3 + c_4 x^4 \n\\ee\ncontains the coefficients\n$$\n c_0 = 1 \\; , \\qquad c_1 = -1 \\; , \\qquad c_2 = 0.6 \\; , \\qquad\nc_3 = -0.257143 \\; , \\qquad c_4 = 0.085714 \\; .\n$$\nIn the short-wave limit, one has\n\\be\n\\label{8.3}\n S(x) \\simeq \\frac{B}{x} \\qquad ( x \\ra \\infty ) \\; ,\n\\ee\nwith the amplitude\n\\be\n\\label{8.4}\n B = \\frac{1}{3} \\; .\n\\ee\n\nThe reconstruction of the short-wave amplitude by Pad\\'{e} approximants\nleads to senseless negative values. Factor approximants give $B_4= 0.097$, \nand the power-transformed factors yield two solutions, $0.179$ and $0.329$.\nIterated roots, at low orders, overestimate the amplitude, giving\n$B_2 = 0.745$, $B_3 = 0.642$, and $B_4 = 0.590$. The same happens for the \npower-transformed roots yielding the values close to $0.6$. However, the higher \norders of the iterated roots converge to value (8.4). For instance, the \nseventh-order iterated root approximant gives a very good accuracy, \nwith $B_7 = 0.330$. \n\n\n\\subsection{Fluctuating fluid string}\n\n\nThere exists an important class of systems, called fluid membranes \n\\cite{Sei1997}, which finds wide applications in chemistry, biology, medicine,\nand in a variety of technological applications. First, let us consider a model \nof a fluid string that is a cartoon of a one-dimensional membrane oscillating \nbetween two rigid walls \\cite{Edw1965, Doi2001}. The free energy of the \nstring coincides with the ground-state energy of a quantum particle in a \none-dimensional rigid potential \\cite{Kle1999, Kas2002}. This energy, as a \nfunction of a finite wall stiffness $g$, can be represented as\n\\be\n\\label{8.5}\n E(g) = \\frac{\\pi^2}{8g^2} \\left ( 1 + \\frac{g^2}{32} +\n\\frac{g}{4} \\; \\sqrt{ 1 + \\frac{g^2}{64} } \\right ) \\; .\n\\ee\nThe low-stiffness expansion results in\n\\be\n\\label{8.6}\n E_k(g) = \\frac{\\pi^2}{8g^2} \\left ( 1 + \\sum_{n=1}^k a_n g^n \n\\right ) \\; ,\n\\ee\nwith the coefficients\n$$\na_1 = \\frac{1}{4} \\; , \\qquad a_2 = \\frac{1}{32} \\; , \\qquad\na_3 = \\frac{1}{512} \\; , \\qquad a_4 = 0 \\; ,\n$$\n$$\na_5 = - \\; \\frac{1}{131072} \\; , \\qquad a_6 = 0 \\; , \\qquad\na_7 = \\frac{1}{16777216} \\; .\n$$\nThe case of interest corresponds to rigid walls, when the stiffness tends \nto infinity. For such rigid walls, the energy is\n\\be\n\\label{8.7}\n E(\\infty) = \\frac{\\pi^2}{128} = 0.077106 \\; .\n\\ee\n\nPad\\'{e} approximants are not applicable for this problem, giving negative \nvalues of the large-stiffness energy. Factor approximants give positive \nvalues, although overestimating the energy, e.g., $E^*_4(\\infty) = 0.15$. Iterated \nroots yield $E^*_2(\\infty) = 0.039$, $E^*_3(\\infty) = 0.051$, and \n$E^*_4(\\infty) = 0.058$. Corrected iterated roots give \n$E^*_{2\/2} (\\infty) = 0.169$ and power-transformed iterated roots, \n$E^*_4(\\infty) = 0.065$. Taking more terms in the expansion improves the \naccuracy. Thus, iterated roots of higher orders yield $E^*_5(\\infty) = 0.062$, \n$E^*_6(\\infty) = 0.065$, and $E^*_7(\\infty) = 0.067$. The most accurate result \nis obtained by employing the doubly renormalized iterated roots,\ngiving $E_2^{**}(\\infty) = 0.07237$. The variational perturbation theory, to \nsixth order, gives \\cite{Kas2006} the value $0.076991$. \n\n\n\\subsection{Fluctuating fluid membrane}\n\nIn the case of a two-dimensional membrane, its pressure can be calculated by\nperturbation theory with respect to the wall stiffness \\cite{Kas2006}, which \nyields\n\\be\n\\label{8.8}\n p_k(g) = \\frac{\\pi^2}{8g^2} \\left ( 1 + \n\\sum_{n=1}^k a_n g^n \\right ) \\; ,\n\\ee\nwith the coefficients\n$$\na_1 = \\frac{1}{4} \\; , \\qquad a_2 = \\frac{1}{32} \\; , \\qquad\na_3=2.176347\\times 10^{-3} \\; ,\n$$\n$$\n a_4=0.552721\\times 10^{-4} \\; , \\qquad \na_5=-0.721482\\times 10^{-5} \\; , \\qquad \na_6=-1.777848\\times 10^{-6} \\; .\n$$\nThe rigid-wall limit, calculated by means of the Monte Carlo simulations \n\\cite{Gom1989} is found to be\n\\be\n\\label{8.9}\n p(\\infty) =0.0798 \\pm 0.0003 \\; .\n\\ee\n\nPad\\'{e} approximants are again not applicable, resulting in negative values\nof pressure. Factor approximants of low orders overestimate the limit, e.g., \nthe fourth order giving $0.312$. To higher orders, factor approximants become \nslightly better, but still overestimating the pressure. Iterated roots of low \norders give $p^*_2(\\infty)=0.039$, $p^*_3(\\infty)=0.053$, and \n$p^*_4(\\infty)=0.061$ and power-transformed iterated roots in fourth order, \n$0.068$. Taking into account all available coefficients improves the results. \nFor instance, in the case of the iterated roots, we have $p^*_5(\\infty)=0.067$, \n$p^*_6(\\infty)=0.071$. Doubly renormalized iterated roots give \n$p^{**}_3(\\infty) = 0.0792$, which is the most accurate result. This is to be \ncompared with the value of $0.0821$ from the variational perturbation theory\n\\cite{Kas2006}, which overestimates the Monte Carlo result (8.9). \n\n\n\\subsection{Two-dimensional polymer chain} \n\n\nAn important characteristic of polymer chains is their expansion factor, \nthat is, the ratio of the mean-square end-to-end distance of the chain, with \ninteractions between its segments, to the value of the mean-square end-to-end\ndistance of the chain, without such interactions. Two-dimensional polymers\nare often met in chemistry and biology. For such polymers, perturbation theory \nwith respect to weak interactions can be developed \\cite{Mut1984, Mut1987} and, \nin a certain limiting case, can be reduced to a series in a single \ndimensionless interaction parameter $g$. For a two-dimensional polymer chain, \nperturbation theory results \\cite{Mut1984} in the expansion factor \n\\be\n\\label{8.10}\n F(g) \\simeq 1 + \\sum_{n=1}^4 a_n g^n \\; ,\n\\ee\nwith the coefficients\n$$\n a_1 = \\frac{1}{2} \\; , \\qquad a_2 = -0.12154525 \\; , \\qquad\na_3=0.02663136 \\; , \\qquad a_4=-0.13223603 \\; .\n$$\nIn the strong-interaction limit \\cite{Li1995}, one has\n\\be\n\\label{8.11}\nF(g) \\simeq Bg^\\bt \\qquad (g \\ra \\infty ) \\; ,\n\\ee\nwith the critical exponent\n\\be\n\\label{8.12}\n \\bt = 1 \\; .\n\\ee\nOne also considers the critical index\n\\be\n\\label{8.13}\n \\nu \\equiv \\frac{1}{2} \\left ( 1 + \\frac{\\bt}{2} \\right ) \\; ,\n\\ee\nwhich here is $\\nu = 0.75$.\n\nCalculating the critical amplitude, we have the following. Factor \napproximants are complex, but the power-transformed factor approximant\nat fourth order gives $0.31$. Iterated roots yield $B_2 = 0.08$, with\nthe higher orders being complex. The corrected iterated roots yield\n$B_{2\/2} = 0.09$. The exact value of the amplitude $B$ is not known, \nbecause of which we cannot evaluate the accuracy of the approximants.\nBut, as we see, all approximants give the values of order $0.1$. \n\n\n\\subsection{Three-dimensional polymer coil}\n\nIn the case of a three-dimensional polymer coil, perturbation theory\n\\cite{Mut1984} for the expansion factor leads to series (8.10), however with\nthe coefficients\n$$\na_1 = \\frac{4}{3} \\; , \\qquad a_2 =-2.075385396 \\; , \\qquad\na_3 = 6.296879676 \\; ,\n$$\n$$\na_4 = -25.05725072 \\; , \\qquad a_5=116.134785 \\; , \\qquad\na_6 = - 594.71663 \\; .\n$$\nThe strong-coupling limit \\cite{Mut1987} is\n\\be\n\\label{8.14}\n F(g) \\simeq 1.531 g^{0.3544} \\qquad ( g \\ra \\infty) \\; ,\n\\ee\nwhich yields for the critical index (8.13) $\\nu = 0.5866$. Numerical fitting\n\\cite{Mut1987} for the whole range of interactions results in the formula\n\\be\n\\label{8.15}\n F(g) = \\left ( 1 + 7.524 g + 11.06 g^2 \\right )^{0.1772} \\; .\n\\ee\n\nEmploying four terms in the weak-coupling expansion gives for the factor \napproximants the amplitude $B_4 = 1.548$ and for power-transformed factor \napproximants, $1.535$. Iterated roots yield \n$B_2 = 1.543$, $B_3 = 1.549$, $B_4 = 1.538$. Corrected iterated roots result\nin $B_{2\/2} = 1.544$ and power-transformed iterated roots, in $B_4 = 1.535$.\nDoubly renormalized iterated roots give $1.530$. Higher-order approximants\nimprove the results, but already at fourth order all these approximants are\nclose to the numerical value $B = 1.531$. The accuracy of Pad\\'{e} \napproximants is several orders worse \\cite{Glu2003}.\n\n\n\n\\section{Calculation of critical exponents}\n\nIn the previous sections, we have concentrated on the calculation of \ncritical amplitudes, with known critical exponents, by extrapolating \nthe small-variable perturbative expansions to the large-variable limit, \nemploying the techniques of self-similar approximants. Now we show how the\ncritical exponents can also be found by using these techniques. \n\n\n\\subsection{Scheme of general approach}\n\n\nWhen a function, for asymptotically large variable, behaves as\n\\be\n\\label{9.1}\n f(x) \\simeq B x^\\bt \\qquad ( x \\ra \\infty ) \\; ,\n\\ee\nthen the critical exponent can be represented by the limit\n\\be\n\\label{9.2}\n \\bt = \\lim_{x\\ra\\infty} x \\; \\frac{d}{dx} \\; \\ln f(x) \\; .\n\\ee\n\nAssuming that the small-variable expansion for the function is given by the\nsum $f_k(x)$, as in Eq. (2.2), we have the corresponding small-variable \nexpression for the critical exponent\n\\be\n\\label{9.3}\n \\bt_k(x) = x \\; \\frac{d}{dx} \\; \\ln f_k(x) \\; ,\n\\ee\nwhich can be expanded in powers of $x$, leading to \n\\be\n\\label{9.4}\n\\bt_k(x) = \\sum_{n=0}^k b_n x^n \\; .\n\\ee\n \nApplying the method of self-similar approximants to expansion (9.4), as has \nbeen done above, we get a self-similar approximant\n$\\beta_k^*(x)$ whose limit, being by definition finite,\n$$\n \\bt_k^*(x) \\ra const \\qquad ( x \\ra \\infty ) \\; ,\n$$\ngives us the sought approximate expression for the critical exponent \n\\be\n\\label{9.5}\n \\bt_k^* = \\lim_{x\\ra\\infty} \\bt_k^*(x) \\; .\n\\ee\n\nNote that the value of the critical amplitude $B$ does not need to be\nconsidered at all. Below, we illustrate this method of calculating the\ncritical exponents by concrete examples. \n\n\n\\subsection{One-dimensional anharmonic oscillator}\n\n\nLet us consider, as in Sec. 6.2, the model of the one-dimensional anharmonic \noscillator whose mathematical structure is typical for many applied \nproblems, yielding strongly divergent perturbation series.\n\nThe exact critical exponent, as follows from Eq. (6.7), is\n$$\n \\bt = \\frac{1}{3} \\; .\n$$\nIn addition to the coefficients $c_n$ of Sec. 6.2, we shall analyze the \nhigher-order terms of sum (6.6), with the coefficients\n$$\nc_5 = \\frac{916731}{256} \\; , \\qquad \nc_6 = - \\; \\frac{65518401}{1024} \\; , \\qquad\nc_7 = \\frac{2723294673}{2048} \\; ,\n$$\n$$\n c_8 = -\\; \\frac{1030495099053}{32786} \\; , \\qquad\nc_9 = \\frac{54626982511455}{65536} \\; , \\qquad\nc_{10} = -24478940702.8 \\; .\n$$\n\nEmploying the scheme of Sec. 9.1, we find, for the critical exponent, the \nfactor approximants $\\beta_4^*=0.241$, $\\beta_7^*=0.303$, and $\\beta_8^*=0.282$. \nIterated roots result in $\\beta_2^*=0.397$, $\\beta_3^*=0.181$, but $\\beta_4^*$\nis complex. Corrected iterated roots yield $\\beta_{2\/2}^* = 0.307$, \n$\\beta_{2\/3}^* = 0.328$, $\\beta_{2\/4}^* = 0.310$, $\\beta_{2\/5}^* = 0.346$, and \n$\\beta_{2\/6}^* = 0.305$. Power-transformed roots give two solutions, $0.156$ \nand $0.238$. Doubly renormalized iterated roots of second order lead to \n$0.319$. As we see, the self-similar approximants are rather accurate, being \nclose to $0.3$. \n\n\n\\subsection{Three-dimensional polymer coil}\n\n\nAs another example, we consider the three-dimensional polymer coil of Sec. 8.5.\nThe exponent found numerically, according to Eq. (8.14), is \n$$\n \\bt=0.3544 \\; .\n$$\n\nFollowing the scheme of Sec. 9.1, we obtain the self-similar approximants \nfor the critical exponent. Factor approximants yield $\\beta_3^* = 0.343$, \n$\\beta_4^* = 0.346$, and $\\beta_5^* = 0.349$. Iterated roots result in \n$\\beta_2^* = 0.345$, $\\beta_3^* = 0.343$, $\\beta_4^* = 0.351$, and \n$\\beta_5^* = 0.349$. Power-transformed iterated roots give two solutions, \n$0.285$ and $0.349$ and corrected iterated roots give $\\beta_{1\/4}^* = 0.348$, \n$\\beta_{2\/2}^* = 0.345$, $\\beta_{3\/2}^* = 0.349$. Doubly-renormalized \niterated roots yield $\\beta_4^{**} = 0.353$, $\\beta_5^{**} = 0.355$. All \nthese approximants are close to the numerical value $\\beta = 0.3544$. \n\n\n\\section{Equation of state}\n\nThe problems, considered in previous sections, were related to the cases\nwhen it was necessary to find the large-variable behaviour of the studied \nfunctions. However, generally, self-similar approximation theory allows us to\nderive approximants valid for the whole range of the variable. To illustrate \nthis, we show below how it is possible to construct an equation of state, \nproviding a good description in the whole region of densities.\n\nLet us consider a system of quantum hard spheres \\cite{Kel1996} characterized\nby the $s$-wave scattering length $a_s$ corresponding to the diameter of a\nhard sphere. The ground-state energy, in the limit of low density $\\rho \\ra 0$, \nis given \\cite{Lee1957} by the asymptotic expression\n\\be\n\\label{10.1} \n \\frac{E}{N} \\simeq 2\\pi\\; \\frac{\\rho a_s}{m} \\left ( 1 +\n\\frac{128}{15\\sqrt{\\pi}} \\; \\sqrt{\\rho a_s^3 } \\right ) \\; ,\n\\ee\nwhere $m$ is a sphere mass. The density can increase up to the value $\\rho_0$,\nwhen the system of the spheres becomes close packed. For a primitive hexagonal\nclose packing, such as producing a face-centred cubic arrangement, \n\\be\n\\label{10.2}\n \\rho_0 = \\frac{\\sqrt{2}}{a_s^3} \\; .\n\\ee\nIn the close-packed limit, the energy behaves as\n\\be\n\\label{10.3}\n \\frac{E}{N} \\simeq \\frac{B}{2m} \\left ( \\rho^{-1\/3} - \\rho_0^{-1\/3}\n\\right ) \\; ,\n\\ee\nwith the experimental value\n\\be\n\\label{10.4}\nB \\equiv 2^{2\/3} \\pi^2\n\\ee\nfound by Cole \\cite{Col1967}.\n\nTo rewrite the low-density asymptotic expression in a more convenient way, we\nintroduce the variable $x$ by the relation\n\\be\n\\label{10.5}\n \\frac{\\rho}{\\rho_0} = \\frac{x^6}{(1+x^2)^3} \\; .\n\\ee\nAs is seen, $x \\ra 0$ when $\\rho \\ra 0$ and $x \\ra \\infty$ when $\\rho \\ra \\rho_0$. \nWith the new variable, expansion (10.1) for $x \\ra 0$ takes the form\n\\be\n\\label{10.6}\n \\frac{E}{N} \\simeq 2\\pi\\; \\frac{\\rho_0 a_s}{m} \\; x^6 \\left ( 1 -\n3x^2 + \\frac{128}{15\\sqrt{\\pi}} \\; \\sqrt{\\rho_0 a_s^3 }\\; x^3 + 6x^4 - \\;\n\\frac{192}{5} \\; \\sqrt{\\rho_0 a_s^3} \\; x^5 \\right ) \\; ,\n\\ee\nwhile the close-packed limit reads as\n\\be\n\\label{10.7}\n \\frac{E}{N} \\simeq \\frac{\\pi^2}{ma_s^2} \\; x^4 \\qquad (x\\ra\\infty) \\; .\n\\ee\nUsing the iterated root approximant of second order for expansion (10.6), we get\n\\be\n\\label{10.8}\n \\frac{E^*_2}{N} = 2\\pi\\; \\frac{\\rho_0 a_s}{m} \\; x^6 \\left ( \n1 + A_2 x^2 \\right )^{-1} \\; ,\n\\ee\nwith $A_2 = 2 \\sqrt{2} \/ \\pi$ corresponding to limit (10.7). Inverting the change\nof the variable (10.5), we return to the initial variable, that is, to density,\nobtaining the equation of state\n\\be\n\\label{10.9}\n \\frac{E^*_2}{N} = 2\\pi\\; \\frac{\\rho a_s}{m} \\left [ 1 - \\left (\n\\frac{\\rho}{\\rho_0} \\right )^{1\/3} \\right ]^{-2}\n\\left [ 1 + b \\left (\\frac{\\rho}{\\rho_0} \\right )^{1\/3} \\right ]^{-1} \\; ,\n\\ee\nin which\n\\be\n\\label{10.10}\nb = \\frac{2\\sqrt{2}}{\\pi} - 1 \\; .\n\\ee\nThis equation {\\it exactly} coincides with the empirical equation called the \nmodified London equation \\cite{Sol1994} that is in very good agreement with the\nGreen function Monte Carlo computer simulations for the many-body hard-sphere\nfluid \\cite{Kal1974}. Higher orders of the self-similar iterated root approximants,\nas we have checked, do not essentially change the accuracy of the equation of \nstate (10.9) that already gives a perfect agreement with computer simulations. \n \n \n\n\\section{Conclusion}\n\n\nWe have considered the problem of extrapolating perturbation-theory expansions,\nobtained for asymptotically small variable $x \\ra 0$, to the large-variable\nlimit $x \\ra \\infty$. For this purpose, we have applied the theory of \nself-similar approximations, concentrating on six different variants, resulting \nin self-similar factor approximants (Sec. 3.1), self-similar root approximants \n(Sec. 3.2), iterated root approximants (Sec. 3.3), corrected root approximants\n(Sec. 3.4), self-similar power-transformed approximants (Sec. 3.5), and doubly\nrenormalized self-similar approximants (Sec. 3.6).\n\nPad\\'{e} approximants are shown to be much less accurate than the self-similar \napproximants, and often not applicable at all. In some cases, more refined \ntechniques, such as the Kleinert variational perturbation theory, employing \ncontrol functions introduced through a variable transformation, can give better \naccuracy, although they are essentially more complicated. However, our main aim \nhere has been the analysis of the validity of the approximants that could provide \ngood accuracy, at the same time being sufficiently simple for calculations and \nyielding explicit analytical formulas. \n \nIn order to demonstrate the wide applicability of the self-similar approximants,\nwe treated a number of examples of rather different nature. In the majority \nof cases, the approximants yield close results and provide good accuracy of \nextrapolation. In general, their accuracy is essentially higher than that of \nPad\\'{e} approximants. In some cases, the latter are not applicable at all, \ngiving qualitatively wrong results, while self-similar approximants do work in\nsuch cases. \n\nComparing different variants of the analyzed self-similar approximants, we see\nthat power-transformed approximants often lead to multiple solutions for the \nsought parameters, because of which they are less convenient than other \napproximants enjoying unique solutions. The doubly renormalized approximants, \nalthough improving the final results, are cumbersome allowing only for their \ncomplicated numerical calculation. The self-similar factor approximants and \niterated root approximants seem to be the most convenient for the purpose of\nthe considered extrapolation. \n\nHaving to hand several methods of self-similar extrapolation is important\nbecause of the following reason. A problem under consideration can be so \ncomplicated that the exact answer is not known and only a few terms of \nperturbation theory are available, then it is rather difficult to judge the \naccuracy of the approximation used. However, if different methods give close \nresults, this serves as an argument that the obtained approximations are \nreliable. \n\nFinally, we have considered problems whose large-variable behaviour is\nof power-law type. We are aware that there exists another class of problems\npossessing exponential behaviour and also demonstrating the Stokes phenomenon.\nFor the problems of this class, it is necessary to use another variant of the\nself-similar approximation theory, involving self-similar exponential\napproximants \\cite{YukGlu1998, Yuk1998}. These, as has been demonstrated \nin the cited papers, make it possible to derive accurate approximations for\nthe functions of exponential behaviour as well as to treat problems \naccompanied by the Stokes phenomenon. We do not address such problems here \nbut they have been studied in our previous publications \\cite{YukGlu1998, Yuk1998}.\n\n\n\\vskip 1cm\n{\\bf Acknowledgement}\n\n\\vskip 3mm\n\nOne of the authors (V.I.Y.) is grateful for useful discussions to E.P. \nYukalova and for financial support, to the Russian Foundation for Basic \nResearch.\n\n\n\n\\newpage\n \n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{The Amoroso distribution family}\nThe {\\bf Amoroso} (generalized gamma, Stacy-Mihram) distribution~\\cite{Amoroso1925,Johnson1994} is a four parameter, continuous, univariate, unimodal probability density, with semi-infinite range. The functional form in the most straightforward parameterization is\n\\begin{align}\n\\label{Amoroso} \n \\text{Amoroso}(x| a, \\theta, \\alpha, \\beta) \n&=\n\\frac{1}{\\Gamma(\\alpha)} \n\\left|\\frac{\\beta}{\\theta}\\right|\n\\left(\\frac{x-a}{\\theta}\\right)^{\\alpha \\beta -1}\n\\exp \\left\\{\n- \\left(\\frac{x-a}{\\theta}\\right)^{\\beta}\n\\right\\}\n\\\\ \\notag\n& \\text{for } x,\\ a,\\ \\theta,\\ \\alpha,\\ \\beta\\ \\text{in } \\mathbb{R}, \n\\ \\alpha>0, \\ \n\\\\ \\notag\n& \\text{support } x \\geq a \\ \\text{if}\\ \\theta > 0, \\ x\\leq a \\ \\text{if}\\ \\theta < 0 .\n\\end{align}\n\nThe Amoroso distribution was originally developed to model lifetimes~\\cite{Amoroso1925}. It occurs as the Weibullization of the standard gamma distribution (\\ref{Gamma}) and, with integer $\\alpha$, in extreme value statistics (\\ref{GenFisherTippett}). The Amoroso distribution is itself a limiting form of various more general distributions, most notable the generalized beta and generalized beta prime distributions~\\cite{McDonald1984}.\n\nA useful and important property of the Amoroso distribution is that many common and interesting probability distributions are special cases or limiting forms (See Table~\\ref{AmorosoTable}). (Informally, an ``interesting distribution'' is one that has acquired a name, which generally indicates that the distribution is the solution to one or more interesting problems.) This provides a convenient method for systemizing a significant fraction of the probability distributions that are encountered in practice, provides a consistent parameterization for those distributions, and to obviates the need to enumerate the properties (mean, mode, variance, entropy and so on) of each and every specialization. \n\n\n\\paragraph*{Notation:} The four real parameters of the Amoroso distribution consist of a location parameter~$a$, \na scale parameter~$\\theta$, and two shape parameters,~$\\alpha$ and~$\\beta$. Whenever these symbols appears in special cases or limiting forms, they refer directly to the parameters of the Amoroso distribution.\nThe shape parameter $\\alpha$ is positive, and in many specializations an integer, $\\alpha=n$, or half-integer, $\\alpha=\\tfrac{k}{2}$. The negation of a standard parameter is indicated by a bar, e.g. $\\bar{\\beta} = -\\beta$. The chi, chi-squared and related distributions are traditionally parameterized with the scale parameter $\\sigma$, where $\\theta= (2\\sigma^2)^{1\/{\\beta}}$, and $\\sigma$ is the standard deviation of a related normal distribution. Additional alternative parameters are introduced as necessary. \n \nWe write $\\text{Amoroso}(x| a, \\theta, \\alpha, \\beta)$ for a density function, $\\text{Amoroso}(a, \\theta, \\alpha, \\beta)$ for the corresponding random variables, and $X\\sim\\text{Amoroso}(a, \\theta, \\alpha, \\beta)$ to indicate that two random variables have the same probability distribution~\\cite{Gelman2004}. Recall that a ``random variable'' is an unbound function from events to values, whereas the probability density function maps from values to probabilities. \n\n\n\n\n\n\n\\begin{table}[tph!]\n\\label{AmorosoTable}\n\\caption{The Amoroso family of distributions.}\n\\begin{align*}\n \\text{Amoroso}(x| a, \\theta, \\alpha, \\beta) \n= &\n\\frac{1}{\\Gamma(\\alpha)} \n\\left|\\frac{\\beta}{\\theta}\\right|\n\\left(\\frac{x-a}{\\theta}\\right)^{\\alpha \\beta -1}\n\\exp \\left\\{\n- \\left(\\frac{x-a}{\\theta}\\right)^{\\beta}\n\\right\\}\n\\\\ \\notag\n& \\text{for } x,\\ a,\\ \\theta,\\ \\alpha,\\ \\beta\\ \\text{in } \\mathbb{R}, \n\\ \\alpha>0, \\ \n\\\\ \\notag & k,\\ n\\ \\text{positive integers}\n\\\\ \\notag\n& \\text{support } x \\geq a \\ \\text{if}\\ \\theta > 0, \\ x\\leq a \\ \\text{if}\\ \\theta < 0 .\n\\end{align*}\n\n\n\\begin{tabular}{llccccl}\n(\\ref{Amoroso}) &Amoroso & $a$ & $\\theta$ & $\\alpha$ & $\\beta$\n\\\\ \\hline\n(\\ref{Stacy}) & Stacy & $0$ & . & . & . \\\\\n(\\ref{GenFisherTippett}) & gen. Fisher-Tippett & . & . & $n$ & . \\\\\n(\\ref{FisherTippett}) & Fisher-Tippett & . & . & 1 & . \\\\\n(\\ref{Frechet}) &Fr\\'{e}chet & . & . & 1 & $<\\!\\!0$ \\\\\n(\\ref{GenFrechet}) & generalized Fr\\'{e}chet & . & . & $n$ & $<\\!\\!0$ \\\\\n(\\ref{ScaledInvChi}) &scaled inverse chi& 0 & . & $\\tfrac{1}{2}k$ & -2 \\\\\n(\\ref{InvChi}) & inverse chi & 0 & $\\frac{1}{\\sqrt{2}}$ & $\\tfrac{1}{2}k$ & -2 \\\\\n(\\ref{InvRayleigh}) & inverse Rayleigh & $0$ & . & $1$ & -2 \\\\\n(\\ref{PearsonV}) & Pearson type V & . & . & . & -1 \\\\\n(\\ref{InvGamma}) & inverse gamma & 0 & . & . & -1 \\\\\n(\\ref{ScaledInvChiSqr}) & scaled inverse chi-square & 0 & . & $\\tfrac{1}{2}k$ & -1 \\\\\n(\\ref{InvChiSqr}) & inverse chi-square & 0 & $\\frac{1}{2}$ & $\\tfrac{1}{2}k$ & -1 \\\\\n(\\ref{Levy}) & L\\'{e}vy & . & . & $\\frac{1}{2}$ & -1 \\\\\n(\\ref{InvExp}) & inverse exponential & 0 & . & 1 & -1 \\\\\n(\\ref{PearsonIII}) &Pearson type III & . & . & . & 1 \\\\\n(\\ref{Gamma}) &gamma & $0$ & . & . & $1$ \\\\\n(\\ref{Gamma}) & Erlang & $0$ & $>\\!\\!0$ & $n$ & $1$ \\\\\n(\\ref{StdGamma}) &standard gamma & 0 & 1 & . & 1 \\\\ \n(\\ref{ScaledChiSqr}) & scaled chi-square & 0 & . & $\\tfrac{1}{2}k$ & 1 \\\\\n(\\ref{ChiSqr}) & chi-square & 0 & 2 & $\\tfrac{1}{2}k$ & 1 \\\\\n(\\ref{ShiftExp}) & shifted exponential & . & . & 1 & 1 \\\\ \n(\\ref{Exp}) & exponential & $0$ & . & $1$ & $1$ \\\\\n(\\ref{Exp}) &standard exponential & 0 & 1 & 1 & 1 \\\\ \n(\\ref{Gamma}) & Wien & 0 & . & 4& 1 \\\\\n(\\ref{Nakagami}) & Nakagami & . & . & . & $2$ \\\\\n(\\ref{ScaledChi}) &scaled chi& 0 & . & $\\tfrac{1}{2}k$ & 2 \\\\\n(\\ref{Chi}) & chi & 0 & $\\sqrt{2}$ & $\\tfrac{1}{2}k$ & 2 \\\\\n(\\ref{HalfNormal}) & half-normal & 0 & . & $\\tfrac{1}{2}$ & 2 & \\\\ \n(\\ref{Rayleigh}) & Rayleigh & 0 & . & 1 & 2 \\\\\n(\\ref{Maxwell}) & Maxwell& 0 & . & $\\frac{3}{2}$ & 2 \\\\\n(\\ref{WilsonHilferty}) &Wilson-Hilferty& 0 & . & . & 3 \\\\\n(\\ref{GenWeibull}) & generalized Weibull & . & . & $n$ & $>\\!\\!0$ \\\\\n(\\ref{Weibull}) & Weibull & . & . & 1 & $>\\!\\!0$ \\\\\n(\\ref{PseudoWeibull}) & pseudo-Weibull & . & . & $1$+$\\tfrac{1}{\\beta}$ & $>\\!\\!0$ \\\\\n(\\ref{StretchedExp}) & stretched exponential & 0 & . & 1 & $>\\!\\!0$ & \\\\\n\n\n\n\\\\\n & \\underline{Limits} \\\\\n(\\ref{LogGamma}) & log-gamma &. & . & . & . &$\\lim_{\\beta\\rightarrow\\infty}$ \\\\\n(\\ref{PowerLaw}) & power law &.&.& $\\tfrac{1-p}{\\beta}$& . &$\\lim_{\\beta\\rightarrow0}$ \\\\\n(\\ref{LogNormal}) & log-normal & . & . &$\\tfrac{1}{(\\beta\\sigma)^2}$ & . &$\\lim_{\\beta\\rightarrow0}$ \\\\\n(\\ref{Normal}) & normal & . & . & . & 1 &$\\lim_{\\alpha\\rightarrow\\infty}$ \\\\\n\\\\\n\\\\\n\\\\\n\\\\\n\\\\\n\\end{tabular} \n\n\\end{table}\n\n\n\n\n\n\\subsection{Special cases: Miscellaneous}\n\n\n\\dist{Stacy} (hyper gamma, generalized Weibull, Nukiyama-Tanasawa, generalized gamma, generalized semi-normal, hydrograph, Leonard hydrograph, transformed gamma) distribution~\\cite{Stacy1962,Dadpay2007}:\n\\begin{align}\n\\label{Stacy}\n\\text{Stacy}(x | \\theta, \\alpha, \\beta) \n=& \\frac{1}{\\Gamma(\\alpha)} \\left|\\frac{\\beta}{\\theta}\\right| \\left(\\frac{x}{\\theta}\\right)^{\\alpha\\beta-1} \n\\exp \\left\\{ -\\left(\\frac{x}{\\theta}\\right)^{\\beta} \\right\\}\n\\\\=& \\text{Amoroso}(x| 0, \\theta, \\alpha, \\beta) \\notag\n\\end{align}\nIf we drop the location parameter from $\\text{Amoroso}$, then we obtain the \nStacy, or generalized gamma distribution, the parent of the gamma family of distributions.\nIf $\\beta$ is negative then the distribution is {\\bf generalized inverse gamma}, the parent of various inverse distributions, including the inverse gamma (\\ref{InvGamma}) and inverse chi (\\ref{InvChi}). \n\nThe Stacy distribution is obtained as the positive even powers, modulus, and powers of the modulus of a centered, normal random variable (\\ref{Normal}), \n\\[\n\\text{Stacy}\\left((2\\sigma^2)^{\\tfrac{1}{\\beta}} ,\\tfrac{1}{2}, \\beta\\right) \\sim \\Big|\\text{Normal}(0,\\sigma)\\Big|^{\\tfrac{2}{\\beta}}\n\\]\nand as powers of the sum of squares of $k$ centered, normal random variables. \n\\[\n\\text{Stacy}\\left( (2\\sigma^2)^{\\tfrac{1}{\\beta}} ,\\tfrac{1}{2}k, \\beta\\right) \\sim \\left( \\sum_{i=1}^{k} \\Big(\\text{Normal}(0,\\sigma)\\Big)^2\\right)^{\\tfrac{1}{\\beta}}\n\\]\n\n\\dist{Stretched exponential} distribution~\\cite{Laherrere1998}:\n\\begin{align}\n\\label{StretchedExp}\n\\text{StretchedExp}(x|\\theta,\\beta) = & \n\\frac{\\beta}{|\\theta|}\n\\left(\\frac{x}{\\theta}\\right)^{\\beta -1}\n\\exp\\left\\{\n- \\left(\\frac{x}{\\theta}\\right)^{\\beta}\n\\right\\}\n\\\\ \\notag & \\text {for } \\beta>0\n\\\\= & \\text{Weibull}(x| 0, \\theta, \\beta) \\notag \n \\\\= & \\text{Amoroso}(x| 0, \\theta,1, \\beta) \\notag \n\\end{align}\nStretched exponentials are an alternative to power laws for modeling fat tailed distributions. For $\\beta=1$ we recover the exponential distribution (\\ref{Exp}), and $\\beta=0$ a power law distribution (\\ref{PowerLaw}). \n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[scale=1]{pdfStretchedExpPDF}\n\\end{center}\n\\caption{$\\text{\\rm Amoroso}(x|0,1,1,\\beta)$, stretched exponential}\n\\end{figure}\n\n\n\n\\dist{Pseudo-Weibull} distribution~\\cite{Voda1989}:\n\\begin{align}\n\\label{PseudoWeibull}\n\\text{PseudoWeibull}(x|\\theta,\\beta) \n=& \\frac{1}{\\Gamma(1+\\tfrac{1}{\\beta})} \\frac{\\beta}{|\\theta|} \\left(\\frac{x}{\\theta}\\right)^{\\beta} \n\\exp \\left\\{ -\\left(\\frac{x}{\\theta}\\right)^{\\beta} \\right\\}\n\\\\ & \\text{for } \\beta>0 \\notag \n\\\\=& \\text{Amoroso}(x| 0, \\theta, 1+\\tfrac{1}{\\beta}, \\beta) \\notag\n\\end{align}\nProposed as another model of failure times. \n\n\n\n\n\n\\subsection{Special cases: Positive integer $\\beta$}\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[scale=1]{pdfAmorosoBetaPDF}\n\\end{center}\n\\caption{$\\text{\\rm Amoroso}(x|0,1,2,\\beta)$}\n\\end{figure}\n\n\n\n\\dist{Gamma} ($\\Gamma$) distribution~\\cite{Pearson1893, Pearson1895, Johnson1994} : \n\\begin{align}\n\\label{Gamma}\n\\text{Gamma}(x | \\theta, \\alpha) \n&= \\frac{1}{\\Gamma(\\alpha)|\\theta|} \\left(\\frac{x}{\\theta}\\right)^{\\alpha-1} \\exp\\left\\{-\\frac{x}{\\theta}\\right\\} \\\\\n&=\\text{PearsonIII}(x | 0, \\theta, \\alpha) \\notag \\\\\n&=\\text{Stacy}(x| \\theta, \\alpha,1) \\notag \n\\\\&= \\text{Amoroso}(x| 0, \\theta, \\alpha, 1) \\notag \n\\end{align}\nThe name of this distribution derives from the normalization constant.\nThe gamma distribution often appear as a solution to problems in statistical physics. For example, the energy density of a classical ideal gas, or the {\\bf Wien} (Vienna) distribution $\\text{Wien}(x|T)=\\text{Gamma}(x|T,4)$, an approximation to the relative intensity of black body radiation as a function of the frequency. The {\\bf Erlang} (m-Erlang) distribution~\\cite{Erlang1909} is a gamma distribution with integer $\\alpha$, which models the waiting time to observe $\\alpha$ events from a Poisson process with rate $1\/\\theta$ ($\\theta>0$).\n\n\n\nGamma distributions obey an addition property:\n\\begin{align*}\n\\text{Gamma}(\\theta, \\alpha_1) + \\text{Gamma}(\\theta,\\alpha_2) \\sim \\text{Gamma}(\\theta,\\alpha_1+\\alpha_2)\n\\end{align*}\nThe sum of two independent, gamma distributed random variables (with common $\\theta$'s, but possibly different $\\alpha$'s) is again a gamma random variable~\\cite{Johnson1994}.\n\n\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[scale=1]{pdfGammaPDF}\n\\end{center}\n\\caption{$\\text{\\rm Gamma}(x|\\tfrac{1}{\\alpha},\\alpha)$ (unit variance) }\n\\end{figure}\n\n\n\n\n\n\\dist{Standard gamma} (standard Amoroso) distribution~\\cite{Johnson1994}: \n\\begin{equation}\n\\text{StdGamma}(x|\\alpha) = \\frac{1}{\\Gamma(\\alpha)} x^{\\alpha-1} e^{-x}\n\\label{StdGamma}\n\\end{equation}\nThe Amoroso distribution can be obtained from the standard gamma distribution by the Weibull change of variables, $x \\mapsto \\left(\\tfrac{x-a}{\\theta}\\right)^\\beta$.\n\\[\n\\text{Amoroso}(a ,\\theta,\\alpha,\\beta) \\sim\na+\\theta \\Big[{\\text{StdGamma}}(\\alpha)\\Big]^{1\/\\beta} \n\\]\n \n\n\n\n\\dist{Pearson type III} \\noindent distribution~\\cite{Pearson1895, Johnson1994}:\n\\begin{align}\n\\label{PearsonIII}\n\\text{PearsonIII}(x | a , \\theta, \\alpha) \n=& \\frac{1}{\\Gamma(\\alpha)|\\theta|} \\left(\\frac{x-a }{\\theta}\\right)^{\\alpha-1} \n\\exp\\left\\{-\\left(\\frac{x-a }{\\theta}\\right)\\right\\}\n\\\\\n=& \\text{Amoroso}(x| a , \\theta, \\alpha, 1) \\notag\n\\end{align}\nThe gamma distribution with a location parameter.\n\n\n\n\n\n\\dist{Exponential} (Pearson type X, waiting time, negative exponential) distribution~\\cite{Johnson1994}:\n\\begin{align}\n\\label{Exp}\n\\text{Exp}(x | \\theta) \n&= \\frac{1}{|\\theta|} \\exp\\left\\{-\\frac{x}{\\theta}\\right\\} \\\\\n&= \\text{Gamma}(x| \\theta, 1) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\theta, 1, 1) \\notag\n\\end{align}\nAn important property of the exponential distribution is that it is memoryless: the conditional probability given that $x>c$, where $c$ is a positive content, is again an exponential distribution with the same scale parameter. The only other distribution with this property is the geometric distribution~\\cite{Evans2000}, the discrete analog of the exponential distribution. With $\\theta=1$ we obtain a {\\bf standard exponential} distribution. See also shifted exponential (\\ref{ShiftExp}), stretched exponential (\\ref{StretchedExp}) and inverse exponential (\\ref{InvExp}).\n\n\\dist{Shifted exponential} distribution~\\cite{Johnson1994}:\n\\begin{align}\n\\label{ShiftExp}\n\\text{ShiftExp}(x |a , \\theta) \n&= \\frac{1}{|\\theta|} \\exp\\left\\{-\\left(\\frac{x-a }{\\theta}\\right)\\right\\}\\\\\n&= \\text{PearsonIII}(x | a , \\theta, 1) \\notag \\\\\n&= \\text{Amoroso}(x| a ,\\theta,1,1) \\notag \n\\end{align}\nThe exponential distribution with a location parameter. \n\n\n\n\\dist{Nakagami} (generalized normal, Nakagami-m) distribution~\\cite{Nakagami1960}:\n\\begin{align}\n\\label{Nakagami}\n \\text{Nakagami}(x | a , \\theta, m) \n& =\n \\frac{2}{\\Gamma(\\tfrac{m}{2}) |\\theta| }\n\\left(\\frac{x-a }{\\theta}\\right)^{m -1}\n\\exp \\left\\{\n- \\left(\\frac{x-a }{\\theta}\\right)^{2}\n\\right\\}\n\\\\ \\notag\n& = \\text{Amoroso}(x|a,\\theta, \\tfrac{m}{2} ,2)\n\\notag\n\\end{align}\nUsed to model attenuation of radio signals that reach a receiver by multiple paths~\\cite{Nakagami1960}.\n\n\n\n\n\\dist{Half-normal} (semi-normal, positive definite normal, one-sided normal) distribution~\\cite{Johnson1994}:\n\\begin{align}\n\\label{HalfNormal}\n\\text{HalfNormal}(x | \\sigma ) \n&= \\frac{2}{\\sqrt{2\\pi \\sigma^2}} \n\\exp\\left\\{-\\left( \\frac{x^2}{2\\sigma^2}\\right) \\right\\} \\\\\n&=\\text{ScaledChi}(x | \\sigma, 1) \\notag \\\\\n&= \\text{Stacy}(x| \\sqrt{2\\sigma^2} ,\\tfrac{1}{2},2) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\sqrt{2\\sigma^2} , \\tfrac{1}{2}, 2) \\notag \n\\end{align}\nThe modulus of a normal distribution with zero mean and variance $\\sigma^2$.\n\n\n\n\n\\dist{Chi-square} ($\\chi^2$) distribution~\\cite{Fisher1924,Johnson1994}:\n\\begin{align}\n\\label{ChiSqr}\n\\text{ChiSqr}(x | k) \n&= \\frac{1}{2\\Gamma(\\tfrac{k}{2})} \\left(\\frac{x}{2}\\right)^{\\tfrac{k}{2}-1} \n\\exp\\left\\{-\\left(\\frac{x}{2}\\right)\\right\\} \\\\\n& \\qquad \\text{for positive integer } k \\notag \\\\\n&= \\text{Gamma}(x| 2,\\tfrac{k}{2}) \\notag \\\\\n&= \\text{Stacy}(x|2, \\tfrac{k}{2},1) \\notag \\\\\n&= \\text{Amoroso}(x| 0, 2, \\tfrac{k}{2}, 1) \\notag \n\\end{align}\nThe distribution of a sum of squares of $k$ independent standard normal random variables. The chi-square distribution is important for statistical hypothesis testing in the frequentist approach to statistical inference.\n\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[scale=1]{pdfChiSqr}\n\\end{center}\n\\caption{$\\text{\\rm ChiSqr}(x|k)$}\n\\end{figure}\n\n\n\n\n\\dist{Scaled chi-square} distribution~\\cite{Lee2009}:\n\\begin{align}\n\\label{ScaledChiSqr}\n\\text{ScaledChiSqr}(x | \\sigma, k) \n&= \\frac{1}{2\\sigma^2\\Gamma(\\tfrac{k}{2})} \\left(\\frac{x}{2\\sigma^2}\\right)^{\\tfrac{k}{2}-1} \n\\exp\\left\\{-\\left(\\frac{x}{2\\sigma^2} \\right)\\right\\} \\\\\n& \\qquad \\text{for positive integer } k \\notag \\\\\n&= \\text{Stacy}(x|2\\sigma^2, \\tfrac{k}{2},1) \\notag \\\\\n&=\\text{Gamma}(x|2\\sigma^2, \\tfrac{k}{2}) \\notag \\\\\n&= \\text{Amoroso}(x| 0, 2\\sigma^2, \\tfrac{k}{2}, 1) \\notag \n\\end{align}\nThe distribution of a sum of squares of $k$ independent normal random variables with variance $\\sigma^2$.\n\n\\dist{Chi} ($\\chi$) distribution~\\cite{Johnson1994}:\n\\begin{align}\n\\label{Chi}\n\\text{Chi}(x | k) \n&= \\frac{ \\sqrt{2}}{\\Gamma(\\tfrac{k}{2})} { \\left(\\frac{x}{\\sqrt{2}}\\right)}^{k-1} \n\\exp\\left\\{ -\\left( \\frac{x^2}{2} \\right)\\right\\} \n\\\\\n& \\qquad \\text{for positive integer } k \\notag \\\\\n& = \\text{ScaledChi}(x|1,k) \\notag \\\\\n&= \\text{Stacy}(x|\\sqrt{2}, \\tfrac{k}{2}, 2) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\sqrt{2} , \\tfrac{k}{2}, 2) \\notag \n\\end{align}\nThe root-mean-square of $k$ independent standard normal variables, or the square root of a chi-square random variable.\n\\[\n\\text{Chi}(k) \\sim \\sqrt{\\text{ChiSqr}(k)}\n\\]\n\n\\dist{Scaled chi} (generalized Rayleigh) distribution~\\cite{Miller1964,Johnson1994}:\n\\begin{align}\n\\text{ScaledChi}(x | \\sigma, k) \n&= \\frac{2}{\\Gamma(\\tfrac{k}{2}) \\sqrt{2\\sigma^2}} { \\left(\\frac{x}{\\sqrt{2\\sigma^2}}\\right)}^{k-1} \n\\exp\\left\\{-\\left(\\frac{x^2}{2\\sigma^2}\\right)\\right\\} \n\\notag\n\\\\\n& \\qquad \\text{for positive integer } k \\notag \\\\\n&= \\text{Stacy}(x|\\sqrt{2\\sigma^2}, \\tfrac{k}{2},2) \n\\label{ScaledChi}\n\\\\\n&= \\text{Amoroso}(x | 0, \\sqrt{2\\sigma^2}, \\tfrac{k}{2}, 2) \n\\notag \n\\end{align}\nThe root-mean-square of $k$ independent and identically distributed normal variables with zero mean and variance~$\\sigma^2$. \n\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[scale=1]{pdfBeta2PDF}\n\\end{center}\n\\caption{$\\text{\\rm Amoroso}(x|0,1,\\alpha,2)$}\n\\end{figure}\n\n\n\n\\dist{Rayleigh} distribution~\\cite{Strutt1880,Johnson1994}:\n\\begin{align}\n\\label{Rayleigh}\n\\text{Rayleigh}(x | \\sigma) \n&= \\frac{1}{\\sigma^2 }\\ x\\ \\exp\\left\\{-\\left(\\frac{x^2}{2 \\sigma^2}\\right)\\right\\} \n\\\\\n&=\\text{ScaledChi}(x | \\sigma, 2) \\notag \\\\\n&= \\text{Stacy}(x| \\sqrt{2\\sigma^2} ,1,2) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\sqrt{2\\sigma^2} , 1, 2) \\notag \n\\end{align}\n The root-mean-square of two independent and identically distributed normal variables with zero mean and variance $\\sigma^2$. \n For instance, wind speeds are approximately Rayleigh distributed, since the horizontal components of the velocity are approximately normal, and the vertical component is typically small~\\cite{Justus1978}. \n\n\n\\dist{Maxwell} (Maxwell-Boltzmann, Maxwell speed) distribution~\\cite{Maxwell1860, Abramowitz1965}:\n\\begin{align}\n\\label{Maxwell}\n\\text{Maxwell}(x | \\sigma) \n&= \\frac{\\sqrt{2}}{\\sqrt{\\pi} \\sigma^3}\\ x^2 \\exp\\left\\{-\\left(\\frac{x^2}{2\\sigma^2}\\right)\\right\\} \n \\\\\n&=\\text{ScaledChi}(x | \\sigma, 3) \\notag \\\\\n&= \\text{Stacy}(x| \\sqrt{2\\sigma^2} ,\\tfrac{3}{2},2) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\sqrt{2\\sigma^2} , \\tfrac{3}{2}, 2) \\notag \n\\end{align}\nThe speed distribution of molecules in thermal equilibrium. The root-mean-square of three independent and identically distributed normal variables with zero mean and variance $\\sigma^2$.\n\n\n\n\\dist{Wilson-Hilferty} distribution~\\cite{Wilson1931,Johnson1994}:\n\\begin{align}\n\\label{WilsonHilferty}\n\\text{WilsonHilferty}(x | \\theta, \\alpha) \n&= \\frac{3}{\\Gamma(\\alpha)|\\theta|} \\left(\\frac{x}{\\theta}\\right)^{3 \\alpha-1} \\exp\\left\\{-\\left(\\frac{x}{\\theta}\\right)^{3}\\right\\}\n\\\\ \n&= \\text{Stacy}(x|\\theta, \\alpha, 3) \n\\notag \n\\\\ &= \\text{Amoroso}(x| 0, \\theta, \\alpha, 3) \\notag\n\\end{align}\nThe cube root of a gamma variable follows the Wilson-Hilferty distribution~\\cite{Wilson1931}, which has been used to approximate a normal distribution if $\\alpha$ is not too small.\nA related approximation using quartic roots of gamma variables~\\cite{Hawkins1986} leads to $\\text{Amoroso}(x| 0, \\theta, \\alpha, 4)$.\n\n\n\n\\subsection{Special cases: Negative integer $\\beta$}\n\n\n\n\n\n\n\\dist{Pearson type V} distribution~\\cite{Pearson1901}:\n\\begin{align}\n\\label{PearsonV}\n\\text{PearsonV}(x |a, \\theta, \\alpha) \n=&\n\\frac{1}{\\Gamma(\\alpha)\\left|{\\theta}\\right|} \n\\left(\\frac{\\theta}{x-a }\\right)^{\\alpha +1}\n \\exp\\left\\{-\\left( \\frac{\\theta}{x-a }\n\\right)\\right\\} \n\\notag \n\\\\\n=& \\text{Amoroso}(x| a , \\theta, \\alpha, -1) \n\\end{align}\nWith negative $\\beta$ we obtain various ``inverse'' distributions related to distributions with positive $\\beta$ by the reciprocal transformation $ (\\tfrac{x-a}{\\theta} ) \\mapsto (\\tfrac{\\theta}{x-a} )$.\nPearson's type V is the inverse of Pearson's type III distribution.\n\n\n\n\\dist{Inverse gamma} (Vinci) distribution~\\cite{Johnson1994}:\n\\begin{align}\n\\label{InvGamma}\n\\text{InvGamma}(x | \\theta, \\alpha) \n&= \\frac{1}{\\Gamma(\\alpha) |\\theta|} \\left(\\frac{\\theta}{x}\\right)^{\\alpha+1} \n \\exp\\left\\{-\\left( \\frac{\\theta}{x} \\right)\\right\\} \n\\\\\n&= \\text{Stacy}(x| \\theta, \\alpha,-1)\\notag \\\\\n& = \\text{PearsonV}(x|0,\\theta,\\alpha) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\theta, \\alpha, -1) \\notag \n\\end{align}\nOccurs as the conjugate prior for an exponential distribution's scale parameter~\\cite{Johnson1994}, or the prior for variance of a normal distribution with known mean~\\cite{Gelman2004}.\n\n\\dist{Inverse exponential} distribution~\\cite{Kleiber2003}:\n\\begin{align}\n\\label{InvExp}\n\\text{InvExp}(x | \\theta) \n&= \\frac{|\\theta|}{x^2 } \\exp\\left\\{-\\left( \\frac{\\theta}{x} \\right)\\right\\} \\\\\n&= \\text{InvGamma}(x| \\theta, 1) \\notag \\\\\n&= \\text{Stacy}(x|\\theta,1,-1) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\theta, 1, -1) \\notag \n\\end{align}\nNote that the name ``inverse exponential'' is occasionally used for the ordinary exponential distribution (\\ref{Exp}).\n\n\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[scale=1]{pdfAmorosoBetaNegPDF}\n\\end{center}\n\\caption{$\\text{\\rm Amoroso}(x|0,1,2,\\beta)$, negative $\\beta$.}\n\\end{figure}\n\n\\dist{L\\'{e}vy} distribution (van der Waals profile)~\\cite{Feller1971}: \n\\begin{align}\n\\label{Levy}\n\\text{L\\'{e}vy}(x | a, c) \n&= \\sqrt{\\frac{c}{2\\pi}} \\frac{1}{(x-a)^{3\/2}} \\exp\\left\\{-\\frac{c}{2(x-a)}\\right\\} \n\\\\\n&= \\text{PearsonV}(x|a,\\tfrac{c}{2},\\tfrac{1}{2}) \\notag \\\\\n&= \\text{Amoroso}(x| a, \\tfrac{c}{2}, \\tfrac{1}{2}, -1) \\notag \n\\end{align}\nThe L\\'{e}vy distribution is notable for being stable: a linear combination of identically distributed L\\'{e}vy distributions is again a L\\'{e}vy distribution. The other stable distributions with analytic forms are the normal distribution (\\ref{Normal}), which is also a limit of the Amoroso distribution, and the Cauchy distribution~\\cite{Johnson1994}, which is not. L\\'{e}vy distributions describe first passage times in one dimensional Brownian diffusion~\\cite{Feller1971}.\n\n\n\\dist{Scaled inverse chi-square} distribution~\\cite{Gelman2004}:\n\\begin{align}\n\\label{ScaledInvChiSqr}\n\\text{ScaledInvChiSqr}(x | \\sigma, k) \n=& \\frac{2 \\sigma^2}{\\Gamma(\\tfrac{k}{2}) } \\left(\\frac{1}{2 \\sigma^2x}\\right)^{\\frac{k}{2}+1} \n\\exp\\left\\{-\\left( \\frac{1}{2 \\sigma^2x} \\right)\\right\\}\n\\\\\n&\\qquad \\text{for positive integer } k \\notag \\\\\n&= \\text{InvGamma}(x| \\tfrac{1}{2 \\sigma^2}, \\tfrac{k}{2}) \\notag \\\\\n&= \\text{PearsonV}(x|0,\\tfrac{1}{2 \\sigma ^2},\\tfrac{k}{2}) \\notag \\\\\n&= \\text{Stacy}(x| \\tfrac{1}{2 \\sigma ^2},\\tfrac{k}{2}, -1) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\tfrac{1}{2 \\sigma ^2}, \\tfrac{k}{2}, -1) \\notag \n\\end{align}\nA special case of the inverse gamma distribution with half-integer $\\alpha$. Used as a prior for variance parameters in normal models~\\cite{Gelman2004}.\n\n\n\n\n\\dist{Inverse chi-square} distribution~\\cite{Gelman2004}: \n\\begin{align}\n\\label{InvChiSqr}\n\\text{InvChiSqr}(x | k) \n=& \\frac{2}{\\Gamma(\\tfrac{k}{2}) } \\left(\\frac{1}{2x}\\right)^{\\frac{k}{2}+1} \\exp\\left\\{-\\left( \\frac{1}{2x} \\right)\\right\\}\n \\\\\n&\\qquad \\text{for positive integer } k \\notag \\\\\n& = \\text{ScaledInvChiSqr}(x|1,k)\\notag \\\\\n&= \\text{InvGamma}(x| \\tfrac{1}{2}, \\tfrac{k}{2}) \\notag \\\\\n&= \\text{PearsonV}(x|0,\\tfrac{1}{2},\\tfrac{k}{2}) \\notag \\\\\n&= \\text{Stacy}(x|\\tfrac{1}{2}, \\tfrac{k}{2},-1) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\tfrac{1}{2}, \\tfrac{k}{2}, -1) \\notag \n\\end{align}\nA standard scaled inverse chi-square distribution.\n\n\n\n\\dist{Scaled inverse chi} distribution~\\cite{Lee2009}:\n\\begin{align}\n\\label{ScaledInvChi}\n\\text{ScaledInvChi}(x | \\sigma, k) \n&= \\frac{2 \\sqrt{2 \\sigma ^2} }{ \\Gamma(\\tfrac{k}{2})} { \\left(\\frac{1}{\\sqrt{2 \\sigma^2} x}\\right)}^{k+1} \\exp\\left\\{-\\left(\\frac{1}{2 \\sigma^2 x^2} \\right)\\right\\}\n\\\\\n&= \\text{Stacy}(x|\\tfrac{1}{\\sqrt{2 \\sigma^2}}, \\tfrac{k}{2}, -2) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\tfrac{1}{\\sqrt{2 \\sigma^2}}, \\tfrac{k}{2}, -2) \\notag \n\\end{align}\nUsed as a prior for the standard deviation of a normal distribution.\n\n\n\\dist{Inverse chi} distribution~\\cite{Lee2009}: \n\\begin{align}\n\\label{InvChi}\n\\text{InvChi}(x | k) \n&= \\frac{2\\sqrt{2} }{ \\Gamma(\\tfrac{k}{2})} { \\left(\\frac{1}{\\sqrt{2} x}\\right)}^{k+1} \\exp\\left\\{-\\left(\\frac{1}{2 x^2} \\right)\\right\\}\n\\\\\n&= \\text{Stacy}(x| \\tfrac{1}{\\sqrt{2}}, \\tfrac{k}{2}, -2) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\tfrac{1}{\\sqrt{2}} , \\tfrac{k}{2}, -2) \\notag \n\\end{align}\nThe standard inverse chi distribution.\n\n\n\\dist{Inverse Rayleigh} distribution~\\cite{Evans2000}:\n\\begin{align}\n\\label{InvRayleigh}\n\\text{InvRayleigh}(x | \\sigma) \n&= 2 \\sqrt{2 \\sigma ^2} \\left(\\frac{1}{\\sqrt{2 \\sigma^2} x}\\right)^{3} \\exp\\left\\{-\\left(\\frac{1}{2 \\sigma^2 x^2} \\right)\\right\\}\n\\\\\n&= \\text{Stacy}(x|\\tfrac{1}{\\sqrt{2 \\sigma^2}}, 1, -2) \\notag \\\\\n&= \\text{Amoroso}(x| 0, \\tfrac{1}{\\sqrt{2 \\sigma^2}}, 1, -2) \\notag \n\\end{align}\nThe inverse Rayleigh distribution has been used to model a failure time~\\cite{Voda1972}.\n\n\n\n\n\n\n\n\\subsection{Special cases: Extreme order statistics}\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[scale=1]{pdfEVD}\n\\end{center}\n\\caption{Extreme value distributions}\n\\end{figure}\n\n\n\n\\dist{Generalized Fisher-Tippett} distribution~\\cite{Smirnov1949,Barndorff-Nielsen1963}:\n\\begin{align}\n\\label{GenFisherTippett} \n \\text{GenFisherTippett}(x| a, \\omega, n, \\beta) \n\\notag\n&=\n\\frac{n^n}{\\Gamma(n)} \n\\left|\\frac{\\beta}{\\omega}\\right|\n\\left(\\frac{x-a}{\\omega}\\right)^{n \\beta -1}\n\\exp \\left\\{\n- n \\left(\\frac{x-a}{\\omega}\\right)^{\\beta}\n\\right\\}\n\\\\\n& \\quad \\text{for positive integer } n\n\\\\ \\notag\n& = \\text{Amoroso}(x|a,{\\omega}\/{n^{\\frac{1}{\\beta} }},n,\\beta)\n\\end{align}\nIf we take $N$ samples from a probability distribution, then asymptotically for large $N$ and $n\\ll N$, the distribution of the $n$th largest (or smallest) sample follows a generalized Fisher-Tippett distribution. The parameter $\\beta$ depends on the tail behavior of the sampled distribution. Roughly speaking, if the tail is unbounded and decays exponentially then $\\beta$ limits to $\\infty$, if the tail scales as a power law then $\\beta<0$, and if the tail is finite $\\beta>0$~\\cite{Gumbel1958}. In these three limits we obtain the Gumbel (\\ref{Gumbel}, \\ref{GenGumbel}), Fr\\'{e}chet (\\ref{Frechet}, \\ref{GenFrechet}) and Weibull (\\ref{Weibull},\\ref{GenWeibull}) families of extreme value distribution (Extreme value distributions types I, II and III) respectively. If $\\beta\/\\omega$ is negative we obtain distributions for the $n$th maxima, if positive then the $n$th minima.\n\n\n\n\\dist{Fisher-Tippett} (Generalized extreme value, GEV, von Mises-Jenkinson, von Mises extreme value) distribution~\\cite{Fisher1928, Mises1936, Gumbel1958,Johnson1995}: \n\\begin{align}\n\\label{FisherTippett}\n \\text{FisherTippett}(x| a , \\omega, \\beta) \n&=\n\\left|\\frac{\\beta}{\\omega}\\right|\n\\left(\\frac{x-a}{\\omega}\\right)^{ \\beta -1}\n\\exp \\left\\{\n- \\left(\\frac{x-a}{\\omega}\\right)^{\\beta}\n\\right\\}\n\\\\ \\notag & = \\text{GenFisherTippett}(x|a, \\omega, 1, \\beta)\n\\\\ \\notag & = \\text{Amoroso}(x|a, \\omega, 1, \\beta)\n\\end{align}\nThe asymptotic distribution of the extreme value from a large sample. The superclass of type I, II and III (Gumbel, Fr\\'{e}chet, Weibull) extreme value distributions~\\cite{Mises1936}. This is the distribution for maximum values with $\\beta\/\\omega<0$ and minimum values for $\\beta\/\\omega>0$.\n\n\nThe maximum of two Fisher-Tippett random variables (minimum if $\\beta\/\\omega>0$) is again a Fisher-Tippett random variable. \n\\begin{align*}\n\\max\\Big[ \\text{FisherTippett}(a,\\omega_1,\\beta), \\text{FisherTippett}(a, \\omega_2,\\beta) \\Big]&\\\\ \\sim \n \\text{FisherTippett}(a, \\frac{(\\omega_1^{\\beta} + \\omega_2^{\\beta} )^{1\/\\beta}}{\\omega_1 \\omega_2},\\beta) \n\\end{align*}\nThis follows because taking the maximum of two random variables is equivalent to multiplying their cumulative distribution functions, and the Fisher-Tippett cumulative distribution function is $\\exp \\left\\{\n- \\left(\\frac{x-a}{\\omega}\\right)^{\\beta}\n\\right\\}$.\n\n\n\\dist{Generalized Weibull} distribution~\\cite{Smirnov1949,Barndorff-Nielsen1963}:\n\\begin{align}\n\\label{GenWeibull}\n\\text{GenWeibull}(x | a , \\omega, n, \\beta) \n&=\t\\frac{n^n}{\\Gamma(n)} \\frac{ \\beta}{| \\omega |} \\left(\\frac{x-a }{\\omega}\\right)^{n \\beta-1} \\exp\\left\\{-n \\left(\\frac{x-a }{\\omega}\\right)^{ \\beta}\\right\\} \n\\\\ \\notag &\\quad \\text{for } \\beta>0 \n\\\\ \\notag & = \\text{GenFisherTippett}(x|a, \\omega, n, \\beta)\n\\\\ \\notag\n&= \\text{Amoroso}(x| a , {\\omega}\/{n^{\\frac{1}{\\beta} }}, n, \\beta) \n\\end{align}\nThe limiting distribution of the $n$th smallest value of a large number of identically distributed random variables that are at least~$a$. \nIf $\\omega$ is negative we obtain the distribution of the $n$th largest value.\n\n\n\\dist{Weibull} (Fisher-Tippett type III, Gumbel type III, Rosin-Rammler, Rosin-Rammler-Weibull, extreme value type III, Weibull-Gnedenko) distribution~\\cite{Weibull1951,Johnson1995}: \n\\begin{align}\n\\label{Weibull}\n\\text{Weibull}(x | a ,\\omega, \\beta) \n&=\t\\frac{\\beta}{| \\omega |} \\left(\\frac{x-a }{\\omega}\\right)^{\\beta-1} \\exp\\left\\{-\\left(\\frac{x-a }{\\omega}\\right)^{\\beta}\\right\\} \n\\\\ \\notag &\\quad \\text{for } \\beta>0 \n\\\\ \\notag\n& = \\text{FisherTippett}(x| a, \\omega, \\beta) \n\\\\ \\notag\n&= \\text{Amoroso}(x| a , \\omega, 1, \\beta) \n\\end{align}\nThis is the limiting distribution of the minimum of a large number of identically distributed random variables that are at least~$a$. If $\\omega$ is negative we obtain a {\\bf reversed Weibull} (extreme value type III) distribution for maxima.\nSpecial cases of the Weibull distribution include the exponential ($\\beta=1$) and Rayleigh ($\\beta=2$) distributions.\n\n\n\\dist{Generalized Fr\\'{e}chet} distribution~\\cite{Smirnov1949,Barndorff-Nielsen1963}:\n\\begin{align}\n\\label{GenFrechet}\n\\text{GenFr\\'{e}chet}(x | a , \\omega, n, \\bar{\\beta}) \n \\notag \n&=\t\\frac{n^n}{\\Gamma(n)} \\frac{\\bar{\\beta}}{| \\omega |} \\left(\\frac{x-a }{\\omega}\\right)^{-n\\bar{\\beta}-1} \n\\exp\\left\\{-n\\left(\\frac{x-a }{\\omega}\\right)^{-\\bar{\\beta}}\\right\\} \n\\\\ &\\quad \\text{for } \\bar{\\beta}>0 \n\\\\ \\notag\n& = \\text{GenFisherTippett}(x|a, \\omega, n, -\\bar{\\beta})\n\\\\ \\notag\n&= \\text{Amoroso}(x| a , {\\omega}\/{n^{\\frac{1}{\\beta} }},n,-\\bar{\\beta}),\n\\end{align}\nThe limiting distribution of the $n$th largest value of a large number identically distributed random variables whose moments are not all finite and are bounded from below by $a$. (If the shape parameter $\\omega$ is negative then minimum rather than maxima.)\n\n\n\\dist{Fr\\'{e}chet} (extreme value type II , Fisher-Tippett type II, Gumbel type II, inverse Weibull) distribution~\\cite{Frechet1927,Gumbel1958}:\n\\begin{align}\n\\label{Frechet}\n\\text{Fr\\'{e}chet}(x | a , \\omega, \\bar{\\beta}) \n&=\t\\frac{\\bar{\\beta}}{| \\omega |} \\left(\\frac{x-a }{\\omega}\\right)^{-\\bar{\\beta}-1} \n\\exp\\left\\{-\\left(\\frac{x-a }{\\omega}\\right)^{-\\bar{\\beta}}\\right\\}\n\\\\ \\notag &\\quad \\text{for } \\bar{\\beta}>0 \n\\\\ \\notag\n& = \\text{FisherTippett}(x| a, \\omega, -\\bar{\\beta}) \n\\\\ \\notag \n&= \\text{Amoroso}(x| a , \\omega,1,-\\bar{\\beta})\\notag\n\\end{align}\nThe limiting distribution of the largest of a large number identically distributed random variables whose moments are not all finite and are bounded from below by $a$. (If the shape parameter $\\omega$ is negative then minimum rather than maxima.)\nSpecial cases of the Fr\\'{e}chet distribution include the inverse exponential ($\\bar{\\beta}=1$) and inverse Rayleigh ($\\bar{\\beta}=2$) distributions.\n \n\n\n\n\n\n\n\\subsection{Properties}\n\\begin{align*}\n\\text{support} \\quad & x \\geq a & \\theta > 0\n\\\\\n& x\\leq a & \\theta < 0 \n \\\\\n\\text{cdf} \\quad \n\n 1-Q(\\alpha, \\left(\\tfrac{x - a }{\\theta}\\right)^{\\beta}) \n& \\tfrac{\\beta}{\\theta}>0\n\\\\\n&\n Q(\\alpha, \\left(\\tfrac{x - a }{\\theta}\\right)^{\\beta}) \n& \\tfrac{\\beta}{\\theta}<0\n\\\\\n\\text{mode} \\quad& a+ \\theta (\\alpha-\\tfrac{1}{\\beta})^{\\frac{1}{\\beta}} \n& \\alpha \\beta \\geq 1\n\\\\ & a & \\alpha \\beta \\le 1\n\\\\\n\\text{std. moments}\\quad & \\frac{\\Gamma(\\alpha+\\frac{r}{\\beta})}{\\Gamma(\\alpha)} \\quad ( a=0, \\theta=1) \n& \\alpha + \\tfrac{n}{\\beta} \\geq 0\n\\\\\n\\text{mean} \\quad& a + \\theta \\frac{\\Gamma(\\alpha+\\frac{1}{\\beta})}{\\Gamma(\\alpha)} \n& \\alpha + \\tfrac{1}{\\beta} \\geq 0\n\\\\\n\\text{variance} \\quad& \\theta^2 \\left[ \\frac{\\Gamma(\\alpha+\\frac{2}{\\beta})}{\\Gamma(\\alpha)} - \n\\frac{\\Gamma(\\alpha+\\frac{1}{\\beta})^2}{\\Gamma(\\alpha)^2} \\right]\n& \\alpha + \\tfrac{2}{\\beta} \\geq 0\n \\\\\n\\text{entropy} \\quad& \n\\ln \\frac{\\theta \\Gamma(\\alpha)}{|\\beta|} +\\alpha + \\left( \\frac{1}{\\beta} - \\alpha\\right) \\psi(\\alpha) &\n\\text{\\cite{Dadpay2007}}\n\\end{align*}\nHere, cdf is the cumulative distribution function, \n$Q(\\alpha,x)= \\Gamma(\\alpha,x) \/\\Gamma(\\alpha)$ is the regularized gamma function~\\cite{Abramowitz1965},\n$\\Gamma(\\alpha,x) = \\int_x^{\\infty} t^{\\alpha-1} e^{-t} dt$ is the incomplete gamma integral~\\cite{Abramowitz1965},\nand\n$\\psi(x)=\\frac{d}{dx} \\ln \\Gamma(x)$ is the digamma function~\\cite{Abramowitz1965}, the logarithmic derivative of the gamma function.\n Important special cases and limits include\n$\\Gamma(\\tfrac{1}{2})=\\sqrt{\\pi}$, $\\Gamma(\\tfrac{1}{2}, x) = \\sqrt{\\pi} \\text{erfc}(\\sqrt{x})$ and $\\Gamma(1,x) = \\exp(-x)$.\nThe derivative of the regularized gamma function is $\\tfrac{d}{dx} Q(\\alpha,x) = -\\tfrac{1}{\\Gamma(\\alpha)} x^{\\alpha-1} e^{-x}$. \n\n\nThe profile of the Amoroso distribution is bell shaped for $\\alpha\\beta\\geq1$, and otherwise L- or J- shaped with the mode at the boundary. The moments are undefined if the side conditions are not satisfied. Expressions for skew and kurtosis are not simple, but can be deduced from the moments if necessary.\n\nThe Amoroso distribution can be obtained from the standard gamma distribution (\\ref{StdGamma}) with the change of variables, $x \\mapsto \\left(\\tfrac{x-a}{\\theta}\\right)^\\beta$. Therefore, Amoroso random numbers can be obtained by sampling from the standard gamma distribution, for instance using the Marsaglia-Tsang fast gamma method~\\cite{Marsaglia2001} and applying the appropriate transformation~\\cite{Knuth1997}.\n\n\n\n\n\n\n\\pagebreak[4]\n\\section{Log-gamma distributions}\n\\label{SecLogGamma}\n\n\n\n\n\n\nThe {\\bf log-gamma} (Coale-McNeil) distribution~\\cite{Bartlett1946,Prentice1974,Johnson1995} is a three parameter, continuous, univariate, unimodal probability density with infinite range. The functional form in the most straightforward parameterization is\n\\begin{align}\n\\label{LogGamma}\n\\text{LogGamma}(x|\\nu, \\lambda, \\alpha) \n&=\n\\frac{1}{ \\Gamma(\\alpha) |\\lambda|} \\exp\\left\\{ \\alpha \\left(\\frac{x-\\nu}{\\lambda}\\right) - \\exp\\left(\\frac{x-\\nu}{\\lambda}\\right) \\right\\} \n\\\\ \\notag\n& \\qquad \\text{for } x,\\ \\nu,\\ \\lambda,\\ \\alpha,\\ \\text{in } \\mathbb{R}, \n\\ \\alpha>0, \\ \n\\\\ \\notag\n& \\qquad \\text{support } -\\infty \\leq x \\leq \\infty\n\\notag\n\\end{align}\nThe three real parameters consist of a location parameter $\\nu$, a scale parameter $\\lambda$, and a shape parameter $\\alpha$, which is inherited directly from the Amoroso distribution.\n\nThe name ``log-gamma'' arises because the standard log-gamma distribution is the logarithmic transform of the standard gamma distribution\n\\begin{align*}\n\\text{StdLogGamma}(\\alpha) &\\sim \\ln\\Big( \\text{StdGamma}(\\alpha) \\Big)\n\\\\\n\\text{LogGamma}(\\nu, \\lambda, \\alpha) &\\sim \\ln\\Big( \\text{Amoroso}(0, e^{\\nu},\\alpha, \\tfrac{1}{\\lambda}) \\Big)\n\\end{align*}\nNote that this naming convention is the opposite of that used for the log-normal distribution (\\ref{LogNormal}). The name ``log-gamma'' has also been used for the antilog transform of the generalized gamma distribution, which leads to the unit-gamma distribution~\\cite{Gupta2004}.\n\n\nThe log-gamma distribution is a limit of the Amoroso distribution (\\ref{Amoroso}), and itself has a number of important limits and special cases (Table II), which we will discuss below. \n\\begin{align}\n&\\text{LogGamma}(x|\\nu, \\lambda, \\alpha) \n\\\\ \\notag & = \\lim_{\\beta\\rightarrow\\infty} \n\\frac{1}{\\Gamma(\\alpha) |\\lambda|} \n\\left(1+ \\frac{1}{\\beta}\\frac{x-\\nu }{\\lambda}\\right)^{\\alpha\\beta-1}\n\\!\\!\\exp\\left\\{ - \\left(1+ \\frac{1}{\\beta}\\frac{x-\\nu }{\\lambda}\\right)^{\\beta} \\right\\}\n\\\\ \\notag &= \\lim_{\\beta\\rightarrow\\infty} \\text{Amoroso}(\\nu-\\beta \\lambda,\\beta \\lambda, \\alpha,\\beta)\n\\notag\n\\end{align}\nRecall that $\\lim_{\\beta\\rightarrow\\infty} (1+ \\tfrac{x}{\\beta})^{\\beta} = \\exp(x) $.\n\n\n\n\\begin{table}\n\\caption{The log-gamma family of distributions.}\n\\begin{align*}\n&\\text{LogGamma}(x|\\nu, \\lambda, \\alpha) \n\\\\&=\n\\frac{1}{ \\Gamma(\\alpha) | \\lambda |} \\exp\\left\\{ \\alpha \\left(\\frac{x-\\nu}{\\lambda}\\right) - \\exp\\left(\\frac{x-\\nu}{\\lambda}\\right) \\right\\} \\notag\n\\\\ \\notag\n& \\qquad \\text{for } x,\\ \\nu,\\ \\lambda,\\ \\alpha,\\ \\text{in } \\mathbb{R}, \n\\ \\alpha>0, \\ \n\\\\ \\notag\n& \\qquad \\text{support } -\\infty \\leq x \\leq \\infty\n\\notag\n\\end{align*}\n\n\\begin{tabular}{llcccl}\n(\\ref{LogGamma}) & log-gamma & $\\nu$ & $\\lambda$ & $\\alpha$ \n\\\\ \\hline\n(\\ref{StdLogGamma}) & standard log-gamma & $0$ & $1$ & $\\alpha$ \\\\\n(\\ref{LogChiSqr}) & log-chi-square &$\\ln 2 $ & $1$ & $\\tfrac{k}{2}$ \\\\ \n(\\ref{GenGumbel}) &generalized Gumbel & . & . & $n$ & \\\\\n(\\ref{Gumbel}) &Gumbel & . & . & 1 & \\\\\n(\\ref{BHP}) &BHP & . & . & $\\frac{\\pi}{2}$ & \\\\\n(\\ref{StdGumbel}) &standard Gumbel & 0 & -1 & 1 & \\\\ \n\\\\\n & \\underline{Limits} \\\\\n(\\ref{Normal}) & normal & . & . & $\\alpha$& $\\lim_{\\alpha\\rightarrow\\infty}$ \\\\\n\n\\end{tabular}\n\\end{table}\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[scale=1]{pdfLogGamma}\n\\end{center}\n\\caption{$\\text{\\rm LogGamma}(x|0,1,\\alpha)$}\n\\end{figure}\n\n\n\\subsection{Special cases}\n\n\n\\dist{Standard log-gamma} distribution:\n\\begin{align}\n\\label{StdLogGamma}\n\\text{StdLogGamma}(x| \\alpha) \n=&\n\\frac{1}{\\Gamma(\\alpha) } \\exp\\left\\{ \\alpha x - \\exp (x) \\right\\} \n\\\\ \\notag =& \\text{LogGamma}(x|0,1,\\alpha)\n\\end{align} \nThe log-gamma distribution with zero location and unit scale.\n\n\\dist{Log-chi-square} distribution~\\cite{Lee2009}:\n\\begin{align}\n\\label{LogChiSqr}\n\\text{LogChiSqr}(x|k) \n &= \\notag\n\\frac{1}{2^{\\frac{k}{2}} \\Gamma(\\frac{k}{2})} \\exp\\left\\{ \\frac{k}{2} x - \\frac{1}{2} \\exp(x) \\right\\} \n\\\\ & \\qquad \\text{ for positive integer } k\n\\\\\n&= \\text{LogGamma}(x|\\ln 2, 1 ,\\tfrac{k}{2}) \n\\notag\n\\end{align}\nThe logarithmic transform of the chi-square distribution~(\\ref{ChiSqr}).\n\n\\dist{Generalized Gumbel} distribution~\\cite{Gumbel1958,Johnson1995}: \n\\begin{align}\n\\label{GenGumbel}\n\\text{GenGumbel}(x|u,\\bar{\\lambda},n) \n &= \\notag\n\\frac{n^n}{\\Gamma(n) |\\bar{\\lambda}|} \\exp\\left\\{ - n \\left(\\frac{x-u}{\\bar{\\lambda}}\\right) - n \\exp\\left(- \\frac{x-u}{\\bar{\\lambda}}\\right) \\right\\} \n\\\\ & \\qquad \\text{ for positive integer } n\n\\\\\n&= \\text{LogGamma}(x|u+\\bar{\\lambda} \\ln n,-\\bar{\\lambda},n) \n\\notag\n\\end{align}\nThe limiting distribution of the $n$th largest value of a large number of unbounded identically distributed random variables whose probability distribution has an exponentially decaying tail.\n\n\n\\dist{Gumbel} (Fisher-Tippett type I, Fisher-Tippett-Gumbel, FTG, Gumbel-Fisher-Tippett, log-Weibull, extreme value (type I), doubly exponential, double exponential) distribution~\\cite{Fisher1928,Gumbel1958, Johnson1995}:\n\\begin{align}\n\\label{Gumbel}\n\\text{Gumbel}(x|u,\\bar{\\lambda}) \n&=\n\\frac{1}{|\\bar{\\lambda}|} \\exp\\left\\{ -\\left(\\frac{x-u}{\\bar{\\lambda}}\\right) - \\exp\\left(-\\frac{x-u}{\\bar{\\lambda}}\\right) \\right\\} \n\\\\\n&= \\text{LogGamma}(x|u,-\\bar{\\lambda},1) \n\\notag\n\\end{align}\nThis is the asymptotic extreme value distribution for variables of ``exponential type'', unbounded with finite moments~\\cite{Gumbel1958}.\nWith positive scale $\\bar{\\lambda}>0$, this is an extreme value distribution of the maximum, with negative scale $\\bar{\\lambda}<0$ ($\\lambda>0$) an extreme value distribution of the minimum. Note that the Gumbel is sometimes defined with the negative of the scale used here.\n\nNote that the term ``double exponential distribution'' can refer to either the Gumbel or Laplace~\\cite{Johnson1995} distributions.\nThe Gompertz distribution is a truncated Gumbel distribution~\\cite{Johnson1995}. \n\n\\dist{Standard Gumbel} (Gumbel) distribution~\\cite{Gumbel1958}:\n\\begin{align}\n\\label{StdGumbel}\n\\text{StdGumbel}(x) \n=&\n \\exp\\left\\{- x - e^{-x} \\right\\} \\\\\n=& \\text{LogGamma}(x|0,-1,1) \\notag\n\\end{align}\nThe Gumbel distribution with zero location and a unit scale.\n\n\n\n\n\n\n\n\n\\dist{BHP} (Bramwell-Holdsworth-Pinton) distribution~\\cite{Bramwell1998}:\n\\begin{align}\n\\label{BHP}\n\\text{BHP}(x|\\nu,\\lambda) \n&=\n\\frac{1}{\\Gamma(\\tfrac{\\pi}{2}) |\\lambda|} \\exp\\left\\{ \\frac{\\pi}{2}\\left(\\frac{x-\\nu}{\\lambda}\\right) - \\exp\\left(\\frac{x-\\nu}{\\lambda}\\right) \\right\\} \n\\\\\n&= \\text{LogGamma}(x|\\nu,\\lambda,\\frac{\\pi}{2}) \n\\notag\n\\end{align}\nProposed as a model of rare fluctuations in turbulence and other correlated systems.\n\n\n\\subsection{Properties}\n\n\\begin{align*}\n\\text{support}\\quad & -\\infty \\leq x \\leq +\\infty & \\\\\n\\text{cdf} \\quad & 1- Q(\\alpha, e^{\\frac{x-\\nu}{\\lambda} }) & \\text{ for } \\lambda>0 \n\\\\ & Q(\\alpha, e^{\\frac{x-\\nu}{\\lambda} }) & \\text{ for } \\lambda<0 \n\\\\\n\\text{cgf} \\quad & \\nu t + \\ln \\frac{\\Gamma(\\alpha+\\lambda t)}{\\Gamma(\\alpha)} & \\text{\\cite{Johnson1995}}\n\\\\\n\\text{mode} \\quad & \\nu -\\lambda \\ln \\alpha\n\\\\\n\\text{mean} \\quad & \\nu+ \\lambda \\psi(\\alpha) & \n\\\\\n\\text{variance} \\quad & \\lambda^2 \\psi_1(\\alpha) & \n\\\\\n\\text{skew} \\quad & \\text{sgn}(\\lambda) \\psi_2(\\alpha) \/ \\psi_1(\\alpha)^{3\/2} \n\\\\\n\\text{kurtosis} \\quad & \\psi_3(\\alpha) \/ \\psi_1(\\alpha)^{2}\n \\\\\n\\text{entropy} \\quad & \\ln \\Gamma(\\alpha) |\\lambda| - \\alpha \\psi(\\alpha) + \\alpha & \n\\end{align*}\nHere, cdf is the cumulative distribution function, cgf the cumulant generating function, $\\ln \\text{E}[ \\exp( t X)]$, $\\text{sgn}(z)$ is the sign function, $\\psi_n(z)= \\tfrac{d^n}{dz^n} \\ln \\Gamma(z)$ is the polygamma function and $\\psi(z)\\equiv\\psi_0(z)$ is the digamma function.\n\n\n\\section{Miscellaneous limits}\n\n\\label{SecMisc}\n \n\\dist{Log-normal} (Galton, Galton-McAlister, antilog-normal, logarithmic-normal, logarithmico-normal, Cobb-Douglas, $\\Lambda$) distribution~\\cite{Galton1879, McAlister1879, Johnson1994}: \n \\begin{align}\n \\label{LogNormal}\n \\text{LogNormal}(x|a, \\vartheta,\\sigma) \n&= \\frac{\\vartheta}{\\sqrt{2\\pi \\sigma^2}} \\left(\\frac{x-a}{\\vartheta}\\right)^{-1} \\exp\\left\\{-\\frac{1}{2\\sigma^2} \\left( \\ln \\frac{x-a}{\\vartheta} \\right)^2 \\right\\}\n\\\\ \\notag & \n= \\lim_{\\beta\\rightarrow0} \\text{Amoroso}(x| a, \\vartheta (\\beta\\sigma)^{2\/\\beta} ,1\/(\\beta\\sigma)^2, \\beta)\n\\end{align}\nThe log-normal distribution is a limiting form of the gamma family. To see this, make the requisite substitutions, \n\\begin{align*}\n& \\text{Amoroso}(x| a, \\vartheta (\\beta\\sigma)^{2\/\\beta} ,1\/(\\beta\\sigma)^2, \\beta) \\\\\n&\\propto\n \\left(\\frac{x-a}{\\vartheta}\\right)^{-1} \n\\exp\\left\\{\n\\frac{1}{\\beta\\sigma^2} \\ln \\frac{x-a}{\\vartheta} \n- \\frac{1}{\\beta^2\\sigma^2} \\exp \\left[ \\beta \\ln \\frac{x-a}{\\vartheta} \\right]\n\\right\\}\n\\notag\n\\end{align*}\nand in the limit ${\\beta\\rightarrow0}$ expand the second exponential to second order in $\\beta$.\n\nWith $a=0$, $\\vartheta=1$, $\\sigma=1$ we obtain the {\\bf standard log-normal} (Gibrat) distribution~\\cite{Gibrat1931}.\nThe {\\bf two-parameter lognormal} distribution ($a=0$) arises from the multiplicative version of the central limits theorem: When the sum of independent random variables limits to normal, the product of those random variables limits to log-normal.\nThe log-normal distribution maps to the normal distribution with the transformation $x\\mapsto \\exp(x)$.\n\\begin{align*}\n\\text{LogNormal}(a, \\vartheta, \\sigma) &\\sim \\exp\\Big(\\text{Normal}(\\ln \\vartheta,\\sigma)\\Big) +a\n\\end{align*}\n\n \n \n \\dist{Normal} (Gauss, Gaussian, bell curve, Laplace-Gauss, de Moivre, error, Laplace's second law of error, law of error) distribution~\\cite{Moivre1733,Johnson1994}:\n\\begin{align}\n\\label{Normal}\n\\text{Normal}(x|\\mu,\\sigma) \n&=\n\\frac{1}{\\sqrt{2 \\pi \\sigma^2}} \\exp\\left\\{ - \\frac{( x-\\mu)^2}{2\\sigma^2} \\right\\}\n \\\\ \\notag\n&= \\lim_{\\alpha\\rightarrow\\infty} \\text{Amoroso}(x| \\mu- \\sigma\\sqrt{\\alpha}, \\sigma\/\\sqrt{\\alpha}, \\alpha, 1)\n \\\\ \\notag\n&= \\lim_{\\alpha\\rightarrow\\infty} \\text{LogGamma}(x| \\mu-\\sigma\\sqrt{\\alpha}\\ln\\alpha, \\sigma\\sqrt{\\alpha}, \\alpha) \n\\end{align}\nWith $\\mu=0$ and $\\sigma = 1\/ \\sqrt{2} h$ we obtain the {\\bf error function} distribution, and\nwith $\\mu=0$ and $\\sigma=1$ we obtain the {\\bf standard normal} ($\\Phi$, $z$, unit normal) distribution. In the limit that $\\sigma\\rightarrow\\infty$ we obtain an unbounded {\\bf uniform} (flat distribution, and in the limit $\\sigma\\rightarrow0$ we obtain a {\\bf delta} (degenerate) distribution. \n\nThe normal distribution is a limit of the Amoroso~\\cite{Johnson1994} and log-gamma distributions~\\cite{Prentice1974}. For Amoroso, make the requisite substitutions,\n\\begin{align*}\n& \\text{Amoroso}(x| \\mu- \\sigma\\sqrt{\\alpha}, \\sigma\/\\sqrt{\\alpha}, \\alpha,1) \\\\\n&\\propto \\\n\\exp\\left\\{ -\\sqrt{\\alpha} \\frac{x-\\mu}{\\sigma} + (\\alpha-1) \\ln \\left( 1+ \\frac{1}{\\sqrt{\\alpha}} \\frac{x-\\mu}{\\sigma} \\right) \\right\\}\n\\end{align*}\nand expand the logarithm as $\\ln(1+x) = x - \\tfrac{x^2}{2} + O(x^3)$.\n\n\n\n\n\n\n\\dist{Power-law} (Pearson type XI, fractal) distribution~\\cite{Pearson1916}: \n\\begin{align}\n\\label{PowerLaw}\n\\text{PowerLaw}(x|p) \n&\\propto \\frac{1}{(x-a)^p}\n\\\\&= \\lim_{\\alpha\\rightarrow\\infty} \\text{Amoroso}(a,\\theta,\\alpha,(1-p)\/\\alpha) \\notag\n\\end{align}\nImproper (unnormalizable) power law distributions are obtained as a limit of the gamma distribution family.\n If $p=0$ we obtain the {\\bf half-uniform} distribution over the positive numbers; if $p=1$ we obtain {\\bf Jeffreys} distribution~\\cite{Jeffreys1948}, used as an uninformative prior in Bayesian probability~\\cite{Jaynes2003}. \n\n~\n\n~\n\n~\n\n\n\\paragraph*{Acknowledgments:}\nI am grateful to David Sivak, Edward E. Ayoub and Francis J. O'Brien for spotting various typos and errors, and, as always, to Avery Brooks for many insightful observations. In curating this collection of distributions, I have benefited mightily from Johnson, Kotz, and Balakrishnan's monumental compendiums~\\cite{Johnson1994, Johnson1995}, Eric Weisstein's MathWorld, and the myriad pseudo-anonymous contributors to Wikipedia.\nThis project was supported, in part, by Award Number R01GM092992 from the National Institute Of General Medical Sciences.\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbcmh b/data_all_eng_slimpj/shuffled/split2/finalzzbcmh new file mode 100644 index 0000000000000000000000000000000000000000..dd5a26ab38b60213d39240f74c4c7882477832d7 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbcmh @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nRecently four groups \\cite{SDSS}, using data from the\nSloan Digital Sky Survey\\footnote{http:\/\/www.sdss.org\/}, published evidence\nfor features in the matter power spectrum on scales of $100\\,$Mpc.\nThese features, long predicted, hold the promise of another route to\nunderstanding the expansion history of the universe and the influence\nof dark energy \\cite{EisReview}.\n\nOscillations in the baryon-photon fluid at $z\\sim 10^3$ lead to a\nseries of almost harmonic peaks in the matter power spectrum, or a\nbump in the correlation function, arising from a preferred scale in\nthe universe: the sound horizon.\n(A description of the physics leading to the features can be found in\n\\cite{EHSS} or Appendix A of \\cite{MeiWhiPea}; a comparison of the Fourier\nand configuration space pictures is presented in \\cite{ESW06}.)\nIt was pointed out in Refs.~\\cite{CooHuHutJof,Eis03} that this scale could\nbe used as a standard ruler to constrain the distance-redshift relation, the\nexpansion of the universe and dark energy. Numerous authors\n\\cite{Fisher} have now observed that a high-$z$ galaxy survey\\footnote{It\nis even possible that such oscillations could be seen in the Ly-$\\alpha$\nforest \\cite{Davis} or in very large cluster surveys \\cite{Ang}.} covering\nupwards of several hundred square degrees could place interesting\nconstraints on dark energy.\nKey to realizing this is the ability to accurately predict the physical scale\nat which the oscillations appear in the power spectrum plus the means to\nlocalize those primordial features in observations of galaxy spectra which\nnecessarily involve galaxy bias, non-linear evolution and redshift space\ndistortions.\nThe former problem seems well in hand \\cite{SSWZ,EisWhi}.\nPreliminary investigations of the latter problem were presented in\nRefs.~\\cite{Ang,Millenium,PMBaryon,SeoEis05,GuzBer}.\nWe continue these investigations in this paper using a large set of\nhigh resolution N-body simulations.\n\nThe outline of the paper is as follows: \\S\\ref{sec:sim} describes\nour N-body simulations and the construction of the mock galaxy catalogs\nusing halo model methods. It also presents some basic properties of the\ngalaxy clustering. \\S\\ref{sec:models} introduces the models for\nthe 2-point function that we consider in this paper. \\S\\ref{sec:dxi}\nintroduces a new configuration space band-power statistic which we believe\nis useful for BAO work while \\S\\ref{sec:methodology} introduces our fitting\nmethodology. Our primary results are described in \\S\\ref{sec:results}.\nSome preliminary investigations of the reconstruction method of \\cite{ESSS06}\nare described in \\S\\ref{sec:reconstruction} and our conclusions are\npresented in \\S\\ref{sec:conclusions}.\n\n\\section{Simulations} \\label{sec:sim}\n\nWe need an ``event generator'' which can be used to develop methods for\ngoing from observations of galaxies to cosmology. Ideally this tool\nwould encode many of the complications we expect in real observations\nbut be based on a known cosmology. To this end we use a series of\nsimulations of a $\\Lambda$CDM cosmology ($\\Omega_M=0.3=1-\\Omega_\\Lambda$,\n$\\Omega_B=0.046$, $h=0.7$, $n=1$ and $\\sigma_8=0.9$). The linear theory\nmass spectrum was computed by evolution of the coupled Einstein, fluid\nand Boltzmann equations using the code described in \\cite{Boltz}. (A\ncomparison of this code to CMBfast \\cite{CMBfast} is given in \\cite{SSWZ}.)\nFor this model the sound horizon\\footnote{We caution the reader that the\ndefinition of the sound horizon, like that of the epoch of last scattering,\nis one of convention. Unfortunately several conventions exist in the\nliterature. Along with fitting formulae of limited accuracy this makes it\ndifficult to compare quoted numbers at the percent level. We define $s$ as\nthe integral of the sound speed up to the redshift where $\\tau=1$, excluding\nthe contribution from $z1$ the mean number of satellites,\n$N_{\\rm sat}=N(M)-1$, is computed for the halo and a Poisson random number,\n$n_{\\rm sat}$, drawn.\nThen $n_{\\rm sat}$ dark matter particles, chosen at random, are anointed as\ngalaxies. Our fiducial model thus has the satellite galaxies tracing the\ndark matter in the halo.\nThe galaxy velocity is taken to be the particle velocity, thus the satellites\nhave no velocity bias. However since the central galaxy is nearly at rest\nwith respect to the halo, the population as a whole has a different velocity\nfield than the dark matter.\nThe characteristic mass, $M_\\star$, for our models at $z=1$ is\n$2\\times 10^{11}\\,h^{-1}M_\\odot$. As all of our tracer galaxies live in\nhalos more massive than $M_\\star$ they have biases greater than 1.\n\nWe follow \\cite{PMBaryon} and choose a simple two-parameter form for $N(M)$:\n\\begin{equation}\n N(M) = \\Theta(M-M_{\\rm min})\\ \\frac{\\left(M-M_{\\rm min}\\right)+M_1}{M_1}\n\\label{eqn:hod}\n\\end{equation}\nwhere $\\Theta(x)$ is the Heaviside step function.\nIf we take $M_1\\to\\infty$ our catalog reduces to the catalog of halos more\nmassive than $M_{\\rm min}$. By holding $\\bar{n}$ fixed\nwe can specify a 1-parameter sequence of models with varying $M_{\\rm min}$,\nlarge-scale bias, $\\langle b\\rangle$, or galaxy weighted mean halo mass\n$\\langle M\\rangle$ (see Table \\ref{tab:hod10}).\nIn comparing the models with different HODs it is important to remember that\nwe hold $\\bar{n}$ fixed within each sequence, so variations in mean halo mass,\nsatellite fraction, bias etc are highly correlated.\nTo test the dependence on the slope of the satellite contribution we\nalso ran some models where $n_{\\rm sat}\\propto M^{1\/2}$. The parameters of\nthese models are listed in Table \\ref{tab:hod05}.\nBoth theoretical \\cite{ConWecKra} and observational \\cite{YanMadWhi}\nresults suggest that at higher redshift $M_{\\rm min}\\approx M_1$. These\nmodels have the larger biases, mean halo masses and satellite fractions.\n\n\\begin{table}\n\\begin{center}\n\\begin{tabular}{cccc|cccc}\n\\multicolumn{4}{c|}{$\\bar{n}=10^{-3.0}\\,h^3\\,{\\rm Mpc}^{-3}$} &\n\\multicolumn{4}{c}{$\\bar{n}=10^{-3.5}\\,h^3\\,{\\rm Mpc}^{-3}$} \\\\\n$\\log_{10}M_{\\rm min}$ & $\\log_{10}M_1$ &\n $\\langle b\\rangle$ & $\\log_{10}\\langle M\\rangle$ &\n$\\log_{10}M_{\\rm min}$ & $\\log_{10}M_1$ &\n $\\langle b\\rangle$ & $\\log_{10}\\langle M\\rangle$ \\\\\n 12.8 & 13.0 & 2.0 & 13.3 &\n 13.2 & 13.0 & 2.6 & 13.7 \\\\\n 12.7 & 13.5 & 1.9 & 13.2 &\n 13.1 & 13.5 & 2.4 & 13.6 \\\\\n 12.6 & 14.0 & 1.8 & 13.2 &\n 13.0 & 14.0 & 2.3 & 13.5 \\\\\n 12.6 & 14.5 & 1.8 & 13.1 &\n 13.0 & 14.5 & 2.2 & 13.4\n\\end{tabular}\n\\end{center}\n\\caption{The HOD parameters used for $z=1$ catalogs with\n$n_{\\rm sat}\\propto M^{1\/2}$. Units and labels are as\nin Table \\protect\\ref{tab:hod10}.}\n\\label{tab:hod05}\n\\end{table}\n\nWhile the galaxy models above are not prescriptive, or likely even\nclose to ``right'', they are physically well motivated, easy to adjust and\nlead to galaxy catalogs with non-linear, scale-dependent, stochastic\nbiasing and redshift space distortions -- many of the complications we\nwill face in observations of the universe.\n\nThe statistics of counts in cubical cells allow us to infer the\nstochasticity of the bias and the degree to which the 1-point\ndistribution of the galaxies is Poisson. We find that the\ngalaxy-mass cross-correlation coefficient \\cite{DekLah}, $r$, rises from\n$0.6-0.7$ (depending on sample) on scales of $1\\,h^{-1}$Mpc to $>95\\%$ on\nscales larger than $10\\,h^{-1}$Mpc. The variance of the counts divided\nby their mean, which would be unity for a Poisson distribution, exceeds\none on scales $1-30\\,h^{-1}$Mpc with the largest value ($\\approx 20$)\non the largest scales. This excess is easily understood as extra power\ncoming from large-scale clustering of the galaxies. On Mpc scales the\nvalue is very close to Poisson for the less biased galaxies and close to\n$2$ for the more biased samples.\n\n\\section{BAO models} \\label{sec:models}\n\nThe cosmological signal that interests us is primarily contained in the\ntwo-point statistics of the galaxy density field, and we shall concentrate\non these statistics henceforth. We begin by considering measurements in\nreal space, such as would be relevant to photometric or 2D surveys,\nand then include redshift space distortions which are relevant for\nspectroscopic surveys. \n\n\\subsection{Real space}\n\nThere are now several models in the literature relating the (non-linear)\ngalaxy power spectrum to the (linear) dark matter power spectrum.\nWe have critically compared these to the power spectra and correlation\nfunctions measured in our mock surveys.\nTo our knowledge the performance of these models in matching the shape of\nthe power spectra and correlation function has not been compared to mock\ncatalogs produced with a wide range of HOD schemes. In particular we find\nthat the correlation function is a very discriminating statistic, because it\nis sensitive to translinear scales.\nAs we discuss in \\S\\ref{sec:results} some of the models do not match the\nclustering of our mock galaxy samples, particularly for highly biased or rare\nsamples.\n\nWe now describe the five models we've investigated in this paper.\nThe simplest is the linear bias model, which is often motivated by\narguments like those presented in \\cite{SchWei}.\nThe linear bias model asserts that\n\\begin{equation}\n \\Delta^2_{\\rm gal}(k)=b^2 \\Delta^2_{\\rm lin}(\\alpha k)\n\\label{eqn:linbias}\n\\end{equation} \nwhere $\\Delta^2(k)$ denotes the dimensionless power spectrum, or variance\nper $\\ln k$:\n\\begin{equation}\n \\Delta^2(k)\\equiv \\frac{k^3P(k)}{2\\pi^2} \\qquad .\n\\end{equation}\nThe parameter $b$ in Eq.~\\ref{eqn:linbias} is the large scale galaxy bias\nand $\\Delta^2_{\\rm lin}(k)$ is the linear dark matter power spectrum.\nIn this study we have introduced the parameter $\\alpha$, which scales the\nwave number in Eqs.~\\ref{eqn:linbias}, \\ref{eqn:lrg}, \\ref{eqn:hmi}\nand \\ref{eqn:esw}, to parameterize small changes in the cosmology that\nresult in a stretch in the baryon signature.\nWe introduce this parameter in order to study potential degeneracies between\nthe other model parameters, which depend on the HOD, and the inferred\ncosmology.\nWhen $\\alpha\\ne 1$ the inferred length scale differs from the true length\nscale, leading to an incorrect estimate of the sound horizon and hence the\nconstraints on dark energy. We will use biases and errors on $\\alpha$ as\nan indicator of how well the sound horizon can be measured.\nTo translate the error in $\\alpha$ into an error on dark energy parameters\nwe need to make further assumptions. As a rough rule of thumb: if we assume\na constant equation of state, $w$, for the dark energy the fractional error\nin $w$ is five times that in $\\alpha$.\n \nIt has been observed several times in the literature \n\\cite{PMBaryon,SeoEis05,Coo04} that the large-scale bias may not be constant\nat the $1\\%$ level. Halo bias \\cite{PeaksBias} will have a small scale\ndependence even for weakly non-linear scales.\nThe distribution of galaxies within dark matter halos and halo exclusion\neffects also lead to small changes in large-scale power.\nThe shifting of galaxy positions on $\\sim 10\\,h^{-1}$Mpc scales leads to\na smearing of the amplitude of the oscillations in the power spectrum.\nSeveral attempts have been made to model these effects.\n\nIn \\cite{BlaGla03} a method was introduced to empirically\nfit the scale dependence, using the form \n\\begin{equation}\n \\Delta^2(k)= \\Delta^2_{\\rm ref}( k)\n \\left[1+Ak\\,\\exp\\left\\{-\\left(\\frac{ k}{k_s}\\right)^{1.4}\\right\\}\n \\sin\\left( \\frac{2 \\pi k}{k_A} \\right) \\right]\n\\label{eqn:blagla}\n\\end{equation}\nHere $A$ and $k_A$ are fit parameters in the decaying sinusoid used to\ncharacterize the baryon oscillations, $k_s\\equiv 0.1\\,h\\,{\\rm Mpc}^{-1}$\nis a constant and $\\Delta^2_{\\rm ref}(k)$ is a reference spectrum including\nthe effects of Silk damping but excluding the oscillations. The reference\nspectrum is from the fitting formula in \\cite{EisHu99}.\nSince we are fitting non-linear power spectra we additionally allow a linear\nramp in power when doing our fits. This approximates the broad-band power\nremoval suggested by \\cite{BlaGla03} without correlating the errors.\nThe fits do prefer a positive slope to this extra factor.\nIn Eq.~(\\ref{eqn:blagla}) a shift in $k_A$ corresponds to a shift in the\nsound horizon, so $k_A$ replaces the $\\alpha$ in our previous expression.\nWhile the true spectrum cannot be accurately fit by Eq.~(\\ref{eqn:blagla}),\nif we concentrate around the second peak $k_A\\simeq 0.058\\,h{\\rm Mpc}^{-1}$\nprovides a good fit to the peak position for our input model.\nAn alternative definition, used by \\cite{BlaGla03}, is\n$k_A=2\\pi\/s\\simeq 0.063\\,h{\\rm Mpc}^{-1}$ a difference of about 10\\%.\nIf we use the fitting function, Eq.~(26), of \\cite{EisHu98} for $s$ instead\nwe find $k_A\\simeq 0.060\\,h{\\rm Mpc}^{-1}$; midway between the former two\nvalues.\nThe latter approximation comes closest to our best fit $k_A$ (see below) so\nwe shall use that -- but we note again the uncertainty in quoted values of\n$s$ in the literature.\nIn our fitting we shall assume that $\\Delta_{\\rm ref}$ is known, and use the\ncorrect cosmological parameters for our runs.\n\nA recent analysis of the clustering of Luminous Red Galaxies (LRGs) in the\nSloan Digital Sky Survey (SDSS) instead used the fitting function\n\\cite{Pad06,Cole05}\n\\begin{equation}\n \\Delta^2(k)=b^2\\,\\Delta_{\\rm lin}^2(k) \\frac{1+Qk^2}{1+ak}\n \\qquad {\\rm for\\\/} \\ k<0.5\\,h\\,{\\rm Mpc}^{-1}\n\\label{eqn:lrg}\n\\end{equation}\nIn this description, the parameter $Q\\sim 10$ governs the scale dependence of\nthe bias and $a=1.7\\,h^{-1}$Mpc is a constant ($a$ becomes $1.4\\,h^{-1}$Mpc\nin redshift space). To test for shifts in the sound\nhorizon we again replace $k$ with $\\alpha k$. Note that for small $k$\nthis model looks like the quadratic, multiplicative bias model used in\n\\cite{PMBaryon}.\n\nIn \\cite{ToyModel}, motivated by analytic arguments using the halo model,\nthe scale dependence was modeled by adding a term to the expression in\nEq.~\\ref{eqn:linbias} proportional to $k^3$. \nIn \\cite{SeoEis05} a similar treatment was used for slightly different\nreasons, involving $k^3$ times a quadratic in $k$.\nFor $k\\ll 1$ the leading order term will dominate and the two expressions\nare similar. We consider\n\\begin{equation}\n \\Delta_{\\rm gal}^2(k) = b^2 \\Delta_{\\rm lin}^2(\\alpha k)\n e^{-(\\alpha k\/k_2)^2} + \\left(\\alpha k\/ k_1\\right)^3 \\qquad .\n\\label{eqn:hmi}\n\\end{equation}\nThe parameters $b$ and $k_1$ are the galaxy weighted large-scale halo bias\nand the amplitude of the 1-halo term, and the parameter $k_2$ governs a\nsuppression due to halo profiles and exclusion.\nWe introduce $k_2$ because very little clustering power on small scales\nis due to galaxies in separate halos. It is most necessary for those\nmodels with low $M_1$, i.e.~many satellite galaxies, the strongest 1-halo\nterms, the largest mean halo mass, but the shape of the transition between\nthe 2- and 1-halo terms is not well constrained. We will consider a\ndifferent transition below.\nSince the baryon oscillation signal is present in $\\Delta_{\\rm lin}^2(k)$, it\nis clear that the one halo term, which is a description of the impact of\nnon-linear physics, decreases the contrast of the oscillations.\nBecause the parameters $b$, $k_1$,and $k_2$ all depend on the HOD, the\ncontrast of the oscillations will depend somewhat on the type of galaxies\nbeing selected in the BAO survey, and on the mean redshift of the survey.\n\nAnother way of viewing the effects of non-linearities and \ngalaxy bias is found in \\cite{ESW06}.\nThat analysis seeks to describe the gradual erasure of the acoustic peak\nsignature by considering the\ndistribution of Lagrangian displacements of galaxies.\nThe authors of \\cite{ESW06} showed that to simultaneously model the smearing\ndue to galaxy displacement, and also the correct level of small scale power,\nit is useful to add back the broad-band power that is filtered out by the\nexponential suppression in Eq.~\\ref{eqn:hmi}.\nThe fitting form from \\cite{EisHu99} is again used for this purpose leading to\n\\begin{equation}\n \\Delta_{\\rm gal}^2(k) = b^2 \\Delta_{\\rm lin}^2(\\alpha k)\n e^{-(\\alpha k\/k_2)^2} + \\left(\\alpha k\/k_1\\right)^3 +\n \\left( 1-e^{-(\\alpha k\/k_2)^2} \\right) b^2 \\Delta^2_{\\rm ref}(\\alpha k)\n\\label{eqn:esw}\n\\end{equation}\nThe role of $k_2$ in this form differs from its role in Eq. \\ref{eqn:hmi}\nin that here it controls the erosion of the baryon wiggles\ndue to galactic motions on $\\sim 10\\,h^{-1}$Mpc scales, while \npreserving the overall shape of the two halo portion of the\npower spectrum in the trans-linear regime.\n\n\\subsection{Redshift space}\n\nLittle work has been done on extending these models to redshift space.\nIn redshift space the power is enhanced, by a $z$-dependent factor, on\nlarge scales due to supercluster infall \\cite{Kaiser} and suppressed on\nsmall scales due to virial motions within halos\\footnote{This statement\nassumes a $k$-space picture. In configuration space, on large scales,\noverdensities are the sites of convergent flows, so redshift space\ndistortions `sharpen' structure. The correlation function thus falls\noff more quickly along the line-of-sight than transverse to it.}.\nTo include the dependence on the angle with respect to the line of sight,\n$\\mu\\equiv\\cos\\theta=\\hat{k}\\cdot\\hat{r}$, we decompose $\\Delta^2(\\vec{k})$\ninto Legendre polynomials, $P_\\ell(\\mu)$, as\n\\begin{equation}\n \\Delta^2(k,\\mu) \\equiv \\sum_{\\ell=0}^{\\infty} \\Delta_\\ell^2(k)P_\\ell(\\mu)\n\\label{eqn:deltal}\n\\end{equation}\nso\n\\begin{equation}\n \\Delta_\\ell^2(k) = \\frac{2\\ell+1}{2} \\int_{-1}^{+1}d\\mu\\ \n \\Delta^2(k,\\mu)P_\\ell(\\mu)\n\\end{equation}\nSymmetry along the line-of-sight implies that the coefficients for odd $\\ell$\nmodes vanish.\n\nOn very large scales $(k\\to 0)$ supercluster infall modifies the power\nspectrum as \\cite{Kaiser,HamiltonReview}\n\\begin{equation}\n \\Delta^2_{\\rm red}(k,\\mu) = \\Delta^2_{\\rm real}(k)\n \\left(1+\\beta\\mu^2\\right)^2,\n\\end{equation}\nwhere $\\mu\\equiv\\hat{k}\\cdot\\hat{r}$ and\n$\\beta\\equiv f(\\Omega)\/b\\simeq \\Omega^{0.6}\/b$ assuming a scale-independent\nbias. Here $f$ is the dimensionless linear growth rate of the growing mode\nin linear perturbation theory which can be approximated as $\\Omega^{0.6}$\n\\cite{Pee80}.\nThe coefficients of the first two multipole moments are thus\n\\begin{equation}\n \\Delta^2_0(k) = \\left(1 + \\frac{2}{3}\\beta +\n \\frac{1}{5}\\beta^2\\right)\\Delta_{\\rm real}^2(k)\n\\end{equation}\nand\n\\begin{equation}\n \\Delta^2_2(k) = \\left( \\frac{4}{3}\\beta +\n \\frac{4}{7}\\beta^2\\right)\\Delta_{\\rm real}^2(k).\n\\end{equation}\nTo capture the smaller-scale angular dependence one often adds a high-$k$\ncutoff; popular choices include Lorentzian, Gaussian or exponential, e.g.\n\\begin{equation}\n \\Delta_{\\rm red}^2(k,\\mu) = \\Delta_{\\rm real}^2(k)\n \\left(1 + \\beta \\mu^2\\right)^2 e^{-k\\sigma|\\mu|}\n\\end{equation}\nWe will refer to these modifications collectively as ``streaming models''\n\\cite{HamiltonReview}.\nIn general $\\beta$ and $\\sigma$ are regarded simply as parameters to be fit\nto the data.\nThe analytic expressions for the resulting multipole moments are\nstraightforward but lengthy and will not be reproduced here.\n\nA more empirical model for the angular dependence is explored in \\cite{HatCol},\nwho found the quadrupole-to-monopole ratio in N-body simulations can be fit by\n\\begin{equation}\n \\frac{\\Delta^2_2}{\\Delta^2_0} =\n \\frac{\\frac{4}{3}\\beta+\\frac{4}{7}\\beta^2}\n {1+\\frac{2}{3}\\beta+\\frac{1}{5}\\beta^2}\n \\left[1-\\left(\\frac{k}{k_{\\rm rs}}\\right)^{1.22}\\right],\n\\label{eqn:hatcol}\n\\end{equation}\nwhere $k_{\\rm rs}$ is a free parameter analogous to the $\\sigma$ parameter\nin streaming models. Though the authors of \\cite{HatCol} do not discuss\nthe shape of the small-scale downturn in $\\Delta_\\ell^2(k)$, an additional\nparameter needs to be introduced if we are to predict the run of\n$\\Delta_\\ell^2(k)$ with $k$. Further discussion of the interplay between\nthe large-scale enhancement and small-scale suppression can be found in\n\\cite{SheDia01,HaloRed}.\n\nIn \\S\\ref{sec:redresult}, we will investigate the accuracy with which each\nthese forms reproduces the large- and intermediate-scale\n($k\\le 0.2\\,h\\,{\\rm Mpc}^{-1}$)\nangular dependence of the power spectrum in redshift space.\nOur approach also allows us to examine the effects of the HOD on the\nredshift-space distortions, with the hope of using this additional\ninformation to reduce degeneracies between cosmology and galaxy physics.\nWe do not consider in this paper how modeling of the anisotropic clustering\nallows us to constrain the line-of-sight and transverse distance scales\nseparately.\n\n\\section{A configuration space band-power estimator} \\label{sec:dxi}\n \nFor each of the forms in Eqs.~\\ref{eqn:linbias}-\\ref{eqn:esw}\nthere is a corresponding model for the correlation function $\\xi(r)$;\nthe probability, in excess of random, of finding a pair of tracers at\nseparation $r$.\nThe correlation function is related to the dimensionless power spectrum as\n\\begin{equation}\n \\xi(r) =\\int \\frac{dk}{k}\\ \\Delta^2(k)\\ j_0(kr)\n \\simeq \\int \\frac{dk}{k}\\ \\Delta^2(k)\n \\left[ 1 - \\frac{(kr)^2}{6} + \\cdots \\right]\n\\label{eqn:xi_wt}\n\\end{equation}\nwhere $j_0$ is the zeroth spherical Bessel function and the series expansion\nis valid for $kr\\ll 1$.\nMost of the scale dependence in the galaxy bias can be traced to the fact that\ngalaxies and dark matter transition from one to two halo dominance\nat disparate scales \\cite{ToyModel}.\nIn Fourier space this is spread over a range of $k$, but in configuration\nspace the 1-halo term is confined to scales much smaller than the scales of\ninterest to us. Beyond $2-3\\,h^{-1}$Mpc more than 99.9\\% of the contribution\nto $\\xi(r)$ comes from the 2-halo term for all of our models.\nFor this reason we expect less scale dependence in the bias in configuration\nspace than in Fourier space (see also \\cite{GuzBer}), as shown in\nFig.~\\ref{fig:rbias}.\n\n\\begin{figure}\n\\begin{center}\n\\resizebox{5.5in}{!}{\\includegraphics{match.ps}}\n\\end{center}\n\\caption{An estimate of the scale-dependence of the bias in configuration\nspace. The different symbols are $\\Delta\\xi$ (see text) for different HOD\nmodels (from Table~\\protect\\ref{tab:hod10}) each divided by a constant bias to\nmatch near $100\\,h^{-1}$Mpc. The degree to which the shapes match indicates\nhow well each can be considered a constant times the dark matter correlation\nfunction. The lower panel shows the residuals from one of the models, taken\nas a template.}\n\\label{fig:rbias}\n\\end{figure}\n\nFormally the correlation function, $\\xi(r)$, and the power spectrum, $P(k)$,\nare a Fourier transform pair.\nHowever the simulation volume is a periodic cube and our signal has support\nonly for $k$-modes which are integer multiples of the fundamental mode in\neach dimension.\nBecause of this restriction the correlation function computed in the box\ndiffers from the true continuum correlation function on scales approaching\nthe box size. The modulation in power is non-trivial even on $100\\,$Mpc\nscales where we would like to work.\nFortunately the box is large enough to contain the modes of interest for\nbaryon oscillations and the difficulty is purely a technical issue. We\nchoose to proceed by computing a quantity containing the same information\nas $\\xi(r)$ but which is less sensitive to the low-$k$ modes.\nSpecifically we compute\n\\begin{equation}\n \\Delta\\xi(r) \\equiv \\bar{\\xi}(0.7\\,h{\\rm Mpc}^{-1}$.\nHowever, since the correlations are so small in the $k$-range of interest\nwe use Eq.~(\\ref{eqn:modecounting}) in the fits.\n\nWe compute our power spectra in redshift space assuming the distant observer\napproximation for all outputs and use the periodicity of the simulation to\nremap positions. For the isotropically averaged spectra the power ratios\ndo not exactly recover the results of Ref.~\\cite{Kaiser} on large scales.\nWhether we should expect to reach \nthe those limits on the scales relevant to baryon\noscillations remains in doubt -- see Ref.~\\cite{Sco04} and references\ntherein for further discussion.\nOn small scales we are able to compute the $\\Delta^2_\\ell$ by direct\nsummation on the Cartesian $k$ grid, however on large scales we do not have\nenough modes. For this reason we perform a least squares fit for the\n$\\Delta^2_\\ell$ up to $\\ell=6$ for each of the $k$ bins.\nThe line-of-sight angular dependence of the power spectrum on large scales\nis simple, as expected: the resulting Legendre coefficients above $\\ell=4$\nare small for $k\\le 0.3\\,h\\,{\\rm Mpc}^{-1}$.\n\n\n\\subsection{Configuration space}\n\n\\begin{figure}\n\\begin{center}\n\\resizebox{2.7in}{!}{\\includegraphics{cmp_rxi.ps}}\n\\resizebox{2.7in}{!}{\\includegraphics{cmp_dxi.ps}}\n\\end{center}\n\\caption{A comparison of the different ways of computing $\\xi(r)$ and\n$\\Delta\\xi(r)$ discussed in the text. In both panels open circles represent\ndirect pair counting in the periodic box, filled triangles the Landy-Szalay\nestimator and open squares the Fourier transform method. For the\n$\\Delta\\xi(r)$ plot we also show the estimate of $\\Delta\\xi$ where $\\bar{\\xi}$\nis obtained from counts in spheres as the stars. The different estimators\ndiffer in $\\xi(r)$ at small $r$, so for the $\\Delta\\xi$ plot we have added an\n$r^{-1}$ term to make $r^2\\Delta\\xi=100$ at $r=100\\,h^{-1}$Mpc for all\nof the lines. For $\\xi(r)$ there is a noticeable shortfall in the power\nestimated by FFT methods at large $r$ which is largely absent for\n$\\Delta\\xi$.}\n\\label{fig:xi_cmp}\n\\end{figure}\n\nWe can compute $\\xi(r)$ either by directly counting pairs as a function of\nseparation in the periodic volume, \nusing FFT techniques in the periodic volume, or counting pairs and using the\nLandy \\& Szalay \\cite{LanSza} estimator\n\\begin{equation}\n \\xi(r) = \\frac{\\langle DD\\rangle - 2\\langle DR\\rangle + \\langle RR\\rangle}\n {\\langle RR \\rangle}\n \\qquad {\\rm pairs\\ in\\ }(r;dr)\n\\end{equation}\nin sub-volumes with vacuum boundary conditions -- here $D$ refers to the\ndata, $R$ refers to a random catalog with the same selection and the angled\nbrackets indicate the number of pairs in a given shell $(r;dr)$.\nA comparison of the different techniques is shown in Fig.~\\ref{fig:xi_cmp}.\nFor the FFT method we use a Fourier grid with $512^3$ points.\nBoth CIC and NGP charge assignment \\cite{HocEas} give the same answer for\n$\\xi(r)$ well above the grid scale.\nFor the Landy \\& Szalay method we used a random catalog with $10\\times$\nas many points as the data -- increasing the number did not alter the\nresults -- but in computing $\\langle RR\\rangle$ we only searched for pairs\naround the first $N_{\\rm data}$ random points.\nSince the $\\langle DR\\rangle$ point is limited to $N_{\\rm data}$ points\nthere is no loss in accuracy from limiting the $\\langle RR\\rangle$ term\nsimilarly but the cost scales as $N_{\\rm data}\\times N_{\\rm random}$ rather\nthan $N_{\\rm random}^2$.\nIn what follows we shall use the direct pair counting estimate of the\ncorrelation function, in bins of width $3\\,h^{-1}$Mpc. We note in passing\nthat estimating $\\xi$ from future surveys will be non-trivial as we wish\nto work at very large scales with fine radial resolution.\n\nFormally $\\bar{\\xi}(0$ and $r_2>0$ the $1\/\\bar{n}^2$ shot-noise terms are\n\\begin{equation}\n \\frac{1}{2\\pi V\\bar{n}^2}\n \\left[ \\frac{3}{r_1^3}\\Delta\\xi(r_2) + \\frac{3}{r_2^3}\\Delta\\xi(r_1) +\n \\frac{3}{r_>^3}\\Delta\\xi(r_<) + \\frac{1+\\xi(r_1)}{r_1^2}\\delta(r_1-r_2)\\right]\n\\end{equation}\nwhere $r_<$ the lesser of $r_1$ and $r_2$ and $r_>$ is the greater.\nThe $\\delta$-function in the last term is rendered finite when we estimate\n$\\xi$ in bins of finite width. For small bins we can replace $\\delta(r_1-r_2)$\nwith the inverse of the bin width. Since $\\xi\\ll 1$ at large scales this\ngives $r_1^{-2}\\Delta r^{-1}$ along the diagonal.\nFinally the $1\/\\bar{n}^3$ term is simply\n\\begin{equation}\n \\left(\\frac{1}{4\\pi}\\right)^2 \\frac{1}{V\\bar{n}^3}\n \\ \\frac{3}{r_1^3}\\frac{3}{r_2^3}\n\\end{equation}\nWhile we show in \\S\\ref{sec:results} that making this Gaussian assumption\ndoes not bias the results for $\\alpha$, in future it would be better to use\na large set of mock catalogs to estimate the covariance. From our limited\nnumbers of realizations, and using the non-linearly processed Gaussian fields,\nwe found that the shape and amplitude of the analytic expression were within\n${\\mathcal O}(25\\%)$ of the numerical results for scales near\n$100\\,h^{-1}$Mpc. The agreement at smaller scales was considerably worse.\n\nIn configuration space there are no peculiar issues with estimating the $\\mu$\ndependence of $\\xi(s,\\mu)$ on the scales of interest. We bin $\\xi(s,\\mu)$\nin 15 bins in $|\\mu|$ and then sum to get $\\xi_\\ell(s)$. As \nwas found for $P(k)$, the\nmodes beyond the quadrupole are small at large $s$. Performing a least\nsquares fit to $\\ell=0$, 2 and 4 yields the same result to better than a\npercent for $\\ell=0$, 2 and a few percent for the very small $\\ell=4$ mode.\nWe find in general that $\\Delta\\xi$ is much closer to spherical than $\\xi$\ndue to the large contribution from $\\bar{\\xi}(s)$. For example from\n$75\\,h^{-1}$Mpc to $125\\,h^{-1}$Mpc, $\\Delta\\xi_2\/\\Delta\\xi_0$ falls from\napproximately $0.05$ to $\\simeq 0.02$ for our fiducial $b\\simeq 2$ catalog.\nThe contours of both $\\xi$ and $\\Delta\\xi$ are shown in\nFigure \\ref{fig:xicont}.\n\n\\begin{figure}\n\\begin{center}\n\\resizebox{2.7in}{!}{\\includegraphics{butterfly_xi.eps}}\n\\resizebox{2.7in}{!}{\\includegraphics{butterfly_dxi.eps}}\n\\end{center}\n\\caption{Contours of $\\xi(r_p,\\pi)$ (left) and $\\Delta\\xi(r_p,\\pi)$ (right)\nfor one of our $b\\simeq 2$ catalogs. Contours are equally spaced in log,\nand dotted lines indicate negative values. Here $r_p$ measures separations\nacross the line-of-sight and $\\pi$ along it.}\n\\label{fig:xicont}\n\\end{figure}\n\n\\section{Results}\\label{sec:results}\n\n\\subsection{Dark matter}\n\nWe begin by presenting\\footnote{We thank Wayne Hu for suggesting we\nmake this point explicitly.} the power spectrum of the dark matter, in\nreal space, at the present epoch ($z=0$) from one of the simulations:\nFig.~\\ref{fig:pk_fit}.\nAlso on the plot we show the results of two ans\\\"{a}tze for non-linear\nspectra, that of Peacock \\& Dodds \\cite{PD96} based on an idea by\nHamilton et al.~\\cite{HKLM}, and another\nbased on halo model ideas \\cite{HaloFit}.\nThe former is seen to be a bad approximation as it implicitly assumes\nthat there exists a $1-1$ mapping between linear and non-linear power.\nWhile not an issue for smoothly varying spectra, this causes problems when\nthe spectrum contains features such as the baryon oscillations. In reality\nmode coupling erases features, whereas the mapping procedure enhances them.\nWe could reduce some of the discrepancy by using a broad band measure of\nthe slope in the fitting function, but the underlying problem still remains.\nThe halo-model based methods perform better in this regard, as expected\n\\cite{Sel00}, since they model the non-linear power with an integral over\nthe linear theory power spectrum. None of the fitting formulae approach\npercent level accuracy in the non-linear regime.\n\n\\begin{figure}\n\\begin{center}\n\\resizebox{5.5in}{!}{\\includegraphics{pk_fit.ps}}\n\\end{center}\n\\caption{The real space power spectrum of the dark matter at $z=0$ for\none of our simulations along with two ans\\\"{a}tze commonly used in the\nliterature (see text). The lower panel shows the ratio of the fits and\nN-body points to the smooth spectrum of \\protect\\cite{EisHu99}.}\n\\label{fig:pk_fit}\n\\end{figure}\n\n\\subsection{Galaxies}\n\nNow we turn to the mock galaxy catalogs.\nWe show the results at $z=1$ for one of our HOD prescriptions, with\n$\\bar{n}=10^{-3}\\,h^3{\\rm Mpc}^{-3}$ and $b\\simeq 2$, in Fig.~\\ref{fig:pk_run7}\nalong with the predictions of linear theory multiplied by $b^2$.\nThe power is biased on large scales and shows a clear excess on small scales. \nWe shall now try to fit this behavior using the models described in\n\\S\\ref{sec:models}. Our results for the sound horizon parameter $\\alpha$\nare reported in Table \\ref{tab:alpha}.\n\nWe have tried several methods for performing these fits. We fit to both\n$P(k)$ and $\\Delta\\xi(r)$. For the power spectrum we use errors from\nEq.~(\\ref{eqn:modecounting}), since they agree with the errors estimated from\nthe dispersion among the octants.\nFor the correlation function we use the analytic expression of\n\\S\\ref{sec:methodology}.\nThe multi-dimensional fitting was done using the Levenberg-Marquardt algorithm\n\\cite{NumRec}. We experimented with several implementations and found good\nconvergence with both analytic and numerically computed derivatives. From\nthese fits we also obtain an estimate of the parameter \ncovariance matrix from the\ncurvature of the likelihood around the best fit.\nTo test the Gaussianity of the likelihood surface we also ran Markov-Chain\nMonte-Carlo fits (see e.g.~\\cite{MCMC} for an introduction) for the power\nspectra for each model.\nWe provide comparisons of each of these methods for the different models below.\n\n\\begin{figure}\n\\begin{center}\n\\resizebox{5.5in}{!}{\\includegraphics{pk_run7.ps}}\n\\end{center}\n\\caption{The real space power spectrum of one of our mock galaxy catalogs\nwith $b\\simeq 2$ at $z=1$. The solid line shows the predictions of linear\ntheory, multiplied by $b^2$.}\n\\label{fig:pk_run7}\n\\end{figure}\n\nWe begin with the linear bias model (Eq.~\\ref{eqn:linbias}) fit to\n$P(k)$ over the range $0.022\\pi\/\\lambda(\\tau)$ in the turbulent power spectrum given in \\cref{eq:power-spectrum}, and we should account, in the GW production, for the viscous dissipation of the kinetic energy at each scale $k$ in addition to the overall free decay described by \\cref{eq:VelEvMod,eq:XiEvMod}.\nHowever, as we will discuss in \\cref{sec:gw-instant}, the bulk of the GW signal is generated faster than the turbulent decay: therefore, the dynamics at the viscous scale plays no role in practice in the present analysis, and it is justified to neglect it altogether.\n\n\n\\subsection{Unequal time correlator of the velocity field}\n\\label{sec:UETCvelocity}\n\nTogether with the overall free-decay, we also need to model the time decorrelation of the turbulent velocity field.\nThis is encapsulated by the normalized velocity UETC\n\\begin{equation}\n\t\\label{eq:NorVelUETC}\n\t\\tdec(k, \\tau, \\zeta) \\equiv \\frac{\\UETCv(k,\\tau,\\zeta)}{\\sqrt{\\Psdv(k,\\tau)\\,\\Psdv(k,\\zeta)}}.\n\\end{equation}\nThe first model to have been developed was Kraichnan's random sweeping approximation \\cite{kraichnan:1964},\nin which the decorrelation function in the inertial range is a Gaussian in the variable $k\\overline{v}|\\tau - \\zeta| $,\nfor small time differences.\nThe thinking behind the model is that modes are decorrelated by being\n``swept'' by the large-scale flow, whose rms velocity is $\\overline{v}$.\nThis model has good numerical support in the inertial range for both stationary and decaying turbulence (see for example \\Refs{sanada_random_1992,he_computation_2004,Gorbunova:2021cpn}).\nWe review it in \\cref{sec:kraichnan}.\n\nHowever, for GW production calculations, we need to model the decorrelation around the peak of the velocity power spectrum.\nTo this end, we study --- for the first time with hydrodynamical simulations --- the decorrelation of the turbulent velocity field outside the inertial range, as far as possible in the low wavenumber part of the spectrum.\nThe simulation results are presented in \\cref{sec:results-uetc}, and show that a good model is obtained by replacing the rms velocity in the Gaussian with a time and scale dependent average decorrelation velocity, based on\nthe sweeping velocity proposed in Ref.~\\cite{kaneda_lagrangian_1993}, which we present in \\cref{sec:kaneda}.\n\nFurthermore, we also need to take into account the formal requirement that the UETCs (velocity and anisotropic stress) must be positive definite kernels, which is not manifest in the simple Gaussian model.\nThis will be presented in \\cref{sec:PosKer}.\n\n\\subsubsection{Kraichnan random sweeping approximation}\n\\label{sec:kraichnan}\n\nThe random sweeping approximation provides an explanation for the origin of the Gaussian functional form for the decorrelation.\nKraichnan's sweeping hypothesis is based on the assumption that\nFourier modes of the velocity field\nin the inertial range are advected without deformation by a locally uniform velocity field $\\vb{V}$, which may be time-dependent\n\\cite{kraichnan:1964}.\nThis amounts to neglecting the non-linear terms and writing\n\\begin{equation}\n\t\\pdv{\\vb{v}}{\\tau} + i \\qty[\\vb{k} \\vdot \\vb{V}(\\tau)] \\vb{v} \\simeq 0.\n\t\\label{eq:sweeping-ode}\n\\end{equation}\nSince $\\vb{V}$ is locally uniform, the evolution of modes with different wavenumbers \\(\\vb{k}\\) is decoupled.\nThe turbulent velocity field can then be explicitly integrated: from \\cref{eq:sweeping-ode} one finds\n\\begin{equation}\n\t\\vb{v}(\\vb{k}, \\tau) \\simeq \\vb{v}(\\vb{k},\\zeta) \\exp(-i\\int_{\\zeta}^{\\tau}\\vb{k}\\vdot \\vb{V}(s) \\dd{s}),\n\\end{equation}\nwhere $\\zeta < \\tau$.\nUnder the assumption that the locally uniform velocity field $\\vb{V}$ is statistically independent of the turbulent velocity field $\\vb{v}$ at the initial time, the unequal time correlator of $\\vb{v}$ can be expressed as\n\\begin{equation}\n\t\\UETCv(k,\\tau,\\zeta) \\simeq \\Psdv(k,\\zeta) \\ev{\\exp(-i\\int_{\\zeta}^{\\tau}\\vb{k}\\vdot \\vb{V}(s) \\dd{s})}.\n\\end{equation}\nThe average of the exponential can be calculated assuming that its argument is a Gaussian random variable.\nIt then follows that the velocity field decorrelates with a characteristic Gaussian law\\footnote{We recall that for a Gaussian random variable, \\begin{equation}\n\t\t\\ev{\\exp(X)} = \\ev{\\sum_{n=0}^{\\infty} \\frac{X^{n}}{n!}} = \\sum_{k=0}^{\\infty}\\frac{\\ev{X^{2}}^{k}}{2^{k} k!} = \\exp(\\frac{\\ev{X^{2}}}{2}).\n\t\\end{equation}},\n\\begin{equation}\n\t\\UETCv(k,\\tau,\\zeta) \\simeq \\Psdv(k,\\zeta) \\exp\\qty(- \\frac{1}{2} \\ev{X^2}),\n\\end{equation}where\n\\begin{equation}\n\tX^{2} \\equiv \\iint_{\\zeta}^{\\tau} (\\vb{k} \\vdot \\vb{V}(s))(\\vb{k} \\vdot \\vb{V}(s')) \\dd{s} \\dd{s'}.\n\\end{equation}\nThis expression can be further simplified assuming that on average the locally uniform velocity has the same amplitude in the three spatial directions \\cite{kraichnan:1964}, leading to\n\\begin{equation}\n\t\\UETCv(k,\\tau,\\zeta) \\simeq \\Psdv(k,\\tau) \\exp\\qty[-\\frac{1}{2} k^2 v_\\mathrm{sw}^2(\\tau, \\zeta)(\\tau - \\zeta)^2],\n\t\\label{eq:expvsweep}\n\\end{equation}\nwhere $v_\\mathrm{sw}(\\tau,\\zeta)$ is the average sweeping velocity which takes the form \\cite{he_computation_2004,dong_study_2008}\n\\begin{equation}\n\tv_\\mathrm{sw}^{2}(\\tau, \\zeta) = \\frac{1}{3 (\\tau-\\zeta)^{2}}\\iint_{\\zeta}^{{\\tau}} \\ev{\\vb{V}(s)\\cdot \\vb{V}(s')} \\dd{s} \\dd{s'}.\n\t\\label{eq:sweep-equal-time}\n\\end{equation}\nKraichnan's model assumes stationary turbulence\nand sets the sweeping velocity to the rms velocity divided by $\\sqrt{3}$, under the hypothesis of statistical isotropy \\cite{kraichnan:1964,wilczek_wave-numberfrequency_2012}.\nSince this model has good numerical support in the inertial range \\cite{sanada_random_1992,he_computation_2004,Gorbunova:2021cpn}, any good model should reduce to\n\\begin{equation}\n\tv_\\mathrm{sw}^{2} (\\tau, \\tau) = C_v^2\\,\\frac{ \\overline{v}^2(\\tau)}{3}\n\t\\label{eq:Cv2}\n\\end{equation}\nat equal time and in the inertial range, with $C_v^2$ a numerical factor whose value will be inferred from simulations.\nAs presented in \\cref{sec:results-uetc}, simulations indicate $C_v^2 \\simeq 1$, providing a good test of the random sweeping approximation and of statistical isotropy.\n\n\\subsubsection{Scale dependent sweeping velocity}\n\\label{sec:kaneda}\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics{figures\/ir-sweeping.pdf}\n\t\\caption{Extension of the decorrelation velocity at scales larger than the integral scale, following the model given in \\cref{eq:Vlarge}. It interpolates smoothly between $\\overline{v}^2\/3$ in the inertial range as in \\cref{eq:Cv2} and $2\\overline{v}^2\/15$ on large scales. These limits are shown with the black dashed lines.}\n\t\\label{fig:IRsweep}\n\\end{figure}\n\n\n\n\nThe random sweeping approximation reviewed in \\cref{sec:kraichnan}\napplies in the inertial range.\nHowever, in order to evaluate the GW generation, we need to model the time decorrelation of the velocity field on all scales (see~for example \\cref{eq:unequal-stress-final}).\nThe decorrelation dynamics at scales larger than the integral scale in freely decaying turbulence have received little attention: numerical studies such as those performed in \\Refs{he_computation_2004,dong_study_2008}, for example, study decorrelation only in the inertial range.\nThe largest scale analysed in \\Refa{sanada_random_1992} is $k=1$, corresponding to the scale of the forcing, namely the peak of the power spectrum: it appears from this analysis that even this scale decorrelates more slowly than those of the inertial range.\n\nIn the context of the literature dedicated to GW production by turbulence, various decorrelation models have been proposed, see \\Refs{Gogoberidze:2007an,Caprini:2009yp,Niksa:2018ofa}.\n\\Refa{Caprini:2009yp} assumed that the large scales do not decorrelate. The only time dependence of the velocity spectrum outside the inertial range was therefore due to the free decay, and an exponential decorrelation was inserted for wavenumbers in the inertial range by means of a step function. This introduced a non-physical discontinuity (see~Eq.~(57) of \\cite{Caprini:2009yp}).\nFurthermore, as pointed out in \\Refa{Niksa:2018ofa}, \\Refs{Gogoberidze:2007an,Caprini:2009yp} used the Lagrangian eddy turnover time as typical decorrelation time,\nwhich is scale-dependent in the inertial range, and therefore\nin conflict with the numerical results of \\Refs{sanada_random_1992,he_computation_2004}.\n\n\\Refa{Niksa:2018ofa} recommended the use of the\nmodel of Ref.~\\cite{kaneda_lagrangian_1993},\nwhich improves Kraichnan's random sweeping approximation by taking interactions between modes into account.\nThe result is an Eulerian eddy turnover time which is weakly scale-dependent for wavenumbers up to $\\order{10^2}$ times that corresponding to the integral scale.\nWe adopt the same form for the Eulerian eddy turnover time\nto construct our model of the velocity UETC, but\ncorrect it to ensure the mathematical consistency of\nthe correlation, as discussed in \\cref{sec:PosKer}.\n\n\nFollowing \\Refs{kaneda_lagrangian_1991,kaneda_lagrangian_1993},\nwe define an instantaneous scale-dependent decorrelation velocity\n\\begin{equation}\n\tv_\\mathrm{dc}^2(k, \\tau) \\equiv\n\t\\int_0^\\infty h\\qty(\\frac{q}{k}) \\Psv(q, \\tau) \\dd{\\ln q},\n\t\\label{eq:Vlarge}\n\\end{equation}\nwhere the function $h(x)$ is given by\\footnote{Note that the function $h(x)$ of \\Refs{kaneda_lagrangian_1991,kaneda_lagrangian_1993} has a factor of $2$ difference due to our definition of the power spectrum in \\cref{eq:kinetic-energy}. }\n\\begin{equation}\n\th(x) = \\frac{1}{48}(13-8x^2+3x^4) + \\frac{1}{32 x} (1-x^2)^3 \\ln \\frac{1+x}{\\abs{1-x}}.\n\t\\label{eq:weird-h}\n\\end{equation}\nNote that the calculations performed in \\Refa{kaneda_lagrangian_1993} in principle apply only to the inertial range. We will see that extending it to lower wavenumbers is justified by the numerical data.\n\n\\cref{eq:Vlarge} reduces to $v_\\mathrm{dc}^2(\\tau)\\simeq \\overline{v}(\\tau)^2 \/ 3$ far in the inertial range, consistent with the result of the random sweeping hypothesis, and gives $C_v^2=1$ (see~\\cref{eq:Cv2}).\nHowever, it also has another term, relevant at scales closer to the energy containing scale $\\xi$, which is proportional to $- v_k^2\\sim - \\Psv(k)$.\nThis negative contribution comes from the analysis at small time intervals and appears to slow down the process of decorrelation.\nIn order to account for decorrelation at all scales, one can therefore attempt to use \\cref{eq:Vlarge} as an extension of the sweeping velocity \\cite{Niksa:2018ofa}.\nPushing \\cref{eq:Vlarge} to large scales, one can appreciate that $v_\\mathrm{dc}(k,\\tau)$ provides a continuous interpolation from $\\overline{v}^2 \/ 3$ in the inertial range to $2\\overline{v}^2 \/ 15$ at large scales, as shown in \\cref{fig:IRsweep}.\nIn the semi-analytical GW evaluation, we will solve the integral of \\cref{eq:Vlarge} numerically, although a good analytical fit to $v_\\mathrm{dc}(k,\\tau)$ is (we recall that $K=\\mathcal{A}k\\xi(\\tau)$)\n\\begin{equation}\n\tv_\\mathrm{dc}^2(k, \\tau) \\simeq \\frac{\\overline{v}^2}{3} \\qty(\\frac{1+0.2 K}{\\sqrt{5\/2} + 0.2 K})^2,\n\t\\label{eq:vdcapprox}\n\\end{equation}\nthat we compare to the numerical result in \\cref{fig:IRsweep} (note that this fit differs slightly from what was given in \\Refa{Niksa:2018ofa}).\n\nIn order to describe how the relevant scales in the velocity spectrum decorrelate, one might substitute, in the exponential of \\cref{eq:expvsweep}, the decorrelation velocity given in \\cref{eq:Vlarge}.\nThis is the approach adopted in \\Refa{Niksa:2018ofa}.\nHowever, this breaks the time symmetry of the UETC $\\UETCv(k,\\tau,\\zeta)$ (see~\\cref{eq:velocity-ps}). To restore this symmetry,\nthe decorrelation velocity entering the exponential in \\cref{eq:expvsweep} must depend on both times. \nThe next section is devoted to ensuring the symmetry and positivity requirements that are necessary to model a valid UETC.\n\n\n\n\\section{Unequal time correlators as positive kernels}\n\\label{sec:PosKer}\n\nThe two-point correlation function of any random variable must be a positive kernel \\cite{Genton:2002}, in other words it must satisfy certain properties --- most notably Mercer's theorem.\nThis applies to the turbulent velocity field UETC \\cref{eq:velocity-ps}, as well as to $P_{\\tilde{\\Pi}}(k, \\tau,\\zeta)$ \\cref{eq:Pi}, describing the random anisotropic stresses arising from the turbulent field.\nThe model of the velocity field --- and consequently of the anisotropic stress --- UETCs that we construct in this section ensures that they are positive kernels.\nAccording to Mercer's theorem, this implies that the GW energy density power spectrum is indeed also positive, see~\\cref{eq:sbgw}.\nThis issue had already been raised in \\Refa{Caprini:2009yp}, but it is analysed here in more depth directly on the velocity field UETC.\n\n\n\\subsection{The unequal time correlation function as a non-stationary Gaussian kernel}\n\\label{sec:mercer}\n\nAs we have discussed in \\cref{sec:free-decay-model}, the turbulent rms velocity and integral scale are evolving in time, while the turbulent source decorrelates.\nGWs are therefore generated by a non-stationary, random process, whose UETC we need to model.\nTo do so, we make use of some general properties of two-point correlators of stochastic processes, which we review in the following.\n\nThe two-point correlator of a complex valued\nstochastic process $\\phi(\\tau)$ is defined by\n\\begin{equation}\n\tK(\\tau_1, \\tau_2) \\equiv \\ev{\\phi^*(\\tau_1) \\phi(\\tau_2)}.\n\\end{equation}\nWe assume that the real and imaginary part of the stochastic process are uncorrelated, in which case $K(\\tau_1, \\tau_2)$ is real.\n$K(\\tau_1, \\tau_2)$ is a positive semi-definite kernel which satisfies several properties: symmetry $K(\\tau_1, \\tau_2)=K(\\tau_2, \\tau_1)$; the Cauchy-Schwartz inequality; and the Mercer condition (see \\emph{e.g}\\onedot} \\def\\Eg{\\emph{E.g}\\onedot~\\Refa{Genton:2002})\n\\begin{equation}\n\t\\iint_{I\\times I} \\dd{\\tau_1} \\dd{\\tau_2} f^*(\\tau_1) f(\\tau_2) K(\\tau_1,\\tau_2)\n\t\\geq 0\\,,\n\t\\label{eq:mercer-condition}\n\\end{equation}\nfor any interval $I$.\nThis follows from the fact that, for any well-behaved function $f$, the stochastic variable\n$X = \\int \\dd{\\tau} f(\\tau) \\phi(\\tau)$ must have positive variance $\\langle \\abs{X}^2 \\rangle \\geq 0$.\nOne can show that the linear combination and\nmultiplication of two kernels yield another kernel~\\cite{1909RSPTA.209..415M}.\n\n\nThe velocity UETC $\\UETCv(k,\\tau,\\zeta)$ is the two-point correlation function of the stochastic process $v_i(\\vb{k}, \\tau)$ (see~\\cref{eq:velocity-ps}), and is therefore a positive semi-definite kernel.\nIn \\cref{sec:UETCvelocity} we analysed the time evolution and decorrelation properties of the velocity field,\nbased on our understanding of the turbulence physical process.\nIn this section, we explain how the fact that a UETC must be a positive semi-definite kernel leads to our choice of the decorrelation velocity at unequal times $v_\\mathrm{dc}(k,\\tau,\\zeta)$, and how to symmetrize $\\UETCv(k,\\tau,\\zeta)$.\n\n\nThe simplest example of a positive semi-definite kernel is the separable kernel $K(\\tau_1, \\tau_2) = g^*(\\tau_1) g(\\tau_2)$, which\nfactorizes Mercer's condition, \\cref{eq:mercer-condition}, into a modulus.\nThis type of kernel was already proposed in the context of GW generation by turbulence and primordial magnetic fields in Refs~\\cite{Caprini:2009yp,Caprini:2009fx,Caprini:2009pr}, where it is referred to as the coherent assumption.\nAnother common form is the stationary kernel $K(\\tau_1, \\tau_2) =K(\\tau_1-\\tau_2)$.\nStationary kernels are positive if and only if there exist a positive finite function $F$ such that~$K(\\tau_1-\\tau_2) = \\int \\cos[\\omega (\\tau_1-\\tau_2)] F(\\omega) \\dd{\\omega}$.\nFrom this result one can easily see that a kernel of the form $K(\\tau_1, \\tau_2) \\propto \\delta(\\tau_1-\\tau_2)$ is positive definite: it is referred to as incoherent in Refs.~\\cite{Caprini:2009yp,Caprini:2009pr}.\n\n\nMost importantly for us, stationary kernels also include the Gaussian kernel $$K(\\tau_1, \\tau_2) = \\exp[-\\alpha(\\tau_1-\\tau_2)^2],$$ with $\\alpha>0$, which is the form provided by the Kraichnan sweeping scenario \\cref{eq:expvsweep}.\nHowever, here we would like to model the power spectrum of freely decaying turbulence, which is, by definition, non-stationary.\nA simple but well-defined departure from stationary kernels is provided by locally stationary kernels, of the form $K(\\tau_1, \\tau_2) = K_1\\qty({\\tau_1+\\tau_2}\/{2}) K_2\\qty(\\tau_1-\\tau_2)$,\nwhere $K_1$ is a non-negative function and $K_2$ a positive stationary kernel \\cite{Silverman:1957}.\nThe UETC of a freely-decaying turbulent velocity field, though, cannot assume this form.\nThis is because the sweeping velocity depends explicitly on time, as can be seen from \\cref{eq:expvsweep}.\nFor the problem at hand, we therefore also need to go beyond local stationarity.\n\nWe do so by introducing process-convolution kernels~\\cite{higdon:1999}.\nThis specific class of non-stationary kernels is defined through a two-valued\ncomplex function $g(\\tau,u)$ as follows:\n\\begin{equation}\n\tK(\\tau_1, \\tau_2) = \\int g^*(\\tau_1, u) g(\\tau_2, u) \\dd{u}.\n\t\\label{eq:pckernel}\n\\end{equation}\nIt is easy to show that process-convolution kernels satisfy Mercer's condition \\cref{eq:mercer-condition}.\nThe process-convolution kernel adapted to our case is the non-stationary Gaussian kernel, defined with the function\n\\begin{equation}\n\tg(\\tau,u) = \\qty(\\frac{2}{\\pi J(\\tau)})^ {1\/4}\\exp[-\\frac{(\\tau-u)^2}{J(\\tau)}],\n\t\\label{eq:gtwovalue}\n\\end{equation}\nwhere $J(\\tau)$ is a free function.\n\n\\subsection{Symmetrized unequal time correlator of the velocity field}\n\\label{sec:unequal-time-velocity}\n\nTo define the kernel representing the velocity unequal time correlator, we choose for a given $k$\n\\begin{equation}\n\tJ_k(\\tau) = \\frac{1}{[k\\,v_\\mathrm{dc}(k,\\tau)]^2},\n\\end{equation}\nwhere $v_\\mathrm{dc}(k,\\tau)$\nis given in \\cref{eq:Vlarge}.\nInserting this definition into \\cref{eq:gtwovalue} and further into \\cref{eq:pckernel}, one finds a family of non-stationary Gaussian kernels\n\\begin{equation}\n\tK_k(\\tau,\\zeta) = \\sqrt{\\frac{2\\, v_\\mathrm{dc}(k, \\tau) v_\\mathrm{dc}(k, \\zeta) }{v_\\mathrm{dc}^2(k, \\tau) + v_\\mathrm{dc}^2(k, \\zeta)}}\n\t\\exp[-k^2 (\\tau-\\zeta)^2 \\frac{v_\\mathrm{dc}^2(k,\\tau) v_\\mathrm{dc}^2(k,\\zeta)}{v_\\mathrm{dc}^2(k,\\tau)+v_\\mathrm{dc}^2(k,\\zeta)}].\n\t\\label{eq:kernelvdc}\n\\end{equation}\nNote that these kernels satisfy $K_k(\\tau, \\tau) = 1$, and are therefore of a suitable form to be used as a normalized UETC $R(k,\\tau,\\zeta)$.\n\nWe can now use this result to construct a UETC $\\UETCv(k,\\tau,\\zeta)$ which satisfies the Mercer condition.\nFirst we note that the UETC evaluated at equal times is the power spectrum,\n$\\UETCv(k,\\tau,\\tau) = \\Psdv(k, \\tau)$.\nHence, multiplying the kernel \\cref{eq:kernelvdc} by\n$\\sqrt{\\Psdv(k, \\tau)\\Psdv(k, \\zeta)}$\nwill give a function of the two times which correctly\nreduces to the power spectrum at equal times.\nThis function is also a kernel,\nas $\\sqrt{\\Psdv(k, \\tau)\\Psdv(k, \\zeta)}$ is a separable kernel, and the product of two kernels is a kernel.\nOur model is then\n\\begin{equation}\n\t\\UETCv\\qty(k, \\tau, \\zeta) =\n\t\\sqrt{\\Psdv(k, \\tau)\\Psdv(k, \\zeta)} \\sqrt{\\frac{2 v_\\mathrm{dc}(k, \\tau) v_\\mathrm{dc}(k, \\zeta) }{v_\\mathrm{dc}^2(k, \\tau) + v_\\mathrm{dc}^2(k, \\zeta)}}\n\t\\exp[- \\frac{1}{2}k^2 (\\tau-\\zeta)^2 v_\\mathrm{dc}^2(k,\\tau, \\zeta)],\n\t\\label{eq:unequal-velocity-ps}\n\\end{equation}\nwhere the average decorrelation velocity $v_\\mathrm{dc}(k,\\tau,\\zeta)$ is given by the harmonic average of the equal-time decorrelation velocity:\n\\begin{equation}\n\tv_\\mathrm{dc}^{2}(k,\\tau,\\zeta)=2\\,\\frac{v_\\mathrm{dc}^{2}(k,\\tau)v_\\mathrm{dc}^{2}(k,\\zeta)}{v_\\mathrm{dc}^{2}(k,\\tau)+v_\\mathrm{dc}^{2}(k,\\zeta)}. \\label{eq:vsweepcomplete}\n\\end{equation}\nNote that this is symmetric in the two times $\\tau$ and $\\zeta$.\n\nTo summarize, we have shown that the velocity UETC of \\cref{eq:unequal-velocity-ps} is a positive kernel, \\emph{i.e}\\onedot} \\def\\Ie{\\emph{I.e}\\onedot a well-defined two point correlator.\nThis guarantees that the anisotropic stress UETC $P_{\\tilde{\\Pi}}(k,\\tau,\\zeta)$ of \\cref{eq:unequal-stress-final} is also a positive kernel, being the product of two positive kernels.\nFinally, the integrand of \\cref{eq:sbgw} is also a positive kernel. Indeed, $\\mathcal{G}$ is a separable kernel since it results from the square of \\cref{eq:hdot-short}.\nAs a consequence, the GW energy density power spectrum defined in \\cref{eq:sbgw} is always positive, as it should be.\n\nHaving managed to model the decorrelation of the freely-decaying turbulence with \\cref{eq:unequal-velocity-ps}, we can go beyond the approach of \\Refa{Caprini:2009yp}.\nIn that paper the shear stress decorrelation was modelled directly in the anisotropic stress\nUETC as a top-hat function of the kind $\\Theta[k\\abs{\\tau-\\zeta} - x_c]$.\nWe remark that such a function is not a positive kernel, since it is the Fourier transform of a sinc function:\nin order to derive a positive definite GW power spectrum, one must indeed take $x_c <\\pi$\n\\cite{Caprini:2009yp,Caprini:2009pr}.\n\n\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=0.49\\textwidth]{figures\/V-0000.png}\n\t\\includegraphics[width=0.49\\textwidth]{figures\/V-0001.png}\n\t\\caption{\\emph{Left panel}: Slice through simulation ($\\mathrm{A}'$) showing the velocity initial conditions in real space. \\emph{Right panel}: Same slice as the left panel but\n after a time $\\Delta \\tau=20.6 \\tau_{\\xi_*}$ has elapsed.}\n\t\\label{fig:simslices}\n\\end{figure}\n\n\n\\section{Direct numerical simulations}\n\\label{sec:numerical}\n\nWe carry out a series of direct hydrodynamical simulations in order to\nstudy the freely-decaying turbulent flow, in particular the UETCs of\nthe velocity field and its overall time evolution, as well as the SGWB signal produced. For our\nsimulations, we use a modified version of the relativistic\nhydrodynamics code SCOTTS, previously developed to study the coupled\nevolution of the scalar field and the relativistic fluid, and the\nproduction of GWs during a thermal phase\ntransition~\\cite{Hindmarsh:2013xza,Hindmarsh:2015qta,Hindmarsh:2017gnf,Cutting:2019zws}.\nHere we are mainly interested in the dynamics of the fluid after the\nphase transition has completed. We therefore\ninitialize the scalar field to lie in the broken phase at the start of\nthe simulation. Given the form of the effective potential and equation\nof state in Ref.~\\cite{Cutting:2019zws}, this leaves us\nsolely with the evolution of the\nrelativistic fluid.\n\nIdeally, one would want to simulate the coupled\nfield-fluid model~\\cite{Enqvist:1991xw}, in order to observe the\ngeneration of turbulence at all stages from the bubble collisions onwards~\\cite{Cutting:2019zws}.\nIn this work, however, we do not analyse the\ncomplete system up to fully developed turbulence. We rather\nconcentrate on the\ndecay of turbulence,\ninserting the turbulence\nparameters (\\emph{e.g}\\onedot} \\def\\Eg{\\emph{E.g}\\onedot kinetic energy and integral scale) as input values,\ndisconnected from the rest of the phase transition dynamics.\nHere, we will initialize only the vortical component of the\nfluid velocity.\nThis permits a cleaner analysis of the results and comparison to the\nsemi-analytical modelling of \\cref{sec:free-decay-model,sec:PosKer}.\nWe defer studying the compressional component and\nsimulating the turbulence generation to future work.\n\nIn the rest of this section, we specify the equations of motion of the fluid that are solved by the code, and then explain how we fix the initial conditions and evaluate the UETCs. Finally, we discuss how we compute GW power spectra from the simulations.\n\n\n\\subsection{Hydrodynamic evolution}\n\\label{sec:hydroeq}\n\nGiven that we are discarding the scalar field dynamics, the energy momentum tensor of the system becomes that of a perfect fluid,\n\\begin{equation}\n\tT^{\\mu\\nu}= (\\epsilon + p) U^\\mu U^\\nu + g^{\\mu\\nu}p\\text,\n\\end{equation}\nwith $\\epsilon$ the internal energy density in the fluid, $p$ the\npressure, $U^\\mu=\\gamma(1,\\vb{v})$ the fluid four-velocity and with\nLorentz factor $\\gamma = 1\/\\sqrt{1-v^2}$.\nThe equation of state is\nthat of an ultrarelativistic gas with $p=\\epsilon\/3$. It has been\nshown that the hydrodynamic equations of motion of an\nultrarelativistic fluid in an expanding universe expressed in comoving\ncoordinates and conformal time are the same as the hydrodynamic\nequations in Minkowski space-time, provided the dynamical quantities\nare replaced by appropriately scaled\nvariables~\\cite{Brandenburg:1996fc}. However, the source term for the\nGWs does not scale in the same way. Therefore, the Minkowski\nspace-time code SCOTTS\ncan be used to study hydrodynamic turbulence in an expanding background, but the output GW power spectrum can be matched to the expanding background result only for sources which are short-lived compared with the Hubble time \\cite{Hindmarsh:2015qta}.\n\nThe dynamical quantities that the code evolves are the fluid energy density $E=\\gamma \\epsilon$ with equation of motion~\\cite{Hindmarsh:2015qta}\n\\begin{equation}\n\t\\dot{E} + \\partial_{i}(Ev^i) + p\\left[\\dot{\\gamma} + \\partial_i(\\gamma v^i)\\right] = 0\\text,\n\\end{equation}\nand the fluid momentum density $Z_i = \\gamma^2(\\epsilon + p)v_i$, the components of which evolve according to\n\\begin{equation}\n\t\\dot{Z_i} + \\partial_j(Z_i v^j) + \\partial_i p = 0\\text.\n\\end{equation}\nThe evolution algorithm follows the approach taken in\nRef.~\\cite{WilsonMatthews} with a leapfrog and operator splitting\napproach to updating the dynamical quantities.\nFor this work we\nuse a van Leer scheme for the advection\nupdate~\\cite{VANLEER1977276,Anninos:2002gz}, whereas earlier uses of\nthe SCOTTS code used an upwind donor cell scheme. For all of our simulations we pick the lattice\nspacing and timestep such that their ratio is $\\dd x\/\\dd \\tau = 2.5$. We denote\nthe starting time of our simulations as $\\tau = \\tdevel$.\nNote that while our simulations do not contain a physical viscosity,\nthe introduction of a lattice spacing $\\dd x$ effectively sets a\ndissipation scale. This is due to the numerical viscosity introduced\nby the discretization of the fluid equations. Therefore,\nsimulations with larger $\\xi_*\/\\dd x$ have a larger effective\nReynolds number.\n\n\n\\subsection{Initial conditions of the simulation}\n\\label{sec:initial}\nIn our simulations the velocity field $\\vb{v}$ is not necessarily divergence-free. However, as outlined in the rest of this section, we initialize the velocity field to be divergence-free in our simulations. In \\cref{sec:appendix-sim-vrms-comp} we show that in this case the longitudinal component of the velocity field remains negligible throughout our simulations, and so we treat $\\vb{v}$ as divergence-free in subsequent sections.\n\nAt the start of our simulations, we initialize the fluid energy density $E$ everywhere. For our simulations we choose to set $E\\, \\dd x^4 = 1\/16$. Next we initialize the velocity field of the simulations in Fourier space. We do this indirectly by initializing the spatial components of the four-velocity $\\vb{U}=\\gamma \\vb{v}$, such that\n\\begin{equation}\n\t\\vb{U}_{\\vb{x}} = \\frac{1}{L^3}\\sum_{\\vb{k}} \\vb{U}_{\\vb{k}} \\e^{i \\vb{k} \\cdot \\vb{x}},\n\\end{equation}\nwhere $\\vb{x}$ and $\\vb{k}$ refer to the discrete real and momentum space coordinates on the lattice, and $L^3$ is the total number of lattice sites.\nEach mode $\\vb{U}_{\\vb{k}}$ is randomly distributed and follows Gaussian statistics with mean zero and variance determined by the desired power spectrum serving as initial condition.\nInitializing $\\vb{U}_{\\vb{k}}$ rather than $\\vb{v}_{\\vb{k}}$ prevents individual modes of $\\vb{v}_{\\vb{k}}$ being drawn from the tails of the Gaussian with unphysical velocities larger than one.\nThe $\\mathbf{U}_{\\vb{k}}$ field is then projected onto its vortical component with the projector $\\bot_{ij}(\\vu{k})$, see \\cref{eq:PSsim}.\nSince $\\vb{U}$ is real, we impose that $\\vb{U}_{\\vb{k}} = \\vb{U}^* _{-\\vb{k}}$.\nOnce the $\\vb{U}_{\\vb{k}}$ field is correctly initialized we perform a Fourier transform to find the real space configuration of the $\\vb{U}$ field, and from this we can calculate the velocity field $\\vb{v}$.\n\n\nThis method allows us to initialize the simulation with arbitrary choices for the $\\vb{U}$ field power spectrum $\\mathcal{P}_U(k,\\tau) = {k^3} P_U(k,\\tau)\/{\\pi^2}$.\nHere $P_U(k,\\tau)$ corresponds to the spectral density of the $\\vb{U}$ field, defined via the equal time correlator (see~also \\cref{eq:velocity-sd})\n\\begin{equation}\n\t\\langle U_{i}(\\vb{k},\\tau) U_{j}^*(\\vb{q},\\tau) \\rangle = (2\\pi)^3 \\bot_{ij}(\\vu{k}) \\delta(\\vb{k}-\\vb{q})P_{U}(k,\\tau)\\text,\n\\end{equation}\nwhere in our initial conditions we have set the longitudinal component of $\\mathbf{U}$ to zero.\nWe initialize the simulations with a relativistic version of the von K\\'arm\\'an~spectrum \\cref{eq:power-spectrum}, supplemented with a high-wavenumber cutoff:\n\\begin{equation} \\label{eq:sim-input}\n\t\\mathcal{P}_U(k,\\tdevel) = C \\frac{(k\/\\tilde{k})^5}{[1 + (k\/\\tilde{k})^2]^{17\/6}} \\exp\\left[-\\left(\\frac{k}{k_\\mathrm{max}}\\right)^2\\right]\\text.\n\\end{equation}\nThe two constants $\\tilde{k}$ and $C$ determine respectively the peak location and amplitude.\nThe values of $C$ and $\\tilde{k}$ are chosen so that the initial rms velocity $\\overline{v}_*=\\sqrt{ \\overline{v}^2(\\tdevel)}$ (defined in \\cref{eq:kinetic-energy}) and integral scale $\\xi_* = \\xi(\\tdevel)$ (defined in \\cref{eq:integral-scale}) extracted from the simulation will be close to a desired value.\nA list of parameters for the simulations we perform is given in \\cref{tab:list}.\nIn \\cref{eq:sim-input} we insert an exponential cutoff to ensure that the power does not extend to the lattice scale, with $k_\\mathrm{max}\\, \\dd{x}=\\pi\/4$ used in our simulations.\n\n\n\\begin{table}\n\t\\centering\n\t\\resizebox{\\textwidth}{!}{%\n\t\t\\begin{tabular}{c D{.}{.}{-1} D{.}{.}{-1} D{.}{.}{-1} D{.}{.}{-1} D{.}{.}{-1} c c c c c c c}\n\t\t\t$L^3$ &\n\t\t\t\\multicolumn{1}{c}{$\\overline{v}_*$} &\n\t\t\t\\multicolumn{1}{c}{$\\xi_* \/ \\dd{x}$} &\n\t\t\t\\multicolumn{1}{c}{$L \\dd{x}\/\\xi_*$} &\n\t\t\t\\multicolumn{1}{c}{$(\\tend-\\tdevel)\/\\tau_{\\xi_*}$} &\n\t\t\t\\multicolumn{1}{c}{$(\\tuetc-\\tdevel)\/\\tau_{\\xi_*}$} &\n\t\t\t\\multicolumn{1}{c}{$\\deltuetc\/\\tau_{\\xi_*}$} &\n\t\t\tC &\n\t\t\t$\\tilde{k} \\dd{x}$\n\t\t\t & Label \\\\\n\t\t\t\\hline\n\t\t\t$4096^3$ & 0.0988 & 29.8 & 138 & 99.6 & 5.97 & $1.08\\,\\times\\, 10^{-2}$ & 1.99 & $9.19\\times10^{-3}$ &\n\t\t\t(A) \\\\\n\t\t\t\\hline\n\t\t\t$2048^3$ & 0.0298 & 8.08 & 253 & 99.5 & 4.97 & $1.18\\,\\times\\, 10^{-3}$ & 0.995 & $4.06\\times10^{-2}$ &\n\t\t\t(B) \\\\\n\t\t\t$2048^3$ & 0.0274 & 57.2 & 35.8 & 12.9 & 6.47 & $9.18\\,\\times\\, 10^{-4}$ & 0.129 & $3.28\\times10^{-3}$ &\n\t\t\t(C) \\\\\n\t\t\t\\cline{2-9}\n\t\t\t$2048^3$ & 0.0984 & 8.08 & 253 & 97.4 & 4.87 & $1.32\\,\\times\\, 10^{-2}$ & 0.974 & $4.06\\times10^{-2}$ &\n\t\t\t(D) \\\\\n\t\t\t$2048^3$ & 0.0955 & 64.4 & 31.8 & 11.9 & 0.594 & $1.02\\,\\times\\, 10^{-2}$ & 0.119 & $3.28\\times10^{-3}$ &\n\t\t\t(E) \\\\\n\t\t\t\\cline{2-9}\n\t\t\t$2048^3$ & 0.279 & 8.11 & 253 & 103 & 4.13 & $1.18\\,\\times\\, 10^{-1}$ & 1.03 & $4.06\\times10^{-2}$ &\n\t\t\t(F) \\\\\n\t\t\t$2048^3$ & 0.289 & 70.9 & 28.9 & 11.7 & 0.466 & $9.18\\,\\times\\, 10^{-2}$ & 0.117 & $3.28\\times10^{-3}$ &\n\t\t\t(G) \\\\\n\t\t\t\\hline\n\t\t\t$2048^3$ & 0.0974 & 28.4 & 72.0 & 103 & 6.17 & $1.08\\,\\times\\, 10^{-2}$ & 2.06 & $9.19 \\times 10^{-3}$ &\n\t\t\t($\\mathrm{A}'$) \\\\\n\t\t\t$1024^3$ & 0.0942 & 25.4 & 40.4 & 111 & 6.69 & $1.08\\,\\times\\, 10^{-2}$ & 2.23 & $9.19 \\times 10^{-3}$ &\n\t\t\t($\\mathrm{A}''$) \\\\\n\t\t\\end{tabular}}\n\t\\caption{List of the simulations used in this work. We list\n\t\tthe number of sites in the lattice for each simulation\n\t\t$L^3$, the initial rms velocity $\\overline{v}_*=\\sqrt{\n\t\t\t\t\\overline{v}^2(\\tdevel)}$ (see \\cref{eq:kinetic-energy}) and\n\t\tintegral scale $\\xi_*=\\xi(\\tdevel)$ (see\n\t\t\\cref{eq:integral-scale}) extracted from the simulation\n\t\ton the initial timestep. The relative size of each\n\t\tsimulation to the integral scale $L \\dd{x}\/\\xi_*$ is\n\t\tgiven. We also list the final time of each simulation\n\t\t$\\tend$, the UETC reference time $\\tuetc$ and the UETC output interval $\\deltuetc$, all given in\n\t\tunits of the eddy turnover time at the integral scale\n\t\t$\\tau_{\\xi_*} = \\xi_* \/ \\overline{v}_*$. Finally we list the parameters\n\t\tfor the simulation input power spectrum as given in\n\t\t\\cref{eq:sim-input}. Simulations ($\\mathrm{A}'$) and ($\\mathrm{A}''$) are\n\t\tused to test the effects of finite volume and are discussed\n\t\tin \\cref{sec:appendix-finite-vol}.}\n\t\\label{tab:list}\n\\end{table}\n\n\\subsection{Velocity power spectra in the simulations}\n\\label{sec:sim-velps}\nDuring the simulation we regularly output the rotational velocity power spectra $\\Psv(k,\\tau)$.\nWe validate that the longitudinal piece of the velocity correlator $\\Psdv(k,\\tau)$ is negligible over the full duration of all our simulations, providing it is initialized to be zero, see \\cref{sec:appendix-sim-vrms-comp}.\nTo find the velocity power spectrum within our simulations, we first perform a discrete Fourier transform on the real space field to obtain the Fourier modes\n\\begin{equation}\n\t\\vb{v}_{\\vb{k}} = \\sum_{\\vb{x}} \\vb{v}_{\\vb{x}} \\e^{- i \\vb{x} \\cdot \\vb{k}}\\text.\n\\end{equation}\nThe rotational power spectrum is then approximated by projecting and binning these modes in momentum space.\nThe power spectrum for the $r$-th bin is constructed via\n\\begin{equation}\n\t\\Psv(k_r,\\tau) = \\frac{1}{L^6} \\frac{k_r}{\\Delta k} \\sum_{r \\leq \\tfrac{|\\vb{k}|}{\\Delta k}\\mathcal{H}_*$.\nThe wavenumbers accessible in the simulations satisfy $k\\xi_*>2\\pi\\xi_*\/(L \\dd{x}) > 0.03$ (see \\cref{tab:list}), and\nwe will therefore compare with semi-analytical results calculated setting $\\mathcal{H}_*\\xi_*=0.001$ (see \\cref{sec:numintegration}).\n\n\n\n\\section{Results: evolution of the velocity field}\n\\label{sec:results-vel}\n\n\nIn this section, we verify the model of decaying turbulence presented in \\cref{sec:free-decay-model} with the hydrodynamic simulations.\nWe compare the time evolution laws of the turbulent kinetic energy and correlation scale with the simulation results in \\cref{sec:ResETC}.\nThen in \\cref{sec:results-uetc}, we validate the symmetrized velocity field decorrelation function constructed in \\cref{sec:UETCvelocity,sec:PosKer}, and thereby show that it can also describe decorrelation at large scales, outside the inertial range.\nNote that the turbulence model we adopted has been developed in the context of non-relativistic turbulence.\nThe highest value of the initial rms velocity that we have tested in the simulations is $\\overline{v}_* \\simeq 0.3$ (see~\\cref{tab:list}), finding good agreement with the predictions of the non-relativistic turbulence modelling, as far as the UETC, free decay evolution and SGWB spectra are concerned.\n\n\\subsection{Velocity power spectrum and averaged quantities}\n\\label{sec:ResETC}\n\n\\begin{figure}\n\t\\centering \\subfloat[Simulation (A), unrescaled velocity power\n\t\tspectrum.\\label{fig:unrescaled}]{\n\t\t\\includegraphics[width=0.45\\textwidth]{figures\/power_spectra}}\n\t\\subfloat[Power spectrum rescaled with \\cref{eq:pv_scaled_one_scale}.\\label{fig:rescaledonescale}]{\n\t\t\\includegraphics[width=0.465\\textwidth]{figures\/pspec_scaled.pdf}}\n\t\\caption{Evolution of the velocity power spectrum from simulation\n\t\t(A). \\emph{Left panel}: unrescaled power\n\t\tspectrum. The solid black line is the\n\t\tinitial condition, \\cref{eq:power-spectrum} and coloured lines\n\t\tshow the time evolution from $\\tau=\\tdevel$ up to $\\tau = \\tend$\n\t\twith interval $\\Delta\\tau = 9.96\\, \\tau_{\\xi_*}$. Lighter colours refer to later times.\n\t\t\\emph{Right panel}:\n power spectrum rescaled according to \\cref{eq:pv_scaled_one_scale}. The curves are plotted starting from $\\tau-\\tdevel\\simeq 40\\,\\tau_{\\xi_*}$, at fixed intervals $\\Delta \\tau = 1.99 \\tau_{\\xi_*}$.\n\t\t\\label{fig:Pv_and_first_rescale}}\n\\end{figure}\n\nHere we use the results from simulation (A) (\\cref{tab:list}) to investigate the time evolution of the velocity power spectrum, and the values of the parameters $\\beta$, $p$ and $q$, introduced in \\cref{sec:free-decay-evol}.\n\nIn \\cref{fig:Pv_and_first_rescale} we show the time evolution of the velocity power spectrum. \\cref{fig:unrescaled} shows the velocity power spectrum in its raw form, whilst \\cref{fig:rescaledonescale} shows the power spectrum rescaled with \\cref{eq:pv_scaled_one_scale}, indicating that there is indeed only one principal length scale in the flow, given our initial condition.\nThe self-similarity is established starting from about $40 \\tau_{\\xi_*}$, the time to which the first power spectrum line in \\cref{fig:rescaledonescale} corresponds.\nNote that the large-scale slope of the late-time power spectrum is significantly less steep than the $k^5$ corresponding to the Batchelor power spectrum imposed in the initial condition \\cref{eq:power-spectrum}: therefore, the large-scale power spectrum is not constant in time in the simulation, before about $40 \\tau_{\\xi_*}$.\nWhile the large-scale slope could be subject to finite volume effects, its constancy in time seems to be well established at late times.\n\n\nTurning to the form of the velocity power spectrum at wavenumbers higher than the peak, we see from \\cref{fig:Pv_and_first_rescale}\nevidence for a power law in the range $1 \\lesssim k\\xi_* \\lesssim 5 $.\nThis power law is approximately consistent with a Kolmogorov power spectrum ($k^{-2\/3}$) at late times.\nAlthough this was also the power spectrum in the initial conditions, one can see that this power law is lost in the early evolution,\nbut later regained.\nWe do not have the inertial range\nto give a firm value for the late-time power law index.\nFor wavenumbers above $k\\xi_* \\gtrsim 5 $, we see a small build up in power followed by a sharp fall-off for $k\\xi_* \\gtrsim 10$.\nThe fall-off is set by the viscous damping scale of the numerical scheme, while the build up seems to be an associated feature of the van Leer advection scheme\\footnote{The upwind donor cell advection scheme previously used was associated with a fall-off in power at smaller $k\\xi_*$ consistent with it being a lower order advection scheme.}.\n\nIn \\cref{fig:vrms-xi-beta} we plot the\ncombination $\\overline{v}^2 \\xi^{1+\\beta}$, predicted to be constant when the large-scale power spectrum is constant as well, see \\cref{eq:vxigeneral}.\nIt can be seen that this combination indeed\nremains approximately constant after about $40\\tau_{\\xi_*}$, provided $\\beta=3$.\nConsequently, in \\cref{fig:rescaledbeta3} we analyse again the self similarity of the power spectrum, this time rescaled according to \\cref{eq:Pvxi} setting $\\beta=3$. Again, we only plot the rescaled spectra for $\\tau-\\tdevel\\gtrsim 40\\,\\tau_{\\xi_*}$.\nIt appears that the self-similarity condition \\cref{eq:Pvxi} is satisfied by the simulation with $\\beta=3$.\nThis result is consistent with the findings of \\Refa{Brandenburg:2016odr}.\nHowever, the large-scale power spectrum is less steep than $k^4$, which is the power law which would be inferred setting $\\beta=3$.\n\nSimulation (A), therefore, seems broadly consistent with the self-similar behaviour outlined in \\cref{eq:Pvxi}, at late times.\nWe have seen that $\\beta$ is not set by the slope of the power spectrum at low wavenumbers in the initial condition; rather, it seems to be part of the dynamics of self-similarity. We would need even larger lattices\nto study in detail the evolution of the power spectrum at low wavenumbers,\nwhere there appear to be correlations established at scales greater than the integral scale.\nLimitations of the size of the computational domain are known to possibly affect the interpretation of the turbulent decay features \\cite{doi:10.1063\/1.4901448}.\n\n\\begin{figure}\n\t\\centering\n\t\\subfloat[Evolution of $\\overline{v}^2 \\xi^{1+\\beta}$. \\label{fig:vrms-xi-beta}]{\\includegraphics[width=0.45\\textwidth]{figures\/globals}}\n\t\\subfloat[Power spectrum rescaled with \\cref{eq:Pvxi}, assuming\n\t\t$\\beta=3$.\\label{fig:rescaledbeta3}]{\n\t\t\\includegraphics[width=0.465\\textwidth]{figures\/collapse-beta3}}\n\t\\caption{\n\t\t\\emph{Left panel}: evolution of $\\overline{v}^2 \\xi^{1+\\beta}$ for different values of $\\beta$ in simulation (A). As shown in \\cref{eq:vxigeneral}, this quantity is expected to remain constant, thus indicating that $\\beta \\simeq 3$.\n\t\t\\emph{Right panel}:\n\t\tPower spectrum rescaled with\n\t\t\\cref{eq:Pvxi}, using $\\beta=3$.\n\t\tThe power spectra are plotted from $\\tau-\\tdevel\\simeq 40\\,\\tau_{\\xi_*}$ onward, at fixed interval $\\Delta \\tau = 1.99 \\tau_{\\xi_*}$.\n\t\t\\label{fig:collapse}}\n\t\\label{fig:beta3}\n\\end{figure}\n\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=0.47\\textwidth]{figures\/evolution}\n\t\\caption{Evolution of the velocity and integral scale in simulation (A).\n\t The black-dashed line showcases \\cref{eq:VelEvMod,eq:XiEvMod} for $\\mathcal{N}_\\mathrm{e}=5$, $p=4\/3$ and $q=1\/3$, where the values of $p$ and $q$ correspond to $\\beta=3$; see the relationships in \\cref{eq:pchialpha,eq:qchialpha}.\n\t\t\\label{fig:xi-vrms-evol}}\n\\end{figure}\n\n\nIn \\cref{fig:xi-vrms-evol} we show the ratios\n$\\overline{v}^2(\\tau)\/\\overline{v}_*^2$ and $\\xi(\\tau)\/\\xi_*$ extracted from simulation (A), where $\\overline{v}^2(\\tau)$ and $\\xi(\\tau)$ are calculated by integrating the velocity power spectra of \\cref{fig:unrescaled}, according to \\cref{eq:kinetic-energy,eq:integral-scale}.\nWe also show the predicted evolution according to \\cref{eq:VelEvMod,eq:XiEvMod} with $\\mathcal{N}_\\mathrm{e} = 5$ (black, dashed lines), where this specific value has been chosen to match the simulation result.\nWe fix the decay exponents in \\cref{eq:VelEvMod,eq:XiEvMod} to $p=4\/3$ for the kinetic energy and $q=1\/3$ for the integral scale. These exponent values are provided by the condition $\\beta=3$ from \\cref{eq:pchialpha,eq:qchialpha} and provide a good fit to the simulation outcome.\nWe shall see later in \\cref{sec:numintegration} that the precise values of the decay exponents do not have a significant effect on the final SGWB spectrum.\nInstead, we will see in \\cref{sec:GWcontinuous} that the initial phase preceding the generation of the turbulence plays a much larger role in determining the spectral shape.\n\n\n\\begin{figure}\n\t\\centering\n\t\\subfloat[Instantaneous exponents $(p,q)$.\\label{fig:pq-diagram}]{\\includegraphics[width=0.49\\textwidth]{figures\/beta}}\n\t\\subfloat[Time evolution $p(\\tau)$, $q(\\tau)$.\\label{fig:p-q-evol}]{\\includegraphics[width=0.46\\textwidth]{figures\/scales}}\n\t\\caption{\\emph{Left panel}: Trajectory of the instantaneous exponents $(p,q)$ in simulation (A). Time is represented by the colour scheme: early times are shown with lighter shades and late times with darker shades, starting at $\\tau=\\tdevel$ and with interval $\\Delta \\tau \\simeq 2 \\tau_{\\xi_*}$ between markers.\n\t\tThe dark solid line represents the scale-invariance line $p=2(1-q)$, \\cref{eq:pVsq}. The coloured dashed, dotted and dash-dotted lines show the self-similarity relations $p=(1+\\beta)q$ for various choices of $\\beta$. \\emph{Right panel}: Evolution of the instantaneous kinetic energy and integral scale exponents $(p, q)$ as a function of time in simulation (A). The dash-dotted line shows the values expected for $p$ and long dashed line for $q$, with $\\beta = 3$.}\n\t\\label{fig:pq-explore}\n\\end{figure}\n\nWe further analyse the behaviour of the power law exponents $p$ and $q$ of the kinetic energy and integral scale in \\cref{fig:pq-explore}.\nIn \\cref{fig:pq-diagram}, we adopt the same approach as \\Refa{Brandenburg:2016odr} and plot the state of the system in $(p,q)$ space\nfor a range of times during the simulation.\nThe values of $p$ and $q$ are found by solving \\cref{eq:VelEvMod,eq:XiEvMod} for $p$ and $q$ in terms of the measured values of $\\overline{v}^2(\\tau)$, \\eqref{eq:kinetic-energy}, and $\\xi(\\tau)$, \\eqref{eq:integral-scale}.\nThe scale-invariance line $p = 2(1 - q)$, derived in \\cref{eq:pVsq}, is represented by the black line in \\cref{fig:pq-diagram}.\nThe self-similarity lines $p=(1+\\beta)q$ are shown for various values of $\\beta$ as dashed lines.\nAs in clear from the figure, the point $(p,q)$ is still evolving at the end of the simulation.\nHowever, the evolution is moving in the direction of the intersection of the self-similarity line $p = (1+\\beta)q$ with $\\beta=3$, and the scale invariance line, $p =2(1 -q)$.\nIn \\cref{fig:p-q-evol}, we display the evolution of $p$ and $q$\nagainst time, and also show the values that can be predicted from requiring scale-invariance and self-similarity for $\\beta = 3$, namely $p = 4\/3$ and $q =1\/3$.\n\n\n\n\n\n\n\\subsection{Unequal time velocity correlations}\n\\label{sec:results-uetc}\n\n\n\nIn this subsection we report on our results for the decorrelation of the velocity field at different times. We introduce a new model for the unequal time correlator and test it against the numerical simulations.\n\n\nOur model for the normalized UETC is\nobtained by combining \\cref{eq:expvsweep,eq:Vlarge,eq:vsweepcomplete}\ngiving\n\\begin{equation}\n\t\\tdec_\\mathrm{A+}(k,\\tau,\\zeta) =\n\t\\frac{v_\\mathrm{dc}(k,\\tau,\\zeta)}{\\sqrt{v_\\mathrm{dc}(k, \\tau) v_\\mathrm{dc}(k, \\zeta)}}\n\t\\exp\\qty[-\\frac{1}{2} k^2\n\t\tv_\\mathrm{dc}^{2}(k,\\tau,\\zeta)(\\tau-\\zeta)^2].\n\\end{equation}\nThe factor in front of the exponential guarantees that the shear stress UETC is a positive definite kernel and hence that the resulting gravitational wave power spectrum is positive definite, as explained in \\cref{sec:mercer}.\nThis is to be compared with the model of \\Refa{Niksa:2018ofa},\n\\begin{equation}\n\t\\label{eq:RNSSdef}\n\t\\tdec_\\mathrm{NSS}(k,\\tau,\\zeta) =\n\t\\exp\\qty[-\\frac{1}{2} k^2\n\t\tv_\\mathrm{dc}^{2}(k,\\zeta)(\\tau-\\zeta)^2], \\quad \\tau > \\zeta \\, .\n\\end{equation}\n\n\\begin{figure}\n\t\\centering\n\t\\subfloat[Model of \\Refa{kaneda_lagrangian_1993}. ]{\\includegraphics[width=0.48\\textwidth]{figures\/uetcs_A_kaneda.pdf}}\n\t\\subfloat[Our model. ]{\\includegraphics[width=0.48\\textwidth]{figures\/uetcs_A.pdf}}\n\t\\caption{Comparison of normalized unequal time correlator models with data from simulation (A): on the left, the model of \\Refa{kaneda_lagrangian_1993} as used in \\Refa{Niksa:2018ofa}, on the right our model. The $y$-axis displays the combination of data and model functions which should produce a Gaussian\n\tcurve in the argument of the $x$-axis;\n\tthis is discussed in further detail in connection with \\cref{eq:Rfigure2b}.\n\tThe solid dark line is the prediction of the model in each case.}\n\t\\label{fig:decorr}\n\\end{figure}\n\n\n\nIn \\cref{fig:decorr}, we compare the two models to data from simulation (A), by\nplotting combinations of data and model functions which should be of Gaussian form.\nResults from the other simulations are given in \\cref{sec:appD}.\nThe construction of the numerical UETC, $\\UETCv(k,\\tau,\\zeta)$\nis described in \\cref{sec:sim-uetc}; in our simulations we measure the correlation of the velocity field at times $\\zeta = \\tuetc$ and $\\tau$.\n\nFor our model, the data is plotted as the quantity\n\\begin{equation}\n\t\\tgauss(k, \\tau, \\tuetc) \\equiv \\frac{\\UETCv(k,\\tau,\\tuetc)}{\\sqrt{\\Psdv(k,\\tau)\\,\\Psdv(k,\\tuetc)}} \\sqrt{\\frac{v_\\mathrm{dc}^2(k, \\tau) + v_\\mathrm{dc}^2(k, \\tuetc)}{2 v_\\mathrm{dc}(k, \\tau) v_\\mathrm{dc}(k, \\tuetc) }},\n\t\\label{eq:Rfigure2b}\n\\end{equation}\nagainst the combination $k\\,v_\\mathrm{dc}(k,\\tau,\\tuetc)(\\tau-\\tuetc)$,\nwhile for the model of \\Refa{Niksa:2018ofa}, we plot the normalized velocity UETC \\eqref{eq:NorVelUETC}\nas a function of $k\\,v_\\mathrm{dc}(k,\\tuetc)(\\tau-\\tuetc)$. We choose a range of values for the wavenumber $k$, both larger and smaller than the inverse integral scale $\\xi_*$.\nThe reference time is taken to be approximately one initial eddy turnover time, $\\tuetc \\simeq \\tau_{\\xi_*} = \\xi_*\/\\overline{v}_*$ (see \\cref{tab:list}).\nThe success of the models can be judged by how well all the data from simulations collapses onto a Gaussian in the $x$-axis argument,\nindicated by the black lines.\n\nFrom \\cref{fig:decorr}\nit appears that both models work well in the inertial range $k\\xi_* \\gg 1$.\nThis demonstrates that\nthe sweeping model, with sweeping velocity $\\overline{v}\/\\sqrt{3}$,\ncharacterizes very well the decorrelation of the system at high wavenumbers, and that we may take $C_v^2 = 1$ in \\cref{eq:Cv2}.\nThis also indicates that models using the scale-dependent Lagrangian eddy turn-over time\n$v^2_\\mathrm{dc}(k,\\tuetc) \\propto \\Psv(k, \\tuetc)$\nin the inertial range \\cite{Kamionkowski:1993fg,Gogoberidze:2007an}\ngive a poor fit in the inertial range.\n\nOn the other hand, the decorrelation model based on \\cref{eq:RNSSdef} degrades somewhat for $ 0.125\\leq k\\xi_*\\leq 1$.\nOur decorrelation model is a significant improvement on these scales, which we ascribe partly to the fact that the average decorrelation velocity takes into account\nthe slowing of the decorrelation with the decay of the kinetic energy.\n\nBecause of limitations in the dynamical range of the simulations, it is not possible to analyse very small values of $k\\xi_*$.\nHowever, we will see that the GW power spectrum decreases at small $k\\xi_*$, and so it is less important to model accurately the UETC at wavenumbers well below the peak.\n\nNote that according to recent work in \\Refa{Gorbunova:2021cpn}, the Gaussian form of the random sweeping hypothesis should change to an exponential form\nat large time differences.\nAgain, this effect should be most influential at low $k$, below the peak of the power spectrum. We leave further study of UETC models for future work.\n\n\n\\section{Results: the gravitational wave spectrum}\n\\label{sec:sgwbresults}\n\nThis section is organized as follows.\nIn \\cref{sec:stationary} we study a stationary source, meaning that we assume that $\\UETCv(p,\\tau,\\zeta)$ only depends on the time difference $\\tau-\\zeta$.\nThis is not the correct model for freely decaying turbulence, but we consider it nonetheless since it is useful to illustrate some properties of the GW power spectrum of a freely decaying source.\n\nWe present the case of freely decaying turbulence in the subsequent \\cref{sec:gw-instant,sec:GWcontinuous}.\n\\cref{sec:gw-instant} contains the main results of our work, and is dedicated to the case of instantaneously generated turbulence, meaning that we insert as initial conditions a fully developed turbulent spectrum.\nFinally, in \\cref{sec:GWcontinuous}, we go beyond the instantaneous generation scenario, and include a growth phase for the turbulence kinetic energy.\n\n\nBefore continuing, we first set the basis for the calculation of the stochastic GW background that will follow.\nCombining \\cref{eq:sbgw,eq:unequal-stress-final}, one obtains (recall that $\\vb{q} = \\vb{k} - \\vb{p}$)\n\\begin{equation}\n\t\\eval{\\dv{{\\Omega}_\\mathrm{gw}}{\\ln k}}_{\\tau} =\n\t\\frac{k^3}{3 \\pi^5}\n\t\\int \\dd[3]{p}\n\t\\qty[1 + (\\vu{k}\\vdot \\vu{p} )^2] \\qty[1 + (\\vu{k}\\vdot \\vu{q})^2]\n\t\\iint_{\\tini}^{\\tau} \\mathcal{G}(k,\\tau,\\eta,\\zeta)\n\t\\UETCv(p, \\eta, \\zeta) \\UETCv(q, \\eta, \\zeta)\n\t\\frac{\\dd{\\eta}}{\\eta}\\frac{\\dd{\\zeta}}{\\zeta}.\n\t\\label{eq:OmPv}\n\\end{equation}\nFrom this expression, it appears that the computation of the GW background involves performing a five-dimensional integration for each mode $k$.\nIntegrating over the azimuthal angle of $\\vb{p}$ immediately gives a factor of $2\\pi$ and one is left with a four-dimensional integration.\nTechnical details on how to tackle this four-dimensional integration are presented in \\cref{sec:4dinteg}.\nIn particular, we advocate the change of variables $\\vb{p}=\\vb{k}\/2 + \\vb{h}$ (and hence $\\vb{q} = \\vb{k}\/2 - \\vb{h}$): this makes the symmetry around the zero of the cosine of the declination angle $\\alpha = \\hat{h}\\cdot\\hat{k}$ explicit.\n\n\n\nAfter the complete decay of turbulence, occurring at a time that we denote $\\tfin$, the GW power spectrum in the radiation era evolves as\n\\begin{equation}\n\t\\eval{\\dv{\\Omega_\\mathrm{gw}}{\\ln k}}_{\\tau\\geq \\tfin} = \\frac{8}{3 \\pi^2}\n\tk^3\n\t\\iint_{\\tini}^{\\tfin} \\mathcal{G}(k,\\tau, \\eta,\\zeta) P_{\\tilde{\\Pi}}(k, \\eta,\\zeta)\\frac{\\dd{\\eta}}{\\eta} \\frac{\\dd{\\zeta}}{\\zeta}\\label{eq:gwradera}.\n\\end{equation}\nThis can be obtained by matching the sourced and free solutions of \\cref{eq:gw-propagation} at time $\\tfin$.\nNote that the function $\\mathcal{G}$ of \\cref{eq:green_with_all_terms} depends explicitly on $\\tau$, meaning that the GW spectrum in principle still depends on time after the source has decayed.\nWe are interested in the GW spectrum at a time $\\tau$ in the radiation era long after the source has decayed, and such that all relevant wavenumbers are inside the horizon, $k\\tau>1$.\nThis way we can neglect the second and third terms in \\cref{eq:green_with_all_terms}, proportional to $1\/(k\\tau)$ and $1\/(k\\tau)^2$, since they become subdominant with respect to the first one.\nFurthermore, we can expand the first term of \\cref{eq:green_with_all_terms} to obtain\n\\begin{align}\n\t\\eval{\\dv{\\Omega_\\mathrm{gw}}{\\ln k}}_{\\tau\\geq \\tfin}\n\t= \\, & \\frac{4}{3 \\pi^2} k^3 \\iint_{\\tini}^{\\tfin} \\cos k(\\eta - \\zeta) P_{\\tilde{\\Pi}}(k, \\eta,\\zeta)\\frac{\\dd{\\eta}}{\\eta} \\frac{\\dd{\\zeta}}{\\zeta} \\nonumber \\\\\n\t & + \\frac{4}{3 \\pi^2} k^3 \\cos(2k\\tau) \\iint_{\\tini}^{\\tfin} \\sin k(\\zeta + \\eta) P_{\\tilde{\\Pi}}(k, \\eta,\\zeta)\\frac{\\dd{\\eta}}{\\eta} \\frac{\\dd{\\zeta}}{\\zeta} \\nonumber \\\\\n\t & + \\frac{4}{3 \\pi^2} k^3 \\sin(2k\\tau) \\iint_{\\tini}^{\\tfin} \\cos k(\\zeta + \\eta ) P_{\\tilde{\\Pi}}(k, \\eta,\\zeta)\\frac{\\dd{\\eta}}{\\eta} \\frac{\\dd{\\zeta}}{\\zeta} + \\order{(k\\tau)^{-1}} \\label{eq:green-approx}\n\\end{align}\nThe residual time dependence consists of irrelevant, rapid oscillations in time.\nWe can therefore perform a time average of the free SGWB\nsolution over a long time interval around $\\tau$, satisfying $\\Delta\n\\tau \\gg 1\/k$ for every interesting $k$, to obtain\n\\begin{equation}\n\t\\dv{\\Omega_\\mathrm{gw}}{\\ln k}\n\t\\approx \\frac{4}{3 \\pi^2} k^3 \\iint_{\\tini}^{\\tfin} \\cos k(\\eta - \\zeta) P_{\\tilde{\\Pi}}(k, \\eta,\\zeta)\\frac{\\dd{\\eta}}{\\eta} \\frac{\\dd{\\zeta}}{\\zeta}\\,.\n\t\\label{eq:OmPvAVG}\n\\end{equation}\nThis is the effectively time-independent GW power spectrum that we aim to calculate.\nRecall that\n$\\Omega_\\mathrm{gw}$ is normalized to the radiation energy density, see \\cref{eq:sbgw}.\nTherefore, long after the source has decayed,\nthe averaged energy density of the GW signal would be expected to decrease as $a(\\tau)^{-4}$.\n\n\\subsection{The stationary assumption}\n\\label{sec:stationary}\n\nIn this section, we simplify the evaluation of the GW spectrum integral of \\cref{eq:OmPvAVG} by assuming that the turbulence is stationary.\nUnder this assumption, the rms velocity $\\overline{v}$ and the integral scale $\\xi$ remain constant in time.\nWe further set the decorrelation velocity to $v_\\mathrm{dc}\\equiv \\overline{v}_* \/ \\sqrt{3}$ on all scales.\nIn other words, we neglect the $k$-dependence in \\cref{eq:vdcapprox}.\nMotivated by what we see in the simulations, we also set $C_v^2=1$ in \\cref{eq:Cv2}.\nConsequently, the only time dependence left in the UETC in \\cref{eq:unequal-velocity-ps} is the time difference in the exponential.\nIn order to evaluate \\cref{eq:OmPvAVG} under the hypothesis that the source is stationary, it therefore becomes natural to change the time integration variables to $\\tau_\\mathrm{mid}= (\\tau+\\zeta)\/2$ and $\\tau_\\mathrm{diff}=\\tau-\\zeta$, where the integration over the latter is symmetric around zero.\nWe give the form that \\cref{eq:OmPvAVG} takes under this stationary assumption as \\cref{eq:Omstationary}.\n\nIn order to proceed analytically, we rely on the exponential decorrelation, and extend the integration limits of\n$\\tau_\\mathrm{diff}$ to the interval $[0,\\infty[$.\nFurthermore, one can also assume that the time difference $\\tau_\\mathrm{diff}$ is small compared to the absolute time $\\tau_\\mathrm{mid}$.\nUnder these two further assumptions, which are not strictly speaking a direct consequence of stationarity, one can integrate \\cref{eq:Omstationary} analytically.\n\nA stationary source in principle lasts forever.\nHowever, this is not very realistic in the early Universe setting.\nWe will therefore fix the source duration to a finite value that we express in terms of $\\mathcal{N}_\\mathrm{cut}$, the number of eddy turnover times for which the turbulence lasts.\nIn other words, we integrate from $\\tdevel$ until $\\tdevel+\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*}$.\nThe result then depends explicitly on $\\mathcal{N}_\\mathrm{cut}$.\n\nIn \\cref{sec:analytical} we provide the details of the integration procedure.\nThe asymptotic behaviour of the resulting SGWB spectrum, and its scaling with the turbulence parameters are given by\n\\begin{align}\n\t{\\dv{{\\Omega}_\\mathrm{gw}}{\\ln k}}\n\t\\qty(K \\ll 1) & =\n\tc_1\\,\\frac{\\mathcal{N}_\\mathrm{cut}\\,(\\mathcal{H}_*\\xi_*)^2}{\\overline{v}_*+\\mathcal{N}_\\mathrm{cut}\\mathcal{H}_*\\xi_*}\\, \\overline{v}_*^6\\, \\qty(\\frac{K}{\\overline{v}_*})^3; \\label{eq:stat-ir} \\\\\n\t{\\dv{{\\Omega}_\\mathrm{gw}}{\\ln k}}\n\t\\qty(K \\gg 1) & = \\frac{\\mathcal{N}_\\mathrm{cut}\\,(\\mathcal{H}_*\\xi_*)^2}{\\overline{v}_*+\\mathcal{N}_\\mathrm{cut}\\mathcal{H}_*\\xi_*}\\,\n\t\\qty[c_2\\,\\overline{v}_*^6\\,\n\t\\qty(\\frac{K}{\\overline{v}_*})^{-\\frac{7}{3}}+\n\tc_3\\,\n\t\\overline{v}_*^{4\/3}\\,\n\t\\exp(-\\frac{3}{2 \\overline{v}_*^2})\n\t\\qty(\\frac{K}{\\overline{v}_*})^{-\\frac{5}{3}} ] \\label{eq:stat-uv}\n\\end{align}\n(note that $K=\\mathcal{A}\\xi k$, and the value of the numerical coefficients $c_i$ are given in \\cref{sec:analytical}).\n\n\\cref{eq:stat-ir,eq:stat-uv} show that the scaling of the GW signal with the parameter $\\mathcal{H}_*\\xi_*$ depends on the source duration.\nIf the sourcing lasts less than one Hubble time,\nmeaning $\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*}=\\mathcal{N}_\\mathrm{cut} \\xi_*\/\\overline{v}_*< \\mathcal{H}_*^{-1}$, one can neglect the second term in the denominator of \\cref{eq:stat-ir,eq:stat-uv}, and the SGWB scaling becomes quadratic in $\\mathcal{H}_*\\xi_*$.\nOn the other hand, if the source lasts longer than one Hubble time, the scaling is linear in $\\mathcal{H}_*\\xi_*$\n(see for example \\Refs{Hindmarsh:2015qta,Hindmarsh:2017gnf,Caprini:2009yp,Caprini:2006jb,Caprini:2019egz}).\nIf we had set the duration of the source to infinity, as required in principle by the stationary assumption, we would have consistently\nfound a linear dependence on $\\mathcal{H}_*\\xi_*$.\n\nGravitational wave generation from sound waves also presents the same duration-dependent scaling with $\\mathcal{H}_*\\xi_*$.\nThis was previously derived within the sound shell model \\cite{Hindmarsh:2015qta,Hindmarsh:2019phv,Hindmarsh:2020hop,Guo:2020grp}.\nIn the sound shell model one can also assume stationarity and set $\\mathcal{N}_\\mathrm{cut} \\sim 1$ as in \\Refa{Guo:2020grp}.\nNote that, in the acoustic case, the decorrelation time depends on the sound speed, instead of on the rms fluid velocity.\nThe typical lifetime of the source is connected to the kinetic energy.\nFor the turbulent case, low velocities correspond to longer eddy turnover times $\\tau_{\\xi_*}=\\xi_*\/\\overline{v}_*$, and therefore sources that last longer.\nThe kinetic energy is in turn expected to be connected to the PT strength (see for example \\Refa{Ellis:2020awk}).\nThe latter then ultimately influences the scaling of the SGWB amplitude \\cite{Caprini:2019egz}.\n\nThe turbulent kinetic energy also naturally enters the SGWB amplitude, through $\\overline{v}_*$.\nIn particular, the stationary assumption leads to a stronger scaling with $\\overline{v}_*$ than both the case of instantaneous generation (see \\cref{sec:gw-instant}) and the SGWB signal arising from sound waves \\cite{Hindmarsh:2019phv,Hindmarsh:2020hop}: at large scales and\/or for low initial turbulent velocity, \\cref{eq:stat-ir,eq:stat-uv} show that the SGWB amplitude from turbulence within the stationary assumption scales as $\\overline{v}_*^6$, as opposed to $\\overline{v}_*^4$ for both sound waves and an instantaneous turbulence generation (as we shall see in \\cref{sec:gw-instant}).\n\nWe remark that the value of $\\overline{v}_*$ also influences the high-frequency slope of the SGWB, see~\\cref{eq:stat-uv}. This is a consequence of the exponential decorrelation in the velocity power spectrum \\cref{eq:unequal-velocity-ps}. For large $\\overline{v}_* > 0.5$, the second term in \\cref{eq:stat-uv} dominates: consequently, the SGWB spectrum becomes shallower, and its amplitude scales as $\\overline{v}_*^{4\/3}$.\nThe slope of the SGWB at low wavenumber, on the other hand, is due to the steep (causal) increase of the velocity power spectrum \\cref{eq:power-spectrum}, causing the convolution in \\cref{eq:unequal-stress-final} to be flat for $k< 1\/\\xi_*$ (uncorrelated in space for scales larger than $\\xi_*$) \\cite{Caprini:2006jb}.\nThe complete spectral shape can be obtained by integrating numerically \\cref{eq:stationary}: it smoothly interpolates between the slopes given by \\cref{eq:stat-ir,eq:stat-uv}, peaking at the wavenumber $k \\simeq 2 \\overline{v}_* \/ (\\mathcal{A}\\xi_*)$.\nExamples of spectra are given in \\cref{fig:stationary}.\n\n\n\\subsection{Instantaneous turbulence generation}\n\\label{sec:gw-instant}\n\nIn this section, we consider turbulence generated instantaneously, and freely decaying afterwards.\nWe consider a situation where gravitational wave production begins when the fluid has\nthe velocity spectrum of fully developed turbulence, at time $\\tdevel$.\nWe use three different methods to compute the SGWB spectrum.\nFirst, we measure the SGWB in the direct numerical simulations.\nSecond, we numerically integrate \\cref{eq:OmPvAVG} inserting the model for freely-decaying turbulence derived in the first part of this paper, summarized in \\cref{eq:unequal-velocity-ps} (see also section \\cref{sec:results-vel}).\nThird, we assume that the GW source is constant in time, and analytically integrate \\cref{eq:green-approx} under this assumption\\footnote{The reason why we do not perform the time average in this case will be clarified in \\cref{sec:const}.}.\nWe show that there is good agreement between the three methods for the resulting SGWB.\nIn particular, the analytical approximation provides an easy-to-use formula, \\cref{eq:constant_approx}, in which the scaling with the turbulence parameters (initial rms velocity, integral scale, duration) is apparent.\n\n\n\n\\subsubsection{Direct numerical simulations}\n\\label{sec:results-gw-sim}\n\nIn \\cref{fig:gwps-sim-vrms-0.1} we present GW power spectra from three simulations with $\\overline{v}_*\\approx 0.1$, corresponding to\nsimulations (A), (D) and (E) in \\cref{tab:list}. Equivalent\nplots for the other simulations are given in\n\\cref{sec:appendix-sim-gws}. We explore different regions of the power spectrum with each simulation.\nSimulation (D) has a large value\nof $L \\dd{x}\/\\xi_*$ and is therefore able\nto probe the GW power spectrum around the peak and towards smaller\n$k$, whereas simulation (E) is better able to probe the inertial range due to the smaller value of $\\dd\nx\/\\xi_*$.\nSimulation (A) has more lattice sites, allowing for a larger dynamic range. For this simulation we chose $L \\dd{x}\/\\xi_*$ and $\\dd x\/\\xi_*$ such that they lie in between the values of simulations (D) and (E). Simulation (A) then probes the peak of the spectrum and intermediate wavenumbers, confirming the power laws at low and high wavenumbers in these regions.\n\nThe SGWB power spectrum builds up very\nrapidly - this is further discussed in \\cref{sec:const}; its shape reflects the initial power spectrum, $\\mathcal{P}_U$ of \\cref{eq:sim-input}, representing fully developed turbulence.\n\\begin{figure}\n\t\\centering\n\t\\subfloat[Simulation (A), $\\Delta\\tau\/\\tau_{\\xi_*} = 3.98$.]{\\includegraphics[width=0.45\\textwidth]{figures\/A_gwspec.pdf}} \\\\\n\t\\subfloat[Simulation (D), $\\Delta\\tau\/\\tau_{\\xi_*} = 4.87$.]{\\includegraphics[width=0.45\\textwidth]{figures\/D_gwspec.pdf}}\n\t\\subfloat[Simulation (E), $\\Delta\\tau\/\\tau_{\\xi_*} = 0.594$.]{\\includegraphics[width=0.45\\textwidth]{figures\/E_gwspec.pdf}}\n\t\\caption{Gravitational wave power spectrum from our simulations with\n\t\t$\\overline{v}_* \\approx 0.1$.\n\t\tIn the y-axis, we divide the by $(\\mathcal{H}_*\\xi_*)^2$ since the simulations are in flat space-time, see~\\cref{eq:OmGWsimul} and the following discussion.\n\t\tThe coloured lines show the gravitational wave power spectrum\n\t\tat intervals $\\Delta \\tau$ starting from $\\tau - \\tdevel = \\Delta \\tau$ and finishing at $\\tau = \\tend$ .\n\t\tDarker shades correspond to later times. The black dashed line\n\t\tshows an average over the gravitational wave power spectrum in the\n\t\tlast\n\t\thalf of the elapsed simulation time.\n\t\tThe red dashed line shows a $k^1$ power-law, while the blue dashed line shows a $k^{-8\/3}$.\n\t\tWe have cut off the\n\t\tspectrum at high wavenumbers as it progressively gets polluted due to\n\t\tnumerical precision errors in projecting $\\dot{u}_{ij}(\\vb{k})$ to\n\t\t$\\dot{h}_{ij}(\\vb{k})$.\\label{fig:gwps-sim-vrms-0.1} }\n\\end{figure}\n\nBy averaging over the gravitational wave power spectrum in the latter\nhalf of each simulation, we are able to smooth out time-dependent\noscillations in the GW power spectrum. This averaged power spectrum is shown\nby the black dashed line in \\cref{fig:gwps-sim-vrms-0.1}. A clear $k^{-8\/3}$ power\nlaw is found for the inertial range in simulations (A) and (E), and an approximate $k^1$\npower law at small wavenumbers is seen in (A) and (D).\nTaking a time average over the second half of the simulation is justified since the simulation wavenumbers satisfy $k>2\/(\\tend-\\tdevel)$ for all simulations.\nThis can be verified by comparing the k-range in \\cref{fig:gwps-sim-vrms-0.1,fig:gwps-sim-vrms-0.3,fig:gwps-sim-vrms-0.03} with the values given in \\cref{tab:list} (see also \\cref{tab:notation}).\n\nWhen we compute the power spectrum from our simulations, we bin modes which have a wavenumber within a given shell of $k$,\nas given by \\cref{eq:metric-pspec-dns}.\nAt late times, the frequency of the oscillations of each mode increases and the modes in each bin begin to oscillate out of phase. Consequently, our simulations average over these oscillations and do not capture them, even when we do not perform an average over time. This process can clearly be seen for high wavenumbers in \\cref{fig:gwps-sim-vrms-0.1}.\n\nTo show the variation of the GW power spectrum for different $\\overline{v}_*$\nand for different choices of $\\xi_*\/\\dd x$, we plot the averaged GW\npower spectrum from all simulations in \\cref{fig:gwps-sim-multi}. As can be seen,\nsimulations with different choices of $\\xi_*\/\\dd x$ explore different\nparts of the spectrum, with small $\\xi_*\/\\dd x$ mapping out low\nwavenumbers and the peak of the spectrum, and larger $\\xi_*\/\\dd x$\nexploring the inertial range.\nIndeed, for $\\overline{v}_*\\simeq0.1$ we find good agreement between simulations (A) and (D) from low wavenumbers up to the vicinity of the peak, and with (E) at high wavenumbers.\nThis gives us confidence\nthat the results\nobtained for other $\\overline{v}_*$ are also valid.\n\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=0.7\\textwidth]{figures\/gw_multi_example.pdf}\n\t\\caption{Averaged GW power spectra for simulations (A)-(G) from \\cref{tab:list}. The coloured lines shown here correspond to averaging the GW power spectra over the last half of the simulations. Simulations (B) and (C) have $\\overline{v}_*\\simeq0.03$, (A), (D) and (E) have $\\overline{v}_*\\simeq0.1$, and (F) and (G) have $\\overline{v}_*\\simeq0.3$. A cut off has been applied to the spectrum at high wavenumbers due to numerical precision noise.\n\t}\n\t\\label{fig:gwps-sim-multi}\n\\end{figure}\n\n\nIn subsequent sections we will show how these simulations can be compared with the results of the four dimensional numerical integration, and with the analytical result obtained under the assumption of a constant source in time.\nWe plot all three approaches in \\cref{fig:sim-analytic-comp} and find good agreement.\n\n\\subsubsection[GW spectrum via numerical integration of the\n\tanisotropic stress UETC]{Gravitational wave spectrum via numerical integration of the anisotropic stress UETC}\n\\label{sec:numintegration}\n\nWe have developed a numerical method allowing exact integration of the time-averaged SGWB spectrum, \\cref{eq:OmPvAVG}.\nThis includes integration of the full angular dependence as well, improving on previous attempts~\\cite{Caprini:2009yp,Niksa:2018ofa}.\nWe insert \\cref{eq:unequal-velocity-ps} into \\cref{eq:unequal-stress-final}, and in turn into \\cref{eq:OmPvAVG}.\nMaking use of \\cref{eq:velocity_ps_mathcal}, the initial spectral density $\\Psdv(k,\\tini)$ is set to the\nvon K\\'arm\\'an\\ spectrum \\cref{eq:power-spectrum},\nrepresenting fully developed incompressible turbulence.\n\nWe also need to insert the time evolution laws for $\\overline{v}^2(\\tau)$ and $\\xi(\\tau)$.\nIn accordance with the initial conditions adopted in the simulations (see \\cref{sec:initial}), we assume instantaneous generation of turbulence.\nSpecifically, prior to $\\tdevel$ the velocity field is zero everywhere, then at time $\\tdevel$, turbulence appears fully developed with initial rms velocity $\\overline{v}_*$ and initial correlation scale $\\xi_*$.\nTurbulence then starts decaying on a timescale of order $\\tau_{\\xi_*}$, following \\cref{eq:VelEvMod,eq:XiEvMod}.\nTo summarize, for the instantaneous generation scenario:\n\\begin{align}\n\t & \\overline{v}^2(\\tau)=\n\t\\begin{cases}\n\t\t0 & \\text{if } \\tau < \\tdevel \\\\\n\t\t\\overline{v}_* ^2 \\qty(\\dfrac{\\tau - \\tdevel +\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}}{\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}})^{-p} & \\text{if } \\tau > \\tdevel\n\t\\end{cases}\n\t\\quad\\text{and} \\quad\n\t\\xi(\\tau) =\n\t\\begin{cases}\n\t\t\\text{Not defined} & \\text{if } \\tau < \\tdevel \\\\\n\t\t\\xi_*\\qty(\\dfrac{\\tau - \\tdevel +\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}}{\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}})^{q} & \\text{if } \\tau > \\tdevel\n\t\\end{cases}.\n\t\\label{eq:disc_evol}\n\\end{align}\n\nIn the following we set $\\mathcal{N}_\\mathrm{e}=5$, the value that best fits the simulations, see \\cref{fig:xi-vrms-evol}. For the decay exponents $p,~q$, we tested both sets of values that one infers from \\cref{eq:pchialpha,eq:qchialpha}, setting $\\beta=3$ and $\\beta=4$.\nThe unequal time decorrelation velocity appearing in the exponential of \\cref{eq:unequal-velocity-ps} is expressed in terms of the equal time one as in \\cref{eq:vsweepcomplete}, with $v_\\mathrm{dc}(k,\\tau)$ given in \\cref{eq:Vlarge}.\nThe four-dimensional integral of \\cref{eq:OmPvAVG} is calculated with Monte Carlo integration with importance sampling, using the VEGAS algorithm~\\cite{1978JCoPh..27..192L, Lepage:2020tgj}, an iterative and adaptive Monte Carlo scheme.\nMore details on the implementation are given in \\cref{sec:4dinteg}.\n\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=\\textwidth]{figures\/figure_10.pdf}\n\t\\caption{\n\t\tExamples of GW power spectra computed via numerical integration of \\cref{eq:OmPvAVG} under the assumption of instantaneous turbulence generation (see \\eqref{eq:disc_evol}).\n\t\tThe spectra are computed using different values of the initial rms velocity, $\\overline{v}_*$, the initial integral scale relative to the Hubble scale, $\\mathcal{H}_*\\xi_*$.\n\t\tEach panel corresponds to a different value of $\\overline{v}_*$, as specified by the title, and each colour indicates a different value of $\\mathcal{H}_*\\xi_*$, as specified by the legend in the bottom right panel.\n\t\tThe solid lines are computed setting $\\beta = 3$, whereas the dashed lines setting $\\beta = 4$.\n\t\tWe recall that the evolution of $\\overline{v}(\\tau)$ and $\\xi(\\tau)$ are determined by $\\beta$ through \\cref{eq:pchialpha,eq:qchialpha}.}\n\t\\label{fig:varying-beta}\n\\end{figure}\n\n\nAs discussed at the end of \\cref{sec:free-decay-evol}, turbulence is fully dissipated after about $1000\\tau_{\\xi_*}$, for reasonable choices of the initial parameters $T_*,~\\overline{v}_*,~ \\xi_*\\mathcal{H}_*$.\nHowever, there is no need to time-integrate \\cref{eq:OmPvAVG} for so long: the bulk of the GW power spectrum is sourced on a much shorter timescale.\nThis is due to two reasons.\nFirst, the generation of GWs is very localized in time: GWs with wavenumber $k$ are produced by the source on a timescale $\\order{1\/k}$, since the Green's function in \\cref{eq:OmPv}, given in \\cref{eq:green_with_all_terms}, has period $2\\pi\/k$.\nSecond, the decay of turbulence occurs over a timescale of the order of the initial eddy turnover time $\\tau_{\\xi_*}$, which effectively cuts off the source (more precisely, about five $\\tau_{\\xi_*}$, since $\\mathcal{N}_\\mathrm{e}\\simeq 5$ is the value that best fits the simulations - see \\cref{sec:free-decay-evol}).\nAll GW wavenumbers with $k\\gtrsim 1\/(\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*})$ are therefore sourced before the turbulent kinetic energy has decayed appreciably. This includes the peak region of the SGWB spectrum.\nOn the other hand, GW wavenumbers with $k\\lesssim 1\/(\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*})$ are excited when the decay of the turbulent kinetic energy has already occurred: GW production on these wavenumbers is therefore negligible. This is the large scale spectral region, proportional to $k^3$.\nWe therefore expect the GW power spectrum to stop growing after a timescale of the order of $1\/(\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*})$.\nWe have numerically checked that the integration in \\cref{eq:OmPv} indeed converges to a fixed result after a few tens of $\\tau_{\\xi_*}$.\n\nIn the code, we actually integrate for $100 ~ \\tau_{\\xi_*}$: therefore, in practice we output the SGWB spectra long after the source has decayed, when the spectral amplitude does not grow in time any longer (the integral\nin \\cref{eq:OmPvAVG} has converged).\nFurthermore, the $k$-range of interest is such that $k>1\/(100\\,\\tau_{\\xi_*})$ for every chosen value of $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$, as can be appreciated from \\cref{fig:varying-beta}.\nTherefore, we are allowed to integrate directly the time-averaged GW spectrum \\cref{eq:OmPvAVG}.\nWe have indeed checked that numerically integrating the full expression \\cref{eq:green-approx} provides a GW spectrum which is oscillating around the amplitude of the integral of the time-averaged GW spectrum, \\cref{eq:OmPvAVG}, shown in \\cref{fig:varying-beta}.\nWe do not plot these spectra, as they do not convey any additional information.\n\n\\cref{fig:varying-beta} shows the GW power spectrum computed from the numerical integration code, for different values of the parameters $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$ and for both $\\beta=3$ and $\\beta=4$.\nIt can be appreciated that the peak amplitude scales like $(\\mathcal{H}_*\\xi_*)^2$.\nThe spectral peak is independent of the value of $\\overline{v}_*$, and corresponds to the peak of the velocity power spectrum \\cref{eq:power-spectrum}, occurring at $k\\xi_*\\simeq 2.7\/\\mathcal{A}$.\nThis is consistent with the finding of Ref.~\\cite{Caprini:2009fx} for a source which is discontinuous in time.\n\nThe spectra which have $\\mathcal{H}_*\\xi_* = 10^{-3}$ in \\cref{fig:varying-beta} are\nminimally affected by the expansion of the Universe: the chosen values of $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$ satisfy $100\\,\\tau_{\\xi_*} \\leq \\mathcal{H}_*^{-1}$, and so the dynamics take place fully at sub-horizon scales.\nIt is therefore appropriate to compare these spectra to the time-averaged spectra resulting from the simulations, in the region $k>\\mathcal{H}_*$ (see \\cref{eq:OmGWsimul} and discussion thereafter). We will do so in~sub-\\cref{sec:compamethods}.\nOn the other hand, for larger values of $\\mathcal{H}_*\\xi_*$ in \\cref{fig:varying-beta}, we integrate for longer than one Hubble time.\n\n\nWe find that the actual values of the exponents $p$ and $q$ do not substantially impact the SGWB spectrum, indicating that the precise details of the turbulent decay are unimportant in this respect.\nThis is due to the nature of the free decay of turbulence in combination with the typically short timescale associated with GW production.\n\\cref{fig:varying-beta} shows the SGWB spectrum from the numerical integration for both $\\beta=3$ and $\\beta=4$.\nFor all values of $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$ the spectra only differ in the region $k< 1\/(\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*})$, as expected for reasons we have outlined above.\nFor the same reasons, we can safely ignore the role of the viscous scale $\\lambda$ in all our evaluations.\n\n\n\n\n\\subsubsection{Constant source approximation}\n\\label{sec:const}\n\nIn this section, we show that the GW source can be approximated as almost constant in time while the majority of the GW signal is being generated.\nThis has already been pointed out in \\Refa{RoperPol:2022iel}, in the case of MHD turbulence.\nSince the Green's function in \\cref{eq:OmPv} has period $2\\pi\/k$, the GW production for each wavenumber $k$ occurs on a timescale of the order of $1\/k$.\nTherefore, over the typical decay time for the turbulence source, namely $\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}$ -- as we assumed for \\cref{eq:disc_evol} --\nthere is sourcing of gravitational waves for wavenumbers $k>1\/(\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}) \\equiv \\overline{v}_*\/(\\mathcal{N}_\\mathrm{e}\\xi_*)$.\nThis constitutes a large fraction of the GW spectrum, including the region around the peak $k\\,\\xi_*\\simeq 2.7\/\\mathcal{A}$.\nConsequently, the SGWB signal on the scales of interest -- specifically, around the peak -- is fully established before the turbulence has decayed appreciably.\nOn scales larger than the peak, on the other hand, the GW production is less efficient due to the decay of the source.\nOn even larger scales, one expects the $k^3$ increase typical of uncorrelated sources in both time and space \\cite{Caprini:2009fx}.\n\nWe assume in what follows that the turbulent GW source is instantaneously generated, remains constant for a duration $\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*}$, and sharply turns off at a time $\\tdevel+\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*}$.\nThe number $\\mathcal{N}_\\mathrm{cut}$ is of the same order as $\\mathcal{N}_\\mathrm{e}$, and we set $\\mathcal{N}_\\mathrm{cut}$ to $7$ as it gives the best fit to the result of the numerical integration.\nWe neglect both the overall time dependence of the turbulent fluid velocity power spectrum and its time decorrelation in \\cref{eq:unequal-velocity-ps}\nThis means that the anisotropic stress spectral density \\cref{eq:unequal-stress-final} becomes time-independent, and the time and momentum integrals in \\cref{eq:OmPv} decouple.\nWe can then rewrite \\cref{eq:gwradera} as\n\\begin{equation}\n\t\\eval{\\dv{{\\Omega}_\\mathrm{gw}}{\\ln k}}_{ \\tdevel+\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*}}\n\t=\\frac{8}{3\\pi^2}\\,k^3\\,\\mathcal{T}_\\mathrm{gw}(k,\\tdevel,\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*})\\, P_{\\tilde\\Pi}(k,\\tdevel) \\,.\\label{eq:SGWBfinconst}\n\\end{equation}\nLet us first analyse the part due to the double time integral $\\mathcal{T}_\\mathrm{gw}$.\nWe evaluate it at the source turn-off:\n\\begin{equation}\n\t\\mathcal{T}_\\mathrm{gw}(k,\\tdevel,\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*}) \\equiv \\iint_{\\tdevel}^{\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}} \\mathcal{G}(k, \\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}, \\eta, \\zeta)\\,\\frac{\\dd{\\eta}}{\\eta}\\frac{\\dd{\\zeta}}{\\zeta}.\n\\end{equation}\nRestricting our consideration of this integral to wavenumbers satisfying $k>1\/(\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*})$, we are again entitled to drop the second and third term from the Green's function \\cref{eq:green_with_all_terms}, to obtain~\\cite{RoperPol:2022iel}:\n\\begin{align}\n\t\\mathcal{T}_\\mathrm{gw}(k,\\tdevel,\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*}) & =\\iint_{\\tdevel}^{\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}} \\cos k(\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*} - \\eta)\\cos k(\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*} - \\zeta) \\frac{\\dd{\\eta}}{\\eta}\\frac{\\dd{\\zeta}}{\\zeta} \\label{eq:mathT} \\\\\n\t & = \\left\\{\\cos k(\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}) \\qty[\\cosi (k (\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*})) -\\cosi(k \\tdevel)] \\right. \\nonumber \\\\\n\t & \\qquad + \\left.\\sin k(\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}) \\qty[\\sini (k (\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*} ))-\\sini(k \\tdevel)]\\right\\}^2.\n\\end{align}\nThe upper limits of both time integrals must be set to $\\tfin=\\tdevel+\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}$ in order to match the results of both the simulations and the numerical integration (see~\\cref{sec:compamethods}).\nAs pointed out in \\Refa{RoperPol:2022iel}, the abrupt switching off of the source means that the evolution after $\\tfin$ would give an enhancement of the SGWB power at high frequency, which would break this agreement.\nSince we evaluate the time integrals at $\\tfin$, we cannot perform a time average; this is why we do not use \\cref{eq:OmPvAVG} in this Section.\n\nIn \\Refa{RoperPol:2022iel}, a simplified expression for \\cref{eq:mathT} has been found, tuned to interpolate the envelope of the $k$-oscillations.\nIn this work, we want to compare with the results of the simulations (\\cref{sec:results-gw-sim}) and of the numerical integration (\\cref{sec:numintegration}), which both evaluate the time-averaged SGWB.\nWe therefore adapt the result of \\Refa{RoperPol:2022iel} to account for time averaging, as follows:\n\\begin{equation}\n\t\\mathcal{T}_\\mathrm{gw}(k,\\tdevel,\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*})\\approx\n\t\\begin{cases}\n\t\t\\ln^2\\qty(1 + \\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*} \/ \\tdevel) & \\text{ for}~\\sqrt{2}\\, k<1\/(\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*})\\,, \\\\\n\t\t\\ln^2\\qty[1 + (\\sqrt{2}k\\tdevel)^{-1}] & \\text{ for}~\\sqrt{2}\\, k>1\/(\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}) \\,.\n\t\\end{cases}\n\t\\label{eq:tgw-approx}\n\\end{equation}\nThe transition occurs at the wavenumber corresponding to the source lifetime, namely $\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}$ (see \\Refa{Caprini:2009fx}).\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics{figures\/pi_dimless_ps.pdf}\n\t\\caption{Anisotropic stress spectral density $P_{\\tilde{\\Pi}}(k,\\tdevel)$ at the initial time in the constant source approximation, from the exact integration of \\cref{eq:Piintegral} (black solid line) and the analytical approximation \\cref{eq:Piapprox} (red dashed line). We also show $P_{\\tilde{\\Pi}}(k,\\tau)$ extracted from simulation (A) (colored solid lines) between $\\tau=\\tdevel$ and $\\tau=\\tend$ with interval $\\Delta \\tau = 9.96 \\tau_{\\xi_*}$. Lighter colours indicate later times.}\n\t\\label{fig:plotPi}\n\\end{figure}\n\nWe now turn to the description of the anisotropic stress spectral density at initial time $P_{\\tilde\\Pi}(k,\\tdevel)$, also entering \\cref{eq:SGWBfinconst}.\nThis is given by\nthe convolution in momentum of \\cref{eq:unequal-stress-final} (without residual time dependence).\nIt takes the form (we use the notation of \\cref{sec:analytical}):\n\\begin{align}\n\tP_{\\tilde\\Pi}(k,\\tdevel) & =\\frac{\\pi^2}{2}\\mathcal{A}^3{\\xi_*^3}\n\t\\int_0^\\infty \\dd H\\,H^2\n\t\\int_0^1 \\dd\\alpha \\,\n\t\\frac{\\mathrm{Proj}(H,\\alpha)}{\\mathrm{s}^3(H,\\alpha)\\mathrm{s}^3(H,-\\alpha)}\\Psv[\\mathrm{s}(H,\\alpha), \\tdevel]\\Psv[\\mathrm{s}(H,-\\alpha), \\tdevel]\\label{eq:Piintegral}\n\t\\\\\n\t & \\simeq\n\t\\frac{P_{\\tilde\\Pi}(0)}{1+\\qty(\\frac{\\mathcal{A} k\\xi_* }{3.0})^2+\\qty(\\frac{\\mathcal{A} k\\xi_* }{3.5})^{11\/3}}\\,,\\label{eq:Piapprox}\n\\end{align}\nwhere the factors of $3.0$ and $3.5$ come from an analytical fit, tuned to match the exact numerical integration of \\cref{eq:Piintegral}, and $P_{\\tilde\\Pi}(0)$ is the value of the anisotropic stress spectral density at $k=0$: %\n\\begin{equation}\n\tP_{\\tilde\\Pi}(0)= \\mathcal{B}^2\\mathcal{A}^3\\,\\overline{v}_*^4\\,{\\xi_*^3} \\int_0^\\infty \\dd{H} \\frac{1024\\,\\sqrt[3]{2}\\,\\pi^2 H^6}{(4 H^2+4)^{17\/3}} \\int_0^1 \\dd{\\alpha} (\\alpha^2+1)^2= \\mathcal{B}^2\\mathcal{A}^3\\,\\overline{v}_*^4\\,{\\xi_*^3}\\,\\, \\frac{7 \\pi^{5\/2} \\,\\Gamma \\left(\\frac{13}{6}\\right)}{8\\, \\Gamma \\left(\\frac{17}{3}\\right)}\\,.\n\\end{equation}\nIn \\cref{fig:plotPi}, we compare the anisotropic stress spectral density from simulation (A) with both the numerical result \\cref{eq:Piintegral}, and the analytical approximation\n\\cref{eq:Piapprox}.\nAs can be appreciated from the figure, at the initial time $\\tdevel$ there is excellent agreement between the simulation result and the anisotropic stress spectral density derived in the context of our turbulent model.\nFor illustration purposes, in \\cref{fig:plotPi} we also display the time evolution of the anisotropic stress spectral density from simulation (A): it is decaying, due in turn to the decay of the turbulent source itself.\n\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=\\textwidth]{figures\/figure_12.pdf}\n\t\\caption{Reproduction of \\cref{fig:varying-beta}, where here we also show the constant source approximation given in \\cref{eq:constant_approx} for different values of $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$. The solid lines match those of \\cref{fig:varying-beta} (with $\\beta=3$), whereas the dashed lines give the constant source approximation for an equivalent value of $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$. We fix $\\mathcal{N}_\\mathrm{cut}=7$ in the constant source approximation \\cref{eq:constant_approx} for all values of $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$.\n\t}\n\t\\label{fig:SGWB_num_int}\n\\end{figure}\n\nThe spectral shape of the SGWB from a constant source, \\cref{eq:SGWBfinconst}, is given by the combination of the $k$-dependence coming from both $\\mathcal{T}_\\mathrm{gw}(k,\\tdevel,\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*})$ and $P_{\\tilde{\\Pi}}(k,\\tdevel)$.\nRecalling that the values of $\\mathcal{A}$ and $\\mathcal{B}$ can be found in \\cref{eq:constants1,eq:constants2}, we find\n\\begin{multline}\n\t\\dv{{\\Omega}_\\mathrm{gw}}{\\ln k}\n\t= \\frac{7 \\sqrt{\\pi} \\,\\Gamma(13\/6)}{3 \\Gamma(17\/3)} \\frac{(\\mathcal{A}\\, k\\, \\xi_*)^3 \\mathcal{B}^2\\,\\overline{v}_*^4}{1+\\qty(\\mathcal{A} \\,k\\,\\xi_* \/ 3.0)^2+\\qty(\\mathcal{A}\\, k\\,\\xi_* \/ 3.5)^{11\/3}} \\\\\n\t\\times \\begin{cases}\n\t\t\\ln^2\\qty(1 + \\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*} \\mathcal{H}_*) & \\text{ for}~\\sqrt{2} k<1\/(\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}) \\\\\n\t\t\\ln^2\\qty[1 +\\mathcal{H}_*\/ (\\sqrt{2}\\,k)] & \\text{ for}~\\sqrt{2}\\, k>1\/(\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*})\n\t\\end{cases}\\text.\n\t\\label{eq:constant_approx}\n\\end{multline}\nThe $k$-dependence of $\\mathcal{T}_\\mathrm{gw}(k,\\tdevel,\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*})$ has been described in Ref.~\\cite{Caprini:2009fx} in the context of coherent sources,\nmeaning those for which the UETC can be factorized.\n\\cref{eq:mathT} is effectively a time-domain Fourier cosine transform, therefore the decay properties of $\\mathcal{T}_\\mathrm{gw}(k,\\tdevel,\\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*})$ at large $k$ depend on its time differentiability -- and in the case being studied here, it is discontinuous in time.\nThis gets combined with the behaviour of $P_{\\tilde{\\Pi}}(k,\\tdevel)$, which is uncorrelated for wavenumbers $k< 1\/(\\mathcal{A}\\xi_*)$, while at high $k$ it shares the same power-law decrease as $\\Psdv(k,\\tdevel)$ (hence, for an initially von K\\'arm\\'an spectrum, it decreases as $k^{-11\/3}$ at high $k$) \\cite{Caprini:2007xq}.\nAltogether, for instantaneous turbulence generation of a von K\\'arm\\'an spectrum, the expected slopes for low $\\overline{v}_*$ are (with notation $K=\\mathcal{A}\\xi_* k$):\n\\begin{align}\n\t\\text{if}~~\\mathcal{N}_\\mathrm{cut}\\mathcal{H}_*\\xi_*<\\overline{v}_* \\,, \\qquad &\n\t\\dv{{\\Omega}_\\mathrm{gw}}{\\ln k} \\propto \\label{eq:Omslopesprediction}\n\t\\begin{cases}\n\t\t\\mathcal{N}_\\mathrm{cut}^2\\, \\overline{v}_*^2\\, (\\mathcal{H}_*\\xi_*)^2\\, K^3 & \\text{ for}~\\mathcal{N}_\\mathrm{cut} K\\ll\\overline{v}_* \\,, \\\\\n\t\t\\overline{v}_*^4\\, (\\mathcal{H}_*\\xi_*)^2\\, K^{1} & \\text{ for}~ \\overline{v}_* \\ll \\mathcal{N}_\\mathrm{cut} K \\text{ and } K \\ll 1 \\,, \\\\\n\t\t\\overline{v}_*^4\\, (\\mathcal{H}_*\\xi_*)^2\\, K^{-8\/3} & \\text{ for}~ 1 \\ll K \\,,\n\t\\end{cases} \\\\\n\t\\text{if}~~\\mathcal{N}_\\mathrm{cut}\\mathcal{H}_*\\xi_*>\\overline{v}_*\\,, \\qquad &\n\t\\dv{{\\Omega}_\\mathrm{gw}}{\\ln k} \\propto \\label{eq:OmslopespredictionLONG}\n\t\\begin{cases}\n\t\t\\overline{v}_*^4 K^3 \\ln^2\\qty(1 + \\mathcal{N}_\\mathrm{cut} \\mathcal{H}_*\\xi_* \/ \\overline{v}_*) & \\text{ for}~\\mathcal{N}_\\mathrm{cut} K\\ll\\overline{v}_* \\,, \\\\\n\t\t\\overline{v}_*^4 K^3 \\ln^2\\qty[1 + \\mathcal{A}\\mathcal{H}_*\\xi_*\/ (\\sqrt{2}\\,K)]\n\t\t & \\text{ for}~\\overline{v}_*\/\\mathcal{N}_\\mathrm{cut} \\ll K\\ll \\mathcal{A}\\mathcal{H}_*\\xi_* \\,, \\\\\n\t\t\\overline{v}_*^4\\, (\\mathcal{H}_*\\xi_*)^2\\, K^{1} & \\text{ for}~ \\mathcal{A}\\mathcal{H}_*\\xi_* \\ll \\mathcal{N}_\\mathrm{cut} K \\text{ and } K \\ll 1 \\,, \\\\\n\t\t\\overline{v}_*^4\\, (\\mathcal{H}_*\\xi_*)^2\\, K^{-8\/3} & \\text{ for}~ 1 \\ll K \\,.\n\t\\end{cases}\n\\end{align}\nThis behaviour is in accordance with that found in \\Refs{Caprini:2009fx,RoperPol:2022iel}.\nNote that the $P_{\\tilde\\Pi}\/k^2$ slope observed in \\Refa{Brandenburg:2019uzj} at high $k$ can be interpreted in light of what has been discussed above as well as the results of \\Refa{Caprini:2009fx}.\n\nIn \\cref{fig:SGWB_num_int}, we plot the constant source approximation, \\cref{eq:constant_approx}, and compare it to the result of the full numerical integration presented in \\cref{sec:numerical}.\nAs discussed above, we set $\\mathcal{N}_\\mathrm{cut}=7$ to match the SGWB spectra obtained from the numerical integration.\nThe spectral shapes of the SGWBs evaluated under the constant source approximation are in good agreement with those produced with the numerical integration,\nover the tested range of values for the input parameters $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$.\nThe discrepancy around the transition between regimes at $k = 1\/(\\sqrt{2} \\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*})$, see~\\cref{eq:constant_approx},\nis most notable for sources that last longer than one Hubble time (meaning $\\mathcal{N}_\\mathrm{cut}\\tau_{\\xi_*}\\mathcal{H}_*>1$), where the intermediate power law for wavenumbers smaller than the peak is less pronounced.\nNote that in \\Refa{RoperPol:2022iel} it was possible to adjust the source duration (i.e.~the value of $\\mathcal{N}_\\mathrm{cut}$) in the constant approximation, as a function of $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$. This was done by comparison with the SGWBs output by the simulations, which covered alsp the large scale part of the spectrum.\nHere, on the other hand, we fix $\\mathcal{N}_\\mathrm{cut}=7$ for all initial $\\overline{v}_*$ and $\\mathcal{H}_*\\xi_*$.\n\nThe UETC of decaying turbulence \\cref{eq:unequal-velocity-ps} has both a factorizable (coherent) component and a decorrelating one.\nThe coherent component shapes the SGWB spectrum at large scales and around the peak.\nOn the other hand, the decorrelation influences the spectral shape at small scales, as discussed in \\cref{sec:stationary}.\nThe discrepancy that can be observed in \\cref{fig:SGWB_num_int} at high wavenumber for large $\\overline{v}_*$\ncan therefore be interpreted in light of what found under the assumption of a purely stationary source, see~the asymptotic behaviour at high $k$ of \\cref{eq:stat-uv}.\nThe constant source approximation, in which the decorrelation is absent, predicts a steeper power law -- given in \\cref{eq:Omslopesprediction} -- than that found by the numerical integration, which contains the full time evolution of the source.\n\nAll in all, it emerges that both the overall decay of turbulence as well as its decorrelation play a relatively minor role in shaping the SGWB signal: the first influences the spectral shape only at large scales $k\\xi_*< \\overline{v}_*\/\\mathcal{N}_\\mathrm{e}$, while the second does so only for large initial velocity $\\overline{v}_*$ and at small scales $k\\xi_*\\gg 1$.\n\n\n\\subsubsection{Comparison of the SGWB spectra}\n\\label{sec:compamethods}\n\n\\begin{figure}\n\t\\centering\n\t{\\includegraphics[width=0.8\\textwidth]{figures\/sim-analytic-gw-comp.pdf}}\n\t\\caption{GW power spectrum for instantaneous turbulence generation. The gray lines show the analytical approximation of \\cref{eq:constant_approx} based on a constant source lasting for $\\mathcal{N}_\\mathrm{cut} = 7$ eddy turnover times. The black lines show the result of the 4d numerical integration of \\cref{sec:numintegration}. From top to bottom, these lines correspond to $\\overline{v}_*=0.3$, $\\overline{v}_*=0.1$ and $\\overline{v}_*=0.03$ respectively. In all cases we fix $\\mathcal{H}_* \\xi_* = 0.001$.\n\t\tWe also show the averaged GW power spectra for simulations (A)-(G) from \\cref{tab:list}, which are plotted using colored lines as specified in the legend.\n\t\tThe GW power spectra from simulations has been cut off at high wavenumbers due to numerical precision noise.\n\t}\n\t\\label{fig:sim-analytic-comp}\n\\end{figure}\n\n\nIn \\cref{sec:results-gw-sim,sec:numintegration,sec:const}, we have presented three different methods of estimating the GW power spectrum when turbulence is generated instantaneously.\nWe first presented the GW spectra from direct numerical simulations, then we performed an exact numerical integration and finally computed an analytical approximation assuming a constant source.\n\nIn this section, we compare the GW spectra obtained from the three different methods.\nThe comparison is performed (i) long after the source has dissipated and (ii) for the lowest value of $\\mathcal{H}_*\\xi_*$\nfor which we performed the numerical integration, namely $\\mathcal{H}_*\\xi_* = 10^{-3}$. Condition (i) is necessary to perform the time average, while\ncondition (ii) is necessary since the simulations neglect the effect of expansion. For more detail, we refer the reader to the discussions at the end of \\cref{sec:gw-numerical,sec:numintegration}.\n\nWe plot the late-time averaged GW power spectra from our simulations in \\cref{fig:sim-analytic-comp}, and compare them to those computed in the full numerical integration and in the constant source approximation.\nWe show the GW power spectra from the numerical integration and the constant source approximation for $\\overline{v}_*=0.03\\text,~0.1$ and $0.3$.\nThere is excellent agreement between the SGWBs obtained with the three methods, for all the values of $\\overline{v}_*$ we simulated.\nThey are all consistent with respect to the GW power spectrum amplitude, the breadth of the peak, the peak location at $k\\xi_* \\simeq 2.7\/\\mathcal{A}$ and the power laws at low and high wavenumbers.\nIn particular, they all agree in the regions we expect the simulations to be accurate, namely at low wavenumbers for simulations (B), (D) and (F), and at high wavenumbers for simulations (C), (E) and (G). Simulation (A), which is our largest simulation, has close agreement with the other methods for the full range of wavenumbers depicted.\nWe have therefore validated that the numerical integration and the constant approximation are\naccurate in the limit of $\\mathcal{H}_*\\xi_*\\ll1$. Furthermore, as they include the effect of expansion, we expect them to also be accurate outside this limit.\nAs discussed in the previous section and depicted in \\cref{fig:SGWB_num_int}, there is a discrepancy between the constant source approximation and the full numerical integration for wavenumbers close to the kink at $k = 1\/(\\sqrt{2} \\mathcal{N}_\\mathrm{cut} \\tau_{\\xi_*})$. This can also be seen in \\cref{fig:sim-analytic-comp}.\n\nAs the constant source approximation displays excellent agreement around the peak and obtains the correct asymptotic power laws, \\cref{eq:constant_approx} can be used as a simple approximation to the GW power spectrum which does not require simulations.\n\n\n\\subsection{Phase of turbulence growth: results from the numerical integration}\n\\label{sec:GWcontinuous}\n\n\\begin{figure}\n\t\\centering\n\t\\begin{tikzpicture}[scale=2,ultra thick, node distance=2mm]\n\n\t\t\\coordinate(begin) at (0,0);\n\t\t\\coordinate(onset) at (1,0);\n\t\t\\coordinate(onset2) at (0,1);\n\t\t\\coordinate(end) at (3,3);\n\t\t\\coordinate(tau) at (4,0);\n\t\t\\coordinate(zeta) at (0,3.5);\n\t\t\\coordinate(fin) at (3,0);\n\t\t\\coordinate(fin2) at (0,3);\n\t\t\\node (zone1) at (0.5, 0.5) {$1$};\n\t\t\\node (zone2) at (2, 2) {$2$};\n\t\t\\node (zone3) at (2, 0.5) {$3$};\n\t\t\\node (zone4) at (0.5, 2) {$4$};\n\n\n\t\t\\draw (begin) rectangle (end);\n\t\t\\draw[dashed] (onset) -- +(0, 3);\n\t\t\\draw[dashed] (onset2) -- +(3, 0);\n\t\t\\draw[-latex] (begin) -- +(tau);\n\t\t\\draw[-latex] (begin) -- +(zeta);\n\n\t\t\\node [below=of begin] {$\\tini$};\n\t\t\\node [below=of onset] {$\\tdevel=\\tini + \\tgro$};\n\t\t\\node [left=of onset2] {$\\tdevel=\\tini + \\tgro$};\n\t\t\\node [below=of tau] {$\\eta$};\n\t\t\\node [left=of zeta] {$\\zeta$};\n\t\t\\node [below=of fin] {$\\tau$};\n\t\t\\node [left=of fin2] {$\\tau$};\n\n\t\\end{tikzpicture}\n\n\t\\caption{Diagram for the evolution of turbulence in terms of $\\eta$ and $\\zeta$. The injection of kinetic energy starts at $\\tini$ and turbulence develops in region $1$ on a timescale $\\tgro$, the value of which depends on the model: it might correspond to the PT duration $\\beta^{-1}$, or to the eddy turnover time $\\tau_{\\xi_*}$. In region $2$, the turbulence is freely decaying. The growth and free decay phases are correlated: regions $3$ and $4$ also contribute to the production of GWs.}\n\t\\label{fig:two-time}\n\\end{figure}\n\nIn this section we go beyond the scenario of instantaneous turbulence generation, considered so far, and include a growth phase for the turbulence kinetic energy.\nSince the Reynolds number in the early Universe is very large (of the order of $10^{13}$ at the electroweak scale, see \\cref{sec:numintegration}), turbulence is expected to arise from vorticity generated during the PT \\cite{Cutting:2020nla}, or due to the interaction of shocks \\cite{Pen:2015qta,Dahl:2021wyk}.\nIn order to model the GW source properly, one would have to model the onset of turbulence by simulating the complete system of scalar field and fluid. We leave this complicated problem for future work.\n\nIn the present analysis we model the (purely vortical) turbulence growth phase heuristically.\nThis allows us to gauge in a simple way the importance of this phase, as far as the SGWB spectral shape is concerned.\n\\Refs{Caprini:2009fx,Caprini:2009yp,Caprini:2009pr} have demonstrated analytically that time continuity of the GW sourcing process must be ensured, since it plays a role in shaping the SGWB signal, and Refs.~\\cite{Pol:2019yex,Brandenburg:2021aln,RoperPol:2021xnd} also obtained different SGWB spectral shapes in the forced case.\nAs we shall see, we confirm this result in the present work, via the numerical integration of the SGWB source (see \\cref{sec:numintegration}).\n\nStarting from the initial time $\\tini$, we assume that turbulence is sourced on a timescale $\\tgro$. At time $\\tdevel=\\tini + \\tgro$, turbulence is fully developed, and then starts decaying (see~\\cref{fig:two-time}).\nThe duration of the growth phase would depend on the particular mechanism sourcing the turbulence. If the sourcing process is related to the PT dynamics, one would expect the duration of the growth phase to be of the order of the duration of the PT.\nAlternatively, one can assume that it takes approximately one initial eddy turnover time $\\tau_{\\xi_*} = \\xi_* \/ \\overline{v}_*$ to build the turbulent cascade.\nThis is the assumption we make in the following, namely that $\\tgro = \\tau_{\\xi_*}$.\n\nConcerning the correlations of the velocity field, the Gaussian decorrelation scenario of \\cref{sec:kraichnan,sec:unequal-time-velocity} holds in principle only in the free-decay phase.\nHowever, in the absence of a better model, we assume that \\cref{eq:unequal-velocity-ps} is valid also during the growth phase, and in the mixed regions.\nTherefore, in the UETC of \\cref{eq:unequal-velocity-ps}, the times $\\tau$ and $\\zeta$ can pertain either both to the growth phase, or both to the free decay phase, or to the two phases mixed (see~\\cref{fig:two-time}).\n\nWe consider two scenarios for the generation of turbulence: a $\\mathcal{C}^0$ {growth phase} and a $\\mathcal{C}^{1}$ {growth phase}.\nFirst we adopt the model of Ref.~\\cite{Caprini:2009pr} and suppose that the vortical kinetic energy grows linearly in time before turbulence enters the free-decay phase. Furthermore, we assume that the integral scale remains constant during the growth phase, and starts growing during the phase of free decay in the $\\mathcal{C}^0$ scenario:\n\\begin{equation}\n\t\\begin{split}\n\t\t\\overline{v}^2(\\tau) & = \\overline{v}_* ^2 \\begin{cases}\n\t\t\t\\dfrac{\\tau - \\tini}{\\tgro} & \\text{if } \\tau < \\tdevel \\\\\n\t\t\t\\qty(\\dfrac{\\tau - \\tdevel +\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}}{\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}})^{-p} & \\text{if } \\tau > \\tdevel\n\t\t\\end{cases} \\\\\n\t\t\\text{and} \\quad\n\t\t\\xi(\\tau) & = \\xi_*\\begin{cases}\n\t\t\t1 & \\text{if } \\tau < \\tdevel \\\\\n\t\t\t\\qty(\\dfrac{\\tau - \\tdevel +\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}}{\\mathcal{N}_\\mathrm{e}\\tau_{\\xi_*}})^{q} & \\text{if } \\tau > \\tdevel.\n\t\t\\end{cases}\n\t\\end{split}\n\t\\label{eq:C0}\n\\end{equation}\n\nIn the second scenario the kinetic energy and the integral scale evolve in such a way that they are both continuous and differentiable at time $\\tdevel$. The kinetic energy is also continuous and differentiable at initial time $\\tini$. This scenario requires defining two extra functions.\nFor the growth phase of the kinetic energy we use a smooth step function with the properties $\\smoothstep(1) = 1$, $\\smoothstep'(1) = 0$ (and also, $\\smoothstep(0)=\\smoothstep'(0) = 0$):\n\\begin{equation}\n\t\\smoothstep(x) = \\begin{cases}\n\t\t0 & x< 0 \\\\\n\t\t3x^2-2x^3 & 01.\n\t\\end{cases}\n\\end{equation}\nTo connect with the decay phase, we use a smooth power law with the property $\\smoothpl(1, p) = 1$ and $\\smoothpl'(1, p) = 0$:\n\\begin{equation}\n\t\\smoothpl(x, p) = (1 - p) x^p + p x^{p-1}\\, ,\n\\end{equation}\nwhere $p<1$.\nUsing the above functions, we set for the $\\mathcal{C}^1$ scenario:\n\\begin{equation}\n\t\\begin{split}\n\t\t\\overline{v}^2(\\tau) & = \\overline{v}_*^2 \\begin{cases}\n\t\t\t\\smoothstep\\qty(\\dfrac{\\tau - \\tini}{\\tgro}) & \\text{if } \\tau < \\tdevel \\\\\n\t\t\t\\smoothpl\\qty(\\dfrac{\\tau - \\tdevel+\\mathcal{N}_\\mathrm{e} \\tau_{\\xi_*}}{\\mathcal{N}_\\mathrm{e} \\tau_{\\xi_*}}, -p) & \\text{if } \\tau > \\tdevel\n\t\t\\end{cases} \\\\\n\t\t\\text{and} \\quad \\xi(\\tau) & = \\xi_* \\begin{cases}\n\t\t\t1 & \\text{if } \\tau < \\tdevel \\\\\n\t\t\t\\smoothpl\\qty(\\dfrac{\\tau - \\tdevel+\\mathcal{N}_\\mathrm{e} \\tau_{\\xi_*}}{\\mathcal{N}_\\mathrm{e} \\tau_{\\xi_*}}, q) & \\text{if } \\tau > \\tdevel.\n\t\t\\end{cases}\n\t\\end{split}\n\t\\label{eq:C1}\n\\end{equation}\n\nDifferent choices for the growth phase lead to substantially different spectral shapes in the resulting SGWB.\nThis is demonstrated by \\cref{fig:c0vsc1}, showing the SGWB with both the $\\mathcal{C}^0$ and $\\mathcal{C}^1$ growth phases, as well as for the instantaneous generation scenario.\nWhile the spectra at large scales are comparable, inserting a continuous growth phase shifts the spectral peak position from the characteristic length scale of the source, $k_\\mathrm{peak}\\simeq \\xi_*^{-1}$, to the characteristic timescale of the source, $k_\\mathrm{peak}\\simeq \\tau_{\\xi_*}^{-1}$: the peak position becomes therefore velocity-dependent~\\cite{Caprini:2009fx}.\nThis behaviour is caused by the component in the velocity field UETC \\cref{eq:unequal-velocity-ps} which is factorizable in time,\ntermed the ``coherent'' case in \\Refa{Caprini:2009fx}; see the discussion below \\cref{eq:constant_approx}.\nFurthermore, the addition of a growth phase which is continuous in time leads to a\nreduction in the GW power, and a steeper slope at high frequencies,\nas shown in \\cref{fig:gwps-smooth} (in this figure we only plot the results for the $\\mathcal{C}^1$ growth phase).\nSince the peak now corresponds to the inverse eddy turnover time, the $k^1$ region observed in the instantaneous growth scenario never develops, as illustrated by \\cref{fig:c0vsc1}.\nNote that the spectra in the $\\mathcal{C}^0$ and $\\mathcal{C}^1$ growth phase are almost identical, contrary to what was observed in \\Refa{Caprini:2009fx}. Indeed, we would not expect \\Refa{Caprini:2009fx}\nto capture all the complexity of the spectral changes due to the insertion of a continuous growth phase, since it studied a very simplified case in which the anisotropic stress UETC was separable in time.\n\nAt first, it may seem counter-intuitive that the addition of a growth phase decreases the energy in GWs, given Mercer's condition (see \\cref{sec:mercer}).\nWe illustrate this using \\cref{fig:two-time}: Mercer's condition \\cref{eq:mercer-condition} applies to intervals of the form $I\\times I$, hence the contributions from the regions $1$ and $2$ are positive.\nHowever, the contributions from the regions $3$ and $4$ can very well be negative, lowering the total GW energy.\nThis effect depends crucially on the correlations between the growth and the free decay phase. We leave the study of the growth of turbulence from more realistic initial conditions to a future work.\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=\\textwidth]{figures\/figure_14.pdf}\n\t\\caption{Gravitational wave power spectrum in the instantaneous generation scenario (solid lines), with a $\\mathcal{C}^0$ growth phase (dashed lines) and with a $\\mathcal{C}^1$ growth phase (dotted lines).\n\t\tFrom bottom to top, $\\overline{v}_* = 0.1, 0.3$ and $0.6$.\n\t\tThe left panel shows $\\mathcal{H}_* \\xi_* = 10^{-3}$, the middle panel $\\mathcal{H}_* \\xi_* = 10^{-2}$ and the right panel $\\mathcal{H}_* \\xi_* = 10^{-1}$.}\n\t\\label{fig:c0vsc1}\n\\end{figure}\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=\\textwidth]{figures\/figure_15.pdf}\n\t\\caption{Gravitational wave power spectrum in the scenario with $\\mathcal{C}^1$ growth phase.\n\t\tEach panel displays a different value for the initial integral scale $\\mathcal{H}_* \\xi_*$ (as specified in the panels titles), and each line corresponds to $\\overline{v}_* = 0.1, 0.2, 0.3, 0.4, 0.5,$ and $0.6$ from bottom to top.\n\t}\n\t\\label{fig:gwps-smooth}\n\\end{figure}\n\n\\section{Discussion}\n\\label{sec:discussion}\n\n\nIn this paper, we have studied the GW signal generated by a\nphase of freely decaying vortical turbulence in a relativistic fluid.\nWe were motivated by the fact that thermal first order phase transitions occurring in the early Universe are expected to lead to a turbulent flow, either directly from vorticity generation\nduring bubble collisions, or from\nthe interaction of shocks following the acoustic phase.\nSince the sourcing of the turbulent flow remains an active area of research, we have chosen to focus on GW production in the phase of turbulent free decay, inserting as our initial conditions a fully developed turbulent spectrum, as if the turbulence generation was instantaneous.\nWhile this is not the most realistic assumption, it\nsimplified\nour analysis,\nallowing us to build a thorough understanding of the temporal and spatial structure of both the velocity field and the subsequent GW signal.\nWe thereby derived a model for the UETC of the velocity field in freely decaying turbulence.\nThe GW source, built in the context of this model, was then integrated to compute the GW spectrum.\nThe validity of our model and of the integration technique was supported with a series of direct numerical simulations\nusing the Minkowski space relativistic hydrodynamics code SCOTTS. The simulations were prepared with equivalent initial conditions to our model, namely a fully developed turbulent spectrum.\n\n\nThe velocity of the turbulent fluid was assumed to follow the von K\\'arm\\'an\\ power spectrum, interpolating between a causal $k^5$ slope at low wavenumbers and a Kolmogorov $k^{-2\/3}$ one at high wavenumbers.\nThis basic choice influences the spectral shape of the anisotropic stress power spectrum and consequently of the GW power spectrum.\nConcerning the velocity UETC, motivated by the Kraichnan sweeping model,\nwe assumed that the velocity field decorrelation is Gaussian in time, with a timescale set by the Eulerian eddy turnover time.\nWe implemented the decorrelation at larger scales, around the spectral peak, by extending the decorrelation velocity according to \\Refa{kaneda_lagrangian_1993}.\nWe further addressed the modelling of decorrelation in the context of the theory of positive kernels, and propose to model the UETC of the turbulent velocity field as a Gibbs kernel.\nThis both consistently enforces time symmetry in the UETC, and guarantees that the GW power spectrum is non-negative.\nWe have validated our model for the velocity field decorrelation\nwith a series of relativistic hydrodynamic simulations performed with SCOTTS, with the initial root-mean-squared velocity, $\\overline{v}_*$, ranging from $0.03$ to $0.3$.\nWe used our simulations also to study the time evolution of both the kinetic energy and\nthe integral scale, allowing us to fix the number of eddy turnover times that it takes for the turbulence to decay.\n\nWe have reviewed the analytical computation of the SGWB from a purely vortical fluid, arriving at an equation for the GW power spectrum in terms of an integral over the Green's function and the anisotropic stress UETC, which is provided by the turbulence model we have developed.\nThe computation of the GW spectrum could then be performed without any further approximation using Monte Carlo integration with importance sampling.\nVarying the exponents of the turbulence decay laws had limited impact on the resulting GW spectrum.\nWe therefore conclude that our predictions are robust with respect to this unknown.\nFor instantaneously generated turbulence, we found excellent agreement between the GW signal evaluated with direct numerical integration and the one extracted from our direct numerical simulations.\n\nTo conclude our exploration of the GW signal in instantaneously generated turbulence, we studied the case of a turbulent source which is constant in time.\nAs the GW spectrum at high wavenumbers and around the peak is built up on short timescales in comparison to the decay of the source, the constant source approximation works well in this region.\nThe GW power spectrum derived within the constant source approximation is in very good agreement with both\nthe direct numerical simulation result and the power spectrum arising from direct numerical integration of the turbulence model we have built.\nWe provide an analytical form for the SGWB signal from turbulence, \\cref{eq:constant_approx}, which has the advantage that it can be readily used without the need to perform simulations or the challenging computation of 4-dimensional integrals.\n\nFinally, we explored how the GW spectrum was modified by inserting a growth phase for the turbulence, therefore removing the time discontinuity inherent in the instantaneous generation assumption.\nWe considered in particular a $\\mathcal{C}^0$ growth phase and a $\\mathcal{C}^{1}$ growth phase.\nThese models, although simplistic, allowed us to show how the GW signal might\nbe modified by the turbulence generation phase.\nIn contrast to the decay law exponents, the form of the initial growth phase has a large effect on the GW power spectrum: flows with equivalent $\\overline{v}_*$ can lead to GW signals with different spectral shapes and amplitudes, if the initial condition differs.\nThis underlines the importance of understanding the turbulence formation mechanism in the context of first order phase transitions.\n\nFuture works will therefore focus on the generation of turbulence --- which we showed is crucial to make precise predictions for future GW experiments --- and on the inclusion of the longitudinal velocity field and a magnetic field.\n\n\\section*{Acknowledgements}\n\nThe work has been performed under the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 Programme; in particular, PA gratefully acknowledges the support of Mark Hindmarsh and the Department of Physics of the University of Helsinki and the computer resources and technical support provided by CSC.\nThis collaboration has been made possible thanks to the generosity of CNRS through the International Emerging Actions program 2020.\nPA and CC have completed most of this project while affiliated at the APC laboratory, Paris.\nDJW (ORCID ID 0000-0001-6986-0517) was supported by Academy of\nFinland grant nos. 324882 and 328958; DC (ORCID ID 0000-0002-7395-7802) was supported by Academy of\nFinland grant nos. 328958 and 345070; KR (ORCID ID 0000-0003-2266-4716) was supported by Academy of Finland grants 319066, 320123 and 308791.\nThe work of PA (ORCID ID 0000-0002-4814-1406) was partially supported by the Wallonia-Brussels Federation Grant ARC \\textnumero 19\/24 - 103.\nWe acknowledge PRACE for awarding us access to HAWK at GCS@HLRS, Germany.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nAn accurate, fast and robust estimation of 6D object pose is of great importance to many application fields such as robotic manipulation, augmented reality, scene interpretation, and autonomous driving. In robotics, for example, the 6D object pose facilitates spatial reasoning and allows an end-effector to act upon the object. In an augmented reality scenario, object pose can be used to enhance one's perception of reality by augmenting objects with extra information such as hints for assembly.\n\nThe introduction of consumer and industrial grade RGB-D sensors have allowed for substantial improvement in 6D object pose estimation as these sensors concurrently capture both the appearance and geometry of the scene. However, there still remain challenges to be addressed, including robustness against occlusion and clutter, scalability to multiple objects, and fast and reliable object modelling, including capturing of reflectance properties. Extending contemporary methods to work reliably and with sufficient execution speed in an industrial setting is still an open problem. Many recent methods focus on specific rigid objects, but pose estimation of deformable or articulated objects and of object categories is also an important research direction.\n\n\\begin{figure}[!t]\n\\begin{center}\n \\includegraphics[width=1.0\\columnwidth]{teaser.jpg}\n \\caption{\\label{fig:6d} Estimation of 6D pose, i.e. 3D translation and 3D rotation, of a specific rigid object. This task is considered in the BOP benchmark~\\cite{hodan2018bop}.}\n\\end{center}\n\\end{figure}\n\nThe field of 6D object pose estimation has gained more attention last years. A big achievement for the field is the best paper award of ECCV 2018 given to Martin Sundermeyer, Zoltan Marton, Maximilian Durner, Manuel Brucker, and Rudolph Triebel for their work titled \\emph{Implicit 3D Orientation Learning for 6D Object Detection from RGB Images}.\n\nThe 4th edition of the International Workshop on Recovering 6D Object Pose~\\cite{r6d2018}\\footnote{The previous editions were held at ICCV 2015~\\cite{r6d2015}, ECCV 2016~\\cite{r6d2016}, and ICCV 2017~\\cite{r6d2017}.} was organized in conjunction with ECCV 2018 and was attended by 100+ people from both academia and industry who shared up-to-date advances and discussed open problems. Four invited speakers talked about their current work (Section~\\ref{sec:invited_talks}), the BOP benchmark for 6D object pose estimation was introduced (Section~\\ref{sec:bop}), and the accepted workshop papers were presented.\n\nThe workshop covered the following topics: (a)~6D object pose estimation (a.k.a. 3D object detection) and tracking, (b)~3D object modeling and reconstruction, (c)~surface representation and registration, (d)~robustness to occlusion and background clutter, (e)~multiple object instance detection, (f)~pose estimation of non-rigid objects and object categories, (g)~robotic grasping and grasp affordances, and (h)~object manipulation and interaction.\n\nMany methods for 6D pose estimation of specific rigid objects (Fig.~\\ref{fig:6d}) have been published recently, but were usually compared with only a few competitors on a small subset of datasets. It had been therefore unclear which methods perform well and in which scenarios. To capture the \\emph{status quo} of the field, we organized the SIXD Challenge~\\cite{sixd2017} at the 3rd workshop edition held at ICCV 2017~\\cite{r6d2017}. The results submitted to the challenge were published in the BOP benchmark paper~\\cite{hodan2018bop} and presented at the 4th workshop edition.\n\nPapers submitted to the workshop were peer-reviewed. Out of 13 submissions, the program committee accepted 10 papers which were introduced at the workshop through oral and poster presentations. The best paper award was given to \\emph{Image-to-Voxel Model Translation with Conditional Adversarial Networks} by Vladimir Knyaz, Vladimir V. Kniaz, and Fabio Remondino.\n\n\n\\section{Invited talks} \\label{sec:invited_talks}\n\nThe invited talks were given by Federico Tombari from Technical University of Munich, Kostas Bekris from Rutgers University, Bertram Drost from MVTec, and Thibault Groueix from Ecole Nationale des Ponts et Chauss\u00e9es. The talks are summarized below and the slides are available on the workshop website~\\cite{r6d2018}.\n\n\\subsection{From 3D Descriptors to Monocular 6D Pose: What Have We Learned? -- \\textit{Federico Tombari}}\n\nWhile 6D rigid pose estimation has been an important research task for more than twenty years, recently the design of new algorithms is more and more focused on overcoming the limitations provided by real world applications, so to bridge the gap between lab research and products. This translates to the necessity to move on from the simplified scenario of estimating the pose of a single object on a clutter-less planar surface, towards scenarios with high clutter and occlusion~\\cite{tan2017looking}. At the same time, algorithms need to process input data at a very high frame rate, so to reduce the latency of the output and avoid lagging, provide accurate pose estimation (e.g. to reduce jitter during tracking) and cope with resource-limited hardware architectures such as mobile phones or embedded computers. This is particularly motivated by applications that already have a strong market interest such as augmented reality, personal\/industrial robotics and autonomous driving. A disruptive technology that strongly influenced the field of 6D rigid pose estimation in the past 5 years is deep learning. Hence, the talk presented an overview of the current trends and results regarding the use of deep learning for this task, in view of overcoming the aforementioned limitations. In particular, it highlighted two main research directions where deep learning has been leveraged to improve the state of the art: i) the definition of 3D descriptors for 3D data; ii) 6D object pose estimation from RGB (or monocular) data. \n\nAs for the first aspect, we briefly went over the development of 3D descriptors for unorganized 3D representations, starting from the handcrafted ones \\cite{tombari10SHOT,rusu09FPFH} until the more recent \"learned\" ones. An important aspect regarding the influence of deep learning on this field is that certain 3D representations such as point clouds and 3D meshes, frequently utilized for 6D rigid pose estimation tasks, are not well suited to convolutions due to their intrinsic unorganized nature. Hence, an important step in the direction of learning 3D features was the introduction of methods such as PointNet \\cite{qi2017pointnet} and \\cite{wang18DGCNN}, that, conversely to approaches such as 3D Match \\cite{zeng173DMatch} and \\cite{khoury17CGN}, can directly operate on point clouds or meshes without the need to either voxelize or histogram the data. The recent global and fully-convolutional architecture for point cloud processing and scene understanding proposed in \\cite{rethage18} was also introduced. \n\nAs for the second research direction, the intuition of estimating the 6D pose of an object based on monocular information relies on the fact that humans can often have a rough idea of the pose of the objects in the surrounding 3D space simply from monocular cues, provided that they are familiar with the shape of the objects. Several works have recently explored this direction, which were briefly introduced and compared in terms of characteristics. One distinctive trait that differentiates such approaches is the type of output that the network is trained to infer: from the 8 corners of the projected bounding box \\cite{rad2017bb8,tekin18} to the regression of the 6D pose \\cite{xiang18posecnn,do18deep6dpose} or the classification of the viewpoint and in-plane rotations \\cite{kehl2017ssd}. The method in \\cite{manhardt18} was also introduced, that proposes to learn the 6D pose refinement from pairs of RGB patches using a CNN. Finally, the extension of monocular 6D pose estimation to the autonomous driving domain was also discussed, by referencing recent directions such as \\cite{chen16Mono3D,chen153DOP}. \n\nIn conclusion, deep learning appears as a powerful tool for 6D rigid pose estimation, although seems still strongly limited by open issues such as generalizability, learning of geometric invariance and computational efficiency (especially in a field where most applications need to deal with resource-limited hardware). Monocular pose estimation can be promisingly carried out via deep learning, although not yet as accurately as with a depth sensor, as also showcased by a qualitative comparison on a real sequence between the two tracking approaches in \\cite{manhardt18} (monocular) and \\cite{tan2017looking} (RGB-D). Finally, deploying monocular 6D pose estimation jointly with monocular semantic SLAM such as CNN-SLAM \\cite{tateno17CNNSLAM}, which also leverages deep learning to obtain dense semantic reconstruction, appears as an interesting direction to explore towards full (i.e. semantic+geometry) scene understanding from monocular data. \n\n\n\\subsection{Towards Robust 6D Pose Estimation: Physics-based Reasoning and Data Efficiency -- \\textit{Kostas Bekris}}\n\nTowards the objective of robust 6 DoF pose estimation at an accuracy\nand speed level that allows robots to manipulate objects in their\nsurroundings, this talk focused on warehouses tasks, such as picking\nfrom bins, packing and sorting. Warehouses can be seen as a stepping\nstone between the success story of robotics in manufacturing and the\nvision of deploying robots in everyday human environments. Warehouses\ninvolve a large variety of objects, which can appear in general,\nunpredictable configurations, as well as cluttered scenes and tight\nspaces, such as shelving units, which limit the observability of\non-board sensors and introduce occlusions. They allow, however, access\nto known object models, which frequently correspond to standard\ngeometric shapes, due to packaging.\n\nIn these setups, it is critical for a robot to both utilize\nphysics-based reasoning to achieve 3D scene-level understanding as\nwell as minimize the dependence of solutions to excessive human\nlabeling, which negatively impacts scalability. With these priorities\nin mind, the talk highlighted a pipeline for robust 6D pose\nestimation, which includes the following four steps: 1) semantic object segmentation given physically realistic synthetic data, 2) pose hypothesis generation through robust point registration, 3) pose improvement via consideration of physical constraints at the scene level, and 4) lifelong self-learning through active physical interaction with objects.\n\nRecently, deep learning methods, such as those employing Convolutional\nNeural Networks (CNN's), have become popular for object\ndetection \\cite{ren2015faster,redmon2016you} and pose estimation\n\\cite{kehl2017ssd,xiang2017posecnn}, outperforming alternatives in\nmost benchmarks. These results are typically\nobtained by training CNN's using datasets with a large number of labeled images. Such datasets, however, need to be\ncollected in a way that captures the intricacies of the environment\nthe robot is deployed in, such as lighting conditions, occlusion and clutter. This motivates the development of synthetic dataset that can capture the known parameters of the environment and generate data accordingly, while avoid overfitting to\nthe unknown parameters.\n\n\\emph{The first component of the proposed pipeline} is to use a\nphysics engine in the synthetic dataset generation pipeline\n\\cite{mitash2017self}. The physics engine defines environmental\nconstraints on object placement, which naturally capture in the\ntraining set, the distribution of object poses that can realistically\nappear during testing. Furthermore, a physics engine is a convenient\ntool to parameterize the unknown scene features, such as illumination.\nA randomization over such parameters is very effective in avoiding overfitting to synthetic textures of objects.\n\nGiven semantic object segmentation, the problem of estimating the 6D object\nposes involves geometric reasoning regarding the position\nand orientation of the detected objects. Solutions that became popular\nin the context of the Amazon Picking Challenge (APC)\n\\cite{Correll:2016aa}, use a Convolutional Neural Network (CNN) for object segmentation \\cite{Princeton,hernandez2016team}\nfollowed by a 3D model alignment step using point cloud\nregistration techniques \\cite{mellado2014super,icp}. The quality of\nthe pose estimate, however, can still suffer due to over-reliance on\nthe learned models.\n\n\\emph{The second insight of the talk} is that CNN output can be\nseen as a probability for an object to be visible at each pixel. These\nsegmentation probabilities can then be used during the registration\nprocess to achieve robust and fast pose estimation. This requires\nsampling a base of points on a point cloud segment, such that all\npoints on the base belong to the target object with high\nprobability. The resulting approach, denoted as ``Stochastic Congruent\nSets'' (StoCS) \\cite{Mitash:2018aa}, builds a\nprobabilistic graphical model given the obtained soft segmentation and\ninformation from the pre-processed geometric object models. The\npre-processing corresponds to building a global model descriptor that\nexpresses oriented point pair features \\cite{drost2010model}. This\ngeometric modeling, not only biases the base samples to lie within the\nobject bound, but is also used to constrain the search for finding the\ncongruent sets, which provides a substantial computational benefit.\n\n\\emph{The third key observation of the talk} is to treat\nindividual-object predictions with some level of uncertainty and\nperform a global, scene-level optimization process that takes object\ninteractions into account \\cite{Mitash:2018ab}. This information\narises from physical properties, such as respecting gravity and\nfriction as well as the requirement that objects do not penetrate one\nanother. In particular, a Monte Carlo Tree Search (MCTS) process\nutilizes local detections to achieve scene-level optimization. It\ngenerates multiple candidate poses per object and then searches over\nthe cartesian product of these individual object pose candidates to\nfind the optimal scene hypothesis. The scene is evaluated according to\na score defined in terms of similarity of the rendered hypothesized\nscenes against input data. The search performs constrained local\noptimization via physics correction and ICP \\cite{icp}. Through\nthis physical reasoning, the resulting pose estimates for the objects\nare of improved accuracy and by default consistent. \n\nOnce the system has access to an object detector and a pose estimation\nprocess, it can already be deployed for the desired task.\nNevertheless, as the system performs its task, it also gets access to\ndata in the operation domain, which it did not have access to during\ntraining. This data could be very useful in further improving the\nperformance of the system but they are not labelled.\n\n\\emph{The fourth aspect of the talk} is a solution for automatically\nlabeling real images acquired from multiple viewpoints using a robotic\nmanipulator \\cite{mitash2017self}. A robotic manipulator\nautonomously collects multi-view images of real scenes and labels them\nautomatically using the object detector trained with the above\nphysics-based simulation tool. The key insight is the fact that the\nrobot can often find a good viewing angle that allows the detector to\naccurately label the object and estimate its pose. The object's\npredicted pose is then used to label images of the same scene taken\nfrom more difficult and occluded views. The transformations between\ndifferent views are known because they are obtained by moving the\nrobotic manipulator. Overall, the data can be added to the existing\nsynthetic dataset to re-train the model for better performance as part\nof a lifelong, self-learning procedure.\n\n\n\\subsection{Detecting Geometric Primitives in 3D Data -- \\textit{Bertram Drost}}\n\nEven though methods based on (deep) learning lead to major advances in many areas of computer vision over the last years, the top-performing methods for 6D object detection are still hand-crafted, classic methods. This is evident from the recent results on the BOP benchmark (Section~\\ref{sec:bop}).\n\nMethods that currently perform best on BOP are based on a voting scheme that can be interpreted as a meet-in-the-middle between RANSAC and a generalized Hough transform.\nThe base method~\\cite{drost2010model} uses a local parametrization for the object pose, where an oriented scene point (reference point) is fixed and assumed to be on the target object. The remaining local parameters are then the point on the model surface that corresponds to the reference point and the rotation around the normal vector, which combined represent three degrees of freedom. The optimal local parameters are recovered using a voting scheme. For this, the reference point is paired with neighboring scene points, and similar point pairs on the model are searched using an efficient hashing scheme. Each such match then casts a vote.\nWhile the base method shows good results for 3D shapes with a distintive geometry, it has weaknesses for shapes that are symmetric of strongly self-similar. This is mostly because point pairs on such shapes are no longer very discriminative. The voting thus finds all symmetric poses simultaneously, which is both slower and less robust.\n\nThe talk presented a way of adapting the base method for geometric primitive shapes - spheres, planes, cylinders, and to some extend cones.\nFirst, the local parameter space was reduced by removing duplicate entries due to symmetries. For example, since a sphere is identical no matter the corresponding point on its surface or the rotation around the normal, the local parameter space becomes zero-dimensional, i.e. a single counter. Additionally, the local parameters can be extended by shape parameters such as the radius of a sphere or cylinder, or the opening angle of a cone. Second, instead of using a hashing scheme for matching point pairs between scene and model, an explicit point pair matching can be used thanks to the explicit nature of the geometric primitives.\nThose changes make the method both faster and more robust for such geometric primitives, while also allowing the recovery of shape parameters.\n\n\n\\subsection{Parameteric Estimation of 3D Surfaces and Correspondences -- \\textit{Thibault Groueix}}\n\nPose of a rigid object has 6 degrees of freedom and is well defined.\nA broader class of objects are non-rigid shapes such as articulated robots or humans. Articulated robots have an additional degree of freedom for each joint. Those additional intrinsic parameters also have to be estimated. In this case, it is easy to manually design a parametrization with N degrees of freedom, where each parameter naturally encodes the angle of a joint. Humans are much harder to parameterize and doing it manually is hard. We propose to learn the parameterization~\\cite{groueix20183d}.\n\nTo that end, we want to map each shape $S$ of category $C$, represented as a point cloud, to a parameter space. A relevant prior on deformable shapes is the ability to find a neutral template, already encoding lots of general information on humans.\nTo learn a parameterization, we use autoencoder neural networks. The encoder is a PointNet~\\cite{qi2017pointnet} and the decoder is a Shape Deformation Network~\\cite{groueix20183d} that deforms the template by iteratively transforming each point on the template to a point in 3D. Given correspondence annotation between a training example and the template, the L2 distance between the generated and the input point clouds is penalized. Annotation of correspondences is expensive and can be avoided through an unsupervised reconstruction loss, the chamfer distance, and a proper regularization. The parameterization is a mapping from the parameter space to the point cloud space, and is given by the learned decoder.\n\nAt test time, the parameters associated with this parametrization can be estimated using the encoder. We propose to use this estimate as an initialization for a local exploration of the parameter space through gradient descent. The learned parametrization is evaluated on the task of 3D correspondences on the FAUST dataset~\\cite{bogo2014faust} and achieves state-of-the-art-results. It also displays strong robustness to holes and noise in the data.\n\nThe method is not limited to humans, but applies to any category of shape for which a template can be found. For broader category without natural templates such as chairs, or general furniture, the template can be replaced by a set a square patches~\\cite{groueix2018atlasnet}. The assumption is that any object can be reconstruct through the deformation of a set of patches. The proposed parametrization, Atlasnet, is learned on Shapenet and applied for the task of Single View Reconstruction, leading to state-of-the-art results. This confirms that learning a parametrization can be the key to extending 6D pose estimation beyond rigid objects.\n\n\n\\section{BOP: Benchmark for 6D Object Pose Estimation} \\label{sec:bop}\n\nThe BOP benchmark considers the task of 6D pose estimation of a rigid object from a single RGB-D input image, when the training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: (i) eight datasets~\\cite{hinterstoisser2012accv,brachmann2014learning,tejani2014latent,doumanoglou2016recovering,hodan2017tless,rennie2016dataset} in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, (ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, (iii) an evaluation of 15 diverse recent methods that captures the state of the art, and (iv) an online evaluation system at \\texttt{\\href{http:\/\/bop.felk.cvut.cz}{bop.felk.cvut.cz}} that is open for continuous submission of new results.\n\nThe evaluation shows that methods based on point-pair features~\\cite{vidal2018sixd,drost2010model}, introduced in 2010, currently perform best. They outperform template matching methods~\\cite{hodan2015detection}, learning-based methods~\\cite{brachmann2014learning,brachmann2016uncertainty,kehl2016deep,tejani2014latent} and methods based on 3D local features~\\cite{buch2016local,buch2017rotational}.\nOcclusion is a big challenge for current methods, as shown by scores dropping swiftly already at low levels of occlusion. As another important future research directions, our analysis identified robustness against object symmetries and similarities, varying lighting conditions, and noisy depth images.\n\n\\subsection{Future Plans}\n\nVarious possible extensions of the BOP benchmark regarding datasets, problem\nstatement and evaluation metrics were discussed at the workshop and are summarized in the following paragraphs.\n\nThe benchmark was started with the simple task of 6D localization of a single instance of a single object. This task allowed to evaluate most of the recent methods out of the box and is a common denominator of the other 6D localization variants -- a single instance of multiple objects, multiple instances of a single object, and multiple instances of multiple objects. The plan is to move step by step and add the more complicated variants to the online evaluation system in the near future, as well as the 6D detection task, where no prior information about the object presence is provided~\\cite{hodan2016evaluation}. Note that while it is easy to extend the evaluation to other tasks, it is non-trivial to extend the methods.\n\nThe plan is also to keep adding new datasets, e.g.~ \\cite{drost2017introducing}, and for every new dataset to reserve a set of test images for which the ground-truth annotation will be private. The datasets that are currently included in BOP are fully public.\n\nAnother topic of discussion were the pose-error functions. In the BOP benchmark, 6D pose estimates are evaluated using the Visible Surface Discrepancy (VSD) which measures misalignment of the visible part of the object surface.\nOne limitation of the current version of VSD is that it does not consider color. The same holds for the other pose-error functions commonly used in the literature~\\cite{hodan2016evaluation}. While the alignment of color texture is less relevant for a number of robotic applications, it may be important for augmented-reality applications. The plan is to fix this limitation in a new version of VSD with an extended pixel-wise test checking not only the distance of the object surface but also its color. \nWe are open to other feedback and discussion about the evaluation methodology.\n\n\\section{Conclusions}\n\nThis document summarized the 4th International Workshop on Recovering 6D Object Pose which was organized at ECCV 2018 and featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.\n\nAn accurate, fast and robust method for 6D object pose estimation is still in need, as is evident from the current scores on the BOP benchmark~\\cite{hodan2018bop}.\nWe would like to encourage authors of relevant methods to keep submitting results to the online evaluation system at \\texttt{\\href{http:\/\/bop.felk.cvut.cz}{bop.felk.cvut.cz}}. 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Phys.} {{\\bf #1} {(#2)} {#3}}}\n\\newcommand{\\ibid}[3]{{\\sl ibid.} {{\\bf #1} {#2} {#3}}}\n\\newcommand{\\mbox{\\sl et al. }}{\\mbox{\\sl et al. }}\n\\newcommand{\\PE}{\\ \\\\ {\\centering {\\large Per Elmfors} \\\\ \\ \\\\\n { NORDITA \\\\\n Blegdamsvej 17 \\\\\n DK-2100 K\\o benhavn \\O \\\\\n Denmark \\\\\n E-mail: elmfors@nordita.dk \\\\ }}}\n\n\n\\newcommand{{\\rm T}}{{\\rm T}}\n\\newcommand{\\cL^0_{eff}}{{\\cal L}^0_{eff}}\n\\newcommand{\\cL_{eff}^{\\beta,\\mu}}{{\\cal L}_{eff}^{\\beta,\\mu}}\n\\newcommand{\\cL_{0}^{\\beta,\\mu}}{{\\cal L}_{0}^{\\beta,\\mu}}\n\\newcommand{\\cL_{1}^{\\beta,\\mu}}{{\\cal L}_{1}^{\\beta,\\mu}}\n\\normalsize\n\\renewcommand{\\theequation}{\\thesection.\\arabic{equation}}\n\\newcommand{\\AUTHORS}{\\ {\\centering\n{\\large Per Elmfors}\\footnote{Email address: elmfors@nordita.dk.} \\\\\n{\\sl NORDITA \\\\\n Blegdamsvej 17 \\\\\n DK-2100 Copenhagen \\O, Denmark \\\\ }\n{\\large\nDavid Persson\\footnote{Email address: tfedp@fy.chalmers.se.} and\nBo-Sture Skagerstam}\\footnote{Email address\naddress:tfebss@fy.chalmers.se. Research supported by the Swedish\nNational Research Council under contract no. 8244-103} \\\\\\\n\\vspace*{1mm}\n {\\sl Institute of Theoretical Physics \\\\\n Chalmers University of Technology and\\\\\nUniversity of G\\\"oteborg\\\\\n S-412 96 G\\\"oteborg, Sweden\\\\ }}}\n\n\\begin{document}\n\\large\n\\thispagestyle{empty}\n\\begin{flushright} NORDITA--93\/35 P \\\\\n G\\3teborg ITP 92--22 \\\\\n April 1993 \\end{flushright}\n\\begin{center}\n\\normalsize\n{\\LARGE\\bf QED Effective Action at Finite Temperature\n and Density\\\\}\n\\end{center}\n\\vspace*{0.5cm}\n\\AUTHORS\n\\begin{center}\n{\\bf Abstract} \\\\\n\\end{center}\n{\\normalsize\nThe QED effective action at finite temperature and density is\ncalculated to all orders in an external homogeneous and\ntime-independent magnetic\nfield in the weak coupling limit. The free energy, obtained\nexplicitly, exhibit the expected de\\ Haas -- van\\ Alphen oscillations.\nAn effective coupling at finite temperature and density is derived in\na closed form and is compared with renormalization group results.\n}\n\\newpage\n\\normalsize \\setcounter{page}{1}\n\\begin{center} \\section{Introduction}\n\\setcounter{equation}{0}\n\\label{intro}\n\\end{center}\nLarge magnetic fields are relevant in a number of physical\nsystems like supernovas\\cite{Ginzburg91}, where\n$B={\\cal O} (10^{10}){\\rm T}$, neutron stars \\cite{ShapiroT83}, where\n$B={\\cal O} (10^{8}){\\rm T}$, or white\nmagnetic dwarfs \\cite{Angel78} in which case\n$B={\\cal O}\n(10^{4}){\\rm T}$. (As a reference the electron mass in units of\ntesla is $m^2={\\cal O}(10^9){\\rm T}$.) The radiative corrections to the magnetic\nmoment of a Dirac fermion\nhas been estimated in the presence of such large magnetic fields and it was\nargued that they are extremely small \\cite{Skagerstam91,Studenikin90}.\nIt has recently been shown that a plasma at thermal equilibrium can sustain\nlarge fluctuations of the electromagnetic fields. For instance, in\nthe primordial Big-Bang\nplasma, the\namplitude of magnetic field (zero frequency) fluctuations at the time of\nthe\nprimordial nucleosynthesis can be as large as\n$B = {\\cal O}(10^{10}) {\\rm T}$\n\\cite{TajimaCSK92}. Other systems with large magnetic fields present are\nmergers of massive black holes \\cite{NatayanPP92}, where\n$B={\\cal O} (10^{13}){\\rm T}$ or superconducting strings \\cite{Witten85}, where\n$B={\\cal O} (10^{14}){\\rm T}$ or even larger. At the electroweak\nphase transition in\nthe\nvery early universe it has, furthermore, been argued that very large magnetic\nfields,\n$B={\\cal O} (10^{19}){\\rm T}$, can be generated due to\ngradients in the Higgs field\n\\cite{Vachaspati91}.\n\nIn many of these systems one has to consider the effects of thermal\nenvironments.\nCalculation of the QED effective potential, i.e. the free energy, has been\nattempted before\neither at finite temperature \\cite{Dittrich79,Rojas92} or at\nfinite chemical potential \\cite{ChodosEO90}. In\nthe latter case the effective action is unfortunately not complete but the\ncorrect form is\npresented here. At finite chemical potential and for sufficiently small\ntemperatures,\nthe QED effective action should exhibit a certain periodic dependence of\nthe the\nexternal field, i.e. the well-known de\\ Haas -- van\\ Alphen\noscillations in condensed\nmatter\nphysics. This was not obtained in Ref.\\cite{ChodosEO90}.\n\nLet ${\\cal L}_{eff}$ denote the QED effective action for a constant B-field\nat finite temperature $T=1\/\\beta$ and chemical potential $\\mu$. We\ncalculate\nthis effective action\nto all orders in $eB$ but with no virtual photons present, i.e. we\nconsider the weak coupling limit.\nA more detailed analysis will be\npresented elsewhere \\cite{ElmforsPS93}.\n\n\\begin{center}\\section{Derivation of the Effective Action ${\\cal L}_{eff}$}\n\\setcounter{equation}{0}\n\\label{derivLB}\n\\end{center}\nThe basic relation we need in order to derive the\none-loop correction to the effective Lagrangian\nis the identity\n\\begin{equation}\n\\frac{\\partial{\\cal L}_{eff}}{\\partial m}= i {\\rm Tr\\,} S_F(x;x)~~~,\n\\label{BI}\n\\end{equation}\nwhere $S_F(x;x')$ is the fermion propagator in the external magnetic\nfield\nand the trace is over spinor indices. It can be constructed from\nthe solutions of the Dirac equation\n$(i\\partial\\!\\!\\!\/-eA\\!\\!\\!\/-m)\\psi(x)=0$ in such a way that\n\\begin{equation}\n\\begin{array}{c}\nS_F(x;x')=\\langle 0|{\\bf T } [\\psi(x)\\overline{\\psi}(x')]|0\\rangle\\ .\n\\label{SF}\n\\end{array}\n\\end{equation}\nEquation (\\ref{BI}) determines the vacuum part,\n${\\cal L}_{1}={\\cal L}_{eff}(T=\\mu =0,B)$, of the effective action\nwhich should be added to the tree-level, ${\\cal L}_{0}=-B^2\/2$. At finite\ntemperature and\ndensity we simply replace the time-ordered vacuum expectation values\nin \\Eqref{SF} by a\nthermal average.\nIt can be shown that this replacement corresponds to the conventional\ncalculational rules of\nthermo field dynamics. The solutions of the Dirac equation with an external\nconstant\nmagnetic field parallel to the $z-$axis are the standard relativistic\nLandau levels with energy spectrum given by\n\\begin{equation}\nE_n(k_z) = \\sqrt{m^2+k^2_z+2eBn}\\ ,\n\\end{equation}\nwhere $n=0,1,2,...$ and $k_z$ is the momentum parallel to the magnetic\nfield. The construction of the propagator is similar to the zero\ntemperature case \\cite{KobayashiS83} except that the propagating\nparticles can now be exchanged with the heatbath. We find\n\\begin{equation}\n\\label{SF11}\n{\\rm Tr\\,} S_F(x;x)= \\sum_{n=0}^\\infty \\int \\frac{d\\omega dk_y dk_z}{(2\\pi)^3}\n[\\Delta+(\\Delta^*-\\Delta)f_F(\\omega)]\\ 2m(I_n+I_{n-1})\\ ,\n\\end{equation}\nwhere we have introduced the scalar propagator\n\\begin{equation}\n\\Delta = \\inv{\\omega^2-k_z^2-m^2-2eBn+i\\epsilon}\\ .\n\\end{equation}\nThe thermal distribution $f_F(\\omega)$ is given by\n\\begin{equation}\nf_F(\\omega) = \\frac{\\theta(\\omega)}{e^{\\beta(\\omega-\\mu)}+1}\n+\\frac{\\theta(-\\omega)}{e^{\\beta(\\mu-\\omega)}+1}\\ ,\n\\end{equation}\nand we use the notation\n\\begin{equation}\nI_n = \\left( \\frac{eB}{\\pi} \\right)^{1\/2} \\exp \\left[\n - eB \\left( x \\!-\\! \\frac{k_{y}}{eB} \\right)^{2} \\right]\n \\frac{1}{n!} H_{n}^2 \\left[ \\sqrt{2eB} \\left( x \\!-\\! \\frac{k_{y}}\n {eB} \\right) \\right]\\ ,\n\\end{equation}\nwhere the functions $H_n$ are Hermite polynomials, and we define\n$I_{-1}=0$. From the propagator in \\Eqref{SF11} we get both\nthe vacuum correction ${\\cal L}_{1}$ and a thermal correction\n$\\cL_{eff}^{\\beta,\\mu}$. It is well-known that a real-time formalism at\nfinite temperature requires a doubling of the degrees of\nfreedom and it can be shown that \\Eqref{SF11} is the 11-component\nof the matrix propagator in thermo field dynamics \\cite{UmezawaMT82}.\nHere we only need the 11-component for the one-loop calculation.\nThe vacuum part of \\Eqref{SF11} that survives when\n$f_F(\\omega)\\rightarrow 0$ reproduces the old result by Schwinger\n\\cite{Schwinger51}\n\\begin{equation}\n{\\cal L}_{1} = -\\frac{1}{8\\pi^{2}} \\int_{0}^{\\infty}\n \\frac{ds}{s^3}exp(-m^{2}s) \\left(esB\\coth(esB) -1 -\n\\frac{1}{3}(esB)^2\\right)~~.\n\\end{equation}\nHere ${\\cal L}_{1}$ has been renormalized by adding\na second order polynomial in $eB$. We stress that the physics behind\nthis renormalization is related to the fact that the coefficient in\nfront of the quadratic term is proportional to the square of inverse\n(bare) coupling. This renormalization corresponds to a charge\nrenormalization as well as a wave function renormalization in such a way\nthat $eB$ is invariant. This charge renormalization also leads to the weak\ncoupling\nexpansion of the QED $\\beta$-function, i.e.\n\\begin{equation}\n\\lambda\\frac{d}{d\\lambda}\\alpha(\\lambda) = \\beta (\\alpha(\\lambda)) =\n\\frac{2}{3\\pi}\\alpha^{2}(\\lambda) + {\\cal O}(\\alpha^{3}(\\lambda))~~~,\n\\label{BETAF}\n\\end{equation}\nwhere $\\lambda$ is a momentum scale factor.\nIn order to calculate the thermal part $\\cL_{eff}^{\\beta,\\mu}$ of the effective action, we\nhave to be\ncareful with the convergence and the analytical structure. We therefore\nlet the sum over the quantum number $n$ only go to a finite $N$ and take\nthe limit $N\\rightarrow\\infty$ at the end. This gives\n\\begin{equation}\n\\label{SFbmu}\n{\\rm Tr\\,} S_F^{\\beta,\\mu}= \\lim_{N\\rightarrow\\infty} i\\,\\frac{mB}{\\pi^{3\/2}}\n\\,{\\rm Im}\\int_{-\\infty}^{\\infty}\\frac{d\\omega}{2\\pi}\n\\int_0^\\infty\\frac{ds}{s^{1\/2}}e^{i\\frac{3\\pi}{4}}\ne^{-is(\\omega^2-m^2-i\\epsilon)} \\left[\\frac{1+e^{i2sB}}{1-e^{i2sB}}-\n\\frac{2 e^{i2NsB}}{1-e^{i2sB}}\\right]\\ .\n\\end{equation}\nThe poles in the last factor cancel for finite\n$N$, and we cannot let $N\\rightarrow\\infty$ in a naive\nway before deforming the $s$ integration contour to the\nimaginary axis. After integrating \\Eqref{SFbmu} with\nrespect to $m$, to get $\\cL_{eff}^{\\beta,\\mu}$, and being careful\nwith the convergence when deforming the contours of integration we\narrive at $\\cL_{eff}^{\\beta,\\mu} =\\cL_{0}^{\\beta,\\mu} +\\cL_{1}^{\\beta,\\mu}$, where\n\\begin{equation}\n\\cL_{0}^{\\beta,\\mu} =\n\\inv{3\\pi^2}\\int_{-\\infty}^\\infty d\\omega\n\\theta(\\omega^2-m^2)f_F(\\omega)(\\omega^2-m^2)^{3\/2} \\ ,\n\\end{equation}\nis the ideal gas contribution in absence of the external field $B$,\nand\n\\begin{eqnarray}\n\\label{Lbmueff}\n\\cL_{1}^{\\beta,\\mu} &=&\n\\int_{-\\infty}^\\infty d\\omega\n\\theta(\\omega^2-m^2)f_F(\\omega)\n\\Biggl[\\inv{4\\pi^{5\/2}}\\int_0^\\infty\\frac{ds}{s^{5\/2}}e^{-s(\\omega^2-m^2)}\n(seB\\coth (seB) -1)\\nonumber\\\\\n &&-\n\\inv{2\\pi^3}\\sum_{n=1}^\\infty \\left(\\frac{eB}{n}\\right)^{3\/2}\n\\sin\\left(\\frac{\\pi}{4}-\\frac{\\pi n}{eB}(\\omega^2-m^2)\\right) \\Biggr]\\ .\n\\end{eqnarray}\nThis is the main result of our paper. The term with the\nsum over $n$ was neglected in Ref.\\cite{ChodosEO90} and we\nshow in Section~\\ref{physical} that it is essential to keep\nthis term in order to get the\ncorrect physical result.\n\nThe finite temperature part of the effective action is directly related to the\nfree energy of a gas of relativistic fermions in a constant $B$-field.\nIf $Z(B,T,\\mu)$ is the corresponding partition function, without the\ncontribution from the thermal photon gas, we can also write\n\\begin{eqnarray}\n\\label{F}\n\\cL_{eff}^{\\beta,\\mu}=\\frac{\\log Z(B,T,\\mu)}{\\beta V}\n&=&\\frac{eB}{\\beta(2\\pi)^2}\\sum_{\\lambda=1}^2\\sum_{n=0}^\\infty\n\\int_{-\\infty}^\\infty dk\\left\\{\\log(1+e^{-\\beta(E_{\\lambda,n}-\\mu)})\\right.\n\\nonumber \\\\\n&+&\n\\left. \\log(1+e^{-\\beta(E_{\\lambda,n}+\\mu)})\\right\\}\\, ,\n\\end{eqnarray}\nwhere $E_{\\lambda,n}=\\sqrt{m^2+k^2+2eB(n+\\lambda-1)}$,\nand $\\lambda$ labels the spin of the fermions.\nFor $\\Abs{\\mu} \\leq m$, \\Eqref{F} can be rewritten in the\nphysically less transparent way\n\\begin{equation}\n\\label{Ditt}\n\\cL_{eff}^{\\beta,\\mu}=\\frac{1}{(2\\pi)^2}\n\\sum_{l=1}^\\infty (-1)^l\\int_0^\\infty\n\\frac{ds}{s^3}\\exp(-\\frac{\\beta^2l^2}{4s}-m^2s)\neBs\\coth(seB)\\frac{\\cosh(\\beta l\\mu)}{2}\\ ,\n\\end{equation}\nwhich for $\\mu = 0 $ also is an equation given in \\cite{Dittrich79}.\n However, it\nis not obvious, when written in this form, to see how\n to extract the physical\ncontents, and how to generalize $\\cL_{eff}^{\\beta,\\mu}$\nto $\\Abs{\\mu}\\geq m$, since then it appears to be divergent.\nIn particular we notice that the high $T$ behaviour given in\n\\cite{Dittrich79} is not correct. After a Poisson resummation in $l$,\nrewriting the sum over $l$ as a contour integral and and carefully\ndeforming the contours it is, however, possible to show that\n\\Eqref{Ditt} is equal to \\Eqref{Lbmueff} which, of course, is valid for all\n$T$ and $\\mu$.\n\\begin{center}\n\\section{The Physical Content of ${\\cal L}_{eff}$}\n\\setcounter{equation}{0}\n\\label{physical}\n\\end{center}\nThere are several dimensionful parameters related to ${\\cal L}_{eff}$, i.e.\n$T,\\ \\mu,\\ m$, and $B$, that can be large or small\ncompared to each other. We shall only focus on a few\nof these limits which we think are particularly\ninteresting.\n\nThe second term in \\Eqref{Lbmueff} has an oscillatory\nbehaviour that we can explore in the limit where\n$\\{T=0,eB\\ll \\mu^2-m^2\\ll m^2\\}$. This is a non-relativistic\nlimit (in the sense that the kinetic energy is much smaller\nthan $m$) with a degenerate Fermi sea and a weak external field.\nThe oscillating part ${\\cal L}_{osc}$ of $\\cL_{1}^{\\beta,\\mu}$ can be integrated in this\napproximation for which we obtain\n\\begin{equation}\n\\label{Losc}\n{\\cal L}_{osc}=-\\frac{(eB)^{5\/2}}{4\\pi^4 m}\\sum_{n=1}^\\infty\n\\inv{n^{5\/2}}\\cos\\left(\\frac{\\pi}{4}-n\\pi\\frac{\\mu^2-m^2}{eB}\\right)\\ .\n\\end{equation}\nThe oscillation frequency of this periodic function\nagrees with the one derived by Onsager \\cite{Onsager52}\nfor the de\\ Haas -- van\\ Alphen effect. Equation (\\ref{Lbmueff}) describes\nthe full relativistic generalization of this effect. The distance\nbetween the magnetic field of two\nadjacent minima of the magnetization is determined\nby\n\\begin{equation}\n\\Abs{\\inv{eB_i}-\\inv{eB_{i+1}}}= \\frac{2\\pi}{A}\\ ,\n\\end{equation}\nwhere $A$ is the area of an extremal cross section of the\nFermi sea.\n\nIn the limit of strong field, $\\{eB\\gg T^2,m^2,\\mu^2-m^2\\}$,\nwe can see from \\Eqref{F} that only the lowest Landau level\ncontribute and $\\cL_{eff}^{\\beta,\\mu}$ goes like a linear function of $eB$.\nWe shall now reproduce this result from \\Eqref{Lbmueff}\nand it turns out to be rather non--trivial. The leading $B$\ndependence in the first term in \\Eqref{Lbmueff} is obtained by\nscaling out $eB$ and taking $eB\\rightarrow \\infty $ in the remainder.\nThe total contribution is, apart from the thermal integration,\n\n\\begin{equation}\n\\frac{(eB)^{3\/2}}{4\\pi^{5\/2}}\\left[\n\\int_0^\\infty \\frac{dx}{x^{5\/2}}(x\\coth x -1)-\\sqrt{\\frac{2}{\\pi}}\n\\sum_{n=1}^\\infty\\inv{n^{3\/2}}\\right]\\ ,\n\\end{equation}\nbut this is actually identically zero. The next subleading\nterm can be shown to be\n\\begin{equation}\n\\cL_{1}^{\\beta,\\mu} = \\frac{eB}{2\\pi^2}\\int_{-\\infty}^\\infty\nd\\omega\\theta(\\omega^2-m^2)f_F(\\omega) \\sqrt{\\omega^2-m^2}~~~,\n\\end{equation}\nwhich is exactly the leading term from \\Eqref{F}. This\ncalculation shows that the oscillatory term in \\Eqref{Lbmueff}\nis absolutely necessary to cancel the $B^{3\/2}$ term and to give\nthe correct linear term.\n\nHaving shown that the thermal corrections in \\Eqref{Lbmueff}\nare correct and comprehensible in physical terms, we now\naddress the question of when they are important, i.e. when\nthey dominate over the vacuum correction. For\n$eB\\gg m^2,T^2,\\mu^2$, the vacuum correction goes like\n\\begin{equation}\n\\label{LARGEB}\n{\\cal L}_{1} \\approx \\frac{(eB)^2}{24\\pi^2}\\log\\left(\\frac{eB}{m^2}\\right)\\ ,\n\\end{equation}\nand it dominates over $\\cL_{1}^{\\beta,\\mu}$. However, when\n$\\{T=0,eB\\ll \\mu^2-m^2\\ll m^2\\}$, we have\n\\begin{equation}\n\\label{SMALLB}\n{\\cal L}_{1} \\approx \\frac{(eB)^2}{360\\pi^2}\n\\left(\\frac{eB}{m^2}\\right)^2\\ ,\n\\end{equation}\nand\n\\begin{equation}\n\\cL_{1}^{\\beta,\\mu} \\approx \\frac{(eB)^2}{12\\pi^2}\\log \\left(\\frac{\\Abs{\\mu}}{m}\n +\\sqrt{\\frac{\\mu^2}{m^2}-1}\\, \\right)\n \\approx \\frac{(eB)^2}{12\\pi^2}\n\\left(\\frac{3\\pi^2 n}{m^3}\\right)^{1\/3}\\ ,\n\\end{equation}\nwhere $en$ is the charge density, and where we have neglected ${\\cal L}_{osc}$.\nThe density correction $\\cL_{1}^{\\beta,\\mu}$\ntherefore dominates over ${\\cal L}_{1}$ when\n\\begin{equation}\n\\left(\\frac{ n}{m^3}\\right)^{1\/3} \\gg\n\\inv{30(3\\pi^2)^{1\/3}}\\left(\\frac{eB}{m^2}\\right)^2\\ .\n\\end{equation}\nWhen $T^2\\gg m^2\\gg eB$, we have that\n\\begin{equation}\n\\cL_{1}^{\\beta,\\mu} \\approx \\frac{(eB)^2}{24\\pi^2}\\log\\left( \\frac{T^2}{m^2}\\right)\\ ,\n\\end{equation}\nand we do not agree with the high temperature and weak field\nlimit in \\cite{Dittrich79}.\n(We notice the similarity of our result with $\\cL^0_{eff}$ for\n$eB\\gg m^2$.) In this case the thermal contribution $\\cL_{1}^{\\beta,\\mu}$\ndominates over ${\\cal L}_{1}$ as given by Eq.(\\ref{SMALLB}) when\n\\begin{equation}\n\\frac{T}{m}\\gg \\exp\\left[\\inv{30}\\left(\\frac{eB}{m^2}\\right)^2\\right]\n\\approx 1\\ .\n\\end{equation}\n\nAnother useful way of extracting the physical information from\n${\\cal L}_{eff}$ is to define an effective coupling constant as\n\\cite{Schwinger51,ChodosOS88}\n\\begin{equation}\n\\label{effalp}\n\\frac{1}{\\alpha(T,\\mu,B)}=\\inv{\\alpha}\n-\\frac{1}{\\alpha B}\\frac{\\partial {\\cal L}_{eff}}{\\partial B}\\ ,\n\\end{equation}\nin analogy with the definition of the renormalized\ncoupling in the vacuum sector\nin connection with \\Eqref{BETAF}. Special care has\nto be taken when evaluating the\nderivative\nof the oscillating term in Eq.(\\ref{Lbmueff}).\nIn the limit when $eB=0$, we obtain the effective coupling\n$\\alpha(T,\\mu) = \\alpha(T,\\mu,B=0)$ given by\n\\begin{equation}\n\\label{AlphaTmu}\n\\frac{1}{\\alpha(T,\\mu)}= \\frac{1}{\\alpha } - \\frac{2}{3\\pi}\\int\n_{-\\infty}^{\\infty}d\\omega\n\\frac{\\theta(\\omega^{2}-m^{2})}{\n\\sqrt{\\omega^{2}-m^{2}}}f_{F}(\\omega)~~~.\n\\end{equation}\nWhen $T=0$, we therefore get an effective coupling\n$\\alpha(\\mu) = \\alpha(T=0,\\mu)$ such that\n\\begin{equation}\n\\label{Alphamu}\n\\frac{1}{\\alpha(\\mu)}= \\frac{1}{\\alpha }\n-\\frac{2}{3\\pi}\\log \\left( \\frac{|\\mu|}{m} + \\sqrt{\\frac{\\mu ^{2}}{m^{2}} -\n1}~\\right)\\ .\n\\end{equation}\nIn the limit $\\mu=0$, we find the following asymptotic behaviour\nof the corresponding effective coupling $\\alpha(T) = \\alpha(T,\\mu = 0)$:\n\\begin{equation}\n\\label{AlphaT}\n\\frac{1}{\\alpha(T)} = \\frac{1}{\\alpha } -\\frac{4}{3\\pi}\n\\int\n_{\\beta m}^{\\infty}\n\\frac{dx}{\n\\sqrt{x^{2}-(\\beta m)^{2}}}\\frac{1}{e^{x} + 1}\n\\approx \\frac{1}{\\alpha }\n-\\frac{2}{3\\pi}\\log \\left(\\frac{T}{m}\\right)~~~,\n\\end{equation}\nfor $T \\gg m $.\nIt is now clear that (only) for $\\mu \\gg m$ and $T \\gg m$ the\neffective couplings\n$\\alpha (\\mu)$ and $\\alpha (T)$ are solutions to the renormalization group\nequation (\\ref{BETAF}) when $\\lambda $ is identified with $\\mu$ and $T$\nrespectively\n(see in this context e.g. Refs.\\cite{Morley79,Rojas92}).\nWe also note that Eq.(\\ref{LARGEB}) leads to an effective\ncoupling $\\alpha(B) = \\alpha(T=0,\\mu = 0,B)$\nwith an asymptotic behaviour\n\\begin{equation}\n\\frac{1}{\\alpha(B)} \\approx \\frac{1}{\\alpha }\n-\\frac{1}{3\\pi}\\log \\left(\\frac{eB}{m^{2}}\\right)~~~.\n\\end{equation}\nThe effective coupling defined in \\Eqref{effalp} can also be extracted\nfrom the residue of the thermal Debye-screened photon propagator\n(see Ref.\\cite{Morley79}).\n\nWe have only considered a few particular limits\nin this paper and there are many more to explore in\ndifferent physical situations. All information needed to\ndo that is contained\nin \\Eqref{Lbmueff}.\n\n\n\\begin{center}\\section{Conclusions}\n\\label{concl}\n\\setcounter{equation}{0}\n\\end{center}\nWe have established the correct form of the one-loop\nQED effective action at finite temperature and density\nto all orders in a constant external magnetic field, and the\nresult differs from earlier attempts. From the form of\n$\\cL_{eff}^{\\beta,\\mu}$ presented in \\Eqref{Lbmueff} we have checked several\nlimits that can be understood from a physical point of view.\nA great advantage with our expression for $\\cL_{eff}^{\\beta,\\mu}$ is that\nthe thermal distribution function $f_F(\\omega)$ occurs explicitly.\nThis means that it is easy to study other thermal situations\nby simply replacing $f_F(\\omega)$ with some other distribution.\n\nThe importance of the thermal correction depends on the value\nof $B$, $T$ and $\\mu$. In many physically interesting cases\nthey are all large compared to $m$ and often of the same\norder of magnitude, which makes it difficult to obtain\nanalytical approximations. It is, however, straightforward\nto use \\Eqref{Lbmueff} for numerical calculations.\n\nEven though the correction to the free energy is small\ncompared to the value without the external field there\nare other quantities that are effected by the presence of\nthe heatbath. For instance, the magnetization of a degenerate\nFermi sea as was briefly discussed\nin Section~\\ref{physical}. One could also expect that\nQED radiative corrections at\nfinite\ntemperature and density and with the strong magnetic fields\ndiscussed in the Introduction could\neffect the\nelectroweak transition rates, relevant for the Big-Bang primordial\nnucleosynthesis. We will\nreturn to this issue elsewhere.\n\nWe have, furthermore, calculated an effective coupling constant\ndefined from the part of $\\cL_{eff}^{\\beta,\\mu}$ which is quadratic in $eB$.\nIt satisfies asymptotically a naive zero temperature\nrenormalization group\nequation where the renormalization scale is replaced by\n$T$, $\\mu$ or $\\sqrt{eB}$.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:intro}\n\nFeynman diagrams and the associated integrals are at the computational core of quantum field theory and their evaluation attracts considerable attention see e..g \\cite{SmirnovBooks}. Two important and well-known methods for their computation are Integration By Parts (IPB) \\cite{ChetyrkinTkachov1981} and Differential Equations (DE) \\cite{DE}. The more recent method called Symmetries of Feynman Integrals (SFI) \\cite{SFI} is closely related to them. It considers a Feynman integral $I$ associated with a diagram of fixed topology yet most general parameters, namely masses and kinematical invariants denoted collectively by $X$. Given a diagram SFI defines a set of partial differential equations in $X$, with the idea that the integral can be determined as a solution of this equation set (at least partially), rather than through direct integration. \n\nThe method was applied to the two-loop vacuum diagram (or ``diameter diagram\") \\cite{SFI, locus}, the 1-loop propagator diagram (``bubble\") \\cite{bubble} and most recently to the vacuum seagull diagram, a certain 3 loop vacuum diagram, where novel evaluations were obtained for certain 3 mass scales. \n\nThe SFI method has aspects of geometrical symmetry as the equation set defines a continuous group $G$ which acts on parameter space $X$ and had a rather direct geometrical interpretation. More specifically, $G$ consists of all possible linear redefinitions of the loop currents and external momenta which preserve the propagator hyperspace in the space of quadratics in currents, see \\cite{locus,VacSeagull} for definitions.\n\nGenerically, on orbits of $G$ in $X$ SFI reduces the computation of the Feynman integral to a line integral over simpler diagrams (up to some boundary or initial condition such as a base point). However, at some locus in $X$ the equations set is singular and produces algebraic rather than differential equations \\cite{locus}. On some components of this locus the algebraic equation allows to reduce $I$ into a linear combination of simpler diagrams (instead of a line integral).\n\nThe purpose of this note is to present algebraic methods for determining the singular locus and when relevant, the reduction to simpler diagrams. We start in section \\ref{sec:prelim} by recalling from linear algebra the role of maximal minors and their decomposition as well as the LU decomposition. In section \\ref{sec:reduction} we proceed to apply a generalization of these methods to the SFI equation set and to obtain our results. In section \\ref{sec:demon} the algebraic methods are demonstrated on the diameter diagram. We conclude with a discussion in section \\ref{sec:disc}.\n\n\n\\section{Linear algebra preliminaries}\n\\label{sec:prelim}\n\nIn this section we review maximal minors and the LU decomposition in linear algebra. This will serve as a basis for the next section.\n\n\\subsection*{Warm up}\n\nAs a first warm up consider an $n+1$ by $n$ matrix of rank $n$ (the maximal possible rank) \\begin{eqnarray}\nT^a_{~i}, && ~~ a=1 \\dots n+1,\\, i=1 \\dots n \\nonumber \\\\\n\\mbox{rk}(T) &=& n ~.\n\\end{eqnarray}\n The $n+1$ rows are linearly dependent, yet any $n$ of them are independent. Therefore there exists a single vector $l_a$ which is in the left hand side null subspace (kernel) of $T$, namely \\begin{equation}\n l_a\\, T^a_{~i} = 0\n \\label{l_null1}\n \\end{equation}\nMoreover, the left null vector $l_a$ can be expressed in terms of $T$ as follows \\begin{equation}\nl_a = \\eps_{a a_1 \\dots a_n}\\, \\eps^{i_1 \\dots i_n}\\, T^{a_1}_{~i_1} \\dots T^{a_n}_{~i_n}\n\\label{def:null1}\n\\end{equation}\n This expression tells us that component $a$ of $l$ is gotten by erasing row $a$ in $T$, computing the determinant (or minor) of the resulting square matrix, and finally multiplying by an appropriate sign. \n \nTo see why definition (\\ref{def:null1}) satisfies (\\ref{l_null1}) note that since each component of $l_a$ is given by an $n$ by $n$ determinant then for any column $i$ of $T$ $l_a\\, T^a_{~i}$ represents an expansion of an $n+1$ by $n+1$ determinant gotten by joining with $T$ its column $i$. Since this matrix has a repeated column its determinant vanishes thereby proving the point. \n\nTo motivate (\\ref{def:null1}) consider normalizing one of the component of $l_a$ to unity, say $l_{n+1}=1$. Now the remaining components are determined and can be expressed through Cramer's rule as a ratio of minors. Multiplying this vector by the common denominator (the minor gotten by erasing row $n+1$ of $T$) we arrive at (\\ref{def:null1}).\n\nSummarizing this first warm up example, it shows how maximal minors can be used to represent the null subspace of a matrix. \n\n\\vspace{.5cm} \\noindent {\\bf Example 2}. As a second warm up example consider an $n+1$ by $n+1$ matrix $T^a_{~i}$ of (sub-maximal) rank $n$, namely \\begin{eqnarray}\nT^a_{~i}, && ~~ a=1 \\dots n+1,\\, i=1 \\dots n+1 \\nonumber \\\\\n\\mbox{rk}(T) &=& n ~.\n\\end{eqnarray}\nGiven its rank there should be a single left null vector $l_a$ and a single right null vector $r^i$ ($l_a,\\, r^i$ are unique up to rescaling), namely \\begin{equation}\n l_a\\, T^a_{~i} =0 \\qquad T^a_{~i}\\, r^i =0 \n \\end{equation}\n\nIn order to find expressions for $l_a$ and $r^i$ one proceeds as follows. One defines the adjugate of $T$, $M$, such that each of its components $M^i_{~a}$ is the minor gotten by erasing row $a$ and column $i$ of $T$ followed by multiplication by the sign $(-1)^{i+a}$. Equivalently \\begin{equation}\nM^i_{~a} = \\eps^{i i_1 \\dots i_n}\\, \\eps_{a a_1 \\dots a_n}\\, T^{a_1}_{~i_1} \\dots T^{a_n}_{~i_n} ~~.\n\\label{def:null2}\n\\end{equation}\n\nFor $T$ of any rank we have \\begin{eqnarray}\nT^a_{~i} \\, M^i_{~b} &=& \\det(T) \\delta^a_{~b} \\label{TM} \\\\\nM^i_{~a} \\, T^a_{~j} \\, &=& \\det(T) \\delta^i_{~j} \\label{MT}\n\\end{eqnarray}\nWhen $\\det(T) \\neq 0$ the adjugate matrix leads to the inverse matrix through $T^{-1}=M \/ \\det(T)$. However, we assumed that $\\mbox{rk}(T)=n$ and hence $\\det(T)=0$. (\\ref{TM}) implies that each column $b$ of $M$ is in the right null subspace. Since all right null vectors are proportional to $r^i$ we must have $M_i^{~a} = r^i\\, \\tilde{l}_a$ where each component of $\\tilde{l}_a$ represents a possible rescaling factor and together they form the components of some row vector $\\tilde{l}$. Similarly, from (\\ref{MT}) one deduces that $M^i_{~a}$ must be of the form $M^i_{~a} = \\tilde{r}^i\\, l_a$ for some column vector $\\tilde{r}^i$. Together (\\ref{TM}, \\ref{MT}) imply that \\begin{equation}\nM^i_{~a} =c\\, r^i\\, l_a\n\\label{Factorization1}\n\\end{equation}\nwhere $c$ is some non-zero constant. This tells us that the adjugate matrix, which is a matrix composed out of maximal minors, necessarily factorizes into a right null vector times a left null vector, and this is the purpose of this example. \n\n\n\\subsection{Factorization of maximal minors}\n\nAfter the warm up we proceed to the general case of a matrix of general size, $m$ by $n$, and a general rank $r$, namely \n\\begin{eqnarray}\nT^a_{~i}, && ~~ a=1 \\dots m,\\, i=1 \\dots n \\nonumber \\\\\n\\mbox{rk}(T) &=& r ~.\n\\label{def:T}\n\\end{eqnarray}\n \nMotivated by the previous examples we define \\begin{equation}\nM^I_{~A} := \\eps^{I i_1 \\dots i_r}\\, \\eps_{A a_1 \\dots a_r}\\, T^{a_1}_{~i_1} \\dots T^{a_r}_{~i_r} ~~ \\\\\n\\label{def:M}\n\\end{equation}\nwhere $I=(i_{r+1} \\dots i_n)$ and $A=(a_{r+1} \\dots a_m)$ are multi-indices. The components of $M$ are the maximal minors of $T$. The following theorem describes the special properties of $M^I_{~A}$. It is the main point of the current section and will be useful in the next.\n\n\\vspace{.5cm} \\noindent {\\bf Maximal minor factorization theorem}. Given any matrix $T^a_{~i}$ as in (\\ref{def:T}) its associated maximal minors $M^I_{~A}$ defined in (\\ref{def:M}) satisfy \\begin{enumerate}\n\n\\item[(i)] $M$ is null, namely \\begin{eqnarray}\nT^a_{~i} \\, M^{i I'}_{~B} &=& 0 \\label{M_r_null} \\\\\nM^I_{~a A'} \\, T^a_{~j} \\, &=& 0 \\label{M_l_null}\n\\end{eqnarray}\nwhere $I'$ is a multi index with $n-r-1$ indices and $A'$ has $m-r-1$ indices. Since $M^I_{~A}$ is anti-symmetric in both $I$ and $A$ the contracted index can be interchanged with any other.\n\n\\item[(ii)] $M$ factorizes as \\begin{equation}\nM^I_{~A} =c\\, r^I\\, l_A\n\\label{Factorization}\n\\end{equation}\nwhere $r^I$ and $l_A$ are antisymmetric and unique up to an overall multiplicative scalar.\n\n\\end{enumerate}\n\nThis theorem generalizes example 1 which demonstrated the minors to be null, and example 2 which demonstrated factorization. For completeness we include a proof.\n\n\\vspace{.5cm} \\noindent {\\bf Proof}. $M$ is seen to be null by an argument analogous to that in example 1, namely the product $M \\cdot T$ represents the expansion of the wedge product of $r+1$ columns of $T$, which must vanish given that $\\mbox{rk}(T)=r$ (and similarly for $T \\cdot M$).\n\nAn alternative proof for the null property would be to note that for minors that are not necessarily maximal multiplication by $T$ gives larger minors through the determinant expansion formula as follows \\begin{eqnarray}\nT^a_{~i} \\, M^{i I'}_{~b B'} &=& \\delta^a_{[b}\\, M^{I'}_{~B']} \\nonumber \\\\\nM^{i I'}_{~a A'} \\, T^a_{~j} \\, &=& \\delta^{[i}_{~j}\\, M^{I']}_{~A'} ~.\n\\end{eqnarray}\nNow, once we assume $M$ to be maximal the larger minors all vanish and we recover (\\ref{M_r_null},\\ref{M_l_null}).\n\nIn order to see why $M$ factorizes, recall that $T$ defines a right null subspace of dimension $m-r$. It can be described as the span of a set of independent vectors $r_1^{i_1}, \\dots, r_{m-r}^{i_{m-r}}$. However, this description is considerably redundant as it can be replaced by any other spanning set. An alternative way to identify a subspace is through the wedge product \\begin{equation}\nr^I \\equiv r^{i_1 \\dots i_{m-r}} := \\bigwedge_{j=1}^{m-r} r_j^{i_j} ~.\n\\end{equation}\n The wedge product defines an antisymmetric tensor (or multi-vector) $r^{i_1 \\dots i_{m-r}}$ which can be called the null right tensor. It satisfies \\begin{equation}\nT^a_{~i}\\, r^{i_1 \\dots i_{m-r-1} i} = 0\n\\end{equation}\nand it is the only such tensor which satisfies this equation up to an overall multiplicative factor. In this sense it characterizes the subspace in a unique and hence non-redundant way. \n\nThe uniqueness of of the right null tensor together with right null property of $M^I_{~B}$ for any value of the multi-index $B$, as described in (\\ref{M_r_null}), implies that $M^I_{~B} = r^I\\, \\tilde{l}_B$ for some tensor $\\tilde{l}_B$. Repeating the argument for the left null space, in analogy with example 2, implies the argued factorization, namely (\\ref{Factorization}), where $l_A$ is the left null tensor of $T$, and $c$ is a free constant whose value depends on the chosen normalizations for $r^I$ and $l_A$.\n\nThis completes the proof of the maximal minor factorization theorem. We now proceed to a discussion of several related comments.\n\n\\vspace{.5cm} \\noindent {\\bf Matrix of polynomials}. Below we shall be interested in a matrix $T=T(x)$ whose entries are polynomials in some variables denoted collectively by $x$. In this case each component of $M^I_{~A}$ is a polynomial in $x$ and one can be more specific about the choice of the scalar $c$: it can be chosen to be the greatest common divisor of all the polynomials $M^I_{~A}(x)$, namely \\begin{equation}\nc(x) = \\gcd \\( M^I_{~A} (x) \\) ~.\n\\label{def:cx}\n\\end{equation}\n $c(x)$ is unique up to multiplication by a number (a field element).\n\n\\vspace{.5cm} \\noindent {\\bf Gauss elimination}. The computation of each component of the maximal minor tensor $M^I_{~A}$ requires to evaluate a determinant. It is well known that evaluating determinants through their definition is computationally costly and a more efficient method is provided by the Gauss elimination method where through elementary operations on rows (or columns) the matrix $T^a_{~i}$ can be brought into an upper triangular form. This implies that $L^{-1}\\, T = U$ where $U$ is the upper triangular form, and $L^{-1}$ is a lower triangular matrix which records all the row operation carried on $T$. Since the row operations are invertible one also has \\begin{equation}\nT= L\\, U ~,\n\\label{LU_decomp}\n\\end{equation}\n which is known as an ``LU decomposition of $T$''. \n\nThe LU decomposition above is useful since $T$ and $U$ share the same right null subspace, yet the triangular form of $U$ makes it immediate to determine it.\n\nSimilarly Gauss elimination can be applied to columns to obtain (a possibly different) LU decomposition. Column operations produce a matrix $L$ of the same size as $T$ together with a square $U$, while row operations produce the opposite sizes, and hence if T is non-square the two decompositions are necessarily different.\n\nAltogether, minors and null subspaces can be computed either directly from the definitions (\\ref{def:M},\\ref{Factorization}) or by using an LU decomposition (which is essentially Gauss elimination). The choice of method depends on computational convenience. When applying an LU decomposition to a matrix of polynomials the $L,U$ factor generically would become rational (a ratio of polynomials0, yet in such a way that the minors remain polynomial.\n\n\\vspace{.5cm} \\noindent {\\bf Dualization}. The definition of the tensor of minors $M^I_{~A}$ in (\\ref{def:M}) can be thought to involve two steps -- first a wedge product of $T$ with several copies of itself, followed by a dualization on both the $a$ and $i$ indices. Both steps are performed by the $\\eps$ tensors -- first assuring projection onto the antisymmetric sector and then performing dualization. Here we note that the wedge product is the more essential step, while dualization is convenient in the common case when the $r \\equiv \\mbox{rk} (T)$ is close to either $m$ or $n$.\n\n\\section{Reduction of a Feynman Integral}\n\\label{sec:reduction}\n\nAfter reviewing the factorization of maximal minors, we proceed to apply it to the Symmetries of Feynman Integrals (SFI) method \\cite{SFI}. \n\n\\vspace{.5cm} \\noindent {\\bf Set-up}. SFI considers a Feynman diagram as a function of its most general possible parameters, namely the masses and the kinematical invariants, and a general spacetime dimension $d$. The parameter space is denoted by $X$ and each diagram is associated with set of partial differential equations in $X$. \n\nSchematically, the SFI equations are of the form \\begin{equation}\nc^a\\, I + (T^a)^j_{~i}\\, x_j\\, {\\partial}} \\def\\dag{\\dagger^i\\, I = J^a\n\\label{SFI_schematic}\n\\end{equation}\nwhere $a=1, \\dots , \\dim(G)$ labels each equation in the set, $c^a=c^a(d)$ are constants (namely, are independent of $X$), the matrices $(T^a)^j_{~i}$ define a representation of a group $G$ on $X$, the range of $i,j$ is given by $i,j = 1, \\dots, \\dim(X)$, ${\\partial}} \\def\\dag{\\dagger^i={\\partial}} \\def\\dag{\\dagger\/{\\partial}} \\def\\dag{\\dagger x_i$ and finally $J^a$ are terms composed of simpler diagrams. The group $G$ is called the SFI group and it is defined by the topology of the diagram in a natural way.\n\nHere we focus on the differential term $(T^a)^j_{~i}\\, x_j\\, {\\partial}} \\def\\dag{\\dagger_i\\, I$ which is fully defined by the representation of $G$ on $X$. We define the matrix $Tx$ by \\begin{equation}\n \\( Tx \\) ^a_{~i} := (T^a)^j_{~i}\\, x_j\n \\label{def:Tx}\n \\end{equation}\nThis matrix will be the object of our study and it will correspond to the general matrix $T^a_{~i}$ in the previous section. The size of the matrix $Tx$ is given by $m=\\dim(G)$ by $n=\\dim(X)$.\n\n\\vspace{.5cm} \\noindent {\\bf Factorization}. At any specific point in $x \\in X$ we may determine the rank of $Tx$ which equals the dimension of the tangent space to the G-orbit at $x$ and hence to the dimension of this G-orbit\n\\begin{equation}\n r(x) := {\\rm rk}(Tx) \\equiv \\dim \\( {\\rm G-orbit} (x) \\) ~.\n\\end{equation}\nThe we evaluate the maximal minors \\begin{equation}\nM^I_{~A}(x) := \\eps_{A a_1 \\dots a_r}\\, \\eps^{I i_1 \\dots i_r}\\, Tx^{a_1}_{~i_1} \\dots Tx^{a_r}_{~i_r} ~~ \\\\\n\\label{def:Mx}\n\\end{equation}\n(gotten by substituting $T \\to Tx$ in the general definition (\\ref{def:M}) ).\n\nFactorization of maximal minors (\\ref{Factorization}) in the presence of polynomials (\\ref{def:cx}) implies \\begin{equation}\nM^I_{~A}(x) =S(x)\\, Orb^I(x)\\, Stb_A(x)\n\\label{MxFactor}\n\\end{equation}\nwhere all the terms are defined up to a multiplicative $x$-independent constant. The notation reflects the interpretation of the various terms in the context of $Tx$ as we proceed to explain.\n\n$S(x)$ is the factor common to all minors, denoted by $c(x)$ in (\\ref{Factorization},\\ref{def:cx}). At zeroes of $S(x)$ all the minors vanish reflecting a drop in the rank of $Tx$. We refer to this zero locus as the singularity locus, and accordingly the notation $S(x)$ was chosen to stand for singular.\n\n$Orb^I(x)$ is the right null tensor, denoted by $r^I$ in (\\ref{Factorization}). This null subspace is composed of cotangent vectors at $x$ which annihilate all the rows in $Tx$ and hence annihilate (are perpendicular to) the orbit of $G$ through $x$. The notation $Orb$ refers to this relation with the orbit. By its definition, the orbit tensor includes the differentials of all group invariants, namely $Orb \\wedge dInv=0$ where $Inv$ is any invariant of $G$.\n\n$Stb_A(x)$ is the left null tensor, denoted by $l^A$ in (\\ref{Factorization}). Vectors in this subspace represent a combination of equations (rows of $Tx$) whose action vanishes at $x$ and is hence known as the stabilizer of the group. Correspondingly, the notation $Stb$ stands for stabilizer. Note that a multiplication of the SFI equation set from the left by a stabilizer vector generates by definition an equation with no derivatives, namely an algebraic rather than differential equation. For some $x$ the inner product of a stabilizing vector $Stb_a$ with $c^a$, the vector of constants in (\\ref{SFI_schematic}), is non-zero thereby generating a simple equation for $I$ which yields a reduction of $I$ to a linear combination of simpler integrals \\cite{locus}.\n \nThe preceding interpretation of the terms in (\\ref {MxFactor}) is summarized by the following list \\\\\n\\begin{tabular}{lcl}\n$c(x)$, the common factor for minors & $\\to$ & $S(x)$ whose zeroes are the singularity locus \\\\\nright null space & \t$\\to$ & co-orbit form subspace\t\\\\\nleft null space & \t$\\to$ & Stabilizer subspace. \t\n\\end{tabular} \n\nEquation (\\ref{MxFactor}) is the main result of this paper, providing a systematic way to compute the singularity locus, the orbit and its invariants and\/or the group stabilizer through minors of $Tx$. Some stabilizers provide a reduction of the diagram under study into a linear combination of simpler ones (as discussed above). Computationally, an LU decomposition (\\ref{LU_decomp}) might be performed to facilitate the evaluation of minors (see also a demonstration in the next section).\n\nSince all terms in (\\ref{MxFactor}) are polynomials in the parameters $x$ we obtain a useful relation between the degrees with respect to $x$ \\begin{equation}\n r = {\\rm deg}_x\\, S + {\\rm deg}_x\\, Orb + {\\rm deg}_x\\, Stb\n\\label{deg_balance}\n \\end{equation}\nwhere $r= {\\rm deg}_x\\, M^I_{~A}(x) ={\\rm rk}(Tx) \\equiv \\dim \\( {\\rm G-orbit} (x) \\)$. \n\n\\vspace{.5cm} \\noindent {\\bf From maximal rank to lower ones}.\nLet us proceed to examine in more detail various loci in $X$ which correspond to a given rank of $Tx$, namely G-orbits of various dimensions.\n\n\\vspace{.5cm} \\noindent {Maximal rank}. Denote the highest possible rank by $Rk$ namely \\begin{equation}\nRk = \\max_x {\\rm rk} (Tx) ~.\n\\label{def:Rk}\n\\end{equation} \n$Rk$ is the generic rank, namely it is achieved in an open set in $X$.\n\nClearly $Rk$ is bounded by the size of $Tx$ namely $Rk \\le \\min \\{\\dim (G),\\dim(X)\\}$. If the inequality is exhausted such that $Rk = \\dim (G) \\le \\dim(X)$ then the stabilizer is trivial, namely $Stb =1$ in (\\ref{MxFactor}), and $Orb$ is non-trivial telling us about $G$ invariants. Similarly if $Rk=\\dim (X) \\le \\dim(G)$ then $Orb$ is trivial and hence the G-orbit is co-dimension zero, implying maximal effectiveness for SFI, while $Stb$ is non-trivial providing us with algebraic equations (probably an algebraic constraint on the sources).\n\n\\vspace{.5cm} \\noindent {Next to maximal rank}. Factorization at maximal rank defines the singularity function $S(x)$. One proceeds to factorize $S(x) = S_1(x)\\, S_2(x) \\dots$. Each factor of $S(x)$ defines a component of the singular locus over which the rank is reduced, generically to $Rk-1$. On any component $c=1,2,\\dots$ where $S_c(x)=0$ factorization of maximal minors (\\ref{MxFactor}) may be re-applied as follows \\begin{equation}\n M_{c~~\\tilde{A}}^{~\\tilde{I}}(x) = \\hat{S}_c(x)\\, Orb_c^{~\\tilde{I}}(x)\\, Stb_{c~\\tilde{A}}(x) \\mod S_c(x)\n\\label{Factor_at_sing}\n \\end{equation}\n where $\\tilde{I},\\tilde{A}$ are larger multi-indices. This time the minors are smaller, and accordingly the stabilizer and orbit subspaces strictly contain the respective maximal rank subspaces. In addition the factorization holds only at $S_c(x)=0$ or equivalently $\\mod S_c(x)$.\n\n\\vspace{.5cm} \\noindent {Second next to maximal rank}. It could be interesting to proceed to even lower rank at the locus of intersection of several singularity components and to determine the reduction of the Feynman diagram over there.\n\n\\vspace{.5cm} \\noindent {\\bf Original motivation and chronology}. The idea for this paper appeared while studying the 2-loop propagator diagram (sometimes called lizard eye or marshmallow) \\cite{InProgress}, yet notions closely related to maximal minors had appeared earlier in SFI papers and in a prominent way: the wedge product in \\cite{locus} and the determinant in \\cite{VacSeagull}.\n\n\\section{Demonstration}\n\\label{sec:demon}\n\nIn this section we demonstrate the algebraic method of the previous section on the 2-loop vacuum diagram shown in fig. \\ref{fig:diameter} which we call the diameter diagram. This provides a simple and non-trivial demonstration.\n\n\\begin{figure}\n\\centering \\noindent\n\\includegraphics[width=4cm]{diameter_fig}\n\\caption[]{The diameter diagram.}\n\\label{fig:diameter}\n\\end{figure}\n\nThe parameter space consists of the three possible masses-squared $X=\\(x_1,\\, x_2,\\, x_3 \\)$ where $x_i:=(m_i)^2, ~i=1,2,3$. The SFI equation set was found in \\cite{SFI} eq. (6.9) from which we can read \\begin{equation}\nTx = \\[ \\begin{array}{ccc}\n -x_1\t\t& -x_2\t\t& -x_3 \t\\\\\n x_2-x_3\t& x_2\t\t& -x_3 \t\\\\\n -x_1 \t& x_3-x_1\t\t& x_3\t\\\\\n x_1 \t\t& -x_2\t\t& x_1- x_2 \\\\\n \\end{array} \\] \n \\label{def:Tx_diam}\n\\end{equation}\nThere are 4 equations and 3 parameters so $Tx$ is $4$ by $3$.\n\n\\vspace{.5cm} \\noindent {\\bf Maximal rank}. At a generic point the rank of $Tx$ is 3 and so the G-orbit is 3d. Computing the 3-minors according to (\\ref{def:Mx}) we find \\begin{equation}\nM_a = \\lambda} \\def\\hxi{{\\hat \\xi} \\[ \\begin{array}{cccc} 0 & x_1 & x_2 & x_3 \\end{array} \\]\n\\label{diam_factor}\n\\end{equation}\nwhere \\begin{equation}\n \\lambda} \\def\\hxi{{\\hat \\xi} := x_1^2 + x_2^2 + x_3^2 - 2 x_1 x_2 - 2 x_1 x_3 - 2 x_2 x_3\n\\end{equation}\nis the Heron formula \/ K\\\"all\\'en invariant. \n\nComparing the expression for the minors with the general factorization (\\ref{MxFactor}) we identify \\begin{eqnarray}\n S &=& \\lambda} \\def\\hxi{{\\hat \\xi} \\label{s_diam} \\\\\n Stb &=& \\[ \\begin{array}{cccc} 0 & x_1 & x_2 & x_3 \\end{array} \\]\n \\label{stb_diam}\n \\end{eqnarray}\nso the singularities are the zeroes of $\\lambda} \\def\\hxi{{\\hat \\xi}$, thereby reproducing \\cite{SFI} eq. (6.16) and \\cite{locus} eq. (4.9), while the expression for the stabilizer reproduces \\cite{locus} eq. (4.12). Multiplying the equation set by this stabilizer produces a relation between sources which is valid everywhere in $X$. The degree balance (\\ref{deg_balance}) which corresponds to (\\ref{diam_factor}) is \\begin{equation}\n3 = 2 +1 ~.\n\\end{equation}\n\n\\vspace{.5cm} \\noindent {\\bf LU decomposition}. Alternatively, we may compute the minors and the associated factors through the LU decomposition. To obtain the stabilizer group (null left subspace) we may operate on the right, that is on columns. As a starting point it is convenient to reorder the rows of $Tx$ (\\ref{def:Tx_diam}) as \\begin{equation}\n\\widetilde{Tx} = \\[ \\begin{array}{ccc}\n x_1 \t& -x_2\t\t& x_1- x_2 \\\\\n -x_1 \t& x_3-x_1\t\t& x_3\t\\\\\n x_2-x_3\t& x_2\t\t& -x_3 \t\\\\\n -x_1\t\t& -x_2\t\t& -x_3 \t\\\\\n \\end{array} \\] \n \\label{Tx_diam_reorder}\n\\end{equation}\nAfter operating on columns one gets a lower triangular form \\begin{equation}\n \\[ \\begin{array}{ccc}\n x_1 \t& 0\t\t& 0 \\\\\n -x_1 \t& 1\t\t& 0\t\\\\\n x_2-x_3\t& -x_2\/x_1\t\t& 0\t\\\\\n -x_1\t\t& x_2\/x_3\t\t& -\\lambda} \\def\\hxi{{\\hat \\xi} \t\\\\\n \\end{array} \\] \n\\end{equation}\nConsidering the third row we notice that all 3-minors are proportional to $\\lambda} \\def\\hxi{{\\hat \\xi}$ consistent with the singularity factor (\\ref{s_diam}). Moreover, from this form a left null vector can be read $\\[ \\begin{array}{cccc} x_3\/x_1 & x_2\/x_1 & 1 & 0 \\end{array} \\]$. After multiplying by $x_1$ and reordering to account for the ordering of $\\widetilde{Tx}$ we reproduce the stabilizer (\\ref{stb_diam}).\n\nFor completeness we present also the triangulation of $\\widetilde{Tx}$ through operations on rows. In this case we reach the upper triangular form \\begin{equation}\n U = \\[ \\begin{array}{ccc}\n x_1 \t& -x_2\t& x_1-x_2 \\\\\n 0\t \t& -2 s^3\t& 2 s^2\t\\\\\n 0\t\t& 0\t\t& 0\t\\\\\n 0\t\t& 0\t\t& -\\frac{2}{s^3}\\, \\lambda} \\def\\hxi{{\\hat \\xi} \t\\\\ \n \\end{array} \\] ~.\n\\end{equation}\nwhere we have introduced the notation \\begin{equation}\n s^i := -\\frac{{\\partial}} \\def\\dag{\\dagger}{4\\, {\\partial}} \\def\\dag{\\dagger x_i}\\, \\lambda} \\def\\hxi{{\\hat \\xi} \n \\end{equation} \nOnly one minor is non-zero -- the one gotten by erasing row 3, and it is indeed proportional to $\\lambda} \\def\\hxi{{\\hat \\xi}$ (and the denominator cancels). To find the stabilizer we must record the row operations performed through the lower triangular matrix \\begin{equation}\n L^{-1} = \\[ \\begin{array}{ccc}\n x_1 \t& -x_2\t& x_1-x_2 \\\\\n 0\t \t& -2 s^3\t& 2 s^2\t\\\\\n 0\t\t& 0\t\t& 0\t\\\\\n 0\t\t& 0\t\t& -\\frac{2}{s^3}\\, \\lambda} \\def\\hxi{{\\hat \\xi} \t\\\\ \n \\end{array} \\] \n\\end{equation}\nwhich satisfies $L^{-1}\\, \\widetilde{Tx} = U$. Now $\\tilde{s}=\\[ \\begin{array}{cccc} 0 & 0 & 1 & 0 \\end{array} \\]$ is a left null vector for $U$. Multiplication by $L^{-1}$ on its left reproduces the stabilizer (\\ref{stb_diam}), after some rescaling and reordering just as before.\n\n\n\\vspace{.5cm} \\noindent {\\bf Next to maximal rank}. At the singular\/algebraic locus $\\lambda} \\def\\hxi{{\\hat \\xi}=0$ the rank of $Tx$ and hence the dimension of the G-orbit reduces to 2 and one computes the maximal minors tensor \\begin{equation}\nN^i_{~ab} := \\( M_\\lambda} \\def\\hxi{{\\hat \\xi}\\)^i_{~ab}\n\\label{def:N}\n\\end{equation} by omitting column $i$ and rows $a,b$. \n\nAccording to the general procedure (\\ref{Factor_at_sing}) $N^i_{~ab}$ factorizes. We start by noticing that the co-orbit 1-form $Orb_\\lambda} \\def\\hxi{{\\hat \\xi} \\equiv Orb$ can be anticipated. By definition at $\\lambda} \\def\\hxi{{\\hat \\xi}=const$ the 1-form $d\\lambda} \\def\\hxi{{\\hat \\xi} \\equiv -4\\, s^i \\, dx_i$ annihilates all vectors tangent to the locus, and hence we recognize $Orb$ to be\n\\begin{equation}\nOrb^i = \\[ \\begin{array}{c}\ns^1 \\\\\ns^2 \\\\\ns^3 \\\\\n\\end{array} \\]\n\\label{Orb_lam}\n\\end{equation}\n\nNext, $N^{i}_{~ab}$ should be divided by $Orb$ to yield $Stb_\\lambda} \\def\\hxi{{\\hat \\xi}$. Since this factorization holds for $\\lambda} \\def\\hxi{{\\hat \\xi}=0$, but not for all $x$, some more algebra is required. Factoring, say, $M^3_{~ab}$ by $s^3 \\mod \\lambda} \\def\\hxi{{\\hat \\xi}$ can be done by eliminating one the coordinates, for instance $x_3$, in terms of $x_1,\\, x_2$ but this introduces square roots and makes the algebra awkward. Instead one can work $\\mod \\lambda} \\def\\hxi{{\\hat \\xi}$ and write $M^3_{~ab}(x)=s^3\\, Stb_{\\lambda} \\def\\hxi{{\\hat \\xi}~ab}(x) + \\lambda} \\def\\hxi{{\\hat \\xi}\\, k_{ab}(x)$ where $k_{ab}$ is some matrix. Now it is easier to impose $s^3=0$ by substituting $x_3 \\to x_1+x_2$. This allows to determine $k_{ab}$ which happens to be a matrix of constants, and now $ Stb_{\\lambda} \\def\\hxi{{\\hat \\xi}}$ can be determined to be\n\\begin{equation}\n Stb_{\\lambda} \\def\\hxi{{\\hat \\xi}~ab} = \\[ \\begin{array}{cccc}\n 0 \t\t& x_1\t& x_2\t& x_3 \t \\\\\n -x_1\t&0\t \t& s^3\t& -s^2\t\\\\\n -x_2\t& -s^3\t& 0\t\t& s^1 \t\\\\\n -x_3\t& s^2\t& -s^1 \t& 0 \t\\\\ \n \\end{array} \\] ~.\n\\label{Stb_lam}\n\\end{equation}\n\nSince at $\\lambda} \\def\\hxi{{\\hat \\xi}=0$ ${\\rm rk} \\( Tx \\) =2$ the stabilizer subgroup is 2d and hence the tensor $Stb_\\lambda} \\def\\hxi{{\\hat \\xi}$ tensor has rank 2, namely it is a bi-vector. Its first row confirms that it includes $Stb$ (\\ref{stb_diam}), the general stabilizer which is valid for all $x$ and leads to the algebraic constraint for the sources. \n\nAny of the remaining three rows can be used to obtain the algebraic solution, namely the reduction of the diameter to simpler diagrams. Their sum reproduces eq. (4.10) of \\cite{locus}. However, we notice that if we pick one of them, say the 1st, it becomes apparent that $\\mod \\lambda} \\def\\hxi{{\\hat \\xi}$ the expression for the algebraic solution simplifies to \\begin{equation} \n\\left. I \\right|_\\lambda} \\def\\hxi{{\\hat \\xi} = \\frac{1}{d-3} \\[ s^3\\, j'(x_1)\\, j'(x_2) + cyc. \\]\n\\label{I_lam}\n\\end{equation}\nwhere the notation is the same as in \\cite{locus}. The simplification occurs due to a non-manifest cancellation of the denominator $\\mod \\lambda} \\def\\hxi{{\\hat \\xi}$ in eq. (4.11). \n\nSummarizing the factorization at $\\lambda} \\def\\hxi{{\\hat \\xi}=0$ we have \\begin{equation}\nN^i_{~ab} = Orb^i\\, Stb_{\\lambda} \\def\\hxi{{\\hat \\xi}~ab} \\mod \\lambda} \\def\\hxi{{\\hat \\xi}\n\\label{N_factor}\n\\end{equation}\nwhere $Orb$ is given in (\\ref{Orb_lam}) and $\\left. Stb_{\\lambda} \\def\\hxi{{\\hat \\xi}~ab} \\right|_\\lambda} \\def\\hxi{{\\hat \\xi}$ in (\\ref{Stb_lam}). There is no non-trivial scalar common factor so $ \\hat{S}_\\lambda} \\def\\hxi{{\\hat \\xi}(x) =1$. The degree balance reads \\begin{equation}\n2 = 1 + 1 ~.\n\\end{equation}\n\n\n\\section{Summary and Discussion}\n\\label{sec:disc}\n\nIn this paper we analyzed certain algebraic aspects of the SFI equation set, and showed that factorization of maximal minors is useful to determine the singular locus, the G-orbit together with the G-invariants and the stabilizer (\\ref{MxFactor}). On some orbits the latter provides a reduction of the diagram under study into a sum of simpler diagrams. Factorization can be performed over any G-orbit, including those whose dimension is lower than the generic value.\n\nThe method was illustrated through the diameter diagram. \n\nWe end with two comment. First, at the algebraic locus we have an exact solution. It would be interesting to develop a perturbation theory in its vicinity. \n\nSecondly, the procedure described in this paper, depends only on the representation of $G$ on $X$ and it involves minors which are antisymmetric and hence suggest fermionic variables. In these respects it is similar to Group Cohomology, where the ghost and anti-ghosts are fermionic. It would be interesting to find out whether this similarity is more substantial.\n\n\\subsection*{Acknowledgments}\n\nIt is a pleasure to thank Subhajit Mazumdar, Lior Oppenheim, Amit Schiller and Ruth Shir and for collaboration on related projects and for comments on a presentation.\n\nThis research was supported by the ``Quantum Universe'' I-CORE program of the Israeli Planning and Budgeting Committee.\n\n\\bibliographystyle{unsrt}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbjlj b/data_all_eng_slimpj/shuffled/split2/finalzzbjlj new file mode 100644 index 0000000000000000000000000000000000000000..a9814cd0479d092c9ef1801daed1559bfa8a882c --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbjlj @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\\label{sec1}\nThe influence of disorder on phase transitions and critical phenomena has\nattracted a considerable interest in the last decades. In the absence of\nfrustration, it is now well established that a first-order phase transition\nis smoothed by the introduction of randomness and can be made continuous\nat large enough disorder strength~\\cite{ImryWortis}. In 2D, an infinitesimal\ndisorder is sufficient to remove any discontinuity~\\cite{HuiBerker,%\nAizenman,Aizenman2}. When the pure system undergoes already a continuous\nphase transition, the Harris criterion predicts that the universality class\nof the pure model will be affected by disorder\nif the specific heat diverges, i.e. if the critical exponent $\\alpha$\nis positive~\\cite{Harris74}. In this context, the $q$-state Potts model\nhas been a useful toy model, because it displays a rich phase diagram\ninvolving two lines of respectively first and second-order phase transition.\nAlong the latter, the universality class depends on the number of states $q$.\nOn the practical side, efficient Monte Carlo and transfer matrix\nalgorithms exist for this model and Conformal Invariance can be used in\n2D in combination with Renormalisation Group (RG).\n \nIn comparison, correlated disorder was much less studied. Nevertheless\nin some experimental situations, impurities cannot be considered as\nuncorrelated. This is in particular the case when they carry an electric\ncharge or a magnetic moment and are coupled via an electromagnetic interaction.\nOn the theoretical side, Weinrib and Halperin studied the $\\phi^4$ model\nwith a random mass and showed that a new RG fixed point, distinct from the\nrandom and the pure ones, emerges in the phase diagram when the correlations\nof this mass decay algebraically~\\cite{Weinrib}.\nFor a sufficiently slow decay of these disorder correlations,\nthe new fixed point becomes stable. Denoting $a$ the exponent of the\nalgebraic decay of disorder correlations, the perturbation is relevant\nwhen $a4$, was shown to be smoothed and replaced\nby a continuous transition. However, the new universality class was shown\nto be $q$-independent, a feature shared by the strong-disorder fixed point\nof the $q$-state Potts model with a layered McCoy-Wu-like disorder. This\nresult is remarkably different from the continuous increase of the\nmagnetic scaling dimension $x_\\sigma$ observed for the Potts model with\nan uncorrelated disorder. More intriguing is the fact that the phase diagram\ndisplays a Griffiths phase, as in the McCoy-Wu model, where the magnetic\nsusceptibility diverges with the lattice size. Interestingly, such a phase\nhas been predicted by Weinrib and Halperin, but only above the upper\ncritical dimension $d_c=4$. Finally, the hyperscaling relation ${\\gamma\\over\\nu}\n=d-2{\\beta\\over\\nu}$ was observed to be broken in the Griffiths phase,\nas a result of large disorder fluctuations.\n\nHowever, these observations were made for finite systems so one cannot exclude\nthe possibility that, at much larger lattice sizes, the Griffiths phase\ncollapse into a single point, the critical point, where the hyperscaling\nrelation would be restored. Moreover, the estimate of the correlation length\nexponent $\\nu$ is incompatible with Weinrib-Halperin exact result $\\nu=2\/a$,\nwhich substantiates the idea that the lattice sizes considered could be too\nsmall and that a cross-over would be observed at much larger lattice sizes.\nOn the other hand, no significant evolution of the Griffiths phase could be\nobserved in the range of lattice sizes studied~\\cite{PRE}. Moreover, the\nconspiracy of two amplitudes that leads to the violation of the hyperscaling\nrelation is well verified and no sign of deviation at large lattice\nsizes is observed.\n\nSince larger lattice sizes are not accessible by Monte Carlo simulations,\nwe turn our attention in this work to larger exponents $a$ of the disorder\ncorrelations. The fact that Weinrib-Halperin predictions were confirmed by\nMonte Carlo simulations of the 3D Ising model with $a=2$ could indicate\nthat finite-size effects are weaker for larger values of $a$.\nIn refs~\\cite{EPL,PRE}, only small values of $a$ were considered\nbecause disorder configurations were generated by simulating an auxiliary\nAshkin-Teller model on a self-dual line where its critical exponents\nare known exactly. The polarisation density was then used to construct the\ncouplings $J_{ij}$ of the Potts model. Disorder correlations correspond\ntherefore to the polarisation-polarisation correlations of the auxiliary\nAshkin-Teller model. When moving along the self-dual line, only exponents\nin the range $a\\in [1\/4;3\/4]$ can be obtained.\n\nIn this work, we present new data for disorder correlation exponents\n$a=1\/3$ and $2\/3$ obtained by using an auxiliary Ashkin-Teller model.\nIn order to investigate the possible existence of a cross-over towards the\nWeinrib-Halperin fixed point, we considered also the values $a\\simeq 1.036$\nand $a=2$ obtained using the 3D and 4D Ising models as auxiliary models to\ngenerate the disorder configurations. In the first section, details about\nthe models and the Monte Carlo simulations are given. In the second section,\nthe behaviour of the magnetic susceptibility $\\bar\\chi$ is discussed.\nAs already observed in Ref.~\\cite{PRE}, $\\bar\\chi$ diverge\nalgebraically with the lattice size in a broad interval of temperatures,\nidentified as a Griffiths phase, when $a$ is sufficiently small.\nA simple explanation of this phenomena is to assume that disorder\nfluctuations induce a spreading of local transition temperatures. Because\nthese fluctuations vanish as $L^{-a\/2}$, this would imply that a single\npeak would be recovered at large lattice sizes. Moreover, the smaller the\nexponent $a$ and the larger the lattice sizes needed to observe a single peak.\nIn the second section,\nnumerical evidence is given that disorder fluctuations are not sufficient\nto explain the observed Griffiths phase, and therefore, that the latter\nphase cannot be expected to collapse as $L^{-a\/2}$. In the third section,\nthe possibility of a cross-over controlled by the amplitude of disorder\ncorrelations is considered. These amplitudes are compared for the\ndifferent disorder correlations considered and, then different disorder\nstrengths are studied. Finally, conclusions follow.\n\n\\section{Models and simulation}\n\\label{sec2}\nThe classical $q$-state Potts model is the lattice spin model defined by the\nHamiltonian~\\cite{Potts,Wu}\n \\begin{equation}\n H=-J\\sum_{(i,j)} \\delta_{\\sigma_i,\\sigma_j},\\hskip 1truecm\n \\sigma_i=0,1,\\ldots,q-1\n \\end{equation}\nwhere the spin $\\sigma_i$ takes $q$ possible values and is located on the\n$i$-th node of the lattice. The sum extends over all pairs $(i,j)$ of nearest\nneighbours on the lattice. In the following, the Potts model will be considered\non the square lattice. As mentioned in the introduction, the phase transition\nis continuous for $q\\le 4$ and of first-order when $q>4$. We will restrict\nourselves to the case $q=8$, i.e. a point in the regime of first-order\ntransition. Disorder is now introduced as bond-dependent random exchange\ncouplings $J_{ij}$. The Hamiltonian becomes\n \\begin{equation}\n H=-\\sum_{(i,j)} J_{ij}\\delta_{\\sigma_i,\\sigma_j}.\n \\end{equation}\nThe spatial correlations between these couplings is assumed to decay\nalgebraically with an exponent $a$ at large distance:\n \\begin{equation}\n \\overline{J_{ij}J_{kl}}-\\overline{J_{ij}}\\ \\!\\overline{J_{kl}}\n \\sim |\\vec r_i-\\vec r_k|^{-a}.\n \\end{equation}\nFor convenience, we will restrict ourselves in the following to a binary\ncoupling distribution, i.e. $J_{ij}=J_1$ or $J_2$. The presence of disorder\ncorrelations does not affect the self-duality condition of the random Potts\nmodel. Imposing $J_1$ and $J_2$ to be equiprobable and self-dual of each other,\nthe self-dual line is given by the condition~\\cite{Kinzel}\n \\begin{equation}\n \\big(e^{\\beta J_1}-1\\big)\\big(e^{\\beta J_2}-1\\big)=q.\n \\end{equation}\n\nThe coupling configurations are generated by independent Monte Carlo simulations\nof two auxiliary models: the Ising and Ashkin-Teller models. The former is\ndefined by the Hamiltonian\n \\begin{equation}\n H=-J_{IM}\\sum_{(i,j)} \\sigma_i\\sigma_j,\\hskip 1truecm\\sigma_i=\\pm 1\n \\end{equation}\nand is equivalent to the $q=2$ Potts model. It is well known that this model\nundergoes a second-order phase transition in any dimension $d>1$. We considered\nhypercubic lattices of dimension $d=3$ and $d=4$. A few thousand spin\nconfigurations are generated at the critical point, corresponding to $\\beta\nJ_{IM}\\simeq 0.221655$ for $d=3$~\\cite{Pelissetto} and $\\beta J_{IM}\\simeq 0.149694$\nfor $d=4$~\\cite{Stauffer97}. For each spin configuration, a two-dimensional\nsection is cut and random couplings for the 2D Potts model are constructed as\n \\begin{equation}\n J_{ij}={J_1+J_2\\over 2}+{J_1-J_2\\over 2}\\sigma_i\n \\end{equation}\nfor each pair $(i,j)$ of nearest neighbours in the 2D section. Note that,\nat any site $i$, two couplings, in two different directions, are identical.\nBy construction, disorder correlation functions $\\overline{J_{ij}J_{kl}}\n-\\overline{J_{ij}}\\ \\!\\overline{J_{kl}}$ decay as the spin-spin correlation\nfunctions of the auxiliary Ising model. Therefore, the decay is algebraic\nat large distances with an exponent $a=2\\beta\/\\nu\\simeq 1.036$ for the 3D\nIsing model~\\cite{Pelissetto} and $a=2$ for the 4D Ising model.\nNote that, in the second case, the exponent $a$ is equal to the dimension\n$d=2$ of the Potts model. Therefore, according to Weinrib and Halperin,\ndisorder correlations are expected to be irrelevant and the system falls\ninto the same universality class as the\nPotts model with independent random couplings.\n\nThe second auxiliary model is the 2D Ashkin-Teller model defined by the\nHamiltonian~\\cite{AshkinTeller,Fan}\n \\begin{equation}\n H=-\\sum_{(i,j)} \\big[J_{AT}\\sigma_i\\sigma_j+J_{AT}\\tau_i\\tau_j+\n K_{\\rm AT}\\sigma_i\\sigma_j\\tau_i\\tau_j\\big],\\hskip 1truecm\n \\sigma_i,\\tau_i=\\pm 1\n \\end{equation}\nand corresponding to two Ising models coupled by their energy densities.\nOn the square lattice, the model is self-dual along the line of the phase\ndiagram given by $e^{-2K_{\\rm AT}}=\\sinh 2J_{\\rm AT}$. Thanks to a mapping onto\nthe eight-vertex model, the critical exponents are known exactly along this\nline. The random couplings for the Potts model are constructed from the\npolarisation density as\n \\begin{equation}\n J_{ij}={J_1+J_2\\over 2}+{J_1-J_2\\over 2}\\sigma_i\\tau_i.\n \\end{equation}\nThe disorder correlations therefore decay as the polarisation-polarisation\ncorrelation functions of the auxiliary Ashkin-Teller model. In this work, we\nconsidered two points on the self-dual line of the Ashkin-Teller model ($y=0.50$\nand $y=1.25$ in the language of the eight-vertex model) corresponding to\nexponents $a=1\/3$ and $a=2\/3$.\n\nThe above-described spin models were simulated using Monte Carlo cluster\nalgorithms to reduce the critical slowing-down. For the Ising and Potts models,\nthe Swendsen-Wang algorithm was employed~\\cite{SW}. The Ashkin-Teller was\nsimulated using a cluster algorithm introduced by Wiseman and\nDomany~\\cite{Salas,Salas2}.\n\n\\section{Griffiths phase and disorder fluctuations}\n\\label{sec3}\nThe magnetic susceptibility $\\chi$ of a finite system undergoing a continuous\nphase transition in the thermodynamic limit is expected to display a peak whose\nmaximum diverges with the lattice size $L$ as $L^{\\gamma\/\\nu}$. The location of\nthis maximum goes towards the critical temperature $T_c$ in the limit of an\ninfinite system. A very different situation was observed in the 2D Potts\nmodel with strongly correlated disorder~\\cite{PRE}. As can be seen on\nfigure~\\ref{Fig2}, two peaks are present for $a=1\/3$ and $2\/3$. The data\nshow an algebraic increase of the average magnetic susceptibility for all\ntemperatures between these two peaks. For this reason, this region was\nconjectured to be a Griffiths phase, similar to the one observed in the\nMcCoy-Wu model. The absence of any evolution of the location of the two\npeaks was reported in the case $a=0.4$. In contrast, figure~\\ref{Fig2}\nshows a slow evolution in the case $a=2\/3$. Since only lattice sizes up\nto $L=128$ were studied, the possibility of a collapse of the Griffiths\nphase into a single point in the thermodynamic limit cannot be excluded.\nMoreover, such a collapse is even more clearly seen on figure~\\ref{Fig2}\nfor $a\\simeq 1.036$. Two peaks are still visible but they tend to come closer\nwhen the lattice size is increased. It seems natural in this case to assume\nthat the two peaks will merge into a single one at larger lattice sizes.\nFor disorder correlations with a faster decay $a=2$, only one peak is observed\n(Fig.~\\ref{Fig2}) and its location tends towards the critical value\n$\\beta_c=1$, expected from self-duality arguments.\n\n\n\\begin{figure}[ht]\n\\centering\n\\psfrag{chi}[Bc][Bc][1][1]{$\\overline{\\chi}$}\n\\psfrag{beta}[tc][tc][1][0]{$\\beta$}\n\\psfrag{L=24}[Bc][Bc][1][0]{\\tiny $L=24$}\n\\psfrag{L=32}[Bc][Bc][1][0]{\\tiny $L=32$}\n\\psfrag{L=64}[Bc][Bc][1][0]{\\tiny $L=64$}\n\\psfrag{L=48}[Bc][Bc][1][0]{\\tiny $L=48$}\n\\psfrag{L=96}[Bc][Bc][1][0]{\\tiny $L=96$}\n\\psfrag{L=128}[Bc][Bc][1][0]{\\tiny $L=128$}\n\\includegraphics[width=7.25cm]{Fig2a.eps}\\quad\n\\includegraphics[width=7.25cm]{Fig2b.eps}\\par\n\\includegraphics[width=7.25cm]{Fig2c.eps}\\quad\n\\includegraphics[width=7.25cm]{Fig2d.eps}\n\\caption{Average magnetic susceptibility of the 8-state Potts model\nwith different disorder correlation exponents ($a=1\/3$, $2\/3$, $1.036$ and $2$\nfrom left to right and top to bottom) for a disorder strength $r=J_1\/J_2=7.5$.\nThe different curves correspond to different lattice sizes.\nNote that the scale of the $y$-axis is logarithmic.}\n\\label{Fig2}\n\\end{figure}\n\nAs mentioned in the introduction, it may be assumed that the width of the\nGriffiths phase is due to large disorder fluctuations. It seems indeed natural\nto assume that the first peak is caused by the ferromagnetic ordering\nof large clusters with a high concentration of weak bonds $J_2$ while\nthe second one corresponds to clusters of strong bonds $J_1$. Such large\nclusters are more probable when disorder correlations decay slowly.\nIn the following, disorder fluctuations will be compared for the\ndifferent values of $a$ considered. To be more specific, consider the\ngeneral case of a lattice model with an energy density denoted\n$\\epsilon_{ij}=\\epsilon(\\sigma_i,\\sigma_j)$ on the edge between the\nspins on sites $i$ and $j$. The weak disorder limit of the partition\nfunction can be calculated using the replica trick:\n \\begin{equation}\n \\overline{\\ln{\\cal Z}}=\\lim_{n\\rightarrow 0} {1\\over n}\\big(\n \\overline{{\\cal Z}^n}-1\\big).\n \\end{equation}\nIntroducing the interaction energy $\\epsilon_{ij}^\\alpha=\\epsilon\n(\\sigma_i^\\alpha,\\sigma_j^\\alpha)$ between the two spins $\\sigma_i^\\alpha$\nand $\\sigma_j^\\alpha$ of the $\\alpha$-th replica, the partition function\nof $n$ replicas reads\n \\begin{equation}\n \\overline{{\\cal Z}^n}=\\sum_{\\{\\sigma_i^\\alpha\\}}\n \\overline{e^{-\\beta \\sum_{(i,j),\\alpha}J_{ij}\\epsilon_{ij}^\\alpha}}\n \\simeq \\sum_{\\{\\sigma_i^\\alpha\\}}\n e^{-\\beta \\sum_{(i,j),\\alpha}\\overline{J_{ij}}\\epsilon_{ij}^\\alpha\n +{\\beta^2\\over 2}\\sum_{(i,j),(k,l),\\atop\\alpha,\\beta}\n \\big(\\overline{J_{ij}J_{kl}}-\\overline{J_{ij}}\\ \\!\\overline{J_{kl}}\\big)\n \\epsilon_{ij}^\\alpha\\epsilon_{kl}^\\beta+\\ldots}.\n \\label{DevPartF}\n \\end{equation}\nThe first contribution of disorder to the partition function involves\nthe correlations $\\overline{J_{ij}J_{kl}}-\\overline{J_{ij}}\\ \\!\n\\overline{J_{kl}}$, and is obviously a function of $a$. In order to\ncharacterise the disorder strength by a scalar, we considered the sum\nof these correlations, which also corresponds to the fluctuations\nof the couplings:\n \\begin{equation}\n \\Delta J_2=\\overline{\\Big[{1\\over N}\n \\sum_{(i,j)}(J_{ij}-\\bar J)\\Big]^2}^{1\/2}\n \\label{DefSigma}\n \\end{equation}\nwhere $N=2L^2$ is the number of bonds of the square lattice. Since the\ncouplings $J_{ij}$ are constructed from the polarisation density $\\sigma_i\n\\tau_i$ of the auxiliary Ashkin-Teller model (for $a=1\/3$ and $2\/3$), or\nfrom the magnetisation density $\\sigma_i$ of the auxiliary Ising model\n(for $a\\simeq 1.036$ and $2$), $\\Delta J_2$ is related, up to a prefactor\n${J_1-J_2\\over 2}$, to the fluctuations of the polarisation, or magnetisation,\ndensity. Therefore, $\\Delta J_2$ is expected to scale as\n \\begin{equation}\n \\Delta J_2\\sim L^{-a\/2}\n \\label{ScaleSigma}\n \\end{equation}\nfor both auxiliary models. This result is obtained by expanding the square\nin equation~(\\ref{DefSigma}) and integrating out the disorder correlations\nin the continuum limit. Up to a further factor $L^d$, $(\\Delta J_2)^2$ is\nalso proportional to the electric or magnetic susceptibility of the\nAshkin-Teller and Ising models. The hyperscaling relation for these\nauxiliary models leads to $\\Delta J_2\\sim L^{-\\beta\/\\nu}$ where the\nexponents $\\beta\/\\nu$ is equal to $a\/2$ by construction of the random\ncouplings. Equation~(\\ref{ScaleSigma}) shows that $\\Delta J_2$\nbehaves as a shift of the critical temperature $|T_c(L)-T_c(\\infty)|$\nin a finite system. Indeed, one expects $|T_c(L)-T_c(\\infty)|\n\\sim L^{-1\/\\nu}$ and, at the Weinrib-Halperin fixed point, $\\nu=2\/a$.\n\n\n\\begin{figure}[ht]\n\\centering\n\\psfrag{s1}[Bc][Bc][1][1]{$\\Delta J_1$}\n\\psfrag{s2}[Bc][Bc][1][1]{$\\Delta J_2$}\n\\psfrag{s4}[Bc][Bc][1][1]{$\\Delta J_4$}\n\\psfrag{L}[tc][tc][1][0]{$L$}\n\\psfrag{a=1\/3}[Bc][Bc][1][0]{\\tiny $a=1\/3$}\n\\psfrag{a=2\/3}[Bc][Bc][1][0]{\\tiny $a=2\/3$}\n\\psfrag{a=1.036}[Bc][Bc][1][0]{\\tiny $a\\simeq 1.036$}\n\\psfrag{a=2}[Bc][Bc][1][0]{\\tiny $a=2$}\n\\includegraphics[width=4.85cm]{Fig5a.eps}\n\\includegraphics[width=4.85cm]{Fig5b.eps}\n\\includegraphics[width=4.85cm]{Fig5c.eps}\n\\caption{Fluctuations $\\Delta J$ (up to a factor $(J_1-J_2)\/2$) of the\naverage random couplings versus the lattice size $L$. The different\ncurves correspond to different disorder correlations, i.e. to different\nexponents $a=1\/3$, $2\/3$, $1.036$ and $2$ (from top to bottom).}\n\\label{Fig5}\n\\end{figure}\n\nThe variance of the average coupling $\\Delta J_2$ is plotted on\nfigure~\\ref{Fig5} versus the lattice size in the four cases $a=1\/3$,\n$2\/3$, $1.036$ and $2$. Note that in the last two cases ($a\\simeq 1.036$\nand $a=2$), only the magnetisation in the two-dimensional section that\nwas used to construct the exchange couplings $J_{ij}$ is considered.\nAs expected, an algebraic decay with an exponent compatible with $a\/2$\nis observed. On figure~\\ref{Fig2}, the collapse of the two peaks of the\nmagnetic susceptibility is observed for $a\\simeq 1.036$ for lattice sizes\n$L\\sim {\\cal O}(10^2)$ when $a\\simeq 1.036$. According to figure~\\ref{Fig5},\nthis corresponds to disorder fluctuations of order $\\Delta J_2\\simeq 0.06$.\nFor $a=1\/3$ and $2\/3$, none of the lattice sizes that were considered\ncorrespond to so small disorder fluctuations. Indeed, $\\Delta J_2=0.159(4)$\nwhen $a=1\/3$ for the largest lattice size $L=128$ and $\\Delta J_2=0.090(3)$\nwhen $a=2\/3$. This strengthens the idea that the collapse will be\nobserved for larger lattice sizes for $a=1\/3$ and $2\/3$. Using the\nscaling law (\\ref{ScaleSigma}), one can even predict these sizes\nto be of the order of $L^*\\simeq 128(0.06\/0.16)^{-2\/a}\\simeq 44,000$\nfor $a=1\/3$ and $L^*\\simeq 128(0.06\/0.09)^{-2\/a}\\simeq 432$ for $a=2\/3$.\nOn the other hand, disorder fluctuations are small for $a=2$ ($\\Delta J_2\n=0.0618(4)$ already for $L=24$), smaller than for $a\\simeq 1.036$ with\nlattice sizes $L\\sim {\\cal O}(10^2)$. \n\nThese results do not depend on the quantity used to measure disorder\nfluctuations. The scaling law (\\ref{ScaleSigma}) suggests to use the\norder parameter, polarisation $\\overline{|\\sigma_i\\tau_i|}$\nor magnetisation $\\overline{|\\sigma_i|}$, of the auxiliary models as an\nalternative measure of the fluctuations of the couplings. This quantity will\nbe denoted $\\Delta J_1$ in the following. $\\Delta J_1$ and $\\Delta J_2$ give\nessentially the same information and, as can be seen on figure~\\ref{Fig5},\ntake sensibly the same value, but $\\Delta J_1$ presents the advantage of\nbeing more stable numerically. More surprising is the fact that the same\nconclusions can be drawn from the second contribution of disorder\nto the partition function (\\ref{DevPartF}). Expanding further, the next\nterm will involve the connected four-point correlation function\n$\\overline{J_iJ_jJ_kJ_l}_c$ of disorder. This quantity was assumed to\nbe irrelevant by Weinrib and Halperin. We considered the fourth-order\ncumulant\n \\begin{equation}\n \\Delta J_4=\\left(3\\overline{\\Big[{1\\over N}\\sum_{i,j} (J_{ij}-\\bar J)\n \\Big]^2}^2-\\overline{\\Big[{1\\over N}\\sum_{i,j} (J_{ij}-\\bar J)\n \\Big]^4}\\right)^{1\/4}.\n \\end{equation}\nAs can be seen on figure~\\ref{Fig5}, no qualitative difference between\nthe three quantities $\\Delta J_1$, $\\Delta J_2$ and $\\Delta J_4$ is\nobserved.\n\n\nHowever, there are small differences between the four cases $a=1\/3,2\/3,1.036$\nand $a$ that cannot be explained only in terms of disorder fluctuations.\nThe value of $\\Delta J$ for the largest lattice size $L=128$ at $a=2\/3$ is\nclose to the one estimated for $L=48$ at $a\\simeq 1.036$. Therefore, the average\nsusceptibility should look qualitatively the same for $a=2\/3$ at $L=128$\nand for $a\\simeq 1.036$ at $L=48$. It is not clear that it is indeed the\ncase on figure~\\ref{Fig5}. Moreover, a nice collapse of the magnetic\nsusceptibility is observed at large $\\beta$ for $a=1\/3$ and $2\/3$ while\nit is not case for $a\\simeq 1.036$ and 2. Stronger statements might be\nformulated by comparing thermodynamic quantities displaying universal\nproperties. The natural candidate is the 4th-order Binder cumulant\n \\begin{equation}\n U_M=1-{\\overline{\\langle m^4\\rangle}\n \\over 3\\overline{\\langle m^2\\rangle^2}}\n\\end{equation}\nwhose value at the intersection of two curves with respect to\ntemperature is expected to be universal in the thermodynamic limit.\nA notable difference between the different values of $a$ is that the\ncrossing points occur for inverse temperatures $\\beta$ well below\n$\\beta_c=1$ when $a=1\/3$ and $2\/3$ and very close to $\\beta_c=1$\nwhen $a\\simeq 1.036$ and $2$. Unfortunately, the error bars are large\nand do not allow to be conclusive.\n\n\\begin{figure}[ht]\n\\centering\n\\psfrag{Rm}[Bc][Bc][1][1]{$\\overline{\\langle m\\rangle^2}\n\/\\overline{\\langle m\\rangle}^2-1$}\n\\psfrag{beta}[tc][tc][1][0]{$\\beta$}\n\\psfrag{L=24}[Bc][Bc][1][0]{\\tiny $L=24$}\n\\psfrag{L=32}[Bc][Bc][1][0]{\\tiny $L=32$}\n\\psfrag{L=64}[Bc][Bc][1][0]{\\tiny $L=64$}\n\\psfrag{L=48}[Bc][Bc][1][0]{\\tiny $L=48$}\n\\psfrag{L=96}[Bc][Bc][1][0]{\\tiny $L=96$}\n\\psfrag{L=128}[Bc][Bc][1][0]{\\tiny $L=128$}\n\\includegraphics[width=7.25cm]{Fig3a.eps}\\quad\n\\includegraphics[width=7.25cm]{Fig3b.eps}\\par\n\\includegraphics[width=7.25cm]{Fig3c.eps}\\quad\n\\includegraphics[width=7.25cm]{Fig3d.eps}\n\\caption{Ratio $\\overline{\\langle m\\rangle^2}\/\\overline{\\langle m\\rangle}^2-1$\nof the 8-state Potts model with different disorder correlation exponents\n($a=1\/3$, $2\/3$, $1.036$ and $2$ from left to right and top to bottom)\nfor a disorder strength $r=J_1\/J_2=7.5$. The different curves correspond\nto different lattice sizes.}\\label{Fig3}\n\\end{figure}\n\nAnother quantity displaying universal properties is the ratio~\\cite{Wiseman}\n \\begin{equation}\n R_m={\\overline{\\langle m\\rangle^2}-\\overline{\\langle m\\rangle}^2\n \\over \\overline{\\langle m\\rangle}^2}\n \\end{equation}\nthat measures the sample-to-sample fluctuations of magnetisation.\nOutside of a critical point, all disorder realisations are expected\nto lead to the same average magnetisation $\\langle m\\rangle$ in the\nthermodynamic limit. Therefore, the ratio $R_m$ vanishes as $L\\rightarrow\n+\\infty$ and magnetisation is said to be self-averaging. This is no longer\ntrue at a fixed point where disorder is relevant. In this case, $R_m$\ngoes towards a finite value in the thermodynamic limit. This limit is\nexpected to be a universal quantity~\\cite{Aharony}. Numerical data\nfor this ratio $R_m$ are plotted on figure~\\ref{Fig3}. Two distinct\nbehaviours are observed. For $a=1\/3$ and $a=2\/3$, $R_m$ displays\na peak in the paramagnetic phase (small $\\beta=1\/k_BT$), followed by\na broad shouldering. The latter extends over a range of temperatures\nwhich roughly corresponds to the range between the two peaks of the\naverage magnetic susceptibility (see figure~\\ref{Fig2}). Interestingly,\nthe estimates of $R_m$ at any temperature in this shouldering are\ncompatible, within error bars, for all lattices sizes $L\\in[32;128]$.\nUnless a sudden decay of $R_m$ occurs at much larger lattices sizes,\nwe are led to the conclusion that magnetisation is a non-self-averaging\nquantity in the whole range of temperatures between the two peaks\nof the susceptibility. This conclusion is consistent with the assumption\nof the existence of a Griffiths phase. On the other hand, for $a\\simeq\n1.036$ and $a=2$, the peak in the paramagnetic phase is softer and is\nnot followed by a shouldering but by a monotonous decay. More interesting\nis the fact that the curves corresponding to different lattice\nsizes cross each other at a single point, close to the self-dual point\n$\\beta_c=1$. This is consistent with the existence of a unique critical\npoint at $\\beta_c=1$. Would it be possible that, in the case $a=2\/3$,\nthe shouldering disappears at large lattice sizes to be replaced by\na monotonous decay with a single crossing point for different lattice\nsizes? If the coupling fluctuations $\\Delta J$ provides a measure of\nthe width of the Griffiths phase as discussed above, it should also\ndetermine the range of temperatures around $\\beta_c$ for which $R_m$\nis finite and size-independent. Then the ratio $R_m$ should look\nsimilar for $a=2\/3$ at $L=128$ and for $a\\simeq 1.036$ at $L=48$.\nThis is definitely not the case on figure~\\ref{Fig3}. Therefore, the\nGriffiths phase is not solely the consequence of disorder fluctuations\nand there is no reason to expect the Griffiths phase to collapse into a\nsingle point as $L^{-a\/2}$.\n\n\\section{Griffiths phase and disorder strength}\nAll data presented in the previous section correspond to a disorder strength\n$r=J_1\/J_2=7.5$. Because the two peaks of the susceptibility were interpreted\nas the ordering of macroscopic clusters with a majority of strong, or\nweak, couplings, the disorder strength $r$ controls the width of the Griffiths\nphase. One can therefore wonder whether disorder is not too strong\nin the cases $a=1\/3$ and $2\/3$ which implies that a cross-over to the\nWeinrib-Halperin fixed point would be observed at larger lattice sizes.\nFor the Potts model with uncorrelated disorder, strong scaling corrections\ndepending on $r=J_1\/J_2$ were indeed observed. Accurate estimates of the critical\nexponents became accessible only after an appropriate disorder strength was\ndetermined. The by-far most\nefficient technique was, in this case, to compute an effective central charge\n$c_{\\rm eff}$ by transfer matrix techniques and search for the maximum of\n$c_{\\rm eff}$. The central charge is unfortunately difficult to measure by\nMonte Carlo simulations. Consequently, we will restrict ourselves to\nobserve the effect of a variation of the disorder strength $r$.\n\n\\begin{figure}[ht]\n\\centering\n\\psfrag{s2}[Bc][Bc][1][1]{$(2-a)(\\Delta J_2)^2 L^{a}$}\n\\psfrag{L}[tc][tc][1][0]{$L$}\n\\psfrag{a=1\/3}[Bc][Bc][1][0]{\\tiny $a=1\/3$}\n\\psfrag{a=2\/3}[Bc][Bc][1][0]{\\tiny $a=2\/3$}\n\\psfrag{a=1.036}[Bc][Bc][1][0]{\\tiny $a\\simeq 1.036$}\n\\psfrag{a=2}[Bc][Bc][1][0]{\\tiny $a=2$}\n\\includegraphics[width=7.25cm]{Fig8.eps}\n\\caption{Amplitude $w$ of disorder correlations (up to a factor $(J_1-J_2)^2\/4$)\nversus the lattice size $L$. The different curves correspond to different\ndisorder correlations, i.e. to different exponents $a=1\/3$, $2\/3$\nand $1.036$.}\n\\label{Fig8}\n\\end{figure}\n\nWeinrib and Halperin considered disorder correlations of the form\n \\begin{equation}\n C(r_{ik})=\\overline{J_{ij}J_{kl}}-\\overline{J_{ij}}\\ \\!\\overline{J_{kl}}\n =v\\delta(\\vec r_i-\\vec r_k)+{w\\over |r_{ik}|^a}\n \\label{CorrelationWH}\n \\end{equation}\nwhere the two amplitudes $v$ and $w$ are irrelevant scaling fields at the\nlong-range random fixed point. When simulating a finite system with an\namplitude $w$ much larger (or much weaker) than the value $w^*$ taken at the\nfixed point, the critical behaviour may be affected by strong scaling\ncorrections. Indeed, in the neighbourhood of the Weinrib-Halperin random\nfixed point, the free energy density can be assumed to scale under\na dilatation with a scale factor $b$ as:\n \\begin{equation}\n f(t,h,1\/L,w)=b^{-d}f\\big(b^{y_t}t,b^{y_h}h,b\/L,b^{y_w}(w-w^*)\\big)\n \\end{equation}\nwhere $t=|T-T_c|$ is the reduced temperature and $h$ the magnetic field.\nAt the critical point, i.e. $t=h=0$, and with $b=L$, the magnetic\nsusceptibility $\\chi=-{\\partial^2f\\over\\partial h^2}$ scales as\n \\begin{equation}\n \\chi(1\/L,w)=L^{\\gamma\/\\nu}{\\cal F}\\big(L^{y_w}(w-w^*)\\big)\n \\end{equation}\nwhere $\\gamma\/\\nu=2y_h-d$. The scaling function ${\\cal F}$ involves a cross-over\nlength $\\ell\\sim (w-w^*)^{-1\/y_w}$ associated to disorder. The dominant\nfinite-size scaling behaviour $\\chi\\sim L^{\\gamma\/\\nu}$ will be hidden by\nscaling corrections if $L\\ll\\ell$.\n\nIn the previous section, the fluctuations of the average coupling have been\ncompared for different exponents $a$. To compare now the amplitudes $w$ of\ndisorder correlations, note that integrating out disorder correlations leads\non one hand to\n \\begin{equation}\n {1\\over N^2}\\sum_{ij,kl}\\big[\\overline{J_{ij}J_{kl}}\n -\\overline{J_{ij}}\\ \\!\\overline{J_{kl}}\\big]\n \\simeq {1\\over L^2}\\int_{L^2} C(\\vec r)d^2\\vec r\n \\simeq {w\\over L^2}\\int_0^L {rdr\\over r^a}\n =w{L^{-a}\\over 2-a}\n \\end{equation}\nwhile on the other hand, the same quantity is equal to $(\\Delta J_2)^2$\naccording to equation (\\ref{DefSigma}).\nThe amplitude $w$ can therefore be recomputed as $w\\simeq (2-a)\n(\\Delta J_2)^2L^a$. This estimate is plotted on figure~\\ref{Fig8}.\nNote that the amplitude $w$ is not plotted for $a=2$ because the definition\nis inappropriate in this case (the integration of the correlations involves\na logarithm) and leads to $w=0$. As can be seen on figure~\\ref{Fig8},\nthe amplitude $w$ does not evolve monotonously with $a$. This should not\nbe a surprise because the couplings have been generated from different\nauxiliary models. The amplitude $w$ for $a\\simeq 1.036$ lies in between the\namplitudes for $a=1\/3$ and $a=2\/3$. Therefore, the Griffiths phase\nand the small $\\nu$ exponents reported in~\\cite{PRE} for $a=1\/3$ and\n$a=2\/3$ cannot be explained as the result of strong scaling corrections.\nIndeed, if one assume that the amplitude $w$ is close to $w^*$ in the case\n$a\\simeq 1.036$, which would explain why the collapse of the two peaks\nof $\\bar\\chi$ is observed for reachable lattice sizes, one can conceive that\nthe Griffiths phase is the result of too strong disorder correlations,\ni.e. $w>w^*$, in the case $a=1\/3$. However, it is hard to understand how\nweak correlations, i.e. $w0$. This is seldom a problem in concrete situations.\n\n\\begin{proof}\nAs in the proof of Proposition \\ref{prop:loc-sparse2convex-pre}, given $\\vec f$, we consider the compact, convex, symmetric set \n\\begin{equation*}\n K:=\\cave{\\vec f}_{X}, \n\\end{equation*}\nand denote by $\\mathcal E_K$ its John ellipsoid such that\n\\begin{equation*}\n \\mathcal E_K\\subset K\\subset\\sqrt{n}\\mathcal E_K. \n\\end{equation*}\n\n\\subsubsection*{Case: $\\mathcal E_K$ is non-degenerate}\nLet $R_K$ be a linear transformation such that $R_K\\mathcal E_K=\\bar B_{\\mathbb{R}^n}$, the closed unit ball of $\\mathbb{R}^n$.\nLet $(\\vec e_i)_{i=1}^n$ be some orthonormal basis of $\\mathbb{R}^n$.\nAs in \\eqref{eq:tvecfvecg-pre}, we then write\n\\begin{equation}\\label{eq:tvecfvecg}\n\\begin{split}\n t_{Q_0}(\\vec f,\\vec g)\n &=t_{Q_0}(R_K^{-1}R_K\\vec f,\\vec g)\n =t_{Q_0}(R_K\\vec f,R_K^{-t}\\vec g) \\\\\n &=\\sum_{i=1}^n t_{Q_0}(R_K\\vec f\\cdot\\vec e_i,R_K^{-t}\\vec g\\cdot\\vec e_i)\n =:\\sum_{i=1}^n t_{Q_0}(f_i,g_i),\n\\end{split}\n\\end{equation}\nwhere $f_i$ and $g_i$ are as in Lemma \\ref{lem:fi-vs-convex}.\n\nIt is from this point on that the present proof requires some elaboration compared to the proof of Proposition \\ref{prop:loc-sparse2convex-pre}. According to assumption \\eqref{it:1scaleDomSca}, for each of the pairs of functions $f_i:=R_K\\vec f\\cdot\\vec e_i$ and $g_i:=R_K^{-t}\\vec g\\cdot\\vec e_i$, we can find disjoint $\\hat Q_{i,k}\\subset Q_0$ with $\\sum_k\\abs{\\hat Q_{i,k}}\\leq\\eps\\abs{Q_0}$ and such that: whenever $Q_j\\subset Q_0$ are disjoint, not strictly contained in any $\\hat Q_{i,k}$, and cover all $\\hat Q_{i,k}$, then\n\\begin{equation}\\label{eq:tfigi}\n \\abs{t_{Q_0}(f_i,g_i)-\\sum_j t_{Q_j}(f_i,g_i)}\\leq C\\Norm{f_i}{X}\\Norm{g_i}{Y}.\n\\end{equation}\nWe make the following specific choice of the cubes $Q_j$: Let $\\{Q_j\\}_{j=1}^\\infty$ be the maximal cubes among $\\{\\hat Q_{i,k}\\}_{1\\leq i\\leq n}^{1\\leq k<\\infty}$. Then\n\\begin{equation*}\n \\sum_j\\abs{Q_j}\\leq\\sum_{i=1}^n\\sum_{k=1}^\\infty\\abs{\\hat Q_{i,k}}\n \\leq\\sum_{k=1}^n\\eps\\abs{Q_0}\n = n\\eps\\abs{Q_0},\n\\end{equation*}\nand \\eqref{eq:tfigi} holds with these $Q_j$ for each $i=1,\\ldots,n$. Using \\eqref{eq:tvecfvecg}, and observing that it also holds with $Q_0$ replaced by $Q_j$, it follows that\n\\begin{equation*}\n\\begin{split}\n &\\abs{t_{Q_0}(\\vec f, \\vec g)-\\sum_j t_{Q_j}(\\vec f,\\vec g)}\n \\leq\\sum_{i=1}^n \\abs{t_{Q_0}(f_i,g_i)-\\sum_j t_{Q_j}(f_i,g_i)} \\\\\n &\\qquad\\leq C\\sum_{i=1}^n\\Norm{f_i}{X}\\Norm{g_i}{Y}\n \\leq Cn^{3\/2}\\cave{\\vec f}_X\\cdot\\cave{\\vec g}_Y,\n\\end{split}\n\\end{equation*}\nusing Lemma \\ref{lem:fi-vs-convex} in the last step.\nThis completes the proof under the assumption that $\\mathcal E_K$ is non-degenerate.\n\n\\subsubsection*{Case: $\\mathcal E_K$ is degenerate}\nThis follows the corresponding case in the proof of Proposition \\ref{prop:loc-sparse2convex-pre} almost verbatim. Like there, let $H:=\\lspan K$, and let $P$ denote the orthogonal projection of $\\mathbb{R}^n$ onto $H$. We then have \\eqref{eq:tfg-vs-tfPg} for each $t=t_Q$, as well as \\eqref{eq:caveg-vs-cavePg}. So it is again enough to prove the claim with $P\\vec g$ in place of $\\vec g$, and hence we may assume without loss of generality that also $\\vec g=P\\vec g$. But then we can repeat the argument in the non-degenerate case, but with $\\mathbb{R}^n$ replaced by its subspace $H$ throughout; within this subspace, $\\mathcal E_K\\subset H$ is non-degenerate, and the previous case applies to give the desired result.\n\\end{proof}\n\n\n\\section{From single-scale bounds to global bounds}\n\nThis passage is by now a relatively routine part of the theory, but we include some details for completeness. The following lemma is again stated in an operator-free, and even function-free way, simply as a criterion for dominating a real number by sum over a sparse collection. A more concrete situation for applying this criterion is presented afterwards.\n\n\\begin{lemma}\\label{lem:loc2glob}\nConsider numbers $a\\in\\mathbb{R}$ and $a_Q,c_Q\\in\\mathbb{R}$ indexed by dyadic cubes $Q\\in\\mathscr D$, with the following properties:\n\\begin{enumerate}\n \\item\\label{it:cover} There is a family $\\mathscr Q$ of disjoint dyadic cubes such that\n\\begin{equation*}\n a=\\sum_{Q\\in\\mathscr Q}a_Q.\n\\end{equation*}\n \\item\\label{it:local} For some $\\delta\\in(0,1)$ and each $Q\\in\\mathscr D$ that is contained in some $P\\in\\mathscr Q$, there is a family of disjoint $Q_k\\in\\mathscr D(Q)$ such that\n\\begin{equation*}\n \\sum_k\\abs{Q_k}\\leq\\delta\\abs{Q},\\qquad\n \\Babs{a_{Q}-\\sum_k a_{Q_k}}\\leq c_{Q}.\n\\end{equation*}\n \\item\\label{it:limit} For some $\\alpha,C\\in[1,\\infty)$ and each $Q\\in\\mathscr D$ that is contained in some $P\\in\\mathscr Q$, we have $\\abs{a_Q}\\leq C\\abs{Q}^\\alpha$.\n\\end{enumerate}\nThen there is a $(1-\\delta)$-sparse family of dyadic cubes $\\mathscr S$ such that\n\\begin{equation*}\n \\mathscr S\\subset\\bigcup_{Q\\in\\mathscr Q}\\mathscr D(Q),\\qquad \\abs{a}\\leq \\sum_{S\\in\\mathscr S}c_S.\n\\end{equation*}\n\\end{lemma}\n\n\\begin{remark}\\label{rem:loc2glob}\nIf $\\mathscr Q=\\{Q_0\\}$ consists of a single cube only, then condition \\eqref{it:cover} is automatic with $a=a_{Q_0}$.\n\\end{remark}\n\n\\begin{proof}\nLet $\\mathscr Q\\subset\\mathscr D$ be a disjoint collection provided by assumption \\eqref{it:cover}. For each $P\\in\\mathscr Q$, denote $\\mathscr S_0(P):=\\{P\\}$.\n Assuming that a disjoint $\\mathscr S_j(P)\\subset\\mathscr D(P)$ has already been constructed, for each $Q\\in\\mathscr S_j(P)$, let $\\mathscr S'(Q):=\\{Q_k\\}_{k=1}^\\infty$ be the collection provided by assumption \\eqref{it:local}, and let $\\mathscr S_{j+1}(P):=\\bigcup_{Q\\in\\mathscr S_j(P)}\\mathscr S'(Q)$. Let also \n $\\mathscr S(P):=\\bigcup_{j=0}^\\infty\\mathscr S_j(P)$, and $\\mathscr S:=\\bigcup_{P\\in\\mathscr Q}\\mathscr S(P)$.\n\nFor $Q\\in\\mathscr S$, let $E(Q):=Q\\setminus\\bigcup_{R\\in\\mathscr S'(Q)}R$. From the construction it is clear that these sets $E(Q)$ are pairwise disjoint, and by assumption \\eqref{it:local} we have $\\abs{E(Q)}\\geq(1-\\delta)\\abs{Q}$.\n\nBy telescoping, for each $P\\in\\mathscr Q$, we have\n\\begin{equation*}\n a_P=\\sum_{j=0}^{k-1}\\sum_{Q\\in\\mathscr S_j(P)}\\Big(a_Q-\\sum_{R\\in\\mathscr S'(Q)}a_R\\Big)+\\sum_{S\\in\\mathscr S_k(P)}a_S.\n\\end{equation*}\nand hence, using assumptions \\eqref{it:local} and \\eqref{it:limit},\n\\begin{equation*}\n \\abs{a_P}\\leq\\sum_{j=1}^{k-1}\\sum_{Q\\in\\mathscr S_j(P)}c_Q+\\sum_{S\\in\\mathscr S_k(P)} C\\abs{S}^\\alpha\n\\end{equation*}\nBy an elementary inequality and induction, we have\n\\begin{equation*}\n \\sum_{S\\in\\mathscr S_k(P)} \\abs{S}^\\alpha\n \\leq\\Big(\\sum_{S\\in\\mathscr S_k(P)} \\abs{S}\\Big)^\\alpha\\leq(\\delta^k\\abs{P})^\\alpha,\n\\end{equation*}\nand hence\n\\begin{equation*}\n \\abs{a_P}\\leq\\lim_{k\\to\\infty}\\sum_{j=1}^{k-1}\\sum_{Q\\in\\mathscr S_j(P)}c_Q=\\sum_{Q\\in\\mathscr S(P)}c_Q.\n\\end{equation*}\nSubstituting this into assumption \\eqref{it:cover}, we obtain the claim.\n\\end{proof}\n\n\\begin{lemma}\\label{lem:easyConds}\nSuppose that $t$ is a bilinear form on $L^\\infty_c(\\mathbb{R}^d;E)\\times L^\\infty_c(\\mathbb{R}^d;H)$, and moreover bounded with respect to the norm of $L^p(\\mathbb{R}^d;E)\\times L^q(\\mathbb{R}^d;H)$ for some exponents with $1\/p+1\/q\\geq 1$. For $(\\vec f,\\vec g)\\in L^\\infty_c(\\mathbb{R}^d;E)^n\\times L^\\infty_c(\\mathbb{R}^d;H)^n$, the numbers\n\\begin{equation*}\n a=t(\\vec f,\\vec g),\\quad a_Q=t(1_{3Q}\\vec f,1_Q \\vec g)\n\\end{equation*}\nsatisfy assumptions \\eqref{it:cover} and \\eqref{it:limit} of Lemma \\ref{lem:loc2glob}, provided that $\\mathscr D$ is a dyadic system without quadrants.\n\\end{lemma}\n\n\\begin{proof}\nSince $\\mathscr D$ is without quadrants, each $Q\\in\\mathscr D$ is contained in some (large enough) $R\\in\\mathscr D$ that contains $\\supp \\vec f$. Thus the collection $\\mathscr Q$ of maximal cubes that do not contain $\\supp \\vec f$ form a cover of $\\mathbb{R}^d$. By maximality, it follows that $\\supp \\vec f\\subset 3Q$, and hence $\\vec f=1_{3Q}\\vec f$ for every $Q\\in\\mathscr Q$. On the other hand, any $Q$ with $\\ell(Q)<\\operatorname{diam}(\\supp \\vec f)$ cannot contain $\\supp\\vec f$; hence any $Q$ with $\\ell(Q)<\\frac12\\operatorname{diam}(\\supp \\vec f)$ cannot be among the maximal cubes $\\mathscr Q$, and thus every $Q\\in\\mathscr Q$ will have to satisfy $\\ell(Q)\\geq\\frac12\\operatorname{diam}\\vec f$. Since $\\vec g\\in L^\\infty_c(\\mathbb{R}^d;F)^n$, there are only finitely many $Q\\in\\mathscr Q$ with $1_Q \\vec g\\neq 0$. Hence, without any issues of convergence, we can write\n\\begin{equation*}\n t(\\vec f,\\vec g)=t\\Big(\\vec f,\\sum_{Q\\in\\mathscr Q}1_Q \\vec g\\Big)=\\sum_{Q\\in\\mathscr Q}t(\\vec f,1_Q \\vec g)=\\sum_{Q\\in\\mathscr Q}t(1_{3Q}\\vec f,1_Q \\vec g),\n\\end{equation*}\nwhich is condition \\eqref{it:cover}.\n\nIf $n=1$, the assumed boundedness directly implies that\n\\begin{equation*}\n \\abs{t(1_{3Q}f,1_Q g)}\\leq C\\Norm{1_{3Q}f}{L^p(\\mathbb{R}^d;E)}\\Norm{1_Q g}{L^{q}(\\mathbb{R}^d;F)}\n \\leq C 3^{d\/p}\\Norm{f}{\\infty}\\Norm{g}{\\infty}\\abs{Q}^{1\/p+1\/q},\n\\end{equation*}\nwhere $\\alpha:=1\/p+1\/q\\geq 1$, as required for condition \\eqref{it:limit}. In general, if $(\\vec e_i)_{i=1}^n$ is an orthonormal basis of $\\mathbb{R}^n$ and $\\vec f=\\sum_{i=1}^n f_i\\vec e_i$ and similarly for $\\vec g$, we have\n\\begin{equation*}\n \\abs{t(1_{3Q}\\vec f,1_Q \\vec g)}\\leq\\sum_{i=1}^n\\abs{t(1_{3Q}f_i,1_Q g_i)}\n \\leq Cn 3^{d\/p}\\Norm{\\vec f}{\\infty}\\Norm{\\vec g}{\\infty}\\abs{Q}^{1\/p+1\/q},\n\\end{equation*}\nusing the previous bound in each component and trivial bounds like $\\Norm{f_i}{\\infty}\\leq\\Norm{\\vec f}{\\infty}$.\n\\end{proof}\n\nWe are finally ready to state a semi-generic convex body domination principle. Condition \\eqref{it:1scaleDomScaNew} below is a typical intermediate estimate in a number of sparse domination proofs for different operators. The conclusion is that it is already good enough to conclude convex body domination as well.\n\n\\begin{corollary}\\label{cor:1scale2cbd}\nLet $E$ and $H$ be Banach spaces, and suppose that $t$ is a bilinear form defined on $F\\times G:=L^\\infty_c(\\mathbb{R}^d;E)\\times L^\\infty_c(\\mathbb{R}^d;H)$ and bounded with respect to the norm of $L^p(\\mathbb{R}^d;E)\\times L^q(\\mathbb{R}^d;H)$ for some exponents with $1\/p+1\/q\\geq 1$, and suppose that \n\\begin{enumerate}\n \\item\\label{it:1scaleDomScaNew} for all $(f,g)\\in F\\times G$ and all $Q\\in\\mathscr D$, there are disjoint $\\hat Q_k\\subset Q$ with $\\sum_k\\abs{\\hat Q_k}\\leq\\eps\\abs{Q}$ and such that: whenever $Q_j\\subset Q$ are disjoint, not strictly contained in any $\\hat Q_k$, and cover all $\\hat Q_k$, then\n\\begin{equation}\\label{eq:1scaleDomScaNew}\n \\abs{t(1_{3Q}f,1_{Q}g)-\\sum_j t(1_{3Q_j}f,1_{Q_j}g)}\\leq c\\Norm{f}{X(Q)}\\Norm{g}{Y(Q)}\\abs{Q}\n\\end{equation}\nfor some norms $\\Norm{\\ }{X(Q)}$ on $L^\\infty_c(\\mathbb{R}^d;E)$ and $\\Norm{\\ }{Y(Q)}$ on $L^\\infty_c(\\mathbb{R}^d;H)$.\n\\end{enumerate}\nThen for all $(\\vec f,\\vec g)\\in F^n\\times G^n$, there is a $(1-\\eps_n)$-sparse collection $\\mathscr S\\subset\\mathscr D$ such that\n\\begin{equation*}\n \\abs{t(\\vec f,\\vec g)}\\leq c_n\\sum_{S\\in\\mathscr S}\\cave{\\vec f}_{X(S)}\\cdot\\cave{\\vec g}_{Y(S)}\\abs{S},\n\\end{equation*}\nwhere $\\eps_n=n\\eps$ and $c_n=cn^{3\/2}$.\n\\end{corollary}\n\n\\begin{proof}\nLet us begin by considering a fixed cube $Q=Q_0\\in\\mathscr D$. We observe that assumption \\eqref{it:1scaleDomScaNew} of the present corollary coincides with condition \\eqref{it:1scaleDomSca} of Proposition \\ref{prop:loc-sparse2convex} with\n\\begin{equation*}\n t_Q(f,g):=t(1_{3Q}f,1_{Q}g),\\qquad C=c\\abs{Q},\\qquad X=X(Q),\\qquad Y=Y(Q).\n\\end{equation*}\nHence the said proposition, applied to each fixed $Q=Q_0\\in\\mathscr D$ at a time, implies:\n\\begin{enumerate}\\setcounter{enumi}{1}\n \\item\\label{it:1scaleDomScaVecNew} For all $(\\vec f,\\vec g)\\in L^\\infty_c(\\mathbb{R}^d;E)^n\\times L^\\infty_c(\\mathbb{R}^d;H)^n$ and all $Q\\in\\mathscr D$, there are disjoint $Q_k\\subset Q$ with $\\sum_k\\abs{Q_k}\\leq\\eps_n\\abs{Q}$ and such that\n\\begin{equation*}\n \\abs{t(1_{3Q}\\vec f,1_{Q}\\vec g)-\\sum_j t(1_{3Q_j}\\vec f,1_{Q_j}\\vec g)}\\leq c_n\\cave{\\vec f}_{X(Q)}\\cdot \\cave{g}_{Y(Q)}\\abs{Q},\n\\end{equation*}\nwhere $\\eps_n=n\\eps$ and $c_n=cn^{3\/2}$.\n\\end{enumerate}\n\nLet us then consider a fixed pair $(\\vec f,\\vec g)\\in L^\\infty_c(\\mathbb{R}^d;E)^n\\times L^\\infty_c(\\mathbb{R}^d;H)^n$. We observe that condition \\eqref{it:1scaleDomScaVecNew} above coincides with condition \\eqref{it:local} of Lemma \\ref{lem:loc2glob} with the choices\n\\begin{equation*}\n a_Q=t(1_{3Q}\\vec f,1_{Q}\\vec g),\\qquad c_Q= c_n\\cave{\\vec f}_{X(Q)}\\cdot \\cave{g}_{Y(Q)}\\abs{Q},\\qquad \\delta=\\eps_n.\n\\end{equation*}\nOn the other hand, Lemma \\ref{lem:easyConds} shows that these same $a_Q$, together with $a:=t(\\vec f,\\vec g)$, also satisfy conditions \\eqref{it:cover} and \\eqref{it:limit} of Lemma \\ref{lem:loc2glob}. Thus, all assumptions, and hence the conclusions, of Lemma \\ref{lem:loc2glob} are valid for the said quantities, and these conclusions agree with the claims of the result that we are proving. The proof is thus complete.\n\\end{proof}\n\nTo facilitate the discussion of consequences of Corollary \\ref{cor:1scale2cbd}, we give\n\n\\begin{definition}\\label{def:cbd}\nSuppose that a pair of normed spaces $(X(Q),Y(Q))$ is associated to every dyadic cube $Q\\in\\mathscr D$.\nWe say that a bilinear form $t:F\\times G\\to\\mathbb{R}$ satisfies the $(X(Q),Y(Q))$ convex body domination of order $n\\in\\mathbb{N}$ if $F\\subseteq X(Q)$ and $G\\subseteq Y(Q)$ for every $Q\\in\\mathscr D$, and if for every $(f,g)\\in F^n\\times G^n$, there exists a $\\delta_n$-sparse collection $\\mathscr S\\subset\\mathscr D$ such that\n\\begin{equation*}\n \\abs{t(\\vec f,\\vec g)}\\leq C_n\\sum_{Q\\in\\mathscr S}\\abs{Q}\\cave{\\vec f}_{X(Q)}\\cdot\\cave{\\vec g}_{Y(Q)}.\n\\end{equation*}\nWe say that $t:F\\times G\\to\\mathbb{R}$ satisfies the $(X(Q),Y(Q))$ convex body domination if it satisfies this for every $n\\in\\mathbb{N}$. We say that an operator $T:F\\to G^*$ satisfies these properties if its associated bilinear form $t(f,g):=\\pair{Tf}{g}$ does.\n\\end{definition}\n\n\nLet us now consider some examples:\n\n\\begin{example}[Calder\\'on--Zygmund operators]\\label{ex:CZO}\nLet $T$ be a Dini--Calder\\'on--Zygmund operator, i.e., $T$ is $L^2(\\mathbb{R}^d)$ bounded and has the representation\n\\begin{equation*}\n Tf(x)=\\int_{\\mathbb{R}^d}K(x,y)f(y)\\ud y,\\qquad x\\notin\\supp f,\n\\end{equation*}\nwhere $\\abs{K(x,y)}\\leq c\\abs{x-y}^{-d}$ and, for $\\abs{x-x'}\\leq\\frac12\\abs{x-y}$,\n\\begin{equation}\\label{eq:CZomega}\n \\abs{K(x,y)-K(x',y)}+\\abs{K(y,x)-K(y,x')}\\leq\\omega\\Big(\\frac{\\abs{x-x'}}{\\abs{x-y}}\\Big)\\frac{1}{\\abs{x-y}^d},\n\\end{equation}\nwhere $\\omega:[0,\\frac12]\\to[0,\\infty)$ is increasing, subadditive, and satisfies the Dini condition\n\\begin{equation*}\n \\int_0^{1\/2}\\omega(t)\\frac{\\ud t}{t}<\\infty.\n\\end{equation*}\nThen \\eqref{it:1scaleDomScaNew} of Corollary \\ref{cor:1scale2cbd} holds for $t(f,g)=\\pair{Tf}{g}$ and $E=H=\\mathbb{R}$ and $X(Q)=\\avL^1(3Q)$, $Y(Q)=\\avL^1(Q)$, even in a stronger form. Namely, on the left oif \\eqref{eq:1scaleDomScaNew}, we have\n\\begin{equation}\\label{eq:Lerner}\n\\begin{split}\n &\\Babs{\\Bpair{T(1_{3Q}f)}{1_{Q}g}-\\sum_j \\pair{T(1_{3Q_j}f)}{1_{Q_j}g}} \\\\\n &\\leq\\BNorm{1_QT(1_{3Q}f)-\\sum_j 1_{Q_j}T(1_{3Q_j}f)}{L^\\infty(Q)}\\Norm{g}{L^1(Q)},\n\\end{split}\n\\end{equation}\nand even the $L^\\infty$ norm here is dominated by $\\Norm{f}{\\avL^1(3Q)}$, as essentially shown in \\cite[(3.4)]{Lerner:NYJM}. (Strictly speaking, \\cite[(3.4)]{Lerner:NYJM} is formally slightly weaker, but a straightforward modification of the argument gives the desired version, as observed in \\cite[Proof of Theorem 3.4]{NPTV:convex}.) Thus Corollary \\ref{cor:1scale2cbd} says that a Dini--Calder\\'on--Zygmund operator satisfies $(\\avL^1(3Q),\\avL^1(Q))$ convex body domination, but this was of course already known from \\cite{NPTV:convex} by essentially the same argument.\n\\end{example}\n\n\\begin{example}[Banach space -valued Calder\\'on--Zygmund operators]\\label{ex:CZO-Banach}\nLet $T$ be as in Example \\ref{ex:CZO} but now acting on the Bochner space $L^2(\\mathbb{R}^d;E)$ of Banach space $E$ -valued functions, and with an operator-valued kernel $K(x,y)\\in\\bddlin(E)$ satisfying the same estimates as above but for the operator norm in place of the absolute value, e.g., $\\Norm{K(x,y)}{\\bddlin(E)}\\leq c\\abs{x-y}^{-d}$. It is in general a difficult problem to check the $L^2(\\mathbb{R}^d;E)$-boundedness of such an operator, but we now take this as an assumption. For $g\\in L^2(\\mathbb{R}^d;E^*)$, we have \\eqref{eq:Lerner} with $L^\\infty(Q;E)$ and $L^1(Q;E^*)$ in place of $L^\\infty(Q)$ and $L^1(Q)$, and the same proof of \\cite[(3.4)]{Lerner:NYJM} (with same modifications pointed out in \\cite[Proof of Theorem 3.4]{NPTV:convex}) shows that the $L^\\infty(Q;E)$ norm is dominated by $\\Norm{f}{\\avL^1(3Q;E)}$. Thus we find that \\eqref{it:1scaleDomScaNew} of Corollary \\ref{cor:1scale2cbd} also holds with $X(Q)=\\avL^1(3Q;E)$ and $Y(Q)=\\avL^1(Q;E^*)$. The resulting sparse domination (i.e., case $n=1$ of the conclusion of Corollary \\ref{cor:1scale2cbd}) was known before, first in \\cite{HH:14} for a slightly smaller class of kernels, and since \\cite[discussion on page 193]{Lacey:elem} in the present generality. However, the convex body domination in this Banach space -valued setting is completely new.\n\\end{example}\n\n\n\\begin{example}[Operators with grand maximal function control]\\label{ex:grand}\nLet $1 \\leq q \\leq r$ and $s \\geq 1$. Suppose that $T$ is a linear operator\n\\begin{equation}\\label{eq:TLcL1loc}\n T:L^\\infty_c(\\mathbb{R}^d)\\to L^1_{\\loc}(\\mathbb{R}^d),\n\\end{equation}\nthat $T$ has weak type $(q,q)$, and that the bi-sublinear maximal operator\n\\begin{equation*}\n \\mathcal M_T(f,g)(x):=\\sup_{Q\\owns x} \\fint_Q \\abs{T(1_{(3Q)^c}f)}\\cdot\\abs{g}\n\\end{equation*}\nmaps boundedly $\\mathcal M_T:L^r\\times L^s\\to L^{\\nu,\\infty}$, where $1\/\\nu=1\/r+1\/s$. Then condition \\eqref{it:1scaleDomScaNew} of Corollary \\ref{cor:1scale2cbd} holds for $t(f,g)=\\pair{Tf}{g}$ and $E=H=\\mathbb{R}$ and $X(Q)=\\avL^r(3Q)$, $Y(Q)=\\avL^s(Q)$. This result is essentially contained in the proof of \\cite[Theorem 3.1]{Lerner:rough}, where it appears as an intermediate step towards the sparse domination (i.e., case $n=1$ of the conclusion of Corollary \\ref{cor:1scale2cbd}) for such operators. The extension to convex body domination was recently achieved in \\cite{MRR:22}, so Corollary \\ref{cor:1scale2cbd} only reproduces a known result here. A key example of concrete operators satisfying these assumptions consists of rough homogeneous singular integrals\n\\begin{equation*}\n Tf(x)=\\int_{\\mathbb{R}^d}\\frac{\\Omega(y)}{\\abs{y}^d}f(x-y)\\ud y,\n\\end{equation*}\nwhere $\\Omega(y)=\\Omega(y\/\\abs{y})$ is a bounded function with vanishing average over the unit sphere.\n\nAs in Example \\ref{ex:CZO-Banach}, the abstract result above, involving a priori bounds of $T$ and $\\mathcal M_T$, extends straightforwardly to the Banach space -valued setting; however, verifying these bounds for concrete operators such as the rough homogeneous singular integrals may present a problem in this generality, since the scalar-valued versions depend on deep results of Seeger \\cite{Seeger:rough}, which so far lack a Banach space -valued extension.\n\\end{example}\n\n\n\\section{Matrix-weighted inequalities for Banach space -valued operators}\\label{sec:L2WE}\n\nA matrix weight is a locally integrable function $W:\\mathbb{R}^d\\to\\mathbb{R}^{n\\times n}$ that is a.e.\\ positive definite -valued. The space $L^p(W)$ consists of all measurable $\\vec f:\\mathbb{R}^d\\to\\mathbb{R}^n$ such that $W^{1\/p}\\vec f\\in L^p(\\mathbb{R}^d;\\mathbb{R}^n)$, and $\\Norm{\\vec f}{L^p(W)}:=\\Norm{W^{1\/p}\\vec f}{L^p(\\mathbb{R}^d;\\mathbb{R}^n)}$.\n\nFor a Banach space $E$, we extend this definition in a natural way: The space $L^p(W;E^n)$ consists of all measurable $\\vec f:\\mathbb{R}^d\\to E^n$ such that $W^{1\/p}\\vec f\\in L^p(\\mathbb{R}^d;E^n)$, and $\\Norm{\\vec f}{L^p(W;E^n)}:=\\Norm{W^{1\/p}\\vec f}{L^p(\\mathbb{R}^d;E^n)}$. Here, at each $x\\in\\mathbb{R}^d$, we define $(W^{1\/p}\\vec f)(x)\\in E^n$ as the vector with components $(W^{1\/p}\\vec f)_i(x):=\\sum_{j=1}^n (W^{1\/p}(x))_{ij}f_j(x)$, i.e., the matrix multiplication on $\\mathbb{R}^n$ is extended to $E^n$ in the natural way.\n\nWe now concentrate on $p=2$. For two matrix weights $W,V:\\mathbb{R}^d\\to\\mathbb{R}^{n\\times n}$, we define\n\\begin{equation*}\n [W,V]_{A_2}:=\\sup_Q\\abs{\\ave{W}_Q^{1\/2}\\ave{V}_Q^{1\/2}}^2,\\qquad\n [W]_{A_2}:=[W,W^{-1}]_{A_2},\n\\end{equation*}\nwhere we denote the operator norm in $\\mathbb{R}^{n\\times n}\\simeq\\bddlin(\\mathbb{R}^n)$ simply by $\\abs{\\ }$. We denote by $A_2(\\mathbb{R}^d;\\mathbb{R}^n)$ the class of matrix weights $W:\\mathbb{R}^d\\to\\mathbb{R}^{n\\times n}$ for which $[W]_{A_2}<\\infty$. We also define\n\\begin{equation*}\n [W]_{A_\\infty}:=\\sup_{\\vec e\\in\\mathbb{R}^n}[x\\mapsto \\vec e\\cdot W(x)\\vec e]_{A_\\infty},\n\\end{equation*}\nwhere on the right we have $A_\\infty$ ``norms'' of some scalar weights, defined as usual by\n\\begin{equation*}\n [w]_{A_\\infty}:=\\sup_Q\\frac{1}{w(Q)}\\int_Q M(1_Q w).\n\\end{equation*}\nAccording to \\cite[Remark 4.4]{NPTV:convex}, we have\n\\begin{equation}\\label{eq:Ainfty-vs-A2}\n [W]_{A_\\infty}\\leq 4[W]_{A_2}.\n\\end{equation}\n\nAs a consequence of the Banach space -valued convex body domination from Example \\ref{ex:CZO-Banach}, we obtain:\n\n\\begin{theorem}\\label{thm:L2WE}\nLet $E$ be a Banach space, and $T\\in\\bddlin(L^2(\\mathbb{R}^d;E))$ be a Dini--Calder\\'on--Zygmund operator with $\\bddlin(E)$-valued kernel. For any $W\\in A_2(\\mathbb{R}^d;\\mathbb{R}^n)$, the operator $T$ extends boundedly to $L^2(W;E^n)$ and satisfies\n\\begin{equation*}\n \\Norm{T}{\\bddlin(L^2(W;E^n))}\\leq c_{n,T}([W]_{A_2}[W]_{A_\\infty}[W^{-1}]_{A_\\infty})^{1\/2}\\leq c_{n,T}[W]_{A_2}^{3\/2}.\n\\end{equation*}\n\\end{theorem}\n\nNote that Theorem \\ref{thm:L2WE} applies to a general Banach space $E$, but contains the (difficult) a priori boundedness hypothesis that $T\\in\\bddlin(L^2(\\mathbb{R}^d;E))$. Concrete examples are available in the class of UMD spaces, treated in detail in \\cite{HNVW}.\n\n\\begin{corollary}\\label{cor:L2WE}\nLet $E$ be a UMD space, and $T\\in\\bddlin(L^2(\\mathbb{R}^d))$ be a scalar-valued Calder\\'on--Zygmund operator with a H\\\"older-type modulus of continuity $\\omega(t)=c t^\\delta$, $\\delta\\in(0,1]$ in \\eqref{eq:CZomega}. For any $W\\in A_2(\\mathbb{R}^d;\\mathbb{R}^n)$, the operator $T$ extends boundedly to $L^2(W;E^n)$ and satisfies\n\\begin{equation*}\n \\Norm{T}{\\bddlin(L^2(W;E^n))}\\leq c_{n,E,T}([W]_{A_2}[W]_{A_\\infty}[W^{-1}]_{A_\\infty})^{1\/2}\\leq c_{n,E,T}[W]_{A_2}^{3\/2}.\n\\end{equation*}\nIn particular, this estimate holds when $T$ is the classical Hilbert transform.\n\\end{corollary}\n\n\\begin{proof}\nWe reduce Corollary \\ref{cor:L2WE} to Theorem \\ref{thm:L2WE} with the help of the $T(1)$ theorem of David and Journ\\'e \\cite{DJ:T1}, and its extension to UMD spaces by Figiel \\cite{Figiel:T1}. By the (easy half of) the David--Journ\\'e theorem, the assumptions on $T$ imply that that $T$ satisfies the so-called weak boundedness property as well as $T(1),T^*(1)\\in\\BMO(\\mathbb{R}^d)$. Then, by Figiel's theorem, an operator satisfying these conditions and the Calder\\'on--Zygmund kernel assumptions extends boundedly to $L^2(\\mathbb{R}^d;E)$, for any UMD space $E$. Thus $T$ satisfies the assumptions, and hence the conclusions, of Theorem \\ref{thm:L2WE}, and we are done.\n\\end{proof}\n\nThese results, even just for the Hilbert transform, and even in their qualitative form (i.e., just concluding the boundedness of $T$, without specifying any concrete bound for the norm), are completely new in the combined setting of matrix weights and Banach spaces. For $E=\\mathbb{R}$ and the Hilbert transform $T$, the qualitative form of Corollary \\ref{cor:L2WE} is due to Treil and Volberg \\cite{TV:angle}. The quantitative form for $E=\\mathbb{R}$ was obtained by Nazarov et al.\\ \\cite{NPTV:convex}, and this is the best that is known at the time of writing. For scalar-weights, the power $3\/2$ can be replaced by $1$ \\cite{Hytonen:A2}, and the product of $[W]_{A_\\infty}$ and $[W^{-1}]_{A_\\infty}$ by their sum \\cite{HytPer}, but extending these to the general matrix case consists of the outstanding open ``matrix $A_2$ conjecture''.\n\nTurning to the proof of Theorem \\ref{thm:L2WE}, we begin with:\n\n\\begin{remark}[Without loss of generality, we assume that $E$ is reflexive]\\label{rem:RNP}\nSince Theorem \\ref{thm:L2WE} is about the bounded extension of an operator, it suffices to prove an a priori estimate on a dense subspace of functions $\\vec f$. In particular, we can assume that each component $f_i$ takes its values in a finite-dimensional subspace of $E$. Since any finite-dimensional space is reflexive, {\\em we make the standing assumption, without loss of generality, that $E$ is reflexive}. (Note that this is automatic in Corollary \\ref{cor:L2WE} in any case, since UMD spaces are reflexive \\cite[Theorem 4.3.3]{HNVW}.) Under this assumption, we have $L^1(Q;E)^*=L^\\infty(Q;E^*)$ (see \\cite[Theorems 1.3.10 and 1.3.21]{HNVW}), which is convenient in view of calculations involving the convex bodies $\\cave{\\ }_{\\avL^1(Q;E)}$.\n\\end{remark}\n\n\\begin{lemma}\n\\begin{equation*}\n\\begin{split}\n \\abs{Q} &\\cave{W^{1\/2}\\vec f}_{\\avL^1(3Q;E)}\\cdot \\cave{V^{1\/2}\\vec g}_{\\avL^1(Q;E^*)} \\\\\n &\\leq \\int \\Big(1_Q(x)\\fint_{3Q}\\abs{V^{1\/2}(x)W^{1\/2}(y)}\\Norm{\\vec f(y)}{E^n}\\ud y\\Big)\\Norm{\\vec g(x)}{\\vec E^{*n}}\\ud x\n\\end{split}\n\\end{equation*}\n\\end{lemma}\n\n\\begin{proof}\nUnder the standing assumption from Remark \\ref{rem:RNP}, we evaluate consider a generic element of the convex body on the left with $\\phi\\in\\bar B_{L^\\infty(Q;E^*)}$ and $\\psi\\in\\bar B_{L^\\infty(Q;E)}$:\n\\begin{equation*}\n\\begin{split}\n &\\abs{Q}\\Babs{\\fint_{3Q} W^{1\/2}(y)\\pair{\\vec f(y)}{\\phi(y)}\\ud y\\cdot \\fint_Q V^{1\/2}(x)\\pair{\\vec g(x)}{\\psi(x)}\\ud x} \\\\\n &=\\abs{Q}\\Babs{\\fint_Q\\fint_{3Q} V^{1\/2}(x)W^{1\/2}(y)\\pair{\\vec f(y)}{\\phi(y)}\\cdot\\pair{\\vec g(x)}{\\psi(x)}\\ud y\\ud x} \\\\\n &\\leq\\int_Q\\fint_{3Q} \\abs{V^{1\/2}(x)W^{1\/2}(y)}\\Norm{\\vec f(y)}{E^n}\\Norm{\\vec g(x)}{\\vec E^{*n}}\\ud y\\ud x.\\qedhere\n\\end{split}\n\\end{equation*}\n\\end{proof}\n\nSumming over a sparse collection, we obtain\n\\begin{equation}\\label{eq:cbd-vs-L}\n\\begin{split}\n &\\sum_{Q\\in\\mathscr S} \\abs{Q} \\cave{W^{1\/2}\\vec f}_{\\avL^1(3Q;E)}\\cdot \\cave{V^{1\/2}\\vec g}_{\\avL^1(Q;E^*)} \\\\\n &\\leq \\int\\Big(\\sum_{Q\\in\\mathscr S}1_Q(x)\\fint_{3Q}\\abs{V^{1\/2}(x)W^{1\/2}(y)}\\Norm{\\vec f(y)}{E^n}\\ud y\\Big)\\Norm{\\vec g(x)}{\\vec E^{*n}}\\ud x \\\\\n &=:\\int \\tilde L(\\Norm{\\vec f}{E^n})(x)\\Norm{\\vec g(x)}{\\vec E^{*n}}\\ud x,\n\\end{split}\n\\end{equation}\nwhere $\\tilde L$, here acting on the scalar-valued function $y\\mapsto\\Norm{\\vec f(y)}{E^n}$, is an operator denoted by the same symbol in \\cite[(5.8)]{NPTV:convex}. By \\cite[Lemma 5.6]{NPTV:convex}, we have\n\\begin{equation}\\label{eq:NPTV-L}\n \\Norm{\\tilde L}{\\bddlin(L^2)}\\leq C([W,V]_{A_2}[W]_{A_\\infty}[V]_{A_\\infty})^{1\/2}.\n\\end{equation}\n\nBy duality and standard changes of variables, which present no essential difference in the Banach space -valued setting, an estimate of the form\n\\begin{equation*}\n \\Norm{T\\vec f}{L^2(V;E^n)}\\leq N\\Norm{\\vec f}{L^2(V;E^n)}\n\\end{equation*}\nis equivalent to\n\\begin{equation}\\label{eq:dualBound2prove}\n \\pair{T(W^{1\/2}\\vec f)}{V^{1\/2}\\vec g}\\leq N\\Norm{\\vec f}{L^2(\\mathbb{R}^d;E^n)}\\Norm{\\vec g}{L^2(\\mathbb{R}^d;E^{*n})}.\n\\end{equation}\nIf $T$ is an in Theorem \\ref{thm:L2WE}, it satisfies the $(\\avL^1(3Q;E),\\avL^1(Q;E^*))$ convex body domination by Example \\ref{ex:CZO-Banach}, which means that the left-hand side of \\eqref{eq:dualBound2prove} is dominated by the left-hand side of \\eqref{eq:cbd-vs-L}, and hence, by \\eqref{eq:cbd-vs-L} and \\eqref{eq:NPTV-L}, we have\n\\begin{equation*}\n N\\leq c_{n,T}([W,V]_{A_2}[W]_{A_\\infty}[V]_{A_\\infty})^{1\/2}.\n\\end{equation*}\nThis is the desired bound, and concludes the proof of Theorem \\ref{thm:L2WE}.\n\n\\section{Convex domination and generalised commutators}\\label{sec:commu}\n\nFor an operator $T$ and two vector functions $\\vec a=(a_1,\\ldots,a_n)$ and $\\vec b=(b_1,\\ldots,b_n)$, let us consider the operator\n\\begin{equation*}\n \\vec a\\cdot T\\vec b:f\\mapsto\\vec a\\cdot T(\\vec b f)=\\sum_{i=1}^n a_i T(b_i f).\n\\end{equation*}\nWe are mainly interested in the boundedness on $L^p(\\mathbb{R}^d)$, or a weighted $L^p(w)$, or between two such spaces, and the case when $T$ is a singular integral operator bounded on the space. However, we do not require that $a_i,b_i\\in L^\\infty(\\mathbb{R}^d)$, and hence the pointwise multipliers $f\\mapsto b_i f$ and $g\\mapsto a_i g$, and the compositions $f\\mapsto a_iT(b_i f)$, may be unbounded operators. Nevertheless, their sum $\\vec a\\cdot T\\vec b$ may still be bounded, thanks to cancellation between different terms.\n\nA case that has been much studied in the literature consists of $\\vec b=(1,b)$ and $\\vec a=(b,-1)$, in which case\n\\begin{equation*}\n \\vec a\\cdot T(\\vec b f)= bTf-T(bf)=[b,T]f\n\\end{equation*}\nis the {\\em commutator} of $b$ and $T$, whose $L^p(\\mathbb{R}^d)$-boundedness is characterised by $b\\in\\BMO(\\mathbb{R}^d)$, the space of functions of bounded mean oscillation, which is strictly larger than $L^\\infty(\\mathbb{R}^d)$, and contains in particular functions like $b(x)=\\log\\abs{x}$.\n\nBy dualising with a function $g$, and denoting by $t(f,g)=\\pair{Tf}{g}$ the bilinear form of $T$, we arrive at\n\\begin{equation*}\n \\pair{\\vec a\\cdot T(\\vec b f)}{g}=\\sum_{i=1}^n\\pair{T(b_i f)}{a_i g}\n =t(\\vec b f,\\vec a g),\n\\end{equation*}\nwhere the action of the bilinear form is extended to vector-valued functions as before. To be precise, if $t$ in defined on $F\\times G$, we should now require that\n\\begin{equation*}\n f\\in F_{\\vec b}:=\\{f\\in F: b_i f\\in F\\text{ for all }i=1,\\ldots,n\\},\n\\end{equation*}\nand $g\\in G_{\\vec a}$, defined similarly. If $F\\supseteq L^\\infty_c(\\mathbb{R}^d)$, then clearly $F_{\\vec b}$ contains in particular all $f\\in L^\\infty_c(\\mathbb{R}^d)$ with $\\supp f\\subseteq E_N:=\\{\\abs{\\vec b}\\leq N\\}$ for any $N\\in\\mathbb{N}$. For a.e.\\ finite-valued $b_i$, the union $\\bigcup_{N\\in\\mathbb{N}}E_N$ covers $\\mathbb{R}^d$ up to a null set, it is immediate that $F_{\\vec b}$ is dense in any $L^p(w)$ with finite $p$. \n\n\n\\begin{lemma}\\label{lem:cbd-implies}\nSuppose that $T$ satisfies the $(X(Q),Y(Q))$ convex body domination. Then for all relevant functions, we have\n\\begin{equation}\\label{eq:cbd-implies}\n \\abs{\\pair{\\vec{a}\\cdot T(\\vec{b}f)}{g}}\n \\leq C\\sum_{Q\\in\\mathscr S}\\abs{Q}\\cave{\\vec b f}_{X(Q)}\\cdot\\cave{\\vec a g}_{Y(Q)}.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThis is immediate by applying definition to $\\vec f=\\vec b f$ and $\\vec g=\\vec a g$.\n\\end{proof}\n\nWe take a closer look at the case when $X(Q)=Y(Q)=\\avL^1(\\gamma Q)$.\n\n\\begin{lemma}\\label{lem:cbdL1-further}\nFor all $s,t\\in(1,\\infty)$ and all functions in the relevant spaces, we have\n\\begin{equation*}\n \\cave{\\vec b f}_{\\avL^1(Q)}\\cdot\\cave{\\vec a g}_{\\avL^1(Q)}\n \\leq\\Norm{(x,y)\\mapsto\\vec a(x)\\cdot\\vec b(y)}{\\avL^{(s,t)}_{\\min}(Q\\times Q)}\n \\Norm{f}{\\avL^{t'}(Q)}\\Norm{g}{\\avL^{s'}(Q)},\n\\end{equation*}\nwhere\n\\begin{equation*}\n \\Norm{F}{\\avL^{(s,t)}_{\\min}(Q\\times Q)}\n :=\\begin{cases} \\Big(\\fint_Q\\Big[\\fint_Q\\abs{F(x,y)}^s\\ud x\\Big]^{t\/s}\\ud y\\Big)^{1\/t}, & \\text{if }s\\leq t, \\\\\n \\Big(\\fint_Q\\Big[\\fint_Q\\abs{F(x,y)}^t\\ud y\\Big]^{s\/t}\\ud x\\Big)^{1\/s}, & \\text{if }t\\leq s. \\end{cases} \n\\end{equation*}\n\\end{lemma}\n\n\\begin{proof}\nThe generic element of $\\cave{\\vec b f}_{X(Q)}\\cdot\\cave{\\vec a g}_{Y(Q)}$ has the following form, where $\\phi,\\psi\\in\\bar B_{L^\\infty(Q)}$:\n\\begin{equation*}\n\\begin{split}\n &\\fint_Q \\vec b(y)f(y)\\phi(y)\\ud y\\cdot\\fint_Q \\vec a(x)g(x)\\psi(x)\\ud x \\\\\n &=\\fint_Q\\fint_Q(\\vec a(x)\\cdot\\vec b(y))f(y)g(x)\\phi(y)\\psi(x)\\ud x\\ud y,\n\\end{split}\n\\end{equation*}\nand hence\n\\begin{equation*}\n\\begin{split}\n &\\cave{\\vec b f}_{X(Q)}\\cdot\\cave{\\vec a g}_{Y(Q)}\n \\leq\\fint_Q\\fint_Q\\abs{\\vec a(x)\\cdot\\vec b(y)} \\abs{f(y)}\\abs{g(x)}\\ud x\\ud y \\\\\n &\\leq\\Norm{(x,y)\\mapsto a(x)\\cdot b(y)}{Z}\\Norm{(x,y)\\mapsto f(y)g(x)}{Z^*},\n\\end{split}\n\\end{equation*}\nfor either choice of\n\\begin{equation*}\n (Z,Z^*)\\in\\{(\\avL^s_x(Q;\\avL^t_y(Q)),\\avL^{s'}_x(Q;\\avL^{t'}_y(Q))),\n (\\avL^t_y(Q;\\avL^s_x(Q)),\\avL^{t'}_y(Q;\\avL^{s'}_x(Q)))\\},\n\\end{equation*}\nby H\\\"older's inequality for mixed-norm $L^p$ spaces. By Fubini's theorem, we have\n\\begin{equation*}\n \\Norm{(x,y)\\mapsto f(x)g(y)}{Z^*}\n =\\Norm{f}{\\avL^{t'}(Q)}\\Norm{g}{\\avL^{s'}(Q)}\n\\end{equation*}\nin either case, and hence, taking the minimum over the two choices of $Z$, we arrive at the factor\n\\begin{equation*}\n \\min_Z\\Norm{(x,y)\\mapsto b(x)\\cdot a(y)}{Z}=\\Norm{(x,y)\\mapsto\\vec b(x)\\cdot\\vec a(y)}{\\avL^{(s,t)}_{\\min}(Q\\times Q)}.\n\\end{equation*}\n\\end{proof}\n\n\\begin{proposition}\\label{prop:gen-commu}\nLet $T$ be an operator that satisfies the $(\\avL^1(\\gamma Q),\\avL^1(\\gamma Q))$ convex body domination. Let $\\vec a,\\vec b\\in L^1_{\\loc}(\\mathbb{R}^d)^n$ be functions such that\n\\begin{equation*}\n A_{s,t}:=\\sup_{Q}\\Norm{(x,y)\\mapsto \\vec a(x)\\cdot\\vec b(y)}{\\avL^{(s,t)}_{\\min}(Q\\times Q)}<\\infty.\n\\end{equation*}\nThen $\\vec a\\cdot T\\vec b$ extends to a bounded operator on $L^p(\\mathbb{R}^d)$ for all $p\\in(t',s)$. In particular, if $A_s:=A_{s,s}<\\infty$ for some $s\\in(2,\\infty)$, then $\\vec a\\cdot T\\vec b$ extends boundedly to $L^2(\\mathbb{R}^d)$.\n\\end{proposition}\n\n\\begin{proof}\nCombining Lemmas \\ref{lem:cbd-implies} and \\ref{lem:cbdL1-further}, we find that\n\\begin{equation*}\n\\begin{split}\n \\abs{\\pair{\\vec aT(\\vec b f)}{g}}\n &\\leq C\\sum_{Q\\in\\mathscr S}\\abs{Q}\\cave{\\vec b f}_{\\avL^1(\\gamma Q)}\\cdot\\cave{\\vec a g}_{\\avL^1(\\gamma Q)} \\\\\n &\\leq C\\sum_{Q\\in\\mathscr S}\\abs{Q}\\Norm{(x,y)\\mapsto a(x)\\cdot b(y)}{\\avL^{(s,t)}_{\\min}(Q\\times Q)}\n \\Norm{f}{\\avL^{t'}(Q)}\\Norm{g}{\\avL^{s'}(Q)} \\\\\n &\\leq C\\sum_{Q\\in\\mathscr S}\\frac{\\abs{E(Q)}}{\\delta}A_{s,t}\\inf_Q M_{t'}f\\inf_Q M_{s'}g \\\\\n &\\leq\\frac{C A_{s,t}}{\\delta}\\sum_{Q\\in\\mathscr S}\\int_{E(Q)}M_{t'}f M_{s'}g\n \\leq\\frac{C A_{s,t}}{\\delta}\\int_{\\mathbb{R}^d}M_{t'}f M_{s'}g \\\\\n &\\leq\\frac{C A_{s,t}}{\\delta}\\Norm{M_{t'}f}{L^p(\\mathbb{R}^d)}\\Norm{M_{s'}g}{L^{p'}(\\mathbb{R}^d)},\n\\end{split}\n\\end{equation*}\nwhere\n\\begin{equation*}\n \\Norm{M_{t'}f}{L^p(\\mathbb{R}^d)}\\lesssim_{t,p}\\Norm{f}{L^p(\\mathbb{R}^d)},\\qquad\n \\Norm{M_{s'}g}{L^{p'}(\\mathbb{R}^d)}\\lesssim_{s,p}\\Norm{g}{L^{p'}(\\mathbb{R}^d)}\n\\end{equation*}\nfor $p>t'$ and $p'>s'$, where the latter is equivalent to $p2$. On the other hand, by Proposition \\ref{prop:gen-commu}, another sufficient condition for the same conclusion is $A_s<\\infty$.\n\nLet us compare the two. Adding and subtracting terms and multiplying out, we find that\n\\begin{equation*}\n\\begin{split}\n &(b^1(x)-b^1(y))(b^2(x)-b^2(y)) \\\\\n &=[(b^1(x)-\\ave{b^1}_Q)-(b^1(y)-\\ave{b^1}_Q)][(b^2(x)-\\ave{b^2}_Q)-(b^2(y)-\\ave{b^2}_Q)] \\\\\n &=(b^1(x)-\\ave{b^1}_Q)(b^2(x)-\\ave{b^2}_Q)+(b^1(y)-\\ave{b^1}_Q)(b^2(y)-\\ave{b^2}_Q) \\\\\n &\\qquad-(b^1(x)-\\ave{b^1}_Q)(b^2(y)-\\ave{b^2}_Q)-(b^1(y)-\\ave{b^1}_Q)(b^2(x)-\\ave{b^2}_Q).\n\\end{split}\n\\end{equation*}\nTaking $\\avL^s(Q\\times Q)$ and then supremum over $Q$ on both sides, we deduce that\n\\begin{equation*}\n A_s\\leq 2(T_s+S_s),\n\\end{equation*}\nso that the new criterion provided by Proposition \\ref{prop:gen-commu} is at least as sharp as that of \\cite[(1.1)]{HLO:20}, and it seems less obvious to make any estimate in the other direction. Perhaps more importantly, the new condition $A_s<\\infty$ arises more ``naturally'' as an instance of a general principle.\n\n(Let us note that there is a more general criterion \\cite[Theorem 3.10]{HLO:20}, where the $\\avL^s$ norms in $S_s$ and $T_t$ are replaced by more general Orlicz norms. On the other hand, it is apparent that similar generalisations could be achieved in Proposition \\ref{prop:gen-commu}: what we used was the boundedness of the rescaled maximal operators $M_{t'}$ on $L^p(\\mathbb{R}^d)$ for $p>t'$, and this could be replaced having an Orlicz maximal operator $M_A$ with the same mapping property. A characterisation of this property in terms of the so-called $B_p$ condition on the Orlicz function $A$ is a classical result of P\\'erez \\cite{Perez:95}; this very result is used in \\cite{HLO:20}; see \\cite[Proposition 3.8]{HLO:20}.)\n\\end{example}\n\nLet us finally consider an ``exotic'' example with no obvious predecessor in the existing literature. We begin with a lemma:\n\n\\begin{lemma}\\label{lem:BMOpowers}\nSuppose that $0\\leq b\\in\\BMO(\\mathbb{R}^d)$. If $0\\leq\\alpha,\\beta$ and $\\alpha+\\beta\\leq 1$, then\n\\begin{equation*}\n B(x,y):=b(x)^\\alpha b(y)^\\beta - b(x)^\\beta b(y)^\\alpha\n\\end{equation*}\nsatisfies\n\\begin{equation*}\n \\Big(\\fint_Q\\fint_Q\\abs{B(x,y)}^p\\ud x\\ud y\\Big)^{1\/p} \\leq (2\\Norm{b}{\\BMO^p(\\mathbb{R}^d)})^{\\alpha+\\beta}.\n\\end{equation*}\n\\end{lemma}\n\n\\begin{proof}\nLet $\\gamma:=\\min(\\alpha,\\beta)\\in[0,\\frac12]$ and $\\delta:=\\max(\\alpha,\\beta)-\\gamma\\in[0,1]$. Then\n\\begin{equation*}\n \\abs{B(x,y)}=b(x)^\\gamma b(y)^\\gamma \\abs{b(x)^\\delta-b(y)^\\delta}.\n\\end{equation*}\nWe observe the following elementary inequality:\n\\begin{equation}\\label{eq:ud-vd}\n \\abs{u^\\delta-v^\\delta}\\leq\\frac{\\abs{u-v}}{\\max(u,v)^{1-\\delta}},\\qquad\\forall u,v\\geq 0,\\ \\delta\\in[0,1].\n\\end{equation}\nIndeed, by symmetry and homogeneity, it is enough to consider $u=1$ and $v\\in[0,1]$, in which case we are reduced to proving that\n\\begin{equation*}\n 1-v^\\delta\\leq 1-v,\n\\end{equation*}\nwhich is immediate from the fact that $v\\leq v^\\delta$ for $v,\\delta\\in[0,1]$.\n\nUsing \\eqref{eq:ud-vd}, and noting that $\\delta+2\\gamma=\\alpha+\\beta\\in[0,1]$, it follows that\n\\begin{equation*}\n\\begin{split}\n \\abs{B(x,y)} &\\leq b(x)^\\gamma b(y)^\\gamma\\frac{\\abs{b(x)-b(y)}}{\\max(b(x),b(y))^{1-\\delta}} \n \\leq \\frac{\\abs{b(x)-b(y)}}{\\max(b(x),b(y))^{1-\\delta-2\\gamma}} \\\\\n &= \\Big(\\frac{\\abs{b(x)-b(y)}}{\\max(b(x),b(y))}\\Big)^{1-\\delta-2\\gamma}\\abs{b(x)-b(y)}^{\\delta+2\\gamma}\n \\leq\\abs{b(x)-b(y)}^{\\alpha+\\beta},\n\\end{split}\n\\end{equation*}\nand hence\n\\begin{equation*}\n\\begin{split}\n \\Big( &\\fint_Q\\fint_Q\\abs{B(x,y)}^p\\ud x\\ud y\\Big)^{1\/p}\n \\leq \\Big(\\fint_Q\\fint_Q\\abs{b(x)-b(y)}^p\\ud x\\ud y\\Big)^{(\\alpha+\\beta)\/p} \\\\\n &\\leq\\Big[\\Big(\\fint_Q\\abs{b(x)-c}^p\\ud x\\Big)^{1\/p}+\\Big(\\fint_Q\\abs{b(y)-c}^p\\ud y\\Big)^{1\/p}\\Big]^{\\alpha+\\beta}\n\\end{split}\n\\end{equation*}\nfor all constants $c$.\n\\end{proof}\n\n\\begin{corollary}\nLet $T$ be an operator satisfying $(\\avL^1(\\gamma Q),\\avL^1(\\gamma Q))$ convex body domination, let $0\\leq b\\in\\BMO(\\mathbb{R}^d)$ and $0\\leq\\alpha,\\beta$ with $\\alpha+\\beta\\leq 1$. Then\n\\begin{equation*}\n \\Norm{b^\\alpha T(b^\\beta f)-b^\\beta T(b^\\alpha f)}{L^p(\\mathbb{R}^d)}\\lesssim_p \\Norm{b}{\\BMO(\\mathbb{R}^d)}^{\\alpha+\\beta}\\Norm{f}{L^p(\\mathbb{R}^d)}.\n\\end{equation*}\n\\end{corollary}\n\n\\begin{proof}\nBy Proposition \\ref{prop:gen-commu} with $s=t$, the $L^p(\\mathbb{R}^d)$ operator norm of $f\\mapsto b^\\alpha T(b^\\beta f)-b^\\beta T(b^\\alpha f)$ is dominated by\n\\begin{equation*}\n A_s:= \\sup_Q\\Norm{(x.y)\\mapsto b(x)^\\alpha b(y)^\\beta-b(x)^\\beta b(y)^\\alpha}{\\avL^s(Q\\times Q)}\n\\end{equation*}\nif $p\\in(s',s)$, i.e., if $s>\\max(p,p')$. By Lemma \\ref{lem:BMOpowers} and the John--Nirenberg inequality, we have\n\\begin{equation*}\n A_s\\leq(2\\Norm{b}{\\BMO^s(\\mathbb{R}^d)})^{\\alpha+\\beta}\\lesssim_s\\Norm{b}{\\BMO(\\mathbb{R}^d)}^{\\alpha+\\beta},\n\\end{equation*}\nand fixing (say) $s=2\\max(p,p')$, we obtain a dependence on $p$ only.\n\\end{proof}\n\n\n\\begin{remark}\nAside from the examples already discussed, the generalised commutators $\\vec a\\cdot T\\vec b$ also arise in the following question studied by Bloom \\cite{Bloom:85,Bloom:89}. Suppose that a matrix weight $W$ is given in the diagonalised form $W = U^*\\Lambda U$, where $U$ is unitary, $\\Lambda$ is diagonal, and the diagonal entries $\\lambda_k$ of $\\Lambda$ are scalar $A_2$ weights. What does one need to know about $U$ in order to conclude that $W\\in A_2$? (According to \\cite[Theorem 4.2]{Bloom:89}, the condition that $\\lambda_k\\in A_2$ is necessary for $W\\in A_2$, if in addition $U$ is assumed to be continuous.)\n\nLet $T$ be the Hilbert transform, or another operator whose boundedness on the matrix-weighted $L^2(W)$ characterises $W\\in A_2$. By connecting the $L^2(W)$ boundedness of $T$ to the boundedness of the classical commutators $[T,\\bar u_{ij}]$ between the weighted spaces $L^2(\\lambda_i)$ and $L^2(\\lambda_k)$ (sic: the condition involves triplets of indices $(i,j,k)$), \\cite[Theorem 5.1]{Bloom:85} shows that $u_{ij}\\in\\BMO_{\\sqrt{\\lambda_i\/\\lambda_k}}$ (a weighted BMO space, nowadays commonly referred to as Bloom-type BMO) is a sufficient condition. In the special case of $2\\times 2$ matrices, it is also necessary by \\cite[Theorem 4.3]{Bloom:89} but, over 30 years since these contributions, the general case seems to remain open. (The author is grateful to Amalia Culiuc for bringing this question to his attention \\cite{Culiuc:22}.)\n\nHere is a possible approach to the problem. As is well known, the $L^2(W)$ boundedness of $T$ is equivalent to the (unweighted) $L^2$ boundedness of\n\\begin{equation*}\n W^{1\/2}TW^{-1\/2}=U^*\\Lambda^{1\/2}UTU^*\\Lambda^{-1\/2}U.\n\\end{equation*}\nMultiplication by $U$ and $U^*$ is isometric on $L^2$, and the $L^2$ boundedness of a matrix of operators is equivalent to the $L^2$ boundedness of each of the components\n\\begin{equation*}\n (\\Lambda^{1\/2}UTU^*\\Lambda^{-1\/2})_{ij}\n =\\sum_{k=1}^n\\lambda_i^{1\/2}u_{ik}T\\bar u_{jk}\\lambda_j^{-1\/2}\n =\\lambda_i^{1\/2}\\vec u_i\\cdot T\\bar {\\vec u}_j\\lambda_j^{-1\/2},\n\\end{equation*}\nwhere $i,j=1,\\ldots,n$ and $\\vec u_i=(u_{ik})_{k=1}^n$. These are operators of the form $\\vec{a}\\cdot T\\vec{b}$ that we have studied here and, up to this point, we kept an exact equivalence with the original question; the question then would be, whether we can give useful conditions on the boundedness of these operators. A further equivalent condition is of course the two-weight boundedness\n\\begin{equation*}\n \\vec u_i\\cdot T\\bar {\\vec u}_j:L^2(\\lambda_j)\\to L^2(\\lambda_i),\\qquad i,j=1,\\ldots,n,\n\\end{equation*}\nwhere the spaces are more complicated, but the multipliers are simply rows of the unitary matrix $U$.\n\\end{remark}\n\n\\begin{remark}\nWe have concentrated in this section on the application of convex body domination---an inherently vector-valued theory---to questions of generalised commutators acting on scalar-valued functions. We have made this choice for two reasons: to make the case that this vector-valued theory is useful even for such scalar-valued applications, and not to obscure the relatively simple basic philosophy behind too many technicalities of notation. This said, it is quite plain that the presented ideas can be immediately generalised to the case of vector-valued functions $\\vec f$ and $\\vec g$ (in place of scalar $f$ and $g$) and matrix-valued multipliers $A$ and $B$ (in place of the vectors $\\vec a$ and $\\vec b$). In the particular case of the classical-style commutator $[T,B]$ with a matrix-valued function, this idea has been developed in \\cite{IPT:commu}. \n\\end{remark}\n\n\n\\section{Stopping times and maximal functions involving convex bodies}\n\nThe aims of this final section are two-fold. Concretely, we establish a convex-body analogue of a result of Nieraeth \\cite{Nie:19}, which shows that the estimation of sums over sparse collection that arise in the usual sparse domination is equivalent to the estimation of certain maximal functions. On the way of achieving this, we develop some convex-body versions of the typical stopping time arguments involving averages of scalar-valued functions; these might have some independent interest elsewhere.\n\nWe begin with an estimate of a sum of convex-body ``norms'' over disjoint subsets.\n\n\\begin{lemma}\\label{lem:stoppingAux}\nLet $p,q\\in[1,\\infty)$ and $\\frac1r:=\\frac1p+\\frac1q$. Let $Q_i\\in\\mathscr D(Q_0)$ be disjoint cubes. Then\n\\begin{equation*}\n \\sum_{i=1}^\\infty\\big(\\cave{\\vec f}_{L^p(Q_i)}\\cdot\\cave{\\vec g}_{L^q(Q_i)}\\big)^r\n \\leq n^{\\max(r,1)+r\/2}\\big(\\cave{f}_{L^p(Q)}\\cdot\\cave{g}_{L^q(Q)}\\big)^r.\n\\end{equation*}\n\\end{lemma}\n\nNote that for $p,q\\in[1,\\infty)$, we have $\\frac1r=\\frac1p+\\frac1q\\leq 1+1=2$, and hence\n\\begin{equation*}\n n^{\\max(r,1)+r\/2}\n =\\big(n^{\\max(1,1\/r)+1\/2}\\big)^r\\leq \\big(n^{5\/2}\\big)^r.\n\\end{equation*}\n\n\\begin{proof}\nFor orientation, let us begin with the proof in the case $n=1$, i.e., with $\\Norm{\\ }{}$ in place of $\\cave{\\ }$ throughout. By H\\\"older's inequality with $1=\\frac{r}{p}+\\frac{r}{q}$, we have\n\\begin{equation*}\n\\begin{split}\n \\sum_{i=1}^\\infty &\\big(\\Norm{f}{L^p(Q_i)}\\Norm{g}{L^q(Q_i)}\\big)^r\n =\\sum_{i=1}^\\infty\\big(\\Norm{f}{L^p(Q_i)}^p\\big)^{r\/p}\\big(\\Norm{g}{L^q(Q_i)}^q\\big)^{r\/q} \\\\\n &\\leq\\Big(\\sum_{i=1}^\\infty\\Norm{f}{L^p(Q_i)}^p\\Big)^{r\/p}\\Big(\\sum_{i=1}^\\infty\\Norm{g}{L^q(Q_i)}^q\\Big)^{r\/q} \\\\\n &\\leq\\Big(\\Norm{f}{L^p(Q_0)}^p\\Big)^{r\/p}\\Big(\\Norm{g}{L^q(Q_0)}^q\\Big)^{r\/q}\n =\\Big(\\Norm{f}{L^p(Q_0)}\\Norm{g}{L^q(Q_0)}\\Big)^{r}.\n\\end{split}\n\\end{equation*}\n\nIn the general case of the lemma, let\n\\begin{equation*}\n A_i:=\\cave{\\vec f}_{L^p(Q_i)}\n =\\Big\\{\\int_{Q_i}\\phi_i\\vec f:\\Norm{\\phi_i}{L^{p'}(Q_i)}\\leq 1\\Big\\},\\quad B_i:=\\cave{\\vec g}_{L^q(Q_i)}.\n\\end{equation*}\nThen we observe that\n\\begin{equation*}\n\\begin{split}\n \\cave{\\vec f}_{L^p(Q)}\n &=\\Big\\{\\int_{Q}\\phi\\vec f:\\Norm{\\phi}{L^{p'}(Q)}\\leq 1\\Big\\} \\\\\n &\\supseteq\\Big\\{\\sum_{i=1}^\\infty a_i\\int_{Q_i}\\phi_i\\vec f:\\Norm{\\phi_i}{L^{p'}(Q_i)}\\leq 1, \\Norm{(a_i)}{\\ell^{p'}}\\leq 1\\Big\\} \\\\\n &=\\Big\\{\\sum_{i=1}^\\infty a_i A_i: \\Norm{(a_i)}{\\ell^{p'}}\\leq 1\\Big\\}=:\\bigoplus_{\\ell^p}A_i=:A,\n\\end{split}\n\\end{equation*}\nand similarly\n\\begin{equation*}\n \\cave{\\vec g}_{L^q(Q)}\\supseteq\\bigoplus_{\\ell^q}B_i=:B.\n\\end{equation*}\nHence, the lemma is reduced to proving that\n\\begin{equation*}\n \\sum_{i=1}^\\infty\\big(A_i\\cdot B_i\\big)^r\n \\leq n^{\\max(r,1)+r\/2}\\big(A\\cdot B\\big)^r,\\quad A:=\\bigoplus_{\\ell^p}A_i,\\quad B:=\\bigoplus_{\\ell^q}B_i.\n\\end{equation*}\n\nLet $\\mathcal E_A$ be the John ellipsoid of $A$, and let $R_A\\mathcal E_A=\\bar B_{\\mathbb{R}^n}$. Since $A_i\\cdot B_i=R_A A_i\\cdot R_A^{-t}B_i$, The claim above is equivalent to a version where each $A_i$ is replaced by $R_A A_i$ and each $B_i$ by $R_A^{-t}B_i$. Hence, without loss of generality, we assume that $\\mathcal E_A=\\bar B_{\\mathbb{R}^n}$ to begin with, hence $\\bar B_{\\mathbb{R}^n}\\subseteq A\\subseteq \\sqrt{n}\\bar B_{\\mathbb{R}^n}$. Thus\n\\begin{equation*}\n A\\cdot B\\supset \\bar B_{\\mathbb{R}^n}\\cdot B=[-M,M],\\quad\\text{where}\\quad M:=\\max\\{\\abs{\\vec b}:\\vec b\\in B\\}.\n\\end{equation*}\nOn the other hand, if $(\\vec e_j)_{j=1}^n$ is some orthonormal basis of $\\mathbb{R}^n$, then\n\\begin{equation*}\n\\begin{split}\n A_i\\cdot B_i &=\\{\\vec a\\cdot \\vec b:\\vec a\\in A_i,\\vec b\\in B_i\\} \\\\\n &=\\Big\\{\\sum_{j=1}^n(\\vec a\\cdot\\vec e_j)(\\vec b\\cdot\\vec e_j):\\vec a\\in A_i,\\vec b\\in B_i\\} \n \\subseteq\\sum_{j=1}^n (A_i\\cdot \\vec e_j)(B_i\\cdot\\vec e_j),\n\\end{split}\n\\end{equation*}\nor, using the identification of $[-s,s]$ with $s$,\n\\begin{equation*}\n A_i\\cdot B_i \\leq \\sum_{j=1}^n (A_i\\cdot \\vec e_j)(B_i\\cdot\\vec e_j).\n\\end{equation*}\nThus\n\\begin{equation*}\n (A_i\\cdot B_i)^r \\leq \\sum_{j=1}^n \\big((A_i\\cdot \\vec e_j)(B_i\\cdot\\vec e_j)\\big)^r,\\quad r\\in(0,1],\n\\end{equation*}\nand\n\\begin{equation*}\n \\Big(\\sum_{i=1}^\\infty (A_i\\cdot B_i)^r\\Big)^{1\/r}\\leq\\sum_{j=1}^n \\Big(\\sum_{i=1}^\\infty \\big((A_i\\cdot \\vec e_j)(B_i\\cdot\\vec e_j)\\big)^r\\Big)^{1\/r},\\quad r\\in[1,\\infty).\n\\end{equation*}\nIn the sum over $i$, we use H\\\"older's inequality as in the toy model in the beginning:\n\\begin{equation*}\n\\begin{split}\n \\sum_{i=1}^\\infty&\\big((A_i\\cdot \\vec e_j)(B_i\\cdot\\vec e_j)\\big)^r\n =\\sum_{i=1}^\\infty\\big((A_i\\cdot \\vec e_j)^p\\big)^{r\/p}\\big((B_i\\cdot\\vec e_j)^q\\big)^{r\/q} \\\\\n &\\leq\\Big(\\sum_{i=1}^\\infty(A_i\\cdot \\vec e_j)^p\\Big)^{r\/p}\\Big(\\sum_{i=1}^\\infty(B_i\\cdot\\vec e_j)^q\\Big)^{r\/q} \\\\\n &=\\sup\\Big\\{\\Big(\\sum_{i=1}^\\infty a_i A_i\\cdot\\vec e_j\\Big)^{1\/r}\\Big(\\sum_{i=1}^\\infty b_i B_i\\cdot\\vec e_j\\Big)^{1\/r}:\n \\Norm{(a_i)}{\\ell^{p'}}\\leq 1,\\Norm{(b_i)}{\\ell^{q'}}\\leq 1\\Big\\} \\\\\n &=(A\\cdot \\vec e_j)^r(B\\cdot\\vec e_j)^r\n\\end{split}\n\\end{equation*}\nHere\n\\begin{equation*}\n A\\cdot\\vec e_j\\subseteq \\sqrt{n}\\bar B_{\\mathbb{R}^n}\\cdot\\vec e_j=[-\\sqrt{n},\\sqrt{n}],\\quad A\\cdot\\vec e_j\\leq \\sqrt{n},\n\\end{equation*}\nand clearly\n\\begin{equation*}\n B\\cdot\\vec e_j\\leq M.\n\\end{equation*}\nAltogether, writing $s:=\\max(r,1)$, we have\n\\begin{equation*}\n\\begin{split}\n \\Big(\\sum_{i=1}^\\infty(A_i\\cdot B_i)^r\\Big)^{1\/s}\n &\\leq \\sum_{j=1}^n \\Big[\\sum_{i=1}^\\infty(A_i\\cdot \\vec e_j)^r(B_i\\cdot\\vec e_j)^r\\Big]^{1\/s} \\\\\n &\\leq \\sum_{j=1}^n \\Big[ (A\\cdot \\vec e_j)^r(B\\cdot\\vec e_j)^r\\Big]^{1\/s} \n \\leq n [ n^{r\/2} M^r]^{1\/s},\n\\end{split}\n\\end{equation*}\nand hence\n\\begin{equation*}\n \\sum_{i=1}^\\infty(A_i\\cdot B_i)^r\\leq n^s n^{r\/2} M^r= n^{\\max(1,r)+r\/2}(A\\cdot B)^r,\n\\end{equation*}\nwhich remained to be proved.\n\\end{proof}\n\nThe following lemma is a convex-body analogue of the basic principle underlying the simplest stopping time constructions: for a function on a cube $Q_0$, the total measure of the subcubes, where the average of a function is much bigger than on the whole $Q_0$, can be at most a fraction of the measure of $Q_0$.\n\n\\begin{lemma}\\label{lem:convexStopping}\nLet $A,p,q\\in[1,\\infty)$ and let $Q_i\\in\\mathscr D(Q_0)$ be disjoint cubes such that\n\\begin{equation*}\n \\cave{\\vec f}_{\\avL^p(Q_i)}\\cdot\\cave{\\vec g}_{\\avL^q(Q_i)}\\geq A\\cave{\\vec f}_{\\avL^p(Q_0)}\\cdot\\cave{\\vec g}_{\\avL^q(Q_0)}.\n\\end{equation*}\nThen\n\\begin{equation*}\n \\sum_{i=1}^\\infty\\abs{Q_i}\\leq \\frac{n^{\\max(r,1)+r\/2}}{A^{r}}\\abs{Q_0},\\qquad \\frac1r:=\\frac1p+\\frac1q.\n\\end{equation*}\n\\end{lemma}\n\n\\begin{proof}\nDirectly from the definition, it is easy to extend the basic identity $\\Norm{f}{\\avL^p(Q)}=\\abs{Q}^{-1\/p}\\Norm{f}{L^p(Q)}$ to convex bodies as\n\\begin{equation}\\label{eq:avLvsL}\n \\cave{\\vec f}_{\\avL^p(Q)}=\\abs{Q}^{-1\/p}\\cave{\\vec f}_{L^p(Q)}.\n\\end{equation}\nFrom this, the assumption of the lemma can be rewritten as\n\\begin{equation*}\n \\abs{Q_i}^{-1\/p-1\/q} \\cave{\\vec f}_{L^p(Q_i)}\\cdot\\cave{\\vec g}_{L^q(Q_i)}\\geq A\\abs{Q_0}^{-1\/p-1\/q}\\cave{\\vec f}_{\\L^p(Q_0)}\\cdot\\cave{\\vec g}_{\\L^q(Q_0)},\n\\end{equation*}\nor, rearranging,\n\\begin{equation*}\n \\abs{Q_i}\\leq \\frac{A^{-r}\\abs{Q_0}}{\\big(\\cave{\\vec f}_{\\L^p(Q_0)}\\cdot\\cave{\\vec g}_{\\L^q(Q_0)}\\big)^r}\n \\big(\\cave{\\vec f}_{L^p(Q_i)}\\cdot\\cave{\\vec g}_{L^q(Q_i)}\\big)^r.\n\\end{equation*}\nSumming over $i$ and using Lemma \\ref{lem:stoppingAux}, we obtain the claim.\n\\end{proof}\n\nWe now obtain the following proposition, which is a convex body analogue of a result of Nieraeth \\cite[Prop. 2.7; especially Eq. (2.7) for $m=1$]{Nie:19}. It says that estimating the sums over sparse collections, like those that arise from convex body domination, is equivalent to estimating related bi-sublinear maximal operators. In \\cite[Prop. 2.7]{Nie:19}, the result is formulated as a set of equivalent conditions for a tuple of weights. The formulation below has no reference to weights as such, but as soon as one starts asking questions about the boundedness of either side on spaces like $L^s(W)\\times L^{s'}(W')$, the proposition guarantees that one can equally well study this boundedness for the other side of the equivalence.\n\n\\begin{proposition}\nFor all $\\delta\\in(0,1)$, all dimensions $d,n\\geq 1$, exponents $p,q\\in[1,\\infty)$, and functions $\\vec f\\in L^p_{\\loc}(\\mathbb{R}^d)^n$, $\\vec g\\in L^q_{\\loc}(\\mathbb{R}^d)^n$, we have the two-sided estimate\n\\begin{equation*}\n \\sup_{\\mathscr S}\\sum_{Q\\in\\mathscr S}\\cave{\\vec f}_{\\avL^p(Q)}\\cdot\\cave{\\vec g}_{\\avL^q(Q)}\\abs{Q}\n \\eqsim\\BNorm{\\sup_{Q\\in\\mathscr D}1_Q\\cave{\\vec f}_{\\avL^p(Q)}\\cdot\\cave{\\vec g}_{\\avL^q(Q)}}{L^1(\\mathbb{R}^d)},\n\\end{equation*}\nwhere the supremum is taken over all $\\delta$-sparse collections of dyadic cubes in $\\mathbb{R}^n$, and the implied constants depend only on $n,p,q$, and $\\delta$.\n\\end{proposition}\n\n\\begin{proof}\nWith $\\vec f\\in L^p_{\\loc}(\\mathbb{R}^d)^n$ and $\\vec g\\in L^q_{\\loc}(\\mathbb{R}^d)^n$ fixed, let us denote\n\\begin{equation*}\n a_Q:=\\cave{\\vec f}_{\\avL^p(Q)}\\cdot\\cave{\\vec g}_{\\avL^q(Q)}.\n\\end{equation*}\nThe estimate $\\lesssim$ is immediate: From $\\delta$-sparseness, we have $\\abs{Q}\\leq\\delta^{-1}\\abs{E(Q)}$ for some disjoint sets $E(Q)$, and hence\n\\begin{equation*}\n \\sum_{Q\\in\\mathscr S}a_Q\\abs{Q}\n \\leq\\frac{1}{\\delta}\\sum_{Q\\in\\mathscr S}a_Q\\abs{E(Q)}\n =\\frac{1}{\\delta}\\int_{\\mathbb{R}^d}\\sum_{Q\\in\\mathscr S}a_Q 1_{E(Q)}\n \\leq\\frac{1}{\\delta}\\int_{\\mathbb{R}^d}\\sup_{Q\\in\\mathscr D}a_Q 1_Q.\n\\end{equation*}\n\nThe estimate $\\gtrsim$ needs a bit more. By monotone convergence, it is enough to consider $\\mathscr D(Q_0)$ in place of $\\mathscr D$. Let $\\mathscr S_0:=\\{Q_0\\}$. For some $A>1$ to be chosen and $Q\\in\\mathscr D(Q_0)$, let $\\mathscr S'(Q)$ consist of all maximal $Q'\\in\\mathscr D(Q)$ such that $a_{Q'}>A a_Q$. By maximality, the cubes $Q'\\in\\mathscr S'(Q)$ are disjoint. By Lemma \\ref{lem:convexStopping}, we have\n\\begin{equation*}\n \\sum_{Q'\\in\\mathscr S'(Q)} \\abs{Q'}\\leq\\frac{n^{\\max(1,r)+r\/2}}{A^r}\\abs{Q}\\leq(1-\\delta)\\abs{Q},\\qquad\\frac1r:=\\frac1p+\\frac1q,\n\\end{equation*}\nprovided that $A$ is chosen large enough, depending on $n,p,q$, and $\\delta$. Hence, defining inductively $\\mathscr S_{j+1}:=\\bigcup_{Q\\in\\mathscr S_j}\\mathscr S'(Q)$ and $\\mathscr S:=\\bigcup_{j=0}^\\infty\\mathscr S_j$, we find that $\\mathscr S$ is $\\delta$-sparse. If $Q\\in\\mathscr D(Q_0)$ and $S\\in\\mathscr S$ is the minimal stopping cube that contains $Q$, then $a_Q\\leq Aa_S$ by the way that the cubes $S\\in\\mathscr S$ were chosen, hence\n\\begin{equation*}\n \\sup_{Q\\in\\mathscr D(Q_0)}1_Q a_Q\\leq \\sup_{S\\in\\mathscr S}1_S Aa_S\\leq A\\sum_{S\\in\\mathscr S}1_S a_S,\n\\end{equation*}\nand thus\n\\begin{equation*}\n \\BNorm{\\sup_{Q\\in\\mathscr D(Q_0)}1_Q a_Q}{L^1(\\mathbb{R}^d)}\\leq A\\BNorm{\\sum_{S\\in\\mathscr S}1_S a_S}{L^1(\\mathbb{R}^d)}\n =A\\sum_{S\\in\\mathscr S}a_S\\abs{S}.\\qedhere\n\\end{equation*}\n\\end{proof}\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nThe electronic transport properties of a molecular device depend much on the\nunderlying nuclear configuration and the electron-phonon coupling\\cite{galperin-review}.\nThe mutual interaction between them induces Joule heating in the molecule,\nwhich may have important consequences on the functionality and reliability of\na molecular device. Study of the heat transport by phonons in molecular\njunctions is crucial for a better understanding of their electronic\ntransport properties. Phonon thermal transport itself is also interesting.\nDetailed understanding of the underlying transport mechanism is especially\nuseful for the design of novel phononic\ndevices\\cite{li:184301,science-thermal}. Furthermore, combined study of the\nelectronic and phononic transport in molecular junctions is the first step\ntoward the design of molecular thermoelectric\ndevices\\cite{reddy07}. Great experimental progress has been\nmade in these directions recently, which enables one to measure the thermal\nand thermoelectric transport properties of molecular\njunctions\\cite{wangzh07,reddy07}. Advances in experimental technique call for\nnew theoretical method to predict the thermal conductance of molecular\njunctions. Although semiempirical or minimal model calculation is helpful to\nunderstand the underlying physics\\cite{segal:6840,buldum99}. For a detailed\nquantitative study, a parameter-free, first-principle method is highly\ndesirable\\cite{mingo08}. Furthermore, for many molecular structures there\nexists no empirical inter-atom potential.\n\nIn this paper we introduce such a method based on a standard quantum chemistry\nsoftware, the Gaussian03 parckage\\cite{gaussian03}. Given the molecule\nstructure, we can obtain the force constant matrices after performing the\nenergy minimization. The thermal conductance can be calculated via available\ntheoretical methods. Among them are the nonequilibrium Green's function (NEGF)\nmethod\\cite{wang:033408,wang-2007,wang-review-epjb,mingo06,yamamoto06}, which\nhas been successfully used to predict the electronic conductance of molecular\njunctions\\cite{taylor01,brandbyge02,damle01}. To study the transport of each\nphonon mode, we will also use the mode-matching method\\cite{ando91}, which is\nequivalent to the NEGF method in the ballistic limit\\cite{khomyakov05}. Using\nthese methods, we first compare the thermal conductance of a benzene ring\namide-linked with two $(6,0)$ single-walled carbon nanotubes (SWCNTs) under\ndifferent compression or stretching conditions. Then we study the effect of\nSWCNT terminating group at the cutting edges. Finally, we compare the phonon\ntransmission probability and the thermal conductance of benzene and alkane\nchains with the same leads. Although the electron contribution to the thermal\nconductance may be comparable with or larger than that of phonons, inclusion of\nthis effect is out of the scope of present study. \n\n\n\\section{Molecular structure and theoretical method}\n\\label{sec:theory}\nIn this section we first introduce the system Hamiltonian and the procedure to\nobtain it from the Gaussian03 package. Then we briefly outline the NEGF, the\nmode-matching method, and their relationship. For a detailed discussion, we\nrefer the reader to Ref.~\\onlinecite{wang-review-epjb}. The system we are\ninterested in is a molecular junction connected with two periodic semi-infinite\nleads at both sides. It is a standard treatment to divide the whole structure\ninto three parts: the center junction and two leads acting as thermal baths.\nThe boundaries between the center and the baths may be at arbitrary positions,\nand not correspond to any physical interface. But it is desirable to include\npart of the leads into the center region, since we need to make sure that there\nis no direct interaction between the two baths, which is required by the NEGF\nformalism. By doing this, we can also include the charge transfer effect\nbetween the leads and the molecule junction. In this setup, the system\nHamiltonian can be written as\n\\begin{equation}\n\\label{eq:h}\n{\\cal H} = \\!\\!\\!\\!\\!\\sum_{\\alpha=L,C,R}\\!\\!\\!\\!\\!H_\\alpha + (u^L)^T V^{LC} u^C + (u^C)^TV^{CR} u^R + V_n,\n\\end{equation}\nwhere $H_{\\alpha} = \\frac{1}{2} {(\\dot{u}^\\alpha)}^T \\dot{u}^\\alpha +\n\\frac{1}{2} {(u^\\alpha)}^T K^\\alpha u^\\alpha$ represents harmonic\noscillators, $u^\\alpha$ is a column vector consisting of all the mass normalised\ndisplacement variables in region $\\alpha$, and $\\dot{u}^\\alpha$ is the\ncorresponding conjugate momentum. $K^\\alpha$ is the spring constant matrix in the tight-binding form\nand $V^{LC}=(V^{CL})^T$ is the coupling matrix of the left lead to the\ncentral region; similarly for $V^{CR}$. $V_n$ is the nonlinear interaction\nin the center, which could be $V_n = \\frac{1}{3}\\sum_{ijk}t_{ijk}u_iu_ju_k$\nfor cubic nonlinearality. We ignore the nonlinear interaction in this paper and only briefly discuss its effect in Sec.~\\ref{sec:results}.\n\nWe study the phonon thermal conductance of benzene and alkane chains covalently\nbonded with two $(6,0)$ SWCNTs via the amide group. This is relevant to a\nrecent experimental setup\\cite{guo06}, where the SWCNT is oxidatively cutted,\nand the cutting edges are covalently bonded with molecular chains via the amide\ngroup. To get the force constants of the system, we do two separate runs for\nthe center and the leads using Gaussian03. For the center, we include extra one\nand a half periods of SWCNT at each side, which is terminated by hydrogen atoms\nat the outer boundaries. The cutting edges may be terminated by H or COOH\ngroup. We optimize the center at the level of b3lyp density functional method\nusing the 6-31G basis set. We first relax the structure by constraining the\nleads to be coaxial to get an optimized distance between the two leads. Then we\nfix the outer half period of the SWCNTs and the H atoms at both sides and allow\nall other atoms to move freely. The optimized structure is used to get the\ndynamical matrix. The inset of Fig.~\\ref{fig:coohh} shows the optimized\nbenzene structure terminated by the H and COOH groups at the cutting edges. For\nthe lead, it is desirable to use periodic boundary condition to compute the\ndynamical matrix, while this is out of the ability of Gaussian03. So we\noptimise $7$ SWCNT periods and extract the force constant from the central\nperiod to minimize the finite size effect. To connect the molecule with the two\nleads, we remove the outer fixed atoms in the molecule and connect the\nremaining period of SWCNT with one semi-infinite SWCNT at each side. For the\ncoupling matrix between the center and the leads, we only include coupling\nbetween one period of SWCNT atoms in the center and one period in the leads.\nThis is a good approximation so long as we include enough SWCNT atoms into the\ncenter molecule. \n\nIn the NEGF method as described in\nRefs.~\\onlinecite{wang:033408,wang-2007,wang-review-epjb,mingo06,yamamoto06}, thermal\nconductance of the molecular junction can be calculated from the \nLandauer formula ($\\hbar=1$ throughout the formula)\n\\begin{equation}\n\\label{eq:caroli}\n\\sigma = \n\\frac{1}{2\\pi} \\int_0^\\infty \\!\\!\\!d\\omega\\, \\omega\\, T[\\omega] \n\\frac{\\partial f(\\omega)}{\\partial T}\n\\end{equation}\nwith the transmission coefficient\n\\begin{eqnarray}\nT[\\omega] &=& \n{\\rm Tr} \\bigl\\{ G^r \\Gamma_L G^a \\Gamma_R \\bigr\\}. \n\\label{eq-eff-trans}\n\\end{eqnarray}\n$f(\\omega)$ is the Bose-Einstein distribution function.\nThe retarded Green's function $G^r$ is obtained from \n\\begin{equation}\n\tG^r[\\omega] = \\left( (\\omega+i0^+)^2-K^C-\\Sigma^r_L[\\omega]-\\Sigma^r_R[\\omega] \\right)^{-1},\n\t\\label{eq:dyson1}\n\\end{equation}\nwhere the retarded self-energy of lead $\\alpha$ is \n\\begin{equation}\n\t\\Sigma^r_\\alpha[\\omega] = V^{C\\alpha}g^r_\\alpha[\\omega]V^{\\alpha C},\n\t\\label{eq:sel}\n\\end{equation}\nand the lead surface Green's function $g^r_\\alpha[\\omega]$ can be calculated\nfrom the generalized eigenvalue method\\cite{wang-review-epjb}, e.g., \n\\begin{equation}\ng^r_R[\\omega] = \\left( (\\omega+i0^+)^2-k^R_{11}-k^R_{01}F^+_R(1) \\right)^{-1}.\n\t\\label{eq:surface}\n\\end{equation}\n$k^R_{11}$ and $k^R_{01}$ are the diagonal and off-diagonal parts of the\nright lead dynamical matrix. Their sizes are determined by the degrees of\nfreedom $M$ in each period of the lead. The matrix $F^+_R(s)$\ntranslates to the right the displacement in the\n$n$th period to the $(n+s)$th period $u^+_R(n+s)=F^+_R(s)u^+_R(n)$. \nIt is constructed from the eigen values and vectors of the generalized eigen value problem\n\\begin{equation}\n\\label{eq:eigen}\n\\left( \\begin{array}{cc} (\\omega+i0^+)^2I\\!\\!-\\!\\!k^R_{11} & -I \\\\\n k^R_{10} & 0 \n \\end{array}\\right) \n\\left( \\begin{array}{c} \\epsilon \\\\\n \\zeta \n \\end{array}\\right) = \\lambda\n\\left( \\begin{array}{cc} k^R_{01} & 0 \\\\\n 0 & I \n \\end{array}\\right) \n\\left( \\begin{array}{c} \\epsilon \\\\\n \\zeta \n \\end{array}\\right)\n\\end{equation}\nas $F^+_R(s)=E^+_R\\Lambda_+^s(E^+_R)^{-1}$. Here $I$ is an $M\\! \\times\\! M$\nidentity matrix. The diagonal matrix $\\Lambda_+^s$ consists of all the eigen\nvalues $|\\lambda_+^s| < 1$, and $E^+_R$ the corresponding eigen vectors $E^+_R\n= (\\varepsilon^+_1,\\varepsilon^+_2,\\cdots,\\varepsilon^+_{M'})$. Note that $M'$\nmay be less than $M$, in which case the matrix inverse becomes pseudo-inverse.\nA similar left-translation matrix $F^{-}_R(s)$ can be constructed from\n$\\Lambda_{-}^s$ and $E_R^-$, which include all the eigen values $|\\lambda_-^s|\n> 1$ (excluding infinity) and the corresponding eigen vectors.\n\nWhile the NEGF method is systematic and suitable to take into account the\nnonlinear interaction, the transmission coefficient from it is the sum of\nall the eigen modes from the leads. Thus it is difficult to analyse the\ncontribution from each mode. The mode-matching method provides another way\nto calculate the transmission coefficient in the ballistic\nlimit\\cite{ando91,khomyakov05,wang-review-epjb}. Single mode transmission and\nmode mixing effect can be studied by this method. The transmission matrix is\ngiven by\n\\begin{equation}\n\tt^{RL}_{mn} = \\sqrt{\\frac{v_{R,m}^+a_L}{v_{L,n}^+a_R}}\\tau^{RL}_{mn},\n\t\\label{eq:mm1}\n\\end{equation}\nand\n\\begin{equation}\n\t\\tau^{RL} = (E^+_R)^{-1}g^r_RV^{RC}G^rV^{CL}g^r_Lk_{10}^L(F^{+}_L(-1)-F^{-}_L(-1))E^+_L,\n\t\\label{eq:mm2}\n\\end{equation}\nwhere $v^+_{\\alpha,m}$ is the group velocity of the $m$th right propagating\nmode for the lead $\\alpha$. While the mode indices $m$ and $n$ are only for\npropagating modes, the matrices $E^{\\pm}_\\alpha$ include all the propagating\nand evanescent modes. The total transmission coefficient as in the Landauer\nformula is $T[\\omega] = {\\rm Tr} \\bigl\\{ (t^{RL})^\\dagger t^{RL} \\bigr\\}$.\nSimilar relations hold for waves incidented from the right lead and transmitted\nto the left $t^{LR}_{nm}$. \n\nThe NEGF and the mode-matching method are exactly equivalent in the ballistic\ncase as show in Ref.~\\onlinecite{khomyakov05}. All the matrices needed by\nEqs.~(\\ref{eq:mm1}--\\ref{eq:mm2}) can be obtained by solving the generalized\neigen value problem Eq.~(\\ref{eq:eigen}). It is interesting to note that by\ndoing a singular value decomposition on the transmission matrix $t$ we can get\nthe transmission eigenchannel information without any other efforts, for which\ndifferent methods have been developed in the electronic transport\nliterature\\cite{inglesfield:155120}.\n\n\\section{Numerical results and discussion}\n\\label{sec:results}\nWe now present our numerical results. We begin with the simplest case where\nthere is only one benzene ring at the center. We compare the thermal\nconductance of the molecular junction at different compression or stretching\nconfigurations by changing the distance between the two leads. The purpose of\nthis study is twofold. Firstly, we want to study the effect of the compression\nor stretching on the thermal conductance. Secondly, our optimization process\ndiscussed in Sec.~\\ref{sec:theory} can not ensure that we have reached the\nlowest energy configuration while keeping the two SWCNTs to be coaxial. This is because we have fixed the position of the outer C and H atoms at both sides. If the thermal conductance is not sensitive to the\ndistance between the two leads, our results make sense even if we do not find the\nlowest energy configuration. As we can see in Fig.~\\ref{fig:confs}, this is\nindeed the case. In Fig.~\\ref{fig:confs}, from $1$ to $5$ the distance between the two carbon\natoms connecting to the amide groups is $10.07$, $10.21$, $10.38$, $10.51$,\n$10.64$ \\AA, respectively. The inset shows the atom configuration of the five\ncases. In the compression states ($10.07$ and $10.21$ \\AA), the relative position between the benzene ring and the SWCNT leads changes compared to the full relaxed ($10.38$ \\AA) and stretching ($10.51$ and $10.64$ \\AA) states (inset of Fig.~\\ref{fig:confs}). Although the atom configurations change a lot, the ballistic thermal\nconductance of the molecular junction only changes slightly in all the\ntemperature range studied here. So we can conclude that the ballistic thermal\nconductance of the molecular junction is not sensitive to small compression or\nstretching of the molecule.\n\\begin{figure} \n\\includegraphics[scale=0.6]{fig1.eps}\n\\caption{\\label{fig:confs} Ballistic thermal conductance of the benzene junction terminated by H atoms at different configurations. $3$ is the full relaxed structure. $1-2$ are in the compression state, and $3-4$ are in the stretching state.}\n\\end{figure}\n\nIn a related experiment\\cite{guo06}, the cutting edge carbon atoms are\nexpected to be saturated by the COOH group, not the H group shown in\nFig.~\\ref{fig:confs}. In Ref.~\\onlinecite{renw07}, the authors show that the\nelectron transmission is largely affected by the terminating groups. It is also\ninteresting to know how the terminating group affects the quantum thermal\nconductance. We still use the single benzene structure to study this problem.\nFigure~\\ref{fig:coohh} shows the thermal conductance of the two configurations.\nThe full relaxed structures are shown in the inset. The electrical conductance\nis mainly determined by the energy channel near the chemical potential, while\nthe thermal conductance is jointly contributed by many phonon modes. The\nterminating group only has a large influence on the high energy (short\nwavelength) optical phonon modes. The low energy (long wavelength) phonon\nmodes are not sensitive to the local environment at the cutting edges. As a\nresult, the thermal conductance only shows quantitative difference, which\nbecomes larger at high temperatures.\n\\begin{figure}\n\\includegraphics[scale=0.6]{fig2.eps}\n\\caption{\\label{fig:coohh} Ballistic thermal conductance of the benzene junctions terminated by H and COOH group.}\n\\end{figure}\n\nWe now compare the conductance of the benzene rings and that of the alkane\nchains, with the SWCNTs terminated by H atoms. We expect that the COOH\nterminating structures show similar behavior. To ensure the two molecules have\ncomparable length, we include two benzene rings for the benzene structure and\n$8$ CH$_2$ groups for the alkane chain. The total transmission probability and\nthe thermal conductance are shown in Figs.~\\ref{fig:3} and \\ref{fig:4},\nrespectively. Since the nonlinear interaction is not considered here, phonon\nmodes with different energies are independent of each other. The transmission\nprobability is nonzero only in the overlapped energy range of the SWCNT leads\nand the center molecule. Strong coupling with the leads gives rise to wide\nbroadening and strong shifting of the discrete molecule phonon energy. It is\nhard to make a one-to-one correspondence between the transmission peaks and the\nisolated molecule phonon eigen frequencies. This is especially true for low\nenergy phonon modes, which have relatively large spatial extent. The high\nenergy modes are highly localized and show sharp peaks in the transmission\nspectrum. Due to the highly localized nature and low Bose-Einstein weighting,\nthese modes can hardly transfer energy. There is a wide zero transmission gap\naround $0.07$ eV for the alkane chain, which is a characteristic of the alkane\nchain phonon spectrum\\cite{segal:6840}.\n\n\n\\begin{figure}\n\\includegraphics[scale=1.0]{fig3.eps}\n\\caption{\\label{fig:3} Phonon transmission probability as a function of energy for the benzene and alkane chains.}\n\\end{figure}\n\n\\begin{figure}\n\\includegraphics[scale=1.0]{fig4.eps}\n\\caption{\\label{fig:4} Ballistic thermal conductance as a function of temperature for the benzene and alkane chains. The inset shows the crossing point of the thermal conductance at about $38$ K.}\n\\end{figure}\n\n\\begin{figure}\n\\includegraphics[scale=0.8]{fig5.eps}\n\\caption{\\label{fig:5} Transmission probability of different phonon branches: (a) TA modes, (b) LA mode, (c) twisting mode, (d) lowest optical modes.}\n\\end{figure}\n\nTo gain further insight into the transmission spectrum, we also study the\nsingle mode transmission using the mode-matching method. Figure~\\ref{fig:5}\nshows the transmission of several important branches in SWCNT phonon spectrum: Two transverse acoustic (TA) phonon\nmodes, one longitudinal acoustic (LA) mode, one twisting mode and the lowest two degenerate optical modes. These\nlow frequency modes are expected to contribute much to the thermal\nconductance. The degenerate phonon modes of SWCNTs show different transmission\nin both cases. This is consistent with the fact that the junction structure\ndestroys the degeneracy in transverse directions. At the energy range below\n$0.01$ eV, the main contribution comes from the TA\nmodes of the SWCNT. The alkane chain shows larger transmission than the\nbenzene chain in this energy range. Above $0.01$ eV, the benzene chain shows\nlarger transmission in most cases. The twisting mode shows the largest\ndifference in two structures. While it can hardly be transmitted by the\nalkane chain, it has a large contribution in the benzene chain.\n\nThe thermal conductance of the molecular junction depends not only on the\ntransmission coefficient, but also on the phonon occupation number, which is\nreflected as the Bose-Einstein distribution in the Landauer formula. According\nthe analysis of the transmission spectrum, we may expect that the alkane chain\nhas larger thermal conductance at low temperatures, while at higher\ntemperatures the order reverses. This is confirmed in Fig.~\\ref{fig:4}. The\ncrossing temperature is about $38$ K. The room temperature thermal conductance\nis about $0.075$ nW\/K for alkane chains and $0.125$ nW\/K for benzene chains.\nIn a recent experiment\\cite{wangzh07}, the thermal conductance of alkane chain\nis found to be smaller than our theoretical value. This difference comes from\nthe effect of the leads. We are using SWCNTs here, while in the experiment it\nis the bulk gold connected with the alkane chain via the sufur atom. At least\ntwo factors from the leads may account for this decrease. The first is the\nsmaller phonon spectrum overlap between gold and alkane chain, and the second\nis the weaker coupling between them.\n\nSome comments are worthwhile. The nonlinear interaction may change the\ntransmission spectrum, and lead to a decrease of the thermal conductance. A\nperturbative analysis of the single benzene structure shows that the room\ntemperature thermal conductance drops about $30\\%$ of the ballistic\nvalue if we include the cubic force constant calculated from Gaussion03.\nMean-filed approximation in the NEGF formalism only works well for simple\nstructures with relatively weak nonlinear interaction\\cite{wang-review-epjb}. It\nfails to converge in the self-consistent iteration in present case. So it is\nstill a challenge to find a good approximation for the nonlinear\nself-energies in NEGF method.\n\n\\section{Conclusions} In this paper, we introduce a straightforward method to\ncalculate the phononic thermal conductance of molecular junctions in the\nballistic regime from first-principles. The force constant matrices are\nobtained from Gaussian03 quantum chemistry software. The phonon transmission\nand thermal conductance are calculated using the NEGF or mode-matching method.\nFurther information can be obtained from the transmission spectrum of each\nsingle mode. Using this method we show that the ballistic thermal conductance\nof the benzene ring amide linked with SWCNTs is not sensitive to the distance\nbetween the two SWCNTs. The benzene rings show larger thermal conductance\nthan the alkane chains. This method is general, and can be easily applied to\nother material systems.\n\n\\begin{acknowledgments}\nThe authors thank Nan Zeng for discussions. JTL is grateful to the hospitality\nof Prof. J. C. Cao at Shanghai Institute of Microsystem and Information\nTechnology, where this paper was finished. This work was supported in part\nby a Faculty Research Grant (R-144-000-173-101\/112) of National University of\nSingapore.\n\\end{acknowledgments}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\n\\subsection{Motivation}\n\\label{sec:bwp_intro}\nOur adaptive bandwidth allocation (BA) model is motivated by a new feature in 5G NR called bandwidth part (BWP)~\\cite[Section 6.10]{NR_BWP_2019_1},\n\\cite[Section 4.4.5]{NR_BWP_2019_2}. The BWP concept is based on the division of a wide bandwidth into multiple \\textit{contiguous} smaller chunks of bandwidth. The main aim of this division is to let the number of bandwidth chunks used by a wireless user depend on its type at a given time, namely on its current needs in terms of data rate or constraints in terms of hardware complexity and power consumption. This flexibility on what is allocated to users makes of BWP a new dimension of radio spectrum sharing. \n\n\n\n\\begin{table}[]\n\\caption{Types of wireless devices, wireless applications, and heterogeneous throughput demands. The numbers in parentheses refer to 2017 and 2022, respectively. (Sources: Cisco VNI Mobile, 2019~\\cite{Cisco_VNI} and MediaTek white paper~\\cite{mediatek}.)}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\n\\multirow{3}{*}{} & \\multicolumn{2}{l|}{\\!\\!\\!\\! Wireless devices~\\cite{Cisco_VNI}} & \\multicolumn{2}{l|}{\\!\\!\\! Wireless applications~\\cite{mediatek}\\!\\!\\!} \\\\ \\cline{2-5} \n & \\multirow{2}{*}{\\begin{tabular}[c]{@{}l@{}}\\!\\!\\! \\% Share \\\\ \\!\\!\\!(2017, 2022)\\!\\!\\!\\end{tabular}} & \\multirow{2}{*}{\\begin{tabular}[c]{@{}l@{}}\\!\\!\\! \\% Growth \\!\\!\\!\\\\ \\!\\!\\!(2017, 2022)\\!\\!\\!\\!\\!\\end{tabular}} & \\multicolumn{2}{l|}{\\multirow{2}{*}{\\!\\!\\!\\!\\! Throughput demand (Mbps)}\\!\\!\\!} \\\\\n & & & \\multicolumn{2}{l|}{} \\\\ \\hline\n\\multirow{2}{*}{\\begin{tabular}[c]{@{}l@{}}\\!\\!\\! \\!Smartphones \\\\ + phablets\\!\\!\\end{tabular}} & \\multirow{2}{*}{\\!\\!\\!(50, 44)} & \\multirow{2}{*}{\\!\\!\\!(88, 93)} & \\multirow{2}{*}{\\begin{tabular}[c]{@{}l@{}}\\!\\!\\! \\!\\!Streaming\\!\\!\\!\\!\\!\\!\\!\\!\\! \\\\ \\!\\!\\!\\!\\! video\\end{tabular}} & \\!\\!\\!Video (1.5) \\\\ \\cline{5-5} \n & & & & \\!\\!\\!HD video (5) \\\\ \\hline\n\\multirow{2}{*}{\\!\\!\\!M2M} & \\multirow{2}{*}{\\!\\!\\!(11, 31)} & \\multirow{2}{*}{\\!\\!\\!(1.8, 2.2)} & \\multirow{2}{*}{\\begin{tabular}[c]{@{}l@{}}\\!\\!\\!\\!\\! Online \\\\ \\!\\!\\!\\!\\! gaming\\end{tabular}} & \\!\\!\\!Min (1) \\\\ \\cline{5-5} \n & & & & \\!\\!\\!Full (25) \\\\ \\hline\n\\multirow{3}{*}{\\!\\!\\!Nonsmartphones \\!\\!\\!\\!\\!} & \\multirow{3}{*}{\\!\\!\\!(34, 10)} & \\multirow{3}{*}{\\!\\!\\!(1.3, 0.3)} & \\multirow{3}{*}{\\begin{tabular}[c]{@{}l@{}}\\!\\!\\!\\!\\! Video \\\\\\!\\!\\! \\!\\!\\! service\\\\ \\!\\!\\!\\!\\! \\& sharing \\!\\!\\!\\end{tabular}} & \\!\\!\\!Min (0.5) \\\\ \\cline{5-5} \n & & & & \\!\\!\\!HD video (2) \\\\ \\cline{5-5} \n & & & & \\begin{tabular}[c]{@{}l@{}}\\!\\!\\!HD video \\\\ \\!\\!\\!sharing\\! (10)\\end{tabular} \\\\ \\hline\n\\multirow{3}{*}{\\!\\!\\!Tablets} & \\multirow{3}{*}{\\!\\!\\!(2, 3)} & \\multirow{3}{*}{\\!\\!\\!(4.6, 2.9)} & \\multirow{3}{*}{\\begin{tabular}[c]{@{}l@{}}\\!\\!\\!\\!\\! VoIP \\end{tabular}} & \\!\\!\\!Voice (0.1) \\\\ \\cline{5-5} \n & & & & \\!\\!\\!HD video (1.5)\\!\\!\\!\\!\\! \\\\ \\hline\n\\multirow{2}{*}{\\!\\!\\!PCs} & \\multirow{2}{*}{\\!\\!\\!(2, 1)} & \\multirow{2}{*}{\\!\\!\\!(4.3, 1.6)} & \\multirow{2}{*}{\\begin{tabular}[c]{@{}l@{}}\\!\\!\\!\\!\\! Social \\\\ \\!\\!\\!\\!\\! media\\end{tabular}} & \\multirow{2}{*}{\\!\\!\\!Text (0.3)} \\\\\n & & & & \\\\ \\hline\n\\end{tabular}\n\\label{tab:device_types}\n\\end{table}\n\nThe BWP setting requires the allocation of a set of contiguous bandwidth chunks. But in general, e.g., in carrier aggregation in LTE, there is no restriction on a user to use contiguous bandwidth chunks~\\cite{CA}. In this paper, we propose a general adaptive BA model that allows one to analyze both the non-contiguous bandwidth chunk allocation of LTE and the contiguous case of BWP.\n\nSuch a bandwidth adaptation depending on the user type is particularly important in future wireless networks, e.g., $5$G networks, which need to accommodate a larger variety of wireless devices (see Table~\\ref{tab:device_types}), running in turn a larger variety of wireless applications with highly heterogeneous throughput demands (see Table~\\ref{tab:device_types}). More specifically, as shown in Table~\\ref{tab:device_types}, mobile video streaming constitutes the majority of wireless traffic and requires higher data rate and hence wider spectrum allocation\nin the adaptive BA setting. In contrast, text and e-mail applications have lower data rate requirements and would thus be allocated less bandwidth. For users of the latter type, the use of wide bandwidth leads to high costs, in particular, high idling power consumption by radio-frequency (RF) and baseband signal processing circuitry. Hence, the use of different bandwidth sizes allows a balance between data rate variations and power consumed by users.\n\nOne typical use case is that of web browsing, where the user is active for a short time on wide bandwidth to accommodate the bursty traffic (download of a web page with pictures), and then stays active on narrow bandwidth for the time until it again encounters bursty traffic situation. This power-saving feature of adaptive BA helps making wide bandwidth operations energy more efficient. \n\n\nThe benefits of the flexibility offered by adaptive BA are hence multiple. Not only this adaptation saves energy for those applications and devices with lower data rate requirement or bursty traffic, but this in turn diminishes the interference incurred by other nearby devices of all types. Indeed, due to the broadcast nature of the wireless medium, users interact with each other through mutual interference. The wider the bandwidth, the higher the interference. Hence the bandwidth adaption has two competing effects. On the positive side, a wider bandwidth increases the signal power and hence the throughput. On the negative side, it increases the interference power, which has a detrimental collective effect. As a result, it is fair to say that there is no global understanding of the effect of adaptive BA on the per-type and the overall performance. \n\nTo the best of our knowledge, there is no analytical study of a resource allocation model that captures the features of BWP. The existing studies on BWP (discussed in Section~\\ref{sec:rel_work}) are limited to the investigation of power savings and bandwidth switching using simulations. The study of the key wireless network performance metrics is an open area. In particular, there is currently no known way to predict the effect of the BA on the performance of a user of a given type.\n\nThe main motivation of the present paper can now be stated in simple terms: it is to provide a statistical model allowing one to analyze adaptive bandwidth allocation motivated by BWP in device-to-device (D2D) wireless networks, and more precisely to predict the key performance metrics of the typical user of a given type in this context. It is appropriate to stress the analogy with the theory of differentiated services in wireline networks (DiffServ~\\cite{DiffServ}), which was instrumental in classifying and managing different types (classes) of network traffic and in predicting their interactions. The aim of this paper is to make a first step in the direction of a quantitative theory for BWP-based bandwidth allocation and the management of different types of network traffic in this class of wireless networks.\n\n\n\\subsection{Contributions}\n\\textbf{An adaptive BA model.} The first contribution of this paper is a stochastic model for adaptive BA motivated by BWP in infrastructureless wireless networks,\\footnote{In 5G NR, although the BWP feature is proposed for cellular networks, we focus on an infrastructureless network (a simpler one compared to the cellular network) since this paper is the first attempt to analytically study a bandwidth allocation scheme motivated by BWP.} where the type of the user is determined by the number of bandwidth chunks it uses which in turn adapts to the current user needs. As already explained, our model accommodates the following two BA approaches.\n\\begin{itemize}\n\\item Contiguous BA: A user that needs $k$ bandwidth chunks can select any set of $k$ \\textit{contiguous} chunks uniformly at random. Such a contiguous bandwidth allocation may be used in the BWP setting in 5G NR.\n\\item Random BA: A user that needs $k$ bandwidth chunks can select any set of $k$ chunks uniformly at random. Such a bandwidth allocation has applications in LTE resource allocation, e.g., carrier aggregation~\\cite{CA}.\n\\end{itemize} \nAlso, our adaptive BA model allows us to quantify service differentiation in wireless networks by capturing interaction among different types of wireless users. Specifically, the model allows one to calculate the performance achieved by each type of user.\n\n\\textbf{Performance analysis.} Using tools from stochastic geometry for D2D networks, we derive analytical expressions for key wireless network performance metrics, namely, success probability, meta distribution of the signal-to-interference ratio (SIR), Shannon throughput, and Shannon throughput per Joule. These expressions permit the evaluation of per-type and overall performance metrics.\n\n\\textbf{Different performance viewpoints.} Our model allows one to analyze the bandwidth allocation from the viewpoints of both users and operators. Per-type performance, although relevant to operators, is more important for users, while overall performance might be important too from the operator's viewpoint due to, e.g., the link between this and pricing. Thus, the machinery proposed here to predict both could be useful to help an operator make a choice between the following options: 1) allocate the entire bandwidth, 2) adaptively allocate bandwidth chunks. \n\n\\textbf{The mean model.} The proposed BA model introduces additional randomness due to the probabilistic selection of the set of bandwidth chunks depending on the user type. We show that the increased variability in traffic due to adaptive BA may improve the performance for the same mean interference and the same mean signal powers. This is particularly useful when comparing two networks based on adaptive BA but with different mix of user types. \n\n\\textbf{Service differentiation.} We show that adaptive BA leads to a roughly egalitarian service in Shannon throughput per Hz and to a linear service differentiation in aggregated Shannon throughput.\n\n\n\n\\subsection{Related work}\n\\label{sec:rel_work}\nIn wireline networks, traffic-flow characterization has received significant attention for networks consisting of users running different types of applications. To understand the behavior of heterogeneous flows and their interactions, flows have been classified based on their features, e.g., traffic size (as \\textit{elephant} and \\textit{mouse})~\\cite{traffic_size_Thompson, traffic_size_Papagiannaki, traffic_size_Estan}.\nFor wireless networks as well, in the adaptive BA setting, one can make an analogy to elephants and mice: users needing wide bandwidth can be viewed as \\textit{elephants}, while users needing small bandwidth as \\textit{mice}.\n\n\nAs alluded to earlier, the $3$GPP has very recently considered the inclusion of BWP in $5$G NR to enable spectrum flexibility and power savings. The literature on how BWP affects power savings, throughput, and reliability is very limited. For instance, \\cite{Jeon_BWP_2018} discusses power savings due to BWP. Since a user need not transmit or receive outside the bandwidth allocated to it, the user consumes less power in some scenarios, for example, involving bursty traffic. The work in~\\cite{Fuad_BWP} shows that the bandwidth switching to save power results in increased latency and decreased throughput for low load and bursty traffic. The work in~\\cite{Arslan_reliability} studies the effect of BWP on reliability and fairness in wireless networks. But these works on BWP use simulations as a tool to evaluate the performance based on the dynamic BWP management. \n\nThis paper is focused on adaptive BA for D2D networks. There exists a large number of works on spectrum allocation including multi-channel scenarios in standalone D2D networks or D2D networks sharing spectrum with cellular networks. For instance, see~\\cite{Stefanatos_BWP,Kyasanur_BWP,Sun_BWP} and references therein. In relation to heterogeneity among devices in a D2D network based on allocated bandwidth, \\cite{Yuan_BWP} focuses on the dynamic allocation of bandwidth to unlicensed users based on data rate demand, provided the allocated spectrum is unoccupied by licensed users and other unlicensed users. This results in an orthogonal bandwidth allocation to avoid mutual interference. Using a simulation approach, the work in \\cite{Hamidouche_BWP} takes battery life into account and tries to maximize the number of completed transmissions as a function of already allocated bandwidth. \n\nStochastic geometry, which is the main tool used in this paper, has been extensively used to model and analyze both infrastructureless (e.g., D2D) and infrastructure (e.g., cellular) networks~\\cite{bb_book,mh_book,SG_tut,SG_cellular}. Especially, Poisson point process (PPP)-based models are very popular for the analysis of wireless networks. Heterogeneous PPP based cellular networks were in particular discussed in \\cite{SG_het}. These models feature several types of base stations and a single class of users associating e.g. to the closest base station. In contrast, the setting discussed here features different types of transmitters with dedicated receivers and adapting their bandwidths to their needs.\n\nIn \\cite{Jindal_BWP} the authors aim to maximize the density of successful transmissions given an outage constraint at the typical user. The user always selects one bandwidth chunk uniformly at random irrespective of its need. For the same bandwidth partitioning scheme as~\\cite{Jindal_BWP}, \\cite{Haenggi_BWP_2014} analyzes the mean local delay and \\cite{sanket_globecom} exhibits the tradeoff between the density of successful transmissions and the mean local delay. As in~\\cite{Jindal_BWP}, the work in~\\cite{Lu_BWP} maximizes the density of successful transmissions, but for frequency-selective channels, where again only one bandwidth chunk is selected for the transmission. To the best of our knowledge, no concrete adaptive BA models capturing BWP features and accommodating different types of users are available in the literature.\n\n\n\n\\section{System Model}\n\\label{sec:models}\n\n\\iffalse\n\\begin{figure}\n\\centering\n\\includegraphics[scale=.45]{PBN}\n\\caption{A snapshot of the Poisson bipolar network with $\\lambda = 1\/25$ and $R = 2$. A cross `$\n\\times$' represents a transmitter and a circle `$\\circ$' represents a receiver. Arrows represent the links between transmitters and their associated receivers.}\n\\label{fig:snapshot}\n\\end{figure} \n\\fi\n\\subsection{Network model}\nWe consider infrastructureless wireless networks such as ad hoc, D2D, and machine-to-machine (M2M) networks.\\footnote{As shown in Table~\\ref{tab:device_types}, in year 2022, $31\\%$ of mobile devices are expected to belong to M2M networks.} The transmitters are randomly located according to a homogeneous PPP $\\Phi \\subset \\mathbb{R}^2$ of intensity $\\lambda$. Each transmitter has a receiver at fixed distance $R$ in a random direction~\\cite{bb_book}. Since the homogeneous PPP is stationary, one can just focus on the reference link between a receiver at the origin $o$ and its associated transmitter at $x_0 \\in \\Phi$ with $\\|x_0\\| = R$. Averaging over $\\Phi$, this representative link becomes the {\\em typical} link in that it has the same statistical properties as those obtained by averaging over all other links in the network.\n\nA transmission is subject to some path loss, where the path-loss function is given by $\\ell(r)$ for distance $r$. Furthermore, the transmissions experience Rayleigh fading, where the channel power gain is an exponential random variable with mean $1$. Let $h_x$ denote the channel power gain between the typical receiver at the origin and the transmitter at $x \\in \\Phi$ due to fading. Then $h_x \\sim \\exp(1)$. We focus on the interference-limited scenario, where the noise power is negligible compared to the interference power.\n\\subsection{Bandwidth allocation model}\n\\label{sec:bwp_model}\nIn this subsection, we describe our two BA models.\n\nLet $W$ be the total bandwidth available to users. Without loss of generality, we assume $W = 1$. \n\n\\textbf{Random BA model.} In the case without continuity requirement, our adaptive BA model is as follows:\n\\begin{itemize}\n\\item[(1)] The total bandwidth is divided into $K$ orthogonal chunks of equal bandwidth of $1\/K$.\n\\item[(2)] Depending on how many chunks a transmitter uses, the transmitters are categorized into $K$ types. A type-$i$ transmitter selects $i$ ($ 1\\leq i \\leq K$) chunks for its transmission. In other words, the type of the user is decided by the amount of bandwidth used by that user. Let $\\mathcal{T} = \\lbrace \\mathcal{T}_1, \\mathcal{T}_2, \\dotsc, \\mathcal{T}_K \\rbrace$, where $\\mathcal{T}_i$ is the set of all subsets $\\mathcal{T}_{i,q}$ of $[K] \\triangleq \\lbrace 1, 2, \\dotsc, K\\rbrace$ of cardinality $i$, i.e., $|\\mathcal{T}_{i,q}| = i$ with $q = 1, 2, \\dotsc, {K \\choose i}$. Here, ${K \\choose i}$ is the number of possible ways of selecting $i$ chunks from $K$ chunks, i.e., $|T_i| = {K \\choose i}$. For example, for $K = 3$, a type-$2$ transmitter, i.e., $i = 2$, can select two from three chunks $1$, $2$, and $3$, i.e., we have $\\mathcal{T}_2 = \\lbrace \\lbrace 1, 2\\rbrace, \\lbrace 2, 3\\rbrace, \\lbrace1, 3\\rbrace\\rbrace$.\n\\item[(3)] A type-$i$ transmitter further selects a set of chunks from $\\mathcal{T}_i$ uniformly at random. For the aforementioned example with $K = 3$, a type-$2$ transmitter selects $2$ chunks for transmission, and it does so by selecting one of the possible sets of chunks from $\\lbrace 1, 2\\rbrace, \\lbrace 2, 3\\rbrace$, and $\\lbrace 1, 3\\rbrace$ at random.\n\\item[(4)] Each transmitter independently decides to be of $i$th type with probability $p_i$ with $\\sum_{i = 1}^{K} p_i = 1$.\n\\end{itemize}\n\n\\textbf{Contiguous BA model.} The case of contiguous bandwidth chunks is a variant of the above random BA model because the set of contiguous chunks is a subset of $\\mathcal{T}_i$. For a type-$i$ user, there are $K-i+1$ sets of contiguous chunks. Again, a natural way for type-$i$ user to select one of $K-i+1$ sets of contiguous chunks is to do so uniformly at random.\\footnote{The contiguous BA model is also random in nature but with a restriction that only contiguous bandwidth chunks are allocated.} For example, for $K = 3$ and $i = 2$, we have $\\mathcal{T}_2 = \\lbrace \\lbrace 1, 2\\rbrace, \\lbrace 2, 3\\rbrace\\rbrace$, and the type-$2$ transmitter can select one set from $\\lbrace 1, 2\\rbrace$ and $\\lbrace 2, 3\\rbrace$ uniformly at random.\n\n\n\\textbf{User types.} For both BA models, the probabilities $p_i$, $1 \\leq i \\leq K$, quantify service requirements. Specifically, these probabilities can be obtained from the statistical analysis of the user traffic. For instance, $p_i$ could be set according to what proportion of user's data traffic consists of large data transfer such as video streaming and what proportion consists of small traffic such as text or e-mail transmissions. As Table~\\ref{tab:device_types} in Section~\\ref{sec:bwp_intro} shows, a user's traffic consists of traffic from different wireless applications with heterogeneous throughput demands. Thus, the probabilities $p_i$ could be set according to the user's throughput demands. Let us consider a use case: \\cite{Cisco_VNI} reports that, in 2018 approximately 63\\% of the traffic of a mobile user was video content, and it will grow to 79\\% in 2022. Due to higher throughput demand for video, a mobile user is likely to request a wider bandwidth (and hence a large number of bandwidth chunks). \n\n\n\n\\subsection{Signal-to-interference ratio (SIR)}\n\\label{sec:sir}\nIn interference-limited wireless networks, many key performance metrics are based on SIR. The SIR at the typical receiver located at the origin $o$ with respect to its associated transmitter at $x_0 \\in \\Phi$ is given by $\\mathsf{SIR}_o \\triangleq \\frac{S}{I}$, where $S$ and $I$ are the received signal power and the interference power at the origin, respectively. \n\nThe received SIR depends on the type of the typical user because the signal and interference powers are functions of the bandwidth allocated to the user. Without loss of generality, we condition on the fact that the typical transmitter is of type $k$, i.e., it uses $1 \\leq k \\leq K$ chunks for transmission. \n\n\\textbf{Signal power}: We assume that a transmitter spends power $P$ per chunk used for a transmission.\\footnote{This assumption is in line with the proposed BWP model for $5$G, where the transmit power is mentioned in terms of the power spectral density.} Hence, $P$ is expressed in Joule-s$^{-1}$-Hz$^{-1}$. The received signal power at the typical receiver is given by $S_k = kPh_{x_0}\\ell(x_0)$, since the typical transmitter selects $k$ chunks for transmission. Here, $h_{x_0}\\sim\\exp(1)$ denotes the channel power gain on the typical link.\n\n\\textbf{Interference power}: \n Let $t_x^{(k)}$ denote the number of such overlapping chunks between an interferer at $x \\in \\Phi$ and the typical transmitter (of type $k$). Then, the interference power at the typical receiver is given by\n\\begin{align}\nI_k = \\sum_{x \\in \\Phi\\setminus\\lbrace x_0 \\rbrace} P t_x^{(k)} h_x \\ell(x).\n\\label{eq:intf_pow_main}\n\\end{align} \nOur assumption in this paper is that a type-$i$ interfering transmitter selects $i$ chunks with probability $p_i$ independently of other transmitters. Hence, the original PPP $\\Phi\\setminus\\lbrace x_0\\rbrace$ of interferers can be split into $K$ independent PPPs of intensities $\\lambda p_i$, $i = 1, 2, \\dotsc, K$. Let $\\Phi_{i} \\setminus \\lbrace x_0\\rbrace$ denote the PPP of type-$i$ interferers. In random BA, the typical transmitter selects a set $T_{k, u}$ of $k$ chunks at random with $1 \\leq u \\leq {K \\choose k}$. In contiguous BA, the typical transmitter selects a set $T_{k, u}$ of $k$ contiguous chunks at random with $1 \\leq u \\leq K-k+1$. Also, in random BA, a type-$i$ interferer selects a set $T_{i,v}$ of $i$ chunks with $1 \\leq v \\leq {K \\choose i}$ at random and independently. Similarly, in contiguous BA, a type-$i$ interferer selects a set $T_{i,v}$ of $i$ contiguous chunks with $1 \\leq v \\leq K-i+1$ at random and independently. Then the interference power from the transmitter at $x \\in \\Phi\\setminus \\lbrace x_0\\rbrace$ to the typical receiver is $P|T_{k, u} \\cap T_{i, v}|h_x\\ell(x)$, where $|T_{k, u} \\cap T_{i, v}|$ is the number of overlapping chunks between a type-$i$ interferer and the typical transmitter. Let $0 \\vee (i+k-K) \\leq t \\leq k \\wedge i \\leq K$ denote the number of overlapping chunks between a type-$i$ interfering transmitter and the typical transmitter, where $0 \\vee (i+k-K)$ means $\\max(0, i+k-K)$ and $k \\wedge i$ means $\\min(k, i)$. Note that $t$ is a random variable since the typical and interfering transmitters select $k$ and $i$ chunks, respectively, uniformly at random. For notation simplicity, we do not always indicate the dependence of $t$ on $k$ and $i$. \n\nDepending on the number of overlapping chunks $t$, the PPP $\\Phi_i$ can further be partitioned into $t$ independent PPPs denoted by $\\Phi_{i, t}$. The PPP $\\Phi_{i, t}$ corresponds to transmitters located at $x \\in \\Phi_i$ that have $0 \\vee (i+k-K) \\leq t \\leq k \\wedge i$ chunks in common with the typical transmitter. Consequently, the interference power received at the typical receiver at the origin from type-$i$ interferers is given by\n\\begin{align}\nI_{k,i} = \\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\sum_{x \\in \\Phi_{i,t}\\setminus \\lbrace x_0 \\rbrace} tPh_x\\ell(x).\n\\label{eq:intf_pow_i}\n\\end{align}\nThe total interference power at the typical receiver of type $k$ follows as $I_k = \\sum_{i = 1}^{K} I_{k,i}$.\n\n\n\n\\textbf{SIR expression}: Following the expressions of signal and interference powers, the SIR experienced at the typical user of type $k$ can be expressed as\n\\begin{align}\n\\mathsf{SIR}_o^{(k)} = \\frac{kh_{x_0}\\ell(x_0)}{\\sum_{i = 1}^{K} \\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\sum_{x \\in {\\Phi_{i,t}}\\setminus \\lbrace x_0 \\rbrace} th_x\\ell(x)}.\n\\label{eq:sir_main_ex}\n\\end{align}\nThe transmit power $P$ per chunk vanishes from \\eqref{eq:sir_main_ex}. \n\n\n\n\n\n\n\\section{Performance Metrics}\nIn this section, we define and discuss the three performance metrics that we consider in this work. These metrics are based on the SIR received at the typical receiver and shed light on different aspects of the performance of users. \n\nAlthough the definitions of the performance metrics are general, the calculation of these metrics depends on the type of the typical user. The overall performance can be evaluated by unconditioning with respect to the type of the typical transmitter. For instance, let $f_k$ be some performance function conditioning on the fact that the typical transmitter is of type $k$. Then, the overall performance $f$ of the typical user is given by $f = \\sum_{k = 1}^{K} p_k f_k$. For the notation simplicity, in this section, we do not explicitly show the dependence of the performance metrics on $k$ while defining them. However, we shall reintroduce the parameter `$k$' in the performance analysis done in Section~\\ref{sec:perf_ana}. \n\n\\subsection{Success probability}\n\\begin{definition}[Success probability]\nThe success probability of the typical user at the origin $o$ is the complementary cumulative distribution function (ccdf) of the SIR, which is\n\\begin{align}\np_{\\rm s}(\\theta) \\triangleq \\mathbb{P}^{!}_{o}(\\mathsf{SIR}_o > \\theta),\n\\label{eq:p_suc}\n\\end{align}\nwhere $\\theta \\in \\mathbb{R}^{+}$ is the target SIR threshold. \n\\end{definition}\n\\noindent Here, $\\mathbb{P}^{!}_{o}(\\cdot)$ denotes the reduced Palm probability of the receiver point process. $p_{\\rm s}$ is an outage-based performance metric: if the received SIR at the typical receiver is larger than $\\theta$, the transmission is considered successful. \n\nWhen the underlying point process is ergodic, $p_{\\rm s}$ can also be interpreted as the fraction of concurrent transmissions that achieve an SIR greater than $\\theta$ in each realization of the network. In other words, $p_{\\rm s}$ is nothing but a {\\em spatial average} in that it is evaluated by taking a certain expectation over the point process. This average is certainly very useful in wireless networks, but it does not provide information about individual user success probabilities. \nHence, to analyze the fine-grained performance, we need to quantify how individual user success probabilities are distributed around the average $p_{\\rm s}$. The meta distribution of the SIR defined below is one such fine-grained performance metric in wireless networks~\\cite{Haenggi_MD_2016}.\n\n\\subsection{Meta distribution of the SIR}\n\\label{sec:md_sir}\nWe are interested in the random variable $P_{\\rm s}$ defined as $P_{\\rm s}(\\theta, \\Phi) \\triangleq \\mathbb{P}(\\mathsf{SIR} > \\theta \\mid \\Phi)$, where the conditional probability is taken over the fading and the random channel access scheme of interferers determined by the BA model. This conditional random variable is the probability that the fading and the random channel access scheme yielding an SIR at least $\\theta$ for the user under consideration for a given realization of $\\Phi$. Hence, $P_{\\rm s}$ is the success probability of that user conditioned on the point process $\\Phi$. The distribution of $P_{\\rm s}$ obtained by taking an expectation over the point process is the meta distribution of the SIR. Formally, it is defined as follows.\n\\begin{definition}[Meta distribution]\nThe meta distribution of the SIR is the distribution function\n\\begin{align}\n\\bar{F}(\\theta, x) \\triangleq \\mathbb{P}^{!}_{o}(P_{\\rm s}(\\theta, \\Phi) > x), \\quad \\theta \\in \\mathbb{R}^{+}, x \\in [0, 1].\n\\label{eq:md_def}\n\\end{align}\n\\end{definition}\n\n\\noindent The meta distribution $\\bar{F}(\\theta, x)$ is the probability that the user under consideration has a reliability at least $x$ for the target SIR threshold of $\\theta$, where the reliability is the conditional success probability $P_{\\rm s}(\\theta, \\Phi)$. When the underlying point process is ergodic (such as the PPP), $\\bar{F}(\\theta, x)$ can be interpreted as the fraction of users that achieve the target SIR of $\\theta$ with probability at least $x$. The parameter $x$ can be viewed as the target reliability. Note that the standard success probability $p_{\\rm s}(\\theta)$ given in \\eqref{eq:p_suc} is the mean of the conditional random variable $P_{\\rm s}(\\theta, \\Phi)$. Hence, the meta distribution of the SIR $\\bar{F}(\\theta, x)$ provides much sharper SIR performance compared to its mean $p_{\\rm s}(\\theta)$.\n\n\n\\subsection{Shannon throughput}\nThe success probability $p_{\\rm s}(\\theta)$ and the meta distribution $\\bar{F}(\\theta, x)$ correspond to the binary event whether the SIR is larger than some threshold $\\theta$ or not. Hence, they fail to use the SIR values larger than $\\theta$ and reduce the SIR threshold $\\theta$ to avoid outages if the SIR value is smaller than $\\theta$. Instead, a transmitter can (if possible) adapt to channel conditions and adjust the SIR threshold $\\theta$ to the maximum value such that $\\mathsf{SIR} \\geq \\theta$ (alternatively, adapt the coding rate). In this case, Shannon throughput is a more suitable performance metric to quantify the performance in wireless networks.\n\\begin{definition}[Shannon throughput]\nFor the bandwidth $B$ used by a user, the Shannon throughput is\n\\begin{align}\n\\mathcal{R} \\triangleq B\\mathbb{E}(\\log_2(1+\\mathsf{SIR})).\n\\label{eq:shannon_throughput}\n\\end{align} \n\\end{definition}\n\\noindent The Shannon throughput is the average of the instantaneous throughput $B\\log_2(1+\\mathsf{SIR})$ of a random user in the network with adaptive coding, where the SIR corresponds to that random user. The Shannon throughput is expressed in bits\/s.\n\n\\section{Performance Analysis}\n\\label{sec:perf_ana}\nIn this section, we calculate the expressions of the performance metrics.\n\n\\subsection{Success probability}\nWe obtain a closed-form expression of the success probability. Although our analysis can be generalized to arbitrary path loss models, we first focus on the standard power-law path loss function given as $\\ell(x) = \\|x\\|^{-\\alpha}$ with $\\alpha > 2$ being the path loss exponent. We later provide a simple closed-form expression of the success probability for the bounded path loss function $\\ell(x) = (c_0 +\\|x\\|^\\alpha)^{-1}$ with $c_0 > 0$.\n\\begin{lemma}\n\\label{lem:suc_prob_k}\nConditioned on the typical transmitter being of type $k$, i.e., when $x_0 \\in \\Phi_k$, the success probability $p_{\\rm s}^{(k)}$ of the typical receiver at the origin can be expressed as\n\\begin{align}\np_{\\rm s}^{(k)}(\\theta) & = \\prod_{i = 1}^{K} p_{\\rm s} ^{(k,i)}(\\theta),\n\\label{eq:cond_suc}\n\\end{align}\nwhere $p_{\\rm s} ^{(k,i)}$ is the success probability of the typical receiver due to the interference from type-$i$ interferers only conditioned on the typical transmitter being of type $k$.\n\\end{lemma}\n\\begin{IEEEproof} \nSee Appendix~\\ref{app:suc_prob_k}.\n\\end{IEEEproof}\nThe probabilities $p_{\\rm s} ^{(k,i)}$ quantify the performance of differentiated services. \nSpecifically, as shown in the following theorem, by just playing with the values of $k$ and $i$, one can investigate how elephants (users with wider bandwidth) affect the performance of mice (users with smaller bandwidth) and vice-versa. The following theorem gives a simple closed-form expression of $p_{\\rm s} ^{(k,i)}$ that allows one to quantify the effect of type-$i$ users on the success probability of a type-$k$ user.\n\\begin{theorem}\n\\label{thm:suc_ki}\nLet us condition on the typical transmitter being a type-$k$ user. For the power-law path loss model, the success probability $p_{\\rm s} ^{(k,i)}$ of the typical user is\n\\begin{align}\np_{\\rm s} ^{(k,i)}(\\theta) = \\exp\\left(-\\lambda p_i C \\theta^{\\delta} \\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}p^{(t)}_{k,i}\\left(\\frac{t}{k}\\right)^\\delta\\right),\n\\end{align}\nwhere $C \\triangleq \\pi R^2 \\Gamma(1+\\delta)\\Gamma(1-\\delta)$ with $\\delta \\triangleq 2\/ \\alpha$ and $p^{(t)}_{k,i}$ is the probability that an interferer of type $i$ has $0 \\vee (i+k-K) \\leq t \\leq k\\wedge i$ chunks in common with the typical transmitter, conditioned on the fact that the latter is a type-$k$ user. For the random BA case, we have\n\\begin{align}\np^{(t)}_{k,i} = \\frac{{k \\choose t}{K-k \\choose i-t}}{{K \\choose i}},\n\\label{eq:pt}\n\\end{align}\nwhereas for the contiguous BA case,\n\n{{\\small\\begin{align}\np^{(t)}_{k,i} =\\begin{cases}\n\\frac{2(K+t-k-i+1)}{(K-k+1)(K-i+1)} & \\mathrm{if}~t < k \\wedge i~\\mathrm{and}~K \\geq k+i-t\\\\\n0 & \\mathrm{if}~t < k \\wedge i~\\mathrm{and}~K < k+i-t\\\\\n\\frac{i-k+1}{K-k+1} & \\mathrm{if}~t = k~\\mathrm{and}~k \\leq i \\\\\n\\frac{k-i+1}{K-i+1} & \\mathrm{if}~t = i~\\mathrm{and}~k > i \\\\\n\\frac{(K-k-i+1)(K-k-i+2)}{(K-k+1)(K-i+1)} & \\mathrm{if}~t = 0~\\mathrm{and}~K \\geq k+i.\n\\end{cases} \n\\label{eq:pt_cont}\n\\end{align}}}\n\\end{theorem}\n\\begin{IEEEproof}\nSee Appendix~\\ref{app:suc_ki}.\n\\end{IEEEproof}\nFinally, unconditioning on the type of the typical transmitter yields the overall success probability $p_{\\rm s} = \\sum_{k = 1}^{K}p_k p_{\\rm s}^{(k)}(\\theta)$ as\n\\begin{align}\np_{\\rm s}(\\theta) = \\sum_{k = 1}^{K} p_k \\exp\\!\\left(\\!\\!-\\lambda C \\theta^{\\delta}\\sum_{i = 1}^{K}p_i \\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}p^{(t)}_{k,i} \\left(\\frac{t}{k}\\right)^\\delta\\right).\n\\label{eq:int_suc} \n\\end{align}\n\n\\textbf{Bounded path loss function}: For the bounded path loss function $\\ell(x) = (c_0 + \\|x\\|^{\\alpha})^{-1}$ with $c_0 > 0$, we have \n{{\\small\n\\begin{align}\np_{\\rm s}^{(k,i)}(\\theta) = \\exp\\!\\left(\\!\\!- p_i C_b\\theta \\!\\!\\!\\!\\!\\!\\!\\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\!\\!\\!\\!\\!\\!\\!\\!p^{(t)}_{k,i}\\!\\left(\\frac{t}{k}\\right)\\!\\! \\!\\left(\\frac{\\theta t}{k}(c_0 + R^{\\alpha}) + c_0\\!\\right)^{\\delta{-}1}\\! \\right)\\!\\!,\n\\end{align}}}\\vspace*{-4mm}\n\n\\noindent where $C_b \\triangleq \\lambda \\pi (c_0+R^{\\alpha})\\Gamma(1+\\delta)\\Gamma(1-\\delta)$. The proof follows the one of Theorem~\\ref{thm:suc_ki}. The standard path loss function $\\ell(x) = \\|x\\|^{-\\alpha} $ is a special case of the bounded path loss function $(c_0 + \\|x\\|^{\\alpha})^{-1}$ with $c_0 = 0$. The equations \\eqref{eq:cond_suc} and \\eqref{eq:int_suc} are also valid for the bounded path loss case.\n\n\\subsection{Meta distribution of the SIR}\n\nThe direct calculation of the SIR meta distribution, i.e., the ccdf in \\eqref{eq:md_def}, is impossible. Hence we take an indirect route, where we first calculate $b$th moments ($b \\in \\mathbb{C}$) of $P_{\\rm s}$ and use those moments to obtain the ccdf $\\bar{F}(\\theta, x)$ accurately using the Gil-Pelaez theorem~\\cite{Gil_Pelaez} or approximately by matching the first and second moments to those of the beta distribution~\\cite{Haenggi_MD_2016}. In other words, averaging over the point process $\\Phi$ and averaging over the fading and the random channel access scheme are done separately. This is unlike the calculation of the success probability $p_{\\rm s}$, where averaging over $\\Phi$, the fading, and the random channel access scheme are done simultaneously.\n\n\\begin{theorem}\n\\label{thm:Mb_k}\nConditioning on the typical transmitter being of type $k$, the $b$-th moment of $P_{\\rm s}^{(k)}$ is\n{{\\small\\begin{align*}\nM_{b}^{(k)} = \\exp\\!\\!\\left(\\!\\!-2\\pi\\lambda \\!\\!\\int_{0}^{\\infty}\\!\\!\\!\\left[\\!1-\\!\\!\\left(\\sum_{i = 1}^{K}p_i\\!\\!\\!\\!\\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\!\\frac{p^{(t)}_{k,i}}{1+\\theta \\frac{t}{k}\\frac{\\ell(r)}{\\ell(x_0)}}\\!\\!\\right)^{b}\\right]\\!\\mathrm{d}r\\!\\!\\right)\\!\\!,\n\\end{align*}}}\nwhere $p^{(t)}_{k,i}$ is given by \\eqref{eq:pt} for random BA and by \\eqref{eq:pt_cont} for contiguous BA.\n\\end{theorem}\n\\begin{IEEEproof}\nSee Appendix~\\ref{app:Mb_k}.\n\\end{IEEEproof}\n\n\\subsection{Shannon throughput}\n\\begin{theorem}\nConditioning on the typical transmitter being of type $k$, the Shannon throughput $\\mathcal{R}^{(k)}$ is given by\n\\begin{align}\n\\mathcal{R}^{(k)} = \\frac{k}{K} \\int_{0}^{\\infty} p_{\\rm s}^{(k)}(2^y-1)~\\mathrm{d}y,\n\\end{align}\nwhere $p_{\\rm s}^{(k)}$ is given by~\\eqref{eq:cond_suc}.\n\\end{theorem}\n\\begin{IEEEproof}\nA transmitter of type $k$ has $k\/K$ Hz of bandwidth for the transmission. Hence, we have\n\\begin{align}\n\\mathcal{R}^{(k)} &= \\frac{k}{K}\\int_{0}^{\\infty} \\mathbb{P}(\\log_2(1 + \\mathsf{SIR}_o) > y \\mid x_0 \\in \\Phi_k)~\\mathrm{d}y \\nonumber \\\\\n&= \\frac{k}{K} \\int_{0}^{\\infty} p_{\\rm s}^{(k)}(2^y-1)~\\mathrm{d}y.\n\\label{eq:Shannon_throughput_no_joule}\n\\end{align}\n\\end{IEEEproof}\n\nThe overall Shannon throughput of the typical user is hence\n$\\mathcal{R} = \\sum_{k = 1}^{K} p_k \\mathcal{R}^{(k)}.$\n\n\\textbf{Shannon throughput per Joule}: In $5$G NR, a key motivation for proposing the inclusion of BWP is power savings through adaptive bandwidth allocation. Hence, we consider Shannon throughput per Joule of the energy spent, which is the Shannon throughput divided by the spent power $kP$. Conditioning on the typical transmitter being of type $k$, Shannon throughput per Joule $\\mathcal{R}^{(k)}_{J}$ is obtained by dividing \\eqref{eq:Shannon_throughput_no_joule} by $kP$ as\n\\begin{align}\n\\mathcal{R}^{(k)}_{J} = \\frac{1}{KP}\\int_{0}^{\\infty} p_{\\rm s}^{(k)}(2^y-1)~\\mathrm{d}y.\n\\end{align}\nHere, the Shannon throughput is expressed in bits\/Joule.\n\\begin{remark}\nNote that, in this paper, the Shannon throughput per Joule is equivalent to the Shannon throughput per Hertz in that they only differ in the multiplicative constant $P$. This is due to the assumption that a transmitter spends power $P$ per chunk. Thus, for a given type of the user, normalizing by the transmit power to obtain the Shannon throughput per Joule is equivalent to normalizing by the bandwidth used by the user. \n\\end{remark}\n\n\n\n\n\n\n\\section{The Mean Model}\n\\label{sec:mean_model}\n\n\\subsection{Comparison of two networks with adaptive BA}\nSuppose the network operator has decided to use adaptive BA. There are several BA configurations to choose from based on the values of the probabilities $p_k$. From the operator's viewpoint, it is natural to investigate how two networks with different $p_k$ fare in terms on the overall performance. To fairly compare two networks employing adaptive BA, it is natural to do so for the same mean signal and the same mean interference powers.\n\n\\textbf{Mean signal power}: For our BA model, since the typical transmitter is of type $k$ with probability $p_k$, the mean signal power at the typical receiver is given by\n\\begin{equation}\n\\bar{S}_{k} = P\\ell(x_0)\\sum_{k = 1}^{K}p_kk.\n\\label{eq:mean_sig_bwp}\n\\end{equation}\nFor another network with different $p_k$, say $p_k'$, we might have to adjust the transmit power per chunk $P'$ to have the mean signal power $\\bar{S}_{k}'$ same as $\\bar{S}_{k}$. From \\eqref{eq:mean_sig_bwp}, it follows that\n\\begin{equation}\nP' = \\frac{P\\sum_{k = 1}^{K}k p_k}{\\sum_{k = 1}^{K}k p_k'} \n\\label{eq:eq:_power}\n\\end{equation}\nleads to $\\bar{S}_{k} = \\bar{S}_{k}'$.\n\n\\textbf{Mean interference power}: For our BA model, conditioned on the fact that the typical user is of type $k$, the mean interference power is obtained by taking the expectation of $I_k$ given in \\eqref{eq:intf_pow_main} as\\vspace*{-3mm}\n\n{{\\small \\begin{align}\n\\mathbb{E}[I_k] &= \\sum_{i = 1}^{K}\\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\mathbb{E}\\left[\\sum_{x \\in \\Phi_{i,t}\\setminus \\lbrace x_0 \\rbrace} tP\\ell(x)\\right] \\nonumber\\\\\n&\\overset{(a)}{=} \\lambda\\pi \\delta c_0^{\\delta - 1}\\Gamma(\\delta)\\Gamma(1-\\delta)P\\sum_{i = 1}^{K}p_i \\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i} tp_{k,i}^{(t)},\n\\label{eq:cond_mean_intf}\n\\end{align}}}\\vspace{-3mm}\n\n\\noindent where $(a)$ is obtained by the straightforward application of the Campbell's theorem for the PPP $\\Phi_{i,t}$~\\cite{bb_book} and using the bounded path loss function $\\ell(x) = (c_0 +\\|x\\|^{\\alpha})^{-1}$ with $\\delta \\triangleq 2\/\\alpha$.\\footnote{The standard path loss function $\\ell(x) = \\|x\\|^{-\\alpha}$ follows from the bounded path loss function as $c_0 \\to 0$.} Unconditioning on the type of the typical user yields the mean interference power at the typical receiver as\n\\begin{align}\n\\bar{I} = \\sum_{k = 1}^{K} p_k \\mathbb{E}[I_k].\n\\label{eq:mean_intf}\n\\end{align}\n\n\\begin{figure*}[t]\n\t\\begin{minipage}{0.32\\linewidth}\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width = 2in,height=1.3in]{suc_prob_N_3_uniform_alpha_4\n\t\t\t\\caption{\\footnotesize Per-type success probability $p_{\\rm s}^{(k)}$ versus the SIR threshold $\\theta$. $p_k = 1\/K = 1\/3$.}\n\t\t\t\\label{fig:suc_prob_N_3_uniform_alpha_4}\n\t\t\\end{center}\n\t\\end{minipage}\n\t\\hspace{0.01\\linewidth}\n\t\\begin{minipage}{0.32\\linewidth}\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width = 2in,height=1.3in]{overall_suc_prob_K_3_alpha_4\n\t\t\t\\caption{\\footnotesize Overall success probability $p_{\\rm s}$ versus the SIR threshold $\\theta$.}\n\t\t\t\\label{fig:overall_suc_prob_K_3_alpha_4}\n\t\t\\end{center}\n\t\\end{minipage}\n\t\\hspace{0.01\\linewidth}\n\t\\begin{minipage}{0.32\\linewidth}\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width = 2in,height=1.3in]{md_basic\n\t\t\t\\caption{\\footnotesize The SIR meta distribution $\\bar{F}(\\theta, x)$ versus the reliability threshold $x$. $p_k = 1\/K = 1\/3$.}\n\t\t\t\\label{fig:md_basic}\n\t\t\\end{center}\n\t\\end{minipage}\n\\end{figure*}\n\n\\begin{figure*}[t]\n\t\\begin{minipage}{0.32\\linewidth}\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width = 2in,height=1.3in]{shannon_rate_joule_no_joule_no_mean\n\t\t\t\\caption{\\footnotesize Per-type Shannon throughput versus intensity $\\lambda$. $P = 2$ and $p_k = 1\/K = 1\/3$. For solid curves: $k = 1, 2, 3$ (from bottom to top). For dashed curves: $k = 1, 2, 3$ (from top to bottom).}\n\t\t\t\\label{fig:shannon_rate_joule_no_joule_no_mean}\n\t\t\\end{center}\n\t\\end{minipage}\n\t\\hspace{0.01\\linewidth}\n\t\\begin{minipage}{0.32\\linewidth}\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width = 2in,height=1.3in]{Overall_shannon_joule_no_joule\n\t\t\t\\caption{\\footnotesize Overall Shannon throughput versus intensity $\\lambda$. $P = 2$.}\n\t\t\t\\label{fig:Overall_shannon_joule_no_joule}\n\t\t\\end{center}\n\t\\end{minipage}\n\t\\hspace{0.01\\linewidth}\n\t\\begin{minipage}{0.32\\linewidth}\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width = 2in,height=1.3in]{egalitarian\n\t\t\t\\caption{\\footnotesize Per-type Shannon throughput versus the type $k$ of the user for random and contiguous bandwidth allocation. $K = 10$, $p_i = 1\/K$.}\n\t\t\t\\label{fig:Mean_interference_contiguous}\n\t\t\\end{center}\n\t\\end{minipage}\n\\end{figure*}\n\n\n\nFor another adaptive BA-based network with probabilities $p_k'$, the mean interference power $\\bar{I}'$ can be calculated by replacing $p_k$ by $p_k'$ and $\\lambda$ by $\\lambda'$ in \\eqref{eq:cond_mean_intf} and \\eqref{eq:mean_intf}, where $\\lambda'$ is the intensity of the PPP corresponding to the network with $p_k'$. The intensity $\\lambda'$ is chosen such that the mean interference powers for two networks with $p_k$ and $p_k'$ are the same, i.e., $\\bar{I} = \\bar{I}'$. Hence, from \\eqref{eq:cond_mean_intf} and \\eqref{eq:mean_intf}, it follows that\n\\begin{align}\n\\lambda' = \\frac{\\lambda P\\sum_{k = 1}^{K}p_k \\sum_{i = 1}^{K}p_i \\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i} tp_{k,i}^{(t)}}{P'\\sum_{k = 1}^{K}p_k' \\sum_{i = 1}^{K}p_i' \\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i} tp_{k,i}^{(t)}},\n\\label{eq:eq_lambda}\n\\end{align}\nwhich results in $\\bar{I} = \\bar{I}_K$.\n\n\\subsection{Service differentiation}\nWe now quantify the service differentiation resulting from adaptive BA. We focus on mean values of signal and interference powers to estimate the Shannon throughput (data rate) a user gets depending on its type. As shown later in the paper in Section~\\ref{sec:var}, such a mean-based approach sheds light on the effect of traffic variability on the performance.\n\\begin{proposition}\nThe random BA is roughly egalitarian in Shannon throughput per Hertz and leads to a linear service differentiation in aggregate Shannon throughput.\n\\end{proposition}\nThis proposition is based on the following observation. For a user of type $k$, the mean signal power is $k$ times the mean signal power of a user of type $1$. Also, noting that \\eqref{eq:pt} is the probability mass function of the hypergeometric distribution, the term $\\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i} tp_{k,i}^{(t)} = ik\/K$ in \\eqref{eq:cond_mean_intf} is the mean of the hypergeometric distribution. As a result, the mean interference at a user of type $k$ is $k$ times that at a user of type $1$. Hence, if we only think in terms of mean values, all users, regardless of their type, have the same mean signal-to-mean interference ratio (MSMIR), and hence roughly the same (egalitarian) Shannon throughput per Hertz. Of course, since a user of type $k$ has a bandwidth $k$ times larger than that of a user of type 1, a user benefits linearly from its type in terms of aggregated Shannon throughput. This proposition is illustrated by Fig.~\\ref{fig:Mean_interference_contiguous}.\n\nSimilarly, for the contiguous BA as well, as Fig.~\\ref{fig:Mean_interference_contiguous} shows, the adaptive BA provides a roughly egalitarian service in Shannon throughput per Hertz and a linear service differentiation in aggregate Shannon throughput.\n\n\\section{Results and Discussions}\nIn this section, we discuss the results for the adaptive BA model. We divide this section in two parts. The first part provides results that focus on the per-type and the overall performance in the adaptive BA setting. The second part integrates the mean model discussed in Section~\\ref{sec:mean_model}. \n\nWithout loss of generality, we assume the following model parameters. The number of bandwidth chunks is $K = 3$ unless otherwise mentioned. The intensity of the PPP is $\\lambda = 0.2$. For the bounded path loss model, the path loss exponent is $\\alpha = 4$ with $c_0 = 1$. The desired link distance is $R = 1$. Other parameters are given in the captions of the relevant figures. Unless otherwise mentioned, all plots correspond to the random BA model.\n\n\\subsection{Per-type and overall performance}\n\n\n\\textbf{Success probability.} Fig.~\\ref{fig:suc_prob_N_3_uniform_alpha_4} shows the per-type success probability. A type-$1$ user experiences a smaller interference compared to type-$2$ and type-$3$ users since it uses only one chunk selected randomly. Hence, a type-$1$ user achieves the highest success probability. As a consequence, when we uncondition on the type of the user and calculate the overall success probability, the network model where a user always selects only one chunk at random (the model with $p_1 = 1$) achieves the highest overall success probability, while a network model without adaptive BA (the case with $p_3 = 1$) performs the worst (see Fig.~\\ref{fig:overall_suc_prob_K_3_alpha_4}). Other cases of adaptive BA lie in between these two extreme cases of $p_1 = 1$ and $p_3 = 1$.\n\n\\textbf{Meta distribution.} Fig.~\\ref{fig:md_basic} plots the per-type SIR meta distribution against the reliability $x$ for different target SIR thresholds $\\theta$. The curves in~Fig.~\\ref{fig:md_basic} allow one to make precise statements about the fraction of users achieving an SIR of $\\theta$ with reliability $x$. Notice that the same trend as the per-type success probability in Fig.~\\ref{fig:suc_prob_N_3_uniform_alpha_4} holds for the SIR meta distribution, i.e., type-$1$ users outperform type-$2$ and type-$3$ users irrespective of the SIR threshold value. In other words, the fraction of type-$1$ users that achieve a reliability of $x$ for a given SIR threshold $\\theta$ is higher than the fractions of type-$2$ and type-$3$ users. For instance, the fraction of users achieving an SIR of $-5$ dB with reliability $60\\%$ is $0.78$ for type-$1$ users, $0.74$ for type-$2$ users, and $0.73$ for type-$3$ users. We skip the discussion on the overall SIR meta distribution since it follows a similar trend as that of the overall success probability in~Fig.~\\ref{fig:overall_suc_prob_K_3_alpha_4}.\n \n\\textbf{Shannon throughput.}\nFor the Shannon throughput defined in~\\eqref{eq:shannon_throughput}, as shown by solid curves in Fig.~\\ref{fig:shannon_rate_joule_no_joule_no_mean}, a new trend emerges for the per-type Shannon throughput, where a type-$3$ user outperforms type-$2$ and type-$1$ users. This trend occurs because a higher allocated bandwidth boosts the per-type Shannon throughput, which overcomes the increased interference due to higher bandwidth. Here, note that the users of higher types achieve a higher Shannon throughput at the expense of larger transmit power $kP$ (hence more power consumption), which grows linearly with $k$ (the type of the user). \n\n\\textbf{Shannon throughput per Joule.}\\footnote{Note that, as mentioned in Remark $1$, the trends observed here in the Shannon throughput per Joule hold true for the Shannon throughput per Hertz as well.}\nAs shown by dashed curves in Fig.~\\ref{fig:shannon_rate_joule_no_joule_no_mean}, the trend reverses when the per-type Shannon throughput is normalized by the transmit power, i.e., a type-$1$ user achieves a higher Shannon throughput than type-$2$ and type-$3$ users. This reveals the per-type throughput performance per Joule of energy spent, which is useful in understanding the effect of power consumption on the per-type throughput performance.\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig.~\\ref{fig:Overall_shannon_joule_no_joule} shows that the intensity $\\lambda$ of the PPP plays a key role in determining the overall Shannon throughput. For small intensity $\\lambda$, as shown by solid curves in Fig.~\\ref{fig:Overall_shannon_joule_no_joule}, the network model with no adaptive BA (the model with $p_3 = 1$) achieves a higher overall Shannon throughput than two networks employing adaptive BA with $p_k = 1\/3$ and $p_1 = 1$. In contrast, for large $\\lambda$, the trend reverses in that the networks with adaptive BA outperform the network with no adaptive BA. The reason behind this behavior is that: for a small $\\lambda$, the interference power is relatively small. Hence, a wide bandwidth in the network without adaptive BA boosts the Shannon throughput and overcomes the negative impact of increased interference due to higher bandwidth. Whereas for large $\\lambda$, the effect of increased interference dominates and the network with adaptive BA achieves a higher Shannon throughput. \n\nAlso, again similar to the per-type Shannon throughput case, the network without adaptive BA achieves a higher Shannon throughput for small $\\lambda$ at the expense of larger transmit power $kP$ (hence more power consumption). Thus, when the Shannon throughput is normalized by the transmit power, the network with the least interference power (the network with $p_1 = 1$ in this case) achieves the highest Shannon throughput.\n\n\n\\begin{figure*}[t]\n\t\\begin{minipage}{0.32\\linewidth}\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width = 2in,height=1.3in]{suc_prob_mean_model\n\t\t\t\\caption{\\footnotesize Overall success probability $p_{\\rm s}(\\theta)$ versus the SIR threshold $\\theta$. }\n\t\t\t\\label{fig:suc_prob_mean_model}\n\t\t\\end{center}\n\t\\end{minipage}\n\t\\hspace{0.01\\linewidth}\n\t\\begin{minipage}{0.32\\linewidth}\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width = 2in,height=1.3in]{shannon_rate_3_10_7_10_N_3_mean_model\n\t\t\t\\caption{\\footnotesize Overall Shannon throughput versus intensity $\\lambda$. $P = 2$. }\n\t\t\t\\label{fig:shannon_rate_3_10_7_10_N_3_mean_model}\n\t\t\\end{center}\n\t\\end{minipage}\n\t\\hspace{0.01\\linewidth}\n\t\\begin{minipage}{0.32\\linewidth}\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width = 2in,height=1.3in]{shannon_rate_3_10_7_10_N_3_Joule_mean_model\n\t\t\t\\caption{\\footnotesize Overall Shannon throughput per Joule versus intensity $\\lambda$. $P = 2$.}\n\t\t\t\\label{fig:shannon_rate_3_10_7_10_N_3_Joule_mean_model}\n\t\t\\end{center}\n\t\\end{minipage}\n\\end{figure*}\n\\textbf{Service differentiation.} Fig.~\\ref{fig:Mean_interference_contiguous} plots the per-type aggregated Shannon throughput and per-Hertz Shannon throughput against the user type $k$. The plots confirm Proposition $1$ that both random and contiguous BA result in a roughly equal Shannon throughput per Hertz to users of different types and a linear increase in aggregated throughput with user type. As shown by the dashed curves in Fig.~\\ref{fig:shannon_rate_joule_no_joule_no_mean}, this egalitarian property holds for different intensities $\\lambda$ of the PPP. Also, as $k$ increases, the Shannon throughput per Hertz decreases slightly. This can be attributed to the fact that a type-$k$ user experiences interference with a larger variance than a type-$j$ user if $k< j$, and such a higher variance is generally beneficial to most metrics as shown in~\\cite{Lee_F}. \nAnother interesting observation from Fig.~\\ref{fig:Mean_interference_contiguous} is that random BA results in a better Shannon throughput compared to contiguous BA since the former leads to higher traffic variability that benefits users in terms of data rates. We discuss this observation in more detail in Section~\\ref{sec:var}.\n\n\n\n\\subsection{Benefits of adaptive BA}\n\nWe now briefly discuss the benefits of adaptive BA from these numerical results. Let us start with the user performance viewpoint. It should be clear from Figs.~\\ref{fig:suc_prob_N_3_uniform_alpha_4} and \\ref{fig:md_basic} that smaller users (i.e., users of type 1) get a better success probability and a better meta distribution than bigger ones. Note that the improvement is more pronounced for higher SIR thresholds. Fig.~\\ref{fig:shannon_rate_joule_no_joule_no_mean} shows that the Shannon throughput per Joule is also better for smaller users than for\nbigger ones. We conclude that adaptive BA brings the expected service differentiation and protection of small users, both in terms of success probability and Shannon throughput per Joule. As for bigger users, we see in Fig.~\\ref{fig:shannon_rate_joule_no_joule_no_mean} that they nevertheless get a better Shannon throughput than smaller ones, to the\nexpense of a higher power consumption (proportional to the number of chunks they use). One also gets from first principles that the biggest users get less interference and hence a better Shannon throughput in the scenario with adaptive BA than in the scenario without adaptive BA. Hence, at least for this performance metric, adaptive BA is beneficial to bigger users as well.\n\nConsider now the point of view of operators, namely overall performance, which can be linked to revenue. When comparing the uniform case to the case with $p_3 = 1$ (no adaptive BA case) in Fig.~\\ref{fig:overall_suc_prob_K_3_alpha_4}, we see that the overall success probability is higher in the situation with adaptive BA than in the situation without.\nFig.~\\ref{fig:Overall_shannon_joule_no_joule} actually shows that the same conclusion holds for the overall Shannon throughput per Joule. We conclude that adaptive BA should be beneficial to operators as well. These conclusions are not limited to this special case with three chunks as the ordering of the curves is in fact the same when varying the number of chunks and\/or the other model parameters.\n\n\n\n\n\n\\subsection{Increasing traffic variability may improve performance}\n\\label{sec:var}\nSuppose the operator has decided to use adaptive BA and is interested in comparing the overall performance of two networks with different probabilities $p_k$. Consider a first network with uniformly distributed $p_k$, for $K = 3$, $p_k = 1\/K = 1\/3$. For the second network, also with $K = 3$, assume that $p_3 = 0.7$ and $p_1 = 0.3$. The choice of $p_k$ for the second network is inspired from the estimated proportion of the video traffic for year 2020~\\cite{Cisco_VNI}. It is expected that approximately $70\\%$ of mobile traffic will correspond to videos needing a wide bandwidth. Hence, $p_3 = 0.7$ corresponds to video traffic, while $p_1 = 0.3$ corresponds to non-video traffic due to applications requiring smaller bandwidths. For the second network, the transmit power per chunk $P'$ and the intensity $\\lambda'$ are calculated from \\eqref{eq:eq:_power} and \\eqref{eq:eq_lambda}, respectively, such that both networks have the same mean interference and the same mean signal powers.\n\nCompared to the first network, the second one has a more variable distribution of powers and hence a more variable interference. From Figs.~\\ref{fig:suc_prob_mean_model}, \\ref{fig:shannon_rate_3_10_7_10_N_3_mean_model}, and \\ref{fig:shannon_rate_3_10_7_10_N_3_Joule_mean_model}, it is clear that the second and more variable traffic network outperforms the first one in terms of all performance metrics: success probability, Shannon throughput, and Shannon throughput per Joule. \n\nFor the Shannon throughput case, the trends in both the Shannon throughput and the Shannon throughput per Joule with intensity $\\lambda$ are the same (see Figs.~\\ref{fig:shannon_rate_3_10_7_10_N_3_mean_model} and \\ref{fig:shannon_rate_3_10_7_10_N_3_Joule_mean_model}). This is different from the case without equality of the mean values as shown in Fig.~\\ref{fig:Overall_shannon_joule_no_joule}, where we observed that, for small $\\lambda$, the trend in Shannon throughput per Joule is opposite of that in Shannon throughput. Also, there is no a crossover in curves of Shannon throughput with $\\lambda$ in the model with balanced means.\n\n\n\n\n\n\n\n\n\n\n\\section{Concluding Remarks and Future Directions}\nThis paper proposes a first analytic model for the prediction of the adaptive BA inspired by BWP. The proposed model allowed us to show that adaptive BA should benefit to both small and big users.\nWe showed that small users are well protected by adaptive BA in terms of success probability, meta distribution of the SIR, and Shannon throughput per Joule. On the other hand, big users achieve a better Shannon throughput than in the situation without adaptive BA. The analysis of overall performance allowed us to show that adaptive BA should also be beneficial to operators. Also, we observe that adaptive BA is roughly egalitarian per Hertz and leads to a linear service differentiation in aggregated Shannon throughput. \n\nThere are several future directions of research. A natural extension is to study adaptive BA in cellular settings. It would also be interesting to see how to strategically assign bandwidth chunks to users based on their local environment. Finally, this work is limited to a snapshot analysis of the network. The inclusion of dynamics will certainly be of great interest as well.\n\n\\section*{Acknowledgment}\nThe authors would like to thank Luis Guilherme Uzeda Garcia, Fuad Abinader, Andrea Marcano, and Dalia Popescu from Nokia Bell Labs, Nozay, France for the initial discussion on this problem and bringing to our attention the lack of analytical formulation of BWP.\n\n\n\n\\appendices\n\\section{Proof of Lemma~\\ref{lem:suc_prob_k}}\n\\label{app:suc_prob_k}\nConditioned on $x_0 \\in \\Phi_k$, the success probability $p_{\\rm s}^{(k)}$ is\\vspace*{-3mm}\n\n{{\\small \\begin{align*}\n&p_{\\rm s}^{(k)}(\\theta) = \\mathbb{P}(\\mathsf{SIR}_{o}^{(k)} > \\theta \\mid x_0 \\in \\Phi_k) \\nonumber \\\\\n&= \\mathbb{P}\\!\\left(\\!h_{x_0} > \\theta \\frac{\\sum_{i = 1}^{K} \\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\sum_{x \\in \\Phi_{\\Phi_{i,t}}\\setminus \\lbrace x_0 \\rbrace}th_x \\ell(x)}{k\\ell(x_0)}\\!\\right) \\nonumber \\\\\n&= \\mathbb{E}\\!\\left[\\!\\exp\\!\\left(\\!\\!-\\frac{\\theta \\sum_{i = 1}^{K} \\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\sum_{x \\in \\Phi_{\\Phi_{i,t}}\\setminus \\lbrace x_0 \\rbrace}t h_x\\ell(x)}{k\\ell(x_0)}\\!\\right)\\!\\!\\right]\\!\\!.\n\\end{align*}}}\nAveraging over interfering fading channels, it follows that\n\\begin{equation}\np_{\\rm s}^{(k)}(\\theta) =\\prod_{i=1}^{K} \\underbrace{\\prod_{t = 0 \\vee (i+k-K)}^{k \\wedge i} \\mathbb{E}\\left[\\prod_{x \\in \\Phi_{i,t} \\setminus \\lbrace x_0 \\rbrace} \\frac{1}{1+\\frac{\\theta t \\ell(x)}{k\\ell(x_0)}}\\right]}_{p_{{\\rm s} \\mid k}^{(i)}(\\theta)}.\n\\label{eq:suc_ki_inter}\n\\end{equation}\nThe probability $p_{\\rm s}^{(k,i)}$ can be interpreted as the success probability of the typical receiver due to interference from type-$i$ interferers only conditioned on the typical transmitter being of type $k$. The reason behind this interpretation is that the expression of $p_{\\rm s}^{(k,i)}$ in \\eqref{eq:suc_ki_inter} can be obtained by calculating $\n\\mathbb{P}\\left(\\frac{S}{I_{k,i}} > \\theta\\right) = \\mathbb{P}\\left(\\mathsf{SIR}_o^{(k, i)} > \\theta\\right)$, where $S$ is signal power and $I_{k,i}$ given by \\eqref{eq:intf_pow_i} is the interference power received at the typical receiver only from type-$i$ interferers. Thus, $\\mathsf{SIR}_o^{(k,i)}$ is the SIR at the typical receiver when the interference from type-$i$ interferers only is taken into account.\n\n\\section{Proof of Theorem~\\ref{thm:suc_ki}}\n\\label{app:suc_ki}\nContinuing from \\eqref{eq:suc_ki_inter}, we have\n\\begin{align}\np_{\\rm s}^{(k,i)}(\\theta) = \\prod_{t = 0 \\vee (i+k-K)}^{k \\wedge i} \\mathbb{E}\\left[\\prod_{x \\in \\Phi_{i,t} \\setminus \\lbrace x_0 \\rbrace} \\frac{1}{1+\\frac{\\theta t \\ell(x)}{k\\ell(x_0)}}\\right].\n\\end{align}\nFor the power-law path loss model $\\ell(r) = r^{-\\alpha}$, by the probability generating functional (PGFL) of the PPP, it follows that\n\\begin{align}\np_{\\rm s}^{(k,i)}(\\theta) = \\hspace*{-6mm}\\prod_{t =0 \\vee (i+k-K)}^{k \\wedge i}\\hspace*{-6mm}\\exp\\left(-\\lambda_{i,t}\\int_{\\mathbb{R}^2}\\left(1- \\frac{1}{1+\\frac{t\\theta R^{\\alpha} \\|x\\|^{-\\alpha}}{k}}\\right)\\mathrm{d}x\\right)\\!\\!,\n\\label{eq:suc_ki_int}\n\\end{align}\nwhere $\\lambda_{i,t}$ is the intensity of the point process $\\Phi_{i,t}$. Here, $\\lambda_{i,t} = \\lambda_i p_{k,i}^{(t)}$ since the PPP $\\Phi_i$ of interferers of type $i$ is partitioned into $t$ independent PPPs of interferers of type $i$ having $t$ common chunks with the typical transmitter. Solving the integral in \\eqref{eq:suc_ki_int} and substituting $\\lambda_i = \\lambda p_i$, we have the desired expression of $p_{\\rm s}^{(k,i)}$.\n\n\n\\section{Proof of Theorem~\\ref{thm:Mb_k}}\n\\label{app:Mb_k}\nConditioning on the typical transmitter $x_0$ being of type $k$, the conditional success probability $P_{\\rm s}^{(k)}$ is given as\n\\begin{align*}\nP_{\\rm s}^{(k)}(\\theta) &= \\mathbb{P}(\\mathsf{SIR}_o^{(k)} > \\theta \\mid \\Phi) \\nonumber \\\\\n&= \\mathbb{P}\\left(h_{x_0} > \\theta \\frac{\\sum_{x \\in \\Phi\\setminus\\lbrace x_0 \\rbrace} t_x h_x \\ell(x)}{k\\ell(x_0)} \\mid \\Phi\\right) \\\\ \n& = \\prod_{x \\in \\Phi\\setminus\\lbrace x_0\\rbrace} \\mathbb{E}\\left[\\exp\\left(-\\theta \\frac{ t_x h_x \\ell(x)}{k\\ell(x_0)}\\right) \\mid \\Phi\\right],\n\\end{align*}\nwhere the expectation is taken over the random channel access scheme of interferers determined by the BWP model and the fading. Recall that, for the BWP model, each interferer can be of type $i$ with probability $p_i$. Then, a type-$i$ interferer can have $ 0 \\vee (i+k-K)\\leq t \\leq k \\wedge i$ chunks common with the typical transmitter with probability $p^{(t)}_{k,i}$. Thus, by averaging over the channel access scheme, it follows that\\vspace*{-3mm} \n\n{{\\small \\begin{align*}\nP_{\\rm s}^{(k)}(\\theta) = \\hspace*{-3mm}\\prod_{x \\in \\Phi \\setminus \\lbrace x_0 \\rbrace}\\!\\!\\!\\mathbb{E}\\!\\left[\\sum_{i = 1}^{K}p_i\\hspace*{-3mm}\\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\hspace*{-3mm}p^{(t)}_{k,i}\\exp\\!\\left(-\\theta \\frac{ t h_x \\ell(x)}{k\\ell(x_0)}\\right)\\mid \\Phi\\right]\\!\\!.\n\\end{align*}}}\nNow, averaging over the fading on interfering channels yields\n\\begin{align*}\nP_{\\rm s}^{(k)}(\\theta) = \\prod_{x \\in \\Phi \\setminus \\lbrace x_0 \\rbrace}\\sum_{i = 1}^{K}p_i\\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\frac{p^{(t)}_{k,i}}{1+\\theta \\frac{t}{k}\\frac{\\ell(x)}{\\ell(x_0)}}.\n\\end{align*}\nThe $b$th ($b \\in \\mathbb{C}$) moment of $P_{\\rm s}^{(k)}$ can be expressed as\n{{\\small\\begin{align*}\nM_{b}^{(k)} = \\exp\\!\\!\\left(\\!\\!-2\\pi\\lambda \\!\\!\\int_{0}^{\\infty}\\!\\!\\left[\\!1-\\!\\left(\\sum_{i = 1}^{K}p_i\\hspace*{-3mm}\\sum_{t = 0 \\vee (i+k-K)}^{k \\wedge i}\\hspace*{-1mm}\\frac{p^{(t)}_{k,i}}{1+\\theta \\frac{t}{k}\\frac{\\ell(r)}{\\ell(x_0)}}\\!\\!\\right)^{b}\\right]\\!\\mathrm{d}r\\!\\!\\right)\\!\\!,\n\\end{align*}}}\nwhen making use of the PGFL of the PPP.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\section{Introduction}\n\n\nIn this article we present a new expression for certain elements in the Hecke algbra, related to the $q$-analogue of the Young symmetrizer, which generate its irreducible representations. We will obtain this new expression using Cherednik's fusion\nprocedure. This method originates from the work of Jucys\n\\cite{J}, and has already been used by Nazarov and Tarasov\n\\cite{N1, N2, NT}. However our approach differs by minimising the\nnumber of auxiliary parameters needed in the fusion procedure.\nThis is done by considering hooks of Young diagrams, rather than\ntheir rows or columns as in \\cite{N1, N2, NT}.\n\n\nLet $H_n$ be the finite \ndimensional Hecke algebra over the\nfield $\\mathbb{C}(q)$ of rational functions in $q$,\nwith the generators $T_1, \\ldots , T_{n-1}$ and the relations \n\\begin{equation}\\label{Hecke1}\n(T_i-q)(T_i+q^{-1})=0;\n\\end{equation}\n\\begin{equation}\\label{Hecke2}\nT_i T_{i+1} T_i=T_{i+1} T_i T_{i+1};\n\\end{equation}\n\\begin{equation}\\label{Hecke3}\nT_i T_j=T_j T_i,\n\\quad\nj\\neq i, i+1\n\\end{equation}\nfor all possible indices $i$ and $j$.\n\nThe generators $T_1, \\ldots , T_{n-1}$ are invertible since \n\\begin{equation}\\label{Tinverse}\nT^{-1}_i=T_i-q+q^{-1}\n\\end{equation}\ndue to (\\ref{Hecke1}). \n\nFor any index $i=1, \\ldots , n-1$ let $\\sigma_i=(i, i+1)$ be \nthe adjacent transposition in the symmetric group $S_n$. \nTake any element $\\sigma \\in S_n$\nand choose a reduced decomposition $\\sigma=\\sigma_{i_1}\\ldots \\sigma_{i_l}$.\nAs usual put $T_\\sigma=T_{i_1}\\ldots T_{i_l}$,\nthis element of the algebra $H_n$ does not depend on the\nchoice of reduced decomposition of $\\sigma$ due to (\\ref{Hecke2}) and\n(\\ref{Hecke3}). The element of maximal length in\n$S_n$ will be denoted by $\\sigma_0$. We will write $T_0$ instead of \n$T_{\\sigma_0}$ for short. The elements $T_\\sigma$ form a basis of $H_n$ \nas a vector space over the field $\\mathbb{C}(q)$. We will also use the \nbasis in $H_n$ formed by the elements~$T_\\sigma^{-1}$.\n\n\n\nA \\emph{partition} of $n$ is a sequence of weakly decreasing integers $\\lambda_1 \\geqslant \\lambda_2 \\geqslant \\cdots \\geqslant \\lambda_k$ whose sum is equal to $n$. The \\emph{Young diagram} of a\npartition $\\lambda$ is the set of boxes $(i, j) \\in \\mathbb{Z}^2$\nsuch that $1 \\leqslant j \\leqslant \\lambda_i$. In drawing such\ndiagrams we let the first coordinate $i$ increase as one goes\ndownwards, and the second coordinate $j$ increase from left to\nright. For example the partition $\\lambda = (3,3,2)$ gives the\ndiagram \\[ \\yng(3,3,2)\\]\n\nIf $(i,j)$ is a box in the diagram of $\\lambda$, then the\n$(i,j)$-\\emph{hook} is the set of boxes in $\\lambda$\n\\[ \\{ (i, j') : j' \\geqslant j \\} \\cup \\{ (i', j)\n: i' \\geqslant i \\}, \\] We call the $(i,i)$-hook the\n\\emph{$i^{\\mbox{\\small th}}$ principal hook}.\n\nA \\emph{standard tableau}, $\\Lambda$, is a filling of the diagram\n$\\lambda$ in which the entries are the numbers 1 to $n$, each\noccurring once. If the box $(i,j)$ contains $a$ we define the \\emph{content} of the box to be $c_a(\\Lambda) = j-i$.\n\n\nThe $\\mathbb{C}(q)$-algebra $H_n$ is semisimple; see \\cite[Section 4]{GU}\nfor a short proof of this well known fact. \nThe simple ideals of $H_n$ are\nlabeled by partitions $\\lambda$ of $n$, like the equivalence classes of \nirreducible representations of the symmetric group $S_n$.\n\nIn this article, for any standard tableau $\\Lambda$ of shape $\\lambda$ we \nwill construct\na certain non-zero element $F_\\Lambda\\in H_n$. Under left multiplication by\nthe elements of $H_n$, the left ideal $H_nF_\\Lambda\\subset H_n$ is an \nirreducible $H_n$-module. The $H_n$-modules $V_\\lambda$\nfor different partitions $\\lambda$ are pairwise non-equivalent; see\nCorollary \\ref{C3.6}. At $q=1$, the algebra $H_n(q)$ specializes\nto the group ring $\\mathbb{C}S_n$, where $T_\\sigma$ becomes the permutation $\\sigma \\in S_n$. The $H_n(q)$-module\n$V_\\lambda$ then specializes to the irreducible representation of \n$S_n$, coresponding to the partition $\\lambda$, \\cite{Y1}.\n\nOur construction of $V_\\lambda$ employs a certain limiting process called \nthe \\emph{fusion procedure}. The idea of this construction goes \nback to \\cite[Section 3]{C2} were no proofs were given however.\nThe element $F_\\Lambda$ is related to the $q$-analogue\nof the Young symmetrizer in the group ring $\\mathbb{C} S_n$\nconstructed in \\cite{Gy}.\n\n\n\n\nFor each $i=1, \\ldots , n-1$ introduce the $H_n$-valued rational function in \ntwo variables $a \\neq 0$, $a \\neq b \\in\\mathbb{C}(q)$\n\\begin{equation}\\label{q-smallf}\nF_i(a, b)=T_i+\\frac{q-q^{-1}}{a^{-1}b - 1}.\n\\end{equation}\n\n\nNow introduce $n$ variables $z_1, \\ldots , z_n\\in\\mathbb{C}(q)$.\nEquip the set of all pairs $(i, j)$\nwhere $1\\leqslant i \\alpha_2\n> \\dots > \\alpha_d > 0$ and the columns of $\\beta(\\lambda)$\nby $\\beta_1 > \\beta_2 > \\dots > \\beta_d > 0$, then we have the\nfollowing alternative notation for $\\lambda$;\n\\[ \\lambda = ( \\alpha | \\beta ), \\]\nwhere $\\alpha = (\\alpha_1, \\dots , \\alpha_d)$ and $\\beta =\n(\\beta_1, \\dots , \\beta_d)$.\n\nHere $d$ denotes the length of the side of the \\emph{Durfee\nsquare} of shape $\\lambda$, which is the set of boxes\ncorresponding to the largest square that fits inside $\\lambda$,\nand is equal to the number of principal hooks in $\\lambda$. In our\nexample $d=2$ and $\\lambda = (2, 1 | 3, 2)$.\n\nWe may consider the following identity as a dual of the Giambelli\nidentity.\n\\[ \\textrm{Ind}_{H_{h_1} \\times H_{h_2} \\times \\cdots\n\\times H_{h_d}}^{H_n} V_{(\\alpha_1 | \\beta_1)} \\otimes\nV_{(\\alpha_2 | \\beta_2)} \\otimes \\cdots \\otimes V_{(\\alpha_d |\n\\beta_d)} \\cong \\bigoplus_{\\mu} (V_{\\mu})^{\\oplus D_{\\mu\n\\lambda}},\\] where $h_i$ is the length of the $i^{\\mbox{\\small th}}$\nprincipal hook, and the sum is over all partitions of $n$. This is\na decomposition of the induced representation of the tensor\nproduct of modules of hook shape. Further these hooks are the\nprincipal hooks of $\\lambda$. The coefficients, $D_{\\mu \\lambda}$,\nare non-negative integers, and in particular $D_{\\lambda \\lambda}\n=1$.\n\nOn the subspace $\\mathcal{H}_\\Lambda$, if $z_i \/ z_j \\notin\nq^\\mathbb{Z}$ when $i$ and $j$ are in different principal hooks of\n$\\Lambda$ then the above induced module may be realised as the\nleft ideal in\n$H_n$ generated by $F_\\Lambda(z_1, \\dots, z_n)$.\\\\\nThe irreducible representation $V_\\lambda$ appears in the\ndecomposition of this induced module with coefficient 1, and is\nthe ideal of $H_n$ generated by $F_\\Lambda(z_1, \\dots ,\nz_n)$ when $z_1 = z_2 = \\dots = z_n$.\n\nHence, in this way, our hook fusion procedure relates to the\nGiambelli identity in the same way that Cherednik's original\nfusion procedure relates to the Jacobi-Trudi identity. Namely, it\nprovides a way of singling out the irreducible component\n$V_\\lambda$ from the above induced module.\n\n\nThe fusion procedure was originally developed in the study of\naffine Hecke algebras, \\cite{C1}. Our results may be regarded as an\napplication of the representation theory of these algebras,\n\\cite{OV}. Descriptions of the fusion procedure for the Symmetric group may be found in \\cite{NT} and \\cite{GP}. The hook fusion procedure for the Symmetric group was considered in \\cite{Gr}.\n\n\nAcknowledgements and thanks go to Maxim Nazarov for his\nsupervision, and for introducing me to this subject. I would also\nlike to thank EPSRC for funding my research.\n\n\\section{Fusion Procedure for a Young Diagram}\n\n\n\nWe fill a diagram $\\lambda$ by hooks to form a tableau $\\Lambda^\\circ$\nin the following way: For the first principal hook we fill the\ncolumn with entries $1$, $2$, \\dots , $r$ and then fill the row\nwith entries $r+1$, $r+2$, \\dots , $s$. We then fill the column of\nthe second principal hook with $s+1$, $s+2$, \\dots , $t$ and fill\nthe row with $t+1$, $t+2$, \\dots , $x$. Continuing in this way we\nform the hook tableau.\n\n\n{\\addtocounter{definition}{1} \\bf Example \\thedefinition .} On the\nleft is the hook tableau of the diagram $\\lambda = (3,3,2)$, and\non the right the same diagram with the content of each box.\n\n\\begin{normalsize}\n\n\\begin{center}\n\\begin{picture}(50,50)\n\\put(0,30){\\framebox(15,15)[r]{ 1 }}\n\\put(15,30){\\framebox(15,15)[r]{ 4 }}\n\\put(30,30){\\framebox(15,15)[r]{ 5 }}\n\\put(0,15){\\framebox(15,15)[r]{ 2 }}\n\\put(15,15){\\framebox(15,15)[r]{ 6 }}\n\\put(30,15){\\framebox(15,15)[r]{ 8 }}\n\\put(0,0){\\framebox(15,15)[r]{ 3 }}\n\\put(15,0){\\framebox(15,15)[r]{ 7 }}\n\\end{picture}\n\\qquad \\qquad \\qquad\n\\begin{picture}(50,50)\n\\put(0,30){\\framebox(15,15)[r]{ 0 }}\n\\put(15,30){\\framebox(15,15)[r]{ 1 }}\n\\put(30,30){\\framebox(15,15)[r]{ 2 }}\n\\put(0,15){\\framebox(15,15)[r]{ -1 }}\n\\put(15,15){\\framebox(15,15)[r]{ 0 }}\n\\put(30,15){\\framebox(15,15)[r]{ 1 }}\n\\put(0,0){\\framebox(15,15)[r]{ -2 }}\n\\put(15,0){\\framebox(15,15)[r]{ -1 }}\n\\end{picture}\n\\end{center}\n\\end{normalsize}\nTherefore the sequence $(c_1(\\Lambda^\\circ), c_2(\\Lambda^\\circ), \\dots , c_8(\\Lambda^\\circ))$ is given by $(0,\n-1, -2, 1, 2, 0 , -1, 1)$. {\\nolinebreak \\hfill \\rule{2mm}{2mm}\n\nConsider (\\ref{q-bigf}) as a rational function of the variables\n$z_1, \\dots , z_n$ with values in $H_n$. Using the substitution \\begin{equation}\\label{substitution} w_i = q^{c_i(\\Lambda)}z_i, \\end{equation} the factor\n$F_{i}(w_a, w_b)$ has a pole at $z_a = z_b$ if and\nonly if the numbers $a$ and $b$ stand on the same diagonal of a\ntableau $\\Lambda$. We then call the pair $(a, b)$ a\n\\emph{singularity}. And we call the corresponding term $F_{i}(w_a,w_b)$ a \\emph{singularity term}, or\nsimply a singularity.\n\nLet $a$ and $b$ be in the same principal hook of $\\Lambda$. If $a$ and $b$\nare next to one another in the column of the hook then, on\n$\\mathcal{H}_\\lambda$, $F_{i}(w_a,w_b) = T_{i} - q$. Since \\begin{equation}\\label{P-}(T_i - q)^2 = -(q+q^{-1})(T_i-q)\\end{equation}\nthen $\\frac{-1}{q+q^{-1}} F_{i}(w_a,w_b)$ is an\nidempotent. Denote this idempotent $P^-_i$.\n\nSimilarly, if $a$ and $b$ are next to one another in\nthe same row of the hook then $F_{i}(w_a,w_b)= T_{i} +\nq^{-1}$. And since \\begin{equation}\\label{P+}(T_i + q^{-1})^2 = (q+q^{-1})(T_i+q^{-1})\\end{equation} then\n$\\frac{1}{q+q^{-1}} F_{i}(w_a,w_b)$ is an idempotent. Denote this idempotent $P^+_i$.\\\\\n\nWe also have\n\\begin{equation}\\label{q-inverse} F_i(a, b) F_i(b, a)=1-\\frac{(q-q^{-1})^2 ab}{(a-b)^2}.\n\\end{equation}\nTherefore, if the contents $c_a(\\Lambda)$ and $c_b(\\Lambda)$ differ by a number\ngreater than one, then the factor $F_{i}(w_a,w_b)$\nis invertible in $H_n$ when $z_a = z_b \\neq 0$ for all values of $q$.\n\nThe presence of singularity terms in the product $F_\\Lambda(z_1,\n\\dots , z_n)$ mean this product may or may not be regular on the\nvector subspace of $\\mathcal{H}_\\lambda$ consisting of all tuples\n$(z_1, \\dots , z_n)$ such that $z_1 = z_2 = \\dots = z_n \\neq 0$. Using\nthe following lemma, we will be able to show that $F_\\Lambda(z_1,\n\\dots , z_n)$ is indeed regular on this subspace.\n\n\\begin{lemma}\\label{q-regular} Restriction of the rational function $F_i(a, b)F_{i+1}(a, c)F_i(b, c)$ to the set of\n$(a, b, c)$ such that $a=q^{\\pm 2}b$, \nis regular at $a = c \\neq 0$. \\end{lemma}\n\\begin{proof} Let us expand the product at the left hand side of (\\ref{q-triple}) in the\nfactor\n$F_{i+1}(a, c)$. By the definition (\\ref{q-smallf}) we will get the sum\n\\[\nF_i(a, b)T_{i+1}F_i(b, c)+\n\\frac{q-q^{-1}}{a^{-1}c-1}F_i(a, b)F_i(b, c).\n\\]\nHere the restriction to $a=q^{\\pm 2}b$ of the first summand is\nevidently regular at $a=c$. After the substitution \n$b=q^{\\mp2}a$, the second summand takes the form\n\\[\n\\frac{q-q^{-1}}{a^{-1}c-1}\n\\bigl( T_i\\mp q^{\\pm1}\\bigr)\n\\biggl(\nT_i+\\frac{q-q^{-1}}{q^{\\pm2}a^{-1} c-1}\n\\biggr)\n=\n\\frac{q-q^{-1}}{a^{-1} c-q^{\\mp2}}\n( q^{\\pm1}\\mp T_i).\n\\]\nThe rational function of $a, c$ at the right hand side\nof the last displayed equality is also evidently regular at $a=c$. \\end{proof}\n\nIn particular, if the middle term on the left hand side of (\\ref{q-triple}) is a singularity\nand the other two terms are an appropriate idempotent and\n\\emph{triple term}, then the three term product, or \\emph{triple} is regular at $z_1 = z_2 =\n\\dots = z_n \\neq 0$. we may now prove the first statement of Theorem\n\\ref{q-fulltheorem}.\n\n\\begin{proposition}\\label{q-jimtheorem1} The restriction of the rational function\n$F_\\Lambda (z_1, \\dots , z_n)$ to the subspace\n$\\mathcal{H}_\\lambda$ is regular at $z_1 = z_2 = \\dots = z_n \\neq 0$.\n\\end{proposition}\n\n\\begin{proof}\nConsider any standard tableau $\\Lambda'$ obtained from the tableau $\\Lambda$\nby an adjacent transposition of its entries, say by $\\sigma_k\\in S_n$.\nUsing the relations (\\ref{q-triple}) and (\\ref{q-commute}), we derive\nthe equality of rational functions in the variables $z_1, \\ldots , z_n$\n\\[\nF_\\Lambda(z_1, \\ldots , z_n)F_{n-k}\n\\bigl( \nq^{2c_{k+1}(\\Lambda)}z_{k+1}, q^{2c_k(\\Lambda)}z_k\n\\bigr)\n=\n\\]\n\\begin{equation}\\label{q-jimtheorem1equation}\nF_k\n\\bigl( \nq^{2c_k(\\Lambda)}z_k, q^{2c_{k+1}(\\Lambda)}z_{k+1}\n\\bigr)\nF_{\\Lambda^{\\prime}}(z'_1, \\ldots ,z'_n),\n\\end{equation}\nwhere the sequence of variables $(z'_1, \\ldots ,z'_n)$\\ is obtained from\nthe sequence $(z_1, \\ldots , z_n)$ by exchanging the terms $z_k$ and \n$z_{k+1}$. Observe that\n\\[\n(z'_1, \\ldots ,z'_n)\\in\\mathcal{H}_{\\Lambda'}\n\\quad\\Leftrightarrow\\quad\n(z_1, \\ldots , z_n)\\in\\mathcal{H}_\\Lambda.\n\\]\nAlso observe that here $| c_k(\\Lambda)-c_{k+1}(\\Lambda)|\\geqslant2$\nbecause the tableaux $\\Lambda$ and $\\Lambda'$ are standard.\nTherefore the functions \n\\[\nF_k\n\\bigl( \nq^{2c_k(\\Lambda)}z_k, q^{2c_{k+1}(\\Lambda)}z_{k+1}\n\\bigr)\n\\ \\quad\\textrm{and}\\ \\quad\nF_{n-k}\n\\bigl( \nq^{2c_{k+1}(\\Lambda)}z_{k+1}, q^{2c_k(\\Lambda)}z_k\n\\bigr)\n\\]\nappearing in the equality (\\ref{q-jimtheorem1equation}),\nare regular at $z_k=z_{k+1} \\neq 0$.\nMoreover, their values at $z_k=z_{k+1} \\neq 0$ are invertible\nin the algebra $H_n$, see the relation (\\ref{q-inverse}). \nDue to these two observations, the equality (\\ref{q-jimtheorem1equation})\nshows that Proposition \\ref{q-jimtheorem1} is equivalent to its counterpart for\nthe tableau $\\Lambda'$ instead of $\\Lambda$.\n\nLet us take the hook tableau $\\Lambda^\\circ$ of shape $\\lambda$. \nThere is a chain $\\Lambda,\\Lambda', \\ldots ,\\Lambda^\\circ$ of standard tableaux\nof the same shape $\\lambda$, such that each subsequent tableau in the \nchain is\nobtained from the previous one by an adjacent transposition of the \nentries.\nDue to the above argument, it now suffices to prove Proposition \\ref{q-jimtheorem1} \nonly in the case $\\Lambda=\\Lambda^\\circ$.\n\n\nWe will prove the statement by reordering the factors of the\nproduct $F_{\\Lambda^\\circ} (z_1, \\dots , z_n)$, using relations\n(\\ref{q-triple}) and (\\ref{q-commute}), in such a way that each\nsingularity is part of a triple which is regular at $z_1 = z_2 =\n\\dots = z_n \\neq 0$, and hence the whole of $F_{\\Lambda^\\circ} (z_1, \\dots ,\nz_n)$ will be manifestly regular.\n\nDefine $g(a,b)$ to be the following; \\[ g(a,b) = \\left\\{\n\\begin{array}{ccc}\n F_{b-a} (w_a, w_b) & \\textrm{if} & a 0$ (i.e. $v$ is above the steps)\nthen $F_{v-u}(w_u, w_v)$ is tied to the singularity\n$F_{v-u+1}(w_{u-1}, w_v)$ as a triple term. To\nshow divisibility by $F_{v-u}(w_u, w_v)$ in this case\nwe need an alternative expression for $F_{\\Lambda^\\circ}(z_1, \\dots ,\nz_n)$ that is regular when $z_1 = z_2 = \\dots = z_n \\neq 0$. Define a\npermutation $\\tau$ as follows,\n\n\\[ \\tau = \\prod_{i = u, \\dots, s}^\\rightarrow \\left( \\prod_{j= s+1, \\dots,\nv}^\\leftarrow (i j) \\right)\n\\phantom{XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX}\n\\]\n\\[ = \\left(\n\\normalsize \\begin{array}{ccccccccccccccccc}\n 1 & 2 & \\dots & u-1 & u & u+1 & \\dots & & \\dots & \\dots & & \\dots & v-1 & v & v+1 & \\dots & n \\\\\n 1 & 2 & \\dots & u-1 & s+1 & s+2 & \\dots & v-1 & v & u & u+1 & \\dots & s-1 & s & v+1 & \\dots & n \\\\\n \\end{array} \\right) \\]\n\n\nFrom the definition of $C_1$ in (\\ref{q-cproduct}) we now define\n$C'_1 = \\psi_\\tau C_1$, where $\\psi_\\tau$ is a homomorphism such that\n\\[ \\psi_\\tau F_{j-i}(w_i,w_j) = F_{j-i}(w_i,w_{\\tau j}). \\]\n\n\nDefine $R'_1$ as,\n\\begin{eqnarray*}\nR'_1 & = & \\prod_{i= r+2, \\dots, u-1}^\\leftarrow \\left(\n\\prod_{j=s+1, \\dots ,v}^\\rightarrow F_{i+j-s-r-1}(w_i,w_j) \\right) \\cdot \\prod_{i=\ns+1, \\dots, t-1}^\\rightarrow \\left( \\prod_{j=i+1, \\dots\n,t}^\\rightarrow f_{j-i+1}(w_i,w_j) \\right) \\\\\n && \\times \\left( \\prod_{j=s+1, \\dots\n,t}^\\leftarrow F_{t+1-j}(w_{r+1},w_j) \\right) \\left( \\prod_{j=t+1, \\dots\n,v}^\\rightarrow F_{j-s}(w_{r+1},w_j) \\right) \\\\\n && \\times \\prod_{i= r+1,\n\\dots, s-1}^\\rightarrow \\left( \\prod_{j=i+1, \\dots ,s}^\\rightarrow\nF_{j-i+v-s}(w_i,w_j) \\right) \\cdot \\prod_{j= v+1, \\dots, n}^\\rightarrow\n\\left( \\prod_{i=r+1, \\dots ,s}^\\rightarrow F_{j-i}(w_i,w_j) \\right). \\\\\n\\end{eqnarray*}\n\nFinally, define $C'_2$ as, \\[ C'_2 = \\prod_{j= t+1, \\dots,\nn}^\\rightarrow \\left( \\prod_{i=s+1, \\dots ,t}^\\rightarrow F_{j-i}(w_i,w_j)\n\\right). \\]\n\nThen, \\[ F_\\Lambda(z_1, \\dots , z_n) = \\prod_{i = u, \\dots ,\ns}^\\leftarrow \\left( \\prod_{j=s+1, \\dots, v}^\\rightarrow F_{i+j-s-1}(w_i,w_j)\n\\right) \\cdot C'_1 R'_1 C'_2 R_2 \\cdot \\prod_{i=3}^d C_iR_i, \\]\nwhere $d$ is the number of principal hooks of $\\lambda$.\n\nThe product $C'_1 R'_1 C'_2 R_2 \\cdot \\prod C_iR_i$ is regular at\n$z_1 = z_2 = \\dots z_n \\neq 0$ since, as before, for any singularity\n$(a,b)$ the terms $F_{i}(w_a,w_b)F_{i-1}(w_{a+1},w_b)$ can be replaced by the triple\n$P_{i-1}^\\pm F_{i}(w_a,w_b)F_{i-1}(w_{a+1},w_b)$ for some index $i$ -- except in the\nexpression $R'_1$ where the terms $F_{i-1}(w_a,w_l)F_{i}(w_a,w_b)$ are replaced by\n$F_{i-1}(w_a,w_l)F_{i}(w_a,w_b)P_{i-1}^+$, where $l$ is the entry to the\nimmediate left of $b$. Note that $l = b-1$ when $c_b(\\Lambda^\\circ) > 1$ and $l =\ns+1$ when $c_b(\\Lambda^\\circ) = 1$. \\\\\nAnd so by letting $z_1 = z_2 = \\dots =z_n \\neq 0$ we see that $F_{\\Lambda^\\circ}$\nis divisible on the left by \\[ \\prod_{i = u, \\dots , s}^\\leftarrow\n\\left( \\prod_{j=s+1, \\dots, v}^\\rightarrow F_{i+j-s-1}(q^{2c_i(\\Lambda^\\circ)}, q^{2c_j(\\Lambda)})\n\\right).\n\\]\n\\end{proof}\n\n\\begin{proposition}\\label{q-jimtheorem3} Suppose the numbers $u < v$ stand next to each\nother in the same row of the hook tableau $\\Lambda^\\circ$ of shape\n$\\lambda$. Let $r$ be the last entry in the column containing $u$.\nIf $c_u > 0$ then the element $F_{\\Lambda^\\circ} \\in H_n$ is\ndivisible on the left by $F_{u}(q^{2c_u(\\Lambda^\\circ)}, q^{c_v(\\Lambda^\\circ)}) = T_{u} +\nq^{-1}$. If $c_u \\leqslant 0$ then the element $F_{\\Lambda^\\circ} \\in\nH_n$ is divisible on the left by the product\n\\[ \\prod_{i = u, \\dots, r}^\\leftarrow \\left( \\prod_{j= r+1, \\dots,\nv}^\\rightarrow F_{i+j-r-1}(q^{c_i(\\Lambda^\\circ)}, q^{c_j(\\Lambda^\\circ)}) \\right) \\]\n\\end{proposition}\n\nWe omit the proof of this proposition as it is very similar to\nthat of Proposition \\ref{q-jimtheorem2}. \n\n\n\\begin{lemma}\\label{q-divisibilitybyadjacenttransposition} Let $\\Lambda$ and $\\tilde{\\Lambda}$ be tableaux of the same shape such that $k= \\Lambda(a,b) = \\Lambda(a+1,b)-1$ and $\\tilde{k}=\\tilde{\\Lambda}(a,b) = \\tilde{\\Lambda}(a+1,b)-1$. Then $F_\\Lambda \\in H_n$ is divisible on the left by $T_k - q$ if and only if $F_{\\tilde{\\Lambda}} \\in H_n$ is divisible on the left by $T_{\\tilde{k}} - q$. \\end{lemma}\n\n\\begin{proof}\nLet $\\sigma$ be the permutation such that $\\tilde{\\Lambda}=\\sigma\\cdot\\Lambda$.\nThere is a decomposition\n$\\sigma=\\sigma_{i_N}\\ldots\\sigma_{i_1}$ such that for each $M=1, \\ldots , N-1$\nthe tableau $\\Lambda_{ M}=\\sigma_{i_M}\\ldots\\sigma_{i_1}\\cdot\\Lambda$ is\nstandard. Note that this decomposition is not necessarily reduced.\n\nDenote $F_\\Lambda$ by $F_k(q^{2c_k(\\Lambda)},q^{2c_{k+1}(\\Lambda)}) \\cdot F$. Then, by using the relations\n(\\ref{q-triple}) and (\\ref{q-commute}) and Proposition \\ref{q-jimtheorem1}, we have the following chain of equalities:\n\n\\[\nF_{\\tilde{k}}\n\\bigl( \nq^{2c_{\\tilde{k}}(\\tilde{\\Lambda})}, q^{2c_{\\tilde{k}+1}(\\tilde{\\Lambda})}\n\\bigr)\n\\ \\cdot\\ \n\\prod_{M=1, \\ldots , N}^{\\longleftarrow}\nF_{ n- i_M}\n\\bigl( \nq^{2 c_{i_M+1}(\\Lambda_M)}\n, \nq^{2 c_{i_M}(\\Lambda_M)}\n\\bigr) \\cdot F = \\]\n\\[\\prod_{M=1, \\ldots , N}^{\\longleftarrow}\nF_{ i_M}\n\\bigl( \nq^{2 c_{i_M}(\\Lambda_M)}\n, \nq^{2 c_{i_M+1}(\\Lambda_M)}\n\\bigr)\n\\ \\cdot\\ \nF_{ k}\n\\bigl( q^{2c_k(\\Lambda)}, q^{2c_{k+1}(\\Lambda)}\\bigr) \\cdot F =\\]\n\\[\n\\prod_{M=1, \\ldots , N}^{\\longleftarrow}\nF_{ i_M}\n\\bigl( \nq^{2 c_{i_M}(\\Lambda_M)}\n, \nq^{2 c_{i_M+1}(\\Lambda_M)}\n\\bigr)\n\\ \\cdot\\ F_\\Lambda\\ =\n\\]\n\\[\nF_{\\tilde{\\Lambda}}\\ \\cdot\n\\prod_{M=1, \\ldots , N}^{\\longleftarrow}\nF_{ n- i_M}\n\\bigl( \nq^{2 c_{i_M+1}(\\Lambda_M)}\n, \nq^{2 c_{i_M}(\\Lambda_M)}\n\\bigr)\n\\]\n\nHence divisibility by $T_k - q$ for $F_\\Lambda$ implies its counterpart for the tableau\n$\\tilde{\\Lambda}$ and the index $\\tilde{k}$, and vice versa.\nHere we also use the equalities\n\\[\nF_{ k}\n\\bigl( q^{2c_k(\\Lambda)}, q^{2c_{k+1}(\\Lambda)}\\bigr)\n=T_k-q,\n\\]\\[\nF_{\\tilde{k}}\n\\bigl( \nq^{2c_{\\tilde{k}}(\\tilde{\\Lambda})}, q^{2c_{\\tilde{k}+1}(\\tilde{\\Lambda})}\n\\bigr)\n=T_{\\tilde{k}}-q.\n\\]\n\\end{proof}\n\n\\begin{corollary}\\label{q-divisibilitycorollary}\nIf $k=\\Lambda(a, b)$ and $k+1=\\Lambda(a+1, b)$\nthen the element $F_\\Lambda\\in H_n$ is divisible on the left by $T_k-q$. If $k=\\Lambda(a, b)$ and $k+1=\\Lambda(a, b+1)$\nthen the element $F_\\Lambda\\in H_n$ is divisible on the left by \n$T_k+q^{-1}$.\n\\end{corollary}\n\n\\begin{proof}\nDue to Lemma \\ref{q-divisibilitybyadjacenttransposition}\nit suffices to prove the first part of Corollary \\ref{q-divisibilitycorollary} for only one \nstandard tableau $\\Lambda$ of shape $\\lambda$. Therefore, using Proposition \\ref{q-jimtheorem2} and taking $\\tilde{\\Lambda}$ to be the hook tableau $\\Lambda^\\circ$ of shape $\\lambda$ we have shown the first part of Corollary \\ref{q-divisibilitycorollary} in the case $c_v(\\Lambda) < 0$.\n\nNext let $\\Lambda^{\\circ}(a,b)= u$, $\\Lambda^{\\circ}(a+1,b) = v$ and $s$ be the last entry in the row containing $u$. Then for $c_v({\\Lambda^\\circ}) \\geqslant 0$ Proposition \\ref{q-jimtheorem2} showed that $F_{\\Lambda^\\circ} \\in H_n$ is divisible on the left by \n\\begin{equation}\\label{q-divisibilitycorollaryequation2} \\prod_{i = u, \\dots, s}^\\leftarrow \\left( \\prod_{j= s+1, \\dots,\nv}^\\rightarrow F_{i+j-s-1}(q^{2c_i(\\Lambda^\\circ)}, q^{2c_j(\\Lambda^\\circ)}) \\right) \\end{equation}\nPut $k=u+v-s-1$,\nthis is the value of the index $i+j-s-1$ in (\\ref{q-divisibilitycorollaryequation2})\nwhen $i=u$ and $j=v$. Let $\\Lambda$ be the tableau\nsuch that $\\Lambda^\\circ$ is obtained from \nthe tableau $\\sigma_k\\cdot\\Lambda$ by the permutation\n\\[\n\\prod_{i = u, \\ldots, s}^{\\longleftarrow}\\,\n\\biggl(\\ \n\\prod_{j = s+1, \\ldots, v}^{\\longrightarrow}\n\\sigma_{ i+j-s-1} \\biggr).\n\\]\nThe tableau $\\Lambda$ is standard. Moreover, then\n$\\Lambda(a, b)=k$ and $\\Lambda(a+1, b)=k+1$.\nNote that the rightmost factor in the product (\\ref{q-divisibilitycorollaryequation2}),\ncorresponding to $i=u$ and $j=v$, is\n\\[\nF_{u+v-s-1}\n\\bigl( q^{2c_u(\\Lambda^\\circ)}, q^{2c_{v}(\\Lambda^\\circ)}\\bigr)\n=\nT_k - q.\n\\]\nDenote by $F$ the product of all factors in (\\ref{q-divisibilitycorollaryequation2})\nbut the rightmost one. Further, denote by $G$ the product obtained\nby replacing each factor in $F$\n\\[\nF_{i+j-s-1}\n\\bigl(q^{2c_i(\\Lambda^\\circ)}, q^{2c_j(\\Lambda^\\circ)}\\bigr)\n\\]\nrespectively by\n\\[\nF_{n-i-j+s+1}\n\\bigl(q^{2c_j(\\Lambda^\\circ)}, q^{2c_i(\\Lambda^\\circ)}\\bigr).\n\\]\nThe element $F \\in H_n$ is invertible, and we have\n\\[ F \\cdot F_\\Lambda = F_{\\Lambda^\\circ} \\cdot G = F \\cdot (T_k - q) \\cdot (C'_1R'_1C'_2R_2 \\prod C_iR_i) \\cdot G,\\] where the final equality is as described in Proposition \\ref{q-jimtheorem2}. Therefore the divisibility of \nthe element $F_{\\Lambda^\\circ}$ on the left by the product (\\ref{q-divisibilitycorollaryequation2})\nwill imply the divisibility of the element $F_\\Lambda$ on the left by \n$T_k - q$.\n\nThis shows the required divisibility for the tableau $\\Lambda = \\sigma \\sigma_k \\cdot \\Lambda^{\\circ}$. Using Lemma \\ref{q-divisibilitybyadjacenttransposition} again concludes the proof of the first part of Corollary \\ref{q-divisibilitycorollary}. \n\nThe second part of Corollary \\ref{q-divisibilitycorollary} may be shown similarly.\n\\end{proof}\n\n\n\n\n\\section{Generating irreducible representations of $H_n$}\n\nFor every standard tableau $\\Lambda$ of shape $\\lambda$ \nwe have defined an element $F_\\Lambda$ of the algebra $H_n$. \nLet us now assign to $\\Lambda$ another element of $H_n$,\nwhich will be denoted by $G_\\Lambda$. \n\nLet $\\rho\\in S_n$ be the permutation such \nthat $\\Lambda=\\rho\\cdot\\Lambda^\\circ$, that is\n$\\Lambda(a, b)=\\rho(\\Lambda^\\circ(a, b))$ for all possible $a$ and $b$. For any $j=1, \\ldots , n$ denote by $\\mathcal{B}_j$ the subsequence of the sequence\n$\\rho(1), \\ldots ,\\rho(n)$ consisting of all $i\\rho^{-1}(j)$. Denote by $\\mathcal{A}_j$ the result of reversing\nthis subsequence. Using induction on the length of the element $\\rho\\in S_n$, one can prove that\n\\[\nF_\\Lambda(z_1, \\ldots , z_n)\\ =\\ \nG_\\Lambda(z_1, \\ldots , z_n)\\ \\ \\times\n\\]\\[\n\\prod_{j=1,\\ldots, n}^{\\longleftarrow}\n\\biggl(\\ \n\\prod_{k=1,\\ldots,|\\mathcal{A}_j|}^{\\longleftarrow}\n\\ F_{ n-j+k}\n\\bigl( q^{2c_i(\\Lambda)}z_i, q^{2c_j(\\Lambda)}z_j\\bigr)\n\\biggr)\n\\ \\quad\\textrm{where}\\ \\quad \ni=\\mathcal{A}_j(k).\n\\]\nHence restriction of $G_\\Lambda(z_1, \\ldots , z_n)$ \nto the subspace $\\mathcal{H}_\\Lambda\\subset\\mathbb{C}(q)^{n}$\nis regular on the line $(z_1 = \\cdots = z_n \\neq 0)$ due to Proposition \\ref{q-jimtheorem1}.\nThe value of that restriction is our element $G_\\Lambda\\in H_n$ \nby definition. \n\nNote that $G_{\\Lambda^\\circ}=F_{\\Lambda^\\circ}$. Denote by $V_\\lambda$ the left ideal in the algebra $H_n$\ngenerated by the element $F_{\\Lambda^\\circ}$. The elements $G_\\Lambda\\in H_n$ for all\npairwise distinct standard tableaux $\\Lambda$ of shape $\\lambda$\nform a basis in the vector space $V_\\lambda$, \\cite{N1}. Let us now consider the left ideal $V_\\lambda\\subset H_n$ as \n$H_n$-module. Here the algebra $H_n$ acts via left multiplication.\n\nThe following theorems are stated without proofs. All proofs can in found in \\cite[Section 3]{N1}.\n\n\\begin{theorem}\\label{C3.6}\n{\\bf\\hskip-6pt.\\hskip1pt} \nThe $H_n$-modules $V_\\lambda$ for different partitions $\\lambda$ of $n$\nare irreducible and pairwise non-equivalent.\n\\end{theorem}\n\nFurthermore we have the following proposition about the elements $G_\\Lambda$;\n\n\\begin{proposition}\\label{Vkappa}\nThe vector $G_\\Lambda\\in V_\\lambda$ belongs to the $H_k$-invariant subspace\nin $V_\\lambda$, equivalent to the $H_k$-module $V_{\\kappa}$ where\nthe partition $\\kappa$ is the shape of the tableau obtained by\nremoving from $\\Lambda$ the entries $k+1, \\ldots , n$.\n\\end{proposition}\n\nThe properties of the vector $G_\\Lambda$ given \nby Proposition \\ref{Vkappa} for $k=1, \\ldots , n-1$,\ndetermine this vector \nin $V_\\lambda$ uniquely up to a non-zero factor from $\\mathbb{C}(q)$. \nThese properties can be restated for any irreducible\n$H_n$-module $V$ equivalent to $V_\\lambda$.\nExplicit formulas for the action of the generators $T_1, \\ldots , T_{n-1}$\nof $H_n$ on the vectors in $V$ determined by these properties,\nare known; cf.\\ \\cite[Theorem 6.4]{M}.\n\nSetting $q=1$, the algebra $H_n$ specializes to the symmetric group ring\n$\\mathbb{C}S_n$. The element $T_\\sigma\\in H_n$ then specializes to the\npermutation $\\sigma\\in S_n$ itself. \nThe proof of Proposition \\ref{q-jimtheorem1} demonstrates that the coefficients\nin the expansion of the element $F_\\Lambda\\in H_n$ relative to the basis\nof the elements $T_\\sigma$, are regular at $q=1$ as rational functions\nof the parameter $q$. Thus the specialization of the element\n$F_\\Lambda\\in H_n$ at $q=1$ is well defined. The same is true for the\nelement $G_\\Lambda\\in H_n$.\nThe specializations at $q=1$ of the basis vectors $G_\\Lambda\\in V_\\lambda$\nform the \\emph{Young seminormal basis} in the corresponding\nirreducible representation of the group $S_n$.\nThe action of the generators $\\sigma_1, \\ldots ,\\sigma_{n-1}$ of $S_n$ on the \nvectors of the latter basis was first given by \\cite[Theorem IV]{Y2}.\nFor the interpretation of the elements $F_\\Lambda$ and $G_\\Lambda$ using\nrepresentation theory of the affine Hecke algebra $\\widehat{H}_n$, \nsee \\cite[Section 3]{C2} and references therein.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbnpo b/data_all_eng_slimpj/shuffled/split2/finalzzbnpo new file mode 100644 index 0000000000000000000000000000000000000000..677cd42b29cda6b91535e3846e3442e04f14fc14 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbnpo @@ -0,0 +1,5 @@ +{"text":"\\section{\\large Supplemental Material}\n\n\n\\subsection{Time Dependencies}\n\n\\subsubsection{Intermediate Times}\n\nIn our paper, we discussed the frequency dependence of the\noptical conductivity $\\text{Re} \\, \\sigma(\\omega)$ rather than\nthe time dependence of the spin-current autocorrelation function\n$C(t)$ as such. However, we determined $\\text{Re} \\, \\sigma(\\omega)$\nvia the finite-time Fourier transform of $C(t)$. Furthermore, it is\ninstructive to discuss the real-time decay of $C(t)$. Thus, we\nshow in Fig.\\ \\ref{FigS1} the time-dependent data underlying\nFig.\\ 3 in our paper.\n\nClearly, for all $0.5 \\leq W\/J \\leq 4.0$ and the two $\\Delta = 1.0$\nand $1.5$ depicted, the initial value $C(0)$ agrees well with the\nsum rule $J^2\/8$, which also shows the accuracy of our DQT approach. \nFor all $W$ depicted, the initial decay of $C(t)$ is fast with a\nrelaxation time $\\tau \\, J \\ll 10$. It is clearly visible that $C(t)$\ndevelops oscillatory behavior for large $W$, which is the origin\nof the maximum $\\sigma_\\text{max}$ located at the position\n$\\omega_\\text{max}$, as discussed in our paper. However, all\noscillations fully decay on a time scale $t \\, J \\leq 40$ and\nthere is no signature of a conserved Drude-weight contribution\nto $C(t)$ in the long-time limit. Therefore, $t \\, J \\leq\n40$ data is sufficient to precisely determine the $\\omega$\ndependence of $\\text{Re} \\, \\sigma(\\omega)$ in general and the\nvalue of $\\sigma_\\text{dc}$ in particular.\n\n\\subsubsection{Long Times}\n\nIn Fig.\\ \\ref{FigS2} (a) we show $C(t)$ at $\\Delta = 1$ and\n$W\/J = 3.5$ for even longer times $t J \\leq 400$ and as many\nas $N = 30000$ disorder realizations in a system of size $L=18$.\nClearly, $C(t)$ is practically zero for $t J \\gtrsim 50$. Thus, while\ntaking into account $t J \\leq 400$ in the Fourier transform certainly\nincreases frequency resolution, we find no change of the linear\nfrequency dependence down to a rather small scale of frequency, see\nFig.\\ \\ref{FigS2} (b). It is worth mentioning that the $W\/J = 3.5$\ncalculation depicted in Fig.\\ \\ref{FigS2} took about $20$ CPU years in\ntotal.\n\nIn Fig.\\ \\ref{FigS2} we also depict high-resolution data for the\nsame set of parameters except for $W\/J = 2.0$, $2.5$ and $N=5000$, cf.\\\nFig.\\ 4 in the main text. Apparently, this data is well described by power\nlaws with the exponent $\\alpha$ being close to 1 in both cases.\n\n\\begin{figure*}[t]\n\\centering\n\\includegraphics[width=1.95\\columnwidth]{FigS1.eps}\n\\caption{(Color online) Data underlying Fig.\\ 3 of the main text: Real-time decay of the\nhigh-temperature current autocorrelation function $C(t)$ of the spin-$1\/2$ Heisenberg\nchain at (a)-(d) $\\Delta = 1.0$ and (e)-(h) $\\Delta = 1.5$ in the transition from (a),\n(e) small disorder $W \/ J = 0.5$ over intermediate disorder (b), (f) $W \/J = 1.0$, (c),\n(g) $W \/ J = 2.0$ to strong disorder (d), (h) $W \/ J = 4.0$, as obtained numerically\nfor the grand-canonical ensemble $\\langle S^z \\rangle = 0$ and system sizes $L = 22$\nand $24$. The results shown are averaged over $N = 200$ different disorder realizations\nusing a uniform distribution $[-W,W]$. The sum rule is $C(0) \/ J^2 = 0.125$. In all\ncases (a)-(h), $C(t)$ decays fully on a time scale $t \\, J \\leq 40$. Damping of $C(t)$\nafter its first zero crossing causes the linear $\\omega$ dependence $\\text{Re} \\,\n\\sigma(\\omega) \\approx \\sigma_\\text{dc} + a |\\omega|$.}\n\\label{FigS1}\n\\end{figure*}\n\n\\begin{figure}[b]\n\\centering\n\\includegraphics[width=0.85\\columnwidth]{FigS2.eps}\n\\caption{(Color online) (a) Real-time decay of $C(t)$ for very long times $t J \\leq\n400$, averaged over as many as $N=30000$ disorder realizations in a system of size\n$L=18$. Remaining parameters: $\\Delta = 1$, $W\/J=3.5$, and $\\beta J \\to 0$. (b) Fourier\ntransform of (a) and, additionally, for $W\/J = 2.0, 2.5$ in a log-log plot. Remaining\nparameters: identical to (a) except for $N = 5000$. Note that $\\sigma_{\\mathrm dc}$ is\nsubtracted from the Fourier transform. Power-law fits are also indicated.}\n\\label{FigS2}\n\\end{figure}\n\n\\subsection{Binary Disorder}\n\nOur paper focused on local magnetic fields $B_r$ drawn at\nrandom from a uniform distribution in the interval $[-W,W]$. To\ndemonstrate that the results presented do not depend on the\nspecific distribution used, we repeat the $\\Delta=1.5$ calculations\nfor $W\/J = 1.0$ and $W\/J = 2.0$ in Fig.\\ 3 (f) and (g) for\na binary distribution with the same width, i.e., $B_r = \\pm \\sqrt{3}\n\\, W$. In Fig.\\ \\ref{FigS3} we compare the corresponding results.\nEvidently, the low-$\\omega$ behavior of the optical conductivity\n$\\text{Re} \\, \\sigma(\\omega)$ is the same for both distributions,\nwhile differences can only be seen in the high-$\\omega$ behavior,\nemerging for strong disorder $W$. These differences are not relevant\nfor the physics discussed in our paper.\n\n\\begin{figure}[b]\n\\centering\n\\includegraphics[width=0.85\\columnwidth]{FigS3.eps}\n\\caption{(Color online) Optical conductivity $\\text{Re} \\, \\sigma(\\omega)$\nfor binary and uniform distribution and disorder strength (a)\n$W\/J = 1$ and (b) $W\/J = 2$. Remaining parameters: $\\Delta=1.5$,\n$\\beta \\, J \\to 0$, $L \\leq 24$, $t \\, J \\leq 40$, and $N=200$.\nClearly, the low-$\\omega$ behavior does not depend on the specific\nprobability distribution used, while high-$\\omega$ differences\nemerge for large $W$.}\n\\label{FigS3}\n\\end{figure}\n\n\\subsection{Finite-Size Effects:\\\\ Comparison to Clean Systems}\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.85\\columnwidth]{FigS4.eps}\n\\caption{(Color online) Optical conductivity $\\text{Re} \\, \\sigma(\\omega)$ for\ncases without disorder but with (a) $\\Delta' = 0.5$ (non-integrable) and (b)\n$\\Delta' = 0.0$ (integrable). Remaining parameters: $\\Delta=1.5$, $\\beta\n\\, J \\to 0$, $L \\leq 34$, $t \\, J \\leq 20$, and $N=1$. The solid line in\n(b) is the perturbative result of \\cite{Ssteinigeweg2011}.\nInset: (a) for $t \\, J \\leq 100$, i.e., higher $\\omega$ resolution.}\n\\label{FigS4}\n\\end{figure}\n\nWe found in our paper that the optical conductivity $\\text{Re} \\,\n\\sigma(\\omega)$ has a maximum $\\sigma_\\text{max} > \\sigma_\\text{dc}$\nlocated at a position $\\omega_\\text{max} > 0$. Moreover, we observed\nlittle finite-size effects for large $L \\geq 22$. Even though not\nexpected, we cannot exclude a very slow convergence to the thermodynamic\nlimit $L \\to \\infty$. Note that estimating potential finite-size effects\non the basis of the non-interacting case $\\Delta = 0$ is not meaningful\nfor two reasons: First, for $\\Delta = 0$ and $W > 0$, the localization\nlength represents a natural scale for finite-size effects but is absent\nin the thermal phase of the $\\Delta > 0$ problem. Second, also the case\n$\\Delta = W = 0$ is well-known to feature huge finite-size effects\nbecause of the highly degenerated spectrum. Moreover, the mean free\npath is infinitely large.\n\n\\subsubsection{Non-Integrable Systems}\n\nTo provide further evidence for finite-size effects being negligibly small,\nwe compare to results for cases without disorder, i.e., $W = 0$. For such\ncases, and $\\Delta = 1.5$, the diffusion constant can be estimated\nperturbatively along the lines of \\cite{Ssteinigeweg2011},\nyielding $D\/J \\sim 0.6$. This value of $D$ corresponds to a mean free path\nof a few lattice sites, i.e., the mean free path is small compared to\ntypical system sizes considered. Thus, finite-size effects are most likely\nrelated to the Hilbert-space dimension being finite and not to a physical\nlength scale as such. Note that this line of reasoning is also meaningful\nfor disordered but thermal cases.\n\nWe again break the integrability of the XXZ spin-$1\/2$ chain but now by\nadding to Eq.\\ (1), where $W=0$, the next-to-nearest neighbor interaction\n\\begin{equation}\nH' = J \\, \\Delta' \\sum_{r=1}^L S_r^z S_{r+2}^z \\label{NNN}\n\\end{equation}\nwith the anisotropy $\\Delta'$. Adding Eq.\\ (\\ref{NNN}) does not break\ntranslation invariance and does not change the form of the spin-current\noperator.\n\nIn Fig.\\ \\ref{FigS4} (a) we depict the high-temperature optical conductivity\n$\\text{Re} \\, \\sigma(\\omega)$ for $\\Delta = 1.5$ and $\\Delta' = 0.5$, for\na large $L = 30$ and a small enough $L = 22$ to illustrate the role of\nfinite-size effects. It is clearly visible that, as long as $L \\ll 30$,\n$\\sigma_\\text{dc}$ decreases with $L$. Hence, together with the overall\nconvergence at frequencies $\\omega \/ J \\gtrsim 0.4$, Fig.\\ \\ref{FigS4}\n(a) proves $\\sigma_\\text{dc} < \\sigma_\\text{max}$ in another model. Note\nthat the largest-subspace dimension for $L = 30$ is comparable to the one\nof the $L=26$ disordered model.\n\nThis $\\omega$ dependence of $\\text{Re} \\, \\sigma(\\omega)$ has been found\nalso in \\cite{Smierzejewski2011} using Lanczos diagonalization\nand, for spin-$1\/2$ ladders, in \\cite{Skarrasch2015} using\ntime-dependent density-matrix renormalization group.\n\n\\subsubsection*{Integrable Systems}\n\nEventually, we contrast all our results presented so far against the\nlarge finite-size effects in the integrable cases $W = \\Delta' = 0$,\nas shown in Fig.\\ \\ref{FigS4} (b) for $\\Delta = 1.5$. Here, $\\text{Re}\n\\, \\sigma(\\omega)$ is governed by finite-size effects at $\\omega = 0$\nand $\\omega > 0$. Furthermore, these finite-size effects depend on\nthe $\\omega$ resolution used, see the inset of Fig.\\ \\ref{FigS4} (b).\nThus, a very careful analysis is needed to determine correctly the\nthermodynamic limit \\cite{Sprelovsek2004, Ssteinigeweg2011, Skarrasch2014}, \nyielding the dc value $\\sigma_\\text{dc} \/ \\beta \\, J\\sim 0.15$. While\nthis value is depicted in Fig.\\ 4 (a) of our paper, none of\nour actual results rely on any kind of extrapolation.\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.85\\columnwidth]{FigS5.eps}\n\\caption{(Color online) \\label{FigS5} Optical conductivity $\\text{Re} \\,\n\\sigma(\\omega)$ at high temperatures $\\beta \\to 0$, as calculated by ED\n($L=14$), FTLM ($L=22$), and DQT ($L=22$), for the isotropic point $\\Delta\n=1.0$ and various disorder strengths (a) $W\/J = 0.5$ ,$1.0$ and (b) $W\/J =\n2.0$, $4.0$. ED data for $W\/J = 1.0$, $4.0$ is taken from\n\\cite{Sgopalakrishnan2015}.}\n\\end{figure}\n\n\\subsection{Comparison to Exact and Lanczos Diagonalization}\n\nTo additionally confirm the DQT results presented in our paper, we present\nresults from two other numerical techniques: standard exact diagonalization\n(ED) and the finite-temperature Lanczos method (FTLM) \\cite{Sprelovsek2013}.\nBoth numerical techniques have direct access to the frequency domain. While\nED data is binned in channels of width $\\delta \\omega \/J = 0.005$,\nthe resolution of FTLM depends on the number of Lanczos steps $M$ and\nthe energy span $\\Delta E$, i.e., $\\delta\\omega \\propto \\Delta E\/M$.\nHere, we use $M=400$. Furthermore, we use $10$ initial random vectors for\neach of the $N=100$ disorder realizations, to decease any remaining\nstatistical error associated with the initial state. \n\nIn Fig.\\ \\ref{FigS5} we show ED ($L=14$) and FTLM ($L=22$) data for\n$\\Delta = 1.0$, together with the DQT data ($L=22$) presented in the\nmain text. The overall agreement of all methods for $W\\ge1$ is\nremarkably good, despite the smaller system size $L=14$ accessible to ED.\nFor small $W$, significant finite-size effects are visible for $L=14$.\nNote that ED data for $W\/J = 1.0, 4.0$ is taken from \\cite{Sgopalakrishnan2015}.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Secure Minimum Energy Routing}\n\\label{sec:algorithms}\n\nIn this section, we investigate the secure minimum energy routing\nproblem, where the cost of a path is given by~\\eqref{eq:pcost}. We\nbegin by establishing that it is NP-hard. Then, by exploiting the\nstructure of the optimal solution, we employ dynamic programming to\nobtain a pseudo-polynomial time algorithm that provides an exact\nsolution. This means that the problem is weakly NP-hard~\\cite{garey},\nthus fully polynomial time approximate schemes are possible.\nAccordingly, we conclude the section by presenting a fully polynomial\ntime $\\epsilon$-approximation algorithm for the problem,\nwhich takes an approximation parameter $\\epsilon > 0$ and after\nrunning for time polynomial in the size of the network and in\n$1\/\\epsilon$, it returns a path whose cost is at most $(1+\\epsilon)$\ntimes more than the optimal value.\n\n\n\\subsection{Computational Complexity}\nWe first show that our routing problem is NP-hard\nvia a reduction from the partition problem.\n\n\\begin{theorem}\nProblem SMER is NP-hard.\n\\end{theorem}\n\n\\begin{proof}\nWe describe a polynomial time reduction of the Partition\nproblem~\\cite{garey} to SMER. Given a set of integers $\\mc{S}=\n\\set{k_1, k_2, \\ldots, k_n}$, with $\\sum_{i=1}^{n} k_i = 2\\cdot K$,\nthe Partition problem is to decide whether there is a subset\n$\\mc{S'}$ of $\\mc{S}$ such that $\\sum_{i\\in \\mc{S'}} k_i = K$.\n\nGiven an instance $\\mc{S}= \\set{k_1, k_2, \\ldots, k_n}$ of the\nPartition problem, with $\\sum_{i=1}^{n} k_i = 2\\cdot K$, we\nconstruct the following network. The set of nodes is identical to\n$\\mc{S}$. For $i=1$ to $n-1$, we interconnect node $k_i$ to node\n$k_{i+1}$ with two links, as follows: an ``upper'' link\n$\\ell_i^{(u)}$, to which we assign $\\mc{C}_1(\\ell_i^{(u)})= 2\\cdot K\n\\cdot k_i$ and $\\mc{C}_2(\\ell_i^{(u)})= 0$, and a ``lower'' link\n$\\ell_i^{(w)}$, to which we assign $\\mc{C}_1(\\ell_i^{(w)})= 0$ and\n$\\mc{C}_2(\\ell_i^{(w)})= k_i$.\n\n\\begin{lemma}\nThe answer to the Partition problem is affirmative {\\em iff} the\nsolution to SMER in the constructed network, \\ie\\ the minimum value\nof $(\\ref{eq:pcost2})$ of a path between nodes $k_1$ and $k_n$,\nequals $3 \\cdot K^2$.\n\\end{lemma}\n\n\\begin{proof}\nA path $\\Pi$ between nodes $k_1$ and $k_n$ consists of a (possibly\nempty) set of ``upper'' links $\\mc{U}$ and a (possibly empty) set\nof ``lower'' links $\\mc{W}$. Let $\\mc{S}_u$ and $\\mc{S}_w$ be,\ncorrespondingly, the sets of indices of the links in $\\mc{U}$ and in\n$\\mc{W}$, \\ie\\ $i\\in \\mc{S}_u$ {\\em iff} $\\ell_i^{(u)}\\in \\mc{U}$\nand $i\\in S_w$ {\\em iff} $\\ell_i^{(w)}\\in \\mc{W}$. Clearly,\n$\\mc{S}_u \\cup \\mc{S}_w = \\mc{S}$. The cost of the path, per\n$(\\ref{eq:pcost2})$, is given by:\n\\begin{equation}\n\\label{eq:nph1}\n\\begin{split}\n \\mc{C}(\\Pi)\n &= \\sum_{\\ell_i^{(u)}\\in \\mc{U}} \\mc{C}_1(\\ell_i^{(u)}) + \\sum_{\\ell_i^{(w)}\\in \\mc{W}} \\mc{C}_1(\\ell_i^{(w)})\\\\\n &\\quad+ \\Big( \\sum_{\\ell_i^{(u)}\\in \\mc{U}} \\mc{C}_2(\\ell_i^{(u)}) + \\sum_{\\ell_i^{(w)}\\in \\mc{W}} \\mc{C}_2(\\ell_i^{(w)}) \\Big)^2, \\\\\n &= \\sum_{i\\in \\mc{S}_u} (2\\cdot K \\cdot k_i) + \\sum_{i\\in \\mc{S}_w} 0\n + \\Big( \\sum_{i\\in \\mc{S}_u} 0 + \\sum_{i\\in \\mc{S}_w} k_i \\Big)^2\n \\eqend\n\\end{split}\n\\end{equation}\nConsider first the case $\\sum_{i\\in \\mc{S}_u} k_i \\geq \\sum_{i\\in\n\\mc{S}_w} k_i$. Since $\\sum_{i\\in \\mc{S}_u} k_i + \\sum_{i\\in\n\\mc{S}_w} k_i = 2\\cdot K$, denote: $\\sum_{i\\in \\mc{S}_u} k_i = K +\n\\delta$, $\\sum_{i\\in \\mc{S}_w} k_i = K - \\delta$, for some $\\delta\n\\geq 0$. Then, from (\\ref{eq:nph1}), we have:\n\\begin{equation*}\n\\label{eq:nph2}\n\\begin{split}\n \\mc{C}(\\Pi)\n = 2\\cdot K \\cdot (K+\\delta) + \\Big( K-\\delta \\Big)^2 = 3 \\cdot K^2 + \\delta^2\n \\eqend\n\\end{split}\n\\end{equation*}\nConsider now the case $\\sum_{i\\in \\mc{S}_u} k_i < \\sum_{i\\in\n\\mc{S}_w} k_i$. It follows similarly that\n\\begin{equation*}\n\\label{eq:nph3}\n \\mc{C}(\\Pi) = 3 \\cdot K^2 + \\delta^2 \\eqend\n\\end{equation*}\nWe conclude that the length of a path between nodes $k_1$ and $k_n$\nis at least $3 \\cdot K^2$, and, furthermore, that value is attained\n{\\em iff} the set $\\mc{S}$ can be partitioned into two subsets\n$\\mc{S}_u$ and $\\mc{S}_w$, such that $\\sum_{i\\in \\mc{S}_u} k_i =\n\\sum_{i\\in \\mc{S}_w} k_i$, \\ie\\ {\\em iff} there is a subset $\\mc\n{S'}= \\mc{S}_u$ of $\\mc{S}$ such that $\\sum_{i\\in \\mc{S'}} k_i =\nK$, and the lemma follows.\n\\end{proof}\nSince the Partition problem is NP-complete~\\cite{garey}, the\n\\mbox{theorem} follows.\n\\end{proof}\n\n\n\\subsection{Pseudo-Polynomial Time Exact Algorithm}\nFirst, scale the values of the $\\mc{C}_2(\\ell)$'s for any link $\\ell$\nin the network so that they are all integers.\\fnote{\\mbox{The value\nof ``$1$'' is determined by the precision at which we compute\n$\\mc{C}_2(\\ell)$'s}.} Let $B$ denote an upper-bound on the sum of the\n$\\mc{C}_2(\\ell)$'s on any simple path. A trivial bound is given by\n$B=(N-1)\\cdot\\mc{C}_2^{max}$, where $N$ is the number of nodes in the\nnetwork and $\\mc{C}_2^{max}$ is the maximum value of $\\mc{C}_2(\\ell)$\namong all network links.\nIn a network with $N$\nnodes, $\\mc{C}_2^{max}$ can be computed in $O(N^2)$ time via a\nbrute-force search.\n\nOur algorithm, termed DP-SMER, is listed below. \\mbox{DP-SMER} iterates\nover all values of $\\mc{C}_2(\\ell)$, \\ie\\\n$\\mc{C}_2(\\ell)=1,2,\\ldots,B$, and for each value of\n$\\mc{C}_2(\\ell)$, it minimizes $\\sum \\mc{C}_1(\\ell)$. Upon return,\nthe algorithm returns the cost of the optimal path from source $s$\nto destination $d$ along with the structure $\\Pi$ that contains the\nnetwork nodes that form the path.\n\n\\begin{algorithm}\n\\caption{DP-SMER (source $s$, dest. $d$, network $\\mc{N}$).}\n\\label{alg:dp-smer}\n\\renewcommand{\\algorithmiccomment}[1]{\/*~#1~*\/}\n\\begin{algorithmic}\n \\STATE \\COMMENT{path cost from $s$ to itself is always $0$}\n \\FOR {$b = 1 \\to B$}\n \\STATE $C_s(b) = 0$\n \\ENDFOR\n \\STATE \\COMMENT{initial path cost from $s$ to any other node is infinite}\n \\FORALL {$n_i \\in \\mc{N}$, $n_i \\neq s$}\n \\FOR {$b = 1 \\to B$}\n \\STATE $C_i(b) = \\infty$\n \\ENDFOR\n \\ENDFOR\n\n\n \\FOR {$b = 1 \\to B$}\n \\STATE \\COMMENT{all node pairs can form a link and be neighbors}\n \\FORALL {$n_i \\in \\mc{N}$}\n \\FORALL {$n_j \\in \\mc{N}$}\n \\STATE \\COMMENT{update path cost via the neighboring nodes}\n \\IF {$b+\\mc{C}_2(\\ell_{ij}) \\leq B$}\n \\STATE $ t = C_{i}(b) + \\mc{C}_1(\\ell_{ij})$\n \\IF {$t < C_{j}(b+\\mc{C}_2(\\ell_{ij}))$}\n \\STATE $\\Pi_j(b+\\mc{C}_2(\\ell_{ij})) = i$ \\COMMENT {set $n_j$'s parent to $n_i$}\n \\STATE $C_{j}(b+\\mc{C}_2(\\ell_{ij})) = t$ \\COMMENT {update path cost}\n \\ENDIF\n \\ENDIF\n \\ENDFOR\n \\ENDFOR\n \\ENDFOR\n\n \\STATE \\COMMENT{include the ``$b$'' component, \\ie\\ $\\mc{C}_2$, in the path costs}\n \\FOR {$b = 1 \\to B$}\n \\STATE $\\hat{C}_d(b) = C_d(b) + b^2$\n \\ENDFOR\n\n \\STATE \\COMMENT{choose the best value for reaching the destination}\n \\STATE $\\displaystyle b^* = \\arg \\min_{b} \\hat{C}_d(b)$\n \\STATE {\\bf return} $[\\hat{C}_d(b^*), \\Pi(b^*)]$\n\\end{algorithmic}\n\\end{algorithm}\n\n\n\\begin{theorem}\n\\label{theo:DP} DP-SMER runs in time $O(N^2 \\cdot B)$, where $N$ is\nthe number of nodes in the network. Upon completion, the algorithm\nreturns an optimal solution to Problem SMER.\n\\end{theorem}\n\\begin{proof}\nThe first claim follows by noting that the computational complexity\nis dominated by an iteration on all values $1,2,\\ldots,B$, and for\neach such iteration, iterating on all pairs of nodes.\n\nWe turn to consider the second claim. First, it can be established,\nby induction on the values of $b$, that, upon completion of the\n$b$-th iteration of the main loop of the algorithm, for all nodes\n$n_i$, $C_i(b)$ is the length of a shortest path with respect to the\nmetric of the $\\mc{C}_1(\\ell_{ij})$ values, among all paths between\nthe source $s$ and node $n_i$, whose length with respect to the\nmetric of the $\\mc{C}_2(\\ell_{ij})$ values is precisely $b$.\\fnote{We\nnote that this shortest path may be non-simple, \\ie\\, include loops,\ndue to the potentially negative values of $\\mc{C}_1(\\ell_{ij})$'s;\nnonetheless, it is a finite path, and, furthermore, the optimal path\nreturned by the last step of DP-SMER is guaranteed to be simple, due\nto the monotonicity property explained at the end of Section\n\\ref{sec:pathcost}.} Furthermore, it is easy to verify that the\nvalues of $\\hat{C}_N (b)$, computed at the next step of the\nalgorithm, stand for the lengths of the above shortest paths with\nrespect to the metric considered by Problem SMER.\n\nNow, let $\\Pi^*$ be an optimal solution (\\ie\\ a path) to Problem\nSMER, and denote by $C^*$ its length with respect to the metric\nconsidered by SMER. Furthermore, denote $b^* =\n\\sum_{\\ell_{ij}\\in\\Pi^*} \\mc{C}_2(\\ell_{ij})$. It is easy to verify\nthat $\\Pi^*$ is a shortest path with respect to the metric of the\n$\\mc{C}_1(\\ell_{ij})$ values, among all paths between the source $s$\nand the destination $d$, whose length with respect to the metric of\nthe $\\mc{C}_2(\\ell_{ij})$ values is precisely $b^*$. Therefore, upon\ncompletion of the above steps of the algorithm, we will have\n$\\hat{C}_N (b^*) = C^*$; moreover, since $\\Pi^*$ is an optimal\nsolution to SMER, it must hold that $\\hat{C}_N (b^*) \\leq \\hat{C}_N\n(b)$ for all values of $b$. The theorem follows.\n\\end{proof}\n\n\n\\subsection{Fully Polynomial Time $\\epsilon$-Approximation}\nAs in the previous section, we scale the values of the\n$\\mc{C}_2(\\ell)$'s for any link $\\ell$ in the network so that they\nare all integers and denote by $B$ an upper-bound on the sum of the\n$\\mc{C}_2(\\ell)$'s on any simple path.\n\nThe above pseudo-polynomial solution indicates that SMER is only\nweakly NP-hard (see~\\cite{garey}), which enables us to apply\nefficient, $\\epsilon$-optimal approximation schemes of polynomial\ntime complexity, similar to the case of the widely investigated\nRestricted Shortest Path problem (RSP, see,\n\\eg~\\cite{Lorenz99asimple} and references therein). The RSP problem\nconsiders a network where each link has two metrics, say ``cost''\nand ``delay'', and some ``bound'' on the end-to-end delay. Then, for\na given source-destination pair, the problem is to find a path of\nminimum cost among those whose delay do not exceed the delay bound.\nThis weakly NP-hard problem admits efficient $\\epsilon$-optimal\napproximation schemes of polynomial\ncomplexity,~\\eg~\\cite{Lorenz99asimple}.\n\nWe turn to specify our approximation scheme for Problem SMER by a\nsimple employment of any solution to the RSP problem.\\fnote{Other\nsolutions, of reduced computational complexity, can be established,\nyet their structure is somewhat more complex.} First, a technical\ndifficulty arises in applying RSP approximation schemes to Problem\nSMER. Recall that while link costs as given by~\\eqref{eq:lcost} are\nnon-negative, $\\mc{C}_1(\\ell)$ can be negative for some links\n$\\ell$. In RSP, specifically in the approximation scheme\nof~\\cite{Lorenz99asimple}, it is assumed that link costs are\nnon-negative. Nevertheless, we show that the original network with\npossibly negative link weights can be safely transformed (\\ie\\\nwithout affecting the identity of the solution) to an expanded\nnetwork with non-negative link weights, by employing the following\npre-processing step:\n\\begin{algorithm}\n\\caption{Expand\\_Network (source $s$, network $\\mc{N}$).}\n\\begin{enumerate}\n\\item Add the source node $s$ to the expanded network.\n\\item For each node $u$ $(u \\neq s)$ in the original network,\nadd $N-1$ replicas denoted by $u(1), u(2), \\ldots, u(N-1)$ to the\nexpanded network.\n\n\\item For each link $\\ell_{su}$ from node $s$ to node $u$ in the original network,\nadd a link from node $s$ to node $u(1)$ in the expanded network with\nthe same metrics as for the original link.\n\n\\item For each link $\\ell_{uv}$ in the original network, where \\mbox{$u \\neq s, u \\neq d,\nv \\neq s$}, and for each $h=1,\\ldots, N-2$, add a link between node\n$u(h)$ and node $v(h+1)$ in the expanded network with the same\nmetrics as for the original link.\n\n\\item For each link $\\ell$ in the expanded network, add some (identical to all links)\nbias $\\delta \\ge 0$\nto each link cost $\\mc{C}_1(\\ell)$ so that the new link costs would\nbe non-negative.\n\\end{enumerate}\n\\end{algorithm}\n\nThe following lemmas establish the relation between the shortest\npaths in the original network and the shortest paths in the expanded\nnetwork.\n\\begin{lemma}\n\\label{l:exp}\nA path that is shortest w.r.t. the biased metric $(\\mc{C}_1(\\ell) +\n\\delta)$ among those that obey a bound on the $\\sum \\mc{C}_2(\\ell)$\nand have precisely $h$ hops, is also shortest w.r.t. the unbiased\nmetric $\\mc{C}_1(\\ell)$ among those that obey the same bound on\n$\\sum \\mc{C}_2(\\ell)$ and have precisely $h$ hops.\n\\end{lemma}\n\\begin{proof}\nSuppose that this is not true. That is, there are paths $\\Pi$ and\n$\\Pi'$, both obeying the bound on $\\sum \\mc{C}_2(\\ell)$ and with $h$\nhops, in such a way that $\\Pi'$ is a shortest path with the bias yet\n$\\Pi$ is shorter without the bias. Therefore, $\\sum_{\\ell \\in \\Pi}\n\\mc{C}_1(\\ell) < \\sum_{\\ell \\in \\Pi'} \\mc{C}_1(\\ell)$, yet\n$\\sum_{\\ell \\in \\Pi} (\\mc{C}_1(\\ell) + \\delta) \\ge \\sum_{\\ell \\in\n\\Pi'} (\\mc{C}_1(\\ell) + \\delta)$. However, the second inequality can\nbe rewritten as:\\linebreak $\\sum_{\\ell \\in \\Pi} \\mc{C}_1(\\ell) + h \\cdot\n\\delta \\ge \\sum_{\\ell \\in \\Pi'} \\mc{C}_1(\\ell) + h \\cdot \\delta$,\nwhich contradicts the first inequality.\n\\end{proof}\n\n\\begin{lemma}\n\\label{l:hops} A shortest path from source $s$ to node $d(h)$ in the\nexpanded network has precisely $h$ hops.\n\\end{lemma}\n\\begin{proof}\nThe proof follows from the fact that the $i$-th hop on the shortest path\nfrom $s$ to $d(h)$ has to go from some node $v(i-1)$ to some node\n$u(i)$ (see the network expansion procedure).\n\\end{proof}\n\nThus, to compute an $\\epsilon$-optimal solution to Problem SMER, for\nevery bound on $\\sum \\mc{C}_2(\\ell)$, we find the shortest path with\n$h=1,\\ldots,N-1$ hops in the expanded network by repeatedly\nemploying an approximation solution to the RSP problem.\nFor a given\napproximation value $\\epsilon>0$, let \\mbox{$\\eta=\\epsilon\/3$}.\nFurthermore, let $L$ be the smallest integer for which $\\lceil\n(1+\\eta)^L \\rceil \\geq B$. Our algorithm, called $\\epsilon$-SMER, is\nlisted below. In this algorithm, $\\epsilon$-RSP refers to an\n$\\epsilon$-optimal approximation solution for the RSP problem.\n\n\\begin{algorithm}\n\\caption{$\\epsilon$-SMER (error $\\epsilon$, source $s$, dest. $d$,\nnet. $\\mc{N}$).}\n\\renewcommand{\\algorithmiccomment}[1]{\/* #1 *\/}\n\\begin{algorithmic}\n \\STATE $\\mc{N}_x$ = Expand\\_Network($s$, $\\mc{N}$)\n\n \\FORALL {$\\ell \\in \\mc{N}_x$}\n \\STATE $cost(\\ell) = \\mc{C}_1(\\ell)$\n \\STATE $delay(\\ell) = \\mc{C}_2(\\ell)$\n \\ENDFOR\n\n \\FOR {$l = 1 \\to L$}\n \\STATE $delay\\_bound = \\lceil (1+\\eta)^l \\rceil$\n \\STATE \\COMMENT{compute the approximate $h$-hop path}\n \\FOR {$h = 1 \\to N-1$}\n \\STATE $[C(l, h), \\Pi(l, h)] =$ $\\epsilon$-RSP$(\\epsilon, s, d(h), \\mc{N}_x)$\n \\STATE \\COMMENT{compute the actual cost as per SMER metric}\n \\STATE $\\hat{C}(l,h) = (C(l,h) - h \\cdot \\delta) + \\lceil (1+\\eta)^l \\rceil^2$\n \\ENDFOR\n \\ENDFOR\n\n \\STATE \\COMMENT{choose the best $l$ and $h$ for reaching the destination}\n \\STATE $\\displaystyle (l^*,h^*) = \\arg \\min_{l,h} \\hat{C}(l,h)$\n \\STATE {\\bf return} $[\\hat{C}(l^*,h^*), \\Pi(l^*,h^*)]$\n\\end{algorithmic}\n\\end{algorithm}\n\nIn the $\\epsilon$-SMER algorithm, for each considered delay bound\n$\\lceil (1+\\eta)^l \\rceil$, $N-1$ instances of the approximation\nsolution to the RSP problem, for the same bound, are run on the\nexpanded network: in each instance $h$, we consider $s$ to be the\nsource and $d(h)$ to be the destination. Using Lemma~\\ref{l:hops},\nit is straightforward to verify that, in each instance $h$, the RSP\napproximation obtains a solution that satisfies the required delay\nbound with the restriction that the path has {\\em precisely} $h$\nhops (in both the expanded and the original network).\n\nTherefore, per considered bound on the $\\mc{C}_2(\\ell)$ metric and\nper possible number of hops up to $N-1$, we get an $\\epsilon$-optimal\npath with respect to the original metric $\\mc{C}_1(\\ell)$ (of\nprecisely that many hops). It follows from Lemmas~\\ref{l:exp} and\n~\\ref{l:hops}, that, by comparing all solutions (for all considered\nbounds on the $\\mc{C}_2(\\ell)$ metric and number of hops $h$), we\nwill find a shortest $\\epsilon$-optimal path that corresponds to an\n$\\epsilon$-optimal solution to SMER. This is established next\nthrough the following lemmas and theorem.\n\n\\begin{lemma}\n\\label{l:eps1} Let $\\Pi^*$ be an optimal solution (path) to SMER.\nDenote by $\\mc{C}(\\Pi^*)$ and $\\mc{C}(\\hat{\\Pi})$, the costs, per\nthe SMER metric, of the optimal solution and of the solution\nobtained by $\\epsilon$-SMER, correspondingly. Then:\n\\begin{equation}\n\\label{eq:eps1}\n \\mc{C}(\\hat{\\Pi}) \\leq (1+\\epsilon) \\cdot \\mc{C}(\\Pi^*)\n \\eqend\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nLet $\\bar{l}$ be the smallest integer such that\n\\begin{equation}\n\\label{eq:eps2}\n \\Big( \\sum_{\\ell \\in \\Pi^*} \\mc{C}_2(\\ell)\n \\Big)^2 \\leq \\Big( \\lceil {(1+\\eta)^{\\bar{l}}} \\rceil \\Big)^2\n \\eqend\n\\end{equation}\nNote that this implies that:\n\\begin{equation}\n\\label{eq:eps3}\n (1+\\eta)^2 \\Big( \\sum_{\\ell \\in \\Pi^*}\n \\mc{C}_2(\\ell) \\Big)^2 \\geq \\Big( \\lceil (1+\\eta)^{\\bar{l}} \\rceil\n \\Big)^2\n \\eqend\n \\end{equation}\nLet $\\bar{h}$ be the number of hops of $\\Pi^*$. By construction,\n$\\Pi (\\bar{l},\\bar{h})$ is an $\\epsilon$-optimal approximation for\nRSP, for ``costs'' $\\mc{C}_1(\\ell)$, ``delays'' $\\mc{C}_2(\\ell)$,\n``delay bound'' $\\lceil (1+\\eta)^{\\bar{l}} \\rceil$ and precisely\n$\\bar{h}$ hops. Moreover, by (\\ref{eq:eps2}), the path $\\Pi^*$ obeys\nthis bound. Therefore:\n\\begin{equation}\n \\sum_{\\ell \\in \\Pi (\\bar{l},\\bar{h})} \\mc{C}_1(\\ell) + \\bar{h} \\cdot \\delta \\leq\n (1+\\epsilon) \\sum_{\\ell \\in \\Pi^*} \\mc{C}_1(\\ell) + \\bar{h} \\cdot \\delta,\n \\end{equation}\nor, equivalently,\n\\begin{equation}\n\\label{eq:eps4}\n \\sum_{\\ell \\in \\Pi (\\bar{l},\\bar{h})} \\mc{C}_1(\\ell)\n \\leq (1+\\epsilon) \\sum_{\\ell \\in \\Pi^*} \\mc{C}_1(\\ell)\n \\eqend\n \\end{equation}\nSince $\\Pi (\\bar{l},\\bar{h})$ obeys the ``delay bound'' $\\lceil (1+\\eta)^{\\bar{l}}\n\\rceil$, we have:\n\\begin{equation}\n\\label{eq:eps5}\n \\Big( \\sum_{\\ell \\in \\Pi (\\bar{l},\\bar{h})}\n \\mc{C}_2(\\ell) \\Big)^2 \\leq \\Big( \\lceil (1+\\eta)^{\\bar{l}} \\rceil \\Big)^2\n \\eqend\n \\end{equation}\nCombining (\\ref{eq:eps3}), (\\ref{eq:eps4}) and (\\ref{eq:eps5}), we\nhave:\n\\begin{equation}\n\\label{eq:eps6}\n\\begin{split}\n &\\mc{C}(\\hat{\\Pi}) \\leq \\mc{C}( \\Pi (\\bar{l},\\bar{h}))\\\\\n &\\quad\\leq (1+\\epsilon) \\sum_{\\ell \\in \\Pi^*} \\mc{C}_1(\\ell) +\n (1+\\eta)^2 \\Big( \\sum_{\\ell \\in \\Pi^*} \\mc{C}_2(\\ell) \\Big)^2,\n\\end{split}\n\\end{equation}\nwhere the first transition is due to the way that $\\hat{\\Pi}$ is\nchosen. Since $\\eta={\\epsilon\\over 3}$, for small values of\n$\\epsilon$ (precisely, $\\epsilon < 3$), (\\ref{eq:eps6}) implies:\n\\begin{equation}\n\\label{eq:eps7} \\mc{C}(\\hat{\\Pi}) \\leq (1+\\epsilon) \\cdot\n\\mc{C}(\\Pi^*),\n\\end{equation}\nas required.\n\\end{proof}\n\n\\begin{lemma}\n\\label{l:eps2} The computational complexity of $\\epsilon$-SMER is\n\\mbox{$O(A\\cdot {1\\over \\epsilon} \\cdot \\log (B) \\cdot N^3)$}, where\n$O(A)$ is the computational complexity of the employed approximation\nscheme for RSP.\n\\end{lemma}\n\\begin{proof}\nLet $M$ be the number of links in the original network. Each time we\nemploy the RSP approximation scheme, we would incur a computational\ncomplexity of $O(A)$, where $A$ corresponds to a network with $N$\nnodes and $M$ links.\n\nFor each value of $l=1\\ldots,L$, we call the RSP approximation as\nfollows: once for a network with $N$ nodes and $O(N)$ links (\\ie\\\nfor the network that contains $s$ and all the $u(1)$'s), once for a\nnetwork with roughly $2N$ nodes and $M$ links (\\ie\\ for the network\nthat contains, in addition to the above, all the $u(2)$'s and links\nof the form $(u(1),v(2))$, once for a network with roughly $3N$\nnodes and $2M$ links (\\ie\\ for the network that contains, in\naddition to the above, all the $u(3)$'s and links of the form\n$(u(2),v(3))$, and so on up to, once (the $(N-1)$-th time) for a\nnetwork with roughly $(N-1)N$ nodes and $(N-2)M$ links. The above\n$N-1$ instances (more precisely, all but the first, which can be\nneglected due to smaller complexity) aggregate to:\n\n\n\\begin{equation}\n\\begin{split}\n &O\\big(A\n \\cdot (2\\cdot1+3\\cdot2+\\cdots+N\\cdot(N-1)) \\big) \\\\\n &\\quad= O\\big( A \\cdot \\sum_{i=1}^{N-1} i(i+1) \\big)\n = O\\big( A \\cdot N^3 \\big)\n \\eqend\n\\end{split}\n\\end{equation}\n\nThe proof follows by noting that $L=O({1 \\over \\epsilon} \\cdot \\log\n(B))$.\n\\end{proof}\n\n\n\\begin{theorem}\n$\\epsilon$-SMER is an $\\epsilon$-optimal approximation scheme of\npolynomial complexity. In particular, when employing the\napproximation solution of~\\cite{Lorenz99asimple} to the RSP problem,\n$\\epsilon$-SMER runs in $O(N^6 \\cdot (\\log \\log N + {1 \\over\n\\epsilon}) \\cdot {1\\over \\epsilon} \\cdot \\log (B))$ time.\n\\end{theorem}\n\\begin{proof}\nThe RSP scheme of~\\cite{Lorenz99asimple} has computational\ncomplexity of $O\\big( (N \\cdot M \\cdot (\\log \\log N+1\/\\epsilon)\n\\big)$ for $N$ nodes and $M$ links. Depending on the limit on the\ntransmission power at each node, in worst-case we have $M=O(N^2)$,\n\\ie\\ all nodes may be neighbors\\fnote{Note that, typically, the network is sparse, \\ie\\ $M \\ll N^2$, hence\nthe dependency on $N$ is more like $N^5$.}.\nThe proof then follows from\nLemmas~\\ref{l:eps1}~and~\\ref{l:eps2}.\n\\end{proof}\n\nMore efficient versions of $\\epsilon$-SMER should be possible,\nyet our goal has been to show that fully polynomial time $\\epsilon$-approximation schemes\n(FPTAS) exist for the NP-hard problem SMER.\n\n\n\\subsection{Distributed Implementation}\nWhile it is not discussed in this paper, our routing algorithms can\nbe implemented in a distributed manner following standard techniques\nof distance-vector routing. Note that the power allocation at the\nphysical layer is a local operation performed by the transmitting\nnode of each link based on the information from the routing algorithm\nand topological information (collected, for instance, through\nneighbor discovery before running the routing algorithm).\n\n\\section{Conclusion}\n\\label{sec:conclusion}\n\nThis paper studied the problem of secure minimum energy routing in\nwireless networks. It was shown that while the problem is\n\\mbox{NP-hard}, it admits exact pseudo-polynomial and fully polynomial time\n$\\epsilon$-approximation algorithmic solutions.\nFurthermore, using simulations, we showed that our algorithms\nsignificantly outperform security-agnostic algorithms based on\nminimum energy routing. Finally, we note that our work can be\npotentially extended to incorporate other secrecy models. Such extensions are left for future work.\n\n\\section{Introduction}\n\\label{sec:intro}\n\nProtecting the secrecy of user messages has become a major concern\nin modern communication networks. Due to the propagation properties\nof the wireless medium, wireless networks can potentially make the\nproblem more challenging by allowing an eavesdropper to have\nrelatively easy access to the transmitted message if countermeasures\nare not employed. Our goal is to provide everlasting\nsecurity in this wireless environment; that is, we will consider\nmethods that will prevent an eavesdropper from ever decoding a\ntransmitted message - even if the eavesdropper has the capability\nto record the signal and attempt decryption over many years (or decades).\nThere are two different classes of security techniques of interest\nhere: cryptographic approaches based on computational complexity,\nand information-theoretic approaches that attempt to obtain\nperfect secrecy. Both have advantages and disadvantages\nfor the desired everlasting security in the wireless environment.\n\nThe traditional solution to providing security in a wireless\nenvironment is the cryptographic approach: assume that the\neavesdropper will get the transmitted signal without distortion,\nbut the desired recipient who shares a key with the transmitter\nis able to decode the message easily, while the eavesdropper\nlacking the key must solve a \\emph{hard} problem that is beyond\nher\/his computational capabilities~\\cite{stinson2006cryptography}.\nSince the eavesdropper is assumed to get the transmitted signal\nwithout distortion, cryptography addresses the key challenge in the\nwireless environment of thwarting an eavesdropper very near the\ntransmitter. However, such an approach faces the concern that the\neavesdropper can store the signal, and, then, with later advances in\ncomputational capabilities or by breaking the encryption scheme, obtain\nthe message. The desire for everlasting security then motivates\nadding countermeasures at the physical layer that inhibit even the\nrecording of the encrypted message by the eavesdropper that combine\nwith cryptography to facilitate a defense-in-depth approach~\\cite{NSA_defense}.\n\nIn the information-theoretic approach to obtain perfect\nsecrecy~\\cite{shannon1949communication}, the goal is to guarantee\nthat the eavesdroppers can never extract information from the\nmessage, regardless of their computational capability.\nWyner~\\cite{wyner1975wire} and succeeding\nauthors~\\cite{leung1978gaussian,csiszar1978broadcast} showed that\nperfect secrecy is possible if the channel conditions between the\ntransmitter and receiver were favorable relative to the channel\nconditions between the transmitter and eavesdropper. In this\nso-called \\emph{wiretap} channel, perfect secrecy at a positive rate\nwith no pre-shared key is possible~\\cite{wyner1975wire}. This\nclearly satisfies the requirement for everlasting secrecy, but it\nrelies on favorable channel conditions that are difficult (if not\nimpossible) to guarantee in a wireless environment. Hence,\ninformation-theoretic secrecy requires a network design which\ninhibits reception at the eavesdropper while supporting reception at\nthe desired recipient.\n\nOur work supports both a cryptographic (computational) approach or\ninformation-theoretic approach. Per above, it is advantageous in\neither case to seek or create conditions so as to inconvenience\nreception at eavesdropper(s) while facilitating communication of the\nlegitimate system nodes. This has been actively considered in the\nliterature on the physical layer of wireless networks over the last\ndecade, with approaches based on both\nopportunism~\\cite{maurer93,bloch2008} and active channel\nmanipulation~\\cite{goel08,tekin2008general} being employed. Most of\nthese works have arisen in the information-theoretic community and\nconsidered small networks consisting of a source, destination,\neavesdropper, and perhaps a relay\nnode(s)~\\cite{bloch2008,goel08,tekin2008general,dong10,elgamal08,\nDennis2011JSAC}. More recently, there has been the active\nconsideration of large networks with the introduction of the secrecy\ngraph to consider secure\nconnectivity~\\cite{haenggi2008secrecy,pinto2008physical,pinto2009wireless}\nand a number of approaches to throughput scaling versus security\ntradeoffs~\\cite{liang2009secrecy, koyluoglu2010secrecy,\nvasudevan2010security}. Hence, whereas there has been a significant\nconsideration of small single- and two-hop networks and\nasymptotically large multi-hop networks, there has been almost no\nconsideration of the practical multi-hop networks that lie between\nthose two extremes. It is this large and important gap that this\npaper fills.\n\nConsider a network where system nodes communicate with each other\nwirelessly, possibly over multiple hops, such as in wireless mesh\nnetworks and ad hoc networks. A set of {\\em eavesdroppers} try to passively listen\nto communications among legitimate network nodes. To prevent the\neavesdroppers from successfully capturing communications between\nlegitimate nodes, mechanisms to thwart such are employed at the\nphysical layer of the network. Two nodes that wish\nto communicate securely may need to do so over multiple hops in\norder to thwart eavesdroppers or simply because the nodes are\nnot within the reach of each other. While we make no argument about\nthe optimality or practicality of any specific physical layer\nsecurity mechanism, for the sake of concreteness, we focus on\n\\emph{cooperative jamming}, which has received considerable attention\n~\\cite{goel08,tekin2008general,dong10,elgamal08,Dennis2011JSAC,swindle11}.\nIn cooperative jamming, whenever a node transmits a message, a\nnumber of cooperative nodes, called {\\em jammers}, help the node conceal its\nmessage by transmitting a carefully chosen signal to raise the\nbackground \\emph{noise} level and degrade the eavesdropping channels.\nBecause our general philosophy applies to any physical layer\napproach, the framework can be extended to include other forms\nof physical layer security. However, some of the attractive features\nof cooperative jamming that motivated us to study this technique include:\n\\begin{enumerate}\n\\item Opportunistic techniques~\\cite{maurer93,bloch2008} that exploit the time-varying wireless channel may suffer from excessive delays depending on the rate of channel fluctuations. For applications that require security without an excessive delay, active channel manipulation such as cooperative jamming should be adopted. The price to be paid, in this case, is the increased interference due to jamming.\n\n\\item Multi-antenna systems can also be used to jam eavesdroppers~\\cite{yates07,goel08}. However, the use of multiple antennas on every wireless device may not be feasible due to cost and size (\\eg\\ wireless sensors). Cooperative jamming is a distributed alternative to multi-antenna systems.\n\n\\item Node cooperation, while requiring a more complex physical layer, is incorporated in commercial wireless technologies such as LTE. Thus, we envision that cooperative jamming can be implemented in practice, as was demonstrated in a limited form (single jammer) in~\\cite{katabi11}.\n\n\\item Anonymous wireless communication is a challenging problem. Cooperative jamming can potentially be utilized for wireless anonymous communication, as it creates confusion for wireless localization techniques~\\cite{banerjee11}.\n\\end{enumerate}\n\nIn this general case, the main questions are: (1) how to choose the\nintermediate nodes that form a multi-hop path from the source node to\nthe destination node, and (2) how to configure each hop at the\nphysical layer with respect to the security and throughput\nconstraints of the path. Specifically, the problem we consider in\nthis paper is how to find a {\\em minimum cost} path between a source\nand destination node in the network, while guaranteeing a\npre-specified lower bound on the {\\em end-to-end secrecy} and {\\em\ngoodput} of the path. The cost of a path can be defined in terms of\nvarious system parameters. In a wireless network, transmission power\nis a critical factor affecting the throughput and lifetime of the\nnetwork. While increasing the transmission power results in increased\nlink throughput, excessive power actually results in high levels of\ninterference, hence reducing the network throughput due to\ninefficient spacial reuse. With cooperative jamming at the physical\nlayer, transmission power is even more important due to the\nadditional interference caused by jamming signals if they need to be\nemployed. Thus, in this work, we consider the amount of end-to-end\ntransmission power as the cost of a path with the objective of\nfinding secure paths that consume the least amount of energy. In\nturn, such paths, by minimizing interference in the network, result\nin \\emph{higher} throughput. Note that solutions employing power\nonly at the nodes transmitting the messages (and no cooperative\njamming) are part of the space over which the optimization will be\nperformed; thus, if it is more efficient to not employ cooperative\njamming, such a solution will be revealed by our algorithms.\n\nWhile it might seem that physical layer security techniques can be\nextended to multi-hop networks by implementing them on a\nhop-by-hop basis, in general, such extensions sacrifice performance\nor are not feasible. The eavesdropping probability on a link\nis a function of the power allocation on that link. A hop-by-hop\nimplementation is unable to determine the optimal eavesdropping\nprobability and consequently power allocation for each link in\norder to satisfy the end-to-end constraints (\\ie\\ the chicken-egg problem).\nMoreover, a hop-by-hop approach overlaid on a shortest path routing algorithm\nmight pay an enormous penalty to mitigate eavesdroppers on some links (\\eg\\\nby routing through a node with one or more links, that, because\nof system geometry, are very vulnerable to nearby eavesdroppers).\nA routing algorithm that is designed in conjunction with\nphysical layer security can selectively employ links that are\neasier to secure when it is power-efficient to do so and, in\nsuch a way, minimize the impact of the security constraint on\nend-to-end throughput.\n\n\n\nOur main contributions can be summarized as follows:\n\\begin{itemize}\n\\item We formulate the secure minimum energy routing problem with end-to-end security and goodput constraints as a constrained\noptimization of transmission power at the physical layer and link\nselection at the network layer.\n\n\\item We prove that the secure minimum energy routing problem is \\mbox{NP-hard}, and develop\nexact and $\\epsilon$-approximate solutions of, respectively, pseudo-polynomial and fully-polynomial time complexity for the problem.\n\n\\item We show how cooperative jamming can be used to establish a secure link between two nodes in the presence of multiple eavesdroppers or probabilistic information about potential eavesdropping locations by utilizing random linear coding at the network layer.\n\n\\item We provide simulation results that demonstrate the significant energy savings\nof our algorithms compared to the combination of security-agnostic minimum energy\nrouting and physical layer security.\n\\end{itemize}\n\nThe rest of the paper is organized as follows. Our system model is\ndescribed in Section~\\ref{sec:sysmodel}. The optimal link and path\ncost are analyzed in Sections~\\ref{sec:linkcost}\nand~\\ref{sec:pathcost}. Our routing algorithms are presented in\nSection~\\ref{sec:algorithms}. Simulation results are discussed in\nSection~\\ref{sec:simulation}. Section~\\ref{sec:related} presents an\noverview of some related work, while Section~\\ref{sec:conclusion}\nconcludes the paper.\n\n\\section{Secure Link Cost}\n\\label{sec:linkcost}\n\nThe link cost is composed of two components: (1) the source power, and (2)\nthe jammers' power. Let $\\mc{C}(\\ell_k)$ denote the cost of link\n$\\ell_k=\\enc{S_k, D_k, \\mc{E}_k, \\mc{J}_k}$ under the constraint of\neavesdropping probability $\\pi_k$. Then, $\\mc{C}(\\ell_k)$ is given by:\n\\begin{eqnarray}\n \\mc{C}(\\ell_k) = P_S^{(k)} + P_J^{(k)},\n\\end{eqnarray}\nwhere $P_S^{(k)}$ and $P_J^{(k)}$ denote, respectively, the average\nsource and jammers power on link $\\ell_k$. In the following\nsubsections, we will compute the optimal values of $P_S^{(k)}$ and\n$P_J^{(k)}$ subject to a given $\\pi_k$.\n\n\n\\subsection{Source Transmission Power}\nAssume that the (complex) fading channel coefficient $h_{S_k,D_k}$ is\nknown at the source $S_k$ of the given link $\\ell_k$. Because we are\ntrying to maintain a fixed rate (and, hence, a fixed received power),\nthe source will attempt to invert the channel using power control.\nHowever, for a Rayleigh frequency-nonselective fading channel, as\nassumed here, the expected required power for such an inversion goes\nto infinity, and, hence {\\em truncated channel inversion} is employed\n\\cite[Pg.~112]{goldsmith2005}. In truncated channel inversion, the\nsource maintains the required link quality except for extremely bad\nfades, where the link goes into outage. When a link is in a bad fade,\nthe source will need to wait until the link improves before\ntransmitting the packet and delay will be incurred. To limit the\ndelay, we maintain a given outage probability $\\rho$ per link. Then,\nfor a given packet, we need to transmit at rate $R = \\lambda \/ (1 -\n\\rho)$ to maintain the desired goodput $\\lambda$. Associated with\nthat rate $R$ is the SINR threshold $\\gamma_D = 2^{R}- 1$ required\nfor successful reception at the link\ndestination~\\cite{tse05wireless}.\n\nLet $P_S^{(k)}$ denote the average transmission power of $S_k$, and\nlet $P_S^{(k)}(|h_{S_k,D_k}|^2)$ denote the power used for a given\npacket as a function of the power $|h_{S_k,D_k}|^2$ in the fading\nchannel between $S_k$ and $D_k$. Per above, below some threshold\n$\\tau$, the source will wait for a better channel. From the\nRayleigh fading model employed,\n$|h_{S_k,D_k}|^2$ is exponential with parameter\n$1\/d_{S_k,D_k}^{\\alpha}$; hence, $\\tau = - \\ln(1 - \\rho) \\cdot d_{S_k,D_k}^{\\alpha}$ and truncated channel inversion yields:\n\\begin{equation}\n P_S^{(k)}(|h_{S_k,D_k}|^2) = \\begin{cases}\n \\frac{\\gamma_D}{|h_{S_k,D_k}|^2} \\cdot d_{S_k, D_k}^{\\alpha},\n & |h_{S_k,D_k}|^2 \\geq \\tau \\\\\n 0, & |h_{S_k,D_k}|^2 < \\tau\n \\end{cases}\n\\end{equation}\nThen, the average power employed on the link is given by:\n\\begin{eqnarray}\n\\label{eq:ps}\n P_S^{(k)} & = & \\frac{1}{1 - \\rho} \\int_{\\tau}^{\\infty} \\frac{\\gamma_D}{x} \\cdot d_{S_k,D_k}^{\\alpha}\n e^{-x} dx \\nonumber\\\\\n & = & \\gamma_D d_{S_k,D_k}^{\\alpha} \\frac{1}{1 - \\rho} \\int_{\\tau}^{\\infty} \\frac{e^{-x}}{x} dx \\nonumber\\\\\n & = & \\gamma_D \\, k_{\\rho} \\, d_{S_k,D_k}^{\\alpha},\n\\end{eqnarray}\nwhere $k_{\\rho}$ is a constant that depends on the parameter\n${\\rho}$. Hence, for a fixed network parameter $\\rho$ (which also\ndetermines $\\gamma_D$), the average power consumed on a given link\n$\\ell_k$ to achieve the secure goodput $\\lambda$ is proportional to\n$d_{S_k, D_k}^{\\alpha}$.\n\n\n\\subsection{Jammers' Transmission Power}\nOur physical layer security primitive described in\nSection~\\ref{sec:sysmodel} can provide security only against a single\neavesdropper at a fixed location. To achieve security in the presence\nof multiple eavesdroppers or uncertainty about the location of\neavesdroppers, we utilize \\emph{random linear coding}\\fnote{Other\nforms of coding, such as dividing each message to smaller\nchunks~\\cite{shamir79}, can be equally incorporated in our\nalgorithm.} on each link.\n\nConsider link $\\ell_k$ between transmitter $S_k$ and receiver $D_k$\nwith the associated set of potential eavesdropping locations\n$\\mc{E}_k=\\set{E_1, \\ldots, E_{|\\mc{E}_k|}}$. Transmitter $S_k$\nperforms coding over $|\\mc{E}_k|$ messages accumulated in its buffer\nfor transmission to $D_k$. To generate a coded message, $S_k$ selects\na random subset of the messages in its buffer and adds them together\n(module-$2$). To recover the original messages, the receiver needs to\ncollect $|\\mc{E}_k|$ linearly independent coded messages. In order to\ntransmit only linearly independent coded messages, $S_k$ keeps track\nof the coded messages it has transmitted so far. Let $m_i$ denote the\n$i$-th coded message that is being transmitted to $D_k$. To securely\ntransmit $m_i$, $S_k$ employs the cooperative jamming primitive of\nSection~\\ref{sec:sysmodel} assuming that there is an eavesdropper in\nlocation $E_i$. Since each coded message is hidden from at least one\neavesdropping location, it is guaranteed that an eavesdropper located\nat location $E_i$, for all $E_i \\in \\mc{E}_k$, will not be able to\nobtain any information about the original messages.\n\nIn the following subsections, we compute the optimal jamming power\nper link. The derivation for the case of multiple eavesdroppers\nrelies on the jamming power computed for the single eavesdropper\ncase.\n\n\\subsubsection{Single Eavesdropper}\nBecause slow frequency non-selective fading is assumed and\nthe channel to the eavesdropper is unknown, there is some probability\nthat the eavesdropper will obtain the message by achieving a received\nSINR greater than a threshold $\\gamma_E$.\nLet $\\pi_k(|h_{S_k,D_k}|^2)$ denote the probability the eavesdropper\nachieves SINR greater than threshold $\\gamma_E$ for a given source to\ndestination channel $h_{S_k,D_k}$ (recall that the source power will\nfluctuate as $h_{S_k,D_k}$ fluctuates, and this will impact the\ninterception probability at the eavesdropper). Because we want to\navoid placing limitations on the capabilities of the eavesdropper,\nassume that the eavesdropper receiver is noiseless. Let $P_J^{(k)}$\nand $P_J^{(k)}(|h_{S_k,D_k}|^2)$ denote the average and instantaneous\ntransmission power allocated to jammers in $\\mc{J}_k$, respectively.\nThen, using~\\eqref{eq:success}, it is obtained that\n\\begin{equation*}\n \\pi_k(|h_{S_k,D_k}|^2) =\n \\quad \\frac{1}\n {1 + \\frac{\\gamma_E d_{S_k, E_k}^{\\alpha}}\n {P_S^{(k)}(|h_{S_k,D_k}|^2)}\n \\big( \\sum_{J_i \\in \\mc{J}_k} \\frac{1}{d_{J_i, E_k}^{\\alpha}} \\big)\n P_J^{(k)}(|h_{S_k,D_k}|^2)} \\eqend\n\\end{equation*}\nNow, to maintain a given $\\pi_k$, it is sufficient to\nmaintain $\\pi_k(|h_{S_k,D_k}|^2) = \\pi_k$ across all\n$|h_{S_k,D_k}|^2$. Under this condition, recognizing that both\n$P_S^{(k)}(|h_{S_k,D_k}|^2)$ and $P_J^{(k)}(|h_{S_k,D_k}|^2)$ are\nproportional to $|h_{S_k,D_k}|^2$, we have:\n\n\\begin{equation}\n\\begin{split}\n P_J^{(k)}(|h_{S_k,D_k}|^2) =\n \\frac{(1\/\\pi_k - 1) \\, P_S^{(k)}(|h_{S_k,D_k}|^2)}\n {\\gamma_E \\, d_{S_k,E_k}^{\\alpha} (\\sum_{J_i \\in \\mc{J}_k} \\frac{1}{d_{J_i, E_k}^{\\alpha}})}\n ,\n\\end{split}\n\\end{equation}\nand, taking expectations yields\n\\begin{equation}\n\\label{eq:P_J}\n P_J^{(k)} = \\frac{1\/\\pi_k - 1}\n {\\gamma_E d_{S_k,E_k}^{\\alpha} (\\sum_{J_i \\in \\mc{J}_k} \\frac{1}{d_{J_i,E_k}^{\\alpha}})}\n P_S^{(k)} ,\n\\end{equation}\nand,\n\\begin{equation}\n\\label{eq:pi1}\n \\pi_k = \\frac{1}\n {1 + \\frac{\\gamma_E d_{S_k,E_k}^{\\alpha}}{P_S^{(k)}} \\big(\\sum_{J_i \\in \\mc{J}_k} \\frac{1}{d_{J_i, E_k}^{\\alpha}}\\big)P_J^{(k)} }\n \\eqend\n\\end{equation}\n\n\n\\subsubsection{Multiple Eavesdroppers}\nRecall that our objective is to compute the minimum jamming power for\nthe link.\nLet $\\pi_k(i)$ denote the successful eavesdropping probability on\nlink $\\ell_k$ conditioned on having an eavesdropper at location\n$E_i$. The unconditional eavesdropping probability $\\pi_k$ on link\n$\\ell_k$ is then given by the approximate relation $\\pi_k =\n\\sum_{E_i \\in \\mc{E}_k} p_k(E_i) \\cdot \\pi_k(i)$, where $p_k(E_i)$ is\nthe probability of having an eavesdropper at location $E_i$. Since\njamming power depends on the location of the eavesdroppers, by\noptimally allocating jamming power to each potential eavesdropping\nlocation, we can minimize the total jamming power across all\neavesdropping locations for a given link.\n\nThe minimum jamming power for link $\\ell_k$ over all eavesdropping\nlocations $\\mc{E}_k$ is given by the solution of the following\noptimization problem:\n\\begin{equation}\n\\begin{split}\n &\\min_{P_J^{(k)}(i)} \\sum_{E_i \\in \\mc{E}_k} P_J^{(k)}(i) \\\\\n &s.t. \\quad\n \\displaystyle\\sum_{E_i \\in \\mc{E}_k} p_k(E_i) \\cdot \\pi_k(i) = \\pi_k,\n\\end{split}\n\\end{equation}\nwhere $P_J^{(k)}(i) = \\sum_{J_j \\in \\mc{J}_k} P_j^{(k)}(i)$ is the\njamming power conditioned on the eavesdropping location $E_i$, \\ie\\\nthe jamming power during the transmission of the coded message $m_i$.\nDefine $\\phi_k(i)$ as follows\n\\begin{equation}\n \\phi_k(i) = \\frac{\\gamma_E}{\\gamma_S k_{\\rho}} \\big(\\frac{d_{S_k, E_i}}{d_{S_k, D_k}}\\big)^{\\alpha} \\sum_{J_j \\in \\mc{J}_k} \\frac{1}{d_{J_j, E_i}^{\\alpha}}\n \\eqend\n\\end{equation}\nAfter substituting for $\\pi_k(i)$ using~\\eqref{eq:pi1}, we obtain the\nfollowing optimization problem:\n\\begin{equation}\n\\label{e:multi}\n\\begin{split}\n &\\min_{P_J^{(k)}(i)} \\sum_{E_i \\in \\mc{E}_k} P_J^{(k)}(i) \\\\\n &s.t. \\quad\n \\displaystyle\\sum_{E_i \\in \\mc{E}_k}\n \\frac{p_k(E_i)}\n {1 + \\phi_k(i) P_J^{(k)}(i) }\n = \\pi_k\n \\eqend\n\\end{split}\n\\end{equation}\nThe optimization variables in this optimization problem are the\njamming powers $P_J^{(k)}(i)$. \nThe Lagrangian for the link cost\noptimization problem is expressed as follows\n\\begin{equation*}\n\\begin{split}\n &L(P_J^{(k)}(1), \\ldots, P_J^{(k)}(|\\mc{E}_k|), \\nu)\\\\\n & = \\sum_{E_i \\in \\mc{E}_k}\n P_J^{(k)}(i)\n + \\nu\n \\Big(\n \\displaystyle\\sum_{E_i \\in \\mc{E}_k}\n \\frac{p_k(E_i)}\n {1 + \\phi_k(i) P_J^{(k)}(i) } \n - \\pi\n \\Big)\n \\eqend\n\\end{split}\n\\end{equation*}\nUsing the Lagrange multipliers technique, it is obtained that\n\\begin{equation}\n\\label{eq:LL1}\n \\frac{\\partial L}{\\partial P_J^{(k)}(i)}\n = 1 - \\nu\n \\frac{\\phi_k(i) p_k(E_i)}\n {(1 + \\phi_k(i) P_J^{(k)}(i))^2} ,\n\\end{equation}\nand,\n\\begin{equation}\n\\label{eq:LL2}\n \\frac{\\partial L}{\\partial \\nu}\n = \\sum_{E_i \\in \\mc{E}_k}\n \\frac{p_k(E_i)} {1 + \\phi_k(i) P_J^{(k)}(i) }\n - \\pi\n \\eqend\n\\end{equation}\nUsing \\eqref{eq:LL1}, we have\n\\begin{equation}\n\\label{eq:LL1-s}\n \\frac{p_k(E_i)} {1 + \\phi_k(i) P_J^{(k)}(i) }\n =\n \\frac{\\sqrt{p_k(E_i)}}{\\sqrt{\\nu \\phi_k(i)}}\n \\eqend\n\\end{equation}\nBy substituting in~\\eqref{eq:LL2}, it follows that\n\\begin{equation}\n \\sum_{E_i \\in \\mc{E}_k} \\sqrt{\\frac{p_k(i)}{\\nu \\phi_k(i)}} = \\pi ,\n\\end{equation}\nand, therefore,\n\\begin{equation}\n \\frac{1}{\\sqrt{\\nu}}\n = \\frac{\\pi}{\\sum_{E_i \\in \\mc{E}_k} \\sqrt{\\frac{p_k(i)}{\\phi_k(i)}} }\n \\eqend\n\\end{equation}\nIt is then obtained that\n\\begin{equation}\n \\pi_k(i) = \\frac{1}{\\phi_k(i)} \\frac{1\/\\sqrt{\\frac{p_k(E_i)}{\\phi_k(i)}}}\n { \\sum_{E_i \\in \\mc{E}_k} \\sqrt{\\frac{p_k(E_i)}{\\phi_k(i)}}} \\, \\pi_k,\n\\end{equation}\nand,\n\\begin{equation}\n\\label{eq:pji}\n P_J^{(k)}(i) =\n \\frac{1}{\\pi_k} \\sqrt{\\frac{p_k(E_i)}{\\phi_k(i)}} \\sum_{E_i \\in \\mc{E}_k} \\sqrt{\\frac{p_k(E_i)}{\\phi_k(i)}} - \\frac{1}{\\phi_k(i)}\n \\eqend\n\\end{equation}\nFor a given link $\\ell_k$ and eavesdropping probability $\\pi_k$, we\ncan use~\\eqref{eq:pji} to compute the optimal jamming power\nallocation for each coded message $m_i$. Consequently, the average\njamming power per message on link $\\ell_k$ is given by:\n\\begin{equation}\n\\label{eq:pj1}\n\\begin{split}\n P_J^{(k)} &= \\frac{1}{|\\mc{E}_k|} \\sum_{E_i \\in \\mc{E}_k} P_J^{(k)}(i)\\\\\n &= \\frac{1}{\\pi_k} \\frac{1}{|\\mc{E}_k|} \\Bigg( \\sum_{E_i \\in \\mc{E}_k} \\sqrt{\\frac{p_k(E_i)}{\\phi_k(i)}} \\Bigg)^2\n - \\frac{1}{|\\mc{E}_k|} \\sum_{E_i \\in \\mc{E}_k} \\frac{1}{\\phi_k(i)}\n \\eqend\n\\end{split}\n\\end{equation}\n\n\n\\subsection{Discussion}\n\\subsubsection{Colluding Eavesdroppers}\nWhile we considered the case of non-colluding\neavesdroppers here, our model can be extended to handle\ncolluding eavesdroppers by requiring that at least of the coded messages be protected against all\neavesdroppers. Let $\\mc{E}_k=\\set{E_1, \\ldots, E_{|\\mc{E}_k|}}$ denote the set of colluding eavesdroppers. Assume that on link $\\ell_k$, $B_k$ messages are coded together for transmission, \\ie\\ $B_k$ is the length of the coding block. Then, the probability that a coded message $m$ is captured by all eavesdroppers is given by $\\prod_{E_i \\in \\mc{E}_k} \\pi_k(i)$. Thus, the probability that at least one message out of the $B_k$ coded messages is not received by all eavesdroppers is given by\n\\begin{equation}\n 1 - \\Big( \\prod_{E_i \\in \\mc{E}_k} \\pi_k(i) \\Big)^{B_k}\n \\eqend\n\\end{equation}\nTo satisfy the link eavesdropping constraint $\\pi_k$, the following relation should be satisfied\n\\begin{equation}\n 1 - \\Big( \\prod_{E_i \\in \\mc{E}_k} \\pi_k(i) \\Big)^{B_k} = \\pi_k,\n\\end{equation}\nwhich yields\n\\begin{equation}\n \\prod_{E_i \\in \\mc{E}_k} \\pi_k(i) = \\sqrt[B_k]{\\pi_k}\n \\eqend\n\\end{equation}\nThis constraint can be used in the optimization problem~\\eqref{e:multi} to compute the optimal link cost for the case of colluding eavesdroppers.\n\nAn interesting observation is that\n\\begin{equation}\n \\lim_{B_k \\rightarrow \\infty} \\pi_k(i) = 1, \\quad \\text{for all $E_i \\in \\mc{E}_k$}\n \\eqend\n\\end{equation}\nThat is, by increasing the length of the coding block, the link cost can be significantly reduced. The cost to be paid is in terms of increased transmission delay.\n\n\n\\subsubsection{End-to-End Coding}\nRather than looking at individual links in isolation and then\nperforming hop-by-hop coding, we can perform coding on an end-to-end\nbasis only at the source node. Then by repeatedly finding paths that\nare secure against single eavesdropping per link, the source can\nsecurely communicate with the destination through multiple paths.\nThis approach is appropriate if there are only a few potential\neavesdropping locations in the network. If the maximum number of\neavesdropping locations per link is $m$, then the running time of\nthis approach is $m$ times that of the routing algorithm with single\neavesdropping location per link.\n\n\\section{Secure Path Cost}\n\\label{sec:pathcost} In this section, using the link cost formulation\nof the previous section, we formulate the optimal cost of a\n\\emph{given} path $\\Pi$ subject to an end-to-end eavesdropping\nprobability $\\pi$.\nThe problem essentially is to divide $\\pi$ across the links forming $\\Pi$ so that the path cost is minimized.\n\n\\subsection{Optimal Path Cost}\nConsider a given path $\\Pi$. We find the optimal cost of path $\\Pi$\nby solving the optimization problem~\\eqref{eq:smer-p}. Consider link\n$\\ell_k \\in \\Pi$, where $\\ell_k = \\enc{S_k, D_k, \\mc{E}_k,\n\\mc{J}_k}$. Define $x_k$ and $y_k$ as follows:\n\\begin{align*}\n x_k = \\frac{1}{\\sqrt{|\\mc{E}_k|}} \\sum_{E_i \\in \\mc{E}_k} \\sqrt{\\frac{p_k(E_i)}{\\phi_k(i)}},\n\\end{align*}\nand,\n\\begin{align*}\n y_k = \\frac{1}{|\\mc{E}_k|} \\sum_{E_i \\in \\mc{E}_k} \\frac{1}{\\phi_k(i)}\n \\eqend\n\\end{align*}\nUsing the results obtained in the previous subsection, the following\nrelation holds:\n\\begin{align*}\n \\pi_k = \\frac{x^2_k}{y_k + P_J^{(k)}}\n \\eqend\n\\end{align*}\nBy substituting the above expressions in the optimal routing\nformulation described in~\\eqref{eq:smer-p}, the following\noptimization problem is obtained for minimizing the cost\n$\\mc{C}(\\Pi)$ of route $\\Pi$:\n\\begin{equation}\n\\begin{split}\n &\\displaystyle\\min_{P_J^{(k)}} \\sum_{\\ell_k \\in \\Pi} P_S^{(k)} + P_J^{(k)} \\\\\n &s.t. \\quad\n \\displaystyle\\sum_{\\ell_k \\in \\Pi}\n \\Big(\n \\frac{x^2_k} {y_k + P_J^{(k)}}\n \\Big)\n = \\pi\n \\eqend\n\\end{split}\n\\end{equation}\nThe optimization variables in this optimization problem are jamming\npowers $P_J^{(k)}$. \nThe Lagrangian for the routing\noptimization problem is expressed as follows\n\\begin{equation*}\n\\begin{split}\n &L(P_J^{(1)}, \\ldots, P_J^{(K)}, \\nu)\\\\\n & = \\sum_{\\ell_k \\in \\Pi}\n \\big( P_S^{(k)} + P_J^{(k)} \\big)\n + \\nu\n \\Big(\n \\sum_{\\ell_k \\in \\Pi}\n \\Big(\n \\frac{x^2_k} {y_k + P_J^{(k)}}\n \\Big)\n - \\pi\n \\Big)\n \\eqend\n\\end{split}\n\\end{equation*}\nUsing the Lagrange multipliers technique, it is obtained that\n\\begin{equation}\n\\label{eq:L1}\n \\frac{\\partial L}{\\partial P_J^{(k)}}\n = 1 - \\nu\n \\frac{x^2_k} {(y_k + P_J^{(k)})^2} ,\n\\end{equation}\nand,\n\\begin{equation}\n\\label{eq:L2}\n \\frac{\\partial L}{\\partial \\nu}\n = \\sum_{\\ell_k \\in \\Pi}\n \\Big(\n \\frac{x^2_k} {y_k + P_J^{(k)}}\n \\Big)\n - \\pi\n \\eqend\n\\end{equation}\nUsing \\eqref{eq:L1}, we have\n\\begin{equation}\n\\label{eq:L1-s}\n \\frac{x^2_k} {y_k + P_J^{(k)}}\n =\n \\frac{x_k}{\\sqrt{\\nu}}\n \\eqend\n\\end{equation}\nBy substituting in~\\eqref{eq:L2}, it follows that\n\\begin{equation}\n \\sum_{\\ell_k \\in \\ell} \\frac{x_k}{\\sqrt{\\nu}} = \\pi ,\n\\end{equation}\nand, therefore,\n\\begin{equation}\n \\frac{1}{\\sqrt{\\nu}}\n = \\frac{\\pi}{\\sum_{\\ell_k \\in \\Pi} x_k}\n \\eqend\n\\end{equation}\nAfter substitution in~\\eqref{eq:L1-s}, the following relation for\nthe optimal eavesdropping probability $\\pi_k$ on link $\\ell_k$ is\nobtained\n\\begin{equation}\n\\label{eq:pi}\n \\pi_k = \\frac{x_k}\n {\\sum_{\\ell_i \\in \\Pi} x_i} \\, \\pi\n \\eqend\n\\end{equation}\nFor a given route $\\Pi$ and end-to-end eavesdropping probability\n$\\pi$, we can use \\eqref{eq:pi} to divide $\\pi$ between links\n$\\ell_k \\in \\Pi$. Having computed $\\pi_k$, the optimal power allocated to\njammers on link $\\ell_k$ is given by the following expression:\n\\begin{equation}\n\\label{eq:pj}\n P_J^{(k)} = \\frac{1}{\\pi} \\cdot x_k \\sum_{\\ell_i \\in \\Pi} x_i - y_k\n \\eqend\n\\end{equation}\nUsing the above expression for $P_J^{(k)}$, the cost of link $\\ell_k\n\\in \\Pi$ is expressed as\n\\begin{equation}\n\\label{eq:lcost}\n \\mc{C}(\\ell_k)\n = \\big( (\\gamma_S k_{\\rho}) \\cdot d_{S_k, D_k}^{\\alpha} - y_k \\big)\n + \\frac{1}{\\pi} \\big( x_k \\sum_{\\ell_i \\in \\Pi} x_i \\big)\n \\eqend\n\\end{equation}\nConsequently, the cost of secure route $\\Pi$ is given by:\n\\begin{equation}\n\\label{eq:pcost}\n \\mc{C}(\\Pi)\n = \\sum_{\\ell_k \\in \\Pi} \\big( (\\gamma_S k_{\\rho}) \\cdot d_{S_k, D_k}^{\\alpha} - y_k \\big)\n + \\frac{1}{\\pi} \\big( \\sum_{\\ell_k \\in \\Pi} x_k \\big)^2\n \\eqend\n\\normalsize\n\\end{equation}\nTo this end, for a given route $\\Pi$ between the source and\ndestination, the optimal cost of $\\Pi$ subject to the end-to-end\neavesdropping constraint $\\pi$ is given by~\\eqref{eq:pcost}. The\noptimal cost is achieved by allocating $P_S^{(k)}$ and $P_J^{(k)}$ to\neach link $\\ell_k \\in \\Pi$ using~\\eqref{eq:ps} and \\eqref{eq:pj},\nrespectively. Such a power allocation scheme would result in minimum\ncost, while guaranteeing that the eavesdropping constraint would be\nsatisfied. Thus, SMER is reduced to finding a path, among all\npossible paths between the source and destination, that minimizes the\noptimal path cost~\\eqref{eq:pcost}. The following proposition\nformally states this result.\n\\begin{propose}\nSMER with end-to-end eavesdropping and goodput constraint $\\pi$ and\n$\\lambda$, respectively, is equivalent to finding a path that\nminimizes the optimal path cost $\\mc{C}(\\Pi)$ as given\nby~\\eqref{eq:pcost}.\n\\end{propose}\n\n\\subsection{Optimal Path Cost Structure}\nDefine $\\mc{C}_1(\\ell_k)$ and $\\mc{C}_2(\\ell_k)$ as follows:\n\\begin{equation}\n\\begin{split}\n \\mc{C}_1(\\ell_k) &= (\\gamma_S k_{\\rho}) \\cdot d_{S_k, D_k}^{\\alpha} - y_k,\\\\\n \\mc{C}_2(\\ell_k) &= \\frac{1}{\\sqrt{\\pi}} \\cdot \\sum_{\\ell_k \\in \\Pi} x_k \\eqend\n\\end{split}\n\\end{equation}\nThen the optimal path cost~\\eqref{eq:pcost} can be expressed as\n\\begin{equation}\n\\label{eq:pcost2}\n \\mc{C}(\\Pi)\n = \\sum_{\\ell_k \\in \\Pi} \\mc{C}_1(\\ell_k)\n + \\Big( \\sum_{\\ell_k \\in \\Pi} \\mc{C}_2(\\ell_k) \\Big)^2\n \\eqend\n\\end{equation}\n\n\nIt is important to note that, while the $\\mc{C}_1(\\ell_k)$'s may\nassume negative values, the path cost structure in (\\ref{eq:pcost2})\nis monotonous in the number of links, \\ie\\ if a path $\\hat{\\Pi}$ is a\nsubset of a path $\\Pi$, then $\\mc{C} (\\hat{\\Pi}) < \\mc{C} (\\Pi)$.\nThis is because $\\pi <1$, and it can be shown that\n$\\big( \\sum_{\\ell_k \\in \\Pi} x_k \\big)^2 > \\sum_{\\ell_k \\in \\Pi} y_k$.\nConsequently, (\\ref{eq:pcost2}) is minimized by a {\\em simple} path.\n\n\\section{Related Work}\n\\label{sec:related}\n\nA survey of prior work is presented in this section.\n\n\\spar{Secure Routing in Multi-hop Networks} While there are numerous\nworks on secure routing in wireless networks (see,\n\\eg~\\cite{guizani08} and references therein), their focus is on\npreventing malicious attacks that disrupt the operation of the\nrouting protocol using application level mechanisms such as\nauthentication and cryptography. The focus of this paper, on the\nother hand, is on secure transmission of messages via the most\ncost-effective paths in the network, which is orthogonal to the\nsecure routing problem considered in the existing literature.\n\n\n\\spar{Wireless Physical Layer Security}\nThe idea behind physical layer security is to exploit the\ncharacteristics of the wireless channel such as fading to provide\nsecure wireless communications.\nThe foundations of information\ntheoretic security, which is the theoretical basis for physical layer\nsecurity, were laid by Wyner and\nothers~\\cite{wyner1975wire,leung1978gaussian,csiszar1978broadcast}\nbased on Shannon's notion of perfect\nsecrecy~\\cite{shannon1949communication}.\nIn the classical wiretap\nmodel of Wyner, to achieve a strictly positive secrecy rate, the\nlegitimate user should have some advantage over the eavesdropper in\nterms of SNR. Later, Maurer~\\cite{maurer93} proved that even when a\nlegitimate user has a worse channel than an eavesdropper, it is\npossible to have secure communication.\nWhile some physical layer\nsecurity techniques allow for opportunistic exploitation of the\nspace\/time\/user diversity for secret\ncommunications~\\cite{maurer93,bloch2008}, others actively manipulate\nthe wireless channel to block eavesdroppers by employing techniques\nsuch as multiple antennas~\\cite{yates07} and\njamming~\\cite{tekin2008general,elgamal08}. While some of these\ntechniques have been successfully implemented in practical\nsystems~\\cite{katabi11}, physical layer security is focused on very\nspecial network topologies, \\eg\\ single-hop networks. In this work,\nwe have developed algorithms to extend these techniques to multi-hop\nnetworks.\n\n\n\\spar{Scaling Laws in Large Secure Networks} Motivated\nby~\\cite{gupta00}, recently, throughput scaling versus security\ntradeoffs have been investigated in the context of large wireless\nnetworks~\\cite{liang2009secrecy, koyluoglu2010secrecy,\nvasudevan2010security, Dennis2011JSAC}. Specifically, for cooperative\njamming when the eavesdroppers are uniformly distributed, it was\nshown that if the number of eavesdroppers grows sub-linearly with\nrespect to the number of legitimate nodes, a positive throughput for\nsecure communication is achievable~\\cite{Dennis2011JSAC}.\n\n\n\\spar{Security Based on Network Topology}\nWhen there is sufficient path diversity in a network, different\nmessages can be routed over different parts of the network in the\nhope that an eavesdropper would be incapable of capturing all\nmessages from across the network. To exploit network diversity for\nsecurity, various techniques based on multi-path\nrouting~\\cite{fang09,krunz10} and network\ncoding~\\cite{yeung02,jain04} have been investigated. While such techniques are suitable for wired networks,\ntheir application in wireless networks is challenging due to lack of\npath diversity at the source or destination of a communication\nsession. Moreover, there are considerable complications when splitting a flow among several paths, in particular,\nat the granularity of a single session.\nMoreover, network topology, in wireless networks, is a\nfunction of power allocation at the physical-layer and propagation\nenvironment, \\eg\\ fading.\nNevertheless, our approach is\ncomplimentary to these techniques, by providing a mechanism to find a\nminimum cost path that is information-theoretically secure,\nregardless of the network diversity.\n\n\n\\section{Simulation Results}\n\\label{sec:simulation}\n\n\\subsection{Simulation Environment}\nWe have implemented our routing algorithms in a custom-built\nsimulator to study their performance in a variety of network\nscenarios. We simulate a wireless network, in which nodes are\ndistributed uniformly at random in a square of area $5 \\times 5$ with\nnode density $\\sigma=3$. We also place a number of eavesdroppers in\nthe network with density $\\sigma_E$, as described later. We consider\none eavesdropper per link. We keep the number of eavesdroppers\nconsiderably less than that of the legitimate nodes in order to be\nable to establish secure routes as we put a limit on the maximum\ntransmission power of each node. Every node has a maximum\ntransmission power that is set in such a way that the resulting\nnetwork becomes connected (the absolute value of the maximum power\ndoes not affect the results). We choose two nodes $s$ and $d$ located\nat the lower left and the upper right corners of the network,\nrespectively, and find paths from $s$ to $d$. We then compute the\ntotal amount of energy consumed on each path using different routing\nalgorithms. The performance metric ``energy savings'' refers to the\npercentage difference between total energy used by different\nalgorithms with respect to the benchmark. For simulation purposes, we\nset $\\pi=0.1$, $\\sigma_E=1$, $N_0=1$, $\\gamma_D = 0.8$, and $\\gamma_E\n= 0.6$, unless otherwise specified. The numbers reported are obtained\nby averaging over $10$ simulation runs with different seeds.\n\n\n\\subsection{Simulated Algorithms}\nIn addition to\nDP-SMER and \\mbox{$\\epsilon$-SMER}, we have also implemented a\nsecurity-agnostic algorithm based on minimum energy routing as a\nbenchmark to measure energy savings achieved by our algorithms. The\nbenchmark algorithm, called {\\em security-agnostic shortest path\nrouting (SASP)}, is described below. Note that some of the\noptimizations described in Sections~\\ref{sec:linkcost}\nand~\\ref{sec:pathcost} have been incorporated in SASP, making it a\nconsiderably efficient benchmark (see Subsection~\\ref{sub:alloc}).\n\n\\begin{algorithm}\n\\caption{SASP (source $s$, dest. $d$, network $\\mc{N}$).}\n\\begin{enumerate}\n\\item Find a shortest path in terms of transmission power between\n$s$ and $d$ ignoring eavesdroppers. The standard Dijkstra's\nalgorithm can be used for this purpose.\n\n\\item Use~\\eqref{eq:pi} to allocate an optimal eavesdropping probability\nto each link of the computed path.\n\n\\item Use~\\eqref{eq:pj} to allocate sufficient power to jammers on each link\nwith respect to the allocated eavesdropping probabilities in step\n(2).\n\\end{enumerate}\n\\end{algorithm}\n\n\n\\subsection{Results and Discussion}\n\n\\spar{Effect of Eavesdropper Location on Link Cost}\nFor a fixed link between two nodes, the source transmission power is\nalso fixed as obtained in~\\eqref{eq:ps}. Thus, the cost of the link\ndepends only on the jamming power which is a function of the\neavesdropper location as given by~\\eqref{eq:pj1}.\nFig.~\\ref{fig:onelink} shows the cost of establishing a secure link\nbetween source $S$ (placed at the center) and destination $D$ for\ndifferent eavesdropper locations and $\\pi = 0.001$. In the figure,\nthe color intensity at each point is proportional to the amount of\nenergy required to establish the link if the eavesdropper is placed\nat that point. Clearly, by some maneuvering around an eavesdropper,\na significant reduction in energy cost can be achieved as the\neavesdropper becomes almost ineffective in some locations. This is\nthe main idea behind this work.\n\n\\begin{figure}[ht]\n \\centering\n \\subfloat[Path-loss exponent $\\alpha=2$.]{\n \\includegraphics[width=7cm]{link_a2}\n \\label{fig:onelinka2}\n }\n \\hspace{1mm}\n \\subfloat[Path-loss exponent $\\alpha=4$.]{\n \\includegraphics[width=7cm]{link_a4}\n \\label{fig:onelinka4}\n }\n \\caption{Effect of eavesdropper location on link cost.}\n \\label{fig:onelink}\n\\end{figure}\n\n\n\\spar{Effect of Optimal Secrecy Allocation on Path Cost}\n\\label{sub:alloc} For a fixed path subject to an end-to-end secrecy\nrequirement $\\pi$, the optimal eavesdropping probability assigned to\neach link of the path is given by~\\eqref{eq:pi}, which in turn\ndetermines the optimal jamming power allocated to each link of the\npath using~\\eqref{eq:pj}. Specifically, this is how power allocation\nis performed in SASP in order to minimize power consumption.\nAlternatively, a simple heuristic is to divide $\\pi$ equally across\nthe links. That is, if the path contains $h$ links, then each link\n$\\ell_k$ is allocated sufficient jamming power to satisfy the\neavesdropping probability $\\pi_k = \\pi\/h$. In\nFig.~\\ref{fig:allocation}, we have depicted energy savings that can\nbe achieved ``solely'' by optimal secrecy allocation compared to\nequal allocation for a fixed path that is computed by SASP.\nInterestingly, as the number of eavesdroppers increases or the\nsignal propagation becomes more restricted, optimal secrecy\nallocation becomes even more important, achieving energy savings of\nup to $72\\%$ ($47\\%$) for $\\alpha=4$ ($\\alpha=2$) in the simulated\nnetwork.\n\n\\begin{figure}[ht]\n \\centering\n \\subfloat[Effect of eavesdropper density.]{\n \\includegraphics[width=8cm]{path_density}\n \\label{fig:optdens}\n }\n \\subfloat[Effect of eavesdropping prob.]{\n \\includegraphics[width=8cm]{path_prob}\n \\label{fig:optprob}\n }\n \\caption{Energy savings achieved by optimal secrecy allocation.}\n \\label{fig:allocation}\n\\end{figure}\n\n\n\\spar{Non-uniform Eavesdropper Placement}\nTo gain more insight about the behavior of different routing\nalgorithms, in this experiment, rather than randomly distributing\neavesdroppers in the network, we strategically place them close to\nthe line that connects the source and destination. Ideally, SMER and\n$\\epsilon$-SMER should avoid the shortest path that crosses the\nnetwork diagonally. This is indeed the behavior observed in the\nsimulations as depicted in Fig.~\\ref{fig:nonuniform} (`\\scalebox{0.6}{$\\bigstar$}'\ndenotes an eavesdropper). As expected, SASP blasts right through the\neavesdroppers, while SMER, $0.1$-SMER and $1.0$-SMER route around\nthem resulting in $88\\%$, $86\\%$ and $85\\%$ energy savings,\nrespectively.\n\\begin{figure}[ht]\n \\centering\n \\includegraphics[width=8cm]{snapshot}\n \\caption{Snapshot of paths computed by different algorithms.}\n \\label{fig:nonuniform}\n\\end{figure}\n\n\n\\spar{Uniform Eavesdropper Placement} In this experiment,\neavesdroppers are placed in the network uniformly at random. As seen\nin Fig.~\\ref{fig:uniform}, our algorithms consistently outperform\nSASP for a wide range of eavesdropper densities and eavesdropping\nprobabilities. In particular, energy savings of up to $99\\%$ and\n$98\\%$ (for $\\alpha=4$) can be achieved by SMER and $0.1$-SMER,\nrespectively.\n\n\\begin{figure}[ht]\n \\centering\n \\subfloat[Effect of eavesdropper density.]{\n \\includegraphics[width=8cm]{uniform_density}\n \\label{fig:uniform1}\n }\n \\subfloat[Effect of eavesdropping prob.]{\n \\includegraphics[width=8cm]{uniform_prob}\n \\label{fig:uniform2}\n }\n \\caption{Energy savings with uniform eavesdropper placement.}\n \\label{fig:uniform}\n\\end{figure}\n\n\n\\spar{Effect of Network Size}\nFig.~\\ref{fig:size} shows the energy savings achieved by different\nalgorithms in networks with varying sizes. The ``network dimension''\nrefers to the length of one side of the square area that contains\nthe network nodes. As observed from the figure, the energy saving\nis an increasing function of the network size. Interestingly, as the\nnetwork size increases, the effect of the propagation environment\ndiminishes in such a way that energy savings for $\\alpha=2$ and\n$\\alpha=4$ converge to the same numbers as opposed to the previous\nscenarios. As the network size increases so does the average length\nof the path (in terms of the number of hops) between the source and\ndestination nodes. Those paths that are longer provide more\nopportunities for energy savings on each link of the path resulting\nin increased overall energy savings. This effect works in favor of\n$\\alpha=2$ as well as $\\alpha=4$. However, given the high values of\nenergy savings for $\\alpha=4$ (due to longer paths compared to\n$\\alpha=2$), the effect of longer paths is more prominent for\n$\\alpha=2$.\n\\begin{figure}[ht]\n \\centering\n \\subfloat[Non-uniform eavesdrop. placement.]{\n \\includegraphics[width=8cm]{network_size_diag}\n \\label{fig:size_diag}\n }\n \\subfloat[Uniform eavesdrop. placement.]{\n \\includegraphics[width=8cm]{network_size}\n \\label{fig:size_uniform}\n }\n \\caption{Effect of network size on energy savings.}\n \\label{fig:size}\n\\end{figure}\n\n\n\\spar{Effect of Jamming Set} The cardinality of the jamming set\naffects the power allocation to jammers. In this experiment, we\nchange the number of jammers that participate in secure transmissions\non each link and compute the energy savings achieved by different\nalgorithms. Figs.~\\ref{fig:jammers}\\subref{fig:jammers_diag}\nand~\\ref{fig:jammers}\\subref{fig:jammers_uniform}, respectively, show\nthe energy savings achieved for non-unform and uniform placement of\neavesdroppers. Interestingly, in these scenarios, a small number of\njammers, namely $2$, is sufficient to obtain most of the benefits of\ncooperative jamming, which should greatly simplify any practical\nimplementation.\n\n\\begin{figure}[ht]\n \\centering\n \\subfloat[Non-uniform eavesdrop. placement.]{\n \\includegraphics[width=8cm]{jammers_diag}\n \\label{fig:jammers_diag}\n }\n \\subfloat[Uniform eavesdrop. placement.]{\n \\includegraphics[width=8cm]{jammers_uniform}\n \\label{fig:jammers_uniform}\n }\n \\caption{Effect of the jamming set on energy savings.}\n \\label{fig:jammers}\n\\end{figure}\n\n\\section{System Model and Assumptions}\n\\label{sec:sysmodel}\n\nConsider a wireless network with arbitrarily distributed nodes.\nWe assume that each node (legitimate or eavesdropper) is equipped\nwith a single omni-directional antenna.\nA $K$-hop route $\\Pi$ between a source and a destination in the\nnetwork is a sequence of $K$ links connecting the source to the\ndestination\\fnote{Terms ``path'' and ``route'' are used\ninterchangeably throughout the paper.}. We use the notation $\\Pi =\n\\angles{\\ell_1, \\ldots, \\ell_K}$ to refer to a route that is formed\nby $K$ links $\\ell_1$ to $\\ell_K$. A link $\\ell_k \\in \\Pi$ is formed\nbetween two nodes $S_k$ and $D_k$ on route $\\Pi$. We assume that\nevery link $\\ell_k$ is exposed to a set of (potential) eavesdroppers\ndenoted by $\\mc{E}_k$. Whenever $S_k$ transmits a message to $D_k$,\na set of trusted nodes, called jammers, cooperate with $S_k$ to\nconceal its message from the eavesdroppers in $\\mc{E}_k$ by jamming\n$S_k$'s signal at the eavesdroppers. The set of the jammers\ncooperating with $S_k$ to conceal its transmissions from $\\mc{E}_k$\nis denoted by $\\mc{J}_k = \\set{J_1\\ldots, J_{|\\mc{J}_k|}}$, where $|\\mc{A}|$\ndenotes the cardinality of set $\\mc{A}$. The set of\njammers is potentially different for different links. Throughout the\npaper, we use the notation $\\enc{S_k, D_k, \\mc{E}_k,\n\\mc{J}_k}$ to identify link $\\ell_k$.\n\nIn the following subsections, we describe the models considered in\nthis paper for the wireless channel, eavesdroppers, physical-layer\nsecurity and end-to-end routing. For notational simplicity, we may\ndrop the link index $k$ whenever there is no ambiguity.\n\n\n\\subsection{Wireless Channel Model}\nConsider the discrete-time equivalent model for a transmission from node\n$S$ to node $D$. Let $x_S$ be the normalized (unit-power) symbol\nstream to be transmitted by $S$, and let $y_D$ be the received\nsignal at node $D$. We assume that transmitter $S$ is able to\ncontrol its power $P_S$ in arbitrarily small steps, up to some\nmaximum power $\\pmax$. Let $n_D$ denote the receiver noise at $D$, where\n$n_D$ is assumed to be a complex Gaussian random variable with\n$\\mathbb{E}\\left[|n_D|^2\\right] = N_0$. The received signal at $D$\nis expressed as\n\\begin{equation}\n y_D = \\sqrt{P_S} \\, h_{S,D} \\, x_S + n_D,\n\\end{equation}\nwhere $h_{S,D}$ is the complex channel gain between $S$ and $D$. The\nchannel gain is modeled as $h_{S,D} = |h_{S,D}|e^{\\theta_{S,D}}$,\nwhere $|h_{S,D}|$ is the channel gain magnitude and $\\theta_{S,D}$\nis the uniform phase. We assume a non line-of-sight environment, implying\nthat $|h_{S,D}|$ has a Rayleigh distribution, and that\n$\\mathbb{E}[|h_{S,D}|^2] = 1\/d_{S,D}^\\alpha$, where $d_{S,D}$ is the\ndistance between nodes $S$ and $D$,\nand $\\alpha$ is the path-loss exponent (typically between $2$ and\n$6$). This is the standard narrowband fading channel model\nemployed in the physical layer literature~\\cite{tse05wireless,goldsmith2005}.\n\n\n\\subsection{Adversary Model}\nWe limit our attention to passive eavesdroppers as in prior\nwork~\\cite{goel08,tekin2008general,dong10,elgamal08,Dennis2011JSAC,swindle11}.\nAlthough there are other forms of adversarial behavior, their\nconsideration is beyond the scope of this paper.\n\nWhile the literature on physical layer security often assumes not\nonly eavesdropper locations but also either perfect\n(\\eg~\\cite{tekin2008general}) or imperfect (\\eg~\\cite{swindle11})\nknowledge at the transmitters and jammers of the complex channel\ngains of the eavesdropping channels (\\ie\\ availability of\ninstantaneous eavesdropper channel state information (CSI)), we\nconsider the more realistic scenario, in which CSI for eavesdropping\nchannels is not available. In addition, our model requires only the\nknowledge of {\\em potential} eavesdropping locations in the network,\nyet we show that it provides guaranteed security by employing coding\nin conjunction with cooperative jamming.\n\nSpecifically, we assume that each link $\\ell_k$ is subject to\npotential eavesdropping from a set of locations denoted by $\\mc{E}_k\n= \\set{E_1, \\ldots, E_{|\\mc{E}_k|}}$, where the probability of\neavesdropping from location $E_i$ is given by $p(E_i)$ for $0 \\le\np(E_i) \\le 1$. This is a considerably general model that can be used\nto represent a wide range of eavesdropping scenarios\\fnote{Although\nour model cannot be applied to every possible scenario, it is more\ngeneral compared to the models in the literature on physical layer\nsecurity\n(see~\\cite{goel08,tekin2008general,dong10,elgamal08,Dennis2011JSAC,swindle11},\nand references therein).}. For example, setting all $p(E_i)$'s to $1$\nfor a link models multiple eavesdroppers for that link. Other\nexamples include, for example, military scenarios where the locations\nof enemy installations are known, or wireless networks where a\nmalicious user(s) has been detected. In general, for any given link,\nthere is only a limited region around the link that can be exploited\nfor eavesdropping. By dividing the effective eavesdropping region to\na few smaller areas~\\cite{tessellation}, one can compute the most\neffective eavesdropping location within each area, and consequently,\nconstruct the set of eavesdropping locations for that link.\n\n\\subsection{Physical Layer Security Model}\nConsider a secure link formed between source $S$ and receiver $D$\nwith the help of jammers $\\mc{J}$. For the moment, we assume that\ncooperative jamming is implemented at the physical layer to deal with\na \\emph{single} eavesdropper $E$ located at a \\emph{fixed} position.\nLater, in Section~\\ref{sec:linkcost}, we show how this physical layer\nprimitive can be used to provide security against multiple\neavesdroppers or unknown eavesdropping locations.\n\nWhen node $S$ transmits a message, there are multiple ways in which\ncooperative jamming by system nodes can be exploited, ranging from\nrelatively simple noncoherent techniques to sophisticated beamforming\ntechniques~\\cite{mostafa_twireless}. Since the {\\em implementation}\nof beamforming in other contexts, with the same challenges of\nsynchronization in the wireless environment, is advancing rapidly\n\\cite{beamform_overview,beamform_implementation}, we assume that the\njammers cooperatively \\emph{beamform} a common artificial noise\nsignal $z$ to the receiver in such a way that their signals cancel\nout at the receiver \\cite{brown_null}. The noise signal $z$ is\ntransmitted in the \\emph{null~space} of the channel vector\n $\\mx{h}_D = [h_{J_1,D}, h_{J_2,D}, \\ldots, h_{J_{|\\mc{J}|},D}]^\\mathrm{T}$\nwhere, $h_{J_i,D}$ denotes the channel gain between jammer $J_i$ and\ndestination $D$ and $\\mx{A}^\\mathrm{T}$ denotes the conjugate transpose of vector\n$\\mx{A}$. Thus, the signal transmitted by the jammers can be\nexpressed as\n $\\mathbf{s}_J = \\mathbf{h}_D^\\perp \\, z$,\nwhere $\\mathbf{h}_D^\\perp$ is a vector chosen in the null\nspace of $\\mathbf{h}_D$. It follows that the total transmission\npower of the jammers is given by $P_J = \\parallel\\mathbf{h}_D^\\perp\\parallel^2$.\nAssuming that the source node transmits with power $P_S$, the\nsignals received at the destination and the eavesdropper are given\nby\n\\begin{equation}\n\\begin{split}\n\\label{eq:rsignals}\n y_D &= \\sqrt{P_S} \\, h_{S,D} \\, x_S + n_D,\\\\\n y_E &= \\sqrt{P_S} \\, h_{S,E} \\, x_S + \\mathbf{h}_E^\\mathrm{T} \\mathbf{h}_D^\\perp z + n_E,\n\\end{split}\n\\end{equation}\nwhere, $\\mathbf{h}_E = [h_{J_1,E}, h_{J_2,E}, \\ldots,\nh_{J_{|\\mc{J}|},E}]^\\mathrm{T}$ represents the channel gain vector between\nthe jammers and the eavesdropper, and $n_D$ and $n_E$ denote the\ncomplex Gaussian noise at the destination and eavesdropper,\nrespectively, with $\\E{|n_D|^2} = \\E{|n_E|^2} = N_0$.\n\nAlthough the jammers try to prevent the eavesdropper from\nsuccessfully receiving the message, there is still some probability\nthat the eavesdropper actually obtains the message due to the fact\nthat the channel to the eavesdropper is \\emph{unknown} in our model, \\ie\\ $\\mx{h}_E$ and\n$h_{S,E}$ are unknown. Recalling that the signal-to-interference-plus-noise\nratio ($\\sinr$) at the destination is controlled via power control, let\n$\\gamma_E$ denote the minimum required $\\sinr$ at the eavesdropper\nin order to violate the security constraints of the protocol\n(e.g. for the cryptographic case, the $\\sinr$ above which the eavesdropper\ncan record a meaningful version of the transmitted signal; in the\ninformation-theoretic case, the $\\sinr$ above which, for a given wire-tap code,\nthe equivocation does not equal the entropy of the message.) Let $\\sinr_E$\ndenote the $\\sinr$ at the eavesdropper. We have\n\\begin{equation}\n\\label{eq:success_beaming}\n\\begin{split}\n &\\pr{ \\sinr_E \\ge \\gamma_E}\n =\n \\pr{\n \\tfrac{ P_S|h_{S,E}|^2 }{ N_0 + \\mathbf{h}_E^\\mathrm{T} \\mathbf{h}_D^\\perp {\\mathbf{h}_D^\\perp}^\\mathrm{T} \\mathbf{h}_E }\n \\ge \\gamma_E }\\\\\n &\\qquad= \\mathbb{E}_{\\mathbf{h}_E}\n \\left[\n \\pr{\n \\frac{ P_S|h_{S,E}|^2 }{ N_0 + \\mathbf{h}_E \\mathbf{h}_D^\\perp {\\mathbf{h}_D^\\perp}^\\mathrm{T} \\mathbf{h}_E^\\mathrm{T} }\n \\ge \\gamma_E \\Big|\\, \\mathbf{h}_E }\n \\right] \\\\\n &\\qquad= \\mathbb{E}_{\\mathbf{h}_E}\n \\big[\n e^{- \\frac{\\gamma_E d_{S,E}^\\alpha}{P_S} \\mathbf{h}_E^\\mathrm{T} \\mathbf{h}_D^\\perp {\\mathbf{h}_D^\\perp}^\\mathrm{T} \\mathbf{h}_E }\n \\big]\n e^{- \\frac{\\gamma_E N_0 d_{S,E}^\\alpha}{P_S}} ,\n \\end{split}\n\\end{equation}\nwhere $\\mathbb{E}_{\\mathbf{h}_E}$ means expectation with respect to\nchannel gain vector $\\mathbf{h}_E$. Using the results on quadratic forms~\\cite[Eq.~14]{turin} to calculate the expectation, it is obtained that ($\\mathbf{I}_N$ is\nthe identity matrix of size $N$)\n\\begin{equation}\n\\label{eq:success}\n\\begin{split}\n \\pr{ \\sinr_E \\ge \\gamma_E}\n &\\le \\frac{ e^{- \\frac{\\gamma_E N_0 d_{S,E}^\\alpha}{P_S}} }\n { |\\mathbf{I}_N + \\frac{\\gamma_E d_{S,E}^\\alpha}{P_S}(\\sum_{J_i \\in \\mc{J}}{\\frac{1}{d_{J_i,E}^\\alpha}}) \\mathbf{\\mathbf{h}_D^\\perp {\\mathbf{h}_D^\\perp}^\\mathrm{T}}| } \\\\\n &= \\frac{ e^{ -N_0 \\gamma_E \\frac{d_{S,E}^\\alpha}{P_S} } }\n { 1 + \\frac{\\gamma_E d_{S,E}^\\alpha}{P_S}(\\sum_{J_i \\in \\mc{J}}{\\frac{1}{d_{J_i,E}^\\alpha}}) P_J }\n \\eqend\n\\end{split}\n\\end{equation}\nwhere the final expression is derived from Sylvester's determinant\ntheorem:\n\\[\\det(\\mathbf{I}_m + \\mathbf{A}\\mathbf{B}) = \\det(\\mathbf{I}_n + \\mathbf{B}\\mathbf{A} ),\\]\nfor $\\mathbf{A}$ and $\\mathbf{B}$ being $m\\times n$ and $n\\times m$\nmatrices, respectively, and the fact that $P_J =\n{\\mathbf{h}_D^\\perp}^\\mathrm{T} \\mathbf{h}_D^\\perp$ (see\n\\eqref{eq:pj1}).\n\nIn the remainder of the paper, we use~\\eqref{eq:success} in equality form to compute the eavesdropping probability for a given jamming power $P_J$. While this results in a (slightly)\nconservative power allocation, it is sufficient to satisfy the security requirement of\neach link.\n\n\n\n\\subsection{Routing Model}\nConsider a $K$-hop route $\\Pi = \\angles{\\ell_1, \\ldots, \\ell_K}$ between a\nlegitimate source and destination in the network. Let $\\mc{L}$ denote the set\nof all possible routes between the source and destination. Let\n$\\mc{C}(\\Pi)$ denote the cost of route $\\Pi$, where the cost of a\nroute is defined as the summation of the costs of the links forming the\nroute. With slight abuse of the notation, we use $\\mc{C}(\\ell_k)$ to\ndenote the cost of link $\\ell_k$ as well. The secure routing problem\nis then defined as follows.\n\n\\bigskip\\noindent {\\bf SMER: Secure Minimum Energy Routing Problem}\\\\\n{\\em Consider a wireless network and a set of eavesdroppers\ndistributed in the network. Given a source and destination, find a\nminimum energy path $\\Pi^*$ between the source and destination\nsubject to constraints $\\pi$ and $\\lambda$ on the end-to-end successful eavesdropping\nprobability and goodput on the path respectively.}\n\\bigskip\n\nLet $\\lambda(\\Pi)$ and $\\lambda(\\ell_k)$ denote, respectively, the goodput\nof path $\\Pi$ and link $\\ell_k \\in \\Pi$. Then $\\lambda(\\Pi)$ can be expressed as\n \\[ \\lambda(\\Pi) = \\min_{\\ell_k \\in \\Pi} \\lambda(\\ell_k) \\eqend\\]\nSince goodput of a link is an increasing function of the transmission\npower of the transmitter of that link, a necessary condition for minimizing power over the path $\\Pi$\nis given by $\\lambda(\\ell_k) = \\lambda$, for all $\\ell_k \\in \\Pi$, \\ie\\\nall links should just achieve the minimum goodput $\\lambda$. Thus, our power allocation scheme (see Section~\\ref{sec:linkcost}) establishes links that achieve exactly the minimum required goodput $\\lambda$. Consequently, the constraint on the end-to-end goodput is satisfied by any path in the network, and hence does not need to be explicitly considered when solving SMER.\nAs such, SMER can be formally described by the following optimization problem:\n\\begin{equation}\n\\begin{split}\n\\label{eq:smer}\n \\Pi^* = \\, &\\displaystyle\\arg \\min_{\\Pi \\in \\mc{L}} \\sum_{\\ell_k \\in \\Pi} \\mc{C}(\\ell_k) \\\\\n &s.t. \\quad \\pr{\\text{eavesdropping on route $\\Pi$}} \\le \\pi,\n\\end{split}\n\\end{equation}\nfor some pre-specified $\\pi$ ($0 < \\pi < 1$). The constraint on\nthe route eavesdropping probability in the above optimization\nproblem can be expressed in terms of the eavesdropping probability\non individual links $\\ell_k$ that form the route $\\Pi$, as\n $\\prod_{\\ell_k \\in \\Pi} (1 - \\pi_k) \\ge 1-\\pi$,\nwhere $\\pi_k$ ($ 0 < \\pi_k < 1$) denotes the successful\neavesdropping probability on link $\\ell_k$. We use the following\nresult to convert the above inequality constraint to an equality\nconstraint in the routing problem~\\eqref{eq:smer}.\n\n\\begin{lemma}\nThe cost of route $\\Pi$ is a monotonically\nincreasing function of\n$\\prod_{\\ell_k \\in \\Pi} (1 - \\pi_k)$.\n\\end{lemma}\n\\begin{proof}\nConsider a path $\\Pi$ between the source and destination nodes.\nDefine the end-to-end secrecy of path $\\Pi$, denoted by\n$\\omega(\\Pi)$, as follows:\n\\begin{equation}\n \\omega(\\Pi) = \\prod_{\\ell_k \\in \\Pi} \\omega_k,\n\\end{equation}\nwhere $\\omega_k = (1 - \\pi_k)$.\n\nFirst, we show that the link cost $\\mc{C}(\\ell_k)$ is a\nmonotonically increasing function of the the link secrecy $\\omega_k$\nfor every link $\\ell_k \\in \\Pi$. Let $P_S^{(k)}$ and $P_J^{(k)}$\ndenote the source and jamming powers allocated to the link $\\ell_k$,\nrespectively. In Section~\\ref{sec:linkcost}, we show that: (i)\n$P_S^{(k)}$ is a constant for a given link independent of the link\nsecrecy, and (ii) $P_J^{(k)}$ is a function of the link secrecy and\nis given by\n\\begin{equation}\n P_J^{(k)} = c_k \\cdot \\frac{\\omega_k}{1 - \\omega_k},\n\\end{equation}\nwhere $c_k$ is some constant independent of $\\omega_k$. Thus, for a\nfixed link $\\ell_k$, the link cost $\\mc{C}(\\ell_k) = P_S^{(k)} +\nP_J^{(k)}$ depends on $\\omega_k$ only through the jamming power\n$P_J^{(k)}$. Taking the derivative on the link cost with respect to\n$\\omega_k$ results in the following relation:\n\\begin{equation}\n \\frac{d}{d \\omega_k} \\, \\mc{C}(\\ell_k) = c_k \\cdot \\frac{1}{(1 - \\omega_k)^2} > 0,\n\\end{equation}\nindicating that the link cost is an increasing function of the link\nsecrecy $\\omega_k$.\n\nLet $\\mc{C}^*(\\Pi)$ and $\\mc{C}(\\Pi)$ denote the optimal cost of the\npath $\\Pi$ computed by solving the optimization\nproblem~\\eqref{eq:smer-p}, with equality and inequality constraints,\nrespectively. Furthermore, let $\\omega^*(\\Pi)$ and $\\omega(\\Pi)$\ndenote the corresponding end-to-end path secrecies. We present a\nproof of the lemma based on contradiction by assuming that the\noptimal path cost with the inequality constraint is less than that\nwith the equality constraint. That is, we assume that\n\\begin{equation}\n \\mc{C}(\\Pi) \\le \\mc{C}^*(\\Pi),\n\\end{equation}\nwhile,\n\\begin{equation}\n \\omega(\\Pi) > \\omega^*(\\Pi) \\eqend\n\\end{equation}\n\nNext, by manipulating the link secrecy allocation vector $[\\omega_1,\n\\ldots, \\omega_k, \\ldots, \\omega_K]$, we construct a new link\nsecrecy allocation vector that satisfies the equality constraint,\nwhile having a cost smaller than $\\mc{C}^*(\\Pi)$. To this end,\nconsider some arbitrary link $\\ell_k$, and replace $\\omega_k$ by a\nnew $\\omega'_k$ as follows\n\\begin{equation}\n \\omega'_k = \\frac{\\omega^*}{\\omega} \\cdot \\omega_k\n \\eqend\n\\end{equation}\nSince $\\omega^* < \\omega$, it follows that $\\omega'_k < \\omega_k$.\nConsequently, the new cost of the link $\\ell_k$ with link secrecy\n$\\omega'_k$ is less than $\\mc{C}(\\ell_k)$, which in turn indicates\nthat the new path cost with secrecy allocation vector $[\\omega_1,\n\\ldots, \\omega'_k, \\ldots, \\omega_K]$ is less than $\\mc{C}(\\Pi)$.\nTherefore, we have\n\\begin{equation}\n \\mc{C}'(\\Pi) < \\mc{C}(\\Pi) < \\mc{C}^*(\\Pi),\n\\end{equation}\nand,\n\\begin{equation}\n \\omega(\\Pi) > \\omega'(\\Pi) = \\omega^*(\\Pi) \\eqend\n\\end{equation}\nThe proof follows by noting that this contradicts the assumption\nthat $\\mc{C}^*(\\Pi)$ is the minimum cost of path $\\Pi$ with the\nequality constraint.\n\\end{proof}\n\nThus, to minimize the cost of the optimal route, the inequality\nconstraint can be substituted by the equality constraint\n$\\prod_{\\ell_k \\in \\Pi} (1 - \\pi_k) = 1-\\pi$. On each link $\\ell_k$,\nit is desirable to keep the successful eavesdropping probability\n$\\pi_k$ close to $0$. In this case, the product $\\prod_{\\ell_k \\in\n\\Pi} (1 - \\pi_k)$ can be approximated by the expression $1 -\n\\sum_{\\ell_k \\in \\Pi} \\pi_k$. By substituting the approximate\nlinearized constraint in the routing problem, the following\noptimization problem is obtained\n\\begin{equation}\n\\begin{split}\n\\label{eq:smer-p}\n \\Pi^* = \\, &\\displaystyle\\arg \\min_{\\Pi \\in \\mc{L}} \\sum_{\\ell_k \\in \\Pi} \\mc{C}(\\ell_k) \\\\\n &s.t. \\quad \\displaystyle \\sum_{\\ell_k \\in \\Pi}\n \\pi_k = \\pi\n \\eqend\n\\end{split}\n\\end{equation}\nIn the rest of the paper, we focus on\nthis optimization \\mbox{problem}.\nWe first show that the problem is, in general,\nNP-hard and then develop exact and approximate algorithms to\nsolve it.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nMolecules secreted and internalized by Eukaryotic cells follow well defined routes, the secretory or endocytic pathways, along which they are exposed to a succession of biochemical environments by sequentially visiting different membrane-bound organelles \\cite{kelly1985pathways}. Different organelles have different membrane compositions, as well as a distinct set of membrane-associated proteins, refered to henceforth as the membrane {\\em identity}. Interestingly, it has been shown that the identity of some organelles changes with time ; for example, the early endosome (a compartment digesting newly internalized content) has a different identity from the late endosome, which then becomes a lysosome \\cite{zerial:2001}. One fundamental issue underlying the organization of intracellular transport is whether progression along the various pathways occurs by exchange between organelles of fixed biochemical identities (via the budding and scission of carrier vesicles), or by the biochemical maturation of the organelles themselves \\cite{kelly1985pathways,zerial:2001}. This question is particularly debated for the Golgi apparatus, where proteins undergo post-transcriptional maturation and sorting. The Golgi is divided into early ({\\em cis}), middle ({\\em medial}) and late ({\\em trans}) $\\unit{\\mu m}$-size compartments called cisternae. In yeast, each cisterna appears to undergo independent biochemical maturation from a {\\em cis} to a {\\em trans} identity in less than 1 min \\cite{matsuura:2006,losev:2006}. In higher eukaryotes, the cisternae form a tight and polarized stack with {\\em cis} and {\\em trans} ends, through which proteins travel in about 20 min \\cite{emr:2009}. Whether transport through the stack occurs by inter-cisternal exchange or by the maturation of entire cisternae remains controversial \\cite{emr:2009}.\n\nMaturation in an organelle membrane causes different membrane identities to transiently coexist and may trigger the formation of transient membrane domains. Membrane components have indeed been seen to segregate into domains in both yeast \\cite{matsuura:2006,losev:2006} and mammalian Golgi cisternae \\cite{patterson:2008}.\n This is the case of proteins of the Rab family, though to be essential identity labels of cellular organelles \\cite{zerial:2001}. The so-called {\\em Rab cascade}, in which the activation of one Rab inactivates the preceding Rab along the pathway, is thought to permit the sequential maturation of the organelle identity \\cite{rivera:2009}. Domains could also emerge from the maturation of ceramids (present in cis-Golgi) into sphingomyelin (present in trans-Golgi), as these two species are known to lead to domain formation on vesicles \\cite{sot:2006}. Finally, there is a continuous gradient of membrane thickness from cis to trans Golgi compartments \\cite{mitra:2004} and thickness mismatch can lead to phase separation in model membranes \\cite{heberle:2013}.\n \n It has been argued that membrane domains in organelles could undergo budding and scission, and hence control inter-organelle transport \\cite{pfeffer:2010b}. This raises the interesting possibility that the rate of domain formation could control the rate of transport. To quantitatively assess this possibility, we studied transient domain formation in an ideal two-component membrane. We consider an irreversible transformation (maturation) $A\\rightarrow B$ taking place between two components, with $A$ and $B$ representing distinct biochemical identities, and we investigate the phase behavior of a such membrane.\n\nThe kinetics of phase separation in binary mixtures have been abundantly studied \\cite{bray:1994}. In the context of fluid membranes, hydrodynamic flows in the membrane and the surrounding media make the problem quite complex. Several dynamical regimes have been reported, and a unified picture of has not yet emerged \\cite{camley:2010,fan:2010}. For deformable fluid membranes such as cellular membranes, the budding of membrane domains \\cite{lipowsky:1992} makes the dynamics of phase separation even more complex \\cite{sunil:1998,sunil:2001,laradji:2004}. Here, we study transient phase separation on flat membranes, and we implement membrane deformability at a phenomenological level by introducing a critical domain size beyond which flat domains are unstable. If domains reach such a size, they undergo a budding transition and may serve as transport intermediates, provided a scission mechanism ({\\em e.g} the activity of specialized proteins such as dynamin \\cite{ferguson:2012}) separates budded domains from the rest of the membrane.\n\nThe budding of membrane domains may for instance be driven by the line energy associated with domain boundaries \\cite{lipowsky:1992}, expressed as the domain line tension $\\gamma$ times the boundary length. Budding is resisted by the membrane bending rigidity $\\kappa$ and surface tension $\\sigma$, and will occur for a finite range of domain size $R$ \\cite{pio_regul}:\\begin{equation}\n4 \\frac{\\kappa}{\\gamma} < R < 2 \\frac{\\gamma}{\\sigma}\\, .\n\\label{range}\n\\end{equation}\nFor typical values of the parameters: $\\kappa\\simeq10k_{{}_{\\rm B}}T$ and $\\gamma\\simeq1\\unit{pN}$, the lower bound is a fraction of the typical size of Golgi cisternae ($\\sim\\unit{\\mu m}$), and the scenario of a line tension induced domain pinching appears realistic. The upper bound could be restrictive in a system with low area\/volume ratio, where pinching might increase membrane tension and lead to incomplete buds. This is often observed in artificial vesicles, but this constraint does not appear stringent in organelles such as the Golgi. We assume henceforth that the rate of membrane deformation is much faster than the rate of chemical maturation, so that only the lower bound of \\eq{range} is relevant. \n\n\nIn this article, we show that, in a membrane undergoing irreversible maturation, the maximal size of transient domains follows a power law with respect to the maturation rate. First, we illustrate two modes of domain growth in fluid membranes. We then show the influence of maturation on domain growth, and the predicted power laws are then confirmed numerically. Because budding depends on domain size, this means that organelle transport can be controlled by the maturation rate.\n\n\n\n\\section{Phase separation kinetics in fluid membranes}\n\n In order to elucidate the role of maturation, we only consider phase separation in a flat fluid membrane and we disregard the influence of the surrounding fluid. This approximation is valid for domains smaller than $\\sim\\eta_2\/\\eta_3$, where $\\eta_3$ is the three-dimensional viscosity of the surrounding fluid (the cytoplasm and the lumen of cellular organelles in the cell), and $\\eta_2$ is the two-dimensional viscosity of the membrane \\cite{saffman:1975}. For biological membranes, one expects $\\eta_2\/\\eta_3\\gtrsim\\unit{\\mu m}$.\n\nDomain growth is initially dominated by the so-called ``Ostwald ripening'', where large domains grow by adsorbing diffusing matter evaporated from smaller domains. At later time, hydrodynamic effects, leading in particular to domain coalescence, dominate the growth. In both cases, after an early nucleation stage, the domain size $R_c$ increases with time according to a power law \\cite{bray:1994}:\n\\begin{eqnarray}\nR_c^{1\/\\alpha-1} \\dot{R_c} \\propto \\tilde D(\\bar\\phi) \\quad:\\quad R_c\\sim t^\\alpha.\n\\label{powerlaw}\n\\end{eqnarray}\nThe exponent $\\alpha$ and the transport coefficient $\\tilde D$ depend on the dominant growth process, and the latter also depends on composition, line tension, and component mobility. \n\n\\subsection{Thermodynamics}\n\nThe thermodynamics of phase separation in a two-component membrane containing distinct biochemical identities $A$ and $B$ can be studied using a local order parameter $\\phi$ varying continuously between $\\phi=0$ for A-rich and $\\phi=1$ for B-rich membrane regions. We use the classical Landau free energy \\cite{bray:1994}:\n\\begin{eqnarray}\n&\\mathcal{F}=\\int d^2 \\mathbf{r} \\left[ V [\\phi(\\mathbf{r})] + \\frac{1}{2} \\zeta\\| \\boldsymbol{\\nabla} \\phi \\|^2 \\right] \\label{compofreeE} \\\\\n&V [\\phi] =\\frac{k_{{}_{\\rm B}}T}{a^2}\\left(\\phi \\log{\\phi} + (1-\\phi) \\log{(1-\\phi)}\\right)+ \\frac{K}{2a^2} \\phi ( 1- \\phi), \\nonumber\n\\end{eqnarray}\nwhere $a$ is a molecular size. This energy is the sum of an interfacial term (of parameter $\\zeta$) and a potential term $V$ that includes the translational entropy and the interaction between the two phases (repulsive if $K>0$). \n\n\nPhase separation occurs spontaneously inside the spinodal region of the phase diagram, defined by $K> k_{{}_{\\rm B}}T\/(\\bar{\\phi} (1-\\bar{\\phi}))$, where $\\bar{\\phi}$ is the mean value of $\\phi$ in the system \\cite{chaikin:1995}. Within this region, the interface between A-rich and B-rich domains is sharp and the energy of interaction reduces to a line energy characterised by the line tension $\\gamma$ (see Supplementary Information - S.I. - for more details). \n\n\\begin{figure}[b]\n \\includegraphics[width=7.5cm]{fig1} \n \\caption{\\small (color online) Phase diagram of a binary mixture undergoing chemical maturation. The blue line represents the spinodal line. The chemical reaction (maturation) increases the mean order parameter $\\bar{\\phi}$ from $0$ to $1$.}\n \\label{spinomat}\n\\end{figure}\n\\subsection{Domain growth by Ostwald ripening}\n\n For a conserved order parameter, the dynamics of the order parameter is described by a Cahn-Hilliard equation \\cite{bray:1994}:\n\\begin{equation}} \\def\\ee{\\end{equation}\n\\partial_t \\phi = D\\boldsymbol{\\nabla}^2\\mu\/k_{{}_{\\rm B}}T \\qquad \\mu =a^2\\delta \\mathcal{F} \/\\delta \\phi\\ , \n\\label{cahnhilliard}\n\\ee\nwhere $\\mu$ is the chemical potential associated with $\\phi$, and $D$ is the monomer diffusion coefficient. Within the two-phases region of \\fig{spinomat}, \\eq{cahnhilliard} produces domains with sharp boundaries. Domains larger than a critical size $R_c$ grow at the expense of smaller domains, and inter-domain exchange occurs by monomer diffusion through the bulk phase. \n\nProvided diffusion is much faster than the evaporation of the smallest domains, Lifschitz, Slyozov and Wagner (LSW) have shown that the characteristic domain size should obey the scaling law $R_c \\sim t^{1\/3}$ \\cite{lifshitz:1961,wagner:1961}. This can be qualitatively explained: if diffusion is fast, the order parameter profile outside the domain boundary satisfies the quasi-static approximation $\\boldsymbol{\\nabla}^2\\mu=0$, and the chemical potential inside domains is related to the line tension by $\\mu \\approx \\gamma \/ R_c$ \\cite{bray:1994}. If there is only one length scale $R_c$ in the system, mass conservation implies $\\dot R_c\\sim \\nabla\\mu\\sim \\mu\/R_c$. Identifying ${\\bf \\nabla.}$ with $1\/R_c$, one finds (see S.I.):\n\\begin{eqnarray}\nR_c^2 \\dot{R_c} \\propto D \\gamma a^2\/k_{{}_{\\rm B}}T \\label{rcsigma} \\, ,\n\\label{LS}\n\\end{eqnarray}\nleading to the classical Lifschitz-Slyozov-Wagner (LSW) dynamical scaling $R_c \\sim t^{1\/3}$ \\cite{lifshitz:1961,wagner:1961}. The size distribution of domains growing by Ostwald ripening is strongly peaked around the size $R_c$ \\cite{bray:1994}. Though this was strictly shown in dimension $3$ or higher, it has been confirmed numerically in two dimension \\cite{huse:1986}.\n\n A similar scaling has been observed numerically {\\em at steady state} in the presence of reversible reaction $A \\rightleftarrows B$, time being replaced by the inverse of the reaction rate \\cite{glotzer:1995}. In the following we show analytically and numerically that a similar scaling also exists for irreversible reactions.\n\n\n\n\\subsection{Domain growth by coalescence}\n\n\n The role of hydrodynamics on phase separation in fluid membranes is still controversial, despite considerable recent attention \\cite{camley:2011,fan:2010}. For off-critical mixtures ($\\phi\\ne 1\/2$), hydrodynamic correlations result in the diffusion and coalescence of entire domains. At the scaling level \\cite{bray:1994}, the area of the largest domain can at most double at each coalescence event: $\\dot R_c \\le R_c\/\\tau_D$. The typical collision time $\\tau_D=L^2\/D_d$ depends on the domain diffusion coefficient $D_d$ and the typical area per domain $L^2\\sim R_c^2\/\\bar\\phi$. Finally, one finds :\n\\begin{equation}} \\def\\ee{\\end{equation}\nR_c\\dot R_c\\propto D_d\\bar\\phi\\ .\n\\label{hydro}\n\\ee\nIf viscous dissipation is mostly due to membrane hydrodynamics, the domain diffusion coefficient $D_d$ is only weakly dependent on domain size \\cite{saffman:1975}. One thus expects $R_c\\sim t^{1\/2}$ for constant composition, which dominates Ostwald ripening at long times.\n\nThe size distribution of domains can be studied using the Smoluchowski coagulation equation \\cite{smo:1916}:\n\\begin{eqnarray}\n\\partial_t C_n=J_n-kC_n N+\\frac{k}{2}\\sum_{m=1}^{n-1}C_{m}C_{n-m}\\cr {\\rm with} \\quad N=\\sum_{m=1}^\\infty C_m\n\\label{master}\n\\end{eqnarray}\nwhere $C_n$ is the concentration of domains containing $n$ monomers (if $R$ is the domain size: $n\\sim R^2$), $k$ is a typical diffusion rate, and where domain scission has been neglected. This model has been studied extensively for different forms of the diffusion rate \\cite{davies:1999}. Following the assumption that the diffusion coefficient of a domain is independent of its size \\cite{saffman:1975}, we choose a constant diffusion rate $k=D_d\/a^2$. $J_n$ is a source and sink term allowing for the creation or removal of domains \\cite{turner:2005}.\n\nIn the absence of maturation ($\\bar\\phi=$const., $J_n=0$), the size distribution is well approximated by an exponential with a characteristic domain size $\\bar n\\sim \\bar\\phi kt$, giving the domain radius $\\bar R(t)\\sim \\sqrt{D_d\\bar\\phi t}$, in agreement with \\eq{hydro}.\n\nThe domain size distribution is modified by the presence of sources and sinks. It has been shown in \\cite{turner:2005} that choosing a source and sink term that conserves the average concentration $\\bar\\phi$ (i.e. $J_n=j_{in}\\delta_{n,1}-k_{\\rm off}C_n$) produces a steady-state power-law distribution $C_n\\sim n^{-3\/2}$, up to a characteristic domain size beyond which the distribution is exponential. The characteristic size obeys a scaling reminiscent of \\eq{hydro}: $R_c\\propto \\sqrt{D_d\\bar\\phi\/k_{\\rm off}}$.\n\n\\section{Transient phase separation under irreversible maturation}\n\nMaturation corresponds to the increase of $\\bar{\\phi}$ with time from $\\bar{\\phi}=0$ to $\\bar{\\phi}=1$ due to a chemical reaction. If $K>4 k_{{}_{\\rm B}}T$, the spinodal line (\\fig{spinomat}) is crossed twice, first at $\\bar{\\phi}=\\phi_a$ when phase separation starts, then at $\\bar{\\phi}=\\phi_b$ where the system tends to be homogenous once again . \nWe ask whether domains larger than a critical budding size, for instance given by \\eq{range}, can form during the time the system is prone to phase separation ({\\em i.e.} while $\\phi_a <\\bar\\phi <\\phi_b$). \n\n\\subsection{Dynamical scaling}\n \nFor Ostwald ripening, the quasi-static approximation above assumes that the order parameter profile outside domains adjusts quasi-statically to domain growth ($\\boldsymbol{\\nabla}^2\\phi=0$ in the bulk). In a maturing membrane,\nthe average membrane composition evolves according to $\\partial_t \\bar\\phi= k_r (1-\\bar\\phi)$, and the quasi-static composition profile satisfies :\n\\begin{equation}} \\def\\ee{\\end{equation}\nD\\boldsymbol{\\nabla}^2\\phi+k_r(1-\\phi)=0 \\, ,\\ee\n which defines a characteristic length scale $\\lambda_D=\\sqrt{D\/k_r}$. \nFor small domains $R\\ll\\lambda_D$, \nmaturation does not modify the concentration profile, but merely changes the mean density outside the domains. Thus \\eq{LS} should hold, with a transport coefficient now depending on time through the mean concentration $\\bar\\phi$. This is shown with more details in the S.I.\n\nIn the regime dominated by coalescence, \\eq{hydro}, which assumes a single characteristic domain size, may be used with a time-dependent $\\bar\\phi$ under the assumption that the number of domains varies little between two coalescence events. This approximation is shown to be valid below.\n\nThe extent of phase separation can be characterized by the maximum size $R_{max}$ a domain can reach during the transient phase separation. Here, we are interested in membrane domains that may undergo budding, namely domains of the minority phase surrounded by the majority phase. Domains are thus of the mature species below $\\phi=1\/2$ and of the immature species for $\\phi>1\/2$ (see insets \\fig{spinomat}), and the maximum domain size occurs for $\\phi=1\/2$. Integrating \\eq{powerlaw}, one finds:\n\\begin{equation}\nR_{max}^{1\/\\alpha} \\propto \\int_{\\phi_a}^{1\/2} \\tilde D (\\bar{\\phi}) \\frac{d\\bar\\phi }{\\dot{\\bar\\phi}}= \\frac{1}{k_r} \\int_{\\phi_a}^{1\/2} \\frac{\\tilde D(\\bar\\phi)}{1-\\bar{\\phi}} d\\bar{\\phi} .\n\\label{scaling}\n\\end{equation}\nThe {\\em maximum} size of transient domains in a maturing membrane is thus predicted to follow the dynamical scaling law observed for domain growth under fixed composition (\\eq{powerlaw}), where the maturation rate replaces $1\/t$: $R_{max}\\sim k_r^{-\\alpha}$. This dynamical scaling is reminiscent of the scaling observed {\\em at steady state} in the presence of reversible reaction $A \\rightleftarrows B$ \\cite{glotzer:1995} or continuous recycling \\cite{turner:2005}. That it is also applicable to irreversible reactions is remarkable, since the kinetics of domain growth in a membrane undergoing maturation do not follow the same scaling, as $\\bar\\phi$ changes with time. This kinetics is not easily obtained from scaling arguments for Ostwald ripening, as the $\\bar\\phi$ dependence of \\eq{LS} is not straightforward (see S.I.). For domain coalescence, a simple integration of \\eq{hydro} with $\\bar\\phi\\simeq k_r t$, valid at early time, shows that one expects $R_c(t)\\simeq\\sqrt{D_d k_r}t$. This linear growth contrasts with the $\\sim t^{1\/2}$ scaling in the absence of maturation.\n\n\n \\begin{figure}[t]\n \\includegraphics[width=8.5cm]{fig2} \n \\caption{\\small Growth by Ostwald ripening. Snapshots of Monte Carlo simulations of domain formation in an Ising model without maturation for different times (top row) and with maturation for different maturation rates (bottom row). The average concentration is $\\bar\\phi=1\/2$ in all cases, \n and the interaction parameter $J=0.75 k_{{}_{\\rm B}}T$ corresponds to a physiological line tension $\\gamma\\simeq4\\unit{pN}$ (see S.I.)}\n \\label{num}\n\\end{figure}\n\n\\subsection{Numerical results - Ostwald ripening}\n\n\n\\begin{figure}[b]\n \\includegraphics[width=8.5cm]{fig3} \n \\caption{\\small Growth by Ostwald ripening. Domain size (in units of the molecule size $a$) as a function of time ($\\circ$) and maximum domain size as function of the inverse maturation rate $1\/k_r$ ($\\times$), from simulations with $J=0.75$. Time is in units of the diffusion time $a^2 \/ D$ ($\\approx 10^{-5} s$). The dashed line corresponds to $k_r^{-1\/3}$. The critical budding size is estimated based on the line tension driven budding scenario (\\eq{range})}\n \\label{Lct}\n\\end{figure}\n\nThe prediction of \\eq{scaling} was tested numerically (simulation details are given in the S.I.). Without hydrodynamics, we performed Monte Carlo simulations of the Ising model with nearest-neighbor interaction (parameter $J$) and a discrete order parameter $s$ ($0$ or $1$). Monomer diffusion is implemented using Kawasaki dynamics (spin exchange between nearer neighbors), known to produce the LSW growth in a system without maturation \\cite{huse:1986,2005:krzakala}. Maturation is implemented by letting each site with $s$=0 become a $s$=1 site with a probability $k_r dt$ at each time step (of duration $dt$). Snapshots of the simulations, shown in \\fig{num}, highlight the similarity between a system without maturation (fixed $\\bar\\phi$) after a time $t$, and a system which reaches the same concentration by maturation (after a time $t=-\\log(1-\\bar\\phi)\/k_r$, shown for $\\bar\\phi=1\/2$). \n\\fig{Lct} compares the variation of the average domain size with time in a system without maturation (with $\\bar\\phi=1\/2$) and the variation of the maximum domain size with respect to the inverse maturation rate in a system undergoing maturation.\nBoth the LSW scaling without maturation ($R\\sim t^{1\/3}$), and our predicted scaling with maturation ($R_{max}\\sim k_r^{-1\/3}$) are apparent at late stages. Strikingly, prefactors of the power law appear very similar with or without maturation. \n \n\\subsection{Numerical results - Diffusion and coalescence}\n \n \nGrowth by coalescence was studied numerically by solving the Master equation \\eq{master} with the source term $J_n=\\delta_{n,1} k_r(1-\\bar\\phi)$ (with $\\bar\\phi=\\sum_n nC_n$), corresponding to monomers being constantly ``created'' by maturation (see S.I. for details of the numerical scheme). The system contains no mature components at $t=0$ ($C_n(t=0)=0\\ \\forall\\ n$). We find that the domain size distribution crosses over from a power-law $C_n=A n^{-3\/2}$ for small $n[l]^{} \\ar@<0.6ex>[l]_{} &\\mathbf{D}(R)\\ar@<-0.6ex>[l]^{} \\ar@<0.6ex>[l]_{}}$ and $\\xymatrix{\n\\mathbf{K}_{ex}(\\mathcal{GP})\\ar[r]^{} & \\mathbf{K}(\\mathcal{GP}) \\ar[r]^{}\\ar@<-0.6ex>[l]^{} \\ar@<0.6ex>[l]_{} &\\mathbf{D}(R)\\ar@<-0.6ex>[l]^{} \\ar@<0.6ex>[l]_{}}$.\n\nIf the underlying ring is left-Gorenstein, it follows from \\cite{Chen10} that $\\mathbf{K}(\\mathcal{GP})\\simeq \\mathbf{K}(\\mathcal{GI})$; we also recovered this equivalence in \\cite{Ren19}. However, we do not know if it is true that $\\mathbf{K}_{ex}(\\mathcal{GP})\\simeq \\mathbf{K}_{ex}(\\mathcal{GI})$. We remark that one can not get an answer by simply restricting the equivalent functor $\\mathbf{K}(\\mathcal{GP})\\simeq \\mathbf{K}(\\mathcal{GI})$ in \\cite{Chen10}, or by the methods in \\cite{Ren19}.\n\n\n\n\\section { \\bf The proof of the theorem}\n\nThroughout the paper, let $R$ be a left-Gorenstein ring. All modules are left $R$-modules.\n\nLet $\\mathcal{A}$ be an abelian category with enough projectives and injectives. A pair of classes $(\\mathcal{X}, \\mathcal{Y})$ in $\\mathcal{A}$ is a cotorsion pair provided that $\\mathcal{X} = {^\\perp}\\mathcal{Y}$ and $\\mathcal{Y} = \\mathcal{X}^{\\perp}$, where $^{\\perp}\\mathcal{Y} = \\{X \\mid \\mathrm{Ext}^{1}_{\\mathcal{A}}(X, Y) = 0,~~\\forall~~Y\\in \\mathcal{Y}\\}$ and\n$\\mathcal{X}^{\\perp} = \\{Y \\mid \\mathrm{Ext}^{1}_{\\mathcal{A}}(X, Y) = 0,~~\\forall~~X\\in \\mathcal{X}\\}$.\n\nThe cotorsion pair $(\\mathcal{X}, \\mathcal{Y})$ is complete provided that for any $M\\in \\mathcal{A}$,\nthere exist short exact sequences $0\\rightarrow Y\\rightarrow X \\stackrel{f}\\rightarrow M \\rightarrow 0$ and $0\\rightarrow M\\stackrel{g}\\rightarrow Y^{'} \\rightarrow X^{'} \\rightarrow 0$ with $X, X^{'}\\in \\mathcal{X}$ and $Y, Y^{'}\\in \\mathcal{Y}$. In this case, for any $N\\in \\mathcal{X}$, $\\mathrm{Hom}_{\\mathcal{A}}(N, f): \\mathrm{Hom}_{\\mathcal{A}}(N, X)\\rightarrow \\mathrm{Hom}_{\\mathcal{A}}(N, M)$ is surjective since $\\mathrm{Ext}^{1}_{\\mathcal{A}}(N, Y) = 0$, and then $f: X\\rightarrow M$ is said to be a special $\\mathcal{X}$-precover of $M$. Dually, $g: M\\rightarrow Y^{'}$ is called a special $\\mathcal{Y}$-preenvelope of $M$.\n\nBy \\cite[Theorem 2.2]{Hov02}, an abelian model structure on $\\mathcal{A}$ is equivalent to a triple $(\\mathcal{A}_{c}, \\mathcal{A}_{tri}, \\mathcal{A}_{f})$ of subcategories, for which $\\mathcal{A}_{tri}$ is thick and both $(\\mathcal{A}_{c}, \\mathcal{A}_{f}\\cap \\mathcal{A}_{tri})$ and $(\\mathcal{A}_{c}\\cap \\mathcal{A}_{tri}, \\mathcal{A}_{f})$ are complete cotorsion pairs; see also \\cite[Chapter VIII]{BR07}. In this case, $\\mathcal{A}_{c}$ is the class of cofibrant objects, $\\mathcal{A}_{tri}$ is the class of trivial objects and $\\mathcal{A}_{f}$ is the class of fibrant objects. The model structure is called ``abelian'' since it is compatible with the abelian structure of the category in the following way: (trivial) cofibrations are monomorphisms with (trivially) cofibrant cokernel, (trivial) fibrations are epimorphisms with (trivially) fibrant kernel, and weak equivalences are morphisms which factor as a trivial cofibratin followed by a trivial fibration.\n\nFor convenience, we will use the triple $(\\mathcal{A}_{c}, \\mathcal{A}_{tri}, \\mathcal{A}_{f})$ to denote the corresponding model structure. The following is immediate from \\cite[Section 2]{Bec14} or \\cite[Theorem 4.7]{Gil08}.\n\n\\begin{lemma}\\label{lem 1}\nOn the category $\\mathrm{Ch}(R)$ of complexes, there is a singular contraderived model structure\n$\\mathcal{M}_{sing}^{ctr} = (ex\\widetilde{\\mathcal{P}}, (ex\\widetilde{\\mathcal{P}})^{\\perp}, \\mathrm{Ch}(R))$, and a singular coderived model structure $\\mathcal{M}_{sing}^{co} = (\\mathrm{Ch}(R), {^{\\perp}}(ex\\widetilde{\\mathcal{I}}), ex\\widetilde{\\mathcal{I}})$.\n\\end{lemma}\n\nFor a bicomplete abelian category $\\mathcal{A}$ with the model structure $\\mathcal{M} = (\\mathcal{A}_{c}, \\mathcal{A}_{tri}, \\mathcal{A}_{f})$, the associated homotopy category $\\mathrm{Ho}(\\mathcal{M})$ is constructed by localization with respect to weak equivalences. The homotopy category of an abelian model category is always a triangulated category. There is an equivalence of categories $\\mathrm{Ho}(i): \\mathcal{A}_{cf}\/\\omega = {\\mathcal{A}_{cf}\/\\sim}\\rightarrow \\mathrm{Ho}(\\mathcal{M})$ induced by the inclusion functor $i: \\mathcal{A}_{cf}\\rightarrow \\mathcal{A}$, where $\\mathcal{A}_{cf}=\\mathcal{A}_{c}\\cap \\mathcal{A}_{f}$, $f\\sim g: M\\rightarrow N$ if $g-f$ factors through an object in $\\omega = \\mathcal{A}_{c}\\cap \\mathcal{A}_{tri}\\cap \\mathcal{A}_{f}$; see e.g. \\cite[Section 1.2]{Hov99}.\n\n\\begin{corollary}\\label{cor 1}\nThere are equivalences $\\mathrm{Ho}(\\mathcal{M}_{sing}^{ctr})\\simeq \\mathbf{K}_{ex}(\\mathcal{P})$ and\n$\\mathrm{Ho}(\\mathcal{M}_{sing}^{co})\\simeq \\mathbf{K}_{ex}(\\mathcal{I})$.\n\\end{corollary}\n\n\\begin{proof}\nWe use $\\widetilde{\\mathcal{P}}$ (resp. $\\widetilde{\\mathcal{I}}$) to denote the subcategory of contractible complexes of projective (resp. injective) modules. It is well known that a complex $P\\in \\widetilde{\\mathcal{P}}$ if and only if $P$ is exact and each $\\mathrm{Ker}d_{i}^{P}$ is a projective module; similarly, complexes in $\\widetilde{\\mathcal{I}}$ are characterized. Note that for any chain maps $f$ and $g$, if $g-f$ factors through a complex in $\\widetilde{\\mathcal{P}}$ (or, a complex in $\\widetilde{\\mathcal{I}}$), then $f$ is chain homotopic to $g$, denoted by $f\\sim g$. Since $ex\\widetilde{\\mathcal{P}}\\cap (ex\\widetilde{\\mathcal{P}})^{\\perp}=\\widetilde{\\mathcal{P}}$ and $ex\\widetilde{\\mathcal{I}}\\cap {^{\\perp}}(ex\\widetilde{\\mathcal{I}}) = \\widetilde{\\mathcal{I}}$, the equivalences hold directly.\n\\end{proof}\n\nLet $F=\\Lambda\\Omega$ and $G=\\Lambda\\Theta$ be functors on $\\mathrm{Ch}(R)$, where $\\Omega$ and $\\Theta$ are functors from $\\mathrm{Ch}(R)$ to $\\mathrm{Mod}(R)$ such that for any $X\\in \\mathrm{Ch}(R)$, $\\Omega(X)= X_0\/\\mathrm{Im}d_{1}^{X}$ and $\\Theta(X)= \\mathrm{Ker}d_{0}^{X}$. Let $\\Lambda: \\mathrm{Mod}(R)\\rightarrow \\mathrm{Ch}(R)$ be a functor which sends every module to a stalk complex concentrated on degree zero.\n\n\n\\begin{lemma}\\label{lem 2}\nLet $X$, $Y$ be any $R$-complexes, and $f: X\\rightarrow Y$ a monomorphism of complexes. If $f$ is a quasi-isomorphism, then $\\Omega(f)$ is also a monomorphism of $R$-modules.\n\\end{lemma}\n\n\\begin{proof}\nWe consider the following commutative diagram\n$$\\xymatrix{\n0\\ar[r] & \\mathrm{Ker}d_{0}^{X} \/ \\mathrm{Im}d_{1}^{X} \\ar[r]\\ar[d] & X_{0} \/ \\mathrm{Im}d_{1}^{X} \\ar[r]\\ar[d]_{\\Omega(f)}\n& X_{0} \/ \\mathrm{Ker}d_{0}^{X}\\ar[r]\\ar[d] &0\\\\\n0\\ar[r] & \\mathrm{Ker}d_{0}^{Y} \/ \\mathrm{Im}d_{1}^{Y} \\ar[r] & Y_{0} \/ \\mathrm{Im}d_{1}^{Y} \\ar[r] & Y_{0} \/ \\mathrm{Ker}d_{0}^{Y}\\ar[r] &0 }$$\nSince $f$ is a quasi-isomorphism, we have an isomorphism induced by $f$:\n$$\\mathrm{H}_0(f): \\mathrm{H}_0(X)=\\mathrm{Ker}d_{0}^{X} \/ \\mathrm{Im}d_{1}^{X}\\longrightarrow \\mathrm{Ker}d_{0}^{Y} \/ \\mathrm{Im}d_{1}^{Y}=\\mathrm{H}_0(Y).$$\nThe chain map $f$ is monic, then the induced map of modules $X_{0} \/ \\mathrm{Ker}d_{0}^{X}\\cong \\mathrm{Im}d_{0}^{X}\\longrightarrow \\mathrm{Im}d_{0}^{Y}\\cong Y_{0} \/ \\mathrm{Ker}d_{0}^{Y}$ is also monic. Hence, by the ``Five Lemma'' for the above diagram, we get that $\\Omega(f): X_{0} \/ \\mathrm{Im}d_{1}^{X}\\longrightarrow Y_{0} \/ \\mathrm{Im}d_{1}^{Y}$ is a monomorphism. We mention that it is also direct to check injectivity of $\\Omega(f)$ by diagram chasing.\n\\end{proof}\n\nFor model categories $\\mathcal{C}$ and $\\mathcal{D}$, recall that an adjunction $(F, G): \\mathcal{C}\\rightarrow \\mathcal{D}$ is a Quillen adjunction if $F$ is a left Quillen functor, or equivalently $G$ is a right Quillen functor. That is, $F$ preserves cofibrations and trivial cofibrations, or $G$ preserves fibrations and trivial fibrations.\n\n\n\\begin{proposition}\\label{prop 1}\n$(F, G): (\\mathrm{Ch}(R), \\mathcal{M}_{sing}^{ctr})\\rightarrow (\\mathrm{Ch}(R), \\mathcal{M}_{sing}^{co})$ is a Quillen adjunction.\n\\end{proposition}\n\n\\begin{proof}\nLet $X$, $Y$ be any $R$-complexes. It follows from \\cite[Lemma 3.1]{Gil04} that $(\\Omega, \\Lambda): \\mathrm{Ch}(R)\\rightarrow \\mathrm{Mod}(R)$\nand $(\\Lambda, \\Theta): \\mathrm{Mod}(R)\\rightarrow \\mathrm{Ch}(R)$ are adjunctions. Then we have the following natural isomorphisms:\n$\\mathrm{Hom}_{\\mathrm{Ch}(R)}(F(X), Y)\\cong \\mathrm{Hom}_{R}(\\Omega(X), \\Theta(Y))\\cong \\mathrm{Hom}_{\\mathrm{Ch}(R)}(X, G(Y))$.\nThis implies that $(F, G): \\mathrm{Ch}(R)\\rightarrow \\mathrm{Ch}(R)$ is an adjunction.\n\nIt suffices to show that $F$ preserves cofibration and trivial cofibration. Let $f: X\\rightarrow Y$ be a cofibration in $\\mathcal{M}_{sing}^{ctr}$, i.e. $f$ is a monomorphism with $\\mathrm{Coker}f \\in ex\\widetilde{\\mathcal{P}}$. This yields that $f$ is a quasi-isomorphism, and by Lemma \\ref{lem 2}, $\\Omega(f)$ is monic. Then, we have an exact sequence $$0\\longrightarrow F(X)\\stackrel{F(f)}\\longrightarrow F(Y)\\longrightarrow F(\\mathrm{Coker}f)\\longrightarrow 0.$$ Since every complex is a cofibrant object in $\\mathcal{M}_{sing}^{co}$, this implies that $F(f)$ is a cofibration.\n\nNow suppose $f: X\\rightarrow Y$ is a trivial cofibration in $\\mathcal{M}_{sing}^{ctr}$, i.e. $f$ is a monomorphism with $\\mathrm{Coker}f \\in ex\\widetilde{\\mathcal{P}}\\cap (ex\\widetilde{\\mathcal{P}})^{\\perp}= \\widetilde{\\mathcal{P}}$. Then we have an exact sequence $$0\\longrightarrow F(X)\\stackrel{F(f)}\\longrightarrow F(Y)\\longrightarrow F(\\mathrm{Coker}f)\\longrightarrow 0.$$\nNote that $\\Omega(\\mathrm{Coker}f)$ is a projective module. For any complex $I\\in ex\\widetilde{\\mathcal{I}}$, it is easy to show that any chain map $F(\\mathrm{Coker}f) = \\Lambda\\Omega(\\mathrm{Coker}f)\\rightarrow I$ is null homotopic, and then $F(\\mathrm{Coker}f)\\in {^{\\perp}}(ex\\widetilde{\\mathcal{I}})$. Thus $F(f)$ is a trivial cofibration in $\\mathcal{M}_{sing}^{co}$. This completes the proof.\n\\end{proof}\n\n\nRecall that a module $M$ is Gorenstein projective if $M$ is a syzygy of a totally acyclic complex of projective modules; and dually, Gorenstein injective modules are defined; see \\cite{EJ00}. We use $\\mathcal{GP}$ and $\\mathcal{GI}$ to denote the classes of Gorenstein projective and Gorenstein injective modules, respectively. It is widely accepted that over a left-Gorenstein ring, $(\\mathcal{GP}, \\mathcal{W})$ and $(\\mathcal{W}, \\mathcal{GI})$ are complete cotorsion pairs, where $\\mathcal{W}$ is the class of modules with finite projective (injective) dimension. In \\cite[Theorem 2.7]{Ren18} we show that the cotorsion pair $(\\mathcal{GP}, \\mathcal{W})$ is cogenerated by a set, i.e. there exists a set $S$ such that $\\mathcal{W} =\\{S\\}^{\\perp}$. This also implies the completeness of $(\\mathcal{GP}, \\mathcal{W})$, and generalizes the Gorenstein projective model structure of $\\mathrm{Mod}(R)$ in \\cite[Theorem 8.3 and 8.6]{Hov02} from Iwanaga-Gorenstein rings to left-Gorenstein rings.\n\n\\begin{lemma}\\label{lem 3}\nLet $X$, $Y$ be complexes in $ex\\widetilde{\\mathcal{P}}$, and $f: X\\rightarrow Y$ a chain map. If $F(f)$ is a weak equivalence in $\\mathcal{M}_{sing}^{co}$, then $f$ is a weak equivalence in $\\mathcal{M}_{sing}^{ctr}$.\n\\end{lemma}\n\n\\begin{proof}\nIn the model category $(\\mathrm{Ch}(R), \\mathcal{M}_{sing}^{ctr})$, we can factor $f: X\\rightarrow Y$ as a trivial cofibration $i: X\\rightarrow Z$ followed by a fibration $p: Z\\rightarrow Y$. By Proposition \\ref{prop 1}, $F(i)$ is a trivial cofibration in $\\mathcal{M}_{sing}^{co}$, and then $F(i)$ is a weak equivalence. Then $F(f) = F(p)F(i)$ is a weak equivalence if and only if so is $F(p)$.\n\nLet $L = \\mathrm{Coker}i$ and $K = \\mathrm{Ker}p$. It follows from the exact sequence $0\\longrightarrow X\\stackrel{i}\\longrightarrow Z\\longrightarrow L\\longrightarrow 0$ that $Z\\in ex\\widetilde{\\mathcal{P}}$, where $X\\in ex\\widetilde{\\mathcal{P}}$ and $L\\in ex\\widetilde{\\mathcal{P}}\\cap (ex\\widetilde{\\mathcal{P}})^{\\perp} = \\widetilde{\\mathcal{P}}$. Moreover, it follows from the exact sequence $0\\longrightarrow K\\longrightarrow Z\\stackrel{p}\\longrightarrow Y\\longrightarrow 0$ that $K\\in ex\\widetilde{\\mathcal{P}}$.\n\nLet $M$ be any Gorenstein injective module. Then there exists a totally acyclic complex of injective module, saying $I$, such that $M\\cong \\Theta(I)$. It follows from \\cite[Lemma 4.2]{Gil08} that there is an isomorphism\n$$\\mathrm{Ext}^{1}_{\\mathrm{Ch}(R)}(F(K), I) = \\mathrm{Ext}^{1}_{\\mathrm{Ch}(R)}(\\Lambda\\Omega(K), I)\\cong \\mathrm{Ext}^{1}_{R}(\\Omega(K), \\Theta(I)).$$\nSince $F(p)$ is a weak equivalence, $F(K) = \\mathrm{Ker}(F(p)) \\in {^{\\perp}(ex\\widetilde{\\mathcal{I}})}$. For any Gorenstein injective module $M$, it yields that $\\mathrm{Ext}^{1}_{R}(\\Omega(K), M) = 0$. Since $(\\mathcal{W}, \\mathcal{GI})$ is a cotorsion pair, we get that $\\Omega(K)$ is a module of finite projective dimension. Moreover, $\\Omega(K)$ is a Gorenstein projective module since $R$ is a left-Gorenstein ring and $K\\in ex\\widetilde{\\mathcal{P}}$ is a totally acyclic complex of projective modules. By \\cite[Proposition 10.2.3]{EJ00}, the projective dimension of any Gorenstein projective module is either zero or infinity, so $\\Omega(K)$ is a projective module. Considering exact sequences $0\\rightarrow \\mathrm{Ker}d_{i}^{K}\\rightarrow K_{i}\\rightarrow \\mathrm{Ker}d_{i-1}^{K}\\rightarrow 0$ inductively, we can prove each syzygy of $K$ is projective, that is, $K$ is a complex in $\\widetilde{\\mathcal{P}}$. This implies that $p: Z\\rightarrow Y$ is a trivial fibration, and hence $f = pi$ is a weak equivalence, as desired.\n\\end{proof}\n\n\\begin{lemma}\\label{lem 4}\nLet $Y$ be an exact complex of injective $R$-modules. Then $\\varepsilon: FG(Y)\\rightarrow Y$ is a weak equivalence in $\\mathcal{M}_{sing}^{co}$, where $\\varepsilon$ is the counit of the adjoint pair $(F, G)$.\n\\end{lemma}\n\n\\begin{proof}\nFor $Y$, $G(Y)=\\Lambda\\Theta(Y)= \\cdots\\rightarrow 0 \\rightarrow \\mathrm{Ker}d_0^Y\\rightarrow 0\\rightarrow\\cdots$ is a stalk complex with $\\mathrm{Ker}d_0^Y$ concentrated in degree zero. It is easy to see that $FG(Y) = G(Y)$. Then the map $\\varepsilon: FG(Y)\\rightarrow Y$ is given by a natural embedding $\\varepsilon_0: \\mathrm{Ker}d_0^Y\\rightarrow Y_0$ and $\\varepsilon_i =0$ for any $i\\neq 0$.\nLet $C= \\mathrm{Coker}\\varepsilon$. Then $C=\\cdots\\longrightarrow Y_2\\stackrel{d_2^Y}\\longrightarrow Y_1\\stackrel{0}\\longrightarrow \\mathrm{Im}d_0^Y\\stackrel{\\iota}\\longrightarrow Y_{-1}\\stackrel{d_{-1}^Y}\\longrightarrow Y_{-2}\\longrightarrow\\cdots$, where $\\iota$ is an embedding. Let $Y_{\\sqsupset}=\\cdots\\rightarrow Y_2\\stackrel{d_2^Y}\\rightarrow Y_1\\rightarrow 0$ be a hard truncation, $D=0\\rightarrow \\mathrm{Im}d_0^Y\\stackrel{\\iota}\\rightarrow Y_{-1}\\stackrel{d_{-1}^Y}\\rightarrow Y_{-2}\\rightarrow\\cdots$. Then there is an exact sequence of complexes $0\\longrightarrow Y_{\\sqsupset}\\longrightarrow C\\longrightarrow D\\longrightarrow 0$.\n\nLet $E$ be any $R$-complex in $ex\\widetilde{\\mathcal{I}}$. Since $R$ is left-Gorenstein, then $E$ is totally acyclic, and for any $Y_i$, $\\mathrm{Hom}_{R}(Y_i, E)$ is an exact complex. By \\cite[Lemma 2.4]{CFH06}, the complex $\\mathrm{Hom}_{R}(Y_{\\sqsupset}, E)$ is exact.\nNote that $D$ is an exact sequence, and then $\\mathrm{Hom}_{R}(D, E_i)$ is an exact complex for any $i\\in \\mathbb{Z}$. By \\cite[Lemma 2.5]{CFH06}, the complex $\\mathrm{Hom}_{R}(D, E)$ is exact. Moreover, it follows from the short exact sequence\n$$0\\longrightarrow \\mathrm{Hom}_{R}(D, E)\\longrightarrow \\mathrm{Hom}_{R}(C, E)\\longrightarrow \\mathrm{Hom}_{R}(Y_{\\sqsupset}D, E)\\longrightarrow 0$$\nthat the complex $\\mathrm{Hom}_{R}(C, E)$ is exact. This implies that every map from $C$ to any complex in $ex\\widetilde{\\mathcal{I}}$ is null homotopic. Then $C\\in {^{\\perp}}ex\\widetilde{\\mathcal{I}}$. Hence, $\\varepsilon: FG(Y)\\rightarrow Y$ is a trivial cofibration in $\\mathcal{M}_{sing}^{co}$, and moreover, $\\varepsilon$ is a weak equivalence.\n\\end{proof}\n\n\n\\begin{lemma}\\label{lem 5}\nLet $Y$ be an exact complex of injective $R$-modules. Then $F(q): FQG(Y)\\rightarrow FG(Y)$ is a weak equivalence in $\\mathcal{M}_{sing}^{co}$, where $q: QG(Y)\\rightarrow G(Y)$ is a cofibrant replacement in the model category $(\\mathrm{Ch}(R), \\mathcal{M}_{sing}^{ctr})$.\n\\end{lemma}\n\n\\begin{proof}\nFor $Y$, $G(Y) = FG(Y) = \\cdots\\rightarrow 0 \\rightarrow \\mathrm{Ker}d_0^Y\\rightarrow 0\\rightarrow\\cdots$. By the completeness of the cotorsion pair $(\\mathcal{GP}, \\mathcal{W})$, there is an exact sequence of $R$-modules $0\\rightarrow W\\rightarrow M\\rightarrow \\mathrm{Ker}d_0^Y\\rightarrow0$ with $M\\in \\mathcal{GP}$ and $W\\in \\mathcal{W}$. Consider the totally acyclic complex $P$ of $M$, we have a short exact sequence $0\\rightarrow K\\rightarrow P\\stackrel{q}\\rightarrow G(Y)\\rightarrow 0$, see the following diagram\n$$\\xymatrix@C=20pt@R=10pt{\nK=\\cdots \\ar[r] &P_{1}\\ar[dd]_{=}\\ar[r]^{} &K_{0}\\ar[dd]\\ar@{-->}[rd]^{\\pi}\\ar[rr]^{} & &P_{-1}\\ar[r]\\ar[dd]_{=}&P_{-2}\\ar[r]\\ar[dd]_{=}&\\cdots \\\\\n & & & W\\ar@{-->}[ur]\\ar@{-->}[dd]^{}\\\\\nP= \\cdots \\ar[r] &P_{1}\\ar[dd]_{}\\ar[r]^{} &P_{0}\\ar[dd]\\ar@{-->}[rd]^{}\\ar[rr]^{} & &P_{-1}\\ar[r]\\ar[dd]_{}&P_{-2}\\ar[r]\\ar[dd]_{}&\\cdots\\\\\n & & & M\\ar@{-->}[ur]\\ar@{-->}[dd]^{}\\\\\nG(Y) = \\cdots \\ar[r] &0\\ar[r]^{} &\\mathrm{Ker}d_0^Y\\ar@{==}[rd]\\ar[rr]^{} & &0\\ar[r]_{} &0\\ar[r]&\\cdots\\\\\n & & & \\mathrm{Ker}d_0^Y \\ar@{-->}[ur]\n }$$\n\nLet $K_{0\\supset}= \\cdots\\rightarrow P_2\\rightarrow P_1\\rightarrow \\mathrm{Ker}\\pi\\rightarrow 0$ and $K_{\\subset0}= 0\\rightarrow W\\rightarrow P_{-1}\\rightarrow P_{-2}\\rightarrow\\cdots$. Then there is a short exact sequence of complexes $0\\longrightarrow K_{0\\supset}\\longrightarrow K\\longrightarrow K_{\\subset0}\\longrightarrow 0.$\nLet $T$ be any complex in $ex\\widetilde{\\mathcal{P}}$. Note that $T$ is totally acyclic. Then it follows from\n\\cite[Lemma 2.5]{CFH06} that the complex $\\mathrm{Hom}_{R}(T, K_{\\subset0})$ is exact, and this implies that $K_{\\subset0}\\in (ex\\widetilde{\\mathcal{P}})^{\\perp}$. Note that $K_{0\\supset}$ is an exact complex. For any morphism\n$f: T\\rightarrow K_{0\\supset}$, we consider the following diagram:\n$$\\xymatrix@C=40pt{\n\\cdots \\ar[r] &T_{2}\\ar[d]_{f_2}\\ar[r]^{} &T_{1}\\ar[d]_{f_1}\\ar[r]^{}\\ar@{-->}[ld]_{s_1} &T_{0}\\ar[r]\\ar[d]_{f_{0}}\\ar@{-->}[ld]_{s_0}\n&T_{-1}\\ar[r]\\ar[d]^{}\\ar@{-->}[ld]_{s_{-1}} &\\cdots \\\\\n\\cdots \\ar[r] &P_{2}\\ar[r] &P_{1}\\ar[r]^{} &\\mathrm{Ker}\\pi \\ar[r]&0\\ar[r]&\\cdots\n }$$\nLet $s_i=0$ for any $i< 0$. Since $d_1^K: P_1\\rightarrow \\mathrm{Ker}\\pi$ is an epic and $T_0$ is a projective module, there is a map\n$s_0: T_0\\rightarrow P_1$ such that $f_0 = d_{1}^{K}s_0$. Since $d_{1}^{K}(f_{1} - s_{0}d_{1}^{T}) = d_{1}^{K}f_{1} - d_{1}^{K}s_{0}d_{1}^{T} = d_{1}^{K}f_{1} - f_{0}d_{1}^{T} = 0$, then $f_{1} - s_{0}d_{1}^{T}: T_1\\rightarrow \\mathrm{Ker}d_{1}^{K}$, and there exists a map $s_1: T_1\\rightarrow P_2$ such that $f_{1} - s_{0}d_{1}^{T} = d_{2}^{K}s_{1}$. Analogous to comparison theorem, we inductively get homotopy maps $\\{s_i\\}$ such that $f$ is null homotopic. Then $K_{0\\supset}\\in (ex\\widetilde{\\mathcal{P}})^{\\perp}$. Thus, we have $K\\in (ex\\widetilde{\\mathcal{P}})^{\\perp}$.\nNote that for any object in the model category $(\\mathrm{Ch}(R), \\mathcal{M}_{sing}^{ctr})$, its cofibrant replacement is precisely a special $ex\\widetilde{\\mathcal{P}}$-precover. Then it follows from the short exact sequence $0\\rightarrow K\\rightarrow P\\stackrel{q}\\rightarrow G(Y)\\rightarrow 0$ that $P$ is a cofibrant replacement of $G(Y)$, and we can set $QG(Y) = P$.\n\nNote that $F(K)= \\cdots \\rightarrow 0\\rightarrow W\\rightarrow 0\\rightarrow\\cdots$. Since $W$ is a module of finite projective dimension, for any complex $E\\in ex\\widetilde{\\mathcal{I}}$, $\\mathrm{Hom}_{R}(W, E)$ is exact. This implies that $F(K)\\in {^{\\perp}}(ex\\widetilde{\\mathcal{I}})$.\nFor $F(K)$, there is an exact sequence $0\\rightarrow F(K)\\rightarrow I\\rightarrow L\\rightarrow 0$ with $I\\in ex\\widetilde{\\mathcal{I}}$ and $L\\in {^{\\perp}}(ex\\widetilde{\\mathcal{I}})$. We consider the following push-out diagram:\n$$\\xymatrix@C=20pt@R=20pt{ & 0\\ar[d] & 0\\ar[d] \\\\\n0 \\ar[r]^{} &F(K) \\ar[d] \\ar[r] & F(P) \\ar@{-->}[d]_{i}\n \\ar[r]^{F(q)} &FG(Y) \\ar@{=}[d] \\ar[r] &0 \\\\\n0 \\ar[r] & I \\ar@{-->}[r] \\ar[d] & J \\ar[r]^{p} \\ar[d] & GF(Y) \\ar[r] & 0\\\\\n & L \\ar[d] \\ar@{=}[r] & L\\ar[d]\\\\\n & 0 & 0\n }$$\nIt is clear that $i$ is a trivial cofibration. By the left column, we have $I\\in {^{\\perp}}(ex\\widetilde{\\mathcal{I}})$. Then $I\\in ex\\widetilde{\\mathcal{I}}\\cap {^{\\perp}}(ex\\widetilde{\\mathcal{I}})$, and $p$ is a trivial fibration. Hence $F(q) = pi$ is a weak equivalence in $\\mathcal{M}_{sing}^{co}$.\n\\end{proof}\n\n\\subsection*{The proof of the theorem}\nIt follows from Proposition \\ref{prop 1} that $(F, G): (\\mathrm{Ch}(R), \\mathcal{M}_{sing}^{ctr})\\longrightarrow (\\mathrm{Ch}(R), \\mathcal{M}_{sing}^{co})$ is a Quillen adjunction.\nBy \\cite[Corollary 1.3.16]{Hov99}, there is a useful criterion for checking the given Quillen adjunction is a Quillen equivalence. Specifically, we need to show that $F$ reflects weak equivalences between cofibrant objects in $\\mathcal{M}_{sing}^{ctr}$ (i.e. complexes in $ex\\widetilde{\\mathcal{P}}$), see Lemma \\ref{lem 3}; moreover, for every fibrant object $Y$ in $\\mathcal{M}_{sing}^{co}$ (i.e. $Y\\in ex\\widetilde{\\mathcal{I}}$), we need to show that the composition $FQG(Y)\\stackrel{F(q)}\\rightarrow FG(Y)\\stackrel{\\varepsilon}\\rightarrow Y$ is a weak equivalence, where $\\varepsilon$ is the counit of the adjunction $(F, G)$, and $q: QG(Y)\\rightarrow G(Y)$ is a cofibrant replacement of $G(Y)$, see Lemma \\ref{lem 4} and \\ref{lem 5}.\n\n\n\\begin{ack*}\nThe author is supported by National Natural Science Foundation of China (11871125), Natural Science Foundation of Chongqing (cstc2018jcyjAX0541) and the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN201800509).\n\\end{ack*}\n\n\\bigskip\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec:introduction}\nIntermediate mass (IM) stars, namely stars whose mass is in the 2 to 8\nM$_\\odot$ range, are crucial in studies of star formation because they\nconstitute the link between low- and high-mass stars\n\\citep{DiF97,Man97,Man00}, and, therefore, can help to understand if\nand how much different are the processes at work in the two ends. On\nthe one hand, low mass stars are can be formed isolated or in loose\ngroups of few objects per cubic parsec \\citep{Gom93}, while high-mass\nstars are usually found to form in tight clusters\n\\citep[e.g.][]{Hil98}. IM stars, on the other hand, are also found in\nclusters \\citep[e.g.][]{Tes98,Ner07,Fue07}, with a smooth transition\ntowards the low mass star, loose cluster regime for star masses around\n3.5 M$_\\odot$ \\citep{Tes99}. \\citet{Tes99} also concluded that IM\nstars mark the transition from low density aggregates of $\\mathrel{\\hbox{\\rlap{\\hbox{\\lower4pt\\hbox{$\\sim$}}}\\hbox{$<$}}}$ 10\nstars per cubic parsec of T Tauri stars to dense clusters of $\\gtrsim$\n10$^3$ stars per cubic parsec associated with early-type stars. In\nagreement with the different observed environments, several authors\nhave proposed that high mass stars are formed by coalescence of lower\nmass stars, whereas other authors favor the ``monolithic''\nformation (see for example the recent review by \\citet{Beu07}). In\nthis context, the IM stars study can greatly help the debate. Indeed,\ndue to their intermediate position, the study of IM protostars will\nprovide crucial information on the transition between the two\nformation regimes as well as on the limits of the low mass and high\nmass formation scenarios. Finally, IM stars are among the dominant\nsources of the Inter-Stellar FUV field \\citep[e.g.][]{Hab68,Gon75},\nwhich regulates the phases of the ISM in the Galaxy, and, in turns,\nthe overall Galaxy star formation process and history. Despite the\nfar-reaching importance of IM stars, very little is known about the\nformation and first evolutionary stages of these stars. The situation\nis so bad that to date we do not have a satisfying sample of Class 0\nIM objects, namely objects representing the first phases of stellar\nformation, where the protostar is embedded in its envelope and its\nluminosity is dominated by the accretion luminosity, nor a systematic\nstudy of their physical structure, as it is the case for low mass\nClass 0 sources \\citep[e.g.][]{Cec07,Dif07}. This article is the\nfirst of a series that aims to fill this gap in our knowledge.\n\nIn this context, the Orion Molecular Cloud 2 (OMC2), the closest known\nregion where high to low mass star formation is going on, represents a\nprecious laboratory for these studies. Observed first by\n\\citet{Gat74}, OMC2 is located 15' ($\\sim$ 2 pc) North of the Orion\nnebula. It has a filamentary structure, elongated in the direction\nnorth-south, with active star formation concentrated in the central\nand densest region, shielded from the UV radiation from newly formed\nOB stars \\citep{Joh90}. The mass of the cloud amounts to about 1500\nM$_\\odot$ \\citep{Mez90}. Several extensive studies have shown that\nOMC2 is a rich star forming region, which harbors several young\nprotostars, including several Class 0 candidates\n\\citep{Ali95,Chi97,Lis98,Johnstone99,Rei99}. Observations of\nmolecular lines have revealed several outflows emanating from the\nyoung protostars in the region. Many studies have focused on the\noutflows \\citep[e.g.][]{Wil03} and their impact on the cloud\n\\citep{Aso00,Wu05}. Only few of these studies, in contrast, have\naddressed the problem of the chemical structure of the forming stars\nin OMC2 \\citep{Johnstone03}.\n\nAmong the several protostars in OMC2, FIR4 stands out as the brightest\nsubmillimeter source \\citep{Mez90}. Located almost at the center of\nthe cloud, FIR4 is also a bright IRAS source and a VLA radio source\n\\citep{Rei99}. All these characteristics led \\citet{Rei99} to define\nFIR4 ``a bona fide Class 0 source''. The FIR4 integrated luminosity\nwas estimated to be about 400 L$_\\odot$ and the envelope mass is about\n35 M$_\\odot$. Such values led to identify FIR4 as an {\\it intermediate\n mass protostar} \\citep{Johnstone03}. Because of its vicinity and its\nrelatively bright molecular lines, FIR4 is an ideal source for a\ndetailed study of the physical and chemical structure of an IM\nprotostar. Existing dust continuum and molecular line observations\npoint to an envelope with at least two components: a warm component\nwith an average temperature of about 40 K and a colder component at\nabout 15 K \\citep{Mez90,Johnstone03}. \\citet{Jor06} modeled the 850\n$\\mu$m SCUBA map towards this source to reconstruct its temperature\nand density profiles. Based on the observed CO and H$_2$CO millimeter\nline emission, \\citet{Jor06} concluded that the FIR4 envelope is\nilluminated by an external FUV field amounting to $1\\times10^{4}$\ntimes the Interstellar FUV field. However, their interpretation\nsuffers of some contradictions emphasized by the same authors. For\nexample, such an intense FUV field would heat up the whole envelope to\na temperature larger than 25 K, the CO freezing temperature \\citep{Obe05}, in contradiction with the measured average CO\nabundance, ten times lower than the canonical value, which would\nrather testify for a large CO-frozen region \\citep{Jor06}. In\naddition, the maps of the fine structure lines of the O and C$^+$\natoms together with the CO 1-0 line led \\citet{Her97} to conclude that\nthe OMC2 region is illuminated by a FUV field 500 times the\nInterstellar field.\n\nGiven this puzzling situation, we decided to derive again the\ntemperature and density profiles of FIR4 by taking into account more\ndata than those considered by \\citet{Jor06} (\\S\n\\ref{sec:dust-dens-temp}). Using the derived dust temperature and\ndensity profiles, we then computed the gas temperature profile, by\nequating the heating and cooling terms across the envelope (\\S\n\\ref{sec:gas-temp-prof}). As shown by several authors\n\\citep[e.g.][]{Cec96,Dot97}, the gas cooling in protostellar envelopes\nis dominated by the emission from the rotational lines of CO and, more\nimportant, H$_2$O together with the fine structure lines of\nOI. Actually, water is a key molecule in the gas thermal balance for\ntwo reasons. First, in the warm regions where the grain mantles\nsublimate, it is the most abundant molecule; second, given its\nrelatively large dipole moment, water is a very powerful line emitter\nand, consequently, gas coolant. Given its major role in the\nprediction of the gas temperature profile, we discuss the dependence\nof the derived gas temperature on the assumed water abundance profile,\nwhich is poorly known. Not surprising, FIR4 is in fact one of the few\nsources where the full spectrum between 50 and 2000 GHz is planned to\nbe observed at high spectral resolution with the Heterodyne Instrument\nfor the Far Infrared (HIFI) on board Herschel\n(http:\/\/herschel.esac.esa.int\/), to be launched in 2009. HSO, and\nspecifically the high resolution interferometer HIFI, will allow to\nobserve the water lines in the 500 to 2000 GHz range with\nunprecedented spectral and spatial resolution. Motivated by the\nHerschel mission, we report the predicted water line spectrum for the\ndifferent assumed water abundance profiles, and discuss the\nobservability by HIFI and PACS (\\S\n\\ref{sec:predicted-water-line}). Section \\ref{sec:conclusion}\nconcludes the article.\n\n\\section{Dust density and temperature profiles}\\label{sec:dust-dens-temp}\nIn this section, we derive the dust density and temperature profiles\nby modelling the 350, 450 and 850 $\\mu$m maps of the region, plus the\nSpectral Energy Distribution (SED) from the millimeter to the\nMid-Infrared (MIR) wavelength range. We first describe the\nobservations we used in our analysis (\\S \\ref{sec:cont-emiss-data})\nand then the modeling (\\S \\ref{sec:cont-emiss-dusty}) and the result\nof the modeling (\\S \\ref{sec:dust-results}).\n\n\\subsection{Continuum emission: observational data}\\label{sec:cont-emiss-data}\nIn our analysis, we use the maps of the continuum emission at 850, 450\nand 350 $\\mu m$ obtained at JCMT and CSO respectively. In addition, we\ntake into account the Spectral Energy Distribution (SED) of FIR4 from\n24 to 850 $\\mu$m obtained considering also the IRAS and Spitzer\nobservations.\n\n\\noindent\n{\\it a) 850, 450 and 350 $\\mu m$ maps}\\\\\nWe retrieved the 450 and 850 $\\mu$m maps obtained by \\citet{Johnstone99}\n at the 15 m James Clerk Maxwell Telescope (JCMT) with the\nfocal-plane instrument SCUBA (Submillimeter Common-User Bolometer\nArray). The spatial resolution of the maps is 7.5$\"$ and 14.8$\"$ at\n450 and 850 $\\mu$m respectively. The calibration uncertainty and\nnoise levels are estimated by those authors $\\mathrel{\\hbox{\\rlap{\\hbox{\\lower4pt\\hbox{$\\sim$}}}\\hbox{$<$}}}$ 10\\% and 0.04\nJy beam$^{-1}$ at 850 $\\mu$m and $\\mathrel{\\hbox{\\rlap{\\hbox{\\lower4pt\\hbox{$\\sim$}}}\\hbox{$<$}}}$ 30\\% and 0.3 Jy\nbeam$^{-1}$ at 450 $\\mu$m, respectively. The 350 $\\mu$m map was\nobtained by \\citet{Lis98} at the 10.4 m telescope of the Caltech\nSubmillimeter Observatory (CSO). The instrument used was the bolometer\ncamera SHARC. The resolution of the map is 12$\"$. The calibration\nuncertainty has been evaluated $\\sim$ 25\\%-30\\%. The three maps are\nreported in Fig. \\ref{maps}.\n\\begin{figure*} \\centering\n\\rotatebox{270}{\\includegraphics[width=10cm]{maps.ps}}\n\\caption{Continuum emission maps around OMC2-FIR4 at 850 $\\mu$m (left\n panel), 450 $\\mu$m (middle panel) and 350 $\\mu$m (right\n panel). The contours mark the continuum flux from 5 \\% to 75 \\% of\n the peak emission by steps of 5 \\%. The hatched regions have been\n excluded when computing the brightness profile of the FIR4 envelope\n (see text). The position of the three protostars in the regions,\n FIR3, FIR4 and FIR 5 are marked in the central panel\n figure. \\label{maps}}\n\\end{figure*}\nThey show the envelope surrounding\/forming FIR4 which extends for\nabout 20$\"$, but also the presence of two sources: FIR3, 25$\"$ North,\nand FIR5, 25$\"$ South. To evaluate the continuum brightness profile of\nthe FIR4 envelope, we averaged the continuum flux over ring at the\nsame distance from the FIR4 center, excluding the regions contaminated\nby the presence of FIR3 and FIR5 (dashed regions in\nFig. \\ref{maps}). The resulting brightness profiles are shown in\nFig. \\ref{fit}. Note that in the analysis of the envelope emission (\\S\n\\ref{sec:cont-emiss-dusty}) we subtracted the cloud contribution,\nestimated to be $\\sim$ 0.001, $\\sim$ 0.03 and $\\sim$ 0.05 Jy\narcsec$^{-2}$ at 850, 450 and 350 $\\mu$m respectively. Furthermore, in\norder to take into account that the SCUBA and SHARC maps were obtained\nwith the chop throw of 65$\"$ and 90-120$\"$ respectively, we only\nconsidered the inner 60$\"$ in our analysis.\n\n\\noindent\n{\\it b) SED}\\\\\nThe SED points at 850, 450 and 350 $\\mu$m, shown in Fig. \\ref{fit},\nwere obtained integrating the continuum emission over the envelope. We\nattributed an uncertainty of $\\sim$ 30 \\% to them to account for the\nuncertainty in the envelope size. We also considered the IRAS fluxes\nat 60 and 100 $\\mu$m, respectively, extracted from the IRAS maps at\nthese wavelengths. The evaluation of the fluxes was done using the\nmethod previously employed for the maps at 850, 450 and 350 $\\mu$m,\nnamely excluding the same regions (dashed regions in Fig. \\ref{maps})\nto limit the contamination by FIR3 and FIR5 and integrating over the\nrings. We also subtracted the cloud contribution, estimated to be\n$\\sim$ 0.06 and $\\sim$ 0.07 Jy arcsec$^{-2}$ at 60 and 100 $\\mu m$\nrespectively. To account for the possible contamination of FIR3 and\nFIR5 due to the large beam of IRAS and the non-sphericity of the\nsource, we took an uncertainty of 50 \\% on the fluxes. Finally, we\nalso considered the integrated flux at 24 $\\mu$m extracted from the\nSpitzer Space Telescope's Multiband Imaging Photometer (MIPS) maps. To\nthis end, we retrieved the observations from the Spitzer Science\narchive ({\\it http:\/\/ssc.spitzer.caltech.edu\/archanaly\/}). The\nobservations were obtained the 6th October 2006 as part of the Program\n``Infrared Properties of Edge-on Young Stellar Object Disks'' (AOR:\n30765, PI: Karl Stapelfeldt). The data reduction was performed using\nthe pipeline S16.0.1. The flux, ($5.0\\pm2.5$ Jy), in Fig. \\ref{fit}\nwas obtained by integration over a 15$\"$ radius.\n\n\\subsection{Continuum emission: modeling}\\label{sec:cont-emiss-dusty}\nTo derive the dust physical structure, namely the dust temperature and\ndensity profiles, we used the 1D radiative transfer code DUSTY\n\\citep{Ive97}. Briefly, giving as input the temperature of the\ncentral object and a dust density profile, DUSTY computes\nself-consistently the dust temperature profile and the dust\nemission. The comparison between the computed 350, 450, 850 $\\mu$m\nbrightness profiles (namely the brightness versus the distance from\nthe center of the envelope) and SED with the observed profiles and SED\n(see previous paragraph) makes it possible to constrain the density\nprofile and, consequently, the temperature profile of the envelope.\n\nTo be compared with the observations, the theoretical emission is\nconvolved with the beam pattern of the telescope. Following the\nrecommendations for the relevant telescope, the beam is assumed to be\na combination of gaussian curves: at 850 $\\mu$m, we use HPBWs of\n14.5$\"$, 60$\"$, and 120$\"$, with amplitudes of 0.976, 0.022, and 0.002\nrespectively; at 450 $\\mu$m, the HPBWs are 8$\"$, 30$\"$, and 120$\"$\nwith amplitude ratios of 0.934, 0.06, and 0.006, respectively\n\\citep{San01}; at 350 $\\mu$m, we use HPBWs of 12$\"$ and 22$\"$, with\namplitude ratios of 0.7, 0.3, respectively \\citep{Hun96}.\n\nWe assumed that the envelope density follows a power law:\n\\begin{equation} \\label{density_power_law}\n n(r)=n_0 \\times \\left( \\frac{r_0}{r} \\right)^\\alpha\n\\end{equation}\nwhere the power law index, $\\alpha$, is a free parameter of the model,\nas well as the density $n_0$, the density at $r_0$. Besides, the\nenvelope starts at a radius R$_{in}$ and extends up to R$_{out}$. Both\nR$_{in}$ and R$_{out}$ are free parameters of the model. The last\ninput to DUSTY is the temperature of the central source, T$_*$, here\nassumed to be 5000 K. We verified that the choice of this last\nparameter does not influence the results. Finally, the opacity of the\ndust as function of the wavelength is another parameter of\nDUSTY. Following numerous previous studies\n\\citep{Van99,Eva01,Shi02,You03}, we adopted the dust opacity\ncalculated by \\citet{Oss94}, specifically their OH5 dust model, which\nrefers to grains coated by ice.\n\nIn summary, the output of DUSTY depends on $\\alpha$, $n_0$, R$_{in}$\nand R$_{out}$. In practice, the DUSTY input parameters are the power\nlaw index, $\\alpha$, the optical thickness at 100 $\\mu$m,\n$\\tau_{100}$, the ratio between the inner and outer radius, Y\n(=R$_{out}$\/R$_{in}$) and the temperature at the inner radius\nT$_{in}$. The optical thickness is, in turn, proportional to the dust\ncolumn density which depends on $n_0$ and the physical thickness of\nthe envelope. Note that, since the beam of the available maps are\nrelatively large ($\\geq 7.5\"$ which corresponds to a linear length of\n$\\geq 3300$ AU), the inner region of the envelope is relatively\nunconstrained by the available observational data. In practice, we\nobtain a lower limit to T$_{in}$ of 300 K: any larger value would give\nsimilar results. Finally, as explained in \\citet{Ive97}, DUSTY gives\nscaleless results (which makes it very powerful because the same grid\nof models can be applied to different sources). This means that to\ncompare the DUSTY output with actual observations, it is necessary to\nscale the output by the source bolometric luminosity L$_{bol}$ and the\ndistance. Note that the bolometric luminosity is in fact estimated by\nintegrating the emission over the full spectrum. By definition, this\ncan only be done when the entire SED is known. This is exactly one of\nthe outputs of the modeling. So we re-evaluated the luminosity of FIR4\niteratively from the best fit model, by minimizing the\n$\\chi^2$\\footnote{Note that, in the case of OMC2-FIR4, integrating the\n model SED gives the same results than integrating under the observed\n SED.}. We anticipate here that the new value is 1000 rather than 400\nL$_\\odot$, where we assumed the most recent estimation of the\ndistance, namely\n($437\\pm19$) pc \\citep{Hir07}.\\\\\n\nWe run a grid of models to cover the parameter space as reported in\nTable \\ref{DUSTY_input}. The same grid of models were run for four\nvalues of the illuminating FUV field : G$_0$ = 1, 10, 100 and 1000.\nIn all cases, we used the Inter-Stellar Radiation Field (ISRF)\nconstructed by \\citet{Eva01} : combination of the radiation field\nintroduced by \\citet{Bla94} with that of \\citet{Dra78}. Note that,\nsince DUSTY makes the assumption of isotropic scattering, the computed\nMIR emission is largely overestimated in presence of strong\nexternal fields (Elitzur, private communication). To solve this\nproblem, we followed the suggestion by \\citet{You05} to neglect the\nscattering, artificially putting it to zero.\n\\begin{table}[h] \\centering\n\\begin{tabular}{|ll|} \\hline \\hline \nParameter & Range \\\\ \\hline\n$\\alpha$ & 0.2-3.9 \\\\ \nY & 100-2200 \\\\ \n$\\tau_{100}$ & 0.1-4.6 \\\\\nT$_{in}$ & 300 K \\\\\nT$_*$ & 5000 K \\\\ \\hline \n\\end{tabular}\n\\caption{Range of the input parameters to DUSTY covered in the present\n study. The range of the $\\alpha$, Y and $\\tau_{100}$ parameters\n is covered by increasing by 20\\% their respective value at each\n step of the grid. Note that T$_{in}$ and T$_{_*}$ are kept fixed\n as they do not influence the results (see text). \\label{DUSTY_input}}\n\\end{table}\nThe best fit model has been found minimizing the $\\chi^2$ with an\niterated two-steps procedure. First, we use the observed brightness\nprofiles at 350, 450 and 850 $\\mu m$ to constrain $\\alpha$ and Y,\nassuming a value for $\\tau_{100}$. Second, we constrain the \noptical thickness $\\tau_{100}$ by comparing the computed and\nobserved SED, assuming the $\\alpha$ and Y of the previous step. The\nnew $\\tau_{100}$ is used for a new iteration and so on. In practice,\nthe iteration converges in two steps. This is because the normalized\nbrightness profiles very weakly depend on $\\tau_{100}$, while they\nvery much depend on the sizes of the envelope and on the slope of the\ndensity profile (see also \\citet{Jor02} and \\citet{Sch02}). On the\ncontrary, the optical thickness depends mostly on the\nabsolute column density of the envelope, constrained by the SED.\n\n\n\\subsection{Results}\\label{sec:dust-results} \n We run four grids of models, as discussed separately below: a)\n with a standard illumination FUV field (G$_o$=1) and b) with a\n 10,100,1000 times enhanced field (G$_o$=10,100,1000) (see\n Introduction). In paragraph c), we also discuss why larger G$_o$\n were not considered, and in paragraph d) we summarize the results.\\\\\n\n\\noindent\n{\\it a) G$_o$=1}\\\\\nTable \\ref{best_fit_no_ISRF} presents the set of parameters $\\alpha$,\nY and $\\tau_{100}$, which better reproduce the observations \nassuming G$_o$=1. Figure \\ref{fit} shows the relevant derived\nbrightness profiles and SED against the observed ones. Figure\n\\ref{fig:chi2-contour} shows the $\\chi^2$ contours plots obtained by\nconsidering separately the brightness profiles at 350, 450 and 850\n$\\mu m$, and by combining the three profiles together. Figure\n\\ref{X2SED} shows the $\\chi^2$ dependence on the $\\tau_{100}$\nparameter.\n\\begin{table}[h] \\centering\n\\begin{tabular}{l|ccc|cc} \\hline \\hline \nObservation & $\\alpha$ & Y & $\\tau_{100}$ & $\\chi _{red}^{2}$ & $\\nu$\\\\ \\hline\n850 $\\mu$m profile & 1.4 & 160 & - & 0.72 & 10 \\\\\n450 $\\mu$m profile & 0.6 & 120 & - & 0.63 & 10 \\\\\n350 $\\mu$m profile & 0.5 & 170 & - & 0.47 & 10 \\\\ \nAll profiles & 0.6 & 120 & - & 1.24 & 36 \\\\ \nSED & - & - & 0.6 & 0.55 & 3 \\\\ \\hline\n\\end{tabular}\n\\caption{Best fit parameters for the case G$_o$=1. Note that $\\chi\n _{red}^{2} = \\chi ^{2}\/\\nu$ where $\\nu$ is the number of degrees of\n freedom. The first line reports the best fit obtained using only the\n 850 $\\mu$m brightness profile; second line, using the 450 $\\mu$m\n brightness profile; third line, using the 350 $\\mu$m brightness\n profile; fourth line gives the best fit using the three profiles;\n the last line gives the best fit using the\n SED.\\label{best_fit_no_ISRF}}\n\\end{table}\n\\begin{figure*} \\centering\n\\rotatebox{90}{\\includegraphics[width=12cm]{FIR4_fit.ps}}\n\\caption{Observed brightness profiles at 350 (upper left panel), 450\n (upper right panel), 850 $\\mu$m (lower left panel) and SED (lower\n right panel). The curves report the best fit obtained in the two\n cases G$_o$=1 (solid line) and 1000 (dashed line). The dashed-dotted lines represent the beam pattern of the telescope adopted at 350 450 and 850 $\\mu$m. Note that\n the SED plot reports the ISO-LWS spectrum between 45 and 200 $\\mu\n m$ for completeness, although it has not been considered in the\n $\\chi^2$ analysis, due to the relative larger calibration\n uncertainty compared to the IRAS data.}\\label{fit}\n\\end{figure*}\n\\begin{figure*} \\centering\n\\centerline{\\rotatebox{90}{\\includegraphics[width=12cm]{X2_map_G1.ps}}}\n\\caption{$\\chi ^{2}_{red}$ contour plots (Y,$\\alpha$) for the models\n with G$_{o}$=1. In these computations, $\\tau_{100}$ is equal to\n 0.6. The contours show the loci of the $\\chi ^{2}_{red}$ values\n equal to 1.1, 1.5, 2.5 and 5 times the minimum $\\chi ^{2}_{red}$.\n The upper left panel is obtained by comparing the model predictions\n with the 850 $\\mu$m brightness profile; the upper right panel refers\n to the 450 $\\mu$m profile; the lower left panel refers to the 350\n $\\mu$m profile; the lower right panel makes use of the three\n profiles. \\label{fig:chi2-contour}}\n\\end{figure*}\n\\begin{figure} \\centering\n\\centerline{\\rotatebox{0}{\\includegraphics[width=10cm]{X2_SED_G1.ps}}}\n\\caption{$\\chi^2_{red}$ versus $\\tau_{100}$. In these computations, Y\n is equal to 120 and $\\alpha$ is equal to 0.6. \\label{X2SED}}\n\\end{figure}\n\nThe three $\\chi _{350}^{2}$, $\\chi _{450}^{2}$ and $\\chi _{850}^{2}$\ncontour plots point to a value of Y around 100-200. Conversely, the\n$\\chi _{350}^{2}$ and $\\chi _{450}^{2}$ contour plots constraint\n$\\alpha$ to a lower value than 1, around 0.5-0.6, whereas the $\\chi\n_{850}^{2}$ would rather indicate a larger value for $\\alpha$,\nalthough the value 0.6 is still acceptable. Note that the solution\nfound by \\citet{Jor06} relies on the 850 $\\mu$m profile only, and,\ntherefore, gives a large $\\alpha$ value, consistent with our\n $\\chi _{850}^{2}$ plot. The $\\chi _{SED}^{2}$ plot\n(Fig.\\ref{X2SED}) points to a value of $\\tau_{100}$ of 0.6. In\n minimizing the $\\chi _{SED}^{2}$, we varied the source luminosity\n from 400 to 1500 L$_\\odot$. The best fit is\n obtained for a source luminosity equal to 1000 L$_\\odot$.\\\\\n\n\\noindent\n{\\it b) G$_o$=10,100,1000}\\\\\nThe best fit values of $\\alpha$ and Y for cases of an enhanced\nillumination UV field are presented in Figure \\ref{X2all_ISRF}.\n\\begin{figure*} \\centering\n\\rotatebox{90}{\\includegraphics[width=6cm]{cont_all_850um.ps}}\n\\rotatebox{90}{\\includegraphics[width=6cm]{cont_all_450um.ps}}\n\\rotatebox{90}{\\includegraphics[width=6cm]{cont_all_350um.ps}}\n\\rotatebox{90}{\\includegraphics[width=6cm]{cont_all_tot.ps}}\n\\caption{$\\chi ^{2}_{red}$ contour plots (Y,$\\alpha$) for the models\n with G$_{o}$=1 (black lines), 10 (blue lines), 100 (green lines) and\n 1000 (red lines). In these computations, $\\tau_{100}$ is equal to\n 0.6. The contours show the loci of the $\\chi ^{2}_{red}$ values\n equal to 2.5 times the minimum $\\chi ^{2}_{red}$. From top to\n bottom: $\\chi^2$ contours of the 850 $\\mu$m, 450 $\\mu$m, 350 $\\mu$m\n and the three together.\\label{X2all_ISRF}}\n\\end{figure*}\nThe first thing to notice is that the $\\chi _{all}^{2}$ does not\nchange appreciably for G$_o$ equal to 1,10,100 or 1000: the minimum\n$\\chi _{all_{red}}^{2}$ value is 1.24, 1.23, 1.19 and 1.20 for\nG$_o$=1, 10, 100 and 1000 respectively. In other words, the available\ncontinuum observations, both the profiles and the SED, cannot\ndistinguish which of the four models is better. Furthermore, \nFigure \\ref{X2all_ISRF} shows that the $\\chi _{all}^{2}$ contour\nplots point to the same Y and $\\alpha$ values. Similarly, the\n$\\tau_{100}$ value is 0.6 for the four cases G$_o$=1, 10, 100 and\n1000. The situation is illustrated in Fig. \\ref{fit}, where the best\nfit predictions are compared to the observations for the two cases\nG$_o$=1 and 1000. Both models reproduce fairly well the observations,\nas implicit in the best-$\\chi^2$ similar values. Note, however, that\nthe G$_o$=1000 case predicts slightly larger fluxes, due to the\nenhanced temperature at the border of the envelope.\\\\\n\n\\noindent\n{\\it c) larger G$_o$ }\\\\\n We did not explore in detail the case of larger G$_0$ for three\n reasons. The first one is that previous line observations showed\n that the FUV field in the OMC2 region is ``only'' 500 times the\n Interstellar field. Indeed, \\cite{Her97} mapped the OMC-2 cloud in\n the CII-157 $\\mu$m, OI-63 and -146 $\\mu$m lines with the\n spectrometer FIFI on board the Kuiper Airborne Observatory. They\n detected extended emission associated with the Photo-Dissociation\n Region (PDR) enveloping the whole OMC-2 molecular cloud. These\n authors concluded that OMC-2 is illuminated by a FUV field whose\n intensity is G$_0$ $\\sim$ 500. Note that this is the FUV field\n impinging on the cloud and that the effective G$_0$ seen by the FIR4\n envelope is probably lower than this. The second reason is that\nvarying G$_0$ from 1 to 10$^3$ does not improve the $\\chi^2$\nvalue. The third reason is that the G$_0$=10$^4$ case suffers of\nsevere convergence problems, and it was not possible to derive a large\nenough number of runs for a meaningful $\\chi^2$\nanalysis.\\\\\n\n\\noindent\n{\\it d) Summary of the adopted solution}\\\\\n Table \\ref{best_fit_phy_param} summarizes the value of the best\n fit parameters, obtained by considering all the profiles and the SED\n $\\chi^2$ contour plots and assuming the G$_0$=1 case. Some relevant\n physical quantities are quoted in the same table. Fig. \\ref{T_n.ps}\n shows the dust density and temperature profiles of the best fit\n models with G$_0$=1 and 1000 respectively. Note that the dust\n temperature in the skin of the envelope is larger by $\\sim$ 20-30 K\n in the case G$_0$=1000 with respect to the G$_0$=1 case. This\n increase concerns a relatively small region, of a few thousand\n AU. \\citet{Jor06} found a larger warm region, of about\n 10$^4$ AU, because of the steeper adopted density distribution\n ($\\alpha$=2): in this case, the FUV photons can penetrate deeper\n into the envelope.\n\\begin{table}\n $$\n \\begin{array}{p{0.5\\linewidth}r}\n \\hline\n \\multicolumn{2}{c}{\\mathrm{Fixed\\ input\\ parameters}} \\\\\n \\hline\n \\noalign{\\smallskip}\n Distance, $d$\t & 437\\,\\mathrm{pc}\t \\\\\n Stellar temperature, $T_{\\star}$ & 5000\\,\\mathrm{K}\\\\\n Dust temperature at $r_{\\mathrm{i}}$, $T_{\\mathrm{d}}(r_{\\mathrm{i}})$ & 300\\,\\mathrm{K}\\\\\n Dust opacity (OH5) at 100\\,$\\mu$m, $\\kappa_{100}$ & 86.5\\,\\mathrm{cm}^2\\mathrm{g}^{-1} \\\\\n \\hline\n \\noalign{\\smallskip}\n \\multicolumn{2}{c}{\\mathrm{Best\\ fit\\ parameters}} \\\\\n \\hline\n \\noalign{\\smallskip}\n Luminosity, $L$ & 1000\\,\\mathrm{L}_{\\sun}\\\\\n Dust optical depth at 100\\,$\\mu$m, $\\tau_{100}$ & 0.6 \\\\\n Density power law index, $\\alpha$ & 0.6 \\\\\n Envelope thickness, $r_{\\mathrm{out}}$\/$r_{\\mathrm{i}}$ & 120 \\\\\n \\hline\n \\noalign{\\smallskip}\n \\multicolumn{2}{c}{\\mathrm{Physical\\ quantities}} \\\\\n \\hline\n \\noalign{\\smallskip}\n Inner envelope radius, $r_{\\mathrm{in}}$ \t & 100\\,\\mathrm{AU}\\\\\n Outer envelope radius, $r_{\\mathrm{out}}$ \t & 12000\\,\\mathrm{AU}\\\\\n Radius at T$_{dust}$ = 100 K, $r_{\\mathrm{100K}}$ & 440\\,\\mathrm{AU}\\\\\n H$_2$ density at $r_{\\mathrm{100K}}$, $n_0$ \t & 4.3\\times 10^{6}\\,\\mathrm{cm}^{-3} \\\\\n Envelope mass, $M_{\\mathrm{env}}$\t\t & 30\\,\\mathrm{M}_{\\sun} \\\\\n \\hline\n \\noalign{\\smallskip}\n \\end{array}\n $$\n \\caption[]{ Summary of the dust radiative transfer analysis of\n OMC2-FIR4. The first part lists the fixed input parameters, \n the second part reports the best fit parameters, and some relevant \n physical quantities corresponding to the\n best fit model are reported in the third part.}\n \\label{best_fit_phy_param}\t \n\\end{table}\n\\begin{figure} \\centering\n\\rotatebox{0}{\\includegraphics[width=9cm]{T.ps}}\n\\rotatebox{0}{\\includegraphics[width=9cm]{n.ps}}\n\\caption{Dust temperature (upper panel) and H$_{2}$ density (lower panel) \nprofiles from the best fit obtained in the two cases G$_0$=1 and 1000. The \nplain line and the dotted line represent the cases G$_0$=1 and 1000 \nrespectively.}\\label{T_n.ps}\n\\end{figure}\n\n\n\\section{Gas temperature profile}\\label{sec:gas-temp-prof}\n\n\\subsection{Model description}\n\\citet{Cec96}, \\citet{Dot97} and \\citet{Mar02} showed that the gas is\nthermally decoupled from dust in the inner regions of low and high\nmass protostellar envelopes. The reason for that is the large water\nabundance in the gas phase caused by the sublimation of the grain\nmantles. The same phenomenon may occur in the envelopes of\nintermediate mass protostars, so we explicitly computed the gas\ntemperature profile of the envelope surrounding FIR4. For that we\nexplicitly computed the equilibrium temperature by equating the gas\ncooling and heating terms at each radius. Following the method\ndescribed in \\citet{Cec96}, we considered heating from the gas\ncompression (due to the collapse), dust-gas collisions and\nphoto-pumping of H$_2$O and CO molecules by the IR photons emitted by\nthe warm dust close to the center\\footnote{Cosmic rays ionization is a\n minor heating term in the protostellar envelopes.}. The cooling is\nmainly due to rotational lines from H$_2$O and CO, plus the fine\nstructure lines from O. Therefore, the gas temperature depends on the\nabundance of these three species. In practice, though, only the water\nabundance is a real parameter of the model, because the CO and O lines\nare optically thick in the range of the CO and O abundances typical of\nprotostellar envelopes. For this reason, we computed various cases for\nthe water abundance, as it is generally poorly constrained in\nprotostellar envelopes, and totally unconstrained in FIR4 (see \\S\n\\ref{sec:predicted-water-line}). We adopted a step function for the\nwater abundance profile to simulate the jump caused by the ices\nsublimation. The jump is assumed to occur at 100 K. We considered the\nH$_2$O abundance (with respect to H$_2$) X(H$_2$O)$_{out}$ in the\nouter envelope, where T$\\leq$ 100 K, equal to 10$^{-7}$, 10$^{-8}$ and\n10$^{-9}$. We also considered three cases for the abundance in the\ninner region X(H$_2$O)$_{in}$, 10$^{-4}$, 10$^{-5}$ and\n10$^{-6}$. Finally, we studied the case with G$_0$ = 1000. The run\nparameters are summarized in Table \\ref{tab:abu-cool}.\n\\begin{table}[tbh]\n \\centering\n \\begin{tabular}{|cccccc|}\n \\hline\n Model & X(CO) & X(O) & X(H$_2$O)$_{out}$& X(H$_2$O)$_{in}$ & G$_0$\\\\ \\hline\n 1$^a$ & $1\\times10^{-4}$ & $5\\times10^{-4}$ & $1\\times10^{-8}$ & $1\\times10^{-5}$ & 1\\\\\n 2 & $1\\times10^{-4}$ & $5\\times10^{-4}$ & $1\\times10^{-8}$ & $1\\times10^{-6}$ & 1\\\\\n 3 & $1\\times10^{-4}$ & $5\\times10^{-4}$ & $1\\times10^{-8}$ & $1\\times10^{-4}$ & 1\\\\\n 4 & $1\\times10^{-4}$ & $5\\times10^{-4}$ & $1\\times10^{-9}$ & $1\\times10^{-5}$ & 1\\\\\n 5 & $1\\times10^{-4}$ & $5\\times10^{-4}$ & $1\\times10^{-7}$ & $1\\times10^{-5}$ & 1\\\\\n 6 & $1\\times10^{-4}$ & $5\\times10^{-4}$ & $1\\times10^{-7}$ & $1\\times10^{-4}$ & 1\\\\\n 7 & $1\\times10^{-4}$ & $5\\times10^{-4}$ & $1\\times10^{-8}$ & $1\\times10^{-5}$ & 1000\\\\ \\hline\n \\end{tabular}\n \\caption{The different run models (column 1). Column 2 to 5 report \n the adopted abundances of the main gas coolants: X(CO), X(O) and the \n H$_2$O abundance in the T$\\geq$100 K region X(H$_2$O)$_{in}$ and outer \n region X(H$_2$O)$_{out}$. Column 5 reports the FUV illuminating \n field G$_0$. Note: $^a$ Model 1 is the reference for the studies of\n the water line spectrum presented in \\S \\ref{sec:predicted-water-line}. \n \\label{tab:abu-cool}}\n\\end{table}\n\nTo compute the cooling from the lines we used the code described in\n\\citet{Cec96,Cec03} and \\citet{Par05}. The same code has been used in\nseveral past studies, whose results have been substantially confirmed\nby other groups (e.g. the analysis on IRAS16293-2422 by\n\\citep{Sch02}). Briefly, the code is based on the escape probability\nformalism in presence of warm dust (see \\citet{Tak83}), where the\nescape probability $\\beta$ is computed at each point by integrating\nthe line and dust absorption over the solid angle $\\Omega$ as follows:\n\\begin{equation} \n \\beta = \\frac{k_\\mathrm{d}}{k_\\mathrm{L} + k_\\mathrm{d}} + \n \\frac{k_\\mathrm{L}}{(k_\\mathrm{L} + k_\\mathrm{d})^2} \\int d\\mu \n \\frac{1-\\exp \\left[ - \\left( k_\\mathrm{L} + k_\\mathrm{d} \\right) \n \\Delta L_\\mathrm{th} \\right]} {\\Delta L_\\mathrm{th}} \n\\end{equation} \n\\noindent \nwhere $k_\\mathrm{L}$ and $k_\\mathrm{d}$ are the line and dust\nabsorption coefficients respectively, and $\\Delta L_\\mathrm{th}$ is\nthe line trapping region, given by the following expressions:\n\\begin{equation} \n \\Delta L_\\mathrm{th} = 2 \\Delta v_\\mathrm{th} \n \\left( \\frac{v}{r} \\left| 1-\\frac{3}{2} \\mu^2 \\right| \\right)^{-1} \n\\end{equation} \n\\noindent\nin the infalling region of the envelope (where $\\mathrm{arcos} \\left(\n\\mu \\right)$ is the angle with the radial outward direction) and\n\\begin{equation} \n \\Delta L_\\mathrm{th} = r \\left( 1 - \\frac{r}{R_\\mathrm{env}} \\right) \n\\end{equation} \n\\noindent \nin the static region (where $R_\\mathrm{env}$ is the envelope radius).\n In the present calculations, we assumed that the entire envelope is\ncollapsing in free-fall towards a central object of 2 M$_{\\sun}$. In\npractice, the photons emitted by the dust can be absorbed by the gas\nand can pump the levels of the water molecules. This, indeed, is an\nimportant factor in the population of the water levels, and, for the\nhighest energy levels, even the dominant one (\\S\n\\ref{sec:predicted-water-line}). In addition, H$_2$O and CO\n molecules can be pumped by absorption of the NIR photons emitted by\n the innermost warm dust. Since the densities and temperatures of the\n regions of the envelope targeted by this study are not enough to\n populate the levels at the vibrational states, the effect of the NIR\n photons is an extra heating of the gas, as described in the\n Ceccarelli et al. (1996) article. Note that the code takes into\n account the dust with temperatures up to 1500 K, by following the\n algorithm described in Ceccarelli et al. (1996).\n\nFor the collisional coefficients of water with hydrogen molecules, we\nused the data by \\citet{Fau07} available for the temperature range\n20-2000K. This data set includes quasi-classical results for the\nhighest rates (those larger than 10$^{-12}$ cm$^3$s$^{-1}$) and quantum\nscaled H$_2$O-He results for the lowest rates. Recent quantum\ncalculations on ortho-H$_2$O by Dubernet and co-workers have shown\nthat the quasi-classical rates can be in error by as much as a factor\nof 100 but that, in general, they are accurate to within a factor of\n1-3 \\citep{Dub09}. It should be noted that\nthe rates of \\citet{Fau07} are currently the only complete and\nconsistent set of data for both ortho- and para-H$_2$O colliding with\nH$_2$. We also note that these rates have been recently extrapolated\nin order to cover energy levels and temperatures up to 5000K\n\\citep{Fau08}. Since the ortho to para conversion process of H$_2$ is\nchemical rather than radiative, the ortho-to-para ratio H$_2$ OPR is\nhighly uncertain in the interstellar medium. Here we assume that in\nwarmer gas it is in Local Thermal Equilibrium and, therefore, follows\nthe Boltzmann distribution:\n\\begin{equation}\nOPR = \\frac{(2I_o+1) \\Sigma (2J+1) \\exp(-\\frac{E_o(J)}{kT})}\n {(2I_p+1) \\Sigma (2J+1) \\exp(-\\frac{E_p(J)}{kT})}\n\\end{equation}\nwhere $I_o$ and $I_p$ are the total nuclear spin, corresponding to\nwhether the hydrogen nuclear spins are parallel ($I_o = 1$,\n$\\uparrow\\uparrow$) or anti-parallel ($I_p = 0$,\n$\\uparrow\\downarrow$). The sum in the numerator and denominator\nextends over all ortho and para levels J, respectively. Similarly to\nH$_2$, water comes in the ortho and para forms. In this case, since\nthe water is the dominant gas coolant only in the regions where the\ndust temperature exceeds 100 K, we assumed OPR equal to 3, strictly\nvalid for gas temperatures larger than 60 K. Since the water lines\nare optically thick, the cooling depends on the velocity field,\nassumed to be that of an envelope collapsing in free-fall towards a\ncentral object of 2 M$_{\\sun}$ (see above). We checked the influence\nof our results against this assumption, running a case with a constant\nvelocity field of 0.5 km\/s. The difference in the gas temperature\nbetween the two cases never exceeds 10 K.\n\n\n\\subsection{Results}\\label{sec:results-gastemp}\nFigure \\ref{tg.ps} shows the computed gas temperature profile obtained\nwith different values of X(H$_2$O)$_{in}$ in the case G$_0$ = 1.\n\\begin{figure}\n\\rotatebox{0}{\\includegraphics[width=9cm]{Tg.ps}}\n\\caption{The gas temperature profile of the collapsing envelope of\n OMC2-FIR4. The different curves refer to different values of the\n inner envelope water abundance X(H$_2$O)$_{in}$: $1\\times10^{-6}$\n (dotted), $1\\times10^{-5}$ (dashed) and $1\\times10^{-4}$\n (dotted-dashed) respectively. In these computations,\n X(H$_2$O)$_{out}$ is $1\\times10^{-8}$ and G$_0$=1. The solid line\n refers to the dust temperature profile.\\label{tg.ps}}\n\\end{figure}\n\\begin{figure}\n\\rotatebox{0}{\\includegraphics[width=9cm]{heating.ps}}\n\\rotatebox{0}{\\includegraphics[width=9cm]{cooling.ps}}\n\\caption{Heating (top panel) and cooling (bottom panel) rates as\n function of the radius, computed assuming that the inner H$_2$O\n abundance is equal to $1\\times10^{-5}$ while the outer abundance is\n $1\\times10^{-8}$. \\label{thermal.ps}}\n\\end{figure}\n Figure \\ref{thermal.ps} shows the different contributions to the\n heating and cooling rates. Similarly to what had been found in low\nmass protostars \\citep{Cec96,Cec00,Mar02}, the gas temperature tracks\nthe dust temperature in the outer envelope while gas and dust are\ndecoupled in the inner part of envelope, where the icy grain mantles\nsublimate. The heating is dominated by compression of the\n collapsing gas across the entire envelope, even though the dust-gas\n collisions becomes comparable to the compression heating in the\n inner envelope. Although important in the very inner regions, the\n H$_2$O photo-pumping never dominates the heating contrarely to what\n happens in the studied low mass protostars. The cooling, on the\n other hand, is dominated by H$_2$O line emission in the inner\n envelope, by the OI line emission in the intermediate region and by\n the CO in the outermost regions of the envelope.\nNote that the increased water abundance causes an increased cooling\nof the gas, which brings the equilibrium gas temperature to lower\nvalues than the dust temperature. This phenomenon, already predicted\nin low mass protostars, is much more marked in the FIR4 case, leading\nto more than 100\\% of difference (with respect to the gas temperature)\nin the dust and gas temperatures for the case of the highest water\nabundance ($1\\times10^{-4}$). For example, at 100 AU the dust\ntemperature is 300 K, whereas the gas temperature varies from 200 to\n80 K depending on the assumed X(H$_2$O)$_{in}$, $1\\times10^{-6}$ and\n$1\\times10^{-4}$ respectively. The phenomenon is more marked in FIR4\nthan in the studied low mass protostars because of the relatively\nlower density of the region where the icy grain mantles sublimate in\nFIR 4 than in the low mass protostars, or, in other words, because the\nFIR4 envelope is warmer. Note that we obtain similar results also for\nlarger illuminating FUV fields.\n\n We emphasize that this result is a consequence of the derived\n shallow dependence of the density distribution, which is constrained\n from the fit of the maps. The dependence is strictly valid only at\n scales larger than the smaller telescope beam, namely 8$\"$\n (equivalent to a radius of about 1700 AU) and the SED fit only gives\n the total column density, which, coupled with the density dependence\n on the radius (constrained by the maps), constrains the density at\n these scales. While we cannot exclude the presence of a denser\n compact object hidden by the envelope, it seems unlikely that the\n envelope density gradient increases inwards, because this would be\n unphysical.\n\nClearly, the water abundance in the inner region of FIR4 will have a\ngreat impact not only on the emerging water spectrum but also on the\nemerging line spectrum of any molecule (abundant in the inner region),\nand has to be correctly taken into account to give reliable molecular\nabundances. Conversely, given the large effect, in principle\nappropriate multiline observations of any molecule will be able to\nconstrain the inner region water abundance and the present model\npredictions. Note that varying the outer abundance X(H$_2$O)$_{out}$\ndoes not have effect on the gas temperature, as in the outer region\nthe cooling is dominated by the CO and O lines.\n\n\n\n\n\n \\section{Predicted water line spectrum}\\label{sec:predicted-water-line}\n\\subsection{Reference model}\nHere we report and discuss the predicted spectrum of our reference\nmodel. Next paragraph will discuss how it depends on the parameters of\nthe model. We adopted the Model 1 of Table \\ref{tab:abu-cool} as\nreference model . We first discuss the general water line spectrum by\nmeans of the synthetic rotational diagram, and then we discuss the\nspecific predictions for the two spectrometers on board Herschel: HIFI\nand PACS.\n\\begin{figure} \\centering\n\\rotatebox{0}{\\includegraphics[width=9cm]{rot_diag.ps}}\n\\caption{Synthetic rotational diagram derived from the water line emission \n using the reference model (Model 1, Table \\ref{tab:abu-cool})\n integrated over the whole envelope. \n Crosses and diamonds trace the ortho and para water, respectively.\n\\label{fig:rot-diagr}}\n\\end{figure}\n\nFigure \\ref{fig:rot-diagr} shows the synthetic rotational diagram\nderived from the line emission integrated over the whole envelope. As\nexpected, \n the theoretical points do not lie on a compact and straight\n line, reflecting the different line optical depths, the gradients\nin density and temperature of the envelope and non-LTE effects. An\nilluminating example is represented by the fundamental transitions of\nthe ortho and para water lines at 557 and 1113 GHz respectively. We\nwill discuss these two lines in detail because, first, they will\ncertainly be important observational diagnostics and, second, they\noffer a great pedagogic case. The situation is illustrated in\nFig.\\ref{fig:h2o-profile}, where we report the profile of the emission\nof the ortho and para H$_2^{16}$O and H$_2^{18}$O fundamental lines as\nfunction of the radius. Figure \\ref{fig:h2o-profile-2}, with the beta\nescape probability as function of the radius for the two fundamental\nH$_2^{16}$O lines, also greatly helps to interpret the emerging line\nfluxes for the two lines. The ortho-H$_2^{16}$O fundamental line\nemission (Fig.\\ref{fig:h2o-profile}) peaks at the border of the\nenvelope and it decreases inwards because of the decreasing emitting\nvolume. The para-H$_2$O fundamental line shows approximately the same\nbehavior. If the lines were optically thin and LTE populated, the\nexpected flux ratio of the para over ortho fundamental line would be\nbetween 3 and 4 for a temperature between 50 and 200 K. Any departure\nfrom this value originates from a combination of line opacity and\nnon-LTE effects. In the outer region the ratio is lower than 1: the\npara-H$_2$O line is optically thin, whereas the ortho-H$_2$O lines is\nmoderately optically thick (Fig.\\ref{fig:h2o-profile-2}). Therefore,\nthe much lower emission of the para-H$_2$O line with respect to the\northo-line is due to the non-LTE population effect, more accentuated\nin the para-H$_2$O line. The situation is reversed in the inner\nregion, where ices sublimate: the para-H$_2$O fundamental line becomes\nabout ten times brighter than the ortho-H$_2$O fundamental line\nbecause of the line opacity, which is much larger in the ortho-H$_2$O\nline than in the para-H$_2$O line (Fig.\\ref{fig:h2o-profile-2}). In\nfact, the increase in the water abundance by a factor 1000 gives rise to a jump in the\nline emission by a factor 3 in the ortho-H$_2$O line and 30 in the\npara-H$_2$O line, and this can only be due to the larger opacity of\nthe ortho-H$_2$O line as the excitation conditions do not change when\nices sublimate. In summary, the emission from the water lines is due,\nin principle, to a rather complex combination of line opacity, non-LTE\neffects and emitting volume (namely temperature and density\ngradient). Evidently, the intensity ratio of lines from the\nH$_2^{16}$O and H$_2^{18}$O isotopologues is far to give the\n``opacity'' of the line, as it is a combination of the penetration of\nthe line and the opacity itself.\n\\begin{figure} \\centering\n\\rotatebox{0}{\\includegraphics[width=9cm]{h2o_ground_prof.ps}}\n\\rotatebox{0}{\\includegraphics[width=9cm]{prof_h2_18o.ps}}\n\\caption[] {Emission profile, $R\\frac{dF}{dR}$ of the ortho (solid\n line) and para (dashed line) water fundamental lines at 557 and 1113\n GHz respectively as function of the radius, in the case of the\n reference model (see Table \\ref{tab:abu-cool}). The plotted quantity\n $R\\frac{dF}{dR}$ is the contribution of each shell to the flux\n integrated over the whole envelope. H$_2^{16}$O and H$_2^{18}$O\n emission profiles are represented on the top and bottom panel,\n respectively.\\label{fig:h2o-profile}}\n\\end{figure}\n\n\\begin{figure} \\centering\n\\rotatebox{0}{\\includegraphics[width=9cm]{ratio_h2o_h2_18o.ps}}\n\\caption[] {Ratio of the H$_2^{16}$O escape probability over the\n H$_2^{18}$O escape probability of the ortho (solid line) and para\n (dashed line) water fundamental lines obtained with the reference\n model (model 1 of Table \\ref{tab:abu-cool}) at 557 and 1113 GHz\n respectively as function of the radius.\\label{fig:h2o-profile-2}}\n\\end{figure}\n\n Table \\ref{tab:spe-ref} lists the predicted water line fluxes\nfor the two spectrometers on board Herschel: HIFI and PACS. Note that,\nin both cases, we computed the signal after convolving the theoretical line intensity map \nwith the instrument beam which vary\n from 39$\"$ to 13$\"$ with the frequency varying from 500 GHz to 2000\n GHz (HIFI frequency range) and from 13$\"$ to 5$\"$ for wavelengths\n from 210 $\\mu$m and 60 $\\mu$m (PACS wavelength range).\n\n\\begin{table}[h]\n \\centering\n \\begin{tabular}{|ccc|}\n \\hline\n PACS range & & \\\\ \\hline\n Transition & Wavelength & Flux \\\\\n & ($\\mu$m) & (erg s$^{-1}$ cm$^-2$) \\\\ \\hline\n H$_2^{16}$O & & \\\\\n 2$_{ 2 1} \\rightarrow $ 3$_{ 3 0}$ & 66.44 & 2.99E-14 \\\\\n 2$_{ 2 0} \\rightarrow $ 3$_{ 3 1}$ & 67.09 & 1.13E-14 \\\\\n 3$_{ 0 3} \\rightarrow $ 3$_{ 3 0}$ & 67.27 & 2.28E-14 \\\\\n 2$_{ 1 2} \\rightarrow $ 3$_{ 2 1}$ & 75.38 & 1.55E-13 \\\\\n 3$_{ 1 2} \\rightarrow $ 4$_{ 2 3}$ & 78.74 & 4.51E-14 \\\\\n 2$_{ 1 1} \\rightarrow $ 3$_{ 2 2}$ & 89.99 & 5.09E-14 \\\\\n 4$_{ 0 4} \\rightarrow $ 5$_{ 1 5}$ & 95.63 & 1.05E-14 \\\\\n 4$_{ 1 4} \\rightarrow $ 5$_{ 0 5}$ & 99.49 & 4.21E-14 \\\\ \n 1$_{ 1 1} \\rightarrow $ 2$_{ 2 0}$ & 100.98 & 7.80E-14 \\\\ \n 1$_{ 1 0} \\rightarrow $ 2$_{ 2 1}$ & 108.07 & 1.43E-13 \\\\\n 3$_{ 0 3} \\rightarrow $ 4$_{ 1 4}$ & 113.54 & 8.45E-14 \\\\\n 4$_{ 2 3} \\rightarrow $ 4$_{ 3 2}$ & 121.72 & 1.14E-14 \\\\\n 3$_{ 1 3} \\rightarrow $ 4$_{ 0 4}$ & 125.36 & 5.34E-14 \\\\\n 4$_{ 1 4} \\rightarrow $ 4$_{ 2 3}$ & 132.41 & 2.92E-14 \\\\\n 3$_{ 2 1} \\rightarrow $ 3$_{ 3 0}$ & 136.49 & 1.35E-14 \\\\\n 2$_{ 0 2} \\rightarrow $ 3$_{ 1 3}$ & 138.53 & 7.84E-14 \\\\\n 3$_{ 1 3} \\rightarrow $ 3$_{ 2 2}$ & 156.20 & 2.25E-14 \\\\\n 2$_{ 1 2} \\rightarrow $ 3$_{ 0 3}$ & 174.62 & 7.42E-14 \\\\\n 1$_{ 0 1} \\rightarrow $ 2$_{ 1 2}$ & 179.53 & 1.17E-13 \\\\\n 2$_{ 1 2} \\rightarrow $ 2$_{ 2 1}$ & 180.49 & 4.97E-14 \\\\ \\hline\nHIFI range & & \\\\ \\hline\n Transition & Frequency & Flux \\\\\n & (GHz) & (K Km s$^{-1}$) \\\\ \\hline\n H$_2^{16}$O & & \\\\\n 1$_{ 0 1} \\rightarrow $ 1$_{ 1 0}$ & 556.96 & 14.8 \\\\\n 2$_{ 0 2} \\rightarrow $ 2$_{ 1 1}$ & 752.04 & 1.06E+00 \\\\\n 1$_{ 1 1} \\rightarrow $ 2$_{ 0 2}$ & 987.95 & 1.31E+00 \\\\\n 3$_{ 0 3} \\rightarrow $ 3$_{ 1 2}$ & 1097.34 & 1.46E+00 \\\\\n 0$_{ 0 0} \\rightarrow $ 1$_{ 1 1}$ & 1113.35 & 4.32E+00 \\\\\n 2$_{ 2 1} \\rightarrow $ 3$_{ 1 2}$ & 1153.09 & 2.50E+00 \\\\\n 3$_{ 1 2} \\rightarrow $ 3$_{ 2 1}$ & 1162.93 & 6.97E-01 \\\\\n 2$_{ 1 1} \\rightarrow $ 2$_{ 2 0}$ & 1228.81 & 5.33E-01 \\\\ \n 4$_{ 1 3} \\rightarrow $ 4$_{ 2 2}$ & 1207.62 & 2.45E-01 \\\\ \n 5$_{ 1 4} \\rightarrow $ 5$_{ 2 3}$ & 1410.65 & 1.47E-01 \\\\\n 2$_{ 1 2} \\rightarrow $ 2$_{ 2 1}$ & 1660.99 & 2.33E+00 \\\\\n 4$_{ 0 4} \\rightarrow $ 4$_{ 1 3}$ & 1602.23 & 1.46E-01 \\\\\n 1$_{ 0 1} \\rightarrow $ 2$_{ 1 2}$ & 1669.87 & 5.46E+00 \\\\\n 2$_{ 1 2} \\rightarrow $ 3$_{ 0 3}$ & 1716.83 & 3.37E+00 \\\\\n 5$_{ 2 3} \\rightarrow $ 5$_{ 3 2}$ & 1867.75 & 3.13E-02 \\\\\n H$_2^{18}$O & & \\\\\n 1$_{ 0 1} \\rightarrow $ 1$_{ 1 0}$ & 556.96 & 1.88E-01 \\\\\n & & \\\\\n 0$_{ 0 0} \\rightarrow $ 1$_{ 1 1}$ & 1113.35 & 1.48E-01 \\\\\n & &\\\\ \\hline\n \\end{tabular}\n \\caption{Predictions of the line fluxes (after subtraction of the continuum) \n of the water lines observable with the Herschel spectrometers, HIFI and \n PACS. The predictions refer to the reference model (model 1 of Table \n \\ref{tab:abu-cool}). \\label{tab:spe-ref}}\n\\end{table}\n\n Based on the (preliminary) sensitivities reported on the Herschel\n Observation Planning Tool HSpot: {\\it\n http:\/\/herschel.esac.esa.int\/Tools.shtml}), several ortho and\npara lines are predicted to be detectable by the two Herschel\nspectrometers: about a dozen in the HIFI frequency range and twice\nmore in the PACS wavelength range. The H$_2^{18}$O ortho and para\nlines are also predicted to be detectable by HIFI, and 100 and 20\ntimes less bright than the respective lines of the H$_2^{16}$O\nrespectively. Note the counter-intuitive result: the para-H$_2^{16}$O\nline seems to be more optically thick than the ortho-H$_2^{16}$O line!\nAs explained above, this is not the case, of course: the line\nintensity ratio (from which the line optical depth is usually derived) is due to the combination of optical depth plus\nexcitation (non-LTE) effects, and the final result is not easily predictable. In our\nreference model, no observable line is predicted to be in\nabsorption.\n\n\\subsection{Other models}\nHere we explore the sensitivity of the results reported in the\nprevious paragraph against the variation of the three main parameters\nof the model: the water abundance in the inner (X(H$_2$O)$_{in}$) and\nouter (X(H$_2$O)$_{out}$) envelope, and the illuminating FUV field\nG$_o$. \n\nFigures \\ref{hifi_ratio_xout} and \\ref{hifi_ratio_xin} show the ratio\nbetween the line intensities of the reference model (Model 1 of Table\n\\ref{tab:abu-cool}) and the line intensities predicted by models with\ndifferent X(H$_2$O)$_{out}$ and X(H$_2$O)$_{in}$ respectively. As\nnoted by other authors \\citep{Cec00,Mar02}, lines with upper level\nenergies lower than about 200 cm$^{-1}$ are sensitive to\nX(H$_2$O)$_{out}$ and insensitive to X(H$_2$O)$_{in}$, because these\nlines mostly originate in the outer envelope for excitation and line\nopacity reasons. A variation of a factor 10 in X(H$_2$O)$_{out}$ leads\nto an almost similar variation in the line intensity of the lowest\nlying lines. The higher the upper level energy the smaller the\nvariation. Conversely, lines with upper level energies larger than\nabout 200 cm$^{-1}$ are sensitive to X(H$_2$O)$_{in}$ and insensitive\nto X(H$_2$O)$_{out}$. In this case, variations by a factor 10 in\nX(H$_2$O)$_{in}$, going from $1\\times10^{-6}$ to $1\\times10^{-5}$, can\nlead to variations in the lines fluxes even 100 times larger. This\nextreme variation, 10 times larger than the difference in the\nX(H$_2$O)$_{in}$ variation, occurs to some lines in the 50-200 $\\mu$m\nwavelength range. This phenomenon is due to the fact that those lines\nare in absorption rather than in emission in the region just after the\nices sublimation, resulting in an additional decrease of the emerging\nline flux. The higher the X(H$_2$O)$_{in}$ the smaller the absorption\ndepth. When X(H$_2$O)$_{in}$ reaches $1\\times10^{-5}$ the absorption\nregion generally vanishes. In addition, many high lying lines are\n prevalently populated by absorption of the photons emitted by the\n dust, so that they are particularly sensitive to the dust\n continuum.\n\\begin{figure} \\centering\n\\rotatebox{90}{\\includegraphics[width=6cm]{ratio_var_Xout_o.ps}}\n\\rotatebox{90}{\\includegraphics[width=6cm]{ratio_var_Xout_p.ps}}\n\\caption[] {Ratio between the line fluxes of the reference model\n (model 1 of Table \\ref{tab:abu-cool}) and the line fluxes predicted\n by models with same X(H$_2$O)$_{in}$ but different\n X(H$_2$O)$_{out}$. Flux ratios between model 4 (model 5) and model 1\n are represented by squares (triangles). Filled and empty symbols\n refer to the lines emitted in the PACS and HIFI bands,\n respectively. The upper (lower) panel reports ortho (para) water\n line intensities ratios. \\label{hifi_ratio_xout}}\n\\end{figure}\n\\begin{figure} \\centering\n\\rotatebox{90}{\\includegraphics[width=6cm]{ratio_var_Xin_o.ps}}\n\\rotatebox{90}{\\includegraphics[width=6cm]{ratio_var_Xin_p.ps}}\n\\caption[] {Ratio between the line fluxes of the reference model\n (model 1 of Table \\ref{tab:abu-cool}) and the line fluxes predicted\n by models with same X(H$_2$O)$_{out}$ but different\n X(H$_2$O)$_{in}$. Flux ratios between model 2 (model 3) and model 1\n are represented by squares (triangles). Filled and empty symbols\n refer to the lines emitted in the PACS and HIFI bands,\n respectively. The upper (lower) panel reports ortho (para) water\n line flux ratios. \\label{hifi_ratio_xin}}\n\\end{figure}\n\nNote that as mentioned in the Sect. \\ref{sec:cont-emiss-dusty}, the inner region of the envelope is relatively unconstrained by the available observational data. Therefore we derived water line spectrum predictions varying the power law index of the density profile $\\alpha$ of about $\\sim$ 30$\\%$ in the inner part. We observed a variation of the line intensity of a factor 5 - 10 for the transitions with upper level energy $\\gtrsim$ 300$-$400 cm$^{-1}$ and lesser than 2 for the lower lines.\nFinally, the predicted line intensities do not vary appreciably when\nthe illuminating FUV field changes from 1 to 1000. Therefore, observations of \nwater lines will be extremely helpful in constraining the water abundance across\nthe envelope, but will not be sensitive to the illuminating FUV field.\n\n\n\\subsection{Effect of gas-dust thermal decoupling}\n\nAs presented in \\S \\ref{sec:gas-temp-prof}, the large quantity of\nwater vapor injected into the gas in the inner part of the envelope\ncauses a dramatic decoupling between the dust and gas temperatures\n(see Fig. \\ref{tg.ps}). Obviously, this effect has a great impact in\nthe interpretation of the water line emission. This is illustrated in\nFigures \\ref{ratio_decoup}, where we report the ratio of the water\nline intensities obtained by considering the gas temperature\nself-consistently computed (model 1) over the case where T$_{gas}$ is\nassumed to be equal to T$_{dust}$.\n\\begin{figure} \\centering\n\\rotatebox{90}{\\includegraphics[width=6cm]{ratio_decoup_T_o-h2o_ref.ps}}\n\\rotatebox{90}{\\includegraphics[width=6cm]{ratio_decoup_T_p-h2o_ref.ps}}\n\\caption[]{Ratios between the line fluxes of the reference model\n (model 1) and the reference model with gas-dust non-thermally\n decoupled (namely T$_{gas}$=T$_{dust}$, as function of the upper\n level energy of the transition. Upper and lower panels show ortho\n and para H$_2$O lines, respectively. \\label{ratio_decoup}}\n\\end{figure}\nAssuming artificially T$_{gas}$ = T$_{dust}$ leads to differences in\nline fluxes up to two orders of magnitude. Since the decoupling\noccurs in the inner part of the envelope, the larger the upper level\nenergy the larger the difference, for both ortho and para lines. Note\nthat the fluxes of the two fundamental ortho and para lines are not\naffected by the T$_{gas}$ = T$_{dust}$ choice. Finally, we did the\nsame study with the model 2 ( X(H$_2$O)$_{in}$ = 1O$^{-6}$). In this\ncase, due to the smaller X(H$_2$O)$_{in}$, the decoupling is less\nimportant than in the previous one, leading to changes in lines\nintensities up to one order of magnitude.\n\n\n\\subsection{Constraints from ISO data}\\label{sec:iso-data:water}\nObservations of FIR4 were obtained by the spectrometer ISO-LWS in the\ngrating mode (spectral resolution $\\sim$200) and Fabry-Perot (spectral\nresolution $\\sim 10^{4}$). We retrieved the data from the ISO Data\nArchive ({\\it http:\/\/iso.esac.esa.int\/ida\/}). No water lines are\ndetected. Table \\ref{ISO_upper} summarizes the upper limits obtained\nfor the predicted brightest lines together with the predictions of the\nreference model and model 6.\n\\begin{table*}\n \\centering\n\\begin{tabular}{|cc|c|c|cc|} \\hline \n&&Model 1 &Model 6 & ISO & \\\\\nWavelength & Transition & Intensity & Intensity & Instrument & Upper Limit (3$\\sigma$) \\\\\n ($\\mu$m) & J$_{K_- K_+} \\rightarrow$ J'$_{K'_- K'_+}$ & (erg.s$^{-1}$.cm$^{-2}$) & (erg.s$^{-1}$.cm$^{-2}$) && (erg.s$^{-1}$.cm$^{-2}$) \\\\ \\hline\northo &&&&& \\\\\n75.4957 & 8$_{5 4} \\rightarrow$ 8$_{4 5}$ & 2.6E-20 & 1.9E-19 & LWS04 & 9.78e-12 \\\\\n108.073 & 2$_{2 1} \\rightarrow$ 1$_{1 0}$ & 1.3E-12 & 1.5E-12 & LWS04 & 2.21e-12 \\\\ \n179.527 & 2$_{1 2} \\rightarrow$ 1$_{0 1}$ & 1.2E-12 & 7.0E-12 & LWS04 & 2.56e-11 \\\\ \n113.538 & 4$_{1 4} \\rightarrow$ 3$_{0 3}$ & 8.3E-13 & 8.7E-13 & LWS01 & 1.2e-11 \\\\ \npara &&&&& \\\\\n100.983 & 2$_{2 0} \\rightarrow$ 1$_{1 1}$ & 7.3E-13 & 9.1E-13 & LWS01 & 8.37e-12 \\\\\n138.527 & 3$_{1 3} \\rightarrow$ 2$_{0 2}$ & 7.3E-13 & 7.3E-13 & LWS01 & 4.14e-12 \\\\\n156.197 & 3$_{2 2} \\rightarrow$ 3$_{1 3}$ & 2.2E-13 & 3.4E-13 & LWS01 & 1.60e-12 \\\\ \\hline\n\\end{tabular}\n\\caption{The brightest lines predicted by models 1 and 6 (the model with the largest water abundance) compared with the upper limits derived by the ISO observations.) \\label{ISO_upper}}\n\\end{table*}\nUnfortunately, the ISO sensitivity is not enough to put sensible\nconstraints to the water abundance across the FIR4 envelope.\n\n\n\n\\section{Concluding remarks}\\label{sec:conclusion}\nWe have analyzed in great detail the continuum emission from the\nIntermediate Mass protostar OMC2-FIR4, with the aim of deriving the\nphysical structure of its envelope, a mandatory first step for further\nstudies to understand the formation process. Our analysis led to a new\nestimate of the FIR4 luminosity, which is 1000 L$_\\odot$. The density\nof the envelope surrounding FIR4 has a shallow dependence on the\nradius, the density power law index being only 0.6. Since\n systematic studies of the IM protostars envelopes have not been\n published yet, we can tentatively compare the FIR4 envelope with low\n and high mass protostellar envelopes, where similar studies have been\n carried out. Specifically, \\citet{Jor02} analyzed 18 Class 0 and I\n sources and found that the average power law index $\\alpha$ in Class\n 0 sources is $1.3\\pm0.4$ while in Class I sources it is $1.7\\pm0.1$,\n significatively larger than the value we found in FIR4. Similarly,\n \\citet{Van00} studied a sample of high mass protostars\n and found $\\alpha=1.4\\pm0.4$. One has to notice that, however, there\n are exceptions, with sources in both low and high mass showing a\n smaller than unity $\\alpha$ value: L1527 ($\\alpha=0.6$) and L483\n ($\\alpha=0.9$) in the Class 0 sources, GL7009S ($\\alpha=0.5$) in the\n high mass protostars sample. It is not clear what makes these\n sources ``anomalous'': the presence of strong asymmetries \\citep{Jor02} have been suggested as possible reason. The\n case of FIR4 seems to fall in this ``anomalous sources'' category,\n and further studies, possibly on chemistry, are needed to say more.\n\n Giving the suggestion by \\citet{Jor06} that a strong FUV field\n (G$_0$=$1\\times10^{4}$) illuminates the FIR4 envelope, we explored\n the cases of different FUV fields. As already noted by the same\n authors, however, the dust continuum cannot really distinguish\n whether a strong illuminating FUV field is impinging on the\n envelope. In fact, \\citet{Jor06} adopted a steeper density distribution ($\\alpha$=2) which allows the FUV photons to penetrate deeper into the envelope. Their conclusions were based on\n submillimeter lines from CO and H$_2$CO, which would be exceedingly\n bright if they were emitted in the envelope. They attributed the\n lines to the warm gas at the border of the envelope, heated up by\n the hypothetical large FUV field. However, as discussed in \\S\n \\ref{sec:dust-results}, OI and CII maps by \\citet{Her97} showed\n that the entire OMC2 region is illuminated by a G$_0$=500 FUV field,\n which would imply an even lower FUV field on the FIR4 envelope. One\n has also to notice here that large scale maps by \\citet{sch82} show\n that the CO (1$\\rightarrow$0) line is bright ($\\sim$40 K) over the\n whole OMC2 region, a fact that lead \\citet{Her97} to attribute the\n CO emission to the PDR associated with the cloud. In addition,\n several outflows are known to \"pollute\" the CO emission in the\n region, in particular the one originating from FIR3 (25$\"$\n North of FIR4: Fig. \\ref{maps}) and reaching FIR4 and FIR5\n \\citep{Wil03}. \n All the above considerations together lead to\nconclude that the FUV field impinging FIR4 is not anomalously large\nand less than 500. Therefore, giving the presence of a bright PDR\n and a ``polluting'' outflow from FIR3, caution is needed in\n interpreting the low lying water lines, as much as lines from any\n molecule, separately from the whole molecular cloud emission.\n\nOne major motivation of the present work is the prediction of the\nwater line spectrum from FIR4, as this source will be observed in the\n500-2000 GHz frequency range by the incoming Herschel Space\nObservatory (FIR4 is a target of the Key Program ``HIFI Spectral\nSurveys of Star Formation Regions'': {\\it\n http:\/\/www-laog.obs.ujf-grenoble.fr\/heberges\/hs3f\/}). In the present\nstudy, we have shown that water is indeed a key molecular species,\nbecause of its great impact on the gas cooling in the region where the\ndust temperature exceeds 100 K, the sublimation temperature of the\ndust grain ices. The large quantity of water vapor injected into the\ngas by the sublimated ices very efficiently cools the gas, causing a\ndramatic decoupling between the dust and gas temperatures. Depending\non the abundance of the injected water vapor, the difference in the\ntemperature can be as high as 50 K at the sublimation radius (namely\n50\\%!) and even larger going inward. For example, at 100 AU the dust\ntemperature is predicted to be around 280 K whereas the gas\ntemperature is 80 K if the water abundance is\n$1\\times10^{-4}$. Obviously, this has a great impact in the\ninterpretation of the water line emission as much as the emission from\nany molecular species emitting in the inner region. In fact, the\ncomparison of the water line emission between the case where dust and\ngas are assumed to be thermally coupled and the case where the gas\ntemperature is self-consistently computed shows that the difference of\nthe line intensity can reach two orders of magnitudes for lines with\nlarge upper level energies (namely the lines excited in the innermost\nregion, where gas and dust decouple). Therefore, our important second\nconclusion is that caution has to be applied in interpreting the line\nemission from FIR4, as much as any source with a similar luminosity\nand envelope structure. Gas and dust temperature can be very different\nand in order to derive correct molecular abundances (including water\nabundance) they have to be both estimated, accounting for all terms of\nheating and cooling. Avoiding that may lead to very wrong conclusions.\n\n\\bigskip\n\\begin{acknowledgements}\n We warmly thank Moshe Elitzur for his valuable help in using the\n DUSTY code. We also wish to thank Neal Evans and Doug Johnstone for\n helpful discussions, and Doug Johnstone and Darek Lis for providing\n us with the JCMT and CSO continuum maps of the OMC2-FIR4 region. We thank an anonymous referee and Malcolm Walmsley for comments which helped improving the manuscriptOne of us (N.Crimier) is supported by a fellowship of the Minist\\`ere de\nl'Enseignement Sup\\'erieur et de la Recherche. \n\\end{acknowledgements}\n\n\n\n\\bibliographystyle{aa}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbsuh b/data_all_eng_slimpj/shuffled/split2/finalzzbsuh new file mode 100644 index 0000000000000000000000000000000000000000..5f61e088852b1851484ceb9386c5074470cbe3d4 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbsuh @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nLarge Language Models (LMs) \nachieve remarkable accuracy and generalization ability when fine tuned for NLP tasks~\\citep{peters2018deep,devlin2018bert,liu2019roberta,lan2020albert,raffel2020exploring}. They are also capable zero- and few-shot learners ~\\citep{brown2020gpt3}, with the ability to generalize to tasks not seen during training.\nA reliable way to improve LM accuracy in all of these settings is by scaling up: increasing the number of parameters and the amount of computation used during training and inference~\\citep{raffel2020exploring,brown2020gpt3,fedus2021switch}. In fact, some generalization properties only emerge in very large models, including much improved zero- and few-shot learning~\\citep{brown2020gpt3}.\n\nUnfortunately, the corresponding growth in computational resources required to train state-of-the-art language models is a barrier for many in the research community~\\cite{schwartz2019green}.\nThere is also a concern about the environmental costs associated with training and deploying such models~\\citep{strubell2019energy,gupta2021chasing,bender2021dangers,patterson2021carbon} motivating research into more efficient model designs~\\citep{lepikhin2021gshard,fedus2021switch,Lewis2021BASELS}.\n\n\\begin{figure}[t!]\n\\centering\n\\includegraphics[width=\\linewidth]{figs\/efficiency-gain.pdf}\n\\caption{\n\\textbf{Estimate of how much more efficient MoEs are relative to dense models.} A speedup factor of $y$ indicates that an MoE model can match the performance of the corresponding dense model---trained with $x$ ZFLOPs---using $y$ times less compute (i.e., $x\/y$ ZFLOPs). We estimate this factor according to validation perplexity for in-domain language modeling, the Pile perplexity for out-of-domain language modeling, and average accuracy across 6 tasks for zero-shot priming. See \\S\\ref{subsubsec:efficiency-gain} for more details.\n}\n\\label{fig:efficiency-gain}\n\\end{figure}\n\n\\emph{Sparse} models allow for increased number of learnable parameters without the associated computational costs. \nFor example, sparsely gated mixture of experts (\\emph{MoE})~\\cite{lepikhin2021gshard} have been successfully used for language modeling and machine translation~\\citep{lepikhin2021gshard,Lewis2021BASELS,roller2021hash}, but are yet to be shown effective for fine-tuning~\\cite{fedus2021switch} as well as zero- and few-shot learning. \nWe hypothesize that sparse models are comparatively accurate to dense models but at a much lower computational footprint. \nTo measure this claim, we train traditional dense and MoE language models ranging in size from several hundred million parameters to more than one trillion parameters and present a careful empirical comparison of these models on downstream tasks in zero-shot, few-shot and fully supervised settings.\n\nAs shown in Figure \\ref{fig:efficiency-gain}, we find that MoE models can indeed achieve similar downstream task performance as dense models at a fraction of the compute. For models with relatively modest compute budgets, a MoE model can perform on par with a dense model that requires almost four times as much compute.\nDownstream task performance improves with scale for both MoE models and dense models.\nWhile we observe that the performance gap narrows as we increase model size, even at larger compute budgets ($\\sim$ 5000 GPU days), our largest MoE model (1.1T parameters) outperforms a dense model with similar computational cost (6.7B parameters).\nWe further compare and contrast the performance of dense and sparse models with similar computational signatures and observe some performance variations across tasks and domains, suggesting this an interesting area for future research. In summary, our contributions are:\n\\begin{itemize}[leftmargin=*]\n \\item We present a comprehensive study of sparse models for zero and few-shot learning at scale; \n \\item We demonstrate that even at scale sparse MoE models can yield competitive zero and few-shot performance at a fraction of the computation for model training and inference;\n \\item We observe some differences in how dense and sparse models generalize at scale suggesting complementary behaviour that could be an interesting future research direction.\n\\end{itemize}\n\n\n\n\\section{Background and Related Work}\n\n\\subsection{Large Language Models \/ GPT-3}\n\nProgress in the field of NLP has been driven by increasingly large Language Models (LMs) pretrained on large text datasets. While numerous variations have been proposed, such LMs are predominantly based on the transformer architecture~\\citep{vaswani2017attention}. Models are pretrained by hiding parts of the input: predicting the next word sequentially left-to-right, masking words in the text~\\citep{devlin2018bert,liu2019roberta}, or perturbing and\/or masking spans~\\citep{lewis-etal-2020-bart,raffel2020exploring}. The resulting models can be quickly adapted to perform new tasks at high accuracy by fine-tuning on supervised data~\\citep{devlin2018bert,liu2019roberta}.\n\nRecently, GPT-3~\\citep{brown2020gpt3} demonstrated that large LMs can perform zero- and few-shot learning without fine-tuning through in-context learning. Notably, many of these in-context zero- and few-shot learning behaviors emerge or amplify at scale. Concurrent to our work, \\citet{rae2022scaling} and \\citet{smith2022using} further explore scaling dense language models. %\n\n\n\\subsection{Sparse models}\nOne drawback of dense model scaling is that it grows increasingly computationally expensive. To more efficiently increase model capacity, conditional compute strategies have been developed \\citep{bengio2013estimating,davis2013low,cho2014exponentially,bengio2015conditional}, where each input activates a subset of the model. Recent work \\citep{Lewis2021BASELS,lepikhin2021gshard,fedus2021switch,fan2021beyond} has studied different conditional compute strategies that work well with Transformer models for natural language tasks. In this work, we focus on Sparsely Gated Mixture of Expert (MoE) models \\citep{shazeer2017outrageously,lepikhin2021gshard}. Sparse MoE models replace the dense feed forward network block in every alternate Transformer layer with an MoE layer. The MoE layer has a routing gate that learns which tokens are to be mapped to which set of experts (we use top-2 experts). To ensure scalability and training efficiency, it is also common to include a weighted gate loss term as in \\citet{lepikhin2021gshard} to the cross entropy loss to encourage the tokens to be uniformly distributed to the experts. \nConcurrent to our work, \\citet{du2021glam}, \\citet{rajbhandari2022deepspeedmoe} and \\citet{clark2022unified} also study MoE scaling.\n\n\\begin{table*}[ht]\n\\begin{center}\n\\begin{small}\n\\addtolength{\\tabcolsep}{-2.5pt}\n\\begin{tabular}{crrrrrrrrrrrrrrrrrr}\n\\toprule\n& \\multicolumn{5}{c}{GPT-3 (dense)} && \\multicolumn{5}{c}{Ours (dense)} && \\multicolumn{5}{c}{Ours (MoE)} & \\\\\n\\cmidrule{2-6} \\cmidrule{8-12} \\cmidrule{14-18}\n& \\multicolumn{1}{c}{\\emph{size}} & \\multicolumn{1}{c}{\\emph{cost}} & \\multicolumn{1}{c}{$l$} & \\multicolumn{1}{c}{$h$} & \\multicolumn{1}{c}{$e$} &\n& \\multicolumn{1}{c}{\\emph{size}} & \\multicolumn{1}{c}{\\emph{cost}} & \\multicolumn{1}{c}{$l$} & \\multicolumn{1}{c}{$h$} & \\multicolumn{1}{c}{$e$} &\n& \\multicolumn{1}{c}{\\emph{size}} & \\multicolumn{1}{c}{\\emph{cost}} & \\multicolumn{1}{c}{$l$} & \\multicolumn{1}{c}{$h$} & \\multicolumn{1}{c}{$e$} &\n\\\\\n\\midrule\n& 125M & 0.36 & 12 & 768 & -- &\n& 125M & 0.36 & 12 & 768 & -- &\n& 15B & 0.43 & 12 & 768 & 512 &\n\\\\\n& 355M & 1.06 & 24 & 1024 & -- &\n& 355M & 1.06 & 24 & 1024 & -- &\n& 52B & 1.30 & 24 & 1024 & 512 &\n\\\\\n& 760M & 2.13 & 24 & 1536 & -- &\n& \\multicolumn{5}{c}{---} &\n& \\multicolumn{5}{c}{---} &\n\\\\\n& 1.3B & 3.57 & 24 & 2048 & -- &\n& 1.3B & 3.57 & 24 & 2048 & -- &\n& 207B & 4.53 & 24 & 2048 & 512 &\n\\\\\n& 2.7B & 7.08 & 32 & 2560 & -- &\n& 2.7B & 7.08 & 32 & 2560 & -- &\n& \\multicolumn{5}{c}{---} &\n\\\\\n& 6.7B & 17.12 & 32 & 4096 & -- &\n& 6.7B & 17.12 & 32 & 4096 & -- &\n& 1.1T & 22.27 & 32 & 4096 & 512 &\n\\\\\n& 13B & 32.67 & 40 & 5120 & -- &\n& 13B & 32.67 & 40 & 5120 & -- &\n& \\multicolumn{5}{c}{---} &\n\\\\\n& 175B & 430.17 & 96 & 12288 & -- &\n& \\multicolumn{5}{c}{---} &\n& \\multicolumn{5}{c}{---} &\n\\\\\n\\bottomrule\n\\end{tabular}%\n\\end{small}\n\\end{center}\n\\caption{\\textbf{Dense and mixture of expert (MoE) model details}. \\emph{size}: number of parameters, \\emph{cost}: training ZFLOPs, $l$: layers, $h$: hidden dimension, $e$: number of experts. All models are trained for 300B tokens with a sequence length of 2048 tokens. Models within the same row are roughly comparable. We estimate the training cost in ZFLOPs analytically (see Appendix~\\ref{app:flops}).}\n\\label{tab:models}\n\\end{table*}\n\n\\subsection{Zero-shot and Few-shot Learning} \\label{subsec:background_fewshot}\n\nRecent works \\citep{schick-schutze-2021-exploiting,radford2019language} have directly evaluated LMs on unseen tasks successfully (zero-shot learning), by recasting their task inputs as cloze-style prompt completion tasks. This is in contrast to the traditional approach of augmenting LMs with task-specific heads, followed by supervised fine-tuning \\citep{devlin2018bert,raffel2020exploring}. Subsequently, \\citet{brown2020gpt3} demonstrated that priming LMs with a few input-output examples (few-shot learning) before careful prompting can improve task performance, that grows with model scale without any fine-tuning, and this gave rise to new resources for prompt engineering \\citep{bach2022promptsource}. In this paper, we contrast the zero-shot, few-shot, and fully supervised fine-tuning performance of dense and MoE models. Finally, \\citet{schick2021smalllanguagemodels} perform few-shot learning by few-shot fine-tuning using pattern-exploiting training, whose efficiency can be improved by performing partial fine-tuning of a small number of additional task-specific parameters instead \\citep{lester2021power,li2021prefix,houlsby2019parameter}. %\n\n\n\n\\subsection{Large-scale training}\n\nMany of the models we consider in this work are too big to be trained using standard data parallel techniques, since parameter storage would exceed the usable memory of a single GPU.\nWe adopt several techniques to make these models feasible to train, including pure FP16 training, activation checkpointing and fully sharded data parallel training. These techniques are described in more depth in Appendix~\\ref{app:scaling}.\n\n\\section{Experimental Setup}\n\n\\subsection{Models}\n\nWe train autoregressive (decoder-only) transformer models that roughly match the sizes and architecture explored in~\\citet{brown2020gpt3}. Model sizes are summarized in Table~\\ref{tab:models}.\nWe use pre-normalization transformer blocks~\\citep{baevski2018adaptive,child2019generating} and GELU activations~\\citep{hendrycks2016gelu}.\nWe differ from \\citet{brown2020gpt3} in two ways: (1) we use only dense attention, while they alternate between dense and locally banded sparse attention; and (2) we train our models with sinusoidal positional embeddings, following Shortformer~\\cite{press2020shortformer}.\\footnote{Early experiments found this to produce comparable results with fewer learned parameters.}\n\nWe also train MoE models that mirror our dense model configurations (see the third set of columns in Table~\\ref{tab:models}), so that comparisons are approximately matched in terms of the number of floating point operations (\\emph{FLOP}s).\nOur MoE models follow the design proposed in \\citet{lepikhin2021gshard} with alternating dense and expert layers and top-2 expert selection.\nWe use 512 experts in each expert layer ($E=512$).\nEach expert has a \\emph{capacity} of $\\frac{C \\cdot B}{E}$ tokens, where $C$ is a \\emph{capacity factor} that we set to $2$ and $B$ is the total batch size in tokens. Capacity refers to the maximum number of tokens that are routed to each expert.\nOnce an expert is at capacity for a given batch, additional tokens are considered to be ``overflowed\" with their representations passed-through via the residual connection.\n\n\\citet{fedus2021switch} report instability training large MoE models and suggest rescaling the initial model weights, which we do not find necessary.\nWe instead observe that expert parameters have an $E$-times smaller batch size relative to dense (data parallel) parameters and accordingly rescale expert gradients by a factor $\\frac{1}{\\sqrt{E}}$.\nThis rescaling aligns with theory suggesting that an $E$-times increase in batch size should be accompanied by a $\\sqrt{E}$ increase in learning rate~\\citep{krizhevsky2014one}.\n\n\nFollowing \\citet{brown2020gpt3}, we train our models for 300B tokens\\footnote{While we control the total number of tokens to be the same as \\citet{brown2020gpt3}, our pretraining data is not the same. See \\S\\ref{subsec:pretraining_data} for further details.}\nwith a context size (sequence length) of 2048 tokens.\nThe batch size and learning rate are set according to the model size following \\citet{brown2020gpt3}.\nWe linearly warm-up the learning rate from $0$ over the first 375M tokens and linearly decay back to $0$ over the remaining tokens.\nWe use the Adam optimizer~\\cite{kingma2014adam} with $\\beta_1=0.9$, $\\beta_2=0.98$, $\\epsilon=10^{-8}$, weight decay of 0.01 and dropout of 0.1.\\footnote{We note that our 355M dense and 52B MoE models (FLOPs-matched) were trained without dropout, which we find slightly improves performance at smaller scale.}\n\nWe train our models in PyTorch~\\cite{paszke2017automatic} using \\textsc{fairseq}~\\citep{ott2019fairseq}.\n\n\\subsection{Pretraining data}\n\\label{subsec:pretraining_data}\n\nWe pretrain our models on a union of six English-language datasets, including the five datasets used to pretrain RoBERTa~\\citep{liu2019roberta} and the English subset of CC100, totalling 112B tokens corresponding to 453GB:\n\\begin{itemize}[leftmargin=*]\n\\item \\textbf{BookCorpus}~\\citep{zhu2015bookcorpus} consists of more than 10K unpublished books (4GB);\n\\item \\textbf{English Wikipedia}, excluding lists, tables and headers (12GB);\n\\item \\textbf{CC-News}~\\citep{nagel2016ccnews} contains 63 millions English news articles crawled between September 2016 and February 2019 (76GB);\n\\item \\textbf{OpenWebText}~\\citep{gokaslan2019openwebtext}, an open source recreation of the WebText dataset used to train GPT-2 (38GB);\n\\item \\textbf{CC-Stories}~\\citep{trinh2018simple} contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas (31GB);\n\\item \\textbf{English CC100}~\\citep{wenzek-etal-2020-ccnet}, a dataset extracted from CommonCrawl snapshots between January 2018 and December 2018, filtered to match the style of Wikipedia (292GB).\n\\end{itemize}\nWe encode our data using the same Byte-Pair Encoding (BPE) as GPT-2~\\citep{radford2019language} and RoBERTa~\\citep{liu2019roberta} with a vocabulary of 50K subword units.\n\n\\subsection{Evaluation}\n\nWe evaluate models in terms of their in-domain and out-of-domain perplexity, as well as downstream task performance. \n\n\\subsubsection{Perplexity Evaluation}\n\nWe first evaluate our models on their ability to predict the next token in a sequence as measured by perplexity. Similar to training, we concatenate all documents in a given dataset using empty lines as separators, split the resulting sequence into non-overlapping blocks of 2048 tokens, and score each block independently.\\footnote{One limitation of this approach is that the first tokens in each block have limited context, as they do not condition on tokens from preceding blocks. Although more expensive, better results could be obtained using a sliding window approach. Nevertheless, this form of chunking the input is standard in language model evaluation.}\n\nWe evaluate and report perplexity in both \\textbf{in-domain} and \\textbf{out-of-domain} settings.\nIn-domain, we sample a held-out subset of the combined pretraining data (\\S\\ref{subsec:pretraining_data}).\nFor out-of-domain we use data from The Pile \\citep{gao2021thepile}, a public dataset that combines data from 22 diverse sources (e.g., ArXiv, Github, OpenSubtitles, etc.).\nWe report perplexities on the official test set of each individual subset, as well as the average across all subsets.\n\n\n\\subsubsection{Downstream Evaluation} \\label{subsec:downstream}\n\nWe target models that can perform downstream tasks well. Recent work shows that good perplexity performance does not always align with good performance on downstream tasks~\\cite{tay2021scale}. Hence, we evaluate our models accordingly.\n\n\\paragraph{Benchmarks.}\nWe evaluate our models on a subset of the tasks considered in \\citet{brown2020gpt3}.\nAs GPT-3 performance varies greatly across tasks and model sizes, we focus on tasks for which GPT-3 either demonstrated consistent gains from scaling, or consistent gains going from zero-shot to few-shot settings.\n\n\\textbf{Few-shot:} we use WinoGrande~\\citep{sakaguchi2020winogrande}, StoryCloze~\\citep{mostafazadeh-etal-2016-corpus} and OpenBookQA~\\citep{mihaylov-etal-2018-suit}, the only non-generation tasks for which \\citet{brown2020gpt3} reported meaningful gains over zero-shot at our scale.\\footnote{Defined as an improvement of at least 2 accuracy points over zero-shot learning and the majority class baseline for at least one GPT-3 model no bigger than 6.7B.} We exclude SuperGLUE, since we were not able to reproduce results reported in \\citet{brown2020gpt3} using the public GPT-3 API.\\footnote{Different from other tasks, we were not able to reproduce GPT-3 results on SuperGLUE using the OpenAI API and our evaluation protocol. The authors confirmed that they used a different evaluation protocol for SuperGLUE through personal correspondence.}\n\n\\textbf{Zero-shot:} in addition to the 3 few-shot tasks, we evaluate on ReCoRD~\\citep{zhang2018record}, HellaSwag~\\citep{zellers-etal-2019-hellaswag} and PIQA~\\citep{bisk2020piqa}. \\citet{brown2020gpt3} reported strong results and monotonic improvements from scaling on these tasks.\n\n\\paragraph{Evaluation protocol.} Following \\citet{brown2020gpt3}, we report results on the development set for all tasks except OpenBookQA and StoryCloze, for which we use the test set. For few-shot learning, we report the average results across 25 runs, randomly sampling a different set of few-shot examples from the training set each time.\\footnote{StoryCloze does not have a training set, so we follow \\citet{brown2020gpt3} and sample few-shot examples from the development set instead.} For priming, we further shuffle the few-shot examples for each test instance. Following \\citet{brown2020gpt3}, we use k=50 few-shot examples for WinoGrande, k=70 for StoryCloze and k=100 for OpenBookQA.\nIn cases where this exceeds the maximum context length for the model, we truncate the prompt keeping the maximum number of full examples that fit.\n\n\\paragraph{Baselines.} We compare to the published GPT-3 numbers \\citep{brown2020gpt3} as our primary baseline. To validate our experimental framework, we also evaluate GPT-3 leveraging the OpenAI API using our own evaluation code and settings. Unfortunately, the correspondence between model sizes and model names in the OpenAI API is not published. We follow other published work \\citep{gao2021thepile} and guess the correspondence based on our results from the public API as compared to results in \\citet{brown2020gpt3} (see \\S\\ref{subsec:zero_shot_results}).\n\n\\paragraph{Methods.} We compare both priming and fine-tuning-based approaches.\n\\begin{itemize}[leftmargin=*]\n\\item \\textbf{Priming:} We use a language model to separately score each label choice using the same templates as \\citet{brown2020gpt3}, and pick the one with the highest score. For few-shot learning, we use a single newline to separate examples. Our scoring function follows the description in \\citet{brown2020gpt3}:\n\\begin{itemize}\n\\item{\\bf For WinoGrande}, we take the log-likelihood of the common suffix of the different candidates.\n\\item{\\bf For OpenBookQA}, we normalize by the unconditional probability of each candidate by taking $\\frac{p(\\mathtt{completion}|\\mathtt{context})}{p(\\mathtt{completion}|\\mathtt{answer\\_context})}$, where we use the string \\textit{``Answer: ''} as answer\\_context.\n\\item{\\bf For ReCoRD}, we take the sum of per-token log-probabilities.\\footnote{This is different from \\citet{brown2020gpt3}, who take the average per-token log-probability for this task. This worked worse in our preliminary experiments.}\n\\item{\\bf For all the other tasks}, we take the average of per-token log-probabilities, ignoring the common prefix of the different candidates.\n\\end{itemize}\n\\item \\textbf{Fine-tuning:} Although supervised fine-tuning of pre-trained LMs on task specific training data, $\\mathcal{D}$, requires updating and storage of all model parameters per task, the process typically produces significant task specific performance improvements. We contrast the fine-tuning performance of sparse models and their dense counterparts following \\cite{radford2018gpt}, which applies an additional task-specific linear layer $W_y$ on the representation from the final transformer block for each input candidate separately, followed by a softmax layer.\nWe fine-tune all model parameters using the entire training set (fully supervised learning). In addition to our zero-shot tasks, we also evaluate on 3 widely-used classification tasks: BoolQ~\\citep{clark-etal-2019-boolq}, MNLI~\\citep{williams-etal-2018-broad} and SST-2~\\citep{socher-etal-2013-recursive}. More details are in Appendix \\ref{sec:fine_tuning_settings}.\n\n\\end{itemize}\n\n\n\n\\begin{figure*}[t]\n \\centering\n \\begin{subfigure}[b]{0.485\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/ppl-valid.pdf}\n \\caption{In-domain (validation)}\n \\label{fig:ppl-valid}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{0.485\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/ppl-thepile.pdf}\n \\caption{Out-of-domain (the Pile)}\n \\label{fig:ppl-thepile}\n \\end{subfigure}\n \\hfill\n \\caption{\\textbf{Language modeling perplexity.} For the Pile, we report the average perplexity across the 22 subsets.}\n \\label{fig:ppl}\n\\end{figure*}\n\n\\begin{figure*}[t]\n \\centering\n \\begin{subfigure}[b]{0.485\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/efficiency-gain-ppl.pdf}\n \\caption{Language modeling (the Pile)}\n \\label{fig:efficiency-gain-ppl}\n \\end{subfigure}\n \\hfill\n \\begin{subfigure}[b]{0.485\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/efficiency-gain-zeroshot.pdf}\n \\caption{Zero-shot priming}\n \\label{fig:efficiency-gain-zeroshot}\n \\end{subfigure}\n \\hfill\n \\caption{\n \\textbf{Estimate of how much more efficient MoEs are relative to dense models in representative datasets.} A speedup factor of $y$ indicates that an MoE model can match the performance of the corresponding dense model using $y$ times less compute. Refer to \\S\\ref{subsubsec:efficiency-gain} for more details.\n }\n \\label{fig:efficiency-gain-datasets}\n\\end{figure*}\n\n\\subsubsection{MoE speedup factor}\n\\label{subsubsec:efficiency-gain}\n\nWe hypothesize that sparse models can achieve comparable performance at a smaller compute budget. As such, it is informative to measure how much more efficient MoEs are at achieving a specific performance level relative to dense models. We estimate how many FLOPs $\\flop (t)$ the model needs to achieve performance $t$ in a particular task (as measured by perplexity for language modeling and accuracy for downstream tasks) using either an MoE or a dense model. Given that we only have discrete observations, we estimate exact missing values by interpolating on a logarithmic scale as follows:\n$$ \\flop(t) = \\exp \\left( \\log \\flop_{lo}(t) + r \\left(\\log \\flop_{hi}(t) - \\log \\flop_{lo}(t) \\right) \\right)$$\nwhere $r = \\frac{t - t_{lo}}{t_{hi} - t_{lo}}$, $t_{lo}$ and $t_{hi}$ are the closest performance to $t$ from the available models while being lower and higher than $t$, respectively, and $\\flop_{lo}(t)$ and $\\flop_{hi}$ are their corresponding training cost in ZFLOPs.\n\nThe interpolation gives us matching performance levels for dense and MoE models. We use them to compute the MoE speedup factor $\\flop_{dense}(t) \/ \\flop_{moe}(t)$. For example, if a dense model requiring 20 ZFLOPs achieves a performance of $90\\%$ on a given task and a MoE model requiring 5 ZFLOPs achieves the same performance, then the formula produces saving factor of 4. We visualize the savings curve using $\\flop_{dense}(t)$ in the $x$ axis, which allows us to contrast speedup in different tasks in a comparable scale.\n\n\n\n\n\n\n\\begin{table}[t]\n\\begin{center}\n\\begin{small}\n\\addtolength{\\tabcolsep}{-2.5pt}\n\\resizebox{0.48\\textwidth}{!}{\n\\begin{tabular}{cr|cccccc|c}\n\\toprule\n&& RE & HS & PI & WG & SC & OB & avg \\\\\n\\midrule\n\\multirow{8}{*}{\\shortstack{GPT-3 \\\\ (paper)}}\n& 125M & 70.8 & 33.7 & 64.6 & 52.0 & 63.3 & 35.6 & 53.3 \\\\\n& 355M & 78.5 & 43.6 & 70.2 & 52.1 & 68.5 & 43.2 & 59.4 \\\\\n& 760M & 82.1 & 51.0 & 72.9 & 57.4 & 72.4 & 45.2 & 63.5 \\\\\n& 1.3B & 84.1 & 54.7 & 75.1 & 58.7 & 73.4 & 46.8 & 65.5 \\\\\n& 2.7B & 86.2 & 62.8 & 75.6 & 62.3 & 77.2 & 53.0 & 69.5 \\\\\n& 6.7B & 88.6 & 67.4 & 78.0 & 64.5 & 77.7 & 50.4 & 71.1 \\\\\n& 13B & 89.0 & 70.9 & 78.5 & 67.9 & 79.5 & 55.6 & 73.6 \\\\\n& 175B & 90.2 & 78.9 & 81.0 & 70.2 & 83.2 & 57.6 & 76.9 \\\\\n\\midrule\n\\multirow{4}{*}{\\shortstack{GPT-3 \\\\ (API)}}\n& ada & 77.4 & 42.9 & 70.3 & 52.9 & 68.6 & 41.0 & 58.9 \\\\\n& babb. & 83.1 & 55.1 & 74.5 & 59.4 & 73.3 & 45.6 & 65.2 \\\\\n& curie & 87.1 & 67.8 & 77.1 & 64.3 & 77.7 & 50.8 & 70.8 \\\\\n& davi. & -- & 78.8 & 80.0 & 70.0 & 83.1 & 58.8 & -- \\\\\n\\midrule\n\\multirow{6}{*}{\\shortstack{Ours \\\\ (dense)}}\n& 125M & 69.3 & 33.7 & 65.3 & 52.1 & 66.0 & 35.4 & 53.6 \\\\\n& 355M & 78.1 & 46.2 & 70.6 & 54.2 & 71.0 & 42.0 & 60.4 \\\\\n& 1.3B & 83.5 & 58.4 & 74.6 & 58.1 & 76.8 & 49.4 & 66.8 \\\\\n& 2.7B & 85.8 & 65.9 & 76.6 & 61.4 & 78.2 & 49.6 & 69.6 \\\\\n& 6.7B & 87.5 & 70.2 & 78.2 & 64.7 & 80.5 & 51.8 & 72.2 \\\\\n& 13B & 88.5 & 73.7 & 79.0 & 67.6 & 80.9 & 55.4 & 74.2 \\\\\n\\midrule\n\\multirow{4}{*}{\\shortstack{Ours \\\\ (MoE)}}\n& 15B & 77.8 & 53.2 & 74.3 & 53.4 & 73.6 & 42.0 & 62.4 \\\\\n& 52B & 83.4 & 64.9 & 76.8 & 57.4 & 75.9 & 51.0 & 68.2 \\\\\n& 207B & 86.0 & 70.5 & 78.2 & 60.9 & 78.1 & 50.8 & 70.7 \\\\\n& 1.1T & 88.0 & 78.6 & 80.3 & 66.4 & 81.8 & 55.2 & 75.0 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\end{small}\n\\end{center}\n\\caption{\n\\textbf{Zero-shot priming accuracy.}\n\\textit{GPT-3 (paper)} results taken from \\citet{brown2020gpt3}, all the other results were obtained by us as described in \\S\\ref{subsec:downstream}.\n\\texttt{RE}: ReCoRD, \\texttt{HS}: HellaSwag, \\texttt{PI}: PIQA, \\texttt{WG}: WinoGrande, \\texttt{SC}: StoryCloze, \\texttt{OB}: OpenBookQA.\nWe do not evaluate the largest GPT-3 model (davinci) on \\texttt{RE} given the high price.\n}\n\\label{tab:zeroshot}\n\\end{table}\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=\\linewidth]{figs\/zeroshot-avg.pdf}\n\\caption{\n\\textbf{Zero-shot priming accuracy averaged across 6 tasks as a function of compute cost.} Each point corresponds to a different, fully-trained model (see Table \\ref{tab:models}).\n\\textit{GPT-3 (paper)} results taken from \\citet{brown2020gpt3}.\n}\n\\label{fig:zeroshot}\n\\end{figure}\n\n\n\n\\section{Results and Analysis}\\label{sec:results}\n\n\n\\subsection{Language modeling perplexity}\n\nWe report our perplexity results in Figure \\ref{fig:ppl}, and visualize the speedup curves in representative subsets of the Pile \\citep{gao2021thepile} in Figure \\ref{fig:efficiency-gain-ppl}. Refer to Appendix \\ref{app:full-results} for full results for all the 22 subsets of the Pile.\n\nWe observe that all MoE models outperform their dense counterparts in all datasets, but their advantage greatly varies across domains and models. MoEs are most efficient when evaluated in-domain, where they are able to match the performance of dense models trained with 8-16x more compute (see Figure \\ref{fig:efficiency-gain}). The improvement is more modest in out-of-domain settings, bringing a speedup of 2-4 on the Pile. This is reflected in Figure \\ref{fig:ppl}, where the gap between the MoE and dense curves is substantially smaller in out-of-domain settings. Moreover, the advantage of MoEs over dense models decreases at scale: MoEs need $\\sim$4 times less compute to match the performance of dense models trained with 2-6 ZFLOPs, but the speedup is $\\sim$2 for dense models trained with $\\sim$30 ZFLOPs. %\n\nWe also observe large difference across the subsets of the Pile, which correspond to different domains. As shown in Figure \\ref{fig:efficiency-gain-ppl}, MoEs obtain the largest speedups in subsets that are closest to the training corpus (e.g., CommonCrawl). The efficiency gains are more moderate but still remarkable for other domains like ArXiv and OpenSubtitles. Our largest MoE model barely outperforms its dense counterpart on DM Mathematics (7.63 vs. 7.66 perplexity), which is arguably very different from the training domain.\n\n\n\\subsection{Downstream task evaluation}\n\n\\subsubsection{Zero-shot learning}\\label{subsec:zero_shot_results}\n\nWe report zero-shot results in Table \\ref{tab:zeroshot}, and visualize how the different model families scale in Figure \\ref{fig:zeroshot}.\n\nOur dense models perform at par with their GPT-3 counterparts. This is consistent across different tasks, with our models doing marginally better on average. We are thus able to match \\citet{brown2020gpt3} despite some notable differences in our setup (e.g., different training corpus), establishing a solid baseline to evaluate MoE models on downstream tasks. Similarly, when using our own code to evaluate the strongest GPT-3 API backend (\\textit{davinci}), we obtain numbers that replicate those reported in the original paper for their largest model, which reinforces that our evaluation settings are comparable to \\citet{brown2020gpt3}.\\footnote{We assume that \\textit{ada} corresponds to the 355M model, \\textit{babbage} corresponds to the 1.3B model, and \\textit{curie} corresponds to the 6.7B model based on the API evaluation results.}\n\n\nAs with language modeling, MoEs outperform their dense counterparts for all datasets and model sizes. But, once again, we find the advantage narrows at scale as illustrated in Figure \\ref{fig:zeroshot}. Similar to the domain differences in language modeling, we observe differences across downstream tasks. As shown in Figure \\ref{fig:efficiency-gain-zeroshot}, MoEs obtain significant speedups in certain tasks like HellaSwag and PIQA, but this improvement is more modest in other tasks such as ReCoRD and Winogrande.\n\n\n\n\n\n\n\n\\subsubsection{Few-shot learning}\n\nWe report our few-shot results in Table \\ref{tab:fewshot} and plot the corresponding improvement over zero-shot in Figure \\ref{fig:fewshot}.\n\nOur dense baselines perform at par or slightly better than GPT-3. We observe that the improvement over zero-shot is bigger for larger models, further supporting that certain capabilities in language models emerge at scale \\citep{brown2020gpt3}. Finally, we find that our larger MoE models also benefit from few-shot learning, outperforming their dense counterparts in all conditions. However, the improvements going from zero-shot to few-shot are smaller for MoE models compared to their dense counterparts. For example, the average for the 6.7B dense model improves by 3.6 points to 69.3 going from zero-shot to few-shot, whereas the corresponding 1.1T model improves by 2.3 points yielding 70.1.\n\n\\begin{table}[t!]\n\\begin{center}\n\\begin{small}\n\\addtolength{\\tabcolsep}{-2pt}\n\\resizebox{0.48\\textwidth}{!}{\n\\begin{tabular}{cr|ccc|c}\n\\toprule\n&& WG & SC & OB & avg \\\\\n\\midrule\n\\multirow{8}{*}{\\shortstack{GPT-3 \\\\ (paper)}}\n& {125M} & 51.3 \\scriptsize{--0.7} & 62.3 \\scriptsize{--1.0} & 37.0 \\scriptsize{+1.4} & 50.2 \\scriptsize{--0.1} \\\\\n& {355M} & 52.6 \\scriptsize{+0.5} & 70.2 \\scriptsize{+1.7} & 43.6 \\scriptsize{+0.4} & 55.5 \\scriptsize{+0.9} \\\\\n& {760M} & 57.5 \\scriptsize{+0.1} & 73.9 \\scriptsize{+1.5} & 48.0 \\scriptsize{+2.8} & 59.8 \\scriptsize{+1.5} \\\\\n& {1.3B} & 59.1 \\scriptsize{+0.4} & 76.1 \\scriptsize{+2.7} & 50.6 \\scriptsize{+3.8} & 61.9 \\scriptsize{+2.3} \\\\\n& {2.7B} & 62.6 \\scriptsize{+0.3} & 80.2 \\scriptsize{+3.0} & 55.6 \\scriptsize{+2.6} & 66.1 \\scriptsize{+2.0} \\\\\n& {6.7B} & 67.4 \\scriptsize{+2.9} & 81.2 \\scriptsize{+3.5} & 55.2 \\scriptsize{+4.8} & 67.9 \\scriptsize{+3.7} \\\\\n& {13B} & 70.0 \\scriptsize{+2.1} & 83.0 \\scriptsize{+3.5} & 60.8 \\scriptsize{+5.2} & 71.3 \\scriptsize{+3.6} \\\\\n& {175B} & 77.7 \\scriptsize{+7.5} & 87.7 \\scriptsize{+4.5} & 65.4 \\scriptsize{+7.8} & 76.9 \\scriptsize{+6.6} \\\\\n\\midrule\n\\multirow{6}{*}{\\shortstack{Ours \\\\ (dense)}}\n& {125M} & 52.2 \\scriptsize{+0.1} & 64.7 \\scriptsize{--1.3} & 35.0 \\scriptsize{--0.4} & 50.7 \\scriptsize{--0.5} \\\\\n& {355M} & 53.7 \\scriptsize{--0.5} & 72.2 \\scriptsize{+1.1} & 42.0 \\scriptsize{+0.0} & 56.0 \\scriptsize{+0.2} \\\\\n& {1.3B} & 60.1 \\scriptsize{+2.0} & 78.6 \\scriptsize{+1.9} & 49.4 \\scriptsize{+0.0} & 62.7 \\scriptsize{+1.3} \\\\\n& {2.7B} & 63.9 \\scriptsize{+2.5} & 82.1 \\scriptsize{+3.8} & 53.2 \\scriptsize{+3.6} & 66.4 \\scriptsize{+3.3} \\\\\n& {6.7B} & 67.6 \\scriptsize{+2.9} & 83.2 \\scriptsize{+2.7} & 57.0 \\scriptsize{+5.2} & 69.3 \\scriptsize{+3.6} \\\\\n& {13B} & 71.0 \\scriptsize{+3.5} & 85.0 \\scriptsize{+4.1} & 59.5 \\scriptsize{+4.1} & 71.8 \\scriptsize{+3.9} \\\\\n\\midrule\n\\multirow{4}{*}{\\shortstack{Ours \\\\ (MoE)}}\n& {15B} & 52.5 \\scriptsize{--0.9} & 71.4 \\scriptsize{--2.1} & 42.2 \\scriptsize{+0.2} & 55.4 \\scriptsize{--0.9} \\\\\n& {52B} & 58.1 \\scriptsize{+0.7} & 77.5 \\scriptsize{+1.6} & 48.9 \\scriptsize{--2.1} & 61.5 \\scriptsize{+0.1} \\\\\n& {207B} & 62.8 \\scriptsize{+1.9} & 81.1 \\scriptsize{+3.0} & 52.4 \\scriptsize{+1.6} & 65.4 \\scriptsize{+2.2} \\\\\n& {1.1T} & 68.6 \\scriptsize{+2.3} & 83.9 \\scriptsize{+2.1} & 57.7 \\scriptsize{+2.5} & 70.1 \\scriptsize{+2.3} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\end{small}\n\\end{center}\n\\caption{\n\\textbf{Few-shot priming accuracy and absolute improvement over zero-shot.}\n\\textit{GPT-3 (paper)} results taken from \\citet{brown2020gpt3}, all the other results were obtained by us as described in \\S\\ref{subsec:downstream}.\n\\texttt{WG}: WinoGrande, \\texttt{SC}: StoryCloze, \\texttt{OB}: OpenBookQA.}\n\\label{tab:fewshot}\n\\end{table}\n\n\n\\begin{figure}[t!]\n\\centering\n\\includegraphics[width=\\linewidth]{figs\/fewshot-improvement.pdf}\n\\caption{\n\\textbf{Absolute accuracy improvement going from zero-shot to few-shot}, averaged across 3 tasks. Each point corresponds to a different, fully-trained model (see Table \\ref{tab:models}).\n\\textit{GPT-3 (paper)} results taken from \\citet{brown2020gpt3}.\n}\n\\label{fig:fewshot}\n\\end{figure}\n\n\n\n\n\n\\subsubsection{Supervised Fine-Tuning}\nTable \\ref{tab:finetuning} contrasts full fine-tuning performance of MoE models with their dense counterparts on 8 datasets, using zero-shot accuracy as a baseline for reference. We did not fine-tune the 6.7B and 13B dense models and the 1.1T MoE models, owing to their high resource needs. As expected, supervised fine-tuning yields substantial performance benefits for all dense models across all datasets, over zero-shot performance. In contrast, although fine-tuning of MoE models produces substantial benefits for Storycloze, BoolQ, SST-2, MNLI and some improvements on OpenBookQA, it results in worse performance for HellaSwag, PIQA, and Winogrande. For the cases where we see improvements, the accuracy of fine-tuned MoE models approach that of their corresponding dense models. For this comparison, we fine-tune MoE models exactly as we do the dense models. While MoE models may benefit from alternative fine-tuning approaches, for example, selective fine-tuning of the expert or non-expert parameters, we leave such exploration to future work. \n\n\\begin{table}[t!]\n\\begin{center}\n\\begin{small}\n\\addtolength{\\tabcolsep}{-2.5pt}\n\\resizebox{0.48\\textwidth}{!}{\n\\begin{tabular}{cr|cccc|ccc}\n\\toprule\n&& \\multicolumn{4}{c|}{Ours (Dense)} & \\multicolumn{3}{c}{Ours (MoE)} \\\\\n&& 125M & 355M & 1.3B & 2.7B & 15B & 52B & 207B \\\\ %\n\\midrule\n\\multirow{2}{*}{\\texttt{SC}}\n& zero-shot & 66.0 & 71.0 & 76.8 & 78.2 & 73.6 & 75.9 & 78.1 \\\\ %\n& fine-tune & 87.8 & 89.5 & 93.8 & 97.0 & 80.3 & 84.9 & 80.9 \\\\ %\n\\midrule\n\\multirow{2}{*}{\\texttt{OB}}\n& zero-shot & 35.4 & 42.0 & 49.4 & 49.6 & 42.0 & 51.0 & 50.8 \\\\ %\n& fine-tune & 50.6 & 59.0 & 67.4 & 70.8 & 51.2 & 51.4 & 51.0 \\\\ %\n\\midrule\n\\multirow{2}{*}{\\texttt{BQ}}\n& zero-shot & 56.1 & 58.6 & 58.7 & 60.3 & 60.9 & 56.0 & 54.2 \\\\ %\n& fine-tune & 73.2 & 75.2 & 79.6 & 84.6 & 71.6 & 75.3 & 77.5 \\\\ %\n\\midrule\n\\multirow{2}{*}{\\texttt{MN}}\n& zero-shot & 46.2 & 52.1 & 55.3 & 56.0 & 49.3 & 52.1 & 52.6 \\\\ %\n& fine-tune & 80.9 & 84.3 & 84.1 & 88.9 & 77.7 & 81.2 & 78.7 \\\\ %\n\\midrule\n\\multirow{2}{*}{\\texttt{SST-2}}\n& zero-shot & 50.9 & 50.9 & 51.6 & 51.9 & 51.6 & 50.9 & 50.9 \\\\ %\n& fine-tune & 92.9 & 92.9 & 94.8 & 93.4 & 89.3 & 90.1 & 90.3 \\\\ %\n\\midrule\n\\multirow{2}{*}{\\texttt{HS}}\n& zero-shot & 33.7 & 46.2 & 58.4 & 65.9 & 53.2 & 64.9 & 70.5 \\\\ %\n& fine-tune & 50.7 & 64.8 & 74.1 & 90.0 & 37.3 & 45.4 & 42.2 \\\\ %\n\\midrule\n\\multirow{2}{*}{\\texttt{PI}}\n& zero-shot & 65.3 & 70.6 & 74.6 & 76.6 & 74.3 & 76.8 & 78.2 \\\\ %\n& fine-tune & 68.2 & 71.7 & 71.2 & 80.3 & 66.3 & 66.1 & 68.3 \\\\ %\n\\midrule\n\\multirow{2}{*}{\\texttt{WG}}\n& zero-shot & 52.1 & 54.2 & 58.1 & 61.4 & 53.4 & 57.4 & 60.9 \\\\ %\n& fine-tune & 65.7 & 63.3 & 67.4 & 69.5 & 50.2 & 56.0 & 50.4 \\\\ %\n\\bottomrule\n\\end{tabular}}\n\\end{small}\n\\end{center}\n\\caption{\n\\textbf{Fully supervised fine-tuning accuracy compared with zero-shot accuracy.} \\texttt{SC}: StoryCloze, \\texttt{OB}: OpenBookQA, \\texttt{BQ}: BoolQ, \\texttt{MN}: MNLI, \\texttt{HS}: HellaSwag, \\texttt{PI}: PIQA, \\texttt{WG}: WinoGrande. Largest models omitted owing to their high resource utilization.}\n\\label{tab:finetuning}\n\\end{table}\n\n\n\n\\section{Conclusion}\n\nWe present results for scaling sparse Language Models up to 1.1T parameters. We observe that up to this scale sparse models offer better performance vs. computation trade-off when compared to their dense counterparts for language modeling, zero- and few-shot learning. While the gap begins to close at scale our biggest sparse model outperforms its dense counterpart where the latter requires twice as much computation. These results confirm that sparse MoE models can provide an alternative to widely used dense architectures that saves computation and reduces model energy consumption.\n\n\n\n\\section*{Ethical considerations}\nPrevious work~\\cite{sheng2019woman,bordia2019identifying,nadeem2020stereoset,de2021stereotype} has observed that language models absorb bias and toxicity represented in the training data. So as to better understand the potential harms of our models in this front, we evaluated them on StereoSet~\\cite{nadeem2020stereoset} and CrowS-Pairs~\\cite{nangia-etal-2020-crows}, and report our results in Appendix \\ref{app:potential_harms}. Our results show that the percentage of bias and stereotype in dense and MoE models is comparable, especially at scale.\nMoreover, in general, we note worse performance (more bias\/stereotyping) at larger scales. This observation points to more research needed in order to mitigate such behavior. Intuitively however, we believe that sparse models may be inherently more controllable -- e.g.~designing specific experts -- than dense models. We leave this line of investigation for future research. \n\nAnother concern of scaling language models is the energy usage and the associated environmental impact required for training, which we discuss in detail in Appendix \\ref{app:co2}. Nevertheless, our work shows that MoEs can be a more compute-efficient alternative to traditional dense models, which could alleviate the environmental impact of future scaling efforts.\nMoreover, by releasing all of our pre-trained language models, we believe we have alleviated some exploration burden for the community and the environment, allowing for more efficient offsets for other researchers.\n\nIn the spirit of transparency and allowing for maximal replicability and accountability, we include data and model cards together with our code. \n\n\n\\section*{Limitations}\n\nOur study is limited to one specific MoE configuration. In particular, our sparse and dense models use the same hyperparameters and model structure, closely following GPT-3. However, it is possible that this configuration is suboptimal for scaling MoE models. Similarly, we did not explore different MoE-specific hyperparameters, such as the number of experts. Finally, while our work reveals that the performance gap between MoE and dense models varies greatly across tasks and domains, the specific factors that make certain tasks more favorable for MoEs remain unclear.\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nEnergy levels, eigenvalues of a Hamiltonian, play the most primary role in\ndetermining properties of a quantum system. When energy levels cross\nor avoided cross as parameters of a Hamiltonian vary, various interesting\nphenomena happen. For example, if two instantaneous energy levels of a \ntime-dependent Hamiltonian are avoided crossing, the non-adiabatic tunneling \ncalled the Landau-Zener tunneling between them takes place~\\cite{Landau}. \nClosely related to this, the runtime of adiabatic quantum computation is \ninversely proportional to the square of the energy gap between the ground \nand first exited levels~\\cite{Farhi01}. An eigenstate encircling \nadiabatically degeneracy points accumulates a Berry phase in addition to \na dynamical phase~\\cite{Berry84,Shapere}. In quantum chemistry, a conical \nintersection of electronic energy surfaces of molecules plays a key role \nin understanding ultrafast radiationless reactions~\\cite{Yarkony96,Kuppermann}.\nA quantum phase transition, a dramatic change in a ground state as parameters \nof a system vary, is related with crossings or avoided crossings of\ntwo lowest energy levels~\\cite{Vojta03}. Kais {\\it et al}. have shown that \nthe finite size scaling method can be used for studying the critical behavior, \ni.e., the level degeneracy or absorption, of a few-body quantum Hamiltonian \n$H(\\lambda_1,\\cdots,\\lambda_k)$ as a function of a set of parameters \n$\\{\\lambda_i\\}$~\\cite{kais0,kais12}. These parameters could be the external \nfields, inter-atomic distances, nuclear charges for stability of negative \nions, cluster size, and optical lattice parameters such as the potential \ndepth~\\cite{kais34}. Thus, it is important to develop a way of finding level \ncrossings and to understand how eigenstates or relevant physical quantities \nchange at crossing or avoided crossings.\n\nRecently Bhattacharya and Raman presented a powerful algebraic method for \nfinding level crossings without solving an eigenvalue problem \ndirectly~\\cite{Bhattacharya06}. Along with this mathematical \nway, it is necessary to understand what physical quantities can be used to \ndetect or characterize crossings or avoided crossings. First of all, the \nmeasurement of a Berry phase could be a good way to detect level crossings \nbecause it due to level crossings. It is well known that avoided crossings \nor glancing intersections are not the source of Berry phases~\\cite{Yarkony96}.\nIs there any way that Berry phases can detect {\\it avoided level \ncrossings} ? Here we show that the marginal Berry phase of an entangled\nstate could be an indicator to avoided level crossings.\n\nThe entropy is an another indicator to level crossings. Since level crossing \nor avoided crossings are accompanied with a drastic change in eigenstates, \nany contents of information on relevant eigenstates may also vary. The Shannon \nentropy of the electron density measures the delocalization or the lack of \nstructure in the respective distribution. Thus the Shannon entropy is maximal \nfor uniform distribution, that is, for an unbound system, and is minimal when\nthe uncertainty about the structure of the distribution is minimal~\\cite{kais5}.\nGonz\\'alez-F\\'erez and Dehesa showed that the Shannon entropy could be \nused as an indicator of avoided crossings~\\cite{Gonzalez03}. The von Neumann \nentropy, the quantum version of the Shannon entropy, is a good entanglement \nmeasure for a bipartite pure state. In quantum information, much attention \nhas been paid to the relation between entanglement and quantum phase \ntransitions~\\cite{Amico08,kais6}. Recently one of the authors investigated \nthe relation between entanglement, Berry phases, and level crossings for two \nqubits with the XY-type interaction and found that the level crossing is not \nalway accompanied with the abrupt change in entanglement~\\cite{scoh08}. \n\nIn this paper, in order to study how entanglement and Berry phases vary at \nlevel crossings, we consider the Breit-Rabi Hamiltonian describing a hyperfine \ninteraction of electron and nuclear spins in a uniform magnetic \nfield~\\cite{Breit-Rabi}. It is shown that the von Neumann entropy of the\nelectron (or nuclear) spin is maximum at avoided crossings. \nIt is demonstrated that the significant changes in Berry phases and \nentanglement are closely related to level crossings. \nWe show that the marginal Berry phase of the electron (or nuclear) spin \ncould be a good indicator to avoided level crossings. \nThe marginal Berry phase has nodal points at the avoided crossing points. \n\nThe paper is organized as follows. In Sec.~\\ref{section_BR}, the Breit-Rabi\nHamiltonian is introduced. In Sec.~\\ref{section_hydrogen}, as a specific\napplication of the Breit-Rabi Hamiltonian, we consider a hyperfine interaction\nbetween a nuclear spin $1\/2$ and an electron spin $1\/2$ of a hydrogen atom \nin a magnetic field. We analyze the close relation between entanglement,\nBerry phases, and level crossings. In Sec.~\\ref{section_sodium}, we make \nan similar analysis for a sodium atom with a nuclear spin $3\/2$ and an \nelectron spin $1\/2$. In Sec.~\\ref{section_conclusion}, we summarize the main \nresults.\n\n\\section{Breit-Rabi Hamiltonian}\n\\label{section_BR}\n\nLet us consider an atom with a single valence electron in the ground state \nwith orbital angular momentum $L=0$. In the presence of a uniform magnetic \nfield $B$ in the $z$ direction, its atomic spectrum is described by \nthe Breit-Rabi Hamiltonian~\\cite{Breit-Rabi}, which is given by the sum of \nthe hyperfine interaction between a nuclear spin $\\bf I$ and an electron \nspin $\\bf S$ and their Zeeman couplings to the magnetic field \n\\begin{equation}\nH = A\\, {\\bf I}{\\bm \\cdot}{\\bf S} + (a S_z + b I_z)B \\,,\n\\label{Hamil_BR}\n\\end{equation}\nwhere $A$ is the hyperfine coupling constant, $a=\\gamma_e\\hbar$, and \n$b=\\gamma_n\\hbar$. Here $\\gamma_e$ and $\\gamma_n$ are the electron and \nnuclear gyromagnetic ratios, respectively. The electron spin operator \n${\\bf S}$ and the nuclear spin operator ${\\bf I}$ are measured in the unit \nof $\\hbar$. \n\nThe Breit-Rabi Hamiltonian~(\\ref{Hamil_BR}) is well studied to describe double\nresonance in nuclear magnetic resonance~\\cite{Slichter} and the muon spin \nrotation in semiconductors~\\cite{Patteson88}. Although simple and well\nunderstood, it still continues to provide new insights. Recently Bhattacharya \nand Raman found a new class of invariants of the Breit-Rabi\nHamiltonian~\\cite{Bhattacharya06}. As will be shown here, it is a prime example\nfor showing the close relation between level crossings, entanglement, and \ngeometric phases. Also it is related to a Hamiltonian of \nelectron spin qubits in quantum dots~\\cite{Loss98} where the Heisenberg \ninteraction between two electron spins can be turned on and off to \nimplement the controlled-not gate.\n\nBefore applying the Hamiltonian~(\\ref{Hamil_BR}) to specific systems, let us\nlook at its general properties. If $B =0$, then $H$ commutes with \nboth the square of the total spin operator ${\\bf J}^2$ and $J_z$, where the \ntotal spin operator is defined by ${\\bf J} \\equiv {\\bf I} + {\\bf S}$. However,\nfor $B\\ne 0$, due to the fact that $a \\ne b$, the Hamiltonian~(\\ref{Hamil_BR})\nno longer commutes with ${\\bf J}^2$, but still commutes with $J_z$. So the \neigenvalue $m$ of $J_z$ is a good quantum number for the Breit-Rabi \nHamiltonian. With ladder operators, $S_\\pm = S_x \\pm i S_y$ and \n$I_\\pm = I_x \\pm i I_y$, the Hamiltonian~(\\ref{Hamil_BR}) can be rewritten as\n\\begin{equation}\nH = A\\,I_zS_z + \\frac{A}{2}(S_{+}I_{-} + S_{-}I_{+}) + B (a S_z + b I_z)\\,.\n\\label{Hamil_BR2}\n\\end{equation}\nLet us use a simple notation $\\ket{m_S,m_I}$ to represent the product state \n$\\ket{S,m_S}\\otimes\\ket{I,m_I}$, where $\\ket{S,m_S}$ is an eigenstate of\n${\\bf S}^2$ and $S_z$, and $\\ket{I,m_I}$ is an eigenstate of ${\\bf I}^2$ and \n$I_z$. The first and third terms in Eq.~(\\ref{Hamil_BR2}) give the diagonal \nmatrix elements \n\\begin{subequations}\n\\begin{equation}\n\\bra{m_S\\,m_I}H\\ket{m_S\\,m_I} \n= f(m_S, m_I) = A\\,m_S\\,m_I + m_S\\,aB + m_I\\,bB \\,.\n\\end{equation}\nThe second term in Eq.~(\\ref{Hamil_BR2}) corresponds to the off-diagonal \nmatrix elements\n\\begin{align}\n&\\bra{m_S'\\,m_I'}S_{+}I_{_-}\\ket{m_S\\,m_I} \\nonumber\\\\ \n&=\\sqrt{(S-m_S)(S+m_s+1)}\\,\\sqrt{(I+m_I)(I-m_I+1)}\\,\n \\delta_{m_S',m_S+1}\\delta_{m_I',m_I-1}\\,.\n\\end{align}\n\\label{matrix_element}\n\\end{subequations}\nSince $m_S'-m_S =1$ and $m_I' - m_I= -1$ (or vice versa), one has the \nselection rule, $\\Delta m = (m_S'+m_I') -(m_S+m_I) =0$, that is, the magnetic\nquantum number $m=m_S + m_I$ is conserved. This implies that the \nHamiltonian~(\\ref{Hamil_BR}) is block diagonal in the basis set \n$\\{\\ket{m_S,m_I}\\}$ ordered by $m$. \n\n\\section{The Hydrogen Atom in a Uniform Magnetic Field}\n\\label{section_hydrogen}\n\n\\subsection{Eigenvalues and Eigenstates}\n\nAs a simple but real system described by the Hamiltonian~(\\ref{Hamil_BR}), \nlet us consider the interaction between the nuclear spin $I=1\/2$ and the \nelectron spin $S=1\/2$ of a hydrogen atom in a uniform magnetic field.\nSince $H$ commutes with the $z$-component of the total spin operator, \n$J_z = S_z + I_z$, it is convenient to arrange the product basis \n$\\{\\ket{m_S,m_I}\\}$ in the decreasing order of the magnetic quantum number \n$m$ of $J_z$ as \n$\\left\\{\\ket{\\tfrac{1}{2},\\tfrac{1}{2}}, \\ket{\\tfrac{1}{2},-\\tfrac{1}{2}},\n\\ket{-\\tfrac{1}{2},\\tfrac{1}{2}}, \\ket{-\\tfrac{1}{2},-\\tfrac{1}{2}}\\right\\}$. \nBy means of Eqs.~(\\ref{matrix_element}), the Breit-Rabi Hamiltonian for a\nhydrogen atom can be written in the ordered basis\n\\begin{align}\nH\n= \\frac{1}{4}\n\\begin{pmatrix}\nA + 2(a+b)B & 0 & 0 & 0 \\\\\n0 & -A+2(a-b)B & 2A & 0 \\\\\n0 & 2A & -A -2(a-b)B & 0 \\\\\n0 & 0 & 0 & A -2(a+b)B\n\\end{pmatrix}\\,.\n\\label{Hamil_Hydrogen}\n\\end{align}\nThe Hamiltonian~(\\ref{Hamil_Hydrogen}) is block diagonal, so it is \nstraightforward to obtain its eigenvalues and eigenvectors.\nThe subspace of $m=\\pm 1$ is spanned by \n$\\left\\{ \\ket{\\tfrac{1}{2},\\tfrac{1}{2}}, \n \\ket{-\\tfrac{1}{2},-\\tfrac{1}{2}}\\right\\}$. \nThe block Hamiltonian on this subspace is already diagonal and has its \neigenvalues and eigenvectors\n\\begin{subequations}\n\\label{H_eigen1}\n\\begin{align}\nE_{\\pm 1} &= \\frac{A}{4} \\pm \\frac{1}{2}(a + b)B \\,,\\\\\n\\ket{E_{\\pm 1}} &= \\ket{\\pm\\tfrac{1}{2},\\pm\\tfrac{1}{2}},\n\\label{H_eva}\n\\end{align}\n\\end{subequations}\nwhere the subscripts `$\\pm 1$' in $E_{\\pm 1}$ denote the magnetic quantum \nnumber $m=\\pm 1$. The block Hamiltonian with $m=0$ is defined on the subspace \nof $\\left\\{ \\ket{\\tfrac{1}{2}, -\\tfrac{1}{2}}, \n \\ket{\\tfrac{1}{2}, -\\tfrac{1}{2}} \\right\\}$ and is written as \n\\begin{align}\nH_{m=0} \n&= \\frac{1}{4}\n \\begin{pmatrix}\n -A +2(a-b)B & 2A \\\\\n 2A & -A - 2(a-b)B\n \\end{pmatrix}\\,.\n\\end{align}\nOne can interpret the Hamiltonian $H_{m =0}$ as that of a spin in an \neffective magnetic field in the $x$-$z$ plane, ${\\bf B}_{\\rm eff} \n\\equiv (A\/2, 0, (a-b)B\/2)$. \nThe eigenvalues and eigenvectors of $H_{m=0}$ can be written easily as\n\\begin{subequations}\n\\label{H_eigen0}\n\\begin{align}\nE_{0}^{\\pm} \n&= -\\frac{A}{4} \\pm \\frac{1}{2}\\sqrt{(a-b)^2 B^2 + A^2}\\,, \\\\\n\\ket{E_{0}^{+}} \n&= \\phantom{+} \\cos\\frac{\\alpha}{2}\\, \\ket{ \\tfrac{1}{2},-\\tfrac{1}{2}} \n + \\sin\\frac{\\alpha}{2}\\, \\ket{-\\tfrac{1}{2}, \\tfrac{1}{2}}\\,,\n\\label{H_evb} \\\\\n\\ket{E_{0}^{-}} \n&= - \\sin\\frac{\\alpha}{2}\\, \\ket{ \\tfrac{1}{2},-\\tfrac{1}{2}} \n + \\cos\\frac{\\alpha}{2}\\, \\ket{-\\tfrac{1}{2}, \\tfrac{1}{2}}\\,,\n\\label{H_evc}\n\\end{align}\n\\end{subequations}\nwhere $\\tan\\alpha\\equiv \\frac{A}{(a-b)B}$. In a weak magnetic field limit \n(so called the Zeeman region), the Zeeman energy is smaller\nthan the hyperfine coupling. At $B= 0$, i.e., $\\alpha = \\pi\/2$, \nthe ground eigenstate $\\ket{E_{0}^{-}}$ becomes the singlet state,\n$\\ket{E_{0}^{-}} = \\frac{1}{\\sqrt{2}}\\left(\\ket{-\\tfrac{1}{2},\\tfrac{1}{2}} -\n\\ket{\\tfrac{1}{2}, -\\tfrac{1}{2}} \\right)$. In a strong magnetic field called\nthe Paschen-Back region, the Zeeman couplings are dominant. That is, in \nlimit of $B\\to \\infty$, one has $\\alpha \\to 0$ and $\\ket{E_{0}^{-}} \\to\n\\ket{-\\tfrac{1}{2},\\tfrac{1}{2}}$.\n\nThe eigenvalues and eigenstates of the Breit-Rabi Hamiltonian for a hydrogen \natom, Eqs.~(\\ref{H_eigen1}) and (\\ref{H_eigen0}) depend on two parameters: \nthe hyperfine constant $A$ and the magnetic field $B$. The hyperfine constant \n$A$ of the hydrogen atom in vacuum is positive. However, if a hydrogen atom\nis in an inert gas, the hyperfine constant $A$ could be \nnegative~\\cite{Foner60}, resembling the spin-spin coupling constant in \na Heisenberg model. We assume that $A$ as well as $B$ varies and can be \nnegative. To this end, $A$ in Eqs.~(\\ref{H_eigen1}) and (\\ref{H_eigen0}) is \nreplaced by $fA$ with $-1\\le f\\le 1$, so $A$ is still kept the positive \nconstant in vacuum. If $f$ is negative, so the hyperfine constant.\n\nDepending on $f$ and $B$, the ground state of the \nHamiltonian~(\\ref{Hamil_Hydrogen}) is given either by $\\ket{E_{\\pm 1}}$ or by \n$\\ket{E_{0}^{-}}$. It is convenient to plot the energy levels normalized by \n$A$. Then, Eqs.~(\\ref{H_eigen1}) and (\\ref{H_eigen0}) become\n$E_{\\pm1}\/A = \\frac{f}{4} \\pm \\frac{1}{2}(a' + b') B$ and \n$E_{0}^{\\pm}\/A= -\\frac{f}{4} \\pm \\frac{1}{2}\\sqrt{(a'-b')^2 B^2 + f^2}$,\nwhere $a' \\equiv a\/A \\approx 19.767\\,\\,\\text{T}^{-1}$ and \n $b'\\equiv b\/A \\approx -0.03\\,\\,\\text{T}^{-1}$ are taken \nform Ref.~\\cite{Arimondo77}, and $B$ is measured in the unit of tesla. \nThe energy levels $E_m\/A$ are plotted as functions of $B$ for $f=1$ in \nFig.~\\ref{Fig1} (a) and for $f=-0.5$ in Fig.~\\ref{Fig1} (b). For $f\\ge 0$, \nthe ground level is $E_{0}^{-}$. For $f<0$, two levels, $E_{0}^{\\pm}$ and\n$E_{\\pm1}$, with different magnetic quantum numbers cross at \n$f =\\frac{2a'b'}{a'-b'} |B|$. Fig.~\\ref{Fig2} (a) shows the energy gap \n$\\Delta\/A$ between the ground and first exited states as a function of $f$ \nand $B$, where we take $a' =0.1\\,\\,\\text{T}^{-1}$ and \n$b' = -0.01\\,\\,\\text{ T}^{-1}$ to see clearly the phase diagram of \nthe ground state of the Hamiltonian~(\\ref{Hamil_Hydrogen}) determined by \nthe magnetic quantum number $m$. As shown in Fig.~\\ref{Fig2} (a), the energy\ngap $\\Delta\/A$ vanishes along the lines defined by \n$f = \\frac{2a'b'}{a'-b'} |B|$ \nand the negative $f$ axis. In the region of $f <\\frac{2a'b'}{a'-b'} |B|$, \nthe ground state becomes either $\\ket{E_{+1}}$ or $\\ket{E_{-1}}$ with the \nmagnetic quantum number $m=1$ or $m=-1$, respectively. On the other hand, \nthe ground state in the region defined by $f >\\frac{2a'b'}{a'-b'} |B|$ is \ngiven by $\\ket{E_{0}^{-}}$ with the magnetic quantum number $m=0$.\nOne can see that the magnetic quantum number $m$ of the ground state \nchanges abruptly at the level crossing points.\n\n\\subsection{Entanglement}\n\nLet us discuss the relation between level crossings and entanglement. \nEntanglement refers to the quantum correlation between subsystems and has no \nclassical analog~\\cite{book-Gruska,kais7}. When level crossing happens as the \nparameter of the Hamiltonian varies, the ground state changes drastically. \nEntanglement as a physical quantity may also undergo a significant change. \nHowever, entanglement is not always a good indicator to level crossing as \nshown in Ref.~\\cite{scoh08}.\n\nFirst, let us examine the relation between entanglement and level crossings \nfor each eigenstate. The von Neumann entropy $S$ of a subsystem is a good \nentanglement measure for a pure bipartite system. If $\\ket{\\psi_{AB}}$ is \na quantum state of a system composed of two subsystems $A$ and $B$, the \nentanglement between $A$ and $B$ is measured by the von Neumann entropy of \nthe subsystem,\n$S(\\rho_A) = -{\\rm tr}(\\rho_A\\log\\rho_A)= S(\\rho_B) \n = -{\\rm tr}(\\rho_B\\log\\rho_B)$, where the reduced density matrix \n$\\rho_A$ of the subsystem $A$ is obtained by tracing out the degrees of \nfreedom of $B$ as $\\rho_A ={\\rm tr}_B( \\ket{\\psi_{AB}} \\bra{\\psi_{AB}} )$. \nIf the ground state is given by $\\ket{E_{\\pm 1}}$, i.e., a product state, \nthen the von Neumann entropy $S$ of the electron (or nuclear) spin \nis zero. On the other hand, for the quantum state $\\ket{E_0^{\\pm}}$ of \nthe electron and nuclear spins, the von Neumann entropy of the electron \n(or nuclear) spin can be written as\n\\begin{align}\nS(\\rho_A) = -\\frac{1+\\cos\\alpha}{2}\\,\\log_2\\frac{1+\\cos\\alpha}{2}\n -\\frac{1-\\cos\\alpha}{2}\\,\\log_2\\frac{1-\\cos\\alpha}{2}\\,.\n\\end{align}\nFig.~\\ref{Fig1} (c) shows the von Neumann entropy of the electron (or nuclear) \nspin for each eigenstate as a function of $B$. For the eigenstates\n$\\ket{E_{0}^{\\pm}}$, it is maximum at $B=0$, i.e., at the avoided crossing\npoint. This is analogous to the sharp change in Shannon entropy at avoided \ncrossing in Ref.~\\cite{Gonzalez03}.\n\nNow, let us look at how entanglement changes at level crossings as the\nparameters of the Hamiltonian vary. Fig.~\\ref{Fig2} (b) plots the von Neumann \nentropy $S$ of the electron (or nuclear ) spin for the ground state as \na function of $f$ and $B$. Across the level crossing line, \n$f=\\frac{2a'b'}{a'-b'}|B|$, the von Neumann entropy changes abruptly. \nFor $f>0$, $S$ becomes 1 as $B$ goes to 0. Along the line of $f=0$, \nthe von Neumann entropy $S$ vanishes even though there is no level crossing. \n\n\\subsection{Berry Phase}\n\\label{hydrogen_berry}\n\nAn instantaneous eigenstate encircling the energy level crossing points \nacquires the Berry phase in addition to the dynamical phase. The information\non the level crossings is encoded in the Berry phase. At $B=0$ and $f=1$, the \ntwo levels $E_{\\pm1}$ cross and the other two levels $E_{0}^{\\pm}$ are avoided \ncrossing. Also $E_{\\pm1}$ and $E_{0}^{-}$ cross at $f=\\frac{2a'b'}{a'-b'}|B|$. \nHere we focus on the Berry phase due to the level crossing or avoided crossing \nat $B=0$.\n\nDue to the fact that $a\\gg |b|$, an electron spin rotates much faster than a \nnuclear spin. We assume the magnetic field ${\\bf B}$ is rotated slowly enough\nfor both the electron and nuclear spins to evolve adiabatically. The magnetic\nfield ${\\bf B} = B\\,{\\bf\\hat{n}}$ in the direction of ${\\bf\\hat{n}} = \n(\\sin\\theta\\cos\\phi, \\sin\\theta\\sin\\phi,\\cos\\theta)$ is constructed starting \nfrom ${\\bf B} = B\\,{\\bf \\hat{z}}$. First, it is rotated about the $y$ axis by \nangle $\\theta$. And it is subsequently rotated about the $z$ axis by angle \n$\\phi$. By applying SU(2) rotations corresponding to the above SO(3) rotations \non the Hamiltonian~(\\ref{Hamil_BR}), one obtains the Breit-Rabi Hamiltonian in \nthe magnetic field ${\\bf B} = B\\, {\\bf\\hat{n}}$\n\\begin{align}\nH(\\theta,\\phi) \n= A\\,{\\bf I}{\\bm \\cdot}{\\bf S} \n+ a\\,{\\bf B}{\\bm \\cdot}{\\bf S} + b\\,{\\bf B}{\\bm \\cdot}{\\bf I}\\,.\n\\label{Hamil_Berry}\n\\end{align}\nThe hyperfine interaction $A\\,{\\bf I}{\\bm \\cdot}{\\bf S}$ is spherical\nsymmetric, so the eigenvalues and eigenvectors of the \nHamiltonian~(\\ref{Hamil_Berry}) are identical to those of the \nHamiltonian~(\\ref{Hamil_Hydrogen}) except replacing \n$\\ket{\\pm\\frac{1}{2}}$ by $\\ket{{\\bf \\hat{n}};\\pm\\frac{1}{2}}$.\nHere $\\ket{{\\bf \\hat{n}};\\pm\\frac{1}{2}}$ are eigenstates of \n${\\bf\\hat{n}}{\\bm \\cdot}{\\bf S}$ or ${\\bf\\hat{n}}{\\bm \\cdot}{\\bf I}$. \nIf the magnetic field ${\\bf B}$ is rotated slowly about the $z$ axis by $2\\pi$ \nto make a cone with a solid angle $\\Omega = 2\\pi(1-\\cos\\theta)$, then the\ninstantaneous eigenstate $\\ket{{\\bf \\hat{n}};\\pm\\frac{1}{2}}$ follows it and\naccumulates the Berry phase $\\beta_\\pm =\\mp\\frac{1}{2}\\Omega$. The total Berry \nphase $\\beta$ of electron and nuclear spins is the sum of two phases acquired \nby each one. It depends on the magnetic quantum number $m$ \n\\begin{align}\n\\beta = \\left\\{ \\begin{array}{cl}\n \\mp\\Omega \\quad &\\text{for $m=\\pm 1$} \\,,\\\\\n 0 \\quad &\\text{for $m=0$} \\,.\n \\end{array} \\right.\n\\end{align}\nAs expected, the Berry phase is nonzero only for real crossings, i.e., \n$m=\\pm1$. Fig.~\\ref{Fig2} (c) plots the total Berry phase as a function of \n$B$ and $f$ and shows that the total Berry phase jumps at the level crossings.\nThe zero Berry phase of the eigenstates $\\ket{E_{0}^{\\pm}(\\theta,\\phi)}$ can \nbe understood in two ways. First, two levels $E_{0}^{\\pm}$ are avoided \ncrossing at $B =0$, so it is zero. Another view is as follows.\nSince $\\ket{E_{0}^{\\pm}}$ is a superposition of \n$\\ket{\\tfrac{1}{2},-\\tfrac{1}{2}}$ and $\\ket{-\\tfrac{1}{2},\\tfrac{1}{2}}$, \nthe Berry phase of the electron spin is opposite to that of the nuclear spin \nand they cancel each other. \n\t \nAlthough the entangled states $\\ket{E_{0}^{\\pm}}$ of electron and nuclear spins \naccumulates no Berry phase, each subsystem (electron spin or nuclear spin) can \nget nonzero marginal Berry phases of mixed states. Following the studies on\ngeometric phase of mixed states~\\cite{Sjoqvist00,Sjoqvist05} and the relation \nbetween entanglement and marginal Berry phases~\\cite{Yi04a,Yi04b,Sjoqvist05}, \nwe investigate the relation between avoided level crossings, marginal Berry \nphases, and entanglement. For an adiabatic cyclic evolution parameterized by\n$\\bf x$, an instantaneous eigenstate of a bipartite system $AB$ can be \nexpressed in a Schmidt decomposition $\\ket{\\psi({\\bf x})} = \\sum_{i=1}^{M}\n\\sqrt{p_i} \\ket{e_i({\\bf x})} \\otimes\\ket{f_i({\\bf x})}$, where \n$\\{\\ket{e_i({\\bf x})}\\}_{i=1}^{N_A}$ is an orthonormal basis for a subsystem\n$A$, $\\{\\ket{f_i({\\bf x})}\\}_{i=1}^{N_B}$ for a subsystem $B$, \n$M\\le\\min\\{N_A,N_B\\}$, and $\\sum_{i=1}^M p_i =1$. Here our attention is\nrestricted to the case that the Schmidt coefficients $\\sqrt{p_i}$ are\nindependent of ${\\bf x}$. After an adiabatic cyclic evolution implemented by\n${\\bf x}(0) = {\\bf x}(T)$, the total Berry phase of the bipartite system $AB$ \nis given by \n\\begin{align}\n\\beta=\\sum_{i=1}^{M} p_i \\left(\\beta^{A}_{i} + \\beta^{B}_{i}\\right)\\,,\n\\label{berry_sum}\n\\end{align}\nwhere $\\beta^{A}_{i} = i\\oint_C d{\\bf x}{\\cdot} \\bra{e_i({\\bf x})} \n \\nabla_{\\bf x} \\ket{e_i({\\bf x})}$. Then the marginal \nmixed state Berry phase $\\Gamma_A$ of a subsystem $A$ is defined by \n\\begin{align}\n\\Gamma_A =\\arg\\sum_{i}p_i \\exp\\left(i\\beta^A_i\\right)\\,.\n\\label{berry_mixed}\n\\end{align}\n\nWith Eqs.~(\\ref{berry_sum}) and~(\\ref{berry_mixed}), let us analyze how the \ntotal Berry phase and the marginal Berry phase of $\\ket{E_0^{-}}$ depend on\n$B$. The two Schmidt coefficients are given by $p_1 = \\sin^2\\tfrac{\\alpha}{2}$ \nand $p_2=\\cos^2\\tfrac{\\alpha}{2}$. It is easy to obtain the marginal \nBerry phase of the electron spin \n$\\Gamma_e = \\arctan\\left(\\cos\\alpha\\tan\\frac{\\Omega}{2}\\right)$\nand the average Berry phase of the electron spin \n$\\beta_e \\equiv p_1\\beta_1^e + p_2 \\beta_2^e =\\frac{\\Omega}{2}\\cos\\alpha$. \nIn the limit of $B\\gg1$, i.e., $\\alpha\\to 0$, one has $\\ket{E_0^{-}} \\to\n\\ket{-\\tfrac{1}{2},\\tfrac{1}{2}}$ and $\\beta_e={\\Omega}\/{2}$. Also the marginal\nBerry phase of the electron spin is given by $\\Gamma_e = {\\Omega}\/{2}$ for \n$0\\le \\theta <\\frac{\\pi}{2}$. Fig.~\\ref{Fig3} plots $\\Gamma_e$\nas a function of $B$ and the azimuthal angle $\\theta$. The marginal Berry \nphase of the electron spin jumps at $\\theta=\\pi\/2$ and $B=0$.\nThe node at $B=0$ corresponds to the avoided crossing. \n\n\\section{The Sodium Atom in a Uniform Magnetic Field}\n\\label{section_sodium}\n\n\\subsection{Energy Spectrum}\nNow we consider an $^{23}$Na atom in its $3S_{1\/2}$ ground state in the \npresence of a uniform magnetic field $B$ along the $z$ axis. The nuclear \nand electron spins of an $^{23}$Na atom are $I=2\/3$ and $S=1\/2$, respectively.\nAs in Sec.~\\ref{section_hydrogen}, it is convenient to arrange \nthe product basis $\\{\\ket{m_S,m_I}\\}$ in the decreasing order of \nthe magnetic quantum number $m$ of $J_z$ as follows.\n$\\left\\{\\ket{\\frac{1}{2},\\frac{3}{2}} \\right\\}$,\n$\\left\\{\\ket{\\frac{1}{2},\\frac{1}{2}}, \\ket{-\\frac{1}{2},\\frac{3}{2}}\\right\\}$,\n$\\left\\{\\ket{\\frac{1}{2},-\\frac{1}{2}},\\ket{-\\frac{1}{2},\\frac{1}{2}}\\right\\}$, \n$\\left\\{\\ket{\\frac{1}{2},-\\frac{3}{2}},\\ket{-\\frac{1}{2},-\\frac{1}{2}}\\right\\}$,\nand $\\left\\{\\ket{-\\frac{1}{2},-\\frac{3}{2}} \\right\\}$. For example, \n$\\left\\{ \\ket{\\frac{1}{2},\\frac{1}{2}}, \\ket{-\\frac{1}{2},\\frac{3}{2}}\\right\\}$ \nspans the subspace of $m=1$. In this ordered basis set, the \nHamiltonian~(\\ref{Hamil_BR}) for the sodium atom can be represented by \na block-diagonal matrix\n\\begin{align}\nH = \n\\begin{pmatrix}\nf({\\frac{1}{2},\\frac{3}{2}}) & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n0 & f(\\frac{1}{2},\\frac{1}{2}) & \\frac{\\sqrt{3}}{2}A & 0 & 0 & 0 & 0 & 0 \\\\\n0 &\\frac{\\sqrt{3}}{2}A & f(\\frac{-1}{2},\\frac{3}{2}) & 0 & 0 & 0 & 0 & 0 \\\\\n0 & 0 & 0 & f(\\frac{1}{2},\\frac{-1}{2}) & \\frac{A}{2} & 0 & 0 & 0 \\\\\n0 & 0 & 0 & \\frac{A}{2} & f(\\frac{-1}{2},\\frac{1}{2}) & 0 & 0 & 0 \\\\\n0 & 0 & 0 & 0 & 0 & f(\\frac{1}{2},\\frac{-3}{2}) & \\frac{\\sqrt{3}}{2}A & 0 \\\\\n0 & 0 & 0 & 0 & 0 & \\frac{\\sqrt{3}}{2}A & f(\\frac{-1}{2},\\frac{-1}{2}) & 0 \\\\\n0 & 0 & 0 & 0 & 0 & 0 & 0 & f(\\frac{-1}{2},\\frac{-3}{2}) \n\\end{pmatrix},\n\\label{Hamil_Sodium}\n\\end{align}\nwhere $f(m_S,m_I) \\equiv A\\,m_S\\,m_I + m_S\\,aB + m_I\\,bB$.\nEach block is at most a $2\\times 2$ matrix and can be easily diagonalized.\nFirst, consider the subspace of $m = \\pm 2$. The corresponding eigenvalues \nand eigenvectors can be written as\n\\begin{subequations}\n\\label{Na_m2}\n\\begin{align}\nE_{\\pm 2} &= \\frac{3}{4}A \\pm \\frac{1}{2}(a+3b)B\\,,\\\\\n\\ket{E_{\\pm 2}} &= \\ket{\\pm\\tfrac{1}{2},\\pm\\tfrac{3}{2}} \\,. \n\\label{Na_ev2}\n\\end{align}\n\\end{subequations}\nNotice that Eqs.~(\\ref{Na_m2}) are comparable to Eqs.~(\\ref{H_eigen1}).\nSecond, in the subspace with $m= 1$ spanned by \n$\\left\\{\\ket{\\frac{1}{2},-\\frac{1}{2}}, \\ket{-\\frac{1}{2},\\frac{1}{2}}\\right\\}$, \none obtains the eigenvalues and eigenvectors,\n\\begin{subequations}\n\\label{Eq_plus}\n\\begin{align}\nE_{+1}^{\\pm} \n&= -\\frac{A}{4} + bB \\pm \\frac{1}{2}\\sqrt{\\bigl(A + (a-b)B\\bigr)^2 + 3A^2}\\,,\\\\\n\\ket{E_{+1}^+} \n&= \\phantom{+} \\cos\\frac{\\alpha_1}{2}\\, \\ket{ \\tfrac{1}{2},\\tfrac{1}{2}} \n + \\sin\\frac{\\alpha_1}{2}\\, \\ket{-\\tfrac{1}{2},\\tfrac{3}{2}}\\,,\n\\label{Na_p1p}\\\\\n\\ket{E_{+1}^-}&= -\\sin\\frac{\\alpha_1}{2}\\, \\ket{ \\tfrac{1}{2},\\tfrac{1}{2}} \n + \\cos\\frac{\\alpha_1}{2}\\, \\ket{-\\tfrac{1}{2},\\tfrac{3}{2}}\\,,\n\\label{Na_p1m}\n\\end{align}\n\\end{subequations}\nwhere $\\tan\\alpha_1\\equiv \\frac{\\sqrt{3} A}{A + (a-b)B}$. \nThird, the Hamiltonian of $m=-1$ is defined on the subspace \n spanned by $\\left\\{ \\ket{\\frac{1}{2},-\\frac{3}{2}}, \n\\ket{-\\frac{1}{2},-\\frac{1}{2}} \\right\\}$. \nIts eigenvalues and eigenstates are given by\n\\begin{subequations}\n\\label{Eq_minus}\n\\begin{align}\nE_{-1}^{\\pm} &= -\\frac{A}{4} -bB \n \\pm \\frac{1}{2}\\sqrt{\\bigl(A - (a-b)B\\bigr)^2 + 3A^2}\\,,\\\\\n\\ket{E_{-1}^+} \n&= \\phantom{+} \\cos\\frac{\\alpha_2}{2}\\, \\ket{-\\tfrac{1}{2},-\\tfrac{1}{2}} \n + \\sin\\frac{\\alpha_2}{2}\\, \\ket{ \\tfrac{1}{2},-\\tfrac{3}{2}}\\,,\n\\label{Na_m1p} \\\\\n\\ket{E_{-1}^-} \n&= - \\sin\\frac{\\alpha_2}{2}\\, \\ket{-\\tfrac{1}{2},-\\tfrac{1}{2}} \n + \\cos\\frac{\\alpha_2}{2}\\, \\ket{ \\tfrac{1}{2},-\\tfrac{3}{2}} \\,,\n\\label{Na_m1m}\n\\end{align}\n\\end{subequations}\nwhere $\\tan\\alpha_2\\equiv \\frac{\\sqrt{3}A}{A -(a-b)B}$. Note that\nEqs.~(\\ref{Eq_minus}) can be obtained from Eqs.~(\\ref{Eq_plus}) by replacing \n$B$ with $-B$. Finally, the subspace of $m=0$ is spanned by \n$\\left\\{\\ket{\\frac{1}{2},-\\frac{1}{2}},\\ket{\\frac{1}{2},-\\frac{1}{2}}\\right\\}$. \nThe corresponding eigenvalues and eigenvectors are given by\n\\begin{subequations}\n\\label{Na_ev0}\n\\begin{align}\nE_{0}^{\\pm} &= -\\frac{A}{4} \\pm \\frac{1}{2}\\sqrt{(a-b)^2B^2 + 4A^2} \\,,\\\\\n\\ket{E_0^+} &= \n\\phantom{+} \\cos\\frac{\\alpha_0}{2}\\, \\ket{ \\tfrac{1}{2},-\\tfrac{1}{2}} \n + \\sin\\frac{\\alpha_0}{2}\\, \\ket{-\\tfrac{1}{2}, \\tfrac{1}{2}} \\,, \n\\label{Na_0p} \\\\\n\\ket{E_0^-} &= \n - \\sin\\frac{\\alpha_0}{2}\\, \\ket{ \\tfrac{1}{2},-\\tfrac{1}{2}} \n\t+ \\cos\\frac{\\alpha_0}{2}\\, \\ket{-\\tfrac{1}{2}, \\tfrac{1}{2}}\\,,\n\\label{Na_0m}\n\\end{align}\n\\end{subequations}\nwhere $\\tan\\alpha_0\\equiv \\frac{A}{(a-b)B}$. As expected, Eqs.~(\\ref{Na_ev0})\nis very similar to Eqs.~(\\ref{H_eigen0}) in the case of a hydrogen atom.\n\n\n\\subsection{Entanglement}\n\nLet us examine the relation between entanglement and level crossings \nor avoided crossings for a sodium atom. \nWith the values of the parameters $A$, $a$, and $b$ of the $^{23}$Na \natom in Ref.~\\cite{Arimondo77}, energy levels $E_{m}^{\\pm}\/A$ are plotted\nin Fig.~\\ref{Fig4} (a). The von Neumann entropies of the electron \n(or nuclear) spin for each eigenstates are shown in Fig.~\\ref{Fig4} (b).\nThe ground state is given by $\\ket{E_{+1}^{-}}$ for $B>0$ and \n$\\ket{E_{-1}^{-}}$ for $B<0$. Two levels, $E_{+1}^{+}$ and \n$E_{+1}^{-}$, are avoided crossing and maximally entangled at \n$A -(a-b)B= \\sqrt{3}A$. Another two levels, $E_{-1}^{+}$ and $E_{-1}^{-}$, \nare avoided crossing and maximally entangled at $A + (a-b)B= \\sqrt{3}A$.\nTwo levels with $m=0$, $E_{0}^{\\pm}$ are avoided crossing and maximally\nentangled at $B=0$. Two levels $E_{\\pm2}$ show real crossing at $B=0$ and have\nzero von Neumann entropies. Again one can see that the eigenstate is maximally\nentangled at the avoided crossing point. This is analogous to the results \nin Ref.~\\cite{Gonzalez03}, where Shannon entropy is used as an indicator of\navoided crossings.\n\n\n\\subsection{Berry phase}\n\nAs in Sec.~\\ref{hydrogen_berry}, let us consider an adiabatic cyclic evolution\nof nuclear and electron spins of a sodium atom by rotating the magnetic \nfield $B\\,{\\bf\\hat{n}}$ slowly. For a adiabatic rotation keeping the azimuthal \nangle $\\theta$ constant and varying the polar angle $\\phi$ from $0$ to $2\\pi$,\nthe instantaneous eigenstates accumulates the total Berry phases proportional \nto the magnetic quantum number $m$, $\\beta = \\mp m \\Omega$. In contrast to a\nhydrogen atom, the ground state is given either by $\\ket{E_{+1}^{-}}$ or by\n$\\ket{E_{-1}^{-}}$ with $m=\\pm 1$, so it acquires the total Berry phase $\\beta\n=\\mp\\Omega$.\n\nLet us analyze how the marginal Berry phase of the entangled state is related \nto the avoided crossings. We focus on the eigenstate, $\\ket{E_{+1}^{-}}$. It has \ntwo Schmidt coefficients, \n$p_1 = \\sin^2\\tfrac{\\alpha_1}{2}$ and $p_2=\\cos^2\\tfrac{\\alpha_1}{2}$.\nWith Eq.~(\\ref{berry_sum}), one obtains the total phase as a sum of the Berry \nphases acquired by nuclear and electron spins with weights of the Schmidt \ncoefficients,\n\\begin{align}\n\\beta = \\sin^2\\tfrac{\\alpha_1}{2}\n \\left( -\\tfrac{\\Omega}{2} - \\tfrac{\\Omega}{2}\\right)\n + \\cos^2\\tfrac{\\alpha_1}{2}\n \\left(+\\tfrac{\\Omega}{2} - \\tfrac{3\\Omega}{2}\\right)\n = -\\Omega\\,.\n\\end{align}\nFrom Eq.~(\\ref{berry_mixed}), one obtains the marginal Berry phases of an\nelectron spin $\\Gamma_e$ and of nuclear spin $\\Gamma_n$\n\\begin{subequations}\n\\begin{align}\n\\Gamma_n &= \\arg\\bigl[\\,\n \\sin^2\\tfrac{\\alpha_1}{2}\\,e^{-i\\Omega\/2} \n\t + \\cos^2\\tfrac{\\alpha_1}{2}\\,e^{-i3\\Omega\/2}\\, \\bigr] \\,,\\\\\n\\Gamma_e &=\\arctan\\left[\\,\\cos\\alpha_1\\tan\\tfrac{\\Omega}{2}\\,\\right]\\,.\n\\end{align}\n\\end{subequations}\nFig.~\\ref{Fig5} plots the marginal Berry phase of a nuclear spin $\\Gamma_n$ \nas a function of $B$ and the azimuthal angle $\\theta$. In the limit of \n$B\\gg 1$, i.e., $\\alpha_1\\to 0$, one has $\\ket{E_{+1}^{-}} \\to\n\\ket{-\\tfrac{1}{2}, \\tfrac{3}{2}}$, $\\Gamma_e=\\Omega\/2$, and\n$\\Gamma_n=-3\\Omega\/2$. It is clearly seen that \nthe node of the marginal Berry phase of a nuclear (or electron) spin \ncorresponds to the avoid crossing at $A + (a-b)B = \\sqrt{3}A$. Thus it could \nbe expected that the marginal Berry phase of a subsystem for an entangled \nstate has a node at avoided crossings.\n\n\n\\section{Conclusions}\n\\label{section_conclusion}\n\nWe have considered the Breit-Rabi Hamiltonians for hydrogen and sodium atoms, \ndescribing the hyperfine interaction between a nuclear spin and an electron \nspin in the presence of a magnetic field. We have examined the relation between\nlevel crossings, entanglement, and Berry phases. It is shown that entanglement \nbetween nuclear and electron spins is maximum at avoided crossing points. \nThe Berry phase and the von Neumann entropy change abruptly at level\ncrossings as the parameters of the Breit-Rabi Hamiltonian for a hydrogen atom \nvary. An entangled state encircling the avoided crossing acquires \nthe marginal Berry phase of an electron (or nuclear) spin like an eigenstate \nmoving around the real crossing accumulates a Berry phase.\nWe have shown that the nodal points of the marginal Berry phase of \nan entangled state corresponds to the avoided crossing points.\n\n\n\\begin{acknowledgments}\nThis work is in part supported by the visitor program of Max Planck Institute \nfor the Physics of Complex Systems. We would like also to thank the Binational \nIsrael-US Foundation (BSF) for financial support. \n\\end{acknowledgments}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:introduction}\n\n\\emph{Science} is commonly described as the ``discovery of natural laws through experimentation and observation''.\nResearchers in the natural sciences increasingly turn to machine learning (ML) to aid the discovery of natural laws from observational data alone, which is often abundantly available, hoping to bypass expensive and cumbersome targeted experimentation.\nWhile there may be fundamental limitations to what can be extracted from observations alone,\nrecent successes of ML in the entire range of natural sciences provide ample reason for excitement.\nIn this work, we focus on ordinary differential equations, a ubiquitous description of dynamical natural laws in physics, chemistry, and systems biology.\nFor a first order ODE $\\dot{y} := \\nicefrac{\\partial y}{\\partial t} = f(y, t)$, we call~$f$ (which uniquely defines the ODE) the underlying dynamical law.\nInformally, our goal is then to infer~$f$ in symbolic form given discrete time-series observations of a single solution $\\{y_i := y(t_i)\\}_{i=1}^n$ of the underlying ODE.\n\nContrary to ``black-box-techniques'' such as Neural Ordinary Differential Equations (NODE)~\\citep{chen2018neural} that aim at inferring a possible~$f$ as an arguably opaque neural network, we focus specifically on symbolic regression.\nFrom the perspective of the sciences, a law of nature is useful insofar as it is more broadly applicable than to merely describe a single observation.\nIn particular, the reason to learn a dynamical law in the first place is to dissect and understand it as well as to make predictions about situations that differ from the observed one.\nFrom this perspective, a symbolic representation of the law (in our case the function~$f$) has several advantages over block-box representations: they are compact and directly interpretable, they are amenable to analytic analysis, they allow for meaningful changes and thus enable assessment of interventions and counterfactuals.\n\nIn this work we present NSODE, a sequence-to-sequence transformer that maps observed trajectories, i.e., numeric sequences of the form $\\{(t_i, y_i)\\}_{i=1}^n$, directly to symbolic equations as strings, e.g., \\texttt{\"y**2+1.64*cos(y)\"}, which is the prediction for $f$.\nThis example directly highlights the benefit of symbolic representations in that the $y^2$ and $\\cos(y)$ terms tell us something about the fundamental dynamics of the observed system; the constant \\texttt{1.64} will have semantic meaning in a given context and we can, for example, make predictions about settings in which this constant takes a different value.\n\n\\begin{figure*}\n \\centering\n \\vspace{-7mm}\n \\includegraphics[width=1.0\\textwidth]{figs\/pipeline_new.pdf}\n \\caption{An overview illustration of the data generation (top) and training pipeline (bottom). Our dataset stores solutions in numerical (non-binarized) form on the entire regular solution time grid.}\n \\label{fig:overview}\n \\vspace{-2mm}\n\\end{figure*}\n\n\\section{Background and Related Work}\n\\label{sec:related_work}\nWhile NODE \\citep{chen2018neural} (with a large body of follow up work) is perhaps the most prominent method to learn ODEs from data in black-box form, we focus on various works that infer governing laws in symbolic form.\nClassically, symbolic regression aims at regular functional relationships (mapping $(x, f(x))$ pairs to~$f$ instead of mapping trajectories $(t, y(t))$ to the governing ODE $\\dot{y}=f(y, t)$) and has been approached by heuristics-based search, most prominently via genetic programming \\citep{koza}. Genetic programming randomly evolves a population of prospective mathematical expressions over many iterations and mimics natural selection by keeping only the best contenders across iterations, where superiority is measured by user-defined and problem-specific fitness functions.\nMore recently, symbolic regression has been approached with machine learning methods which exploit gradient information to optimize within the space of (finite) compositions of pre-defined basis functions.\n\\citet{sindy} use linear regression to identify a (sparse) linear combination of basis functions that yields the best fit for the observed data, while other approaches use neural networks with a diverse set of activation functions \\citep{eql2, pdenet2, rodenet}.\nAll these techniques deploy strong sparsity-promoting regularizers and fit a separate model for each observed trajectory.\n\nAlternatively, one can train a model to directly output the symbolic expressions.\nSupervised learning with gradient-based optimization for this approach is challenged by the formulation of a differentiable loss that measures the fit between the predicted symbolic expression and the observed data.\nThus, prior work resorted to reinforcement learning \\citep{deepsymres} or evolutionary algorithms \\citep{atkinson2019data, costa2021fast} for gradient-free optimization.\nFurthermore, inspired by common properties of known natural laws, \\citet{aifeynman2} devise a hybrid approach that combines a gradient-free heuristic search with neural network-based optimization. This approach has been extended to work with dynamical systems by \\citet{weilbach2021inferring}.\n\nThe closest works to ours use pre-trained, attention-based sequence-to-sequence models for symbolic regression \\emph{of functional relationships} \\citep{nesymres,SymbolicGPT2021,kamienny2022end, vastl2022symformer}.\nThey exploit the fact that symbolic expressions for (multi-variate) scalar functions can be both generated and evaluated on random inputs cheaply, resulting in essentially unlimited training data.\nLarge data including ground-truth expressions in symbolic form allow for a differentiable cross-entropy loss based directly on the symbols of the expression, instead of the numerical proximity between evaluations of the predicted and true expression.\nWhile the cross-entropy loss works well for operators and symbols (e.g. \\texttt{+,exp,sin,x,y}), a naive implementation is inefficient for numerical constants, e.g., \\texttt{1.452}. Previous works therefore resort to one of two strategies: \n1) represent all constants with a special \\texttt{} token when training the sequence-to-sequence model and predict only the presence of a constant. Actual values are then inferred in a second, subsequent parameter estimation step where the structure of an expression is held fixed and only constants are optimized.\nThis second optimization procedure comes with substantial computational cost as constants have to be fit per inferred expression. In particular, we highlight that it does not transfer to inferring ODEs as it would require to first solve the predicted ODE $\\dot{y} = \\hat{f}(y)$ to obtain predicted $\\{\\hat{y}_i\\}_{i=1}^n$ values that can be compared to the set of observations $\\{y_i\\}_{i=1}^n$. While differentiable ODE solvers exist, optimizing constants this way is prohibitively expensive and typically highly unstable. \n2) A second popular strategy consists in rounding constants within the range of interest so that they can be represented with a finite number of tokens. This second strategy avoids a subsequent optimization step and enjoys clever encoding schemes with improved token efficiency yet represents values with an inherent loss in precision.\nAs an alternative, we develop a representation based on a ``two-hot'' encoding which avoids subsequent optimization steps as well as rounding.\n\n\\section{Method}\n\\label{sec:method}\n\n\\xhdr{Problem setting}\nGiven observations $\\{(t_i, y_i)\\}_{i=1}^n$ of a trajectory $y: [t_1, t_n] \\to \\ensuremath \\mathbb{R}$ that is a solution of the ODE $\\dot{y} = f(y)$, we aim to recover the function~$f$ in symbolic form---in our case as a string.\nIn this work, we focus on time-invariant (or autonomous) ODEs (i.e.,~$f(y, t) = f(y)$).\nSuch settings are a good starting point for investigation as they are commonly studied and can be thought of as ``evolving on their own'' without external driving forces or controls, i.e., once an initial condition is fixed the absolute time does not directly influence the evolution.\nWe explicitly assume that the observed system actually evolves according to an ODE in canonical form~$\\dot{y} = f(y)$ such that~$f$ can be expressed in closed form using the mathematical operators seen during training (see \\cref{sec:data}).\nIn this paper we restrict ourselves to the rich class of non-linear, scalar, first-order, autonomous ODEs but we discuss extensions of NSODE to higher-order systems of coupled non-autonomous ODEs in \\cref{app:extensions}.\n\n\\subsection{Data Generation}\n\\label{sec:data}\n\n\\xhdr{Sampling symbolic expressions}\n\\label{xhdr:sampling}\nTo exploit large-scale supervised pretraining we generate a dataset of $\\sim$63M ODEs in symbolic form along with numerical solutions for randomly sampled initial values.\nSince we assume ODEs to be in canonical form $\\dot{y} = f(y)$, generating an ODE is equivalent to generating a symbolic expression $f(y)$.\nWe follow \\citet{lample2019deep}, who sample such an expression $f(y)$ as a unary-binary tree, where each internal node corresponds to an operator and each leaf node corresponds to a constant or variable.\nThe algorithm consists of two phases: (1) A unary-binary tree is sampled uniformly from the distribution of unary-binary trees with up to $k\\in \\ensuremath \\mathbb{N}$ internal nodes, which crucially does not overrepresent small trees corresponding to short expressions. Here the maximum number of internal nodes $\\ensuremath K$ is a hyperparameter of the algorithm.\n(2) The sampled tree is ``decorated'', that is, each binary internal node is assigned a binary operator, each unary internal node is assigned a unary operator, and each leaf is assigned a variable or constant. \nHence, we have to specify a distribution over the $\\ensuremath N_{\\mathrm{bin}}$ binary operators, one over the $\\ensuremath N_{\\mathrm{una}}$ unary operators, a probability $\\ensuremath p_{\\mathrm{sym}} \\in (0,1)$ to decide between symbols and constants, as well as a distribution $\\ensuremath p_{\\mathrm{c}}$ over constants.\nFor constants we distinguish explicitly between sampling an integer or a real value.\nTogether with $\\ensuremath K$, these choices uniquely determine a distribution over equations $f$ and are described in detail in \\cref{app:modeltraining}.\n\\Cref{fig:overview} depicts an overview of the data generation procedure.\n\nThe pre-order traversal of a sampled tree results in the symbolic expression for~$f$ in prefix notation.\nAfter conversion to infix notation, we simplify each expression using the computer algebra system SymPy \\citep{sympy}, and filter out constant equations~$f(y) = c$ as well as expressions that contain operators or symbols that were not part of the original distribution. \nWe call the structure modulo the value of the constants of such an expression a \\textbf{skeleton}.\nAny skeleton containing at least one binary operator or constant can be represented by different unary-binary trees.\nVice versa many of the generated trees will be simplified to the same skeleton. To ensure diversity and to mitigate potential dataset bias towards particular expressions, we discard duplicates on the skeleton level. To further cheaply increase the variability of ODEs we sample $\\ensuremath N_{\\mathrm{const}}$ unique sets of constants per skeleton.\nWhen sampling constants we take care not to modify the canonical expression by adhering to the rules listed in \\cref{app:constant_rules}.\nWe provide summary statistics on operator frequencies and expression complexities for the generated dataset in \\cref{app:datastats}. Here, \\textbf{complexity} refers to overall count of symbols (e.g., $y$, or constants) as well as operators in an expression, a simple yet common measure in the symbolic regression literature.\n\n\\xhdr{Computing numerical solutions}\nWe obtain numerical solutions for all ODEs via SciPy's interface \\citep{scipy} to the LSODA software package \\citep{lsoda} with both relative and absolute tolerances set to $10^{-9}$.\nWe solve each equation on a fixed time interval $t \\in [0, \\ensuremath T]$ and store solutions on a regular grid of $\\ensuremath N_{\\mathrm{grid}}$ points.\nFor each ODE, we sample up to $\\ensuremath N_{\\mathrm{iv}}$ initial values $y(0) = y_0$ uniformly from $\\ensuremath (y_0^{\\min}, y_0^{\\max})$.\\footnote{Due to a timeout per ODE, fewer solutions may remain in cases when the numerical solver fails repeatedly.}\nWhile LSODA attempts to select an appropriate solver, numerical solutions still cannot be trusted in all cases.\nTherefore, we check the validity of solutions via the following quality control check: we use 9th order central finite differences to approximate the temporal derivative of the solution trajectory (on the same temporal grid as the proposed solution), denoted by $\\dot{y}_{\\mathrm{fd}}$, and filter out any solution for which $\\|\\dot{y}_{\\mathrm{fd}} - \\dot{y} \\|_{\\infty} > \\epsilon$, where we use $\\epsilon = 1$.\n\n\\subsection{Model Design Choices}\n\\label{sec:model}\n\nNSODE consists of an encoder-decoder transformer with architecture choices listed in \\cref{app:modeldesign}.\nWe provide a visual overview in \\cref{fig:overview}.\n\n\\xhdr{Representing input trajectories}\nA key difficulty in feeding numerical solutions $\\{y_i\\}_{i=1}^n$ as input is that their range may differ greatly both within a single solution as well as across ODEs.\nFor example, the linear ODE $\\dot{y} = c \\cdot y$ for a constant~$c$ is solved by an exponential $y(t) = y_0 \\exp(c t)$ for initial value $y(0) = y_0$, which may span many orders of magnitude on a fixed time interval.\nTo prevent numerical errors and vanishing or exploding gradients caused by the large range of values, we assume each representable 64-bit float value is a token and use its IEEE-754 encoding as the token representation \\citep{nesymres}.\nWe thus convert all pairs $(t_i, y_i)$ to their IEEE-754 64 bit representations, channel them through a linear embedding layer before feeding them to the encoder.\n\n\\xhdr{Representing symbolic expressions}\nThe target sequence (i.e., the string for the symbolic expression of~$f$) is tokenized on the symbol-level.\nWe distinguish two cases: (1) \\emph{Operators and variables:} for each operator and variable we include a unique token in the vocabulary. \n(2) \\emph{Numerical constants:} constants may come from both discrete (integers) as well as continuous distributions, as for example in \\texttt{y**2+1.64*cos(y)}. Hence, it is unfeasible to include individual tokens ``for each constant''.\nNaively tokenizing on the digit level, i.e., representing real values literally as the sequence of characters (e.g., \\texttt{\"1.64\"}), not only significantly expands the length of target sequences and thus the computational cost, but also requires a variable number of prediction steps for every single constant.\n\nInstead, we take inspiration from \\citet{schrittwieser2020mastering} and encode constants in a \\emph{two-hot} fashion.\nWe fix a finite homogeneous grid on the real numbers $x_1 < x_2 < \\ldots < x_m$ for some~$m \\in \\ensuremath \\mathbb{N}$, which we add as tokens to the vocabulary.\nThe grid range $(x_1, x_m)$ and number of grid points $m$ are hyperparameters that can be set in accordance to the problems of interest. Our choices are described in \\cref{app:modeldesign}.\n\nFor any constant $c$ in the target sequence we then find $i \\in \\{1, \\ldots, m-1\\}$ such that $x_i \\le c \\le x_{i+1}$ and encode~$c$ as a distribution supported on $x_i, x_{i+1}$ with weights $\\alpha, \\beta$ such that $\\alpha x_i + \\beta x_{i+1} = c$.\nThat is, the target in the cross-entropy loss for a constant token is not a strict one-hot encoding, but a distribution supported on two (neighboring) vocabulary tokens resulting in a lossless encoding of continuous values in $[x_1, x_m]$.\nThis two-hot representation can be used directly in the cross-entropy loss function.\n\n\\xhdr{Decoding constants}\nWhen decoding a predicted sequence, we check at each prediction step whether the argmax of the logits corresponds to one of the~$m$ constant tokens $\\{x_1, \\ldots, x_m\\}$.\nIf not, we proceed by conventional one-hot decoding to obtain predicted operators and variables.\nIf instead the argmax corresponds to, for example, $x_i$, we also pick its largest-logit neighbor ($x_{i-1}$ or $x_{i+1}$; suppose $x_{i+1}$), renormalize their probabilities by applying a softmax to all logits and use the resulting two probability estimates as weights $\\alpha, \\beta$.\nConstants are then ultimately decoded as $\\alpha x_i + \\beta x_{i+1}$.\n\n\\subsection{Evaluation and Metrics}\n\\label{sec:metrics}\n\n\\xhdr{Sampling solutions}\nTo infer a symbolic expression for the governing ODE of a new observed solution trajectory $\\{(t_i, y_i)\\}_{i=1}^n$, all the typical policies such as greedy, sampling, or beam search are available.\nIn our evaluation, we use beam search with 1536 beams and report top-$k$ results with $k$ ranging from 1 to 1536.\n\n\\xhdr{Metrics}\nWe evaluate model performance both numerically and symbolically.\nFor numerical evaluation we follow \\citet{nesymres}: suppose the ground truth ODE is given by $\\dot{y} = f(y)$ with (numerical) solution $y(t)$ and the predicted ODE is given by $\\hat{\\dot{y}} = \\hat{f}(y)$.\nTo compute numerical accuracy we first evaluate $f$ and $\\hat{f}$ on $\\ensuremath N_{\\mathrm{eval}}$ points in the interval $[\\min(y(t)), \\max(y(t))]$ (i.e., the interval traced out by the observed solution), which yields function evaluations $\\texttt{gt}=\\{\\dot{y}_i\\}_{i=1}^{\\ensuremath N_{\\mathrm{eval}}}$ and $\\texttt{pred}=\\{\\hat{\\dot{y}}_i\\}_{i=1}^{\\ensuremath N_{\\mathrm{eval}}}$.\nWe then assess whether \\texttt{numpy.allclose}\\footnote{\\texttt{numpy.allclose} returns True if \\texttt{abs(a - b) <= (atol + rtol * abs(b))} holds element-wise for elements $a$ and $b$ from the two input arrays.\nWe use \\texttt{atol=1e-10} and \\texttt{rtol=0.05}; $a$ corresponds to predictions, $b$ corresponds to ground truth.} returns \\texttt{True} as well as whether the coefficient of determination $\\mathrm{R}^2 \\geq 0.999$.\\footnote{For observations $y_i$ and predictions $\\hat{y}_i$ we have $\\mathrm{R}^2 = 1 - (\\sum_i (y_i - \\hat{y}_i)^2) \/ (\\sum_i (y_i - \\overline{y})^2 )$.}\nNumerical evaluations capture how closely the predicted function approximates the ground truth function within the interval $[\\min(y(t)), \\max(y(t))]$.\n\nHowever, a key motivation for symbolic regression is to uncover a \\emph{symbolic} mathematical expression that governs the observations.\nTesting for symbolic equivalence between ground truth expression $f(y)$ and a predicted expression $\\hat{f}(y)$ is unsuitable in the presence of real-valued constants as even minor deviations between true and predicted constants render the equivalence false.\nInstead, we regard the predicted expression $\\hat{f}(y)$ to be symbolically correct if $f(y)$ and $\\hat{f}(y)$ can be made equivalent by modifying only the values of constants in the predicted expression $\\hat{f}(y)$.\nThis is implemented using SymPy's \\texttt{match} function.\nIn order not to alter the structure of the predicted expression, we constrain modifications of constants such that all constants remain non-zero and retain their original sign.\nThis definition is thus primarily concerned with the structure of an expression, rather than precise numerical agreement.\nOnce the structure is known, the inference problem becomes conventional parameter estimation.\nWe report percentages of samples in a given test set that satisfies any individual metric (numerical and symbolic), as well as percentages satisfying symbolic and numerical metrics simultaneously.\n\n\\section{Experiments}\n\\label{sec:experiments}\n\n\\subsection{Benchmark Datasets}\\label{sec:datasets}\nWe construct several test sets to evaluate model performance and generalization in different settings.\n\\begin{itemize}[leftmargin=*,topsep=0pt,itemsep=0pt]\n \\item \\textbf{testset-iv}: Our first test set assesses generalization within initial values not seen during training.\n It consists of 5793 ODEs picked uniformly at random from our generated dataset but re-sampled initial values. We also employ the following constraints via rejection sampling: (a) All skeletons in testset-iv are unique. (b) As the number of unique skeletons increases with the number of operators, we allow at most 2000 examples per number of operators (with substantially fewer unique skeletons existing for few operators).\n \n \\item \\textbf{testset-constants}: Our second test set assesses generalization within unseen initial values and constants.\n It consists of 2720 ODEs picked uniformly at random from our dataset (ensuring unique skeletons and at most 1000 examples per number of operators as above), but re-sampled intial values and constants.\n \n \\item \\textbf{testset-skeletons}: In principle, we can train NSODE on all possible expressions (using only the specified operators and number ranges) up to a specified number of operators.\n However, even with the millions examples in our dataset, we have by far not exhausted the huge space of possible skeletons (especially for larger numbers of operators).\n Hence, our third test set contains 100 novel random ODEs with skeletons that were never seen during training. \n \n \\item \\textbf{testset-iv-163}: This is a subset of testset-iv motivated by the fact that most symbolic regression models we want to compare to require a separate optimization for every individual example, which was computationally infeasible for our testset-iv.\n For a fair comparison, we therefore subsampled up to 10 ODEs per complexity uniformly at random, yielding 163 examples.\n \n \\item \\textbf{testset-textbook}: To assess how NSODE performs on ``real problems'', we manually curated 12 scalar, non-linear ODEs from Wikipedia pages, physics textbooks, and lecture note from university courses on ODEs.\n These equations are listed in \\cref{tab:textbook_equations} in \\cref{app:textbook_equations}.\n We note that they are all extremely simple compared to the expressions in our generated dataset in that they are ultimately mostly low order polynomials, some of which with one fractional exponent.\n \n \\item \\textbf{testset-classic}:\n To validate our approach on existing datasets we turn to benchmarks in the classic symbolic regression literature (inferring just the functional relationship between input-ouput pairs) and simply interpret functions as ODEs. In particular we include all scalar function listed in the overview in \\cite{mcdermott2012genetic} which includes equations from many different benchmarks \\cite{keijzer2003improving, koza, koza1994genetic, uy2011semantically, vladislavleva2008order}.\n For example, we interpret the function $f(y) = y^3 + y^2 + y$ from \\citet{uy2011semantically} as an autonomous ODE $\\dot{y}(t) = f(y(t)) = y(t)^3 + y(t)^2 + y$, which we solve numerically for a randomly sampled initial value as described before.\n\\end{itemize}\n\n\\subsection{Baselines}\\label{sec:baselines}\nWe compare our method to recent popular baselines from the literature (see \\cref{sec:related_work}).\nWe briefly describe them including some limitations here and defer all details to \\cref{app:baselines}.\nFirst, no baseline is suited directly to infer dynamical laws, but only to infer functional relationships.\nTherefore, all baselines fit a separate regression function mapping $y(t) \\mapsto \\hat{\\dot{y}}(t)$ per individual ODE, using the coefficient of determination $\\mathrm{R}^2$ as optimization objective.\nSince derivatives $\\hat{\\dot{y}}(t)$ are typically not observed, we approximate them via finite differences using PySindy \\citep{pysindy}.\nHence, all these methods crucially rely on regularly sampled and noise-free observations, whereas our approach can easily be extended to take those into account (see \\cref{app:extensions}).\n\n\\begin{itemize}[leftmargin=*,topsep=0pt,itemsep=0pt]\n \\item \\textbf{Sindy} \\citep{sindy}: Sindy builds a (sparse) linear combination of a fixed set of (non-linear) basis functions.\n The resulting Lasso regression is efficient, but suffers from limited expressiveness.\n In particular, Sindy cannot easly represent nested functions or non-integer powers as all non-linear expressions have to be added explicitly to the set of basis functions.\n We cross-validate Sindy over a fairly extensive hyperparameter grid of 800 different combinations for each individual trajectory.\n \n \\item \\textbf{GPL}\\footnote{\\ttfamily \\url{gplearn.readthedocs.io\/}} (genetic programming):\n GPL(earn) maintains a population of programs each representing a mathematical expression.\n The programs are mutated for several generations to heuristically optimize a user defined fitness function.\n While not originally developed for ODEs, we can apply GPLearn on our datasets by leveraging the finite difference approximation.\n We use a population size of 1000 and report the best performance across all final programs.\n Compared to sindy, GPLearn is more expressive yet substantially slower to fit.\n \n \\item \\textbf{AIFeynman} \\citep{aifeynman, aifeynman2}: AIFeynman is a physics-inspired approach to symbolic regression that exploits the insight that many famous equations in natural sciences exhibit well-understood functional properties such as symmetries, compositionality, or smoothness.\n AIFeynman implements a neural network based heuristic search that tests for such properties in order to identify a symbolic expression that fits the data.\n For every test sample AIFeynman computes a pareto front of solutions that trade off complexity versus accuracy.\n We report the best performance across all functions on the pareto front. \n Notably, AIFeynman performs quite an exhaustive search procedure such that running it even on a single equation took on the order of tens of minutes.\n\\end{itemize}\n\n\\subsection{Results}\\label{sec:results}\n\n\\begin{figure}\n\\centering\n\\begin{subfigure}{1\\textwidth}\n \\centering\n \\includegraphics[width=0.65\\linewidth]{figs\/iv_const_skel\/legend_iv_const_skel.pdf}\n\\end{subfigure}%\n\\\\ \\vspace{0.25cm}\n\\begin{subfigure}{.25\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{figs\/iv_const_skel\/iv_const_skel_isclose.pdf}\n \\caption{allclose}\n\\end{subfigure}%\n\\begin{subfigure}{.25\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{figs\/iv_const_skel\/iv_const_skel_r2.pdf}\n \\caption{R$^2 \\geq 0.999$}\n\\end{subfigure}%\n\\begin{subfigure}{.25\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{figs\/iv_const_skel\/iv_const_skel_skelrec.pdf}\n \\caption{skeleton recovery}\n\\end{subfigure}%\n\\begin{subfigure}{.25\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{figs\/iv_const_skel\/iv_const_skel_skelrec_isclose.pdf}\n \\caption{skel. recov. \\& allclose}\n\\end{subfigure}%\n\\caption{Numerical and symbolic performance evaluation on testset-iv.}\n\\label{fig:results}\n\\end{figure}\n\n\n\\xhdr{Model Performance}\n\\Cref{fig:results} shows NSODE's performance on our testset-iv, testset-constants, and testset-skeletons according to our numerical and symbolic metrics as well as combined skeleton recovery and allclose as we vary $k$ in the top-k considered candidates of the beam search.\nInvesting sufficient test-time-compute (i.e., considering many candidates) continuously improves performance.\nWhile we capped $k$ at 1536 due to memory limitations, we did not observe a stagnation of the roughly logarithmic scaling of all performance metrics with $k$.\nThis cannot be attributed to ``exhaustion effects'', where one may assume that all possible ODEs will eventually be among the candidates, because (a) the space of possible skeletons grows much faster than exponentially, and (b) the numerical metrics are extremely sensitive also to the predicted constant values in continuous domains.\n\nAs one may expect, performance decreases as we move from only new initial conditions, to also sampling new constants, to finally sampling entirely unseen skeletons.\nOn testset-iv with $k=1536$ we achieve about 50\\% skeleton recovery and still successfully recover more than a third skeletons of testset-skeletons with similar numbers for allclose.\nThe fact that the combined metric (symbolic + numerical) is only about half of that indicates that numerical and symbolic fit are indeed two separate measures, none of which need to imply the other.\nHence, a thorough evaluation of both is crucial to understand model performance in symbolic regression tasks.\n\n\\begin{table}[h!]\n\\centering\n\\caption{Comparing NSODE to the baselines. Results are average percentages across dataset. GPLearn often generates extremely long expressions which take SymPy up to half a minute to parse during evaluation. We denote this extra time in gray.}\\label{tab:resultsummary}\n\\begin{tabularx}{\\columnwidth}{l@{\\hspace{8pt}}l@{\\hspace{8pt}}Y@{\\hspace{6pt}}Y@{\\hspace{8pt}}Y@{\\hspace{0pt}}Y}\n\\toprule \\rowcolor{white}\nDataset & Metric & NSODE & Sindy & GPLearn & AIFeynman \\\\ \n\\midrule\n & skel-recov & \\bf 37.4 & 3.7 & 2.5 & 14.1 \\\\ \n & R$^2 \\geq 0.999$ & 24.5 & 31.9 & 3.7& \\bf 49.7 \\\\ \niv-163 & allclose & 42.3 & 25.8 & 14.7 & \\bf 55.8 \\\\ \n & skel-recov \\& R$^2 \\geq 0.999$ & \\bf 15.3 & 3.1 & 1.8 & 13.5 \\\\ \n & skel-recov \\& allclose & \\bf 15.3 & 3.1 & 1.8 & 13.5 \\\\ \n & runtime [s] & 5.4 & \\bf0.4 & 29 {\\color{gray}+22} & 1203.6 \\\\\n\\midrule\n & skel-recov & 41.7 & 33.3 & 8.3 & \\bf 91.7 \\\\ \n & R$^2 \\geq 0.999$ & 16.7 & 50 & 0.0 & \\bf 75 \\\\ \ntextbook & allclose & 25 & 58.3 & 8.3 & \\bf 75 \\\\ \n & skel-recov \\& R$^2 \\geq 0.999$ & 33.3 & 41.7 & 0 & \\bf 66.7 \\\\ \n & skel-recov \\& allclose & 8.3 & 33.3 & 1.8 & \\bf 66.7 \\\\\n & runtime [s] & 6 & \\bf1 & 23 {\\color{gray}+22} & 1267.1 \\\\\n\\bottomrule\n\\end{tabularx}%\n\\end{table}\n\n\\xhdr{Comparison to baselines}\nIn \\cref{tab:resultsummary} we compare NSODE to all baselines using $k=1536$ in our beam search; full results on all datasets can be found in \\cref{app:detailedresults}.\nWe also include the average wallclock runtime per expression for each of the datasets.\n\nFirst, we note that on our subsampled testset-iv-163, NSODE outperforms competing approaches in terms of skeleton recovery by a wide margin and also performs best in terms of joint skeleton recovery and numerical measures, which is a strong indication of actually having recovered the governing ODE accurately.\nBy spending over 200x more time on its exhaustive heuristic search, AIFeynman manages to outperform NSODE in terms of numerical accuracy ($\\mathrm{R}^2$ and allclose).\n\\Cref{fig:distribution} shows the number of skeletons recovered by each method given the complexity of equations, results for other datasets can be found in \\cref{app:detailedresults}. \\footnote{Due to simplification, complexity is not upper bounded by the number of nodes in a unary-binary tree.}\nWhile AIFeynman and Sindy recover some of the low complexity expressions, NSODE is the only method to also recover some of the more complex skeletons. \n\nOn testset-textbook, AIFeynman outperforms all other methods on numerical and symbolic metrics. This can be understood with regard to the dataset where $8\/12$ expressions are polynomials with the remaining 4\/12 expressions having a polynomial skeleton with fractional or negative exponents. These expressions are particularly favorable for the heuristics implemented by AIFeynman which explicitly attempt to fit a polynomial to the data.\nHowever, even on these simple examples AIFeynman takes over 200x longer than our method, which in turn clearly outperforms Sindy and GPLearn in terms of skeleton recovery.\n\n\\begin{figure}\n\\centering\n\\begin{subfigure}{1\\textwidth}\n \\centering\n \\includegraphics{figs\/complexity_vs_recovery\/legend2.pdf}\n\\end{subfigure}%\n\\\\\n\\begin{subfigure}{.25\\textwidth}\n \\centering\n \\includegraphics[width=.99\\linewidth]{figs\/complexity_vs_recovery\/complexity_vs_recovery_nsode_163.pdf}\n \\caption{NSODE}\n\\end{subfigure}%\n\\begin{subfigure}{.25\\textwidth}\n \\centering\n \\includegraphics[width=.99\\linewidth]{figs\/complexity_vs_recovery\/complexity_vs_recovery_aifeynman_163.pdf}\n \\caption{AIFeynman}\n\\end{subfigure}%\n\\begin{subfigure}{.25\\textwidth}\n \\centering\n \\includegraphics[width=.99\\linewidth]{figs\/complexity_vs_recovery\/complexity_vs_recovery_sindy_163.pdf}\n \\caption{Sindy}\n\\end{subfigure}%\n\\begin{subfigure}{.25\\textwidth}\n \\centering\n \\includegraphics[width=.99\\linewidth]{figs\/complexity_vs_recovery\/complexity_vs_recovery_gplearn_163.pdf}\n \\caption{GPLearn}\n\\end{subfigure}%\n\\caption{Correctly recovered skeletons by each method on testset-iv-163 per complexity. AIFeynman and Sindy are mostly able to recover some of the low complexity skeletons, while NSODE performs much better also on higher complexities. GPLearn fails to recover most skeletons.}\\label{fig:distribution}\n\\vspace{-2mm}\n\\end{figure}\n\n\\section{Conclusion}\n\\label{sec:conclusion}\n\nWe have developed a flexible and scalable method to infer ordinary differential equations $\\dot{y} = f(y)$ from a single observed solution trajectory.\nOur method follows the successful paradigm of large-scale pretraining of attention-based sequence-to-sequence models on essentially unlimited amounts of simulated data, where the inputs are the observed solution $\\{(t_i,y_i)\\}_{i=1}^n$ and the output is a symbolic expression for~$f$ as a string.\nOnce trained, our method is orders of magnitude more efficient than similarly expressive existing symbolic regression techniques that require a separate optimization for each instance and achieves strong performance in terms of skeleton recovery especially for complex expressions on various benchmarks.\n\n\\bibliographystyle{abbrvnat}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction} \\label{secIntro}\n\nThe affine Kac-Moody superalgebra $\\AKMSA{gl}{1}{1}$ is an attractive candidate for study. On the one hand, its highest weight theory is particularly easy to analyse. On the other, one is naturally led to study indecomposable modules of the type that arise in logarithmic conformal field theory{}. In \\cite{CR:GL11}, we reviewed and consolidated what was known about this superalgebra, drawing in particular upon the previous works \\cite{RozQua92,Rozansky:1992td,SalGL106,Creutzig:2007jy,CS09,Creutzig:2009zz,Creutzig:2010ne}.\n\nOne motivation for undertaking this work was to understand how one could reconcile the observation that conformal field theories{} with $\\AKMSA{gl}{1}{1}$ symmetry appeared to admit only continuous spectra, whereas one might expect that the Wess-Zumino-Witten{} model on the real form $\\SLSG{U}{1}{1}$ would have the same symmetry, but a discrete spectrum. Another was to understand whether $\\AKMSA{gl}{1}{1}$ could be related to other infinite-dimensional algebras, thus providing relationships between certain (logarithmic) conformal field theories{}. For the first question, we were able to show that certain discrete spectra seem to be consistent provided one \\emph{extends} the chiral algebra appropriately. For the second, we identified a certain $\\AKMA{u}{1}$-coset of $\\AKMSA{gl}{1}{1}$ as the chiral algebra of the well-known $\\beta \\gamma$ ghost system. Previous work \\cite{RidSL210} then links $\\AKMSA{gl}{1}{1}$ to the affine Kac-Moody algebra $\\AKMA{sl}{2}_{-1\/2}$ \\cite{RidSL208,RidFus10}, the triplet algebra $\\func{\\alg{W}}{1,2}$ of Gaberdiel and Kausch \\cite{GabRat96} and the symplectic fermions algebra \\cite{KauSym00} ($\\AKMSA{psl}{1}{1}$).\n\nThis article describes a certain family of \\emph{extended algebras} of $\\AKMSA{gl}{1}{1}$. In \\cite{CR:GL11}, we noted that the fusion rules give rise to an infinite family of simple currents labelled by $n \\in \\mathbb{R}$ and $\\ell \\in \\mathbb{Z}$. It follows that these algebra extensions may be computed algorithmically \\cite{RidSU206,RidMin07}. Here, we perform the computations up to a certain order, using a well-known free field realisation \\cite{Guruswamy:1999hi}. More precisely, we study the resulting W-algebras and show that, for certain infinite families of $n$ and $\\ell$, there is a bosonic subalgebra which we conjecture to be the $W^{\\brac{2}}_N$ algebra of Feigin and Semikhatov \\cite{Feigin:2004wb}.\n\n\\section{$\\SLSA{gl}{1}{1}$ and its representations} \\label{secFinAlg}\n\n\\subsection{Algebraic Structure}\n\nThe Lie superalgebra $\\SLSA{gl}{1}{1}$ consists of the endomorphisms of the super vector space $\\mathbb{C}^{1 \\mid 1}$ equipped with the standard graded commutator. It is convenient to choose the following basis,\n\\begin{equation} \\label{eqngl11DefRep}\nN = \\frac{1}{2} \n\\begin{pmatrix}\n1 & 0 \\\\\n0 & -1\n\\end{pmatrix}\n, \\qquad E = \n\\begin{pmatrix}\n1 & 0 \\\\\n0 & 1\n\\end{pmatrix}\n, \\qquad \\psi^+ = \n\\begin{pmatrix}\n0 & 1 \\\\\n0 & 0\n\\end{pmatrix}\n, \\qquad \\psi^- = \n\\begin{pmatrix}\n0 & 0 \\\\\n1 & 0\n\\end{pmatrix}\n,\n\\end{equation}\nin which $N$ and $E$ are parity-preserving (bosonic) whereas $\\psi^+$ and $\\psi^-$ are parity-reversing (fermionic). The non-vanishing brackets are then\n\\begin{equation} \\label{eqngl11Rels}\n\\comm{N}{\\psi^{\\pm}} = \\pm \\psi^{\\pm}, \\qquad \\acomm{\\psi^+}{\\psi^-} = E.\n\\end{equation}\nWe note that $E$ is central, so this superalgebra is not simple. In fact, $\\SLSA{gl}{1}{1}$ does not decompose as a direct sum of ideals. Equivalently, the adjoint representation of $\\SLSA{gl}{1}{1}$ is reducible, but indecomposable.\n\nThe standard non-degenerate bilinear form $\\killing{\\cdot}{\\cdot}$ on $\\SLSA{gl}{1}{1}$ is given by the supertrace of the product in the defining representation \\eqref{eqngl11DefRep}. With respect to the basis elements \\eqref{eqngl11DefRep}, this form is\n\\begin{equation}\n\\killing{N}{E} = \\killing{E}{N} = 1, \\qquad \\killing{\\psi^+}{\\psi^-} = -\\killing{\\psi^-}{\\psi^+} = 1,\n\\end{equation}\nwith all other combinations vanishing. From this, we compute the quadratic Casimir $Q \\in \\uealg{\\SLSA{gl}{1}{1}}$ (up to an arbitrary polynomial in the central element $E$). We find it convenient to take\n\\begin{equation} \\label{eqnDefCasimir}\nQ = NE + \\psi^- \\psi^+.\n\\end{equation}\n\n\\subsection{Representation Theory} \\label{secFinRep}\n\nThe obvious triangular decomposition of $\\SLSA{gl}{1}{1}$ regards $\\psi^+$ as a raising (annihilation) operator, $\\psi^-$ as a lowering (creation) operator, and $N$ and $E$ as Cartan elements. A highest weight state{} of a $\\SLSA{gl}{1}{1}$-representation is then defined to be an eigenstate of $N$ and $E$ which is annihilated by $\\psi^+$. Such states generate Verma modules in the usual way and as $\\psi^-$ squares to zero in any representation, every Verma module has dimension $2$. If $\\brac{n,e}$ denotes the weight (the $N$- and $E$-eigenvalues) of a highest weight state{} generating a Verma module, then its unique descendant will have weight $\\brac{n-1,e}$. We will denote this Verma module by $\\VerMod{n-1\/2,e}$, remarking that the convention of characterising a highest weight module{} by the \\emph{average} $N$-eigenvalue of its states, rather than that of the highest weight state{} itself, turns out to symmetrise many of the formulae to follow.\n\nSuppose now that $\\ket{v}$ is a (generating) highest weight state{} of $\\VerMod{n,e}$. It satisfies\n\\begin{equation}\n\\psi^+ \\psi^- \\ket{v} = \\acomm{\\psi^+}{\\psi^-} \\ket{v} = E \\ket{v} = e \\ket{v},\n\\end{equation}\nso the descendant $\\psi^- \\ket{v} \\neq 0$ is a singular vector if and only if $e=0$. Verma modules are therefore irreducible for $e \\neq 0$, and have irreducible quotients of dimension $1$ when $e = 0$. Modules with $e \\neq 0$ are called \\emph{typical} while those with $e = 0$ are \\emph{atypical}. We will denote a typical irreducible by $\\TypMod{n,e} \\cong \\VerMod{n,e}$ and an atypical irreducible by $\\AtypMod{n}$. Our convention of labelling modules by their average $N$-eigenvalue leads us to define the latter to be the irreducible quotient of $\\VerMod{n-1\/2,0}$. This is summarised in the short exact sequence\n\\begin{equation} \\label{ESFinV}\n\\dses{\\AtypMod{n-1\/2}}{\\VerMod{n,0}}{\\AtypMod{n+1\/2}}\n\\end{equation}\nand structure diagram\n\\begin{equation}\n\\parbox[c]{0.4\\textwidth}{\n\\begin{tikzpicture}[auto,thick,\n\tnom\/.style={circle,draw=black!20,fill=black!20,inner sep=2pt}\n\t]\n\\node (q1) at (0,0) {$\\AtypMod{n+1\/2}$};\n\\node (s1) at (3,0) {$\\AtypMod{n-1\/2}$};\n\\node at (-2,0) [nom] {$\\VerMod{n,0}$};\n\\node at (-1.25,0) {$:$};\n\\draw [->] (q1) to node {$\\psi^-$} (s1);\n\\end{tikzpicture}\n} \\ .\n\\end{equation}\nSuch diagrams illustrate how the irreducible composition factors of a module are combined, with arrows indicating (schematically) the action of the algebra.\n\nAtypical modules also appear as submodules of larger indecomposable modules. Of particular importance are the four-dimensional projectives\\footnote{We mention that the typical irreducibles are also projective in the category of finite-dimensional $\\SLSA{gl}{1}{1}$-modules.} $\\ProjMod{n}$ whose structure diagrams take the form\n\\begin{equation} \\label{picStaggered}\n\\parbox[c]{0.28\\textwidth}{\n\\begin{center}\n\\begin{tikzpicture}[auto,thick,\n\tnom\/.style={circle,draw=black!20,fill=black!20,inner sep=2pt}\n\t]\n\\node (top) at (0,1.5) [] {$\\AtypMod{n}$};\n\\node (left) at (-1.5,0) [] {$\\AtypMod{n+1}$};\n\\node (right) at (1.5,0) [] {$\\AtypMod{n-1}$};\n\\node (bot) at (0,-1.5) [] {$\\AtypMod{n}$};\n\\node at (0,0) [nom] {$\\ProjMod{n}$};\n\\draw [->] (top) to node [swap] {$\\psi^+$} (left);\n\\draw [->] (top) to node {$\\psi^-$} (right);\n\\draw [->] (left) to node [swap] {$\\psi^-$} (bot);\n\\draw [->] (right) to node {$-\\psi^+$} (bot);\n\\end{tikzpicture}\n\\end{center}\n}\n.\n\\end{equation}\nWe remark that these modules may be viewed as particularly simple examples of staggered modules \\cite{RidSta09}. Indeed, they may be regarded as extensions of highest weight modules{} via the exact sequence\n\\begin{equation} \\label{ESFinP}\n\\dses{\\VerMod{n+1\/2,0}}{\\ProjMod{n}}{\\VerMod{n-1\/2,0}},\n\\end{equation}\nand one can verify that the Casimir $Q$ acts non-diagonalisably on $\\ProjMod{n}$, taking the generator associated with the top $\\AtypMod{n}$ factor to the generator of the bottom $\\AtypMod{n}$ factor, while annihilating the other states.\n\n\\subsection{The Representation Ring}\n\nThe relevance of the projectives $\\ProjMod{n}$ is that they appear in the representation ring generated by the irreducibles.\\footnote{It is perhaps also worth pointing out that the adjoint representation of $\\SLSA{gl}{1}{1}$ is isomorphic to $\\ProjMod{0}$.} The tensor product rules governing this ring are \\cite{RozQua92}\n\\begin{equation} \\label{RepRing}\n\\begin{gathered}\n\\AtypMod{n} \\otimes \\AtypMod{n'} = \\AtypMod{n+n'}, \\qquad\n\\AtypMod{n} \\otimes \\TypMod{n',e'} = \\TypMod{n+n',e'}, \\qquad\n\\AtypMod{n} \\otimes \\ProjMod{n'} = \\ProjMod{n+n'}, \\\\\n\\TypMod{n,e} \\otimes \\TypMod{n',e'} = \n\\begin{cases}\n\\ProjMod{n+n'} & \\text{if $e+e'=0$,} \\\\\n\\TypMod{n+n'+1\/2,e+e'} \\oplus \\TypMod{n+n'-1\/2,e+e'} & \\text{otherwise,}\n\\end{cases}\n\\\\\n\\TypMod{n,e} \\otimes \\ProjMod{n'} = \\TypMod{n+n'+1,e} \\oplus 2 \\: \\TypMod{n+n',e} \\oplus \\TypMod{n+n'-1,e}, \\qquad\n\\ProjMod{n} \\otimes \\ProjMod{n'} = \\ProjMod{n+n'+1} \\oplus 2 \\: \\ProjMod{n+n'} \\oplus \\ProjMod{n+n'-1}.\n\\end{gathered}\n\\end{equation}\nThere are other indecomposables which may be constructed from submodules and quotients of the $\\ProjMod{n}$ by taking tensor products. We will not need them and refer to \\cite{GotRep07} for further discussion.\n\n\\section{$\\AKMSA{gl}{1}{1}$ and its Representations} \\label{secAffine}\n\n\\subsection{Algebraic Structure} \\label{secAffAlg}\n\nOur conventions for $\\SLSA{gl}{1}{1}$ carry over to its affinisation $\\AKMSA{gl}{1}{1}$ in the usual way. Explicitly, the non-vanishing brackets are\n\\begin{equation}\n\\comm{N_r}{E_s} = r k \\delta_{r+s,0}, \\qquad \\comm{N_r}{\\psi^{\\pm}_s} = \\pm \\psi^{\\pm}_{r+s}, \\qquad \\acomm{\\psi^+_r}{\\psi^-_s} = E_{r+s} + r k \\delta_{r+s,0},\n\\end{equation}\nwhere $k \\in \\mathbb{R}$ is called the level and $r,s \\in \\mathbb{Z}$. We emphasise that when $k \\neq 0$, the generators can be rescaled so as to normalise $k$ to $1$:\n\\begin{equation} \\label{eqnLevelScaling}\nN_r \\longrightarrow N_r, \\qquad E_r \\longrightarrow \\frac{E_r}{k}, \\qquad \\psi^{\\pm}_r \\longrightarrow \\frac{\\psi^{\\pm}_r}{\\sqrt{k}}.\n\\end{equation}\nAs in the more familiar case of $\\AKMA{u}{1}$, we see that the actual value of $k \\neq 0$ is not physical.\n\nThe Virasoro generators are constructed using (a modification of) the Sugawara construction. Because the quadratic Casimir of $\\SLSA{gl}{1}{1}$ is only defined modulo polynomials in $E$, one tries the ansatz \\cite{RozQua92}\n\\begin{equation} \\label{eqnDefT}\n\\func{T}{z} = \\mu \\func{\\normord{NE + EN - \\psi^+ \\psi^- + \\psi^- \\psi^+}}{z} + \\nu \\func{\\normord{EE}}{z},\n\\end{equation}\nfinding that this defines an energy-momentum tensor if and only if $\\mu = 1\/2k$ and $\\nu = 1\/2k^2$. Moreover, the $\\AKMSA{gl}{1}{1}$ currents $\\func{N}{z}$, $\\func{E}{z}$ and $\\func{\\psi^{\\pm}}{z}$ are found to be Virasoro primaries of conformal dimension $1$ and the central charge is zero.\n\nThe structure theory of highest weight modules{} for $\\AKMSA{gl}{1}{1}$ turns out to be particularly accessible because of certain automorphisms. These consist of the automorphism $\\mathsf{w}$ which defines the notion of conjugation and the family \\cite{SalGL106} of spectral flow automorphisms $\\sigma^{\\ell}$, $\\ell \\in \\mathbb{Z}$. Explicitly,\n\\begin{equation}\n\\begin{aligned}\n\\func{\\mathsf{w}}{N_r} &= -N_{r}, \\\\\n\\func{\\sigma^{\\ell}}{N_r} &= N_r,\n\\end{aligned}\n\\qquad\n\\begin{aligned}\n\\func{\\mathsf{w}}{E_r} &= -E_{r}, \\\\\n\\func{\\sigma^{\\ell}}{E_r} &= E_r - \\ell k \\delta_{r,0},\n\\end{aligned}\n\\qquad\n\\begin{aligned}\n\\func{\\mathsf{w}}{\\psi^{\\pm}_r} &= \\pm \\psi^{\\mp}_{r}, \\\\\n\\func{\\sigma^{\\ell}}{\\psi^{\\pm}_r} &= \\psi^{\\pm}_{r \\mp \\ell},\n\\end{aligned}\n\\qquad\n\\begin{aligned}\n\\func{\\mathsf{w}}{L_0} &= L_0. \\\\\n\\func{\\sigma^{\\ell}}{L_0} &= L_0 - \\ell N_0.\n\\end{aligned}\n\\end{equation}\nThese automorphisms may be used to construct new modules $\\func{\\mathsf{w}^*}{\\mathcal{M}}$ and $\\func{\\sigma^*}{\\mathcal{M}}$ by twisting the action of the algebra on a module $\\mathcal{M}$:\n\\begin{equation} \\label{eqnInducedAction}\nJ \\cdot \\tfunc{\\mathsf{w}^*}{\\ket{v}} = \\func{\\mathsf{w}^*}{\\tfunc{\\mathsf{w}^{-1}}{J} \\ket{v}}, \\qquad J \\cdot \\tfunc{\\sigma^*}{\\ket{v}} = \\func{\\sigma^*}{\\tfunc{\\sigma^{-1}}{J} \\ket{v}} \\qquad \\text{($J \\in \\AKMSA{gl}{1}{1}$).}\n\\end{equation}\nNote that $\\func{\\mathsf{w}^*}{\\mathcal{M}}$ is precisely the module conjugate to $\\mathcal{M}$.\n\n\\subsection{Representation Theory} \\label{secAffRep}\n\nWe can now define affine highest weight states{}, affine Verma modules $\\AffVerMod{n,\\ell}$, and their irreducible quotients as before. We remark only that \\eqref{eqnLevelScaling} suggests that we characterise modules by the invariant ratio $\\ell = e\/k$ rather than by the $E_0$-eigenvalue $e$. The affine highest weight state{} $\\ket{v_{n,\\ell}}$ of $\\AffVerMod{n,\\ell}$, whose weight (its $N_0$- and $E_0\/k$-eigenvalues) is $\\brac{n + \\tfrac{1}{2}, \\ell}$, has conformal dimension\n\\begin{equation} \\label{eqnConfDim}\n\\Delta_{n,\\ell} = n \\ell + \\frac{1}{2} \\ell^2.\n\\end{equation}\nOf course, this formula also applies to singular vectors. Again, the label $n$ refers to the average $N_0$-eigenvalue of the zero-grade subspace of $\\AffVerMod{n,\\ell}$, generalising the labelling convention of \\secref{secFinRep}.\n\nVerma modules for $\\AKMSA{gl}{1}{1}$ are infinite-dimensional and their characters have the form\n\\begin{equation} \\label{eqnCharVerma}\n\\ch{\\AffVerMod{n,\\ell}}{z;q} = \\traceover{\\AffVerMod{n,\\ell}} z^{N_0} q^{L_0} = z^{n+1\/2} q^{\\Delta_{n,\\ell}} \\prod_{i=1}^{\\infty} \\frac{\\brac{1 + z q^i} \\brac{1 + z^{-1} q^{i-1}}}{\\brac{1 - q^i}^2}.\n\\end{equation}\nFor the irreducible quotients, the case with $\\ell = 0$ is particularly easy. As in \\secref{secFinRep}, we regard $\\brac{n,\\ell}$ (and modules so-labelled) as being \\emph{typical} if $\\AffVerMod{n,\\ell}$ is irreducible and \\emph{atypical} otherwise.\n\\begin{proposition} \\label{prop:ell=0}\nThe affine Verma module $\\AffVerMod{n,0}$ has an exact sequence\n\\begin{equation}\n\\dses{\\AffAtypMod{n-1\/2,0}}{\\AffVerMod{n,0}}{\\AffAtypMod{n+1\/2,0}}\n\\end{equation}\nin which the $\\AffAtypMod{n,0}$ are (atypical) irreducibles whose characters are given by\n\\begin{equation} \\label{eqnCharVac}\n\\ch{\\AffAtypMod{n,0}}{z;q} = z^n \\prod_{i=1}^{\\infty} \\frac{\\brac{1 + z q^i} \\brac{1 + z^{-1} q^i}}{\\brac{1 - q^i}^2}.\n\\end{equation}\n\\end{proposition}\n\\begin{proof}\nSince $\\ell = 0$, every singular vector of $\\AffVerMod{n,0}$ has dimension $0$ by \\eqnref{eqnConfDim}. The space of singular vectors is thus spanned by $\\ket{v_{n,0}}$ and $\\psi^-_0 \\ket{v_{n,0}}$. Taking the quotient by the module generated by $\\psi^-_0 \\ket{v_{n,0}}$ gives a module with a one-dimensional zero-grade subspace. The only singular vector is then the highest weight state{}, so this quotient is irreducible. We denote it by $\\AffAtypMod{n+1\/2,0}$ as its zero-grade subspace has $N_0$-eigenvalue $n+\\tfrac{1}{2}$. Its character follows trivially. The submodule of $\\AffVerMod{n,0}$ generated by $\\psi^-_0 \\ket{v_{n,0}}$ is not a Verma module because $\\bigl( \\psi^-_0 \\bigr)^2 \\ket{v_{n,0}} = 0$. It must therefore be a proper quotient of $\\AffVerMod{n-1,0}$ and, by the above argument, the only such quotient is the irreducible $\\AffAtypMod{n-1\/2,0}$. The exact sequence follows.\n\\end{proof}\nFor $\\ell \\neq 0$, one proves by direct calculation \\cite{CR:GL11} that for $0 < \\abs{\\ell} < 1$, $\\AffVerMod{n,\\ell}$ is irreducible. In other words, the corresponding irreducibles are typical, hence we denote them by $\\AffTypMod{n,\\ell}$. For $\\abs{\\ell} \\geqslant 1$, the structure of the Verma modules now follows from considering the induced action of the spectral flow automorphisms. More precisely, one proves \\cite{CR:GL11} that any Verma module is isomorphic to a twisted version of a Verma module with $-1 < \\abs{\\ell} < 1$ (or the conjugate of such a Verma module). We summarise the result as follows.\n\\begin{proposition}\nWhen $\\ell \\notin \\mathbb{Z}$, the affine Verma module $\\AffVerMod{n,\\ell}$ is irreducible, $\\AffVerMod{n,\\ell} \\cong \\AffTypMod{n,\\ell}$, so its character is given by \\eqnref{eqnCharVerma}. When $\\ell \\in \\mathbb{Z}$, the affine Verma module $\\AffVerMod{n,\\ell}$ has an exact sequence\n\\begin{equation}\n\\begin{gathered}\n\\dses{\\AffAtypMod{n+1,\\ell}}{\\AffVerMod{n,\\ell}}{\\AffAtypMod{n,\\ell}} \\qquad \\text{($\\ell = +1,+2,+3,\\ldots$),} \\\\\n\\dses{\\AffAtypMod{n-1,\\ell}}{\\AffVerMod{n,\\ell}}{\\AffAtypMod{n,\\ell}} \\qquad \\text{($\\ell = -1,-2,-3,\\ldots$),}\n\\end{gathered}\n\\end{equation}\nin which the $\\AffAtypMod{n,\\ell}$ are (atypical) irreducibles whose characters are given by\n\\begin{equation} \\label{eqnCharAtyp}\n\\ch{\\AffAtypMod{n,\\ell}}{z;q} = \n\\begin{cases}\n\\displaystyle \\frac{z^{n+1\/2} q^{\\Delta_{n,\\ell}}}{1 + zq^{\\ell}} \\prod_{i=1}^{\\infty} \\frac{\\brac{1 + z q^i} \\brac{1 + z^{-1} q^{i-1}}}{\\brac{1 - q^i}^2} & \\text{($\\ell = +1,+2,+3,\\ldots$),} \\\\\n\\displaystyle \\frac{z^{n+1\/2} q^{\\Delta_{n,\\ell}}}{1 + z^{-1} q^{-\\ell}} \\prod_{i=1}^{\\infty} \\frac{\\brac{1 + z q^i} \\brac{1 + z^{-1} q^{i-1}}}{\\brac{1 - q^i}^2} & \\text{($\\ell = -1,-2,-3,\\ldots$).}\n\\end{cases}\n\\end{equation}\n(The exact sequence and character for $\\ell = 0$ was given in \\propref{prop:ell=0}.)\n\\end{proposition}\n\\noindent Note that the $\\AffVerMod{n,\\ell}$ with $\\ell \\in \\mathbb{Z}$ have a non-trivial singular vector at grade $\\abs{\\ell}$. We emphasise that the $\\AffAtypMod{n,\\ell}$ with $\\ell \\neq 0$ therefore possess a two-dimensional zero-grade subspace.\n\nThis description of the Verma modules, their irreducible quotients and characters relies upon being able to identify the result of applying the spectral flow automorphisms to modules. For irreducibles, we have\n\\begin{equation}\n\\tfunc{\\bigl( \\sigma^{\\ell'} \\bigr)^*}{\\AffTypMod{n,\\ell}} = \\AffTypMod{n-\\ell',\\ell+\\ell'}, \\qquad \\tfunc{\\bigl( \\sigma^{\\ell'} \\bigr)^*}{\\AffAtypMod{n,\\ell}} = \\AffAtypMod{n-\\ell'+\\func{\\varepsilon}{\\ell+\\ell'}-\\func{\\varepsilon}{\\ell},\\ell+\\ell'},\n\\end{equation}\nwhere we introduce a convenient variant $\\varepsilon$ of the sign function on $\\mathbb{Z}$, defined by taking $\\func{\\varepsilon}{\\ell}$ to be $\\tfrac{1}{2}$, $0$ or $-\\tfrac{1}{2}$ according as to whether $\\ell \\in \\mathbb{Z}$ is positive, zero or negative, respectively.\n\n\\subsection{Fusion} \\label{secAffFus}\n\nThe fusion rules of the irreducible $\\AKMSA{gl}{1}{1}$-modules (among others) were first deduced in \\cite{Creutzig:2007jy} using three-point functions computed in a free field realisation and a conjectured completeness of the spectrum. These rules and the spectrum conjecture were confirmed in \\cite{CR:GL11} through a direct argument involving the Nahm-Gaberdiel-Kausch fusion algorithm \\cite{NahQua94,GabInd96} and spectral flow. The fusion ring generated by the irreducibles may be understood \\cite{Quella:2007hr} as a ``constrained lift'' of the representation ring \\eqref{RepRing} of $\\SLSA{gl}{1}{1}$ where the constraints are effectively implemented by spectral flow. Explicitly, the rules are\n\\begin{equation} \\label{Fusion}\n\\begin{gathered}\n\\AffAtypMod{n,\\ell} \\mathbin{\\times} \\AffAtypMod{n',\\ell'} = \\AffAtypMod{n+n'-\\func{\\varepsilon}{\\ell,\\ell'},\\ell+\\ell'}, \\quad\n\\AffAtypMod{n,\\ell} \\mathbin{\\times} \\AffTypMod{n',\\ell'} = \\AffTypMod{n+n'-\\func{\\varepsilon}{\\ell},\\ell+\\ell'}, \\quad\n\\AffAtypMod{n,\\ell} \\mathbin{\\times} \\AffProjMod{n',\\ell'} = \\AffProjMod{n+n'-\\func{\\varepsilon}{\\ell,\\ell'},\\ell+\\ell'}, \\\\\n\\AffTypMod{n,\\ell} \\mathbin{\\times} \\AffTypMod{n',\\ell'} = \n\\begin{cases}\n\\AffProjMod{n+n'+\\func{\\varepsilon}{\\ell+\\ell'},\\ell+\\ell'} & \\text{if $\\ell+\\ell'=0$,} \\\\\n\\AffTypMod{n+n'+1\/2,\\ell+\\ell'} \\oplus \\AffTypMod{n+n'-1\/2,\\ell+\\ell'} & \\text{otherwise,}\n\\end{cases}\n\\\\\n\\AffTypMod{n,\\ell} \\mathbin{\\times} \\AffProjMod{n',\\ell'} = \\AffTypMod{n+n'+1-\\func{\\varepsilon}{\\ell'},\\ell+\\ell'} \\oplus 2 \\: \\AffTypMod{n+n'-\\func{\\varepsilon}{\\ell'},\\ell+\\ell'} \\oplus \\AffTypMod{n+n'-1-\\func{\\varepsilon}{\\ell'},\\ell+\\ell'}, \\\\\n\\AffProjMod{n,\\ell} \\mathbin{\\times} \\AffProjMod{n',\\ell'} = \\AffProjMod{n+n'+1-\\func{\\varepsilon}{\\ell,\\ell'},\\ell+\\ell'} \\oplus 2 \\: \\AffProjMod{n+n'-\\func{\\varepsilon}{\\ell,\\ell'},\\ell+\\ell'} \\oplus \\AffProjMod{n+n'-1-\\func{\\varepsilon}{\\ell,\\ell'},\\ell+\\ell'}.\n\\end{gathered}\n\\end{equation}\nHere, we have defined $\\func{\\varepsilon}{\\ell , \\ell'} = \\func{\\varepsilon}{\\ell} + \\func{\\varepsilon}{\\ell'} - \\func{\\varepsilon}{\\ell + \\ell'}$ for convenience.\n\nThese fusion rules also introduce the indecomposable modules $\\AffProjMod{n,\\ell}$ which are the counterparts of the projective $\\SLSA{gl}{1}{1}$-modules $\\ProjMod{n}$ discussed in \\secref{secFinRep}.\\footnote{More precisely, $\\AffProjMod{n,0}$ is the affine counterpart to $\\ProjMod{n}$ and the remaining $\\AffProjMod{n,\\ell}$ are obtained by spectral flow.} The $\\AffProjMod{n,\\ell}$ are staggered with structure diagram\n\\begin{equation} \\label{picAffineStaggered}\n\\parbox[c]{0.28\\textwidth}{\n\\begin{center}\n\\begin{tikzpicture}[auto,thick,\n\tnom\/.style={circle,draw=black!20,fill=black!20,inner sep=2pt}\n\t]\n\\node (top) at (0,1.5) [] {$\\AffAtypMod{n,\\ell}$};\n\\node (left) at (-1.5,0) [] {$\\AffAtypMod{n+1,\\ell}$};\n\\node (right) at (1.5,0) [] {$\\AffAtypMod{n-1,\\ell}$};\n\\node (bot) at (0,-1.5) [] {$\\AffAtypMod{n,\\ell}$};\n\\node at (0,0) [nom] {$\\AffProjMod{n,\\ell}$};\n\\draw [->] (top) to (left);\n\\draw [->] (top) to (right);\n\\draw [->] (left) to (bot);\n\\draw [->] (right) to (bot);\n\\end{tikzpicture}\n\\end{center}\n}\n\\end{equation}\nand a non-diagonalisable action of the Virasoro mode $L_0$. It follows that conformal field theories{} whose spectra contain typical modules will also contain such $\\AffProjMod{n,\\ell}$ (by fusion), and so will be \\emph{logarithmic}.\n\n\\section{W-Algebras extending $\\AKMSA{gl}{1}{1}$} \\label{secExtAlg}\n\n\\subsection{Chiral Algebra Extensions}\n\nOur search for extended algebras is guided by the following considerations: First, note that if we choose to extend by a zero-grade field associated to any irreducible $\\AKMSA{gl}{1}{1}$-module, then we must include the rest of its zero-grade fields in the extension. Second, the fields we extend by should be closed under conjugation. Third, extending by fields from typical irreducibles will lead to logarithmic behaviour in the extended chiral algebra because fusing typicals with their conjugates yields the staggered indecomposable $\\AffProjMod{0,0}$.\n\nIt seems then that the most tractable extensions will involve zero-grade fields from atypical modules $\\AffAtypMod{n,\\ell}$ and their conjugates $\\AffAtypMod{-n,-\\ell}$. The simplest extension we could hope for would involve a single atypical and its conjugate and have the further property that these extension fields generate no new fields at the level of the commutation relations. This may be achieved for extension fields of integer or half-integer conformal dimension by requiring that the operator product expansions{} of the zero-grade fields of $\\AffAtypMod{n,\\ell}$ are regular. From the fusion rules \\eqref{Fusion}, we obtain\n\\begin{equation}\n\\AffAtypMod{n,\\ell} \\mathbin{\\times} \\AffAtypMod{n,\\ell} = \\AffAtypMod{2n - \\func{\\varepsilon}{\\ell},2\\ell},\n\\end{equation}\nfrom which it follows that the zero-grade fields of $\\AffAtypMod{n,\\ell}$ will have regular operator product expansions{} with one another if $2 \\: \\Delta_{n,\\ell} \\leqslant \\Delta_{2n - \\func{\\varepsilon}{\\ell},2\\ell}$, that is, if\n\\begin{equation}\\label{eqdim}\n\\abs{\\ell} \\leqslant 2 \\: \\Delta_{n,\\ell}.\n\\end{equation}\nWe may take $\\ell$ positive without loss of generality. Further, we require that the conformal dimension of the extension fields be a positive half-integer (so $2n\\ell \\in \\mathbb{Z}$). \\eqnref{eqdim} then implies that there are $m$ distinct possibilities to extend by fields of dimension $m\/2$.\nWe denote by $\\alg{W}_{n,\\ell}$ the algebra obtained upon extending $\\AKMSA{gl}{1}{1}$ by the atypical module $\\AffAtypMod{n,\\ell}$ and its conjugate $\\AffAtypMod{-n,-\\ell}$.\n\n\\subsection{Characters of Extended Algebras}\n\nThe complete extended algebra also contains normally-ordered products of the extension fields and their descendants. Indeed, the extended algebra $\\alg{W}_{n,\\ell}$ may be identified, at least at the level of graded vector spaces, with the orbit of the $\\AKMSA{gl}{1}{1}$ vacuum module under fusion by the simple current modules $\\AffAtypMod{n,\\ell}$ and $\\AffAtypMod{-n,-\\ell}$. In other words,\n\\begin{equation}\n\\alg{W}_{n+1\/2,\\ell} = \\AffAtypMod{0,0} \\oplus \\bigoplus_{m=1}^{\\infty} \\bigl( \\AffAtypMod{mn+1\/2,m\\ell} \\oplus \\AffAtypMod{-mn-1\/2,-m\\ell} \\bigr).\n\\end{equation}\nThe character of the extended vacuum module is therefore\n\\begin{equation} \\label{eqnCharW}\n\\begin{split}\n\\ch{\\alg{W}_{n+1\/2,\\ell}}{y;z;q} &= \\ch{\\AffAtypMod{0,0}}{y,z;q} + \\sum_{m=1}^\\infty \\Bigl[ \\ch{\\AffAtypMod{mn+1\/2, m \\ell}}{y;z;q} + \\ch{\\AffAtypMod{-mn-1\/2, -m \\ell}}{y;z;q} \\Bigr] \\\\\n&= z \\sum_{m \\in \\mathbb{Z}} \\frac{y^{m \\ell} z^{mn} q^{\\brac{mn+1\/2} m \\ell + m^2 \\ell^2 \/ 2}}{1 + z q^{m \\ell}} \\cdot \\prod_{i=1}^{\\infty} \\frac{\\brac{1 + z q^i} \\brac{1 + z^{-1} q^{i-1}}}{\\brac{1 - q^i}^2}.\n\\end{split}\n\\end{equation}\nHere, we have introduced an additional formal variable $y$ in order to keep track of the eigenvalues of $E_0 \/ k$. One can likewise identify the irreducible modules of the extended algebra with the other orbits of the extension modules. We will not consider these modules, their characters, nor their interesting modular properties here, but will return to this in a future publication.\n\n\\subsection{Free Field Realisations} \\label{appFreeFields}\n\nThe affine Kac-Moody superalgebra $\\AKMSA{gl}{1}{1}$ has two well-known free field realizations, the standard Wakimoto realization \\cite{SalGL106} and one constructed from a pair of symplectic fermions, a euclidean boson, and a lorentzian boson \\cite{Guruswamy:1999hi}. An explicit equivalence between the two realisations was established in \\cite{CR09}. Here, we review the latter one.\n\nWe take the symplectic fermions $\\chi^{\\pm}$ and bosons $Y$, $Z$ to have the following operator product expansions{}:\n\\begin{equation}\n\\func{\\chi^+}{z} \\func{\\chi^-}{w} = \\frac{1}{\\brac{z-w}^2} + \\text{ regular terms}, \\qquad \n\\func{\\partial Y}{z} \\func{\\partial Z}{w} = \\frac{1}{\\brac{z-w}^2} + \\text{ regular terms}\n\\end{equation}\n(the others are regular). The $\\AKMSA{gl}{1}{1}$ current fields are then given by\n\\begin{equation} \\label{eqnGL11FFR}\n\\func{E}{z} = k \\func{\\partial Y}{z}, \\qquad \\func{N}{z} = \\func{\\partial Z}{z}, \\qquad \\func{\\psi^{\\pm}}{z} = \\sqrt{k} \\vertop{\\pm \\func{Y}{z}} \\func{\\chi^{\\pm}}{z},\n\\end{equation}\nand a moderately tedious computation shows that the $\\AKMSA{gl}{1}{1}$ energy momentum tensor \\eqref{eqnDefT} indeed corresponds to the sum of those of the bosonic and symplectic fermion systems.\n\nIt remains to construct the $\\AKMSA{gl}{1}{1}$ primaries that generate our extended algebras. As these correspond to atypical modules, this is relatively straight-forward. First, we introduce some convenient notation:\n Let $X_{n,\\ell}$ be the bosonic linear combination $n Y + \\ell Z$ and define composite fields $F^{\\pm}_r$, with $r \\in \\mathbb{N}$, by $F^{\\pm}_0 = 1$ and $F^{\\pm}_r = \\normord{F^{\\pm}_{r-1} \\partial^{r-1} \\chi^{\\pm}}$ for $r \\geqslant 1$. The conformal dimension of $F^{\\pm}_r$ is then $\\tfrac{1}{2} r \\brac{r+1}$. The zero-grade fields of the atypicals $\\AffAtypMod{n,\\ell}$ for $\\ell > 0$ have conformal dimension $\\Delta_{n,\\ell} = \\ell \\brac{n + \\ell \/ 2}$ and are realised by\n\\begin{equation}\nV_{n,\\ell}^+ = \\vertop{X_{n+1\/2,\\ell}} F^-_{\\ell-1}, \\qquad V_{n,\\ell}^- = \\vertop{X_{n-1\/2,\\ell}} F^-_{\\ell}.\n\\end{equation}\nThis follows from their operator product expansions{} with the $\\AKMSA{gl}{1}{1}$ currents:\n\\begin{equation}\n\\begin{aligned}\n\\func{N}{z} \\func{V_{n,\\ell}^{\\pm}}{w} &= \\frac{\\brac{n \\pm 1\/2} \\: \\func{V_{n,\\ell}^{\\pm}}{w}}{z-w} + \\ldots , \\\\\n\\func{E}{z} \\func{V_{n,\\ell}^{\\pm}}{w} &= \\frac{\\ell k \\: \\func{V_{n,\\ell}^{\\pm}}{w}}{z-w} + \\ldots ,\n\\end{aligned}\n\\qquad\n\\begin{aligned}\n\\func{\\psi^+}{z} \\func{V_{n,\\ell}^-}{w} &= \\brac{-1}^{\\ell - 1} \\ell ! \\frac{\\sqrt{k} \\: \\func{V_{n,\\ell}^+}{w}}{z-w} + \\ldots , \\\\\n\\func{\\psi^-}{z} \\func{V_{n,\\ell}^+}{w} &= \\frac{(-1)^{\\ell-1}}{\\brac{\\ell-1}!} \\frac{\\sqrt{k} \\: \\func{V_{n,\\ell}^-}{w}}{z-w} + \\ldots ,\n\\end{aligned}\n\\end{equation}\nthe others being regular. The zero-grade fields of the conjugate module $\\AffAtypMod{-n,-\\ell}$ are realised as\n\\begin{equation}\nV_{-n,-\\ell}^+ = \\vertop{X_{-n+1\/2,-\\ell}} F^+_{\\ell}, \\qquad V_{-n,-\\ell}^- = \\vertop{X_{-n-1\/2,-\\ell}} F^+_{\\ell-1}.\n\\end{equation}\nTheir operator product expansions{} with the current fields are similar.\n\n\\subsection{The Extended Operator Product Algebra}\n\nIn order to compute the leading contributions to the extended algebra operator product expansions{}, we need the expansion of the bosonic vertex operators. To second order, this is\n\\begin{multline} \\label{eqnVertexOPE}\n\\vertop{\\func{X_{n,\\ell}}{z}} \\vertop{\\func{X_{n',\\ell'}}{w}} = \\brac{z-w}^{n\\ell'+n'\\ell} \\biggl[ \\vertop{\\func{X_{n+n',\\ell+\\ell'}}{w}} + \\normord{\\func{\\partial X_{n,\\ell}}{w} \\Vertop{\\func{X_{n+n',\\ell+\\ell'}}{w}}} \\brac{z-w} \\Biggr. \\\\\n\\Biggl. + \\frac{1}{2} \\normord{\\Bigl( \\func{\\partial X_{n,\\ell}}{w} \\func{\\partial X_{n,\\ell}}{w} + \\func{\\partial^2 X_{n,\\ell}}{w} \\Bigr) \\Vertop{\\func{X_{n+n',\\ell+\\ell'}}{w}}} \\brac{z-w}^2 + \\ldots \\biggr].\n\\end{multline}\nNote that it follows that $\\vertop{\\func{X_{n,\\ell}}{w}}$ and $\\vertop{\\func{X_{n',\\ell'}}{w}}$ will be mutually bosonic when $n\\ell' + n'\\ell$ is an even integer and mutually fermionic when $n\\ell' + n'\\ell$ is odd. The implication of this for the statistics of the extended algebra generators $V_{n,\\ell}^{\\pm}$ and $V_{-n,-\\ell}^{\\pm}$ is a little subtle. It turns out that when $2n \\ell$ is even, these generators may be consistently assigned a bosonic or fermionic parity --- $\\alg{W}_{n,\\ell}$ is a superalgebra. In fact, $V_{n,\\ell}^+$ and $V_{-n,-\\ell}^-$ will be fermions and $V_{n,\\ell}^-$ and $V_{-n,-\\ell}^+$ will be bosons in this case. However, when $2n \\ell$ is odd, such an assignment is impossible --- $\\alg{W}_{n,\\ell}$ is \\emph{not} a superalgebra. In this case, separately taking $V_{n,\\ell}^+$ and $V_{-n,-\\ell}^-$ to be bosons and $V_{n,\\ell}^-$ and $V_{-n,-\\ell}^+$ to be fermions is consistent, but the mutual locality of a boson and a fermion will now be $-1$ instead of $+1$. We will remark further on this subtlety in \\secref{secExamples}.\n\nWe moreover need the leading terms of certain operator product expansions{} of the $F^{\\pm}_r$. In particular,\n\\begin{equation} \\label{eqnCompositeSFOPEs}\n\\begin{split}\n\\func{F^+_r}{z} \\func{F^-_r}{w} &= \\brac{z-w}^{-r \\brac{r+1}} \\biggl[ \\mu_r^{\\brac{0}} + \\mu_{r-1}^{\\brac{2}} \\normord{\\func{\\chi^+}{w} \\func{\\chi^-}{w}} \\brac{z-w}^2 + \\ldots \\biggr] , \\\\\n\\func{F^-_{r-1}}{z} \\func{F^+_r}{w} &= \\brac{z-w}^{-\\brac{r-1} \\brac{r+1}} \\biggl[ \\mu_{r-1}^{\\brac{1}} \\: \\func{\\chi^+}{w} + \\ldots \\biggr] , \\\\\n\\func{F^-_r}{z} \\func{F^+_{r-1}}{w} &= \\brac{z-w}^{-\\brac{r-1} \\brac{r+1}} \\biggl[ \\mu_{r-1}^{\\brac{1}} \\: \\func{\\chi^-}{w} + \\ldots \\biggr] ,\n\\end{split}\n\\end{equation}\nwhere the coefficients $\\mu_r^{\\brac{a}}$, for $a = 0$, $1$, $2$, are given by\n\\begin{equation} \\label{eq:coeff}\n\\mu_r^{\\brac{a}} = \\sum_{\\sigma \\in \\group{S}_r} \\brac{-1}^{\\abs{\\sigma}} \\prod_{i=1}^r \\brac{i + \\func{\\sigma}{i} + a - 1}! = \\prod_{i=1}^r \\brac{i-1}! \\brac{i+a}!\n\\end{equation}\nThis last equality follows from recognising the $\\mu_r^{\\brac{a}}$ as determinants of Hankel matrices for which LU-decompositions are easily found. In detail, consider the $r \\times r$ matrix $\\func{A_r}{a}$, for a non-negative integer $a$, with entries $\\brac{\\func{A_r}{a}}_{ij} = \\brac{i+j+a-1}!$ Defining $r \\times r$ matrices $\\func{L_r}{a}$ and $\\func{U_r}{a}$ by\n\\begin{equation}\n\\brac{\\func{L_r}{a}}_{ij} = \\frac{\\brac{i+a}!}{\\brac{j+a}!} \\binom{i-1}{j-1}, \\qquad \n\\brac{\\func{U_r}{a}}_{ij} = \\brac{i-1}! \\brac{j+a}! \\binom{j-1}{i-1},\n\\end{equation}\nand noting that $\\func{L_r}{a}$ is lower-triangular with diagonal entries equal to $1$ and $\\func{U_r}{a}$ is upper-triangular, we see that $\\func{L_r}{a} \\func{U_r}{a}$ is an LU-decomposition of $\\func{A_r}{a}$:\n\\begin{equation}\n\\begin{split}\n\\brac{\\func{L_r}{a} \\func{U_r}{a}}_{ij} &= \\sum_{k=1}^r \\frac{\\brac{i+a}! \\brac{i-1}! \\brac{j+a}! \\brac{j-1}!}{\\brac{k+a}! \\brac{k-1}! \\brac{i-k}! \\brac{j-k}!} = \\brac{j+a}! \\brac{i-1}! \\sum_{k=1}^r \\binom{i+a}{k+a} \\binom{j-1}{k-1} \\\\\n&= \\brac{j+a}! \\brac{i-1}! \\binom{i+j+a-1}{i-1} = \\brac{\\func{A_r}{a}}_{ij}.\n\\end{split}\n\\end{equation}\nSince $\\det \\: \\func{L_r}{a} = 1$, we obtain $\\det \\: \\func{A_r}{a} = \\det \\: \\func{U_r}{a} = \\prod_{i=1}^r \\brac{i-1}! \\brac{i+a}!$ and hence \\eqnref{eq:coeff}.\n\nWe are now in a position to obtain the leading contributions to the\noperator product expansions{} of the extension fields $V_{n,\\ell}^{\\pm}$ and their conjugates $V_{-n,-\\ell}^{\\mp}$. Since we assume \\eqref{eqdim}, there are only four non-regular expansions and these take the form\n\\begin{equation} \\label{eqnGenExtAlgOPEs}\n\\begin{split}\n\\func{V_{n,\\ell}^+}{z} \\func{V_{-n,-\\ell}^+}{w} &= \\frac{\\mu_{\\ell-1}^{\\brac{1}} \\: \\func{\\psi^+}{w} \/ \\sqrt{k}}{\\brac{z-w}^{2 \\Delta_{n,\\ell} - 1}} + \\ldots , \\\\\n\\func{V_{-n,-\\ell}^-}{z} \\func{V_{n,\\ell}^+}{w} &= \\mu_{\\ell-1}^{\\brac{0}} \\Biggl[ \\frac{1}{\\brac{z-w}^{2 \\Delta_{n,\\ell}}} - \\frac{\\func{\\partial X_{n+1\/2,\\ell}}{w}}{\\brac{z-w}^{2 \\Delta_{n,\\ell} - 1}} + \\frac{\\ell \\brac{\\ell-1}}{2} \\frac{\\normord{\\func{\\chi^+}{w} \\func{\\chi^-}{w}}}{\\brac{z-w}^{2 \\Delta_{n,\\ell} - 2}} \\Biggr. \\\\\n& \\mspace{90mu} \\Biggl. + \\frac{1}{2} \\frac{\\normord{\\func{\\partial X_{n+1\/2,\\ell}}{w} \\func{\\partial X_{n+1\/2,\\ell}}{w}} - \\func{\\partial^2 X_{n+1\/2,\\ell}}{w}}{\\brac{z-w}^{2 \\Delta_{n,\\ell} - 2}} + \\ldots \\Biggr] , \\\\\n\\func{V_{-n,-\\ell}^+}{z} \\func{V_{n,\\ell}^-}{w} &= \\mu_{\\ell}^{\\brac{0}} \\Biggl[ \\frac{1}{\\brac{z-w}^{2 \\Delta_{n,\\ell}}} - \\frac{\\func{\\partial X_{n-1\/2,\\ell}}{w}}{\\brac{z-w}^{2 \\Delta_{n,\\ell} - 1}} + \\frac{\\ell \\brac{\\ell+1}}{2} \\frac{\\normord{\\func{\\chi^+}{w} \\func{\\chi^-}{w}}}{\\brac{z-w}^{2 \\Delta_{n,\\ell} - 2}} \\Biggr. \\\\\n& \\mspace{90mu} \\Biggl. + \\frac{1}{2} \\frac{\\normord{\\func{\\partial X_{n-1\/2,\\ell}}{w} \\func{\\partial X_{n-1\/2,\\ell}}{w}} - \\func{\\partial^2 X_{n-1\/2,\\ell}}{w}}{\\brac{z-w}^{2 \\Delta_{n,\\ell} - 2}} + \\ldots \\Biggr] , \\\\\n\\func{V_{n,\\ell}^-}{z} \\func{V_{-n,-\\ell}^-}{w} &= \\frac{\\mu_{\\ell-1}^{\\brac{1}} \\: \\func{\\psi^-}{w} \/ \\sqrt{k}}{\\brac{z-w}^{2 \\Delta_{n,\\ell} - 1}} + \\ldots\n\\end{split}\n\\end{equation}\nHere, we have used \\eqref{eq:coeff} to evaluate the ratios $\\mu_{r-1}^{\\brac{2}} \/ \\mu_r^{\\brac{0}} = \\tfrac{1}{2} r \\brac{r+1}$ appearing in these expansions.\n\n\\subsection{Examples} \\label{secExamples}\n\nLet us now illustrate the results of the above calculations with a few simple examples. First, \\eqref{eqdim} tells us that the extended algebra $\\alg{W}_{n,\\ell}$ will be unique if we insist that the extension fields have conformal dimension $\\tfrac{1}{2}$. Indeed, this requires $\\ell = 1$ and $n=0$. We are therefore extending $\\AKMSA{gl}{1}{1}$ by the fields associated with the atypical modules $\\AffAtypMod{0,1}$ and $\\AffAtypMod{0,-1}$. Since $2n \\ell = 0$ is even, the generators of the resulting extended algebra, $\\alg{W}_{0,1}$, may be assigned a definite parity: $\\varkappa = V_{0,1}^+$ and $\\bar{\\varkappa} = V_{0,-1}^-$ are odd, $\\beta = V_{0,1}^-$ and $\\gamma = -V_{0,-1}^+$ are even. The expansions \\eqref{eqnGenExtAlgOPEs} become\n\\begin{equation}\n\\begin{aligned}\n\\func{\\varkappa}{z} \\func{\\bar{\\varkappa}}{w} &= \\frac{1}{z-w} + \\func{N}{w} + \\frac{1}{2k} \\func{E}{w} + \\ldots , \\\\\n\\func{\\beta}{z} \\func{\\gamma}{w} &= \\frac{1}{z-w} + \\func{N}{w} - \\frac{1}{2k} \\func{E}{w} + \\ldots ,\n\\end{aligned}\n\\qquad\n\\begin{aligned}\n\\func{\\beta}{z} \\func{\\varkappa}{w} = +\\frac{\\func{\\psi^+}{w}}{\\sqrt{k}} + \\ldots , \\\\\n\\func{\\gamma}{z} \\func{\\bar{\\varkappa}}{w} = -\\frac{\\func{\\psi^-}{w}}{\\sqrt{k}} + \\ldots ,\n\\end{aligned}\n\\end{equation}\nwhich we recognise as a free complex fermion $\\brac{\\varkappa,\\bar{\\varkappa}}$ and a $\\beta \\gamma$ ghost system. Because the mixed operator product expansions{} are regular, $\\alg{W}_{0,1}$ decomposes into the direct sum of the chiral algebras of these theories.\n\nIf we choose to extend by dimension $1$ fields, then there are two distinct choices: $n = \\tfrac{1}{2}$ and $\\ell = 1$ or $n = \\tfrac{1}{2}$ and $\\ell = -2$. We expect a current algebra symmetry in both cases. Indeed, if we set $\\mathbf{H} = N + E \/ \\ell k$ and $\\mathbf{Z} = N - E \/ \\ell k$, then we discover that the $\\brac{\\mathbf{H} , \\mathbf{Z}}$-weights of the $\\AKMSA{gl}{1}{1}$ currents and the extension fields $V_{n,\\ell}^{\\pm}$, $V_{-n,-\\ell}^{\\pm}$ precisely match the $\\brac{\\mathbf{H} , \\mathbf{Z}}$-weights of the adjoint representation of $\\SLSA{sl}{2}{1}$.\\footnote{Here, $\\mathbf{H}$ and $\\mathbf{Z}$ should be associated with the matrices $\\diag \\set{1,-1,0}$ and $\\diag \\set{1,1,2}$ in the defining representation of $\\SLSA{sl}{2}{1}$.} Moreover, we have\n\\begin{equation}\n\\func{\\mathbf{H}}{z} \\func{\\mathbf{H}}{w} = \\frac{2 \/ \\ell}{\\brac{z-w}^2} + \\ldots , \\qquad \\func{\\mathbf{Z}}{z} \\func{\\mathbf{Z}}{w} = \\frac{-2 \/ \\ell}{\\brac{z-w}^2} + \\ldots ,\n\\end{equation}\nand $\\func{\\mathbf{H}}{z} \\func{\\mathbf{Z}}{w}$ regular, which suggests that the extended algebra will be $\\AKMSA{sl}{2}{1}$ at level $1 \/ \\ell$.\n\nChecking this for the choice $\\ell = -2$ is easy. As $2n \\ell = -2$ is even, $\\alg{W}_{1\/2,-2}$ admits a superalgebra structure. Moreover, the fusion rules\n\\begin{equation}\n\\AffAtypMod{0,1} \\mathbin{\\times} \\AffAtypMod{0,1} = \\AffAtypMod{-1\/2,2}, \\qquad \\AffAtypMod{0,-1} \\mathbin{\\times} \\AffAtypMod{0,-1} = \\AffAtypMod{1\/2,-2}\n\\end{equation}\nimply that $\\alg{W}_{1\/2,-2}$ is a subalgebra of the extended algebra $\\alg{W}_{0,1}$ considered above. One readily checks that by taking normally-ordered products, the $\\beta \\gamma$ ghost fields of $\\alg{W}_{0,1}$ generate the bosonic subalgebra $\\AKMA{sl}{2}_{-1\/2} \\subset \\AKMSA{sl}{2}{1}_{-1\/2}$, the complex fermion gives the $\\AKMA{u}{1}$-subalgebra, and the mixed products yield the remaining fermionic currents. This establishes the superalgebra isomorphism $\\alg{W}_{1\/2,-2} \\cong \\AKMSA{sl}{2}{1}_{-1\/2}$.\n\nThe computation when $\\ell = 1$ is, however, more subtle because $2n \\ell = 1$ is odd, so $\\alg{W}_{1\/2,1}$ does not admit the structure of a superalgebra. To impose the correct parities on the extended algebra currents, we must adjoin an operator-valued function $\\mu$ which is required to satisfy\n\\begin{equation} \\label{eqCocycle}\n\\mu_{a,b} \\mu_{c,d} = (-1)^{ad} \\mu_{a+b,c+d}, \\qquad \\text{($a,b,c,d \\in \\mathbb{Z}$).}\n\\end{equation}\nNote that the algebra generated by these operators has unit $\\mu_{0,0}$. The currents are then given by\n\\begin{equation}\n\\begin{aligned}\n\\mathbf{E} &= +\\mu_{1,1} V_{1\/2,1}^+, \\\\\n\\mathbf{F} &= -\\mu_{-1,-1} V_{-1\/2,-1}^-,\n\\end{aligned}\n\\qquad\n\\begin{aligned}\n\\mathbf{H} &= N + E\/k, \\\\\n\\mathbf{Z} &= N - E\/k,\n\\end{aligned}\n\\qquad\n\\begin{aligned}\n\\mathbf{e}^+ &= -\\mu_{1,0} \\psi^+ \/ \\sqrt{k}, \\\\\n\\mathbf{f}^- &= +\\mu_{-1,0} \\psi^- \/ \\sqrt{k},\n\\end{aligned}\n\\qquad\n\\begin{aligned}\n\\mathbf{f}^+ &= \\mu_{0,-1} V_{-1\/2,-1}^+, \\\\\n\\mathbf{e}^- &= \\mu_{0,1} V_{1\/2,1}^-,\n\\end{aligned}\n\\end{equation}\nand routine computation now verifies that these currents indeed generate $\\AKMSA{sl}{2}{1}_1$.\n\nAs our final example, we briefly consider the case of extensions of conformal dimension $\\tfrac{3}{2}$. There are now three distinct choices, corresponding to $n=1$, $\\ell=1$, or $n=-\\tfrac{1}{4}$, $\\ell=2$, or $n=-1$, $\\ell=3$. The latter choice again results in an extended algebra which is a subalgebra of $\\alg{W}_{0,1}$ because\n\\begin{equation}\n\\AffAtypMod{0,1} \\mathbin{\\times} \\AffAtypMod{0,1} \\mathbin{\\times} \\AffAtypMod{0,1} = \\AffAtypMod{-1,3}.\n\\end{equation}\nBoth $\\alg{W}_{1,1}$ and $\\alg{W}_{-1,3}$ are superalgebras, while $\\alg{W}_{-1\/4,2}$ is not. We expect, however, that a modification similar to \\eqref{eqCocycle} will restore the superalgebra parity requirements. We will not analyse this in any detail as our interest in $\\Delta_{n,\\ell} = \\tfrac{3}{2}$ lies not with the full extended algebra, but rather with one of its subalgebras.\n\nWe start with the superalgebras $\\alg{W}_{1,1}$ and $\\alg{W}_{-1,3}$. Both $V_{-n,-\\ell}^+$ and $V_{n,\\ell}^-$ are bosonic and upon defining\n\\begin{equation}\n\\begin{gathered}\n\\mathsf{g}^+ = \\sqrt{\\frac{3 \\alpha \\brac{3 \\alpha - 1}}{2 \\mu_{\\ell}^{\\brac{0}}}} \\: V_{-n,-\\ell}^+, \\qquad \n\\mathsf{g}^- = \\sqrt{\\frac{3 \\alpha \\brac{3 \\alpha - 1}}{2 \\mu_{\\ell}^{\\brac{0}}}} \\: V_{n,\\ell}^-, \\\\\n\\mathsf{j} = -\\alpha \\partial X_{n-1\/2,\\ell}, \\qquad \n\\mathsf{t} = \\frac{\\alpha}{2} \\normord{\\partial X_{n-1\/2,\\ell} \\partial X_{n-1\/2,\\ell}} - \\frac{\\ell \\brac{\\ell + 1}}{2} \\frac{\\alpha \\brac{3 \\alpha - 1}}{\\alpha + 1} \\frac{\\normord{\\psi^+ \\psi^-}}{k},\n\\end{gathered}\n\\end{equation}\nwhere\n\\begin{equation}\n\\alpha = \\frac{1}{\\brac{2n-1} \\ell},\n\\end{equation}\nwe obtain the defining relations of the \\emph{Bershadsky-Polyakov algebra} $W_3^{\\brac{2}}$ \\cite{PolGau90,BerCon91}:\n\\begin{equation}\n\\begin{gathered}\n\\func{\\mathsf{g}^+}{z} \\func{\\mathsf{g}^-}{w} = \\frac{\\brac{K+1} \\brac{2K+3}}{\\brac{z-w}^3} + \\frac{3 \\brac{K+1} \\func{\\mathsf{j}}{w}}{\\brac{z-w}^2} + \\frac{3 \\func{\\normord{\\mathsf{j} \\mathsf{j}}}{w} + \\tfrac{3}{2} \\brac{K+1} \\func{\\partial \\mathsf{j}}{w} - \\brac{K+3} \\func{\\mathsf{t}}{w}}{z-w} + \\ldots , \\\\\n\\func{\\mathsf{j}}{z} \\func{\\mathsf{g}^{\\pm}}{w} = \\frac{\\pm \\func{\\mathsf{g}^{\\pm}}{w}}{z-w} + \\ldots , \\qquad \n\\func{\\mathsf{j}}{z} \\func{\\mathsf{j}}{w} = \\frac{\\brac{2K+3}\/3}{\\brac{z-w}^2} + \\ldots , \\\\\n\\func{\\mathsf{t}}{z} \\func{\\mathsf{g}^{\\pm}}{w} = \\frac{3}{2} \\frac{\\func{\\mathsf{g}^{\\pm}}{w}}{\\brac{z-w}^2} + \\frac{\\func{\\partial \\mathsf{g}^{\\pm}}{w}}{z-w} + \\ldots , \\qquad \n\\func{\\mathsf{t}}{z} \\func{\\mathsf{j}}{w} = \\frac{\\func{\\mathsf{j}}{w}}{\\brac{z-w}^2} + \\frac{\\func{\\partial \\mathsf{j}}{w}}{z-w} + \\ldots , \\\\\n\\func{\\mathsf{t}}{z} \\func{\\mathsf{t}}{w} = \\frac{-\\brac{2K+3} \\brac{3K+1} \/ 2 \\brac{K+3}}{\\brac{z-w}^4} + \\frac{2 \\func{\\mathsf{t}}{w}}{\\brac{z-w}^2} + \\frac{\\func{\\partial \\mathsf{t}}{w}}{z-w} + \\ldots\n\\end{gathered}\n\\end{equation}\nHere, the $\\AKMA{sl}{3}$-level $K = \\tfrac{3}{2} \\brac{\\alpha - 1}$ is $0$ for $\\alg{W}_{1,1}$ and $-\\tfrac{5}{3}$ for $\\alg{W}_{-1,3}$. The central charge of the $W_3^{\\brac{2}}$-subalgebra is in both cases $-1$.\n\nFor $\\alg{W}_{-1\/4,2}$, this procedure does not yield a Bershadsky-Polyakov algebra because $V_{-n,-\\ell}^+$ and $V_{n,\\ell}^-$ are, in this case, mutually fermionic. Rather, these fields generate a copy of the $\\mathcal{N} = 2$ superconformal algebra of central charge $-1$. Instead, we must consider the mutually bosonic fields $V_{n,\\ell}^+$ and $V_{-n,-\\ell}^-$. Taking\n\\begin{equation}\n\\mathsf{g}^{+} = \\sqrt{3} \\: V_{1\/4,-2}^-, \\quad \\mathsf{g}^{-} = \\sqrt{3} \\: V_{-1\/4,2}^+, \\quad \\mathsf{j} = -\\partial X_{1\/4,2}, \\quad \\mathsf{t} = \\frac{1}{2} \\normord{\\partial X_{1\/4,2} \\partial X_{1\/4,2}} - \\frac{1}{k} \\normord{\\psi^+ \\psi^-}\n\\end{equation}\nin particular, now leads to the Bershadsky-Polyakov algebra of level $0$ and central charge $-1$. (In contrast, $V_{n,\\ell}^+$ and $V_{-n,-\\ell}^-$ are fermionic in both $\\alg{W}_{1,1}$ and $\\alg{W}_{-1,3}$, generating copies of the $\\mathcal{N} = 2$ superconformal algebra with central charges $1$ and $-1$, respectively.)\n\n\\subsection{$W^{\\brac{2}}_N$-subalgebras}\n\nIn the previous section, we found the Bershadsky-Polyakov algebra $W^{\\brac{2}}_3$, at certain levels, appearing as a subalgebra of the extended algebras $\\alg{W}_{1,1}$, $\\alg{W}_{-1\/4,2}$ and $\\alg{W}_{-1,3}$. We now generalise this observation. The algebra $W^{\\brac{2}}_3$ is defined \\cite{PolGau90,BerCon91} as the Drinfel'd-Sokolov reduction of $\\AKMA{sl}{3}$ corresponding to the non-principal embedding of $\\SLA{sl}{2}$ in $\\SLA{sl}{3}$. Feigin and Semikhatov \\cite{Feigin:2004wb} found that it could also be realised as a subalgebra of $\\AKMSA{sl}{3}{1} \\oplus \\AKMA{u}{1}$ commuting with an $\\AKMA{sl}{3}$-subalgebra. They then studied a generalisation $W^{\\brac{2}}_N \\subset \\AKMSA{sl}{N}{1} \\oplus \\AKMA{u}{1}$ which commutes with the obvious $\\AKMA{sl}{N}$-subalgebra.\n\nWhen $N=1$, these generalisations reduce to the chiral algebra of the $\\beta \\gamma$ ghost system. For $N=2$, one gets $\\AKMA{sl}{2}$, and as mentioned above, $N=3$ recovers the Bershadsky-Polyakov algebra. The examples studied in \\secref{secExamples} therefore lead us to the plausible conjecture that the $W^{\\brac{2}}_N$ algebras of Feigin and Semikhatov may be realised, at least for certain levels, as subalgebras of certain of our extended algebras $\\alg{W}_{n,\\ell}$. We mention that there is a second construction of these $W^{\\brac{2}}_N$ algebras, but restricted to the critical level $K=-N$ (see \\eqref{eq:W_NC}), starting from the affine superalgebra $\\AKMSA{psl}{N}{N}$ at (critical) level $0$ \\cite{CGL}.\n\nFeigin and Semikhatov only computed the first few terms of the defining operator product expansions{} of $W^{\\brac{2}}_N$. We will compare these terms with those obtained from our extended algebras, finding decidedly non-trivial agreement. Our findings will, however, be stated as conjectures because the full operator product expansion{} of $W^{\\brac{2}}_N$ is not currently known. $W^{\\brac{2}}_N$ is generated by two fields $\\mathcal{E}^{\\pm}_N$ of dimension $\\tfrac{1}{2} N$, a $\\AKMA{u}{1}$-current $\\mathcal{H}_N$ and an energy-momentum tensor $\\mathcal{T}_N$. The defining expansions are:\n\\begin{equation}\\label{eq:W2nope}\n\\begin{split}\n\\func{\\mathcal{H}_N}{z} \\func{\\mathcal{H}_N}{w} &= \\frac{\\brac{N-1} K\/N + N-2}{\\brac{z-w}^2} + \\ldots , \\qquad\n\\func{\\mathcal{H}_N}{z} \\func{\\mathcal{E}^{\\pm}_N}{w} = \\pm \\frac{\\func{\\mathcal{E}^{\\pm}_N}{w}}{z-w} + \\ldots , \\\\\n\\func{\\mathcal{E}^+_N}{z} \\func{\\mathcal{E}^-_N}{w} &= \\frac{\\lambda_{N-1}}{\\brac{z-w}^N} + \\frac{N \\lambda_{N-2} \\func{\\mathcal{H}_N}{w}}{\\brac{z-w}^{N-1}} - \\frac{\\brac{K+N} \\lambda_{N-3} \\func{\\mathcal{T}_N}{w}}{\\brac{z-w}^{N-2}} \\\\\n&\\mspace{-20mu} + \\frac{\\lambda_{N-3}}{\\brac{z-w}^{N-2}} \\sqbrac{\\frac{N \\brac{N-1}}{2} \\func{\\normord{\\mathcal{H}_N \\mathcal{H}_N}}{w} + \\frac{N \\bigl( \\brac{N-2} \\brac{K+N-1} - 1 \\bigr)}{2} \\func{\\partial \\mathcal{H}_N}{w}} + \\ldots\n\\end{split}\n\\end{equation}\nHere, $\\lambda_m = \\prod_{i=1}^m \\bigl( i \\brac{K+N-1} - 1 \\bigr)$, $K$ is the level of the $W^{\\brac{2}}_N$ algebra, and the central charge is given by\n\\begin{equation} \\label{eq:W_NC}\nC = -\\frac{\\bigl( \\brac{K+N} \\brac{N-1} - N \\bigr) \\bigl( \\brac{K+N} \\brac{N-2} N - N^2 + 1 \\bigr)}{K+N}.\n\\end{equation}\n\nSuppose first that $2n\\ell$ is even, so we can consider the bosonic subalgebra generated by the fields\n\\begin{equation}\n\\mathcal{E}^+_N = \\sqrt{\\frac{\\lambda_{N-1}}{\\mu_{\\ell}^{\\brac{0}}}} \\: V_{-n,-\\ell}^+, \\qquad \\mathcal{E}^-_N = \\sqrt{\\frac{\\lambda_{N-1}}{\\mu_{\\ell}^{\\brac{0}}}} \\: V_{n,\\ell}^-.\n\\end{equation}\nEvaluating the operator product expansion{} of these fields using \\eqref{eqnGenExtAlgOPEs} and comparing with \\eqref{eq:W2nope}, we find that the first two singular terms agree provided that $N = 2 \\Delta_{n,\\ell}$ and $\\mathcal{H}_N = -\\partial X_{n-1\/2,\\ell} \/ \\brac{2n-1} \\ell$. This also fixes the $W^{\\brac{2}}_N$ level $K$. Comparing the third terms fixes the form of the $W^{\\brac{2}}_N$ energy-momentum tensor $\\mathcal{T}_N$ and $\\mathcal{H}_N$ is then verified to have dimension $1$. However, the $\\mathcal{E}^{\\pm}_N$ only have the required dimension $\\tfrac{1}{2} N = \\Delta_{n,\\ell}$ if $n=1$ or $2n+\\ell=1$.\\footnote{There is a third solution, $\\Delta_{n,\\ell} + \\ell + 1 = 0$, but this is invalid as we require $\\ell, \\Delta_{n,\\ell} > 0$.} These constraints also let us check that $\\mathcal{T}_N$ is an energy-momentum tensor and the central charge turns out to be $C=-1$. When $2n\\ell$ is odd, we instead consider the bosonic subalgebra generated by\n\\begin{equation}\n\\mathcal{E}^+_N = \\sqrt{\\frac{\\lambda_{N-1}}{\\mu_{\\ell-1}^{\\brac{0}}}} V_{-n,-\\ell}^-, \\qquad \\mathcal{E}^-_N = \\sqrt{\\frac{\\lambda_{N-1}}{\\mu_{\\ell-1}^{\\brac{0}}}} \\: V_{n,\\ell}^+.\n\\end{equation}\nA similar analysis reveals that this subalgebra agrees with $W^{\\brac{2}}_N$ up to the first three terms in the operator product expansions{} provided that $N = 2 \\Delta_{n,\\ell}$ and either $\\ell = 1$ or $\\ell = 2$.\\footnote{Taking $n = -\\tfrac{1}{2} \\brac{\\ell + 1}$ also satisfies these requirements, but then $2n\\ell$ is necessarily even. Moreover, there is again a solution of the form $\\Delta_{n,\\ell} - \\ell + 1 = 0$, but it is easy to check that it leads to the wrong operator product expansion{} of $\\mathcal{T}_N$ with itself.} In the first case, $C=1$; in the second, $C=-1$.\n\nWe summarise our findings as follows:\n\\begin{conjecture}\nThe extended algebra $\\alg{W}_{n,\\ell}$ has a subalgebra isomorphic to $W^{\\brac{2}}_N$ of level $K$ when:\n\\begin{itemize}\n\\item $\\ell = 1$ and $n = 0,1,2,\\ldots$ Then, $N = 2n + 1$ and $K = -2 \\brac{n-1} \\brac{2n+1} \/ \\brac{2n-1}$.\n\\item $\\ell = 1$ and $n = \\tfrac{1}{2}, \\tfrac{3}{2}, \\tfrac{5}{2}, \\ldots$ Then, $N = 2n + 1$ and $K = - \\brac{2n^2 - 1} \/ n$.\n\\item $\\ell = 2$ and $n = -\\tfrac{3}{4}, -\\tfrac{1}{4}, \\tfrac{1}{4}, \\ldots$ Then, $N = 4 \\brac{n+1}$ and $K = -2 \\brac{n+1} \\brac{4n+1} \/ \\brac{2n+1}$.\n\\item $n = -\\tfrac{1}{2} \\brac{\\ell - 1}$ and $\\ell = 1,2,3,\\ldots$ Then, $N = \\ell$ and $K = -\\brac{\\ell^2 - \\ell - 1} \/ \\ell$.\n\\end{itemize}\n\\end{conjecture}\n\\noindent Note that the examples considered in \\secref{secExamples} exhaust the $W^{\\brac{2}}_N$-subalgebras with $N \\leqslant 3$ except for $\\ell = 2$ and $n = -\\tfrac{3}{4}$. This latter case is excluded if one insists, as we did with \\eqref{eqdim}, that the operator product expansion{} of $\\mathcal{E}^{\\pm}$ with itself is regular. We mention that Feigin and Semikhatov actually computed the first \\emph{four} terms of the $W^{\\brac{2}}_N$ operator product expansions{}, finding in the fourth term a Virasoro primary field $\\mathcal{W}_N$ of dimension $3$ and $\\mathcal{H}_N$-weight $0$. We have extended \\eqnTref{eqnVertexOPE}{eqnCompositeSFOPEs}{eqnGenExtAlgOPEs} to compute $\\mathcal{W}_N$ in our extended algebras and have checked that for each $\\ell$ and $n$ appearing in our conjecture, this field indeed has the required properties. It follows that our conjecture has been verified for all $N \\leqslant 4$.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction.}\nA recent analysis of the Low Energy Neutrino Anomaly (LNA) \\cite{RNA11},\\cite{GiuLev10} led to a \nchallenging claim that this anomaly can be explained in terms of a new \nfourth neutrino with a much larger mass\nsquared difference. Assuming that the neutrino mass eigenstates are non \ndegenerate one finds\\cite{RNA11}\\cite{GiuLev10}:\n \\barr\n \\Delta m^2_{31}&\\approx& \\Delta m^2_{32}=|m_3^2-m_2^2|,\\nonumber\\\\ \\Delta m^2_{41}&\\approx& \\Delta m^2_{42}=|m_4^2-m_2^2|>1.5\\mbox{(eV)}^2\n \\label{deltam}\n \\earr\nwith a mixing angle:\n\\beq \n\\sin^2{2 \\theta_{14}}=0.14\\pm 0.08 (95\\%).\n\\eeq\n\nIt is obvious that this new neutrino should contribute to the oscillation \nphenomenon. In the present paper we will assume that the new neutrino is sterile, that is it does not participate in weak interaction. Even then, however, it has an effect on neutrino oscillations since it will tend to decrease the electron neutrino flux. This makes the analysis of oscillation experiments more \nsophisticated. In all the previous experiments the oscillation length is \nmuch larger than the size of the detector. So one is able to see the effect only \nif the detector is placed in the right distance from the source. It is, \nhowever, possible to design an experiment with an oscillation length of the \norder of the size of the detector, as it was proposed in \\cite{VERGIOM06},\\cite{VERNOV10}. This is \nequivalent to many standard experiments done simultaneously. \nThe main \nrequirements are as follows \\cite{VERNOV10}:\n\\begin{itemize}\n\\item The neutrinos should have as low as possible energy so that the oscillation \nlength can be minimized. At the same time it should not be too low, so that \nthe neutrino-electron cross section is sizable.\n\\item A monoenergetic neutrino source has the advantage that some of the features \nof the oscillation pattern are not washed out by the averaging over a \ncontinuous neutrino spectrum.\n\\item The life time of the source should be suitable for the experiment to be \nperformed. If it is too short, the time available will not be adequate for \nthe execution of the experiment. If it is too long, the number of counts \nduring the data taking will be too small. Then one will face formidable \nbackgrounds and\/or large experimental uncertainties.\n\\item The source should be cheaply available in large quantities. Clearly a \ncompromise has to be made in the selection of the source.\n\\end{itemize}\nAt low energies the only neutrino detector, which is sensitive to neutrino \noscillations, is one, which is capable of detecting recoiling electrons\\cite{VERGIOM06} or nuclei \\cite{VGN-NC11}:\n\nThe aim of this article is to show that the existence of a new fourth \nneutrino can be verified experimentally by the direct measurements\nof the oscillation curves for the monoenergetic neutrino-electron \nscattering. It can be done point-by-point within the dimensions of the detector, \nthus providing what we call neutrino oscillometry \\cite{VERNOV10},\\cite{VERGIOMNOV}. \n\nThe electron neutrino, produced in weak interactions, can be expressed in \nterms of the standard mass eigenstates as follows:\n\\barr\n\\nu_e&=&\\cos_{\\theta_{14}}\\left[\\cos{\\theta_{12}} \\cos{\\theta_{13}}~\\nu_1+\\sin{\\theta_{12}} \\cos{\\theta_{13}}\\,\\nu_2+\\right .\\nonumber\\\\\n&&\\left . \\sin{\\theta_{13}}~ e^{i\\delta} \\nu_3 \\right]+\\sin{\\theta_{14}}e^{i\\delta_4} \\nu_4\n\\label{nue},\n\\earr\n where $\\sin{ \\theta_{13}}$ is a small quantity constrained by the\nCHOOZ experiment and $\\sin{ \\theta_{14}}$ is the small mixing angle proposed for the resolution of LNA\\cite{RNA11},\\cite{GiuLev10}.\n We can apply a four neutrino oscillation analysis to write, under the approximations of Eq. \\ref{deltam},\n the $\\nu_e$ disappearance oscillation probability as follows:\n\\barr\n P(\\nu_e \\rightarrow \\nu_e )&\\approx&1- \n \\left [\n \\sin ^2 {2\\theta_{12}}\n\\sin^2 {(\\pi \\frac{L}{L_{21}})}\\right .\\nonumber\\\\\n&+& \\left . \\sum_{n=3}^4 \\sin ^2{2\\theta _{1n}}\\sin^2{ (\\pi \\frac{L}{L_{n2}})} \\right]\n \\label{disap}\n\\earr\nwith\n\\beq \nL_{ij}=\\frac{4 \\pi E_{\\nu}}{m_i^2-m_j^2}.\n\\label{OscLength}\n\\eeq\nSince the oscillation lengths are very different, $L_{42}< 1.5\\mbox{(eV)}^{2} $\\cite{RNA11}, i.e. very \nlarge by neutrino mass standards, the oscillation length can be quite small \neven for quite energetic neutrinos. \n\n\\begin{table}[htbp]\n\\caption{\nProposed candidates for a new neutrino oscillometry at the \nspherical gaseous TPC. \nTabulated nuclear data have been taken from \\cite{AUDI03}, other data have been \ncalculated in this work (see the text for details. The mass of the source was assumed to be 0.1Kg).\n\\label{table1}}\n\\begin{center}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\hline\n& & & & & & &\\\\\nNucli-& \n$T_{1\/2}$ \\par & \n$Q_{EC}$ & \n$E_{\\nu }$ & \n$L_{32}$& \n$L_{42}$ & \n$\\sigma(0,x) $& \n$N_{\\nu }$ \\\\\n& & & & && $10^{-45}$&\\\\\nde& \n(d)& \n(keV)& \n(keV)& \n(m)& \n(m)& \ncm$^2$& \n(s$^{-1})$ \\\\\n\\hline\n$^{37}$Ar& \n35 & \n814& \n811& \n842& \n1.35& \n5.69& \n$3.7\\times 10^{17}$ \\\\\n\\hline\n\n$^{51}$Cr& \n27.7 & \n753& \n747& \n742& \n1.23& \n5.12& \n$4.1\\times 10^{17}$ \\\\\n\\hline\n$^{65}$Zn& \n244 & \n1352& \n1343& \n1330& \n2.22& \n10.5& \n$3.0\\times 10^{16}$ \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{center}\n\\end{table}\nIn other words, unlike the case involving $\\theta_{13}$ previously discussed \n\\cite{VERGIOM06},\\cite{VERNOV10},\\cite{VERGIOMNOV}, one can now choose much higher neutrino energy sources and thus \nachieve much higher cross sections. Thus our best candidates, see in Table \\ref{table1}, are \nnuclides, which emit monoenergetic neutrinos with energies higher than many \nhundreds of keV. Columns 2 and 3 show the decay characteristics of the \ncorresponding nuclides \\cite{NDSH}. The neutrino energies in column 4 have been \ncalculated by using equation (\\ref{Eq12}) taking $Q_{EC}$ from \\cite{AUDI03} and $B_{i}$ \nfrom \\cite{LARKINS}. For these nuclides the capture is strongly predominant between the \nground states, thus $E^{\\ast }$ =0. Columns 5 and 6 give the oscillation \nlengths for the third and the fourth neutrino states. One can see that \n$L_{32}$ and $L_{42}$ are very different and that the two oscillation curves \ncan be disentangled.\nThe maximum energy of the recoiling electron can be \ncalculated by use of Eq. (2.4) in \\cite{VERNOV10}. Column 7 shows the neutrino-electron \ncross-sections calculated by the use of formula (\\ref{sigmatot2}). The last column presents \nthe neutrino source intensities which can be reasonably produced by \nirradiation of the corresponding targets of stable nuclides in the high flux \nnuclear reactors. \n\nThe goal of the experiment is to scan the monoenergetic neutrino electron \nscattering events by measuring the electron recoil counts in a function of \ndistance from the neutrino source prepared in advance at the reactor\/s. This \nscan means point-by-point determination of scattering events along the \ndetector dimensions within its position resolution.\n\nIn the best cases these events can be observed as a smooth curve, which \nreproduces the neutrino disappearance probability.\nIt is worthwhile to note again that the \noscillometry is suitable for monoenergetic neutrino, since it deals with a \nsingle oscillation length $L_{32}$ or $L_{42}$. This is obviously not a case \nfor antineutrino, since, in this instance, one extracts only an effective \noscillation length. Thus some information may be lost due to the folding \nwith the continuous neutrino energy spectrum.\n\nTable \\ref{table1} clearly shows that the oscillation lengths for a new neutrino \nproposed in \\cite{RNA11}, \\cite{GiuLev10} are much smaller compared to those previously considered \\cite{VERGIOMNOV} in connection with $\\theta_{13}$. They can thus be directly measured within \nthe dimensions of detector of reasonable sizes. One of the very promising \noptions could be the STPC proposed \nin \\cite{VERGIOM06}. If necessary, a spherical Micromegas based on the micro-Bulk \n technology \\cite{ADRIAM10},\n which will be developed in the near future, can be employed in the STPC. In fact a large detector 1.3 m in diameter has already been developed and it is under operation at the LSM (Laboratoire Souterrain de Modane) underground laboratory. The device provides sub-keV energy threshold and good energy resolution. \n A thin 50\n micron polyamide foil will be used as bulk material to fabricate the\n detector structure. This detector provides an excellent energy \n resolution, can\n reach high gains at high gas pressure (up to 10 bar) and has the advantage that its \n radioactivity\n level \\cite{CEBRIAN10} should fulfill the requirements of the proposed \n experiment.\n \n In this spherical chamber with a modest radius of a few meters the shielded\nneutrino source can be situated in the center of the sphere.\nThe details of shielding, like the amount and the type of material surrounding the neutrino source, which is required to reach an \nappropriate background level, is under study. The electron \ndetector is also placed around the center of the smaller sphere with radius \n$r \\approx 1$m. The sphere volume out of the detector position is filled with a \ngas (a noble gas such as Ar or preferably Xe, which has a higher number of \nelectrons). The recoil electrons are guided by the strong electrostatic \nfield towards the Micromegas-detector \\cite{Giomataris},\\cite{GIOMVER08}. Such type of device has an \nadvantage in precise position determination (better than 0.1 m) and in \ndetection of very low electron recoils in 4$\\pi$-geometry (down to a few \nhundreds of eV, that well suits to the nuclides of table \\ref{table1}).\n\nAssuming that we have a gas target under pressure $P$ and temperature $T_0$, the number of electrons in STPC can be determined by formula: \n\\beq\nn_e= Z\\frac{P}{kT_0}=4.4\\times 10^{27}m^{-3} \\frac{P}{10~{\\mbox{Atm}}}\\frac{Z}{18}\\frac{300}{T_0},\n\\eeq\nwhere $Z$ is the atomic number, while\n $P$ and $T_{0}$ stand for a gas pressure and \ntemperature.\n\n\nSince in the resolution of neutrino anomaly one can employ sources with quite \nhigh energy neutrinos of hundreds of keV, one expects large cross sections. \nTherefore a modest size source, so that it can easily fit inside the \ninner sphere of the detector, and a modest size detector say of radius of 4 \nm and pressure of 10 bar can be adequate. We will thus employ these \nparameters in this calculation and assume a running time equal to the life \ntime of the source. The result obtained for one of the candidates, nuclide \n$^{51}$Cr, is shown in Fig. \\ref{fig1}. This nuclide has previously been considered for oscillation measurements \\cite{VERNOV10}, \\cite{RNA11}, \\cite{GiuLev10}.\n\nAs can be seen from this figure the oscillometry curves are well \ndisentangled for different values of mixing angle $\\theta_{14 }$, which shows the \nfeasibility of this method for identification of the new neutrino existence \nas such. \n\nThe sensitivity for determination of $\\theta_{14 }$ can be deduced also from the \ntotal number of events in the fiducial volume of detector. After integration \nof equation (\\ref{eventsph}) over $L$ from 0 to 4 m it can be written in the form:\n\\barr\nN_{0} &=& A + B {\\sin^2{ (2\\theta_{14})}},\\quad A=N_{\\nu} n_e R_0 \\sigma(0,x),\\nonumber\\\\\n \\frac{B}{A}&=&- \\left [\\frac{1}{2}-\\frac{0.067}{R_0} x \\sin \\left(\\frac{7.45 R_0}{x}\\right) \\right ].\n\\label{Eq14}\n\\earr\nThus for 55 days of measurements with $^{51}$Cr we find:\n $ A=1.59\\times 10^{4}$ and $B = -7.56 \\times 10^{3}$ . \n\nTaking these values we determined the sensitivity of $\\sin{^{2 }(2\\theta_{14})}$ = \n0.05 within 99{\\%} of confidence level reachable after two months of data handling in the STPC. This \nvalue is quite enough to access the validity of a new neutrino existence. \n\\begin{figure}[!ht]\n \\begin{center}\n \\includegraphics[width=3.3in,height=2.2in]{fig1.eps}\n\\hspace*{-0.0cm} { $L \\rightarrow$ meters}\\\\\n \\caption{ \n\n Oscillation spectra with different values of $\\sin{^{2}(2\\theta_{14})}$= \n0.07, 0.17 and 0.27 on the corresponding colored curves with the \nstatistical corridor of 1$\\sigma$. The values on the y-axis are obtained for 55 \ndays of measurement with a $^{51}$Cr source and an Ar target under a pressure of 10 bar. In all \ncases we have included distances up to $1.5\\times L_{42}$. The pattern is repeated \ntwo times up to the radius of the sphere $R_{0}$= 4 m.\n } \n \\label{fig1}\n \\end{center}\n \\end{figure} \n\nThe results presented in Fig. \\ref{fig1}\ndid not take into consideration the electron energy threshold of 0.1 keV, \nwhich is too small in comparison with the neutrino energy and the average \nelectron recoil energy. We neglected also the Solar background of 2 counts \nper day derived from the measured Borexino results \\cite{BOREXINO8}, \\cite{BOREXINO9}. It is obvious that STPC should be installed in an underground laboratory surrounded with appropriate shield against rock radioactivity.\n\n\nIn conclusion, we propose to use the oscillometry method for direct \nobservation of the fourth neutrino appearance. The calculations and analysis \nshows that neutrino oscillometry with the gaseous STPC is a powerful tool \nfor identification of a new neutrino in the neutrino-electron scattering. \nSince the expected mass-difference for this neutrino is rather high, the \ncorresponding oscillation length is going to be sufficiently small for 1 MeV neutrino energy so that it can \nbe fitted into the dimensions of a spherical detector with the radius of a \nfew meters. The neutrino oscillometry can be implemented in this detector \nwith the use of the intense monochromatic neutrino sources which can be \nplaced at the origin of sphere and suitably shielded. The gaseous STPC with the Micromegas \ndetection has a big advantage in the 4$\\pi$-geometry and in very good position \nresolution (better than 0.1 m) with a very low energetic threshold ($\\approx \n$ 100 eV). The most promising candidates for oscillometry have been \nconsidered. The sensitivity for one of them, e.g. $^{51}$Cr, to the mixing angle \n$\\theta_{14}$ is estimated as $\\sin{^{2}(2\\theta_{14})}$ = 0.05 with the 99{\\%} of \nconfidence, which can be reached after two months of data handling. This value can be pushed further down by using renewable sources. The observation of the oscillometry curve suggested in this work will be a \ndefinite manifestation of the existence of a new type of neutrino, like the one\n recently proposed by the analysis of the low energy neutrino anomaly.\n\nA help of D. Nesterenko in preparation of this manuscript is very much \nacknowledged.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzdazc b/data_all_eng_slimpj/shuffled/split2/finalzzdazc new file mode 100644 index 0000000000000000000000000000000000000000..0c04a413f662100fb86ee85a18ea5ca18344f1c8 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzdazc @@ -0,0 +1,5 @@ +{"text":"\\section{Historical Introduction}\n\nThe late 1500's and early 1600's were a remarkable period in the evolution of human thought. It might be reasonably argued that during this period Galileo Galilee put into practice the modern scientific method for describing and understanding natural processes. An equally important advancement in our way of thinking about the world was an emerging conviction of the universality of causes. This extraordinary new way of understanding the world around us is often associated with a slightly later period and with Isaac Newton. The notion that the laws of nature applied equally everywhere was indeed imagined in this earlier period by Johannes Kepler. In particular Kepler proposed that the principles that governed the movement of the planets was the same as on Earth. Kepler's thinking of a universal nature of physical properties both celestial and terrestrial is evident in his own words \\cite{Holton88} : ``I am occupied with the investigation of the physical causes. My aim in this is to show that the celestial machine is to be likened not to a divine organism but rather to a clockwork ..., insofar as nearly all the manifold movements are carried out by means of a single, quite simple magnetic force, as in the case of a clockwork all motion are caused by a simple weight. Moreover, I show how this physical conception is to be presented through calculation and geometry.\" Kepler's way of thinking about the motions of the planets and the universality of the laws of physics would be completely recognizable to every modern physicist.\n\n\\begin{figure}[H]\n\\centering\n\\includegraphics[width=75mm]{Faraday.jpeg}\n\\caption{This figure is from Faraday's laboratory notebook describing the apparatus he constructed to conduct experiments on the relationship between gravity and electromagnetic induction \\cite{Faraday1885}. \\label{FaradayFig}}\n\\end{figure}\n\nWhile Kepler's conviction of the relationship between the motion of the planets and processes on Earth helped inspire our modern way of thinking, he was of course mistaken in making the association between gravity and a ``simple magnetic force''. However, even this mistake was an inspired effort to describe the world around us in terms of physical causes. The universality of physical principles quickly became a central theme in the development of physics and an inspiration for Newton and those that followed. This expectation of the universality of celestial and terrestrial processes and Kepler's expectation of universality in a connection between magnetism and the motion of the planets is evident in Faraday's experimental investigations. Some time around the 1850's Faraday conducted experiments to demonstrate the possible connection between the gravitational field and the electromagnetic field. Faraday constructed an experimental apparatus in an effort to measure the magnitude of electromagnetic induction associated with a gravitational field as shown in Fig. \\ref{FaradayFig}. Faraday's results failed to demonstrate any relation between gravity and electricity but his commitment to this idea of universality was unwavering, \\cite{Faraday1885}, ``Here end my trials for the present. The results are negative. They do not shake my strong feeling of the existence of a relation between gravity and electricity, though they give no proof that such a relation exists.\"\n\nWhile Faraday's experiments were not successful, later theoretical research by Skobelev \\cite{Skobelev75} in 1975 supported Faraday's ``strong feeling\" by demonstrating a non-zero amplitude for the interaction of gravitons and photons in both scattering and annihilation. This association between gravity and electromagnetism was also described around the same time by Gibbons \\cite{gibbons} in noting that, ``Indeed since a `graviton' presumably in some sense carries light-like momentum the creation of one or more particles with time-like or light-like momentum would violate the conservation of momentum unless the created particles were massless and precisely aligned with the momentum of the graviton\". These kinematic restrictions for conversion of massless particles have also been studied more recently and in greater detail by Fiore and Modanese \\cite{Fiore96,Modanese95}. The processes of graviton and photon interaction described by Skobelev and Gibbons are exceedingly small \\cite{Skobelev75} but are non-zero. Our more recent research has expanded on this interaction of gravity\/gravitons and electromagnetism\/photons through annihilation and scattering processes by recognizing the contribution of the external gravitational field associated with a gravitational wave \\cite{Jones15,Jones16,Jones17,Jones18,Gretarsson18}. Our study of the vacuum production of light by a gravitational wave differs from Skobelev in that the amplitudes of the ``tree level diagrams\" would be dependent on the strength of the external gravitational field or equally the strain amplitude of the gravitational wave. This type of semi-classical conversion process between gravitational and electromagnetic fields was described more broadly by Davies \\cite{Davies01} ``One result is that rapidly changing gravitational fields can create particles from the vacuum, and in turn the back-reaction on the gravitational dynamics operates like a damping force.\" The back-reaction on the gravitational wave was shown to be small compared to the gravitational wave luminosity but sufficient to be detectable under the right circumstances \\cite{Jones17}. \n\nIn this brief review we will specifically outline the relationship between gravity and electricity for the special case of gravitational and electromagnetic radiation. While we take a historical perspective leading to current research no effort will be made to present the historical formalism. Instead we will present the ideas relating the association between gravitational and electromagnetic radiation and in particular the vacuum production of electromagnetic radiation by a gravitational wave using modern notation and mathematical formalism.\n\n\n\\section{Electromagnetism and light}\n\nA general review of the research on the relationship between gravity and electricity would completely preclude any possibility of being brief. We will instead focus our attention on the radiation regimes. The current understanding of the radiation regime for electricity began with James Clerk Maxwell's modification of Ampere's law \\cite{Maxwell2} to include the displacement current. This modification led to a wave equation solution to the equations of electromagnetism. Maxwell immediately recognized this wave equation as a description of the phenomena of light. In keeping with our intent to discuss the historical development of gravitational wave production of electromagnetic radiation using modern notation, the equations describing electromagnetic radiation will be presented in a form that is completely covariant. The Maxwell equations are written in terms of tensor relations and will have the same form in Minkowski space and curved space-time. The physical properties of electromagnetic radiation, such as luminosity, will be developed in terms of Newman-Penrose scalars \\cite{Newman61,Teukolsky73}, in a form that is well suited for the comparison of electromagnetic and gravitational radiation \\cite{Jones17}.\n\nIn order to write the field equations for electrodynamics in a suitable \nform for curved space-time two tensors are defined in terms of the \nelectric and magnetic fields. The field strength tensor \n\\cite{Ellis73,Senego98,Hogan09,Palenzuela10,Lehner09,Lehner12_85,\nLehner12_86,Lehner16} is defined as \\footnotemark \\footnotetext{Great \ncare is required with sign conventions in any covariant representation. \nThis is particularly true in the case of Maxwell's equations and here we \nare following Palenzuela {\\it et al.} \\cite{Palenzuela10}, which is consistent\nwith our metric. It is prudent to check the signs by confirming that the \ncovariant relations reduce correctly to the Maxwell equations in a \nLorentz inertial frame.}\n\n\\begin{equation}\nF^{\\mu \\nu } = u^{\\mu} E^{\\nu} - u^{\\nu} E^{\\mu} + e^{\\mu \\nu \\alpha \n\\beta} B_{\\alpha} u_{ \\beta},\n\\label{Faraday}\n\\end{equation}\n\n\\noindent and the dual to the field strength tensor as,\n\n\\begin{equation}\n^*F^{\\mu \\nu } = u^{\\mu} B^{\\nu} - u^{\\nu} B^{\\mu} - e^{\\mu \\nu \n\\alpha \\beta} E_{\\alpha} u_{ \\beta},\n\\label{dFaraday}\n\\end{equation}\n\n\\noindent where $e^{\\mu \\nu \\alpha \\beta}$ is the ``Levi-Civita \npseudotensor of the space-time\" and $u_{\\nu}$ is the field frame 4-\nvelocity. The covariant expression for the field strength tensor \n\\eqref{Faraday} and the dual \\eqref{dFaraday} was originally developed \nby Ellis \\cite{Ellis73}. While these expressions are perhaps not widely \nknown, expanding \\eqref{Faraday} in a Lorentz inertial frame produces \nthe expected components for the field strength tensor. This covariant \nform of the field strength tensor has proven to be very useful in \nstudies of the relation between gravity and electromagnetism \n\\cite{Hogan09,Palenzuela10,Lehner09,Lehner12_85,Lehner12_86,Lehner16}. \nConversely, the electric and magnetic fields are found from the \ncontractions of the tensors with the 4-velocity,\n\n\\begin{equation}\nE^{\\mu} = F^{\\mu \\nu } u_{ \\nu},~ ~ B^{\\mu} = {^*}F^{\\mu \\nu } u_{ \\nu}.\n\\label{EBfieldsF}\n\\end{equation}\n\n\\noindent Using the field strength tensor and its dual the Maxwell \nequations can be written in a covariant form that is the same in both Minkowski space and curved \nspace-time. The inhomogeneous Maxwell equations ({\\it i.e.} Gauss's law and Ampere's) \nlaw are,\n\n\\begin{equation}\n\\frac{1}{\\sqrt{-\\left| g_{\\mu \\nu} \\right|}} \\partial_{\\nu} \\left( \\sqrt{-\\left| g_{\\mu \\nu} \\right|} F^{\\mu \\nu } \n\\right)= 4 \\pi J^{\\mu}.\n\\label{Maxwell1}\n\\end{equation}\n\n\\noindent where $ \\left| g_{\\mu \\nu} \\right|= det [g_{\\mu \\nu}]$ is the determinant of the metric. The homogeneous \nGauss's law for magnetism and Faraday's law are \\cite{Greiner96, \nPalenzuela10},\n\n\\begin{equation}\n\\partial_{\\nu} \\left( \\sqrt{-\\left| g_{\\mu \\nu} \\right|} ~{^*}F^{\\mu \\nu } \\right) = 0.\n\\label{Maxwell2}\n\\end{equation}\n\n\\noindent The covariant form of the conservation law is,\n\n\\begin{equation}\n\\partial_{\\mu} \\left( \\sqrt{-\\left| g_{\\mu \\nu} \\right|} J^\\mu \\right)= 0.\n\\label{Conservation}\n\\end{equation}\n\n\\noindent The field strength tensor can also be expressed in terms of \nthe electromagnetic 4-vector potential,\n\n\\begin{equation}\nF_{\\mu \\nu } = \\partial_{\\mu} A_{\\nu} - \\partial_{\\nu} A_{\\mu} ~.\n\\label{FourPotential}\n\\end{equation}\n\n\\noindent Maxwell came to the wave equation from the bottom up by recognizing that the displacement current term was missing from the traditional form of Ampere's law. In the modern notation the wave equation is a mathematical identity in the absence of source terms in the Maxwell equations \\cite{Tsaga05}. \n\nThe covariant form of the equations for the electromagnetic field appears naturally in the radiative expression for electrodynamics in the Newman-Penrose formalism \\cite{Newman61,Teukolsky73} through the introduction of the Newman-Penrose electromagnetic scalar, to be discussed shortly. In order to provide a means of comparison between electromagnetic and gravitational radiation using the Newman-Penrose formalism we will require the Lagrangian density for the electromagnetic field in curved space-time. Including the electric source terms the Lagrangian density is,\n\n\\begin{equation}\n \\mathcal{L}_\n{em} = - \\frac{1}{4}\\left( {\\partial _\\nu A_\\mu - \\partial _\\mu A_\\nu } \\right)\\left( {\\partial ^\\nu A^\\mu - \n\\partial ^\\mu A^\\nu } \\right) +J_\\mu A^\\mu.\n\\label{emLagrangian}\n\\end{equation}\n\n\\noindent The Lagrangian density can be simplified using the Lorenz gauge \\cite{Greiner96}, $\\partial_\\mu A^\\mu = 0$, and by restricting our attention to be source free ({\\it i.e.} $J_\\mu =0$) so that \\eqref{emLagrangian} becomes,\n\n\\begin{equation}\n \\mathcal{L}_{em} = - \\frac{1}{2}\\partial _\\mu A_\\nu \\partial ^\\mu\n A^\\nu .\n\\label{emLagrangianLG}\n\\end{equation}\n\n\\noindent Since we are only considering the radiation regime for the \nfield equations, we assume a plane wave solution for the \nelectromagnetic field and a massless vector field can then be \nexpressed in terms of a mode expansion \\cite{Greiner96} as follows,\n\n\\begin{equation}\nA_\\mu \\left( {k,\\lambda ,x} \\right) = \\epsilon_{\\mu} \n^{(\\lambda)} \\phi ^{(\\lambda)} \\left( {k ,x} \\right).\n\\label{ModeExpN}\n\\end{equation}\n\n\\noindent with $k$ being the momentum, $x$ being the \nspace-time coordinates and $\\epsilon ^{(\\lambda)}$ being the \npolarization vectors with $\\lambda = 0, 1, 2, 3$. For example, the two \ntransverse modes are often labeled $\\lambda =1 , 2$ with $\\epsilon \n_\\mu ^{(1)} = (0,1,0,0)$ and $\\epsilon _\\mu ^{(2)} = (0,0,1,0)$. The \n$\\lambda = 0, 3$ components are the time-like and longitudinal polarizations. The\nfour polarization vectors satisfy the orthogonality relationship\n$\\epsilon _\\mu ^{(\\lambda)} \\epsilon ^{\\mu ~(\\lambda')} = \n\\eta ^{\\lambda \\lambda'}$, with $\\eta ^{\\lambda \\lambda'}$ being the\nMinkowski metric.\n\nOne can define a complex scalar field using the $\\lambda = 1, 2$ \ncomponents of the real scalar fields {\\it i.e.} \n$\\phi ^{(1,2)} (k, x)$ as $\\varphi =\\frac{1}{\\sqrt{2}}\n(\\phi ^{(1)} + i \\phi ^{(2)})$. In terms of this complex scalar field \nthe Lagrange density of \\eqref{emLagrangianLG} can be written as\n\\begin{equation}\n\\label{emLagrangian2}\n{\\cal L}_{em} = - \\partial_\\mu \\varphi \\partial ^\\mu \\varphi ^* ~,\n\\end{equation}\nwhich is the Lagrange density for a massless complex scalar field. Below we will use a massless,\ncomplex scalar field as a stand-in for the massless photon. This substitution is justified \nby equation \\eqref{emLagrangian2}\n\nThe field equations following from the Lagrange density in \\eqref{emLagrangian2} are, \n\n\\begin{equation}\n\\frac{1}{{\\sqrt{-\\left| g_{\\mu \\nu} \\right|}}} \\partial_{\\mu} \\sqrt{-\\left| g_{\\mu \\nu} \\right|} g^{\\mu \\nu} \\partial_{\\nu}\\varphi = 0.\n\\label{eomvarphi}\n\\end{equation}\n\n\\noindent This expression for the field equations is the same for both curved space-time and for Minkowski space-time. If we restricted our attention to plane waves propagating in Minkowski space-time the solution to \\eqref{eomvarphi} takes the form, \n\n\\begin{equation}\n\\varphi = B e^{i \\text{k} u} + C,\n\\label{PlaneWave}\n\\end{equation}\n\n\\noindent where the $z$ axis is assumed to be along the direction of propagation, $B$ and $C$ are constants, and $k$ is the wave number. The solution \\eqref{PlaneWave} is written in the standard light cone coordinate $u = z - t$ and $\\partial_{z} - \\partial_{t} = 2 \\partial_{u}$ \\cite{Jones16}.\n\nThe previous discussion provides an outline of Maxwell's electromagnetic radiation in modern covariant form. The principle goal of this review is to realize Faraday's expectation of a relationship between gravity and electricity. We will demonstrate this relationship for gravitational and electromagnetic radiation by comparing the gravitational radiation luminosity to the luminosity of the corresponding electromagnetic radiation produced from the vacuum by the gravitational radiation. We have found that the Newman-Penrose formalism is a good method for calculating the luminosity of both gravitational radiation and the corresponding electromagnetic radiation. The electromagnetic luminosity is presented here in terms of the Newman-Penrose formalism and the gravitational radiation luminosity will be presented in the following section.\n\nThe radiated electromagnetic power per unit solid angle is found from the projection of the electromagnetic field strength onto the elements of a null tetrad ($l_\\mu, m^\\mu, n^\\mu , \\bar m^\\mu$) which gives us the electromagnetic Newman-Penrose scalar $\\Phi _2$. In the Newman-Penrose formalism the power per unit solid angle of emission for electromagnetic radiation is written as \\cite{Teukolsky73,Lehner09},\n\n\\begin{equation}\n\\frac{d E_{em} }{dt d\\Omega} = \\mathop {lim}\\limits_{r \\to \\infty } \\frac{{r^2 }}{{4\\pi }}\\left| {\\Phi _2 } \\right|^2.\n\\label{emFlux0}\n\\end{equation}\n\n\\noindent The Newman-Penrose electromagnetic scalar in \\eqref{emFlux0} \\cite{Newman61,Teukolsky73,Lehner09,Lehner12_86} is,\n\n\\begin{equation}\n\\Phi _2 = F_{\\mu \\nu } \\bar m^\\mu n^\\nu.\n\\label{Phi2}\n\\end{equation}\n\n\\noindent The null tetrad of the Newman-Penrose formalism in \\eqref{Phi2} can be defined as \\cite{Lehner12_85},\n\n\\begin{equation}\n\\begin{array}{*{20}c}\n {l^\\mu = \\frac{1}{{\\sqrt 2 }}\\left( {1,0,0,1} \\right),} & {n^\\mu = \\frac{1}{{\\sqrt 2 }}\\left( {1,0,0, - 1} \\right),} \\\\\n {m^\\mu = \\frac{1}{{\\sqrt 2 }}\\left( {0,1,i,0} \\right),} & {\\bar m^\\mu = \\frac{1}{{\\sqrt 2 }}\\left( {0,1, - i,0} \\right),} \\\\\n\\end{array}\n\\label{null1}\n\\end{equation}\n\n\\noindent and\n\n\\begin{equation}\n l \\cdot n = - 1,~~m \\cdot \\bar m = 1 ,~~\nl \\cdot l = n \\cdot n = m \\cdot m = \\bar m \\cdot \\bar m = 0.\n\\label{null2}\n\\end{equation}\n\n\\noindent The electromagnetic field strength tensor in \\eqref{Phi2} is $F_{\\mu \\nu } = \\partial _\\mu A_\\nu {\\kern 1pt} - \\partial _\\nu A_\\mu$, where $A_\\mu = \\epsilon ^{(\\lambda)} _\\mu \\phi ^{(\\lambda )} \\left( {t,z} \\right)$, and the plane polarization vectors are $\\epsilon ^{(1)} _\\mu = \\left(0, 1, 0, 0 \\right), \\;\\epsilon ^{(2)} _\\mu = \\left(\n0, 0, 1,0 \\right)$ \\cite{Jones17}. The electric and magnetic fields are determined by taking the derivatives of the scalar field \\eqref{PlaneWave}: $\\,\\partial _t \\varphi = - i k B e^{i k \\left( {z - t} \\right)}$ and $\\partial_z \\varphi = i k B e^{i k \\left( {z - t} \\right)}$. Collecting terms for the Newman-Penrose scalar of the ``out\" state of \\eqref{PlaneWave} \\cite{Lehner12_85,Jones17},\n\n\\begin{equation}\n\\Phi _2 = F_{\\mu \\nu } \\bar m^\\mu n^\\nu =\n\\frac{1}{{\\sqrt 2 }}e^{ - i\\frac{\\pi }{4}} \\left( { \\partial _z \\varphi - \\partial _t \\varphi} \\right) =\ni e^{ - i\\frac{\\pi }{4}} {\\sqrt 2 } k B e^{i k \\left( {z - t} \\right)}.\n\\label{emNPscalar0}\n\\end{equation}\n\n\\noindent The square of the electromagnetic scalar is then,\n\n\\begin{equation}\n\\left| {\\Phi _2 } \\right|^2 = 2 k^2 B^2.\n\\label{emNPscalar}\n\\end{equation}\n\nThe square of the Newman-Penrose scalar in \\eqref{emNPscalar} is proportional to the electromagnetic flux, $F_{em} \\sim \\left| {\\Phi _2 } \\right|^2 $. In Section \\ref{gRad} on gravitational radiation and in Section \\ref{production} on scalar field production we will show that by using the Newmam-Penrose scalars for the gravitational and the electromagnetic fields respectively, one can compare the fluxes of gravitational and electromagnetic radiation \\cite{Jones17}.\n\n\\section{Gravitational radiation} \\label{gRad}\n\nA wave like solution to the vacuum equations for general relativity exist similar to that of electromagnetism \\cite{Schutz00}. This was recognized by Einstein soon after the development of general relativity and proposed even earlier by Poincar{\\' e} \\cite{Smoot16}. Initially there was doubt as to whether or not gravitational waves were physically real. Unlike the production of electromagnetic radiation there is no dipole source for gravitational radiation. This is because mass dipole production of radiation would violate conservation of 4-momentum \\cite{Schutz00,Smoot16}. However, there are also quadrupole source terms which lead to a wave solution and does not violate any conservation principles \\cite{Schutz00}.\n\n\nSince our interest here is in the relation between gravity and electromagnetism, in the radiation regime, we will restrict our attention to the plane wave solution of general relativity. The metric of a gravitational plane wave traveling in the $+z$ direction and with the $+$ polarization\ncan be written as \\cite{Schutz},\n\n\\begin{equation}\nds^2 = g_{\\mu \\nu} dx^{\\mu} dx^{\\nu} = -dt^2 + dz^2 + f^2 dx^2 + g^2 dy^2, \n\\label{GWmetric}\n\\end{equation}\n\n\\noindent were we set $c=1$. This metric is oscillatory with $f = 1 + \\varepsilon (u) $, $g = 1 - \\varepsilon(u)$ with $\\varepsilon(u) =h_+ e^{iku}$. The coefficient $h_+$ is the gravitational wave strain amplitude and $k$ is the wave number. The coordinate variable in the metric is the standard light cone coordinate $u=z-t$. This metric only includes the ``plus\" polarization. Similar to electromagnetic radiation there are two degrees of freedom corresponding to two polarization states for gravitational radiation. The two polarization states for gravitational radiation are ``plus\" and ``cross\" polarization. They differ by an angle of $\\frac{\\pi}{4}$ in contrast to a phase angle difference of $\\frac{\\pi}{2}$ for electromagnetism \\cite{Schutz00, Schutz09}. Including the ``cross\" polarization would not change our discussion.\n\n\\begin{figure}[H]\n\\centering\n{\\caption{The ``discovery paper\" spectrogram of gravitational waves produced by a binary black hole in-spiral \\cite{LIGO}.} \\label{spectrogram}}\n{\\includegraphics[width=12cm]{SpectrogramBW.jpeg}}\n\\end{figure}\n\nThe luminosity of the gravitational radiation can be calculated using the Newman-Penrose formalism \\cite{Teukolsky73,Lehner09}. The relevant scalar for gravitational radiation is a projection of the Riemann tensor onto elements of a null tetrad. This projection is identified as the gravitational Newman-Penrose scalar $\\Psi _4$. The power per unit of solid angle for the gravitational radiation is written in terms of the gravitational scalar as,\n\n\\begin{equation}\n\\frac{d E_{gw} }{dt d\\Omega} = \\mathop {lim}\\limits_{r \\to \\infty } \\frac{{r^2 }}{{16\\pi k^2 }}\\left| {\\Psi _4 } \\right|^2 .\n\\label{GWluminsity}\n\\end{equation}\n\n\\noindent Substituting the metric for the gravitational wave \\eqref{GWmetric} into the Riemann tensor for an outgoing gravitational plane wave in vacuum the gravitational Newman-Penrose scalar \\cite{Teukolsky73,Jones17} is,\n\n\\begin{equation}\n\\Psi _4 = - R_{\\alpha \\beta \\gamma \\delta } n^\\alpha \\bar m^\\beta n^\\gamma \\bar m^\\delta = f\\partial_{u}^{2} f- g\\partial_{u}^{2} g .\n\\label{Psi}\n\\end{equation}\n\n\\noindent The partial derivatives, $\\partial_{u}$, are with respect to the light cone coordinate, $u$. For the plane wave metric, where $\\varepsilon = h_+ e^{iku}$, the gravitational Newman-Penrose scalar and the square are given by,\n\n\\begin{equation}\n \\Psi _4 = - 2 h_+ k^2 e^{ik\\left( {z - t} \\right) } \\rightarrow |\\Psi _4 | ^2 = 4 h_+ ^2 k^4.\n \\label{Psi4}\n \\end{equation}\n\n\\noindent The Newman-Penrose scalars in \\eqref{emNPscalar} and \\eqref{Psi4} provide a convenient method for the comparison of the power of the gravitational radiation and the counterpart vacuum production of electromagnetic radiation which will follow. The flux of the gravitational wave can be calculated from \\eqref{GWluminsity} and \\eqref{Psi} as \\cite{Schutz,Schutz09,Gretarsson18},\n\n\\begin{equation}\nF_{gw}= \\frac{c^3}{16 \\pi G} \\left| \\dot \\varepsilon \\right| = \\frac{c^3 h_+ ^2 \\omega^2}{16 \\pi G},\n \\label{GWfluxh}\n \\end{equation}\n \n \\noindent which is a function of the strain amplitude $h$ and gravitational wave frequency $\\omega = 2 \\pi f$.\n \nThis review of the relationship between gravity and electromagnetism in the radiation regime might be considered of academic interest only except for the recent and remarkable discovery by the LIGO scientific collaboration of gravitational waves \\cite{LIGO}. The spectrogram from the discovery papers is provided in Fig. \\ref{spectrogram} for the binary in-spiral of two black holes. The detection of gravitational waves makes the discussion of the potential production of electromagnetic radiation by gravitational waves immediately relevant to current research in both the fundamental relation between gravity and electromagnetism as well as potential applications in astrophysics. If there were any lingering doubt about the certainty of the detection of gravitational waves the more recent detection of gravitational waves from a kilonova event \\cite{ligo2} has laid these doubts to rest. The kilonova event was first identified through the detection of gravitational waves by the LIGO scientific collaboration. The kilonova was immediately verified across the electromagnetic spectra through the coordination of an international collaboration of observatories based around the World and in space. The kilonova observations have not only eliminated any reasonable doubt of the existence of gravitational waves but also ushered in a new era of ``multi-messenger\" astronomy and astrophysics.\n\n\n\\section{Electromagnetic fields in gravitational wave background}\n\n\\subsection{Gravitational waves and uniform magnetic field}\n\nThe first modern attempt to connect gravitational radiation and \nelectromagnetic fields was work by Gertsenshtein \\cite{Gertsenshtein60}\nwhich considered the linearized Einstein field equations coupled to an \nelectromagnetic plane wave.\n\n\\begin{equation}\n\\label{gert-1}\n\\Box {\\tilde h}^{\\mu \\nu} = -\\kappa t^{\\mu \\nu} ~,\n\\end{equation}\n\n\\noindent where $t^{\\mu \\nu} = \\frac{1}{4 \\pi} (F^{\\mu \\tau} F^\\nu _\\tau -\ng^{\\mu \\nu} F^{\\alpha \\beta} F_{\\beta \\alpha})$ is the energy-momentum\ntensor for the electromagnetic field, ${\\tilde h}^{\\mu \\nu} = h^{\\mu \\nu} \n- \\frac{1}{2} g^{\\mu \\nu} h$ is the trace reduced metric deviation \nof the metric tensor ({\\it i.e.} $g^{\\mu \\nu} = \\eta ^{\\mu \\nu} + h^{\\mu \n\\nu}$), and $\\kappa = 16 \\pi G$. Now the proposal in \n\\cite{Gertsenshtein60} was to generate gravitational waves by sending \nelectromagnetic waves through a constant magnetic field. This potential \neffect was compared to radio physics phenomenon of wave resonance. The \nidea being that despite the weak coupling of gravity one could \nnevertheless generate some significant amount of gravitational radiation \nby this method. \n\nNow if one takes the electromagnetic field to have a constant magnetic \nfield part (whose field strength tensor we denote by $F^{(0) \\mu \\nu}$) \nand a plane wave part (whose field strength we denote by $F ^{\\mu \\nu}$),\nand if we feed this into \\eqref{gert-1}, dropping the squared terms in\n$F^{(0) \\mu \\nu}$ and $F^{\\mu \\nu}$ and keeping only the cross terms \nwe arrive at\n\n\\begin{equation}\n\\label{gert-2}\n\\Box {\\tilde h}^{\\mu \\nu} = -\\frac{\\kappa}{2} \\left( F^{(0) \\mu \\tau} \nF^{\\nu} _\\tau - \\frac{1}{4} g^{\\mu \\nu} F^{(0) \\alpha \\beta} F_{\\alpha \\beta} \\right) ~.\n\\end{equation}\n\n\\noindent One now assumes that the electromagnetic plane wave field and\ngravitational field propagate along the $z$ direction with wave number \n$k$ and have the form\n\n\\begin{equation}\n\\label{gert-3}\nF^{\\mu \\nu} = b(z) \\epsilon^{\\mu \\nu} e^{i(kz-\\omega t)} ~~~;~~~\n{\\tilde h}^{\\mu \\nu} = a(z) \\zeta ^{\\mu \\nu} \\sqrt{\\frac{\\kappa}{k^2}}\ne^{i(kz - \\omega t)} ~,\n\\end{equation}\n\n\\noindent where $\\epsilon ^{\\mu \\nu} , \\zeta ^{\\mu \\nu}$ are the \nelectromagnetic and gravitational polarization tensors respectively. \nUsing \\eqref{gert-3} in \\eqref{gert-2} and assuming slowly varying\namplitudes $a(z) , b(z)$ one obtains the following relationship \nbetween the amplitudes\n\\begin{equation}\n\\label{gert-4}\ni \\frac{da (z)}{dz} = \\sqrt{\\frac{\\kappa}{16}} F^{(0) \\mu \\nu}\n\\epsilon_{\\beta \\nu} \\zeta^{\\beta} _\\mu b(z) ~.\n\\end{equation}\nUnder the assumption that $b(x) \\approx const.$ \\eqref{gert-4} can be \nintegrated to obtain $a(x)$ as\n\\begin{equation}\n\\label{gert-5}\n\\left| \\frac{a (z)}{b(0)} \\right|^2 = \\frac{\\kappa}{16 \\pi^2} \nB_0 ^2 T^2 ~,\n\\end{equation}\nwhere $B_0 \\simeq | F^{(0) \\mu \\nu} |$ is the constant magnetic field \nstrength, $T$ is the time that the electromagnetic wave traverses the\nuniform magnetic field, and $b(0)$ is the initial amplitude of the\nelectromagnetic wave. If one takes the cosmological sized magnetic fields\n($B_0 \\simeq 10 ^{-5}$ G) and assumes cosmological times for the \nelectromagnetic wave to travel through this constant magnetic field\n($T \\simeq 10^7$ years) one finds that the ratio of gravitational to \nelectromagnetic amplitude is of the order $|a\/b|^2 \\simeq 10^{-17}$. One \ncould increase this by having stronger magnetic fields and\/or longer \nperiods of travel. \n\nOne of the most interesting features of the above mechanism is that the\ngravitational wave frequency is the same as that of the electromagnetic \nwave. This gives the possibility of generating very high frequency \ngravitational waves compared to the ``natural'' sources of gravitational \nwaves from the first series of direct detections-- merging black hole, \nmerging neutron stars. These natural or astrophysical sources have \nfrequencies in the 100s to 1000s of Hertz, whereas electromagnetic \nradiation has a much broader range of frequencies which have been\nobserved -- from 1000s of Hertz to Gigahertz and beyond.\n\nIn the original work by Gertsenshtein \\cite{Gertsenshtein60} the focus \nwas on generating gravitational waves from electromagnetic waves. In this\nreview our focus is the exact opposite -- we are interested in \nelectromagnetic radiation generated from gravitational waves. This \nreversed possibility was pessimistically noted by Gertsenshtein with the\nconcluding comment ``From general relativity follows also the possibility\nof the inverse conversion of gravitational waves into light waves, \nbut this problem is hardly of interest.'' Nevertheless, several years \nafter Gertsenshtein's paper, Lupanov \\cite{lupanov67} did examine the \ninverse process of generating electromagnetic waves from gravitational\nwaves. \n\nWe will follow the work in \\cite{lupanov67} by examining the reverse \nprocess of electromagnetic radiation generated from gravitational waves. \nThere are two reasons for our focus on the reverse process: (i) \nelectromagnetic radiation, even weak radiation, is easier\nto detect, and (ii) we argue, beginning in the next subsection, that the\nconversion of gravitational waves to electromagnetic radiation occurs \neven in vacuum.\n\nBefore concluding this subsection we mention that there is more recent work\nin the spirit of Gertsenshtein's work \\cite{Gertsenshtein60}\nwhere the magnetic field is replaced by a Bose-Einstein condensate\n\\cite{fuentes}. In this case the interaction of the gravitational\nwave with the Bose-Einstein condensate is conjectured to lead to \nthe creation of phonons, just as in Gertsenshtein's work the \ninteraction of the gravitational wave with the magnetic field\nlead to the creations of photons. This creation of phonons\nwith a Bose-Einstein condensate has been put forward as a \npotential alternative mechanism to interferometers like LIGO\nto detect gravitational waves. \n\n\n\\subsection{Feynman diagram approach to gravitational and electromagnetic\nradiation}\n\nTo check the assertion that electromagnetic radiation can be created in \nvacuum from gravitational radiation we turn to tree-level Feynman \ndiagrams for graviton-photon scattering. The four basic diagrams for this \nprocess are given in Fig. \\eqref{Skobelev} with curly lines \nrepresenting gravitons and wavy lines represent photons. The original \ncalculation was carried out by Skobelev \\cite{Skobelev75} with more \nrecent and more extensive calculations being found in references \n\\cite{Bohr14}. The diagrams in Fig. \\eqref{Skobelev} represent\n$graviton + photon \\to graviton + photon$ scattering. By rotating the \ndiagrams one can get $graviton + graviton \\to photon + photon$ or\n$photon + photon \\to graviton + graviton$ which can be viewed as \ncreation of photons (gravitons) from gravitons (photons). \n\n\n\\begin{figure}[!h]\n\\centering\n\\includegraphics[width=110mm]{Skobelev.jpeg}\n\\caption{Tree level Feynman diagrams of graviton-photon transitions \n\\cite{Skobelev75}. Wavy lines represent photons and curly lines \nrepresent gravitons \\label{Skobelev}}\n\\end{figure}\n\nIn \\cite{Skobelev75} the process $graviton + photon \\to graviton + \nphoton$ is calculated first. After a long calculation the \ndifferential scattering cross section is found to be\n\\begin{equation}\n\\label{sko-1}\n\\frac{d \\sigma}{d \\cos \\Theta} =\\frac{\\kappa ^2 \\omega ^2}{64 \\pi}\n\\left( \\frac{1+ \\cos ^8 (\\Theta \/2) }{\\sin ^4 (\\Theta \/ 2)} \\right) ~,\n\\end{equation}\nwhere $\\kappa = 16 \\pi G$ as previously, $\\omega$ is the energy of \nthe system, and $\\Theta$ is the scattering angle.\n\nNow the process of interest in this review is where gravitational waves \ncreate electromagnetic waves or photons. In the Feynman diagram language \nthis means $graviton + graviton \\to photon + photon$. This process \ncan be obtained by rotating the diagrams in Fig. \\ref{Skobelev} by $90^0$ degrees. Upon \ndoing this the differential cross section for $graviton + graviton \\to \nphoton + photon$ is found to be \\cite{Skobelev75}\n\\begin{equation}\n\\label{sko-3}\n\\frac{d \\sigma}{d \\cos \\Theta} =\\frac{\\kappa ^2 \\omega ^2}{64 \\pi}\n(\\cos ^8 (\\Theta \/2) + \\sin ^8 (\\Theta \/ 2) ) ~.\n\\end{equation}\nIntegrating \\eqref{sko-3} to obtain the total cross section \n\\begin{equation}\n\\label{sko-4}\n\\sigma =\\frac{\\kappa ^2 \\omega ^2}{160 \\pi} ~.\n\\end{equation}\nIf one takes the energy of the system to be the rest mass energy of \nthe electron, $\\omega = m_e c^2$, \\footnote{This energy is much larger\nthan the energy implied by the frequencies of the observed gravitational \nwave signals \\cite{LIGO}. For the energy associated with the frequencies\nimplies by the LIGO observations the cross section would be even smaller\n} one finds \\cite{Skobelev75} that $\\sigma \\simeq 10^{-110} cm ^2$. \nThis is a very small number and indicates that at the level of individual\nphotons and gravitons this is not a large effect. However, given the \nenormous energy of the observed gravitational wave signals, which implies a\nlarge number of gravitons, we will argue that there are cases where the \nsmall cross section of \\eqref{sko-4} may be compensated for by the \nlarge number of gravitons\/strength of the gravitational wave. \n\n\\subsection{Massive Scalar field in Gravitational Plane Wave Background}\n\nThe idea of particle creation from a time dependent space-time was \nfirst considered in a series of papers by Parker \\cite{parker-1,parker-2,parker-3} which investigated particle production \nfrom the time dependent FRW cosmological space-time. The next major \ntime-dependent space-time to be studied in terms of particle production \nwas the gravitational plane wave space-time. The initial studies were \ncarried out by Gibbons \\cite{gibbons} and Deser \\cite{deser}, who \nconsidered the production of a massive scalar field in a pulsed \ngravitational wave space-time. Reference \\cite{garriga} has a \nmore extensive discussion of particle creation from a gravitational \nwave background, again in the context of a massive scalar field. \nThe conclusion of all of these works was that massive scalar \nfields would not be created from such a plane wave gravitational \nbackground. \n\nThis conclusion, of no particle creation from a plane gravitational \nwave background, appears at odds with recent work \\cite{Jones15,Jones16,Jones17,Jones18}. However these recent\nworks focus on the case of massless particles whereas references\n\\cite{gibbons,deser,garriga} focus on massive particles.\nAs noted by Gibbons one can already guess that the production of a \nmassive field from a gravitational plane wave would be forbidden\nby energy momentum conservation. A massless graviton can not \ndecay\/transform into massive particles since this would violate \nenergy-momentum conservation. It is the same reason that forbids \na photon from decaying into an electron-positron pair in free space.\n(A photon can decay\/transform into electron-positron pair in the\npresence of a heavy nuclei which acts to conserve energy and momentum). \n\nGeneral studies of when one type of massless field quanta can \ndecay\/transform into other massless field quanta can be found\nin \\cite{Modanese95} and \\cite{Fiore96}. Using energy-momentum \nconservation these two works show that some number of massless\nquanta can transform into some number of other massless field quanta\nso long as the incoming and outgoing particles lie along the same \ndirection. This is consistent with the Feynman diagram calculations\nof reference \\cite{Skobelev75} where the decay of gravitons to photons\n({\\it i.e.} $graviton + graviton \\to photon + photon$) is possible so long as the\nmomenta of all particles lie along the same direction. This is also the\ncondition under which the creation of massless fields\/field quanta \noccurs in references \\cite{Jones15,Jones16,Jones17,Jones18}.\n\nIn the rest of this section we review the calculations of \\cite{gibbons,deser,garriga} which demonstrate the absence of\nparticle creation when the \nparticles are massive, since this will provide a nice segue to\nthe case of massless particles. We will follow the notation of\nreference \\cite{garriga}.\n\nGarriga and Verdaguer \\cite{garriga} begin by considering the plane\nwave metric of the form\n\\begin{equation}\n\\label{garr-1}\nds^2 = -dt^2 + dz^2 + g_{ab} (z, t) dx^a dx^b = \n-du dv + g_{ab} (u) dx^a dx^b ~,\n\\end{equation}\nwhere in the last expression the metric has been transformed to light\nfront coordinates defined as $u=z-t$ and $v=z+t$ with $c=1$. The \nindices $a, b =1, 2$ and run over the $x, y$ directions, which are \nperpendicular to the $+z$ direction of travel of the gravitational wave.\nFor a gravitational wave traveling in the $-z$ direction one would take\nthe metric components to be functions of the light front coordinate\n$v$ {\\it i.e.} $g_{ab} (v)$.\n\nNext a massive scalar field, $\\varphi$, is placed in the metric given by \n\\eqref{garr-1}. The equation for a massive scalar field in a curved\nbackground is given by\n\\begin{equation}\n\\label{garr-2}\n\\left [ \\frac{1}{{\\sqrt { -\\left| {g_{\\mu \\nu } } \\right|} }}\\left( \n{\\partial _\\mu g^{\\mu \\nu } \\sqrt { - \\left| {g_{\\mu \\nu } } \\right|} \n\\partial _\\nu } \\right) - m^2 \\right] \\varphi = 0.\n\\end{equation}\nUsing the metric \\eqref{garr-1} in equation \\eqref{garr-2}\nand applying separation of variables, one finds solutions for the \nscalar field of the form\n\\begin{equation}\n\\label{garr-3}\n\\varphi (u, v, x^a) = \n\\frac{1}{(p_{-})^{1\/2} (det g_{ab} (u))^{1\/4} (2 \\pi)^{3\/2} }\n\\exp \\left[i p_a x^a - i p_{-} v -\\frac{i}{4 p_{-}} \n\\int _0 ^u (g_{ab} p^a p^b +m^2) du\\right]~,\n\\end{equation}\nwith $p_{-}$ and $p_a$ being separation constants which physically\ncorrespond to momenta connected with the coordinates,\n$v$ and $x^a$ respectively. \n\nThe next step is to calculate the Bogoliubov coefficients \\cite{davies} \nfor this scalar field for a sandwich space-time {\\it i.e.} one has a \nplane wave space-time for $u_1 < u < u_2 <0$, sandwiched between two \nMinkowski space-times for $uu_2$. The \n``in'' and ``out'' states for this sandwich space-time are \\cite{garriga}\n\\begin{eqnarray}\n\\label{garr-4}\n\\varphi ^{in} (u, v, x^a) &=& \n\\frac{1}{(p_{-})^{1\/2} (2 \\pi)^{3\/2} }\n\\exp \\left[i p_a x^a - i p_{-} v -\\frac{i}{4 p_{-}} \n(p_a p^a +m^2) u + i \\Delta \\right]~,\\\\\n\\varphi ^{out} (u,v, x^a) &=&\n\\frac{1}{(k_{-})^{1\/2} (2 \\pi)^{3\/2} }\n\\exp \\left[i k_a x^a - i k_{-} v -\\frac{i}{4 k_{-}} \n(k_a k^a +m^2) u \\right]~,\n\\label{garr-5}\n\\end{eqnarray}\nwhere $\\Delta$ is a constant phase. For the exact expression for this\nphase as well as for the full details of the calculation and some \nsubtleties in the definition of the coordinates we refer the reader to\n\\cite{garriga}. The light front momentum $p_-$ and $k_-$ are given by \n\\begin{equation}\n\\label{garr-5a}\np_- = \\frac{\\omega_p - p_z}{2} ~~~;~~~ k_-=\\frac{\\omega_k - k_z}{2} ~,\n\\end{equation}\nwith $p_z , k_z$ being the three-momentum in the $z$ direction and \n$\\omega_p = \\sqrt{{\\bf p}^2 +m^2}$ and $\\omega_k = \\sqrt{{\\bf k}^2\n+m^2}$ are the energies associated with the wave solutions. \n\nFrom \\eqref{garr-4} and \\eqref{garr-5} one can calculate the\nBogoliubov beta coefficients to be\n\\begin{equation}\n\\label{garr-6}\n\\beta = - \\langle \\varphi ^{in} | \\varphi ^{out ~*}\\rangle \\propto \n\\delta (p_- + k_-) ~,\n\\end{equation}\nwith the Dirac delta being a function of $p_- , k_-$ coming from \nintegration over $dv$. Now if the scalar field is massive it is easy \nto see, using the expressions for $\\omega _p \\omega_k$ in equation \n\\eqref{garr-5a} that\n$p_- + k_- \\ne 0$ so that $\\beta =0$. However, if $m=0$ and the 3-\nmomentum are in the same direction ${\\bf p} = p_z = {\\bf k} = k_z$\nthen $p_- + k_- =0$ and $\\beta \\ne 0$ meaning that production of \nthe scalar field from the gravitational wave occurs. This is \nconsistent with the Feynman\ndiagram calculations of \\cite{Skobelev75,Bohr14} as well as the\ndiscussion in term of energy-momentum conservation of \nparticle decay\/production\/scattering of massless fields \n\\cite{Modanese95,Fiore96}. In the next section we investigate \nin more detail the possibility of producing massless fields\/particles \nfrom a gravitational wave background. \n\n\\section{Particle Production from a Gravitational Wave Background}\n\nIn this section we review some recent work \\cite{Jones15,Jones16,Jones17,Jones18} on the production of massless fields from\ngravitational wave backgrounds. We use a massless scalar field\nto carry out the analysis, but our results also apply to the more\nrealistic case of a massless vector field from the results and\ndiscussion around equations \\eqref{emLagrangian}, \\eqref{emLagrangianLG}, \n\\eqref{ModeExpN}, and \\eqref{emLagrangian2}.\nWe also look at the response of an Unruh-Dewitt \ndetector in a gravitational plane wave background which supports the\npicture of gravitons decaying\/transforming into photons. \n \n\\subsection{Scalar field production} \\label{production}\n\nWe now repeat some of the calculations of the previous section but for a \nmassless scalar field. We will follow the work of \\cite{Jones16}. For\nthe gravitational plane wave background we take the metric of \n\\eqref{garr-1} to have the more specific form\n\n\\begin{equation}\n\\label{jones-1}\nds^2 = -dt^2 + dz^2 + f^2 (z,t) dx^2 + g^2 (z,t) dy^2 = \n-du dv + f^2 (u) dx^2 + g^2 (u) dy^2 ~,\n\\end{equation}\nwhere we have again transformed to light front coordinates, $u, v$ and \ntaken $c=1$. The form of the metric in \\eqref{jones-1} assumes the\nplus-polarization for the gravitational plane wave, which we take \nwithout loss of generality. We further assume that the ansatz functions\nhave a oscillatory behavior of the form $f(u) = 1 + h_+ e^{iku}$ and\n$g(u) =1 - h_+ e^{iku}$, where $h_+$ is the dimensionless amplitude\nof the plus polarization and $k$ is the gravitational wave \nnumber. With $m=0$ the field equation for $\\varphi$ from \n\\eqref{garr-2} becomes\n\n\\begin{equation}\n\\label{jones-2}\n\\frac{1}{{\\sqrt { -\\left| {g_{\\mu \\nu } } \\right|} }}\\left( \n{\\partial _\\mu g^{\\mu \\nu } \\sqrt { - \\left| {g_{\\mu \\nu } } \\right|} \n\\partial _\\nu } \\right) \\varphi = 0.\n\\end{equation}\n\nUsing the plane wave metric from \\eqref{jones-1} along with the oscillatory form of the ansatz functions $f(u), g(u)$ equation \\eqref{jones-2} becomes\n\n\\begin{equation}\n \\left( {4F (ku) \\partial _u \\partial _v - 4ikG (ku) \\,\\partial _v + \n H(ku) \\left( {\\partial _x^2 + \\partial _y^2 } \\right)} \\right)\\varphi \n = 0,\n\\label{jones-3}\n\\end{equation}\n\n\\noindent where the functions $F(ku), G(ku), H(ku)$, are given by\n\n\\begin{equation}\n\\begin{array}{l}\n \\quad F\\left( {ku} \\right) \\equiv \\left( 1 - h_ + ^2 e^{2iku} \\right) \n ^2, \\\\ \n \\quad G\\left( {ku} \\right) \\equiv h_ + ^2 e^{2iku} \\left( 1 -\n h_ + ^2 e^{2iku} \\right), \\\\ \n \\quad H\\left( {ku} \\right) \\equiv \\left( {1 + h_ + ^2 e^{2iku} } \n \\right). \\\\ \n \\end{array}\n\\label{jones-4}\n\\end{equation}\n\n \\noindent In arriving at \\eqref{jones-3} we have assumed that the\n behavior of $\\varphi$ in the perpendicular $x, y$ directions are the \n same so that\n $\\partial _x \\varphi = \\partial _y \\varphi$ and \n $\\partial _x ^2 \\varphi = \\partial _y ^2 \\varphi$.\n\nTo solve \\eqref{jones-3} we employ separation of variables as\n$\\varphi (u, v, x, y) = U(u) V(v) X(x) Y(y)$. The ansatz functions\nin the $v, x, y$ directions are plane waves of the form\n\n\\begin{equation}\n X (x) = e^{ik_{xy} x} ~~~;~~~ Y(y) = e^{ik_{xy} y} ~~~;~~~~ \n V(v) = e^{ik_v v} ~,\n\\label{jones-5}\n\\end{equation}\n\n\\noindent where we have enforced the equality of the $x$ and $y$ \ndirections by taking a common wave number, $k_{xy}$. With this \nset up and the solutions from \\eqref{jones-5} the solution for $U(u)$\nis \\cite{Jones16}\n\\begin{equation}\nU = B e^{\\frac{\\lambda }{k}} e^{ \\frac{- \\lambda }{{k\\left( {1 - h_ + \n^2 e^{2iku} } \\right)}}} \\left( {1 - h_ + ^2 e^{2iku} } \n\\right)^{\\frac{1}{2}\\left( {\\frac{\\lambda }{k} - 1} \\right)} e^{ - \ni\\lambda u} + C ~,\n \\label{jones-6}\n\\end{equation} \n\\noindent with $B, C$ being integration constants and \n$\\lambda = \\frac{k_{xy}^2}{2 k_v}$. Putting equations\n\\eqref{jones-5} \\eqref{jones-6} together, and taking $C=-B$ the \nscalar field in the plane wave background becomes\n\\begin{equation}\n\\varphi (u, v, x, y) = B e^{\\frac{\\lambda }{k}} e^{ - \\frac{\\lambda }\n{{k\\left( {1 - \nh_ + ^2 e^{2iku} } \\right)}}} \\left( {1 - h_ + ^2 e^{2iku} } \n\\right)^{\\frac{1}{2}\\left( {\\frac{\\lambda }{k} - 1} \\right)} e^{ - \ni\\lambda u} e^{ik_v v} e^{ik_{xy} x} e^{ik_{xy} y} - B.\n\\label{jones-7}\n\\end{equation}\nIn the limit $h_+ \\to 0$ ({\\it i.e.} the gravitational background is \nturned off) the scalar field in \\eqref{jones-7} becomes\n\\begin{equation}\n\\varphi _0 (t,x,y,z) = \nB e^{ - i\\lambda u} e^{ik_v v} e^{ik_{xy} x} e^{ik_{xy} y} \n-B \\rightarrow\n B e^{i\\left( {k_v + \\lambda } \\right)t} e^{i\\left( {k_v - \\lambda } \n \\right)z} e^{ik_{xy} x} e^{ik_{xy} y} -B ~,\n \\label{jones-8}\n\\end{equation}\n\\noindent where in the last step we have converted back to the original\n$t,x,y,z$ coordinates. One can see that $k_v + \\lambda$ plays the role\nof the wave energy and $k_v - \\lambda$ momentum in the $z$ direction. \nThe result in \\eqref{jones-8} is expected, since if the gravitational\nwave background is turned off one should recover a plane wave traveling\nin free space, which is what the solution in \\eqref{jones-8} represents. \n\nTaking the limit where all the wave numbers\/momenta go to zero \n({\\it i.e.} $k_v, \\lambda, k_{xy} \\to 0$) in equation \\eqref{jones-7}\none would expect the scalar field to vanish. However on taking this\nlimit one finds instead that \n\\begin{equation}\n\\varphi (u, v, x, y) = B \\left[\\left( {1 - h_ + ^2 e^{2iku} } \n\\right)^{-\\frac{1}{2}} - 1 \\right] \\approx \\frac{B}{2} h_ + ^2 e^{2iku}\n+ \\frac{3B}{8} h_ + ^4 e^{4iku} ~.\n\\label{jones-9}\n\\end{equation}\nThe result in \\eqref{jones-9} shows that even when one tries to take\nthe wave to its vacuum state, namely $k_v , \\lambda, k_{xy} \\to 0$,\nthere is a non-vanishing and non-trivial scalar field. This \nnon-vanishing scalar field is the field\/field quanta created by the \ngravitational wave background. Note that if one takes $h_+ \\to 0$\nin \\eqref{jones-9} that one does get the expected value for the scalar\nfield $\\varphi \\to 0$ \\footnote{Setting the constant $C=-B$ in\n\\eqref{jones-6} is done to get $\\varphi \\to 0$ in this limit. If one takes \n$C=0$ the $h_+ \\to 0$ limit of \\eqref{jones-9} would be\n$\\varphi \\to B$ which is also a vacuum solution to the wave equation\nfor the massless $\\varphi$, but having $\\varphi \\to 0$ is more ``natural\".}\nThe four-current associated with $\\varphi$ is given by the standard\nexpression $j_\\mu = -i (\\varphi \\partial _\\mu \\varphi ^* - \\varphi^* \n\\partial_\\mu \\varphi)$. Inserting this solution from \\eqref{jones-7} in the expression for the four-current and time averaging gives \\cite{Jones16}\n\\begin{equation}\n\\langle j_\\mu \\rangle = -2 B^2 \\lambda - B^2 h_+ ^4 \\left(\n\\frac{9}{2} \\frac{\\lambda ^3}{k^2} -\\frac{12 \\lambda ^2}{k}\n+ \\frac{13}{2} \\lambda - k \\right) ~.\n\\label{jones-10}\n\\end{equation}\nThe constant $B$ is determined by choosing a normalization condition or\nconvention. Following references \\cite{stahl} and \\cite{Jones16} \nwe pick the normalization condition $B = \\frac{1}{\\sqrt{2 k V}}$. \nOther possible normalization conditions for $B$ are \ndiscussed in \\cite{halzen}. With this normalization the vacuum \nscalar field from \\eqref{jones-9} reads\n\\begin{equation}\n\\varphi (u, v, x, y) = \\frac{1}{\\sqrt{2 k V}}\n\\left[\\left( {1 - h_ + ^2 e^{2iku} } \n\\right)^{-\\frac{1}{2}} - 1 \\right] \\approx \n\\frac{1}{2 \\sqrt{2 k V}} h_ + ^2 e^{2iku}\n\\left( 1 + \\frac{3}{4} h_ + ^2 e^{2iku} \\right) ~.\n\\label{jones-11}\n\\end{equation}\nTime averaging this vacuum current from \\eqref{jones-10} gives\n\\begin{equation}\n\\langle j_\\mu \\rangle = \\frac{\\rm{sign} (k) h_+ ^4 }{2 V}~.\n\\label{jones-12}\n\\end{equation}\n\n\\noindent In \\eqref{jones-11} we are using a normalization that assumes the \nscalar field is in a box of volume $V$. In the previous section we took \n$B=\\frac{1}{(2 \\pi) ^{3\/2}}$ -- see \\eqref{garr-4} \\eqref{garr-5}.\n\nEquation \\eqref{jones-10} gives the effect, in terms of the \nfour-current, of passing a massless scalar field through a \ngravitational wave. On setting all the energy-momentum of the\nscalar field to zero one finds, from equations \\eqref{jones-11} and \n\\eqref{jones-12}, that the scalar field and scalar field current\ndo not vanish. This represents the production \nof scalar field\/scalar field quanta from the gravitational wave\nbackground. \n\nFollowing \\cite{Jones17} the ratio of the \nproduced electromagnetic radiation \\eqref{emFlux0} \nto the gravitational \\eqref{GWluminsity} radiation can be written \ndown in terms of electromagnetic and gravitational Newman-Penrose\nscalars from \\eqref{Phi2} and \\eqref{Psi4}, \n\n\\begin{equation}\n\\frac{{dE_{em} }}{{dE_{gm} }} = \\frac{{\\left( {\\frac{1}{{4\\pi }}\\left| {\\Phi _2 } \\right|^2 } \\right)}}{{\\left( {\\frac{1}{{16\\pi k^2 }}\\left| {\\Psi_4 } \\right|^2 } \\right)}} = \\frac{F_{em}}{F_{gw}}.\n\\label{emgwRatioA}\n\\end{equation}\n\n\\noindent Switching to a normalization where $B=1$ in \\eqref{jones-9} \nthe amplitude of the leading term of the scalar field is $\\frac{h_+^2}{2}$.\nUsing this in the expression for $|\\Phi_2|^2$ calculated \nin \\eqref{emNPscalar} we get $|\\Phi_2|^2 = 2 k^2 h_+^4$. \nNext from \\eqref{Psi4} we recall that \n$|\\Psi _4 |^2 = 4 h_+^2 k^4$ for a gravitational plane we. Using all this\nin \\eqref{emgwRatioA} yields a relationship between the electromagnetic wave \nflux and gravitational wave flux\n\n\\begin{equation}\nF_{em} = 2 h_+^2 F_{gw}.\n\\label{emgwRatioB}\n\\end{equation}\n\n\\noindent Since the gravitational radiation is proportional to $h_+^2$ the electromagnetic production will be proportional to $h_+^4$. The result in equation \\eqref{emgwRatioB} is consistent with the result in equation\n\\eqref{jones-12}.\n\n\\subsection{Unruh-Dewitt detector approach}\n\nAnother approach to study the connection between gravitational and \nelectromagnetic radiation is through the use of an Unruh-DeWitt detector\n\\cite{unruh-det,dewitt-det}. An Unruh-DeWitt detector is a two-state, \nquantum system which is placed in a given space-time background. If the\nUnruh-DeWitt detector is excited from the low energy state to the \nhigh energy state, this is taken to indicate that the given space-time\nhas produced field quanta in order to excite this transition. \nTwo common examples of the use of an Unruh-DeWitt detector are placing \nit in the Schwarzschild space-time \\cite{davies,hawking} of a black hole or \nplacing it the Rindler space-time of an accelerating observer \\cite{unruh}. \nIn the first case the Unruh-DeWitt detector will detect the photons from \nHawking Radiation and in the second case the Unruh-DeWitt detector will \ndetect the photons from Unruh Radiation.\n\nIn this subsection we will summarize the work of reference \\cite{Jones15}\nwhich calculates the response of an Unruh-DeWitt detector interacting with \na plane gravitational wave. The expression for the spectrum, $S(E)$, \nof an Unruh-DeWitt detector is given by \n\\begin{equation}\n\\label{UD-1}\nS(E) = n_{general} - n_{inertial} = 2 \\pi \\rho (E) F(E) ~.\n\\end{equation}\nIn equation \\eqref{UD-1} $n_{general}$ and $n_{inertial}$ are the\nphoton density of a general space-time and inertial space-time\nrespectively. The difference between these two is a measure of the\nphotons created due to the general space-time. The terms $\\rho (E)$\nand $F(E)$ are, respectively, the density of states and response \nfunction both as a function of energy \\cite{davies,letaw,akhmedov,wilburn,rad}. \n\nThe detector response function is given by\n\\begin{equation}\n\\label{UD-2}\nF(E) = \\int _{-\\infty} ^{+\\infty} e^{-i \\Delta \\tau \\Delta E}\n(G^+ _{general} (\\Delta \\tau) - G^+ _{inertial} (\\Delta \\tau))\nd (\\Delta \\tau ) ~,\n\\end{equation}\nwhere we recall that $\\hbar =1$ and $c=1$ in the above\nformulas. $\\Delta E = E_{up} - E_{down}$ is the energy difference \nbetween the two states of the Unruh-DeWitt detector. For simplicity \nwe assume $E_{down} = 0$ so that $\\Delta E \\to E_{up} \\to E$ and \nthus the response function is written as just a function of $E$. \nThe terms $G^+ _{general} (\\Delta \\tau)$ and $G^+ _{inertial} \n(\\Delta \\tau))$ are the Wightman functions \\cite{davies,Jones15} for the detector path in a general space-time and \nthe detector path in the inertial space-time. The\nWightman function depends on the proper time difference \n$\\Delta \\tau$ \nfor the path through the given space-time. The space-time path for \nthe inertial detector is $x^\\mu (\\Delta \\tau ) = (\\Delta \\tau , 0, \n0, 0)$. The Wightman function for this inertial detector is\n\\begin{equation}\n\\label{UD-3}\nG_{inertial} ^+ = \\frac{1}{4 \\pi ^2 x^\\mu x_\\mu} =\\frac{1}{4 \\pi ^2 \n\\Delta \\tau ^2} ~.\n\\end{equation}\nFor a gravitational wave traveling in the $+z$ direction and having\n$+$ polarization the space-time path is given by $x^\\mu (\\Delta \n\\tau ) = ( \\gamma \\Delta \\tau , \\Delta x, 0, 0)$, where $\\Delta x$ \nis the spatial displacement of the detector due to the gravitational \nwave and $\\gamma ^{-2} = 1 - \\Delta {\\dot x} ^2$. Without loss of \ngenerality the detector is taken to be aligned along the $x$ direction. \nThe Wightman function for the gravitational wave is \n\\begin{equation}\n\\label{UD-4}\nG_{wave} ^+ = \\frac{1}{4 \\pi ^2 x^\\mu x_\\mu} =\\frac{1}{4 \\pi ^2 \n(\\gamma ^2 \\Delta \\tau ^2 - \\Delta x^2)} ~.\n\\end{equation}\nUsing these two Wightman functions from \\eqref{UD-3} \\eqref{UD-4} in \n\\eqref{UD-2} we find\n\\begin{equation}\n\\label{UD-5}\nF(E) = \\frac{1}{2\\pi^2 }\\int _0 ^{+\\infty} \\cos (E \\Delta \\tau)\n\\left( \\frac{1}{ (\\gamma ^2 \\Delta \\tau ^2 - \\Delta x^2)}- \n\\frac{1}{\\Delta \\tau ^2} \\right)\nd (\\Delta \\tau ) ~.\n\\end{equation}\nTo evaluate \\eqref{UD-5} we need to give $\\Delta x$ as a function of \n$\\Delta \\tau$. This is done using the ${\\dot x} = (1 + \\frac{1}{2}h)$ \n\\cite{EFTaylor} which is the expression for the trajectory along a null \ngeodesic, to first order, for a gravitational wave background \ncharacterized by the amplitude $h (\\Delta \\tau, \\theta, \\psi) = h_0 \n\\sin^2 (\\theta) \\sin (2 \\psi ) \\sin (\\omega \\Delta \\tau )$. The angles\n$\\theta$ and $\\psi$ give the orientation of the axis of the detector \nwith respect to the incoming gravitational wave \\cite{hendry}. The \nseparation $\\Delta x$ between the particle undergoing geodesic \nmotion in the gravitational wave background characterized by \n$h (\\Delta \\tau, \\theta, \\psi)$ and an inertial observer is then\ngiven by $\\Delta x = (1 + \\frac{1}{2}h) \\Delta \\tau - \\Delta \\tau =\n\\frac{1}{2}h (\\Delta \\tau, \\theta, \\psi) \\Delta \\tau$. Using these\nresults in \\eqref{UD-5} and integrating over $\\Delta \\tau$ as well as \nintegrating over the orientation direction $\\theta$ and $\\psi$ gives\nthe detector response function as \\cite{Jones15}\n\\begin{equation}\n\\label{UD-6}\nF(E) = \\frac{3 \\pi}{256} h_0 ^2 (2 \\omega - E) \\Theta (2 \\omega - E),\n\\end{equation}\nwhere $\\Theta$ is the Heaviside step function. Thus $F(E) = 0 $ when\n$E > 2 \\omega$ which is a similar type of cut-off to that in \nmuon decay \\cite{halzen,griffiths}. This suggests a picture of\ngravitons ``decaying\" into photons -- $graviton + graviton \\to photon + \nphoton$ or $graviton \\to graviton + photon + photon$.\n\nUsing the response function from \\eqref{UD-6} and a density of\nstates $\\rho (E) = \\frac{E^2}{2 \\pi^2}$ \\cite{letaw} the spectrum can \nbe found from \\eqref{UD-1} as\n\\begin{equation}\n\\label{UD-7}\nS(E) = \\frac{3 }{256 \\pi \\hbar ^3 c^3} E^2 h_0 ^2 \n(2 \\hbar \\omega - E) \\Theta (2 \\hbar \\omega - E) ~.\n\\end{equation}\nWe have restored factors of $\\hbar$ and $c$ temporarily. The\nfunctional form of the spectrum from \\eqref{UD-7} is that of a\n$Beta (3,2)$ distribution which is reminiscent of particle decays. This\nagain supports the picture of gravitons decaying into photons. \n\nThe analysis of the present subsection is different from the proceeding\nsubsection in that here we place an Unruh-DeWitt detector\nin the presence of a gravitational plane wave background, whereas in\nthe previous subsection we focused on the response of the vacuum to \na gravitational wave. The Unruh-DeWitt calculation is closer in spirit to \nthe work of Gertsenshtein \\cite{Gertsenshtein60} where the gravitational \nwave interacts with a magnetic field. In both these cases there is some \nphysical object -- the Unruh-DeWitt detector or a magnetic field -- which \ninteracts with the gravitational wave. In the previous subsection the \ngravitational wave interacts with the quantum vacuum. Nevertheless all of \nthese calculations indicate that\na gravitational wave can create electromagnetic radiation, or in\nparticle language that gravitons can transform\/decay into photons. The work in \\cite{Calmet16}\nalso looks into this possibility of gravitons decaying\/transforming in to photons and thus\nweakening the gravitational wave.\n\n\\section{Possible Observational Consequences\/Signatures}\n\nWhile gravitational waves have only been directly detected very recently, electromagnetic \nradiation has been observed for all of human existence. If gravitational waves produce \ncounterpart electromagnetic radiation as is outlined above, it is natural to ask what the \npossible observable consequence of this would be. In this section we address two possible observational\nconsequence: (i) the attenuation\/decay of the gravitational wave due to production electromagnetic radiation;\n(ii) the direction detection of the electromagnetic radiation produced by the gravitational wave. \n\n\\subsection{Decay\/attenuation of the gravitational wave}\n\nIf electromagnetic waves are produced from a gravitational wave, as suggested above, this\nshould weaken and attenuate the gravitational wave since this electromagnetic radiation must be\ncreated at the expense of the gravitational wave \\cite{Jones15,Jones16}. This is similar \nto how a black hole is conjectured to lose mass as a result of Hawking radiation -- the \nHawking radiation comes at the expense of the mass of the black hole. \n\nOne can connect particle\/field production rate, $\\Gamma$, with a current, $j_\\mu$, as in equation\n\\eqref{jones-12} via the relationship \\cite{stahl,nikolic,frob}\n\\begin{equation}\n \\label{decay-1}\n \\frac{\\Gamma}{V} \\Delta T \\approx | j_\\mu | ~,\n\\end{equation}\nwhere $\\Delta T$ is some characteristic time for the system and $V$ is the volume. Using $|j_\\mu| = \\frac{h_+ ^4}{2 V}$\nfrom \\eqref{jones-12} and taking $\\Delta T \\approx \\frac{1}{\\omega}$ (where $\\omega$ is the frequency of the gravitational\nwave) as the characteristic time we arrive at\n\\begin{equation}\n \\label{decay-2}\n \\Gamma \\approx \\frac{\\omega h_+ ^4}{2}~.\n\\end{equation}\nIf we denote the number of gravitons in the volume $V$ as, $N_g$ one can write out a rate of change of $N_g$ as\n\\begin{equation}\n \\label{decay-3}\n \\frac{d N_g}{dt} = - \\Gamma N_g \\to c \\frac{d N_g}{dz} = - \\Gamma N_g ~.\n\\end{equation}\nIn the last step we have replaced $dt$ by $dz\/c$ since we want the decay as a function of distance rather than \ntime. Taking the number of gravitons to be proportional to the amplitude squared \\footnote{This is similar to QED where\nthe number of photons is proportional to the square of the vector potential -- $N_\\gamma \\propto A_\\mu A^\\mu$}\n({\\it i.e.} $N_g \\propto h_+ ^2$) and using the expression of $\\Gamma$ from \\eqref{decay-2} we arrive at an\nequation for how the amplitude, $h_+$, varies with distance, $z$,\n\\begin{equation}\n \\label{decay-4}\nc \\frac{d {h_+ ^2}}{dz} = - \\frac{\\omega h_+ ^4}{2} (h_+ ^2) \\to \\frac{dh_+}{dz} = - \\frac{k h_+ ^5}{4}~.\n\\end{equation}\nOne can solve \\eqref{decay-4} for $h_+ (z)$ and find\n\\begin{equation}\n \\label{decay-5}\nh_+ (z) = (k z + K_0) ^{-1\/4}~,\n\\end{equation}\nwhere $K_0 = (h_+ ^{(0)} ) ^{-4}$ and $h_+ ^{(0)}$ is the reference amplitude at $z=0$. From equation \n\\eqref{decay-5} one sees that the fall off of $h_+$ as a function of distance, $z$, is very slow. This is expected,\nsince this slow fall off tells us that the transformation of gravitational radiation (gravitons) into electromagnetic\nradiation (photons) is a very weak process. If a gravitational wave background did not produce electromagnetic radiation\nthen $h_+ (z)$ should remain constant (recall that in this plane wave approximation we do not take into account\nthe $\\frac{1}{r}$ fall of a real three dimensional wave). \n\nTo get an idea of how weak the effect is we can calculate the ``half-distance\", $\\Lambda$, which we define \nas the distance for the amplitude of the plane wave to fall to half of its initial value, $h_+ ^{(0)}$. \nTaking $\\omega \\approx 300$ Hz, the approximate frequency of the signal from the first LIGO detection\n\\cite{LIGO}, gives $k = \\frac{\\omega}{c} = 10 ^{-6}$ m. Setting $h_+ (\\Lambda) = \\frac{1}{2} h_+ ^{(0)}$ \ngives the ``half-distance\" as\n\\begin{equation}\n \\label{decay-6}\n\\Lambda = \\frac{15}{k (h_+ ^{(0)} )^4} = \\frac{1.5 \\times 10^7}{ (h_+ ^{(0)} )^4} \\rm{m}~,\n\\end{equation}\nIf one sets the ``half-distance\", $\\Lambda$, equal to the size of \nthe observable Universe -- $\\Lambda = 10^{27}$ m -- then \nequation \\eqref{decay-6} gives an amplitude of $h_+ ^{(0)} \n\\approx 10^{-5}$, which is a very large amplitude. Equation\n\\eqref{decay-6} implies that as $h_+ ^{(0)}$ gets larger the \nhalf-distance, $\\Lambda$, gets smaller. Taking $h_+ ^{(0)} \n\\approx 10^{-3}$ would give $\\Lambda \\approx 10 ^{19}$ m, which \nis 100 times smaller than the size of the Milky Way. We also want to stress again that\nthe above estimates based on equation \\eqref{decay-6} are for planes waves and do not take into account the\n$\\frac{1}{r}$ fall off of a more realistic three dimensional wave, but regardless they\nindicate that the decay\/attenuation of the gravitational wave due to vacuum production of\nelectromagnetic radiation is a small effect, except perhaps close to the source where \none might have amplitudes like $h_+ ^{(0)} \\approx 10^{-3}$ or larger. \n\n\\subsection{Detection of electromagnetic radiation produced by gravitational waves}\n\nNext we look at the possibility of directly detecting the electromagnetic radiation that is\nproduced from the vacuum by the gravitational wave. Looking at \\eqref{jones-9} one can \nsee that the counterpart electromagnetic radiation production would have twice the\nfrequency of the gravitational wave. From \\eqref{jones-9} one can see there are also components that\nare at four times the frequency of the gravitational wave, but they are down by an extra factor of\n$h_+ ^2$ compared to the component at twice the frequency. \n\nThe first problem that occurs in potentially detecting the counterpart electromagnetic radiation is that\nit will have a very low frequency (VLF) and thus a very large wavelength. For example the discovery paper \n\\cite{LIGO} reported frequencies for the gravitational wave on the order of $100~\\rm{Hz}$. Even doubling this,\nthe electromagnetic radiation would have a frequency and wavelength of $200~\\rm{Hz}$ and $1.5 \\times 10 ^6 \\rm{m}$\nrespectively. \n\nA second problem with detecting the VLF counterpart electromagnetic radiation is that there are various cutoff\nfrequencies due to the plasma in space. In the illustration and table below we give the plasma cutoffs for a detector\nlocated in one of three locations: on the Earth, in space but near Earth's orbit, and finally in interstellar space\nas shown in Fig. \\eqref{CutoffsFig}. \n\n\\begin{figure}[H]\n\\centering\n\\includegraphics[width=130mm]{Interstellar.jpeg}\n\\begin{tabular}{|c|c|}\n\\hline ~~ Region ~~ & \\ ~~ Observable frequency range ~~ \n\\\\ \n\\hline On Earth & \\ $> 10 $ MHz \n\\\\ \n\\hline Interplanetary space (near Earth's orbit) & \\ $>$ $20~\\rm{kHz} - 30~\\rm{kHz} $ \n\\\\\n\\hline Interstellar space (outside the heliosphere) & $ > 2 $ kHz \n\\\\ \n\\hline\n\\end{tabular}\n\\caption{Log scale illustration of the regions of space within our solar system and galaxy \\cite{UCSB} \\label{MediumFig} and the associated plasma frequency cutoff in each region \\cite{Jones17,Lacki10}.}\n\\label{CutoffsFig}\n\\end{figure}\n\nFor a detector on Earth one can detect electromagnetic radiation with a frequency of $10~\\rm{MHz}$ or larger. \nAssuming this electromagnetic wave came from production by a gravitational wave, this would require a\ngravitational wave frequency of $5~\\rm{MHz}$. Since non-exotic gravitational waves sources are expected to have \nfrequencies that are orders of magnitude lower than this, this rules out an Earth based detector for such VLF electromagnetic \nradiation. \n\nFor a detector at the outer edge of the Solar System, near interstellar space, one has a plasma cutoff of \n$2~\\rm{kHz}$ which would require a gravitational wave frequency of $1~\\rm{kHz}$. The fundamental ({\\it i.e.} \nf-modes) of neutron star quakes have frequencies in the range $1 - 3~\\rm{kHz}$ \\cite{Kokkotas97} and thus upon \ndoubling this frequency could produce counterpart VLF electromagnetic radiation above the $2~\\rm{kHz}$ cutoff. \nIn fact \nthe Voyager probes did detect such VLF electromagnetic radiation \\cite{Kurth84} in the range of $2-3 ~\\rm{kHz}$\nshowing that detection of such VLF electromagnetic radiation is possible \\footnote{The source of the Voyager \ndetection of this VLF electromagnetic radiation was a mystery for some time, but the source of this\nVLF radiation is now thought to be due to the interaction of the solar wind with ions in the outer heliosphere \nduring times of intense solar activity \\cite{Kurth03, Webber09}.} Thus\none could detect counterpart VLF electromagnetic radiation from\nthe $f$-modes of neutron star quakes if the neutron star were\nclose enough. However, it requires sending a probes to the edge of\nthe Solar System or beyond. \n\nGiven that sending probes to the edge of the Solar System is costly both in terms of time and money \none could ask if there are gravitational wave sources which would give rise to VLF electromagnetic \nradiation, which could be detected near Earth's orbit. From Fig. \\eqref{CutoffsFig} one can see that for\ndetection one needs the electromagnetic radiation to have a frequency greater than $20 - 30$ kHz. This implies that\nthe gravitational wave vacuum producing the electromagnetic radiation would need to have a frequency in the range\n$10-15$ kHz. Theoretical models show that of gravitational wave of this kHz frequency range could be \nproduced from neutron star oscillations \\cite{Kokkotas97,Kokkotas01}. There are different types of \nneutron star oscillation modes. Three\nof the most common are: (i) {\\it p}-modes or ``pressure modes\" \\cite{Kokkotas97} with a frequency \nrange $5 - 9~ \\rm{kHz}$; (ii) {\\it f}-modes or ``fundamental modes\" \\cite{Kokkotas97} with a\nfrequency range of $1 - 3~\\rm{kHz}$; (iii) {\\it w}-modes or ``space-time modes\" \\cite{Anderson96} with \na frequency range of $8 - 16~\\rm{kHz}$ or greater. From this list of oscillation modes \nthe {\\it w}-modes have the most promising frequency range in terms of detection of the \ncounterpart VLF electromagnetic radiation. \n\nWe now want to give a rough estimate of the strength of the electromagnetic flux produced by \ngravitational waves coming from a {\\it w}-mode oscillation of a neutron star quake. First from\n\\cite{abadie} the gravitational wave amplitude at Earth for {\\it f}-mode generated gravitational waves \nfrom a neutron star that is $1$ kpc or $3\\times 10^{19}$ m distant from Earth would be of order\n$h_+ \\sim 10^{-23}$. The associated {\\it w}-modes gravitational wave amplitude is expected to be\ndown from this by at least one order of magnitude $h_+ \\sim 10^{-24}$. Using this {\\it w}-mode \namplitude and the $1\/r$ fall off relation \n\\begin{equation}\n\\label{detect-1}\n h_+ = 10^{-24} \\frac{1 ~ {\\rm kpc}}{r}\n\\end{equation}\nwe can determine the amplitude at some point close to the source. For this we take \n$r^{(0)}=3 \\times 10^4$ m \\cite{Jones17} -- this is far enough from the neutron star that the plane wave \napproximation we have used throughout should apply, at least roughly. Using \\eqref{detect-1} and \n$r^{(0)}=3 \\times 10^4$ m we find that the {\\it w}-mode amplitude near the source would be of \norder $h_+^{(0)} \\sim 10^{-9}$. Using this amplitude, a frequency of 10 kHz in the {\\it w}-mode\nrange $8-16$ kHz, we can determine the flux of the gravitational wave near the source \n({\\it i.e.} at $r^{(0)}=3 \\times 10^4$ m) using the formula \\cite{Schutz96}\n\\begin{equation}\n \\label{detect-2}\n F^{(0)} _{gw} = \\frac{c^3}{16 \\pi G} |{\\dot \\epsilon}|^2 = \n \\left( 3 \\times 10 ^{35} \\frac{W s^2}{m^2} \\right) h_+^2 f^2 =\n 3 \\times 10^{25} \\frac{W}{m^2} ~,\n\\end{equation}\nwhere $\\epsilon = h_+ e^{iku}$ as defined below equation \\eqref{GWmetric}. We can now calculate\nthe flux of the counterpart VLF electromagnetic radiation using \\eqref{detect-2} in\n\\eqref{emgwRatioB} to give \n\\begin{equation}\n \\label{detect-3}\n F^{(0)} _{em} = 2 \\times (10^{-9})^2 \\times (3 \\times 10^{25} \\frac{W}{m^2})\n = 6.0 \\times 10^7 \\frac{W}{m^2}~.\n\\end{equation}\nFrom \\eqref{detect-2} and \\eqref{detect-3} we find that $F^{(0)} _{gw} \\gg F^{(0)} _{em}$\nwhich is expected -- the electromagnetic radiation produced is much smaller than the\ngravitational wave which produced it. However, $F^{(0)} _{em}$ is nevertheless large enough\nthat even taking into account the $1\/r$ fall off one could potentially detect this\nelectromagnetic radiation at the location of the Earth's orbit. Taking\nthe flux $F^{(0)} _{em}$ from \\eqref{detect-3} one can determine the flux at the \nlocation of the Earth assuming that the neutron star is 1 kpc away.\n\\begin{equation}\n \\label{detect-4}\n F_{em}= F^{(0)} _{em} \\left( \\frac{r^{(0)}}{1 kpc}\\right) \\sim \n 6.0 \\times 10 ^{-23} \\frac{W}{m^2} ~,\n\\end{equation}\nwhere $r^{(0)} = 3 \\times 10 ^4$ m from before. A flux of the magnitude in\n\\eqref{detect-4} could be detected \\cite{Jones17} and given the frequency range\nof the {\\it w}-modes the associated VLF electromagnetic radiation would \nhave a\nfrequency that is above the plasma cutoff at the location of Earth's\norbit as given in Fig. \\eqref{CutoffsFig}. Thus the proposal to detect such\nthe hypothesized co-produced VLF electromagnetic radiation, coming {\\it w}-modes of\nneutron star quakes, would be to place a satellite capable of detecting such\nradiation near earth's orbit \\cite{Jones17, Gretarsson18}. The old Explorer 49\nsatellite was capable of detecting such VLF electromagnetic radiation. The Explorer\n49 satellite was a Lunar orbiting satellite which was periodically occulted\nby the Moon in order to block out interference from Solar emissions. The\noccultation allows one to detect weak signals like \\eqref{detect-4} above\nthe interference from the Sun. \n \n\\section{Conclusion and Future prospects}\n\nFor over 100 years there was nothing to support Faraday's expectation of a relationship between \ngravity and electromagnetism. However, in the past 50 years we have seen the development of considerable \ntheoretical support for this relationship and in particular the relation between gravitational and \nelectromagnetic radiation. The earliest work was by Gertsenshtein \\cite{Gertsenshtein60} \ndemonstrating that electromagnetic radiation can produce gravitational radiation. This \nwas followed in 1975 by Skobelev \\cite{Skobelev75} who calculated the small but non-zero \namplitudes for the $graviton + graviton \\to photon + photon$ processes. Beginning around the same time as reference \\cite{Skobelev75}, there was work that \nexamined the production of electromagnetic fields\/multiple photons from a gravitational background \\cite{unruh-det,dewitt-det,hawking,unruh,Jones15}. Rrcently we have worked on calculations of the production of electromagnetic radiation \nby gravitational waves propagating in vacuum \\cite{Jones16,Jones17,Jones18}. Perhaps \nin the next 50 years we will see empirical evidence of the relation between \ngravitational and electromagnetic radiation by either direct or indirect observation.\n\nThe most promising possibility for direct observation is the detection of VLF counterpart \nproduction by gravitational waves from neutron star quakes \\cite{Jones17}. This would \nonly be possible using space based detectors such as the Voyager missions \n\\cite{Kurth84,Kurth03,Webber09} or with a lunar occulted detector similar to the \nExplorer 49 mission \\cite{Gretarsson18}. However, detection of counterpart production \nlocally would be limited to the highest frequencies of the counterpart production \nfrom neutron star gravitational waves. Detectors in the outer heliosphere would be \nmuch more effective and rather remarkably the Voyager space craft are still making \nobservations in the $2-4~\\rm{kHz}$ range of expected counterpart production \\cite{Gurnett15}.\n\nEven without direct observation it is possible that the counterpart production of electromagnetic radiation would have important applications in astrophysical processes. One intriguing possibility is in the energetics of core collapse supernovae. The prompt production of gravitational waves from the core collapse would produce gravitational waves with quadruple amplitudes on the order of $1~\\rm{m}$ and strain amplitude of something like $10^{-5} - 10^{-4}$ in the star layers just outside the core. These strain amplitudes have the potential of producing counterpart radiation of sufficient energy to contribute to the energetics of the supernovae. Previous work on fully general relativistic magnetohydrodynamics \\cite{Font07} (MHD) have assumed the ``ideal\" MHD condition. This assumption suppresses any potential production of electromagnetic radiation from the strong gravitational wave background. More recent work \\cite{Obergaulinger14,Just18,Obergaulinger18} on the effects of magnetic fields and rotation on the energetics of core collapse supernovae have not been fully general relativistic and again could not include the energy from production of electromagnetic radiation by the outgoing gravitational wave. Fully general relativistic MHD simulations have been implemented \\cite{Lehner12_86} for collapsing hyper-massive neutron stars but not for the study of core collapse supernovae. It is possible that the energy associated with electromagnetic production by gravitational waves outside the iron core could contribute importantly to the supernova, but only fully general relativistic MHD simulations would account for this phenomena in the processes of core collapse and explosion.\n\nSince the production of electromagnetic radiation by gravitational waves is so fundamental it is likely that further study of this phenomena could illuminate our understanding of nature. One recent example of the potential importance of production of photons in a gravitational wave background is the investigation of graviton-photon oscillations in alternatives to general relativity \\cite{Cembranos18, ejlli}. This investigation did not directly study counterpart production and potential general relativity violations but does describe the significance of this phenomena in investigating theories of gravity. Counterpart production by gravitational waves \\cite{Ricciardone17} could also be important in studies of cosmology. Following the Planck epoch the Standard Model fields were still massless\nfor some time. It would be interesting to consider the production of the massless Standard Model particles by primordial gravitational waves during the grand unification epoch and prior to the Standard Model particles acquiring mass via the Higgs mechanism.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{To Do list}\n\n\\section{Introduction}\n\\label{sec:introduction}\n\nIn the study of CP violation by CKM unitary triangle analysis,\nhadron matrix elements of four-fermion operators,\nsuch as $B_{\\rm K}$, play a vital role.\nAccurate calculations of this quantity from first principles\nare an important task for the lattice QCD community.\nIn such calculations, having chiral symmetry\nis crucial to avoid an operator mixing problem\nwhich causes uncontrollable systematic errors.\nAlthough lattice chiral fermions\n \\cite{Kaplan:1992bt,Shamir:1993zy,Neuberger:1997fp}\nare a clean formulation,\nthey require enormous computing power to perform dynamical simulations.\nIn comparison, ordinary fermion formulations, like Wilson type fermions\nand starggared fermions are relatively cheap.\nNowadays, however, thanks to the development of computer architecture\nand algorithms, dynamical simulations with\nlattice chiral fermions have become feasible\neven for three flavors \\cite{Noaki:2008gx}.\nIn particular,\nthe RBC\/UKQCD collaboration \\cite{Allton:2008pn} is currently using\ndomain-wall fermions (DWFs) to compute $B_{\\rm K}$.\nIn the course of their computation,\nthere are many sources of systematic errors which one has to control.\nAmong them, the non-perturbative renormalization (NPR)\ncould be serious.\nAt the moment, the collaboration has been using conventional schemes, such as,\nthe RI\/MOM scheme and its variants \\cite{Aoki:2007xm,Sturm:2009kb}.\nHowever, these schemes potentially contain ``large scale\nproblem\" which requires a quite large lattice volume.\nTo avoid such difficulties,\na new scheme was invented, known as the Schr\\\"odinger functional\n(SF) scheme \\cite{Luscher:1992an}.\nThis scheme provides a reliable way of estimating errors in the NPR.\nIf one wants to use this scheme for the renormalization\nof $B_K$ given by the RBC collaboration,\nfirst of all, one has to formulate DWF in the SF setup.\nThis is the purpose of this paper.\n\n\n\nWhile chiral fermions are useful\nfor computing the bare $B_{\\rm K}$\nto avoid the mixing problem, a formulation for such fermions\nin the SF setup was a non-trivial task\nbecause SF boundary conditions break chiral symmetry explicitly.\nWe will address this issue in the next section.\nHowever, Taniguchi \\cite{Taniguchi:2004gf} made the first attempt to formulate overlap fermions\nby using an orbifolding technique.\nSubsequently he provided a formulation for domain-wall fermions\nand then he and his collaborators \\cite{Nakamura:2008xz} calculated\na renormalized $B_{\\rm K}$ in quenched QCD.\nSint \\cite{Sint:2010eh} developed such\ntechniques by combining with a flavor twisting trick.\nHowever, these orbifolding formulations are constrained by\nthe requirement that the number of flavors be even.\nThus, apparently such formulations\nare incompatible with current trends toward dynamical three flavor simulations.\nTo overcome this difficulty, L\\\"uscher \\cite{Luscher:2006df} gave\na completely different approach\nrelying on a universality argument,\ndimensional power counting and symmetry considerations.\nSome perturbative calculations were performed in Ref.~\\cite{Takeda:2007ga}.\nA crucial property of this formulation is that\nthere is no restriction on the number of flavors.\nSince only overlap fermions were considered in Ref.~\\cite{Luscher:2006df},\nour main purpose here is to formulate\nthe other chiral fermions, namely, domain-wall fermions.\n\n\n\nThe rest of the paper is organized as follows.\nSection \\ref{sec:formulation} gives the formulation of domain-wall\nfermions in the SF setup, after a brief\nreview of the universality argument.\nWe present several pieces of numerical evidence\nin Section \\ref{sec:spectrum} and \\ref{sec:oneloop}\nto show that our formulation is working properly.\nWe also discuss the lattice artifacts\nfor the step scaling function in Section \\ref{sec:SSF}.\nIn the last section, we conclude by giving some remarks and outlook.\n\n\n\n\n\n\\section{Formulation}\n\\label{sec:formulation}\nIn the following, we assume that the reader\nis familiar with the SF in QCD \\cite{Luscher:1992an,Sint:1993un}.\nAfter giving a brief reminder of the universality argument,\nwe give a formulation for DWF\nand finally check the chiral symmetry breaking structure numerically.\n\n\\subsection{Universality argument}\n\nIn the massless continuum theory,\nthe Dirac operator $D$ satisfies the anti-commutation relation with\n$\\gamma_5$\n\\begin{equation}\n\\gamma_5D+D\\gamma_5=0.\n\\label{eqn:Dg5}\n\\end{equation}\nThe above is true even in the SF setup, although the boundary conditions,\n\\begin{eqnarray}\nP_+ \\psi(x)=0 &\\mbox{ at }& x_0=0,\n\\label{eqn:SFBC0}\n\\\\\nP_- \\psi(x)=0 &\\mbox{ at }& x_0=T,\n\\label{eqn:SFBCT}\n\\end{eqnarray}\nwith $P_\\pm=(1\\pm\\gamma_0)\/2$,\nbreak chiral symmetry explicitly.\nEq.(\\ref{eqn:Dg5}) means that the operator itself\ndoes not know about boundary conditions.\nIn the continuum theory, information such as boundary conditions\nis embedded in the Hilbert space.\nIn fact, the corresponding propagator,\nwhich is a solution of the inhomogeneous equation,\n\\begin{equation}\nDS(x,y)=\\delta(x-y),\n\\end{equation}\nfails to satisfy the anti-commutation relation.\nInstead, it follows\n\\begin{eqnarray}\n\\lefteqn{\\gamma_5S(x,y)+S(x,y)\\gamma_5=}\\nonumber\\\\\n&&\n\\int_{z_0=0}d^3{\\bf z} S(x,z)\\gamma_5 P_- S(z,y)\n+\n\\int_{z_0=T}d^3{\\bf z} S(x,z)\\gamma_5 P_+ S(z,y).\n\\label{eqn:Sg5}\n\\end{eqnarray}\nThis can be derived by using partial integration\non the SF manifold which has two boundaries\nat time slice $x_0=0$ and $T$.\nThe non-vanishing right-hand side in eq.(\\ref{eqn:Sg5})\nshows an explicit chiral symmetry breaking.\nSince such a breaking term is supported\nonly on the time boundaries,\nthe chiral symmetry is preserved in a bulk.\n\n\nIf someone naively tries to formulate\nchiral fermions on the lattice,\none may define an overlap operator, for example,\nwith the Wilson kernel in the SF setup \\cite{Sint:1993un}.\nHowever such an operator immediately satisfies\nthe Ginsberg-Wilson relation and thus cannot reproduce\neq.(\\ref{eqn:Sg5}) in the continuum limit.\nThis indicates that such naive formulation\ndoes not work and furthermore may\nbelong to another boundary universality class\nwhich is not what we want.\nIn this way, it is a non-trivial task to\nformulate chiral fermions in the SF setup.\n\n\nSome years ago, L\\\"uscher \\cite{Luscher:2006df} proposed a clever way\nto overcome this situation.\nFirst, consider the relation for the propagator\nin eq.(\\ref{eqn:Sg5}).\nThis indicates that the GW relation has to be modified\nby boundary effects.\nThus one has to find a modified overlap operator\nwhich breaks the GW relation near the time boundaries and correctly\nreproduces eq.(\\ref{eqn:Sg5}) in the continuum limit.\nActually, finding such a modified operator is not so hard.\nHowever, a new question naturally arising\nis how the SF boundary conditions emerge.\nFor the Wilson fermion case \\cite{Sint:1993un},\nbecause there is a transfer matrix,\nit is natural for fermion fields\nto follow the SF boundary conditions.\nHowever for chiral fermions,\nthere is no such transfer matrix\nwhich can be defined from nearest neighbor interaction\nin the time direction.\nTherefore it is not an easy task.\n\n\nL\\\"uscher \\cite{Luscher:2006df} gave another point of view\nto see how fields respect the boundary condition.\nIn the quantum field theory,\nthe correlation function can tell you\nwhat kinds of boundary conditions are imposed.\nAs an example, let us see\nhow the boundary conditions emerge for Wilson fermions\nwhose action is given by\n\\begin{eqnarray}\nS_{\\rm w}\n&=&\n\\sum_x\\bar\\psi(x)\nD_{\\rm w}(m)\n\\psi(x),\n\\\\\nD_{\\rm w}(m)\n&=&\n\\frac{1}{2}\n\\left[\n\\sum_\\mu (\\nabla_\\mu+\\nabla^{\\ast}_\\mu)\\gamma_\\mu\n-a\\sum_\\mu \\nabla_\\mu^{\\ast}\\nabla_\\mu\n\\right]+m,\n\\end{eqnarray}\nwhere $\\nabla_\\mu$ and $\\nabla_\\mu^{\\ast}$ are\nforward and backward covariant difference operators respectively,\n\\begin{eqnarray}\n\\nabla_\\mu\\psi(x)\n&=&\n\\frac{1}{a}\n\\left[\nU(x,\\mu)\\psi(x+a\\hat\\mu)-\\psi(x)\n\\right],\n\\\\\n\\nabla_\\mu^{\\ast}\\psi(x)\n&=&\n\\frac{1}{a}\n\\left[\n\\psi(x)-U(x-a\\hat\\mu,\\mu)^{-1}\\psi(x-a\\hat\\mu)\n\\right].\n\\end{eqnarray}\nIn the SF setup, the sum over $x$ in the action is a little bit subtle.\nWe assume that the dynamical fields are \n$\\psi(x)$ with $a\\le x_0 \\le T-a$ and\nthe fields $\\psi(x)$ with $x_0 \\le 0$ and $T\\le x_0$ are set to zero.\nFor this setup,\nthe propagator may be defined by\n\\begin{eqnarray}\n\\langle\\eta(x)\\bar\\psi(y)\\rangle&=&a^{-4}\\delta_{x,y},\n\\label{eqn:etapsi}\n\\\\\n\\eta(x)&=&\\frac{\\delta S_{\\rm w}}{\\delta\\bar\\psi(x)}.\n\\label{eqn:etaS}\n\\end{eqnarray}\nFor $2a \\le x_0 \\le T-2a$, eq.(\\ref{eqn:etaS}) turns out to be\n\\begin{equation}\n\\eta(x)=D_{\\rm w}(m)\\psi(x).\n\\end{equation}\nOn the other hand, at $x_0=a$, we obtain\n\\begin{eqnarray}\n\\eta(x)&=&\n\\frac{1}{a}P_+\\psi(x)-\\nabla_0P_-\\psi(x)\n\\nonumber\\\\\n&+&\n\\frac{1}{2}\n\\left[\n \\sum_k (\\nabla_k+\\nabla^{\\ast}_k)\\gamma_k\n-a\\sum_k \\nabla_k^{\\ast}\\nabla_k\n\\right]\n\\psi(x)\n+m\\psi(x).\n\\label{eqn:eta}\n\\end{eqnarray}\nBy substituting eq.(\\ref{eqn:eta}) into eq.(\\ref{eqn:etapsi}) with $x\\neq y$\nwe obtain\n\\begin{equation}\n\\frac{1}{a}P_+\\langle\\psi(x)\\bar\\psi(y)\\rangle|_{x_0=a}\n-\n\\nabla_0P_-\\langle\\psi(x)\\bar\\psi(y)\\rangle|_{x_0=a}\n+...\n=0.\n\\end{equation}\nIn the continuum limit, the first term is dominant\n\\begin{equation}\n\\frac{1}{a}P_+\\langle\\psi(x)\\bar\\psi(y)\\rangle|_{x_0=0}=0.\n\\end{equation}\nThis shows that in the naive continuum limit,\nthe Dirichlet type boundary condition ($P_+\\psi|_{x_0=0}=0$)\nis stable against the Neumann one ($\\nabla_0P_-\\psi|_{x_0=0}=0$), and in the end\nthe SF boundary conditions in eq.(\\ref{eqn:SFBC0}) emerge.\nIt is plausible that similar things happen also for the chiral fermions case,\nas long as the locality and symmetry\nare kept in a proper way,\nalthough we expect that the coefficient of the lowest dimensional operators\n($\\frac{1}{a}P_+\\psi$)\nmay be different from the above case,\nand more higher dimensional terms may appear in eq.(\\ref{eqn:eta}).\nThe important point here is that\ncontinuum SF boundary conditions emerge dynamically\nin the continuum limit of the correlation function.\nThis boundary condition is natural\nand automatically guaranteed to emerge\nfrom the dimensional order counting argument.\nTherefore, when we construct chiral fermions in the SF,\nwe only have to prepare a modified operator\nby introducing an additional term\nwhich breaks the chiral symmetry near the time boundaries.\nOnce this is fulfilled, then\nsuch an operator automatically\nturns out to be the desired one in the continuum limit without fine tuning.\nA final important note is that the form of the boundary term is irrelevant\nas long as it will go into a preferred boundary universality class.\nTherefore, there is a large amount of freedom when choosing boundary terms\nand one can use this freedom for practical purposes.\n\n\n\nFollowing these guiding principles,\nL\\\"uscher \\cite{Luscher:2006df} proposed the operator:\n\\begin{eqnarray}\n\\bar{a}D_{\\rm N}&=&1-\\frac{1}{2}(U+\\tilde U),\n\\\\\nU&=&A(A^{\\dag}A+caP)^{-1\/2},\\hspace{7mm}\\tilde U = \\gamma_5 U^{\\dag} \\gamma_5,\n\\\\\nA&=&1+s-aD_{\\rm w}(0),\\hspace{7mm}\\bar{a}=a\/(1+s),\n\\label{eqn:overlap}\n\\end{eqnarray}\nwith the parameter in the range $|s|<1$.\n$D_{\\rm w}(0)$ is the massless Wilson operator in the SF.\nThe key point here is the presence of the $P$\nterm in the inverse square root which is given by\n\\begin{equation}\naP(x,y)\n=\n\\delta_{{\\bf x},{\\bf y}}\\delta_{x_0,y_0}(\\delta_{x_0,a}P_-+\\delta_{x_0,T-a}P_+).\n\\end{equation}\nNote that this term is supported near the time boundaries\nand thus called a boundary operator.\nThe presence of this term breaks the GW relation explicitly\nand the breaking is given by\n\\begin{eqnarray}\n\\Delta_{\\rm B}=\\gamma_5D_{\\rm N}+D_{\\rm N}\\gamma_5-\\bar{a}D_{\\rm N}\\gamma_5D_{\\rm N}.\n\\end{eqnarray}\nIt was shown in ref.~\\cite{Luscher:2006df} that this term is local and\nsupported in the vicinity of the boundaries up to the\nexponentially small tails.\n\n\nAlthough this operator breaks chiral symmetry explicitly,\nother symmetries (the discrete rotational symmetries, $C$, $P$ and $T$,\nflavor symmetry and so on)\nhave to be maintained\nsince the boundary conditions in eq.(\\ref{eqn:SFBC0},\\ref{eqn:SFBCT}) are\ninvariant under these symmetries.\nIn addition, this operator has $\\gamma_5$-Hermiticity.\nIn this way, the universality formulation\ncan avoid breaking important symmetries, such as the flavor symmetry.\nThis is a distinctive feature of this formulation\ncompared with the orbifolding technique,\nwhere flavor symmetries cannot be maintained\nor, there is a constraint on the number of flavors.\n\n\n\nBefore leaving this subsection,\nlet us summarized the guiding principles\nof formulating chiral fermions in the SF setup.\nWhat we learned from this construction\nis that, for an original chiral fermion operator,\none has to introduce an additional term\nto break the chiral symmetry and\nthen demand that\nsuch breaking only appears near the time boundaries.\nFurthermore, one must\nmaintain important symmetries as well as $\\gamma_5$-Hermiticity.\nOnce these conditions are fulfilled,\nit is automatically guaranteed that the such a lattice operator\nwill correctly reproduce the continuum results\naccording to the universality argument.\n\n\n\\subsection{Formulation of domain-wall fermions }\nLet us apply the guiding principles given in the previous subsection\nto domain-wall fermions.\nWe propose a massless\\footnote{The mass term can be introduced\nin the usual way, namely\n$a^4m_{\\rm f}\\sum_{{\\bf x}}\\sum_{x_0=a}^{T-a}[\\bar\\psi(x,1)P_R\\psi(x,L_s)+\\bar\\psi(x,L_s)P_L\\psi(x,1)]$.}\ndomain-wall fermion action\n\\begin{equation}\nS=\na^4\\sum_{x,x^\\prime}\\sum^{L_s}_{s,s^\\prime=1}\n\\bar\\psi(x,s)(D_{\\rm DWF})_{xs,x^\\prime s^\\prime}\\psi(x^\\prime,s^\\prime),\n\\end{equation}\nwhere a massless operator with $L_s=6$\nfor example\\footnote{We restrict ourselves to an even number of $L_s$,\nwhich is the case usually implemented.}\nin four dimensional block form is given by\n\\begin{equation}\naD_{\\rm DWF}=\n\\left[\n\\begin{array}{cccccc}\na\\tilde{D}_{\\rm w}&-P_L&0&0&0&cB\\\\\n-P_R&a\\tilde{D}_{\\rm w}&-P_L&0&cB&0\\\\\n0&-P_R&a\\tilde{D}_{\\rm w}&-P_L+cB&0&0\\\\\n0&0&-P_R-cB&a\\tilde{D}_{\\rm w}&-P_L&0\\\\\n0&-cB&0&-P_R&a\\tilde{D}_{\\rm w}&-P_L\\\\\n-cB&0&0&0&-P_R&a\\tilde{D}_{\\rm w}\\\\\n\\end{array}\n\\right],\n\\label{eqn:DWF}\n\\end{equation}\nwith the chiral projections,\n\\begin{equation}\nP_{R\/L}=(1\\pm\\gamma_5)\/2.\n\\end{equation}\nWe also assume that the dynamical fields\nare $\\psi(x,s)$ with $a\\le x_0 \\le T-a$.\nThe block elements in eq.(\\ref{eqn:DWF}) are four dimensional operators\nand $a\\tildeD_{\\rm w}$ is given by\n\\begin{equation}\na\\tilde{D}_{\\rm w}=aD_{\\rm w}(-m_5)+1.\n\\end{equation}\nThe domain-wall height parameter usually takes a value in a range $00$, $v_{j}$, $z_{j,0}$, and $\\phi_{j,0}$, $j=1,\\,\\ldots,\\,N$,\nwe have that\n\\[\n\\psi(z,\\,t)=\\sum_{j=1}^{N}u_{j}(z,\\,t)\\,,\n\\]\nwhere the $u_{j}$'s are the solutions of the system of $N$ equations\n\\[\n\\sum_{k=1}^{N}\\,\\frac{1\/\\gamma_{j}+\\gamma_{k}^{*}}{\\lambda_{j}+\\lambda_{k}^{*}}u_{k}=1\\,,\\quad j=1,\\,\\ldots,\\,N,\n\\]\nwhere\n\\[\n\\lambda_{j}=A_{j}\/2+i v_{j}\n\\] and\n\\[\n\\gamma_{j}=\\exp\\left[\\lambda_{j}(z-z_{j,0})+i\\lambda_{j}^{2}t\/2+i\\phi_{j,0}\\right]\\,.\n\\]\nHere the parameters $z_{j,0}$, and $\\phi_{j,0}$ are \\emph{almost but not quite}\nthe position and phase, at $t=0$, of the $j$th soliton when it is spatially\nseparated from the others (see below), but the first two are precisely its norm\nand velocity, as we now explain. The norm of $\\psi$ is $\\sum_{j=1}^{N} A_{j}$;\nas $t\\to \\pm \\infty$, if the velocities $v_{j}$ are all distinct, $\\psi$\nbecomes a sum of $N$ 1-soliton solution, of norms $A_{j}$, traveling at\nvelocities $v_{j}$. More precisely, in the limit as the $j$th soliton becomes\nmore and more spatially separated from the others, its form converges to\n\\[\n\\frac{A_{j}}{2}\\sech\n\\left[\\frac{A_{j}}{2}(z-z_{j})+q_{j}\\right]\\exp[i(\\phi_{j}+\\Psi_{j})]\\,,\n\\]\nwhere\n\\begin{align*}\nz_{j}&=z_{j,0}+v_{j}t\\,,\n\\\\\n\\phi_{j}&=v_{j}(z-z_{j})+\\frac{1}{2}\\left(A_{j}^{2}\/4+v_{j}^{2}\\right)t+\\phi_{j,0}\\,,\n\\end{align*}\nand the real numbers $q_{j}$ and $\\Psi_{j}$ capture the interaction with the\nother solitons. They are given as\n\\[\nq_{j}+i\\Psi_{j}=\\sum_{\\substack{k=1 \\\\\nk\\neq j}}^{N}\\sign (z_{k}-z_{j})\\ln\n\\frac{A_{j}+A_{k}+2i(v_{j}-v_{k})}{A_{j}-A_{k}+2i(v_{j}-v_{k})}\\,,\n\\]\nwhere $\\sign{z}$ is -1, 0, or +1 if $z<0$, $z=0$, or $z>0$, respectively.\n\nNow we see that the parameters $z_{j,0}$, and $\\phi_{j,0}$ \\emph{differ},\nthrough $q_{j}$ and $\\Psi_{j}$, respectively, from the actual position and\nphase at $t=0$ of the $j$th soliton when it is spatially separated from the\nothers. But the actual position and phase at $t=0$ is easily characterized:\nsince the displacements $q_{j}$ depend only on the ordering of the spatial\npositions of the solitons (and not on the magnitudes of the relative\ndistances), it follows that if one wants isolated solitons to sit at\n$\\bar{z}_{0,j}$ at $t=0$, one may proceed as follows: first set each $z_{j,0}$\nto $\\bar{z}_{0,j}$, and compute the $q_{j}$'s. Then set\n$z_{j,0}=\\bar{z}_{0,j}+2q_{j}\/A_{j}$, and proceed to compute the $N$-soliton\nsolution; a similar correction may be applied for the initial phases.\n\nThe case of degenerate $\\lambda_{j}$, when two or more constituent solitons\nhave both the same norm and the same velocity, may be treated by taking the\nappropriate limit of the system of $N$ equations above. In this case one\nobtains solutions that are qualitatively different from those discussed thus\nfar. For example, in the two-soliton case, as $t\\to \\pm \\infty$, one finds \\cite{zakharov1972_118} that\nthe distance between the solitons increases proportionally to $\\ln (A^{2} t)$\n(in natural units). Since here the solitons separate on\ntheir own on the time scale of $1\/A^{2}$, the collision experiment should last\nshorter than that. On the other hand, the breather needs to start sufficiently\nfar from the barrier so that it begins in an approximately integrable regime,\nand it needs to be sufficiently slow so that the kinetic energy per particle is\nmuch less than the chemical potential. It turns out that these constraints are\nimpossible to satisfy simultaneously, and thus the degenerate case is not of\ninterest for us.\n\n\n\n\\label{conjecture_argument}\n\n\n\n\n\\ssection{Derivation of the exact expression for $d\\lambda\/dt$, Eq.~(5\\xspace) in the main text}\n\\label{derivation_of_DlambdaDt}\n\nWe are dealing with the 1D nonlinear Schr{\\\"o}dinger equation in Eq.~(1\\xspace) in the main text,\n\\begin{multline}\ni\\hbar\\frac{\\partial}{\\partial t}\\Psi(z,\\,t)=-\\frac{\\hbar^{2}}{2m}\\frac{\\partial^{2}}{\\partial z^{2}}\\Psi(z,\\,t)\n\\\\\n+g_{\\textrm{1D}}N_{\\textrm{a}}|\\Psi(z,\\,t)|^{2}\\Psi(z,\\,t)+\\tilde{V}(z,\\,t)\\Psi(z,\\,t)\\,,\n\\label{td_NLSE}\n\\end{multline}\nwhere $g_{\\textrm{1D}}<0$, \\emph{in the presence of} the external integrability-breaking potential $\\tilde{V}(z,\\,t)$ (note that here we will allow this external potential to explicitly depend on time). In order to facilitate comparison with Ref.~\\onlinecite{Kivshar1989_763}, which treats the same kind of problem, we will work in the units in which $\\hbar=1$, $m=1\/2$, and $|g_{\\textrm{1D}}|N_{\\textrm{a}}=2$. Thus the NLSE becomes\n\\begin{multline}\ni\\frac{\\partial}{\\partial t} \\psi(z,\\,t)\n=\n\\left[- \\frac{\\partial^2}{\\partial z^2} - 2 |\\psi(z,\\,t)|^2 \\right] \\psi(z,\\,t)\n\\\\\n + \\epsilon v_{ext}(z,t) \\psi(z,\\,t)\n\\label{td_NLSE_perturbed}\n\\,,\n\\end{multline}\nwhere the external potential in (\\ref{td_NLSE}) is factorized as\n\\begin{eqnarray*}\n&&\n\\tilde{V}(z,\\,t) \\equiv V_{0} v_{ext}(z,t)\n\\\\\n&&\n\\max_{z}[v_{ext}(z,\\,0)]=1\n\\,,\n\\end{eqnarray*}\nand the small parameter $\\epsilon$ is\n\\begin{eqnarray*}\n\\epsilon \\equiv \\frac{2 \\hbar^2 V_{0}}{(g_{\\textrm{1D}}N_{\\textrm{a}})^{2} m}\n\\,.\n\\end{eqnarray*}\n\nThe first Lax operator reads\n\\begin{eqnarray}\n\\hat{\\cal L}\n=\n\\left(\n\\begin{array}{cc}\n\\hat{L} & \\hat{M}\n\\\\\n-\\hat{M}^{\\dagger} & -\\hat{L}\n\\end{array}\n\\right)\n\\label{Lax}\n\\end{eqnarray}\nwith\n\\begin{eqnarray}\n&&\n\\hat{L} = -i \\frac{\\partial}{\\partial z}\n\\label{Lax_L}\n\\\\\n&&\n\\hat{M} = \\psi^{*}(z,\\,t)\n\\label{Lax_M}\n\\,\\,.\n\\end{eqnarray}\nFor each instance of time $t$, one can set up an eigenstate-eigenvalue problem, which is the central object of interest of this derivation:\n\\begin{eqnarray*}\n\\hat{\\cal L} | w \\!\\!\\succ = \\lambda | w \\!\\!\\succ\n\\,\\,,\n\\end{eqnarray*}\nwhere\n\\begin{eqnarray*}\n| w \\!\\!\\succ = \\left(\\begin{array}{c} u(z,\\,t) \\\\ v(z,\\,t) \\end{array}\\right)\n\\,.\n\\end{eqnarray*}\n\nTo convert expressions in this text to the ones appearing in Ref.~\\onlinecite{Kivshar1989_763}, one\nshould use the following replacement table:\n\\begin{center}\n\\begin{minipage}{.3\\textwidth}\n\\begin{eqnarray*}\n&&\nz \\to x\n\\\\\n&&\n\\psi(z,\\,t) \\to u(x,\\,t)\n\\\\\n&&\nu(z,\\,t) \\to \\psi^{(1)}(x,\\,t)\n\\\\\n&&\nv(z,\\,t) \\to \\psi^{(2)}(x,\\,t)\n\\\\\n&&\nv_{ext}(z,\\,t) \\psi(x,\\,t) \\to i (P[u])(x,\\,t)\\,.\n\\end{eqnarray*}\n$\\left.\\right.$ \\vspace{-.5\\baselineskip}\n\\end{minipage}\n\\end{center}\\vspace{\\baselineskip}\n\n\\ssubsection{Relevant functional analysis}\nFrom the fact that $\\hat{L}$ is Hermitian, $\\hat{L}^{\\dagger} = \\hat{L}$, it follows that the Lax operator (\\ref{Lax}) possesses the following property:\n\\begin{eqnarray*}\n\\hat{\\cal L}^{\\dagger} = \\hat{\\sigma}_{3} \\hat{\\cal L} \\hat{\\sigma}_{3}\n\\,.\n\\end{eqnarray*}\nThis property induces a particular Hermitian form $\\inner{\\cdot}{\\cdot}$, a pseudo-inner product:\n\\begin{align}\n\\inner{\\kket{w_{1}}}{\\kket{w_{2}}}&=\\prec \\!\\!\n w_{1} | w_{2}\n\\!\\!\\succ\n\\equiv\n\\langle u_{1} | u_{2} \\rangle - \\langle v_{1} | v_{2} \\rangle\n\\notag\n\\\\\n&=\n\\int \\! dz \\,\n\\{\n u_{1}^{*}(z) u_{2}(z) - v_{1}^{*}(z) v_{2}(z)\n\\}\n\\label{scalar_product}\n\\,\\,.\n\\end{align}\nThis Hermitian form lacks the property of being positive definite (i.e. lacks the property that, for all $| w \\rangle$, $\\langle w | w \\rangle \\ge 0$ and $\\langle w | w \\rangle = 0$ if and only if $| w \\rangle = | 0 \\rangle$). The rest of the inner product axioms, on the other hand, remain intact:\n\\begin{eqnarray*}\n&&\\prec \\!\\! w_{2} | w_{1} \\!\\!\\succ = \\prec \\!\\! w_{2} | w_{1} \\!\\!\\succ^{*}\n\\\\\n\\text{and}&&\n\\\\\n&&\\prec \\!\\! w_{1} | a w_{2} + b w_{3}\\!\\!\\succ = a\\! \\prec \\!\\! w_{1} | w_{2} \\!\\!\\succ + b\\! \\prec \\!\\! w_{1} | w_{3}\\!\\!\\succ\\,.\n\\end{eqnarray*}\nThe Lax operator $\\hat{\\cal L}$ from Eq.~(\\ref{Lax}) is symmetric with respect to this form:\n\\begin{eqnarray}\n\\inner{\\kket{w_{1}}}{ \\hat{\\cal L}\\kket{w_{2}}}=\\inner{ \\hat{\\cal L}\\kket{w_{1}}}{\\kket{w_{2}}}\n\\label{pseudo-Hermiticity}\n\\,\\,.\n\\end{eqnarray}\nThe property above justifies a standard notation\n$\n\\inner{\\kket{w_{1}}}{ \\hat{\\cal L}\\kket{w_{2}}}\n\\equiv\n\\prec \\!\\!\n w_{1} | \\hat{\\cal L} | w_{2}\n\\!\\!\\succ\n$\nthat we are going to employ below.\n\nThe pseudo-Hermiticity property (\\ref{pseudo-Hermiticity}) implies the following properties of the eigenstates of $\\hat{\\cal L}$: Let\n$\\hat{\\cal L} | w_{1} \\!\\!\\succ = \\lambda_{1} | w_{1} \\!\\!\\succ$,\n$\\hat{\\cal L} | w_{2} \\!\\!\\succ = \\lambda_{2} | w_{2} \\!\\!\\succ$, and\n$\\hat{\\cal L} | w \\!\\!\\succ = \\lambda | w \\!\\!\\succ$.\nThen\n\\begin{itemize}\n\\item[1.]\neigenvectors whose eigenvalues are not complex conjugates of each other are mutually orthogonal,\n\\begin{eqnarray*}\n&&\n\\lambda_{1}^{*} \\not= \\lambda_{2}^{} \\,\\, \\Rightarrow \\,\\,\\, \\prec \\!\\! w_{1} | w_{2} \\!\\!\\succ = 0\\,;\n\\end{eqnarray*}\n\\item[2.]\nnon-zero norm eigenstates of $\\hat{\\cal L}$ correspond to real eigenvalues\n(a corollary of the above):\n\\begin{eqnarray*}\n&&\n\\lambda^{*} \\not= \\lambda^{} \\,\\, \\Rightarrow \\,\\,\\, \\prec \\!\\! w | w \\!\\!\\succ = 0\\,.\n\\end{eqnarray*}\n\\end{itemize}\nNote that the eigenspectrum of $\\hat{\\cal L}$ is not necessarily complete. In cases when the Lax operator (\\ref{Lax})\nrepresents a linear stability analysis equation of a nonlinear PDE, the missing states are associated with the continuous\nsymmetries of the PDE that is broken by the solution in question \\cite{castin2009_317}. (For a ``flat'' condensate, $\\psi(z) = \\mbox{const}$,\nwe found one missing state; there could be more. There seem to be none for a single soliton.)\n\nThe operator (\\ref{Lax}) also possesses properties specific to a particular form of the matrix elements, Eqs.~(\\ref{Lax_L}) and (\\ref{Lax_M}):\n\\begin{eqnarray*}\n&&\n\\hat{L}^{*} = - \\hat{L}\n\\,\\,.\n\\end{eqnarray*}\nThis property implies that:\n\\begin{itemize}\n\\item[1.]\nreal eigenvalues $\\lambda$ are doubly degenerate. The corresponding eigenstates,\n\\begin{eqnarray*}\n&&\n| w \\!\\!\\succ \\stackrel{\\cdot}{=} \\left(\\begin{array}{c}u(z) \\\\ v(z)\\end{array}\\right)\n\\\\\n&&\n| \\tilde{w} \\!\\!\\succ \\stackrel{\\cdot}{=} \\left(\\begin{array}{c} \\tilde{u}(z) \\\\ \\tilde{v}(z)\\end{array}\\right)\n\\,\\,,\n\\end{eqnarray*}\nare related\nby\n\\begin{eqnarray*}\n&&\n\\tilde{u}(z) = - v^{*}(z)\n\\\\\n&&\n\\tilde{v}(z) = + u^{*}(z)\n\\,\\,.\n\\end{eqnarray*}\nHere,\n\\begin{eqnarray*}\n&&\n\\hat{\\cal L}| w \\!\\!\\succ \\stackrel{\\cdot}{=} \\lambda | w \\!\\!\\succ\n\\\\\n&&\n\\hat{\\cal L}| \\tilde{w} \\!\\!\\succ \\stackrel{\\cdot}{=} \\lambda | \\tilde{w} \\!\\!\\succ\n\\,.\n\\end{eqnarray*}\n\n\\item[2.]\nComplex eigenvalues $\\lambda$ come in complex conjugate pairs, $\\lambda_{+}$, $\\lambda_{-}$ such that $\\lambda_{-} = (\\lambda_{+})^{*}$. The corresponding eigenstates,\n\\begin{eqnarray*}\n&&\n| w_{+} \\!\\!\\succ \\stackrel{\\cdot}{=} \\left(\\begin{array}{c} u_{+}(z) \\\\ v_{+}(z)\\end{array}\\right)\n\\\\\n&&\n| w_{-} \\!\\!\\succ \\stackrel{\\cdot}{=} \\left(\\begin{array}{c} u_{-}(z) \\\\ v_{-}(z)\\end{array}\\right)\n\\,\\,,\n\\end{eqnarray*}\nare related\nby\n\\begin{eqnarray}\n&&\nu_{-}(z) = - (v_{+})^{*}(z)\n\\nonumber\n\\\\\n&&\nv_{-}(z) = + (u_{+})^{*}(z)\n\\label{plus-minus_relation}\n\\,\\,.\n\\end{eqnarray}\nHere,\n\\begin{eqnarray*}\n&&\n\\hat{\\cal L}| w_{+} \\!\\!\\succ \\stackrel{\\cdot}{=} \\lambda_{+} | w_{+} \\!\\!\\succ\n\\\\\n&&\n\\hat{\\cal L}| w_{-} \\!\\!\\succ \\stackrel{\\cdot}{=} \\lambda_{-} | w_{-} \\!\\!\\succ\n\\\\\n&&\n\\lambda_{-} = (\\lambda_{+})^{*}\n\\,.\n\\end{eqnarray*}\n\\end{itemize}\n\nWithin the context of the Inverse Scattering Transform, the wavefuction $\\psi(x,\\,t)$ in the parent NLSE, Eq.~(\\ref{td_NLSE}), is assumed to be localized in space, while the\neigenstates of the Lax operator $\\hat{\\cal L}$ of Eq.~(\\ref{Lax}) are required to be finite at $x=\\pm\\infty$.\nIn this case, the real eigenvalues of $\\hat{\\cal L}$ form a continuum spectrum, while the complex eigenvalues are discrete.\nFinally, in the parent NLSE, the complex eigenvalues correspond to\nthe solitonic part of the scattering data, while the real eigenvalues correspond to the thermal noise.\n\nFrom now on, we will assume that the eigenstates of $\\hat{\\cal L}$ with substantially complex eigenvalues (i.e. the ``discrete spectrum'', or ``bound states'') are normalized\nas\n\\begin{eqnarray}\n\\prec \\!\\! w_{-} | w_{+} \\!\\!\\succ = 1\\,.\n\\label{normalization}\n\\end{eqnarray}\n\nFor the case of a single soliton,\n\\begin{eqnarray*}\n&&\n\\psi(z) = -i \\, \\mbox{sech}(x)\n\\,,\n\\end{eqnarray*}\nthe corresponding eigenvalues and eigenstates are\n\\begin{eqnarray*}\n&&\n\\lambda_{+} = - \\frac{i}{2}\n\\\\\n&&\n| w_{+} \\!\\!\\succ \\stackrel{\\cdot}{=} \\frac{+i}{\n2\n} \\exp(x\/2) \\left(\\begin{array}{c} -1 + \\tanh(x) \\\\ \\mbox{sech}(x) \\end{array}\\right)\n\\end{eqnarray*}\nand\n\\begin{eqnarray*}\n&&\n\\lambda_{-} = + \\frac{i}{2}\n\\\\\n&&\n| w_{-} \\!\\!\\succ \\stackrel{\\cdot}{=} \\frac{-i}{\n2\n} \\exp(x\/2) \\left(\\begin{array}{c} -\\mbox{sech}(x) \\\\ -1 + \\tanh(x) \\end{array}\\right)\n\\,.\n\\end{eqnarray*}\n\n\\ssubsection{The Hellmann-Feynman theorem}\nLet $\\hat{\\cal L}$ depend on a parameter $\\xi$: $\\hat{\\cal L} = \\hat{\\cal L}(\\xi)$. Its discrete eigenvalues $\\lambda_{\\pm}$ and the corresponding eigenstates, $| w_{\\pm} \\!\\!\\succ$ then also depend on $\\xi$:\n$\\lambda_{\\pm} = \\lambda_{\\pm}(\\xi)$, and\n$| w_{\\pm} \\!\\!\\succ = | w_{\\pm}(\\xi) \\!\\!\\succ$.\n\nLet us express the eigenvalue $\\lambda_{+}$ as\n\\begin{eqnarray*}\n\\lambda_{+}(\\xi) = \\prec \\!\\! w_{-}(\\xi) | \\hat{\\cal L}(\\xi) | w_{+}(\\xi) \\!\\!\\succ\\,.\n\\end{eqnarray*}\nThen, the derivative of $\\lambda_{+}$ with respect to $\\xi$ becomes\n\\begin{multline*}\n\\frac{d}{d\\xi} \\lambda_{+}\n=\n\\inner{\\frac{d}{d\\xi}\\kket{w_{-}}}{\\hat{\\cal L}\\kket{w_{+}}}\n\\\\\n+\n\\inner{\\kket{w_{-}}}{\\left(\\frac{d}{d\\xi}\\hat{\\cal L}\\right)\\kket{w_{+}}}+\n\\inner{\\kket{w_{-}}}{\\hat{\\cal L}\\frac{d}{d\\xi}\\kket{w_{+}}}\n\\end{multline*}\nAs in the proof of the usual Hellmann-Feynman theorem, the sum of the first and the last term will turn out to be proportional to the derivative of the norm; and since the norm is one, the derivative is zero. Indeed, the first term gives\n\\begin{align*}\n\\inner{\\frac{d}{d\\xi}\\kket{w_{-}}}{\\hat{\\cal L}\\kket{w_{+}}}\n&\n=\n\\inner{\\frac{d}{d\\xi}\\kket{w_{-}}}{\\lambda_{+}\\kket{w_{+}}}\n\\\\\n&\n=\n\\lambda_{+}\\inner{\\frac{d}{d\\xi}\\kket{w_{-}}}{\\kket{w_{+}}}\\,.\n\\end{align*}\nSimilarly, the last term gives\n\\begin{align*}\n\\inner{\\kket{w_{-}}}{\\hat{\\cal L}\\frac{d}{d\\xi}\\kket{w_{+}}}\n&\n=\n\\inner{\\hat{\\cal L}\\kket{w_{-}}}{\\frac{d}{d\\xi}\\kket{w_{+}}}\n\\\\\n&\n=\n\\inner{\\lambda_{-}\\kket{w_{-}}}{\\frac{d}{d\\xi}\\kket{w_{+}}}\n\\\\\n&\n=\n(\\lambda_{-})^{*}\\inner{\\kket{w_{-}}}{\\frac{d}{d\\xi}\\kket{w_{+}}}\\,.\n\\end{align*}\nBut $(\\lambda_{-})^{*}= \\lambda_{+}$; thus, the sum of the two terms gives\n\\begin{multline*}\n\\lambda_{+}\\,\\left[\\inner{\\frac{d}{d\\xi}\\kket{w_{-}}}{\\kket{w_{+}}}+\\inner{\\kket{w_{-}}}{\\frac{d}{d\\xi}\\kket{w_{+}}}\\right]\n\\\\\n=\\lambda_{+}\\,\\frac{d}{d\\xi}\\inner{\\kket{w_{-}}}{\\kket{w_{+}}}=\\lambda_{+}\\,\\frac{d}{d\\xi}\\,1=0\\,.\n\\end{multline*}\nThus, we get the following generalization of the Hellmann-Feynman theorem:\n\\begin{equation}\n\\frac{d}{d\\xi} \\lambda_{+} = \\prec \\!\\! w_{-} | \\left(\\frac{d}{d\\xi} \\hat{\\cal L}\\right) | w_{+} \\!\\!\\succ\\,.\n\\label{HF}\n\\end{equation}\n\n\n\\ssubsection{The exact expression for $d\\lambda\/dt$ from the Hellmann-Feynman theorem}\nLet us set\n\\begin{eqnarray*}\n&&\n\\xi = t\n\\\\\n&&\n\\hat{\\cal L}(t)\n=\n\\left(\n\\begin{array}{cc}\n-i \\frac{\\partial}{\\partial z} & \\psi^{*}(z,\\,t)\n\\\\\n-\\psi^{}(z,\\,t) & +i \\frac{\\partial}{\\partial z}\n\\end{array}\n\\right)\n\\\\\n&&\n\\hat{\\cal L}(t) | w(t)\\!\\!\\succ = \\lambda(t) | w(t)\\!\\!\\succ\n\\\\\n&&\n\\frac{d}{dt}\\hat{\\cal L}(t) =\n\\\\\n&&\n\\left(\n\\begin{array}{cc}\n0 & +(F[\\psi] + \\epsilon P[\\psi])^{*}(z,\\,t)\n\\\\\n-(F[\\psi] + \\epsilon P[\\psi])^{}(z,\\,t) & 0\n\\end{array}\n\\right)\n\\\\\n&&\n| w(t) \\!\\!\\succ =\n\\left(\n\\begin{array}{cc}\nu(z,\\,t)\n\\\\\nv(z,\\,t)\n\\end{array}\n\\right)\n\\\\\n&&\n2 \\int_{-\\infty}^{+\\infty} u(z,\\,t) v(z,\\,t) = 1\n\\,,\n\\end{eqnarray*}\nwhere $F[\\psi](z,\\,t) = -i \\left[- \\frac{\\partial^2}{\\partial z^2} - 2 |\\psi(z,\\,t)|^2 \\right] \\psi(z,\\,t)$, and $P[\\psi](z,\\,t) = -i v_{ext}(z,\\,t)\\psi(z,\\,t)$.\nAccording to the Hellmann-Feynman theorem, Eq.~(\\ref{HF}), the time derivative of the Lax eigenvalue is\n\\begin{eqnarray*}\n\\frac{\\partial}{\\partial t}&& \\lambda\n=\n\\\\\n&&\n- \\int_{-\\infty}^{+\\infty} \\! dz \\,\n\\left\\{\n\tu_{+}^2(z,\\,t) \\epsilon P[\\psi](z,\\,t)\n\\right.\n\\\\\n&&\\hspace{10em}\\left.\n- v_{+}^2(z,\\,t) \\epsilon^{*} P^{*}[\\psi](z,\\,t)\n\\right\\}\n\\\\\n&&\n=\n(+i)\n\\int_{-\\infty}^{+\\infty} \\! dz \\,\n\\left\\{\n\t\\epsilon u^2(z,\\,t)\\psi(z,\\,t)\n\\right.\n\\\\\n&&\\hspace{10em}\\left.\n+ \\epsilon^{*} v^2(z,\\,t) \\psi^{*}(z,\\,t)\n\\right\\}v_{ext}(z,\\,t)\n\\,.\n\\end{eqnarray*}\nNotice that the contribution to $d\\lambda\/dt$ from $F[\\psi]$ disappears. Indeed this contribution\ndescribes the time derivative of the Lax eigenvalue in the time evolution according to the {\\it unperturbed} NLS; this derivative\nindeed vanishes as a consequence of integrability of the NLSE.\n\n\nA translation to the Kivshar-Malomed\nnotation system of Ref.~\\onlinecite{Kivshar1989_763} gives\n\\begin{multline*}\n\\frac{\\partial}{\\partial t} \\lambda_{n}\n=\n- \\frac{1}{2}\n\\frac{1}\n{\\int_{-\\infty}^{+\\infty} \\! dz \\, \\psi^{(1)}(x,\\,t) \\psi^{(2)}(x,\\,t) }\n\\\\\n\\times \\int_{-\\infty}^{+\\infty} \\! dz \\,\n\\left\\{\n\t(\\psi^{(1)})^2(x,\\,t,\\,\\lambda_{n}) \\epsilon P[\\psi](x,\\,t)\n\\right.\n \\\\\n\\left.\n - (\\psi^{(2)})^2(x,\\,t) \\epsilon^{*} P^{*}[\\psi](x,\\,t)\n\\right\\}\n\\,.\n\\end{multline*}\n\\mbox{}\\\\\n\\mbox{}\\\\\n\\mbox{}\\\\\n\\mbox{}\\\\\n\\mbox{}\\\\\n\\mbox{}\\\\\n\n\n\n\n\n\\newpage\n\n\\begin{widetext}\n\n\\begin{center}\n\\textbf{\\textsc{\\relsize{1}Extended Data}}\n\\end{center}\n\n\\vfill\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[width=0.9\\textwidth,keepaspectratio=true,draft=false]{Fig_4_01a_font}\n\\end{center}\n\\caption { \\label{Fig_4_01a}\n\\textbf{Integrable vs. nonintegrable case.} The initial state for the nonintegrable\ncase was prepared by time-propagating the breather at rest while the\nnonlinearity was slowly ramped from $|\\psi|^{2}$ to $|\\psi|^{2p}$ with $p=3\/2$.\nThe result was Galilei-boosted and scattered off of a Gaussian barrier whose\nwidth (for numerical reasons) was twice the reference value. All other\nparameters were at their reference values, including the number of particles. Also plotted is the integrable case, all of whose parameters are at their\nreference values. In particular, the number of particles is four times smaller than that for Fig.~3 in the main text, resulting in a degraded, but\nstill noticeable plateau at around 25\\% transmission. In the nonintegrable\ncase, no plateau can be discerned.\n }\n\\end{figure}\n\n\\vfill\n\\mbox{}\n\\newpage\n\\mbox{}\n\\vfill\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[width=0.9\\textwidth,keepaspectratio=true,draft=false]{Fig_4_01b_font}\n\\end{center}\n\\caption { \\label{Fig_4_01b}\n\\textbf{Dependence on the phase of the\nbreathing cycle.} The $y$-axis: the value of $E_{\\text{kin}}\/V_{0}$ for which\ntransmission jumps from 0 to 1\/4; the $x$-axis: the time offset in the\nbreathing cycle for the initial state, relative to that in\nFig.~2 in the main text; all other parameters are as in that Figure.\n }\n\\end{figure}\n\n\n\\vfill\n\\mbox{}\n\\newpage\n\\mbox{}\n\\vfill\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[width=0.9\\textwidth,keepaspectratio=true,draft=false]{Fig_4_01c_font}\n\\end{center}\n\\caption { \\label{Fig_4_01c}\n\\textbf{Transmission plot for a three-soliton breather solution.} The constituent solitons have norms 1\/6,\\, 1\/3,\\, and 1\/2 (1:2:3 norm ratio, which does \\emph{not} belong to the sequence of odd number ratios). The dash-dotted horizontal lines are at transmissions values of 1\/6, 1\/2, and 1, corresponding, respectively, to only the smallest, norm-1\/6 soliton being transmitted, to the norm-1\/6 and norm-1\/3 solitons being transmitted, and to all three constituent solitons being transmitted.\n }\n\\end{figure}\n\n\\vfill\n\\mbox{}\n\\newpage\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[width=0.9\\textwidth,keepaspectratio=true,draft=false]{solitons_and_breather_workv}\n\\end{center}\n\\caption { \\label{Fig_4_01d}\n\\textbf{Uncoupled solitons vs. breather.} How the ``staircase'' plot of Fig.~3 in the main text would look if the constituent solitons of the breather were completely uncoupled, as compared to now it is in reality. \\textbf{a,} The transmission plots for the scattering of single solitons, of norms $1\/4$ and $3\/4$, off a barrier. All parameters are as in Fig.~3 in the main text, with barrier width $w=w_{0}$. \\textbf{b,} dashed line: the weighted sum of the single-soliton transmission curves from panel a, with the norms used as weights. Solid line: the transmission curve for the breather for the same set of parameters. This is the same curve as the $w=w_{0}$ curve in Fig.~3 in the main text.\n }\n\\end{figure}\n\n\\newpage\n\\mbox{}\n\\vfill\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[width=0.9\\textwidth,keepaspectratio=true,draft=false]{phase_imprinting_split_05_workv}\n\\end{center}\n\\caption { \\label{Fig_4_01e}\n\\textbf{Superheated integrability in the context of phase imprinting.} At $t=0$, the breather wavefunction is multiplied by the space-dependent pure phase $e^{i\\epsilon \\varphi(x)}$. Here \\mbox{$\\epsilon=0.01$} and $\\varphi(x) = \\sqrt{\\frac{2}{L}}\\sqrt{\\frac{3}{2M}}\\sum_{m=1}^{M}\\left[c_{m}\\cos (2\\pi m x\/L)+s_{m}\\sin (2\\pi m x\/L)\\right]$, with \\mbox{$L=16$} and \\mbox{$M=5$}; $c_{m}$ and $s_{m}$ were drawn from the uniform distribution on $[-1,\\,1]$, and in the realization shown here had the values $(c_{1},\\,\\ldots,\\,c_{5})=(0.307,\\, 0.622,\\, 0.648,\\, -0.738,\\, 0.304)$ and $(s_{1},\\,\\ldots,\\,s_{5})=(0.353,\\, -0.0422,\\, -0.794 ,\\, -0.746,\\, 0.721)$. The function $\\varphi(x)$ is plotted in the inset. The main plot shows the time evolution of the density $|\\psi|^{2}$, during which the constituent solitons separate. Once they are well-separated, one can verify that their norms are 0.25 and 0.75. In the context of Eq.~(5) in the main text: if one uses the approximation $e^{i\\epsilon \\varphi(x)}\\approx 1 + i\\epsilon \\varphi(x)$, then the process depicted in this Figure corresponds to $v_{\\text{ext.}}(x,\\,t)\\,\\psi(x,t)=i\\delta(t)\\,\\varphi(x)\\,\\lim_{\\tau\\to t^{-}}\\psi(x,\\tau)$. Just as in the case of collision with a barrier, the separation of scales between the real and imaginary parts of the Lax-operator eigenvalues $\\lambda$, which correspond to the constituent solitons, again imply that the soliton velocities ($\\sim \\text{Re}~\\lambda$) change but norms ($\\sim \\text{Im}~\\lambda$) do not.\n }\n\\end{figure}\n\n\\vfill\n\\mbox{}\n\\newpage\n\n\\end{widetext}\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{Intro}\n The heavy ion collisions produce matter at extreme temperatures and densities where \nit is expected to be in the form of Quark Gluon Plasma \n(QGP), a phase in which the quarks and gluons can move far beyond the size of a nucleon \nmaking color degrees of freedom dominant in the medium. \n The experimental effort to produce such matter started with low energy CERN accelerator \nSPS and evolved through voluminous results \nfrom heavy ion collision at Relativistic Heavy Ion Collider (RHIC) \\cite{INTRO_Arsene, INTRO_Back, INTRO_Adams, INTRO_Adcox}.\nThe recent results from Large Hadron Collider (LHC) experiments \\cite{QGP_Tc} are \npointing towards formation of high temperature system in many ways similar to the matter\nproduced at RHIC. \n One of the most important signal of QGP is the suppression of \nquarkonium states \\cite{SATZ}, both of the charmonium ($J\/\\psi$, $\\psi(2S)$, $\\chi_{c}$, etc) \nand the bottomonium ($\\Upsilon(1S)$ , $\\Upsilon(2S)$, $\\chi_{b}$, etc) families. This is thought to be a \ndirect effect of deconfinement, when the binding potential between the constituents of a quarkonium state, \na heavy quark and its antiquark, is screened by the colour charges of the surrounding light quarks and gluons. \n The ATLAS and CMS experiments have carried out detailed quarkonia measurements in PbPb collisions \nwith the higher energy and luminosity available at the LHC.\n The ATLAS measurements \\cite{ATLAS} show suppression of inclusive $J\/\\psi$ with high transverse momenta $p_T$ \nin central PbPb collisions compared to peripheral collisions at $\\sqrt s_{NN} = 2.76$ TeV. \n Similarly, CMS measured a steady and smooth decrease of suppression \nof prompt $J\/\\psi$ as a function of centrality with nuclear modification factor $R_{\\rm AA}$ remaining $<$ 1 even \nin the peripheral bin \\cite{JCMS}. \n\n The melting temperature of the quarkonia states depends on their binding energy. The ground states, \n$J\/\\psi$ and $\\Upsilon(1S)$ are expected to dissolve at significantly higher temperatures than the \nmore loosely bound excited states. The difference in binding energies among different quarkonia indicate that\nthey melt in a hot QGP at different temperatures and the quarkonium spectrum can\nserve as plasma thermometer \\cite{SATZ2,Mocsy_Strik}.\n The $\\Upsilon(2S)$ and $\\Upsilon(3S)$ have smaller binding energies as compared to ground\nstate $\\Upsilon(1S)$ and hence are expected to dissolve at a lower temperature. \n With the 2011 PbPb run the CMS published results on sequential suppression of \n$\\Upsilon(nS)$ states as a function of centrality \\cite{CMSU2} with enlarged statistics\nover their first measurement \\cite{UCMS}\nwhere a suppression of the excited $\\Upsilon$ states with respect to the ground state have been observed \nin PbPb collisions compared to pp collisions at $\\sqrt s_{NN} = 2.76$ TeV.\n\n The quarkonia yields in heavy ion collisions are also modified due to non-QGP effects such as\nshadowing, an effect due to change of the parton distribution functions inside the nucleus,\nand dissociation due to nuclear or comover interactions \\cite{Vogt}. Due to higher mass, the \nnuclear suppression is expected to be less for bottomonia over charmonia.\n If large number of heavy quarks are produced in initial heavy ion collisions at LHC energy \nthis could even lead to enhancement of quarkonia via statistical recombination \\cite{Rapp1,Rapp2}. \n The effect of regeneration is expected to be less significant for bottomonia as compared \nto charmonia since bottom quarks are much smaller in number as compared to charm quarks. \n In addition, due to higher bottom mass the bound state properties obtained from \npotential models are more reliable. Thus recent years witness a shift in the \ninterest to bottomonia. \n The ratios of the yields of excited states to the ground states is considered \neven more robust QGP probe as the cold nuclear matter effects if any cancel out and can be \nneglected in the ratios. The calculation of ratios of $\\Upsilon$ states was also made in \nfew works e.g. \\cite{UPsi_Blaiz,UPsi_Guni} in past which showed that the $p_T$ dependence \nof such ratio would show large variations and this would be a direct probe of the QGP. \n \n In this paper, we calculate the bottomonia suppression due to color screening in an expanding\nQGP using the model by Chu and Matsui \\cite{CHU},\nwhich takes into account the finite QGP lifetime and spatial extent. \n We start by describing the properties of quarkonia obtained from potential models and then \ngive a brief description of the model which is extended to get the survival \nprobabilities of $\\Upsilon$ states as a function of centrality of the collisions. \n Finally we compare the model calculations with the experimental data recently \nmeasured by the CMS experiment.\n\n\\section{Properties of the $\\Upsilon$ states from potential models}\nInteraction between the heavy quark and its antiquark inside the quarkonium at zero temperature \ncan be described by Cornell potential \\cite{QPOT1, QPOT2,UPsi_KarschMehr}.\nThe solution of the Schrodinger equation for such potential gives mass, bound state radius and\nthe formation time $\\tau_{F}$, the time needed to form a bound state after the production of heavy quark pairs.\n All parameters obtained with zero temperature \npotential using the parameter values given in \\cite{UPsi_KarschMehr,QPROP} are summarized in \nfirst three rows of Table I, which describe well the experimentally \nobserved quarkonia spectroscopy. \n\nThe potential model can be extended to finite temperature with the main assumption that medium effects can be \naccounted for as a temperature-dependent potential. Instead of just looking at the individual\nbound states (at $T$ = 0 where quarkonium is well defined), one could rather obtain a unified treatment of bound states, \nthreshold and continuum by determining the spectral function. \n Using a class of screened potentials based on lattice calculations of the static quark-antiquark free energy, \nspectral functions at finite temperature are calculated in a work \\cite{UPsi_Mocsy2,UPsi_Mocsy3} and it was found that all \nquarkonium states, except the 1S bottomonium, dissolve in the deconfined phase at temperatures smaller than 1.5$T_C$.\nAn upper limit on binding energy and the thermal width of different quarkonia states are then estimated using \nspectral functions in the quark-gluon plasma. \n Corresponding upper bounds on their dissociation temperatures $T_{D}$ \\cite{UPsi_Mocsy3} are\ngiven in second last row of Table I. We used slightly lower values of $T_{D}$ given in the last row \nto obtain a good match with measured $R_{\\rm AA}$. \n\\renewcommand{\\arraystretch}{1.4}\n\\begin{table}[ph]\n\\tbl{Quarkonia properties from non-relativistic potential theory \\cite{UPsi_KarschMehr,UPsi_Mocsy3}.}\n{\\begin{tabular}{@{}cccccc@{}} \\toprule \n\\hline\\noalign{\\smallskip}\n {\\rm Bottomonium properties} & $\\Upsilon(1S)$ & $\\chi_b(1P)$ & $\\Upsilon(2S)$ & $\\Upsilon(3S)$ & $\\chi_b(2P)$ \\\\\n\\hline\\noalign{\\smallskip}\n {\\rm Mass~[GeV\/$c^{2}$]} & 9.46 & 9.99 & 10.02 & 10.36 & 10.26 \\\\\n\\hline\n{Radius \\rm [fm]} & 0.28 & 0.44 & 0.56 & 0.78 &0.68 \\\\\n\\hline \n$\\tau_{F}$ \\rm [fm] \\cite{UPsi_KarschMehr} & 0.76 & 2.60 & 1.9 & 2.4 & \\\\\n\\hline \n$T_D$ \\rm [GeV] upper limit \\cite{UPsi_Mocsy3} & 2~$T_C$ & 1.3~$T_C$ & 1.2~$T_C$ & 1~$T_C$ & \\\\\n\\hline\n$T_D$ \\rm [GeV] used in the present work & 1.8~$T_C$ & 1.15~$T_C$ & 1.1~$T_C$ & 1.0~$T_C$ & \\\\\n\\hline\n\\end{tabular} }\n\\label{prop}\n\\end{table}\n\n\\section{Quarkonia suppression in finite size QGP}\n The bottomonia survival probabilities due to color screening in an expanding QGP\nare estimated using a dynamical model which takes into account \nthe finite lifetime and spatial extent of the system \\cite{CHU}. The competition between the resonance formation \ntime $\\tau_{F}$ and the plasma characteristics such as temperature, lifetime and spatial extent decide the \n$p_{T}$ dependence of the survival probabilities of $\\Upsilon$ sates. We describe the essential \nsteps used to develop the model which is then extended to get \nthe survival probabilities as a function of centrality of the collision.\n\n The model assumes that quark gluon plasma is formed at some initial entropy density \n$s_0$ corresponding to initial temperature $T_0$ at time $\\tau_{0}$ which undergoes an \nisentropic expansion by Bjorken's hydrodynamics~\\cite{UPsi_Bjork}. The plasma cools to an entropy density $s_D$ \ncorresponding to the dissociation temperature $T_D$ in time $\\tau_{D}$ which is given by \n\\begin{equation}\\label{bjork}\n \\tau_{D} = \\tau_0 \\left( { s_0 \\over s_D} \\right) = \\tau_0 \\left( \\frac{T_{0}}{T_{D}} \\right)^{3},\n\\end{equation}\n As long as $\\tau_{D}$\/$\\tau_{F}$ $>$ 1, quarkonium formation will be suppressed. \n\n In the finite system produced in heavy ion collision, the suppression and entropy depend on the size \nof the system. The initial entropy density is assumed to be dependent on radius $R$ (decided by the\ncentrality of the collision) of the QGP \\cite{CHU} as \n\\begin{equation}\\label{eprofile}\n s_0(r) = s_0 ~\\left(1 - \\left(\\frac{r}{R}\\right)^2\\right)^{1\/4},\n\\end{equation}\n Using Eq.~(\\ref{bjork}) and Eq.~(\\ref{eprofile}) one can obtain the $r$ dependence of $\\tau_D$ as\n\\begin{eqnarray}\\label{tDr}\n \\tau_{D}(r) & = & \\tau_{D}(0)\\left(1 - \\left(\\frac{r}{R}\\right)^2\\right)^{1\/4}.\n\\end{eqnarray}\nwhere $\\tau_{D}(0)$ is the value of $\\tau_{D}$ for resonances produced in the center of the system.\n\n Let a $Q\\overline{Q}$ pair is created at the position ${\\rm \\bf r}$ in the transverse plane with a \ntransverse momentum ${\\rm \\bf p_T}$ and transverse energy $E_{T}$ = $\\sqrt{M^2 + p_{T}^2}$.\n The $\\Upsilon$ formation time is $\\tau_{F}\\gamma$ which on equating with the screening duration $\\tau_{D}(r)$ given \nin Eq~(\\ref{tDr}) one obtains the critical radius $r_D$, which is the boundary of the suppression region as \n\\begin{equation}\n r_D = R\\left(1 - \\left(\\frac{\\gamma \\tau_{F}}{\\tau_{D}(0)}\\right)^{4}\\right)^{1\/2}.\n\\end{equation}\nwhere $\\gamma$ = $E_T\/M$ is the Lorentz factor associated with the transverse motion of the pair. \n A bottom-quark pair can escape the screening region $r_D$ and form $\\Upsilon$ if the position at \nwhich it is created satisfies\n\\begin{equation}\\label{taumax}\n| {\\rm \\bf r} + {\\tau_{F} {\\rm \\bf p_{T}} \\over M} | > r_D,\n\\end{equation}\nwhere the screening region $r$ $<$ $r_D$ is shrinking because of the cooling of the system.\n Defining $\\phi$ to be the angle between ${\\rm \\bf p_{T}}$ and ${\\rm \\bf r}$, the Eq.~(\\ref{taumax}) leads to a range \nof $\\phi$ for which the bottom-quark pair can escape:\n\\begin{equation}\\label{cosmax}\n {\\rm cos} \\, \\phi \\ge z ~~~~{\\rm where } ~~~~ \\nonumber \\\\ \n z = \\frac{ r_D^2 - r^2 - (\\tau_{F}p_{T}\/M)^2}{2r \\,(\\tau_{F}p_{T}\/M)},\n\\end{equation}\n With this we can then calculate probability for the pair created at ${\\rm \\bf r}$ with transverse momentum ${\\rm \\bf p_T}$\nto survive as\n\\begin{eqnarray}\n\\phi(r,p_{T}) & = 1 & \\,\\,\\, z\\le -1 \\nonumber \\\\\n & = \\left( { {\\rm cos}^{-1}z \\over \\pi} \\right) & \\,\\,\\, |z| < 1 \\nonumber \\\\\n & = 0 & \\,\\,\\, z\\ge 1, \\nonumber \n\\end{eqnarray}\n If the probability $\\rho(r)$ of a quark pair to be created at $r$ which is symmetric in transverse plane \nis parameterized as\n\\begin{equation}\n\\rho(r) = \\left(1 - \\left( {r \\over R} \\right)^2\\right)^{1\/2},\n\\end{equation}\nthe survival probability of quarkonia becomes \n\\begin{equation}\nS(p_{T}, R) = \\frac{\\int_0^Rdr~r~\\rho(r)~\\phi(r,p_{T})}{\\int_0^Rdr~r~\\rho(r)}.\n\\end{equation}\n\nThe survival probability as a function of centrality can be obtained by integrating over \n$p_T$ as follows \n\\begin{equation} \n S(N_{\\rm part}) = \\int S(p_{T}, R(N_{\\rm part}) ) \\, Y(p_T) \\,dp_T.\n\\end{equation}\nHere Y($p_T$) is $p_T$ distribution (normalized to one) obtained from Pythia. \nThe size $R = R(N_{\\rm part})$ as a function of centrality is obtained in terms of the radius of the Pb \nnucleus given by $R_0 = r_0\\, A^{1\/3}$($r_0 = 1.2 \\,$ fm) and the total number of participants $N_{\\rm part0}=2A$ in head-on collisions as \n\\begin{equation}\\label{rnpart}\nR(N_{\\rm part}) = R_0 \\, \\sqrt{N_{\\rm part} \\over N_{\\rm part0} }.\n\\end{equation}\nWe assumed initial temperature $T_0$ is the temperature in 0-5\\% central collisions and calculated it\nfor a given initial time $\\tau_0$ by\n\\begin{equation}\\label{Int1}\nT_{0}^{3}\\tau_{0} = \\frac{3.6}{4a_{q}\\pi R_{0-5\\%}^{2}}\\left(\\frac{dN}{d\\eta}\\right)_{0-5\\%},\n\\end{equation}\nHere $(dN\/d\\eta)_{0-5\\%}$ = 1.5$\\times$1600 obtained from the charge particle multiplicity measured in \nPbPb collisions at 2.76 TeV \\cite{MULT} and $a_{q}$ = 37$\\pi^{2}$\/90 is the degrees of freedom we take in \nquark gluon phase. Using Eq.~(\\ref{rnpart}) we can obtain the transverse size of the system\nfor 0-5\\% centrality as $R_{0-5\\%}$ = 0.92$R_0$. \nFor $\\tau_{0}$ = 0.1 fm\/$c$, we obtain $T_{0}$ as 0.65 GeV using Eq.~(\\ref{Int1}). \nThe critical temperature is taken as $T_{C}$ = 0.170 GeV \\cite{QGP_Tc}. \nThe initial temperature as a function of centrality is calculated by \n\\begin{equation}\\label{Int2}\nT(N_{\\rm part})^3 = T_0^3 \\, \\left({dN\/d\\eta \\over N_{\\rm part}\/2}\\right) \/ \\left({dN\/d\\eta \\over N_{\\rm part}\/2}\\right)_{0-5\\%}.\n\\end{equation}\nwhere $(dN\/d\\eta)$ is the multiplicity as a function of number of participants measured by ALICE experiment \\cite{MULT}. \nBoth ALICE and CMS \\cite{CMSmult} measurements on multiplicity agree well with each other.\n Equation~(\\ref{Int2}) giving the variation of initial temperature as a function of centrality \ndiffers from the approach taken in the work of Ref.~\\cite{STR1} where it is taken to vary\nas a third root of number of participants.\n\\begin{figure}\n\\begin{center}\n\\includegraphics[width=0.70\\textwidth]{Fig1_dNdEta.eps}\n\\caption{\na) Measured $(dN\/d\\eta)\/(N_{\\rm part}\/2)$ ~\\cite{MULT} as a function \nof $N_{\\rm part}$ along with the function $(dN\/d\\eta)\/(\\pi R^2)$. \n(b) The initial temperature obtained from measured multiplicity using Eq.~(\\ref{Int2})\n}\n\\label{fig:upsiRatio1}\n\\end{center}\n\\end{figure}\n\\begin{figure}\n\\begin{center}\n \\includegraphics[width=0.48\\textwidth]{Fig2a_rD1s.eps}\n \\includegraphics[width=0.48\\textwidth]{Fig2b_rD2s.eps} \\\\\n\\caption{ The screening radius $r_D$ (in fm) as a function of $p_T$ for $R=6.8$ fm \n(corresponding to head-on collisions) and $R=3.7$ fm (corresponding to minimum bias collisions)\nfor (a) $\\Upsilon(1S)$ and (b) $\\Upsilon(2S)$.\n The straight lines $|$ ${\\rm \\bf r} + {\\tau_{F} {\\rm \\bf p_{T}} \\over M}$ $|$\nmark the distance a bottom quark pair (created at $r=0$) will travel before forming a bound state.\n The mesh region in both the figures marks the escape region for \nbottom quark pair in case of head-on collisions and total shaded (mesh+lines) region marks\nthe escape region in case of minimum bias collisions. }\n\\label{fig:upsiRatio2}\n\\end{center}\n\\end{figure}\n\n The nuclear modification factor, $R_{\\rm AA}$ is obtained from survival probability taking into account \nthe feed-down corrections as follows,\n\n\\begin{eqnarray}\n R_{\\rm AA}(3S) &=& S(3S) \\nonumber \\\\\n\n R_{\\rm AA}(2S) &=& f_1~S(2S) + f_2~S(3S) \\nonumber \\\\\n R_{\\rm AA}(1S) &=& g_1 ~S(1S) + g_2~S(\\chi_b(1P)) + g_3~S(2S) + g_4~S(3S)\n\\end{eqnarray}\nThe factors $f$'s and $g$'s are obtained from CDF measurement \\cite{CDF}. The values \nof $g_{1}$, $g_{2}$, $g_{3}$ and $g_{4}$ are 0.509, 0.27, 0.107 and 0.113 respectively.\nHere it is assumed that the survival probabilities of $\\Upsilon(3S)$ and $\\chi_{b}$(2P) \nare same and $g_4$ is their combined fraction. \nThe values of $f_{1}$ and $f_{2}$ are taken as 0.50 guided by the work from Ref.~\\cite{STR2}. \n\\begin{figure*}\n\\begin{center}\n\\includegraphics[width=0.48\\textwidth]{Fig3a_Surv_MB.eps}\n\\includegraphics[width=0.48\\textwidth]{Fig3b_SurvFd_MB_3s.eps}\\\\\n\\caption{ (a) The survival probability as a function of \n$p_{T}$ for $\\Upsilon(1S)$, $\\Upsilon(2S)$, $\\Upsilon(3S)$ and $\\chi_{b}(1P)$ for $R=3.7$ fm \n(corresponding to average $N_{\\rm part}$ = 114 for minimum bias collisions). \n (b) The nuclear modification factor \nfor $\\Upsilon(1S)$, $\\Upsilon(2S)$ and $\\Upsilon(3S)$ which is obtained from survival probabilities including \nfeed down corrections. The solid squares are $\\Upsilon(1S)$ $R_{\\rm AA}$ measured in the minimum \nbias PbPb collisions at $\\sqrt{s_{NN}} = 2.76$ TeV by CMS experiment \\cite{JCMS}. \n}\n\\label{fig:upsiRatio3}\n\\end{center}\n\\end{figure*}\n\\begin{figure*}\n\\begin{center}\n\\includegraphics[width=0.65\\textwidth]{Fig4_RaaALL.eps} \n\\caption{The nuclear modification factor, $R_{\\rm AA}$\nas a function of $N_{\\rm part}$ for $\\Upsilon(1S)$, $\\Upsilon(2S)$ and $\\Upsilon(3S)$. The solid squares and circles are \nmeasured $R_{\\rm AA}$ by CMS experiment in PbPb collisions at $\\sqrt{s_{\\rm NN}}$ = 2.76 TeV \\cite{CMSU2} for \n$\\Upsilon(1S)$ and $\\Upsilon(2S)$ respectively and solid triangles are the minimum bias data points. \nThe boxes at unity are the common systematic uncertainties in pp luminosity \nmeasurement and the pp yield. The lines(solid for $\\Upsilon(1S)$ , dashed for $\\Upsilon(2S)$ and dotted for $\\Upsilon(3S)$) \nrepresent the present model calculations. }\n\\label{fig:upsiRatio4}\n\\end{center}\n\\end{figure*}\n\n\\section{Results and discussions}\n Figure~\\ref{fig:upsiRatio1} (a) shows measured $(dN\/d\\eta)\/(N_{\\rm part}\/2)$ ~\\cite{MULT} as a function \nof $N_{\\rm part}$. The function $(dN\/d\\eta)\/(\\pi R^2)$ gives the multiplicity divided by transverse \narea obtained using Eq.(\\ref{rnpart}). Figure~\\ref{fig:upsiRatio1} (b) gives the initial temperature \nobtained from measured multiplicity using Eq.~(\\ref{Int2}). Except in peripheral collisions, the initial \ntemperature has weak dependence on centrality of collisions. \nFigure~\\ref{fig:upsiRatio2} demonstrates working of the model. It shows\nthe screening radius $r_D$ (in fm) as a function of $p_T$ for $R=6.8$ fm \n(corresponding to head-on collisions) and $R=3.7$ fm (corresponding to minimum bias collisions)\nfor (a) $\\Upsilon(1S)$ and (b) $\\Upsilon(2S)$.\n The straight lines $|$ ${\\rm \\bf r} + {\\tau_{F} {\\rm \\bf p_{T}} \\over M}$ $|$\nmark the distance a bottom quark pair (created at $r=0$) will travel before forming a bound state.\n The mesh region in both the figures marks the escape region for \nbottom quark pair in case of head-on collisions and total shaded (mesh+lines) region marks\nthe escape region in case of minimum bias collisions. \n If $r$ is non-zero, the region where a bottomonium can escape screening, enlarges.\n\n Figure~\\ref{fig:upsiRatio3} (a) shows the survival probability as a function of \n$p_{T}$ for $\\Upsilon(1S)$, $\\Upsilon(2S)$, $\\Upsilon(3S)$ and $\\chi_{b}(1P)$ for $R=3.7$ fm \n(corresponding to average $N_{\\rm part}$ = 114 for minimum bias collisions). \n The survival probability $S(p_{T})$ has a unique $p_T$ dependence decided by the \n$T_D$ and $\\tau_{F}$ of each $\\Upsilon$ state. \n In general, the survival probabilities of resonance states increase with increasing $p_T$ \nand become unity at different $p_T$ for different states corresponding to complete survival. \n Since $\\Upsilon(1S)$ is expected to dissolve at a higher temperature it has more probability to survive\nthe plasma region even at lower p$_{T}$ as compared to the cases of other bottomonia states.\n The model gives very similar survival probabilities for $\\Upsilon(2S)$ and $\\Upsilon(3S)$.\nThis is due to the fact that $\\Upsilon(3S)$ has large formation time even though its \ndissociation temperature is smaller in comparison to $\\Upsilon(2S)$.\n Figure~\\ref{fig:upsiRatio3} (b) shows the nuclear modification factor \nfor $\\Upsilon(1S)$, $\\Upsilon(2S)$ and $\\Upsilon(3S)$ which is obtained from survival probabilities \nincluding feed down corrections. \n The solid squares are $\\Upsilon(1S)$ $R_{\\rm AA}$ measured in the minimum bias PbPb collisions at \n$\\sqrt{s_{NN}} = 2.76$ TeV by CMS experiment \\cite{JCMS}. \n The model reproduces the trend of the $p_T$ dependence of low statistics \nmeasurements of $R_{\\rm AA}$ from 2010 PbPb collisions by CMS. \n\n Figure~\\ref{fig:upsiRatio4} shows the nuclear modification factor, $R_{\\rm AA}$\nas a function of $N_{\\rm part}$ for $\\Upsilon(1S)$, $\\Upsilon(2S)$ and $\\Upsilon(3S)$. The solid squares and circles are \nmeasured $R_{\\rm AA}$ by CMS experiment in PbPb collisions at $\\sqrt{s_{\\rm NN}}$ = 2.76 TeV \\cite{CMSU2} for \n$\\Upsilon(1S)$ and $\\Upsilon(2S)$ respectively and solid triangles are the minimum bias data points. \nThe lines(solid for $\\Upsilon(1S)$, dashed for $\\Upsilon(2S)$ and dotted for $\\Upsilon(3S)$) represent \nthe present model calculations. \nThe common systematic uncertainties in pp luminosity measurement and the pp yield are \nrepresented by the boxes at unity. The model correctly reproduces the measured nuclear modification \nfactors of both $\\Upsilon(1S)$ and $\\Upsilon(2S)$ for \nall centralities using the parameters given in the Table I. The \nsurvival probabilities for $\\Upsilon(2S)$ and $\\Upsilon(3S)$ are very similar.\n\\begin{figure}\n\\begin{center}\n\\includegraphics[width=0.65\\textwidth]{Fig5_DRatio.eps}\n\\caption{Double ratio, $[\\Upsilon(2S)\/\\Upsilon(1S)]_{PbPb}$\/$[\\Upsilon(2S)\/\\Upsilon(1S)]_{pp}$ as a \nfunction of $N_{\\rm part}$ measured by CMS experiment \\cite{CMSU2} \nalong with the present calculation (solid line). The box at unity is\nthe common systematic uncertainty in the pp yield. \n}\n\\label{fig:upsiRatio5}\n\\end{center}\n\\end{figure}\n\n We also calculated the ratio of $R_{\\rm AA}$ of $\\Upsilon(2S)$ to that of $\\Upsilon(1S)$ \nwhich is equivalent to the so called double ratio $[\\Upsilon(2S)\/\\Upsilon(1S)]_{PbPb}$\/$[\\Upsilon(2S)\/\\Upsilon(1S)]_{pp}$.\nThe double ratio has the advantage that the effects such as initial-state nuclear effects and regeneration\nwhich we ignore in our calculations are supposedly canceled out. \n Figure~\\ref{fig:upsiRatio5} shows the double ratio measured by CMS experiment \\cite{CMSU2} \nalong with the present calculation. The calculations reproduce the measured double ratio \neven for the most peripheral data point.\n\n The most important parameters in above study are formation time \nand dissociation temperatures of bottomonia states. There are reliable calculations of formation time \nobtained from zero temperature potential models which reproduce the bottomonia spectroscopy very well. \nUpper limits are available for dissociation temperatures which are obtained from potential models \nat finite temperature. We used slightly lower values of the dissociation temperature to get a good\ndescription of the measured nuclear modification factors of $\\Upsilon(1S)$ and $\\Upsilon(2S)$. \n The dynamics of the system is affected by the initial conditions which in the present calculations are \nfixed using measured charged particle multiplicity at LHC.\nThere can be suppression due to initial nuclear effects which we assume to be \nmuch smaller than that due to colour screening and hence are ignored in the present work. \n The calculations of shadowing in PbPb show that it will effect the bottomoina yields by \napproximately 20 \\% for most central collisions \\cite{Shadow}. Thus, the dissociation temperatures \nobtained by us are still considered to be the upper limits. Conversly there are other views which say that \n$\\Upsilon$ ground state is not much affected by the color screening \nup to the temperatures of $\\sim 3-4T_C$ and regeneration of the states are not negligible at the LHC \\cite{Rapp}.\n The bottom quark mass is 10 times higher than the temperature we \nare considering for the system and hence the regeneration effect can be safely ignored in calculating\nnuclear modification for bottomonia. The uncertainties in the measurements of feed-down fractions \nwould introduce uncertainties in the calculated nuclear modification factor. \n Finally we mention that the uncertainties arising from the effects other than colour screening are small and supposedly \nwill have little or no effect on the double ratio.\n\n\\section{Conclusions}\n In summary, we calculate the survival probabilities of $\\Upsilon$ states and obtain the nuclear modification\nfactors due to colour screening in an expanding quark gluon plasma of finite lifetime and size produced \nduring PbPb collisions $\\sqrt{s_{NN}}=$ 2.76 TeV.\n The formation time and dissociation temperatures of bottomonia states \nobtained from potential models are used as input parameters in the model. \nWe used slightly lower values of the dissociation temperatures to get a good\ndescription of the measured nuclear modification factors of $\\Upsilon(1S)$ and $\\Upsilon(2S)$. \n The model reproduces the centrality dependence of measured nuclear modification \nfactors of $\\Upsilon(1S)$ and $\\Upsilon(2S)$ and the double ratio very well at $\\sqrt{s_{\\rm NN}}$ = 2.76 TeV.\n The trend of $p_T$ dependence of low statistics measurements of nuclear modification factor \nfrom 2010 PbPb collisions of CMS is reproduced as well. \n The uncertainties arising from effects other than colour screening are assumed to be \nsmall and supposedly will have little or no effect on the double ratio calculations.\n\\section{Acknowledgement}\nWe thank Vineet Kumar, Ramona Vogt and CMS heavy ion colleagues for many fruitful discussion.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nIn the last fifteen years, large dimensional stationary factor models have achieved great success in the economic profession, especially in forecasting macroeconomic variables \\citep[see, e.g.,][]{Nowcasting}, and are now a common tool in several policy institutions. However, macroeconomic time series are typically non-stationary due to the presence of common and idiosyncratic stochastic trends, and the practice of differencing the data to achieve stationarity is a problem that not always has a clear-cut solution. Take for example the case of the unemployment rate, which is a highly-persistent time series, but at the same time economic theory forbids it to have a unit root; or, take as another example the case of inflation, which shows periods of high-persistence in the late 70s early 80s, while more recently displays clear mean reversion. To avoid the risk of over- or under-differencing data, a Non-Stationary Dynamic Factor Model (NS-DFM) is then desirable, and it is studied in this paper. \n\nThe NS-DFM proposed in this paper captures several features of macroeconomic data as it takes into account the presence of common trends generating permanent fluctuations in the economy, as well as common transitory forces generating cyclical fluctuations. More technically, in our model, the common factors are a cointegrated vector process, thus containing both $I(1)$ trends and stationary components. Moreover, the NS-DFM addresses the possible presence of idiosyncratic trends, as well as the presence of secular (linear) trends, which can have either a constant slope (deterministic linear trends) or a time-varying slope (local linear trends). \n\nIn this paper, we study estimation of the NS-DFM by Quasi Maximum Likelihood (QML) implemented through the Expectation Maximization (EM) algorithm and the Kalman smoother (KS). Specifically, we extend the results in \\citet{BLqml} for the stationary case, to prove that when the common factors are the only source of non-stationarity, the common component estimated at a given point in time and for a given unit is $\\min(\\sqrt n,\\sqrt T)$-consistent. We also discuss extensions to the cases of (i) unit roots in the idiosyncratic components, and (ii) local levels and local linear trends. \n \nEstimation is implemented in two steps. First, given the observed data, by means of the KS we estimate the conditional mean of the latent factors, which, together with its associated conditional covariance matrix, we use to compute the expected log-likelihood of the model (E-step).\\footnote{In a non-stationary setting the existence of the conditional mean of the factor as a minimizer of the mean-squared prediction error has been proved by \\citet[Theorem 1]{hannan67} and \\citet[Theorem 1]{sobel67}.} Second, we maximize the expected log-likelihood with respect to the loadings and the other parameters of the model (M-step). The use of an iterative procedure to extract unobserved components in the case of non-stationary data was proposed since the original work by \\citet{kalman60}. Although this is not the first paper using these techniques for non stationary data, this is the first paper to address consistency of factors. Moreover, QML estimation of autoregressive processes with unit roots is a classical problem studied at length by the literature \\citep{simsstockwatson,johansen91}.\\footnote{Solutions based on spectral analysis are also in \\citet{bell84} and \\citet{CT76}.}\n\nEstimation of NS-DFMs has also been studied by \\citet{baing04} and \\citet{BLL2} by PC analysis on differenced data. Both approaches are designed to account for non-stationary idiosyncratic components; however, only the latter is designed to deal with linear deterministic trends. \\citet{bai04} has used a factor model to estimate common trends via PC on data in levels. However, because of its nature, that approach is valid only if all idiosyncratic components are stationary, i.e., only if data are cointegrated.\n\nCompared to those PC based estimators, our approach has a number of practical advantages. First, it allows estimating the model even in the presence of missing values, which is crucial when using the model in real-time because macroeconomic data are published with delays and at non-synchronized dates. Second, it allows estimating jointly stochastic trends as well as (deterministic or local) linear trends, whereas \\citet{baing04} and \\citet{BLL2} are forced to remove the deterministic trends before running PC analysis. Third, it allows having time-varying parameters, such as, for example, the slope of linear trends. Fourth, it allows putting restrictions on the parameters, such as national accounts identities, or restrictions coming from economic theory.\n\nFrom a theoretical point of view, our estimator converges at a faster rate than those of \\citet{baing04} and \\citet{BLL2}. However, this faster convergence does not come for free. Indeed, our estimator is based on stronger assumptions than those of PC analysis: namely, it is derived under the assumption that we know which idiosyncratic components are $I(1)$ and which ones are stationary, and which series have a linear trend component. Under this assumption, we can model the $I(1)$ idiosyncratic components, and the time-varying slopes or means, as additional latent states in the KS, thus allowing to simultaneously estimate the entire model. This strategy is shown to work well in practice, provided the number of additional latent states is not too large.\n\nThe use of the EM in time series dates back to \\citet{SS77}, \\citet{shumwaystoffer82}, \\citet{watsonengle83}, \\citet{quahsargent93}, and \\citet{SAZ13}, among others. However, with the exception of the last two, none of the above works has considered the case of non-stationary data. Moreover, to the best of our knowledge, no asymptotic theory exists for the setting considered in this paper.\n\nThe rest of the paper proceeds as follows: in Section \\ref{sec:modelNS}, we present the NS-DFM and its assumptions. Estimation is outlined in Section \\ref{sec:estNS} where we also prove consistency. The extension to non-stationary idiosyncratic states is discussed in Section \\ref{sec:idioI1}. Numerical results are in Section \\ref{sec:mc2}. Section \\ref{sec:conclusion} concludes.\n\n\\section{Model and assumptions}\\label{sec:modelNS}\nWe define a NS-DFM driven by $q$ factors as \n\\begin{align}\nx_{it}&=\\alpha_{it}+\\beta_{it} t+\\bm b_i^\\prime(L) \\bm f_t +\\xi_{it},\\label{eq:NSDFM1}\\\\\n\\bm f_t&= \\bm{\\mathcal A}(L)\\bm f_{t-1}+\\bm u_t,\\label{eq:NSDFM2}\\\\\n\\xi_{it}&=\\rho_i\\xi_{it-1}+e_{it},\\label{eq:NSDFM3}\\\\\n\\alpha_{it}&=\\alpha_{it-1}+\\omega_{it},\\label{eq:NSDFM5}\\\\\n\\beta_{it}&=\\beta_{it-1}+\\eta_{it},\\label{eq:NSDFM4}\n\\end{align}\nfor $i=1,\\ldots, n$, and $t=1,\\ldots, T$. We let $\\chi_{it}=\\bm b_i^\\prime(L) \\bm f_t$. Then, $\\bm\\chi_{nt}=(\\chi_{1t}\\cdots\\chi_{nt})^\\prime$ is the common component, $\\bm\\xi_{nt}=(\\xi_{1t}\\cdots\\xi_{nt})^\\prime$ the idiosyncratic component, $\\bm {\\mathcal B}_n(L)=(\\bm b_1(L)\\cdots\\bm b_n(L))^\\prime$ the $n\\times q$ polynomial matrix of factor loadings, $\\bm f_t=(f_{1t}\\cdots f_{qt})^\\prime$ the $q$ factors, $\\bm u_t=(u_{1t}\\cdots u_{qt})^\\prime$ the $q$ common shocks, $\\mathbf e_{nt}=(e_{1t}\\cdots e_{nt})^\\prime$ the idiosyncratic shocks, and we also define $\\bm\\omega_{nt}=(\\omega_{1t}\\cdots\\omega_{nt})^\\prime$ and $\\bm\\eta_{nt}=(\\eta_{1t}\\cdots\\eta_{nt})^\\prime$.\n\n\nWe make the following assumptions.\n\n\\begin{ass}\\label{ass:dynamic}\n\\begin{inparaenum}[(a)]\n\\item for all $i\\in\\mathbb N$ and $z\\in\\mathbb C$, $\\bm b_i(z)=\\sum_{k=0}^s \\bm b_{ik}z^k$, such that $\\bm b_{ik}$ are $q\\times 1$ and $s$ is a finite integer with $s\\ge 0$;\n\\item for all $n\\in\\mathbb N$, let $\\bm{\\mathcal B}_{kn}=(\\bm b_{1k}\\cdots \\bm b_{nk})^\\prime$, then $\\lim_{n\\to\\infty}\\Vert n^{-1}\\bm{\\mathcal B}_{kn}^\\prime\\bm{\\mathcal B}_{kn}-\\bm\\Sigma_{k}\\Vert=0$, with $\\bm\\Sigma_{k}$ being $q\\times q$, and $\\bm \\Sigma_0$ positive definite, while $\\mbox{rk}(\\bm\\Sigma_k)\\le q$ for $k=1,\\ldots,s$, \nmoreover, for all $i\\in\\mathbb N$ and $k=0,\\ldots, s$, $\\Vert\\bm b_{ik}\\Vert \\le M_B$ for some finite positive real $M_B$ independent of $i$ and $k$; \\item $\\bm\\Gamma^{\\Delta f}=\\E_{\\varphi_n}[\\Delta\\bm f_t\\Delta\\bm f_t^\\prime]$ is $q\\times q$ positive definite and there exists a finite positive real $M_f$, such that $\\Vert\\bm\\Gamma^{\\Delta f}\\Vert\\le M_f$;\n\\item $q$ is a finite positive integer, such that $q0$ is the relevant one, as there is full agreement in the economic profession that while some fluctuations in the economy are permanent (common trends), some others are only temporary.\n\nAssumption \\ref{ass:modelNS} characterizes the innovations of the model. In particular, by part (c) the idiosyncratic innovations $e_{it}$ are allowed to be mildly cross-correlated, thus implying that $\\Delta x_{it}$ follows an approximate factor model. Moreover, by part (e) we allow some series to be driven by a time-varying intercept and\/or a trend with time-varying slope, modeled as in a local level and local linear trend model, respectively \\citep[Section 2.3.6, page 45]{harvey90}. Notice that, if we set $\\sigma_{i\\eta}^2=0$, then the trend becomes deterministic with slope $\\beta_{i0}$, which is fixed to a constant by Assumption \\ref{ass:modelNS}(h), and similarly if we set $\\sigma_{i\\omega}^2=0$, we have a deterministic, hence constant, intercept term equal to $\\alpha_{i0}$. Finally, by parts (d) and (f) all innovations are independent. Notice that gaussianity is not strictly needed, but it is a reasonable assumption in macroeconomics.\n\nUnder these assumptions, it can be shown that the covariance matrix of the differenced common component $\\Delta\\bm\\chi_{nt}$ has at least $q$ and at most $q(s+1)$ eigenvalues that diverge linearly as $n\\to\\infty$. In particular, letting the covariance matrix of $\\Delta \\bm \\chi_n$ be $\\bm\\Gamma_n^{\\Delta\\chi}$, and denoting as $\\mu_{jn}^{\\Delta\\chi}$ the $j$-th largest eigenvalue of $\\bm\\Gamma_n^{\\Delta\\chi}$, Assumptions \\ref{ass:dynamic}(b) and \\ref{ass:dynamic}(c) imply that, for $j=1,\\ldots, q$,\n\\begin{equation}\\label{eq:divevaldiff}\n\\underline K_j\\le \\lim\\inf_{n\\to\\infty} n^{-1} \\mu_{jn}^{\\Delta\\chi}\\le\\lim\\sup_{n\\to\\infty} n^{-1} \\mu_{jn}^{\\Delta\\chi}\\le \\overline K_j, \n\\end{equation}\nfor some positive reals $\\underline K_j$ and $\\overline K_j$. Moreover, letting the covariance matrix of $\\Delta \\bm \\xi_n$ be $\\bm\\Gamma_n^{\\Delta\\xi}$, and denoting as $\\mu_{jn}^{\\Delta\\xi}$ the $j$-th largest eigenvalue of $\\bm\\Gamma_n^{\\Delta\\xi}$, Assumption \\ref{ass:modelNS}(c), implies that\n\\begin{equation}\n\\sup_{n\\in\\mathbb N} \\mu_{1n}^{\\Delta\\xi}\\le M_\\xi, \\label{eq:divevaldiff2}\n\\end{equation}\nfor some positive real $M_\\xi$. From \\eqref{eq:divevaldiff} and \\eqref{eq:divevaldiff2}, and Assumption \\ref{ass:modelNS}(e), by Weyl's inequality, the $q$ largest eigenvalues of the covariance matrix of $\\Delta \\mathbf x_{nt}$ diverge linearly in $n$, while all other $(n-q)$ eigenvalues stay bounded for all $n\\in\\mathbb N$. \n\nMoreover, it can be shown that the $q$ largest eigenvalues of the spectral density of $\\Delta \\mathbf x_{nt}$ diverge with $n$ at all frequencies, but at zero-frequency, where, due to the presence of common trends, only $(q - d)$ eigenvalues diverge, all the others eigenvalues being bounded for all $n$ and all frequencies. Hence, by looking at the eigenvalues of the spectral density matrix of $\\Delta \\mathbf x_{nt}$ we can determine $q$ and $d$ (see \\citealp{hallinliska07}, and \\citealp{BLL2}, respectively). Moreover, notice that when all factors are pervasive at all lags, i.e., in Assumption \\ref{ass:dynamic}(b) we let $\\mbox{rk}(\\bm\\Sigma_k)=q$ for all $k=0,\\ldots,s$, then \\eqref{eq:divevaldiff} holds for all $j=1,\\ldots,q(s+1)$. Therefore, by looking at the eigenvalues of the covariance matrix of $\\Delta \\mathbf x_{nt}$, we can also determine $s$ \\citep{dagostinogiannone12}.\n\nThe model defined in \\eqref{eq:NSDFM1}-\\eqref{eq:NSDFM4} has $q$ latent states, given by the common factors $\\bm f_t$, and additional latent states given by those idiosyncratic components that are autocorrelated as in \\eqref{eq:NSDFM3}, and by the time-varying intercepts and trend slopes as in \\eqref{eq:NSDFM5} and \\eqref{eq:NSDFM4}. These additional latent states are such that they satisfy the following assumption. \n\nIn other words, we are assuming that some, but not all, idiosyncratic components are $I(1)$, and that some, but not all, series have a time-varying intercept and\/or a linear trend with time-varying slope. For simplicity, we are also assuming that stationary idiosyncratic components are serially uncorrelated. \n\nWe then make the following identifying assumptions.\n\n\\begin{ass} \\label{ass:identNS}\nLet $\\mathbf M_n^{\\Delta \\chi}$ be the $q\\times q$ diagonal matrix with elements $\\mu_{1n}^{\\Delta \\chi},\\ldots,\\mu_{qn}^{\\Delta \\chi}$, and let \n$\\mathbf V_n^{\\Delta\\chi}$ be the $n\\times q$ matrix having as columns the corresponding normalized eigenvectors. Then: \\begin{inparaenum}[(a)]\n \\item $\\Delta \\bm f_t=(\\mathbf M_n^{\\Delta\\chi})^{-1\/2}\\mathbf V_n^{\\Delta\\chi\\prime} \\Delta\\bm\\chi_{nt}$;\n \\item the entries of $\\mathbf M_n^{\\Delta\\chi}$ are such that they satisfy \\eqref{eq:divevaldiff} and $\\overline K_{j+1}<\\underline K_j$ for $j=1,\\ldots, q-1$;\n \\item the entries of $\\mathbf V_n^{\\Delta\\chi}$ are such that $[\\mathbf V_n^{\\Delta\\chi}]_{1j}>0$ for all $j=1,\\ldots, q$.\n\n\\end{inparaenum}\n\\end{ass}\n \nParts (a) and (b) are standard in factor model literature for stationary processes and allow to identify the differenced factors up to a multiplication by a sign \\citep[see, e.g.,][]{FGLR09,FLM13}. We identify the first difference of the factors with the $q$ normalized principal components of $\\Delta\\bm\\chi_{nt}$ and this implies in Assumption \\ref{ass:dynamic}(b) that $\\bm\\Gamma^{\\Delta f}=\\mathbf I_q$. It can then be seen that the following must hold for the loadings\n\\begin{align}\n\\mathbf V_n^{\\Delta\\chi\\prime} \\bm{\\mathcal B}_{0n}= (\\mathbf M_n^{\\Delta\\chi})^{1\/2},\n\\end{align}\ntherefore we can choose $\\bm{\\mathcal B}_{0n}=\\mathbf V_n^{\\Delta\\chi}(\\mathbf M_n^{\\Delta\\chi})^{1\/2}$, and in Assumption \\ref{ass:dynamic}(a) we have that $\\bm\\Sigma_0$ is diagonal with entries given by $\\lim_{n\\to\\infty} (n^{-1}\\mu_{jn}^{\\Delta \\chi})$, which as requested are finite and positive because of \\eqref{eq:divevaldiff}. Part (c) is a way to fix the sign indeterminacy in the identification of the factors. Once $\\Delta\\bm f_t$ and $\\bm{\\mathcal B}_{0n}$ are identified, then the remaining loadings are obtained by projecting $\\Delta\\mathbf x_{nt}$ onto the lagged factors.\n\n\nThe identifying restrictions in Assumption \\ref{ass:identNS} are particularly useful for initializing the EM algorithm with the PC estimator (see the next section). However, it has to be stressed that this identification does not provide any economic meaning to the factors. In other words we are not interested here in giving any interpretation of the factors, but we are just interested in the common component, which is always identified.\n\n\n\\section{Estimation and asymptotic properties}\\label{sec:estNS}\nThroughout the rest of the section we assume to observe the $nT$-dimensional vector $\\bm X_{nT}=(\\mathbf x_{n1}^\\prime\\cdots\\mathbf x_{nT}^\\prime)^\\prime$ satisfying \\eqref{eq:NSDFM1}-\\eqref{eq:NSDFM4}. In order to derive an estimator of the common component, we need to estimate the factors vector $\\bm f_T=(\\bm f_{1}^\\prime\\cdots \\bm f_T^\\prime)^\\prime$ and the vector containing the true values of all parameters is \n \\[\n \\bm\\varphi_n=\\l(\\text{vec}(\\bm{\\mathcal B}_{0n}\\cdots \\bm{\\mathcal B}_{sn})^\\prime, \\text{vech}(\\bm\\Gamma_n^e)^\\prime, \\rho_1,\\ldots, \\rho_{n_1} ,\\text{vec}(\\bm{\\mathcal A}_1\\cdots \\bm{\\mathcal A}_p)^\\prime, \\text{vech}(\\bm\\Gamma^u)^\\prime,\\sigma_{1\\omega}^{2}\\cdots\\sigma^{2}_{n_a\\omega},\\sigma_{1\\eta}^{2}\\cdots\\sigma^{2}_{n_b\\eta}\\r)^\\prime, \n \\]\n where, without loss of generality, we assumed that $\\mathcal I_1=\\{1,\\ldots, n_1\\}$, $\\mathcal I_a=\\{1,\\ldots, n_a\\}$, and $\\mathcal I_b=\\{1,\\ldots, n_b\\}$.\n\nIn this Section, we provide asymptotic results when $n_1=0$, $n_a=0$, and $n_b=0$, thus assuming that all idiosyncratic component are stationary and that no time-varying term is present. At first sight this might seem as a strong requirement, but notice that in our framework introducing non-stationary idiosyncratic components and\/or local levels and\/or local linear trends implies just adding latent states. We discuss this extension in Section \\ref{sec:idioI1}. Moreover, in \\ref{app:prestNS}, we give all details of the EM algorithm together with explicit expressions for all estimators in the general case. \n\nWithout loss of generality, we fix $s=1,$ and we fix the VAR order in \\eqref{eq:NSDFM2} to $p=2$, thus $\\bm{\\mathcal A}(L)\\equiv(\\bm{\\mathcal A}_1 L+\\bm{\\mathcal A}_2L^2)$, and, in this way the stationary component of $\\bm f_t$ follows a non-trivial dynamics. For simplicity, we also assume that $\\alpha_{i0}=0$ and $\\beta_{i0}=0$. \n\nThe EM algorithm is an iterative procedure, which starts with an initial value of the parameters $\\widehat{\\bm\\varphi}_n^{(0)}$, and at each iteration $k\\ge 0$ produces estimates of the factors $\\bm f_{t|T}^{(k)}$ (KS and E-step) and of the parameters $\\widehat{\\bm\\varphi}_n^{(k+1)}$ (M-step). When the EM algorithm converges, say at iteration $k^*$, it gives the estimated common component $\\widehat{\\chi}_{it}=\\widehat{\\bm b}_{i0}^{(k^*+1)\\prime}\\bm f_{t|T}^{(k^*+1)}+\\widehat{\\bm b}_{i1}^{(k^*+1)\\prime}\\bm f_{t-1|T}^{(k^*+1)}$. \n\nMore in detail, the NS-DFM in \\eqref{eq:NSDFM1}-\\eqref{eq:NSDFM2} can be written as\n\\begin{align}\n\\mathbf x_{nt}&=\\l(\\bm{\\mathcal B}_{0n}\\ \\bm{\\mathcal B}_{1n}\\r)\n\\l(\\begin{array}{c}\n\\bm f_t\\\\\n\\bm f_{t-1}\n\\end{array}\n\\r)+\\mathbf e_{nt},\\label{eq:SSNS001}\\\\\n\\l(\\begin{array}{c}\n\\bm f_t\\\\\n\\bm f_{t-1}\n\\end{array}\n\\r)&=\\l(\\begin{array}{cc}\n\\bm{\\mathcal A}_1&\\bm{\\mathcal A}_2\\\\\n\\mathbf I_q&\\mathbf 0_{q\\times q}\n\\end{array}\n\\r)\n\\l(\\begin{array}{c}\n\\bm f_{t-1}\\\\\n\\bm f_{t-2}\n\\end{array}\n\\r)\n+\\l(\\begin{array}{c}\n\\bm u_t\\\\\n\\mathbf 0_q\n\\end{array}\n\\r),\\label{eq:SSNS002}\n\\end{align} \nBy defining $\\mathbf F_t=(\\bm f_t^\\prime\\; \\bm f_{t-1}^\\prime)^\\prime$ and $\\bm\\lambda_i=(\\bm{b}_{0i}^\\prime\\;\\bm{b}_{1i}^\\prime)^\\prime$, we see that, for given values of the parameters $\\widehat{\\bm\\varphi}_n^{(k)}$, we can easily estimate the factors via the KS applied to the state-space form in \\eqref{eq:SSNS001}-\\eqref{eq:SSNS002}. The estimated states are then $\\mathbf F_{t|T}^{(k)}=\\E_{\\widehat{\\varphi}_n^{(k)}}[\\mathbf F_t|\\bm X_{nT}]$, the first $q$-components of which give $\\bm f_{t|T}^{(k)}=\\E_{\\widehat{\\varphi}_n^{(k)}}[\\bm f_t|\\bm X_{nT}]$. Then, using the output of the KS, we can compute the expected log-likelihood, which is maximized by the loadings estimator $\\widehat{\\bm \\lambda}_{i}^{(k+1)}\\equiv(\\widehat{\\bm b}_{i0}^{(k+1)\\prime} \\; \\widehat{\\bm b}_{i1}^{(k+1)\\prime})^\\prime$, such that\n\\begin{align}\n\\widehat{\\bm \\lambda}_{i}^{(k+1)}\\!=\n\\l\\{\\sum_{t=1}^T\\E_{\\widehat{\\varphi}_n^{(k)}}\\!\\!\\l[\\l(\\begin{array}{ll}\n\\bm f_t\\bm f_t^\\prime&\\bm f_t\\bm f_{t-1}^\\prime\\\\\n\\bm f_{t-1}\\bm f_t^\\prime&\\bm f_{t-1}\\bm f_{t-1}^\\prime\n\\end{array}\\r)\\!\\!\\bigg\\vert \\bm X_{nT}\n\\r]\\r\\}^{\\!\\!-1}\\!\\!\\!\n\\l\\{\\sum_{t=1}^T\\E_{\\widehat{\\varphi}_n^{(k)}}\\!\\!\\l[\\l(\\begin{array}{l}\n\\bm f_tx_{it}\\\\\n\\bm f_{t-1}x_{it}\n\\end{array}\\r)\\!\\!\\bigg\\vert \\bm X_{nT}\n\\r]\\r\\}.\\label{eq:param1NS}\n\\end{align}\n\nThe initial value of the parameters $\\widehat{\\bm\\varphi}_n^{(0)}$ is determined as follows. For the loadings and the factors we use the approach proposed in \\citet{BLL2}, which makes use of the $q$ leading PCs of the model in first differences. Two comments are worth making. \nFirst, it important to stress that initializing the model in first differences (including when determining $q$ and $s$) is crucial, since it allows us to use PCs without incurring in spurious effects due to the presence of idiosyncratic unit roots (\\citealp{OW19}), or linear trends (\\citealp{ngCG}). Second, in light of the previous comment, this approach provides consistent estimates of the loadings, even in the case in which Assumption \\ref{ass:I1} is satisfied with $n_1>0$ and $n_b>0$, but for constant intercepts and trend slopes \\citep[see also][in the case of no linear trends]{baing04}. In particular, our initialization delivers estimates of $\\alpha_{i0}$ and $\\beta_{i0}$, which, together with a given small initial value of the variances $\\widehat{\\sigma}_{i\\omega}^{2(0)}$ and $\\widehat{\\sigma}_{i\\eta}^{2(0)}$, can be used to update the slope state in \\eqref{eq:NSDFM4}. Notice that the pre-estimators of those initial conditions do not need to be consistent for our results to hold.\nThe initialization is completed by estimating the parameters of \\eqref{eq:NSDFM2} from an unrestricted VAR fitted on the estimated factors. This is a valid procedure when estimating an autoregressive model for cointegrated data (see \\citealp{simsstockwatson}). Consistency of the pre-estimators of the loadings and VAR coefficients is proved in \\citet[Lemma 3 and Proposition 2]{BLL2} (see also \\ref{app:consN}). \n\nFinally, notice also that we initialize the KF by setting the initial value of the covariance of the factors, ${\\mathbf P}_{0|0}$, to a very large value, as suggested by \\citet[Section 3.3.4, page 121]{harvey90}. \n\nConsistency of the estimated common component follows.\n\\newpage\n\\begin{prop}\\label{th:chiNS}\nUnder Assumptions \\ref{ass:dynamic}, \\ref{ass:modelNS}, \\ref{ass:identNS}, and if $\\mbox{rk}(\\bm\\Sigma_k)=q$ for all $k=0,\\ldots,s$, and $n_1=0$, $n_a=0$, and $n_b=0$, as $n,T\\to\\infty$, for any given $i=1,\\ldots, n$ and $k=0,1$, $\\min(\\sqrt n,\\sqrt T)\\Vert \\widehat{\\bm b}_{ki}-\\bm b_{ki}\\Vert = O_p(1)$, \nand, for any given $t=\\bar t,\\ldots, T$,\n$\\min(\\sqrt n,\\sqrt T)\\Vert \\widehat{\\bm f}_{t|T}-\\bm f_{t}\\Vert= O_p(1)$. Moreover, $\\min(\\sqrt n,\\sqrt {T})\\,\\Vert \\widehat{\\chi}_{it}-{\\chi}_{it} \\Vert = O_p(1)$,\nfor any given $i=1,\\ldots,n$ and $t=\\bar t,\\ldots, T$, with $\\bar t\\ge 2$.\n\\end{prop}\n\n\n\nThe convergence rate depends on different ingredients. First, we show that the KS reaches a steady state within $\\bar t$ periods, where $\\bar t$ depends on the initial value $\\mathbf P_{0|0}$ and, as shown in Section \\ref{sec:mc2}, $\\bar t$ is typically very small. Then, for the KS we show that, given the true parameters, the factors are $\\sqrt N$-consistent. Third, given the true factors, the loadings estimator are consistent, with convergence rate $T$ for the loadings of the $I(1)$ components of the factors and convergence rate $\\sqrt T$ for the loadings of the stationary \ncomponent of the factors. As a result, for any given $i$, the whole loadings vector is $\\sqrt T$-consistent, unless $d=q$, in which case each all $q$ factors are random walks and then the loadings vector would be $T$-consistent. \n\nUnder the assumption $n_1=0$, $n_a=0$, and $n_b=0$, our model is equivalent to the model studied in \\citet{bai04}, who considers estimation by means of PCs in levels. In this respect, we notice that the rates in Proposition \\ref{th:chiNS} are very similar to those in \\citet[Theorem 6 for the case $d0$, and $\\E_{\\varphi_n}[\\bm\\nu_{mt}\\bm\\nu_{mt-k}] = 0_{m\\times m}$ for all $k\\ne 0$. If $i\\notin\\mathcal I_m$, then \\eqref{eq:NSDFM1} stays the same. Moreover, we leave the dynamics of the factors in \\eqref{eq:NSDFM2} unchanged, while we change \\eqref{eq:NSDFM3} to\n\\begin{align}\n\\xi_{it}&=\\xi_{it-1} + e_{it},\\;\\text{ if }\\; i\\in \\mathcal I_1,\\;\\text{ and }\\; \\xi_{it}= e_{it},\\;\\text{ if }\\; i\\notin \\mathcal I_1,\\label{eq:NSDFM3bis}\n\\end{align}\nwhere Assumptions \\ref{ass:modelNS}(b) and \\ref{ass:modelNS}(c) still hold, and $\\E_{\\varphi_n}[\\nu_{it} e_{js}]=0$, for all $t,s\\in\\mathbb Z$, all $i\\in\\mathcal I_m$ and all $j=1,\\ldots, n$.\nFinally, according to \\eqref{eq:NSDFM5} and \\eqref{eq:NSDFM4}, we have the state equations\n\\begin{align}\n&\\alpha_{it}=\\alpha_{it-1} + \\omega_{it},\\;\\text{ if }\\; i\\in \\mathcal I_a,\\label{eq:NSDFM5bis}\\\\\n&\\beta_{it}=\\beta_{it-1} + \\eta_{it},\\;\\text{ if }\\; i\\in \\mathcal I_b,\\label{eq:NSDFM4bis}\n\\end{align}\nsuch that $\\E_{\\varphi_n}[\\nu_{it} \\omega_{js}]=0$, and $\\E_{\\varphi_n}[\\nu_{it} \\eta_{js}]=0$, for all $t,s\\in\\mathbb Z$, all $i\\in\\mathcal I_m$, and all $j\\in\\mathcal I_a$ or $j\\in\\mathcal I_b$. \n\nThe model, which has as measurement equation either \\eqref{eq:NSDFM1} or \\eqref{eq:NSDFM1bis} if $i\\in\\mathcal I_m$, and which has as state equations \\eqref{eq:NSDFM2}, and, if needed, also equations \\eqref{eq:NSDFM3bis}, \\eqref{eq:NSDFM5bis} and \\eqref{eq:NSDFM4bis}, has a compact state space form which is given in \\ref{app:prestNS}, together with the details on its estimation via the EM algorithm. In particular, letting $w_{it}=\\alpha_{it}+\\beta_{it}t+\\xi_{it}$, for all $i\\in\\mathcal I_m$ we show that, at a given iteration $k\\ge 0$ of the EM algorithm, the M-step gives the loadings estimators:\n\\begin{align}\n\\widehat{\\bm \\lambda}_{i}^{(k+1)}\\!=\n\\l\\{\\sum_{t=1}^T\\E_{\\widehat{\\varphi}_n^{(k)}}\\!\\!\\l[\\l(\\begin{array}{ll}\n\\bm f_t\\bm f_t^\\prime&\\bm f_t\\bm f_{t-1}^\\prime\\\\\n\\bm f_{t-1}\\bm f_t^\\prime&\\bm f_{t-1}\\bm f_{t-1}^\\prime\n\\end{array}\\r)\\!\\!\\bigg\\vert \\bm X_{nT}\n\\r]\\r\\}^{\\!\\!-1}\\!\\!\\!\n\\l\\{\\sum_{t=1}^T\\E_{\\widehat{\\varphi}_n^{(k)}}\\!\\!\\l[\\l(\\begin{array}{l}\n\\bm f_t(x_{it}-w_{it})\\\\\n\\bm f_{t-1}(x_{it}-w_{it})\n\\end{array}\\r)\\!\\!\\bigg\\vert \\bm X_{nT},\n\\r]\\r\\}.\\nonumber\n\\end{align}\nwhere $\\bm\\lambda_i=(\\bm{b}_{0i}^\\prime\\;\\bm{b}_{1i}^\\prime)^\\prime$, while for $i\\notin\\mathcal I_m$ the loadings estimator is the same as in \\eqref{eq:param1NS}. Formulas for all other estimators are given in \\ref{app:prestNS}. In order to be able to compute $\\widehat{\\bm \\lambda}_{i}^{(k+1)}$, we have to estimate the $m$ additional latent states $w_{it}$ and therefore we also need modify the KS accordingly (see \\ref{app:prestNS} for details). \n\nIn \\ref{app:misidioNS}, we provide an overview of the challenges involved by this task and we provide an informal derivation of the conditions necessary for consistent estimation, together with the related convergence rates. Three main results emerge. First, the new latent states can be recovered only if they display also some degree of cross-sectional correlation, as if they were driven by some common factor which is weakly pervasive for the whole panel. The intuition is that, if the additional latent states are completely uncorrelated across the components of $\\mathbf x_{nt}$, then pooling many series does not help in recovering them, since their effect is always dominated by the factors. \n\nSecond, when the previous condition is verified, then we can still achieve $\\sqrt n$-consistency for the estimated factors (as in the proof of Proposition \\ref{th:chiNS}), regardless of $m$, but provided that the variance of the measurement error $\\nu_{it}$ in \\eqref{eq:NSDFM1bis} is fixed in such a way that $\\phi=o(n^{-1})$, that is, it is asymptotically negligible. Indeed, the presence of $\\nu_{it}$ represents a mis-specification of the original model in \\eqref{eq:NSDFM1}, which needs to be introduced only as a numerical device, since the KF is not be defined if $\\phi=0$. The smaller is $\\phi$, the smaller the effect of the mis-specification is, and, therefore, the estimation of the factors is unaffected by the additional states. \n\nAs a consequence of this result, our estimator converges at a faster rate than those proposed by \\citet{baing04} and \\citet{BLL2}, which are based on PC analysis on the differenced data. This faster convergence rate comes from the fact that we distinguish \\textit{a priori} between $I(1)$ and stationary idiosyncratic components. By contrast, due to differencing the estimator of \\citet{baing04} and \\citet{BLL2} essentially treat all idiosyncratic components as if they were $I(1)$. Of course, for the implementation of our estimator, it is crucial to be able to determine consistently which idiosyncratic component is $I(1)$---for example, using the test for idiosyncratic unit roots proposed by \\citet{baing04}.\n\nThird, to achieve consistency of the additional latent states a necessary condition is $mn^{-1}\\to 0$. This reflects the obvious intuition that the more latent states we need to estimate, the worse the performance of our estimator is going to be. Moreover, $\\sqrt n$-consistency for the new states can be obtained for any $m$, but only if we choose an even smaller value of $\\phi$, namely $\\phi=o((m\\sqrt n)^{-1})$. \n \nWe conclude with three remarks. First, the requirement that the new latent states display some degree of cross-sectional correlation is perfectly in line with Assumption \\ref{ass:modelNS}(c) according to which the idiosyncratic components can be cross-correlated. Moreover, we can relax Assumptions \\ref{ass:modelNS}(e) and \\ref{ass:modelNS}(g) to allow for some correlation across the innovations $e_{it}$, $\\omega_{it}$, and $\\eta_{it}$ in \\eqref{eq:NSDFM3bis}, \\eqref{eq:NSDFM5bis} and \\eqref{eq:NSDFM4bis}. Indeed, it is reasonable to assume that local linear trends are shared by real variables (e.g., GDP and GDI), or that local levels are more apt to capture time-varying mean of groups of variables belonging, for example, to the labor market. Nevertheless, as shown in the proof of Proposition \\ref{th:chiNS}, the fact that we estimate the $I(1)$ idiosyncratic components without modeling the cross-correlation between their innovations, will add miss-specification to our model, but will not affect the consistency of our estimates. \n\nSecond, as a far as estimation of the parameters given estimates of the states is concerned, we conjecture that nothing changes with respect to the results used in the proof of Proposition \\ref{th:chiNS}, provided the states estimators are $\\sqrt n$-consistent. Third, since the above are just asymptotic arguments, the choice of $\\phi$ is not straightforward. A common way to proceed consists in initializing $\\phi$ to be very small for all $m$ additional states and then update its estimate at each iteration of the EM algorithm, thus adding $m$ additional parameters. This is the way we implement the EM algorithm in the next section (see also \\ref{app:prestNS}).\n\n\n\n\n\n\n\n\n\\section{MonteCarlo results}\\label{sec:mc2}\nThroughout, we let $n\\in\\{75,100,200,300\\}$, $T\\in\\{75,100,200,300\\}$, $q\\in\\{2,4\\}$, and $s\\in\\{0,1\\}$, and we simulate data according to \\eqref{eq:NSDFM1}, \\eqref{eq:NSDFM2}, \\eqref{eq:NSDFM3}, and \\eqref{eq:NSDFM4} as follows.\n\nFirst, the factor loadings are such that $[\\bm{\\mathcal B}_{kn}]_{ij} \\sim N(1,1)$ for $k=0,\\ldots, s$, and then if $s=1$, for all $j=1,\\ldots q$, we take $n\/2$ randomly selected elements of $[\\bm{\\mathcal B}_{1n}]_{\\cdot j}$ and we set them to zero. Second, for the common factors we set the VAR order $p=2$, and to generate $\\bm{\\mathcal A}(L)$ we use the Smith-McMillan factorization according to which $\\bm{\\mathcal A}(L)=\\mathbfcal{U}(L) \\mathbfcal{M}(L) \\mathbfcal{V}(L)$, where $\\mathbfcal{M}(L)= \\mbox{diag} \\left( (1-L)\\mathbf I_{q-d}, \\mathbf I_d\\right)$, $\\mathbfcal{V}(L)=\\mathbf I_q$, and $\\mathbfcal{U}(L)=(\\mathbf I_q-\\mathbfcal{U}_1 L)$, where $\\mathbfcal{U}_1=\\mu\\,\\widetilde{\\mathbfcal{U}}_1(\\nu^{(1)}(\\widetilde{\\mathbfcal{U}}_1))^{-1}$, where the diagonal elements of $\\widetilde{\\mathbfcal{U}}_1$ are drawn from a uniform distribution on $[0.5,0.8]$, while the off-diagonal elements from a uniform distribution on $[0,0.3]$, and $\\mu=0.5$. In this way, $\\bm f_t$ follows a VAR(2) with $q-d$ unit roots, or, equivalently, a VECM(1), where the number cointegration relations is set to $d=1$. The common innovations are such that $\\bm u_t\\stackrel{iid}{\\sim} \\mathcal{N}(\\mathbf 0_q,\\mathbf I_q)$, or $\\bm u_t\\stackrel{iid}{\\sim} t_4(\\mathbf 0_q,\\mathbf I_q)$.\n\nThird, each idiosyncratic component follows an AR(2) with roots $\\rho_{i1}$ and $\\rho_{i2}$, such that $\\rho_{i1}=1$ if $\\xi_{it}\\sim I(1)$, while $\\rho_{i1}=0$ otherwise, and $\\rho_{i2}$ is drawn from a uniform distribution on $[0.2,0.6]$. We randomly select $n_1$ idiosyncratic components to have a unit root, with $n_1\\in\\{0,25,50,75,100\\}$, provided $n_10$, while, if $\\tau=0$, $\\bm\\Gamma^e_n$ is diagonal with entries drawn from a uniform distribution on $[0.5,1.5]$. We set $\\tau\\in\\{0,0.5\\}$.\n\nFourth, we randomly select $n_b$ variables to have a non-zero linear trend, with $n_b\\in\\{0,25,50,75,100\\}$, provided $n_b0$. Similarly we do not add idiosyncratic states even when $\\delta>0$. In other words, we always estimate a mis-specified model and, in this way, we are able to assess how robust our-estimators are with respect to mis-specifications. \n\nIn Table \\ref{tab:PttNS}, we report for different values of $n$ and for $t=1,\\ldots, 10$, the trace of the one-step-ahead, KF, and KS MSEs when $q=2$, $s=1$, $T=100$, $\\tau=0.5$, and $\\delta=0.2$ (serially and cross-correlated idiosyncratic components). The MSEs are computed using the true simulated value of the parameters in order to verify numerically convergence to the steady-state. First, as $n$ grows, the one-step-ahead MSE reaches a steady state within maximum five time periods and $\\mbox{tr}(\\mathbf P_{t|t-1})\/q\\simeq 1$. This is consistent with the fact that due to the presence of unit roots we inizialize the filter with a vary large value of $\\mathbf P_{0|0}$. Second, the KF and KS MSEs are very similar and both decrease to zero as $n$ grows and $\\mbox{tr}(\\mathbf P_{t|t})n\/q$ and $\\mbox{tr}(\\mathbf P_{t|T})n\/q$, computed when $t=10$, stabilize as $n$ grows thus showing that the rate of decrease is $n$.\n\n\\begin{table}[t!]\n\\setlength{\\tabcolsep}{0\\textwidth}\n\\caption{Simulation results NS-DFM}\\label{tab:PttNS} \\centering \\smallskip\n\n\\textsc{Kalman filter and Kalman smoother MSEs} \\smallskip\n\n\\scriptsize\n\\vskip .2cm\n\\begin{tabular}{L{.04\\textwidth} L{.08\\textwidth} | C{.11\\textwidth}C{.11\\textwidth}C{.11\\textwidth}C{.1\\textwidth}C{.11\\textwidth}C{.11\\textwidth}C{.11\\textwidth}C{.11\\textwidth}}\n\\multicolumn{10}{c}{Serially and cross-correlated idiosyncratic ($\\tau=0.5$, $\\delta=0.2$), $n_1=0$, $n_b=0$, $q=2$, $s=1$}\\\\\n\\multicolumn{10}{c}{Gaussian innovations}\\\\[-4pt]\n\\\\\n\\hline\n\\hline\n&&\\\\[-4pt]\n \t&$n$&\t$5$\t&\t$10$\t&\t$25$\t&\t$50$\t&\t$75$\t&\t$100$\t&\t$200$\t&\t$300$\t\\\\[4pt]\n\\hline\n&&\\\\[-4pt]\n\\multicolumn{2}{l}{$\\mbox{tr}(\\mathbf P_{0|0})\/q$}\t\\vline \n\t&\t147.6161\t&\t154.1094\t&\t133.2495\t&\t152.7333\t&\t138.4666\t&\t109.5626\t&\t120.8897\t&\t134.7097\t\\\\[4pt]\n\\hline\n&&\\\\[-4pt]\n&$t=1$\t&\t2.874610\t&\t1.601115\t&\t1.132925\t&\t1.076572\t&\t0.981511\t&\t1.020192\t&\t0.947337\t&\t0.980840\t\\\\\n&$t=2$\t&\t2.768656\t&\t1.543904\t&\t1.110419\t&\t1.064744\t&\t0.971928\t&\t1.014110\t&\t0.945185\t&\t0.979096\t\\\\\n\\multirow{4}{*}{\\rotatebox{90}{$\\mbox{tr}(\\mathbf P_{t|t-1})\/q$}}\n&\t$t=3$\t&\t2.748894\t&\t1.537389\t&\t1.109305\t&\t1.064269\t&\t0.970874\t&\t1.013479\t&\t0.945034\t&\t0.978928\t\\\\\n&\t$t=4$\t&\t2.746597\t&\t1.536414\t&\t1.109239\t&\t1.064245\t&\t0.970735\t&\t1.013398\t&\t0.945022\t&\t0.978908\t\\\\\n&\t$t=5$\t&\t2.746357\t&\t1.536248\t&\t1.109235\t&\t1.064244\t&\t0.970716\t&\t1.013387\t&\t0.945021\t&\t0.978906\t\\\\\n&\t$t=6$\t&\t2.746329\t&\t1.536220\t&\t1.109235\t&\t1.064244\t&\t0.970714\t&\t1.013386\t&\t0.945020\t&\t0.978906\t\\\\\n&\t$t=7$\t&\t2.746325\t&\t1.536215\t&\t1.109235\t&\t1.064244\t&\t0.970713\t&\t1.013385\t&\t0.945020\t&\t0.978906\t\\\\\n&\t$t=8$\t&\t2.746324\t&\t1.536214\t&\t1.109235\t&\t1.064244\t&\t0.970713\t&\t1.013385\t&\t0.945020\t&\t0.978906\t\\\\\n&\t$t=9$\t&\t2.746324\t&\t1.536214\t&\t1.109235\t&\t1.064244\t&\t0.970713\t&\t1.013385\t&\t0.945020\t&\t0.978906\t\\\\\n&\t$t=10$\t&\t2.746324\t&\t1.536214\t&\t1.109235\t&\t1.064244\t&\t0.970713\t&\t1.013385\t&\t0.945020\t&\t0.978906\t\\\\\n[4pt]\n\\hline\n&&\\\\[-4pt]\n&\t$t=1$\t&\t1.665780\t&\t0.548360\t&\t0.161240\t&\t0.107121\t&\t0.049408\t&\t0.036570\t&\t0.015982\t&\t0.011897\t\\\\\n&\t$t=2$\t&\t1.386910\t&\t0.400762\t&\t0.116701\t&\t0.079362\t&\t0.037964\t&\t0.030533\t&\t0.011515\t&\t0.007687\t\\\\\n\\multirow{4}{*}{\\rotatebox{90}{$\\mbox{tr}(\\mathbf P_{t|t})\/q$}}\n&\t$t=3$\t&\t1.369813\t&\t0.393790\t&\t0.114352\t&\t0.078298\t&\t0.037113\t&\t0.030087\t&\t0.011295\t&\t0.007466\t\\\\\n&\t$t=4$\t&\t1.366557\t&\t0.392627\t&\t0.114174\t&\t0.078221\t&\t0.037001\t&\t0.030029\t&\t0.011276\t&\t0.007446\t\\\\\n&\t$t=5$\t&\t1.365967\t&\t0.392442\t&\t0.114163\t&\t0.078216\t&\t0.036985\t&\t0.030021\t&\t0.011274\t&\t0.007443\t\\\\\n&\t$t=6$\t&\t1.365898\t&\t0.392411\t&\t0.114163\t&\t0.078216\t&\t0.036983\t&\t0.030020\t&\t0.011274\t&\t0.007443\t\\\\\n&\t$t=7$\t&\t1.365890\t&\t0.392406\t&\t0.114163\t&\t0.078216\t&\t0.036983\t&\t0.030020\t&\t0.011274\t&\t0.007443\t\\\\\n&\t$t=8$\t&\t1.365889\t&\t0.392405\t&\t0.114163\t&\t0.078216\t&\t0.036983\t&\t0.030020\t&\t0.011274\t&\t0.007443\t\\\\\n&\t$t=9$\t&\t1.365889\t&\t0.392404\t&\t0.114163\t&\t0.078216\t&\t0.036983\t&\t0.030020\t&\t0.011274\t&\t0.007443\t\\\\\n&\t$t=10$\t&\t1.365889\t&\t0.392404\t&\t0.114163\t&\t0.078216\t&\t0.036983\t&\t0.030020\t&\t0.011274\t&\t0.007443\t\\\\\n[4pt]\n\\hline\n&&\\\\[-4pt]\n\\multicolumn{2}{l}{$\\mbox{tr}(\\mathbf P_{10|10})n\/q$}\t\\vline &\t3.414722\t&\t1.962022\t&\t1.427032\t&\t1.955403\t&\t1.386863\t&\t1.501011\t&\t1.127376\t&\t1.116435\t\\\\[4pt]\n\\hline\n&&\\\\[-4pt]\n&\t$t=1$\t&\t0.938577\t&\t0.368873\t&\t0.110011\t&\t0.074161\t&\t0.028644\t&\t0.017577\t&\t0.010943\t&\t0.008560\t\\\\\n&\t$t=2$\t&\t0.708279\t&\t0.253087\t&\t0.073988\t&\t0.051268\t&\t0.021393\t&\t0.014262\t&\t0.007372\t&\t0.005221\t\\\\\n\\multirow{4}{*}{\\rotatebox{90}{$\\mbox{tr}(\\mathbf P_{t|T})\/q$}}\n&\t$t=3$\t&\t0.697724\t&\t0.250786\t&\t0.072065\t&\t0.050450\t&\t0.020933\t&\t0.014096\t&\t0.007206\t&\t0.005061\t\\\\\n&\t$t=4$\t&\t0.696501\t&\t0.250387\t&\t0.071913\t&\t0.050388\t&\t0.020872\t&\t0.014075\t&\t0.007192\t&\t0.005046\t\\\\\n&\t$t=5$\t&\t0.696180\t&\t0.250328\t&\t0.071903\t&\t0.050384\t&\t0.020864\t&\t0.014072\t&\t0.007190\t&\t0.005045\t\\\\\n&\t$t=6$\t&\t0.696142\t&\t0.250318\t&\t0.071903\t&\t0.050384\t&\t0.020863\t&\t0.014072\t&\t0.007190\t&\t0.005045\t\\\\\n&\t$t=7$\t&\t0.696138\t&\t0.250317\t&\t0.071903\t&\t0.050384\t&\t0.020863\t&\t0.014072\t&\t0.007190\t&\t0.005045\t\\\\\n&\t$t=8$\t&\t0.696137\t&\t0.250316\t&\t0.071903\t&\t0.050384\t&\t0.020863\t&\t0.014072\t&\t0.007190\t&\t0.005045\t\\\\\n&\t$t=9$\t&\t0.696137\t&\t0.250316\t&\t0.071903\t&\t0.050384\t&\t0.020863\t&\t0.014072\t&\t0.007190\t&\t0.005045\t\\\\\n&\t$t=10$\t&\t0.696137\t&\t0.250316\t&\t0.071903\t&\t0.050384\t&\t0.020863\t&\t0.014072\t&\t0.007190\t&\t0.005045\t\\\\\n[4pt]\n\\hline\n&&\\\\[-4pt]\t\t\t\t\t\t\t\t\t\t\t\n\\multicolumn{2}{l}{$\\mbox{tr}(\\mathbf P_{10|T})n\/q$}\t\\vline &\t1.740343\t&\t1.251582\t&\t0.898783\t&\t1.259595\t&\t0.782350\t&\t0.703592\t&\t0.719033\t&\t0.756698\t\\\\[4pt]\n\\hline\n\\hline\n\n\\end{tabular}\n\\end{table}\n\nIn Table \\ref{tab:mc1NS} and in Table \\ref{tab:mc1NSt}, we report the relative MSE of our estimator over the MSE of the common component estimators obtained by PC as in \\citet{bai04}, and by PC in first differences as in \\citet{baing04} and \\citet{BLL2}. Overall our estimator outperforms the others with the exception of the latter, which is show to perform better when $n_1$ becomes very large and about the same order of magnitude as $n$. This reflects the additional computational burden of our estimator which requires increasing the number of latent states when the idiosyncratic components are non-stationary and therefore we must include their dynamics in the model.\n\n\\begin{table}[ht!]\n\\setlength{\\tabcolsep}{0\\textwidth}\n\\caption{Simulation results - Common components}\\label{tab:mc1NS}\n\\centering\n\\small \\textsc{Relative Mean Squared Errors} \\smallskip\n\n\\footnotesize\n\\begin{tabular}{C{.085\\textwidth}C{.085\\textwidth}C{.085\\textwidth}C{.085\\textwidth} | C{.11\\textwidth} C{.11\\textwidth} C{.11\\textwidth} | C{.11\\textwidth} C{.11\\textwidth} C{.11\\textwidth} }\n\\multicolumn{10}{c}{Serially and cross correlated idiosyncratic components ($\\tau=0.5$, $\\delta =0.2$). Gaussian innovations}\\\\\n\\hline\n\\hline\n&&&&\\multicolumn{3}{c|}{$q=2$, $s=0$}&\\multicolumn{3}{c}{$q=2$, $s=1$}\\\\\\hline\n$n$ & $T$ & $n_1$& $n_b$& \\tiny{B}& \\tiny{BN}& \\tiny{BLL}& \\tiny{B}& \\tiny{BN}& \\tiny{BLL}\\\\\n\\hline\n75\t&\t75\t&\t0\t&\t0\t&\t0.58\t&\t0.00\t&\t0.28\t&\t0.54\t&\t0.01\t&\t0.53\t\\\\\n100\t&\t100\t&\t0\t&\t0\t&\t0.54\t&\t0.00\t&\t0.22\t&\t0.54\t&\t0.00\t&\t0.49\t\\\\\n200\t&\t200\t&\t0\t&\t0\t&\t0.45\t&\t0.00\t&\t0.12\t&\t0.59\t&\t0.00\t&\t0.39\t\\\\\n300\t&\t300\t&\t0\t&\t0\t&\t0.40\t&\t0.00\t&\t0.08\t&\t0.63\t&\t0.00\t&\t0.32\t\\\\\n\\hline\n75\t&\t75\t&\t25\t&\t25\t&\t0.01\t&\t0.02\t&\t0.44\t&\t0.04\t&\t0.04\t&\t0.83\t\\\\\n100\t&\t100\t&\t25\t&\t25\t&\t0.01\t&\t0.01\t&\t0.47\t&\t0.02\t&\t0.02\t&\t0.68\t\\\\\n200\t&\t200\t&\t25\t&\t25\t&\t0.00\t&\t0.00\t&\t0.53\t&\t0.01\t&\t0.01\t&\t0.66\t\\\\\n300\t&\t300\t&\t25\t&\t25\t&\t0.00\t&\t0.00\t&\t0.77\t&\t0.00\t&\t0.00\t&\t0.73\t\\\\\n\\hline\n75\t&\t75\t&\t50\t&\t50\t&\t0.05\t&\t0.21\t&\t1.55\t&\t0.11\t&\t0.23\t&\t1.47\t\\\\\n100\t&\t100\t&\t50\t&\t50\t&\t0.02\t&\t0.12\t&\t1.45\t&\t0.06\t&\t0.12\t&\t1.53\t\\\\\n200\t&\t200\t&\t50\t&\t50\t&\t0.00\t&\t0.02\t&\t0.82\t&\t0.01\t&\t0.02\t&\t0.75\t\\\\\n300\t&\t300\t&\t50\t&\t50\t&\t0.00\t&\t0.01\t&\t0.93\t&\t0.00\t&\t0.01\t&\t0.80\t\\\\\n\\hline\n100\t&\t100\t&\t75\t&\t75\t&\t0.03\t&\t0.23\t&\t1.44\t&\t0.08\t&\t0.23\t&\t1.48\t\\\\\n200\t&\t200\t&\t75\t&\t75\t&\t0.00\t&\t0.03\t&\t0.80\t&\t0.02\t&\t0.06\t&\t1.52\t\\\\\n300\t&\t300\t&\t75\t&\t75\t&\t0.00\t&\t0.02\t&\t1.11\t&\t0.01\t&\t0.02\t&\t0.92\t\\\\\n\\hline\n200\t&\t200\t&\t100\t&\t100\t&\t0.01\t&\t0.12\t&\t1.87\t&\t0.04\t&\t0.15\t&\t2.08\t\\\\\n300\t&\t300\t&\t100\t&\t100\t&\t0.00\t&\t0.03\t&\t1.08\t&\t0.01\t&\t0.04\t&\t1.45\t\\\\\n\\hline\n\\hline\n\\end{tabular}\n\n\\begin{tabular}{p{\\textwidth}}\\tiny\nThis table reports relative MSEs of the QML estimator proposed in this paper over the MSE of the common component estimators obtained by PC as in \\citet{bai04} (B), and by PC in first differences as in \\citet{baing04} (BN) and \\citet{BLL2} (BLL).\n\\end{tabular}\n\\end{table}\n\n\n\\begin{table}[t!]\n\\setlength{\\tabcolsep}{0\\textwidth}\n\\caption{Simulation results NS-DFM - Common components}\\label{tab:mc1NSt}\n\\centering\n\\small \\textsc{Relative Mean Squared Errors} \\smallskip\n\n\\footnotesize\n\\begin{tabular}{C{.085\\textwidth}C{.085\\textwidth}C{.085\\textwidth}C{.085\\textwidth} | C{.11\\textwidth} C{.11\\textwidth} C{.11\\textwidth} | C{.11\\textwidth} C{.11\\textwidth} C{.11\\textwidth} }\n\\multicolumn{10}{c}{Serially and cross correlated idiosyncratic components ($\\tau=0.5$, $\\delta =0.2$). Student $t_4$ innovations}\\\\\n\\hline\n\\hline\n&&&&\\multicolumn{3}{c|}{$q=2$, $s=0$}&\\multicolumn{3}{c}{$q=2$, $s=1$}\\\\\n$n$ & $T$ & $n_1$& $n_b$&Rel-MSE&Rel-MSE&Rel-MSE&Rel-MSE&Rel-MSE&Rel-MSE\\\\\n&&&&\\tiny B&\\tiny BN&\\tiny BLL&\\tiny B&\\tiny BN&\\tiny BLL\\\\\n\\hline\n75\t&\t75\t&\t0\t&\t0\t&\t0.58\t&\t0.00\t&\t0.30\t&\t0.58\t&\t0.01\t&\t0.57\t\\\\\n100\t&\t100\t&\t0\t&\t0\t&\t0.55\t&\t0.00\t&\t0.25\t&\t0.56\t&\t0.00\t&\t0.53\t\\\\\n200\t&\t200\t&\t0\t&\t0\t&\t0.45\t&\t0.00\t&\t0.12\t&\t0.60\t&\t0.00\t&\t0.40\t\\\\\n300\t&\t300\t&\t0\t&\t0\t&\t0.41\t&\t0.00\t&\t0.09\t&\t0.67\t&\t0.00\t&\t0.35\t\\\\\n\\hline\n75\t&\t75\t&\t25\t&\t25\t&\t0.01\t&\t0.02\t&\t0.43\t&\t0.06\t&\t0.04\t&\t0.83\t\\\\\n100\t&\t100\t&\t25\t&\t25\t&\t0.01\t&\t0.01\t&\t0.39\t&\t0.03\t&\t0.02\t&\t0.70\t\\\\\n200\t&\t200\t&\t25\t&\t25\t&\t0.00\t&\t0.00\t&\t0.40\t&\t0.01\t&\t0.00\t&\t0.64\t\\\\\n300\t&\t300\t&\t25\t&\t25\t&\t0.00\t&\t0.00\t&\t0.56\t&\t0.01\t&\t0.00\t&\t0.68\t\\\\\n\\hline\n75\t&\t75\t&\t50\t&\t50\t&\t0.06\t&\t0.17\t&\t1.51\t&\t0.14\t&\t0.17\t&\t1.45\t\\\\\n100\t&\t100\t&\t50\t&\t50\t&\t0.04\t&\t0.11\t&\t1.57\t&\t0.09\t&\t0.10\t&\t1.46\t\\\\\n200\t&\t200\t&\t50\t&\t50\t&\t0.00\t&\t0.01\t&\t0.57\t&\t0.01\t&\t0.01\t&\t0.69\t\\\\\n300\t&\t300\t&\t50\t&\t50\t&\t0.00\t&\t0.01\t&\t0.63\t&\t0.01\t&\t0.01\t&\t0.71\t\\\\\n\\hline\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n100\t&\t100\t&\t75\t&\t75\t&\t0.04\t&\t0.18\t&\t1.36\t&\t0.12\t&\t0.21\t&\t1.48\t\\\\\n200\t&\t200\t&\t75\t&\t75\t&\t0.01\t&\t0.02\t&\t0.65\t&\t0.03\t&\t0.05\t&\t1.41\t\\\\\n300\t&\t300\t&\t75\t&\t75\t&\t0.00\t&\t0.01\t&\t0.71\t&\t0.01\t&\t0.01\t&\t0.81\t\\\\\n\\hline\n200\t&\t200\t&\t100\t&\t100\t&\t0.02\t&\t0.10\t&\t1.68\t&\t0.06\t&\t0.13\t&\t1.99\t\\\\\n300\t&\t300\t&\t100\t&\t100\t&\t0.00\t&\t0.02\t&\t0.72\t&\t0.02\t&\t0.04\t&\t1.39\t\\\\\n\\hline\n\\hline\n\\end{tabular}\n\n\\begin{tabular}{p{\\textwidth}}\\tiny\nThis table reports relative MSEs of the QML estimator proposed in this paper over the MSE of the common component estimators obtained by PC as in \\citet{bai04} (B), and by PC in first differences as in \\citet{baing04} (BN) and \\citet{BLL2} (BLL).\n\\end{tabular}\n\\end{table}\n\nSome clarifications on the competing methods considered are necessary in order to interpret the results in Table \\ref{tab:mc1NS} and in Table \\ref{tab:mc1NSt} (we refer to the original papers for details). First, notice that all alternative approaches considered here do not allow for dynamic loadings, so here they are implemented by computing the first $q(s+1)$ PCs. \n\nSecond, despite the common practice in the literature, \\citet{bai04} did not propose its approach for factor model estimation, but rather to estimate common trends, and it is based on the crucial assumption of all idiosyncratic components being stationary. Indeed, we see from Table \\ref{tab:mc1NS} that when $n_1>0$ this approach fails completely. \n\nThird, the \\citet{baing04} approach delivers estimates of the common component which are obtained \n\\begin{inparaenum}[($i$)]\n\t\\item by detrending the data by estimating the slope of the trend with the mean of the data in first difference; then\n\t\\item by estimating the factors in first differences; and, finally,\n\t\\item by cumulating the differenced estimator to obtain an estimate of the levels. \n\\end{inparaenum} \nAs such, this estimator is always subject to a location shift---it can be shown to converge to a Brownian bridge. Notice that this approach was introduced to test for the presence of unit roots rather than for factor model estimation, and, while the test is unaffected by location shifts, the use of the cumulated estimator for other scopes is not justified in general. As we see from Table \\ref{tab:mc1NS}, this approach fails to consistently reconstruct the common component in all cases considered. \n\nFourth, the approach in \\citet{BLL2} is based on the same ideas of \\citet{baing04}, but it takes care of the above mentioned issues related to detrending and cumulation, and, therefore, it is a valid alternative.\n\n\n\\section{Concluding remarks}\\label{sec:conclusion}\n\nThis paper considers estimation of large non-stationary approximate dynamic factor models by means of the Expectation Maximization algorithm, implemented jointly with the Kalman smoother. In our model the factors are a cointegrated vector process, thus containing both common $I(1)$ trends and stationary (cyclical) components. We show that, as the cross-sectional dimension $n$ and the sample size $T$ diverge to infinity, the common factors, the factor loadings, and the common component estimated are $\\min(\\sqrt n,\\sqrt T)$-consistent at each $i$ and $t$.\n\nFurthermore, we show that the model can be extended to account for the possible presence of idiosyncratic trends, as well as the presence of secular (linear) trends, which can have either a constant slope (deterministic linear trends) or a time-varying slope (local linear trends). Consistent estimation of this case is also considered. \n\nFinally, the results in this paper provides the theoretical background for the application considered in \\citet{OGAP}, where the NS-DFM is used to estimate the output gap in the US.\n\n\\singlespacing\n{\\small{\n\\setlength{\\bibsep}{.2cm}\n\\bibliographystyle{chicago}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzdwlg b/data_all_eng_slimpj/shuffled/split2/finalzzdwlg new file mode 100644 index 0000000000000000000000000000000000000000..83b16d7f31160c633ddb98ef47002bf9f85392f0 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzdwlg @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nThroughout this paper $R$ is a commutative noetherian ring, $\\frak a$ is an ideal of $R$. Given a Serre subcategory $\\cS$ of $R$-modules, an $R$-module $M$ is said to be $\\cS$-$\\frak a$-{\\it cofinite} if $\\Supp M\\subseteq V(\\frak a)$ and $\\Ext_R^i(R\/\\frak a, M)\\in\\cS$ for all integers $i\\geq 0$. Let $\\cN$ be the subcategory of finitely generated $R$-modules.\n\n The extension subcategory induced by $\\cN$ and $\\cS$ is denoted by $\\cN\\cS$, consisting of those $R$-modules $M$ for which there exist an exact sequence $0\\To N\\To M\\To S\\To 0$ such that $N\\in\\cN$ and $S\\in\\cS$. It has been proved by [Y] that $\\cN\\cS$ is Serre. A well-known example for these subcategories is $\\cN\\cA$, the subcategory of {\\it minimax modules} studied by [Z] where $\\cA$ is the subcategory of artinian modules. Another example is $\\cN\\cF$, the subcategory of FSF modules introduced by [Q], where $F$ consists of all modules of finite support. When $\\cS=0$, an $\\cN\\cS$-$\\frak a$-cofinite module was known as an $\\frak a$-cofinite module which is defined for the first time by Hartshorne [H], giving a negative answer to a question of [G, Expos XIII, Conjecture 1.1]. Many people [BNS, M1, M2, NS] studied $\\frak a$-cofiniteness in various cases. \n\nThe main aim of this paper is to extend the fundamental results about $\\frak a$-cofinite modules at small dimensions to $\\cN\\cS$-$\\frak a$ cofinite modules.\n We recall that a Serre subcategory $\\cS$ satisfies the condition $C_{\\frak a}$ if for every $R$-module $M$, the following implication holds.\n\\begin{center}\n$C_{\\frak a}$: If $\\Gamma_{\\frak a}(M)=M$ and $(0:_M{\\frak a})$ is in $\\cS$, then\n$M$ is in $\\cS$.\n\\end{center}\n\nFor every $\\frak p\\in\\Spec R$, we denote by $\\cS({\\frak p})$ the smallest Serre subcategory of $R_{\\frak p}$-modules containing $\\cS_{\\frak p}=\\{M_{\\frak p}|\\hspace{0.1cm} M\\in\\cS\\}$. For every $R$-module $M$, $\\dim M$ means the dimension of $\\Supp_RM$ which is the length of the longest chain of prime ideals in $\\Supp_RM$. In Section 2, we prove the following result. \n\\begin{Theorem}\nLet $(R,\\frak m)$ be a local ring, let $S(\\frak p)$ satisfy the condition $\\frak pR_{\\frak p}$ for each $\\frak p\\in V(\\frak a)$. If $M$ is an $R$-module of dimension $d$ such that $\\Ext_R^i(R\/\\frak a,M)\\in\\cN\\cS$ for each $i\\leq d$, then $\\Ext_R^i(N,M)\\in\\cN\\cS$ for each $i\\geq 0$ and each finitely generated $R$-module $N$ with $\\dim N\\leq 2$ and $\\Supp_RN\\subseteq V(\\frak a)$.\n\\end{Theorem}\n Assume that $M$ is an $R$-module such that $\\Supp_R M\\subseteq V(\\frak a)$. Melkersson [M1, Theorem 2.3] showed that if $\\dim R\/\\frak a=1$, then $M$ is $\\frak a$-cofinite if and only if $\\Hom_R(R\/\\frak a,M)$ and $\\Ext_R^1(R\/\\frak a,M)$ are finitely generated. The above theorem generalizes this result when $R$ is a local ring and $S(\\frak p)$ satisfies the condition $\\frak pR_{\\frak p}$ for each $\\frak p\\in V(\\frak a)$. Indeed we deduce that if $\\dim R\/\\frak a=1$, then $M$ is $\\cN\\cS$-$\\frak a$-cofinite if and only if $\\Hom_R(R\/\\frak a,M)$ and $\\Ext_R^1(R\/\\frak a,M)\\in\\cN\\cS$. Moreover, if $R$ is a local ring of dimension $2$ such that $\\cS(\\frak p)$ satisfies the condition $C_{\\frak pR_{\\frak p}}$ for every prime ideal $\\frak p$ of $R$ with $\\dim R\/\\frak p\\leq 1$, then $M$ is $\\cN\\cS$-$\\frak a$-cofinite if and only if $\\Hom_R(R\/\\frak a,M)$ and $\\Ext_R^1(R\/\\frak a,M)\\in\\cN\\cS$. \n\nBahmanpour et all [BNS, Theorem 3.5] showed that if $R$ is a local ring such that $\\dim R\/\\frak a=2$, then $M$ is $\\frak a$-cofinite if and only if $\\Ext_R^i(R\/\\frak a,M)$ are finitely generated for $i=0,1,2$. As another conclusion, we generalize this result when $S(\\frak p)$ satisfies the condition $\\frak pR_{\\frak p}$ for each $\\frak p\\in V(\\frak a)$. To be more prcise, we deduce that if $\\dim R\/\\frak a=2$, then $M$ is $\\cN\\cS$-$\\frak a$-cofinite if and only if $\\Ext_R^i(R\/\\frak a,M)\\in\\cN\\cS$ for $i=0,1,2$. Moreover, if $R$ is a local ring of dimension $3$ such that $\\cS(\\frak p)$ satisfies the condition $\\frak pR_{\\frak p}$ for every prime ideal $\\frak p$ with $\\dim R\/\\frak p\\leq 2$, then $M$ is $\\cN\\cS$-$\\frak a$-cofinite if and only if $\\Ext_R^i(R\/\\frak a,M)\\in\\cN\\cS$ for $i=0,1,2$. We prove the following result about local cohomology which generalizes [NS, Theorem 3.7]. For the basic properties and unexplained terminology of local cohomology, we refer the reader to the textbook by Brodmann and Sharp [BS]. \n\n\\begin{Theorem}\nLet $(R,\\frak m)$ be a local ring, let $\\frak a$ be an ideal of $R$ such that $\\dim R\/\\frak a=2$ and let\n$S(\\frak p)$ satisfy the condition $\\frak pR_{\\frak p}$ for each $\\frak p\\in V(\\frak a)$. If $n$ is a non-negative integer such that $\\Ext_R^i(R\/\\frak a,M)\\in\\cN\\cS$ for all $i\\leq n+1$, then the following conditions are equivalent.\n \n{\\rm (i)} $H_{\\frak a}^i(M)$ is $\\cN\\cS$-$\\frak a$-cofinite for all $i0$). Assume that $d>0$ and we proceed by induction on $i$. If $\\dim R\/\\frak b=0$, since $\\Ext_R^i(R\/\\frak b,M)\\in\\cN\\cS$, using a similar argument as mentioned above, $\\Ext_R^i(R\/\\frak b,M)\\in\\cS$ for all $i\\leq d$; and hence it follows from [AM, Theorem 2.9] that $H_{\\frak b}^i(M)\\in\\cS$ for all $i\\geq 0$; and hence the assertion is clear in this case. If $\\dim R\/\\frak b=1$, it follows from [AS, Theorem 3.5] that $H_{\\frak b}^i(M)$ is $\\cN\\cS$-$\\frak b$-cofinite for all $i1$, since $\\Gamma_{\\frak a}(M)\\in\\cN\\cS$, it follows from $(\\dag_i)$ and the previous isomorphisms that $\\Ext_R^j(R\/\\frak a,Q)\\in\\cN\\cS$ for all $i\\leq n$ so that (i) and (ii) are equivalent for $Q$ and non-negative integer $n-1$. Now, using again the previous isomorphisms, the conditions (i) and (ii) are equivalent for $M$ and non-negative integer $n$.\n\\end{proof}\n\n\n\\medskip\n\\begin{Corollary}\nLet $R$ be an local ring with $\\dim R\/\\frak a\\leq 2$, let $n$ be a non-negative integer such that $\\Ext_R^i(R\/\\frak a,M)\\in\\cN\\cF$ for all $i\\leq n+1$. Then the following conditions are equivalent.\n \n{\\rm (i)} $H_{\\frak a}^i(M)$ is $\\cN\\cF$-$\\frak a$-cofinite for all $i0$ and the result has been proved for all values smaller than $i$. Then $E_2^{p,0}=\\Ext_{\\overline{R}}^p(\\overline{R}\\otimes_RR\/\\frak a+\\Gamma_{\\frak a}(R),\\overline{M})\\in\\cN\\cS$ for all $0\\leq p2$, the $\\overline{R}$-module $E_r^{p,q}$ is a subquotient of $E_{r-1}^{p,q}$ and so an easy induction yields that $E_r^{p,q}\\in\\cN\\cS$ for all $r\\geq 2$ so that $E_{\\infty}^{p,q}\\in\\cN\\cS$ for all $p,q\\geq 0$ . For any $0\\leq t\\leq n$, there is a finite filtration \n$$0=\\Phi^{t+1}H^t\\subset \\Phi^tH^t\\subset\\dots\\subset\\Phi^1H^t\\subset \\Phi^0H^t\\subset H^t$$ \nsuch that $\\Phi^pH^t\/\\Phi^{p+1}H^t\\cong E_{\\infty}^{p,t-p}$ where $0\\leq p\\leq t$. Since $E_{\\infty}^{p,t-p}\\in\\cN\\cS$ for all $0\\leq p\\leq t$ and $t\\geq 0$, we deduce that $H^t\\in\\cN\\cS$ for all $t\\geq 0$; and hence $\\overline{M}$ is $\\cN\\cS$-$\\frak a$-cofinite. On the other hand, since $(0:_M\\frak a^n)\\in\\cN\\cS$, we conclude that $M$ is $\\cN\\cS$-$\\frak a$-cofinite.\n\\end{proof}\n\\medskip\n\n\\begin{Corollary}\nLet $R$ be a local ring of dimension $2$ such that $\\cS$ satisfies the condition $C_{\\frak a}$ for every ideal $\\frak a$ of $R$ with $\\dim R\/\\frak a\\leq 1$. Then $R$ admits the condition $P_1^{\\cN\\cS}(\\frak a)$ for every ideal $\\frak a$ of $R$. \n\\end{Corollary}\n\\begin{proof}\nIt follows from [AS, Theorem 3.2] and \\cref{t22} that $R$ admits the condition $P_1^{\\cN\\cS}(\\frak a)$ for all ideals with $\\dim R\/\\frak a\\leq 1$. Now, the result follows from \\cref{td}.\n\\end{proof}\n\n\n\\medskip\n\\begin{Corollary}\nLet $R$ be a local ring of dimension $2$ such that $\\cS(\\frak p)$ satisfies the condition $\\frak pR_{\\frak p}$ for every prime ideal $\\frak p$ with $\\dim R\/\\frak p\\leq 1$. Then $R$ admits the condition $P_2^{\\cN\\cS}(\\frak a)$ for every ideal $\\frak a$ of $R$. \n\\end{Corollary}\n\\begin{proof}\nIt follows from \\cref{t22} that $R$ admits the condition $P_1^{\\cN\\cS}(\\frak a)$ for all ideals $\\frak a$ with $\\dim R\/\\frak a\\leq 1$. Now, the result follows from \\cref{td}.\n\\end{proof}\n\\medskip\n\n\\begin{Corollary}\nLet $R$ be a local ring of dimension $3$ such that $\\cS(\\frak p)$ satisfies the condition $\\frak pR_{\\frak p}$ for every prime ideal $\\frak p$ with $\\dim R\/\\frak p\\leq 2$. Then $R$ admits the condition $P_2^{\\cN\\cS}(\\frak a)$ for every ideal $\\frak a$ of $R$. \n\\end{Corollary}\n\\begin{proof}\nIt follows from \\cref{t21} that $R$ admits the condition $P_2^{\\cN\\cS}(\\frak a)$ for all ideals with $\\dim R\/\\frak a\\leq 2$. Now, the result follows from \\cref{td}.\n\\end{proof}\n\n\\medskip\n\\begin{Corollary}\nLet $R$ be a local ring of dimension $3$. Then $R$ admits the condition $P_2^{\\cN\\cF}(\\frak a)$. \n\\end{Corollary}\n\\begin{proof}\nIt follows from \\cref{f} that $R$ admits the condition $P_2^{\\cN\\cF}(\\frak a)$ for all ideals with $\\dim R\/\\frak a=2$. Now, the result follows from \\cref{td}.\n\\end{proof}\n\n\\section{Cofiniteness with respect to a new dimension}\n\nFor every $R$-module $M$, it is clear that $\\Supp_RM\\subseteq V(\\Ann_RM)$ and for the case where $M$ is finitely generated, they are equal. We define the {\\it upper dimension} of $M$ and we denote it by $\\overline{\\dim}M$ which is $\\overline{\\dim}M=\\dim R\/\\Ann_RM$. Clearly $\\dim M\\leq \\overline{\\dim}M$. We first recall some results which are needed in this section. \n\n\\begin{Lemma}\\label{del}\nLet $S$ be a finitely generated $R$-algebra and let $M$ be an $S$-module. Then $M$ is $\\frak a$-cofinite if and only if $M$ is $\\frak a S$-cofinite (as an $S$-module).\n\\end{Lemma}\n\\begin{proof}\nSee [DM, Proposition 2].\n\\end{proof}\n\\medskip\n\\begin{Lemma}\nLet $S$ be a finitely generated $R$-algebra and let $M$ be an $S$-module. Then $M$ satisfies the condition $P^{\\cN}_n(\\frak a)$ if and only if $M$ satisfies the condition $P_n^{\\cN}(\\frak aS)$. \n\\end{Lemma}\n\\begin{proof}\nSee [KS, Proposition 2.15].\n\\end{proof}\n\n\n\n\\medskip\n\\begin{Lemma}\\label{mel}\nLet $M$ be an $R$-module such that $\\Supp_RM\\subseteq V(\\frak a)$. Then $M$ is Artinian and $\\frak a$-cofinite if and only if $(0:_M{\\frak a})$ has finite length.\n\\end{Lemma}\n\\begin{proof}\nSee [M1, Proposition 4.1].\n\\end{proof}\nFor every non-negative integer $n$, we denote by $\\cD_{\\leq n}$, the subcategory of all $R$-modules $M$ such that $\\overline{\\dim} M\\leq n$. We also denote by $\\cG$, the subcategory of all $R$-modules $F$ such that $V(\\Ann_RF)$ is a finite set. \n\n\\medskip\n\\begin{Lemma}\\label{ser}\nIf $0\\To N\\To M\\To M\/N\\To 0$ is an exact sequence of $R$-modules, Then $V(\\Ann_RM)=V(\\Ann_RN)\\cup V(\\Ann_RM\/N)$.\n\\end{Lemma}\n\\begin{proof}\nSince $\\Ann_RM\\subseteq \\Ann_RN\\cap\\Ann_R M\/N$, we conclude that $V(\\Ann_RN)\\cup V(\\Ann_RM\/N)\\subseteq V(\\Ann_RM)$. Now assume that $\\frak p\\in V(\\Ann_RM)$ and $\\frak p\\notin V(\\Ann_RN)$. Then there exists $r\\in\\Ann_RN\\setminus \\frak p$ and so for every $x\\in\\Ann_R M\/N$, we have $rx\\in\\Ann_RM\\subseteq \\frak p$ which implies that $x\\in\\frak p$. Consequently, $\\frak p\\in V(\\Ann_RM\/N)$. \n\\end{proof}\n\n\\medskip\n\n \n\n\\begin{Corollary}\nThe following conditions hold.\n\n{\\rm (i)} For every non-negative integer $n$, the suncategory $\\cD_{\\leq n}$ is Serre.\n\n{\\rm (ii)} The subcategory $\\cG$ is Serre.\n\\end{Corollary}\n\\begin{proof}\nThe proof is is straightforward by \\cref{ser}.\n\\end{proof}\n\n\n\\medskip \n\n\\begin{Proposition}\nLet $M$ be an $R$-module with $\\overline{\\dim}M=2$. Then $M$ is $\\frak a$-cofinite if and only if $\\Hom_R(R\/\\frak a,M)$ and $\\Ext_R^1(R\/\\frak a,M)$ are finitely generated. \n\\end{Proposition}\n\\begin{proof}\nLet $\\overline {R}=R\/\\Ann_RM$. Using [KS, Proposition 2.15] we may assume that $\\dim R=2$. Now, the result follows from [NS, Corollary 2.4].\n\\end{proof}\n\n\\medskip \n\n\\begin{Proposition}\nLet $M$ be an $R$-module with $\\overline{\\dim}M=3$. Then $M$ is $\\frak a$-cofinite if and only if $\\Ext_R^i(R\/\\frak a,M)$ is finitely generated for $i=0,1,2$. \n\\end{Proposition}\n\\begin{proof}\nLet $\\overline {R}=R\/\\Ann_RM$. Using [KS, Proposition 2.15] we may assume that $\\dim R=3$. Now, the result follows from [NS, Corollary 2.5].\n\\end{proof}\n\\medskip\n\n\\begin{Proposition}\\label{dim1}\nThe subcategory of $\\frak a$-cofinite modules in $\\cD_{\\leq 1}$ is Serre.\n\\end{Proposition}\n\\begin{proof}\n If $\\overline{\\dim} M=0$, then $\\dim M=0$ and so the module $(0:_M\\frak a)$ has finite length. Thus it follows from \\cref{mel} that $M$ is $\\frak a$-cofinite. Now, assume that $\\overline{\\dim} M=1$ and so $\\dim \\overline{R}=1$ where $\\overline{R}=R\/\\Ann_RM$. It is clear that $(0:_M\\frak a\\overline{R})=(0:_M\\frak a)$ is finitely generated and $\\Supp_{\\overline{R}}M\\subseteq V(\\overline{\\frak a})$. It follows from [M1, Proposition 4.5] that $M$ is $\\overline{\\frak a}$-cofinite. Finally, using \\cref{del}, $M$ is $\\frak a$-cofinite. The second assertion is straightforward. \n\\end{proof}\n\n\n\\medskip\n\\begin{Corollary}\nLet $M\\in\\cN\\cG$ with $\\Supp_RM\\subseteq V(\\frak a)$. Then $M$ is $\\frak a$-cofinite if and only if $(0:_M\\frak a)$ is finitely generated. \n\\end{Corollary}\n\\begin{proof}\nSince $M\\in\\cN\\cF$, there exists an exact sequence of $R$-modules $0\\To N\\To M\\To F\\To 0$ such that $N$ is finitely generated and $F$ has finite support. We notice that $(0:_F\\frak a)$ is finite, and it suffices to show that $F$ is $\\frak a$-cofinite and so we may assume that $V(\\Ann_RM)$ is a finite set so that $\\overline{\\dim}M\\leq 1$. It follows from [M1, Proposition 4.5] that $M$ is $\\overline{\\frak a}$-cofinite where $\\overline{R}=R\/\\Ann_RM$ and $\\overline{\\frak a}=\\frak a\\overline{R}$. Now \\cref{del} implies that $M$ is $\\frak a$-cofinite. \n\\end{proof}\n\\medskip\n\n\\begin{Proposition}\n The subcategory of $\\frak a$-cofinite modules in $\\cD_{\\leq 2}$ is abelian.\n\\end{Proposition}\n\\begin{proof}\nAssume that $f:M\\To N$ be a morphism of $\\frak a$-cofinite modules and assume that $K=\\Ker f, I=\\Im f$ and $C=\\Coker f$. \n The assumption implies that $(0:_I\\frak a)=(0:_I\\frak a\\overline{R})$ is finitely generated $\\overline{R}$-module where $\\overline{R}=R\/\\Ann_RM$. If $\\dim \\overline{R}=0$, the module $(0:_I\\frak a)$ has finite length and so $I$ is Artinian. Now, \\cref{mel} implies that $I$ is $\\frak a$-cofinite. If $\\dim\\overline{R}=1$, it follows from \\cref{dim1} that $I$ is $\\frak a\\overline{R}$-cofinite as $M$ is $\\frak a\\overline{R}$-cofinite; and hence $I$ is $\\frak a$-cofinite using \\cref{del}. If $\\dim\\overline{R}=2$, it follows from [NS, Corollary 2.6] that $I$ is $\\frak a\\overline{R}$-cofinite and so is $\\frak a$-cofinite by using \\cref{del}. Now, using the exact sequences of $R$-modules $$0\\To K\\To M\\To I\\To 0;$$$$0\\To I\\To N\\To C\\To 0;$$ it is straightforward to show that $K$ and $C$ are $\\frak a$-cofinite modules.\n\\end{proof}\n\n\n\\medskip\n\n\\begin{Proposition}\nThe kernel and cokernel of a homomorphism $f:M\\To N$ of $\\frak a$-cofinite modules in $\\cD_{\\leq 3}$ is $\\frak a$-cofinite if and only if $(0:_{\\Coker f}\\frak a)$ is finitely generated.\n\\end{Proposition}\n\\begin{proof}\nBy the assumption we have $\\dim R\/\\Ann_RM\\leq 3$ and also $\\dim R\/\\Ann_RN\\leq 3$. If we put $\\frak b=\\Ann_RM\\cap \\Ann_RN$ and $\\overline{R}=R\/\\frak b$, we have $\\dim \\overline{R}\\leq 3$ and further $M$ and $N$ are $R\/\\frak b$-module. Clearly $(0:_{\\Coker f}\\frak a\\overline{R})$ is a finitely generated $\\overline{R}$-module. It follows from [NS, Theorem 2.8] that \n$\\ker f$ and $\\Coker f$ are $\\frak a\\overline{R}$-cofinite and so using \\cref{del}, they are $\\frak a$-cofinite.\n\\end{proof}\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{}\n \n\\noindent An exciting area where biology meets technology is evolutionary robotics \\cite{nolfi2000evolutionary,Vargas-2014,doncieux2015evolutionary}. The key idea behind the field is to optimize the design of robots through evolutionary computing \\cite{eiben2003introduction-to}. Using artificial evolution for robot design has a strong rationale.\n\n\\begingroup\n\\addtolength\\leftmargini{0.1cm}\n\\begin{quote}\n{\\bf As natural evolution has produced successful life forms for practically all possible environmental niches on Earth, it is plausible that artificial evolution can produce specialised robots for various environments and tasks.}\n\\end{quote}\n\\endgroup\n\n\n\\noindent The long-term vision foresees a technology where robots for a certain application can be `bred' through iterated selection and reproduction cycles until they satisfy the users' criteria. This approach is not meant to replace classic engineering when designing robots for structured environments with known conditions. However, for complex, unstructured environments with (partially) unknown and possibly changing conditions evolution offers great advantages \\cite{howard2019evolving}. A crucial difference between a system of evolving robots and a usual evolutionary algorithm is that the members of the population are not digital objects in a virtual space, but physical artefacts in the real-world. Such a system goes beyond evolutionary computing and implements the Evolution of Things with several new challenges rooted in the physical incarnation \\cite{Eiben2012Embodied-Artifi,eiben2015evolutionary}.\n\nAnother property distinguishing robot evolution from mainstream evolutionary optimization is that robots have agency, i.e., they are active artefacts that exhibit autonomous behavior. To this end, it is important to note that a robot is a combination of its body (morphology, hardware) and its brain (controller, software), and the behavior depends on both \\cite{Louise2011Beyond,weigmann2012does}. This implies that the evolution of robots should concern both the bodies and the brains \\cite{pfeifer2007body}. \n\nThus, in a full-fledged evolutionary robot system the morphologies as well as the controllers undergo evolution. This is in stark contrast with the current practice. Evolutionary robotics today is mainly concerned with evolving the controllers of simulated robots \\cite{radhakrishna2018survey}. Systems where morphologies and controllers of robots evolve simultaneously are rare and they work in simulation \\cite{Auerbach_Bongard2014Environmental}. Occasionally, an organism evolved in software is constructed in the real world, using hardware \\cite{lipson2000automatic} or `wetware' \\cite{Kriegman2020}, but the evolutionary process is simulated. This evolve-then-construct approach inevitably runs into the reality gap \\cite{jakobi1995noise}. On the other hand, there exist systems where physical objects are evolved in the real-world, but these are passive artefacts without agency \\cite{Rieffel-Sayles-2010,Kuehn-Rieffel-2012}. Complete systems where real robots undergo evolution are still ahead of us, but with the development of 3D-printing, rapid prototyping, and automated assembly such systems are becoming feasible, at least in an academic setting \\cite{brodbeck2015morphological,hale2019robot, jelisavcic2017real,Vujovic2017}. \n\n\\begin{figure}[!htbp]\n \\centering\n \\includegraphics[width=.33\\textwidth]{images\/Triangle-of-Life.pdf}\n \\caption{Generic system architecture for robot evolution conceptualized by the Triangle of Life{}.\n }\n\t\\label{fig:system}\n\\end{figure} \n\nA generic model to underpin ``evolving robots in real time and real space'' has been described recently \\cite{eiben2013triangle}. \nThis model, called the Triangle of Life{}, captures the universal life cycle of a robot, not from birth to death because that would not be a cycle, but from conception (being conceived) to conception (conceiving offspring). This cycle consists of three stages: morphogenesis (from conception to birth), infancy (from birth to becoming a fertile adult), and mature life (during which the robot can mate and conceive offspring multiple times), cf. Figure \\ref{fig:system}. \n\n\nA key insight behind this paper is that including a learning stage is not an arbitrary design choice, it is pivotal for mitigating a general problem. Namely, while it can be assumed that the parents had well-matching bodies and brains (otherwise they had not been fit enough to be selected for mating), in general it cannot be assumed that crossover preserves the good match. The mis-match in the offspring may be severe (e.g., having more sensors than there are inputs in the controller) or moderate (e.g., only requiring some parameter tuning in the brain), but in any case it is important to adjust and optimize the inherited brain quickly after `birth'. \n\nThe problem we highlight here is inherent to morphological robot evolution where a large number and variety of robots is produced through consecutive generations. All these robots with different and unpredictable morphologies have to learn the tasks required by the given application. Thus, a morphologically evolving robot system needs a learning method that works in any of the possible robots and finds a good controller efficiently. \n\n\n\n\n\nComparing with current studies, the main contributions of the paper are the following. \nFirstly, a new type of controller architecture based on coupled oscillators with sensory feedback that can be customized to any modular robot driven by joints. \nIn such a type of controller, a generic method is used to specify a frame of reference (coordinates for the robot modules) for any given body in our design space.\nA steering mechanism based on scaling the activation signals, depending on the coordinates of the joint and the angle between the direction to the target and the robot's heading, is used to drive the robot joints. \nThese controllers can be used in a closed-loop approach and steer a robot towards a target regardless of the specific shape of modular robots. \nThis makes it possible to generate a closed-loop controller for a modular robot with an arbitrary shape.\nSecondly, a generic learning method that allows modular robots with arbitrary shapes to learn approaching a target. \nWe validate our method with three different robots, rather than a fixed and special shape robot, in three different scenarios. To this end, we use three modular robots, a `spider', a `gecko', and their `baby' created by an evolutionary robotics project in our lab \\cite{jelisavcic2017real}. \n\n\n\n\n\n\n\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=0.49\\textwidth]{images\/all_robots.pdf}\n \\caption{The three robots in our test suite after \\cite{jelisavcic2017real}. The spider (b) and gecko (c) are the parents, the baby (a) is the offspring. (d) exhibits the overall network topologies of the controllers and the coordinates of the joints in the baby (left), spider (middle), and gecko (right) robot. The core block (head) is red, other blocks are black. Joints are represented by circles.\n \n (e) shows the inner components of the spider robot.}\n \\label{fig:robots}\n\\end{figure}\n\n\n\n\n\\section{Good locomotion and how to learn it}\n\\label{sec:loco-learn}\n\nThe existing studies in learning locomotion on modular roobts can be divided into two categories in terms of the controllers, open-loop and closed-loop. The dominant approaches are using Central Pattern Generators (CPGs) \\cite{Ijspeert2008learning}.\nMost papers studied gait learning with real robots using an open-loop controller with no sensory feedback from the environment \\cite{Kamimura2005Automatic,Bongard2006Science,Kyrre2015real-world,Thakker2014ReBis}. \nFor closed-loop controllers, joint angle and foot contact are typically used to be the sensory feedback. \nThe studies \\cite{owaki2013simple,owaki2017quadruped,nordmoen2019evolved} used a CPG-based controller and foot contact from force sensors on each robot leg to produce coordinated gaits and increase its adaptability on various terrains. \nInertial measurement \\cite{wang2005motion,seo2010cpg,barasuol2013reactive,sartoretti2018central}, joint angles \\cite{kimura2007adaptive}, and touch sensing \\cite{righetti2008pattern,ajallooeian2013central} are used to be sensory feedback in CPG-based controllers to adjust robot behaviours for desired tasks. \nIn particular, the combination of a CPG-based controller and camera is used to achieve the closed-loop control for directed locomotion in a hexapod robot \\cite{shaw2019workspace}.\nSimilarly, there are other studies \\cite{wu2013neurally,Ijspeert2007From} that use various sensory feedback and controllers to achieve closed-loop control for different tasks.\nAlthough these studies proposed closed-loop controllers with various sensory feedback for locomotion, they focus on learning locomotion on a fixed shape robot such as a hexapod robot, a fish robot, a salamander robot. It is still generally lacks generic methodologies for integrating sensory feedback to adapt the locomotion for a modular robot with an arbitrary shape.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn this paper, we consider the problem of targeted locomotion on modular robots with arbitrary shapes. This is important, challenging, and novel. Targeted locomotion is important, because for many applications robots should be capable of going to a point of interest, be it a charging station, an object to fetch, or another robot to mate with. Targeted locomotion on evolvable modular robots is challenging because the number and the spatial arrangement of the joints, the length and branches of the limbs can vary and the overall shape can be irregular. This makes the simple adoption of the steering policy for wheeled robots --to turn left (right) apply more force to the wheels on the right (left)-- highly non-trivial. Finally, this problem is also novel; to our best knowledge there are no publications addressing this. \n\n\n\\subsection{Robots with a sense of direction}\n\\label{sec:loco}\n\nTo enable targeted locomotion of modular robots with arbitrary morphologies, we need a generic way of defining a \\textit{frame of reference}. To this end we define a coordinate system based on the fact that our robots have a core module with a camera, see Figure \\ref{fig:robots}. By definition, the `head', that is, the core module is the origin $(0,0)$ and the direction of the camera determines `North'. Now we can assign the coordinates $\\{(1,0), (2,0), ...\\}$ and $\\{(-1,0), (-2,0), ...\\}$ to the modules (passive bricks or active joints) as we go to East and West, respectively.\nSimilarly, if we go North and South we set the coordinates to $\\{(0,1), (0,2), ...\\}$ and $\\{(0,-1), (0,-2), ...\\}$, respectively. The coordinates of the three robots in this paper are shown in Figure \\ref{fig:robots}.\n\n\n\n\nTo make robots see the target we use a system proposed in \\cite{lan2018ICARCV}. When a target is detected its direction $\\alpha \\in [-\\beta, \\beta]$ w.r.t. the robot can be determined, where $[-\\beta, \\beta]$ is the cameras field of vision. This information can be combined with the frame of reference defined above: If $\\alpha < 0$, then the target is on the left; otherwise it is on the right.\n\nFollowing the literature we use coupled oscillators to control the joints of a robot and we arrange these oscillators in a network to form Central Pattern Generators (CPGs) \\cite{ijspeert2008central, steuer2019central}. Such oscillators generate tightly-coupled patterns of neural activity that drive rhythmic and stereotyped locomotion behaviors like walking, swimming, flying in vertebrate species and they have been proven to perform well in modular robots as well \\cite{hultborn2007spinal,ijspeert2008central,Ijspeert2007From}.\n\nTo provide useful information from the environment we introduce the notion of a sensory oscillator as shown in Figure \\ref{fig:framework} (a). \nSuch a sensory oscillator drives one joint in the robot by two coupled neurons $x$ and $y$, and an $out$-neuron that together generate a signal regulated by three weights as usual. \nSubsequently, the extra (square shaped) node generates the actual signal $sig$ for the joint by applying a function $f$ to the sensory information $sen$ and the signal of the $out$-neuron.\nThe novelty of this model lies in the extra node that considers the sensory input before generating the signal that actually drives the joint. The model is general, there is no restriction on the sensory input(s) and $f$ can be any function depending on the task at hand.\n\nThe subsystems described above can be combined into an adequate control system that enables the modular robots to move towards a target. To this end, we use the angle to the target $\\alpha$ as input and generate a scaling factor $d_p(\\alpha)$ and define a function $f$ to adjust the signals of the joints on the left and right side of the robots as necessary. The exact details are described in the Methods section, the overall effect is that if the target is on the left (right), then the joints on the left (right) apply less force and make the robot turn in the correct direction. The middle joints are never modified. The overall architecture is exhibited in Figure \\ref{fig:framework} (b).\n\\begin{figure}[!htbp]\n\t \\centering\n\t \\includegraphics[width=\\columnwidth]{images\/framework.pdf}\n\t\t\\caption{(a) A sensory oscillator with three neurons, $x$-neuron, $y$-neuron, $out$-neuron and an extra node $f$. The function $f$ combines the raw control signal of the $x$-neuron with the sensory information $sen$. (b) Overall scheme of steering by sending control commands to actuators depending on their lateral position and the direction of the target.}\n\t\t\\label{fig:framework}\n\\end{figure}\nThe overall control system of a robot is a CPG network where sensory oscillators of neighboring joints are connected as shown in Figure \\ref{fig:robots} (d).\n\n\n\\subsection{Learning method}\n\\label{sec:learn}\n\nLearning a task in our system amounts to finding proper weights for the controller. This boils down to optimizing a black-box objective function. Bayesian optimization (BO) is the state-of-the-art method in terms of data efficiency, but its computational complexity grows cubically with respect to the number of observations \\cite{jasper2012practical}. \nAlternatively, Evolutionary Algorithms (EA) take constant time for generating candidate solutions \\cite{eiben2015evolutionary}. This makes their overhead much less computation-intensive than that of the BO, at the expense of data efficiency. \n\nTo get the best of both worlds, here we use a combined algorithm, the Bayesian Evolutionary Algorithm (BEA), that starts with BO and runs this until the time efficiency becomes too low. At this point, a certain subset $S$ of the solutions generated so far is selected by considering their quality and diversity. This set $S$ is then used as the initial population for the EA that is run until a good solution is found or the given computing budget is exhausted. We refer the reader to the Methods section for further details.\n\n\n\n\\section{Experiments}\n\\label{sec:experiments}\nWe employed the BEA to learn adequate controllers in simulation. Then we tested the learned controllers in three test scenarios: 1) approaching a fixed target, 2) following a moving target, and 3) following another robot that is following a moving target. Let us note that the robots do not change or tune their controllers between scenarios. \n\nWe used the customized framework \\emph{Revolve} \\cite{hupkes_2018_revolve} to learn appropriate controllers for directed locomotion at a zero angle (thus: straight forward) on the virtual gecko, spider, and baby robots.\nThe fitness function for the BEA was a combination of the deviation from the required direction and the distance covered during the test period, see the Methods section for details. The duration of a test period was 60 seconds and the BEA was allowed to perform 1500 evaluations. \nDespite the heavy simulations, the computing times were acceptable, approximately one hour was enough to complete one run on a Linux PC with a 3.0GHz CPU, 64Gb RAM, and 32 cores with multi-threading. Conducting the whole learning process on the real robots would take several days, $1500 \\times 60$ seconds, plus overhead for re-positioning the robots between tests, charging the batteries, and handling breakdowns.\n\n\n\n\\subsection{Scenario 1: fixed target}\n\\label{sec:fixed}\nThe controllers learned in simulation are validated on the real robots, the spider, the gecko, and the baby. In the first series of experiments, we tested each robot with three different setups, one with a fixed target to the left, one with the target straight ahead of the robot, one with the target to the right. In all cases, the target was in the field of view of the robot camera (Raspberry Pi Camera Module v2) at the start of the test. We repeated each test five times and displayed the observed trajectories in Figure \\ref{fig:trajectories}.\nThe experiments were recorded with an overhead camera above the test arena, as shown in the supplementary videos.\nThe plots show a consistent behavior for all robots and test cases. The path of the spider shows that it is `wobbling', see the top left plot in Figure \\ref{fig:trajectories}. This is understandable because its middle-line lies over two of its limbs, hence straight forward movement is very hard without zigzagging. Comparing the blue curves with the other colors we can also see that the robots approach the target in the center faster. This result can be easily explained, because for a target on one of the sides, the robot must turn and that costs extra time.\n\n\\begin{figure}[!htbp]\n\t\\centering\n\t \\begin{adjustbox}{max width=0.49\\textwidth}\n\t\\begin{tabular}{c c c}\t\t\\includegraphics[width=0.4\\textwidth]{images\/spider9_trajectory_all.pdf}&\n\t\t\\includegraphics[width=0.4\\textwidth]{images\/gecko7_trajectory_all.pdf}&\n\t\t\\includegraphics[width=0.4\\textwidth]{images\/babyA_trajectory_all.pdf} \\\\\n\t\\end{tabular}\n \\end{adjustbox}\n \\begin{adjustbox}{max width=0.49\\textwidth}\n\t\\begin{tabular}{c c c}\n\t\t\\includegraphics[width=0.4\\textwidth]{images\/spider9_moving_tra_all.pdf}&\n\t\t\\includegraphics[width=0.4\\textwidth]{images\/gecko7_moving_tra_all.pdf}&\n\t\t\\includegraphics[width=0.4\\textwidth]{images\/babyA_moving_tra_all.pdf} \\\\\n\t\\end{tabular}\n \\end{adjustbox}\n\t\\caption{Trajectories of the spider (left), the gecko (middle), and the baby (right). The solid lines show the average trajectories (five runs). The hexagonal bins show the number that robots located in the hexagonal area for all five repetitions, where the location of robots are collected per 0.5 second. Top row: moving towards a fixed target, the coloured circles. Bottom row: following a moving target. The red line shows the path of the target robot, the blue one belongs to the `chaser'. \n\n\t}\n\t\\label{fig:trajectories}\n\\end{figure}\n\n\\subsection{Scenario 2: moving target}\n\\label{sec:moving}\nIn the second series of experiments, we tested each robot with a moving target. To this end, we used a wheeled Robobo robot that was pre-programmed to drive a given trajectory. In the initial position, the Robobo was approximately 30 cm ahead of the modular robot and started to drive to the right, then it turned and drove to the left. The bottom row of Figure \\ref{fig:trajectories} shows the trajectories after five repetitions with each modular robot. Additionally, we recorded the experiments with an overhead camera above the test arena (see Supplementary Video). These data indicate that the modular robots were able to follow the Robobo in all cases. This demonstrates that the controllers learned for a simple task (moving straight ahead) were applicable in a different and more difficult task. In turn, this proves the usefulness of our new controllers based on an internal frame of reference and sensory oscillators. \n\n\n\\begin{figure*}[!ht]\n\t\\centering\n \\begin{adjustbox}{max width=0.95\\textwidth}\n\t\\begin{tabular}{c c c c c}\n\t\t\\includegraphics[width=0.385\\textwidth,angle=90]{images\/babyA2gecko1_1.png}& \\hspace{-4mm}\n \\includegraphics[width=0.385\\textwidth,angle=90]{images\/babyA2gecko1_2.png}& \\hspace{-4mm}\n\t\t\\includegraphics[width=0.385\\textwidth,angle=90]{images\/babyA2gecko1_3.png}& \\hspace{-4mm}\n\t\t\\includegraphics[width=0.385\\textwidth,angle=90]{images\/babyA2gecko1_4.png}& \\hspace{-4mm}\n\t\t\\includegraphics[width=0.385\\textwidth,angle=90]{images\/babyA2gecko1_5.png} \\\\\n\t\t\\includegraphics[width=0.363\\textwidth,angle=90]{images\/gecko2spider1_1.png}& \\hspace{-4mm}\n\t\t\\includegraphics[width=0.363\\textwidth,angle=90]{images\/gecko2spider1_2.png}& \\hspace{-4mm}\n\t\t\\includegraphics[width=0.363\\textwidth,angle=90]{images\/gecko2spider1_3.png}& \\hspace{-4mm}\n\t\t\\includegraphics[width=0.363\\textwidth,angle=90]{images\/gecko2spider1_4.png}& \\hspace{-4mm}\n\t\t\\includegraphics[width=0.363\\textwidth,angle=90]{images\/gecko2spider1_5.png} \\\\\n\t\\end{tabular}\n \\end{adjustbox}\n\t\\caption{Still images of the double moving target experiments. Top row: the gecko follows the hand-held target and the baby follows the gecko. Bottom row: the spider follows the hand-held target and the gecko follows the spider. The hand-held target is the red cylinder at the end of a white stick. The red and blue lines show the trajectories of the first and the second robot, respectively.}\n\t\\label{fig:double-overhead}\n\\end{figure*}\n\n\\subsection{Scenario 3: double moving target}\n\\label{sec:d-moving}\n\nIn the third series of real-world experiments, we challenged the modular robots even further. In this setup, we replaced the Robobo used in the previous experiments by one of the modular robots and hand-held a target in front of it. Another modular robot was placed on the regular starting position. Then the robots started at the same time, the first one following the target hand-held by the experimenter, the second one following the first one. In Figure \\ref{fig:double-overhead}, we show two of these tests, the case of the gecko following the hand-held target and the baby following the gecko and the case of the spider following the hand-held target and the gecko following the spider. \nThe figure captures the test by five still images taken by the overhead camera, more information is shown in Supplementary Video. These show that robots can follow a target even if it is an irregularly moving irregular shape (another modular robot). The trajectories also disclose the speed differences. In the first test, the distance between the gecko and the baby is gradually growing, which indicates that the baby is a little slower than the gecko. In particular, the baby encountered a little difficulty in turning right. This is not surprising, given that its morphology with the long right limb is not perfectly symmetrical. In the second test we see the opposite, the second robot is closing in on the first one, and before the end of the experiment the gecko hits the spider.\n\n\n\n\n\n\\section{Discussion}\n\\label{sec:discussion}\n\n\n\nThe wider context of this study is full-fledged robot evolution, where both morphologies and controllers evolve. We note that a morphologically evolving robot system needs a learning component that works in any of the possible robots and adjusts the inherited controller (or a randomly initialized controller) to the given morphology and task. \n\nOur experimental work is, in essence, a feasibility study addressing this general issue within our system of evolvable robots and a specific task. \nThe concrete problem we tackle is to enable targeted locomotion, that is, approaching and following an object, in modular robots without any assumption on their specific morphology. This forms a good test case because it is morphology dependent, challenging for robots with random shapes, and practically relevant. The solution we deliver is based on an internal frame of reference and the use of oscillators with sensory feedback to drive the joints. These are combined into a control system that modulates the force in the servo motors depending on the robots angle to the target and the location of the joint in the robots body. The overall control system is a CPG-based network where neighboring joints of the robot are connected. The other ingredient of the solution is a learning method that can find good parameter values for any given CPG-based network. \n\nTo validate our approach we designed nine test cases by three robots with different morphologies and three scenarios, one with a fixed target, one with a moving target, and one with a double moving target. The robots learned a good controller for moving straight forward and these controllers were evaluated in the test scenarios.\n\nIn the first scenario, all robots approached the target accurately, even though the spider exhibited a little offset to the right, cf. Figure \\ref{fig:trajectories} and Supplementary Video. In the second scenario, the target moved to the right first and turned to the left halfway the test. All three robots could adjust their trajectory and followed the target with a little delay as can be seen in Figure \\ref{fig:trajectories}. This proves that the sensory-motor feedback loop they learned for one basic skill (straight forward movement) was generalizable to a more dynamic and complex task.\n\nIn the third scenario, a modular robot had to follow another modular robot that followed a hand-held target. This test further confirmed the adequacy of the learned controllers and illuminated the differences between the locomotion abilities. For instance, the baby followed the gecko well in a left turn, but it fell behind when turning right because of the longer right-front limb as shown in the fifth frame of Figure \\ref{fig:double-overhead}.\nIn the other test, the gecko followed the spider easily because of the wiggly locomotion of the spider and the relatively high speed of the gecko.\n\nThe results of the experiments demonstrate that the new type of oscillators and CPGs with external feedback in combination with the internal frame of reference empower robots with different morphologies to perform object following. Our frame of reference is in essence a 2-dimensional coordinate system, where the origin and the `North' are defined by specific features in our robots, the head module and the direction of the camera, respectively. This concept can be easily extended to three dimensions and to different robots with other features. For instance, a designated head module is not required, the origin can be defined by any reasonable principle that applies to the given morphologies. Likewise, while using the direction of the camera to define `North' is a natural choice in our application, a coordinate system can be defined for robots without a camera as well. \n\nFurther extensions and generalizations are possible regarding the feedback from the environment. Although in this study we use a camera, our approach is applicable in a wide range of robot systems, because the sensory oscillators can handle inputs from different sensors, e.g., accelerometers, gyroscopes, IR sensors, sonars. Furthermore, in this paper all joints on the same side are treated the same way, cf. Equations \\ref{eq:l_joint_downscale} and \\ref{eq:r_joint_downscale}, but it is possible to define a variant where the exact coordinates, for instance the distance to the `spinal cord', are also taken into account. \n\nLast but not least, improvements are possible by employing another learning method. The BEA we use here is not application specific, in principle any derivative-free black-box optimization algorithm for learning the adequate parameter values for the controller is applicable. In our view, it is very important to consider both sample efficiency and time efficiency, that is, the number of trials or evaluations as well as the time needed to achieve a decent result. In this paper we used a budget of 1500 trials and the BEA spent about one hour on learning. This is certainly acceptable and delivers the proof of concept we aimed for, but we are convinced that these figures can be improved by more advanced methods. \n\nA particular aspect here is the dichotomy between the real-world and simulations. To this end, it is important to note that the use of simulations does not invalidate the concept of physical robot evolution. In fact, simulations can be used in both the evolutionary and the learning loop.\nAs outlined in \\cite{howard2019evolving} and \\cite{hale2019robot} there are great potential advantages in evolving real and simulated robots simultaneously. If the simulated and real robots share the same genetic language then we can `cross-breed' them, using a virtual `mother' and a physical `father'. In such a hybrid evolutionary system physical evolution is accelerated by the virtual component that can find good robot features with less time and resources than physical evolution, while simulated evolution benefits from the influx of genes that are tested favourably in the real world. Additionally, even if we have clean physical evolution and all robot `children' are produced in the real world, the learning process in the Infancy stage can use simulations to obtain a good controller for the given robot `child'. The learned controllers can suffer from the reality gap, which can be mitigated by a subsequent real-world learning process on the physical robot. The advantage of the combination is that virtual learning can be on a meso-scale, spending quite a few trials (e.g., hundreds, like in our case) and the real-world process on a micro-scale with much fewer trials (perhaps just a few dozens). For the sake of the argument, if 100 real-world trials were enough, then the learning process could be completed on the real robots in an afternoon. The current paper is on the simulated side. The controllers learned in simulation just worked for us here, thus we had no need to add a real-world learning process. \n\n\n\n\nReflecting from a broader perspective, this work provides an approach for learning tasks inside an evolutionary process that produces various morphologies. Our approach is generic, applicable to various types of evolvable robot systems and different tasks. For example, it can be applied in a system for evolving robots for exploring and decommissioning nuclear power plants \\cite{hale2019robot}, as well as in more fundamental studies regarding the evolution of embodied intelligence. \n\n\n\n\n\n\n\n\\section{Methods}\n\\label{sec:methods}\n\n\\subsection*{The general system}\n\\label{subsec:general_framework}\n\nThe robots in our system are based on RoboGen \\cite{auerbach2014robogen}, they consist of a core component that hosts the controller board, the battery and a camera, 3D-printed passive bricks, and joints driven by servo motors \\cite{jelisavcic2017real}. Figure \\ref{fig:robots} displays the three robots we use in the current experiments. The camera provides information about the environment that is processed by a new type of control system based on sensory oscillators that activate the servos. \nA schematic representation of the system is presented in Figure \\ref{fig:framework} (b). \nIn the next subsections, we discuss the details of each component of the system.\n\\subsection*{Robot Vision}\n\\label{subsec:robot_vision}\n\nThe closed-loop controllers are based on visual input delivered by a camera. The robot vision system must work accurately in real-time on our Raspberry Pi Camera Module v2, and ideally be power efficient. The two pivotal steps for this system are the recognition of objects of interest, and the calculation of the angle of a target object w.r.t. the orientation of the robot.\n\n\\paragraph{Object recognition} \nHere, we follow the approach proposed in \\cite{lan2018ICARCV,lan2019evolving} that allows to quickly recognize targets with low-performance hardware.\nThe method consists of two components:\n\\begin{itemize}[nolistsep]\n \\item Detection of region of interests (ROIs) using \\textit{Fast ROIs Search} proposed in \\cite{lan2018ICARCV}.\n \\item Object recognition using Histogram of Gradients (HOG) as a feature extractor and Support Vector Machines (SVM) with the linear kernel as a detection method.\n\\end{itemize}\nWe decided to use the combination of HOG and SVM instead of Deep Neural Networks since HOG and SVM perform much faster. In this project, we used the implementation provided by OpenCV.\n\n\\paragraph{Angle to target object}\nOnce a target is detected, its relative position from the robot's perspective can be expressed by the angle $\\alpha \\in [-\\beta, \\beta]$, where $\\beta$ is the angle determined by the field of vision of the given camera. The angle to the target $\\alpha$ is the angle between the orientation of the robot (i.e, the camera) and the target object. If the target is on the left-hand side of the robot's face, then $\\alpha$ is negative, whereas if the target is on the right-hand side of the robot's face, $\\alpha$ is positive. The value of $\\alpha$ is zero if the target is straight ahead of the robot. \nGiven an image registered by the Raspberry Pi Camera Module v2 with the parameters (the field of view ($2 \\times \\beta$) is $\\ang{62.2}$, the resolution is $3280 \\times 2464$ where 3280 is the number of pixel columns ($\\mathcal{N}_c$)), where the coordinate of the target can be recognized by the robot vision as $(x,y)$ in pixels, $\\alpha$ is calculated from this image by\n\\begin{equation}\n \\alpha = \\frac{\\arctan(\\frac{x - \\mathcal{N}_c \/ 2} {\\mathcal{F}})} { \\pi \\times 180 }\n\\end{equation}\nwhere $\\mathcal{F}$ is a potential inherent factor that depends on the camera, can be calculated by \n\\begin{equation}\n \\mathcal{F} = \\frac{ \\mathcal{N}_c \/2}{\\tan(\\frac{\\beta}{180 \\times \\pi})}\n\\end{equation}\n\n\n\n\n\n\nDue to the limited camera's field of view in the real world, the robots have to process the situation that the target is out of the camera's field of view. \nFor such a situation, we expect the robots to search the target until the target is in the camera's field of view.\nTo this end, we use two solutions: 1) If the target is out of the camera's field of view initially, the robots have to search the target until it is in the camera's field of view. For this purpose, the initial value of $\\alpha$ can be set as $\\beta\/2$ (or $-\\beta\/2$) for turning right (or left).\n2) If the target escapes from the camera's field of view during locomotion, the robots keep the previous behaviours to follow the target until the target comes back to the field of view. \nThat is, $\\alpha$ preserves the previous value if no target is in the field of view except the initial stage.\n\n\n\n\n\n\n\n\n\n\\subsection*{Controller}\n\\label{subsec:controller}\n\n\\paragraph{Modeling joints as oscillators}\nThe key element of the controller is the model of a single joint. Here, we propose a new oscillator with sensory feedback to properly represent oscillatory behavior often seen in nature \\cite{ijspeert2008central}.\nThe controller composed of new oscillators works in a closed-loop, in which the robot's action can be changed according to the sensory feedback about the target in the environment.\nIn general, the sensory input to the new oscillator can be generated by any sensors.\n\nA sensory oscillator has an $x$-neuron, a $y$-neuron, an $out$-neuron, and an extra node that implements a function $f$.\nFor each time step, neuron $x$ ($y$) feeds its activation value multiplied by the weight $w_{xy}$ ($w_{yx}$) to the neuron $y$ ($x$).\nAt a time step $t$, the changes of the activation value of an $x$-neuron and a $y$-neuron can be calculated as $\\Delta x_{(t)} = w_{yx}y_{(t-1)}$ and $\\Delta y_{(t)} = w_{xy}x_{(t-1)}$ respectively, \nwhere $t-1$ represents the last time step. \nThe $x$-neuron and the $y$-neuron generate the activation values $x_{(t)}$ and $y_{(t)}$ of oscillatory patterns over time according to the following expression:\n\\begin{align}\n \\begin{split}\n x_{(t)} = x_{(t-1)} + \\Delta x_{(t)} \\\\\n \ty_{(t)} = y_{(t-1)} + \\Delta y_{(t)} \n \\end{split}\n \\label{eq:activation}\n\\end{align}\n\n\\par The $x$-neuron feeds its activation value multiplied by the weight $w_{xo}$ to the $out$-neuron.\nThe $out$-neuron applies an activation function to generate the activation value.\nFor the oscillator in the CPG-based controller of modular robots with joints driven by servo motors, the activation values of the $out$-neurons have to meet two conditions due to the limited rotating angle of the joints. \nFirst, the activation value of $out$-neuron must be periodic that repeatedly returns to its initial condition.\nAccording to the stability criterion for linear dynamical systems \\cite{bubnicki2005modern}, it is beneficial to take $w_{yx} = - w_{xy}$. Such values of parameters lead to periodic signals that do not explode over time.\nIn this study, we use the predefined initial values $(x_{(0)}, y_{(0)}) = (-\\frac{1}{2}\\sqrt{2}, \\frac{1}{2}\\sqrt{2})$, and $(w_{xy}, w_{yx}) = (0.5, -0.5)$, but they can be randomly initialized except 0. \nSecond, the activation value of $out$-neuron should be bounded in an interval.\nTherefore, we use a variant of the sigmoid function, the hyperbolic tangent function ($tanh$), as the activation function of $out$-neurons to bound the output value in $[-1,1]$.\nAt a time step $t$, the $tanh$ activation value of $out$-neuron can be calculated as follows:\n\\begin{equation}\n out_{(t)} = \\frac{e^{x_{(t)}} - e^{- x_{(t)}}}{e^{x_{(t)}} + e^{- x_{(t)}}} .\n \\label{eq:output}\n\\end{equation}\n\n\nFinally, the new oscillator executes an extra operation $f$ that combines the activation value of $out$-neuron and the external sensory signal $sen$ to produce a signal $sig$, i.e., $sig = f(sen, out)$.\nIn general, $f$ could be any function, here we use multiplication of $out$ and $sen$.\nHence, at a time step $t$, the $sig$ value can be calculated by:\n\\begin{equation}\n sig_{(t)} = sen_{(t)} \\times out_{(t)}\n \n \\label{eq:multiplication}\n\\end{equation}\nThe rationale behind our new oscillator model is to allow the inclusion of a sensory signal for a closed-loop control. We refer to this new model as the sensory oscillator (see Figure \\ref{fig:framework} (a)). \n\n\n\\paragraph{Network of oscillators}\n\nFor modular robots, e.g., the robots in Figure \\ref{fig:robots}, there are multiple joints affecting each other for achieving the actions.\nIn other words, we deal with a network of joints (oscillators) and we must take into account the influence of other oscillators.\nIn the CPG-based network controller, the neighboring oscillators are connected to each other as the blue arrow lines shown in Figure \\ref{fig:robots} (d).\nAs a result, we have a further expression for a single oscillator by including the neighborhood instead of Equation \\ref{eq:activation}.\nFor the $i$-th sensory oscillator, the activation values $x_{i(t)}$ of its $x$-neuron and $y_{i(t)}$ of its $y$-neuron can be calculated as:\n\\begin{align}\n \\begin{split}\n x_{i(t)} &= x_{i(t-1)} + \\Delta x_{i(t)} + \\sum_{j \\in \\mathcal{N}_i} x_{j(t-1)} w_{ji}\\\\\n \ty_{i(t)} &= y_{i(t-1)} + \\Delta y_{i(t)} \n \\end{split}\n \\label{eq:network}\n\\end{align}\nwhere \n$\\mathcal{N}_i$ is the set of indices of the oscillators connected to the $i$-th oscillator,\n$w_{ji}$ is the weight between $i$-th oscillator and $j$-th oscillator.\nThe connected oscillators in the CPG-based controller cooperate to achieve the desired task.\nThe number of weights that need to be optimized for the controllers of the robots, spider, gecko, and baby are 18, 13, and 16, respectively.\n\n\n\\paragraph{Steering}\nThe usual steering policy for wheeled robots is relatively easy, for left (right) turn the force needs to be reduced on the left (right) wheel. Here we generalize this idea to modular robots with no assumptions about the morphology. Our method allows for scaling the activation signals for the joints depending on the coordinates of the joint and the angle $\\alpha$ between the direction to the target and the robots heading. The key idea is to use a scaling factor \n\\begin{equation}\nd_p(\\alpha) = \\left(\\frac{\\beta - | \\alpha |}{\\beta}\\right)^p ,\n\\label{eq:cp}\n\\end{equation}\nwhere $p > 0$ is a user parameter that determines how strongly we penalize the deviation $\\alpha$. \nIn this study, we use the value of 7 for the parameter $p$ by the experiments of parameter tuning.\nRecall that robots field of vision is the region between $-\\beta$ and $\\beta$, hence $\\alpha \\in [-\\beta, \\beta]$, and $\\alpha < 0$ means that the target is on the left, $\\alpha > 0$ means the target is on the right.\n\nThis scaling factor is used to modify the signals to the joints on the left as follows: \n\\begin{equation}\n sig = \\begin{cases}\n d_p(\\alpha) \\cdot out & \\textit{if } \\alpha < 0 \\\\\n out & \\textit{if }\\alpha \\ge 0\n \\end{cases}\n \\label{eq:l_joint_downscale}\n\\end{equation}\nand, analogously, the signal for the joints on the right is modified as follows:\n\\begin{equation}\n sig = \\begin{cases}\n out & \\textit{if } \\alpha < 0 \\\\\n d_p(\\alpha) \\cdot out & \\textit{if }\\alpha \\ge 0 .\n \\end{cases}\n \\label{eq:r_joint_downscale}\n\\end{equation}\nThe signals for the middle joints are never modified.\n\n\nObserve that by these formulas we define a specific implementation of the square shaped extra node within a sensory oscillator in Figure \\ref{fig:framework}. We use $d_p(\\alpha)$ as the sensory information $sen$ and the function $f$ is simple multiplication. To our knowledge, this is a novel method. The only other existing work that is remotely similar is that of \\cite{Ijspeert2007From}, where an internal signal is used to modify the working of oscillators for two predefined locomotions, walking and swimming.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\begin{figure*}[!tbp]\n \\centering\n \\begin{adjustbox}{max width=0.99\\textwidth}\n\t\\begin{tabular}{c c c}\n\t \\Large{spider} & \\Large{gecko} & \\Large{baby} \\\\\n\t \\includegraphics[width=0.4\\textwidth]{images\/spider9_bo_ea_bea.pdf} &\n\t\t\\includegraphics[width=0.4\\textwidth]{images\/gecko7_bo_ea_bea.pdf} &\n\t\t\\includegraphics[width=0.4\\textwidth]{images\/babyA_bo_ea_bea.pdf} \n\t\\end{tabular}\n \\end{adjustbox}\n \\caption{The performance of Bayesian optimization (blue), an evolutionary algorithm (black), and the BEA (green) on the directed locomotion task in simulation. In each plot we provide the mean value (a solid line) with two standard deviations (a shadowed region). The purple dashed lines present the switch points. The plots on the left, in the middle and on the right represent results for spider, gecko, and baby, respectively.}\n \\label{fig:bea_comparison}\n\\end{figure*}\n\n\\subsection*{Fitness function for directed locomotion}\n\\label{sec:function}\nThe fitness function used here is to evaluate the performance of controllers for the task of directed locomotion. This task is defined by a target direction $\\gamma$ that the robot has to follow. The good fitness function needs to combine two objectives: minimizing deviation with respect to the target direction $\\gamma$ and maximizing speed (i.e., displacement) over the evaluation period (60 seconds in our experiments). \nTo calculate the fitness value we need \n\\begin{itemize}[noitemsep,nolistsep]\n \\item the robots starting position $p_0$ at the beginning of the evaluation period,\n \\item the robots final position $p_1$ at the end of the evaluation period,\n \\item the deviation angle $\\delta$ between the target direction $\\gamma$ and the line drawn between $p_0$ and $p_1$,\n \\item the total length $L$ of the trajectory travelled during the evaluation period (which is not the distance between $p_0$ and $p_1$).\n\\end{itemize} \n\nThe fitness value is then composed of several components. Component one is to maximize the distance travelled in the right direction. This distance can be expressed by the value $E_1 = d(p_0,p_1) \\times cot(\\delta)$, where $d(x,y)$ is the Euclidean distance between two points $x$ and $y$ and $cot$ is the cotangent function. Another component is to minimize the deviation w.r.t. the target angle. This can be expressed not only by $\\delta$ but also by the distance of the of the final position $p_1$ from the ideal trajectory starting at $p_0$ and following the target direction. This distance can be expressed as $E_2 = d(p_0,p_1) \\times tan(\\delta)$, where $tan$ is the tangent function. The third component is to reward locomotion in a straight line. This can be simply captured by the value $E_3 = d(p_0,p_1) \/ (L + \\epsilon$), where $\\epsilon$ is an infinitesimal constant. This value is maximized when the length $L$ of the travelled trajectory equals the distance between the starting point $p_0$ and the end point $p_1$. Combining these components into one formula we obtain the following fitness function:\n\n\\begin{equation}\n\\label{eq:fitness}\nF = E_3 \\cdot \\Big{(} \\frac{E_1}{\\delta + 1} - w \\cdot E_2 \\Big{)},\n\\end{equation}\nwhere $w > 0$ is a penalty factor. This function maximized when $d(p_0,p_1)$ is maximal, $\\delta$ (and hence $E_2$) is zero, and $L = d(p_0,p_1)$.\n\nThe fitness of a given controller in a robot is established by running the robot with that controller, measuring $p_0, p_1, \\delta$ and $L$ and calculating the value of $F$ as defined by Equation \\ref{eq:fitness}.\n\n\\subsection*{The Bayesian-Evolutionary Algorithm}\n\\label{subsec:BEA}\n\nThe Bayesian-Evolutionary Algorithm (BEA) consists of three stages: BO, switching, EA. In the first stage, i.e., the early iterations, Bayesian optimization is employed because its computation time is not yet large. In this paper we use standard Bayesian optimization from the flexible high-performance library Limbo \\cite{cully2018limbo}, using a Gaussian process with a Mat\\'ern 5\/2 kernel, the length scale $\\theta = 0.2$, and a GP-UCB acquisition function. This setting outperformed other hyperparameter settings in our preliminary experiments on parameter tuning.\n\nThe stage of switching starts when the time efficiency of BO drops to lower than that of EA. To determine the switch point we monitor the quality gain per time interval during the search process. This can be defined for any interval of $n$ consecutive iterations (objective function evaluations). If $t_1, \\dots, t_n$ denote the time instances of these iterations and $f_1, \\dots, f_n$ the resulting objective function values, then the gain over this time interval is $\\mathcal{G} = \\frac{f_n - f_1}{t_n - t_1}$. \nWe tested the gain of BO and EA on several well-known objective functions and found that a good moment to switch generally lies in the interval between 190 and 300 iterations.\nIn this study, the switch is triggered at 300 evaluations. \n\nTo seed the EA with a good initial population, we aim for quality (which can be exploited) as well as diversity (which assures appropriate exploration). Hence, if the intended population size of the EA is $K$, then we use K-means clustering in the top $50\\%$ of solutions found by BO and transfer the best solution in each cluster to the EA. \n\nIn the third stage, the search is continued by an evolutionary algorithm. In general, this can be any EAs, but here we use an evolution strategy where the mutation step-size is self-adaptive, but is also controlled by the quality gain per time interval. The idea is to use smaller mutation step size for exploitation when the gains are relatively large and larger mutation step sizes for exploration when gains are small over a period of iterations. \nFor parameters of the EA in BEA, the mutation rate is 0.8, the population size is 10, the tournament size is 2.\n\n\n\n\n\n\n\nWe performed additional experiments to compare the performance of the BEA against its components separately, namely, the BO and the EA. The goal of these additional experiments is to indicate the benefit of our approach compared to using either the BO or the EA alone. We report the performance of the three methods in \\autoref{fig:bea_comparison}. Additionally, we present a comparison of the BEA with the BO and the EA in terms of the achieved fitness value and the computational time in \\autoref{tab:comparison}. We notice that the BEA obtains around $20-30\\%$ better fitness value than the BO and around $45-70\\%$ better fitness value than the EA. Obviously, the BEA is slower than the EA (by around $10-20\\%$), but it is significantly faster than the BO (by about $70\\%$). Eventually, we see that the proposed optimization procedure allows to not only significantly reduce time complexity of the optimization as originally planned, but also leads to a better exploration\/exploitation, and, eventually, to better results than the standalone BO\n\nIn conclusion, we want to stress out the novelty of the proposed optimization strategy. First, the idea of combining BO and EAs is not widely-used. Typically, BO is applied to parameter tuning of EAs \\cite{karroblack, roman2016bayesian}. Here, we propose to optimize the initial population of the EA using BO. Second, running both algorithms one after the other is not necessarily beneficial. The crucial step is to decide about the moment to switch from a computationally heavy, but accurate procedure, to a lightweight generate-and-test method. Here, we discussed how to accomplish that by monitoring the time efficiency. Third, we propose heuristics to \\textit{transfer} solutions found by BO to the initial population of the EA. Last, we further propose a new self-adaptive mutation operation that takes into account information about the progress of the procedure, i.e., the gain in the fitness function value.\n\n\\begin{table}[!tbp]\n \\centering\n \\small\n \\setlength{\\tabcolsep}{8pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{l|l|ccc}\n \\multicolumn{2}{c|}{} & \\multicolumn{1}{c}{spider9} & \\multicolumn{1}{c}{gecko7} & \\multicolumn{1}{c}{babyA} \\\\ \n \\hline\n best fitness ($\\uparrow$) & BO & 122.9\\% & 135.6\\% & 133.6\\% \\\\\n of BEA w.r.t. & EA & 145.9\\% & 169.7\\% & 158.7\\% \\\\ \n \n \\hline\n comp. time ($\\downarrow$) & BO & 31.0\\% & 33.8\\% & 32.4\\% \\\\\n of BEA w.r.t. & EA & 112.2\\% & 119.6\\% & 110.4\\% \\\\ \n \\end{tabular}\n \\caption{Simulation based comparison. Upper half: The performance of BEA in terms of fitness over a full run w.r.t. BO and EA defined by BEA\/BO and BEA\/EA respectively. Lower half: BEA vs. BO and the EA in terms of computation time defined as BEA\/BO and BEA\/EA respectively.}\n \\label{tab:comparison}\n\\end{table}\n\n\\section*{Data and code availability}\nThe data and code in this study can be provided by the corresponding author \nupon reasonable request.\nA Supplementary Video is available for this paper at \\url{https:\/\/youtu.be\/U9n86ngVe-4}.\n\n\n\\bibliographystyle{unsrt}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:intro}\nUnderstanding isotopic abundances on a large scale is a major field of interest which has received a great deal of attention for its application to terrestrial environments (ocean, meteorites), the solar system (planets, comets) and in galactic interstellar space. The variation of isotopic ratios may give us some information about the link between solar system objects and galactic interstellar environments as discussed by \\cite{aleon:10}.\n We principally focus our study on interstellar environments where low temperature conditions may significantly impact the isotopic ratios of the molecular content. Isotopic molecules are detected in a variety of environments and offer an additional tool to determine physical conditions as they usually do not suffer from opacity problems.\n \n Early modeling studies on {\\tC} and {\\dO} isotopic enrichment were performed by \\cite{langer:84} who introduced different isotopic exchange reactions, relying on previous theoretical and experimental studies by \\citealt{watson:76,smith:80}. Possible effects of selective photodissociation of CO have subsequently been emphasized \\citep{glassgold:85,lebourlot:93,visser:09}, which tend to increase the {\\nCO\/\\tCO} ratio. The use of the CN radical as a tracer of {\\dC\/\\tC} isotopic ratio has been raised by \\cite{savage:02,milam:05}, who studied the corresponding gradient as a function of the galactic distance. The actual value of the {\\dC\/\\tC} isotopic ratio in the local InterStellar Medium (ISM) is assumed to be 68 \\citep{milam:05}.\n \n The possibility of nitrogen isotopic fractionation in interstellar clouds has been investigated by \\cite{terzieva:00} who suggested various {\\fN} isotopic exchange reactions. \\cite{rodgers:04,rodgers:08b,rodgers:08a} used these suggested reaction rate constants to predict nitrogen isotopic fractionation in chemical models of dense interstellar molecular cores. They specifically discussed the role of the atomic to molecular nitrogen ratio in the fractionation process and the possible link between nitrogen hydrides and CN containing molecules (nitriles).\nThe corresponding observations are sparse however and difficult as the elemental {\\nN\/\\fN} ratio is high (441 $\\pm$ 5), as determined by the recent Genesis solar wind sampling measurement \\citep{marty:11} and assumed to hold in the local ISM. In addition, the zero point energy (ZPE) differences involved in nitrogen fractionation reactions are small and the predicted corresponding chemical enrichment is moderate. \n\\cite{lucas:98} reported {\\fHCN} absorption in diffuse clouds located in front of distant quasars with a {\\nHCN\/\\fHCN} ratio of 270$\\pm$ 27, close to value reported for the Earth. However, various new observations of isotopic nitrogen containing molecules have been reported, including {\\fN} substituted ammonia and deuterated ammonia \\citep{gerin:09,lis:10} , the diazenylium ion (\\NNHp) \\citep{bizzocchi:10,bizzocchi:13,daniel:13}, CN and HCN \\citep{ikeda:02,pillai:07,adande:12,hilyblant:13,daniel:13}, HCN and HNC \\citep{wampfler:14}. The strong depletion found in {\\fN} variants of the {\\NNHp} isotopologue strongly contradicts model predictions \\citep{gerin:09}, which motivates a reinvestigation of the chemical processes at work. With this in mind, the link between deuterated chemistry and the possible role of ortho\/para molecular hydrogen has been \nstudied by \\cite{wirstrom:12}. \n \nWe analyse in Section~\\ref{sec:reac} the various possible isotopic exchange reactions that are involved for carbon and nitrogen containing molecules. Indeed, most nitrogen fractionation observational results for CN containing molecules involve only {\\tC} and\n{\\fN} species so that the measure of the nitrogen isotopic ratio assumes a fixed {\\dC\/\\tC} fraction. We examine and extend the pioneering study of \\cite{terzieva:00}\nand check for the possible presence of barriers in the entrance channels of isotopic exchange reactions through theoretical calculations. \nWe also update the zero point energy (ZPE) values involved and derive the corresponding exothermicity values.\nWe present our new chemical model in Section~\\ref{sec:model} and compare with available observations and other models. Our conclusions are presented in Section~\\ref{sec:conclusion}\n\n\\section{Chemical reactions involving isotopic substitutes of {\\dC} and \\nN.}\n\\label{sec:reac}\n\\subsection{{\\tC} and {\\fN} exchange reactions}\nAt very low temperatures, isotopic exchange reactions may only occur if no barrier is present between the interacting atoms, ions and molecules or if tunnelling plays an important role. Experimental information is crucial and we constrain the evaluation of rate constants using that information. If no experimental data are available, we apply theoretical methods to determine the presence of a barrier: A first technique uses\n DFT (Density Functional Theory) calculations (with the hybrid M06-2X functional developed by \\cite{zhao:08} which is well suited for thermochemical calculations, associated to the cc-pVTZ basis set using GAUSSIAN09 software). The alternative is provided by the MRCI+Q method (with the aug-cc-pVTZ basis set).\nFor barrierless cases, we derive the reaction rate constants by using a simple capture theory \\citep{georgievskii:05} for both ion-neutral and neutral-neutral reactions.\nWe consider four different families of isotopic exchange reactions:\n\\begin{itemize}\n\\item{{\\emph{A : direct reactions}}. The proton transfer in the $\\NNHp + \\NfiN \\rightarrow \\NfNHp + \\NN$ reaction can serve as an example. In this case and for reactions without a barrier, the reaction rate coefficient of the forward reaction is equal to the capture rate constant multiplied by a probability factor {\\it{f(B, M)}}, $f(B,M)$ depending on the rotational constant, mass and symmetry values of the reactants and products. In reactions involving{\\fN} and {\\tC} the mass ratio of reactants and products are very close and $f(B,M) \\cong \\sigma_{\\rm{entrance~channel}} \/ \\sigma _{\\rm{exit~channels} }$} (The symmetry number $\\sigma$ is equal to the number of pathways). The reverse reaction is calculated from the equilibrium constant $K$, as in \\cite{terzieva:00}: $K = k_f\/k_r=f(B,M) \\times exp( \\Delta E \/ kT)$.\n \n\\item{{\\emph{B : reactions involving adduct formation leading to direct products without isomerization}}. As an example, we refer to \n$\\fNp + \\NN \\rightarrow \\NfiN + \\Np$. We first assume that the high pressure rate constant is equal to the capture rate constant (for reactions without a barrier). We apply statistical theory for the system at thermal equilibrium so that $ k_f + k_r = k_{capture} $. From the equilibrium constant expression, we then derive \n$ k_f= k_{capture} \\times \\frac{f(B,M)}{[ f(B,M) + exp(- \\Delta E \/ kT)] }$ and $ k_r= k_{capture} \\times \\frac{exp(- \\Delta E \/ kT)}{[f(B,M) +exp(- \\Delta E \/ kT)]}$} \n\n\\item{{\\emph{C : reactions involving adduct formation with isomerization pathways}}. Such a case holds for \n$\\tC + \\nHCN \\rightarrow \\dC + \\tHCN$. We again assume that the high pressure rate constant is given by capture theory (for reactions without a barrier). The isotopic isomerization reaction competes with the dissociation of the adduct. The rate constant depends on the location of the transition state and statistical calculations are generally required to estimate the isomerization reaction rate constant.}\n \\item{{\\emph{D : other reactive exothermic channels exist}}}. The exchange reaction is discarded generally (the possibility of N atom exchange in the {\\fN} + CN and in {\\fN} + {\\CCN} reactions is discussed).\n\\end{itemize}\nThe knowledge of the exoergicity values $\\Delta E$ is also a major concern. They are obtained from the differences of the ZPEs between products and reactants. We recall in the Appendix the corresponding expressions and derive their values by using the most recent determinations of spectroscopic constants\n \n We summarize in Table \\ref{tab:exch} the different isotopic exchange reactions considered and display the corresponding reaction rate constants. Detailed information is provided in the online material on the theoretical methods used for the different systems. The reactions involving {\\fN} are displayed in the upper part of the Table. We also consider {\\tC} isotopic exchange reactions in the lower part of Table \\ref{tab:exch}. \n %\n \\begin{table*}[h]\n\\caption{Isotopic exchange reactions.} \n\\label{tab:exch} \n\\begin{center} \n\\begin{tabular}{l l l l l c l} \n\\hline\\hline \n \nlabel \/ & \\multicolumn{3}{c}{Reaction} & k$_f$ $^*$ & $f(B,M)$ $^*$ & $\\Delta$E $^*$ \\\\\ncomment & \\multicolumn{3}{c}{ } & (cm$^{3}$ s$^{-1}$) & & (K)\\\\\n\\hline \n(1) A & {\\bf{$\\NfiN + \\NNHp$}} & $\\rightleftharpoons$ & {\\bf{$ \\NfNHp + \\NN$}} & 2.3 $\\times$ 10$^{-10}$ & 0.5 & 10.3 \\\\\n (2) A &{\\bf{$\\NfiN + \\NNHp$}} & $\\rightleftharpoons$ & {\\bf{$ \\fNNHp + \\NN$}} & 2.3 $\\times$ 10$^{-10}$ & 0.5 & 2.1 \\\\\n (3) A &{\\bf{$\\NfiN + \\fNNHp$}} & $\\rightleftharpoons$ & {\\bf{$ \\NfNHp + \\NfiN$}} & 4.6 $\\times$ 10$^{-10}$ & 1 & 8.1 \\\\\n(4) B & {\\bf{$\\fNp + \\NN$}} & $\\rightleftharpoons$ & {\\bf{$ \\Np + \\NfiN$}} & 4.8 $\\times 10 ^{-10} \\times \\frac{2}{2+exp(-28.3\/T)}$ &2 & 28.3 \\\\\n(5) C & {\\bf{$\\fN + \\CNCp$}} & $\\rightleftharpoons$ & {\\bf{$ \\CfNCp + \\nN$}} & 3.8 $\\times 10 ^{-12} \\times (\\frac{T}{300})^{-1} $ & 1 & 38.1 \\\\ \n(6) D & {\\bf{$\\fNp + \\nNO$}} & $\\rightleftharpoons$ & {\\bf{$ \\Np + \\fNO$}} & no react & - & 24.3 \\\\\n(7) barrier & {\\bf{$\\fN + \\NNHp$}} & $\\rightleftharpoons$ & {\\bf{$\\nN + \\NfNHp $}} & no react & - & 38.5 \\\\ \n(8) barrier & {\\bf{$\\fN + \\NNHp$}} & $\\rightleftharpoons$ & {\\bf{$\\nN + \\fNNHp $}} & no react & - & 30.4 \\\\ \n(9) barrier & {\\bf{$ \\fNNHp + \\nH $}} & $\\rightleftharpoons$ & {\\bf{$\\nH + \\NfNHp $}} & no react & - & 8.1 \\\\ \n (10) barrier & {\\bf{$\\fN + \\HCNHp$}} & $\\rightleftharpoons$ & {\\bf{$\\nN + \\HCfNHp $}} & no react & - & 37.1 \\\\ \n (11) D & {\\bf{$\\fN + \\nCN$}} & $\\rightleftharpoons$ & {\\bf{$ \\nN + \\CfiN$}} & upper limit : 2.0 $\\times$ 10$^{-10}$ $\\times$ & 1 & 22.9 \\\\ \n & & & & (T\/300)$^{1\/6}$ $\\times$ $\\frac{1}{1+exp(-22.9\/T)}$ & & \\\\\n (12) B & {\\bf{$\\fN + \\CCN$}} & $\\rightleftharpoons$ & {\\bf{$ \\nN + \\CCfN $}} & 1.6 $\\times 10 ^{-10} \\times (T\/300)^{1\/6} \\times$ & 1 & 26.7 \\\\ \n & & & & $\\frac{1}{1+exp{\\bf{(-26.7\/T)}}}$ & & \\\\\n(13) D & {\\bf{$\\fN + \\nNO$}} & $\\rightleftharpoons$ & {\\bf{$\\nN + \\fNO$}} & - & - & 24.3 \\\\ \n\\hline \n\\hline \n(14) B & {\\bf{$\\tCp + \\nCO$}} & $\\rightleftharpoons$ & {\\bf{$ \\Cp + \\tCO$}} & 6.6 $\\times 10 ^{-10} \\times (T\/300)^{-0.45} $& 1 & 34.7 \\\\\n & & & & $\\times$ exp(-6.5\/T) $ \\times$ $ \\frac{1}{1+exp(-34.7\/T)}$ & & \\\\\n(15) A & {\\bf{$\\tCO + \\HCOp$}} & $\\rightleftharpoons$ & {\\bf{$ \\nCO + \\HtCOp$}} & 2.6 $\\times 10 ^{-10} \\times (T\/300)^{-0.4} $ & 1 & 17.4 \\\\ \n(16) B & {\\bf{$\\tCp + \\nCN$}} & $\\rightleftharpoons$ & {\\bf{$ \\Cp + \\tCN$}} & 3.82 $\\times 10 ^{-9} \\times (T\/300)^{-0.4}$ & 1 & 31.1 \\\\ \n & & & & $ \\times$ $ \\frac{1}{1+exp(-31.1\/T)}$ & & \\\\ \n (17) B & {\\bf{$\\tC + \\nCN$}} & $\\rightleftharpoons$ & {\\bf{$ \\dC + \\tCN$}} & 3.0 $\\times 10 ^{-10} \\times \\frac{1}{1+exp(-31.1\/T)}$ & 1 & 31.1 \\\\ \n (18) C & {\\bf{$\\tC + \\nHCN$}} & $\\rightleftharpoons$ & {\\bf{$ \\dC + \\tHCN$}} & no react & - & 48.4 \\\\ \n(19) B & {\\bf{$\\tC + \\nCC$}} & $\\rightleftharpoons$ & {\\bf{$ \\dC + \\tCC$}} & 3.0 $\\times 10 ^{-10} \\times \\frac{2}{2+exp(-26.4\/T)}$ & 2 & 26.4 \\\\ \n (19) barrier &{\\bf{$ \\tCH + \\nCO $}} & $\\rightleftharpoons$ & {\\bf{$ \\tCO + \\nCH$}} & no react & - & 28.6 \\\\ \n \\hline \n\\end{tabular}\n\\end{center}\n$^*$ k$_f$ is the forward reaction rate coefficient. The reverse reaction rate coefficient, k$_r$ , is obtained by k$_r$ = $\\frac{k_f}{f(B,M)}$ exp(- $\\Delta$E\/T).\n\\end{table*}\nTable \\ref{tab:exch} shows two main discrepancies compared to previous studies by \\cite{terzieva:00} : The exchange reactions between atomic {\\fN} and {\\NNHp}, {\\HCNHp} are found to be unlikely to occur as significant barriers arise in the complex formation step. A similar result is obtained for {\\fNp} exchange with NO, whereas these reactions had been included in \\cite{terzieva:00}. The exchange reaction between atomic {\\fN} and CN, which was suggested by \\cite{rodgers:08b}, is found to be plausible.\nAdditional possibilities of exchange have also been considered such as the reaction between {\\fN} and {\\CCN}. As far as {\\tC} possible fractionation is concerned, we find that CN could be enriched in {\\tC} through the exchange reactions of CN with {\\tC} and {\\tCp}. However, such a mechanism does not hold for HNC as atomic carbon is found to react with HNC \\citep{loison:14}. $^{13}$C\nenrichment of HCN is also found to be unlikely as the calculated transition state lies above the entrance level in the hypothetical isomerization process (C mechanism). \n \n \\subsection{Ammonia synthesis}\n Ammonia synthesis proceeds mainly through a chain of reactions starting with {\\Np} and {\\HH}, as the reaction between N and {\\HHHp} has been shown to be inefficient \\citep{milligan:00}.\n \\label{sec:ammonia}\n \\subsubsection{The {\\Np} + {\\HH} reaction and isotopic substitutions}\n\\label{sec:nph2}\nThis almost thermoneutral reaction deserves a special mention and has received considerable attention. \\cite{lebourlot:91} first pointed out the possible role of ortho-{\\HH} in the interstellar chemistry of ammonia as the energy of its J=1 rotational level almost compensates the small endothermicity of the reaction $ \\Np + \\HH \\rightarrow \\NHp + \\nH$. \\cite{dislaire:12} subsequently reanalysed the experimental data \\citep{marquette:88} and suggested new separate expressions for the reaction rate with {p-\\HH} and o-\\HH. Similar results were obtained by \\cite{zymak:13}, who also emphasized the possible role of the fine structure level of \\Np. We follow the prescription derived by \\cite{dislaire:12} and extend their analysis to deuterated forms and those including {\\fN} , as displayed in Table \\ref{tab:npHH}. In the case of {\\fN} substituted compounds, we have taken into account the (small) additional term due to the change in ZPE. \n\\begin{table*}[h]\n\\caption{Reaction rate coefficients of {\\mbox{N$^{+}$}} + {\\HH} and isotopic variants.} \n\\label{tab:npHH} \n\\centering \n\\begin{tabular}{l c l l c } \n\\hline\\hline \n \n\\multicolumn{3}{c}{Reaction} & k (cm$^{3}$ s$^{-1}$)& Comment\\\\\n\\hline \n{\\bf{$\\Np + p-\\HH$}} & $\\rightarrow$ & {\\bf{$ \\NHp + \\nH$}}& 8.35 $\\times$ 10$^{-10}$ $\\times$ exp(-168.5\/T) & \\citealt{dislaire:12}\\\\ \n{\\bf{$\\Np + o-\\HH$}} & $\\rightarrow$ & {\\bf{$ \\NHp + \\nH$}}& 4.2 $\\times$ 10$^{-10}$ $\\times$ (T\/300)$^{-0.17}$ $\\times$ exp(-44.5\/T) & \\citealt{dislaire:12} \\\\ \n{\\bf{$\\fNp + p- \\HH$}} & $\\rightarrow$ & {\\bf{$ \\fNHp + \\nH$}}& 8.35 $\\times$ 10$^{-10}$$\\times$ exp(-164.3\/T)& see text \\\\ \n{\\bf{$\\fNp + o- \\HH$}} & $\\rightarrow$ & {\\bf{$ \\fNHp + \\nH$}}& 4.2 $\\times$ 10$^{-10}$ $\\times$ (T\/300)$^{-0.17}$ $\\times$ exp(-39.7\/T) & see text \\\\ \n{\\bf{$\\Np + \\HD $}} & $\\rightarrow$ & {\\bf{$ \\NDp + \\nH$}}& 3.17 $\\times$ 10$^{-10}$ $\\times$ exp(-16.3\/T) & \\citealt{marquette:88} \\\\ \n{\\bf{$\\Np + \\HD $}} & $\\rightarrow$ & {\\bf{$ \\NHp + \\nD$}}& 3.17 $\\times$ 10$^{-10}$ $\\times$ exp(-594.3\/T) & see text\\\\ \n{\\bf{$\\fNp + \\HD $}} & $\\rightarrow$ & {\\bf{$ \\fNDp + \\nH$}}& 3.17 $\\times$ 10$^{-10}$ $\\times$ exp(-9.3\/T) & see text \\\\ \n{\\bf{$\\fNp + \\HD $}} & $\\rightarrow$ & {\\bf{$ \\fNHp + \\nD$}}& 3.17 $\\times$ 10$^{-10}$ $\\times$ exp(-589.5\/T) & see text \\\\ \n{\\bf{$\\Np + \\DD $}} & $\\rightarrow$ & {\\bf{$ \\NDp + \\nD$}}& 2.37 $\\times$ 10$^{-10}$ $\\times$ exp(-197.9\/T) & \\citealt{marquette:88} \\\\ \n{\\bf{$\\fNp + \\DD $}} & $\\rightarrow$ & {\\bf{$ \\fNDp + \\nD$}}& 2.37 $\\times$ 10$^{-10}$ $\\times$ exp(-190.9\/T) & \\citealt{marquette:88} \\\\ \n \\hline \n\\end{tabular}\n\\end{table*}\n These expressions should be used with caution as in their kinetic expression, we consider that the exponential term represents the enthalpy difference between the products and reactants. The capture rate constant of {\\fNp} + HD reaction is about (2\/3)$^{0.5}$\nsmaller than the rate of the {\\fNp} + {\\HH} reaction, due to the different mass dependences. The formation of {\\fNDp} is favored at low temperatures.\n\\subsubsection{ {\\NHHHp} + \\HH}\nThe final step of ion-molecule reactions leading to ammonia formation is the reaction between {\\NHHHp} and \\HH, giving \\NHHHHp. Although exothermic, this reaction has a strong temperature dependence, displaying a minimum at T $\\sim$ 100K and a slow increase at lower temperatures \\citep{barlow:87}, which is interpreted by the presence of a barrier to complex formation, as discussed in \\cite{herbst:91}. At temperatures close to 10K, the reaction is likely to proceed through tunneling which may take place or H atom abstraction. However, the {\\NHHHp} reaction with D$_2$ is found to be slower when the temperature decreases as tunneling is not efficient with deuterium. We thus reconsider the isotopic variants of this reaction, as shown in Table \\ref{tab:nhhhp}, where we give the present reaction rates compared to previous values which were derived from \\cite{anicich:86}.\nThese values are indeed different from those used in our previous studies \\citep{roueff:05} where we assumed the same rate for the channels resulting from the reactions of {\\NHHHp} and isotopologues with HD, based on pure statistical considerations. \n We discuss the resulting modifications in Section \\ref{sec:model}.\n\\begin{table*}[h]\n\\caption{Reaction rate coefficients of {\\NHHHp} + {\\HH} and isotopic variants at T = 10K.} \n\\label{tab:nhhhp} \n\\begin{center} \n\\begin{tabular}{l c l c c} \n\\hline\\hline \n \n\\multicolumn{3}{c}{Reaction} & \\multicolumn{2}{c}{k (cm$^{3}$ s$^{-1}$)} \\\\\n & & & present work & old value (*) \\\\\n\\hline \n{\\bf{$\\NHHHp + \\HH$}} & $\\rightarrow$ & {\\bf{$ \\NHHHHp + \\nH$}}& 8.2 $\\times$ 10$^{-13}$ & 2.4 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$\\NHHHp + \\HD$}} & $\\rightarrow$ & {\\bf{$ \\NHHHHp + \\nD$}}& 8.2 $\\times$ 10$^{-13}$ & 1.2 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$\\NHHHp + \\HD$}} & $\\rightarrow$ & {\\bf{$ \\NHHHDp + \\nH$}}& 1.0 $\\times$ 10$^{-13}$ & 1.2 $\\times$ 10$^{-12}$ \\\\ \n{\\bf{$\\NHHHp + \\DD$}} & $\\rightarrow$ & {\\bf{$ \\NHHHDp + \\nD$}}& 1.0 $\\times$ 10$^{-13}$ & 2.4 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$ \\NHHDp + \\HH$}} & $\\rightarrow$ & {\\bf{$ \\NHHHDp + \\nH$}}& 8.2 $\\times$ 10$^{-13}$ & 2.4 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$ \\NHHDp + \\HD$}} & $\\rightarrow$ & {\\bf{$ \\NHHHDp + \\nD$}}& 8.2 $\\times$ 10$^{-13}$ & 1.2 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$ \\NHHDp + \\HD$}} & $\\rightarrow$ & {\\bf{$ \\NHHDDp + \\nH$}}& 1.0 $\\times$ 10$^{-13}$ & 1.2 $\\times$ 10$^{-12}$ \\\\ \n{\\bf{$ \\NHHDp + \\DD$}} & $\\rightarrow$ & {\\bf{$ \\NHDDDp + \\nH$}}& 1.0 $\\times$ 10$^{-13}$ & 2.4 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$ \\NHDDp + \\HH$}} & $\\rightarrow$ & {\\bf{$ \\NHHDDp + \\nH$}}& 8.2 $\\times$ 10$^{-13}$ & 2.4 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$ \\NHDDp + \\HD$}} & $\\rightarrow$ & {\\bf{$ \\NHHDDp + \\nD$}}& 8.2 $\\times$ 10$^{-13}$ & 1.2 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$ \\NHDDp + \\HD$}} & $\\rightarrow$ & {\\bf{$ \\NHDDDp + \\nH$}}& 1.0 $\\times$ 10$^{-13}$ & 1.2 $\\times$ 10$^{-12}$ \\\\ \n{\\bf{$ \\NHDDp + \\DD$}} & $\\rightarrow$ & {\\bf{$ \\NHDDDp + \\nD$}}& 1.0 $\\times$ 10$^{-13}$ & 2.4 $\\times$ 10$^{-12}$ \\\\ \n{\\bf{$\\NDDDp + \\HH$}} & $\\rightarrow$ & {\\bf{$ \\NHDDDp + \\nH$}}& 8.2 $\\times$ 10$^{-13}$ & 2.4 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$\\NDDDp + \\HD$}} & $\\rightarrow$ & {\\bf{$ \\NHDDDp + \\nD$}}& 8.2 $\\times$ 10$^{-13}$ & 1.2 $\\times$ 10$^{-12}$ \\\\ \n {\\bf{$\\NDDDp + \\HD$}} & $\\rightarrow$ & {\\bf{$ \\NDDDDp + \\nH$}}& 1.0 $\\times$ 10$^{-13}$ & 1.2 $\\times$ 10$^{-12}$ \\\\ \n{\\bf{$\\NDDDp + \\DD$}} & $\\rightarrow$ & {\\bf{$ \\NDDDDp + \\nD$}}& 1.0 $\\times$ 10$^{-13}$ & 2.4 $\\times$ 10$^{-12}$ \\\\ \n \\hline \n\\end{tabular}\n\\end{center}\n(*) \\cite{roueff:05}\n\\end{table*}\nIdentical reaction rate coefficients are used for the $^{15}$N isotopically substituted reactions. \n\n \\section{Models}\n\\label{sec:model}\n\\subsection{General features}\nChemical reactions involving nitrogen atoms and CH, CN and OH have been studied experimentally at low temperatures and shown to be less efficient than previously thought at low temperatures \\citep{daranlot:12,daranlot:13}, which was confirmed by theoretical studies.\nThe corresponding reaction rate constants have been implemented in the KIDA chemical data base \\citep{wakelam:13} and we have updated our chemical network accordingly. We also include the reactions discussed in \\cite{loison:14} in their study of HCN \/ HNC chemistry. We explicitly include deuterium, {\\tC} and {\\fN} molecular compounds in our chemical network. The reactions displayed in Table \\ref{tab:exch}, have been included, which allows us to test the hypothesis of a constant {\\dC}\/{\\tC} isotopic ratio to derive the {\\nN\/\\fN} ratio in {\\CfiN} containing molecules. \nWe take into account the role of the ortho\/para ratio of molecular {\\HH} in the {\\HHDp} + {\\HH} and {\\Np} ({\\fNp}) + {\\HH} chemical reactions in an approximate way: we do not compute the full ortho\/para equilibrium as in the models of \\citealt{flower:06,pagani:11,faure:13} and rather introduce it as a model parameter, which can be varied. \\cite{faure:13} have found a value of 10$^{-3}$ for temperatures below 15K\n\nApart from exchange reactions, reactions involving isotopic molecules are assumed to have the same total rate constant as those involving the main isotope except for the reaction of {\\fNp} with H$_{2}$\/HD\/D$_{2}$. The various reaction channels are obtained from statistical considerations, in the absence of experimental information. We restrict carbon containing molecules to 3 carbon atoms, nitrogen containing molecules to 2 nitrogen atoms and consider full deuteration as in our previous studies \\citep{roueff:05}. Within these constraints, the number of species considered in the model is 307 linked through more than 5400 chemical reactions. \n\\begin{table}[h]\n\\caption{Model definitions. The { C \/ \\tC} and {N \/ \\fN} ratios are respectively taken as 68 \\citep{milam:05} and 440\n \\citep{marty:11}. } \n\\label{tab:model} \n\\centering \n\\begin{tabular}{lll } \n\\hline\\hline \n & Model (a) & Model (b) \\\\\n \\hline\ndensity {\\mbox{n$_H$}} (cm$^{-3}$) & 2 $\\times$ 10$^4$ & 2 $\\times$ 10$^5$ \\\\\nTemperature (K) & 10 & 10 \\\\\ncosmic ionization rate per H$_2$ (s$^{-1}$) & 1.3 $\\times$ 10$^{-17}$ & 1.3 $\\times$ 10$^{-17}$ \\\\\n\\hline\nHe {\/} H & 0.1 & 0.1 \\\\ \nC {\/} H & 4.15 $\\times$ 10$^{-5}$ & 1.4 $\\times$ 10$^{-5}$ \\\\ \nN {\/} H & 6.4 $\\times$ 10$^{-5}$ & 2.1 $\\times$ 10$^{-5}$ \\\\ \nO {\/} H & 6 $\\times$ 10$^{-5}$ & 2.0 $\\times$ 10$^{-5}$ \\\\ \nS {\/} H & 8.0 $\\times$ 10$^{-8}$ & 8.0 $\\times$ 10$^{-8}$ \\\\\nFe \/ H & 1.5 $\\times$ 10$^{-9}$ & 1.5 $\\times$ 10$^{-9}$ \\\\ \n \\hline \n \\hline \n\\end{tabular}\n\\end{table}\nWe consider two different models as displayed in Table \\ref{tab:model}. Model (a) may be considered as a template of TMC1, and assumes a density of hydrogen nuclei n$_H$ = 2 $\\times$ 10$^4$ cm$^{-3}$. The elemental abundance of carbon relative to hydrogen nuclei is taken as 4.15 $\\times$ 10$^{-5}$ to reproduce the derived relative abundance of CO \\citep{ohishi:92}. We derive the oxygen elemental abundance by imposing a C\/O ratio of 0.7 appropriate for TMC1 and take the nitrogen elemental abundance used by \\cite{legal:14} in their work on nitrogen chemistry. The elemental abundance of sulfur is not well constrained and we have taken the low metal case value of 8.0 $\\times$ 10$^{-8}$.\nModel (b) is more representative of a pre stellar core with a density of 2 $\\times$ 10$^5$ cm$^{-3}$ similar to L134N or Barnard 1 (B1) where the elemental abundances of carbon, oxygen and nitrogen are reduced by a factor of 3\nto account for depletion.\n T~=~10K in both cases and the cosmic ionization rate $\\zeta$ per H$_2$ is 1.3 $\\times$ 10$^{-17}$ \\textbf{s$^{-1}$} as in \\cite{legal:14}.\n\\subsection{Results}\n\nWe summarize our results obtained with a 10$^{-3}$ value of the o\/p ratio of {\\HH} in Table \\ref{tab:res} and give some observational values for comparison. Time dependent effects may be visualized\n from the values reported at 10$^6$ years and at steady state for model (a). \n Steady state is reached after a few 10$^7$ and 10$^6$ years respectively for models (a) and (b).\n As there are fewer $^{15}$N enrichment reactions than previously assumed \\citep{terzieva:00}\n most nitrogen containing species are found to have isotopic abundance ratios close to the solar value ($^{14}$N\/$^{15}$N = 440) \ngiven by the Genesis mission \\citep{marty:11}. \n\\begin{table*}[h]\n\\caption{Model (a) and (b) results and observations. ss means stationary state. The value of the o\/p ratio of {\\HH} is taken as 10$^{-3}$. } \n\\label{tab:res} \n\\centering \n\\begin{tabular}{l|cc|c|lll} \n\\hline\\hline \n & \\multicolumn{2}{c|}{Model (a)} & Model (b) & TMC1 & L1544 & B1\\\\\n & t= 10$^6$ yrs& ss & ss & & & \\\\ \n\n \\hline\nelectronic fraction & 1.4 $\\times$ 10$^{-8}$ & 2.8 $\\times$ 10$^{-8}$ & 1.7 $\\times$ 10$^{-8}$ & & & \\\\\nN \/ \\fN & 440 & 456 & 455 & & & \\\\ \n2 $\\times$ N$_2$ \/ \\fN N & 438 &431& 437& & & \\\\\nNH \/ \\fNH & 429 & 428 & 421 & & & \\\\ \nNH \/ ND & 16 & 31 & 9 & & & \\\\ \n\\NHHH \/ \\HH & 6.7 10$^{-9}$ & 1.3 10$^{-9}$ & 6.0 10$^{-9}$& 2 10$^{-8}$ $^{(8)}$ & & \\\\\n\\NHHH \/ \\fNHHH & 333 & 386 & 387 & & & 300$^{+ 55}_{-40}$ $^{(1)}$\\\\\n\\NHHD \/ \\HH & 3.8 10$^{-10}$ & 5.8 10$^{-11}$ & 3.3 10$^{-10}$ & 4 10$^{-10}$ $^{(9)}$& & \\\\\n\\NHHD \/ \\fNHHD & 215 & 276 & 336 & & & 230$^{+ 105}_{-55}$ $^{(1)}$ \\\\\n\\NHHH \/ \\NHHD & 18 & 22 & 18 & 50 $^{(9)}$& & \\\\\n\\NNHp \/ \\HH & 4.8 10$^{-10}$ & 1.3 10$^{-10}$ & 2.1 10$^{-10}$ & 5 10$^{-10}$ $^{(8)}$ & & \\\\\n\\NNHp \/ \\NfNHp & 431 & 430 & 423 & &1050$^{\\pm 220}$ $^{(2)}$ & 400$^{+ 100}_{-65}$ $^{(1)}$\\\\\n\\NNHp \/ \\fNNHp & 437 & 432 & 433 & & 1110$^{\\pm 240}$ $^{(2)}$ & $>$ 600 $^{(1)}$ \\\\\n\\NNHp \/ \\NNDp & 16 & 29 & 8.6 & 12.5 $^{(9)}$ & & 2.9 $^{(1)}$ \\\\\nCN \/ \\HH & 6.8 10$^{-9}$ & 5.5 10$^{-9}$ & 1.2 10$^{-9}$ & 3 10$^{-8}$ $^{(8)}$ & & \\\\\nCN \/ \\tCN & 67 & 84 & 63 & & &50$^{+19}_{-11}$ $^{(1)}$\\\\\nCN \/ \\CfiN & 430 & 449 & 445 & & & 240$^{+135}_{-65}$ $^{(1)}$\\\\\n\\tCN \/ \\CfiN & 6.4 & 5.3 & 7.0 & & 7.5$^{\\pm 1}$ $^{(3)}$ &\\\\\nHCN \/ \\HH & 7.4 10$^{-9}$& 5.9 10$^{-10}$ & 5.4 10$^{-10}$ & 2 10$^{-8}$ $^{(8)}$ & & \\\\\nHCN \/ \\tHCN & 93 & 168 & 114 & & & 30$^{+7}_{-4}$ $^{(1)}$\\\\\nHCN \/ \\fHCN & 398 & 445 & 453 & & & 165$^{+30}_{-20}$ $^{(1)}$\\\\\n \\tHCN \/ \\fHCN & 4.3 & 2.6 & 4.0 & 2 - 4.5 $^{(5)}$ & & 5.5 $\\pm$1 $^{(1)}$ \\\\\nHCN \/ DCN & 43 & 96 & 22 & & & 20$^{+6}_{-10}$ $^{(1)}$\\\\\nHNC \/ \\HH & 5.6 10$^{-9}$ & 7.4 10$^{-10}$ & 8.4 10$^{-10}$ & 2 10$^{-8}$ $^{(8)}$ & & \\\\\nHNC\/ \\tHNC & 93 & 180 & 121 & 54 - 72 $^{(4)}$ & & 20$^{+5}_{-4}$ $^{(1)}$\\\\\nHNC\/ \\fHNC & 405 & 442 & 446 & 250 - 330 $^{(4)}$ & & 75$^{+25}_{-15}$ $^{(1)}$ \\\\\n{\\tHNC} \/ {\\fHNC} & 2.5 & 1.75 & 3.7 & 4.6 $\\pm$ 0.6 $^{(4)}$ & & 3.7 $\\pm$ 1 $^{(1)}$ \\\\\nHNC\/ DNC & 23 & 66 & 16 & & & 2.9$^{+1.1}_{-0.9}$ $^{(1)}$\\\\\nNO \/ \\HH & 1. 10$^{-7}$ & 3.1 10$^{-8}$ & 4.1 10$^{-8}$ & 2.7 10$^{-8}$ $^{(10)}$ & & \\\\\nNO \/ {\\fN}O & 438 & 451 & 446 & & & \\\\ \nCO \/ \\HH & 8.1 10$^{-5}$ & 8.0 10$^{-5}$ & 2.8 10$^{-5}$ & 8 10$^{-5}$ $^{(8)}$ & & \\\\\n {CO} \/ {\\tC}O & 68 & 67.4 & 68 & & & \\\\\nCH \/ \\HH & 1.3 10$^{-9}$ & 1.7 10$^{-9}$& 1.7 10$^{-10}$ & 2 10$^{-8}$ $^{(8)}$ & & \\\\\n{CH} \/ {\\tC}H & 74 & 154 & 71 & $>$ 71 $^{(6)}$ & & \\\\\n\\HCOp \/ \\HH & 2.5 10$^{-9}$ & 3.610$^{-10}$ & 5.7 10$^{-10}$ & 8 10$^{-9}$ $^{(8)}$ & & \\\\\n{\\HCOp} \/ {\\HtCOp} & 56 & 65 & 56 & & & 59 $^{(7)}$ \\\\\n{\\HCOp} \/ {\\DCOp} & 15 & 29 & 8.4 & 50 $^{(9)}$ & & \\\\\n\\hline \n \\hline \n\\end{tabular}\n\\tablebib{(1)~\\citet{daniel:13};\n(2) \\citet{bizzocchi:13}; (3) \\citet{hilyblant:13}; (4) \\citet{liszt:12}\n; (5) \\cite{hilyblant:13b}; (6) \\citet{sakai:13b}, (7) \\citet{hirano:14} assuming CO\/ C$^{18}$O=500,\n(8) \\cite{ohishi:92}, (9) \\cite{tine:00}, (10) \\cite{gerin:93}.\n}\n\\end{table*}\n\\section{Discussion}\nWe display the time dependence of various isotopic ratios and fractional abundances relative to {\\HH} and discuss the chemical behavior involved \nin the fractionation processes for the two reported models. We first\n consider in Figure \\ref{fig:nn2} the reservoirs of nitrogen, atomic and molecular nitrogen as well as {\\NNHp} ions which are chemically linked to \\NN.\n \\begin{figure*\n \\centering\n \\includegraphics[width=14cm]{Fig_1a.pdf}\n \n \\caption{ Upper panel : time dependence of N \/ {\\fN} isotopic ratios in atomic and molecular nitrogen and {\\NNHp} ions. (a) and (b) correspond to the models defined in Table \\ref{tab:model}; the black heavy dotted line represents the elemental N \/ \\fN. Lower panel : time dependence of the fractional abundances relative to {\\HH} of N, {\\NN} and {\\NNHp} for models (a) and (b) . The value reported for {\\NNHp} towards TMC1 \\citep{ohishi:92} is displayed as a horizontal green dashed line.}\n \\label{fig:nn2}%\n \\end{figure*}\nAtomic nitrogen becomes depleted in {\\fN} after about 10$^5$ years whereas molecular nitrogen is slightly enriched. \n These evolution times are also required to build significant amounts of molecular compounds which compare satisfactorily with available observations. \n The overall dependence of {\\NNHp} follows closely that of {\\NN} as it is formed from {\\NN} + {\\HHHp} reaction, with a slight decoupling between {\\fNNHp} and {\\NfNHp} at long evolution times. \nWe find that the isotopic ratio of the {\\NNHp} ions displays an almost constant value close to the solar value after some 10$^5$ years. They are in good agreement with observations in B1 but disagree by a factor of 2 for L1544.\n The trend that {\\fNNHp} is less abundant than {\\NfNHp} is reproduced in our results, as a result of the differences of endothermicity in their reactions with \\NN. We checked that introducing \\fN$_2$ and species containing two {\\fN} atoms had no effect on these ratios.\nWe could not find any gas-phase mechanism able to generate \n such a large ratio in pre-stellar core conditions. The high isotopic ratio found in L1544 implies an equivalently large ratio for molecular nitrogen, which is in strong contradiction with our findings.\nThese results are markedly different from those derived by \\cite{hilyblant:13} who found a moderate {\\fN} enrichment as these authors introduced the {\\fN} + {\\NNHp} fractionation reaction which we have found not to occur. \\footnote{These authors also interchanged the endothermicities of the {\\fNNHp} and {\\NfNHp} reactions with N and {\\NN}.\n\\subsection{Nitrogen hydrides}\n\\label{sec:NH}\nWe display in Figure \\ref{fig:nh3-fN} the time evolution of the isotopic ratios of nitrogen hydrides and of their fractional abundances relative to \\HH. {\\NHHH} and {\\NHH} have a very similar behavior as they both result from the reaction chain starting with the {\\Np} + {\\HH} reactions. \n \\begin{figure*\n \\centering\n \\includegraphics[width=14cm]{Fig_2.pdf}\n \n \\caption{ Upper panel : Time dependence of N \/ {\\fN} isotopic ratios in nitrogen hydrides. (a) and (b) correspond to the models defined in Table \\ref{tab:model}; The black heavy dotted line represents the elemental N \/ \\fN.\n Lower panel : time dependence of the fractional abundances relative to {\\HH} of NH, {\\NHH}, {\\NHHH} and {\\NHHD} for models (a) and (b). The values reported for {\\NHHH} \\citep{ohishi:92} and {\\NHHD} \\citep{tine:00} towards TMC1 are displayed as horizontal blue and red dashed lines respectively in the left panel.} \\label{fig:nh3-fN}%\n \\end{figure*}\n The species NH$_2$ and NH$_3$ are found to be enriched in $^{15}$N due to the $^{15}$N$^{+}$ + o-H$_{2}$ reaction which has a slightly smaller (weak) endothermicity, as reported in Table \\ref{tab:npHH}, than the \ncorresponding $^{14}$N$^{+}$ + o-H$_2$ reaction, a difference which slightly favors $^{15}$NH$^{+}$ formation. Nevertheless, despite the large uncertainties regarding \nthe rate of the N$^{+}$ + H$_2$ reaction, our results are in fair agreement with the results of \\cite{daniel:13} for prestellar core B1.\nAs the N$^{+}$ + HD $\\rightarrow$ ND$^{+}$ + H reaction has a smaller endothermicity than the N$^{+}$ + H$_{2}$ $\\rightarrow$ NH$^{+}$ + H reaction, \nthe modeled $^{14}$NH$_2$D \/ $^{15}$NH$_2$D ratio behaves somewhat differently than the $^{14}$NH$_3$ \/ $^{15}$NH$_3$ ratio. The ratio exhibits large variations around 10$^5$ years and becomes smaller than that of {\\NHHH} at large times and at steady state.\nThe observed values are compatible with calculations at sufficiently large times. The values of this ratio reported in \\cite{gerin:09} have been found to be too large as a result of assuming a single excitation rotational temperature. The future availability of collisional excitation rates of {\\NHHD} by {\\HH} \\citep{daniel:14} may give rise to additional changes.\n NH does not follow the same trend as NH$_2$ and NH$_3$ and is only slightly enriched in \\fN\n As discussed by \\cite{hilyblant:13}, NH is mainly formed \n from the dissociative recombination (DR) of \\NNHp, a reaction which has been recently revisited by \\cite{vigren:12}\n who derive a branching ratio towards NH of 7\\%.\n We have checked that this analysis still holds for model (b) even if the reaction of {\\NNHp} with CO becomes more efficient.\nThe formation route of NH through NH$_2^+$ recombination may take over for highly depleted CO. \n\n\\subsection{Nitriles and isonitriles}\nDeriving {\\fN} isotopic ratios of CN, HCN and HNC from observations is a difficult challenge as the transitions of the main isotopologues are optically thick.\nThen, most of the reported observational values of the {\\nN} \/ {\\fN} molecular ratios \n are obtained from the ratios of the minor isotopologues $^{13}$CN \/ C$^{15}$N, H$^{13}$CN \/ HC$^{15}$N \n and HN$^{13}$C \/ H$^{15}$NC which is subsequently multiplied by an assumed C \/ {\\tC} value, usually taken as 68 \\citep{milam:05}. \n\n\\subsubsection{ {\\tC}\/ {\\fN} ratios}\n %\n We test these hypotheses in our models by explicitly introducing fractionation reactions of \\tC, as discussed in Section \\ref{sec:reac}.\n Table \\ref{tab:res} shows that\nthe {\\tC} isotopic ratios of CN, HCN and HNC vary both with time and density. \n \\begin{figure*\n \\centering\n \\includegraphics[width=14cm]{Fig_3.pdf}\n \n\n \\caption{Time evolution of \\tC \/ {\\fN} ratios in CN, HCN and HNC for models (a) and (b). The \\tC\/{\\fN} elemental ratio is displayed as heavy dotted line.}\n \\label{fig:1315}%\n \\end{figure*}\nFigure \\ref{fig:1315} displays the $^{13}$CN \/ C$^{15}$N, H$^{13}$CN \/ HC$^{15}$N \n and HN$^{13}$C \/ H$^{15}$NC ratios as a function of time for the two considered models.\n The deviation from the elemental ratio of 6.48 is\n significant for HCN, HNC and CN. \n\n %\n\\subsubsection{{\\tC} chemistry}\nWe now consider the time dependences of the $^{12}$C \/ $^{13}$C isotopic ratios in CN, HCN and HNC species as displayed in Figure \\ref{fig:tCN}.\nThe time dependent ratio displays large variations, which is\n due to the fact that there are various reactions incorporating $^{13}$C \nin the molecules.\nThe elemental value of the ratio (68) is fulfilled in a narrow range around 1 Myr for model (a) and 2 $\\times$ 10$^5$ yrs for model (b) but steady state values are significantly different except for CN. \n \\begin{figure*\n \\centering\n \\includegraphics[width=14cm]{Fig_4.pdf}\n \n \\caption{time dependence of C \/ {\\tC} isotopic ratios in CN, HCN and HNC. (a) and (b) correspond to the models defined in Table \\ref{tab:model}}\n \\label{fig:tCN}%\n \\end{figure*}\n %\nThis relatively complex behavior results from the many different reaction channels involved in {\\tC} chemistry. \nWe then also consider other {\\tC} containing species and display the {\\dC} \/ {\\tC} ratio in C, CH, CO and HCO$^+$\nin Figure \\ref{fig:tC} as well as their fractional abundances relative to \\HH.\n \\begin{figure*\n \\centering\n \\includegraphics[width=14cm]{Fig_5.pdf}\n \n \\caption{ Upper panel : Time dependence of C \/ {\\tC} isotopic ratios in C, CH, CO and HCO$^+$. The black heavy dotted line represents the elemental {\\dC} \/ \\tC.\n Lower panel : Time dependence of the fractional abundances relative to {\\HH} of C, \\Cp, CO, CH and {\\HCOp}, for models (a) and (b). The observational values towards TMC1 \\citep{ohishi:92} are displayed as horizontal dashed lines with corresponding colors in the left panel. (a) and (b) correspond to the models defined in Table \\ref{tab:model}}\n \\label{fig:tC}%\n \\end{figure*}\nThe $^{12}$C\/$^{13}$C isotopic ratios of the various molecules are highly dependent\non the evolution time. \nThe transition from gas-phase atomic carbon toward CO controls the $^{13}$C enrichment. As long as there is still a relatively \nhigh carbon atom concentration in the gas phase, there is enough free $^{13}$C to allow strong enrichment of CN\nthrough the {\\tC} + CN reaction. When CO molecules become the reservoir of carbon, even if in that case the $^{13}$C concentration is low, the $^{13}$C$^{+}$ + $^{12}$CO $\\rightarrow$ \n$^{12}$C$^{+}$ + $^{13}$CO reaction still leads to a small $^{13}$CO enrichment \\citep{langer:90,milam:05}. Although this small excess is not measurable in CO, significant amounts of $^{13}$C are locked up in CO and most of the other carbon containing species become depleted in {\\tC}, as found for CH and other carbon chains. \n This effect is seen in Figure \\ref{fig:tC} for the isotopic ratio of CH in model (a) at steady state where CO is slightly enriched,\nleading to a significant depletion of {\\tC} in CH. \nOur results \nfor C and {\\HCOp} are similar to those of \\cite{furuya:11} who studied the {\\tC} fractionation of multiple carbon chains by \nexplicitly introducing the dependence of the {\\tC} position in the chain.\nWe see that\n{\\HCOp} is marginally enriched in {\\tC} at steady state whereas HCN and HNC are significantly depleted in \\tC. \n We also note that CN and {\\HCOp} may react via proton transfer to give CO + HNC$^+$ and have checked\n, at the DFT level, the absence of any barrier. The corresponding reaction rate is 2.2 $\\times$ 10$^{-9}$ ($\\frac{T}{300}$)$^{-0.4}$\n cm$^{3}$ s$^{-1}$ when using the capture rate theory. This reaction has not been included in any chemical database up to now. \n \nHNC has been shown recently to react with atomic carbon \\citep{loison:14}, which leads to the different steady state isotopic ratios obtained for HCN and HNC. \n The CN chemistry is then somewhat decoupled from that of HCN and HNC. HCN and HNC are formed at relatively long times via CN + H$_3$$^{+}$ $\\rightarrow$ HNC$^{+}$ \/HCN$^{+}$ + H$_2$, followed immediately by HNC$^{+}$ + H$_2$ $\\rightarrow$ HCNH$^{+}$ + H giving back HCN and HNC via DR \\citep{mendes:12}. With the adopted elemental abundances, the main CN destruction reactions are however O + CN and N + CN so that the HCNH$^{+}$\/HCN$^{+}$\/HNC$^{+}$\/HCN\/HNC\/CN \n network is not a closed system in contrast to models including coupled gas-grain chemistry \\citep{loison:14}.\n\n %\n \\subsubsection{ {\\fN} fractionation} \nWe now display also the N \/ {\\fN} fractionation in nitriles as well as that of NO in Figure~\\ref{fig:15CN}.\n \\begin{figure*\n \\centering\n \\includegraphics[width=14cm]{Fig_6.pdf}\n \n \\caption{Time dependence of N \/ {\\fN} isotopic ratios in CN, HCN, HNC and NO. (a) and (b) correspond to the models defined in Table \\ref{tab:model}}\n \\label{fig:15CN}%\n \\end{figure*}\nThe time dependences of the isotopic ratios are markedly different in the two models, except for NO. Whereas CN, HCN and HNC are somewhat enriched in {\\fN} for model (a) both at intermediates times and steady state, the opposite result is obtained in model (b) after 10$^{5}$ years. We display as well the time dependences of the fractional abundances of these molecules in Figure \\ref{fig:td} in order to better understand the previously described differences observed in the various fractionation ratios. \n \\begin{figure*\n \\centering\n \\includegraphics[width=14cm]{Fig_7.pdf}\n \n \\caption{Time dependence of CN, HCN, HNC and their isotopologues. (a) and (b) correspond to the models defined in Table \\ref{tab:model}}\n \\label{fig:td}%\n \\end{figure*}\n\nOur model (a) is intended to be representative of TMC1, a moderately dense cloud in an early evolutionary stage. \nWe see that the various abundances and ratios are very sensitive to the chosen \"age\", assumed to be the relevant chemical evolution time. Considering a TMC1 age of 1Myr leads to a reasonable agreement with the sparse observations available, see Table \\ref{tab:res}. \nModel (b) is more likely to be representative of a denser evolved molecular cloud such as L134N, L1544, Barnard B1, ... where elemental C, O, N are partially depleted through sticking on grains. Steady state values obtained with significant depletion conditions \\citep{roueff:05} may be used as corresponding proxies. We see that ammonia isotopologues are satisfactorily accounted for in our model. The agreement between observed and calculated \\tC \/ {\\fN} ratios is somewhat misleading as the modeled value mainly results from a significant depletion in the {\\tC} species. The ratios involving N \/ {\\fN} are found close to the elemental value in our model (b) at steady state (although {\\NHH} and {\\NHHH} are somewhat enriched in {\\fN} through the {\\fNp} + {\\HH} reaction as explained in subsection \\ref{sec:NH}), which is in agreement with the fact that no significant \ngas phase fractionation mechanisms have been found. The occurrence of small ratios in B1 observations \\citep{daniel:13},\nif real, implies other mechanisms at work. An obvious suggestion lies in the processes involved in adsorption\/{\\bf{desorption}} on grains and possible surface reactions.\n\\subsection{Role of the {\\HH} ortho to para ratio}\nWe now test the role of the value of the ortho\/para ratio of molecular hydrogen (OPR) in the context of ammonia chemistry and fractionation determination. We run two additional models in the frame of models (a) and (b) by changing the OPR by a factor of 10 \nboth upwards and downwards. The abundances of p-{\\HH} and o-{\\HH} are expressed respectively as\nn(p-{\\HH}) = $\\frac{1}{1+\\rm{OPR}}$ n(\\HH) and \nn(o-{\\HH})~=$\\frac{\\rm{OPR}}{1+\\rm{OPR}}$ n(\\HH).\nThis ratio is introduced, in addition to the reaction of {\\HH} with {\\Np} (\\fNp), in the reverse\nreaction of the fractionation of \\HHHp, namely the \\HHDp + {\\HH} reaction which plays a significant role in the deuterium \nfractionation of various molecules \\citep{pagani:11} as shown below:\n\\\\\n\\\\\n\\begin{tabular}{llll}\n{\\bf{$\\HHDp + \\rm{p}-\\HH$}} & $\\rightarrow$ & {\\bf{$ \\HHHp + \\HD$}}& , k$_1$ (cm$^3$ s$^{-1}$)\\\\\n{\\bf{$\\HHDp + \\rm{o}-\\HH$}} & $\\rightarrow$ & {\\bf{$ \\HHHp + \\HD$}}& , k$_2$ (cm$^3$ s$^{-1}$)\\\\\\ \n\\end{tabular}\n\\\\\nwith k$_1$ = 2.0 $\\times$ 10$^{-9}$ $\\times$ exp(-232\/T) and \nk$_2$ = 2.0 $\\times$ 10$^{-9}$ $\\times$ exp(-61.5\/T) . \n\\begin{table*}[h]\n\\caption{Dependence of the fractionation ratios on the o\/p ratio of {\\HH} for model (a). ss means stationary state. } \n\\label{tab:opa} \n\\centering \n\\begin{tabular}{|l|cc|cc|cc|} \n\\hline\\hline \n & \\multicolumn{2}{c|}{OPR = 10$^{-4}$} & \\multicolumn{2}{c|}{OPR = 10$^{-3}$} &\\multicolumn{2}{c|}{OPR = 10$^{-2}$}\\\\\n & t= 10$^6$ yrs& ss & t= 10$^6$ yrs& ss & t= 10$^6$ yrs& ss \\\\ \n\n \\hline\nelectronic fraction & 5.1 $\\times$ 10$^{-8}$ & 2.2 $\\times$ 10$^{-7} $ &4.8 $\\times$ 10$^{-8}$ & 2.1 $\\times$ 10$^{-7}$ & 3.7 $\\times$ 10$^{-8}$ & 4.2 $\\times$ 10$^{-8}$ \\\\\nN \/ \\fN & 440 & 456 & 440 & 456 & 440 & 452 \\\\ \n2 $\\times$ N$_2$ \/ \\fN N & 438& 430 & 438 &431& 438 & 437 \\\\\nNH \/ \\fNH & 431 & 429 & 429 & 428 & 426 & 418 \\\\ \nNH \/ ND & 16 & 31 & 16 & 31 & 18 & 23 \\\\ \n\\NHHH \/ \\fNHHH & 345 & 409 & 333 & 386 & 374 & 395 \\\\\n\\NHHD \/ \\fNHHD & 206 & 267 & 215 & 276 & 265 & 303 \\\\\n\\NHHH \/ \\NHHD & 8 & 14 & 17 & 22 & 43 & 63 \\\\\n\\NNHp \/ \\NfNHp &431 & 429 & 431 & 430 & 429 & 421\\\\\n\\NNHp \/ \\fNNHp & 437 & 432 & 437 & 432 & 436 & 433 \\\\\n\\NNHp \/ \\NNDp & 16 & 30 & 16 & 29 & 17 & 21 \\\\\nCN \/ \\tCN & 67 & 85 & 67 & 84 & 67 & 70 \\\\\nCN \/ \\CfiN & 432 & 449 & 430 & 449 & 429 & 434 \\\\\nHCN \/ \\tHCN & 92 & 168 & 93 & 168 & 93 & 165 \\\\\nHCN \/ \\fHCN & 401 & 453 & 398 & 445 & 400 & 413 \\\\\nHCN \/ DCN & 44 & 95 & 43 & 96 & 40 & 55 \\\\\nHNC\/ \\tHNC & 91 & 178 & 93 & 180 & 97 & 195 \\\\\nHNC\/ \\fHNC & 410 & 451 & 405 & 442 & 405 & 410 \\\\\nHNC\/ DNC & 23 & 66 & 23 & 66 & 22 & 32 \\\\\n\n{\\HCOp} \/ {\\HtCOp} & 57& 66 & 56 & 65 & 54 & 55 \\\\\n{\\HCOp} \/ {\\DCOp} & 15 & 30 & 15 & 29 & 16 & 19 \\\\\n\\hline \n \\hline \n\\end{tabular}\n\\end{table*}\n %\n \\begin{table}[h]\n\\caption{Dependence of the fractionation ratios on the o\/p ratio of {\\HH} for model (b) at steady state. } \n\\label{tab:opb} \n\\centering \n\\begin{tabular}{l|c|c|c} \n\\hline\\hline \n & {OPR = 10$^{-4}$} & {OPR = 10$^{-3}$} & {OPR = 10$^{-2}$}\\\\\n\n \\hline\nelectronic fraction & 2.2$\\times$ 10$^{-8}$ & 1.7 $\\times$ 10$^{-8} $ &1.1 $\\times$ 10$^{-8}$ \\\\\nN \/ \\fN & 457 & 455 & 445 \\\\ \n2 $\\times$ N$_2$ \/ \\fN N & 436& 437 & 438 \\\\\nNH \/ \\fNH & 425 & 421 & 418 \\\\ \nNH \/ ND & 9 & 9 & 11 \\\\ \n\\NHHH \/ \\fNHHH &396 & 387 & 416 \\\\\n\\NHHD \/ \\fNHHD & 322& 336 & 376 \\\\\n\\NHHH \/ \\NHHD &11 & 18 & 33 \\\\\n\\NNHp \/ \\NfNHp &425 & 423 & 417 \\\\\n\\NNHp \/ \\fNNHp & 434 & 433 & 433 \\\\\n\\NNHp \/ \\NNDp & 8.7 & 8.6 & 9.7 \\\\\nCN \/ \\tCN & 65 & 63 & 60 \\\\\nCN \/ \\CfiN & 449& 445 & 438 \\\\\nHCN \/ \\tHCN & 115 & 114 & 107 \\\\\nHCN \/ \\fHCN & 467 & 453 & 458 \\\\\nHCN \/ DCN & 25 & 22 & 18 \\\\\nHNC\/ \\tHNC & 120 & 121 & 118 \\\\\nHNC\/ \\fHNC & 461 & 446 & 453 \\\\\nHNC\/ DNC & 19 & 16 & 12 \\\\\n\n{\\HCOp} \/ {\\HtCOp} & 58& 56 &51 \\\\\n{\\HCOp} \/ {\\DCOp} & 8.6& 8.4 & 9.4 \\\\\n\\hline \n \\hline \n\\end{tabular}\n\\end{table}\n\nTables \\ref{tab:opa} and \\ref{tab:opb} display the fractionation values for the three different assumed values of the OPR for models (a) and (b)\nand Figures \\ref{fig:opa} and \\ref{fig:opb} display the corresponding time evolutions. We see that the curves are \nalmost superposable for times less than 1 Myr for model (a) and less than several 10$^5$ yrs for model (b).\n The variations are significative at steady state. We find that the competition between the destruction channels of {\\Np}\nthrough its reactions with o-{\\HH} and CO plays a major role. If o-{\\HH} is the most efficient destruction channel, which occurs typically for \n$\\rm{OPR} > 200 \\times x_{CO} $ at 10K, where x$_{CO}$ is the fractional abundance of CO relative to \\HH,\nformation of {\\NHHHHp} proceeds efficiently. In the opposite case {\\Np} is mainly destroyed through reaction with CO to yield {\\COp}, leading rapidly to {\\HCOp}, and {\\NOp} (which does not react with \\HH). As the DR rate coefficients of polyatomic ions increase significantly for larger polyatomic ions (the {\\NHHHHp} + e$^-$ reaction is 3 times more rapid than {\\HCOp} + e$^-$ and 20 times more rapid than {\\HHHp} + e$^-$), these two different channels impact the electron abundances and then affect many other species. \n Moreover, the reaction between {\\NHHH} and {\\HHHp} leads to {\\NHHHHp} + \\HH. {\\NHHHHp} then reacts mainly with electrons, recycling back to {\\NHHH} which subsequently converts to {\\NHHHHp}, amplifying the electron loss through DR. \n These effects occur when CO becomes the main carbon reservoir, for sufficiently large evolution times. \nAs a result, the electron fraction\nis found to decrease when the OPR increases at large evolution times and at steady state.\nThe decreasing electron abundance with increasing OPR values acts to diminish the effect of all DR reactions\nand as a result to increase the abundances of {\\HHHp} and {\\HHDp} since DR is the main destruction channel in our conditions. The abundance of {\\HHDp} is even more enhanced through the {\\HHHp} + HD reaction. Then the abundances of other deuterated molecular ions produced through deuteron transfer reactions of abundant neutrals with {\\HHDp} are increased as well, which impacts the subsequent formation of neutral deuterated compounds produced in the DR reactions. Eventually, the HCN\/DCN and HNC\/DNC ratios are shown to decrease for increasing OPR values.\nThis example demonstrates the complexity of the interplay between the different chemistries.\n \n \\begin{figure*\n \\centering\n \\includegraphics[width=15cm]{Fig_8.pdf}\n \n \\caption{Time dependence of fractionation ratios of CN, HCN, HNC in model (a) for 3 different OPR values. Black heavy dotted line: elemental ratio; green : OPR=10$^{-4}$, purple : OPR = 10$^{-3}$, red : OPR =10$^{-2}$}\n \\label{fig:opa}%\n \\end{figure*}\n \\begin{figure*\n \\centering\n \\includegraphics[width=15cm]{Fig_9.pdf}\n \n \\caption{Time dependence of fractionation ratios of CN, HCN, HNC in model (b) for 3 different OPR values. Black heavy dotted line: elemental ratio; green : OPR=10$^{-4}$, purple : OPR = 10$^{-3}$, red : OPR =10$^{-2}$}\n \\label{fig:opb}%\n \\end{figure*}\n\\section{Conclusions}\n\\label{sec:conclusion}\n We have built for the first time an isotopically substituted gas phase chemical network including D, {\\tC} and {\\fN} species and included it in a time dependent chemical model. Our model is based on a careful analysis of the possible gas phase mechanisms involved in carbon and nitrogen fractionation by scrutinizing the few available experimental studies and performing DFT and ab-initio quantum calculations on hypothetical reactions to check the possible presence of barriers in the reaction channels. One important result obtained is that the fractionation reaction of {\\fN} with {\\NNHp} is unlikely, due to the presence of a barrier, in contrast to the previous hypothesis made by \\cite{terzieva:00}. As a result, the modeled isotopic ratios involved in the isotopologues of \\NNHp are found to be very close to the elemental values and are similar to each other, in contradiction with observations towards L1544 \\citep{bizzocchi:13}. The availability of new collisional rate coefficients\n for the {\\NNHp - \\HH} system \\citep{lique:15} may however modify these conclusions. \n We also discarded the {\\fN} + {\\HCNHp} and {\\fN} + NO exchange reactions through similar arguments. Tentative reaction rate coefficients are also proposed for carbon fractionation reactions involving {\\tCp} and {\\tC} with CN and {\\nCC}.\nAdditionally, we have explicitly considered the various isotopologues involved in N$^{+}$ + {\\HH} reaction, assuming that the energy defect involved in the reactions of {\\Np} with para-{\\HH} is a \"real\" endothermicity. This leads to a slight decline of the exponential term when {\\fNp} reacts with {\\HH} and with HD compared to {\\Np}. This explains satisfactorily that {\\fNHHD} is found to be more enriched in {\\fN} than {\\fNHHH} in the observations. \nComparison between observations of nitriles and isonitriles and simulated values is much more questionable, as carbon and nitrogen chemistries are interdependent. Observed isotopic ratios are usually large and suffer from large error bars due to opacity effects in the main isotopologue and difficulties linked to nuclear spin effects. Whereas the various isotopologues follow a similar evolution, the isotopic ratios display significant variations due to slight shifts in the position of the maximum fractional abundances. \nA reasonable agreement {\\bf{is}} obtained between the observed {\\tC} \/ {\\fN} ratios for most of the species in L134N and Barnard B1 and steady state model values.\nOur model results show a strong depletion in {\\tC} and a near elemental ratio for {\\nN} \/ {\\fN}, whereas observations are usually interpreted by assuming an elemental ratio for {\\tC} containing species which leads to the incorrect assumption of {\\fN} enrichment. These considerations\nare undeniably dependent on the chosen elemental abundances, and in particular to the assumed C\/O ratio. \n We additionally point out a somewhat unexpected effect of the ortho to para ratio of \\HH, which affects significantly the fractional ionization, and consequently the level of deuterium fractionation through the respective orders of magnitude of DR rate coefficients of polyatomic molecular ions. The importance of coupling C, O and N chemistries is emphasized. %\n\\section*{Acknowledgments} \n We acknowledge support of PCMI (Programme National de Physique et Chimie du Milieu Interstellaire). This work has been partially funded by the Agence Nationale de la\nRecherche (ANR) research project IMOLABS (ANR-13-BS05-0008). \n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nA major outstanding puzzle in modern physics is the nature of dark matter (DM) \\cite{pdg2018}. Despite the ever-improving sensitivities of direct detection experiments, the simplest dark matter candidates have not been observed, motivating searches for a wider range of possible dark sectors. Moreover, challenges that simple cold DM candidates face on sub-galactic scales \\cite{Kuhlen:2012ft,Bullock:2017xww} might be relieved with more complex dark sectors. For instance, self-interacting dark matter has been investigated to address small-scale challenges such as the core-cusp problem \\cite{Spergel:1999mh,Loeb:2010gj}.\n\nNearly all current dark matter detection strategies, ranging from direct-detection efforts in the laboratory to indirect signals from DM annihilation (or decay), are based on the assumption that the dark matter is distributed around the universe as a gas of free particles with a large number density. This picture naturally emerges if self-interactions within the dark sector are weak, but is not strictly prescribed by existing observational constraints. The most stringent limits arise from observations of the Bullet Cluster, which restrict the self-interaction cross section per unit mass to be $\\sigma_{\\chi\\chi}\/m_{\\rm DM} \\lesssim 1 ~{\\rm cm}^2\/{\\rm g}$ \\cite{Spergel:1999mh}. It is apparent that the limit on $\\sigma_{\\chi\\chi}$ itself is significantly weakened if dark matter is clustered into composite states with large masses $m_{\\rm DM}$.\n\nIf the dark sector has strong self-interactions, it would undergo a nucleosynthesis process in the early universe much like the nuclei of the standard model (SM), whereby individual particles coalesce to form large composite states \\cite{Hardy:2014mqa,Wise:2014ola}. SM nucleosynthesis suffers from a number of theoretical accidents (such as the deuterium bottleneck) that render certain light elements unstable and thereby inhibit the production pathway of ultra-heavy elements. Still, the SM manages to produce large composite systems \\cite{ghiorso1955new}. It is thus not surprising that a completely unconstrained dark sector could also produce large composite objects. We will refer to these composite states as ultra heavy dark matter (UHDM) \\cite{Gresham:2017cvl,Grabowska:2018lnd}.\n\nDirect searches for canonical DM in the form of a gas of small particles leverage the large influx of these particles in a detector to compensate for their small cross section with SM particles. For example, about $10^{16}$ WIMP particles of mass 100 GeV would pass through a ${\\rm m}^3$ detector in a year, allowing for the direct detection of WIMP-nucleon scattering cross sections as low as $10^{-45}\\text{ cm}^2$ \\cite{Roszkowski_2018}. UHDMs, on the other hand, would arrive with significantly lower flux and require a different detection strategy. Thus, an experimental strategy for UHDM detection should leverage generic signatures of large composite objects instead of focusing on the specifics of any one composite dark matter model, as the rich dynamics of an interacting dark sector can produce a plethora of models. Accordingly, we focus here on the fact that the many constituent particles of a UHDM will enhance its cross section with SM matter such that each rare UHDM incidence could result in a spectacular event with a distinct signature imitated by no SM effect. The detector sensitivity is therefore less important in such scenarios and can be traded for greater spacetime dimensions that maximize the probability of a rare UHDM transit. In this paper, our primary focus is to establish the detectability of such a signal. While we provide an example of a dark matter model that yields such a signal, it is straightforward to construct other examples of ultra-heavy particles that can cause similar damage (e.g., Q-balls \\cite{Kusenko:1997si}). \n\nDamage tracks left by particles passing through the Earth over geological times could be recorded by ancient rock samples buried underground. For this reason, such geological samples have been proposed and used as \"natural particle detectors\" in the past, including for magnetic monopoles \\cite{Fleischer1969,Eberhard1971,Kovalik1986,Jeon:1995rf,fleischer1969jan,fleischer1969aug,price1984,price1986}, WIMPs \\cite{Snowden-Ifft1995,Collar:1994mj,engel1995,SnowdenIfft:1997hd,Baum:2018tfw,Drukier:2018pdy,Edwards:2018hcf}, and neutrinos \\cite{Baum:2019fqm,Jordan:2020gxx}. The geological exposure times of ancient rock detectors range from the present back to the time they were last heated naturally to the point of annealing, which can be up to $\\sim 10^9$ years and is thus much longer than typical DM direct detection laboratory experiments (by factors of up to $10^9$). This advantage makes geological DM detectors ideal probes of sparser, higher-mass composite DM. As we discuss in Sec. \\ref{sec:feasibility}, searches with $10 ~{\\rm m}^2$ of billion-year old rock would probe DM masses up to $m_{\\rm DM} \\sim 10^{28}~{\\rm GeV}$. A major challenge for such a detection strategy is the ability to efficiently identify DM signatures in a large volume of rock and distinguish them from geological, radioactive, and cosmic ray backgrounds. Such discrimination is significantly simpler in searches for UHDMs, since the extremely long and continuous cylindrical damage patterns they generically leave are qualitatively different from the sporadic defects due to expected backgrounds.\n\nHere, we assess the use of geologically old quartz samples as solid-state particle detectors to search for damage tracks left by UHDMs. Quartz, a crystalline polymorph of silica SiO$_2$, is one of the most abundant and well-studied minerals in the lithosphere \\cite{gotze2012application}. Defects and damage tracks can be resolved down to the micron scale with SEM-CL: a scanning electron microscope (SEM) combined with a cathodoluminescence (CL) detector. Imaging provided by the SEM is supplemented with spectral information from CL, which reveals the nature of trace elements and point defects in the quartz \\cite{stevens2009cathodoluminescence}. This modality has already proved successful at providing answers to key geological questions \\cite{vasyukova2013,macrae2013hyperspectral,leeman2012study,spear2009cathodoluminescence,ackerson2018low,hamers2017scanning,ackerson2015trace}. The technical advantages of SEM-CL mapping, as well as the considerable literature on its application in quartz, make it an appropriate choice for our readout method.\n\nNote that a similar search for long damage tracks was performed by Price and Salamon \\cite{price1986} in ancient mica crystals with null results. While they used this result to constrain the abundance of magnetic monopoles, the experiment is also sensitive to UHDMs with masses $m_{\\rm DM}\\lesssim 10^{26}\\,{\\rm GeV}$ \\cite{Bhoonah:2020dzs}. The readout method used in their experiment requires acid etching prior to microscopy in order to enlarge damage tracks and render them visible in an optical microscope. Such an etching process also enlarges background signals in the form of naturally-occurring lattice defects. Hence, the success of the experiment hinges on the low level of background in the samples and its scalability is limited by the availability of sufficiently pristine mica crystals. By contrast, the lack of etching in our proposed SEM-CL readout and the more readily available quartz mineral that meets our requirements allow us to scan over larger sample areas and extend the search pioneered by Price and Salamon to higher DM masses.\n\nThe rest of the discussion is organized as follows. In Sec.~\\ref{sec:feasibility} we present an experimental realization of the UHDM detection method sketched above, identify optimal samples, and assess its model-independent sensitivity. We then demonstrate its ability to probe a simple composite UHDM model, taking into account various existing constraints, in Sec.~\\ref{sec:model}. Finally, we conclude in Sec.~\\ref{sec:conclusion}.\n\n\\section{Detection Feasibility}\n\\label{sec:feasibility}\nIn this section, we investigate experimental issues for UHDM detection with quartz. Based on the considerations below, we establish the criteria for UHDM signatures to be robustly detectable with microscopy of about $\\SI{}{\\SI{}{\\micro}\\meter}$-resolution. We discuss the discovery reach of this approach in Sec. \\ref{sec:sensitivity} (see equations \\eqref{eqn:dragforcebound}-\\eqref{eqn:meltingenergypernucleus}).\n\n\\begin{figure}[htbp!]\n \\centering\n \\includegraphics[width=1\\columnwidth]{fig1.pdf}\n \\caption{{Schematic of the proposed readout method.} {\\bf (a)} A quartz sample of size $\\sim{\\rm cm}^2 \\times {\\rm mm}$. The black straight line illustrates a damage track as a result of an ultra-heavy composite dark matter (UHDM) particle passing through the sample. The sample is sectioned into multiple sections of thickness $\\sim100~\\SI{}{\\SI{}{\\micro}\\meter}$. We show several sections where the top and bottom surfaces are highlighted, which would be scanned using SEM-CL. {\\bf (b)} Correlated damage spots of micron-scale diameter over a macroscopic (mm-scale or longer) distance, between sections is the unique signature of the ultra-heavy DM particle interaction with quartz. Note that the probability of background features coincidently aligning reduces exponentially with the number of correlated layers. For a realistic feature density of $1000\/{\\rm cm}^2$, simulations show that correlations of 4 layers efficiently rejects false positive signals.}\n \\label{fig:readout}\n\\end{figure}\n\n\\subsection{Damage Tracks}\nSolid-state systems \\cite{fleischer1964,fleischer1965,lannunziata2012} have been used as particle track detectors with applications ranging from nuclear science to geophysics, as such tracks yield information about the history of the sample and properties of the impinging particles \\cite{fleischer1965a}. For example, DM detection using crystal damage tracks has been proposed as a directional signal in semiconductors such as diamond, where a WIMP scattering event would give rise to damage tracks of $\\sim ~30-100 ~{\\rm nm}$ in length; the directional information in these tracks could enable such a detector to probe below the \"neutrino floor\" \\cite{Rajendran:2017ynw,Marshall:2020azl}. Paleodetection, which looks for damage tracks of a similar size in ancient rock samples, has also been investigated for WIMP detection \\cite{Baum:2018tfw,Drukier:2018pdy,Edwards:2018hcf}.\n \n\nWe propose using ancient quartz as a detector for ultra-heavy composite dark matter (UHDMs). As we discuss in Sec. \\ref{sec:sensitivity}, each UHDM could deposit enough energy to locally melt nearby quartz along its trajectory. Since quartz nucleation under ambient conditions is a very slow process \\cite{buckley2018nucleation}, the melted region would solidify into amorphous silica without recrystallizing. Detecting such amorphous micro-regions within quartz samples is feasible with SEM-CL, where defects in the tetrahedrally coordinated SiO$_2$ microstructure contribute to CL emission \\cite{stevens2013cathodoluminescence}. Even in the absence of melting, the same SEM-CL method would in principle be sensitive to linear tracks of lattice distortions left by UHDMs. However, quantitatively characterizing the sensitivity of this method to such tracks requires further study of the backgrounds in natural quartz, which we leave for future work. Thus, in what follows we focus on detecting UHDMs that can cause melting.\n\n\\subsection{Quartz Samples and Backgrounds}\n\\label{sec:background}\n\nThe signature of the proposed UHDM detection method is a long damage track, of micron-scale cross section, extending through the entire length of the quartz sample (see Figure \\ref{fig:readout}a). This signature has the distinct advantage that no known mechanism produces such a track, allowing for strong geometric rejection of background signals. A variety of effects may induce localized disruption of the crystal lattice on the micron scale, such as extended growth defects or radioactive decays, but these localized features cannot pass through the entire macroscopic crystal. And although particles with low interaction probability (e.g., neutrinos or relativistic cosmic rays) can pass through an entire sample, their low nuclear cross sections yield dispersed individual damage events rather than a continuous, micron-scale-diameter track. Fractures induced by historical geological stresses could similarly pass continuously through an entire sample, but would in general be two- or three- rather than one-dimensional, and would not leave behind amorphous quartz within the damage track.\n\nTo take advantage of the distinctive extended geometry of a UHDM signal, we propose a multi-phase scanning readout, where we search for correlated feature positions at multiple depths in the sample (see Figure \\ref{fig:readout}). This allows us to reject backgrounds, which have an exponentially suppressed likelihood of lying along a single line (as shown by simulations discussed in Figure \\ref{fig:readout}). An expected background signal, given the imaging resolution of our method, is from the presence of radioactive isotopes such as uranium, which lead to fission tracks and alpha recoil damage within the crystal lattice. These processes leave behind halos of size $\\sim~10~ \\SI{}{\\SI{}{\\micro}\\meter}$ \\cite{Bower2016}, which are readily detectable using our proposed SEM-CL protocol (see Figure \\ref{fig:CLimage}d). In a single two-dimensional SEM-CL scan, these could mimic a UHDM damage track, but would be disqualified as UHDM signals by lack of correlated damage in subsequent slices. The presence of some radioactivity-induced features is potentially beneficial to our analysis, as their preservation would indicate that recent annealing events would not have removed older, UHDM-induced features. As such, if the fission track age can be determined from the host quartz, the absence of UHDM-induced features implies the lack of UHDM interaction events since the time of occurrence of the fission track.\n\nQuartz samples with low impurity levels are essential for reducing background levels. High-resolution cathodoluminescence (CL) studies reveal both the microstructure of the samples and trace element inclusions. Titanium (Ti) and aluminum (Al) are the two of the most abundant impurities in quartz. Ti is the dominant CL activator while Al is not generally considered an activator \\cite{tailby2018,leeman2012study}. Trace element studies show that quartz samples of different geological origins have a wide range of Ti and Al concentrations. Low-temperature hydrothermal vein quartz (HVQ) has the lowest trace element concentrations: quartz typically has a Ti concentration of a few 100 ppm; but this number could be as low as 6 ppm for an HVQ sample, which simultaneously has a low Al concentration \\cite{rusk2008trace,ackerson2015trace}. Here, we characterize preliminary measurements to demonstrate that low-Ti vein quartz samples are a suitable choice for our proposed experiment (see Figure \\ref{fig:CLimage}).\n\n\\begin{figure*}\n\\includegraphics[width=2\\columnwidth]{fig2.png\n\\caption{\\label{fig:CLimage}Example quartz sample characterization. SEM-CL images of two samples, {\\bf (a)} magmatic quartz from Bishop Tuff with Ti concentration $51 \\pm 6$ ppm, and {\\bf (b)} vein quartz from Jack Hills with Ti concentration $5.2 \\pm 6.5$ ppm, measured on a mass spectrometer. The scan rate is $20~{\\rm s}\/{\\rm mm}^2$ with $1.5~\\SI{}{\\SI{}{\\micro}\\meter}$ resolution for magmatic quartz and $5~{\\rm s}\/{\\rm mm}^2$ with $3~\\SI{}{\\SI{}{\\micro}\\meter}$ resolution for vein quartz (we forecast the full-scale UHDM experiment time and resources using these values). In (b) we identify a few high-count pixels in the vein quartz image, which demonstrates the possibility of high-resolution detection of concentrated CL emission. The inset shows a zoomed-in image of the region of interest with high-count pixels. These pixels could be a melting track intersection, which needs to be investigated by correlating multiple sections as described in the text. {\\bf (c)} Normalized histogram of the pixel counts in arbitrary units for each of the two sample SEM-CL images. Vein quartz shows a lower CL noise level as well as smaller variation, making it a suitable target for our detection proposal. {\\bf (d)} SEM-CL signal from a uranium halo (measured in a different quartz sample from those shown in (a) and (b)). Microscopic uranium inclusions have decayed over time; the fission products from these inclusions create crystal lattice damage, which emits cathodoluminescence (CL) upon excitation by the SEM. The CL signal from an ultra-heavy composite dark matter (UHDM) particle track would also result from crystal lattice defects at and around the track of melted quartz. Any such uranium halos in a UHDM search would be disqualified as potential damage tracks by lack of correlated damage in other slices of the sample.}\n\\end{figure*}\n\nHVQ has yet other advantages. Hydrothermal fluid flow is commonly localized along fracture systems, fault systems, and shear zones that can produce vast arrays of quartz veins. When a fracture or fault remains open and under hydrothermal pressure for a sufficient period of time, hydrothermal vein quartz grows as large, euhedral, and high-purity crystals. These properties will enable us to analyze a large net exposure with the proposed protocol, using serial sectioning and SEM-CL scanning of large samples, with background rejection via correlation of damage spots across layers (see Figure \\ref{fig:readout}b).\n\nHVQ from the Jack Hills of Western Australia is an ideal source of quartz for the DM search. The siliciclastic units at Jack Hills contain numerous, large quartz veins that appear as prominent surface features (i.e., clusters of milky white outcrops that can form very localized topographic highs) observed throughout the range. The veins are generally either undeformed or very weakly deformed, and often show an abundance of high-purity, gem-quality quartz crystals. These HVQ systems can reach impressive sizes at several locations within the Jack Hills from cm-scale to 50 meters wide \\cite{spaggiarietal2007jack}. Several of the vein systems can be followed for several kilometers and appear to be associated with major episodes of brittle faulting. The combined work of Rasmussen et al. \\cite{rasmussen2010situ} and Spaggiari \\cite{spaggiari2007jack} provide strong evidence that units (including the hydrothermal quartz veins) at Jack Hills, particularly in the vicinity of the \"classic\" W74 location, have likely remained at temperature conditions less than 330-420 $^{\\circ}$C for $\\sim$ 1.7 Gyr. The fact that the tectonic environment can be evaluated in detail \\cite{trail2016li,cavosie2004internal,baxter1984jack,spaggiari2007jack} (including thermometry and age dating) and consistently demonstrates equilibria at such low temperature (i.e., at or below greenschist facies), is to the best of our knowledge unique to Jack Hills, making it an ideal source of HVQ for our proposed measurements.\n\n\\subsection{Experimental Protocol}\n\\label{sec:protocol}\n\nThe proposed experimental protocol is as follows:\n\\begin{enumerate}\n \\item Identify quartz samples that are (i) old, having last annealed no less than 1 Gyr ago; and (ii) clean, with low CL noise level and less than a few thousand micron-scale resolved CL features per cm$^2$.\n \\item Prepare about $10^4$ samples of size $\\sim {\\rm cm}^2 \\times {\\rm mm}$ (lateral area $\\times$ length) that satisfy the above conditions and prepare each of the samples into sections of thickness $\\sim 100~\\SI{}{\\SI{}{\\micro}\\meter}$ (see Figure \\ref{fig:readout}a). Polish the top and bottom surfaces of each section, then scan them with SEM-CL.\n \\item Search for correlated damage spots, across the first few sections, that are aligned, section-to-section, along a straight line (see Figure \\ref{fig:readout}b).\n \\item If such a damage track of interest is identified, perform a dedicated search in subsequent sections to reject false positives\n \\item Repeat steps 3 and 4 for all the ${\\rm cm}^2 \\times {\\rm mm}$ samples.\n\\end{enumerate}\nThe scanning rate with SEM-CL depends on sample properties such as the concentration of CL activators. Given a typical data acquisition time of $\\sim$ 100 min per ${\\rm cm}^2$ with $\\SI{}{\\SI{}{\\micro}\\meter}$ resolution (for example see Figure \\ref{fig:CLimage}), we plan the experiment in three stages:\n\\begin{itemize}\n \\item Quartz-$1 \\, {\\rm m}^2$: About two years of experiment time with four SEM-CL apparatuses will be required to scan samples with a total area of about 1 m$^2$. The total quartz exposure of $\\sim 1\\, {\\rm m}^2\\,{\\rm Gyr}$ for such a search would probe UHDMs of mass $m_{\\rm DM} \\lesssim 10^{27}~{\\rm GeV}$. This first stage search would probe a currently unconstrained mass range with a new technique; see Figure \\ref{fig:param_space}.\n \\item Quartz-$10 \\, {\\rm m}^2$: 20 SEM apparatuses running for about four years would provide a total quartz exposure to UHDMs of $\\sim 10\\, {\\rm m}^2\\,{\\rm Gyr}$, yielding sensitivity $m_{\\rm DM} \\lesssim 10^{28}~{\\rm GeV}$.\n \\item Quartz-$100 \\, {\\rm m}^2$: 100 SEM apparatuses running for about eight years would provide a total quartz exposure to UHDMs of $\\sim 100\\, {\\rm m}^2\\,{\\rm Gyr}$, yielding sensitivity $m_{\\rm DM} \\lesssim 10^{29}~{\\rm GeV}$.\n\\end{itemize}\n\n\\subsection{Model-Independent Sensitivity}\n\\label{sec:sensitivity}\nThe proposed\nexperiment would be sensitive to a wide range of ultra-heavy dark matter (UHDM) candidates, independent of the underlying dark sector microphysics, that (1) pass through the quartz sample with sufficiently high probability while (2) depositing enough energy in a sufficiently concentrated way to melt a micron-size lateral region. \n\nGiven a DM candidate of mass $m_{\\rm DM}$, we can estimate the expected number of DM transits in a sample of area $L\\times L$ over a duration $T$ to be\n\\begin{equation}\n N \\sim 1 \\left(\\frac{10^{29}\\text{ GeV}}{m_{\\rm DM}}\\right) \\left(\\frac{L}{10\\,{\\rm m}}\\right)^2 \\left(\\frac{T}{10^9\\text{ year}}\\right)\\label{eqn:eventrate}\n\\end{equation}\nbased on the local DM density, $\\rho_{\\rm DM}\\approx 0.3\\,{\\rm GeV}\/\\text{cm}^3$. As described in the previous sections, the quartz samples under consideration are roughly $T\\sim 10^{9}\\text{ year}$ old, and a 100 ${\\rm m}^2$ sample area can be scanned in stage three. The requirement that $N\\gtrsim 1$ imposes an upper bound on the UHDM mass, $m_{\\rm DM}\\lesssim 10^{29}\\text{ GeV}\\sim 100\\text{ kg}$. The advantage afforded by the large spacetime volume of such a long-lived sample is manifest. \n\nAn UHDM moving through the Earth will collide with and deposit energy to SM particles along its path. The energy $E_1$ imparted to each SM nucleus can go as high as the kinematical limit of $10~{\\rm keV}$ (corresponding to nuclei acquiring twice the velocity of the UHDM in a collision) depending on how elastic these collisions are, while the stopping power $dE\/dx$ depends on $E_1$ as well as the UHDM radius. For simplicity, we assume in our estimates that the UHDM travels at least a few kilometers deep into the Earth's surface while maintaining its Milky Way virial velocity of $v_{\\rm DM}\\sim 10^{-3}c$. This amounts to an upper bound on the energy deposition rate\n\\begin{equation}\n \\frac{dE}{dx}\\lesssim 10^{13}\\frac{{\\rm MeV}}{\\SI{}{\\angstrom}}\\left(\\frac{m_{\\rm DM}}{10^{29}\\text{ GeV}}\\right).\\label{eqn:dragforcebound}\n\\end{equation}\nMost of the deposited energy will likely go to SM nuclei. Only a tiny portion will go directly to electrons, whose low mass limits their kinetic energy gain (for kinematics reasons) and whose coupling to DM is severely limited by astrophysical and cosmological constraints \\cite{Green:2017ybv}. The nuclei and electrons will then thermalize, leading to a loosening of molecular bonds as the electrons acquire more energy, and eventually cause melting. Due to thermal diffusion, the melted region will enlarge and cool. What ultimately remains, in the case of quartz, is a long cylindrical trail of amorphous silica, precisely the kind of damage that is detectable with the method outlined above.\n\n\nIn order to leave a robustly detectable damage trail, the UHDM must deposit sufficient energy per unit length $dE\/dx$ exceeding the required latent heat to melt each unit-length segment of a micron-radius cylinder. This amounts to a $dE\/dx$ threshold for robust detection of\n\\begin{equation}\n \\frac{dE}{dx}\\gtrsim \\frac{{\\rm MeV}}{\\SI{}{\\angstrom}}\\,.\\label{eqn:meltingenergydeposition}\n\\end{equation}\nSee Figure \\ref{fig:param_space}a for model-independent sensitivity projections. Further, since quartz has a melting point of $10^4~\\rm K \\sim 1~{\\rm eV}$ and energy tends to spread outward, the energy deposition must be sufficiently localized that the energy $E_1$ gained by each nucleus is greater than the melting temperature, i.e. it must lie in the range\n\\begin{equation}\n 1\\,{\\rm eV}\\lesssim E_1 \\lesssim 10\\, {\\rm keV} \\label{eqn:meltingenergypernucleus}\n\\end{equation}\nwhere the upper bound is the kinematical limit for energy transfer per nucleus (with mass number $A\\sim 10$).\n\n\\begin{figure}[htbp!]\n \\centering\n \\includegraphics[width=\\columnwidth]{fig3.png}\n \\caption{{Sensitivity projections for the proposed ultra heavy dark matter (UHDM) search.} {\\bf (a)} Model-independent reach of the geological-quartz detector proposal expressed as stopping power $dE\/dx$ vs mass $m_{\\rm DM}$ of a passing UHDM particle, together with the existing constraints from MACRO for energy deposition per nucleus $E_1\\sim 1~{\\rm eV}$ \\cite{Ambrosio:2004ub, Scholz:2016} as well as from damage track searches in ancient mica \\cite{price1986}. The vertical and slanted boundaries of the quartz-detectable parameter space (for different effective detector areas) stem from the requirements of an $O(1)$ probability of transit, Eq.~\\eqref{eqn:eventrate}, and a negligible slowing of the UHDM up to a $1\\text{ km}$ depth, Eq.~\\eqref{eqn:dragforcebound}, respectively. The black horizontal line indicates the melting threshold for a micron-sized lateral region, Eq.~\\eqref{eqn:meltingenergydeposition}, above which robust detection is possible. {\\bf (b)} Parameter space of the UHDM model considered in Sec.~\\ref{sec:model}. \\textit{Left:} reach on coupling $g_{\\rm n}$ vs DM mass $m_{\\rm DM}$. \\textit{Right:} reach on coupling $g_{\\rm n}$ vs mediator mass $m_\\phi$. Also shown are existing constraints from ancient mica \\cite{price1986}, fifth force experiments \\cite{Green:2017ybv}, and stellar cooling of SN1987A \\cite{Green:2017ybv} and horizontal branch (HB) stars \\cite{Green:2017ybv} (note that the stellar cooling bounds are model-dependent \\cite{DeRocco:2020xdt}). In these $g_{\\rm n}$ plots, we set $g_\\chi$ to its upper bound $m_\\phi\/\\Lambda_\\chi$ from Eq.~\\eqref{stability}.}\n \\label{fig:param_space}\n\\end{figure}\n\n\n\\section{Example UHDM Model}\n\\label{sec:model}\n\nIn this section, we consider an example of a simple, ultra-heavy composite dark matter (UHDM) state \\cite{Grabowska:2018lnd} that can give rise to the desired damage tracks while being consistent with existing experimental and observational constraints. These composite objects consist of $N_\\chi$ dark fermions $\\chi$ whose mass, inverse size, and binding energy to form the UHDM are determined by a single scale $\\Lambda_\\chi$. It follows that they have mass $m_{\\rm DM} \\sim N_\\chi \\Lambda_\\chi$ and size $R \\sim N_\\chi^{1\/3} \\Lambda_\\chi^{-1}$. We assume that the fermions $\\chi$ interact with standard model nucleons $\\psi_{\\rm n}$ through a repulsive\\footnote{Attractive DM-nucleon interactions are just as compelling as the repulsive interaction considered here. We note that the attractive interactions might have more complicated dynamics as nuclei may get trapped and accumulate inside the UHDM.} Yukawa interaction mediated by a scalar $\\phi$ of mass $m_\\phi$:\n\\begin{equation}\n \\mathcal{L}\\supset \\frac{1}{2}m_\\phi^2\\phi^2+g_{\\rm n}\\phi\\bar{\\psi}_{\\rm n}\\psi_{\\rm n}-g_{\\chi}\\phi\\bar{\\chi}\\chi\\,.\n\\end{equation}\nWe show that UHDMs with the following properties satisfy the robust detectability criteria detailed in Sec.~\\ref{sec:sensitivity} without running afoul of any existing constraints:\\footnote{Due to various constraints, this parameter space has a complicated geometry. Here we simply identified the lower and upper limits for each parameter.}\n\\begin{align}\n 10^{26} \\,{\\rm GeV} \\lesssim& m_{\\rm DM} \\lesssim 10^{29} \\,{\\rm GeV}\\label{eqn:massrange}\\\\\n 10 \\text{ nm}\\lesssim& R\\lesssim 1 \\text{ cm}\\label{eqn:radiusrange}\\\\\n 0.1\\,{\\rm eV}\\lesssim& m_\\phi\\lesssim {\\rm MeV} \\,\\leftrightarrow\\, \\,\n 10^2\\text{ fm}\\lesssim m_\\phi^{-1}\\lesssim 1\\,\\SI{}{\\SI{}{\\micro}\\meter}\n \\label{eqn:mediatorrange}\\\\ \n 100 \\,{\\rm keV} \\lesssim& \\Lambda_\\chi \\lesssim 10 \\,{\\rm GeV}\\,.\\label{eqn:Lambdarange}\n\\end{align}\nThis allows us to probe wide ranges of the couplings $g_{\\rm n}$ and $g_{\\chi}$. Two slices of this parameter space are shown in Figure~\\ref{fig:param_space}b. Eq.~\\eqref{eqn:massrange} follows from Eq.~\\eqref{eqn:eventrate} and ancient mica constraints; Eq.~\\eqref{eqn:radiusrange} follows from\n\\eqref{eqn:meltingenergydeposition}, \\eqref{eqn:meltingenergypernucleus}, \\eqref{eqn:E1dEdx}, and the quartz sample size of 1 cm; Eq.~\\eqref{eqn:mediatorrange} follows from fifth force constraints and the requirement that the UHDM-nucleus interaction be treated classically; Eq.~\\eqref{eqn:Lambdarange} follows from Eqs.~\\eqref{eqn:massrange} and \\eqref{eqn:radiusrange}.\n\n\\subsection{Detectability with Quartz}\nThe optimal UHDM detection signature is expected for mediators with a range $m_\\phi^{-1}$ satisfying $\\Lambda_\\chi^{-1}\\ll m_\\phi^{-1} \\lesssim \\SI{}{\\SI{}{\\micro}\\meter}$, since this is the intermediate regime where the UHDM-nucleon coupling is enhanced by the number of constituents $\\chi$ of the UHDM within the range of the mediator $(m_\\phi^{-1}\/\\Lambda_\\chi^{-1})^3$ while simultaneously evading existing fifth force constraints. For simplicity of analysis we only consider part of the parameter space where $m_\\phi^{-1}\\ll R$. In doing so, we limit the UHDM's cross section to be at most geometrical.\n\nAn SM nucleus located inside the UHDM only sees the composite dark matter particle's constituents $\\chi$ within the range of the mediator $ m_\\phi^{-1}\\ll R$. Hence, to the SM nucleus each point in the bulk of the UHDM is just like any other, yielding a potential energy $V(r)$ as a function of the distance $r$ from the center of the UHDM with the following profile:\n\\begin{equation}\n V(r)=\\begin{cases}\n +V_0, &rR\n \\end{cases}\n\\end{equation}\nwhere at the boundary $r\\approx R$ the potential drops to zero exponentially over a length scale of order $m_\\phi^{-1}$, and \n\\begin{align}\n V_0\\sim \\left(\\frac{\\Lambda_\\chi}{m_\\phi}\\right)^3\\frac{g_{\\chi}(10g_{\\rm n})}{m_\\phi^{-1}} \\label{eqn:V0}\n\\end{align}\nfor SM nuclei with mass number $A\\sim 10$. As a result, from the perspective of a nucleus the UHDM is just a constant potential hill moving at a velocity $v_{\\rm DM}\\sim 10^{-3}c$.\n\nSince the de Broglie wavelengths $(10\\,{\\rm MeV})^{-1}$ of the SM nuclei are smaller than the mediator ranges $m_\\phi^{-1}$ of interest, we can treat the UHDM-nucleus interactions classically. When $V_0\\gtrsim 10 \\,{\\rm keV}$, the potential $V_0$ prevents any nucleus from entering the UHDM. The UHDM-nucleus collisions are thus elastic, and the energy $E_1$ transferred to a nucleus saturates the kinematical limit $E_1\\sim 10\\, {\\rm keV}$. If $V_0\\lesssim 10 \\,{\\rm keV}$, on the other hand, the nuclei can easily climb the potential hill, and the collisions between a nucleus and the UHDM's surface will be inelastic. When a nucleus encounters the surface of the UHDM, it receives a force $F\\sim V_0\/m_\\phi^{-1}$ due to the gradient of the Yukawa potential. This force is exerted throughout the duration $\\tau\\sim m_\\phi^{-1}\/v_{\\rm DM}$ of the collision, resulting in a nearly-instantaneous momentum kick $p_1\\sim F\\tau$ which translates to the kinetic energy $E_1\\sim 10\\,{\\rm keV}\\left(V_0\/10\\,{\\rm keV}\\right)^2$ per nucleus. To sum up, the energy imparted to a nucleus after the passage of a UHDM is\n\\begin{equation}\n E_1\\sim 10\\,{\\rm keV} \\times \\text{min}\\left[1,\\left(\\frac{V_0}{10 \\,{\\rm keV}}\\right)^2\\right].\\label{eqn:E1V0}\n\\end{equation}\nUsing a lattice spacing of about $5\\SI{}{\\angstrom}$ for quartz, the energy deposition rate then follows:\n\\begin{equation}\n \\frac{dE}{dx}\\sim \\frac{E_1}{5\\SI{}{\\angstrom}}\\left(\\frac{R}{5\\SI{}{\\angstrom}}\\right)^2. \\label{eqn:E1dEdx}\n\\end{equation}\nHaving linked the model parameters with the quantities characterizing quartz damage tracks, the detectable parameter space can be evaluated based on the considerations in Sec.~\\ref{sec:sensitivity} (see Figure \\ref{fig:param_space}b).\n\n\\subsection{Existing Constraints}\n\\label{sec:existing_constraints}\n\\subsubsection{The mediator}\nPast experiments and observations have placed limits on the coupling $g_{\\rm n}$ of the mediator $\\phi$ to standard model nucleons with varying severity for different masses $m_\\phi$ of the mediator.These include: collider constraints on the meson decay rate, laboratory {\\it fifth-force} searches, and stellar cooling bounds from observations of the SN1987A event and horizontal branch (HB) stars. Note, however, that the stellar cooling bounds are model-dependent \\cite{DeRocco:2020xdt}. The following parameter space is thus ruled out \\cite{Green:2017ybv}:\n\n\\begin{itemize}\n \\item Meson decay: $g_{\\rm n}\\gtrsim 10^{-6}$, $m_\\phi\\lesssim 100\\,{\\rm MeV}$.\n \\item Fifth force: $g_{\\rm n}\\gtrsim 10^{-12} (m_\\phi\/{\\rm eV})^3$, $m_\\phi\\lesssim 100\\,{\\rm eV}$.\n \\item SN1987A: $3\\times10^{-10}\\lesssim g_{\\rm n}\\lesssim 3\\times10^{-7}$, $m_\\phi\\lesssim 30\\,{\\rm MeV}$.\n \\item HB stars: $g_{\\rm n}\\gtrsim 10^{-13}$, $m_\\phi\\lesssim 100\\,{\\rm keV}$.\n\\end{itemize}\nFurthermore, the couplings of UHDM constituents $\\chi$ to the mediator $\\phi$ add extra self-interactions among $\\chi$ that may destabilize the UHDM. In order for the UHDM to be stable the mediated self-interaction potential $g_\\chi^2\\Lambda_\\chi^3m_\\phi^{-2}$ of a single $\\chi$ must not exceed the binding energy $\\Lambda_\\chi$. This puts an upper bound on the coupling $g_\\chi$ of $\\chi$ to the mediator $\\phi$:\n\\begin{equation}\n g_{\\chi}\\lesssim \\frac{m_\\phi}{\\Lambda_\\chi}.\\label{stability}\n\\end{equation}\n\n\n\\subsubsection{Direct detection}\nOf the currently and previously running direct-detection DM experiments, MACRO puts a strong constraint on our scenario due to its large volume. MACRO is a scintillator experiment with spacetime dimensions of about $10^3\\text{ m}^2\\times 10\\text{ years}$ corresponding to $m_{\\rm DM} \\lesssim 10^{22}\\,{\\rm GeV}$ for 1 event over its decade-long lifespan. It is sensitive to energy depositions $\\gtrsim 10\\,{\\rm MeV}\/{\\rm cm}$ \\textit{to electrons} \\cite{Ambrosio:2004ub}. When a nucleus receives energy $E_1$ from interaction with a UHDM, only some fraction $Q(E_1)$, called the quenching factor, of that energy effectively goes to the electrons tied to the nucleus. It is this relatively small fraction of energy that is responsible for the processes of scintillation and ionization that may occur subsequently. We can translate MACRO's $10\\,{\\rm MeV}\/{\\rm cm}$ detection threshold to a sensitivity \\textit{to nuclear energy depositions} via effecting an increase by the quenching factor $Q(E_1)$ \\cite{Scholz:2016}. \n\nAn even more stringent bound on our model arises from direct searches for long damage tracks in muscovite mica crystals \\cite{price1986}. The non-observation of tracks extending beyond naturally occurring defects and radioactivity damage was originally used to constrain the abundance of magnetic monopoles, but also limits the UHDM parameter space. This past mica search involved total sample area $\\sim 1200 ~{\\rm cm}^2$ with sample ages $\\simeq 5 \\times 10^8 \\,{\\rm yr}$, corresponding to a dark matter reach of $m_{\\rm DM} \\lesssim 10^{26}~{\\rm GeV}$. The energy deposition threshold for detection in this experiment via etching and optical microscopy was identified as $dE\/dx \\gtrsim 6~{\\rm GeV}\/{\\rm cm}$. \n\n\\subsubsection{Astrophysical and Cosmological limits}\nIndirect limits can also be placed on the couplings $g_{\\rm n}$ and $g_{\\chi}$ from the limits on DM-baryon and DM-DM cross sections. DM-baryon interactions in the early universe can affect baryon acoustic oscillations and is therefore constrained by CMB and LSS observations. This puts an upper bound on the DM-baryon momentum-transfer cross section that would be observed today: $\\sigma_{\\chi\\rm b}\/m_{\\rm DM}\\lesssim 10^{-3}\\text{ cm}^2\/\\text{g}$ \\cite{Dvorkin:2013cea}. Astronomical observations of the Bullet Cluster also place a limit on the DM-DM momentum-transfer cross section: $\\sigma_{\\chi\\chi}\/m_{\\rm DM}\\lesssim 1\\text{ cm}^2\/\\text{g}$ \\cite{Spergel:1999mh}. Since we are mainly interested in UHDMs with geometrical cross sections of order $\\SI{}{\\SI{}{\\micro}\\meter}^2$ and masses up to $10^{29}~{\\rm GeV} (100 ~{\\rm kg})$, these astrophysical and cosmological observations only impose significant constraints on the low mass side of our parameter space. Moreover, these constraints are alleviated if UHDMs constitute less than $10\\%$ of the total dark matter mass, in which case the maximum detectable mass would also be lowered by an order of magnitude.\n\n\\section{Conclusion and Outlook}\n\\label{sec:conclusion}\n\nGiven the diverse range of theoretically well-motivated dark sectors, it is critical to perform searches with techniques that are sensitive to a broad class of dark-sector phenomena. In this paper, we propose a detection method for ultra-heavy composite dark matter particles (UHDMs). Our proposed experiment is based on mapping damage tracks in ancient quartz samples with SEM-CL scanning. This method has two significant advantages: (1) the billion-year exposure time of such samples enables us to probe dark matter candidates with masses as high as $10^{29}~{\\rm GeV} (100 ~{\\rm kg})$, surpassing the reach of existing direct-detection experiments, and (2) the distinctly-long cylindrical damage trails left by such UHDMs are easily distinguished from other features at the relevant scales.\n\nIn this work, we focus on detecting long tracks of amorphous silica in quartz samples expected from passing UHDMs that impart enough energy to cause melting. In future work, we will consider the feasibility of extending the experimental sensitivity to energy deposition rates below the melting threshold. For that purpose, we intend to carry out a number of studies including: (i) signal calibration by artificially creating damage tracks in synthetic quartz samples with a high-power pulsed laser of variable intensity and comparing it with the resulting CL signal levels; and (ii) noise calibration by preparing a set of quartz samples, natural and synthetic, with different concentrations of CL activators and analysing their CL emission rates. These studies will provide us a better understanding of the signal-to-noise ratio as seen in SEM-CL imaging, which will allow us to better estimate the detection threshold.\n\nOur proposed experiment is largely agnostic to the detailed microphysics of the dark sector, as long as it results in long damage tracks in geological quartz. To demonstrate the projected reach of the proposed approach, we considered a QCD-like dark sector that interacts with the standard model repulsively via a light mediator. The particle spectrum of this theory includes heavy bound states, composed of a large number of elementary dark fermions, which could create interesting targets for detection. We identified experimentally-detectable regions of the parameter space that satisfy various limits derived from phenomenological considerations as well as past observations. In future work, it would be interesting to delineate a broader range of dark matter models than can lead to similar damage patterns in ancient rock.\n\n\n\\begin{acknowledgments}\nThis work was supported by the DOE QuANTISED program under Award No. DE-SC0019396; the Army Research Laboratory MAQP program under Contract No. W911NF-19-2-0181; and the University of Maryland Quantum Technology Center. SR is supported by the NSF under grant PHY-1818899, the SQMS Quantum Center and DOE support for MAGIS. \n\\end{acknowledgments}\n\n\\bibliographystyle{apsrev4-1}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nIn the study of nonnegative scalar curvature, one would like to formulate some notion of ``weak'' nonnegative scalar curvature for metrics that are not necessarily smooth. The gold standard for a notion of weak nonnegative scalar curvature would be something like Alexandrov spaces as a weak notion of spaces with lower bounds on sectional curvature. A good notion of weak nonnegative scalar curvature would be one that implies the same consequences as ``classical'' nonnegative scalar curvature---for example, the positive mass theorem in the asymptotically flat case, or topological restrictions in the compact case. \n\nAn important theorem in this direction was proved by P.~Miao \\cite{Miao:2002}, generalizing an earlier result of H.~Bray \\cite[Section 6]{Bray:2001}. See also \\cite[Section~3]{Shi-Tam:2002} for the spin case, as well as a more recent proof by D.~McFeron and G.~Sz\\'{e}kelyhidi~\\cite{McFeron-Szekelyhidi}. \n\n\\begin{thm}\\label{hypersurface}\nLet $M^n$ be a smooth manifold such that $n<8$ or $M$ is spin.\\footnote{The only point of this assumption is to make sure that the classical positive mass theorem is valid on $M$. If the positive mass theorem is true in all dimensions, then this hypothesis can safely be eliminated} Let $S$ be a smooth closed hypersurface in $M$, and let $g$ be a complete asymptotically flat metric on $M$ such such that $g$ is $C^2$ \\emph{up to} $S$ from each side of it (but not necessarily \\emph{across} it) and $C^{2,\\alpha}_\\mathrm{loc}$ away from $S$. \n\nNear each point of $S$, $S$ divides $M$ into two sides, which we will call $A$ and $B$. Let $H_A$ be the mean curvature vector of $S$ as computed by the metric on the $A$ side, and similarly define $H_B$.\n\nIf $g$ has nonnegative scalar curvature on the complement of $S$, and at each point of~$S$, $H_A-H_B$ either points toward side $A$ or is zero, then the mass of $g$ is nonnegative in each end.\n\\end{thm}\n\nNote that the hypotheses of the theorem above require $g$ to be Lipschitz everywhere. One way to interpret this theorem is that when the singular set of $g$ is a hypersurface $S$ whose induced metric is well-defined regardless of which ``side'' of $S$ one uses to compute it, then the correct notion of weak nonnegative scalar curvature on $S$ is the pointwise mean curvature comparison condition that appears as a hypothesis of the theorem above. \n\nIn this article we consider singular sets of lower dimension and ponder what conditions on $S$ correspond to weak nonnegative scalar curvature. We find that if $S$ has low enough dimension, then no further conditions are needed.\n\\begin{thm}\\label{maintheorem}\nLet $M^n$ be a smooth manifold such that $n<8$ or $M$ is spin. Let $g$ be a complete asymptotically flat Lipschitz metric on $M$, and let $S$ be a bounded subset whose $n\/2$-dimensional lower Minkowski content is zero.\nIf $g$ has bounded $C^2$-norm and nonnegative scalar curvature on the complement of $S$, then the mass of $g$ is nonnegative in each end. \n\\end{thm}\nSee Section \\ref{definitions} for the definition of Minkowski content. For now, recall that Minkowski content equals Hausdorff measure for well-behaved sets (\\textit{e.g.} submanifolds).\n\nThere is also a $W^{1,p}$ version of this theorem.\n\\begin{thm}\\label{w1p}\nLet $M^n$ be a smooth manifold such that $n<8$ or $M$ is spin. Let $p>n$, let $g$ be a complete asymptotically flat $W^{1,p}_{\\mathrm{loc}}$ (and hence continuous) metric on $M$, and let $S$ be a bounded subset whose $\\frac{n}{2}(1-\\frac{n}{p})$-dimensional lower Minkowski content is zero. If $g$ has bounded $C^2$-norm and nonnegative scalar curvature on the complement of $S$, then the mass of $g$ is nonnegative in each end. \n\\end{thm}\n\nIt might seem surprising that one does not have to place any other conditions on the behavior of $g$ at singular set, but as we will see in the proof, the Lipschitz (or $W^{1,p}$) condition is very restrictive. Essentially, $g$ is too regular for the scalar curvature to be truly singular on a small set. We use the same technique as in \\cite{Miao:2002}. Note that if $S$ is a closed submanifold, the proofs of Theorems \\ref{maintheorem} and \\ref{w1p} are much simpler.\n\nThe dimensional restriction of $n\/2$ seems to an unnecessary artifact of the conformal method used in the proof. Also note that Theorems \\ref{maintheorem} and \\ref{w1p} do not include rigidity results for Euclidean space.\n\\begin{conj}\nLet $M^n$ be a smooth manifold such that $n<8$ or $M$ is spin. Let $g$ be a complete asymptotically flat Lipschitz metric on $M$, and let $S$ be a bounded subset whose $(n-1)$-dimensional lower Minkowski content is zero.\nIf $g$ has bounded $C^2$-norm and nonnegative scalar curvature on the complement of $S$, then the mass of $g$ is nonnegative in each end. Moreover, if the mass of any end is zero, then $(M,g)$ must be isometric to Euclidean space.\n\\end{conj}\nOne might try to prove the spin case of this conjecture using a spinor argument, following \\cite{Shi-Tam:2002}. Or one might try to prove the conjecture using Ricci flow as in \\cite{McFeron-Szekelyhidi}. One advantage of the Ricci flow method is that it is more likely to produce a rigidity result.\n\n\\section{Definitions}\\label{definitions}\n\\begin{defin}\nLet $g$ be a continuous Riemannian metric on a smooth manifold $M^n$ where $n\\geq3$. Then $(M,g)$ is an \\emph{asymptotically flat manifold} if and only if there is a compact set $K\\subset M$ such that $M\\smallsetminus K$ is a disjoint union of ends, $E_\\ell$, such that\neach end is diffeomorphic to $\\rr^n$ minus a ball, and in each of\nthese coordinate charts, the metric $g_{ij}$ is $C^2$ and satisfies \n\\begin{align*}\ng_{ij}&=\\delta_{ij}+O(|x|^{-\\sigma})\\\\ \ng_{ij,k}&=O(|x|^{-\\sigma-1})\\\\\ng_{ij,kl}&=O(|x|^{-\\sigma-2})\\\\\n R_g&=O(|x|^{-\\tau}), \n\\end{align*} \nfor some $\\sigma>(n-2)\/2$ and $\\tau>n$, where the commas denote partial derivatives in the coordinate chart, and $R_g$ denotes the scalar curvature of~$g$.\n\nWe define the \\emph{mass} of each end $E_\\ell$ by the formula\n\\[m(E_\\ell,g)={1\\over\n2(n-1)\\omega_{n-1}}\\lim_{\\rho\\to\\infty}\\int_{S_\\rho}\n\\sum_{i,j=1}^n(g_{ij,i}-g_{ii,j})\\nu_j d\\mu,\\]\n where $\\omega_{n-1}$ is the area of the standard unit\n$(n-1)$-sphere, $S_{\\rho}$ is the coordinate sphere in $E_\\ell$ of radius\n$\\rho$, $\\nu$ is its outward unit normal, and $d\\mu$ is the Euclidean area\nelement on $S_{\\rho}$. The mass is well-defined on each end of an asymptotically flat manifold.\n\\end{defin}\n\n\\begin{defin}\nFor a subset $S$ of a Riemannian manifold$(M^n, g)$, the \\emph{$m$-dimensional lower Minkowski content} of $S$ is \n\\[ \\liminf_{\\epsilon\\to0} \\frac{\\mathcal{L}_g^n(S_\\epsilon)}{\\alpha_{n-m}\\epsilon^{n-m}}\\]\nwhere $\\mathcal{L}_g^n$ is Lebesgue measure with respect to $g$, $S_\\epsilon$ is the $\\epsilon$-neighborhood of $S$, and $\\alpha_{n-m}$ is the volume of the unit ball in $\\rr^{n-m}$.\n\\end{defin}\nThe $m$-dimensional lower Minkowski content provides an upper bound (up to constant) for $m$-dimensional Hausdorff measure, and they are the same for rectifiable sets (see \\cite[Chapter 3.2]{Federer-book} for details). In particular, the condition of zero Minkowski content in Theorems \\ref{maintheorem} and \\ref{w1p} is only slightly stronger than the condition of zero Hausdorff measure.\n\n\n\\section{Proof of Theorem \\ref{maintheorem}}\\label{main}\n\nFirst, we briefly sketch out the proof, which is straightforward. Choose $M^n$, $g$, and $S$ as in the statement of Theorem \\ref{maintheorem}. We mollify the metric $g$ to get a smooth metric $g_\\epsilon$ in such a way that $g_\\epsilon=g$ outside of the $2\\epsilon$-neighborhood $S_{2\\epsilon}$. The precise smoothing of $g$ does not matter much. The only important property of the smoothing is that, using the hypotheses on~$g$, we have that $g_\\epsilon$, $g_\\epsilon^{-1}$, and $\\partial g_\\epsilon$ are bounded independently of $\\epsilon$, while $\\partial\\partial g_\\epsilon=O(\\epsilon^{-1})$, with respect to a particular atlas. By the formula for the scalar curvature of $g_\\epsilon$, it follows that $R_{g_\\epsilon}=O(\\epsilon^{-1})$. The hypothesis about Minkowski content tells us (roughly) that the volume of $S_\\epsilon$ is $o(\\epsilon^{n\/2})$. Thus\n\\begin{equation}\\label{goal}\n\\int_{S_{2\\epsilon}} |R_{g_\\epsilon}|^{n\/2}\\,dg=o(1).\n\\end{equation}\nFrom there, a standard argument (as in \\cite{Miao:2002}) tells us that we can conformally deform $g_\\epsilon$ to have nonnegative scalar curvature, without changing the mass too much. Applying the classical positive mass theorem to the new, smooth manifold of nonnegative scalar curvature, we find that the original manifold $(M,g)$ has mass greater than a small negative number that is $o(1)$ in $\\epsilon$. Taking the limit as $\\epsilon$ approaches zero, the result follows. In what follows, we describe an explicit smoothing that yields \\eqref{goal}.\n\nWe choose a finite atlas $U_1,\\ldots,U_N$ for $M$. By asymptotic flatness and continuity of $g$, we can choose these $U_k$ so that $g$ is uniformly equivalent to the background Euclidean metric of each patch. That is, on each coordinate patch, we have \n\\[ C^{-1}\\delta_{ij}\\leq g_{ij}\\leq C\\delta_{ij} \\]\nas positive definite symmetric bilinear forms. \nWe choose a partition of unity $\\psi_1,\\ldots, \\psi_N$ subordinate to this cover. \nOn each patch $U_k$, we will define a smoothing $g^k_\\epsilon$ of $g$ that is defined on the support of $\\psi_k$, which we denote $U'_k$. We then obtain a smoothing $g_\\epsilon$ of $g$ by defining $g_\\epsilon=\\sum_{k=1}^N \\psi_k g^k_\\epsilon$. \n\n\\begin{notation}In what follows, we will use a generic constant $C$ to mean some large number that may depend on $(M,g)$ and the choices of $U_k$ and $\\psi_k$. The only thing that will be important to us is that $C$ is independent of $\\epsilon$.\n\\end{notation}\nGiven a coordinate patch $U_k$, we wish to define $g^k_\\epsilon$. Let $\\varphi$ be a nonnegative smooth function supported on the unit ball in $\\rr^n$ whose integral is $1$. The standard way to smooth $g$ is to convolve it with \n\\[\\varphi_\\epsilon(x) := \\epsilon^{-n}\\varphi(x\/\\epsilon).\\]\n However, we want to smooth $g$ in such a way that it does not change $g$ away from a neighborhood of $S$. In order to do that, we need the following simple lemma. \n\n\\begin{lem}\\label{sigma}\nFor each $\\epsilon>0$, on each coordinate patch $U_k$, there exists a nonnegative smooth function $\\sigma$ such that $\\sigma=\\epsilon$ on the Euclidean neighborhood $S_\\epsilon$ of $S$ in $U_k$, and $\\sigma=0$ outside $S_{2\\epsilon}$, while $|\\partial\\sigma|\\le 3$ and $|\\partial\\partial\\sigma|\\le C\\epsilon^{-1}$ everywhere.\n\\end{lem}\n\\begin{proof}\nDefine a continuous function \n\\[s(x)=\\left\\{\\begin{array}{ll}\n\\epsilon & \\text{for }x\\in S_{4\\epsilon\/3}\\\\\n5\\epsilon - 3\\dist(x,S) & \\text{for }x\\in S_{5\\epsilon\/3}\\smallsetminus S_{4\\epsilon\/3}\\\\\n0 & \\text{for }x\\notin S_{5\\epsilon\/3}\n\\end{array}\\right.\\]\nThen we can define \n\\[\\sigma(x)=\\int_{\\rr^n} s(x-y)\\varphi_{\\epsilon\/6} (y)\\,dy.\\]\nClearly, $\\sigma$ is a nonnegative smooth function such that $\\sigma=\\epsilon$ on $S_\\epsilon$ and $\\sigma=0$ outside $S_{2\\epsilon}$. We just need to check the bounds on derivatives.\n\\begin{align*}\n|\\sigma(x_1)-\\sigma(x_2)| & \\leq\\int_{\\rr^n} |s(x_1-y)-s(x_2-y)|\\varphi_{\\epsilon\/6} (y)\\,dy \\\\\n&\\leq \\int_{\\rr^n} 3 |x_1-x_2| \\varphi_{\\epsilon\/6} (y)\\,dy\\\\\n&=3|x_1-x_2|,\n\\end{align*}\nwhere we used the Lipschitz property of $s$ in the second line. Thus $|\\partial\\sigma|\\le 3$.\n\nWe know that \n\\begin{align*}\n \\partial\\sigma(x)&= \\int_{\\rr^n} s(x-y) \\partial\\varphi_{\\epsilon\/6}(y)\\,dy \\\\ \n&=\\int_{\\rr^n} s(x-y) \\left(\\frac{6}{\\epsilon}\\right)^{n+1} \\partial\\varphi\\left(\\frac{6y}{\\epsilon}\\right)\\,dy \n\\end{align*}\nArguing as above, \n\\begin{align*}\n|\\partial\\sigma(x_1)-\\partial\\sigma(x_2)| \n&\\leq \\int_\\rr^n 3|x_1-x_2| \\left(\\frac{6}{\\epsilon}\\right)^{n+1} \\left|\\partial\\varphi \\left(\\frac{6y}{\\epsilon}\\right)\\right|\\,dy \\\\\n& = \\frac{18}{\\epsilon} |x_1-x_2| \\int_{\\rr^n} |\\partial\\varphi (y)|\\,dy \\\\\n&=\\frac{C}{\\epsilon}|x_1-x_2|.\n\\end{align*}\n Thus $|\\partial\\partial\\sigma|\\le C\\epsilon^{-1}$.\n\\end{proof}\n\nFor small enough $\\epsilon$, we define $g^k_\\epsilon$ on the patch $U'_k$ by the formula\n\\[ (g^k_\\epsilon)_{ij}(x)=\\int_{\\rr^n}g_{ij}(x-\\sigma(x)y)\\varphi(y)\\,dy \n=\\int_{\\rr^n}g_{ij}(y)\\varphi_{\\sigma(x)}(x-y)\\,dy.\\]\nKeep in mind that the function $\\sigma$ described by the lemma above depends on the patch $U_k$, the singular set $S$, and $\\epsilon$. Clearly, each component of $g^k_\\epsilon$ is smooth. We now claim that $|\\partial g^k_\\epsilon|\\le C$ and $|\\partial\\partial g^k_\\epsilon|\\le C\\epsilon^{-1}$. For ease of notation, let us prove these inequalities for each component, individually. The lemma below proves the claim.\n\n\\begin{lem}\\label{convolve}\nLet $\\sigma$ be the function described in Lemma \\ref{sigma}, and let $f$ be a Lipschitz function on $U_k$ that has bounded $C^2$-norm on the complement of $S$. For small enough $\\epsilon$, if we define the function\n\\[ f_\\epsilon(x)=\\int_{\\rr^n}f(x-\\sigma(x)y)\\varphi(y)\\,dy \n=\\int_{\\rr^n}f(y)\\varphi_{\\sigma(x)}(x-y)\\,dy\\]\non the set $U'_k$, then $|\\partial f_\\epsilon|\\le C$ and $|\\partial\\partial f_\\epsilon|\\le C\\epsilon^{-1}$, where $C$ may depend on the supremum and Lipschitz constant of $f$.\n\\end{lem}\n\\begin{proof}\nOn the complement of $S_\\epsilon$, the result follows easily from differentiating the first formula for $f_\\epsilon$ above and using the $C^2$ bound on $f$ and the bounds on $|\\partial\\sigma|$ and $|\\partial\\partial\\sigma|$ from Lemma \\ref{sigma}. So we need only consider the region $S_\\epsilon$. But in this region we have $\\sigma(x)=\\epsilon$ by construction, and therefore\n\\[ f_\\epsilon(x)\n=\\int_{\\rr^n}f(y)\\varphi_\\epsilon(x-y)\\,dy \n\\]\nis just the usual mollification formula. Then since $f$ is Lipschitz, a standard computation shows that $|\\partial f_\\epsilon|$ is bounded. Moreover, for $x\\in S_\\epsilon$,\n\\begin{align*}\n\\partial f_\\epsilon (x)\n&=\\int_{\\rr^n} f(y) \\partial\\varphi_\\epsilon(x-y)\\,dy\\\\\n&=\\int_{\\rr^n} f(y) \\epsilon^{-n-1}\\partial\\varphi\\left(\\frac{x-y}{\\epsilon}\\right)\\,dy\\\\\n&=\\int_{\\rr^n} f(x-\\epsilon y) \\epsilon^{-1}\\partial\\varphi(y)\\,dy.\n\\end{align*}\nSo for any $x_1,x_2\\in S_\\epsilon$, \n\\begin{align*}\n|\\partial f_\\epsilon(x_1)-\\partial f_\\epsilon(x_2)|\n&=\\left| \\int_{\\rr^n} [f(x_1-\\epsilon y)-f(x_2-\\epsilon y)] \\epsilon^{-1}\\partial\\varphi(y)\\,dy\\right|\\\\\n&\\le \\int_{\\rr^n} |f(x_1-\\epsilon y)-f(x_2-\\epsilon y)| \\epsilon^{-1}|\\partial\\varphi(y)|\\,dy\\\\\n&\\le \\int_{\\rr^n}C|x_1-x_2| \\epsilon^{-1}|\\partial\\varphi(y)|\\,dy\\\\\n&\\le \\frac{C}{\\epsilon}|x_1-x_2|.\n\\end{align*}\nThe result follows.\n\\end{proof}\n\nSetting $g_\\epsilon=\\sum_{k=1}^N \\psi_k g^k_\\epsilon$, Lemma \\ref{convolve} implies that in each coordinate chart~$U_k$, \n$|\\partial (g_\\epsilon)_{ij}|\\le C$ and $|\\partial\\partial (g_\\epsilon)_{ij}|\\le C\\epsilon^{-1}$ for some $C$ independent of~$\\epsilon$. From looking at how scalar curvature depends on the metric, it is clear that $|R_{g_\\epsilon}|\\le C\\epsilon^{-1}$ for some $C$. Meanwhile, $g=g_\\epsilon$ outside $S_{2\\epsilon}$, and by our assumption on the Minkowski content of $S$, we have \n\\begin{align*}\n\\int_{S_{2\\epsilon}} |R_{g_\\epsilon}|^{n\/2}\\,dg &\\le \\mathcal{L}_g^n(S_{2\\epsilon})\\sup |R_{g_\\epsilon}|^{n\/2} \\\\\n&= o(\\epsilon^{n\/2}) O(\\epsilon^{-n\/2})\\\\\n&=o(1),\n\\end{align*}\nwhich is our desired estimate \\eqref{goal}. The rest of the proof of Theorem \\ref{maintheorem} proceeds exactly as in \\cite{Miao:2002}. (Technically, since we are using \\emph{lower} Minkowski content, it is inaccurate to say that $\\mathcal{L}_g^n(S_{2\\epsilon})= o(\\epsilon^{n\/2})$, but the argument still works since we only need to use a subsequence of $\\epsilon$'s approaching zero. Also, we were careless about the distinction between defining $S_\\epsilon$ using the metric $g$ versus the Euclidean metric on each chart, but by uniform equivalent of metrics, this sloppiness is inconsequential.)\n\n\\section{Proof of Theorem \\ref{w1p}}\n\nThe proof of the $W^{1,p}$ version of Theorem \\ref{maintheorem} requires only slight modification. Choose $M^n$, $g$, $S$, and $p$ as in the statement of Theorem \\ref{maintheorem}. First, observe that because of the $C^2$ bounds on $g$, we can see that $|R_{g_\\epsilon}|=O(\\epsilon^{-1})$ on the complement of $S_\\epsilon$, just as in the Lipschitz case, and we now have even better bounds on $\\mathcal{L}^n_g(S_{2\\epsilon})$, so we have\n\\[\\int_{S_{2\\epsilon}\\smallsetminus S_\\epsilon} |R_{g_\\epsilon}|^{n\/2}\\,dg=o(1).\\] \n Therefore, in order to establish \\eqref{goal}, it is sufficient to show that\n\\begin{equation}\\label{newgoal}\n\\int_{S_\\epsilon} |R_{g_\\epsilon}|^{n\/2}\\,dg=o(1).\n\\end{equation}\n\n\nNext we use a $W^{1,p}$ version of Lemma \\ref{convolve}.\n\\begin{lem}\\label{convolve-w1p}\nLet $\\sigma$ be the function described in Lemma \\ref{sigma}, and let $f\\in W^{1,p}_{\\mathrm{loc}}(U_k)$ such that $f$ has bounded $C^2$-norm on the complement of $S$. For small enough $\\epsilon$, if we define the function\n\\[ f_\\epsilon(x)=\\int_{\\rr^n}f(x-\\sigma(x) y)\\varphi(y)\\,dy \n=\\int_{\\rr^n}f(y)\\varphi_{\\sigma(x)}(x-y)\\,dy\\]\non the set $U'_k$, then $\\|\\partial f_\\epsilon\\|_{L^p(S_\\epsilon\\cap U'_k)} \\le C$ and \n$|\\partial\\partial f_\\epsilon|\\le C\\epsilon^{-1-\\frac{n}{p}}$, where $C$ may depend on the $W^{1,p}$ norm of $f$.\n\\end{lem}\n\\begin{proof}\nRecall that for $x\\in S_\\epsilon$, $\\sigma(x)=\\epsilon$, so that the formula for\n$f_\\epsilon$ is the usual mollification formula. A standard argument using H\\\"{o}lder's inequality shows that\n\\[ \\|\\partial f_\\epsilon\\|_{L^p(S_\\epsilon \\cap U'_k)} \\le \\|\\partial f\\|_{L^p(S_{2\\epsilon}\\cap U_k)}\\le C.\\]\nFor any $x\\in S_\\epsilon$, if $q$ is chosen so that $\\frac{1}{p}+\\frac{1}{q}=1$, then\n\\begin{align*}\n|\\partial\\partial f_\\epsilon(x)|\n&= \\left|\\partial\\partial \\int_{\\rr^n} f(x-y)\\varphi_\\epsilon(y)\\,dy\\right|\\\\\n&= \\left|\\partial\\int_{\\rr^n} \\partial f(x-y)\\varphi_\\epsilon(y)\\,dy\\right|\\\\\n&= \\left|\\partial\\int_{\\rr^n} \\partial f(y)\\varphi_\\epsilon(x-y)\\,dy\\right|\\\\\n&= \\left|\\int_{S_{2\\epsilon}\\cap U_k} \\partial f(y) \\partial\\varphi_\\epsilon(x-y)\\,dy\\right|\\\\\n&\\le \\|\\partial f\\|_{L^p(S_{2\\epsilon}\\cap U_k)} \\left(\\int_{B_\\epsilon(x)} |\\partial\\varphi_\\epsilon(x-y)|^q\\,dy\\right)^{\\frac{1}{q}}\\\\\n&\\le C\\left(\\epsilon^{n} (\\epsilon^{-n-1})^q\\right)^{\\frac{1}{q}}\\\\\n&= C\\epsilon^{-1-\\frac{n}{p}}.\n\\end{align*}\n\\end{proof}\nSince the scalar curvature is a contraction of $\\partial\\partial g + g^{-1}*g^{-1}*\\partial g *\\partial g$, on any of the coordinate patches, we can use Lemma \\ref{convolve-w1p} to estimate\n\\begin{align*}\n \\int_{S_\\epsilon\\cap U_k} |R_{g_\\epsilon}|^{n\/2}\\,dg\n &\\le C\\int_{S_\\epsilon\\cap U_k} |\\partial\\partial g_\\epsilon|^{n\/2}\\,dx + C\\int_{S_\\epsilon\\cap U_k} |\\partial g_\\epsilon|^{n}\\,dx \\\\\n &\\le \\mathcal{L}^n(S_\\epsilon\\cap U_k) \\left(C\\epsilon^{-1-\\frac{n}{p}}\\right)^{n\/2} + o(1)\\\\\n &= o\\left(\\epsilon^{\\frac{n}{2}\\left(1+\\frac{n}{p}\\right)}\\right) \\epsilon^{-\\frac{n}{2}\\left(1+\\frac{n}{p}\\right)}+o(1)\\\\\n &=o(1).\n \\end{align*}\n As in Section \\ref{main}, there is some justifiable carelessness in the computation above. And once again, the result of the proof follows exactly as in \\cite{Miao:2002}.\n\n\\bibliographystyle{hamsplain}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzegcp b/data_all_eng_slimpj/shuffled/split2/finalzzegcp new file mode 100644 index 0000000000000000000000000000000000000000..7aa1a91f3b10de59affc69e23d169ecd956d876c --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzegcp @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nMagnetic fields permeate the Universe and often play an important role in the dynamics of astrophysical processes \\citep{crutcher:12, vlemmings:13, crutcher:19}. It is difficult to directly observe magnetic fields; one typically has to use the polarization properties of the observed light \\citep[e.g.,][]{han:17}. For (sub)millimeter interferometers, such as ALMA, magnetic field detection is done mostly through dust \\citep[e.g.,][]{hull:17} and line polarization observations \\citep[e.g.,][]{vlemmings:17}. However, it has recently become increasingly clear that dust polarization does not always faithfully trace the magnetic field morphology, but instead it can be affected by processes such as self-scattering \\citep{kataoka:15, kataoka:17}. Line polarization observations are not affected by such processes, and therefore they likely trace the magnetic field structure of the observed region. However, to interpret line polarization observations, modelers have to defer to the theory of \\citet{goldreich:81}, which relies on the large velocity gradient (LVG) approximation, and therefore they cannot treat three-dimensional (3D, magnetic field) structures. \n\nIn this paper, we present POlarized Radiative Transfer Adapted to Lines (PORTAL)\\footnote{The source code of PORTAL is available on GitHub at \\url{https:\/\/github.com\/blankhaar\/PORTAL}.}, which is a 3D polarized radiative transfer code that simulates the emergence of polarization in the emission of atomic or molecular (sub)millimeter lines. PORTAL can be used in stand-alone mode or process the output of regular 3D radiative transfer codes. We are able to model the emergence of linear polarization in (sub)millimeter lines through two main approximations: (i) the strong magnetic field approximation and (ii) the anisotropic intensity approximation.\\ We show that both of them are valid in the majority of astrophysical regions. \n\nRegular radiative transfer models of astrophysical environments only take the total radiation intensity and its effect on the local isotropic populations into account \\citep{vandertak:07, brinch:10}. The local populations are determined by the balance of collisional and radiative events that both excite and de-excite the populations of the molecular and atomic species. Collisional events are isotropic and a function of the density and temperature of the environment. At the outset, for unaligned quantum states, spontaneous emission events are also isotropic, that is,~the direction of the next spontaneously emitted photon of a certain molecule is random. The probability of absorption of those randomly directed photons, however, need not be isotropic \\citep{goldreich:81}. Anisotropy in the local absorption of photons aligns the quantum states that are associated with the line-transition, which in turn leads to polarization in the emission \\citep{morris:85, landi:06}. By considering the directional dependance of the photon-escape probability in a medium with an anisotropic velocity gradient, \\citet{goldreich:81} showed that radiation emitted from such a system is partially polarized. This effect is known in the literature as the Goldreich-Kylafis (GK) effect. It is strongest for lines with optical depth around unity in regions where the collisional rates are not so high as to quench the molecular or atomic alignment. Provided the magnetic field precession rate is 10-100 times stronger than radiative and collisional rates, which we show to be the case in most astrophysical regions in Section \\ref{sec:comp_align}, the line polarization traces the magnetic field projected onto the plane of the sky with a $90^{\\circ}$ ambiguity. \n\nNumerical modeling of the GK effect has been based on the theory presented in \\citet{goldreich:82}. In such models, the perpendicular and parallel components (with respect to the projected magnetic field direction) of the radiation field are propagated through a medium with an anisotropic velocity gradient. The velocity-gradient is so strong that the large velocity gradient (LVG) approximation can be employed. The LVG escape probability is a function of the velocity-gradient and is therefore anisotropic. This leads to alignment in the molecular or atomic states associated with the transition under investigation. Because of this, the emitted radiation is partially polarized. \\citet{deguchi:84} later showed that in order to accurately model the GK effect, it is vital to perform comprehensive (polarized) excitation modeling of the molecular or atomic quantum states and also of the ones that are not associated with the transition under investigation. \\citet{cortes:05} showed that an external anisotropic radiation source, such as a nearby stellar object, can enhance the polarized emission significantly. These numerical models only considered the one-dimensional propagation of polarized radiation, and the representation of the radiation field in perpendicular and parallel components is only valid when the magnetic field direction is constant over the investigated path. Furthermore, because of its heavy reliance on the LVG approximation, numerical modeling based on \\citet{goldreich:82} can only consider the introduction of anisotropy in the escape probability through an anisotropic velocity gradient. In light of recently developed polarimetric capabilities of interferometers, such as ALMA, these types of approximations cannot be afforded anymore. Rather, one needs comprehensive modeling of the 3D radiative transfer and its anisotropy, taking both the spatial and velocity structure into account for the astrophysical region under investigation as well as the 3D structure of the magnetic field. \n\nIn this paper, we demonstrate how such modeling can be attained. By using two (main) approximations, we show that regular (nonpolarized) radiative transfer codes can be extended with polarization capabilities. In Section 2, we introduce these approximations and show their simplifying impact on the theory of line polarization. In Section 3, we show how our PORTAL code provides the option of computing the emerging polarization using the output from a regular 3D radiative transfer code, in particular LIME \\citep{brinch:10}. In Section 4, we present the capabilities of PORTAL through the simulation of the emergence of polarization in a protoplanetary disk and a collapsing sphere. We discuss our results in Section 5 and conclude in Section 6.\n\n\n\\section{Theory}\nWe describe the introduction of anisotropy in the molecular or atomic populations through an anisotropic radiation field using the formalism of \\citet{landi:06}. We make the following approximations: \n\nFirst, we assume the magnetic field precession rate is way higher than collisional and radiative rates. We call this the strong magnetic field approximation. The magnetic precession rate is in the order of s$^{-1}$\/mG for diamagnetic (i.e.,~weakly magnetizable) molecules. Typical collisional rates are on the order of $10^{-5} \\left(\\frac{n_{H_2}}{10^6 \\ \\mathrm{cm}^{-3}}\\right)\\ \\mathrm{s}^{-1}$ and radiative rates are on the order of $10^{-4} \\ \\mathrm{s}^{-1}$ for a transition at $100$ GHz with a dipole moment of $0.1$ Debye, which is shone upon isotropically by $400$ Kelvin black-body radiation. Therefore, for almost all molecules, magnetic field interactions already dominate at very weak magnetic fields ($\\mu$G). Under the assumption of a strong magnetic field, many terms in the polarized density-equations can be dropped \\citep{landi:06}. The strong magnetic field approximation is also invoked by \\citet{goldreich:81}. In Section \\ref{sec:comp_align} we discuss special cases where a dominant magnetic field cannot be assumed.\n\nSecond, we assume that only the total intensity of the radiation has an influence on the (polarized) populations of the molecular or atomic states. This is a reasonable assumption if the polarization fraction is low, which is corroborated by polarization observations of molecular emission lines. We refer to this approximation as the anisotropic intensity approximation. We discuss the validity of the anisotropic intensity approximation in more detail in Section \\ref{sec:anis_int}, where we also compare our modeling with that of \\citet{goldreich:81}, who take the influence of both the Stokes-I and -Q parameters on the alignment of the molecular states into account. \n \nThese assumptions lead to significant simplifications in the theory behind the alignment of molecular and atomic quantum states and the radiation with which they interact. They allow for the implementation of such a model as an extension to a regular line radiative transfer code. In the following, we introduce the formalism that we used to model the alignment to molecular or atomic quantum states. After this, we outline how aligned quantum states influence the propagation of polarized radiation. \n\\subsection{Polarized statistical equilibrium equations}\nThe polarizing mechanism we focus on is the anisotropic radiation field. Mathematically, anisotropy in the radiation field that affects the quantum state alignment is most easily described in terms of an irreducible tensor-element expansion. The irreducible tensor components of the radiation field, which are in direction $\\Omega$ and at the position $\\boldsymbol{r}$, are obtained as \\citep{landi:84}\n\\begin{align}\n\\mathcal{J}^K_Q (\\boldsymbol{r},\\nu , \\Omega) = \\sum_j \\mathcal{T}^K_Q(j,\\Omega) S_j(\\boldsymbol{r},\\nu,\\Omega),\n\\label{eq:irred_J}\n\\end{align}\nwhere $K$ represents the irreducible tensor rank and $Q$ is its projection, $S_j (\\nu,\\Omega)$ are the Stokes-parameters at frequency $\\nu,$ and $j$ runs over all four Stokes parameters. We define the Stokes parameters in relation to the complex electric field vector components as \n\\begin{subequations}\n\\begin{align}\nI = |E_x|^2 + |E_y|^2 , \\\\\nQ = |E_x|^2 - |E_y|^2 , \\\\ \nU = 2\\mathrm{Re}\\left[E_x E_y^* \\right], \\\\\nV = 2\\mathrm{Im}\\left[E_x E_y^* \\right], \n\\end{align} \n\\end{subequations}\nwhere $x$ and $y$ refer to the axes that are perpendicular to the propagation direction, $z$, and each other. In this work, we consistently chose the axis of $x$ along the rejection of the (local) magnetic field direction from the propagation direction. The transformation coefficients $\\mathcal{T}^K_Q(j,\\Omega)$ are defined in equation~(A6) from \\citet{landi:84}. If we only consider alignment by Stokes-I radiation and if we furthermore assume a dominant magnetic field, only the $K=0,2$ and $Q=0$ components are of interest \\citep{landi:06}. Under these conditions, the irreducible tensor components of the radiation field reduce to\n\\begin{subequations}\n\\begin{align}\n\\mathcal{J}^0_0(\\boldsymbol{r},\\nu,\\Omega) &= I(\\boldsymbol{r},\\nu,\\Omega), \\\\\n\\mathcal{J}^2_0(\\boldsymbol{r},\\nu,\\Omega) &= \\sqrt{\\frac{1}{2}}P_2 (\\mu ) I(\\boldsymbol{r},\\nu,\\Omega),\n\\end{align}\n\\label{eq:J_int}\n\\end{subequations}\nwhere $\\Omega = (\\theta,\\phi)$ is expressed in terms of the inclination and azimuth angles that are gauged with respect to the magnetic field direction. The quantity $P_2 (\\mu)$ is the second-order Legendre polynomial and $\\mu=\\cos \\theta$. The solid-angle integrated tensors at position $\\boldsymbol{r}$ are readily obtained as\n\\begin{subequations}\n\\begin{align}\nJ^0_0 (\\boldsymbol{r},\\nu) &= \\frac{1}{4\\pi}\\int_{-1}^1 d \\mu \\int_0^{2\\pi} d\\phi \\ I(\\boldsymbol{r},\\nu,\\mathrm{acos}(\\mu),\\phi), \\\\ \nJ^2_0 (\\boldsymbol{r},\\nu) &= \\frac{1}{4\\pi \\sqrt{2}} \\int_{-1}^1 d \\mu \\ P_2 (\\mu) \\int_0^{2\\pi} d\\phi \\ I(\\boldsymbol{r},\\nu,\\mathrm{acos}(\\mu),\\phi). \n\\end{align}\n\\label{eq:int_rad_tens} \n\\end{subequations}\nIn the following, we refer to the ratio $J_0^0 (\\boldsymbol{r},\\nu) \/ J_0^2 (\\boldsymbol{r},\\nu)$ as the relative alignment of the radiation field. For an isotropic radiation field ($I(\\boldsymbol{r},\\nu,\\Omega) = I(\\boldsymbol{r},\\nu)$), it should be noted that only the (isotropic) $J^0_0 (\\boldsymbol{r},\\nu)$-term survives.\n\nJust as for the radiation field, we represent the molecular or atomic quantum states as irreducible tensor elements in order to most optimally utilize their symmetry properties. Quantum states are denoted as $\\rho^K_Q (\\alpha J)$, where $K$ is the rank of the irreducible tensor element and $Q$ is its projection. The total angular momentum of the associated quantum state is $J$ and all other quantum numbers characterizing the quantum state are collected in $\\alpha$. The rank $K$ is positive and restricted to values of $\\leq 2J$. The elements $K\\geq 1$ of the population tensor relate to the alignment of the quantum state and the $K=0$ element relates to the population of the quantum state. Under the assumption of a strong magnetic field, we can neglect all but the $Q=0$ projection elements. Because of the symmetry of the radiation field, we only have to take elements into account where $K$ is even. \\citet{landi:06} presented the statistical equilibrium equations for the polarized quantum state $\\rho^K_0 (\\alpha, J)$ under the following conditions: \n\\begin{align}\n\\dot{\\rho}^K_0 (\\alpha J) &= \\sum_{\\alpha_l J_l K_l} \\rho^{K_l}_0 (\\alpha_l J_l) \\left[ [t_A]_{\\alpha J K}^{\\alpha_l J_l K_l} + \\sqrt{\\frac{[J_l]}{[J]}} \\delta_{K,K_l} [C_I^{(K)}]_{\\alpha J}^{\\alpha_l J_l} \\right] \\nonumber \\\\ \n&+ \\sum_{\\alpha_u J_u K_u} \\rho^{K_u}_0 (\\alpha_u J_u) \\left[ [t_S]_{\\alpha J K}^{\\alpha_u J_u K_u} + [t_E]_{\\alpha J K}^{\\alpha_u J_u K_u} \\right. \\nonumber \\\\ \n&+ \\left. \\sqrt{\\frac{[J_u]}{[J]}} \\delta_{K,K_u} [C_S^{(K)}]_{\\alpha J}^{\\alpha_u J_u} \\right] \\nonumber \\\\\n&- \\sum_{K'} \\rho^{K'}_0 (\\alpha J) \\left[ [r_A]_{\\alpha J K K'} + [r_E]_{\\alpha J K K'} + [r_S]_{\\alpha J K K'} \\right. \\nonumber \\\\\n&+ \\left. \\delta_{KK'}\\left( \\sum_{\\alpha_u J_u} [C_I^{(0)}]_{\\alpha_u J_u}^{\\alpha J} + \\sum_{\\alpha_l J_l} [C_S^{(0)}]_{\\alpha_l J_l}^{\\alpha J} + D^{(K)} (\\alpha J)\\right) \\right].\n\\label{eq:stateq}\n\\end{align}\nIn Eq.~(\\ref{eq:stateq}), the rate of radiative absorption events toward the $\\rho^K_0 (\\alpha, J)$ from lower level $ \\rho^{K_l}_0 (\\alpha_l J_l)$ is given by $[t_A]_{\\alpha J K}^{\\alpha_l J_l K_l}$ \nand the collisional contribution is $[C_I^{(K)}]_{\\alpha J }^{\\alpha_l J_l }$. The rate of stimulated and spontaneous emission events toward the $\\rho^K_0 (\\alpha, J)$ from upper level $ \\rho^{K_u}_0 (\\alpha_u J_u)$ are given by $[t_S]_{\\alpha J K}^{\\alpha_u J_u K_u}$ and $[t_E]_{\\alpha J K}^{\\alpha_u J_u K_u}$,\nand the collisional contribution is $[C_S^{(K)}]_{\\alpha J}^{\\alpha_u J_u}$. The rates of absorption, stimulated emission, and spontaneous emission from the level $\\rho^K_0 (\\alpha, J)$ to all other levels is given by $[r_A]_{\\alpha J K K'}$, $[r_S]_{\\alpha J K K'}$, and $[r_E]_{\\alpha J K K'}$.\nFinally, the collisional depolarization rates are $D^{(K)}(\\alpha J)$. More detailed expressions for the radiative rates from Eq.~(\\ref{eq:stateq}) can be found in equations 7.20 from \\cite{landi:06}. By assuming a steady-state, $\\dot{\\rho}^K_0 (\\alpha J) = 0$, the statistical equilibrium equations can be solved as a linear set of equations. The solution yields the quantum state populations, including their relative alignment. \n\nWe should note that the statistical equilibrium equations of Eq.~(\\ref{eq:stateq}) are isomorphic to those presented in \\citet{deguchi:84}. While \\citet{deguchi:84} set up the statistical equilibrium equations in the standard angular momentum basis $\\ket{jm}$, where $j$ is the total angular momentum of the eigenstate and $m$ is its projection, we worked in a spherical tensor representation. We refer to \\citet{landi:06} for a detailed discussion on the relation between the two representations. We chose to work in a spherical tensor representation because of its symmetry properties. The properties of the spherical tensor expansion of both the molecular (or atomic) states and the radiation are such that truncation of higher-order $K$-terms in the $\\rho^K_0 (\\alpha J)$-expansion can be done with minimal loss of accuracy in the description of the statistical equilibrium equations for our system. Such truncation is not possible in the representation that \\citet{deguchi:84} used, and it results in a rapid and unmitigable increase in computational effort when high angular momentum states are considered. \n\\subsection{Polarized radiative transfer}\nAfter having obtained the (aligned) quantum state populations, we can evaluate their impact on the radiation propagation. Because of the strong magnetic field, (locally) only Stokes-Q radiation is produced.\nThe propagation of radiation around frequency, $\\nu_{\\alpha' J' , \\alpha J}$, associated with a transition $\\alpha' J' \\to \\alpha J$, can be described by\n\\begin{align}\n\\frac{d}{ds} \\boldsymbol{I}_{\\nu} = -\\boldsymbol{\\kappa}_{\\nu}^{\\alpha' J' , \\alpha J} \\boldsymbol{I}_{\\nu} + \\boldsymbol{\\epsilon}^{\\alpha' J' , \\alpha J},\n\\label{eq:polrad} \n\\end{align} \nwhere $\\boldsymbol{I}_{\\nu}=[I_{\\nu},Q_{\\nu},U_{\\nu},V_{\\nu}]$ is the Stokes vector and the propagation matrix \n\\begin{align}\n\\boldsymbol{\\kappa}_{\\nu}^{\\alpha' J' , \\alpha J} = \\begin{bmatrix}\\eta_I^{\\alpha' J' , \\alpha J} (\\nu) & \\eta_Q^{\\alpha' J' , \\alpha J} (\\nu) & 0 & \\eta_V^{\\alpha' J' , \\alpha J} (\\nu) \\\\ \\eta_Q^{\\alpha' J' , \\alpha J} (\\nu) & \\eta_I^{\\alpha' J' , \\alpha J} (\\nu) & 0 & 0 \\\\ 0 & 0 & \\eta_I^{\\alpha' J' , \\alpha J} (\\nu) & 0 \\\\ \\eta_V^{\\alpha' J' , \\alpha J} (\\nu) & 0 & 0 & \\eta_I^{\\alpha' J' , \\alpha J} (\\nu) \\end{bmatrix} \n\\label{eq:kappa_mat}\n\\end{align}\nis significantly simplified if one assumes a dominant magnetic field. Because we only consider diamagnetic molecules with Zeeman splitting that are far weaker than the thermal broadening, the production of Stokes-V radiation through the Zeeman effect is negligible and we set $\\eta_V^{\\alpha' J' , \\alpha J} (\\nu) \\to 0$. Thus, in PORTAL, we only consider the propagation of linearly polarized radiation. The expressions for the $\\eta$-elements of Eq.~(\\ref{eq:kappa_mat}) are \\citep{landi:84} \n\\begin{subequations}\n\\begin{align}\n\\eta_I^{\\alpha' J' , \\alpha J} (\\nu) &= \\frac{h \\nu_{\\alpha' J',\\alpha J}}{4\\pi} B_{\\alpha' J' , \\alpha J} \\phi_{\\nu_{\\alpha' J' , \\alpha J}}(\\nu) \\left\\{ \\left( \\mathcal{N}_{\\alpha' J'} - \\mathcal{N}_{\\alpha J} \\frac{[J']}{[J]}\\right) \\right. \\nonumber \\\\\n&+ \\left. \\left(\\mathcal{N}_{\\alpha' J'} w_{J'J}^{(2)} \\sigma^2_0 (\\alpha' J') - \\frac{[J']}{[J]} \\mathcal{N}_{\\alpha J} w_{JJ'}^{(2)} \\sigma^2_0 (\\alpha J) \\right) \\right. \\nonumber \\\\\n& \\times \\left. \\frac{3 \\cos^2 \\theta - 1}{2\\sqrt{2}} \\right\\}, \\\\\n\\eta_Q^{\\alpha' J' , \\alpha J} (\\nu) &= -\\frac{h \\nu_{\\alpha' J',\\alpha J}}{4\\pi} B_{\\alpha' J' , \\alpha J} \\phi_{\\nu_{\\alpha' J' , \\alpha J}} (\\nu) \\left( \\mathcal{N}_{\\alpha' J'} w_{J'J}^{(2)} \\sigma^2_0 (\\alpha' J') \\right. \\nonumber \\\\ \n&- \\left. \\frac{[J']}{[J]}\\mathcal{N}_{\\alpha J} w_{JJ'}^{(2)} \\sigma^2_0 (\\alpha J) \\right) \\frac{3\\sin^2 \\theta }{2\\sqrt{2}},\n\\end{align}\n\\label{eq:eta}\n\\end{subequations}\nwhere $\\mathcal{N}_{\\alpha J} = \\mathcal{N} [J]^{1\/2} \\rho_0^0 (\\alpha J)$ is the number density of the quantum state $\\alpha J$, and $\\phi_{\\nu_{\\alpha' J' , \\alpha J}}$ denotes the normalized line-profile centered at $\\nu_{\\alpha' J' , \\alpha J}$ in frequency-space. The symbols \n\\begin{align}\nw_{J'J}^{(2)} = (-1)^{1+J+J'}\\sqrt{3[J']}\\begin{Bmatrix} 1 & 1 & 2 \\\\ J' & J' & J\\end{Bmatrix} \\nonumber\n\\end{align}\nwere introduced by \\citet{landi:84}. The quantity between curly brackets is a Wigner-6j symbol \\citep{biedenharn:81}. We use the short-hand notation\n$\n\\sigma_0^2 (\\alpha J) = \\rho_0^2 (\\alpha J) \/ \\rho_0^0 (\\alpha J)\n$ \nfor the relative alignment of the quantum state $\\alpha J$. The spontaneous emission events in the polarized radiative transfer equations of Eq.~(\\ref{eq:polrad}) are represented in the $\\boldsymbol{\\epsilon}$-vector. The spontaneous emission contribution to the Stokes-U is zero in the strong magnetic field limit we consider. The Zeeman effect for diamagnetic molecules is way smaller than the thermal broadening, so we can set $\\epsilon_V^{\\alpha' J', \\alpha J} \\to 0$. The contributions to the Stokes-I and -Q parameters are \\citep{landi:84}\n\\begin{subequations}\n\\begin{align}\n\\epsilon_I^{\\alpha' J' , \\alpha J} (\\nu) &= \\frac{h \\nu_{\\alpha' J',\\alpha J}}{4\\pi} A_{\\alpha' J' , \\alpha J} \\phi_{\\nu_{\\alpha' J' , \\alpha J}} (\\nu) \\nonumber \\\\\n& \\times \\mathcal{N}_{\\alpha' J'} \\left\\{ 1 + w_{J'J}^{(2)} \\sigma^2_0 (\\alpha' J') \\frac{3 \\cos^2 \\theta - 1}{2\\sqrt{2}} \\right\\}, \\\\\n\\epsilon_Q^{\\alpha' J' , \\alpha J} (\\nu) &= -\\frac{h \\nu_{\\alpha' J',\\alpha J}}{4\\pi} A_{\\alpha' J' , \\alpha J} \\phi_{\\nu_{\\alpha' J' , \\alpha J}} (\\nu) \\nonumber \\\\\n&\\times\\mathcal{N}_{\\alpha' J'} w_{J'J}^{(2)} \\sigma^2_0 (\\alpha' J') \\frac{3\\sin^2 \\theta }{2\\sqrt{2}}.\n\\end{align}\n\\label{eq:eps}\n\\end{subequations}\n\\section{Methods}\n The formalism that we present in the previous section can be used to simulate the emergence of polarization in spectral lines using an (isotropic) excitation model as input. It can be directly used by assuming LTE excitation, or alternatively, the atomic and molecular excitation from any (3D) radiative transfer code can be input. We outline in the following how we used the LIME radiative transfer code \\citep{brinch:10}. \n\nLIME is a Monte Carlo 3D radiative transfer code that works with a (weighted) randomly chosen grid. A physical structure can be input, whereupon a random grid is chosen that is weighed over the molecular density and other parameters \\citep{ritzerveld:06}. After a number of Monte Carlo radiative transfer iterations, which are sped up by an accelerated lambda iteration \\citep{rybicki:91}, the simulation converges on a molecular and atomic excitation over all of the nodes in the simulation. Subsequently, this solution can be ray traced to simulate an image of the physical structure under investigation. \n\nRather than directly ray tracing the excitation solution, we used it to thoroughly map out the local anisotropy of the radiation field throughout the simulation. With the local anisotropy parameters of the radiation field, we modeled the polarized excitation of the molecular or atomic states under investigation. Having the polarized excitation mapped out throughout the simulation, we performed a polarized ray-tracing to obtain a polarized image of the physical structure under investigation. \n\nIn the following, we outline in more detail how we implemented PORTAL. In the first paragraph, we discuss setting up the polarized statistical equilibrium equations using the output of a line radiative transfer code. In order to do this, we dedicated most of our attention to the mapping of the local anisotropy of the radiation fields. In the second paragraph, we detail the polarized radiative transfer that was performed in the polarized ray-tracing. Especially for simulations with nonuniform magnetic fields, it is crucial to pay extra attention to the frame of reference of the polarized radiation and the proper way to relate different frames of reference. \n\n In PORTAL, we used the anisotropic intensity approximation and formulated the polarized statistical equilibrium equations in terms of irreducible tensor elements. This approach differs from other efforts such as LinePol \\citep[][(submitted)]{kuiper:20}, which builds on LIME, is optimized for CO, and uses the formalism of \\citet{goldreich:82} to describe the propagation of polarized radiation and its interaction with the molecular medium. LinePol takes two polarization modes of the radiation into account and uses a polarized accelerated lambda iteration scheme to obtain the state-populations in the simulation. At minimal cost to the accuracy of our results (see Sections \\ref{sec:polsee} and \\ref{sec:anis_int}), the approximations in PORTAL speed up the simulation tremendously and lead to the possibility to treat more complex systems. PORTAL allows for complex geometries, magnetic field structures, and the treatment of molecules with extensive energy structures.\n\n\\subsection{Polarized statistical equilibrium equations}\n\\label{sec:polsee}\nThe quantum state alignment is dependent on the local anisotropy of the radiation field, so it is important to obtain a good angular sampling of the radiation field at the location of the simulation nodes. Different angular integrations of Eqs.~(\\ref{eq:int_rad_tens}) for the case of an internal source of radiation (e.g., a central stellar object) and the case of no internal radiation source were used. For the latter, the local angular integration was performed as\n\\begin{subequations}\n\\begin{align}\nJ^0_0 (\\boldsymbol{r},\\nu) &= \\frac{1}{4\\pi}\\sum_{i=1}^{N_{\\mu}} w_i^{\\mu} \\sum_{j=1}^{N_\\phi(\\mu_i)} w_j^{\\phi} I(\\boldsymbol{r},\\nu,\\mathrm{acos}( \\mu_i),\\phi_j), \\\\ \nJ^2_0 (\\boldsymbol{r},\\nu) &= \\frac{1}{4\\pi\\sqrt{2}} \\sum_{i=1}^{N_{\\mu}} w_i^{\\mu} P_2 (\\mu_i) \\sum_{j=1}^{N_\\phi (\\mu_i)} w_j^{\\phi} I(\\boldsymbol{r},\\nu,\\mathrm{acos}(\\mu_i),\\phi_j) \n\\end{align}\n\\end{subequations}\nwhere $\\mu_i$ and $w_i^{\\mu}$ are the coordinates and the weights, which were taken from the $N_{\\mu}$-point Gaussian quadrature rule. The integration over $\\phi$ was performed over $N_{\\phi} (\\mu)\\propto \\sqrt{1-\\mu^2}$ equidistant points all with weight $2\\pi\/N_\\phi$. \n\nIn the case of an internal radiation source, it should be appreciated that the solid angle associated with the radiation coming from this internal source is well-defined. Therefore, the solid angle integration was divided up into rays coming from the internal radiation source; the number of rays is proportional to the solid-angle of the internal source $\\Delta \\Omega_* = \\pi(|\\boldsymbol{r}|\/R_*)^2$ and all of the other rays were distributed equally over the remaining sphere surface.\n\nThe local radiation field parameters of a node at the position $\\boldsymbol{r}$, summarized in $J^0_0 (\\boldsymbol{r},\\nu)$ and $J^2_0 (\\boldsymbol{r},\\nu)$, were obtained by ray tracing $N$ rays with direction $\\boldsymbol{k}_{\\mu,\\phi}$ to that node. The parameters $\\mu$ were chosen with respect to the magnetic field direction ($\\boldsymbol{b} \\cdot \\boldsymbol{k}_{\\mu,\\phi} = \\mu$). The angles $\\phi$ were gauged with respect to a canonical direction not parallel to the magnetic field. The choice of the canonical direction is free as the angle $\\phi$ is integrated out without weighing (see Eq.~\\ref{eq:J_int}). The ray-tracing was performed using the molecular populations that were output by LIME, while also using some of the relevant LIME-input parameters, such as (local) temperature, (local) velocity, and gridding. The ray-tracing yielded the local radiation field parameters that were subsequently used to obtain the quantum state populations and alignment.\n\nThe quantum state populations and alignment were obtained from the statistical equilibrium equations (SEE) given in Eq.~(\\ref{eq:stateq}). The SEE are a balance of the radiative and collisional transition events. The radiative transition events are dependent on local parameters for the (an)isotopic radiation field at frequencies of all of the allowed transitions and their associated Einstein coefficients. Collisional rates are dependent on the temperature-dependent collisional cross-sections and (local) number densities of the relevant collisional partners. \nThe relevant Wigner coupling symbols were calculated using the WIGXJPF package \\citep{johansson:16}. The SEE were formulated in terms of a set of linearly dependent equations and were subsequently solved via an LQ decomposition (using the LAPACK libraries, \\citet{LAPACK}) under the following physical constraint: $\\sum_i [j_i]^{1\/2} \\rho_0^0 (\\alpha_i j_i) = 1$. The solutions also included the isotropic populations that were compared to the LIME-output. We found that neglecting the quantum state alignment terms, $\\rho_0^k (\\alpha j)$ with $k>2$, introduces an error of $\\sim 1\\% $ in the state-alignment expressions, and for $k>4$ this error is already reduced to $\\sim 1\\permil$. In general, the quantum state alignment can be neglected for terms, $\\rho_0^k (\\alpha j)$ with $k>6$, with virtually no loss in precision and with great reduction of computational effort as a consequence\\footnote{For example, the dimensionality of the polarized SEE for the first 41 rotational levels of CO reduced from 861 to 151 by setting $k_{\\mathrm{max}}=6$.}.\n\n\\subsection{Polarized radiative transfer}\nThe quantum state populations and alignment obtained from the SEE were used to compute the (polarized) absorption and emission factors for each node in the simulation. The angle $\\theta$ in Eqs.~(\\ref{eq:eta}-\\ref{eq:eps}) was obtained from the local magnetic field direction and the ray-trace direction. The ray-trace direction was chosen by defining an inclination angle and azimuth angle. The polarized radiation was gauged with respect to a canonical axis, $\\boldsymbol{\\chi}_{\\mathrm{global}}$, perpendicular to the ray-tracing direction. The local and global Stokes parameters are related as \\citep{landi:06} \n\\begin{align}\n\\begin{pmatrix} Q_{\\mathrm{local}} \\\\ U_{\\mathrm{local}} \\end{pmatrix} = \\begin{pmatrix} \\cos 2\\alpha \\ & \\sin 2\\alpha \\\\ -\\sin 2\\alpha & \\cos 2\\alpha \\end{pmatrix} \\begin{pmatrix} Q_{\\mathrm{global}} \\\\ U_{\\mathrm{global}} \\end{pmatrix},\n\\label{eq:globtoloc}\n\\end{align}\nand $I_{\\mathrm{local}} = I_{\\mathrm{global}}$. In Eq.~(\\ref{eq:globtoloc}), $\\alpha$ is the angle between $\\boldsymbol{\\chi}_{\\mathrm{global}}$ and $\\boldsymbol{\\chi}_{\\mathrm{local}}$ and the local reference axis is the unit vector along the rejection of the local magnetic field direction from the ray-tracing direction. \n\nThe local Stokes-parameters were propagated using the polarized radiative transfer equations. Equations~(\\ref{eq:polrad}-\\ref{eq:kappa_mat}) show that only the Stokes-Q and -I coefficients are coupled in the polarized radiative transfer. That means that the propagation of the Stokes-U radiation is simply $U(s) = U(0) e^{-\\eta_I s}$. To evaluate the propagation of the other Stokes parameters, $\\boldsymbol{i} = [I,Q]$, the evolution operator formalism of \\citet{landi:06} was used, where the propagation is described by\n\\begin{align}\n\\boldsymbol{i}(s) = \\int_0^s ds' \\ \\boldsymbol{O}(s,s') \\boldsymbol{\\epsilon}(s') + \\boldsymbol{O}(s,0) \\boldsymbol{i}(0),\n\\end{align} \nand where\n\\[\n\\boldsymbol{O}(s,s') = e^{\\int_{s'}^s ds'' \\ \\eta_I} \\begin{bmatrix} \\cosh \\left( \\int_{s'}^s ds'' \\ \\eta_Q \\right) & -\\sinh \\left( \\int_{s'}^s ds'' \\ \\eta_Q \\right) \\\\ \n -\\sinh \\left( \\int_{s'}^s ds'' \\ \\eta_Q \\right) & \\cosh \\left( \\int_{s'}^s ds'' \\ \\eta_Q \\right) \\end{bmatrix}\n\\]\nis the evolution operator (see Chapter 8 of \\citet{landi:84}). The propagation for each crossed cell was considered, and within such a propagation, the coefficients $\\eta_I$ and $\\eta_Q$ as well as $\\epsilon_I$ and $\\epsilon_Q$ are constant. It is then straightforward to evaluate the integrals inside the evolution operator as well as the integral over the evolution operator: $\\int_0^s ds'\\ \\boldsymbol{O}(s,s')$. Having done so, the propagation of the Stokes-I and -Q within a single cell is given by \n\\begin{subequations}\n\\begin{align}\nI(s) &= o_{I} \\epsilon_I + o_Q \\epsilon_Q + \\left[ \\cosh (\\eta_Q s) I(0) - \\sinh(\\eta_Q s) Q(0) \\right]e^{-\\eta_I s}, \\\\\nQ(s) &= o_{Q} \\epsilon_I + o_I \\epsilon_Q + \\left[ \\cosh (\\eta_Q s) Q(0) - \\sinh(\\eta_Q s) I(0) \\right]e^{-\\eta_I s}, \n\\end{align} \n\\end{subequations}\nwhere\n\\begin{align}\no_I &= \\frac{\\eta_I}{\\eta_I^2 - \\eta_Q^2} \\left(1 - \\left[\\cosh(\\eta_Q s) + \\frac{\\eta_Q}{\\eta_I} \\sinh(\\eta_Q s)\\right] e^{-\\eta_I s}\\right), \\nonumber \\\\\no_Q &= -\\frac{\\eta_Q}{\\eta_I^2 - \\eta_Q^2} \\left(1 - \\left[\\cosh(\\eta_Q s) + \\frac{\\eta_I}{\\eta_Q} \\sinh(\\eta_Q s)\\right]e^{-\\eta_I s} \\right) \\nonumber \n\\end{align}\nare the factors that were obtained from integrating the elements of the evolution operator. \n\n\n\\section{Simulations}\nWe applied PORTAL to known astrophysical problems. We consider the standard problem of a spherically symmetric collapsing molecular cloud, and we investigate the emergence of polarization in molecular lines through an anisotropic radiation field in a standard protoplanetary disk system. It should be noted that neither of these problems illustrate the full 3D capabilities of PORTAL. We focus, however, on these models because of their more straightforward interpretation and we leave more complex modeling for further work.\n \n\\subsection{Collapsing spherical cloud}\nA benchmark problem in radiative transfer modeling, which furthermore allows for local anisotropy to establish itself in the radiation field, is the problem of a collapsing spherical cloud. We consider the emergence of polarization in HCO$^+$ lines. The density, velocity, and temperature distribution are taken from the \\citet{shu:77} collapse model, using the same parameters as \\citet{zadelhoff:02}. Only the ground vibrational state of HCO$^+$ is considered. We assume a uniform HCO$^+$ abundance of $10^{-9}$ and assume constant turbulent broadening of $200 \\ \\mathrm{m\/s}$. We assume that a strong radial magnetic field (origin: center of mass) permeates the cloud. \n\nFirst of all, an overview of the relevant isotropic and anisotropic interactions is instrumental to an eventual discussion of the quantum state alignment and radiation polarization characteristics. We report the cumulative radiative and collisional rates of the $J=2$ and $J=3$ level of HCO$^+$ in Figure \\ref{fig:sphere_rates}. Of the different interactions, only stimulated emission and absorption are anisotropic interactions. Using the spherical symmetry of the collapsing sphere-problem, we only plotted the rates as a function of the distance to the center. We observe that for the inner regions of the collapsing sphere, collisions become dominant as the density of this regions increases. Even though there is appreciable alignment of the radiation field, the quantum states do not align themselves because of the dominant isotropizing collisions. From about 400 AU, radiative interactions take over as the dominant interaction and the quantum states align themselves. We also give the magnetic precession rate for a magnetic field of $1$ mG and 1 $\\mathrm{\\mu G}$ and note that for a HCO$^+$-molecule in the collapsing sphere, the magnetic field can be taken to define the symmetry axis when it is $\\sim 10-100 \\times$ stronger than other interactions. From Figure \\ref{fig:sphere_rates}, we estimate this to be the case at magnetic field strengths of $\\sim 10-100 \\ \\mathrm{\\mu G}$.\n\nIn the same Figure \\ref{fig:sphere_rates}, we plotted the relative anisotropy of the radiation field and the relative alignment of the quantum states $J=2$ and $J=3$. We note that the radiation anisotropy increases, thus moving away from the collapsing-sphere center. The radiation anisotropy in the collapsing sphere is partly a result of the density structure and partly the result of the velocity structure. Both structures are spherically symmetric, but this spherical symmetry is only manifest when the center is taken as the origin. For any cell that is not located at the center of the collapsing sphere, the radiation field is therefore anisotropic. Higher anisotropy in the radiation is associated with a stronger alignment of the quantum states.\n\\begin{figure}[h!]\n \\centering\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/sphere_rates_j2.png}\n \\caption{}\n \\end{subfigure}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/sphere_rates_j3.png}\n \\caption{}\n \\end{subfigure}\n \\caption{Plot of the collapsing sphere's interaction rates (collisional, absorption, and stimulated emission as well as spontaneous emission) and relative alignment (radiative and quantum state) as a function of the radius for (a) the $J=2$ level and $J=2-1$ transition and (b) the $J=3$ level and $J=3-2$ transition. The interaction rates should be read from the left axis, the relative alignment from the right axis.}\n \\label{fig:sphere_rates}\n\\end{figure}\n\nWe report the azimuthally averaged total intensity and polarization fraction of the HCO$^+$ $J=3-2$ and $J=2-1$ transitions in Figure \\ref{fig:sphere_frac}. Indeed, we note that close to the center of the collapsing sphere, the polarization fraction is the lowest and gradually increases when moving outward. Polarization fractions are above $1 \\%$ for a radial distance greater than $600$ AU for the $J=3-2$ transition and $900$ AU for the $J=2-1$ transition. We report the associated spectra at $R=1400$ AU in Figure \\ref{fig:sphere_spec}. We observe that the linear polarization spectra roughly follow the spectral shape of the total intensity. \n\n\\begin{figure}[h!]\n \\centering\n \\includegraphics[width=0.5\\textwidth]{.\/figures\/sphere_frac.png}\n \\caption{Total and polarized emission intensity (in Kelvin) of a collapsing sphere as a function of the radial distance.}\n \\label{fig:sphere_frac}\n\\end{figure}\n\n\\begin{figure}[h!]\n \\centering\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/sphere_spec_21.png}\n \\caption{}\n \\end{subfigure}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/sphere_spec_32.png}\n \\caption{}\n \\end{subfigure}\n \\caption{Spectra of the (polarized) intensity (in Kelvin) of the (a) $J=2-1$ and (b) $J=3-2$ transitions from a collapsing sphere. The spectra are azimuthally averaged at $1400$ AU.}\n \\label{fig:sphere_spec}\n\\end{figure}\n\nThe polarization angles are oriented in a radial fashion along the magnetic field lines. One should be aware that for a radial magnetic field, the angle between the magnetic field lines and the propagation direction toward the observer is a function of the propagation position. Accordingly, at the magic angle of $\\theta_{\\mathrm{magic}} \\approx 54.7$ or $z\/R = \\frac{1}{\\sqrt{3}}$, the propagation elements $\\eta_Q$ flip sign and some of the earlier produced polarization is negated.\n\\subsection{Protoplanetary disk}\nThe protoplanetary disk is a prime example of an anisotropic astrophysical structure. Both the anisotropy in the density and velocity structure produce a locally anisotropic radiation field. Magnetic fields in the protoplanetary disk have been conjectured through dust-polarization observations \\citep{stephens:17}, and recently, stringent limits have been put on the magnetic field strength through ALMA line circular polarization observations \\citep{vlemmings:19}. \n\nWe consider the polarization of $^{12}$CO in a general toy model of a protoplanetary disk having a number-density distribution of \n\\begin{align}\nn_{\\mathrm{H}_2}(r_c,h) = 4\\times 10^{14} \\left(\\frac{h}{\\mathrm{AU}}\\right)^{-2.25} e^{-50 \\frac{(h\/\\mathrm{AU})^{2}}{(r_c\/\\mathrm{AU})^{2.5}}} \\ \\mathrm{m}^{-3}, \n\\end{align}\nwhere $r_c$ is the radial distance and $h$ is the height. The disk is assumed to be rotating, resulting in a model velocity-field of\n\\begin{align}\n\\boldsymbol{v}(\\boldsymbol{r})= v(\\cos \\phi \\hat{\\boldsymbol{x}} - \\sin \\phi \\hat{\\boldsymbol{y}}), \n\\end{align}\nwhere\n\\[\nv = 2.11 \\times 10^4 \\left(\\frac{r_c}{\\mathrm{AU}}\\right)^{-1} \\ \\mathrm{m\/s},\n\\]\nand $\\tan \\phi = y\/x$. The temperature is given by\n\\begin{align}\nT(r_c) = 400 \\left(\\frac{r_c}{\\mathrm{AU}} \\right)^{-\\frac{1}{2}} \\ \\mathrm{K}.\n\\end{align} \nFurthermore, we assume a constant CO abundance of $10^{-3}$ and a constant turbulent doppler broadening of $b_{\\mathrm{turb}}=200 \\ \\mathrm{m\/s}$. We only take the vibrational ground-state of $^{12}$CO into account. We neglect any line-overlap with transitions from other species. We explore the emergence of polarization in a protoplanetary disk for three types of (strong) magnetic fields: radial, toroidal, and poloidal.\n\nWe note that perhaps this toy model of the protoplanetary disk does not capture all features of the protoplanetary disk that are important in considering the polarization of thermal lines. For instance, we neglect to represent the inner midplane region by optically thick dust, so that the anisotropic radiation field resulting therefrom is not accounted for. Also, by not taking vibrationally excited levels and the transitions between different vibrational levels into account, we fail to include their significant aligning interactions (see Section \\ref{sec:comp_align}). We explore more detailed and thorough modeling of protoplanetary disk regions in future work. These results should be taken as a simplified, but generally indicative, model of the mechanisms involved in the polarization of thermal line radiation of radiation by a magnetic field in protoplanetary disk regions. \n \nIt is important to map out the rates of isotropic and anisotropic interactions in order to understand the relative alignment of the molecules or atoms. Because of the cylindrical symmetry of the protoplanetary disk, we are able to analyze the interaction rates as a function of the radial distance and the height. In Figure \\ref{fig:pp_rates}, we report the cumulative radiative and collisional rates for the $J=3$ level of CO. The rates are plotted as a function of $r_c$ for different height-cross sections. We also report the magnetic precession rate of a $1\\ \\mathrm{\\mu G}$ and a $1 \\ \\mathrm{mG}$ magnetic field. It is apparent that magnetic interactions dominate other interactions and that we are justified in choosing the projection-axis along the magnetic field direction. Further, we observe a dominance of collisions over other interactions in a large region of the inner parts of the protoplanetary disk. In the disk midplane, isotropic collisions dominate the radiative interactions in the disk, but this dominance becomes weaker with the radial distance. In the outer parts of the disk, where the density drops, collisions become weaker and radiative events dominate.\n\nIn Figure \\ref{fig:pp_rates} we also plotted the relative anisotropy of the radiation field resonant with the $J=3-2$-transition and the relative alignment of the $J=3$ state. Both of these parameters are defined with respect to a toroidal magnetic field configuration. The radiation anisotropy is strongest in the outer parts of the disk and weakest in the bulk of the disk. The same dependence is seen for the relative alignment of the quantum states. The relative anisotropy of the radiation is almost constant as a function of the radial distance at a height of $1$ AU. This is because the disk is optically thick in the midplane. The local angular radiation profile is not isotropic because of the temperature gradient. Due to dominant collisions, the quantum state alignment in the midplane is not large enough to significantly polarize radiation that is coming through. \n\n\\begin{figure}[h!]\n \\centering\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/pp_rates_1.png}\n \\caption{}\n \\end{subfigure}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/pp_rates_40.png}\n \\caption{}\n \\end{subfigure}\n\\caption{Plot of the protoplanetary disk's interaction rates (collisional, absorption, and stimulated emission as well as spontaneous emission) and relative alignment (radiative and quantum state, with respect to a toroidal magnetic field) as a function of the radial distance for (a) $1$ AU height and (b) $40$ AU height. The interaction rates should be read from the left axis, the relative alignment from the right axis.}\n \\label{fig:pp_rates}\n\\end{figure}\n\n\\begin{figure*}[h!]\n \\centering\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/rad_vec2.png}\n \\caption{}\n \\label{fig:pp_contour_rad}\n \\end{subfigure}\n ~\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/tor_vec2.png}\n \\caption{}\n \\label{fig:pp_contour_tor}\n \\end{subfigure}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/rad_angle.png}\n \\caption{}\n \\label{fig:pp_angle_rad}\n \\end{subfigure}\n ~\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\textwidth]{.\/figures\/tor_angle.png}\n \\caption{}\n \\label{fig:pp_angle_tor}\n \\end{subfigure}\n \\caption{Contour plots of (the logarithm of) the total intensity (in Kelvin) of a protoplanetary disk. The disk is viewed face on [(a) and (b)] and at an inclination of $45^o$ [(c) and (d)]. We overlayed the intensity plot with polarization vectors from PORTAL simulations that come from a radial magnetic field (a,c) and a toroidal magnetic field (b,d). Polarization vector lengths scale with the polarization fraction.}\n \\label{fig:pp_contour}\n\\end{figure*}\n\n\\begin{figure}[h!]\n \\centering\n \\includegraphics[width=0.5\\textwidth]{.\/figures\/pp_frac.png}\n \\caption{(Polarized) emission intensity (in Kelvin) of a protoplanetary disk as a function of the radial distance. The polarized intensity is plotted for three different magnetic field configurations and the disk is seen face on.}\n \\label{fig:pp_frac}\n\\end{figure}\n\nWe analyzed the emergence of polarization through two different magnetic field configurations: toroidal and radial. In Figure \\ref{fig:pp_contour_rad} we report the contour map of the $J=3-2$ CO-transition at $345.8$ GHz of the total intensity (in Kelvins) overlayed with polarization vectors resulting from the polarized emission of CO aligned with a radial magnetic field. The polarization vectors are scaled with respect to the polarization fraction and are parallel to the radial configuration of the magnetic field. Figure \\ref{fig:pp_contour_tor} gives the polarization map coming from a toroidal magnetic field. We note that the polarization fraction for the face-on view of the protoplanetary disk is cylindrically symmetric.\n\nIt is striking that the polarization vector maps viewed face on, for both the toroidal and radial magnetic field, have the same configurations. This similarity can be traced back to the anisotropy introduced in the molecular states via the anisotropic radiation field, $J^2_0 (\\boldsymbol{r})$ (Eq.~\\ref{eq:int_rad_tens}). When performing the integration to acquire $J^2_0 (\\boldsymbol{r})$, the $\\mu$-angle is gauged with respect to the magnetic field direction. The different gauges with respect to the toroidal and radial magnetic field configurations lead to the $J^2_0 (\\boldsymbol{r})$, which is associated with the toroidal magnetic field, to be negative, while the $J^2_0(\\boldsymbol{r})$ of the radial magnetic field is positive. Thus, in the region where polarization is produced, where furthermore the angle between propagation and the magnetic field $\\theta_{\\mathrm{prop}} > \\theta_{\\mathrm{magic}}$ for both magnetic field configurations, this gives rise to perpendicular and parallel orientations of the polarization vectors with respect to the toroidal and radial magnetic fields, that is,~polarization vectors that are identically oriented. Only when we view the disk at a significant inclination are we able to discern the orientation of the magnetic field from its polarization vectors, which can be seen in Figures \\ref{fig:pp_angle_tor} and \\ref{fig:pp_angle_rad}. \n\nThe polarization maps of a protoplanetary disk viewed at a $45^o$ inclination show large polarization fractions for the poloidal and toroidal magnetic field configurations. Lower but still significant polarization fractions are seen to emerge from the radial magnetic field configuration. The highest polarization fractions occur at the edges of the protoplanetary disk. In the disk midplane, almost no polarization arises. This effect can be ascribed to the high optical depth from this region; it should, however, also be noted that our method underestimates the polarization fraction coming from optically thick regions (see Section \\ref{sec:anis_int}). \n\nFor the face-on view of a protoplanetary disk that is permeated by a poloidal magnetic field, no significant polarization emerges even though the quantum states are aligned. This is because for a large part of the disk, the magnetic field is almost aligned along the propagation direction. When this is the case, the propagation coefficients are $\\eta_Q \\to 0$, and no polarization is produced. When the disk is viewed at a significant inclination, the poloidal magnetic field produces a large polarization fraction. \n\nFigure \\ref{fig:pp_frac} is a plot of the azimuthally averaged polarization fraction as a function of the radial distance. Near the center of the proto-planetary disk, the polarization fraction is low and increases as one moves outward. The maximum polarization fraction of the protoplanetary disk viewed face on is $\\sim 0.5 \\%$, but polarization fractions up to $\\sim 9\\%$ are observed when the disk is viewed at an inclination of $45^o$. We analyze the azimuthally averaged ($r_c = 50$ AU) spectrum of the total (polarized) intensity in Figure \\ref{fig:pp_spec}. The polarization roughly follows the spectral shape of the total intensity.\n\n\\begin{figure}[h!]\n \\centering\n \\includegraphics[width=0.5\\textwidth]{.\/figures\/pp_spec.png}\n \\caption{Spectrum of the (polarized) intensity (in Kelvin) of the $J=3-2$ transition from a protoplanetary disk permeated by a toroidal magnetic field. The spectrum is azimuthally averaged at $60$ AU and the disk is seen face on.}\n \\label{fig:pp_spec}\n\\end{figure}\nIt is a general trend that high-frequency transitions have a larger tendency to emit polarized radiation. This is because the radiative rates scale with the frequency. Radiative interactions of high-frequency transitions therefore tend to dominate over collisional interactions. At the same time, the transition optical depth falls (generally) with the transition frequency; for transitions that are too optically thin, radiation intensity is too low to align the quantum states. \n\n\\section{Discussion}\nThe anisotropic intensity approximation and the strong magnetic field approximation are central to the quality of the method we employed in PORTAL. We discuss these two approximations in the following two subsections. We discuss general remarks about the simulations of astrophysical regions using PORTAL in Section \\ref{sec:general_remarks}. \n\\subsection{The anisotropic intensity approximation}\n\\label{sec:anis_int}\nOur method heavily relies on the approximation that it is only the anisotropy in the total intensity that contributes to the alignment of the molecular or atomic states under investigation. We call this approximation the anisotropic intensity approximation. We were able to directly compare the anisotropic intensity approximation to the LVG problem of \\citet{goldreich:81}. \\citet{goldreich:81} accounted for the influence of the anisotropy of both the Stokes I and Stokes Q on the quantum state alignment. In the GK approach, the Stokes U component of the radiation field is neglected because the LVG method can only treat a constant magnetic field. The comparison is summarized in Figure \\ref{fig:GK_compare}.\n\\begin{figure}[h!]\n \\centering\n \\includegraphics[width=0.5\\textwidth]{.\/figures\/compare_GK.png}\n \\caption{Comparison of the polarization fraction computed through the GK method (solid line) and the radiation anisotropy method we employ in this paper (dotted line). For more details on the simulation parameters, see \\citet{goldreich:81}. We consider a $J=1-0$ transition at $100$ GHz, with a strong magnetic field along the $\\hat{z}$-axis and a velocity gradient of $10^{-9}$ s$^{-1}$ in the $xy$-plane. We consider a temperature of $T=10$ K. Three ratios for the collision-radiative rates are considered and denoted inside the figure. The polarization fraction was computed for a ray traveling along the $\\hat{x}$-axis.}\n \\label{fig:GK_compare}\n\\end{figure}\n\nWe note that below polarization fractions of $2\\%$, our method agrees with the GK effect for any optical depth. Furthermore, for low optical depth, $\\tau < 0.3$, our method reproduces the GK effect very well regardless of the polarization fraction. It is only for very high degrees of polarization and large optical depths that the polarization fraction obtained through the anisotropic intensity approximation starts to deviate from the GK polarization fraction. For strongly polarized lines ($p_L > 6\\%$), the polarization fraction can be underestimated by up to a factor $1.5$ for $\\tau > 1$ and this underestimation is sustained with increasing $\\tau$. We note that the polarization angle is identical for both methods.\n\nThe anisotropic intensity approximation loses its quality through the following: (i) the fact that a significant part of the radiation is polarized, which has an impact on the irreducible tensor representation of the radiation field (see Eq.~\\ref{eq:irred_J}), and (ii) that this simplification subsequently impacts the source function, resulting in a magnification of the error. The latter error is particularly manifest at high optical depths, and it is also a consequence of the local approximation of an LVG-like problem. We expect this error to be ameliorated when the local approximation is abandoned as in PORTAL. \n\nThe polarization of (sub)millimeter lines through the GK effect has been observed in a number of sources. For most line observations, the observed polarization fraction is lower than $2\\%$ \\citep{lai:03}. This can be taken as a direct indicator of the quality of the anisotropic intensity approximation. There is a fraction of emission lines for which high polarization fractions are observed; the most strongly polarized emission lines go up to $13 \\%$ \\citep{vlemmings:12, cortes:05}. The large polarization fractions are most probably due to large sources of external radiation in the vicinity. \n\nOne avenue to remedy the anisotropic intensity approximation is to iteratively perform the inward ray-tracing steps (see Section \\ref{sec:polsee}) for all radiative polarization modes and perform the irreducible tensor integration as Eq.~(\\ref{eq:irred_J}). After each iteration, the alignment of the quantum states for each cell is recomputed until convergence is attained. We plan to implement such a scheme in a later version of PORTAL, although this will significantly increase the calculation time.\n\\subsection{The strong magnetic field approximation}\n\\label{sec:comp_align}\nThe symmetry axis of the molecular and atomic states determines the (projected) direction of polarization. In our models, it is assumed that the symmetry axis is along the local magnetic field direction. This requires the magnetic precession rate to be $10$-$100$ times stronger than other directional interaction rates. If an alternative directional interaction is about as strong or stronger than the magnetic precession rate, then the symmetry axis of the quantum states is rotated. \n\nThe magnetic precession rate for a nonparamagnetic molecule is given by \n\\begin{align}\ng\\Omega = 4.8 g_{\\mathrm{mol}} \\left(\\frac{B}{\\mathrm{mG}} \\right) \\ \\mathrm{s}^{-1}, \n\\end{align} \nwhere $g_{\\mathrm{mol}}$ is the molecular g-factor: A dimensionless factor that determines the coupling of the molecule to the magnetic field. For linear molecules, $g_{\\mathrm{mol}}$ is the same for all rotational levels. The molecular g-factors of CO and HCO$^+$ that we consider in this work are $g^{\\mathrm{CO}} = -0.269$ \\citep{flygare:71} and $g^{\\mathrm{HCO}^+}=0.006$.\\footnote{\nWe computed the g-factor of HCO$^+$ using quantum chemical techniques since no experimental data are available. The quantum chemical calculations were performed at the CCSD(T) level of theory, using aug-cc-pVTZ basis sets, with the CFOUR program package \\citep{CFOUR}. We used a linear geometry of $r_{\\mathrm{CO}}=1.112$ \\AA and $r_{\\mathrm{CH}}=1.095$ \\AA. We note that the molecular g-factor of HCO$^+$ is anomalously low. Indeed, the only polarimetric observation of HCO$^+$ yielded no detection \\citep{glenn:97}. This could be an effect of weak Zeeman precession. However, one should not forget that HCO$^+$ has in fact a hyperfine structure, where each hyperfine-transition has its own g-factor \\citep[see, for instance,][]{lankhaar:18} that only averages to the rotational g-factor if all hyperfine-transitions have line-strengths proportional to the hyperfine-resolved Einstein A-coefficients.\n}\n\nWe compare the magnetic precession rate ($1$ mG and $1$ $\\mathrm{\\mu G}$) to the cumulative rate of stimulated emission in Figures \\ref{fig:sphere_rates} and \\ref{fig:pp_rates}. For the problems we considered, the magnetic precession rate is dominant over all other interactions and it is justified to assume that the quantum state symmetry axis is along the magnetic field direction.\n\nEarlier, we saw that HCO$^+$ had an exceptionally low magnetic moment. Conversely, the dipole moment of HCO$^+$ is very large. Thus radiative interactions for such a molecule are very strong, and therefore also a strong magnetic field is required to justify the dominant magnetic field approximation. Indeed, for a large region of the collapsing sphere, a $1 \\ \\mathrm{\\mu G}$ magnetic field would not determine the HCO$^+$ symmetry axis. We stress that for molecules that have strong radiative interactions, one should be extra vigilant and check the relevant interaction rates to verify that the magnetic field truly defines the symmetry axis of the quantum states and thus if the polarization vectors do indeed trace the magnetic field structure. \n\nIt is conceivable that a strong external radiation field that has a large angular size, such as a large stellar object, determines the quantum state symmetry axis. The directional rate of interaction of a general lower quantum state, $1$, by an external black-body radiation source at the solid angle $\\Delta \\Omega_*$ and with the temperature $T_*$ is \\citep{nedoluha:92,morris:85} \n\\begin{align}\nR_{12} = \\frac{g_2}{g_1} A_{21} \\left[e^{h\\nu_{21} \/ k_B T_*} - 1 \\right]^{-1} \\Delta \\Omega_*,\n\\end{align}\nwhere $g_i$ is the degeneracy of level $i$ and $A_{21}$ and $\\nu_{21}$ are the Einstein coefficient and frequency of the transition from upper level $2$ to lower level $1$. It is apparent from this expression that (sub)millimeter lines have relatively low interaction rates. Rather, vibrational transitions in the IR region have associated directional interaction rates that are far greater and are more likely to compete with magnetic interactions to determine the symmetry axis of the quantum states. For instance, the interaction rate of the $(v,J)$, $(0,0)\\to (1,1)$ transition of CO is $\\sim 7.8 \\ \\mathrm{s}^{-1}$ when it is excited by a $2000$ Kelvin black-body radiation source at $\\Delta \\Omega_* = 1$ sr. The rate drops quadratically with the distance to the external radiation source and it is not corrected for absorption. We implemented a module in PORTAL that can incorporate the interactions resulting from a bright external source of radiation through vibrational transitions. This is particularly important when investigating the circumstellar envelopes of evolved stars \\citep{morris:85, ramos:05}\n\nThe strong magnetic field approximation should be abandoned when multiple directional interactions have similar interaction rates. In that case, one must comprehensively model all anisotropies affecting the quantum state alignment. This can be done at the expense of a computational effort as it increases the dimensionality of the problem greatly. For example, in treating the first $41$ rotational levels of a linear rotor and by setting $k_{\\mathrm{max}}=6$, the dimensionality of the SEE would increase from $151$ to $1086$, provided that we neglect orientation elements of uneven $k$. The general theory of setting up the complete SEE can be found in Chapter 7 of \\citet{landi:06}. \n\\subsection{General remarks}\n\\label{sec:general_remarks}\n\\subsubsection{(Sub)millimeter line polarization in astrophysical regions}\nIt is clear from our calculations that the only requirement for the emergence of polarized emission is a source that has some form of anisotropy. This anisotropy may come from the velocity field, which has already been explored by \\citet{goldreich:81}, but it is not necessarily limited to this. To present the capabilities of PORTAL, we computed the emergence of polarized radiation in a protoplanetary disk and a collapsing sphere. In the protoplanetary disk, anisotropy mostly comes from the density structure. For the collapsing sphere, anisotropy comes from both the velocity-field and the density structure.\n\nFurthermore, we confirm the earlier observation of \\citet{goldreich:81}, which is that namely around optical depths of unity, the polarization of line emission is the strongest. The physical reason behind this is that for sources with some sort of anisotropy, around $\\tau \\sim 1,$ this anisotropy is most manifest in the local radiation field. Subsequently leading to the highest polarization degrees. \n\n\\subsubsection{Sampling} \nThe sampling of the space that we used is identical to the sampling used by LIME in which a random sampling, weighed by the density-structure, of the space is performed and neighboring cells are found through a Voronoi tessellation. We found that the extensive angular sampling that we performed to compute the local anisotropic radiation field generally requires a higher sampling of the space than would be necessary if one is generating a nonpolarized image. We found that for insufficient sampling of the space, strong local variation in the polarization fractions manifest themselves even though similar variations would not be visible in the total intensity. Also, local $90^o$ flips of the polarization vectors can be a product of sampling of the surrounding space that is too sparse. For a source with symmetry in both the magnetic field and the radiative transfer structure, it can be prudent to use symmetrical averages in the case of a sampling that is too sparse.\n\n\\subsubsection{Collisions} \nIn order for appreciable polarization in the emission from astrophysical regions to be produced, one requires the rate of (isotropic) collisions to be relatively low. When collisions occur more than $100$ times as frequent as the aligning absorption and stimulated emission events, no observable polarization is produced. Polarization is therefore not produced in regions of high number density and temperature. In general, regions that are in local thermal equilibrium show no appreciable polarization in their emission. \n\nIn the astrophysical problems that we analyzed, we represented collisions only by their rank-0 elements, that is, we assumed all magnetic substates to be equally pumped. At the same time, we assumed no depolarization through elastic collisions. The systematic errors of both assumptions are opposite. Such an approximation for the alignment characteristics of collisional rates is a common assumption in the modeling of alignment of quantum states \\citep{landi:06}. Indeed, collisional rates resolved at the level of magnetic substates are not readily available, even though it is possible to compute these using modern quantum-dynamical methods \\citep{alexander:79, faure:12, landi:06}. \n\n\n\\subsubsection{External radiation} \nWe found that an external source of directional radiation enhances the polarization appreciably. Similar conclusions have also been drawn in maser polarization theory \\citep{lankhaar:19} and also for the GK effect \\citep{deguchi:84, cortes:05}. In particular, \\citet{cortes:05} found that they could explain the $90^o$-flip in polarization angle between the CO $J=1-0$ and the $J=2-1$ transitions through the anisotropic radiation coming from an external source. We confirm that this is one possible explanation, but we stress that there are other avenues to attain such a polarization effect. According to our theory, this $90^o$-flip is most generally explained by the $\\eta_Q^{1-0}$ and the $\\eta_Q^{2-1}$ elements being of opposite signs. This does not necessarily require an external radiation source. \n\nIt should be emphasized that polarization enhancement through external radiation is most manifest when hot objects irradiate high-frequency transitions, such as vibrational lines. It is also the case for such transitions that are most likely to compromise the strong magnetic field approximation (see Section \\ref{sec:comp_align}). In this work, we have abstained from including higher vibrational states when computing the polarization maps, but we will further explore this when we use PORTAL in conjunction with more detailed models of astrophysical regions and the involved radiative processes. \n\n\\subsubsection{Alternative routes to polarization} \nDust emission is often observed to be partially polarized. This has been seen in protoplanetary disks \\citep{hull:17}, in circumstellar envelopes of evolved stars \\citep{vlemmings:17}, and molecular clouds \\citep{soler:13}. Polarized emission from dust follows from its alignment. Dust can get aligned to the magnetic field through the process of radiative torque alignment \\citep{draine:97}, but alignment to a strong external source of radiation \\citep{lazarian:07} or through self-scattering \\citep{kataoka:15} is also possible. \n\nThe dust polarization is indicative of the alignment and therefore does not always trace the (projected) magnetic field direction. Polarization fractions are observed to be up to a few percent. In PORTAL, we neglected the contribution of the dust polarization to the molecular state alignment because we used the anisotropic intensity approximation. In the ray-tracing step, we implemented the dust polarization module outlined in \\citet{padovani:12} and added it to the regular line polarization ray-tracing. We have found in the simulations we present here that the contribution of the dust polarization around the line-frequency is negligible because the line-opacity is some orders of magnitude greater than the dust opacity. This means that for strong enough magnetic fields (see Section \\ref{sec:comp_align}), line polarization faithfully traces the (projected) magnetic field direction with 90$^o$ ambiguity.\n\nRecently, it has been proposed that through forward scattering of radiation by a collective of molecules, a phase difference can be induced to the parallel and perpendicularly polarized components of the radiation field \\citep{houde:13}. The phase difference subsequently leads to a conversion of Stokes-U to Stokes-V radiation. This process, called anisotropic resonant scattering, would lead to the production of circular polarization at the cost of linear polarization, and it also changes the polarization angle. Observational evidence for this phenomenon is accruing \\citep{hezareh:13, chamma:18}. Anisotropic resonant scattering is typically thought to occur in a foreground cloud, between the observer and the source of polarized line emission \\citep{houde:13}, but it could also be a feature of the radiative transfer inside the source. A better estimate of the relative strength of anisotropic resonant scattering has to be developed before we can evaluate the importance of this effect on the emergence of linear polarization in thermal line emission. \n\n\\subsubsection{Ground state alignment.} \n\\citet{yan:06} showed that polarization can emerge in atomic (hyper)fine-structure lines through (i) a strong magnetic field that defines the symmetry axis and (ii) an external UV radiation field that induces directional transitions, aligning the quantum states. If the pumping rate is much lower than the spontaneous decay rates of the excited states, only the ground state of the atomic system is aligned. Collisions and stimulated emission events are neglected in the formalism of ground state alignment (GSA). Through neglecting collisions and stimulated emission events and adapting an idealized geometry, \\citet{yan:06} are able to formulate semianalytical expressions for the polarization fractions emerging from atomic lines. GSA has been proposed as a polarizing mechanism for atomic lines in the ISM \\citet{zhang:18}.\n\nPORTAL builds on the same theory as GSA, but it explicitly incorporates the effect of collisions and stimulated emission events. Furthermore, instead of assuming that a radiation field only comes from an external source, PORTAL maps out the full 3D radiation field structure of the medium in which the investigated species is embedded. In this work, we focus on the polarized radiative transfer of (sub)millimeter molecular and atomic lines because its radiative transfer does not involve any scattering \\citep{brinch:10}. We plan to extend our model to also incorporate the emergence of polarization in atomic fine-structure lines, where we will pay special attention to scattering in the radiative transfer of these systems.\n\n\\section{Conclusions}\nWe present PORTAL, a 3D polarized radiative transfer program that is adapted to lines. The program uses the strong magnetic field approximation and the anisotropic intensity approximation, both of which we show to hold for the majority of relevant astrophysical problems. PORTAL can be used in stand-alone mode using an LTE estimate of the molecular or atomic excitation. Alternatively, the output of existing 3D radiative transfer programs can be input in PORTAL. \n\nTo outline PORTAL's capabilities, we computed the polarization maps of a collapsing sphere and a simple protoplanetary disk model. The polarization spectrum of a collapsing sphere shows polarization in its spectral lines up to $2\\%$ with the associated polarization vectors aligned with the projected magnetic field direction. The protoplanetary disk when viewed face on shows polarization fractions up to $\\sim 0.5 \\%$, but the polarization fraction rises to $\\sim 9\\%$ at significant inclinations. The polarization vectors resulting from a radial and toroidal magnetic field configuration are identical for a face-on view of the protoplanetary disk, and they can only be distinguished when viewed at a significant inclination. In forthcoming papers, we plan to use PORTAL to analyze the emergence of polarization in spectral lines in more detailed models of protoplanetary disks, to a molecular outflow, and to the circumstellar envelopes of AGB stars.\n\n\\begin{acknowledgements} Support for this work was provided by the Swedish Research Council (VR). Simulations were performed on resources at the Chalmers Centre for Computational Science and Engineering (C3SE) provided by the Swedish National Infrastructure for Computing (SNIC). Ko-Yun Huang and Athol Kemball are acknowledged for sharing the results of their GK code. The authors thank Luis Velilla Prieto for helpful comments on a first draft of the manuscript. We thank the referee (Martin Houde) for comments that improved the paper.\n \\end{acknowledgements}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{When a star cluster meets a cloud}\n\\label{sec:1}\nWe study the encounters between stars clusters and giant molecular\nclouds (GMCs) \\cite{gieles06b}. The effect of these encounters has\npreviously been studied analytically for two cases: 1) head-on\nencounters, for which the cluster moves through the centre of the GMC\n\\cite{1987gady.book.....B} and 2) distant encounters, where the\nencounter distance $p>3\\mbox{$R_{\\rm n}$}$, with $p$ the encounter parameter and \\mbox{$R_{\\rm n}$}\\ the\nradius of the GMC \\cite{1958ApJ...127...17S}. We introduce an\nexpression for the energy gain of the cluster due to GMC encounters\nvalid for all values of $p$ and \\mbox{$R_{\\rm n}$}\\ of the form\n\n\\begin{equation}\n\\Delta E \\simeq \\frac{4.4\\,\\mbox{$r^2_{\\rm h}$}}{\\left(p^2+\\sqrt{\\mbox{$r_{\\rm h}$}\\,\\mbox{$R_{\\rm n}$}^{3}}\\right)^2}\\,\\left(\\frac{G\\mbox{$M_{\\rm n}$}}{\\mbox{$V_{\\rm max}$}}\\right)^2\\,M_c.\n\\label{eq:1}\n\\end{equation}\nHere \\mbox{$V_{\\rm max}$}\\ is the maximum relative velocity, $\\mbox{$M_{\\rm n}$}$ is the mass of the\nGMC, $G$ is the gravitational constant and \\mbox{$r_{\\rm h}$}\\ and \\mbox{$M_{\\rm c}$}\\ are the\nhalf-mass radius and mass of the cluster, respectively. We perform\n$N$-body simulations of encounters with different $p$ and compare the\nresulting ${\\rm \\Delta} E$ of the cluster to\nEq.~\\ref{eq:1}. Fig.~\\ref{fig:1} shows the very good agreement between\nsimulations and predictions of Eq.~1. Snapshots of one simulation are\nshown in Fig.~2.\n\n\\begin{figure*}\n\\centering\n\\includegraphics[height=3.8cm]{de_p.ps}\n\\caption{\\mbox{${\\rm \\Delta} E\/|E_0|$}\\ of a cluster as a function of $p$. The $N$-body results\n are shown with diamonds. The result of \\cite{1987gady.book.....B}\n and \\cite{1958ApJ...127...17S} for head-on and distant encounters\n are shown as a filled circle and as a dashed line,\n respectively. Eq.~\\ref{eq:1} is shown as a full line.}\n\\label{fig:1} \n\\end{figure*}\n\n\\begin{figure*}\n\\centering\n\\includegraphics[height=2.35cm]{snap_distant_5frames.ps}\n\\caption{Simulation of a close encounter between a GMC (grey shades)\nand a star cluster. The snapshots are viewed in the centre-of-mass\nframe of the cluster. \\mbox{$a_{\\rm c}$}\\ is the Plummer radius of the cluster.}\n\\label{fig:2} \n\\end{figure*}\n\\vspace{-0.8cm}\n\\section{The cluster disruption time}\nFrom the simulations we find that the fractional mass loss ($\\mbox{${\\rm \\Delta} M\/|M_0|$}$) is only\n25\\% of \\mbox{${\\rm \\Delta} E\/|E_0|$}. This is because stars escape with velocities much higher\nthan the escape velocity. Defining the cluster disruption time as\n$\\mbox{$t_{\\rm dis}$}=\\mbox{$M_{\\rm c}$}\/\\dot{\\mbox{$M_{\\rm c}$}}$, we find a cluster disruption time of the form\n\n\\begin{equation}\n\\mbox{$t_{\\rm dis}$} = 2.0\\,S\\left(\\mbox{$M_{\\rm c}$}\/10^4\\,\\mbox{$M_{\\odot}$}\\right)^{\\gamma}{\\mbox{Gyr}},\n\\label{eq:2}\n\\end{equation}\nwith $S\\equiv1$ for the solar neighbourhood and scales with the global\n GMC density ($\\mbox{$\\rho_{\\rm n}$}$) as $S\\propto\\mbox{$\\rho_{\\rm n}$}^{-1}$. The index $\\gamma$ is\n defined as $\\gamma=1-3\\lambda$, with $\\lambda$ the index that relates\n the cluster half-mass radius to its mass ($\\mbox{$r_{\\rm h}$} \\propto\n \\mbox{$M_{\\rm c}$}^{\\lambda}$). The observed shallow relation between cluster\n radius and mass (e.g. $\\lambda\\simeq0.1$), makes the index\n ($\\gamma=0.7$) similar to the index found both from observations\n \\cite{2005A&A...441..117L} and from simulations of clusters\n dissolving in tidal fields ($\\gamma\\simeq0.62$). The constant of 2.0\n Gyr, which is the disruption time of a $10^4\\,\\mbox{$M_{\\odot}$}$ cluster in the\n solar neighbourhood, is about a factor of 3.5 shorter than found from\n earlier simulations of clusters dissolving under the combined effect\n of the galactic tidal field and stellar evolution. It is only slightly\n higher than the observationally determined value of 1.3 Gyr\n \\cite{2005A&A...441..117L}, suggesting that the combined effect of\n tidal field and encounters with GMCs can explain the lack of old open\n clusters in the solar neighbourhood \\cite{1958RA......5..507O}. GMC\n encounters can also explain the (very) short disruption time that was\n observed for star clusters in the central region of M51\n \\cite{2005A&A...441..949G}, since there $\\mbox{$\\rho_{\\rm n}$}$ is an order of\n magnitude higher than in the solar neighbourhood.\n\n\t \n\\vspace{-0.3cm}\n\\input{referenc}\n\n\n\\printindex\n\\end{document}\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\\subsection{The Ever-Increasing Threat of Cyber Crimes}\nIn recent years, the risk of cyber crimes has emerged as a top global concern. \nThis is made evident by the world economic forum's annual global risk report \\citep{wef2020}, which regularly places cyber attacks and theft of data in its ``Top 5 global risks in terms of likelihood''.\nOver the years, the frequency and severity of cyber attacks have increased significantly globally, and they are projected to continue to increase in the future.\nRecently, Cybersecurity Ventures estimated the cost of cyber crimes to rise to 10.5 trillion USD annually by 2025 \\citep{morgan2020cybercrimecost}, up from a world economic forum estimate of 3 trillion USD for 2015. \nFurthermore, see a detailed descriptive analysis of cyber crimes in the business sector over the last 15 years in \\citep{shevchenko2022nature}.\n\nCyber crime can be initiated by various actors, including malicious actors within institutions and external adversaries such as rogue nation states, hackers, and cyber terrorists.\nThese attacks come in a variety of forms, ranging from denial-of-service (DoS) attacks \\citep{gupta2017taxonomy}, malware \\citep{tailor2017comprehensive}, ransomware \\citep{tailor2017comprehensive}, blackmail \\citep{rid2012cyber}, extortion \\citep{young1996cryptovirology}, and more \\citep{craigen2014defining, husak2018survey}.\nSuch criminal activities are being perpetrated on a massive scale, hitting individuals as well as organisations. A large array of organisations worldwide are targeted by cyber attacks, including government agencies, universities, financial sectors, and private corporations. \nIn particular, these attacks can affect important infrastructure units that play a key role in security and safety, such as emergency services and health care.\nSuch attacks have caused breaches and significant damages to those organisations, and often adversely affect downstream users of the compromised organisations and services.\nThe damages incurred include losses attributed to outcomes such as business interruption, loss of data, reduced reputation and trust of the organisation, legal liabilities, intellectual property theft, and potential for loss of life. \n\nThe seriousness of cyber attacks has been reflected in the {U.S.} President's executive order on \\textit{Strengthening the Cybersecurity of Federal Networks and Critical Infrastructure}, which calls for a cybersecurity framework that can ``support the cybersecurity risk management efforts of the owners and operators of the Nation's critical infrastructure\".\nCyber risk from a financial and insurance perspective has also been developed under international banking and insurance regulations. \nThe Basel~III banking Accords cover cyber risk as a key component of Operational Risk capital modelling and adequacy, and the Solvency~II insurance regulations discuss the significance of an emerging cyber insurance threat that affects insurers as well as reinsurers.\nFor example, see an overview of cyber risk from a financial and insurance perspective in \\cite{peters2018understanding, malavasi2022cyber}.\n\n \n\\subsection{Lack of Product Standardisation in the Cyber Risk Insurance Market} \nAlthough many security solutions have been developed and implemented in order to detect and prevent cyber attacks, achieving a complete security protection is not feasible \\citep{lu2018managing}.\nTo address this problem, there is an increasing demand to develop the market for cyber risk insurance and to understand the structuring of insurance products that will facilitate risk transfer strategies in the context of cyber risk; see discussions in \\cite{peters2018understanding, peters2017statistical, marotta2017cyber, bohme2010modeling} and the references therein. \n\n\nAs one can see from surveys such as \\cite{marotta2017cyber} and \\cite{shetty2010competitive}, the scope of such a marketplace is still very much in its infancy, despite the fact that financial institutions rank cyber losses in their top three loss events systematically when reporting Operational Risk loss events under Basel II\/III to national regulators. \nThe reason that the cyber risk insurance market has yet to emerge with standardised products is largely due to differences in opinion as how best to mitigate and reserve against these loss events. \nFrom a technology perspective, it is common to attempt to mitigate such events in contrast to insurance or capital reserving; see discussions in \\cite{bandyopadhyay2009managers}. \nFrom a financial risk perspective, practitioners often opt for Tier I capital reserving and avoid insurance products, as the capital reduction from Operational Risk insurance is capped under Basel regulations with a haircut of 20\\%; see discussions in \\cite{peters2011impact}. This disincentivises them to purchase insurance products that have excessive premiums. \nFrom the insurance industry's perspective, there is a lack of market standardisation on insurance contract specifications that would avoid excessive premiums to be charged when the bespoke insurance products are designed. \n\n\n \n\\subsection{Our Contributions}\n\nThe three aforementioned perspectives are beginning to change and we believe that it is a suitable time to revisit the perennial question of how best to set up an insurance marketplace for cyber loss events. \nIn this paper, we introduce for the first time a Bonus-Malus framework for cyber risk insurance that provides IT-specific incentive mechanisms for encouraging sound IT governance and technology developments. \nSpecifically, we explore a class of Bonus-Malus systems in which an insured enjoys a discount in the cost of risk transfer as a result of their upfront expenses in risk reduction in the form of self-mitigation measures; and like-wise an insurer benefits from encouraging risk reduction in their risk pools to provide competitive pricing of insurance premium. \nUnder this framework, we also demonstrate how to develop loss models and decision models under uncertainty.\n\nTo illustrate this framework and the associated decision problems, let us consider an organisation which provides a service that is exposed to the threat of distributed denial-of-service (DDoS) attacks.\nSuch an organisation may opt to use network traffic filtering as well as a content distribution network, and may use multiple servers to balance the network load. \nAs such, there are various countermeasures for mitigating the threats of DDoS attacks and ensuring the availability of the service, but each of such self-mitigation measures adds to the upfront costs of risk reduction. \nTherefore, the organisation needs to determine the quantum of its security (i.e., risk reduction) budget and distribute this across these self-mitigation measures. We argue that this budget for risk reduction needs to and can be determined in tandem with risk transfer decisions, i.e., the purchase of cyber risk insurance.\nMoreover, a cyber risk insurer can incorporate incentive mechanisms in their insurance policies to encourage such risk reduction provision through offsetting the expenses of risk reduction by a discount in the pricing of the insurance premium.\nThis highlights the need for a comprehensive framework in which losses incurred by cyber threats can be realistically modelled and the rational decisions of organisations in the face of cyber threats can be quantitatively analysed.\n\n\n\nOur main contributions are as follows: \n\\begin{enumerate}\n \\item We introduce the Bonus-Malus system to cyber risk insurance as a mechanism to provide incentive for the insured to adopt self-mitigation measures against cyber risk. \n\\item We develop a mathematical model of cyber losses and cyber risk insurance, and subsequently analyse the optimal cybersecurity provisioning process of the insured under the stochastic optimal control framework. \n\\item We develop an efficient algorithm based on dynamic programming to accurately solve the stochastic optimal control problem, under the assumption that the loss severity follows a truncated version of the g-and-h distribution. We also formally prove the correctness of the proposed algorithm. \n\\item We demonstrate through a numerical experiment that a properly designed cyber risk insurance contract with a Bonus-Malus system can resolve the issue of moral hazard, and can provide benefits for the insurer.\n\\end{enumerate}\n\nThe rest of the paper is organised as follows. \nSection~\\ref{sec:relatedwork} discusses related studies in the literature.\nIn Section~\\ref{sec:model}, we introduce the mathematical model of cyber losses and cyber risk insurance with a Bonus-Malus system. In Section~\\ref{sec:stocoptctr}, we present the optimal cybersecurity provisioning process and the dynamic programming algorithm. In Section~\\ref{sec:g-and-h}, we introduce the g-and-h distribution and use it as the model for loss severity. We present results from the numerical experiment in Section~\\ref{sec:exp}. Finally, Section~\\ref{sec:conclusion} concludes the paper. \n\n\\section{Related Work}\n\\label{sec:relatedwork}\nRecently, many studies analysed cyber risk insurance from a technology perspective. \nCybersecurity frameworks involving cyber risk insurance have been developed for specific IT systems, \nincluding computer networks \\citep{fahrenwaldt2018pricing,xu2019cybersecurity}, \nheterogeneous wireless network \\citep{lu2018cyber}, wireless cellular network \\citep{lu2018managing}, plug-in electric vehicles \\citep{hoang2017charging}, cloud computing \\citep{chase2017scalable}, and fog computing \\citep{feng2018evolving}. \n\nSome studies considered the interplay between self-mitigation measures (i.e., risk reduction) and cyber risk insurance, e.g., \\cite{pal2010analyzing,pal2014will,pal2017security,khalili2018designing,dou2020insurance}. \nThese studies investigated two important challenges in cyber risk insurance: risk interdependence and moral hazard. They found that in order to incentivise the insured to invest in self-mitigation measures, some form of contract discrimination, i.e., adjusting the insurance premium based on the insured's security investment, is necessary. \n\\cite{yang2014security,schwartz2014cyber,zhang2017bi} investigated these challenges in a networked environment, where cyber attacks can spread between neighbouring nodes, further complicating these challenges.\n\nThere are also studies which took the insured's perspective and analysed the security provisioning process using dynamic models. \n\\cite{chase2017scalable} developed a framework based on stochastic optimisation to jointly provision cyber risk insurance and cloud-based security services across multiple time periods in cloud computing applications. \n\\cite{zhang2018optimal} modelled the decisions on self-protections of the insured by a Markov decision process and investigated the problem of insurance contract design.\n\nA critical drawback of many of the existing studies is that they neglected the highly uncertain nature of losses incurred by cyber incidents. These studies relied on over-simplified assumptions, e.g., by modelling cyber loss as: a fixed amount \\citep{pal2010analyzing,pal2014will,yang2014security,hoang2017charging,feng2018evolving,dou2020insurance}, random with finite support \\citep{chase2017scalable,zhang2018optimal,lu2018managing}, or random with a simple parametric distribution \\citep{zhang2017bi,khalili2018designing}. These assumptions limit the practicality of these studies, and their results remain conceptual and non-applicable to realistic insurance loss modelling under a classical Loss Distribution Approach (LDA) framework \\citep{moscadelli2004modelling, peters2006bayesian, maillart2010heavy, shevchenko2013loss, eling2019actual, zeller2022comprehensive}.\nLDA is one of the most common modelling methods in the Advanced Measurement Approach (AMA) under the Basel II Accords. \nUnder the LDA framework, the probability distributions of loss severity (i.e., the impact of a single loss event) and annual loss frequency are modelled and estimated separately. The aggregate annual loss is thus modelled by a compound distribution. \nFor detailed discussions about LDA and a comparison with the Internal Measurement Approach, see \\citep{frachot2001loss} and the references therein.\nIn addition, see \\citep{dutta2006tale} for an evaluation of specific distributional assumptions in LDA and their impacts on the estimation of Operational Risk capital. \nIn our study about the design of cyber risk insurance contracts, we have built LDA into our cyber loss model and we have also incorporated a quantitative model for the effects of self-mitigation measures on cyber losses (see Section~\\ref{ssec:lossmodel} and Section~\\ref{ssec:mitigation}).\n\nMoreover, many of the aforementioned studies do not take into account the interplay between the upfront costs of risk prevention, the consequent reduced risks, and the possibility to exploit this interplay to design practical cyber risk insurance products.\nA review of cyber insurance product prospectus by major insurers, such as AIG's CyberEgde\\footnote{\\url{https:\/\/www.aig.com\/business\/insurance\/cyber-insurance\/}, accessed on 2020-12-10}, Allianz's Cyber Protect\\footnote{\\url{https:\/\/www.agcs.allianz.com\/solutions\/financial-lines-insurance\/cyber-insurance.html}, accessed on 2020-12-10}, and Chubb's Cyber Enterprise Risk Management (Cyber ERM)\\footnote{\\url{https:\/\/www.chubb.com\/us-en\/business-insurance\/cyber-enterprise-risk-management-cyber-erm.html}, \\linebreak accessed on 2020-12-10} also indicates that the insurance products in the market have yet to explicitly factor in the benefits of upfront risk reduction, or to offset those costs against that of risk transfer.\nFor the first time, we introduce the Bonus-Malus system which is frequently used in vehicle insurance products to address this gap in cyber risk insurance product design. \nIn vehicle insurance, Bonus-Malus systems are experience rating systems in which an insured who had one or more accidents is penalised by premium surcharges or \\textit{maluses} and an insured who had a claim-free year is rewarded with premium discounts or \\textit{bonuses} \\citep{lemaire1995bonus, neuhaus1988bonus}; see, e.g., \\citep{baione2002development, ragulina2017bonus, gomez2018multivariate, tzougas2018bonus} for discussions about the design and analysis of Bonus-Malus systems.\nA key characteristic of Bonus-Malus systems is the bonus hunger mechanism \\citep{holtan2001optimal, charpentier2017optimal, tzougas2018bonus}, i.e., under a Bonus-Malus system, an insured is willing to carry small losses themself in order to avoid premium surcharges in the future.\nOur optimal cybersecurity provisioning model in Section~\\ref{sec:stocoptctr} captures this aspect of Bonus-Malus systems by allowing the insured to decide whether to make a claim at the end of each policy year.\n\n\\section{Cyber Risk Insurance Policy and Bonus-Malus System}\n\\label{sec:model}\n\n\nLet us first present an overview of our cyber risk insurance model.\nTo begin, let us specify the frequency and severity model under the Loss Distribution Approach (LDA) that defines the financial loss process resulted from cyber loss events.\nWe consider $T\\in\\N$ consecutive years, and we assume that throughout each year $t$, the insured may suffer a random number ($N_t \\in \\N$) of cyber loss events arising from cyber attacks. Their loss amounts are denoted by $X^{(t)}_1,\\ldots,X^{(t)}_{N_t}$. The aggregate annual cyber loss in year $t$ is therefore $\\sum_{k=1}^{N_t}X^{(t)}_{k}$. \nThe insured has several choices to attempt to mitigate these cyber loss events and reduce the risk, including enhancing the security and resilience of their IT infrastructure and reserving Tier~I capital to cover the incurred losses.\nIn regards to the internal IT infrastructure, it will be assumed that the insured has the option to adopt a self-mitigation measure that can reduce the severity of cyber loss events up to a fixed amount of loss. \nIn addition, the insured can choose to purchase a cyber risk insurance policy which gives the insured the right to claim the aggregate cyber loss incurred, up to a maximum cap imposed by the insurance contract, in an agreed interval of time (typically annually), minus a deductible. \nPlease refer to Table~\\ref{tab:model-notations} for notations used in the cyber risk insurance model.\n\n\\begin{table}\n\\begin{center}\n\\caption{Notations in the cyber risk insurance model}\n\\label{tab:model-notations}\n\\vspace{0.5em}\n\\begin{tabular}{ll}\n$T$ & number of policy years \\\\\n$N_t$ & number of cyber loss events in year $t$ \\\\\n$X^{(t)}_1,\\ldots,X^{(t)}_{N_t}$ & loss amounts of the cyber loss events in year $t$ \\\\\n$\\mathcal{W}$ & space representing annual loss frequency and severities \\\\ \n$(\\Omega,\\mathcal{F}_T,\\PROB,(\\mathcal{F}_t)_{t=0:T})$ & filtered probability space \\\\ \n$W_t$ & loss frequency and severities in year $t$ \\\\\n$\\psi_N(\\cdot)$ & probability generating function of the loss frequency \\\\ \n$F_X(\\cdot)$ & distribution function of the loss severity \\\\ \n$\\mathcal{D}$ & set representing self-mitigation measures \\\\\n$\\beta(d)$ & annual investment required by the self-mitigation measure $d\\in\\mathcal{D}$ \\\\\n$\\gamma(d)$ & loss reduction effect of the self-mitigation measure $d\\in\\mathcal{D}$ \\\\\n$L(d,w)$ & aggregate annual cyber loss with the self-mitigation measure $d\\in\\mathcal{D}$ \\\\\n$\\delta_{\\mathrm{i}\\mathrm{n}}(t)$ & initial sign-on fee of cyber risk insurance in year $t$ \\\\\n$\\delta_{\\mathrm{o}\\mathrm{u}\\mathrm{t}}(t)$ & withdrawal penalty of cyber risk insurance in year $t$ \\\\\n$\\delta_{\\mathrm{r}\\mathrm{e}}$ & re-activation penalty of cyber risk insurance \\\\\n$\\mathcal{B}$ & set representing Bonus-Malus levels \\\\\n$\\mathcal{I}$ & set representing states of the cyber risk insurance contract \\\\\n$\\mathcal{B}\\mathcal{M}(\\cdot,\\,\\cdot)$ & transition rules of the Bonus-Malus system \\\\\n$\\mathcal{B}\\mathcal{M}_0(\\cdot,\\,\\cdot)$ & transition rules of the Bonus-Malus system for inactive contract \\\\\n$p^{\\mathcal{B}\\mathcal{M}}(b,t)$ & insurance premium in Bonus-Malus level $b$ in year $t$ \\\\\n$l^{\\mathcal{B}\\mathcal{M}}_{\\mathrm{d}\\mathrm{t}\\mathrm{b}}(b,t)$ & insurance deductible in Bonus-Malus level $b$ in year $t$ \\\\\n$l^{\\mathcal{B}\\mathcal{M}}_{\\max}(b,t)$ & maximum insurance compensation in Bonus-Malus level $b$ in year $t$ \\\\\n$\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,l)$ & insurance compensation in Bonus-Malus level $b$ in year $t$ with loss $l$\n\\end{tabular}\n\\end{center}\n\\end{table}\n\n\n\\subsection{Cyber Loss Model}\n\\label{ssec:lossmodel}\n\nLet us define $\\mathcal{W}:=\\bigcup_{n\\in\\Z_+}\\{n\\}\\times\\R^n_+$ to be the space representing all possible combinations of realisations of the number of cyber loss events per year (frequency) and individual loss amounts per event (severities).\nLet $\\mathfrak{B}(\\mathcal{W}):=\\sigma\\big(\\bigcup_{n\\in\\Z_+}\\{(n,B):B\\in\\mathfrak{B}(\\R^n_+)\\}\\big)$ be a $\\sigma$-algebra on $\\mathcal{W}$, where $\\mathfrak{B}(\\R^n_+)$ denotes the Borel subsets of $\\R^n_+$. \nLet us consider the space $\\Omega:=(\\mathcal{W})^T=\\underbrace{\\mathcal{W}\\times\\cdots\\times\\mathcal{W}}_{T \\text{ times}}$. For each $\\omega=(w_1,\\ldots,w_T)=\\big(\\big(n_1,x^{(1)}_1,\\ldots,x^{(1)}_{n_1}\\big),\\ldots,$ $\\big(n_T,x^{(T)}_1,\\ldots,x^{(T)}_{n_T}\\big)\\big)\\in\\Omega$, we define $W_t(\\omega):=w_t$ and $N_t(\\omega):=n_t$ for $t=1,\\ldots,T$. \nLet $\\PROB_1$ be a probability measure on $(\\mathcal{W},\\mathfrak{B}(\\mathcal{W}))$, where the subscript ``1'' indicates that it is a probability measure for the cyber loss events occurring in a single year.\nLet $\\PROB:=\\underbrace{\\PROB_1\\otimes\\cdots\\otimes\\PROB_1}_{T\\text{ times}}$, and let $(\\mathcal{F}_t)_{t=0:T}$ be a filtration on $\\Omega$, defined by \n$\\mathcal{F}_0:=\\{\\emptyset,\\Omega\\}$, $\\mathcal{F}_t:=\\sigma((W_s)_{s=1:t})$. Then, $(\\Omega,\\mathcal{F}_{T},\\PROB,(\\mathcal{F}_t)_{t=0:T})$ is a filtered probability space. Under these definitions, $W_1,\\ldots,W_T$ are independently and identically distributed ({i.i.d.})\\ random variables. \nLet $\\psi_{N}(s):=\\EXP[s^{N_1}]$ denote the probability generating function (pgf) of the loss frequency distribution. We assume that for $t=1,\\ldots,T$, \n\\begin{align}\n\\begin{split}\n&\\phantom{=}\\;\\;\\PROB\\Big[\\Big\\{\\omega=\\big(\\big(n_1,x^{(1)}_1,\\ldots,x^{(1)}_{n_1}\\big),\\ldots,\\big(n_T,x^{(T)}_1,\\ldots,x^{(T)}_{n_T}\\big)\\big):n_t=n,\\;x^{(t)}_k\\le z_k,\\forall 1\\le k\\le n\\Big\\}\\Big]\\\\\n&=\\PROB[N_t=n]\\prod_{k=1}^nF_X(z_k),\n\\end{split}\n\\end{align}\nwhere $F_X(\\cdot)$ is the distribution function of the severity distribution. This implies that given the loss frequency in a year, the individual loss amounts in that year are i.i.d. We assume that the severity distribution has finite expectation, i.e., $\\int_{\\R_+}|x|\\DIFFM{F_X}{\\DIFF x}<\\infty$. \nFor convenience, we write $W_t=\\big(N_t,X^{(t)}_1,\\ldots,X^{(t)}_{N_t}\\big)$. \nWe use $W$ to denote a random variable that has the same distribution as $W_1,\\ldots,W_T$. \nSimilarly, we use $N$ to denote a random variable that has the same distribution as $N_1,\\ldots,N_T$, and we use $X$ to denote a random variable that has the distribution function $F_X$. \n\n\\subsection{Self-Mitigation Measures}\n\\label{ssec:mitigation}\n\nLet us assume that there exists $D\\in\\N$ different self-mitigation measures, and the insured makes the decision to either adopt one of the self-mitigation measures or to not adopt any self-mitigation measure at the beginning of each year. The self-mitigation measure $d\\in\\mathcal{D}:=\\{0,1,\\ldots,D\\}$ requires an annual investment of $\\beta(d)\\in\\R_+$ per year, and reduces the severity of each cyber loss by up to $\\gamma(d)\\in\\R_+$, that is, the severity of a loss will be decreased from $X$ to\\footnote{Throughout the paper, we use the following notations: $(x)^+:=\\max\\{x,0\\}$, $x\\vee y:=\\max\\{x,y\\}$ and $x\\wedge y:=\\min\\{x,y\\}$.} $\\left(X-\\gamma(d)\\right)^+$ with the adoption of the self-mitigation measure $d$. We assume that $\\beta(0)=\\gamma(0)=0$. \nThus, if the insured decides to adopt the self-mitigation measure $d$ in a year, then the total loss suffered by the insured that year is given by\n\\begin{align}\nL(d,w):=\\sum_{k=1}^{n}\\left(x_k-\\gamma(d)\\right)^+,\n\\label{eqn:yearlyloss}\n\\end{align}\nwhen the corresponding loss frequency and severities that year are $w=(n,x_1,\\ldots,x_n)\\in\\mathcal{W}$.\n\n\n\\subsection{Cyber Risk Insurance Policy}\n\\label{ssec:cyberinsurance}\nLet us now consider a cyber risk insurance contract that lasts for $T$ years. \nAt the beginning of each year, the insured decides whether to activate the contract. In the case that the contract has been activated in a previous year, this corresponds to the insured deciding whether to continue the contract. If the contract is activated, the insured pays a premium to the insurer at the start of the year, in exchange for insurance coverage throughout the year. If the insured decides to withdraw from the contract, they no longer pays the premium and the contract is deactivated so that the insured receives no coverage. \nWe further assume that the insured pays the insurer an initial sign-on fee for fixed costs and contract origination the first time a contract is initiated, in addition to the premium, and that this amount varies deterministically over time and will be denoted by $\\delta_{\\mathrm{i}\\mathrm{n}}(t)\\ge 0$ in year $t$. This can be used to incentivise the insured to activate the contract early. \nFurthermore, we also assume that the insured pays the insurer a deterministic and time-dependent penalty, denoted by $\\delta_{\\mathrm{o}\\mathrm{u}\\mathrm{t}}(t)\\ge0$, when they withdraws from the contract in year $t$. Once withdrawn, the insured may re-activate the contract in a later year with a fixed penalty $\\delta_{\\mathrm{r}\\mathrm{e}}\\ge0$. \n\nSuppose that the aggregate cyber loss suffered by the insured in a year is $L$. At the end of the year, the insured decides whether to make a claim to the insurer. Once the claim is processed, the insured receives a payment of $(L-l_{\\mathrm{d}\\mathrm{t}\\mathrm{b}})^+\\wedge l_{\\max}$ from the insurer as compensation, that is, the insured covers the loss up to the deductible $l_{\\mathrm{d}\\mathrm{t}\\mathrm{b}}\\ge 0$, and the insurer covers all of the remaining loss up to the maximum compensation $l_{\\max}\\ge0$. \n\n\\subsection{Bonus-Malus System}\n\\label{ssec:bonusmalus}\n\nWe now introduce the Bonus-Malus system to cyber risk insurance contracts. Let us assume that there are $B\\in\\N$ Bonus-Malus levels in the contract, denoted by $\\mathcal{B}:=\\{-\\underline{B},\\ldots,-1,0,1,\\ldots,\\overline{B}\\}$, where $\\underline{B}+\\overline{B}+1=B$. \nHere, the level $0$ is the initial Bonus-Malus level, and the lower the Bonus-Malus level, the higher the experience rating.\nConsequently, a negative Bonus-Malus level grants the insured premium discounts, while a positive Bonus-Malus level penalises the insured by premium surcharges.\nAt $t=0$, the insured starts in the initial Bonus-Malus level, denoted by $b_0=0$. At the end of the $t$-th year, given that the contract is still active, the insurer determines the Bonus-Malus level of the insured based on their previous level $b_{t-1}$ and the amount of insurance claim $C_t$ that was given out to the insured in the $t$-th year, that is, $b_{t}=\\mathcal{B}\\mathcal{M}(b_{t-1},C_t)$, where $\\mathcal{B}\\mathcal{M}:\\mathcal{B}\\times\\R_+\\to\\mathcal{B}$ denotes the deterministic rules that are transparent to the insured at the signing of the contract. We make the assumption that $\\mathcal{B}\\mathcal{M}(b,C)$ is non-decreasing in $C$ for each $b\\in\\mathcal{B}$. \nEven when the insured has withdrawn from the contract, we assume that their Bonus-Malus level is still updated annually. \nConcretely, let us define $\\mathcal{I}:=\\{\\mathrm{n}\\mathrm{o},\\mathrm{o}\\mathrm{n},\\mathrm{o}\\mathrm{f}\\Tf_1,\\ldots,\\mathrm{o}\\mathrm{f}\\Tf_{T}\\}$ as the set of all possible states of the cyber risk insurance contract. In $\\mathcal{I}$, ``$\\mathrm{n}\\mathrm{o}$'' denotes that the contract has not been signed yet, ``$\\mathrm{o}\\mathrm{n}$'' denotes that the contract is active, and ``$\\mathrm{o}\\mathrm{f}\\Tf_y$'' denotes that the contract is withdrawn where $y\\in\\Z_+$ is a counter variable that is updated annually as long as the insured does not re-activate the contract. \nLet $\\mathcal{B}\\mathcal{M}_{0}:\\mathcal{B}\\times\\mathcal{I}\\to\\mathcal{B}\\times\\mathcal{I}$ be a deterministic transition function that represents the update rules after the insured withdraws from the contract. At the end of the $t$-th year, given that the contract is inactive, the insurer determines the Bonus-Malus level $b_t$ and the insurance state $i_t$ of the insured based on their Bonus-Malus level and the insurance state in the previous year, that is, $(b_t,i_t)=\\mathcal{B}\\mathcal{M}_0(b_{t-1},i_{t-1})$. Since no such update is possible before the insured activates the contract, it is required that $\\mathcal{B}\\mathcal{M}_{0}(b,\\mathrm{n}\\mathrm{o})=(b,\\mathrm{n}\\mathrm{o})$ for all $b\\in\\mathcal{B}$. We will formally model the evolution of $(b_t,i_t)_{t=0:T}$ by a controlled stochastic process in Section~\\ref{ssec:provisionprocess}.\n\nWith the addition of the Bonus-Malus system to the cyber risk insurance contract, we assume that the premium depends on both time and the current Bonus-Malus level of the insured, and is given by $p^{\\mathcal{B}\\mathcal{M}}(b,t)$, where $p^{\\mathcal{B}\\mathcal{M}}:\\mathcal{B}\\times\\{1,\\ldots,T\\}\\to\\R_+$ is a deterministic function that is increasing in the first argument. The deductible and the maximum compensation are also assumed to be dependent on both time and the Bonus-Malus level, and are given by deterministic functions $l_{\\mathrm{d}\\mathrm{t}\\mathrm{b}}^{\\mathcal{B}\\mathcal{M}}:\\mathcal{B}\\times\\{1,\\ldots,T\\}\\to\\R_+$ and $l_{\\max}^{\\mathcal{B}\\mathcal{M}}:\\mathcal{B}\\times\\{1,\\ldots,T\\}\\to\\R_+$. We define the function $\\lambda^{\\mathcal{B}\\mathcal{M}}:\\mathcal{B}\\times\\{1,\\ldots,T\\}\\times\\R_+\\to\\R_+$ by\n\\begin{align}\n\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,l):=(l-l_{\\mathrm{d}\\mathrm{t}\\mathrm{b}}^{\\mathcal{B}\\mathcal{M}}(b,t))^+\\wedge l_{\\max}^{\\mathcal{B}\\mathcal{M}}(b,t)\n\\end{align}\nto simplify the notation for the claimable loss from the insurer. \n\n\\begin{remark}\nOur loss model and insurance model are particularly suitable for analysing cyber risk insurance, due to the nature of cyber loss events.\nFirstly, the compound loss model in LDA is vital for capturing the low frequency and high impact nature of certain cyber loss events \\citep{maillart2010heavy, biener2015insurability, eling2017data, eling2015modelling, eling2019actual}. \nIn particular, this loss model allows us to use the highly flexible g-and-h distribution for the loss severity, in order to capture the right-skewness and the heavy-tail of cyber losses.\nThis provides adequate modelling flexibility so that one can tailor this loss model, particularly the skewness and the tail-index of the loss distribution, according to specific kinds of cyber threats and different business lines. \nWe will discuss the details of the g-and-h distribution in Section~\\ref{sec:g-and-h}.\nSecondly, as studies such as \\citep{oughton2019stochastic, armenia2021dynamic} have discussed, despite that complete mitigation of cyber risk is impractical, prevention of crippling damages from cyber attacks, which often include secondary damages to downstream customers, can be achieved with relatively low cybersecurity expenses. \nFor example, countermeasures such as the adoption of two-factor authentication and the regular update or reconfiguration of the software system \\citep{pate2018cyber} can often result in significant reduction of the risk of intrusion. \nMoreover, the adoption of self-mitigation measures generates positive externalities since it improves the overall security of the cyberspace. \nDue to these factors, there is a strong motive for cyber risk insurers to offer incentive mechanisms in their insurance products to encourage such upfront risk reduction effort and to alleviate the problem of moral hazard.\nOverall, the combination of this flexible loss model and the incentive mechanisms built into the Bonus-Malus system is novel in the context of cyber risk insurance. \nOn the other hand, the i.i.d.\\ assumption on the individual loss amounts in our model means that it does not account for the systemic aspect of cyber risk.\nWe would also like to remark that the general specifications of our cyber risk insurance model make it also applicable to other idiosyncratic risk types which can be partially prevented by self-mitigation measures. \nDespite that, our analyses and discussions about our model will be carried out in the context of cyber risk, and we will not generalise our model to other risk types in order not to obscure the main objective of this paper.\n\\label{rmk:cyber-specificity}\n\\end{remark}\n\n\n\\section{Optimal Cybersecurity Provisioning and Stochastic Optimal Control}\n\\label{sec:stocoptctr}\n\n\\subsection{Cybersecurity Provisioning Process}\n\\label{ssec:provisionprocess}\nNow, having introduced the model for cyber losses and cyber risk insurance, we consider the problem of optimal cybersecurity provisioning from the insured's point of view. \nIt is assumed that the cybersecurity provisioning process takes place for $T$ consecutive years (same as the length of the cyber risk insurance contract).\nBefore the first year, the state of the cyber risk insurance contract is initialised to ``$\\mathrm{n}\\mathrm{o}$'', i.e., $i_0=\\mathrm{n}\\mathrm{o}$, and the Bonus-Malus level is initialised to level~$0$, i.e., $b_0=0$.\nSubsequently, each year $t\\in\\{1,\\ldots,T\\}$ consists of the three following stages:\n\\begin{enumerate}\n\\item \\textbf{Provision Stage.} The insured decides: (i) the self-mitigation measure to adopt in this year, denoted by $d_t\\in\\mathcal{D}$ and (ii) whether to activate\/withdraw\/re-activate the cyber risk insurance contract, denoted by $\\iota_t\\in\\{0,1\\}$. Then, depending on the decision $\\iota_t$, a premium payment $p^{\\mathcal{B}\\mathcal{M}}(b_{t-1},t)$ and\/or a sign-on fee $\\delta_{\\mathrm{i}\\mathrm{n}}(t)$, a withdraw penalty $\\delta_{\\mathrm{o}\\mathrm{u}\\mathrm{t}}(t)$, or a re-activation penalty $\\delta_{\\mathrm{r}\\mathrm{e}}$ will be incurred, as stated in Section~\\ref{ssec:cyberinsurance} and Section~\\ref{ssec:bonusmalus}.\n\n\\item \\textbf{Operation Stage.} The random cyber loss events and the corresponding cyber losses suffered by the insured in this year, denoted by $W_t$, is realised in this stage according to the model described in Section~\\ref{ssec:lossmodel} and Section~\\ref{ssec:mitigation}. The aggregate cyber loss incurred is given by $L(d_t,W_t)$.\n\n\\item \\textbf{Claim Stage.} If the insurance contract is active, i.e., $\\iota_t=1$, the insured decides whether or not to make a claim, denoted by $j_t\\in\\{0,1\\}$. In the case where a claim is made, i.e., $j_t=1$, the insured receives a compensation from the insurer according to the aggregate cyber loss that year, given by $\\lambda^{\\mathcal{B}\\mathcal{M}}(b_{t-1},t,L(d_t,W_t))$. Subsequently, the cyber risk insurance contract state $i_t$ and the Bonus-Malus level $b_t$ are updated based on the decisions $\\iota_t$ and $j_t$, as described in Section~\\ref{ssec:bonusmalus}. \nConcretely, if $\\iota_t=1$, then the insurance contract state will be ``$\\mathrm{o}\\mathrm{n}$'' and the Bonus-Malus level will be updated according to the function $\\mathcal{B}\\mathcal{M}(\\cdot,\\,\\cdot)$, i.e., $i_t=\\mathrm{o}\\mathrm{n}$, $b_t=\\mathcal{B}\\mathcal{M}\\big(b_{t-1},j_t\\lambda^{\\mathcal{B}\\mathcal{M}}(b_{t-1},t,L(d_t,W_t))\\big)$.\nIf $\\iota_t=0$, then the insurance contract state and the Bonus-Malus level will be updated according to the function $\\mathcal{B}\\mathcal{M}_0(\\cdot,\\,\\cdot)$, i.e., $(b_t,i_t)=\\mathcal{B}\\mathcal{M}_0(b_{t-1},i_{t-1})$.\n\\end{enumerate}\n\n\\begin{table}\n\\begin{center}\n\\caption{Notations in the optimal cybersecurity provisioning process and stochastic optimal control}\n\\label{tab:dp-notations}\n\\vspace{0.5em}\n\\begin{tabular}{ll}\n$d_t$ & decision of self-mitigation measure adopted in year $t$ \\\\\n$\\iota_t$ & decision to activate\/withdraw\/re-activate the insurance contract in year $t$ \\\\\n$j_t$ & decision of whether to make a claim in year $t$ \\\\\n$\\Pi$ & set containing all admissible decision policies \\\\ \n$f_t(b,i,d,\\iota,j,w)$ & state transition function in year $t$ in stochastic optimal control \\\\ \n$g_t(b,i,d,\\iota,j,w)$ & cost function in year $t$ in stochastic optimal control \\\\\n$(b^\\pi_t,i^\\pi_i)_{t=0:T}$ & controlled stochastic process representing the Bonus-Malus and insurance states \\\\ \n$e^{-r}$ & discount factor in stochastic optimal control \\\\\n$V^\\pi_t$ & expected discounted future cost at year $t$ in stochastic optimal control \\\\ \n$\\mathbb{V}_0$ & the optimal value of the stochastic optimal control problem \\\\\n$\\mathcal{V}_t(b,i)$ & value function in dynamic programming \\\\\n$\\widehat{d}_t(b,i)$ & one-stage optimal decision of self-mitigation measure in year $t$ \\\\\n$\\widehat{\\iota}_t(b,i)$ & one-stage optimal decision of cyber risk insurance in year $t$ \\\\\n$\\widehat{j}_t(b,i,w)$ & one-stage optimal decision of insurance claim in year $t$ \\\\\n$P^\\star_t\\big[(b,i)\\rightarrow(b',i')\\big]$ & transition kernel of the optimally controlled process \\\\\n$\\overline{P}^\\star_t(b,i)$ & marginal state occupancy probability of the optimally controlled process \\\\\n$\\zeta^{(m)}_t(b,i,w)$ & a quantity of interest that depends on the state and the losses \\\\\n$\\overline{\\zeta}^{(m)}_t$ & expected value of a quantity of interest \\\\\n$\\overline{Z}_{\\zeta^{(m)}}$ & aggregate expected value of a quantity of interest\n\\end{tabular}\n\\end{center}\n\\end{table}\n\n\n\n\nNext, in order to formally define an optimal decision policy, let us introduce the stochastic optimal control formulation of the cybersecurity provisioning process. Please refer to Table~\\ref{tab:dp-notations} for notations used in the cybersecurity provisioning process and the stochastic optimal control formulation.\nLet\n\\begin{align}\n\\begin{split}\n\\Pi:=\\Big\\{\\pi=(d_t,\\iota_t,&j_t)_{t=1:T}:d_t:\\Omega\\to\\mathcal{D},\\;\\iota_t:\\Omega\\to\\{0,1\\} \\text{ are }\\mathcal{F}_{t-1}\\text{-measurable},\\\\\n&j_t:\\Omega\\to\\{0,1\\} \\text{ is }\\mathcal{F}_t\\text{-measurable},\\;\\{\\iota_t=0,j_t=1\\}=\\emptyset,\\text{ for }t=1,\\ldots,T\\Big\\}\n\\end{split}\n\\end{align}\ndenote the set of all admissible decision policies. The conditions in the above definition are explained as follows.\n\\begin{itemize}\n\\item The decisions $d_t$ and $\\iota_t$ are made before observing $W_t$, hence may depend on all available information up to year $t-1$.\n\\item The decision $j_t$ is made after observing $W_t$, hence may depend on all available information up to year $t$.\n\\item The condition $\\{\\iota_t=0,j_t=1\\}=\\emptyset$ requires that the insured may only make a claim (i.e., $j_t=1$) when the insurance contract is activated (i.e., $\\iota_t=1$) in year $t$. \n\\end{itemize}\nFor $t=1,\\ldots,T$, let $f_t:\\mathcal{B}\\times\\mathcal{I}\\times\\mathcal{D}\\times\\{0,1\\}\\times\\{0,1\\}\\times\\mathcal{W}\\to\\mathcal{B}\\times\\mathcal{I}$ be the state transition function for each year $t$, given by\n\\begin{align}\n\\begin{split}\nf_t(b,i,d,\\iota,j,w):=\\begin{cases}\n\\big(\\mathcal{B}\\mathcal{M}(b,j\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,w))),\\mathrm{o}\\mathrm{n}\\big) &\\text{if }\\iota=1,\\\\\n\\mathcal{B}\\mathcal{M}_0(b,i) &\\text{if }\\iota=0,\n\\end{cases}\n\\end{split}\n\\label{eqn:socstate}\n\\end{align}\nthat is, $f_t(b,i,d,\\iota,j,w)$ returns the Bonus-Malus level and the insurance state in year $t$ given that the Bonus-Malus level and the insurance state in year $t-1$ are $b$ and $i$, the decisions in year $t$ are $d$, $\\iota$, $j$, and the cyber loss events in year $t$ are $w$.\nFor $t=1,\\ldots,T$, let $g_t:\\mathcal{B}\\times\\mathcal{I}\\times\\mathcal{D}\\times\\{0,1\\}\\times\\{0,1\\}\\times\\mathcal{W}\\to\\R_+$ be the cost function for each year $t$, given by \n\\begin{align}\n\\begin{split}\ng_t(b,i,d,\\iota,j,w)&:=\\beta(d)+\\iota p^{\\mathcal{B}\\mathcal{M}}(b,t)+\\delta_{\\mathrm{i}\\mathrm{n}}(t)\\INDI_{\\{i=\\mathrm{n}\\mathrm{o},\\iota=1\\}}+\\delta_{\\mathrm{o}\\mathrm{u}\\mathrm{t}}(t)\\INDI_{\\{i=\\mathrm{o}\\mathrm{n},\\iota=0\\}}\\\\\n&\\hspace{1.5cm}+\\delta_{\\mathrm{r}\\mathrm{e}}\\INDI_{\\{i\\ne\\mathrm{o}\\mathrm{n},i\\ne\\mathrm{n}\\mathrm{o},\\iota=1\\}}+L(d,w)-\\iota j\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,w)),\n\\end{split}\n\\label{eqn:soccost}\n\\end{align}\nwhere $\\beta(d)$ is the investment of adopting the self-mitigation measure $d$, $\\iota p^{\\mathcal{B}\\mathcal{M}}(b)$ corresponds to the cyber risk insurance premium, $\\delta_{\\mathrm{i}\\mathrm{n}}(t)\\INDI_{\\{i=\\mathrm{n}\\mathrm{o},\\iota=1\\}}+\\delta_{\\mathrm{o}\\mathrm{u}\\mathrm{t}}(t)\\INDI_{\\{i=\\mathrm{o}\\mathrm{n},\\iota=0\\}}+\\delta_{\\mathrm{r}\\mathrm{e}}\\INDI_{\\{i\\ne\\mathrm{o}\\mathrm{n},i\\ne\\mathrm{n}\\mathrm{o},\\iota=1\\}}$ corresponds to the sign-on\/withdrawal\/re-activation costs, $L(d,w)$ is the aggregate cyber loss, and $\\iota j\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,w))$ corresponds to the compensation from the insurer. \n\nSubsequently, for any decision policy $\\pi=(d_t,\\iota_t,j_t)_{t=1:T}\\in\\Pi$, let us define the $(\\mathcal{F}_t)_{t=0:T}$-adapted controlled stochastic process $\\big(b^{\\pi}_t,i^{\\pi}_t\\big)_{t=0:T}$ as follows:\n\\begin{align}\n\\begin{split}\n(b^\\pi_0,i^\\pi_0)&:=(0,\\mathrm{n}\\mathrm{o}),\\\\\n(b^\\pi_t,i^\\pi_t)&:=f_t(b^\\pi_{t-1},i^\\pi_{t-1},d_t,\\iota_t,j_t,W_t)\\quad\\text{for }t=1,\\ldots,T.\n\\end{split}\n\\label{eqn:markovprocess}\n\\end{align}\nThen, $g_t(b^\\pi_{t-1},i^\\pi_{t-1},d_t,\\iota_t,j_t,W_t)$ corresponds to the cybersecurity cost in year $t$. For $\\pi\\in\\Pi$, and $t=1,\\ldots,T$, define $V^{\\pi}_t$ by\n\\begin{align}\nV^\\pi_t:=\\EXP\\Big[\\textstyle\\sum_{s=t+1}^Te^{-(s-t)r}g_s(b^\\pi_{s-1},i^\\pi_{s-1},d_s,\\iota_s,j_s,W_s)\\Big|\\mathcal{F}_{t}\\Big],\n\\end{align}\nwhere $0\\alpha_t(b,b')\\right\\}$. \\COMMENT{the set of potential insurance compensation amounts such that the optimal decision is to make a claim and the updated Bonus-Malus level will be $b'$; see also Remark~\\ref{rmk:dpalgo}}\\label{alglin:dp-L-def} \\\\\n}\n\\nl \\For{$i\\in\\mathcal{I}$}{\n\\nl \\label{alglin:dp-forloop-d}\\For{$d\\in\\mathcal{D}$}{\n\\nl $H_t(b,i,d,1)\\leftarrow \\mathcal{V}_t(\\underline{b},\\mathrm{o}\\mathrm{n})-\\sum_{\\underline{b}\\le b'\\le\\overline{b}}\\EXP\\bigg[\\INDI_{\\{\\mathcal{B}\\mathcal{M}(b,\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,W)))=b'\\}}\\Big(\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,W))-\\alpha_t(b,b')\\Big)^+\\bigg]$.\\label{alglin:dp-H-def1} \\\\\n\\nl $H_t(b,i,d,0)\\leftarrow \\mathcal{V}_t\\big(\\mathcal{B}\\mathcal{M}_0(b,i)\\big)$. \\COMMENT{$H_t(b,i,d,\\iota)$ for $\\iota\\in\\{0,1\\}$ are temporary values to simplify the one-stage optimisation problem in Line~\\ref{alglin:dp-d-iota-def}; see also Remark~\\ref{rmk:dpalgo}}\\label{alglin:dp-H-def2} \\\\\n}\n\\nl $(\\widehat{d}_t(b,i),\\widehat{\\iota}_t(b,i))\\leftarrow\\argmin_{d\\in\\mathcal{D},\\,\\iota\\in\\{0,1\\}} \\bigg\\{\\beta(d)+\\iota p^{\\mathcal{B}\\mathcal{M}}(b,t)+\\delta_{\\mathrm{i}\\mathrm{n}}(t)\\INDI_{\\{i=\\mathrm{n}\\mathrm{o},\\iota=1\\}}+\\delta_{\\mathrm{o}\\mathrm{u}\\mathrm{t}}(t)\\INDI_{\\{i=\\mathrm{o}\\mathrm{n},\\iota=0\\}}+\\delta_{\\mathrm{r}\\mathrm{e}}\\INDI_{\\{i\\ne\\mathrm{o}\\mathrm{n},i\\ne\\mathrm{n}\\mathrm{o},\\iota=1\\}}+\\EXP\\big[L(d,W)\\big]+H_t(b,i,d,\\iota)\\bigg\\}$.\\label{alglin:dp-d-iota-def} \\\\\n\\nl $\\widehat{j}_t(b,i,w)\\leftarrow\\INDI_{\\{\\widehat{\\iota}_t(b,i)=1\\}}\\INDI_{\\bigcup_{\\underline{b}\\le b'\\le\\overline{b}}\\mathcal{L}_t(b,b')}\\big(\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(\\widehat{d}_t(b,i),w))\\big)$.\\label{alglin:dp-j-def} \\\\\n\\nl $\\mathcal{V}_{t-1}(b,i)\\leftarrow e^{-r}\\min_{d\\in\\mathcal{D},\\,\\iota\\in\\{0,1\\}} \\bigg\\{\\beta(d)+\\iota p^{\\mathcal{B}\\mathcal{M}}(b,t)+\\delta_{\\mathrm{i}\\mathrm{n}}(t)\\INDI_{\\{i=\\mathrm{n}\\mathrm{o},\\iota=1\\}}+\\delta_{\\mathrm{o}\\mathrm{u}\\mathrm{t}}(t)\\INDI_{\\{i=\\mathrm{o}\\mathrm{n},\\iota=0\\}}+\\delta_{\\mathrm{r}\\mathrm{e}}\\INDI_{\\{i\\ne\\mathrm{o}\\mathrm{n},i\\ne\\mathrm{n}\\mathrm{o},\\iota=1\\}}+\\EXP\\big[L(d,W)\\big]+H_t(b,i,d,\\iota)\\bigg\\}$.\\label{alglin:dp-value-update} \\\\\n\\nl $P^\\star_t\\big[(b,i)\\rightarrow(b',i')\\big]\\leftarrow 0$ for all $(b',i')\\in\\mathcal{B}\\times\\mathcal{I}$. \\COMMENT{initialise all transition probabilities to 0} \\\\\n\\nl \\If{$\\widehat{\\iota}_t(b,i)=1$}{\n\\nl \\For{$\\underline{b}0$ is the scale parameter, $g\\in\\R$ is the skewness parameter, and $h\\ge0$ is the kurtosis parameter. \nIn this paper, we assume that the parameters $\\alpha$, $\\varsigma$, $g$, and $h$ are fixed and known. \nBy (\\ref{eqn:g-and-hdef}), the distribution function of $\\widetilde{X}$ is given by\n\\begin{align}\n\\begin{split}\nF_{\\widetilde{X}}(x):=\\PROB[\\widetilde{X}\\le x]=\\Phi\\left(Y_{g,h}^{-1}\\left(\\tfrac{x-\\alpha}{\\varsigma}\\right)\\right),\n\\end{split}\n\\end{align}\nwhere $Y_{g,h}^{-1}$ denotes the inverse function of $Y_{g,h}$, and $\\Phi$ denotes the distribution function of the standard normal distribution. \nEven though $Y_{g,h}^{-1}$ cannot be expressed analytically, it can be efficiently evaluated using a standard root-finding procedure such as the bisection method and the Newton's method. Therefore, we treat $Y_{g,h}^{-1}$ as a tractable function. \nThe g-and-h distribution has the property that the $m$-th moment of $\\widetilde{X}$ exists when $h<\\frac{1}{m}$ (see, e.g., Appendix~D of \\cite{dutta2006tale}). Since we consider losses that are positively skewed and have finite expectation, from now on, we assume that $g>0$ and $0\\le h<1$. \n\nSince cyber losses are positive, we introduce a truncated version of the g-and-h distribution. \n\\begin{definition}[Truncated g-and-h distribution] For $\\alpha\\in\\R,\\varsigma>0,g>0,h\\in[0,1)$, the random variable $X$ has truncated g-and-h distribution with parameters $\\alpha,\\varsigma,g,h$, denoted by $X\\sim\\text{Tr-g-and-h}(\\alpha,\\varsigma,g,h)$, if $X$ has distribution function \n\\begin{align}\nF_X(x):=\\PROB[X\\le x]=\\PROB[\\widetilde{X}\\le x|\\widetilde{X}>0],\n\\label{eqn:tr-g-and-hdef}\n\\end{align}\nwhere $\\widetilde{X}\\sim\\text{g-and-h}(\\alpha,\\varsigma,g,h)$. \n\\label{def:tr-g-and-h}\n\\end{definition}\n\nThe next lemma shows some useful properties of the truncated g-and-h distribution.\n\\begin{lemma}\nSuppose that $X\\sim\\text{Tr-g-and-h}(\\alpha,\\varsigma,g,h)$ for $\\alpha\\in\\R,\\varsigma>0,g>0,h\\in[0,1)$. Then, the following statements hold.\n\\begin{enumerate}[label=(\\roman*)]\n\\item The distribution function of $X$ is given by\n\\begin{align}\n\\begin{split}\nF_{X}(x)=\\begin{cases}\n\\frac{F_{\\widetilde{X}}(x)-F_{\\widetilde{X}}(0)}{1-F_{\\widetilde{X}}(0)} & \\text{if }x>0,\\\\\n0 & \\text{if }x\\le0,\n\\end{cases}\n\\end{split}\n\\label{eqn:tr-g-and-h-df}\n\\end{align}\nwhere $F_{\\widetilde{X}}$ is defined in (\\ref{eqn:g-and-hdef}). \n\\label{slem:tr-g-and-h1}\n\\item Suppose that $U\\sim\\text{Uniform}[0,1]$, and let\n\\begin{align}\nX_U:=\\alpha+\\varsigma Y_{g,h}\\Big(\\Phi^{-1}\\Big(U+(1-U)F_{\\widetilde{X}}(0)\\Big)\\Big),\n\\label{eqn:tr-g-and-h-inv}\n\\end{align}\nthen $X_U\\sim\\text{Tr-g-and-h}(\\alpha,\\varsigma,g,h)$. \n\\label{slem:tr-g-and-h2}\n\\item For any $\\gamma\\ge0$, the expectation $\\EXP\\big[(X-\\gamma)^+\\big]$ is given by:\n\\begin{align}\n\\begin{split}\n\\EXP\\big[(X-\\gamma)^+\\big]&=\\frac{\\varsigma}{(1-F_{\\widetilde{X}}(0))g\\sqrt{1-h}}\\Bigg[\\exp\\left(\\frac{g^2}{2(1-h)}\\right)\\Phi\\left(\\left(\\frac{g}{1-h}-Y_{g,h}^{-1}\\left(\\tfrac{\\gamma-\\alpha}{\\varsigma}\\right)\\right)\\sqrt{1-h}\\right)\\\\\n&\\qquad-\\Phi\\left(-Y_{g,h}^{-1}\\left(\\tfrac{\\gamma-\\alpha}{\\varsigma}\\right)\\sqrt{1-h}\\right)\\Bigg]+\\frac{(\\alpha-\\gamma)(1-F_{\\widetilde{X}}(\\gamma))}{1-F_{\\widetilde{X}}(0)}.\n\\end{split}\n\\label{eqn:tr-g-and-h-mom}\n\\end{align}\n\\label{slem:tr-g-and-h3}\n\\end{enumerate}\n\\label{lem:tr-g-and-h}\n\\end{lemma}\n\\begin{proof}\nSee Appendix~\\ref{apx:proofs}. \n\\end{proof}\n\nLemma~\\ref{lem:tr-g-and-h}\\ref{slem:tr-g-and-h2} allows us to efficiently generate random samples from the severity distribution $F_X$, thus allowing us to approximate the distribution of quantities of interest in Example~\\ref{exp:quantities} via Monte Carlo.\nLemma~\\ref{lem:tr-g-and-h}\\ref{slem:tr-g-and-h3} shows that the assumption~\\ref{srmk:comp1} in Remark~\\ref{rmk:comp} is satisfied as long as the expected value of the frequency distribution, i.e., $\\EXP[N]$, is also tractable. \nLemma~\\ref{lem:tr-g-and-h}\\ref{slem:tr-g-and-h1} provides the distribution function of $X$ that can be used to approximate the distribution function of $L(d,W)$. Concretely, by adopting the fast Fourier transform (FFT) approach with exponential tilting (see, e.g., \\cite{embrechts2009panjer,cruz2015fundamental}), we approximate the distribution function of $L(d,W)$, denoted by $F_{L(d,W)}$, by a finitely supported discrete distribution $\\widehat{F}_{L(d,W)}(x)=\\sum_{j\\in\\mathcal{A}}p^{(d)}_j\\INDI_{\\big\\{a^{(d)}_j\\le x\\big\\}}$, where $\\big(a^{(d)}_j\\big)_{j\\in\\mathcal{A}}\\subset\\R_+$ is a finite set of atoms and $\\big(p^{(d)}_j\\big)_{j\\in\\mathcal{A}}$ are the corresponding probabilities. \nThe details of the FFT approach with exponential tilting are shown in Algorithm~\\ref{alg:fft}. \nAfter obtaining $(\\widehat{F}_{L(d,W)})_{d\\in\\mathcal{D}}$ from Algorithm~\\ref{alg:fft}, the quantities $\\EXP\\!\\left[\\INDI_{I}(\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,W)))\\big(\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,W))-\\alpha\\big)^+\\!\\right]$ and $\\PROB\\left[\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,W))\\in I\\right]$ in Remark~\\ref{rmk:comp} can be approximated by finite sums:\n\\begin{align*}\n\\EXP\\!\\left[\\INDI_{I}(\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,W)))\\big(\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,W))-\\alpha\\big)^+\\!\\right]&\\approx\\sum_{j\\in\\mathcal{A}}p^{(d)}_j\\!\\INDI_{I}\\big(\\lambda^{\\mathcal{B}\\mathcal{M}}\\big(b,t,a^{(d)}_j\\big)\\big)\\big(\\lambda^{\\mathcal{B}\\mathcal{M}}\\big(b,t,a^{(d)}_j\\big)-\\alpha\\big)^+,\\\\\n\\PROB\\!\\left[\\lambda^{\\mathcal{B}\\mathcal{M}}(b,t,L(d,W))\\in I\\right]&\\approx\\sum_{j\\in\\mathcal{A}}p^{(d)}_j\\!\\INDI_{I}\\big(\\lambda^{\\mathcal{B}\\mathcal{M}}\\big(b,t,a^{(d)}_j\\big)\\big).\n\\end{align*}\nOne may increase the granularity parameter $K_{\\mathrm{g}\\mathrm{r}}$ in Algorithm~\\ref{alg:fft} to increase the precision of numerical approximation. \nConsequently, assumptions~\\ref{srmk:comp2} and \\ref{srmk:comp3} in Remark~\\ref{rmk:comp} are satisfied, and hence, Algorithm~\\ref{alg:dpconcrete} is tractable and efficient in this setting. In particular, Algorithm~\\ref{alg:fft} only needs to be executed once before executing Algorithm~\\ref{alg:dpconcrete}. \n\n\\begin{algorithm}[t]\n\\KwIn{$\\mathcal{D}$, $F_X(\\cdot)$, $\\psi_N(\\cdot)$, $\\gamma(\\cdot)$, $\\overline{l}$, $K_{\\mathrm{g}\\mathrm{r}}\\in\\N$, $\\theta>0$}\n\\KwOut{$(a^{(d)}_j,p_j^{(d)})_{j\\in\\mathcal{A},d\\in\\mathcal{D}}$, $\\widehat{F}_{L(d,W)}(x)=\\sum_{j\\in\\mathcal{A}}p^{(d)}_j\\INDI_{\\{a^{(d)}_j\\le x\\}}$ for each $d\\in\\mathcal{D}$}\n\\nl $\\varepsilon\\leftarrow(2^{K_{\\mathrm{g}\\mathrm{r}}}-1)^{-1}\\overline{l}$, $\\mathcal{A}\\leftarrow\\{0,1,\\ldots,2^{K_{\\mathrm{g}\\mathrm{r}}}-1\\}$. \\\\\n\\nl \\For{$d\\in\\mathcal{D}$}{\n\\nl $a^{(d)}_j\\leftarrow j\\epsilon$ for each $j\\in\\mathcal{A}$. \\\\\n\\nl $f^{(d)}_{j}\\leftarrow \\exp(-j\\theta)\\left[F_{X,d}(j\\varepsilon+\\frac{1}{2}\\varepsilon)-F_{X,d}(j\\varepsilon-\\frac{1}{2}\\varepsilon)\\right]$ for each $j\\in\\mathcal{A}$, where $F_{X,d}(y):=F_X\\big(y+\\gamma(d)\\big)\\INDI_{\\{y\\ge 0\\}}$. \\\\\n\\nl $\\varphi^{(d)}_j\\leftarrow\\sum_{k\\in\\mathcal{A}}\\exp(i\\pi2^{1-K_{\\mathrm{g}\\mathrm{r}}} jk)f^{(d)}_k$ for each $j\\in\\mathcal{A}$ via the FFT algorithm. \\\\\n\\nl $\\psi^{(d)}_j\\leftarrow\\psi_N(\\varphi^{(d)}_j)$ for each $j\\in\\mathcal{A}$. \\\\\n\\nl $p^{(d)}_j\\leftarrow\\exp(j\\theta)2^{-K_{\\mathrm{g}\\mathrm{r}}}\\sum_{k\\in\\mathcal{A}}\\exp(-i\\pi2^{1-K_{\\mathrm{g}\\mathrm{r}}} jk)\\psi^{(d)}_k$ for each $j\\in\\mathcal{A}$ via the inverse FFT algorithm. \\\\\n}\n\\nl \\Return $(a^{(d)}_j,p_j^{(d)})_{j\\in\\mathcal{A},d\\in\\mathcal{D}}$, $\\widehat{F}_{L(d,W)}(x)=\\sum_{j\\in\\mathcal{A}}p^{(d)}_j\\INDI_{\\{a^{(d)}_j\\le x\\}}$ for each $d\\in\\mathcal{D}$.\\\\\n \\caption{{\\bf Fast Fourier Transform Approach with Exponential Tilting for Approximating $F_{L(d,W)}$} (see \\cite{embrechts2009panjer})}\n \\label{alg:fft}\n\\end{algorithm}\n\n\\section{Numerical Experiments}\n\\label{sec:exp}\n\nIn Section~\\ref{sec:stocoptctr} and Section~\\ref{sec:g-and-h}, we formulated the optimal cybersecurity provisioning problem as a finite horizon stochastic optimal control problem, and developed a dynamic programming algorithm, i.e., Algorithm~\\ref{alg:dpconcrete}, to efficiently solve the problem under the assumption that the loss severity follows the truncated g-and-h distribution. Algorithm~\\ref{alg:dpconcrete} not only computes the optimal cybersecurity provisioning policy $\\pi^\\star$ for the insured, but also computes related quantities of interest, including the marginal state occupancy probabilities $\\big(\\overline{P}^\\star_t\\big)_{t=0:T}$ of the optimally controlled process $\\big(b^{\\pi^\\star}_t,i^{\\pi^\\star}_t\\big)_{t=0:T}$ as well as other quantities of interest such as those illustrated in Example~\\ref{exp:quantities}.\nAs discussed in Section~\\ref{ssec:pricing}, these quantities can guide the insurer when designing a suitable cyber risk insurance contract with a Bonus-Malus system. \nIn this section, we demonstrate how Algorithm~\\ref{alg:dpconcrete} aids the insurer when designing a cyber risk insurance contract and the benefits of the Bonus-Malus system by a numerical experiment.\\footnote{The code used in this work for the experiment is available on GitHub: \\url{https:\/\/github.com\/qikunxiang\/CyberInsuranceBonusMalus}} \nIn particular, we investigate two aspects of the cyber risk insurance contract with Bonus-Malus discussed in Section~\\ref{ssec:pricing}. \nThe first aspect is the issue of moral hazard, that is, whether the presence of the cyber risk insurance contract disincentivises the adoption of self-mitigation measures. \nThe second aspect is whether the Bonus-Malus system provides benefits to the insurer in terms of increased customer retention rates and expected profits.\n\n\\subsection{Experimental Settings}\n\nWe assume that all monetary quantities, including the severity of cyber loss events, the insurance premium, and the annual investment required by the self-mitigation measures are adjusted to the scale of the insured (e.g., their average annual revenue) and are unit-free. \nWe consider insurance policies that last for 20 years, that is, $T=20$. The discount factor $e^{-r}$ is fixed at 0.95. \nIn the cyber loss model, we let the frequency distribution be the Poisson distribution with rate 0.8. We set the severity distribution to be $\\text{Tr-}g\\text{-and-}h(\\alpha=0,\\varsigma=1,g=1.8,h=0.15)$, where the $g$ and $h$ parameters are set to be similar to those estimated in \\cite{dutta2006tale} from real Operational Risk data (see Table~8 of \\cite{dutta2006tale}). \nWe would like to remark that the heaviness of the tail of the loss severity distribution (i.e., the parameter $h$ in the truncated $g$-and-$h$ distribution) determines the probability of extreme risk events and is crucial in the computation of capital estimate \\citep{dutta2006tale}. Therefore, it is important that we specify a realistic value of the parameter $h$.\nIn Algorithm~\\ref{alg:fft}, we fix $\\overline{l}=10000$, $K_{\\mathrm{g}\\mathrm{r}}=20$, $\\theta=\\frac{20}{2^{K_{\\mathrm{g}\\mathrm{r}}}}=3.0518\\times10^{-4}$. \n\nFor simplicity, we consider the situation where only a single self-mitigation measure is available, that is, $D=1$. This self-mitigation measure requires an annual investment of 0.5, and has the effect of preventing 70\\% of the incoming cyber loss events and decreasing the severity of the remaining events by the 70th percentile of the severity distribution, that is, $\\beta(1)=0.5$, $\\gamma(1)=F^{-1}_X(0.7)$, where $X\\sim\\text{Tr-}g\\text{-and-}h(\\alpha=0,\\varsigma=1,g=1.8,h=0.15)$. \nWe consider the following simple cyber risk insurance policy with Bonus-Malus system. Let $\\mathcal{B}=\\{-2,-1,0,1\\}$, and let the functions $\\mathcal{B}\\mathcal{M}(b_{t-1},C_t)$ and $\\mathcal{B}\\mathcal{M}_0(b_{t-1},i_{t-1})$ be specified in Table~\\ref{tab:bm} below.\n\n\\begin{table}[h]\n\\label{tab:bm}\n\\caption{The $\\mathcal{B}\\mathcal{M}(\\cdot,\\cdot)$ and $\\mathcal{B}\\mathcal{M}_0(\\cdot,\\cdot)$ functions that represent the Bonus-Malus update rules}\n\\begin{center}\n\\begin{tabular}{|c|c|c|c|}\n\\hline \n\\multicolumn{2}{|c|}{\\multirow{2}{*}{$\\mathcal{B}\\mathcal{M}(b_{t-1},C_t)$}} & \\multicolumn{2}{|c|}{$C_t$} \\\\ \n\\cline{3-4}\n\\multicolumn{2}{|c|}{} & $=0$ & $>0$ \\\\ \n\\cline{1-4}\n\\multirow{4}{*}{$b_{t-1}$} & $-2$ & $-2$ & $1$ \\\\ \n\\cline{2-4}\n & $-1$ & $-2$ & $1$ \\\\ \n\\cline{2-4}\n & $0$ & $-1$ & $1$ \\\\ \n\\cline{2-4}\n & $1$ & $0$ & $1$ \\\\ \n\\hline \n\\end{tabular}\n\\hspace{1cm}\n\\begin{tabular}{|c|c|c|c|}\n\\hline \n\\multicolumn{2}{|c|}{\\multirow{2}{*}{$\\mathcal{B}\\mathcal{M}_0(b_{t-1},i_{t-1})$}} & \\multicolumn{2}{|c|}{$i_{t-1}$} \\\\ \n\\cline{3-4}\n\\multicolumn{2}{|c|}{} & $\\text{on}$ & $\\text{off}_1$ \\\\ \n\\cline{1-4}\n\\multirow{4}{*}{$b_{t-1}$} & $-2$ & $(-2,\\text{off}_1)$ & $(-1,\\text{off}_1)$ \\\\ \n\\cline{2-4}\n & $-1$ & $(-1,\\text{off}_1)$ & $(0,\\text{off}_1)$ \\\\ \n\\cline{2-4}\n & $0$ & $(0,\\text{off}_1)$ & $(0,\\text{off}_1)$ \\\\ \n\\cline{2-4}\n & $1$ & $(1,\\text{off}_1)$ & $(0,\\text{off}_1)$ \\\\ \n\\hline \n\\end{tabular}\n\\end{center}\n\\end{table}\nThe above settings mean that when the contract is activated, the insured is migrated to level 1 in the subsequent policy year whenever a claim is made. When the insured does not make any claim in a policy year, their policy is migrated downwards by one level in the subsequent policy years until it reaches level $-2$. When the contract is deactivated, if the insured's policy is in level 1, it is migrated back to level 0 after one year. Otherwise, the policy is migrated upwards by one level each year until it reaches level 0. \nIn the experiment, we let the base premium $p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}$ be an adjustable parameter that is varied between 0 and 7 with an increment of $0.005$, and set the premium to be $60\\%,80\\%,100\\%,150\\%$ of the base premium for the Bonus-Malus levels $-2,-1,0,1$, respectively. That is, we let $p^{\\mathcal{B}\\mathcal{M}}(-2,t)=0.6p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}$, $p^{\\mathcal{B}\\mathcal{M}}(-1,t)=0.8p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}$, $p^{\\mathcal{B}\\mathcal{M}}(0,t)=p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}$, $p^{\\mathcal{B}\\mathcal{M}}(1,t)=1.5p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}$ for all $t\\in\\{1,\\ldots,T\\}$. We fix the maximum compensation to be 1000, that is, $l^{\\mathcal{B}\\mathcal{M}}_{\\max}(b,t)=1000$ for all $b\\in\\mathcal{B},t\\in\\{1,\\ldots,T\\}$.\nWe set the deductible to be 0.5 for all but the final policy year, and set the deductible to be 5 for the final policy year, that is, $l^{\\mathcal{B}\\mathcal{M}}_{\\mathrm{d}\\mathrm{t}\\mathrm{b}}(b,t)=0.5$ for all $b\\in\\mathcal{B},t\\in\\{1,\\ldots,T-1\\}$ and $l^{\\mathcal{B}\\mathcal{M}}_{\\mathrm{d}\\mathrm{t}\\mathrm{b}}(b,T)=5$ for all $b\\in\\mathcal{B}$. This is to prevent an issue caused by the finite horizon. Since after the final policy year there is no future benefit from the insurance policy and the insured is not incentivised to adopt the self-mitigation measure, a higher deductible is used as the incentive in the final policy year. \nIn addition, we let $\\delta_{\\mathrm{i}\\mathrm{n}}(t)=0.75(t-16)^+$, $\\delta_{\\mathrm{o}\\mathrm{u}\\mathrm{t}}(t)=3+\\frac{5}{19}(t-1)$, and $\\delta_{\\mathrm{r}\\mathrm{e}}=3$. This setting has the effect of incentivising the insured to activate the insurance contract early on, and disincentivising withdrawal when close to the final policy year. \nAs a baseline for comparison, we also consider another cyber risk insurance policy without the Bonus-Malus system, which can be modelled by letting $\\mathcal{B}=\\{0\\}$. We fix the premium to be the base premium $p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}$, and leave everything else identical to the policy with the Bonus-Malus system. \n\n\\subsection{Results and Discussion}\n\nFigure~\\ref{fig:exp1policy} shows the expected number of years the insured's policy spends in each of the Bonus-Malus levels or being uninsured and the expected number of years the insured adopts the self-mitigation measure. The two panels compare the cyber risk insurance contract with the Bonus-Malus system with the one without.\nWith the contract that does not have the Bonus-Malus system, the decisions of the insured are completely deterministic, that is, they do not depend on the realisation of the cyber loss events. When $p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}\\le4.410$, the optimal strategy of the insured is to purchase the cyber risk insurance policy every year and only adopt the self-mitigation measure in the final policy year (due to the higher deductible in the final policy year). When $p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}\\ge4.415$, the optimal strategy of the insured is to never purchase the cyber risk insurance policy and always adopt the self-mitigation measure. Therefore, without the Bonus-Malus system, the issue of moral hazard is present and the insured will treat the cyber risk insurance policy and the self-mitigation measure as substitute goods. \nOn the other hand, when the Bonus-Malus system is introduced to the cyber risk insurance contract, the decisions of the insured depend on the realisation of the cyber loss events. When $4.495\\le p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}\\le4.930$, the optimal strategy of the insured is to always purchase the cyber risk insurance policy and adopt the self-mitigation measure. When $4.935\\le p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}\\le5.050$, the optimal strategy of the insured is to always adopt the self-mitigation measure but withdraw from the insurance contract when the expected future cost exceeds the expected future benefit of the insurance policy. As a result, the retention rate, i.e., the expected proportion of years the insured remains in the contract, drops when the base premium is increased. When $p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}\\ge5.055$, the optimal strategy of the insured is to never purchase the cyber risk insurance policy and always adopt the self-mitigation measure. Hence, compared with the contract without Bonus-Malus, the contract with Bonus-Malus incentivises the insured to adopt the self-mitigation measure in addition to purchasing the cyber risk insurance policy. \n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.48\\linewidth]{figures\/exp_ori_wo_BM1-eps-converted-to.pdf}\n~\n\\includegraphics[width=0.48\\linewidth]{figures\/exp_ori_w_BM1-eps-converted-to.pdf}\n\\caption{The retention rate of the cyber risk insurance policy and the expected years of adoption of the self-mitigation measure versus the base premium.}\n\\label{fig:exp1policy}\n\\end{figure}\n\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.48\\linewidth]{figures\/exp_ori_wo_BM2-eps-converted-to.pdf}\n~\n\\includegraphics[width=0.48\\linewidth]{figures\/exp_ori_w_BM2-eps-converted-to.pdf}\n\\caption{The discounted total expected loss prevented by the self-mitigation measure and the discounted expected profit (measured by the quantity $\\overline{Z}_{\\mathrm{i}\\mathrm{n}\\mathrm{s}}-\\overline{Z}_{\\mathrm{c}\\mathrm{p}}$) of the insurer versus the base premium. Left panel: the contract without the Bonus-Malus system. The dashed lines indicate the highest base premium before the insured chooses not to purchase the cyber risk insurance policy. Right panel: the contract with the Bonus-Malus system. The dashed lines indicate the highest base premium before the retention rate drops below 100\\%. The dotted lines indicate the highest base premium before the insured chooses not to purchase the cyber risk insurance policy.}\n\\label{fig:exp1profit}\n\\end{figure}\n\nFigure~\\ref{fig:exp1profit} compares both the expected value of the discounted total loss prevented by the self-mitigation measure and the expected value of the discounted profit of the insurer measured by the quantity $\\overline{Z}_{\\mathrm{i}\\mathrm{n}\\mathrm{s}}-\\overline{Z}_{\\mathrm{c}\\mathrm{p}}$ (defined in Section~\\ref{ssec:pricing}) in the two policies. The left panel of Figure~\\ref{fig:exp1profit} shows the case without Bonus-Malus. In that case, when $p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}\\le4.410$, the insured will always purchase the cyber risk insurance policy but will only adopt the self-mitigation measure in the final policy year. Hence, the discounted total expected loss prevented stays at $0.505$, while the discounted expected profit of the insurer increases as the base premium increases. When $p^{\\mathcal{B}\\mathcal{M}}_{\\text{base}}\\ge4.415$, the insured will not purchase the insurance policy but will always adopt the self-mitigation measure. As a result, the discounted total expected loss prevented will be $17.183$ but the insurer will earn no profit. The most the insurer can gain before losing the insured is $-10.510$, when the base premium is set to $4.410$. \nIn contrast, in the case with the Bonus-Malus system, as shown in the right panel of Figure~\\ref{fig:exp1profit}, the insurer can gain a discounted expected profit of at most $-0.860$ while always retaining the insured (i.e., the insured will never withdraw from the contract), when the base premium is set to $4.930$. The insurer can gain a discounted expected profit of at most $-0.006$ before losing the insured, when the base premium is set to $5.050$. With both of these base premiums, the insured will always adopt the self-mitigation measure, resulting in a discounted total expected loss prevention of~$17.183$. \n\nOverall, this experiment demonstrates two benefits of the Bonus-Malus system. First, the presence of the Bonus-Malus system in the cyber risk insurance contract incentivises the insured to adopt the self-mitigation measure. This results in a considerable increase in the prevention of cyber losses, which enhances the overall security of the cyberspace. Second, the Bonus-Malus system benefits the insurer, since it allows the insurer to gain more profit from the cyber risk insurance policy while remaining attractive to the insured. \nTo show that the observations from the results in this experiment and the conclusions drawn do not depend on the specific choice of the $h$ parameter in the loss severity distribution (which is the most impactful parameter in the truncated g-and-h distribution), and that they also do not depend on our distributional assumption, we have repeated the same experiment with slightly modified settings.\nIn the first modified setting, the $h$ parameter in the truncated g-and-h distribution is set to $0.10$, $0.20$, or $0.25$. \nIn the second modified setting, the loss severity distribution is replaced by a log-normal distribution where the parameters are determined by matching the first two moments to $\\text{Tr-}g\\text{-and-}h(\\alpha=0,\\varsigma=1,g=1.8,h=0.15)$. \nThe results obtained under these modified settings turned out to be very similar to the results in the original experiment.\\footnote{The results under these modified settings are available in the online appendix on GitHub: \\url{https:\/\/github.com\/qikunxiang\/CyberInsuranceBonusMalus}}\nThis shows that the benefits of the Bonus-Malus system in the experiment are in fact present across various loss distributional assumptions.\n\n\\section{Conclusion}\n\\label{sec:conclusion}\n\nThis paper motivated the joint consideration of risk reduction and risk transfer decisions in the face of cyber threats. We introduced a cyber risk insurance contract with a Bonus-Malus system to provide incentive mechanisms to promote the adoption of cyber risk mitigation practices. We developed a model based on the stochastic optimal control framework to analyse how a rational insured allocates funds between self-mitigation measures and a cyber risk insurance policy. A dynamic programming-based algorithm was then developed to efficiently solve this decision problem. \nA numerical experiment demonstrated that this novel type of insurance contract can incentivise the adoption of self-mitigation measures and can allow the insurer to profit more from the policy while remaining attractive to the insured. \nFuture research could investigate the effects of the risk profile, i.e., the characteristics of the loss distribution such as the heaviness of its tail, on the effectiveness of the Bonus-Malus system and how one can tailor Bonus-Malus-based insurance contracts for different risk profiles. \n\n\n\\section*{Acknowledgments}\n\\noindent\nAriel Neufeld gratefully acknowledges the financial support by his Nanyang Assistant Professorship Grant (NAP Grant) \\emph{Machine Learning based Algorithms in Finance and Insurance}.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction and related Research}\n\nDiscrete-time queueing networks are used to model a variety of scenarios, ranging from traffic control over parallel computing to wireless communication. They are closely related to the canonical control system\n\\begin{equation}\n\t\\label{eq::usual_control}\n\tx_{t+1} = A x_t + B_t v_t + D_t w_t\n\\end{equation}\nwith some significant differences: i) The controls $v_t$ are binary in nature and linearly constrained by $C v_t \\leq c$, e.g. due to the interference properties of wireless channels. ii) The state lives on the discrete set $x_t \\in \\mathbb{N}^n$ where it exhibits no inertia ($A = I$). iii) And crucially, the matrices $B_t$ and $D_t$ behave stochastically, implying that the effect of a control decision is not certain. Together with the class of back-pressure control policies, those systems form a well investigated subclass of control problems.\n\nThe prototype back-pressure policy, that we will call the max-weight policy ($\\mathsf{MW}$), was first introduced in \\cite{Tassiulas1992}, where the authors also proved its much praised property of being \\textit{throughput optimal}. This means, that $\\mathsf{MW}$ can manage any load of traffic, provided this load can somehow be supported by the network topology.\nOver time, many variations of $\\mathsf{MW}$ where developed, e.g. to allow for a generalized control objective \\cite{Meyn2009} \\cite{Kasparick2018}, or to increase its performance in special cases like networks with input-queued switches or time-varying channels \\cite{Mckeown1999} \\cite{Neely2005}. Specific shortcomings of $\\mathsf{MW}$, like e.g. high end-to-end delay, where investigated in \\cite{Khan2009} \\cite{Subramanian2007} \\cite{Ying2011} and later partially remedied by \\cite{Neely2005} \\cite{Ying2011} \\cite{Huang2013} \\cite{Xiong2011}, using e.g. shortest path algorithms to reduce delay especially in low traffic scenarios.\n\nIn this paper, we propose a novel control policy that is predictive in nature and that we will call predictive network control ($\\mathsf{PNC}$). It can be regarded as a generalization of $\\mathsf{MW}$, since it contains $\\mathsf{MW}$ as a special case. But while $\\mathsf{MW}$ and all its derivations are \\textit{myopic}, i.e. only aim to improve the system state for the \\textit{immediate} next time slot, $\\mathsf{PNC}$ aims to improve the system state for \\textit{multiple} time slots up until a prediction horizon. This leads to the calculation of an entire optimal \\textit{trajectory} of control vectors. However, instead of applying the entire trajectory for the next few time slots, only the first control vector is applied to the system and the process repeats in the consecutive time slot. This allows the controller to react to any unforeseen changes in the control system \\cite{Mayne2000}.\n\nSuch a control scheme is called model-predictive control (MPC), and therefore $\\mathsf{PNC}$ is a realization of MPC, tailored specifically towards queueing networks. MPC itself is a well established branch of control theory and can cope very easily with hard constraints and non-linearities, making it particularly suited for our control problem. However, its advantages are payed for by high requirements on computational resources.\nSo far, there has only been one attempt to bring MPC to queueing networks: In \\cite{VanLeeuwaarden2010a} the authors focus on a special case of the standard model, in which only the arrivals to the system are of stochastic nature. The investigation is limited to numerical simulations, which show better system performance (smoother time behavior) for a designed MPC controller compared to simple feedback control laws.\nSince our queueing network will include a much higher degree of stochastics, we will not follow up on their work.\n\nBecause a queueing network misses any inertia ($A = I$), a predictive control scheme can only tap into its full potential, if the stochastics for $B_t$ (or $D_t$) are complex enough. E.g. if both matrices behave according to white-noise, prediction over more than the next time slot yields close to no improvement over myopic strategies. Hence, the benefit of a predictive control scheme usually increases with complexity of the system model. Therefore, we let $B_t$ (the matrix which is responsible for the topology and the quality of the links between the nodes of the network) be governed by a discrete-time Markov chain (DTMC) and a Bernoulli trial. This gives the opportunity to model long-term and short-term effects, respectively. Take e.g. wireless relay networks with user mobility: here, short-term interference leading to packet loss can be modeled by the Bernoulli trial, while long-term change in channel quality due to the mobility can be expressed by the DTMC \\cite{Guzman2019}.\n\nControl systems, in which the model parameters change according to a DTMC are called Jump-Markov systems (JMS). (Since simple feedback controllers cannot detect this change, JMS are usually controlled with MPC controllers). There exist several control approaches towards JMS, covering cases with linear \\cite{Park2002} \\cite{Chitraganti2014} \\cite{Tonne2017a} and even nonlinear system dynamics \\cite{Liu2015} \\cite{Tonne2017}, where the referenced works mainly differ in the choice of considered constraints.\nHowever, all these works deal with conventional control systems, where the controller usually tries to follow a reference trajectory and noise ($w_t$) represents a stochastic disturbance with zero first moment. In contrast, from the perspective of queueing networks, the noise term represents the arrival of packets\/customers whose first moment is strictly positive, and the controller tries to maintain finite queues for any given arrival (hence, there is no need for a reference trajectory). For that reason, prior work on JMS is only partially applicable to our systems. To the best of our knowledge, we are the first to consider both JMS and MPC in the context of discrete-time queueing networks.\n\nOur \\textbf{contribution} is three-folded: i) We develop a JMS-adapted discrete-time queueing network and introduce a family of predictive control policies, based on the paradigms of MPC. ii) We proof throughput optimality (the equivalent of stability) for the most simple of our predictive control policies, thereby implying the same for the rest. And iii) we show the benefit of these policies over the conventional back-pressure control ($\\mathsf{MW}$), using numerical simulation. In particular, our policies seem to maintain their throughput optimality in networks with synchronized queues, making them unique.\n\n\\section{System Model \\& Prerequisites}\n\n\\subsection{System Model}\n\nWe begin by stating the constituting equation for our system model and clarify its components afterwards. Similar to the conventional control system, a discrete-time queueing network can be expressed by its one-step evolution and associated constraints\n\\def\\hspace{0.5mm}{\\hspace{0.5mm}}\n\\begin{gather}\n \\label{eq::basic_queueing}\n q_{t+1} = q_t + R M_t v_t + a_t\n \\\\[1ex]\n \\nonumber\n \\text{subject to}\n \\\\[1ex]\n \\nonumber\n \\left(\n\t\\begin{aligned}\n\t C v_t & \\leq c \\\\\n\t - R^- v_t & \\leq q_t\n \\end{aligned}\n \\right)\n \\hspace{0.5mm} \\text{and} \\hspace{0.5mm}\n \\left(\n \\begin{gathered}\n\t M_t \\sim \\mathcal{B}(W^{s_t})\n\t \\\\\n\t W^{s_t} \\in \\{W^1 ,\\dots W^{n_s} \\}\n\t \\\\\n\t (s_t) \\sim \\operatorname{DTMC}(\\{1,\\dots n_s\\},P,s_0)\n \\end{gathered}\n \\right)\n\\end{gather}\nThe \\textit{queue vector} $q_t = \\left( q_t^1 \\dots q^{n_q}_t \\right)^\\intercal \\in \\mathcal{Q} = \\mathbb{N}^{n_q}$ represents the system- (or queue-) state, where $q_t^i$ counts the number of packets, waiting in queue $i = 1,\\dots n_q$ in time slot $t$. Each queue itself is a node of the network.\n\nIn any time slot, packets can be transmitted from one queue to another if there exists a directed link between the two and the link is activated.\nThere are $n_v$ links, each of which can be represented by a vector $r^j \\in \\left\\{ -1,0,+1 \\right\\}^{n_q}$ ($j = 1,\\dots n_v$), that, by superpositioning with $q_t$, transfers a packet from one queue ($\\{-1\\}$) to another ($\\{+1\\}$). All links are collected as columns in the routing matrix $R \\in \\left\\{ -1,0,+1 \\right\\}^{n_q \\times n_v}$ which therefore holds the topology.\n\n[Remark:\nIn \\textit{conventional} networks, a link has exactly \\textit{one} $\\{-1\\}$ entry (origin) and \\textit{at most one} $\\{+1\\}$ entry (destination). This implicit constraint is a prerequisite for all back-pressure policies to develop their throughput optimality. Though we will also use this constraint throughout the paper, our novel control policies seem to maintain their throughput optimality even when it is violated (see section \\ref{subsec::synchronized_queues}), allowing us to control networks with synchronized queues.]\n\nThe controller may activate a link in a given time slot via the binary control vector $v_t \\in \\{ 0 , 1 \\}^{n_v}$.\nIf we could activate all links simultaneously ($v_t = \\mathbf{1}_{n_v}$), the control problem would become trivial. However, we are usually constrained in the activation (e.g. due to interference properties) by the \\textit{constituency constraint} $Cv_t \\leq c$. The dimensions of $C$ and $c$ are case dependent, their entries are from the set $\\mathbb{N}$. Furthermore, a packet can only be scheduled for transmission, if it is present at the corresponding queue, hence a packet may only traverse a single link per time slot. We will refer to this as the \\textit{positiveness constraint}, which is readily implemented by considering the maximum one-step efflux of the system, which is $R^- v_t$, where $R^-$ is equal to $R$ without its positive entries. Naturally, the maximum efflux cannot drain more packets than are actually present: $q_t + R^- v_t \\geq 0$. Note that this also guarantees that $q_t \\in \\mathbb{N}^{n_q}$.\n\nFor clarification, we refer to \\figref{fig::min_example}. Here, we stated topology and constituency matrices and derived the corresponding constraints. \nGiven only $C$ and $c$, both components of $v_t$ could be active simultaneously. However, if $q^2$ is empty ($q^2 = 0$), it is not possible to activate the second link $r^2$.\n\n\\def\\comR{\n\t$\n\tR = \\begin{pmatrix}\n\t-1 & 0 \\\\ +1 & -1\n\t\\end{pmatrix}\n\t$\n}\n\\def\\comC{\n\t$\n\tC = \\begin{pmatrix}\n\t0 & 0\n\t\\end{pmatrix}\n\t$\n}\n\\def\\comc{\n\t$ c = 1 $\n}\n\\def\\comRC{\n\t$ \\begin{pmatrix}\n\t1 & 0 \\\\ 0 & 1 \n\t\\end{pmatrix} v_t \\leq q_t$\n}\n\\def\\comCC{\n\t$ \\begin{pmatrix}\n\t\t0 & 0 \n\t\\end{pmatrix} v_t \\leq 1$\n}\n\\def\\comA{\n\t$\\Downarrow$\n}\n\n\\begin{figure}[htbp]\n\t\\centering\n\n\t\\includegraphics[]{network_example_f.pdf}\n\t\\caption{Minimal Example of a Queueing Network}\n\t\\label{fig::min_example}\n\\end{figure}\n\n\nStill, even an activated link $r^j$ might fail in its transmission, leaving source and destination queue unchanged. This is modeled by a stochastic variable $m^j_t \\in \\{0,1\\}$ which is Bernoulli distributed (coin-flip) with probability $\\e{m}^j_t \\in [0,1]$. I.e. $m^j_t \\sim \\mathcal{B}(\\e{m}^j_t)$. For a succinct notation, we collect all those quantities in the diagonal matrices $M_t = \\operatorname{diag}_{j = 1 ,\\dots n_v}\\{m^j_t\\}$ and $\\e{M}_t = \\operatorname{diag}_{j = 1 ,\\dots n_v}\\{\\e{m}^j_t\\}$ respectively such that $M_t \\sim \\mathcal{B}(\\e{M}_t)$ and of course $\\E{M_t} = \\e{M}_t$. \n\nThe Bernoulli trials on $\\e{M}_t$ are intended to model short-term stochastics. For long-term stochastics, we let $\\e{M}_t$ be picked from a predetermined set $\\mathcal{W} = \\{ W^1,W^2,\\dots \\}$ of weight matrices $W^i$ according to a DTMC $(s_t)$. If $\\mathcal{S} = \\{1 ,\\dots n_s\\}$ is the index set of $\\mathcal{W}$, we have $(s_t) \\sim \\operatorname{DTMC}(\\mathcal{S},P,s_0)$ where $P$ and $s_0$ are transition matrix and initial state, respectively. The entire selection process can therefore be expressed as $\\e{M}_t = W^{s_t}$. If $\\sigma_t$ describes a distribution for the DTMC, we have $\\lim_{t\\to\\infty} \\sigma_t = \\pi$, which we assume to be the only stable distribution, with $\\pi^{\\R{s}}$ being the average probability of $s_t = {\\R{s}}$.\n\nThe task for a controller is to steer the packets through the network to their destination nodes. Once reached, the packets leave the system, which can be modeled via links $r^j$ without the $\\{+1\\}$ entry. At the same time, new packets are created directly at the queues through an \\textit{arrival vector} $a_t \\in \\mathbb{N}^{n_q}$ of possibly stochastic nature. We call $\\e{a} = \\E{a_t}$ the \\textit{arrival rate} and make the usual assumption, that there is an upper bound, such that always $a_t \\leq \\hat{a}$.\n\nFinally, we remark that if different packets are destined for different final destination nodes, they are of different \\textit{class} (or belong to a different \\textit{flow}). Each class has to have its separate network of queues in order for the packets to be distinguishable. Thus, for each class a new copy of the system would have to be employed. While many authors model this by adding an additional dimension (the dimension of all classes) to all quantities, we will just assume, that the so far described system model already consists of those copies, stacked in a suitable way, thereby avoiding the introduction of another dimension to the system model.\n\n\\subsection{Control Policies and Throughput Optimality}\n\nWe already defined $q_t \\in \\mathcal{Q}$ and $s_t \\in \\mathcal{S}$. Note that $(s_t)$ is a DTMC and $q_{t-1}$ is not needed for a prediction of future states once $q_t$ is known. If we further assume the arrival vector to be independent of past realizations, there is no reason for a controller to use any but the last known realizations of $q_t$ and $s_t$ for its decision making.\nIf we also define the set of all control vectors by\n\\begin{equation}\n\t\\mathcal{V} = \\Set{ v \\in \\{0,1\\}^{n_v} | \n\t\t\\begin{aligned}\n\t C v & \\leq c \\\\\n\t \n \t\\end{aligned}\n }\n\\end{equation}\nthen we can express a control policy $\\phi$ as a mapping from the set of relevant, observed quantities onto the set of control vectors: $\\phi: \\mathcal{Q} \\times \\mathcal{S} \\to \\mathcal{V}$. In some cases, however, it makes sense to incorporate a stochastic process into the policy itself, in order to circumvent the discreteness of $\\mathcal{V}$. This way, given a fixed pair of observations $(q',s')$, we can not only access a fixed $v' = \\phi(q',s') \\in \\mathcal{V}$, but on average \\textit{any} predetermined element $\\sum_{v \\in \\mathcal{V}} \\lambda^v v$ of the convex hull $\\conv{\\mathcal{V}}$. Hence we define a \\textbf{control policy} as\n\\begin{equation}\n\t\\phi: \\mathcal{Q} \\times \\mathcal{S} \\times \\Omega \\to \\mathcal{V}\n\\end{equation}\nwhere $\\Omega$ is the sample space of the underlying stochastic process. (Remember that a control policy is only valid, if it complies with $R^- \\phi(q_t,s_t,\\omega_t) \\leq q_t$.)\n\nWe say that a control policy $\\phi$ \\textbf{stabilizes} a system for a given arrival rate $\\e{a}$ if it can compensate the arrival rate on average:\n\\begin{equation}\n\\label{eq::def_stability}\n\\begin{aligned}\n\t\\mathbf{0} &= \\lim_{\\tau \\to \\infty} \\frac{1}{\\tau} \\sum_{t=1}^\\tau \\left( \\vphantom{\\frac{1}{2}} a_t + R M_t \\phi(q_t,s_t,\\omega_t) \\right)\n\t\\\\\n\t&= \\hspace{10mm} \\e{a} \\hspace{10.7mm} + \\sum_{\\R{s} \\in \\mathcal{S}} \\pi^\\R{s} RW^\\R{s} \\phi(q_t,\\R{s},\\omega_t)\n\\end{aligned}\n\\end{equation}\nComparing with the system equation \\eqref{eq::basic_queueing}, this implies that the average queue state remains bounded.\n\nFinally, a control policy $\\phi$ is throughput optimal, if it stabilizes a given system for any arrival rate $\\e{a}$ for which at least one other (possibly unknown) policy stabilizes the system. This can readily be expressed by noting that a policy $v_t = \\phi(s_t,\\omega_t)$ can on average, for every state of $\\mathcal{S}$ separately, excess any predetermined element in the interior of the convex hull $\\conv{\\mathcal{V}}$. Note that this excludes the boundary of $\\conv{\\mathcal{V}}$, because due to the positiveness constraints, no policy can guarantee to never be forced to be idle (meaning $v_t = \\mathbf{0}$). Naturally, there are no other options for the average control vector than those in $\\conv{\\mathcal{V}}$.\nThus, $\\phi$ is \\textbf{throughput optimal}, if it stabilizes the system for all $\\e{a}$ with\n\\begin{equation}\n\t\\label{eq::def_to}\n\t\\e{a} + \\sum_{\\R{s} \\in \\mathcal{S}} \\pi^\\R{s} RW^\\R{s} \\sum_{v \\in \\mathcal{V}} \\lambda^{\\R{s},v} v = -\\varepsilon \\mathbf{1}_{n_q}\n\t\\hspace{10mm}\n\t\\begin{aligned}\n\t\\lambda^{\\R{s},v} & \\geq 0 \\\\\n\t\\sum_v \\lambda^{\\R{s},v} & \\leq 1\n\t\\end{aligned}\n\\end{equation}\nwhere $\\varepsilon > 0$ is used to exclude the boundary of $\\conv{\\mathcal{V}}$.\n\n\\section{Predictive Network Control ($\\mathsf{PNC}$)}\n\nInspired by the common MPC paradigms, our novel control policy, $\\mathsf{PNC}$, works in 3 steps: i) An entire trajectory of optimal control decisions from $t$ up until $t+H-1$ is calculated as the result of a minimization of an objective function $J$. Here, $J$ is a function of the next $H$ future system states, which we can only \\textit{predict}. $H$ is called the prediction horizon. ii) Only the first (i.e. the immediate) control decision in this trajectory is actually applied to the system. iii) The process repeats (discarding the rest of the just calculated trajectory). W.l.o.g., for the rest of the paper, we assume the current time slot to be $t=0$.\n\nThe most often encountered objective is the sum of squares, which in our case translates to\n\\begin{equation}\n\t\\label{eq::pnc_objective}\n\tJ(q_0,s_0) = \\CE{ \\sum_{t=1}^{H} q_t^Tq_t }{q_0,s_0}\n\\end{equation}\nUsing this definition, minimizing $J$ means minimizing the amount of packets in the network, which can only be done by delivering the packets to their destinations.\nOver the system evolution \\eqref{eq::basic_queueing}, $J$ will be influenced by the choice of control vectors via\n\\begin{equation}\n\t\\label{eq::actual_expected_evolution}\n \\CE{q_{T}}{q_0,\\sigma_0}\n = q_0 + \\sum_{t = 0}^{T-1} \\sum_{\\R{s} \\in \\mathcal{S}} \\left( \\sigma_0 P^{(t)} \\right)^\\R{s} R W^\\R{s}\n v_t\n + T\\e{a}\n\\end{equation}\nwhere $\\sigma_0$ is the distribution corresponding to the initial state $s_0$ and $\\left( \\sigma_0 P^{(t)} \\right)^\\R{s}$ stands for the $\\R{s}$-th entry of the predicted distribution in time slot $t$. \n\nNote, that the prediction can be implemented in three different ways, varying in precision and required effort:\n\ni) The first one is the \\textit{true prediction}, which assigns a control vector to \\textit{every} time slot (up until $H$) and \\textit{every} possible set of realizations of the quantities in the system evolution. Since the ensemble of these realizations in time slot $t$ forms $q_{t+1}$ and $s_{t+1}$, this would mean making $v_t$ a function of $q_t$ and $s_t$ for the remainder of the prediction. The number of control vectors, required for such a prediction amounts to $H \\cdot n_s \\cdot k_q$, where $k_q$ is the number of all possible queue states, that can be realized in a single time slot (likely to depend itself on prior queue state, realization of arrival and Bernoulli trial). This obviously requires the maximum amount of computational resources but allows us to truly find the optimal control trajectory that minimizes $J$.\n\nii) In contrast, a \\textit{relaxed prediction} uses only a minimum of control vectors. I.e. in every time slot, a single control vector is chosen and thus $v_t$ is only a function of $t$ for the remainder of the prediction. (Using even less control vectors would not constitute a meaningful prediction for our purposes.) This amounts to only $H$ control vectors being required for the prediction, speeding up the calculation of an optimal control trajectory to minimize $J$ considerably. However, said trajectory might be sub-optimal compared to the true prediction from before and as a consequence the control performance might be worse.\n\niii) Finally, a mixture of both cases could be implemented, finding a balance between computational complexity and control performance. E.g. one could consider every future DTMC state $s_t$ (up until $s_H$) leading to $H \\cdot n_s$ control vectors that have to be determined in order to minimize $J$.\n\nWe will define or policy via case ii), i.e. the relaxed prediction and explain the reasoning for this in the end of the section. For what follows, we substitute the control vector $v_t$ with $u_t$ to emphasize, that this is not the actual control of the queueing network but rather the one used for the prediction. Hence, $u_t$ is a quantity that is used internally to define the $\\mathsf{PNC}$ policy according to\n\\begin{multline}\n\t\\label{eq::pnc_expected_evolution}\n \\CE{q_{T}}{q_0,\\sigma_0}\n = q_0 + \\sum_{t = 0}^{T-1} \\sum_{\\R{s} \\in \\mathcal{S}} \\left( \\sigma_0 P^{(t)} \\right)^\\R{s} R W^\\R{s} u_t + T\\e{a}\n\\end{multline}\nIf we define the trajectory\n\\begin{equation}\n \\label{eq::def_w_tilde}\n \\tr{u}_0^{H-1} = \\begin{pmatrix}\n u_0 \\\\\n \\vdots \\\\\n u_{H-1}\n \\end{pmatrix}\n\\end{equation}\nand substitute it together with \\eqref{eq::pnc_expected_evolution} into \\eqref{eq::pnc_objective}, the objective can be rewritten as\n\\begin{equation*}\n J(q_0,s_0) = J_1(q_0) + J_2(q_0,s_0) \\tr{u}_0^{H-1} + \\left( {\\tr{u}_0^{H-1}} \\right)^\\intercal J_3(s_0) \\tr{u}_0^{H-1}\n\\end{equation*}\nAs can be seen, $J_1(q_0)$ will not be influenced by the minimization over $\\tr{u}_0^{H-1}$, and $J_3(s_0)$ will stay bounded, since it is not dependent on $q_0$. Therefore, for large enough $q_0$, the linear term $J_2(q_0,s_0) \\tr{u}_0^{H-1}$ will always dominate the minimization over $\\tr{u}_0^{H-1}$, making it prudent to define our actual objective only over this linear term. This simplifies the minimization from a quadratic to a linear one. (Note that a similar step is also taken in the definition of the $\\mathsf{MW}$ policy.) Expanding the remaining constraints from the original system in a straight forward way, we end up with the following definition of the $\\mathsf{PNC}$ policy:\n\\begin{equation}\n\t\\label{eq::pnc_policy_def}\n\\begin{gathered}\n \\phi^{\\mathsf{PNC}}(q_0,s_0) = \\operatorname{first} \\argmin_{\\tr{u}_0^{H-1}} \\ J_2(q_0,s_0) \\tr{u}_0^{H-1}\n \\\\[2ex]\n \\text{subject to} \\qquad\n \\begin{aligned}\n \\tr{C} \\tr{u}_0^{H-1} &\\leq \\tr{c}\n \\\\\n \\tr{A} \\tr{u}_0^{H-1} &\\leq \\tr{b}(q_0)\n \\\\\n \\tr{u}_0^{H-1} &\\in \\{0,1\\}^{Hn_v}\n \\end{aligned}\n\\end{gathered}\n\\end{equation}\nwhere $\\operatorname{first} \\argmin ()$ expresses that only the first argument of the trajectory is used as output. An overview of the utilized quantities can be found in Table~\\ref{table_01}.\n\nChoosing $H=1$, we end up with the common $\\mathsf{MW}$ policy, which is not surprising, since its definition also involves a quadratic objective. And indeed, $\\mathsf{PNC}$ would merely be the extension of $\\mathsf{MW}$ over multiple time slots, if the $\\mathsf{PNC}$ controller would follow a once calculated optimal trajectory to its end (i.e. for $H$ time slots). However, $\\mathsf{PNC}$ recalculates this trajectory each time slot, thereby discarding its entire tail. This results in a much improved behavior of $\\mathsf{PNC}$ (see section \\ref{subsec::synchronized_queues}) but also makes it impossible to infer any properties from $\\mathsf{MW}$ to $\\mathsf{PNC}$. For more comparisons between the two policies, we refer to \\cite{Schoeffauer2018a} and \\cite{Schoeffauer2019}.\n\nWe continue with the main theorem of this paper, which states throughput optimality of the $\\mathsf{PNC}$ policy. Note that this automatically implies throughput optimality for every other MPC controller, that uses a more precise prediction (under the same constraints and objective function).\n\\begin{theorem}\n\\label{theorem::to_of_pnc}\nThe $\\mathsf{PNC}$ policy \\eqref{eq::pnc_policy_def} is throughput optimal for the system \\eqref{eq::basic_queueing}.\n\\end{theorem}\n\n\\begin{table*}[ht!]\n\\caption{Extended Formulas for the Optimization Problem}\n\\centering\n\\normalsize\n\\begin{tabular}{|p{0.3\\linewidth}|p{0.64\\linewidth}|}\n\\hline\n\\hline\n\\begin{center}\nExpected value of weight matrix $W^{s_t}$\n\\end{center}\n&\n\\begin{center}\nLinear objective $J_2$\n\\end{center}\n\\\\\n\\vspace{-2.8mm}\n\\begin{equation}\n\\begin{gathered}\n \\e{W}_t(s_0) = \n\t\\CE{W^{s_t}}{s_0} \n\t\\\\ =\n \\left( \\sigma_0 P^{(t)} \\otimes I_{n_s} \\right)\n \\begin{pmatrix}\n\t\tW^1 \\\\ W^2 \\\\ \\vdots \\\\ W^{n_s}\n \\end{pmatrix}^\\intercal\n \\end{gathered}\n\\end{equation}\n&\n\\begin{equation}\n \\label{eq::J2_formula}\n J_2 = \n 2q_0^\\intercal R\n\t\\begin{pmatrix}\n\t\tH \\e{W}_0(s_0)\n\t\t\\\\\n\t\t(H-1) \\e{W}_1(s_0)\n\t\t\\\\\n\t\t\\vdots\n\t\t\\\\\n\t\t \\e{W}_{H-1}(s_0)\n\t\\end{pmatrix}^\\intercal\n\t+ \\e{a}^\\intercal R\n\t\\begin{pmatrix}\n\t\t(H+1)(H-0) \\e{W}_0(s_0)\n\t\t\\\\\n\t\t(H+2)(H-1) \\e{W}_1(s_0)\n\t\t\\\\\n\t\t\\vdots\n\t\t\\\\\n\t\t2H \\e{W}_{H-1}(s_0)\n\t\\end{pmatrix}^\\intercal\n\\end{equation}\n\\\\\n\\hline\n\\begin{center}\nConstituency constraints\n\\end{center}\n&\n\\begin{center}\nPositiveness constraints\n\\end{center}\n\\\\\n\\begin{equation}\n\t\\label{eq::table_constituency}\n\\underbrace{\n \t\t\\left( \\vphantom{\\frac{1}{2}} I_{H} \\otimes C \\right) \n\t}_{\\displaystyle \\tr{C} } \t\n \t\\tr{u}_0^{H-1} \\leq\n\t\\underbrace{ \t\n \t\t\\mathbf{1}_{H} \\otimes c \\vphantom{\\left( \\vphantom{\\frac{1}{2}} I_{H} \\otimes C \\right) }\n \t}_{\\displaystyle \\tr{c} \\vphantom{\\tr{C}} }\n\\end{equation}\n&\n\\vspace{-2.6mm}\n\\begin{equation}\n\t\\label{eq::table_positiveness}\n \\underbrace{\n \\begin{pmatrix}\n R^- & & & \\\\\n R & R^- & & \\\\\n \\vdots & & \\ddots \\\\\n R & \\dots & R & R^-\n \\end{pmatrix}\n }_{\\displaystyle \\tr{A}}\n \\tr{u}_0^{H-1}\n \\leq \n \\underbrace{\n \\begin{pmatrix}\n \tq_0 \\\\ q_0 + \\e{a} \\\\ \\vdots \\\\ q_0 + (H-1) \\e{a}\n \\end{pmatrix}\n }_{\\displaystyle \\tr{b}(q_0) }\n\\end{equation}\n\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\label{table_01}\n\\end{table*}\n\n\\section{Proof of Theorem \\ref{theorem::to_of_pnc}}\n\\label{sec::proof}\n\nWe will now prove, that $\\phi^\\mathsf{PNC}$ is throughput optimal. And in contrast to the usual stability-related proofs employed for MPC controllers, we will not rely on a terminal set.\n\n\\subsection{Preliminaries}\n\nIt will often become necessary to upper and lower-bound certain expressions. We will use $K_i \\in \\mathbb{R}_+$, $i\\in \\mathbb{N}$ to denote those bounds or variables, whose values are of no further interest and are obvious to calculate. Crucially, any $K_i$ will be independent of the initial system state $q_0$!\n\nWe will use gothic letters to express realizations of random variables, such that e.g. $\\R{s_t}$ is a realization of $s_t$, hence $\\R{s_t} \\in \\mathcal{S}$. And because the corresponding set of realizations will always be very clear from the context, we will use the succinct notation $\\sum_{\\R{s_t}}$ instead of $\\sum_{\\R{s_t} \\in \\mathcal{S}}$ for the sum of all realizations (as is needed for e.g. expressing the expectation).\n\nGiven a trajectory $\\tr{x}$ of control vectors of certain length, we use $\\tr{x} \\in \\mathcal{P}(q_0)$, to express that $\\tr{x}$ abides to the positiveness constraints $\\tr{A} \\tr{x} \\leq \\tr{b}(q_0)$, where $\\tr{A}$ and $\\tr{b}(q_0)$ are defined as in \\eqref{eq::table_positiveness}, expect for a possibly different value of $H$ (depending on the length of $\\tr{x}$). Analogue, $\\tr{x} \\in \\mathcal{C}$ will express, that $\\tr{x}$ abides to the constituency constraints as in \\eqref{eq::table_constituency}.\n\nFinally, we make the definitions $\\Delta_0^T := q_T - q_0$. Keep in mind, that analogue to $q_t$ being a function of all prior stochastics and controls, $\\Delta_0^T$ is of course a function of all stochastics and controls in the time slot $0,\\dots T-1$. With the definition $\\norm{q_t} := q_t^\\intercal q_t$ (which is not meant to be a norm) this gives raise to\n\\begin{equation}\n\t\\label{eq::to_sub_to_q0}\n\t\\norm{q_T} = \\norm{\\Delta_0^T} + \\norm{q_0} + 2q_0^\\intercal \\Delta_0^T\n\\end{equation}\nNote, that this decomposition corresponds to the one for the objective function $J$ and we can now restate the objective of the $\\mathsf{PNC}$ policy, $J_2$, as\n\\begin{equation}\n\t\\label{eq::to_objective_better}\n\tJ_2(q_0,s_0) = \\CE{ \\sum_{t=1}^{H} 2q_0^\\intercal \\Delta_0^t }{q_0,s_0}\n\\end{equation}\nWith this notation we formulate the next lemmas, needed for the proof.\n\n\\begin{lemma}\nThe difference $\\Delta_0^T$ between two queue states is bounded (element-wise) by\n\\begin{equation}\n\t\\label{eq::to_gen_bounds}\n\t- T n_v \\mathbf{1}_{n_q}\n\t\\leq\n\t\\Delta_0^T\n\t\\leq\n\tT n_v \\mathbf{1}_{n_q} + T \\hat{a}\n\\end{equation}\nleading to\n\\begin{equation}\n\t\\label{eq::to_quad_to_lin}\n\t\\norm{q_0} + 2q_0^\\intercal \\Delta_0^T\n\t\\leq\n\t\\norm{q_T} \n\t\\leq \\norm{q_0} + 2q_0^\\intercal \\Delta_0^T + K_1\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nBetween time slots $0$ and $T$ we have at best a constant efflux of $n_v$ or at worst a constant influx of $n_v + \\hat{a}$ packets per queue per time slot (since there are at most $n_v$ links to fill or drain any given queue).\n\\end{proof}\n\n\\begin{lemma}\n\\label{lemma::diff}\nThe difference between the minimization that originates from the definition of the $\\mathsf{PNC}$ policy (using the formulation from \\eqref{eq::to_objective_better}), and the same minimization but without considering any positiveness constraints can be bounded by\n\\begin{gather}\n\\label{eq::to_lemma}\n\\begin{aligned}\n \\min_{ \\tr{u}_0^{H-1} \\in \\mathcal{C} \\cap \\mathcal{P}(q_0) }\n \t&\\CE{ \n \t\t\\sum_{t=1}^{H} 2q_0^\\intercal \\Delta_0^t\n \t}\n \t{\n \t\tq_0,s_0\n \t}\n \\\\\n -\n \\min_{ \\tr{u}_0^{H} \\in \\mathcal{C} } \\hspace{6mm}\n \t&\\CE{ \n \t\t\\sum_{t=1}^{H} 2q_0^\\intercal \\Delta_0^t\n \t}\n \t{\n \t\tq_0,s_0\n \t} \n\\end{aligned}\n \\\\ \\nonumber\n \\leq\n \\left( Hn_v^2-n_v \\right) \\left( H + 1 \\right)\n = K_2\n\\end{gather}\n\\end{lemma}\n\\begin{proof}\nClearly, the maximum deficit, that $\\Delta_0^t$ can generate is (element-wise) less than $t n_v \\mathbf{1}_{n_q}$ (all links drain a queue over $t$ steps).\nFor a single queue, the \\textit{most} efflux in $H$ time slots is $Hn_v$ packets. \nHence, if $q_0 \\geq Hn_v \\mathbf{1}_{n_q}$, a control trajectory \\textit{cannot} violate the positiveness. Conversely, if a link cannot be activated due to the positiveness constraints, at least one entry of $q_0$ must be smaller than $H n_v$.\n\nDue to the linearity, the largest difference in the minimizations will be found, if $q_0 = (H n_v - 1) \\mathbf{1}_{n_q}$ (possibly denying any activation for the minimization with the positiveness constraints). This, together with the initial bound on $\\Delta_0^t$ leads directly to\n\\begin{equation}\n\t\\sum_{t=1}^H 2 (H n_v - 1) \\mathbf{1}_{n_q}^\\intercal t n_v \\mathbf{1}_{n_q} = \\left( Hn_v^2-n_v \\right) \\left( H + 1 \\right)\n\\end{equation}\nwhich is an upper bound for the difference in question.\n\\end{proof}\n\nFinally, the following theorem will allow us to express our definition of stability by the means of a Ljapunov function.\n\\begin{lemma}\n\t\\label{lemma::foster}\n\tA policy $\\phi$ stabilizes the system \\eqref{eq::basic_queueing} under a certain arrival rate $\\e{a}$, if we can find a function $f: \\mathcal{Q} \\times \\mathcal{S} \\to \\mathbb{R}_+$ with the property\n\t\\begin{equation}\n\t\t\\label{eq::to_drift}\n \t\\CE{ f(q_{1},s_{1}) - f(q_0,s_0) }{q_0} \\leq K_4 - K_5 \\mathbf{1}_{n_q}^\\intercal q_0\n\t\\end{equation}\n\\end{lemma}\n\\begin{proof}\nWithout further ado, we take expectations and sum \\eqref{eq::to_drift} over multiple time slots to obtain the following sequence of arguments\n\\begin{gather}\n\t\\nonumber\n \\E{f(q_T,s_T)} - \\E{f(q_0,s_0)}\n \\leq T K_4 - K_5 \\sum_{t = 0}^{T - 1} \\E{ \\mathbf{1}_{n_q}^\\intercal q_t }\n\\\\\n\t\\nonumber\n \\Longrightarrow \\qquad\n -\\E{f(q_0,s_0)} \n \\leq T K_4 - K_5 \\sum_{t = 0}^{T - 1} \\E{ \\mathbf{1}_{n_q}^\\intercal q_t }\n\t\\hphantom{\\Longrightarrow \\qquad} \n\\\\\n \\Longrightarrow \\qquad\n \\frac{1}{T} \\sum_{t=0}^{T-1} \\E{ \\mathbf{1}_{n_q}^\\intercal q_t } \n \\leq\n \\frac{K_4}{K_5} + \\frac{\\E{f(q_0)}}{T K_5}\n \\hphantom{\\Longrightarrow }\n\\\\\n\t\\nonumber\n \\Longrightarrow \\qquad\n \\lim_{T\\to\\infty}\n \\frac{1}{T} \\sum_{t=0}^{T-1} \\E{ q_t } \\leq\n \\frac{K_4}{K_5} \\mathbf{1}_{n_q} \n \\hphantom{\\Longrightarrow \\qquad}\n\\end{gather}\nSince $\\E{q_t} \\geq \\mathbf{0}$ always, and the difference between consecutive states is bounded, all elements of the sequence $(\\E{q_t})$ must be bounded. From there, the stability condition \\eqref{eq::def_stability} follows immediately. (Note that $\\E{\\cdot}$ is the expectation of the stochastic in the system model and not over time.)\n\\end{proof}\n\n\\subsection{Main Proof}\n\nWe now start with the main part of the proof. We will define a Ljapunov function $f(q_t,s_t)$ and show that \\textit{if} the system is governed by the $\\mathsf{PNC}$ policy, $f$ fulfills Lemma \\ref{lemma::foster} for any possibly stabilizable arrival rate (see \\eqref{eq::def_to}).\n\nFor a $\\mathsf{PNC}$ policy with horizon $H+1$, we employ the following Ljapunov function:\n\\begin{equation}\n \\label{eq::to_f_tilde}\n\\begin{gathered} \n f(q_0,s_0) =\n \\min_{ \\tr{z}_0^{H-1} \\in \\mathcal{C}_z } \n \\CE{ \n \t\t\\sum_{t=1}^{H} \\norm{q_{t}}\n }{q_0,s_0}\n\\end{gathered}\n\\end{equation}\nA few remarks are in order: i) The minimization in $f$ mimics the $\\mathsf{PNC}$ policy, but is in fact independent of it. ii) The horizon of the minimization of $f$ is chosen to be one step smaller than that of the $\\mathsf{PNC}$ policy. iii) The control vectors are now denoted by $z$ instead of $v$ or $u$, because they run independent of the actual control $v_t$ or the predicted control inside the $\\mathsf{PNC}$ controller $u_t$. Crucially, the control trajectory $\\tr{z}_0^{H-1}$ is state sensitive regarding the DTMC $(s_t)$ of the weight matrices and is \\textit{not} constraint by the positiveness constraints $\\mathcal{P}$. I.e.\n\\begin{multline}\n\t\\label{eq::to_expected_evolution}\n \\CE{q_{T}}{q_0,\\sigma_0}\n = q_0 + \\sum_{t = 0}^{T-1} \\sum_{\\R{s} \\in \\mathcal{S}} \\left( \\sigma_0 P^{(t)} \\right)^\\R{s} R W^\\R{s} z_t(\\R{s}) + T\\e{a}\n\\end{multline}\n\nThis last point is important: the minimization of the actual $\\mathsf{PNC}$ policy (with horizon $H+1$) uses the control trajectory $\\tr{u}_0^{H}$, which assigns to each time slot of the prediction \\textit{a single} control vector $u_t \\in \\mathcal{V}$. In contrast and per definition, the minimization in the Ljapunov function $f$ uses the control trajectory $\\tr{z}_0^{H-1}$, which assigns to each time slot \\textit{and} each possible realization of $s_t$ a control vector $z_t(s_t) \\in \\mathcal{V}$. For succinct notation we define $z_t^\\R{s} := z_t(s_t = \\R{s})$. The trajectory of the control vectors $z_t^\\R{s}$ is defined by first stacking over all realization of $(s_t)$ and then over all time slots:\n\\begin{equation}\n\t\\tr{z}_0^{H} \n\t=\n\t\\begin{pmatrix}\n\t\tz'_0 \\\\\n\t\tz'_1 \\\\\n\t\t\\vdots \\\\\n\t\tz'_H\n\t\\end{pmatrix}\n\t,\\qquad \\text{with} \\qquad\n\tz'_t =\n\t\\begin{pmatrix}\n\t\tz_t^1 \\\\\n\t\tz_t^2 \\\\\n\t\t\\vdots \\\\\n\t\tz_t^{n_s}\n\t\\end{pmatrix}\n\\end{equation}\n\nThe constraint $\\tr{z}_0^{H-1} \\in \\mathcal{C}_z$ in the definition of $f$ expresses, that each single control vector $z_t^\\R{s}$ is constraint by the constituency in the usual way ($C z_t^\\R{s} \\leq c$), and therefore $\\mathcal{C}_z$ is a straight forward expansion of $\\mathcal{C}$.\n\nWe can now start expressing the first term of \\eqref{eq::to_drift} (for now conditioning on $s_0$ as well) as\n\\begin{gather}\n\t\\label{eq::to_simple_first_term}\n\\CE{ f (q_1,s_1) }{ q_0,s_0 }\t =\n\t\\CE{\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C} }\n\t\t\\CE{\n\t\t\t\\sum_{t=2}^{H+1} \\norm{q_t}\n\t\t}{\n\t\t\tq_1 , s_1\n\t\t}\n\t}{\n\t\tq_0,s_0\n\t}\n\\end{gather}\nNote that this term is shifted in time. The control $v_0$, that leads from $q_0$ to $q_1$ is exactly the control, that is defined by the control policy $v_0 = \\phi^\\mathsf{PNC}$ and that actually affects the network. In contrast, the dummy controls $\\tr{z}_1^H$ are part of the function $f$, do not affect the actual network and therefore are independent of the chosen policy.\n\nUsing \\eqref{eq::to_sub_to_q0} the inner term of \\eqref{eq::to_simple_first_term} can become\n\\begin{align*}\n\t\\sum_{t=2}^{H+1} \\norm{q_t}\n\t&=\n\t\\sum_{t=2}^{H+1} \\Big(\n\t\t\\norm{q_0} + \\norm{\\Delta_0^t} + 2q_0^\\intercal \\Delta_0^t\n\t\\Big)\n\t\\\\\n\t&\\leq\n\tH \\norm{q_0} + K_6\n\t+\n\t\\sum_{t=2}^{H+1} \\Big(\n\t\t2q_0^\\intercal \\Delta_0^t\n\t\\Big)\n\\end{align*}\nThe individual sums of $\\Delta_0^t$ can be bounded according to \\eqref{eq::to_gen_bounds} by some constant $K_6$ which is unaffected by the minimization or the expectation from \\eqref{eq::to_simple_first_term}. The same holds for $\\norm{q_0}$ if we notice, that conditioning on $q_1$ is the same as conditioning on $q_0$ \\textit{and} $\\Delta_0^1$, since $q_1 = q_0 + \\Delta_0^1$. Hence, both terms can be pulled to the left-hand-side of \\eqref{eq::to_simple_first_term}, as seen in the first two lines of \\eqref{eq::to_long}. In what follows, we will step by step dissolve the outer expectation and expand the sum, which is possible, since the minimization is linear in $\\tr{z}_1^H$ and the constraints act on each control vector separately:\n\\begin{align}\n\t\\label{eq::to_long}\n\t& \\hspace{5mm}\n\t\\CE{ f (q_1,s_1) }{ q_0,s_0 } - H \\norm{q_0} - K_6 \n\\\\ \n\t\\nonumber\n\t& \\leq \n\t\\mathbb{E} \\Bigg[\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\\mathbb{E} \\Bigg[\n\t\t\t\\sum_{t=2}^{H+1}\t\t\t\n\t\t\t\t2q_0^\\intercal \\Delta_1^t\n\t\t\\, \\Bigg| \\,\n\t\t\tq_1 , s_1\n\t\t\\Bigg]\n\t\\, \\Bigg| \\,\n\t\tq_0,s_0\n\t\\Bigg]\n\\\\ \n\t\\nonumber\n\t& = \n\t\\mathbb{E} \\Bigg[\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z}\n\t\t\\mathbb{E} \\Bigg[\n\t\t\t\\sum_{t=2}^{H+1}\t\t\t\n\t\t\t\t2q_0^\\intercal \\Delta_1^t\n\t\t\\, \\Bigg| \\,\n\t\t\tq_0 ,\\Delta_0^1, s_1\n\t\t\\Bigg]\n\t\\, \\Bigg| \\,\n\t\tq_0,s_0\n\t\\Bigg]\n\\\\ \n\t\\nonumber\n\t& = \n\t\\sum_{\\R{q_0},\\R{s_1}}\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\\mathbb{E} \\Bigg[\n\t\t\t\\sum_{t=2}^{H+1}\t\t\t\n\t\t\t\t2q_0^\\intercal \\Delta_1^t\n\t\t\\, \\Bigg| \\,\n\t\t\t\\R{q_0} , \\R{s_1}\n\t\t\\Bigg]\n\t\\CP{\n\t\t\\R{q_0} , \\R{s_1}\n\t}{\n\t\tq_0,s_0\n\t}\n\\\\\n\t\\nonumber\n\t& \\hspace{1mm}\n\t\\begin{aligned}\n\t=\n\t\\sum_{\\R{q_0},\\R{s_1}}\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\t\\mathbb{E} \\Bigg[\n\t\t\t\\sum_{t=1}^{H}\n\t\t\t\\sum_{\\tau = t}^{H}\t\t\t\n\t\t\t\t2q_0^\\intercal \\left(\n\t\t\t\tR M_t z_t^{s_t} + a_t \\right)\n\t\t\\, \\Bigg| \\,\n\t\t\t\\R{q_0} , \\R{s_1}\n\t\t\\Bigg] &\n\t\\\\\n\t\\cdot\t\n\t\\CP{\n\t\t\\R{q_0}\n\t}{\n\t\tq_0\n\t}\n\t\\CP{\n\t\t\\R{s_1}\n\t}{\n\t\ts_0\n\t} \\, &\n\t\\end{aligned}\n\\\\ \n\t\\nonumber\n\t& \\hspace{1mm}\n\t\\begin{aligned}\n\t=\n\t\\sum_{\\R{s_1}}\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\t\\mathbb{E} \\Bigg[\n\t\t\t\\sum_{t=1}^{H}\n\t\t\t\\sum_{\\tau = t}^{H}\t\t\t\t\n\t\t\t\t2q_0^\\intercal \\left(\n\t\t\t\tR M_t z_t^{s_t} + a_t \\right)\n\t\t\\, \\Bigg| \\,\n\t\t\tq_0 , \\R{s_1}\n\t\t\\Bigg] &\n\t\\\\\n\t\\cdot\n\t\\CP{\n\t\t\\R{s_1}\n\t}{\n\t\ts_0\n\t} \\, &\n\t\\end{aligned}\n\\\\ \n\t\\nonumber\n\t& \\hspace{1mm}\n\t\\begin{aligned}\n\t=\n\t\\sum_{\\R{s_1}}\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\t\\sum_{t=1}^{H}\n\t\t\t\\sum_{\\tau = t}^{H}\t\n\t\t\t\\sum_{\\R{s_t}}\t\t\t\n\t\t\t\t2 q_0^\\intercal \\left(\n\t\t\t\tR W^\\R{s_t} z_t^\\R{s_t} + a_t \\right)\n\t\t\\CP{ \\R{s_t} }{\\R{s_1}} &\n\t\\\\[-2ex]\n\t\\cdot\n\t\\CP{\n\t\t\\R{s_1}\n\t}{\n\t\ts_0\n\t} &\n\t\\end{aligned}\n\\\\ \n\t\\nonumber\n\t& \\hspace{1mm}\n\t\\begin{aligned}\n\t=\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\t\\sum_{\\R{s_1}}\n\t\t\t\\sum_{t=1}^{H}\n\t\t\t\\sum_{\\tau = t}^{H}\t\n\t\t\t\\sum_{\\R{s_t}}\t\t\t\n\t\t\t\t2q_0^\\intercal \\left(\n\t\t\t\tR W^\\R{s_t} z_t^\\R{s_t} + a_t \\right)\n\t\t\\CP{ \\R{s_t} }{\\R{s_1}} &\n\t\\\\[-2ex]\n\t\\cdot\n\t\\CP{\n\t\t\\R{s_1}\n\t}{\n\t\ts_0\n\t} &\n\t\\end{aligned}\n\\\\ \n\t\\nonumber\n\t& = \n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\t\\sum_{t=1}^{H}\n\t\t\t\\sum_{\\tau = t}^{H}\t\n\t\t\t\\sum_{\\R{s_t}}\t\t\t\n\t\t\t\t2q_0^\\intercal \\left(\n\t\t\t\tR W^\\R{s_t} z_t^\\R{s_t} + a_t \\right)\n\t\t\\CP{ \\R{s_t} }{s_0}\n\\\\ \n\t\\nonumber\n\t& = \n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\\mathbb{E} \\Bigg[\n\t\t\t\\sum_{t=1}^{H}\n\t\t\t\\sum_{\\tau = t}^{H}\t\n\t\t\t\t2q_0^\\intercal \\left(\n\t\t\t\tR M_{t} z_t^{s_t} + a_t \\right)\n\t\t\\, \\Bigg| \\,\n\t\t\tq_0,s_0\n\t\t\\Bigg]\n\\\\ \n\t\\nonumber\n\t& = \n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\\mathbb{E} \\Bigg[\n\t\t\t\\sum_{t=2}^{H+1}\n\t\t\t\t2q_0^\\intercal \n\t\t\t\t\\Delta_1^t\n\t\t\\, \\Bigg| \\,\n\t\t\tq_0,s_0\n\t\t\\Bigg]\n\\\\ \n\t\\nonumber\n\t& =\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t\\mathbb{E} \\Bigg[\n\t\t\t\\sum_{t=1}^{H+1}\n\t\t\t\t2q_0^\\intercal \n\t\t\t\t\\Delta_0^t\n\t\t\t\t- 2q_0^\\intercal \\Delta_0^1\n\t\t\\, \\Bigg| \\,\n\t\t\tq_0,s_0\n\t\t\\Bigg]\n\\\\ \n \\label{eq::to_last_long}\n\t& \\leq\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z}\n\t\t\\mathbb{E} \\Bigg[\n\t\t\t\\sum_{t=1}^{H+1}\n\t\t\t\t2q_0^\\intercal \n\t\t\t\t\\Delta_0^t\n\t\t\\, \\Bigg| \\,\n\t\t\tq_0,s_0\n\t\t\\Bigg]\n\t-\n\t\\min_{ \\tr{z}_0^{0} \\in \\hphantom{\\mathcal{C}} \\mathclap{\\mathcal{C}_z} }\n\t\\mathbb{E} \\Bigg[\t\n\t2q_0^\\intercal \\Delta_0^1\n\t\\, \\Bigg| \\,\n\t\tq_0,s_0\n\t\\Bigg]\n\\end{align}\nThough $\\Delta_0^1$ is steered by $v_0$ (the actual control of the system), every policy has to abide to the constituency, which allows for the last term to be formulated over $\\tr{z}_0^0 \\Leftrightarrow z_0^{s_0}$. Still, the first term of \\eqref{eq::to_last_long} depends on $v_0$ through $\\Delta_0^t$ so that we can rewrite it as\n\\begin{multline}\n\\label{eq::to_elim_a}\n\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\\mathbb{E} \\Bigg[\n\t\t\\sum_{t=1}^{H+1}\n\t\t\t2q_0^\\intercal \n\t\t\t\\Delta_0^t\n\t\\, \\Bigg| \\,\n\t\tq_0,s_0\n\t\\Bigg]\n\t=\n\t\\frac{ \\left( H+1 \\right) \\left( H+2 \\right) }{2} \\e{a}\n\\\\\n\t\t+\n\t\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t2q_0^\\intercal R \\left[\n\t\t \\sum_0^H\n\t\t W^{s_0} v_0\n\t\t +\n\t\t \\sum_{t=1}^H \\sum_{\\tau=t}^{H} \\sum_{\\R{s_t}} W^\\R{s_t} z_t^\\R{s_t} \\CP{\\R{s_t}}{s_0}\n\t\t\\right]\n\\end{multline}\n\nTo interface the $\\mathsf{PNC}$ policy \\eqref{eq::pnc_policy_def}, we need to incorporate the positiveness constraints. To that end, we introduce a transformation in variables, centered around the idea, that each set $\\{ z_t^1,\\dots z_t^{n_s} \\}$ can be expressed by a common part $\\mu_t$, and $n_s$ differences $\\delta_t^i$:\n\\begin{equation}\n\\begin{gathered}\n\tz_t^i = \\mu_t + \\delta_t^i \\in \\mathcal{C}_z\n\t\\\\\n\t\\text{for} \\qquad t = 1,\\dots H \\qquad \\text{and} \\qquad i = 1 ,\\dots n_s\n\t\\\\\n\t\\text{with} \\qquad \\mu_t,\\delta_t^i \\in \\{0,1\\}^{n_v} \\qquad \\text{and} \\qquad C \\mu_t \\leq c \n\\end{gathered}\n\\end{equation}\nWe define a suitable stacking of these new variables in such a way that\n$\\tr{z}_1^H = \\tr{\\mu}_1^H + \\tr{\\delta}_1^H$ and write $\\tr{\\delta}_1^H \\in \\mathcal{C}_z \\setminus \\tr{\\mu}_1^H $, to express that $\\tr{\\delta}_1^H$ has to abide to usual constituency, if $\\tr{\\mu}_1^H$ has already been chosen (i.e. for each $\\delta_t^i$ separately it must hold that $C \\delta_t^i \\leq c - C \\mu_t$).\n\nNext, we substitute these variables into the last term of \\eqref{eq::to_elim_a}.\n\\def\\hspace{1cm}{\\hspace{1cm}}\n\\def\\hspace{3mm}{\\hspace{3mm}}\n\\begingroup\n\\allowdisplaybreaks\n\\begin{align}\n \\nonumber\n &\n\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t2q_0^\\intercal R \n\t\t\\Bigg[\n\t\t\\sum_{0}^{H} W^{s_0} v_0\n\t\t +\n\t\t\\sum_{t=1}^H \\sum_{\\tau=t}^{H} \\sum_{\\R{s_t}} W^\\R{s_t} z_t^\\R{s_t} \\CP{\\R{s_t}}{s_0}\n\t\t\\Bigg]\n\\\\ \\nonumber\n\t&\n\t=\n\t\\min_{ \\tr{\\mu}_1^H + \\tr{\\delta}_1^H \\in \\mathcal{C}_z }\n\t\t2q_0^\\intercal R \n\t\t\\Bigg[\n\t\t\\sum_{0}^{H} W^{s_0} v_0\n\t\t\\\\ \\nonumber\n\t\t& \\hspace{1cm} +\n\t\t\\sum_{t=1}^H \\sum_{\\tau=t}^{H} \\sum_{\\R{s_t}} W^\\R{s_t} \\left( \\mu_t + \\delta_t^\\R{s_t} \\right) \\CP{\\R{s_t}}{s_0}\n\t\t\\Bigg]\n\\\\ \\nonumber\n\t&\n\t=\n\t\\min_{ \\tr{\\delta}_1^H \\in \\mathcal{C}_z \\setminus \\tr{\\mu}_1^H }\n\t\\hspace{3mm}\n\t\\min_{ \\vphantom{\\tr{\\delta}_1^H} \\tr{\\mu}_1^H \\in \\mathcal{C} }\n\t\t2q_0^\\intercal R \n\t\t\\Bigg[\n\t\t\\sum_{0}^{H} W^{s_0} v_0\n\t\t\\\\ \\nonumber\n\t\t& \\hspace{1cm} +\n\t\t\\sum_{t=1}^H \\sum_{\\tau=t}^{H} \\left( \\sum_{\\R{s_t}} W^\\R{s_t} \\CP{\\R{s_t}}{s_0} \\right) \\mu_t \n\t\t\\\\ \\nonumber\n\t\t& \\hspace{1cm} +\n\t\t\\sum_{t=1}^H \\sum_{\\tau=t}^{H} \\sum_{\\R{s_t}} W^\\R{s_t} \\delta_t^\\R{s_t} \\CP{\\R{s_t}}{s_0}\n\t\t\\Bigg]\n\\\\ \\nonumber\n\t&\n\t=\n\t\\min_{ \\tr{\\delta}_1^H \\in \\mathcal{C}_z \\setminus \\tr{\\mu}_1^H }\n\t\\hspace{3mm}\n\t\\min_{ \\vphantom{\\tr{\\delta}_1^H} \\tr{\\mu}_1^H \\in \\mathcal{C} }\n\t\t2q_0^\\intercal R \n\t\t\\Bigg[\n\t\t\\sum_{0}^{H} W^{s_0} v_0\n\t\t\\\\ \\nonumber\n\t\t& \\hspace{1cm} +\n\t\t\\sum_{t=1}^H \\sum_{\\tau=t}^{H} \\e{W}_t(s_0) \\mu_t\n\t\t\\\\ \\nonumber\t\n\t\t& \\hspace{1cm} +\t\t\n\t\t\\sum_{t=1}^H \\sum_{\\tau=t}^{H} \\sum_{\\R{s_t}} W^\\R{s_t} \\delta_t^\\R{s_t} \\CP{\\R{s_t}}{s_0}\n\t\t\\Bigg]\n\\\\ \\nonumber\n\t&\n\t\\leq\n\t\\min_{ \\tr{\\delta}_1^H \\in \\mathcal{C}_z \\setminus \\tr{\\mu}_1^H }\n\t\\hspace{3mm}\n\t\\min_{ \\vphantom{\\tr{\\delta}_1^H} \\left( \\mathbf{0}_{n_v}^\\intercal , \\left( \\tr{\\mu}_1^H\\right )^\\intercal \\right)^\\intercal \\in \\mathcal{C} \\cap \\mathcal{P}(q_0)}\n\t\t2q_0^\\intercal R \n\t\t\\Bigg[\n\t\t\\cdot\n\t\t\\Bigg]\n\\\\ \\nonumber\n\t&\n\t\\overset{\\mathclap{\\mathsf{PNC}}}{=}\n\t\\min_{ \\tr{\\delta}_1^H \\in \\mathcal{C}_z \\setminus \\tr{\\mu}_1^H }\n\t\\hspace{3mm}\n\t\\min_{ \\vphantom{\\tr{\\delta}_1^H} \\left( v_0^\\intercal , \\left( \\tr{\\mu}_1^H\\right )^\\intercal \\right)^\\intercal \\in \\mathcal{C} \\cap \\mathcal{P}(q_0) }\n\t\t2q_0^\\intercal R \n\t\t\\Bigg[\n\t\t\\cdot\n\t\t\\Bigg]\n\\\\ \\nonumber\n\t&\n\t\\overset{\\mathclap{\\text{Lemma \\ref{lemma::diff}}}}{\n\t\\leq\n\t}\n\tK_7 +\n\t\\min_{ \\tr{\\delta}_1^H \\in \\mathcal{C}_z \\setminus \\tr{\\mu}_1^H }\n\t\\hspace{3mm}\n\t\\min_{ \\vphantom{\\tr{\\delta}_1^H} \\left( v_0^\\intercal , \\left( \\tr{\\mu}_1^H\\right )^\\intercal \\right)^\\intercal \\in \\mathcal{C} }\n\t\t2q_0^\\intercal R \n\t\t\\Bigg[\n\t\t\\cdot\n\t\t\\Bigg]\n\\\\ \\nonumber\n\t&\n\t\\leq\n\tK_7 +\n\t\\min_{ \\tr{\\delta}_1^H \\in \\mathcal{C}_z \\setminus \\tr{\\mu}_1^H }\n\t\\hspace{3mm}\n\t\\min_{ \\vphantom{\\tr{\\delta}_1^H} v_0 \\in \\mathcal{C} }\n\t\\hspace{3mm}\n\t\\min_{ \\vphantom{\\tr{\\delta}_1^H} \\tr{\\mu}_1^H \\in \\mathcal{C} }\n\t\t2q_0^\\intercal R \n\t\t\\Bigg[\n\t\t\\cdot\n\t\t\\Bigg]\n\\\\ \\nonumber\n\t&\n\t=\n\tK_7 +\n\t\\min_{ \\tr{z}_1^H \\in \\mathcal{C}_z }\n\t\\hspace{3mm}\n\t\\min_{ v_0 \\in \\mathcal{C} }\n\t\t2q_0^\\intercal R \n\t\t\\Bigg[\n\t\t\\sum_{0}^{H} W^{s_0} v_0\n\\\\ \\nonumber\n\t\t& \\quad +\t\t\n\t\t\\sum_{t=1}^H \\sum_{\\tau=t}^{H} \\sum_{\\R{s_t}} W^\\R{s_t} z_t^\\R{s_t} \\CP{\\R{s_t}}{s_0}\n\t\t\\Bigg]\n\\\\ \\nonumber\n\t&\n\t=\n\tK_7 + \n\t\\min_{ \\tr{z}_0^H \\in \\mathcal{C}_z }\n\t2q_0^\\intercal R \n\t\t\\sum_{t=0}^H \\sum_{\\tau=t}^{H} \\sum_{\\R{s_t}} W^\\R{s_t} z_t^\\R{s_t} \\CP{\\R{s_t}}{s_0}\n\\\\\n \\label{eq::to_intro_PNC}\n\t&\n\t=\n\tK_7 + \n\t\\min_{ \\tr{z}_0^H \\in \\mathcal{C}_z }\n\t\t\\CE{ \\sum_{t=1}^{H+1} 2q_0^\\intercal R \\Delta_0^t }{q_0,s_0}\n\\end{align}\nNote that we used the fact that $\\mu_t$, once separated from the $\\delta_t^i$ in a suitable manner, can be identified with the dummy control $u_t$ from the definition of the $\\mathsf{PNC}$ policy \\eqref{eq::pnc_objective}. Hence, the equality marked with the $\\mathsf{PNC}$ label \\textit{only} holds under the $\\mathsf{PNC}$ policy, since it chooses $v_0$ in such a way that the entire first term is minimized over the trajectory $\\tr{z}_0^H$ instead of only $\\tr{z}_1^H$.\n\nIf we now combine the results of \\eqref{eq::to_long}, \\eqref{eq::to_elim_a} and \\eqref{eq::to_intro_PNC} we get\n\\begin{gather}\n\t\\label{eq::to_first_drift_term}\n\t\\CE{ f (q_1,s_1) }{ q_0,s_0 } - H \\norm{q_0} - K_8 \n\t\\\\\n\t\\nonumber\n\t\\leq\n\t\\min_{ \\tr{z}_0^{H} \\in \\hphantom{\\mathcal{C}} \\mathclap{\\mathcal{C}_z} }\n\t\\mathbb{E} \\Bigg[\n\t\t\\sum_{t=1}^{H+1} 2q_0^\\intercal \\Delta_0^t\n\t\\, \\Bigg| \\,\n\t\tq_0,s_0\n\t\\Bigg]\n\t-\n\t\\min_{ \\tr{z}_0^{0} \\in \\hphantom{\\mathcal{C}} \\mathclap{\\mathcal{C}_z} }\n\t\\mathbb{E} \\Bigg[\t\n\t2q_0^\\intercal \\Delta_0^1\n\t\\, \\Bigg| \\,\n\t\tq_0,s_0\n\t\\Bigg]\n\\end{gather}\nwhich completes the derivation for the first term of \\eqref{eq::to_drift}.\n\nIn a similar but much easier fashion, the second term of \\eqref{eq::to_drift} can be reshaped into\n\\begin{equation}\n\t\\label{eq::to_second_drift_term}\n\\begin{gathered}\n\t\\CE{ f (q_0,s_0) }{ q_0,s_0 } - H \\norm{q_0}\n\t\\\\\n\t\\geq\n\t\\min_{ \\tr{z}_0^{H-1} \\in \\mathcal{C}_z }\n\t\\mathbb{E} \\Bigg[\n\t\t\\sum_{t=1}^{H} 2q_0^\\intercal \\Delta_0^t\n\t\\, \\Bigg| \\,\n\t\tq_0,s_0\n\t\\Bigg]\n\\end{gathered}\n\\end{equation}\nCombining \\eqref{eq::to_first_drift_term} and \\eqref{eq::to_second_drift_term} results in\n\\begin{gather*}\n\t\\CE{ f (q_1,s_1) - f (q_0,s_0) }{ q_0,s_0 }\n\\\\\n\t\\leq\n\tK_{8} +\n\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\\mathbb{E} \\Bigg[\n\t\t2q_0^\\intercal \\Delta_1^{H+1}\n\t\\, \\Bigg| \\,\n\t\tq_0,s_0\n\t\\Bigg]\n\\end{gather*}\nTo alleviate the outer conditioning on $s_0$ we take the expectation $\\CE{\\cdot}{q_0}$ on both sides, conditioned only on $q_0$, and swap minimization and expectation operator:\n\\begin{gather*}\n\t\\CE{ f (q_1,s_1) - f (q_0,s_0) }{ q_0 }\n\\\\\n\t\\leq\n\tK_{9} +\n\t\\mathbb{E} \\Bigg[\n\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\\mathbb{E} \\Bigg[\n\t\t2q_0^\\intercal \\Delta_1^{H+1}\n\t\\, \\Bigg| \\,\n\t\tq_0,s_0\n\t\\Bigg]\n\t\\, \\Bigg| \\,\n\t\tq_0\n\t\\Bigg]\n\\\\\n\t\\leq\n\tK_{9} +\n\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\\mathbb{E} \\Bigg[\n\t\t2q_0^\\intercal \\Delta_1^{H+1}\n\t\\, \\Bigg| \\,\n\t\tq_0\n\t\\Bigg]\n\\end{gather*}\nFinally, recall that for throughput optimality, this expression has to be negative for each $\\e{a}$ that can be expressed via \\eqref{eq::def_to}. Substituting this we obtain\n\\begin{gather*}\n\t\\CE{ f (q_1,s_1) - f (q_0,s_0) }{ q_0 }\n\\\\\n\\begin{aligned}\n\t& \\leq\n\tK_{9} +\n\t\\min_{ \\tr{z}_1^{H} \\in \\mathcal{C}_z }\n\t\t2q_0^\\intercal \\left(\n\t\t\\sum_{t=1}^{H}\n\t\t\t\\sum_{\\R{s}} \\pi^\\R{s} R W^\\R{s} z_t^\\R{s} + H \\e{a}\n\t\t\\right)\n\\\\ &\n\\begin{multlined}\n\t\\leq\n\tK_{9} +\n\t\\min_{ z' \\in \\mathcal{C}_z }\n\t\t2Hq_0^\\intercal \\Bigg(\n\t\t\\sum_{\\R{s}} \\pi^\\R{s} RW^\\R{s} z^\\R{s}\n\t\\\\\n\t\t- \\varepsilon \\mathbf{1}_{n_q}\n\t\t- \\sum_{\\R{s}} \\pi^\\R{s} R W^\\R{s} \\sum_{v \\in \\mathcal{V}} \\lambda^{\\R{s},v} v\n\t\t\\Bigg)\n\\end{multlined}\t\n\\end{aligned}\t\n\\end{gather*}\nBecause the minimization is linear, the optimum is found on the boundary and thus the first term in the bracket (which is subject to minimization) will at least cancel out the last term, leaving us with\n\\begin{align*}\n\t\\CE{ f (q_1,s_1) - f (q_0,s_0) }{ q_0 }\n\t& \\leq\n\tK_{9}\n\t-\n\t2 H \\varepsilon q_0^\\intercal \\mathbf{1}_{n_q}\n\t\\\\\n\t& \\leq\n\tK_{9}\n\t-\n\tK_{10} \\mathbf{1}_{n_q}^\\intercal q_0 \n\\end{align*}\nwhich fulfills lemma \\ref{lemma::foster} and therefore proves throughput optimality of our $\\mathsf{PNC}$ policy.\n\\\\\n\\rightline{$\\Box$}\n\n\\section{Exemplary Applications of PNC}\n\\label{sec::examples}\n\n\\subsection{Dynamic Topology}\n\nWe employ a scenario as depicted in \\figref{fig::ext_1_scenario}, where a mobile user equipment (UE) crosses multiple sectors, each one designated to a specific access point (AP). In each sector, the UE can only communicate with the corresponding AP. The APs are connected to a global network from which they receive packets that they are supposed to transmit to the UE.\nThe derived queueing network is shown in \\figref{fig::ext_1_model}. We use a most simplified model to yield easily interpretable results: First, the DTMC is deterministic which allows us to fix the time behavior of the transmission success probabilities $\\e{m}^j_t$ of the links. Second, we model this deterministic time behavior as binary sequences which are depicted in \\figref{fig::ext_1_links}. This corresponds to the case, in which the UE travels with constant velocity along a known path and the sectors do not overlap. The UE remains in each sector for exactly 3 time slots, where it experiences perfect channel quality (guaranteed transmission success).\nFurther we assume that a single packet is created every second time slot at $q^1$, which represents the entire arrival to the system.\n\\begin{figure}[htbp]\n \\centering\n \\includegraphics[]{motivation_DTMC_f.pdf}\n \\caption{Extension 1 - Scenario}\n \\label{fig::ext_1_scenario}\n\\end{figure}\n\\begin{figure}[htbp]\n \\centering\n \\includegraphics[]{motivation_DTMC_model_f.pdf}\n \\caption{Example 1 - Queueing Network}\n \\label{fig::ext_1_model}\n\\end{figure}\n\\begin{figure}[htbp]\n \\centering\n \\includegraphics[]{motivation_DTMC_links_f.pdf}\n \\caption{Example 1 - Link Probabilities}\n \\label{fig::ext_1_links}\n\\end{figure}\n\nThe simulation results in \\figref{fig::ext_1_simu_2} depict the accumulated amount of packets send (blue) and received by the UE (green and red). For visualization purposes, we averaged the resulting step functions, so that they are presented as lines. It can be observed that only around 33\\% of the packets reach the UE for the conventional back pressure policy, $\\mathsf{MW}$, (red). (Note that $\\mathsf{MW}$ can be expressed as a special case of the $\\mathsf{PNC}$ policy, in which the horizon is $H=1$.) The other 66\\% remain at already past base stations. This high packet loss is due to $\\mathsf{MW}$ requiring time to establish its throughput optimality. Indirectly, $\\mathsf{MW}$ functions by using misplaced packets as an indicator for later control decisions. The presented example, however, is based on a transient event where this indicator function of misplaced packets is only of limited use.\n\nAs can be seen, using the novel $\\mathsf{PNC}$ with horizon $H=2$, (the lowest green line) already nearly doubles the amount of packets that arrive at the UE to 60\\%. For $H=5$ we reach 80\\%, a significant performance boost.\n\\begin{figure}[htbp]\n \\centering\n \\includegraphics[]{motivation_DTMC_simu_f.pdf}\n \\caption{Example 1 - Simulation with Multiple Policies}\n \\label{fig::ext_1_simu_2}\n\\end{figure}\n\n\\subsection{Networks with Synchronized Queues}\n\\label{subsec::synchronized_queues}\n\nThe following application is motivated by fact, that though $\\mathsf{MW}$ performs poorly in networks with dynamic topology, it is still able to achieve throughput optimality in the long run, if we assume $\\mathsf{MW}$ to be sensitive to the current state of the DTMC. And it is only fair to make this assumption, since we assume the same for our $\\mathsf{PNC}$ policy. Hence, throughput optimality seems to be shared by both policies, if we talk about conventional networks. However, in the next example, we forgo conventional networks and introduce \\textit{synchronized} queues. In networks with synchronized queues, only $\\mathsf{PNC}$ seems to maintain its throughput optimality while $\\mathsf{MW}$ fails, giving a strong incentive to employ the $\\mathsf{PNC}$ policy.\n\nQueues are \\textit{synchronized} (or \\textit{paired}), if they can only be served at the same time. This can be useful, if one wants to\nexploit constructive interference \\cite{Timotheou2016} or\nmodel parallel processing tasks in computing \\cite{Evdokimova2018} and social matchmaking \\cite{Buke2015}.\nWhile synchronized queues have been studied on their own \\cite{Harrison1973} \\cite{Fayolle1979} \\cite{Borst2008} \\cite{DeCuypere2014}, there has not been any research on how they behave in a network. Indeed, \\cite{Schoeffauer2018a} presents a simple example, proving that $\\mathsf{MW}$ loses its throughput optimality if the network contains only a single pair of synchronized queues. The reason for that can be found equation A.18 from the original proof in \\cite{Tassiulas1992}, which loses its generality. In layman's terms, the original proof is based on the fact, that the evolution of the queue vector constitutes a DTMC by itself. And for conventional networks, there exists a \\textit{finite} set of states (of that DTMC) which can be shown to be recurrent. This makes the entire DTMC recurrent which corresponds to throughput optimality. However, introducing synchronized queues, the finite set grows to infinite size, invalidating this correspondence. This leads to the questions, in how far back-pressure policies are suited for such networks and if there exists another policy, which guarantees throughput optimality.\n\nTo illustrate that $\\mathsf{PNC}$ might be that policy, we refer to the example, depicted in \\figref{fig::constructive_interference_set_up}. Set-up and thereof derived queueing network are shown on the left and right side, respectively. The example consists of an access point (AP) $q_1$ that can either transmit solitary (link $r^1$), or initiate synchronized transmission (link $r^3$) with a neighboring AP $q_2$. The synchronized transmission uses constructive interference and thus achieves higher throughput. However, before synchronized transmission can be initiated, the data packets have to be shared (link $r^2$), i.e. copied from $q_1$ to $q_2$.\n\\begin{figure}[htbp]\n \\centering\n \\includegraphics[]{motivation_LT_f.pdf}\n \\caption{Example 2 - Scenario and Queueing Network}\n \\label{fig::constructive_interference_set_up}\n\\end{figure}\n\nFor comprehensiveness, we use a constant success probability matrix $\\e{M}$, i.e. we do not make use of an DTMC to select different matrices from $\\mathcal{W}$. The $\\e{m}^j$ (which are the diagonal elements of $\\e{M}$) are chosen in such a way, that it is beneficial to copy (share) the data and then transmit together, instead of broadcasting the data directly. \nSpecifically, we set $\\e{m}^1 = \\frac{1}{4}$ and $\\e{m}^2 = \\e{m}^3 = 1$ and assume all links to be disjunct. Note, that we can neglect $q^3$ in all further discussions, since it only symbolizes the destination queue.\n\nFor this simple example, it is prudent to forgo working in terms of the \\textit{control vector} $v_t$ and instead use the \\textit{control option} $u_t$, which we define to be $u_t = R \\e{M} v_t$ (this $u_t$ is not related to the one, that was used in the definition of the $\\mathsf{PNC}$ policy). We have $u_t \\in \\mathcal{U}$, which can be derived from the system description without further ado to be\n\\begin{equation}\n \\mathcal{U} =\n \\Set{\n u^0 , u^1 , u^2 , u^3\n }\n = \n \\Set{\n \\begin{pmatrix} 0 \\\\ 0 \\end{pmatrix} ,\n \\begin{pmatrix} -1 \\\\ 0 \\end{pmatrix} ,\n \\begin{pmatrix} 0 \\\\ 4 \\end{pmatrix} ,\n \\begin{pmatrix} -4 \\\\ -4 \\end{pmatrix}\n }\n\\end{equation}\nwhere we scaled all elements of $\\mathcal{U}$ with the factor $4$ to simplify any calculations. We have $u^1, u^2, u^3$ represent single transmission, data sharing, and joint transmission, respectively and in each time slot, the controller may only choose one of these options to influence the expected queue state via $\\CE{q_{t+1}}{q_t} = q_t + u_t + a_t$.\n\nRegarding \\eqref{eq::def_to}, it is now very easy to express the set of all arrival rates $\\e{a}$, for which there exists a policy that stabilizes the system. We call this set the maximum stability region $\\mathcal{A}$ and have\n\\begin{equation}\n\t\\begin{aligned}\n\t\\mathcal{A} :&= \\Set{\n\t\t\\e{a} : \\quad\n\t\t\\e{a} + \\sum_{u \\in \\mathcal{U}} \\lambda^u u = -\\mathbf{1} \\varepsilon, \\quad\n\t\t\\begin{gathered}\n\t\t\t\\varepsilon > 0\n\t\t\t\\\\\n\t\t\t\\sum \\lambda^u \\leq 1\n\t\t\\end{gathered}\n\t}\n\t\\\\\n\t&=\n\t\\Set{\n\t\t\\e{a} : \\quad\n\t\t\\e{a} + \\sum_{u \\in \\mathcal{U}} \\lambda^u u = \\mathbf{0}, \\quad \\hspace{4.5mm}\n\t\t\t\\sum \\lambda^u < 1\n\t}\n\t\\end{aligned}\n\\end{equation}\nRemember that throughput optimality is accomplished, if a policy can stabilize the system for all arrival rates in $\\mathcal{A}$.\nA graphical representation of $\\mathcal{A}$ is given in green on the left side of \\figref{fig::constructive_interference_stab_region}.\n\nNow, let us assume that there is no arrival at $q^2$, i.e. $\\e{a}^2 = 0$.\nUsing the control options $u^2$ and $u^3$ in alternating sequence (given that there are enough packets in $q^1$ to do so) would yield an efflux of $4$ packets every $2$ time slot, thus an efflux of $2$ packets per time slot. The corresponding point is shown on the right side of \\figref{fig::constructive_interference_stab_region}.\nIt is easy to check, that no other sequence of control options can match this efflux.\n\nHowever, conventional back-pressure policies like $\\mathsf{MW}$ are not able to access the control option $u^2$, resulting in the loss of its throughput optimality in this example. The only arrival rates, that $\\mathsf{MW}$ \\textit{can} stabilize are those in the red triangle on the right side of \\figref{fig::constructive_interference_stab_region}.\n\n\\begin{figure}[htbp]\n \\centering\n \n \\includegraphics[]{stab_region_simple_f.pdf}\n \\vspace{0mm}\n \\centering\n \\caption{Example 2 - Stability Regions}\n \\label{fig::constructive_interference_stab_region}\n\\end{figure}\n\nIn contrast, $\\mathsf{PNC}$ is able to select the missing control option $u^2$ and simulations suggest, that it stabilizes the example for any strictly positive arrival rate $\\e{a}$ from $\\mathcal{A}$. To substantiate this claim we refer to \\figref{fig::simu_comparison}. Here, we simulated the queue state $q^1$ over time $t$ for 3 different arrival rates $\\e{a}$ under 3 different policies. The respective positions of those $\\e{a}$ regarding $\\mathcal{A}$ are depicted in \\figref{fig::simu_points}. As for the policies, we chose $\\mathsf{MW}$ and $\\mathsf{PNC}$. Also, we added a third control policy, labeled $\\mathsf{fPNC}$ for \\textit{fixed} $\\mathsf{PNC}$. This policy mimics the $\\mathsf{PNC}$ policy, except that it uses the entire calculated control trajectory before repeating the optimization. In contrast, $\\mathsf{PNC}$ repeats the optimization every time slot again.\n\nAs predicted, we have $\\mathsf{MW}$ not stabilizing the blue and green arrival rates. Furthermore, it can be seen, that $\\mathsf{fPNC}$ loses some stabilizing properties with increasing horizon as the green arrival rate cannot be stabilized with $H=3$ (this is related to the horizon not being an even number). This proves, that the MPC paradigm of repeating the optimization in every step (and thereby discarding the rest of the trajectory) is an essential part in the $\\mathsf{PNC}$ policy.\n\n\\begin{figure}[htbp]\n \\centering\n \\includegraphics[]{stab_region_points_f.pdf}\n \\caption{Example 2 - Selected arrival rates for simulation; Corresponding stability regions: (red -- $\\mathsf{MW}$), (red+blue -- $\\mathsf{fPNC}_{H=3}$), (red+blue+green -- $\\mathsf{PNC}$, $\\mathsf{fPNC}_{H=2}$)}\n \\label{fig::simu_points}\n\\end{figure}\n\\begin{figure}[htbp]\n \\centering\n \\includegraphics[]{horizon_dependent_stability_f.pdf}\n \\caption{Example 2 - \n Queue state (system state) $q^1$ as a function of time $t$ for various (color-coded) arrival rates}\n \\label{fig::simu_comparison}\n\\end{figure}\n\n\\section{Conclusion}\nWe successfully modify a discrete-time queueing network with a JMS, i.e. with an additional DTMC that changes network parameters (even topology) on a mid- to long-term time scale. We then introduce a novel family of predictive control policies, $\\mathsf{PNC}$, based on the paradigms of MPC, and devise a special implementation of the underlying prediction, that allows the policy to be executed in the fastest way possible. The policy is especially well suited to control the mentioned systems and outperforms conventional control approaches as is illustrated in numerical simulations. In our main contribution, we prove throughput optimality of $\\mathsf{PNC}$. Looking ahead, we see an intriguing application in networks that consist of synchronized queues (e.g. found in parallel computing or manufacturing chains). Those networks still elude known control strategies but seem to be stabilizable under $\\mathsf{PNC}$ policies with suitably chosen prediction horizon.\n\n\\section*{Acknowledgment}\nThis work is part of and thereby funded by the DFG Priority Program 1914\n\n\\bibliographystyle{ieeetr}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction and Motivation} \n Information on hyperon resonances is generally not as extensive as for nucleon resonances. The study of $\\overline K N\\rightarrow \\overline K N$, $\\overline K N\\rightarrow \\pi \\Lambda$, and $\\overline K N\\rightarrow \\pi \\Sigma$ could lead to the better understanding of $\\Lambda^*$s and $\\Sigma^*$s.\n\n Most previous partial-wave analyses (PWAs) of $\\overline K N\\rightarrow \\overline K N$, $\\overline K N\\rightarrow \\pi \\Lambda$, and $\\overline K N\\rightarrow \\pi \\Sigma$ {\\cite{Armenteros1969, Conforto1971, Horn1975_1, Hemingway1975, Baillon1975, Gopal1977}}, were based on the assumption that partial-wave amplitudes could be represented by a simple sum of resonant and background terms. Such an assumption violates unitarity of the partial-wave $S$-matrix. In this work, we report on our investigation of the reactions $K^-p\\rightarrow K^-p$ and $K^-p\\rightarrow \\overline K^0n$, $K^-p\\rightarrow \\pi^0\\Lambda$, and $K^-p\\rightarrow\\pi^+\\Sigma^-$, $K^-p\\rightarrow\\pi^0\\Sigma^0$, and $K^-p\\rightarrow\\pi^-\\Sigma^+$ via single-energy analyses and a subsequent energy-dependent analysis. All available differential cross section, polarization, polarized cross section, and cross-section data up to a maximum c.m.\\ energy of about 2.1 GeV were fitted. In order to ensure that our amplitudes had a relatively smooth variation with energy, we introduced several constraints that will be described in detail below. The determination of resonance parameters from our subsequent energy-dependent analysis is discussed in Ref. \\cite{manoj2013}.\n\n\n \n \n\n \n \\section{Formalism and Fitting Procedures}\n Here, we summarize the formalism for the single-energy partial-wave analyses.\n The differential cross section $\\rm d\\sigma \/\\rm d\\Omega$ and polarization $P$ for unpolarized scattering of spin-0 mesons off spin-$\\frac12$ nucleons are given by \\cite{bransden73}\n \\begin{equation}\n \\frac{{\\rm d}\\sigma}{{\\rm d}\\Omega} = {\\lambdabar}^2(|f|^2+|g|^2)~,\n \\end{equation}\n \\begin{equation}\n P\\frac{{\\rm d}\\sigma}{{\\rm d}\\Omega} =2{\\lambdabar}^2\\rm Im(fg^\\ast)~,\n \\end{equation}\n where $\\lambdabar = {\\hbar}\/{k}$,\n with $k$ the magnitude of c.m.\\ momentum of the incoming meson.\n Here, $f = f(W,\\theta)$ and $g = g(W,\\theta)$ are the usual spin-non-flip and spin-flip amplitudes at c.m.\\ energy $W $ and meson c.m.\\ scattering angle $\\theta$. In terms of partial waves, $f$ and $g$ can be expanded as\n \n \\begin{equation}\n f(W,\\theta) = \\sum_{l=0}^{\\infty} [(l+1)T_{l+} + lT_{l-}]P_l(\\cos\\theta)~,\n \\end{equation}\n \\begin{equation}\n g(W,\\theta) = \\sum_{l=1}^{\\infty} [T_{l+} - T_{l-}]P_l^1(\\cos\\theta)~,\n \\end{equation}\n where $l$ is the initial orbital angular momentum, $P_l(\\cos\\theta)$ is a Legendre polynomial and $P_l^1(\\cos\\theta) = \\sin\\theta \\cdot {\\rm d} P_l(\\cos\\theta)\/{\\rm d}(\\cos\\theta$). The total angular momentum for the amplitude $T_{l+}$ is $J=l+\\frac12$, while that for the amplitude $T_{l-}$ is $J=l-\\frac12$.\n For the initial $\\overline K N$ system, we have $I = 0$ or $I =1$ so that the amplitudes $T_{l\\pm}$ can be expanded in terms of isospin amplitudes as \n \\begin{equation}\n T_{l\\pm} = C_{0}T^{0}_{l\\pm} + C_{1}T^{1}_{l\\pm}~,\n \\end{equation}\n \\newline\n where $T^I_{l\\pm}$ are partial-wave amplitudes\n with isospin $I$ and total angular momentum $J = l\\pm\\frac12$ with $C_I$ the appropriate isospin Clebsch-Gordon coefficients for a given reaction.\n For $K^- p \\rightarrow K^- p$, for example, we have $C_{0} = {\\frac12}$ and $C_{1}={\\frac12}$.\n\\newline\n\n The total $K^- p$ cross section is given by $\\sigma_{\\rm total} = 4\\pi {\\lambdabar}^2 {\\rm Im} f(W, 0)$, or\n\\begin{equation}\\label{eq:Sigma_total}\n\\sigma_{\\rm total} = 4\\pi \\lambdabar^2 \\sum_{l=0}^{\\infty}[(l+1) {\\rm Im}T_{l^+} + l{ \\rm Im}T_{l^-}],\n\\end{equation}\nwhere here the $T_{l\\pm}$ are partial-wave amplitudes for elastic $\\overline KN$ scattering.\n\n\n The integrated cross section for a particular two-body reaction is\n\\begin{equation}\n\\sigma=4\\pi {\\lambdabar}^2 \\sum_{l=0}^{\\infty} [(l+1) |T_{l^+}|^2+l |T_{l^-}|^2] .\n\\end{equation}\n\n Tables I, II, and III summarize the available quantity and types of data in each energy bin for the three reactions $\\overline K N\\rightarrow \\overline K N$, $\\overline K N\\rightarrow \\pi \\Lambda$, and $\\overline K N\\rightarrow \\pi \\Sigma$, respectively. \n Single-energy fits were performed separately for (i) $K^-p\\rightarrow K^-p$ and $K^-p\\rightarrow \\overline K^0n$, for (ii) $K^-p\\rightarrow \\pi^0\\Lambda$, and for (iii) $K^-p\\rightarrow\\pi^+\\Sigma^-$, $K^-p\\rightarrow\\pi^0\\Sigma^0$, and $K^-p\\rightarrow\\pi^-\\Sigma^+$. In each case the available data were analyzed in c.m.\\ energy bins of width 20 MeV. This choice of bin width was appropriate because the data for smaller widths had unacceptably low statistics and for larger widths, some amplitudes varied too much over the energy spread of the bin. \n \n\n\n\n\\begin{table*}[htbp]\n\\caption{Summary of database for $\\overline{K} N \\rightarrow \\overline{K} N$. Column 1 lists the central energy $W_0$ of each energy bin, columns 2 and 3 list the number of differential cross-section data points in each bin for $K^-p\\rightarrow K^-p$ and $K^-p\\rightarrow \\overline K^0n$, respectively, column 4 lists the number of polarization data points for $K^-p\\rightarrow K^-p$ in each bin, column 5 lists the number of polarized cross-section data points for $K^-p\\rightarrow K^-p$ in each bin, column 6 lists the number of integrated cross-section data points for $K^-p\\rightarrow K^-p$ and $K^-p\\rightarrow \\overline K^0n$ in each bin, column 7 lists the number of $K^-p$ total cross-section data points in each bin, column 8 lists the total number of data points for all kinds of data in each bin, and column 9 lists the references for the measurements referred to in columns 2-5.}\n\\begin{center}\n\\begin{ruledtabular}\n\\begin{tabular}{ccccccccc}\n\\multicolumn{1}{c}{$W_{0}$} & \\multicolumn{1}{c}{{${d\\sigma}\/{d\\Omega}$ } } & \\multicolumn{1}{c}{${d\\sigma}\/{d\\Omega}$ { } }& \\multicolumn{1}{c}{{\\mbox{$P$}}}& \\multicolumn{1}{c}{{\\mbox{$P$}${d\\sigma}\/{d\\Omega}$ }} & \\multicolumn{1}{c}{\\multirow{2}{*}{$\\sigma$}}& \\multicolumn{1}{c}{\\multirow{2}{*}{$\\sigma_{\\rm total}$}} & \\multicolumn{1}{c}{ Total} & \\multicolumn{1}{c}{\\multirow{2}{*}{ References}} \\\\\n\\multicolumn{1}{c}{\\rm (MeV)} & ($K^- p$) &($\\overline{K}^0 n$) & ($K^- p$)& ($K^- p$)& & & \\multicolumn{1}{c}{ No.} & \\multicolumn{1}{c}{} \\\\\n\n\\hline\n1480\t&\t 57&\t 24&\t&\t\t&15\t&\t5& 96\t&\t \\cite{Mast1976}\\\\\n1500\t&\t114&\t120&\t&&\t\t23\t&\t8& 257\t\t& \\cite{Mast1976}\\\\\n1520\t&\t100&\t100&\t&&\t\t28\t& 10& 228\t\t& \\cite{Mast1976}\\\\\n1540\t&\t178&\t120&\t&&\t\t25\t&\t8& 323\t\t& \\cite{Armenteros1970, Mast1976}\\\\\n1560&\t117&\t100&\t&&\t\t14\t&\t5& 231\t\t& \\cite{Armenteros1970, Alston1978_1, CrystalBall2005}\\\\\n1580&\t78&\t112&\t&\t&\t10\t&\t3& 200\t\t\t& \\cite{Armenteros1970, Alston1978_1, CrystalBall2005}\\\\\n1600&\t79&\t116&\t&\t&\t14\t&\t6& 209\t\t\t& \\cite{Armenteros1970, Alston1978_1, CrystalBall2005}\\\\\n1620&\t147&\t120&\t&&\t\t14\t&\t6& 281\t\t& \\cite{Armenteros1970, Adams1975, Alston1978_1, CrystalBall2005}\\\\\n1640\t&\t113&\t148&\t&&\t\t14\t&\t6& 275\t\t& \\cite{Armenteros1970, Adams1975, Alston1978_1, CrystalBall2005}\\\\\n1660\t&\t149&\t132&\t&&\t\t19\t&\t6& 300\t\t& \\cite{Armenteros1970, Adams1975, Alston1978_1, CrystalBall2005}\\\\\n1680\t&\t194&\t210&\t&&\t\t30\t&\t9& 434\t\t& \\cite{Armenteros1968, Armenteros1970, Conforto1971, Adams1975, Alston1978_1, CrystalBall2005}\\\\\n1700\t&\t150&\t112&\t&&\t\t11\t&\t6& 283\t\t& \\cite{Armenteros1968, Armenteros1970, Conforto1971, Adams1975, Alston1978_1}\\\\\n1720\t&\t150&\t150&\t&&\t\t20\t&\t6& 320\t\t& \\cite{Armenteros1968, Conforto1971, Adams1975, Alston1978_1}\\\\\n1740\t&\t216&\t241&\t&27&\t\t29\t&\t6& 513\t\t & \\cite{Armenteros1968, Albrow1971, Conforto1971, Jones1975, Adams1975, Alston1978_1}\\\\\n1760&\t176&\t193&\t&26&\t\t25\t&\t4& 420\t\t & \\cite{Armenteros1968, Albrow1971, Conforto1971, Jones1975, Adams1975, Alston1978_1}\\\\\n1780\t&\t185&\t196&\t&27&\t\t34\t&\t9& 442\t\t & \\cite{Armenteros1968, Andersson1970, Conforto1971, Jones1975, Conforto1976, Alston1978_1}\\\\\n1800&\t132&\t79&\t&52&\t\t18\t&\t4& 281\t\t\t& \\cite{Armenteros1968, Conforto1971, Jones1975, Conforto1976}\\\\\n1820&\t146&\t60&\t&27&\t\t18\t&\t5& 251\t\t\t& \\cite{Armenteros1968, Albrow1971, Conforto1971, Conforto1976}\\\\\n1840&\t185&\t80&\t&27&\t\t23\t&\t9& 315\t\t\t& \\cite{Armenteros1968, Andersson1970, Conforto1971, Conforto1976}\\\\\n1860&\t227&\t100&\t&30&\t\t22\t&\t4& 379\t\t & \\cite{Armenteros1968, Andersson1970, Conforto1971, Griselin1975, Conforto1976}\\\\\n1880&\t266&\t120&\t&28&\t\t21\t&\t6& 435\t\t & \\cite{Armenteros1968, Albrow1971, Conforto1971, Griselin1975, Conforto1976}\\\\\n1900\t&\t175&\t60&\t&56&\t\t18\t&\t6& 309\t\t\t& \\cite{Armenteros1968, Andersson1970, Albrow1971, Conforto1971, Griselin1975, Conforto1976}\\\\\n1920\t&\t146&\t60&\t&27&\t\t17\t&\t3& 250\t\t\t& \\cite{Andersson1970, Griselin1975, Conforto1976}\\\\\n1940&\t110&\t80&\t&30&\t\t18\t&\t5& 238\t\t\t& \\cite{Albrow1971, Griselin1975, Conforto1976}\\\\\n1960&\t64&\t40&\t23&&\t\t14\t&\t4& 141\t\t& \\cite{Daum1968, Griselin1975, Conforto1976}\\\\\n1980&\t34&\t20&\t&&\t\t11\t&\t3& 65\t\t& \\cite{Griselin1975, Abe1975}\\\\\n2000&\t23&\t20&\t23&&\t\t9\t&\t3& 75\t\t& \\cite{Daum1968, Abe1975}\\\\\n2020\t&\t23&\t&\t23&&\t\t12\t&\t5& 58\t\t& \\cite{Daum1968}\\\\\n2040\t&\t54&\t&\t22&&\t\t10\t&\t4& 86\t\t& \\cite{Daum1968, Abe1975}\\\\\n2060&\t23&\t&\t23&&\t\t10\t&\t3& 56\t\t& \\cite{Daum1968}\\\\\n2080\t&\t22&\t&\t22&&\t\t9\t&\t3& 53\t\t& \\cite{Daum1968}\\\\\n2100&\t53&\t&\t22&&\t\t7\t&\t2& 83\t\t& \\cite{Daum1968, Abe1975}\\\\\n2120\t&\t46&\t&\t23&46&\t\t12\t&\t5& 104\t\t& \\cite{Daum1968, Andersson1970}\\\\\n2140&\t23&\t&\t23&&\t\t5\t&\t2& 51\t\t& \\cite{Daum1968}\\\\\n2160&\t32&\t&\t&&\t\t14\t&\t5& 46\t\t& \\cite{Abe1975}\\\\\n\\end{tabular}\n\\label{Table:KN}\n\\end{ruledtabular}\n\\end{center}\n\\end{table*}\n\n\\begin{table*}[htbp]\n\\caption{Summary of database for $\\overline{K} N \\rightarrow \\pi \\Lambda$. Column 1 lists the central energy $W_0$ of each energy bin, column 2 lists the number of differential cross-section data points in each bin for $K^-p\\rightarrow \\pi^0\\Lambda$, column 3 lists the number of polarization data points in each bin, column 4 lists the number of polarized cross-section data points in each bin, column 5 lists the number of integrated cross-section data points in each bin, column 6 lists the total number of data points for all kinds of data in each bin, and column 7 lists the references for the measurements referred to in columns 2-4.}\n\\begin{center}\n\\begin{ruledtabular}\n\\begin{tabular}{ccccccc\n\\multicolumn{1}{c}{$W_{0}$} & \\multicolumn{1}{c}{\\multirow{2}{*}{${d\\sigma}\/{d\\Omega}$}} & \\multicolumn{1}{c}{\\multirow{2}{*}{\\mbox{$P$}}} & \\multicolumn{1}{c}{\\multirow{2}{*}{\\mbox{{$P$}${d\\sigma}\/{d\\Omega}$}}} & \\multicolumn{1}{c}{\\multirow{2}{*}{$\\sigma$}} & \\multicolumn{1}{c}{ Total} & \\multicolumn{1}{c}{\\multirow{2}{*}{ References}} \\\\\n\\multicolumn{1}{c}{\\rm (MeV)} & & & & & \\multicolumn{1}{c}{ No.} & \\multicolumn{1}{c}{} \\\\\n\\hline\n1480\t&\t\t& &\t\t&\t4\t&\t4\t\t\t&\t\t \\cite{Baldini1988}\\\\\n1500&\t\t& &\t\t&\t8\t&\t8\t\t\t&\t\t \\cite{Baldini1988}\\\\\n1520\t&\t\t& &\t\t&\t9\t&\t9\t\t\t&\t\t \\cite{Baldini1988}\\\\\n1540&\t40\t& &\t16\t&\t7\t&\t63\t\t\t&\t\t \\cite{Armenteros1970}\\\\\n1560&\t76\t&16&\t26\t&\t4\t&\t122\t\t\t&\t \\cite{Armenteros1970, CrystalBall2005}\\\\\n1580&\t56\t&16&\t19\t&\t2\t&\t93\t\t\t&\t \\cite{Armenteros1970, CrystalBall2005}\\\\\n1600\t&\t56\t&16&\t20\t&\t5\t&\t97\t\t\t&\t \\cite{Armenteros1970, CrystalBall2005}\\\\\n1620\t&\t56\t&16&\t20\t&\t4\t&\t96\t\t\t&\t \\cite{Armenteros1970, CrystalBall2005}\\\\\n1640&\t81\t&32&\t20\t&\t6\t&\t139\t\t\t&\t \\cite{Armenteros1970, Baxter1973, CrystalBall2005}\\\\\n1660\t&\t74\t&16&\t19\t&\t9\t&\t118\t\t\t&\t \\cite{Armenteros1970, Baxter1973, CrystalBall2005}\\\\\n1680\t&\t114\t&22&\t28\t&\t13\t&\t177\t\t\t&\t \\cite{Armenteros1968, Armenteros1970, Baxter1973, CrystalBall2005}\\\\\n1700\t&\t58\t&7 &\t\t8\t&\t13\t&\t86\t\t\t\t&\\cite{Armenteros1968, Armenteros1970, Baxter1973}\\\\\n1720\t&\t101\t&27&\t\t&\t9\t&\t137\t\t\t&\t \\cite{Armenteros1968, Baxter1973, Jones1975}\\\\\n1740&\t185\t&65&\t\t&\t12\t&\t262\t\t\t&\t \\cite{Armenteros1968, Baxter1973, Jones1975}\\\\\n1760\t&\t138\t&54&\t\t&\t11\t&\t203\t\t\t&\t \\cite{Armenteros1968, Baxter1973, Jones1975}\\\\\n1780&\t160\t&88&\t\t&\t10\t&\t258\t\t\t&\t \\cite{Armenteros1968, Jones1975, Conforto1976}\\\\\n1800\t&\t80\t&46&\t\t&\t4\t&\t130\t\t\t&\t \\cite{Armenteros1968, Jones1975, Conforto1976}\\\\\n1820\t&\t60\t&35&\t\t&\t4\t&\t99\t\t\t&\t \\cite{Armenteros1968, Conforto1976}\\\\\n1840&\t80\t&36&\t\t&\t5\t&\t121\t\t\t&\t \\cite{Armenteros1968, Conforto1976}\\\\\n1860&\t100\t&26&\t\t&\t7\t&\t133\t\t\t&\t \\cite{Armenteros1968, Griselin1975, Conforto1976}\\\\\n1880\t&\t120\t&44&\t\t&\t7\t&\t171\t\t\t&\t \\cite{Armenteros1968, Griselin1975, Conforto1976}\\\\\n1900\t&\t60\t&18&\t\t&\t5\t&\t83\t\t\t&\t \\cite{Armenteros1968, Griselin1975, Conforto1976}\\\\\n1920\t&\t80\t&21&\t\t&\t7\t&\t108\t\t\t&\t \\cite{Berthon1970, Griselin1975, Conforto1976}\\\\\n1940\t&\t100\t&23&\t\t&\t8\t&\t131\t\t\t&\t \\cite{Berthon1970, Griselin1975, Conforto1976}\\\\\n1960\t&\t60\t&24&\t\t&\t5\t&\t89\t\t\t&\t \\cite{Berthon1970, Griselin1975, Conforto1976}\\\\\n1980&\t40\t&6&\t\t&\t5\t&\t51\t&\t\t\t \\cite{Berthon1970, Griselin1975}\\\\\n2000\t&\t40\t&13&\t&\t4\t&\t57\t&\t\t\t \\cite{Berthon1970, Griselin1975}\\\\\n2020\t&\t20\t&9&\t\t&\t4\t&\t33\t\t&\t \\cite{Berthon1970}\\\\\n2040\t&\t20\t&8&\t\t&\t2\t&\t30\t\t&\t \\cite{Berthon1970}\\\\\n2060&\t30\t&6&\t\t&\t3\t&\t39\t\t&\t \\cite{Berthon1970, London1975}\\\\\n2080&\t40\t&8&\t\t&\t3\t&\t51\t\t&\t \\cite{Berthon1970, London1975}\\\\\n2100&\t30\t&&\t\t&\t2\t&\t32\t\t&\t \\cite{London1975}\\\\\n2120\t&\t70\t&11&\t&\t4\t&\t85\t\t&\t \\cite{Berthon1970, London1975}\\\\\n2140&\t\t& &\t\t&\t\t&\t0\t \t &\tNA\\\\\n2160\t&\t40\t&9&\t\t&\t2\t&\t51\t\t&\t \\cite{Berthon1970}\\\\\n\\end{tabular}\n\\label{Table:Lambda}\n\\end{ruledtabular}\n\\end{center}\n\\end{table*}\n\n\\begin{table*}[htbp]\n\\caption{Summary of database for $\\overline{K} N \\rightarrow \\pi \\Sigma$. Column 1 lists the central energy $W_0$ of each energy bin, columns 2, 3, and 4 list the number of differential cross-section data points in each bin for $K^-p\\rightarrow \\pi^+\\Sigma^-$, $K^-p\\rightarrow \\pi^0\\Sigma^0$, and $K^-p\\rightarrow \\pi^-\\Sigma^+$, respectively, column 5 and 6 list the number of polarization data points for $K^-p\\rightarrow \\pi^0\\Sigma^0$ and $K^-p\\rightarrow \\pi^-\\Sigma^+$, respectively, in each bin, column 7 and 8 list the number of polarized cross-section data points for $K^-p\\rightarrow \\pi^0\\Sigma^0$ and $K^-p\\rightarrow \\pi^-\\Sigma^+$, respectively, in each bin, column 9 lists the number of integrated cross-section data points for $K^-p\\rightarrow \\pi^+\\Sigma^-$, $K^-p\\rightarrow \\pi^0\\Sigma^0$, and $K^-p\\rightarrow \\pi^-\\Sigma^+$ in each bin, column 10 lists the total number of data points for all kinds of data in each bin, and column 11 lists the references for the measurements referred to in columns 2-8.}\n\\begin{center}\n\\begin{ruledtabular}\n\\begin{tabular}{ccccccccccc\n\\multicolumn{1}{c}{$W_{0}$} & \\multicolumn{1}{c}{{${d\\sigma}\/{d\\Omega}$}} & \\multicolumn{1}{c}{{${d\\sigma}\/{d\\Omega}$}} & \\multicolumn{1}{c}{{${d\\sigma}\/{d\\Omega}$}} & \\multicolumn{1}{c}{{\\mbox{$P$}}}& \\multicolumn{1}{c}{{\\mbox{$P$}}} & \\multicolumn{1}{c}{{\\mbox{{$P$}${d\\sigma}\/{d\\Omega}$}}}& \\multicolumn{1}{c}{{\\mbox{{$P$}${d\\sigma}\/{d\\Omega}$}}} & \\multicolumn{1}{c}{{$\\sigma$}} & \\multicolumn{1}{c}{ Total} & \\multicolumn{1}{c}{{ References}} \\\\\n\\multicolumn{1}{c}{\\rm (MeV)} &{($\\pi^+ \\Sigma^-$}) &{($\\pi^0 \\Sigma^0$}) & {($\\pi^- \\Sigma^+$}) & {($\\pi^0\\Sigma^0$})& {($\\pi^- \\Sigma^+$})& {($\\pi^0\\Sigma^0$})& {($\\pi^- \\Sigma^+$})& & \\multicolumn{1}{c}{ No.} & \\multicolumn{1}{c}{} \\\\\n\\hline\n1480&\t\t&\t\t&\t\t&&&\t\t& &\t13\t&\t13\t\t&\t \\cite{Baldini1988}\\\\\n1500\t&\t\t&\t\t&\t\t&&&\t\t&\t&21\t&\t21\t\t&\t \\cite{Baldini1988}\\\\\n1520\t&\t\t&\t\t&\t\t&&&\t\t&\t&23\t&\t23\t\t&\t \\cite{Baldini1988}\\\\\n1540\t&\t40\t&\t19\t&\t40\t&&&\t19\t&18\t&22\t&\t158\t\t&\t \\cite{Armenteros1970}\\\\\n1560\t&\t60\t&\t39\t&\t60\t&9&&\t30\t&30&\t12\t&\t240\t\t&\t \\cite{Armenteros1970, CrystalBall2005, CrystalBall2008_1}\\\\\n1580&\t40\t&\t29\t&\t40\t&9&\t&20\t&20\t&6\t&\t164\t\t&\t \\cite{Armenteros1970, CrystalBall2005, CrystalBall2008_1}\\\\\n1600&\t40\t&\t29\t&\t40\t&9&\t&20\t&20\t&10\t&\t168\t\t&\t \\cite{Armenteros1970, CrystalBall2005, CrystalBall2008_1}\\\\\n1620\t&\t40\t&\t29\t&\t40\t&9&\t&20\t&20\t&13\t&\t171\t\t&\t \\cite{Armenteros1970,CrystalBall2005, CrystalBall2008_1}\\\\\n1640\t&\t40\t&\t48\t&\t40\t&18&&\t20\t&20&\t11\t&\t\t&\t \\cite{Armenteros1970, Baxter1973, CrystalBall2005, CrystalBall2008_1}\\\\\n1660\t&\t40\t&\t49\t&\t40\t&9&\t&20\t&\t19&17\t&\t194\t\t&\t \\cite{Armenteros1970, Baxter1973, CrystalBall2005, CrystalBall2008_1}\\\\\n1680\t&\t80\t&\t49\t&\t80\t&9&\t&30\t&29\t&25\t&\t302\t\t&\t \\cite{Armenteros1968, Armenteros1970, Baxter1973, CrystalBall2005, CrystalBall2008_1}\\\\\n1700\t&\t40\t&\t30\t&\t40\t&&\t&10\t&9\t&16\t&\t145\t\t&\t \\cite{Armenteros1968, Armenteros1970, Baxter1973}\\\\\n1720&\t76\t&\t30\t&\t75\t&&10&\t\t&&\t15\t&\t206\t&\t \\cite{Armenteros1968, Baxter1973, Jones1975}\\\\\n1740&\t154\t&\t20\t&\t157\t&&24&\t\t&&\t24\t&\t379\t\t&\t \\cite{Armenteros1968, Baxter1973, Jones1975}\\\\\n1760\t&\t114\t&\t20\t&\t114\t&&25&\t\t&&\t20\t&\t293\t\t&\t \\cite{Armenteros1968, Baxter1973, Jones1975}\\\\\n1780\t&\t148\t&\t\t&\t147\t&&34&\t\t&&\t24\t&\t353\t\t&\t \\cite{Armenteros1968, Jones1975, Conforto1976}\\\\\n1800\t&\t72\t&\t\t&\t74\t&&22&\t\t&&\t10\t&\t178\t\t&\t \\cite{Armenteros1968, Jones1975, Conforto1976}\\\\\n1820&\t60\t&\t\t&\t60\t&&15&\t\t&&\t11\t&\t146\t\t&\t \\cite{Armenteros1968, Conforto1976}\\\\\n1840\t&\t80\t&\t\t&\t80\t&&13&\t\t&&\t14\t&\t187\t\t&\t \\cite{Armenteros1968, Conforto1976}\\\\\n1860&\t100\t&\t\t&\t100\t&&11&\t\t&&\t18\t&\t229\t\t&\t \\cite{Armenteros1968, Griselin1975, Conforto1976}\\\\\n1880&\t120\t&\t\t&\t120\t&&24&\t\t&&\t18\t&\t282\t\t&\t \\cite{Armenteros1968, Griselin1975, Conforto1976}\\\\\n1900&\t60\t&\t\t&\t60\t&&14&\t\t&&\t8\t&\t142\t\t&\t \\cite{Armenteros1968, Griselin1975, Conforto1976}\\\\\n1920\t&\t80\t&\t\t&\t79\t&&15&\t\t&&\t12\t&\t186\t\t&\t \\cite{Berthon1970, Griselin1975, Conforto1976}\\\\\n1940\t&\t99\t&\t\t&\t100\t&&14&\t\t&&\t17\t&\t230\t\t&\t \\cite{Berthon1970, Griselin1975, Conforto1976}\\\\\n1960\t&\t60\t&\t\t&\t59\t&&17&\t\t&&\t10\t&\t146\t\t&\t \\cite{Berthon1970, Griselin1975, Conforto1976}\\\\\n1980\t&\t40\t&\t\t&\t38\t&&\t&\t\t&\t&7\t&\t85\t\t&\t \\cite{Berthon1970, Griselin1975}\\\\\n2000\t&\t40\t&\t\t&\t40\t&&\t&\t&\t&9\t&\t89\t&\t\t \\cite{Berthon1970, Griselin1975}\\\\\n2020\t&\t19\t&\t\t&\t19\t&&\t&\t&\t&5\t&\t43\t\t&\t \\cite{Berthon1970}\\\\\n2040&\t20\t&\t\t&\t16\t&&\t&\t&\t&2\t&\t38\t\t&\t \\cite{Berthon1970}\\\\\n2060\t&\t19\t&\t10\t&\t18\t&&\t&\t&\t&5\t&\t52\t\t&\t \\cite{Berthon1970, London1975}\\\\\n2080\t&\t20\t&\t10\t&\t19\t&&\t&\t&\t&5\t&\t54\t\t&\t \\cite{Berthon1970, London1975}\\\\\n2100\t&\t\t&\t26\t&\t\t&&\t&\t&\t&3\t&\t29\t\t&\t \\cite{London1975}\\\\\n2120\t&\t39\t&\t8\t&\t37\t&&\t&\t&\t&4\t&\t88\t\t&\t \\cite{Berthon1970, London1975}\\\\\n2140\t&\t\t&\t\t&\t\t&&\t&\t&\t\t&\t0 \t\t &\t NA\\\\\n2160&\t36\t&\t\t&\t35\t&&\t&\t&\t&5\t&\t76\t\t&\t \\cite{Berthon1970}\\\\\n\\end{tabular}\n\\label{Table:Sigma}\n\\end{ruledtabular}\n\\end{center}\n\\end{table*}\n \n \n The general qualitative behavior of the partial-wave amplitudes that we wanted to determine is known from earlier partial-wave analyses. Therefore it was convenient to make use of this information in our single-energy fits. In 2007, one of us (J. Tulpan) completed a multichannel fit {\\cite{Tulpan2007}} of published partial-wave amplitudes for $\\overline{K}N$ scattering to several final states, including $\\overline{K}N$, $\\overline{K}^*N$, $\\overline{K}\\Delta$, $\\pi \\Lambda$, $\\pi \\Lambda(1520)$, $\\pi \\Sigma$, and $\\pi \\Sigma(1385)$. The channels $\\sigma\\Lambda$, $\\sigma\\Sigma$, and $\\eta\\Sigma$ (for $S_{11}$) were included as ``dummy channels\", where $\\sigma$ denotes the broad isoscalar S-wave $\\pi \\pi$ interaction. Also, $\\eta\\Lambda$ was included for $S_{01}$. Our fit of $S_{01}$ amplitudes included data for $\\sigma(K^-p\\rightarrow\\eta\\Lambda)$ up to a c.m.\\ energy of 1685 MeV (see Fig.\\ \\ref{etalambda}). The dummy channels were channels without data and were included to satisfy unitarity in some partial waves.\nTulpan's work resulted in an energy-dependent solution that is consistent with { $S$}-matrix unitarity.\nWe refer to his solution as the {\\it initial global fit}. Within each energy bin, each partial-wave amplitude with a given isospin amplitude was approximated by a first-order Taylor series expansion:\n\\begin{equation}\n T(W) = T(W_0) + T'(W_0)(W - W_0)\n\\end{equation} \n where $W$ is the c.m.\\ energy of the data point in the bin and $W_0$ is the central energy of the bin. Here, for simplicity $T(W)$ denotes an isospin amplitude $T_{l\\pm}^I$. The complex $T$-matrix amplitude $T(W_0)$ belongs to the parameter set to be optimized at c.m.\\ energy $W_0$, and $T'(W_0)$ is called the slope parameter. During fits, the slope parameter was held fixed so that the real and imaginary parts of $T(W_0)$ were our fitting parameters.\nDuring our initial single-energy partial-wave analyses, we calculated the slope parameters $T'(W_0)$ from the initial global fit and kept these parameters constant in our fits.\n\n\\begin{figure}[htpb]\n\\scalebox{0.35}{\\includegraphics{.\/etalambda.pdf}}\n\\caption{Integrated cross section for $K^-p\\rightarrow\\eta\\Lambda$ compared with the results of our energy-dependent fit. Data are from Starostin 2001 \\cite{starostin2001}.}\n\\label{etalambda}\n\\end{figure}\n\nBecause the database is somewhat sparse, additional constraints were introduced in order to determine partial-wave amplitudes with a reasonably smooth variation with energy.\nTo decrease the number of free parameters to be searched, we also held fixed the very small {$ T$}-matrix amplitudes (those with $|{ T}(W_0)|<0.05$).\nThis constraint is expected to introduce only a small bias to our final energy-dependent partial-wave solution.\n\nAs an additional constraint, we held fixed the ${ D}_{03}$ amplitudes for $\\overline{K}N \\rightarrow \\overline{K}N$ and $\\overline{K}N \\rightarrow \\pi \\Sigma$ at the values from the initial global fit in the bin with $W_0=1520$ MeV. This constraint was introduced because of the well-known narrow $\\Lambda (1520)$ resonance, which has a width of only about 16 MeV.\nEven with this constraint, we ultimately concluded that we could not determine reliable amplitudes in this bin for the reactions $\\overline{K}N \\rightarrow \\overline{K}N$ and $\\overline{K}N \\rightarrow \\pi \\Sigma$.\n\nFinally, in our single-energy fits, we introduced a {\\it penalty term} to the $\\chi^2$ function that we minimized. This penalty term constrained our fitted amplitudes from differing greatly from the values of the initial global fit. \nFor calculating the uncertainties in our single-energy amplitudes, we carried out a {\\it zero-iteration} fit in which the initial values of all amplitudes were replaced by the values determined by our $\\chi^2$ minimization procedure.\nIn the zero-iteration fit, all partial-wave amplitudes except ${ G}_{17}$ were treated as free parameters.\nThe ${ G}_{17}$ amplitude was held fixed in this procedure to remove the ambiguity in determining the global overall phase of our amplitudes. Spin-9\/2 waves were not needed in our solution.\n\nOnce we had obtained a complete set of amplitudes for $\\overline{K}N$, $\\pi \\Lambda$, and $\\pi \\Sigma$ reactions from our single-energy analyses, we carried out global multichannel energy-dependent fits using a procedure similar to that of Tulpan \\cite{Tulpan2007}.\nThe key new ingredient is that our global fit (details of how the partial-wave $S$-matrix was constructed can be found in Ref. \\cite{manoj12}) used our own single-energy amplitudes for the $\\overline{K}N$, $\\pi \\Lambda$, and $\\pi \\Sigma$ channels.\nFor other final states,\nwe used the same input information as Tulpan did from Refs. \\cite{Gopal1977, Cameron1978, Cameron1977, Cameron1978_1, Litch1974}. We assumed the same uncertainties used by Tulpan {\\cite{Tulpan2007}} for obtaining the inital global fit ($\\pm 0.025$ for $\\overline{K}N$, $\\pm 0.035$ for $\\pi \\Lambda$ and $\\pi \\Sigma$, and $\\pm 0.050$ otherwise). These uncertainties were necessary because previous published partial-wave amplitudes were without error bars. These uncertainties were estimated by comparing like partial-wave amplitudes from different energy-dependent analyses and estimating the average differences for the real and imaginary parts. The smaller error bars implied the analyses agreed well with each other and the larger error bars implied the analyses agreed less well with each other.\n\nWe reduced the number of free amplitudes for a new set of single-energy solutions. At this stage, our free amplitudes included only $ S_{01}, S_{11}, P_{01}, P_{11}, P_{13},$ and $ D_{03}$.\nAll other amplitudes were held fixed at the values determined from our first new global fit.\nIn addition, the slope parameters were recalculated based on the new global fit and kept constant during this stage of the single-energy analyses.\nWe were able to obtain excellent agreement with the observables.\nNext, we repeated our global energy-dependent analysis to refit the new set of single-energy amplitudes for $ S_{01}, S_{11}, P_{01}, P_{11}, P_{13},$ and $ D_{03}$.\nWe then compared our new predictions with the observables in our single-energy fits.\nStill the agreement was less than satisfactory, so we carried out yet another round of single-energy analyses.\nAt this stage, we successfully reduced our free amplitudes to include only $ S_{01}, S_{11}$, and $ P_{01}$.\nAll other amplitudes were unchanged at the values from our last global fit, and slope parameters were again recalculated from the last global fit, and then held fixed in the single-energy fits.\n\n\\section{\\emph{\\bf RESULTS AND DISCUSSION }}\nThe final single-energy fits resulted in an excellent agreement with all observables ($\\rm d\\sigma\/{\\rm d\\Omega}$, $P$, $P\\rm d\\sigma\/{\\rm d\\Omega}$, and $\\sigma$) yielding a fairly smooth set of partial-wave amplitudes within the energy range of our analysis. The energy-dependent solutions were finally used to compare with the observable data.\nFigures 2 - 7 show representative energy-dependent results for the differential cross section of each $\\overline KN$ reaction included in our single-energy fits. \nThe cross sections are shown as a function of $\\cos\\theta$, where $\\theta$ is the c.m.\\ scattering angle of the meson.\nFigure 2 shows the comparison of differential cross section data for $K^-p\\rightarrow K^-p$ with our energy-dependent solution at four lab momenta of 514, 935, 1165, and 1483 MeV. Although the data are from the 1960s and 1070s \\cite{Armenteros1970, Conforto1971, Conforto1976, Daum1968} they are in excellent agreement with our solution. For $K^- p \\rightarrow \\overline K^0 n$ (Fig.\\ 3) the Crystal Ball data with smaller error bars at $ P_{\\rm Lab}$ = 514 MeV and 714 MeV are well described by our solution in the forward and backward angles with a slight under fitting in the intermediate angles. The other data \\cite{Jones1975, Griselin1975} at $P_{\\rm Lab}$ = 936 MeV and 1434 MeV with larger error bars are in good agreement with our energy-dependent solution. Similarly, Fig.\\ 4 shows the excellent agreement between our solution and differential cross section data at $P_{\\rm Lab}$ = 514, 750, 1153, and 1465 MeV for $K^-p\\rightarrow \\pi^0\\Lambda$. Figure 5 shows a comparison of data from Refs. \\cite{Armenteros1970, Armenteros1968, Conforto1976, Berthon1970} with our solution for $K^-p\\rightarrow \\pi^+\\Sigma^-$. Except for some under representation of data at $P_{\\rm Lab}$ = 1245 MeV we have an excellent agreement with the data. Figures 6 and 7 show an excellent agreement of our energy-dependent solution with the differential cross section data at various lab momenta of kaons for $K^-p\\rightarrow \\pi^0\\Sigma^0$ and $K^-p\\rightarrow \\pi^-\\Sigma^+$, respectively.\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_11_514.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_11_935.pdf}}\n\\vspace{-25.5mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_11_1165.pdf}} \n\\vspace{-20mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_11_1483.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow K^- p$ differential cross section. Data are from Armenteros 1970 \\cite{Armenteros1970}, Conforto 1971 \\cite{Conforto1971}, and Duam 1968 \\cite{Daum1968}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_12_514.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_12_750.pdf}}\n\\vspace{-25.5mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_12_1165.pdf}} \n\\vspace{-20mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_12_1434.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\overline K^0 n$ differential cross section. Data are from Prakhov 2009 \\cite{CrystalBall2005}, Conforto 1976 \\cite{Conforto1976}, and Griselin 1975 \\cite{Griselin1975}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_2_514.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_2_750.pdf}}\n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_2_1153.pdf}} \n\\vspace{-20mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_2_1465.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^0\\Lambda$ differential cross section. Data are from Prakhov 2009 \\cite{CrystalBall2005}, Armenteros 1968 \\cite{Armenteros1968}, and Berthon 1970 \\cite{Berthon1970}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_31_495.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_31_935.pdf}}\n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_31_1245.pdf}} \n\\vspace{-20mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_31_1462.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^+\\Sigma^-$ differential cross section. Data are from Armenteros 1970 \\cite{Armenteros1970}, Armenteros 1968 \\cite{Armenteros1968}, Conforto 1976 \\cite{Conforto1976}, and Berthon 1970 \\cite{Berthon1970}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_32_495.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_32_560.pdf}}\n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_32_659.pdf}} \n\\vspace{-20mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_32_714.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^0\\Sigma^0$ differential cross section. Data are from Armenteros 1970 \\cite{Armenteros1970} and Prakhov 2009 \\cite{CrystalBall2005}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_33_495.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_33_935.pdf}}\n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_33_1245.pdf}} \n\\vspace{-25mm}\n\\vspace{3mm}\n\\scalebox{0.35}{\\includegraphics{.\/dSigma_33_1462.pdf}}\n\\vspace{-2mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^-\\Sigma^+$ differential cross section. Data are from Armenteros 1970 \\cite{Armenteros1970}, Armenteros 1968 \\cite{Armenteros1968}, Conforto 1976 \\cite{Conforto1976}, and Berthon 1970 \\cite{Berthon1970}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\nFigures 8, 9, 10, and 11 show representative energy-dependent fit results for the polarization in reactions $K^- p \\rightarrow K^- p$, $K^- p \\rightarrow \\pi\\Lambda$, $K^- p \\rightarrow \\pi^0\\Sigma^0 $, and $K^- p \\rightarrow \\pi^- \\Sigma^+$, respectively. The polarizations are shown as a function of $\\cos\\theta$, where $\\theta$ is the c.m.\\ scattering angle of the meson. Figure 8 shows the excellent agreement of our energy-dependent solution with the $K^-p\\rightarrow K^-p$ polarization at $P_{\\rm Lab}$ = 1383, 1483, 1584, 1684 MeV from Ref. \\cite{Daum1968}. For $K^-p\\rightarrow\\pi^0\\Lambda$ (Fig.\\ 9) our solution is in good agreement with the polarization data at $P_{\\rm Lab}$ = 514, 936, and 1165 MeV. Our solution also agrees well the Crystal Ball data at $P_{\\rm Lab}$ = 714 MeV at forward angles but there is a slight under representation of data at backward angles. Similarly, Fig.\\ 10 shows a very good agreement between our solution and the $K^-p\\rightarrow \\pi^0\\Sigma^0$ polarization data within the given uncertainties at $P_{\\rm Lab}$ = 514, 581, 687, and 750 MeV, all from Crystal Ball Collaboration. Finally, Fig.\\ 11 shows a comparison of our solution with the $K^-p\\rightarrow \\pi^-\\Sigma^+$ polarization data at $P_{\\rm Lab}$ = 862, 936, 1001, and 1125 MeV. Except for small forward angles at $P_{\\rm Lab}$ = 1001 MeV, we have good agreement with the data.\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_11_1383.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_11_1483.pdf}}\n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_11_1584.pdf}} \n\\vspace{-25mm}\n\\vspace{3mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_11_1684.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow K^-p$ differential cross section. Data are from Daum 1968 \\cite{Daum1968}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_2_514.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_2_714.pdf}}\n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_2_936.pdf}} \n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_2_1165.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^0\\Lambda$ differential cross section. Data are from Armenteros 1970 \\cite{Armenteros1970}, Armenteros 1968 \\cite{Armenteros1968}, Conforto 1976 \\cite{Conforto1976}, and Berthon 1970 \\cite{Berthon1970}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_32_514.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_32_581.pdf}}\n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_32_687.pdf}} \n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_32_750.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^-\\Sigma^+$ differential cross section. Data are from Armenteros 1970 \\cite{Armenteros1970}, Armenteros 1968 \\cite{Armenteros1968}, Conforto 1976 \\cite{Conforto1976}, and Berthon 1970 \\cite{Berthon1970}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_33_862.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_33_936.pdf}}\n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_33_1001.pdf}} \n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/Polar_33_1125.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^-\\Sigma^+$ differential cross section. Data are from Armenteros 1970 \\cite{Armenteros1970}, Armenteros 1968 \\cite{Armenteros1968}, Conforto 1976 \\cite{Conforto1976}, and Berthon 1970 \\cite{Berthon1970}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\nFigures 12, 13, 14, and 15 show representative energy-dependent fit results for the polarized cross section in reactions $K^- p \\rightarrow K^- p$, $K^- p \\rightarrow \\pi\\Lambda$, $K^- p \\rightarrow \\pi^0\\Sigma^0 $, and $K^- p \\rightarrow \\pi^- \\Sigma^+$, respectively. The polarized cross sections are shown as a function of $\\cos\\theta$, where $\\theta$ is the c.m.\\ scattering angle of the meson.\nWithin the uncertainties associated with the polarized cross section data our results are in good agreement with the data for these reactions.\n\n\\begin{comment}\n\\begin{figure}[htpb]\n\\vspace{-10mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_11_865.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_11_1082.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_11_1330.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_11_1732.pdf}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow K^- p$ polarized cross section. Data are from Albrow 1971 \\cite{Albrow1971}, Andersson 1970 \\cite{Andersson1970}, and Armenteros 1970 \\cite{Armenteros1970}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\\end{comment}\n\n\\begin{figure}[htpb]\n\\vspace{-15mm}\n\\scalebox{0.35}{\\includegraphics{.\/PdSigma_11_865.pdf}}\n\\vspace{-1mm}\n\\vspace{-25mm}\n\\scalebox{0.35}{\\includegraphics{.\/PdSigma_11_1082.pdf}}\n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/PdSigma_11_1330.pdf}} \n\\vspace{-25mm}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/PdSigma_11_1732.pdf}}\n\\vspace{-5mm}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow K^- p$ polarized cross section. Data are from Albrow 1971 \\cite{Albrow1971}, Andersson 1970 \\cite{Andersson1970}, and Armenteros 1970 \\cite{Armenteros1970}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\\begin{figure}[htpb]\n\\vspace{-10mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_2_514.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_2_554.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_2_637.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_2_719.pdf}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^0\\Lambda$ polarized cross section. Data are from Armenteros 1970 \\cite{Armenteros1970}}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\n\\begin{figure}[H]\n\\vspace{-10mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_32_495.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_32_597.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_32_699.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_32_793.pdf}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^0\\Sigma^0 $ polarized cross section. Data are from Armenteros 1970 \\cite{Armenteros1970}.}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\\begin{figure}[H]\n\\vspace{-10mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_33_495.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_33_597.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_33_699.pdf}\n\\vspace{-1mm}\n\\vspace{-20mm}\n\\includegraphics[width=0.51\\textwidth]{PdSigma_33_793.pdf}\n\\caption{(Color online) Representative results of our energy-dependent fit for the $K^- p \\rightarrow \\pi^- \\Sigma^+$ polarized cross section. Data are from Armenteros 1970 \\cite{Armenteros1970}}\n\\label{fig:dSigma_11_New}\n\\end{figure}\n\n\n Figure 16 shows our prediction for the total $K^-p\\rightarrow$ cross section.\n Figure 17 shows our prediction for the $K^-p\\rightarrow K^-p$ and $K^-p\\rightarrow \\overline K^0n$ integrated cross sections, Fig.\\ 18 shows our prediction for the $K^-p\\rightarrow \\pi^0\\Lambda$ integrated cross section, and Fig. 19 shows our prediction for the $K^-p\\rightarrow \\pi^+\\Sigma^-$, $K^-p\\rightarrow \\pi^0\\Sigma^0$, and $K^-p\\rightarrow\\pi^-\\Sigma^+$ integrated cross sections. Figures 17 and 18 do not show predictions for c.m.\\ energies below 1540 MeV because it was not possible to obtain single-energy amplitudes in this region where only integrated cross-section data are available.\n\n\\begin{figure}[H]\n\\begin{center}\n\\vspace{-1mm}\n\\scalebox{0.35}{\\includegraphics{.\/All1.pdf}}\n\\caption{(Color online) Total $K^-p$ cross section compared with the results of our energy-dependent fit. Data are from Baldini 1988 \\cite{Baldini1988}}\n\\label{fig:dSigma_11_New}\n\\end{center}\n\\end{figure}\n \n\\begin{figure}[htpb]\n\\begin{center}\n\\vspace{-9.5mm}\n\\scalebox{0.35}{\\includegraphics{.\/Sigma1_11.pdf}}\n\\vspace{-10mm}\n\\vspace{-5mm}\n\\scalebox{0.35}{\\includegraphics{.\/Sigma11_12.pdf}}\n\\vspace{-10mm}\n\\caption{(Color online) Integrated cross sections for $K^- p \\rightarrow K^-p$ and $K^- p \\rightarrow \\overline K^0n$ compared with the results of our energy-dependent fit. Data are from Baldini 1988 \\cite{Baldini1988}}\n\\label{fig:dSigma_11_New}\n\\end{center}\n\\end{figure}\n\n\n\\begin{figure}[htpb]\n\\begin{center}\n\\vspace{-9.08mm}\n\\scalebox{0.35}{\\includegraphics{.\/Sigma21_2.pdf}}\n\\caption{(Color online) Integrated cross section for $K^- p \\rightarrow \\pi^0\\Lambda$ compared with the results of our energy-dependent fit. Data are from Baldini 1988 \\cite{Baldini1988}.}\n\\label{fig:dSigma_11_New}\n\\end{center}\n\\end{figure}\n\n\n\n\\begin{figure}[htpb]\n\\begin{center}\n\\vspace{-9.5mm}\n\\scalebox{0.35}{\\includegraphics{.\/Sigma1_31.pdf}}\n\\vspace{-10mm}\n\\vspace{-5mm}\n\\scalebox{0.35}{\\includegraphics{.\/Sigma1_32.pdf}}\n\\vspace{-10mm}\n\\vspace{-5mm}\n\\scalebox{0.35}{\\includegraphics{.\/Sigma31_33.pdf}}\n\\vspace{-10mm}\n\\caption{(Color online) Integrated cross sections for $K^- p \\rightarrow \\pi^+\\Sigma^-$, $K^- p \\rightarrow \\pi^0\\Sigma^0$, and $K^- p \\rightarrow \\pi^-\\Sigma^+$ compared with the results of our energy-dependent fit. Data are from Baldini 1988 \\cite{Baldini1988}.}\n\\label{fig:dSigma_11_New}\n\\end{center}\n\\end{figure}\n\n\\clearpage\n\\section{Summary and Conclusions}\nWe have investigated $\\overline KN\\rightarrow \\overline KN$, $\\overline K N\\rightarrow \\pi\\Lambda$, and $\\overline KN\\rightarrow \\pi\\Sigma$ reactions through single-energy analyses constrained by a global unitary energy-dependent fit from threshold to a c.m.\\ energy of 2.1 GeV. We found partial waves up to G-waves necessary to describe the available data for the reactions. This work was motivated, in part, by the relatively recent measurements for $K^-p\\rightarrow \\overline K^0n$, $K^-p\\rightarrow \\pi^0\\Lambda$, $K^-p\\rightarrow\\pi^0\\Sigma^0$, and $K^-p\\rightarrow \\eta\\Lambda$ by the Crystal Ball Collaboration. We were successful in describing these data in addition to older data from constrained single-energy analyses. The partial-wave amplitudes thus extracted were used in our global multichannel fit. A discussion of the resonance parameters from this global fit, which is the most comprehensive multichannel fit to date for $\\overline K N$ scattering reactions, is presented in a separate publication \\cite{manoj2013}. \n\n\n\\acknowledgements{This work was supported by the U.S. Department of Energy Grant No. DE-FG02-01ER41194. \n \n ","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzegrq b/data_all_eng_slimpj/shuffled/split2/finalzzegrq new file mode 100644 index 0000000000000000000000000000000000000000..b7d4504507ccfb4eea5a361521f62417b4ab7887 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzegrq @@ -0,0 +1,5 @@ +{"text":"\n\n\\section{Overview}\n\n\nThe Smooth 4-Dimensional Poincar\\'e Conjecture (SPC4)\nproposes that every\nclosed smooth 4-manifold $\\Sigma$ that is homotopy equivalent to $\\sphere{4}$\nis diffeomorphic to the standard $\\sphere{4}$.\nBy work of Freedman \\cite{freedman1982topology},\nit is known that if $\\Sigma$ is homotopy equivalent to $\\sphere{4}$,\nthen $\\Sigma$ is in fact homeomorphic to $\\sphere{4}$.\nIn stark contrast to the SPC4,\nit might be the case that every compact smooth 4-manifold\nadmits infinitely many distinct smooth structures.\nThe existence of an exotic homotopy 4-sphere is equivalent\nto the existence of an exotic\ncontractible compact manifold with\n$\\sphere{3}$ boundary \\cite[p.\\ 113]{MR0190942},\nhenceforth called an \\emph{exotic homotopy 4-ball}.\n\nOne possible approach to proving that a proposed exotic homotopy 4-ball $\\mathcal{B}$\nis in fact exotic is to find a knot $K \\subset \\sphere{3} = \\partial \\mathcal{B}$,\nsuch that there is a smooth properly embedded disk\n$\\disk{2} \\hookrightarrow \\mathcal{B}$,\nwith $\\partial \\disk{2}$ mapped to $K$,\nwhere $K$ is not smoothly slice in the usual sense\nin the standard 4-ball $\\ball{4}$.\nA knot is (topologically\/smoothly) slice in $\\ball{4}$ \nif and only if it is null-concordant in\n$\\sphere{3} \\times {I} = \\sphere{3} \\times [0, 1]$,\ni.e.\\ there is a properly embedded (locally flat\/smooth) cylinder\n$\\sphere{1} \\times {I} \\hookrightarrow \\sphere{3} \\times {I}$\nwhose oriented boundary is\n$K \\subset \\sphere{3} \\times \\{ 0 \\}$ together with\nthe unknot $U \\subset \\sphere{3} \\times \\{ 1 \\}$.\nAnother way of thinking about this strategy is\nthat we want to find a knot $K$ in\n$\\sphere{3} = \\partial \\mathcal{B}$\nthat bounds a properly embedded smooth disk in $\\mathcal{B}$\nbut does not bound any such disk that is contained in a \ncollar $\\sphere{3} \\times {I}$ of the boundary of $\\mathcal{B}$.\nIn this case, to verify the sliceness of $K$, we have to go ``deep'' into $\\mathcal{B}$. \n\nAn easier task might be to find a homology 4-ball $X$\nwith $\\sphere{3}$ boundary such that there is a knot in the boundary\nthat bounds a smooth properly embedded disk in $X$ but not in $\\ball{4}$,\nhowever, this is also an open problem.\nIn \\cite{Freedman_2010}, the authors investigate the possibility of proving\nthat a homotopy 4-ball $\\mathcal{B}$ with $\\sphere{3}$ boundary is exotic by taking a\nknot in the boundary that bounds a smooth properly embedded disk in $\\mathcal{B}$\nand computing the $s$-invariant of $K$, in the hopes that $s(K) \\neq 0$,\nwhereby they could then conclude that $\\mathcal{B}$ is exotic.\nUnfortunately for this approach as noted in the paper,\nit turns out that the homotopy 4-ball that they were studying\nwas in fact diffeomorphic to $\\ball{4}$, see \\cite{akbulut2010cappell}.\nIt is still open whether the $s$-invariant can obstruct the\nsliceness of knots in $\\ball{4}$ that are slice in some homotopy 4-ball,\nas is noted in the corrigendum to \\cite{kronheimer2013gauge}.\n\nMotivated by this, we make the following definitions:\nFor a 3-manifold $M^3$ containing a knot\n$K \\colon \\sphere{1} \\hookrightarrow M$, we say\nthat \\emph{$K$ is null-concordant in $M \\times {I}$}\nif there is a smoothly properly embedded annulus\n$\\sphere{1} \\times {I} \\hookrightarrow M \\times I$\ncobounding $K \\subset M \\times \\{ 0 \\}$ on one end\nand an unknot contained in a 3-ball $U \\subset B^3 \\subset M \\times \\{ 1 \\}$\non the other.\nEquivalently, $K \\subset M \\times \\{ 0 \\}$ bounds a smoothly properly\nembedded disk in $M \\times {I}$.\n\n\\begin{definition}[Deep slice\/Shallow slice]\n Let $X^{4}$ be a smooth compact 4-manifold with nonempty boundary $\\partial X$.\n We call a knot $K \\subset \\partial X$ \\emph{slice} in $X$ if there is a smooth properly embedded disk in $X$\n with boundary $K$.\n We call a knot $K \\subset \\partial X$ \\textit{shallow slice} in $X$\n if there is a smooth properly embedded disk in $\\partial X \\times I$\n with boundary $K$ -- this is equivalent\n to $K$ being null-concordant in the collar $\\partial X \\times {I}$.\n If $K$ is slice in $X$ but not shallow slice,\n we will call it \\textit{deep slice} in $X$.\n See \\autoref{fig:deep_shallow_schematic} for a schematic\n illustration of these definitions.\n\\end{definition}\n\n\\begin{figure}\n \\centering\n \\begin{overpic}[width=0.35\\textwidth]{pictures\/deep_slice_schematic}\n \\definecolor{blue-green}{rgb}{0.0, 0.87, 0.87}\n \\put(34, 96){\\color{blue} $K$}\n \\put(6, 87){$\\partial X$}\n \\put(35, 74){\\Large \\color{blue-green} ?}\n \\put(70, 80.5){\\color{gray} collar}\n \\put(5, 40){$X^4$}\n \\put(50, 20){\\color{blue} $\\Delta^2$}\n \\end{overpic}\n \\caption{\n Schematic of a deep slice disk $\\Delta^2$ (blue)\n in a 4-manifold $X^4$,\n with boundary the knot $K \\subset \\partial X$.\n The knot $K$ is called \\emph{deep slice}\n if it does not bound a properly embedded disk in a collar\n of the boundary, indicated by the (light blue) dashed lines.\n }\n \\label{fig:deep_shallow_schematic}\n\\end{figure}\n \nIn this language,\nProblem 1.95 on Kirby's list \\cite{kirby1995problems}\n(attributed to Akbulut)\ncan be reformulated as follows:\nAre there contractible smooth 4-manifolds\nwith boundary an integral homology 3-sphere\nwhich contain deep slice knots that are null-homotopic in the boundary?\nNote that any knot that is not nullhomotopic in the boundary\nwill not be shallow slice and\nthus if it is slice, it will be deep slice.\nFor this reason we will be looking for deep slice knots\nthat are null-homotopic in the boundary.\nWe will often consider our knots\nto be contained in 3-balls in the boundary,\nwhich we call \\emph{local knots},\nso we can freely consider them in the boundary of any 4-manifold and\ndiscuss if they are slice there.\nTo avoid confusion\nwhen we say that a (local) knot in a 3-manifold $M^3$ is slice\nwe will usually qualify it with ``in $X^4$''.\n\n\n\n\n\\subsection{Outline}\n\nIn the first part of this paper\nwe will restrict ourselves to the smooth category,\nstarting in \\autoref{sec:nonexistence_deep_slice}, \nwhere we discuss a condition that guarantees that some 4-manifolds\nhave no deep slice knots and related results.\nIn \\autoref{sec:existence_deep_slice}, we prove that every 2-handlebody\nhas a deep slice knot in its boundary.\nTo do this we employ the Wall self-intersection number\nand a result of Rohlin\nwhich we discuss briefly.\n\nIn \\autoref{sec:universal_slicing}, we recall the Norman-Suzuki trick\nand observe that every 3-manifold bounds a 4-manifold\nwhere every knot in the boundary bounds a properly embedded disk.\nIn contrast, if we restrict to slice disks trivial in\nrelative second homology, \nwe will see that every compact topological $4$-manifold\nwith boundary $\\sphere{3}$\ncontains a knot which\ndoes not bound a null-homologous topological slice disk.\nWe finish with some questions and suggestions for\nfurther directions in \\autoref{sec:questions}.\n\n\n\n\n\\subsection{Conventions}\n\nIn the literature, \nproperly embedded slice disks in a $4$-manifold $X$ are \noften assumed to be null-homologous\nin $H_{2}(X, \\partial X)$.\nWe will make this extra assumption on homology only\nin \\autoref{sec:universal_slicing} when discussing the ``universal slicings''.\nFor the first part \\emph{deep slice} and \\emph{shallow slice}\nwill describe the existence of\na embedded disks with the relevant properties\nwithout conditions on the homology class.\n\nStarting from\nan $n$-manifold $M^{n}$ without boundary,\nwe obtain a \\emph{punctured $M$}\n(more precisely a \\emph{bounded punctured $M$})\nby removing\na small open $n$-ball $M^{\\circ} \\coloneqq M \\setminus \\interior \\disk{n}$,\nwhich yields a\nmanifold with boundary\n$\\partial M^{\\circ} = \\sphere{n-1}$.\nObserve that a punctured $M$ is the same\nas a connected sum\n$M^{\\circ} \\cong M \\# \\disk{n}$\nwith a $n$-ball.\n\n\\subsection{Acknowledgments}\n\nThe authors would like to thank\nAnthony Conway, Rob Kirby, Mark Powell, \nArunima Ray and Peter Teichner for helpful conversations,\ntheir encouragement and guidance.\nBR would like to thank Thorben Kastenholz\nfor asking about the decidability of the embedded genus problem\nin 4-manifolds, which motivated \\autoref{sec:universal_slicing}.\nWe are especially grateful to\nAkira Yasuhara for pointing us to related literature\nand the work of Suzuki.\nWe are also especially grateful to the anonymous referee for numerous helpful\nsuggestions that improved the exposition and a suggestion that\nlead to our subsequent work relating the techniques in this paper\nto the embeddings of traces of surgeries on links. \nThe Max Planck Institute for Mathematics in Bonn\nsupported us financially and with a welcoming research environment.\n\n\\section{Nonexistence of deep slice knots}\n\\label{sec:nonexistence_deep_slice}\n\n\n\\noindent For starters, we have:\n\\begin{proposition} \\label{1-handles}\nThere are no deep slice knots in\n$\\natural^k \\sphere{1} \\times \\ball{3}$.\n\\end{proposition}\n\n\\begin{proof}\nLet\n$K \\subset \\#^k \\sphere{1} \\times \\sphere{2} \n= \\partial(\\natural^k \\sphere{1} \\times \\ball{3})$\nsuch that $K$ is slice in $\\natural^k \\sphere{1} \\times \\ball{3}$.\nThen, thinking of $\\natural^k \\sphere{1} \\times \\ball{3}$\nas a wedge of $k$ copies of $\\sphere{1}$ thickened to be 4-dimensional,\nif $D$ is any slice disk for $K$ we can isotope $D$ such that\nit does not intersect a one-dimensional wedge of circles that\n$\\natural^k \\sphere{1} \\times \\ball{3}$ deformation retracts onto.\nTherefore, $D$ can be isotoped to be contained in\na collar neighborhood of the boundary $\\#^k \\sphere{1} \\times \\sphere{2}$\nand thus $K$ is shallow slice. \n\\end{proof}\n\n\nThe following might be a surprise,\nas one could expect that additional topology\nin a 3-manifold $M^3$\ncreates more room for concordances:\n\\begin{proposition}[{Special case of \\cite[Prop. 2.9]{nagel2019smooth}}]\n \\label{prop:local_knot_slice}\n If a local knot $K \\subset \\ball{3} \\subset M^{3}$\n is null concordant in $M^{3} \\times {I}$,\n then $K$ is null concordant in $\\sphere{3} \\times {I}$. \n\\end{proposition}\n\n\\begin{proof}[Proof sketch]\n Let $D$ be a properly embedded disk in\n $M \\times {I}$ with boundary $K$\n and let $\\widetilde{M}$ be the universal cover of $M$.\n Then $D$ lifts to a properly\n embedded disk $\\widetilde{D} \\subset \\widetilde{M} \\times {I}$.\n Further, since $K$ is contained in a 3-ball $B$,\n all of the lifts of $K$ to $\\widetilde{M}$ are just copies of $K$,\n and therefore, the boundary of $\\widetilde{D}$\n is a copy of $K$, considered inside of $\\widetilde{M}$.\n As a consequence of geometrization \\cite{perelman2003finite},\n we know that every universal cover\n of a punctured compact 3-manifold smoothly embeds into $\\sphere{3}$,\n as was observed in \\cite[Lem.\\ 2.11]{boden2017concordance}.\n It follows then that there is an embedding\n $\\widetilde{M} \\times {I} \\hookrightarrow \\sphere{3} \\times {I}$.\n But then the image of $\\widetilde{D}$\n under this embedding shows that $K$ bounds a disk in $\\sphere{3} \\times {I}$. \n\\end{proof}\n\nWe have added a proof of this proposition here to\nhighlight that this lifting argument \nbreaks down in the case of higher genus\nsurfaces if their inclusion\ninduces a nontrivial map on fundamental groups.\nIf $K$ bounds a genus $g$ surface \nwith one boundary component $\\Sigma_{g, 1}$\nin $M \\times {I}$, we can only\nlift this to the universal cover (and subsequently find\na genus $g$ surface for $K$ in $\\sphere{3}$ via this method)\nunder the condition that the inclusion\nof $\\Sigma_{g, 1}$ in $M \\times {I}$ is $\\pi_{1}$-trivial.\nSo this argument does not work if the surface\nreally ``uses the extra topology of $M$''.\n\n\\begin{example}\n Take a non-orientable\n 3-manifold $M$ containing the connected\n sum $K \\# K$ of two copies of\n a local invertible knot $K$ with\n smooth 4-ball genus $g^{4}(K \\# K) \\ge 2$.\n As an explicit example,\n $K$ a left-handed trefoil will work,\n and we illustrate the following\n in \\autoref{fig:torus_movie}.\n Describe an embedded torus in\n $M \\times {I}$ with the motion picture method:\n Use the connected sum band to split the sum with a saddle.\n Then let one of the summands travel around an orientation\n reversing loop in $M$\n while leaving the other one fixed.\n The summand traveling around the loop\n was reflected in the process and since it is invertible\n it is isotopic to\n $-K = r \\overline{K}$\n in a 3-ball neighborhood in $M$.\n Fusing the summands back together\n along a connected sum band\n we now obtain\n $K \\# -K$ as a local knot.\n Finally cap this off with the usual ribbon disk\n for the connected sum of a knot with\n its concordance inverse. Therefore $g^{M \\times I}(K \\# K) \\leq 1$ and by\n \\autoref{prop:local_knot_slice} in fact $g^{M \\times I}(K \\# K) = 1$.\n The 4-ball genus $g^{4}(K \\# K) \\ge 2$\n of this example is strictly\n larger than its\n \\emph{$(M^{3} \\times {I})$-4-genus},\n which we define as\n \\[\n g^{M \\times {I}}(J)\n \\coloneqq\n \\min \n \\{ \n g \\mid\n \\exists \\text{ smooth proper embedding }\n \\Sigma_{g, 1} \\hookrightarrow M \\times I\n \\text{ with }\n \\partial \\Sigma_{g, 1} = J \\subset M \\times \\{ 0 \\}\n \\}\n \\]\n Observe that in this notation\n the usual 4-ball genus is\n $g^{4} = g^{\\sphere{3} \\times {I}}$\n and we can rephrase \\autoref{prop:local_knot_slice} as\n $g^{M \\times {I}}(K) = 0$ implies\n $g^4(K) = 0$\n for local knots $K$.\n Similar notions of 4-genera\n were introduced in Celoria's investigation\n of almost-concordance \\cite[Def.\\ 12]{celoria2018concordance}.\n\\end{example}\n\n\\begin{figure}[ht]\n \\begin{subfigure}{.495\\textwidth}\n \\centering\n \n \\begin{overpic}[width=\\textwidth]{pictures\/torus_movie_frame_1}\n \\label{fig:torus_movie_frame_1}\n \\end{overpic}\n \\caption{\n Saddle move to separate the summands\n of $K \\# K$.\n }\n \\end{subfigure}\n \n \\begin{subfigure}{.495\\textwidth}\n \\centering\n \n \\begin{overpic}[width=\\textwidth]{pictures\/torus_movie_frame_2}\n \\label{fig:torus_movie_frame_2}\n \\end{overpic}\n \\caption{\n One of the summands travels around an orientation\n reversing loop in $M$.\n }\n \\end{subfigure}\n \n \\begin{subfigure}{.495\\textwidth}\n \\centering\n \n \\begin{overpic}[width=\\textwidth]{pictures\/torus_movie_frame_3}\n \\label{fig:torus_movie_frame_3}\n \\end{overpic}\n \\caption{\n It returns mirrored, now add a fusion band.\n }\n \\end{subfigure}\n \n \\begin{subfigure}{.495\\textwidth}\n \\centering\n \n \\begin{overpic}[width=\\textwidth]{pictures\/torus_movie_frame_4}\n \\label{fig:fig:torus_movie_frame_4}\n \\end{overpic}\n \\caption{\n Finish off the movie with the standard\n ribbon disk for $K \\# -K$.\n }\n \\end{subfigure}\n \n \n \\caption{\n Four frames of the movie of a properly embedded\n punctured torus in $M^3 \\times {I}$ with boundary\n $K \\# K \\subset M \\times \\{ 0 \\}$,\n where $M$ is a non-orientable 3-manifold.\n }\n \\label{fig:torus_movie}\n\\end{figure}\n\n\nIt would be interesting to find an example \nof an orientable $3$-manifold $M^3$ where the\n$g^{M \\times {I}}(K)$ genus of\nsome local knot $K \\subset \\disk{3} \\subset M$\nis strictly smaller than\nthe $4$-ball genus $g^{4}(K)$, or prove that no such $M$ exists.\nLocal $K$ satisfy $g^{M \\times {I}}(K) \\le g^4(K)$\nas cobordisms in $\\sphere{3} \\times {I}$\ncan be embedded into $M \\times {I}$.\nBecause of \\autoref{prop:local_knot_slice}\nan example where these values differ can only appear\nfor $g^{4}(K) \\ge 2$.\nMoreover, as we will see in \\autoref{thm:embedding_4-sphere_shallow}\nsuch an $M$ would necessarily not embed in $S^4$. \nAnother special case is treated in\n\\cite[Thm.\\ 2.5]{Davis_2018} where \na handle cancellation argument shows that\nthere is no difference\nfor local knots in $M = \\sphere{1} \\times \\sphere{2}$,\nthat is the equality \n$g^{\\sphere{1} \\times \\sphere{2} \\times {I}} = g^{4}$ holds\n(and also analogous statements for $\\#^{k} \\sphere{1} \\times \\sphere{2}$).\nTopological concordance in $\\sphere{1} \\times \\sphere{2} \\times {I}$\nis investigated in \\cite{friedl2019satellites}.\n\n\nWe now give a criterion that shows that certain 4-manifolds\nhave no local deep slice knots in the boundary.\nThis idea is also contained in \\cite[Thm.\\ 0]{suzuki1969localknots}\nand its variants.\n\n\\begin{proposition} \n \\label{thm:embedding_4-sphere_shallow}\n Let $X^4$ be a compact 4-manifold with a \n local knot $\\gamma \\subset \\ball{3} \\subset \\partial X$\n that is slice in $X$.\n If there is a cover of $X$ which can be smoothly embedded into $\\sphere{4}$,\n then $\\gamma \\subset \\ball{3} \\hookrightarrow \\sphere{3} = \\partial \\ball{4}$\n is slice in $\\ball{4}$.\n Hence, $\\gamma$ is shallow slice in $X$. \n\\end{proposition}\n\n\\begin{proof}\n Let $\\widetilde{X}$ be a cover of $X$\n with an embedding $\\widetilde{X} \\subset \\sphere{4}$ into $\\sphere{4}$\n and let $\\widetilde{D}$ be a lift of a slice disk for $\\gamma$ to\n $\\widetilde{X}$\n with $\\widetilde{\\gamma} = \\partial \\widetilde{D}$.\n Note that the knot $\\widetilde{\\gamma}$ is the same as $\\gamma$,\n since $\\gamma$ is contained in a 3-ball and the only covers of a 3-ball\n are disjoint unions of 3-balls. Puncture $S^4$ by removing a small ball $B$ close to $\\widetilde{\\gamma}$ and such that $\\widetilde{\\gamma}$ can be connected by an annulus disjoint from $\\tilde{X}$ to $\\partial B$ and such that the other end of the annulus is (the mirror image of) $K \\subset \\partial B$.\n Then since $S^4 - \\interior{B} \\cong B^4$, \n the annulus together with $\\widetilde{D}$ show that $K$ is slice in\n the $B^4$ which is the complement of the small ball. \n Therefore $\\gamma$ is shallow slice in $X$. \n\\end{proof}\n\nAs an example, \\autoref{thm:embedding_4-sphere_shallow}\nimplies that $\\natural^k \\sphere{2} \\times \\disk{2}$\ncontains no deep slice local knots,\nsince these manifolds can all be embedded in $\\sphere{4}$.\nHowever, these manifolds all contain deep slice knots, necessarily non-local, \nas will be seen shortly.\nAdditionally, we have:\n\\begin{corollary}\n \\label{cor:universal_cover_S4_no_deep_slice}\n Suppose that $X$ is a closed smooth 4-manifold with universal cover $\\mathbb{R}^4$ or $\\sphere{4}$,\n and let $X^{\\circ}$ denote the punctured version.\n Then $X^{\\circ}$ has no deep slice knots. \n\\end{corollary}\n\n\n\\section{Existence of deep slice knots}\n\\label{sec:existence_deep_slice}\n\nA \\emph{2-handlebody} is a 4-manifold whose handle decomposition\ncontains one 0-handle, some nonzero number of 2-handles\nand no handles of any other index.\nExamples of this are knot traces,\nwhere a single $2$-handle is attached along a framed knot\nto the 4-ball.\nIn this section, we prove:\n\\begin{theorem} \n \\label{existence}\n Every 2-handlebody $X$ contains a null-homotopic\n deep slice knot in its boundary. \n\\end{theorem}\n\n\\begin{remark}\n For the special case of the $2$-handlebody\n $\\disk{2} \\times \\sphere{2}$\n the existence of such knots was\n already observed in\n \\cite[Thm.\\ B]{Davis_2018}\n (here only winding number $w = 0$ gives null homotopic knots).\n Furthermore the authors construct an infinite family of slice knots\n which are pairwise different in topological concordance in\n a collar of the boundary.\n\\end{remark}\n\n\\autoref{existence} breaks up naturally into two cases depending on whether\nthe boundary has nontrivial $\\pi_1$ or not (i.e. if it is or is not $S^3$).\nIn the case where $\\pi_1(\\partial X) \\neq 1$,\nthere is a concordance invariant for knots in arbitrary 3-manifolds,\nclosely related to the Wall self-intersection number\n(see \\cite{wall1999surgery}, \\cite{freedman1990topology},\nand \\cite{schneiderman2003algebraic}),\nthat will allow us to show that some obviously slice knots are not shallow slice.\nIn the case where $\\pi_1 (\\partial X)$ is trivial,\nand therefore by the \n3-dimensional Poincar\\'e conjecture \\cite{perelman2003finite}\n$\\partial X = \\sphere{3}$, the Wall self-intersection number is of no use.\nHowever, in this case, the consideration of whether a knot\nthat is slice in $X$ is deep slice in $X$ is related\nto the existence of spheres representing various homology\nclasses in the manifold obtained by closing $X$ off with a 4-handle. \n\n\\begin{remark}\n If there was a direct proof\n that every closed homotopy 3-sphere\n smoothly bounds a contractible 4-manifold,\n then we would not need to\n invoke the $3$-dimensional Poincar\\'e conjecture.\n\\end{remark}\n\nFollowing \\cite{yildiz2018note} and \\cite{schneiderman2003algebraic},\nwe briefly introduce the Wall self-intersection number\nin the setting that we will be working in, and state some of its basic properties.\nLet $Y^3$ be a closed oriented 3-manifold and let\n$\\gamma \\colon \\sphere{1} \\hookrightarrow Y$ be a knot in $Y$.\nLet $\\mathcal{C}_\\gamma(Y)$ denote the set of concordance classes\nof oriented knots in $Y$ that are freely-homotopic to $\\gamma$.\nIn particular $\\mathcal{C}_{U}(Y)$\ndenotes the set of concordance classes of\noriented null-homotopic knots in $Y$,\nwhere we write $U$ for the local unknot in $Y$.\nGiven an oriented null-homotopic knot $K \\subset Y$,\nby transversality there exists an oriented immersed disk\n$D$ in $Y \\times {I}$ with boundary\n$K \\subset Y \\times \\{ 0 \\} = Y$\nthat has only double points of self-intersection.\nLet $\\star \\in Y$ denote a basepoint which we implicitly use for\n$\\pi_1(Y) = \\pi_1(Y \\times {I})$ throughout.\nChoose an arc, which we will call a whisker, from $\\star$ to $D$.\nFor each double point of self-intersection $p \\in D$\nchoose a numbering of the two sheets of $D$ that intersect at $p$.\nThen let $g_p \\in \\pi_1(Y)$ be the homotopy class of the loop in $Y \\times {I}$\nobtained by starting at $\\star$, taking the whisker to $D$,\ntaking a path to $p$ going in on the first sheet,\ntaking a path back to where the whisker meets $D$ that leaves $p$ on the second sheet,\nand then returning to $\\star$ using the whisker.\nNote that changing the order of the two sheets would transform $g_p$ to $g_p^{-1}$.\nAlso, since $K$ and $Y$ are oriented,\n$D$ and $Y \\times {I}$ obtain orientations\nwith the convention that $K \\subset Y \\times \\{ 0 \\} = Y$,\nand therefore, for every self-intersection point $p \\in D$,\nthere is an associated sign which we will denote by $\\operatorname{sign}(p)$. \n\n\\noindent Let\n\\[\n \\widetilde{\\Lambda}\n \\coloneqq\n \\frac{ \\mathbb{Z}[\\pi_1(Y)] }\n\t{ \\langle \\{g - g^{-1} \\mid g \\in \\pi_1(Y)\\} \\rangle \\oplus \\mathbb{Z}[1] }\n\\]\nwere the quotient is a quotient as abelian groups.\nThe \\emph{Wall self-intersection number} of $K$ is defined to be \n\\[\n \\mu(K) = \\sum_p \\operatorname{sign}(p) \\cdot g_p \\in \\widetilde{\\Lambda}\n\\]\nSee \\cite{schneiderman2003algebraic}\nfor a proof that it is independent of the choice of $D$, the choice of whisker,\nand the choice of orderings of the sheets of $D$ around the double points.\nFurther, $\\mu$ is a concordance invariant in $Y \\times {I}$,\nand therefore defines a map:\n\\[\n \\mu \\colon \\mathcal{C}_{U}(Y) \\to \\widetilde{\\Lambda}\n\\]\nNotice that if $g \\in \\pi_1(Y)$ and $g \\neq 1$ then $g$ is also nonzero in $\\widetilde{\\Lambda}$. \n\n\\begin{proof}[{Proof of \\autoref{existence}, Case 1}]\nWe are now in position to handle \\autoref{existence}\nin the case where $\\pi_1(\\partial X) \\neq 1$.\nNow $X$ is described by attaching 2-handles to $\\disk{4}$\nalong some framed link $L \\subset \\partial \\disk{4}$.\nSince $\\pi_1(\\partial X)$ is \n(normally) generated by the meridians of $L$\nand $\\pi_1(\\partial X) \\neq 1$, there is some meridian $\\gamma$ of\n$L$ that is nontrivial in $\\pi_1(\\partial X)$.\nNotice that if we are given a 2-handlebody described by a framed link $L$\nand $K$ is a knot in the boundary of the 2-handlebody that is shown in the\nframed link diagram as an unknot (possibly linked with $L$),\nthen $K$ is slice in the 2-handlebody -- just forget all of the other\n2-handles and take an unknotting disk\nwhose interior has been pushed into the 0-handle. \nNow, take $K$ in $\\partial X$ to be a\nWhitehead double of $\\gamma$ as in \\autoref{fig:meridian_Whitehead_double},\nwhich is a null-homotopic knot in the boundary. \nBy the previous observation, since $K$ is unknotted in the boundary of the 0-handle,\n$K$ is slice in $X$.\nAdditionally, one computes that $\\mu(K) = \\gamma \\neq 1 \\in \\widetilde{\\Lambda}$,\nfor example using the null-homotopy in \\autoref{fig:Whitehead_double_nullhomotopy}.\nTherefore, $K$ is not null-concordant in $\\partial X$, so $K$ is deep slice in $X$.\n\\end{proof}\n\n\\begin{figure}\n \\centering\n \\begin{overpic}[width=0.9\\textwidth]{pictures\/meridian_Whitehead_double}\n \\put(5, 18){$L_{i}$}\n \\put(15, 10){\\color{blue} $\\gamma$}\n \\put(36.5, 25){$\\star$}\n \\put(63, 10){\\color{blue} $\\operatorname{Wh}(\\gamma) = K$}\n \\put(63, 18){$L_{i}$}\n \\put(94, 25){$\\star$}\n \\end{overpic}\n \\caption{The Whitehead double of a nontrivial meridian\n $\\gamma$ to one of the surgery link components is deeply slice in $X$.}\n \\label{fig:meridian_Whitehead_double}\n\\end{figure}\n\n\\begin{figure}\n \\centering\n \\begin{overpic}[width=0.9\\textwidth]{pictures\/Whitehead_double_nullhomotopy}\n \\end{overpic}\n \\caption{\n Track of a homotopy from the Whitehead double of $\\gamma$\n to the unknot giving an immersed disk with a\n single double point (red in the middle frame).\n The red double point loop based at the green basepoint\n calculates that $\\mu(K) = \\gamma$.\n }\n \\label{fig:Whitehead_double_nullhomotopy}\n\\end{figure}\n\nNotice that if $\\pi_1(\\partial X) = 1$,\nthen $\\mu$ is of no use since $\\widetilde{\\Lambda} = 0$.\nNow assume that $\\pi_1(\\partial X) = 1$ so that $\\partial X = \\sphere{3}$.\nAgain $X$ is obtained by attaching 2-handles to some framed link $L$.\nLet $\\widehat{X}$ denote the closed 4-manifold\nobtained by closing off $X$ with a 4-handle.\nWe will need a lemma on surfaces in 2-handlebodies,\nwhose statement is\nstandard and could alternatively be concluded from\nthe KSS-normal form for surfaces\nas in \\cite[{Thm.\\ 3.2.7}]{kamada2017surface} and \\cite{MR672939}.\n\\begin{lemma}\n \\label{lem:disk_in_the_bottom}\n Let $X$ be a closed smooth 4-manifold with a handle decomposition consisting\n of only 0-, 2-, and 4-handles,\n with exactly one 0-handle and one 4-handle.\n Every element of $H_2(X;\\mathbb{Z})$ can be represented\n by a smooth closed orientable surface whose\n intersection with the union of the 0- and 2-handles of $X$\n is a single disk.\n\\end{lemma}\n\n\\begin{proof}\t\nLet $X_{\\le 2}$ denote the union of the 0- and 2-handles of $X$,\nso that $X = X_{\\le 2} \\cup \\ball{4}$.\nFor every 2-handle $h_i$, there is an element $H_2(X; \\mathbb{Z})$\nobtained by taking the co-core disk $D_i$ for $h_i$\nand capping it off\nwith an orientable surface in the 4-handle. \nLet $\\{F_i\\}$ denote a choice of these surfaces, one for each 2-handle.\nThese surfaces form a basis for $H_2(X;\\mathbb{Z})$ and note\nthat each has the desired property that $F_i \\cap X_{\\le 2} = D_i$ is a disk.\n\nGiven an arbitrary element $x \\in H_2(X; \\mathbb{Z})$, we have\n$x = a_1 [F_1] + \\cdots + a_n[F_n]$ for some $a_i \\in \\mathbb{Z}$.\nTherefore, by taking parallel copies of the $F_i$ for each summand,\nwe can find an immersed (possibly disconnected)\norientable surface $F'$ representing $x$,\nwith $F' \\cap X_{\\le 2}$ a union of $\\sum \\abs{a_{i}}$ disjoint disks.\nBy taking arcs in $\\partial X_{\\le 2}$ that connect\nthe different boundaries of the disks all together,\nand attaching tubes to $F'$ along these arcs,\nwe obtain a connected orientable immersed surface $F''$ representing $x$\nwhose intersection with $X_{\\le 2}$ is now a disk.\nIn particular, the tubing is done so that half of the tube is contained in $X_{\\le 2}$ and the other half is in the 4-handle, and therefore $F'' \\cap X_{\\le 2}$ is the result of boundary summing together the disks in $F' \\cap X_{\\leq 2}$.\n\nTo make $F''$ into an embedded surface,\nwe can resolve the double points in the 4-handle,\nby increasing the genus,\nand arrive at a surface\nrepresenting $x$ with the desired property. \n\\end{proof}\n\nThe main ingredient for the proof of the second case of\n\\autoref{existence} is the following theorem of Rohlin,\nand in particular the corollary that follows.\nRohlin's theorem has been used in a similar way\nto study slice knots in punctured connected sums\nof projective spaces, for example in \\cite{yasuhara1991torusknot} \nand \\cite{yasuhara1992complexplane}.\n\\begin{theorem}[{Rohlin, \\cite{rokhlin1971two}}]\n\t\\label{Rohlin}\n\tLet $X$ be an oriented closed smooth 4-manifold with $H_1(X; \\mathbb{Z}) = 0$.\n\tLet $\\psi \\in H_2(X; \\mathbb{Z})$ be an element that is divisible by 2,\n\tand let $F$ be a closed oriented surface of genus $g$\n\tsmoothly embedded in $X$ that represents $\\psi$. Then\n\t$$\n\t\t4g \\geq | \\psi \\cdot \\psi - 2\\sigma(X) | - 2b_2(X)\n\t$$\n\\end{theorem}\n\n\\begin{corollary}\n \\label{cor:no_sphere}\n Let $X$ be a closed smooth 4-manifold with $H_1(X; \\mathbb{Z}) = 0$, and $H_2(X; \\mathbb{Z}) \\neq 0$. Then there exists a homology class $\\psi \\in H_2(X; \\mathbb{Z})$ that cannot be represented by a smoothly embedded sphere. \n\\end{corollary}\n\n\\begin{proof}[Proof of \\autoref{cor:no_sphere}]\nTo apply \\autoref{Rohlin}, we must find a homology class $\\psi$\nthat is divisible by 2 where the right hand side\n$| \\psi \\cdot \\psi - 2\\sigma(X) | - 2b_2(X) > 0$.\nSince the intersection form on $X$ is unimodular,\nthere exists some element $\\alpha$ with $\\alpha \\cdot \\alpha \\neq 0$.\nFrom Poincar{\\'e} duality together with the universal coefficient\ntheorem and our hypothesis that $H_{1}(X; \\mathbb{Z}) =0$, we know that $H_{2}(X; \\mathbb{Z})$ \nis torsion free.\nThen by taking $k$ to be a sufficiently large integer,\nwe can make $|(2k \\alpha) \\cdot (2k \\alpha) - 2 \\sigma(X) |$ arbitrarily large.\nBy taking $\\psi = 2k \\alpha$, the result follows. \n\\end{proof}\n\n\\begin{proof}[{Proof of \\autoref{existence}, Case 2}]\nBy \\autoref{cor:no_sphere}, let $\\psi \\in H_2(\\widehat{X}; \\mathbb{Z})$\nbe a homology class that can not be represented by an embedded sphere.\nUsing \\autoref{lem:disk_in_the_bottom}, let $F$ be a smooth closed orientable surface\nrepresenting $\\psi$ whose intersection with $X = \\widehat{X}_{\\le 2}$ is a disk $D$,\nas illustrated schematically in \\autoref{fig:surface_schematic}.\n\\begin{figure}\n \\centering\n \\begin{overpic}[width=0.45\\textwidth]{pictures\/surface_in_handlebody_schematic}\n \\definecolor{blue-green}{rgb}{0.0, 0.87, 0.87}\n \\put(73, 88){\\color{blue} $\\partial D$}\n \\put(-11.5, 60.5){\\color{gray} $\\sphere{3} \\times {I}$}\n \\put(60, 69){4-h.}\n \\put(81, 33){$X = \\widehat{X}_{\\le 2}$}\n \\put(60, 36){2-h.}\n \\put(60, 28){0-h.}\n \\put(36, 20){\\color{blue} $D$}\n \\end{overpic}\n \\caption{\n Schematic of the blue surface $F$\n in the 2-handlebody, intersecting $X=$ the\n union of the 0- and 2-handles\n in a disk $D$. If $\\partial D$\n was shallow slice (dashed light blue) in $X$,\n disk $D$ union the shallow slice disk\n flipped into the 4-handle (solid light blue)\n would be an impossible sphere representative\n of the homology class of $F$.\n }\n \\label{fig:surface_schematic}\n\\end{figure}\nThen $\\partial D \\subset \\partial X$ is deep slice in $X$,\nsince otherwise the surface obtained by intersecting $F$\nwith the 4-handle could be replaced with a disk\nwithout altering the homology class,\nviolating the assumption that $\\psi$ cannot be represented\nby an embedded sphere.\nTo see that the homology class is not altered,\nobserve that in any 2-handlebody the homology class of a surface\nis determined by how it intersects the 0- and 2-handles. \nAlso observe that in this case the deep slice knot\n$\\partial D \\subset \\partial X = \\sphere{3}$\nis local.\nThis concludes the proof of \\autoref{existence}.\n\\end{proof}\n\n\n\\section{Universal slicing manifolds do not exist}\n\\label{sec:universal_slicing}\n\nThe Norman-Suzuki trick\n\\cite[Cor.\\ 3]{MR246309}, \\cite[Thm.\\ 1]{suzuki1969localknots}\ncan be used to show\nthat any knot $K \\subset \\sphere{3}$ bounds\na properly embedded disk in a punctured\n$\\sphere{2} \\times \\sphere{2}$:\nThe track of a null-homotopy of $K$ in $\\disk{4}$\ncan be placed in the punctured $\\sphere{2} \\times \\sphere{2}$\nwhich gives a disk\nthat we can assume to be a generic immersion,\nmissing $\\sphere{2} \\vee \\sphere{2} \\subset (\\sphere{2} \\times \\sphere{2})^{\\circ}$,\nand with a finite number\nof double points.\nBy tubing into the spheres\n$\\sphere{2} \\times \\{ \\textrm{pt} \\}, \\{ \\textrm{pt} \\} \\times \\sphere{2}$\nwe can remove all the intersections\n-- but observe that\nthis changes the homology class of the disk.\n\n\\begin{proposition}\n \\label{prop:all_slice}\n\tLet $M^3$ be a closed orientable 3-manifold.\n\tThere exists a compact orientable 4-manifold $X^4$ constructed\n\twith only a 0-handle and 2-handles, with $\\partial X = M$\n\tsuch that every knot in $M$ is slice in $X$.\n\\end{proposition}\n\n\\begin{proof}\n\tStart by taking any compact 4-manifold $X'$\n\twith only 0-, 2-handles\n\tand boundary $M$\n\tand let $X = X' \\# (\\sphere{2} \\times \\sphere{2})$.\n\tLet $K \\subset M = \\partial X$ be a knot.\n\tSince $X'$ and $X$ are simply connected,\n\t$K$ bounds an immersed disk which we can assume\n\tlives completely in the $X'$-summand of the connected sum.\n\tNow the Norman-Suzuki trick works to remove intersection points\n\tof the immersion\n\tby tubing into the coordinate spheres\n\tof the $\\sphere{2} \\times \\sphere{2}$-summand.\n\\end{proof}\n\n\\begin{remark}\n In contrast to the homologically nontrivial\n disks constructed in the Norman-Suzuki trick,\n a knot is slice via a null-homologous disk in\n some connected sum $\\#^{n} \\sphere{2} \\times \\sphere{2}$\n if and only if its Arf-invariant is zero.\n $\\operatorname{Arf} K = 0$ implies that the knot is band-pass\n equivalent to the unknot, and a band pass\n can be realized by sliding the (oppositely oriented) strands\n of a pair of bands over the coordinate spheres\n in a $\\sphere{2} \\times \\sphere{2}$ factor.\n Conway-Nagel \\cite{conway2020stably}\n defined and studied the minimal number of summands\n needed \n to find a disk \n in a punctured $\\#^{n} \\sphere{2} \\times \\sphere{2}$.\n\\end{remark}\n\n\\noindent \\textbf{Convention:} From now until the\nend of this section, properly embedded\nslice disks $\\Delta^{2} \\subset X^{4}$ in a $4$-manifold\nare always required\nto be \\textbf{null-homologous}.\nWe will still add the qualifier ``null-homologous''\nin the statements to emphasize this.\nSince our obstructions work in the topologically locally\nflat category, we will formulate everything in this more general setting.\n\n\\begin{definition}\n A knot $K \\subset \\sphere{3}$ is\n \\emph{(topologically\/smoothly)\n null-homologous slice in the (topological\/smooth)\n $4$-manifold $X^{4}$} with\n $\\partial X = \\sphere{3}$,\n if $K = \\partial \\Delta$, where\n $\\Delta^{2} \\subset X$ is a (locally flat\/smooth) properly embedded\n disk such that\n $[\\Delta, \\partial \\Delta] = 0 \\in H_{2}(X, \\partial X)$.\n\\end{definition}\n\nOne way of studying if a knot $K$ is slice in $\\disk{4}$\nis to approximate $D^4$ by varying the $4$-manifold $X$.\nBy restricting the intersection form\nand looking at simply-connected 4-manifolds $X$\nthis gives rise to various filtrations of the\nknot concordance group\n(notably the $(n)$-solvable filtration $\\mathcal{F}_{n}$\nof Cochran-Orr-Teichner \\cite{COT2003}\nand the positive and negative variants\n$\\mathcal{P}_{n}, \\mathcal{N}_{n}$ \\cite{MR3109864}).\n\nWe say that the properly embedded disk\n$\\Delta$ is \\emph{null-homologous}\nif its fundamental class\n$[\\Delta, \\partial \\Delta] \\in H_{2}(X, \\partial X)$\nis zero.\nSince by Poincar{\\'e} duality the intersection pairing \n$H_{2}(X) \\otimes_{\\mathbb{Z}} H_{2}(X, \\partial X) \\xrightarrow{\\pitchfork} \\mathbb{Z}$\nis non-degenerate, \na null-homologous disk is characterized\nby the property that it intersects all closed second homology classes\nalgebraically zero times.\nFor slicing in arbitrary $4$-manifolds,\nwe here restrict to null-homologous disks\nto exclude constructions\nas in the Norman-Suzuki trick.\n\nFor every fixed knot $K \\subset \\sphere{3}$,\nthere is a $4$-manifold in which $K$ is null-homologically slice.\nNorman \\cite[Thm.\\ 4]{MR246309} already\nobserves that it is possible\nto take as the 4-manifold a punctured connected sum\nof the twisted 2-sphere bundles $\\sphere{2} \\widetilde{\\times} \\sphere{2}$.\nSimilarly, \\cite[Lem.\\ 3.4]{cochran1986unknotting} discuss that\nfor any knot $K \\subset \\sphere{3}$\nthere are numbers $p, q \\in \\mathbb{N}$\nsuch that $K$ is null-homologous slice\nin the punctured connected sum \n$\\#^{p} \\CP{2} \\#^{q} \\overline{\\CP{2}}$ of\ncomplex projective planes.\nThe argument starts with a sequence of positive and negative\ncrossing changes leading from $K$ to the unknot, \nand then realizes say a positive crossing change\nby sliding a pair of oppositely\noriented strands over the $\\CP{1}$\nin a projective plane summand. The track of this\nisotopy, together with a disk bounding the final unknot\ngives a motion picture of a null-homologous slice disk.\nSince both positive and negative crossing changes might be\nnecessary, it is important that both orientations\n$\\CP{2}, \\overline{\\CP{2}}$ are allowed to appear in the connected sum.\n\nIn view of $(\\sphere{2} \\times \\sphere{2})^{\\circ}$\nwhere every knot in the boundary bounds a disk\n(which is rarely null-homologous)\nand $(\\#^{p} \\CP{2} \\#^{q} \\overline{\\CP{2}})^{\\circ}$,\nin which we find plenty of null-homologous disks\n(but only know how many summands $p, q$ we need\nafter fixing a knot on the boundary)\na natural question concerns the\nexistence of a\n\\emph{universal slicing}\nmanifold.\nIs there a fixed compact, smooth, oriented\n$4$-manifold $V^{4}$ with $\\partial V = \\sphere{3}$\nsuch that any knot $K \\subset \\sphere{3}$\nis slice in $V$ via a null-homologous disk?\nIt turns out that a signature estimate shows such\na universal solution cannot exist.\n\\begin{theorem}\n\t\\label{thm:no_universal_slicing}\n\tAny compact oriented $4$-manifold $V^{4}$ \n\twith $\\partial V = \\sphere{3}$\n\tcontains a knot\n\tin its boundary\n\tthat is not \n\ttopologically null-homologous slice in $V$.\n\\end{theorem}\n\n\\begin{remark}\n If we drop the assumption that $V$ should be compact,\n a punctured infinite connected sum of projective planes\n does the job:\n \\begin{equation*}\n \\disk{4} \\#^{\\infty} (\\CP{2} \\# \\overline{\\CP{2}})\n \\end{equation*}\n For any fixed knot on the\n boundary there\n is a compact slice disk in a finite stage\n \\begin{equation*}\n \\disk{4} \\#^{k} \\CP{2} \\#^{l} \\overline{\\CP{2}} \\# \\disk{4}\n \\subset\n \\disk{4} \\#^{\\infty} (\\CP{2} \\# \\overline{\\CP{2}}).\n \\end{equation*}\n\\end{remark}\n\nThe remainder of this section is concerned with\na proof of \\autoref{thm:no_universal_slicing}.\nAs preparation, let us specialize a result\n\\cite[Thm.\\ 3.8]{conway2020stably},\nwhich is a generalization of the Murasugi-Tristram inequality\nfor links bounding surfaces in $4$-manifolds,\nto the case of knots.\nHere $\\sigma_{\\omega}(K)$ is the\n\\emph{Levine-Tristram signature} of the knot $K$,\ndefined as the signature\nof the hermitian matrix\n$(1-\\omega)V + (1 - \\overline{\\omega})V^T$,\nwhere $V$ is a Seifert matrix of $K$\nand $\\omega$ a unit complex number not equal to $1$.\nReferences for this signature include\n\\cite{MR253348}, \\cite{tristram1969some}\nand the recent survey \\cite{conway2019levine}.\nThe following inequality only holds for specific values of $\\omega$,\nand will adopt the notation\n$\\sphere{1}_{!}$ for unit complex numbers\n$\\omega \\in \\sphere{1} - \\{ 1 \\}$\nwhich do not appear as a zero of an integral Laurent polynomial\n$p \\in \\mathbb{Z}[t, t^{-1}]$ with $p(1) = \\pm 1$.\n\\begin{theorem}[{\\cite[Special case of Thm.\\ 3.8]{conway2020stably}}]\n Let $X$ be a closed oriented topological $4$-manifold\n with $H_{1}(X; \\mathbb{Z}) = 0$.\n If $\\Sigma \\subset (\\sphere{3} \\times {I}) \\# X$\n is a null-homologous (topological)\n cobordism between two knots\n $K \\subset \\sphere{3} \\times \\{ 0 \\}$ and\n $-K' \\subset - (\\sphere{3} \\times \\{ 1 \\})$,\n each contained\n in one of the two boundary component $\\sphere{3}$'s\n of $(\\sphere{3} \\times {I}) \\# X$, then\n \\begin{equation*}\n \\abs{ \\sigma_{K'}(\\omega) - \\sigma_{K}(\\omega) + \\sign(X) }\n - \\chi(X) + 2\n \\le\n - \\chi(\\Sigma)\n \\end{equation*}\n for all $\\omega \\in \\sphere{1}_{!}$.\n\\end{theorem}\n\n\n\\noindent For $K \\subset \\partial X^{\\circ}$ which is\nnull-homologous slice in $X$ and $\\Sigma$ an annulus,\nwe can further simplify:\n\\begin{corollary}\n \\label{cor:MT_general_manifold}\n Let $X$ be a closed topological $4$-manifold with\n $H_{1}(X; \\mathbb{Z}) = 0$.\n If the knot $K \\subset \\sphere{3}$ is topologically\n null-homologous slice in $X^{\\circ}$ then\n for $\\omega \\in \\sphere{1}_{!}$ we have\n \\begin{equation*}\n \\abs{ \\sigma_{K}(\\omega) + \\sign(X) }\n - \\chi(X) + 2\n \\le\n 0\n \\end{equation*}\n\\end{corollary}\n\nTo prove \\autoref{thm:no_universal_slicing}\nit will be enough to obstruct the sliceness of a single knot\nin the boundary.\nThe strategy is to use surgery to trivialize $H_{1}$,\nthen pick the knot $K$ in the original manifold boundary\nand arrive at a contradiction to \\autoref{cor:MT_general_manifold}\nin the surgered manifold if $K$ was null-homologous slice.\n\n\\begin{proof}[Proof of \\autoref{thm:no_universal_slicing}]\n\tLet $V$ be a compact topological $4$-manifold\n\twith boundary $\\sphere{3}$, we want to\n\tfind a knot in its boundary which is not slice.\n\tPick a set of disjointly embedded loops\n\t$\\gamma_{1}, \\ldots, \\gamma_{l}$ in $V$ whose homology classes\n\tgenerate $H_{1}(V)$.\n\tIf $V$ already satisfies $H_{1}(V) = 0$,\n\tset $l=0$ for the remainder of the proof and\n\tomit the surgery altogether.\n\tLet $K$ be a knot in $\\sphere{3}$ whose signature\n\t(at the unit complex number $\\omega = -1$)\n\tsatisfies\n\t\\begin{equation*}\n\t\t\\abs{\\sigma_{K}(-1)} \\ge \\abs{\\sign(V)} + \\abs{\\chi(V)} + 2l.\n\t\\end{equation*}\n\tNote that\n\tthe constant on the right hand side only depends\n\ton the signature, Euler characteristic,\n\tand number of generators of $H_{1}(V)$,\n\tand not on the knot $K$.\n For example, since signature is additive\n under connected sum,\n the self-sum $K_{n} = \\#^{n} K$ with $n$ large enough\n has arbitrarily high signature at $\\omega = -1$\n if we start with a $K$\n that has positive signature $\\sigma_{K}(-1)$\n (for example, taking $K$ to be the left-handed trefoil knot).\n\t\n\tSuppose that $K$ is slice in $V$ via a null-homologous disk $\\Delta$.\n\tBeing null-homologous in\n\tthe relative second homology group\n\tmeans geometrically that there is a locally flat embedded 3-manifold\n\t$M^3$ with boundary the slice disk $\\Delta$ union\n\ta Seifert surface for $K$ in the boundary $\\sphere{3}$,\n\tsee \\cite[Lem.\\ 8.14]{lickorish1997introduction}.\n\tWe can remove the closed components from $M$,\n\twhat remains is a 3-manifold with nonempty boundary in $V$.\n\tGenerically the embedded circles $\\gamma_1, \\ldots, \\gamma_l$\n\twill intersect the\n\t$3$-manifold $M$ in points, but we can push these intersection\n\tpoints off the boundary of $M$\n\tvia an isotopy of the curves in $V$.\n\tWe will still keep the notation\n\t$\\gamma_1, \\ldots, \\gamma_l$ for the isotoped curves\n\twhich are now disjoint from $M$.\n\tEssentially, this finger move supported in a\n\tneighborhood of $M$ is guided by pairwise disjoint arcs in $M$\n\tconnecting the intersections points to the boundary.\n\t\n\tPerform surgery on the loops\n\t$\\gamma_{1}, \\ldots, \\gamma_{l}$,\n\ti.e.\\ for each $\\gamma_{i}$ remove an open\n\ttubular neighborhood $\\nu(\\gamma_{i}) \\cong \\sphere{1} \\times \\interior \\disk{3}$\n\tand glue\n\tcopies of $\\disk{2} \\times \\sphere{2}$ to the\n\tnew $\\sphere{1} \\times \\sphere{2}$ boundary components\n\tvia the identity map\n\t$\\sphere{1} \\times \\sphere{2} \\rightarrow \\sphere{1} \\times \\sphere{2}$.\n\tAfter this surgery we have a compact $4$-manifold $V'$ with $H_{1}(V')=0$,\n\tand the original disk $\\Delta$ survives into $V'$\n\tin which we will call it $\\Delta'$.\n\tObserve that this ``new'' disk $\\Delta'$ is still null-homologous\n\tin $V'$, since the $3$-manifold is still present after\n\tthe surgery.\n Each circle surgery in a $4$-manifold increases\n the Euler characteristic by 2, thus $\\chi(V') = \\chi(V) + 2l$.\n By construction, the 4-manifolds $V$ and $V'$\n are cobordant, and so their signatures $\\sign(V') = \\sign(V)$ agree.\n\t\n\tStarting with a knot $K$\n\twith large enough signature, if there existed\n\ta null-homologous $\\Delta'$,\n\tsince $H_{1}(V') = 0$:\n\t\\begin{equation*}\n\t \\abs{ \\sigma_{K}(-1) + \\sign(V') } - \\chi(V') + 2\n\t =\n\t \\abs{ \\sigma_{K}(-1) + \\sign(V) } - (\\abs{\\chi(V)} + 2l) + 2\n\t >\n\t 0\n\t\\end{equation*}\n\twhich contradicts the inequality in\n\t\\autoref{cor:MT_general_manifold}.\n\tTherefore $\\Delta$ for $K$ cannot exist.\n\\end{proof}\n\n\\begin{remark}\n Earlier sources for results\n in the smooth category\n include Gilmer and Viro's\n \\cite{MR603768} version\n of the Murasugi-Tristram inequality for the classical signature\n as stated in\n \\cite[Thm.\\ 3.1]{yasuhara1996homologyclasses}.\n Our preference for using\n \\cite{conway2020stably} in the proof of\n \\autoref{thm:no_universal_slicing} comes from\n the result being stated in the topological\n locally flat category.\n\\end{remark}\n\n\n\\section{Speculation and Questions}\n\\label{sec:questions}\n\n\\subsection{Connection to other conjectures}\n\nAn alternative approach to the SPC4 is to find a compact 3-manifold $M$\nthat embeds smoothly in some homotopy 4-sphere $\\Sigma^{4}$, but not in $\\sphere{4}$.\nNotice that if a smooth integral\nhomology sphere $M$ smoothly embeds in $\\Sigma$,\nthen $M$ is the boundary of a smooth\nhomology 4-ball \\cite[Prop.\\ 2.4]{MR3653313}.\nHowever, there is no known example of a 3-manifold $M$ that is the boundary of\na smooth homology 4-ball but that does not embed into $\\sphere{4}$.\nBoth this and the approach in the introduction\nare hung up at the homological level.\nFurther discussion of knots in homology spheres\nand concordance in homology cylinders\ncan be found in, for example, \\cite{hom2018knot}, \\cite{davis2019concordance}.\n\n\\autoref{cor:universal_cover_S4_no_deep_slice} has some relevance to\nthis which we now discuss (a similar discussion also appears in a comment by\nIan Agol on Danny Calegari's blogpost \\cite{scharlemannonschoenflies}).\nThe unsolved Schoenflies conjecture proposes that if $\\mathcal{S} \\subset \\sphere{4}$\nis a smoothly embedded submanifold with $\\mathcal{S}$ homeomorphic to $\\sphere{3}$,\nthen $\\mathcal{S}$ bounds a submanifold $B \\subset \\sphere{4}$\nthat is diffeomorphic to $\\disk{4}$.\nThe SPC4 implies the Schoenflies conjecture. \n\n\\begin{question} \\label{all embed}\n\tDoes every exotic homotopy 4-ball $\\mathcal{B}$ smoothly embed into $\\sphere{4}$?\n\\end{question}\n\nNote that if the answer to \\autoref{all embed} is yes,\nthen the Schoenflies conjecture implies the SPC4 and hence the\ntwo conjectures are equivalent: If any homotopy 4-ball\nwould embed into $\\sphere{4}$ and thus,\nby the Schoenflies conjecture, would be diffeomorphic to $\\disk{4}$,\nhence all homotopy 4-balls would be standard,\nso all homotopy 4-spheres would be standard.\nWe have:\n\\begin{observation}\n If the answer to \\autoref{all embed} is yes,\n then no homotopy 4-ball can have deep slice knots. \n\\end{observation}\n\nThus by \\autoref{cor:universal_cover_S4_no_deep_slice},\nif the answer to \\autoref{all embed} is yes,\nthe approach towards SPC4 mentioned in this section would never succeed.\nSimilarly, there would be no 3-manifold that would smoothly embed into a homotopy 4-sphere\nbut not into $\\sphere{4}$. This is because any such embedding into a homotopy sphere avoids a standard 4-ball and after removing this ball the complement is a homotopy 4-ball which we assume embeds into $S^4$, so this approach to SPC4 would also be a dead end. \n\n\\subsection{More questions}\n\n\n\n\\begin{question}\n\tAre there any 2-handlebodies $X$ other that\n\t$\\natural^k (\\sphere{2} \\times \\ball{2}), k \\ge 0$,\n\twith the property that all $K \\subset \\ball{3} \\subset X$ that are slice in $X$\n\tare also slice in $\\ball{4}$?\n\tIn other words, are there always deep slice local knots\n\twhen $X \\neq \\natural^k (\\sphere{2} \\times \\ball{2})$? \n\\end{question}\n\nOne strategy for answering this question would be to start with a framed link $L$\ndescribing a 2-handlebody other than $\\natural^k (\\sphere{2} \\times \\ball{2})$\nand to handle-slide $L$ to a new framed link $L'$ that \ncontains a knot $K$ that is not slice in $\\ball{4}$.\nThen this knot $K$ when considered in a 3-ball\n$K \\subset \\ball{3} \\subset \\partial X$ is an example of such a deep slice knot in $X$.\nThis strategy fails to find any non-slice knots $K$ (as it must)\nfor $\\natural^k (\\sphere{2} \\times \\disk{2})$ when we start with $L$ being\nthe 0-framed unlink -- since then all resulting knots $K$\nwill be ribbon hence slice in $\\ball{4}$. \n\nIn view of the $2$-handlebodies constructed in\n\\autoref{prop:all_slice}, \none could ask whether this extension of the Norman-Suzuki trick is\nthe only way to make any knot in the boundary of a manifold\nbound an embedded disk:\n\\begin{question}\n\tIf $X$ is a 2-handlebody with the property that every knot in the boundary\n\tof $X$ is slice in $X$\n\t(no assumption on the relative homology class of the disk), \n\tdoes it follow that $X$ decomposes\n\tas $X = X_0 \\# (S^2 \\times S^2)$ or $X = X_0 \\# (S^2 \\widetilde{\\times} S^2)$?\n\tMore generally, what about the same question\n\twithout the hypothesis that $X$ be a 2-handlebody?\n\\end{question}\n\n\\bibliographystyle{alpha}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction and main results}\n\nThe model of Lipschitz percolation was introduced in \\cite{DiDoGrHoSc-10}. \nSince its introduction it has been the subject of numerous articles \nand has shown various connections and applications to other topics such as lattice embeddings, plaquette, entanglement and comb percolation or the pinning of interfaces in random media (see e.g.\n\\cite{GrHo-10}, \\cite{DiDoSc-11}, \\cite{GrHo-12}, \\cite{GrHo-12b}, \\cite{HoMa-14}).\nIn the present article we investigate the critical probability for the existence of a Lipschitz surface of open sites\nthat lies above a hyperplane which is tilted (along one or several\ncoordinate axes) by an angle $\\gamma$. We are particularly interested in the asymptotics of this critical probability as\n$d\\to \\infty$ and $\\gamma \\uparrow \\pi\/4$. An immediate consequence of our results is the existence of non-negative stationary supersolutions to the problem\n$$\nu_t(x,t) = \\Delta u(x,t) + f(x, \\bar a\\cdot x + u(x,t),\\omega) + F\n$$\nfor $\\bar a\\in(-\\alpha,\\alpha)^d$ and $F>0$ independent of $\\bar a$ for some $\\alpha>0$ in the sense of \\cite{DiDoSc-11}, i.e., where $f$ describes randomly placed local obstacles. This setting is related to the study of singular homogenization problems, since -- as a cell problem -- it determines the effective velocity $H(\\bar a)$ of an interface with slope $\\bar a$.\n\nOur context is that of site percolation in $\\ensuremath{\\mathbb{Z}}^{d+1}$ with parameter $p \\in [0,1]$. That is, $\\Omega:= \\{0,1\\}^{\\mathbb Z^{d+1}}$ is the set of configurations and the corresponding probability distribution $\\P_p$ is the product measure of Bernoulli distributions with parameter $p$. A site $x \\in \\mathbb Z^{d+1}$ is called \\emph{open} (with respect to $\\omega$) if $\\omega(x)=1$, and \\emph{closed} \nif $\\omega(x)=0$.\n \nOur main object of study are Lipschitz functions and surfaces defined as follows. A function $F: \\ensuremath{\\mathbb{Z}}^d\\rightarrow \\ensuremath{\\mathbb{Z}}$ is called \\emph{Lipschitz} if for any $\\bar x,\\bar y \\in \\ensuremath{\\mathbb{Z}}^d$ the implication \n\\begin{align*}\n \\Vert \\bar x-\\bar y \\Vert_1 =1 \\Rightarrow \\vert F(\\bar x) - F(\\bar y)\\vert \\leq 1\n\\end{align*}\nholds true. We use the term \\emph{Lipschitz surface} to refer to a subset of $\\ensuremath{\\mathbb{Z}}^{d+1}$ that is the graph of a Lipschitz function. Furthermore, given a realization $\\omega \\in \\Omega$, we\n call the Lipschitz surface \\emph{open} if all sites \n in the Lipschitz surface are open in the sense of site percolation, i.e., if $\\omega(\\bar x, F(\\bar x))= 1$ for all ${\\bar x \\in \\ensuremath{\\mathbb{Z}}^d}$.\n\nIt was proven in \\cite{DiDoGrHoSc-10} that the event of existence of an open Lipschitz surface completely contained in the upper half-plane $\\ensuremath{\\mathbb{Z}}^d\\times\\ensuremath{\\mathbb{N}}$ undergoes a phase transition. That is, for any dimension $d \\ge 1$ there exists a critical probability $p_L(d) \\in (0,1)$ such that the following holds: For $p < p_L(d)$ one has that $\\P_p$-a.s. there exists no open Lipschitz\nsurface in $\\ensuremath{\\mathbb{Z}}^d\\times\\ensuremath{\\mathbb{N}}$, whereas for $p > p_L(d)$ one has that $\\P_p$-a.s. there exists an open Lipschitz surface in $\\ensuremath{\\mathbb{Z}}^d\\times\\ensuremath{\\mathbb{N}}$.\n Furthermore, an upper bound for $p_L(d)$ and tail estimates for the height of the minimal surface were established\n for $p$ sufficiently large. These results\n were improved in \\cite{GrHo-12}, where in particular exponential tails for the height of the minimal Lipschitz surface \n have been established for all $p>p_L(d)$. The results were complemented with an asymptotic lower bound yielding $1\/d$ as the correct order of magnitude for $1-p_L(d)$. \n Applications and related results can be found in \\cite{DiDoSc-11}, \\cite{\nGrHo-12b}, \\cite{GrHo-10}.\n\nWhile the investigation of Lipschitz percolation up to now has been focused on Lipschitz surfaces that stay above the plane\n$L:=\\ensuremath{\\mathbb{Z}}^d\\times\\{0\\}$, we are interested in the effect of \\lq tilting\\rq\\, this plane. To make this more precise let us define \n for any $d \\in \\ensuremath{\\mathbb{N}}, \\, \\alpha \\in [0,1)$ and $\\eta \\in \\{-1,0,+1\\}^d$ the \\emph{tilted planes}\n \\begin{align*}\n L^{\\alpha,d}_{\\eta} := \\Big\\{ (x_1, \\ldots, x_{d+1}) \\in \\ensuremath{\\mathbb{Z}}^{d+1} \\mid x_{d+1} = \n \\Big \\lfloor \\alpha \\sum_{i=1}^d \\eta_ix_i \\Big \\rfloor\\Big\\}.\n \\end{align*}\nFor computational convenience we introduce the parameter $\\alpha$ as in the above definition, instead of directly working with the angle $\\gamma $ by which a plane is tilted along all the coordinate axes in direction $e_i$ for which $\\eta_i = 1$, $1 \\le i \\le d,$\nin the above choice of $\\eta$ (and $-\\gamma$ in the case that $\\eta = -1$). However, given $\\eta,$ there is a natural\n one-to-one correspondence between $\\alpha$ and \nthe angle $\\gamma$. Also, note that the case of $\\alpha =0$ as well as the case $\\eta = 0$ correspond to $\\gamma = 0$ and thus to standard Lipschitz percolation.\nThe restriction to $\\alpha \\in [0,1)$, resp. $\\gamma < \\pi\/4$, is natural, once one realizes that for \n$\\eta \\ne 0$, $\\alpha \\geq 1$ (resp. $\\gamma \\geq \\pi\/4$), and any $p<1$, $\\P_p$-a.s. there exists no open Lipschitz surface above the plane $L^{\\alpha,d}_{\\eta}$. \n\nIn the study of Lipschitz percolation above tilted planes, the related concept of Lipschitz percolation above \\lq inverted pyramids\\rq\\ turns out to be helpful. Thus, we introduce for any $d \\in \\ensuremath{\\mathbb{N}}, \\, \\alpha \\in [0,1)$ and $\\eta \\in \\{-1,0,+1\\}^d$ the \n\\emph{inverted pyramid} $\\nabla_\\eta^{\\alpha, d}$ as\n\\begin{align*}\n& \\nabla^{\\alpha, d}_\\eta \n:=\n\t\\Big \\{(x_1, \\dots, x_{d+1}) \\in \\mathbb Z^{d+1} \\mid x_{d+1} = \n\t\\max_{\\substack{\\eta' \\in \\{-1,0,+1\\}^d\\\\ \\Vert \\eta' \\Vert_1 = \\Vert \\eta \\Vert_1}} \n\t\\Big \\{ \\Big \\lfloor \\alpha \\sum_{i=1}^d \\eta_i' x_i \\Big \\rfloor \\Big \\} \\Big \\}.\n\\end{align*}\nWe can now formulate our main result:\n\n\\begin{theorem}\n \\label{thm:mainthm_intro}\n There exists a phase transition for both Lipschitz percolation above planes and\n Lipschitz percolation above inverted pyramids, and their critical probabilities coincide. This critical probability $p_L(\\alpha,d,\\eta)$ is nontrivial and depends on $\\eta$ only via\n $\\Vert \\eta \\Vert_1$. Furthermore,\n \\begin{equation}\\label{eq:mr_d}\n 1-p_L(\\alpha, d, \\eta) \\asymp d^{-\\frac{1}{1-\\alpha}}, \\qquad \\text{ as } d \\rightarrow \\infty, \n \\end{equation}\n and\n\\begin{equation}\\label{mr_alpha}\n 1-p_L(\\alpha, d, \\eta) \\asymp (1-\\alpha)^d, \\qquad \\text{ as }\\alpha \\rightarrow 1. \n \\end{equation}\n\\end{theorem}\nHere we write $f(s) \\asymp g(s)$ as $ s\\rightarrow \\bar s$ for two functions $f$ and $g$\n if there exist positive and finite constants $c,C$ such that $\\liminf_{s \\rightarrow \\bar s} f(s)\/g(s) \\geq c$ and $\\limsup_{s \\rightarrow \\bar s} f(s)\/g(s) \\leq C$.\n\nFor the reader's convenience, Theorem \\ref{thm:mainthm_intro} is a concise\nsummary of the principal asymptotics for $p_L(\\alpha, d, \\eta)$ obtained in this article. The actual asymptotics\n we obtain are more precise and will be given as individual results below.\n\nThe article is structured as follows.\nSection \\ref{sec:Lipschitz} is concerned with general results on Lipschitz percolation in the set-up of tilted planes.\n Proposition \\ref{prop:equiv} establishes the non-trivial phase transition for $p_L(\\alpha, d, \\eta)$,\n whereas Lemma \\ref{lem:mon} exposes the monotonicity relations for the individual parameters.\n\nSection \\ref{sec:bounds} outlines all bounds on the critical probabilities separated into two subsections, one for lower and one for upper bounds. Using the notation of \\eqref{eq:qDef}, the asymptotics\n\\eqref{eq:mr_d} and \\eqref{mr_alpha} follow by combining Propositions \\ref{prop:allAlphaUBp} and \\ref{prop:UBqAsympD}, \nas well as Propositions \\ref{prop:lbq_alpha} and \\ref{prop:UBqAsympAlpha}, respectively. As explained in Proposition \\ref{prop:D1}, for $d=1$ we obtain the exact asymptic behavior for $\\alpha \\rightarrow 1$. In addition, Proposition \\ref{prop:qLBkDepOnd} provides lower bounds for the critical probabilities, depending on how the number of tilted axes behaves asymptotically with the dimension. \n\nThe corresponding proofs and further auxiliary results are contained in Section \\ref{sec:proofs}.\n\n\n\\section{Further notation and auxiliary results} \\label{sec:Lipschitz}\n\nWe begin by defining the events to be considered and to this end denote by $L^{\\alpha, d}_{\\eta, \\geq}$ the upper half space above $L^{\\alpha, d}_{\\eta}$.\nFor this purpose, denote the set of all Lipschitz functions by $\\Lambda$.\n\n\\begin{definition}\n Let $\\mathsf{LIP}^{\\alpha, d}_{\\eta}$ denote the event that there exists an open Lipschitz surface \ncontained in $L^{\\alpha, d}_{\\eta,\\ge}$, i.e.,\n\t\\[\\mathsf{LIP}^{\\alpha, d}_{\\eta} := \n\t\\Big \\{ \\omega \\in \\Omega \\mid \\exists F \\in \\Lambda : \\forall \\bar{x} \\in \\mathbb Z^{d}: \\omega((\\bar{x}, F(\\bar{x})))=1 \\text{ and } F(\\bar{x}) > \\Big \\lfloor \\alpha \\sum_{i=1}^d \\eta_i \\bar{x}_i \\Big \\rfloor \\Big \\}.\\]\nSimilarly to the case of planes we use $\\mathsf{LIP}(\\nabla^{\\alpha, d}_{\\eta})$ to denote the event of existence of a Lipschitz surface above the inverted pyramid $\\nabla_\\eta^{\\alpha, d}$, i.e.,\n\\begin{align*}\n&\\mathsf{LIP}(\\nabla^{\\alpha, d}_{\\eta}):= \\\\\n& \\Big \\{\\omega \\in \\Omega \\mid \\exists F \\in \\Lambda : \\forall \\bar{x} \\in \\mathbb Z^{d} : \\omega((\\bar{x}, F(\\bar{x})))=1 \n\t\\text{ and } F(\\bar{x}) > \n\t\\max_{\\substack{\\eta' \\in \\{-1,0,+1\\}^d\\\\ \\Vert \\eta' \\Vert_1 = \\Vert \\eta \\Vert_1}} \n\t \\Big \\{ \\Big\\lfloor \\alpha \\sum_{i=1}^d \\eta_i' \\bar x_i \\Big \\rfloor \\Big \\} \\Big\\}.\n\t\\end{align*}\n\\end{definition}\n\n\\begin{proposition}\n \\label{prop:equiv}\nFor any $d\\ge 1,$ $\\alpha \\in [0,1)$ and $\\eta \\in \\{-1,0,+1\\}^d$, there exists a critical probability \n $p_L(\\alpha,d, \\eta) \\in (0,1)$ \nsuch that\n\\begin{align}\\label{eq:planePhaseTrans}\n& \\P_p(\\mathsf{LIP}(\\nabla^{\\alpha, d}_{\\eta})) = \\mathbb P_p(\\mathsf{LIP}^{\\alpha, d}_{\\eta}) = \\begin{cases}\n\t0, \\; p\\in [0,p_L(\\alpha,d, \\eta)),\\\\\n 1, \\; p \\in (p_L(\\alpha,d,\\eta ),1].\n \\end{cases}\n\\end{align}\nIn fact, for any $\\eta' \\in \\{-1,0,1\\}$ with $\\Vert \\eta \\Vert_1 = \\Vert \\eta'\\Vert_1$,\n\\begin{align} \\label{eq:pNormDep}\np_L(\\alpha,d, \\eta) = p_L(\\alpha,d, \\eta' ).\n\\end{align}\nTherefore, $p_L(\\alpha,d, \\eta)$ depends on $\\eta$ only through the number of nonzero entries.\n\\end{proposition}\n\nThis means that there exists a phase transition for both Lipschitz percolation above tilted planes and above inverted pyramids, and their critical probabilities coincide.\nDue to \\eqref{eq:pNormDep} it is convenient to define $p_L(\\alpha, d,k) := p_L(\\alpha,d,\\eta)$ for any $\\eta \\in \\{-1,0,+1\\}$ such that $\\Vert \\eta \\Vert_1 = k\\in\\{0,\\ldots,d\\}$. Furthermore, we set\n\\begin{equation} \\label{eq:qDef}\nq_L(\\alpha, d,k) := 1-p_L(\\alpha, d,k).\n\\end{equation}\nFor notational convenience we will formulate most of our results for $q_L$ instead of $p_L$ since the latter usually tends to $1$ and hence the former to $0$.\n\n\\begin{proof}[Proof of Proposition \\ref{prop:equiv}]\nFirst observe that due to the symmetries of $\\ensuremath{\\mathbb{Z}}^d$ and the i.i.d.-product structure of $\\P_p$, the quantity $\\P_p(\\mathsf{LIP}^{\\alpha, d}_{\\eta})$ depends on $\\eta$ only through $\\Vert \\eta \\Vert_1$. Thus, if the postulated critical probabilities exist,\nthen they must fulfill \\eqref{eq:pNormDep}.\n\nWe now start with showing the second equality in \\eqref{eq:planePhaseTrans} for some $p_L(\\alpha,d, \\eta) \\in [0,1]$.\nSince $\\mathsf{LIP}^{\\alpha, d}_{\\eta}$ is an \nincreasing event, it is immediate that $\\mathbb P_p(\\mathsf{LIP}^{\\alpha, d}_{\\eta})$ is nondecreasing in $p.$\nTherefore, it is sufficient to show \n that it takes values in $\\{0,1\\}$ only.\n\nDefine the shift $\\theta: \\omega \\mapsto \\omega(\\cdot, \\ldots, \\cdot +1)$ \nin the $(d+1)$-st coordinate. Then $\\theta$ is measure preserving for $\\P_p$ and ergodic with respect to $\\P_p.$ As a consequence, since $\\theta^{-1}(\\mathsf{LIP}^{\\alpha, d}_{\\eta}) \\subset \\mathsf{LIP}^{\\alpha, d}_{\\eta}$ and $\\P_p(\\theta^{-1}(\\mathsf{LIP}^{\\alpha, d}_{\\eta}))=\\P_p(\\mathsf{LIP}^{\\alpha, d}_{\\eta})$, the event $\\mathsf{LIP}^{\\alpha, d}_{\\eta}$ is $\\P_p$-a.s. invariant with respect to $\\theta$, i.e. $\\P_p(\\mathsf{LIP}^{\\alpha, d}_{\\eta} \\triangle \\theta^{-1}(\\mathsf{LIP}^{\\alpha, d}_{\\eta}))=0,$ and by Proposition 6.15 in \\cite{Breiman} this already implies \n\\begin{align*}\n \\P_p ( \\mathsf{LIP}^{\\alpha, d}_{\\eta} )\\in \\{0,1\\}.\n\\end{align*}\nThis establishes the second equality in \\eqref{eq:planePhaseTrans} for some $p_L(\\alpha,d, \\eta) \\in [0,1]$.\n\nIn order to obtain the first equality of \\eqref{eq:planePhaseTrans}, due to the \nsecond\nequality in \\eqref{eq:planePhaseTrans} and\n$\\mathsf{LIP}(\\nabla^{\\alpha, d}_{\\eta}) \\subseteq \\mathsf{LIP}^{\\alpha, d}_{\\eta},\n$\nit remains to show\nthat\n$\n \\mathbb P_p(\\mathsf{LIP}^{\\alpha, d}_{\\eta}) = 1$ implies\n$\\P_p(\\mathsf{LIP}(\\nabla^{\\alpha, d}_{\\eta})) = 1.$\nBy symmetries, $ \\mathbb P_p(\\mathsf{LIP}^{\\alpha, d}_{\\eta}) = 1$\n already yields\n\\begin{equation*}\n\\P_p \\Big( \\bigcap_{\\substack{\\eta' \\in \\{-1,0,+1\\}^d\\\\ \\Vert \\eta' \\Vert_1 = \\Vert \\eta \\Vert_1}} \\mathsf{LIP}^{\\alpha, d}_{\\eta'} \\Big) = 1.\n\\end{equation*}\nNote that the pointwise maximum of Lipschitz functions is a Lipschitz function again and thus \n \\begin{align*}\n \\bigcap_{\\substack{\\eta' \\in \\{-1,0,+1\\}^d\\\\ \\Vert \\eta' \\Vert_1 = \\Vert \\eta \\Vert_1}} \\mathsf{LIP}^{\\alpha, d}_{\\eta'} \\subseteq \\mathsf{LIP}(\\nabla^{\\alpha, d}_{\\eta}).\n \\end{align*}\n Thus \\eqref{eq:planePhaseTrans} holds true.\n \n It remains to show the nontriviality of the phase transition, i.e., that $p_L(\\alpha,d, \\eta) \\in (0,1).$\nProposition \\ref{prop:allAlphaUBp} below in particular shows that $p_L(\\alpha, d, d) < 1$ for all $\\alpha \\in [0,1)$ and $d \\ge 1$; hence, using \\eqref{eq:etaMon} below, we deduce $p_L(\\alpha, d, k) < 1$ for all $0 \\le k \\le d.$ On the other hand, $p_L(\\alpha, d, k) > 0$ for all $0 \\le k \\le d$ follows from the fact that the critical probability for the existence of an infinite connected component in the $1$-norm\nin $(d+1)$-dimensional Bernoulli site-percolation (which is a lower bound for $p_L(\\alpha, d, k)$) is strictly positive.\n\\end{proof}\n\nUsing the above result one can obtain some simple but helpful monotonicity results for the critical probabilities. \n\n\\begin{lemma} \\label{lem:mon}\n For all $d \\in \\mathbb N,$ and $\\alpha, \\alpha' \\in [0,1)$ such that $\\alpha \\leq \\alpha'$, we have \n\\begin{equation} \n\\forall\\, k = 0, \\ldots, d: \\quad p_L({\\alpha},d, k) \\leq p_L({ \\alpha'}, d, k), \\label{eq:alphaMon}\n\\end{equation}\n\\begin{equation}\n \\forall\\, k = 0, \\ldots, d:\\quad p_L({\\alpha}, d, k)\n \\leq p_L({\\alpha}, d+1, k), \\label{eq:dMon}\n \\end{equation}\n and\n \\begin{equation}\n\\forall\\, k = 0, \\ldots, d-1:\\quad p_L({\\alpha},d, k) \\leq p_L({\\alpha}, d, k+1). \\label{eq:etaMon} \n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nWe start by proving the monotonicity in $\\alpha$, which is best seen considering Lipschitz surfaces above inverted pyramids. Note that for $\\alpha' \\geq \\alpha$, one has $ \\nabla^{\\alpha',d}_\\eta \\geq \\nabla^{\\alpha, d}_\\eta,$ in the sense that for any $(\\bar y, y_{d+1}^{\\alpha'}) \\in \\nabla^{ \\alpha',d}_\\eta$ and $(\\bar y, y_{d+1}^{\\alpha}) \\in \\nabla^{\\alpha,d}_\\eta$ we have $y^{ \\alpha'}_{d+1} \\geq y^{\\alpha}_{d+1}$. Hence $\\mathsf{LIP}(\\nabla^{\\alpha', d}_{\\eta}) \\subseteq\n\\mathsf{LIP}(\\nabla^{\\alpha, d}_{\\eta}),$ which implies \\eqref{eq:alphaMon}.\n\nOn the other hand, to prove \\eqref{eq:dMon} choose $\\eta \\in \\{-1,0,+1\\}^{d+1}$ with $\\Vert \\eta \\Vert_1 = k,$ and let $1 \\le j \\le\nd+1$ be such that $\\eta_j = 0.$\nThen \\eqref{eq:dMon}\n follows directly from the fact that the cross section of a Lipschitz surface in $L^{\\alpha, d+1}_{\\eta, \\ge}$ with $\\ensuremath{\\mathbb{Z}}^{j-1} \\times\n\\{0\\}\\times\\mathbb Z^{d-j+1}$ mapped to $\\ensuremath{\\mathbb{Z}}^d$ by eliminating the $j$-th coordinate\nis again a Lipschitz surface contained in $L^{\\alpha, d}_{\\eta^{(j)}, \\ge}$, for $\\eta^{(j)}:=(\\eta_1, \\ldots, \\eta_{j-1},\\eta_{j+1}, \\ldots, \\eta_{d+1})$, combined with the fact that $\\Vert \\eta^{(j)} \\Vert_1 = k$ and \\eqref{eq:pNormDep}.\n\nLastly, \\eqref{eq:etaMon} follows from the fact that for any $1\\le j \\le d,$ $ \\nabla^{\\alpha, d}_{\\eta_{j \\to 0}} \\geq \\nabla^{\\alpha, d}_\\eta$ in the above sense and thus $\\mathsf{LIP}(\\nabla^{\\alpha, d}_{\\eta_{j \\to 0}}) \\supset \\mathsf{LIP}(\\nabla^{\\alpha, d}_{\\eta})$,\nwhere $\\eta_{j \\to 0}$ is obtained from $\\eta$ by replacing the $j$-th coordinate by $0.$\n\\end{proof}\n\n\n\\section{Bounds on the Critical Probabilities} \\label{sec:bounds}\n\nFor functions $f,g$ we write $f(s) \\lesssim g(s)$ as $s\\rightarrow \\bar s$, if $\\limsup_{s \\rightarrow \\bar s} f(s)\/g(s) \\leq 1$, \nwe write $f(s) \\gtrsim g(s)$ as $s\\rightarrow \\bar s$, if $\\liminf_{s \\rightarrow \\bar s} f(s)\/g(s) \\geq 1$, and\nasymptotic equivalence is denoted by\n $f(s) \\sim g(s), \\,s\\rightarrow \\bar s$ (i.e., if $f(s) \\lesssim g(s)$ and $f(s) \\gtrsim g(s)$ as $s\\rightarrow \\bar s$). With this notation we can write the results on the bounds in \\cite{GrHo-12} as\n\\begin{align} \\label{eq:GH12}\n\\begin{split}\n q_L(0,d,0) &\\geq (8d)^{-1}, \\quad \\text{ for all } d \\in \\ensuremath{\\mathbb{N}},\\\\\n q_L(0,d,0) &\\lesssim (2d)^{-1}, \\quad \\text{ as } d\\rightarrow \\infty.\n \\end{split}\n\\end{align}\n\n\\subsection{Lower Bounds for $q_L(\\alpha, d, k)$ }\n\n\n\\begin{proposition}[General bound]\\label{prop:allAlphaUBp}\n For any $d \\geq 1$ and $\\alpha \\in [0,1)$ one has\n \\begin{align*\n q_L(\\alpha, d, d) \\geq \\frac{1}{2} (4d)^{-\\frac{1}{1-\\alpha}}.\n \\end{align*}\n\\end{proposition}\n\n\nNote that for $\\alpha =0$ this is exactly the lower bound of \\eqref{eq:GH12}.\nIn a similar way one can find bounds for the critical probability in the case that the number $k$ of axes along which the plane is tilted depends on the dimension $d$:\n\\begin{proposition} \\label{prop:qLBkDepOnd}\n Consider a function $\\varphi : \\ensuremath{\\mathbb{N}} \\to \\ensuremath{\\mathbb{N}}_0$ with $\\varphi(d) \\le d$ for all $d \\in \\ensuremath{\\mathbb{N}}$. \n\\begin{enumerate}\n \\item If for some $\\alpha \\in [0,1)$ one has that $\\varphi (d) \\in o(d^{1-\\alpha})$ as $d\\rightarrow \\infty$, then\n\\begin{align*}\n q_L(\\alpha,d,\\varphi(d)) \\gtrsim\\frac{1}{8} d^{-1}, \\quad \\text{ as }d\\rightarrow \\infty.\n\\end{align*}\n \\item If for some\n $\\alpha \\in [0,1)$ and $c \\in [0,1]$ one has $\\varphi(d) \\sim cd^{1-\\alpha}$ as $d\\rightarrow \\infty$, \n then there exists a constant $C(c,\\alpha)>0$ such that \n\\begin{align*}\n q_L(\\alpha,d,\\varphi(d)) \\gtrsim C(c,\\alpha) d^{-1}, \\quad \\text{ as }d\\rightarrow \\infty.\n\\end{align*}\n \\item If for some\n $c \\in (0,1]$ one has\n $\\varphi(d) \\sim cd$ as $d \\rightarrow \\infty$, then for $\\alpha \\in (0,1)$,\n\\begin{align*}\n q_L(\\alpha, d, \\varphi(d)) \\gtrsim \\frac{1}{4} (1-\\alpha)(cd)^{-\\frac{1}{1-\\alpha}}, \\quad \\text{ as }d\\rightarrow \\infty.\n\\end{align*}\n\\end{enumerate}\n\\end{proposition}\n\n\\begin{remark}\n The constant in Proposition \\ref{prop:qLBkDepOnd}, (b), satisfies $C(c,0) = C(0,\\alpha)= 1\/8$ for any $c \\in [0,1]$, $\\alpha \\in [0,1)$;\n this is what one would hope for, given that these cases correspond to standard Lipschitz percolation.\n \n The bound in Proposition \\ref{prop:qLBkDepOnd}, (c), is an improvement compared to Proposition \\ref{prop:allAlphaUBp} at the expense of being of asymptotic nature only. \n\\end{remark}\n\n\n\\begin{proposition}[]\\label{prop:lbq_alpha}\n For each $d \\geq 1$ and each $k = 1,\\ldots,d$ there exists a constant $C(k,d)>0$ such that for all $\\alpha \\in [0,1)$ one has\n \\begin{align*\n q_L(\\alpha,d,k) \\geq C(k,d)(1-\\alpha)^k.\n \\end{align*}\n \\end{proposition}\n \n \\begin{proposition}\\label{prop:D1}\n For $d=1$ one has $q_L(\\alpha,1,1) \\gtrsim (1-\\alpha)$ as $\\alpha \\rightarrow 1$, which together with Proposition \\ref{prop:UBqAsympAlpha} below yields\n \\begin{align*}\n q_L(\\alpha,1,1) \\sim (1-\\alpha), \\qquad \\text{ as } \\alpha \\rightarrow 1.\n \\end{align*}\n \\end{proposition}\n \n\\subsection{Upper Bounds for $q_L(\\alpha, d, k)$}\n \n\n \\begin{proposition}[Asymptotic behavior for $d \\rightarrow \\infty$]\\label{prop:UBqAsympD}\n For every $\\alpha \\in [0,1)$ there exists a constant $C(\\alpha)$ such that\n \\begin{align*}\n q_L(\\alpha,d,d) \\lesssim C(\\alpha) d^{-\\frac{1}{1-\\alpha}}, \\qquad \\text{ as } d\\rightarrow \\infty.\n \\end{align*}\n More precisely, $C(\\alpha) = \\theta^{\\frac{1}{1-\\alpha}}\/({\\rm e}^{\\theta} -1)$, where $\\theta$ is the unique solution to \n $\\theta {\\rm e}^{\\theta}\/({\\rm e}^{\\theta} -1) = 1\/(1-\\alpha)$ and $C(0) = 1$.\n \\end{proposition}\n\n\n \\begin{proposition}[General bound]\\label{prop:UBqAsympAlpha}\n For any $\\alpha \\in [0,1)$ and $d \\in \\ensuremath{\\mathbb{N}}$ \n \\begin{align*}\n q_L(\\alpha,d,d) \\leq \\frac{d!(1-\\alpha)^d}{1+d!(1-\\alpha)^d} \\leq d!(1-\\alpha)^d.\n \\end{align*}\n \\end{proposition}\n \\begin{remark}\nSince $q_L(\\alpha, d,k) \\leq q_L(\\alpha, k,k)$ by Lemma \\ref{lem:mon}, Proposition \\ref{prop:UBqAsympAlpha}\nimmediately implies upper bounds for $q_L(\\alpha, d, k)$ for any $k=1,\\ldots,d$ also.\n \\end{remark}\n \n \n\n\\section{Proofs} \\label{sec:proofs}\n\nAs explained in \\cite{DiDoGrHoSc-10} and \\cite{GrHo-12} for standard Lipschitz percolation, the lowest open Lipschitz surface (above $L^{\\alpha,d}_{\\eta}$) may be constructed as a blocking surface to a certain type of paths called (admissible) $\\lambda$-paths. This characterization is the core of the proofs in this section.\n\nDenote by $e_1, \\ldots, e_{d+1} \\in \\ensuremath{\\mathbb{Z}}^{d+1}$ the standard basis vectors of $\\ensuremath{\\mathbb{Z}}^{d+1}$.\n\n\\begin{definition} \\label{def:adLamPa}\n For $x,y \\in \\ensuremath{\\mathbb{Z}}^{d+1}$ a \\emph{$\\lambda$-path} from $x$ to $y$ is any finite sequence $x=u_0, \\ldots, u_n=y$ \n of distinct sites in $\\ensuremath{\\mathbb{Z}}^{d+1}$ such that for all $ i = 1, \\ldots,n$\n \\begin{align*}\n u_i - u_{i-1} \\in \\{e_{d+1}\\} \\cup \\{-e_{d+1} \\pm e_{j} \\mid j = 1, \\ldots, d\\}.\n \\end{align*}\n Such a path will be called \\emph{admissible (with respect to $\\omega$)}, if for all $i = 1, \\ldots, n$ the following implication holds:\n \\begin{align*}\n \\text{ If } u_i-u_{i-1} = e_{d+1}, \\text{ then } u_i \\text{ is closed (with respect to $\\omega$).}\n \\end{align*}\n\\end{definition}\nFor any $x,y \\in \\ensuremath{\\mathbb{Z}}^{d+1}$ denote by $x \\rightarrowtail y$ the event that there exists an admissible $\\lambda$-path from $x$ to $y$. \nWe then define for all $ x \\in \\ensuremath{\\mathbb{Z}}^d$, $\\alpha \\in [0,1)$, $d \\in \\ensuremath{\\mathbb{N}}$ and $\\eta \\in \\{-1,0,+1\\}$ the function\n\\begin{align} \\label{eq:LSfromlambda}\n\\; F^{\\alpha, d}_{\\eta}(\\bar x):= \\sup\\{n \\in \\ensuremath{\\mathbb{Z}} \\mid \\exists y \\in L^{\\alpha, d}_{\\eta}:\\; y \\rightarrowtail (\\bar x, n) \\} + 1.\n\\end{align}\nNote that the graph of $F$ is contained in $L^{\\alpha,d}_{\\eta}$. As in \\cite{DiDoGrHoSc-10} and \\cite{GrHo-12}, it is easy to see that \nthe function defined in \\eqref{eq:LSfromlambda} \ndescribes a Lipschitz function whose graph consists of open sites,\nif and only if it is finite for all $\\bar x \\in \\ensuremath{\\mathbb{Z}}^d$. This in turn holds true if and only if it is finite at $\\bar x = 0$. Thus, in the analysis of the existence of an open Lipschitz surface we can focus on the behavior of $F^{\\alpha, d}_{\\eta}(0)$ as defined above.\n\nIt will be useful to define $L^{\\alpha, d}_{\\eta}(h) := L^{\\alpha,d}_{\\eta} + he_{d+1}$ and denote by $\\mathcal L^{\\alpha,d}_{\\eta}(h)$ the random set of sites in $L^{\\alpha, d}_{\\eta}(h)$ reachable by an admissible $\\lambda$-path started in the origin.\nWe have taken the practice of marking elements of $\\ensuremath{\\mathbb{Z}}^d$ with a bar as in $\\bar x \\in \\ensuremath{\\mathbb{Z}}^d$ in order to distinguish them from canonical elements $x \\in \\ensuremath{\\mathbb{Z}}^{d+1}$. In the same vein, for $x = (x_1, \\ldots, x_{d+1})\\in \\ensuremath{\\mathbb{Z}}^{d+1}$, we use $\\bar x$ to refer to $(x_1, \\ldots, x_d)$ as well as $(\\bar x,x_{d+1})$ to denote $x$. In addition, by a slight abuse of notation we use $0$ to denote the origin of $\\ensuremath{\\mathbb{Z}}, \\ensuremath{\\mathbb{Z}}^d$ and $\\ensuremath{\\mathbb{Z}}^{d+1}$. As we have tacitly done above already,\n it will be necessary to distinguish between $\\ensuremath{\\mathbb{N}}$ and $\\ensuremath{\\mathbb{N}}_0$. For a set $A$ we will use $\\vert A \\vert$ to denote its cardinality. \n\nIn addition, due to the symmetries of $\\ensuremath{\\mathbb{Z}}^d$ and the product structure of $\\P_p$, we will w.l.o.g.\n from now on assume that for any $k = 1, \\ldots, d$, the vector $\\eta$ is of the form\n\\begin{align*}\n \\eta = (\\underbrace{1, \\ldots,1}_{ k \\text{ times}},\\underbrace{0,\\ldots,0}_{d-k \\text{ times}}).\n\\end{align*}\n\n\\subsection{Lower Bounds for $q_L(\\alpha, d, k)$}\n\n\nWe begin with a criterion ensuring the existence of an open Lipschitz surface by providing suitable conditions for the $\\P_p$-a.s. finiteness of $F^{\\alpha, d}_{\\eta}$ as defined in \\eqref{eq:LSfromlambda}.\n\\begin{lemma}[Criterion for existence of an open Lipschitz surface]\\label{lem:surfaceExistence}\nLet $F^{\\alpha, d}_{\\eta}$ be defined as in \\eqref{eq:LSfromlambda}. Then, for any $\\bar x \\in \\ensuremath{\\mathbb{Z}}^d$ and $h \\in \\ensuremath{\\mathbb{N}}$,\n\\begin{equation} \\label{eq:tailEstCond}\n\\P_p \\Big ( F^{\\alpha,d}_{\\eta}(\\bar{x}) - \\Big \\lfloor \\alpha \\sum_{i=1}^d \\eta_i \\bar{x}_i \\Big \\rfloor \\ge h \\Big) \\le \\ensuremath{\\mathbb{E}}_p [\\vert \\mathcal L_{\\eta}^{\\alpha, d}(h-2) \\vert ].\n\\end{equation} \n In particular, if\n \\begin{align} \\label{eq:summability}\n \\lim_{h \\rightarrow \\infty} \\ensuremath{\\mathbb{E}}_p[\\vert \\mathcal L^{\\alpha,d}_{\\eta}(h) \\vert ] = 0,\n \\end{align}\nthen \n\\begin{equation} \\label{eq:surfaceEx}\n\\P_p(\\mathsf{LIP}^{\\alpha, d}_{\\eta}) = 1.\n\\end{equation}\n\\end{lemma}\n\n\n\\begin{proof}[Proof of Lemma \\ref{lem:surfaceExistence}]\nIn order to prove \\eqref{eq:tailEstCond} we start by observing that \n for every $\\bar{x} \\in \\mathbb Z^{d},$ \n\\begin{align} \\label{eq:stochDom}\n\\text{the random variable }F^{\\alpha,d}_{\\eta}(0)+1 \\text{ stochastically dominates }\nF^{\\alpha,d}_{\\eta}(\\bar{x}) - \\Big \\lfloor \\alpha \\sum_{i=1}^d \\eta_i \\bar{x}_i \\Big \\rfloor,\n\\end{align}\nwhere the $+1$ stems from lattice effects.\nNow we estimate \n\\begin{align*}\n \\P_p(F^{\\alpha,d}_{\\eta}(0) \\ge h + 1) & = \\mathbb P_p \t\\Big( \\exists z \\in L^{\\alpha, d}_{\\eta}\\,: \\, z \\rightarrowtail (0, h) \\Big) \n\t\t\\leq \\sum_{z \\in L^{\\alpha, d}_{\\eta}} \\mathbb P_p ( z \\rightarrowtail (0,h) )\\\\\n\t\t & \\le\t\\sum_{z \\in L^{\\alpha, d}_{\\eta}(h)} \\mathbb P_p ( 0 \\rightarrowtail z ) = \\ensuremath{\\mathbb{E}}_p[\\vert \\mathcal L_{\\eta}^{\\alpha, d}(h) \\vert ]. \n\\end{align*}\nIn combination with \\eqref{eq:stochDom}, this supplies us with \n\\eqref{eq:tailEstCond} which finishes the proof. Note that we used the fact that if a site \n $x = (\\bar x, h)$ with $h \\ge \\Big \\lfloor \\alpha \\sum_{i=1}^d \\eta_i \\bar{x}_i \\Big \\rfloor$ is reachable from $L^{\\alpha, d}_{\\eta}$ by an admissible $\\lambda$-path, then so is any site \n$x = (\\bar x, i)$ with $\\Big \\lfloor \\alpha \\sum_{i=1}^d \\eta_i \\bar{x}_i \\Big \\rfloor \\le i \\le h$. This stems from the observation that if we remove the last step the admissible $\\lambda$-path took in the upward direction and then trace it, we obtain again an admissible $\\lambda$-path reaching the site right below $x$.\n\nThe fact that \\eqref{eq:summability} implies \\eqref{eq:surfaceEx} follows immediately from \\eqref{eq:tailEstCond} in combination with\nthe\nobservation below \\eqref{eq:LSfromlambda}.\n\\end{proof}\n\n\nThe common core of the proofs of Propositions \\ref{prop:allAlphaUBp} and \\ref{prop:qLBkDepOnd} can be summarized in the following, somewhat technical lemma.\n\n\\begin{lemma}[A general lower bound]\\label{lem:LBwithPi}\n Let $\\alpha \\in [0,1)$, $d \\in \\ensuremath{\\mathbb{N}}$ and $k = 0, \\ldots, d$. Then for any choice of\n \\begin{align} \\label{eq:pchoice}\n p_1, p_2, p_3, p_4 \\in (0,1) \\text{ such that } \\sum_{i=1}^4 p_i = 1\n \\end{align}\n we obtain \n\\begin{align}\\label{eq:LBqWithp_i}\n q_L(\\alpha, d,k) \\geq \\min\\left\\{\\frac{1}{k} p_1 \\sqrt{p_2 p_3}\\, ,\\, p_1 \\Big( \\frac{p_3}{k}\\Big)^{\\frac 1{1-\\alpha}}\\, ,\\, \\frac{p_1p_4}{2(d-k)}\\right\\}.\n\\end{align}\n\\end{lemma}\n Note that the above holds true for all possible choices of our parameters -- in particular for $ k \\in \\{0,d\\}$ -- if we use the convention of $1\/0=\\infty$. This somewhat unelegant agreement may be justified in this case as it avoids the need of repeating analogous \n computations without the respective terms.\n\n\n \n \\begin{proof}[Proof of Lemma \\ref{lem:LBwithPi}]\n In order to obtain the existence of an open Lipschitz surface and thus the lower bound through Lemma \\ref{lem:surfaceExistence}, we will show the following estimate under appropriate assumptions on $q = 1-p$:\n \nFor $d \\ge 1,$ $\\alpha \\in [0,1)$, $k = 1, \\ldots, d$ and $q$\nsmaller than the right-hand side of \\eqref{eq:LBqWithp_i},\n there exist constants $\\delta \\in (0,1)$ and $C>0$ such that for all $h\\in \\ensuremath{\\mathbb{N}},$\n\\begin{equation} \\label{eq:tailEst}\n \\ensuremath{\\mathbb{E}}_p [\\vert \\mathcal L_{\\eta}^{\\alpha, d}(h) \\vert ] \\leq C\\delta^{ h-1}.\n \\end{equation}\n \nWe will say that the $j$-th step of a $\\lambda$-path $(u_n)$ is \\emph{positive downward}, if $u_j-u_{j-1} \\in \\{-e_{d+1} + e_l \\mid l = 1, \\ldots, k\\}$ and \\emph{negative downward} if $u_j-u_{j-1} \\in \\{-e_{d+1} - e_l \\mid l = 1, \\ldots, k\\}$ and use $D^+=D^+(u)$, resp $D^-=D^-(u)$ to denote the number of these steps. In analogy, $D=D(u)$ will denote the number of \\emph{downward} steps such that $u_j-u_{j+1} \\in \\{-e_{d+1} \\pm e_l \\mid l = k+1, \\ldots, d\\}$ and $U = U(u)$ will be the number of \\emph{upward} steps, i.e., those for which $u_j -u_{j-1} = e_{d+1}$.\n\nNow for any natural numbers $U,$ $D^+,$ $D^-$ and $D$, the number of $\\lambda$-paths \nstarting in the origin with $U$ upward steps as well as $D^+$ positive, $D^-$ negative and $D$ neutral downward steps, respectively, can be estimated from above by\n\\[\\binom{U+D^++D^-+D}{U,D^+,D^-,D}k^{D^++D^-}(2(d-k))^D.\\]\nThus the expected number of such paths which are admissible can be upper bounded by\n\\begin{equation} \\label{eq:expNrPathUB}\n\\binom{U+D^++D^-+D}{U,D^+,D^-,D}k^{D^++D^-}(2(d-k))^Dq^U.\n\\end{equation}\nIn addition, due to the multinomial theorem, for any $p_1,\\ p_2,\\ p_3,\\ p_4$ chosen as in \\eqref{eq:pchoice} we have\n\\begin{align*}\n{\\binom{U+D^++D^-+D}{U,D^+,D^-,D} p_1^U p_2^{D^+} p_3^{D^-} p_4^D \\le 1,}\n\\end{align*}\nand hence\n\\begin{align} \\label{eq:multinomEst}\n\\binom{U+D^++D^-+D}{U,D^+,D^-,D} \\le \\Big(\\frac{1}{p_1}\\Big)^U \\Big(\\frac{1}{p_2}\\Big)^{D^+} \\Big(\\frac{1}{p_3}\\Big)^{D^-}\\Big(\\frac{1}{p_4}\\Big)^D.\n\\end{align}\nIn order to simplify notation, note that the \\lq best strategy\\rq \\ for admissible $\\lambda$-paths is to go for the negative orthant in the first $d$ coordinate axes, in the sense that\n\\begin{equation*}\n\\sum_{y \\in L^{\\alpha, d}_{\\eta}(h)}\t\\mathbb P_p(0 \\rightarrowtail y) \t\n \\le 2^d \\sum_{y \\in L^{\\alpha, d}_{\\eta}(h) \\cap ((-\\ensuremath{\\mathbb{N}}_0)^d \\times \\ensuremath{\\mathbb{Z}})}\t\\mathbb P_p(0 \\rightarrowtail y) \t.\n\\end{equation*}\n Since at each downward step of a $\\lambda$-path the $(d+1)$-st coordinate of the path is decreased by one, the total number $U(u)$ \n of upward steps of a $\\lambda$-path $(u_n)$ starting in 0 and ending in $L^{\\alpha, d}_{\\eta}(h) \\cap ((-\\ensuremath{\\mathbb{N}}_0)^d \\times \\ensuremath{\\mathbb{Z}})$ fulfills\n\\begin{align*} \nU(u) & = D^+(u) + D^-(u) + D(u) + \\floor{\\alpha (D^+(u)-D^-(u))} + h\n\\end{align*}\nand\n$$\nD^+(u)-D^-(u) \\leq 0.\n$$\nUsing \\eqref{eq:expNrPathUB} and \\eqref{eq:multinomEst} and choosing $q 0$, choosing $p_1 = 1\/2, p_2=p_3=\\varepsilon\/2$ and $p_4 = 1\/2-\\varepsilon$, we get\n\\begin{align*}\n \\liminf_{d \\rightarrow \\infty} q_L(\\alpha, d, \\varphi(d)) d\\geq \\frac{1}{2^2}\\left(\\frac{1}{2}-\\varepsilon\\right).\n\\end{align*}\nSince this is true for any $\\varepsilon >0$, the claim follows.\n\n\\textit{(b)} Now consider the case that\nfor some $c \\in [0,1]$ and $\\alpha >0$\none has $\\varphi(d) \\sim c d^{1-\\alpha}$ as $d\\rightarrow \\infty$. Then the second and third term on the right-hand side of \\eqref{eq:LBqWithp_i} are asymptotically equivalent and smaller than \nthe first term. Hence, they dictate the bound. The claim then holds for any feasible choice of $p_1,\\ldots, p_4$ and\n\\begin{align*}\n C(\\alpha,c):= \\min\\left\\{p_1 \\left(\\frac{p_3}{c}\\right)^{\\frac{1}{1-\\alpha}}, \\frac{1}{2}\\frac{p_1 p_4}{1-c}\\right\\}.\n\\end{align*}\nFor $\\alpha = 0$ we have to take into consideration all three terms of the right-hand side of \\eqref{eq:LBqWithp_i}, and thus obtain the claim with \n\\begin{align*}\n C(0,c):= \\min\\left\\{\\frac{p_1 \\sqrt{p_2p_3}}{c} , p_1 \\frac{p_3}{c}, \\frac{1}{2}\\frac{p_1 p_4}{1-c}\\right\\}.\n\\end{align*}\n\n\\textit{(c)} Now assume that for some $c \\in (0,1]$ one has\n $\\varphi(d) \\sim c d$ as $d\\rightarrow \\infty$. In this case, the second term on the right-hand side of\n\\eqref{eq:LBqWithp_i} is the asymptotically decisive contribution. Again, for any $\\varepsilon >0$, choosing\n\\begin{align*}\n p_1 = \\frac{1-\\alpha}{2-\\alpha} - 2\\varepsilon, \\quad p_2 = p_4 = \\varepsilon, \\quad \\text{and} \\quad p_3 = 1-\\frac{1-\\alpha}{2-\\alpha} = \\frac{1}{2-\\alpha}\n\\end{align*}\nyields\n\\begin{align*}\n \\liminf_{d \\rightarrow \\infty} q_L(\\alpha, d, \\varphi(d)) d^{\\frac{1}{1-\\alpha}} \\geq \\left(\\frac{1-\\alpha}{2-\\alpha} - 2\\varepsilon\\right)\\left(\\frac{1}{2-\\alpha}\\frac{1}{c}\\right)^{\\frac{1}{1-\\alpha}}.\n\\end{align*}\nSince $\\varepsilon$ was arbitrary, \n\\begin{align*}\n \\liminf_{d \\rightarrow \\infty} q_L(\\alpha, d, \\varphi(d)) d^{\\frac{1}{1-\\alpha}} & \\geq \\frac{1-\\alpha}{2-\\alpha}\\left(\\frac{1}{2-\\alpha}\\frac{1}{c}\\right)^{\\frac{1}{1-\\alpha}}\\\\\n\t\t\t\t\t\t\t\t\t\t & = (1-\\alpha)\\left(1-\\frac{1-\\alpha}{2-\\alpha} \\right)^{\\frac{2-\\alpha}{1-\\alpha}}\\left(\\frac{1}{c}\\right)^{\\frac{1}{1-\\alpha}} \\geq (1-\\alpha)\\frac{1}{4}\\left(\\frac{1}{c}\\right)^{\\frac{1}{1-\\alpha}}.\n\\end{align*}\n\\end{proof}\n\n\nThe next step is to prove Proposition \\ref{prop:lbq_alpha}.\n\\begin{proof}[Proof of Proposition \\ref{prop:lbq_alpha}]\n We will again want to apply Lemma \\ref{lem:surfaceExistence}. In order to derive an upper bound for the expectation in \\eqref{eq:summability}, instead of \n directly looking at $\\lambda$-paths, we will consider a coarse-grained version of them and estimate the probability of these paths reaching a certain height. \n The reason for coarse-graining is the following: if $q$ is approximately equal to $q_L(\\alpha,d,k)$, then an admissible $\\lambda$-path starting in 0 (say) will on average pick up at most $1-\\alpha$ closed sites per horizontal step and if $q$ is slightly above $q_L(\\alpha,d,k)$, then such a path will certainly exist. When $\\alpha$ is very close to one, then the average number of sites which such a path visits between two successive visits of closed sites\nwill be of the order $(1-\\alpha)^{-1}$ (which is large). If $d \\ge 2$, then there will automatically be lots of admissible $\\lambda$-paths visiting exactly the same closed sites (in the same order) but taking different routes in between\nsuccessive visits to closed sites, the factor increasing to infinity as $\\alpha$ approaches 1. This means that estimating the probability that there exists an admissible $\\lambda$-path (with a certain property) by the expected number of\nsuch paths (via Markov's inequality) becomes very poor when $\\alpha$ is close to 1. Therefore, we will define larger boxes in $\\ensuremath{\\mathbb{Z}}^{d+1}$ and define equivalence classes of paths by just observing the sequence of larger boxes they visit. The boxes will then be tuned such that the number of closed sites inside a box is of order one. \n\n Recall that w.l.o.g. we assume $\\eta_i \\in \\{0,1\\}$, $i = 1, \\ldots, d$. To facilitate\n reading, we have structured the proof into three steps.\n \n \\noindent \\textit{\\uline{Step 1:} Coarse-grained $\\lambda$-paths.} In order to define the abovementioned paths we partition $\\ensuremath{\\mathbb{Z}}^{d+1}$ by dividing $\\ensuremath{\\mathbb{R}}^{d+1}$ into boxes as illustrated in Figure \\ref{fig:only}:\nDefine \n\\begin{align*}\n B^{\\alpha,d,\\eta}_0:= \\Big\\{ r \\in \\ensuremath{\\mathbb{R}}^{d+1} \\, \\mid \\,& \\forall i =1, \\ldots, d:\\; \\big ( \n \\eta_i = 0 \\Rightarrow r_i \\in [0,1) \\big) \\land \\big(\\eta_i \\neq 0 \\Rightarrow r_i \\in [0, ({1-\\alpha})^{-1} ) \\big),\\\\\n\t\t\t\t\t & \\qquad \\qquad r_{d+1} \\in \\Big( \\alpha \\sum_{i=1}^d \\eta_ir_i -1,\\alpha \\sum_{i=1}^d \\eta_ir_i\\Big]\\Big\\}\n\\end{align*}\nand likewise for $a \\in \\ensuremath{\\mathbb{Z}}^{d+1}$ set $B^{\\alpha,d,\\eta}_a:= B^{\\alpha,d,\\eta}_0 + v(a)$, where\n\\begin{align*}\n v(a):\t& = \\sum_{i\\,:\\, \\eta_i=0} a_ie_i + \\sum_{i\\,:\\, \\eta_i\\neq0} a_i\\frac{1}{1-\\alpha}(e_i + \\alpha\\eta_ie_{d+1}) + a_{d+1}e_{d+1}\\\\\n\t& = \\sum_{i\\,:\\, \\eta_i=0} a_ie_i + \\sum_{i\\,:\\, \\eta_i\\neq0} a_i\\frac{1}{1-\\alpha}e_i + \\left(\\sum_{i \\, : \\, \\eta_i\\neq0}a_i\\frac{\\alpha}{1-\\alpha}\\eta_i + a_{d+1}\\right)e_{d+1}.\n\\end{align*}\n\\begin{SCfigure} \\label{fig:only}\n\\includegraphics[width=.7\\textwidth]{coarse}\n\\caption{$L^{\\alpha,d}_{\\eta}$ is marked by the black dots and the corresponding coarse-grained boxes are hatched. $B^{\\alpha,d,\\eta}_0$ is double hatched.}\n\\end{SCfigure}\nNote that these boxes are translations of $B^{\\alpha,d,\\eta}_0$ shifted either in the direction of $e_{d+1}$ or parallel to the inclination of $L^{\\alpha,d}_{\\eta}$ and are such that $\\ensuremath{\\mathbb{Z}}^{d+1}=\\bigcup_{a \\in \\ensuremath{\\mathbb{Z}}^{d+1}} \\big( B^{\\alpha,d,\\eta}_a \\cap \\ensuremath{\\mathbb{Z}}^{d+1}\\big),$ where the union is over disjoint sets. For any $y\\in \\ensuremath{\\mathbb{Z}}^d$ the coordinates of the box it is contained in are given by $a(y)\\in \\ensuremath{\\mathbb{Z}}^{d+1}$ as\n\\begin{align*}\n a_i(y):=\\begin{cases}\n y_i,\t& i=1,\\ldots,d, \\eta_i =0,\\\\\n \\lfloor (1-\\alpha)y_i\\rfloor,\t& i=1,\\ldots,d, \\eta_i \\neq0,\\\\\n y_{d+1}-\\lfloor \\alpha \\sum_{i=1}^d\\eta_iy_i\\rfloor,\t& i=d+1.\n \\end{cases}\n\\end{align*}\nWe will refer to these as the \\emph{coarse-grained coordinates}. Note that they describe the position of the boxes relative to $L^{\\alpha,d}_{\\eta}$. Note that for $y \\in \\ensuremath{\\mathbb{Z}}^d$ the $(d+1)$-st coordinate of its coarse-grained coordinates $a(y)$ gives its height (or distance in the \n$(d+1)$-st coordinate) relative to $L^{\\alpha, d}_\\eta$.\nSince $\\alpha,d$ and $\\eta$ are fixed for this proof, we will often drop the superscripts for the sake\nof better readability. With the above partition\nof $\\ensuremath{\\mathbb{Z}}^{d+1}$ at hand,\n we can now define coarse-grained $\\lambda$-paths.\n A \\emph{coarse-grained $\\lambda$-path} is any path that takes values in $\\bigcup_{a \\in \\ensuremath{\\mathbb{Z}}^{d+1}} \\{B_a\\},$\nsuch that it can go from $B_a$ to $B_{a'}$ in one time step if and only if \n\\begin{align}\n a'-a \\in \t&\\; \\{ e_{d+1}\\} \\cup \\{-\\eta_ie_i\\mid i=1,\\ldots,d, \\eta_i\\neq0 \\} \\label{eq:cgsteps}\\\\\n\t\t& \\qquad\\cup \\{ - e_{d+1}\\}\\cup \\{\\pm e_i - e_{d+1}\\mid i=1,\\ldots,d\\} \\cup \\{\\eta_ie_i -2e_{d+1} \\mid i=1,\\ldots,d, \\eta_i\\neq0 \\}. \\notag\n\\end{align}\nIn particular, if we sample a standard $\\lambda$-path only on the boxes $\\{B_a\\},$ $a \\in \\ensuremath{\\mathbb{Z}}^{d+1}$, it visits,\nthen this supplies us with a coarse-grained $\\lambda$-path (however, there might be coarse-grained $\\lambda$-paths that\ncannot be obtained by this sampling procedure).\nWe call a box $B_{a}$ \\emph{closed} (with respect to $\\omega$) if and only if $\\omega(x) = 0$ for at least one $x \\in B_a.$\nSimilarly to the case of $\\lambda$-paths, we will call a coarse-grained $\\lambda$-path \\emph{admissible} if for each of its \n\\emph{upward steps}, i.e., those steps for which $a'-a = e_{d+1}$, the box $B_{a'}$ \nis closed. Now since the above sampling procedure maps admissible $\\lambda$-paths to\nadmissible coarse-grained $\\lambda$-paths, the existence of an admissible $\\lambda$-path from some $x \\in \\ensuremath{\\mathbb{Z}}^{d+1}$ to $y\\in \\ensuremath{\\mathbb{Z}}^{d+1}$ implies the existence of an admissible coarse-grained $\\lambda$-path from $B_{a(x)}$ to $B_{a(y)}$. We\ntherefore investigate the behavior of these coarse-grained $\\lambda$-paths more closely.\n\n\\noindent \\textit{ \\uline{Step 2:} An estimate for coarse-grained $\\lambda$-paths.}\n Recalling \\eqref{eq:cgsteps}, note that there is only one kind of step in a coarse-grained $\\lambda$-path that will not \n change its height relative to $L_\\eta^{\\alpha, d}$, i.e., its coarse-grained coordinate in the $(d+1)$-st dimension, namely those of the form $ -\\eta_ie_i$ with $i$ such that $\\eta_i\\neq0$. Use $\\mathsf{CG}(M)$ to denote the set of all coarse-grained $\\lambda$-paths starting with $B_0$ of length $M\\in \\ensuremath{\\mathbb{N}}$ whose endpoint, i.e. its last box, is above or intersects\n $L^{\\alpha,d}_{\\eta}$. For $\\pi \\in \\mathsf{CG}(M)$, use $U=U(\\pi)$ to denote the number of its \\lq up\\rq\\,-steps, i.e., those steps that increase the $(d+1)$-st coarse-grained coordinate. Similarly, use $D=D(\\pi)$ to denote the number of steps that decrease the $(d+1)$-st\n coarse-grained coordinate (possibly by more than 1) and $D^i_0=D^i_0(\\pi)$ the number of steps in each dimension $i=1,\\ldots,d$, that do \\emph{not} alter the \n $(d+1)$-st coarse-grained coordinate. Due to the natural restrictions on the movements, $D^i_0 = 0$ for any $i$ such that $\\eta_i =0$. We can now make the following observation: In order for $\\pi$ to end in a box above or intersecting\n $L^{\\alpha,d}_{\\eta}$, we necessarily have \n$$\nU \\geq D.\n$$\n In addition, observe that due to the length of the boxes in the corresponding directions being $1\/(1-\\alpha)$, between two steps of type $D^i_0$ (for the same $i$) there needs to be at least one step of type $D$ or $U$ (not $D^j_0, j\\neq i$). This implies that \n $$\n D^i_0 \\leq D+U+1.\n $$\n Therefore, for a coarse-grained $\\lambda$-path $\\pi \\in \\mathsf{CG}(M)$, recalling that it ends above \n or intersecting $L^{\\alpha,d}_{\\eta}$,\n\\begin{align} \\label{eq:estU}\n\\begin{split}\n M \t& = U + D + \\sum_{i=1}^{d} D^i_0 \\leq 2U + \\Vert \\eta\\Vert_1 (2 U + 1) = 2U(k+1) + k \\\\ \n \\Longleftrightarrow \\qquad U\t& \\geq \\frac{M-k}{2(k+1)}.\n \\end{split}\n\\end{align}\nThus, we will now estimate the probability of the event on the right-hand side in the above display. Write $m(\\pi)$ for the number of \\emph{distinct} boxes visited by a path $\\pi \\in \\mathsf{CG}(M)$. Then the exponential Chebychev inequality yields for any $\\beta >0$ and \n $\\gamma \\in (0,1)$ that\n \n\\begin{align} \\label{eq:EstClosedSitesInPath}\n\\P_p(& \\text{there exists } \\pi \\in \\mathsf{CG}(M) \n\\text{ whose boxes contain at least } \\gamma M \\text{ closed sites})\\notag\\\\\n\t& \\leq \\sum_{ \\pi \\in \\mathsf{CG}(M)} \\P_p(\\text{boxes of $\\pi$ contain at least } \\gamma M \\text{ closed sites})\\notag\\\\\n\t& \\leq \\sum_{ \\pi \\in \\mathsf{CG}(M)} \\frac{1}{\\exp(\\beta \\gamma M)} \\ensuremath{\\mathbb{E}}_p[\\exp(\\beta ( \\# \\text{ of closed sites in boxes of } \\pi))] \\notag\\\\\n\t& = \\sum_{ \\pi \\in \\mathsf{CG}(M)} \\frac{1}{\\exp(\\beta \\gamma M)} \\ensuremath{\\mathbb{E}}_p[\\exp(\\beta ( \\# \\text{ of closed sites in $m(\\pi)$ distinct boxes}))] \\notag\\\\ \n\t& = \\sum_{ \\pi \\in \\mathsf{CG}(M)} \\frac{1}{\\exp(\\beta \\gamma M)} (\\ensuremath{\\mathbb{E}}_p[\\exp(\\beta ( \\# \\text{ of closed sites in } B_0))])^{m(\\pi)} \\notag\\\\\n\t& = \\sum_{ \\pi \\in \\mathsf{CG}(M)} \\frac{1}{\\exp(\\beta \\gamma M)} (\\exp(\\beta)q + (1-q))^{\\lceil \\frac{1}{1-\\alpha}\\rceil^km(\\pi)} \\notag\\\\\n\t& \\leq \\sum_{ \\pi \\in \\mathsf{CG}(M)} \\frac{1}{\\exp(\\beta \\gamma M)} (\\exp(\\beta)q + (1-q))^{\\lceil \\frac{1}{1-\\alpha}\\rceil^kM} \\notag\\\\\n\t& \\leq (2(2d+1))^M \\frac{1}{\\exp(\\beta \\gamma M)} (\\underbrace{\\exp(\\beta)q + (1-q)}_{ \\leq \\exp(q (\\exp(\\beta)-1))})^{\\lceil \\frac{1}{1-\\alpha}\\rceil^kM} \\notag\\\\\n\t& \\leq \\exp \\Big (M \\Big (\\log(4d+2)- \\beta\\gamma + q(\\exp(\\beta)-1)\\left(\\frac{2-\\alpha}{1-\\alpha}\\right)^k \\Big ) \\Big),\n\\end{align}\nwhere in the penultimate inequality we estimated the total number of coarse-grained $\\lambda$-paths of length $M$ by $(2(2d+1))^M.$\nObserve that, choosing $\\beta = \\frac{1+\\epsilon}{\\gamma}\\log(4d+2)$ for some $\\epsilon >0$ the expression inside the exponential is negative if, and only if, \n\\begin{align}\\label{eq:uglyboundonq}\n -\\epsilon\\log(4d+2) +& q(\\exp(\\frac{1+\\epsilon}{\\gamma}\\log(4d+2))-1) \\left(\\frac{2-\\alpha}{1-\\alpha}\\right)^k < 0 \\notag\\\\\n \\Leftrightarrow \\qquad q & < \\frac{\\epsilon \\log(4d+2)}{\\exp((1+\\epsilon)\\gamma^{-1}\\log(4d+2))-1}\\left(\\frac{1-\\alpha}{2-\\alpha}\\right)^k.\n\\end{align}\n\n\\noindent\\textit{\\uline{Step 3:} Returning to $\\lambda$-paths.}\nIn order to apply Lemma \\ref{lem:surfaceExistence} we need to estimate the probability of reaching a site $y \\in L^{\\alpha,d}_{\\eta}(h)$ with an admissible $\\lambda$-path. Recall that coarse-grained $\\lambda$-paths were defined in such a way that\n the existence of an admissible $\\lambda$-path from $0 \\in \\ensuremath{\\mathbb{Z}}^{d+1}$ to $y \\in \\ensuremath{\\mathbb{Z}}^{d+1}$ implies the existence of an admissible coarse-grained $\\lambda$-path from $B_0$ to $B_{a(y)}$. This path then has length $M$ at least $\\Vert a(y) \\Vert_1$ and thus\n\\begin{align*}\n M \t& \\geq \\Vert a(y) \\Vert_1 \\\\\n\t& \\geq \\sum_{i=1}^d \\vert a_i(y)\\vert + h\\\\\n\t& = \\sum_{i\\,:\\, \\eta_i=0} \\vert y_i \\vert + \\sum_{i\\,:\\, \\eta_i \\neq 0} \\vert \\lfloor(1-\\alpha)y_i\\rfloor \\vert +h\\\\\n\t& \\geq \\sum_{i\\,:\\, \\eta_i=0} \\vert y_i \\vert + \\sum_{i\\,:\\, \\eta_i \\neq 0} ((1-\\alpha)\\vert y_i \\vert - 1) +h\\\\ \n\t& \\geq (1-\\alpha)\\Vert \\bar y \\Vert_1 - k + h.\n\\end{align*}\nTherefore, for any $h \\in \\ensuremath{\\mathbb{N}}$ and $y \\in L^{\\alpha,d}_{\\eta}(h)$ using \\eqref{eq:estU} in the third step,\n\\begin{align*}\n \\P_p(&0 \\rightarrowtail y)\t \\leq \\P_p(\\text{there exists an admissible coarse-grained $\\lambda$-path from $B_0$ to $B_{a(y)}$})\\\\\n\t\t\t\t& \\leq \\P_p(\\text{there exists } \\pi \\in \\mathsf{CG}( (1-\\alpha)\\Vert \\bar y \\Vert_1 - k + h ) \\text{ admissible})\\\\\n\t\t\t\t& \\leq \\P_p(\\text{there exists } \\pi \\in \\mathsf{CG}( (1-\\alpha)\\Vert \\bar y \\Vert_1 - k + h ) \\\\\n\t\t\t\t& \\qquad\\quad \\text{ whose boxes contain at least } \\frac{(1-\\alpha)\\Vert \\bar y \\Vert_1 - k + h-k}{2(k+1)} \\text{ closed sites})\\\\\n\t\t\t\t& \\leq \\exp\\Big(\\big((1-\\alpha)\\Vert \\bar y \\Vert_1 - k + h\\big)\\\\\n\t\t\t\t& \\qquad \\qquad \\quad \\times \\big(-\\epsilon\\log(4d+2) + q(\\exp((1+\\epsilon)4(k+1)\\log(4d+2))-1) \\left(\\frac{2-\\alpha}{1-\\alpha}\\right)^k\\big)\\Big),\n\\end{align*}\nwhere\n we choose $h \\geq 3k$ and set $\\gamma := \\frac{1}{4(k+1)}$ to apply \\eqref{eq:EstClosedSitesInPath} for the last inequality. Assuming \n\\begin{align*}\n q < \\underbrace{\\frac{\\epsilon \\log(4d+2)}{\\exp((1+\\epsilon)4(k+1)\\log(4d+2))-1}\\frac{1}{2^k}}_{=:C(k,d,\\epsilon)}(1-\\alpha)^k\n\\end{align*}\n\\eqref{eq:uglyboundonq} holds and combining the observations above we can estimate \\eqref{eq:summability} by \n \\begin{align*}\n \\sum_{y \\in L^{\\alpha,d}_{\\eta}(h)} \\P_p(0 \\rightarrowtail y)\t& \\leq \\sum_{y \\in L^{\\alpha,d}_{\\eta}(h)} \\exp\n \\Big( ((1-\\alpha)\\Vert \\bar y \\Vert_1 - k + h)\\\\\n\t\t\t\t\t\t\t\t& \\qquad \\qquad \\times (\\underbrace{-\\epsilon\\log(4d+2) + q(\\exp(\\frac{1+\\epsilon}{\\gamma}\\log(4d+2)-1) \\lceil\\frac{1}{1-\\alpha}\\rceil^k}_{=:\\bar c(k,d,\\epsilon,\\alpha, q)=\\bar c < 0 }) \\Big) \\\\\n\t\t\t\t\t\t\t\t& = \\exp((-k+h)\\bar c)\\sum_{y \\in L^{\\alpha,d}_{\\eta}(h)}\\exp((1-\\alpha)\\Vert \\bar y \\Vert_1\\bar c)\\\\\n\t\t\t\t\t\t\t\t& \\leq \\exp((-k+h)\\bar c)\\underbrace{\\sum_{i=1}^{\\infty}\\exp((1-\\alpha)i\\bar c)(2d+1)^i}_{<\\infty}.\n \\end{align*}\nThus\n\\begin{align*}\n \\lim_{h\\rightarrow \\infty} \\ensuremath{\\mathbb{E}}_p[\\vert \\mathcal L^{\\alpha,d}_{\\eta}\\vert ] = \\lim_{h\\rightarrow \\infty} \\sum_{y \\in L^{\\alpha,d}_{\\eta}(h)} \\P_p(0 \\rightarrowtail y) = 0.\n\\end{align*}\nTherefore, the assumptions of Lemma \\ref{lem:surfaceExistence} hold which implies the existence of an open Lipschitz surface. Hence,\n\\begin{align*}\n q_L(\\alpha,k,d) \\geq C(k,d,\\epsilon)(1-\\alpha)^k.\n\\end{align*}\nNote that for our result, any $\\varepsilon >0$ is sufficient. However, the optimal $\\varepsilon$ is given by $\\varepsilon = \\frac{1 + h}{4(k+1)\\log(4d+2)}$, where $h$ is such that $-\\exp(-1-4(k+1)\\log(4d+2))=h\\exp(h)$.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Proposition \\ref{prop:D1}]\n\nIn order to prove the lower bound for $q_L(\\alpha,1,1)$ we show the existence of an open Lipschitz surface for sufficiently small $q$ by analyzing the existence of an admissible $\\lambda$-path starting in $L^{\\alpha,1}_{(1)}$ reaching the site $(0,h)$ for large $h \\in \\ensuremath{\\mathbb{N}}_0$. Writing $x \\overset{A}{\\rightarrowtail} y$ for the event of existence of an admissible $\\lambda$-path from $x \\in \\ensuremath{\\mathbb{Z}}^2$ to $y \\in \\ensuremath{\\mathbb{Z}}^2$ that only uses sites in the set $A \\subseteq \\ensuremath{\\mathbb{Z}}^2$, we observe that\n\\begin{align}\\label{eq:est1}\n \\P_p(L^{\\alpha,1}_{(1)} \\rightarrowtail (0,h)) & = \\P_p(L^{\\alpha,1}_{(1)} \\overset{L^{\\alpha,1}_{(1), \\geq}}{\\rightarrowtail} (0,h)) \\leq 2 \\P_p\\big(\\bigcup_{n \\in \\ensuremath{\\mathbb{N}}_0}\\big\\{(n, \\floor{\\alpha n}) \\overset{L^{\\alpha,1}_{(1), \\geq}}{\\rightarrowtail} (0,h)\\big\\}\\big) \\notag \\\\\n\t\t\t\t\t\t& \\leq 2 \\sum_{n =0}^{\\infty}\\P_p((n, \\floor{\\alpha n}) \\overset{L^{\\alpha,1}_{(1), \\geq}}{\\rightarrowtail} (0,h)),\n\\end{align}\nfor any $h \\in \\ensuremath{\\mathbb{N}}_0$. Therefore we need to find suitable upper bounds for the summands. \n\nA first helpful bound, albeit without the restriction on the space, can be obtained similarly to \\eqref{eq:expNrPathUB}. Observe that any $\\lambda$-path from $(n, \\floor{\\alpha n})$ to $(0,h)$ must have made a total of $4k + \\ceil{(2-\\alpha)n} + h$ steps for some $k \\in \\ensuremath{\\mathbb{N}}_0$: $n+k$ to the downward left, $k$ to the downward right and $n-\\floor{\\alpha n} + h + 2k$ upwards. Then, counting the number of admissible $\\lambda$-paths under consideration\n\\begin{align}\\label{eq:genBound}\n \\P_p((n, \\floor{\\alpha n}) \\overset{L^{\\alpha,1}_{(1), \\geq}}{\\rightarrowtail} (0,h)) \t& \\leq \\P_p((n, \\floor{\\alpha n}) \\rightarrowtail (0,h))\\\\\n\t\t\t\t\t\t\t\t\t\t\t&\\leq \\sum_{k \\in \\ensuremath{\\mathbb{N}}_0} \\binom{2n + h - \\floor{\\alpha n} + 4k}{n+k, k, n-\\floor{\\alpha n} + h + 2k} q^{n-\\floor{\\alpha n} + h + 2k}.\n\\end{align} \nThis upper bounds the terms for small $n$ in \\eqref{eq:est1}, but can also be used to obtain an adequate estimate for large $n$. This is, however, more elaborate: For $n \\in \\ensuremath{\\mathbb{N}}_0$ define\n\\begin{align*}\n A_n & := \\{-n, -(n-1), \\ldots, -1, 0, 1, \\ldots \\}\\times \\ensuremath{\\mathbb{Z}},\\\\\n Y_n & := \\max\\{ r \\in \\ensuremath{\\mathbb{Z}} \\mid (0,0) \\overset{A_n}{\\rightarrowtail }(-n,r) \\}.\n\\end{align*}\n$Y_n$ is the height of the highest site above $-n$ reachable by an admissible $\\lambda$ path started in $0$ under the restriction of using only the sites in $A_n$. Now note that denoting by $\\bar Y_0$ a copy of $Y_0$, independent of $(Y_n)_{n \\in \\ensuremath{\\mathbb{N}}_0}$,\n\\begin{align} \\label{eq:StDom}\n \\bar Y_0 \\text{ stochastically dominates } Y_{n+1} - (Y_n -1) \\text{ under } \\P_p(\\cdot\\mid Y_i, i \\leq n)\n\\end{align}\nsince the conditioning can be seen as discarding those paths in the construction using any site below the $Y_i, i \\leq n$. Therefore,\n a closer study of the distribution of $\\bar Y_0$ seems advisable. Using \\eqref{eq:genBound},\n\\begin{align} \\label{eq:YdistrUB}\n\\begin{split}\n \\P_p(\\bar Y_0 \\geq m) \t& \\leq \\P_p((0,0) \\rightarrowtail (0,m)) \\\\\n\t\t\t& \\leq q^m + \\sum_{k \\in \\ensuremath{\\mathbb{N}}}3^{ m + 4k}q^{m + 2k} \\\\\n\t\t\t& \\leq q^m + (3q)^m \\frac{(9q)^2}{1-(9q)^2},\n\\end{split}\n\\end{align}\nfor $q < 1\/9$. Hence, we can upper bound the expectation\n\\begin{align*}\n \\ensuremath{\\mathbb{E}}_p[\\bar Y_0]\t& \\leq q + \\sum_{m = 2}^{\\infty} q^m + \\frac{(9q)^2}{1-(9q)^2}\\sum_{m =1}^{\\infty}(3q)^m \\\\\n\t\t& \\leq q + Cq^2\n\\end{align*}\nfor a suitable $C>0$ and small $q$. As a consequence, assuming $q$ sufficiently small for \n\\begin{align}\\label{eq:condq}\nq + Cq^2 - 1< -\\alpha \n\\end{align}\n to hold, \\eqref{eq:StDom} and a large deviation principle (the required exponential moments exist due to \\eqref{eq:YdistrUB})\n yield the existence of $c_1, c_2>0$ such that\n\\begin{align*}\n \\P_p( Y_n \\geq -\\alpha n)\t& \\leq c_1\\exp( - n c_2).\n\\end{align*}\nObserve that an admissible $\\lambda$ path started in some $(n, \\floor{\\alpha n})$ and reaching $\\{0\\}\\times\\ensuremath{\\mathbb{N}}_0$ going only through $L^{\\alpha,1}_{(1), \\geq}$ has only used sites to right of $\\{0\\}\\times\\ensuremath{\\mathbb{Z}}$ until the first time it hits $\\{0\\}\\times\\ensuremath{\\mathbb{N}}_0$. Hence,\n\\begin{align*}\n \\P_p((n, \\floor{\\alpha n}) \\overset{L^{\\alpha,1}_{(1), \\geq}}{\\rightarrowtail} (0,h)) & \\leq \\P_p((n, \\floor{\\alpha n}) \\overset{L^{\\alpha,1}_{(1), \\geq}}{\\rightarrowtail} \\{0\\}\\times \\ensuremath{\\mathbb{N}}_0)\\\\\n\t\t & \\leq \\P_p(Y_n \\geq -n \\alpha) \\leq c_1\\exp( - n c_2).\n\\end{align*}\nThis is the last component needed to estimate \\eqref{eq:est1} as it allows us to choose $N \\in \\ensuremath{\\mathbb{N}}$ such that for any $h \\in \\ensuremath{\\mathbb{N}}$\n\\begin{align*}\n \\sum_{n = N}^{\\infty} \\P_p((n, \\floor{\\alpha n}) \\overset{L^{\\alpha,1}_{(1),\\geq}}{\\rightarrowtail} (0,h) )\\leq \\frac{1}{8}.\n \\end{align*}\nOn the other hand, using \\eqref{eq:genBound} again, we may now choose $H$ sufficiently large such that for all $h \\geq H,$ \n\\begin{align*}\n \\sum_{n = 0}^{N-1} \\P_p((n, \\floor{\\alpha n}) \\overset{L^{\\alpha,1}_{(1),\\geq}}{\\rightarrowtail} (0,h) )\\leq \\frac{1}{8}.\n\\end{align*}\nHence, by \\eqref{eq:est1} choosing $q$ as in \\refeq{eq:condq} implies\n\\begin{align*}\n \\P_p(L^{\\alpha,1}_{(1)} \\rightarrowtail (0,h)) \\leq \\frac{1}{2}\n\\end{align*}\nfor all $h \\geq H$ and thus $q < q_L(\\alpha, 1, 1)$. \n\nThe corresponding upper bound is already given by Proposition \\ref{prop:UBqAsympAlpha}.\n\\end{proof}\n\n\\subsection{Upper Bounds for $q_L(\\alpha, d, k)$}\n\nIt will be useful in this section to consider what we call reversed $\\lambda$-paths. A sequence of sites $x_0, x_1, \\ldots, x_n \\in \\ensuremath{\\mathbb{Z}}^{d+1}$ is called an (admissible) \\emph{reversed $\\lambda$-path}, if $x_n, x_{n-1},x_{n-2}, \\ldots, x_0$ is an (admissible) $\\lambda$-path in the sense of Definition \\ref{def:adLamPa}. \n\nFurthermore, the proof of Proposition \\ref{prop:UBqAsympD} will take advantage of a comparison to so-called $\\rho$-percolation, see e.g. \\cite{MeZu-93} and \\cite{KeSu-00}. Here the setting is that of oriented site-percolation in $\\ensuremath{\\mathbb{Z}}^d$, i.e., where in addition to our standard setting\nof Bernoulli site percolation we assume the nearest neighbor edges of $\\ensuremath{\\mathbb{Z}}^d$ to be oriented in the direction of the positive coordinate vectors\n(which is the sense of orientation for the rest of this section). We say that \\emph{$\\rho$-percolation occurs} for $\\omega \\in \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^d}$ if there exists an oriented nearest neighbor path $0=\\bar x_0, \\bar x_1, \\ldots$ in $\\ensuremath{\\mathbb{Z}}^d$ starting in the origin, such that \n\\begin{align*}\n \\liminf_{n \\rightarrow \\infty} \\frac{1}{n} \\sum_{i=1}^n (1-\\omega(\\bar x_i)) \\geq \\rho.\n\\end{align*}\nAny such path is called a \\emph{$\\rho$-path}.\nThe probability of the existence of such a path exhibits a phase transition in the parameter $q$ and the corresponding critical probability is denoted by $q_c(\\rho, d)$. Theorem 2 in \\cite{KeSu-00} states that for every $\\rho \\in (0, 1]$,\n\\begin{align} \\label{eq:RhoConst}\n \\lim_{d \\rightarrow \\infty} d^{\\frac{1}{\\rho}}q_c(\\rho, d) = \\frac{\\theta^\\frac{1}{\\rho}}{e^{\\theta} -1} =:R(\\rho),\n\\end{align}\nwhere $\\theta$ is the unique solution to $\\theta e^{\\theta}\/(e^{\\theta} -1) = 1\/\\rho$, and $R(1)=1$. Note that we have interchanged the role of `open' and `closed' (and thus $p$ and $q$) with respect to \\cite{KeSu-00} in order to adapt the result to its application in our proof. \n\nBefore turning to the proof of Proposition \\ref{prop:UBqAsympD}, we observe a useful property of the critical probability of $\\rho$-percolation.\n\n\n\\begin{lemma}[Continuity of $q_c$]\\label{lem:rho}\n The critical probability of $\\rho$-percolation is continuous in $\\rho$, i.e. for any $d \\in \\ensuremath{\\mathbb{N}}$ the map \n \\begin{align} \\label{eq:qcCont}\n[0,1) \\ni \\rho \\mapsto q_c(\\rho, d)\n \\end{align}\nis continuous.\n\\end{lemma}\n\n\n\\begin{proof}[Proof of Lemma \\ref{lem:rho}]\n Since $d$ is fixed and we only consider $\\ensuremath{\\mathbb{Z}}^d$ in this proof, the index is dropped for better readability. It is easy to see that the event of $\\rho$-percolation also undergoes a phase-transition in $\\rho$ (for fixed $q$) and thus we define\n\\begin{align*}\n \\rho_c(q):= \\sup\\{\\rho \\mid \\P_{1-q}(\\rho\\text{-percolation occurs}) = 1\\}.\n\\end{align*}\nNote that strict monotonicity of $\\rho_c(q)$ for $q \\in [0,\\bar q]$, where $\\bar q := \\sup\\{ q\\mid \\rho_c(q) <1\\}$, \nwould imply the desired continuity of $q_c(\\rho)$ on $[0,1)$. In order to prove this strict monotonicity, we will, however, first consider a different quantity: Still in the setting of oriented percolation in $\\ensuremath{\\mathbb{Z}}^d$, for any $\\omega \\in \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^d}$ \nlet\n\\begin{align*\n Y_{0,n}(\\omega) := \\max \\Big\\{\\, r \\in \\ensuremath{\\mathbb{N}}_0 \\mid \\exists \\text{ directed nearest neighbor path }& 0 = x_0, x_1, \\ldots, x_{n}:\\\\\n\t\t\t\t\t\t & \\qquad \\qquad \\, \\sum_{i=1}^{n} (1- \\omega(x_i)) = r\n\t\t\t\t\t\t \\Big\\},\n\\end{align*}\nand denote by $\\hat X_n$ the site with the lowest lexicographical order that is the endpoint of such a directed nearest neighbor path on which the value of $Y_{0,n}$ is attained. Then, for $m\\geq n$ define\n\\begin{align*\n Y_{n,m}(\\omega) := \\max \\Big\\{\\, r \\in \\ensuremath{\\mathbb{N}}_0 \\mid \\exists \\text{ directed nearest neighbor path }& \\hat X_n = x_0, x_1, \\ldots, x_{m-n}:\\\\\n\t\t\t\t\t\t & \\qquad \\qquad \\, \\sum_{i=1}^{m-n} (1- \\omega(x_i)) = r\n\t\t\t\t\t\t \\Big\\}.\n\\end{align*}\n\nBy the Subadditive Ergodic Theorem (see e.g. \\cite{Durrett}, Theorem 6.6.1)\n the sequence $(Y_{0,n}\/n)_{n \\in \\ensuremath{\\mathbb{N}}}$ converges $\\P_{1-q}$-a.s. and in $L^1(\\P_{1-q})$ to a (deterministic) limit that we denote by $\\gamma(q)$. In fact, \n \\begin{equation} \\label{eq:limitEquality}\n\\gamma (q) = \\rho_c(q).\n \\end{equation}\n To see this, fix $q \\in (0,1)$ and choose $\\rho < \\rho_c(q)$. \nThen for $\\P_{1-q}$-almost any $\\omega \\in \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^d}$ there exists an oriented nearest neighbor path $X_1(\\omega), X_2(\\omega), \\ldots$ such that \n\\begin{align*}\n \\rho \\leq \\liminf_{n \\rightarrow \\infty} \\frac{1}{n} \\sum_{i=1}^n (1-\\omega(X_i(\\omega))).\n\\end{align*}\nSince by definition $ \\sum_{i=1}^n (1-\\omega(X_i(\\omega))) \\leq Y_{0,n}(\\omega)$\nfor $\\P_{1-q}$-almost all $\\omega\\in \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^d}$ and $n \\in \\ensuremath{\\mathbb{N}}$, taking the limes inferior on both sites gives $\\rho \\leq \\gamma(q)$, which implies $\\rho_c(q) \\leq \\gamma(q)$. To prove the converse inequality, choose, for any $\\varepsilon >0$ an $N \\in \\ensuremath{\\mathbb{N}}$ such that $\\frac{1}{N}\\ensuremath{\\mathbb{E}}_{1-q}[Y_{0,N}] \\geq \\gamma(q)-\\varepsilon$. For any $\\omega \\in \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^d}$ let $X_1(\\omega),X_2(\\omega),\\ldots, X_N(\\omega)$ be an (oriented nearest neighbor) path, such that $Y_{0,N} = \\sum_{i=1}^{N} (1- \\omega(X_i(\\omega)))$. Using i.i.d. copies of $(X_1, \\ldots, X_N)$, one can construct an infinite oriented nearest neighbor path $(\\widetilde X_i)_{i \\in \\ensuremath{\\mathbb{N}}_0}$ with the property that by the law of large numbers\n\\begin{align*}\n \\lim_{n \\rightarrow \\infty} \\frac{1}{n} \\sum_{i=1}^n \\widetilde X_i = \\frac{1}{N}\\ensuremath{\\mathbb{E}}_{1-q}[Y_{0,N}] \\geq \\gamma(q)-\\varepsilon \\qquad \\P_{1-q}\\text{-a.s.}.\n\\end{align*}\nThus $\\gamma(q) - \\varepsilon \\leq \\rho_c(q)$ and since $\\varepsilon$ was arbitrary, $\\gamma(q) \\leq \\rho_c(q)$, which in combination with the above establishes \\eqref{eq:limitEquality}.\n\nThe strict monotonicity of $\\gamma(\\cdot)$ (and thus $q_c(\\cdot)$) can now be proven through a suitable coupling argument. Denote by $\\mathcal U_{[0,1]}$ the uniform measure on the interval $[0,1]$ and define $\\mu:= \\mathcal U_{[0,1]}^{\\otimes \\ensuremath{\\mathbb{Z}}^{d+1}}$ as the product measure on the space $\\mathcal W := [0,1]^{\\ensuremath{\\mathbb{Z}}_d}$. For any $w \\in \\mathcal W$, $q \\in (0,1)$ and $n \\in \\ensuremath{\\mathbb{N}}_0$ define\n\\begin{align*}\n Y^q_n(w): = \\max \\Big \\{r \\in \\ensuremath{\\mathbb{N}}_0 \\mid \\exists \\text{ directed nearest neighbor path }& 0 = x_0, x_1, \\ldots, x_{n}:\\\\\n\t\t\t\t\t\t & \\qquad \\qquad \\, \\sum_{i=}^{n} \\mathds{1}_{[0,q]}(w(x_i)) = r\n\t\t\t\t\t\t \\Big \\}.\n\\end{align*}\nObserve that $\\mathcal L_{\\mu} ((Y^q_n)_{n \\in \\ensuremath{\\mathbb{N}}_0}) = \\mathcal L_{\\P_{1-q}}((Y_{0,n})_{n \\in \\ensuremath{\\mathbb{N}}_0})$, where $\\mathcal L_{\\nu}$ denotes the law with respect to the measure $\\nu$. Therefore \n\\begin{align*}\n \\lim_{n \\rightarrow \\infty} \\frac{1}{n} Y^q_n = \\gamma(q) \\qquad \\mu\\text{-a.s. and in } L^1(\\mu).\n\\end{align*}\nAs before, for any $q \\in (0,1)$, $w \\in \\mathcal W$ and $n \\in \\ensuremath{\\mathbb{N}}_0$, let $X^{q,n}_1(w), \\ldots, X^{q,n}_n(w)$ be an oriented nearest neighbor path such that $Y^q_n = \\sum_{i=1}^n \\mathds{1}_{[0,q]}(w(X^{q,n}_i(w)))$. Choose $0\\leq q < q' \\leq \\bar q$, then\n\\begin{align} \\label{eq:DiffInY}\n Y^{q'}_n = \\sum_{i=1}^n \\mathds{1}_{[0,q']}(w(X^{q',n}_i(w))) & \\geq \\sum_{i=1}^n \\mathds{1}_{[0,q']}(w(X^{q,n}_i(w))) \\notag\\\\\n\t\t\t\t\t\t\t & = Y^q_n + \\sum_{i=1}^n \\mathds{1}_{[q,q']}(w(X^{q,n}_i(w))).\n\\end{align}\nSet $\\mathcal F_q := \\sigma(w \\mapsto \\mathds{1}_{[0,q]} (w(x)) \\mid x \\in \\ensuremath{\\mathbb{Z}}^d)$. Then, obviously, the $Y^q_n$ are $\\mathcal F_q$-measurable and the $\\mathds{1}_{[0,q]}(w(X^{q,n}_i(w)))$, $1 \\le i \\le n,$ are independent given $\\mathcal F_q$. In addition,\n\\begin{align*}\n \\mu(\\mathds{1}_{[q,q']}(w(X^{q,n}_i(w))) = 1 \\mid \\mathcal F_q) = \\frac{q'-q}{1-q} \\mathds{1}_{\\{w(X^{q,n}_i(w))>q\\}}.\n\\end{align*}\nThus using \\eqref{eq:DiffInY} we obtain\n\\begin{align*}\n \\ensuremath{\\mathbb{E}}_{\\mu}[Y^{q'}_n-Y^q_n \\mid \\mathcal F_q] \\geq \\ensuremath{\\mathbb{E}}_{\\mu}\n \\Big [ \\sum_{i=1}^n \\mathds{1}_{[q,q']}(w(X^{q,n}_i(w)))\\mid \\mathcal F_q \\Big] = \\left( n - Y^q_n\\right)\\frac{q'-q}{1-q},\n\\end{align*}\nwhere $\\ensuremath{\\mathbb{E}}_{\\mu}$ denotes the expectation with respect to $\\mu$. Using the $L_1(\\mu)$ convergence\n\\begin{align*}\n \\gamma(q') - \\gamma(q) & = \\lim_{n \\rightarrow \\infty} \\ensuremath{\\mathbb{E}}_{\\mu}\n \\Big[ \\ensuremath{\\mathbb{E}}_{\\mu} \\Big[\\frac{1}{n}\\left(Y^{q'}_n - Y^q_n\\right)\\mid \\mathcal F_q \\Big ] \\Big] \\\\\n\t\t\t& \\geq \\lim_{n \\rightarrow \\infty} \\ensuremath{\\mathbb{E}}_{\\mu} \\Big[ \\frac{1}{n} \\left(n-Y^q_n\\right) \\frac{q'-q}{1-q} \\Big]\\\\\n\t\t\t& = (1-\\gamma(q))\\frac{q'-q}{1-q}\n\\end{align*}\nand the right-hand side is positive, since $\\gamma(q)=\\rho_c(q) < 1$ for $q < \\bar q$. This shows the strict monotonicity\nof the function $\\rho_c$ on $[0,\\overline q]$ and hence implies \\eqref{eq:qcCont}.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Proposition \\ref{prop:UBqAsympD}]\n\nWe will compare $\\rho$-paths in $\\ensuremath{\\mathbb{Z}}^d$ with reversed admissible $\\lambda$-paths in $\\ensuremath{\\mathbb{Z}}^{d+1}$.\n To this end define for any $\\omega \\in \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^{d+1}}$ and $\\bar x \\in \\ensuremath{\\mathbb{Z}}^d$ the quantity\n\\begin{align*}\n H_{\\omega}(\\bar x): = \\min\\Big\\{h \\in \\ensuremath{\\mathbb{N}}_0 \\mid \\exists & \\text{ an oriented \n nearest neighbor path } 0=\\bar x_0, \\ldots, \\bar x_m=\\bar x \\in \\ensuremath{\\mathbb{Z}}^d, \\\\\n\t\t\t\t\t\t & \\text{ and a sequence } 0=h_0, \\ldots, h_m = h \\in \\ensuremath{\\mathbb{N}}_0 \\text{ s.t. } \\\\\n\t\t\t\t\t\t & \\qquad \\qquad h_{i+1} = \\begin{cases}\n\t\t\t\t\t\t\t\t\t h_i, & \\text{ if }\\omega(\\bar x_i, h_i) = 0,\\\\\n\t\t\t\t\t\t\t\t\t h_i+1, & \\text{ otherwise}.\n\t\t\t\t\t\t\t\t\t\\end{cases} \\quad \\Big\\}.\n\\end{align*}\nA second's thought reveals that this map is defined in such\na way that there is an admissible $\\lambda$-path from $(\\bar x, H_{\\omega}(\\bar x))$ to the origin,\n which takes advantage of many closed sites in the\n configuration $\\omega$. (It is, however, not optimal, as it does not make use of consecutive \\lq piled up\\rq\\ closed sites in one step.) With this we can then define a map $T : \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^{d+1}} \\rightarrow \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^d}$ as\n\\begin{align*}\n (T(\\omega)) (\\bar x) := \\begin{cases}\n \\omega(\\bar x, H_{\\omega}(\\bar x)), & \\text{ if } \\bar x \\in \\ensuremath{\\mathbb{N}}_0^d,\\\\\n \\omega(\\bar x, 0), & \\text{ otherwise.}\n \\end{cases}\n\\end{align*}\nThe purpose of $T$ is to map a configuration $\\omega \\in \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^{d+1}}$ to a configuration $\\bar \\omega \\in \\{0,1\\}^{\\ensuremath{\\mathbb{Z}}^d}$, \nfor which there exists an oriented path picking up almost as many closed sites as the oriented reversed admissible $\\lambda$-path in $\\omega$ with lowest $(d+1)$-st coordinate. In order to be more precise, we add an index to the probability measure used to indicate the space it is defined on. \nI.e., $\\P_{p,d}$ will denote the Bernoulli product-measure on $\\ensuremath{\\mathbb{Z}}^d$ with parameter $p$. Since the value of $H(\\bar x)$ only depends on the state of the sites $\\bar y \\in \\ensuremath{\\mathbb{N}}_0^d$ with $\\Vert \\bar y \\Vert < \\Vert \\bar x \\Vert$, $\\P_{p,d+1} \\circ T^{-1} = \\P_{p,d}$. Thus, if $q > q_c(\\rho,d)$, we have that\n\\begin{align}\\label{eq:rhopercas}\n 1 \t& = \\P_{p,d}(\\text{$\\rho$-percolation occurs}) \\notag\\\\\n\t& = \\P_{p,d+1} \\Big(\\text{there exists an admissible reversed $\\lambda$-path } 0 = (\\bar x_0, h_0), (\\bar x_1, h_1), \\dots \\\\\n\t& \\hspace{300pt} \\text{ s.t. } \\limsup_{n \\rightarrow \\infty} \\frac{1}{n} h_n \\leq 1-\\rho \\Big). \\notag\n\\end{align}\nNow choose $\\rho > 1-\\alpha$ and set $\\delta := 1- \\rho + (\\alpha - (1-\\rho))\/2 \\in ( 1-\\rho, \\alpha)$. Then \\eqref{eq:rhopercas} implies the existence of a (deterministic) $N \\in \\ensuremath{\\mathbb{N}}$ such that for all $n \\geq N,$\n\\begin{align*}\n\\P_{p,d+1}\\Big(& \\text{there exists an admissible reversed $\\lambda$-path } \\\\ \n\t & \\qquad 0 = (\\bar x_0, h_0), (\\bar x_1, h_1), \\dots, (\\bar x_n, h_n) \\text{ s.t. } h_n \\leq \\delta n\\Big) \\geq \\frac{1}{2}.\n\\end{align*}\nNote that if there exists an admissible reversed $\\lambda$-path from the origin to some $(\\bar x_n, h_n)$ with $h_n \\leq \\delta n$, then there actually exists an admissible $\\lambda$-path from $L^{\\alpha,d}_{\\eta} - \\floor{(\\alpha - \\delta)n}e_{d+1} $ to the origin. Thus, by translation invariance of $\\P_{p,d+1}$, we obtain that\n\\begin{align*}\n \\forall n \\geq N:\\; \\P_{p,d+1} \\big( L^{\\alpha,d}_{\\eta} \\rightarrowtail (0, \\floor{(\\alpha - \\delta)n}) \\big) \\geq \\frac{1}{2}\n\\end{align*}\nwhich, since $\\alpha - \\delta >0$, implies\n\\begin{align*}\n \\P_{p,d+1}\\left( (\\mathsf{LIP}^{\\alpha,d}_{\\eta})^c\\right) = \\lim_{n \\rightarrow \\infty} \\P_{p,d+1} \\big( L^{\\alpha,d}_{\\eta} \\rightarrowtail (0, (\\alpha - \\delta)n ) \\big) \\geq \\frac{1}{2}.\n\\end{align*}\nBy Proposition \\ref{prop:equiv} we deduce that $\\P_p\\left(\\mathsf{LIP}^{\\alpha,d}_{\\eta}\\right) = 0$ and\nhence $q \\geq q_L(\\alpha, d,d)$. We have thus shown that for any $\\rho > 1-\\alpha$ one has $ \\;q_c(\\rho,d) \\geq q_L(\\alpha,d,d)$. Since $q_c(\\rho,d)$ is continuous in $\\rho$ by Lemma \\ref{lem:rho},\nthen the claim follows from \\eqref{eq:RhoConst}. \n\\end{proof}\n\n\n\\begin{lemma}[Criterion for non-existence of an open Lipschitz surface] \\label{lem:CritLSNonExist}\n For any $\\alpha >0$, and $d \\in \\ensuremath{\\mathbb{N}}$ define \n \\begin{align*}\n T := \\inf \\{ m \\in \\ensuremath{\\mathbb{N}}_0 \\mid \\exists \\bar x \\in \\ensuremath{\\mathbb{N}}_0^d:\\; \\Vert \\bar x \\Vert_1 = m \\text{ and } (\\bar x, \\Vert \\bar x \\Vert_1) \\text{ is closed}\\}.\n \\end{align*}\nIf for $p \\in (0,1)$ one has \n\\begin{align}\\label{eq:CritLSNonExist}\n \\ensuremath{\\mathbb{E}}_p[T] < \\frac{1}{1-\\alpha},\n\\end{align}\nthen $\\P$-a.s. there exists no open Lipschitz surface and $q= 1-p \\geq q_L(\\alpha,d,d)$. \n\\end{lemma}\nCondition \\eqref{eq:CritLSNonExist} has an intuitive interpretation: $1\/(1-\\alpha)$ is the number of \\lq downward-diagonal\\rq\\ steps a $\\lambda$-path can take before decreasing its distance to the plane with inclination $\\alpha$ by one. $\\ensuremath{\\mathbb{E}}_p[T]$ on the other hand is the expected number of such steps an admissible $\\lambda$ path must take before encountering a closed site and thus being able to take an upwards step. \\eqref{eq:CritLSNonExist} therefore means that this path will -- on average -- encounter a closed site strictly before decreasing its distance to the plane by one, thus increasing the distance in the long run and preventing the existence of an open Lipschitz surface above it.\n\n\n\\begin{proof}\nAs in the proof of Proposition \\ref{prop:UBqAsympD}, the idea is to construct admissible reversed $\\lambda$-paths starting in $0$ such that their endpoints (i.e. the starting points of the respective $\\lambda$-paths) are arbitrarily far below $L^{\\alpha,d}_{\\eta}$. With a simple shifting argument we can then see that the Lipschitz surface would, with probability bounded away from $0$, have to have arbitrarily large height in $0$ and can therefore almost surely not exist.\n \n We begin with the construction of the \\emph{reversed} $\\lambda$-paths. To this end, set $ X_0:=Y_0:=0$. Let $(\\bar z_i)_{i \\in \\ensuremath{\\mathbb{N}}_0}$ be an ordering of $\\ensuremath{\\mathbb{N}}_0^d$ compatible with $\\Vert \\cdot \\Vert_1$ in the sense that $\\Vert z_{i+1} \\Vert_1 \\geq \\Vert z_i \\Vert_1$, for all $i \\in \\ensuremath{\\mathbb{N}}_0$. Then define for any $n \\in \\ensuremath{\\mathbb{N}}_0$,\n \\begin{align*}\n \\iota_{n+1} \t& := \\inf\\{i \\in \\ensuremath{\\mathbb{N}}_0 \\mid (\\bar z_i, \\Vert\\bar z_i \\Vert_1) + Y_n \\text{ is closed}\\},\\\\\n X_{n+1} \t& := (\\bar z_{\\iota_n}, \\Vert \\bar z_{\\iota_n}\\Vert_1), \\\\\n Y_{n+1}\t& := Y_n + X_{n+1} - e_{d+1}.\n \\end{align*}\nBy construction, there always exists an admissible $\\lambda$-path from any $Y_n$ to 0. Note also that $(\\iota_n)_{n \\in \\ensuremath{\\mathbb{N}}}$ and $(X_n)_{n \\in \\ensuremath{\\mathbb{N}}}$ are i.i.d. sequences where $\\iota_1$ is geometric on $\\ensuremath{\\mathbb{N}}_0$ with parameter $q$ and $\\Vert \\bar X_1 \\Vert_1 = X_1 \\cdot e_{d+1}$\nis distributed as $T$. \n\nWe are now interested in the height of the starting points of these $\\lambda$-paths relative to $L^{\\alpha,d}_{\\eta}$. This is given by \n\\begin{align*}\n H(n)\t& := \\floor{\\alpha \\Vert \\bar Y_n \\Vert_1} - Y_n\\cdot e_{d+1}\\\\\n\t& = \\Big \\lfloor \\alpha \\sum_{j = 1}^n \\Vert \\bar X_j \\Vert_1 \\Big \\rfloor - \\sum_{j=1}^n ( X_n - e_{d+1})\\cdot e_{d+1}\\\\\n\t& = \\Big \\lfloor \\alpha \\sum_{j = 1}^n \\Vert \\bar X_j \\Vert_1 \\Big \\rfloor - \\sum_{j = 1}^n \\Vert \\bar X_j \\Vert_1 + n.\n\\end{align*}\n The law of large numbers then yields\n \\begin{align*}\n \\lim_{n \\rightarrow \\infty} \\frac{1}{n}H(n) = (\\alpha - 1)\\ensuremath{\\mathbb{E}}_p[T] +1 \\qquad \\P_p\\text{-a.s.}\n \\end{align*}\nand the right-hand side is strictly negative by assumption. Thus with $\\Delta := -\\left( (\\alpha - 1)\\ensuremath{\\mathbb{E}}_p[T] +1 \\right)\/2 >0$ we have in particular the existence of a deterministic $N \\in \\ensuremath{\\mathbb{N}}$ such that\n\\begin{align*}\n \\forall n \\geq N: \\; \\P_p( H(n) \\leq - \\Delta n) \\geq \\frac{1}{2}.\n\\end{align*}\nNow note that on the event $\\{H(n) \\leq - \\Delta n\\}$ there exists an admissible $\\lambda$-path starting in $L^{\\alpha,d}_{\\eta} - \\Delta n e_{d+1}$ and reaching 0, since $Y_n$ is below the plane $L^{\\alpha,d}_{\\eta} - \\Delta n e_{d+1}$. Hence, by translation invariance of $\\P_p$ we have that\n\\begin{align*}\n \\forall n \\geq N: \\; \\P_p(L^{\\alpha,d}_{\\eta} \\rightarrowtail (0, \\Delta n) \\geq \\frac{1}{2}\n\\end{align*}\nwhich implies\n\\begin{align*}\n \\P_p\\left( (\\mathsf{LIP}^{\\alpha,d}_{\\eta})^c\\right) = \\lim_{n \\rightarrow \\infty} \\P_p(L^{\\alpha,d}_{\\eta} \\rightarrowtail (0, \\Delta n) \\geq \\frac{1}{2}.\n\\end{align*}\nBy Proposition \\ref{prop:equiv}, $\\P_p\\left(\\mathsf{LIP}^{\\alpha,d}_{\\eta}\\right) = 0$ and $p \\leq p_L(\\alpha,d,d)$, i.e., $q \\geq q_L(\\alpha, d,d)$. \n\\end{proof}\n\n\n\\begin{proof}[Proof of Proposition \\ref{prop:UBqAsympAlpha}]\n Recall the ordering $(\\bar z_i)_{i \\in \\ensuremath{\\mathbb{N}}_0}$ of $\\ensuremath{\\mathbb{N}}_0^d$ compatible with $\\Vert \\cdot \\Vert_1$ from the proof of Lemma \\ref{lem:CritLSNonExist} and define the random variable\n \\begin{align*}\n \\iota_1 := \\inf\\{i \\in \\ensuremath{\\mathbb{N}}_0 \\mid (\\bar z_i, \\Vert\\bar z_i \\Vert_1) \\text{ is closed}\\}, \n \\end{align*}\n which has a geometric distribution on $\\ensuremath{\\mathbb{N}}_0$ with parameter $q$. With $B(j):= \\{ \\bar x \\in \\ensuremath{\\mathbb{N}}_0^d \\mid \\Vert \\bar x \\Vert_1 \\leq j\\}$ denoting the ball with radius $j \\in \\ensuremath{\\mathbb{N}}_0$, define the function \n \\begin{align*}\n r(i) := \\inf\\{j \\in \\ensuremath{\\mathbb{N}}_0 \\mid \\vert B(j) \\vert - 1 \\geq i\\}\n \\end{align*}\nthat gives the radius of the smallest ball such that its cardinality (without the origin) is larger than\nor equal to a given $i \\in \\ensuremath{\\mathbb{N}}_0$. Note that $r(\\iota_1)$ is distributed as $T$,\n for $T$ defined in Lemma \\ref{lem:CritLSNonExist}.\nUsing\n\\begin{align*}\n \\vert B(j) \\vert = \\binom{j+d}{d} \\geq \\frac{(j+1)^d}{d!}\n\\end{align*}\nwe obtain\n\\begin{align*}\n i \\geq \\vert B(r(i)-1) \\vert \\geq \\frac{r(i)^d}{d!}\n\\end{align*}\n and can thus upper bound the expectation\n \\begin{align*}\n \\ensuremath{\\mathbb{E}}_p[T] = \\ensuremath{\\mathbb{E}}_p[r(\\iota_1)] \t& \\leq \\left( d!\\ensuremath{\\mathbb{E}}_p[\\iota_1] \\right)^{\\frac{1}{d}} \\leq \\left( d!\\left(\\frac{1}{q}-1\\right)\\right)^{\\frac{1}{d}},\n \\end{align*}\n where we used Jensen's inequality in the first inequality.\nThe right-hand side is strictly smaller than $1\/(1-\\alpha)$ if and only if \n\\begin{align*}\n q > \\frac{d!(1-\\alpha)^d}{1+d!(1-\\alpha)^d}.\n\\end{align*}\nThus Lemma \\ref{lem:CritLSNonExist} then implies that for such values of $q$ no open Lipschitz surface can exist, i.e. $q \\geq q_L(\\alpha,d,d)$, and the claim follows.\n\\end{proof}\n\n\n\\subsection*{Acknowledgement}\nWe thank Patrick W. Dondl for helpful suggestions and valuable discussions.\n\n\\bibliographystyle{alpha}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nMartin-L\u00f6f type theory can be characterized in a syntax independent way as the initial category with families (cwf) with extra structure for the type formers \\cite{castellan:tlca2015,castellan:lmcs}. The main contribution of this note is a similar syntax independent characterization of the notion of finitely presented generalized algebraic theory as the initial cwf with extra structure.\n\nGeneralized algebraic theories (gats) were introduced by Cartmell in his PhD thesis \\cite{cartmell:phd} as a dependently typed generalization of many sorted algebraic theories. Each gat is specified by a signature with (possibly infinite) sets of sort symbols, operator symbols, and equations. Cartmell's definition of gats \\cite{cartmell:phd,cartmell:apal} is based on a notion of {\\em derived rule} expressed in terms of a traditional syntactic system for dependent type theory. He also defines a notion of model whereby sort symbols are interpreted as families of sets.\n\nCategories with families (cwfs) \\cite{dybjer:torino} were introduced as a new notion of model of dependent type theory. Cwfs arise by reformulating the notion of category with attributes in Martin Hofmann's sense \\cite{hofmann:csl}. The key point is that cwfs arise as models of a certain generalized algebraic theory closely related to Martin-L\u00f6f's substitution calculus \\cite{martinlof:gbg92}. As such the notion of cwf becomes a useful intermediary between traditional syntactic systems for dependent type theory and a variety of categorical notions of model.\n\nThe gat of cwfs is thus a kind of idealized formal system of dependent type theory. In contrast to Martin-L\u00f6f's substitution calculus, and other syntactic systems for dependent type theory, it is {\\em not} formulated in terms of grammars and inference rules for the forms of judgment of type theory. Instead it is formulated in terms of the sort symbols (corresponding to the judgment forms), operator symbols (corresponding to the formation, introduction, and elimination rules), and equations (corresponding to the equality rules for the type formers) of the gat. Some of the general reasoning (about equality, substitution, and assumptions) is taken care of by the underlying infrastructure of dependent types. This makes it possible to abstract away from details in the formulation of grammars and inference rules. In contrast to the various syntactic systems, the gat of cwfs has a canonical flavour. \n\nIn this note we explore the interdependence between gats and cwfs. We already explained that cwfs can be defined as models of a gat. \nIn the other direction, the notion of gat relies on the notion of cwf, in the sense that the latter models the underlying infrastructure of dependent types. \n\n\n\n\\subsection*{Plan of the paper}\n\nIn Section 2 we recall the definition of the category $\\mathbf{CwF}$ of categories with families and morphisms preserving cwf-structure on the nose. Section 3 contains our main definition of a syntax independent notion of valid signature $\\Sigma$ for a gat and the category $\\mathbf{CwF}_\\Sigma$ of cwfs with a $\\Sigma$-structure. In Section 4 we construct an initial object ${\\mathcal T}_\\Sigma$ in $\\mathbf{CwF}_\\Sigma$. In Section 5 we show several examples of gats: for monoids, categories, cwfs, and cwfs with extra structure for one universe. We point out that cwfs with extra structure for gats of monoids, categories, cwfs are cwfs with an internal monoid, category, and cwf, respectively. We also sketch how to extend our approach to some countably presented gats, and show the example of contextual cwfs, a variant of Cartmell's contextual categories \\cite{cartmell:phd,cartmell:apal}. Finally, in Section 6 we discuss related work, for example relating to Voevodsky's initiality conjecture \\cite{voevodsky:initiality} and Altenkirch and Kaposi's quotient inductive-inductive types \\cite{altenkirch:qiits}.\n\nOur development can be formulated in a constructive set theory,\nas described for instance by Aczel \\cite{MR519801}, although the set theory\nwe use for formulating the notion of cwf with a $\\Sigma$-structure is probably\nmuch weaker. As emphasized by Voevodsky~\\cite{voevodsky:initiality}, we study structures invariant\nunder {\\em isomorphisms} and not under {\\em equivalences}, and it is actually misleading\nto call them ``category'' (and this is why Voevodsky used the term ``$C$-system''\nfor what Cartmell called ``contextual category'').\nAs he also noticed, this\nimportant distinction between categories and notions invariant under isomorphisms becomes\nprecise in the setting of univalent foundations where not all collections of objects\nare constructed from sets.\n\n\n\\subsection*{Remarks on terminology and notation}\nLike Cartmell, we have chosen to use the term {\\em sort symbol} from many-sorted universal algebra. However, in our semantic notion of signature sort symbols are interpreted as {\\em type families} in a cwf. A cwf consists of a base category where the objects of the base category are (semantic) contexts and the morphisms are (semantic) substitutions. Moreover, we have a family-valued presheaf mapping contexts to families of (semantic) terms indexed by (semantic) types. Thus the reader should be aware of the mismatch between the word {\\em sort} from universal algebra and the word {\\em type} in the cwf semantics.\n\nAnother possible source of confusion is that cwfs appear on two different levels. In Section \\ref{sec:def_cwf} we recall the definition of cwf in set-theoretic metalanguage, where we use $\\mathrm{Ty}$ to denote the family of types indexed by contexts and $\\mathrm{Tm}$ to denote the family of terms indexed by contexts and types. This notion of cwf is then used to define the semantic notions of signature and category of models of a gat. Then in Section \\ref{gat-cwf} we define the gat of {\\em internal cwfs}. This gat has sort symbols $\\mathrm{ty}$ for {\\em internal types} and $\\mathrm{tm}$ for {\\em internal terms} using lower case to highlight the difference from $\\mathrm{Ty}$ and $\\mathrm{Tm}$ in the model cwf. \n\nFurthermore, we often use the same notation both on the semantic and the syntactic level. For example, in Section \\ref{gat-sig-mod}, where we are syntax independent, the letter $S$ denotes a semantic sort symbol, whereas in Section \\ref{initial-gat}, where we construct the initial model, it denotes a syntactic sort symbol.\n\n\n\\section{Categories with families}\\label{sec:def_cwf}\n\n\\subsection{The category of cwfs and strict cwf-morphisms}\n\n\n\\begin{definition}\\label{def:catFam}\n$\\textbf{Fam}$ is a category whose objects are\nset-indexed families of sets, denoted as $(U_x)_{x\\in X}$.\nA morphism of $\\textbf{Fam}$ with source $(U_x)_{x\\in X}$ and target $(V_y)_{y\\in Y}$\nconsists of a re-indexing function $f: X\\to Y$ together with a family\n$(g_x)_{x\\in X}$ of functions $g_x : U_x \\to V_{f(x)}$.\n\\end{definition}\n\nThe next step is to define the category $\\mathbf{CwF}$.\nWe split this definition in two: first the objects,\nwhich are called \\emph{categories with families}, in Definition~\\ref{def:Cwfobj},\nand then the morphisms in Definition~\\ref{def:Cwfmor}.\nSince $\\mathbf{CwF}$ has been developed as a categorical framework for the semantics of\ntype theory, much of the terminology (contexts, substitutions,\ntypes, terms) refers to the syntax of type theory,\nsuggesting the intended interpretation of this syntax in the\nso-called $\\mathbf{CwF}$-semantics.\n\nThe main novelty of this note is to use $\\mathbf{CwF}$ as a framework\nto define a new notion of a generalized algebraic theory.\nContexts, substitutions, types, and terms also make\nsense in relation to gats.\n\n\\begin{definition}\\label{def:Cwfobj}\nA category with families (cwf) consists of the following data:\n\n\\begin{itemize}\n\\item A category ${\\mathcal C}$;\n\n\\item A $\\textbf{Fam}$-valued presheaf on ${\\mathcal C}$, that is, a functor\n$T : \\C^\\op \\to \\textbf{Fam}$;\n\n\\item A terminal object $1\\in {\\mathcal C}$, and unique maps\n$\\tuple{}_\\Gamma \\in {\\mathcal C}(\\Gamma, 1)$ for all objects $\\Gamma$ of ${\\mathcal C}$;\n\n\\item Operations ${.\\,},~\\tuple{\\_,\\_},~\\mathrm{p}$ and $\\mathrm{q}$\nexplained in the following paragraphs.\nThese four operations and their associated equations\nare referred to as \\emph{context comprehension}.\n\\end{itemize}\n\nWe let $\\Gamma, \\Delta,\\ldots$ range over objects of ${\\mathcal C}$,\nand refer to them as \\emph{contexts}.\nWe let $\\delta, \\gamma,\\ldots$ range over morphisms,\nand refer to them as \\emph{substitutions}.\nWe refer to $1$ as the \\emph{empty} context; the terminal maps\n$\\tuple{}_\\Gamma$ represent the \\emph{empty} substitutions.\n\nIf $T(\\Gamma) = (U_x)_{x\\in X}$, we write $\\mathrm{Ty}(\\Gamma)$ for the set $X$.\nWe call the elements of $\\mathrm{Ty}(\\Gamma)$ \\emph{types in context $\\Gamma$},\nand let $A, B, C$ range over such types.\nFurthermore, for $A \\in \\mathrm{Ty}(\\Gamma)$, we write $\\mathrm{Tm}(\\Gamma, A)$ for the set $U_A$\nand call the elements of $\\mathrm{Tm}(\\Gamma, A)$\n\\emph{terms of type $A$ in context $\\Gamma$}.\n\nFor $\\gamma : \\Delta \\to \\Gamma$,\nthe functorial action of $T$ yields a morphism\n\\[\nT(\\gamma) \\in \\textbf{Fam}\\left((\\mathrm{Tm}(\\Gamma, A))_{A\\in \\mathrm{Ty}(\\Gamma)},\n (\\mathrm{Tm}(\\Delta, B))_{B\\in \\mathrm{Ty}(\\Delta)}\\right)\n\\]\nconsisting of a reindexing function $\\_\\,[\\gamma] : \\mathrm{Ty}(\\Gamma) \\to\n\\mathrm{Ty}(\\Delta)$ referred to as \\emph{substitution in types}, and for each $A\\in\n\\mathrm{Ty}(\\Gamma)$ a function $\\_\\,[\\gamma] : \\mathrm{Tm}(\\Gamma, A) \\to \\mathrm{Tm}(\\Delta,\nA[\\gamma])$, referred to as \\emph{substitution in terms}.\n\nNow we turn to the explanation of the operations\n${.\\,},~\\tuple{\\_,\\_},~\\mathrm{p},~\\mathrm{q}$.\nGiven $\\Gamma \\in {\\mathcal C}$, $A \\in \\mathrm{Ty}(\\Gamma)$, $\\gamma : \\Delta \\to \\Gamma$,\nand $a\\in \\mathrm{Tm}(\\Delta, A[\\gamma])$, we have\n\\[\n\\Gamma . A \\in {\\mathcal C}\n\\quad\\qquad\n\\mathrm{p}_{\\Gamma, A} : \\Gamma . A \\to \\Gamma\n\\quad\\qquad\n\\mathrm{q}_{\\Gamma, A} \\in \\mathrm{Tm}(\\Gamma. A, A[\\mathrm{p}_{\\Gamma,A}])\n\\quad\\qquad\n\\tuple{\\gamma, a}_A : \\Delta \\to \\Gamma . A.\n\\]\nWe call $\\Gamma . A$ the \\emph{extended} context\nand $\\tuple{\\gamma, a}_A$ the \\emph{extended} substitution.\n\nThe operations ${.\\,},~\\tuple{\\_,\\_},~\\mathrm{p},~\\mathrm{q}$\nsatisfy the following universal property:\n$\\tuple{\\gamma, a}_A$ is the unique substitution satisfying\n\\[\n\\mathrm{p}_{\\Gamma, A} \\circ \\tuple{\\gamma, a}_A = \\gamma\n\\qquad \\text{and}\\qquad\n\\mathrm{q}_{\\Gamma, A} [\\tuple{\\gamma, a}_A] = a\\,.\n\\]\nWe refer (colloquially) to $\\mathrm{p}$ as the \\emph{first projection},\nand to $\\mathrm{q}$ as the \\emph{second projection}.\n{Note that the first equation implies that\n$\\mathrm{Tm}(\\Delta,A[\\mathrm{p}_{\\Gamma,A}][\\tuple{\\gamma, a}]) = \\mathrm{Tm}(\\Delta,A[\\gamma])$\nso that $\\mathrm{q}_{\\Gamma, A} [\\tuple{\\gamma, a}]$ and $a$ are elements of the same set.}\nHere and below, subscripts are omitted from ${.\\,},~\\tuple{\\_,\\_},~\\mathrm{p},~\\mathrm{q}$\nwhen they can be reconstructed from the context (no pun intended).\n(End Definition~\\ref{def:Cwfobj}.)\n\\end{definition}\n\nA cwf is thus a structure $({\\mathcal C},1,\\tuple{},T,.\\, , \\tuple{\\_,\\_},\\mathrm{p}, \\mathrm{q})$,\nsubject to equations, for the category and the presheaf, and universal\nproperties, formulated purely equationally, for the terminal object and for context comprehension.\nThe morphisms to be defined next preserve this structure,\neven in a strict way, `on the nose'.\nWe often shorten the notation of a cwf to $({\\mathcal C},T)$, or even just ${\\mathcal C}$,\nleaving the remaining structure implicit.\n\n\\begin{definition}\\label{def:Cwfmor}\nA \\emph{(strict) cwf-morphism $F$ between cwfs $({\\mathcal C},T_{\\mathcal C})$ and $(\\mathcal{D},T_\\mathcal{D})$}\nconsists of\n\n\\begin{itemize}\n\n\\item A functor $F_\\mathrm{fun} : {\\mathcal C} \\to \\mathcal{D}$;\n\\item A natural transformation $F_\\mathrm{nat} : T_{\\mathcal C} \\Rightarrow (T_\\mathcal{D} \\circ F_\\mathrm{fun}^\\text{op})$;\n\\item The terminal object is preserved on the nose: $F_\\mathrm{fun}(1_{{\\mathcal C}}) = 1_{\\mathcal{D}}$;\n\\item Context comprehension is preserved on the nose, see below.\n\\end{itemize}\n\nSince $F_\\mathrm{nat}$ is a natural transformation between $\\textbf{Fam}$-valued presheaves,\n$F_\\mathrm{nat}$ has a component for any object $\\Gamma$ of ${\\mathcal C}$, and\nthese components are morphisms in $\\textbf{Fam}(T_C(\\Gamma),T_\\mathcal{D}(F_\\mathrm{fun}(\\Gamma)))$.\nRecall that morphisms in $\\textbf{Fam}$ consist of a reindexing function\nand a family of functions. It is convenient to denote $F_\\mathrm{fun}$,\nall reindexing functions, as well as all members of the families of functions,\nsimply by $F$. Thus we have $F(A) \\in \\mathrm{Ty}_\\mathcal{D}(F(\\Gamma))$\nand $F(a) \\in \\mathrm{Tm}_\\mathcal{D}(F(\\Gamma), F(A))$, for all $\\Gamma$\nand $A\\in\\mathrm{Ty}_{\\mathcal C}(\\Gamma)$ and $a\\in \\mathrm{Tm}_{\\mathcal C}(\\Gamma, A)$.\n\nNaturality of $F_\\mathrm{nat}$\namounts to preservation of substitution, {i.e.}, for all\n$\\gamma : \\Delta \\to \\Gamma$ in ${\\mathcal C}$, we have\n\\[\nF(A[\\gamma]) = F(A)[F(\\gamma)] \\qquad \\qquad\nF(a[\\gamma]) = F(a)[F(\\gamma)]\\,.\n\\]\n\nLast but not least, we turn to the preservation of context comprehension\non the nose, and require\n\\[\nF(\\Gamma. A) = F(\\Gamma). F(A) \\qquad\nF(\\mathrm{p}_{\\Gamma, A}) = \\mathrm{p}_{F(\\Gamma), F(A)} \\qquad\nF(\\mathrm{q}_{\\Gamma, A}) = \\mathrm{q}_{F(\\Gamma), F(A)}\\,.\n\\]\n\nNote that the universal property implies that\n$F(\\tuple{\\gamma,a}) = \\tuple{F(\\gamma),F(a)}$.\nThe same is true for the terminal maps:\n$F(\\tuple{}_\\Gamma) = \\tuple{}_{F(\\Gamma)}$.\n(End Definition~\\ref{def:Cwfmor}.)\n\\end{definition}\n\nSmall cwfs with strict cwfs-morphisms form a category, written $\\mathbf{CwF}$.\n\n\\section{Signatures and models of generalized algebraic theories}\\label{gat-sig-mod}\n\nWe now come to the main point of this note.\nWe define how to build a valid gat signature $\\Sigma$ and the associated\ncategory $\\mathbf{CwF}_{\\Sigma}$ of cwfs with a $\\Sigma$-structure.\nEach object of $\\mathbf{CwF}_{\\Sigma}$ is a cwf with extra structure and\neach morphism is a cwf-morphism preserving $\\Sigma$-structure.\nFor this definition, we will need the following auxiliary notions.\n\nA {\\em uniform family of contexts} is a family $\\Gamma = (\\Gamma_{{\\mathcal C}})$ with $\\Gamma_{\\mathcal C}$ a context in \n${\\mathcal C}$ for each ${\\mathcal C} \\in \\mathbf{CwF}_{\\Sigma}$, such that\n$F(\\Gamma_{\\mathcal C}) = \\Gamma_\\mathcal{D}$ for all morphisms $F \\in \\mathbf{CwF}_{\\Sigma}({\\mathcal C},\\mathcal{D})$.\nIf $\\Gamma$ is such a family, a {\\em uniform family of types} over $\\Gamma$ is a\nfamily of types $A = (A_{{\\mathcal C}})$ with $A_{{\\mathcal C}}$ a type over $\\Gamma_{{\\mathcal C}}$ and\n$F(A_{{\\mathcal C}}) = A_{\\mathcal{D}}$ for all morphisms $F \\in \\mathbf{CwF}_{\\Sigma}({\\mathcal C},\\mathcal{D})$.\nFinally, given $\\Gamma$ and $A$, a {\\em uniform family of terms} is a family\nof terms $a = (a_{{\\mathcal C}})$ with $a_{\\mathcal C} \\in \\mathrm{Tm}_{{\\mathcal C}}(\\Gamma_{{\\mathcal C}},A_{{\\mathcal C}})$ such that\n$F(a_{{\\mathcal C}}) = a_{\\mathcal{D}}$ for all morphisms $F \\in \\mathbf{CwF}_{\\Sigma}({\\mathcal C},\\mathcal{D})$. \n\n\\begin{remark}\nUniform families appear in Freyd's proof of the adjoint functor theorem \\cite{freyd:abelian}, in Reynolds' \\cite{reynolds:impredicative} and Reynolds and Plotkin's construction \\cite{plotkin-reynolds} of an initial algebra for an endofunctor from an impredicative encoding of an inductive type, and in Awodey, Frey, and Speight's \\cite{awodey:impredicative} construction of an impredicative encoding of a higher inductive type. The common idea in these works is to first construct a weakly initial object and then the initial object is obtained by taking uniform families.\n\\end{remark}\n\n\\begin{definition}\\label{def-sig-mod}\nWe define inductively (actually inductive-recursively) how to build a valid signature $\\Sigma$ and the category $\\mathbf{CwF}_\\Sigma$ of cwfs with a $\\Sigma$-structure and cwf-morphisms that preserve $\\Sigma$-structure. First, the base case:\n\\begin{description}\n\\item[The empty signature] The empty signature $\\emptyset$ is valid and $\\mathbf{CwF}_\\emptyset = \\mathbf{CwF}$.\n\\end{description}\nAssume now that we have defined $\\Sigma$ as a valid signature and the associated\ncategory $\\mathbf{CwF}_{\\Sigma}$.\nThen we can add a new sort symbol, or a new operator symbol, or a new equation, to get a new valid signature,\nas follows:\n\\begin{description}\n\\item[Adding a sort symbol]\n Let $\\Gamma = (\\Gamma_{\\mathcal C})$ be a uniform family of contexts indexed by ${\\mathcal C} \\in \\mathbf{CwF}_{\\Sigma}$.\n Then we can extend $\\Sigma$ with a new sort symbol $S$ relative to $\\Gamma$, to obtain\n the gat $\\Sigma' = (\\Sigma,(\\Gamma,S))$.\n The objects of $\\mathbf{CwF}_{\\Sigma'}$ are pairs $({\\mathcal C},S_{{\\mathcal C}})$, where ${\\mathcal C}$ is an object of $\\mathbf{CwF}_{\\Sigma}$\n and $S_{{\\mathcal C}} \\in \\mathrm{Ty}_{\\mathcal C}(\\Gamma_{{\\mathcal C}})$.\n A morphism in $\\mathbf{CwF}_{\\Sigma'}(({\\mathcal C},S_{{\\mathcal C}}), (\\mathcal{D},S_{\\mathcal{D}}))$\n is a morphism $F \\in \\mathbf{CwF}_{\\Sigma}({\\mathcal C},\\mathcal{D})$ such that $F(S_{\\mathcal C}) = S_\\mathcal{D}$.\n\\item[Adding an operator symbol]\n If $\\Gamma$ is a uniform family of contexts and $A$ a uniform family of\n types over $\\Gamma$, \n \n then we can extend $\\Sigma$ with a new operator\n symbol $f$ relative to $\\Gamma$ and $A$, to obtain\n the gat $\\Sigma' = (\\Sigma,(\\Gamma,A,f))$.\n An object of $\\mathbf{CwF}_{\\Sigma'}$\n is a pair $({\\mathcal C},f_{{\\mathcal C}})$ where ${\\mathcal C}$ is an object in $\\mathbf{CwF}_{\\Sigma}$ and $f_{{\\mathcal C}} \\in \\mathrm{Tm}_{\\mathcal C}(\\Gamma_{\\mathcal C},A_{\\mathcal C})$.\n A morphism in $\\mathbf{CwF}_{\\Sigma'}(({\\mathcal C},f_{{\\mathcal C}}),(\\mathcal{D},f_{\\mathcal{D}}))$ is a morphism $F \\in \\mathbf{CwF}_{\\Sigma}({\\mathcal C},\\mathcal{D})$ such that $F(f_{{\\mathcal C}}) = f_{\\mathcal{D}}$\n\\item[Adding an equation]\n If $\\Gamma$ is a uniform family of contexts,\n $A$ is a uniform family of types over $\\Gamma$\n and $a,a'$ are uniform families of terms in $A$, \n \n then we can extend $\\Sigma$ with a new equation $a = a'$ relative to $\\Gamma$ and $A$, to obtain\n the gat $\\Sigma' = (\\Sigma,(\\Gamma,A,a,a'))$.\n In this case,\n $\\mathbf{CwF}_{\\Sigma'}$ is a full subcategory of $\\mathbf{CwF}_{\\Sigma}$. An object ${\\mathcal C}$ in\n $\\mathbf{CwF}_{\\Sigma'}$ is an object ${\\mathcal C}$ of $\\mathbf{CwF}_\\Sigma$ such that $a_{{\\mathcal C}} = a'_{{\\mathcal C}}$.\n\\end{description}\n\n\\end{definition}\n\nThis definition is {\\em syntax independent}. In the next subsection we then show the purely {\\em syntactic} construction of an {\\em initial object} ${\\mathcal T}_{\\Sigma}$ in $\\mathbf{CwF}_{\\Sigma}$ (for an arbitrary valid signature $\\Sigma$) in terms of grammars and inference rules. A context in ${\\mathcal T}_{\\Sigma}$ will be an equivalence class $[ \\Gamma ]$ of raw contexts, and similarly for substitutions, types, and terms. To give a uniform\n family of contexts $\\Gamma_{\\mathcal C}$ is then equivalent to giving a context $[ \\Gamma ] \\in {\\mathcal T}_{\\Sigma}$, since $\\Gamma_{\\mathcal C} = \\inte{ [ \\Gamma ] }_{\\mathcal C}$ where $\\inte{-}_{\\mathcal C}$ is the interpretation morphism from ${\\mathcal T}_\\Sigma$ to ${\\mathcal C}$.\nUniform families of types and terms arise from types and terms in ${\\mathcal T}_\\Sigma$ in a similar way.\n\nWe refer to Section \\ref{monoids} where we show a simple example: the construction of a signature $\\Sigma$ for internal monoids and its associated category of models $\\mathbf{CwF}_\\Sigma$ of cwfs with an internal monoid. We also show how to construct the initial cwf with an internal monoid ${\\mathcal T}_\\Sigma$. \n\n\\begin{remark}\nCartmell's notion of gat \\cite{cartmell:phd,cartmell:apal} also makes it possible to stipulate equations between type expressions. However, neither of our examples makes use of this extra generality. In particular, in Section \\ref{sec:examples} we present the gat of cwfs with extra structure for $\\mathsf{N}, \\Pi$ and a first universe $\\mathsf{U}_0$ without needing type equations. The reason is that\nequations between types become equations between terms in our rendering\nof dependent type theory as a gat. See Remark \\ref{remark:typeequations} in Section \\ref{sec:u-example} for more explanation.\n\nLike Cartmell, we could consider gats with type equations, but we prefer not to make such equations part of our notion.\n\n\n\\end{remark}\n\n\n\n\n\\section{The construction of an initial object in $\\mathbf{CwF}_\\Sigma$}\\label{initial-gat}\n\nIn Section 3 we gave a {\\em syntax independent specification} of a generalized algebraic theory as the initial object of the category $\\mathbf{CwF}_\\Sigma$ of models of a (semantic) signature $\\Sigma$. Now we show our main theorem: the {\\em syntactic construction} of such an initial object ${\\mathcal T}_\\Sigma$. This construction is done in several steps. We first define the ``raw\" syntactic expressions. Then we define four families of partial equivalence relations (pers) over those raw expressions, corresponding to the four equality judgments. The term model ${\\mathcal T}_\\Sigma$ is obtained by quotienting with these pers.\n\nThis theorem can be viewed as a generalization of Birkhoff's completeness theorem for equational logic \\cite{birkhoff}:\n\\begin{theorem}\nThe category $\\mathbf{CwF}_\\Sigma$ has an initial object ${\\mathcal T}_\\Sigma$,\nfor every valid signature $\\Sigma$.\n\\end{theorem}\n\nThe construction of ${\\mathcal T}_\\Sigma$ will be by induction on the construction of $\\Sigma$. It is based on construction of initial cwfs in \\cite{castellan:tlca2015,castellan:lmcs} and we refer the reader to those papers for more details. Here we only provide a sketch and focus on how to extend the construction to ${\\mathcal T}_\\Sigma$.\n\nFor each $\\Sigma$ we will define the following.\n\\begin{itemize}\n\\item\nA grammar for the {\\em raw syntax}, that is, raw contexts in ${\\tt Ctx}_\\Sigma$, raw substitutions in ${\\tt Sub}_\\Sigma$, raw types in ${\\tt Ty}_\\Sigma$, and raw terms in ${\\tt Tm}_\\Sigma$.\n\\item\nA system of inference rules that generate four families of partial equivalence relations (pers) by a mutual inductive definition:\n$$\n\\Gamma = \\Gamma' \\vdash_\\Sigma\n\\qquad\n\\Gamma \\vdash_\\Sigma A = A'\n\\qquad\n\\Delta \\vdash_\\Sigma \\gamma = \\gamma' : \\Gamma\n\\qquad\n\\Gamma \\vdash_\\Sigma a = a' : A\n$$\nwhere $\\Gamma, \\Gamma' \\in {\\tt Ctx}_\\Sigma, \\gamma, \\gamma' \\in {\\tt Sub}_\\Sigma, A, A' \\in {\\tt Ty}_\\Sigma,$ and $a,a' \\in {\\tt Tm}_\\Sigma$. These pers correspond to valid equality judgments of a variable-free version of dependent type theory with explicit substitutions based on the cwf-combinators. The ordinary judgments will be defined as the reflexive instances of these equality judgments. For example $\\Gamma \\vdash_\\Sigma$ (meaning ``$\\Gamma$ is a valid context\") is defined as the reflexive instance $\\Gamma = \\Gamma \\vdash_\\Sigma$.\n\\item\nA cwf ${\\mathcal T}_\\Sigma$ is then constructed from the equivalence classes of derivable judgments. For example, the contexts in ${\\mathcal T}_\\Sigma$ are equivalence classes $[\\Gamma]$, such that $\\Gamma \\vdash_\\Sigma$. We will show that ${\\mathcal T}_\\Sigma$ is a cwf with a $\\Sigma$-structure, that is, an object of $\\mathbf{CwF}_\\Sigma$.\n\\item\nA $\\mathbf{CwF}_\\Sigma$-morphism $\\inte{-} : {\\mathcal T}_\\Sigma \\to {\\mathcal C}$ for every ${\\mathcal C} \\in \\mathbf{CwF}_\\Sigma$. This is the {\\em interpretation morphism}. This morphism is a partial function defined by induction on the raw syntax, that (whenever it is defined) maps raw contexts to contexts in ${\\mathcal C}$, raw substitutions to substitutions in ${\\mathcal C}$, raw types to types in ${\\mathcal C}$, and raw terms to terms in~${\\mathcal C}$. We show that these partial functions preserve the partial equivalence relations so that we can define the interpretation morphism on the equivalence classes. Finally we show that it indeed is a $\\mathbf{CwF}_\\Sigma$-morphism and the unique such into ${\\mathcal C}$.\n\\end{itemize}\n\nWe begin with the construction for the base case: the {\\bf empty signature} $\\emptyset$.\n\\begin{itemize}\n\\item\nWe start with the {\\em raw syntax}. This following grammar for raw contexts, raw substitutions, raw types, and raw terms.\n\\begin{eqnarray*}\n\\Gamma \\in {\\tt Ctx}_\\emptyset &::=& 1 \\ |\\ \\Gamma. A\\\\\n\\gamma \\in {\\tt Sub}_\\emptyset \\ &::=& \\gamma \\circ \\gamma \\ |\\ \\mathrm{id}_\\Gamma \\ |\\ \\langle\\rangle_\\Gamma \\ |\\ \\mathrm{p}_{A} \\ |\\ \\langle \\gamma, a \\rangle_A\\\\\nA \\in {\\tt Ty}_\\emptyset &::=& A[\\gamma]\\\\\na \\in {\\tt Tm}_\\emptyset &::=& a[\\gamma] \\ |\\ \\qI_A\n\\end{eqnarray*}\nThese grammars generate a language of {\\em cwf-combinators}.\n\\item\nThe system of inference rules is displayed in \\cite{castellan:tlca2015,castellan:lmcs}. It is a system of {\\em general rules}, rules for dependent type theory which come before we introduce any sort symbols and operator symbols and equations (or any rules for the type formers of intuitionistic type theory). We do not have room here to display them, but note that they can be divided into four groups:\n\\begin{itemize}\n\\item the per rules, amounting to symmetry and transitivity for the four forms of equality judgments;\n\\item preservation rules for judgments, amounting to substitution of equals for equals (an example of such a rule is the {\\em type equality rule});\n\\item congruence rules for operators expressing that the cwf-combinators preserve equality;\n\\item conversion rules for the cwf-combinators.\n\\end{itemize}\n\\item\nNote that the initial cwf ${\\mathcal T}_\\emptyset$ is rather uninteresting: its category of contexts contains only a terminal object (the empty context), and there are no types and terms. Nevertheless, the grammar and inference rules used in its definition form a starting point. The grammar for raw types and raw terms will be extended each time we add a new sort symbol or operator symbol, respectively. For each such new symbol and each new equation we will add a new inference rule. As a consequence we will generate a non-trivial ${\\mathcal T}_\\Sigma$.\n\\end{itemize}\n\nAssume now for the induction step that we have defined the grammar, the inference rules, ${\\mathcal T}_\\Sigma$ and the interpretation morphism $\\inte{-} : {\\mathcal T}_{\\Sigma} \\to {\\mathcal C}$ in $\\mathbf{CwF}_\\Sigma$.\nLet $\\Sigma'$ be $\\Sigma$ extended by a new sort symbol, a new operator symbol, or a new equation. We shall now explain how to define ${\\mathcal T}_{\\Sigma'}$.\n\\begin{description}\n\\item[Adding a sort symbol] If $\\Gamma \\vdash_\\Sigma$, then we can introduce a new sort symbol $S$ in the context $\\Gamma$ representing the sequence of types of the arguments of $S$.\n\\begin{itemize}\n\\item\nWe add a new production for raw types\n$$\nA ::= S\n$$\nto the productions for ${\\mathcal T}_\\Sigma$.\n\\item\nWe add the inference rule\n\\begin{mathpar}\n \\inferrule\n {}\n {\\Gamma \\vdash_{\\Sigma'} S}\n \\end{mathpar}\nto the inference rules for ${\\mathcal T}_\\Sigma$.\n\\item\nWe define $S_{{\\mathcal T}_{\\Sigma'}} = [S]$, so that $ {\\mathcal T}_{\\Sigma'}$ has a $\\Sigma'$-structure $({\\mathcal T}_\\Sigma,[S])$.\n\\item\nWe extend the definition of the interpretation morphism $\\inte{-}$ to an interpretation morphism $\\inte{-}' : {\\mathcal T}_{\\Sigma'} \\to {\\mathcal C}$ by\n$$\n\\inte{[S]}' = S_{\\mathcal C}\n$$\nIt follows that this is a morphism in $\\mathbf{CwF}_{\\Sigma'}$ and that it is unique.\n\\end{itemize}\n\n\\item[Adding an operator symbol] If $\\Gamma \\vdash_\\Sigma A$, then we can introduce a new operator symbol $f$, where the context $\\Gamma$ represents the sequence of types of the arguments and $A$ is the type of the result.\n\\begin{itemize}\n\\item\nWe add a new production for raw terms\n$$\na ::= f\n$$\nto the productions for ${\\mathcal T}_\\Sigma$.\n\\item\nWe add the inference rule\n\\begin{mathpar}\n \\inferrule\n {}\n {\\Gamma \\vdash_{\\Sigma'} f : A}\n \\end{mathpar}\nto the inference rules for ${\\mathcal T}_\\Sigma$.\n\\item\nWe define $f_{{\\mathcal T}_{\\Sigma'}} = [f]$, so that $ {\\mathcal T}_{\\Sigma'}$ has a $\\Sigma'$-structure $({\\mathcal T}_\\Sigma,[f])$.\\item\nWe extend the definition of the interpretation morphism $\\inte{-}$ to an interpretation morphism $\\inte{-}' : {\\mathcal T}_{\\Sigma'} \\to {\\mathcal C}$ by\n$$\n\\inte{[f]}' = f_{\\mathcal C}\n$$\nIt follows that this is a morphism in $\\mathbf{CwF}_{\\Sigma'}$ and that it is unique.\n\\end{itemize}\n\n\\item[Adding an equation] If $\\Gamma \\vdash_\\Sigma a : A$ and $\\Gamma \\vdash_\\Sigma a' : A$ we can introduce a new equation $a = a'$.\n\\begin{itemize}\n\\item\n${\\mathcal T}_\\Sigma'$ has the same productions as ${\\mathcal T}_\\Sigma$.\n\\item\nWe add the inference rule\n\\begin{mathpar}\n \\inferrule\n {}\n {\\Gamma \\vdash_{\\Sigma'} a = a' : A}\n \\end{mathpar}\nto the inference rules for ${\\mathcal T}_\\Sigma$.\n\\item\n${\\mathcal T}_{\\Sigma'}$ is based on the same raw syntax as ${\\mathcal T}_\\Sigma$ but the equivalence relation has changed. To show that ${\\mathcal T}_{\\Sigma'} \\in \\mathbf{CwF}_{\\Sigma'}$ we just need to show that $[ a ] = [ a' ]$ but this follows from the inference rule $\\Gamma \\vdash_{\\Sigma'} a = a' : A$.\n\\item\nIn order to define $\\inte{-}'$ we first define the partial function on the raw syntax to be identical to the partial function on the raw syntax for $\\inte{-}$. We then prove that this partial function preserves the extended partial equivalence relation and define $\\inte{-}'$ on the new equivalence classes. It follows that $\\inte{-}'$ is unique.\n\\end{itemize}\n\\end{description}\nThis concludes the proof of the theorem. \\qed\n\n\n\n\\section{Examples of generalized algebraic theories}\\label{sec:examples}\n\nWe will now display the sort symbols, operator symbols, and equations for the generalized algebraic of internal monoids, internal categories and of internal cwfs. We will then show how to add operator symbols and equations when adding internal notions of $\\Pi, \\mathsf{N}$ and a universe closed under $\\Pi$ and $\\mathsf{N}$ to the gat of internal cwfs. The reason for prefacing these notions by the word ``internal\" is that the models of the theories are internal monoids, categories, and cwfs in $\\mathbf{CwF}_\\Sigma$ for the respective signatures for these theories. Moreover, internal monoids, categories, and cwfs in the cwf~$\\Set$ are small monoids, categories, and cwfs, respectively. Note that the cwfs defined in Section 2 need not be small, and hence not internal cwfs in the cwf~$\\Set$.\n\n\nWe begin by using the recipe in Definition \\ref{def:Cwfmor} to construct the semantic signature for internal monoids and its associated category of models, that is, of cwfs with an internal monoid. We then follow the recipe in Definition \\ref{initial-gat} and construct the initial cwf with an internal monoid. \n\nFor ease of readability, we will only present the sort symbols, operator symbols, and equations in the remaining examples by using an informal notation with named variables, rather than the formal notations using cwf-combinators employed in Definitions \\ref{def:Cwfmor} and \\ref{initial-gat}.\n\nOur final example is the gat of internal contextual cwfs, a variant of Cartmell's contextual categories. The contexts in such contextual cwfs come with a length $n$. We sketch how this can be axiomatized as a gat with countably many sort symbols $\\mathrm{ctx}_n, \\mathrm{sub}_n, \\mathrm{ty}_n, \\mathrm{tm}_n$ for an external natural number $n$ (and similarly for the operator symbols and equations). We also indicate how our framework can be extended to cover such gats.\n\n\\subsection{Internal monoids}\\label{monoids}\n The one-sorted algebraic theory of monoids has two operator symbols,\n$\\mathrm{e}$ for identity and $\\mathrm{*}$ for composition, and associativity and identity laws as equations.\nAs any other one-sorted algebraic theory, the theory of monoids yields a\ngeneralized algebraic theory. In ordinary notation with variables it might be rendered as follows, where $\\mathrm{M}$ is the only sort:\n\\begin{eqnarray*}\n&\\vdash& \\mathrm{M}\\\\\n&\\vdash& e : \\mathrm{M}\\\\\nx, y : \\mathrm{M} &\\vdash& \\mathrm{*}(x,y) : \\mathrm{M}\\\\\ny : \\mathrm{M} &\\vdash& \\mathrm{*}(\\mathrm{e},y) = y : \\mathrm{M}\\\\\nx : \\mathrm{M} &\\vdash& \\mathrm{*}(x,\\mathrm{e}) = x : \\mathrm{M}\\\\\nx, y, z : \\mathrm{M} &\\vdash& \\mathrm{*}(\\mathrm{*}(x,y),z) = \\mathrm{*}(x,\\mathrm{*}(y,z)) : \\mathrm{M}\n\\end{eqnarray*}\n\nWe now show how the corresponding semantic signature for monoids $\\Sigma$ and its associated category of models $\\mathbf{CwF}_\\Sigma$ are constructed step-wise in the sense of Definition \\ref{def-sig-mod}.\n\nAs always, we begin with the empty signature $\\emptyset$ and its category of models $\\mathbf{CwF}_\\emptyset = \\mathbf{CwF}$.\n\\begin{description}\n\\item[Adding the sort symbol $\\mathrm{M}$] Each cwf ${\\mathcal C}$ has a chosen empty context (terminal object) $1_{\\mathcal C}$. Since cwf-morphisms preserve empty contexts on the nose, $1 = (1_{\\mathcal C})$ is a uniform family of contexts in $\\mathbf{CwF}_\\emptyset$. Hence we can introduce a new constant sort symbol $\\mathrm{M}$ in the empty context. The resulting signature is\n$$\n\\Sigma_1 = (\\emptyset, (1,\\mathrm{M}))\n$$\nThe objects of $\\mathbf{CwF}_{\\Sigma_1}$ are pairs $({\\mathcal C},\\mathrm{M}_{\\mathcal C})$, where ${\\mathcal C}$ is a cwf and $\\mathrm{M}_{\\mathcal C} \\in \\mathrm{Ty}_{\\mathcal C}(1_{\\mathcal C})$.\n\\item[Adding the operator symbol for the identity]\nSince, morphisms in $\\mathbf{CwF}_{\\Sigma_1}$ preserve both empty contexts $1_{\\mathcal C}$ and types $\\mathrm{M}_{\\mathcal C}$ on the nose, we have a uniform family of contexts $1 = (1_{\\mathcal C})$ and a uniform family of types $\\mathrm{M} = (\\mathrm{M}_{\\mathcal C})$ in $\\mathbf{CwF}_{\\Sigma_1}$. Hence we can introduce a new constant operator symbol $e$ (the identity of the monoid). The resulting signature is\n$$\n\\Sigma_2 = (\\Sigma_1, (1,\\mathrm{M},e))\n$$\nThe objects of $\\mathbf{CwF}_{\\Sigma_2}$ are triples $({\\mathcal C},\\mathrm{M}_{\\mathcal C},e_{\\mathcal C})$, where ${\\mathcal C}$ is a cwf, $\\mathrm{M}_{\\mathcal C} \\in \\mathrm{Ty}_{\\mathcal C}(1_{\\mathcal C})$ and $e_{\\mathcal C} \\in \\mathrm{Tm}_{\\mathcal C}(1_{\\mathcal C},\\mathrm{M}_{\\mathcal C})$.\n\\item[Adding the operator symbol for composition]\nAgain using that cwf-morphisms preserve all cwf-structure and $\\mathrm{M}_{\\mathcal C}$, we deduce that we have a uniform family of contexts $1.\\mathrm{M}.\\mathrm{M}[\\mathrm{p}]$ and a uniform family of types $\\mathrm{M}[\\mathrm{p}][\\mathrm{p}]$ in $\\mathbf{CwF}_{\\Sigma_2}$. Thus we can add a binary operator symbol $\\mathrm{*}$. The resulting signature is\n$$\n\\Sigma_3 = (\\Sigma_2, (1.\\mathrm{M}.\\mathrm{M}[\\mathrm{p}],\\mathrm{M}[\\mathrm{p}][\\mathrm{p}],\\mathrm{*}))\n$$\nThe objects of $\\mathbf{CwF}_{\\Sigma_3}$ are quadruples $({\\mathcal C},\\mathrm{M}_{\\mathcal C},e_{\\mathcal C},*_{\\mathcal C})$, where ${\\mathcal C}$ is a cwf, $\\mathrm{M}_{\\mathcal C} \\in \\mathrm{Ty}_{\\mathcal C}(1_{\\mathcal C})$, $e_{\\mathcal C} \\in \\mathrm{Tm}_{\\mathcal C}(1_{\\mathcal C},\\mathrm{M}_{\\mathcal C})$, and $*_{\\mathcal C} \\in \\mathrm{Tm}_{\\mathcal C}((1.\\mathrm{M}.\\mathrm{M}[\\mathrm{p}])_{\\mathcal C},(\\mathrm{M}[\\mathrm{p}][\\mathrm{p}])_{\\mathcal C})$.\n\\item[Adding the left identity law]\nFurthermore, we extend the signature with the equations stating that $\\mathrm{e}$ is a left identity as follows:\n$$\n\\Sigma_4 = (\\Sigma_3, (1.\\mathrm{M}, \\mathrm{M}[\\mathrm{p}], \\mathrm{*}[\\tuple{\\tuple{\\tuple{},\\mathrm{e}[\\tuple{}]},\\mathrm{q}}], \\mathrm{q}))\n$$\nThe uniform family of context $1.\\mathrm{M}$ expresses that the equation has one variable of type $\\mathrm{M}$, the uniform family of types $\\mathrm{M}[\\mathrm{p}]$ expresses that the two sides of the equation have type $\\mathrm{M}$, and the uniform families of terms $\\mathrm{*}[\\tuple{\\tuple{\\tuple{},\\mathrm{e}[\\tuple{}]},\\mathrm{q}}]$ and $\\mathrm{q}$ express the two sides of the equation.\n$\\mathbf{CwF}_{\\Sigma_4}$ is the full subcategory of $\\mathbf{CwF}_{\\Sigma_3}$ with objects ${\\mathcal C}$\nsuch that $(\\mathrm{*}[\\tuple{\\tuple{\\tuple{},\\mathrm{e}[\\tuple{}]},\\mathrm{q}}])_{\\mathcal C} = \\mathrm{q}_{\\mathcal C}$.\n\\item[Adding the right identity and the associativity laws]\nFinally we add the right identity equation and the associativity equation to get the signatures $\\Sigma_5$ and $\\Sigma_6$. We omit the details. \n\\end{description}\nWe call the resulting signature for internal monoids $\\Sigma = \\Sigma_6$. The category $\\mathbf{CwF}_\\Sigma$ is the category of cwfs with an {\\em internal monoid}. This is a cwf-version of the notion of internal monoid which can be defined in any category with finite products. Ordinary (small) monoids come out as internal monoids in $\\Set$, the cwf of small sets.\n\nWe also sketch the construction of the initial object ${\\mathcal T}_\\Sigma$ of $\\mathbf{CwF}_\\Sigma$ following the recipe for introducing sort symbols, operators symbols, and equations in Section \\ref{initial-gat}. (We omit the index $\\Sigma$ in $\\vdash_\\Sigma$.)\n\\begin{description}\n\\item[Adding the sort symbol $\\mathrm{M}$]\nFirst, we have $1 \\vdash$ for the empty signature, so we can\nadd a production for the constant sort symbol $\\mathrm{M}$ and the inference rule:\n\\begin{eqnarray*}\n1 &\\vdash& \\mathrm{M}\n\\end{eqnarray*}\nFor later use we infer $1.\\mathrm{M} \\vdash$ and, using $\\mathrm{p}:1.\\mathrm{M} \\to 1$, $1.\\mathrm{M}\\vdash\\mathrm{M}[\\mathrm{p}]$,\nso $1.\\mathrm{M}.\\mathrm{M}[\\mathrm{p}] \\vdash$.\n\\item[Adding the operator symbol for identity]\nWe then add a production for the constant operator symbol $\\mathrm{e}$ and the inference rule:\n\\begin{eqnarray*}\n1 &\\vdash& \\mathrm{e} : \\mathrm{M}\n\\end{eqnarray*}\nAgain for later use we infer $1.\\mathrm{M}\\vdash \\mathrm{e}[\\mathrm{p}] :\\mathrm{M}[\\mathrm{p}]$.\nNote that here $\\mathrm{p} = \\tuple{}$, the empty substitution $1.\\mathrm{M} \\to 1$,\nsince there is only one substitution $1.\\mathrm{M} \\to 1$.\n\\item[Adding the operator symbol for composition]\nWe then add a production for the binary operator symbol $\\mathrm{*}$.\nUsing another $\\mathrm{p}: 1.\\mathrm{M}.\\mathrm{M}[\\mathrm{p}] \\to 1.\\mathrm{M}$ (note the different type),\nwe can derive $1.\\mathrm{M}.\\mathrm{M}[\\mathrm{p}] \\vdash \\mathrm{M}[\\mathrm{p}][\\mathrm{p}]$, so we can add the inference rule\n\\begin{eqnarray*}\n1.\\mathrm{M}.\\mathrm{M}[\\mathrm{p}] &\\vdash& \\mathrm{*} : \\mathrm{M}[\\mathrm{p}][\\mathrm{p}]\n\\end{eqnarray*}\nNote that we project $\\mathrm{M}$ on the right twice, reflecting that $\\mathrm{*}$ is binary.\n\\item[Adding the left identity law]\nWe can derive $1.\\mathrm{M} \\vdash \\mathrm{q} : \\mathrm{M}[\\mathrm{p}]$. With some effort,\nusing previous inferences, we can derive\n$1.\\mathrm{M} \\vdash \\mathrm{*}[\\tuple{\\tuple{\\tuple{},\\mathrm{e}[\\tuple{}]},\\mathrm{q}}] : \\mathrm{M}[\\mathrm{p}]$.\nHence we can add the inference rule for the equation ($\\mathrm{e}$ is a left identity):\n\\begin{eqnarray*}\n1.\\mathrm{M} &\\vdash& \\mathrm{*}[\\tuple{\\tuple{\\tuple{},\\mathrm{e}[\\tuple{}]},\\mathrm{q}}] = \\mathrm{q} : \\mathrm{M}[\\mathrm{p}]\n\\end{eqnarray*}\n\\item[Adding the right identity and the associativity laws]\nWe omit the details.\n\\end{description}\nThe resulting initial object ${\\mathcal T}_\\Sigma = {\\mathcal T}_{\\Sigma_6}$ is generated by a system of grammar and inference rules for dependent type theory with an internal monoid. In this theory we can prove statements such as \n$$\n\\Gamma \\vdash a : \\mathrm{M}\n$$\nstating that $a$ is a well-formed monoid expression in the context $\\Gamma$ and\n$$\n\\Gamma \\vdash a = a': \\mathrm{M}\n$$\nstating that $a = a'$ is a derivable equation between monoid expressions in the context $\\Gamma$. Note that both contexts and monoid expressions use cwf-combinators and are variable-free. \n\nThis example illustrates that gats indeed generalise ordinary algebraic theories.\nThe remaining examples will use dependent types in an essential way. However, for reasons of readability we will from now on only use ordinary notation with named variables. Hopefully, it is clear from the above how to formally construct the corresponding semantic signatures, categories of models, and initial models using cwf-combinators. For example, these constructions for the theory of internal categories are similar to the constructions for the theory of internal monoids.\n\n\n\\subsection{Internal categories} The gat of categories was one of Cartmell's motivating examples. It has the following sort symbols, operator symbols, and equations. Again, note that in our case the models are internal categories in a cwf. To emphasize the difference between the internal notions of category and cwf and the external notions (introduced in Section 2), our notation for sort symbols in the gat of internal cwfs use lower case letters ($\\mathrm{obj}, \\mathrm{hom}, \\mathrm{ty}, \\mathrm{tm}$). This is in contrast to the upper case letters for the external versions ($\\mathrm{Ty}, \\mathrm{Tm}$). We will however overload notation for operator symbols, and for example use $\\circ$ both for the cwf-combinator and for the operator symbol in the gat of internal categories.\n\nSort symbols:\n\\begin{eqnarray*}\n&\\vdash& \\mathrm{obj}\\\\\n\\Delta, \\Gamma : \\mathrm{obj} &\\vdash& \\mathrm{hom}(\\Delta,\\Gamma)\\\\\n\\end{eqnarray*}\n\nOperator symbols:\n\\begin{eqnarray*}\n\\Gamma : \\mathrm{obj} &\\vdash& \\mathrm{id}_\\Gamma : \\mathrm{hom}(\\Gamma,\\Gamma)\\\\\n\\Xi,\\Delta,\\Gamma : \\mathrm{obj}, \\gamma : \\mathrm{hom}(\\Delta,\\Gamma), \\delta : \\mathrm{hom}(\\Xi,\\Delta) &\\vdash&\n\\gamma \\circ \\delta : \\mathrm{hom}(\\Xi,\\Gamma)\n\\end{eqnarray*}\n\nEquations:\n\\begin{eqnarray*}\n\\Delta, \\Gamma : \\mathrm{obj}, \\gamma : \\mathrm{hom}(\\Delta,\\Gamma) &\\vdash& \\mathrm{id}_\\Gamma \\circ \\gamma = \\gamma : \\mathrm{hom}(\\Delta,\\Gamma)\\\\\n\\Delta, \\Gamma : \\mathrm{obj}, \\gamma : \\mathrm{hom}(\\Delta,\\Gamma) &\\vdash& \\gamma \\circ \\mathrm{id}_\\Delta = \\gamma : \\mathrm{hom}(\\Delta,\\Gamma)\\\\\n\\Theta, \\Xi,\\Delta,\\Gamma : \\mathrm{obj}, \\gamma : \\mathrm{hom}(\\Delta,\\Gamma), \\delta : \\mathrm{hom}(\\Xi,\\Delta), \\xi : \\mathrm{hom}(\\Theta,\\Xi) &\\vdash&\n(\\gamma \\circ \\delta) \\circ \\xi = \\gamma \\circ (\\delta \\circ \\xi): \\mathrm{hom}(\\Theta,\\Gamma)\n\\end{eqnarray*}\nNote that composition is officially an operator symbol with five arguments. In the official notation we should write $\\gamma \\circ_{\\Xi,\\Delta,\\Gamma} \\delta$, but we suppress the context arguments $\\Xi,\\Delta,\\Gamma$. We will do so for some other operations too.\n\nThe rendering of the gat of categories in cwf-combinator language and the proof that it indeed yields a valid signature are similar to what they were for the gat of monoids. The inference rules for the two sort symbols in cwf-combinator language are\n\\begin{eqnarray*}\n1 &\\vdash& \\mathrm{obj}\\\\\n1.\\mathrm{obj}.\\mathrm{obj}[\\mathrm{p}] &\\vdash& \\mathrm{hom}\n\\end{eqnarray*}\nand the operator symbols for identity\n\\begin{eqnarray*}\n1.\\mathrm{obj} &\\vdash& \\mathrm{e} : \\mathrm{hom}[\\tuple{\\mathrm{id}_{1.\\mathrm{obj}},\\mathrm{q}_{1,\\mathrm{obj}}}]\n\\end{eqnarray*}\nWe omit the verbose cwf-renderings of the operator symbol for composition and the equations.\n\nA cwf with extra structure for the generalized algebraic theory of categories is a cwf with an {\\em internal category}. This is a cwf-based analogue of the usual notion of internal category in a category with finite limits. As shown by Martin Hofmann \\cite{hofmann:csl,hofmann:cambridge}, every category with finite limits yields a category with attributes, and hence a cwf. However, not every cwf has finite limits. To achieve this we need more structure. As shown by Clairambault and Dybjer \\cite{ClairambaultD11,ClairambaultD14} the 2-category of categories with finite limits is biequivalent to the 2-category of democratic cwfs that support $\\Sigma$-types and extensional identity types.\n\nAn internal category in the cwf $\\Set$ of small sets is a small category.\n\n\n\\subsection{Internal cwfs}\\label{gat-cwf}\n\nThe gat of internal cwfs is obtained by extending the gat of internal categories with new sort symbols, operator symbols, and equations for a family valued functor, and then new operator symbols and equations for a terminal object, and context comprehension. We here rename the sort $\\mathrm{obj}$ of objects of the category of contexts to $\\mathrm{ctx}$.\n\n\\subsubsection{The extension with a family valued functor}\n\\mbox{ }\n\nSort symbols:\n\\begin{eqnarray*}\n\\Gamma : \\mathrm{ctx} &\\vdash& \\mathrm{ty}(\\Gamma)\\\\\n\\Gamma : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma) &\\vdash& \\mathrm{tm}(\\Gamma,A)\n\\end{eqnarray*}\n\nOperator symbols:\n\\begin{eqnarray*}\n\\Gamma,\\Delta : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma), \\gamma : \\mathrm{hom}(\\Delta,\\Gamma) &\\vdash&\nA[\\gamma] : \\mathrm{ty}(\\Delta)\\\\\n\\Gamma,\\Delta : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma), \\gamma : \\mathrm{hom}(\\Delta,\\Gamma), a:\\mathrm{tm}(\\Gamma,A) &\\vdash& a[\\gamma] : \\mathrm{tm}(\\Delta,A[\\gamma])\n\\end{eqnarray*}\n\nEquations:\n\\begin{eqnarray*}\n\\Gamma : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma) &\\vdash& A[\\mathrm{id}_\\Gamma] = A : \\mathrm{ty}(\\Gamma)\\\\\n\\Gamma : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma), a:\\mathrm{tm}(\\Gamma,A) &\\vdash& a[\\mathrm{id}_\\Gamma] = a : \\mathrm{tm}(\\Gamma,A)\\\\\n\\Xi,\\Delta,\\Gamma : \\mathrm{ctx}, \\delta : \\mathrm{hom}(\\Xi,\\Delta), \\gamma : \\mathrm{hom}(\\Delta,\\Gamma),\nA:\\mathrm{ty}(\\Gamma) &\\vdash& A[\\gamma\\circ\\delta] = A[\\gamma][\\delta]: \\mathrm{ty}(\\Xi)\\\\\n\\Xi,\\Delta,\\Gamma : \\mathrm{ctx}, \\delta : \\mathrm{hom}(\\Xi,\\Delta), \\gamma : \\mathrm{hom}(\\Delta,\\Gamma),\nA:\\mathrm{ty}(\\Gamma), a:\\mathrm{tm}(\\Gamma,A) &\\vdash&\na[\\gamma\\circ\\delta] = a[\\gamma][\\delta]: \\mathrm{tm}(\\Xi,A[\\gamma\\circ\\delta])\n\\end{eqnarray*}\n\n\\subsubsection{The extension with a terminal object}\nNo new sorts are required.\n\nOperator symbols:\n\\begin{eqnarray*}\n&\\vdash& 1 : \\mathrm{ctx}\\\\\n\\Gamma : \\mathrm{ctx} &\\vdash& \\tuple{}_\\Gamma : \\mathrm{hom}(\\Gamma,1)\n\\end{eqnarray*}\n\nEquations:\n\\begin{eqnarray*}\n &\\vdash& \\mathrm{id}_1 = \\tuple{}_1 : \\mathrm{hom}(1,1)\\\\\n\\Gamma,\\Delta : \\mathrm{ctx}, \\gamma : \\mathrm{hom}(\\Delta,\\Gamma) &\\vdash&\n\\tuple{}_\\Gamma\\circ\\gamma = \\tuple{}_\\Delta : \\mathrm{hom}(\\Delta,1)\n\\end{eqnarray*}\n(The latter two equations are better for term rewriting than the\nobvious single one expressing the uniqueness of $\\tuple{}_\\Gamma$.)\n\n\\subsubsection{The extension with context comprehension}\n\nNo new sorts are required.\n\nOperator symbols:\n\\begin{eqnarray*}\n\\Gamma : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma) &\\vdash& \\Gamma. A : \\mathrm{ctx}\\\\\n\\Gamma,\\Delta : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma), \\gamma : \\mathrm{hom}(\\Delta,\\Gamma), a:\\mathrm{tm}(\\Delta,A[\\gamma]) &\\vdash& \\tuple{\\gamma,a} : \\mathrm{hom}(\\Delta,\\Gamma. A)\\\\\n\\Gamma : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma) &\\vdash& \\mathrm{p}: \\mathrm{hom}(\\Gamma. A,\\Gamma)\\\\\n\\Gamma : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma) &\\vdash& \\mathrm{q}: \\mathrm{tm}(\\Gamma. A,A[\\mathrm{p}])\n\\end{eqnarray*}\n\nEquations:\n\\begin{eqnarray*}\n\\Gamma,\\Delta : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma), \\gamma : \\mathrm{hom}(\\Delta,\\Gamma), a:\\mathrm{tm}(\\Delta,A[\\gamma]) &\\vdash& \\mathrm{p}\\circ\\tuple{\\gamma,a} = \\gamma : \\mathrm{hom}(\\Delta,\\Gamma)\\\\\n\\Gamma,\\Delta : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma), \\gamma : \\mathrm{hom}(\\Delta,\\Gamma), a:\\mathrm{tm}(\\Delta,A[\\gamma]) &\\vdash& \\mathrm{q}[\\tuple{\\gamma,a}] = a : \\mathrm{tm}(\\Delta,A[\\gamma]) \\\\\n\\Gamma,\\Delta,\\Xi : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma), \\gamma : \\mathrm{hom}(\\Delta,\\Gamma), a:\\mathrm{tm}(\\Delta,A[\\gamma]), \\delta : \\mathrm{hom}(\\Xi,\\Delta) &\\vdash&\n\\tuple{\\gamma,a} \\circ \\delta = \\tuple{\\gamma\\circ\\delta,a[\\delta]} :\n\\mathrm{hom}(\\Xi,\\Gamma. A) \\\\\n\\Gamma : \\mathrm{ctx}, A:\\mathrm{ty}(\\Gamma) &\\vdash&\n\\mathrm{id}_{\\Gamma. A} = \\tuple{\\mathrm{p},\\mathrm{q}} : \\mathrm{hom}(\\Gamma. A,\\Gamma. A)\n\\end{eqnarray*}\n(If $\\mathrm{p}\\circ\\delta = \\gamma$ and $\\mathrm{q}[\\delta]=a$, we get\n$\\tuple{\\gamma,a}=\\tuple{\\mathrm{p}\\circ\\delta, \\mathrm{q}[\\delta]} = \\tuple{\\mathrm{p},\\mathrm{q}}\\circ\\delta =\n\\delta$, the uniqueness requirement of the universal property.\nHowever, the equation for surjective pairing is not left-linear and with\na variable on one side, which is not good for rewriting.)\n\nA cwf with extra structure supporting the generalized algebraic theory of cwfs is a cwf with an\n\\emph{internal cwf}. An internal cwf in the cwf $\\Set$ of small sets is a {\\em small cwf},\nthat is, it is a cwf in the ordinary sense (see Definition~\\ref{def:Cwfobj})\nexcept that it has small sets of objects, morphisms, types, and terms.\n\nAn example of a cwf with an internal cwf is provided by the cwf $\\Set$ of small sets with an internal category of very small sets. We can make this precise if we work in set theory with two Grothendieck universes $\\mathrm{V}_0 \\in \\mathrm{V}_1$. We call the members of $\\mathrm{V}_1$ ``small sets\" and the members of $\\mathrm{V}_0$ ``very small sets\". The category of contexts of the cwf $\\Set$ is the usual category of small sets, by which we here mean that its objects are in $\\mathrm{V}_1$. Moreover, the types are also the small sets in $\\mathrm{V}_1$. To get an internal cwf, we interpret its sort of objects $\\mathrm{ctx}$ as the small set $\\mathrm{V}_0$ of very small sets, and the sorts of types $\\mathrm{ty}(\\Gamma)$ also as $\\mathrm{V}_0$.\n\n\n\\subsection{Internal cwfs with $\\Pi$-types}\nWe add three operator symbols in addition to the operator symbols for cwfs in Section 5.2 and 5.3:\n\\begin{eqnarray*}\n\\Gamma : \\mathrm{ctx}, A : \\mathrm{ty}(\\Gamma), B : \\mathrm{ty}(\\Gamma.A)&\\vdash& \\Pi(A,B) : \\mathrm{ty}(\\Gamma)\\\\\n\\Gamma : \\mathrm{ctx}, A : \\mathrm{ty}(\\Gamma), B : \\mathrm{ty}(\\Gamma.A), b : \\mathrm{tm}(\\Gamma.A, B) &\\vdash& \\lambda(b) : \\mathrm{tm}(\\Gamma,\\Pi(A,B))\\\\\n\\Gamma : \\mathrm{ctx}, A : \\mathrm{ty}(\\Gamma), B : \\mathrm{ty}(\\Gamma.A), c : \\mathrm{tm}(\\Gamma,\\Pi(A,B)), a : \\mathrm{tm}(\\Gamma, A) &\\vdash& \\mathsf{app}(c,a) : \\mathrm{tm}(\\Gamma, B[\\tuple{\\mathrm{id},a}])\n\\end{eqnarray*}\n(again omitting some of the official arguments)\nand equations for $\\beta, \\eta$ (also omitting the context and type of the equality judgment)\n \\begin{eqnarray*}\n \\mathsf{app}(\\lambda(b),a) &=& b[\\tuple{\\mathrm{id},a}]\\\\\n \\lambda(\\mathsf{app}(c[\\mathrm{p}],\\mathrm{q})) &=& c\n\\end{eqnarray*}\nand commutation with respect to substitution.\n\\begin{eqnarray*}\n\\Pi(A,B)[ \\gamma ] &=& \\Pi(A [ \\gamma ], B[ \\gamma^+ ])\\\\\n\\lambda(b) [ \\gamma ] &=& \\lambda(b[\\gamma^+ ])\\\\\n\\mathsf{app}(c,a) [ \\gamma ] &=& \\mathsf{app}(c[ \\gamma ], a[ \\gamma ] )\n\\end{eqnarray*}\nwhere $\\gamma^+ = \\tuple{\\gamma \\circ \\mathrm{p}, \\mathrm{q}}$.\n\n\\subsection{Internal cwfs with $\\Pi$ and $\\mathsf{N}$}\nFurthermore, we add the operator symbol\n\\begin{eqnarray*}\n\\Gamma : \\mathrm{ctx} &\\vdash& \\mathsf{N}_\\Gamma : \\mathrm{ty}(\\Gamma) \n\\end{eqnarray*}\nWe also add operator symbols for $0, \\mathrm{s}, \\mathrm{R}$ and the equations for $\\mathrm{R}$ and for commutativity with substitution, but omit the details. Note that the type of the primitive recursion operator $\\mathrm{R}$ relies on the signature for $\\Pi$-types.\n\n\\subsection{Internal cwfs with $\\mathsf{U}_0$ closed under $\\Pi$ and $\\mathsf{N}$}\\label{sec:u-example}\nWe add four more operator symbols\n\\begin{eqnarray*}\n\\Gamma : \\mathrm{ctx} &\\vdash& (\\mathsf{U}_0)_\\Gamma : \\mathrm{ty}(\\Gamma)\\\\\n\\Gamma : \\mathrm{ctx}, a : \\mathrm{tm}(\\Gamma,(\\mathsf{U}_0)_\\Gamma) &\\vdash& {\\mathrm{T}_0}(a) : \\mathrm{ty}(\\Gamma)\\\\\n\\Gamma : \\mathrm{ctx} &\\vdash& (\\mathsf{N}^0)_\\Gamma : \\mathrm{tm}(\\Gamma,(\\mathsf{U}_0)_\\Gamma) \\\\\n\\Gamma : \\mathrm{ctx},\na : \\mathrm{tm}(\\Gamma,(\\mathsf{U}_0)_\\Gamma),\nb : \\mathrm{tm}(\\Gamma \\cdot \\mathrm{T}_0(a), (\\mathsf{U}_0)_\\Gamma))\n&\\vdash&\n \\Pi^0(a,b) : \\mathrm{tm}(\\Gamma,(\\mathsf{U}_0)_\\Gamma)\n\\end{eqnarray*}\n$(\\mathsf{U}_0)_\\Gamma$ is the universe (a type) relative to the context $\\Gamma$; $\\mathrm{T}_0$ is the decoding operation mapping a term in the universe to the corresponding type; $\\mathsf{N}^0$ is the code for $\\mathsf{N}$ in the universe, and $\\Pi^0$ forms codes for $\\Pi$-types in the\n universe. (Note that we have dropped the context argument of $\\mathrm{T}_0$ and $\\Pi^0$.)\n\nWe add the decoding equations:\n\\begin{eqnarray*}\n\\mathrm{T}_0(\\mathsf{N}^0_\\Gamma) &=& \\mathsf{N}_\\Gamma\\\\\n\\mathrm{T}_0(\\Pi^0(a,b)) &=& \\Pi(\\mathrm{T}_0(a),\\mathrm{T}_0(b))\n\\end{eqnarray*}\nand the equations for preservation of substitution:\n\\begin{eqnarray*}\n{(\\mathsf{U}_0)}_\\Gamma [ \\gamma ] &=& {(\\mathsf{U}_0)}_\\Delta\\\\\n\\mathrm{T}_0(a) [ \\gamma ] &=& \\mathrm{T}_0(a[ \\gamma ] )\\\\\n\\mathsf{N}^0_\\Gamma [ \\gamma ] &=&\\mathsf{N}^0_\\Delta\\\\\n\\Pi^0(a,b)[ \\gamma ] &=& \\Pi^0(a [ \\gamma ], b[ \\gamma^+ ])\n\\end{eqnarray*}\n\n\\begin{remark}\\label{remark:typeequations}\nNote that all equations are between {\\em terms} in the gat of cwfs with extra structure for $\\mathsf{N}, \\Pi,$ and $\\mathsf{U}_0$; we do not need the extra generality of stipulating type equations as discussed in the introduction. For example, $\\mathrm{T}_0(\\mathsf{N}^0_\\Gamma) = \\mathsf{N}_\\Gamma$ is an equation between {\\em internal} types, that is, terms of type $\\mathrm{ty}(\\Gamma)$.\n\\end{remark}\n\n\\begin{remark}\nAlso note that the gat for the universe is inevitably {\\em \\`a la Tarski} in the sense that we distinguish between types and terms in a cwf and we must have an operation decoding a term into a type. However, Martin-L\u00f6f's distinction between {\\em \\`a la Russell} and {\\em \\`a la Tarski} \\cite{martinlof:padova} is a distinction between two different formulation of the raw syntax and inference rules of type theory.\n\\end{remark}\n\n\\subsection{A possible refinement to internal contextual cwfs}\n\nOur treatment can be adapted to some non finitely presented gats.\nIf we have an increasing sequence of signatures $\\Sigma_n$ we can consider their\nunion.\nFor instance, we can describe a gat of contextual cwfs \\cite{castellan:lambek} (similar to Cartmell's contextual categories and Voevodsky's $C$-systems) by\nthe following stratification of the theory of cwfs. We replace the sort $\\mathrm{ctx}$\nby a sequence of sorts $\\mathrm{ctx}_0,\\,\\mathrm{ctx}_1,\\,\\dots ,$ where $\\mathrm{ctx}_n$ represents the sort\nof contexts of length $n$ and a corresponding sequence of sorts\n$\\mathrm{ty}_n(\\Gamma)$ for $\\Gamma$ in $\\mathrm{ctx}_n$\nand $\\mathrm{tm}_n(\\Gamma,A)$ for $A$ in $\\mathrm{ty}_n(\\Gamma)$. Context extension $\\Gamma.A$ is now in $\\mathrm{ctx}_{n+1}$\nif $A$ is in $\\mathrm{ty}_n(\\Gamma)$ and so on.\nWe also add {\\em destructors}: we have\n$\\mathrm{ft}(\\Gamma)$ in $\\mathrm{ctx}_n$\nand $\\mathrm{st}(\\Gamma)$ in $\\mathrm{ty}_n(\\mathrm{ft}(\\Gamma))$\nwith $\\Gamma = \\mathrm{ft}(\\Gamma).\\mathrm{st}(\\Gamma)$.\nSimilarly we have a stratification of the sort of substitutions\n$\\mathrm{hom}_{n,m}(\\Delta,\\Gamma)$ for $\\Delta$ in $\\mathrm{ctx}_n$ and $\\Gamma$ in $\\mathrm{ctx}_m$.\nThe resulting models are {\\em internal contextual cwfs} in a cwf.\n\n\\begin{remark}\nGeneralized algebraic presentations of contextual categories (C-systems) have been given by Voevodsky \\cite{voevodsky:c-systems} and Cartmell \\cite{cartmell:gat-contextual}.\n\\end{remark}\n\n\n\n\\section{Related work}\n\nStreicher \\cite{streicher:semtt} defined {\\em doctrines of constructions} (contextual categories with suitable extra structure) as a notion of model of the Calculus of Constructions. He also constructed a term model and remarked that it is an initial object in a category of doctrines of constructions.\nRecently, Brunerie et al \\cite{brunerie:initiality} presented a formalized proof in the Agda system that a formal system for Martin-L\u00f6f type theory forms an initial object in a category of contextual categories with extra structure for\nthe type formers.\n\nMore generally, Voevodsky \\cite{voevodsky:initiality} outlined a new vision of the theory of syntax and semantics of dependent type theories. In this vision formal systems for dependent type theory are proved to be initial in suitable categories of models ({\\em the initiality conjecture}). The above-mentioned contributions by Streicher and Brunerie et al are two examples of such characterizations. However, Voevodsky's aim was to go further and characterize a whole class of type theories and prove a general initiality result for them with the aim to form the basis for a general metatheory of dependent type theory. Our work can be viewed as a contribution to Voevodsky's programme, since we prove an initiality theorem for the whole class of finitely presented generalized algebraic theories. A characterization of a more general class of dependent type theories and their initial models has been proposed by Uemura \\cite{uemura:general-framework}. Another related contribution is Palmgren and Vickers' \\cite{palmgrenvickers} construction of initial models of essentially algebraic theories.\n\nAltenkirch and Kaposi \\cite{altenkirch:qiits} gave several examples of {\\em quotient inductive-inductive types (qiits)}. Their main example is a definition of dependent type theory with $\\Pi$-types and a universe, as a simultaneous definition in the Agda system \\cite{agda-wiki} of the data types $\\mathrm{Ctx}$ of contexts, $\\mathrm{Sub}(\\Delta,\\Gamma)$ of substitutions, $\\mathrm{Ty}(\\Gamma)$, and $\\mathrm{Tm}(\\Gamma,A)$ of terms. Their definition is {\\em inductive-inductive} \\cite{nordvallforsberg:iids}, since the index sets of $\\mathrm{Sub}, \\mathrm{Ty},$ and $\\mathrm{Tm}$ are generated simultaneously, and as a consequence are not indexed inductive definitions in the usual sense where the index sets are fixed in advance. Furthermore, it is a quotient inductive-inductive type since they also have constructors for identity types, as in a {\\em higher inductive type}.\nThere is a close relationship between this qiit and our initial internal cwf with $\\Pi$-types and a universe. Like our definition, their qiit-definition uses cwf-combinators. Moreover, our sort symbols correspond to their formation rules (data type constructors), our operator symbols correspond to their introduction rules (constructors), and our equations correspond to their propositional identities. However, the fact that our equations are judgmental equalities while theirs are propositional identities is an important difference. As a consequence they need to use transport maps when moving between identical types. \n\n\nKaposi, Kov{\\'{a}}cs, and Altenkirch \\cite{kaposi:qiits} developed a general theory of qiits. This includes a notion of signature for a qiit, the notion of an algebra of such a signature, and a construction of initial algebras. For these constructions they work in cwfs with $\\Pi$-types, identity types, and a universe. This is in contrast to our work which is based on plain cwfs without extra structure for type formers. Although gats and qiits are related notions, neither is a generalization of the other. Gat is a basic notion independent of Martin-L\u00f6f type theory, whereas qiit is the latest in a series of generalizations of inductive type (inductive family, inductive-recursive type and family, inductive-inductive type, higher inductive type) extending intensional Martin-L\u00f6f type theory.\n\n\n\n\n\\subsection*{Acknowledgements} \nWe are grateful to the anonymous referees for constructive criticism and pointers to related work. We would also like to thank Andrej Bauer and John Cartmell for further feedback.\n\n\\input{BCDE-ArXiV.bbl}\n\\end{document}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\section*{rf-spectroscopy protocol : Theoretical link between the spectral function $A(\\mc{E},\\bk)$ and the transfer rate $\\Gamma$.}\n\n\nThe aim of this section is to give the explicit derivation of Eq.(1) of the main text. We start with the general expression of the spectral function for non-interacting disordered systems. Second, we derive the expression of transfer rate following the Fermi golden rule and discuss the validity of the approach. Last, we discuss the effect of the inter-atomic interactions in the mean-field regime.\n\n\\subsection{Theoretical expression for $A(\\mc{E},\\bk)$.}\n\n\nThe spectral function for the energy $\\mc{E}_\\delta$ can be generally expressed as \\cite{bruus}:\n\n\\begin{equation}\n A(\\mc{E}_\\delta,\\bk) = - \\frac{1}{\\pi}\\,\\mathrm{Im}\\, \\overline{G}(\\mc{E}_\\delta,\\mathbf{k})= \\overline{\\bra{\\mathbf{k}} \\delta ( \\mc{E}_\\delta - H ) \\ket{\\mathbf{k}}}\n \\label{Ak_def_general}\n\\end{equation}\nwhere $\\overline{\\dotsb}$ denotes the averaging over disorder realizations, ${G}$ is the retarded Green's function and $H=\\bs{p}^2\/2m +V(\\bs{r})$ is the single particle Hamiltonian of the system. If one introduces the eigenstates $\\ket{\\psi_\\alpha}$ of the Hamiltonian $H$ at energy $\\mathcal{E}_\\alpha$, Eq.~(\\ref{Ak_def_general}) can be rewritten as: \n\\begin{equation}\n A(\\mc{E}_\\delta,\\bk) = \\overline{\\sum_\\alpha |\\langle \\mathbf{k} | \\psi_\\alpha \\rangle |^2\\ \\delta(\\mc{E}_\\delta-\\mc{E}_\\alpha)} \n \\label{Ak_sum_eigenstates}\n\\end{equation}\nFor a single realization of the disorder $V(\\bs{r})$, the overlap function $| \\langle \\mathbf{k} | \\psi_\\alpha \\rangle |^2$ exhibits large fluctuations with the energy $\\mc{E}_\\alpha$. However, once disorder averaging is performed, the fluctuations vanish and the overlap function becomes a smooth function. One can then factor the averaged coupling $\\overline{ |\\langle \\mathbf{k} | \\psi_\\delta \\rangle |^2}$ at the energy $\\mc{E}_\\delta$ from the sum, yielding:\n\\begin{eqnarray}\n A(\\mc{E}_\\delta,\\bk) & \\sim & \\overline{ |\\langle \\mathbf{k} | \\psi_\\delta \\rangle|^2} \\cdot \\overline{\\sum_\\alpha \\delta(\\mc{E}_\\delta-\\mc{E}_\\alpha)} \\nonumber \\\\\n& \\sim & \\overline{ |\\langle \\mathbf{k} | \\psi_\\delta \\rangle |^2} \\cdot \\rho (\\mathcal{E}_\\delta)\\; , \\label{Eq_Ak_rho}\n\\end{eqnarray}\nwhere $ \\rho(\\mc{E})$ is the disorder averaged density of states.\n\n\n\\begin{figure}\n\\includegraphics[width=0.9\\columnwidth]{figS1.pdf}\n\\caption{\\textbf{Measurement scheme of the spectral function}. The initial state is a BEC created in the disorder insensitive internal state $\\ket{1}$. Its spatial wavefunction is approximately $\\ket{\\mathbf{k}=0}$. A weak rf field drives the transition from the initial state to the internal final state $\\ket{2}$ that is sensitive to the disordered potential $V(\\bs{r})$, with a transfer rate $\\Gamma$. The spatial wavefunctions of the final states are denoted $\\psi_\\alpha$. These targeted states have an energy centered around the resonant condition $\\mathcal{E}_\\delta=\\hbar \\delta$, where $\\delta$ is the rf-detuning with respect to the bare transition frequency between states $\\ket{1}$ and $\\ket{2}$. The energy spread around $\\mathcal{E}_\\delta$ is Fourier limited by the duration $t$ of the coupling: $\\Delta \\mathcal{E}=\\hbar\/t$. }\n\\label{figSupp_scheme}\n\\end{figure}\n\n\\subsection{Expression of the transfer rate $\\Gamma$}\n\n\nFor clarity, the rf-transfer scheme presented in the letter is reproduced here in figure~\\ref{figSupp_scheme}, with some additional details. We consider the simple case where the two internal states $\\ket{1}$ and $\\ket{2}$ are directly coupled by an oscillating rf magnetic field at frequency $\\omega_\\mathrm{rf} $. The two-photon rf transition used in the experiment (see Fig.~\\ref{fig:freeRes}) is described in the specific section ``Rf-spectroscopy protocol: Experimental details\" below. The magnetic dipolar interaction is $\\hat{W}= \\hat{W}_0 \\,e^{-i\\omega_\\mathrm{rf} t}\\, +\\, \\mathrm{c.c}$, where $\\hat{W}_0= \\hbar \\Omega \\, |2\\rangle\\langle1| $ is the static coupling and $\\Omega $ the Rabi frequency.\n\nA key feature is that the energy spectrum of the targeted states in the disordered potential can be considered as a continuum, due to the finite energy resolution $\\Delta \\mc{E}=\\hbar\/t$, where $t$ is the duration of the rf pulse (see Fig.~\\ref{figSupp_scheme}). If the Rabi frequency $\\Omega$ is weak enough, the transfer rate can be expressed by the Fermi golden rule (see e.g.~Ref.~\\cite{grynberg2010introduction}): \n\\begin{equation}\n\\Gamma=\\frac{2\\pi}{\\hbar} \\sum_{f}|\\braket{f | \\hat{W}_0 | i }|^2\\;\\delta_\\mathrm{t}(E_f- E_i -\\hbar \\omega_\\mathrm{rf}).\\label{Eq_rof}\n\\end{equation}\nHere $\\ket{i}=\\ket{1}\\ket{\\psi_\\mathrm{BEC}}$ is the initial state of \\emph{ total} energy $E_i$ and $\\ket{f}=\\ket{2}\\ket{\\psi_\\alpha}$ refers to the final states of \\emph{ total }energy $E_f$. \nThe function $\\delta_t (E)=2\\hbar\\sin^2(Et\/2\\hbar)\/\\pi t E^2$ is a narrow function of width $\\Delta \\mc{E}=\\hbar\/t$ [see Fig.~(\\ref{figSupp_scheme})]. \nFor long durations, it behaves essentially as a Dirac function, with $\\lim_{t\\rightarrow+\\infty} \\delta_t(E) = \\delta(E)$. Since the coupling between the initial and final states writes $\\braket{f | \\hat{W}_0 | i }=\\hbar \\Omega \\cdot \\braket{\\psi_\\alpha | \\psi_\\mathrm{BEC}}$, Eq.~(\\ref{Eq_rof}) can be directly simplified as:\n\\begin{equation}\n\\Gamma(\\delta)=2\\pi \\hbar \\Omega^2 \\sum_{\\alpha}|\\braket{\\psi_\\alpha |\\psi_\\mathrm{BEC} }|^2\\;\\delta_\\mathrm{t}(\\mc{E}_\\alpha-\\mc{E}_\\delta),\\label{Eq_rof_overlapp}\n\\end{equation}\nwhere we used the relation $E_f- E_i -\\hbar \\omega_\\mathrm{rf}=\\mc{E}_\\alpha-\\mc{E}_\\delta$, with $\\mc{E}_\\delta=\\hbar \\delta$ is the final \\emph{external} energy targeted by the rf frequency (see fig.~\\ref{figSupp_scheme}).\n\nIn order to make the link with the expression~(\\ref{Eq_Ak_rho}) of the spectral functions, we use two assumptions. First, we identify the BEC's wavefunction with a purely uniform one, i.e. $\\ket{\\psi_\\mathrm{BEC}}\\sim\\ket{\\mathbf{k}=0}$. This assumption is meaningful since the BEC coherence length is much longer than the correlation length $\\sigma$ of the disordered potential. \nSecond, we use an ``ergodic'' hypothesis, i.e. replace the averaging over states within the band of width $\\Delta \\mc{E}$ by an averaging over different realizations of the disordered potential. \nAs for the ensemble averaging, the energy averaging smoothes the overlap function $|\\braket{\\psi_\\alpha | \\mathbf{k}=0 }|^2$, which becomes a slowly varying function at the energy scale given by the resolution. One can thus replace the discrete sum by an integral involving the averaged density of states $\\rho(\\mc{E})$~\\footnote{Note that additional disorder averaging could be performed in the experiment. However, the energy averaging for a single disorder realization already reduces fluctuations considerably, and no residual fluctuations were observed in the experiment.}. Altogether we obtain:\n \\begin{equation}\n\\Gamma(\\delta)=2\\pi \\hbar \\Omega^2 \\; \\overline{|\\braket{\\psi_\\delta | \\mathbf{k}=0 }|^2}\\; \\rho(\\mc{E}_\\delta) \\propto A(\\mc{E}_\\delta,\\bk=0) \\; .\\label{Eq_Gamma_Aek}\n\\end{equation}\n\n\n\n\n\nThe derivation of Eq.~(\\ref{Eq_Gamma_Aek}) relies on the assumption that the overlap function $\\overline{|\\braket{\\psi_\\delta |\\mathbf{k}=0 }|^2}$ is a slowly varying function of the energy. The characteristic energy scale $\\hbar \\Delta_\\mathrm{w}$ of variation of this overlap function is the energy width of the continuum that is coupled to the initial state. In our specific case, it is nothing but the width of the spectral function $A(\\mc{E},\\bk=0).$\nThe generic case of an initial state coupled to a continuum of finite width has been studied in great details in the literature, see e.g. Ref.~\\cite{grynberg2010introduction}. \nThe Fermi golden rule is valid provided that the Rabi frequency $ \\Omega $ remains much smaller than the continuum width, i.e.:\n\\begin{equation} \n\\Omega \\ll \\Delta_\\mathrm{w}\\; .\n\\label{Eq_crit_Rabi}\n\\end{equation} \nOtherwise the system cannot differentiate the continuum from a genuine discrete two level system, and Rabi oscillations take place. In all cases, the condition $\\Omega\\ll \\Delta_\\mathrm{w}$ has been carefully checked in the experiment. In particular we verified that the shape of the measured spectral function is not modified when $\\Omega$ is varied.\n \n Another parameter is the duration $t$ of the rf pulse. Here we choose $t$ long enough for the energy resolution $\\Delta \\mc{E}$ to be much smaller that the width of the spectral function, and short enough to deplete only a very small fraction of the initial state (typically a few percent). Altogether, we operate in the regime:\n \\begin{equation} \n \\hbar\/\\Delta_\\mathrm{w} \\ll t \\ll 1\/\\Gamma\n\\label{Eq_crit_duration}\n\\end{equation} \n\n\\subsection*{Raman \\emph{v}s rf coupling scheme}\n\nIn the protocol described in figure~\\ref{figSupp_scheme}, we measure the spectral functions at $\\mathbf{k}=0$ because the rf field carries a negligible momentum. The scheme can be adapted to measure the spectral function with a finite momentum $\\mathbf{k}$ by using a two-photon Raman transition instead (see e.g.~\\cite{Dao07,Dao09}). In that case, a net momentum of $\\mathbf{\\Delta k}=\\mathbf{k}_2-\\mathbf{k}_1$, where $\\mathbf{k}_{1,2}$ are the respective wave numbers of the Raman beams, is transferred to the atoms. By varying the angle between the two lasers, the momentum transfer can be tuned between $0$ and $2 k_\\mathrm{L}$, where $k_1\\sim k_2\\equiv k_\\mathrm{L}$.\nFormally, the coupling matrix element writes now as $\\braket{f | \\hat{W}_0 | i }=\\hbar \\Omega_\\mathrm{eff} \\cdot \\braket{\\psi_\\alpha| e^{i \\mathbf{\\Delta k}\\cdot \\bs{r}} | \\psi_\\mathrm{BEC}}$, where $\\Omega_\\mathrm{eff}$ is the effective Rabi coupling. Taking once again $\\ket{\\psi_\\mathrm{BEC}}\\sim \\ket{\\mathbf{k}=0}$, one has $\\braket{f | \\hat{W}_0 | i }=\\hbar \\Omega_\\mathrm{eff} \\cdot \\braket{\\psi_\\alpha |\\mathbf{\\Delta k}}$ and $\\Gamma \\propto A(\\mathbf{\\Delta k},\\mc{E})$. \n\n\n\\subsection*{Effect of mean-field interactions and the harmonic trap}\n\n\n\nThe above derivation of Eq.~(\\ref{Eq_Gamma_Aek}) can be extended to interacting particles, provided that the interactions can be treated at the mean-field level (i.e. for dilute atomic samples). In that case, the interactions play the role of an effective potential that should be taken into account in the energy resonance condition $E_f=E_i+\\hbar \\omega_\\mathrm{rf}$.\n\nThe short range interactions depend on the atomic internal states $(i,j)$. They are fully characterized by the scattering lengths $a_{ij}$, or equivalently by the interaction parameters $g_{ij}=4\\pi\\hbar^2 a_{ij}\/m$. More precisely, the mean-field interaction energy for one particle immersed in the BEC writes $\\mc{E}_{i,\\mathrm{mf}}=g_{11} \\, n_\\mathrm{BEC}(\\bs{r})$ where $n_\\mathrm{BEC}(\\bs{r})$ is the BEC density. Once the particle has been transferred in state $\\ket{2}$, it experiences the mean-field $1-2$ interaction with the remaining atoms in the BEC, together with the $2-2$ with the atoms already transferred. For weak rf coupling, the atomic density in state $\\ket{2}$ is very low and the latter interaction can be neglected. Since $a_{11}\\simeq a_{12}$ for $^{87}$Rb atoms~\\cite{Egorov13}, the mean-field interaction energy is the same in states $\\ket{1}$ and $\\ket{2}$ and cancels out in the energy resonance condition. Similarly, the shallow harmonic trapping potential is equal in both states and also cancels out.\n\n\n \n\\section*{Rf-spectroscopy protocol: Experimental details}\n\nThe following subsections give experimental details on the BEC parameters, the implementation of the two-photon rf transition used in the experiments and the transfer energy resolution. \n\n\\subsection*{Preparation of the BEC}\n\n\nThe BEC is created starting from $^{87}$Rb atoms in the hyperfine state $\\ket{1}\\equiv\\ket{F=1,m_F=-1}$ that are confined in a crossed optical dipole trap formed by two orthogonal laser beams at a wavelength of $1064\\,\\mathrm{nm}$ (as described in Ref.~\\onlinecite{jendrzejewski2012b}). In addition, a magnetic field gradient $B'=30.46\\,\\mathrm{G}.\\mathrm{cm}^{-1}$ is applied in the vertical direction in order to counteract gravity. We use forced evaporation to obtain an almost pure and dilute BEC with around $2\\times 10^5$ atoms, the trapping frequencies being around $\\omega_\\mathrm{T}\/2\\pi\\sim10\\,\\textrm{Hz}$ in all directions. The BEC's chemical potential is $\\mu_\\mathrm{BEC}=g_{11}\\, n_\\mathrm{BEC}(\\bs{r}=0)\\sim h \\times 140$~Hz (see section ``Effect of mean-field atomic interaction\" above), and the Thomas-Fermi (TF) radius is $R_\\mathrm{TF}=\\sqrt{2\\mu_\\mathrm{BEC}\/ m \\omega_\\mathrm{T}^2}\\;\\sim18\\,\\mu\\textrm{m}$. \n\nThe BEC's size $R_\\mathrm{TF}$ is much larger than the typical correlation length $\\sigma$ of the laser speckle potential (see section `` Statistical properties : measurement of the spatial auto-correlation function\" below).\n\n\\begin{figure}\n\\includegraphics[width=0.99\\columnwidth]{figS2.pdf}\n\\caption{\\textbf{Two-photon transition scheme.} (a) Two-photon transition between the ``clock states'' $\\ket{1}$ and $\\ket{2}$ (via the intermediate state $\\ket{0}$) of the ground state hyperfine structure of $^{87}$Rb. The two-photon transition uses a microwave magnetic field at frequency $\\omega_\\mathrm{MW}$ and a rf magnetic field at frequency $\\omega_\\mathrm{rf}$. (b) Two-photon resonance in the absence of disordered potential for a coupling duration $t=100$~ms. The resonance curve has a Fourier limited width $\\Delta \\mc{E}\/h\\sim 10$~Hz. }\n\\label{fig:freeRes}\n\\end{figure}\n\n\n\\subsection*{Two-photon rf transition}\n\n\nThe hyperfine ``clock states'' $\\ket{1}\\equiv\\ket{F=1,m_F=-1}$ and $\\ket{2}\\equiv\\ket{F=2,m_F=1}$ used in the experiment are shown in Fig.~\\ref{fig:freeRes}(a). We suppressed the broadening effect of magnetic field fluctuations by working at a bias field $B=3.23\\,\\textrm{G}$, for which the magnetic moments of $\\ket{1}$ and $\\ket{2}$ are equal \\cite{Harber02}.\n\n\nDue to selection rules (the angular momentum difference is $\\Delta m_F=2$), these two states can only be coupled using a two-photon rf transition via the intermediate state $\\ket{0}\\equiv\\ket{F=2,m_F=0}$. This ``two-photon'' transition is realized with (i) a microwave magnetic field at frequency $\\omega_{\\textrm{MW}}\/2\\pi \\sim 6831.9\\,\\textrm{MHz}$ that drives the $\\ket{1}\\rightarrow\\ket{0}$ transition and (ii) a rf field at frequency $\\omega_{\\textrm{rf}}\/2\\pi\\sim 2.8\\,\\textrm{MHz}$ that drives the $\\ket{0}\\rightarrow \\ket{2} $ transition.\n\nThe single photon transition to the intermediate state $\\ket{0}$ was detuned by $\\delta_{\\mathrm{1ph}} \/ 2\\pi\\sim 0.5$~MHz. In that case, the entire system can be seen as an effective 2-level system, with an effective Rabi frequency $\\Omega= \\Omega_{\\mathrm{MW}}\\Omega_{\\mathrm{rf}}\/\\delta_{\\mathrm{1ph}}$, where $\\Omega_{\\mathrm{MW}}$ and $\\Omega_{\\mathrm{rf}}$ are the Rabi frequencies associated to each transitions. $\\Omega$ was measured by observing two-photon Rabi-oscillations from $\\ket{1}$ to $\\ket{2}$ in the absence of disordered potential. In practice, we adjusted the coupling $\\Omega$ and the duration $t$ of the transfer for each disorder amplitude $V_0$ in order to fulfill the criteria~(\\ref{Eq_crit_Rabi}) and~(\\ref{Eq_crit_duration}). Consistently, the maximum number of transferred atoms stayed fairly small in these conditions (on the order of $5\\times10^3$ atoms, i.e.~a few percent of the atoms in the initial BEC). Note that the effective Rabi frequency ranged up at maximum to $\\Omega\/ 2\\pi\\sim 80$~Hz, i.e.~much lower than the single photon detuning $\\delta_{\\mathrm{1ph}}$.\n\n\n\\subsection*{Transfer resolution}\n\nThe transfer resolution of the two-photon rf transition results from the convolution of the resolution associated with the transfer time, i.e. $ \\Delta \\mathcal{E}=\\hbar \/ t$. \nAdditional sources of broadening are estimated to be negligible:\n(i) The broadening due to the magnetic field gradient across the size of the condensate is estimated of the order of $1\\,\\textrm{Hz}$ (ii) The residual broadening due to difference of mean-field interaction between the excited $\\ket{2}$ and initial $\\ket{1}$ state is \nof the order of $(g_{12}-g_{11})n_\\mathrm{BEC}(\\bs{r})\\sim (1-a_{12}\/a_{11})\\;\\mu_\\mathrm{BEC}\/h$ (see section ``Effect of mean-field atomic interaction\" above). Since $a_{12}\/a_{11}\\approx0.976$ \\cite{Egorov13}, an upper bound is $\\sim 3$~Hz.\n\n Altogether, the transfer resolution is then Fourier limited for our experimental conditions. In practice, we adapted the resolution to the typical energy span of the spectral function for each disorder amplitude $V_0$, so that it does not affect the observed profile. More precisely, the duration was varied from $t=5$~ms ($\\Delta \\mc{E}\/h =200$~Hz for the strongest disorder amplitude $V_0\/h\\sim4$~kHz) to $t=100$~ms at maximum ($\\Delta \\mc{E}\/h =10$~Hz for the lowest disorder $V_0\/h\\sim60$~Hz). As an example, Fig.~\\ref{fig:freeRes} shows the two-photon resonance in the absence of disordered potential for the largest duration time, i.e. $t=100$~ms.\n\n\n\n\\vspace{3ex}\n\\section*{Laser speckle disordered potential: experimental characterization and numerical simulation}\n\n\\subsection*{State dependent disordered potential}\n\nIn order to obtain a state dependent disordered potential we used a laser speckle tuned close to the optical transition $F=2 \\rightarrow F'=3$ (see Figs.~2 and 1(a) in the main article), with a detuning $\\Delta_\\mathrm{L}$ much smaller than the hyperfine splitting of the ground state separating the ``clock states'' $|\\Delta|\\ll\\Delta_{\\textrm{HFS}}$ (similar to the ``tune-in'' scheme as described by \\cite{leblanc07}). \nWith this configuration, the selectiveness $V_{\\ket{2}}\/V_{\\ket{1}}$ of the disorder potential for $\\ket{2}$ is roughly given by $\\Delta_{\\textrm{HFS}}\/|\\Delta|\\approx 100$.\n\n\\begin{figure}\n\\includegraphics[width=0.99\\columnwidth]{figS3.pdf}\n\\caption{\\textbf{Amplitude of the disordered potential.} (a) Hyperfine structure of the excited state and detunings of the laser speckle ($\\Delta_\\mathrm{red}$ and $\\Delta_\\mathrm{blue}$ for the red-detuned and blue-detuned case respectively). (b) Disorder amplitude $V_0$ in state $\\ket{2}$ as a function of the speckle laser frequency around the $F=2 \\rightarrow F'=3$ resonance.}\n\\label{fig:excState}\n\\end{figure}\n\n\nBeing close to the resonance, the precise hyperfine structure of the excited state has to be taken into account, as shown in panel (a) of Fig.~\\ref{fig:excState} of the present supplemental material. Furthermore, the speckle laser was frequency offset-locked to a saturation spectroscopy stabilized laser, allowing us to control its detuning with an accuracy of $1\\,\\mathrm{MHz}$. The exact detuning for the attractive and repulsive disordered potentials was calibrated by performing two-photon rf-spectroscopy of atoms in homogeneous light field (instead of a speckle field) produced by the same laser. The absolute light shifts created by the red and blue detuned light were adjusted to be equal.\nThe experimentally determined values for $\\Delta_{\\textrm{blue}}=2\\pi\\times81\\,\\textrm{MHz}$ and $\\Delta_{\\textrm{red}}=-2\\pi\\times 73\\,\\textrm{MHz}$ are in agreement with an optical dipole potential model taking all excited hyperfine states into account [see Fig.~ \\ref{fig:excState}(b)].\n\nIn order to calibrate precisely the disorder amplitude $V_0$, we took benefit of the excellent agreement between experiments and numerics. The calibration was done by adjusting our measured spectral function with the numerically-computed spectral function at $V_0\/h=-121\\,\\textrm{Hz}$ (attractive laser speckle disorder). It leads to a calibration of the disorder amplitude with a 4$\\%$ precision. Note that this calibration corresponds to a global correction of about 14$\\%$ compared to an independent calibration based on photometric measurements. Such deviation is not surprising (it is comparable to the one found in~\\cite{semeghini2014}) since the light intensity field cannot be measured at the location of the atoms (experiments take place inside a vacuum chamber). Here we estimate that the uncertainty mainly originates from a slight mis-estimation of the spatial extension of the laser speckle field.\n\n\nIn practice, the laser speckle power was varied between $0.1$ and $10\\,\\mu\\textrm{W}$ to vary the disorder amplitude from $V_0\/h\\sim60$~Hz to $V_0\/h\\sim4$~kHz (see Fig.~2 of the main text). Despite the proximity to the resonance, no heating or losses due to inelastic scattering were observed at the short time scales considered in the experiments. \n\n\n\\subsection*{Statistical properties of the disordered potential: measurement of the spatial auto-correlation function}\n\nThe laser speckle is created by passing a laser beam through a diffusive plate. As illustrated in Fig.~\\ref{fig:corr}(a), the incoming wave that illuminates the diffusive plate is converging around the position $d$ of the atoms, such that they experience the Fraunhofer's diffraction pattern of ground plate (i.e.~the so-called Fourier speckle configuration~\\cite{goodman2007}). The intensity profile of the illumination on the diffusive plate is a Gaussian shape, of waist $w$ (radius at $1\/e^2$), truncated by a circular diaphragm of diameter $D$. The estimated geometrical parameters are: $D=20.3(1)$~mm, $w=9(1)$~mm and $d=15.2(5) $~mm. The diaphragm sets the maximal numerical aperture to $\\mathrm{NA}=\\sin(\\theta_\\mathrm{max})=0.55(2)$.\n\n\n\n\n\n\n In order to characterize the laser speckle field, the random intensity pattern was recorded at the position of the atoms with an high-resolution optical microscope, see Fig.~\\ref{fig:corr}(b). The measurement was done \\emph{ex-situ}, i.e. outside the vacuum chamber, but with an exact replica of the geometrical configuration. The extracted two-point correlation functions in the transverse ($z$ direction, same along $y$) and longitudinal directions ($x$ direction) are shown as blue squares in Fig.~\\ref{fig:corr}(c), together with the half-width-at-half-maximum (HWHM) lengths. They correspond to $\\mathrm{HWHM}_\\perp \\approx 0.42(1)\\, \\mu\\mathrm{m}$ and $\\mathrm{HWHM}_\\parallel \\approx 2.05(5)\\,\\mu\\mathrm{m}$. More details about the experimental configuration and the laser speckle characterization can be found in Ref.~\\onlinecite{richard15}.\n\nFor consistency with previous theoretical work~\\cite{pasek2016anderson}, we define the correlation lengths of the speckle potential as $\\sigma_{\\parallel,\\perp} = \\mathrm{HWHM}_{\\parallel,\\perp} \/ 1.39156$,\nwhich yields $\\sigma_\\perp \\approx 0.306\\, \\mu\\mathrm{m}$ and $\\sigma_\\parallel \\approx 1.45\\, \\mu\\mathrm{m}$. These values are used for the calculation of the correlation energy $\\mathcal{E}_\\sigma = \\hbar^2\/m(\\sigma_\\perp^2 \\sigma_\\parallel)^{2\/3}\\sim441~\\mathrm{Hz} $ (see main text).\n\n\\bigskip\n\n\n\n\n\n\\subsection*{Numerical simulation of the laser speckle}\n\nWe designed the numerical disorder to reproduce the experimental geometry represented in Fig.~\\ref{fig:corr}(a). In particular, the numerical speckle generation takes into account the presence of a diaphragm, and hence deviates from the usual pure Gaussian and Lorentzian character (see e.g.~\\cite{goodman2007}) for the two-point correlation function of the intensity in the transverse and longitudinal directions [see Fig.~\\ref{fig:corr}(c)]. \n\\begin{widetext}\nTo do so, the generation of the numerical disorder was done through the use of a phase mask:\n\\begin{equation}\\label{supsingle}\n\\mathcal{P} (\\mathbf k) =\\delta(|\\mathbf{k}|-k_L) \\exp\\left[-\\frac{(\\tan\\theta)^2}{(w\/d)^2}\\right] \\Theta \\left[ \\frac{D}{2 d} - \\tan|\\theta| \\right],\n\\end{equation}\nwhere $k_\\mathrm{L}$ is the wavenumber of the monochromatic laser beam, $\\theta \\in [-\\pi\/2,\\pi\/2]$ the angle between the wavevector $\\mathbf{k}$ and the beam axis, $w$ the beam waist, $D$ the diaphragm diameter and $d$ the position where the illumination field converges.\n\\end{widetext}\nThe numerical potential was then generated starting from a local random complex field $E_{u}(\\mathbf r)$, whose real and imaginary parts are uncorrelated Gaussian random variables.\nThe convolution of this random field with the phase mask in Eq.~(\\ref{supsingle}) yields the proper statistical distribution of the amplitude of the electric field~\\cite{goodman2007}\n\\begin{equation}\\label{sup2}\nE(\\mathbf r)= \\sum_{\\mathbf k}E_u(\\mathbf k) \\mathcal{P} (\\mathbf k) e^{i \\mathbf k \\cdot \\mathbf r},\n\\end{equation}\nwhich reproduces the coherent superposition of the various plane waves scattered by the diffusive plate. \nParameters of the numerical phase mask, Eq.~(\\ref{supsingle}), that yield the best agreement with measured two-point correlation functions of the speckle intensity, see Fig.~\\ref{fig:corr}(b), are $D = 20.4\\,\\mathrm{mm}$, $d = 15.2\\,\\mathrm{mm}$ and $w = 9.9\\,\\mathrm{mm}$, in excellent agreement with the experimentally-inferred values. \n\n\\begin{figure}[b!]\n\\includegraphics[width=1\\columnwidth]{figS4.pdf}\n\\caption{\\textbf{Laser speckle characterization.} (a) Schematic representation of the experimental configuration used for the speckle generation, with $\\mathrm{NA}=\\sin(\\theta_\\mathrm{max})=0.55(2)$ (see text). (b) 3D representation of the experimental laser speckle field recorded with an high aperture microscope at the position of the atoms. (c) Transverse and longitudinal correlation functions of the laser speckle, respectively along the $z$ (same along $y$) and $x$ directions. Blue squares: experimental autocorrelation functions extracted from the measurement shown in (b). Red solid lines: autocorrelation function for numerically simulated laser speckle [following Eqs.~(\\ref{supsingle}) and~(\\ref{sup2})]. Horizontal black arrows indicates the half-maxima $\\mathrm{HWHM}_\\perp=0.42(1)$~$\\mu$m and $\\mathrm{HWHM}_\\parallel=2.05(5)$~$\\mu$m of the correlation function.}\n\\label{fig:corr}\n\\end{figure}\n\n\n\n\\section*{Numerical computation of the spectral function} \n\n\n\\subsection*{Method}\n\nWe start with the definition, Eq.~(\\ref{Ak_def_general}), of the spectral function $A(\\mathcal{E},\\mathbf{k}) = \\overline{\\bra{\\mathbf{k}} \\delta ( \\mathcal{E} - H ) |\\mathbf k \\rangle} = - \\frac{1}{\\pi}\\,\\mathrm{Im}\\, \\overline{G}(\\mc{E}_\\delta,\\mathbf{k})$. The spectral function can be evaluated numerically in many ways. The most common in the literature starts from the spectral, so-called Lehmann, representation of the Green's function (as used in, e.g., Ref.~\\onlinecite{semeghini2014}). We choose to use instead the temporal representation of the Green's function:\n\\begin{equation}\n\\label{Ak_temp}\nA(\\mathcal{E},\\mathbf{k}) = \\frac{1}{\\pi\\hbar}\\,\\mathrm{Re}\\,\\int_0^{\\infty} \\overline{\\langle \\mathbf{k}|\\mathrm{e}^{-\\mathrm{i} H t\/\\hbar}|\\mathbf k \\rangle} \\mathrm{e}^{\\mathrm{i} \\mathcal{E}t\/\\hbar}\\ \\mathrm{d} t.\n\\end{equation}\nThe numerical computation of the spectral function $A(\\mathcal{E},\\mathbf{k})$ thus amounts to: (1) Computing the overlap between an initial plane-wave excitation $\\ket{\\mathbf{k}}$ \nand its time-evolution under the (disordered) Hamiltonian $H$; (2) Averaging this overlap over multiple disorder configurations (typically between 2000 and 4000 realizations depending on the disorder amplitude); (3) Using the Fourier transform to go from time- to energy-representation.\n\n\nFor the time-propagation algorithm, we use an iterative scheme based on the expansion of the evolution operator $\\exp (-i H\\Delta t \/\\hbar)$ over a time-step $\\Delta t$ in a series of Chebyshev polynomials of the Hamiltonian \\cite{roche1997,fehske2009,trappe2015}.\nIn order to check the numerical accuracy of our results, we verify that they satisfy the first three sum rules of the spectral function, namely \\cite{trappe2015}:\n\\begin{align}\n\\int \\mathrm{d} \\mathcal{E}\\, A_\\mathbf{k} (\\mathcal{E}) &= 1,\\\\\n\\int \\mathrm{d} \\mathcal{E}\\, \\mathcal{E} A_\\mathbf{k} (\\mathcal{E}) &= \\frac{\\hbar^2\\mathbf{k}^2}{2m} + \\overline{V},\\\\\n\\int \\mathrm{d} \\mathcal{E}\\, \\mathcal{E}^2 A_\\mathbf{k} (\\mathcal{E}) &= \\left( \\frac{\\hbar^2\\mathbf{k}^2}{2m} + \\overline{V} \\right)^2 + \\overline{\\delta V^2},\n\\end{align}\nwhere $\\overline{\\delta V^2}=V_0^2$ is the variance of the speckle potential distribution.\n\n\n\n\n\n\n\n\\subsection*{Effect of the residual disorder in state $\\ket{1}$}\n\nIn the experiment, the residual disorder on state $\\ket{1}$ is of the order of $0.01|V_0|$ (see section ``State dependent disordered potential'' above). \nFor the largest value of $|V_0|$ ($\\approx h\\times 4\\,\\mathrm{kHz}$), it results in a potential around 40 Hz on the state $\\ket{1}$ that perturbs the initial BEC (of chemical potential $\\mu_\\mathrm{BEC}\/h\\sim 140 $~Hz), and consequently the transfer $|1\\rangle \\to |2\\rangle$. This effect can be approximately taken into account in the numerical calculation of the spectral function.\nTo do so, we performed a preliminary propagation of the initial $\\ket{\\mathbf{k}=0}$ state in the presence of the same disorder realization but with residual amplitude $V_{\\ket{1}}=-0.01|V_0|$ (which is always blue-detuned since $\\Delta_\\mathrm{HFS}\\gg\\Delta_\\mathrm{L}$, see Fig.~1 of the main text), before performing the time-evolution under the disordered Hamiltonian for atoms in state $\\ket{2}$ [i.e. step (1) in the above-described numerical procedure]. \nThe duration of this preliminary propagation was taken to half the experimental transfer time, though different durations had little influence on the resulting spectral function. This is because the product $|V_{\\ket{1}}|t\/h$ is at maximum of the order of $0.2,$ so that the state $\\ket{1}$ is only slightly distorted by the residual disorder over the duration of the experiment. The resulting spectral functions are displayed as solid brown lines in Panels (I.f) and (II.f) of Fig.~2 of the main text.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sect:intro}\n\nIn \\citet{paper1}, henceforth Paper~I, we described the design and\nmaintenance of the Virtual Observatory (VO) Registry as a distributed\ninformation system. Conceptually, it is a collection of, by now, about\n15000 registry records. To give the Registry's users --\nastronomers, the library\ncommunity, or even the general public -- access to this collection,\nfacilities have to be provided that allow focused queries against it.\nThis includes common bibliographic constraints (by author,\ntitle or abstract term, year, etc), but also constraints specific to a\nregistry mainly concerned with\ndata services (e.g., supported protocols or query parameters,\nmetadata of published tables). In the design of such facilities,\nseveral challenges have to be addressed:\n\n\\begin{enumerate}\n\\item different users have very different expectations and requirements\n\\item the underlying data collection (i.e., the set of registry records)\nis changing over time\n\\item the underlying data structure is fairly complex, and evolves\nitself as new standards and techniques are introduced in the VO\n\\item as many uses require only a small subset of the types of metadata\ncontained, partial resource descriptions should be retrievable\n\\item the total data set cannot efficiently be transferred to clients as\na whole\n\\item registry records are frequently authored by persons not entirely\nfamiliar with the data model, resulting in inconsistent quality\n\\end{enumerate}\n\nIn consequence, no single \\emph{user} interface to the Registry can be\nsufficient. Instead, the VO community designed \\emph{client}\ninterfaces,\ni.e., network endpoints with rigorously defined behavior and\nsemantics, designed for use by\nprograms that then present the actual user interfaces to Registry data.\n\nWe will begin this paper with a brief review of the various client\ninterfaces that are or were used in the VO (section~\\ref{sect:history}).\nIn section~\\ref{sect:clients}, we proceed to describe the use some\nselected clients make of these facilities and the ways they apply and\nexpose information obtained from the registry. A major part of the\npaper, section~\\ref{sect:regtap}, is devoted to a thorough discussion of\nthe Registry Relational Model (RegTAP for short), one of the two\nregistry interfaces currently being developed and deployed in response to the\ndeficiencies of previous standards. In section~\\ref{sect:fulltext}, the\nother new-generation interface is described. \n\nWhile laying out some common use cases of Registry data\nin section~\\ref{sect:commonqueries} we also point out\ncommon query patterns.\nSection~\\ref{sect:openissues} concludes with some speculation about\nprobable future developments.\n\nIn the following, we refer to common Registry standard texts by their\nabbreviated names as introduced in Paper~I, and again the capitalized\nword ``Registry'' refers to the abstract concept, while concrete\nservices are written in lower case (e.g., a ``publishing registry'').\nConcepts from VOResource and its extensions are written in\n\\vorent{small caps}.\n\n\\section{History}\n\\label{sect:history}\n\nAlthough only explicitly written down in 2011, \nthe use cases collected on the IVOA\nwiki \\citep{wiki:regusecase} outline some of the challenges faced by the\ndesigners of the first client interfaces to the registry in the\nmid-2000s -- finding tables containing columns with certain physics,\nlocating services implementing certain protocols, and the like.\n\nWhile on the maintenance side of the registry the ecosystem\naround OAI-PMH \\citep{std:OAIPMH} \nprovided guidance for many technology choices, \nin developing the client interfaces much more new ground had to be broken.\nFor instance, the OPACs (Online Public Access Catalogs; see\n\\citet{kani2008userperc} for a treatment from about the time of RI1\ndesign)\nestablished in the library community, while comparable for the purpose of\nlocating information resources,\ncould not efficiently address the use cases, and no\nbroadly accepted standard for client, rather than user, interfaces to\nOPACs, lent itself to adoption by the VO community.\n\nGiven that the interface to be designed was expected to be expressive\nenough for requests of the type ``find all TAP services exposing a table\nhaving some word in the description and a column with a given \nUCD\\footnote{Unified Content Descriptors or\nUCDs in the VO denote phyiscal concepts like ``angular distance'' or\n``radio flux'' in a simple formal language \\citep{std:UCD}}'',\nit was determined fairly early on that an interface based on\nsimple, atomic parameters would not be sufficent, and\nRegistry information \ncrucial to certain\ndiscovery tasks would not be queryable through it. \nClient interfaces making explicit too much of the\nunderlying data model would also unduly restrict future developments of\nthat data model. Thus, at least one\ninterface to the Registry would have to support a full query\nlanguage. Since the Registry data model was defined in XML Schema, \nan obvious choice for the query language was XQuery\n\\citep{std:XQUERY}, a language that essentially extends SQL concepts to\nquerying XML trees.\n\nHowever, factors against the adoption of XQuery included:\n\\begin{itemize}\n\\item the heavy use VOResource makes of XML\nnamespaces, which tended to make queries hard to write by hand; \n\\item the much\nlarger installed base of relational databases compared to XQuery-capable\nengines (compounded by the fact that translating XQuery to a given\nrelational schema is hard);\n\\item the desire to open up the full registry data\nmodel to queries written by end users, i.e., astronomers.\nAs it was expected that many\nof these would familiarize themselves with the VO's SQL dialect ADQL\n(Astronomical Data Query Language; \\citet{std:ADQL}), \nrequiring yet another query language\nfor Registry access appeared undesirable.\n\\end{itemize}\n\nWith these considerations, it was decided to base the primary\nRegistry interface on conventional relational technology.\n\nWhile the complex queries XQuery and ADQL allow were needed for\nidentified use cases, it was also acknowledged that ``Google-like''\nsearches -- more or less loose matching of words in documents modelled\nas bags of words -- was the dominant mode of searching for resources\noutside of the VO in the targeted\nuser base. At least if common ``comfort'' features like stemming or\nphrase searches are desired, this type of search is hard or\nimpossible to simulate through plain ADQL given its very basic set of\ntext search capabilities. Therefore, a keyword search\noperation with significant freedom for implementors was also defined.\n\nThe result of these considerations was section 2 of RI1\n\\citep{std:RI1}.\nIt defines two required search operations\n\\textit{Search} (with constraints in ADQL) and \\textit{KeywordSearch}\n(with operator-defined matching of keywords against an\noperator-ex\\-ten\\-sible minimal set of fields) as well as an optional\n\\textit{XQuerySearch} operation. All search operations return\neither identifier lists or sequences of full resource records in OAI-PMH\nstyle. In addition, two OAI-PMH-like operations were defined,\n\\textit{GetResource} to obtain a resource record from an identifier, and\n\\textit{GetIdentity} to discover metadata about the registry service\nitself.\n\nSeveral implementations of the standards are available; services are\nprovided by STScI, ESA, and AstroGrid.\n\nAs the RI1 design significantly predates the final standardizations of both\nADQL \\citep{std:ADQL} and the transport protocol for queries and results\n-- that was eventually defined in the TAP standard \\citep{std:TAP} --, \nRI1 further defined an ad-hoc transport based on the RPC\nmechanism SOAP, and it adopted ADQL at a time when experiments were\nunderway with passing ADQL statements to client interfaces\nin parsed (XML) form. In\nconsequence, modern TAP clients cannot use registry endpoints, and\nwriting queries in the aging XML serialization of ADQL became at least\ndifficult as software components translating SQL expressions into the\nXML forms went unmaintained.\n\nFurther critique came from implementor feedback\n\\citep[e.g.,][]{talk:topcatri1} and was collected together with the use\ncases \\citep{wiki:regusecase}. For instance, in practice the\nuse of a restricted set of XPath to specify constraints instead of\ndefining an actual relational schema\nlead to severe interoperability problems between\ndifferent registries, which were further exacerbated by not specifying\nrules for case folding. The apparent flexibility towards registry\nextensions provided by the XPath-based column references also did not\npay off as originally expected since registries still needed to do\ninternal mapping as registry extensions were developed. In contrast\nto the (optional) XQuery interfaces, the (mandatory) ADQL interfaces\nfrequently lagged behind standards deployment.\n\nIn this situation, the most advanced Registry clients relied on the\noptional XQuery\ninterface or even used entirely proprietary interfaces.\n\nAs TAP services entered the registry in the early 2010s, RI1's response\nformat also became a liability. Registry records contain table\nmetadata, and with TAP services exposing many tables, resource records \nof several megabytes are not exceptional. \nThis made relatively common\nqueries like ``Retrieve basic metadata on all TAP services'' expensive\nin terms of transfer time and processing required.\n\nTherefore, starting in 2011, it was decided to design a new\nRegistry interface, dubbed ``RESTful'' to contrast it from the RI1\nSOAP-based protocol. \nWith TAP and ADQL now available, a replacement of the RI1\n\\textit{Search} operation was mainly a matter of designing a schema and\na mapping to this schema from VOResource. This can be seen as creating a\nsecond serialization of an abstract\ndata model implicit in VOResource's XML schema files.\n\nThe combination of a defined\nschema and a TAP service had a model in ObsCore \\citep{std:OBSCORE}.\nThe resulting new standard (``RegTAP''), \ndiscussed in section~\\ref{sect:regtap},\nis in the last phases of IVOA peer review as this article\nis written.\n\nA replacement for the \\textit{KeywordSearch} operation is also being\ndeveloped. Here, the wide availability of feature-rich fulltext engines\nsuch as Apache Lucene\noffers the possibility of enriching the bag-of-words model\nand allows some advanced operators as well. We will revisit\nthis development in section~\\ref{sect:fulltext}.\n\n\n\\section{Registry Use in Clients}\n\\label{sect:clients}\n\nMany VO clients integrate Registry access, frequently without \nadvertising the actual source of the data. Depending on the scope of\nthe application, different parts of Registry metadata are used, and\ndifferent presentations of this information appear appropriate. In the\nfollowing, we look at Registry usage in a number of, we believe,\nrepresentative applications, concluding with an in-depth look at\nTOPCAT's use of the registry.\n\n\\begin{figure}[thm]\n\\begin{center}\n\\includegraphics[width=0.35\\textwidth]{taphandle.png}\n\\end{center}\n\\caption{TAPHandle uses registry information to provide \ninput completion for TAP service access URLs, where the completion items\nare complemented by additional metadata.}\n\\label{fig:TAPHandle}\n\\end{figure}\n\nTAPHandle\\footnote{Online at \\texttt{http:\/\/saada.unistra.fr\/taphandle}.}\nis a TAP client operated through web browsers\n\\citep{2014ASPC..485...15M}. It uses the Registry\nto discover all registered TAP services. With this information, it can\nprovide input completion in the selector for the TAP service queried\n(Fig.~\\ref{fig:TAPHandle}), thus facilitating simple discovery tasks\n(``I want to query the CADC TAP server''). As it is a TAP client, \nis is natural for TAPHandle \nto use RegTAP as its Registry interface. Indeed, its use case is one\nof the standard\ntasks identified in the collection of requirements for a\nrevised Registry interface \\citep{wiki:regusecase}.\nIt uses a hard-wired RegTAP endpoint, performing essentially a\nsingle query per session within its\nserver component. Thus, TAPHandle users are isolated from technical\ndetails of registry access and are also not exposed to visible registry\nqueries.\n\nSimilarly, the spectral analysis tool VOSpec \\citep{2005ASPC..347..198O}\nqueries the registry for all services implementing specific standards\n(spectral and line access, in this case),\nbut since in contrast to TAPHandle\nit has no server-side component, it does so directly\nfrom the user's client, using one of two built-in registry endpoints\nimplementing the RI1 \\textit{Search} operation. Data extraction from\nthe registry records retrieved is\nperformed with an\nXSLT stylesheet. The discovered resources are\npresented to the user in a tree view for individual selection or\nde-selection. This UI is employed both in the selection of the spectral\nservices and in the selection of servers providing information on the\nlocation of spectral lines.\n\n\\begin{figure}[th]\n\\begin{center}\n\\includegraphics[width=0.25\\textwidth]{splat.png}\n\\end{center}\n\\caption{SPLAT's rendering of the metadata of spectral services in the\nVO: Various metadata obtained from the registry records are made\nselectable through checkboxes.}\n\\label{fig:SPLAT}\n\\end{figure}\n\nAnother VO-enabled spectral tool, SPLAT \\citep{2014ascl.soft02008C}, takes this\napproach somewhat further by exposing \\vorent{dataSource} (from\nSimpleDALRegExt, allowing, for instance, the separation of theoretical and\nobservational services) and \\vorent{waveband} from VODataService via\ncheckboxes in its UI (Fig.~\\ref{fig:SPLAT}). The UI shown is built from\na simple query for all services implementing SSAP. SPLAT furthermore\nallows users to add, as it were, private registry records (e.g., for\nunpublished services) that are then integrated into this interface. \n\nA drawback of hiding actual registry queries in this way is that\nmetadata quality of the resource records directly influences the user\nexperience for the application itself. For instance, when a resource\nrecord author neglected to give correct waveband metadata, users knowing\na certain resource serves optical spectra were frequently confused when\nthe service was deselected after restricting queries to optical data.\n\nThe VO client Aladin \\citep{soft:Aladin} supporting the major VO\nprotocols could build upon a registry-like system called GLU that\npredates the definition of the VO Registry \\citep{2003ASPC..295...43F}.\nGLU, with automatic mirror selection and an Aladin-customized\nmetadata format, to this day distributes Registry\ninformation to Aladin. Registry records enter GLU not through a client\ninterface but rather by harvesting an OAI-PMH endpoint. The operators\nof the GLU system at CDS perform additional curation, e.g., by removing invalid\nrecords or records for known-defective services. By removing all\nresource metadata not immediately relevant to the client,\nAladin can keep, in effect, a local cache of the entire\nGLU content, which is impractical for\nthe actual Registry content, as that would currently\nentail managing and updating\nseveral hundreds of megabytes.\nResponsiveness is further enhanced by persisting this data\nbetween executions of Aladin.\n\nThere are also clients specifically built around the Registry. One of the most\nadvanced to date is VOExplorer \\citep{2008ASPC..394..159T}, developed by\nthe UK's VO project\nAstroGrid in the late 2000s as part of its VODesktop suite.\nIn its user interface it guides users\nin the construction of constraints, mixing menu-based selection with\nfree-text queries as appropriate.\nVOExplorer communicates with registries via the XQuery client interface. \nBy thus retrieving only parts of the full VOResource record it\nsignificantly reduces network traffic compared to a RI1 \\emph{Search}\nclient. When a full VOResource record is required, it is cached \nfor use in future query results.\n\nThough the major discovery protocols defined while VODesktop was still\nbeing developed are supported, the application's focus is clearly the\nintegration of Registry data into a workflow, and it offloads\nvisualization to specialized clients by use of the SAMP\ninter-application communication protocol \\citep{std:SAMP}. Regrettably,\nVODesktop's development ceased in 2009, with the demise of\nthe AstroGrid project.\n\nTo replace the comprehensive graphical Registry UI provided by\nVODesktop,\nWIRR\\footnote{online at \\texttt{http:\/\/dc.g-vo.org\/WIRR}.} \nwas developed. It is essentially a\nbrowser-based query builder for RegTAP, where, much like in VODesktop,\nthe user can successively\nadd constraints on the search results. Notable constraint types include\nqueries for\nresources containing columns with specific UCDs, ``inverted queries''\nto obtain registry information from a service's access URL -- which\nis useful for finding contact information when services fail --, or query\nwith regular expressions on IVORNs. Even more than VODesktop,\nWIRR\nrelies on external applications to use the resources found, employing\nSAMP messages for transmitting resource lists. TOPCAT is one application\nthat already supports these.\n\nTo support Registry use from within custom user programs, libraries have\nbeen written that encapsulate details of registry access. Given the\nwidespread adoption of Astropy \\citep{2013A&A...558A..33A}, we note here\nthe registry functions within the Astropy affiliated\npackage PyVO \\citep{2014ascl.soft02004G}. For Registry access, it\ncontains a single function \\texttt{regsearch} that supports constraints\nby keywords (essentially, a full-text search\nwithin resource record text fields), service types (e.g., image or\nspectral service) and wavebands. The function also has a parameter to\npass in custom SQL fragments executed within the VAO's registry. Due to\nthe limitations of RI1 standard client protocols, a custom,\nVOTable-based interface is employed at the moment, with a change\nto RegTAP in the back-end planned.\n\nFinally, there are uses of Registry client interfaces not directly\nconnected to actual VO clients. As an example we mention VO\nFresh\\footnote{Online at http:\/\/dc.g-vo.org\/regrss},\nan RSS feed of metadata for services newly\npublished or updated\nin the Registry; new resources are also announced through microblogging\nservices. VO Fresh\ninitially obtained registry information from a full registry's OAI-PMH\nendpoint but moved to obtaining registry information through RegTAP as\nthat became available.\n\n\\subsection{Case Study: TOPCAT}\n\nTOPCAT \\citep{2005ASPC..347...29T}\nis a tool for analysis of astronomical tables.\nPart of its function is to provide a user-friendly GUI for\nacquiring tabular data from Virtual Observatory services,\nmost importantly\nTAP \\citep{std:TAP} and Cone Search \\citep{std:SCS},\nbut also SIA \\citep{std:SIAP} and SSA \\citep{std:SSAP}.\nTo achieve this, it needs the Registry to locate\nservices with the relevant capabilities and to allow the user\nto assess their suitability for the science job at hand.\n\nFrom a user point of view, TOPCAT's registry interaction consists\nof selecting a particular type of data service,\noptionally supplying some keywords to match against one or\nmore of a handful of fixed resource metadata fields,\nand dispatching a search which results in\npresentation of basic metadata for each matching service.\nThe user then peruses this list and selects one of the returned\nservices for subsequent use in the application.\n\n\\begin{figure}[th]\n\\begin{center}\n\\includegraphics[width=0.85\\columnwidth]{topcat.png}\n\\end{center}\n\n\\caption{TOPCAT's Registry interface: \nthe user specifies a query using\na selector for registry service endpoint\nand interface protocol (RegTAP or RI1),\na keyword text field,\nand a set of checkboxes for what resource fields to match against.\nAn additional constraint is the service type,\nwhch depends on the context these widgets are shown in.\nBelow the input widgets is a listing of matched resources\nfrom which one may be selected.\n``Accept Resource List'' allows filling the\nresource selector from SAMP messages.}\n\\label{fig:topcat}\n\\end{figure}\n\nTOPCAT makes only a single type of registry query to support\nthis functionality, the user interface to which is illustra\\-ted in\nFig.~\\ref{fig:topcat}: \nlocate all registry resources which offer\na fixed standard capability (e.g.\\ TAP) and which\nsatisfy zero or more additional user search constraints\n(e.g.\\ ``Title contains the term UKIDSS''),\nand for each one return a small fixed amount of metadata\n(ID, Title, Publisher, Access URL and a few others).\nThere is other information stored in the Registry records\nthat TOPCAT may require, such as {\\tt vs:CatalogService}\nrecords describing table and column metadata.\nHowever, for newer VO protocols such information is also available from the\nregistered data services themselves, and TOPCAT prefers to acquire\nit from the latter source, since it may be more reliable and\nis also available for unregistered services.\n\nImplementing these queries in RI1 presented some difficulties.\nThe {\\em KeywordSearch\\\/} operation is unsuitable since keyword\nsearches cannot be combined with restrictions on service type.\nThe {\\em XQuerySearch\\\/} operation offers suitable functionality,\nbut being an optional part of the standard it is only available\nfrom a subset of registry services\n(in fact, only the AstroGrid implementation),\nand so would\nhave restricted the choice of registries with which the tool could interact.\nThe only remaining option is the {\\em Search\\\/} operation.\nSyntax and semantics of the fields to match in the required\nADQL queries were somewhat under-documented, and\nthere is a problem with the way case sensitivity is defined,\nbut the most serious issue is that {\\em Search\\\/}\nalways returns the whole, perhaps large, record\nfor each matched resource, the bulk of which is not needed.\nPatchy service implementation quality also contributed to make\nRI1-based registry interaction generally slow and unreliable.\n\nWith the introduction of RegTAP,\nregistry interaction is much improved.\nThe user interface is almost unchanged, but queries are\nmore precise,\nthanks to more careful mapping of the RM data model\ninto its relational counterpart,\nand much faster,\nsince it is possible to restrict the query response to\nitems of interest only.\nThis latter point can lead to a reduction of two orders of magnitude\nin the required data transfer.\nTo give an admittedly drastic -- but in practice not uncommon --\nexample, the response size for a query\nfor all TAP services registered in May 2014\nwent down to about $150\\,\\rm kB$ from previously roughly\n$25\\,\\rm MB$.\n\nNote that although for both RI1 and RegTAP the client uses an essentially\nSQL-like language to select resources,\nwhat the user sees is a keyword-based or ``Google-like'' interface.\nMapping from the latter to the former can result in verbose\nquery text, but this text is not difficult for client code to generate.\nTherefore, for this purpose there has been no requirement for an\nessentially keyword-like client interface to the registry.\n\nThere is scope for richer interaction with the registry from TOPCAT,\nfor instance queries on fine-grained metadata (column UCDs)\nor more detailed display of descriptive or curation metadata\nfrom selected records.\nThese options may be explored in the future.\n\n\\section{The Registry Relational Model}\n\\label{sect:regtap}\n\n\\begin{figure*}[t]\n\\begin{center}\n\\includegraphics[width=0.75\\textwidth]{schema.pdffig}\n\\end{center}\n\\caption{A sketch of the database schema of the relational registry,\nadapted from \\citet{adasspaper}.\nThe arrows indicate foreign key relationships, the ``attributes''\nenumerate the fields most likely interesting to clients or scientists\nwriting queries. When joining through\n\\rtent{relationship} (red) in the discovery of data collection access services\n(cf.~section~\\ref{sect:relationship}), the green tables would be\n``resource-bound'', the blue ones ``capability-bound'', whereas the\nyellow ones might be either resource- or capability-bound depending on\nquery semantics.}\n\\label{fig:schema}\n\\end{figure*}\n\nThe Registry Relational Model -- briefly called RegTAP for mainly\ntechnical reasons -- is the successor to the \\textit{Search} method in\nRI1. It essentially defines a relational schema and rules to map\nVOResource records into this schema. Using TAP as an access\nprotocol and ADQL as the query language, this is enough to completely\ndefine a client interface to the registry.\n\nA sketch of this relational schema is given in Fig.~\\ref{fig:schema}.\nAlthough the authors first experimented with alternative structures that would\nhave been derived from VOResource algorithmically \\citep{wiki:vormap},\nit turned out design considerations did not lend themselves to\nformalization, as discussed in the next subsection.\n\n\\subsection{Design Goals and their consequences}\n\nIn the following discussion of RegTAP's design, the model is derived from\nseveral partly conflicting design goals, which are written\n\\designgoal{slanted} in the following. Additionally, RegTAP names are \nmarked up in\n\\rtent{slanted typewriter}, while we continue to write\nVOResource concepts\nin \\vorent{small caps}.\n\nWhile RegTAP attempts to \\designgoal{represent all concepts} of\nVOResource that could plausibly be of use in locating resources, it is\nnot a full relational mapping of VOResource. An overarching design goal\nwas to \\designgoal{keep the model compact}. In version~1, the model\ndefines 13 tables, a number that would have been significantly higher\nfor a full mapping without proportionally adding discovery capabilities.\nFrom VOResource and its current extensions, the model primarily left out:\n\n\\begin{itemize}\n\\item From TAPRegExt \\citep{std:TAPREGEXT} the descriptions of user defined \nfunctions; these say what extra functions are available in ADQL\nqueries, how to call them, and what they do. Representing this would\nhave required an extra table, which appeared hard to justify given that\nno major discovery scenarios were found for this metadata (it is there\nfor TAP client use, and the TAP clients get the information directly\nfrom the service's capabilities endpoint).\n\\item Also from TAPRegExt the declaration of how clients can upload\ntables into TAP services, with a similar rationale.\n\\item From StandardsRegExt \\citep{std:STDREGEXT} the \nenumerations of the input\nparameters defined by the standard itself. They do not appear\nvaluable for discovery given the moderate number of existing standards.\nRepresenting them would, however, break the simple foreign \nkey relationship between interface\nand capability if they were kept in the interface table, and an\nadditional relatively complex table otherwise.\n\\item Also from StandardRegExt the detailed information on\nthe versions of documents issued. This would have required an extra\ntable, and again, given the moderate number of standards, no credible\ndiscovery scenario is apparent.\n\\item VOResource's ability to have multiple access URLs for a single\ninterface; this feature has essentially not been used in practice, and\nkeeping it would have introduced another join in all queries for access\nURLs and hence the vast majority of current Registry queries.\nIt is planned to drop the feature in future VOResource\nversions; resources that actually need multiple access URLs for a single\ninterface would then have to represent each endpoint as an interface of\nits own.\n\\end{itemize}\n\nTo leverage existing VOResource expertise, RegTAP tries to\n\\designgoal{follow VOResource names}. However, again compromises had to\nbe made\nto meet some other design goals. First, as the subject domain of\nVOResource partly coincides with the data definition language of SQL,\nmany of the terms in VOResource are reserved words in database engines.\nAs RegTAP also tries to \\designgoal{avoid requiring delimited\nidentifiers}\\footnote{Delimited identifiers in SQL, syntactically marked\nby enclosing the identifier name with double quotes, allow using\narbitrary strings as column names and also suspend SQL's case folding.}\nfor\nusability reasons (e.g., difficult to understand parse errors resulting\nfrom forgotten quotes), conflicting names were amended with tags\nindicating entities' roles. In this way, VOResource's \\vorent{table} becomes\n\\rtent{res\\_table}, and \\vorent{column} \\rtent{ta\\-ble\\_column}.\n\nAnother important design goal was to \\designgoal{hide foreign key\nrelationships}. This is again a usability concern -- having to\nwrite explicit join conditions would necessitate a more intimate\nfamiliarity with the data model than can be expected from a\npossibly casual user. Instead, query writers should need\nonly to identify\ncolumns of interest and then use \\texttt{NATURAL JOIN} to build their\nquery's \\texttt{FROM} clause.\n\nThis implies more name mangling, as in VOResource many elements can be\nchildren of different parents, for instance \\vorent{type},\n\\vorent{name}, \\vorent{description}. Again,\ndisambiguation is effected\nusing tags prepended with an underscore, indicating the\nsource table, abbreviated when names would attain excessive length.\nThus, \\vorent{description} in \\vorent{resource} becomes\n\\rtent{res\\_description}, whereas in \\vorent{capability} it becomes\n\\rtent{cap\\_description}. Only the two co\\-lumn-like tables\n(\\rtent{table\\_column} and \\rtent{intf\\_param}) are an exception. \nThis implies that \\rtent{intf\\_param} and\n\\rtent{table\\_co\\-lumn} are the only tables that cannot be naturally\njoined.\n\nThe key used for joining is obvious for all tables directly referencing\n\\rtent{resource}, as Registry semantics ensure \\rtent{ivoid} -- the\nrecord's IVORN -- is a\nsuitable primary key for that table. However, RegTAP also has foreign\nkeys into the tables \\rtent{capability}, \\rtent{interface}, and\n\\rtent{res\\_schema}, for which VOResource does not provide suitable\nprimary keys, as the respective relationships are represented by lexical\ninclusion in XML.\nRegTAP instead introduces surrogate keys, the nature of which is\nimplementation-defined. Hence queries should never explicitly use them,\nand since the tables are naturally joinable, they have no reason to do\nso. In general, as the declaration of primary and foreign keys has no\nimpact on service behavior, RegTAP makes no requirements in this area\nbut restricts itself to recommendations.\n\nA further design goal requiring changes to VOResource names is that\n\\designgoal{quoting must not hurt}. It is not uncommon that SQL authors\nand query generators\nemploy delimited identifiers when they do not need to.\nIn these cases, mixed-case column names\neasily lead to execution errors that again may not be easy to understand.\nTherefore, all identifiers in the standard are completely lowercase. Internal\ncapitalization to indicate compound words is not uncommon in VOResource,\nhowever. In RegTAP compound words are concatenated with underscores,\nsuch that, for instance, \\vorent{relationshipType} becomes\n\\rtent{relationship\\_type}.\n\nIf only for reasons of ease of implementation across different back-end\ndatabase engines, it was important for us to \\designgoal{not grossly\nviolate the relational model}. However, an analogue of the\nobject-relational impedance mismatch impacts RegTAP as well: for VOResource,\nbeing an XML application, hierarchy and sequences are natural and easy. In\na relational model, these translate into foreign keys and extra tables\nand thus complicate the schema. In order to avoid an inflation of\ntables, RegTAP supports what in effect are arrays of simple strings.\n\nThese are only used where \nvalues are taken from controlled vocabularies, specifically for\n\\vorent{level} and \\vorent{type} from \\vorent{content},\n\\vorent{waveband} and \\vorent{rights} from \\vorent{Resource},\n\\vorent{flag} from \\vorent{column}, and \\vorent{queryType} from\n\\vorent{interface}. Here, multiple values from VOResource are\nconcatenated with hash characters (\\#). To allow reliable querying in\nthese columns,\nRegTAP services must implement an ADQL user defined function called\n\\texttt{ivo\\_hashlist\\_has}. We specifically did not use that pattern\nfor \\vorent{subject}, as its vocabulary, while governed by a\nrecommendation to use the IVOA Thesaurus, is deliberately open -- indeed, it\nis well conceivable that, for instance, hashtags might at some point be\nused here -- and it stands to reason that\ncomplex queries over \\rtent{res\\_subject} will be performed\nwhen clients make use of, say,\nontologies that may themselves be represented in database tables.\n\nIn a model so heavily dealing with natural language, another violation\nof strict relationality is almost unavoidable: Treating text as, at\nleast, bags of words. RegTAP therefore requires conforming services to\noffer a user-defined function\n\n\\begin{verbatim}\nivo_hasword(txt VARCHAR(*), pat VARCHAR(*)) \n -> INTEGER\n\\end{verbatim}\n\n\\noindent that returns true at least when \\texttt{pat} is present in\n\\texttt{txt}. Operators are urged to match \\texttt{pat} to \\texttt{txt}\nin an information-retrieval (IR) sense (i.e., ``Google-style'' as\ndocument vectors). This is the main violation of RegTAP's design goal that\n\\designgoal{different registries yield identical results} for identical\nqueries. This\nviolation is regrettable, as experience shows that users are at least\nconfused if their familiar result lists change after\na change in the registry endpoint used by their client. However,\ngiven that IR facilities in back-end databases are inconsistent with each\nother and an independent implementation of them is nontrivial, the\ndesign goal that the standard \\designgoal{does not exclude a \nmajor database back-end}\noverrode the consistency concern.\n\nA final salient design goal is that\n\\designgoal{Registry extensions are possible without schema updates}.\nRegistry extensions change the XML schema, and hence RegTAP would have \nto represent arbitrary XML trees within a fixed relational schema if\nthis design goal were to be fully achieved. The\nresult would have been very hard to query indeed. RegTAP's designers\ntherefore identified a\nsubset of extensions that is relatively straightforward\nin queries, powerful enough to satisfy foreseeable use cases, and\nreasonably compact: atomic values in 1:n relationships over either\nresource or capability. The result is RegTAP's \\rtent{res\\_detail} table.\n\nThis table on the one hand references resources or capabilities by their\n\\rtent{ivoid} and, as appropriate, the surrogate key on\n\\rtent{capability} (which is NULL\nfor items pertaining to the entire resource). On the other hand it\ncontains keys (\\rtent{detail\\_xpath}) and values\n(\\rtent{detail\\_value}). The keys in this table are essentially XPath\nexpressions within the resource record, much like the references in RI1\nquery constraints. The values are always strings, even when the VOResource\nelements represented have other types. \n\nThus, a data collection's \\vorent{accessURL} child is accessible through\nthe key \\texttt{\/accessURL}, the maximum size of files returned from an\nimage service (defined in SimpleDALRegExt) is retrieved as its decimal\nserialization under the key \\texttt{\/capability\/maxFileSize}, and the\nauthorities managed by a registry are in, if necessary multiple, rows\nwith the key \\texttt{\/managedAuthority}.\n\nWhen mapping existing Registry extensions, it was found this was\nsufficient to express the concepts contained with the exceptions\noutlined above. A full list of the keys from the registry extensions\npublished before RegTAP is given in \\citet{std:RegTAP}, and future registry\nextensions should specify which additional keys they define.\n\n\n\\subsection{Addressing Particular Issues}\n\nIn going from VOResource to a relational schema, properties of either\nthe relational or the XML model or restrictions of the query language\nforced us to introduce additional rules for several entities. We\nmention some major special cases in this subsection.\n\n\\paragraph{Case issues} A particular challenge in the mapping rules\nfrom VOResource to RegTAP were case-insensitive values. For instance,\nIVORN \\citep{std:VOID}, UCDs, and utypes\nin current VO usage \\citep{note:utypeusage} all have to be compared\nignoring case. Even if ADQL had an operator for case-insensitive string\ncomparison, having to consider case issues in comparisons would\ninvite bugs in queries that are hard to detect -- when a query author\nforgets that a column must be compared ignoring case, the queries\nmight still return some records and thus appear to work. RegTAP\ntherefore mandates that all such values must be lowercased during ingestion. In\nthis way, queries not taking into account case insensitivity will at\nleast reliably produce an empty result list. RegTAP further\ncase-normalizes other columns filled from controlled vocabularies to\nbe as consistent as possible. Only columns intended\nfor presentation (essentially the descriptions and titles, role name,\nand subject) and those where case normalization might lead to\nambiguities (mainly \\rtent{detail\\_xpath} and \\rtent{detail\\_value}) are\nexempt from normalization. Where case normalized comparisons are desired\nfor such mixed-case columns, RegTAP offers a UDF \\rtent{ivo\\_nocase\\_match} in\naddition to \\rtent{ivo\\_hasword} (that ignores case as well).\n\n\\paragraph{Order} While in most parts of VOResource, the order implied\nin XML trees is irrelevant and thus no particular attention is necessary\nin the translation to the sets of the relational model, \\vorent{creator}\nis an exception. Typically used to convey authorship information, order\nthere matters to many data providers. Rather than add sequencing\ncapabilities to \\rtent{res\\_role}, RegTAP adds a \ncolumn \\rtent{creator\\_seq} to \\rtent{resource} that contains a\npre-for\\-mat\\-ted author list. This has the additional benefit that clients\ndo not need worry about reconciling the (correct) practice of having one\nauthor per \\vorent{creator} element with the (widespread) practice of\nincluding multiple names in one element\nin order to produce a flat author list at least for display purposes; \nany necessary special\nhandling happens at the registry.\n\n\\paragraph{QNames} Several VOResource values are really XML qualified\nnames (QNames). This concerns some fairly fundamental VOResource\nconcepts, in particular the types of resources, interfaces, and\ncapabilities. For instance, a query might be interested in locating all\nresources that are VODataService \\vorent{DataCollection}s. These are\nidentified by having an XML schema type of\n\\texttt{\\{http:\/\/www.ivoa.net\/xml\/VO\\-Data\\-Ser\\-vice\/v1.1\\}Da\\-taCollection},\nusing the conventional notation that prepends the namespace part of\na QName in curly brackets. As this notation is cumbersome for input,\nserialized XML maps the namespace URIs to namespace prefixes. \nVOResource and extensions strongly\nrecommend the use of canonical prefixes that would, for instance, bind\nthe prefix ``\\texttt{vs}'' to\nthe VODataService namespace. Hence, the above name becomes a much more\nmanageable \\texttt{vs:Data\\-Collection}. Unfortunately, the canonical\nprefixes are not mandatory in VOResource, which means that registries\nmight use entirely different prefixes, and indeed, in registry practice,\nseveral do.\n\nAs long as the RegTAP ingestor knows which attributes contain QNames --\nand that is defined in VOResource XML schema files --, it can, however,\nunify prefixes by turning the namespace prefixes of the instance\ndocument into namespace URIs and then translating them back into the\ncanonical prefixes. To ensure consistent results over registries,\nRegTAP requires this prefix normalization. \nEssentially, the recommendation to use its\ncanonical prefix contained in all VOResource standards becomes a hard\nrequirement for RegTAP.\n\n\\section{Full-text Based Registry Interface}\n\\label{sect:fulltext}\n\nIn parallel with the relatively complex RegTAP interface, a successor to\nRI1's \\textit{KeywordSearch} is also being developed.\nThis full-text based registry addresses the difficulty of\nextracting information from the previous registry interface. As field\nvalues describing resources are mostly text, a full-text search engine\nsuch as the Apache Lucene library -- as used in popular server components\nlike ElasticSearch or Apache Solr --\nis suitable to index and search\nthe contents of\nthe registry. \nIt was therefore decided to develop a RESTful\nAPI using ElasticSearch as a client interface to the Registry. The\nrequirements were to fulfill the registry use cases defined by the IVOA\nRegistry Working Group \\citep{wiki:regusecase} and to support\nthe web clients developed at VO-Paris Data\nCentre, among them several Registry curation tools. \nThe full specification of the RESTful interface is currently maintained\nat \\texttt{http:\/\/api.vo.obspm.fr\/registry\/}. That page also provides\nseveral examples. \n\n\n\\subsection{Query Interface}\n\nThe \\texttt{\/search} method is derived from the \\textit{Search} and \n\\textit{KeywordSearch}\noperations of the RI1 searching interface. It allows querying common\nRegistry items individually or all together. \nFor requests specifying multiple constraints\nlogical AND is used by default, but a logical OR is available by adding\na parameter ``orValues'' in the query string\n\nThe initial set of fields to filter a request has been extended to\nfulfill the requirements of all clients using it. One of the VO client\nuse cases is to find services by capability type (Spectrum, Image, TAP,\netc),\nand specific words or expressions from its\n\\vorent{description}, \\vorent{content.subject}, \\vorent{title}, or \n\\vorent{shortName} attributes. Other useful selection criteria come\nfrom \\vorent{curation}'s \\vorent{publisher}, \\vorent{creator.name}, \nand \\vorent{contributor}.\nThe \\vorent{coverage} is also used to filter\nservices in spectral, time, and soon in spatial domain. The spatial\ncoverage actually is an optional field in the resource description and\nnot well described in the service declaration. However this information\nwill soon be available in a standard way, using the HEALPix Multi-Order\nCoverage maps (cf.~\\ref{sect:coverage}).\n\n\nAs the developers of the full-text search based interfaces are also data\ncurators in the VO Registry, some specific information was kept and is\navailable for selected resources:\n\n\\begin{itemize}\n\\item the IVORN of the resource record\n\\item the dates of the publication and last update of the resource\nrecord\n\\item the registry where the resource has been declared and which is responsible for this record\n\\item information from \\vorent{validation}.\n\\end{itemize}\n\n\\subsection{Query Examples}\n\nA prototype service implementing the full-text query is being maintained\nat VO Paris\\footnote{Access URL\n\\texttt{http:\/\/voparis-registry.obspm.fr\/vo\/ivoa\/1\/voresources\/search}.}.\nThe following example queries can be executed there by passing them as\nURL query strings; for reasons of readability, the query strings are\nshown here not URL-encoded.\n\n\\begin{itemize}\n\\item Search all resources containing the keyword ``infrared'':\\\\\n{\\small\n\\hspace*{1em}\\texttt{keywords=infrared}\n}\n \n\\item Ditto, but only return services implementing the Simple Image\nAccess protocol:\\\\\n{\\small\n\\hspace*{1em}\\texttt{keywords=infrared}\\\\\n\\hspace*{2em}\\texttt{+\"ivo:\/\/ivoa.net\/std\/SIA\"}\n}\n \n\\item Search for all\nresources published by the Centre de donn\u00e9es de Strasbourg (CDS)\nimplementing the Simple Cone Search protocol, with a\n\\vorent{contentLevel} of Research,\nand return the 100 resources starting from match number 200:\\\\\n{\\small\n\\hspace*{1em}\\texttt{keywords=publisher:cds}\\\\\n\\hspace*{2em}\\texttt{+standardid:\"ivo:\/\/ivoa.net\/std\/ConeSearch\"}\\\\\n\\hspace*{2em}\\texttt{+contentlevel:Research}\\\\\n\\hspace*{1em}\\texttt{\\&max=100}\\\\\n\\hspace*{1em}\\texttt{\\&from=200}\n}\n \n\\item Return the full resource record for some IVORN:\\\\\n{\\small\n\\hspace*{1em}\\texttt{identifier=ivo:\/\/vopdc.obspm\/luth\/exoplanet}\n}\n \n\\end{itemize}\n\n\\subsection{Service Response}\n\nAs the aim of the full-text query interface is to provide the simplest\nsystem for VO application developers, and most of the new clients are\nJavaScript based, the API returns query results formatted in JSON\nresponses. Those responses give back the most useful fields as a subset\nof the whole service declaration plus a link to the original VOResource\nXML file in the registry. The subset of information returned is \nrelatively compact and has proven sufficient for the clients already\nusing the API.\n\n\n\n\\section{Common Registry Queries}\n\\label{sect:commonqueries}\n\nVOResource is a complex data model that sometimes\noffers multiple ways of expressing apparently very\nsimilar concepts. Driven by both registry record authoring practices and\nthe queries employed by popular clients, some usage patterns have evolved that\nshould be followed for successful Registry use. Other patterns are\nrecommended for ease of use. We describe the\npatterns in RegTAP terms, but most would be equally\napplicable to endpoints speaking XQuery, and partly even to\nkeyword-based services.\n\nWhat is special to RegTAP is the query construction technique. The way\nthe schema is designed, one looks for the fields to be constrained and\nfor the fields to be retrieved in a schema description such as\nthe RegTAP specification, an implementing service's\n\\texttt{TAP\\_SCHEMA}, or its VOSI table metadata. The query can then be\nwritten by collecting all source tables, concatenating\nthem by \\texttt{NATURAL JOIN} and treating the result as a single table.\n\n\\subsection{Locating Standard Services}\n\\label{sect:capquery}\n\nA very common type of query is finding \\vorent{service}s implementing a\ncertain standard. In VOResource terms, it is actually not the service\nbut one of its capabilities that complies with a standard. For instance,\na service could at the same time implement a cone search for telescope\npointings, and two image services each conforming to a specific version\nof SIAP. Each facility is then represented as a different\n\\vorent{capability}.\n\nThe \\rtent{capability} table offers two ways to identify the kind of\ninterface -- one could constrain \\rtent{cap\\_type} or\n\\rtent{standard\\_id}. The correct constraint is on \\rtent{standard\\_id},\nas it would be perfectly legal to register an SSA service, say, with a\n\\rtent{cap\\_type} of \\texttt{vr:capability} (i.e., the minimal capability\ndescription only consisting of a standard identifier and the\ninterfaces). While the record would miss essential metadata, clients\nshould have no trouble operating a service registred in this way, and\nhence the Registry query should find it. All known clients' queries by\nservice type follow the pattern of matching against the standard\nidentifier.\n\nAs the standard identifer is an IVORN, it needs to be lowercased in\nqueries for RegTAP. So, to locate all services (say, by their IVORN and\ntitles) having SSA interfaces, the query would look like this:\n\n\\begin{lstlisting}\nSELECT ivoid, res_title\nFROM rr.resource\n NATURAL JOIN rr.capability\nWHERE\n standard_id='ivo:\/\/ivoa.net\/std\/ssa'\n\\end{lstlisting}\n\nOther relevant standard identifiers are given in the respective\nspecifications or in one of the examples in RegTAP.\n\n\\subsection{Locating Standard Interfaces}\n\\label{sect:q-standardintf}\n\nLocating the capability is not enough to operate a service. In\naddition, the endpoint -- which in VO practice is identified by an\naccess URL -- needs to be located. A single capability can have\nmultiple interfaces, and while this practice is not recommended,\nthere are resource records that have capabilities\ndeclaring adherence to a standard with interfaces for web browsers in\naddition to the standard interface.\n\nVOResource's \\vorent{interface} element has a \\vorent{role} attribute to\ndistinguish the standard interfaces from custom ones. For the former,\n\\vorent{role} would contain a special string formed according to certain\nrules. In practice, many resource record authors have neglected to set\n\\vorent{role}, and therefore actual clients started to ignore it.\nCurrent VO practice therefore is to regard the (hopefully unique)\ninterface of type \\vorent{vs:ParamHTTP} as the interface exposing the\nstandard.\n\n\nHence, the pattern to locate interfaces complying to standards right now\nis, in RegTAP (this time looking for TAP interfaces):\n\n\\begin{lstlisting}\nSELECT ivoid, access_url \nFROM rr.capability\n NATURAL JOIN rr.interface\nWHERE standard_id='ivo:\/\/ivoa.net\/std\/tap'\n AND intf_type='vs:paramhttp'\n\\end{lstlisting}\n\nWe expect this pattern to be stable, mainly because the development of\nStandardsRegExt now very strongly suggests that services supporting\nmultiple versions of a single standard will have to treat each such\ninterface in a single capability. Hence, distinguishing different\nversions by the \\vorent{role} attribute (or the \\rtent{intf\\_role}\ncolumn in RegTAP) appears dispensable, and assuming the\n\\vorent{vs:ParamHTTP} interface within a standard capability must be\nthe standard service endpoint is straightforward and robust.\n\n\\subsection{Query by Physics}\n\nA type of discovery query not yet widely supported in Registry UIs\nis the query by\nphysics. The fact that resource records can and in many cases do\ncontain table metadata giving \nUCDs helps locating resources exposing a certain type of data.\nAs UCDs follow a grammar that ADQL does not understand, it is frequently\nadvisable to use wildcards in such queries. For instance,\ncolumns containing infrared magnitudes could be found like this:\n\n\\begin{lstlisting}\nSELECT name, ucd, column_description\nFROM rr.table_column\nWHERE ucd LIKE 'phot.mag;em.i\n\\end{lstlisting}\n\nTo illustrate again RegTAP's principle of natural joins, let us\nshow how to add a constraint on the embedding table here:\n\n\\begin{lstlisting}\nSELECT name, ucd, column_description,\n table_description\nFROM rr.table_column\n NATURAL JOIN rr.res_table\nWHERE 1=ivo_hasword(\n table_description, 'quasar')\n AND ucd LIKE 'phot.mag;em.i\n\\end{lstlisting}\n\n\\noindent\n-- then constraining on tables accessible via TAP is simply a matter of\ncombining select list and the FROM and WHERE clauses\nfrom subsection~\\ref{sect:q-standardintf} with this query.\n\n\\subsection{A Sketch of TOPCAT's Query}\n\nBy way of example, we present a query submitted by TOPCAT to\nacquire service metadata for presentation to the user,\nincorporating some user-supplied constraints.\nThe following ADQL would locate TAP services concerning galaxies:\n\n\\begin{lstlisting}\nSELECT ivoid, short_name, res_title, \n reference_url, base_role, role_name, \n email, intf_index, access_url, \n standard_id, cap_type, cap_description, \n std_version, res_subjects\nFROM rr.resource AS res\n NATURAL JOIN rr.interface\n NATURAL JOIN rr.capability\n NATURAL LEFT OUTER JOIN rr.res_role\n NATURAL LEFT OUTER JOIN (\n SELECT \n ivoid, \n ivo_string_agg(res_subject, ', ')\n AS res_subjects\n FROM rr.res_subject GROUP BY ivoid\n ) AS sbj\nWHERE \n standard_id='ivo:\/\/ivoa.net\/std\/tap'\n AND intf_type='vs:paramhttp' \n AND (\n 1=ivo_hasword(res_title, 'galaxy')\n OR 1=ivo_hasword(res_subjects, 'galaxy')))\n\\end{lstlisting}\n\nNote how in this query outer joins are used to make sure rows are\nreturned even for records that, for instance, do not give roles. In the\ncase of \\rtent{res\\_subject}, VOResource guarantees that at least one\nsubject must always be present, so doing an outer join here should\nnot be necessary. On the other hand, in particular in queries executed\non behalf of a UI, it is good practice to assume minor violations of\nVOResource will be present in the Registry.\n\nThe sub-query for \\rtent{res\\_subjects} also shows an example for how to\nreduce the number of rows transferred by server-side aggregation.\nAnother application for this pattern could be, using suitable strings\nas separators, retrieving pairs of capability identifiers and their\naccess URLs.\n\n\\section{Open Issues}\n\\label{sect:openissues}\n\nEven after the introduction of RegTAP, Registry development is not\ncompleted. In addition to Registry extensions as new service and\nresource types are defined within the VO, several fields of work are\ncurrently actively being explored. We discuss them here as they delineate\nwhat the Registry should be doing but does not do so far, as well as\nto document approaches tried in the VO to problems that\nmay similarly arise in other communities.\n\n\n\\subsection{Data Collection and Relationships}\n\\label{sect:relationship}\n\nSome TAP services today expose dozens or hundreds of\ntables\\footnote{Examples for such services include\n\\texttt{ivo:\/\/org.gavo.dc\/tap},\n\\texttt{ivo:\/\/nasa.heasarc\/services\/xamin}, as well as the\nTAP interface to VizieR.}. In ObsCore\nservices\\footnote{The CADC TAP service \\texttt{ivo:\/\/cadc.nrc.ca\/tap}\nbelongs in this category.} \\citep{std:OBSCORE}, data from many\nindividual data collections are queryable through a single endpoint. In\nthe same way, some SIAP services make data from several individual\nobservatories accessible.\n\nIn all these cases, the contributing data collections should all be\npresent with their full metadata in the Registry. Using GAVO's Lens\nImage Archive\\footnote{\\texttt{ivo:\/\/org.gavo.dc\/lensunion\/q\/im}} as an\nexample, a title query for one of the contributing data collections,\nMiNDSTEp, say, should yield the full metadata for the data\ncollection\\footnote{In this case,\n\\texttt{ivo:\/\/org.gavo.dc\/danish\/red\/data}.}, and clients should, from\nthere, be able to infer the access URL of the service exposing the data.\n\nThe \\vorent{DataCollection} type of VODataService provides a type for\nsuch cases, and through \\vorent{relationship} -- in this case, with a\n\\vorent{relationshipType} of \\texttt{servedBy} -- the associate data\nservice can be successfully located.\n\nHowever, client support for querying through \\vorent{relationship}\nhas been lacking, even in the most\nadvanced registry clients. WIRR at least shows the presence of\nrelated resources\nexplicitly, but an additional query is required to retrieve them.\nAlso, a query for ``Image services exposing data from\nMiNDSTEp'' would fail unless the registry record of the embedded service\nwere carefully crafted. In RegTAP, writing ADQL for queries that would\nsimultaneously find ``direct'' (i.e., services exposing exactly one data\ncollection) and ``indirect'' (i.e., data collection metadata managed\nseparately from service metadata) services is at least highly nontrivial\n\\citep{talk:uneasy}. To understand why joining tables through\nrelationship requires great care, consider again\nFig.~\\ref{fig:schema}. The colors there distinguish between\n``capability-bound'' metadata that in such queries would have to be\nqueried from the service (e.g., access URL, capability ids,\naccepted parameters) and ``resource-bound'' metadata that needs to come\nfrom the data collection itself (e.g., description, title, or UCDs from\na published table). The two tables that can reference both\n\\rtent{resource} and \\rtent{capability}, shown in yellow in the figure,\nadditionally complicate query construction. In any case,\nnatural joins of tables from different groups will not produce\nmeaningful results, thus requiring\nquery authors to add explicit join conditions.\n\nThese difficulties have spurred activity to consider changing VOResource\nsuch that clients do not need to follow relationships to locate access URLs.\nA reasonable solution explored was adding\n(some of) the capabilities of the data service to the resource\nrecords of the data collections themselves. That, however, leads to an\ninflation of such capabilities that will make the very common queries\nfor all resources implementing a certain standard as laid out in\nsubsection \\ref{sect:capquery} much harder to handle for clients. For\ninstance, to prepare for an all-VO query for images, clients would have\nfilter out duplicate access URLs in order to avoid querying a service\nexposing $n$ resources $n$ times (instead of once).\n\nThe least burdensome solution is still to be found. Discussions within the\nRegistry working group are currently investigating the use of ``auxiliary''\nstandard identifiers for the capabilities on the data collections,\nwhich would, by lexical convention, facilitate the discovery of either\nunique services (by using the standard identifiers already in use) or\nall endpoints exposing data constrained by further metadata (by using an\nappropriate and index-friendly regular expression).\n\n\\subsection{Education and Internationalization}\n\nIn the context of work done within the IVOA working group on Education\n\\citep{note:edumatters}, the issue of multilinguality arose. While in\nprofessional astronomy, all-English metadata seems sufficient and,\nindeed, preferable, the situation is not as clear when certain resources\n-- in this case, educational material -- should be made discoverable for\neducators or even the general public. For instance, if a worked-out use-case\non open clusters is available in Italian, should it not be\ndiscoverable by querying for ``Ammasso Aperto''? This would entail\nallowing the relevant text fields (\\vorent{title}, \\vorent{description},\npossibly \\vorent{subject}) to be present multiple times in resource\nrecords, each element containing text in a different language, and it\nwould probably also entail allowing language constraints in client\ninterfaces to avoid losing precision due to homographs in different\nlanguages. Alternatively, different registries might be set up for\ndifferent languages.\n\nSo far, the Education WG only plans to allow discovering which language specific\nresources are available in rather than supporting queries in non-English\nlanguages. If, however, takeup of Registry technologies outside of the\nresearch community were to increase, the issue would have to be\nrevisited, presumably from both the client and the data model side.\n\n\\subsection{Coverage in Space and Time}\n\\label{sect:coverage}\n\nVODataService allows the specification of resource coverage,\ni.e., the spatial area covered on the sky as well as the ranges in time\nand spectrum, in resource records. Apart from the controlled vocabulary\nin \\vorent{waveband}, this is done through embedding STC-X\n\\citep{note:STCX} within registry records. No standard way of querying\nthis information exists to date. An attempt to include coverage\ninformation through four tables giving sets of coordinate intervals per\nresource as proposed in \\citet{talk:ri2} did not gain much traction,\npartly because of the flexibility and complexity of the underlying STC\ndata model, partly because it was felt that for spatial coverage,\ncoordinate ranges in the equatorial system were too inflexible to be\ngenerally useful even for discovery purposes. An obvious example\nillustrating the shortcomings would be a survey along the galactic\nequator: either many ICRS ranges would have to be given, or the coverage\nwould be dramatically overrepresented.\n\nIn the meantime, multi-order coverage maps \\citep[][MOCs]{std:MOC}\nwere developed as a standard way of representing spatial coverages. Work is\nongoing on how these could be integrated into VOResource on the data\nmodel side and exposed to the clients; if these were to be included in\nan extension to RegTAP, \nan ideally indexable way of representing MOCs in databases would\nbe required, and no technically feasible solution has been proposed so\nfar.\n\n\\section{Conclusions}\n\nThe VO Registry is an essential source of metadata about the services\nand data that can be used within the VO, and no non-trivial interaction\nwith the VO can take place without using its discovery capabilities.\nMany VO clients embed Registry information and protocols in various\nforms.\n\nBy necessity, standardization of the Registry protocols occurred\nrelatively early in the history of the VO. While standards on the\nserver side have held out very well, the early standards on the client\nside have, in the meantime, proved insufficient for today's advanced\nRegistry use.\n\nThis resulted in the creation of second-generation client interfaces.\nIn this article, we have discussed principles and design goals of the\ntwo currently developed interfaces.\nThe keyword search interface provides a simple language to\nconstrain results that accomodates users' habits in taking up patterns\nfrom general search engines, with a response format designed for easy\nintegration into browser-based applications.\nRegTAP, on the other hand, is a relatively faithful mapping of\nessentially the entire data model to a relational database schema,\ntargeted towards \n``thick'' clients and expert users writing ADQL queries by hand.\n\nWhile RegTAP goes much further than RI1 in defining the mapping between\nthe XML schema that defines the Registry data model and the relational\nmodel that is in practice used to represent the data set in queryable\nform, there are still some small areas where the relational schema and\nits XML counterpart do not precisely match each other's expressiveness,\nprecluding, for instance, roundtrip ingestion and recreation of registry\nrecords through a RegTAP tableset. We propose as a lesson to be learned\nfrom this that future data modelling efforts should be done in an\nimplementation-neutral language with well-defined and well-understood\nmappings to the common implementation\nlanguages. Within the VO, an effort is underway to enable this\n\\citep{std:VODML}.\n\nThis is not to say that the Registry model needs fundamental work or a\ntechnology switch any time soon. The new interfaces to the Registry\nexpose its functionality fairly completely and interoperably\nbetween their implementations, and building on\nproven technologies like TAP and JSON, they also lower the cost of\nintegrating VO registry information into client programs.\n\nSome open issues remain in the registry's client interface; the most\nurgent ones are probably the formulation of contraints on spatial\ncoverage and the handling of capabilities associated with data\ncollections.\n\n\\section*{Acknowledgements}\n\nWe thank Laurent Michel, Pierre Fernique, and Pedro Osuna for providing\ninformation on the Registry interfaces of TAPHandle, Aladin, and VOSpec.\n\nThis work was in part supported by the German Astrophysical Virtual\nObservatory GAVO, BMBF grant 05A11VH3.\n\n\\section*{References}\n\n\\bibliographystyle{elsarticle-harv}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzemgj b/data_all_eng_slimpj/shuffled/split2/finalzzemgj new file mode 100644 index 0000000000000000000000000000000000000000..2ff8fef29659c94815c72bcfcc21f333ba38b933 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzemgj @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\n\nThe study of isometric group actions on Riemannian manifolds has seen a number of important applications in Riemannian geometry.\n\nMany of them fall under the umbrella of the so-called \\emph{Grove's program}, whose goal is to study the properties of Riemannian manifolds with non-negative (or even almost non-negative) sectional curvature in the presence of symmetry. This program has been extremely fruitful both in producing new examples of manifolds with non-negative sectional curvature, and in proving important conjectures in the area when some symmetry is added (cf. \\cite{KWW21}, \\cite{GKS20}, \\cite{FGT17}, \\cite{GW14}, \\cite{GZ00}, \\cite{GVZ11}, \\cite{Dea11}, etc.)\n\nThe concept of an isometric group action can be generalized by a \\emph{singular Riemannian foliation}, \nwhich roughly speaking is the partition of a Riemannian manifold into smooth and equidistant submanifolds of possibly varying dimensions, called leaves (and the leaves can be thought as a generalization of the orbits of an isometric group action). \nIt turns out that, while being more flexible than group actions (cf. for example \\cite{Rad14}), singular Riemannian foliations\nstill retain a lot of the same structure of isometric group actions (cf. \\cite{MR19}, \\cite{GGR15}, \\cite{GR15}, \n\\cite{CM20}, \\cite{Mor19}, etc.).\n\nGiven the action of a compact Lie group, the orbits are homogeneous spaces and thus have a very restricted topology, \nwhich can be employed to extrapolate topological properties of the ambient manifold (e.g. \\cite{GZ12} and \\cite{GYW19}). \nIn \\cite{GYW19}, the authors ask to what extent the leaves of a singular Riemannian foliation on a non-negatively curved space \nare also topologically restricted. In \\cite{GGR15}, Galaz-Garcia and the first author proved that if $(M,\\mathcal{F})$ is a closed singular Riemannian foliation on a compact, simply connected Riemannian manifold $M$, then the fundamental group of a generic leaf is a product $A\\times K_2$ \nof an abelian group $A$ and a 2-step nilpotent 2-group $K_2$ - in particular, it is nilpotent. In the present paper, \nwe continue exploring the topology of the leaves of singular Riemannian foliations $(M,\\mathcal{F})$.\n\nThe first result states that if $M$ is simply connected, then a generic leaf $L_0$ of $\\mathcal{F}$ is a \\emph{nilpotent space}, i.e. $\\pi_1(L_0)$ acts nilpotently on $\\pi_n(L_0)$ for all $n>1$:\n\n\\begin{maintheorem}\\label{main-thm:leaves-nilpotent}\nIf $(M,\\mathcal{F})$ is a closed singular Riemannian foliation on a compact, simply connected Riemannian manifold $M$, \nthen the principal leaves of $\\mathcal{F}$ are nilpotent spaces. Furthermore, all leaves are finitely covered by a nilpotent space.\n\\end{maintheorem}\n\nThis answers the first part of Problem 4.8 in \\cite{GYW19}:\n\\begin{nonumberquestion}\nLet $\\mathcal{F}$ be a closed singular Riemannian foliation on a closed (simply connected) Riemannian manifold M of almost non-negative curvature. Are the leaves of $\\mathcal{F}$ finitely covered by a nilpotent space, which moreover is rationally elliptic?\n\\end{nonumberquestion}\n\nOur result does not in fact use the curvature assumption. On the rationally elliptic part of the question, we make the following remarks:\n\\begin{enumerate}\n\\item The very question of whether the leaves are rationally elliptic, only makes sense the moment we know that the leaves are (virtually) nilpotent spaces: these are in fact the spaces on which rational homotopy theory applies, and the rational dichotomy of rationally elliptic vs. rationally hyperbolic spaces holds.\n\\item Assuming the question above to be true, and applying it to the product foliation $(M\\times \\mathbb{S}^n,M\\times \\{pts.\\})$ with $M$ simply connected and almost non-negatively curved, would imply that every simply connected, almost non-negatively curved Riemannian manifold is rationally elliptic, which is the statement of the celebrated (and out of reach) Bott-Halperin-Grove Conjecture. \nIn particular, the rationally elliptic part of the question is so far out of reach.\n\\end{enumerate}\n\nThe second result analyzes more in detail the structure of the fundamental group of a generic leaf $L_0$ \nof a singular Riemannian foliation $(M,\\mathcal{F})$ with $M$ simply connected:\n \n\\begin{maintheorem}\\label{main-thm:non-abelian part}\nLet $(M,\\mathcal{F})$ be a closed singular Riemannian foliation on a compact, simply connected Riemannian manifold $M$. \nIf $L_0$ is a principal leaf of $\\mathcal{F}$, then the non-abelian part $K_2$ of the fundamental group of $L_0$ is of the form \n$$K_2\\cong (\\prod_{j=1}^s \\Z_{2^{a_j}}\\times \\Z_2^b\\times \\prod_{i=1}^k G_i)\/({\\Z_2^{c}\\times\\Z_4^{d}}),$$\nwhere each $G_{i}$ is isomorphic to a central product of copies of $Q_8$, with possibly one copy of $D_8$ or $\\Z_4$.\n\\end{maintheorem}\n\nThe groups $G_i$ in the theorem are called \\emph{generalized extraspecial}. These 2-groups already occur as fundamental groups of orbits of orthogonal representations and hence are impossible to avoid (e.g. $\\mathrm{SO}(3)$ acting on $\\mathbb{S}^4$), \nsee also a family of examples from Section \\ref{SS:examples}.\n\nFinally, we extend Theorem A from \\cite{GGR15} by showing that when $M$ has virtually nilpotent fundamental group, the leaves of any closed singular Riemannian foliation $(M,\\mathcal{F})$ have virtually nilpotent fundamental group as well:\n\n\\begin{maintheorem}\\label{main-thm:virtually nilpotent}\nSuppose $(M,\\mathcal{F})$ is a closed singular Riemannian foliation on compact Riemannian manifold $M$ with virtually nilpotent fundamental group. Then the leaves of $\\mathcal{F}$ have virtually nilpotent fundamental group as well.\n\\end{maintheorem}\n\nIn the fundamental paper \\cite{KPT10}, the authors show that every Riemannian manifold with almost non-negative \nsectional curvature is finitely covered by a nilpotent space. With this in mind, Theorem \\ref{main-thm:virtually nilpotent} gives the following steaightforward corollary:\n\n\\begin{maincorollary}\nGiven a closed singular Riemannian foliation $(M, \\mathcal{F})$ on an almost non-negatively curved manifold $M$, the leaves have virtually nilpotent fundamental group.\n\\end{maincorollary}\n\n\nThis paper is organized as follows. In Section \\ref{S:preliminaries}, we collect some preliminaries about topological results \nfor singular Riemannian foliations, and the main notation for bilinear and quadratic forms we need in the proof of Theorem \\ref{main-thm:non-abelian part}. In Section \\ref{S:topology of leaves}, we prove Theorem \\ref{main-thm:leaves-nilpotent}. \nIn Section \\ref{S:fundamental group}, we prove Theorem \\ref{main-thm:non-abelian part} and provide a family of examples showing that the generalized extraspecial groups can indeed appear in the fundamental group of principal orbits of orthogonal representations. Finally, in Section \\ref{S:nilpotent fundamental group}, we prove Theorem \\ref{main-thm:virtually nilpotent}.\n \n\\section{Preliminaries}\\label{S:preliminaries}\n\n\\subsection{Singular Riemannian foliations}\n\nLet $M$ be a Riemannian manifold. A singular Riemannian foliation on $M$ is a partition $\\mathcal{F}$\nof $M$ into connected, injectively immersed submanifolds called leaves such that every geodesic that starts perpendicular \nto a leaf remains perpendicular to all the leaves it meets, and moreover, M admits a family of smooth vector fields \nthat spans the leaves at all points.\n\nA singular Riemannian foliation is called closed if all of its leaves are closed in $M$. \nGiven a singular Riemannian foliation $(M,\\mathcal{F})$ on a complete manifold $M$ we define the \\emph{dimension \nof $\\mathcal{F}$}, denoted $\\dim\\mathcal{F}$, as the maximal dimension of its leaves. \nThe codimension of $\\mathcal{F}$ is defined by $\\dim M-\\dim\\mathcal{F}$.\n\nA leaf $L$ of the foliation $\\mathcal{F}$ is called regular if its dimension is maximal, or equivalently, $\\dim L=\\dim\\mathcal{F}$.\nThe union of all regular leaves is an open, dense and connected submanifold, which is called the principal stratum of $M$ \nand is denoted by $M_0$. The union of all other leaves is called the singular stratum of $(M,\\mathcal{F})$ \nand the connected components of the singular stratum are called singular strata. \n\nFor a closed singular Riemannian foliation $(M,\\mathcal{F})$, the canonical projection $\\pi:M\\to M\/{\\mathcal{F}}$\ninduces a metric space structure on the leaf space $M\/{\\mathcal{F}}$, where the metric \nis given by $d_{M\/{\\mathcal{F}}}(\\pi(p),\\pi(q))=d_M(L_p,L_q)$. If in addition all the leaves of $\\mathcal{F}$ are regular, \nthen the leaf space is a Riemannian orbifold. In particular, given a closed singular Riemannian foliation $(M,\\mathcal{F})$, \nthe quotient space ${M_0}\/{\\mathcal{F}}$ is an orbifold.\n\nWe then call a leaf $L\\subset M_0$ \\emph{principal} if it projects to a manifold point of $M_0\/\\mathcal{F}$. Clearly, the set of principal leaves is open and dense in $M_0$.\n\n\n\\subsection{Slice Theorem}\\label{SS:Slice Theorem}\nIn this section we describe the structure of a singular Riemannian foliation around a leaf. For more details, we refer the interested reader to \\cite{MR19}.\n\nLet $(M, \\mathcal{F})$ be a closed singular Riemannian foliation, let $p\\in M$, and let $L_p$ denote the leaf through $p$. Define the \\emph{horizontal space to $\\mathcal{F}$ at $p$}, $\\nu_pL_p\\subseteq T_pM$, as the subspace perpendicular to $T_pL_p$. Then there exists a singular Riemannian foliation $(\\nu_pL_p,\\mathcal{F}_p)$ called the \\emph{infinitesimal foliation of $\\mathcal{F}$ at $p$}, with two important properties:\n\\begin{enumerate}\n\\item $\\mathcal{F}_p$ is invariant under rescalings,\n\\item In an $\\epsilon$-neighbourhood $\\nu_p^{\\epsilon}L_p$ of the origin in $\\nu_pL_p$, the exponential map $\\exp_p:\\nu_p^\\epsilon L_p\\to M$ takes the leaves of $\\mathcal{F}_p$ onto the connected components of the intersections $L\\cap \\exp \\nu_p^\\epsilon L_p$, with $L\\in \\mathcal{F}$. \n\\end{enumerate}\nFurthermore, there is a group of isometries $K\\subseteq O(\\nu_pL_p)$, sending leaves of $L_p$ to (possibly different) leaves of $\\mathcal{F}_p$, with the property that for any $v\\in \\nu_p^\\epsilon L_p$, the leaf $L_v\\in \\mathcal{F}_p$ satisfies the following:\n\\[\n\\exp_p(K\\cdot L_v)=L_{\\exp_p(v)}\\cap \\exp_p\\nu_p^\\epsilon L_p\n\\]\nIn other words, two leaves of $\\mathcal{F}_p$ are in the same $K$-orbit if and only if they exponentiate to different connected components of an intersection $L \\cap \\exp_p\\nu_p^\\epsilon L_p$, for some $L\\in \\mathcal{F}$.\n\nIn \\cite{MR19}, the following Slice Theorem establishes a model for a singular Riemannian foliation around a leaf:\n\\begin{nonumbertheorem}[Foliated Slice Theorem]\nGiven a closed singular Riemannian foliation $(M, \\mathcal{F})$ and a point $p\\in M$, let $(\\nu_pL_p, \\mathcal{F}_p)$ be the infinitesimal foliation of $\\mathcal{F}$ at $p$. Then there exists a compact Lie group $K\\subset O(\\nu_pL_p)$ and a principal $K$-bundle $P\\to L_p$ such that the foliation $\\mathcal{F}$ in an $\\epsilon$-neighbourhood of $L_p$ is foliated diffeomorphic to\n\\[\n(P\\times_K\\nu_pL,P\\times_K\\mathcal{F}_p)\n\\]\n\\end{nonumbertheorem}\n\nIt follows directly from the Slice Theorem that all principal leaves are diffeomorphic to each other, and for any leaf $L_p$, there is a locally trivial fiber bundle $L_0\\to L_p$ from a principal leaf $L_0$, whose fiber is an orbit $K\\cdot L_v$ for some principal point $v\\in (\\nu_pL_p, \\mathcal{F}_p)$, and it consists of a finite disjoint union of principal leaves of $\\mathcal{F}_p$.\n\n\n\n\\subsection {The Molino bundle}\\label{SS:molino}\n\nLet $(M,\\mathcal{F})$ be a closed singular Riemannian foliation of codimension $q$ on a compact Riemannian manifold $M$. \nThe principal $\\mathrm{O}(q)$-bundle $\\hat{M}\\to M_0$, where $\\hat{M}$ is the collection of orthonormal frames \nof ${TM_0}\/{T\\mathcal{F}}$, is called the Molino bundle. The foliation $\\mathcal{F}$ lifts to a foliation \n$\\hat{\\mathcal{F}}$ on $\\hat{M}$ whose leaves are diffeomorphic to the leaves of $\\mathcal{F}$ \non an open dense set. Moreover, the leaves of $\\hat{\\mathcal{F}}$ are given by fibers of a submersion \n$\\theta:\\hat{M}\\to W$, where $W$ is the frame bundle of the orbifold ${M_0}\/{\\mathcal{F}}$. \n\nConsider the fibration $\\hat{\\theta}:{\\hat{M}}_{\\mathrm{O}(q)}\\to W_{\\mathrm{O}(q)}$ induced by $\\theta$,\nwhere ${\\hat{M}}_{\\mathrm{O}(q)}={\\hat{M}}\\times_{\\mathrm{O}(q)}\\mathrm{EO}(q)$ and $W_{\\mathrm{O}(q)}=W\\times_{\\mathrm{O}(q)}\\mathrm{EO}(q)$ denote the Borel constructions of $\\hat{M}$ and $W$, respectively. \nNote that $\\hat{\\theta}:{\\hat{M}}_{\\mathrm{O}(q)}\\to W_{\\mathrm{O}(q)}$ and $\\theta:\\hat{M}\\to W$ \nhave the same fibers and hence the fiber of $\\hat{\\theta}$ is diffeomorphic to $L_0$, where $L_0$ is a principal leaf \nof $\\mathcal{F}$. In addition, ${\\hat{M}}_{\\mathrm{O}(q)}$ is homotopy equivalent to ${\\hat{M}}\/{\\mathrm{O}(q)}=M_0$ \nand $W_{\\mathrm{O}(q)}$ coincides with the Haefliger's classifying space $B$ of ${M_0}\/{\\mathcal{F}}$. \nTherefore, we get the following fibration (up to homotopy):\n$$L_0\\overset{\\iota_0}{\\rightarrow}M_0\\overset{\\hat{\\theta}}{\\rightarrow}B.$$\n\n\\subsection{Bilinear and quadratic forms over $\\Z_2$}\\label{SS:quadratic}\n\nLet $V$ be a finite dimensional vector space over a field $F$. A quadratic form on $V$ is a map $Q:V\\to F$ \nsuch that $Q(\\lambda v)=\\lambda^2 Q(v)$ for all $\\lambda\\in F$ and $v\\in V$, and moreover, the map \n$B_Q:V\\times V\\to F$ defined by $B_Q(u,v)=Q(u+v)-Q(u)-Q(v)$ is a bilinear form. \nGiven a basis $\\{v_1,\\ldots,v_{\\ell}\\}$ of $V$, it follows that\n\\begin{equation}\\label{eq:quadratic form}\nQ(x_1v_1+\\ldots+x_{\\ell}v_{\\ell})=\\sum_{i=1}^{\\ell} Q(v_i)x_i^2+\\sum_{1\\leq i2$,\nwe can assume that we only have singular strata of codimension $\\leq 2$. Furthermore, it is known that there are no strata \nof codimension one, which reduces $\\mathcal{F}$ to only having strata of codimension two. \n\nLet $\\Sigma_1,\\ldots,\\Sigma_m$ denote the singular strata of $\\mathcal{F}$ of codimension two.\nFor $i=1,\\ldots, m$, choose a singular leaf $L'_i$ in $\\Sigma_i$, and let $L_i$ be a principal leaf at some distance $\\epsilon_i$ \nfrom $L'_i$. For $\\epsilon_i$ small enough, the foot-point projection $\\pi_i:L_i\\to L'_i$ is a circle bundle.\nFix a point $p_i\\in L_i$, and let $[c_i]\\in \\pi_1(L_i,p_i)$ be the element represented by the fiber $c_i$ \nof $\\pi_i$ through $p_i$.\n\nFixing a principal leaf $L_0$ and $p_0\\in L_0$, we can choose, for each $i=1,\\ldots, m$, a diffeomorphism $h_i:L_i\\to L_0$, \nand define $k_i=(h_i)_*([c_i])\\in\\pi_1(L_0,p_0)$. The group $K$ generated by the elements $k_i$ is then a normal subgroup \nof $\\pi_1(L_0,p_0)$. Furthermore, there exists a homotopy fibration\n\\[\nL_0\\overset{\\iota_0}{\\rightarrow}M_0\\overset{\\hat{\\theta}}{\\rightarrow}B,\n\\]\nas described in Section \\ref{SS:molino}. One has the following (see the proof of Theorem A in \\cite{GGR15}):\n\\begin{enumerate}\n\\item $\\pi_1(L_0,p_0)$ is generated by the subgroup $K$ and the image of the boundary map \n$\\partial:\\pi_2(B,b_0)\\to\\pi_1(L_0,p_0)$.\n\\item $H:=\\im(\\partial)$ is central in $\\pi_1(L_0,p_0)$\n\\item Any two non-commuting generators $k_i$ and $k_j$ of $K$ satisfy $k_ik_j=k_j^{-1}k_i$.\n\\item Let $N\\subseteq K$ be the subgroup generated by the non-central $k_i$'s, and let $Z_{(2)}$ \ndenote the Sylow $2$-subgroup of $Z(K)$. Then $\\pi_1(L_0,p_0)$ is nilpotent, and equal to $A\\times K_2$, \nwhere $A$ is abelian and $K_2=N\\cdot Z_{(2)}$.\n\\end{enumerate}\n\n\\subsection{Proof of Theorem \\ref{main-thm:leaves-nilpotent}}\n\nAs discussed in Section \\ref{SS:known-results}, the principal leaves of $\\mathcal{F}$ have nilpotent fundamental groups. \nAs a first step towards the proof of Theorem \\ref{main-thm:leaves-nilpotent}, we prove that the principal leaves \nare nilpotent spaces:\n\n\\begin{proposition}\\label{P:princ-leaves-nilp}\nSuppose $(M,\\mathcal{F})$ is a closed singular Riemannian foliation on a compact, simply connected Riemannian manifold $M$. \nLet $L_0$ denote a principal leaf of $\\mathcal{F}$ and let $p_0\\in L_0$. Then $\\pi_1(L_0,p_0)$ acts trivially on $\\pi_n(L_0,p_0)$ \nfor $n\\geq 2$.\n\\end{proposition}\n\n\\begin{proof}\nLet $[\\gamma]\\in\\pi_1(L_0,p_0)$ and $[\\omega]\\in\\pi_n(L_0,p_0)$. The goal is to prove that $[\\gamma]$\nacts trivially on $[\\omega]$. By the discussion in Section \\ref{SS:known-results}, we may assume that either $[\\gamma]\\in H$ \nor $[\\gamma]=k_i$ for some $i$. \n\\par \nFirst, consider the case in which $[\\gamma]=k_i$ for some $i$. Note that ${\\bf p}_i:=\\pi_i\\circ h_i^{-1}:L_0\\to L'_i$ \nis a circle bundle whose fiber is represented by $k_i$. This means that $k_i\\in\\ker(({\\bf p}_i)_*)$, where $({\\bf p}_i)_*$\nis the induced map on $\\pi_n$. Hence we have:\n$$({\\bf p}_i)_*([\\gamma]\\cdot[\\omega])=({\\bf p}_i)_*(k_i\\cdot[\\omega])=(({\\bf p}_i)_*(k_i))\\cdot(({\\bf p}_i)_*([\\omega]))=({\\bf p}_i)_*([\\omega]).$$\nBy the long exact sequence of homotopy groups associated to the fibration \n$\\mathbb{S}}\\newcommand{\\Ca}{\\mathrm{Ca}}\\newcommand{\\pp}{\\mathbb{P}^1\\to L_0\\overset{{\\bf p}_i}{\\rightarrow} L'_i$, it follows that the homomorphism \n$({\\bf p}_i)_*$ is injective in $\\pi_n$ for $n\\geq 2$. \nThis, together with $({\\bf p}_i)_*([\\gamma]\\cdot[\\omega])=({\\bf p}_i)_*([\\omega])$, implies that $[\\gamma]$ \nacts trivially on $[\\omega]$.\n\\par\nSuppose now that $[\\gamma]\\in H=\\im(\\partial)$ and choose $[\\beta]\\in\\pi_2(B,b_0)$ such that $[\\gamma]=\\partial([\\beta])$.\nConsider the fibration \n$$L_0\\overset{\\iota_0}{\\rightarrow}M_0\\overset{\\hat{\\theta}}{\\rightarrow}B.$$\nNote that the action of $\\pi_1(L_0,p_0)$ on $\\pi_n(L_0,p_0)$ satisfies $[\\gamma]\\cdot[\\omega]=(\\iota_0)_*([\\gamma])\\cdot[\\omega]$ (see \\cite[Exercise 4.3.10]{Hat02}). Therefore, \n$$[\\gamma]\\cdot[\\omega]=(\\iota_0)_*([\\gamma])\\cdot[\\omega]=(\\iota_0)_*(\\partial([\\beta]))\\cdot[\\omega]=e\\cdot[\\omega]=[\\omega].$$\nThis completes the proof.\n\\end{proof}\n\nMoving to the non-principal leaves, we first prove that every leaf has a virtually nilpotent fundamental group.\n\n\\begin{lemma}\\label{L:other-leaves}\nSuppose $(M,\\mathcal{F})$ is a closed singular Riemannian foliation with principal leaf $L_0$. If $\\pi_1(L_0)$ is virtually nilpotent, \nthen so is the fundamental group $\\pi_1(L)$ of every leaf $L$ of $\\mathcal{F}$.\n\\end{lemma}\n\n\\begin{proof}\nFor any leaf $L$ of $\\mathcal{F}$, the foliated Slice Theorem (cf. Section \\ref{SS:Slice Theorem}) implies that there is a fibration $L_0\\to L$ whose fiber $F$ \nhas finitely many connected components. From the long exact sequence in homotopy one then has\n\\[\n\\pi_1(L_0)\\to \\pi_1(L)\\to \\pi_0(F)\n\\]\nfrom which it follows that $\\pi_1(L)$ is a finite extension of a quotient of $\\pi_1(L_0)$, therefore it is virtually nilpotent as well.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem \\ref{main-thm:leaves-nilpotent}]\nThe statement about principal leaves has been proved in Proposition \\ref{P:princ-leaves-nilp}, so we now have to only consider non-principal leaves.\n\nGiven a leaf $L$, choose $p\\in L$. Recall that, by the Foliated Slice Theorem (cf. \\ref{SS:Slice Theorem}), there is a locally trivial fibration $\\phi:L_0\\to L$ whose fiber $F$ has finitely many connected components, all diffeomorphic to a principal leaf of the infinitesimal foliation $(\\nu_p L_p,\\mathcal{F}_p)$. Furthermore, the action $\\pi_1(L)\\to \\operatorname{Diff}(F)$ induces an action $\\pi_1(L)\\to \\operatorname{Aut}(\\pi_*(F))$, which factors as $\\pi_1(L)\\stackrel{\\psi}{\\to} \\pi_0(K)\\to \\operatorname{Aut}(\\pi_*(F))$. In particular:\n\\begin{enumerate}\n\\item The subgroup $G_1:=\\ker\\psi \\subseteq \\pi_1(L)$ has finite index in $\\pi_1(L)$ and it acts trivially on $\\pi_*(F)$.\n\\item The fibration induces a map $\\pi_1(L_0)\\stackrel{\\phi_*}{\\to} \\pi_1(L)\\to \\pi_0(F)$. Thus $G_2:=\\phi_*(\\pi_1(L_0))$ is a nilpotent subgroup of $\\pi_1(L)$ with finite index.\n\\end{enumerate}\nConsider $G:=G_1\\cap G_2\\subseteq \\pi_1(L)$, which is by the points above a nilpotent subgroup with finite index. We will now show that $G$ acts nilpotently on each $\\pi_n(L)$, i.e. the \\emph{lower central series} $\\Gamma^m_G(\\pi_n(L))\\subseteq\\pi_n(L)$ defined iteratively by\n\\[\n\\Gamma_G^1(\\pi_n(L))=\\pi_n(L), \\qquad \\Gamma_G^{m+1}(\\pi_n(L))=\\{\\gamma\\cdot \\alpha-\\alpha\\mid \\gamma\\in G,\\,\\alpha\\in \\Gamma_G^{m}(\\pi_n(L))\\}\n\\]\neventually becomes trivial.\n\nConsider the long exact sequence\n\\[\n\\cdots\\to\\pi_n(F)\\to\\pi_n(L_0)\\stackrel{\\phi_*}{\\to}\\pi_n(L)\\stackrel{\\partial}{\\to}\\pi_{n-1}(F)\\to\\cdots\n\\]\nLet $\\alpha\\in \\pi_n(L)$, and $\\gamma=\\phi_*(\\gamma_0)\\in G$, where $\\gamma_0\\in \\pi_1(L_0)$. Recall that $\\partial(\\gamma\\cdot \\alpha)=\\gamma\\cdot \\partial(\\alpha)$, where the action on the left is $\\pi_1(L)$ acting on $\\pi_*(L)$, while on the right we have the $\\pi_1(L)$-action on $\\pi_*(F)$. Since $G\\subseteq G_1$, we have\n\\[\n\\partial(\\gamma\\cdot\\alpha)=\\partial (\\alpha)\\quad\\Rightarrow \\quad \\partial(\\gamma\\cdot \\alpha-\\alpha)=0\n\\]\nand therefore\n\\[\n\\Gamma_G^2(\\pi_n(L))\\subseteq \\ker (\\partial)=\\phi_*(\\pi_n(L_0))=\\phi_*(\\Gamma_{\\pi_1(L_0)}^1(\\pi_n(L_0))).\n\\]\nFinally, we notice that if $\\alpha=\\phi_*(\\alpha_0)$ with $\\alpha_0\\in \\pi_n(L_0)$ then\n\\[\n\\gamma\\cdot \\alpha=(\\phi_*(\\gamma_0))\\cdot(\\phi_*(\\alpha_0))=\\phi_*(\\alpha_0)\\Rightarrow \\gamma\\cdot \\alpha-\\alpha=\\phi_*(\\gamma_0\\cdot \\alpha_0-\\alpha_0).\n\\]\n\nBy induction on $m$, one then has\n\\[\n\\Gamma_G^{m+1}(\\pi_n(L))\\subseteq \\phi_*\\big(\\Gamma_{\\pi_1(L_0)}^{m}(\\pi_n(L_0))\\big).\n\\]\nSince by Proposition \\ref{P:princ-leaves-nilp}, $\\Gamma_{\\pi_1(L_0)}^{2}(\\pi_n(L_0))=0$, we have $\\Gamma_G^{3}(\\pi_n(L))=0$ which proves that $G$ acts nilpotently on $\\pi_n(L)$, hence finishing the proof.\n\\end{proof}\n\n\\section{Fundamental groups of the principal leaves}\\label{S:fundamental group}\n\nThis section consists of two parts. The first part is devoted to the proof of Theorem \\ref{main-thm:non-abelian part}.\nIn the second part, we provide examples of singular Riemannian foliations whose principal leaves have fundamental groups \nof the form discussed in Theorem \\ref{main-thm:non-abelian part}.\n\nSuppose that $(M,\\mathcal{F})$ is a closed singular Riemannian foliation on a compact, simply connected Riemannian manifold $M$. \nFix a principal leaf $L_0$ of $\\mathcal{F}$ and $p_0\\in L_0$. Let $N$ and $K_2$ be the subgroups of $\\pi_1(L_0,p_0)$ \ndiscussed in Section \\ref{SS:known-results}.\n\nConsider the graph $\\Gamma$ with vertices the generators of $N$ and an edge between $k_i$ and $k_j$ \nif and only if $k_ik_jk_i^{-1}=k_j^{-1}$. Note that for every generator $k_i$ of $N$, there exists another generator \nwhich does not commute with $k_i$. Therefore, $\\Gamma$ does not contain any isolated vertices. \nNote moreover that for every connected component $\\Gamma_i$ of $\\Gamma$, all vertices of $\\Gamma_i$ \nsquare to the same element $c_i$. In addition, by proof of Theorem A in \\cite{GGR15}, for any generator $k_i$ of $N$,\nwe have $k_i^4=1$ and $k_i^2$ is central in $K$. Therefore, $c_i$ is a central element of $N$ of order two.\nAltogether, we get that there is a map $C:\\pi_0(\\Gamma)\\to Z(N)$ defined by $C(\\Gamma_i)=c_i$.\n\n\\begin{notation}\\label{N:N_c}\nFrom now on, we fix an element $c$ of $Z(N)$ which is of the form $k_i^2$ for some generator $k_i$ of $N$.\nMoreover, $N_c$ denotes the subgroup of $N$ that is generated by all the vertices in $\\Gamma_c:=C^{-1}(c)$.\n\\end{notation}\n\nRecall that given a group $G$, the \\emph{Frattini subgroup} $\\Phi(G)$ is the intersection of all the maximal subroups of $G$. Furthermore, we recall the following:\n\n\\begin{definition}\\label{def:Generalized ES}\nA $2$-group $G$ is called \\emph{generalized extraspecial} if $\\Phi(G)$ is central, and $\\Phi(G)=[G,G]=\\Z_2$.\n\\end{definition}\n\nWe prove two important properties of the groups $N_c$. \n\n\\begin{lemma}\\label{L:abt-N_c}\nLet $\\{N_c\\}_{c\\in \\textrm{Im}(C)}$ be the collection of groups defined above. Then:\n\\begin{enumerate}\n\\item For $c\\neq c'$, the groups $N_{c}$ and $N_{c'}$ commute.\n\\item Each $N_c$ is a generalized extraspecial $2$-group.\n\\end{enumerate}\n\\end{lemma}\n\n\\begin{proof}\nFirst, we prove Statement (1). Let $k_1,\\ldots, k_{\\ell}$ be the generators of $N_c$, and $k'_1,\\ldots, k'_r$ be the generators\nof $N_{c'}$. As vertices of $\\Gamma$, there is no edge between any $k_i$ and any $k'_j$, which means that each $k_i$ commutes with any $k'_j$ in $K$. Hence the result follows.\n\\par\nAs for Statement (2), if $k_1,\\ldots, k_{\\ell}$ denote the generators of $N_c$, then $V={N_c}\/{\\langle c\\rangle}$ \nis isomorphic to $\\Z_2^{\\ell}$ and is generated by $[k_1],\\ldots,[k_{\\ell}]$. \nIt follows that $N_c$ fits into a short exact sequence\n\\[\n1\n\\to \\langle c\\rangle \\to N_c\\to V\\to 1\n\\]\nand in particular one has that both $N_c^2:=\\langle g^2\\mid g\\in N_c\\rangle$ and the commutator subgroup $[N_c,N_c]$ \ncoincide with $\\langle c \\rangle\\simeq \\Z_2$. Therefore, the same is true for the Frattini subgroup $\\Phi(N_c)$\nsince for a $2$-group $G$, one has $\\Phi(G)=G^2\\cdot [G,G]$.\n\\end{proof}\n\nGiven generalized extraspecial groups $G_1$ and $G_2$, with Frattini subgroups generated by $c_1$ and $c_2$, respectively, define the \\emph{central product} $G_1*G_2$ by $G_1*G_2:={(G_1\\times G_2)}\/\\langle (c_1,c_2)\\rangle$. \nThis is again a generalized extraspecial group, since\n\\[\\Phi(G_1*G_2)=\\Phi(G_1)\\times_{\\Z_2}\\Phi(G_2) \\cong \\Z_2.\\]\n\nThe $*$ operation is furthermore associative, and thus it makes sense to define, for a generalized extraspecial group $G$, \nthe central product powers\n\\[\n(G)^{*m}:=\\underbrace{G*G*\\ldots*G}_\\text{m\\textrm{ times}}\n\\]\n\nGeneralized extraspecial 2-groups are, as the name suggests, a generalization of \\emph{extraspecial $2$-groups}, \nthat is 2-groups such that $\\Phi(G)=Z(G)=[G,G]\\cong \\Z_2$. These groups have been thoroughly studied at least since the 60's \\cite{Hal56}. They are extremely simple: an extraspecial group has the form $(Q_8)^{*m}$ or $(Q_8)^{*(m-1)}*D_8$ \nfor some $m\\geq 1$, where $Q_8$ is the quaternion group and $D_8$ is the dihedral group of order $8$\n(cf. Theorem 2.2.11 of \\cite{LGM05}). It then follows from Lemma 3.2 in \\cite{Sta02} that\n\n\\begin{theorem}\nA generalized extraspecial $2$-group is of the form $G\\times \\Z_2^n$, where $G$ is one of\n\\[\nQ_8^{*m},\\qquad Q_8^{*(m-1)}*D_8,\\qquad Q_8^{*(m-1)}*\\Z_4.\n\\]\n\\end{theorem}\n\n\\subsection{The associated quadratic form}\\label{SS:associated quadratic form}\n\nLet $G$ be a generalized extraspecial $2$-group with $\\Phi(G)=G^2=\\langle c\\rangle$,\nand let $V:=G\/{\\langle c\\rangle}$. It is easy to check that $V$ is a vector space over $\\Z_2$. \n\nDefine the function $Q_G:V\\to\\Z_2$ by $Q_G([g])=k$, where $g^2=c^k$. Since $c$ is central in $G$ and has order two, \nfor any $g\\in G$, we have $(cg)^2=cgcg=c^2g^2=g^2$ and thus $Q_G([cg])=Q_G([g])$. \nTherefore, $Q:=Q_G$ is well-defined and in fact a quadratic form as defined in Section \\ref{SS:quadratic}. \nFurthermore, the bilinear form $B_Q$ associated to $Q$ (cf. Section \\ref{SS:quadratic}) satisfies \n$$ghg^{-1}h^{-1}=c^{B_Q([g],[h])},~{\\text{for}}~g,h\\in G.$$ \nIn order to see this, note that both $g^2$ and $h^2$ are central elements of $G$. Therefore,\n$$c^{B_Q([g],[h])}=c^{Q([g]+[h])}c^{-Q([g])}c^{-Q([h])}=(gh)^2g^{-2}h^{-2}=ghg^{-1}h^{-1}.$$\n\n\nThe quadratic form of each generalized extraspecial group can be explicitly computed. For this, consider the quadratic forms:\n\\begin{alignat*}{3}\n & H_+: \\Z_2^2\\to \\Z_2 && \\qquad H_-:\\Z_2^2\\to\\Z_2 && \\qquad Q_1:\\Z_2\\to \\Z_2\\\\\n & H_+(x,y)=xy && \\qquad H_-(x,y)=x^2+y^2+xy && \\qquad Q_1(x)=x^2.\n\\end{alignat*}\nWe have the following:\n\n\\begin{proposition}\\label{prop:quad-forms}\nSuppose that $G$ is a generalized extraspecial $2$-group and let $V:= G\/\\Phi(G)$. \n\\begin{enumerate}\n\\item If $G=(Q_8)^{*m}$, then $V\\simeq\\Z_2^{2m}$ and\n\\[\nQ_G=H_-^{\\oplus m}=\\begin{cases}\nH_+^{\\oplus m}& m~{\\rm{even}}\\\\\nH_-\\oplus H_+^{\\oplus (m-1)} & m~{\\rm{odd}}\n\\end{cases}\n\\]\n\\item If $G=(Q_8)^{*(m-1)}*D_8$, then $V\\simeq\\Z_2^{2m}$ and\n\\[\nQ_G=H_-^{\\oplus (m-1)}\\oplus H_+=\\begin{cases}\nH_+^{\\oplus m} & m~{\\rm{odd}}\\\\\nH_-\\oplus H_+^{\\oplus (m-1)}& m~{\\rm{even}}\n\\end{cases}\n\\]\n\\item If $G=(Q_8)^{m}*\\Z_4$, then $V\\simeq\\Z_2^{2m+1}$ and\n\\[\nQ_G=H_+^{\\oplus m}\\oplus Q_1=H_-^{\\oplus m}\\oplus Q_1\\]\n\\item If $G=G'\\times \\Z_2^n$ with $G'$ as in the previous points, then $V\\simeq V'\\oplus \\Z_2^n$ and $Q_G=Q_{G'}\\oplus0^{\\oplus n}$.\n\\end{enumerate}\n\\end{proposition}\n\n\\begin{proof}\nThis proposition follows easily from the following straightforward facts:\n\\begin{enumerate}\n\\item For $G=Q_8$, $G\/\\Phi(G)\\simeq \\Z_2^2$ and $Q_G=H_-$.\n\\item For $G=D_8$, $G\/\\Phi(G)\\simeq \\Z_2^2$ and $Q_G=H_+$.\n\\item For $G=\\Z_4$, $G\/\\Phi(G)\\simeq \\Z_2$ and $Q_G=Q_1$.\n\\item Given $G_1$ and $G_2$ with quotients $V_i=G_i\/\\Phi(G_i)$, one has\n\\[\n(G_1*G_2)\/\\Phi(G_1*G_2)=V_1\\oplus V_2\\quad\\textrm{and}\\quad Q_{G_1*G_2}=Q_{G_1}\\oplus Q_{G_2}.\n\\]\n\\item Given $G$ with quotient $V=G\/\\Phi(G)$, one has\n\\[\n(G\\times \\Z_2^n)\/\\Phi(G\\times \\Z_2^n)\\simeq V\\oplus \\Z_2^n\\quad\\textrm{and}\\quad Q_{G\\times \\Z_2^n}=Q_G\\oplus 0^{\\oplus n}.\n\\qedhere\\]\n\\end{enumerate}\n\\end{proof}\n\n\\begin{remark}\\label{remark:N_c}\nThe group $N_c$ discussed above is generated by elements of order four, that is the $k_i$'s. \nMoreover, for each $k_i$, there exists $k_j$ such that $k_ik_jk_i^{-1}k_j^{-1}=c$.\nThis is reflected in the corresponding quadratic form $Q:V\\to\\Z_2=\\{0,1\\}$ as follows.\nThere exists a basis $\\{v_1,\\ldots,v_{\\ell}\\}$ of $V\\cong\\Z_2^\\ell$ with the property that $Q(v_i)=1$ for all $i$, \nand for each $v_i$, there exists $v_j$ such that $B_Q(v_i,v_j)=1$. We call such quadratic forms \\emph{admissible}. \n\\end{remark}\n\nThe next step consists of understanding which of the quadratic forms in Proposition \\ref{prop:quad-forms} is admissible. \nWe start by reducing the problem to quadratic forms without trivial summands:\n\\begin{lemma}\\label{lem:splitting}\nLet $Q:V\\to\\Z_2$ be a quadratic form. If there exists a splitting $V=V_1\\oplus V_2$ such that $Q$ splits as $Q=q\\oplus 0^{\\oplus n}$ with $Q|_{V_1}=q$ and $Q|_{V_2}=0^{\\oplus n}$, then $Q$ is admissible if and only if $q$ is admissible.\n\\end{lemma}\n\n\\begin{proof}\nSuppose that $Q$ is admissible and choose a basis\n$$\\{(v_1,w_1),\\ldots, (v_{m+n},w_{m+n})\\}$$\nof $V_1\\oplus V_2$ with the property that $Q(v_i,w_i)=1$, and for every $(v_i,w_i)$ there exists $(v_j,w_j)$\nwith $B_Q((v_i,w_i),(v_j,w_j))=1$. After possibly rearranging basis elements of $V_1\\oplus V_2$, \nwe may assume that $\\{v_1,\\ldots, v_m\\}$ forms a basis for $V_1$. \nSince $Q(v_i,w_i)=q(v_i)$ and $B_Q((v_i,w_i),(v_j,w_j))=B_q(v_i,v_j)$, the basis $\\{v_1,\\ldots, v_m\\}$ of $V_1$ \nis admissible for $q$. On the other hand, if $\\{v_1,\\ldots, v_m\\}$ is admissible for $q$ and $\\{w_1,\\ldots, w_n\\}$ \nis any basis of $V_2$, then \n$$\\{(v_i,{\\bf{0}})\\mid i=1,\\ldots, m\\}\\cup\\{(v_1,w_j)\\mid j=1,\\ldots, n\\},$$\nforms an admissible basis for $Q$.\\end{proof}\n\nWe now apply Lemma \\ref{lem:splitting} to classify the admissible quadratic forms.\n\n\\begin{theorem}\\label{thm:admissible}\nAny admissible quadratic form $Q:\\Z_2^{\\ell}\\to\\Z_2$ is isometric to one of the following, up to orthogonal sum \nwith $0^{\\oplus n}$:\n\\begin{equation}\\label{eq:admissible}\nH_+^{\\oplus m}~(m\\geq 2),\\qquad H_-\\oplus H_+^{\\oplus m-1},\\qquad H_+^{\\oplus m}\\oplus Q_1~(m\\geq 2).\n\\end{equation}\n\\end{theorem}\n\n\\begin{proof}\nSince the quadratic forms over $\\Z_2$ are classified (see Proposition \\ref{prop:quadratic form}), \nwe only need to check the admissibility condition. By Lemma \\ref{lem:splitting}, we may assume that $Q$ does not split \nas $q\\oplus 0^{\\oplus m}$. We break the proof into cases.\n\n\\smallskip\n\n{\\textbf{Case 1:}} $Q=H_-\\oplus H_+^{\\oplus m-1}$, where $2m=\\ell$. The quadratic form $Q$ is given by\n$$Q(x,y,z_1,z_2,\\ldots,z_{2m-2})=x^2+xy+y^2+z_1z_2+\\ldots+z_{2m-3}z_{2m-2}.$$\nLet $e_1,\\ldots,e_{\\ell}$ denote the standard basis elements of $\\Z_2^{\\ell}$ and consider the following basis:\n\\begin{align*}\n & v_1=e_1+e_2, \\quad v_2=e_3+e_4,\\quad\\ldots\\quad v_{m}=e_{2m-1}+e_{2m},\\\\\n & v_{m+1}=e_1,\\\\\n & v_{m+2}=e_1+e_3, \\quad v_{m+3}=e_1+e_5,\\quad \\ldots\\quad v_{2m}=e_1+e_{2m-1}.\n\\end{align*}\nThen $Q(v_i)=1$ for all $i$, and for every $v_i$, there exists $v_j$ such that $B_Q(v_i,v_j)=1$. Hence $Q$ is admissible. \n\n\\smallskip\n\n{\\textbf{Case 2:}} $Q=H_+^{\\oplus m}$, where $2m=\\ell$. Note that the only element of $\\Z_2^2$ \nthat is mapped to $1$ by $H_+$ is $(1,1)$. Therefore, $H_+$ is not admissible. However, if $m\\geq 2$, \nthen the following basis of $\\Z_2^{\\ell}$ is admissible for $Q$:\n\\begin{align*}\n &v_1=e_1+e_2, \\quad v_2=e_3+e_4\\quad \\ldots\\quad v_{m}=e_{2m-1}+e_{2m},\\\\\n &v_{m+1}=e_1+e_{2m-1}+e_{2m},\\\\\n & v_{m+2}=e_1+e_2+e_4, \\quad v_{m+3}=e_3+e_4+e_6,\\ldots \\\\\n &v_{2m}=e_{2m-3}+e_{2m-2}+e_{2m}.\n\\end{align*}\n\n\\smallskip\n\n{\\textbf{Case 3:}} $Q=H_+^{\\oplus m}\\oplus Q_1$, where $m\\geq 2$ and $2m+1=\\ell$. \nLet $\\{v_1,\\ldots, v_{2m}\\}$ denote the basis constructed for $H_+^{\\oplus m}$ in Case 2, \nand let $v_{2m+1}=e_1+e_{2m+1}$. Then $\\{v_1,\\ldots, v_{2m+1}\\}$ forms an admissible basis for $Q$.\n\nFor $Q=H_+\\oplus Q_1$, the elements with non-zero quadratic form are $(1,1,0)$, $(1,0,1)$, $(0,1,1)$, $(0,0,1)$. \nAmong these, the only vectors with non-zero bilinear form are the first three, which are linearly dependent \nand thus do not form a basis. Hence $H_+\\oplus Q_1$ is not admissible.\n\\end{proof}\n\nRecall that the group $N_c$ (cf. Notation \\ref{N:N_c}) is a generalized extraspecial group with an admissible basis. \nFrom the previous theorem, we then get:\n\n\\begin{corollary}\\label{C:N_c}\nIf $N_c$ is a generalized extraspecial group whose corresponding quadratic form is admissible,\nthen, up to a direct product with copies of $\\Z_2$, the group $N_c$ is isomorphic to one of the following:\n\\begin{equation}\\label{eq:N_c}\n(Q_{8})^{*m_1},\\qquad (Q_{8})^{*(m_1-1)}*D_8~(m_1\\geq 2),\\qquad (Q_{8})^{*m_1}*\\Z_4~(m_1\\geq 2).\n\\end{equation}\n\\end{corollary}\n\n\\begin{proof}\nThis follows trivially by comparing the quadratic forms in Proposition \\ref{prop:quad-forms} with the classification of admissible quadratic forms in Theorem \\ref{thm:admissible}.\n\\end{proof}\n\nFinally, we prove Theorem \\ref{main-thm:non-abelian part}. \n\n\n\n\\begin{proof}[Proof of Theorem \\ref{main-thm:non-abelian part}]\nFix $p_0\\in L_0$. As discussed in Section \\ref{SS:known-results}, the non-abelian part $K_2$ of $\\pi_1(L_0,p_0)$ \nis a $2$-group of the form $K_2=N\\cdot Z_{(2)}$, where $N$ is generated by the non-central generators of $K$\nand $Z_{(2)}$ denotes the Sylow $2$-subgroup of $Z(K)$. Furthermore, by the discussion in Section \\ref{S:fundamental group}, $N=N_{c_1}\\cdot \\ldots\\cdot N_{c_k}$, where the elements $c_i\\in Z(K)$ have order two.\nBy Corollary \\ref{C:N_c}, each $N_{c_i}$ is of the form $G_i\\times \\Z_2^{a_i}$, where $G_i$ is one of the groups \nlisted in Equation \\eqref{eq:N_c}. Let $a=\\sum_i a_i$. Finally, since all the groups $N_{c_i}$ commute \nwith one another by Lemma \\ref{L:abt-N_c}, one has $N_{c_i}\\cap N_{c_j}\\subseteq Z(N_{c_i})\\cap Z(N_{c_j})$, \nand $Z(N_{c_i})\\subseteq Z(K_2)$. Therefore\n\\[\nK_2\\cong (Z_{(2)}\\times \\prod_{i=1}^kN_{c_i})\/Z'=(Z_{(2)}\\times \\Z_2^a\\times \\prod G_i)\/Z',\n\\]\nwhere $Z'\\subseteq Z_{(2)}\\times \\prod_i Z(N_{c_i})$ is the subgroup of $K_2$ generated by the intersections $H_{ij}=N_{c_i}\\cap N_{c_j}$ and $H_{0j}=Z_{(2)}\\cap N_{c_j}$. Since the groups $H_{ij}$, $H_{0j}$ are all abelian \nand central, commute with one another, and have elements of order $2$ or $4$ (because $Z(N_{c_i})=\\Z_2^{a_i}\\times \\Z_2$ \nor $\\Z_2^{a_i}\\times \\Z_4$) it follows that $Z'=\\Z_2^\\alpha\\times Z_4^\\beta$ for some $\\alpha$ and $\\beta$.\n\\end{proof}\n\n\n\\subsection{Examples of fundamental groups of principal leaves}\\label{SS:examples}\n\nThe family of examples below shows that the non-abelian groups $G_i$ discussed in Theorem \\ref{main-thm:non-abelian part} \nactually arise as fundamental groups of principal leaves of homogeneous singular Riemannain foliations.\n\nLet $\\{e_1,\\ldots, e_n\\}$ be the standard basis of $\\mathbb{R}}\\newcommand{\\C}{\\mathbb{C}}\\newcommand{\\HH}{\\mathbb{H}^n$. The Clifford algebra $Cl(0,n)$ on $\\mathbb{R}}\\newcommand{\\C}{\\mathbb{C}}\\newcommand{\\HH}{\\mathbb{H}^n$\nis defined as the associative algebra generated by $e_1,\\ldots, e_n$, where multiplication of the elements $e_i$ \nis given by:\n$$e_i^2=-1,\\hspace{0.3cm}e_ie_j=-e_je_i.$$\nConsider the subset $E(n)=\\{\\pm e_{i_1}\\ldots e_{i_{2k}}\\}\\subseteq Cl(0,n)$ containing products of even numbers of the $e_i$'s. This is easily seen to be a group under the product of $Cl(0,n)$. In \\cite{CHM09}, Czarnecki, Howe, and McTavish prove that for the action of $G=\\mathrm{SO}(n)\\times\\mathrm{SO}(n)$ \non $M_{n\\times n}(\\mathbb{R}}\\newcommand{\\C}{\\mathbb{C}}\\newcommand{\\HH}{\\mathbb{H})$ defined by $(g,h)\\cdot A=g^TAh$, the fundamental group of a principal orbit is of the form $E(n)\\times\\Z_2$. In this section, we investigate the structure of $E(n)$.\n\n\\begin{lemma}\nLet $G_{0,n-1}$ be the group defined by generators $-1,e_1,\\ldots,e_{n-1}$ and relations\n$$(-1)^2=1,\\qquad (e_i)^2=-1,\\hspace{0.5cm}[e_i,e_j]=-1~(i\\neq j),\\qquad [e_i,-1]=1.$$\nThen the groups $E(n)$ and $G_{0,n-1}$ are isomorphic.\n\\end{lemma}\n\n\\begin{proof}\nWe have: \n$$G_{0,n-1}=\\{\\pm e_{i_1}\\ldots e_{i_{\\ell}}\\mid 1\\leq i_j\\leq n-1, e_i^2=-1, e_ie_j=-e_je_i\\}.$$\nGiven an ordered set $I=(i_1,\\ldots, i_m)$ with indices $i_j$ in $\\{1, \\ldots, n-1\\}$, let $e_I=e_{i_1}\\ldots e_{i_m}$.\nNotice that if $I=(i_1,\\ldots, i_m)$ and $J=(j_1,\\ldots, j_p)$, then $e_Ie_J=e_{I\\cup J}$,\nwhere $I\\cup J=(i_1,\\ldots, i_m,j_1,\\ldots, j_p)$. Now, define the map $\\psi:G_{0,n-1}\\to E(n)$ by\n\\begin{equation*}\n\\psi(e_I)=\n\\begin{cases}\ne_I & |I|~{\\text{even}}\\\\\ne_{I\\cup\\{n\\}} & |I|~{\\text{odd}}\n\\end{cases}\n\\end{equation*}\nFirst, we claim that $\\psi(e_Ie_J)=\\psi(e_I)\\psi(e_J)$ for multi-indices $I$ and $J$.\n\n\\smallskip \n\n{\\textbf{Case 1.}} $|I|$ and $|J|$ are both even. In this case, we have:\n$$\\psi(e_Ie_J)=\\psi(e_{I\\cup J})=e_{I\\cup J}=e_Ie_J=\\psi(e_I)\\psi(e_J).$$\n\n\\smallskip \n\n{\\textbf{Case 2.}} $|I|$ and $|J|$ are both odd. In this case, we have:\n$$\\psi(e_Ie_J)=\\psi(e_{I\\cup J})=e_{I\\cup J}=e_Ie_J=e_Ie_J(-e_ne_n)=e_{I\\cup\\{n\\}}e_{J\\cup\\{n\\}}=\\psi(e_I)\\psi(e_J).$$\n\n\\smallskip \n\n{\\textbf{Case 3.}} If $|I|$ is even and $|J|$ is odd, then\n$$\\psi(e_Ie_J)=\\psi(e_{I\\cup J})=e_{I\\cup J\\cup\\{n\\}}=e_Ie_{J\\cup\\{n\\}}=\\psi(e_I)\\psi(e_J).$$\n\n\\smallskip \n\n{\\textbf{Case 4.}} If $|I|$ is odd and $|J|$ is even, then\n$$\\psi(e_Ie_J)=\\psi(e_{I\\cup J})=e_{I\\cup J\\cup\\{n\\}}=e_{I\\cup\\{n\\}}e_J=\\psi(e_I)\\psi(e_J).$$\n\n\\smallskip\n\nTherefore, $\\psi$ is a homomorphism. It is easy to see that $\\psi$ is injective, and hence an isomorphism\nsince the groups $G_{0,n-1}$ and $E(n)$ have the same order.\n\\end{proof}\n\nThe groups $G_{0,n-1}$ have been classified by Salingaros \\cite{Sal81,Sal82,Sal84} (cf. \\cite{AVW18}).\nWe use this classification to write the group $E(n)\\cong G_{0,n-1}$ as a central product.\nThis gives rise to the following list for fundamental groups of the principal orbits of the $G$-action on $M_{n\\times n}(\\mathbb{R}}\\newcommand{\\C}{\\mathbb{C}}\\newcommand{\\HH}{\\mathbb{H})$:\\\\\n\\begin{equation*}\nE(n)\\times\\Z_2\\cong\n\\begin{cases}\n((Q_8)^{*\\frac{n-4}{2}}*D_8)\\times \\Z_2^2 & n\\equiv 0~({\\text{mod}}~8)\\vspace{0.1cm}\\\\\n(Q_8)^{*\\frac{n-1}{2}}\\times\\Z_2 & n\\equiv 1, 3~({\\text{mod}}~8)\\vspace{0.1cm}\\\\\n((Q_8)^{*\\frac{n-2}{2}}*\\Z_4)\\times\\Z_2 & n\\equiv 2, 6~({\\text{mod}}~8)\\vspace{0.1cm}\\\\\n(Q_8)^{*\\frac{n-2}{2}}\\times\\Z_2^2 & n\\equiv 4~({\\text{mod}}~8)\\vspace{0.1cm}\\\\\n((Q_8)^{*\\frac{n-3}{2}}*D_8)\\times\\Z_2 & n\\equiv 5, 7~({\\text{mod}}~8)\n\\end{cases}\n\\end{equation*}\n\nWe do not know, however, whether \\emph{all} groups in Theorem \\ref{main-thm:non-abelian part} do in fact arise \nas fundamental groups of principal leaves in a simply connected manifold.\n\\smallskip\n\n\\section{Virtually nilpotent fundamental group}\\label{S:nilpotent fundamental group}\n\nIn this section, we consider singular Riemannian foliations $(M,\\mathcal{F})$, where the fundamental group of $M$ is virtually nilpotent.\nAs the following example shows, the fundamental group of a principal leaf is not necessarily nilpotent in this case.\n\n\\begin{example}\nLet $\\hat{M}={\\mathbb{C}}^2\\times{\\mathbb{S}}^1$ and consider the homogeneous foliation $\\hat{\\mathcal{F}}$ \non $\\hat{M}$ induced by the linear action of $T^3=T^2\\times S^1$. Let $M={\\hat{M}}\/{\\Z_2}$, where \nthe non-trivial element $g$ of $\\Z_2$ acts by $g\\cdot(z_1,z_2,t)=({\\bar{z}}_1,{\\bar{z}}_2,t+\\frac{1}{2})$.\nNote that $M$ inherits a singular Riemannian foliation $\\mathcal{F}={\\hat{\\mathcal{F}}}\/{\\Z_2}$.\n\\par\nThe manifold $M$ is orientable, and is homotopy equivalent to ${\\mathbb{S}}^1$. In particular, $M$ is nilpotent.\nHowever, the principal leaf of $\\mathcal{F}$ is $T^3\/{\\Z_2}$ which has fundamental group\n$$G=\\Z^2\\rtimes\\Z=\\langle a, b, c: cac^{-1}=a^{-1}, cbc^{-1}=b^{-1}, ab=ba\\rangle.$$\nSince $G_{\\ell}=\\langle a^{2^{\\ell}}, b^{2^{\\ell}}\\rangle$ for any $\\ell$, $G$ is not nilpotent.\n\\end{example}\n\nNevertheless, in what follows, we prove that the fundamental groups of the leaves contain a nilpotent subgroup of finite index. \n\n\\begin{notation}\nThroughout the rest of this section, $L_0$ denotes a principal leaf of $\\mathcal{F}$. Furthermore, we fix $p_0\\in L_0$, \nand $K=\\langle k_1,\\ldots, k_m\\rangle$ denotes the normal subgroup of $\\pi_1(L_0,p_0)$ discussed at the beginning \nof Section \\ref{S:fundamental group}. Recall that there is a homotopy fibration\n$$L_0\\overset{\\iota_0}{\\rightarrow}M_0\\overset{\\hat{\\theta}}{\\rightarrow}B.$$\nwhich induces a long exact sequence\n\\[\n0\\to H\\to \\pi_1(L_0,p_0)\\stackrel{(\\iota_0)_*}{\\to} \\pi_1(M_0,p_0)\\stackrel{\\hat{\\theta}_*}{\\to} \\pi_1(B,b)\\to 1,\n\\]\nwhere $H=\\partial(\\pi_2(B))$, as well as an action of $\\pi_1(B,b)$ on $L_0$. Denote by $\\hat{K}$ the group generated by $H$ \nand $c\\cdot K$, for $c\\in \\pi_1(B,b)$. Notice that for every $\\gamma\\in \\pi_1(M_0,p_0)$ with $c=\\hat{\\theta}_*(\\gamma)$, \nand every $g\\in\\pi_1(L_0,p_0)$, $(\\iota_0)_*(c\\cdot g)=\\gamma (\\iota_0)_*(g)\\gamma^{-1}$.\n\\end{notation}\n\\begin{lemma}\\label{lemma:central}\nLet $(M,\\mathcal{F})$ be a closed singular Riemannian foliation on a compact Riemannian manifold $M$.\nIf $\\pi_1(M)$ is $n$-step nilpotent, then $(\\pi_1(L_0,p_0))_{n+1}\\subseteq \\hat{K}$, where $(\\pi_1(L_0,p_0))_{n+1}$ \ndenotes the $(n+1)$-th group in the lower central series of $\\pi_1(L_0,p_0)$.\n\\end{lemma}\n\n\\begin{proof}\nSince removing strata of codimension $> 2$ does not change the fundamental group of $M$, we can assume that $M$ \nonly contains singular strata of codimension $\\leq 2$. In particular, we use the notation and results in Section \n\\ref{SS:known-results}.\n\nLetting $\\iota:L_0\\to M$ denote the inclusion, one then has\n\\[\n\\iota_*((\\pi_1(L_0,p_0))_{n+1})\\subseteq (\\pi_1(M,p_0))_{n+1}=1.\n\\]\nTherefore, given any curve $\\alpha$ representing an element of $(\\pi_1(L_0,p_0))_{n+1}$, there exists a disk \n$\\bar{\\iota}:\\mathbb{D}^2\\to M$ extending $\\iota(\\alpha)$. By transversality, this can be deformed to only intersect, \ntransversely, the singular strata $\\Sigma_1,\\ldots,\\Sigma_m$ of codimension 2, and the intersection consists of finitely many points $\\{q_1,\\ldots q_r\\}$ with $q_j\\in \\Sigma_{i_j}$. For each $j=1,\\ldots, r$, let $q'_j$ be a point in $\\bar\\iota(\\mathbb{D}^2)$ \nclose to $q_j$, let $u_j$ be a curve in $\\bar\\iota(\\mathbb{D}^2)$ connecting $p_0$ to $q'_j$, and let $\\psi_j$ a small loop \nin $\\bar\\iota(\\mathbb{D}^2)$ based at $q'_j$, turning once around $q_j$. Finally, let $\\gamma_j=u_j\\star \\psi_j\\star u_j^{-1}$. Then:\n\\begin{enumerate}\n\\item For each $i=1,\\ldots, r$, $[\\gamma_j]\\in \\pi_1(M_0,p_0)$ is conjugate to $(\\iota_0)_*(k_{i_j})$ \nwith $k_{i_j}\\in K\\subseteq \\pi_1(L_0,p_0)$. By the discussion before the proposition, it follows that $[\\gamma_j]=(\\iota_0)_*(c_j\\cdot k_{i_j})$ for some $c_j\\in \\pi_1(B,b)$.\n\\item $(\\iota_0)_*[\\alpha]=[\\gamma_1]\\star\\cdots\\star[\\gamma_r]=(\\iota_0)_*((c_1\\cdot k_{i_1})\\star\\cdots\\star (c_r\\cdot k_{i_r}))$ in $\\pi_1(M_0,p_0)$.\n\\end{enumerate}\n\nSince $H=\\ker((\\iota_0)_*)$, it follows that $[\\alpha]=h((c_1\\cdot k_{i_1})\\star\\cdots\\star (c_r\\cdot k_{i_r}))$ \nfor some $h\\in H$. In particular, $[\\alpha]\\in \\hat{K}$, and therefore $(\\pi_1(L_0,p_0))_{n+1}\\subseteq \\hat{K}$.\n\\end{proof}\n\nWe are finally ready to prove that if $(M, \\mathcal{F})$ is a closed singular Riemannian foliation with $\\pi_1(M)$ virtually nilpotent, then the fundamental group of every leaf is virtually nilpotent as well.\n\n\\begin{proof}[Proof of Theorem \\ref{main-thm:virtually nilpotent}]\nNotice that if $\\pi:\\hat{M}\\to M$ is a finite cover, and $(\\hat{M},\\hat{\\mathcal{F}})$ is the lifted singular Riemannian foliation, one has that a leaf $\\hat{L}\\in \\hat{\\mathcal{F}}$ has virtually nilpotent fundamental group if and only the corresponding leaf $\\pi(\\hat{L})\\in \\mathcal{F}$ does. Therefore, up to replacing $M$ with a finite cover $\\hat{M}$, we can assume that $\\pi_1(M)$ is nilpotent.\n\nLet $L_0$ be a principal leaf, and consider the Hurewicz homomorphism $h:\\pi_1(L_0,p_0)\\to H_1(L_0;\\Z)$ and let $G=h^{-1}(2\\cdot H_1(L_0;\\Z))$. \nClearly, $G$ has finite index in $\\pi_1(L_0,p_0)$, Since $\\pi_1(L_0,p_0)\/G\\cong H_1(L_0;\\Z)\/2\\cdot H_1(L_0;\\Z)$ \nis finite. We claim that if $\\pi_1(M)$ is $n$-step nilpotent, then $G$ is $(n+1)$-step nilpotent.\n\nBy Lemma \\ref{lemma:central}, $G_{n+1}\\subseteq G\\cap \\hat{K}$. The proof is complete once we prove that $G$ \ncommutes with $\\hat{K}$. Notice that $\\hat{K}$ is generated by $H$, and elements of the form $c\\cdot k_i$ for $c\\in \\pi_1(B,b)$ \nand $k_i$ one of the generators of $K$. Recall that $H$ is central in $\\pi_1(L_0,p_0)$ (in particular, $G$ commutes with $H$),\nand for each $g\\in \\pi_1(L_0,p_0)$, $gk_ig^{-1}=k_i^{\\pm 1}$. Since $\\pi_1(B,b)$ acts on $\\pi_1(L_0,p_0)$ \nby group automorphisms, it also follows that for every $g\\in \\pi_1(L_0,p_0)$, $g(c\\cdot k_i)g^{-1}=(c\\cdot k_i)^{\\pm 1}$.\n\nNotice that if $g(c\\cdot k_i)g^{-1}=(c\\cdot k_i)^\\epsilon$ (for $\\epsilon=\\pm1$), then $g^{-1}(c\\cdot k_i)g=(c\\cdot k_i)^{\\epsilon}$ as well. In particular, for every $g_1,g_2\\in \\pi_1(L_0,p_0)$, and every $(c\\cdot k_i)\\in \\hat{K}$, one has:\n\\[\n[g_1,g_2]\\cdot (c\\cdot k_i)[g_1,g_2]^{-1}=(c\\cdot k_i).\n\\]\nThe main observation is that, by definition, any element $g\\in G$ can be written as $g=g_3^2[g_1,g_2]\\cdots [g_{2k-1},g_{2k}]$ \nfor some $g_1,\\ldots g_{2k}\\in \\pi_1(L_0,p_0)$ and therefore, for any generator $(c\\cdot k_i)$ of $\\hat{K}$, one has:\n\\begin{align*}\ng(c\\cdot k_i)g^{-1}=&g_3^2[g_1,g_2]\\cdots [g_{2k-1},g_{2k}](c\\cdot k_i)[g_{2k-1},g_{2k}]^{-1}\\cdots [g_1,g_2]^{-1}g_3^{-2}\\\\\n=&g_3^2(c\\cdot k_i)g_3^{-2}=g_3(c\\cdot k_i)^\\epsilon g_3^{-1}\\\\\n=&(c\\cdot k_i)^{\\epsilon^2}=(c\\cdot k_i).\n\\end{align*}\nTherefore, $G$ commutes with $\\hat{K}$ and hence $G_{n+2}=[G,G_{n+1}]\\subseteq [G,\\hat{K}]=\\{1\\}$.\n\nThis proves that the principal leaves of $\\mathcal{F}$ have virtually nilpotent fundamental group. The corresponding statement for the non-principal leaves then follows from Lemma \\ref{L:other-leaves}.\n\\end{proof}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nDirect photon production is of special importance in relativistic heavy-ion collisions (for reviews, see \\cite{Gale:2009gc,Chatterjee:2009rs}). Since photons couple with the quark-gluon plasma (QGP) only through the electromagnetic interaction, their mean free path is much longer than the dimension of the reaction zone and they can escape from it carrying the information of QGP at the instance of their emission. These photons are called thermal photons, whose observation is regarded as an indirect evidence for the formation of hot thermalized states of quarks and gluons in relativistic heavy-ion collisions \\cite{Shuryak:1978ij,Kapusta:1991qp}. There are still other various photon sources over the time evolution in a heavy-ion collision event in addition to thermal photons from the QGP phase. For example, at the very initial stage, partonic hard collisions (such as the gluon Compton scattering $gq\\to \\gamma q$ and quark annihilation $q\\bar q \\to \\gamma g$) produce the photons, which are called prompt photons \\cite{Owens:1986mp,Turbide:2007mi,Klasen:2013mga}. At the late stage after hadronization, scattering processes between hadrons also produce photons \\cite{Kapusta:1991qp,Holt:2015cda}. Thus, identifying and quantifying photon sources become a vital issue for extracting direct information on QGP from the photon observation.\n\n\nMeasurements of the photons in relativistic heavy-ion collisions have been performed both at RHIC and LHC, and the results of transverse momentum ($p_T$) distributions $dN^\\gamma\/dp_\\perp^2 dy$ and elliptic flow coefficients ($v_2^\\gamma$) at mid-rapidity $y\\sim 0$ are reported \\cite{Adare:2014fwh,Adare:2015lcd,Adam:2015lda,Lohner:2012ct} (see also \\cite{David:2019wpt} for recent experimental review). From the exponential slopes of the $p_T$ distributions, ``{\\it effective temperatures}'' of produced photons at RHIC ($\\sqrt{s_{NN}}=200~$GeV) and LHC ($\\sqrt{s_{NN}}=2.76~$TeV) are found to be $T_{\\rm eff}\\sim 230~$MeV and $T_{\\rm eff}\\sim 300$~MeV, respectively. Very interestingly, the elliptic flow $v_2^\\gamma$ of the direct photon distribution is found be as large as that of hadrons. Notice that although the ``decay photons'' produced in hadron decays (mainly due to $\\pi^0\\to 2 \\gamma$) have been subtracted from the total yield to obtain the direct photon yield, it is still a mixture of photons from various sources. Despite many theoretical attempts to reproduce these experimental results, any theoretical model so far seems to be incapable of explaining the photon data adequately. For example, hydrodynamic models which explain the hadronic spectra and anisotropic flow very well, tend to underpredict the amplitude of photon elliptic flow (e.g., see \\cite{Chatterjee:2021gwa}). In particular, even an up-to-date hydrodynamic model calculation with various possible effects included still underestimates the photon yield \\cite{Paquet:2017wji,Gale:2021emg}. Other model calculations such as blast-wave type fireball model and ideal hydrodynamic model also give rise to smaller thermal photon yield and elliptic flow than the experimental data by PHENIX an ALICE \\cite{vanHees:2014ida}. Furthermore, the parton-hadron-string dynamics (PHSD) model shares the same tendency in direct photon yield and elliptic flow \\cite{Linnyk:2015tha}. Meanwhile, the pre-equilibrium Glasma stage is discussed in Refs.~\\cite{Berges:2017eom,Monnai:2019vup} as a possible photon production source, but its contribution is also likely to suppress the elliptic flow.\n\nThis situation is referred to as the \"direct photon puzzle\".\nThe difficulty comes from two seemingly contradictive aspects of the photon data, large yields and strong collective flow. The large yield could be attributed to early stage of the evolution with higher temperatures, while the strong collective flow prefers large photon emission at later stage when momentum anisotropy of QGP is well developed. Therefore, it is not easy to explain these two points within a single theoretical model. However we should note that there is a reservation about the experimental results because the large yield of photons measured by PHENIX has not been confirmed by STAR.\n\nGiven this situation, it should be very worthwhile to examine another source of photons which has been overlooked so far and is inherent to late stages of the QGP time evolution. This brought us to think of a possibility of photon radiations at hadronization of QGP, which is, in fact, natural from the viewpoint of ordinary electromagnetic plasmas. It is well-known that an electromagnetic plasma radiates numerous photons when it goes back to an atomic gas. This process is called the ``radiative recombination\" and is seen in various astrophysical situations (for an overview see \\cite{graham2012recombination, hahn1997electron}. This is natural because photon emission is advantageous for satisfying energy conservation in the formation of bound states (electrically neutral atoms). We can expect similar phenomena occur in the hadronization processes, where valence quarks and antiquarks form bound states. In the present paper, we formulate photon production at the hadronization stage in analogy with the radiative recombination in electromagnetic plasmas and investigate properties of the produced photons. Photon production during QCD phase transition and hadronization has been attracting much attention indeed, and been discussed in different frameworks, for example, in \\cite{vanHees:2014ida,Campbell:2015jga,Young:2015adw}. \n\n\nThe present paper is organized as follows. In the next section, we explain the radiative recombination in ordinary electromagnetic plasma and comment on similarities to and differences from the QGP. Then in Sec.~III, we provide the basic theoretical framework for radiative recombination applied to hadronization.\nNumerical results are presented in Sec.~IV, where we include the thermal photon contributions obtained by a hydrodynamic simulation to compare the results to the observed data.\nDiscussions and summary are given in Sec.~V. \n\nIn Appendix A, we present photon production formulas in radiative recombination model.\nIn Appendix B, we give a brief description of thermal photon calculation in a hydrodynamic model \\cite{Miyachi-Nonaka}.\n\n\n\n\\section{Radiative recombination in electromagnetic plasmas}\n\nIn this section, we explain what is known about radiative recombination in ordinary plasmas and in other relevant processes, which are helpful to understand how the radiative recombination in QGP could be formulated. The primary and simplest example reads an electromagnetic plasma made of electrons and protons. When the temperature is decreased, the plasma decays and neutralizes into a hydrogen gas through the microscopic mechanism of the radiative recombination $e^-+p^+\\to {\\rm H}^0 +\\gamma$. Emission of a photon compensates the energy difference between the initial continuum state and the final bound state. The ordinary plasma flashes when it decays. This process is well-known in plasma physics and is also called ``free-bound transition\" \\cite{fujimoto2004plasma}. The secondary example is the glow discharge which is seen as a typical picture of a plasma. The third example is radiative recombination in the early universe: There was a ``recombination era\" and without treating the radiative recombination we would not be able to accurately evaluate the ``recombination temperature'' which is essential for understanding the cosmic microwave background (CMB) data \\cite{weinberg2008cosmology}. The fourth example is gas nebulae, such as Orion Nebula, which have beautiful radiations containing continuum spectra due to the radiative recombination \\cite{osterbrock2006astrophysics}. Lastly, in addition to these examples induced by the electromagnetic interaction, we also have examples in nuclear reactions. We know that in the sun there are two important processes called the ``pp chain\" and the ``CNO cycle,\" both of which include formation of bound states accompanied by a photon emission (such as $D + p \\to {}^{3}{\\rm He}+\\gamma$ in the pp chain and $p+{}^{12}{\\rm C}\\to {}^{13}{\\rm N}+\\gamma$ in the CNO cycle) \\cite{clayton1968principles}. All these examples indicate that radiation is naturally inherent to the formation of bound states, which suggests the possibility of photon emissions at hadronization, provided that hadronization can be modeled as a coalescence process of valence partons.\n\n\n\nThere are two key equations for the description of radiative recombination in electromagnetic plasmas \\cite{rybicki2008radiative,weinberg2008cosmology}. One is the Kramers-Milne relation which relates recombination cross section to that of its inverse process (photo-ionization $\\gamma + A \\to e^- + A^+$) and corresponds to the detailed balance relation in thermal equilibrium states. The other is the Saha equation which gives ionization ratio $X=n_{\\rm ion}\/(n_{\\rm ion}+n_{\\rm atom})$ between the atom and ion numbers, $n_{\\rm atom}, n_{\\rm ion}$, as a function of a temperature when the electrons, ions, and atoms are in thermal equilibrium. The Kramers-Milne relation is useful because the photo-ionization rate is easily measured by experiments, and the Saha equation applies to an ordinary plasma because one can control the lifetime of a plasma much longer than the microscopic time scale of radiation reaction. These relations are, unfortunately, not suitable to the hadronization from QGP in heavy-ion collisions since it should be treated as a nonequilibrium process. Hadronization occurs in a small lump of QGP at the time scale of the strong interaction, and therefore the photons are just emitted without re-absorption. To this situation we cannot use the relations which assume a system in bulk equilibrium. \n\n\nWhen the density of an electromagnetic plasma is relatively high, another type of recombination will be possible. It is the ``three-body recombination\" $2 e^- + A^+\\to e^- + A$, where the energy conservation is satisfied by the spectator electron. \nIf one defines the effective photon emission rate at some density, it will be reduced by the presence of the three-body, or in general, multi-body recombination. We may expect similar phenomena in the hadronization process. Formation of bound states without photon emission will be possible if the valence partons interact with other particles in the medium. We will be able to absorb this kind of effects into an effective recombination rate including density dependence. In fact, as we will discuss later, since we treat the recombination rate as a parameter, we expect that such kind of effects are included in the overall normalization parameter which is determined to fit the experimental data. Alternatively, effects of multi-body recombination without a photon emission will be described by ``off-shell'' valence partons. However, within our framework in the present analysis, it is not easy to define off-shell partons in QGP. \n\nLastly, we comment on a very similar process in $e^+e^-$ collisions. Recall that the $e^+e^-$ collisions have been used to discover new particles by changing the invariant mass. Pronounced resonances such as $\\rho, \\omega, \\phi$ and $J\/\\psi$ mesons are measured with energies {\\it below} the invariant mass of the $e^+e^-$ system. This is called the {\\it radiative return}, and is quite important for the analysis of $R$-ratio around threshold and $(g-2)$ of leptons (for reviews, see \\cite{Actis:2010gg, Druzhinin:2011qd}). Notice that the formation of a resonance below the $e^+ e^-$ invariant mass is accompanied by photon emissions and the typical radiative return is represented as \n$\ne^+ + e^- \\to \\text{hadrons} + n\\gamma\\, ,\n$\nwhere the number $n$ of emitted photons is not necessarily one, $n\\ge 1$. In particular, a clean process with a single meson and a single photon \n$\ne^++e^-\\to \\text{meson} + \\gamma\n$ \nis experimentally measured. For example, $J\/\\psi$ production was observed at BaBar \\cite{Aubert:2003sv}, and more recently $\\chi_c$ and $\\eta_c$ at BESIII \\cite{Ablikim:2014hwn, Ablikim:2017ove}. The counterpart in purely QED case such as $e^+e^-\\to \\mu^+\\mu^-\\gamma$ can be perturbatively calculated (though quite tedious), and one can study interplay between the initial state radiation and the final state radiation. On the other hand, hadron production suffers from ambiguity related to the coupling between the virtual photon and a composite hadron. For example, $e^++e^- \\to \\gamma^* \\to \\text{meson}+\\gamma$ includes the transition of a virtual photon into a meson which may be phenomenologically described by the vector meson dominance. Heavy quarkonium production will have less ambiguity, but the NRQCD formalism developed for the calculation of heavy-quarkonium production involves nonperturbative matrix elements (see for example, \\cite{Chung:2008km, Sang:2009jc}). \n\n\nThere are Monte-Carlo generators for the radiative return called PHOKHARA (for real photon emission) and EKHARA (for virtual photon emission) \\cite{Czyz:2017veo}. These generators treat radiation by the vertices like $meson^*\\to meson + \\gamma\\, ({\\rm or}\\ \\gamma^*)$ which are given by effective lagrangian \\cite{Czyz:2012nq}. Here, $meson^*$ could be a virtual (or off-shell) meson. This kind of picture will be useful in our problem. Another lesson from the radiative return is that the final state could involve several hadrons, typically light mesons such as pions and kaons. For example, final states with four mesons like $\\pi^+ \\pi^- K^+ K^-$ were extensively studied \\cite{Actis:2010gg}. We expect similar multiple hadron production in the radiative hadronization. As we will discuss in the next section, we will adopt the ``Recombination model\" and modify it so that it allows photon emission. This recombination model provides the number of produced mesons by the overlapping between a $q\\bar q$ state and a meson state, and implicitly assumes a single meson production from a $q\\bar q$ state. However, the multiple hadron production seen in the $e^+e^-$ collisions suggests that the one-to-one correspondence between a $q\\bar q$ state and a meson should not exactly hold. Still, we may be able to effectively absorb such effects into the recombination rate. We should be aware that the overall recombination rate could contain many different physical effects. Having said all these suggestions and caveats, we are now ready for the problem how to formulate the radiative recombination at hadronization. \n\n\n\n\n\\section{Radiative hadronization: formulation}\n\nHadronization is a nonperturbative process because it takes place around the critical temperature $T\\sim T_c$ and the typical strong coupling $\\alpha_s(T\\sim T_c)$ is not small. It is also a nonequilibrium phenomenon in the sense mentioned in the previous section. One of the possible frameworks to describe the hadronization will be to work in an effective theory that includes both hadronic and constituent-quark degrees of freedom (see \\cite{Young:2015adw} for an analysis in the quark-meson coupling theory). Within this framework, one can compute the hadron production cross sections similarly to the radiation return. We will, however, take an alternative approach. In fact, as already commented before, we know a simple and phenomenologically successful model for hadronization which is based on the coalescence of constituent quark degrees of freedom. It is the recombination model and we utilize it to describe the radiative hadronization. Below we first explain briefly the basic strategy of the recombination model, and then discuss how to modify it to include the photon emission. \n\n\n\n\n\\subsection{Recombination model}\n\nThe recombination\/coalescence models provide a phenomenological description of hadronization for hadron production in the intermediate $p_T$ region ($2 \\simle p_T \\simle 5$~GeV\/c) and give a natural explanation for intriguing phenomena such as the anomalous baryon\/meson ratio and constituent quark number (CQN) scaling of the elliptic flow \\cite{Fries:2003vb,Greco:2003xt,Molnar:2003ff,Hwa:2003bn} (for a review, see \\cite{Fries:2008hs}). There are several recombination\/coalescence models with some differences \\cite{Fries:2003kq, Greco:2003mm,Hwa:2004ng}, and we will adopt the ReCo model that was developed by Duke group \\cite{Fries:2003kq}. \n\n\nThe ReCo model starts by defining the number of hadrons that one can find in the quark\/antiquark distributions. For example, the total number of mesons is defined as an overlap between the meson state $|M;\\bm{P}\\rangle$ and the reduced two-body density matrix $\\hat \\rho_{ab}$ which represents the partonic system with partons, $a$ and $b$, undergoing hadronization:\n\\begin{eqnarray}\nN_M=\\sum_{ab}\\int \\frac{d^3\\bm{P}}{(2\\pi)^3}\\, \\langle M; \\bm{P}|\\, \n\\hat \\rho_{ab} \\, |M;\\bm{P}\\rangle \\, ,\n\\end{eqnarray}\nwhere summation is taken over all the possible combinations of partons $a, b$ that have nonzero overlap with a mesonic state.\nIn this sense, the ReCo model simply projects the partonic picture of the QGP onto the hadron picture, and does not describe dynamical processes of hadron formation. However, since the formula includes matrix elements like $\\langle M;\\bm{P}|\\bm{r}_1,\\bm{r}_2\\rangle$ with $|\\bm{r}_1,\\bm{r}_2\\rangle$ being a state having a quark at $\\bm{r}_1$ and an antiquark at $\\bm{r}_2$, it allows for an intuitive understanding of coalescence processes\\footnote{Note however that this matrix element will be interpreted as a quark-antiquark component of a meson wavefunction.} like $q\\bar q\\to M(\\text{meson})$ and $qqq\\to B(\\text{baryon})$. \n\n\nAfter some manipulations (see \\cite{Fries:2003kq} for details), one obtains the momentum distribution of mesons made of partons $a$ and $b$ as \n\\begin{equation}\nE\\frac{dN_M}{d^3\\bm{P}}=C_M \\int_\\Sigma \\frac{P\\cdot u(R)}{(2\\pi)^3}\\int_0^1 dx\\, w_a(R;x\\bm{P})\\, \\left|\\phi^{}_M(x)\\right|^2\\, w_b(R;(1-x)\\bm{P}) \\, , \n\\label{dN\/dP}\n\\end{equation}\nwhere $P^\\mu=(E,\\bm{P})$ is the four-momentum of the meson $M$, and $R$ is a four-vector specifying a point on the hypersurface $\\Sigma$ where the hadronization takes place, and $u^\\mu (R)$ is a unit vector orthogonal to the hypersurface $\\Sigma$ at $R$. The function $\\phi_M(x)$ is the light-cone wavefunction of a meson with $x$ being the momentum fraction of one of the two quarks, and $w_a(R;x\\bm{P})$ is a one-particle phase space distribution of parton $a$,\n\n\nWe assume the longitudinal boost-invariant expansion (Bjorken expansion) of the QGP so that the hadronization hypersurface $\\Sigma$ has a constant longitudinal proper time $\\tau=\\sqrt{t^2-z^2}=\\, $const and a point $R^\\mu$ on it is specified as\n$$\nR^\\mu = (t,x,y,z)=(\\tau \\cosh \\eta,\\, \\rho \\cos \\phi,\\, \\rho \\sin \\phi ,\\, \\tau \\sinh \\eta)\\, \n$$\nwith the space-time rapidity $\\eta$, the transverse radial coordinate $\\rho$, and the azimuthal angle $\\phi$. Then the forward normal vector $u^\\mu(R)$ orthogonal to the hypersurface $u\\cdot dR|_{\\tau= \\rm const.} =0$, is given by $u^\\mu(R)=(\\cosh \\eta,0,0,\\sinh \\eta)$.\n\n\nRegarding $\\phi_M(x)$, we expect that our results are insensitive to its details and take $|\\phi_M(x)|^2=\\delta(x-1\/2)$ for analytic evaluation and $\\phi_M(x) =\\sqrt{30}\\, x(1-x)$ for numerical evaluation. Notice that both examples of the wavefunction have a peak at $x=1\/2$, and therefore $w_a(R,\\tfrac{1}{2}\\bm{P})w_b(R,\\tfrac{1}{2}\\bm{P})\\sim {\\rm e}^{-P\/T}$ gives rise to the dominant configuration in the $x$ integration. \nThe overall factor $C_M$ counts state degeneracy. The corresponding formula for baryon production has the factor $C_B$ and the wavefunctions of the three-quark state should have a peak around $x=1\/3$, and therefore $w_a(R,\\tfrac{1}{3}\\bm{P})w_b(R,\\tfrac{1}{3}\\bm{P})w_c(R,\\tfrac{1}{3}\\bm{P})\\sim {\\rm e}^{-P\/T}$. Thus, this model predicts that the ratio of the proton to the pion yield at the common $P_T$ at mid-rapidity, $R_{p\/\\pi}\\equiv \\frac{dN_p}{d^2P_Tdy}\\big{\/}\\frac{dN_{\\pi^0}}{d^2P_Tdy}$, is essentially given by a ratio $C_B\/C_M$, which amounts to $\\sim 2$. This is indeed consistent with the experimental result known as the anomalous baryon\/meson ratio, which is in contrast to the expectation from parton fragmentation processes in perturbative QCD, $R_{p\/\\pi}\\sim 0.2$.\n\n\nFor $w_a(R;x\\bm{P})$, \nwe assume that at the onset of hadronization the quarks\/antiquarks are in local thermal equilibrium with a fluid flow velocity $\\bar v^\\mu(R)$, which we parameterize as \n\\begin{equation}\n\\bar v^\\mu(R)=(\\cosh \\eta^{}_L \\cosh \\eta^{}_T,\\, \\sinh \\eta^{}_T \\cos \\phi, \\, \\sinh \\eta^{}_T \\sin \\phi,\\, \\sinh \\eta^{}_L \\cosh \\eta^{}_T)\\, , \\label{normalized_flow_vector}\n\\end{equation}\nwhere $\\eta^{}_L$ and $\\eta^{}_T$ are longitudinal and transverse flow rapidities, respectively. This four-velocity is normalized as $\\bar v_\\mu \\bar v^\\mu=1$.\nIn the Bjorken expansion, which we assume in this work, the longitudinal flow rapidity $\\eta_L$ is identified with the space-time rapidity $\\eta$, i.e., $\\eta^{}_L=\\eta$, and the longitudinal flow velocity is $v_L=\\tanh \\eta = z\/t$. On the other hand, the transverse rapidity $\\eta_T$ is related to the transverse flow velocity $v^{}_T=\\sqrt{v_x^2+v_y^2}$ by\n\\begin{equation}\n v^{}_T=\\tanh \\eta^{}_T \\, ,\n \\label{transverse_velocity}\n\\end{equation}\nat mid-rapidity ($\\cosh \\eta_L = 1$).\nWe assume that $v_T$ is independent of the transverse radius $\\rho$ for computational simplicity.\n\n\nBy using this flow velocity $\\bar v^\\mu(R)$, we take the one-body phase space distribution of parton $a$ as \n\\begin{equation}\nw_a(R;p)=\\gamma_a \\, {\\rm e}^{-p\\cdot \\bar v(R)\/T} {\\rm e}^{-\\eta^2 \/2\\Delta^2}\nf(\\rho, \\phi)\\, ,\n\\label{phase_space_dist}\n\\end{equation}\nwhere $\\gamma_a$ is the fugacity factor of parton $a$.\nThe factor ${\\rm e}^{-\\eta^2 \/2\\Delta^2} f(\\rho, \\phi)$ describes the spatial profile of the hot medium.\nFor central collisions, one may simply assume a constant profile $f(\\rho,\\phi)=\\theta(\\rho_0-\\rho)$\nwithin the transverse radius $\\rho_0$ of the fireball at the recombination time $\\tau$.\nIn more general cases, the transverse profile will be adjusted to reproduce the collision-centrality dependence of observed meson yields.\nMeanwhile, the $\\eta$-dependence may be ignored as far as the mid rapidity region is concerned. These approximations will be used in analytic evaluations of the ReCo model below.\n\n\n\nWe have assumed that the quark momentum distribution in $w_a$ has the thermal profile\n${\\rm e}^{-p \\cdot \\bar v(R)\/T}$\nof temperature $T$, boosted by the collective flow $\\bar v(R)$.\nWe introduce elliptic anisotropy in this momentum distribution by a weak modulation of the transverse flow rapidity $\\eta_T$\naround the mean $\\overline{\\eta}_T$:\n\\begin{align}\n \\eta_T(\\phi;p_T)=\\overline{\\eta}_T (1 - h(p_T)\\cos 2\\phi) \\, .\n\\label{etaT_phi}\n\\end{align}\nThe modulation amplitude $h(p_T)$ is assumed to be $p_T$-dependent:\n\\begin{align}\n h(p_T)=\\frac{\\alpha}{1+(p_T\/p_0)^a}\n\\label{hpt-profile}\n\\end{align}\nwith $\\alpha=(1-r)\/(1+r)$ fixed by the transverse aspect ratio $r$ of the almond-shape collision zone,\nand with $a$ and $p_0$ the constant parameters controlling the momentum dependence.\nWe then calculate the parton distribution eq.~(\\ref{phase_space_dist}),\nto find the elliptic flow coefficient $v_2^a(p^{}_T)$ of parton $a$ defined by \n\\begin{eqnarray}\nw_a(R;p)=\\overline w_a(R;p)\\Big(1+2v_2^a(p^{}_T)\\cos 2\\phi \\Big)\\, ,\n\\end{eqnarray}\nwhere $\\overline w_a(R;p)$ is the part independent of the azimuthal angle $\\phi$.\nInserting this distribution into the formula (\\ref{dN\/dP}), we can compute the elliptic flow coefficient for\na meson transverse momentum ($P_T$) distribution at mid-rapidity:\n\\begin{equation}\n v_2^M(P_T)\\equiv \\langle \\cos 2\\Phi \\rangle^{}_{P_L=0}\n =\\frac{{\\displaystyle \\int d\\Phi \\cos 2\\Phi \\left(\\frac{dN_M}{d^2P_TdP_L}\\Big|_{P_L=0}\\right)} }\n {{\\displaystyle \\int d\\Phi \\left(\\frac{dN_M}{d^2P_TdP_L}\\Big|_{P_L=0}\\right)}}\\, ,\n\\end{equation}\nwhere $\\Phi$ is the azimuthal angle of the produced meson momentum.\nIf we adopt the $\\delta$-function approximation for the light-cone wavefunction $|\\phi_M(x)|^2=\\delta(x-1\/2)$\nand assume a universal elliptic flow coefficient for all quark flavors,\n$v_2^q(p_T)\\equiv v^a_2(p_T)$,\nthen the elliptic flow of the meson momentum distribution is analytically evaluated as \n\\begin{eqnarray}\nv_2^M(P_T)=\\frac{2\\, v^q_2(\\frac12 P_T)}{1+2\\, v^q_2(\\frac12 P_T)^2}\n\\, ,\n\\end{eqnarray}\nwhich simplifies further for $v^q_2(p_T)\\ll 1$ to\n\\begin{eqnarray}\nv_2^M(P_T)\\simeq 2v^q_2(P_T\/2)\\, .\n\\end{eqnarray}\nSimilarly, for baryons one finds \n\\begin{eqnarray}\nv_2^B(P_T)\\simeq 3v^q_2(P_T\/3)\\, .\n\\end{eqnarray}\nTherefore, the elliptic flow coefficient of a hadron with $n$ constituents satisfies\nthe following scaling:\n\\begin{eqnarray}\nv_2^h(P_T)\\simeq nv^q_2(P_T\/n)\\, .\n\\label{eq:CQNscaling}\n\\end{eqnarray}\nIf we plot $v_2^h(P_T)\/n$ as a function of $P_T\/n$ for various hadrons, the results will collapse into a single curve which is determined by the quark elliptic flow coefficient $v_2^q$. This ``CQN scaling\" is indeed observed in experimental data, and is regarded as one of the evidences for the quark recombination in hadronization and also for formation of a thermalized QGP in relativistic heavy-ion collision experiments. Nevertheless, as is obvious from the above derivation of the scaling, it appears only in an idealized situation and a certain deviation is expected from the scaling limit even when the recombination mechanism dominates in hadronization. Such deviations will have different sources, from which we can extract physical information on hadronization.\n\n\n\nAs we emphasized before, the recombination model describes the meson (baryon) formation as a 2-to-1 (3-to-1) process. If we take this literally, it implies that the model violates the energy conservation law because the invariant mass of the initial state with two (three) partons cannot be the same as the energy of a bound state. In Ref.~\\cite{Fries:2003kq}, it is argued that the energy conservation would be preserved by the effects of interactions with the medium (which generates a width in parton dispersion) and that omission of explicit treatment of such effects would not significantly affect the bulk features of hadron production.\nThe medium effect on the recombination reminds us of the three-body recombination in electromagnetic plasmas, where one of the three is just a spectator to keep the energy conservation.\n\n\nBut, at the same time, we notice another process which satisfies the energy conservation law in electromagnetic plasmas. It is the radiative recombination. The counterpart process in hadronization should be also possible.\\footnote{While it is possible to make energy conserved within a dynamical model including the effects of resonances as developed in \\cite{Ravagli:2007xx}, we will take a different picture. } In the next subsection we will discuss how to modify the ReCo model so that it describes the radiative hadronization. \n\n\n\\subsection{Radiative hadronization model}\n\nWe modify the ReCo model so as to allow for a photon emission, which we call radiative ReCo model.\nThen, the meson formation process becomes a 2-to-2 process,\n\\begin{equation}\n q+\\bar q \\to M+\\gamma\n \\, ,\n \\label{radiative_hadronization}\n\\end{equation}\nwhich fulfills the energy conservation law. A similar modification for baryon formation with a photon emission: $qqq\\to B + \\gamma$ should be also possible.\n\n\n\\begin{figure}[t] \n\\begin{center}\n\\vspace*{-1mm}\n \\includegraphics[width=0.7\\hsize]{NewReCo.pdf}\n\\end{center}\n\\caption{Radiative ReCo model with a photon emission.}\n\\label{fig:ReCo}\n\\end{figure}\n\n\nIn our radiative ReCo model, we re-interpret the original ReCo model \\cite{Fries:2003kq} as a tool for picking up a ``preformed state'' consisting of two partons and assume the preformed state emits a photon to form a bound state (see Fig.~\\ref{fig:ReCo}). Notice that we do not consider this preformed state as any physical resonance but just as an intermediate state in radiative meson production.\n\nThe preformed state consisting of two partons with momenta $p_1^\\mu=(E_1, \\bm{p}_1)$ and $p_2^\\mu=(E_2, \\bm{p}_2)$,\nhas the invariant mass $M_*$ and momentum $\\bm{P}$ determined by\n\\begin{eqnarray}\nE \\equiv \\sqrt{M_*^2+\\bm{P}_{}^2}&=&\\sqrt{m_1^2+\\bm{p}_1^2}\n+\\sqrt{m_2^2+\\bm{p}_2^2}\\, ,\\\\\n\\bm{P}&=&\\bm{p}_1 + \\bm{p}_2\\, ,\n\\end{eqnarray}\nwhere $m_1$ and $m_2$ are the constituent quark masses.\nThe invariant mass $M_*$ is a function of $\\bm{p}_1$ and $\\bm{p}_2$ and is evidently larger than $m_1+m_2$:\n$$\nM_*\\ge m_1+m_2\\, .\n$$\nOn the other hand, in the constituent quark picture, the mass of the bound state $M$ should be smaller than $m_1+m_2$ by the binding energy: $M < m_1+m_2$. The surplus energy of $M_* - M>0$ should be carried away by a photon emission here.\nThen, the number of the photons emitted in the formation of a meson $M$ reads\n\\begin{equation}\nE_\\gamma \\frac{d N_\\gamma}{d^3k}=\\kappa \\int dM_*\\, \\varrho(M_*)\\int d^3P \n\\left(\\frac{dN_{M_*}}{d^3P}\\right)\\left(E_\\gamma \\frac{dn_\\gamma(k; M_*,P)}{d^3k }\\right) \\, ,\n\\label{photon_distribution}\n\\end{equation}\nwhich means that it is given by the product of the number of preformed states and the photon distribution emitted from a preformed state. We explain each ingredient below.\n\nFirst of all, ${dN_{M_*}}\/{d^3P}$ is the number of the preformed states which is characterized by momentum ${\\bm P}$ and invariant mass $M_*$. We evaluate this part by the original ReCo model. Although we do not know the light-cone wavefunction of the preformed state, we expect that the results are insensitive to its details as we already commented concerning the original ReCo model. We use the same light-cone wavefunctions as in the original ReCo model. \n\nNext, $E_\\gamma {dn_\\gamma(k; M_*,P)}\/{d^3k}$ corresponds to the photon distribution emitted from the preformed state moving with the momentum $\\bm{P}$. We adopt a tree picture $M_*\\to M+\\gamma$ which is the leading order with respect to QED coupling $\\alpha$. We assume no specific polarization of the preformed states, and the photon distribution is treated as isotropic in the rest frame of the preformed state. More explicitly, we use the following photon distribution:\n\\begin{equation}\n E_\\gamma \\frac{dn_\\gamma}{d^3 k}\n = \\frac{1}{4\\pi k_0}\\, \\delta (E_{\\gamma _{\\rm CM}} - k_0)\n = \\frac{M_*}{4\\pi k_0}\\, \\delta (k \\cdot P - k_0 M_*), \n\\label{photon_CM_dist}\n\\end{equation}\nwhere $E_{\\gamma _{\\rm CM}}$ is the photon energy\nand $k_0\\equiv (M^2_*-M^2)\/(2M_*)$ is the momentum magnitude of the photon and the meson in the center-of-mass (CM) frame of the preformed state ($M_*$-rest frame) with $M$ being the mass of the accompanying meson. The photon distribution is normalized as $\\int dn_\\gamma =1$.\n\n\nWe also introduced $\\varrho(M_*)$ to represent an invariant mass distribution of the preformed states of two partons. It should have a support for $M_*\\ge m_1+m_2$. Considering the thermal distributions of quarks and antiquarks, we expect that the number of preformed states will rapidly decrease with increasing $M_*$. Therefore, we will replace it with the threshold contribution, {\\it i.e.}, $\\varrho(M_*)= \\delta(M_*-(m_1+m_2))$ in this paper.\n\n\nFinally and importantly, we comment on the overall factor $\\kappa$ which is introduced to reflect other possible effects on radiative hadronization. Consider the recombination process mediated by a preformed state $q+\\bar q \\to M_* \\to X$ where $X$ stands for any physical final states, including the radiative hadronization $X=M+\\gamma$.\nIn general, however, $X$ can be multiple hadron states (with photons), as we discussed previously in relation to the radiation return in $e^+e^-$ collisions.\nOnce we compute all possible diagrams for the decay of $M_*$, we are able to define the ``branching ratio\" for the radiative hadronization which corresponds to the factor $\\kappa$. Since the transition probability for $M_*\\to M+\\gamma$ would be proportional to the QED coupling $\\alpha=1\/137$, one may naively expect that $\\kappa$ would be of the order of $\\alpha$. On the other hand, we also know empirically that the CQN scaling works very well up to several GeV of meson momentum $p_T$. This fact suggests that a single meson formation would be the dominant process over multiple meson production. If so, the single photon emission attached to this dominant process may have different value of $\\kappa$ from the naive expectation.\nTherefore, we leave the overall factor $\\kappa$ as a parameter to be determined\nby the experimental data.\\footnote{One can consider gluon radiation,\n $M+g$, but the gluons are strongly interacting and will be re-absorbed into medium.}\n\n\nWe remark here that the number of mesons is given by the same formula as eq.~\\eqref{photon_distribution},\nwith the last factor replaced by the meson distribution emitted from the preformed state:\n\\begin{equation}\nE_M \\frac{d N_M^{\\rm radReCo}}{d^3K}= \\kappa \\int dM_*\\, \\varrho(M_*)\\int d^3P \n\\left(\\frac{dN_{M_*}}{d^3P}\\right)\n\\left(E_M \\frac{dn_M(K; M_*,P)}{d^3K}\\right) \\, ,\n\\label{meson_distribution}\n\\end{equation}\nwhere $K^\\mu$ is a momentum of the produced meson, satisfying $P_\\mu=K_\\mu + k_\\mu^\\gamma$. The distribution $dn_M\/d^3K$ can be defined in a similar way to the photon case. \n\n\nRecall that the original ReCo model naturally explains the CQN scaling.\nIn our modified ReCo model, on the other hand, the scaling should appear at the level of the distribution of preformed states $M_*$ in the integrand of eq.~(\\ref{meson_distribution}) and\nthe meson production accompanying a photon emission may violate the CQN scaling to some extent.\nWe check this point both analytically and numerically in this paper.\nBut we emphasize here that the main contribution to hadron yield at around 2 GeV is given by the original ReCo model\nand the radiative hadronization \\eqref{meson_distribution} is a subdominant process of order $\\kappa$ at most.\nMoreover, since the photon carries away a fraction of the preformed-state momentum, the meson spectrum of the radiative hadronization is shifted to the lower momentum region and therefore at a given momentum $p_T$ its yield is more suppressed than the value simply expected by the factor $\\kappa$.\nHowever, we stress here that to photon production the radiative hadronization can give a significant contribution.\n\n\n\n\\subsection{Characteristics of the radiative hadronization}\n\n\n\nIn order to understand characteristics of the radiative hadronization, let us evaluate the momentum distributions of photons (\\ref{photon_distribution}) and mesons (\\ref{meson_distribution}) under certain approximations.\n\n\n\\subsubsection{Distribution of the preformed states}\n\n\nWe compute the number of preformed states by using the formula (\\ref{dN\/dP}) of the original ReCo model \\cite{Fries:2003kq} and we recap the formulas of\nReCo model here. The transverse momentum spectrum of the preformed state at mid rapidity $\\eta= 0$ is given by \\cite{Fries:2003kq}\n\\begin{equation}\nE_{M_*}\\left.\\frac{dN_{M_*}}{d^3P}\\right|_{\\eta=0}\\sim C_{M_*} M_{*T} \\frac{\\tau A_T}{(2\\pi)^3} \\; 2\\gamma_a\\gamma_b I_0\\left(\\frac{P_T\\sinh \\eta_T}{T_{\\rm reco}}\\right)\n\\int_0^1 dx|\\phi_{M_*}(x)|^2 k_{M_*}(x,P_T)\n\\end{equation}\nwith \n\\begin{equation}\nk_{M_*}(x,P_T) \\equiv K_1\\left(\\frac{\\cosh \\eta_T}{T_{\\rm reco}}\\left\\{\\sqrt{m_a^2+x^2P_T^2}+\\sqrt{m_b^2+(1-x)^2P_T^2}\\right\\}\\right)\\, ,\n\\end{equation}\nwhere $M_{*T}=\\sqrt{P_T^2+M_*^2}$ is the transverse mass,\n$A_T$ is the transverse area of the parton system at hadronization, representing the collision geometry, and $I_0(x)$ and $K_1(x)$ are the modified Bessel functions.\nThe parameter $T_{\\rm reco}$ is the recombination temperature at which quark recombination $q+\\bar q\\to M_*$ occurs.\nFor analytic evaluation, we simply take $|\\phi_{M_*}(x)|^2=\\delta(x-1\/2)$\nand perform the integration over $x$ for pion production:\n$$\n\\int_0^1 dx|\\phi_{M_*}(x)|^2 k_{M_*}(x,P_T)\\simeq K_1\\left(\\frac{\\cosh \\eta_T}{T_{\\rm reco}}M_{*T}\\right)\\, ,\n$$\nwhere $M_{*T}$ appears in the argument because we have taken $m_a=m_b=m$\nand $M_*= 2m$ (recall that we adopt $\\varrho(M_*)=\\delta (M_*-(m_1+m_2))$). By using the asymptotic forms of the modified Bessel functions $I_0(z)\\sim {\\rm e}^z\/\\sqrt{2\\pi z}$ and $K_1(z)\\sim \\sqrt{\\pi\/2z}\\ {\\rm e}^{-z}$ for large $z\\gg 1$ and large $P_T$ approximation $M_{*T}\\simeq P_T$,\nwe find the following approximate form for the $P_T$ distribution:\n\\begin{equation}\nE_{M_*}\\left.\\frac{dN_{M_*}}{d^3P}\\right|_{\\eta=0}\\sim \n{\\rm e}^{-P_T \/ T_{\\rm eff}^*}\\, ,\n\\label{M*pt_distribution}\n\\end{equation}\nwhere $T_{\\rm eff}^*$ is the effective temperature for the preformed state defined by\n\\begin{equation}\nT_{\\rm eff}^*=T_{\\rm reco}\\ {\\rm e}^{\\eta^{}_T}=T_{\\rm reco} \\, \\sqrt{\\frac{1+v^{}_T}{1-v^{}_T}}\\label{eff_temp}\n\\; .\n\\end{equation} \nThe recombination temperature $T_{\\rm reco}$ is identified with\nthe hadronization temperature in the original ReCo model.\nThe multiplicative factor $e^{\\eta_T}>1$ reflects the effect of the nonzero transverse flow of the quarks,\nwhich blue-shifts the inverse slope parameter of the preformed state distribution\nfrom $T_{\\rm reco}$ to $T_{\\rm eff}^*$.\nFor example, this factor $T_{\\rm eff}^*\/T_{\\rm reco}$ amounts to $\\sim $1.7 for $v_T=0.5$ and $2$ for $v_T=0.6$. \n\n\n\\subsubsection{Transverse momentum distributions of photons and mesons}\n\nGiven the distribution of the preformed states, let us discuss the\nphoton distribution eq.~(\\ref{photon_distribution}) at mid-rapidity $k_L=0$.\nWe can perform the integration over $\\Phi$ in eq.~(\\ref{photon_distribution}) with the $\\delta$-function\nin the photon distribution (\\ref{photon_CM_dist}) in the laboratory frame,\n\\begin{align}\n E_\\gamma \\frac{dn^\\gamma}{d^2k_T dk_L}\n &= \\frac{M_*}{4\\pi k_0}\\delta(\n k E_* - k_T P_T \\cos(\\Phi-\\phi) - k_0 M_*)\n ,\n\\end{align}\nwhere $\\Phi$ ($\\phi$) is the azimuthal angle of the preformed state (photon) from the reaction plane,\nas shown in Fig.~{\\ref{fig:decay}.\nThen we obtain\n\\begin{align}\n \\left .\n E_\\gamma \\frac{dN_\\gamma}{d^2 k_{T} dk_L}\n \\right |_{k_L=0} &=\n\\int_{-P_{L\\rm max}}^{P_{L\\rm max}} dP_L \\int_{P_{T\\rm min}}^{P_{T\\rm max}} d P_T \\, \n\\frac{dN_*}{d^2P_T dP_L}\\,\n\\frac{1}{4\\pi k_0}\n \\frac{2M_*}{k_T |\\sin \\theta|}\n \\, ,\n\\label{PT_integral}\n\\end{align}\nwhere \n\\begin{align}\n \\cos \\theta =\\cos(\\Phi-\\phi)=\n \\frac{k E_{M*} -k_0 M_*}{k_T P_T} \n \\, .\n \\label{eq:theta}\n\\end{align}\nThe integration ranges of the longitudinal and transverse momenta,\n$\\pm P_{L\\rm max}$ and $P_{T\\rm min,max}$, of the preformed state are determined by decay kinematics (See Appendix A).\n\n\n\\begin{figure}[t] \n\\begin{center}\n \\includegraphics[width=0.4\\textwidth,bb=0 0 600 600]{Decay-v3.png}\n\\hspace{0.05\\textwidth}\n\\includegraphics[width=0.35\\textwidth,bb=0 0 450 300]{Decay-align-v4.png}\n\\end{center}\n\\caption{Left: Kinematics of the photon emission from the preformed state, ${\\boldsymbol P} \\to {\\boldsymbol k} + {\\boldsymbol K}$.\n Right: Momentum boost from the CM to the laboratory frame\n for the photon momentum parallel (top) and anti-parallel (bottom) to ${\\boldsymbol P}$.\n}\\vspace{-2mm}\n \\label{fig:decay}\n\\end{figure}\n\n\n\n\nWe derive here approximate formulas valid for large photon momentum $k_T \\gg M_*$.\nThe distribution of the preformed states (\\ref{M*pt_distribution}) is a steeply-falling\nfunction of $P_T$ for large $P_T \\gg T_{\\rm eff}^*$,\nand therefore dominant contribution to the integral comes from the lower end of the $P_T$ integration\nwith $P_L \\simeq 0$.\nIn other words,\nit comes from the configurations in which ${\\boldsymbol P}$ is almost parallel to ${\\bm k}$, \n{\\it i.e.}, $\\cos \\theta \\sim 1$ and $P_L \\simeq 0$.\nIn this configuration,\nthe momentum $k_T$ is simply related with the CM-frame momentum $k_0$ by the transverse boost along $P_T = M_* \\sinh y_T^*$ via\n\\begin{align}\n k_T=k_0 \\cosh y_T^* + k_0 \\sinh y_T^* \\sim 2k_0\\frac{P_T}{M_*}\n =(1-\\frac{M^2}{M_*^2}) P_T\n\\label{mom-shift}\n\\end{align}\nas $\\cosh y_T^* \\sim \\sinh y_T^*=P_T\/M_*$.\nThe momentum deficit is carried by the accompanying meson emitted in the opposite direction in the\n$M_*$-rest frame: $K_T= -k_0 \\cosh y_T^* + \\sqrt{M^2+k_0^2} \\sinh y_T^*\n\\sim\n(M^2\/M_*^2)P_T ,\n$\nand $k_T + K_T = P_T$ holds as it should (Fig.~\\ref{fig:decay} top-right). \n\nSince the number of the radiated photons is proportional to that\nof preformed states, the photon distribution should behave as\n(See Appendix A for more details)\n\\begin{align}\n\\left . E_\\gamma \\frac{dN_\\gamma}{d^2 k_T dk_L}\n\\right |_{k_L=0} \\sim\n\\exp \\left ( -\\frac{P_{T}}{T^*_{\\rm eff}(M_*)} \\right ) \n\\sim\n \\exp \\left ( -\\frac{k_{T}}{T^\\gamma_{\\rm eff}(M_*)} \\right )\n\\end{align}\nwith\n\\begin{align}\n T^\\gamma_{\\rm eff}(M_*) = \\left ( 1-\\frac{M^2}{M_*^2} \\right )\n T^*_{\\rm eff} \\, .\n \\label{gamma_T}\n\\end{align}\nThus we find that the effective temperature of the photon $T^\\gamma_{\\rm eff}$ becomes lower than $T_{\\rm eff}^*$.\n\n\n\nOn the other hand, the main contributions for the meson production by the radiative ReCo model come\nfrom the configuration in which the meson momentum is parallel to $P_T$ in the $M_*$-rest frame\n(See the bottom-right panel in Fig.~\\ref{fig:decay}), \nthe same calculation yields\n\\begin{align}\n K_T = k_0 \\cosh y_T^* + \\sqrt{M^2+k_0^2} \\sinh y_T^* \\sim P_T,\n\\end{align}\nand the accompanied photon has nearly zero energy and momentum $k_T \\sim 0$ due to its massless nature.\nAccordingly, we find\n\\begin{align}\n \\left . E_M \\frac{dN_M}{d^2K_TdK_L}\\right\\vert_{K_L=0}\n \\sim\n \\exp \\left(-\\frac{K_T}{T_{\\rm eff}^{\\rm meson}(M_*)}\\right)\n\\end{align}\nwith\n\\begin{align}\n T_{\\rm eff}^{\\rm meson}(M_*)=T_{\\rm eff}^*\n \\, .\n\\label{eq:meson_T}\n\\end{align}\nThe meson has the same effective temperature as the preformed state, up to some corrections.\n\n\n\nThe radiative hadronization predicts that there is an ordering in the effective temperatures of\nphotons, mesons, and preformed states: \n\\begin{equation}\n T_{\\rm eff}^\\gamma(M_*) < T_{\\rm eff}^{\\rm meson}(M_*)\n \\sim T_{\\rm eff}^* \\, .\n \\label{eq:ordering}\n\\end{equation}\nNote that the thermal exponential form of the photon and meson distributions reflects the shape of the parton distributions\nand thus the origin of higher effective temperatures of photons and mesons than the hadronization temperature $T_{\\rm reco}$\ncan be attributed to the partonic collective flow built up during the QGP evolution.\n\n\n\n\n\\subsubsection{$v_2$ of photons and mesons}\n\nWithin the same approximations, we can evaluate the elliptic flow\ncoefficient for the photons, $v_2^\\gamma(k_T)$, defined by \n\\begin{equation}\n v_2^\\gamma(k_T)\\equiv \\frac\n {{\\displaystyle\n \\int d\\phi \\cos 2\\phi\n \\left(\\left. k \\frac{dN_\\gamma}{d^2k_Tdk_L }\\right|_{k_L=0} \\right)}}\n {{\\displaystyle\n \\int d\\phi\n \\left(\\left. k \\frac{dN_\\gamma}{d^2k_T dk_L }\\right|_{k_L=0}\\right)}}\\, .\n \\label{v2_gamma}\n\\end{equation}\nThe distribution of the preformed state is computed with the original ReCo model,\n\\begin{equation}\n\\frac{dN_*}{d^2P_T dP_L}=\n \\overline{ \\frac{dN_*}{d^2P_T dP_L}}\\,\n \\Big(1+2v_2^*(P_T)\\cos 2\\Phi \\Big)\\, ,\n\\label{v2_preformed}\n\\end{equation}\nwhere \n$\\overline{dN_*\/d^2 P_T dP_L}$ is $\\Phi$-independent part of the spectrum and the nonzero elliptic flow $v_2^*(P_T)$ is inherited from the quark\/anti-quark elliptic flow.\nThen, in place of eq.~(\\ref{PT_integral}), the integration over $\\Phi$ with the $\\delta$-function\nyields\n\\begin{align}\n \\left .\n k \\frac{dN_\\gamma}{d^2 k_{T} dk_L}\n \\right |_{k_L=0} &=\n\\int_{-P_{L\\rm max}}^{P_{L\\rm max}} dP_L \\int_{P_{T\\rm min}}^{P_{T\\rm max}} d P_T \\,\n \\overline{\\frac{dN_*}{d^2P_T dP_L}}\\,\n (1+2v_2^*(P_T) \\cos 2 \\phi \\cos 2 \\theta)\\frac{1}{4\\pi k_0}\n \\frac{2M_*}{k_T |\\sin \\theta|}\n,\n\\label{gamma_dist_2}\n\\end{align}\nwhere we have used $\\sum_{i=\\pm}\\cos 2\\Phi_i = 2 \\cos 2\\phi \\cos 2\\theta$\nwith $\\Phi_\\pm = \\phi \\pm \\theta$.\nWe insert eq.~(\\ref{gamma_dist_2}) into the definition (\\ref{v2_gamma}),\nand evaluate the momentum integral approximately with its threshold value near $P_T \\sim P_{T\\rm min}$ and $P_L \\sim 0$,\nwhere we can put $\\cos 2\\theta \\sim 1$.\nThus we find that, after the $\\phi$ integration, the elliptic flow coefficient is unchanged\nbut its momentum argument is replaced with $k_T = (1-M^2\/M_*^2)P_T$~:\n\\begin{equation}\n v_2^\\gamma(k_T) \\sim v_2^*\\left(\\frac{k_T}{1-M^2\/M_*^2}\\right)\n \\, .\n\\label{photon_v2_scaling}\n\\end{equation}\nIn the same manner, we find the coefficient for the meson distribution in radiative ReCo model as \n\\begin{equation}\nv_2^{\\rm meson}(K_T)\\sim v_2^*\\left(K_T \\right)\\, .\n\\label{meson_v2_scaling}\n\\end{equation} \nWe emphasize here that the elliptic flow coefficients $v_2$ of the photons and mesons are both given by $v_2^*$ of the preformed states, \nwhich approximately satisfies the CQN scaling, $v_2^*(P_T) \\sim 2 v_2^q(P_T\/2)$~(see eq.~\\eqref{eq:CQNscaling}). \n\n\nWe have shown that the effective temperature and the elliptic flow coefficient of \nthe meson distribution are the same as those of the preformed-state distribution, \nwhile for the photon distribution these parameters are estimated in the similar manner but with a simple momentum shift \\eqref{mom-shift}.\nThis means that even if the contributions of the radiative hadronization are added,\nnot only the meson elliptic flow still follows the CQN scaling,\nbut also the photon elliptic flow does so approximately. \n\n\n\n\\section{Numerical results}\n\nFor our numerical study we employ a 2-dimensional (2D) model, neglecting the longitudinal momentum of the preformed state $M_*$ ($P_L=0$),\nsince we have seen that the $P_L$-integration plays only a minor role in the modification of the photon and meson distributions from that of the preformed state.\n\n\nThe photon distribution of 2D radiative ReCo model reads \n\\begin{align}\n \\left .\n k \\frac{d N_\\gamma}{d^2k_T dk_L} \\right |_{k_L=0}\n &=\\kappa \\int_{P_{T\\rm min}}^{P_{T\\rm max}} dP_T \\,\n \\sum_{i=\\pm} \\frac{dN_{M_*}}{d^2P_TdP_L}(P_T, \\Phi_i,0)\n \\frac{1}{2\\pi} \\frac{M_*}{k_T |\\sin \\theta |}\n \\, ,\n \\label{2d_photon_distribution}\n\\end{align}\nwhere \n$\\Phi_\\pm=\\phi\\pm \\theta$ with $\\theta$ defined in eq.~\\eqref{eq:theta}.\nWe generate the distribution of the preformed states of mass $M_*$,\nusing the original ReCo model \\cite{Fries:2003kq,Fries:2003vb}.\nBut we change the recombination temperature $T_{\\rm reco}$ to 155 MeV, which is within the range of the pseudo-critical temperature obtained\nin lattice QCD calculations \\cite{Borsanyi:2010cj,Bazavov:2011nk}.\nAccordingly the transverse flow at hadronization is set to $v_T=0.6$ to reproduce the $p_T$ distribution of the mesons,\nand the freeze-out time $\\tau$ and the fireball radius $\\rho_0$ are also adjusted adequately (see Table \\ref{tab:ParamSet}).\nWe set $M_* = 2M_{ud}$ for the preformed state based on the constituent quark model picture. \n\n\n\n\\begin{table}\n \\begin{tabular}{cccccccccc}\n \\hline\n& $T_{\\rm reco}$~\/MeV & ~ $v_T$ ~ & ~$\\tau$~\/fm ~ & ~ $\\rho_0$~\/fm ~ &~ $\\gamma_{u,d}$~ & ~$\\gamma_{\\bar u,\\bar d}$~ & $M_{ud}$~\/MeV & $p_0$~\/GeV & $a$ \\\\\n \\hline\n RHIC & 155 & 0.6 & 8.0 & 12.5 & 1 & 0.9 & 260 & 1.0 & 2.5\\\\\n LHC & 155 & 0.65 & 15.0 & 20.0 & 1 & 1 & 260 & 1.1 & 2.5\\\\ \n\\hline\n\\end{tabular}\n \\caption{\n Model parameters.\n }\n\\label{tab:ParamSet}\n\\end{table} \n\n\n\\subsection{Model characteristics}\nFirst let us numerically test the characteristics of particle distributions of our radiative ReCo model,\nwhich was discussed in the previous section.\nNote that in this subsection we set the normalization factor $\\kappa=1$ of the radiative ReCo model to study the model characteristics,\nwhile in the next subsections we will adjust the parameter $\\kappa$ so that the model reproduces the observed photon distributions.\n\n\n\\subsubsection{Transverse momentum spectrum}\nWe show $p_T$ distributions of the pions and photons produced by the radiative ReCo model at $\\sqrt{s_{NN}}=200$ GeV\nin Fig.~\\ref{fig:model_char_pion} (left), along with the distribution of the preformed states.\nAt a given momentum, yields of the pions and photons are much lower than that of the preformed state because\neach of them shares a fraction of the momentum of the preformed state,\nand therefore their distributions are shifted to the lower $p_T$ region.\nBut their $p_T$ slopes are similar to each other in the relevant momentum region.\n\n\nWe define the effective temperature $T_{\\rm eff}$ here\nby fitting each of the $p_T$ distributions with an exponential function $\\exp(-p_T\/T_{\\rm eff})$\nin the momentum range $2 < p_T < 5$ GeV.\\footnote{%\n %\n The slope parameter $T_{\\rm eff}$ is centrality-independent in this model\n since the quark distribution \\eqref{phase_space_dist}\n depends on collision centrality only by the weak modulation of the transverse flow \\eqref{etaT_phi}\n and by the overall factor of the hot-zone size $f(r,\\phi)$.\n}~\nIn Fig.~\\ref{fig:model_char_pion} (right) we check the $M_*$ dependence of these effective temperatures,\n$T_{\\rm eff}^{\\pi}$ of pions and $T_{\\rm eff}^\\gamma$ of photons produced in the radiative ReCo model.\nThey follow the ordering eq.~\\eqref{eq:ordering} derived in the previous section.\n$T_{\\rm eff}^\\pi = T_{\\rm eff}^*$ is predicted by eq.~\\eqref{eq:meson_T}, and \nthe difference between $T_{\\rm eff}^{\\pi}$ and $T^*_{\\rm eff}$ may be understood\nas a correction due to subleading $p_T$-dependence of the distributions.\nOn the other hand, $T_{\\rm eff}^\\gamma$ of photons is lower than $T^*_{\\rm eff}$ and\napproaching it with increasing $M_*$, as predicted by eq.~\\eqref{gamma_T}. \n\nOur model restricts the invariant mass of the preformed state to $M^*= 2 M_{ud}=520$~MeV for pion production,\nbut in more general treatments, the preformed states with $M^* \\ge 2M_{ud}$ may well contribute to the pion production.\nHowever, from Fig.~\\ref{fig:model_char_pion} (right), the slope parameter $T_{\\rm eff}$ of the photons and pions from the radiative ReCo model\nseem rather insensitive to the $M_*$ value, as far as $M_*$ is much larger than the meson mass $M$.\n\n\n\n\\begin{figure}[th]\n \\centering\n \\includegraphics[width=7.5cm]{radReCo_Npt_MstMeGam.pdf}\n \\hfil\n\\includegraphics[width=7.5cm]{radReCo_Teff_vs_Mst.pdf}\n \\caption{%\n Left: Comparison of transverse momentum distributions $d^2N\/(2\\pi p_T dp_T dy)$\n of the photons (green bold solid), pions (blue thin solid), and preformed state (black dashed) in radiative ReCo model\n ($M_* = 2M_{ud}$, $\\kappa=1$ with $b=5.5$ fm at $\\sqrt{s_{NN}}=200$ GeV).\n Right: \n$M^*$-dependence of the slope parameters of the photons $T_{\\rm eff}^\\gamma$ (green circle),\n of pions $T_{\\rm eff}^\\pi$ (blue triangle), and of preformed states $T_{\\rm eff}^*$ (black square).\n The parameter $T$ is determined by fitting the function $\\propto e^{-p_T\/T}$ \n to the $p_T$ distributions in the momentum range $2 < p_T < 5$ GeV.\n \\label{fig:model_char_pion} } \n\\end{figure}\n\n\n\nWe show the results for kaon production with $M_* = M_s + M_{ud}$ and $M_s = 460$ MeV in Fig.~\\ref{fig:model_char_kaon},\nwhere the particle yields become smaller at $p_T \\lesssim 2$ GeV than the pion case due to the mass effect. \nMoreover, the $p_T$ slope of the photon distribution is much steeper than those of the kaon and preformed state distributions, \nbecause, unlike the pion mass, the kaon mass $M_K=495$~MeV is comparable to $M_*=720$~MeV here (see eq.~\\eqref{gamma_T}).\nWe see that the photon yield associated with radiative hadronization of the kaons is small compared with that of the pions,\nand we neglect it in this work.\n\n\n\\begin{figure}[th]\n \\centering\n \\includegraphics[width=7.5cm]{radReCo_Npt_MstMeGam_kaon.pdf}\n \\caption{%\n The same plot as in Fig.~\\ref{fig:model_char_pion} (right),\n but associated with kaon production ($M_s =460$ MeV).\n \\label{fig:model_char_kaon}\n }\n\\end{figure}\n\n\n\n\n\n\n\\begin{figure}[th]\n \\centering\n \\includegraphics[width=7.5cm]{radReCo_v2_scaling.pdf}\n \\caption{%\n Rescaled elliptic flow coefficients $v_2(p_T\/n_q)\/n_q$\n of the pions (blue thin solid), photons (green thick solid),\n and preformed state (black dashed) at $b=5.5$ fm with $n_q=2$.\n The flow coefficient $v_2(p_T)$ of the quarks (purple dotted)\n is shown for comparison.\n Parameters are the same as in Fig.~\\ref{fig:model_char_pion}. \n \\label{fig:check_QNS_v2_rhic}\n }\n\\end{figure}\n\n\n\\subsubsection{Elliptic flow $v_2$}\n\nWe introduce an azimuthal angle dependence of the quark\/antiquark flow $v_T(p_T)$\nby the modulation amplitude $h(p_T)$ of the transverse flow rapidity $\\eta_T(\\phi; p_T)$ as in eq.~(\\ref{etaT_phi}).\nThe magnitude of $h(p_T)$ is determined by the aspect ratio of the initial collision zone.\nIn Fig.~\\ref{fig:check_QNS_v2_rhic} shown is the result of the elliptic flow coefficients $v_2$\nof the pions (cyan thin) and photons (green bold) as well as that of the preformed states (black dashed),\nafter divided by the constituent quark number $n_q=2$.\nThe quark\/antiquark elliptic flow coefficient $v_2^q$ is also shown (purple dotted) for comparison.\n\n\nWe find that all these $v_2$ have the same magnitude,\nwhich is taken over from the quark\/antiquark flow through the radiative recombination.\nWe also note that the flow coefficient $v_2^\\pi$ of the pions exactly follows\nthat of the preformed state $v_2^*(p_T)$ at $p_T \\gtrsim 1$ GeV,\nwhile the photon $v_2^\\gamma(p_T)$ lies slightly below them.\nIndeed, we have confirmed that,\nwhen plotted in the shifted momentum $\\bar p_T=p_T\/(1-M^2\/M_*^2)$,\nthe photon $v_2^\\gamma(p_T)$ curve overlaps with that of the preformed states at larger $p_T$,\nas discussed in eq.~\\eqref{photon_v2_scaling}.\n\n\n\n\n\n\n\n\\subsection{RHIC}\n\n\nNext we study the contributions of radiative hadronization to the photon production in Au+Au collisions at $\\sqrt{s_{NN}}=200$ GeV.\nIn order to make a comparison of the photon yield to the direct photon data at RHIC \\cite{Adler:2003qi}, \nwe include the thermal photon contributions evaluated with a 3-dimensional viscous hydrodynamic model with the kinetic freezeout temperature $T_{\\rm fo}=116$ MeV \\cite{Miyachi-Nonaka}\n(see Appendix B).\nWe assign the impact parameter $b=3.0, 5.5, 7.5$ and $9.0$ fm in our model for the centrality classes, 0--10, 10--20, 20--30 and 30--40 $\\%$ of the collision events, respectively, based on the Glauber model estimate \\cite{PHENIX:2003iij}. \n\n\n\nIn Fig.~\\ref{fig:centrality_rhic} we compare the $p_T$ distributions of $\\pi^0$ obtained\nby the ReCo (black solid) and radiative ReCo (cyan dashed) models to PHENIX data at different centralities \\cite{Adler:2003qi}.\nThe parameter $\\kappa=0.2$ of the radiative ReCo model is determined so that the sum of the photons from radiative hadronization and\nthe thermal photons reproduces the observed photon yield (see Fig.~\\ref{fig:photon_centrality_rhic} below).\nWe are reassured here that the original ReCo model reproduces the pion $p_T$ distributions for different centrality classes, in the $p_T$ range from 2 to 4 GeV,\nwhere the quark recombination is regarded as the dominant hadronization mechanism.\nOutside this region, other production mechanisms, hydrodynamic process at the lower $p_T$ and parton fragmentation at the higher $p_T$, are important.\nIn contrast, the contribution from the radiative ReCo model takes only a small fraction of the pion yield (less than 10 \\% of the original ReCo model) at a given momentum $p_T$,\nand it brings no obstruction on the success of the original ReCo model in describing meson production in this momentum region \\cite{Fries:2003kq,Fries:2003vb}. \n\n\n\n\\begin{figure}[th]\n \\centering\n \\includegraphics[width=10cm]{radReCo_Npt_wData-NoGam.pdf}\n \\caption{Transverse momentum distributions of $\\pi^0$\n computed with ReCo model (black solid) and radiative ReCo model (cyan dashed)\n for impact parameter $b=3$, 5.5, 7.5 and 9 fm in Au+Au collisions\n at $\\sqrt{s_{NN}}=200$ GeV.\n The $\\pi^0$ data set of 0--10 \\%, 10--20 \\%, 20--30 \\% and 30--40 \\% centrality classes\n is adopted from \\cite{Adler:2003qi}.\n \\label{fig:centrality_rhic} } \n\\end{figure}\n\n\n\\begin{figure}[th]\n \\centering\n \\includegraphics[width=10cm]{Prompt+Thermal+radReCo_photons_Npt.pdf}\n \\caption{\n Transverse momentum distributions of direct photons \n computed with radiative ReCo model (green dashed) for impact parameter $b=5.5$ (left) and $9$ fm (right)\n in Au+Au collisions at $\\sqrt{s_{NN}}=200$ GeV.\n Thermal photon distribution obtained by a viscous hydrodynamic model (purple dotted),\n rescaled prompt photons (black dot-dashed), and their sum (red solid) are also shown.\n The data is adopted from \\cite{Adare:2014fwh}, and the parameter $\\kappa=0.2$ is determined to fit the data. \n\\label{fig:photon_centrality_rhic}\n}\n\\end{figure}\n\n\n\\begin{figure}[th]\n \\centering\n \\includegraphics[width=10cm]{radReCo_v2pt_pi.pdf}\n \\caption{\n Pion elliptic flow coefficient $v_2$ from ReCo model (black solid)\n and radiative ReCo model (blue dashed) as a function of $P_T$\n for $b=5.5$ fm (left) and $b=9.0$ fm (right) in Au+Au collisions at $\\sqrt{s_{NN}}=200$ GeV. \n Data $\\pi^0$ (blue circles) are taken from PHENIX \\cite{Adare:2013wop}.\n \\label{fig:v2_rhic}\n}\n\\end{figure}\n\n\n\\begin{figure}[th]\n \\centering\n \\includegraphics[width=10cm]{Prompt+Thermal+radReCo_photons_v2pt.pdf}\n \\caption{\n Elliptic flow coefficient $v_2^\\gamma$ of the direct photons (red solid) for impact parameter\n $b=5.5$ (left) and 9 fm (right) in Au+Au collisions at $\\sqrt{s_{NN}}=200$ GeV.\n The $v_2^\\gamma$ of the photons from a viscous hydrodynamic model \n and $v_2^\\gamma$ of the photons from radiative ReCo model are shown in purple dotted and green dashed curves, respectively.\nThe normalization $\\kappa=0.2$ for the radiative ReCo model is adopted.\n Data for direct photons (blue solid stars) is adopted from \\cite{Adare:2015lcd}.\n \\label{fig:v2_photon_rhic}\n}\n\\end{figure}\n\n\n\nNext in Fig.~\\ref{fig:photon_centrality_rhic}\nwe show the $p_T$ distributions of the photons emitted in radiative pion production (green dashed)\nfor $b=5.5$ fm (left panel) and $b=9.0$ fm (right panel),\ntogether with those of the thermal photons (purple dotted) and the prompt photons (black dot-dashed),\nand the total (red solid).\nFor thermal photon production, we adopt the thermal photon rates of QGP in \\cite{Arnold:2001ms} and\nthat of the hadronic phase in \\cite{Holt:2015cda,Turbide:2003si,Heffernan:2014mla},\nand integrate these rates over the evolution profile obtained by a 3D viscous hydrodynamic simulation (See Appendix B for details).\nOur estimate of the thermal photon contribution is consistent with other model studies \\cite{Paquet:2015lta}.\nRegarding prompt photon production in AA collisions, which dominates the total photon distribution at higher $p_T$,\nwe use the empirical fit of the photon distribution in pp collisions, $a_1 (1+p_T^2\/a_2)^{a_3}$ ($a_{1,2,3}$ are constants),\nscaled with the number of nucleon collisions for AA collisions, \nas is done by PHENIX \\cite{Adare:2014fwh}.\nThe experimental data of the direct photons in the 0--20 \\% (for $b=5.5$ fm) and 20--40 \\% (for $b=9.0$ fm) centrality classes\nare taken from PHENIX \\cite{Adare:2014fwh}\\footnote{%\n A new data analysis is published in Ref.~\\cite{PHENIX:2022rsx} and\n consistent with that in Ref.~\\cite{Adare:2014fwh}.\n}. \n\n\nWe set the normalization of the radiative ReCo model to $\\kappa=0.2$ so that the sum of the two photon contributions, thermal radiation and radiative hadronization, reproduces the observed photon yield for $p_T < 3$ GeV.\nIndeed, the photon $p_T$ distributions for two centrality classes $0-20$ \\% and $20-40$ \\% are reproduced fairly well with the same normalization $\\kappa=0.2$.\nWe notice that the photon yield from the radiative ReCo model is estimated to be several times larger than that from the thermal radiation and that the $p_T$ slope of the resultant photon distribution is mostly determined by the contribution from the radiative ReCo model for $2 0.4 \\, \\textrm{arcmin}$. This makes sure that the galaxies are large enough in order for the morphology to be determined. The g-band images for each of these 84\\,723 objects were downloaded from the SDSS Science Archive \\citep[DR12;][]{sdss-dr12}. We used the SDSS mosaicking service\\footnote{https:\/\/dr12.sdss.org\/mosaics\/} which combines the maximum number of scans possible for the final image. The mosaicking service employs Swarp \\citep{swarp}, which aligns and combines the background offsets in the separate images. The result is a deep, background-subtracted image of each galaxy in the g-band. We limited ourselves to a field of view of $1.125\\cdot D_{25}$ since even the deep SDSS mosaics rarely reach the $25\\, \\textrm{mag}\\,\\textrm{arcsec}^{-2}$ surface brightness level. \n \n\\subsection{Data preprocessing}\n \nThe images were star subtracted using PTS\\footnote{http:\/\/www.skirt.ugent.be\/pts}, the python toolkit for SKIRT (\\citealt{skirt}; Verstocken et al., in prep.). This makes use of the SDSS point source catalogue as a prior for star positions, and then tries to find a peak around the positions which resembles a true point source. These point sources are then replaced by the local background using bicubic interpolation.\n\nThe star-subtracted images were used to calculate a few features (such as the total g-band luminosity), further discussed in Sect. \\ref{sec-ml}. After the extraction of these features, we log-scaled the images in order to emphasize lower brightnesses, especially at the outskirts of galaxies. First, the image flux was linearly rescaled to the interval [0, 1]. We then log-scaled the pixels in the following way:\n \n\\begin{equation}\n F' = \\frac{\\log \\left(1 + a F \\right)}{\\log \\left( 1 + a \\right)}.\n\\label{eq-scauto}\n\\end{equation}\n \nHere, $F$ is the original pixel value (between 0 and 1). The log-scaled pixel value $F'$ also ranges from 0 to 1, and these then serve as input for the machine learning (Sect.~\\ref{sec-ml}). The 1 inside the log prevents very small values from dominating the output scale. The \\textit{scaling value a} determines how much lower brightnesses are emphasized. Small values of $a$ ($a < 1$) result in a nearly linear scaling. Large values of $a$ result in a pure log scaling, which boosts fainter regions. The scaling value was determined independently for each of the objects. First, the noise level of the input $F$ was determined by sigma clipping the image, and then defining the noise level as two standard deviations above the mean. The scaling value was then fitted so the output (i.e. log-scaled) noise level equals 0.2. The result is that images with a high signal to noise ($S\/N$) have a larger scaling value, which allows for their fainter features to stand out. Images where the noise is more prevalent get a smaller scaling value, which in turn prevents the noise from being mistaken with a feature of the galaxy. This is demonstrated for a low, median, and high scaling value galaxy in Fig.~\\ref{fig-scauto}, where the bottom row shows the automatic scaling value procedure. The result is a more consistent background, boosting features without blowing up the noise. The background noise value of 0.2 was picked visually to distinguish faint features from noise (see bottom panel of Fig.~\\ref{fig-scauto}). We argue that if humans can distinguish the two, deep neural networks should also be able to learn the difference.\n \n\\begin{figure}\n \\centering\n \\includegraphics[width=\\hsize]{figures\/scauto_examples.pdf}\n \\caption{Demonstration of the automatic scaling value. The rows present different scalings, with the top row being a linear scale (equivalent to a scaling value $a \\ll 1$), the middle row using a constant scaling value of 78 (the median of the automatic scaling values), and the bottom row using the automatic scaling value (which fixes the output noise level to 0.2). For the bottom row, the determined scaling values are given as an inset for the different galaxies. The columns show three different galaxies, which from left to right have an increasing $S\/N$ (and thus an increasing scaling value).}\n \\label{fig-scauto}\n\\end{figure}\n \nSo far, we discussed how the input of the machine learning (the g-band image) was processed. Using GSWLC 2, we combined the Bayesian estimate of the stellar mass with a Bayesian estimate of $L_g$ to produce our target $M\/L$. The Bayesian luminosities were taken directly from the GSWLC SED models. A flat prior over the parameter range of the model grid is used, so the Bayesian values are likelihood-weighted averages. Contrary to a least $\\chi_r^2$ method, this allows us to get an uncertainty on $M\/L$ for each galaxy. It should be noted that there is little difference between best-model (i.e. least $\\chi_r^2$) and Bayesian estimates of the $M\/L$, since stellar mass is one of the parameters that can be derived most accurately from SED fitting \\citep{conroy-sedfit-review}. To further improve the $M\/L$ estimate, GSWLC uses a two-component star-formation history (SFH), which allows for a larger old stellar component. The current SFR then only fixes the young component, without constraining the older stellar population (which happens for a single component SFH). This greatly reduces the outshining bias \\citep{gswlc}.\n \n \\begin{figure}\n \t\\centering\n \t\\includegraphics[width=\\hsize]{figures\/ml_hist.pdf}\n \t\\caption{Histogram of the $M\/L$ values from GSWLC 2, after applying the threshold $D_{25} > 0.4$ arcmin. The galaxies with $\\chi_r^2 >= 5$ were not used for the machine learning. From the remaining galaxies, $59\\,637$ were used for training, $6\\,627$ for validation and $7\\,363$ galaxies for testing, as described in Sect.~\\ref{ssec-optimization-setup}. These samples were randomly drawn, and hence their distribution is similar.}\n \t\\label{fig-ml-hist}\n\\end{figure}\n \nOur sample so far is only limited by the minimum angular size ($D_{25} > 0.4$ arcmin), which results in 84\\,723 galaxies. We found that the distribution on $M\/L$ was quite broad, with some galaxies having $M\/L < 0.1$ and others having $M\/L > 10$ (all $M\/L$ are given in solar units). Most of these outliers can however be removed by setting an upper limit on the fitting $\\chi_r^2$ (i.e. the goodness of the CIGALE fit). A large $\\chi_r^2$ means that even the best model did not fit the observed fluxes well, and hence the resulting properties can be inaccurate. These high $\\chi_r^2$ objects are possible mismatches between optical and UV sources, or sources where the UV was compromised by a lower resolution. We decided to use only galaxies for which the $\\chi_r^2$ was below 5. This significantly reduced the number of outliers: the number of galaxies with a $M\/L$ below 0.1 is now 2 (from 36), while 20 galaxies have a $M\/L$ above 10 (from 50). These two criteria ($\\chi_r^2$ and angular resolution) result in our final sample, which contains 73\\,627 galaxies. This sample has a minimum, median, and maximum $M\/L$ of 0.09, 2.7 and 16.6, while without the $\\chi_r^2$ cut-off we had a minimum and maximum $M\/L$ of 0.04 and 30.8 respectively. The distribution of $M\/L$ can be seen in Fig.~\\ref{fig-ml-hist}, for the different subsamples (see Sect.~\\ref{ssec-optimization-setup}). We clearly see a bimodality, which (after inspecting the individual images) roughly correspond to elliptical galaxies for high $M\/L$ and disk galaxies for low $M\/L$. This distribution is specific for our sample: the low $M\/L$ spirals tend to be less luminous and hence they more often fall outside our selection criteria (both $D_{25}$ and the brightness cut from GSWLC). Our sample has a minimum, median, and maximum pixelsize of 0.08 kpc, 0.44 kpc, and 2.44 kpc respectively. The $D_{25}$ criterion selects mostly the more nearby galaxies, so the median redshift is now 0.05. The median seeing for the SDSS g-band is 1.4 arcsec.\n \n\\subsection{Optimization setup}\n\\label{ssec-optimization-setup}\n\nIn order to learn from the data, the machine learning algorithm minimizes an optimization objective (also called a \\textit{loss function}). Since we have access to a Bayesian $M\/L$ as well as its uncertainty, we decided to use a L1 loss that takes into account the uncertainty on $M\/L$. We denote it with $\\mathcal{L}_1$ to distinguish it from the standard L1 without uncertainty. It is defined as follows:\n\n\\begin{equation}\n\t\\mathcal{L}_1 = \\frac{1}{N}\\sum_{i = 1}^N \\left|\\frac{\\Upsilon_{\\textrm{pred}, i} - \\Upsilon_{\\textrm{true}, i}}{\\Delta \\Upsilon_{\\textrm{true}, i}}\\right|.\n\\label{eq-l1loss}\n\\end{equation}\n\n$\\Upsilon_{\\textrm{true}, i}$ denotes the `true' $M\/L$ for the $i^{\\textrm{th}}$ galaxy, which is the Bayesian estimate from GSWLC. $\\Delta\\Upsilon_{\\textrm{true}, i}$ is the corresponding Bayesian error, and $\\Upsilon_{\\textrm{pred}, i}$ is the value predicted by our machine learning method. $N$ is the number of galaxies in the considered set. We usually define a separate $\\mathcal{L}_1$ for the training, validation, and test set (see below). Optimizing $\\mathcal{L}_1$ is equivalent to optimizing a weighted mean absolute error (MAE), with the weights defined as $w_i = 1 \/ \\left( \\Delta \\Upsilon_{\\textrm{true}, i} \\right)$. Since $\\Delta \\Upsilon_{\\textrm{true}, i}$ is derived from the likelihood over the model grid in CIGALE, and does not take into account systematic uncertainties, some galaxies can have a very low error. In order to prevent a few galaxies from dominating the weights, we used a minimum relative error on the Bayesian $M\/L$ of 5\\% (this affects about a quarter of our sample). Galaxies with a high $M\/L$ typically have a larger $\\Delta \\Upsilon_{\\textrm{true}, i}$. We first experimented with a squared loss ($\\mathcal{L}_2$), but found that this was dominated by a few outliers (mostly samples with a low $M\/L$ and hence a low error on $M\/L$). The $\\mathcal{L}_1$ loss ensures that we focus more on the general trend \\citep{mae}. In Appendix~\\ref{app-loss-functions}, we experiment with different loss functions, and show that our results still apply for other common loss functions (such as the L2 variant $\\mathcal{L}_2$). The $\\mathcal{L}_1$ loss performs well on a range of metrics. We will use the term `loss' to describe the optimization criterion on the training set, while `metric' is used for how well the predictor performs on the test set.\n\nWe split our sample randomly in three parts, thus creating subsets that are representative of the whole $M\/L$ distribution (see Fig.~2). First, 10\\% is kept apart as a test set (7\\,363 galaxies). From the remaining set, another 10\\% is split off as a validation set (6\\,627 galaxies). The other 59\\,637 make up the training set. The goal of the algorithm is to minimize $\\mathcal{L}_1$ (the loss) on unseen examples. The main way this is accomplished is by minimizing $\\mathcal{L}_1$ on the training set. However, care has to be taken that the algorithm does not \\textit{overfit}. Overfitting happens when the model is too complex, and the behaviour of individual training samples is learned (instead of the general trend). The result is a low training set loss but a high test and validation set loss. The purpose of the validation set is to prevent overfitting. We optimize the algorithm's hyperparameters (which determine the model complexity), including the duration of training, by using the ones that produce the lowest validation set loss. This means that the validation set is no longer an unbiased estimate of how our algorithm performs on unseen examples, which is why we set apart a test set. This is a typical setup for machine learning \\citep{ml-goodfellow, ml-python}.\n \n \n\\section{Machine learning}\n\\label{sec-ml}\n\n\\begin{figure*}\n\t\\centering\n\t\\includegraphics[width=17cm]{figures\/pipeline.pdf}\n\t\\caption{Schematic overview of the machine learning pipeline. The black arrows denote the order in which properties are derived from each other. The pink boxes show the optimization objective: three CNNs are optimizing the Galaxy Zoo 2 probabilities, using a mean squared error (L2) loss. The LightGBM and the fourth CNN optimize $M\/L$ according to a $\\mathcal{L}_1$ loss. The initial weights of this last CNN were set to the final values of the first CNN (pretraining). The boxes with the bright green outline were used as features for the LightGBM, after disregarding the ones that always were zero (dead neurons). For each of the neural layers or blocks, the output dimension is provided on the left.}\n\t\\label{fig-pipeline}\n\\end{figure*}\n\n\nOur algorithm can be subdivided in two parts. The first part consists of four CNNs \\citep{lecuncnn} which are trained to detect morphology information. The second part is a gradient boosting machine \\citep[GBM;][]{gradient-boosting}, more specifically Microsoft's LightGBM \\citep{lightgbm}. The GBM combines the morphology information from the CNNs with other information, such as redshift and total image luminosity, in order to predict the $M\/L$. A schematic overview of the complete pipeline can be found in Fig.~\\ref{fig-pipeline}. For more information on the machine learning terminology used here, we refer to Appendix~\\ref{app-terminology}.\n\nThe benefit of using this two-part algorithm is that the task of predicting $M\/L$ from the images is split in two easier tasks. The first part detects what features are present in the image (spiral structure, a bar, a possible merger, etc.). The second part then determines how this morphological information correlates with $M\/L$. We have tried using a single CNN trained on $M\/L$, but this often got stuck in local minima, predicting an average $M\/L$ for all samples. Using this two part algorithm also allows us to better interpret the results, since we can directly correlate the $M\/L$ with the morphology features.\n\n\n\\subsection{CNN - detecting morphology features}\n\\label{ssec-cnn}\n\nCNNs are a type of neural network that make use of the 2D image structure. It consists primarily of convolutional layers, each having multiple convolutional kernels (also called filters). These kernels are trained through gradient based optimization, in order to minimize the training loss. In our networks, most kernels are of size $3\\times 3$; the number of trainable parameters is drastically reduced compared to fully connected layers. The kernels in early layers detect simple features such as edges. The implicit assumption in CNNs is translational invariance: a kernel that detects a feature in one part will detect the same feature in other parts of the image. Throughout the architecture, the image typically gets downscaled, giving rise to higher level features (which in our case can learn to detect spiral structure, bulges, bars, etc.). The final layers are often fully connected (also referred to as dense), combining all features into the final prediction. \n\nWhile the final goal is to learn $M\/L$, we started with training our networks on the morphology information from Galaxy Zoo 2 \\citep[GZ2;][]{gz2,gz2-hart}. Since this made use of the SDSS DR7, we crossmatched GZ2 with our catalogue and used the 58\\,966 galaxies ($\\sim 80$\\% of our sample) for which the sky separation was less than 3.6 arcsec. This sky separation was chosen to cleanly separate our matches (>~99\\% of which are closer than 1 arcsec) from possible mismatches (>~99\\% separated by more than 10 arcsec). While we could probably find a one-to-one relation between each of their DR7 and our DR12 galaxies, it is beneficial to train the morphology on a subsample, in order for the GBM to also learn on training samples for which the morphology is known less precisely (the GBM trains on the full training set, but the CNNs are only trained on the subset that has a GZ2 match). Unlike past endeavors to predict morphology information from GZ2 \\citep{dieleman,sanchez}, our CNN only uses the g-band image. \n\nGZ2 contains 11 questions, with 37 answers in total. We decided to use the weighted vote fractions as probabilities for each answer. We did not use the distance debiased vote fractions, since the GBM has access to redshift and can apply any necessary corrections. Since some questions are only answered after particular answers of previous questions, we converted the weighted vote fractions to unconditional probabilities (i.e. multiplying by the probability of the question being asked). For example, answer four gives the probability of being an edge-on disk, which has to be smaller than or equal to the probability of the galaxy having a disk or feature (answer two, the parent question). For a list of all GZ2 questions and answers, see Fig.~2 of \\citet{gz2-hart}. The last dense layer of all networks have a ReLU activation, making sure the output for an answer is larger than or equal to zero (this is a regression approach also taken in \\citealt{dieleman}). A post-processing layer then takes care of the normalization. First, all answers for a particular question have to sum to one. Then, all answers for that question are multiplied by the estimated probability of that question being asked, determined by the features from higher up answers. This way, the network automatically produces valid unconditional probabilities. All steps of the post-processing layer are differentiable.\n\nInstead of using a single CNN, we used an ensemble of CNNs. Different network architectures will make different errors, and combining the extracted features leads to more robust results \\citep{ensembling}. Since the purpose of the CNNs is to detect the morphology, we used these different CNNs as input to our GBM. The GBM can learn in which scenarios a particular CNN is more accurate than another, and can make use of the combined information. Our final model is based on five extracted feature layers from four different networks. Different setups can lead to similar results, and it might be possible to significantly simplify the setup without too much degradation of the test $\\mathcal{L}_1$. The first architecture is the ResNet50, part of the residual learning framework which won the ILSVRC2015 competition \\citep{resnet}. The residual blocks ensure that only residuals from the previous layer have to be learned, making it possible to build much deeper networks. The second architecture is Xception \\citep{xception}, which was based on an inception architecture \\citep{inception}. The idea is to separate spatial features from depth (channel) features by doing multiple convolutions in parallel, starting from a pointwise convolution. These two networks produce state of the art results on many imaging datasets. We applied the transfer learning technique, starting the network weights from their Imagenet values \\citep{imagenet}. We used the keras python library\\footnote{https:\/\/keras.io\/}, in which these models are already implemented. We only kept the convolutional part, after which we applied global average pooling, a dense-256 layer (i.e. a fully connected layer with 256 neurons) with ReLU activation, followed by a dense-37 layer (matching the 37 answers in GZ2). This was then followed by the probability normalization layer, described above. The optimization objective was to minimize the L2 loss (regular RMSE) of the predicted and ground truth probabilities. The ResNet50 architecture used $197\\times 197$ images as input (the minimum required), while the Xception architecture used $128\\times 128$ images. Prior to the training of these networks, all training samples are scaled to the corresponding resolution (pixel area interpolation for shrinking, bicubic interpolation for zooming), with all networks keeping the same field of view per galaxy. Since these networks are pretrained on ImageNet, which has three input colour channels, we duplicated each image across the three channels to avoid changing the architecture.\n\nA third CNN architecture is a more traditional, shallow network (further called the \\textit{custom} network). It consists of 4 convolutional layers followed by two fully connected layers. The number of channels (depth) in the consecutive layers is: 32, 64, 128, 128, 512, 37. The first three convolutional layers are followed by $2\\times2 $ max pooling, after which dropout is applied \\citep{dropout}. The last convolutional layer is followed by a global max pooling but no further dropout. The first and second convolutional kernels are $5\\times 5$ and $4\\times 4$, respectively, and the last two convolutional layers are $3\\times 3$. No zero padding is applied. This architecture is inspired by \\citet{dieleman}, the main differences being that we only have one input channel and that we do not use their view preprocessing pipeline. The input dimensions are $69\\times 69$. Since there is no pretrained variant of this network, we used Glorot uniform random initialization \\citep{glorot-initialization}.\n\nFor these first three networks, we used the 37 estimated GZ2 answer probabilities as features for the GBM. We then used the ResNet50 architecture to extract more features. First, we extract the 2048 features that followed the convolutional part (before the fully connected layers). Furthermore, we took the whole architecture and replaced the probability normalization layer by a dense-1 layer. This network was then further trained to predict $M\/L$ (minimizing $\\mathcal{L}_1$). This again is a form of transfer learning: we pretrain the network on morphology, and then train on $M\/L$. This makes training easier, and we experienced fewer problems with local minima. From this retrained network, we extract the 37 features from the next to last layer, which no longer directly correspond to the 37 answer probabilities (although they are primed on them). Just like the other networks, these features only make use of the log-scaled (Equation \\ref{eq-scauto}) image, without any extra input such as luminosity or redshift. This means that they are still purely morphological features (i.e. depending only on galaxy structure), even though they do not have a clear interpretation like the GZ2 probabilities do. We will refer to the four CNNs as CNN 1, 2, 3 and 4, where we use the order in which they appear in Fig.~\\ref{fig-pipeline} (from left to right).\n\nIn order to make the networks generalize better, we applied data augmentation at training time. The images were randomly rotated (between 0 and 360 degrees), zoomed (between 0.7 and 1.3), and flipped (horizontal and vertical). This means that for every pass through the training set (epoch), the networks see slightly different images. We used the Adam optimizer \\citep{adam} with Nesterov's momentum. We applied a factor of 0.3 learning rate decay when the validation loss did not improve for 4 epochs, and stopped training after 30 epochs (since the validation loss did not seem to improve further). \n\n\\subsection{GBM - combining all information}\n\\label{ssec-gbm}\n\nSo far, the different CNN architectures produced the following morphological features: the custom CNN, ResNet50 and Xception each produce 37 GZ2 features, the ResNet50 provides 2048 features from the last convolutional layer, and a retrained (on $M\/L$) ResNet50 gives 37 features which are primed on GZ2. In total, these account for 2196 features. In addition, the GBM uses features extracted from the images. The following luminosity features are used, where we used the corresponding flux and multiplied by $4\\pi D^2$ (where $D$ is the distance in Mpc): sum (over all pixels), mean, maximum, minimum, standard deviation, central pixel, mean around central pixel ($5\\times 5$ pixels around the centre), the original image size, and the scaling value (as described in Sect.~\\ref{sec-methods}). After the log-scaling (which also scales the images between 0 and 1), we also extract some image statistics: the sum, mean, standard deviation, central pixel value, and mean around the central pixel. We also added two features which required extra metadata: the redshift and pixel size (in kpc). These allow the network to distinguish between a faint but nearby galaxy and a bright, distant galaxy. After removing 17 features that were always zero (dead neurons, 10 from CNN 3 and 7 from the inner part of CNN 1), we are left with 2195 features. The units of all the features are of no importance at this point: decision trees -- on which the GBM is based -- are scale invariant.\n\nGradient boosting traditionally makes use of decision trees as a base classifier. The trees are built sequentially, where each tree learns from the mistakes made from previous ones (as with ResNets, we learn residuals). LightGBM makes use of several optimizations compared to traditional gradient boosting. Since the morphology features were already extracted via the CNN, training was fast (around five minutes on a dual-core CPU). This allowed us to do 5-fold cross-validation in order to optimize the hyperparameters. We ended up using trees with 40 leaves, with a minimum of 150 samples per leaf. Each tree only had access to a (random) subset of 40\\% of the features, and 80\\% of the data (bagging). The validation set was used for early stopping, in order to prevent overfitting. \n \n \n\\section{Results and discussion}\n\\label{sec-results}\n\n\\subsection{Single band predictions}\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=\\hsize]{figures\/g_predictors.pdf}\n\t\\caption{ \\textit{Left:} Comparing the predicted $M\/L$ to the true $M\/L$ for a gradient boosting machine without morphology (top) and with morphology (bottom). Both predictors only make use of the g-band. The colour of the points is a 2D gaussian kernel density estimate. \\textit{Right:} histogram of dex error (top) and $\\mathcal{L}_1$ (bottom) for each galaxy. Purple is used for the predictor without morphology, while green is used for the predictor that includes morphology. For both quantities, closer to zero is better, positive numbers denote overpredictions, and negative values are underpredictions. The figure only includes galaxies from the test set. The dashed lines show 0.15 dex errors.}\n\t\\label{fig-g-predictors}\n\\end{figure}\n\nOur first goal is to investigate how good a single g-band image can constrain $M\/L$. As described in Sect.~\\ref{ssec-gbm}, our GBM combines the morphology information from the CNNs with other information (luminosity statistics, distance, and pixelsize). To evaluate the benefit of morphology, we compare this pipeline to a similar predictor that does not make use of morphology, nor any other resolved data (such as the luminosity features and the pixel size). Instead, we use two features: the g-band luminosity $L_g$ (calculated from the SDSS modelmag flux and distance), and the redshift. This reference method is shown in the top left panel of Fig.~\\ref{fig-g-predictors}, while our method (including morphology) is shown in the bottom left panel. These panels plot the predicted $M\/L$ against the ground truth (the Bayesian $M\/L$ from GSWLC). Even though the reference method only uses $L_g$ and redshift, it does not perform all that bad. We note that this works differently than using a single 3.4~$\\mu$m band, constant $M\/L$ assumption. This reference estimator can use the trend that low $M\/L$ spirals tend to have fewer stars (and hence they are less luminous) than a typical high $M\/L$ elliptical \\citep{review-hubble-sequence}. As discussed in Sect.~\\ref{ssec-interpretation}, it can also use the redshift feature to make use of Malmquist bias.\n\nIt is however clear that including morphology features clearly improves the results. The test set $\\mathcal{L}_1$ (Eq.~\\ref{eq-l1loss}) improves from 4.52 to 2.29. If we disregard the Bayesian uncertainty on the ground truth, we can use the root mean square logarithmic error (RMSLE), which improves from 0.227 dex to 0.124 dex. Including morphology also leads to less biased estimates, as seen on the right panels of Fig.~\\ref{fig-g-predictors}. For our estimator, 85.4\\% of the test set falls within 0.15 dex, while this is only 55.1\\% for the reference method without morphology. The reference method seems to be biased towards underpredictions (although there is a long tail towards overpredictions extending outside the histogram). This is mainly caused by the lack of predictions above 3.3: it seems like these high $M\/L$ cases are not easily found by $L_g$ and redshift alone. We will see in Sect.~\\ref{ssec-interpretation} that these galaxies mainly correspond to edge-on disks.\n\nInterestingly, the outliers in $\\Upsilon_{\\textrm{pred}, i} \/ \\Upsilon_{\\textrm{true}, i}$ do not necessarily match the outliers regarding $\\mathcal{L}_1$. For example, most underpredictions have a large error on $\\Upsilon_{\\textrm{true}, i}$. The largest outliers in $\\mathcal{L}_1$ (when including morphology) are overestimations. These have a low actual $M\/L$, which often results in a lower error on $M\/L$, and hence these datapoints are punished harder by our loss. A different loss function will weight galaxies differently, but we found that this does not change our conclusions (see Appendix~\\ref{app-loss-functions}). \n \nIncluding morphology allows us to detect galaxies with $M\/L > 3.3$. However, the highest $M\/L$ that is predicted is 5.6 (while the ground truth values run up to 16.6). For one, there are only a limited number of these extreme cases, and it is safer to predict a lower $M\/L$. Moreover, this suggests that there is no easy way to detect these samples from the g-band images alone. These samples also have a larger error on $M\/L$, and hence a more conservative estimate is not punished as hard for these samples.\n \n\\subsection{Interpretation}\n\\label{ssec-interpretation}\n\n\\begin{figure}\n \t\\centering\n \t\\includegraphics[width=\\hsize]{figures\/permutation_importances.pdf}\n \t\\caption{Feature importance ranking, by the amount the validation $\\mathcal{L}_1$ increases after permuting that feature. The CNN 4 features come from the ResNet50 that was retrained on $M\/L$ (the rightmost network in Fig.~\\ref{fig-pipeline}). All luminosity features are derived from the raw (star subtracted but not scaled) images, as described in Sect.~\\ref{ssec-gbm}. \\textit{Top:} standard predictor as described in Sect.~\\ref{sec-ml}, using all features. \\textit{Bottom:} a (freshly trained) predictor which does not make use of CNN 4.}\n \t\\label{fig-feat-importances}\n\\end{figure}\n\nOne of the useful properties of our pipeline is that it decouples morphology extraction and $M\/L$ prediction. The morphology detection by a CNN can be understood by inspecting the different layers. The first layers learn simple features such as edges, while deeper layers can learn to detect spiral arms, bars, or other features \\citep{dieleman}. The LightGBM can be interpreted by looking at the feature importances. These are presented in Fig.~\\ref{fig-feat-importances}. We have used \\textit{permutation importances}, which proved to be a robust feature importance measure in the study of \\citet{permutation-importance}. A certain feature's importance is calculated by permuting all observations of that feature, calculating the validation set $\\mathcal{L}_1$, and subtracting the non-permuted $\\mathcal{L}_1$ from this. The permuting leads to randomizing that feature without losing the distribution's properties. If the GBM heavily relies on a particular important feature, the permutation should increase $\\mathcal{L}_1$ considerably, leading to a larger importance.\n\nFrom the top panel of Fig.~\\ref{fig-feat-importances}, we can see that the ten most important features contain a mix of luminosity features, distance related features, and morphology features. The morphology features which are used the most are the ones from CNN 4, which was retrained to optimize $M\/L$. As discussed further below, these tend to correlate directly with $M\/L$. They no longer directly correspond to the GZ2 probabilities, but since CNN 4 only uses the log-normalized image, its features only depend on the galaxy's morphology. CNN 4 essentially eases the work for the GBM by moving part of the $M\/L$ prediction to that CNN. Since this hinders the interpretability of the model, we also show the feature importance for a GBM that does not make use of CNN 4 (bottom panel of Fig.~\\ref{fig-feat-importances}). This predictor is slightly worse, with a test $\\mathcal{L}_1$ of 2.41 (instead of 2.29). Although simple GZ2 questions such as the presence of galaxy features (e.g. spiral arms) correlate well with $M\/L$ (see below), they are not part of the most important features. Instead, the luminosity statistics seem to be more robust features. Since ellipticals and spirals have different brightness profiles, the luminosity statistics (such as the ratio of the mean centre luminosity and total luminosity) do provide morphological information. The GZ2 features that are most important (if CNN 4 is not present) look for a lack of bulge, and for irregular galaxies. Apparently, these two features can not be easily substituted by luminosity statistics.\n\nDue to the large number of features, the model can be resistant against the removal of some features. For example, if we remove the total luminosity feature (sum over all pixels), there is still the mean around the central pixel luminosity which can serve as a proxy. So if we train the LightGBM after leaving out the total luminosity feature, the test set $\\mathcal{L}_1$ only increases by 0.02. This shows an important difference between the permutation importance and so-called drop-out importance (increase in $\\mathcal{L}_1$ after retraining the model without that feature). If we have highly correlated features, retraining the model without one of those features will allow the similar features to make up for its lack. Our permutation importance measures something different: how important is that feature in the current model. We found that by only using the top 50 features (and redoing the cross-validation), the results stay the same. The computational time, however, decreases dramatically, with training only taking about 40 CPU seconds (from 16.7 CPU minutes) and evaluating on the training set taking only 4.2 CPU seconds (instead of 20 CPU seconds), using two threads on a Intel i5 processor.\n\n\\newcommand\\Tstrut{\\rule{0pt}{2.6ex}} \n\\newcommand\\Bstrut{\\rule[-0.9ex]{0pt}{0pt}}\n\\newcommand{\\TBstrut}{\\Tstrut\\Bstrut}\n\\begin{table}\n\t\\caption{Test set $\\mathcal{L}_1$ for a LightGBM model that uses only the features which are checked. CNN 1-3 refers to the first three CNNs (from the left) in Fig~\\ref{fig-pipeline}, all of which are only trained on the Galaxy Zoo probabilities. CNN 4 is the ResNet50 which was retrained on $M\/L$. When leaving out the distance feature (redshift and pixelsize), we also replace all luminosity features by the corresponding flux features. The baseline $\\mathcal{L}_1$ (i.e. minimizing $\\mathcal{L}_1$ when predicting a single value) is 6.52.} \n\t\\label{tab-feature-removing} \n\t\\centering \n\t\\begin{tabular}{c c c c c} \n\t\t\\hline\\hline \n\t\tCNN 1-3 & CNN 4 & Luminosity & Distance & Test $\\mathcal{L}_1$ \\TBstrut \\\\ \n\t\t\\hline \n\t\t\\checkmark & \\checkmark & \\checkmark & \\checkmark & 2.29 \\Tstrut \\\\\n\t\t& \\checkmark & \\checkmark & \\checkmark& 2.37 \\\\\n\t\t\\checkmark & & \\checkmark & \\checkmark & 2.41 \\\\\n\t\t\\checkmark & \\checkmark & & \\checkmark& 2.32 \\\\\n\t\t\\checkmark & \\checkmark & \\checkmark & & 2.67 \\\\\n\t\t&& \\checkmark& \\checkmark & 3.38 \\\\\n\t\t\\hline \n\t\\end{tabular}\n\\end{table}\n\n\\begin{figure*} \n\t\\centering\n\t\\includegraphics[width=17cm]{figures\/scaling_relations.pdf}\n\t\\caption{Influence of a few features on $M\/L$, for the training set. Due to the large number of datapoints, we use (hexagonal) bins. The opacity of each bin corresponds to the number of galaxies in the bin, in a non-linear way (ensuring that lower densities are still visible). The total luminosity denotes the total g-band luminosity in $L_{\\odot, g}$, and is shown in log space. For the top left panel, the $P_{\\rm{feature}}$ feature from CNN 1 estimates the GZ2 probability of the galaxy having a feature or disk. For the bottom left panel, the morphology is determined from the last layer of CNN 1. A galaxy is defined as irregular if the predicted probability of being irregular is larger than 20\\%, it is considered edge-on if it is not irregular but has a probability of being edge-on larger than 40\\%, and it is a feature or disk if the corresponding probability is larger than 40\\% (but it is not in the previous two categories). The ellipticals are the remaining datapoints. }\n\t\\label{fig-scaling-relations}\n\\end{figure*}\n\nMaybe even more important is what happens when we leave out a group of features. The results of such an ablation study can be seen in Table~\\ref{tab-feature-removing}. We see that a reliable distance estimate (in our case the redshift from SDSS) is quite important. It should be noted that we need a distance estimate to go from $M\/L$ to stellar mass anyway. The luminosity is less important in order to estimate $M\/L$. This does not contradict with the total luminosity being an important feature: as discussed further below, the luminosity features allow the machine to roughly distinguish between high and low $M\/L$. However, in the absence of luminosity features the morphology features can take their place. We also see that CNN 4 and CNN 1-3 complement each other well: we see a clear improvement when all CNNs are combined. The results are clearly worse when no CNN is present (bottom row, $\\mathcal{L}_1 = 3.38$), although this predictor still has access to the resolved luminosity features, allowing it to outperform the reference method (top panel of Fig.~\\ref{fig-g-predictors}; $\\mathcal{L}_1 = 4.52$).\n \nOf course, our model does not use the actual GZ2 probabilities as input: this ensures that no human interaction is required when making new predictions. CNN 1 to 3 exist to estimate the GZ2 probabilities. These estimations are not perfect, and we might wonder what the effect of these errors might be on the final prediction. To determine this, we replaced the custom network (CNN 3) by the actual GZ2 cumulative probabilities. The resulting $\\mathcal{L}_1$ of this cheating model is 2.25 (compared to 2.29 for the standard estimator). This shows that only minor improvements can be made by further improving the GZ2 predictions. It also shows that we can trust our CNNs to make good morphology detections, and hence that decisions made regarding the CNN pipeline are not negatively impacting our further analysis.\n\nWe can see the effect of the different features by looking at their influence on $M\/L$. In Fig.~\\ref{fig-scaling-relations}, we show how the target $M\/L$ correlates with some of the features. These correlations are the driving force behind the machine learning. In the top left panel, we can see that the luminosity feature can distinguish roughly between high $M\/L$ and low $M\/L$ galaxies: galaxies with $L_g < 10^{6.5} L_{\\odot, g}$ tend to have a low $M\/L$, while galaxies with $L_g > 10^{7.5} L_{\\odot, g}$ tend to have a high $M\/L$. With the help of the Galaxy Zoo probabilities estimated by the ResNet50 architecture (CNN 1), we can further distinguish between the two groups even in the case of intermediate luminosities. In this case, the GZ2 probability $P_{\\textrm{feature}}$ is used as a colour scale, where $P_{\\textrm{feature}}$ gives the probability of a morphological feature or disk being present (in contrast to being smooth, or a ``star or artifact''). For constant luminosity, galaxies with features tend to have a lower $M\/L$. One exception is the cloud of feature galaxies with $M\/L > 4$, which is explained in the next paragraph. Looking at lower $M\/L$ (< 2), we see that the probability of the galaxy having a feature increases with luminosity. This can be attributed to a distance-dependent classification bias \\citep{gz2-hart}. Essentially, the spiral structure is hard (or impossible) to see for fainter, more distant galaxies (with a lower $S\/N$). The algorithm can detect these low $S\/N$ galaxies (through luminosity features, redshift and scaling value), and react by predicting a lower $M\/L$ than is typical when $P_{\\rm{feature}}$ is low. \n\nThe bottom left panel is similar to the top left, but has combined three Galaxy Zoo features ($p_{\\textrm{feature}}$, $p_{\\textrm{edge-on}}$ and $p_{\\textrm{irregular}}$) to create four categories. We notice that irregulars have a very low $M\/L$, probably because a merger-triggered star formation burst leads to a young stellar population \\citep{merger-starburst}. As expected, there is the bimodality between disk galaxies and ellipticals, where ellipticals are believed to be more evolved objects with an older stellar population (and hence a higher $M\/L$). The big exception here is edge-on disks, which seem to have a very high $M\/L$. This is the result of our definition of the stellar luminosity $L$, where we directly multiplied the flux by $4\\pi D^2$. Edge-on disks are more attenuated and hence we receive less light, resulting in a higher $M\/L$. While our definition ignores anisotropy (the luminosity now depends on the viewing angle), the $M\/L$ only serves as a bridge to estimate the total stellar mass. The CIGALE models assume no particular geometry. We have inspected the influence of $p_{\\textrm{edge-on}}$ on the total stellar mass $M$, and found no clear trend: these two variables have a Spearman correlation coefficient of only 0.03. This suggests that we can still estimate the stellar mass, even when attenuation in edge-on disks causes the observed luminosity to be lower than the intrinsic luminosity (averaged over all directions).\n\nThe top right panel of Fig.~\\ref{fig-scaling-relations} clearly shows the effects of Malmquist bias. Our main selection criterion is $D_{25} > 0.4$ arcmin. At higher redshift, we only include very large (and thus often luminous) objects. These tend to have a high $M\/L$. The GBM makes use of this bias by predicting a high $M\/L$ for higher redshift galaxies. The result is that for our sample, the predictions are actually more accurate for further away galaxies. This stresses the importance that the test set (or any set on which the machine learning is evaluated) should have the same selection criteria as the training set. We learn by example, and so the assumption is that new samples are similar to the training set. \n\nThe CNN 1 features from the left two panels are actually not often present in the trees of the GBM, due to the presence of CNN 4. CNN 4 was trained to correlate more directly with $M\/L$, as seen in the bottom right panel. The result is that the CNN 4 features are no longer directly interpretable. Leaving out CNN 4 increases the test $\\mathcal{L}_1$ by only 0.12, as seen from Table~\\ref{tab-feature-removing}, so the relations from the left two panels do give us some insight in the behaviour of the machine learning. \n\n\\subsection{Using colour and morphology}\n\\label{ssec-gi-morph}\n\n\\begin{figure*} \n\t\\centering\n\t\\includegraphics[width=17cm]{figures\/gi_predictors.pdf}\n\t\\caption{\\textit{Top left, top right and bottom left}: predicted $M\/L$ vs ground truth for $g-i$ power law, global luminosity GBM and the morphology GBM respectively. Only the galaxies in the test set are shown. \\textit{Bottom left}: histogram of dex errors. }\n\t\\label{fig-gi-predictors}\n\\end{figure*}\n \nSo far, we have shown that it is possible to make reasonable $M\/L$ (and hence stellar mass) predictions with observations in only one band (and ideally a distance estimate). This of course does not replace traditional stellar mass methods, but shows that the morphology of a galaxy does provide valuable information. Now we can wonder: does morphology give the same information as colour, or is there a benefit in using morphology in addition to global g and i luminosities? To investigate this, we added the g-band luminosity $L_g$, i-band luminosity $L_i$ and g - i colour $L_g\/L_i$ as features to the LightGBM. These are derived from the SDSS modelmags, which were also used for the SED fitting \\citep{gswlc, gswlc2}. After training has completed, we selected the 50 features that were used the most in the GBM, and retrained using only those. The result is shown in the bottom left panel of Fig.~\\ref{fig-gi-predictors}. The resulting test set $\\mathcal{L}_1$ is 1.12. We compare this against a standard method to estimate the $M\/L$ from a single colour: a power law between $M\/L$ and $g-i$ colour \\citep{zibetti2009}. The two power law parameters were fit on the training set, minimizing $\\mathcal{L}_1$ (just like the machine learning). The result is shown in the top left panel of Fig.~\\ref{fig-gi-predictors}, although the test metrics exclude two datapoints with extreme mispredictions. This already shows one of the drawbacks of this method: it is not applicable if the two fluxes are `incompatible' (e.g. due to large uncertainties or observational artifacts). To make a fairer comparison, we also compare against a more sophisticated single colour method. Instead of assuming a power law, we used a LightGBM regressor (which was also used for the morphology method). This method made use of four features: redshift, $L_i$, $L_g$ and $L_g \/ L_i$. The last feature is beneficial since the individual decision trees only split based on one feature. This method, which does not make use of morphology, is shown in the top right panel of Fig.~\\ref{fig-gi-predictors} and achieves a $\\mathcal{L}_1$ of 1.26. \n \nThere is a clear improvement when going from a power law to a GBM. The power law is unable to make a good fit for both low and high $M\/L$ (low and high $M\/L$ refer to the bimodality also seen in Fig.~\\ref{fig-ml-hist}). There's also a large number of outliers, and these influence the fit to keep the $\\mathcal{L}_1$ under control. A GBM can easily improve on this: every point in the feature space is assigned a $M\/L$ which minimizes the corresponding $\\mathcal{L}_1$, which hence avoids the bias that can be seen in the power law. This can also be verified by looking at the distribution of dex errors, in the bottom right panel of Fig.~\\ref{fig-gi-predictors}. The $g-i$ power law has a strong tail towards overpredictions: 5.1\\% of galaxies have a logarithmic error larger than 0.15 dex (overpredictions), while only 1.8\\% have a logarithmic error smaller than -0.15 dex (underpredictions). For the GBM method (without morphology), only 2.3\\% have a logarithmic error outside 0.15 dex (over- and underpredictions).\n \nAdding morphology to the GBM (bottom left panel of Fig.~\\ref{fig-gi-predictors}) further reduces the test $\\mathcal{L}_1$ to 1.12, resulting in a better estimator. An important question to investigate is whether a test $\\mathcal{L}_1$ of 1.12 (with morphology) is \\textrm{significantly} better than a test $\\mathcal{L}_1$ of 1.26 (without morphology). The reported $\\mathcal{L}_1$ are the mean $\\mathcal{L}_1$ over the $7\\,363$ test samples. Hence, we can look at the distribution of the individual $\\mathcal{L}_1$. First, we did a Kolmogorov-Smirnov two-sample test, for a one-sided comparison. The null hypothesis, which states that the model with morphology does not have a significantly lower $\\mathcal{L}_1$ than the model without morphology, can be rejected with very high confidence (p-value $7.6\\times 10^{-9}$). Next, we took 100\\,000 bootstrap samples of the $\\mathcal{L}_1$ distribution of both models. The mean $\\mathcal{L}_1$ of each of these bootstrap samples has no overlap for the two models. The bootstrap estimate for the mean $\\mathcal{L}_1$ of the method without morphology is $1.26 \\pm 0.01$, while the estimate for the method with morphology is $1.12 \\pm 0.01$. Hence, we can conclude that adding the morphology features gives a significant improvement.\n \nWe notice most improvement at $M\/L > 4$, which are dominated by edge-on galaxies, as can be seen from the bottom left panel of Fig.~\\ref{fig-scaling-relations}. We can quantify the improvement for each of the morphology classes from that panel. By including morphology features, $\\mathcal{L}_1$ improves from 1.15 to 1.08 for irregulars, from 1.31 to 1.18 for feature (disk) galaxies, from 1.14 to 1.07 for ellipticals, and from 1.52 to 1.14 for edge-on galaxies (the biggest improvement). A single colour often underestimates the stellar mass for these edge-on galaxies, and morphology information can be used to prevent this. \n \nWhen comparing these single colour predictors to the g-band image predictor (bottom left panel of Fig.~\\ref{fig-g-predictors}, $\\mathcal{L}_1 = 2.29$), we see that morphology can not replace colour. It is, however, impressive that a g-band $M\/L$ predictor gets close to a $g-i$ power law, even though the power law is also fitted to this dataset. This refitting was necessary, since the original \\citet{zibetti2009} power law has a test set $\\mathcal{L}_1$ of 7.47 (mainly due to the different SPS model grid than GSWLC). Due to its easy application, a single colour power law is commonly used \\citep[e.g.][]{scpl-2, scpl-1}, but as shown here it should be used with caution. Machine learning techniques can help improve these estimates without needing to do SED fitting at evaluation time. The morphology can help assist predictions of $M\/L$. Since it is available for more nearby galaxies anyway, it can be a valuable improvement over techniques that only use global flux information (and hence dismiss all information from the resolved image).\n\n\\subsection{Morphology assisted $M\/L$ with multiple colours: limitations of the ground truth}\n\n\\begin{figure}\n\t\\centering\n\t\\includegraphics[width=\\hsize]{figures\/ugriz_buildup_morphology.pdf}\n\t\\caption{Comparison of the performance (test set $\\mathcal{L}_1$) of different GBM predictors. The predictors only make use of the denoted broadbands. The yellow bars only make use of global information (luminosity, distance and colours), while the green bars also make use of morphology.}\n\t\\label{fig-ugriz-buildup}\n\\end{figure}\n\nAfter looking at single band and single colour predictors, we might wonder what happens when multiple colours are available. Similar to Sect.~\\ref{ssec-gi-morph}, we train gradient boosting machines with and without morphology. For a sequence of available SDSS broadbands, the results are shown in Fig.~\\ref{fig-ugriz-buildup}. We see that the performance stagnates at an $\\mathcal{L}_1$ of 1, when the predictions are as accurate as the uncertainty on the ground truth. Including morphology improves the results for all cases, although the stagnation at $\\mathcal{L}_1 = 1$ limits the benefit when multiple broadbands are available.\n\nThe problem is that we are limited by our ground truth (i.e. the prediction target): the GSWLC $M\/L$ come from Bayesian SED fitting to \\textrm{global} fluxes. This means that the morphology can only make up for missing broadbands, but not for the uncertainties that come from using only global fluxes. It is possible to apply SED fitting pixel-by-pixel, and then sum the stellar masses of the individual pixels. The assumption that a spectrum is the sum of SSPs with a simple attenuation law applies better to individual pixels than to complete galaxies. So while pixel-by-pixel SED fitting is believed to be more accurate \\citep{unresolved-sed1, unresolved-sed2}, it is also more expensive. Pixel-matched panchromatic datasets are required, where the band with the worst resolution effectively sets the working resolution. A high $S\/N$ is required for all relevant pixels. This method is also much more computationally intensive, and hence the number of models that can be fit is limited. The result is that at the time of writing, no large pixel-by-pixel SED fitted catalogues exist. Should they become available in the future, our method can be retrained which can make it possible to beat global flux methods. \n\nAnother way to improve the ground truth is by using more information. Currently, GSWLC uses the WISE observations to estimate the total infrared luminosity $L_{IR}$ \\citep{gswlc2}. Assuming energy balance, this then constrains the total energy absorbed by the dust, allowing us to make better estimates of the unattenuated stellar spectrum. Although the uncertainty on this $L_{IR}$ estimation is only 0.08 dex, it uses just a single WISE band. This can make it troublesome for galaxies with large uncertainties on that WISE band, or for galaxies where the correction for AGN contribution leads to additional uncertainties. The best way to constrain $L_{IR}$ is still to measure it with FIR observations, and hence galaxies with FIR data will have a slightly better ground truth $M\/L$. In addition to using UV-FIR broadbands, spectroscopy can be used as an additional constraint for the SED fitting \\citep{beagle, beagle-spectroscopy, bagpipes, prospector}. Limiting the training to samples were this additional information (more broadbands and\/or spectroscopy) is available unfortunately implies that the size of the training set will be smaller.\n\nOf course, the best case scenario would be that our ground truth were the actual stellar $M\/L$. Unfortunately, there is no way to directly measure stellar mass, instead of estimating it through SPS. There is however a situation in which we know the stellar mass: cosmological simulations. With radiative transfer, it is possible to create mock observations of these simulated galaxies \\citep[e.g.][]{camps2018}. These then could serve as a good training target, since we no longer have to deal with the limitations of SED fitting. The radiative transfer treats the effects of dust rigorously (in contrast to an empirical attenuation law), and star forming regions can be treated with subgrid prescriptions. Recently, some successes have been achieved with training CNNs on cosmological simulations, while testing them on real galaxies \\citep[e.g.][]{simulation-cnn-1,simulation-cnn-2}. The main limitation of this approach is that there are still discrepancies between the observed and the simulated universes.\n\n \n\\subsection{Applications and discussion}\n\nThe success of using morphology information to predict stellar mass depends on the quality of the images. In this work, we limited ourselves to $D_{25} > 0.4$ arcmin to make sure that we have enough pixels for each galaxy. Upcoming surveys will allow for deeper and higher resolution observations, drastically increasing the number of galaxies that are well resolved. In particular, Euclid will have a very broad optical band ($r+i+z$) which is useful to get deeper images. These will be combined with ground-based photometry ($griz$) and Euclid photometry ($YJH$) \\citep{euclid}. The goal is to have 1.5 billion galaxies with very accurate morphometric information. These will be an excellent target for training and testing morphological stellar mass estimates. Besides Euclid, LSST \\citep{lsst} and WFIRST \\citep{wfirst} will also provide wide-field optical\/NIR imaging which could benefit from our method. As discussed in the previous section, the hardest but most rewarding challenge to solve will be to acquire more accurate ground truth $M\/L$, such as from pixel-by-pixel SED fitting. The morphology can then use the resolved information to improve on a global colour estimate. \n\nGSWLC 1 contains about $700~000$ galaxies and is one of the largest SED fitted catalogues to date. This already shows that even global SED fitting will be computationally challenging for Euclid's 1.5 billion galaxies, without significantly reducing the number of fitted models. A machine learning approach (with or without morphology) can be a good alternative. With the use of a single GPU, CNN evaluation is more than an order of magnitude faster than (global) SED fitting on a 100 core CPU cluster. If we train on pixel-by-pixel SED fits, we further avoid the outshining bias. So training on a small but accurate $M\/L$ subset of Euclid, and evaluating on the remaining $> 1$ billion galaxies seems promising. We found that if we train the GBM with only half of the data, the test $\\mathcal{L}_1$ degrades only slightly to 2.31 (from 2.29), confirming that the quality of the training data is more important than the quantity.\n \nOur pipeline can also be used to predict other galaxy properties, such as SFR or metallicity. For both of these properties, spectroscopy can be a valuable constraint on the ground truth. We hope that this two-step process (CNN + GBM) can further improve our understanding of which morphological properties best correlate with the physical properties of a galaxy. This can then further constrain galaxy evolution models. \n \n\n\\section{Summary and conclusions}\n\\label{sec-conclusions}\n\nWe made use of a machine learning framework to make morphology assisted $M\/L$ predictions. First, we predicted $M\/L$ from a single g-band image. The pipeline can be split in two parts: a first part estimates morphology features such as the probability of the galaxy being featureless, edge-on, merging, etc. This information is then combined with redshift, pixel size, and a few g-band luminosity features in order to predict $M\/L$. We optimized a $\\mathcal{L}_1$ loss that weights down samples with a large uncertainty on $M\/L$. Our best model has a test set $\\mathcal{L}_1$ of 2.29, and a RMSLE of 0.124 dex. The morphology from the g-band can partially make up for a lack of observed colour. These predictions are made possible because featureless ellipticals tend to have a higher $M\/L$ than galaxies with features such as spirals (left two panels of Fig.~\\ref{fig-scaling-relations}). Irregular galaxies tend to have a low small $M\/L$, while highly inclined disk galaxies tend to have a very high $M\/L$. Even though the spiral features can not be detected for more distant, dimmer galaxies, the algorithm is trained to produce unbiased results. \n\nObserving multiple bands does lead to a better constrained $M\/L$. A $g-i$ power law, recalibrated on our dataset achieves a $\\mathcal{L}_1$ of 1.90 (compared to 2.29 for our g-band only method). The $g-i$ power law has trouble fitting both small and large $M\/L$. This can be avoided by using a GBM (or other machine learning method). We find that a GBM that makes use of global $g$ and $i$ fluxes and a distance estimate achieves a $\\mathcal{L}_1$ of 1.26. Including the g-band morphology features further improves the $\\mathcal{L}_1$ to 1.12, showing that morphology information does have an added benefit over only global colours. Even though this improvement is small, we have shown that it is significant. Most of the improvement happens for edge-on disk galaxies. With global fluxes only, it is hard to distinguish a more inclined and hence attenuated galaxy from an older one (both effects lead to redder colours), and we find that the $M\/L$ tends to be underpredicted in those cases.\n\nIn future work, we hope that this machine learning framework can be trained on better target estimates for $M\/L$. Currently, our target values are derived from unresolved fluxes, limiting the benefit of our method over global colour methods. Our method can be fit to reproduce pixel-by-pixel SED fitted $M\/L$, but has less strict requirements on pixel $S\/N$, and is faster at evaluation time. \n\n\n \\begin{acknowledgements}\n We thank the anonymous referee for helpful comments which improved this paper. W.D., S.V. and M.B. gratefully acknowledge support from the Flemish Fund for Scientific Research (FWO-Vlaanderen). W.D. is a pre-doctoral researcher of the FWO-Vlaanderen. Special thanks to the Flemish Supercomputer Centre (VSC) for providing computational resources and support. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III web site is http:\/\/www.sdss3.org\/. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.\n \\end{acknowledgements}\n\n \n\\bibliographystyle{aa}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe Bethe--Sommerfeld conjecture is the following statement: for any $d \\geq 2$ and any periodic function $V:{\\mathbb R}^d \\to {\\mathbb R}$, the spectrum of the Schr\\\"odinger operator \n\\[\nL_V := -\\nabla^2 + V\\]\nhas only finitely many gaps. This was studied by many people with important advances in \\cite{HelMoh98,Karp97,PopSkr81,Skr79,Skr84,Skr85,Vel88}, and culminating in the paper of Parnovskii \\cite{Parn2008AHP}. One way to think about the Bethe--Sommerfeld conjecture is that any energy $E$ that is very large relative to the potential $V$ lies in the spectrum of $L_V$. Since discrete Schr\\\"odinger operators are bounded, the high-energy region is absent, so the appropriate discrete version of the Bethe--Sommerfeld conjecture lies in the region of small $V$. Discrete versions of the conjecture were proved on square lattices by Embree--Fillman in dimension $d=2$ \\cite{EmbFil2017} and by Han--Jitomirskaya in arbitrary dimensions $d \\geq 2$ \\cite{HanJit2017}. In those works, the spectrum of a discrete periodic Schr\\\"odinger operator on the square lattice $\\ell^2({\\mathbb Z}^d)$ with a small potential was shown to consist of at most two intervals. Moreover, they showed that as soon as at least one period of the potential is odd, then the spectrum is an interval, and, in the event that a gap opens perturbatively, it must happen at the exceptional energy $E=0$.\n\nMany interesting physical models occur with different underlying lattice geometries beyond the standard square lattice. One of the most prominent such models is supplied by graphene, a two-dimensional material that consists of carbon atoms at the vertices of a hexagonal lattice. The fascinating properties of graphene have led to a substantial amount of attention in mathematics and physics, see e.g.\\ \\cite{BZ2018,BHJ2018,CGPNG,DelMon2010,FW12,HKR2016,KP07,N11} and references therein. \nIn view of this, we are motivated to study the Bethe--Sommerfeld conjecture for the hexagonal lattice and for the corresponding dual lattice (the triangular lattce).\n\nIn addition to the hexagonal and triangular lattices, we also study the square lattice with next-nearest neighbor interactions, which is motivated by the extended Harper model (EHM). The EHM was proposed by Thouless \\cite{Thouless83} and has also led to a lot of study in mathematics and physics \\cite{AJM17,H17,H18,HJ17,Thouless94,JM15}; it corresponds to an electron in a square lattice that interacts not only with its nearest neighbors but also its next-nearest neighbors.\nIn the following, we will refer to square lattice with next-nearest neighbor interactions as the {\\it EHM lattice}, in order to distinguish it from the standard square lattice.\n\n\n\n\n\n\n{Let us mention in particular the closely related work \\cite{HKR2016}.} In \\cite{HKR2016}, Helffer, Kerdelhu\\'e and Royo-Letelier developed a Chambers analysis for magnetic Laplacians on the hexagonal lattice (and its dual lattice: triangular lattice) with rational flux.\nThey showed that for a non-trivial rational flux $p\/q\\notin{\\mathbb Z}$, the magnetic Laplacians on hexagonal and triangular lattices have non-overlapping (possibly touching) bands.\nThis recovers a similar feature of the square lattice \\cite{BelSim82}.\nHowever, unlike the square lattice that has no touching bands except at the center for $q$ even \\cite{VMou89}, \nthey were able to give an explicit example of non-trivial touching bands for hexagonal and triangular lattices. \nIndeed they showed that the triangular Laplacian has touching bands at energy $E=-\\sqrt{3}$ for $p\/q=1\/6$, and the hexagonal Laplacian has touching bands at energies $E=\\pm \\sqrt{3}$ and $0$ for $p\/q=1\/2$.\nTherefore, the underlying geometry is greatly responsible for the formation of touching bands.\nBut it {has remained} unclear that whether there will be other touching bands for different fluxes (and if any, what are the locations).\nIn our work we are able to give a sharp criterion of the formation of touching bands for the free Laplacians on these lattices and the EHM lattice, see Theorems \\ref{t:bsc:tri}, \\ref{t:bsc:hex} and \\ref{t:bsc:nnn}.\n\nMotivated by these models, we prove the Bethe--Sommerfeld conjecture for the triangular, hexagonal, and EHM lattices. \nSimilar to the square lattice case, we show that small perturbations of the free Laplacian may only open gaps at certain {\\it exceptional energies}.\nOur proof uses the perturb-and-count technique developed in \\cite{HanJit2017}.\nThe overall strategy is to argue by contradiction. \nNamely, we assume two adjacent spectral bands of the free Laplacian have a trivial overlap containing a single energy $E$.\nThen, we carefully choose a Floquet parameter and perturb all the Floquet eigenvalues along two different directions. \nIt is then argued that different directions lead to different counting of eigenvalues that move above\/below $E$, hence a contradiction.\nAt the exceptional energies, we are able to develop a {\\it sharp} criterion, in terms of the periods, of whether the gaps could possibly open {under an infinitesimal perturbation}.\nWe also construct potentials that do open (the theoretically existing) gaps at these exceptional energies.\n\nAlthough the general strategy follows that of \\cite{HanJit2017}, {there are several challenges to overcome in the present work.}:\n\\begin{itemize}\n\\item\nThe Floquet parameters and perturbation directions that we choose in the perturb-and-count technique are strongly model-dependent in a subtle fashion. For example, at non-exceptional energies, we locate Floquet parameters and a perturbation direction in a way such that the Floquet eigenvalues with vanishing linear terms have quadratic terms of the same sign along this direction. \nAt the exceptional energy of the triangular lattice, we choose two directions such that the eigenvalues with vanishing gradients have quadratic terms of different signs along the two directions; for a more detailed discussion, see Remark~\\ref{rem:tri}.\nThis is similar to what was done in \\cite{HanJit2017} for the square lattice case.\nHowever, for the EHM lattice, any direction will lead to the same number of positive and negative quadratic terms; see Remark \\ref{rem:sqn}.\nThis issue is resolved by a new construction: we find a direction that moves approximately $2\/3$ of the degenerate eigenvalues up while the other $1\/3$ move down.\nAll these constructions depend heavily on the Floquet representation of the eigenvalues, and thus get more difficult as the underlying geometry gets more complicated.\n\\item Applying the perturb-and-count ideas directly to the hexagonal lattice is quite difficult,\ndue to the fact that the Floquet eigenvalues do not have simple expressions; compare \\eqref{eq:hexTriBandRel}.\nHowever, one can relate Laplacians and Floquet matrices for the triangular and hexagonal lattices in a fairly elegant fashion ({see \\cite{HKR2016} and our \\eqref{eq:hexSquareTri}}). Thus, we prove the Bethe--Sommerfeld conjecture directly for the triangular lattice and then derive the corresponding statement for the hexagonal lattice via a somewhat soft argument.\n\\item Because of the more complicated structure of the lattices involved, constructing potentials that open gaps at the exceptional energies is substantially more difficult than in the square lattice. \nIn particular, we need to construct (2,2)-periodic potentials that live on eight vertices for the hexagonal lattice, and (3,3)-periodic potential for the EHM lattice.\nIn this paper we develop an {robust} technique to study these finite volume problems in a sharp way.\nIndeed, we can not only prove that a gap exists, but also estimate its size up to a constant factor (see Theorems~\\ref{thm:triExampleGapLength}, \\ref{thm:hexQ}, and \\ref{thm:nnnExGapLength}). \nIn the case of the triangular lattice, we are even able to use our technique \\emph{exactly} compute the gap, not only estimate its size (Theorem~\\ref{thm:triExampleGapLength}).\n\\end{itemize}\n\n\\bigskip\n\n\\subsection{Main Results}\n\nLet us now describe more precisely the setting in which we work and the results that we prove. By a \\textit{graph}, we shall mean a pair $\\Gamma = ({\\mathcal V}, {\\mathcal E})$ where ${\\mathcal V}$ is a nonempty set and ${\\mathcal E}$ is a nonempty subset of ${\\mathcal V} \\times {\\mathcal V}$ with the following properties:\n\\begin{itemize}\n\\item For no $v \\in {\\mathcal V}$ does one have $(v,v) \\in {\\mathcal E}$;\n\\item If $(u,v) \\in {\\mathcal E}$, then $(v,u) \\in {\\mathcal E}$.\n\\end{itemize}\nIf $(u,v) \\in \\mathcal{E}$, we write $u\\sim v$ and we say that $u$ and $v$ are neighbors or neighboring vertices. We think of ${\\mathcal E}$ as the set of \\emph{directed edges}; $(u,v)$ represents the edge that originates at $u$ and terminates at $v$.\n\nGiven such a graph, we consider $\\mathcal{H}_\\Gamma = \\ell^2(\\mathcal{V})$ and the associated \\emph{graph Laplacian} $\\Delta_\\Gamma: \\mathcal{H}_\\Gamma \\to \\mathcal{H}_\\Gamma$, which acts via\n\\[\n[\\Delta_\\Gamma \\psi]_u\n=\n\\sum_{v \\sim u} \\psi_v,\n\\quad\nu \\in \\mathcal{V}, \\; \\psi \\in \\mathcal{H}_\\Gamma.\n\\]\nTechnically, this is the adjacency operator of the graph. Other authors use $\\psi_v - \\psi_u$ where we have only $\\psi_v$. Our convention is slightly more natural for the setting in which we wish to work. Concretely, all of the graphs that we consider in the present work have uniform degree (all vertices in a given graph have the same number of incident edges), and hence leaving off the $-\\psi_u$ term merely costs us a multiple of the identity operator, and it simplifies the appearance of a few calculations.\n\nBy a \\emph{Schr\\\"odinger operator} on $\\Gamma$, we mean an operator of the form $H_Q = H_{\\Gamma,Q} = \\Delta_\\Gamma + Q$, where $Q:{\\mathcal V} \\to {\\mathbb R}$ is a bounded function that acts on ${\\mathcal H}_\\Gamma$ by multiplication:\n\\[\n[Q\\psi]_u\n=\nQ(u) \\psi_u,\n\\quad\nu \\in \\mathcal{V}, \\; \\psi \\in \\mathcal{H}_\\Gamma.\n\\]\n\\begin{figure*}[t]\n\\begin{tikzpicture}[yscale=.6,xscale=.6]\n\\draw [-,line width = .06cm] (0,0) -- (6,0);\n\\draw [-,line width = .06cm] (0,0) -- (0,6);\n\\draw [-,line width=.06cm] (0,2) -- (6,2);\n\\draw [-,line width = .06cm] (2,0) -- (2,6);\n\\draw [-,line width=.06cm] (0,4) -- (6,4);\n\\draw [-,line width = .06cm] (4,0) -- (4,6);\n\\draw [-,line width=.06cm] (0,6) -- (6,6);\n\\draw [-,line width = .06cm] (6,0) -- (6,6);\n\\filldraw[color=black, fill=black](0,0) circle (.2);\n\\filldraw[color=black, fill=black](2,0) circle (.2);\n\\filldraw[color=black, fill=black](4,0) circle (.2);\n\\filldraw[color=black, fill=black](6,0) circle (.2);\n\\filldraw[color=black, fill=black](0,2) circle (.2);\n\\filldraw[color=black, fill=black](2,2) circle (.2);\n\\filldraw[color=black, fill=black](4,2) circle (.2);\n\\filldraw[color=black, fill=black](6,2) circle (.2);\n\\filldraw[color=black, fill=black](0,4) circle (.2);\n\\filldraw[color=black, fill=black](2,4) circle (.2);\n\\filldraw[color=black, fill=black](4,4) circle (.2);\n\\filldraw[color=black, fill=black](6,4) circle (.2);\n\\filldraw[color=black, fill=black](0,6) circle (.2);\n\\filldraw[color=black, fill=black](2,6) circle (.2);\n\\filldraw[color=black, fill=black](4,6) circle (.2);\n\\filldraw[color=black, fill=black](6,6) circle (.2);\n\\end{tikzpicture}\n\\caption{The square lattice.}\n\\end{figure*}\nIn the present work, we study ${\\mathbb Z}^2$-periodic graphs. That is, we consider graphs whose vertices $\\mathcal{V}$ comprise a subset of ${\\mathbb R}^2$ and for which there exist linearly independent translations $\\bm{a}_1, \\bm{a}_2 \\in {\\mathbb R}^2$ which leave $\\Gamma$ invariant. That is to say:\n\\begin{itemize}\n\\item For any vertex $v \\in {\\mathcal V}$, $v + \\bm{a}_j \\in {\\mathcal V}$ for $j=1,2$;\n\\item For any edge $(u,v) \\in {\\mathcal E}$, $(u + \\bm{a}_j,v + \\bm{a}_j) \\in {\\mathcal E}$ for $j=1,2$.\n\\end{itemize}\nWe will then be most interested in studying the case when the potential $Q$ is itself periodic. In general, we will say that $Q:{\\mathcal V} \\to {\\mathbb R}$ is $\\bm{p} = (p_1,p_2)$-periodic for some $p_1,p_2 \\in {\\mathbb Z}_+$ if and only if \n\\[\nQ(u+p_1 \\bm{a}_1) = Q(u + p_2 \\bm{a}_2) = Q(u),\\quad\n\\text{for all } u \\in {\\mathcal V}.\n\\]\n\nThe square lattice is the graph with vertices ${\\mathcal V}_{\\mathrm{sq}} = {\\mathbb Z}^2$ and where \n\\[\n\\bm{n} \\sim \\bm{n}'\n\\iff\n\\| \\bm{n} - \\bm{n'} \\|\n=\n1.\n\\]\nHere and throughout the paper, $\\|\\cdot\\|$ denotes the Euclidean norm on ${\\mathbb R}^2$. It is easy to see that the associated Laplacian acts on $\\ell^2({\\mathbb Z}^2)$ via\n\\[\n[\\Delta_{\\mathrm{sq}} \\psi]_{n,m}\n=\n\\psi_{n-1,m} + \\psi_{n+1,m} + \\psi_{n,m-1} + \\psi_{n,m+1}.\n\\]\n\n\n\n\nPart of the motivation for the present work comes from \\cite{EmbFil2017, HanJit2017, KrugPreprint}. In \\cite{EmbFil2017}, Embree and Fillman showed that if $Q: {\\mathbb Z}^2 \\to {\\mathbb R}$ is $(p_1,p_2)$-periodic and sufficiently small, then $\\sigma(\\Delta_{\\mathrm{sq}}+Q)$ consists of one or two intervals and that the spectrum consists of exactly one interval whenever at least one of $p_1$ or $p_2$ is odd, which generalized the work of Kr\\\"uger, who proved a similar result under the stricter condition that the periods were coprime \\cite{KrugPreprint}. In \\cite{HanJit2017}, Han and Jitomirskaya showed that if $Q:{\\mathbb Z}^d \\to {\\mathbb R}$ is $(p_1,\\ldots,p_d)$-periodic and small, then the same results hold true: the spectrum has no more than one gap and has no gaps as long as at least one period is odd.\n\n\n\n\\subsection{The Triangular Lattice} \n\\begin{figure*}[b]\n \\begin{minipage}{0.45\\textwidth}\n \\centering\n\\begin{tikzpicture}[yscale=.85,xscale=.85]\n\\draw [-,line width = .06cm] (0,0) -- (6,0);\n\\draw [-,line width = .06cm] (0,{sqrt(3)}) -- (6,{sqrt(3)});\n\\draw [-,line width = .06cm] (0,{2*sqrt(3)}) -- (6,{2*sqrt(3)});\n\\draw [-,line width = .06cm] (0,{3*sqrt(3)}) -- (6,{3*sqrt(3)});\n\\draw [-,line width=.06cm] (0,{2*sqrt(3)}) -- (1,{3*sqrt(3)});\n\\draw [-,line width=.06cm] (0,0) -- (3,{3*sqrt(3)});\n\\draw [-,line width=.06cm] (2,0) -- (5,{3*sqrt(3)});\n\\draw [-,line width=.06cm] (4,0) -- (6,{2*sqrt(3)});\n\\draw [-,line width=.06cm] (0,{2*sqrt(3)}) -- (2,0);\n\\draw [-,line width=.06cm] (1,{3*sqrt(3)}) -- (4,0);\n\\draw [-,line width=.06cm] (3,{3*sqrt(3)}) -- (6,0);\n\\draw [-,line width=.06cm] (5,{3*sqrt(3)}) -- (6,{2*sqrt(3)});\n\\draw [->,line width=.06cm,color=blue] (1,{sqrt(3)}) -- (2,{2*sqrt(3)});\n\\filldraw[color=black, fill=black](0,0) circle (.2);\n\\filldraw[color=black, fill=black](2,0) circle (.2);\n\\filldraw[color=black, fill=black](4,0) circle (.2);\n\\filldraw[color=black, fill=black](6,0) circle (.2);\n\\filldraw[color=black, fill=black](1,{sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](3,{sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](5,{sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](0,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](2,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](4,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](6,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](1,{3*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](3,{3*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](5,{3*sqrt(3)}) circle (.2);\n\\draw [->,line width=.06cm,color=blue] (1,{sqrt(3)}) -- (2,{2*sqrt(3)});\n\\draw [->,line width=.06cm,color=blue] (1,{sqrt(3)}) -- (3,{sqrt(3)});\n\\node [above] at (1,{sqrt(3)+.3}) {\\cold{$\\bm{a}_2$}};\n\\node [below] at (1.7,{sqrt(3)-.1}) {\\cold{$\\bm{a}_1$}};\n\\end{tikzpicture}\n\\caption{A portion of the triangular lattice}\\label{fig:trilat}\n\\end{minipage}\n\\hfill\n \\begin{minipage}{0.45\\textwidth}\n \\centering\n\\begin{tikzpicture}[yscale=.75,xscale=.75]\n\\draw [-,line width = .06cm] (0,0) -- (6,0);\n\\draw [-,line width = .06cm] (0,0) -- (0,6);\n\\draw [-,line width=.06cm] (0,2) -- (6,2);\n\\draw [-,line width = .06cm] (2,0) -- (2,6);\n\\draw [-,line width=.06cm] (0,4) -- (6,4);\n\\draw [-,line width = .06cm] (4,0) -- (4,6);\n\\draw [-,line width=.06cm] (0,6) -- (6,6);\n\\draw [-,line width = .06cm] (6,0) -- (6,6);\n\\draw [-,line width = .06cm] (0,6) -- (6,0);\n\\draw [-,line width = .06cm] (2,0) -- (0,2);\n\\draw [-,line width = .06cm] (4,0) -- (0,4);\n\\draw [-,line width = .06cm] (2,6) -- (6,2);\n\\draw [-,line width = .06cm] (6,4) -- (4,6);\n\\filldraw[color=black, fill=black](0,0) circle (.2);\n\\filldraw[color=black, fill=black](2,0) circle (.2);\n\\filldraw[color=black, fill=black](4,0) circle (.2);\n\\filldraw[color=black, fill=black](6,0) circle (.2);\n\\filldraw[color=black, fill=black](0,2) circle (.2);\n\\filldraw[color=black, fill=black](2,2) circle (.2);\n\\filldraw[color=black, fill=black](4,2) circle (.2);\n\\filldraw[color=black, fill=black](6,2) circle (.2);\n\\filldraw[color=black, fill=black](0,4) circle (.2);\n\\filldraw[color=black, fill=black](2,4) circle (.2);\n\\filldraw[color=black, fill=black](4,4) circle (.2);\n\\filldraw[color=black, fill=black](6,4) circle (.2);\n\\filldraw[color=black, fill=black](0,6) circle (.2);\n\\filldraw[color=black, fill=black](2,6) circle (.2);\n\\filldraw[color=black, fill=black](4,6) circle (.2);\n\\filldraw[color=black, fill=black](6,6) circle (.2);\n\\end{tikzpicture}\n\\caption{The triangular lattice after shearing.}\\label{fig:trishear}\n\\end{minipage}\n\\end{figure*}\n\nThe first graph that we consider is the \\emph{triangular lattice}. The graph has vertices\n\\[\n{\\mathcal V}_{\\mathrm{tri}}\n=\n\\set{n \\bm{a}_1 + m \\bm{a}_2 : n,m \\in {\\mathbb Z}},\n\\]\nwhere the generating vectors are\n\\[\n\\bm{a}_1\n=\n\\begin{bmatrix}\n1 \\\\ 0\n\\end{bmatrix},\n\\quad\n\\bm{a}_2\n=\n\\frac{1}{2} \\begin{bmatrix}\n1 \\\\ \\sqrt{3}\n\\end{bmatrix}.\n\\]\nOne then declares $v \\sim w$ for $v,w \\in \\mathcal{V}$ if $\\|v - w\\| = 1$. Thus, every $v \\in \\mathcal{V}$ has 6 neighbors; more specifically, if $v = n\\bm{a}_1 + m \\bm{a}_2$, then $v$ has neighbors\n\\[\n(n \\pm1) \\bm{a}_1 + m \\bm{a}_2, \\quad\nn\\bm{a}_1 + (m\\pm 1) \\bm{a}_2,\\quad\n(n \\pm 1) \\bm{a}_1 + (m\\mp 1) \\bm{a}_2.\n\\]\nConsequently, after identifying $n \\bm{a}_1 + m \\bm{a}_2$ with the point $(n,m) \\in {\\mathbb Z}^2$, we may view the Laplacian on the triangular lattice as an operator on $\\ell^2({\\mathbb Z}^2)$ via\n\\begin{equation} \\label{eq:triLaplacianSqVersion}\n[\\Delta_{\\rm tri} \\psi]_{n,m}\n=\n[\\Delta_{\\rm sq} \\psi]_{n,m}\n+\n\\psi_{n-1,m+1} + \\psi_{n+1,m-1}.\n\\end{equation}\nThis correspondence amounts to shearing and stretching the the triangular lattice, and essentially maps the triangular lattice to the square lattice with skewed next-nearest-neighbor interactions added. See Figures~\\ref{fig:trilat} and \\ref{fig:trishear}.\n\n\n\\begin{theorem}[Bethe--Sommerfeld for the triangular lattice] \\label{t:bsc:tri}\nFor all $\\bm{p} = (p_1,p_2 )\\in {\\mathbb Z}_+^2$, there is a constant $c = c_{\\bm{p}} > 0$ such that, if $Q:{\\mathcal V}_{\\mathrm{tri}} \\to {\\mathbb R}$ is $\\bm{p}$-periodic and $\\|Q\\|_\\infty \\leq c$, the following hold true for $H_Q = \\Delta_{\\mathrm{tri}} + Q$:\n\\begin{enumerate}\n\\item[{\\rm(1)}] $\\sigma(H_Q)$ consists of no more than two intervals.\n\\item[{\\rm(2)}] If at least one of $p_1$ or $p_2$ is odd, then $\\sigma(H_Q)$ consists of a single interval.\n\\end{enumerate}\nMoreover, the gap in the first setting may only open at the energy $E = -2$.\n\\end{theorem}\n\nThis theorem is sharp vis-\\`a-vis the number of intervals in the spectrum and the arithmetic restrictions on the periods. Concretely, we exhibit a $(2,2)$-periodic potential that perturbatively opens a gap at $-2$.\n\n\\begin{theorem} \\label{t:triExamples}\nThere exists $Q:{\\mathcal V}_{\\mathrm{tri}} \\to {\\mathbb R}$ which is $(2,2)$-periodic, such that $\\sigma(H_{\\lambda Q})$ has exactly two connected components for any sufficiently small $\\lambda > 0$.\n\\end{theorem}\n\n\\subsection{The Hexagonal Lattice} The set of vertices of the hexagonal lattice is closely related to that of the triangular lattice. Concretely, define $\\bm{b}_\\pm$ by\n\\[\n\\bm{b}_\\pm\n=\n\\frac{1}{2}\n\\begin{bmatrix}\n 3 \\\\ \\pm \\sqrt{3}\n\\end{bmatrix}.\n\\]\nThen, we obtain the hexagonal lattice by deleting the centers of some of the hexagons formed by the triangular lattice; more precisely,\n\\[\n{\\mathcal V}_{\\mathrm{hex}}\n=\n\\set{n \\bm{a}_1 + m \\bm{a}_2 \\in {\\mathcal V}_{\\mathrm{tri}}:n,m \\in {\\mathbb Z}} \\setminus \\{- \\bm{a}_1 + k \\bm{b}_+ + \\ell \\bm{b}_- : k,\\ell \\in {\\mathbb Z}\\}.\n\\]\nEquivalently, it is not hard to check that $\\{0, \\bm{a}_1\\}$ is a fundamental set of vertices and hence every $v \\in {\\mathcal V}_{\\mathrm{hex}}$ may be written uniquely as either $n \\bm{b}_+ + m \\bm{b}_-$ or $\\bm{a}_1 + n \\bm{b}_+ + m \\bm{b}_-$ for integers $n,m$, so we have\n\\begin{align*}\n{\\mathcal V}_{\\mathrm{hex}}\n& =\n\\set{n \\bm{b}_+ + m \\bm{b}_- : n,m \\in {\\mathbb Z}} \\cup \\set{\\bm{a}_1 + n \\bm{b}_+ + m \\bm{b}_- : n,m \\in {\\mathbb Z}}.\n\\end{align*}\nWe define ${\\mathcal E}_{\\mathrm{hex}}$ by declaring $u \\sim v$ for $u, v \\in {\\mathcal V}_{\\mathrm{hex}}$ if $\\|u - v\\|_2 = 1$. After some calculations, we see that\n\\begin{align*}\n[\\Delta_{\\mathrm{hex}} \\psi]_{n \\bm{b}_+ + m \\bm{b}_-}\n& =\n\\psi_{\\bm{a}_1 + n \\bm{b}_+ + m \\bm{b}_-} + \\psi_{\\bm{a}_1 + n \\bm{b}_+ + (m-1) \\bm{b}_-} + \\psi_{\\bm{a}_1 + (n-1) \\bm{b}_+ + m \\bm{b}_-} \\\\\n[\\Delta_{\\mathrm{hex}} \\psi]_{\\bm{a}_1 + n \\bm{b}_+ + m \\bm{b}_-}\n& =\n\\psi_{n \\bm{b}_+ + m \\bm{b}_-} + \\psi_{n \\bm{b}_+ + (m+1) \\bm{b}_-} + \\psi_{(n+1) \\bm{b}_+ + m \\bm{b}_-}\n\\end{align*}\n\n\n\\begin{figure*}[t]\n\\begin{tikzpicture}[yscale=.75,xscale=.75]\n\\draw [-,line width = .06cm] (0,0) -- (1,{sqrt(3)});\n\\draw [-,line width = .06cm] (0,{2*sqrt(3)}) -- (1,{sqrt(3)});\n\\draw [-,line width = .06cm] (0,{2*sqrt(3)}) -- (1,{3*sqrt(3)});\n\\draw [-,line width = .06cm] (0,{4*sqrt(3)}) -- (1,{3*sqrt(3)});\n\\draw [-,line width = .06cm,color=red] (1,{sqrt(3)}) -- (3,{sqrt(3)});\n\\draw [-,line width = .06cm] (1,{3*sqrt(3)}) -- (3,{3*sqrt(3)});\n\\draw [-,line width = .06cm] (4,{2*sqrt(3)}) -- (6,{2*sqrt(3)});\n\\draw [-,line width = .06cm] (4,0) -- (3,{sqrt(3)});\n\\draw [-,line width = .06cm] (4,{2*sqrt(3)}) -- (3,{sqrt(3)});\n\\draw [-,line width = .06cm] (4,{2*sqrt(3)}) -- (3,{3*sqrt(3)});\n\\draw [-,line width = .06cm] (4,{4*sqrt(3)}) -- (3,{3*sqrt(3)});\n\\draw [-,line width = .06cm] (4,0) -- (6,0);\n\\draw [-,line width = .06cm] (4,{2*sqrt(3)}) -- (6,{2*sqrt(3)});\n\\draw [-,line width = .06cm] (4,{4*sqrt(3)}) -- (6,{4*sqrt(3)});\n\\draw [-,line width = .06cm] (6,0) -- (7,{sqrt(3)});\n\\draw [-,line width = .06cm] (6,{2*sqrt(3)}) -- (7,{sqrt(3)});\n\\draw [-,line width = .06cm] (6,{2*sqrt(3)}) -- (7,{3*sqrt(3)});\n\\draw [-,line width = .06cm] (6,{4*sqrt(3)}) -- (7,{3*sqrt(3)});\n\\filldraw[color=black, fill=black](0,0) circle (.2);\n\\filldraw[color=black, fill=black](4,0) circle (.2);\n\\filldraw[color=black, fill=black](6,0) circle (.2);\n\\filldraw[color=red, fill=red](1,{sqrt(3)}) circle (.2);\n\\filldraw[color=red, fill=red](3,{sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](7,{sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](0,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](4,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](6,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](1,{3*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](3,{3*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](7,{3*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](0,{4*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](4,{4*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](6,{4*sqrt(3)}) circle (.2);\n\\draw [->,line width = .06cm,color=blue] (1.2,{sqrt(3)-.1}) -- (3.8,{.1});\n\\draw [->,line width = .06cm,color=blue] (1.2,{sqrt(3)+.1}) -- (3.8,{2*sqrt(3)-.1});\n\\node at (2,{1.8*sqrt(3)}) {\\cold{$\\bm{b}_+$}};\n\\node at (2,{.2*sqrt(3)}) {\\cold{$\\bm{b}_-$}};\n\\end{tikzpicture}\n\\caption{A portion of the hexagonal lattice. A fundamental domain is highlighted in red.}\\label{fig:hexlat}\n\\end{figure*}\nSee Figure~\\ref{fig:hexlat}. The formula for $\\Delta_{\\mathrm{hex}}$ can be made more compact if we view the associated Hilbert space as \n\\[\n\\ell^2({\\mathbb Z}^2,{\\mathbb C}^2)\n=\n\\set{\\Psi:{\\mathbb Z}^2 \\to {\\mathbb C}^2 : \\sum_{n,m} \\|\\Psi_{n,m}\\|^2 < \\infty},\n\\]\nwhere the standard basis of ${\\mathbb C}^2$ corresponds to the left and right vertices of the fundamental domain, respectively. More precisely, given $\\psi \\in \\ell^2({\\mathcal V}_{\\mathrm{hex}})$, define $\\Psi \\in \\ell^2({\\mathbb Z}^2,{\\mathbb C}^2)$ by \n\\[\n\\Psi_{n,m}\n=\n\\begin{bmatrix}\n\\psi_{n \\bm{b}_+ + m \\bm{b}_-} \\\\ \\psi_{\\bm{a}_1 + n \\bm{b}_+ + m \\bm{b}_-}\n\\end{bmatrix}.\n\\]\nIdentifying $\\ell^2({\\mathcal V}_{\\mathrm{hex}})$ and $\\ell^2({\\mathbb Z}^2,{\\mathbb C}^2)$ in this fashion, the Laplacian for the hexagonal lattice is given by\n\\begin{align*}\n[\\Delta_{\\mathrm{hex}} \\Psi]_{n,m}\n& =\nU(\\Psi_{n,m-1}+ \\Psi_{n-1,m}) + L(\\Psi_{n,m+1}+ \\Psi_{n+1,m}) + J \\Psi_{n,m},\n\\end{align*}\nwhere\n\\[\nU\n=\n\\begin{bmatrix}\n0 & 1 \\\\ 0 & 0\n\\end{bmatrix},\n\\quad\nL \n=\nU^\\top\n=\n\\begin{bmatrix}\n0 & 0 \\\\ 1 & 0\n\\end{bmatrix},\n\\quad\nJ\n=\nU+L\n=\n\\begin{bmatrix}\n0 & 1 \\\\ 1 & 0\n\\end{bmatrix}.\n\\]\n{Equivalently, if we denote by $S_1, S_2 : \\ell^2({\\mathbb Z}^2) \\to \\ell^2({\\mathbb Z}^2)$ the shift operators\n\\[\n[S_1 \\psi]_{n,m}\n=\n\\psi_{n+1,m},\n\\quad\n[S_2\\psi]_{n,m}\n=\n\\psi_{n,m+1},\n\\]\nwe have\n\\[\n\\Delta_{\\mathrm{hex}} \\Psi\n=\n\\begin{bmatrix}\n(S_1^* + S_2^* + {\\mathbb I})\\psi^- \\\\ (S_1 + S_2 + {\\mathbb I})\\psi^+\n\\end{bmatrix}\n\\quad\n\\text{for any} \\quad \n\\Psi \n=\n\\begin{bmatrix} \\psi^+ \\\\ \\psi^- \\end{bmatrix}\n\\in \\ell^2({\\mathbb Z}^2,{\\mathbb C}^2).\n\\]\nAbbreviating somewhat, we write:\n\\begin{equation} \\label{eq:hexDecomp}\n\\Delta_{\\mathrm{hex}}\n=\\begin{bmatrix}\n0 & S_1^* + S_2^* + {\\mathbb I} \\\\\nS_1 + S_2 + {\\mathbb I} & 0\n\\end{bmatrix}.\n\\end{equation}}\n\n\\begin{theorem}[Bethe--Sommerfeld for the hexagonal lattice] \\label{t:bsc:hex}\nFor all $\\bm{p} = (p_1,p_2 )\\in {\\mathbb Z}_+^2$, there is a constant $c = c_{\\bm{p}} > 0$ such that, if $Q:{\\mathcal V}_{\\mathrm{hex}} \\to {\\mathbb R}$ is $\\bm{p}$-periodic and $\\|Q\\|_\\infty \\leq c$, the following statements hold true for $H_Q = \\Delta_{\\mathrm{hex}} + Q$:\n\\begin{enumerate}\n\\item[{\\rm(1)}] $\\sigma(H_Q)$ consists of no more than four intervals.\n\\item[{\\rm(2)}] If at least one of $p_1$ or $p_2$ is odd, then $\\sigma(H_Q)$ consists of no more than two intervals.\n\\end{enumerate}\nMoreover, gaps may only open at $0$ and $\\pm1$ in the first case, and only at zero in the second case.\n\\end{theorem}\n\nMoreover, this theorem is sharp in the following sense: there exists a $(1,1)$-periodic potential $Q_1$ which infinitesimally opens a gap at zero, and there is a $(2,2)$-periodic potential $Q_2$ which infinitesimally opens gaps at $-1$, $0$, and $1$ in the following sense:\n\n\\begin{theorem} \\label{t:hexExamples}\n\\begin{enumerate}\n\\item[{\\rm(1)}] There exists $Q_1: {\\mathcal V}_{\\mathrm{hex}} \\to {\\mathbb R}^2$ which is $(1,1)$-periodic such that $\\sigma(H_{\\lambda Q_1})$ has exactly two connected components for all $\\lambda>0$.\n\\item[{\\rm(2)}] There exists $Q_2: {\\mathcal V}_{\\mathrm{hex}} \\to {\\mathbb R}^2$ which is $(2,2)$ periodic such that $\\sigma(H_{\\lambda Q_2})$ has exactly four connected components for any sufficiently small $\\lambda > 0$.\n\\end{enumerate}\n\\end{theorem}\n\nLet us remark that Theorem~\\ref{t:hexExamples}.(1) is well-known; we merely list it for completeness. The example in Theorem~\\ref{t:hexExamples}.(2) is novel.\n\n\\subsection{The EHM Lattice}\n\nThe EHM lattice also has vertex set ${\\mathcal V}_{\\mathrm{sqn}} = {\\mathcal V}_{\\mathrm{sq}} = {\\mathbb Z}^2$. However, now, one connects $\\bm{n}$ and $\\bm{n}'$ if and only if they are nearest neighbors or next-nearest-neighbors in the square lattice. Equivalently, one declares\n\\[\n\\bm{n} \\sim \\bm{n}'\n\\iff\n\\|\\bm{n} - \\bm{n}' \\|_\\infty\n=\n1.\n\\] \nThe associated Laplacian acts on $\\ell^2({\\mathbb Z}^2)$ via\n\\[\n[\\Delta_{\\mathrm{sqn}} \\psi]_{n,m}\n=\n[\\Delta_{\\mathrm{sq}}]_{n,m}+\\psi_{n-1,m-1}+\\psi_{n-1,m+1}+\\psi_{n+1,m-1}+\\psi_{n+1,m+1}.\n\\]\nSee Figure~\\ref{fig:ehmlat}.\n\\begin{figure*}[t]\n\n\\begin{tikzpicture}[yscale=.6,xscale=.6]\n\\draw [-,line width = .06cm] (0,0) -- (6,0);\n\\draw [-,line width = .06cm] (0,0) -- (0,6);\n\\draw [-,line width=.06cm] (0,2) -- (6,2);\n\\draw [-,line width = .06cm] (2,0) -- (2,6);\n\\draw [-,line width=.06cm] (0,4) -- (6,4);\n\\draw [-,line width = .06cm] (4,0) -- (4,6);\n\\draw [-,line width=.06cm] (0,6) -- (6,6);\n\\draw [-,line width = .06cm] (6,0) -- (6,6);\n\\draw [-,line width = .06cm] (0,0) -- (6,6);\n\\draw [-,line width = .06cm] (2,0) -- (6,4);\n\\draw [-,line width = .06cm] (4,0) -- (6,2);\n\\draw [-,line width = .06cm] (0,2) -- (4,6);\n\\draw [-,line width = .06cm] (0,4) -- (2,6);\n\\draw [-,line width = .06cm] (6,0) -- (6,6);\n\\draw [-,line width = .06cm] (0,6) -- (6,0);\n\\draw [-,line width = .06cm] (2,0) -- (0,2);\n\\draw [-,line width = .06cm] (4,0) -- (0,4);\n\\draw [-,line width = .06cm] (2,6) -- (6,2);\n\\draw [-,line width = .06cm] (6,4) -- (4,6);\n\\filldraw[color=black, fill=black](0,0) circle (.2);\n\\filldraw[color=black, fill=black](2,0) circle (.2);\n\\filldraw[color=black, fill=black](4,0) circle (.2);\n\\filldraw[color=black, fill=black](6,0) circle (.2);\n\\filldraw[color=black, fill=black](0,2) circle (.2);\n\\filldraw[color=black, fill=black](2,2) circle (.2);\n\\filldraw[color=black, fill=black](4,2) circle (.2);\n\\filldraw[color=black, fill=black](6,2) circle (.2);\n\\filldraw[color=black, fill=black](0,4) circle (.2);\n\\filldraw[color=black, fill=black](2,4) circle (.2);\n\\filldraw[color=black, fill=black](4,4) circle (.2);\n\\filldraw[color=black, fill=black](6,4) circle (.2);\n\\filldraw[color=black, fill=black](0,6) circle (.2);\n\\filldraw[color=black, fill=black](2,6) circle (.2);\n\\filldraw[color=black, fill=black](4,6) circle (.2);\n\\filldraw[color=black, fill=black](6,6) circle (.2);\n\\end{tikzpicture}\n\\caption{A portion of the EHM lattice.}\\label{fig:ehmlat}\n\\end{figure*}\n\n\\begin{theorem}[Bethe--Sommerfeld for the EHM lattice] \\label{t:bsc:nnn}\nFor all $\\bm{p} = (p_1,p_2 )\\in {\\mathbb Z}_+^2$, there is a constant $c = c_{\\bm{p}} > 0$ such that, if $Q:{\\mathcal V}_{\\mathrm{sqn}} \\to {\\mathbb R}$ is $\\bm{p}$-periodic and $\\|Q\\|_\\infty \\leq c$, the following hold true for $H_Q = \\Delta_{\\mathrm{sqn}} + Q$:\n\\begin{enumerate}\n\\item[{\\rm(1)}] $\\sigma(H_Q)$ consists of no more than two intervals.\n\\item[{\\rm(2)}] If at least one of $p_1$ or $p_2$ is not divisible by three, then $\\sigma(H_Q)$ consists of a single interval.\n\\end{enumerate}\nMoreover, the gap in the first setting may only open at the energy $E = -1$.\n\\end{theorem}\nThis theorem is also sharp:\n\\begin{theorem} \\label{t:nnnExamples}\nThere exists $Q:{\\mathbb Z}^2 \\to {\\mathbb R}$ which is $(3,3)$-periodic such that $\\sigma(H_{\\lambda Q})$ has exactly two connected components for any sufficiently small $\\lambda>0$.\n\\end{theorem}\n\n\\bigskip\n\nThe remainder of the paper is organized as follows. Section~\\ref{sec:floquet} recalls Floquet theory for ${\\mathbb Z}^2$-periodic graphs. We work with the triangular lattice in Section~\\ref{sec:tri}, proving Theorems~\\ref{t:bsc:tri} and \\ref{t:triExamples}. We then work with the hexagonal lattice in Section~\\ref{sec:hex}, proving Theorems~\\ref{t:bsc:hex} and \\ref{t:hexExamples}. Finally, we conclude with the EHM lattice in Section~\\ref{sec:nnn}, proving Theorems~\\ref{t:bsc:nnn} and \\ref{t:nnnExamples}.\n\n\\section{Floquet Theory for Periodic Schr\\\"odinger Operators on Periodic Graphs}\n\\label{sec:floquet}\n\nLet $\\Gamma= ({\\mathcal V},{\\mathcal E})$ be a ${\\mathbb Z}^2$-periodic graph with translation symmetries $\\bm{a}_1, \\bm{a}_2 \\in {\\mathbb R}^2$, and suppose $Q:{\\mathcal V} \\to {\\mathbb R}$ is $\\bm{p} = (p_1,p_2)$-periodic, that is,\n\\[\nQ(u + p_j \\bm{a}_j)\n=\nQ(u),\\quad\nu \\in {\\mathcal V},\\; j = 1,2.\n\\]\nWe will briefly describe Floquet theory for $H_Q = \\Delta_\\Gamma + Q$, following \\cite{KorSab2014}. The main purpose of this section is to establish notation, so we do not give any proofs. One may write $H_Q$ as a constant-fiber direct integral over the fundamental domain. Concretely, let\n\\[\n{\\mathcal V}_{\\rm f}\n=\n{\\mathcal V} \\cap \\set{s \\bm{a}_1 + t \\bm{a}_2 : 0 \\le s < p_1, \\; 0 \\le t < p_2}.\n\\]\nBy periodicity, $|{\\mathcal V}_{\\rm f}| = P := p_0 p_1 p_2$, where\n\\[\np_0\n=\n|{\\mathcal V} \\cap \\set{s\\bm{a}_1 + t \\bm{a}_2 : 0 \\le s,t < 1}|.\n\\]\nHere, and throughout the paper, we use $|S|$ to denote the cardinality of the set $S$. For each edge $(u,v) \\in {\\mathcal E}$ there exist unique vertices $u_{\\rm f}, v_{\\rm f} \\in {\\mathcal V}_{\\rm f}$ and unique integers $n,m,n',m' \\in {\\mathbb Z}$ with\n\\[\nu = u_{\\rm f} + np_1 \\bm{a}_1 + mp_2 \\bm{a}_2,\n\\quad\nv = v_{\\rm f} + n'p_1\\bm{a}_1 + m'p_2 \\bm{a}_2,\n\\]\nWe then define the \\emph{index} of $(u,v)$ by $\\tau(u,v) = (n'-n,m'-m)$. Finally, for $u,v \\in {\\mathcal V}_{\\rm f}$, we define $B(u,v)$ to be the set of all translates of $v$ that connect to $u$ via an edge of $\\Gamma$:\n\\[\nB(u,v)\n=\n\\set{w \\in {\\mathcal V} : w \\sim u \\text{ and } w = v + np_1 \\bm{a}_1 + mp_2 \\bm{a}_2 \\text{ for some }n,m \\in {\\mathbb Z}}.\n\\]\n\nThen, for each $\\bm{\\theta} =(\\theta_1,\\theta_2) \\in {\\mathbb R}^2$, the corresponding Floquet matrix is a self-adjoint operator on ${\\mathcal H}_{\\rm f}:= \\ell^2({\\mathcal V}_{\\rm f}) = {\\mathbb C}^{{\\mathcal V}_{\\rm f}}$ defined by\n\\begin{equation} \\label{eq:floqMatDef}\n\\langle \\delta_u, H_Q(\\bm{\\theta}) \\delta_v \\rangle\n=\n\\sum_{w \\in B(u,v)} \\exp\\Big(i \\big\\langle \\tau(u,w), \\bm{\\theta} \\big\\rangle\\Big).\n\\end{equation}\nIn the event that the sum in \\eqref{eq:floqMatDef} is empty, $\\langle \\delta_u, H_Q(\\bm{\\theta}) \\delta_v \\rangle = 0$. Clearly, if $\\theta_j' - \\theta_j \\in 2\\pi{\\mathbb Z}$ for $j=1,2$, then $H_Q(\\bm{\\theta}) = H_Q(\\bm{\\theta}')$, so $H_Q(\\bm{\\theta})$ descends to a well-defined function of $\\bm{\\theta} \\in {\\mathbb T}^2 := {\\mathbb R}^2 \/ (2\\pi{\\mathbb Z})^2 \\cong [0,2\\pi)^2$. We will freely use $\\bm{\\theta} \\in {\\mathbb R}^2$ or $\\bm{\\theta} \\in {\\mathbb T}^2$ depending on which is more convenient in a given setting.\n\nInformally, \\eqref{eq:floqMatDef} represents the restriction of $H_Q$ to the discrete torus \n\\[\n({\\mathbb Z}\\bm{a}_1 \\oplus {\\mathbb Z}\\bm{a}_2) \/ (p_1 {\\mathbb Z} \\bm{a}_1 \\oplus p_2 {\\mathbb Z} \\bm{a}_2)\n\\cong\n{\\mathbb Z}_{p_1} \\oplus {\\mathbb Z}_{p_2}.\n\\]\nwith the following boundary conditions: wrapping once around the torus in the positive $\\bm{a}_1$ direction accrues a phase $e^{i\\theta_1}$ and wrapping around once in the positive $\\bm{a}_2$ direction accrues a phase $e^{i\\theta_2}$. More precisely, we may view $H_Q(\\bm{\\theta})$ in the following manner. The operator $H_Q$ acts on the space ${\\mathbb C}^{\\mathcal V}$ of arbitrary (not necessarily square-summable) functions ${\\mathcal V} \\to {\\mathbb C}$. When $Q$ is $(p_1,p_2)$-periodic, then for each $\\bm{\\theta} \\in {\\mathbb T}^2$, $H_Q$ preserves the subspace\n\\[\n\\mathcal{H}(\\bm{\\theta})\n=\n\\set{\\psi \\in {\\mathbb C}^{\\mathcal V} : \\psi(u+p_j \\bm{a}_j) = e^{i\\theta_j} \\psi(u)}.\n\\]\nThen, $H_Q(\\bm{\\theta})$ is equivalent to the restriction of $H_Q$ to ${\\mathcal H}(\\bm{\\theta})$.\n\nFor each $\\bm{\\theta}$, order the eigenvalues of $H_Q(\\bm{\\theta})$ as\n\\[\nE_1(\\bm{\\theta})\n\\leq\n\\cdots\n\\leq\nE_P(\\bm{\\theta})\n\\]\nwith each eigenvalue listed according to its multiplicity. Then, for $1 \\le j \\le P$, the $j$th spectral \\emph{band} of $H_Q$ is defined by\n\\[\nF_j\n=\nF_j(Q)\n:=\n\\mathrm{ran}(E_j)\n=\n\\set{E_j(\\bm{\\theta}) : \\bm{\\theta} \\in {\\mathbb T}^2}\n=\n\\set{E_j(\\bm{\\theta}) : \\bm{\\theta} \\in {\\mathbb R}^2}.\n\\]\n\n\\begin{theorem} \\label{t:floquet}\nWith notation as above,\n\\[\n\\sigma(H_Q)\n=\n\\bigcup_{\\bm{\\theta} \\in {\\mathbb T}^2} H_Q(\\bm{\\theta})\n=\n\\bigcup_{j=1}^P F_j.\n\\]\n\\end{theorem}\n\nWe will use Theorem~\\ref{t:floquet} in the following way. Making the dependence on the potential $Q$ explicit, one may write\n\\[\nF_j = F_j(Q)\n=\n[E_j^-(Q),E_j^+(Q)].\n\\]\nThe key fact is the following: by standard perturbation theory for self-adjoint operators, $E_j^\\pm(Q)$ are 1-Lipschitz functions of $Q$. Here, one views $Q$ as an element of ${\\mathbb R}^P$ and the perturbation is with respect to the uniform metric thereupon. In particular, if an energy $E$ satisfies $E \\in \\mathrm{int}(F_j(Q))$, then $(E-\\delta,E+\\delta) \\subseteq F_j(Q)$ for some positive $\\delta$, and it follows that $E \\in F_j(Q') \\subseteq \\sigma(H_{Q'})$ for any $(p_1,p_2)$-periodic $Q'$ with $\\|Q-Q'\\|_\\infty < \\delta$. Note that here it is very important that one views the periods as fixed: one may only perturb within ${\\mathbb R}^P$ for a fixed $P$. Thus, our analysis revolves around determining for a given energy $E$, whether $E$ belongs to the interior of some band of the Laplacian, where the Laplacian is viewed as a degenerate $(p_1,p_2)$-periodic operator. \n\n\n\n\n\\section{Triangular Laplacian}\\label{sec:tri}\n\nWe view the triangular Laplacian as acting on the square lattice $\\ell^2({\\mathbb Z}^2)$, but with extra connections as in \\eqref{eq:triLaplacianSqVersion}:\n\\begin{align*}\n[\\Delta_{\\rm tri} u]_{n,m}\n& =\nu_{n-1,m} + u_{n+1,m} + u_{n,m-1} + u_{n,m+1} + u_{n-1,m+1} + u_{n+1,m-1} \\\\\n& =\n[\\Delta_{\\rm sq}u]_{n,m} + u_{n-1,m+1} + u_{n+1,m-1}.\n\\end{align*}\nNow, given $p_1,p_2 \\in {\\mathbb Z}_+$, we view $\\Delta_{\\mathrm{tri}}$ as a $\\bm{p}$-periodic operator and perform the Floquet decomposition. Define $P:=p_1p_2$ as in Section~\\ref{sec:floquet}, and put\n\\[\n\\Lambda :=\n{\\mathbb Z}^2 \\cap \\Big( [0,p_1) \\times [0,p_2) \\Big).\n\\]\nFor $\\bm{\\theta} = (\\theta_1,\\theta_2) \\in {\\mathbb R}^2$, it is straightforward to check that\n\\[\n\\sigma(H(\\bm{\\theta}))\n=\n\\set{e_{\\bm{\\ell}}^{\\Lambda}(\\bm{\\theta}) : \\bm{\\ell}\\in \\Lambda },\n\\]\nwhere $\\bm{\\ell}=(\\ell_1,\\ell_2)$ and\n\\[\ne^{\\Lambda}_{\\bm{\\ell}}(\\bm{\\theta})\n=\n2\\cos\\left( \\frac{\\theta_1+2\\pi \\ell_1}{p_1}\\right) \n+ 2\\cos\\left(\\frac{\\theta_2+2\\pi \\ell_2}{p_2}\\right) \n+ 2\\cos\\left(\\frac{\\theta_1 + 2\\pi \\ell_1}{p_1} - \\frac{\\theta_2 + 2\\pi \\ell_2}{p_2}\\right).\n\\]\nLet us point out that one needs to be somewhat careful at this point; namely, $e^\\Lambda_{\\bm{\\ell}}(\\bm{\\theta})$ is not a well-defined function of $\\bm{\\theta} \\in {\\mathbb T}^2$. However, the error incurred in using a different coset representative of $\\bm{\\theta} \\in {\\mathbb T}^2$ is simply a change in the index $\\bm{\\ell}$, and one can check that the \\emph{family} $\\set{e^\\Lambda_{\\bm{\\ell}}(\\bm{\\theta}) : \\bm{\\ell} \\in \\Lambda}$ is a well-defined function on ${\\mathbb T}^2$ (as well it should, since the \\emph{operator} $H(\\bm{\\theta})$ is itself a well-defined function of $\\bm{\\theta} \\in {\\mathbb T}^2$). In any case, the ambiguity disappears when one considers the covering space ${\\mathbb R}^2$, which we do for most of the paper. One could also use the minimal covering space ${\\mathbb R}^2 \/ (p_1{\\mathbb Z} \\oplus p_2 {\\mathbb Z})$ on which the $e_{\\bm{\\ell}}^\\Lambda$ are well-defined, but this does not accrue any benefits vis-\\`a-vis the present work, so we simply use ${\\mathbb R}^2$.\n\nAs in Section~\\ref{sec:floquet}, we label these eigenvalues in increasing order according to multiplicity by\n\\[\nE_1^{\\Lambda}(\\bm{\\theta})\n\\le \nE_2^{\\Lambda}(\\bm{\\theta})\\le \\cdots E_P^{\\Lambda}(\\bm{\\theta})\n\\]\nand denote the $P$ spectral bands by\n\\[\nF_k^{\\Lambda}\n=\n\\set{E_k^{\\Lambda}(\\bm{\\theta}) : \\bm{\\theta} \\in {\\mathbb R}^2},\n\\quad\n1 \\le k \\le P.\n\\]\nStraightforward computations shows that $\\sigma(\\Delta_{\\rm tri})=[-3,6]$, and thus\n\\[\n\\bigcup_{k=1}^P F_k^{\\Lambda}\n=\n[-3,6].\n\\]\nHenceforth, we view $p_1$ and $p_2$ as fixed and so we drop $\\Lambda$ from the superscripts.\nOur main theorem of this section is the following.\n\\begin{theorem}\\label{thm:trimain}\nLet $p_1,p_2 \\in {\\mathbb Z}_+$ be given.\n\\begin{enumerate}\n\\item[{\\rm 1.}]\nEach $E\\in (-3, 6)\\setminus \\{-2\\}$ belongs to $\\mathrm{int}(F_k)$ for some $1\\leq k\\leq P$.\n\\item[{\\rm 2.}] If one of the periods $p_1, p_2$ is odd, then $E=-2$ belongs to $\\mathrm{int}(F_k)$ for some $1\\leq k\\leq P$.\n\\end{enumerate}\n\\end{theorem}\n\\begin{proof}[Proof of Theorem~\\ref{t:bsc:tri}]\nAs already discussed, this follows immediately from Theorem~\\ref{thm:trimain}.\n\\end{proof}\n\\subsection{Proof of Theorem \\ref{thm:trimain}} \nWe will divide the proof into two different cases: $E\\neq -2$ and $E=-2$.\nOur general strategy is to argue by contradiction.\nMore specifically, we assume $E=\\min F_{k+1}=\\max F_k$ for some $1\\leq k\\leq P-1$, and show that this leads to a contradiction. We will use the following two lemmas, whose proofs we provide at the end of the present section.\n\\begin{lemma}\\label{lem:constructiontri}\nFor any $E \\in {[-3,6]}$, there exist $x, y \\in [0,2\\pi)$ such that\n\\begin{align}\n\\label{eq:xyCondA} \\cos(x) + \\cos(y) + \\cos(x-y) & = \\frac{E}{2} \\\\\n\\label{eq:xyCondB} \\sin(x) + \\sin(y) & = 0.\n\\end{align}\nFurthermore, {if $E \\neq -2$}, we have\n\\begin{align}\n\\label{eq:xyCondC}\\cos(x) + \\cos(y)=-1+\\sqrt{E+3} \\neq 0\n\\end{align}\nfor any $x,y$ that satisfy conditions \\eqref{eq:xyCondA} and \\eqref{eq:xyCondB}.\n\\end{lemma}\n\n\n\\begin{lemma}\\label{lem:triJ0empty}\nConsider the following system:\n\\begin{equation} \\label{eq:triJ0syst}\n\\begin{cases}\n\\cos(x) + \\cos(y) + \\cos(x-y) = \\frac{E}{2},\\\\\n\\sin(x)+\\sin(x-y)=0,\\\\\n\\sin(y)-\\sin(x-y)=0.\n\\end{cases}\n\\end{equation}\nFor any $E \\in (-3,6) \\setminus \\{-2\\}$, the solution set of \\eqref{eq:triJ0syst} is empty. For $E = -2$, the solutions of \\eqref{eq:triJ0syst} in $[0,2\\pi)^2$ are $(0,\\pi)$, $(\\pi,0)$ and $(\\pi, \\pi)$.\n\\end{lemma}\n\nWe will use Lemma \\ref{lem:constructiontri} in the $E\\neq -2$ case, and Lemma \\ref{lem:triJ0empty} in the $E=-2$ case.\n\n\\subsubsection{$E\\neq -2$}\\\n\n\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:trimain}.1]\nLet $E \\in (-3,6) \\setminus\\{-2\\}$ be given and suppose for the purpose of establishing a contradiction that $E = \\max F_k = \\min F_{k+1}$ for some $1 \\le k < P$. Let $(x,y)$ denote a solution to \\eqref{eq:xyCondA} and \\eqref{eq:xyCondB} from Lemma \\ref{lem:constructiontri}, and take $\\widetilde{\\bm{\\theta}}=(\\widetilde{\\theta}_1,\\widetilde{\\theta}_2)\\in [0,2\\pi)^2$ and $\\bm{\\ell}^{(1)}=(\\ell_1^{(1)},\\ell_2^{(1)})\\in \\Lambda$ such that \n\\[p_1^{-1}(\\widetilde{\\theta}_1+2\\pi \\ell_1^{(1)})=x,\\, \\ \\text{and }\\ \\ p_2^{-1}(\\widetilde{\\theta}_2+2\\pi \\ell_2^{(1)})=y.\n\\]\nIt is clear that $\\widetilde{\\bm{\\theta}}$ and $\\bm{\\ell}^{(1)}$ are uniquely determined by $x$ and $y$.\nLet us also note that \\eqref{eq:xyCondA} is equivalent to \n\\[e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})=E.\\]\nDefine $\\Lambda_E(\\widetilde{\\bm{\\theta}}) \\subseteq \\Lambda$ to be the set of all $\\bm{\\ell} \\in \\Lambda$ such that $e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}}) = E$. Then $r := |\\Lambda_E(\\widetilde{\\bm{\\theta}})|$ is the multiplicity of $E \\in \\sigma(H(\\widetilde{\\bm{\\theta}}))$ and clearly $\\bm{\\ell}^{(1)} \\in \\Lambda_E(\\widetilde{\\bm{\\theta}})$.\n\n\nSince $E \\in F_k$ by assumption, let $s\\in {\\mathbb Z}\\cap [1,r]$ be chosen so that\n\\[E_{k-s}(\\widetilde{\\bm{\\theta}})0$ small enough such that \n\\[E_{k-s}(\\bm{\\theta})0\\}.\n\\end{aligned}\n\\end{equation}\nConsequently, we always have \n\\begin{align}\\label{eq:sumJbetatri}\n|\\mathcal{J}_{\\bm{\\beta}}^0|+|\\mathcal{J}_{\\bm{\\beta}}^+|+|\\mathcal{J}_{\\bm{\\beta}}^-|=r.\n\\end{align}\nWe also define $\\mathcal{J}_0$ as follows\n\\begin{align}\\label{def:J0tri}\n\\mathcal{J}_0\n=\n{\\mathcal J}_0(\\widetilde{\\bm{\\theta}})\n:=\\{ \\bm{\\ell} \\in \\Lambda_E(\\widetilde{\\bm{\\theta}}) :\\ \\nabla e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}}) = \\bm{0}\\}.\n\\end{align}\nSince $E \\neq -2$, Lemma~\\ref{lem:triJ0empty} clearly implies $\\mathcal{J}_0=\\emptyset$.\n\nWe choose $\\bm{\\beta}_1=(\\beta_{1,1},\\beta_{1,2})=(p_1,p_2)\/\\sqrt{p_1^2+p_2^2}$. Then \\eqref{eq:xyCondB} is equivalent to \n\\[\\bm{\\beta}_1 \\cdot \\nabla e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})=0,\\]\nhence $\\mathcal{J}_{\\bm{\\beta}_1}^0\\neq \\emptyset$. \n\n\nNext we are going to perturb the point $\\widetilde{\\bm{\\theta}}$ and count the eigenvalues.\nSince $\\mathcal{J}_0 = \\emptyset$, we can choose a unit vector $\\bm{\\beta}_2$ such that \n\\begin{align}\\label{eq:beta2trinon-empty}\n\\bm{\\beta}_2\\cdot \\nabla e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}})\\neq 0,\n\\end{align}\nholds for any $\\bm{\\ell} \\in \\Lambda_E(\\widetilde{\\bm{\\theta}})$.\nThus, $\\mathcal{J}_{\\bm{\\beta}_2}^0=\\emptyset$, so one concludes\n\\begin{align}\\label{eq:beta2trinon-empty'}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|+|\\mathcal{J}_{\\bm{\\beta}_2}^-|=r.\n\\end{align}\n\n\n\\subsubsection*{Perturbation along $\\bm{\\beta}_2$}\nWe first perturb the eigenvalues along the $\\bm{\\beta}_2$ direction.\nSince $\\mathcal{J}_{\\bm{\\beta}_2}^0 = \\emptyset$, we will always employ \\eqref{eq:pertgeneralbetatri1order}.\n\nFor $t > 0$ small enough, we have the following.\n\\begin{itemize}\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_2}^+$, we have\n\\[\nE_{k+r-s+1}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_2)\n>\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_2)\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies \n\\begin{align}\\label{eq1:Jbeta2+tri}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|\\leq r-s.\n\\end{align}\n\n\\item If ${\\bm{\\ell} } \\in \\mathcal{J}_{\\bm{\\beta}_2}^-$, we have \n\\[\nE_{k-s}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_2)\n<\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_2)\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta2+tri}\n|\\mathcal{J}_{\\bm{\\beta}_2}^-|\\leq s.\n\\end{align}\n\\end{itemize}\n{In view of \\eqref{eq:beta2trinon-empty'}, Equations~\\eqref{eq1:Jbeta2+tri} and \\eqref{eq2:Jbeta2+tri} imply\n\\begin{equation} \\label{eq:Jbeta2MinusCard+tri}\n|\\mathcal{J}_{\\bm{\\beta}_2}^-|\n=\ns.\n\\end{equation}\nUpon realizing that $\\mathcal{J}_{-\\bm{\\beta}_2}^0 = \\emptyset$ and $\\mathcal{J}_{-\\bm{\\beta}_2}^\\pm = \\mathcal{J}_{\\bm{\\beta}_2}^\\mp$, we may apply the analysis above with $\\bm{\\beta}_2$ replaced by $-\\bm{\\beta}_2$ and conclude that\n\\begin{equation} \\label{eq:JMinusBeta2+tri}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+| = |\\mathcal{J}_{-\\bm{\\beta}_2}^-| = s.\n\\end{equation}\nIn particular, \\eqref{eq:Jbeta2MinusCard+tri} and \\eqref{eq:JMinusBeta2+tri} imply\n\\begin{align}\\label{eq4:Jbeta2tri}\nr=2s.\n\\end{align}} \n\n\\begin{comment}\n\\begin{align}\\label{eq3:Jbeta2+tri}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|=r-s.\n\\end{align}\n\n\n\nWhen $t$ is small enough and $t < 0$, we have the following.\n\\begin{itemize}\n\\item If $(\\ell_m,k_m)\\in \\mathcal{J}_{\\bm{\\beta}_2}^+$, we have\n\\[\nE_{k-s}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n<\ne_{\\ell_m,k_m}(\\widetilde{\\theta}_1+t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies \n\\begin{align}\\label{eq1:Jbeta2-tri}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|\\leq s.\n\\end{align}\n\\item If $(\\ell_m,k_m)\\in \\mathcal{J}_{\\bm{\\beta}_2}^-$, we have \n\\[\nE_{k+r-s+1}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n>\ne_{\\ell_m,k_m}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta2-tri}\n|\\mathcal{J}_{\\bm{\\beta}_2}^-|\\leq r-s.\n\\end{align}\n\\end{itemize}\nTaking \\eqref{eq:beta2trinon-empty'}, \\eqref{eq1:Jbeta2-tri} and \\eqref{eq2:Jbeta2-tri} into account, we have\n\\begin{align}\\label{eq3:Jbeta2-tri}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|=s.\n\\end{align}\nCombining \\eqref{eq3:Jbeta2+tri} with \\eqref{eq3:Jbeta2-tri}, we arrive at\n\\begin{align}\\label{eq4:Jbeta2tri}\nr=2s.\n\\end{align}\n\\end{comment}\n\n\\subsubsection*{Perturbation along $\\bm{\\beta}_1$}\nNow we perturb the eigenvalues along $\\bm{\\beta}_1$.\nThe case when ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_1}^{\\pm}$ is similar to that of $\\bm{\\beta}_2$.\nThe difference here is $\\mathcal{J}_{\\bm{\\beta}_1}^0\\neq \\emptyset$.\n\nBy Lemma~\\ref{lem:constructiontri}, we have\n\\begin{align}\n\\cos\\Big(\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}\\Big) + \\cos\\Big(\\frac{\\widetilde{\\theta}_2 + 2\\pi {\\ell_2}}{p_2}\\Big)\n=\n-1+\\sqrt{E+3}\n\\neq\n0\n\\end{align}\nfor ${\\bm{\\ell} = (\\ell_1,\\ell_2)} \\in \\mathcal{J}_{\\bm{\\beta}_1}^0$.\nThus, by employing \\eqref{eq:pertgeneralbetatri2order}, we obtain\n\\begin{align}\n&e_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\\notag\\\\\n&=\nE - {\\frac{ t^2}{2(p_1^2+p_2^2)}} \\Big(\\cos\\Big(\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}\\Big) + \\cos\\Big(\\frac{\\widetilde{\\theta}_2 + 2\\pi {\\ell_2}}{p_2}\\Big) \\Big) + O(t^3)\\notag\\\\\n& = E - {\\frac{ t^2}{2(p_1^2+p_2^2)}} \\Big(-1+\\sqrt{E+3} \\Big)+O(t^3).\\label{eq:Jbeta10tri}\n\\end{align}\nNotice that the choice of $\\bm{\\beta}_1$ causes the {third} $t^2$ term of \\eqref{eq:pertgeneralbetatri2order} to drop out.\n\nWithout loss of generality, we assume $E\\in (-2, 6)$. The other case can be handled similarly.\nFor $E\\in (-2,6)$, \\eqref{eq:Jbeta10tri} implies that \n\\begin{align}\\label{eq:Jbeta10tri'}\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1) < E = \\min F_{k+1},\n\\end{align}\nholds for $|t|>0$ small enough and for any $\\bm{\\ell} \\in \\mathcal{J}_{\\bm{\\beta}_1}^0$. \n\nCombining \\eqref{eq:Jbeta10tri'} with \\eqref{eq:pertgeneralbetatri1order}, we have the following.\n\nFor $t>0$ small enough,\n\\begin{itemize}\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_1}^+$, we have\n\\[\nE_{k+r-s+1}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\n>\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies \n\\begin{align}\\label{eq1:Jbeta1+tri}\n|\\mathcal{J}_{\\bm{\\beta}_1}^+|\\leq r-s\n=\ns,\n\\end{align}\n{where the equality follows from \\eqref{eq4:Jbeta2tri}.}\n\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_1}^0 \\bigcup \\mathcal{J}_{\\bm{\\beta}_1}^-$, we have \n\\[\nE_{k-s-1}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\n<\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta1+tri}\n|\\mathcal{J}_{\\bm{\\beta}_1}^0|+|\\mathcal{J}_{\\bm{\\beta}_1}^-|\\leq s.\n\\end{align}\n\\end{itemize}\n{In view of \\eqref{eq:sumJbetatri} and \\eqref{eq4:Jbeta2tri}, Equations~\\eqref{eq1:Jbeta1+tri} and \\eqref{eq2:Jbeta1+tri} yield\n\\begin{equation} \\label{eq3:Jbeta1+tri}\n|\\mathcal{J}_{\\bm{\\beta}_1}^+|\n=\n|\\mathcal{J}_{\\bm{\\beta}_1}^0|+|\\mathcal{J}_{\\bm{\\beta}_1}^-|\n=\ns.\n\\end{equation}\nAs before, we may observe that ${\\mathcal J}_{-\\bm{\\beta}_1}^0 = {\\mathcal J}_{\\bm{\\beta}_1}^0$ and ${\\mathcal J}_{-\\bm{\\beta}_1}^\\pm = {\\mathcal J}_{\\bm{\\beta}_1}^\\mp$. Then, the analysis above applied with $\\bm{\\beta}_1$ replaced by $-\\bm{\\beta}_1$ forces\n\\begin{equation} \\label{eq3:Jbeta1-tri}\n|\\mathcal{J}_{\\bm{\\beta}_1}^-|\n=\n|\\mathcal{J}_{\\bm{\\beta}_1}^0|+|\\mathcal{J}_{\\bm{\\beta}_1}^+|\n=\ns.\n\\end{equation}\nTaken together, \\eqref{eq3:Jbeta1+tri} and \\eqref{eq3:Jbeta1-tri} imply $|{\\mathcal J}_{\\bm{\\beta}_1}^0| = 0$, which contradicts ${\\mathcal J}_{\\bm{\\beta}_1}^0 \\neq \\emptyset$.\n}\n\n\\begin{comment}\nTaking \\eqref{eq:sumJbetatri}, \\eqref{eq1:Jbeta1+tri} and \\eqref{eq2:Jbeta1+tri} into account, we have\n\\begin{align}\\label{eq3:Jbeta1+tri}\n|\\mathcal{J}_{\\bm{\\beta}_1}^+|=r-s.\n\\end{align}\n\n\n\nFor $t<0$ small enough, \n\\begin{itemize}\n\\item If $(\\ell_m,k_m)\\in \\mathcal{J}_{\\bm{\\beta}_1}^0 \\bigcup \\mathcal{J}_{\\bm{\\beta}_1}^+ $, we have\n\\[\nE_{k-s}(\\widetilde{\\theta}_1 + t\\beta_{1,1}, \\widetilde{\\theta}_2 + t\\beta_{1,2})\n<\ne_{\\ell_m,k_m}(\\widetilde{\\theta}_1 + t\\beta_{1,1}, \\widetilde{\\theta}_2 + t\\beta_{1,2})\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies \n\\begin{align}\\label{eq1:Jbeta1-tri}\n|\\mathcal{J}_{\\bm{\\beta}_1}^0|+|\\mathcal{J}_{\\bm{\\beta}_1}^+|\\leq s.\n\\end{align}\n\\item If $(\\ell_m,k_m)\\in \\mathcal{J}_{\\bm{\\beta}_1}^-$, we have \n\\[\nE_{k+r-s+1}(\\widetilde{\\theta}_1 + t\\beta_{1,1}, \\widetilde{\\theta}_2 + t\\beta_{1,2})\n>\ne_{\\ell_m,k_m}(\\widetilde{\\theta}_1 + t\\beta_{1,1}, \\widetilde{\\theta}_2 + t\\beta_{1,2})\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta1-tri}\n|\\mathcal{J}_{\\bm{\\beta}_1}^-|\\leq r-s.\n\\end{align}\n\\end{itemize}\nTaking \\eqref{eq:sumJbetatri}, \\eqref{eq1:Jbeta1-tri} and \\eqref{eq2:Jbeta1-tri} into account, we have\n\\begin{align}\\label{eq3:Jbeta1-tri}\n|\\mathcal{J}_{\\bm{\\beta}_1}^0|+|\\mathcal{J}_{\\bm{\\beta}_1}^+|=s.\n\\end{align}\nCombining \\eqref{eq3:Jbeta1+tri} with \\eqref{eq3:Jbeta1-tri}, we arrive at\n\\begin{align}\\label{eq4:Jbeta1tri}\nr=2s-|\\mathcal{J}_{\\bm{\\beta}_1}^0|.\n\\end{align}\nSince $\\mathcal{J}_{\\bm{\\beta}_1}^0\\neq \\emptyset$, \\eqref{eq4:Jbeta1tri} is in contradiction with \\eqref{eq4:Jbeta2tri}.\n\\end{comment}\n\n\\end{proof}\n\n\\subsubsection{$E=-2$}\\\n\nFirst, we would like to make a remark on our strategy of the proof of the $E=-2$ case, and on the importance of one of the periods being odd.\n\\begin{remark}\\label{rem:tri}\nWe will choose $\\widetilde{\\bm{\\theta}}=(\\widetilde{\\theta}_1, \\widetilde{\\theta}_2)$ and $\\bm{\\ell}^{(1)}=(\\ell_1^{(1)}, \\ell_2^{(1)})$ such that $e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})=-2$ and \n$\\nabla e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})=\\bm{0}$. \nLemma \\ref{lem:triJ0empty} yields three possibilities $(p_1^{-1}(\\widetilde{\\theta}_1+2\\pi \\ell_1^{(1)}), p_2^{-1}(\\widetilde{\\theta}_2+2\\pi \\ell_2^{(1)}))=(0,\\pi)$, $(\\pi, 0)$ or $(\\pi, \\pi)$.\nDepending on which one of $p_1, p_2$ is odd, we will choose $(0,\\pi)$ (if $p_1$ is odd), or $(\\pi, 0)$ (if $p_2$ is odd).\nThis choice guarantees that the only eigenvalue located at $-2$ with vanishing gradient is $e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})$.\nConsequently, it suffices to control the second order perturbation of (a single eigenvalue) $e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})$ along a given direction $(\\beta_1, \\beta_2)$.\nWhen $p_1$ is odd, this is equivalent to controlling the sign of the following expression (compare \\eqref{eq:remtri}):\n$$-\\beta_{2}\\Big(\\frac{\\beta_{1}}{p_1}-\\frac{\\beta_{2}}{p_2}\\Big).$$\nWe can easily choose two directions such that the expression above has different signs, which leads to un-even {eigenvalue counts and hence to the desired contradiction}.\n\n{{\\it A posteriori}, the existence of a $(2,2)$-periodic potential satisfying the conclusion of Theorem~\\ref{t:triExamples} implies that this argument must fail if both $p_1$ and $p_2$ are even; let us briefly describe why this must be the case.} If both $p_1, p_2$ are even, there will be three eigenvalues at $-2$ with vanishing gradients, corresponding to all three solutions $(0,\\pi)$, $(\\pi, 0)$, $(\\pi, \\pi)$.\nTrying to control the second order perturbations of all these three eigenvalues along $(\\beta_1, \\beta_2)$ is equivalent to controlling the signs of the following three expressions simultaneously\n\\begin{align*}\n-\\beta_{2}\\Big(\\frac{\\beta_{1}}{p_1}-\\frac{\\beta_{2}}{p_2}\\Big),\\ \\ \n\\beta_{1}\\Big(\\frac{\\beta_{1}}{p_1}-\\frac{\\beta_{2}}{p_2}\\Big),\\ \\ \\text{and}\\ \\ \n\\beta_1\\beta_2.\n\\end{align*}\nA simple inspection of these three expressions yields that two of them are always non-negative with the other one being non-positive. \nTherefore we can never choose two different directions that lead to un-even {eigenvalue counts}.\nThis explains why at least one of the periods must be odd for our argument to work. \n\\end{remark}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:trimain}.2]\nNow let us give a detailed proof. Without loss of generality, assume $p_1$ is odd, let $E = -2$, and assume for the sake of contradiction that $E = \\max F_k = \\min F_{k+1}$ for some $k$.\nWe choose $\\widetilde\\bm{\\theta}$ and $\\bm{\\ell}^{(1)}$ via\n\\begin{align*}\n\\widetilde{\\theta}_1=0,\\ {\\ell_1^{(1)}} = 0,\\ \\ \\ \n(\\widetilde{\\theta}_2, {\\ell_2^{(1)}})=\n\\begin{cases}\n\\Big(0, \\frac{p_2}{2}\\Big),\\ \\ \\text{if } p_2 \\text{ is even},\\\\\n\\Big(\\pi, \\frac{p_2-1}{2}\\Big),\\ \\ \\text{if } p_2 \\text{ is odd}.\n\\end{cases}\n\\end{align*}\n\nWith these choices of $\\bm{\\ell}^{(1)}$ and $\\widetilde{\\bm{\\theta}}$, one can check that $e_{{\\bm{\\ell}^{(1)}}}(\\widetilde\\bm{\\theta}) = -2 = E$. As before, let $r$ denote the multiplicity of $E$ and let $\\Lambda_E(\\widetilde{\\bm{\\theta}})$ denote the set of $\\bm{\\ell} \\in \\Lambda$ with $e_{\\bm{\\ell}}(\\widetilde\\bm{\\theta}) = -2$.\nNote that we also have $\\nabla e_{{\\bm{\\ell}^{(1)}}}(\\widetilde\\bm{\\theta}) = \\bm{0}$, and thus $\\mathcal{J}_0\\neq \\emptyset$. Moreover, we claim that ${\\mathcal J}_0 = \\{\\bm{\\ell}^{(1)}\\}$. To see this, suppose there exists $\\bm{\\ell} \\neq \\bm{\\ell}^{(1)}$ in ${\\mathcal J}_0$. In view of Lemma~\\ref{lem:triJ0empty}, we must have\n\\[\\frac{\\widetilde{\\theta}_1+2\\pi\\ell_1}{p_1}=\\pi,\\]\nwhich implies $p_1 = 2\\ell_1$, which is impossible, since $p_1$ is odd.\nConsequently,\n\\[\\mathcal{J}_0=\n{\\{ \\bm{\\ell}^{(1)} \\}}.\\]\n\nLet us choose $\\bm{\\beta}_1=(\\beta_{1,1},\\beta_{1,2})=(0,1)$ and a unit vector\n\\[\\bm{\\beta}_2=(\\beta_{2,1},\\beta_{2,2}) \\sim (2p_1, p_2)\/\\sqrt{4p_1^2+p_2^2}\\] \nsuch that \n\\begin{align}\\label{eq:beta2triodd1}\n\\beta_{2,2}\\Big(\\frac{\\beta_{2,1}}{p_1}-\\frac{\\beta_{2,2}}{p_2}\\Big)>0,\n\\end{align}\nand\n\\begin{align}\\label{eq:beta2triodd2}\n\\bm{\\beta}_2\\cdot \\nabla e_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}})\\neq 0\\ \\ \\text{holds for any }\\ \\ell \\in \\Lambda_E(\\widetilde{\\bm{\\theta}}) \\setminus \\{\\bm{\\ell}^{(1)}\\}.\n\\end{align}\n\nWe will use \\eqref{eq:beta2triodd1} to control the perturbation of $e_{{\\bm{\\ell}^{(1)}}}(\\widetilde{\\bm{\\theta}})$ along the $\\bm{\\beta}_2$ direction. \nWe also note that \\eqref{eq:beta2triodd2} simply says \n\\begin{align}\\label{eq:beta20triodd}\n\\mathcal{J}_{\\bm{\\beta}_2}^0=\\mathcal{J}_{0}\n=\n\\{{\\bm{\\ell}^{(1)}}\\}.\n\\end{align}\n\n\n\\subsubsection*{Perturbation along $\\bm{\\beta}_2$}\nWe first perturb the eigenvalues along the $\\bm{\\beta}_2$ direction.\\\n\nBy \\eqref{eq:beta20triodd}, we need only consider first-order perturbation theory as in \\eqref{eq:pertgeneralbetatri1order} for $\\bm{\\ell} \\in \\Lambda_E(\\widetilde{\\bm{\\theta}}) \\setminus \\{\\bm{\\ell}^{(1)}\\}$.\nSince ${\\bm{\\ell}^{(1)}} \\in \\mathcal{J}_{0}$, we need to employ \\eqref{eq:pertgeneralbetatri2order} for $e_{{\\bm{\\ell}^{(1)}}}$.\nIndeed, by \\eqref{eq:pertgeneralbetatri2order}, we have for $|t|>0$ small enough,\n\\begin{align}\\label{eq:remtri}\ne_{{\\bm{\\ell}^{(1)}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_2)\n& =\ne_{{\\bm{\\ell}^{(1)}}}(\\widetilde{\\bm{\\theta}})\n-\\frac{t^2}{2}\n\\Bigg[\\frac{\\beta_{2,1}^2}{p_1^2}-\\frac{\\beta_{2,2}^2}{p_2^2}-\\Big(\\frac{\\beta_{2,1}}{p_1}-\\frac{\\beta_{2,2}}{p_2}\\Big)^2\\Bigg]+O(t^3) \\notag\\\\\n& =-2-\\frac{\\beta_{2,2}}{p_2}\\Big(\\frac{\\beta_{2,1}}{p_1}-\\frac{\\beta_{2,2}}{p_2}\\Big)t^2+O(t^3)\\\\\n& <-2 \\notag\\\\\n& = \\min F_{k+1}, \\notag\n\\end{align}\nwhere we used \\eqref{eq:beta2triodd1} in the last inequality.\n\nFor $t > 0$ small enough, we then have the following.\n\\begin{itemize}\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_2}^+$, we have\n\\[\nE_{k+r-s+1}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_2)\n>\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_2)\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies \n\\begin{align}\\label{eq1:Jbeta2+triodd}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|\\leq r-s.\n\\end{align}\n\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_2}^-\\bigcup \\mathcal{J}_0$, we have \n\\[\nE_{k-s}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_2)\n<\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_2)\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta2+triodd}\n|\\mathcal{J}_{\\bm{\\beta}_2}^-|+|\\mathcal{J}_0|\\leq s.\n\\end{align}\n\\end{itemize}\nTaking \\eqref{eq:sumJbetatri}, {\\eqref{eq:beta20triodd},} \\eqref{eq1:Jbeta2+triodd}, and \\eqref{eq2:Jbeta2+triodd} into account, we have\n\\begin{align}\\label{eq3:Jbeta2+triodd}\n{|\\mathcal{J}_{\\bm{\\beta}_2}^-|=s-1}.\n\\end{align}\n{Replacing $\\bm{\\beta}_2$ by $-\\bm{\\beta}_2$ as in previous phases of the argument, we arrive at\n\\begin{equation} \\label{eq3:Jbeta2-triodd}\n|{\\mathcal J}_{\\bm{\\beta}_2}^+|\n=\ns-1.\n\\end{equation}}\n\n\\begin{comment}\nWhen $t$ is small enough and $t < 0$, we have the following.\n\\begin{itemize}\n\\item If $(\\ell_m,k_m)\\in \\mathcal{J}_{\\bm{\\beta}_2}^+\\bigcup \\mathcal{J}_0$, we have\n\\[\nE_{k-s}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n<\ne_{\\ell_m,k_m}(\\widetilde{\\theta}_1+t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies \n\\begin{align}\\label{eq1:Jbeta2-triodd}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|+|\\mathcal{J}_0|\\leq s.\n\\end{align}\n\\item If $(\\ell_m,k_m)\\in \\mathcal{J}_{\\bm{\\beta}_2}^-$, we have \n\\[\nE_{k+r-s+1}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n>\ne_{\\ell_m,k_m}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta2-triodd}\n|\\mathcal{J}_{\\bm{\\beta}_2}^-|\\leq r-s.\n\\end{align}\n\\end{itemize}\nTaking \\eqref{eq:sumJbetatri}, \\eqref{eq1:Jbeta2-triodd} and \\eqref{eq2:Jbeta2-triodd} into account, we have\n\\begin{align}\\label{eq3:Jbeta2-triodd}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|+|\\mathcal{J}_0|=s.\n\\end{align}\n\\end{comment}\nCombining \\eqref{eq3:Jbeta2+triodd} with \\eqref{eq3:Jbeta2-triodd}, we arrive at\n\\begin{align}\\label{eq4:Jbeta2triodd}\n{r\n= |{\\mathcal J}_{\\bm{\\beta}_2}^+| + |{\\mathcal J}_{\\bm{\\beta}_2}^-| + |{\\mathcal J}_0|\n=\n2s-1.}\n\\end{align}\n\n\n\\subsubsection*{Perturbation along $\\bm{\\beta}_1$}\nNow we perturb the eigenvalues along $\\bm{\\beta}_1 = (0,1)$.\nThe case when ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_1}^{\\pm}$ is similar to that of $\\bm{\\beta}_2$.\nThe difference here is the {behavior of} perturbations of $e_{{\\bm{\\ell}^{(1)}}}$ {in the direction $\\bm{\\beta}_1$.}\nIndeed, by \\eqref{eq:pertgeneralbetatri2order}, we have\n\\begin{align*}\ne_{{\\bm{\\ell}^{(1)}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\n&=e_{{\\bm{\\ell}^{(1)}}}(\\widetilde{\\bm{\\theta}})\n-\\frac{t^2}{2}\n\\Bigg[\\frac{\\beta_{1,1}^2}{p_1^2}-\\frac{\\beta_{1,2}^2}{p_2^2}-\\Big(\\frac{\\beta_{1,1}}{p_1}-\\frac{\\beta_{1,2}}{p_2}\\Big)^2\\Bigg]+O(t^3)\\\\\n&=-2+\\frac{t^2}{p_2^2}+O(t^3)\\\\\n&>-2=\\max F_{k}.\n\\end{align*}\nThus, the perturbations of $e_{{\\bm{\\ell}^{(1)}}}$ {in the direction $\\bm{\\beta}_1$} always move up.\n\nFor $t>0$ small enough,\n\\begin{itemize}\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_1}^+\\bigcup \\mathcal{J}_0$, we have\n\\[\nE_{k+r-s+1}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\n>\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies \n\\begin{align}\\label{eq1:Jbeta1+triodd}\n|\\mathcal{J}_{\\bm{\\beta}_1}^+|+|\\mathcal{J}_0| \\leq r-s.\n\\end{align}\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_1}^-$, we have \n\\[\nE_{k-s-1}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\n<\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t\\bm{\\beta}_1)\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta1+triodd}\n|\\mathcal{J}_{\\bm{\\beta}_1}^-| \\leq s.\n\\end{align}\n\\end{itemize}\nIn view of \\eqref{eq:sumJbetatri}, Equations~\\eqref{eq1:Jbeta1+triodd} and \\eqref{eq2:Jbeta1+triodd} yield\n\\begin{align}\\label{eq3:Jbeta1+triodd}\n{|\\mathcal{J}_{\\bm{\\beta}_1}^-|\n=\ns.}\n\\end{align}\n{Applying the usual symmetry argument, we also arrive at $|{\\mathcal J}_{\\bm{\\beta}_1}^+| = s$, which leads to\n\\[\nr\n=\n|{\\mathcal J}_{\\bm{\\beta}_1}^+| + |{\\mathcal J}_{\\bm{\\beta}_1}^-| + |{\\mathcal J}_0|\n=\n2s+1,\n\\]\nwhich in turn contradicts \\eqref{eq4:Jbeta2triodd}.}\n\\end{proof}\n\n\\begin{comment}\nFor $t<0$ small enough, \n\\begin{itemize}\n\\item If $(\\ell_m,k_m)\\in \\mathcal{J}_{\\bm{\\beta}_1}^+ $, we have\n\\[\nE_{k-s}(\\widetilde{\\theta}_1 + t\\beta_{1,1}, \\widetilde{\\theta}_2 + t\\beta_{1,2})\n<\ne_{\\ell_m,k_m}(\\widetilde{\\theta}_1 + t\\beta_{1,1}, \\widetilde{\\theta}_2 + t\\beta_{1,2})\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies \n\\begin{align}\\label{eq1:Jbeta1-triodd}\n|\\mathcal{J}_{\\bm{\\beta}_1}^+|\\leq s.\n\\end{align}\n\\item If $(\\ell_m,k_m)\\in \\mathcal{J}_{\\bm{\\beta}_1}^-\\bigcup \\mathcal{J}_0$, we have \n\\[\nE_{k+r-s+1}(\\widetilde{\\theta}_1 + t\\beta_{1,1}, \\widetilde{\\theta}_2 + t\\beta_{1,2})\n>\ne_{\\ell_m,k_m}(\\widetilde{\\theta}_1 + t\\beta_{1,1}, \\widetilde{\\theta}_2 + t\\beta_{1,2})\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta1-triodd}\n|\\mathcal{J}_{\\bm{\\beta}_1}^-|+|\\mathcal{J}_0|\\leq r-s.\n\\end{align}\n\\end{itemize}\nTaking \\eqref{eq:sumJbetatri}, \\eqref{eq1:Jbeta1-triodd} and \\eqref{eq2:Jbeta1-triodd} into account, we have\n\\begin{align}\\label{eq3:Jbeta1-triodd}\n|\\mathcal{J}_{\\bm{\\beta}_1}^+|=s.\n\\end{align}\nCombining \\eqref{eq3:Jbeta1+triodd} with \\eqref{eq3:Jbeta1-triodd}, we arrive at\n\\begin{align}\\label{eq4:Jbeta1triodd}\nr=2s+|\\mathcal{J}_0|=2s+1.\n\\end{align}\nThis contradicts \\eqref{eq4:Jbeta2triodd}.\n\\end{comment}\n\n\n\n\\subsection{Proof of Lemmas \\ref{lem:constructiontri} and \\ref{lem:triJ0empty}}\\label{sec:constructionlemmaproof}\n\\begin{proof}[Proof of Lemma~\\ref{lem:constructiontri}]\nLet {$E \\in [-3,6]$ be given}, let $x$ be as-yet-unspecified, set $y = 2\\pi - x$, and note that \\eqref{eq:xyCondB} holds. Then, using $y = 2\\pi - x$, we note that\n\\begin{align*}\n\\cos(x) + \\cos(y) + \\cos(x-y)\n& =\n2\\cos(x) + \\cos(2x) \\\\\n& =\n2\\cos(x) + 2\\cos^2(x) - 1.\n\\end{align*}\nSetting $z = \\cos(x)$, we seek to solve $2z+2z^2-1 = E\/2$, which gives\n\\[\nz^2 + z - \\frac{1}{2} - \\frac{E}{4} = 0\n\\implies\nz = \n\\frac{-1 \\pm \\sqrt{3+E}}{2}.\n\\]\nThus, we may take $x$ so that\n\\[\n\\cos(x)\n=\n\\frac{-1+\\sqrt{3+E}}{2}.\n\\]\nIn fact, since $-3 \\le E \\le 6$, we may take $0 \\le x \\le 2\\pi\/3$. Thus, with this choice of $x$ (and $y = 2\\pi - x$), we get \\eqref{eq:xyCondA}. \n\nFinally, suppose $x$ and $y$ solve \\eqref{eq:xyCondA} and \\eqref{eq:xyCondB} for $E \\neq -2$. From \\eqref{eq:xyCondB}, we deduce that either $x+y = 2\\pi$ or $|x-y|=\\pi$. The second option leads to $E = -2$, so we must have $y=2\\pi - x$. Then, $E \\neq -2$ guarantees\n\\[\n\\cos(x) + \\cos(2\\pi - x)\n=\n2\\cos(x)\n=\n-1 + \\sqrt{3+E}\n\\neq 0,\n\\]\nwhich proves \\eqref{eq:xyCondC}.\n\\end{proof}\n\n\n\n\n\\begin{proof}[Proof of Lemma~\\ref{lem:triJ0empty}]\nSuppose that $x$ and $y$ solve\n\\begin{align}\n\\label{eq:triJ0empty:cossum}\n\\cos(x) + \\cos(y) + \\cos(x-y)\n& =\n\\lambda \\\\\n\\label{eq:triJ0empty:sin1}\n\\sin(x) + \\sin(x-y) & = 0 \\\\\n\\label{eq:triJ0empty:sin2}\n\\sin(y) - \\sin(x-y) & = 0\n\\end{align}\nfor some $\\lambda \\in (-3\/2,3)$. Adding \\eqref{eq:triJ0empty:sin1} and \\eqref{eq:triJ0empty:sin2}, we arrive at \n\\[\n\\sin(x) = - \\sin(y).\n\\]\nFor $(x,y) \\in [0,2\\pi)^2$, this forces either $|x-y| = \\pi$ or $x+y = 2\\pi$. In the case $|x-y| = \\pi$, substituting in to \\eqref{eq:triJ0empty:sin1} and \\eqref{eq:triJ0empty:sin2} gives $\\sin(x) = \\sin(y) = 0$, forcing $x,y \\in \\{0,\\pi\\}$. Plugging the various possibilities into \\eqref{eq:triJ0empty:cossum}, one either gets $\\lambda = 3 \\notin (-3\/2,3)$ (when $x=y=0$) or $\\lambda = -1$ (when at least one of $x$ or $y$ is $\\pi$).\n\nAlternatively, if $x = 2\\pi - y$, \\eqref{eq:triJ0empty:sin1} yields $\\sin(x) + \\sin(2x) = 0$, which leads to\n\\[\n\\sin(x)(1 + 2\\cos(x))\n=\n0.\n\\]\nSetting $\\sin(x) = 0$ yields $x \\in \\{0,\\pi\\}$ which leads to the same solutions as before. Setting $1 +2\\cos(x) = 0$ yields $(x,y) = (2\\pi\/3,4\\pi\/3)$ or $(x,y) = (4\\pi\/3, 2\\pi\/3)$. Plugging in either possibility into \\eqref{eq:triJ0empty:cossum} yields\n\\[\n\\cos(x) + \\cos(y) + \\cos(x-y)\n=\n-\\frac{3}{2} \\notin (-3\/2,3),\n\\]\nas claimed.\n\\end{proof}\n\n\n\n\n\n\\subsection{\\boldmath Opening a Gap at $-2$}\nLet us exhibit a $(2,2)$-periodic potential that perturbatively opens a gap at energy $E = -2$ for the triangular lattice.\n\n\\begin{figure*}[t]\n\n\\begin{tikzpicture}[yscale=.8,xscale=.8]\n\\draw [-,line width = .06cm] (0,0) -- (9,0);\n\\draw [-,line width = .06cm] (0,0) -- (0,9);\n\\draw [-,line width=.06cm] (0,9) -- (9,9);\n\\draw [-,line width = .06cm] (9,0) -- (9,9);\n\\draw [-,line width = .06cm] (0,9) -- (9,0);\n\\draw [-,line width = .06cm] (3,0) -- (3,9);\n\\draw [-,line width = .06cm] (6,0) -- (6,9);\n\\draw [-,line width = .06cm] (0,3) -- (9,3);\n\\draw [-,line width = .06cm] (0,6) -- (9,6);\n\\draw [-,line width = .06cm] (0,3) -- (3,0);\n\\draw [-,line width = .06cm] (0,6) -- (6,0);\n\\draw [-,line width = .06cm] (3,9) -- (9,3);\n\\draw [-,line width = .06cm] (6,9) -- (9,6);\n\\filldraw[color=black, fill=black](0,0) circle (.2);\n\\filldraw[color=black, fill=black](0,3) circle (.2);\n\\filldraw[color=black, fill=black](0,6) circle (.2);\n\\filldraw[color=black, fill=black](0,9) circle (.2);\n\\filldraw[color=black, fill=black](3,0) circle (.2);\n\\filldraw[color=red, fill=red](3,3) circle (.2);\n\\filldraw[color=red, fill=red](3,6) circle (.2);\n\\filldraw[color=black, fill=black](3,9) circle (.2);\n\\filldraw[color=black, fill=black](6,0) circle (.2);\n\\filldraw[color=red, fill=red](6,3) circle (.2);\n\\filldraw[color=red, fill=red](6,6) circle (.2);\n\\filldraw[color=black, fill=black](6,9) circle (.2);\n\\filldraw[color=black, fill=black](9,0) circle (.2);\n\\filldraw[color=black, fill=black](9,3) circle (.2);\n\\filldraw[color=black, fill=black](9,6) circle (.2);\n\\filldraw[color=black, fill=black](9,9) circle (.2);\n\\draw [-,line width = .06cm,color=red] (3,3) -- (3,6);\n\\draw [-,line width = .06cm,color=red] (3,3) -- (6,3);\n\\draw [-,line width = .06cm,color=red] (6,3) -- (6,6);\n\\draw [-,line width = .06cm,color=red] (3,6) -- (6,6);\n\\draw [-,line width = .06cm,color=red] (6,3) -- (3,6);\n\\node at (3.8,3.5) {$\\hot{q_1=1}$};\n\\node at (6.8,3.5) {$\\hot{q_2=1}$};\n\\node at (3.8,6.5) {$\\hot{q_3=1}$};\n\\node at (7,6.5) {$\\hot{q_4=-1}$};\n\\end{tikzpicture}\n\\caption{A $(2,2)$ periodic potential on the triangular lattice with a gap at $E = -2$ for all positive coupling constants.}\\label{fig:tri2x2fundDomain}\n\\end{figure*}\n\n\\begin{theorem} \\label{thm:triExampleGapLength}\nDefine\n\\[\nQ_{n,m} \n= \n(-1)^{mn}\n=\n\\begin{cases}\n1 & \\text{ if } m \\text{ or } n \\text{ is even}, \\\\\n-1 & \\text{ if both } m \\text{ and } n \\text{ are odd,}\n\\end{cases}\n\\]\nand denote $H_\\lambda = \\Delta_{\\mathrm{tri}} + \\lambda Q$. For all $\\lambda > 0$, $\\sigma(H_\\lambda)$ has two connected components. Moreover, for all $\\lambda > 0$ sufficiently small, the gap that opens about $E=-2$ is precisely equal to\n\\[\n\\mathfrak{g}_\\lambda\n=\n\\left(-\\sqrt{4+\\lambda^2},-2+\\lambda \\right).\n\\]\nIn particular,\n\\[\n|\\mathfrak{g}_\\lambda|\n=\n\\lambda + \\left( \\sqrt{4+\\lambda^2}-2 \\right)\n\\sim\n\\lambda + \\frac{\\lambda^2}{2},\n\\]\nso the gap opens linearly as $\\lambda \\downarrow 0$.\n\\end{theorem}\n\nThe following lemma will be used:\n\n\\begin{lemma} \\label{lem:triGapLength:trigPolyEst}\nFor all $\\bm{\\theta} \\in {\\mathbb T}^2$ and all $0 \\leq a \\leq 54$,\n\\[\n4(\\sin\\theta_1 + \\sin\\theta_2 - \\sin(\\theta_1+\\theta_2))^2\n+ a(1+\\cos\\theta_1+\\cos\\theta_2+\\cos(\\theta_1+\\theta_2))\n\\geq 0.\n\\]\n\\end{lemma}\n\n\\begin{proof}\nDefine\n\\[\ng(\\theta_1,\\theta_2,a)\n=\n4(\\sin\\theta_1 + \\sin\\theta_2 - \\sin(\\theta_1+\\theta_2))^2\n+ a(1+\\cos\\theta_1+\\cos\\theta_2+\\cos(\\theta_1+\\theta_2)).\n\\]\nWe begin by checking the boundary of ${\\mathbb T}^2 \\times [0,54]$. It is easy to see that $g \\geq 0$ if $a = 0$. For $a = 54$, define $h(\\bm{\\theta}) = g(\\bm{\\theta},54)$. Using the identities \n\\begin{align*}\n\\sin x+\\sin y-\\sin(x+y)&=4\\sin\\left(\\frac{x}{2}\\right) \\sin\\left(\\frac{y}{2}\\right) \\sin\\left(\\frac{x+y}{2}\\right),\\\\\n\\cos x-\\cos (x+y)&=2\\sin\\left(\\frac{y}{2}\\right) \\sin\\left(x+\\frac{y}{2}\\right)\\\\\n\\sin x+\\sin (x+y)&=2\\cos\\left(\\frac{y}{2}\\right) \\sin\\left(x+\\frac{y}{2}\\right),\n\\end{align*}\nwe may simplify $\\nabla h$ to get\n\\begin{align}\\label{eq:A3}\n\\frac{\\partial h}{\\partial \\theta_1}\n& =\n4\\sin\\left(\\theta_1+\\frac{\\theta_2}{2}\\right)\n\\Bigg[\n16\\sin\\left(\\frac{\\theta_1}{2}\\right) \\sin^2\\left(\\frac{\\theta_2}{2}\\right) \n\\sin\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)- 27 \\cos\\left(\\frac{\\theta_2}{2}\\right)\n\\Bigg],\\\\\n\\label{eq:A4}\n\\frac{\\partial h}{\\partial \\theta_2}\n& =\n4\\sin\\left(\\theta_2+\\frac{\\theta_1}{2}\\right)\n\\Bigg[\n16\\sin^2\\left(\\frac{\\theta_1}{2}\\right) \\sin\\left(\\frac{\\theta_2}{2}\\right) \n\\sin\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)- 27 \\cos\\left(\\frac{\\theta_1}{2}\\right)\n\\Bigg].\n\\end{align}\nConsequently, setting $\\nabla h = 0$ leads to four cases. For notational convenience, define\n\\[\n\\alpha\n=\n\\arcsin\\sqrt[4]{\\frac{27}{32}} .\n\\]\n\\noindent \\textbf{Case 1.}\n\\[\\sin\\left(\\theta_1+\\frac{\\theta_2}{2}\\right)=\\sin\\left(\\theta_2+\\frac{\\theta_1}{2}\\right)=0.\\]\nThis implies $\\theta_1 + \\frac{1}{2}\\theta_2 \\in \\pi {\\mathbb Z}$ and $\\theta_2 + \\frac{1}{2}\\theta_1 \\in \\pi {\\mathbb Z}$. Solving the resulting systems for solutions in $[0,2\\pi)$ yields three points:\n\\[\\bm{\\theta}=(0,0),\\ \\left( \\frac{2\\pi}{3},\\frac{2\\pi}{3}\\right),\\ \\left( \\frac{4\\pi}{3},\\frac{4\\pi}{3}\\right).\\]\n\\noindent \\textbf{Case 2.}\n\\[\\sin\\left(\\theta_1+\\frac{\\theta_2}{2}\\right)=16\\sin^2\\left(\\frac{\\theta_1}{2}\\right) \\sin\\left(\\frac{\\theta_2}{2}\\right) \n\\sin\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)- 27 \\cos\\left(\\frac{\\theta_1}{2}\\right)=0.\\]\nAs before, the first condition forces $\\theta_1 + \\frac{1}{2} \\theta_2 \\in \\pi{\\mathbb Z}$. Plugging the various possibilities that this yields into the second condition gives three solutions:\n\\begin{align*}\n\\bm{\\theta}=(\\pi, 0),\\ \\ (2\\alpha,2\\pi - 4\\alpha),\\ \\ (2\\pi-2\\alpha,4\\alpha).\n\\end{align*}\n\\noindent \\textbf{Case 3.}\n\\[\\sin\\left(\\theta_2+\\frac{\\theta_1}{2}\\right)=16\\sin\\left(\\frac{\\theta_1}{2}\\right) \\sin^2\\left(\\frac{\\theta_2}{2}\\right) \n\\sin\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)- 27 \\cos\\left(\\frac{\\theta_2}{2}\\right)=0.\\]\nArguing as in Case~2, there are three solutions:\n\\begin{align*}\n\\bm{\\theta}=(0,\\pi),\\ \\ (2\\pi - 4\\alpha,2\\alpha),\\ \\ (4\\alpha,2\\pi-2\\alpha).\n\\end{align*}\n\\noindent \\textbf{Case 4.}\n\\begin{align}\n\\label{eq:triGapLength:trigPolyEst:Case4a}\n16\\sin^2\\left(\\frac{\\theta_1}{2}\\right) \\sin\\left(\\frac{\\theta_2}{2}\\right) \n\\sin\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)- 27 \\cos\\left(\\frac{\\theta_1}{2}\\right)&=0\\\\\n\\label{eq:triGapLength:trigPolyEst:Case4b}\n16\\sin\\left(\\frac{\\theta_1}{2}\\right) \\sin^2\\left(\\frac{\\theta_2}{2}\\right) \n\\sin\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)- 27 \\cos\\left(\\frac{\\theta_2}{2}\\right)&=0.\n\\end{align}\nMultiply \\eqref{eq:triGapLength:trigPolyEst:Case4a} by $\\sin(\\theta_2\/2)$, multiply \\eqref{eq:triGapLength:trigPolyEst:Case4b} by $\\sin(\\theta_1\/2)$, and subtract the results to obtain\n\\[\n\\sin\\left(\\frac{\\theta_1-\\theta_2}{2}\\right)\n=\n0.\n\\]\nUsing this, we see that the solutions are \n\\begin{align*}\n\\bm{\\theta}=(\\pi, \\pi),\\ \\ (2\\alpha,2\\alpha),\\ \\ (2\\pi - 2\\alpha,2\\pi - 2\\alpha)\n\\end{align*}\nEvaluating $g$ at these points, we find out $\\max h(\\bm{\\theta})=216$ attained at $(0,0)$, \n$\\min h(\\bm{\\theta})= 0$, attained at\n\\[\n\\bm{\\theta}\n=\n\\left( \\frac{2\\pi}{3},\\frac{2\\pi}{3}\\right),\n\\;\n\\left( \\frac{4\\pi}{3},\\frac{4\\pi}{3}\\right),\n\\; (\\pi,0), \\; (0,\\pi), \\; (\\pi,\\pi).\n\\]\n\n\n\nFinally, we need to look at critical points of $g$ in the interior of ${\\mathbb T}^2 \\times [0,54]$. However, this is easy. Any zero of $\\nabla g$ must in particular satisfy $\\frac{\\partial g}{\\partial a} = 0$, which forces\n\\[\n1+\\cos\\theta_1 + \\cos\\theta_2 + \\cos(\\theta_1+\\theta_2)=0,\n\\]\nwhich clearly implies $g \\geq 0$.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:triExampleGapLength}]\nFor $\\bm{\\theta} = (\\theta_1,\\theta_2) \\in {\\mathbb T}^2$, denote by $H_\\lambda(\\bm{\\theta})$ the Floquet matrix corresponding to $H_\\lambda$. Ordering the vertices of the $2\\times 2$ fundamental domain as shown in Figure~\\ref{fig:tri2x2fundDomain}, we obtain\n\\[\nH_\\lambda(\\bm{\\theta}) -(-2+\\varepsilon) {\\mathbb I}\n=\n\\begin{bmatrix}\n2+\\lambda-\\varepsilon & 1+ e^{-i\\theta_1} & 1 + e^{-i\\theta_2}& 1 + e^{-i(\\theta_1+\\theta_2)} \\\\\n1 + e^{i\\theta_1}& 2+\\lambda -\\varepsilon & e^{i\\theta_1} + e^{-i\\theta_2} & 1 + e^{-i\\theta_2} \\\\\n1 + e^{i\\theta_2} & e^{-i\\theta_1} + e^{i\\theta_2} & 2+\\lambda -\\varepsilon & 1 + e^{-i\\theta_1} \\\\\n1 + e^{i(\\theta_1+\\theta_2)}& 1 + e^{i\\theta_2} & 1 + e^{i\\theta_1} & 2 - \\lambda -\\varepsilon\n\\end{bmatrix}\n\\]\n\n\n\n\nFor $\\bm{\\theta} \\in {\\mathbb T}^2$, $\\lambda>0$, and $\\varepsilon \\in {\\mathbb R}$, define\n\\[\np(\\bm{\\theta},\\lambda,\\varepsilon)\n=\n\\det(H_\\lambda(\\bm{\\theta}) - (-2+\\varepsilon) {\\mathbb I}).\n\\]\nAfter some calculations, one observes that\n\\begin{align*}\np(\\bm{\\theta},\\lambda,\\varepsilon)\n=\n-\\lambda^4 & - 4\\lambda^3 + X(\\bm{\\theta}) - 4\\varepsilon \\lambda\\left(3 - \\frac{\\lambda^2}{2}- \\cos\\theta_1 - \\cos\\theta_2 - \\cos(\\theta_1+\\theta_2) \\right) \\\\\n & + 4\\varepsilon^2 (3+3\\lambda - \\cos\\theta_1 - \\cos\\theta_2 - \\cos(\\theta_1+\\theta_2)) \\\\\n & - 2\\varepsilon^3(4 + \\lambda) + \\varepsilon^4,\n\\end{align*}\nwhere \n\\[\nX(\\bm{\\theta}) = \n-4\\big(\\sin\\theta_1+\\sin\\theta_2 - \\sin(\\theta_1+\\theta_2)\\big)^2\n\\]\nClearly $X(\\bm{\\theta}) \\leq 0$ for all $\\bm{\\theta}$, so we have\n\\[\n\\det(H_\\lambda(\\bm{\\theta}) + 2{\\mathbb I})\n=\np(\\bm{\\theta},\\lambda,0)\n\\leq\n-\\lambda^4 - 4\\lambda^3 <0\n\\]\nfor all $\\lambda > 0$; consequently $-2 \\notin \\sigma(H_\\lambda)$ for all $\\lambda > 0$, which proves the first claim of the theorem. Introducing $W_1(\\lambda,\\varepsilon) := -\\lambda^4 -4\\lambda^3 + 2 \\varepsilon\\lambda^3 + 12\\varepsilon^2\\lambda - 2\\varepsilon^3(4+\\lambda) + \\varepsilon^4$, we may rewrite $p$ as\n\\begin{align} \\label{eq:triPrewrittenW1}\np(\\bm{\\theta},\\lambda,\\varepsilon)\n=\n X(\\bm{\\theta}) - 4\\varepsilon(\\lambda-\\varepsilon)(3-\\cos\\theta_1-\\cos\\theta_2-\\cos(\\theta_1+\\theta_2)) +W_1(\\lambda,\\varepsilon).\n \\end{align}\nBy standard eigenvalue perturbation theory, we know that $|g_\\lambda^\\pm+2| \\leq \\lambda$, so we need only concern ourselves with $|\\varepsilon| \\leq \\lambda$. Since $X(\\bm{\\theta}) \\leq 0$ for all $\\bm{\\theta}$ and the second term of \\eqref{eq:triPrewrittenW1} is nonpositive whenever $0 \\leq \\varepsilon \\leq \\lambda$, we arrive at\n\\[\np(\\bm{\\theta},\\lambda,\\varepsilon)\n\\leq\n-\\lambda^4 -4\\lambda^3 + 2 \\varepsilon\\lambda^3 + 12\\varepsilon^2\\lambda - 2\\varepsilon^3(4+\\lambda) + \\varepsilon^4 \n= \n W_1(\\lambda,\\varepsilon)\n\\]\nfor all $\\bm{\\theta} \\in {\\mathbb T}^2$, all $\\lambda > 0$, and all $0 \\leq \\varepsilon \\leq \\lambda$. Moreover, we observe that $p(\\bm{0},\\lambda,\\varepsilon) = W_1(\\lambda,\\varepsilon)$, so this bound is sharp. Factoring $W_1$, we arrive at\n\\[\nW_1(\\lambda,\\varepsilon)\n=\n(\\lambda - \\varepsilon)^2(\\varepsilon^2 - 8\\varepsilon - \\lambda^2 - 4\\lambda).\n\\]\nConsequently, we see that $W_1(\\lambda,\\varepsilon) < 0$ for $\\varepsilon \\in [0,\\lambda)$, which implies that $p(\\bm{\\theta},\\lambda,\\varepsilon) < 0$ for all $\\bm{\\theta} \\in {\\mathbb T}^2$, all $\\lambda > 0$, and all $0 \\le \\varepsilon < \\lambda$; consequently, $[-2,-2+\\lambda) \\cap \\sigma(H_\\lambda) = \\emptyset$, which is to say:\n\\begin{equation} \\label{eq:triGapRightSide}\n[-2,-2+\\lambda) \\subseteq \\mathfrak{g}_\\lambda.\n\\end{equation}\nOn the other hand, $p(\\bm{0},\\lambda,\\lambda) = 0$, so \n\\begin{equation} \\label{eq:triGapRightSide-2+lambda}\n-2+\\lambda \\in \\sigma(H_\\lambda(\\bm{0})) \\subseteq \\sigma(H_\\lambda)\n\\end{equation} \nAlternatively, $-2+\\lambda \\in \\sigma(H_\\lambda)$ is clear from eigenvalue perturbation theory as soon as one has $[-2,2+\\lambda)\\cap \\sigma(H_\\lambda) = \\emptyset$.\n\n\nNow, for $- \\lambda \\leq \\varepsilon \\leq 0$, we have to be more careful with the term\n\\[\nq(\\bm{\\theta},\\lambda,\\varepsilon)\n:=\n-4\\varepsilon(\\lambda - \\varepsilon)(3- \\cos\\theta_1 - \\cos\\theta_2 - \\cos(\\theta_1+\\theta_2)),\n\\]\nas $q$ can be positive when $-\\lambda < \\varepsilon < 0$. Naively, one can bound\n\\[\n3-\\cos\\theta_1-\\cos\\theta_2 - \\cos(\\theta_1+\\theta_2)\n\\leq\n\\frac{9}{2},\n\\]\nwhich leads to the upper bound of $X(\\bm{\\theta}) + q(\\bm{\\theta},\\lambda,\\varepsilon) \\leq -18\\varepsilon(\\lambda - \\varepsilon)$. However, the maximum of $q$ occurs at the global \\emph{minimum} of $X$, so we can do better. Indeed, for $\\lambda > 0$ small and $-\\lambda \\leq \\varepsilon \\leq 0$, we have\n\\begin{equation} \\label{eq:triGapRefinedXQbound}\nX(\\bm{\\theta}) + q(\\bm{\\theta},\\lambda,\\varepsilon) \\leq - 16\\varepsilon(\\lambda-\\varepsilon).\n\\end{equation}\nIn particular, by Lemma~\\ref{lem:triGapLength:trigPolyEst}, the bound in \\eqref{eq:triGapRefinedXQbound} holds for all $\\varepsilon$ such that $-\\lambda \\leq \\varepsilon\\leq 0$ as long as $8 \\lambda^2 < 54$, i.e.\\ $0 < \\lambda < \\frac{3\\sqrt{3}}{2}$. This then leads us to\n\\begin{align*}\np(\\bm{\\theta},\\lambda,\\varepsilon)\n\\leq\nW_2(\\lambda,\\varepsilon)\n:&=\n-\\lambda^4 -4\\lambda^3 + 2 \\varepsilon\\lambda^3 + 12\\varepsilon^2\\lambda - 2\\varepsilon^3(4+\\lambda) + \\varepsilon^4 - 16\\varepsilon(\\lambda-\\varepsilon) \\\\\n& =\nW_1(\\lambda,\\varepsilon) - 16\\varepsilon(\\lambda-\\varepsilon)\n\\end{align*}\nfor $\\lambda > 0$ small and $-\\lambda \\leq \\varepsilon \\leq 0$. Factoring $W_2$ yields\n\\[\np(\\bm{\\theta},\\lambda,\\varepsilon)\n\\leq\nW_2(\\lambda,\\varepsilon)\n=\n(\\varepsilon - \\lambda)(\\varepsilon - \\lambda- 4)(\\varepsilon^2 - 4\\varepsilon - \\lambda^2)\n\\]\nfor $\\lambda > 0$ small and $-\\lambda \\leq \\varepsilon \\leq 0$. It is straightforward to find the roots of $W_2$ and to observe that $W_2(\\lambda,\\varepsilon) < 0$ when\n\\[\n2 - \\sqrt{4+\\lambda^2}\n<\n\\varepsilon\n\\leq \n0.\\]\nAs a result, this implies $p(\\bm{\\theta},\\lambda,\\varepsilon) < 0$ for all $\\bm{\\theta}$, all $\\lambda > 0$ small, and all $\\varepsilon \\in (2-\\sqrt{4+\\lambda^2},0]$, which in turn yields\n\\begin{equation} \\label{eq:triGapLeftSide}\n(-\\sqrt{4+\\lambda^2},-2] \\subseteq \\mathfrak{g}_\\lambda.\n\\end{equation}\nOn the other hand, \n\\[\np\\left((\\pi,\\pi),\\lambda,2 - \\sqrt{4+\\lambda^2}\\right) \n=\n W_2\\left(\\lambda,2 - \\sqrt{4+\\lambda^2}\\right)\n=\n0,\n\\] \nwhich leads us to conclude \n\\begin{equation} \\label{eq:triGapLeftSide-sqrt4+lambda2}\n-\\sqrt{4+\\lambda^2} \\in \\sigma(H_\\lambda(\\pi,\\pi)) \\subseteq \\sigma(H_\\lambda).\n\\end{equation}\nPutting together \\eqref{eq:triGapRightSide}, \\eqref{eq:triGapRightSide-2+lambda}, \\eqref{eq:triGapLeftSide}, and \\eqref{eq:triGapLeftSide-sqrt4+lambda2}, we obtain\n\\[\n\\mathfrak{g}_\\lambda\n=\n\\left(-\\sqrt{4+\\lambda^2},-2+\\lambda \\right)\n\\]\nfor small $\\lambda$, as promised.\n\n\\end{proof}\n\n\n\n\nThe effort involved in proving Lemma~\\ref{lem:triGapLength:trigPolyEst} in order to improve the constant ``18'' to ``16'' is nontrivial, but worthwhile. In particular, this is exactly what enables the exact factorization of $W_2$ and hence the ability to exactly compute the gap edges.\n\n\n\\section{Hexagonal Laplacian} \\label{sec:hex}\n\n\nWe now continue with the Laplacian on the hexagonal lattice. Let $\\Gamma_{\\mathrm{hex}} = ({\\mathcal V}_{\\mathrm{hex}},{\\mathcal E}_{\\mathrm{hex}})$ and\n\\[\n\\bm{b}_\\pm\n=\n\\frac{1}{2} \\begin{bmatrix} 3 \\\\ \\pm \\sqrt{3} \\end{bmatrix}\n\\]\nbe as in the introduction, let periods $p_1,p_2 \\in {\\mathbb Z}_+$ be given, and view $H = \\Delta_{\\mathrm{hex}}$ as a $(p_1,p_2)$-periodic operator.\nFor this setting, there are two vertices of ${\\mathcal V}_{\\mathrm{hex}}$ in $\\set{s\\bm{b}_+ + t \\bm{b}_- : 0 \\le s, t < 1}$, so our Floquet operator $H(\\bm{\\theta})$ will be $P \\times P$ with $P = 2p_1p_2$. As usual, define $\\Lambda = \\big( [0,p_1)\\times[0,p_2)\\big) \\cap {\\mathbb Z}^2$, denote the eigenvalues of $H(\\bm{\\theta})$ by\n\\[\nE_1^\\Lambda(\\bm{\\theta})\n\\leq\n\\cdots\n\\leq E_P^\\Lambda(\\bm{\\theta}),\n\\]\nand let $F_k^\\Lambda$ for $1 \\le k \\le P$ denote the bands of the spectrum. Our main theorem in this section is the following result.\n\n\n\\begin{theorem} \\label{thm:hexmain}\nLet $p_1,p_2 \\in {\\mathbb Z}_+$ be given.\n\\begin{enumerate}\n\\item Every $E \\in (-3,3) \\setminus \\set{-1,0,1}$ belongs to $\\mathrm{int}(F_j)$ for some $1 \\le j \\le P$.\n\\item If at least one of $p_1$ or $p_2$ is odd, then $-1 \\in \\mathrm{int}(F_k)$ and $+1 \\in \\mathrm{int}(F_\\ell)$ for some $1 \\le k \\le \\ell \\le P$\n\\end{enumerate}\n\\end{theorem}\n\n\\begin{proof}[Proof of Theorem~\\ref{t:bsc:hex}]\nThis follows immediately from Theorem~\\ref{thm:hexmain}.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:hexmain}] \nFrom \\eqref{eq:hexDecomp}, we have\n\\[\n\\Delta_{\\mathrm{hex}}\n=\n\\begin{bmatrix}\n0 & S_1^* + S_2^* + {\\mathbb I} \\\\ S_1 + S_2 + {\\mathbb I} & 0 \\end{bmatrix},\n\\]\nwhere $S_j: \\ell^2({\\mathbb Z}^2) \\to \\ell^2({\\mathbb Z}^2)$ denote the shifts\n\\[\n[S_1 \\psi]_{n,m} = \\psi_{n+1,m},\n\\quad\n[S_2 \\psi]_{n,m} = \\psi_{n,m+1}.\n\\]\nIt is easy to see that\n\\[\nS_1 + S_1^* + S_2 + S_2^* + S_1S_2^* + S_1^*S_2 = \\Delta_{\\mathrm{tri}},\n\\]\nthe triangular Laplacian. Thus, a simple calculation shows that\n\\begin{equation} \\label{eq:hexSquareTri}\n[\\Delta_{\\mathrm{hex}}^2 \\Psi]_{\\bm{n}}\n\\begin{bmatrix} [\\Delta_{\\mathrm{tri}} \\psi^+]_{\\bm{n}} + 3\\psi_{\\bm{n}}^+ \\\\ [\\Delta_{\\mathrm{tri}} \\psi^-]_{\\bm{n}} + 3\\psi_{\\bm{n}}^- \\end{bmatrix}\n\\quad\n\\text{for } \\Psi = \\begin{bmatrix} \\psi^+ \\\\ \\psi^- \\end{bmatrix} \\in \\ell^2({\\mathbb Z}^2,{\\mathbb C}^2).\n\\end{equation}\nThis calculation extends to the Floquet matrices, so we see that for each $1 \\le k \\le P$, the bands of $H = \\Delta_{\\mathrm{hex}}$ obey\n\\[\nF^\\Lambda_{k,{\\mathrm{hex}}}\n=\n-F^\\Lambda_{P+1-k,{\\mathrm{hex}}}\n\\]\nand \n\\begin{equation} \\label{eq:hexTriBandRel}\nF_{k,{\\mathrm{hex}}}^\\Lambda\n=\n\\begin{cases}\n\\sqrt{F^\\Lambda_{k - \\frac{P}{2},{\\mathrm{tri}}}+3} & \\frac{P}{2} < k \\leq P \\\\[3mm]\n-\\sqrt{F^\\Lambda_{\\frac{P}{2}+1-k,{\\mathrm{tri}}}+3} & 1 \\leq k \\leq \\frac{P}{2}\n\\end{cases}\n\\end{equation}\nFrom this, we deduce that $E \\in (-3,3)$ lies in the interior of some $F_{k,{\\mathrm{hex}}}$ if and only if $E^2-3$ lies in the interior of some $F_{\\ell,{\\mathrm{tri}}}$. For $E \\in (-3,3) \\setminus \\set{-1,0,1}$, $E^2-3 \\in (-3,6) \\setminus \\{-2\\}$, while $(\\pm1)^2-3 = -2$. Thus, the conclusions of the theorem follow from Theorem~\\ref{thm:trimain}.\n\n\\end{proof}\n\n\\subsection{\\boldmath Opening gaps at $0$ and $\\pm 1$}\n\nDefine the $(1,1)$-periodic potential $Q_1$ on ${\\mathcal V}_{\\mathrm{hex}}$ by $Q_1(\\bm{0}) = 1$ and $Q_1(\\bm{a}_1) = -1$, that is,\n\\[\nQ_1(n \\bm{b}_+ + m \\bm{b}_-) = 1,\n\\quad\nQ_1(\\bm{a}_1 + n \\bm{b}_+ + m \\bm{b}_-) = -1,\n\\quad n,m \\in {\\mathbb Z}.\n\\]\nAfter identifying $\\ell^2({\\mathcal V}_{\\mathrm{hex}})$ with $\\ell^2({\\mathbb Z}^2,{\\mathbb C}^2)$ in the usual way, we get (as an operator) $[Q_1 \\Psi]_{\\bm{n}}= Z\\Psi_{\\bm{n}}$, where\n\\[\nZ=\\begin{bmatrix}\n1 & 0 \\\\ 0 & -1\n\\end{bmatrix}.\n\\]\n\nFrom the calculations $Z U = U = -UZ$ and $Z L= -L = -L Z$, we deduce that $Q_1 \\Delta_{\\mathrm{hex}} + \\Delta_{\\mathrm{hex}} Q_1 = 0$, and hence\n\\[\n(\\Delta_{\\mathrm{hex}} + \\lambda Q_1)^2\n=\n\\Delta_{\\mathrm{hex}}^2 + \\lambda^2 \n\\geq\n\\lambda^2.\n\\]\nConsequently, $(-\\lambda,\\lambda) \\cap \\sigma(\\Delta_{\\mathrm{hex}} + \\lambda Q_1) = \\emptyset$ and there is a gap at zero. In particular, the gap is precisely $(-\\lambda,\\lambda)$, and so opens linearly at the maximal possible rate.\n\\medskip\n\nLet us consider the $(2,2)$-periodic case. We parameterize our potential as $(q_1,\\ldots,q_8) \\in {\\mathbb R}^8$ as shown in Figure~\\ref{fig:hex22period}.\n\\begin{figure*}[t]\n\n\\begin{tikzpicture}[yscale=1]\n\\draw [-,line width = .1cm,color=black] (0,0) -- (1,{sqrt(3)});\n\\draw [-,line width = .1cm,color=red] (0,{2*sqrt(3)}) -- (1,{sqrt(3)});\n\\draw [-,line width = .1cm,color=red] (0,{2*sqrt(3)}) -- (1,{3*sqrt(3)});\n\\draw [-,line width = .1cm] (0,{4*sqrt(3)}) -- (1,{3*sqrt(3)});\n\\draw [-,line width = .1cm,color=red] (1,{sqrt(3)}) -- (3,{sqrt(3)});\n\\draw [-,line width = .1cm,color=red] (1,{3*sqrt(3)}) -- (3,{3*sqrt(3)});\n\\draw [-,line width = .1cm] (4,{2*sqrt(3)}) -- (6,{2*sqrt(3)});\n\\draw [-,line width = .1cm] (4,0) -- (3,{sqrt(3)});\n\\draw [-,line width = .1cm,color=red] (4,{2*sqrt(3)}) -- (3,{sqrt(3)});\n\\draw [-,line width = .1cm,color=red] (4,{2*sqrt(3)}) -- (3,{3*sqrt(3)});\n\\draw [-,line width = .1cm] (4,{4*sqrt(3)}) -- (3,{3*sqrt(3)});\n\\draw [-,line width = .1cm] (4,0) -- (6,0);\n\\draw [-,line width = .1cm,color=red] (4,{2*sqrt(3)}) -- (6,{2*sqrt(3)});\n\\draw [-,line width = .1cm] (4,{4*sqrt(3)}) -- (6,{4*sqrt(3)});\n\\draw [-,line width = .1cm] (6,0) -- (7,{sqrt(3)});\n\\draw [-,line width = .1cm] (6,{2*sqrt(3)}) -- (7,{sqrt(3)});\n\\draw [-,line width = .1cm] (6,{2*sqrt(3)}) -- (7,{3*sqrt(3)});\n\\draw [-,line width = .1cm] (6,{4*sqrt(3)}) -- (7,{3*sqrt(3)});\n\\draw [-,line width = .1cm] (-2,0) -- (0,0);\n\\draw [-,line width = .1cm,color=red] (-2,{2*sqrt(3)}) -- (0,{2*sqrt(3)});\n\\draw [-,line width = .1cm] (-2,{4*sqrt(3)}) -- (0,{4*sqrt(3)});\n\\draw [-,line width = .1cm] (-2,0) -- (-3,{sqrt(3)});\n\\draw [-,line width = .1cm] (-2,{2*sqrt(3)}) -- (-3,{sqrt(3)});\n\\draw [-,line width = .1cm] (-2,{2*sqrt(3)}) -- (-3,{3*sqrt(3)});\n\\draw [-,line width = .1cm] (-2,{4*sqrt(3)}) -- (-3,{3*sqrt(3)});\n\n\n\\filldraw[color=black, fill=black](0,0) circle (.2);\n\\filldraw[color=black, fill=black](4,0) circle (.2);\n\\filldraw[color=black, fill=black](6,0) circle (.2);\n\\filldraw[color=red, fill=red](1,{sqrt(3)}) circle (.2);\n\\filldraw[color=red, fill=red](3,{sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](7,{sqrt(3)}) circle (.2);\n\\filldraw[color=red, fill=red](0,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=red, fill=red](4,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=red, fill=red](6,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=red, fill=red](1,{3*sqrt(3)}) circle (.2);\n\\filldraw[color=red, fill=red](3,{3*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](7,{3*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](0,{4*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](4,{4*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](6,{4*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](-2,{0*sqrt(3)}) circle (.2);\n\\filldraw[color=red, fill=red](-2,{2*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](-2,{4*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](-3,{1*sqrt(3)}) circle (.2);\n\\filldraw[color=black, fill=black](-3,{3*sqrt(3)}) circle (.2);\n\\node at (-2.5,{2*sqrt(3)}){\\hot{$q_1$}};\n\\node at (.5,{2*sqrt(3)}){\\hot{$q_2$}};\n\\node at (.5,{3*sqrt(3)}){\\hot{$q_3$}};\n\\node at (3.5,{3*sqrt(3)}){\\hot{$q_4$}};\n\\node at (.5,{sqrt(3)}){\\hot{$q_5$}};\n\\node at (3.5,{sqrt(3)}){\\hot{$q_6$}};\n\\node at (3.5,{2*sqrt(3)}){\\hot{$q_7$}};\n\\node at (6.5,{2*sqrt(3)}){\\hot{$q_8$}};\n\\end{tikzpicture}\n\\caption{A portion of the hexagonal lattice. A fundamental domain for a $(2,2)$-periodic potential is highlighted in red.}\\label{fig:hex22period}\n\\end{figure*}\n\nWe now turn to the construction of a potential that opens gaps at $0$, $1$, and $-1$ simultaneously. We show that it opens gaps linearly at zero, quadratically at $\\pm1$. Later on, we will show that one cannot open gaps linearly at $\\pm 1$ on both sides.\n\n\\begin{theorem}\\label{thm:hexQ}\nOrder the vertices of a $2\\times 2$ fundamental cell of the hexagonal lattice as shown in Fig.~\\ref{fig:hex22period}, define a $(2,2)$-periodic potential $Q$ by\n\\[\n(q_1,\\ldots,q_8)\n=\n(1,-1,1,2,-2,-1,1,-1),\n\\]\nand denote $H_\\lambda = \\Delta_{\\mathrm{hex}} + \\lambda Q$. Then, for $ |\\lambda| > 0$ sufficiently small, $\\sigma(H_\\lambda)$ consists of four connected components. Moreover, if $\\mathfrak{g}_{E,\\lambda} = (g_{E,\\lambda}^{-}, g_{E,\\lambda}^{+})$ denote the gaps of $\\sigma(H_{\\lambda})$ that open at $E=0,\\pm 1$, one has\n\\[\n \\Big(\\pm 1-\\frac{\\lambda^2}{20}, \\pm 1+\\frac{\\lambda^2}{20}\\Big)\\subset \\mathfrak{g}_{\\pm 1,\\lambda} {\\subseteq \\Big(\\pm 1 - \\frac{1}{2}\\lambda^2,\\pm1 + \\frac{1}{2}\\lambda^2 \\Big)},\\]\nand\n\\[ \\ \\ \\ \\ \\Big(-\\frac{\\lambda}{{5}}, \\frac{\\lambda}{{5}}\\Big)\\subset \\mathfrak{g}_{0,\\lambda} \\subset \\Big(-\\frac{\\lambda}{4}, \\frac{\\lambda}{4}\\Big)\\]\nfor all $|\\lambda| > 0$ sufficiently small.\n\\end{theorem}\n\nWe point that we do not carefully optimize the constants; it is possible to get better constants than $1\/20$, ${1\/2}$, ${1\/5}$, and $1\/4$.\n\n\\begin{proof}\nFor $\\bm{\\theta} = (\\theta_1,\\theta_2) \\in {\\mathbb T}^2$, let $H_\\lambda(\\bm{\\theta})$ denote the Floquet matrix corresponding to $H_\\lambda$. Ordering the vertices of the fundamental domain as in Figure~\\ref{fig:hex22period}, we obtain:\n\\begin{align}\\label{eq:hexH}\nH_\\lambda(\\bm{\\theta})\n=\n\\begin{bmatrix}\n\\lambda & 1 & 0 & e^{-i\\theta_1} & 0 & e^{-i\\theta_2} & 0 & 0 \\\\\n1 & -\\lambda & 1 & 0 & 1 & 0 &0&0 \\\\\n0 & 1 & \\lambda & 1 & 0 & 0 & 0 & e^{-i\\theta_2} \\\\\ne^{i\\theta_1} & 0 & 1 & 2\\lambda & 0 & 0 & 1 & 0 \\\\\n0 & 1 & 0 & 0 & - 2\\lambda & 1 & 0 & e^{-i\\theta_1} \\\\\ne^{i\\theta_2} & 0 & 0 & 0 & 1 & - \\lambda & 1 & 0 \\\\\n0 & 0 &0 & 1 & 0 & 1 & \\lambda & 1 \\\\\n0 & 0 & e^{i\\theta_2} & 0 & e^{i\\theta_1} & 0 & 1 & -\\lambda\n\\end{bmatrix}\n\\end{align}\n\n\nFirst, let us consider the gaps at $E=\\pm 1$.\nCalculations yield\n\\begin{align}\\label{eq:hexsumXZ}\n\\det\\big(H_{\\lambda}(\\bm{\\theta})- (\\pm 1+ s\\lambda^2) {\\mathbb I} \\big)\n= \nX_0^\\pm(\\bm{\\theta}) + X_4^\\pm(\\bm{\\theta},s) \\lambda^4 + X_6^\\pm(\\bm{\\theta},s) \\lambda^6 + O(\\lambda^8)\n\\end{align}\nin which\n\\begin{align*}\nX_0^\\pm(\\bm{\\theta})&=\n-4 (-\\sin(\\theta_1) + \\sin(\\theta_1-\\theta_2) + \\sin(\\theta_2))^2\\\\\nX_4^\\pm(\\bm{\\theta},s)\n&=\n8(s\\pm1)(2s\\mp 1) (3 -\\cos(\\theta_1)-\\cos(\\theta_1-\\theta_2)-\\cos(\\theta_2))\\\\\nX_6^\\pm(\\bm{\\theta},s)\n&=\n-1 \\mp 12 s + 72 s^2 \\mp 16 s^3-4 s^2 (\\pm 4 s+1)(\\cos(\\theta_1) + \\cos(\\theta_1-\\theta_2) + \\cos(\\theta_2)\n\\end{align*}\nIt is clear that \n\\begin{align}\\label{eq:hexX0Z0}\nX_0^\\pm(\\bm{\\theta})\\leq 0 \\quad \\text{for all }\\bm{\\theta} \\in {\\mathbb T}^2.\n\\end{align}\nSince $\\cos(\\theta_1) + \\cos(\\theta_1-\\theta_2) + \\cos(\\theta_2)\\leq 3$, we also have \n\\begin{align}\\label{eq:hexX4Z4}\nX_4^\\pm(\\bm{\\theta},s)\\leq 0 \\quad \\text{for all } \\bm{\\theta} \\in {\\mathbb T}^2, \\; |s|\\leq 1\/2.\n\\end{align}\nWe also have for $|s|\\leq 1\/4$,\n\\[\nX_6^+(\\bm{\\theta},s)\\leq -1-12 s+72 s^2-16 s^3+12 s^2 (4s+1)=:T(s),\n\\]\nand \n\\[\nX_6^-(\\bm{\\theta},s)= X_6^+(\\bm{\\theta},-s) \\leq T(-s).\n\\]\nOne easily checks that $T(s)$ is decreasing on $[-0.05, 0.05]$, and\n\\[T(-0.05)=-0.194.\\]\nHence for $|s|\\leq 0.05$,\n\\begin{align}\\label{eq:hexX6Z6}\nX_6^\\pm(\\bm{\\theta},s)\\leq -0.194.\n\\end{align}\nCombining \\eqref{eq:hexX0Z0}, \\eqref{eq:hexX4Z4}, and \\eqref{eq:hexX6Z6}, we obtain that for $|\\lambda|>0$ sufficiently small, and $|s| \\leq 1\/20$,\n\\[\\det(H_{\\lambda}(\\bm{\\theta})- (\\pm 1+ s\\lambda^2) {\\mathbb I})\\leq -0.1\\lambda^6<0.\\]\nThis proves the claimed lower bound on the gaps at $\\pm 1$.\n\nOn the other hand, let us note that $X_0^\\pm(0,0) =X_0^\\pm(\\pi,\\pi) = 0$, while\n\\begin{align*}\n\\begin{cases}\nX_4^+(\\bm{\\theta}, 0.5)= 0 \\quad \\text{and}\\quad X_6^+((\\pi,\\pi),0.5)=12,\\\\\nX_4^+((0,0),s)=0 \\quad \\text{and}\\quad X_6^+((0,0),-0.5)=28,\\\\\nX_4^-((0,0),s)=0\\quad \\text{and} \\quad X_6^-((0,0),0.5)=28,\\\\\nX_4^-(\\bm{\\theta}, -0.5)=0 \\quad \\text{and}\\quad X_6^-((\\pi, \\pi), -0.5)=12.\n\\end{cases}\n\\end{align*}\nThus for small $\\lambda>0$, we have\n\\begin{align*}\n\\begin{cases}\n\\det(H_\\lambda(\\pi,\\pi)-(1+0.5\\lambda^2){\\mathbb I}) > 0,\\\\\n\\det(H_\\lambda(0,0)-(1-0.5\\lambda^2){\\mathbb I})>0,\\\\\n\\det(H_\\lambda(0,0)-(-1+0.5\\lambda^2){\\mathbb I})>0,\\\\\n\\det(H_\\lambda(\\pi,\\pi)-(-1-0.5\\lambda^2){\\mathbb I})>0.\n\\end{cases}\n\\end{align*}\nWe also easily check that \n\\[X_0^\\pm\\left(\\frac{\\pi}{2},\\pi\\right)=-16,\\]\nwhich implies that for small $\\lambda>0$, we have\n\\[\\det\\left(H_\\lambda\\left(\\frac{\\pi}{2},\\pi \\right)-(\\pm 1\\pm 0.5 \\lambda^2 {\\mathbb I}) \\right)<0.\\]\nWe therefore conclude that \n\\[\\pm 1 + 0.5 \\lambda^2 \\in \\sigma(H_\\lambda) \\text{ and } \\pm 1 - 0.5 \\lambda^2 \\in \\sigma(H_\\lambda),\\]\nwhich proves the upper bounds on the gaps at $\\pm 1$.\n\nNow let us consider the gap at $E=0$. After calculations, we have\n\\begin{align}\\label{eq:hexsumY}\n\\det(H_{\\lambda}(\\bm{\\theta})- s\\lambda {\\mathbb I} )=Y_0(\\bm{\\theta})+Y_2(\\bm{\\theta},s)\\lambda^2+Y_4(\\bm{\\theta},s)+O(\\lambda^6),\n\\end{align}\nwhere \n\\begin{align*}\nY_0(\\bm{\\theta})\n& =\n15 + 2\\cos(2\\theta_1) - 4 \\cos(\\theta_1 - 2\\theta_2) + 2 \\cos(2\\theta_1-2\\theta_2) \n- 4 \\cos(2\\theta_1 - \\theta_2) \\\\ & \\qquad + 2\\cos(2\\theta_2) - 4 \\cos(\\theta_1 + \\theta_2),\n\\end{align*}\n\\[\nY_2(\\bm{\\theta}, s)=2[5-26 s^2+(2+4 s^2)(\\cos(\\theta_1)+\\cos(\\theta_1-\\theta_2)+\\cos(\\theta_2))],\n\\]\nand \n\\[\nY_4(\\bm{\\theta},s)=(1-s^2)[-3 - 42 s^2 + 4 (2 + s^2) (\\cos(\\theta_1)+\\cos(\\theta_1-\\theta_2)+\\cos(\\theta_2))]\n\\]\nWe claim that\n\\begin{equation}\n\\label{eq:hexY0geq0}\nY_0(\\bm{\\theta})\\geq 0\n\\quad\n\\text{for all } \\bm{\\theta} \\in {\\mathbb T}^2.\n\\end{equation} \nLet us see how to use \\eqref{eq:hexY0geq0} to prove the claimed gap at zero and defer the proof of \\eqref{eq:hexY0geq0} for a moment. Using\n\\[\\cos(\\theta_1)+\\cos(\\theta_1-\\theta_2)+\\cos(\\theta_2)\\in \\left[-\\frac{3}{2}, 3\\right],\\]\nwe obtain that for $|s|<1\/5$\n\\begin{align}\\label{eq:hexY2}\nY_2(\\bm{\\theta}, s)\\geq 2(5-26s^2-3(1+2s^2))=4(1-16 s^2)>\\frac{36}{25}.\n\\end{align}\nCombining \\eqref{eq:hexsumY} with \\eqref{eq:hexY2}, we obtain that for $|\\lambda|>0$ sufficiently small\n\\[\\det(H_{\\lambda}(\\bm{\\theta})- s\\lambda\\, {\\mathbb I} )>\\lambda^2.\\]\nThis proves the claimed lower bound of the gap at $0$, modulo the claim that $Y_0(\\bm{\\theta}) \\geq 0$ for all $\\bm{\\theta} \\in {\\mathbb T}^2$. \n\nTo prove the upper bound, we compute\n\\begin{align*}\n\\begin{cases}\nY_0\\left(\\frac{2\\pi}{3}, \\frac{4\\pi}{3}\\right)=0,\\\\\nY_2\\left(\\left(\\frac{2\\pi}{3}, \\frac{4\\pi}{3}\\right), s\\right)=4(1-16 s^2),\\\\\nY_4\\left(\\left(\\frac{2\\pi}{3}, \\frac{4\\pi}{3}\\right), s\\right)=3(s^2-1)(16s^2+5),\n\\end{cases}\n\\end{align*}\nwhich implies that for small $\\lambda>0$,\n\\[\\det\\left(H_{\\lambda}\\left(\\frac{2\\pi}{3}, \\frac{4\\pi}{3}\\right)\\pm 0.25 \\lambda\\, {\\mathbb I} \\right)<0.\\]\nWe also compute that $Y_0(0,0)=9$, which shows for small $\\lambda>0$, \n\\[\\det(H_{\\lambda}(0,0)\\pm 0.25 \\lambda\\, {\\mathbb I})>0.\\]\nThus we conclude that\n\\[\\pm 0.25 \\lambda \\in \\sigma(H_\\lambda),\\]\nwhich proves the claimed upper bound of the gap at $0$.\n\nTo complete the argument, all that remains is to show $Y_0(\\bm{\\theta})\\geq 0$ for all $\\bm{\\theta}\\in {\\mathbb T}^2$.\nTo that end, introduce two auxiliary variables\n\\[\nz := \\cos\\left(\\frac{\\theta_1 - \\theta_2}{2} \\right),\n\\quad\nw := \\cos\\left(\\frac{\\theta_1 + \\theta_2}{2} \\right),\n\\]\nand write $g(z,w)$ to mean $Y_0(\\bm{\\theta})$ in the variables $z$ and $w$. Thus, to optimize $Y_0(\\bm{\\theta})$ on ${\\mathbb T}^2$, it suffices to optimize $g(z,w)$ on the square $[-1,1]^2$. To execute this change of variables, first note the following simple consequences of standard identities:\n\\begin{align*}\n\\cos(2\\theta_1) + \\cos(2\\theta_2)\n& =\n2(2z^2-1)(2w^2-1),\\\\\n\\cos(2\\theta_1 - 2\\theta_2)\n& =\n2(2z^2-1)^2-1,\\\\\n\\cos(\\theta_1 + \\theta_2)\n& =\n2w^2 -1,\\\\\n\\cos(\\theta_1 - 2\\theta_2) + \\cos(2\\theta_1 - \\theta_2)\n& =\n2 zw(4z^2 - 3).\n\\end{align*}\nPutting all this together,\n\\begin{align*}\ng(z,w)\n& =\n15+ 4(2z^2-1)(2w^2-1) - 8zw(4z^2-3)+ 2(2(2z^2-1)^2-1) - 4(2w^2-1).\n\\end{align*}\nIt is easy to check that $g \\geq 0$ holds on the boundary; concretely,\n\\begin{align*}\ng(\\pm 1,w)\n& =\n15 + 4(2w^2-1) \\mp 8w + 2 - 4(2w^2-1) \\\\\n& =\n17 \\mp 8w \\\\\n& \\geq\n17 - 9 \\\\\n& > 0.\n\\end{align*}\nand\n\\begin{align*}\ng(z,\\pm 1)\n& =\n15 + 4(2z^2-1) \\mp 8z(4z^2-3) + (16z^4-16z^2+2) - 4 \\\\\n& =\n16 z^4 \\mp 32 z^3 - 8z^2 \\pm 24z + 9 \\\\\n& =\n(3 \\pm 4z - 4z^2)^2 \\\\\n& \\geq 0.\n\\end{align*}\nSo, we now seek zeros of $\\nabla g$ for $|z| < 1$ and $|w| < 1$.\nOne easily computes $\\partial_z g$ and $\\partial_w g$:\n\\begin{align*}\n\\partial_z g\n& = \n8(w-2z)(3+4z(w-z)) \\\\\n\\partial_w g\n& =\n8(3z - 4z^3 + 4w(z^2-1)).\n\\end{align*}\nSetting $\\partial_w g = 0$ yields\n\\begin{equation} \\label{eq:Y0critical:wval}\nw\n=\n\\frac{4z^3 - 3z}{4(z^2-1)}.\n\\end{equation}\nSince we are working on the interior of $[-1,1]^2$, $z \\neq \\pm 1$ and the denominator does not vanish. Substituting this expression for $w$ into $\\partial_z g$ and simplifying, we get\n\\[\n\\partial_zg \\left(z,\\frac{4z^3 - 3z}{z^2-1} \\right)\n=\n2z \\left( \\frac{1}{(z^2-1)^2} - 16 \\right).\n\\]\nSetting this equal to zero, we obtain three values of $z$ with $|z| <1$: $0$ and $\\pm \\sqrt{3}\/2$. Inserting these $z$ values into \\eqref{eq:Y0critical:wval}, the corresponding $w$ values are all readily seen to be zero. Plugging in the three critical points $(0,0)$ and $(\\pm \\sqrt{3}\/2,0)$ into $g$ yields 25 and 16, respectively, which concludes the proof that $g \\geq 0$ and hence \n\\[\nY_0(\\bm{\\theta}) \\geq 0\n\\]\nfor all $\\bm{\\theta} \\in {\\mathbb T}^2$, proving \\eqref{eq:hexY0geq0}.\n\\end{proof}\n\n\nNext, we show that for any $(2,2)$-periodic potential, it is impossible that it opens linear order gaps on both sides of $E=\\pm 1$ simultaneously.\n\\begin{theorem}\\label{thm:E=pm1linear}\nFor any $(2,2)$-periodic potential $Q$ and any constant $c>0$, the following holds for all sufficiently small $\\lambda>0$:\n\\[ \\left( (-1-c\\lambda, -1+c\\lambda) \\cup (1-c\\lambda, 1+c\\lambda) \\right) \\cap \\sigma(H_{\\lambda})\\neq \\emptyset\\]\n\\end{theorem}\n\\begin{proof}\nLet $(q_1,q_2,\\ldots,q_8)$ be the potential on a $2\\times 2$ fundamental cell, as shown in Fig.~\\ref{fig:hex22period}. \nThe corresponding Floquet matrix $H_{\\lambda}(\\bm{\\theta})$ is \n\\[\nH_\\lambda(\\bm{\\theta})\n=\n\\begin{bmatrix}\n\\lambda q_1 & 1 & 0 & e^{-i\\theta_1} & 0 & e^{-i\\theta_2} & 0 & 0 \\\\\n1 & \\lambda q_2& 1 & 0 & 1 & 0 &0&0 \\\\\n0 & 1 & \\lambda q_3& 1 & 0 & 0 & 0 & e^{-i\\theta_2} \\\\\ne^{i\\theta_1} & 0 & 1 & \\lambda q_4& 0 & 0 & 1 & 0 \\\\\n0 & 1 & 0 & 0 & \\lambda q_5& 1 & 0 & e^{-i\\theta_1} \\\\\ne^{i\\theta_2} & 0 & 0 & 0 & 1 & \\lambda q_6& 1 & 0 \\\\\n0 & 0 &0 & 1 & 0 & 1 & \\lambda q_7& 1 \\\\\n0 & 0 & e^{i\\theta_2} & 0 & e^{i\\theta_1} & 0 & 1 & \\lambda q_8\n\\end{bmatrix}\n\\]\nFor $0<|s|0>X_3^-(\\bm{0},s_0).\\]\nCombining this with \\eqref{eq:X0Y00}, we obtain\n\\begin{equation} \\label{eq:det00>0}\n\\det(H_\\lambda(\\bm{0})-(1+s_0\\lambda) {\\mathbb I})>0\n\\end{equation}\nfor small $\\lambda > 0$. We also have\n\\begin{align}\\label{eq:X0Y0Pi\/4}\nX_0^\\pm((\\pi\/4, 3\\pi\/4), s_0)=-4.\n\\end{align}\nIn particular, \\eqref{eq:X0Y0Pi\/4} implies that \n\\begin{align}\\label{eq:detPi\/4<0}\n\\det(H_\\lambda(\\pi\/4,3\\pi\/4))-(1+s_0\\lambda) {\\mathbb I})<0\n\\end{align}\nfor all $\\lambda \\geq 0$ small.\n\nCombining \\eqref{eq:detPi\/4<0} with \\eqref{eq:det00>0}, for any sufficiently small $\\lambda > 0$, there exists $\\bm{\\theta}$ such that\n\\[\\det(H_{\\lambda}(\\bm{\\theta})-(1+s_0\\lambda) {\\mathbb I})=0.\\]\nHence \n\\[(1-c\\lambda, 1+c\\lambda)\\cap \\sigma(H_{\\lambda})\\neq \\emptyset\\]\nas claimed.\n\\end{proof}\n\n\\section{Square Laplacian with Next-Nearest Neighbor Interactions}\\label{sec:nnn}\nWe now turn our attention to the EHM lattice, whose Laplacian is given by\n\\begin{align*}\n[\\Delta_{\\mathrm{sqn}} u]_{n,m}\n& =\nu_{n-1,m} + u_{n+1,m} + u_{n,m-1} + u_{n,m+1} + u_{n-1,m+1} + u_{n-1,m+1} + u_{n+1,m-1} + u_{n+1,m+1}\\\\\n& =\n[\\Delta_{\\rm sq}u]_{n,m} + u_{n-1,m-1} + u_{n-1,m+1} + u_{n+1,m-1} + u_{n+1,m+1} \\\\\n& =\n[\\Delta_{{\\mathrm{tri}}}u]_{n,m} + u_{n-1,m-1} + u_{n+1,m+1}.\n\\end{align*}\nNow, given $p_1,p_2 \\in {\\mathbb Z}_+$, we define $P = p_1p_2$ and $\\Lambda = {\\mathbb Z}^2 \\cap \\big([0,p_1) \\times [0,p_2)\\big)$ as before and view $\\Delta_{\\mathrm{sqn}}$ as a $(p_1,p_2)$-periodic operator and perform the Floquet decomposition. For $\\bm{\\theta} = (\\theta_1,\\theta_2) \\in {\\mathbb R}^2$, it is straightforward to check that\n\\[\n\\sigma(H(\\bm{\\theta}))\n=\n\\set{e_{\\bm{\\ell}}(\\bm{\\theta}) : \\bm{\\ell}\\in \\Lambda },\n\\]\nwhere $\\bm{\\ell}=(\\ell_1,\\ell_2)$ and\n\\begin{align*}\ne_{\\bm{\\ell}}(\\bm{\\theta})\n=\n2\\cos\\left( \\frac{\\theta_1+2\\pi \\ell_1}{p_1}\\right) \n+ 2\\cos\\left(\\frac{\\theta_2+2\\pi \\ell_2}{p_2}\\right) \n+ &2\\cos\\left(\\frac{\\theta_1 + 2\\pi \\ell_1}{p_1} - \\frac{\\theta_2 + 2\\pi \\ell_2}{p_2}\\right)\\\\\n+ &2\\cos\\left(\\frac{\\theta_1 + 2\\pi \\ell_1}{p_1} + \\frac{\\theta_2 + 2\\pi \\ell_2}{p_2}\\right).\n\\end{align*}\n\\begin{comment}\nWe now consider this as a $(p_1,p_2)$-periodic operator, and hence we seek (generalized) eigenfunctions $u$ with $u_{n+p_1,m} \\equiv e^{2\\pi i \\theta_1} u_{n,m}$ and $u_{n,m+p_2} \\equiv e^{2\\pi i y} u_{n,m}$ with $x,y \\in [0,1]$. Take\n\\[\nu_{n,m}^{(\\ell_1,\\ell_2)}\n=\n\\exp\\left(2\\pi i\\left(n \\frac{x+\\ell}{p_1} + m\\frac{y+k}{p_2} \\right) \\right)\n\\]\nwith $\\ell,k \\in {\\mathbb Z}$ and $0 \\le \\ell < p_1$, $0 \\le k < p_2$. It is straightforward to check that this leads to eigenvalues\n\nFor later use, we compute the derivative of $e_{\\ell,k}$:\n\\[-\\frac{1}{4 \\pi}\n\\nabla e_{\\ell,k}(x,y)\n=\n\\left( \\frac{1}{p_1} \\sin\\left(\\hat{x}\\right) + \\frac{1}{p_1}\\sin\\left(\\hat{x} - \\hat{y}\\right), \\frac{1}{p_2}\\sin(\\hat{y}) - \\frac{1}{p_2}\\sin(\\hat{x} - \\hat{y}) \\right)\n\\]\nwith $\\hat{x} = 2\\pi p_1^{-1} (x+\\ell)$ and $\\hat{y} = 2\\pi p_2^{-1} (y+k)$.\n\\end{comment}\nAs in Section~\\ref{sec:floquet}, we label these eigenvalues in increasing order according to multiplicity by\n\\[\nE_1(\\bm{\\theta})\n\\le \nE_2(\\bm{\\theta})\\le \\cdots E_P(\\bm{\\theta})\n\\]\nand denote the $P$ spectral bands by\n\\[\nF_k\n=\n\\set{E_k(\\bm{\\theta}) : \\bm{\\theta} \\in {\\mathbb R}^2},\n\\quad\n1 \\le k \\le P.\n\\]\nStraightforward computations shows that $\\sigma(\\Delta_{\\mathrm{sqn}})=[-4,8]$, hence \n\\[\n\\bigcup_{k=1}^P F_k\n=\n[-4,8].\n\\]\nOur main theorem of this section is\n\\begin{theorem}\\label{thm:sqnmain}\nLet $p_1,p_2 \\in {\\mathbb Z}_+$ be given.\n\\begin{enumerate}\n\\item[{\\rm 1.}]\nEach $E\\in (-4, 8)\\setminus \\{-1\\}$ belongs to $\\mathrm{int}(F_k)$ for some $1\\leq k\\leq P$.\n\\item[{\\rm 2.}] If one of the periods $p_1, p_2$ is not divisible by three, then $E=-1$ belongs to $\\mathrm{int}(F_k)$ for some $1\\leq k\\leq P$.\n\\end{enumerate}\n\\end{theorem}\n\n\\begin{proof}[Proof of Theorem~\\ref{t:bsc:nnn}]\nThis follows immediately from Theorem~\\ref{thm:sqnmain}.\n\\end{proof}\n\n\\subsection{Proof of Theorem \\ref{thm:sqnmain}} \n\nAs with the proof of Theorem~\\ref{thm:trimain}, we will divide the proof into two different cases: $E\\neq -1$ and $E=-1$ and argue by contradiction.\nTo that end, assume for the sake of establishing a contradiction that $E=\\min F_{k+1}=\\max F_k$ for some $1\\leq k\\leq P-1$.\n\nWe will use the following lemmas, whose proofs we provide at the end of the present section.\n\\begin{lemma}\\label{lem:constructionsqn}\nLet us consider the following system:\n\\begin{align}\\label{eq:xyCondABsqn}\n\\cos(x) + \\cos(y) + \\cos(x-y)+\\cos(x+y) & = \\frac{E}{2} \\\\\n\\sin(x) + \\sin(x-y)+\\sin(x+y) & = 0. \\notag\n\\end{align}\nFor any $E \\in (-4,8) \\setminus \\{-1\\}$, the solution set of \\eqref{eq:xyCondABsqn} in $[0,2\\pi)^2$ satisfies\n\\begin{align}\\label{eq:solutionx=0sqn}\nx=0,\\ \\ 1+2\\cos(y)=\\frac{E+1}{3},\n\\end{align}\nor \n\\begin{align}\\label{eq:solutionx=pisqn}\nx=\\pi,\\ \\ 1+2\\cos(y)=-(E+1).\n\\end{align}\n\\end{lemma}\n\n\\begin{lemma}\\label{lem:sqnJ0empty}\nConsider the following system:\n\\begin{equation} \\label{eq:sqnJ0syst}\n\\begin{cases}\n\\cos(x) + \\cos(y) + \\cos(x+y) +\\cos(x-y) = \\frac{E}{2},\\\\\n\\sin(x)+\\sin(x-y)+\\sin(x+y)=0,\\\\\n\\sin(y)-\\sin(x-y)+\\sin(x+y)=0.\n\\end{cases}\n\\end{equation}\nFor any $E \\in (-4,8) \\setminus \\{0, -1\\}$, the solution set of \\eqref{eq:sqnJ0syst} is empty. \nFor $E=0$, the unique solution of \\eqref{eq:sqnJ0syst} in $[0,2\\pi)^2$ is $(\\pi,\\pi)$.\nFor $E =-1$, the solutions of \\eqref{eq:sqnJ0syst} in $[0,2\\pi)^2$ are $({2\\pi}\/{3},{2\\pi}\/{3})$, $({2\\pi}\/{3},{4\\pi}\/{3})$, $({4\\pi}\/{3},{2\\pi}\/{3})$ and $({4\\pi}\/{3},{4\\pi}\/{3})$.\n\\end{lemma}\n\nWe will use Lemma \\ref{lem:constructionsqn} in the $E\\neq -1$ case, and Lemma \\ref{lem:sqnJ0empty} in the $E=-1$ case.\n\n\\subsubsection{$E\\neq -1$}\\\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:sqnmain}.1]\nLet $E \\in (-4,8)\\setminus\\set{-1}$ be given, and suppose towards a contradiction that $E = \\max F_k = \\min F_{k+1}$ for some $k$. Define $\\widetilde{\\bm{\\theta}}=(\\widetilde{\\theta}_1,\\widetilde{\\theta}_2)\\in [0,2\\pi)^2$ and $\\bm{\\ell}^{(1)}=(\\ell_1^{(1)},\\ell_2^{(1)})\\in \\Lambda$ via\n\\begin{align}\\label{def:ttheellsqnE}\n\\widetilde{\\theta}_1=0,\\ \\ell_1^{(1)}=0,\\ \\frac{\\widetilde{\\theta}_2+2\\pi \\ell_2^{(1)}}{p_2}=\\arccos\\Big(\\frac{E-2}{6}\\Big)\\in (0,\\pi).\n\\end{align}\nNote that since $E\\in (-4, 8)$, we have $\\frac{E-2}{6}\\in (-1,1)$, hence $\\arccos\\Big(\\frac{E-2}{6}\\Big)$ is always well-defined.\nNote also that $\\widetilde{\\theta}_2$ and $\\ell_2^{(1)}$ are uniquely determined.\nUsing \\eqref{def:ttheellsqnE}, one easily checks that \n\\[e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})=E,\\]\nand\n\\begin{align}\\label{eq:sqnJbeta10}\n(1,0)\\cdot \\nabla e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})=0.\n\\end{align}\nAs in the proof of Theorem~\\ref{thm:trimain}, denote $\\Lambda_E(\\widetilde{\\bm{\\theta}}) = \\{\\bm{\\ell} \\in \\Lambda : e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}}) = E\\}$, let $r := |\\Lambda_E(\\widetilde{\\bm{\\theta}})|$ be the multiplicity of $E$ as an eigenvalue of $H(\\widetilde{\\bm{\\theta}})$, and choose $s\\in {\\mathbb Z}\\cap [1,r]$ such that \n\\[E_{k-s}(\\widetilde{\\bm{\\theta}})0$ small enough such that \n\\[E_{k-s}(\\bm{\\theta})0\\}.\n\\end{aligned}\n\\end{equation}\nBy definition, we must have\n\\begin{align}\\label{eq:sumJbetasqn}\n|\\mathcal{J}_{\\bm{\\beta}}^0|+|\\mathcal{J}_{\\bm{\\beta}}^+|+|\\mathcal{J}_{\\bm{\\beta}}^-|=r\n\\end{align}\nfor any $\\bm{\\beta}$. We also define $\\mathcal{J}_0$ as follows\n\\begin{align}\\label{def:J0sqn}\n\\mathcal{J}_0\n={\\mathcal J}_0(\\widetilde{\\bm{\\theta}})\n:=\n\\{\\bm{\\ell} \\in \\Lambda_E(\\widetilde{\\bm{\\theta}}):\\ \\nabla e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}}) = \\bm{0}\\}.\n\\end{align}\nIf $E\\neq 0$, Lemma~\\ref{lem:sqnJ0empty} directly implies $\\mathcal{J}_0=\\emptyset$.\nIf $E=0$, $\\mathcal{J}_0$ is also empty. \nTo see this, suppose on the contrary that $\\bm{\\ell} = (\\ell_1,\\ell_2) \\in {\\mathcal J}_0$.\nLemma~\\ref{lem:sqnJ0empty} implies that\n\\begin{equation} \\label{eq:sqn:J0emptyE=0eq1}\n\\frac{\\widetilde{\\theta}_2+2\\pi \\ell_2}{p_2}=\\pi,\n\\end{equation}\nand \\eqref{def:ttheellsqnE} forces\n\\begin{equation} \\label{eq:sqn:J0emptyE=0eq2}\n\\frac{\\widetilde{\\theta}_2+2\\pi \\ell^{(1)}_2}{p_2}=\\arccos\\left(-\\frac{1}{3}\\right).\n\\end{equation}\nSubtracting \\eqref{eq:sqn:J0emptyE=0eq1} from \\eqref{eq:sqn:J0emptyE=0eq2} yields\n\\[\\frac{\\ell^{(1)}_2-\\ell_2}{p_2}=\\frac{1}{2\\pi}\\arccos\\left(-\\frac{1}{3}\\right)-\\frac{1}{2}.\\]\nHowever, this implies that $(2\\pi)^{-1}\\arccos\\left(-1\/3\\right)$ is a rational number, which contradicts the following well-known fact, whose proof we supply at the end of the present section.\n\\begin{lemma}\\label{lem:arccos1\/3}\n\\[\\frac{1}{2\\pi}\\arccos\\left(-\\frac{1}{3}\\right)\\in {\\mathbb R}\\setminus {\\mathbb Q}.\\]\n\\end{lemma}\nTherefore $\\mathcal{J}_0=\\emptyset$ for any $E\\neq -1$.\n\nWe choose $\\bm{\\beta}_1=(1,0)$. Then \\eqref{eq:sqnJbeta10} implies $\\bm{\\ell}^{(1)} \\in {\\mathcal J}_{\\bm{\\beta}_1}^0$, and hence\n\\begin{align}\\label{eq:beta1sqnnon-empty}\n\\mathcal{J}_{\\bm{\\beta}_1}^0\\neq \\emptyset.\n\\end{align} \n\n\nNext we are going to perturb the point $\\widetilde{\\bm{\\theta}}$ and count the eigenvalues.\nSince $\\mathcal{J}_0=\\emptyset$, we can choose a unit vector $\\bm{\\beta}_2$ such that \n\\begin{align}\\label{eq:beta2sqnnon-empty}\n\\bm{\\beta}_2\\cdot \\nabla e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}})\\neq 0,\n\\end{align}\nholds for any $\\bm{\\ell} \\in \\Lambda_E(\\widetilde{\\bm{\\theta}})$.\nThus $\\mathcal{J}_{\\bm{\\beta}_2}^0=\\emptyset$ and\n\\begin{align}\\label{eq:beta2sqnnon-empty'}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|+|\\mathcal{J}_{\\bm{\\beta}_2}^-|=r.\n\\end{align}\nArguing as in the proof of Theorem~\\ref{thm:trimain}.1, we deduce \\begin{comment}\nWe first perturb the eigenvalues along the $\\bm{\\beta}_2$ direction.\nSince $\\mathcal{J}_{\\bm{\\beta}_2}^0 = \\emptyset$, we will always employ \\eqref{eq:pertgeneralbetasqn1order}.\n\nFor $t > 0$ small enough, we have the following.\n\\begin{itemize}\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_2}^+$, we have\n\\[\nE_{k+r-s+1}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n>\ne_{{\\bm{\\ell}}}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies\n\\begin{align}\\label{eq1:Jbeta2+sqn}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+|\\leq r-s.\n\\end{align}\n\n\\item If ${\\bm{\\ell} } \\in \\mathcal{J}_{\\bm{\\beta}_2}^-$, we have \n\\[\nE_{k-s}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n<\ne_{{\\bm{\\ell}}}(\\widetilde{\\theta}_1 + t\\beta_{2,1}, \\widetilde{\\theta}_2 + t\\beta_{2,2})\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta2+sqn}\n|\\mathcal{J}_{\\bm{\\beta}_2}^-|\\leq s.\n\\end{align}\n\\end{itemize}\n{In view of \\eqref{eq:beta2sqnnon-empty'}, Equations~\\eqref{eq1:Jbeta2+sqn} and \\eqref{eq2:Jbeta2+sqn} imply\n\\begin{equation} \\label{eq:Jbeta2MinusCard+sqn}\n|\\mathcal{J}_{\\bm{\\beta}_2}^-|\n=\ns.\n\\end{equation}\nUpon realizing that $\\mathcal{J}_{-\\bm{\\beta}_2}^0 = \\emptyset$ and $\\mathcal{J}_{-\\bm{\\beta}_2}^\\pm = \\mathcal{J}_{\\bm{\\beta}_2}^\\mp$, we may apply the analysis above with $\\bm{\\beta}_2$ replaced by $-\\bm{\\beta}_2$ and conclude that\n\\begin{equation} \\label{eq:JMinusBeta2+sqn}\n|\\mathcal{J}_{\\bm{\\beta}_2}^+| = |\\mathcal{J}_{-\\bm{\\beta}_2}^-| = s.\n\\end{equation}\nIn particular, \\eqref{eq:Jbeta2MinusCard+sqn} and \\eqref{eq:JMinusBeta2+sqn} imply \\end{comment}\n\\begin{align}\\label{eq4:Jbeta2sqn}\nr=2s.\n\\end{align}\n\n\n\n\\subsubsection*{Perturbation along $\\bm{\\beta}_1$}\nNow we perturb the eigenvalues along $\\bm{\\beta}_1=(1,0)$.\nThe case when ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_1}^{\\pm}$ is similar to that of $\\bm{\\beta}_2$.\nThe difference here is that, according to \\eqref{eq:beta1sqnnon-empty}, $\\mathcal{J}_{\\bm{\\beta}_1}^0\\neq \\emptyset$.\n\nBy Lemma~\\ref{lem:constructionsqn}, we have that for $(\\ell_1,\\ell_2)\\in \\mathcal{J}_{\\bm{\\beta}_1}^0$,\n\\begin{align}\\label{eq:beta1sqnt2sign}\n(E+1)\\Bigg[\n\\cos\\Big(\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}\\Big)\n+&\\cos\\Big(\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}-\\frac{\\widetilde{\\theta}_2 + 2\\pi {\\ell_2}}{p_2}\\Big)\\\\\n&\\qquad +\\cos\\Big(\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}+\\frac{\\widetilde{\\theta}_2 + 2\\pi {\\ell_2}}{p_2}\\Big) \n\\Bigg]\n>0. \\notag\n\\end{align}\nIndeed, if $(\\ell_1,\\ell_2)\\in \\mathcal{J}_{\\bm{\\beta}_1}^0$, \n$(x,y) = (p_1^{-1} (\\widetilde{\\theta}_1 + 2\\pi \\ell_1), p_2^{-1}(\\widetilde{\\theta}_2 + 2\\pi \\ell_2))$ is a solution to \\eqref{eq:xyCondABsqn}.\nHence Lemma~\\ref{lem:constructionsqn} implies that \nwe have \neither \n\\[\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}=0,\\ \\ 1+2\\cos\\Big(\\frac{\\widetilde{\\theta}_2 + 2\\pi {\\ell_2}}{p_2}\\Big)=\\frac{E+1}{3},\\]\nor \n\\[\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}=\\pi,\\ \\ 1+2\\cos\\Big(\\frac{\\widetilde{\\theta}_2 + 2\\pi {\\ell_2}}{p_2}\\Big)=-(E+1).\\]\nClearly, both cases lead to \\eqref{eq:beta1sqnt2sign}.\n\nBy employing \\eqref{eq:pertgeneralbetasqn2order}, we obtain\n\\begin{align}\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t \\bm{\\beta}_1)\n=\nE & - {\\frac{ t^2}{2 p_1^2}} \\Bigg[\n\\cos\\Big(\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}\\Big)\n+\\cos\\Big(\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}-\\frac{\\widetilde{\\theta}_2 + 2\\pi {\\ell_2}}{p_2}\\Big)\\\\\n& \\qquad\\qquad\\qquad\n+\\cos\\Big(\\frac{\\widetilde{\\theta}_1 + 2\\pi {\\ell_1}}{p_1}+\\frac{\\widetilde{\\theta}_2 + 2\\pi {\\ell_2}}{p_2}\\Big) \n\\Bigg] + O(t^3)\\notag\n\\end{align}\nfor $\\bm{\\ell} \\in {\\mathcal J}_{\\bm{\\beta}_1}^0$. Combining this with \\eqref{eq:beta1sqnt2sign}, we obtain that for $|t|>0$ small enough\n\\begin{align}\\label{eq:Jbeta10sqn}\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t \\bm{\\beta}_1)\n\\begin{cases}\n0,\\\\\n>E,\\ \\ \\text{if } E+1<0.\n\\end{cases}\n\\end{align}\nNotice that the choice of $\\bm{\\beta}_1$ causes the second $t^2$ term of \\eqref{eq:pertgeneralbetasqn2order} to drop out.\n\nWithout loss of generality, we assume $E\\in (-1, 8)$. The complementary case when $E\\in (-4, -1)$ can be handled similarly.\nFor $E\\in (-1, 8)$, \\eqref{eq:Jbeta10sqn} implies that \n\\begin{align}\\label{eq:Jbeta10sqn'}\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t \\bm{\\beta}_1)\n<\nE\n=\n\\min F_{k+1},\n\\end{align}\nholds for $|t|>0$ small enough and for any $\\bm{\\ell} \\in \\mathcal{J}_{\\bm{\\beta}_1}^0$. \n\nCombining \\eqref{eq:Jbeta10sqn'} with \\eqref{eq:pertgeneralbetasqn1order}, we have the following.\n\nFor $t>0$ small enough,\n\\begin{itemize}\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_1}^+$, we have\n\\[\nE_{k+r-s+1}(\\widetilde{\\bm{\\theta}} + t \\bm{\\beta}_1)\n>\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t \\bm{\\beta}_1)\n>\nE\n=\n\\max F_k,\n\\]\nwhich implies \n\\begin{align}\\label{eq1:Jbeta1+sqn}\n|\\mathcal{J}_{\\bm{\\beta}_1}^+|\\leq r-s\n=\ns,\n\\end{align}\n{where the equality follows from \\eqref{eq4:Jbeta2sqn}.}\n\n\\item If ${\\bm{\\ell}} \\in \\mathcal{J}_{\\bm{\\beta}_1}^0 \\bigcup \\mathcal{J}_{\\bm{\\beta}_1}^-$, we have \n\\[\nE_{k-s-1}(\\widetilde{\\bm{\\theta}} + t \\bm{\\beta}_1)\n<\ne_{{\\bm{\\ell}}}(\\widetilde{\\bm{\\theta}} + t \\bm{\\beta}_1)\n<\nE\n=\n\\min F_{k+1},\n\\]\nwhich implies\n\\begin{align}\\label{eq2:Jbeta1+sqn}\n|\\mathcal{J}_{\\bm{\\beta}_1}^0|+|\\mathcal{J}_{\\bm{\\beta}_1}^-|\\leq s.\n\\end{align}\n\\end{itemize}\n{In view of \\eqref{eq:sumJbetasqn} and \\eqref{eq4:Jbeta2sqn}, Equations~\\eqref{eq1:Jbeta1+sqn} and \\eqref{eq2:Jbeta1+sqn} yield\n\\begin{equation} \\label{eq3:Jbeta1+sqn}\n|\\mathcal{J}_{\\bm{\\beta}_1}^+|\n=\n|\\mathcal{J}_{\\bm{\\beta}_1}^0|+|\\mathcal{J}_{\\bm{\\beta}_1}^-|\n=\ns.\n\\end{equation}\nAs before, we may observe that ${\\mathcal J}_{-\\bm{\\beta}_1}^0 = {\\mathcal J}_{\\bm{\\beta}_1}^0$ and ${\\mathcal J}_{-\\bm{\\beta}_1}^\\pm = {\\mathcal J}_{\\bm{\\beta}_1}^\\mp$. Then, the analysis above applied with $\\bm{\\beta}_1$ replaced by $-\\bm{\\beta}_1$ forces\n\\begin{equation} \\label{eq3:Jbeta1-sqn}\n|\\mathcal{J}_{\\bm{\\beta}_1}^-|\n=\n|\\mathcal{J}_{\\bm{\\beta}_1}^0|+|\\mathcal{J}_{\\bm{\\beta}_1}^+|\n=\ns.\n\\end{equation}\nTaken together, \\eqref{eq3:Jbeta1+sqn} and \\eqref{eq3:Jbeta1-sqn} imply $|{\\mathcal J}_{\\bm{\\beta}_1}^0| = 0$, which contradicts \\eqref{eq:beta1sqnnon-empty}.\n}\n\\end{proof}\n\n\\subsubsection{$E=-1$}\\\n\nFirst, we would like to make a remark on our strategy of the proof of the $E=-1$ case, and on the importance of one of the period being not divisible by $3$.\n\\begin{remark}\\label{rem:sqn}\nFor the exceptional energy {$E = -1$} of the EHM lattice, we can not use eigenvalues with vanishing gradients to create un-even numbers of counting unless neither $p_1$ nor $p_2$ is divisible by $3$.\nThe reason is the following: suppose {\\it only} $p_1$ is not divisible by $3$ and we choose $\\widetilde{\\bm{\\theta}}=(\\widetilde{\\theta}_1, \\widetilde{\\theta}_2)$ and $\\bm{\\ell}^{(1)}=(\\ell_1^{(1)}, \\ell_2^{(1)})$ such that $e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})=-1$ and $\\nabla e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})=\\bm{0}$. \nLemma \\ref{lem:sqnJ0empty} yields four possibilities $(p_1^{-1}(\\widetilde{\\theta}_1+2\\pi \\ell_1^{(1)}), p_2^{-1}(\\widetilde{\\theta}_2+2\\pi \\ell_2^{(1)}))=(2\\pi\/3,2\\pi\/3)$, $(2\\pi\/3,4\\pi\/3)$, $(4\\pi\/3,2\\pi\/3)$ or $(4\\pi\/3,4\\pi\/3)$.\nWithout loss of generality, we choose $(2\\pi\/3, 2\\pi\/3)$, the other three choices are essentially the same.\nSince $p_2$ is divisible by $3$, there exists $\\bm{\\ell}^{(2)}$, such that \n$(p_1^{-1}(\\widetilde{\\theta}_1+2\\pi \\ell_1^{(2)}), p_2^{-1}(\\widetilde{\\theta}_2+2\\pi \\ell_2^{(2)}))=(2\\pi\/3,4\\pi\/3)$.\nHence $e_{\\bm{\\ell}^{(2)}}(\\widetilde{\\bm{\\theta}})$ is also located at $-1$ with vanishing gradient.\nPerturbing $e_{\\bm{\\ell}^{(1)}}(\\widetilde{\\bm{\\theta}})$ and $e_{\\bm{\\ell}^{(2)}}(\\widetilde{\\bm{\\theta}})$ along a given direction $(\\beta_1, \\beta_2)$ is equivalent to controlling the signs of the following two expressions:\n$$\\beta_1\\beta_2\\ \\ \\text{and}\\ \\ -\\beta_1\\beta_2.$$\nThis means we can never choose two different directions that lead to un-even {counts}.\nTherefore we need to develop a new argument for this case.\n\nIndeed, when $p_1$ is not divisible by $3$, we choose $p_1^{-1}(\\widetilde{\\theta}_1+2\\pi \\ell_1^{(1)})=2\\pi\/3$ and $\\widetilde{\\theta}_2$ such that \n$p_2^{-1}(\\widetilde{\\theta}_2+2\\pi \\ell_2)\\notin \\{2\\pi\/3, 4\\pi\/3\\}$ {{\\it regardless} of the choice of $\\ell_2$}. Such choices guarantee that there are in total $p_2$ eigenvalues located at $-1$, which are \n$\\{e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}}),\\ \\ \\ell_1=\\ell_1^{(1)}\\}$. It then suffices to control the movements of these eigenvalues along any given direction.\nA key observation is that along any direction, approximately $2p_2\/3$ eigenvalues will move up (down) while the other $p_2\/3$ eigenvalues move down (up), see \\eqref{eq:E=-1Jbbe}.\nThis leads to un-even counting that we need.\nLet us point out that if both $p_1, p_2$ are divisible by $3$, this argument does not work {(as it must, given the example constructed in Theorem~\\ref{t:nnnExamples})}: there will be $2p_2$ eigenvalues located at $-1$, and $p_2$ of them move up while the other $p_2$ of them move down along any given direction.\n\\end{remark}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:sqnmain}.2]\nWithout loss of generality, we assume $p_1$ is not divisible by $3$.\nLet $p_j=3p_j'+k_j$, where $p_j',k_j \\in {\\mathbb Z}$ with $0 \\le k_j < 3$ and then define $\\widetilde\\bm{\\theta}$ by\n\\begin{align*}\n\\widetilde{\\theta}_1= \\frac{2\\pi k_1}{3},\\ \\ \n\\widetilde{\\theta}_2={\\frac{k_2+1}{4}\\pi}.\n\\end{align*}\n{As usual, denote $\\Lambda_E(\\widetilde{\\bm{\\theta}}) = \\Lambda_{-1}(\\widetilde{\\bm{\\theta}}) = \\{\\bm{\\ell} \\in \\Lambda : e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}}) = -1\\}$. We first claim that\n\\begin{equation} \\label{eq:sqnE=-1:multiplicity}\n\\Lambda_{-1}(\\widetilde{\\bm{\\theta}})\n=\n\\set{(p_1', \\ell_2) : 0 \\leq \\ell_2 < p_2 \\text{ and } \\ell_2 \\in {\\mathbb Z}}.\n\\end{equation}}\nLet us consider the trigonometric equation\n\\begin{align}\\label{eq:E=-1}\n\\cos(x)+\\cos(y)+\\cos(x-y)+\\cos(x+y)=-\\frac{1}{2}=\\frac{E}{2}.\n\\end{align}\nUsing the identity $\\cos(x-y)+\\cos(x+y)=2\\cos(x) \\cos(y)$, we see that \\eqref{eq:E=-1} is equivalent to \n\\[(2\\cos(x)+1) (2\\cos(y)+1)=0,\\]\nwhose solutions are $\\cos(x)=-1\/2$ or $\\cos(y)=-1\/2$. {With our choice of $\\widetilde{\\bm{\\theta}}$, it is clear that\n\\begin{equation} \\label{eq:E=-1the1ell1}\n\\frac{\\widetilde{\\theta}_1 + 2\\pi p_1'}{p_1}\n=\n\\frac{2\\pi}{3},\n\\quad\n\\cos\\left(\\frac{\\widetilde{\\theta}_1 + 2\\pi p_1'}{p_1}\\right)\n=\n-\\frac{1}{2}.\n\\end{equation}\nConsequently, \n\\begin{equation} \\label{eq:sqn:E=-1:LambdaEeq1}\ne_{(p_1',\\ell_2)}(\\widetilde{\\bm{\\theta}}) = -1 \\quad \\text{for every } 0 \\le \\ell_2 < p_2.\n\\end{equation}}\nDue to our choice of $\\widetilde{\\theta}_2$, we get\n\\begin{align}\\label{eq:E=-1J0}\n\\cos(p_2^{-1}(\\widetilde{\\theta}_2+2\\pi \\ell_2))\\neq -\\frac{1}{2}\\quad\n\\text{for any } \\ell_2 \\in [0,p_2) \\cap {\\mathbb Z}.\n\\end{align}\nIndeed, since $p_2^{-1}(\\widetilde{\\theta}_2+2\\pi\\ell_2) \\in [0,2\\pi)$, $\\cos(p_2^{-1}(\\widetilde{\\theta}_2+2\\pi \\ell_2))=-1\/2$ would force \n\\[\\frac{\\widetilde{\\theta}_2+2\\pi \\ell_2}{p_2} \\in \\set{\\frac{2\\pi}{3}, \\frac{4\\pi}{3}},\\]\nwhich, after doing some algebra, leads to {\n\\[\n3(8\\ell_2+k_2+1) \\in \\{8p_2,16p_2\\},\n\\]\nwhich is plainly impossible, since $\\ell_2,p_2 \\in {\\mathbb Z}$ and $k_2 \\in \\{0,1,2\\}$.}\nAdditionally, due to our choice of $\\widetilde{\\theta}_1$, we also have\n\\begin{align}\\label{eq:E=-1Jbeta10}\n{\\cos(p_1^{-1}(\\widetilde{\\theta}_1+2\\pi \\ell_1))\\neq -1\/2 \\quad\n\\text{for any } \\ell_1 \\in \\big([0,p_1)\\cap{\\mathbb Z}\\big)\\setminus\\{p_1'\\}}.\n\\end{align}\nTo see this, suppose on the contrary that \\eqref{eq:E=-1Jbeta10} fails. This forces\n\\[\\frac{\\widetilde{\\theta}_1+2\\pi \\ell_1}{p_1}=\\frac{4\\pi}{3}\\]\nfor some $0 \\leq \\ell_1 < p_1$ with $\\ell_1\\neq p_1'$. Since\n\\[\\frac{\\widetilde{\\theta}_1+2\\pi p_1'}{p_1}=\\frac{2\\pi}{3},\\]\nthis implies\n\\[\\frac{2\\pi (\\ell_1-p_1')}{p_1}=\\frac{2\\pi}{3},\\]\nwhich is impossible since $p_1$ is not divisible by $3$. Combining \\eqref{eq:E=-1J0} and \\eqref{eq:E=-1Jbeta10} yields\n\\begin{equation} \\label{eq:sqn:E=-1:LambdaEeq2} \n{e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}})\\neq - 1\n\\quad\n\\text{for any } \\bm{\\ell} =(\\ell_1,\\ell_2)\\in\\Lambda \\text{ such that } \\ell_1 \\neq p_1'.}\n\\end{equation}\nTaken together, \\eqref{eq:sqn:E=-1:LambdaEeq1} and \\eqref{eq:sqn:E=-1:LambdaEeq2} imply \\eqref{eq:sqnE=-1:multiplicity}.\n\nLet us choose $\\bm{\\beta}=(\\beta_1,\\beta_2)=(1,0)$. \nWe have that for any $\\bm{\\ell}\\in \\Lambda$:\n\\begin{align*}\n&\\bm{\\beta}\\cdot \\nabla e_{\\bm{\\ell}}(\\widetilde{\\bm{\\theta}})\\\\\n=&-\\sin\\Big(\\frac{\\widetilde{\\theta}_1+2\\pi \\ell_1}{p_1}\\Big)\n-\\sin\\Big(\\frac{\\widetilde{\\theta}_1+2\\pi \\ell_1}{p_1}-\\frac{\\widetilde{\\theta}_2+2\\pi \\ell_2}{p_2}\\Big)\n-\\sin\\Big(\\frac{\\widetilde{\\theta}_1+2\\pi \\ell_1}{p_1}+\\frac{\\widetilde{\\theta}_2+2\\pi \\ell_2}{p_2}\\Big)\\\\\n=&-\\sin\\Big(\\frac{\\widetilde{\\theta}_1+2\\pi \\ell_1}{p_1}\\Big)\\Bigg[1+2\\cos\\Big(\\frac{\\widetilde{\\theta}_2+2\\pi \\ell_2}{p_2}\\Big)\\Bigg].\n\\end{align*}\nBy \\eqref{eq:sqnE=-1:multiplicity}, \\eqref{eq:E=-1the1ell1}, and \\eqref{eq:E=-1J0}, we have the following {for any $\\bm{\\ell} = (\\ell_1, \\ell_2) \\in \\Lambda_{-1}(\\widetilde{\\bm{\\theta}})$}:\n\\[\\sin\\Big(\\frac{\\widetilde{\\theta}_1+2\\pi \\ell_1}{p_1}\\Big)=\\frac{\\sqrt{3}}{2},\\ \\ \\cos\\Big(\\frac{\\widetilde{\\theta}_2+2\\pi \\ell_2}{p_2}\\Big)\\neq -\\frac{1}{2}.\\]\nThis implies\n\\begin{align}\\label{eq:E=-1Jbbe}\n\\mathcal{J}_{\\bm{\\beta}}^0=\\emptyset,\\ \\ \\text{and }\\ \\ \n\\mathcal{J}_{\\bm{\\beta}}^{\\pm}\n=\n\\Bigg\\lbrace \\bm{\\ell} \\in \\Lambda_{-1}(\\widetilde{\\bm{\\theta}}) :\\ \\ \\mp \\frac{1}{2} \\mp \\cos\\Big(\\frac{\\widetilde{\\theta}_2+2\\pi \\ell_2}{p_2}\\Big)>0 \\Bigg\\rbrace.\n\\end{align}\nHence we expect that $|{\\mathcal J}_{\\bm{\\beta}}^+|\\sim p_2\/3$, and $|{\\mathcal J}_{\\bm{\\beta}}^-|\\sim 2p_2\/3$.\nMore precisely, we note that\n\\[\n{{\\mathcal J}_{\\bm{\\beta}}^+\n=\n\\Big\\lbrace (p_1', \\ell_2):\\ \\ \\frac{2\\pi}{3} < \\frac{(k_2+1)\\pi\/4 + 2\\pi \\ell_2}{p_2} < \\frac{4\\pi}{3} \\Big\\rbrace}.\\]\nUsing $p_2=3p_2'+k_2$, we obtain\n\\[\n{{\\mathcal J}_{\\bm{\\beta}}^+\n=\n\\Big\\lbrace (p_1',\\ell_2):\\ \\ p_2'+\\frac{5k_2-3}{24} < \\ell_2 < 2p_2'+\\frac{13k_2-3}{24} \\Big\\rbrace}.\n\\]\nConsequently,\n\\begin{align*}\n{\\mathcal J}_{\\bm{\\beta}}^+=\n\\begin{cases}\n\\{(p_1', \\ell_2):\\ \\ p_2'\\leq \\ell_2 \\leq 2p_2'-1\\},\\ \\ \\text{if } k=0,\\\\\n\\{(p_1', \\ell_2):\\ \\ p_2'+1\\leq \\ell_2 \\leq 2p_2'\\},\\ \\ \\text{if } k=1, 2.\n\\end{cases}.\n\\end{align*}\nTherefore \n\\begin{align}\\label{eq:Jbetapm}\n(|{\\mathcal J}_{\\bm{\\beta}}^+|, |{\\mathcal J}_{\\bm{\\beta}}^-|)=\n\\begin{cases}\n(p_2', 2p_2'),\\ \\ \\text{if } k=0,\\\\\n(p_2', 2p_2'+1),\\ \\ \\text{if } k=1,\\\\\n(p_2', 2p_2'+2),\\ \\ \\text{if } k=2.\n\\end{cases}\n\\end{align}\nNote that $p_2'\\geq 1$ whenever $k_2 = 0$.\nThus, a direct consequence of \\eqref{eq:Jbetapm} is\n\\begin{align}\\label{eq:Jbetapmneq}\n|{\\mathcal J}_{\\bm{\\beta}}^+|\\neq |{\\mathcal J}_{\\bm{\\beta}}^-|.\n\\end{align}\nOn the other hand, since ${\\mathcal J}_{\\bm{\\beta}}^0 = \\emptyset$, following the same argument as in the proof of Theorems~\\ref{thm:trimain}.1 yields $|{\\mathcal J}_{\\bm{\\beta}}^+|=|{\\mathcal J}_{\\bm{\\beta}}^-|$, which contradicts \\eqref{eq:Jbetapmneq}.\n\\end{proof}\n\n\\subsection{Proofs of Lemmas \\ref{lem:constructionsqn}, \\ref{lem:sqnJ0empty}, and \\ref{lem:arccos1\/3}}\n\n\\begin{proof}[Proof of Lemma~\\ref{lem:constructionsqn}]\nLet $x$ and $y$ solve \\eqref{eq:xyCondABsqn} with $E \\neq -1$. The second condition therein yields\n\\[\n\\sin(x) + 2\\sin(x)\\cos(y) = 0,\n\\]\nleading to two possibilities: $\\sin(x) = 0$ or $\\cos(y) = -1\/2$. If $\\sin(x) = 0$, we get $x = 0$ or $x = \\pi$, which yields \\eqref{eq:solutionx=0sqn} and \\eqref{eq:solutionx=pisqn} upon plugging in to the first condition in \\eqref{eq:xyCondABsqn}. In the event that $\\cos(y) = -1\/2$, we arrive at\n\\begin{align*}\n\\cos(x) + \\cos(y) + \\cos(x-y) + \\cos(x+y)\n& =\n\\cos(x) + \\cos(y) + 2\\cos(x)\\cos(y) \\\\\n& =\n\\cos(x) - \\frac{1}{2} - \\cos(x) \\\\\n& =\n-\\frac{1}{2},\n\\end{align*}\nin contradiction with $E \\neq -1$.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Lemma~\\ref{lem:sqnJ0empty}]\nSuppose $x$ and $y$ satisfy \\eqref{eq:sqnJ0syst}. From the proof of Lemma~\\ref{lem:constructionsqn}, the second condition of \\eqref{eq:sqnJ0syst} implies $\\sin(x) = 0$ or $\\cos(y) = -1\/2$. Thus, $x = 0$, $x = \\pi$, $y=2\\pi\/3$, or $y = 4\\pi\/3$. \nWhen $\\sin(x) = 0$, the third condition of \\eqref{eq:sqnJ0syst} forces $\\sin(y) = 0$. The four points so obtained yield $E = 8$ when $(x,y) = (0,0)$, $E=-4$ when $(x,y) = (0,\\pi),(\\pi,0)$ and $E = 0$ when $(x,y) = (\\pi,\\pi)$. \nAlternatively, when $\\cos(y) = -1\/2$, the third condition of \\eqref{eq:sqnJ0syst} yields $\\cos(x) = -1\/2$, which impies $x = 2\\pi\/3$ or $x = 4\\pi\/3$. As in the Proof of Lemma~\\ref{lem:constructionsqn}, the four points corresponding to\n\\[\nx,y \\in \\set{\\frac{2\\pi}{3}, \\frac{4\\pi}{3}}\n\\]\nall yield $E = -1$.\n\\end{proof}\n\n\\begin{proof}[Proof of Lemma~\\ref{lem:arccos1\/3}]\nSuppose \n\\begin{align}\\label{eq1:cos1\/3}\n\\cos\\left(\\frac{2\\pi m}{n}\\right)=-\\frac{1}{3},\n\\end{align}\nfor $m\/n\\in {\\mathbb Q}$.\nLet $T_n(\\cdot)$ denote the $n$-th degree Cheybeshev polynomial so that\n\\begin{align}\\label{eq2:cos1\/3}\nT_n\\left(\\cos\\left(\\frac{2\\pi m}{n}\\right)\\right)=\\cos(2\\pi m)=1.\n\\end{align}\nIt is well-known that $T_n(x)=\\sum_{k=0}^n a_k x^k$, where $a_n=2^{n-1}$ and $a_k\\in {\\mathbb Z}$ for any $k$.\nHence \\eqref{eq1:cos1\/3} and \\eqref{eq2:cos1\/3} imply\n\\[2^{n-1}\\left(-\\frac{1}{3}\\right)^n+\\sum_{k=0}^{n-1} a_k \\left(-\\frac{1}{3}\\right)^{k} =1.\\]\nMultiplying by $(-3)^n$ on both sides of the equation, we obtain\n\\[2^{n-1}-3\\sum_{k=0}^{n-1} a_k (-3)^{n-k-1}=(-3)^n,\\]\nwhich implies $2^{n-1}$ is divisible by $3$.\nContradiction.\n\\end{proof}\n\n\n\n\n\n\n\\subsection{\\boldmath Opening a gap at $-1$}\n\n\\begin{figure*}[b]\n\\begin{tikzpicture}[yscale=0.8, xscale=0.8]\n\\draw [-,line width = .1cm] (0,0) -- (12,0);\n\\draw [-,line width = .1cm] (0,0) -- (0,12);\n\\draw [-,line width= .1cm] (0,3) -- (12,3);\n\\draw [-,line width= .1cm] (3,0) -- (3,12);\n\\draw [-,line width= .1cm] (0,6) -- (12,6);\n\\draw [-,line width= .1cm] (6,0) -- (6,12);\n\\draw [-,line width= .1cm] (0,9) -- (12,9);\n\\draw [-,line width= .1cm] (9,0) -- (9,12);\n\\draw [-,line width= .1cm] (0,12) -- (12,12);\n\\draw [-,line width= .1cm] (12,0) -- (12,12);\n\\draw [-,line width= .1cm] (0,0) -- (12,12);\n\\draw [-,line width= .1cm] (3,0) -- (12,9);\n\\draw [-,line width= .1cm] (6,0) -- (12,6);\n\\draw [-,line width= .1cm] (9,0) -- (12,3);\n\\draw [-,line width= .1cm] (0,3) -- (9,12);\n\\draw [-,line width= .1cm] (0,6) -- (6,12);\n\\draw [-,line width= .1cm] (0,9) -- (3,12);\n\\draw [-,line width= .1cm] (3,0) -- (0,3);\n\\draw [-,line width= .1cm] (6,0) -- (0,6);\n\\draw [-,line width= .1cm] (9,0) -- (0,9);\n\\draw [-,line width= .1cm] (12,0) -- (0,12);\n\\draw [-,line width= .1cm] (12,3) -- (3,12);\n\\draw [-,line width= .1cm] (12,6) -- (6,12);\n\\draw [-,line width= .1cm] (12,9) -- (9,12);\n\\filldraw[color=black, fill=black](0,0) circle (.2);\n\\filldraw[color=black, fill=black](3,0) circle (.2);\n\\filldraw[color=black, fill=black](6,0) circle (.2);\n\\filldraw[color=black, fill=black](9,0) circle (.2);\n\\filldraw[color=black, fill=black](0,3) circle (.2);\n\\filldraw[color=red, fill=red](3,3) circle (.2);\n\\filldraw[color=red, fill=red](6,3) circle (.2);\n\\filldraw[color=red, fill=red](9,3) circle (.2);\n\\filldraw[color=black, fill=black](0,6) circle (.2);\n\\filldraw[color=red, fill=red](3,6) circle (.2);\n\\filldraw[color=red, fill=red](6,6) circle (.2);\n\\filldraw[color=red, fill=red](9,6) circle (.2);\n\\filldraw[color=black, fill=black](0,9) circle (.2);\n\\filldraw[color=red, fill=red](3,9) circle (.2);\n\\filldraw[color=red, fill=red](6,9) circle (.2);\n\\filldraw[color=red, fill=red](9,9) circle (.2);\n\\filldraw[color=black, fill=black](12,0) circle (.2);\n\\filldraw[color=black, fill=black](12,3) circle (.2);\n\\filldraw[color=black, fill=black](12,6) circle (.2);\n\\filldraw[color=black, fill=black](12,9) circle (.2);\n\\filldraw[color=black, fill=black](12,12) circle (.2);\n\\filldraw[color=black, fill=black](0,12) circle (.2);\n\\filldraw[color=black, fill=black](3,12) circle (.2);\n\\filldraw[color=black, fill=black](6,12) circle (.2);\n\\filldraw[color=black, fill=black](9,12) circle (.2);\n\\draw [-,line width= .1cm,color=red] (3,3) -- (3,9);\n\\draw [-,line width= .1cm,color=red] (6,3) -- (6,9);\n\\draw [-,line width= .1cm,color=red] (9,3) -- (9,9);\n\\draw [-,line width= .1cm,color=red] (3,3) -- (9,3);\n\\draw [-,line width= .1cm,color=red] (3,6) -- (9,6);\n\\draw [-,line width= .1cm,color=red] (3,9) -- (9,9);\n\\draw [-,line width= .1cm,color=red] (3,6) -- (6,9);\n\\draw [-,line width= .1cm,color=red] (3,3) -- (9,9);\n\\draw [-,line width= .1cm,color=red] (6,3) -- (9,6);\n\\draw [-,line width= .1cm,color=red] (3,6) -- (6,3);\n\\draw [-,line width= .1cm,color=red] (3,9) -- (9,3);\n\\draw [-,line width= .1cm,color=red] (6,9) -- (9,6);\n\\node at (3.9,3.3){\\hot{$q_1$}};\n\\node at (6.9,3.3){\\hot{$q_2$}};\n\\node at (9.9,3.3){\\hot{$q_3$}};\n\\node at (3.9,6.3){\\hot{$q_4$}};\n\\node at (6.9,6.3){\\hot{$q_5$}};\n\\node at (9.9,6.3){\\hot{$q_6$}};\n\\node at (3.9,9.3){\\hot{$q_7$}};\n\\node at (6.9,9.3){\\hot{$q_8$}};\n\\node at (9.9,9.3){\\hot{$q_9$}};\n\\end{tikzpicture}\n\\caption{A $3\\times 3$ potential on the square lattice that opens a gap at $E=-1$ with small positive positive coupling.}\\label{fig:sqn33period}\n\\end{figure*}\n\n\\begin{theorem} \\label{thm:nnnExGapLength}\nEnumerate the vertices of a $3\\times 3$ fundamental cell of the square lattice as in Figure~\\ref{fig:sqn33period}, denote $r=\\sqrt{4-\\sqrt{15}}$, define a $(3,3)$-periodic potential $Q$ on ${\\mathbb Z}^2$ via\n\\[\n(q_1,\\ldots,q_9)\n=\n\\Big(-r-\\frac{1}{r}+2,\\ -r,\\ -r+\\frac{1}{r}-2,\\ -\\frac{1}{r},\\ 0,\\ +\\frac{1}{r},\\ r-\\frac{1}{r}-2,\\ r,\\ r+\\frac{1}{r}+ 2\\Big),\n\\]\nand denote $H_\\lambda = \\Delta_{\\mathrm{sqn}} + \\lambda Q$. Then, for all $ \\lambda > 0$ sufficiently small, $\\sigma(H_\\lambda)$ consists of two connected components. Moreover, if $\\mathfrak{g}_\\lambda$ denotes the gap that opens at energy $-1$, one has\n\\[\n\\left(-1 - \\frac{\\lambda}{10}, -1 + \\frac{\\lambda}{10} \\right)\n\\subseteq\n\\mathfrak{g}_\\lambda\n\\subseteq\n\\left(-1 - \\frac{\\lambda}{4},-1+\\frac{\\lambda}{4}\\right).\n\\]\nIn particular, the gap opens linearly.\n\\end{theorem}\n\nLet us observe that the proof below can be refined a bit to yield sharper constants than $1\/10$ and $1\/4$.\n\n\\begin{proof}\nFor $\\bm{\\theta} = (\\theta_1,\\theta_2) \\in {\\mathbb T}^2$, let $H_\\lambda(\\bm{\\theta})$ denote the Floquet matrix corresponding to $H_\\lambda$. Ordering the vertices of the fundamental domain as in Figure~\\ref{fig:sqn33period}, we obtain:\n\\[\nH_\\lambda(\\bm{\\theta})\n=\n\\begin{bmatrix}\n\\lambda q_1 & 1 & e^{-i\\theta_1} & 1 & 1 & e^{-i\\theta_1} & e^{-i \\theta_2} & e^{-i\\theta_2} &e^{-i(\\theta_1+\\theta_2)} \\\\\n1 & \\lambda q_2 & 1 & 1 & 1 & 1 & e^{-i \\theta_2}& e^{-i\\theta_2} & e^{-i\\theta_2} \\\\\ne^{i \\theta_1} & 1 & \\lambda q_3& e^{i\\theta_1} & 1 & 1 & e^{i(\\theta_1-\\theta_2)} & e^{-i\\theta_2} & e^{-i\\theta_2}\\\\\n1 & 1 & e^{-i\\theta_1} & \\lambda q_4 & 1 & e^{-i\\theta_1} & 1 & 1 &e^{-i\\theta_1} \\\\\n1 & 1 & 1 & 1 & \\lambda q_5 & 1 & 1 & 1 &1 \\\\\ne^{i\\theta_1} & 1 & 1 & e^{i\\theta_1} & 1 & \\lambda q_6 & e^{i\\theta_1} & 1 & 1 \\\\\ne^{i\\theta_2} & e^{i\\theta_2} & e^{-i(\\theta_1-\\theta_2)} & 1 & 1 &e^{-i\\theta_1} & \\lambda q_7& 1 & e^{-i\\theta_1} \\\\\ne^{i\\theta_2} & e^{i\\theta_2} & e^{i\\theta_2} & 1 & 1 & 1 & 1 & \\lambda q_8 & 1 \\\\\ne^{i(\\theta_1+\\theta_2)} & e^{i\\theta_2} & e^{i\\theta_2} & e^{i\\theta_1} &1 &1 & e^{i\\theta_1} & 1 &\\lambda q_9 \n\\end{bmatrix}.\n\\]\n\\begin{comment}\nStraightforward (Mathematica!) calculations reveal\n\\begin{align*}\n\\det(H_{\\lambda}(\\bm{\\theta}) + {\\mathbb I})\n& =\nQ_0(\\bm{\\theta})+\\sum_{k=1}^9 Q_k(\\bm{\\theta}) \\lambda^k.\n\\end{align*}\nwhere\n\\begin{align*}\nQ_0(\\bm{\\theta})& =\n4096 \\sin^6\\left(\\frac{\\theta_1}{2}\\right) \\sin^6\\left(\\frac{\\theta_2}{2}\\right)\\\\\nQ_1(\\bm{\\theta})& =0\\\\\nQ_2(\\bm{\\theta})&=10240 \\sin^4\\left(\\frac{\\theta_1}{2}\\right) \\sin^4\\left(\\frac{\\theta_2}{2}\\right)\\\\\nQ_3(\\bm{\\theta})&=1024 \\sin^4\\left(\\frac{\\theta_1}{2}\\right) \\sin^4\\left(\\frac{\\theta_2}{2}\\right)\\\\\nQ_4(\\bm{\\theta})&=5824 \\sin^2\\left(\\frac{\\theta_1}{2}\\right) \\sin^2\\left(\\frac{\\theta_2}{2}\\right)\\\\\nQ_5(\\bm{\\theta})&=1024 \\sin^2\\left(\\frac{\\theta_1}{2}\\right) \\sin^2\\left(\\frac{\\theta_2}{2}\\right)\\\\\nQ_6(\\bm{\\theta})&=176 - 80 \\cos(\\theta_1)-80 \\cos(\\theta_2) - 16 \\cos(\\theta_1) \\cos(\\theta_2) - 8 \\sin(\\theta_1) \\sin(\\theta_2)\\\\\nQ_8(\\bm{\\theta})&=12\\\\\nQ_9(\\bm{\\theta})&=0.\n\\end{align*}\nNext we will show $Q_6\\geq 0$, which will lead to $\\det(H_{\\lambda}(\\bm{\\theta}) + {\\mathbb I})\\geq 12\\lambda^8>0$.\n\nTo this end, we compute\n\\begin{align*}\n\\nabla Q_6(\\bm{\\theta})=(&80\\sin(\\theta_1)-8\\cos(\\theta_1) \\sin(\\theta_2)+16\\cos(\\theta_2)\\sin(\\theta_1),\\\\\n &\\qquad 80 \\sin(\\theta_2) -8\\cos(\\theta_2) \\sin(\\theta_1)+16 \\cos(\\theta_1) \\sin(\\theta_2) ).\n\\end{align*}\nSetting $\\nabla Q_6(\\bm{\\theta})=(0,0)$, we arrive tat\n\\begin{align}\n10\\sin(\\theta_1)-\\cos(\\theta_1)\\sin(\\theta_2)+2\\sin(\\theta_1)\\cos(\\theta_2)=0,\\label{eq1}\\\\\n10\\sin(\\theta_2)-\\cos(\\theta_2)\\sin(\\theta_1)+2\\sin(\\theta_2)\\cos(\\theta_1)=0.\\label{eq2}\n\\end{align}\nAdding \\eqref{eq1} and \\eqref{eq2}, we obtain\n\\begin{align}\\label{eq3}\n\\sin\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)\\left(10\\cos\\left(\\frac{\\theta_1-\\theta_2}{2}\\right)+\\cos\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)\\right)=0.\n\\end{align}\nSubtracting \\eqref{eq2} from \\eqref{eq1}, we obtain\n\\begin{align}\\label{eq4}\n\\sin\\left(\\frac{\\theta_1-\\theta_2}{2}\\right)\\left(10\\cos\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)+3\\cos\\left(\\frac{\\theta_1-\\theta_2}{2}\\right)\\right)=0.\n\\end{align}\nSolving \\eqref{eq3} and \\eqref{eq4} yields\n\\[\n\\cos\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)=\\cos\\left(\\frac{\\theta_1-\\theta_2}{2}\\right)=0,\n\\]\nor \n\\[\n\\sin\\left(\\frac{\\theta_1+\\theta_2}{2}\\right)=\\sin\\left(\\frac{\\theta_1-\\theta_2}{2}\\right)=0.\n\\]\nHence all the four solutions are $\\bm{\\theta}=(0,0), (0, \\pi), (\\pi, 0), (\\pi, \\pi)$.\n\nEvaluating $Q_6(\\bm{\\theta})$ at these points yields\n\\[Q_6(0,0)=0,\\ \\ Q_6(0,\\pi)=192,\\ \\ Q_6(\\pi,0)=192,\\ \\ Q_6(\\pi,\\pi)=320.\\]\nIn conclusion, we have shown $Q_6(\\bm{\\theta})\\geq 0$ for all $\\bm{\\theta}$.\nHence $\\det(H_{\\lambda,\\theta_1,\\theta_2} + {\\mathbb I})\\geq 12\\lambda^8>0$, thus $E=-1$ is not in the spectrum.\n\\end{comment}\nFor $s\\in (-1,1)$, let us consider \n\\[\n\\det(H_{\\lambda}(\\bm{\\theta}) + (1+ s\\lambda) {\\mathbb I})\n=\n\\sum_{k=0}^9 X_k(\\bm{\\theta}, s) \\lambda^k.\n\\]\nOur goal is to show $\\det(H_{\\lambda}(\\bm{\\theta}) + (1+ s\\lambda) {\\mathbb I})$ never vanishes for sufficiently small $\\lambda>0$ and for $|s| < 0.1$.\nDirect computations yield\n\\begin{align*}\nX_0(\\bm{\\theta}, s)&=4096 \\sin^6\\left(\\frac{\\theta_1}{2}\\right) \\sin^6\\left(\\frac{\\theta_2}{2}\\right)\\\\\nX_1(\\bm{\\theta}, s)&=0\\\\\nX_2(\\bm{\\theta}, s)&=Y_2(s) \\sin^4\\left(\\frac{\\theta_1}{2}\\right) \\sin^4\\left(\\frac{\\theta_2}{2}\\right)\\\\\nX_3(\\bm{\\theta}, s)&=Y_3(s) \\sin^4\\left(\\frac{\\theta_1}{2}\\right) \\sin^4\\left(\\frac{\\theta_2}{2}\\right)\\\\\nX_4(\\bm{\\theta}, s)&=Y_4(s) \\sin^2\\left(\\frac{\\theta_1}{2}\\right) \\sin^2\\left(\\frac{\\theta_2}{2}\\right)\\\\\nX_5(\\bm{\\theta}, s)&=Y_5(s) \\sin^2\\left(\\frac{\\theta_1}{2}\\right) \\sin^2\\left(\\frac{\\theta_2}{2}\\right)\\\\\nX_6(\\bm{\\theta}, s)&=Y_{6,1}(s)+Y_{6,2}(s)\\cos(\\theta_1)+Y_{6,3}(s)\\cos(\\theta_2)\\\\ & \\qquad +Y_{6,4}(s)\\cos(\\theta_1)\\cos(\\theta_2)+Y_{6,5}(s) \\sin(\\theta_1)\\sin(\\theta_2)\\\\\nX_7(\\bm{\\theta}, s)&=0\\\\\nX_8(\\bm{\\theta}, s)&=Y_8(s)\\\\\nX_9(\\bm{\\theta}, s)&=Y_9(s),\n\\end{align*}\nin which\n\\begin{align*}\nY_2(s)&=512 (20-9 s^2)\\\\\nY_3(s)&=256 (4 - 20 s + 3 s^3)\\\\\nY_4(s)&=16(364 + 144 s - 504 s^2 + 81 s^4)\\\\\nY_5(s)&=16 (64 - 196 s - 48 s^2 + 104 s^3 - 9 s^5)\\\\\nY_{6,1}(s)&=176 + 704 s - 3132 s^2 - 496 s^3 + 1376 s^4 - 96 s^6\\\\\nY_{6,2}(s)&=-80 + (96 \\sqrt{15}-320) s + (1380 +144 \\sqrt{15}) s^2 + 208 s^3 - (584+54 \\sqrt{15}) s^4 + 42 s^6\\\\\nY_{6,3}(s)&=-80 - (320 +96 \\sqrt{15}) s + (1380 - 144 \\sqrt{15}) s^2 + 208 s^3 - (584- 54 \\sqrt{15}) s^4 + 42 s^6\\\\\nY_{6,4}(s)&=-16 - 64 s + 372 s^2 + 80 s^3 - 208 s^4 + 12 s^6\\\\\nY_{6,5}(s)&=8(2s-1)^3\\\\\nY_8(s)&=12 + 32 s - 360 s^2 - 512 s^3 + 1025 s^4 + 96 s^5 - 224 s^6 + 9 s^8\\\\\nY_9(s)&=12 s + 16 s^2 - 120 s^3 - 128 s^4 + 205 s^5 + 16 s^6 - 32 s^7 + s^9.\\\\\n\\end{align*}\nOne simple observation is that \n\\begin{align}\\label{eq:sumY6}\nY_{6,1}(s)+Y_{6,2}(s)+Y_{6,3}(s)+Y_{6,4}(s)=0.\n\\end{align}\nIt is easy to see that for $|s|<0.1$, \n\\[Y_2(s), Y_3(s),Y_5(s)>0.\\]\nIt is easy to compute that\n\\[\nY'_9(s)=12 + 32 s - 360 s^2 - 512 s^3 + 1025 s^4 + 96 s^5 - 224 s^6 + 9 s^8\n=\nY_8(s).\n\\]\nThus, \n\\begin{equation} \\label{eq:Y9prime(s)}\nY_9'(s)>12 - 32 \\times 0.1 - 360 \\times (0.1)^2 - 512 \\times (0.1)^3 - 96 \\times (0.1)^5 - 224\\times (0.1)^6{>4.5}>0\\end{equation}\nfor $|s|<0.1$, which implies\n\\begin{align}\\label{eq:Y9s}\nY_9(s)\\geq Y_9(-0.1)>-1\n\\end{align}\nfor all $|s| < 0.1$. Carefully estimating $Y_4(s)$ and $Y_8(s)$ will help us bound the $\\lambda^6$ order term from below using the AM-GM inequality.\n\\begin{align}\\label{eq:Y4Y8s}\nY_4(s)&\\geq 16(364-144\\times 0.1-504\\times (0.1)^2 {-81 \\times (0.1)^4})>5500,\\\\\nY_8(s)&\\geq 12 - 32 \\times 0.1 - 360 \\times (0.1)^2 - 512 \\times (0.1)^3 - 96 \\times (0.1)^5 - 224 \\times (0.1)^6>4.5. \\notag\n\\end{align}\n{In fact, since $Y_8 = Y_9'$, the second inequality already follows from \\eqref{eq:Y9prime(s)}.}\nFor the $Y_{6,j}$ terms, we have\n\\begin{equation}\\label{eq:Y6s}\n\\begin{aligned}\nY_{6,1}(s)&\\geq 176 - 704 \\times 0.1 - 3132 \\times (0.1)^2 - 496 \\times (0.1)^3 - 96 \\times (0.1)^6>0,\\\\\n Y_{6,2}(s)&\\leq -80 + (96 \\sqrt{15}-320) \\times 0.1 + (1380 +144 \\sqrt{15}) \\times (0.1)^2 \\\\ & \\qquad\\qquad + 208 \\times (0.1)^3 + 42\\times (0.1)^6<0 \\\\\n Y_{6,3}(s)&\\leq -80 + (320 + 96 \\sqrt{15}) \\times 0.1 + (1380 - 144 \\sqrt{15}) \\times (0.1)^2 \\\\ & \\qquad\\qquad+ 208 \\times (0.1)^3 + 42 \\times (0.1)^6<0, \\\\\nY_{6,4}(s)&\\leq -16 + 64 \\times 0.1 + 372 \\times (0.1)^2 + 80 \\times (0.1)^3 + 12 \\times (0.1)^6<0, \\\\\n-14 & \\leq Y_{6,5}(s)< 0.\n\\end{aligned}\n\\end{equation}\nUsing \\eqref{eq:sumY6} and \\eqref{eq:Y6s}, we obtain\n\\begin{align} \\label{eq:X6LB}\n\\notag\nX_6(\\bm{\\theta}) & \\geq Y_{6,1}(s)+Y_{6,2}(s)+Y_{6,3}(s)+Y_{6,4}(s) + Y_{6,5}(s)\\sin(\\theta_1)\\sin(\\theta_2) \\\\\n\\notag\n& =\nY_{6,5}(s)\\sin(\\theta_1)\\sin(\\theta_2) \\\\\n& \\geq\n-14 |\\sin(\\theta_1)\\sin(\\theta_2)|.\n\\end{align}\nIn particular, the first line uses $Y_{6,2}, Y_{6,3}, Y_{6,4} < 0$, the second line uses \\eqref{eq:sumY6}, and the final line uses $-14 \\leq Y_{6,5} < 0$.\n\nNow we combine our estimates together. \nNote that \n\\begin{align}\\label{eq:sumX0235}\nX_0(\\bm{\\theta},s)+X_2(\\bm{\\theta},s)\\lambda^2+X_3(\\bm{\\theta},s)\\lambda^3+X_5(\\bm{\\theta},s)\\lambda^5\\geq 0.\n\\end{align}\nUsing $a^2+b^2\\geq 2|ab|$, we obtain the following from \\eqref{eq:Y4Y8s}\n\\[\nX_4(\\bm{\\theta},s)\\lambda^4+\\frac{1}{2}X_8(\\bm{\\theta},s)\\lambda^8 \\geq 2\\sqrt{2.25\\times 5500} \\left|\\sin\\left(\\frac{\\theta_1}{2}\\right) \\sin\\left(\\frac{\\theta_2}{2}\\right)\\right| \\lambda^6.\n\\]\nUsing $2|\\sin(x\/2)|\\geq 2|\\sin(x\/2)\\cos(x\/2)|=|\\sin(x)|$, we obtain from above that\n\\[X_4(\\bm{\\theta},s) {\\lambda^4} + \\frac{1}{2}X_8(\\bm{\\theta},s) {\\lambda^8} \n\\geq \n55 |\\sin(\\theta_1)\\sin(\\theta_2)|{\\lambda^6}.\\]\nCombining this with \\eqref{eq:X6LB}, we have\n\\begin{equation} \\label{eq:sumX486}\nX_4(\\bm{\\theta},s)\\lambda^4+\\frac{1}{2}X_8(\\bm{\\theta},s)\\lambda^8+X_6(\\bm{\\theta},s)\\lambda^6 \n\\geq\n41 |\\sin(\\theta_1)\\sin(\\theta_2)|\\lambda^6\n\\geq 0.\n\\end{equation}\n\nFinally using \\eqref{eq:Y9s} and \\eqref{eq:Y4Y8s}, we have\n\\begin{align}\\label{eq:sumY89}\n\\frac{1}{2}X_8(\\bm{\\theta},s)\\lambda^8+X_9(\\bm{\\theta},s)\\lambda^9=\\frac{1}{2}Y_8(s)\\lambda^8+Y_9(s)\\lambda^9\\geq 2.25\\lambda^8-\\lambda^9>0.25 \\lambda^8,\n\\end{align}\nprovided that $\\lambda<2$.\nCombining \\eqref{eq:sumX0235}-\\eqref{eq:sumY89}, we have\n\\[\\det(H_{\\lambda}(\\bm{\\theta}) + (1+ s\\lambda) {\\mathbb I}) \\geq 0.25\\lambda^8>0,\\]\nfor any $\\bm{\\theta}\\in {\\mathbb T}^2$ and $|s|<0.1$. \nThis proves the lower bound on the gap.\n\n{For the upper bound, observe that $X_j\\big((\\pi,0),s\\big) = 0$ for all $s$ and for every $0 \\le j \\le 5$ and\n\\[\nX_6\\big((\\pi,0),\\pm 1\/4\\big)\n<-85.\n\\]\nThus, for small $\\lambda > 0$, \n\\[\n\\det\\big(H_\\lambda(\\pi, 0) + (1\\pm\\lambda\/4){\\mathbb I} \\big) < -85\\lambda^6 + O(\\lambda^8)<0.\n\\]\nIt is also clear that $X_0((0,0),s)=4096$,\nwhich implies \n\\[\\det \\big(H_\\lambda(0, 0) +(1\\pm\\lambda\/4){\\mathbb I} \\big) =4096+O(\\lambda)>0.\\]\nThus we conclude that \n\\[1\\pm \\frac{\\lambda}{4}\\in \\sigma(H_\\lambda),\\]\nwhich concludes the proof of the upper bound on the length of the gap.}\n\\end{proof}\n\n\\section*{Acknowledgement}\nWe would like to thank Svetlana Jitomirskaya for comments on an earlier version of the manuscript, and Tom Spencer for useful discussions.\nR.H. would like to thank IAS, Princeton, for its hospitality during the 2017-18 academic year,\nand Virginia Tech for its hospitality during which part of the work was done.\nR.H. is supported in part by the National Science Foundation under Grant No. DMS-1638352. \nJ.F.\\ was supported in part by an AMS-Simons Travel Grant 2016--2018.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nNowadays, the axion mechanism represents our best solution to the long standing strong CP puzzle, that is, the non-observation of CP violation in the strong interactions that should have manifested itself as an electric dipole moment for the neutron~\\cite{Abel:2020pzs}.\n\nThe axion mechanism relies on the spontaneous breaking of a new symmetry, the PQ symmetry~\\cite{PQ}, and on the subsequent realignment of the associated Goldstone boson, the axion~\\cite{Weinberg:1977ma,Wilczek:1977pj}, by strong interaction effects that kills off any CP violation in the QCD Lagrangian. This solution is thus tailored to the problem it is intended to solve and, as such, may appear a bit ad-hoc. In addition, unsuccessful experimental searches for the axion have ruled out its simplest incarnation, leaving us with essentially two classes of scenarios in which the axion is extremely light (well below the eV scale) and very weakly coupled to normal matter: the KSVZ~\\cite{KSVZ} framework in which new very heavy colored fermions are introduced, and the DFSZ~\\cite{DFSZ} scenario in which at least two Higgs doublets are required. Though the strong CP puzzle is extremely serious, additional motivations appear desirable to motivate such departures from the Standard Model (SM) matter content. To that avail, knowing that the axion could also make up for the observed dark matter (DM) offers a strong incentive to pursue this route~\\cite{DMaxion}. \n\nYet, current axion models cannot explain why the DM relic density is so close to that of baryonic matter. Though this may be totally coincidental, it nevertheless suggests a link between DM and baryogenesis~\\cite{Kaplan:1991ah}, another prominent cosmological enigma. Actually, it suggests DM is not foreign to baryon $\\mathcal{B}$ or lepton $\\mathcal{L}$ number (see Ref.~\\cite{Alonso-Alvarez:2021oaj} and references therein for a recent analysis), or that DM is somehow related to $\\mathcal{B}$ being spontaneously broken~\\cite{Dulaney:2010dj}. In parallel, there have been many attempts at involving axions in the baryogenesis mechanism, see e.g. Refs.~\\cite{Craig:2010au,Servant:2014bla,Jeong:2018ucz,Co:2019wyp,Krauss:2022usd,Domcke:2020kcp}, though in general still relying on the SM anomalous $\\mathcal{B}+\\mathcal{L}$ effects. \n\nOur goal here is to go one step further and entangle the PQ symmetry with $\\mathcal{B}$ and $\\mathcal{L}$ from the start. As a matter of principle, accidental symmetries are not particularly attractive, but while we can live with the PQ symmetry, assuming some dynamics hide behind it, $\\mathcal{B}$ and $\\mathcal{L}$ cannot be viable since, as said before, the electroweak non-perturbative dynamics break them, and baryogenesis asks for their violation. By unifying the PQ symmetry with $\\mathcal{B}$ and $\\mathcal{L}$, all three are broken spontaneously, but a single Goldstone field remains, the axion (for some recent works along this line, see Refs.~\\cite{Reig:2018yfd,Ohata:2021rkh}). In this way, the complex scalar field whose pseudoscalar component is the axion becomes charged under $\\mathcal{B}$ and $\\mathcal{L}$ and, at the high scale, protects the model from additional $\\mathcal{B}$ and\/or $\\mathcal{L}$ violation. At the same time, though the axion has no charge, it inherits a $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating phenomenology. Whether this is sufficient to relate the DM and baryonic relic densities remains to be seen, and is beyond the scope of the present paper, but we think these constructions may direct us in the right direction.\n\nIn this paper, we will use scalar and vector leptoquarks and diquarks to entangle the PQ, $\\mathcal{B}$, and $\\mathcal{L}$ symmetries. Such states are well motivated in various theoretical settings (see Ref.~\\cite{Dorsner:2016wpm} for a review) and, furthermore, supported by a number of anomalies like the $W$ boson mass, B decays~\\cite{LQreview} or $(g-2)_{\\mu}$~\\cite{Dorsner:2019itg}, or even combinations of them~\\cite{Athron:2022qpo,Bhaskar:2022vgk}. Our goal is to systematically analyze the $\\mathcal{B}$ and\/or $\\mathcal{L}$ symmetry breaking patterns that can arise combining the DFSZ and KSVZ scenarios with leptoquarks and diquarks and, in each case, to analyze the impact on the axion phenomenology.\n\nThe paper is organized as follows. In section~\\ref{Sec2a}, we briefly introduce the KSVZ and DFSZ axion models and, in Section~\\ref{Sec2b}, discuss in some details the ambiguities arising from the $\\mathcal{B}$ and $\\mathcal{L}$ fermionic currents~\\cite{Quevillon:2020hmx,Quevillon:2020aij}. Then in section~\\ref{Sec2c}, we set up the leptoquark and diquark sector, describing all the possible $\\mathcal{B}$ and $\\mathcal{L}$ explicit breaking patterns achievable with these states. This forms the basis for combining the axion and leptoquark\/diquark sectors in Sec.~\\ref{SecAxLQDQ}. We analyze first the KSVZ setting in Sec.~\\ref{SecKSVZ} and describe the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,\\pm1), (2,0), (1,\\pm3)$ spontaneous breaking patterns, further adding to them a spontaneously generated $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,2)$ seesaw mechanism for neutrino masses. These scenarios are then trivially adapted to the DFSZ setting in Sec.~\\ref{SecDFSZ}. In the final Sec.~\\ref{SecSpont}, we show how to force $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})$ effects to involve one or more axion fields. The phenomenology is then quite different, and we briefly describe some possible consequences for the neutron lifetime anomaly or neutron-antineutron oscillation experiments. Finally, our results are summarized in Sec.~\\ref{Ccl}.\n\n\\section{Axion and leptoquark models}\n\nIn this section, the KSVZ~\\cite{KSVZ} and DFSZ~\\cite{DFSZ} axion models are introduced, and their connection to baryon and lepton numbers, $\\mathcal{B}$ and $\\mathcal{L}$, are detailed. Then, we introduce separately the leptoquarks and diquarks that can be coupled to SM fermions, and discuss how their couplings drive specific $\\mathcal{B}$ and $\\mathcal{L}$ violating patterns. This sets the stage for the next section, where both axion models and leptoquarks\/diquarks will be put together.\n\n\\subsection{Introducing the KSVZ and DFSZ models\\label{Sec2a}}\n\nIn both the KSVZ and DFSZ constructions, the axion emerges as the pseudoscalar component of a complex scalar field. This state is neutral under all the SM gauge interactions, $\\phi=(\\mathbf{1},\\mathbf{1},0)$ under $SU(3)_{C}\\otimes SU(2)_{L}\\otimes U(1)_{Y}$, but its kinetic term is invariant under the rephasing $\\phi\\rightarrow e^{i\\alpha}\\phi$. This invariance is promoted to a\nspontaneously broken symmetry $U(1)_{\\phi}$ by postulating a rephasing invariant scalar potential with the usual Mexican hat shape, $V(\\phi^{\\dagger}\\phi)=\\mu^{2}\\phi^{\\dagger}\\phi+\\lambda(\\phi^{\\dagger}\\phi)^{4}$, $\\mu^{2}<0$ and $\\lambda>0$. In that case, the components of $\\phi$ can be written\n\\begin{equation}\n\\phi=\\frac{1}{\\sqrt{2}}(v_{\\phi}+\\rho)\\exp(i\\eta_{\\phi}\/v_{\\phi})\\ ,\n\\label{PhiPolar}\n\\end{equation}\nwith $\\eta_{\\phi}$ the associated Goldstone boson and $v_{\\phi}^{2}=-\\mu^{2}\/\\lambda$ the vacuum expectation value (VEV). As the breaking scale $v_{\\phi}$ naturally tunes all the $\\eta_{\\phi}$ couplings, it is assumed much higher than the electroweak scale to avoid exclusion bounds.\n\nTo solve the strong CP puzzle, $\\eta_{\\phi}$ must interact with SM particles~\\cite{Weinberg:1977ma,Wilczek:1977pj}, in particular with gluons via a $\\eta_{\\phi}G^{\\alpha,\\mu\\nu}\\tilde{G}_{\\mu\\nu}^{a}$ coupling~\\cite{PQ}. What differentiates the KSVZ and DFSZ models is how these couplings are introduced. The former~\\cite{KSVZ} adds a vector-like colored fermion $\\Psi_{L,R}\\sim(\\mathbf{R},\\mathbf{T},Y)$ for some complex representation $\\mathbf{R}$ of $SU(3)_{C}$, but otherwise arbitrary weak representation $\\mathbf{T}$ and hypercharge $Y$, and postulates the Lagrangian (the rest of the SM couplings are understood)\n\\begin{align}\n\\mathcal{L}_{\\mathrm{KSVZ}} & =\\partial_{\\mu}\\phi^{\\dagger}\\partial^{\\mu\n}\\phi-V(\\phi)+\\bar{\\Psi}_{L,R}(i\\slashed D)\\Psi_{L,R}+(y\\phi\\bar{\\Psi}_{L}\\Psi_{R}+h.c.)\\nonumber\\\\\n& -\\bar{u}_{R}\\mathbf{Y}_{u}q_{L}H-\\bar{d}_{R}\\mathbf{Y}_{d}q_{L}H^{\\dagger\n}-\\bar{e}_{R}\\mathbf{Y}_{e}\\ell_{L}H^{\\dagger}-\\bar{\\nu}_{R}\\mathbf{Y}_{\\nu\n}\\ell_{L}H+h.c.\\ . \\label{KSVZ0}%\n\\end{align}\nThe covariant derivative acting on $\\Psi_{L,R}$ is as appropriate to its chosen gauge quantum numbers. What characterizes this model is first that the Goldstone boson of the PQ symmetry does not mix with that of the $SU(2)_{L}\\otimes U(1)_{Y}$ breaking (the phase of the Higgs doublet $H$). Thus, the axion is simply $a^{0}=\\eta_{\\phi}$, and it has no direct coupling to any of the SM particles. It only couples to $\\Psi_{L}$ and $\\Psi_{R}$, which necessarily have different charges under $U(1)_{\\phi}$. Then, axion to SM gauge boson couplings first arise at one-loop, via anomalous $\\Psi_{L,R}$ triangle loops, while those to SM fermions require a further gauge boson loop. Since $\\Psi_{L,R}$ can be massive in the electroweak unbroken phase, its loops do not break $SU(2)_{L}\\otimes U(1)_{Y}$ and the couplings to gauge bosons have the $SU(2)_{L}\\otimes U(1)_{Y}$ invariant form~\\cite{Georgi:1986df}\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{KSVZ}}^{eff}=-\\frac{1}{16\\pi^{2}v_{\\phi}}a^{0}(g_{s}%\n^{2}d_{L}C_{C}G_{\\mu\\nu}^{a}\\tilde{G}^{a,\\mu\\nu}+g^{2}d_{C}C_{L}W_{\\mu\\nu}%\n^{i}\\tilde{W}^{i,\\mu\\nu}+g^{\\prime2}d_{L}d_{C}C_{Y}B_{\\mu\\nu}\\tilde{B}^{\\mu\n\\nu})\\ , \\label{GaugeCouplAno}\n\\end{equation}\nwith the quadratic invariants and dimensions of the $\\mathbf{R}$ and $\\mathbf{T}$ representations denoted $C_{C,L}$ and $d_{C,L}$, and $C_{Y}=Y^{2}\/4$.\n\nThe DFSZ model~\\cite{DFSZ} does not introduce new fermions, but requires two Higgs doublets. The important couplings are%\n\\begin{align}\n\\mathcal{L}_{\\mathrm{DFSZ}} & =\\partial_{\\mu}\\phi^{\\dagger}\\partial^{\\mu\n}\\phi-V(\\phi^{\\dagger}\\phi)+\\phi^{2}H_{u}^{\\dagger}H_{d}+V(H_{u}^{\\dagger\n}H_{u},H_{d}^{\\dagger}H_{d})\\nonumber\\\\\n& -\\bar{u}_{R}\\mathbf{Y}_{u}q_{L}H_{u}-\\bar{d}_{R}\\mathbf{Y}_{d}q_{L}%\nH_{d}^{\\dagger}-\\bar{e}_{R}\\mathbf{Y}_{e}\\ell_{L}H_{d}^{\\dagger}-\\bar{\\nu}%\n_{R}\\mathbf{Y}_{\\nu}\\ell_{L}H_{u}+h.c.\\ . \\label{DFSZ0}%\n\\end{align}\nThe potentials and Yukawa couplings are invariant under three independent $U(1)$s, corresponding to the rephasing of $\\phi$, $H_{u}$, and $H_{d}$. A combination of these is explicitly removed by the mixing term $\\phi^{2}H_{u}^{\\dagger}H_{d}$ (we could equally take $\\phi H_{u}^{\\dagger}H_{d}$, but at the cost of introducing a new mass scale), so that only two Goldstone bosons arise. Explicitly, if we adopt for $H_{u,d}$ a polar representation similar as in Eq.~(\\ref{PhiPolar}), with their pseudoscalar components denoted as $\\eta_{u,d}$ and their VEVs as $v_{u,d}$, the $\\phi^{2}H_{u}^{\\dagger}H_{d}$ coupling translates as a mass term for the combination $\\pi^{0}\\sim2\\eta_{\\phi}\/v_{\\phi}-\\eta_{u}\/v_{u}+\\eta_{d}\/v_{d}$. One of the two remaining Goldstone bosons is eaten by the $Z$ boson. Since $H_{u,d}$ have the same hypercharge, the would-be Goldstone state $G^{0}$ must be $G^{0}\\sim v_{u}\\eta_{u}+v_{d}\\eta_{d}$. The last remaining Goldstone mode, orthogonal to both $\\pi^{0}$ and $G^{0}$, stays massless and is the axion:\n\\begin{equation}\na^{0}\\sim\\eta_{\\phi}+\\frac{v_{EW}}{v_{\\phi}}\\sin2\\beta(\\cos\\beta\\eta_{u}\n-\\sin\\beta\\eta_{d})+\\mathcal{O}(v_{EW}^{2}\/v_{\\phi s}^{2})\\ , \\label{DFSZA0}%\n\\end{equation}\nwith $\\tan\\beta=v_{u}\/v_{d}$ and $v_{EW}^{2}=v_{u}^{2}+v_{d}^{2}\\approx (246\\,$GeV)$^{2}$. The net result of all this is that the axion components in $H_{u,d}$ are suppressed by $v_{u,d}\/v_{\\phi}$. The leading couplings of the axion to SM particles come from the Yukawa couplings, with\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{DFSZ}}^{eff}=-i\\frac{v_{EW}}{v_{\\phi}}\\sin2\\beta\n\\sum_{f=u,d,e}\\frac{m_{f}}{v_{EW}}\\chi_{P}^{f}\\,a^{0}\\bar{\\psi}_{f}\\gamma\n_{5}\\psi_{f}\\ ,\\ \\ \\chi_{P}^{u}=\\frac{1}{\\tan\\beta}\\ ,\\ \\chi_{P}^{d}=\\chi\n_{P}^{e}=\\tan\\beta\\ . \\label{PseudoCoupl}%\n\\end{equation}\nTo reach this form, the mass terms are identified as $\\sin\\beta v_{EW}\\mathbf{Y}_{u}\\equiv\\sqrt{2}\\mathbf{m}_{u}$ and $\\cos\\beta v_{EW}\\mathbf{Y}_{d,e}\\equiv\\sqrt{2}\\mathbf{m}_{d,e}$ and the fermions are rotated to their mass basis. In the DFSZ setting, the axion couplings to gauge bosons only arise through SM fermion loops. As shown in Ref.~\\cite{Quevillon:2019zrd} (see also Refs.~\\cite{Bonnefoy:2020gyh,Quevillon:2021sfz}), starting from the pseudoscalar couplings in Eq.~(\\ref{PseudoCoupl}), the final couplings to gauge boson do not have the form shown in Eq.~(\\ref{GaugeCouplAno}), but instead explicitly break $SU(2)_{L}\\otimes U(1)_{Y}$ invariance. Naively, this is easily understood since SM fermions only acquire masses after the $SU(2)_{L}\\otimes U(1)_{Y}$ breaking.\n\n\\subsection{Introducing baryon and lepton numbers\\label{Sec2b}}\n\nIn the following, when introducing leptoquark states, baryon and lepton numbers $\\mathcal{B}$ and $\\mathcal{L}$ will play a central role. The purpose in this section is to gather a few important facts about the interplay of these accidental symmetries with the PQ symmetry. Additional information on this topic can be found in Ref.~\\cite{Quevillon:2020hmx}.\n\nBy definition, the $U(1)$ symmetry associated to the axion state is called the PQ symmetry. Given the scalar couplings described in the previous section, the PQ charges of all the scalar states is well-defined in the KSVZ and DFSZ models. Explicitly, we have in the KSVZ setting\n\\begin{equation}\n\\begin{tabular}[c]{ccc}\\hline\nKSVZ & $\\phi$ & $H$\\\\\\hline\n$U(1)_{\\phi}$ & $1$ & $0$\\\\\n$U(1)_{H}$ & $0$ & $1$\\\\\\hline\n\\end{tabular}\n\\ \\ \\ \\ \\ \\Longrightarrow%\n\\begin{tabular}[c]{ccc}\\hline\nKSVZ & $\\phi$ & $H$\\\\\\hline\n$U(1)_{PQ}$ & $1$ & $0$\\\\\n$U(1)_{Y}$ & $0$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nand in the DFSZ, choosing the two independent $U(1)$ symmetries as those associated to Higgs doublet rephasings\\footnote{The PQ charges of $\\phi$, $H_{u}$ and $H_{d}$ are simply the coefficients of $\\eta_{\\phi,u,d}$ in Eq.~(\\ref{DFSZA0}), up to a choice of normalization.},\n\\begin{equation}\n\\begin{tabular}[c]{cccc}\\hline\nDFSZ & $\\phi$ & $H_{u}$ & $H_{d}$\\\\\\hline\n$U(1)_{Hu}$ & $1\/2$ & $1$ & $0$\\\\\n$U(1)_{Hd}$ & $-1\/2$ & $0$ & $1$\\\\\\hline\n\\end{tabular}\n\\ \\ \\ \\ \\ \\Longrightarrow\n\\begin{tabular}[c]{cccc}\\hline\nDFSZ & $\\phi$ & $H_{u}$ & $H_{d}$\\\\\\hline\n$U(1)_{PQ}$ & $(x+1\/x)\/2$ & $x$ & $-1\/x$\\\\\n$U(1)_{Y}$ & $0$ & $1$ & $1$\\\\\\hline\n\\end{tabular}\n\\ \\ \\label{DFSZScalars}\n\\end{equation}\nwith the conventional notation $\\tan\\beta\\equiv1\/x$. Note that the $U(1)_{Y}$ and $U(1)_{PQ}$ charges of the two Higgs doublets are not `orthogonal', reflecting the fact that the original $U(1)_{Hu}$ and $U(1)_{Hd}$ charges for the three states $(\\phi,H_{u},H_{d})$ were not. Also, it is important to keep in mind that though well-defined, these PQ charges are only defined in the electroweak broken phase, since they are function of $x\\equiv v_{d}\/v_{u}$.\n\nFor fermions, identifying the PQ charge is less trivial because the Yukawa couplings allow for two accidental symmetries, $\\mathcal{B}$ and $\\mathcal{L}$ (no particular structure is assumed for $\\mathbf{Y}_{u,d,e,\\nu}$, so individual flavors are not conserved a priori). Looking at the Lagrangian, the KSVZ model prescribes\n\\begin{equation}\n\\begin{tabular}[c]{ccccccccc}\\hline\nKSVZ & $\\Psi_{L}$ & $\\Psi_{R}$ & $q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ &\n$e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{PQ}$ & $\\alpha$ & $\\alpha-1$ & $\\beta$ & $\\beta$ & $\\beta$ & $\\gamma$ &\n$\\gamma$ & $\\gamma$\\\\\n$U(1)_{Y}$ & $Y$ & $Y$ & $1\/3$ & $4\/3$ & $-2\/3$ & $-1$ & $-2$ & $0$\\\\\\hline\n\\end{tabular}\n\\label{KSVZfermions}\n\\end{equation}\nwhere $\\alpha$, $\\beta$, and $\\gamma$ are arbitrary, and correspond to conserved $\\Psi$ number, baryon number, and lepton number, respectively. Similarly, for the DFSZ model,\n\\begin{equation}\n\\begin{tabular}[c]{ccccccc}\\hline\nDFSZ & $q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{PQ}$ & $\\beta$ & $\\beta+x$ & $\\beta-1\/x$ & $\\gamma$ & $\\gamma-1\/x$ &\n$\\gamma+x$\\\\\n$U(1)_{Y}$ & $1\/3$ & $4\/3$ & $-2\/3$ & $-1$ & $-2$ & $0$\\\\\\hline\n\\end{tabular}\n\\label{DFSZfermions}\n\\end{equation}\nSince $\\beta$ and $\\gamma$ are aligned with baryon and lepton numbers, it is tempting to set $\\beta=\\gamma=0$. This is not acceptable. For the DFSZ scenario, all the SM fermions do couple to the axion, but these couplings are not $SU(2)_{L}\\otimes U(1)_{Y}$ invariant. Looking at Eq.~(\\ref{PseudoCoupl}), no value of $\\beta$ or $\\gamma$ make perfect sense since the PQ charge of the Dirac $u$ and $d$ states are different, so that of $q_{L}$ cannot be defined. The situation appears simpler in the KSVZ case, where it seems rather natural to set $\\beta=\\gamma=0$ since the SM fermions are not directly coupled to the scalar field $\\phi$. Yet, even that is not tenable.\n\nTo see this, let us set off a seesaw mechanism~\\cite{TypeI}. Given the quantum numbers of the $\\nu_{R}$ field, we can either allow for a Majorana mass term $M_{R}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, a coupling $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, or a coupling $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$. These three cases are mutually exclusive since they impose different PQ charges to $\\nu_{R}$. Let us consider the $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ case, which in effect identifies the PQ symmetry with lepton number symmetry, and the axion with the Majoron~\\cite{Langacker:1986rj,Shin:1987xc,Clarke:2015bea} (see also \\cite{Heeck:2019guh}). It imposes non-zero values for $\\gamma$~\\cite{Quevillon:2020hmx}\n\\begin{align}\n\\text{KSVZ} & :\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}\\rightarrow\\gamma=\\frac{1}{2}\\ ,\\\\\n\\text{DFSZ} & :\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}\\rightarrow\\gamma=\\frac{1-3x^{2}}{4x}\\ .\n\\end{align}\nIn both cases, the PQ current acquires a component aligned with the lepton number current, $J_{\\mathcal{L}}^{\\mu}=\\bar{\\ell}_{L}\\gamma^{\\mu}\\ell_{L}+\\bar{e}_{R}\\gamma^{\\mu}e_{R}+\\bar{\\nu}_{R}\\gamma^{\\mu}\\nu_{R}$. In other words, $\\ell_{L}$ and\/or $e_{R}$ do end up PQ charged also. Yet, in the KSVZ case, a look at the Lagrangian shows that neither are directly coupled to $\\phi$. Because of $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, the axion does end up coupled to right-handed neutrinos, with a $a^{0}\\rightarrow\\nu_{R}\\nu_{R}$ vertex, but no such $\\Delta\\mathcal{L}=2$ coupling exists with the other leptons since it is forbidden by hypercharge. Only at the cost of extra Higgs doublet insertions could a $a^{0}\\rightarrow\\nu_{L}\\nu_{L}$ exist, as arising from an effective PQ- and hypercharge-neutral operator $\\phi^{\\dagger}H\\ell_{L}H\\ell_{L}$ (or $\\phi^{\\dagger}H_{u}\\ell_{L}H_{u}\\ell_{L}$ in the DFSZ model), while obviously, any $\\Delta\\mathcal{L}=2$ coupling to charged lepton would require either extra gauge fields, or charged Higgs bosons.\n\nThe ambiguous nature of the PQ charges of fermions is not purely academic. In most phenomenological studies of the axion, the starting point is the effective Lagrangian that is obtained by reparametrizing fermion fields to make them PQ neutral (even if that is usually not explicitly stated):\n\\begin{equation}\n\\psi\\rightarrow\\exp(-iPQ(\\psi)a^{0}\/v_{\\phi})\\psi\\ , \\label{ReparamG}%\n\\end{equation}\nwhere $\\psi$ denotes generically the PQ-charged fermions. Since the underlying physics is PQ neutral, this looks innocuous. Yet, it modifies the Lagrangian of the model in two important ways. First, it removes the axion field from Yukawa interactions (both for the SM and heavy fermions, if present), and replace them by shift-symmetric derivative couplings of the axion to the\nfermionic PQ current, as adequate for a Goldstone boson\n\\begin{equation}\n\\delta\\mathcal{L}_{\\text{\\textrm{Der}}}=\\frac{\\partial_{\\mu}a^{0}}{v_{\\phi}%\n}J_{PQ}^{\\mu}\\ ,\\ J_{PQ}^{\\mu}=\\sum_{\\psi}PQ(\\psi)\\bar{\\psi}\\gamma^{\\mu}\\psi\\ .\n\\end{equation}\nSecond, the PQ symmetry being anomalous, the fermion reparametrizations in Eq.~(\\ref{ReparamG}) change the fermionic measure. To account for this, one must introduce anomalous couplings to the gauge bosons,\n\\begin{equation}\n\\delta\\mathcal{L}_{\\text{\\textrm{Jac}}}=\\frac{a^{0}}{16\\pi^{2}v_{\\phi}}\\left(\ng_{s}^{2}\\mathcal{N}_{C}G_{\\mu\\nu}^{a}\\tilde{G}^{a,\\mu\\nu}+g^{2}%\n\\mathcal{N}_{L}W_{\\mu\\nu}^{i}\\tilde{W}^{i,\\mu\\nu}+g^{\\prime2}\\mathcal{N}%\n_{Y}B_{\\mu\\nu}\\tilde{B}^{\\mu\\nu}\\frac{{}}{{}}\\right) \\;,\n\\end{equation}\nwhere the coefficients $\\mathcal{N}_{C,L,Y}$ are functions of the PQ charges of all the fermions, and generically given by\n\\begin{equation}\n\\mathcal{N}_{X}=\\sum_{\\psi}PQ(\\psi)C_{X}(\\psi)\\ ,\n\\end{equation}\nwith $C_{C,L,Y}(\\psi)$ the quadratic invariant of the field $\\psi$ under $SU(3)_{C}$, $SU(2)_{L}$ or $U(1)_{Y}$. The effective Lagrangian\n\\begin{equation}\n\\mathcal{L}_{\\text{\\textrm{Eff}}}=\\delta\\mathcal{L}_{\\text{\\textrm{Jac}}%\n}+\\delta\\mathcal{L}_{\\text{\\textrm{Der}}}\\ , \\label{AxionEL}%\n\\end{equation}\nis in general the basis in which the axion phenomenology is studied, with the common further assumption that $\\delta\\mathcal{L}_{\\text{\\textrm{Der}}}$ is model-dependent and subleading compared to the model independent $\\delta\\mathcal{L}_{\\text{\\textrm{Jac}}}$. Yet, since the PQ charge of the fermions are ambiguous, both $\\delta\\mathcal{L}_{\\text{\\textrm{Der}}}$ and $\\delta\\mathcal{L}_{\\text{\\textrm{Jac}}}$ are also ambiguous. This is most striking in the DFSZ case, where $\\mathcal{N}_{L}\\sim3\\beta+\\gamma$. This conundrum was analyzed in Ref.~~\\cite{Quevillon:2019zrd}, where in particular it was shown that $\\delta\\mathcal{L}_{\\text{\\textrm{Der}}}$ and $\\delta\\mathcal{L}_{\\text{\\textrm{Jac}}}$ do in fact contribute at the same order to physical observables, and that this ensures all the ambiguities in $\\delta\\mathcal{L}_{\\text{\\textrm{Der}}}$ and $\\delta\\mathcal{L}_{\\text{\\textrm{Jac}}}$ cancel each other systematically. This means that the couplings to (chiral) gauge bosons cannot be read off $\\delta\\mathcal{L}_{\\text{\\textrm{Jac}}}$, and that $\\delta\\mathcal{L}_{\\text{\\textrm{Der}}}$ cannot be neglected.\n\nFor our purpose, it is important to emphasize how this translates for the baryon and lepton numbers. Thus, consider the KSVZ scenario with the fermion charges in Eq.~(\\ref{KSVZfermions}), keeping $\\alpha$, $\\beta$, and $\\gamma$ arbitrary, and let us perform the reparametrization of Eq.~(\\ref{ReparamG}) for all the fermions. The PQ current is then identified as\n\\begin{equation}\nJ_{PQ}^{\\mu}=\\bar{\\Psi}_{R}\\gamma^{\\mu}\\Psi_{R}+\\alpha J_{\\Psi}^{\\mu}+3\\beta\nJ_{\\mathcal{B}}^{\\mu}+\\gamma J_{\\mathcal{L}}^{\\mu}\\ ,\n\\end{equation}\nwhere%\n\\begin{align}\nJ_{\\Psi}^{\\mu} & =\\bar{\\Psi}_{L}\\gamma^{\\mu}\\Psi_{L}+\\bar{\\Psi}_{R}%\n\\gamma^{\\mu}\\Psi_{R}=\\bar{\\Psi}\\gamma^{\\mu}\\Psi\\ ,\\\\\nJ_{\\mathcal{B}}^{\\mu} & =\\frac{1}{3}\\bar{q}_{L}\\gamma^{\\mu}q_{L}+\\frac{1}%\n{3}\\bar{u}_{R}\\gamma^{\\mu}u_{R}+\\frac{1}{3}\\bar{d}_{R}\\gamma^{\\mu}d_{R}%\n=\\frac{1}{3}\\bar{u}\\gamma^{\\mu}u+\\frac{1}{3}\\bar{d}\\gamma^{\\mu}d\\ ,\\ \\\\\nJ_{\\mathcal{L}}^{\\mu} & =\\bar{\\ell}_{L}\\gamma^{\\mu}\\ell_{L}+\\bar{e}%\n_{R}\\gamma^{\\mu}e_{R}+\\bar{\\nu}_{R}\\gamma^{\\mu}\\nu_{R}=\\bar{e}\\gamma^{\\mu\n}e+\\bar{\\nu}\\gamma^{\\mu}\\nu\\ .\n\\end{align}\nAt first sight, one may think to discard the vector currents $J_{\\Psi}^{\\mu}$, $J_{\\mathcal{B}}^{\\mu}$, and $J_{\\mathcal{L}}^{\\mu}$ from the derivative interactions since upon integration by part, $\\partial_{\\mu}a^{0}\\bar{\\psi}\\gamma^{\\mu}\\psi=-a^{0}\\partial_{\\mu}\\bar{\\psi}\\gamma^{\\mu}\\psi=-a^{0}\\bar{\\psi}(m-m)\\psi=0$. This is incorrect though. The vector Ward identity does not survive to the presence of chiral gauge interactions. While $J_{\\Psi}^{\\mu}$ can indeed safely be discarded since $\\Psi$ is vector-like, the baryon and lepton currents are anomalous in the presence of chiral gauge fields:\n\\begin{equation}\n\\partial_{\\mu}J_{\\mathcal{B}}^{\\mu}=\\partial_{\\mu}J_{\\mathcal{L}}^{\\mu}%\n=-\\frac{N_{f}}{16\\pi^{2}}\\left( \\frac{1}{2}g^{2}W_{\\mu\\nu}^{i}\\tilde\n{W}^{i,\\mu\\nu}-\\frac{1}{2}g^{\\prime2}B_{\\mu\\nu}\\tilde{B}^{\\mu\\nu}\\right) \\ .\n\\end{equation}\nObviously, these contributions trivially cancel the $\\beta$ and $\\gamma$-dependent Jacobian terms generated by the fermion reparametrization, which have precisely the same form and origin. Thus, in the KSVZ setting, it seems\nthat the sole role of the SM fermions derivative interactions aligned with the $\\mathcal{B}$ and $\\mathcal{L}$ current is to kill the correspondingly spurious anomalous gauge interactions.\n\nThere is a problem in this reasoning though. This cancellation occurs whether a $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling is assumed initially present or not, since the value of $\\gamma$ is irrelevant. This is puzzling since in the presence of $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, the axion should retain some couplings to $\\nu_{R}$. In the above argument, the step at which we lost the $a\\nu_{R}\\nu_{R}$ coupling is in the Ward identity. After the spontaneous symmetry breaking (SSB), $\\mathcal{L}$, as part of the PQ symmetry, is no longer conserved and the equation of motion (EoM) of $\\nu_{R}$ breaks explicitly the anomalous vector Ward identity. In practice, $(\\partial_{\\mu}a^{0}\/v)\\bar{\\nu}_{R}\\gamma^{\\mu}\\nu_{R}$ does generate the $(M_{R}\/v)a^{0}\\nu_{R}\\nu_{R}$ coupling. This means that whether the axion is coupled to $\\nu_{R}$ or not is not apparent at the level of the effective axion Lagrangian, but hides in the EoM of $\\nu_{R}$.\\ Further, these EoM spoil the $1\/v_{\\phi}$ scaling of the effective Lagrangian operators, since they contain terms of $\\mathcal{O}(v_{\\phi})$. Phenomenologically, this failure of the effective interactions to manifestly exhibit all the possible axion interactions is clearly an important point to keep in mind.\n\nTo conclude, let us stress again:\n\n\\begin{itemize}\n\\item The PQ symmetry has some room for $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating effects. In the presence of such violation, the PQ symmetry eats part of the $\\mathcal{B}$ and $\\mathcal{L}$ accidental $U(1)$s, and the PQ current inherits some $J_{\\mathcal{B}}^{\\mu}$ and\/or $J_{\\mathcal{L}}^{\\mu}$ components.\n\n\\item Incorporating a $\\mathcal{B}$ and\/or $\\mathcal{L}$ component in the PQ current does not modify the leading order axion to gauge boson couplings.\n\n\\item The $\\mathcal{B}$ and\/or $\\mathcal{L}$ components of PQ current do not tell us much about the couplings of the axion to SM fermions. Most of the $\\partial_{\\mu}a^{0}J_{\\mathcal{B}}^{\\mu}$ and $\\partial_{\\mu}a^{0}J_{\\mathcal{L}}^{\\mu}$ couplings are just there to cancel spurious local anomalous terms.\n\n\\item Any $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating couplings must break explicitly the (already anomalous) $\\mathcal{B}$ and\/or $\\mathcal{L}$ vector Ward identities. In their presence, the EoM of the SM fermions will ensure the derivative interactions $\\partial_{\\mu}a^{0}J_{\\mathcal{B}}^{\\mu}$ and $\\partial_{\\mu}a^{0}J_{\\mathcal{L}}^{\\mu}$ do include the expected $\\Delta\\mathcal{B}$ and\/or $\\Delta\\mathcal{L}$ couplings of the axion.\n\\end{itemize}\n\nAs we will see in the following, introducing leptoquark states often forces us to entangle $\\mathcal{B}$ and\/or $\\mathcal{L}$ with the PQ symmetry. These points are thus crucial to understand the phenomenological consequences.\n\n\\subsection{Introducing leptoquarks and diquarks\\label{Sec2c}}\n\nLeptoquarks (LQ) are scalars or vectors that couple simultaneously to a quark-lepton pair, while diquarks (DQ) couple to quark pairs (for a review, see e.g. Ref.~\\cite{Dorsner:2016wpm}). Given the quantum numbers of the SM fermions, only a finite number of LQ and DQ can couple to normal matter, and only a few of them can have both LQ and DQ couplings. Though the full list of possible LQ and DQ states is well-known, let us nevertheless go through this construction as it will play an important role in the following, and permits to conveniently introduce our notations.\n\nAll the LQ are color triplets, while DQ are triplets (using $1\\supset 3\\otimes3\\otimes3$) or sexplets (using $1\\supset3\\otimes3\\otimes\\bar{6}$). From the point of view of $SU(2)_{L}$, these states can be either triplet, doublets, or singlets, depending on the involved SM fermions. Once $SU(2)_{L}\\otimes SU(3)_{C}$ contractions are set, the hypercharge is then fixed to accommodate specific couplings to SM fermions. In this regard, one should remember that scalars couple to $\\bar{\\psi}_{L}\\psi_{R}$ or $\\bar{\\psi}_{R}\\psi_{L}$, vectors to $\\bar{\\psi}_{R}\\gamma_\\mu\\psi_{R}$ or $\\bar{\\psi}_{L}\\gamma_\\mu\\psi_{L}$, and that charge conjugation $\\mathrm{C}$ flips the chirality. This means that a scalar can couple to $\\bar{\\psi}_{R}^{\\mathrm{C}}\\psi_{R}$ for example. By constructing all possible pairs of SM leptons, including conjugate fields, the standard list of possible states are recovered, with the scalars LQ states\n\\begin{align}\n(\\mathbf{3},\\mathbf{2},+1\/3) & :S_{2}^{1\/3}\\times(\\bar{d}_{R}\\ell_{L}\\ ,\\ \\bar{q}_{L}\\nu_{R})\\ ,\\nonumber\\\\\n(\\mathbf{3},\\mathbf{2},+7\/3) & :S_{2}^{7\/3}\\times(\\bar{u}_{R}\\ell_{L}\\ ,\\ \\bar{q}_{L}e_{R})\\ ,\\nonumber\\\\\n(\\mathbf{3},\\mathbf{1},-2\/3) & :S_{1}^{2\/3}\\times(\\bar{d}_{R}\\nu_{R}^{\\mathrm{C}}\\ \\ ,\\ \\bar{u}_{R}e_{R}^{\\mathrm{C}}\\ ,\\ \\bar{q}_{L}\\ell_{L}^{\\mathrm{C}})\\ ,\\ \\ (\\mathbf{3},\\mathbf{3},-2\/3):S_{3}^{2\/3}\\times\\bar\n{q}_{L}\\ell_{L}^{\\mathrm{C}}\\ ,\\label{LQ1}\\\\\n(\\mathbf{3},\\mathbf{1},+4\/3) & :S_{1}^{4\/3}\\times\\bar{u}_{R}\\nu_{R}^{\\mathrm{C}}\\ ,\\nonumber\\\\\n(\\mathbf{3},\\mathbf{1},-8\/3) & :S_{1}^{8\/3}\\times\\bar{d}_{R}e_{R}^{\\mathrm{C}}\\ ,\\nonumber\n\\end{align}\nand the vector LQ states$\\ $%\n\\begin{align}\n(\\mathbf{3},\\mathbf{2},+1\/3) & :V_{2,\\mu}^{1\/3}\\times(\\bar{u}_{R}\\gamma^{\\mu}\\ell_{L}^{\\mathrm{C}}\\ ,\\ \\bar{q}_{L}\\gamma^{\\mu}\\nu_{R}^{\\mathrm{C}})\\ ,\\nonumber\\\\\n(\\mathbf{3},\\mathbf{2},-5\/3) & :V_{2,\\mu}^{5\/3}\\times(\\bar{d}_{R}\\gamma^{\\mu}\\ell_{L}^{\\mathrm{C}}\\ ,\\ \\bar{q}_{L}\\gamma^{\\mu}e_{R}^{\\mathrm{C}})\\ ,\\nonumber\\\\\n(\\mathbf{3},\\mathbf{1},+4\/3) & :V_{1,\\mu}^{4\/3}\\times(\\bar{u}_{R}\\gamma^{\\mu}\\nu_{R}\\ ,\\ \\bar{d}_{R}\\gamma^{\\mu}e_{R}\\ ,\\ \\bar{q}_{L}\\gamma^{\\mu}\\ell_{L})\\ ,\\ \\ (\\mathbf{3},\\mathbf{3},+4\/3):V_{3,\\mu}^{4\/3}\\times\\bar{q}%\n_{L}\\gamma^{\\mu}\\ell_{L}\\ ,\\label{LQ2}\\\\\n(\\mathbf{3},\\mathbf{1},10\/3) & :V_{1,\\mu}^{10\/3}\\times\\bar{u}_{R}\\gamma^{\\mu}e_{R}\\ ,\\nonumber\\\\\n(\\mathbf{3},\\mathbf{1},-2\/3) & :V_{1,\\mu}^{2\/3}\\times\\bar{d}_{R}\\gamma^{\\mu}\\nu_{R}\\ .\\nonumber\n\\end{align}\nMany notations exist for these states, in particular $S_{i}$, $\\tilde{S}_{i}$, $\\bar{S}_{i}$ when several states occur with the same $SU(3)_{C}\\otimes SU(2)_{L}$ quantum numbers~\\cite{Dorsner:2016wpm}. Here, we denote all states as color triplets $S_{t}^{y}$ or $V_{t}^{y}$, with $t$ the $SU(2)_{L}$ dimensionality and $y$ the absolute value of the $U(1)_{Y}$ hypercharge. Note also that $V_{1,\\mu}^{2\/3}$ and $S_{1}^{4\/3}$ exist only in the presence of $\\nu_{R}$, and are thus often discarded. Concerning diquarks, there are only six possible combinations of quark fields, leading to%\n\\begin{align}\n(\\mathbf{3},\\mathbf{2},+1\/3) & :V_{2,\\mu}^{1\/3}\\times\\bar{d}_{R}^{\\mathrm{C}}\\gamma^{\\mu}q_{L}\\ ,\\nonumber\\\\\n(\\mathbf{3},\\mathbf{2},-5\/3) & :V_{2,\\mu}^{5\/3}\\times\\bar{u}_{R}^{\\mathrm{C}}\\gamma^{\\mu}q_{L}\\ ,\\nonumber\\\\\n(\\mathbf{3},\\mathbf{1},-2\/3) & :S_{1}^{2\/3}\\times(\\bar{q}_{L}^{\\mathrm{C}}q_{L}\\ ,\\ \\bar{d}_{R}^{\\mathrm{C}}u_{R})\\ ,\\ (\\mathbf{3},\\mathbf{3},-2\/3):S_{3}^{2\/3}\\times\\bar{q}_{L}^{\\mathrm{C}}q_{L}\\ ,\\label{LQ3}\\\\\n(\\mathbf{3},\\mathbf{1},+4\/3) & :S_{1}^{4\/3}\\times\\bar{d}_{R}^{\\mathrm{C}}d_{R}\\ ,\\nonumber\\\\\n(\\mathbf{3},\\mathbf{1},-8\/3) & :S_{1}^{8\/3}\\times\\bar{u}_{R}^{\\mathrm{C}}u_{R}\\ .\\nonumber\n\\end{align}\nAll these states are already present in the LQ list. Note that each of the above quark state can also couple to a DQ transforming like $\\mathbf{\\bar{6}}$ under $SU(3)_{C}$, with the same $SU(2)_{L}\\otimes U(1)_{Y}$ quantum numbers. In that case, they do not have LQ couplings. We will adopt the same notation for these states, relying on the context to make clear whether they transform as $\\mathbf{3}$ or $\\mathbf{\\bar{6}}$.\n\n\n\\begin{table}[t] \\centering\n\\begin{tabular}[c]{l}\n\\begin{tabular}[c]{lllllll}\\hline\n$\\Delta\\mathcal{B}$ & $\\Delta\\mathcal{L}$ & Dim. &\n\\multicolumn{2}{l}{Operators (no $\\nu_{R}$)} & & \\\\\\hline\n$+0$ & $+2$ & $5$ & $H^{\\dagger2}\\ell_{L}^{2}$ & & & \\\\\n$+1$ & $+1$ & $6$ & $q_{L}^{3}\\ell_{L}$ & $u_{R}^{2}d_{R}e_{R}$ & $q_{L}%\nu_{R}d_{R}\\ell_{L}$ & $q_{L}^{2}u_{R}e_{R}$\\\\\n$+1$ & $-1$ & $7$ & $H^{\\dagger}d_{R}^{3}\\ell_{L}^{\\mathrm{C}}$ & $Hd_{R}%\n^{2}q_{L}e_{R}^{\\mathrm{C}}$ & $Hd_{R}^{2}u_{R}\\ell_{L}^{\\mathrm{C}}$ &\n$Hq_{L}^{2}d_{R}\\ell_{L}^{\\mathrm{C}}$\\\\\n$+2$ & $+0$ & $9$ & $d_{R}^{4}u_{R}$ & $d_{R}^{3}u_{R}q_{L}^{2}$ & $d_{R}%\n^{2}q_{L}^{4}$ & \\\\\n$+1$ & $+3$ & $9$ & $u_{R}^{2}q_{L}\\ell_{L}^{3}$ & $u_{R}^{3}\\ell_{L}^{2}%\ne_{R}$ & & \\\\\n$+1$ & $-3$ & $10$ & $Hd_{R}^{3}\\ell_{L}^{\\mathrm{C},3}$ & & &\n\\end{tabular}\n\\\\\n\\begin{tabular}[c]{llllllll}\\hline\n$\\Delta\\mathcal{B}$ & $\\Delta\\mathcal{L}$ & Dim. &\n\\multicolumn{2}{l}{Operators (one $\\nu_{R}$)} & & & \\\\\\hline\n$+0$ & $+2$ & $5$ & $H^{\\dagger2}e_{R}\\nu_{R}$ & & & & \\\\\n$+1$ & $+1$ & $6$ & $q_{L}^{2}d_{R}\\nu_{R}$ & $d_{R}^{2}u_{R}\\nu_{R}$ & & &\n\\\\\n$+1$ & $-1$ & $7$ & $H^{\\dagger}d_{R}^{2}q_{L}\\nu_{R}^{\\mathrm{C}}$ &\n$Hd_{R}q_{L}u_{R}\\nu_{R}^{\\mathrm{C}}$ & $Hq_{L}^{3}\\nu_{R}^{\\mathrm{C}}$ & &\n\\\\\n$+2$ & $+0$ & $9$ & -- & & & & \\\\\n$+1$ & $+3$ & $9$ & $d_{R}u_{R}^{2}\\ell_{L}^{2}\\nu_{R}$ & $d_{R}q_{L}u_{R}%\n\\ell_{L}^{2}\\nu_{R}$ & $u_{R}^{3}e_{R}^{2}\\nu_{R}$ & $u_{R}^{2}q_{L}\\ell\n_{L}e_{R}\\nu_{R}$ & $q_{L}^{2}u_{R}\\ell_{L}^{2}\\nu_{R}$\\\\\n$+1$ & $-3$ & $10$ & $Hd_{R}^{3}\\ell_{L}^{\\mathrm{C}}e_{R}^{\\mathrm{C}}\\nu\n_{R}^{\\mathrm{C}}$ & $Hd_{R}^{2}q_{L}\\ell_{L}^{\\mathrm{C},2}\\nu_{R}%\n^{\\mathrm{C}}$ & & & \\\\\\hline\n\\end{tabular}\n$\\ $\n\\end{tabular}\n$\\ $\n\\caption{Leading effective operators with non-trivial $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})$ charges in the SM, involving no or one $\\nu_R$ field.\nWe do not include redundant patterns, e.g. all the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=n\\times(0,2),n\\times\n(1,1),...$ with $n=2,3,...$ operators, or operators of higher dimensions within each $(\\Delta\\mathcal{B},\\Delta\\mathcal\n{L})$ class. With even more fields, the next unique patterns involve eight fermions, and induce $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,5)$ transitions at dimension 12, and $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-5)$ transitions at dimension 13 (with an extra Higgs field). All these processes involve at least one $\\nu\n_{R}$ field at these orders. Still higher in dimensionality, $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(3,1)$ and $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,7)$ come at the ten-fermion level, via dimension-15 operators. Only the SM Higgs doublet $H$ is used in the Table together with SM fermions, but the extension to the THDM is trivial.}%\n\\label{TableLQBL}\n\\end{table}\n\nIntroducing scalar or vector states that couple to quarks and leptons can impact the accidental $\\mathcal{B}$ and $\\mathcal{L}$ symmetries (for a recent review, see e.g. Ref.~\\cite{Assad:2017iib}). Depending on which states are introduced and, if several of them are present, depending also on how they are coupled, the symmetry pattern can be quite different. Actually, these symmetry patterns are reminiscent of those of the possible effective operators involving SM fields but carrying non-trivial $\\mathcal{B}$ and\/or $\\mathcal{L}$ charges~\\cite{Weinberg,WeinbergPRD22,Weldon:1980gi}. Those are listed in Table~\\ref{TableLQBL}. This connection is easily understood from tree diagrams with the external fermions linked together by virtual LQ\/DQ exchanges\\footnote{The notation LQ\/DQ generically refers to any of the pure LQ, pure DQ, or mixed LQ\/DQ state introduced in Eqs.~(\\ref{LQ1}),~(\\ref{LQ2}), and~(\\ref{LQ3}).}. Obviously, these external fermion states must be $SU(3)_{C}\\otimes SU(2)_{L}\\otimes U(1)_{Y}$ invariant since the LQ\/DQ are. Further, operators with six or less fermions are the most relevant when only renormalizable interactions among the LQ\/DQ are present. Being colored, these states can at most have quadratic or cubic interactions, hence induce four or six fermion interactions. More complicated fermion interactions can arise, but they would require multiple cubic interactions, and would not open additional phenomenologically interesting channels. Indeed, the above set contains already the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,2)$ operators for neutrino masses, $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ operators for neutron-antineutron oscillations, and all the others for proton decay. Note, finally, that one can understand why some states have both LQ and DQ couplings while others do not from the fact that dimension-six operators are necessarily $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$, see Table~\\ref{TableLQBL}. As tree-level exchanges of states with both LQ and DQ couplings (Fig.~\\ref{Fig1}$a$) must match onto these operators, only $V_{2}^{y}$ and $S_{1}^{y}$ can occur since they couple to a quark-lepton (or antiquark-antilepton) pair\\footnote{This condition is sometimes quantified using $\\mathcal{F}=3\\mathcal{B}+\\mathcal{L}$ as a quantum numbers~\\cite{Dorsner:2016wpm}, so that those states with both LQ and DQ couplings have $\\mathcal{F}=\\pm2$, and the others $\\mathcal{F}=0$. We prefer here to use $\\mathcal{B}\\pm\\mathcal{L}$.}.\n\nWith the above picture in mind, let us see in more details how the various\n$(\\Delta\\mathcal{B},\\Delta\\mathcal{L})$ patterns of Table~\\ref{TableLQBL} can arise:\n\n\\begin{enumerate}\n\\item[A.] Exact $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}:$ Whenever a given $S$ or $V$ state with only LQ or DQ coupling is present, $\\mathcal{B}$ and $\\mathcal{L}$ can still be unambiguously defined. The LQ or DQ state simply carries some specific $\\mathcal{B}$ and $\\mathcal{L}$ quantum numbers, but overall, $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ is still exact. This remains true even in the presence of several different states, so long as they do not couple together.\n\n\\item[B.] Exact $U(1)_{\\mathcal{B}-\\mathcal{L}}:$ When a state with both LQ and DQ couplings is present, the symmetry gets reduced to $U(1)_{\\mathcal{B}-\\mathcal{L}}$, with the $\\mathcal{B}-\\mathcal{L}$ quantum numbers $-2\/3$ for $S_{1}^{y}$ and $V_{2}^{y}$, $+1\/3$ and $-1$ for quarks and leptons, respectively. This remains true if more than one DQ\/LQ state is present provided any couplings among them is compatible with these charge assignments, which further requires the $\\mathcal{B}-\\mathcal{L}$ quantum numbers of $S_{2}^{y}$ and $V_{1}^{y}$ to be $+4\/3$. For example, a scenario with $S_{1}^{2\/3}$ and $S_{1}^{4\/3}$ but without an $S_{1}^{2\/3}S_{1}^{2\/3}S_{1}^{4\/3}$ interaction, or with $S_{2}^{7\/3}$, $S_{1}^{2\/3}$ and a coupling $H^{\\dagger}S_{2}^{7\/3}S_{1}^{2\/3}S_{1}^{2\/3}$, or with $S_{2}^{1\/3}$, $S_{1}^{2\/3}$ and a coupling $HS_{2}^{1\/3}S_{1}^{2\/3}S_{1}^{2\/3}$ all preserve $U(1)_{\\mathcal{B}-\\mathcal{L}}$ (note that the antisymmetric color contraction requires at least two different $S_{1}^{2\/3}$). For all these scenarios, the $S$ and\/or $V$ mass has to be pushed at the GUT scale since $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$ operators induce proton decay (Fig.~\\ref{Fig1}$a$).\n\n\\item[C.] Exact $U(1)_{3\\mathcal{B}+\\mathcal{L}}:$ A peculiar situation arises for the $S_{2}^{1\/3}$ state, because the $H^{\\dagger}S_{2}^{1\/3}S_{2}^{1\/3}S_{2}^{1\/3}$ is allowed by all the SM gauge symmetries. In its presence, the active symmetry gets reduced to $U(1)_{3\\mathcal{B}+\\mathcal{L}}$ since $S_{2}^{1\/3}$ is neutral for that specific combination. This situation is again problematic because $H^{\\dagger}S_{2}^{1\/3}S_{2}^{1\/3}S_{2}^{1\/3}$ collapses to $Hd_{R}^{\\dagger3}\\ell_{L}^{3}$ and can induce a $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)$ proton decay. No other combination of scalar leptoquarks contributes to this $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)$ channel.\n\n\\item[D.] No exact $U(1):$ In the presence of two states having different $\\mathcal{B}-\\mathcal{L}$ quantum numbers, there is no remaining symmetry whenever those states have all their gauge-allowed couplings to SM fermions turned on, and when they are coupled together. For example, introducing both $S_{2}^{1\/3}$ and $S_{1}^{2\/3}$ with a $\\mu HS_{2}^{1\/3\\dagger}S_{1}^{2\/3}$ coupling, $U(1)_{\\mathcal{B}}$ and $U(1)_{\\mathcal{L}}$ are entirely broken. As seen earlier, $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$ proton decay is induced by $S_{1}^{2\/3}$, pushing its mass to the GUT range. But the total absence of accidental $U(1)$s means the other classes of $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})$ operators are also generated. The simplest is the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,2)$ operator, generating neutrino masses via the diagram of Fig.~\\ref{Fig1}$b$.\n\n\\item[E.] Exact $U(1)_{\\mathcal{B}}:$ Adding to the scenarios A a seesaw mechanism for neutrino masses, i.e., a $\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ term, then $U(1)_{\\mathcal{L}}$ is explicitly broken but $U(1)_{\\mathcal{B}}$ remains exact, preventing proton decay. The same pattern can be obtained using mixing terms among some carefully chosen LQ\/DQ states, such that an effective neutrino mass term is generated but proton decay cannot occur. For example, introducing $S_{2}^{1\/3}$, $S_{1}^{2\/3}$, the mixing term $\\mu HS_{2}^{1\/3\\dagger}S_{1}^{2\/3}$ but turning off the DQ couplings of $S_{1}^{2\/3}$ (or alternatively, with the mixing term $\\mu S_{2}^{1\/3}S_{2}^{1\/3}S_{1}^{2\/3}$ but turning off the LQ couplings of $S_{1}^{2\/3}$), the dimension-five $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,2)$ operator arises, see Fig.~\\ref{Fig1}$b$. In these scenarios, $S_{2}^{1\/3}$, $S_{1}^{2\/3}$ acquire well defined $\\mathcal{B}$ numbers, $U(1)_{\\mathcal{B}}$ is conserved, and proton decay is forbidden.\n\n\\item[F.] Exact $U(1)_{\\mathcal{B}+\\mathcal{L}}:$ Another possible symmetry pattern corresponds to taking again $S_{2}^{1\/3}$, $S_{1}^{2\/3}$, and the $\\mu HS_{2}^{1\/3\\dagger}S_{1}^{2\/3}$ coupling but turning off the LQ couplings of $S_{1}^{2\/3}$ (or with $\\mu S_{2}^{1\/3}S_{2}^{1\/3}S_{1}^{2\/3}$ but turning off the DQ couplings of $S_{1}^{2\/3}$). In this case, no neutrino masses can be generated, but proton decay is back. Yet, the proton decay channels do not match those induced by the dimension-six Weinberg operators. With the $\\mu HS_{2}^{1\/3\\dagger}S_{1}^{2\/3}$ coupling, the simplest processes lead to the dimension-seven $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ effective operators, see Fig.~\\ref{Fig1}$c$, while the $\\mu S_{2}^{1\/3}S_{2}^{1\/3}S_{1}^{2\/3}$ coupling generates $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ transitions but with an extra lepton-antilepton pair.\n\n\\item[G.] Exact $U(1)_{\\mathcal{L}}:$ Another pattern is obtained by introducing several states but now allowing only for DQ couplings, and turning on some mixing terms (this kind of construction was considered recently e.g. in Refs.~\\cite{Arnold:2012sd,FileviezPerez:2015mlm}). These latter mixings are necessary since otherwise, $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ remains exact. The simplest scenarios are those with $S_{1}^{2\/3}$, $S_{1}^{4\/3}$, and the cubic coupling $\\mu S_{1}^{2\/3}S_{1}^{2\/3}S_{1}^{4\/3}$, or $S_{1}^{4\/3}$, $S_{1}^{8\/3}$, and the cubic coupling $\\mu S_{1}^{4\/3}S_{1}^{4\/3}S_{1}^{8\/3}$. In both cases, only the DQ couplings are allowed, and $S_{1}^{4\/3}$ ($S_{1}^{8\/3}$) must transform as $\\mathbf{\\bar{6}}$ in the first (second) case, respectively. As a result, neither neutrino masses nor proton decay are induced, but the dimension-nine $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ operators do arise, and contribute to neutron-antineutron oscillations, see Fig.~\\ref{Fig1}$d$.\n\n\\item[H.] Exact $U(1)_{3\\mathcal{B}-\\mathcal{L}}:$ As for the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ case, dimension-nine $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,3)$ operators are attainable by taking $S_{1}^{2\/3}$, $S_{1}^{4\/3}$, and the cubic coupling $\\mu S_{1}^{2\/3}S_{1}^{2\/3}S_{1}^{4\/3}$, or $S_{1}^{4\/3}$, $S_{1}^{8\/3}$, and the cubic coupling $\\mu S_{1}^{4\/3}S_{1}^{4\/3}S_{1}^{8\/3}$, but turning on only the LQ couplings (since all LQ transform as $\\mathbf{3}$, the color contraction requires three different LQ to be present). Yet, only interactions involving $\\nu_{R}$ can occur because of the LQ coupling of $S_{1}^{4\/3}$ to $\\bar{u}_{R}\\nu_{R}^{\\mathrm{C}}$, so proton decay is suppressed. The dimension-nine $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,3)$ operators not involving $\\nu_{R}$ require a combination of scalar and vector LQ, for example $S_{1}^{2\/3}V_{2}^{1\/3}V_{2}^{1\/3}$ can induce both $\\bar{q}_{L}\\ell_{L}^{\\mathrm{C}}\\bar{u}_{R}\\gamma_{\\mu}\\ell_{L}^{\\mathrm{C}}\\bar{u}_{R}\\gamma^{\\mu}\\ell_{L}^{\\mathrm{C}}$ and $\\bar{u}_{R}e_{R}^{\\mathrm{C}}\\bar{u}_{R}\\gamma_{\\mu}\\ell_{L}^{\\mathrm{C}}\\bar{u}_{R}\\gamma^{\\mu}\\ell_{L}^{\\mathrm{C}}$.\n\\end{enumerate}\n\n\\begin{figure}[ptb]\n\\begin{center}\n\\includegraphics[height=2.3134in,width=5.3195in]{Fig1.jpg}\n\\caption{LQ\/DQ processes inducing proton decay via $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$ operators ($a.$), a neutrino Majorana mass term ($b.$), proton decay via $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operators ($c.$), and neutron-antineutron oscillations via a $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ operator ($d.$).}%\n\\label{Fig1}\n\\end{center}\n\\end{figure}\n\nThis concludes our list of symmetry patterns. It is quite remarkable that a relatively simple scenario exists for all the possible patterns of Table~\\ref{TableLQBL}, with in each case the `orthogonal' $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})$ pattern remaining as an exact accidental $U(1)$ symmetry. What this list does not show is that actually, not so many other scenarios do exist to generate most of these symmetry-breaking patterns. Indeed, in most cases, allowing for several LQ\/DQ states, both scalar and vector, and some couplings among them, one simply ends up with no accidental symmetries. The interesting situations in which some accidental symmetries do remain are quite constrained, and those can be classified once and for all.\n\nFirst, notice that at the renormalizable level, there are only two classes of couplings among the LQ\/DQ: those with bilinear color contractions, typically $\\mathbf{3}\\otimes\\mathbf{\\bar{3}}$ or $\\mathbf{6}\\otimes\\mathbf{\\bar{6}}$, and those with cubic contractions, typically $\\mathbf{3}\\otimes\\mathbf{3}\\otimes\\mathbf{3}$ or $\\mathbf{3}\\otimes\\mathbf{3}\\otimes\\mathbf{\\bar{6}}$. For the former, barring partial derivatives acting on the LQ\/DQ fields, the only non-trivial LQ\/DQ bilinear couplings compatible with the SM gauge symmetries are%\n\\begin{equation}\nHS_{2}^{1\/3\\dagger}S_{1}^{2\/3}\\ ,\\ HS_{1}^{4\/3\\dagger}S_{2}^{1\/3}%\n\\ ,\\ HS_{2}^{7\/3\\dagger}S_{1}^{4\/3}\\ ,\\ HV_{2,\\mu}^{1\/3\\dagger}V_{1}^{2\/3,\\mu\n}\\ ,\\ HV_{1,\\mu}^{4\/3\\dagger}V_{2}^{1\/3,\\mu}\\ ,\\ HV_{1,\\mu}^{2\/3\\dagger}%\nV_{2}^{5\/3,\\mu}\\ . \\label{LQOpsBmL}\n\\end{equation}\nThe $HS_{2}^{1\/3\\dagger}S_{1}^{2\/3}$ coupling was used to illustrate the symmetry patterns, but all the others are completely similar: $U(1)_{\\mathcal{B}}$ and $U(1)_{\\mathcal{L}}$ are entirely broken when all the LQ\/DQ couplings are present (case D), $U(1)_{\\mathcal{B}}$ stays exact with only LQ couplings (case E), or $U(1)_{\\mathcal{B}+\\mathcal{L}}$ remains if $S_{1}^{y}$ or $V_{2}^{y}$ have only DQ couplings (case F). This last situation is probably the most interesting phenomenologically since each coupling in Eq.~(\\ref{LQOpsBmL}) produces a specific subset of the dimension-seven $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operators in Table~\\ref{TableLQBL}.\n\nFor cubic interactions, though there are a total of $37$ such couplings, most of them involve LQ\/DQ of different $\\mathcal{B}-\\mathcal{L}$ charges and conserve either $U(1)_{\\mathcal{B}-\\mathcal{L}}$ (case B) or $U(1)_{\\mathcal{B}+\\mathcal{L}}$ (case F). Yet, compared to the dimension 6 and 7 operators in Table~\\ref{TableLQBL}, they necessarily produce an extra lepton-antilepton pair. The symmetry patterns typical of six-fermion states, i.e., leading to the dimension 9 or 10 operators in Table~\\ref{TableLQBL}, are obtained with three LQ\/DQ with the same $\\mathcal{B}-\\mathcal{L}$ charge, and this leaves only eight possibilities:\n\\begin{align}\n& S_{1}^{2\/3}V_{2,\\mu}^{1\/3}V_{2}^{1\/3,\\mu}\\ ,\\ S_{1}^{4\/3}V_{2,\\mu}%\n^{1\/3}V_{2}^{5\/3,\\mu}\\ ,\\ S_{1}^{2\/3}S_{1}^{2\/3}S_{1}^{4\/3}\\ ,\\ S_{1}%\n^{4\/3}S_{1}^{4\/3}S_{1}^{8\/3}\\ ,\\label{LQOpsB2}\\\\\n& H^{\\dagger}S_{2}^{1\/3}S_{2}^{1\/3}S_{2}^{1\/3}\\ ,\\ H^{\\dagger}S_{2}%\n^{1\/3}V_{1,\\mu}^{2\/3}V_{1}^{4\/3,\\mu}\\ ,\\ H^{\\dagger}S_{2}^{7\/3}V_{1,\\mu\n}^{\\prime2\/3}V_{1}^{2\/3,\\mu}\\ ,\\ HS_{2}^{1\/3}V_{1,\\mu}^{\\prime2\/3}%\nV_{1}^{2\/3,\\mu}\\ . \\label{LQOpsBL3}%\n\\end{align}\nThe scenarios in the first line lead to $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ or $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,3)$ operators (case G and H), and those in the second line to $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)$ operators (case C). Note that for $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,\\pm3)$ transitions, the LQ must transform as $\\mathbf{3}$, and the color contraction is necessarily antisymmetric. When this is not compensated by an antisymmetric $SU(2)_L$ contraction, the three LQs must be different (hence one of the two $V_{1,\\mu}^{2\/3}$ fields is primed in the last two operators of Eq.~(\\ref{LQOpsBL3})). This does not apply to $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ operators, for which it is always possible to take one of the DQ to transform as a symmetric $\\mathbf{\\bar{6}}$. As a final remark, it should be noted that scalar or vector color-singlet dileptons could also be introduced, opening the door to quartic couplings among the new states, and correspondingly, to eight-fermion $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,\\pm2)$ operators~\\cite{Helset:2021plg}. This will not be considered here.\n\nThroughout this paper, when estimating bounds on LQ\/DQ masses from proton decay or neutron-antineutron oscillations, the LQ\/DQ couplings to SM fermions is assumed flavor universal, or at the very least non-hierarchical in flavor space. As was shown in Ref.~\\cite{MFVBandL}, this is a strong assumption for $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating operators. The $SU(3)^{5}$ flavor\nsymmetry would ask instead for a strong hierarchy because of the systematic presence of the three quark generations in all the operators in Table~\\ref{TableLQBL}. In the present context, such hierarchies would first require LQ\/DQ to carry flavor quantum numbers, and then to extend the minimal flavor violating formalism to the LQ\/DQ sector~\\cite{Davidson:2010uu}. This will not be analyzed here, but such kind of flavor suppression should be kept in mind, especially given the context in B physics. There, a number of puzzles in leptonic and semileptonic decays can be explained by introducing new LQ states with particular flavor hierarchies (for a recent review, see e.g. Ref.~\\cite{LQreview}). Typically, the favored LQ is $V_{1,\\mu}^{4\/3}\\sim(\\mathbf{3},\\mathbf{1},+4\/3)$ thanks to its $q_{L}\\gamma^{\\mu}\\ell_{L}$ couplings, but other states could also occur in principle. The connection of some of these models with axions has been investigated e.g. in Ref.~\\cite{Fuentes-Martin:2019bue} (for some considerations of axions in the context of the B physics anomalies see e.g. \\cite{Baek:2020ovw}, whereas axions in a more broad flavor context have also been studied in Refs.~\\cite{Ema:2016ops,Calibbi:2016hwq,Arias-Aragon:2017eww,Bonnefoy:2020llz}, but to our knowledge, no systematic studies has been performed yet. In the present paper, our goal is mainly to analyze symmetry breaking patterns involving both LQ\/DQ and axions, so the LQ\/DQ couplings to SM fermions will simply be assumed $\\mathcal{O}(1)$ for all flavors whenever deriving bounds on their masses. Turning on non-trivial flavor structures is left for future studies.\n\n\\section{Coupling axions to leptoquarks and diquarks\\label{SecAxLQDQ}}\n\nIn the previous section, we have established the possible global accidental symmetries in the presence of LQ and DQ states. Here, we want to add to these scenarios a KSVZ or DFSZ sector. The consequences are rather different for both models, since the SM fermions can be PQ neutral in the former case, but not in the latter. Yet, so long as the $\\phi$ (and the heavy KSVZ fermions $\\Psi_{L,R}$) are not directly coupled to the LQ\/DQ states, the axion stays rather insensitive to the possible $\\mathcal{B}$ and\/or $\\mathcal{L}$ violation.\n\nTo illustrate this, consider the KSVZ scenario. Without direct couplings of $\\phi$ or $\\Psi_{L,R}$ to the LQ\/DQ states, the $U(1)_{\\phi}$ symmetry stays separate from the accidental symmetries $U(1)_{\\mathcal{B},\\mathcal{L}}$, so the PQ breaking proceeds trivially as\n\\begin{equation}\nU(1)_{\\phi}\\otimes U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}\\overset\n{\\text{Explicit}}{\\rightarrow}U(1)_{\\phi}\\otimes U(1)_{X}\\simeq U(1)_{PQ}%\n\\otimes U(1)_{X}\\overset{\\text{Spontaneous}}{\\rightarrow}U(1)_{X}\\ ,\\ \\\n\\end{equation}\nThe specific LQ\/DQ scenario fixes which accidental symmetry\n\\begin{equation}\nU(1)_{X}=U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}},\\ U(1)_{\\mathcal{B}%\n\\pm\\mathcal{L}}\\ ,U(1)_{\\mathcal{B}}\\ ,\\ U(1)_{\\mathcal{L}}%\n,\\ U(1)_{3\\mathcal{B}\\pm\\mathcal{L}}\\ ,...\\ , \\label{SurvivingU1}%\n\\end{equation}\nsurvives, by introducing couplings that explicitly break $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}\\backslash U(1)_{X}$. Yet, the axion does not break $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ or $U(1)_{X}$, only the dynamics of the SM and LQ\/DQ fields does. Of course, the axion being coupled to SM gauge fields and SM fermions, it does end up coupled to leptoquarks and possibly acquires some $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating decay channels, but this is indirect. A good example for this situation is the KSVZ model with a Majorana mass $M_{R}\\nu_{R}\\nu_{R}$. The Majorana mass term explicitly breaks $U(1)_{\\mathcal{L}}$ at all scale, but such that $U(1)_{X}=U(1)_{\\mathcal{B}}$ stays exact at all scales. Clearly, the axion dynamics does not break $U(1)_{\\mathcal{L}}$, only neutrino masses do. Thus, any $\\Delta\\mathcal{L}=2$ effect would come indirectly, e.g. as in $a^{0}\\rightarrow\\bar{\\nu}_{R}\\nu_{L}\\rightarrow\\nu_{R}\\nu_{L}$. The situation in the DFSZ scenario is similar, though the $U(1)_{PQ}$ arises from a specific combination of $U(1)_{\\phi}$ and $U(1)_{Y}$, see Eq.~(\\ref{DFSZScalars}). This situation also corresponds to that often found in simple GUT models. For example, in $SU(5)$, gauge interactions break $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ down to $U(1)_{\\mathcal{B}-\\mathcal{L}}$ independently of the axion field (for a detailed account of how the PQ, $\\mathcal{B}$, and $\\mathcal{L}$ symmetries are entangled in the $SU(5)$ setting, see Ref.~\\cite{Quevillon:2020aij}).\n\nOur goal is to consider situations in which the symmetry above the PQ scale entangles $U(1)_{\\phi}$ within $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$. Breaking $U(1)_{\\phi}$ spontaneously then means breaking a linear combination of $\\mathcal{B}$ and $\\mathcal{L}$ (or both) spontaneously. Taking again the KSVZ scenario for illustration, this is accomplished by introducing some set of couplings that are only invariant under a subgroup of $U(1)_{\\phi}\\otimes U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$. In most cases of interests, $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ stays active at the high scale, but $\\phi$ carries some definite $\\mathcal{B}$ and\/or $\\mathcal{L}$ quantum numbers, so that the breaking chain becomes\n\\begin{equation}\nU(1)_{\\phi}\\otimes U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}\\overset\n{\\text{Explicit}}{\\rightarrow}U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}%\n}\\simeq U(1)_{PQ}\\otimes U(1)_{X}\\overset{\\text{Spontaneous}}{\\rightarrow\n}U(1)_{X}\\ .\n\\end{equation}\nThe simplest example illustrating this situation is the KSVZ model with the $\\phi^{\\dagger}\\bar{\\nu}^\\mathrm{C}_{R}\\nu_{R}$ couplings, so that $\\phi$ becomes a $(\\mathcal{B},\\mathcal{L})=(0,2)$ state, $U(1)_{PQ}=U(1)_{\\mathcal{L}}$ is spontaneously broken, but $U(1)_{X}=U(1)_{\\mathcal{B}}$ stays exact. Compared to the previous case, the main difference is that the axion has a $\\Delta\\mathcal{L}=2$ coupling $a^{0}\\rightarrow\\nu_{R}\\nu_{R}$ of $\\mathcal{O}(1)$. Of course, phenomenologically, whether one adds $M_{R}\\bar{\\nu}^\\mathrm{C}_{R}\\nu_{R}$ or $\\phi^{\\dagger}\\bar{\\nu}^\\mathrm{C}_{R}\\nu_{R}$ is irrelevant, but this may not be the case for scenarios in which $U(1)_{\\mathcal{B}}$ is spontaneously broken. Our goal here is to systematically study these scenarios, taking advantage of the fact that LQ\/DQ open many routes to entangle $U(1)_{\\phi}$ within $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ at the renormalizable level (with only SM fields, the $\\phi^\\dagger\\bar{\\nu}^\\mathrm{C}_{R}\\nu_{R}$ coupling is the only possibility). Note, finally, that in the KSVZ context, there is actually an extra accidental symmetry corresponding to $\\Psi$ number, $U(1)_{\\Psi}$, that will either survive or be incorporated within $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ via explicit breaking terms independent of $\\phi$. In this way, the final surviving $U(1)_{X}$ accidental symmetry is independent of $U(1)_{\\Psi}$, and still given by Eq.~(\\ref{SurvivingU1}).\n\nIn practice, to entangle the $U(1)_{\\phi}$ symmetry with the accidental symmetries, the strategy is to turn on some direct couplings between $\\phi$ and the LQ\/DQ, and for these latter, to turn on some or all of their couplings to SM fields such that no direct $\\mathcal{B}$ and\/or $\\mathcal{L}$ violation occurs. It is important to stress that we do not assign $U(1)$ charges to the fields. Instead, we let the theory tell us what are the exact accidental $U(1)$ symmetries, and what are the charges of the fields. Indeed, it is well-known that symmetries and charges are entirely fixed given a set of couplings in the Lagrangian, but often one identifies them by inspection, or starts from the charges to infer the allowed couplings. In the present case, as we will see, the set of couplings can be quite large, and the surviving $U(1)$s assign quite intricate charges to the fields. Typically, a naive inspection of the Lagrangian couplings would most likely miss some of the surviving $U(1)$s, or outright fail to identify possible scenarios. In practice, starting from the Lagrangian also provides a very systematic procedure: to find the surviving $U(1)$ symmetries, it suffices to express the charge constraint corresponding to each coupling, and solve this system of equations. When this system is under-determined, each parametric under-determination corresponds to a surviving $U(1)$. The charges of $\\phi$ under these $U(1)$ then tell us which combination is spontaneously broken.\n\n\\subsection{KSVZ scenarios with leptoquarks and diquarks\\label{SecKSVZ}}\n\nOur requirements for the KSVZ scenarios are first that there should be only one Higgs doublet, neutral under the PQ and all accidental symmetries, and no direct mixing of the heavy fermions $\\Psi_{L,R}$ with SM quarks to avoid FCNC or CKM unitarity constraints. Also, our goal is to force proton decay, neutron-antineutron oscillations, or a Majorana mass terms for $\\nu_{R}$ (or more generally, neutrino-less double beta decays~\\cite{Deppisch:2012nb}) to only arise through the spontaneous symmetry breaking of $U(1)_{\\phi}$. Thus, none of these observables should be immediately allowed by LQ\/DQ transitions. Typically, the strategy to achieve this is, starting from some Lagrangian with a specific set of couplings among $\\phi$, some chosen LQ\/DQs, and the SM fermions, to identify the accidental symmetries, and then make sure these accidental symmetries forbid any other renormalizable Lagrangian couplings. This will be made clear going through specific examples. But, before that, let us describe some generic features of the scenarios and their consequence for the axion effective Lagrangian.\n\nIn all scenarios, there will be some $\\phi^{2}S_{i}^{\\dagger}S_{j}$, $\\phi HS_{i}^{\\dagger}S_{j}$, and\/or $\\phi S_{i}S_{j}S_{k}$ couplings. In this representation, the axion ends up coupled to the LQ\/DQ, as can be seen plugging in Eq.~(\\ref{PhiPolar}) in these couplings (remember $\\eta_{\\phi}=a^{0}$ in the KSVZ setting). Importantly, these couplings are never suppressed by the PQ breaking scale, since for example\n\\begin{equation}\n\\phi S_{i}S_{j}S_{k}\\rightarrow\\frac{1}{\\sqrt{2}}(v_{\\phi}+i\\eta_{\\phi}+...)S_{i}S_{j}S_{k}\\ . \n\\label{ExampleSSS}\n\\end{equation}\nThough as a matter of principle, the axion $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating couplings are not suppressed by $v_{\\phi}$, this scale nevertheless indirectly limits them. Indeed, the leading $v_{\\phi}$ term produces a direct coupling among the LQ\/DQ such that one falls into any one of the situations described in Sec.~\\ref{Sec2c}, with some $U(1)_{X}$ smaller than $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ remaining exact. At low energy, these LQ\/DQ couplings can induce $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating processes, hence set rather strong bounds on the LQ\/DQ masses. Now, the largest $v_{\\phi}$ is, the tightest these bounds are, so indirectly, the $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating axion couplings to SM fermions decrease for increasing $v_{\\phi}$.\n\nComing back to the point of principle, one may wonder how is it that the axion couplings is not suppressed by $v_{\\phi}$ in the effective axion Lagrangian language of Eq.~(\\ref{AxionEL}). Indeed, as a result of the $\\phi^{2}S_{i}^{\\dagger}S_{j}$, $\\phi HS_{i}^{\\dagger}S_{j}$, and\/or $\\phi S_{i}S_{j}S_{k}$ couplings, some or all of the LQ\/DQ become charged under $U(1)_{PQ}$. This means that if, along with Eq.~(\\ref{ReparamG}) for the fermions, we reparametrize them as\n\\begin{equation}\nS_{i}\\rightarrow\\exp(-iPQ(S_{i})a^{0}\/v_{\\phi})S_{i}\\ , \n\\label{ReparamS}\n\\end{equation}\nthe axion field is entirely removed from all the Lagrangian couplings. Indeed, the Lagrangian is PQ-symmetric, so the $\\exp(ia^{0}\/v_{\\phi})$ factors always compensate exactly. Their kinetic terms $D_{\\mu}S_{i}^{\\dagger}D^{\\mu}S_{i}$ are not invariant under the reparametrization though, and as for fermions, this is embodied in dimension-five interactions\n\\begin{equation}\n\\delta\\mathcal{L}_{\\text{\\textrm{Der}}}=\\frac{1}{v_{\\phi}}\\partial_{\\mu}%\na^{0}J_{PQ}^{\\mu}\\text{ ,\\ \\ \\ }J_{PQ}^{\\mu}=\\sum_{i}PQ(S_{i})(S_{i}^{\\dagger\n}(D^{\\mu}S_{i})-(D^{\\mu}S_{i}^{\\dagger})S_{i})+...\n\\end{equation}\nThis representation is deceptive because the axion couplings to LQ\/DQ appear suppressed by $v_{\\phi}$. Yet, they are not suppressed because the EoM of the $S_{i}$ have $\\mathcal{O}(v_{\\phi})$ terms, like that coming from a $v_{\\phi}S_{i}S_{j}S_{k}$ coupling in the example of Eq.~(\\ref{ExampleSSS}). The same happens if LQ\/DQ are integrated out before the reparametrization Eq.~(\\ref{ReparamS}). They then do not occur in $J_{PQ}^{\\mu}$, but SM fermions do, and their EoM now have inherited $\\mathcal{O}(v_{\\phi}\/M^{n}$)\nterms for some $n$, with $M$ the LQ\/DQ mass scale. In all cases, the axion keeps its $\\mathcal{O}(v_{\\phi}^{0})$ $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating couplings, as it should.\n\nThis shows explicitly that the shift-symmetric $\\delta\\mathcal{L}_{\\text{\\textrm{Der}}}$ is not well-suited to these scenarios, at least for what concerns couplings to matter fields. For gauge boson, the situation is a bit different. The fermion reparametrization Eq.~(\\ref{ReparamG}) generates spurious anomalous interactions to chiral gauge fields that are cancelled by the anomalies in the $J_{PQ}^{\\mu}$ current, exactly as before, but the LQ\/DQ obviously do not. Thus, for them, the axion effective Lagrangian after the reparametrization of Eq.~(\\ref{ReparamS}) correctly captures the fact that triangle graphs with LQ\/DQ running in the loop are not anomalous, and vanish at the dimension-five level for a massless axion. Thus, none of the axion to gauge boson couplings is affected by the LQ\/DQ at leading order.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}+\\mathcal{L}$}\n\nWe have seen that $\\mathcal{B}+\\mathcal{L}$ is immediately broken whenever a given $S_{i}$ or $V_{i}$ has both LQ and DQ couplings. For example, $S_{1}^{8\/3}$ with its couplings to $\\bar{d}_{R}e_{R}^{\\mathrm{C}}$ and $\\bar{u}_{R}^{\\mathrm{C}}u_{R}$ can induce $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$ operators and proton decay. A possible strategy to adapt this scenario and force these operators to appear only through the SSB of $\\phi$ is to consider two such states, one LQ and one DQ, with a $\\phi$-dependent mixing term:\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}}=\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}^{8\/3}\\bar\n{d}_{R}e_{R}^{\\mathrm{C}}+\\tilde{S}_{1}^{8\/3}\\bar{u}_{R}^{\\mathrm{C}}%\nu_{R}+\\phi^{2}S_{1}^{8\/3\\dagger}\\tilde{S}_{1}^{8\/3}+h.c.\\ , \\label{LagrKSVZ1a}%\n\\end{equation}\nwith $\\mathcal{L}_{\\mathrm{KSVZ}}$ given in Eq.~(\\ref{KSVZ0}), and LQ\/DQ kinetic terms are understood. We also do not write explicitly the LQ\/DQ scalar potential terms made of bilinears like $S_{1}^{8\/3\\dagger}S_{1}^{8\/3}$ or $\\tilde{S}_{1}^{8\/3\\dagger}\\tilde{S}_{1}^{8\/3}$ since those are neutral under any $U(1)$ symmetry. Solving for the $U(1)$ charges of all the fields under the requirement that the Higgs doublet is neutral (to avoid mixing with $U(1)_{Y}$), a triple under-determination remains, which we can identify as\\footnote{Evidently, the normalization of each line is free, but that for $U(1)_{\\mathcal{B}}$ and $U(1)_{\\mathcal{L}}$ is chosen to reproduce conventional quark and lepton $\\mathcal{B}$ and $\\mathcal{L}$ of $1\/3$ and $1$, respectively.}\n\\begin{equation}\n\\begin{tabular}[c]{cccccccccccc}\\hline\n& $\\phi$ & $S_{1}^{8\/3}$ & $\\tilde{S}_{1}^{8\/3}$ & $\\Psi_{L}$ & $\\Psi_{R}$ &\n$q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{\\Psi}$ & $0$ & $0$ & $0$ & $1$ & $1$ & $0$ & $0$ & $0$ & $0$ & $0$ &\n$0$\\\\\n$U(1)_{\\mathcal{B}}$ & $1\/2$ & $1\/3$ & $-2\/3$ & $-1\/2$ & $0$ & $1\/3$ & $1\/3$ &\n$1\/3$ & $0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $1\/2$ & $1$ & $0$ & $-1\/2$ & $0$ & $0$ & $0$ & $0$ &\n$1$ & $1$ & $1$\\\\\\hline\n\\end{tabular}\n\\label{ChargKSVZ1a}\n\\end{equation}\n\n\nWhat this table shows is that $\\phi$ carries a $U(1)_{\\mathcal{B}+\\mathcal{L}}$ charge, which thus gets spontaneously broken, while $U(1)_{\\mathcal{B}-\\mathcal{L}}$ stays exact. This model is essentially identical to that introduced long ago in Ref.~\\cite{WeinbergPRD22}, except that the Goldstone boson is here identified with the axion. This pattern of symmetry breaking is easily understood from the Lagrangian couplings and the diagram in Fig.~\\ref{Fig2}. Plugging in the polar representation of $\\phi$, Eq.~(\\ref{PhiPolar}), the effective operator at the low-scale is\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)}^{eff}=\\exp\n(2ia^{0}\/v_{\\phi})\\frac{v_{\\phi}^{2}}{m_{S}^{2}m_{\\tilde{S}}^{2}}\\bar{u}%\n_{R}^{\\mathrm{C}}u_{R}\\bar{d}_{R}^{\\mathrm{C}}e_{R}+h.c.\\ , \\label{EffHKSVZ1}%\n\\end{equation}\nwhere we have identified $\\eta_{\\phi}$ as the axion $a^{0}$, and denoted the $S_{1}^{8\/3}$ and $\\tilde{S}_{1}^{8\/3}$ masses as $m_{S}$ and $m_{\\tilde{S}}$, respectively. Note well that this operator arises entirely through the SSB: the charges in Eq.~(\\ref{ChargKSVZ1a}) explicitly prevent a DQ coupling for $S_{1}^{8\/3}$, and a LQ coupling for $\\tilde{S}_{1}^{8\/3}$. Expanding the exponential, the leading term involves only SM particles, and contributes to proton decay. Thus, $m_{S}$ and $m_{\\tilde{S}}$ have to be pushed quite high, though a bit lower that in the usual GUT scenarios. For instance, while the scale of the dim-6 operators is typically pushed above $10^{14}$~GeV, we only need $m_{S}\\approx m_{\\tilde{S}}>10^{11}$~GeV when $v_{\\phi}=10^{9}$~GeV. With these parameters, the proton decay modes involving the axion are thus totally negligible. Finally, notice that the axion totally disappears from $\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)}^{eff}$ under the reparametrization Eq.~(\\ref{ReparamG}), with the PQ charges identified as $(\\mathcal{B}+\\mathcal{L})\/2$. As stated earlier, the $v_{\\phi}a^{0}\\bar{u}_{R}^{\\mathrm{C}}u_{R}\\bar{d}_{R}^{\\mathrm{C}}e_{R}$ effective coupling would then hide in the $\\partial_{\\mu}a^{0}J_{PQ}^{\\mu}\/v_{\\phi}$ terms since the quarks and leptons inherit from $v_{\\phi}^{2}\\bar{u}_{R}^{\\mathrm{C}}u_{R}\\bar{d}_{R}^{\\mathrm{C}}e_{R}$ some $\\mathcal{O}(v_{\\phi}^{2})$ terms in their EoM.%\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[height=1.3586in,width=1.5826in]{Fig2.jpg}\n\\caption{Proton decay operator generated by the spontaneous breaking of $U(1)_{\\mathcal{B}+\\mathcal{L}}$.}%\n\\label{Fig2}\n\\end{center}\n\\end{figure}\n\nAs shown in Eq.~(\\ref{ChargKSVZ1a}), $U(1)_{\\Psi}$ remains as an exact accidental symmetry, which means that the $\\mathcal{B}+\\mathcal{L}$ charges of $\\Psi_{L,R}$ are not unambiguously defined. To fix them requires $\\Psi_{L,R}$\nto couple to SM fermions, and this is possible only for some specific gauge quantum numbers. If we further ask that $S_{1}^{8\/3}$ ($\\tilde{S}_{1}^{8\/3}$) should always (never) couple to leptons, the only possibilities are%\n\\begin{equation}%\n\\begin{tabular}\n[c]{c|cccccc}\\hline\n$Y,\\mathcal{B},\\mathcal{L}$ & $S_{1}^{8\/3}\\bar{\\Psi}_{L}\\ell_{L}^{\\mathrm{C}}$\n& $S_{1}^{8\/3}\\bar{\\Psi}_{R}e_{R}^{\\mathrm{C}}$ & $S_{1}^{8\/3}\\bar{\\Psi}%\n_{R}\\nu_{R}^{\\mathrm{C}}$ & $\\tilde{S}_{1}^{8\/3}\\bar{\\Psi}_{L}^{\\mathrm{C}%\n}q_{L}$ & $\\tilde{S}_{1}^{8\/3}\\bar{\\Psi}_{R}^{\\mathrm{C}}u_{R}$ & $\\tilde\n{S}_{1}^{8\/3}\\bar{\\Psi}_{R}^{\\mathrm{C}}d_{R}$\\\\\\hline\n$\\Psi_{L}$ & $-\\dfrac{5}{3},\\dfrac{1}{3},0\\rule[-0.14in]{0in}{0.36in}$ &\n$-\\dfrac{2}{3},\\dfrac{5}{6},\\dfrac{1}{2}$ & $-\\dfrac{8}{3},\\dfrac{5}{6}%\n,\\dfrac{1}{2}$ & $\\dfrac{7}{3},\\dfrac{1}{3},0$ & $\\dfrac{4}{3},\\dfrac{5}%\n{6},\\dfrac{1}{2}$ & $\\dfrac{10}{3},\\dfrac{5}{6},\\dfrac{1}{2}$\\\\\n$\\Psi_{R}$ & $-\\dfrac{5}{3},-\\dfrac{1}{6},-\\dfrac{1}{2}\\rule[-0.14in]%\n{0in}{0.36in}$ & $-\\dfrac{2}{3},\\dfrac{1}{3},0$ & $-\\dfrac{8}{3},\\dfrac{1}%\n{3},0$ & $\\dfrac{7}{3},-\\dfrac{1}{6},-\\dfrac{1}{2}$ & $\\dfrac{4}{3},\\dfrac\n{1}{3},0$ & $\\dfrac{10}{3},\\dfrac{1}{3},0$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThese couplings are mutually exclusive since they impose different hypercharges for $\\Psi_{L,R}$. Also, $S_{1}^{8\/3}\\bar{\\Psi}_{R}e_{R}^{\\mathrm{C}}$ and $\\tilde{S}_{1}^{8\/3}\\bar{\\Psi}_{R}^{\\mathrm{C}}u_{R}$ would allow for direct $\\bar{\\Psi}_{R}d_{R}$ and $\\bar{\\Psi}_{R}u_{R}$ couplings, respectively, hence must be discarded. Note how the peculiar choice of couplings completely twists the $\\mathcal{B},\\mathcal{L}$ charges, in the sense that they do not correspond to the naive assignments of $\\mathcal{B}=1\/3$ and $\\mathcal{L}=0$ one may have expected for the \"heavy quarks\" of the KSVZ mechanism. As said before, the charges of the fields have to be deduced from the set of couplings of the Lagrangian, and not the other way around.\n\nSimilar scenarios can be constructed using $S_{1}^{2\/3}$, $S_{1}^{4\/3}$, $V_{2}^{1\/3}$ or $V_{2}^{5\/3}$. Actually, $S_{1}^{2\/3}$ was considered in Ref.~\\cite{Reig:2018yfd}, though the model built there is more complicated (here the PQ symmetry is directly identified with $\\mathcal{B}+\\mathcal{L}$ and only a single Higgs doublet is introduced instead of four). Each time, two such states are taken, with one having LQ couplings, and the other DQ couplings, and a $\\phi$-driven mixing term is introduced. The only difference in each case is the specific $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$ operator(s) that can be spontaneously generated, see Table~\\ref{TableLQBL}, and thereby, the induced pattern of proton decay modes. In this respect, it is worth to look at the $S_{1}^{4\/3}$ scenario, since it has only LQ couplings to $\\nu_{R}$:\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}}=\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}^{4\/3}\\bar\n{u}_{R}\\nu_{R}^{\\mathrm{C}}+\\tilde{S}_{1}^{4\/3}\\bar{d}_{R}^{\\mathrm{C}}%\nd_{R}+\\phi^{2}S_{1}^{4\/3\\dagger}\\tilde{S}_{1}^{4\/3}+\\phi\\bar{\\nu}%\n_{R}^{\\mathrm{C}}\\nu_{R}+h.c.\\ . \\label{LagrKSVZ1b}%\n\\end{equation}\nLet us also turn on a coupling to $\\Psi_{L,R}$, to fix its charges, and for definiteness, let us take $S_{1}^{4\/3}\\bar{\\Psi}_{L}\\ell_{L}^{\\mathrm{C}}$. Because of the $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling, solving for the $U(1)$ charges of all the fields now leaves a single under-determination:\n\\begin{equation}%\n\\begin{tabular}\n[c]{cccccccccccc}\\hline\n& $\\phi$ & $S_{1}^{4\/3}$ & $\\tilde{S}_{1}^{4\/3}$ & $\\Psi_{L}$ & $\\Psi_{R}$ &\n$q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{PQ}$ & $2$ & $2\/3$ & $-10\/3$ & $5\/3$ & $-1\/3$ & $5\/3$ & $5\/3$ & $5\/3$ &\n$-1$ & $-1$ & $-1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThis time, neither $\\mathcal{B}$ nor $\\mathcal{L}$ survives. Starting from $U(1)_{\\phi}\\otimes U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$, two $U(1)$s are explicitly broken by $\\mathcal{L}_{\\mathrm{KSVZ+LQ}}$, while the remaining exact $U(1)$ is identified with $U(1)_{PQ}$ and spontaneously broken by $\\phi$. The interest in this scenario is that $S_{1}^{4\/3}$ couples only to $\\nu_{R}$, whose mass is pushed at the PQ breaking scale by the $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling. At the low-energy scale, the leading proton decay operator will scale as%\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)}^{eff}=\\exp\n(2ia^{0}\/v_{\\phi})\\frac{v_{\\phi}^{2}}{m_{S}^{2}m_{\\tilde{S}}^{2}}\\bar{d}%\n_{R}d_{R}^{\\mathrm{C}}\\bar{u}_{R}\\nu_{R}^{\\mathrm{C}}+h.c.\\rightarrow\n\\exp(2ia^{0}\/v_{\\phi})\\frac{v_{\\phi}v_{EW}}{m_{S}^{2}m_{\\tilde{S}}^{2}}\\bar{d}%\n_{R}d_{R}^{\\mathrm{C}}\\bar{u}_{R}\\nu_{L}^{\\mathrm{C}}+h.c.\\ .\n\\label{FinalScale11}%\n\\end{equation}\nThanks to this extra suppression, the PQ breaking scale, which is also the neutrino seesaw scale, and the LQ\/DQ mass scale, can all sit at around $10^{9}$ GeV. They could thus naturally have a common UV origin.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}-\\mathcal{L}$}\n\nWith only LQ\/DQ, scenarios in which $\\mathcal{B}-\\mathcal{L}$ is explicitly broken typically arise from any one of the $HS_{i}^{\\dagger}S_{j}$ or $HV_{i}^{\\dagger}V_{j}$ couplings in Eq.~(\\ref{LQOpsBmL}). Those couplings always involve a pure LQ state together with a mixed LQ\/DQ state. The $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ pattern arises when the latter has only DQ couplings. All these scenarios can be adapted to force $\\mathcal{B}-\\mathcal{L}$ to be broken spontaneously instead of explicitly. Let us take the $HS_{2}^{7\/3\\dagger}S_{1}^{4\/3}$ case as an example, the others being totally similar. To entangle the KSVZ symmetry with $\\mathcal{B}-\\mathcal{L}$, we start from the Lagrangian\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}}=\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}^{4\/3}\\bar\n{d}_{R}^{\\mathrm{C}}d_{R}+S_{2}^{7\/3}(\\bar{u}_{R}\\ell_{L}+\\bar{q}_{L}%\ne_{R})+\\phi HS_{2}^{7\/3\\dagger}S_{1}^{4\/3}+h.c.\\ , \\label{LagrKSVZ2}%\n\\end{equation}\nwhere again kinetic terms and LQ\/DQ potential terms are understood. For definiteness, we also include the $S_{1}^{4\/3}\\bar{\\Psi}_{L}\\ell_{L}^{\\mathrm{C}}$ coupling to get rid of $U(1)_{\\Psi}$ and fix the quantum numbers of $\\Psi_{L,R}$. Then, there remain only a $U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ symmetry with charges\n\\begin{equation}\n\\begin{tabular}[c]{cccccccccccc}\\hline\n& $\\phi$ & $S_{2}^{7\/3}$ & $S_{1}^{4\/3}$ & $\\Psi_{L}$ & $\\Psi_{R}$ & $q_{L}$ &\n$u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $1$ & $1\/3$ & $-2\/3$ & $-2\/3$ & $-5\/3$ & $1\/3$ & $1\/3$\n& $1\/3$ & $0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $-1$ & $-1$ & $0$ & $-1$ & $0$ & $0$ & $0$ & $0$ & $1$\n& $1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nand $U(1)_{\\mathcal{B}-\\mathcal{L}}$ is spontaneously broken when $\\phi$ acquires its vacuum expectation value. Note that these charges prevent the LQ couplings of $S_{1}^{4\/3}$ (taking $\\phi HS_{2}^{1\/3\\dagger}S_{1}^{2\/3}$ instead, they would further forbid the $HS_{2}^{1\/3}S_{2}^{1\/3}S_{2}^{1\/3}$ coupling). The final operators are part of the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ dimension-seven ones in Table~\\ref{TableLQBL} because of the Higgs doublet appearing in the $\\phi HS_{2}^{7\/3\\dagger}S_{1}^{4\/3}$ mixing term (see Fig.~\\ref{Fig3}):\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)}^{eff}=\\exp\n(ia^{0}\/v_{\\phi})\\frac{v_{\\phi}}{m_{S}^{2}m_{\\tilde{S}}^{2}}H\\bar{d}_{R}%\nd_{R}^{\\mathrm{C}}(\\bar{u}_{R}\\ell_{L}+\\bar{q}_{L}e_{R})+h.c.\\ .\n\\end{equation}\nThe situation is thus similar to that in Eq.$~$(\\ref{FinalScale11}). Further lowering the LQ\/DQ scale by about an order of magnitude is possible starting from the $HV_{1,\\mu}^{2\/3\\dagger}V_{2}^{5\/3,\\mu}$ coupling, as $V_{1,\\mu}^{2\/3\\dagger}$ couples only to $\\nu_{R}$.\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[height=1.3984in,width=1.9009in]{Fig3.jpg}\n\\caption{Proton decay operator generated by the spontaneous breaking of $U(1)_{\\mathcal{B}-\\mathcal{L}}$.}\n\\label{Fig3}\n\\end{center}\n\\end{figure}\n\nIn this regard, note that all these scenarios are again compatible with a seesaw mechanism. Adding either a $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, or $M_{R}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling to $\\mathcal{L}_{\\mathrm{KSVZ+LQ}}$ in Eq.~(\\ref{LagrKSVZ2}), a single exact $U(1)$ remains at the PQ scale, with charges\n\\begin{equation}\n\\begin{tabular}[c]{ccccccccccccc}\\hline\n& & $\\phi$ & $S_{2}^{7\/3}$ & $\\tilde{S}_{1}^{4\/3}$ & $\\Psi_{L}$ & $\\Psi_{R}$\n& $q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}:$ & $U(1)_{PQ}$ & $2$ & $4\/3$ & $-2\/3$\n& $1\/3$ & $-5\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $-1$ & $-1$ & $-1$\\\\\n$\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}:$ & $U(1)_{PQ}$ & $2\/3$ & $0$\n& $-2\/3$ & $-1$ & $-5\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $1\/3$\\\\\n$M_{R}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}:$ & $U(1)_{PQ}$ & $1$ & $1\/3$ & $-2\/3$\n& $1\/3$ & $-5\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $0$ & $0$ & $0$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nFor all these cases, the axion still emerges as a massless Goldstone boson, and is associated to both $U(1)_{\\mathcal{B}-\\mathcal{L}}$ and $U(1)_{\\mathcal{L}}$ spontaneous breakings.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}$}\n\nThe spontaneous breaking of $\\mathcal{B}$ first arose at the dimension-9 level in Table~\\ref{TableLQBL} since it necessarily involves six fermions. As seen in Sec.~\\ref{Sec2c}, typical scenarios thus require a cubic coupling among DQ states. Let us start with\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}}=\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}^{4\/3}\\bar\n{d}_{R}^{\\mathrm{C}}d_{R}+S_{1}^{8\/3}\\bar{u}_{R}^{\\mathrm{C}}u_{R}+\\phi\nS_{1}^{4\/3}S_{1}^{4\/3}S_{1}^{8\/3}+h.c.\\ , \\label{LagrKSVZ3}%\n\\end{equation}\nwhere $S_{1}^{4\/3}\\sim(\\mathbf{3},\\mathbf{1},+4\/3)$ and $S_{1}^{8\/3}\\sim(\\mathbf{\\bar{6}},\\mathbf{1},-8\/3)$. Though not compulsory, we add the coupling $S_{1}^{8\/3}\\bar{\\Psi}_{L}^{\\mathrm{C}}q_{L}$ to break $U(1)_{\\Psi}$ and fix the charges of $\\Psi_{L,R}$. With this Lagrangian, only two $U(1)$s are exact:\n\\begin{equation}\n\\begin{tabular}[c]{cccccccccccc}\\hline\n& $\\phi$ & $S_{2}^{8\/3}$ & $S_{1}^{4\/3}$ & $\\Psi_{L}$ & $\\Psi_{R}$ & $q_{L}$ &\n$u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $2$ & $-2\/3$ & $-2\/3$ & $1\/3$ & $-5\/3$ & $1\/3$ & $1\/3$\n& $1\/3$ & $0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $0$ & $0$ & $0$ & $0$ & $0$ & $0$ & $0$ & $0$ & $1$ &\n$1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThus, $U(1)_{PQ}=U(1)_{\\mathcal{B}}$ is broken spontaneously by two units, but $U(1)_{\\mathcal{L}}$ stays exact. This model is actually very similar to that of Ref.~\\cite{Barbieri:1981yr} (see also Ref.~\\cite{Berezhiani:2015afa}), except that the Goldstone boson associated to the $U(1)_{\\mathcal{B}}$ breaking is identified with the axion. In turn, the axion ends up coupled to neutron pairs, via the diagram shown in Fig.~\\ref{Fig4a}. The corresponding operator is\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)}^{eff}=\\exp\n(ia^{0}\/v_{\\phi})\\frac{v_{\\phi}}{m_{S^{4\/3}}^{4}m_{S^{8\/3}}^{2}}\\bar{d}%\n_{R}^{\\mathrm{C}}d_{R}\\bar{d}_{R}^{\\mathrm{C}}d_{R}\\bar{u}_{R}^{\\mathrm{C}%\n}u_{R}+h.c.\\ . \\label{ScaleNN}%\n\\end{equation}\nTypical bounds on the scale of the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ operators are at around $100$~TeV~\\cite{Mohapatra:2009wp,Phillips:2014fgb} if the couplings implicit in Eq.~(\\ref{LagrKSVZ3}) are all $\\mathcal{O}(1)$. The PQ scale of $10^{9}$~GeV pushes the DQ scale slightly higher than those $100\\ \\text{TeV}$, but given that their masses appear to the sixth power, this is marginal (less than an order of magnitude). The presence of the axion also leads to an effective operator%\n\\begin{equation}\n\\frac{1}{m_{S^{4\/3}}^{4}m_{S^{8\/3}}^{2}}a^{0}\\bar{d}_{R}^{\\mathrm{C}}d_{R}%\n\\bar{d}_{R}^{\\mathrm{C}}d_{R}\\bar{u}_{R}^{\\mathrm{C}}u_{R}+h.c.\\ \\rightarrow\n\\frac{\\Lambda_{QCD}^{6}}{m_{S^{4\/3}}^{4}m_{S^{8\/3}}^{2}}a^{0}\\bar\n{n}^{\\mathrm{C}}\\gamma_{5}n+h.c.\\ ,\n\\end{equation}\nwith the QCD confinement scale $\\Lambda_{QCD}$ of the order of $300$~MeV. Because the DQ mass scale is pushed rather high by the dimension-nine operator in Eq.~(\\ref{ScaleNN}), this direct coupling is very suppressed. Note, though, that it could have consequences in a cosmological context~\\cite{Mohapatra:2009wp}.%\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[height=1.8844in,width=1.7167in]{Fig4a.jpg}\n\\caption{Neutron-antineutron oscillation operators generated by the spontaneous breaking of $U(1)_{\\mathcal{B}}$.}\n\\label{Fig4a}\n\\end{center}\n\\end{figure}\n\nAs for the previous two scenarios, a seesaw mechanism can be implemented by adding a $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, or $M_{R}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling to the Lagrangian in Eq.~(\\ref{LagrKSVZ3}). For the former two, this identifies the axion as the Majoron~\\cite{Barbieri:1981yr}. The only change is, in some sense, to assign a $\\mathcal{B}$ number to $\\nu_{R}$, hence by extension, to the leptons:\n\\begin{equation}\n\\begin{tabular}[c]{ccccccccccccc}\\hline\n& & $\\phi$ & $S_{2}^{8\/3}$ & $S_{1}^{4\/3}$ & $\\Psi_{L}$ & $\\Psi_{R}$ &\n$q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}:$ & $U(1)_{PQ}$ & $2$ & $-2\/3$ & $-2\/3$\n& $1\/3$ & $-5\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $-1$ & $-1$ & $-1$\\\\\n$\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}:$ & $U(1)_{PQ}$ & $2$ &\n$-2\/3$ & $-2\/3$ & $1\/3$ & $-5\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $1$ & $1$ & $1$\\\\\n$M_{R}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}:$ & $U(1)_{PQ}$ & $2$ & $-2\/3$ &\n$-2\/3$ & $1\/3$ & $-5\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $0$ & $0$ & $0$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nNote that the charges imposed by the presence of $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ open the door to the $S_{1}^{4\/3}\\bar{u}_{R}\\nu_{R}^{\\mathrm{C}}$ coupling also, and thus to direct proton decay via an $S_{1}^{4\/3}$ Fermi interaction. For the other two scenarios, proton decay remains forbidden since all its decay modes include an odd number of leptons, but only $\\Delta\\mathcal{L}=2n$ transitions are made possible by the Lagrangian couplings.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}\\pm3\\mathcal{L}$}\n\nFrom Eq.~(\\ref{LQOpsB2}), it is clear that the scenarios leading to $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ can be adapted to generate $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,3)$ effects. All that is needed is to replace all DQ couplings by LQ couplings. The only difficulty is to account for the antisymmetric color contraction, since LQ necessarily transform as $\\mathbf{3}$ under $SU(3)_{C}$. If we insist on introducing at most two different LQ, the only available scenario is%\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}}=\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}^{2\/3}(\\bar\n{d}_{R}\\nu_{R}^{\\mathrm{C}}+\\bar{u}_{R}e_{R}^{\\mathrm{C}}+\\bar{q}_{L}\\ell\n_{L}^{\\mathrm{C}})+V_{2,\\mu}^{1\/3}(\\bar{u}_{R}\\gamma^{\\mu}\\ell_{L}%\n^{\\mathrm{C}}+\\bar{q}_{L}\\gamma^{\\mu}\\nu_{R}^{\\mathrm{C}})+\\phi S_{1}%\n^{2\/3}V_{2,\\mu}^{1\/3}V_{2}^{1\/3,\\mu}+h.c.\\ . \\label{LagrKSVZ4}%\n\\end{equation}\nAs usual, the $U(1)_{\\Psi}$ is broken explicitly, this time by adding $V_{2,\\mu}^{1\/3}\\bar{\\Psi}_{L}\\gamma^{\\mu}e_{R}^{\\mathrm{C}}$ to force the hypercharge of $\\Psi_{L,R}$ to be different from that of SM quarks. If instead of the LQ couplings, DQ couplings were allowed, this scenario produces the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ symmetry pattern discussed in the previous section. Now, with these LQ couplings and no DQ couplings, the charges are\n\\begin{equation}%\n\\begin{tabular}[c]{cccccccccccc}\\hline\n& $\\phi$ & $S_{1}^{2\/3}$ & $V_{2}^{1\/3}$ & $\\Psi_{L}$ & $\\Psi_{R}$ & $q_{L}$ &\n$u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $1$ & $1\/3$ & $1\/3$ & $1\/3$ & $-2\/3$ & $1\/3$ & $1\/3$ &\n$1\/3$ & $0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $3$ & $1$ & $1$ & $0$ & $-3$ & $0$ & $0$ & $0$ & $1$ &\n$1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThe PQ symmetry is identified with $U(1)_{\\mathcal{B}+3\\mathcal{L}}$, and dimension-nine $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,3)$ proton decay operators appear at the low scale, see Fig.~\\ref{Fig4b}$a$ (a similar LQ model was proposed in Ref.~\\cite{WeinbergPRD22} to break $\\mathcal{B}+3\\mathcal{L}$ spontaneously). The fact that these operators are dimension-nine allows to lower the LQ scale, but qualitatively, this scenario is not very different from the $\\mathcal{B}\\pm\\mathcal{L}$ ones. Also, a seesaw mechanism can be added with either $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ or $M_{R}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, but not with $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ as this would allow back the DQ couplings of both $S_{1}^{2\/3}$ and $V_{2,\\mu}^{1\/3}$. It should be noted that these charges allow for the $D^{\\mu}HS_{1}^{2\/3}V_{2,\\mu}^{1\/3\\dagger}$ and $HD^{\\mu}S_{1}^{2\/3}V_{2,\\mu}^{1\/3\\dagger}$couplings. If not initially present, they are immediately generated via a fermion loop. Yet, these operators carry $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,0)$ and cannot help create simpler proton decay processes. They could turn on some new four-fermion semileptonic FCNC operators though, but these effects are beyond our scope.%\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[height=2.2329in,width=4.9536in]{Fig4b.jpg}\n\\caption{Proton decay operators generated by the spontaneous breaking of $U(1)_{\\mathcal{B}+3\\mathcal{L}}$ ($a.$) and $U(1)_{\\mathcal{B}-3\\mathcal{L}}$ ($b.$).}\n\\label{Fig4b}\n\\end{center}\n\\end{figure}\n\nThe final pattern is $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)$, and this one is quite difficult to induce spontaneously. The operators in Eq.~(\\ref{LQOpsBL3}) being already of dimension four, we cannot proceed as for the other cases and simply multiply them by $\\phi$. One way to proceed is to start with an operator from the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,3)$\nclass in Eq.~(\\ref{LQOpsB2}), and then switch $\\mathcal{L}$ by six units using $\\Delta\\mathcal{L}=2$ operators of Eq.~(\\ref{LQOpsBmL}). For instance, the Lagrangian\n\\begin{align}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}} & =\\mathcal{L}_{\\mathrm{KSVZ}}+S_{2}%\n^{1\/3}(\\bar{d}_{R}\\ell_{L}+\\bar{q}_{L}\\nu_{R})+V_{1,\\mu}^{2\/3}\\bar{d}%\n_{R}\\gamma^{\\mu}\\nu_{R}\\nonumber\\\\\n& \\ \\ \\ \\ +\\phi(HS_{2}^{1\/3\\dagger}S_{1}^{2\/3}+H^{\\dagger}V_{1}%\n^{2\/3\\dagger,\\mu}V_{2,\\mu}^{1\/3}+S_{1}^{2\/3\\dagger}V_{2,\\mu}^{1\/3\\dagger}%\nV_{2}^{1\/3,\\mu\\dagger})+h.c.\\ , \\label{LagrKSVZ5}%\n\\end{align}\ndoes lead to the desired $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)$ pattern, as shown in Fig.~\\ref{Fig4b}$b$. With four LQ states, it is certainly more complex than the other scenarios, though it should be noted that there is a certain symmetric flavor to the presence of $S_{2}^{1\/3}$, $S_{1}^{2\/3}$ and $V_{2,\\mu}^{1\/3}$, $V_{1,\\mu}^{2\/3}$. Also, it is not compulsory for $\\phi$ to appear in all of the last three couplings, but when it does, only some combinations do give a $\\mathcal{B}-3\\mathcal{L}$ charge to $\\phi$. Further adding $V_{1,\\mu}^{2\/3}\\bar{\\Psi}_{R}\\gamma^{\\mu}e_{R}$ to fix the $\\Psi_{L,R}$ quantum numbers, we find%\n\\begin{equation}\n\\begin{tabular}[c]{cccccccccccccc}\\hline\n& $\\phi$ & $S_{1}^{2\/3}$ & $V_{2}^{1\/3}$ & $S_{2}^{1\/3}$ & $V_{1}^{2\/3}$ &\n$\\Psi_{L}$ & $\\Psi_{R}$ & $q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ &\n$\\nu_{R}$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $1\/4$ & $1\/12$ & $1\/12$ & $1\/3$ & $1\/3$ & $7\/12$ &\n$1\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $-3\/4$ & $-1\/4$ & $-1\/4$ & $-1$ & $-1$ & $-3\/4$ & $0$ &\n$0$ & $0$ & $0$ & $1$ & $1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThe PQ symmetry is thus indeed $U(1)_{\\mathcal{B}-3\\mathcal{L}}$. Note that these charges forbid all the SM fermion couplings of $S_{1}^{2\/3}$ and $V_{2}^{1\/3}$, as well as all other possible cubic interactions among the LQ and DQ states\\footnote{Some derivative interactions are possible though, but those necessarily involve the LQs whose SM fermion couplings are forbidden, hence they do not alter the symmetry breaking pattern, and would lead to more suppressed proton decay operators.}. However, given the complicated structure shown in Fig.~\\ref{Fig4b}$b$, the final operators are of dimension 16 instead of dimension 10:\n\\begin{align}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)}^{eff} &\n=\\frac{\\phi^{4}(H^{\\dagger}H)}{m_{S1\/3}^{2}m_{V2\/3}^{4}m_{S2\/3}^{2}%\nm_{V1\/3}^{4}}H^{\\dagger}(\\bar{d}_{R}\\ell_{L}+\\bar{q}_{L}\\nu_{R})\\bar{d}%\n_{R}\\gamma_{\\mu}\\nu_{R}\\bar{d}_{R}\\gamma^{\\mu}\\nu_{R}\\nonumber\\\\\n& \\rightarrow\\frac{v_{\\phi}^{4}v_{EW}^{3}}{m_{S1\/3}^{2}m_{V2\/3}^{4}m_{S2\/3}%\n^{2}m_{V1\/3}^{4}}(\\bar{d}_{R}\\ell_{L}+\\bar{q}_{L}\\nu_{R})\\bar{d}_{R}%\n\\gamma_{\\mu}\\nu_{R}\\bar{d}_{R}\\gamma^{\\mu}\\nu_{R}\\ .\n\\end{align}\nBesides, turning on a seesaw mechanism with $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ (as $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ would allow back some $S_{1}^{2\/3}$ and $V_{2}^{1\/3}$ couplings to SM fermions), a further suppression of $(v_{EW}\/v_{\\phi})^{2}$ to connect two $\\nu_{R}$ to light fermions arises. Altogether, assuming a common scale for all the LQs, their mass can be as low as around 100 TeV when $v_{\\phi}\\approx10^{9}$ GeV. This is much lower than in GUT scenarios, and actually falls within the ballpark of the scale required by neutron-antineutron oscillation from Eq.~(\\ref{ScaleNN}).\n\n\\subsection{DFSZ scenarios with leptoquarks and diquarks\\label{SecDFSZ}}\n\nAll the scenarios discussed in the KSVZ case can readily be adapted to the DFSZ model. Basically, one removes the $\\Psi_{L,R}$ state but introduces a $\\phi^{2}H_{u}^{\\dagger}H_{d}$ coupling, while the $\\phi$ couples to various combinations of LQ\/DQ states exactly as in the KSVZ scenarios. A number of peculiarities are worth mentioning though:\n\n\\begin{enumerate}\n\\item The symmetry patterns are more difficult to analyze in the DFSZ case because the PQ and hypercharge symmetries are entangled, see Eq.~(\\ref{DFSZScalars}). Thus, further entangling $U(1)_{\\mathcal{B}}$ and $U(1)_{\\mathcal{L}}$ with the $U(1)$s associated to $H_{u}$ and $H_{d}$ rephasing blurs the picture completely. In practice, the PQ charges of $\\phi$, $H_{u}$, and $H_{d}$ are always fixed to $PQ(H_{u})=x$, $PQ(H_{d})=-1\/x$, $PQ(\\phi)=\\left( x+1\/x\\right) \/2$, see Eq.~(\\ref{DFSZScalars}), no matter the amount of $U(1)_{\\mathcal{B}}$ and $U(1)_{\\mathcal{L}}$ that is entangled within $U(1)_{PQ}$.\n\n\\item Because $H_{u}$, and $H_{d}$ carry PQ charges, so does the SM fermions, even without the presence of LQ\/DQ states. As shown in Eq.~(\\ref{DFSZfermions}), these charges have ambiguities reflecting the exact accidental symmetries. Thus, any entanglement of $U(1)_{\\mathcal{B}}$ and $U(1)_{\\mathcal{L}}$ with $U(1)_{PQ}$ will be reflected in that arbitrariness. Typically, only one free parameter will remain instead of the $\\beta$ and $\\gamma$ parameters of Eq.~(\\ref{DFSZfermions}). Thus, looking at this remaining arbitrariness permits to identify the combination of $\\beta$ and $\\gamma$, i.e., $U(1)_{\\mathcal{B}}$ and $U(1)_{\\mathcal{L}}$, that has been spontaneously broken.\n\n\\item Because $U(1)_{PQ}$ has always a component within $U(1)_{Hu}\\otimes U(1)_{Hd}$, the PQ charge of the SM fermions are never fully aligned with some combinations of $\\mathcal{B}$ and $\\mathcal{L}$. As a result, LQ states are often restricted to couple to only a single SM fermion LQ or DQ pair. For example, the gauge symmetries allow both $S_{2}^{1\/3}\\bar{d}_{R}\\ell_{L}$ and $S_{2}^{1\/3}\\bar{q}_{L}\\nu_{R}$, but the PQ charge do not since $PQ(\\bar{d}_{R}\\ell_{L})=\\gamma-\\beta+1\/x$ and $PQ(\\bar{q}_{L}\\nu_{R})=\\gamma-\\beta+x$, and this is true independently of $\\beta$ and $\\gamma$. In some cases, this actually makes the choice of LQ\/DQ couplings more natural than in the KSVZ case, since once some of them are selected, the others are immediately forbidden.\n\n\\item With $H_{u,d}$ at hand, many new couplings to LQ\/DQ states are a priori possible already in the scalar potential. For instance, replacing $H$ by $H_{u}$ or $H_{d}$ in any of the couplings in Eqs.~(\\ref{LQOpsB2}) or~(\\ref{LQOpsBL3}) would couple the axion to $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating operators. However, these situations correspond to breaking $U(1)_{\\mathcal{B}}$ and\/or $U(1)_{\\mathcal{L}}$ at the electroweak scale, by entangling them with $U(1)_{Y}$. Indeed, $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating operators would involve $\\eta_{u}$ or $\\eta_{d}$ (the pseudoscalar components of $H_{u}$ and $H_{d}$), and thus also the Would-be Goldstone associated to $U(1)_{Y}$ since $G^{0}\\sim v_{u}\\eta_{u}+v_{d}\\eta_{d}$. The axion has only tiny $\\eta_{u}$ and $\\eta_{d}$ components, see Eq.~(\\ref{DFSZA0}). Turning on some $H_{u}^{\\dagger}H_{d}S_{j}^{\\dagger}S_{i}$ couplings would prevent any $G^{0}$ coupling, but would similarly lead to tiny axion couplings via its $\\cos\\beta\\eta_{u}-\\sin\\beta\\eta_{d}$ component. For these reasons, all the scenarios discussed below start from coupling $\\phi$ to the LQ\/DQ states, so that $\\mathcal{B}$ and\/or $\\mathcal{L}$ are broken at the PQ scale and the axion inherits some large $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating couplings. These scenarios are thus constructed exactly as in the KSVZ case.\n\\end{enumerate}\n\nAfter these general comments, let us briefly go through each of the $\\mathcal{B}$ and\/or $\\mathcal{L}$ spontaneous breaking scenario.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}+\\mathcal{L}$}\n\nBy analogy with the KSVZ scenario, Eq.~(\\ref{LagrKSVZ1a}), let us take%\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{DFSZ+LQ}}=\\mathcal{L}_{\\mathrm{DFSZ}}+S_{1}^{8\/3}\\bar\n{d}_{R}e_{R}^{\\mathrm{C}}+\\tilde{S}_{1}^{8\/3}\\bar{u}_{R}^{\\mathrm{C}}%\nu_{R}+\\phi^{2}S_{1}^{8\/3\\dagger}\\tilde{S}_{1}^{8\/3}+h.c.\\ , \\label{LagrDFSZ1}%\n\\end{equation}\nwith $\\mathcal{L}_{\\mathrm{DFSZ}}$ given in Eq.~(\\ref{DFSZ0}). Solving for the $U(1)$ charges under the constraint that $PQ(H_{u})=x$, $PQ(H_{d})=-1\/x$, which fixes $PQ(\\phi)=\\left( x+1\/x\\right) \/2$, leaves a single under-determination. In this way, we identify the remaining symmetry as $U(1)_{\\mathcal{B}-\\mathcal{L}}$, with%\n\\begin{equation}%\n\\begin{tabular}[c]{ccccccccc}\\hline\n& $S_{1}^{8\/3}$ & $\\tilde{S}_{1}^{8\/3}$ & $q_{L}$ & $u_{R}$ & $d_{R}$ &\n$\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{PQ}$ & $\\dfrac{1}{x}-x\\rule[-0.14in]{0in}{0.36in}$ & $-2x$ & $0$ & $x$\n& $\\dfrac{1}{x}$ & $-\\dfrac{1}{x}-x$ & $-x$ & $-\\dfrac{1}{x}$\\\\\n$U(1)_{\\mathcal{B}-\\mathcal{L}}$ & $-2\/3$ & $-2\/3$ & $1\/3$ & $1\/3$ & $1\/3$ &\n$-1$ & $-1$ & $-1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nwith $\\phi$, $H_{u}$, and $H_{d}$ neutral under $U(1)_{\\mathcal{B}-\\mathcal{L}}$. This shows that $U(1)_{\\mathcal{B}+\\mathcal{L}}\\subset U(1)_{PQ}\\subset U(1)_{Hu}\\otimes U(1)_{Hd}\\otimes U(1)_{\\mathcal{B}}\\otimes U(1)_{\\mathcal{L}}$ is spontaneously broken. Note well that the quoted $U(1)_{PQ}$ charges are just one possible choice, since $U(1)_{\\mathcal{B}-\\mathcal{L}}$ remains as an ambiguity. We could also have written the charges as\n\\begin{equation}%\n\\begin{tabular}[c]{ccccccccc}\\hline\n& $S_{1}^{8\/3}$ & $\\tilde{S}_{1}^{8\/3}$ & $q_{L}$ & $u_{R}$ & $d_{R}$ &\n$\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{PQ}$ & $\\dfrac{1}{x}-x-2\\xi\\rule[-0.14in]{0in}{0.36in}$ & $-2x-2\\xi$ &\n$\\xi$ & $x+\\xi$ & $\\dfrac{1}{x}+\\xi$ & $-\\dfrac{1}{x}-x-3\\xi$ & $-x-3\\xi$ &\n$-\\dfrac{1}{x}-3\\xi$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nwith $\\xi$ the free parameter corresponding to $U(1)_{\\mathcal{B}-\\mathcal{L}}$. We can also see that this corresponds to Eq.~(\\ref{DFSZfermions}) with $\\beta=\\xi$ and $\\gamma=-\\dfrac{1}{x}-x-3\\xi$. This shows that the dimension-five axion to gauge boson couplings are unaffected by the LQ\/DQ, since they are independent of $\\beta$ and $\\gamma$. Also, one should not conclude that the axion does not couple to $q_{L}$, even though that coupling is absent from the axion effective Lagrangian since $PQ(q_{L})$ is set to zero.\n\nConcerning the axion $\\mathcal{B}+\\mathcal{L}$ violating operator, the same effective interactions arises as in the KSVZ scenario, see Eq.~(\\ref{EffHKSVZ1}). This is evident from Fig.~\\ref{Fig2}, which is independent of how the axion emerges. The only difference is that the pseudoscalar component of $\\phi$ is not purely the axion, but this is only a totally negligible $\\mathcal{O}(v_{EW}\/vs)$ effect, see Eq.~(\\ref{DFSZA0}).\n\nFinally, exactly as in the KSVZ scenario, the remaining $U(1)_{\\mathcal{B}-\\mathcal{L}}$ freedom permits to set up a PQ-induced seesaw mechanism by adding $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ or $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$. In both cases, this simply fixes the parameter $\\xi$ and removes the remaining $U(1)_{\\mathcal{B}-\\mathcal{L}}$ ambiguity. Yet, the final PQ charges do not reflect at all the peculiar symmetry breaking pattern, with $U(1)_{\\mathcal{B}+\\mathcal{L}}$ and $U(1)_{\\mathcal{L}}$ being separately, but concurrently, spontaneously broken at the PQ scale. By the way, exactly the same PQ charges arise if $\\phi^{2}S_{1}^{8\/3\\dagger}\\tilde{S}_{1}^{8\/3}$ is replaced by $H_{d}^{\\dagger}H_{u}S_{1}^{8\/3\\dagger}\\tilde{S}_{1}^{8\/3}$, though as discussed before, the symmetry breaking chain is very different, as are the axion couplings.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}-\\mathcal{L}$}\n\nPursuing our adaptation of the KSVZ scenario, let us consider now%\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{DFSZ+LQ}}=\\mathcal{L}_{\\mathrm{DFSZ}}+S_{1}^{4\/3}\\bar\n{d}_{R}^{\\mathrm{C}}d_{R}+S_{2}^{7\/3}\\bar{u}_{R}\\ell_{L}+\\phi H_{u}%\nS_{2}^{7\/3\\dagger}S_{1}^{4\/3}+h.c.\\ . \\label{LagrDFSZ2}%\n\\end{equation}\nBoth fermionic couplings of $S_{2}^{7\/3}$ cannot be present at the same time for the PQ symmetry to exist, so we choose to keep $S_{2}^{7\/3}\\bar{u}_{R}\\ell_{L}$ and discard $S_{2}^{7\/3}\\bar{q}_{L}e_{R}$. Also, we introduced $H_{u}$ in the quartic scalar coupling, but could equally well have used $H_{d}$. From this Lagrangian, the PQ charges are found to be\n\\begin{equation}%\n\\begin{tabular}[c]{ccccccccc}\\hline\n& $S_{1}^{4\/3}$ & $S_{2}^{7\/3}$ & $q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ &\n$e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{PQ}$ & $-\\dfrac{2}{x}\\rule[-0.14in]{0in}{0.36in}$ & $\\dfrac{3x}%\n{2}-\\dfrac{3}{2x}$ & $0$ & $x$ & $\\dfrac{1}{x}$ & $\\dfrac{3}{2x}-\\dfrac{x}{2}$\n& $\\dfrac{5}{2x}-\\dfrac{x}{2}$ & $\\dfrac{3}{2x}+\\dfrac{x}{2}$\\\\\n$U(1)_{\\mathcal{B}+\\mathcal{L}}$ & $-2\/3$ & $-2\/3$ & $1\/3$ & $1\/3$ & $1\/3$ &\n$1$ & $1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nSo, $U(1)_{\\mathcal{B}-\\mathcal{L}}$ is spontaneously broken at the PQ scale, but $U(1)_{\\mathcal{B}+\\mathcal{L}}$ remains. As before, we could rewrite the PQ charge introducing a free parameter to reflect the exact $U(1)_{\\mathcal{B}+\\mathcal{L}}$ symmetry, hence one should not interpret $PQ(q_{L})=0$ as meaning it has no coupling to the axion. The $U(1)_{\\mathcal{B}+\\mathcal{L}}$ ambiguity can then be used to allow for a $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ or $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling, and set up the seesaw mechanism.\n\nThe final $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operator is again one of the dimension-seven operators listed in Table~\\ref{TableLQBL}. Note, though, that because the PQ symmetry restricts the LQ couplings to SM fermions, only a single operator is induced. This is a generic characteristic of the DFSZ implementation: compared to the KSVZ case, it is more restrictive. Phenomenologically, this could show up as definite decay patterns for the proton (if ever observed). Starting from Eq.~(\\ref{LagrDFSZ2}), the operator arising at tree level is\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)}^{eff}=\\frac{1}{m_{S}^{2}m_{\\tilde{S}}^{2}}\\phi H_{u}\\bar{d}_{R}d_{R}^{\\mathrm{C}}\\bar\n{u}_{R}\\ell_{L}+h.c.\\ ,\n\\end{equation}\nNote that some other gauge and PQ invariant operators may arise at higher loops via Yukawa insertions, but those are more suppressed. The leading proton decay operator is thus proportional to $v_{\\phi}v_{u}\/m_{S}^{4}$, and the constraints are similar as in the KSVZ scenario. Concerning the axion coupling, notice that\n\\begin{equation}\n\\phi H_{u}\\ell_{L}\\rightarrow\\frac{1}{2}v_{u}v_{\\phi}\\exp i\\left( \\frac{\\eta\n_{u}}{v_{u}}+\\frac{v_{\\phi}}{v_{\\phi}}\\right) \\nu_{L}\\ ,\n\\end{equation}\nso the combination that occurs in the effective operator is\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)}^{eff}=\\frac\n{v_{\\phi}v_{u}}{m_{S}^{2}m_{\\tilde{S}}^{2}}\\left( 1+i\\frac{G^{0}}{v}%\n+i\\frac{a^{0}}{v_{\\phi}}\\frac{3x^{2}+1}{x^{2}+1}+i\\frac{\\pi^{0}}{v}x\\right)\n\\bar{d}_{R}d_{R}^{\\mathrm{C}}\\bar{u}_{R}\\nu_{L}\\ .\n\\end{equation}\nFor comparison, the $\\mu H_{u}S_{2}^{7\/3\\dagger}S_{1}^{4\/3}$ coupling would lead to\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)}^{eff}=\\frac{\\mu\nv_{u}}{m_{S}^{2}m_{\\tilde{S}}^{2}}\\left( 1+i\\frac{G^{0}}{v}+i\\frac{a^{0}%\n}{v_{\\phi}}\\frac{2x^{2}}{x^{2}+1}+i\\frac{\\pi^{0}}{v}x\\right) \\bar{d}_{R}%\nd_{R}^{\\mathrm{C}}\\bar{u}_{R}\\nu_{L}\\ ,\n\\end{equation}\nwith $\\mu$ some mass scale. The $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operator arises at the $v_{\\phi}$ scale from $\\phi H_{u}S_{2}^{7\/3\\dagger}S_{1}^{4\/3}$, but at a lower scale from $H_{u}S_{2}^{7\/3\\dagger}S_{1}^{4\/3}$ since we would expect $\\mu$ to be at the LQ\/DQ scale, $\\mu\\sim m_{S}$, or even at the electroweak scale, $\\mu\\sim v$. Note that in both cases, the $G^{0}$ enters as expected for a would-be Goldstone, and would disappear in the unitary gauge. The axion coupling is $\\mathcal{O}(v_{EW}\/v_{\\phi})$ compared to the four-fermion operator, exactly like in the KSVZ scenario.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}$}\n\nNeutron-antineutron oscillations can be induced in the same way in the DFSZ and KSVZ models, see Fig.~\\ref{Fig4a}. Starting with\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{DFSZ+LQ}}=\\mathcal{L}_{\\mathrm{DFSZ}}+S_{1}^{4\/3}\\bar\n{d}_{R}^{\\mathrm{C}}d_{R}+S_{1}^{8\/3}\\bar{u}_{R}^{\\mathrm{C}}u_{R}+\\phi\nS_{1}^{4\/3}S_{1}^{4\/3}S_{1}^{8\/3}+h.c.\\ , \\label{LagrDFSZ3}%\n\\end{equation}\nwhere $S_{1}^{4\/3}\\sim(\\mathbf{3},\\mathbf{1},+4\/3)$ and $S_{1}^{8\/3}\\sim(\\mathbf{\\bar{6}},\\mathbf{1},-8\/3)$, we get the PQ charges%\n\\begin{equation}%\n\\begin{tabular}[c]{ccccccccc}\\hline\n& $S_{1}^{4\/3}$ & $S_{1}^{8\/3}$ & $q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ &\n$e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{PQ}$ & $\\dfrac{x}{2}-\\dfrac{5}{6x}\\rule[-0.14in]{0in}{0.36in}$ &\n$\\dfrac{7}{6x}-\\dfrac{3x}{2}$ & $-\\dfrac{x}{4}-\\dfrac{7}{12x}$ & $\\dfrac\n{3x}{4}-\\dfrac{7}{12x}$ & $\\dfrac{5}{12x}-\\dfrac{x}{4}$ & $0$ & $\\dfrac{1}{x}$\n& $x$\\\\\n$U(1)_{\\mathcal{L}}$ & $0$ & $0$ & $0$ & $0$ & $0$ & $1$ & $1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThus, $U(1)_{\\mathcal{B}}$ is broken spontaneously, but $U(1)_{\\mathcal{L}}$ stays exact. The phenomenology is the same as in the KSVZ model, see Fig.~\\ref{Fig4a} and Eq.~(\\ref{ScaleNN}). Majorana neutrino masses can be generated spontaneously with $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$, but not with $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ as the PQ charges of the leptons would then allow for the $S_{1}^{4\/3}\\bar{u}_{R}\\nu_{R}^{\\mathrm{C}}$ coupling, and thereby to tree-level proton decay.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}\\pm3\\mathcal{L}$}\n\nThe last two scenarios are those producing exotic $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,\\pm3)$ proton decay operators. Let us start with the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,3)$ case, and the Lagrangian\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{DFSZ+LQ}}=\\mathcal{L}_{\\mathrm{DFSZ}}+S_{1}^{2\/3}\\bar\n{q}_{L}\\ell_{L}^{\\mathrm{C}}+V_{2,\\mu}^{1\/3}(\\bar{u}_{R}\\gamma^{\\mu}\\ell\n_{L}^{\\mathrm{C}}+\\bar{q}_{L}\\gamma^{\\mu}\\nu_{R}^{\\mathrm{C}})+\\phi^{\\dagger}\nS_{1}^{2\/3}V_{2,\\mu}^{1\/3}V_{2}^{1\/3,\\mu}+h.c.\\ . \\label{LagrDFSZ4}%\n\\end{equation}\nThe $S_{1}^{2\/3}(\\bar{d}_{R}\\nu_{R}^{\\mathrm{C}}+\\bar{u}_{R}e_{R}^{\\mathrm{C}})$ and $S_{1}^{2\/3}\\bar{q}_{L}\\ell_{L}^{\\mathrm{C}}$ couplings cannot both be present, and we take only the latter, while the $V_{2,\\mu}^{1\/3}(\\bar{u}_{R}\\gamma^{\\mu}\\ell_{L}^{\\mathrm{C}}+\\bar{q}_{L}\\gamma^{\\mu}\\nu _{R}^{\\mathrm{C}})$ couplings are compatible with each other. The $U(1)$ charges are then\n\\begin{equation}%\n\\begin{tabular}[c]{ccccccccc}\\hline\n& $S_{1}^{2\/3}$ & $V_{2,\\mu}^{1\/3}$ & $q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$\n& $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{PQ}$ & $\\dfrac{1}{6x}-\\dfrac{x}{2}\\rule[-0.14in]{0in}{0.36in}$ &\n$\\dfrac{x}{2}+\\dfrac{1}{6x}$ & $0$ & $x$ & $\\dfrac{1}{x}$ & $\\dfrac{1}%\n{6x}-\\dfrac{x}{2}$ & $\\dfrac{7}{6x}-\\dfrac{x}{2}$ & $\\dfrac{1}{6x}+\\dfrac\n{x}{2}$\\\\\n$U(1)_{3\\mathcal{B}-\\mathcal{L}}$ & $0$ & $0$ & $1$ & $1$ & $1$ & $-1$ & $-1$\n& $-1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThe $U(1)_{3\\mathcal{B}-\\mathcal{L}}$ symmetry remains, and its orthogonal combination $U(1)_{\\mathcal{B}+3\\mathcal{L}}$ is spontaneously broken. Dimension-nine $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,3)$ proton decay operators thus appear at the low scale (as well as semileptonic $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,0)$ operators since these charges allow for the $D^{\\mu}HS_{1}^{2\/3}V_{2,\\mu}^{1\/3\\dagger}$ couplings). Once more, there is enough room for a seesaw mechanism with $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ and\/or $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$. Depending on the LQ couplings of $S_{1}^{2\/3}$, it is always possible to choose the seesaw operator that sets PQ charges forbidding the DQ couplings of both $S_{1}^{2\/3}$ and $V_{2,\\mu}^{1\/3}$, and thus proton decay via dimension-six operators.\n\nConcerning the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)$ operators, we start from\n\\begin{align}\n\\mathcal{L}_{\\mathrm{DFSZ+LQ}} & =\\mathcal{L}_{\\mathrm{DFSZ}}+S_{2}%\n^{1\/3}\\bar{d}_{R}\\ell_{L}+V_{1,\\mu}^{2\/3}\\bar{d}_{R}\\gamma^{\\mu}\\nu\n_{R}\\nonumber\\\\\n& +\\phi(H_{u}S_{2}^{1\/3\\dagger}S_{1}^{2\/3}+H_{d}^{\\dagger}V_{1}%\n^{2\/3\\dagger,\\mu}V_{2,\\mu}^{1\/3}+S_{1}^{2\/3\\dagger}V_{2,\\mu}^{1\/3\\dagger}\nV_{2}^{1\/3,\\mu\\dagger})+h.c.\\ , \\label{LagrDFSZ5}%\n\\end{align}\nwith the $S_{2}^{1\/3}\\bar{q}_{L}\\nu_{R}$ removed. Several choices are possible for introducing the doublets $H_{u}$ and $H_{d}$ in these couplings, and we opt for the one most symmetrical with the Yukawa couplings, see Eq.~(\\ref{DFSZ0}). Only one of the fermionic couplings of $S_{2}^{1\/3}$ can be turned on, and we choose $S_{2}^{1\/3}\\bar{d}_{R}\\ell_{L}$. Then, the $U(1)$\ncharges are found to be\n\\begin{equation}%\n\\begin{tabular}\n[c]{ccccccccccc}\\hline\n& $S_{1}^{2\/3}$ & $V_{2,\\mu}^{1\/3}$ & $S_{2}^{1\/3}$ & $V_{1,\\mu}^{2\/3}$ &\n$q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{PQ}\\rule[-0.14in]{0in}{0.39in}$ & $\\dfrac{x^{2}+5}{6x}$ & $\\dfrac\n{x^{2}-1}{6x}$ & $\\dfrac{5x^{2}+4}{3x}$ & $\\dfrac{2x^{2}+4}{3x}$ & $0$ & $x$ &\n$\\dfrac{1}{x}$ & $\\dfrac{5x^{2}+1}{-3x}$ & $\\dfrac{5x^{2}-2}{-3x}$ &\n$\\dfrac{2x^{2}+1}{-3x}$\\\\\n$U(1)_{3\\mathcal{B}+\\mathcal{L}}$ & $0$ & $0$ & $0$ & $0$ & $1$ & $1$ & $1$ &\n$1$ & $1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThis time, $U(1)_{3\\mathcal{B}+\\mathcal{L}}$ remains and $U(1)_{\\mathcal{B}-3\\mathcal{L}}$ is spontaneously broken. The induced operator, from a process easily adapted from that of Fig.~\\ref{Fig4b}, is\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)}^{eff}=\\frac\n{\\phi^{4}(H_{d}^{\\dagger}H_{d}^{\\dagger}H_{u})}{m_{S1\/3}^{2}m_{V2\/3}^{4}m_{S2\/3}^{2}%\nm_{V1\/3}^{4}}\\bar{d}_{R}\\ell_{L}\\bar{d}_{R}\\gamma_{\\mu}\\nu\n_{R}\\bar{d}_{R}\\gamma^{\\mu}\\nu_{R}\\ .\n\\end{equation}\nAgain, phenomenologically, there is not much difference between the DFSZ and KSVZ implementation.\n\n\\subsection{Axion-induced proton decay and neutron-antineutron oscillations\\label{SecSpont}}\n\nIn both the KSVZ and DFSZ cases, we can induce spontaneously proton decay or neutron-antineutron oscillations. But, in all the scenarios discussed up to now, the processes involving the axion were $\\mathcal{O}(v_{EW}\/v_{\\phi})$ with respect to that without it. The reason is of course that in all cases, some coupling of $\\phi$ to the LQ\/DQ states was introduced, and $\\phi=(v_{\\phi}+\\rho)\\exp(i\\eta_{\\phi}\/v_{\\phi})\\approx v_{\\phi}+\\rho+ia^{0}$ (see Eq.~(\\ref{ExampleSSS})). The purpose here is to kill off the leading term, leaving only axion-induced $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating processes. The only way to achieve this is to consider derivative couplings of $\\phi$ to pairs of LQ\/DQs, and there are only three renormalizable options\n\\begin{equation}\n\\partial_{\\mu}\\phi S_{2}^{1\/3\\dagger}V_{2}^{1\/3,\\mu}\\ ,\\ \\partial_{\\mu}\\phi\nS_{1}^{2\/3\\dagger}V_{1}^{2\/3,\\mu}\\ ,\\ \\partial_{\\mu}\\phi S_{1}^{4\/3\\dagger\n}V_{1}^{4\/3,\\mu}.\n\\label{DerScenars}\n\\end{equation}\nIn these cases, the axion enters as $\\partial_{\\mu}\\phi\\approx\\partial_{\\mu}\\rho+i\\partial_{\\mu}a^{0}$, without a leading term tuned by $v_{\\phi}$. Though we have not attempted at constructing UV complete models generating such interactions, their structure is evocative of that which could arise if both $\\phi$ and scalar LQ\/DQ were somehow related to the fields giving masses to the vector LQ\/DQ. Such a situation can happen in simple GUT models: In Ref.~\\cite{Wise:1981ry} for example, $\\phi$ is identified with the phase of the complex $H_{24}$ field breaking $SU(5)$ down to the SM gauge group. Note, though, that the PQ breaking scale and the LQ\/DQ mass scale would be related in such models. In the present section, the two will be kept independent, with the latter usually much lower than the former.\n\nLet us see which symmetry breaking patterns can be achieved with these building blocks. We will use the KSVZ setting throughout as the alignments of the PQ with some combination of $\\mathcal{B}$ and $\\mathcal{L}$ are manifest, but the adaptation to the DFSZ scenario is immediate. Also, we will discard $\\Psi_{L,R}$ from the discussion. As in Sec.~\\ref{SecKSVZ}, their charge can always be set separately by introducing some couplings to the LQ\/DQ.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}-\\mathcal{L}$}\n\nThe scenarios with $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operators are immediately obtained using any one of the three couplings in Eq.~(\\ref{DerScenars}). For example, we can take\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}}=\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}^{2\/3}(\\bar\n{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R})+V_{1,\\mu}^{2\/3}%\n\\bar{d}_{R}\\gamma^{\\mu}\\nu_{R}+\\partial_{\\mu}\\phi S_{1}^{2\/3\\dagger}%\nV_{1}^{2\/3,\\mu}+h.c.\\ , \\label{LagrSSB1}%\n\\end{equation}\nand get two active $U(1)$s, with charges\n\\begin{equation}%\n\\begin{tabular}[c]{cccccccccc}\\hline\n& $\\phi$ & $S_{1}^{2\/3}$ & $V_{1,\\mu}^{2\/3}$ & $q_{L}$ & $u_{R}$ & $d_{R}$ &\n$\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $-1$ & $-2\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $0$ &\n$0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $1$ & $0$ & $-1$ & $0$ & $0$ & $0$ & $1$ & $1$ &\n$1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThus, $\\phi$ spontaneously breaks $U(1)_{\\mathcal{B}-\\mathcal{L}}$, leaving $\\mathcal{B}+\\mathcal{L}$ as an exact accidental symmetry. As before, we can add a $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling to generate neutrino masses and forbid the LQ couplings of $S_{1}^{2\/3}$.\n\nThe situation starting from the other derivative interaction is similar, hence we can generate:\n\\begin{align}\n\\partial_{\\mu}\\phi S_{1}^{2\/3\\dagger}V_{1}^{2\/3,\\mu} & \\rightarrow\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)}^{eff}=\\frac\n{1}{m_{S}^{2}m_{V}^{2}}\\partial_{\\mu}\\phi(\\bar{q}_{L}^{\\mathrm{C}}q_{L}%\n+\\bar{d}_{R}^{\\mathrm{C}}u_{R})\\bar{d}_{R}^{\\mathrm{C}}\\gamma^{\\mu}\\nu\n_{R}^{\\mathrm{C}}\\ ,\\\\\n\\partial_{\\mu}\\phi S_{2}^{1\/3\\dagger}V_{2}^{1\/3,\\mu} & \\rightarrow\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)}^{eff}=\\frac\n{1}{m_{S}^{2}m_{V}^{2}}\\partial_{\\mu}\\phi\\bar{d}_{R}^{\\mathrm{C}}\\gamma^{\\mu\n}q_{L}(\\bar{d}_{R}^{\\mathrm{C}}\\ell_{L}^{\\mathrm{C}}+\\bar{q}_{L}^{\\mathrm{C}%\n}\\nu_{R}^{\\mathrm{C}})\\ ,\\\\\n\\partial_{\\mu}\\phi S_{1}^{4\/3\\dagger}V_{1}^{4\/3,\\mu} & \\rightarrow\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)}^{eff}=\\frac\n{1}{m_{S}^{2}m_{V}^{2}}\\partial_{\\mu}\\phi\\bar{d}_{R}^{\\mathrm{C}}d_{R}(\\bar\n{u}_{R}^{\\mathrm{C}}\\gamma^{\\mu}\\nu_{R}^{\\mathrm{C}}+\\bar{d}_{R}^{\\mathrm{C}%\n}\\gamma^{\\mu}e_{R}^{\\mathrm{C}}+\\bar{q}_{L}^{\\mathrm{C}}\\gamma^{\\mu}\\ell\n_{L}^{\\mathrm{C}})\\ .\n\\end{align}\nAll these situations induce $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operators, see Fig.~\\ref{Fig5}$a$, but necessarily involving the axion, with for example\n\\begin{align}\n\\frac{\\partial_{\\mu}\\phi}{m_{S}^{2}m_{V}^{2}}\\bar{d}_{R}\\gamma^{\\mu}%\nq_{L}^{\\mathrm{C}}\\bar{d}_{R}\\ell_{L}+h.c. & \\rightarrow\\frac{1}{m_{S}^{2}%\nm_{V}^{2}}\\partial_{\\mu}a^{0}\\bar{d}_{R}\\gamma^{\\mu}q_{L}^{\\mathrm{C}}\\bar\n{d}_{R}\\ell_{L}+h.c. \\nonumber \\\\ \n & \\rightarrow\\frac{\\Lambda_{QCD}^{3}}{m_{S}^{2}m_{V}^{2}%\n}(\\partial_{\\mu}a^{0}\\bar{p}\\gamma^{\\mu}\\left( 1-\\gamma^{5}\\right)\n\\ell + \\partial_{\\mu}a^{0}\\bar{n}\\gamma^{\\mu}\\left( 1-\\gamma^{5}\\right)\n\\nu + ... + h.c.)\\ ,\n\\label{OpsAPL}\n\\end{align}\nwhere (...) denotes operators with additional light mesons. Given the proton decay bounds, and taking $\\Lambda_{QCD}\\approx 300$~MeV, this imposes a quite high bound $m_{S}\\approx m_{V}>10^{4}$~TeV, close to the PQ breaking scale and quite lower than the GUT scale. With those values, such $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operators cannot affect significantly the phenomenology of the axion, as its coupling to photons or gluons remain much larger.\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[height=1.5766in,width=4.7547in]{Fig5.jpg}%\n\\caption{The $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operators involving one ($a.$) and two axions ($b.$).}%\n\\label{Fig5}%\n\\end{center}\n\\end{figure}\n\nThe situation can be different for an axion-like particle (ALP) with a mass above that of the proton. If the mass is just slightly above but below that of the neutron, $m_{p}-m_{e} 900\\ \\text{GeV} \\left( \\frac{10^{16}\\ \\text{GeV}}{f_a} \\right)^{1\/4}\\;.\n\\end{equation}\nPlugging this in Eq.~(\\ref{neutdec}), the branching ratio for $n\\rightarrow a^{0}+\\nu$ is at around $1\\%$ provided $f_{a}$ is pushed at the GUT scale, $f_a \\approx 10^{16}\\ \\text{GeV}$, see Fig.~\\ref{FigNeut}. Note that for that value of $f_{a}$, the ALP still decays mostly into $\\gamma\\gamma$ as $\\Gamma(a^{0}\\rightarrow p\\bar{\\ell})>\\Gamma(a^{0}\\rightarrow\\gamma\\gamma)$ requires $f_{a}$ about an order of magnitude larger. The $p\\rightarrow\\ell(a^{0\\ast}\\rightarrow\\gamma\\gamma)$ decay can happen only for $\\ell=e,\\mu$, but the underlying LQ couplings could actually exhibit non-trivial flavor hierarchies. If they couple preferentially to the $\\tau$, then proton decay would be forced to occur via more suppressed channels, e.g. via $p\\rightarrow\\pi\\nu_{\\tau}(a^{0\\ast}\\rightarrow\\gamma\\gamma)$, and $f_{a}$ could be brought down by a few orders of magnitude. Thus, an ALP could realize the scenario proposed in Ref.~\\cite{NeutronTau} to solve the neutron lifetime puzzle, though it does not alleviate its inherent fine tuning of the dark particle mass. \n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[width=11cm]{Neutron.jpg}%\n\\caption{Evolution of the $a^{0}\\rightarrow\\gamma\\gamma$, $a^{0}\\rightarrow p\\bar{\\ell}$, $n\\rightarrow a^{0}+\\nu$ and $p\\rightarrow\\ell(a^{0\\ast}\\rightarrow\\gamma\\gamma)$ widths as functions of $f_a$. The dashed line indicates the observed neutron lifetime discrepancy, $\\Gamma_{bottle}-\\Gamma_{beam} \\approx 7.1\\times 10^{-30}$~GeV~\\cite{NeutronTau}. The ALP mass is kept fixed at $m_a =0.9384$~GeV. The LQ\/DQ mass is adjusted so that $\\tau \\left( p\\rightarrow\\ell(a^{0\\ast}\\rightarrow\\gamma\\gamma) \\right) = 10^{32}$~yr. For the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operator, Eq.~(\\ref{OpsAPL}), $m_{S}\\approx m_{V}$ then follows the indicated line (Case I), and must be a bit below the TeV to reproduce the observed $\\Gamma_{bottle}-\\Gamma_{beam}$ discrepancy. Concerning the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$ scenario, for which leptons and antileptons should be interchanged, the extra factor of $f_a$ in Eq.~(\\ref{OpsAPantiL}) sets $m_{S}\\approx m_{V}\\approx 90$~TeV independently of $f_a$ (Case II).}\n\\label{FigNeut}%\n\\end{center}\n\\end{figure}\n\n\nFor heavier ALPs, neutron decay is kinematically closed, and the $\\Gamma(a^{0}\\rightarrow p\\bar{\\ell},n\\bar{\\nu})>\\Gamma(a^{0}\\rightarrow\\gamma\\gamma)$ pattern can arise for lower $f_{a}$ values (though still very large from the axion point of view), with for example $\\Gamma(a^{0}\\rightarrow p\\bar{\\ell},n\\bar{\\nu})>\\Gamma(a^{0}\\rightarrow\\gamma\\gamma)$ for $f_{a}>10^{13}$ GeV if $m_{a}=100~$GeV. This, however, requires also to boost the $a\\rightarrow p\\bar{\\ell},n\\bar{\\nu}$ rate by allowing light LQ\/DQ at around the TeV scale. Even if $\\Gamma(a^{0}\\rightarrow p\\bar{\\ell},n\\bar{\\nu})$ does not dominate, such decay channels could have some cosmological implications. From a baryogenesis point of view, it is interesting to remark that the present scenario has all the necessary ingredients. Provided the LQ\/DQ couple to more than one SM fermion states, several operators will simultaneously contribute to the $a^{0}\\rightarrow p\\bar{\\ell},n\\bar{\\nu}$ and $a^{0}\\rightarrow\\bar{p}\\ell,\\bar{n}\\nu$ decay processes, and since the LQ\/DQ couplings to SM fermions are a priori complex, their rates would be different (slightly, as rescattering is required). In this picture, note that if there are several LQ\/DQ states with a non-trivial mass spectrum, their decay chains may first generate an asymmetry when $m_{S,V}>m_{a}$~\\cite{Arnold:2012sd}, but it would be washed out and regenerated at a lower scale by the ALP decays. Whether this mechanism is sufficient to generate the observed baryon asymmetry is left for a future study.\n\nTo close this section, let us mention another scenario in which $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ operators require two derivative couplings, and proton decay is associated to axion pair production. Specifically, if we start from\n\\begin{align}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}} & =\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}%\n^{2\/3}(\\bar{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R}%\n)+S_{2}^{1\/3}(\\bar{d}_{R}\\ell_{L}+\\bar{q}_{L}\\nu_{R})\\nonumber\\\\\n& \\ \\ \\ \\ +\\partial_{\\mu}\\phi S_{1}^{2\/3\\dagger}V_{1}^{2\/3,\\mu}+\\partial\n_{\\mu}\\phi V_{2}^{1\/3,\\mu\\dagger}S_{2}^{1\/3}+\\phi H^{\\dagger}V_{1,\\mu\n}^{2\/3\\dagger}V_{2}^{1\/3,\\mu}+h.c.\\ ,\n\\end{align}\nthe mixing between $S_{1}^{2\/3}$ and $S_{2}^{1\/3}$ can only occur through that of $V_{1,\\mu}^{2\/3}$ and $V_{2,\\mu}^{1\/3}$. Specifically, with this specific choice of couplings,\n\\begin{equation}%\n\\begin{tabular}[c]{cccccccccccc}\\hline\n& $\\phi$ & $S_{1}^{2\/3}$ & $V_{1,\\mu}^{2\/3}$ & $S_{2}^{1\/3}$ & $V_{2,\\mu\n}^{1\/3}$ & $q_{L}$ & $u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}%\n$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $-1\/3$ & $-2\/3$ & $-1\/3$ & $1\/3$ & $0$ & $1\/3$ & $1\/3$\n& $1\/3$ & $0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $1\/3$ & $0$ & $-1\/3$ & $-1$ & $-2\/3$ & $0$ & $0$ & $0$\n& $1$ & $1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nThanks to these charges, which crucially follow from whether $\\phi$ or $\\phi^{\\dagger}$ are introduced in the couplings, no other SM fermion couplings of the LQ\/DQ, nor any other renormalizable couplings among the LQ\/DQ, is allowed. Turning on $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ does not change this picture, except for the operator $\\phi^{\\dagger}S_{1}^{2\/3}S_{2}^{1\/3}S_{2}^{1\/3}$. This is quite natural looking at the Lagrangian, since $\\phi^{\\dagger}S_{1}^{2\/3}S_{2}^{1\/3}S_{2}^{1\/3}$ followed by $S_{1}^{2\/3}\\rightarrow\\bar{q}_{L}^{\\mathrm{C}}q_{L}$ and $S_{2}^{1\/3}\\rightarrow\\bar{q}_{L}\\nu_{R}$ permits to recover $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ by closing the $q_{L}$ loops. As the $\\phi^{\\dagger}S_{1}^{2\/3}S_{2}^{1\/3}S_{2}^{1\/3}$ and $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ have the same quantum numbers, both carrying $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,2)$, they are both able to generate neutrino masses only, and do not affect the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ pattern.\n\nThe leading operator for proton decay is now (Fig.~\\ref{Fig5}$b$):%\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)}^{eff}=\\frac\n{1}{m_{S}^{4}m_{V}^{4}}\\phi\\partial_{\\mu}\\phi\\partial^{\\mu}\\phi H^{\\dagger\n}(\\bar{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R})(\\bar{d}%\n_{R}^{\\mathrm{C}}\\ell_{L}^{\\mathrm{C}}+\\bar{q}_{L}^{\\mathrm{C}}\\nu\n_{R}^{\\mathrm{C}})\\ ,\n\\end{equation}\nand it contains in particular\n\\begin{equation}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)}^{eff}=\\frac\n{v_{\\phi}v\\Lambda_{QCD}^{3}}{m_{S}^{4}m_{V}^{4}}\\partial_{\\mu}a^{0}%\n\\partial^{\\mu}a^{0}\\bar{p}\\left( 1-\\gamma^{5}\\right) \\ell+...+h.c.\\ .\n\\end{equation}\nPhenomenologically, proton decay is suppressed, even for relatively low $m_{S}\\approx m_{V}$ of $\\mathcal{O}(10~$TeV$)$. On the other hand, if $a^{0}$ is an ALP with twice its mass above the proton but below the neutron mass, this setting is less interesting because the LQ\/DQ masses need to be too low to reach $B(n\\rightarrow a^{0}+a^{0}+\\bar{\\nu})\\approx1\\%$. Whether ALP or axions, the cosmological implications of this scenario would be worth further study though, as the consequences of opening up (possibly CP-violating) $a^{0}+p\\leftrightarrow a^{0}+\\ell$ and $a^{0}+\\bar{p}\\leftrightarrow a^{0}+\\bar{\\ell}$ scattering processes could provide a new baryogenesis mechanism.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}+\\mathcal{L}$}\n\nTo attain $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$ operators, the trick is to start from the previous scenario, but use some additional LQ couplings to switch $\\mathcal{L}$ by two units. Specifically, we can consider%\n\\begin{align}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}} & =\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}%\n^{2\/3}(\\bar{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R})+V_{2,\\mu\n}^{1\/3}(\\bar{u}_{R}\\gamma^{\\mu}\\ell_{L}^{\\mathrm{C}}+\\bar{q}_{L}\\gamma^{\\mu\n}\\nu_{R}^{\\mathrm{C}})\\nonumber\\\\\n& \\ \\ \\ \\ +\\partial_{\\mu}\\phi S_{1}^{2\/3\\dagger}V_{1}^{2\/3,\\mu}+\\phi\nH^{\\dagger}V_{1,\\mu}^{2\/3\\dagger}V_{2}^{1\/3,\\mu}+h.c.\\ . \\label{LagrSSB2}%\n\\end{align}\nProvided $V_{1,\\mu}^{2\/3}$ has no couplings to SM fermions, and only those two interactions among $\\phi$ and the LQ\/DQ are present, two $U(1)$s are present in the Lagrangian, with charges\n\\begin{equation}%\n\\begin{tabular}[c]{ccccccccccc}\\hline\n& $\\phi$ & $S_{1}^{2\/3}$ & $V_{1,\\mu}^{2\/3}$ & $V_{2}^{1\/3,\\mu}$ & $q_{L}$ &\n$u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $-1\/2$ & $-2\/3$ & $-1\/6$ & $1\/3$ & $1\/3$ & $1\/3$ &\n$1\/3$ & $0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $-1\/2$ & $0$ & $1\/2$ & $1$ & $0$ & $0$ & $0$ & $1$ &\n$1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nSo, the $U(1)_{\\mathcal{B}+\\mathcal{L}}$ symmetry is spontaneously broken, while $\\mathcal{B}-\\mathcal{L}$ remains. If neutrino masses are generated by adding the $\\phi\\bar{\\nu}_{R}\\nu_{R}^{\\mathrm{C}}$ coupling, the remaining exact $U(1)$ symmetry suffices to keep off all other interactions among $\\phi$ and the LQ\/DQ, as well as the LQ\/DQ couplings to SM fermions not already present in the Lagrangian, except for a $\\phi^{\\dagger}S_{1}^{2\/3}V_{2}^{1\/3,\\mu}V_{2,\\mu}^{1\/3}$ which carries the same quantum number as $\\phi^{\\dagger}{\\bar{\\nu}_{R}}^{\\mathrm{C}}\\nu_{R}$. Neither is able to open new $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})$ patterns for proton decay.\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[height=1.3517in,width=2.0254in]{Fig5b.jpg}\n\\caption{Axion-induced $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$ operators.}\n\\label{Fig5b}\n\\end{center}\n\\end{figure}\n\nThe leading $\\mathcal{B}+\\mathcal{L}$ violating operator is (see Fig.~\\ref{Fig5b})\n\\begin{align}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)}^{eff} & =\\frac\n{1}{m_{S}^{2}m_{V}^{4}}\\phi\\partial_{\\mu}\\phi H^{\\dagger}(\\bar{q}%\n_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R})(\\bar{u}_{R}^{\\mathrm{C}%\n}\\gamma^{\\mu}\\ell_{L}+\\bar{q}_{L}^{\\mathrm{C}}\\gamma^{\\mu}\\nu_{R})\\nonumber\\\\\n& \\rightarrow\\frac{v_{EW}v_{\\phi}}{m_{S}^{2}m_{V}^{4}}\\partial_{\\mu}a^{0}(\\bar\n{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R})(\\bar{u}%\n_{R}^{\\mathrm{C}}\\gamma^{\\mu}e_{L}+\\bar{d}_{L}^{\\mathrm{C}}\\gamma^{\\mu}\\nu\n_{R})\\ .\n\\label{OpsAPantiL}\n\\end{align}\nPhenomenologically, thanks to the $v_{EW}v_{\\phi}$ from the $\\phi HV_{2,\\mu}^{1\/3\\dagger}V_{1}^{2\/3,\\mu}$ coupling, the LQ\/DQ scale can be lower by about an order of magnitude without violating proton decay bounds. For ALPs, the main difference with the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ scenario is that $f_{a}=v_{\\phi}$ occurs in the $n\\rightarrow a^{0}\\bar{\\nu}$ and $a^0\\rightarrow p\\ell$ amplitudes, but cancels out from the $p\\rightarrow\\bar{\\ell}(a^0\\rightarrow\\gamma\\gamma)$ rate. This means that $m_{S}\\approx m_{V}$ cannot be as low as before, but must above $90$~TeV. Yet, this high scale is compensated in the $n\\rightarrow a^{0}\\bar{\\nu}$ rate by the $v_{\\phi}$ factor, so its branching ratio can still reach $\\mathcal{O}(1\\%)$. Actually, the dependencies of the various rates on $f_a$ stays exactly as depicted in Fig.~\\ref{FigNeut}, but for $m_{S}\\approx m_{V}\\approx 90$~TeV.\n\nNote, finally, that $\\mathcal{B}+\\mathcal{L}$ violating operators are not easily forced to involve pairs of axions. The pattern of LQ\/DQ couplings to SM fermions, and their hypercharge, does not leave many options if only renormalizable operators are allowed. The simplest we could find would require two different Higgs doublets, so would be suitable for the DFSZ model\n\\begin{align}\n\\mathcal{L}_{\\mathrm{DFSZ+LQ}} & =\\mathcal{L}_{\\mathrm{DFSZ}}+S_{1}%\n^{4\/3}\\bar{d}_{R}^{\\mathrm{C}}d_{R}+S_{1}^{2\/3}(\\bar{d}_{R}\\nu_{R}%\n^{\\mathrm{C}}+\\bar{u}_{R}e_{R}^{\\mathrm{C}}+\\bar{q}_{L}\\ell_{L}^{\\mathrm{C}%\n})\\nonumber\\\\\n& \\ \\ \\ \\ +\\partial_{\\mu}\\phi S_{1}^{2\/3\\dagger}V_{1}^{2\/3,\\mu}+\\partial\n_{\\mu}\\phi S_{1}^{4\/3\\dagger}V_{1}^{4\/3,\\mu}+H_{u}^{\\dagger}H_{d}^{\\dagger\n}V_{1,\\mu}^{2\/3\\dagger}V_{1}^{4\/3,\\mu}+h.c.\\ .\n\\end{align}\nAs the phenomenology is similar as that for $\\mathcal{B}-\\mathcal{L}$ violating operators, we do not detail this situation further.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}$}\n\nGiven that we want to start from the derivative couplings, which are all quadratic in the LQ\/DQ, we will need to add at least some cubic interactions. This quickly increases the number of new state needed, and phenomenologically, the longest the chain, the smallest the predicted rate given that LQ\/DQ masses are at least of a few TeV.\n\nThe simplest processes correspond to the skeleton graph $\\partial_{\\mu}\\phi\\rightarrow X_{i}(X_{l}\\rightarrow X_{j}X_{k})$, with the final\n$X_{i}X_{j}X_{k}$ set allowing for $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ transitions, so $X_{i,j,k}=S_{1}^{y}$ or $V_{2}^{y}$ for some $y$. If $X_{l}$ is integrated out, the effective operator involves $\\partial_{\\mu}\\phi X_{i}X_{j}X_{k}$ plus some Higgs fields. The simplest such operators are of dimension six, and only seven of them are compatible with the gauge symmetries,\n\\begin{align}\n& \\partial_{\\mu}\\phi~H^{\\dagger}~(S_{1}^{2\/3}S_{1}^{4\/3}V_{2}^{1\/3,\\mu\n},\\ S_{1}^{4\/3}S_{1}^{4\/3}V_{2}^{5\/3,\\mu},\\ V_{2}^{1\/3,\\mu}V_{2,\\nu}%\n^{1\/3}V_{2}^{1\/3,\\nu})\\ ,\\\\\n& \\partial_{\\mu}\\phi~H~(V_{2}^{5\/3,\\mu}V_{2,\\nu}^{1\/3}V_{2}^{1\/3,\\nu}%\n,\\ S_{1}^{2\/3}S_{1}^{2\/3}V_{2}^{1\/3,\\mu},\\ S_{1}^{2\/3}S_{1}^{4\/3}%\nV_{2}^{5\/3,\\mu},\\ S_{1}^{4\/3}S_{1}^{8\/3}V_{2}^{1\/3,\\mu})\\ ,\n\\end{align}\nwhere $\\partial_{\\mu}\\phi$ could be replaced by $\\partial_{\\mu}\\phi^{\\dagger}$ wherever required. Starting from the three derivative interactions of Eq.~(\\ref{DerScenars}), there are several ways to reach these operators using a $HX_{l}X_{j}X_{k}$ or $H^{\\dagger}X_{l}X_{j}X_{k}$ coupling. Since $X_{l}=S_{2}^{y}$ or $V_{1}^{y}$, these operators alone cannot induce $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ processes. Further, if $X_{l}$ transforms as a $6$, it does not couple to SM fermions hence these operators cannot lead to proton decay either. If $X_{l}$ transforms as a $3$, one must make sure the PQ charges forbid $X_{l}\\rightarrow\\ell q$. All this nevertheless leaves many possible mechanisms, though many of them turn out to be essentially equivalent phenomenologically, so let us take an example.\n\n\n\\begin{figure}[t]\n\\begin{center}\n\\includegraphics[height=1.9787in,width=4.7158in]{Fig6.jpg}\n\\caption{One and two axion induced neutron-antineutron oscillation $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ operators.}\n\\label{Fig6}\n\\end{center}\n\\end{figure}\n\nConsider the Lagrangian\n\\begin{align}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}} & =\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}%\n^{2\/3}(\\bar{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R})+V_{2,\\mu\n}^{1\/3}\\bar{d}_{R}^{\\mathrm{C}}\\gamma^{\\mu}q_{L}\\nonumber\\\\\n& +\\partial_{\\mu}\\phi V_{1}^{2\/3,\\mu\\dagger}S_{1}^{2\/3}+HV_{1,\\mu}^{2\/3}%\nS_{1}^{2\/3}V_{2}^{1\/3,\\mu}+h.c.\\ , \\label{LagrSSB3a}%\n\\end{align}\nwhere $S_{1}^{2\/3}$ and $V_{1}^{2\/3,\\mu}$ transform as $\\mathbf{3}$, but $V_{2}^{1\/3,\\mu}\\sim\\mathbf{\\bar{6}}$ since the final operator $\\partial_{\\mu}\\phi HS_{1}^{2\/3}S_{1}^{2\/3}V_{2}^{1\/3,\\mu}$ would cancel for $V_{2}^{1\/3,\\mu}\\sim\\mathbf{3}$. Dropping the $\\Psi_{L,R}$, as their charge can independently be fixed by turning on some couplings to the LQ, the active $U(1)$s are then\n\\begin{equation}%\n\\begin{tabular}[c]{ccccccccccc}\\hline\n& $\\phi$ & $S_{1}^{2\/3}$ & $V_{2,\\mu}^{1\/3}$ & $V_{1,\\mu}^{2\/3}$ & $q_{L}$ &\n$u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $2$ & $-2\/3$ & $-2\/3$ & $4\/3$ & $1\/3$ & $1\/3$ & $1\/3$\n& $0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $0$ & $0$ & $0$ & $0$ & $0$ & $0$ & $0$ & $1$ & $1$ &\n$1$\\\\\\hline\n\\end{tabular}\n\\ \\label{SponB}%\n\\end{equation}\nTurning on any of the LQ couplings of $S_{1}^{2\/3}$ or $V_{1,\\mu}^{2\/3}$ would break $U(1)_{\\mathcal{B}+\\mathcal{L}}$, and induce proton decay (compare Eq.~(\\ref{LagrSSB3a}) with Eq.~(\\ref{LagrSSB2})). At this level, their presence is thus forbidden by the still active $U(1)_{\\mathcal{B}}$ and $U(1)_{\\mathcal{L}}$ symmetries. For an even stricter protection, the PQ symmetry can be extended to prevent these couplings. It suffices to add a seesaw mechanism with the $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling, something we should do anyway (the $\\phi\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ would instead allow all the LQ couplings). Note that $S_{1}^{2\/3}$ and $V_{2,\\mu}^{1\/3}$ can mix via a $D^{\\mu}HS_{1}^{2\/3\\dagger}V_{2,\\mu}^{1\/3}$ term, but this is inessential since they have the same $\\mathcal{B}$ and $\\mathcal{L}$ quantum numbers. This scenario lead to neutron-antineutron oscillation operators, with the diagram of Fig.~\\ref{Fig6}$a$, corresponding to\n\\begin{align}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)}^{eff} & =\\frac\n{1}{m_{S}^{8}}\\partial_{\\mu}\\phi H(\\bar{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}%\n_{R}^{\\mathrm{C}}u_{R})(\\bar{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}%\n}u_{R})\\bar{d}_{R}^{\\mathrm{C}}\\gamma^{\\mu}q_{L}+h.c.\\ \\nonumber\\\\\n& \\rightarrow\\frac{v\\Lambda_{QCD}^{6}}{m_{S}^{8}}\\partial_{\\mu}a^{0}\\bar\n{n}^{\\mathrm{C}}\\gamma^{\\mu}\\gamma_{5}n + ... + h.c.\\ , \\label{SponB2}%\n\\end{align}\nwhere we have set all the DQ masses to a common $m_{S}$ value. Since there is no associated $n-\\bar{n}$ operator, this scale can in principle be quite low. The best low energy limits come from nuclear transitions, as this operator also contributes to $nn\\rightarrow a$, but those do not push $m_{S}$ well above the TeV scale~\\cite{Heeck:2020nbq}. The main constraint thus come from LHC searches~\\cite{CMS:2020gru,ATLAS:2020dsk,ATLAS:2020xov,CMS:2020wzx,ATLAS:2021oiz}. Note, though, that the generic leptoquark searches may not apply to this case: all these states decay to diquark pairs and, furthermore, $V_{1}^{2\/3,\\mu}$ could end up quite long lived if it is lighter than $V_{2}^{1\/3,\\mu}$ and $S_{1}^{2\/3,\\mu}$, and would show up in channels with at least four jets.\n\nEven if $m_{S}$ can be quite low, at around the TeV say, the $a^{0}nn$ coupling is significantly smaller than the other couplings, including to $n\\bar{n}$, as can be estimated setting $f_{a}\\equiv v_{\\phi}$:\n\\begin{equation}\n\\frac{1}{f_{a}}\\approx\\frac{v\\Lambda_{QCD}^{6}}{m_{S}^{8}}\\Leftrightarrow\nm_{S}\\approx10~\\text{GeV}\\times\\left( \\frac{f_{a}}{10^{9}\\ \\text{GeV}%\n}\\right) ^{1\/8}\\ ,\n\\end{equation}\nfor $\\Lambda_{QCD}\\approx300$ MeV. Even with $f_{a}$ close to the Planck scale, the LQ mass would need to be well below the TeV scale, which would again be ruled out by direct searches. For $m_{S}$ around the TeV, the $a^{0}nn$ coupling is at best $10^{-16}$ smaller than that to $a^{0}n\\bar{n}$. Thus, $a^{0}nn$ does not represent a competitive signature for direct axion searches.\n\nIndirectly, the $a^{0}nn$ coupling may nevertheless open new routes by relying instead on neutron-antineutron oscillation phenomena. Indeed, while $a^{0}nn$ cannot generate $n\\rightarrow\\bar{n}$ in vacuum, oscillations could now be catalyzed by an axion dark matter background. While the typical high frequency of the coherent axion field precludes any observation using standard beam searches for $n-\\bar{n}$ oscillations (the induced $\\delta m_{n-\\bar{n}}$ would average to zero), transient variations of the axion field may be observable in this way. Another possibility would be to exploit the magnetic splitting between $n$ and $\\bar{n}$ states, which in a $1~$T magnetic field would be of about $10^{-7}$~eV~\\cite{Phillips:2014fgb}, larger than the axion mass if $f_{a}>10^{14}$~GeV. Note that the neutron beam go through a 4.6~T magnet in neutron lifetime experiments, Ref.~\\cite{Nico:2004ie,Yue:2013qrc}, and that axion-induced mixing effects, if they occur, would not have been excluded by the recent mirror neutron search of Ref.~\\cite{Broussard:2021eyr}, which relies on hypothesized mirror neutrons capabilities to pass through normal matter.\n\nTwo other features compared to the usual neutron oscillations are worth mentioning: the coupling is axial, $\\bar{n}^{\\mathrm{C}}\\gamma^{\\mu}\\gamma_{5}n$, instead of the usual scalar $\\bar{n}^{\\mathrm{C}}n$ oscillation operator, so the spin dependencies are different~\\cite{Gardner:2014cma}, and the $\\partial_{\\mu}a^{0}\\bar{n}^{\\mathrm{C}}\\gamma^{\\mu}\\gamma_{5}n$ coupling can be CP violating~\\cite{Berezhiani:2015uya,McKeen:2015cuz,Berezhiani:2018xsx} since the DQ couplings are a priori complex, so $n$ and $\\bar{n}$ may react differently to an axionic background. Also, compared to neutron-mirror neutron oscillations, like those invoked to explain the neutron lifetime anomaly~\\cite{Berezhiani:2018eds}, the antineutron would not be invisible but would either decay to antiproton, or annihilate with the surrounding matter. A quantitative analysis of these signatures is clearly called for but would require a detailed study, which go beyond our scope. Also, other manifestations of the $a^{0}nn$ coupling in an astrophysical and cosmological context are left for a future study.\n\nWith only three LQ, another rather simple scenario can lead to the $\\partial_{\\mu}\\phi H^{\\dagger}V_{2}^{1\/3,\\mu}V_{2,\\nu}^{1\/3}V_{2}^{1\/3,\\nu}$ operator by virtual $S_{2}^{1\/3}$ exchanges, and involves only states transforming as $\\mathbf{3}$:\n\\begin{align}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}} & =\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}%\n^{2\/3}(\\bar{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R})+V_{2,\\mu\n}^{1\/3}\\bar{d}_{R}^{\\mathrm{C}}\\gamma^{\\mu}q_{L}\\nonumber\\\\\n& +\\partial_{\\mu}\\phi S_{2}^{1\/3\\dagger}V_{2}^{1\/3,\\mu}+H^{\\dagger}%\nS_{2}^{1\/3}V_{2,\\nu}^{1\/3}V_{2}^{1\/3,\\nu}+h.c.\\ . \\label{LagrSSB3b}%\n\\end{align}\nThe same $U(1)_{\\mathcal{B}}$ charges are found as in Eq.~(\\ref{SponB}), with $V_{1,\\mu}^{2\/3}\\rightarrow S_{2}^{1\/3}$. Also, as before, adding the $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling prevents all the LQ couplings of $V_{2,\\mu}^{1\/3}$, $S_{1}^{2\/3}$, and $S_{2}^{1\/3}$. Proton decay is now forbidden by the existence of the PQ symmetry at the high scale, and does not arise at the low scale thanks to the specific $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ and $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,2)$ symmetry breaking pattern. The final operator is phenomenologically similar to that in Eq.~(\\ref{SponB2}).\n\nMany other choices of DQ states are possible, but they lead to similar patterns. We will not investigate more complicated processes, except for the following that leads to a different phenomenology:\n\\begin{align}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}} & =\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}%\n^{2\/3}(\\bar{q}_{L}^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R})+V_{2,\\mu\n}^{1\/3}\\bar{d}_{R}^{\\mathrm{C}}\\gamma^{\\mu}q_{L}\\nonumber\\\\\n& \\ \\ \\ \\ +\\partial_{\\mu}\\phi S_{2}^{1\/3\\dagger}V_{2}^{1\/3,\\mu}+\\partial\n_{\\mu}\\phi V_{1}^{2\/3,\\mu\\dagger}S_{1}^{2\/3}+\\phi^{\\dagger}S_{2}^{1\/3}V_{2,\\mu}%\n^{1\/3}V_{1}^{2\/3,\\mu}+h.c.\\ .\\label{LagrSSB3c}%\n\\end{align}\nIn some senses, it combines the previous two scenarios, and gives the same charges as in Eq.~(\\ref{SponB}), with $V_{1,\\mu}^{2\/3}$ and $S_{2}^{1\/3}$ having $\\mathcal{B}=4\/3$. Also, the $\\phi^{\\dagger}\\bar{\\nu}_{R}^{\\mathrm{C}}\\nu_{R}$ coupling now suffices to prevent the LQ couplings of the four states, $V_{2,\\mu}^{1\/3}$, $V_{1,\\mu}^{2\/3}$, $S_{1}^{2\/3}$, $S_{2}^{1\/3}$. What differs however is how the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ effects are induced at the low-energy scale. The two derivative couplings are needed, and $\\phi$ further occurs in the cubic DQ coupling, so the leading operator is (see Fig.~\\ref{Fig6}$b$)\n\\begin{align}\n\\mathcal{H}_{(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)}^{eff} & =\\frac\n{1}{m_{S}^{10}}\\phi\\partial_{\\mu}\\phi^\\dagger\\partial_{\\nu}\\phi^\\dagger(\\bar{q}_{L}%\n^{\\mathrm{C}}q_{L}+\\bar{d}_{R}^{\\mathrm{C}}u_{R})\\bar{d}_{R}^{\\mathrm{C}%\n}\\gamma^{\\mu}q_{L}\\bar{d}_{R}^{\\mathrm{C}}\\gamma^{\\nu}q_{L}+h.c.\\ \\nonumber\\\\\n& \\rightarrow\\frac{v_{\\phi}\\Lambda_{QCD}^{6}}{m_{S}^{10}}\\partial_{\\mu}%\na^{0}\\partial^{\\mu}a^{0}\\bar{n}^{\\mathrm{C}}\\gamma_{5}n+...+h.c.\\ .\n\\end{align}\nThough this operator is now of dimension 14 instead of that of dimension 12 in Eq.~(\\ref{SponB2}), the extra suppression is compensated by the $v_{\\phi}$ factor since $v_{\\phi}\\Lambda_{QCD}\/m_{S}^{2}$ is of $\\mathcal{O}(1)$ for $m_{S}$ around the tens of TeV scale and $v_{\\phi}$ at around $10^{6}$~TeV. The nuclear transition bounds are thus similar as in the single axion case, and in any case not competitive with direct collider searches for new colored states. Phenomenologically, neutron-antineutron conversion now requires pairs of axions, and would occur through scattering processes like $a^{0}+n\\leftrightarrow a^{0}+\\bar{n}$ or $n+n\\leftrightarrow a^0+a^0$ and $\\bar{n}+\\bar{n}\\leftrightarrow a^0+a^0$. Though unlikely to be ever observed, these processes could play a cosmological role.\n\n\\subsubsection{Spontaneous breaking of $\\mathcal{B}\\pm3\\mathcal{L}$}\n\nThe $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,3)$ scenarios are trivially obtained from any of the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ Lagrangians of the previous section by switching all DQ couplings to LQ couplings. For example, starting from Eq.~(\\ref{LagrSSB3b}),\n\\begin{align}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}} & =\\mathcal{L}_{\\mathrm{KSVZ}}+S_{1}%\n^{2\/3}(\\bar{d}_{R}\\nu_{R}^{\\mathrm{C}}+\\bar{u}_{R}e_{R}^{\\mathrm{C}}+\\bar\n{q}_{L}\\ell_{L}^{\\mathrm{C}})+V_{2,\\mu}^{1\/3}(\\bar{u}_{R}\\gamma^{\\mu}\\ell\n_{L}^{\\mathrm{C}}+\\bar{q}_{L}\\gamma^{\\mu}\\nu_{R}^{\\mathrm{C}})\\\\\n& +\\partial_{\\mu}\\phi S_{2}^{1\/3\\dagger}V_{2}^{1\/3,\\mu}+H^{\\dagger}%\nS_{2}^{1\/3}V_{2,\\nu}^{1\/3}V_{2}^{1\/3,\\nu}+h.c.\\ ,\n\\end{align}\nleads to the charges\n\\begin{equation}%\n\\begin{tabular}[c]{ccccccccccc}\\hline\n& $\\phi$ & $S_{1}^{2\/3}$ & $V_{2,\\mu}^{1\/3}$ & $S_{1}^{2\/3}$ & $q_{L}$ &\n$u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $-1$ & $-2\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $1\/3$ &\n$0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $-3$ & $-2$ & $1$ & $1$ & $0$ & $0$ & $0$ & $1$ & $1$ &\n$1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nBy analogy, $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)$ transitions can be induced by taking the Lagrangian\n\\begin{equation}\n\\mathcal{L}_{\\mathrm{KSVZ+LQ}}=\\mathcal{L}_{\\mathrm{KSVZ}}+S_{2}^{1\/3}(\\bar\n{d}_{R}\\ell_{L}+\\bar{q}_{L}\\nu_{R})+V_{1,\\mu}^{2\/3}\\bar{d}_{R}\\gamma^{\\mu}%\n\\nu_{R}+\\partial_{\\mu}\\phi V_{1}^{2\/3,\\mu\\dagger}S_{1}^{2\/3}+\\phi S_{1}%\n^{2\/3}S_{2}^{1\/3}S_{2}^{1\/3}+h.c.\\ ,\n\\end{equation}\nwith the charges are\n\\begin{equation}%\n\\begin{tabular}[c]{ccccccccccc}\\hline\n& $\\phi$ & $S_{1}^{2\/3}$ & $S_{2}^{1\/3}$ & $V_{1,\\mu}^{2\/3}$ & $q_{L}$ &\n$u_{R}$ & $d_{R}$ & $\\ell_{L}$ & $e_{R}$ & $\\nu_{R}$\\\\\\hline\n$U(1)_{\\mathcal{B}}$ & $1\/2$ & $-1\/6$ & $1\/3$ & $1\/3$ & $1\/3$ & $1\/3$ & $1\/3$\n& $0$ & $0$ & $0$\\\\\n$U(1)_{\\mathcal{L}}$ & $-3\/2$ & $1\/2$ & $-1$ & $-1$ & $0$ & $0$ & $0$ & $1$ &\n$1$ & $1$\\\\\\hline\n\\end{tabular}\n\\end{equation}\nNote that for each case, additional $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,0)$ couplings involving pairs of LQs are possible, like $\\phi HS_{2}^{1\/3\\dagger}S_{1}^{2\/3}$ or $D^{\\mu}HS_{2}^{1\/3\\dagger}V_{1,\\mu}^{2\/3}$ for the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-3)$ scenario. Those can neither affect the symmetry pattern, nor open new routes for proton decay.\n\nPhenomenologically, these scenarios are very similar to the $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,\\pm1)$ ones described before, so we will not detail them further. The main difference is the extra suppression of proton and neutron decays due to the higher dimensionality of the operators, and of the many particles in the final states. These scenarios thus have essentially the same phenomenology whenever these suppressions can be compensated by lowering the LQ mass scale without violating LHC bounds.\n\n\\section{Conclusions\\label{Ccl}}\n\nIn this paper, the opportunities arising from combining leptoquarks and diquarks with axions have been systematically analyzed. From a phenomenological standpoint, our main results are:\n\n\\begin{enumerate}\n\\item The PQ symmetry of which the axion is the Goldstone boson can be identified with any combination of baryon $\\mathcal{B}$ and lepton $\\mathcal{L}$ numbers. In this way, $\\mathcal{B}$ and $\\mathcal{L}$ appear less accidental, in the sense that one combination of $\\mathcal{B}$ and\/or $\\mathcal{L}$ is spontaneously broken, while the orthogonal combination remains exact and is actually protected by the PQ symmetry. Reminiscent of the possible $\\Delta\\mathcal{B}$ and\/or $\\Delta\\mathcal{L}$ operators made of SM fields (see Table~\\ref{TableLQBL}), the simplest scenarios identify $U(1)_{PQ}$ with $U(1)_{\\mathcal{B}\\pm\\mathcal{L}}$, $U(1)_{\\mathcal{B}\\pm3\\mathcal{L}}$, $U(1)_{\\mathcal{B}}$, or $U(1)_{\\mathcal{L}}$, and induce spontaneously either proton decay, neutron-antineutron oscillations, or a Majorana mass terms for $\\nu_{R}$ (or more generally, neutrinoless double beta decays).\n\n\\item All scenarios can be supplemented with a seesaw mechanism. The axion is then not only the Goldstone boson associated to $U(1)_{\\mathcal{B}\\pm\\mathcal{L}}$, $U(1)_{\\mathcal{B}\\pm3\\mathcal{L}}$, or $U(1)_{\\mathcal{B}}$ breaking, but becomes also the Majoron associated to the $U(1)_{\\mathcal{L}}$ breaking. Though no accidental symmetry (besides of course $U(1)_{PQ}$ itself) remains, each scenario retains a specific phenomenology. For example, when $U(1)_{PQ}$ is identified both with $U(1)_{\\mathcal{B}}$ and $U(1)_{\\mathcal{L}}$, $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2n,0)$ and $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(0,2n)$ transitions are possible, but proton decay cannot occur.\n\n\\item For each pattern of symmetry breaking, it is also possible to prevent axion-free proton decay, neutron-antineutron oscillations, or neutrinoless double beta decays. In other words, one can make sure $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})$ effects always involve at least one axion field. Phenomenologically, $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,1)$ scenarios open the door to $p\\rightarrow a^{0}+\\ell,$ $n\\rightarrow a^{0}+\\nu$, $p\\rightarrow2a^{0}+\\ell,$ $n\\rightarrow2a^{0}+\\nu$, and scattering processes like $a^{0}+(p,n)\\leftrightarrow a^{0}+(\\ell,\\nu)$. Scenarios with $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(1,-1)$ or $(1,\\pm3)$ are similar. Following the strategy proposed in Ref.~\\cite{NeutronTau}, if $a^{0}$ is an ALP of just the right mass, such that proton decay is forbidden but neutron decay is not, these scenarios are able to solve the neutron lifetime puzzle, see Fig.~\\ref{FigNeut}.\n\n\\item When applied to $(\\Delta\\mathcal{B},\\Delta\\mathcal{L})=(2,0)$ operators, being forced to include an axion field could lead to very peculiar effects. The phenomenology of the $\\partial_{\\mu}a^{0}\\bar{n}^{\\mathrm{C}}\\gamma^{\\mu}\\gamma_{5}n$ and $\\partial_{\\mu}a^{0}\\partial^{\\mu}a^{0}\\bar{n}^{\\mathrm{C}}\\gamma_{5}n$ interactions have, to our knowledge, not been investigated in detail yet. Though a dedicated analysis is called for, we do not expect these interactions to be phenomenologically relevant in vacuum, but they could open interesting channels in an axionic dark matter background, or transitions like $n\\rightarrow\\bar{n}+a^{0}$ or $n\\rightarrow\\bar{n}+a^{0}+a^0$ in an intense magnetic field.\n\\end{enumerate}\n\nBesides these phenomenological aspects, we have also analyzed the consequences on the foundations of axion effective Lagrangians. Whenever the axion is associated to some patterns of $U(1)_{\\mathcal{B}}$ and\/or $U(1)_{\\mathcal{L}}$ breaking, the SM fermions become charged under the PQ symmetry. Typically, they thus occur in the usual $f_{a}$-suppressed derivative interactions, but through vector current interactions, $\\partial_{\\mu}a^{0}\\bar{\\psi}\\gamma^{\\mu}\\psi$ (since $\\mathcal{B}$ and $\\mathcal{L}$ are vectorial). Often, these interactions are discarded owing to the naive vector Ward identity, but this is incorrect for two reasons:\n\n\\begin{enumerate}\n\\item[5.] Axion-gauge field interaction are usually expected to be $(g_{X}^{2}\/f_{a})\\mathcal{N}_{X}a^{0}X_{\\mu\\nu}\\tilde{X}^{\\mu\\nu}$, $X=G^{a}$, $W^{i}$, $B$, with $\\mathcal{N}_{X}$ summing up the contribution of all the fields charged under both the PQ symmetry and the $X$ gauge interactions of strength $g_{X}$.\\ Thus, $\\mathcal{N}_{X}$ depend on the SM fermion charges, with in particular $\\mathcal{N}_{W}$ and $\\mathcal{N}_{B}$ depending on how $U(1)_{\\mathcal{B}}$ and\/or $U(1)_{\\mathcal{L}}$ are embedded in $U(1)_{PQ}$. Yet, as shown in Ref.~\\cite{Quevillon:2019zrd}, the SM fermion contributions to $\\mathcal{N}_{W}$ and $\\mathcal{N}_{B}$ arising from $U(1)_{\\mathcal{B}}$ and\/or $U(1)_{\\mathcal{L}}$ systematically cancel with that coming from triangle graphs built on the corresponding $\\partial_{\\mu}a^{0}\\bar{\\psi}\\gamma^{\\mu}\\psi$ interactions.\\ At the end of the day, the $U(1)_{\\mathcal{B}}$ and\/or $U(1)_{\\mathcal{L}}$ components of $U(1)_{PQ}$ do not alter the axion to gauge boson couplings, even though this is not apparent at the level of the effective Lagrangian.\n\n\\item[6.] The counting rule in powers of $1\/f_{a}$, central in constructing the axion effective Lagrangian (see e.g. Ref.~\\cite{Georgi:1986df}), is invalid when $\\mathcal{B}$ and\/or $\\mathcal{L}$ are broken spontaneously along with the PQ symmetry. Indeed, the equations of motion of the SM fermions (or that of the leptoquarks if they have not been integrated out) inherit $\\mathcal{O}((f^{\\alpha})^{n}),$ $n\\geq1$ terms, so that $\\mathcal{O}(f_{a}^{n-1})$ interactions are hidden inside $f_{a}^{-1}\\partial_{\\mu}a^{0}\\bar{\\psi}\\gamma^{\\mu}\\psi$. In practice, in the present paper, all these interactions were suppressed by some relatively high power of the leptoquark masses, which are pushed above the TeV by direct collider searches. Thus, in all the scenarios considered here, the $\\mathcal{B}$ and\/or $\\mathcal{L}$ violating interactions are not expected to be dominant compared to e.g. the two photon or two gluon modes for $f_{a}$ below the Planck scale. Still, as this relative suppression has nothing to do with $f_{a}$, there is no guarantee it always happens.\n\\end{enumerate}\n\nIn conclusion, even if entangling the PQ symmetry with the accidental symmetries of the SM requires new leptoquarks states, and often several of them, these scenarios end up being more economical from a $U(1)$ global symmetry point of view. The axion becomes a central piece, not only solving the strong CP puzzle, and maybe making up for the observed dark matter, but also setting off the seesaw mechanism and introducing potentially CP violating baryon number violation. With all its capabilities, the axion could hold the keys to many of the standing cosmological enigmas.\n\n\\subsubsection*{Acknowledgements} This work is supported by the labex \\textit{Enigmass}, and by the\nCNRS\/IN2P3 Master project \\textit{Axions from Particle Physics to Cosmology}.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzetrp b/data_all_eng_slimpj/shuffled/split2/finalzzetrp new file mode 100644 index 0000000000000000000000000000000000000000..4374d6544dfcf7092fb993a1de935ceca81f0eb8 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzetrp @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction and main results}\nWe start with some notation and terminology. Let\n$(\\R^d,\\<\\cdot,\\cdot\\>,|\\cdot|)$ be the $d$-dimensional Euclidean\nspace, and $\\R^d\\otimes\\R^m$ the collection of all $d\\times m$\nmatrices endowed with the Hilbert-Schmidt norm $\\|\\cdot\\|$. For\nfixed $r_0>0$, $\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r:=C([-r_0,0];\\R^d)$ stands for the family of all\ncontinuous functions $f:[-r_0,0]\\rightarrow\\R^d$ which is a Banach space\nwith the uniform norm $\\|f\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle:=\\sup_{-r_0\\le v\\le 0}|f(v)|$. Given any\ninteger $ p\\ge1$, we use $\\Theta$ to denote a bounded, open and convex\nsubset of $\\R^p$ whose closure is written as $\\bar\\Theta$. Let $\\mathcal\n{P}(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$ be the totality of all probability measures on $\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$. Set\n$\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r):=\\{\\mu\\in\\mathcal\n{P}(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r):\\mu(\\|\\cdot\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2):=\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\\|\\xi\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\mu(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\xi)<\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\\}$.\n$(\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r),\\W_2)$ is a Polish space under the Warsserstein\ndistance $\\mathbb{W}_2$ on $\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$ defined by\n$$\\W_2(\\mu,\\nu):= \\inf_{\\pi\\in \\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r(\\mu,\\nu)} \\bigg(\\int_{\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\\times\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r} \\|\\xi-\\eta\\|^2_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\\pi(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D \\xi,\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D \\eta)\\bigg)^{\\ff 1 2},\\ \\ \\mu,\\nu\\in \\mathcal\n{P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r),$$ where $\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r(\\mu,\\nu)$ is the set of couplings for $\\mu$\nand $\\nu$. As usual, we use $\\lfloor a\\rfloor$ to denote the integer part of $a\\ge0.$\n\n\n\n\n\n\n\n\n\n\n\nThe time evolution for most of stochastic dynamical systems depends\nnot only on the present state but also on the past path. So,\npath-dependent (i.e., functional) SDEs are much more desirable; see,\ne.g., the monograph \\cite{M84}. Since the pioneer work \\cite{IN} due\nto It\\^o and Nisio, path-dependent SDEs have been investigated\nconsiderably owing to their theoretical and practical importance;\nsee, e.g., Hairer et al. \\cite{HMS11}, Wang \\cite{W18} and the\nreferences within.\n\n McKean-Vlasov SDEs, which are SDEs with coefficients dependent on the\n law, were initiated by \\cite{Mc} inspired by Kac's programme in Kinetic\n theory. An excellent and thorough account of the general theory of McKean-Vlasov\n SDEs and their particle approximations can be found in \\cite{sz}. McKean-Vlasov\n SDEs\nare alternatively referred to as mean-field SDEs in the literature, which have\nwide applications in interacting particle systems, optimal control problems,\ndifferential games, just to mention but a few. Recently, McKean-Vlasov SDEs have been\nextensively investigated on, e.g., wellposedness of strong\/weak solutions (cf.\n\\cite{DST,HSS,LMb,MV,W16}), Freidlin-Wentzell large deviation principles (cf. \\cite{DST}), ergodicity (cf. \\cite{B14,EGZ,Ve}), links with nonlinear partial differential\nequations (cf. \\cite{BLPR,HRW,HRW}), and distribution properties (cf.\\cite{Huang,W18}).\n\nOn the other hand, from stochastic and\/or statistical aspects, there exist\nunknown parameters in various type SDEs arising in mathematical modeling (cf.\n\\cite{B08}). Hence, there are vast of investigations paying attention to\nparameter estimations for SDEs via maximum likelihood estimator,\nleast squares estimator (LSE for short), trajectory-fitting estimator, among others.\nSee, for instance, \\cite{Ku04,LS01,M05,P199,SY06}. In the same vein, the parameter estimations for SDEs (without path-dependence) with small noises have been developed very\nwell; see, e.g., \\cite{GS,HL,Long10,L09,LMS,LSS,M10,SM03,U04,U08}, and references\ntherein.\n\nFrom above discussion, it is very natural to consider SDEs together with all four features of path dependence, distribution dependence, small noises and unknown parameter. So,\nin the present work, we focus on the following path-distribution SDE\n\\begin{equation}\\label{eq1}\n\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D X^\\vv(t)=b(X_t^\\vv,\\mathscr{L}_{X_t^\\vv},\\theta)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nt+\\vv\\,\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^\\vv,\\mathscr{L}_{X_t^\\vv})\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(t),\n~~~t>0,~~~~X_0^\\vv=\\xi\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r.\n\\end{equation}\nHerein, $\\vv\\in(0,1)$ is the scale parameter; for fixed $t$,\n$X_t^\\vv(v):=X^\\vv(t+v), v\\in[-r_0,0],$ is called the segment (or\nwindow) process generated by $X^\\vv(t)$; $\\mathscr{L}_{X_t^\\vv}$\nstands for the distribution of $X_t^\\vv$; $b:\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\\times\\mathcal\n{P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)\\times\\Theta\\rightarrow\\R^d$ and $\\sigma} \\def\\ess{\\text{\\rm{ess}}:\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\\times\\mathcal\n{P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)\\rightarrow\\R^d\\otimes\\R^m$ are continuous w.r.t. the\nfirst variable and the second variable;\n $\\Theta\\ni\\theta $ is an unknown parameter whose true value is\n written as\n$\\theta_0\\in\\Theta$; and $(B(t))_{t\\ge0}$ is an $m$-dimensional\nBrownian motion on a filtered probability space\n$(\\OO,\\F,(\\F_t)_{t\\ge0},\\P)$ satisfying the usual conditions, that\nis, $\\F_t$ is non-decreasing (i.e., $\\F_s\\subseteq\\F_t, s\\le t),$\n$\\F_0$ contains all $\\P$-null sets and $\\F_t$ is right continuous\n(i.e., $\\F_t=\\F_{t+}:=\\bigcap_{s\\uparrow t}\\F_s$).\n\n\nTo guarantee the existence and uniqueness of solutions to \\eqref{eq1},\nwe assume that, for any $\\zeta_1,\\zeta_2\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$, $\\mu,\\nu\\in\\mathcal\n{P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, and $\\theta\\in\\Theta,$\n\\begin{enumerate}\n\\item[({\\bf A1})] There exist\n$\\aa_1,\\aa_2>0$ such that\n\\begin{equation*}\n\\<\\zeta_1(0)-\\zeta_2(0),b(\\zeta_1,\\mu,\\theta)-b(\\zeta_2,\\nu,\\theta)\\>\\le\\aa_1\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\aa_2\\mathbb{W}_2(\\mu,\\nu)^2;\n\\end{equation*}\n\n\n\\item[({\\bf A2})] There exist $\\bb_1,\\bb_2>0$ such that\n\\begin{equation*}\n\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta_1,\\mu)-\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta_2,\\nu)\\|^2\\le\n\\bb_1\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\bb_2\\mathbb{W}_2(\\mu,\\nu)^2.\n\\end{equation*}\n\\end{enumerate}\n\nFrom \\cite[Theorem 3.1]{HRW}, \\eqref{eq1} has a unique\nstrong solution $(X^\\vv(t))_{t\\ge-r_0}$ under the assumptions ({\\bf\nA1}) and ({\\bf A2}). For any $\\zeta_1,\\zeta_2\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$,\n$\\mu,\\nu\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, and $\\theta\\in\\Theta,$ if there\nexist $\\aa,\\bb>0$ such that\n\\begin{equation*}\n\\<\\zeta_1(0)-\\zeta_2(0),b(\\zeta_1,\\mu,\\theta)-b(\\zeta_2,\\mu,\\theta)\\>\\le\\aa\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\n\\end{equation*}\nand\n\\begin{equation*}\n|b(\\zeta_2,\\mu,\\theta)-b(\\zeta_2,\\nu,\\theta)|\\le\n\\bb\\mathbb{W}_2(\\mu,\\nu),\n\\end{equation*}\nthen ({\\bf A1}) holds.\n\n\nWithout loss of generality, we arbitrarily fix the time horizontal $T>0$ and assume that there exist positive integers $n,M$ sufficiently large such that\n$\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho:=\\frac{T}{n}=\\ff{r_0}{M}$. Now we define the continuous-time\ntamed Euler-Maruyama (EM) scheme (see, e.g., \\cite{HJK}) associated\nwith \\eqref{eq1}\n\\begin{equation}\\label{q3}\n\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D Y^\\vv(t)=b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\theta)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t+ \\vv\\,\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(t),~~~~t>0\n\\end{equation}\nwith the initial value $Y^\\vv(t)=X^\\vv(t)=\\xi(t)$ for any $\nt\\in[-r_0,0]$, where\n\\begin{itemize}\n\\item $t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho:=\\lfloor t\/\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rfloor\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho$ for $t\\ge0;$\n\n\\item For any $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ and $\\mu\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$,\n\\begin{equation}\\label{e4}\nb^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta,\\mu,\\theta):=\\ff{b(\\zeta,\\mu,\\theta)}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta,\\mu,\\theta)|},~~~~\\aa\\in(0,1\/2];\n\\end{equation}\n\n\\item For $k=0,1,\\cdots,n,$\n$\\bar Y_{k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv=\\{ \\bar Y_{k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv(s):-r_0\\le s\\le0\\}$, a\n$\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$-valued random variable, is defined by\n\\begin{equation}\\label{w2}\n\\bar\nY_{k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv(s)=Y^\\vv((k-i)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\ff{s+i\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\{Y^\\vv((k-i)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\n-Y^\\vv((k-i-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\}\n\\end{equation}\nfor any $s\\in[-(i+1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho,-i\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho]$, $i=0,1,\\cdots,M-1$, that is, $\\bar\nY_{k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv$ is the linear interpolation of the points\n$(Y^\\vv(l\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho))_{l=k-M,\\cdots,k}$.\n\\end{itemize}\n\nWe denote $(Y_t^\\vv)_{t\\ge0}$ by the segment process generated by\n$(Y^\\vv(t))_{t\\ge-r_0}$. It is worthy to point out that $\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ is defined by \\eqref{w2} rather than by $\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}(s)=\\bar Y^\\vv(t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho+s)$ for any $s\\in[-r_0,0]$\n Based on the\ncontinuous-time tamed EM algorithm \\eqref{q3}, we design the\nfollowing contrast function\n\\begin{equation}\\label{eq2}\n\\Psi_{n,\\vv}(\\theta)=\\vv^{-2}\\delta^{-1}\\sum_{k=1}^nP_k^*(\\theta)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta),\n\\end{equation}\nin which, for $k=1,\\cdots,n$,\n\\begin{equation}\\label{w1}\nP_k(\\theta):=Y^\\vv(k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)-Y^\\vv((k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)-b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho,~\n\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv):=(\\sigma} \\def\\ess{\\text{\\rm{ess}}\\si^*)^{-1}(\\bar\nY_{k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv}).\n\\end{equation}\nFor more motivations on the construction of constrast function\nabove, we refer to Ren-Wu \\cite{RW}. To obtain the LSE of\n$\\theta\\in\\Theta$, it is sufficient to choose an element\n$\\hat\\theta_{n,\\vv}\\in\\Theta$ satisfying\n\\begin{equation*}\n\\Psi_{n,\\vv}(\\hat\\theta_{n,\\vv})=\\min_{\\theta\\in\\Theta}\\Psi_{n,\\vv}(\\theta).\n\\end{equation*}\nWhence, for\n\\begin{equation*}\n\\Phi_{n,\\vv}(\\theta):=\\vv^2(\\Psi_{n,\\vv}(\\theta)-\\Psi_{n,\\vv}(\\theta_0)),\n\\end{equation*}\none has\n\\begin{equation}\\label{eq4}\n\\Phi_{n,\\vv}(\\hat\\theta_{n,\\vv})=\\min_{\\theta\\in\\Theta}\\Phi_{n,\\vv}(\\theta).\n\\end{equation}\nWe shall rewrite $\\hat\\theta_{n,\\vv}\\in\\Theta$ such that \\eqref{eq4}\nholds true as\n\\begin{equation*}\n\\hat\\theta_{n,\\vv}=\\arg\\min_{\\theta\\in\\Theta}\\Phi_{n,\\vv}(\\theta),\n\\end{equation*}\nwhich is called the LSE of the unknown parameter\n$\\theta\\in\\Theta$.\n\nTo discuss the consistency of LSE (see Theorem \\ref{th1} below), we\nfurther suppose that, for any $\\zeta,\\zeta_1,\\zeta_2\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$,\n$\\mu,\\nu\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, and $\\theta\\in\\Theta,$\n\\begin{enumerate}\n\n\\item[({\\bf B1})] There exist $q_1,L_1>0$ such that\n\\begin{equation*}\n|b(\\zeta_1,\\mu,\\theta)-b(\\zeta_2,\\nu,\\theta)|\\le\nL_1\\Big\\{(1+\\|\\zeta_1\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_1}+\\|\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_1})\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\mathbb{W}_2(\\mu,\\nu)\\Big\\};\n\\end{equation*}\n\\item[({\\bf B2})] There exist $q_2,L_2>0$ such that\n\\begin{equation*}\n\\sup_{\\theta\\in\\bar\\Theta}\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)(\\zeta_1,\\mu,\\theta)-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)(\\zeta_2,\\nu,\\theta)\\|\\le\nL_2\\Big\\{(1+\\|\\zeta_1\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_2}+\\|\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_2})\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\mathbb{W}_2(\\mu,\\nu)\\Big\\},\n\\end{equation*}\nwhere $(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)$ is the gradient operator w.r.t. the third\nspatial variable;\n\n\\item[({\\bf B3})] $(\\sigma} \\def\\ess{\\text{\\rm{ess}}\\si^*)(\\zeta,\\mu)$ is invertible, and\nthere exist $q_3,L_3>0$ such that\n\\begin{equation*}\n\\|(\\sigma} \\def\\ess{\\text{\\rm{ess}}\\si^*)^{-1}(\\zeta_1,\\mu)-(\\sigma} \\def\\ess{\\text{\\rm{ess}}\\si^*)^{-1}(\\zeta_2,\\nu)\\|\\le\nL_3\\Big\\{(1+\\|\\zeta_1\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_3}+\\|\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_3})\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\mathbb{W}_2(\\mu,\\nu)\\Big\\};\n\\end{equation*}\n\\item[({\\bf B4})] There exists a constant $K>0$ such that\n\\begin{equation*}\n|\\xi(t)-\\xi(s)|\\le K|t-s|,~~~t,s\\in[-r_0,0],\n\\end{equation*}\nwhere $\\xi(\\cdot)$ stands for the initial value of \\eqref{eq1}.\n\\end{enumerate}\n\nIn order to reveal the asymptotic distribution of LSE (see Theorem\n\\ref{th2} below), we in addition assume that\n\\begin{enumerate}\n\\item[({\\bf C})] There exist $q_4,L_4>0$ such that\n\\begin{equation*}\n\\begin{split}\n&\\sup_{\\theta\\in\\bar\\Theta}\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb^*))(\\zeta_1,\\mu,\\theta)-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb^*))(\\zeta_2,\\nu,\\theta)\\|\\\\\n&\\le\nL_4\\Big\\{(1+\\|\\zeta_1\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_4}+\\|\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_4})\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\mathbb{W}_2(\\mu,\\nu)\\Big\\},\n\\end{split}\n\\end{equation*}\nwhere $b^*$ means the transpose of $b.$\n\\end{enumerate}\n\n\n\nNext we consider the following deterministic path-dependent ordinary\nequation\n\\begin{equation}\\label{k1}\n\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D X^0(t)=b(X_t^0,\\mathscr{L}_{X_t^0},\\theta_0)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nt,~~~t>0,~~~X_0^0=\\xi\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r.\n\\end{equation}\nUnder the assumption ({\\bf A1}), \\eqref{k1} is wellposed. In\n\\eqref{k1}, $\\mathscr{L}_{X_t^0}$ is indeed a Dirac's delta measure\nat the point $X_t^0$ as $X_t^0$ is deterministic. To unify the\nnotation, we keep the notation $\\mathscr{L}_{X_t^0}$ in lieu of\n$\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{X_t^0}$. We remark that ({\\bf B4}) is imposed to guarantee\nthat the linear interpolation $\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}$ tends to $X_t^0$\nin the moment sense, see Lemma \\ref{le1} below.\n\n\n\n\n For any random variable $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ with\n$\\mathscr{L}_\\zeta\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, set\n\\begin{equation}\\label{p1}\n\\Gamma(\\zeta,\\theta,\\theta_0):=b(\\zeta,\\mathscr{L}_\\zeta,\\theta_0)\n-b(\\zeta,\\mathscr{L}_\\zeta,\\theta),~~\n~~\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta,\\theta,\\theta_0):=b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta,\\mathscr{L}_\\zeta,\\theta_0)\n-b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta,\\mathscr{L}_\\zeta,\\theta),\n\\end{equation}\nand, for any $\\theta\\in\\Theta,$\n\\begin{equation*}\\label{e0}\n\\Xi(\\theta)=\\int_0^T\\Gamma^*(X_t^0,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\\Gamma(X_t^0,\\theta,\\theta_0)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nt,\n\\end{equation*}\nwhere $(X_t^0)_{t\\ge0}$ is the functional solution to \\eqref{k1}.\n\nThe theorem below is concerned with the consistency of the LSE for\nthe parameter $\\theta\\in\\Theta$, which is the first contribution of\nour work.\n\n\n\\begin{thm}\\label{th1}\n Let $({\\bf A1})-({\\bf A2})$ and $({\\bf B1})-({\\bf B4})$ hold and\nassume further that $\\Xi(\\theta)>0$ for $\\theta\\neq\\theta_0$. Then\n\\begin{equation*}\n\\hat\\theta_{n,\\vv}\\rightarrow\\theta_0~~~~\\mbox{ in probability as }\n \\vv\\rightarrow0 ~~\\mbox{ and }~~n\\rightarrow}\\def\\l{\\ell\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle.\n\\end{equation*}\n\n\\end{thm}\n\nFor $A:=(A_1,A_2,\\cdots,A_p)\\in\\R^p\\otimes\\R^{pd}$ with $A_{k}\\in\n\\R^p\\otimes\\R^d$, $k=1,\\cdots,p,$ and $B\\in\\R^d$, define $A\\circ\nB\\in\\R^p\\otimes\\R^p$ by\n\\begin{equation*}\nA\\circ B=(A_1B,A_2B,\\cdots,A_pB).\n\\end{equation*}\nFor any $\\theta\\in\\Theta$, set\n\\begin{equation}\\label{0z3}\nI(\\theta):=\\int_0^T(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*(X_t^0,\\mathscr{L}_{X_t^0},\\theta)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)(X_t^0,\\mathscr{L}_{X_t^0},\\theta)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t,\n\\end{equation}\n\\begin{equation}\\label{0z2}\n\\begin{split}\nK(\\theta):&=-2\\int_0^T(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\nb^*)(X_t^0,\\mathscr{L}_{X_t^0},\\theta)\\circ\\Big(\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\n\\Gamma(X_t^0,\\theta,\\theta_0)\\Big)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t,\n\\end{split}\n\\end{equation}\nwhere $(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)} b^*):=(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^*))$, and,\nfor any random variable $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ with\n$\\mathscr{L}_\\zeta\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$,\n\\begin{equation}\\label{0s0}\n\\Upsilon(\\zeta,\\theta_0)=(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*(\\zeta,\\mathscr{L}_{\\zeta},\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta,\\mathscr{L}_{\\zeta}).\n\\end{equation}\n\nAnother main result in this paper is presented as below, which\nreveals the asymptotic distribution of $\\hat\\theta_{n,\\vv}.$\n\n\n\\begin{thm}\\label{th2}\n Let the assumptions of Theorem \\ref{th1} hold and suppose\nfurther that $({\\bf C})$ holds and that $I(\\cdot)$ and $K(\\cdot)$\ndefined in \\eqref{0z3} and \\eqref{0z2}, respectively, are\ncontinuous. Then,\n\\begin{equation*}\n\\vv^{-1}(\\hat\\theta_{n,\\vv}-\\theta_0)\\rightarrow\nI^{-1}(\\theta_0)\\int_0^T\\Upsilon(X_t^0,\\theta_0)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(t)~~~~\\mbox{ in\nprobability }\n\\end{equation*}\nas $\\vv\\rightarrow0$ and $n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle$, where $\\Upsilon(\\cdot)$\nis given in \\eqref{0s0}.\n\\end{thm}\n\n\nWith contrast to the existing literature, the innovations of this\npaper lie in:\n\\begin{itemize}\n\\item[(i)] The classical contrast function for LSE is based on EM\nalgorithm. Whereas, under the monotone condition, the EM scheme no\nlonger works. Hence in the present work we adopt a tamed EM method\nto establish the corresponding contrast function. The above is our\nfirst innovation.\n\n\\item[(ii)]For the classical setup, the discrete-time\nobservations at the gridpoints are sufficient to construct the\ncontrast function. Nevertheless, for our present model, the\ndiscrete-time observations are insufficient to establish the\ncontrast function since the SDEs involved are path-dependent. In\nthis paper, we overcome the difficulty mentioned by linear\ninterpolation w.r.t. the discrete-time observations. The above is\nour second innovation.\n\n\\item[(iii)] Our model is much more applicable, which allow the\ncoefficients to be distribution-dependent and weakly monotone. In\nparticular, the drift terms are allowed to be singular (e.g.,\nH\\\"older continuous). The above is our third innovation.\n\n\n\\end{itemize}\n\n\n\nNow, we provide a concrete example to demonstrate Theorems\n\\ref{th1} and \\ref{th2}.\n\\begin{exa}\\label{exa}\nFor any $\\vv\\in(0,1)$, consider the following scalar\npath-distribution dependent SDE\n\\begin{equation}\n\\begin{split}\n\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D X^\\vv(t)&=\\theta^{(1)}+\\theta^{(2)}\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\\Big(-\n(X^\\vv(t))^3+X^\\vv(t)+\\int_{-r_0}^0X^\\vv(t+\\theta)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\theta+\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\n \\zeta(\\theta) \\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\theta\\Big)\\mathscr{L}_{X^\\vv_t}(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta)\\\\\n&\\quad+\\vv\\,\\Big(1+ \\int_{-r_0}^0X^\\vv(t+\\theta)|\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\theta\\Big)\\,\n\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(t),~~~t\\ge0\n\\end{split}\n\\end{equation}\nwith the initial value $X_0^\\vv=\\xi\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ which is Lipschitz,\nwhere, for some $c_10$ is a\ngeneric constant whose value may change from line to line.\n\n\n\\section{Proof of Theorem \\ref{th1}}\\label{sec2}\nTo complete the proof of Theorem \\ref{th1}, we provide some\ntechnical lemmas. The lemma below expounds that the path associated\nwith \\eqref{q3} is uniformly bounded in the $p$-th moment sense.\n\\begin{lem}\n Let $({\\bf A1})$ and $({\\bf A2})$ hold. Then, for any $p>0$ there\nis a constant $C_{p,T}>0$ such that\n\\begin{equation}\\label{r0}\n\\sup_{0\\le t\\le T}\\|X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^p\\le C_{p,T}(1+\\|\\xi\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^p),\n\\end{equation}\nand\n\\begin{equation}\\label{r6}\n\\sup_{0\\le t\\le T}\\E\\Big(\\sup_{-r_0\\le s\\le t}|Y^\\vv(s)|^p\\Big)\\le\nC_{p,T}(1+\\|\\xi\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^p).\n\\end{equation}\n\\end{lem}\n\n\\begin{proof}\nWith the assumption ({\\bf A1}) in hand, the proof of \\eqref{r0} can\nbe achieved by the chain rule and the Gronwall inequality. We\nherein omit the details since it is\n standard. Now we turn to show the argument of\n\\eqref{r6}. By H\\\"older's inequality, it suffices to verify that\n\\eqref{r6} holds for any $p>4.$ By It\\^o's formula, we deduce that\n\\begin{equation*}\n\\begin{split}\n|Y^\\vv(t)|^p&=|Y^\\vv(0)|^p+\\int_0^t\\Big\\{p|Y^\\vv(s)|^{p-2}\\+\n\\ff{p}{2}|Y^\\vv(s)|^{p-2}\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}^*(\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^2\\\\\n&\\quad +\\ff{p(p-2)}{2}|Y^\\vv(s)|^{p-4}|\\sigma} \\def\\ess{\\text{\\rm{ess}}^*(\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})Y^\\vv(s)|^2\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+p\\int_0^t|Y^\\vv(s)|^{p-2}\\\\\\\n&\\le p\\int_0^t|Y^\\vv(s)|^{p-2}\\\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+p\\int_0^t|Y^\\vv(s)|^{p-2}\\\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+\\ff{p(p-1)}{2}\\int_0^t|Y^\\vv(s)|^{p-2}\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^2\n\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+p\\int_0^t|Y^\\vv(s)|^{p-2}\\\\\\\n&=:\\sum_{i=1}^4\\Pi_i(t),~~~~~~t\\in[0,T].\n\\end{split}\n\\end{equation*}\nWhence, for any $t\\ge0$ one has\n\\begin{equation}\\label{w6}\n\\begin{split}\n\\Upsilon(t):=\\E\\Big(\\sup_{-r_0\\le s\\le\nt}|Y^\\vv(s)|^p\\Big)\\le\\|\\xi\\|^p_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\sum_{i=1}^4\\E\\Big(\\sup_{0\\le\ns\\le t}\\Pi_i(s)\\Big).\n\\end{split}\n\\end{equation}\nIn the sequel, we are going to claim that\n\\begin{equation}\\label{w7}\n\\Upsilon(t) \\le 2\\|\\xi\\|^p_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+ c\\,t+c \\int_0^t \\Upsilon(s) \\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{equation}\nIf \\eqref{w7} was true, thus \\eqref{r6} follows directly from\nGronwall's inequality. So, it remains to verify that \\eqref{w7}\nholds true.\n\n\nLet $\\zeta_0(s)\\equiv {\\bf 0}\\in\\R^d$\nfor any $s\\in[-r_0,0].$ For $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ and $\\mu\\in\\mathcal\n{P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, we deduce from ({\\bf A1}) that\n\\begin{equation}\\label{r1}\n\\begin{split}\n\\<\\zeta(0),b(\\zeta,\\mu,\\theta)\\>&=\\<\\zeta(0)-\\zeta_0,b(\\zeta,\\mu,\\theta)-b(\\zeta_0,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0},\\theta)\\>\n+\\<\\zeta(0),b(\\zeta_0,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0},\\theta)\\>\\\\\n&\\le\\aa_1\\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\aa_2\\mathbb{W}_2(\\mu,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2+|\\zeta(0)|^2+|b(\\zeta_0,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0},\\theta)|^2\\\\\n&\\le c\\,(1+\\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\mathbb{W}_2(\\mu,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2),\n\\end{split}\n\\end{equation}\nand from ({\\bf A2}) that\n\\begin{equation}\\label{r2}\n\\begin{split}\n\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta,\\mu)\\|^\n&\\le\n2\\bb_1\\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+2\\bb_2\\mathbb{W}_2(\\mu,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2+2\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta_0,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\|^2\\\\\n&\\le c\\,(1+\\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\mathbb{W}_2(\\mu,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2).\n\\end{split}\n\\end{equation}\nAccording to \\eqref{w2}, we obtain that\n\\begin{equation}\\label{r5}\n\\begin{split}\n&\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\\\\\n&=\\max_{k=0,\\cdots,M-1}\\sup_{-(k+1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le s\\le-k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv(s)|\\\\\n&=\\max_{k=0,\\cdots,M-1}\\sup_{-(k+1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le\ns\\le-k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\Big|\\ff{s+(k+1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}Y^\\vv(t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho-k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)-\\ff{s+k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}Y^\\vv(t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho-(k+1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\Big|\\\\\n&\\le 2\\sup_{-r_0\\le s\\le t}|Y^\\vv(s)|.\n\\end{split}\n\\end{equation}\nFurthermore, recall the Young inequality:\n\\begin{equation}\\label{r3}\na^\\aa b^{1-\\aa}\\le \\aa a+(1-\\aa)b,~~~~~a,b\\ge0,~~\\aa\\in[0,1],\n\\end{equation}\nand the fundamental fact that: for any $q>0$,\n\\begin{equation}\\label{w8}\n\\E|B(t)|^q\\le c\\, t^{q\/2}.\n\\end{equation}\nBy virtue of \\eqref{w2}, we notice that\n\\begin{equation}\\label{r8}\n\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv(0)=Y^\\vv(t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho).\n\\end{equation}\nThen, by exploiting \\eqref{r1}, \\eqref{r5} as well as \\eqref{r8}, it\nfollows from \\eqref{r3} and H\\\"older's inequality that\n\\begin{equation}\\label{w3}\n\\begin{split}\n&\\E\\Big(\\sup_{0\\le s\\le t}\\Pi_1(s)\\Big) \\\\&= p\\,\\E\\Big(\\sup_{0\\le\ns\\le t}\\int_0^s\\ff{|Y^\\vv(u)|^{p-2}}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa |b(\\bar\nY^\\vv_{u_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar\nY^\\vv_{u_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\theta)|}\\\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nu\\Big)\\\\\n&= p\\,\\E\\Big(\\sup_{0\\le s\\le\nt}\\int_0^s\\ff{|Y^\\vv(u)|^{p-2}}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa |b(\\bar\nY^\\vv_{u_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{u_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\theta)|}\\<\\bar\nY^\\vv_{u_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}(0),b(\\bar Y^\\vv_{u_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar\nY^\\vv_{u_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\theta)\\>\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nu\\Big)\\\\\n&\\le c\\,\\E\\Big(\\sup_{0\\le s\\le\nt}\\int_0^s\\ff{|Y^\\vv(u)|^{p-2}}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa| b(\\bar\nY^\\vv_{u_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar\nY^\\vv_{u_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\theta)|}\\Big\\{1+\\|\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le\nc\\int_0^t\\Big\\{1+\\E|Y^\\vv(s)|^p+\\E\\|\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^p\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\le c\\int_0^t\\{1+\\Upsilon(s)\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{split}\n\\end{equation}\nIt is straightforward to see that, for any $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$,\n$\\mu\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, and $\\theta\\in\\Theta$,\n\\begin{equation}\\label{r4}\n|b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta,\\mu,\\theta)|=\\ff{|b(\\zeta,\\mu,\\theta)|}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta,\\mu,\\theta)|}\\le\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{-\\aa}.\n\\end{equation}\nTaking \\eqref{r2} and \\eqref{r4} into consideration and making use\nof \\eqref{w8} and $\\aa\\in(0,1\/2]$, for any $q\\ge2$, we derive that\n\\begin{equation}\\label{r7}\n\\begin{split}\n\\E|Y^\\vv(t)-Y^\\vv(t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^q&\\le\nc\\,\\Big\\{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{q(1-\\aa)}+\\E\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^q\\E|B(t)-B(t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^q\\Big\\}\\\\\n&\\le\nc\\,\\Big\\{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{q(1-\\aa)}+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{q\/2}\\E\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^q\\Big\\}\\\\\n&\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{q\/2}\\Big\\{1+\n\\E\\|\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|^q+\\mathbb{W}_2(\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^q\\Big\\}\\\\\n&\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{q\/2}\\Big\\{1+ \\E\\Big(\\sup_{-r_0\\le s\\le\nt}|Y^\\vv(s)|^q\\Big)\\Big\\},\n\\end{split}\n\\end{equation}\nwhere in the last procedure we have used H\\\"older's inequality and\n\\eqref{r5}. Thus, taking advantage of \\eqref{r4} and \\eqref{r7} and\nemploying H\\\"older's inequality yields that\n\\begin{equation}\\label{w4}\n\\begin{split}\n\\E\\Big(\\sup_{0\\le s\\le t}|\\Pi_2(s)|\\Big)&\\le\np\\E\\int_0^t|Y^\\vv(s)|^{p-2}|Y^\\vv(s)-Y^\\vv(s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|\\cdot|b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\theta)|\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le\np\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{-\\aa}\\int_0^t\\E(|Y^\\vv(s)|^{p-2}|Y^\\vv(s)-Y^\\vv(s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\le\np\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{-\\aa}\\int_0^t\\Big(\\E(|Y^\\vv(s)|^p)\\Big)^{\\ff{p-2}{p}}\\Big(\\E|Y^\\vv(s)-Y^\\vv(s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^{\\ff{p}{2}}\\Big)^{\\ff{2}{p}}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le\np\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\ff{1}{2}-\\aa}\\int_0^t\\Big(\\E(|Y^\\vv(s)|^p)\\Big)^{\\ff{p-2}{p}}\\Big\\{1+\n\\E\\Big(\\sup_{-r_0\\le s\\le\nt}|Y^\\vv(s)|^{\\ff{p}{2}}\\Big)\\Big\\}^{\\ff{2}{p}}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\le c \\int_0^t\\{1+\\Upsilon(s)\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s,\n\\end{split}\n\\end{equation}\nwhere in the last display we used\n $\\aa\\in(0,1\/2]$ and \\eqref{r3}. Next, we observe\nthat\n\\begin{equation}\\label{q1}\n\\begin{split}\n\\E\\Big(\\sup_{0\\le s\\le t}\\Pi_3(s)\\Big)&\\le\n\\ff{p(p-1)}{2}\\int_0^t\\E(|Y^\\vv(s)|^{p-2}\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^2) \\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{split}\n\\end{equation}\nUsing Burkhold-Davis-Gundy's (BDG's for short) inequality and\n\\eqref{r3}, we infer that\n\\begin{equation}\\label{q2}\n\\begin{split}\n\\E\\Big(\\sup_{0\\le s\\le t}\\Pi_4(s)\\Big)&\\le p\\,\\E\\Big(\\sup_{0\\le s\\le\nt}\\Big|\\int_0^s|Y^\\vv(u)|^{p-2}\\\\Big|\\Big)\\\\\n&\\le 4\\ss2\\,p\\,\\E\\Big(\\int_0^t|Y^\\vv(s)|^{2(p-2)}|\\sigma} \\def\\ess{\\text{\\rm{ess}}^*(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})Y^\\vv(s)|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\Big)^{1\/2}\\\\\n&\\le 4\\ss2\\,p\\,\\E\\Big(\\sup_{0\\le s\\le\nt}|Y^\\vv(s)|^p\\int_0^t|Y^\\vv(s)|^{p-2}\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\Big)^{1\/2}\\\\\n&\\le\\ff{1}{2}\\Upsilon(t)+16p^2\\int_0^t\\E(|Y^\\vv(s)|^{p-2}\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^2)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{split}\n\\end{equation}\nSubsequently, one gets from \\eqref{q1} and \\eqref{q2} that\n\\begin{equation}\\label{w5}\n\\begin{split}\n&\\E\\Big(\\sup_{0\\le s\\le t}\\Pi_3(s)\\Big)+\\E\\Big(\\sup_{0\\le s\\le\nt}\\Pi_4(s)\\Big)\\\\&\\le\n\\ff{1}{2}\\Upsilon(t)+c\\int_0^t\\E(|Y^\\vv(s)|^{p-2}\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^2)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le \\ff{1}{2}\\Upsilon(t)+ c\\int_0^t\\{\\E|Y^\\vv(s)|^p+\\E\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^p\\}\n\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\le \\ff{1}{2}\\Upsilon(t)+ c\\int_0^t\\Big\\{1+\\E|Y^\\vv(s)|^p+\\E\\|\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^p+\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^p\\Big\\}\n\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\le \\ff{1}{2}\\Upsilon(t)+ c \\int_0^t\\{1+\\Upsilon(s)\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s,\n\\end{split}\n\\end{equation}\nwhere we have adopted \\eqref{r3} in the second inequality, used\n\\eqref{r2} in the last two step, and utilized H\\\"older's inequality,\nin addition to \\eqref{r5}, in the last procedure. Substituting\n\\eqref{w3}, \\eqref{w4}, and \\eqref{w5} into \\eqref{w6} gives that\n\\begin{equation*}\n\\Upsilon(t) \\le \\|\\xi\\|^p_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+ \\ff{1}{2}\\Upsilon(t)+ c\n\\int_0^t\\{1+\\Upsilon(s)\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{equation*}\nAs a consequence, \\eqref{w7} is now available.\n\\end{proof}\n\n\nThe following lemma shows that the linear interpolation $\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}$ approaches $X_t^0$ in the mean square sense as\n $\\vv$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho$ go to zero.\n\\begin{lem}\\label{le1}\n Assume $({\\bf A1}), ({\\bf A2}), ({\\bf B1})$ and $({\\bf B4})$. Then,\nfor any $\\bb\\in(0,1)$, there exists $c_\\bb>0$\n\n\\begin{equation}\\label{a9}\n \\sup_{0\\le t\\le T}\\E\\|\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\le\n c_\\bb(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb+\\vv^2+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{2\\aa}),\n\\end{equation}\nwhere $\\aa\\in(0,1\/2]$ is introduced in \\eqref{e4}.\n\\end{lem}\n\n\\begin{proof}\nFor any $\\bb\\in(0,1)$ and $t\\in[0,T]$, by H\\\"older's inequality and\n$Y_0^\\vv=X_0^0=\\xi$, we find that\n\\begin{equation}\\label{a7}\n\\begin{split}\n\\E\\|\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2&\\le3\\,\\E\\|Y_t^\\vv-\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+3\\,\\E\\|Y^\\vv_t-X_t^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+3\\,\\E\\|X^\\vv_t-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\\\\n&\\le3\\,\\E\\Big(\\sup_{-r_0\\le v\\le 0}|Y^\\vv(t+v)-\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}(v)|^2\\Big)+3\\,\\E\\Big(\\sup_{0\\le s\\le\nt}|Y^\\vv(s)-X^\\vv(s)|^2\\Big)\\\\\n&\\quad+3\\,\\E\\Big(\\sup_{0\\le s\\le t}|X^\\vv(s)-X^0(s)|^2\\Big)\\\\\n&\\le3\\,\nM^{1-\\bb}\\max_{k=0,\\cdots,M-1}\\,\\Big(\\E\\Big(\\sup_{-(k+1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le\nv\\le-k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}| Y^\\vv(t+v)-\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}(v)|^{\\ff{2}{1-\\bb}}\\Big)\\Big)^{1-\\bb}\\\\\n&\\quad+3\\,\\E\\Big(\\sup_{0\\le s\\le t}|Y^\\vv(s)-X^\\vv(s)|^2\\Big)\n+3\\,\\E\\Big(\\sup_{0\\le s\\le\nt}|X^\\vv(s)-X^0(s)|^2\\Big)\\\\\n&=:\\LL_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\LL_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\LL_3(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho),\n\\end{split}\n\\end{equation}\nwhere $M>0$ such that $M\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho=r_0.$ Hereinafter, we intend to estimate\n$\\LL_i(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)$, $i=1,2,3,$ respectively. In the first place, we\nshall show that\n\\begin{equation}\\label{e3}\n\\LL_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb,~~~~t\\in[0,T].\n\\end{equation}\nFor $t\\in[0,T)$, there is an integer $k_0\\ge0$ such that\n$t\\in[k_0\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho,(k_0+1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho).$ From \\eqref{w2}, it follows that\n\\begin{equation}\\label{a00}\n\\begin{split}\n&\\LL_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\\\&\\le c\\,\nM^{1-\\bb}\\max_{k=0,\\cdots,M-1}\\,\\Big(\\E\\Big(\\sup_{(k_0-k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le\ns\\le(k_0+1-k)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}|\nY^\\vv(s)-Y^\\vv((k_0-k)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^{\\ff{2}{1-\\bb}}\\Big)\\Big)^{1-\\bb}\\\\\n&\\quad+\n c\\,\nM^{1-\\bb}\\max_{k=0,\\cdots,M-1}\\,\\Big(\\E\\Big(\\sup_{(k_0-k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le\ns\\le(k_0+1-k)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}|\nY^\\vv(s)-Y^\\vv((k_0-k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^{\\ff{2}{1-\\bb}}\\Big)\\Big)^{1-\\bb}\\\\\n&\\le\n c\\,\nM^{1-\\bb}\\max_{k=0,\\cdots,M-1}\\,\\Big(\\E\\Big(\\sup_{(k_0-k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le\ns\\le(k_0+1-k)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}|\nY^\\vv(s)-Y^\\vv((k_0-k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^{\\ff{2}{1-\\bb}}\\Big)\\Big)^{1-\\bb}\\\\\n&\\quad+c\\, M^{1-\\bb}\\max_{k=0,\\cdots,M-1}\\,\\Big(\\E|\nY^\\vv((k_0-k)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)-Y^\\vv((k_0-k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^{\\ff{2}{1-\\bb}}\\Big)^{1-\\bb}.\n\\end{split}\n\\end{equation}\nIn case of $k\\ge k_0+1$, by virtue of ({\\bf B4}), one has\n\\begin{equation*}\n\\LL_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\le c\\,M^{1-\\bb}\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^2\\le c\\,r_0^{1-\\bb}\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb.\n\\end{equation*}\nIn terms of ({\\bf B1}), for any $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ and $\\mu\\in\\mathcal\n{P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$,\n\\begin{equation}\\label{q4}\n\\begin{split}\n|b(\\zeta,\\mu,\\theta_0)|&\\le|b(\\zeta,\\mu,\\theta_0)-b(\\zeta_0,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0},\\theta_0)|+|b(\\zeta_0,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0},\\theta_0)|\\\\\n&\\le\nL_1\\Big\\{(1+\\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_1})\\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\mathbb{W}_2(\\mu,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\Big\\}+|b(\\zeta_0,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0},\\theta_0)|.\n\\end{split}\n\\end{equation}\nLet $k'\\ge0$ be an arbitrary integer. For any\n$t\\in[k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho,(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho]$, note from BDG's inequality followed by\nH\\\"older's inequality that\n\\begin{equation*}\n\\begin{split}\n&\\E\\Big(\\sup_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le t\\le\n(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}|Y^\\vv(t)-Y^\\vv(k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^{\\ff{2}{1-\\bb}}\\Big)\\\\&\\le\nc\\,\\E\\Big(\\int_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^{(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}|b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\theta_0)|\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\Big)^{\\ff{2}{1-\\bb}}+c\\,\\E\\Big(\\sup_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le t\\le\n(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\Big|\\int_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^t\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nB(s)\\Big|^{\\ff{2}{1-\\bb}}\\Big)\\\\\n&\\le c\\,\\E\\Big(\\int_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^{(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}|b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\theta_0)|\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\Big)^{\\ff{2}{1-\\bb}}+c\\,\\E\\Big(\\int_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^{(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\Big)^{\\ff{1}{1-\\bb}}\\\\\n&\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\ff{\\bb}{1-\\bb}}\\int_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^{(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\Big\\{\\E|b(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\theta_0)|^{\\ff{2}{1-\\bb}}+ \\E\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^{\\ff{2}{1-\\bb}}\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s,\n\\end{split}\n\\end{equation*}\nwhere in the last display we have used the fact that\n\\begin{equation}\\label{t7}\n|b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta,\\mu,\\theta_0)|\\le|b(\\zeta,\\mu,\\theta_0)|,~~~~\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r,~~~\\mu\\in\\mathcal\n{P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r).\n\\end{equation}\nSubsequently, taking \\eqref{r6}, \\eqref{r2} and \\eqref{q4} into\naccount and making use of H\\\"older's inequality yields that\n\\begin{equation}\\label{d1}\n\\begin{split}\n&\\E\\Big(\\sup_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le t\\le\n(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}|Y^\\vv(t)-Y^\\vv(k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^{\\ff{2}{1-\\bb}}\\Big)\\\\\n&\\le\nc\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\ff{\\bb}{1-\\bb}}\\int_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^{(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\Big\\{1+\\E\\|\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{\\ff{2(1+q_1)}{1-\\bb}}+\n\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^{\\ff{2}{1-\\bb}}\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le\nc\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\ff{\\bb}{1-\\bb}}\\int_{k'\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^{(k'+2)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\Big\\{1+\\E\\|\\bar\nY^\\vv_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{\\ff{2(1+q_1)}{1-\\bb}}\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\ff{1}{1-\\bb}}.\n\\end{split}\n\\end{equation}\nHence, it follows from \\eqref{a00} and \\eqref{d1} with\n$k^\\prime=k_0-k-1$ that\n\\begin{equation*}\n\\begin{split}\n\\LL_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\le c\\, M^{1-\\bb}\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb\n\\end{split}\n\\end{equation*}\nprovided that $k\\le k_0-1$. Whenever $k=k_0$, we deduce from\n\\eqref{a00}, \\eqref{d1} with $k'=0$ as well as ({\\bf B4}) that\n\\begin{equation*}\n\\begin{split}\n\\LL_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)&\\le c\\, M^{1-\\bb}\\Big(\\E\\Big(\\sup_{0\\le s\\le\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}|\nY^\\vv(s)-Y^\\vv(0)|^{\\ff{2}{1-\\bb}}\\Big)\\Big)^{1-\\bb}\\\\\n&\\quad+ c\\, M^{1-\\bb}\\Big(\\E\\Big(\\sup_{-\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\le s\\le0}|\nY^\\vv(s)-Y^\\vv(-\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^{\\ff{2}{1-\\bb}}\\Big)\\Big)^{1-\\bb}\\\\\n&\\quad+c\\, M^{1-\\bb}|\nY^\\vv(0)-Y^\\vv(-\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|^2\\\\\n&\\le c\\, M^{1-\\bb}\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\\\\n&\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb.\n\\end{split}\n\\end{equation*}\nNext, we are going to claim that\n\\begin{equation}\\label{a8}\n\\LL_3(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho) \\le c\\,\\vv^2,~~~~t\\in[0,T].\n\\end{equation}\nFollowing the argument to derive \\eqref{r6}, we deduce that, for\nsome constant $C_{p,T}>0,$\n\\begin{equation}\\label{0r6}\n\\sup_{0\\le t\\le T}\\E\\|X^\\vv_t\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^p\\le C_{p,T}(1+\\|\\xi\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^p).\n\\end{equation}\nBy the It\\^o formula and $X^\\vv_0=X_0^0=\\xi$, we observe that\n\\begin{equation*}\n\\begin{split}\n&|X^\\vv(t)-X^0(t)|^2\\\\\n&=\\int_0^t\\{2\\+\\vv^2\\|\n\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_s^\\vv,\\mathscr{L}_{X_s^\\vv})\\|^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\quad+2\\,\\vv\\int_0^t\\.\n\\end{split}\n\\end{equation*}\nThus, by using BDG's inequality and \\eqref{r3} and noting that\n$X^\\vv_0=X_0^0=\\xi$, we infer from ({\\bf A1}) and \\eqref{r2} that\n\\begin{equation*}\n\\begin{split}\n\\LL_3(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)&\\le2\\int_0^t\\{\\aa_1\\E\\|X_s^\\vv-X_s^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\aa_2\\mathbb{W}_2(\\mathscr{L}_{X_s^\\vv},\\mathscr{L}_{X_s^0})^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\quad+c\\,\\vv^2\\int_0^t\\{1+\\E\\|X_s^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\mathbb{W}_2(\\mathscr{L}_{X_s^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\quad+8\\ss2\\,\\vv\\E\\Big(\\int_0^t|\\sigma} \\def\\ess{\\text{\\rm{ess}}^*(X_s^\\vv,\\mathscr{L}_{X_s^\\vv})(X^\\vv(s)-X^0(s))|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\Big)^{1\/2}\\\\\n&\\le c\\int_0^t\\LL_3(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s+c\\,\\vv^2\\int_0^t\\{1+\\E\\|X_s^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+8\\ss2\\,\\vv\\E\\Big(\\sup_{0\\le s\\le\nt}|X^\\vv(s)-X^0(s)|^2\\int_0^t\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_s^\\vv,\\mathscr{L}_{X_s^\\vv})\\|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\Big)^{1\/2}\\\\\n&\\le\\ff{1}{2}\\LL_3(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+c\\int_0^t\\LL_3(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns+c\\,\\vv^2\\int_0^t\\{1+\\E\\|X_s^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{split}\n\\end{equation*}\nSo, one has\n\\begin{equation*}\n\\LL_3(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho) \\le c\\int_0^t\\LL_3(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns+c\\,\\vv^2\\int_0^t\\{1+\\E\\|X_s^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{equation*}\nThus, \\eqref{a8} follows from \\eqref{0r6} and Gronwall's inequality.\nFinally, we intend to verify that\n\\begin{equation}\\label{s1}\n\\LL_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\le c\\,(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{2\\aa}),~~~~t\\in[0,T].\n\\end{equation}\nAlso, by It\\^o's formula, we derive from $X_0^\\vv=Y_0^\\vv=\\xi$ that\n\\begin{align*}\n|X^\\vv(t)-Y^\\vv(t)|^2&=2\\int_0^t\\\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+2\\int_0^t\\\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+2\\int_0^t\\\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+\\vv^2\\int_0^t\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_s^\\vv,\\mathscr{L}_{X_s^\\vv})-\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})\\|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\quad+2\\,\\vv\\int_0^t\\\\\\\n&=:\\Xi_1(t)+\\Xi_2(t)+\\Xi_3(t)+\\Xi_4(t)+\\Xi_5(t).\n\\end{align*}\nIn view of ({\\bf A1}), we deduce that\n\\begin{equation}\\label{a22}\n\\begin{split}\n\\E\\Big(\\sup_{0\\le s\\le\nt}\\Xi_1(s)\\Big)&\\le2\\int_0^t\\{\\aa_1\\E\\|X_s^\\vv-Y_s^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\aa_2\\mathbb{W}_2(\\mathscr{L}_{X_s^\\vv},\\mathscr{L}_{Y_s^\\vv})^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le c\\,\\int_0^t\\E\\|X_s^\\vv-Y_s^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\le c\\int_0^t\\LL_2(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{split}\n\\end{equation}\nCarrying out a similar argument to derive \\eqref{e3}, for any\n$\\kk>2$, we have\n\\begin{equation}\\label{w0}\n\\sup_{0\\le t\\le T}\\E\\|Y^\\vv_t-\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^\\kk\\le\nc\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\ff{\\kk}{2}-1}.\n\\end{equation}\nTaking ({\\bf A1}), \\eqref{r6} and \\eqref{w0} into consideration\nand applying H\\\"older's inequality that\n\\begin{equation}\\label{a2}\n\\begin{split}\n&\\E\\Big(\\sup_{0\\le s\\le\nt}|\\Xi_2(s)|\\Big)\\\\&\\le\\int_0^t\\{\\E|X^\\vv(s)-Y^\\vv(s)|^2+\n\\E|b(Y_s^\\vv,\\mathscr{L}_{Y_s^\\vv},\\theta_0)-b(\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta_0)|^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le c\\int_0^t\\E|X^\\vv(s)-Y^\\vv(s)|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\quad+c\\int_0^t\\E\\{(1+\\| Y_s^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2q_1}+\\|\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2q_1})\\|Y^\\vv_s-\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\mathbb{W}_2(\\mathscr{L}_{Y_s^\\vv},\\mathscr{L}_{\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})^2\\} \\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le c\\int_0^t\\LL_2(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s+c\\int_0^t\\Big(\\E\\|Y^\\vv_s-\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{\\ff{2}{1-\\bb}}\\Big)^{1-\\bb}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+c\\int_0^t\\Big(\\E\\Big(1+\\| Y_s^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2q_1}+\\|\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2q_1}\\Big)^{\\ff{1}{\\bb}}\\Big)^{\\bb}\\Big(\\E\\|Y^\\vv_s-\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{\\ff{2}{1-\\bb}}\\Big)^{1-\\bb}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb+c\\int_0^t\\LL_2(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{split}\n\\end{equation}\nAccording to \\eqref{e4} and in view of \\eqref{r6} and \\eqref{q4}, it\nfollows from H\\\"older's inequality that\n\\begin{equation}\\label{a3}\n\\begin{split}\n&\\E\\Big(\\sup_{0\\le s\\le\nt}|\\Xi_3(s)|\\Big)\\\\&\\le2\\int_0^t\\E\\{|X^\\vv(s)-Y^\\vv(s)|\\cdot\n|b(\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta_0)-b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta_0)|\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\le c\\int_0^t\\E\\Big\\{|X^\\vv(s)-Y^\\vv(s)|^2+\n \\ff{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{2\\aa}|b(\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta_0)|^4}{(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|\n b(\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta_0)|)^2}\n \\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n &\\le c\\int_0^t\\Big\\{\\E|X^\\vv(s)-Y^\\vv(s)|^2+\n\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{2\\aa}\\E|b(\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta_0)|^4\n \\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n &\\le c\\int_0^t\\Big\\{\\E|X^\\vv(s)-Y^\\vv(s)|^2+\n\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{2\\aa}\\{1+\\E\\|\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(1+q_1)}+\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^4\\}\n \\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n &\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{2\\aa}+c\\int_0^t\\LL_2(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{split}\n\\end{equation}\n Next, owing to $\\vv\\in(0,1)$, $({\\bf A2})$, and\n\\eqref{e3}, one gets that\n\\begin{equation}\\label{a1}\n\\begin{split}\n\\E\\Big(\\sup_{0\\le s\\le t}\\Xi_4(s)\\Big)&\\le\nc\\int_0^t\\{\\E\\|X_s^\\vv-\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\mathbb{W}_2(\\mathscr{L}_{X_s^\\vv},\\mathscr{L}_{\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le c\\int_0^t\\{\\E\\|X_s^\\vv-Y_s^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\E\\|Y_s^\\vv-\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le c\\int_0^t\\{\\LL_1(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\LL_2(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb+c\\int_0^t\\LL_2(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{split}\n\\end{equation}\nNext, for $\\vv\\in(0,1)$, BDG's inequality and Young's inequality\n\\eqref{r3}, besides \\eqref{a1}, give that\n\\begin{equation}\\label{a5}\n\\begin{split}\n&\\E\\Big(\\sup_{0\\le s\\le\nt}|\\Xi_5(s)|\\Big)\\\\&\\le8\\ss2\\,\\E\\Big(\\int_0^t\n|(\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_s^\\vv,\\mathscr{L}_{X_s^\\vv})-\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv}))^*(X^\\vv(s)-Y^\\vv(s))|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\Big)^{1\/2}\\\\\n&\\le8\\ss2\\,\\E\\Big(\\sup_{0\\le\\le\nt}|X^\\vv(s)-Y^\\vv(s)|^2\\int_0^t\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_s^\\vv,\\mathscr{L}_{X_s^\\vv})-\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})\\|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\Big)^{1\/2}\\\\\n&\\le\n\\ff{1}{2}\\LL_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+c\\int_0^t\\E\\|\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_s^\\vv,\\mathscr{L}_{X_s^\\vv})-\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})\\|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\ns\\\\\n&\\le\n\\ff{1}{2}\\LL_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb+c\\int_0^t\\LL_2(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{split}\n\\end{equation}\nThus, \\eqref{a22}, \\eqref{a2}-\\eqref{a5} yield that\n\\begin{equation*}\n\\LL_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\le\\ff{1}{2}\\LL_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\nc\\,(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{2\\aa})+c\\int_0^t\\LL_2(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{equation*}\nNamely,\n\\begin{equation*}\n\\LL_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\le\nc\\,(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{2\\aa})+c\\int_0^t\\LL_2(s,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s.\n\\end{equation*}\nAs a result, we obtain from Gronwall's inequality that\n\\begin{equation}\\label{a6}\n\\LL_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\le c\\,(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{2\\aa}).\n\\end{equation}\nInserting \\eqref{e3}, \\eqref{a8}, and \\eqref{a6} back into\n\\eqref{a7} leads to the desired assertion \\eqref{a9}.\n\\end{proof}\n\n\n\\begin{rem}\n{\\rm The convergence rate of EM scheme for path-independent SDEs\nunder the global Lipschitz condition is one half. Taking $\\aa=1\/2$\nin \\eqref{s1}, we conclude that the convergence rate of the tamed\nEM scheme constructed in \\eqref{q3} is close sufficiently to one\nhalf. This demonstrate the distinct features between path-dependent\nSDEs and path-independent SDEs. }\n\\end{rem}\n\n\nThe lemma below plays a crucial role in revealing the asymptotic\nbehavior of the LSE of the unknown parameter $\\theta\\in\\Theta$.\n\n\n\n\n\n\n\n\n\\begin{lem}\\label{lem}\n Let $({\\bf A1})-({\\bf A2})$ and $({\\bf B1})-({\\bf B4})$ hold. Then,\n\\begin{equation}\\label{t4}\n\\begin{split}\n&\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\sum_{k=1}^n(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\\\\n&\\rightarrow\\Xi(\\theta):=\\int_0^T\\Gamma(X_t^0,\\theta,\\theta_0)^*\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\\Gamma(X_s^0,\\theta,\\theta_0)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nt\n\\end{split}\n\\end{equation}\nin $L^1$ as $\\vv\\rightarrow0$ and $\\delta\\rightarrow0 $ $($i.e.,\n$n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle$$)$. Moreover,\n\\begin{equation}\\label{q6}\n \\sum_{k=1}^n(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta_0) \\rightarrow0\n\\end{equation}\nin $L^2$ as $\\vv\\rightarrow0$.\n\\end{lem}\n\n\n\\begin{proof}\nObserve that\n\\begin{equation*}\n\\begin{split}\n&\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\sum_{k=1}^n(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)-\n\\int_0^T\\Gamma^*(X_t^0,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\\Gamma(X_t^0,\\theta,\\theta_0)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nt\\\\\n&=\\int_0^T\\Big\\{(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\n-\\Gamma^*(X_t^0,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\\Gamma(X_t^0,\\theta,\\theta_0)\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nt\\\\\n&=\\int_0^T \\Big(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)-\\Gamma(X_t^0,\\theta,\\theta_0)\\Big)^*\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\\\\n&\\quad+\\int_0^T\\Gamma(X_t^0,\\theta,\\theta_0)^*\n \\Big(\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)-\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\\Big)\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0) \\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nt\\\\\n&\\quad+\\int_0^T\\Gamma(X_t^0,\\theta,\\theta_0)^*\n\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\\Big(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)-\\Gamma(X_t^0,\\theta,\\theta_0)\\Big) \\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nt\\\\\n&=:J_1(\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+J_2(\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+J_3(\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho).\n\\end{split}\n\\end{equation*}\nFrom ({\\bf B1}) and \\eqref{q4}, a direct calculation shows that,\nfor any random variables $\\zeta_1,\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ with\n$\\mathscr{L}_{\\zeta_1},\\mathscr{L}_{\\zeta_2}\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$,\n\\begin{equation}\\label{t1}\n\\begin{split}\n&|\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta_1,\\theta,\\theta_0)-\\Gamma(\\zeta_2,\\theta,\\theta_0)|\\\\\n&=|b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)-b(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta_0)+\nb(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)-b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)\n|\\\\\n&\\le|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)-b(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta_0)|\n+|b(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)-b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|\\\\\n&\\quad+|b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)-b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)|\n+|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)-b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|\\\\\n&=|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)-b(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta_0)|\n+|b(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)-b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|\\\\\n&\\quad+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa\\Big|\\ff{|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)|}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)|}b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)\\Big|\n+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa\\Big|\\ff{|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|}\nb(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)\\Big|\\\\\n&\\le|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)-b(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta_0)|\n+|b(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)-b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|\\\\\n&\\quad+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\aa}\\{|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta_0)|^2+|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|^2\\}\\\\\n&\\le\nc\\Big\\{(1+\\|\\zeta_1\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_1}+\\|\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_1})\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\mathbb{W}_2(\\mathscr{L}_{\\zeta_1},\\mathscr{L}_{\\zeta_2})\\Big\\}\\\\\n&\\quad+c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\aa}\\Big\\{1+\\|\\zeta_1\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+q_1)}+\\mathbb{W}_2(\\mathscr{L}_{\\zeta_1},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2\\Big\\}.\n\\end{split}\n\\end{equation}\nNext, for a random variable $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ with\n$\\mathscr{L}_\\zeta\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, by \\eqref{q4} and\n\\eqref{t7}, it follows that\n\\begin{equation}\\label{t3}\n\\begin{split}\n|\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta,\\theta,\\theta_0)|+|\\Gamma(\\zeta,\\theta,\\theta_0)\n\\le c\\,\\Big\\{1+\n \\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_1} +\n \\mathbb{W}_2(\\mathscr{L}_\\zeta,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\Big\\}\n \\end{split}\n\\end{equation}\nand, due to ({\\bf B3}), that\n\\begin{equation}\\label{t2}\n\\|\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)\\|\\le\\|\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)-\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(0)\\|+\\|\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(0)\\|\n\\le c\\,\\Big\\{1+\n \\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_3} +\n \\mathbb{W}_2(\\mathscr{L}_\\zeta,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\Big\\}.\n\\end{equation}\nConsequently, combining \\eqref{t1} with \\eqref{t3} and\\eqref{t2},\nfor $q:=q_1\\vee q_3$, we deduce from \\eqref{r0} that\n\\begin{align*}\n&|J_1(\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|+|J_3(\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|\\\\&\\le c\\int_0^T\\Big\\{(1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_1}+\\|X_s^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_1})\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\mathbb{W}_2(\\mathscr{L}_{\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\mathscr{L}_{X_t^0})\\\\\n&\\quad+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\aa}\\Big(1+\\|\\bar\nY_{s_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+q_1)}+\\mathbb{W}_2(\\mathscr{L}_{\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2\\Big)\\Big\\}\\\\\n&\\quad\\times\\Big\\{1 +\\|X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_1}+\n \\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_1} + \\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_0)\\Big\\}\\\\\n&\\quad\\times\\Big\\{1 +\\|X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_3}+\n \\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_3} + \\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\\\\n&\\le c\\int_0^T\\Big\\{(1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^q)\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\ss{\\E\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2}\\Big\\}\\\\\n&\\quad\\times\\Big\\{1+\n \\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+ q)} + \\E\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n&\\quad+c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\aa}\\int_0^T\\Big\\{1+\n \\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(1+ q)} + \\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^4\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t.\n\\end{align*}\nThis, by exploiting \\eqref{r6} and \\eqref{a7} and using H\\\"older's\ninequality, gives that\n\\begin{equation}\\label{t5}\n\\begin{split}\n&\\E|J_1(\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|+\\E|J_3(\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|\\\\&\\le c\\,\\int_0^T\\ss{\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2}\\Big\\{1 +\\E\n \\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{8(1+ q)}\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\\\\n&\\quad+c\\,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\aa}\\int_0^T\\Big\\{1+\\E\n \\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(1+ q)} \\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\\\\n&\\rightarrow0\n\\end{split}\n\\end{equation}\n as $ \\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0$.\nNext, making use of ({\\bf B3}) and \\eqref{t3}, we derive that\n\\begin{equation*}\n\\begin{split}\n |J_2(\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|&\\le c\\int_0^T(1+\n \\|X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_1})\\Big(1+\n \\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_1} + \\ss{\\E\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2}\\Big)\\\\\n&\\quad\\times\\Big((1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_3}+\\|X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_3})\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\ss{\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2}\\Big)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t.\n\\end{split}\n\\end{equation*}\nAgain, using \\eqref{r0}, \\eqref{r6} and \\eqref{a9} and utilizing\nH\\\"older's inequality gives that\n\\begin{equation}\\label{t6}\n\\begin{split}\n\\E|J_2(\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)|&\\le c\\,\\int_0^T\\ss{\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2}\\Big\\{1 +\\E\n \\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(1+ q)}\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\\\\n&\\rightarrow0\n\\end{split}\n\\end{equation}\n as $ \\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0$.\nHence, \\eqref{t4} follows immediately from \\eqref{t5} and\n\\eqref{t6}.\n\n\nIn the sequel, we are going to show that \\eqref{q6} holds. In terms\nof \\eqref{q3}, we obtain that\n\\begin{equation}\\label{q5}\n\\begin{split}\n&\\sum_{k=1}^n(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta_0)\\\\\n&=\\vv\\sum_{k=1}^n(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\n\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y^\\vv_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})(B(k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)-B((k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho))\\\\\n&=\\vv\\int_0^T(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv) \\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(t).\n\\end{split}\n\\end{equation}\nBy the It\\^o isometry and the H\\\"older inequality, we derive from\n\\eqref{r2}, \\eqref{t3}, and \\eqref{t2} that\n\\begin{equation*}\n\\begin{split}\n&\\E\\Big|\\int_0^T(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv) \\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nB(t)\\Big|^2\\\\\n&=\\int_0^T\\E|(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv) \\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\n t\\\\\n &\\le\\int_0^T\\E\\{|\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)|^2\\cdot\\|\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\|^2\\cdot\\| \\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho},\\mathscr{L}_{\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}})\\|^2\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\\\\n&\\le c\\,\\int_0^T\\E\\Big\\{\\Big(1+\\|\\bar\nY^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\mathbb{W}_2(\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2\\Big)\\\\\n&\\quad\\times\\Big(1+\n \\|\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+q_3)} +\n \\mathbb{W}_2(\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2\\Big)\\\\\n&\\quad\\times\\Big(1+\n \\|\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+q_1)} +\n \\mathbb{W}_2(\\mathscr{L}_{\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2\\Big)\\Big\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\\\\n&\\le c\\,\\int_0^T\\{1+\\E\\|\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{8(1+q)}\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t.\n\\end{split}\n\\end{equation*}\nThis, together with \\eqref{r6}, leads to\n\\begin{equation*}\n\\begin{split}\n&\\E\\Big|\\sum_{k=1}^n(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta_0)\\Big|^2\\\\\n&\\le c\\,\\vv^2\\int_0^T\\{1+\\E\\|\\bar Y^\\vv_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{8(1+q)}\\}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\\\\n&\\le c\\,\\vv^2.\n\\end{split}\n\\end{equation*}\nAs a consequence, we obtain \\eqref{q6} immediately.\n\\end{proof}\n\n\n\nSo far, with Lemma \\ref{lem} in hand, we are in the position to\ncomplete the\n\\begin{proof}[ Proof of Theorem \\ref{th1}]\nA direction calculation shows that\n\\begin{equation}\\label{h1}\n\\begin{split}\n&\\Phi_{n,\\vv}(\\theta)\\\\\n&=\\delta^{-1}\\sum_{k=1}^n\\Big\\{P_k^*(\\theta)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta)-P_k^*(\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta_0)\\Big\\}\\\\\n&=\\delta^{-1}\\sum_{k=1}^n\\Big\\{\\Big(P_k(\\theta_0)+(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\Big)^*\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv) \\Big(P_k(\\theta_0)\n+\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar Y_{t_{k-1}}^\\vv,\\theta,\\theta_0)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\Big)\\\\\n&\\quad -P_k^*(\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta_0)\\Big\\}\\\\\n&=2\\sum_{k=1}^n(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta_0)\\\\\n&\\quad+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\sum_{k=1}^n(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0).\n\\end{split}\n\\end{equation}\nBy virtue of Lemma \\ref{lem}, we therefore infer from Chebyshev's\ninequality that\n\\begin{equation*}\n\\sup_{\\theta\\in\\Theta}|-\\Phi_{n,\\vv}(\\theta)-(-\\Xi(\\theta))|\\rightarrow0~~~~\\mbox{\nin probability.}\n\\end{equation*}\n Next, for any\n$\\kk>0,$ due to $\\Xi(\\cdot)>0$,\n\\begin{equation*}\n \\sup_{|\\theta-\\theta_0|\\ge\\kk}(-\\Xi(\\theta))<-\\Xi(\\theta_0)=0.\n\\end{equation*}\nFurthermore, one has $-\\Phi_{n,\\vv}(\n\\hat\\theta_{n,\\vv})\\ge-\\Phi_{n,\\vv}(\\theta_0)=0$. Consequently, we\ndeduce from \\cite[Theorem 5.9]{V98} with\n$M_n(\\cdot)=-\\Phi_{n,\\vv}(\\cdot)$ and $M(\\cdot)=-\\Xi(\\cdot)$ therein\nthat $\\hat\\theta_{n,\\vv}\\rightarrow\\theta_0$ in probability as\n$\\vv\\rightarrow0$ and $n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle$. We henceforth complete the\nproof.\n\\end{proof}\n\n\n\n\n\\section{Proof of Theorem \\ref{th2}}\\label{sec3}\nBefore we start to finish the argument of Theorem \\ref{th2}, we also\nneed to prepare some auxiliary lemmas below. For any random\nvariable $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ with $\\mathscr{L}_\\zeta\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$,\nset\n\\begin{equation*}\n\\Upsilon^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\zeta,\\theta):=(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*\n(\\zeta,\\mathscr{L}_\\zeta,\\theta)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta,\\mathscr{L}_{\\zeta}).\n\\end{equation*}\n\n\n\n\n\n\\begin{lem}\\label{le2}\n Let $({\\bf A1})-({\\bf A2})$ and $({\\bf B1})-({\\bf B4})$ hold. Then,\n\\begin{equation}\\label{v1}\n\\vv^{-1}(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Phi_{n,\\vv})(\\theta)\\rightarrow-2\\int_0^T\\Upsilon(X_t^0,\\theta)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nB(t)~~~~\\mbox{ in probability }\n\\end{equation}\nwhenever $\\vv\\rightarrow0$ and $n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle$, where\n$\\Upsilon(\\cdot,\\cdot)$ is introduced in \\eqref{0s0}.\n\\end{lem}\n\n\\begin{proof}\nBy the chain rule, one infers from \\eqref{q3} and \\eqref{h1} that\n\\begin{equation}\\label{c1}\n\\begin{split}\n&\\vv^{-1}(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Phi_{n,\\vv})(\\theta)\\\\%&=2\\vv^{-1}\\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta_0)\\\\\n&=2\\,\\vv^{-1}\\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\n\\Big\\{P_k(\\theta_0)+\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\Big\\}\\\\\n&=2\\,\\vv^{-1}\\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta)\\\\\n&=-2\\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})\\\\\n&\\quad\\times(B(k\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)-B((k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho))\\\\\n&=-2\\int_0^T\\Upsilon^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(t),\n\\end{split}\n\\end{equation}\nwhere in the last two display we used the fact that\n\\begin{equation}\\label{c2}\n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)=-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}) (\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta).\n\\end{equation}\nTo achieve \\eqref{v1}, in terms of \\cite[Theorem 2.6, P.63]{F98}, it\nis sufficient to claim that\n\\begin{equation}\\label{v2}\n\\int_0^T\\|\\Upsilon^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta)-\\Upsilon(X_t^0,\\theta)\\|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nt\\rightarrow0~~~~\\mbox{ in probability }\n\\end{equation}\nas $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0.$ Observe that\n\\begin{equation*}\n\\begin{split}\n&\\Upsilon^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta)-\\Upsilon(X_t^0,\\theta)\\\\\n&=(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^* (\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*\n(X_t^0,\\mathscr{L}_{X_t^0},\\theta)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0,\\mathscr{L}_{X_t^0})\\\\\n&=\\{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^* (\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*\n(X_t^0,\\mathscr{L}_{X_t^0},\\theta)\\}\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})\\\\\n&\\quad+(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*\n(X_t^0,\\mathscr{L}_{X_t^0},\\theta)\\{\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)-\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\\}\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\n\\mathscr{L}_{\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})\\\\\n&\\quad+(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^* (X_t^0,\\mathscr{L}_{X_t^0},\\theta)\n\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0)\\{\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv})-\\sigma} \\def\\ess{\\text{\\rm{ess}}(X_t^0,\\mathscr{L}_{X_t^0})\\}\\\\\n&=:\\Sigma_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\Sigma_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\Sigma_3(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho).\n\\end{split}\n\\end{equation*}\n\n\n\n\n\n\n\n\n\n\n\nBy a straightforward calculation, for any random variable\n$\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ with $\\mathscr{L}_{\\zeta}\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, one\nhas\n\\begin{equation}\\label{v3}\n\\begin{split}\n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})(\\zeta,\\mathscr{L}_\\zeta,\\theta)&=\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Big(\\ff{b(\\zeta,\\mu,\\theta)}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta,\\mu,\\theta)|}\\Big)\\\\\n&=\\ff{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)(\\zeta,\\mu,\\theta)}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta,\\mu,\\theta)|}-\\ff{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa( b\nb^*)(\\zeta,\\mu,\\theta)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)(\\zeta,\\mu,\\theta)}{|b(\\zeta,\\mu,\\theta)|(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta,\\mu,\\theta)|)^2}.\n\\end{split}\n\\end{equation}\nNext, for any random variables $\\zeta_1,\\zeta_2\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ with\n$\\mathscr{L}_{\\zeta_1},\\mathscr{L}_{\\zeta_2}\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$,\nit follows from \\eqref{v3} that\n\\begin{equation}\\label{v5}\n\\begin{split}\n&\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*\n(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*\n(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)\\|\\\\\n&=\\Big\\|\\ff{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|}-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^* (\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta) -\\ff{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa (\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta) (b\nb^*)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)}{(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|)^2|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|}\\Big\\|\\\\\n&=\\Big\\|\\ff{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*\n(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|}-\\ff{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|\n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*\n(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|}\\\\\n&\\quad-\\ff{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa (\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta) (b\nb^*)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)}{(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|)^2|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|}\\Big\\|\\\\\n&\\le\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)\n(\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)\\|\\\\\n&\\quad+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|\\cdot\\{\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb) (\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)\\|+\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)\n(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)\\|\\},\n\\end{split}\n\\end{equation}\nwhere in the last step we utilized the facts that $\\|A\\|=\\|A^*\\|$\nfor a matrix $A$ and that\n\\begin{equation*}\n\\begin{split}\n&\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta) (b\nb^*)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)\\|^2\\\\\n&=\\mbox{trace}\\Big(((\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta) (b\nb^*)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta))^*(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta) (b\nb^*)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)\\Big)\\\\\n&=\\mbox{trace}\\Big((b\nb^*)^*(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta))((\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)\n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta) (b\nb^*)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)\\Big)\\\\\n&=\\mbox{trace}\\Big(((\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b) (\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)(b\nb^*)(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)(b b^*)^*(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)) \\Big)\\\\\n&=|b(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)|^4\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*\n(\\zeta_1,\\mathscr{L}_{\\zeta_1},\\theta)\\|^2.\n\\end{split}\n\\end{equation*}\nMoreover, from ({\\bf B2}), one has\n\\begin{equation}\\label{v4}\n\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b) (\\zeta_2,\\mathscr{L}_{\\zeta_2},\\theta)\\|\\le\nc\\Big\\{1+\\|\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_2}+\\mathbb{W}_2(\\mathscr{L}_{\\zeta_2},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\Big\\}.\n\\end{equation}\nNow, taking ({\\bf B2}), \\eqref{v5}, and \\eqref{v4}, in addition to\n\\eqref{r2} and \\eqref{t2}, into account yields that\n\\begin{equation*}\n\\begin{split}\n\\|\\Sigma_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\|&\\le c\\Big\\{(1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_2}+\\|X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q_2})\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\mathscr{L}_{X_t^0})\\\\\n&\\quad+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa\\Big(1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_1}+\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\Big)\\Big(1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_2}+\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\Big)\\Big\\}\\\\\n&\\quad\\times\\Big\\{1+\n \\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{1+q_3} +\n \\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\Big\\}\\times\\Big\\{1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})\\Big\\}.\n\\end{split}\n\\end{equation*}\nFor $q:=q_1\\vee q_2\\vee q_3,$ simple calculations and \\eqref{r6}\n give that\n\\begin{equation*}\n\\begin{split}\n\\|\\Sigma_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\|&\\le c\\Big\\{(1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{q})\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\ss{\\E\\|\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv}-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2}\\Big\\}\\\\\n&\\quad\\times\\Big\\{1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+q)}+\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\Big\\}\\\\\n&\\quad+c\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa\\Big\\{1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+q)}+\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\Big\\}^2\\\\\n&\\le c (1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(1+q)})\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\\\\\n&\\quad+c (1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+q)})\\ss{\\E\\|\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv}-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2}+c\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa(1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(1+q)})\\\\\n&=:\\tilde} \\def\\Ric{\\text{\\rm{Ric}}\\LL_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\tilde} \\def\\Ric{\\text{\\rm{Ric}}\\LL_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\tilde} \\def\\Ric{\\text{\\rm{Ric}}\\LL_3(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho).\n\\end{split}\n\\end{equation*}\nFor any $\\rho>0$, by virtue of H\\\"older's inequality, together with\n\\eqref{r6} and \\eqref{a9}, it follows that\n\\begin{equation}\\label{v6}\n\\begin{split}\n&\\P\\Big(\\int_0^T\\|\\tilde} \\def\\Ric{\\text{\\rm{Ric}}\\LL_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\ge\\rho\\Big)\\\\\n&\\le\\P\\Big(c\\int_0^T (1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{8(1+q)})\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\ge\\rho\\Big)\\\\\n&\\le\\P\\Big(c\\int_0^T (1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{9(1+q)})\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\ge\\rho\\Big)\\\\\n&\\le \\ff{c}{\\rho}\\int_0^T (1+\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{18(1+q)})\\ss{\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2} \\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\\\\n&\\rightarrow0\n\\end{split}\n\\end{equation}\nwhenever $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0.$ On the other hand,\nby means of \\eqref{r6}, and \\eqref{a9}, it follows that\n\\begin{equation}\\label{v7}\n\\begin{split}\n\\E\\tilde} \\def\\Ric{\\text{\\rm{Ric}}\\LL_2^2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\E\\tilde} \\def\\Ric{\\text{\\rm{Ric}}\\LL_3^2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)&\\le c (1+\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(1+q)})\\E\\|\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv}-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+c\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa(1+\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{8(1+q)})\\\\\n&\\le c(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\bb+\\vv^2+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\aa})\\\\\n&\\rightarrow0\n\\end{split}\n\\end{equation}\nas $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0.$ As a consequence, we\ninfer from \\eqref{v6} and \\eqref{v7} that\n\\begin{equation}\\label{b1}\n \\int_0^T \\|\\Sigma_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho) \\|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D t\\rightarrow0~~~~\\mbox{ in\n probability }\n\\end{equation}\nwhen $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0.$ Next, taking advantage\nof ({\\bf A2}), ({\\bf B3}), \\eqref{r2}, and \\eqref{v4} leads to\n\\begin{equation*}\n\\begin{split}\n&\\|\\Sigma_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\|^2+\\|\\Sigma_3(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\|^2\\\\&\\le c\\Big\\{1+\\|X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+q)}\\Big\\}\\\\\n&\\quad\\times\\Big\\{(1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2q}+\\|X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2q})\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\mathscr{L}_{X_t^0})^2\\Big\\}\\\\\n&\\quad\\times (1+\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\mathbb{W}_2(\\mathscr{L}_{\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^2)\\\\\n&\\le c\\Big\\{(1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(1+q)}) \\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+\\E\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|^2_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\\Big\\}\\\\\n&\\quad\\times \\Big\\{1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|^2_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\\Big\\}\\\\\n&\\le c (1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{2(2+q)}) \\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle +c(1+\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2)\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|^2_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\\\\\n&=:\\Xi_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)+\\Xi_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho),\n\\end{split}\n\\end{equation*}\nin which we adopted \\eqref{r6} in the last procedure. Via H\\\"older's\ninequality, we obtain from \\eqref{r6} and \\eqref{a9} that\n\\begin{equation}\\label{v8}\n \\E \\Xi_1(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\le c(1+\\E\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(2+q)}) \\ss{\\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2}\\rightarrow0\n\\end{equation}\nas $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0.$ Also, by \\eqref{r6} and\n\\eqref{a9}, one has\n\\begin{equation}\\label{v9}\n \\E \\Xi_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\le c(1+\\E\\|\\bar Y_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2) \\E\\|\\bar\nY_{t_\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv-X_t^0\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2\\rightarrow0\n\\end{equation}\nprovided that $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0.$ Therefore,\n\\eqref{v8} and \\eqref{v9} lead to\n\\begin{equation}\\label{b2}\n \\E \\|\\Sigma_2(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\|^2+ \\E \\|\\Sigma_3(t,\\vv,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)\\|^2\\rightarrow0\n\\end{equation}\nif $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0.$ At last, the desired\nassertion \\eqref{v1} holds from \\eqref{b1} and \\eqref{b2}.\n\\end{proof}\n\n\n\n\\begin{lem}\\label{le3}\n Let $({\\bf A1})-({\\bf A3}), ({\\bf B1})-({\\bf B4})$, and $({\\bf\n C})$\nhold. Then\n\\begin{equation}\\label{c3}\n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta)\\rightarrow\nK_0(\\theta):=K(\\theta)+I(\\theta)~~~~\\mbox{ in probability }\n\\end{equation}\nas $n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle$ and $\\vv\\rightarrow0$, where $I(\\cdot)$ and\n$K(\\cdot)$ are introduced in \\eqref{0z3} and \\eqref{0z2},\nrespectively.\n\\end{lem}\n\n\n\n\\begin{proof}\n From \\eqref{c1} and \\eqref{c2}, we deduce that\n\\begin{equation*}\n\\begin{split}\n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta)& =2\n\\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}(\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*)(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\n\\circ\\Big(\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta)\\Big)\\\\\n&\\quad+2 \\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta P_k)(\\theta)\\\\\n& =-2 \\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}(b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*)(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\n\\circ\\Big(\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta)\\Big)\\\\\n&\\quad+2 \\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^* (\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta).\n\\end{split}\n\\end{equation*}\nFor any random variable $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ with\n$\\mathscr{L}_\\zeta\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, by the chain rule, we\ninfer from \\eqref{v3} that\n\\begin{equation}\\label{e6}\n\\begin{split}\n\\Big(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}(b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*\\Big)(\\zeta,\\mathscr{L}_\\zeta,\\theta)\n&=\\bigg(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\bigg(\\ff{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb^*)}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b|}\\bigg)\\bigg)(\\zeta,\\mathscr{L}_\\zeta,\\theta)\\\\\n&\\quad-\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa\\bigg(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\bigg(\\ff{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^*)( b\nb^*)}{|b|(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b|)^2}\\bigg)\\bigg)(\\zeta,\\mathscr{L}_\\zeta,\\theta)\\\\\n&=(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)} b^*)\n(\\zeta,\\mathscr{L}_\\zeta,\\theta)-\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa\\Theta_1(\\zeta,\\mathscr{L}_\\zeta,\\theta).\n\\end{split}\n\\end{equation}\nNext, the chain rule shows that\n\\begin{equation*}\n\\begin{split}\n& \\Theta_1(\\zeta,\\mathscr{L}_\\zeta,\\theta)\n:= \\bigg(\\ff{|b|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)} b^*)}{1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b|}+\\ff{\\Big(b^*(\\ff{\\partial}{\\partial\\theta_1}b)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*,\\cdots,b^*(\\ff{\\partial}{\\partial\\theta_p}b)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*\\Big)_{p\\times pd}}{|b|(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b|)^2}\\\\\n&\\qquad\\qquad\\quad+\\ff{\\Big((\\ff{\\partial}{\\partial\\theta_1}(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^*))( b\nb^*),\\cdots,(\\ff{\\partial}{\\partial\\theta_p}(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*)( b\nb^*)\\Big)_{p\\times pd}}{|b|(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b|)^2}\\\\\n&\\qquad\\qquad\\quad+\\ff{\\Big((\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*( (\\ff{\\partial}{\\partial\\theta_1}b)\nb^*+b\\ff{\\partial}{\\partial\\theta_1}b^*),\\cdots,(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)^*(\n(\\ff{\\partial}{\\partial\\theta_p}b)\nb^*+b\\ff{\\partial}{\\partial\\theta_p}b^*)\\Big)_{p\\times pd}}{|b|(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b|)^2}\\\\\n&\\quad-\\ff{1+3\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b|}{|b|^3(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|b|)^3}\\Big(\n\\Big(b^*\\Big(\\ff{\\partial}{\\partial\\theta_1}b\\Big)\\Big)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*( b\nb^*),\\cdots,\\Big(b^*\\Big(\\ff{\\partial}{\\partial\\theta_p}b\\Big)\\Big)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)^*( b b^*)\\Big)\\bigg)_{p\\times\npd}(\\zeta,\\mathscr{L}_\\zeta,\\theta).\n\\end{split}\n\\end{equation*}\n Thanks to \\eqref{v3}, it follows\nthat\n\\begin{equation}\\label{e5}\n\\begin{split}\n&\\Big((\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})^*\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)})\\Big)(\\zeta,\\mathscr{L}_\\zeta,\\theta) = \\Big((\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb^*)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb)\\Big)(\\zeta,\\mathscr{L}_\\zeta,\\theta)- \\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\n\\aa}\\Theta_2(\\zeta,\\mathscr{L}_\\zeta,\\theta),\n\\end{split}\n\\end{equation}\nwhere\n\\begin{equation*}\n\\begin{split}\n\\Theta_2(\\zeta,\\mathscr{L}_\\zeta,\\theta):&=\\bigg( \\ff{ (2|\nb|+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{\\aa}| b |^2)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^*) \\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)\n}{(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa| b |)^2} +\\ff{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^*) \\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)( b\n\\,\\, b^*) (\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\n b) }{|\nb |(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa|\nb |)^3} \\\\\n&\\quad+ \\ff{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^*) ( b \\, b^*) \\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb) }{| b |(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa| b |)^3} - \\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{ \\aa} \\ff{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b^*) ( b\n\\, b^*) \\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)( b \\, b^*) (\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b) }{| b\n|^2(1+\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa| b |)^4}\\bigg)(\\zeta,\\mathscr{L}_\\zeta,\\theta).\n\\end{split}\n\\end{equation*}\nThus, taking \\eqref{e5} and \\eqref{e6} into consideration yields\nthat\n\\begin{equation*}\n\\begin{split}\n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta)& = -2\n\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}b^*)(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\n\\circ\\Big(\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)\\Gamma^{(\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho)}(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\theta,\\theta_0)\\Big)\\\\\n&\\quad+2 \\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\sum_{k=1}^n\\Big((\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\nb^*)\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)\\Big)(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\\\\\n&\\quad-2 \\sum_{k=1}^n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}b^*)(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\n\\circ\\Big(\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta_0)\\Big)\\\\\n&\\quad-2\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^\\aa \\sum_{k=1}^n\\Theta_1(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\n\\circ\\Big(\\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv)P_k(\\theta)\\Big)\\\\\n&\\quad- \\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho^{ 1+\\aa}\\sum_{k=1}^n\\Theta_2(\\bar\nY_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv,\\mathscr{L}_{\\bar Y_{(k-1)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho}^\\vv},\\theta)\\\\\n&=:\\sum_{i=1}^5I_i(n,\\vv).\n\\end{split}\n\\end{equation*}\nBy following the argument to derive \\eqref{t4}, we deduce from\n({\\bf A3}) that\n\\begin{equation}\\label{c6}\nI_1(n,\\vv)\\rightarrow K(\\theta) ~~\\mbox{ and\n}~~I_2(n,\\vv)\\rightarrow I(\\theta),~~~~\\mbox{ in probability }\n\\end{equation}\nas $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0$. Notice from ({\\bf A3})\nand \\eqref{q4} that\n\\begin{equation}\\label{c4}\n\\begin{split}\n\\|\\Theta_1\\|(\\zeta,\\mathscr{L}_\\zeta,\\theta)&\\le c\\Big((|b|+\n\\|\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)} b\\|+(1+3 |b|)\\|\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\nb\\|)\\|\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)} b\\|\\Big)(\\zeta,\\mathscr{L}_\\zeta,\\theta)\\\\\n&\\le c(1+\\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(1+q)}+\\mathcal\n{W}_2(\\mathscr{L}_\\zeta,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^4).\n\\end{split}\n\\end{equation}\nOn the other hand, owing to \\eqref{v4}, \\eqref{t2}, and \\eqref{q4},\none has\n\\begin{equation}\\label{c5}\n\\begin{split}\n\\|\\Theta_2\\|(\\zeta,\\mathscr{L}_\\zeta,\\theta)&\\le 2\\Big(|\nb|~\\|\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b\\|^2\\| \\hat\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta)\\| (1+ 2| b | )\n\\Big)(\\zeta,\\mathscr{L}_\\zeta,\\theta)\\\\\n&\\le c(1+\\|\\zeta\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^{4(1+q)}+\\mathcal\n{W}_2(\\mathscr{L}_\\zeta,\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho_{\\zeta_0})^4).\n\\end{split}\n\\end{equation}\nThus, by mimicking the argument of \\eqref{q6}, we obtain from\n\\eqref{c4} that\n\\begin{equation}\\label{c7}\nI_3(n,\\vv)\\rightarrow0~~\\mbox{ and\n}~~I_4(n,\\vv)\\rightarrow0~~~\\mbox{ in probability }\n\\end{equation}\nas $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0$. Furthermore, \\eqref{r6}\nand \\eqref{c5} enable us to get that\n\\begin{equation}\\label{c8}\nI_5(n,\\vv)\\rightarrow0 ~~~\\mbox{ in probability }\n\\end{equation}\nwhenever $\\vv\\rightarrow0$ and $\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\rightarrow0$.\n Thus, the desired\nassertion \\eqref{c3} follows from \\eqref{c6}, \\eqref{c7}, as well as\n\\eqref{c8}.\n\\end{proof}\n\n\n\n\nNow, we move forward to complete the\n\\begin{proof}[ Proof of Theorem \\ref{th2}]\nWith Lemmas \\ref{le2} and \\ref{le3} at hand, the proof of Theorem\n\\ref{th2} is parallel to that of \\cite[Theorem 4.1]{RW}. Whereas, to\nmake the content self-contained, we give an outline of the proof. In\nterms of Theorem \\ref{th1}, there exists a sequence\n$\\eta_{n,\\vv}\\rightarrow0$ as $\\vv\\rightarrow0$ and $n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle$\nsuch that $\\hat\\theta_{n,\\vv}\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)\\subset\\Theta$, $\\P$-a.s. By the Taylor\nexpansion, one has\n\\begin{equation}\\label{a4}\n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Phi_{n,\\vv})(\\hat\\theta_{n,\\vv})=(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Phi_{n,\\vv})(\\theta_0)+D_{n,\\vv}(\\hat\\theta_{n,\\vv}-\\theta_0),~~~\\hat\\theta_{n,\\vv}\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)\n\\end{equation}\nwith\n\\begin{equation*}\nD_{n,\\vv}:=\\int_0^1(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta_0\n+u(\\hat\\theta_{n,\\vv}-\\theta_0))\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D u,~~~~\\hat\\theta_{n,\\vv}\\in\nB_{\\eta_{n,\\vv}}(\\theta_0).\n\\end{equation*}\n Observe that, for $\\hat\\theta_{n,\\vv}\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)$,\n\\begin{equation*}\n\\begin{split}\n\\|D_{n,\\vv}-K_0(\\theta_0)\\|&\\le\\|D_{n,\\vv}-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta_0)\\|+\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta_0)-K_0(\\theta_0)\\|\\\\\n&\\le\\int_0^1\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta_0\n+u(\\hat\\theta_{n,\\vv}-\\theta_0))-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta_0)\\|\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nu\\\\\n&\\quad+ \\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta_0)-K_0(\\theta_0)\\|\\\\\n&\\le \\sup_{\\theta\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)}\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta)-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta_0)\\|+\n\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta_0)-K_0(\\theta_0)\\|\\\\\n&\\le\\sup_{\\theta\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)}\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta)-K_0(\\theta)\\|+\\sup_{\\theta\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)}\\|K_0(\\theta)-K_0(\\theta_0)\\|\\\\\n&\\quad+2\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta_0)-K_0(\\theta_0)\\|.\n\\end{split}\n\\end{equation*}\n This, together\nwith Lemma \\ref{le3} and continuity of $K_0(\\cdot)$, gives that\n\\begin{equation}\\label{a0}\nD_{n,\\vv}\\rightarrow K_0(\\theta_0)~~~~\\mbox{ in probability }\n\\end{equation}\nas $\\vv\\rightarrow0$ and $n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle.$ By following the exact\nline of \\cite[Theorem 2.2]{LSS}, we can deduce that $D_{n,\\vv}$ is\ninvertible on the set\n\\begin{equation*}\n\\Gamma_{n,\\vv}:=\\Big\\{\\sup_{\\theta\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)}\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta)-K_0(\\theta_0)\\|\\le\\ff{\\aa}{2},~~\\hat\\theta_{n,\\vv}\\in\nB_{\\eta_{n,\\vv}}(\\theta_0) \\Big\\}\n\\end{equation*}\nfor some constant $\\aa>0.$ Let\n\\begin{equation*}\n\\mathscr{D}_{n,\\vv}=\\{D_{n,\\vv} \\mbox{ is invertible },\n\\hat\\theta_{n,\\vv}\\in B_{\\eta_{n,\\vv}}(\\theta_0) \\}.\n\\end{equation*}\nBy virtue of Lemma \\ref{le3}, one has\n\\begin{equation}\\label{n1}\n\\lim_{\\vv\\rightarrow0,n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle}\\P\\Big(\\sup_{\\theta\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)}\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta)-K_0(\\theta_0)\\|\\le\\ff{\\aa}{2}\\Big)=1.\n\\end{equation}\nOn the other hand, recall that\n\\begin{equation}\\label{n2}\n\\lim_{\\vv\\rightarrow0,n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle}\\P\\Big(\\hat\\theta_{n,\\vv}\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)\\Big)=1.\n\\end{equation}\nBy the fundamental fact: for any events $A,B$,\n$\\P(AB)=\\P(A)+\\P(B)-\\P(A\\cup B)$, we observe that\n\\begin{equation}\\label{n3}\n\\begin{split}\n1\\ge\\P(\\Gamma_{n,\\vv})&\\ge\\P\\Big(\\sup_{\\theta\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)}\\|(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta^{(2)}\\Phi_{n,\\vv})(\\theta)-K_0(\\theta_0)\\|\\le\\ff{\\aa}{2}\\Big)\\\\\n&\\quad+\\P\\Big(\\hat\\theta_{n,\\vv}\\in\nB_{\\eta_{n,\\vv}}(\\theta_0)\\Big)-1.\n\\end{split}\n\\end{equation}\nThus, taking advantage of \\eqref{n1}, \\eqref{n2} as well as\n\\eqref{n3}, we deduce from Sandwich theorem that\n\\begin{equation}\\label{n4}\n\\P(\\mathscr{D}_{n,\\vv})\\ge \\P(\\Gamma_{n,\\vv})\\rightarrow1\n\\end{equation}\n as $\\vv\\rightarrow0$ and $n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle$. Set\n\\begin{equation*}\nU_{n,\\vv}:=D_{n,\\vv}{\\bf1}_{\\mathscr{D}_{n,\\vv}}+I_{p\\times\np}{\\bf1}_{\\mathscr{D}_{n,\\vv}^c},\n\\end{equation*}\nwhere $I_{p\\times p}$ is a $p\\times p$ identity matrix. For\n$S_{n,\\vv}:=\\vv^{-1}(\\hat\\theta_{n,\\vv}-\\theta_0)$, we deduce from\n\\eqref{a4} that\n\\begin{align*}\nS_{n,\\vv}&=S_{n,\\vv}{\\bf1}_{\\mathscr{D}_{n,\\vv}}+ S_{n,\\vv}{\\bf1}_{\\mathscr{D}_{n,\\vv}^c}\\\\\n&=U_{n,\\vv}^{-1}D_{n,\\vv}S_{n,\\vv}{\\bf1}_{\\mathscr{D}_{n,\\vv}}+ S_{n,\\vv}{\\bf1}_{\\mathscr{D}_{n,\\vv}^c}\\\\\n&=\\vv^{-1}U_{n,\\vv}^{-1}\\{(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Phi_{n,\\vv})(\\hat\\theta_{n,\\vv})-(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Phi_{n,\\vv})(\\theta_0)\\}{\\bf1}_{\\mathscr{D}_{n,\\vv}}\n+ S_{n,\\vv}{\\bf1}_{\\mathscr{D}_{n,\\vv}^c}\\\\\n&=-\\vv^{-1}U_{n,\\vv}^{-1}(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Phi_{n,\\vv})(\\theta_0){\\bf1}_{\\mathscr{D}_{n,\\vv}}\n+ S_{n,\\vv}{\\bf1}_{\\mathscr{D}_{n,\\vv}^c}\\\\\n&\\rightarrow I^{-1}(\\theta_0)\\int_0^T\\Upsilon(X_s^0,\\theta_0)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nB(s),\n\\end{align*}\nas $\\vv\\rightarrow0$ and $n\\rightarrow\\infty$, where in the forth\nidentity we dropped the term\n$(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta\\Phi_{n,\\vv})(\\hat\\theta_{n,\\vv})$ according to the\nnotion of LSE and Fermat's lemma, and the last display follows from\nLemma \\ref{le2}, \\eqref{a0} as well as \\eqref{n4} and by noting\n$K_0(\\theta_0)=I(\\theta_0)$. We therefore complete the proof.\n\\end{proof}\n\n\n\n\\section{Proof of Example \\ref{exa}}\n\n\\begin{proof}[Proof of Example \\ref{exa}]\nIt is sufficient to check all of assumptions in Theorems \\ref{th1}\nand Theorem \\ref{th2} are fulfilled.\n\nFor any $\\zeta,\\zeta'\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$, $\\mu\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$ and\n$\\theta=(\\theta^{(1)},\\theta^{(2)})^*\\in\\Theta_0$, set\n\\begin{equation}\\label{ex1}\nb_0(\\zeta,\\zeta'):=-\\zeta^3(0)+\\zeta(0)+\\int_{-r_0}^0\\zeta(v)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nv+\\int_{-r_0}^0\\zeta'(v)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D v,\n\\end{equation}\n\\begin{equation*}\nb(\\zeta,\\mu,\\theta):=\\theta^{(1)}+\\theta^{(2)}\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\zeta,\\zeta')\\mu(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta') ~~\\mbox{ and\n}~~\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta):=\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta,\\mu):=1+\\int_{-r_0}^0|\\zeta(v)|\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D v.\n\\end{equation*}\nThen, \\eqref{d1} can be reformulated as \\eqref{eq1}. By \\eqref{ex1}\nand H\\\"older's inequality, we find out some constants $c_1,c_2>0$\nsuch that\n\\begin{equation}\\label{d3}\n\\begin{split}\n&\\<\\zeta_1(0)-\\zeta_2(0),b(\\zeta_1,\\mu,\\theta)-b(\\zeta_2,\\mu,\\theta)\\>\\\\\n&= \\theta^{(2)} \\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r \\<\\zeta_1(0)-\\zeta_2(0),b_0(\\zeta_1,\\zeta)-\nb_0(\\zeta_2,\\zeta)\\>\\mu_t(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta) \\\\\n&\\le c_1\\Big\\{|\\zeta_1(0)-\\zeta_2(0)|^2+\\int_{-r_0}^0|\\zeta_1(v)-\\zeta_2(v)|^2\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D v\\Big\\}\\\\\n&\\le c_2 \\,\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2,~~~~~\\mu\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r),\n~~\\zeta_1,\\zeta_2 \\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\n\\end{split}\n\\end{equation}\nNext, we deduce from \\eqref{ex1} that for some constant $c_3>0,$\n\\begin{equation*}\n\\begin{split}\n|b(\\zeta,\\mu,\\theta)-b(\\zeta,\\nu,\\theta)|&\\le\\theta^{(2)}\\Big|\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\zeta,\\zeta_1)\\mu(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta_1)-\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\zeta,\\zeta_2)\\nu(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta_2)\\Big|\\\\\n&\\le\\theta^{(2)}\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r| b_0(\\zeta,\\zeta_1)-\nb_0(\\zeta,\\zeta_2)|\\pi(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta_1,\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta_2)\\\\\n&\\le c_3\\,\\mathbb{W}_2(\\mu,\\nu),~~~~\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r,~~~\\mu,\\nu\\in\\mathcal\n{P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r),\n\\end{split}\n\\end{equation*}\nin which $\\pi\\in\\mathcal {C}(\\mu,\\nu)$. Therefore, ({\\bf A1}) holds\ntrue. Next,\n for any\n$\\zeta_1,\\zeta_2\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$ and $\\mu,\\nu\\in\\mathcal {P}_2(\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r)$, we obtain\nthat\n\\begin{equation*}\n |\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta_1,\\mu)-\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\zeta_2,\\nu) |\\le\\int_{-r_0}^0|\\zeta_1(\\theta)-\\zeta_2(\\theta)|\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\theta\\le r_0\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle.\n\\end{equation*}\nSo ({\\bf A2}) is satisfied. For any\n$\\zeta_1,\\zeta_2,\\zeta^{(1)},\\zeta^{(2)}\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r$, note that\n\\begin{equation}\\label{ex2}\n\\begin{split}\n&|b_0(\\zeta_1,\\zeta^{(1)})-b_0(\\zeta_2,\\zeta^{(2)})|\\\\&\\le\n|\\zeta_1^3(0)-\\zeta_2^3(0)|+|\\zeta_1(0)-\\zeta_2(0)|+\\int_{-r_0}^0|\\zeta_1(v)-\\zeta_2(v)|\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\nv+\\int_{-r_0}^0|\\zeta^{(1)}(v)-\\zeta^{(2)}(v)|\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D v\\\\\n&\\le\nc_4(1+\\zeta_1^2(0)+\\zeta_2^2(0))|\\zeta_1(0)-\\zeta_2(0)|+r_0\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+r_0\\|\n\\zeta^{(1)}-\\zeta^{(2)}\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\\\\\n&\\le\nc_5(1+\\|\\zeta_1\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\|\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2)\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+r_0\\|\n\\zeta^{(1)}-\\zeta^{(2)} \\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\n\\end{split}\n\\end{equation}\nfor some constants $c_4,c_5>0.$ Next, we have\n\\begin{equation}\\label{d4}\n(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b)(\\zeta,\\mu,\\theta)=\\Big(1,\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\zeta,\\zeta')\\mu(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta')\\Big)^*~~~\\mbox{ and\n}~~~(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta(\\nabla} \\def\\pp{\\partial} \\def\\EE{\\scr E_\\theta b))(\\zeta,\\mu,\\theta)={\\bf 0}_{2\\times2},\n\\end{equation}\nwhere ${\\bf 0}_{2\\times2}$ stands for the $2\\times 2$-zero matrix.\nThus, \\eqref{ex2} and \\eqref{d4} enable us to deduce that ({\\bf B2})\nand ({\\bf C}) hold, respectively. Furthermore, due to \\eqref{ex2},\nwe find that\n\\begin{equation*}\n\\begin{split}\n|b(\\zeta_1,\\mu,\\theta)-b(\\zeta_2,\\nu,\\theta)|&\\le\\theta^{(2)}\\Big|\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\zeta_1,\\zeta^{(1)})\\mu(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta^{(1)})-\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\zeta_2,\\zeta^{(2)})\\nu(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta^{(2)})\\Big|\\\\\n&\\le\\theta^{(2)}\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r| b_0(\\zeta_1,\\zeta^{(1)})-\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\zeta_2,\\zeta^{(2)})|\\pi(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta^{(1)},\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta^{(2)})\\\\\n&\\le\nc_6(1+\\|\\zeta_1\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2+\\|\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle^2)\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle+c_6\\mathbb{W}_2(\\mu,\\nu).\n\\end{split}\n\\end{equation*}\nTherefore, we infer that ({\\bf B1}) holds. Next, observe that\n\\begin{equation*}\n|\\sigma} \\def\\ess{\\text{\\rm{ess}}^{-2}(\\zeta_1,\\mu)-\\sigma} \\def\\ess{\\text{\\rm{ess}}^{-2}(\\zeta_2,\\nu)| \\le\nc_7\\|\\zeta_1-\\zeta_2\\|_\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle\n\\end{equation*}\nfor some $c_7>0.$ Consequently, ({\\bf B3}) is true.\n\n\n\n\n\n\n\n\nThe discrete-time EM scheme associated with \\eqref{d1} is given by\n\\begin{equation}\nY^\\vv(t_k)=Y^\\vv(t_{k-1})+\\Big(\\theta^{(1)}+\\theta^{(2)}\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\hat Y^\\vv_{t_{k-1}},\\zeta)\\mathscr{L}_{\\hat\nY^\\vv_{t_{k-1}}}(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta)\\Big)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho+\\vv\\,\\sigma} \\def\\ess{\\text{\\rm{ess}}(\\hat\nY^\\vv_{t_{k-1}})\\triangle B_k,~~~k\\ge1,\n\\end{equation}\nwith $Y^\\vv(t)=X^\\vv(t)=\\xi(t), t\\in[-r_0,0],$ where $(\\hat\nY^\\vv_{t_k})$ is defined as in \\eqref{w2}. According to \\eqref{eq2},\nthe contrast function admits the form below\n\\begin{eqnarray*}\n\\Psi_{n,\\vv}(\\theta)&=&\\vv^{-2}\\delta^{-1}\\sum_{k=1}^n\\ff{1}{(1+|Y^\\vv(t_{k-1})|)^2}\\Big|Y^\\vv(t_k)\n-Y^\\vv(t_{k-1})\\\\\n&&\\qquad\\qquad-\\Big(\\theta^{(1)}+\\theta^{(2)}\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\hat Y^\\vv_{t_{k-1}},\\zeta)\\mathscr{L}_{\\hat\nY^\\vv_{t_{k-1}}}(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta)\\Big)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\Big|^2.\n\\end{eqnarray*}\nObserve that\n\\begin{equation*}\n\\begin{split}\n\\ff{\\partial}{\\partial\\theta^{(1)}}\\Psi_{n,\\vv}(\\theta)&=-2\\,\\vv^{-2}\\sum_{k=1}^n\\ff{1}{(1+|Y^\\vv(t_{k-1})|)^2}\\Big\\{Y^\\vv(t_k)-Y^\\vv(t_{k-1})\\\\\n&\\quad-\\Big(\\theta^{(1)}+\\theta^{(2)}\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r b_0(\\hat\nY^\\vv_{t_{k-1}},\\zeta)\\mathscr{L}_{\\hat\nY^\\vv_{t_{k-1}}}(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta)\\Big)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\Big\\},\n\\end{split}\n\\end{equation*}\nand\n\\begin{equation*}\n\\begin{split}\n\\ff{\\partial}{\\partial\\theta^{(2)}}\\Psi_{n,\\vv}(\\theta)&=-2\\,\\vv^{-2}\\sum_{k=1}^n\\ff{1}{(1+|Y^\\vv(t_{k-1})|)^2}\\Big\\{Y^\\vv(t_k)-Y^\\vv(t_{k-1})\\\\\n&\\quad-\\Big(\\theta^{(1)}+\\theta^{(2)}\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r b_0(\\hat\nY^\\vv_{t_{k-1}},\\zeta)\\mathscr{L}_{\\hat\nY^\\vv_{t_{k-1}}}(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta)\\Big)\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho\\Big\\} \\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r b_0(\\hat\nY^\\vv_{t_{k-1}},\\zeta)\\mathscr{L}_{\\hat Y^\\vv_{t_{k-1}}}(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta).\n\\end{split}\n\\end{equation*}\nSubsequently, solving the equation below\n\\begin{equation*}\n\\ff{\\partial}{\\partial\\theta^{(1)}}\\Psi_{n,\\vv}(\\theta)=\\ff{\\partial}{\\partial\\theta^{(2)}}\\Psi_{n,\\vv}(\\theta)=0,\n\\end{equation*}\nwe then obtain the LSE\n$\\hat\\theta_{n,\\vv}=(\\hat\\theta_{n,\\vv}^{(1)},\\hat\\theta_{n,\\vv}^{(2)})^*$\nof the unknown parameter\n$\\theta=(\\theta^{(1)},\\theta^{(2)})^*\\in\\Theta_0$ with the following \n\\begin{equation*}\n\\hat\\theta_{n,\\vv}^{(1)}=\\ff{A_2A_5-A_3A_4}{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho(A_1A_5-A_4^2)}~~~~~\\mbox{\nand }~~~~~\n\\hat\\theta_{n,\\vv}^{(2)}=\\ff{A_1A_3-A_2A_4}{\\delta} \\def\\DD{\\Delta} \\def\\vv{\\varepsilon} \\def\\rr{\\rho(A_1A_5-A_4^2)},\n\\end{equation*}\nwhere\n\\begin{equation*}\nA_1:=\\sum_{k=1}^n\\ff{1}{(1+|Y^\\vv(t_{k-1})|)^2},~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n~~A_2:=\\sum_{k=1}^n\\ff{Y^\\vv(t_k)-Y^\\vv(t_{k-1})}{(1+|Y^\\vv(t_{k-1})|)^2},\n\\end{equation*}\n\\begin{equation*}\nA_3:=\\sum_{k=1}^n\\ff{(Y^\\vv(t_k)-Y^\\vv(t_{k-1}))\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r b_0(\\hat\nY^\\vv_{t_{k-1}},\\zeta)\\mathscr{L}_{\\hat\nY^\\vv_{t_{k-1}}}(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta)}{(1+|Y^\\vv(t_{k-1})|)^2},~~~A_4:=\\sum_{k=1}^n\\ff{\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r\nb_0(\\hat Y^\\vv_{t_{k-1}},\\zeta)\\mathscr{L}_{\\hat\nY^\\vv_{t_{k-1}}}(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta)}{(1+|Y^\\vv(t_{k-1})|)^2},\n\\end{equation*}\nand\n\\begin{equation*}\nA_5:=\\sum_{k=1}^n\\ff{\\Big(\\int_\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r b_0(\\hat\nY^\\vv_{t_{k-1}},\\zeta)\\mathscr{L}_{\\hat\nY^\\vv_{t_{k-1}}}(\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D\\zeta)\\Big)^2}{(1+|Y^\\vv(t_{k-1})|)^2}.\n\\end{equation*}\nIn terms of Theorem \\ref{th1}, $\\hat\\theta_{n,\\vv}\\rightarrow\\theta$\nin probability as $\\vv\\rightarrow0$ and $n\\rightarrow\\infty$. Next,\nfrom \\eqref{d4}, it follows that\n\\begin{equation*}\nI(\\theta_0)=\\left(\\begin{array}{ccc}\n \\int_0^T\\ff{1}{(1+|X_s^0|)^2}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s & \\int_0^T\\ff{b_0(X_s^0,X_s^0)}{(1+|X_s^0|)^2}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n \\int_0^T\\ff{b_0(X_s^0,X_s^0)}{(1+|X_s^0|)^2}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s & \\int_0^T\\ff{b_0^2(X_s^0,X_s^0)}{(1+|X_s^0|)^2}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D s\\\\\n \\end{array}\n \\right),\n\\end{equation*}\nand, for $\\zeta\\in\\scr C} \\def\\aaa{\\mathbf{r}} \\def\\r{r,$\n\\begin{equation*}\n\\int_0^T\\Upsilon(X_s^0,\\theta_0)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(s)=\\left(\\begin{array}{c}\n \\int_0^T\\ff{1}{1+|X^0(s)|}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(s) \\\\\n \\int_0^T\\ff{ b_0(X_s^0,X_s^0)}{1+|X^0(s)|}\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(s)\\\\\n \\end{array}\n \\right).\n\\end{equation*}\nAt last, according to Theorem \\ref{th2}, we conclude that\n\\begin{equation*}\n\\vv^{-1}(\\hat\\theta_{n,\\vv}-\\theta_0)\\rightarrow\nI^{-1}(\\theta_0)\\int_0^T\\Upsilon(X_s^0,\\theta_0)\\text{\\rm{d}}} \\def\\bb{\\beta} \\def\\aa{\\alpha} \\def\\D{\\scr D B(s)~~~~\\mbox{ in\nprobability }\n\\end{equation*}\nas $\\vv\\rightarrow0$ and $n\\rightarrow\\infty}\\def\\X{\\mathbb{X}}\\def\\3{\\triangle$ provided that $I(\\cdot)$\nis positive definite.\n\n\n\n\n\n\n\n\\end{proof}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction} \nIt is widely believed that the cosmic microwave background (CMB) will\nbecome the premier laboratory for the study of the early universe and\nclassical cosmology. This belief relies on the high precision of the\nupcoming MAP\\footnote{{\\tt http:\/\/map.gsfc.nasa.gov}}\nand Planck Surveyor\\footnote{{\\tt http:\/\/astro.estec.eas.nl\/SA-general\/Projects\/Planck}} satellite missions and the high accuracy of theoretical\npredictions of CMB anisotropies given a definite model for\nstructure formation (\\cite{Hu95}\\ 1995). To realize the potential of the CMB,\naspects of structure formation affecting anisotropies at only the\npercent level in power must be taken into account. \n\nA great uncertainty in models for structure formation is the extent\nand nature of reionization. \nFortunately, this uncertainty is largely not reflected in\nthe CMB anisotropies due to the low optical depth to Thomson\nscattering at the low redshifts in question.\nReionization is known to be essentially\ncomplete by $z \\sim 5$ from the absence of the Gunn-Peterson effect in\nquasar absorption spectra (\\cite{Gun65}\\ 1965). Significant\nreionization before $z\\sim 50$ will be ruled out once the tentative\ndetections of the CMB acoustic peaks at present are confirmed \n(\\cite{Sco95} 1995). It should be possible to\ndeduce the reionization redshift $z_i$ through CMB polarization\nmeasurements (\\cite{Zal97}\\ 1997). \n\nNevertheless, the\nduration of time spent in a partially ionized state will remain\nuncertain. \nMoreover as emphasized by\n\\cite{Kai84} (1984) secondary anisotropies generated by the Doppler\neffect in linear perturbation theory are suppressed on small scales\nfor geometric reasons (gravitational instability generates potential flows, \nleading to cancellations between positive and negative Doppler shifts). Higher\norder effects which are not generally included in the theoretical\nmodeling of CMB anisotropies are likely to be the main source of\nsecondary anisotropies from reionization below the degree scale. Such\neffects rely on modulating the Doppler effect with spatial variations\nin the optical depth. Incarnations of this general mechanism include\nthe Vishniac effect from linear density variations (\\cite{Vis87}\\\n1987), the kinetic Sunyaev-Zel'dovich effect from clusters (\\cite{SZ}\\\n1970), and the effect considered here: the spatial variation of the\nionization fraction.\n\nReionization commences when the first baryonic objects form stars or\nquasars that convert a part of the nuclear or gravitational energy\ninto UV photons. Each such source then blows out an ionization sphere around it. Before these\nregions overlap is a period when the universe is ionized in patches.\nThe extent of this period and the time evolution of the size and\nnumber density of these patches depend on the nature of the ionizing\nengines in the first baryonic objects. Theories of reionization do not\ngive robust constraints \n({\\it cf.} \\cite{Teg94}\\ 1994; \\cite{Ree96}\\ 1996;\n\\cite{Agh96}\\ 1996; \\cite{Loe97}\\ 1997; \\cite{Hai97}\\ 1997; \\cite{Sil98}\\ 1998;\n\\cite{Hai98}\\ 1998). \n\nWe therefore take a phenomenological approach to\nstudying the effects of patchy reionization on the CMB. We introduce\na simple but illustrative three parameter model for the reionization\nprocess based on the redshift of its onset $z_i$, the duration before\ncompletion $\\delta z$, and the typical comoving size of the patches\n$R$. It is then straightforward to calculate the CMB anisotropies\ngenerated by the patchiness of the ionization degree of the\nintergalactic medium.\n\nWe find that only the most extreme models of reionization can produce\ndegree scale anisotropies that are observable in the power spectrum\n given the cosmic\nvariance limitations. A large signal on degree scales requires early\nionization, $z_i\\gtrsim 30$, long duration, $\\delta z\\sim z_i$, and\nionization in very large patches, $R\\gtrsim 30$Mpc. Thus the\npatchiness of reionization is unlikely to affect cosmological\nparameter estimation from the acoustic peaks in the CMB (\\cite{Jun96}\\\n1996;\n\\cite{Zal97}\\ 1997; \\cite{Bon97}\\ 1997).\n \nOn the other hand, the patchy reionization signal on the sub-arcminute\nscale can, in principle, surpass both the primary and the secondary Vishniac\nsignals. These may be detectable by the Planck Surveyor and upcoming \nradio interferometry measurements (\\cite{Par97}\\ 1997) \nif point sources can be removed\nat the $\\Delta T\/T \\sim 10^{-6}$ level. \n\nAn explicit expression for the CMB anisotropies power spectrum\ngenerated in a universe reionized in patches is given \\S \\ref{sec:explicit}. \nSimple\norder of magnitude estimate of the anisotropy from patches is given in\n\\S \\ref{sec:order}. In \\S \\ref{sec:power} we give a rigorous definition of our three-parameter\nreionization model, and calculate the patchy part of the power\nspectrum. We discuss illustrative examples in \\S \\ref{sec:discussion}.\n\n\n\\section{CMB power spectrum}\n\\subsection{Explicit expression}\n\\label{sec:explicit}\nTemperature perturbations $\\Delta \\equiv \\delta T\/T$ are generated by\nDoppler shifts from Thomson scattering. For small optical depths\n\\begin{equation}\n\\Delta =-\\int d{\\bf l}\\cdot {\\bf v}\\sigma _Tnx_e.\n\\label{eqn:fundamental}\n\\end{equation}\nAll quantities here are in physical units. The integral is along the line\nof site, ${\\bf v}$ is the peculiar velocity of matter, $c=1$,\n$\\sigma_T$ is the Thomson cross section, $n$ is the number density of\nfree and bound electrons, $x_e$ is the local ionization fraction. \n\nTo evaluate equation~(\\ref{eqn:fundamental}) explicitly one must specify\nthe cosmological model. For simplicity, we take a universe\nwith critical density in matter throughout; we describe the generalization\nto an arbitrary FRW universe in \\S \\ref{sec:power}. \nWe furthermore use comoving coordinates ${\\bf x}$ and conformal time\n$\\eta\\equiv \\int (1+z)dt= (1+z)^{-1\/2} \\eta_0$, \nwhere $\\eta_0=2\/H_0$ is the present particle horizon. \nWe observe at ${\\bf x}=0$ and conformal time $\\eta_0$\nalong the direction of a unit vector $\\hat{\\gamma}$; light propagation\nis given by ${\\bf x}=\\hat{\\gamma}(\\eta _0-\\eta )$. \nEquation~(\\ref{eqn:fundamental})\ncan be\nwritten as\n\\begin{equation}\n\\Delta (\\hat{\\gamma})=-\\tau_0 \\eta_0^3\n\\int {d\\eta \\over \\eta^4} \\hat{\\gamma}\\cdot {\\bf v}[ \\eta ,\\hat{\\gamma}(\\eta _0-\\eta )] x_e[ \\eta ,\\hat{\\gamma}(\\eta _0-\\eta )] .\n\\end{equation}\nHere $\\tau _0\\equiv \\sigma _Tn_0 \\eta_0$ is\nthe optical depth to Thomson scattering across the present particle horizon.\n\nThe\nscales contributing to the peculiar velocity are still in the linear\nregime, therefore\n\\begin{equation}\n{\\bf v}(\\eta ,{\\bf x})={\\eta \\over \\eta _0}{\\bf v}({\\bf x}),\n\\end{equation}\nwhere ${\\bf v}({\\bf x})$ is the peculiar velocity today. The final\nexplicit expression for the CMB temperature perturbation generated\nduring reionization is\n\\begin{equation}\n\\Delta (\\hat{\\gamma})=-\\tau_0 \\eta_0^2\n\\int {d\\eta \\over \\eta ^3} \\hat{\\gamma}\\cdot {\\bf v}[ \\hat{\\gamma}(\\eta _0-\\eta )] x_e[ \\eta ,\\hat{\\gamma}(\\eta _0-\\eta )] .\n\\label{eqn:temperature}\n\\end{equation}\nThe correlation function of the temperature perturbations is defined\nas\n\\begin{equation}\nC(\\theta )=\\left<\\Delta (\\hat{\\gamma}_1)\\Delta\n(\\hat{\\gamma}_2)\\right>|_{\\hat{\\gamma}_1\\cdot \\hat{\\gamma}_2=\\cos \\theta}.\n\\end{equation}\nWith temperature perturbations given by equation~(\\ref{eqn:temperature}), \nthis becomes\n\\begin{eqnarray}\nC(\\theta )&=&\\tau_0^2 \\eta _0^4 \\int {d\\eta _1\\over \\eta _1^3}\n\\int {d\\eta _2\\over \\eta _2^3} \\big<\n\\hat{\\gamma}_1\\cdot {\\bf v}({\\bf x}_1)\n\\hat{\\gamma}_2\\cdot\n{\\bf v}({\\bf x}_2)\n\\nonumber\\\\ &&\\quad \\times \\, x_e(\\eta _1,{\\bf x}_1)x_e(\\eta _2,{\\bf x}_2)\\big>,\n\\label{eqn:correlationgeneral}\n\\end{eqnarray}\nwhere we denote ${\\bf x}_1\\equiv \\hat{\\gamma}_1(\\eta _0-\\eta _1)$ and\n${\\bf x}_2\\equiv \\hat{\\gamma}_2(\\eta _0-\\eta _2)$.\n\n\\subsection{Order of magnitude estimates}\n\\label{sec:order}\n\nConsider the following patchy reionization scenario. The universe was\nreionized in randomly distributed patches with a characteristic\ncomoving size $R$. The patches appeared at random in space and\ntime. Once a reionized patch appears, it moves with matter. The\naverage ionization fraction, that is the filling fraction of fully\nionized patches, grows monotonically from $X_e=0$ at high redshifts to\n$X_e=1$ at low redshifts. We consider late reionization (optical depth\nto Thomson scattering is small) and small patches (smaller than the\ncharacteristic length scale of the peculiar velocity field). We assume\nthat reionization occurred at redshift $z_i$, and the patchy phase\nduration is given by $\\delta z$.\n\nThe angular scale of the patchy signal is given by the ratio of the\nsize of patches to the distance to them in comoving coordinates, \n$\\theta \\sim R\/(\\eta_0-\\eta_i)$. \nAssuming that the patches are uncorrelated, the spectrum of \nfluctuations should be white noise above this scale which \nagrees with the exact result as we shall see (eq.~[\\ref{eqn:Cl}]). \n\nThe rms CMB temperature fluctuation $\\Delta$ on scales $\\theta$\ndue to the patchiness can be estimated as follows. Since, by\nassumption, different patches are independent, $\\Delta \\sim\nN^{1\/2}\\Delta _p$. Here $N$ is the number of patches on a line of\nsite, $\\Delta _p$ is a temperature fluctuation from one patch, $\\Delta\n_p\\sim \\tau _pv(z_i)$. Here $v(z)=(1+z)^{-1\/2}v(0)$ is the rms peculiar\nvelocity at redshift $z$, and $\\tau _p$ is the optical depth for one\npatch, $\\tau _p \\sim (1+z_i)^2\\sigma _Tn_0R$. The number of patches\n$N\\sim \\delta \\eta\/R\\sim (1+z_i)^{-3\/2}\\delta z\\eta _0\/R$. Collecting\nall the factors, we get the following estimate for the rms\nanisotropies from patches\n\\begin{equation}\n\\Delta \\sim \\tau_0\\left^{1\/2}({R\/\\eta _0})^{1\/2}(1+z_i)^{3\/4} (\\delta z)^{1\/2},\n\\end{equation}\nwhich again \nagrees with the exact result (eq.~[\\ref{eqn:amplitude}], up to a dimensionless\nmultiplier). \n\n\n\\subsection{Power spectrum}\n\\label{sec:power}\n\nWe can factor the general expression for the temperature correlation\n(\\ref{eqn:correlationgeneral}) as\n\\begin{eqnarray}\nC(\\theta )&=&\\tau_0^2 \\eta_0^4 \\int {d\\eta _1\\over \\eta _1^3}\n\\int {d\\eta _2\\over \\eta _2^3}\n\\left<\\hat{\\gamma}_1\\cdot {\\bf v}({\\bf x}_1)\\hat{\\gamma}_2\\cdot {\\bf\nv}({\\bf x}_2)\\right>\n\\nonumber \\\\\n&&\\quad\\times\\,\n\\left.\n\\end{eqnarray}\nThis assumes that $x_e$ and ${\\bf v}$ are independent random\nfields. This is not strictly correct. The ionization fraction $x_e$\nmust be determined by the density perturbation $\\delta$, and the\ndensity perturbation is not independent of the peculiar velocity (for\nexample in the linear regime $\\delta =-{1\\over 2} \\eta \\nabla \\cdot\n{\\bf v}$). However, the ionizing radiation is presumably coming from\nstrongly nonlinear objects, where first stars or quasars are\nlightening up. At high $z$, the length scales where the density is\nnonlinear are $\\ll 10$Mpc comoving, which is much smaller than the\nlength scales contributing to the peculiar velocity. Under the\nassumption of scale separation, velocity and density (and hence $x_e$)\nare indeed independent.\n\nThe correlation function for the local ionization fraction $\\left$\nis not known. Our model parameterizes the correlation function\nthrough the the patch size $R$ and \na mean (cosmic time - dependent) ionization\nfraction $X_e(\\eta )$,\n\\begin{eqnarray}\n&&\\left = X_e(\\eta _1)X_e(\\eta\n_2)\\nonumber\\\\ \n&& \\qquad + [ X_e(\\eta _{\\rm\nmin})-X_e(\\eta _1)X_e(\\eta _2)] e^{-{({\\bf x}_1-{\\bf x}_2)^2\\over\n2R^2}}\\qquad.\n\\label{eqn:xexemodel}\n\\end{eqnarray} \nHere $\\eta _{\\rm min}={\\rm min}(\\eta _1,\\eta _2)$. The Gaussian\nfunction is chosen for simplicity; it could have been any function of\nthe separation $x$ which equals 1 at $x=0$ and gradually turns to zero\nat $x>R$. For the mean ionization fraction $X_e$ we assume a change\nfrom 0 to 1 at a redshift $z_i$, with the transition occurring in a\nredshift interval $\\delta z$. We also assume $\\delta z\\ll\nz_i$ (this is true in all of the models of reionization that we are aware of).\n\n\nCMB anisotropies generated (and erased) due to the spatially constant\npart of the correlation function (\\ref{eqn:xexemodel}) are obviously the same as in the\nmodel with a uniform time-dependent reionization. The spatially\nvarying part is responsible for generating new anisotropies; its\ncontribution to erasing the primary anisotropies is negligible. The\nanisotropy suppression is mainly determined by the total optical depth\nto Thomson scattering and is insensitive to the small-scale structure\nof the ionization fraction $x_e(\\eta ,{\\bf x})$.\n\nThe CMB correlation function due to the patchy part only is\n\\begin{eqnarray}\n\\label{eqn:cthetaintegral}\nC^{\\rm (p)}(\\theta ) &=& \\tau_0^2 \\eta_0^4\\int_{0}^{\\eta _0} {d\\eta\n_1\\over \\eta _1^3} \\int_{0}^{\\eta _0} {d\\eta _2\\over \\eta\n_2^3}I_{12} \\\\ && \\quad\\times \\left<\\hat{\\gamma}_1\\cdot {\\bf\nv}({\\bf x}_1)\\hat{\\gamma}_2\\cdot {\\bf v}({\\bf x}_2)\\right>e^{-{({\\bf\nx}_1-{\\bf x}_2)^2\\over 2R^2}} \\nonumber,\n\\end{eqnarray}\nwhere we denote $I_{12}\\equiv X_e(\\eta _{\\rm min})-X_e(\\eta\n_1)X_e(\\eta _2)$. The correlation function is non-negligible only for\n$|{\\bf x}_1-{\\bf x}_2|\\lesssim R$. By assumption, $R$ is much smaller\nthen the characteristic scale of the peculiar velocity field. Also\n$|{\\bf x}_1-{\\bf x}_2|\\lesssim R$ requires that the lines of sight\n$\\hat{\\gamma}_1$ and $\\hat{\\gamma}_2$ be nearly parallel. Then\n\\begin{equation}\n\\left<\\hat{\\gamma}_1\\cdot {\\bf v}({\\bf x}_1)\\hat{\\gamma}_2\\cdot {\\bf\nv}({\\bf x}_2)\\right>\\approx {1\\over 3}\\left,\n\\end{equation}\nwhere $\\left$ is the mean squared peculiar velocity today. For $z_i\\gg\n1$, the integral (\\ref{eqn:cthetaintegral}) is dominated by small conformal times, and we\nhave $|{\\bf x}_1-{\\bf x}_2|^2\\approx \\theta ^2(\\eta _0-\\eta_i)^2+(\\eta _1-\\eta\n_2)^2$. Then\n\\begin{eqnarray}\nC^{\\rm (p)}(\\theta )&\\approx& {1\\over 3}\\tau_0^2\\eta_0^4\\lefte^{-{(\\eta\n_0 - \\eta_i)^2\\theta ^2\\over 2R^2}}\\nonumber \\\\\n&&\\times \\int_{0}^{\\infty }{d\\eta _1\\over \\eta _1^3}\n\\int_{0}^{\\infty } {d\\eta _2\\over \\eta _2^3}I_{12}e^{-{(\\eta _2-\\eta\n_1)^2\\over 2R^2}}.\n\\end{eqnarray}\nWe assume that $\\eta _i\\delta z\/(1+z_i)\\gg R$ (with $\\eta\n_i\\equiv \\eta (z_i)$) and that during the patchy phase,\n$z_i>z>z_i-\\delta z$, the ionization fraction $X_e$ grows linearly from\n0 to 1 such that eventually both hydrogen and helium are fully ionized. \nThen\n\\begin{equation}\nC^{\\rm (p)}(\\theta )=Ae^{-{\\theta ^2\\over 2\\theta _0^2}},\n\\label{eqn:cthetagen}\n\\end{equation}\nwhere the characteristic angular scale is\n\\begin{equation}\n\\theta _0={R\\over \\eta _0 - \\eta_i} = {R \\over \\eta_0} {(1+z_i)^{1\/2} \\over\n(1+z_i)^{1\/2} - 1},\n\\label{eqn:angularscale}\n\\end{equation}\nand the amplitude is\n\\begin{equation}\nA={\\sqrt{2\\pi }\\over 36} \\tau _0^2\\left{R\\over \\eta _0}\\delta\nz(1+z_i)^{3\/2}.\n\\label{eqn:amplitude}\n\\end{equation}\nNote that a critical matter-dominated universe is assumed in this\nexpression. To generalize this result replace $\\eta_0-\\eta_i$ by\nthe comoving angular diameter distance in \nequation~(\\ref{eqn:angularscale}) and a factor of $(1+z_i)$ in\nequation~(\\ref{eqn:amplitude}) with the appropriate velocity growth\nfactor.\n\nThe power spectrum is given by the spherical harmonics decomposition\n\\begin{equation}\nC_l^{\\rm (p)}=2\\pi \\int d\\cos \\theta P_l(\\cos \\theta )w_p(\\theta ).\n\\end{equation}\nFor $l\\gg 1$,\n\\begin{equation}\nC_l^{\\rm (p)}\\approx 2\\pi \\int_0^{\\infty} \\theta d\\theta J_0(l\\theta )w_p(\\theta\n)=2\\pi A\\theta _0^2e^{-{\\theta _0^2l^2\\over 2}}.\n\\end{equation}\nThe power per octave is\n\\begin{equation}\n{l^2C_l^{\\rm (p)}\\over 2\\pi }=Al^2\\theta _0^2e^{-{\\theta _0^2l^2\\over 2}}.\n\\label{eqn:Cl}\n\\end{equation}\n\nThe anisotropy power reaches the maximal value\n\\begin{equation}\n({l^2C_l^{\\rm (p)}\\over 2\\pi })_{\\rm max}={\\sqrt{2\\pi }\\over 18{\\rm e}} \\tau\n_0^2\\left{R\\over \\eta _0}\\delta z(1+z_i)^{3\/2},\n\\end{equation}\nat\n\\begin{equation}\nl_{\\rm max}={\\sqrt{2} \\eta _0\\over R} [1 - (1+z_i)^{-1\/2}].\n\\end{equation}\n\n\n\\subsection{Discussion}\n\\label{sec:discussion}\n\nThe signal from patchy reionization in our model depends on four\nquantities: the rms peculiar velocity \n$\\left< v^2 \\right>^{1\/2}$ today, the redshift of reionization $z_i$,\nits duration $\\delta z$ and the characteristic comoving size of the patches $R$.\nThe structure formation model specifies\nthe power spectrum of fluctuations which in turn tells us the \nrms peculiar velocity. Let us now consider the patchy reionized signal in \nthe context of a specific model for structure formation. \n\nFor illustrative purposes, let us consider\na cold dark matter model with $h=0.5$, $\\Omega_b =0.1$, and a scale-invariant $n=1$ \nspectrum of initial fluctuations. Normalizing the spectrum to the\nCOBE detection via the fitting formulae of \\cite{Bun97} (1997) (their\nequations [17]-[20]) and employing the analytic fit to the transfer function \nof \\cite{Eis98} (1998) (their equations [15]-[24])\nwe find an rms velocity \nof $\\left^{1\/2}=3.9\\times 10^{-3}$. \nWith the present optical depth of $\\tau_0 = 0.122 \\Omega_b h = 0.0061$,\nwe have a maximal anisotropy of\n\\begin{equation}\n({l^2C_l\\over 2\\pi })_{\\rm max}=2.41\\times 10^{-15}{R\\over {\\rm\nMpc}}\\delta z(1+z_i)^{3\/2},\n\\end{equation}\nat\n\\begin{equation}\nl_{\\rm max}={16958\\over R\/{\\rm Mpc}} [ 1 - (1+z_i)^{-1\/2}].\n\\end{equation}\n\nThe power spectrum of the model in principle also tells us the\nremaining parameters of the ionization: its redshift $z_i$,\nduration $\\delta z$ and typical patch size $R$.\nUnfortunately, these quantities depend on details of the cooling and\nfragmenting of the first baryonic objects to form the ionizing \nengines. We therefore consider $5 \\lesssim z_i \\lesssim 50$ which spans \nthe range of estimates in the literature\n(\\cite{Teg94}\\ 1994; \\cite{Ree96}\\ 1996;\n\\cite{Agh96}\\ 1996; \\cite{Loe97}\\ 1997; \\cite{Hai97}\\ 1997; \\cite{Sil98}\\ 1998;\n\\cite{Hai98}\\ 1998). Reionization, once it commences, is generally completed in\na time short compared with the expansion time at that epoch $\\delta z \/(1 +z_i) \n< 1$ by the coalescence of patches that are small compared with the \nhorizon at the time $R\/\\eta_i\n\\ll 1$ at the time. Again the exact relations depend on the efficiency with\nwhich the first objects form and create ionizing radiation \n(see e.g. \\cite{Teg94}\\ 1994). \n\n\\begin{figure}[htb]\n\\psfig{figure=patchf1.eps,width=3.3in}\n\\caption{CMB anisotropy power spectra in a CDM model with extreme patchiness. \nShown here are the primary anisotropy \nand the patchy reionization anisotropy, eq.~(\\protect\\ref{eqn:Cl}) with\n$z_i=10$, $\\delta z=3$, $R=20$Mpc. These signals are compared with\nthe cosmic variance of the primary anisotropy and the noise of\nthe MAP satellite (in logarithmic bins).}\n\\label{fig:patch10}\n\\end{figure}\n\n\nLet us consider an extreme example of $z_i=10$, $\\delta z=3$, $R=20{\\rm Mpc}$. \nThen the maximal\npower is $\\approx 5.3\\times 10^{-12}$ at $l\\approx 590$, the primary\nsignal at these scales is $\\approx 3\\times 10^{-10}$ -- the\ncontribution of the patchy reionization is small in comparison \n(see Fig.~\\ref{fig:patch10}). However, in light of \nthe high precision measurements expected from the MAP and Planck satellites \nsuch a signal is not necessarily negligible. \nThe ultimate limit of detectability through\npower spectrum measurements is provided by so-called cosmic variance.\nThis arises since we can only measure $2\\ell+1$ realizations\nof any given multipole such that power spectrum estimates will vary by\n\\begin{equation}\n\\delta C_\\ell = \\sqrt{2 \\over 2\\ell+1} C_\\ell^{(\\rm primary)}.\n\\end{equation}\nDetection of a broad feature such as that from patchy reionization is assisted\nin that we may reduce the cosmic variance by averaging over many $\\ell$'s. \nWe show an example of this averaging in Fig.~\\ref{fig:patch10} (lower left boxes).\nIn this model, the patchy reionization signal can be detected at the several\n$\\sigma$ level if cosmic variance were the main source of uncertainty. \nOf course a realistic experiment also has noise and systematic errors.\nWe also show the noise error contributions expected from the MAP experiment\nin Fig.~\\ref{fig:patch10}. \n\nAn important additional source of uncertainty is provided by other\nunknown aspects of the model. Indeed it is hoped that the CMB power spectrum\ncan be used to measure fundamental cosmological parameters to high precision.\nExcess variance from patchy reionization can in principle cause problems for\ncosmological parameter estimation from the CMB if not included in the model.\nIt would remain undetected and produce parameter misestimates if\nits signal can be accurately mimicked by variations in the other parameters.\nFortunately, the angular signature we find here -- $\\ell^2$ white noise until some\ncut off due to the patch size -- does not resemble the signature of other cosmological\nparameters which alter the positions and amplitudes of the acoustic peaks \n(see \\cite{Bon97}\\ 1997; \\cite{Zal97}\\ 1997). Coupled with the small amplitude of the effect on the 10 arcminute to degree scale for even this extreme model, it is unlikely that patchy reionization will significantly affect parameter\nestimation through the CMB. \n\nWe have called the ($z_i=10,\\delta z=3,R=20$) model extreme, because of the size of patches; the reionization redshift and duration would be considered reasonable by a number of theories. For example the early quasar model of Haiman \\& Loeb (1998) does predict $z_i\\sim 10$ and $\\delta z \\sim 3$. However, their ``medium quasar'' emits only $\\sim 10^{67}$ ionizing photons during its life time. These photons cannot ionize a bubble larger than $R\\sim 1$Mpc comoving.\n\nPerhaps more interesting is the case where reionization takes \nplace at a higher redshift with\nfor example \n$z_i=30$, $\\delta z=5$, $R=3{\\rm Mpc}$. The reduction in the patch size\ncauses the signature to move to smaller angles where the primary signal\nis negligible due to dissipational effects at recombination. \nThe increase in the optical depth at this higher redshift is counterbalanced\nby the reduction in the rms fluctuation due to the number of patches along\nthe line of site such that the amplitude of the signal increases\nonly moderately. Here the maximal \npower is $\\approx 6.2\\times10^{-12}$ at $l\\approx 4650$ \n(see Fig.~\\ref{fig:patch30}). Patchy reionization effects exceed \nthe Vishniac signal at these\nscales ($\\approx 3\\times 10^{-12}$) which is believed to be\nthe leading other source of secondary anisotropies \n(\\cite{Hu96}\\ 1996). \n\n\\begin{figure}[htb]\n\\psfig{figure=patchf2.eps,width=3.3in}\n\\caption{CMB anisotropy power spectra in a CDM model with early reionization. \nShown here are the primary anisotropy suppressed by rescattering \nand the patchy reionization anisotropy, eq.~(\\protect\\ref{eqn:Cl}) with\n$z_i=30$, $\\delta z=5$, $R=3$Mpc. These signals are compared with\nthe cosmic variance of the primary anisotropy achievable by an ideal\nexperiment in the absence of galactic and extragalactic foregrounds.}\n\\label{fig:patch30}\n\\end{figure}\n\nAlthough the morphology and amplitude \nof the patchy reionization and Vishniac signals are similar, the Vishniac\neffect is fully specified by the ionization redshift and the spectrum\nof initial fluctuations and hence may be removed once these are determined\nfrom parameter estimation at larger angular scales. Likewise, \nsince the rms peculiar velocity $\\left< v \\right>$ and the ionization\nredshift $z_i$ will be specified by the large scale observations, \nthe amplitude of the signal can be used to estimate\nthe duration of reionization $\\delta z$ and its angular location \nthe typical comoving size of the bubbles $R$. \n\nIn summary, the patchiness of reionization leaves a potentially observable \nimprint on the CMB power spectrum, but one that is unlikely to affect cosmological\nparameter estimation from the acoustic peaks in the CMB. We show how the signature\nscales with the gross properties of reionization -- its redshift, duration, and\ntypical patch size. Observational detection of this signature would provide \nuseful constraints on the presently highly uncertain reionization scenarios but\nwill likely require experiments with angular resolution of an \narcminute or better and foreground subtraction at better than the \n$\\delta T\/T \\sim 10^{-6}$ level.\n\n\\acknowledgements\nWe thank \nR. Juszkiewicz, A. Liddle, M. Tegmark and M. White\nfor discussions. This work\nwas supported by NSF PHY-9513835. WH was also supported by the\nW. M. Keck Foundation.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe fascinating beauty of the theory of the functions of complex variable reveals\nitself, for example, in the harmony of the algebraic fractals on the Euclidian\nplane. It makes many researches look for the analogous number systems, the elements\nof which could be correlated not to the points on the plane but to the points of the\n4-dimensional space-time. In case of the success of such a search, we could really\ntrust the famous Pythagoras saying 'all the existing is number'. On this way, the\ninteresting results were obtained for quaternions [1], biquaternions [2-4], octaves\n[5] and so forth. Nevertheless, none of these number system theories can be compared\neven to the theory of the relatively simple 2-component complex numbers. The main\nreason for this seems to be the lack of the commutativity (and sometimes even of the\nassociativity) of the multiplication in these algebras. Although the authors of this\npaper realize the conceptual bases of all the variety of algebras, the commutativity\nof the multiplication is the integral property of all the principal number systems\nthat contain natural, integer, rational, real and complex numbers. Finally, the\ncommutativity and the associativity of the multiplication are among the axioms of\narithmetic which presents the foundation of mathematics, and it would be strange if\nthe algebraic system which is the most natural for our real world does not\ncorrespond to the rules of regular counting.\n\nOne of the systems free from this drawback is the algebra of the commutative and\nassociative hyper complex numbers, related to the direct sum of the four real\nalgebras, which will be denoted as $H_4$. The algebra of these numbers is isomorphic\nto the algebra of the 4-dimensional square real diagonal matrices, and the\ncorresponding space is a linear Finsler space with the Berwald-Moor metric (the last\nfact was proved by the authors in [6]). It should be mentioned that Finsler space\nwith the Berwald-Moor metric has been known and partially investigated for a long\ntime [7--8].\n\nOne of the main properties of this space is the existence of such a range of the\nparameters that the 3-dimensional distances (from the point of view of the observer\nwho uses the radar method to measure them [9]) correspond to the positively defined\nmetric function the limit of which is the quadratic form [10]. In other words, the\n3-dimensional world observed by an \"$H_4$ inhabitant\" is Euclidian within certain\naccuracy. Moreover, when one passes to the relativistic velocities, the\n4-dimensional intervals between the $H_4$ events present the Minkowski space\ncorrelations [11]. All this makes possible to suggest that the $H_4$ space and the\ncorresponding Finsler geometry can be used as a mathematical model of the real\nspace-time, and maybe this model would be even more productive than the pseudo\nRiemannian constructions prevailing in Physics now.\n\n\\mes\n\nAny hyper complex algebra is completely defined by the multiplication rule for the\nelements of a certain fixed basis. In the H4 number system there is a special --\nisotropic -- basis $e_1, e_2, e_3, e_4$, such that\n \\begin{equation}\\label{gp1}\n e_ie_j=p^k_{ij}e_k \\, \\qquad p^k_{ij} = \\left\\{ \\begin{array}{l}\n 1\\, , \\hbox{\\q if~} \\; i=j=k \\, , \\\\\n 0\\, , \\hbox{\\q else} \\, .\n \\end{array}\n \\right.\n \\end{equation}\n Any analytical function in this basis can be given as\n \\begin{equation}\\label{gp2}\n F(X) = f^1(\\xi^1)e_1 + f^2(\\xi^2)e_2 + f^3(\\xi^3)e_3 + f^4(\\xi^4)e_4 \\, ,\n \\end{equation}\nwhere\n \\begin{equation}\\label{gp3}\n H_4 \\ni X = \\xi^1e_1 + \\xi^2e_2 + \\xi^3e_3 + \\xi^4e_4 \\, ,\n \\end{equation}\nand $f^i$ are four arbitrary smooth functions of a single real variable.\n\nIn $H_4$ there is one more -- orthogonal -- selected basis $1, \\, j, \\, k, \\, jk$,\nwhich is related to the isotropic basis by the following formulas\n \\begin{equation}\\label{gp4}\n \\left. \\begin{array}{l}\n 1 = e_1 + e_2 + e_3 + e_4 \\, , \\\\[2pt]\n j = e_1 + e_2 - e_3 - e_4 \\, , \\\\[2pt]\n k = e_1 - e_2 + e_3 - e_4 \\, , \\\\[2pt]\n jk = e_1 - e_2 - e_3 + e_4 \\, ,\n \\end{array}\n \\right\\}\n \\end{equation}\nwhere {\\it 1} is the unity of algebra, and the corresponding component of the\nanalytical function of the $H_4$ variable is defined by the formula\n \\begin{equation}\\label{gp5}\n u = \\frac{1}{4} \\left[ f^1(\\xi^1) + f^2(\\xi^2) + f^3(\\xi^3) + f^4(\\xi^4) \\right] \\, .\n \\end{equation}\n\nIf $X$ is a radius vector, then the coordinate space $\\xi^1, \\, \\xi^2, \\, \\xi^3, \\,\n\\xi^4$ is a Berwald-Moor space with the length element\n \\begin{equation}\\label{gp6}\n ds = \\sqrt[4]{d\\xi^1d\\xi^2d\\xi^3d\\xi^4} \\equiv\n \\sqrt[4]{g_{ijkl}d\\xi^id\\xi^jd\\xi^kd\\xi^l} \\, ,\n \\end{equation}\nwhere\n \\begin{equation} \\label{gp7}\n g_{ijkl} = \\left\\{\n \\begin{array}{l}\n \\frac{1}{4!} \\, , {~~(i\\ne j\\ne k\\ne l)} , \\\\[9pt]\n \\; 0\\, \\, , {~~(else)} .\n \\end{array} \\right.\n \\end{equation}\nFor this geometry the tangent indicatrix equation is\n \\begin{equation}\\label{gp8}\n g^{ijkl}p_ip_jp_kp_l - 1 = 0 \\, ,\n \\end{equation}\nwhere\n \\begin{equation}\\label{gp9}\n g^{ijkl} = \\left\\{ \\begin{array}{l}\n \\displaystyle\\frac{4^4}{4!} \\, , {~~(i\\ne j\\ne k\\ne l)} , \\\\[9pt]\n \\; 0\\, \\, , \\hbox{~~(else)} ,\n \\end{array} \\right.\n \\end{equation}\n \\begin{equation}\\label{gp10}\n p_i = \\frac{g_{ijkl}d\\xi^jd\\xi^kd\\xi^l}{\\left(g_{mrst}d\\xi^md\\xi^rd\\xi^sd\\xi^t\n \\right)^{3\/4}} \\,\n \\end{equation}\nare the components of the generalized momentum or generalized momenta.\n\nIf we have tensors $p^k_{ij}$, $g_{ijkl}$, $g^{ijkl}$ and vector fields of the\nanalytical functions $F_{(A)}(X)$ of the $H_4$ variables, we could construct the\nmetric tensors in the 4-dimensional space-time in many ways. For example,\n \\begin{equation}\\label{gp11}\n g_{ij}(\\xi) = g_{ijkl}f^k_{(1)}f^l_{(2)} \\, ,\n \\end{equation}\nNow one can investigate the obtained Riemannian geometry. The main drawback of this\napproach is the variety of the ways to construct it.\n\n It is known [12] that if the tangent indicatrix equation is defined as\n \\begin{equation}\\label{gp12}\n \\Phi(p;\\xi) = 0 \\, ,\n \\end{equation}\nthen the geodesics will be the solutions of the canonical system of differential\nequations\n \\begin{equation}\\label{gp13}\n \\dot{\\xi}^i = \\frac{\\partial \\Phi}{\\partial p_i} \\cdot \\lambda(p;\\xi) \\, , \\qquad \\dot{p}_i\n = - \\frac{\\partial \\Phi}{\\partial \\xi^i} \\cdot \\lambda(p;\\xi) \\, ,\n \\end{equation}\n $\\lambda (p;\\xi )\\neq 0$ is an arbitrary smooth function, and a dot above $\\xi^i$ and $p_i$ means the derivation by the evolution parameter, $\\tau$.\n\n\\section{Construction of the metric function\\\\ of the pseudo Riemannian space}\n\nLet us regard a space which is conformally connected to the $H_4$ space, that is to\nthe space with the length element\n \\begin{equation}\\label{gp14}\n ds' = \\kappa(\\xi)\\cdot \\sqrt[4]{g_{ijkl}d\\xi^id\\xi^jd\\xi^kd\\xi^l} \\, ,\n \\end{equation}\nwhere $\\kappa(\\xi) > 0$ is a scalar function which is a contraction-extension\ncoefficient depending on the point.\n\nLet there be a normal congruence of geodesics (world lines). Then there is a scalar\nfunction $S(\\xi)$ (see, e.g. [12]) such that its level hyper surfaces are\ntransversal to this normal congruence of the world lines and this function is a\nsolution of the equation\n \\begin{equation}\\label{gp15}\n g^{ijkl}\\frac{\\partial S}{\\partial \\xi^i}\\frac{\\partial S}{\\partial\n \\xi^j}\\frac{\\partial S}{\\partial \\xi^k}\\frac{\\partial S}{\\partial\n \\xi^l} = \\kappa(\\xi)^4 \\, ,\n \\end{equation}\nwhile the generalized momenta along this congruence of the world lines are related\nto $S(\\xi)$ by\n \\begin{equation}\\label{gp16}\n p_i = \\frac{\\partial S}{\\partial \\xi^i} \\, ,\n \\end{equation}\nThe equations for the world lines obtain the form\n \\begin{equation}\\label{gp17}\n \\dot{\\xi}^i = g^{ijkl}\\frac{\\partial S}{\\partial\n \\xi^j}\\frac{\\partial S}{\\partial \\xi^k}\\frac{\\partial S}{\\partial\n \\xi^l} \\cdot \\lambda(\\xi) \\, ,\n \\end{equation}\nwere $\\lambda (\\xi )\\neq 0$.\n\nIn Physics the function $S(\\xi)$ is called \"action as a function of coordinates\"\\,\nand (\\ref{gp15}) is known as the Hamilton-Jacoby equation. In \\cite{10} the function\n$S(\\xi)$ was called the \\emph{World function}.\n\nIf there is a congruence of the world lines, then the evolution of every point in\nspace is known, particularly, the velocity field is known, but the energy\ncharacteristics of the material objects (observers) corresponding to a given world\nline are not known. The knowledge of the World function $S(\\xi)$ makes it possible\nto calculate the generalized momenta $p_i$, corresponding to the energy\ncharacteristics, and the invariant energy characteristic, $\\kappa(\\xi)$, which has\nalso the meaning of the local contraction-extension coefficient of the plane $H_4$\nspace.\n\nSo, if our world view is the classical mechanics, then any pair out of the three:\nWorld function, congruence of the world lines, Finsler geometry - gives us the\ncomplete knowledge of the World.\n\nLet us construct a twice contravariant tensor {\\it g}{\\it ij}{\\it (?)} in the\nfollowing way:\n \\begin{equation}\\label{gp18}\n g^{ij}(\\xi) = \\frac{1}{\\kappa(\\xi)^4} \\cdot g^{ijkl}\\frac{\\partial\n S}{\\partial \\xi^k}\\frac{\\partial S}{\\partial \\xi^l} \\, .\n \\end{equation}\nSince\n \\begin{equation}\\label{gp19}\n det(g^{ij}(\\xi))= - \\frac{4^4}{3^3 \\kappa(\\xi)^8} \\neq 0 \\, ,\n \\end{equation}\nthen everywhere where the geometry (\\ref{gp14}) is defined, one can construct a\ntensor $g_{ij}(\\xi)$ such that\n \\begin{equation}\\label{gp20}\n g^{ik}(\\xi)g_{kj}(\\xi)=\\delta^i_j \\, ,\n \\end{equation}\n \\begin{equation}\\label{gp21}\n g_{ij}(\\xi) = 4\\cdot \\left(\n \\begin{array}{cccc}\n -2\\left(\\frac{\\partial S}{\\partial \\xi^1}\\right)^2 & \\frac{\\partial S}{\\partial \\xi^1}\\frac{\\partial S}{\\partial \\xi^2} & \\frac{\\partial S}{\\partial \\xi^1}\\frac{\\partial S}{\\partial \\xi^3} & \\frac{\\partial S}{\\partial \\xi^1}\\frac{\\partial S}{\\partial \\xi^4}\n \\\\[9pt]\n \\frac{\\partial S}{\\partial \\xi^1}\\frac{\\partial S}{\\partial \\xi^2} & -2\\left(\\frac{\\partial S}{\\partial \\xi^2}\\right)^2 & \\frac{\\partial S}{\\partial \\xi^2}\\frac{\\partial S}{\\partial \\xi^3} & \\frac{\\partial S}{\\partial \\xi^2}\\frac{\\partial S}{\\partial \\xi^4} \\\\[9pt]\n \\frac{\\partial S}{\\partial \\xi^1}\\frac{\\partial S}{\\partial \\xi^3} & \\frac{\\partial S}{\\partial \\xi^2}\\frac{\\partial S}{\\partial \\xi^3} & -2\\left(\\frac{\\partial S}{\\partial \\xi^3}\\right)^2 & \\frac{\\partial S}{\\partial \\xi^3}\\frac{\\partial S}{\\partial \\xi^4} \\\\[9pt]\n \\frac{\\partial S}{\\partial \\xi^1}\\frac{\\partial S}{\\partial \\xi^4} & \\frac{\\partial S}{\\partial \\xi^2}\\frac{\\partial S}{\\partial \\xi^4} & \\frac{\\partial S}{\\partial \\xi^3}\\frac{\\partial S}{\\partial \\xi^4} & -2\\left(\\frac{\\partial S}{\\partial \\xi^4}\\right)^2\n \\end{array}\n \\right) .\n \\end{equation}\n\nNo doubt that in the same coordinate space $\\xi ^{1} ,\\xi ^{2} ,\\xi ^{3} ,\\xi ^{4}$\nsuch tensor $g_{ij}(\\xi)$ defines a Riemannian or pseudo Riemannian geometry with\nthe length element\n \\begin{equation}\\label{gp22}\n ds'' = \\sqrt{g_{ij}(\\xi)d\\xi^id\\xi^j} \\, .\n \\end{equation}\n\nThe construction of tensor $g_{ij}(\\xi)$ leads directly to the conclusion: the\nchange of geometry (\\ref{gp14}) to the geometry (\\ref{gp22}) does not lead to the\nchange of the initial congruence of the world lines and corresponding World function\n$S(\\xi)$.\n\nTherefore, in our concept one and the same World, i.e. the pair \\{World function;\ncongruence of the world lines\\}, corresponds to a whole class of related but\nqualitatively different Finsler geometries.\n\n\\section{Analyticity condition and the Minkowski space}\n\nLet the World function $S(\\xi)$ be the (unity) component of an analytical function\nof the $H_4$ variable in the orthogonal basis (\\ref{gp4}), that is\n \\begin{equation}\\label{gp23}\n S(\\xi) = \\frac{1}{4} \\left[ f^1(\\xi^1) + f^2(\\xi^2) + f^3(\\xi^3) + f^4(\\xi^4) \\right] \\, .\n \\end{equation}\nThen\n \\begin{equation}\\label{gp24}\n g^{ijkl}\\frac{\\partial S}{\\partial \\xi^i}\\frac{\\partial S}{\\partial\n \\xi^j}\\frac{\\partial S}{\\partial \\xi^k}\\frac{\\partial S}{\\partial\n \\xi^l} = \\frac{\\partial f^1(\\xi^1)}{\\partial \\xi^1}\\frac{\\partial\n f^2(\\xi^2)}{\\partial \\xi^2}\\frac{\\partial f^3(\\xi^3)}{\\partial\n \\xi^3}\\frac{\\partial f^4(\\xi^4)}{\\partial \\xi^4} = \\kappa(\\xi)^4 > 0 \\, ,\n \\end{equation}\nand this leads to the limitation on the functions, {\\it f}{\\it i}:\n \\begin{equation}\\label{gp25}\n \\frac{\\partial f^1(\\xi^1)}{\\partial \\xi^1}\\frac{\\partial\n f^2(\\xi^2)}{\\partial \\xi^2}\\frac{\\partial f^3(\\xi^3)}{\\partial\n \\xi^3}\\frac{\\partial f^4(\\xi^4)}{\\partial \\xi^4} > 0 \\, .\n \\end{equation}\n\nIt follows from (\\ref{gp24}) that the space with the length element (\\ref{gp14}) can\nbe obtained from the space with the length element (\\ref{gp6}) with the help of the\nconformal transformation, which means that the condition of the analyticity of the\nWorld function can be treated in a sense as the condition of the conformal symmetry.\n\nLet us construct tensor $g_{ij}(\\xi)$ following the algorithm developed in the\nprevious section. It turns out that in a region where functions $f^i$ have no\nsingularities there will always be such a coordinate system $x^0, \\, x^1, \\, x^2, \\,\nx^3$ in which the length element $ds''$ has a form\n \\begin{equation}\\label{gp26}\n ds'' = \\sqrt{(x^0)^2 - (x^1)^2 - (x^3)^2 - (x^3)^2} \\, .\n \\end{equation}\n\nLet us express the coordinates $x^0,\\, x^1,\\, x^2,\\, x^3$ in terms of the initial\ncoordinates $\\xi ^{1} ,\\xi ^{2} ,\\xi ^{3} ,\\xi ^{4} $:\n\\begin{equation}\\label{gp27}\n\\left.\n\\begin{array}{l}\n x^0 = \\displaystyle\\frac{\\;\\, 1 \\;}{4} \\left( f^1(\\xi^1) + f^2(\\xi^2) + f^3(\\xi^3) +\nf^4(\\xi^4) \\right) \\, ,\\\\[12pt]\n x^1 = \\displaystyle\\frac{\\sqrt{3}}{4}\\left( f^1(\\xi^1) + f^2(\\xi^2) - f^3(\\xi^3) -\nf^4(\\xi^4) \\right) \\, ,\\\\[12pt]\n x^2 = \\displaystyle\\frac{\\sqrt{3}}{4}\\left( f^1(\\xi^1) - f^2(\\xi^2) + f^3(\\xi^3) -\nf^4(\\xi^4) \\right) \\, ,\\\\[12pt]\n x^3 =\\displaystyle \\frac{\\sqrt{3}}{4}\\left( f^1(\\xi^1) - f^2(\\xi^2) - f^3(\\xi^3) +\nf^4(\\xi^4) \\right) \\, .\n\\end{array}\n\\right\\}\n\\end{equation}\n\nTherefore, to obtain the non-trivial curving of the space-time one should use the World functions with the broken conformal symmetry.\n\n\\section{Newtonian potential}\n\nLet us show that there are World functions that lead to the non-trivial pseudo\nRiemannian 4-dimensional spaces. Let us regard a function\n \\begin{equation}\\label{gp28}\n S(\\xi) = \\frac{1}{4}\\left( \\xi^1 + \\xi^2 + \\xi^3 + \\xi^4 \\right) +\n \\alpha\\cdot\\psi(\\varrho) \\, ,\n \\end{equation}\nwhere $\\alpha$ is the parameter characterizing the break of the analyticity of the\nWorld function (the break of the conformal symmetry in the $H_4$ space), $\\psi$ is\nan arbitrary function of a single argument\n \\begin{equation}\\label{gp29}\n \\varrho = \\sqrt{(y^1)^2+(y^2)^2+(y^3)^2} \\, ,\n \\end{equation}\nand $y^0,\\, y^1,\\, y^2,\\, y^3$ are the coordinates in the orthogonal basis $1, j, k,\njk$:\n \\begin{equation}\\label{gp30}\n \\left.\n \\begin{array}{c}\n y^0 = \\displaystyle \\frac{1}{4}(\\xi^1+\\xi^2+\\xi^3+\\xi^4) \\, , \\\\[12pt]\n y^1 = \\displaystyle \\frac{1}{4}(\\xi^1+\\xi^2-\\xi^3-\\xi^4) \\, , \\\\[12pt]\n y^2 = \\displaystyle \\frac{1}{4}(\\xi^1-\\xi^2+\\xi^3-\\xi^4) \\, , \\\\[12pt]\n y^3 = \\displaystyle \\frac{1}{4}(\\xi^1-\\xi^2-\\xi^3+\\xi^4) \\, .\n \\end{array}\n \\right\\} \\,\n \\end{equation}\nThen the derivatives of the World functions over the coordinates $\\xi^i$ can be\nexpressed in the following way:\n\\begin{equation}\\label{gp31}\n\\left.\n\\begin{array}{c}\n\\displaystyle\\frac{\\partial S}{\\partial \\xi^1} =\n\\displaystyle \\frac{1}{4}\\left[1+\\frac{\\alpha}{\\varrho}\\frac{d\\psi}{d\\varrho}\\left( y^1+y^2+y^3 \\right)\\right] \\, , \\\\[15pt]\n\\displaystyle\\frac{\\partial S}{\\partial \\xi^2} =\n\\displaystyle \\frac{1}{4}\\left[1+\\frac{\\alpha}{\\varrho}\\frac{d\\psi}{d\\varrho}\\left( y^1-y^2-y^3 \\right)\\right] \\, , \\\\[15pt]\n\\displaystyle\\frac{\\partial S}{\\partial \\xi^3} =\n\\displaystyle \\frac{1}{4}\\left[1+\\frac{\\alpha}{\\varrho}\\frac{d\\psi}{d\\varrho}\\left( -y^1+y^2-y^3 \\right)\\right] \\, , \\\\[15pt]\n\\displaystyle\\frac{\\partial S}{\\partial \\xi^4} =\n\\displaystyle\\frac{1}{4}\\left[1+\\frac{\\alpha}{\\varrho}\\frac{d\\psi}{d\\varrho}\\left(\n-y^1-y^2+y^3 \\right)\\right] \\, .\n\\end{array}\n\\right\\} \\,\n\\end{equation}\n\nLet us calculate the components of the metric tensor in coordinates $y^0,\\, y^1,\\,\ny^2,\\, y^3$ using the invariance of the square of the length element\n \\begin{equation}\\label{gp32}\n g_{ij}(\\xi) d\\xi^id\\xi^j = \\tilde{g}_{ij}(y)dy^idy^j\n \\end{equation}\nGrouping the terms, one gets\n \\begin{equation}\\label{gp33}\n \\tilde{g}_{00}= 1 - 3\\alpha^2\\left( \\frac{d\\psi}{d\\varrho} \\right)^2\n \\, , \\qquad \\tilde{g}_{\\beta\\beta_-}=-3\\left\\{1 + \\alpha^2\\left(\n \\frac{d\\psi}{d\\varrho} \\right)^2\\left[ 1 -\n \\frac{4(y^\\alpha)^2}{3\\rho^2}\\right]\\right\\} \\, ,\n \\end{equation}\n \\begin{equation}\\label{gp34}\n 2\\tilde{g_{0\\beta}} = - 4 \\left[\n \\alpha\\frac{d\\psi}{d\\varrho}\\frac{\\;\\; y^\\beta}{\\varrho} +\n 3\\alpha^2\\left(\\frac{d\\psi}{d\\varrho}\\right)^2\\cdot\\frac{y^1y^2y^3}{y^\\beta\\varrho^2}\n \\right] \\, ,\n \\end{equation}\n \\begin{equation}\\label{gp35}\n 2\\tilde{g}_{\\beta\\gamma} = - 4 \\left[ 3\n \\alpha\\frac{d\\psi}{d\\varrho}\\frac{\\;\\, y^\\delta}{\\varrho} +\n \\alpha^2\\left(\\frac{d\\psi}{d\\varrho}\\right)^2\\cdot\\frac{y^\\beta\n y^\\gamma}{\\varrho^2} \\right] \\, ,\n \\end{equation}\nwhere $\\beta,\\, \\gamma,\\, \\delta,\\, = 1, 2, 3$; $\\beta\\equiv\\beta_-$ but no\nsummation is performed here; in the last formula all the indices $\\beta,\\, \\gamma,\\,\n\\delta\\,$ are different.\n\n If $\\alpha = 0$, then\n \\begin{equation}\\label{gp36}\n (\\tilde{g}_{ij}) = diag(1,-3,-3,-3) \\, .\n \\end{equation}\nThis means that the real physical coordinates $x^0,\\, x^1,\\, x^2,\\, x^3$ of the\nspace-time are expressed by the coordinates $y^0,\\, y^1,\\, y^2,\\, y^3$ in the\nfollowing way\n \\begin{equation}\\label{gp37}\n x^0 = y^0 \\, , \\qquad x^\\beta = \\sqrt{3}\\cdot y^\\beta \\, .\n \\end{equation}\n\nLet us pass to the physical coordinates $x^0,\\, x^1,\\, x^2,\\, x^3$:\n \\begin{equation}\\label{gp38}\n \\tilde{g}_{ij}(y)dy^idy^j = \\bar{g}_{ij}(x)dx^idx^j \\, ,\n \\end{equation}\nwhere\n \\begin{equation}\\label{gp39}\n \\bar{g}_{00} = \\tilde{g}_{00} \\, , \\qquad \\bar{g}_{0\\beta} =\n \\frac{1}{\\sqrt{3}}\\cdot \\tilde{g}_{0\\beta} \\, , \\qquad \\bar{g}_{\\beta\\gamma} =\n \\frac{1}{3}\\cdot \\tilde{g}_{\\beta\\gamma} \\,.\n \\end{equation}\n Let us denote\n \\begin{equation}\\label{gp40}\n r = \\sqrt{(x^1)^2+(x^2)^2+(x^3)^2} \\equiv \\sqrt{3}\\cdot\\varrho \\, ,\n \\end{equation}\nThen\n \\begin{equation}\\label{gp41}\n \\bar{g}_{00}= 1 - 9\\alpha^2\\left( \\frac{d\\psi}{dr} \\right)^2 \\, ,\n \\qquad \\bar{g}_{\\beta\\beta_-}=-\\left\\{1 + 3\\alpha^2\\left(\n \\frac{d\\psi}{dr} \\right)^2\\left[ 1 -\n \\frac{4(x^\\alpha)^2}{3r^2}\\right]\\right\\} \\, ,\n \\end{equation}\n \\begin{equation}\\label{gp42}\n 2\\bar{g_{0\\beta}} = - 4 \\left[ \\alpha\\frac{d\\psi}{dr}\\frac{\\;\\;\n x^\\beta}{r} +\n 3\\sqrt{3}\\alpha^2\\left(\\frac{d\\psi}{dr}\\right)^2\\cdot\\frac{x^1x^2x^3}{x^\\beta\n r^2} \\right] \\, ,\n \\end{equation}\n \\begin{equation}\\label{gp43}\n 2\\bar{g}_{\\beta\\gamma} = - 4 \\left[ \\sqrt{3}\n \\alpha\\frac{d\\psi}{dr}\\frac{\\;\\, x^\\delta}{r} +\n \\alpha^2\\left(\\frac{d\\psi}{dr}\\right)^2\\cdot\\frac{x^\\beta x^\\gamma}{r^2} \\right] \\, .\n \\end{equation}\n\nThe metric tensor $\\bar{g}_{ij} (x)=\\bar{g}_{ij} (x^{1} ,x^{2} ,x^{3} )$ depends\nonly on the space coordinates $x^1,x^2,x^3$, and this corresponds to the stationary\ngravitational field, stationary Universe. The probe particle of mass $m$ moves along\nthe geodesic of the pseudo Riemannian space with metric tensor $\\bar{g}_{ij} (x^{1}\n,x^{2} ,x^{3} )$.\n\nLet a particle move in a fixed frame and have velocity much less than the light\nvelocity, $c$:\n \\begin{equation}\\label{gp44}\n \\frac{dx^\\beta}{dt} = v^\\beta \\, , \\qquad |v^\\beta| \\ll c \\, ,\n \\end{equation}\nThe gravitational fields are weak, that is the condition $|v^{\\beta } |<<1$ remains\nvalid for all the time of the particle motion. Let us obtain the Lagrange function,\n{\\it L}, to describe such non-relativistic motion of the probe particle in the weak\ngravity field. To do this, develop the right hand side of the expression\n \\begin{equation}\\label{gp45}\n L = -mc\\cdot \\frac{\\sqrt{\\bar{g}_{ij}(x^1,x^2,x^3)dx^idx^j}}{dt}\n \\end{equation}\nWithin the accuracy of $\\left(\\frac{v}{c}\\right)^2$\n\\begin{equation}\\label{gp46}\nL = -mc^2 \\sqrt{\\bar{g}_{00}} \\cdot \\sqrt{1+ \\frac{1}{\\bar{g}_{00}}\\left(\n2\\bar{g}_{0\\beta}\\frac{v^\\beta}{c} + \\bar{g}_{\\beta\\gamma}\\frac{v^\\beta\nv^\\gamma}{c^2} \\right)} \\, ,\n\\end{equation}\n\\begin{equation}\\label{gp47}\nL \\simeq -mc^2 \\sqrt{\\bar{g}_{00}} \\cdot \\left\\{1+ \\frac{1}{2\\bar{g}_{00}}\\left(\n2\\bar{g}_{0\\beta}\\frac{v^\\beta}{c} + \\bar{g}_{\\beta\\gamma}\\frac{v^\\beta\nv^\\gamma}{c^2} \\right) - \\frac{1}{8\\bar{g}^2_{00}}\\left(\n2\\bar{g}_{0\\beta}\\frac{v^\\beta}{c} \\right)^2\\right\\} \\, .\n\\end{equation}\nOpening the brackets in the right hand side, we get an additive term which is the\nfull time derivative of a certain function {\\it f(r)}, it depends linearly on the\nvelocity components and, thus, it can be omitted. Leaving the same designation for\nthe Lagrange function, we get\n\\begin{equation}\\label{gp48}\nL \\simeq -mc^2 \\sqrt{\\bar{g}_{00}} \\cdot \\left\\{1+ \\frac{1}{2\\bar{g}_{00}}\\cdot\n\\bar{g}_{\\beta\\gamma}\\frac{v^\\beta v^\\gamma}{c^2} - \\frac{1}{8\\bar{g}^2_{00}}\\left(\n2\\bar{g}_{0\\beta}\\frac{v^\\beta}{c} \\right)^2\\right\\} \\, .\n\\end{equation}\n\nOur goal is the Lagrange function of the form\n \\begin{equation}\\label{gp49}\n L = \\frac{m\\vec{v}^2}{2} - U(\\vec{x}) \\, ,\n \\end{equation}\nwhere $U(\\vec{x})$ is the potential energy of the probe particle, $\\vec{x}\\equiv\n(x^{1} ,x^{2} ,x^{3} ),~ \\vec{v}\\equiv (v^{1} ,v^{2} ,v^{3} ),~ r^{2} =\n\\vec{x}^{\\,2}$, $\\vec{v}^{\\,2} =(v^{1} )^{2} +(v^{2} )^{2} +(v^{3} )^{2} \\equiv\nv^{2} $. To reach it we have to make some assumptions about the correlation between\nthe parameter, $\\alpha$ and light velocity:\n \\begin{equation}\\label{gp50}\n \\alpha = \\frac{\\nu}{c} \\, , \\quad \\hbox{when} \\quad c\\rightarrow\n \\infty \\quad \\alpha\\rightarrow 0 \\, .\n \\end{equation}\nBesides, let $\\alpha$ be of the same order (or smaller) with the relation\n$\\left|\\displaystyle\\frac{v}{c}\\right|$. Then leaving only the terms that don't\ndisappear at $c\\to \\infty $ in the (\\ref{gp48}), one gets\n \\begin{equation}\\label{gp51}\n L \\simeq -mc^2 + mc^2 \\frac{9}{2}\\frac{\\nu^2}{c^2} \\left(\n \\frac{d\\psi}{dr} \\right)^2 + m\\cdot \\frac{v^1v^1+v^2v^2+v^3v^3}{2}\\, .\n \\end{equation}\nSince $(-mc^2)$ is a full time derivative of function $(-mc^2\\cdot t)$, we omit it\nand get\n \\begin{equation}\\label{gp52}\n L \\simeq \\frac{m\\vec{v}^2}{2} + \\frac{9m\\nu^2}{2} \\left(\n \\frac{d\\psi}{dr} \\right)^2 \\, .\n \\end{equation}\n\nLet a mass $M$ be motionless in the frame origin, and then the potential energy of\nthe probe particle with mass $m$ located at $x^1,x^2,x^3$ is equal to\n \\begin{equation}\\label{gp53}\n U(r) = - \\gamma \\frac{mM}{r} \\, ,\n \\end{equation}\nwhere $\\gamma$ is the gravitational constant. Comparing (\\ref{gp49}) and\n(\\ref{gp52}), we get the equation for $\\psi(r)$:\n\\begin{equation}\\label{gp54}\n\\frac{9m\\nu^2}{2} \\left(\\frac{d\\psi}{dr} \\right)^2 = \\gamma \\frac{mM}{r} \\qquad\n\\Rightarrow \\qquad \\frac{d\\psi}{dr} = \\pm \\frac{\\sqrt{2\\gamma\nM}}{3\\nu}\\frac{1}{r^{1\/2}} \\, .\n\\end{equation}\nTherefore,\n\\begin{equation}\\label{gp55}\n\\psi(r) = \\pm \\frac{2\\sqrt{2\\gamma M}}{3\\nu}\\cdot r^{1\/2} + \\psi_0 \\qq (\\psi_0 =\nconst).\n\\end{equation}\n\nFinally, the World function is equal to\n\\begin{equation}\\label{gp56}\nS = x^0 \\pm \\frac{2\\sqrt{2\\gamma M}}{3c}\\cdot r^{1\/2} + C_0 \\qq (C_0 = const),\n\\end{equation}\nWhen it performs a conformal transformation of the length element of the plane\nBerwald-Moor space, it induces a pseudo Riemannian geometry in the Minkowski space.\nFor a non-relativistic probe particle of mass {\\it m}, this geometry gives the\nmotion equations for the Kepler problem for the point mass {\\it M} located in the\norigin of the space frame.\n\n The more complicated World function, maybe also leading to the stationary Universe, has the form\n\\begin{equation}\\label{gp57}\nS(\\xi) = \\frac{1}{4}\\left( \\xi^1 + \\xi^2 + \\xi^3 + \\xi^4 \\right)\\left[\n1+\\alpha_1\\cdot\\psi_1(\\varrho) \\right] + \\alpha_2\\cdot\\psi_2(\\varrho) \\, ,\n\\end{equation}\nwhere $\\alpha_A$ are the parameters of the analyticity break of the World function\n(parameters of the conformal symmetry break in the $H_4$ space), $\\psi_A$ are the\narbitrary functions of single argument $\\varrho$ (\\ref{gp29}), (\\ref{gp30}).\n\n \\section*{Conclusion}\n\nThe results obtained in this paper point at the deep correlation between the\nEinstein geometries and Finsler spaces with Berwald-Moor metric. We managed to find\nthe concrete Finsler space with the Berwald-Moor metric which in the limit appeared\nto be related to the curved pseudo Riemannian space with the Newtonian gravitational\npotential. This fact points at the principal possibility to built more interesting\nconstructions, particularly, such Finsler spaces whose limit cases would be the\nknown relativistic solutions.\n\n\\small\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nI was asked to talk about thermal radiation from\nisolated neutron stars (NSs). In this meeting, George Pavlov\nreviewed the X-ray properties of pulsars and thermally emitting NSs\n(see Kaplan et al.~2011), and Wynn Ho discussed central compact\nobjects and their magnetic fields (see Halpern \\& Gotthelf 2010;\nShabaltas \\& Lai 2012; Vigano \\& Pons 2012). Recent works on\ntheoretical modelling of NS surface emission can be found in Potekhin\net al.~(2012) (see also Pavlov et al.~1995; Harding \\& Lai 2006 and\nvan Adelsberg \\& Lai 2006 for reviews). Since these subjects were\nadequately covered in the meeting, I decided to focus on a different\ntopic that did not receive much attention in this meeting but is\nlikely to become increasingly important in the coming decade.\n\n\nMerging NS binaries have been studied since 1970s, with major\nactivities in the relativity community since the early 1990s because\nof their importance as a source of gravitational waves (GWs)\n(e.g. Cutler et al.~1993). They are of great current interest for two\nreasons: (i) Merging NS\/NS or NS\/Black-Hole (BH) binaries have been\nidentified as the leading candidate for the central engine of short\nGRBs\n(Berger 2011). They are also expected to produce optical and radio\ntransients that may be detected by wide-field, fast imaging telescopes\nthat are coming online (e.g. PTF, LSST) in the next few years\n(Nissanke et al.~2012). (ii) After several decades of developmemt and\npromise,\ngravitational wave astronomy in the Hz-kHz band may finally take off\nin the next decade. The initial LIGO reached the design sensitivity\n($h_c\\simeq 10^{-21}$) in 2006, and the enhanced LIGO (with a factor of 2\nreduction in $h_c$) is taking or analysing data. The Advanced LIGO and\nVIRGO are expected to begin observations in 2015 and reach full\nsensitivity (a factor of 10 reduction in $h_c$) in 2018-19 --- at which\ntime the detection of GWs from many merging NS binaries seems\nguaranteed.\n\nThe last three minutes of a NS binary's life may be divided into two\nphases: the inspiral phase, producing quasi-periodic GWs, and the\ncoalescence phase, where physical collision results in ``messy'' GWs.\nThe recent years, 3D simulations of the final merger in full general\nrelativity (GR) have become possible (see Shibata \\& Taniguchi 2006;\nFoucart et at.~2012; Sekiguchi et al.~2012). It has long been\nrecognized that the final merger waveforms can provide a useful probe of NS\nequation of state (EOS; e.g., Cutler et al.~1993; Bildsten \\& Cutler\n1992; Lai \\& Wiseman 1996; Wiggins \\& Lai 2000). The idea is \nsimple: By measuring the ``cut-off'' frequency $\\propto\n(GM_t\/R^3)^{1\/2}$ associated with binary contact or tidal disruption,\ncombined with the precise mass measurement from the inspiral waveform,\none can obtain the NS radius (see Bauswein et al.~2012 for recent \nsimulations which put such a idea into concrete footing; see\nalso Sekiguchi et al.~2012; Faber \\& Rasio 2012 for reviews). \n\nIn the following sections I will focus on the pre-merger phase.\n\n\n\\section{Hydrodynamics of merging NS binaries}\n\nPrior to binary merger, tidal effects may affect the orbital decay and\nthe GWs. There are two types of tides: {\\it equilibrium tides} and\n{\\it dynamical tides}. The equilibrium tides correspond to global deformation \nof the NS, which leads to the interaction potential between the two stars\n(with the NS mass $M$ and radius $R$, the companion mass $M'$ -- treated as a \npoint mass, and the binary separation $a$) \n\\begin{equation}\nV(r)=-{MM'\/a}-{\\cal O}\\left(k_2{{M'}^2R^5\/a^6}\\right),\n\\end{equation}\nwhere $k_2$ is the so-called\nLove number. This would lead to a correction to the number of GW cycles,\n$dN=dN^{(0)}\\left[1-{\\cal O}(k_2M'R^5\/Ma^5)\\right]$. For a Newtonian\npolytropic NS model, simple analytic expressions can be found in Lai et\nal.~(1994). Recent semi-analytic GR calculations of such equilibrium\ntidal effects (including the more precise determination of the Love number)\ncan be found in numerous papers (e.g., Flanagan \\& Hinderer 2008;\nBinnington \\& Poisson 2009; Damour \\& Nagar 2009; Penner et al.~2012, \nFerrari et al.~2012). Obviously\nthis effect is only important at small orbital separations (just prior\nto merger) -- there is some prospect of measuring this, thereby\nconstraining the EOS, but it may be challenging (Damour et al.~2012).\nAt small orbital separations, the quadrupole approximation is not valid; \ntherefore one\nmust use the numerically computed GR quasi-equilibrium binary sequences\nto characterize the tidal effect -- such sequences have been\nconstructed by several groups since the 1990s (e.g., Baumgarte et al.~1998;\nUryu et al.~2009) or use fully numerical simulations.\n\nAnother aspect of the equilibrium tide concerns tidal dissipation,\nwhich leads to a lag of the tidal bulge with respect to the binary\naxis. It was shown already in the 1990s (Bildsten \\& Cutler 1992;\nKochanek 1992) that because of the rapid GW-driven orbital decay,\nviscous tidal lag cannot synchronize the NS spin. Thus the NS will\nbe close to irrotational (approximated as a Riemann-S ellipsoid;\nLai et al.~1994; Wiggins \\& Lai 2000; Ferrari et al.~2012). \nNear the final phase\nof the inspiral, the rapid orbital decay gives rise to a finite lag\nangle (even with zero viscosity), but this cannot synchronize the NS\n(Lai \\& Shapiro 1995; Dall'Osso \\& Rossi 2012).\n\n\nThe situation is more complicated for {\\bf dynamical tides}, which\nmanifest as resonant excitations of internal oscillations of the NS: As\ntwo NSs spiral in, the orbit can momentarily come into resonance with\nthe normal modes (frequency $\\omega_\\alpha$) of the NS:\n\\begin{equation}\n\\omega_\\alpha=m\\Omega_{\\rm orb},\\qquad m=2,3,\\cdots\n\\end{equation}\nBy drawing energy from the orbital\nmotion and resonantly exciting the modes, the rate of inspiral is\nmodified, giving rise to a phase shift in the gravitational\nwaveform. This problem was studied by Reisenegger \\& Goldreich (1994),\nLai (1994) and Shibata (1994) in the case of non-rotating NSs, where\nthe only modes that can be resonantly excited are g-modes (with\ntypical mode frequencies$\\lo 100$~Hz). It was found that the effect is\nsmall for typical NS parameters (mass $M=1.4M_\\odot$ and radius\n$R=10$~km) because the coupling between the g-mode and the tidal\npotential is weak. Ho \\& Lai (1999) studied the effect of NS rotation,\nand found that the g-mode resonance can be strongly enhanced even by a\nmodest rotation (e.g., the phase shift in the waveform $\\Delta\\Phi$\nreaches up to 0.1~radian for a spin frequency $\\nu_s\\lo 100$~Hz).\nThey also found that for a rapidly rotating NS ($\\nu_s\\go 500$~Hz),\nf-mode resonance becomes possible (since the inertial-frame f-mode\nfrequency can be significantly reduced by rotation) and produces a\nlarge phase shift. In addition, NS rotation gives rise to r-mode\nresonance whose effect is appreciable only for very rapid (near\nbreakup) rotations. Lai \\& Wu (2006) further studied resonant\nexcitations of other inertial modes (of which r-mode is a member) and\nfound similar effects. Flanagan \\& Racine (2006) studied the\ngravitomagnetic resonant excitation of r-modes and and found that the\npost-Newtonian effect is more important than the Newtonian tidal\neffect (and that the phase shift reaches 0.1~radian for $\\nu_s\\sim\n100$~Hz). Tsang et al.~(2012) examined crustal modes and found that \nthe GW phase correction is small\/modest and suggested that tidal resonance\ncould shatter the NS crust, giving rise to the pre-cursor of short GRBs.\nTaken together, these studies suggest that for canonical NS\nparameters ($R \\simeq 10$~km, $\\nu_s\\lo 100$~Hz), tidal resonances\nhave a small effect on the gravitational waveform during binary\ninspiral. However, it is important to keep in mind that the effect is \na strong function of $R$ (e.g., $\\Delta\\Phi\\propto R^4$ for g-modes\nand $\\propto R^{3.5}$ for inertial modes). A larger radius ($R\\simeq \n15$~km), appropriate for stiff EOS, \nwould make the effect important. In the case of g-modes, the magnitude\nof the effect depends on the symmetry energy of nuclear matter \nand could be non-negligible (W. Newton \\& D. Lai 2013, in prep).\n\n\n\\section{Electrodynamics of merging NS binaries}\n\nFor magnetic NSs, magnetic interactions may play a role. If the\nbinary is embedded in a vacuum, then the interaction potential is\n$V(r)=-MM'\/a-{\\cal O}(\\mu\\mu'\/a^3)$ (where $\\mu,\\mu'$ are the magnetic\ndipole moments of the two stars). It is easy to check that such\nmagnetic interaction would lead to negligible effect on the GWs unless\nboth NSs have superstrong fields ($\\gg 10^{15}$~G) -- this is unlikely\n(e.g., the double pulsars PSR J0737-3039 has $10^{10}$~G for pulsar A\nand $2\\times 10^{13}$ for pulsar B). \n\n\\begin{figure}[b]\n\\vspace*{-0.8 cm} \n\\begin{center}\n \\includegraphics[width=3.4in]{f1.eps}\n\\vspace*{-1.0 cm} \n \\caption{DC circuit (unipolar induction) \nmodel of magnetic interactions in binary \nsystems {\\it a la} Goldreich \\& Lynden-Bell (1969).}\n \\label{fig1}\n\\end{center}\n\\end{figure}\n\n\nOf course, as in the case of isolated pulsars,\nthe circumbinary environment cannot be vacuum.\nThe following discussion is based on Lai (2012).\nConsider a binary system consisting of a magnetic NS (the\n``primary'', with mass $M$, radius $R$, spin $\\Omega_s$, and magnetic\ndipole moment $\\mu$) and a non-magnetic companion (mass $M_c$, radius\n$R_c$). The orbital angular\nfrequency is $\\Omega$. The magnetic field strength at the surface of\nthe primary is $B_\\star=\\mu\/R^3$. The whole binary system is embedded\nin a tenuous plasma (magnetosphere). We assume for simplicity that\n${\\bf\\Omega}$, ${\\bf\\Omega_s}$ and ${\\mbox{\\boldmath $\\mu$}}$ are all aligned.\nThe motion of the non-magnetic companion relative to the magnetic field of the\nprimary produces an EMF ${\\cal E} \\simeq 2R_c |E|$, where ${\\bf E}= \n{\\bf v}_{\\rm rel}\\times {\\bf B}\/c$, with\n${\\bf v}_{\\rm rel}=(\\Omega-\\Omega_s)a\\,{\\hat{\\mbox{\\boldmath $\\phi$}}}$ and\n${\\bf B}=(-\\mu\/a^3){\\hat{\\bf z}}$.\nThis gives\n${\\cal E}\\simeq {(2\\mu R_c\/ca^2)}\\Delta\\Omega$,\nwhere $\\Delta\\Omega=\\Omega-\\Omega_s$\\footnote{If the magnetic field does\nhave time to fully penetrate the companion star due to the rapid orbital decay,\nthen the EMF will be reduced by a factor of order the ratio of the skin depth\nand the stellar radius. I thank Anatoly Spitkovsky for\npointing this out to me at the IAU conference.}. \nThe EMF drives a current along the magnetic\nfield lines in the magnetosphere, connecting the primary and the companion\nthrough two flux tubes. The current in the circuit is given by\n${\\cal I}={{\\cal E}\/({\\cal R}_{\\rm tot})}$,\nwhere the total resistance of the circuit is\n${\\cal R}_{\\rm tot}={\\cal R}+{\\cal R}_c+2{\\cal R}_{\\rm mag}$,\nwith ${\\cal R},\\,{\\cal R}_c,\\,{\\cal R}_{\\rm mag}$ the resistances of the magnetic\nstar, the companion and the magnetosphere, respectively.\nThese resistances depend on the properties of the binary components and the\nmagnetosphere, and can vary widely for different types of systems.\nThe energy dissipation rate of the system is then\n$\\dot E_{\\rm diss}=2{\\cal I}^2R_{\\rm tot}={2{\\cal E}^2\/{\\cal R}_{\\rm tot}}$,\nwhere the factor of 2 accounts for both the upper and lower sides of the circuit.\n\nThe total magnetic force (in the azimuthal direction) on the companion is\n$F_\\phi\\simeq (2R_c) (2{\\cal I}B_z\/c)$, with $B_z=-\\mu\/a^3$.\nThus the torque acting on the binary's orbital angular momentum is\n$T=\\dot J_{\\rm orb}\\simeq ({4\/c})a\\,R_c{\\cal I}B_z\\simeq\n-({4\\mu R_c\/ca^2})({{\\cal E}\/{\\cal R}_{\\rm tot}})$.\nThe torque on the primary's spin is $I\\dot\\Omega_s=-T$ (where $I$ is\nthe moment of inertia).\nThe orbital energy loss rate associated with $T$ is then\n$\\dot E_{\\rm orb}=T\\Omega$.\n\nThe equations above show that the binary\ninteraction torque and energy dissipation associated with the DC\ncircuit increase with decreasing total resistance ${\\cal R}_{\\rm tot}$.\nIs there a problem for the DC model when ${\\cal R}_{\\rm tot}$ is too small?\nThe answer is yes.\nThe current in the circuit produces a toroidal magnetic field, which\nhas the same magnitude but opposite direction above and below\nthe equatorial plane. The toroidal field just above the\ncompanion star (in the upper flux tube) is $B_{\\phi+}\\simeq -(2\\pi\/c){\\cal J}_r$,\nwhere ${\\cal J}_r\\simeq -4{\\cal I}\/(\\pi R_c)$ is the (height-integrated) surface current.\nThus the azimuthal twist of the flux tube is\n$\\zeta_\\phi=-{B_{\\phi+}\/B_z}=\n={16 v_{\\rm rel}\/(c^2{\\cal R}_{\\rm tot})}$,\nwhere $v_{\\rm rel}=a\\Delta\\Omega=a(\\Omega-\\Omega_s)$.\nClearly, when ${\\cal R}_{\\rm tot}$ is less than $16v_{\\rm rel}\/c^2$,\nthe flux tube will be highly twisted.\n\nGoldreich \\& Lynden-Bell (1969) speculated that the DC circuit would break\ndown when the twist is too large. (For the Jupiter-Io system\nparameters, the twist $|\\zeta_\\phi|\\ll 1$.) \nNumerous works have since confirmed that this is indeed the case.\nTheoretical studies and numerical simulations, usually carried out\nin the contexts of solar flares\nand accretion disks, have shown that as a flux tube is twisted beyond\n$\\zeta_\\phi\\go 1$, the magnetic pressure associated with $B_\\phi$\nmakes the flux tube expand outward and the magnetic fields open up,\nallowing the system to reach a lower energy state (e.g., Aly 1985; \nLynden-Bell \\& Boily 1994;\nLovelace et al.~1995; Uzdensky et al.~2002).\nThus, a DC circuit with $\\zeta_\\phi\\go 1$ cannot be realized: The\nflux tube will break up, disconnecting the linkage between the two\nbinary components.\nA binary system with ${\\cal R}_{\\rm tot}\\lo 16v_{\\rm rel}\/c^2$\ncannot establish a steady-state DC circuit.\nThe electrodynamics is likely rather complex, only\na quasi-cyclic circuit may be possible (Lai 2012; see Aly \\& Kuijpers 1990):\n(a) The magnetic field from the primary penetrates\npart of the companion, establishing magnetic linkage between the two\nstars; (b) The linked fields are twisted by differential rotation, generating\ntoroidal field from the linked poloidal field; (c) As the toroidal magnetic field\nbecomes comparable to the poloidal field, the fields inflate and\nthe flux tube breaks, disrupting the magnetic linkage;\n(d) Reconnection between the inflated field lines relaxes the shear and restore\nthe linkage. The whole cycle repeats.\n\nIn general, we can use the dimensionless azimuthal twist $\\zeta_\\phi$\nto parameterize the magnetic torque and energy dissipation rate:\n\\begin{equation}\nT= {1\\over 2}aR_c^2B_zB_{\\phi+}\n= -\\zeta_\\phi{\\mu^2 R_c^2\\over 2a^5},\\quad\n\\dot E_{\\rm diss} = -T \\Delta\\Omega\n=\\zeta_\\phi\\Delta\\Omega {\\mu^2 R_c^2\\over 2a^5}.\n\\label{eq:emax}\\end{equation}\nThe maximum torque and dissipation are obtained by setting $\\zeta_\\phi\\sim 1$.\nIf the quasi-cyclic circuit discussed in the last paragraph is\nestablished, we would expect $\\zeta_\\phi$ to vary between $0$ and $\\sim 1$.\nNote that in the above, $T$ is negative since we\nare assuming $\\Omega>\\Omega_s$. \n\nGravitational wave (GW) emission drives the orbital decay of the \nNS binary, with timescale\n$t_{\\rm GW}={a\/|\\dot a|}=\n0.012\\left({a\/30\\,{\\rm km}}\\right)^{4}{\\rm s}$,\nwhere we have adopted $M=1.4M_\\odot$ and mass ratio\n$q=M_c\/M=1$.\nThe magnetic torque tends to spin up the primary when $\\Omega$$>$$\\Omega_s$.\nSpin-orbit synchronization is possible only if\nthe synchronization time $t_{\\rm syn}=I\\Omega\/|T|$ is less than\n$t_{\\rm GW}$ at some orbital radii. With\n$I=\\kappa M R^2$, we find\n\\begin{equation}\nt_{\\rm syn}={2\\kappa(1+q)\\over\\zeta_\\phi\\Omega}\n\\left(\\!{GM^2\\over B_\\star^2R^4}\\!\\right)\n\\!\\left(\\!{a\\over R_c}\\!\\right)^2\n\\simeq 2\\times 10^7\\zeta_\\phi^{-1}\\!\\left(\\!{B_\\star\\over 10^{13}\\,{\\rm G}}\n\\!\\right)^{\\!-2}\\!\\left({a\\over 30\\,{\\rm km}}\\right)^{7\/2}{\\rm s},\n\\label{eq:tsyn}\\end{equation}\nwhere on the right we have adopted $\\kappa=0.4$ and $R=R_c=10$~km.\nClearly, even with magnetar-like field strength ($B_\\star\\sim 10^{15}$~G) and\nmaximum efficiency ($\\zeta_\\phi\\sim 1$), spin-orbit synchronization cannot be\nachieved by magnetic torque. For the same reason, the effect of magnetic torque on the\nnumber of GW cycles during binary inspiral is small. \n\nThe energy dissipation rate is\n\\begin{equation}\n\\dot E_{\\rm diss}=\\zeta_\\phi\\left(\\!{v_{\\rm rel}\\over c}\\!\\right){B_\\star^2R^6\nR_c^2c\\over 2a^6}\n= 7.4\\times 10^{44}\\zeta_\\phi\\left(\\!{B_\\star\\over\n10^{13}\\,{\\rm G}}\\!\\right)^{\\!2}\\!\\left(\\!{a\\over 30\\,{\\rm km}}\\!\\right)^{\\!\\!-13\/2}\n\\!{\\rm erg\\,s}^{-1},\n\\end{equation}\nwhere on the right we have used $v_{\\rm rel}\\simeq a\\Omega$ (for\n$\\Omega_s\\ll \\Omega$) and adopted canonical parameters\n($M=M_c=1.4M_\\odot$, $R=R_c=10$~km).\nThe total energy dissipation per $\\ln a$ is\n\\begin{equation}\n{dE_{\\rm diss}\\over d\\ln a}=\\dot E_{\\rm diss}t_{\\rm GW}\n\\simeq 8.9\\times 10^{42}\\zeta_\\phi\\!\\left(\\!{B_\\star\\over\n10^{13}\\,{\\rm G}}\\!\\right)^{\\!2}\\!\\left({a\\over 30\\,{\\rm km}}\\right)^{\\!\\!-5\/2}\n\\!{\\rm erg}.\n\\end{equation}\nSome fraction of this dissipation will emerge as electromagnetic\nradiation counterpart of binary inspiral. It is possible that \nthis radiation is detectable at extragalactic distance. But this will depend \non the microphysics in the magnetosphere, including particle acceleration and \nradiation mechanism (e.g., Vietri 1996; Hansen \\& Lyutikov 2001).\n\nIf one assumes that the magnetosphere resistance is given by the\nimpedance of free space, ${\\cal R}_{\\rm mag}=4\\pi\/c$, then the corresponding twist\nis $\\zeta_\\phi=2v_{\\rm rel}\/(\\pi c)$, which satisfies our upper limit.\nWe then have\n\\begin{equation}\n\\dot E_{\\rm diss}=\\left(\\!{v_{\\rm rel}\\over c}\\!\\right)^2\\!\n{B_\\star^2R^6 R_c^2c\\over \\pi a^6}\n= 1.7\\times 10^{44}\\left(\\!{B_\\star\\over 10^{13}\\,{\\rm G}}\\!\\right)^{\\!2}\n\\!\\left({a\\over 30\\,{\\rm km}}\\right)^{\\!\\!-7}{\\rm erg\/s}.\n\\end{equation}\nThis is in agreement with the estimate of Lyutikov (2011).\n\nThe situation is similar for NS\/BH binaries.\nIn the membrane paradigm (Thorne et al.~1986), a BH\nof mass $M_H$ resembles a sphere of radius $R_c=R_H=2GM_H\/c^2$\n(neglecting BH spin)\nand impedance ${\\cal R}_H=4\\pi\/c$. Neglecting the resistances of the\nmagnetosphere and the NS, the azimuthal twist of the flux tube in the DC\ncircuit is\n$\\zeta_\\phi={4v_{\\rm rel}\/(\\pi c)}$,\nwhich satisfies our upper limit.\nThe energy dissipation rate is (cf. Lyutikov 2011; McWilliams \\& Levin 2011)\n\\begin{equation}\n\\dot E_{\\rm diss}=\\left(\\!{v_{\\rm rel}\\over c}\\!\\right)^2\\!\n{2B_\\star^2R^6 R_H^2c\\over \\pi a^6}\n\\simeq 5.7\\!\\times\\! 10^{42}\\!\\left(\\!{B_\\star\\over 10^{13}\\,{\\rm G}}\\!\\right)^{\\!2}\n\\!\\!\\left(\\!{M_H\\over 10M_\\odot}\\!\\right)^{\\!\\!\\!-4}\n\\!\\!\\!\\left(\\!{a\\over 3R_H}\\!\\right)^{\\!\\!-7}\\!\\!\\!{\\rm erg\\,s}^{-1},\n\\end{equation}\nwhere we have assumed $M_{BH}\/M\\gg 1$.\nAgain, it is uncertain whether this radiation is detectable for binaries\nat extragalactic distances.\n\n\\vspace{1ex}\n{\\bf Acknowledgements}:\nThis work has been supported in part by NSF grants AST-1008245 and\nAST-1211061, and NASA grants NNX12AF85G and NNX10AP19G.\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nStrongly interacting systems in the high energy (or small $x$) limit\nare very nonlinear systems in spite of the smallness of\nthe coupling constant $\\alpha_{\\mathrm{s}}$. This is due to the large phase\nspace available for semihard gluon radiation that increases \nthe occupation numbers of gluonic modes in the hadron or nucleus \nwavefunction. Thus high energy scattering has to be understood in\nterms of gluon recombination and saturation that enforce the unitarity\nrequirements of the $S$-matrix. This happens naturally in\nthe Color Glass Condensate (CGC) effective theory of the\nhigh energy wavefunction. In the context of \ndeep inelastic scattering (DIS) the CGC \nleads to the dipole picture that naturally\ngives a consistent description of both inclusive\nand diffractive scattering.\nThe nonlinearities in high energy scattering are enhanced when the target\nis changed from a proton to a heavy nucleus. Thus there is a great\nopportunity to understand them by studying nuclear DIS in \nnew collider experiments, such as the EIC~\\cite{Deshpande:2005wd} \nor the LHeC~\\cite{Dainton:2006wd}. The particular process we discuss in this\npaper is diffractive DIS on nuclei.\n\n\n\nIn the Good-Walker~\\cite{Good:1960ba} picture of diffraction one needs to identify \nthe states\nthat diagonalize the imaginary part of the $T$-matrix. In the case of \nnuclear DIS at high energy these states are the ones with the virtual photon \nfluctuating into a dipole of a fixed size $r$ and with the nucleons in\nthe nucleus at fixed transverse positions $b_i$. In coherent diffraction\nthe nucleus is required to stay intact, which corresponds to performing\n the average over the nuclear wavefunction at the level of the\nscattering amplitude. \nAveraging the cross section, instead of the amplitude, \nover the nucleon positions allows for the nucleus\nto break up, giving the sum of incoherent and coherent cross sections,\ni.e. the quasielastic cross section. \nFor a more formal discussion of this we point the reader e.g. to \nRef.~\\cite{Caldwell:2009ke}.\nThe $t$-dependence of the incoherent cross section therefore directly \nprobes the fluctuations and correlations in the nuclear wavefunction, which\nhave turned out to be a crucial ingredient in understanding the initial \nconditions in heavy ion collisions \\cite{Miller:2007ri,*Alver:2010gr}.\n\nThe average gluon density probed in the \ncoherent process is very smooth, meaning that the cross section is dominated\nby small values of momentum transfer to the nucleus, $t \\sim - 1\/R_A^2$.\nMeasuring such a small momentum transfer accurately is \nvery challenging.\n At momentum scales corresponding to the nucleon \nsize $t \\sim - 1\/R_p^2$ the diffractive cross section is almost purely incoherent.\nThe larger momentum transfer should\nalso be easier to reconstruct experimentally even without measuring\nthe transverse momentum of the \nnuclear remnants, by accurately reconstructing the outgoing electron\nand $J\/\\Psi$ momenta and using momentum conservation.\nBy taking these processes into account in the detector design\none should be capable of measuring diffractive events at a higher accuracy\nthan was done at HERA.\nIn the dilute limit (for small dipoles) there is no multiple scattering, and the\nincoherent cross section is given by $A$ times the corresponding one for\nprotons. The\ndeviation of the $t$-slope from the proton measures the transverse size of the \nfluctuating areas in the nucleus. \n\n\nIn the black disc limit \nthe nucleus is smooth not only on average, but event-by-event, \nleading to a strong suppression of the incoherent cross section. Incoherent\ndiffraction gets contributions from the edge of the nucleus, making the\ncross section asymptotically behave as $\\sim A^{1\/3}$ in contrast to\n$\\sim A$ in the dilute limit.\nThe suppression in the\nnormalization relative to the proton is a measure of the approach to the unitarity\nlimit in the dipole cross section. It is a clear signal of how individual nucleons\nhave lost their identity in the sense that they cannot be resolved by the virtual photon.\nIt is precisely this suppression that we are proposing to \nuse to quantitatively access saturation effects in the nuclear wavefunction.\nThe purpose of this paper is to provide a realistic estimate of \nthe nuclear suppression in diffractive\ncross sections in a regime that could be measured \nin future nuclear DIS experiments. \n\n\nNuclear DIS data from fixed target experiments, in particular \nE665~\\cite{Adams:1994bw} and NMC~\\cite{Arneodo:1994qb,*Arneodo:1994id} \nhave already been much discussed in the literature as\ndemonstrations of \\emph{color transparency} (see e.g. \nRefs.~\\cite{Frankfurt:1991nx,Frankfurt:1993it,Brodsky:1994kf,Kopeliovich:2001xj,Frankfurt:2005mc}). The form of nuclear modification to the incoherent\ndiffraction in terms of the dipole cross section \nthat we have rederived is not new (see \ne.g.~\\cite{Kopeliovich:1991pu,Kopeliovich:2001xj}).\nSo far, however, less attention\nhas been paid to inelastic diffraction in future DIS experiments.\nThe production \ncross sections have not been calculated using the same\nCGC inspired cross sections that have been used\nsuccessfully to confront HERA data, as we intend to do here.\nIn this work we concentrate on the $J\/\\Psi$ \nbecause its small size means that the interaction\nof the dipole with the target is calculable in weak coupling even at\nsmall $Q^2$.\n\nThe importance of diffraction in understanding gluon saturation has been discussed\nand our basic setup motivated in Ref.~\\cite{Kowalski:2007rw}.\nNuclear modifications \nto the diffractive structure functions, integrated over the momentum transfer\n$t$, were computed in Ref.~\\cite{Kowalski:2008sa}. Vector meson production \nat future DIS experiments was recently \ndiscussed from a more experimental point of view in\nRef.~\\cite{Caldwell:2009ke}, and coherent production cross sections (integrated\nover $t$) calculated in Ref.~\\cite{Goncalves:2009za}.\nAn interesting discussion on coherent and incoherent diffraction\nand gluon saturation in the nucleus can be found in Ref.~\\cite{Tuchin:2008np}.\nIn this study we want to take a step beyond the discussion \nof inclusive diffraction in Refs.~\\cite{Kowalski:2007rw,Kowalski:2008sa}\n to understand the $t$ dependence in more detail.\n\n \n\\section{Dipole cross sections}\n\\label{sec:dipxs}\n\nThere are many dipole cross section parametrizations available \nin the literature, and\nwe have taken for this study two representative samples. One is the \nIIM~\\cite{Iancu:2003ge} dipole cross section, which is a \nparametrization including the most important features \nof BK~\\cite{Balitsky:1995ub,*Kovchegov:1999yj,*Kovchegov:1999ua}\nevolution. The detailed expression for the\ndipole cross section can be found in Ref.~\\cite{Iancu:2003ge};\nwe use here the values of the parameters from the newer\nfit to HERA data including charm~\\cite{Soyez:2007kg}\nthat was also used to compute diffractive structure functions in\nRef.~\\cite{Marquet:2007nf}. We also want to compare\nour results to a parametrization with an eikonalized DGLAP-evolved gluon distribution.\nFor this purpose we will use an approximation of the IPsat dipole \ncross section~\\cite{Kowalski:2003hm,Kowalski:2006hc}. \n\n\nTo extend the dipole cross section from protons to nuclei\n we will take the independent\nscattering approximation that is usually used in Glauber theory \nand write the $S$-matrix as\n\\begin{equation}\\label{eq:sfact}\nS_A({\\mathbf{r}_T},{\\mathbf{b}_T},x) = \\prod_{i=1}^A S_p({\\mathbf{r}_T},{\\mathbf{b}_T}-{\\mathbf{b}_T}_i,x).\n\\end{equation}\nHere we conventionally parametrize the energy dependence of the scattering\namplitude with $x$, the Bjorken variable of the DIS event\\footnote{\nNote that strictly speaking the relation between $x$ and the \nenergy of the dipole-target scattering depends \non $Q^2$, not only $r$. Using $x$ here is justified\nin a high energy approximation\nwhere the energy of the dipole in the target rest frame\nis approximately the same as that of the virtual photon.}.\nThe variables ${\\mathbf{b}_T}_i$ in Eq.~\\nr{eq:sfact} \nare the nucleon coordinates that we will discuss in \nSec.~\\ref{sec:comp}.\nThis independent scattering assumption is\nnatural in IPsat-like parametrizations or the MV~\\cite{McLerran:1994ni} model, \nwhere, denoting $r = |{\\mathbf{r}_T}|,$ $S({\\mathbf{r}_T}) \\sim e^{-r^2 Q_\\mathrm{s}^2\/4}$ with a saturation scale \n$Q_\\mathrm{s}^2$ proportional to the nuclear thickness $T_A(b)$.\nHigh energy evolution, however, introduces an anomalous dimension that leads,\nin the nuclear case, to what could be called leading twist shadowing.\nWith an anomalous dimension\n$S\\sim e^{-(Q_\\mathrm{s} r)^{2\\gamma}}$ with $\\gamma \\neq 1$, a proportionality\n$Q_\\mathrm{s}^2 \\sim T_A(b)$ is not equivalent to Eq.~\\nr{eq:sfact}. A solution to this \nproblem (see also the more detailed discussion in~\\cite{Kowalski:2008sa}) \nwould require a realistic impact parameter dependent solution to the \nBK equation which, we feel fair to say, is not yet available.\nWe point the reader e.g. to Ref.~\\cite{GolecBiernat:2003ym} for a \ndiscussion of the difficulties. These are related to the long distance \nCoulomb tails that, physically, are regulated at the confinement length\nscale that is not enforced in a first principles weak coupling calculation.\nThe effect of BK evolution is important for the CGC description\nof the forward suppression of particle production\nin dAu-collisions at RHIC (for a review see~\\cite{Jalilian-Marian:2005jf}).\nIn our case the difficulty is greater since we are interested\nnot only in the relatively smooth average gluon density, but\nits variations at smaller length scales of the order of the\nproton radius.\nWe thus leave the modifications of Eq.~\\nr{eq:sfact} due to\nthe effects of evolution to a future study.\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{amplitude.pdf}\n\\caption{\nThe $r$-dependence of the different proton dipole cross sections used,\nat $x=0.0001$ and $b=0$.\nAs discussed in Sec.~\\ref{sec:res}, the ``IPnonsat''-curve is\nEq.~\\nr{eq:BEKWfact} linearized in $r^2F(x,r)$.\n} \\label{fig:sigmap}\n\\end{figure}\n\nThe IIM parametrization assumes, either explicitly or implicitly,\na factorizable ${\\mathbf{b}_T}$ dependence \n\\begin{eqnarray}\\label{eq:factbt}\n{\\frac{\\ud \\sigma^\\textrm{p}_\\textrm{dip}}{\\ud^2 \\bt}}({\\mathbf{b}_T},{\\mathbf{r}_T},x) &=& 2 \\left( 1 - S_p({\\mathbf{r}_T},{\\mathbf{b}_T},x)\\right)\n\\\\ \\nonumber\n&=& 2 \\,T_p({\\mathbf{b}_T}) {\\mathcal{N}}(r,x),\n\\end{eqnarray}\nWe take, following Ref.~\\cite{Marquet:2007nf}, a Gaussian profile\n$T_p({\\mathbf{b}_T}) = \\exp\\left(-b^2\/2 B_p\\right)$ with \n$B_p=5.59\\ \\textrm{GeV}^{-2}$ (see Sec.~\\ref{sec:res} for a discussion of \nthis largish numerical value). \n\nIn the IPsat model the impact parameter dependence is\nincluded in the saturation scale as\n\\begin{equation}\\label{eq:unfactbt}\n{\\frac{\\ud \\sigma^\\textrm{p}_\\textrm{dip}}{\\ud^2 \\bt}}({\\mathbf{b}_T},{\\mathbf{r}_T},x)\n = 2\\,\\left[ 1 - \\exp\\left(- r^2 F(x,r) T_p({\\mathbf{b}_T})\\right) \n\\right].\n\\end{equation}\nHere $T_p({\\mathbf{b}_T}) = \\exp\\left(-b^2\/2 B_p\\right)$ \nis the impact parameter profile function in the proton \nwith $B_p=4.0\\ \\textrm{GeV}^2$ and $F$ is proportional to the \nDGLAP evolved gluon distribution~\\cite{Bartels:2002cj}\n\\begin{equation}\nF(x,r^2) = \n\\frac{1}{2 \\pi B_p}\n\\frac{ \\pi^2 }{2 {N_\\mathrm{c}}} \\alpha_{\\mathrm{s}} \\left(\\mu_0^2 + \\frac{C}{r^2} \\right) \nx g\\left(x,\\mu_0^2 + \\frac{C}{r^2} \\right), \n\\label{eq:BEKW_F}\n\\end{equation}\nwith $C$ chosen as 4 and $\\mu_0^2=1.17\\ \\textrm{GeV}^2$ resulting from the \nfit~\\cite{Kowalski:2006hc}. The proton dipole cross sections used are\nplotted in Fig.~\\ref{fig:sigmap} for $x=0.0001$.\n\nWe would generally prefer the unfactorized $b$-dependence\nof Eq.~\\nr{eq:unfactbt} to the factorized one in Eq.~\\nr{eq:factbt}\nbecause it allows for the correct unitarity\nlimit of the scattering amplitude at all impact parameters \n(see the discussion in Ref.~\\cite{Kowalski:2008sa}). \nHowever, there seems to be no clear difference between the two in\nterms of the quality of the description of HERA data, and for the sake \nof computational simplicity we will in this work limit ourselves to\nthe factorized dependence and approximate the IPsat dipole cross section\nby\n\\begin{equation}\\label{eq:BEKWfact}\n{\\frac{\\ud \\sigma^\\textrm{p}_\\textrm{dip}}{\\ud^2 \\bt}}({\\mathbf{b}_T},{\\mathbf{r}_T},x)\n \\approx 2 T_p({\\mathbf{b}_T}) \\,\\left[ 1 - \\exp\\left(- r^2 F(x,r)\\right)\n\\right]\n\\end{equation}\nusing the same $F(x,r)$ defined in Eq.~\\nr{eq:BEKW_F}. This approximation\nbrings the IPsat parametrization to the form Eq.~\\nr{eq:factbt}\nwith ${\\mathcal{N}}(r,x)=\\left[ 1 - \\exp\\left(- r^2 F(x,r)\\right)\\right]$;\nin fact this is the form used already in Ref.~\\cite{Bartels:2002cj}; we however\nuse the gluon distribution from the IPsat fit~\\cite{Kowalski:2006hc} \nfor convenience. Improving this\ndescription goes hand in hand with giving up the approximation\nof independent scatterings off the nucleons, Eq.~\\nr{eq:sfact},\nand is left for future work. As we shall see in the following, these \napproximations enable us to write the cross section for incoherent\ndiffraction in a form which is much simpler to evaluate numerically \nthan one with a general $b$-dependence.\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{totxs-boosted.pdf}\n\\caption{Comparison of the used dipole cross sections to HERA \ndata~\\cite{Chekanov:2004mw,*Aktas:2005xu} on diffractive vector meson production.\n} \\label{fig:hera}\n\\end{figure}\n\n\n\n\\section{Computing diffractive cross sections}\n\\label{sec:comp}\n\n\n\nThe cross section for quasielastic vector meson \nproduction in nuclear DIS is\n\\begin{equation} \\label{eq:xsec}\n\\frac{\\, \\mathrm{d} \\sigma^{\\gamma^* A \\to V A }}{\\, \\mathrm{d} t} \n= \\frac{R_g^2(1+\\beta^2)}{16\\pi} \\Aavg{|{\\mathcal{A}}({x_\\mathbb{P}},Q^2,{\\boldsymbol{\\Delta}_T})|^2}.\n\\end{equation}\nwith $t=-{\\boldsymbol{\\Delta}_T}^2$. \nThe dipole cross section is evaluated at the \nenergy scale corresponding to the rapidity gap between the \nvector meson and the target ${x_\\mathbb{P}}$.\nTo translate this into the photon-target center of mass energy $W$ \nthat is often used to present experimental results note that \n${x_\\mathbb{P}} = (M_{J\/\\Psi}^2 + Q^2)\/(W^2 + Q^2)$.\nThe factor $1+\\beta^2$ accounts for the\nreal part of the scattering amplitude and the factor $R_g^2$ corrects\nfor the skewedness effect, i.e. that the gluons in the target are probed at \nslightly different $x$~\\cite{Shuvaev:1999ce,*Martin:1999wb}. \nFor these corrections\nwe follow the prescription of Ref.~\\cite{Watt:2007nr}, taking them \nas\n\\begin{eqnarray}\n\\beta &=& \\tan \\frac{\\pi \\lambda}{2}\n\\\\\nR_g &=& \\frac{2^{2 \\lambda+3}}{\\sqrt{\\pi}}\\frac{\\Gamma(\\lambda + 5\/2)}{\\lambda+4} \n\\quad \\textrm{ with}\n\\\\\n\\lambda &=& \\frac{\\partial \\ln {\\mathcal{A}}_{T,L}^{\\gamma^*p\\to J\/\\Psi p}}{\\partial \\ln 1\/{x_\\mathbb{P}}}.\n\\end{eqnarray}\nThese corrections depend, in general, on $t$, which we take into account in our calculation. \nFor the full IPsat model $\\lambda$ changes by about 5\\% between $t=0$ and \n$-t=0.5\\ \\textrm{GeV}^2$. For the factorized impact parameter dependence in \nEqs.~\\nr{eq:factbt} and~\\nr{eq:BEKWfact} $\\lambda$ is \nindependent of $t$.\nWe calculate the correction terms from the energy dependence of the nucleon\nscattering amplitudes and use the same values for the nucleus at the \nsame $Q^2,{x_\\mathbb{P}}$. Since the difference in $\\lambda$ extracted from the nucleus \nand the nucleon cross sections is small (compared to the value of $\\lambda$) and \n$R_g$ and $\\beta$ are in themselves corrections to the cross section, this \napproximation is justified. In addition this approximation has the advantage that \nthese corrections cancel on the nucleus\/nucleon cross section ratio.\nThe real part and skewedness corrections, especially $R_g$ are, however, a significant \nfactor in the absolute normalization of the cross section and are \nnecessary for the agreement with HERA data.\n\nThe imaginary part of the scattering amplitude is the Fourier-transform of the\ndipole cross section from ${\\mathbf{b}_T}$ to ${\\boldsymbol{\\Delta}_T}$ contracted with the \noverlap between the vector meson and virtual photon wave functions:\n\\begin{multline}\\label{eq:ampli}\n{\\mathcal{A}}({x_\\mathbb{P}},Q^2,{\\boldsymbol{\\Delta}_T}) \n= \\int \\, \\mathrm{d}^2 {\\mathbf{r}_T} \\int \\frac{\\, \\mathrm{d} z}{4\\pi} \\int \\, \\mathrm{d}^2 {\\mathbf{b}_T} \n\\\\\n\\times [\\Psi_V^* \\Psi](r,Q^2,z)\ne^{-i {\\mathbf{b}_T} \\cdot {\\boldsymbol{\\Delta}_T}} \n{\\frac{\\ud \\sigma_\\textrm{dip}}{\\ud^2 \\bt}}({\\mathbf{b}_T},{\\mathbf{r}_T},{x_\\mathbb{P}}),\n\\end{multline}\nwhere we have followed the normalization convention of~\\cite{Kowalski:2006hc}.\nFor the virtual photon--vector meson wavefunction overlap we\nuse the ``boosted Gaussian'' parametrization from Ref.~\\cite{Kowalski:2006hc}.\nWe have also tested the ``gaus-LC'' wavefunction also used in \nRef.~\\cite{Kowalski:2006hc}. Although the ``boosted Gaussian'' seems preferred\nby HERA data, also the ``gaus-LC'' parametrization is compatible with \nthe data within the experimental errors. The cross sections for the\nproton differ by factors of the order of 10\\%.\nThe interaction of the gluon target with the dipole can in general depend also on \n${\\boldsymbol{\\Delta}_T}$, which introduces terms that couple ${\\mathbf{r}_T},$ ${\\boldsymbol{\\Delta}_T}$ and $z$ in \nEq.~\\nr{eq:ampli}. For the $J\/\\Psi$ and the range in $t$ considered in this paper\n${\\boldsymbol{\\Delta}_T}$ is sufficiently small compared to the relevant values of $1\/r$ that \nwe can neglect this coupling, which simplifies the structure considerably.\nLighter vector mesons would require a more general treatment.\n\nThe average over the positions of the nucleon in the nucleus is denoted here by\n\\begin{equation} \\label{eq:aavg}\n\\Aavg{\\mathcal{O}(\\{ {\\mathbf{b}_T}_i \\})} \n\\equiv \\int \\prod_{i=1}^{A}\\left[ \\, \\mathrm{d}^2 {\\mathbf{b}_T}_i T_A({\\mathbf{b}_T}_i) \\right] \n\\mathcal{O}(\\{ {\\mathbf{b}_T}_i \\}).\n\\end{equation}\nHere $T_A$ is the Woods-Saxon distribution with nuclear radius \n$R_A = (1.12 A^{1\/3}-0.86 A^{-1\/3})\\ \\textrm{fm} $ and surface thickness \n$d=0.54\\ \\textrm{fm}$.\nThis expectation value is equivalent to the average\nover nucleon configurations in a Monte Carlo Glauber \ncalculation.\nWe are assuming that the positions ${\\mathbf{b}_T}_i$ are independent, i.e. \nneglecting nuclear correlations that would be a subject of\ninterest in their own right (see e.g.~\\cite{Alvioli:2009ab}).\nThe coherent cross section is obtained by averaging the amplitude\nbefore squaring it, $|\\Aavg{{\\mathcal{A}}}|^2$, and the incoherent one is\nthe variance $\\Aavg{|{\\mathcal{A}}|^2} - |\\Aavg{{\\mathcal{A}}}|^2$ that measures the fluctuations\nof the gluon density inside the nucleus. Because\n$\\Aavg{{\\mathcal{A}}}$ is a very smooth function of ${\\mathbf{b}_T}$, its Fourier transform \nvanishes rapidly for $\\Delta \\gtrsim 1\/R_A$. Therefore at large $\\Delta$\nthe quasielastic cross section \\nr{eq:xsec} is almost purely incoherent.\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{coherent_q0.pdf}\n\\caption{The quasielastic and coherent diffractive $J\/\\Psi$ cross sections in gold\nnuclei at $Q^2= 0$ and ${x_\\mathbb{P}} = 0.001$.\nShown are \nthe IPsat and IIM parametrizations. We also show the \nresult for the linearized ``IPnonsat'' version \n(used e.g. in Ref.~\\cite{Caldwell:2009ke}) where the incoherent\ncross section is explicitly $A$ times that of the proton. \nOur approximation \\nr{eq:amplisq} \nis not valid for small $|t|$; the corresponding part of the distribution\nhas been left out.\n} \\label{fig:dsigmavst}\n\\end{figure}\n\n\nThe cross section for quasielastic vector meson production is now expressed\nin terms of the dipole scattering amplitude as\n\\begin{multline} \\label{eq:incxsec}\n\\frac{\\, \\mathrm{d} \\sigma^{\\gamma^* A \\to V A^* }}{\\, \\mathrm{d} t} \n= \n\\frac{R_g^2(1+\\beta^2)}{16\\pi} \n\\int \n\\frac{\\, \\mathrm{d} z}{4 \\pi} \\frac{\\, \\mathrm{d} z'}{4 \\pi}\n\\, \\mathrm{d}^2 {\\mathbf{r}_T} \\, \\mathrm{d}^2 {\\mathbf{r}_T}'\n\\\\ \\times\n\\left[ \\Psi^*_V \\Psi \\right] (r,z,Q)\n\\, \\left[ \\Psi^*_V \\Psi \\right](r',z',Q)\n\\\\ \\times\n\\Aavg{ \\left| \\mathcal{A}_{q\\bar{q}}\\right|^2({x_\\mathbb{P}},r,r',{\\boldsymbol{\\Delta}_T}) } \\, .\n\\end{multline}\nWe now average the square of the dipole scattering amplitude over the \nnucleon coordinates, using the assumptions of\nEqs.~\\nr{eq:sfact} and~\\nr{eq:factbt} and taking the large $A$ limit.\nWe are additionally assuming that $T_A$ is a smooth function on the \ndiscance scale defined by $B_p$.\nAveraging the square of the amplitude gives the total quasielastic \ncontribution, but we only keep the terms \nthat contribute at large $|t| \\gg 1\/R_A^2$, which leaves us \nwith the expression \n\\begin{multline}\\label{eq:amplisq}\n\\left| \\mathcal{A}_{q\\bar{q}}\\right|^2({x_\\mathbb{P}},r,r',{\\boldsymbol{\\Delta}_T}) \n=\n16 \\pi B_p \\int \\, \\mathrm{d}^2 {\\mathbf{b}_T} \\sum_{n=1}^A\n\\frac{1}{n} \\binom{A}{n} \n\\\\ \\times \ne^{-B_p {\\boldsymbol{\\Delta}_T}^2\/n}\ne^{-2 \\pi B_p A T_A(b)\n\\left[ {\\mathcal{N}}(r) + {\\mathcal{N}}(r') \\right] } \n\\\\ \\times\n\\left( \\frac{\\pi B_p {\\mathcal{N}}(r){\\mathcal{N}}(r') T_A(b) }\n {1 - 2 \\pi B_p T_A(b)\\left[ {\\mathcal{N}}(r) + {\\mathcal{N}}(r') \\right] } \\right)^n\n.\n\\end{multline}\nNote that Eqs.~\\nr{eq:sfact} and~\\nr{eq:factbt} have enabled us to\nwrite the leading contributions as proportional to the\n(Gaussian) proton impact parameter profile, which can then be \nFourier-transformed analytically. Giving up either of these approximations\nwould force us to numerically Fourier-transform the ``lumpy'' \n$b$-dependence corresponding to a fixed configuration\nof the nucleon positions. This would make the numerical calculation \nmuch more demanding and is left for future work. \n\nThe terms with $n \\geq 2$ correspond to scattering off a system of several \noverlapping nucleons simultaneously, leading to slower suppresion with $|t|$.\nIn practice we have verified numerically that they do not\ncontribute to our results at the values of $t$ we are interested in \n(the $n=2$ contribution is typically $\\lesssim 2\\%$ of the $n=1$-one,\nonly reaching $5\\%$ at $-t\\gtrsim 0.5 \\ \\textrm{GeV}^2$ ) and \nwill neglect them in the following. This leaves us with the expression\n\\begin{multline}\\label{eq:amplisqn1}\n\\left| \\mathcal{A}_{q\\bar{q}}\\right|^2({x_\\mathbb{P}},r,r',{\\boldsymbol{\\Delta}_T}) \n=\n16 \\pi B_p A \\int \\, \\mathrm{d}^2 {\\mathbf{b}_T} \n\\\\ \\times \ne^{-B_p {\\boldsymbol{\\Delta}_T}^2}\ne^{-2 \\pi B_p A T_A(b)\n\\left[ {\\mathcal{N}}(r) + {\\mathcal{N}}(r') \\right] } \n\\\\ \\times\n\\left( \\frac{\\pi B_p {\\mathcal{N}}(r){\\mathcal{N}}(r') T_A(b) }\n {1 - 2 \\pi B_p T_A(b)\\left[ {\\mathcal{N}}(r) + {\\mathcal{N}}(r') \\right] } \\right).\n\\end{multline}\nEquation \\nr{eq:amplisqn1} has a very clear interpretation. The \nsquared amplitude is proportional to $A$ times the squared amplitude\nfor scattering off a proton, corresponding to the dipole scattering \nindependently off the nucleons in a nucleus. This sum of independent\nscatterings is then multiplied by a nuclear attenuation factor\n\\begin{multline}\n\\frac{ e^{-2 \\pi B_p A T_A(b) \\left[ {\\mathcal{N}}(r) + {\\mathcal{N}}(r') \\right] } } \n {1 - 2 \\pi B_p T_A(b)\\left[ {\\mathcal{N}}(r) + {\\mathcal{N}}(r') \\right] } \n\\approx\n\\\\\n e^{-2 \\pi (A-1) B_p T_A(b) \\left[ {\\mathcal{N}}(r) + {\\mathcal{N}}(r') \\right] } ,\n\\end{multline}\nwhich accounts for the requirement that the dipole must \\emph{not}\nscatter inelastically off the other $A-1$ nucleons in the target (otherwise the\ninteraction would not be diffractive). \nNote that the factor \n$4 \\pi B_p {\\mathcal{N}}(r,{x_\\mathbb{P}})={ \\sigma^\\textrm{p}_\\textrm{dip} }(r,{x_\\mathbb{P}})$ is the proton-dipole cross section for a\ndipole of size $r$. Thus this attenuation corresponds to the\nprobability of a dipole with a cross section which is the average \nof dipoles with $r$ and $r'$ to pass though the nucleus.\nA similar expression \ncan be found e.g. in Ref.~\\cite{Kopeliovich:2001xj}.\n\n\n\nFor comparison, the coherent cross section in our approximation is given by\n\\begin{equation} \\label{eq:coh}\n\\frac{\\, \\mathrm{d} \\sigma^{\\gamma^* A \\to V A }}{\\, \\mathrm{d} t} \n=\\frac{R_g^2(1+\\beta^2)}{16\\pi} \\left| \\Aavg{{\\mathcal{A}}({x_\\mathbb{P}},Q^2,{\\boldsymbol{\\Delta}_T})} \\right|^2,\n\\end{equation}\nwhere in the large $A$ and smooth nucleus limit the amplitude is\n\\begin{multline}\\label{eq:cohampli}\n\\Aavg{{\\mathcal{A}}({x_\\mathbb{P}},Q^2,{\\boldsymbol{\\Delta}_T}) }\n= \\int \\frac{\\, \\mathrm{d} z}{4\\pi} \\, \\mathrm{d}^2 {\\mathbf{r}_T} \\, \\mathrm{d}^2 {\\mathbf{b}_T} e^{-i {\\mathbf{b}_T} \\cdot {\\boldsymbol{\\Delta}_T}} \n\\\\\n \\times [\\Psi_V^*\\Psi](r,Q^2,z)\n\\, 2 \\left[ 1-\\exp\\left\\{ - 2 \\pi B_p A T_A(b) {\\mathcal{N}}(r,{x_\\mathbb{P}}) \\right\\} \\right].\n\\end{multline}\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{plot_Q.pdf}\n\\caption{The ``nuclear transparency'' \nratio of cross sections vs. $Q^2$ for IPsat, IIM \nparametrizations at ${x_\\mathbb{P}}= 10^{-2}$ (the upper three curves, blue)\nand $10^{-4}$ (the lower 3 curves, black).\nFor comparison we also include\nwe also include the result if unitarization effects are included \nat the nucleus but not at the nucleon level in the IPsat-parametrization. \n(See text for discussion).\n}\\label{fig:ratiovsq}\n\\end{figure}\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{plot_Q_gaus-lc.pdf}\n\\caption{The ``nuclear transparency'' \nratio of cross sections vs. $Q^2$ using the ``Gaus-LC'' vector meson \nwavefunctions. The labeling is the same as in Fig.~\\ref{fig:ratiovsq}.\n}\\label{fig:ratiovsq_gauslc}\n\\end{figure}\n\n\n\n\n\\section{Results and discussion}\n\\label{sec:res}\n\nWe first test our dipole cross section parametrizations and vector meson wave\nfunctions by comparing them to HERA results~\\cite{Chekanov:2004mw,*Aktas:2005xu} \non diffractive $J\/\\Psi$ production\nthat is known to be well described by dipole model \nfits~\\cite{Kowalski:2006hc,Marquet:2007qa}. The comparison is quite satisfactory, as\ncan be seen from Fig.~\\ref{fig:hera}. In addition to the factorized \napproximation (Eq.~\\nr{eq:BEKWfact}, ``factorized IPsat'' in the figure) that \nwe are using in the rest of this paper, also shown is the result with the original\nIPsat parametrization (Eq.~\\nr{eq:unfactbt}, denoted ``IPsat'' in the figure). The \nfactorized approximation differs from the original one slightly at small \n$Q^2$, but the difference is not significant for our purpose of establishing a \nreasonable baseline for computing nuclear effects.\n\nWe note here that the diffractive slope parameters\nin the parametrizations are different, $B_p=4.0\\ \\textrm{GeV}^{-2}$ for IPsat and \n$B_p= 5.59\\ \\textrm{GeV}^{-2}$\nfor IIM; since these are correlated with the other parameters in the fits leading to\nthe parameter values used we do not wish to alter them here.\nOur approximation of a factorized $b$-dependence with a constant $B$ \ndoes not allow us to describe the observed weak energy and $Q^2$ dependence of the\ndiffractive slope. \nThe larger $B$ that we use for IIM comes from the $\\sigma_0$ normalization\nin a fit to inclusive $F_2$ data, and also agrees with the observed slopes in \ninclusive diffraction at large $\\beta$ and small \n${x_\\mathbb{P}}$~\\cite{Chekanov:2004hy,*Aktas:2006hx} and exclusive $\\rho$ and $\\phi$\ndata~\\cite{Adloff:1999kg,*Chekanov:2005cqa}. The HERA $J\/\\Psi$-data,\non the other hand, has a smaller slope \n$\\sim 4\\ \\textrm{GeV}^{-2}$~\\cite{Chekanov:2004mw,*Aktas:2005xu}. \nThe $t$-slope in\nthe IPsat parametrization is mostly determined by this $J\/\\Psi$-measurement,\nand an agreement with the larger measured slopes for $\\rho$ and $\\phi$\nis obtained by taking into account the larger size of the wavefunctions\nof these lighter mesons.\n\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{plot_x.pdf}\n\\caption{The ``nuclear transparency'' ratio of cross sections vs. ${x_\\mathbb{P}}$ \nusing the IPsat and IIM parametrizations for $Q^2=0$ and $Q^2=10\\ \\textrm{GeV}^2$.\n}\\label{fig:ratiovsx}\n\\end{figure}\n\n\n\nThe differential cross section $\\, \\mathrm{d} \\sigma^{\\gamma^* A \\to J\/\\Psi A}\/\\, \\mathrm{d} t$\n for $A=197$ (gold) as a function of $t$ is presented in Fig.~\\ref{fig:dsigmavst}. \nWe show the cross \nsection at ${x_\\mathbb{P}} = 0.001$ \nfor photoproduction. As we performed\nthe nuclear wavefunction average leading to Eq.~\\nr{eq:amplisq} in the \napproximation where $|t|$ is large, neglecting the coherent contribution,\nwe cannot extend our incoherent curves to small $|t|$. \nFor comparison we show the corresponding\n``IPnonsat'' result where the IPsat model is linearized in $r^2 F(x,r)$. \nThis curve corresponds to the calculation done in Ref.~\\cite{Caldwell:2009ke},\nincluding both the coherent and incoherent contributions, but without the effect\nof multiple scattering off different nucleons (i.e. the incoherent cross section is\nexplicitly $A$ times the one for a proton). \nAs one can see, the nuclear modification\ndue to multiple scattering (resulting mostly from the\nfactor $e^{-2 \\pi B_p A T_A(b) \\left[ {\\mathcal{N}}(r) + {\\mathcal{N}}(r') \\right] } $ in \nEq.~\\nr{eq:amplisq}) is very large. \n In the full black disk limit \nof ${\\mathcal{N}}(r)=1$ this factor becomes $\\approx e^{-0.5 A^{1\/3}}$ and completely\nsupresses the contribution from the center of a large nucleus, leaving only an area\nof $\\approx 2 \\pi d R_A \\sim A^{1\/3}$ contributing to the integral over ${\\mathbf{b}_T}$.\nThus the cross section in \nthe black disc limit behaves as $\\sim A^{1\/3}$ compared to $\\sim A$ in the dilute\nlimit, so a large suppression is to be expected.\n\n\nWe also show in Fig.~\\ref{fig:dsigmavst} the coherent cross sections \n(using Eq.~\\nr{eq:cohampli}). They are also suppressed compared to the linearized \nversion (IPnonsat), but not by as much as the incoherent one. In the linearized\nversion (as can be seen explicitly in Ref.~\\cite{Caldwell:2009ke} where this case\nwas considered) the ratio between the coherent cross section at $t=0$ and the incoherent\none extrapolated to $t=0$ is $A$. In the IPsat model we get\n$270$ ($250$) and in the IIM model $300$ ($270$) at $Q^2=0$ ($Q^2=10\\ \\textrm{GeV}^2$).\nThis would make it slightly easier to measure the first \ndiffractive dip in the coherent cross section, since the background from the incoherent\nprocess is smaller by a factor of 2 than the linearized \nestimate~\\cite{Caldwell:2009ke}.\n\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{ratio_Q.pdf}\n\\caption{\nThe incoherent cross section integrated over the interval $0.1\\ \\textrm{GeV}^2< -t < 0.3 \\ \\textrm{GeV}^2$\ndivided by the coherent cross section integrated over $0 < -t < 0.1 \\ \\textrm{GeV}^2$\nas a function of $Q^2 + M_{J\/\\Psi}^2$.\n} \\label{fig:incohovercohQ}\n\\end{figure}\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{ratio_x.pdf}\n\\caption{\nThe incoherent cross section integrated over the interval $0.1\\ \\textrm{GeV}^2< -t < 0.3 \\ \\textrm{GeV}^2$\ndivided by the coherent cross section integrated over $0 < -t < 0.1 \\ \\textrm{GeV}^2$\nas a function of ${x_\\mathbb{P}}$.\n} \\label{fig:incohovercohx}\n\\end{figure}\n\n\nTo demonstrate the nuclear dependence further we show in Fig.~\\ref{fig:ratiovsq}\nthe ratio of the cross section in a gold nucleus to that in a nucleon as a \nfunction of $Q^2$. Historically this ratio is known as the ``nuclear transparency''.\nIts smallness at low energy, similarly to coresponding quantities in \nhadron-nucleus scattering, is due to the interactions of the $J\/\\Psi$ as it propagates\nthrough the nucleus. The growth of the transparency towards $1$ for increasing\n$Q^2$~\\cite{Adams:1994bw,Arneodo:1994qb,*Arneodo:1994id} is \na demonstration of \\emph{color transparency} (see e.g. \nRef.~\\cite{Frankfurt:1991nx,Frankfurt:1993it,Brodsky:1994kf,Kopeliovich:2001xj,Frankfurt:2005mc,Miller:2010eh}),\nnamely that at large\n$Q^2$ the interacting components of the photon wavefunction are of smaller\nsize $r$ and interact weakly. In our framework color transparency is\nautomatically present in the fact that the dipole cross section approaches zero\nfor $r\\to0$. In Fig.~\\ref{fig:ratiovsq} we also show the result (labeled\n``IPsat, nonsatp'') of using a nonsaturated dipole-nucleon cross section\nin Eq.~\\nr{eq:amplisq}. This corresponds to including \nunitarity effects at the nucleus level but not \nfor a single nucleon. The observed nuclear suppression in this unphysical \nscenario is significantly larger than for the saturated full IPsat\nparametrization, showing the sensitivity of the nuclear transparency\nto saturation effects already at the proton level.\n\nThe IIM parametrization has a much larger nuclear \nsuppression in incoherent diffraction, with the nuclear transparency\nratio close that of an unsaturated dipole-proton cross section.\nTo put this in perspective recall that both parametrizations\ngave an equally good description of the elastic cross section\nmeasured at HERA (Fig.~\\ref{fig:hera}). Since IIM does this with a\nlarger $B_p$ than IPsat, we can infer that the typical ${\\mathcal{N}}$ is smaller, \nso that the elastic cross section $\\sigma^\\textrm{el}\n\\sim B_p {\\mathcal{N}}^2$ is of the same order. \nThe nuclear transparency ratio, on the other hand, depends on the\ntotal dipole-nucleon cross section \n$\\sim B_p {\\mathcal{N}} \\sim \\sigma^\\textrm{el}\/{\\mathcal{N}}$ which is thus \nlarger for IIM. Thus we have a situation where both parametrizations\nhave been fitted to inclusive $F_2$ data\\footnote{Although we have here \napproximated the original IPsat parametrization by factorizing the $b$-dependence.},\nreproduce well the HERA $J\/\\Psi$ cross section, but differ in their\nresult for incoherent diffraction in nuclei. This stresses the importance of\nperforming a global analysis of both inclusive and diffractive data\nto constrain the dipole cross sections, and demonstrates\nthe utility of eventual incoherent diffractive measurements in such \nan analysis.\n\nFigure \\ref{fig:ratiovsq_gauslc} shows the same $Q^2$-dependence\nusing the ``gaus-LC'' wavefunction. It puts more\nweight on large dipole sizes, leading to a stronger nuclear suppression. \nThe cross section ratio typically decreases by $\\sim 0.04$ from \nthe ``boosted Gaussian'' wavefunction, but the\nrelative structure between the different dipole cross sections stays the same.\nThe difference between the cross sections themselves is larger,\nbut much of the it cancels in the ratio. The existing HERA data \nis not precise enough to fully discriminate between different models for\nthe vector meson wavefunction, a situation which should also improve\nwith planned new DIS experiments.\n\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{cohincoh.pdf}\n\\caption{\nThe ratio of the coherent (at $t=0$) and incoherent (at $t=-0.5\\ \\textrm{GeV}^2$,\nbut in our approximation this does not depend on $t$) \ncross sections to the corresponding ones for a proton; normalized with \n$A^2$ and $A$ respectively. Plotted as a function of $A$,\nfor ${x_\\mathbb{P}} = 0.001$ and $Q^2=10\\ \\textrm{GeV}^2$.\n} \\label{fig:cohincoh}\n\\end{figure}\n\n\n\nThe energy dependence of the nuclear suppression (again for $A=197$)\nis shown in Fig.~\\ref{fig:ratiovsx} for both IPsat and IIM parametrizations \nat $Q^2=0$ and $Q^2=10\\ \\textrm{GeV}^2$. Again \nwe see the larger nuclear suppression in the IIM model than in IPsat.\nThe differences in the energy (i.e. ${x_\\mathbb{P}}$) dependence of\nthe two dipole cross sections are more clearly visible in the \nphotoproduction result. This is natural, since in the IPsat model \nthe energy dependence at the initial scale of the DGLAP evolution\n(probed at smaller $Q^2$) is almost flat, in stark contrast to the\ntypical behavior resulting from BK evolution. At higher $Q^2$\nthe difference in the $x$-dependence is smaller, although \nthere the IPsat-model, driven by the DGLAP evolution, \nturns over to a \\emph{faster}\nenergy dependence. We have not extrapolated our curves to higher \nenergies, since there is no prospect of experimental measurements. One does \nhowever see from Fig.~\\ref{fig:ratiovsx} that the curves\ncontinue to go down when extrapolated to smaller ${x_\\mathbb{P}}$. This is to be \nexpected since, as discussed previously, one has not yet reached the \nblack disk limit. \n\nIn a realistic experimental setup it might be possible to detect \nor veto the nuclear breakup even when the momentum transfer\n$t$ is not measured very accurately. In this case it will be interesting \nto understand how the relative magnitudes of the incoherent and coherent\ncross sections behave as a function of $Q^2$ and ${x_\\mathbb{P}}$. Generally when approaching \nthe black disk limit the coherent cross section increases and the incoherent one\ndecreases. The relative change shows, however, a smaller dependence on $Q^2$ and \n${x_\\mathbb{P}}$ than the nucleus\/nucleon cross section ratio. This is shown in our \nparametrization in Figs.~\\ref{fig:incohovercohQ} and~\\ref{fig:incohovercohx}, where\nwe plot the \nthe incoherent cross section integrated over the interval $0.1\\ \\textrm{GeV}^2< -t < 0.3 \\ \\textrm{GeV}^2$\ndivided by the coherent cross section integrated over $0 < -t < 0.1 \\ \\textrm{GeV}^2$\nas a function of $Q^2 + M_{J\/\\Psi}^2$ and ${x_\\mathbb{P}}$.\nFigure \\ref{fig:cohincoh} further demonstrates the relative similarity of the \nnuclear suppression in the coherent and incoherent cross sections. \nShown is the $A$ dependence of the ratios \n$ (\\, \\mathrm{d}\\sigma^A_\\mathrm{incoh}\/\\, \\mathrm{d} t)\/(A \\, \\mathrm{d} \\sigma^p\/\\, \\mathrm{d} t)$\n(which, in our approximation, is independent of $t$)\nand \n$\\left. (\\, \\mathrm{d}\\sigma^A_\\mathrm{coh}\/\\, \\mathrm{d} t)\/(A^2 \\, \\mathrm{d} \\sigma^p\/\\, \\mathrm{d} t)\\right|_{t=0}$\nfor $Q^2=10\\ \\textrm{GeV}^2$ and ${x_\\mathbb{P}}=0.001$. Note that the coherent and the incoherent \ncross sections are normalized by different powers of $A$ and that\nwidth of the coherent peak at small $t$ also depends on $A$.\n\nFigures \\ref{fig:dsigmavst} and \\ref{fig:ratiovsq} are our main result.\nOur calculation uses as input only well tested parametrizations that have been fit\nto existing HERA data and nuclear geometry. We work strictly in the\nsmall $x$-limit which makes our formalism simple and transparent.\nThis paper provides realistic estimates for the absolute cross sections \nthat could be measured in future nuclear DIS experiments. \nWe have, however, made several simplifying assumptions in our calculation, the most \nimportant being a) the factorized impact parameter dependence Eq.~\\nr{eq:factbt},\nb) the assumption of independent scattering off different nucleons\nEq.~\\nr{eq:sfact} and c) neglecting nucleon-nucleon correlations. Including\nthese effects in a physically correct manner and discussing how they could be \nstudied experimentally is left for future work. As can be seen from \nthe values of the nuclear suppression in Figs.~\\ref{fig:ratiovsq} and~\\ref{fig:ratiovsx},\nthe effects of high densities, gluon saturation and unitarity on the\nincoherent cross section are large.\nThus incoherent diffraction in future nuclear DIS experiments will\nbe a sensitive probe of small-$x$ physics.\n\n\n\n\\section*{Acknowledgements}\nWe thank E. Aschenauer, H. Kowalski and W. Horowitz for discussions and K.~J.~Eskola\nfor a careful reading of the manuscript.\nThe work of T.L. has been supported by the Academy of Finland, project \n126604. T.L. wishes to thank the INT at the \nUniversity of Washington for \nits hospitality during the completion of this work.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzhhop b/data_all_eng_slimpj/shuffled/split2/finalzzhhop new file mode 100644 index 0000000000000000000000000000000000000000..a3d8f1cc1e6b13bed22855f82a272e0c8fa047fb --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzhhop @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nLocalising the source of an unknown or uncertain scalar field has attracted significant attention in recent years. Extremum seeking can then be understood as driving the state of an agent or network of agents to the source, and maintaining a steady state in the neighbourhood of this optimal state in the unknown field. The widespread applications include internal combustion engine calibration~\\cite{killingsworth2009hcci}, locating RF leakage\\cite{al2012position}, optimising energy distribution~\\cite{ye2016distributed}, and maximising the output of bioreactors~\\cite{wang1999optimizing}. The main challenge in general is the approximation of the field, or a valid descent direction, with the additional challenge in the multi-agent case of coordinating the agents to improve the estimation. In this work, we consider discrete time extremum seeking, for the more classical continuous time extremum seeking problem see~\\cite{tan2010extremum} and the references therein.\n\nExtremum seeking with a single agent primarily uses ``dither'' or other motion patterns to estimate a descent direction. In~\\cite{cochran2009nonholonomic, zhang2007extremum}, extremum seeking with a single agent is investigated relying only on the measurements of the scalar field, without usage of the agent's position. Both approaches use a sinusoidal dither signal to estimate the gradient of the unknown field. Using finite difference with previous measurements, tracking and estimation error bounds for the minima of a time-varying scalar field are derived in~\\cite{shames2019online}, along with extensive numerical studies using a single agent. A hybrid controller is defined in~\\cite{mayhew2007robust}, conducting a series of line minimisations to construct the descent direction, with stability and convergence results.\n\nUsing a network of agents allows for a more robust estimate of the gradient, as the measurements are typically assumed to be simultaneous and thus unaffected by a time-varying source. In\\cite{biyik2008gradient} a network is used with a single leader determining the estimated gradient, employing a zero mean dither signal, with the followers only keeping formation. The authors show that with a fast dither and slow formation keeping, the followers only track the gradient descent movement of the leader. Using multiple ``leader'' agents and only inter-agent bearing measurements, the authors in~\\cite{zhao2015bearing,zhao2015translational} stabilise a formation in arbitrary dimension with leaders following reference velocities or trajectories. In addition, using only bearing measurements allows for formation scaling and rotation. In multi-agent approaches, the set of measurements from each agent can be used to compute an estimated gradient, assuming a single sensor aboard each agent\\cite{khong2014multi,ogren2004cooperative,skobeleva2018planar,vandermeulen2017discrete,vweza2015gradient}. All of these publications use some form of the simplex gradient\\cite{regis2015calculus}, as do we in this paper. The controller design derived in~\\cite{khong2014multi} uses a centralised extremum seeking controller, with access to all of the agents' measurements, which provides reference velocities to each of the agents. Convergence guarantees are provided for a variety of formation and extremum seeking methods satisfying their assumptions. A centralised controller is implemented in~\\cite{ogren2004cooperative} to track the estimated gradient using least squares estimation and refined by Kalman filtering. The agents are tasked with formation keeping around a virtual leader, which climbs the gradient of the unknown field. However, the problem formulation only considers finite manoeuvres, and the formation may move extremely slowly. For networks of $3$ agents in $2$ dimensions a distributed control law with exponential convergence guarantees is investigated in\\cite{skobeleva2018planar}. The agents in\\cite{vandermeulen2017discrete} use a dynamic consensus algorithm to coordinate the gradient estimation, combined with a zero mean dither to construct a local gradient estimation. Finally, in a series of papers\\cite{circular2015distributed,circular2013consensus,circular2011collaborative,circular2010source}, a group of unicycle agents performing distributed extremum seeking in circular formations is examined. The agents stabilise their formation and gradient estimate using a consensus algorithm, and performs well even with lossy communication and time-varying communication networks. The algorithm described in~\\cite{circular2015distributed} is implemented in Section~\\ref{sec:simul} to compare to the results derived in this paper.\n\n\nThis paper provides a novel analysis of multiagent extremum seeking focused on a time-varying source without using a centralised coordinator or dither motion. This differs from the majority of the literature, which assumes a static or slowly drifting scalar field. We show that given an appropriate choice of the agents' formation, a simple gradient descent algorithm enables the network of agents to converge to a bounded neighbourhood of the time-varying extremum sets. At each iteration, we only assume that the time varying field is represented by a function which has Lipschitz continuous gradient (bounded second derivative), and satisfies the Polyak-\\L{}ojasiewicz inequality. The Polyak-\\L{}ojasiewicz inequality assumption is also weaker than many which are used to provide the linear convergence of gradient descent algorithms, such as convexity or quadratic growth\\cite{plstuff}. The authors' previous investigation into this problem \\cite{michael2020optimisation} included a more complicated control law than is presented here, with results restricted to $2$ dimensions. In this analysis, we simplify the control law, derive stronger convergence guarantees, and broaden the method to arbitrary dimension.\n\nThe paper is organised as follows. Section~\\ref{sec:probForm} is devoted to basic assumptions on the time-varying field and agent dynamics. Section~\\ref{sec:coopGradDesc} discusses the distributed control law for extremum seeking and formation keeping, along with convergence guarantees. Section~\\ref{sec:gradEstimation} provides an example of cooperative gradient estimation, an improvement and generalisation of the results from~\\cite{michael2020optimisation}. We provide numerical studies and comparison in Section~\\ref{sec:simul}, and conclude in Section~\\ref{sec:conclude}.\n\n\\section{Problem Formulation}\\label{sec:probForm}\n\nConsider a network of $n$ agents where $x_k^{(i)} \\in \\mathbb{R}^d$ denotes the position of the $i$-th agent for $i\\in \\{1,...,n\\}$ at iteration $k$. We use bold variables throughout the paper to describe the stacked vector for all agents, i.e. ${\\bf x}_k$ to denote the vector of all agents' states stacked vertically. Let $\\mathcal{G}=(\\mathcal{V},\\mathcal{E})$ be the underlying graph of the network with the vertex set $\\mathcal{V}=\\{1,...,n\\}$ representing the agents and the edge set $\\mathcal{E}\\subseteq \\mathcal{V}\\times\\mathcal{V}$ representing the communication topology. For each agent $i$, we define a set of neighbours $\\mathcal{N}^{(i)}:= \\{ j \\mid (j,i)\\in\\mathcal{E} \\}$ from which agent $i$ receives information at each iteration step.\n\n\\begin{assum}\\label{ass:networkProp}\nAssume that the agent communication graph $\\mathcal{G}=(\\mathcal{V},\\mathcal{E})$ is connected and time invariant.\n\\end{assum}\nThe agents are modeled as single integrators, with dynamics\n\\begin{align}\nx_{k+1}^{(i)} = x_k^{(i)} + \\alpha p_k^{(i)}. \\label{eq:dyn}\n\\end{align}\nThe constant term $\\alpha$ is determined by the time-varying field and is time invariant and uniform across the network. At each iteration $k$, the time-varying field is represented by the function $f_k:\\mathbb{R}^{d}\\rightarrow\\mathbb{R}$ with the non-empty minimiser set $\\mathcal{X}^*_{f_k} := \\textrm{argmin}_{x\\in\\mathbb{R}^{d}} f_k(x)$. The agents can only measure the function value at their location at each iteration, i.e. the value $f_k(x_k^{(i)})$. For any dimension $m\\in\\mathbb{Z}^+$ we define the distance between a point $x\\in\\mathbb{R}^{m}$ and a set $\\mathcal{S}\\subseteq\\mathbb{R}^{m}$ as\n\\begin{align}\n d(x,\\mathcal{S}) = \\inf_{y\\in\\mathcal{S}} ||y-x||, \\label{eq:pointSetDist}\n\\end{align}\nwhere $||\\cdot||$ is the Euclidean norm. Additionally, for a function $h:\\mathcal{D}\\rightarrow\\mathbb{R}$ we will use the shorthand\n\\begin{align}\n h^* := \\inf_{x\\in\\mathcal{D}} h(x), \\label{eq:funcMinVal}\n\\end{align}\nto represent the minimum value of that function.\n\n\\begin{assum}(Differentiability and Lipschitz Gradient):\\label{ass:Lipschitz}\nFor all $k\\geq0$, the functions $f_k:\\mathbb{R}^d \\rightarrow \\mathbb{R}$ are at least once continuously differentiable. The gradients are $L_{f}-$Lipschitz continuous, i.e. there exists a positive scalar $L_{f}$ such that, for all $k \\geq 0, x\\in\\mathbb{R}^d,\\; y\\in\\mathbb{R}^d$, $$|| \\nabla f_k(x) - \\nabla f_k(y) || \\leq L_{f}||x-y||, $$ or equivalently $$ f_k(y) \\leq f_k(x) + \\nabla f_k(x)^T(y-x) + \\frac{L_{f}}{2}||y-x||^2.$$\n\\end{assum}\n\n\\begin{assum}(Polyak-\\L{}ojasiewicz Condition):\\label{ass:polyak}\nFor all $k\\geq0$, there exists a positive scalar $\\mu_{f}$ such that $$\\frac{1}{2}||\\nabla f_k(x)||^2\\geq \\mu_{f}(f_k(x) - f^*_k),$$ for $f^*_k$ defined in~\\eqref{eq:funcMinVal}.\n\\end{assum}\nThe assumption that a function has an $L-$Lipschitz continuous gradient is equivalent to assuming the second derivative has bounded norm, if it is twice differentiable. The Polyak-\\L{}ojasiewicz condition simply requires that the gradient grows faster than a quadratic as we move away from the optimal function value. The Polyak-\\L{}ojasiewicz condition does not require the minima to be unique, although it does guarantee that every stationary point is a global minimum~\\cite{plstuff}. In addition to Assumptions~\\ref{ass:Lipschitz}-\\ref{ass:polyak} on each $f_k$, we quantify the ``speed'' with which the field may vary in the following assumption.\n\\begin{assum}(Bounded Drift in Time):\\label{ass:drift}\nThere exist positive scalars $\\eta_0$ and $\\eta^*$ such that $|f_{k+1}(x)-f_k(x)|\\leq \\eta_0$ for all $x\\in\\mathbb{R}^d$ and $|f^*_k-f^*_{k+1}|\\leq \\eta^*$.\n\\end{assum}\nThe problem of interest is given below.\n\\begin{prob}\\label{prob:onlyProb}\nFor a network of $n$ agents with dynamics~\\eqref{eq:dyn} and communication topology satisfying Assumption~\\ref{ass:networkProp}, let $\\{f_k\\}$ be a sequence of functions with a corresponding sequence of minimiser sets $\\{\\mathcal{X}^*_{f_k}\\}$ satisfying Assumptions~\\ref{ass:Lipschitz}-\\ref{ass:drift}. Given the measurements $\\mathcal{Y}_k^{(i)}=\\{f_k(x_k^{(j)}) \\mid j\\in\\mathcal{N}^{(i)}\\cup\\{i\\}\\}$, find $\\alpha,p_k^{(i)}$ and a constant $M$ for all agents $i\\in\\mathcal{V}$ and for all $k\\geq0$ such that $\\lim\\limits_{k\\rightarrow\\infty}d(x_k^{(i)},\\mathcal{X}^*_{f_k})\\leq M$.\n\\end{prob}\nPrevious research in extremum seeking of stationary sources have resulted in stronger convergence guarantees, such as semi-global practical asymptotic stability~\\cite{mayhew2007robust,khong2014multi,kvaternik2012analytic}. The authors of~\\cite{zhang2007extremum} are able to show local exponential stabilty of their extremum seeking algorithm for slowly moving source, i.e. the source velocity is significantly less than the dither speed. Further, the stability results are only extended for moving sources with periodic or ``almost-periodic'' trajectories. In this paper we have a more general definition of a ``moving source'' than found in the literature, and show that the control law described in the following sections is able to stabilise the steady state tracking error $d(x_k^{(i)},\\mathcal{X}^*_{f_k})$.\n\n\\section{Cooperative Gradient Descent}\\label{sec:coopGradDesc}\n\nIn this section we discuss our primary result, showing that a network of agents cooperating can reach a bounded neighbourhood of the minimiser set. In this section, for simplicity, we assume each agent uses an $\\varepsilon-$\\emph{gradient oracle} at each iteration to construct a step direction.\n\n\\begin{defn}\\label{def:epsOracle}\n\\emph{($\\varepsilon$-gradient oracle):} Given the function $f_k:\\mathbb{R}^{d}\\rightarrow\\mathbb{R}$ and the state of the agents in the network ${\\bf x}_k\\in\\mathbb{R}^{d}$, the oracle returns $O(f_k,{\\bf x}_k,\\mathcal{N}^{(i)}) = \\nabla f_k(x^{(i)}_k) + \\varepsilon_k$.\n\\end{defn}\n\nIn order to motivate the incorporation of formation control, we first analyse the erroneous gradient descent dynamics with input $p^{(i)}_k = -O(f_k,{\\bf x}_k,\\mathcal{N}^{(i)})$,\n\\begin{align}\n\\begin{split}\nx^{(i)}_{k+1} :=& x^{(i)}_{k} + \\alpha (-O(f_k,{\\bf x}_k,\\mathcal{N}^{(i)})) \\\\\n=& x^{(i)}_{k} - \\alpha (\\nabla f_k(x^{(i)}_k) + \\varepsilon_k)),\n\\end{split}\\label{eq:naiveDyn}\n\\end{align}\nand provide the following lemma on the convergence properties of the system.\n\n\\begin{lem}\\label{lem:noisyGradDesc}\nFor a sequence of functions $\\{f_k\\}$ with minimiser sets $\\{\\mathcal{X}^*_{f_k}\\}$ satisfying Assumptions~\\ref{ass:Lipschitz}-\\ref{ass:drift}, the system with dynamics~\\eqref{eq:naiveDyn} satisfies\n\\begin{align}\n\\begin{split}\n\\frac{1}{2}d(x^{(i)}_k,\\mathcal{X}^*_{f_k})^2 &\\leq \\beta(d(x^{(i)}_0,\\mathcal{X}^*_{f_0})^2,k) \\\\\n&\\hspace{-1cm} + \\frac{\\alpha}{2\\mu_{f}}\\sum_{t=0}^{k}(1 - \\alpha\\mu_{f})^{k-t}||\\varepsilon_t||^2 + \\frac{\\eta_{0}+\\eta^*}{\\alpha\\mu_{f}^2}, \\label{eq:noisyGradNeighbourhood}\n\\end{split}\n\\end{align}\nfor $\\beta\\in\\mathcal{KL}$, $\\alpha\\in(0,\\frac{1}{L_{f}}]$ with $L_{f},\\mu_{f}$ from Assumptions~\\ref{ass:Lipschitz}-\\ref{ass:polyak}, and $d(x^{(i)}_k,\\mathcal{X}^*_{f_k})$ defined in~\\eqref{eq:pointSetDist}.\n\\end{lem}\n\n\\begin{pf}\nSee Appendix~\\ref{app:noisyGradDesc}.\n\\end{pf}\n\n\\begin{rem}\n Lemma~\\ref{lem:noisyGradDesc} seems to imply that if $\\alpha$ is chosen to be $\\frac{1}{\\mu_f}$, the impact of the gradient error from steps before $k$ is zero. To understand why this is so, note that the Lipschitz constant $L_{f}$ and Polyak-\\L{}ojasiewicz constant $\\mu_{f}$ satisfy the following\n \\begin{align}\n \\frac{\\mu_{f}}{2}d(x^{(i)}_k,\\mathcal{X}^*_{f_k})^2 \\leq f_k(x) - f^*_k \\leq \\frac{L_{f}}{2}d(x^{(i)}_k,\\mathcal{X}^*_{f_k})^2, \\label{eq:quadraticSqueeze}\n \\end{align}\n see~\\cite{plstuff} for in depth discussion regarding the Polyak-\\L{}ojasiewicz inequality. Requiring that $\\alpha \\leq \\frac{1}{L_{f}}$ then implies $\\alpha \\leq \\frac{1}{\\mu_{f}}$. Therefore, if $\\alpha \\approx \\frac{1}{\\mu_f}$, then we must have that $\\mu_f\\approx L_{f}$ and $f_k$ is approximately a scaled norm as a consequence of~\\eqref{eq:quadraticSqueeze}. For the scaled norm function, the gradient dynamics~\\eqref{eq:naiveDyn} would take the agent directly to the minimiser, except for the error term from the most recent gradient estimate in~\\eqref{eq:noisyGradNeighbourhood} and the drift error term $\\frac{\\eta_0+\\eta^*}{\\mu_f}$.\n\\end{rem}\n\nWe see from Lemma~\\ref{lem:noisyGradDesc} that the system with dynamics~\\eqref{eq:naiveDyn} converges to a neighbourhood dependent on the magnitude of the gradient error terms $||\\varepsilon_k||^2$ and a constant term due to drift. In using function samples to estimate the gradient, the error in estimation is generally a function of the geometry of the samples taken. By incorporating formation control into the dynamics, we are able to bound the error terms $||\\varepsilon_k||^2$. We show a specific example of this in Section~\\ref{sec:gradEstimation}, but make minimal assumptions in this section on the specifics of how to construct a gradient estimate from sample points.\n\nTo expand the focus onto the entire network's behavior, we define the collective time-varying function $F_k:\\mathbb{R}^{nd}\\rightarrow\\mathbb{R}$ as\n\\begin{align*}\nF_k({\\bf x}_k) := \\sum_{i\\in\\mathcal{V}} f_k(x^{(i)}_k),\n\\end{align*}\nand note that $F_k$ satisfies Assumptions~\\ref{ass:Lipschitz}-\\ref{ass:polyak} with the same constants $L_{f},\\mu_{f}$. The time-varying minimiser set of $F_k({\\bf x}_k)$ is\n\\begin{align*}\n\\mathcal{X}^*_{F_k} = \\overbrace{\\mathcal{X}^*_{f_k}\\times\\mathcal{X}^*_{f_k}...\\times\\mathcal{X}^*_{f_k}}^{n}.\n\\end{align*}\nTo incorporate formation control, we use a formation potential function $\\phi({\\bf x}_k):\\mathbb{R}^{nd}\\rightarrow\\mathbb{R}^+$ which takes the full state vector of all agents and returns a scalar which is minimised when the agents are in formation. Let the minimum be $\\phi^* := \\min\\limits_{{\\bf x}\\in\\mathbb{R}^{nd}}\\phi({\\bf x})$.\n\\begin{defn}\\label{def:formFuncs}\nWe define $\\phi:\\mathbb{R}^{nd} \\rightarrow \\mathbb{R}^+$ to be the \\emph{formation potential function} for the network, with minimisers $\\mathcal{X}^*_{\\phi}$, and assume the following properties. The function $\\phi({\\bf x}_k)$\n\\begin{enumerate}\n \\item is continuously differentiable on $\\mathbb{R}^{nd}$ with gradient which is Lipschitz continuous with constant $L_{\\phi}$;\n \\item satisfies the Polyak-\\L{}ojasiewicz inequality (Assumption~\\ref{ass:polyak}), with constant $\\mu_{\\phi} \\geq \\mu_{f}$;\n \\item has gradient component $\\nabla_{x^{(i)}_k} \\phi({\\bf x}_k)$ which is computable using only the state of agent $i$ and neighbours $j\\in\\mathcal{N}_{i}$;\n \\item satisfies $\\phi({\\bf x}_k) \\geq \\frac{c}{2}\\sum_{i\\in\\mathcal{V}}||\\varepsilon^{(i)}_k||^2$ for $c > \\frac{1}{\\mu_{f}}$.\n\\end{enumerate}\n\\end{defn}\nIn the definition of the formation potential functions, the first two conditions ensure that $\\phi({\\bf x}_k)$ shares the minimal properties that make $f_k$ amenable to analysis. The third property ensures that the local information each agent has is sufficient for computation of the descent direction. Finally, the fourth property formalises the connection between the formation and the gradient estimation error. This final property formalises the relationship between the gradient estimation error and the formation. In Section~\\ref{sec:simul} we provide the example $\\phi({\\bf x}_k) = \\phi^* + L_{f} \\sum_{i\\in\\mathcal{V}} ||x^{(i)}-x^{(j)}-\\hat{x}^{(ij)}||^2$, where the terms $\\hat{x}^{(ij)}$ define the optimal formation and the constant $\\phi^*$ ensures condition $4$ from Definition~\\ref{def:formFuncs} is satisfied when the agents are in perfect formation. The constant offset does not change the dynamics, it simply allows $\\phi({\\bf x}_k)$ to bound the gradient error in the convergence analysis, see the proof of Theorem~\\ref{thm:compositeConvergence}.\n\nWith the formation potential function defined, we define the ``composite'' function $\\hat{f}_k:\\mathbb{R}^{nd} \\rightarrow \\mathbb{R}$ as\n\\begin{align}\n\\hat{f}_k({\\bf x}_k) := F_k({\\bf x}_k) + \\phi({\\bf x}_k),\\label{eq:compositeFunc}\n\\end{align}\nwith corresponding minimisers in the set $\\mathcal{X}^*_{\\hat{f}_k}$, and the new system dynamics\n\\begin{align}\nx^{(i)}_{k+1} := x^{(i)}_k - \\alpha(\\nabla_{x^{(i)}_k} \\hat{f}_k + \\varepsilon_k). \\label{eq:compositeDyn}\n\\end{align}\nEach agent can compute the gradient $\\nabla_{x^{(i)}_k} \\phi({\\bf x}_k)$ with only local information, so the gradient of the composite function, being the sum of $f_k$ and $\\phi$, can be estimated by using the same $\\varepsilon-$gradient oracle for $f_k$. Both $F_k$ and $\\phi$ satisfy Assumption~\\ref{ass:Lipschitz} with constants $L_{f},L_{\\phi}$ respectively, and Assumption~\\ref{ass:polyak} with constants $\\mu_{f},\\mu_{\\phi}$. Therefore, the composite function satisfies both Assumptions~\\ref{ass:Lipschitz}-\\ref{ass:polyak} with constants $L_{\\hat{f}} := L_{f}+L_{\\phi}$ and $\\mu_{\\hat{f}} \\geq \\min(\\mu_{f},\\mu_{\\phi}) = \\mu_{f}$.\n\n\\begin{lem}\\label{lem:minimiserRelation}\nFor the composite function $\\hat{f}_k$, as defined in~\\eqref{eq:compositeFunc}, we have\n\\begin{align*}\n\\hat{f}^*_k := \\min_{{\\bf x}\\in\\mathbb{R}^{nd}}\\hat{f}_k({\\bf x}) \\leq \\phi^* + \\frac{\\min(L_{f},L_{\\phi})}{2} d(\\mathcal{X}^*_{F_k},\\mathcal{X}^*_{\\phi})^2,\n\\end{align*}\nwhere we define the distance between the minimiser sets as\n\\begin{align*}\nd(\\mathcal{X}^*_{F_k},\\mathcal{X}^*_{\\phi}) := \\min \\{ ||x^*_{\\phi} - x^*_{F_k}|| \\mid x^*_{\\phi}\\in\\mathcal{X}^*_{\\phi} \\; , \\;x^*_{F_k}\\in \\mathcal{X}^*_{F_k} \\}.\n\\end{align*}\n\\end{lem}\n\n\\begin{pf}\nSee Appendix~\\ref{app:minimiserRelation}.\n\\end{pf}\nThe primary result is given in Theorem~\\ref{thm:compositeConvergence}.\n\n\\begin{thm}\\label{thm:compositeConvergence}\nFor a sequence of functions $\\{\\hat{f}_k\\}$ as defined in~\\eqref{eq:compositeFunc} with minimisers $\\{\\mathcal{X}^*_{\\hat{f}_k}\\}$, the system with dynamics~\\eqref{eq:naiveDyn} satisfies\n\\begin{align*}\n\\begin{split}\n\\frac{1}{2}d(x^{(i)}_{k+1},\\mathcal{X}^*_{\\hat{f}_{k+1}})^2 &\\leq \\beta(d(x^{(i)}_{0},\\mathcal{X}^*_{\\hat{f}_0})^2,k) \\\\\n&\\hspace{-1cm} + \\frac{\\alpha}{c\\mu}\\sum_{t=0}^{k}(1 - \\alpha\\mu')^{k-t}\\hat{f}^*_t + \\frac{\\eta_{0}+\\eta^*}{\\alpha\\mu\\mu'},\n\\end{split}\n\\end{align*}\nfor $\\beta\\in\\mathcal{KL}$, $\\alpha\\in(0,\\frac{1}{L_{\\hat{f}}}]$, and $\\mu' = \\mu_{f}-\\frac{1}{c}$. Therefore, we have\n\\begin{align}\n\\lim_{k\\rightarrow\\infty} \\frac{1}{2}d(x^{(i)}_{k+1},\\mathcal{X}^*_{\\hat{f}_{k+1}})^2 \\leq \\frac{\\underset{k\\rightarrow\\infty}{\\lim} \\sup \\hat{f}^*_k}{\\mu'} + \\frac{\\eta_{0}+\\eta^*}{\\alpha\\mu\\mu'}. \\label{eq:limBound}\n\\end{align}\n\\end{thm}\n\n\\begin{pf}\nSee Appendix~\\ref{app:compositeConvergence}.\n\\end{pf}\nIn a typical gradient descent scenario, we may expect a convergence bound similar to~\\eqref{eq:limBound}, without the $\\limsup \\hat{f}^*_k$ term. However, the gradient being used is only an estimation, and $\\limsup \\hat{f}^*_k$ represent the limit of the ``quality'' of the gradient estimate. Even on a stalimsuptionary function, i.e. $\\eta_0 = \\eta^* = 0$, we would not expect asymptotic convergence with erroneous gradient information.\n\nIn Theorem~\\ref{thm:compositeConvergence}, we demonstrate that by incorporating a formation potential function, which bounds the gradient estimation error, we are able to converge to a bounded neighbourhood of the time varying minimiser set $\\mathcal{X}^*_{\\hat{f}_{k}}$. Further, the system does not require the delineation of leaders and followers, a separate time-scale for the formation-keeping, or any centralised computing to guide the formation.\n\\section{Gradient Estimation and Error}\\label{sec:gradEstimation}\nIn Section~\\ref{sec:coopGradDesc}, we assume that each agent has access to an estimate local gradient $\\nabla f(x_k^{(i)}) + \\epsilon^{(i)}$. In this section, we provide a method by which agent $i$ can estimate $\\nabla f(x_k^{(i)})$ as well as compute an error bound for the estimate. The error bound and gradient estimation method apply to \\emph{any} function which satisfies Assumption~\\ref{ass:Lipschitz}. This method is a significant improvement of our previous work\\cite{michael2020optimisation}, which generalises to any dimension with any number of neighbours. We make the following assumption on the neighbour set.\n\n\\begin{assum}\\label{ass:fullRankNeighbours}\nFor each agent $i\\in\\mathcal{V}$ with state $x^{(i)}_k\\in\\mathbb{R}^d$, the neighbour set cardinality satisfies $|\\mathcal{N}^{(i)}| \\geq d$. Further, the vectors $\\{x^{(l)}_k - x^{(i)}_k\\}_{l\\in\\mathcal{N}^{(i)}}$ span $\\mathbb{R}^d$.\n\\end{assum}\n\nThe requirement that the agents do not arrange on a low dimensional subspace is the primary motivation for incorporating formation control. We define three variables before proceeding, as they will figure heavily in the analysis,\n\\begin{align}\n\\begin{split}\ns^{(ij)}_k &:= \\frac{f_k(x^{(j)}_k) - f_k(x^{(i)}_k)}{||x^{(j)}_k - x^{(i)}_k||}, \\\\\nv^{(ij)}_{k} &:= \\frac{x^{(j)}_k - x^{(i)}_k}{||x^{(j)}_k - x^{(i)}_k||}, \\\\\na^{(ij)}_k &:= \\frac{L_{f}}{2}||x^{(j)}_k - x^{(i)}_k||.\n\\end{split}\n\\label{eq:usefulConstants}\n\\end{align}\nFor an agent $i$, $s^{(ij)}_k$ is the average slope between themselves and agent $j$, $v^{(ij)}_k$ the unit vector pointing from agent $i$ to $j$, and $a^{(ij)}_k$ is the distance between the agents, scaled by the Lipschitz constant $L_{f}$. We will use ${\\bf s}^{(i)}_k,{\\bf a}^{(i)}_k$ to denote the vertically stacked vectors of $s^{(ij)}_k,a^{(ij)}_k$ for all neighbours $j\\in\\mathcal{N}^{(i)}$.\n\n\\begin{lem}\\label{lem:gradPolyhedron}\nFor a function $f_k$ satisfying Assumption~\\ref{ass:Lipschitz} and an agent $i$ with neighbour set $\\mathcal{N}^{(i)}$ satisfying Assumption~\\ref{ass:fullRankNeighbours}, there exists a \\textbf{bounded} polyhedron\n\\begin{align}\n\\mathcal{P}^{(i)}_k := \\{ x\\in\\mathbb{R}^d \\mid \\begin{bmatrix} A^{(i)}_k \\\\ -A^{(i)}_k \\end{bmatrix} x \\leq b^{(i)}_k \\} \\label{eq:polyhedron}\n\\end{align}\nsuch that $\\nabla f(x^{(i)}_k) \\in \\mathcal{P}^{(i)}_k$, for $A^{(i)}_k\\in\\mathbb{R}^{|\\mathcal{N}^{(i)}|\\times d}$ and $b_k\\in\\mathbb{R}^{2|\\mathcal{N}^{(i)}|\\times d}$.\n\\end{lem}\n\n\\begin{pf}\nSee Appendix~\\ref{app:gradPolyhedron}.\n\\end{pf}\nFrom Lemma~\\ref{lem:gradPolyhedron}, there exists a bounded space $\\mathcal{P}^{(i)}_k$ within which the gradient $\\nabla f(x^{(i)}_k)$ must exist. In~\\cite{michael2020optimisation}, we restricted the error bound analysis to $2$ dimensions with $2$ neighbours. The same method is not computationally feasible in higher dimension, as it requires computation of the largest diagonal in the $d$-parallelotope, which has $2^{d-1}$ diagonals. Instead, we compute a ``rounding'' of the polytope by defining an ellipse\n\\begin{align}\nm^{(i)}_{k} &:= \\sqrt{\\sum_{j\\in\\mathcal{N}^{(i)}} (|s^{(ij)}_k-(g^{(i)}_k)^Tv^{(ij)}_k| + a^{(ij)}_k)^2} \\label{eq:ellipseScaling}\\\\\n\\mathcal{E}^{(i)}_k &:= \\left\\{ x\\in\\mathbb{R}^{d} \\mid \\left\\|\\frac{A^{(i)}_k(x-g^{(i)}_k)}{m^{(i)}_k}\\right\\|^2 \\leq 1 \\right\\}, \\label{eq:ellipseDef}\n\\end{align}\nwith $g^{(i)}_k$ the center of $\\mathcal{E}^{(i)}_k$, $A^{(i)}_k$ the matrix defined in Lemma~\\ref{lem:gradPolyhedron}, and $s^{(ij)}_k,v^{(ij)}_k,a^{(ij)}_k$ defined in~\\eqref{eq:usefulConstants}. We define $g^{(i)}_k$ to be\n\\begin{align}\ng^{(i)}_k := ((A^{(i)}_k)^TA^{(i)}_k)^{-1}(A^{(i)}_k)^T{\\bf s}^{(i)}_k. \\label{eq:centerDef}\n\\end{align}\nNote that the center of the ellipse will serve as the gradient estimate for agent $i$, and is equivalent to the simplex gradient\\cite{regis2015calculus} of agent $i$ and its neighbours. In the following theorem we present the main result for this section, an error bounding process which works in arbitrary dimension for any number of neighbours, given Assumption~\\ref{ass:fullRankNeighbours}.\n\n\\begin{thm}\\label{thm:boundingEllipse}\nFor a function $f_k$ satisfying Assumption~\\ref{ass:Lipschitz} and an agent $i$ with neighbour set $\\mathcal{N}^{(i)}$ satisfying Assumption~\\ref{ass:fullRankNeighbours}, let $\\mathcal{P}^{(i)}_k$ be the polytope defined in Lemma~\\ref{lem:gradPolyhedron}. Then $\\mathcal{P}^{(i)}_k\\subseteq \\mathcal{E}^{(i)}_k$, for $\\mathcal{E}^{(i)}_k$ the ellipse defined in~\\eqref{eq:ellipseDef} with center $g^{(i)}_k$ defined in~\\eqref{eq:centerDef}. Further, if $|\\mathcal{N}^{(i)}| = d$, and we assume $B(r,c) = \\{ x\\in\\mathbb{R}^{d} \\mid ||x-c||_2 \\leq r\\}$ is the smallest bounding ball such that $\\mathcal{P}^{(i)}_k \\subseteq B(r,c)$, then\n\\begin{align}\n\\frac{||{\\bf a}_k^{(i)}||}{\\sigma_{\\textrm{max}}(A^{(i)}_k)} \\leq r \\leq \\frac{||{\\bf a}_k^{(i)}||}{\\sigma_{\\textrm{min}}(A^{(i)}_k)} \\label{eq:radiusBounds}\n\\end{align}\nfor $\\sigma_{\\textrm{max\/min}}$ the largest\/smallest singular values of $A^{(i)}_k$ and ${\\bf a}_k^{(i)}$ the vector of $a_k^{(ij)}$ for all $j\\in\\mathcal{N}^{(i)}$.\n\\end{thm}\n\n\\begin{pf}\nSee Appendix~\\ref{app:boundingEllipse}.\n\\end{pf}\nThe result in~\\eqref{eq:radiusBounds} may be interpreted as ``the radius of the smallest bounding ball lies between the largest and smallest radii of $\\mathcal{E}^{(i)}_k$.'' A simple example of the ellipse~\\eqref{eq:ellipseDef} with $2$ neighbours labelled \\emph{uniform scaling} (due to the uniform scaling of the shape matrix) compared the smallest bounding ball is shown in Figure~\\ref{fig:2neigh}.\n\\begin{figure}[thpb]\n \\centering\n \\includegraphics[scale=0.45]{2neighFont9797.pdf}\n \\caption{Ellipse bounding demonstration of Theorem~\\ref{thm:boundingEllipse}. \\label{fig:2neigh}}\n\\end{figure}\n\nGiven that finding the smallest bounding ball which contains a polytope is an NP hard problem, even for the relatively simple centrally symmetric parallelotopes\\cite{bodlaender1990computational}, this approximation is sufficient for the primary goal of gradient estimation. Further, this approximation method gives the smallest $2$-norm bound on the error in the simplest case, with $d$ neighbours distributed in a lattice around agent $i$, as demonstrated in Corollary~\\ref{cor:boundingBall}.\n\n\\begin{cor}\\label{cor:boundingBall}\nIf agent $i$ has neighbour set with cardinality $|\\mathcal{N}^{(i)}|=d$, and $(v^{(ij)}_k)^Tv^{(il)}_k=0$ for all $j,l\\in\\mathcal{N}^{(i)}$ with $j\\neq l$, then $\\mathcal{E}^{(i)}_k$ as defined in~\\eqref{eq:ellipseDef} is the smallest bounding ball such that $\\mathcal{P}^{(i)}_k\\in\\mathcal{E}^{(i)}_k$.\n\\end{cor}\n\n\\begin{pf}\nIf all neighbours are orthogonal, then $A^{(i)}_k$ as defined in Lemma~\\ref{lem:gradPolyhedron} is an orthogonal matrix, i.e. $(A^{(i)}_k)^TA^{(i)}_k = I$. Therefore, $\\mathcal{E}^{(i)}$ is a ball. Further, from Theorem~\\ref{thm:boundingEllipse}, the smallest bounding ball radius lies between the largest and smallest radii of $\\mathcal{E}^{(i)}_k$, which in this case are the same radius. Therefore, $\\mathcal{E}^{(i)}$ is the smallest bounding ball containing $\\mathcal{P}^{(i)}_k$.\n\\end{pf}\nFor any number of neighbours satisfying Assumption~\\ref{ass:fullRankNeighbours}, Theorem~\\ref{thm:boundingEllipse} guarantees a gradient estimation error bound of the form\n\\begin{align}\n||g^{(i)}_k - \\nabla f_k(x^{(i)}_k)|| \\leq \\frac{m^{(i)}_k}{\\sigma_{\\min}(A_k^{(i)})}, \\label{eq:gradErrorBound}\n\\end{align}\nfor $g^{(i)}_k$ the estimated gradient~\\eqref{eq:centerDef} and $m^{(i)}_k$ as defined in~\\eqref{eq:ellipseScaling}.\n\n\\subsection{Bounding Ellipse for large Neighbour Sets}\n\nThe ellipse definition from~\\eqref{eq:ellipseDef} performs well for smaller sets of neighbours, but tends to be conservative when the neighbour set is larger than $d$. We provide an additional bounding ellipse here, which shares many of the useful properties of the ellipse defined in~\\eqref{eq:ellipseDef}, but tends to be significantly less conservative in larger problems. The potentially large scaling factor in the denominator of ellipse definition~\\eqref{eq:ellipseDef} is distributed to each row, rather than applied uniformly, which mitigates some of the inflation from redundant neighbours. We define a matrix $B^{(i)}_{k}\\in\\mathbb{R}^{|\\mathcal{N}^{(i)}|\\times d}$ with the $j$-th row $B^{(i)}_{k}[j]$ defined\n\\begin{align}\nB^{(i)}_{k}[j] := \\frac{(v^{(ij)}_{k})^T}{\\sqrt{|\\mathcal{N}^{(i)}|}(|s^{(ij)}_k-(g^{(i)}_k)^Tv^{(ij)}_{k}| + a^{(ij)}_k)} \\label{eq:otherEllipseRow}\n\\end{align}\nfor $g^{(i)}_k\\in\\mathbb{R}^{d}$ the center of the ellipse. The second ellipsoidal approximation of $\\mathcal{P}^{(i)}_k$ can then be defined as\n\\begin{align}\n\\bar{\\mathcal{E}}^{(i)}_k := \\left\\{ x\\in\\mathbb{R}^{d} \\mid \\left\\|B^{(i)}_k(x-g^{(i)}_k)\\right\\|^2 \\leq 1 \\right\\}, \\label{eq:otherEllipseDef}\n\\end{align}\nIt can be verified that $\\bar{\\mathcal{E}}^{(i)}_k$ defined in~\\eqref{eq:otherEllipseDef} also contains $\\mathcal{P}^{(i)}_k$. However, the radius of the smallest bounding ball is not guaranteed to lie between the largest and smallest eigenvalues, and thus $\\bar{\\mathcal{E}}^{(i)}_k$ does not satisfy the claims of Corollary~\\ref{cor:boundingBall}. For problems with larger sets of neighbours however, the authors note that $\\bar{\\mathcal{E}}^{(i)}_k$ seems to be a tighter approximation of $\\mathcal{P}^{(i)}_k$, based on a large number of trials generating random neighbour sets and comparing the ellipses. An example comparing the ``uniform scaling ellipse'' from~\\eqref{eq:ellipseDef} to the ``row scaling ellipse'' from~\\eqref{eq:otherEllipseDef} is included in Figure~\\ref{fig:4neigh}.\n\\begin{figure}[thpb]\n \\centering\n \\includegraphics[scale=0.5]{4neighFont8838.pdf}\n \\caption{Comparing the bounds~\\eqref{eq:ellipseDef} and~\\eqref{eq:otherEllipseDef}. \\label{fig:4neigh}}\n\\end{figure}\n\n\\section{Simulations}\\label{sec:simul}\n\nIn this section we provide numerical studies to illustrate the results from the previous sections, as well as comparison to another distributed extremum seeking algorithm. For the time varying scalar field, we use convex quadratic functions $f_k(x) = \\frac{1}{2}(x-c(k))^TQ(x-c(k)) + \\zeta^T(x-c(k)) + p$, for positive semi-definite $Q$. The values used in the following plots are\n\\begin{align*}\nQ = \\begin{bmatrix} 2.66 &-0.36 \\\\ -0.35 &1.74 \\end{bmatrix} \\; , \\; &\\zeta = [-1.28,4.66]^T \\;,\\; p = 6.26,\\\\\nc(k) = 10\\sin(\\frac{\\sqrt{2}k}{100}) &+ 10\\sin(\\frac{\\sqrt{3}k}{100}) +\\frac{k}{100},\n\\end{align*}\nwith $L_f,\\mu_f$ the largest and smallest eigenvalues of $Q$ respectively. For the formation control function, we designate a set of neighbours for each agent $\\mathcal{N}^{(i)}$ along with a corresponding set of ideal displacements $\\hat{x}^{(ij)}$. The formation potential function is then\n\\begin{align}\n\\phi({\\bf x}_k) = \\phi^* + L_{f}\\sum_{i\\in\\mathcal{V}}\\sum_{j\\in\\mathcal{N}^{(i)}} ||x^{(i)} - x^{(j)} - \\hat{x}^{(ij)}||^2_2. \\label{eq:formationPotential}\n\\end{align}\nIn~\\cite{michael2020optimisation} we derive the error bound on the gradient estimation in two dimensions, and show that the estimation error is proportional to the distance between the agents, with proportionality constant $L_{f}$, so the the Lipschitz constant $L_{f}$ and the minimum value $\\phi^*$ in~\\eqref{eq:formationPotential} ensure that $\\phi({\\bf x}_k) $ satisfies the assumptions in Definition~\\ref{def:formFuncs}. The minimum value $\\phi^*$ is chosen as an upper bound on the gradient approximation error when the agents are in perfect formation, derived from the gradient estimation error bounds in Theorem~\\ref{thm:boundingEllipse}.\n\nThe simulated methods include the composite method derived in Section~\\ref{sec:coopGradDesc} using two different formations, as well as the consensus for circular formations from~\\cite{circular2015distributed} for comparison. For the composite method, as described in Section~\\ref{sec:coopGradDesc}, we use the simplex gradient as the local gradient estimation method at each iteration, as presented in Algorithm~\\ref{alg:compDyn}.\n\n\\begin{algorithm}\n\\caption{Distributed Composite Dynamics\\label{alg:compDyn}}\n\\begin{algorithmic}\n\\For{$k=1,2,...$}\n \\For{$i\\in\\{1,2,...,n\\}$}\n \\State $g^{(i)}_k = ((A^{(i)}_k)^TA^{(i)}_k)^{-1}(A^{(i)}_k)^T{\\bf s}^{(i)}_k$ \n \\EndFor\n \\For{$i\\in\\{1,2,...,n\\}$}\n \\State $x^{(i)}_{k+1} = x^{(i)}_{k} - \\frac{1}{L}(g^{(i)}_k+\\nabla_{x^{(i)}_k} \\phi({\\bf x}_k))$\n \\EndFor\n\\EndFor\n\\end{algorithmic}\n\\end{algorithm}\n\nThe circular formation controller is presented in Algorithm~\\ref{alg:circDyn}, and is written exactly as in~\\cite{circular2015distributed} accounting for the notation of this paper. The parameters used within Algorithm~\\ref{alg:circDyn} are the same as used in the original paper~\\cite{circular2015distributed}, in the example provided therein without noise. The radius of the formation $D=3$, the rotation velocity $\\omega=1$, $\\epsilon=0.5$ and $\\alpha=1$. The consensus matrix used is also the same as shown in~\\cite{circular2015distributed}, for $6$ agents we have used\n\\begin{align*}\n P = \\begin{bmatrix}\n 0.5 & 0.25 & 0 & 0 & 0 & 0.25 \\\\\n 0.25 & 0.5 & 0.25 & 0 & 0 & 0 \\\\\n 0 & 0.25 & 0.5 & 0.25 & 0 & 0 \\\\\n 0 & 0 & 0.25 & 0.5 & 0.25 & 0 \\\\\n 0 & 0 & 0 & 0.25 & 0.5 & 0.25 \\\\\n 0.25 & 0 & 0 & 0 & 0.25 & 0.5\n \\end{bmatrix}.\n\\end{align*}\n\\begin{algorithm}\n\\caption{Circular Source Seeking\\label{alg:circDyn}}\n\\begin{algorithmic}\n\\For{$i=1,...,n$}\n \\State $h^{(i)}_0 = \\tilde{g}^{(i)}_0=h^{(i)}_{-1}=c^{(i)}_{0}+f_{0}(x^{(i)}_0)(x^{(i)}_0-c^{(i)}_0)$\n \\State $\\phi^{(i)} = i\\frac{2\\pi}{n}$\\;\n\\EndFor\n\\For{$k=1,2,...$}\n \\For{$i=1,...,n$}\n \\State $g^{(i)}_{k}=c^{(i)}_{k}+\\frac{2}{D^2}f(x^{(i)}_{k})(x^{(i)}_{k}-c^{(i)}_{k})$\n \\State $\\tilde{g}^{(i)}_{k}=(1-{\\bf \\alpha})\\tilde{g}^{(i)}_{k-1}+\\alpha\\tilde{g}^{(i)}_{k}$\n \\State $\\tilde{h}^{(i)}_{k}=h^{(i)}_{k-1}+\\tilde{g}^{(i)}_{k-1}-\\tilde{g}^{(i)}_{k-2}$\n \\EndFor\n \\State ${\\bf h_{k}} = (P\\otimes I_{2})(\\bf \\tilde{h}_{k})$\n \\For{$i=1,...,n$}\n \\State $c^{(i)}_{k} = (1-\\varepsilon)c^{(i)}_{k-1}+\\varepsilon h^{(i)}_{k}$\n \\State $x^{(i)}_{k} = c^{(i)}_{k} + D R(\\phi^{(i)}+\\omega k)$\n \\EndFor\n\\EndFor\n\\end{algorithmic}\n\\end{algorithm}\n\nChoosing six agents forces the use of a regular hexagon for~\\cite{circular2015distributed}. We therefore included the composite method using a regular hexagon formation for comparison. The neighbours are chosen to be the adjacent vertices as in Figure~\\ref{fig:formation2d}.\n\\begin{figure}[t!]\n \\centering\n \\begin{subfigure}[t]{0.2\\textwidth}\n \\begin{tikzpicture}\n \n \\node[draw,minimum size=3cm,regular polygon,regular polygon sides=6] (a) {};\n\n \n \\foreach \\x in {1,2,...,6}\n \\fill (a.corner \\x) circle[radius=2pt];\n\n \\end{tikzpicture}\n \\caption{Hexagonal\\label{fig:formation2d}}\n \\end{subfigure}%\n ~\n \\begin{subfigure}[t]{0.2\\textwidth}\n \\begin{tikzpicture}\n \n \\node[draw,minimum size=3cm,regular polygon,regular polygon sides=4] (a) {};\n \n \\node[draw,minimum size=3cm,regular polygon,regular polygon sides=4,right of= a,xshift=1.12cm] (b) {};\n\n \n \\foreach \\x in {1,2,...,4}\n \\fill (a.corner \\x) circle[radius=2pt];\n\n \n \\foreach \\x in {1,4}\n \\fill (b.corner \\x) circle[radius=2pt];\n\n \\end{tikzpicture}\n \\caption{Rectangular\\label{fig:rectangleForm}}\n \\end{subfigure}\n \\caption{Neighbour topology for six agents in two dimensions.}\n\\end{figure}\n\nWhile the circular motion controller in~\\cite{circular2015distributed} requires this hexagonal arrangement for six agents, the framework proposed in this paper is flexible in the choice of formation by changing the ideal displacements $\\hat{x}^{(ij)}$. To this end we also include a rectangular formation, illustrated in Figure~\\ref{fig:rectangleForm}. As shown in~\\cite{michael2020optimisation}, the gradient estimation error bound is a function of the orthogonality of the neighbours as well as the distance between them, so the rectangular formation will have lower gradient estimation error than the hexagonal formation with the same neighbour distances.\n\nFigures~\\ref{fig:trajComp} shows the resulting trajectories from the composite method. We exclude the trajectories from other methods, as they are visually identical. Instead, we include the comparison of the tracking error $\\frac{1}{2}d(x_{k+1},\\mathcal{X}^*_{\\hat{f}_{k+1}})^2$ in Figure~\\ref{fig:minErrorComp} for each method, including the theoretical bounds from Theorem~\\ref{thm:compositeConvergence}.\n\n\\begin{figure}[thpb]\n \\centering\n \\includegraphics[scale=0.45]{traj.pdf}\n \\caption{Agent Trajectories using the composite method from Section~\\ref{sec:coopGradDesc}. \\label{fig:trajComp}}\n\\end{figure}\n\n\\begin{figure*}[thpb]\n \\centering\n \\includegraphics[scale=0.33]{errorComp.pdf}\n \\caption{Comparison of formation distance from the signal source. \\label{fig:minErrorComp}}\n\\end{figure*}\n\nWe can see from Figure~\\ref{fig:minErrorComp} that the theoretical minimiser error bound derived in Theorem~\\ref{thm:compositeConvergence} holds in simulation. All methods exhibit similar performance, including the periodic increases in tracking error, i.e. the five ``bumps'' in Figure~\\ref{fig:minErrorComp}. These coincide with the source accelerating around the curves of the path. The circular formation has higher tracking error, but the method in~\\cite{circular2015distributed} is not explicitly designed to operate on time-varying scalar fields. The rectangular and hexagonal formations using the composite method track nearly identically, although the rectangular formation converges slightly closer to the optimal value set due to the lower gradient error.\n\nAlthough all methods achieve similar performance in tracking the time varying minimiser, the composite method derived in this paper can work in any formation, with any number of agents, in any dimension, without the need to delineate leaders and followers, and is guaranteed to converge to a bounded neighbourhood of the minimiser. Further, we can see from Figure~\\ref{fig:minErrorComp} that formations with orthogonal neighbours improve gradient information, and lead the better source tracking, as the rectangular formation of agents has a lower tracking error by the end of the simulation.\n\nIn Figure~\\ref{fig:gradError}, we show the error of the estimated gradient, as well as the error bound for each agent derived from the results of Theorem~\\ref{thm:boundingEllipse}, defined in~\\eqref{eq:gradErrorBound}.\n\n\\begin{figure}[thpb]\n \\centering\n \\includegraphics[scale=0.37]{gradError.pdf}\n \\caption{Gradient estimation error (black) and estimation error bound (red) for each agent from Figure~\\ref{fig:trajComp} \\label{fig:gradError}}\n\\end{figure}\n\nAs the results from Section~\\ref{sec:coopGradDesc} generalise to any dimension, we provide an example in three dimensions, as well as an implementation of the extremum seeking algorithm from Section~\\ref{sec:coopGradDesc}, at the provided link.\\footnote{\\url{https:\/\/tinyurl.com\/yc4fzpv2}}\n\n\\section{Conclusion}\\label{sec:conclude}\n\nIn this paper we consider a formation of agents tracking the optimum of a time varying scalar field with no gradient information, in arbitrary dimension. At each iteration, the agents take measurements, communicate with their neighbours to estimate a descent direction, and converge to a neighbourhood of the optimum. We derive distributed control laws which drive the agents to a bounded neighbourhood of the optimiser set, without the delineation of leaders\/followers or the use of communication intensive consensus protocols. The method is flexible to the choice of formation and gradient estimation method, and we provide examples using two formations and gradient estimation using the simplex gradient. By blending formation control with extremum seeking, the agents are able to minimise the gradient estimation error, improving the neighbourhood of convergence. We concluded with numerical studies showing that the proposed method is comparable with other extremum seeking methods, converging to a tighter neighbourhood while being more flexible in the choice of formation. Further research will focus on the relaxing of the assumptions on the formation potential functions, allowing for potential functions with non unique minima which do not satisfy the Polyak-\\L{}ojasiewicz inequality, and incorporating time-varying neighbour sets.\n\n\n\n\\bibliographystyle{plain} \n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec:introduction}\n\n\\subsection{Overview and Motivation}\n\n\n\n\nModern technologies enable Internet resources such as routers, computing servers and cables to be abstracted from the physical layer to a \\emph{virtual} layer, facilitating a quick response to demands for setting up communication networks or processing computing jobs. Virtual servers comprising different sets of physical resources are assigned to arriving customers who use these resources for a period of time and then return them to a pool when they depart. \n\n\n\n\nSuch networks are just particular examples of more general systems where users of different types arrive with a desire to be allocated resources of various kinds, to use these resources and then return them. Users are often indifferent to the precise set of resources that they are allocated, they just require allocation of some resources that will enable them to accomplish the task at hand. In such circumstances a network manager has the task of deciding whether an arriving customer should be admitted into the system and, if so, which set of resources should be assigned to satisfying their requirements.\n\nIn this paper we describe and analyze a very general model for such systems. Specifically, we study a system in which $J$ \\emph{resource pools}, each made up of finite numbers of \\emph{resource units} (RUs), await allocation to incoming requests of $L$ different types. We refer to the number of RUs in a resource pool as its \\emph{capacity}. Each resource\npool is potentially shared and \\emph{competed} for by many requests, but \\emph{reservation} of RUs for still-to-arrive requests is also allowed. When a request has been accommodated by a resource pool, an appropriate number of RUs of this type are occupied by the request until it leaves the system. The released RUs can be reused by other requests. A request is permitted to occupy RUs from more than one resource pool simultaneously. In this context, the number of requests of the same type that are accommodated by a group of resource pools varies according to a stochastic process, where the transition rates are affected by the resource allocation policy employed. Several such processes associated with the same resource pool are coupled by its capacity limitations.\n\n\nBy strategically assigning requests to appropriate combinations of RUs, we aim to maximize the long-run average revenue, defined as the difference between the long-run average reward earned by serving the requests and the long-run average cost incurred by using the resource pools. \nSuch a resource allocation problem can be easily applied to a rich collection of classical models, such as loss networks in telecommunications, resource allocation for logistic systems, and job assignment in parallel computing. \n\n\\cite{kelly1991loss} published a comprehensive analysis of \\emph{loss network} models with and without \\emph{alternative routing}. In the latter case, network traffic can be re-routed onto alternative paths when the original path fails or is full. \nIn \\cite{kelly1991loss}, a list of alternative paths as choices of resource pools is given for each call\/request. The alternative paths are selected in turn after if preceding offered paths are unavailable. In contrast, the manager of a typical resource allocation problem described above is potentially able to change the priorities of paths dynamically. How this should be done is a key focus of this paper.\n\n\\begin{figure}[t]\n\\centering\n\\begin{minipage}{.45\\textwidth}\n\\centering\n\\includegraphics[width=0.4\\linewidth]{loss_network.eps}\n\\caption{A simple loss network.}\\label{fig:loss_network}\n\\end{minipage}\n\\begin{minipage}{.45\\textwidth}\n\\centering\n\\includegraphics[width=0.4\\linewidth]{queue_model.eps}\n\\caption{A simple parallel queueing model.}\\label{fig:queue_model}\n\\end{minipage}\n\\vspace{-0.5cm}\n\\end{figure}\nTo illustrate the kind of problem of interest here, consider the simple loss network model shown in Figure~\\ref{fig:loss_network}. Links $a$, $b$ and $c$ are abstracted as resource pools with capacities equal to 1, 3 and 3, respectively: link $a$ consists of one channel as an RU, and links $b$ and $c$ each have 3 channels. Requests asking for a connection from $A$ to $B$ occupying one channel can be served by either path $\\{a\\}$ or $\\{b,c\\}$, but requests requiring two channels for each connection from $A$ to $B$ are able to be accommodated only by path $\\{b,c\\}$. We refer to the former and the latter as type-I and type-II requests, respectively. An arrival of a type-I request results in one of the paths $\\{a\\}$ and $\\{b,c\\}$ being chosen by the optimizer depending on current traffic loads on the three links, where links $b$ and $c$ might be shared with existing type-II requests. Occupied channels or RUs are released immediately and simultaneously when relevant requests are completed. \n\nResource allocation problems with small values of $L$ and $J$, such as the example above, can be modeled by a Markov Decision Processes (MDP), and solved through dynamic programming. However, in real-world applications, where $L$ and $J$ are large, resulting in high dimensionality of the state and action spaces, such an approach is often intractable.\n\n\n\nIn this paper we use an analysis inspired by techniques applied to Restless Multi-Armed Bandit Problems (RMABPs).\nThe standard RMABP consists of parallel MDPs with binary actions (they can either be ``pulled', that is activated, or not), which are competing for a limited possibility of being selected at each decision epoch. Each of the MDPs, referred to as a \\emph{bandit process}, has its own individual state-dependent reward rates and transition probabilities when it is activated and when it is not. \n\nAttempts to solve the problem are faced with exponential growth in the size of the state space as the number of parallel bandit processes increases. \nThis class of problems was described by \\cite{whittle1988restless}, who proposed a heuristic management policy that was shown to be asymptotically optimal under non-trivial extra conditions by~\\cite{weber1990index};\nthis policy approaches optimality as the number of bandit processes tends to infinity.\nThe policy, subsequently referred to as the \\emph{Whittle index policy}, always prioritizes bandit processes with higher state-dependent \\emph{indices} that intuitively represent marginal rewards earned by processes if they are selected.\nThe Whittle indices can be computed independently for each bandit process - a process that imposes significantly reduced computational complexity. The Whittle index policy is scalable to a RMABP with a large number of bandit processes.\nAlso, the asymptotic optimality property, if it is satisfied, guarantees a bounded performance degradation in a large-scale system and is appropriate for large problems where optimal solutions are intractable.\nThe non-trivial extra conditions required by the asymptotic optimality proof in \\cite{weber1990index} are related to proving the existence of a global attractor of a stochastic process.\n\nRMABPs have been widely used in scheduling problems, such as channel detecting (see \\cite{liu2012learning,wang2019whittle} ), job assignments in data centers (see \\cite{fu2016asymptotic}), web crawling (see \\cite{avrachenkov2016whittle}), target tracking (see \\cite{krishnamurthy2007structured,le2010scheduling}) and job admission control (see \\cite{nino2012admission,nino2019resource}).\nHere we treat the resource allocation problem described above as a set of RMABPs coupled by linear inequalities involving random state and action variables.\n\n\n\\subsection{Main Contributions}\n\n\n\n\nWe propose a modified \\emph{index policy} that takes into account the capacity constraints of the problem. The index policy prioritizes combinations of RUs with the highest indices, each of which is a real number representing the marginal revenue of using its associated RUs. The policy is simple, scalable and appropriate for a large scale resource allocation problem.\n\n\n \nOur analysis of asymptotic optimality of the index policy proceeds through a relaxed version of the problem and study of a global attractor of a stochastic process defined in \\eqref{eqn:z_process} below.\nWe prove that the process \\eqref{eqn:z_process} will almost surely converge to a global attractor in the asymptotic regime regardless of its initial point, and hence the index policy is asymptotically optimal if and only if this global attractor coincides with an optimal solution of the resource allocation problem. \nFollowing ideas similar to those of \\cite{weber1990index}, optimality of the global attractor for the resource allocation problem can be deduced from its optimality for the relaxed problem, which can be analyzed with remarkably reduced computational complexity. \n\nA sufficient condition for the global attractor and optimal solution to coincide is that the offered traffic for the entire system is \\emph{heavy} and the resource pools in our system are \\emph{weakly coupled}.\nWe rigorously define these concepts in Section~\\ref{subsec:sufficient_condition}.\nThese results are enunciated in Theorems~\\ref{theorem:main_second}, \\ref{theorem:main} and Corollary~\\ref{coro:main} in Section~\\ref{sec:asym_opt}.\n\nWhen the above-mentioned sufficient conditions are not satisfied, an asymptotically optimal index policy can still exist. In this case, we propose a method that can derive the parameters required by the asymptotically optimal policy.\nAlthough asymptotic optimality is not guaranteed, \nTheorem~\\ref{theorem:main} provides a verifiable sufficient condition, less stringent than the one mentioned above, to check asymptotic optimality of the index policy with adapted parameters. \nWe numerically demonstrate the effectiveness of this method in Section~\\ref{sec:example}.\n\n\nThe index policy exhibits remarkably reduced computational complexity, compared to conventional optimizers, and its potential asymptotic optimality is appropriate for large-scale systems where computational power is a scarce commodity. Furthermore, simulation studies indicate that an index policy can still be good in the pre-limit regime.\nAs mentioned earlier, our problem can be seen as a set of RMABPs coupled by the capacity constraints. When the capacities of all resource pools tend to infinity, the index policy reduces to the Whittle index policy because the links between RMABPs no longer exist.\n\n\nTo the best of our knowledge, no existing work has proved asymptotic optimality in resource allocation problems, where resource competition and reservation are potentially permitted, nor has there been a previous analysis of such a combination of multiple, different RMABPs, resulting in a much higher dimensionality of the state space.\n\n\nThe remainder of the paper is organized as follows.\nIn Section~\\ref{sec:model}, we describe the resource allocation problem.\nIn Section~\\ref{sec:relaxation}, we apply the Whittle relaxation technique. \nIn Section~\\ref{sec:index_policy}, we propose an algorithm to implement an index policy.\nIn Section~\\ref{sec:asym}, we define the asymptotic regime and we prove the asymptotic optimality of the index policy under some conditions.\nTo demonstrate the effectiveness of the proposed policies, numerical results are provided in Section~\\ref{sec:example}.\nIn Section~\\ref{sec:conclusions}, we present conclusions.\n\n\n\n\n\n\\subsection{Relation to the Literature}\nThe classical Multi-Armed Bandit Problem (MABP) is a optimization problem in which only one bandit process (BP) among $K$ BPs can be activated at any one time, while all the other $K-1$ BPs are \\emph{frozen}: an active BP randomly changes its state, while state transitions will not happen to the frozen BPs.\nIn 1974, Gittins and Jones published the well-known \\emph{index theorem} for the MABP \\cite{gittins1974dynamic}, and in 1979, \\cite{gittins1979bandit} proved the optimality of a simple \\emph{index policy}, subsequently referred to as the \\emph{Gittins index policy}.\nUnder the Gittins index policy, an index value, referred to as the \\emph{Gittins index}, is associated with each state of each BP, and the BP with the largest index value is activated, while all the other BPs are frozen.\nMore details about Gittins indices can be found in \\cite[Chapter 2.12]{gittins2011multiarmed} (and the references therein).\n\nThe optimality of the Gittins index policy for the conventional MABP fails for the general case where the $K-1$ BPs that are not selected can also change their states randomly; such a process is known as a Restless Multi-Armed Bandit Process (RMABP).\nThe RMABP was proposed by \\cite{whittle1988restless}.\nThe RMABP allows $M=1,2,\\ldots,K$ BPs to be active simultaneously.\nIn a similar vein to the Gittins index policy, Whittle assigned a state-dependent index value, referred to as the \\emph{Whittle index}, to each BP and always activated the $M$ BPs with the highest indices. \nThe Whittle indices are calculated from a \\emph{relaxed} version of the original RMABP obtained by randomizing the action variables. \n\\cite{whittle1988restless} defined a property of a RMABP, referred to as \\emph{indexability}, under which the \\emph{Whittle index policy} exists.\nWhittle conjectured in \\cite{whittle1988restless} that the Whittle index policy, if it exists, is \\emph{asymptotically optimal}.\n\\cite{papadimitriou1999complexity} proved that the optimization of RMABPs is PSPACE-hard in general;\nnonetheless, \\cite{weber1990index} were able to establish asymptotic optimality of Whittle index policy under mild conditions. \n\n\n\n\\cite{nino2001restless} proposed a Partial Conservation Law\n(PCL) for the optimality of RMABP; this is an\nextension of the General Conservation Law (GCL) published in\n\\cite{bertsimas1996conservation}. \nLater, \\cite{nino2002dynamic} defined a group of problems that satisfies\nPCL-indexibility and proposed a new index policy that improved the\nWhittle index. \nThe new index policy was proved to be optimal for problems with PCL-indexibility. \nPCL-indexibility implies (and is stronger than) Whittle indexibility.\nA detailed survey about the optimality of bandit problems can be found in \\cite{nino2007dynamic}. \n\n\n\\cite{verloop2016asymptotically} proved the asymptotic optimality of the Whittle index policy in an extended version of an RMABP, where BPs randomly arrive and depart the system. She proposed an index policy that was not restricted to Whittle indexable models and numerically demonstrated its near-optimality. \n\\cite{larranaga2015asymptotically} applied this extended RMABP to a queueing problem assuming convex, non-decreasing functions for both holding costs and measured values of people's impatience.\nMore results on asymptotic optimality of index-like polices can be\nfound in \\cite[Chapter IV]{fu2016thesis}. \n\n\n\n\n\n\nAsymptotically optimal policies for cost-minimization problems in network systems using a fluid approximation technique have been considered in \\cite{bauerle2000asymptotic,bauerle2002optimal,stolyar2004maxweight,\nnazarathy2009near} and \\cite{bertsimas2015robust}.\nThe fluid approximation to the stochastic optimization problem can be much simpler than the original.\nA key problem here is to establish an appropriate fluid problem and translate its optimal solution to a policy amenable to the stochastic problem.\nAsymptotic optimality of the translated stochastic policy can be established if the fluid solution provides an upper\/lower bound of the stochastic problem and the policy coincides with this bound asymptotically.\nThe reader is referred to \\cite{meyn2008control} for a detailed description of fluid approximation across various models. \n\n\n\nAlthough the fluid approximation technique helps with asymptotic analysis in a wide range of (cost-minimization) network problems, existing results cannot be directly applied to our problem, where the arrival and departure rates of request queues are state-dependent and capacity violation over resource pools is strictly forbidden.\nOur system is always stable for any offered traffic because of the strict capacity constraints.\nIn our case, the form of the corresponding fluid model remains unclear for generic policies. \nEven given the optimal solution of a well-established fluid model, the synthesis of an explicit policy in the stochastic model remains a challenge.\n\nWe adopt another approach, following the ideas of \\cite{whittle1988restless} and \\cite{weber1990index}.\nOur asymptotic optimality is derived from an optimal solution of a relaxed version of the stochastic optimization problem. The relaxed problem is still a stochastic optimization problem with a discrete state space. We propose a policy based on intuition captured by the relaxed problem, of which the optimal solution provides a performance upper bound of the original problem.\nThen, we prove, under certain conditions, that this policy coincides with the upper bound asymptotically. \nThe detailed analysis comprises the main content of the paper.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{A Resource Allocation Problem}\\label{sec:model}\n\n\n\n\nWe use $\\mathbb{N}_{+}$ and $\\mathbb{N}_{0}$ to denote the sets of positive and non-negative integers, respectively, and for any $N\\in\\mathbb{N}_{+}$, let $[N]$ represent the set $\\{1,2,\\ldots,N\\}$ with $[0]=\\emptyset$.\nLet $\\mathbb{R}$, $\\mathbb{R}_{+}$ and $\\mathbb{R}_{0}$ be the set of all, positive and non-negative reals, respectively.\n\n\n\n\\subsection{System Model}\\label{subsec:model}\n\nRecall that there are $L$ types of requests and $J$ pools of RUs, all\npotentially different, with resource pool $j\\in[J]$ having capacity $C_j$\nRUs that can be dynamically allocated to and released by the $L$ types\nof requests.\n\n\nEach request comes with an associated list of candidate resource combinations. \nSpecifically, requests from \\emph{request type} $\\ell \\in [L]$ can be accommodated by one of a set $\\mathscr{P}_{\\ell}$ of candidate \\emph{patterns}.\nOne of these candidate patterns will be selected by a policy.\nPatterns are indexed by $i\\in\\mathbb{N}_+$. \nIf a request is accommodated by pattern $i$, $w_{j,i}$\nRUs of pool $j\\in [J]$ are occupied until the request is completed and\ndeparts. \nWe can thus identify pattern $i$ with the \\emph{weight vector} $\\bm{w}_i = (w_{j,i})$ that defines its requirement.\nPreemption or re-allocation of requests\nare not allowed.\nA request is blocked if there is not enough capacity on any\nof its corresponding patterns. We might also want to block a request in\nother circumstances, if accepting it would be detrimental to future\nperformance. In either case, we model the situation where a request\nis blocked by assigning it to the dummy pattern $d(\\ell)$ with the weight vector set to $\\bm{0}$.\n\n\n\n\n\n\n\nIt is possible for different RTs to be satisfied by the same pattern (this occurs, in particular with the dummy pattern). In such cases, we consider there to be multiple copies of each pattern, one for each RT that it can satisfy. \nThis enables us to consider the sets $\\mathscr{P}_\\ell$ \nto be mutually\nexclusive; that is,\n$\\mathscr{P}_{\\ell_1}\\cap \\mathscr{P}_{\\ell_2} = \\emptyset$ for any\n$\\ell_1\\neq \\ell_2$. Given\n$|\\mathscr{P}_{\\ell}|$ patterns for each RT $\\ell$, we have in total\n$I = \\sum_{\\ell\\in[L]}|\\mathscr{P}_{\\ell}|$ patterns \nassociated with weight vectors $\\bm{w}_i\\in\\mathbb{N}_0^{J}$,\n$i\\in[I]$.\nFor any pattern $i$, let $\\ell(i)$ be the unique RT that is satisfied by that pattern.\n\nLet $\\mathcal{W}$ be a $J\\times I$ matrix with entries $w_{j,i}$.\nWe assume that there is no row and exactly $L$ columns in $\\mathcal{W}$ with all zero entries.\nEach of these zero columns corresponds to one of the dummy patterns $d(\\ell)$ where requests of type $\\ell\\in[L]$ are blocked.\n\nRequests of RT $\\ell$ arrive at the system sequentially, following a Poisson process, with rates $ \\lambda_{\\ell}$ \nand the occupation times of the requests accommodated by pattern $i\\in\\mathscr{P}_{\\ell}$ are exponentially distributed with parameter $\\mu_{i}$. \nAlthough there might be situations when it is reasonable to assume that the occupation time depends only on the request type $\\ell$, there might also be cases where the lifetime of a request depends on the resources accommodating it, which is why we allow the occupation time distribution to depend on $i$.\nThe RUs used to accommodate a request are occupied and released at the same time.\nNeither the request nor the system knows the lifespan of a request until it is accomplished and departs the system.\n\n\n\nSince there are similarities between our problem and a parallel\nqueueing model, we present a second example to clarify the similarities and differences. \nConsider two\nresource pools corresponding to two queues as illustrated in\nFigure~\\ref{fig:queue_model}, where both capacities are set to three; that is, $J=2$ and $C_1=C_2=3$. There are two types\nof requests: if a type-one request is accommodated in the system, it\nwill simultaneously occupy one RU of both pools;\nand a type-two request can be accommodated by two RUs of either\npool. In other words, $L=2$, $\\mathscr{P}_{1}=\\{1,2\\}$,\n$\\mathscr{P}_2=\\{3,4,5\\}$, patterns $2$ and $5$ are dummy patterns\nwith $\\bm{w}_2=\\bm{w}_5=\\bm{0}$, $\\bm{w}_1=(1,1)$, $\\bm{w}_3=(2,0)$,\n$\\bm{w}_4=(0,2)$ and $I=5$. \n\n\nIn this case, the number of occupied RUs in both resource pools may decrease or increase\nby one simultaneously, or by two exclusively for an arrival or\ndeparture event.\nThe transition rates are affected by the system\ncontroller: if the capacity constraints are not violated, there are\ntwo choices, resource pool one or two, for accommodating a type-two request. The task of a system manager is to find a policy for deciding which of these choices to take in order to maximize some long-term objective.\nEach choice will result in a parallel queueing model with dependencies\nbetween the sizes of queues, between the policy employed and\nqueue transition rates. As mentioned in Section~\\ref{sec:introduction},\nconventional optimization methods cannot be applied directly when $L$ and $J$ are large.\n\n\n\n\n\n\n\\subsection{A Stochastic Optimization Problem}\\label{subsec:RMABPs:general_case}\n\nWe focus here an explanation of\nthe stochastic mechanism of the resource allocation problem.\n\n\nAn \\emph{instantiation} is \ngenerated in the memory of the system controller\nwhen a request of RT $\\ell\\in[L]$ is accommodated by a pattern $i\\in\\mathscr{P}_{\\ell}$. \nOnce the request departs the system, the associated instantiation will\nbe removed from the controller's memory. As requests are accommodated\nand completed, the number of instantiations associated with each\npattern forms a birth-and-death process, indicating the\nnumber of requests being served by this pattern. As mentioned\nin the second example, the birth-and-death processes for all patterns\n$i\\in[I]$ are coupled by capacity constraints and affected by control\ndecisions.\n\n\nLet $N_i(t)$, $t\\geq 0$, represent the number of instantiations\nfor pattern $i$ at time $t$. The\nprocess $N_i(t)$ has state space $\\mathscr{N}_i $ that is a discrete,\nfinite set of possible values. The finiteness of $\\mathscr{N}_i$ derives from the finite\ncapacities $C_j$. \nIf $N_i (t)$ is known for all $i \\in [I]$, \nthe number of occupied RUs in pool $j \\in [J]$ at time $t$ is given by\n$S_j(t)=\\sum_{i \\in [I]} w_{j,i}N_i(t)$, which must be less than $C_j$.\nThe vector $\\bm{N}(t)=(N_i(t):\\ i\\in[I])$ is the state variable of the entire system taking values in\n$\\mathscr{N}\\coloneqq\\prod_{i\\in[I]}\\mathscr{N}_i$,\nwhere $\\prod$ represents Cartesian product.\nSince the state variables are further subject to capacity constraints to be discussed in Section~\\ref{subsubsec:capacity_constraints}, $\\mathscr{N}$ is larger than necessary. With slightly abused notation, we still refer to $\\mathscr{N}$ as the state space of the system. \n\n\\subsubsection{Action Constraints}\\label{subsubsec:action_constraints}\nWe associate an action variable $a_i(\\bm{n})\\in\\{0,1\\}$ with\nprocess $i\\in[I]$ when the system is\nin state $\\bm{n}\\in\\mathscr{N}$, and $\\bm{a}(\\bm{n})=(a_i(\\bm{n}):\\ i\\in[I])$. The action variable $a_i(\\bm{n})$ tells us what to do with a potential new request of type $\\ell(i)$. If $a_i(\\bm{n}) =1$, then such a pattern will be allocated to pattern $i$.\nThe \\emph{action constraint},\n\\begin{equation}\\label{eqn:constraint:action}\n\\sum\\limits_{i\\in\\mathscr{P}_{\\ell}} a_i(\\bm{n}) = 1,~\n\\forall \\ell\\in[L],~\n\\forall \\bm{n}\\in\\mathscr{N},\n\\end{equation}\nensures that exactly one pattern (which may be the dummy pattern $d(\\ell)$) is selected for each RT $\\ell$ and current state $\\bm{n}$.\n\n\nAt any time $t$, we say that the arrival process for pattern $i$ is \\emph{active} or \\emph{passive} according to whether $a_i(\\bm{N}(t))$ is $1$ or $0$ respectively.\nThe birth rate of process $i\\in\\mathscr{P}_{\\ell}$,\n$\\ell\\in[L]$, is $\\lambda_{\\ell}$ if $a_i(\\bm{N}(t))=1$;\nand zero otherwise. The death rate of process $i$ is\n$\\mu_{i} N_{i}(t)$.\nThe time proportion that $a_{d(\\ell)}(\\bm{N}(t))=1$ is the \\emph{blocking probability} for requests of type $\\ell$.\n \n \n\n\\subsubsection{Capacity Constraints}\\label{subsubsec:capacity_constraints}\n\nTo ensure feasibility of an allocation of a request of type $\\ell(i)$ to pattern $i$ when the state is $\\bm{n}$, we need \n\\begin{equation}\\label{eqn:constraint:resourcesi}\n\\mathcal{W}\\left(\\bm{n}+ \\bm{e}_i\\right) \\leq \\bm{C},~\n\\end{equation}\nwhere $\\bm{e}_i$ is a vector with a one in the $i$th position and zeros everywhere else and $\\bm{C}\\in\\mathbb{N}_{+}^{J}$ is a vector with entries $C_j$. In view of the action constraint (\\ref{eqn:constraint:action}), a neat way to collect together the constraints (\\ref{eqn:constraint:resourcesi}) for all $i \\in \\mathscr{P}_{\\ell}$ is to write them in the form\n\\begin{equation}\\label{eqn:resources1}\n\\mathcal{W}\\left(\\bm{n}+\\mathcal{E}_{\\ell}\\bm{a}(\\bm{n})\\right) \\leq \\bm{C},~\\forall \\bm{n}\\in\\mathscr{N},\n\\end{equation}\nwhere $\\mathcal{E}_{\\ell}$ is a diagonal matrix of size $I$ with entries $e_{\\ell,i,i}=1$ if $i\\in\\mathscr{P}_{\\ell}$ and $e_{\\ell,i,i}=0$ if $i\\in[I]\\backslash \\mathscr{P}_{\\ell}$.\n\n\n\nFor two different request types $\\ell_1$ and $\\ell_2$, a constraint of the form \n\\begin{equation}\\label{eqn:resources12}\n\\mathcal{W}\\left(\\bm{n}+\\mathcal{E}_{\\ell_1}\\bm{a}(\\bm{n})+\\mathcal{E}_{\\ell_2}\\bm{a}(\\bm{n})\\right) \\leq \\bm{C},~\\forall \\bm{n}\\in\\mathscr{N},\n\\end{equation} \ncaptures the idea that the action vector $\\bm{a}(\\bm{n})$ must be such that the allocation decisions for $\\ell_1$ and \n$\\ell_2$ ensure enough capacity to implement both of them when both requests arrive simultaneously while the state is\n$\\bm{n}$. Another way to think about this is that, if a request of type $\\ell_1$ is allocated to a non-dummy pattern $i_1$ when the state is $\\bm{n}$, the decision for a request of type $\\ell_2$ when the state is $\\bm{n}$ must satisfy constraint \\eqref{eqn:resources1} when the state is $\\bm{n}+\\bm{e}_{i_1}$. In particular, if there is not enough capacity to accommodate a request of type $\\ell_2$ when the state is $\\bm{n}+\\bm{e}_{i_1}$, then a request of type $\\ell_2$ must be allocated to the dummy pattern $d(\\ell_2)$, when the state is $\\bm{n}$.\nThis can be viewed as giving priority to \\emph{reserving} resources for a type $\\ell_1$ request over a type $\\ell_2$ request\nwhen the state is $\\bm{n}$. \nAs we shall see below, the decision to do this will be made in order to optimize a long-term reward function.\n\n\n\nObserving that $\\sum_{\\ell \\in [L]} \\mathcal{E}_{\\ell} = I$, we see that the constraint \n\\begin{equation}\\label{eqn:constraint:resources}\n\\mathcal{W}\\left(\\bm{n}+ \\bm{a}(\\bm{n})\\right) = \\mathcal{W}\\Bigl(\\bm{n}+ \\bigl(\\sum_{\\ell \\in [L]} \\mathcal{E}_{\\ell}\\bigr) \\bm{a}(\\bm{n})\\Bigr) \\leq \\bm{C},~\n\\forall \\bm{n}\\in\\mathscr{N},\n\\end{equation}\ncan be thought of as an extended version of \\eqref{eqn:resources12}.\nIn \\eqref{eqn:constraint:resources}, requests of all types are taken into account when the state is $\\bm{n}$ and allocation decisions for some types are made in order to reserve resources for other types that turn out to be more profitable in the long run. In particular, resources are reserved for those request types $\\ell$ which are allocated to non-dummy patterns $i$ at the expense of those types that are allocated to less profitable patterns or the corresponding dummy patterns.\nIn this paper, all the results presented are based on capacity constraint~\\eqref{eqn:constraint:resources}.\n\n\nFrom \\eqref{eqn:constraint:resources}, there is an upper bound, $\\min_{j\\in[J]}\\lceil C_j\/w_{j,i} \\rceil$, on the number of instantiations of pattern $i$, and this serves as a bounding state at which no further instantiation of this pattern can be added; that is, $\\mathscr{N}_i=\\{0,1,\\ldots,\\min_{j\\in[J]}\\lceil C_j\/w_{j,i}\\rceil\\}$ and $|\\mathscr{N}_i|=\\min_{j\\in[J]}\\lceil C_j\/w_{j,i}\\rceil +1 <+\\infty$.\nIn this context, Equation~\\eqref{eqn:constraint:resources} implies the condition\n\\begin{equation}\\label{eqn:constraint:resources:zero}\na_i(\\bm{n}) = 0, ~\\text{if}~i\\notin \\{d(\\ell):\\ \\ell\\in[L]\\} \\text{ and } n_i = |\\mathscr{N}_i|-1.\n\\end{equation}\n\n\n\n\\subsubsection{Objective}\nA \\emph{policy} $\\phi$ is defined as a mapping $\\mathscr{N}\\rightarrow \\mathscr{A}$ where\n$\\mathscr{A}\\coloneqq\\prod_{\\ell\\in[L]}\\{0,1\\}^{|\\mathscr{P}_{\\ell}|}$,\ndetermined by the action variables $\\bm{a}(\\bm{n})$ defined above.\nWhen we are discussing a system operating with a given policy $\\phi$, we rewrite the action and state variables as $\\bm{a}^{\\phi}(\\cdot)$ and $\\bm{N}^{\\phi}(t)$, respectively.\n\nBy serving a request of type $\\ell\\in[L]$ and occupying an RU of pool\n$j$ for one unit of time, we gain expected reward $r_{\\ell}$ and pay\nexpected cost $\\varepsilon_j$. \nThe expected reward for a whole service is gained at the moment the service is completed. \nIt corresponds to the situation where a request allocated to pattern $i$ earns reward at rate $r_{\\ell(i)} \\mu_i$ for as long as it is in the system (so that the expected revenue per customer is $(r_{\\ell(i)} \\mu_i) . (1\/\\mu_i) = r_{\\ell(i)}$).\nThe value of $\\epsilon_j$ is the cost per unit time of using a unit of capacity from resource pool $j$ in which case the expected cost of accommodating the request in pool $j$ as part of pattern $i$ is $\\epsilon_j\/\\mu_i$.\nWe seek a policy that\nmaximizes the \\emph{revenue}: the difference between expected reward\nand cost, by efficiently utilizing the limited amount of\nresources. \n\nThe objective is to maximize the long-run average rate of earning revenue, which exists because, for any policy $\\phi$, the process can be modeled by a finite-state Markov chain.\nLet $\\bm{r}=(r_{\\ell}:\\ \\ell\\in[L])$ and $\\bm{\\varepsilon}=(\\varepsilon_{j}:\\ j\\in[J])$. \nFor all $\\ell\\in[L]$ and $i\\in[I]$, define a $L\\times I$ matrix $\\mathcal{U}$ with entries\n$u_{\\ell,i}\\coloneqq \\mu_{i}\\mathds{1}_{i\\in\\mathscr{P}_{\\ell}}$.\nBy the Strong Law of Large Numbers for Continuous Time Markov Chains, see for example \\cite{serfozo2009basics} Theorem 45 in Chapter 4, noting the subsequent discussion of the case where rewards are earned at jump times, the long-run average rate of earning revenue when the policy is $\\phi$ is given by\n\\begin{equation}\nR^{\\phi} \\coloneqq \\mathbb{E}_{\\pi^{\\phi}} \\Bigl[\\bm{r}\\mathcal{U} - \\bm{\\varepsilon}\\mathcal{W}\\Bigr] =\\sum\\limits_{i\\in[I]} \\sum_{n_i\\in\\mathscr{N}_i} \\pi_i^{\\phi}(n_i) \\Bigl(r_{\\ell(i)} \\mu_i - \\sum_{j\\in\\mathscr{J}} w_{j,i} \\varepsilon_j\\Bigr)n_i,\n\\end{equation}\nwhere $\\pi_i^{\\phi}(n_i)$ is the stationary probability that the state of process $i$ is $n_i$ when the policy is $\\phi$. Then we wish to find the policy $\\phi$ that maximizes $R^\\phi$, that is we wish to find\n\\begin{equation}\\label{eqn:objective}\nR \\coloneqq \\max\\limits_{\\phi}R^{\\phi}.\n\\end{equation}\nDefine\n$\\Phi$ to be the set of all policies\nwith the constraints in~\\eqref{eqn:constraint:action} and \\eqref{eqn:constraint:resources} satisfied.\nEach policy in $\\Phi$ is then a \\emph{feasible policy} for our resource allocation problem.\n\n\n\\section{Whittle Relaxation}\\label{sec:relaxation}\nOur resource\nallocation problem with objective function defined by \\eqref{eqn:objective} and constraints given by \\eqref{eqn:constraint:action} and \\eqref{eqn:constraint:resources} can be modeled as a set of RMABPs coupled by\ncapacity constraints.\nWe leave the\nspecification of the RMABPs to Appendix~\\ref{app:RMABPs}.\n\n\n\nIn this section, we provide a theoretical analysis of the resource allocation problem,\nfollowing the idea of Whittle relaxation \\cite{whittle1988restless}.\nIn the vein of a RMABP, \nwe randomize the action variable $\\bm{a}^{\\phi}(\\bm{n})$ so that its elements take values from $\\{0,1\\}$ with probabilities determined by the policy $\\phi$ and relax constraint~\\eqref{eqn:constraint:action} to require that\n\\begin{equation}\\label{eqn:constraint:relax:action}\n\\lim\\limits_{t\\rightarrow +\\infty}\\mathbb{E}\\biggl[\\sum\\limits_{i\\in\\mathscr{P}_{\\ell}} a^{\\phi}_i(\\bm{N}^{\\phi}(t)) \\biggr]= 1,~\n\\forall \\ell\\in[L].\n\\end{equation}\nFollowing similar ideas, we relax \\eqref{eqn:constraint:resources} into two equations:\n\\begin{equation}\\label{eqn:constraint:relax:resources}\n\\lim\\limits_{t\\rightarrow +\\infty}\\mathbb{E}\\biggl[\\mathcal{W}\\Bigl(\\bm{N}^{\\phi}(t)+ \\bm{a}^{\\phi}(\\bm{N}^{\\phi}(t))\\Bigr)\\biggr] \\leq \\bm{C},\n\\end{equation}\nand \n\\begin{equation}\\label{eqn:constraint:dummy}\n\\lim\\limits_{t\\rightarrow+\\infty} \\mathbb{E}\\Bigl[a^{\\phi}_i(\\bm{N}^{\\phi}(t))\\ \\mathds{1}_{N^{\\phi}_i(t)=|\\mathscr{N}_{i}|-1}\\Bigr] = 0,~\\forall i\\in[I]\\backslash\\{d(\\ell):\\ \\ell\\in[L]\\}.\n\\end{equation}\n{\\bf Remark} \nEquation~\\eqref{eqn:constraint:relax:resources} \nis derived by taking expectations for both sides of Equation~\\eqref{eqn:constraint:resources},\nand \\eqref{eqn:constraint:dummy} is a consequence of \\eqref{eqn:constraint:resources:zero}, \nso constraints described by~\\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy} are relaxed versions of the constraints described by~\\eqref{eqn:constraint:resources}.\nThe justification for Equation~\\eqref{eqn:constraint:dummy} will be discussed in Section~\\ref{subsec:asym_regime}, in conjunction with the physical meanings of all variables, when we increase the scale of the entire system.\nWe refer to the problem with objective~\\eqref{eqn:objective}, constraints~\\eqref{eqn:constraint:relax:action}, \\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy} and randomized control variables $\\bm{a}^{\\phi}(\\bm{n})$, for all $\\bm{n}\\in\\mathscr{N}$, as the \\emph{relaxed problem}.\n\n\nA value $a$ in $(0,1)$ can be interpreted as a randomisation between taking $a_i^{\\phi}(\\bm{n}) = 0$ and $a_i^{\\phi}(\\bm{n}) = 1$. Specifically we take $a_i^\\phi(n) = 1$ with probability $a$.\nWe represent the set of policies that correspond to assigning such values $a\\in(0,1)$ as $\\tilde{\\Phi}$.\nFor $n_i\\in \\mathscr{N}_i$, $\\phi\\in\\tilde{\\Phi}$, $i\\in[I]$, define\n\\begin{itemize}\n\\item $\\alpha^{\\phi}_i(n_i) \\coloneqq \\lim\\limits_{t\\rightarrow+\\infty} \\mathbb{E}\\left[a^{\\phi}_i(\\bm{N}^{\\phi}(t))\\ |\\ N^{\\phi}_i(t)=n_i\\right]$, \nwhich is the expectation with respect to the stationary distribution when policy $\\phi$ is used,\nand the vector $\\bm{\\alpha}^{\\phi}_i \\coloneqq (\\alpha^{\\phi}_i(n_i) :\\ n_i\\in\\mathscr{N}_i)$;\n\\item the stationary probability that $N^{\\phi}_i(t)=n_i$ under policy $\\phi$ to be $\\pi_{i,n_{i}}^{\\phi}$, and the vector $\\bm{\\pi}^{\\phi}_i \\coloneqq (\\pi_{i,n_i}^{\\phi}:\\ n_i\\in\\mathscr{N}_i)$.\n\\end{itemize}\nLet $\\bm{\\Pi}^{\\phi}_n \\coloneqq \\left( \\bm{\\pi}^{\\phi}_{i}\\cdot (\\mathscr{N}_i):\\ i\\in[I]\\right)^{T}$ and $\\bm{\\Pi}^{\\phi}_a \\coloneqq \\left(\\bm{\\pi}^{\\phi}_{i}\\cdot \\bm{\\alpha}^{\\phi}_i:\\ i\\in[I]\\right)^{T}$, where \n$(\\mathscr{N}_i)$ represents the vector $(0,1,\\ldots,|\\mathscr{N}_i|-1)$.\nThe Lagrangian function for the optimization problem with objective function (\\ref{eqn:objective}) and constraints \\eqref{eqn:constraint:relax:action}, \\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy} is\n\\begin{multline}\\label{eqn:dual_func}\ng(\\pmb{\\gamma},\\bm{\\nu},\\bm{\\eta})\\coloneqq\n\\max\\limits_{\\phi\\in\\tilde{\\Phi}}\n(\\bm{r}\\mathcal{U}-\\bm{\\varepsilon}\\mathcal{W})\\bm{\\Pi}^{\\phi}_n-\\sum\\limits_{\\ell=1}^{L}\\nu_{\\ell}\\Bigl(\\sum\\limits_{i\\in\\mathscr{P}_{\\ell}}\\bm{\\pi}^{\\phi}_i\\cdot \\bm{\\alpha}^{\\phi}_i -1 \\Bigr)\\\\\n- \\pmb{\\gamma}\\cdot \\Bigl(\\mathcal{W}(\\bm{\\Pi}^{\\phi}_n+\\bm{\\Pi}^{\\phi}_a)-\\bm{C}\\Bigr)\n-\\sum\\limits_{i\\in[I]\\backslash\\{d(\\ell):\\ \\ell\\in[L]\\}}\\eta_i \\pi^{\\phi}_{i,|\\mathscr{N}_i|-1} \\alpha^{\\phi}_{i}(|\\mathscr{N}_i|-1),\n\\end{multline}\nwhere\n$\\bm{\\nu}\\in\\mathbb{R}^{L}$, $\\pmb{\\gamma}\\in\\mathbb{R}_{0}^{J}$ and $\\bm{\\eta}\\in\\mathbb{R}^{I-L}$ are Lagrange multiplier vectors for constraints~\\eqref{eqn:constraint:relax:action}, \\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy}, respectively. \nIn~\\eqref{eqn:dual_func}, the constraints no longer apply to variables $\\bm{\\alpha}^{\\phi}_i$ ($i\\in[I]$) but appear in the maximization as cost items weighted by their Lagrange multipliers.\nFor $i\\in[I]\\backslash\\{d(\\ell):\\ \\ell\\in[L]\\}$,\ndefine functions \n\\begin{multline}\\label{eqn:dual_func_i}\n\\Lambda_{i}(\\phi,\\pmb{\\gamma},\\nu_{\\ell(i)},\\eta_i)\n\\coloneqq (r_{\\ell(i)}\\mu_i-\\bm{\\varepsilon}\\cdot\\bm{w}_i)\\bm{\\pi}^{\\phi}_i\\cdot (\\mathscr{N}_i)-\\nu_{\\ell(i)}\\bm{\\pi}^{\\phi}_i\\cdot \\bm{\\alpha}^{\\phi}_i \n- \\pmb{\\gamma}\\cdot \\bigl(\\bm{w}_i(\\bm{\\pi}^{\\phi}_i\\cdot (\\mathscr{N}_i)+\\bm{\\pi}^{\\phi}_i\\cdot \\bm{\\alpha}^{\\phi}_i)\\bigr)\\\\\n-\\eta_i \\pi^{\\phi}_{i,|\\mathscr{N}_i|-1}\\alpha^{\\phi}_{i}(|\\mathscr{N}_i|-1),\n\\end{multline}\nwhere we recall that $\\bm{w}_i$ is the weight vector of pattern $i$ given by the $i$th column vector of $\\mathcal{W}$; similarly, for $\\ell\\in[L]$, $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$ and $\\eta\\in\\mathbb{R}$, define\n$\\Lambda_{d(\\ell)}(\\phi,\\pmb{\\gamma},\\nu_{\\ell},\\eta) \\coloneqq -\\nu_{\\ell}\\alpha^{\\phi}_{d(\\ell)}(n)$, where $n$ is the only state in $\\mathscr{N}_{d(\\ell)}$.\nFrom Equation \\eqref{eqn:dual_func}, for $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$, $\\bm{\\nu}\\in\\mathbb{R}^L$ and $\\bm{\\eta}\\in\\mathbb{R}^{I-L}$,\n\\begin{equation}\\label{eqn:dual_func2}\ng(\\pmb{\\gamma},\\bm{\\nu},\\bm{\\eta})=\\max\\limits_{\\phi\\in\\tilde{\\Phi}}\\sum\\limits_{i\\in[I]}\\Lambda_{i}(\\phi,\\pmb{\\gamma},\\nu_{\\ell(i)},\\eta_i)\n+\\sum\\limits_{\\ell\\in[L]}\\nu_{\\ell}\n+ \\pmb{\\gamma}\\cdot \\bm{C}.\n\\end{equation}\nwhere $\\eta_{d(\\ell)}$ $(\\ell\\in[L])$ are unconstrained real numbers that are used for notational convenience. \n\n\n\n\n\nIn the maximization problem on the right hand side of~\\eqref{eqn:dual_func2}, there is no constraint that restricts the value of one $\\Lambda_i(\\phi,\\pmb{\\gamma},\\nu_{\\ell(i)},\\eta_i)$ once the others are known. \nAs a result, we can maximize the sum in \\eqref{eqn:dual_func2} by maximizing each of the summands independently. \nWe can thus write \\eqref{eqn:dual_func2} as\n\\begin{equation}\ng(\\pmb{\\gamma},\\bm{\\nu},\\bm{\\eta})=\\sum\\limits_{i\\in[I]}\\max\\limits_{\\phi\\in\\tilde{\\Phi}}\\Lambda_{i}(\\phi,\\pmb{\\gamma},\\nu_{\\ell(i)},\\eta_i)\n+\\sum\\limits_{\\ell\\in[L]}\\nu_{\\ell}\n+ \\pmb{\\gamma}\\cdot \\bm{C},\n\\end{equation}\nbut with the maximum over $\\phi\\in\\tilde{\\Phi}$. \nObserve now that maximizing $\\Lambda_i$ over $\\phi$ is equivalent to choosing $\\bm{\\alpha}^{\\phi}_i(n_i)$ from $[0,1]^{|\\mathscr{N}_i|}$, by interpreting $\\alpha^{\\phi}_{i,n}\\in[0,1]$ as the probability that process $i$ is activated under policy $\\phi$ when it is in state $n$. Thus,\n\\begin{equation}\\label{eqn:dual_problem}\ng(\\pmb{\\gamma},\\bm{\\nu},\\bm{\\eta})\n=\\sum\\limits_{i\\in[I]}\\max\\limits_{\\bm{\\alpha}^{\\phi}_i\\in[0,1]^{|\\mathscr{N}_i|}}\\Lambda_{i}(\\phi,\\pmb{\\gamma},\\nu_{\\ell},\\eta_i)\n+\\sum\\limits_{\\ell\\in[L]}\\nu_{\\ell} + \\pmb{\\gamma}\\cdot \\bm{C}.\n\\end{equation}\n\n\n\nBy slightly abusing notation, we refer to the policy $\\phi$ determined by an action vector $\\bm{\\alpha}^{\\phi}_i$ as the policy for pattern $i$,\nand define $\\Phi_{i}$ as the set of all policies for pattern $i$.\n\n\n\n\n\n\n\\begin{definition}\\label{def:sub-problem}\nThe maximization of $\\Lambda_{i}(\\phi,\\pmb{\\gamma},\\nu_{\\ell},\\eta_i)$ over $\\bm{\\alpha}^{\\phi}_{i}\\in[0,1]^{|\\mathscr{N}_{i}|}$ is the \\emph{sub-problem} for pattern $i\\in[I]$. \n\\end{definition}\n\nFor given $\\pmb{\\gamma}$, $\\bm{\\nu}$ and $\\bm{\\eta}$, the sub-problem for any pattern is an MDP, so that it can be numerically solved by dynamic programming. By solving the sub-problems for all patterns $i\\in[I]$, we obtain $g(\\pmb{\\gamma},\\bm{\\nu},\\bm{\\eta})$.\nFor any $\\pmb{\\gamma}$, $\\bm{\\nu}$ and $\\bm{\\eta}$, the Lagrangian function $g(\\pmb{\\gamma},\\bm{\\nu},\\bm{\\eta})$ is a performance upper bound for the primal problem described in \\eqref{eqn:objective}, \\eqref{eqn:constraint:relax:action}, \\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy}, which is a relaxed version of the original resource allocation problem. \nThus there will be a non-negative gap between this upper bound and the maximized performance of the original problem.\n\n\\subsection{Analytical Solutions}\\label{sec:indexability}\n\\begin{proposition}\\label{prop:a_opt}\nFor given $\\bm{\\nu}$ and $\\pmb{\\gamma}$, there exists $\\bm{E}\\in\\mathbb{R}^{I-L}$ such that, for any $\\bm{\\eta}>\\bm{E}$, a policy of the sub-problem for pattern $i$, referred to as $\\bar{\\varphi}\\in\\Phi_i$, determined by action vector $\\bm{\\alpha}^{\\bar{\\varphi}}_i\\in[0,1]^{|\\mathscr{N}_i|}$ is optimal for this sub-problem, if, for $n\\in\\mathscr{N}_i$,\n\\begin{numcases}{\\alpha^{\\bar{\\varphi}}_{i}(n)}\n=1 &\\text{if }$0<\\lambda_{\\ell}(r_{\\ell}-\\frac{1}{\\mu_{i}}\\sum\\limits_{j\\in \\mathscr{J}_i}\\varepsilon_{j}w_{j,i})\n- (1+\\frac{\\lambda_{\\ell}}{\\mu_{i}})\\sum\\limits_{j\\in \\mathscr{J}_i}w_{j,i}\\gamma_{j}-\\nu_{\\ell} \\text{ and } n< |\\mathscr{N}_i|-1$,\\label{eqn:a_opt:a}\\\\\n\\in [0,1]\n&\\text{if }$0=\\lambda_{\\ell}(r_{\\ell}-\\frac{1}{\\mu_{i}}\\sum\\limits_{j\\in \\mathscr{J}_i}\\varepsilon_{j}w_{j,i})\n- (1+\\frac{\\lambda_{\\ell}}{\\mu_{i}})\\sum\\limits_{j\\in \\mathscr{J}_i}w_{j,i}\\gamma_{j}-\\nu_{\\ell} \\text{ and } n< |\\mathscr{N}_i|-1$,\\label{eqn:a_opt:b}\\\\\n=0&\\text{otherwise},\\label{eqn:a_opt:c}\n\\end{numcases}\nwhere $\\ell=\\ell(i)$.\n\\end{proposition}\nThe proof will be given in Appendix~\\ref{app:prop:a_opt} in the e-companion to this paper.\nIn the maximization of $\\Lambda_i(\\phi,\\pmb{\\gamma},\\nu_{\\ell(i)},\\eta_i)$), the only term of $\\Lambda_i$ dependent on $\\bm{\\eta}$ is $-\\eta_i\\pi^{\\phi}_{i,|\\mathscr{N}_i|-1}\\alpha^{\\phi}_i(|\\mathscr{N}_i|-1)$.\nThe choice of a sufficiently large $\\eta_i$ guarantees that $\\alpha^{\\phi}_i(|\\mathscr{N}_i|-1)$ is $0$ for an optimal policy of the sub-problem, so that constraints~\\eqref{eqn:constraint:dummy} of the relaxed problem are satisfied. \nFor convenience, in what follows we fix $\\bm{\\eta}$ to be one of these large values \nso that $\\alpha^{\\phi}_i(|\\mathscr{N}_i|-1)$ is also fixed to be $0$ for any optimal policy $\\phi$ of the sub-problem for pattern $i$.\nBy slightly abusing notation, in all subsequent equations and discussions, we directly require $\\alpha^{\\phi}_i(|\\mathscr{N}_i|-1)=0$ ($i\\in[I]\\backslash\\{d(\\ell):\\ell\\in[L]\\}$) unless specified otherwise.\n\n\n{\\bf Remark}\nRecall that the action variables $\\bm{\\alpha}^{\\phi}_i$ for any pattern $i\\in[I]$ and policy $\\phi\\in\\Phi_i$ are potentially state-dependent. However, the right hand sides of equations~\\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c} are independent of the state variable $n$ which appears on their left hand side, provided that this is less than $|\\mathscr{N}_i|-1$. This state-independence phenomenon is a consequence of the linearity of the reward and cost rates in the state variable, $N^{\\phi}_i(t)$, for pattern $i\\in[I]\\backslash\\{d(\\ell):\\ \\ell\\in[L]\\}$: from our definition in Section~\\ref{sec:model}, the reward and cost rates of process $i$ in state $N^{\\phi}_i(t)$ are $r_{\\ell(i)}\\mu_{i}N^{\\phi}_i(t)$ and $\\sum_{j\\in\\mathscr{J}_i}\\varepsilon_j w_{j,i}N^{\\phi}_i(t)$, respectively.\nA detailed analysis is provided in the proof of Proposition~\\ref{prop:a_opt}.\n\n\n\n\n\n\nUsing an argument similar to that in \\cite{whittle1988restless}, we can derive from \\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c} an abstracted \\emph{priority} for each \\emph{pattern-state pair} (PS pair) $(i,n)$ with $n\\in\\mathscr{N}_i\\backslash\\{|\\mathscr{N}_i|-1\\}$ and $i\\in[I]$; unlike in \\cite{whittle1988restless}, here, this priority is $(\\pmb{\\gamma},\\bm{\\nu})$-dependent. \nThe priority of a PS pair $(i,n)$ with $n\\in\\mathscr{N}_i\\backslash\\{|\\mathscr{N}_i|-1\\}$\nis determined by the \\emph{index}\n\\begin{equation}\\label{eqn:index_value}\n\\Xi_{i}(\\pmb{\\gamma},\\bm{\\nu})\\coloneqq\\lambda_{\\ell(i)}\\Bigl(r_{\\ell(i)}-\\frac{1}{\\mu_{i}}\\sum\\limits_{j\\in \\mathscr{J}_i}\\varepsilon_{j}w_{j,i}\\Bigr)\n- \\Bigl(1+\\frac{\\lambda_{\\ell(i)}}{\\mu_{i}}\\Bigr)\\sum\\limits_{j\\in \\mathscr{J}_i}w_{j,i}\\gamma_{j}-\\nu_{\\ell(i)},\n\\end{equation}\nand \\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c} can be characterized as comparing $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$ with $0$. \nWhen there is strict inequality in the comparison (that is, the cases described in \\eqref{eqn:a_opt:a} and \\eqref{eqn:a_opt:c}), the value of $\\alpha^\\phi_i(n)$ is specified, since PS pairs $(i,n)$ for all $n\\in\\mathscr{N}_i\\backslash\\{|\\mathscr{N}_i|-1\\}$ correspond to the same $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$ value. \nHowever, there is still freedom to decide different values of $\\alpha^\\phi_i(n)$,\nwhen $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})=0$ (the case described in \\eqref{eqn:a_opt:b}).\nA detailed discussion about priorities of PS pairs corresponding to the same $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$ will be provided in Section~\\ref{subsec:decomposable}.\nBy solving the sub-problem of dummy pattern $d(\\ell)$ $(\\ell\\in[L])$ which involves only one state $n\\in\\mathscr{N}_{d(\\ell)}$, we obtain an optimal policy $\\varphi$ determined by\n\\vspace{-0.1cm}\n\\begin{equation}\\label{eqn:a_opt:dummy}\n\\alpha^{\\varphi}_{d(\\ell)}(n) \\left\\{\\begin{array}{ll}\n=1, &\\text{if } 0 < -\\nu_{\\ell}, \\vspace{-0.1cm}\\\\\n\\in[0,1], &\\text{if }0 = -\\nu_{\\ell},\\vspace{-0.1cm}\\\\\n=0, &\\text{otherwise}. \n\\end{array}\\right.\n\\end{equation}\n\\vspace{-0.1cm}\nThe priority of the state of a dummy pattern is then $\\Xi_{d(\\ell)}(\\pmb{\\gamma},\\bm{\\nu}) \\equiv -\\bm{\\nu}$ for any $\\pmb{\\gamma}$. \n\nIn addition, from Equation~\\eqref{eqn:a_opt:c} in Proposition~\\ref{prop:a_opt}, for any given $\\bm{\\nu}\\in\\mathbb{R}^I$ and $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$, there exists $\\bm{\\eta}\\in\\mathbb{R}^{I-L}$ such that it is optimal to make states $|\\mathscr{N}_i|-1$ passive (that is, $\\alpha^{\\bar{\\varphi}}_i (|\\mathscr{N}_i| - 1) = 0$) for all $i\\in[I]\\backslash\\{d(\\ell):\\ \\ell\\in[L]\\}$. \nAmong all PS pairs $(n,i)$ ($n\\in\\mathscr{N}_i$, $i\\in[I]$), we assign, without loss of generality, the least priority to those PS pairs $(i,|\\mathscr{N}_i|-1)$ for which $i\\in[I]\\backslash\\{d(\\ell):\\ \\ell\\in[L]\\}$. \n\n\nThe policy $\\bar{\\varphi}$ determined by \\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c} and \\eqref{eqn:a_opt:dummy} is optimal for the relaxed problem described by \\eqref{eqn:objective}, \\eqref{eqn:constraint:relax:action}, \\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy}, if the given multipliers $\\bm{\\nu}$ and $\\pmb{\\gamma}$ that appear in \\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c} and \\eqref{eqn:a_opt:dummy} satisfy the \\emph{complementary slackness conditions} of this relaxed problem, defined by \\vspace{-0.3cm}\n\\begin{condition}{Complementary Slackness}\\label{cond:complementary_slackness}\n\\vspace{-0.3cm}\n\\begin{equation}\n\\label{eqn:relaxaction:slack}\n\\nu_{\\ell}\\Bigl(\\sum\\limits_{i\\in\\mathscr{P}_l} \\bm{\\pi}^{\\phi}_i\\cdot\\bm{\\alpha}^{\\phi}_i -1\\Bigr)=0,~\n\\forall l\\in[L],\\vspace{-0.3cm}\n\\end{equation}\nand \\vspace{-0.3cm}\n\\begin{equation}\n\\label{eqn:relaxconstraint:slack}\n\\gamma_{j} \\Bigl(\\bm{\\omega}_j\\cdot \\left(\\bm{\\Pi}^{\\phi}_n+\\bm{\\Pi}^{\\phi}_a\\right)-C_j\\Bigr) = 0,~\\forall j\\in[J], \\vspace{-0.3cm}\n\\end{equation}\nwhere $\\bm{\\omega}_j=(w_{j,i}:\\ i\\in[I])$ is the $j$th row of matrix $\\mathcal{W}$.\\vspace{-0.3cm}\n\\end{condition}\nIn this context, if resource pool $j\\in[J]$ is very popular so that the capacity constraint corresponding to the $j$th row in \\eqref{eqn:constraint:relax:resources} achieves equality, then $\\gamma_j$ is allowed to be positive, leading to a lower value of $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$ than for $\\gamma_j=0$.\nOn the other hand, if resource pool $j\\in[J]$ cannot be fully occupied and the $j$th capacity constraint in \\eqref{eqn:constraint:relax:resources} is satisfied with a strict inequality, then the \ncomplementary slackness condition described in \\eqref{eqn:relaxconstraint:slack} forces $\\gamma_j$ to be zero.\nFollowing this mechanism, when resource pool $j\\in[J]$ is overloaded and its priority is reduced by increasing $\\gamma_j$, the offered traffic to this resource pool will be reduced in line with its priority.\n\n\nIf\nthere exist multipliers $\\bm{\\nu}$, $\\pmb{\\gamma}$ and a policy $\\bar{\\varphi}$ determined by \\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c}, such that the complementary slackness conditions~\\eqref{eqn:relaxaction:slack} and \\eqref{eqn:relaxconstraint:slack} are satisfied by taking $\\phi=\\bar{\\varphi}$, then, by the strong duality theorem, this policy $\\bar{\\varphi}$ is optimal for the relaxed problem;\nthis observation, together with Theorem~\\ref{theorem:main_second} in Section~\\ref{sec:asym_opt}, leads to the existence of an asymptotically optimal policy feasible for the original problem, derived with priorities of patterns induced by the descending order of $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$. More details about the analysis in the asymptotic regime will be provided in Section~\\ref{sec:asym}.\nHere we focus on the non-asymptotic regime, and specifically on the choice and computation of $\\pmb{\\gamma}$ and $\\bm{\\nu}$. \n\n\n\\subsection{Decomposable Capacity Constraints}\\label{subsec:decomposable}\n\n\n\n\n\nIn the general case, it is not clear whether the \nthe complementary slackness conditions~\\eqref{eqn:relaxaction:slack} and \\eqref{eqn:relaxconstraint:slack}\ncan be satisfied and, \neven if they are, what the values of $\\pmb{\\gamma}$ and the corresponding $\\bm{\\nu}$ are.\nMore important is the question of how the multipliers help with proposing the asymptotically optimal policy applicable to the original problem.\n\n\nIn Sections~\\ref{subsec:decomposable} and \\ref{subsec:sufficient_condition}, we concentrate on the complementary slackness conditions and the existence of an optimal policy (for the relaxed problem) satisfying \\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c}.\nRecall that \\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c} intuitively suggest priorities of patterns induced by $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$. Later in Section~\\ref{sec:index_policy}, a policy feasible for the original problem will be proposed based on given priorities of patterns, and its asymptotic optimality will be discussed in Section~\\ref{sec:asym_opt}.\n\n\\subsubsection{Priorities of PS Pairs}\\label{subsubsec:priorities_of_pairs}\n\nAs described in Section~\\ref{sec:indexability}, \nthe priorities of PS pairs are determined by the descending order of $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$, with higher priorities given by higher values of $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$. \nIt may happen that, because of different tie-breaking rules, the same $\\pmb{\\gamma}$ and $\\bm{\\nu}$ lead to different priorities. For given $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$ and $\\bm{\\nu}\\in\\mathbb{R}^L$, let $\\mathscr{O}(\\pmb{\\gamma},\\bm{\\nu})$ represent the set of all rankings of PS pairs according to the descending order of $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$ ($i\\in[I]$). \nAlso, for notational convenience, let $\\mathscr{O}$ represent the set of all PS pair rankings.\n\n\nTo emphasize the priorities of these PS pairs, according to a given ranking $o\\in\\mathscr{O}$, we label all these pairs by their order $\\iota^{o}\\in[N]$ with $N\\coloneqq \\sum_{i\\in[I]} |\\mathscr{N}_i|$ and $(i_{\\iota^o},n_{\\iota^o})$ giving the pattern and the state of the $\\iota^{o}$th PS pair.\nWe will omit the superscript $o$ and use $\\iota$ for notational simplicity unless it is necessary to specify the underlying ranking.\nThere exists one and only one $\\ell\\in[L]$ satisfying $i_{\\iota} \\in \\mathscr{P}_{\\ell}$ for any PS pair labeled by $\\iota$. Such an $\\ell$ is denoted by $\\ell_{\\iota}$.\n\n\n\\IncMargin{1em}\n\\begin{algorithm}\n\\small \n\\linespread{0.4}\\selectfont\n\n\\SetKwFunction{FPriorityPolicy}{PriorityPolicy}\n\\SetKwProg{Fn}{Function}{:}{\\KwRet}\n\\SetKwInOut{Input}{Input}\\SetKwInOut{Output}{Output}\n\\SetAlgoLined\n\\DontPrintSemicolon\n\\Input{a vector of non-negative reals $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$ and a ranking of PS pairs $o\\in\\mathscr{O}$.}\n\\Output{a policy $\\bar{\\varphi}(o)\\in\\tilde{\\Phi}$ determined by action variables $\\bm{\\alpha}^{\\bar{\\varphi}(o)}_i\\in[0,1]^{|\\mathscr{N}_i|}$ for all $i\\in[I]$ and a vector of reals $\\bm{\\nu}(o,\\pmb{\\gamma})$.}\n\n\n\n\\Fn{\\FPriorityPolicy{$o,\\pmb{\\gamma}$}}{\n\n\n $\\bm{\\alpha}^{\\bar{\\varphi}}_i\\gets \\bm{0}$ for all $i\\in[I]$ \\tcc*{Variables $\\bm{\\alpha}^{\\bar{\\varphi}}_i$ determine a policy $\\bar{\\varphi}$}\n Initializing the list of candidate PS pairs as the list of all PS pairs\\;\n $\\iota \\gets 0$ \\tcc*{Iteration variable} \n \\While {$\\iota\\iota$ with $\\ell_{\\iota'}=\\ell_{\\iota}$ from the list of candidate PS pairs\\;\n\t \t}\n \t\t\\ElseIf{$\\exists j\\in[J],\\ \\bm{\\omega}_j\\cdot\\left(\\bm{\\Pi}^{\\bar{\\varphi}}_n+\\bm{\\Pi}^{\\bar{\\varphi}}_a\\right)=C_j$}{\n\t\t\t\\tcc*{If a capacity constraint achieves equality under policy $\\bar{\\varphi}$}\n\t\t\t\\tcc*{determined by updated $\\bm{\\alpha}^{\\bar{\\varphi}}_i$, $i\\in[I]$.}\n\n\t\t remove all PS pairs $\\iota'>\\iota$ with $w_{j,i_{\\iota'}}>0$ from the list of candidate PS pairs\\;\n\t\t}\n\n}\n\n$\\bm{\\alpha}^{\\bar{\\varphi}(o)}_i\\gets \\bm{\\alpha}^{\\bar{\\varphi}}_{i}$ for all $i\\in[I]$\\;\n\n}\n\\caption{Priority-style policy for the relaxed problem}\\label{algo:varphi_gamma}\n\\end{algorithm}\n \\DecMargin{1em}\n\n\nFor any given ranking of PS pairs $o\\in\\mathscr{O}$, we can generate a policy $\\bar{\\varphi}(o)$ with priorities of PS pairs defined by $o$, such that \n\\eqref{eqn:constraint:relax:action}, \\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy} are satisfied:\nthe policy $\\bar{\\varphi}(o)$ is feasible for the relaxed problem but not necessarily feasible for the original problem.\nThe pseudo-code for generating $\\bar{\\varphi}(o)$ is presented in Algorithm~\\ref{algo:varphi_gamma}.\nThe key idea for generating such a $\\bar{\\varphi}(o)$ is to initialize $\\bm{\\alpha}_i^{\\bar{\\varphi}(o)}$ to $\\bm{0}$ for all $i\\in[I]$, and sequentially activate the PS pairs according to their priorities defined by $o$ until either a relaxed action or capacity constraint described in \\eqref{eqn:constraint:relax:action} and \\eqref{eqn:constraint:relax:resources}, respectively, achieves equality. In particular, \n\\begin{enumerate}[label=(\\Roman*)]\n\\item\nif a relaxed action constraint described in \\eqref{eqn:constraint:relax:action} achieves equality by activating PS pairs less than or equal to $\\iota$, then the multiplier $\\nu_{\\ell_{\\iota}}$ is set to $\\Xi_{i_{\\iota}}(\\pmb{\\gamma},\\bm{0})$, and all later PS pairs $\\iota'>\\iota$ with $\\ell_{\\iota'}=\\ell_{\\iota}$ are \\emph{disabled} from being activated and are removed from the \\emph{list of candidate pairs} awaiting later activation; \\label{case:equality:action}\n\\item similarly, if a relaxed capacity constraint described in \\eqref{eqn:constraint:relax:resources} associated with resource pool $j\\in[J]$ achieves equality by activating PS pairs less than or equal to $\\iota$, then all later PS pairs $\\iota'>\\iota$ with $w_{j,i_{\\iota'}}>0$ are disabled and removed from the list of candidate states.\\label{case:equality:capacity}\n\\end{enumerate}\nMaintaining an iteratively updated list of candidate pairs in this way continues until all \naction constraints in \\eqref{eqn:constraint:relax:action} achieve equality: the policy $\\bar{\\varphi}(o)$ is determined by the resulting $\\bm{\\alpha}^{\\bar{\\varphi}(o)}_i$ ($i\\in[I]$), and the multipliers $\\bm{\\nu}$ are updated in \\ref{case:equality:action}.\nThe vector of these multipliers is denoted by $\\bm{\\nu}(o,\\pmb{\\gamma})$.\nThe PS pair labeled by $\\iota$ satisfying the condition described in \\ref{case:equality:capacity} is called the \\emph{critical pair}, with the corresponding resource pool $j$ referred to as the \\emph{critical pool} of PS pair $\\iota$, denoted by $j_{\\iota}(o)$. Note that, from the description in \\ref{case:equality:capacity}, there might be more than one resource pool for which the capacity constraints achieve equality simultaneously while activating PS pair $\\iota$; we choose one of them to be $j_{\\iota}(o)$ and refer to this resource pool as the critical pool of $\\iota$.\nLet $\\mathscr{I}(o)$ represent the set of all critical pairs with respect to the policy $\\bar{\\varphi}(o)$. \n\\begin{lemma}\\label{lemma:critical_pattern}\nFor any $o\\in\\mathscr{O}$ and $\\iota,\\iota'\\in\\mathscr{I}(o)$, if $\\iota \\neq \\iota'$ then $i_{\\iota}\\neq i_{\\iota'}$.\n\\end{lemma}\n\\proof{Proof.}\nConsider critical pairs $\\iota,\\iota'\\in\\mathscr{I}(o)$ with $\\iota\\neq \\iota'$, and assume $\\iota < \\iota'$ without loss of generality. Since $\\iota$ is a critical pair, there is a critical resource pool $j_{\\iota}$ which is fully occupied. In this case, if $i_{\\iota} = i_{\\iota'}$, then pair $\\iota'$ must require some resource units from pool $j_{\\iota}$ and so $\\alpha^{\\bar{\\varphi}(o)}_{\\iota'}=0$.\nPS pair $\\iota'$ cannot be critical, which violates the condition $\\iota'\\in\\mathscr{I}(o)$. \nHence, $i_{\\iota} \\neq i_{\\iota'}$. This proves the lemma.\n\\endproof\n\n\n\n\nRecall, for any ranking $o$, the policy $\\bar{\\varphi}(o)$ must satisfy the action and capacity constraints~\\eqref{eqn:constraint:relax:action}, \\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy}. Also, since \\eqref{eqn:constraint:relax:action} holds, the \ncomplementary slackness conditions \ncorresponding to the action constraints~\\eqref{eqn:relaxaction:slack} are satisfied by taking $\\phi=\\bar{\\varphi}(o)$. However, the complementary slackness conditions \ncorresponding to the capacity constraints~\\eqref{eqn:relaxconstraint:slack} and equations~\\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c} are not necessarily satisfied if we plug in $\\phi=\\bar{\\varphi}(o)$ and $\\pmb{\\gamma}$: the policy $\\bar{\\varphi}(o)$ is a heuristic policy applicable for the relaxed problem defined by \\eqref{eqn:objective}, \\eqref{eqn:constraint:relax:action}, \\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy} derived by intuitively prioritizing PS pairs according to their ranking $o\\in\\mathscr{O}$.\n\nIn Section~\\ref{subsec:sufficient_condition} we shall define a particular class of resource allocation models, for which we can show the complementary slackness conditions are indeed satisfied.\n\n\n\\begin{definition}\\label{define:decomposable}\nThe system said to be \\emph{decomposable} if there exist multipliers $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$, $\\bm{\\nu}\\in\\mathbb{R}^L$ and a ranking $o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{\\nu})$ such that $\\bm{\\nu}=\\bm{\\nu}(o,\\pmb{\\gamma})$ and the complementary slackness conditions~\\eqref{eqn:relaxaction:slack} and \\eqref{eqn:relaxconstraint:slack} are achieved by taking $\\phi=\\bar{\\varphi}(o)$. In this case the optimal values of the dual variables are called \\emph{decomposable values}. \n\\end{definition}\n\n\nRecall that, in the general case, for $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$ and $\\bm{\\nu}\\in\\mathbb{R}^L$, even if $o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{\\nu})$, the policy $\\bar{\\varphi}(o)$ is not necessarily optimal (because it does not necessarily satisfy \\eqref{eqn:a_opt:a}-\\eqref{eqn:a_opt:c}). \nWhen the policy $\\bar{\\varphi}(o)$ is optimal for the relaxed problem, the ranking $o$ can be used to construct an index policy applicable to the original problem (detailed steps are provided in Section~\\ref{sec:index_policy}). Theorem~\\ref{theorem:main_second} (in Section~\\ref{sec:asym_opt}) then ensures that such an index policy is asymptotically optimal.\n\n\\subsubsection{Derivation of the Pair Ranking} \\label{subsubsec:derive_ranking}\n\nWe start with a proposition that shows how the values of the Lagrange multipliers $\\bm{\\nu}$ and $\\pmb{\\gamma}$ can be derived from a knowledge of the critical pair and critical resource pool corresponding to a given order $o\\in \\mathscr{O}$.\n\n\\begin{proposition}\\label{prop:solution_existence}\nFor any given $\\pmb{\\gamma}_0\\in\\mathbb{R}_0^J$ and $o\\in\\mathscr{O}$, the linear equations\n\\begin{equation}\\label{eqn:necessary_gamma}\n\\nu_{\\ell_{\\iota}}(o,\\pmb{\\gamma}_0)=\\Xi_{i_{\\iota}}(\\pmb{\\gamma},\\bm{0}),~\\forall \\iota\\in\\mathscr{I}(o)\n\\vspace{-0.3cm}\n\\end{equation}\nand \\vspace{-0.3cm}\n\\begin{equation}\\label{eqn:necessary_gamma:zero}\n\\gamma_{j} = 0,~\\forall j \\notin \\{j_{\\iota}(o)\\in[J] ~|~\\iota\\in\\mathscr{I}(o)\\}\n\\end{equation}\nhave a unique solution $\\pmb{\\gamma}\\in\\mathbb{R}^J$.\n\\end{proposition}\nThe proof of Proposition~\\ref{prop:solution_existence} will be given in Appendix~\\ref{app:prop:solution_existence} in the e-companion. \nFor a ranking $o\\in\\mathscr{O}$, define an function $\\mathcal{T}^o$ of $\\pmb{\\gamma}_0\\in\\mathbb{R}_0^J$ with respect to $o\\in\\mathscr{O}$: $\\mathcal{T}^o(\\pmb{\\gamma}_0)\\coloneqq \\pmb{\\gamma}$ where $\\pmb{\\gamma}$ is the unique solution of \\eqref{eqn:necessary_gamma} and \\eqref{eqn:necessary_gamma:zero}. \nLet $\\mathcal{T}^o_j(\\pmb{\\gamma}_0)$ represent the $j$th element of $\\mathcal{T}^o(\\pmb{\\gamma}_0)$.\n\n\n\\begin{proposition}\\label{prop:converge_gamma}\nIf there exist $\\pmb{\\gamma}_0\\in\\mathbb{R}_0^J$ and $o\\in\\mathscr{O}(\\pmb{\\gamma}_0,\\bm{0})$ such that $\\mathcal{T}^o(\\pmb{\\gamma}_0)=\\pmb{\\gamma}_0$, then \n$\\pmb{\\gamma}_0$ is a vector of decomposable multipliers and the policy $\\bar{\\varphi}(o)$ based on ranking $o$ is optimal for the relaxed problem defined by \\eqref{eqn:objective}, \\eqref{eqn:constraint:relax:action}, \\eqref{eqn:constraint:relax:resources} and \\eqref{eqn:constraint:dummy}.\n\\end{proposition}\nThe proof of Proposition~\\ref{prop:converge_gamma} will be given in Appendix~\\ref{app:prop:converge_gamma} in the e-companion. Recall that $\\mathscr{I}(o)$ is the set of critical pairs with respect to the policy $\\bar{\\varphi}(o)$, $j_{\\iota}(o)$ is the critical resource pool corresponding to critical pair $\\iota\\in\\mathscr{I}(o)$ according to ranking $o$, and $\\nu_{\\ell_{\\iota}}(o,\\pmb{\\gamma}_0)$ is an output of Algorithm~\\ref{algo:varphi_gamma} when the inputs are $o$ and $\\pmb{\\gamma}=\\pmb{\\gamma}_0$.\n\n{\\bf Remark} Proposition~\\ref{prop:converge_gamma} provides a way of checking decomposability of $\\pmb{\\gamma}_0$ and optimality of $\\bar{\\varphi}(o)$.\nBy Proposition~\\ref{prop:converge_gamma}, any fixed point $\\pmb{\\gamma}_0\\in\\mathbb{R}_0^J$ of the function $\\mathcal{T}^o$ with respect to a ranking $o\\in\\mathscr{O}(\\pmb{\\gamma}_0,\\bm{0})$ is a decomposable vector. \nThe decomposability of $\\pmb{\\gamma}_0$ can be checked without requiring knowledge of any $\\bm{\\nu}\\in\\mathbb{R}^L$. \nAlso, we present the following corollary of Proposition~\\ref{prop:converge_gamma}.\n\\begin{corollary}\\label{coro:converge_gamma}\nFor $\\pmb{\\gamma}_0\\in\\mathbb{R}_0^J$ and $o\\in\\mathscr{O}(\\pmb{\\gamma}_0,\\bm{0})$, if $\\mathcal{T}^o(\\pmb{\\gamma}_0)\\neq \\pmb{\\gamma}_0$, $\\mathcal{T}^o(\\pmb{\\gamma}_0)\\in \\mathbb{R}_0^J$ and $o\\in\\mathscr{O}(\\mathcal{T}^o(\\pmb{\\gamma}_0),\\bm{0})$, then $\\mathcal{T}^o(\\mathcal{T}^o(\\pmb{\\gamma}_0))=\\mathcal{T}^o(\\pmb{\\gamma}_0)$. \n\\end{corollary}\nNote that the hypothesis of Corollary~\\ref{coro:converge_gamma} requires all components of $\\mathcal{T}^o(\\pmb{\\gamma}_0)$ to be nonnegative. This is not such an easy condition to satisfy. \nThe proof of Corollary~\\ref{coro:converge_gamma} will be given in Appendix~\\ref{app:coro:converge_gamma} in the e-companion.\n\n\n\nIn this context, consider a given $\\pmb{\\gamma}_0\\in\\mathbb{R}_0^J$ and a ranking $o\\in\\mathscr{O}(\\pmb{\\gamma}_0,\\bm{0})$. If $\\pmb{\\gamma}_0$ is a fixed point of $\\mathcal{T}^o$, then it is the vector of decomposable multipliers; if it is not but $\\mathcal{T}^o(\\pmb{\\gamma}_0)$ is a nonnegative fixed point of $\\mathcal{T}^o$, then $\\mathcal{T}^o(\\pmb{\\gamma}_0)$ represents the decomposable multipliers. \nHowever, in both cases we need to propose a specific $\\pmb{\\gamma}_0$; it requires prior knowledge of the multipliers, which is, in general, not available.\nSection~\\ref{subsec:sufficient_condition} will discuss a special case where the decomposability is provable and we have a method of deriving the decomposable multipliers.\nHere, to make a reasonably good choice of the Lagrangian multipliers in a general system, we embark on a \\emph{fixed point iteration method}.\n\n\nSince Proposition~\\ref{prop:converge_gamma} requires a fixed point $\\pmb{\\gamma}$ of the function $\\mathcal{T}^o$ with $o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{0})$, we need to iterate not only the value of $\\pmb{\\gamma}$ but also the corresponding ranking $o$ which affects the function $\\mathcal{T}^o$ and should be an element of $\\mathscr{O}(\\pmb{\\gamma},\\bm{0})$.\nFollowing the idea of conventional\nfixed point interation methods, for $k\\in\\mathbb{N}_0$, \nlet $\\pmb{\\gamma}_{k+1}= \\bigl(\\mathcal{T}^{o_k}(\\pmb{\\gamma}_k)\\bigr)^+$ with initial $\\pmb{\\gamma}_0$ and $o_0\\in\\mathscr{O}(\\pmb{\\gamma}_0,\\bm{0})$, \nwhere $(\\bm{v})^+\\coloneqq (\\max\\{0,v_i\\}: i\\in[N])$ for a vector $\\bm{v}\\in \\mathbb{R}^N$ ($N\\in\\mathbb{N}_+$). \nConstruct a ranking $o_{k+1}\\in\\mathscr{O}(\\pmb{\\gamma}_{k+1},\\bm{0})$ according to $o_k$:\nfor any two different PS pairs $(i,n)$ and $(i',n')$ with $\\Xi_i(\\pmb{\\gamma}_{k+1},\\bm{0})=\\Xi_{i'}(\\pmb{\\gamma}_{k+1},\\bm{0})$, $(i,n)$ precedes $(i',n')$ in the ranking $o_{k+1}$ if and only if $(i,n)$ precedes $(i',n')$ in the ranking $o_k$.\nHere, the operation $(\\cdot)^+$ is used to make all the elements of $\\pmb{\\gamma}_{k+1}$ non-negative, so that $\\pmb{\\gamma}_{k+1}$ is feasible for the function $\\mathcal{T}^{o_{k+1}}$.\nThus the ranking $o_{k+1}$ inherits the tie-breaking rule used for $o_k$ so that the difference between $o_k$ and $o_{k+1}$, which must satisfy $o_k\\in\\mathscr{O}(\\pmb{\\gamma}_k,\\bm{0})$ and $o_{k+1}\\in\\mathscr{O}(\\pmb{\\gamma}_{k+1},\\bm{0})$, is minimized.\nCorollary~\\ref{coro:converge_gamma} can be used to check whether the $\\pmb{\\gamma}_{k+1}$ is a fixed point of the function $\\mathcal{T}^{o_k}$.\nAlso, $\\pmb{\\gamma}_{k+1}$ and $o_{k+1}$ are uniquely determined by $\\pmb{\\gamma}_k$ and $o_k$. We can consider $(\\pmb{\\gamma}_k,o_k)$ as an entity which is an argument delivered to the function $\\mathcal{T}^{o_k}(\\pmb{\\gamma}_k)$, and wish to find a fixed point in this sense.\n\n\nIn the general case, the function $\\mathcal{T}^{o_k}(\\pmb{\\gamma}_k)$ is discontinuous in $\\pmb{\\gamma}_k$ and the sequence $\\{\\pmb{\\gamma}_k\\}_{k=0}^{\\infty}$ is heuristically generated with no proof of convergence to a fixed point. \nIn fact, the choice of $\\pmb{\\gamma}_{k+1}= \\bigl(\\mathcal{T}^{o_k}(\\pmb{\\gamma}_k)\\bigr)^+$ may result in the sequence $\\{\\pmb{\\gamma}_k\\}_{k=0}^{\\infty}$ being trapped in oscillations. \nTo avoid this, with slight abuse of notation, we modify the iteration as $\\pmb{\\gamma}_{k+1}= \\bigl(c\\mathcal{T}^{o_k}(\\pmb{\\gamma}_k)+(1-c)\\pmb{\\gamma}_k\\bigr)^+$ with a parameter $c\\in [0,1]$, which captures the effects of exploring the new point $\\mathcal{T}^{o_k}(\\pmb{\\gamma}_k)$.\nNumerical examples of iterating $\\pmb{\\gamma}_k$ will be provided in Section~\\ref{sec:example}.\n\n\n\nWith an upper bound, $U\\in\\mathbb{N}_+$, we take $k^*\\coloneqq \\arg\\min_{k=1,2,\\ldots,U} \\lVert \\pmb{\\gamma}_{k-1}-\\pmb{\\gamma}_k\\rVert$ and consider $o_{k^*}$ as a reasonably good ranking of PS pairs. \nSuch $o_{k^*}$ is pre-computable with computational complexity no worse than $O(U(N^2+J^2))$, where $N^2$ and $J^2$ result from ordering the $N$ pairs and solving the $J$ linear equations, respectively.\nIn Section~\\ref{sec:index_policy}, we show that an index policy feasible for the original problem can always be generated with such an $o_{k^*}$, and the implementation complexity is $O(I)$ in terms of computation and storage. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\vspace{-0.3cm}\n\\subsection{Weakly Coupled Constraints}\\label{subsec:sufficient_condition}\n\\vspace{-0.3cm}\nHere, we discuss a sufficient condition under which the sequence $\\{\\pmb{\\gamma}_k\\}_{k=0}^{\\infty}$ is provably convergent; and, in Section~\\ref{sec:example}, when this condition fails, we show via numerical examples that the sequence might still converge. \n\n\n\\begin{definition}\nRecall the matrix $\\mathcal{W}=(w_{j,i})$ defined in Section~\\ref{subsec:model}.\nWe say that row $j\\in[J]$ is \n\\begin{enumerate}\n\\item a \\emph{type-1} row if there is at most one $i\\in[I]$ with $w_{j,i}>0$; \\vspace{-0.15cm}\n\\item a \\emph{type-2} row if there is more than one $i\\in[I]$ with $w_{j,i}>0$.\n\\end{enumerate} \\vspace{-0.3cm}\n\\end{definition}\nThat is, row $j$ is a type-1 row if resource pool $j$ is not shared by patterns of different types; and is a type-2 row, otherwise.\nDenote by $\\mathscr{J}_i=\\{j\\in[J]\\ |\\ w_{j,i}>0\\}$ the set of resource pools used by pattern $i$. \nWe then define a condition. \\vspace{-0.3cm}\n\\begin{condition}{Weak Coupling}\\label{cond:hypothesis}\nA system is weakly coupled if, for any pattern $i$, there is at most one $j\\in\\mathscr{J}_i$ with row $j$ of $\\mathcal{W}$ being a type-2 row. \\vspace{-0.3cm}\n\\end{condition}\nThis condition implies that there is at most one shared resource pool associated with each pattern. \nIn a weakly coupled system, if pattern $i_1$ shares a resource pool $j_{12}$ with pattern $i_2$ and pattern $i_1$ shares a resource pool $j_{13}$ with pattern $i_3$ then $j_{12}=j_{13}$. \nA system where each of the patterns requires only one resource pool is clearly weakly coupled.\nNote that, in a weakly coupled system, dependencies between state variables of different patterns still exist, because \neach resource pool can be shared by requests of multiple RTs.\n\n\\begin{definition}\\label{define:w_star}\nFor a weakly coupled system define, for each $i\\in[I]\\backslash\\{d(\\ell):\\ell\\in[L]\\}$, $w^*_i=w_{j,i}$ where $j$ is the only resource pool in $\\mathscr{J}_i$ shared with other patterns, if there is one; or any member of the set $\\arg\\min\\limits_{j'\\in\\mathscr{J}_i}\\frac{C_{j'}}{w_{j',i}}$, otherwise.\n\\end{definition}\n\\begin{definition}\\label{define:o_star}\n\nFor a weakly coupled system define, for $\\bm{\\nu}\\in\\mathbb{R}^L$, a set of PS rankings $\\mathscr{O}^*(\\bm{\\nu})\\subset \\mathscr{O}$ such that, for any $o\\in\\mathscr{O}^*(\\bm{\\nu})$, PS pairs $\\iota\\in[N]$ are ranked according to the descending order of \n\\begin{equation}\\label{eqn:main:index}\n\\Xi_{\\iota}^*= \\left\\{\n\\begin{array}{ll}\n\\frac{\\Xi_{i_{\\iota}}(\\bm{0},\\bm{0})-\\nu_{\\ell_{\\iota}}}{w^*_{i_{\\iota}}(1+\\lambda_{\\ell_{\\iota}}\/\\mu_{i_{\\iota}})}, &\\text{if } \\nexists \\ell\\in[L],~i_{\\iota}=d(\\ell),\\\\\n0, & \\text{otherwise},\n\\end{array}\\right.\n\\end{equation} \n\\end{definition}\n\n\n\\begin{proposition}\\label{prop:equal_opt}\nIf the system is weakly coupled and there exists a ranking $o\\in\\mathscr{O}^*(\\bm{0})$ satisfying $\\nu(o,\\bm{0})=\\bm{0}$, then the capacity constraints described in \\eqref{eqn:constraint:relax:resources} are decomposable and the policy $\\bar{\\varphi}(o)$ is optimal for the relaxed problem defined by \\eqref{eqn:objective} and \\eqref{eqn:constraint:relax:action}-\\eqref{eqn:constraint:dummy}.\nIn particular, there exist decomposable multipliers $\\pmb{\\gamma}\\in\\mathbb{R}_0^{J}$ satisfying, for $j\\in[J]$,\n\\begin{enumerate}[label=\\roman*)]\n\\item if there is a critical PS pair $\\iota\\in\\mathscr{I}(o)$ with critical resource pool $j=j_{\\iota}(o)$, and no \n$j'\\neq j$ with $j'\\in\\mathscr{J}_{i_{\\iota}}$ is critical for any other PS pair $\\iota' \\in\\mathscr{I}(o)$, then\n\\vspace{-0.3cm}\n\\begin{equation}\\label{eqn:equal_opt:a}\n\\gamma_j = \\frac{\\Xi_{i_{\\iota}}(\\bm{0},\\bm{0})-\\nu_{\\ell_i}}{w_{j,i_{\\iota}}\\left(1+\\lambda_{\\ell_{\\iota}}\/\\mu_{i_{\\iota}}\\right)};\n\\vspace{-0.3cm}\n\\end{equation}\n\\item if there are critical PS pairs $\\iota$ and $\\iota'$ in $\\mathscr{I}(o)$ with critical resource pools $j=j_{\\iota}(o)\\neq j_{\\iota'}(o)$ and $ j_{\\iota'}(o)\\in\\mathscr{J}_{i_{\\iota}}$, then \\vspace{-0.3cm}\n\\begin{equation}\\label{eqn:equal_opt:b}\n\\gamma_j=\\frac{w_{j_{\\iota'}(o),i_{\\iota}}}{w_{j,i_{\\iota}}}\\left(\\frac{\\Xi_{i_{\\iota}}(\\bm{0},\\bm{0})-\\nu_{\\ell_{\\iota}}}{w_{j_{\\iota'}(o),i_{\\iota}}\\left(1+\\lambda_{\\ell_{\\iota}}\/\\mu_{i_{\\iota}}\\right)}\n-\\frac{\\Xi_{i_{\\iota'}}(\\bm{0},\\bm{0})-\\nu_{\\ell_{\\iota'}}}{w_{j_{\\iota'}(o),i_{\\iota'}}\\left(1+\\lambda_{\\ell_{\\iota'}}\/\\mu_{i_{\\iota'}}\\right)}\\right);\\vspace{-0.3cm}\n\\end{equation}\n\\item otherwise, \\vspace{-0.3cm}\n\\begin{equation}\\label{eqn:equal_opt:c}\n\\gamma_j = 0. \\vspace{-0.3cm}\n\\end{equation}\n\\end{enumerate}\n\\end{proposition}\nThe proof is given in Appendix~\\ref{app:prop:equal_opt} in the e-companion. \nNote that, from Lemma~\\ref{lemma:critical_pattern}, for any critical PS pairs $\\iota,\\iota'\\in\\mathscr{I}(o)$ with $\\iota\\neq \\iota'$, it follows that $i_{\\iota}\\neq i_{\\iota'}$.\nIf the system is weakly coupled, for any $j\\in[J]$, there exist at most two different critical pairs $\\iota\\in\\mathscr{I}(o)$ satisfying $j\\in\\mathscr{J}_{i_{\\iota}}$. Also, in a weakly coupled system, for the second case stated in Proposition~\\ref{prop:equal_opt}, if there are critical PS pairs $\\iota$ and $\\iota'$ in $\\mathscr{I}(o)$ with critical resource pools $j=j_{\\iota}(o)\\neq j_{\\iota'}(o)$ and $ j_{\\iota'}(o)\\in\\mathscr{J}_{i_{\\iota}}$, then $ j_{\\iota}(o)\\notin \\mathscr{J}_{i_{\\iota'}}$ because there is at most one resource pool in $\\mathscr{J}_{i_{\\iota}}$ shared with other patterns.\n\n\n\nIn Proposition~\\ref{prop:equal_opt}, \nthe assumption that the system is weakly coupled constrains the way in which resource pools are shared by different requests. \nThe case where there is an $o\\in\\mathscr{O}^*(\\bm{0})$ with $\\bm{\\nu}(o,\\bm{0})=\\bm{0}$ will occur when the relaxed action constraint~\\eqref{eqn:constraint:relax:action} is satisfied with $\\alpha^{\\bar{\\varphi}(o)}_{d(\\ell)}(n)>0$ for the only $n\\in\\mathscr{N}_{d(\\ell)}$ and for all $\\ell\\in[L]$. \nTo see this, note that\nthe construction of the policy $\\bar{\\varphi}(o)$ guarantees that the resulting multipliers $\\bm{\\nu}(o,\\bm{0})$ will be non-negative, and so\nit follows from~\\eqref{eqn:a_opt:dummy} that $\\alpha^{\\bar{\\varphi}(o)}_{d(\\ell)}(n) > 0$ only if $\\nu_{\\ell}(o,\\bm{0}) = 0$. That is, having $\\nu_{\\ell}(o,\\bm{0}) = 0$ is associated with there being a positive probability that the dummy pattern $d(\\ell)$ is selected in the relaxed system.\nFurthermore, if there is a PS pair $\\iota$ (for a non-dummy pattern $i_{\\iota}\\in\\mathscr{P}_{\\ell}$) which satisfies the condition described in \\ref{case:equality:action}, that is PS pair $\\iota$ causes the relaxed action constraint~\\eqref{eqn:constraint:relax:action} to bite, Algorithm~\\ref{algo:varphi_gamma} will ensure that $\\alpha^{\\bar{\\varphi}(o)}_{i_{\\iota'}}(n_{\\iota'}) = 0$ for all PS pairs $\\iota'$ \nranked lower than $\\iota$ according to the order $o$. In particular, this will cause $\\alpha^{\\bar{\\varphi}(o)}_{d(\\ell)}(n) = 0$ for the only $n\\in\\mathscr{N}_{d(\\ell)}$.\n\nSo if $\\alpha^{\\bar{\\varphi}(o)}_{d(\\ell)}(n) > 0$, it is because the relaxed capacity constraints~\\eqref{eqn:constraint:relax:resources} bite before the relaxed action constraints~\\eqref{eqn:constraint:relax:action}.\nIf this is true for all $\\ell$, then the capacity constraints are biting for every request type, and so we refer to the case where there is an $o\\in\\mathscr{O}^*(\\bm{0})$ with $\\bm{\\nu}(o,\\bm{0})=\\bm{0}$ as a \\emph{heavy traffic} condition.\n\n\n\n\n\n\\begin{condition}{Heavy Traffic}\\label{cond:heavy_traffic}\nThe system is in heavy traffic if there is a ranking $o\\in\\mathscr{O}^*(\\bm{0})$ such that $\\bm{\\nu}(o,\\bm{0})=\\bm{0}$. \\vspace{-0.5cm}\n\\end{condition}\n\n{\\bf Remark} \nThe property of being weakly coupled and in heavy traffic simplifies the analysis of the complementary slackness condition of the relaxed problem. In particular, the index related to a pattern, described in Equation~\\eqref{eqn:index_value}, is affected only by the multipliers of resource pools $j \\in[J]$ with $w_{j,i}>0$. Weak coupling helps reduce the number of such multipliers $\\gamma_j$, so that the index of a pattern is affected by at most one $\\gamma_ j$, which in turn affects other pattern indices. When the system is weakly coupled and in heavy traffic, we can analytically solve the $I$ linear equations~\\eqref{eqn:necessary_gamma} and \\eqref{eqn:necessary_gamma:zero} and derive the $\\phi$ and $\\pmb{\\gamma}$ that satisfy the complementary slackness condition described in equaitons~\\eqref{eqn:relaxaction:slack} and \\eqref{eqn:relaxconstraint:slack}. A detailed discussion is provided in the proof of Proposition~\\ref{prop:equal_opt}.\n\n\nProposition~\\ref{prop:equal_opt} guarantees the decomposability of a system when it is weakly coupled and in heavy traffic.\nThe property of being weakly coupled and in heavy traffic is stronger than necessary for decomposability, but it is simple to check and is satisfied in a number of common resource allocation problems. We consider examples about how to easily define such a system.\n\n\nAs explained above, the heavy traffic property is usually satisfied when the service capacity is not enough (or just enough) to address its high traffic load.\nOn the other hand, the weak coupling specifies the structure of the weight matrix $\\mathcal{W}$. \nFor instance, if each pattern involves only one resource pool (that is, for all $i\\in[I]\\backslash\\{d(\\ell)|\\ell\\in[L]\\}$, $|\\mathscr{J}_i|=1$), then the system is weakly coupled as each resource pool is still potentially shared by requests of different types. \n\n\nWithin the above framework, we can model skill-based resource pooling in call centers (see \\cite{wallace2004resource,cezik2008staffing}) as a weakly coupled resource allocation problem; and when its traffic load is also heavy, the system is decomposable.\nIn each call center, agents are trained for several skills, such as two or three languages, and are able to handle some but not all of the incoming calls. We classify these agents into multiple call centers according to their trained skills; that is, all agents in the same call center have the same skills and are able to serve the same types of calls. In this context, a call corresponds to a request, an agent corresponds to a resource unit, a call center is a resource pool and a call type is a request type.\n\nSince each call is served by an agent with appropriate skills, each pattern consists of only one call center (resource pool) and selecting a pattern means selecting an agent (a resource unit) from the corresponding call center: this problem is weakly coupled. Note that agents of each call center are potentially serving different types of calls simultaneously, and the capacity constraints \\eqref{eqn:constraint:resources} still restrict the system because of the limited number of agents in each call center. \n\n\nIn particular, the trained skills are used to establish the availability of an agent to serve a call, and do not relate to any concept defined in the resource allocation problem. When an agent of call center $j\\in[J]$ is able to serve calls of type $\\ell\\in[L]$, regardless of the skills needed for this service, there is a pattern $i$ in $\\mathscr{P}_{\\ell}$ with $w_{j,i}=1$ and $w_{j',i}=0$ for all $j'\\in[J]\\backslash\\{j\\}$. \nFor instance, a call center has agents who can speak English and Chinese, and there are two types of calls: one requires English or French-speaking agents and the other Chinese or Japanese speakers. A call of either type can be served by an agent of this call center, and many calls of both types can be served by this call center simultaneously. \n\n \n\nOther problems with similar features, such as health-care task scheduling for agents with different qualifications (see \\cite{lieder2015task}) and home health-care scheduling (see \\cite{fikar2017home}), can also be modeled as weakly coupled systems. And, of course, when the systems are also in heavy traffic, they are decomposable.\n\n\nA virtual machine (VM) replacement problem can be modeled as a resource allocation problem (see \\cite{stolyar2013infinite,stolyar2017large}). VM replacement is about consolidating multiple VMs onto a set of physical machines\/servers, where each physical server can usually accommodate more than one VM simultaneously. To consolidate a VM, certain numbers of physical units, such as CPU cycles, memory, disk, or I\/O ports, located on a server will be occupied by this VM until it is completed. The VMs and servers are potentially different, and, because of different server profiles or user preference, a server is not necessarily able to accommodate every VM. \nConsider a simple version, for which the capacity of a server is determined by the total amount of only one type of physical unit: this server has a plentiful supply of the others or is not aware of other physical units.\nIn this case, regarding a VM as a request, a server as a resource pool and a physical unit of the shortage type as a resource unit, we obtain a resource allocation problem that is weakly coupled. \nSimilar problems in computer networks, such as the virtual node embedding (see \\cite{esposito2016distributed}), server provisioning in distributed cloud environments (see \\cite{wei2017data}), and wireless resource scheduling (see \\cite{chen2017wireless}), can potentially be modeled as weakly coupled resource allocation problems. And as before, when the weakly coupled systems are in heavy traffic, the decomposability property holds.\n\n\nAs in \\cite{stolyar2013infinite,stolyar2017large}, for general VM replacement problems, each server capacity is not necessarily constrained by physical units of just one type. As above, we model a VM as a request, a physical unit as a resource unit, and the set of all physical units of the same type located on the same server as a resource pool. In this context, the capacity of each resource pool is determined by the total number of its associated physical units of a given type on a server and the weak coupling property cannot hold in general. \nIt follows that, unlike the preceding examples, the system is not necessarily decomposable.\nHowever, as discussed in Section~\\ref{subsubsec:derive_ranking}, a decomposable system that is not weakly coupled or in heavy traffic can be found by finding a fixed point $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$ of the function $\\mathcal{T}^o$ ($o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{0})$). Numerical examples of such systems will be provided in Section~\\ref{sec:example}.\n\n\n\\section{The Index Policy: Its Implementation in the Non-Asymptotic Regime}\\label{sec:index_policy}\nIn Section~\\ref{sec:relaxation}, we considered the relaxed problem with constraints \\eqref{eqn:constraint:relax:action}-\\eqref{eqn:constraint:dummy}. Here, we return to the original problem with constraints \\eqref{eqn:constraint:action} and \\eqref{eqn:constraint:resources}.\n\n\nFor each RT $\\ell\\in[L]$, we refer to a policy $\\varphi\\in\\Phi$ as an \\emph{index policy} according to PS-pair ranking $o\\in\\mathscr{O}$, if it always prioritizes a candidate process in \na PS pair with a ranking equal or higher than those of all the other candidate processes.\nThis policy $\\varphi$ is applicable to the original problem while, the policy $\\bar{\\varphi}(o)$ proposed in Section~\\ref{subsubsec:priorities_of_pairs} is not in general.\nThe method of implementing such a $\\varphi$ is not unique; for instance, the computation of the ranking of the PS pairs can vary. Here we propose one possible implementation.\n\n\nFor $t>0$, we maintain a sequence of $I$ ordered PS pairs $(i,N^{\\varphi}_i(t))$ ($i\\in[I]$) that are associated with the $I$ patterns, according to the given ranking $o$ and the state vector $\\bm{N}^{\\varphi}(t)$: PS pair $(i,N^{\\varphi}_i(t))$ is placed ahead of $(i',N^{\\varphi}_{i'}(t))$ if and only if the former precedes the latter in the ranking $o$.\nLet $i^o_{\\sigma}(\\bm{N}^{\\varphi}(t))$ ($\\sigma\\in[I]$) represent the pattern associated with the $\\sigma$th PS pair in this ordered sequence.\n\nFor a general ranking $o\\in\\mathscr{O}$, the variables $i^o_{\\sigma}(\\bm{N}^{\\varphi}(t))$ are potentially updated at each state transition. Nonetheless, for the purpose of this paper, we mainly focus on the rankings $o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{\\nu})$ (for some $\\pmb{\\gamma}\\in\\mathbb{R}^J_0$ and $\\bm{\\nu}\\in\\mathbb{R}^L$) that follow the descending order of $\\Xi_i(\\pmb{\\gamma},\\bm{\\nu})$.\nIn this case, the variables $i^o_{\\sigma}(\\bm{N}^{\\varphi}(t))$ are updated only if a pattern $i\\in[I]\\backslash\\{d(\\ell)|\\ell\\in[L]\\}$ transitions into or out of its boundary state $|\\mathscr{N}_i|-1$.\n\n\n\nConsider the capacity constraints\n\\begin{equation}\\label{eqn:constraint:resources:epsilon:1}\n\\sum\\limits_{i'\\in[I]}w_{j,i'}N^{\\varphi}_{i'}(t) + \\sum\\limits_{i'\\in[I],i'\\neq i}w_{j,i'}a^{\\varphi}_{i'}(\\bm{N}^{\\varphi}(t))+ a^{\\varphi}_i(\\bm{N}^{\\varphi}(t)) \\leq \\Bigl\\lceil C_j\\bigl(1-\\bar{\\epsilon}_{j,\\iota(i,N^{\\varphi}_i(t))}\\bigr)\\Bigr\\rceil,~\\forall j\\in[J], i\\in[I],\n\\end{equation}\nwhere $\\iota(i,N^{\\varphi}_i(t))\\in[N]$ represents the order of PS pair $(i,N^{\\varphi}_i(t))$ in the ranking $o$ and $\\bar{\\bm{\\epsilon}}\\in [0,1]^{J\\times [N]}$ is a given matrix of parameters.\nApart from this matrix of parameters, constraints~\\eqref{eqn:constraint:resources:epsilon:1} are the same as constraints~\\eqref{eqn:constraint:resources}.\nAs we shall discuss in Section~\\ref{subsec:policies}, we choose the $\\bar{\\epsilon}_{j,\\iota}$ such that $\\bar{\\epsilon}_{j,\\iota}C_j \\geq w_{j,i_{\\iota}}-1$ and, for any $j\\in[J]$ and PS pairs $\\iota<\\iota'$ with respect to the given ranking $o$, if $w_{j,i_{\\iota}},w_{j,i_{\\iota'}}>0$, then $\\bar{\\epsilon}_{j,\\iota} < \\bar{\\epsilon}_{j,\\iota'}$.\nIn this context, if $\\bar{\\epsilon}_{j,\\iota}C_j \\in [ w_{j,i_{\\iota}}-1,w_{j,i_{\\iota}})$ for all $\\iota\\in[N]$ and $j\\in[J]$, then constraints~\\eqref{eqn:constraint:resources:epsilon:1} reduce to \\eqref{eqn:constraint:resources}; otherwise, they are more stringent than \\eqref{eqn:constraint:resources}.\nThe parameter $\\bar{\\bm{\\epsilon}}$ is used to specify the trajectory of the underlying process $\\bm{N}^{\\varphi}(t)$ when the system is scaled to the asymptotic regime. \nThis specification is required \nfor proving the asymptotic optimality of the index policy $\\varphi$. \n\n\nIn the interests of notational consistency, we shall use the form \\eqref{eqn:constraint:resources:epsilon:1} throughout\nbut, here, since we do not worry about the asymptotic behavior, we consider the case with $\\bar{\\epsilon}_{j,\\iota}C_j \\in [ w_{j,i_{\\iota}}-1,w_{j,i_{\\iota}})$ for all $\\iota\\in[N]$ and $j\\in[J]$ so that \\eqref{eqn:constraint:resources:epsilon:1} reduces to \\eqref{eqn:constraint:resources}.\nA detailed discussion about the scaling procedure and the role of $\\bar{\\bm{\\epsilon}}$ in the asymptotic case will be provided in Section~\\ref{sec:asym}.\n\nUnder the index policy $\\varphi$, we select $L$ patterns to accept new arrivals of $L$ types according to their orders in sequence $i^o_{\\sigma}(\\bm{N}^{\\varphi}(t))$ ($\\sigma\\in[I]$).\nIn particular, at a decision making epoch $t>0$, we initialize $a^{\\varphi}_i(\\bm{N}^{\\varphi}(t))=0$ for all $i\\in[I]$ and a set of \\emph{available patterns} to be $[I]$. \nIf, for $i=i^o_1(\\bm{N}^{\\varphi}(t))$, constraints~\\eqref{eqn:constraint:resources:epsilon:1}\nwill not be violated by setting $a^{\\varphi}_i(\\bm{N}^{\\varphi}(t))=1$, then set $a^{\\varphi}_i(\\bm{N}^{\\varphi}(t))=1$ and remove all patterns associated with request type $\\ell(i)$ from the set of available patterns.\n\n\n\nThe other $L-1$ patterns are selected similarly and iteratively.\nThat is, we look for the smallest $\\sigma\\in\\{2,3,\\ldots,I\\}$ such that \n\\begin{itemize}\n\\item $i^o_{\\sigma}(\\bm{N}^{\\varphi}(t))$ remains in the set of available patterns; and\n\\item the capacity constraints~\\eqref{eqn:constraint:resources:epsilon:1} will not be violated by setting $a^{\\varphi}_i(\\bm{N}^{\\varphi}(t))=1$ where $i=i^o_{\\sigma}(\\bm{N}^{\\varphi}(t))$.\n\\end{itemize}\nIf there is such a $\\sigma$, set $a^{\\varphi}_i(\\bm{N}^{\\varphi}(t))=1$ for $i=i^o_{\\sigma}(\\bm{N}^{\\varphi}(t))$, remove all patterns associated with request type $\\ell(i)$ from the set of available patterns and continue selecting the remaining $L-2$ patterns in the same manner.\nWhen all of the $L$ patterns have been selected we can stop. \nDetailed steps are provided in Algorithm~\\ref{algo:index_policy}, \nwhich has a computational complexity that is linear in $I$.\n\n\n\\IncMargin{1em}\n\\begin{algorithm}\n\\small \n\\linespread{0.4}\\selectfont\n\n\\SetKwFunction{FIndexPolicy}{IndexPolicy}\n\\SetKwProg{Fn}{Function}{:}{\\KwRet $\\bm{a}^{\\varphi}(\\bm{n})$}\n\\SetKwInOut{Input}{Input}\\SetKwInOut{Output}{Output}\n\\SetAlgoLined\n\\DontPrintSemicolon\n\\Input{a ranking of PS pairs $o\\in\\mathscr{O}$ and a given state $\\bm{n}\\in\\mathscr{N}$.}\n\\Output{the action variables $\\bm{a}^{\\varphi}(\\bm{n})$ under the index policy $\\varphi\\in\\Phi$ with respect to ranking $o$ when the system is in state $\\bm{n}$.}\n\\Fn{\\FIndexPolicy{$o,\\bm{n}$}}{\n\t$\\bm{a}^{\\varphi}(\\bm{n}) \\gets \\bm{0}$ \\tcc*{Initializing the action variables}\n\t$\\mathscr{P}\\gets [I]$ \\tcc*{Initializing the set of available patterns}\n\t$\\sigma \\gets 1$ \\tcc*{Starting with the pattern with the highest priority}\n \\While {$\\mathscr{P}\\neq \\emptyset $}{\t\n\t \t $i\\gets i^o_{\\sigma}(\\bm{n})$\\;\n\t\t\t\\If {$i\\in\\mathscr{P}$ {\\bf and} Constraints~\\eqref{eqn:constraint:resources:epsilon:1} are not violated by setting $a^{\\varphi}_i(\\bm{n}) =1$ and $\\bm{N}^{\\varphi}(t)=\\bm{n}$}{\n\t\t\t $a^{\\varphi}_i(\\bm{n})\\gets 1$\\;\n\t\t\t\tRemove all patterns $i'\\in\\mathscr{P}$ with $\\ell(i')=\\ell(i)$ from $\\mathscr{P}$\\;\n\t\t\t}\n\t \t$\\sigma\\gets \\sigma +1$\\;\n\t}\n}\n\\caption{Implementing the index policy $\\varphi$ with respect to ranking $o$.}\\label{algo:index_policy}\n\\end{algorithm}\n \\DecMargin{1em}\n\n\n\nThe performance of $\\varphi$ is mainly determined by the given order $o\\in\\mathscr{O}$. \nBased on later discussion of the asymptotic regime, if the policy $\\bar{\\varphi}(o)$ is optimal for the relaxed problem in the asymptotic regime, then $\\varphi$ is asymptotically optimal for the original problem.\nEven without the proved asymptotic optimality, the ranking $o$ should ensure good performance of $\\varphi$ as it is always rational to prioritize patterns according to their potential profits.\nAs long as there are reasonably good $\\pmb{\\gamma}$ and $\\bm{\\nu}$ leading to a $o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{\\nu})$, which correctly reflects the potential profits of patterns, the performance degradation of $\\bar{\\varphi}(o)$ is likely to be limited for the relaxed problem and close to the optimal solution of the original problem; and the index policy $\\varphi$ derived from $o$ is a promising choice for managing resources.\n\n\nThe selection of $\\pmb{\\gamma}$, $\\bm{\\nu}$ and $o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{\\nu})$ is discussed in Section~\\ref{sec:relaxation}. The key point is to guarantee good performance of $\\bar{\\varphi}(o)$: the policy that is guaranteed to be optimal for the relaxed problem when the system is decomposable.\n\n\n\n\\section{Stochastic Optimization in a Scaled System} \\label{sec:asym}\n\n\nIn this section, we establish asymptotic optimality of $\\varphi$.\n\n\\vspace{-0.3cm}\n\\subsection{Scaling Parameter}\\label{subsec:asym_regime}\n\\vspace{-0.3cm}\nWith a parameter $h\\in\\mathbb{N}_+$, let $\\bm{C}\\coloneqq h\\bm{C}^{0}$, $\\bm{C}^{0}\\in\\mathbb{N}_{+}^{J}$, and the arrival rates scale as $\\bm{\\lambda}\\coloneqq h\\bm{\\lambda}^{0}$, $\\bm{\\lambda}^{0}\\in\\mathbb{R}_{+}^{L}$.\nWe refer to the parameter $h$ as the \\emph{scaling parameter}, and the \\emph{asymptotic regime} as the limiting case with $h\\rightarrow +\\infty$.\n\n\n\nWe split the process associated with pattern $i$ into $h$ identical \\emph{sub-processes}\n$(i,k)$, $k\\in[h]$, and divide $N^{\\phi}_{i}(t)$, the number of instantiations for pattern $i$ under policy $\\phi$ at time $t$, into $h$ pieces.\nThe number of instantiations of the $k$th piece is $N^{\\phi}_{i,k}(t)$, so that $N^{\\phi}_{i}(t) = \\sum_{k=1}^{h}N^{\\phi}_{i,k}(t)$.\nWe refer to $N^{\\phi}_{i,k}(t)$ as the number of instantiations for \\emph{sub-pattern} $(i,k)$.\nThe counting process given by $N^{\\phi}_{i,k}(t)$ ($k\\in [h], i\\in [I]$) has state space \n$\\mathscr{N}_{i}^{0} \\coloneqq \\{0,1,\\ldots,\\min_{j\\in \\mathscr{J}_i}\\lceil C_{j}^{0}\/w_{j,i}\\rceil\\}$.\nFor any dummy pattern $d(\\ell)$, we take $\\mathscr{N}^0_{d(\\ell)}= \\mathscr{N}_{d(\\ell)} = \\{0\\}$.\n\n\n\nThe objective and constraints defined by \\eqref{eqn:objective}, \\eqref{eqn:constraint:action} and \\eqref{eqn:constraint:resources} still apply to the sums of variables $ \\sum_{k=1}^{h}N^{\\phi}_{i,k}(t)\\coloneqq N^{\\phi}_i(t)$, $i\\in[I]$.\nWe say the process associated with pattern $i$ is \\emph{replaced} by the $h$ sub-processes associated with sub-patterns $(i,k)$, $k\\in[h]$.\nEach sub-pattern $(i,k)$ earns reward $r_{\\ell(i)}$ per each served request and the cost rate that a request accommodated by this sub-pattern imposes on resource pool $j\\in[J]$ is $\\varepsilon_jw_{j,i}$; that is,\nthe sub-process $(i,k)$ maintains the same reward and cost rates in states $n\\in\\mathscr{N}_i^0$ as process $i$.\nLet $\\bm{N}_{h}^{\\phi}(t) = (N^{\\phi}_{i,k}(t):\\ i\\in[I], k\\in[h])$ be the state variable after this replacement,\nand $a^{\\phi}_{i,k}(\\bm{N}^{\\phi}_h(t))\\in\\{0,1\\}$ ($i\\in[I],k\\in[h]$) be the action variables with respect to the process $\\bm{N}^{\\phi}_h(t)$.\nTo clarify, we rewrite the objective described in \\eqref{eqn:objective} as\n\\begin{equation}\\label{eqn:objective:h}\n\\max\\limits_{\\phi} \\frac{1}{h}\\sum\\limits_{i\\in[I]} \\sum\\limits_{k\\in[h]}\\sum_{n_i\\in\\mathscr{N}_i} \\pi_{i,k}^{\\phi,h}(n_i) \\Bigl(r_{\\ell(i)} \\mu_i - \\sum_{j\\in\\mathscr{J}} w_{j,i} \\varepsilon_j\\Bigr)n_i,\n\\end{equation}\nwhere $\\pi^{\\phi,h}_{i,k}(n_i)$ represents the stationary probability that the state of sub-process $(i,k)$ is $n_i$ under policy $\\phi$ with scaling parameter $h$.\nWe divide the total revenue earned by all sub-patterns by $h\\in\\mathbb{N}_+$ so that the objective function is always bounded when $h\\rightarrow +\\infty$. \nThe policy $\\phi$ in \\eqref{eqn:objective:h} is determined by the action variables $a^{\\phi}_{i,k}(\\bm{N}^{\\phi}_h(t))$ ($i\\in[I],k\\in[h]$)\nsubject to \\vspace{-0.3cm}\n\\begin{equation}\\label{eqn:constraint:action:h}\n\\sum\\limits_{i\\in\\mathscr{P}_{\\ell}}\\sum\\limits_{k\\in[h]}a^{\\phi}_{i,k}(\\bm{N}^{\\phi}_h(t))=1,~\\forall \\ell\\in[L],~\\forall t \\geq 0, \\vspace{-0.3cm}\n\\end{equation}\nand \\vspace{-0.3cm}\n\\begin{equation}\\label{eqn:constraint:resources:h}\n\\sum\\limits_{i\\in[I]}\\frac{w_{j,i}}{h}\\sum\\limits_{k\\in[h]}\\Bigl(N^{\\phi}_{i,k}(t)+a^{\\phi}_{i,k}(\\bm{N}^{\\phi}_h(t))\\Bigr)\\leq C_j^0,~\\forall j\\in[J],~\\forall t \\geq 0.\n\\end{equation}\nThe constraints in \\eqref{eqn:constraint:resources:h} are obtained by substituting $C_j^0h$ for $C_j$ in the constraints stated in \\eqref{eqn:constraint:resources}, and thus \\eqref{eqn:constraint:resources:h} is equivalent to \\eqref{eqn:constraint:resources}.\nAlso, to guarantee that the maximal value of each $N^{\\phi}_{i,k}(t)$ ($k\\in[h]$), $\\min_{j\\in\\mathscr{J}_i}\\lceil C_j^0\/w_{j,i}\\rceil$, is not exceeded, define, for $k\\in[h]$ and $i\\in[I]\\backslash\\{d(\\ell):\\ \\ell\\in[L]\\}$,\n\\vspace{-0.4cm}\n\\begin{equation}\\label{eqn:constraint:resources:zero:h}\na^{\\phi}_{i,k}(\\bm{N}^{\\phi}_h(t))=0,~\\text{if } N^{\\phi}_{i,k}(t)=|\\mathscr{N}_i^0|-1,\n\\vspace{-0.4cm}\n\\end{equation}\nwhich corresponds to the redundant constraints described in \\eqref{eqn:constraint:resources:zero}.\n\n\n\n\n{\\bf Remark}\nAs in Section~\\ref{sec:introduction}, we activate exactly one sub-process $(i,k)$ ($i\\in\\mathscr{P}_{\\ell}$, $k\\in[h]$) for RT $\\ell\\in[L]$ regardless of the scaling parameter $h\\in\\mathbb{N}_+$.\nThe birth and death rates of this active sub-process are $h\\lambda_{\\ell}^0$ and $N^{\\phi}_{i,k}(t)\\mu_{i}^0$, respectively, so that if $h\\lambda^0_{\\ell} \\gg (|\\mathscr{N}^0_i|-1)\\mu_{i}^0$, the number of instantiations of pattern $i$ will increase rapidly until it is restricted by the capacity constraints.\n\n\nA model with a single active sub-process at any time has different stochastic properties compared to the case where the number of active sub-processes is proportional to $h$ (which was discussed in \\cite{weber1990index}). \nTo illustrate the difference, we present an example in Appendix~\\ref{app:example3} of the e-companion.\n\n\n\n\nThe optimization problem consisting of the $hI$ sub-processes associated with $hI$ sub-patterns, coupled through constraints~\\eqref{eqn:constraint:action:h}-\\eqref{eqn:constraint:resources:zero:h} can be analyzed and relaxed along the same lines as in Section~\\ref{sec:relaxation}. \nLet $\\alpha^{\\phi}_{i,k}(n)\\coloneqq \\lim_{t\\rightarrow +\\infty}\\mathbb{E}\\{a^{\\phi}_{i,k}(\\bm{N}^{\\phi}_h(t))| N^{\\phi}_{i,k}(t)=n\\}$ ($n\\in\\mathscr{N}^0_{i}$, $i\\in[I]$, $k\\in[h]$) represent the action variables of the $hI$ sub-problems for the relaxed problem scaled by $h$. \n\n\nAll the sub-processes corresponding to a given pattern $i\\in[I]$ in the same state $n\\in\\mathscr{N}_i^0$ are equivalent.\nThe controller then is concerned only with the total number of active sub-processes of a given pattern in a given state. \nDefine the random variable $Z^{\\phi,h}_{\\iota}(t)$ to be the proportion of sub-processes in PS pair $\\iota$ at time $t$ under policy $\\phi$ where $h$ is the scaling parameter; that is, \\vspace{-0.3cm}\n\\begin{equation}\\label{eqn:z_process}\nZ^{\\phi,h}_{\\iota}(t)=\\frac{1}{hI}\\Bigl|\\bigl\\{(i,k)\\in[I]\\times[h]\\ \\left|\\ N^{\\phi}_{i,k}(t)=n_{\\iota},\\ i_{\\iota}=i\\right.\\bigr\\}\\Bigr|. \\vspace{-0.3cm}\n\\end{equation} \n\nLet $\\bm{Z}^{\\phi,h}(t)=(Z^{\\phi,h}_{\\iota}(t):\\ \\iota\\in[N])$ and $\\mathscr{Z}$ be the probability simplex $\\{\\bm{z}\\in[0,1]^{N}\\ |\\ \\sum_{\\iota\\in[N]}z_{\\iota}=1\\}$.\nIn this model, the process $\\bm{Z}^{\\phi,h}(t)$ is analogous to the counting process $\\bm{N}^{\\phi}_h(t)$ in the original process.\nWhen the process $\\bm{Z}^{\\phi,h}(t)$ takes value $\\bm{z}\\in\\mathscr{Z}$, it can transition only to a state of the form $\\bm{z}+\\bm{e}_{\\iota,\\iota'}\\in\\mathscr{Z}$ with $i_{\\iota}=i_{\\iota'}$, where $\\bm{e}_{\\iota,\\iota'}\\in \\mathbb{R}^{N}$ is a vector with $\\iota$th element $+1\/hI$, $\\iota'$th element $-1\/hI$ and all the other elements set to zero. \nFor our birth-and-death process, a transition will happen only with $n_{\\iota'} = n_{\\iota}\\pm 1$ corresponding to the arrival and departure of a request, respectively.\nFor any given $h\\in\\mathbb{N}_+$, \nthe state space of the process $\\bm{Z}^{\\phi,h}(t)$ is a subset of $\\mathscr{Z}$ and thus the system is always stable.\nWe refer to the system with $h\\rightarrow +\\infty$ as the \\emph{asymptotic regime}.\n\n\n\nNote that any resource allocation problem in the non-asymptotic regime coincides with a scaled problem described in \\eqref{eqn:objective:h}-\\eqref{eqn:constraint:resources:zero:h} with given $h<+\\infty$. The scaling parameter $h$ is introduced to rigorously specify the trajectory of the entire system going from a non-asymptotic regime to an asymptotic regime.\n\n\\vspace{-0.5cm}\n\n\\subsection{Index Policies in a Scaled System}\\label{subsec:policies}\n\\vspace{-0.3cm}\n\nIn Section~\\ref{sec:index_policy}, for a ranking $o\\in\\mathscr{O}$, we proposed an index policy $\\varphi\\in\\Phi$ for the resource allocation problem in the non-asymptotic regime; this coincides with the problem described in \\eqref{eqn:objective:h}-\\eqref{eqn:constraint:resources:zero:h} with given $h<+\\infty$. \nFor clarity, we translate the description of $\\varphi$ to a policy used for a scaled system with the notation described in Section~\\ref{subsec:asym_regime}.\n\n\nFor a ranking $o\\in\\mathscr{O}$, the index policy $\\varphi$ \\emph{activates} a sub-process in the first PS pair $\\iota\\in[N]$ in ranking $o$ with $Z^{\\varphi,h}_{\\iota}(t)>0$ and the action and capacity constraints holding; that is, $\\varphi$ selects a sub-process $(i_{\\iota},k)$ ($k\\in[h]$) satisfying $N^{\\varphi}_{i_{\\iota},k}(t)=n_{\\iota}$ and sets $a^{\\varphi}_{i_{\\iota},k }(\\bm{N}^{\\varphi}_h(t))$ to $1$. The condition $Z^{\\varphi,h}_{\\iota}(t)>0$ is required because there has to be some sub-processes in PS pair $\\iota$ for us to be able to activate.\nOnce a sub-process in PS pair $\\iota$ is selected for activation, the action constraint~\\eqref{eqn:constraint:action:h} for RT $\\ell_{\\iota}$ achieves equality: there is exactly one active sub-process for a specific RT $\\ell\\in[L]$.\nResource units in associated resource pools are reserved for this activated sub-process in PS pair $\\iota$. \nIn this way, $L$ sub-processes in $L$ different PS pairs will be activated iteratively, according to the ranking $o$, for the $L$ RTs.\n\nUnder the index policy $\\varphi$, the transition matrix of process $\\bm{Z}^{\\varphi,h}(t)$ is determined by the value of $\\sum_{k\\in[h],N^{\\varphi}_{i_{\\iota},k}(t)=n_{\\iota}}a^{\\varphi}_{i_{\\iota},k }(\\bm{N}^{\\varphi}_h(t))$ for each PS pair $\\iota\\in[N]$, which is either $0$ or $1$ and is dependent on $\\bm{N}^{\\varphi}_h(t)$ through only $\\bm{Z}^{\\varphi,h}(t)$.\nDefine $\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z})$, $\\iota\\in[N]$, $\\bm{z}\\in \\mathscr{Z}$, to be the ratio of the number of active sub-processes in PS pair $\\iota$, for which the corresponding sub-patterns are prepared to accept a request, to the total number of sub-processes in this PS pair under $\\varphi$, when the proportions of sub-processes in all PS pairs are currently specified by $\\bm{z}$. \nThat is, at time $t$, for $\\iota\\in[N]$,\n\\begin{equation}\n\\upsilon^{\\varphi,h}_{\\iota}\\bigl(\\bm{Z}^{\\varphi,h}(t)\\bigr)= \\frac{\\sum_{k\\in[h],N^{\\phi}_{i_{\\iota},k}(t)=n_{\\iota}}a^{\\phi}_{i_{\\iota},k}(\\bm{N}^{\\varphi}_h(t))}{IhZ^{\\varphi,h}_{\\iota}(t)},\n\\end{equation}\nwhere we recall that \nthe numerator on the right hand side relies on $\\bm{N}^{\\varphi}_h(t)$ through $\\bm{Z}^{\\varphi,h}(t)\\in\\mathscr{Z}$.\nNote that, for arbitrarily large $h$, the value of $\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z})Ihz_{\\iota}$ ($\\bm{z}\\in\\mathscr{Z}$), representing the number of active sub-processes in PS pair $\\iota$, is never more than $1$ because the policy $\\varphi$ must satisfy the action constraints \\eqref{eqn:constraint:action:h}.\nLet $\\bm{\\upsilon}^{\\varphi,h}(\\bm{z})=(\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z}):\\ \\iota\\in[N])$.\nAlthough different tie-breaking rules lead to the same process $\\bm{Z}^{\\varphi,h}(t)$, \nwe shall stipulate that, when there is more than one sub-process $(i,k)$ ($i\\in [I], k\\in[h]$) in the same PS pair available for activation, we prioritize the one with the smaller value of $k$.\nIn this context, the variables $\\bm{\\upsilon}^{\\varphi,h}(\\bm{z})$, $\\bm{z}\\in\\mathscr{Z}$, provide sufficient information for the index policy $\\varphi$ to make decisions on the counting process $\\bm{N}^{\\varphi}_h(t)$.\n\n\n\n\nLet $\\zeta_{\\iota}^{\\varphi,h}(\\bm{z})$ represent the maximal proportion of sub-processes in PS pair $\\iota$ that can be active if we consider only the capacity constraints defined by \\eqref{eqn:constraint:resources:h} (neglecting the action constraints defined by \\eqref{eqn:constraint:action:h}) with proportions of sub-processes in all PS pairs specified by $\\bm{z}$ under policy $\\varphi$.\nWe obtain that, for $\\iota\\in[N]\\backslash\\{d(\\ell):\\ \\ell\\in[L]\\}$,\n\\begin{equation}\\label{eqn:tilde_z}\n\\zeta^{\\varphi,h}_{\\iota}(\\bm{z}) =\n\\min\\Biggl\\{z_{\\iota},\\max\\biggl\\{0,\\min\\limits_{j\\in\\mathscr{J}_i} \\frac{1}{w_{j,i}Ih}\\Bigl\\lceil hC_{j}^{0}(1-\\epsilon^h_{j,\\iota})-\\sum\\limits_{\\iota'=1}^{N}w_{j,i_{\\iota'}}n_{\\iota'}z_{\\iota'}Ih-\\sum\\limits_{\\iota'\\in \\mathscr{N}_{\\iota}^{+}}w_{j,i_{\\iota'}}\\upsilon^{\\varphi,h}_{\\iota'}(\\bm{z})z_{\\iota'}Ih\\Bigr\\rceil\\biggr\\}\\Biggr\\},\n\\end{equation}\nwhere $\\mathscr{N}_{\\iota}^{+}$, $\\iota\\in[N]$, is the set of PS pairs $\\iota'\\in[N]$ with $\\iota' <\\iota$ (with higher priorities than pair $\\iota$) with respect to ranking $o$, \nand $\\epsilon^h_{j,\\iota}\\in [0,1]$ corresponds to $\\bar{\\epsilon}_{j,\\iota}$ in \\eqref{eqn:constraint:resources:epsilon:1}.\n\nHere, the parameter $\\epsilon^h_{j,\\iota}$ is defined so that\n\\begin{equation}\\label{eqn:epsilon_condition}\n0<\\lim\\limits_{h\\rightarrow +\\infty}\\epsilon^h_{j,\\iota} < \\lim\\limits_{h\\rightarrow +\\infty}\\epsilon^h_{j,\\iota'} \\leq 1\n\\end{equation}\nfor any $\\iota < \\iota'$, $w_{j,i_{\\iota}}>0$ and $w_{j,i_{\\iota'}}>0$.\nWe need $\\epsilon^h_{j,\\iota}$ to indicate the priorities of PS pairs in resource pool $j$, because\nthe last term in \\eqref{eqn:tilde_z},\n$\\sum_{\\iota'\\in \\mathscr{N}_{\\iota}^{+}}w_{j,i_{\\iota'}}\\upsilon^{\\varphi,h}_{\\iota'}(\\bm{z})z_{\\iota'}Ih$,\nis $o(h)$.\nIn particular, \nin order to follow the strict capacity constraints described in \\eqref{eqn:constraint:resources:h}, we need to define the $\\epsilon^h_{j,\\iota}$ so that $\\epsilon^h_{j,\\iota}C_{j}^0 h \\geq w_{j,i_{\\iota}}-1$\nfor all $j\\in[J]$, $\\iota\\in[N]$ and $h\\in\\mathbb{N}_+$ and $\\lim\\limits_{h\\rightarrow+\\infty}\\epsilon^h_{j,\\iota}$ exists.\nLet $\\bm{\\epsilon}^h \\coloneqq (\\epsilon^h_{j,\\iota}:\\ j\\in[J],\\iota\\in[N])$ and $\\bm{\\epsilon} \\coloneqq \\lim\\limits_{h\\rightarrow +\\infty}\\bm{\\epsilon}^h$. \nDefine $\\mathscr{E}^h$, $h\\in\\mathbb{N}_+ \\cup \\{+\\infty\\}$, and $\\Psi$ as the sets of all such vectors $\\bm{\\epsilon}^h$ and sequences of such vectors $\\psi\\coloneqq (\\bm{\\epsilon}^1,\\bm{\\epsilon}^2,\\ldots,)$, respectively.\n\n\n\nEquation \\eqref{eqn:epsilon_condition} specifies possible trajectories of $\\bm{\\epsilon}^h$ as $h\\rightarrow +\\infty$, and is required for subsequent proofs of asymptotic optimality.\nNote that, although the asymptotic regime is a limiting situation, using an asymptotically-optimal policy is likely to be appropriate for systems with finite but large $h$. \n\n\nIn \\eqref{eqn:tilde_z}, the value of $\\zeta^{\\varphi,h}_{\\iota}(\\bm{z})$ is constrained by the remaining capacities of relevant resource pools, the proportion of sub-processes currently in PS pair $\\iota$ and the proportions of active sub-processes in PS pairs with higher priorities.\nRecall that $\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z})$ represents the proportion of active sub-processes in PS pair $\\iota$, for which the corresponding sub-patterns are prepared to accept a request, when the proportions of sub-processes in all PS pairs are currently specified by $\\bm{z}$.\nTogether with the action constraints described in \\eqref{eqn:constraint:action:h}, under an index policy $\\varphi$, \nfor $z_{\\iota}>0$,\\vspace{-0.15cm}\n\\begin{equation}\\label{eqn:probability_active}\n\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z}) = \\frac{1}{z_{\\iota}hI}\n\\min\\biggl\\{\\zeta^{\\varphi,h}_{\\iota}(\\bm{z})hI,\\ \\max\\Bigl\\{0,1- \\sum\\limits_{\\begin{subarray}\\ \\iota'\\in \\mathscr{N}^{+}_{\\iota}\\\\ l_{\\iota'}=l_{\\iota}\\end{subarray}}\\zeta^{\\varphi,h}_{\\iota'}(\\bm{z})hI\\Bigr\\}\\biggr\\}.\n\\end{equation}\nIf $z_\\iota = 0$, then there are no sub-processes in PS pair $\\iota$ and so $\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z})$ can take any value in $[0,1]$ without making a difference to the evolution of the process.\nFor completeness, define, for $\\bm{z}$ with $z_{\\iota}=0$ and $\\bm{z}_{\\iota}^{x} \n\\coloneqq (z_{1},z_{2},\\ldots,z_{\\iota-1},x,z_{\\iota+1},\\ldots,z_{N})$, \n$\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z})=\\lim\\limits_{x\\downarrow 0}\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z}^{x}_{\\iota})$.\nFor any given $\\bm{z}\\in\\mathscr{Z}$, $\\zeta^{\\varphi,h}_{\\iota}(\\bm{z})$ and $\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z})$ can be obtained iteratively using equations \\eqref{eqn:tilde_z} and \\eqref{eqn:probability_active} from $\\iota=1$ to $N$.\n\n\n{\\bf Remark}\nAlthough capacity constraints were not considered in \\cite{weber1990index}, the construction of $\\bm{\\upsilon}^{\\varphi,h}(\\bm{z})$ ($\\bm{z}\\in\\mathscr{Z}$) follows ideas similar to those used in that paper. \nRecall that, for a given ranking, $o\\in\\mathscr{O}$, the policy $\\bar{\\varphi}(o)$ is generated by Algorithm~\\ref{algo:varphi_gamma} and is infeasible for the original problem. \nThis gives rise to the interesting property that values of $\\upsilon^{\\varphi,h}_{\\iota}(\\bm{z})$ and $\\alpha^{\\bar{\\varphi}(o)}_{i_{\\iota},k}(n_{\\iota})$ ($k\\in[h]$) for all $h\\in\\mathbb{N}_+\\cup\\{+\\infty\\}$, are always independent of those of PS pairs $\\iota'$ with $\\iota' > \\iota$: the PS pairs with lower priorities than $\\iota$.\nThe property is important \nfor the proofs of Theorems~\\ref{theorem:main_second} and \\ref{theorem:main}.\n\n\\vspace{-0.3cm}\n\n\\subsection{Asymptotic Optimality}\\label{sec:asym_opt}\n\\vspace{-0.3cm}\n\nFor given $h\\in\\mathbb{N}_+$, define the long-run average revenue normalized by $h$ of the resource allocation problem under policy $\\phi$ to be $R^{\\phi,h}$; that is, \\vspace{-0.2cm}\n\\begin{equation}\nR^{\\phi,h} \\coloneqq \\frac{1}{h}\\sum\\limits_{i\\in[I]} \\sum\\limits_{k\\in[h]}\\sum_{n_i\\in\\mathscr{N}_i} \\pi_{i,k}^{\\phi,h}(n_i) \\Bigl(r_{\\ell(i)} \\mu_i - \\sum_{j\\in\\mathscr{J}} w_{j,i} \\varepsilon_j\\Bigr)n_i, \\vspace{-0.2cm}\n\\end{equation}\nthe objective function described in \\eqref{eqn:objective:h}.\n\\vspace{-0.3cm}\n\\begin{definition}\nWe say that the index policy $\\varphi$ derived from PS ranking $o$ by iterating \\eqref{eqn:tilde_z} and \\eqref{eqn:probability_active} is \\emph{asymptotically optimal} if \n$\n\\lim\\limits_{\\lVert \\bm{\\epsilon}\\rVert \\to \\bm{0}}\\lim\\limits_{h\\rightarrow+\\infty }|R^{\\varphi,h}-\\max\\limits_{\\phi\\in\\Phi}R^{\\phi,h}| = 0$.\n\\end{definition}\nRecall that the index policy $\\varphi$ described in Section~\\ref{subsec:policies} is dependent on the parameter $\\bm{\\epsilon}^h$ with $\\bm{\\epsilon} \\coloneqq \\lim\\limits_{h\\rightarrow +\\infty}\\bm{\\epsilon}^h$. The $\\bm{\\epsilon}$ is used to guarantee strict priorities of the sub-processes in the asymptotic regime as discussed in Section~\\ref{subsec:policies}. \nThe policy $\\bar{\\varphi}(o)$, proposed in Section~\\ref{subsubsec:priorities_of_pairs} for the relaxed problem, is generally not applicable to the original resource allocation problem.\nAlthough the policies $\\bar{\\varphi}(o)$ and $\\varphi$ both rely on the same ranking $o\\in\\mathscr{O}$, they are different policies. \n\\begin{theorem}\\label{theorem:main_second}\nFor given $o\\in\\mathscr{O}$, \n$\\varphi$ derived from $o$ by iterating \\eqref{eqn:tilde_z} and \\eqref{eqn:probability_active} is asymptotically optimal if and only if\n\\vspace{-0.5cm}\n\\begin{equation}\\label{eqn:main_second}\n\\lim\\limits_{h\\rightarrow +\\infty}|R^{\\bar{\\varphi}(o),h} -\\max\\limits_{\\phi\\in\\Phi}R^{\\phi,h}|=0.\n\\end{equation}\n\\end{theorem}\nThe proof is given in Appendix~\\ref{app:theorem:main_second}.\nTheorem~\\ref{theorem:main_second} indicates that asymptotic optimality of $\\varphi$ is equivalent to the convergence between $R^{\\bar{\\varphi}(o),h}$ and the maximized long-run average revenue of the original problem as $h\\rightarrow +\\infty$.\nIt is proved by showing the existence of $\\lim_{h\\rightarrow +\\infty} \\lim_{t \\rightarrow +\\infty}\\mathbb{E}[\\bm{Z}^{\\bar{\\varphi}(o),h}(t)]$ and\na global attractor of the process $\\bm{Z}^{\\varphi,h}(t)$ as $t,h\\rightarrow +\\infty$ and $\\lVert\\bm{\\epsilon}\\rVert\\rightarrow 0$, and specifically that they coincide with each other.\nThe long-run average revenue $R^{\\varphi,h}$ then coincides with $R^{\\bar{\\varphi}(o),h}$ as $h\\rightarrow +\\infty$ and $\\lVert \\bm{\\epsilon}\\rVert \\rightarrow 0$. \n\n\nA similar condition relevant to the global attractor was required in \\cite{weber1990index} for asymptotic optimality of the Whittle index policy in a general RMABP. It does not necessarily hold. However, in our problem, each sub-process is a queueing process with the departure rate increasing in its queue size.\nSuch a sub-process is a special case of a general bandit process.\nWe prove in general that the underlying process $\\bm{Z}^{\\varphi,h}(t)$, regardless of its initial point, will converge to any specified neighborhood of a fixed point almost surely as $t,h\\rightarrow +\\infty$ and $\\lVert \\bm{\\epsilon}\\rVert \\rightarrow 0$.\nDetailed proofs are provided in Appendix~\\ref{app:theorem:main_second}.\n\n\nTheorem~\\ref{theorem:main_second}, in itself, does not provide a verifiable condition.\nThis is given in our next theorem. If there exists $H\\in\\mathbb{R}$ such that, for all $h>H$, the system is decomposable, we say the system is decomposable in the asymptotic regime.\n\\begin{theorem}\\label{theorem:main}\nIf the capacity constraints described in \\eqref{eqn:constraint:resources:h} (or equivalently \\eqref{eqn:constraint:resources}) are decomposable with decomposable multipliers $\\pmb{\\gamma}\\in\\mathbb{R}^J_0$ in the asymptotic regime, then there exist $\\bm{\\nu}\\in\\mathbb{R}^L$ and a PS pair ranking $o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{\\nu})$ such that the index policy $\\varphi$ derived from $o$ is asymptotically optimal.\n\\end{theorem}\nThe proof, based on Theorem~\\ref{theorem:main_second}, is given in Appendix~\\ref{app:theorem:main}. \nRecall that we discussed decomposability of multipliers in Section~\\ref{sec:relaxation}\nand provided examples of provably decomposable systems.\n\n\n{\\bf Remark}\nTheorem~\\ref{theorem:main} binds asymptotic optimality of $\\varphi$ to the decomposability of the relaxed problem. \nFor a decomposable system (see Definition~\\ref{define:decomposable}), there always exists a ranking $o\\in\\mathscr{O}$ such that $\\bar{\\varphi}(o)$ is optimal for the relaxed problem.\nIf a system is decomposable in the asymptotic regime \nthen \\eqref{eqn:main_second} is satisfied.\nThis follows because, for any $h\\in\\mathbb{N}_+\\cup\\{+\\infty\\}$, $R^{\\varphi,h} \\leq \\max_{\\phi\\in\\Phi}R^{\\phi,h} \\leq \\max_{\\phi\\in\\tilde{\\Phi}}R^{\\phi,h}$ where $\\Phi$ and $\\tilde{\\Phi}$ are the sets of feasible policies for the original and relaxed problem, and $R^{\\varphi,h}$ coincides with $R^{\\bar{\\varphi}(o),h}$ as $h\\rightarrow +\\infty$ and $\\lVert\\bm{\\epsilon}\\rVert \\rightarrow 0$.\n\n\n\n\nSimilarly, we say the system is in heavy traffic in the asymptotic regime if there exists $H\\in\\mathbb{R}$ such that, for all $h>H$, the system is in heavy traffic.\n\\vspace{-0.3cm} \n\\begin{corollary}\\label{coro:main}\nIf the system is in heavy traffic in the asymptotic regime and is weakly coupled,\nthen there exist decomposable multipliers $\\pmb{\\gamma}\\in\\mathbb{R}^J_0$, satisfying \\eqref{eqn:equal_opt:a}-\\eqref{eqn:equal_opt:c}, and a PS pair ranking $o\\in\\mathscr{O}^*(\\bm{0})$,\nso that the index policy $\\varphi$ derived from $o$ is asymptotically optimal. \\vspace{-0.3cm}\n\\end{corollary}\nThe proof, invoking Theorem~\\ref{theorem:main} and Proposition~\\ref{prop:equal_opt}, is given in Appendix~\\ref{app:coro:main}. \nIn particular, the PS pair ranking $o$, described in Corollary~\\ref{coro:main}, exists in closed form: its PS pairs are ranked according to the descending order of $\\Xi_{\\iota}^*$ with $\\bm{\\nu}=\\bm{0}$. \n\n\n\n\\section{Numerical Results}\\label{sec:example}\n\n\nWe demonstrate by simulation the performance of the index policy $\\varphi$, defined in Section~\\ref{subsec:policies} (or equivalently, defined in Section~\\ref{sec:index_policy} for a given $h<+\\infty$), in systems that are not weakly coupled or in heavy traffic in comparison with baseline policies.\n\n\n\nIn this section, the confidence intervals of all the simulated average revenues at the $95\\%$ level based on the Student's t-distribution are maintained within $\\pm 3\\%$ of the observed mean.\nWe recall that the capacities $\\bm{C}$ and arrival rates $\\bm{\\lambda}$ are scaled by the scaling parameter $h$.\n\n\n\n\n\n\n\n\n\n\nAlong with the fixed point iteration method proposed in Section~\\ref{subsubsec:derive_ranking},\nwe have been able to find systems which are not weakly coupled or in heavy traffic but are decomposable.\nHere, we provide two examples, where $L$ and $J$ are sampled uniformly from the sets $\\{2,3,4,5\\}$ and $\\{10,11,\\ldots,20\\}$, respectively.\nLet\n$\\epsilon_M = \\max_{j\\in[J],\\iota\\in[N]}\\epsilon_{j,\\iota}$.\nWe refer to an index policy $\\varphi$ with specific $\\epsilon_M\\in [0,1]$ as INDEX($\\epsilon_M$).\n\n\n\\begin{figure}[t]\n\\centering\n\\subfigure[]{\\includegraphics[width=0.45\\linewidth]{OR-OPT-seed457-v3.eps}\\label{fig1:opt_v1}}\n\\subfigure[]{\\includegraphics[width=0.45\\linewidth]{OR-OPT-seed406-v3.eps}\\label{fig1:opt_v3}}\n\\caption{Relative difference of a specific policy to $R(o_{k^*})$ against the scaling parameter of the system: (a) diverse performance and non-zero decomposable multipliers; (b) similar performance and non-zero decomposable multipliers; and (c) zero decomposable multipliers.}\\label{fig:fig1}\n\\vspace{-0.5cm}\n\\end{figure}\n\n\nWe consider three baseline policies: two greedy policies that prioritize patterns with maximal reward rates and minimal cost rates, and one policy randomly uniformly selecting an available pattern for each request type. We refer to the three policies as \\emph{Max-Reward}, \\emph{Min-Cost} and \\emph{Random}.\nThe Max-Reward and Min-Cost policies are in fact index policies with PS pairs ranked according to the descending order of their reward rates and the ascending order of their cost rates, respectively. \nThe Random policy was proposed by \\cite{stolyar2017large} for a VM replacement problem, aiming to minimize the system blocking probabilities in the case with finite capacities.\nIt is not a feasible policy of the original problem with capacity constraints \\eqref{eqn:constraint:resources} because it does not reserve resource units for a specific pattern that is more profitable than the others.\nWhen there are not enough resource units in a pool to accommodate multiple request types that have chosen their patterns involving this pool, the Random policy will always assign the resource units to the request that arrives first. \n\n\nIn Figure~\\ref{fig:fig1}, we compare the performance of INDEX(0), INDEX($0.01$), the baseline policies and $\\bar{\\varphi}(o_{k^*})$, where $o_{k^*}$ is the ranking of the multipliers $\\pmb{\\gamma}_{k^*}$ resulting from the fixed point iteration method (described in Section~\\ref{subsubsec:derive_ranking}) with parameter $c=0.5$ and initial point $\\pmb{\\gamma}_0=\\bm{0}$. \nThe system parameters are listed in Appendix~\\ref{app:simulation:opt} and are generated by pseudo-random functions. \nThe discovered multipliers $\\pmb{\\gamma}_{k^*}$ for simulations in Figures~\\ref{fig1:opt_v1} and \\ref{fig1:opt_v3} are \n$(269.555,0,0,0,0,273.11,0,$ $347.995,0,0,0,8.323\\times 10^{-7},9.726\\times 10^{-5},0)$ and $\\bm{0}$,\nrespectively, satisfying $\\mathcal{T}^{o_{k^*}}(\\pmb{\\gamma}_{k^*})=\\pmb{\\gamma}_{k^*}$ in the asymptotic regime.\nBy Proposition~\\ref{prop:converge_gamma}, these $\\pmb{\\gamma}_{k^*}$ are decomposable multipliers and, by Theorem~\\ref{theorem:main}, the index policies derived from the rankings $o_{k^*}$ are asymptotically optimal.\nLet $R(o)\\coloneqq \\lim\\limits_{h\\rightarrow +\\infty}R^{\\bar{\\varphi}(o),h}$ ($o\\in\\mathscr{O}$) of which the existence is guaranteed in the proof of Theorem~\\ref{theorem:main_second}.\nFor the decomposable systems with $h<+\\infty$ and $\\bar{\\varphi}(o_{k^*})$ optimal for the relaxed problem in the asymptotic regime, the asymptotic long-run average revenue, $R(o_{k^*})$, is no less than the optimum of the original problem: $R(o_{k^*})$ is an upper bound of $R^{\\phi,h}$ for any $\\phi\\in\\Phi$.\n\nFigure~\\ref{fig:fig1} illustrates the relative difference of average revenues,\n$\\bigl(R(o_{k^*}) - R^{\\phi,h}\\bigr)\/R(o_{k^*})$ for $\\phi=\\text{INDEX}(0),\\text{INDEX}(0.01)$, Max-Reward, Min-Cost and Random,\nagainst the scaling parameter $h$. \n\n\nIn this context, there are two aspects of performance evaluation presented in Figure~\\ref{fig:fig1}.\nFirst, we see the performance of the index policies in the non-asymptotic regime by comparing their long-run average revenues with an upper bound on the optimum. \nIn particular, Figures~\\ref{fig1:opt_v1} and \\ref{fig1:opt_v3} show that INDEX($0.01$) significantly outperforms INDEX(0) for large $h$: the small but positive $\\bm{\\epsilon}$ does affects the performance of $\\varphi$.\nThe performance of INDEX($0.01$) is close to the upper bound of the optimal solution with relative difference less than $5\\%$ for $h$ greater than $50$ in all three examples: its performance degradation against the optimal solution is limited in the non-asymptotic regime.\n\n\nOn the other hand, by comparing to $R(o_{k^*})$, a trend of coincidence between $R^{\\text{INDEX}(0.01),h}$ and $R(o_{k^*})$ is observed in Figure~\\ref{fig:fig1} as $h$ increases from $1$ to $100$, consistent with the proved asymptotic optimality of $\\varphi$.\nRecall that the examples presented in Figure~\\ref{fig:fig1} are not for systems with weak coupling or heavy traffic but the index policy $\\varphi$ is still proved to be asymptotically optimal here.\nAlso, the performance of $\\varphi$ is close to the optimum without requiring extremely large $h$.\n\n\n\n\\begin{figure}[t]\n\\vspace{-0.5cm}\n\\centering\n\\subfigure[]{\\includegraphics[width=0.45\\linewidth]{OR-nonOPT-seed10-max50-v3.eps}\\label{fig2:nonopt_v1}}\n\\subfigure[]{\\includegraphics[width=0.45\\linewidth]{OR-nonOPT-seed10-scale50-v3.eps}\\label{fig2:nonopt_v2}}\n\\caption{(a) Relative difference of a specific policy to $R(o_{k^*})$ against scaling parameter of the system; (b) Relative difference of a specific policy to $R(o_k)$ against $k$.}\\label{fig:fig2}\n\\vspace{-0.5cm}\n\\end{figure}\n\n\nIn Figure~\\ref{fig:fig2}, we consider another example with multipliers that are not decomposable (that is, $\\mathcal{T}^{o_{k^*}}(\\pmb{\\gamma}_{k^*})\\neq \\pmb{\\gamma}_{k^*}$).\nSimilar to Figure~\\ref{fig:fig1}, in Figure~\\ref{fig2:nonopt_v1}, we plot the relative difference of revenue of INDEX(0), INDEX($0.01$) and the baseline policies to $R(o_{k^*})$ against the scaling parameter; while, in Figure~\\ref{fig2:nonopt_v2}, fixing the scaling parameter $h=50$, we illustrate curves of the relative differences, $\\bigl(R(o_k)-R^{\\phi,h}\\bigr)\/R(o_k)$ ($\\phi=\\text{INDEX}(0),\\text{INDEX}(0.01), \\text{Max-Reward}, \\text{Min-Cost}, \\text{Random}$), against the number of iterations $k$ for the fixed point iteration method. \nNote that the rankings $o_k$ are potentially different as $k$ varies, so as $R(o_k)$.\nIn Figure~\\ref{fig2:nonopt_v1}, the INDEX($0$) and INDEX($0.01$) are proposed based on the ranking $o_{k^*}$, while, with slightly abused notation, in Figure~\\ref{fig2:nonopt_v2}, INDEX($0$) and INDEX($0.01$) represent the index policies $\\varphi$, which are derived from the rankings $o_k$ associated with the varying $k$, with $\\epsilon_M=0$ and $0.01$, respectively. \nThe system parameters for the simulations in Figure~\\ref{fig:fig2} are listed in Appendix~\\ref{app:simulation:opt}. \n\n\nFigure~\\ref{fig2:nonopt_v1} can be read in a similar way to Figure~\\ref{fig:fig1} except that $R(o_{k^*})$ is not a proved upper bound for the average revenue for the original problem.\nHere, INDEX($0$) and INDEX($0.01$) perform similarly and numerically converge to $R(o_{k^*})$ as $h$ increases although the system is not necessarily decomposable. \nThe convergence is consistent with Theorem~\\ref{theorem:main_second} which generally holds without requiring decomposability.\nOn the other hand, for each finite $h$ (which corresponds to the non-asymptotic regime), \nINDEX($0$) and INDEX($0.01$) significantly outperform all the other baseline policies, although the system is not proved to be decomposable, and their performance advantages are likely to maintain as $h$ continues increasing.\n\nFigure~\\ref{fig2:nonopt_v2} illustrates the performance trajectory as the iteration number $k$ (the x-axis) for the fixed point iteration method increases for a system with $h=50$ (in the non-asymptotic regime).\nRecall that, for the simulations presented here, the average revenues of INDEX($0$) and INDEX($0.01$) and $R(o_k)$ are varying with $k$ while all of the baseline policies are independent of $k$. \nWe observe a shape jump on the curves between $k=1$ and $5$.\nThis is caused by the initial setting, $\\pmb{\\gamma}_0=\\bm{0}$, which is not a good choice of multipliers.\nAfter several steps of the iteration method, the curves in Figure~\\ref{fig2:nonopt_v2} become almost flat; \nthat is, the values of $R(o_k)$, $R^{\\text{INDEX}(0)}$ and $R^{\\text{INDEX}(0.01)}$ become relatively stable for $k=5$ to $50$. \nAlso, in Figure~\\ref{fig2:nonopt_v2}, after the performance becomes stable, INDEX($0$) and INDEX($0.01$) achieve clearly higher long-run average revenues than those of the baseline policies: given the poor setting at the beginning, the fixed point iteration method can still lead to a reasonably good ranking $o_{k^*}$ and its associated multipliers $\\pmb{\\gamma}_{k^*}$.\n\n\n\n\\vspace{-0.3cm}\n\\section{Conclusions}\\label{sec:conclusions}\n\\vspace{-0.3cm}\n\n\nWe have modeled a resource allocation problem, described by \\eqref{eqn:objective}, \\eqref{eqn:constraint:action} and \\eqref{eqn:constraint:resources}, as a combination of various RMABPs coupled by limited capacities\nof the shared resource pools, which are shared, competed for, and reserved by requests. \nThis presents us with an optimization problem for a stochastic system, aimed at maximizing the long-run average revenue by dynamically accommodating requests into appropriate resource pools.\n\n\n\n\nUsing the ideas of Whittle relaxation \\cite{whittle1988restless} and the asymptotic optimality proof of \\cite{weber1990index},\nwe have proved the asymptotic optimality of an index policy (referred to as $\\varphi$) if the capacity constraints are decomposable with multipliers $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$ (Theorem~\\ref{theorem:main}).\nThe asymptotic optimality is proved based on the existence of a global attractor $\\bm{z}\\in\\mathscr{Z}$ for the underlying process $\\bm{Z}^{\\varphi,h}(t)$ as $h\\rightarrow +\\infty$ and $\\bm{\\epsilon}\\rightarrow \\bm{0}$. \nWe have proved in general that such a global attractor exists, and then proposed a necessary and sufficient condition for asymptotic optimality in Theorem~\\ref{theorem:main_second}: the performance of the attractor $\\bm{z}$ approaches the optimum of the original problem in the asymptotic regime.\nThis condition holds if the system is decomposable. \n\n\nWe have proved a sufficient condition, described as the property of being weakly coupled and in heavy traffic, \nfor the existence of such decomposable multipliers as well as the asymptotic optimality of policy $\\varphi$ (Corollary~\\ref{coro:main}).\nThe property is not necessary, but is easy to verify and covers a significant class of resource allocation problems. We have listed examples of systems with the property satisfied in Section~\\ref{subsec:sufficient_condition}.\n\n\n\n\nIn a general system, we have proposed a fixed point method to fine tune the multipliers $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$ and a ranking $o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{0})$. \nWe have proved that, if there exists a fixed point $\\pmb{\\gamma}\\in\\mathbb{R}_0^J$ of the function $\\mathcal{T}^o$ satisfying $o\\in\\mathscr{O}(\\pmb{\\gamma},\\bm{0})$, then this $\\pmb{\\gamma}$ is a vector of decomposable multipliers.\nWe have successfully discovered the decomposable multipliers in some situations without assuming weak coupling or heavy traffic by applying the fixed point method.\nAlso, in Section~\\ref{sec:example}, we have compared the index policy $\\varphi$ with different parameter $\\bm{\\epsilon}$ to baseline policies through simulations for systems that are not weakly coupled or in heavy traffic in the non-asymptotic regime. The index policy achieves clearly higher performance than the baseline policies.\nTo the best of our knowledge, no existing work provides rigorous asymptotic optimality for a resource allocation problem where dynamic allocation, competition and reservation are permitted.\n\n\\vspace{-0.3cm}\n\n\n\n\\section*{Acknowledgment}\nJing Fu's and Peter Taylor's research is supported by the Australian Research Council (ARC) Centre of Excellence for the Mathematical and Statistical Frontiers (ACEMS) and ARC Laureate Fellowship FL130100039.\n\\vspace{-0.1cm}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\bibliographystyle{apalike}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{}\n\nThe necessity to generalize the Dirac equations and spinors in momentum space\nto free unstable spin-$1\/2$ particles has recently been recognized in\nconnection with the wave-function renormalizations of mixed systems of Dirac\n\\cite{Kniehl:2008cj,Kniehl:2014dra} and Majorana fermions\n\\cite{Kniehl:2014gfa}.\nIn this report, we discuss their construction when the fundamental requirement\nof Lorentz covariance is taken into account.\nWe also derive the generalized adjoint Dirac equations and spinors, and explain\nthe very simple relation that exists, in our formulation, between the\ngeneralized Dirac equations and spinors and the corresponding expressions for\nstable fermions.\\footnote{%\nFor brevity, spin-$1\/2$ particles are henceforth called fermions.}\nWe illustrate the application of the generalized spinors by evaluating the\nprobability density. \n\nDefining the complex mass $M$ of the unstable fermion as the zero of the\ninverse propagator, a frequently used parametrization is \\cite{Smith:1996xz}\n\\begin{equation}\nM=m-i\\frac{\\Gamma}{2},\n\\label{eq:pole}\n\\end{equation}\nwhere $m$ and $\\Gamma$ are its mass and width, respectively.\n\nWe define the four-momentum of the unstable fermion according to\n\\begin{equation}\np^0=M\\gamma c,\\qquad\n\\vec{p}=M\\gamma\\vec{v},\n\\label{eq:p}\n\\end{equation}\nwhere $\\gamma=(1-\\vec{v}^{\\,2}\/c^2)^{-1\/2}$ and $\\vec{v}$ is the particle's\nvelocity in the chosen inertial frame.\nSince Eq.~(\\ref{eq:p}) differs from the expressions of special relativity\nfor stable fermions by only the constant factor $M\/m$, $p^\\mu=(p^0,\\vec{p}\\,)$\ntransforms as a four-vector.\nEquation~(\\ref{eq:p}) can also be written as\n\\begin{equation}\np^\\mu=Mu^\\mu,\n\\label{eq:u}\n\\end{equation}\nwhere $u^\\mu=\\gamma(c,\\vec{v}\\,)$ is the four-velocity.\nFrom Eqs.~(\\ref{eq:p}) and (\\ref{eq:u}) one finds the basic relation\\footnote{%\nIn this paper, we adopt the notations and conventions of\nRefs.~\\cite{bjorken,mandl}.}\n\\begin{equation}\np_\\mu p^\\mu={p^0}^2-\\vec{p}^{\\,2}=M^2c^2.\n\\label{eq:ps}\n\\end{equation}\nEvaluating the real and imaginary parts of $p^0$ from Eq.~(\\ref{eq:ps}) and\nusing the relation $\\mathop{\\mathrm{Im}}\\nolimits\\vec{p}^{\\,2}=-[m\\Gamma\/(m^2-\\Gamma^2\/4)]\\mathop{\\mathrm{Re}}\\nolimits\\vec{p}^{\\,2}$\nthat follows from Eqs.~(\\ref{eq:pole}) and (\\ref{eq:p}), one can express $p^0$\nin terms of $\\mathop{\\mathrm{Re}}\\nolimits\\vec{p}^{\\,2}$, $m$, and $\\Gamma$ as\n\\begin{equation}\np^0=\nM\\left[\\frac{\\mathop{\\mathrm{Re}}\\nolimits\\vec{p}^{\\,2}+(m^2-\\Gamma^2\/4)c^2}{m^2-\\Gamma^2\/4}\\right]^{1\/2}.\n\\label{eq:p0}\n\\end{equation}\nIn the limit $\\Gamma\\to0$, $\\vec{p}^{\\,2}$ is real, $p^0=E\/c$, and\nEq.~(\\ref{eq:p0}) becomes\n\\begin{equation}\nE=(\\vec{p}^{\\,2}c^2+m^2c^4)^{1\/2},\n\\end{equation}\nthe well-known energy-momentum relation for stable particles.\nIn the rest frame, $\\vec{p}=\\vec{0}$ and Eq.~(\\ref{eq:p0}) reduces to $p^0=Mc$,\nin agreement with Eq.~(\\ref{eq:p}) when $\\vec{v}=\\vec{0}$.\n\nIn momentum space, the generalizations of the Dirac equations to free unstable\nfermions are\n\\begin{equation}\n(\\slashed{p}-Mc)u_r(\\vec{p}\\,)=0,\\qquad\n(\\slashed{p}+Mc)v_r(\\vec{p}\\,)=0,\n\\label{eq:dirac}\n\\end{equation}\nwhere $\\slashed{p}=p_\\mu\\gamma^\\mu$ and $r=1,2$ labels the two independent\nsolutions.\nRecalling Eq.~(\\ref{eq:p}), we note that Eq.~(\\ref{eq:dirac}) can be derived by\nmultiplying the corresponding Dirac equations for stable fermions by $M\/m$.\nThe four independent solutions can be written explicitly in the form\n\\begin{equation}\nu_r(\\vec{p}\\,)=\\left(\\frac{p^0+Mc}{2Mc}\\right)^{1\/2}\\left(\n\\begin{array}{c}\n\\chi_r \\\\\n\\frac{\\vec{\\sigma}\\cdot\\vec{p}}{p^0+Mc}\\chi_r\n\\end{array}\\right),\n\\qquad\nv_r(\\vec{p}\\,)=\\left(\\frac{p^0+Mc}{2Mc}\\right)^{1\/2}\\left(\n\\begin{array}{c}\n\\frac{\\vec{\\sigma}\\cdot\\vec{p}}{p^0+Mc}\\chi_r^\\prime \\\\\n\\chi_r^\\prime\n\\end{array}\\right),\n\\label{eq:spinor}\n\\end{equation}\nwhere $\\sigma^i$ are the Pauli matrices and $\\chi_r$ and $\\chi_r^\\prime$ are\ntwo-dimensional constant and orthogonal spinors frequently chosen as\n\\begin{equation}\n\\chi_1=\\chi_2^\\prime=\\left(\n\\begin{array}{c}\n1 \\\\\n0\n\\end{array}\\right),\n\\qquad\n\\chi_2=\\chi_1^\\prime=\\left(\n\\begin{array}{c}\n0 \\\\\n1\n\\end{array}\\right).\n\\label{eq:chi}\n\\end{equation}\nWith this choice, $u_r(\\vec{p}\\,)$ and $v_r(\\vec{p}\\,)$ are eigenstates of the\n$z$ component of spin in the rest frame of the fermion, with eigenvalue\n$+\\hbar\/2$ (spin up) for $u_1(\\vec{p}\\,)$ and $v_2(\\vec{p}\\,)$ and $-\\hbar\/2$\n(spin down) for $u_2(\\vec{p}\\,)$ and $v_1(\\vec{p}\\,)$.\n\nIncluding the space-time dependencies, the plane-wave solutions associated with\nthe spinors $u_r(\\vec{p}\\,)$ and $v_r(\\vec{p}\\,)$ are\n$u_r(\\vec{p}\\,)\\exp(-ip\\cdot x)$ and $v_r(\\vec{p}\\,)\\exp(ip\\cdot x)$,\nrespectively.\nUsing Eqs.~(\\ref{eq:pole})--(\\ref{eq:u}), we have\n\\begin{equation}\ne^{-ip\\cdot x}=e^{-imu\\cdot x}e^{-(\\Gamma\/2)u\\cdot x}.\n\\label{eq:exp}\n\\end{equation}\nThe first factor on the right-hand side of Eq.~(\\ref{eq:exp}) is the space-time\ndependence in the stable case, while the second factor reflects the fact that\nthe fermion is unstable.\nThe amplitude $u_r(\\vec{p}\\,)\\exp(-imu\\cdot x)$ is a solution of the usual\nDirac equation for stable fermions and is, therefore, time-reversal invariant.\nBy contrast, the second factor,\n$\\exp[-\\Gamma\\gamma(c^2t-\\vec{v}\\cdot\\vec{x})\/2]$, is not invariant under the\ntime-reversal transformation $t\\to-t$ and $\\vec{v}\\to-\\vec{v}$.\n\nAnother simple way to show that the generalized Dirac equations are not\ninvariant under time reversal is the following:\nwe recall that the operator that relates the wave functions at times $t$ and\n$t^\\prime=-t$ is antiunitary, namely of the form $KU$, where $U$ is a unitary\nmatrix and $K$ means complex conjugation.\nIf the Hamiltonian $H(t)$ at time $t$ involves the complex mass $M$, as is the\ncase in the formulation of the generalized Dirac equations, when $K$ acts on\n$H(t)$ it transforms $M\\to M^*$.\nAs a consequence, $H(t^\\prime)$ differs from $H(t)$ by the same change\n$M\\to M^*$, and the proof of time-reversal invariance, explained for instance\nin Ref.~\\cite{bjorken}, breaks down.\n\nThe Hermitian adjoints of Eq.~(\\ref{eq:dirac}) are\n\\begin{equation}\n\\bar{u}_r(\\vec{p}\\,)(\\slashed{p}^*-M^*c)=0,\\qquad\n\\bar{v}_r(\\vec{p}\\,)(\\slashed{p}^*+M^*c)=0,\n\\label{eq:diracc}\n\\end{equation}\nwhere\n\\begin{equation}\n\\bar{u}_r(\\vec{p}\\,)=u_r^\\dagger(\\vec{p}\\,)\\gamma^0,\\qquad\n\\bar{v}_r(\\vec{p}\\,)=v_r^\\dagger(\\vec{p}\\,)\\gamma^0\n\\end{equation}\nare the usual adjoint spinors and $\\slashed{p}^*=p_\\mu^*\\gamma^\\mu$.\nAt first sight, the presence of the complex conjugates $p_\\mu^*$ and $M^*$ seems\nto complicate matters.\nHowever, we note from Eq.~(\\ref{eq:u}) that $p^\\mu\/M=u^\\mu$ is real.\nTherefore, we have the important relation\n\\begin{equation}\n\\left(\\frac{p^\\mu}{M}\\right)^*=\\frac{p^\\mu}{M}.\n\\label{eq:ratio}\n\\end{equation}\nInserting $p_\\mu^*=(M^*\/M)p_\\mu$ and multiplying by $M\/M^*$,\nEq.~(\\ref{eq:diracc}) becomes\n\\begin{equation}\n\\bar{u}_r(\\vec{p}\\,)(\\slashed{p}-Mc)=0,\\qquad\n\\bar{v}_r(\\vec{p}\\,)(\\slashed{p}+Mc)=0.\n\\label{eq:diraca}\n\\end{equation}\nThe four generalized Dirac equations shown in Eqs.~(\\ref{eq:dirac}) and\n(\\ref{eq:diraca}) were postulated in Ref.~\\cite{Kniehl:2014dra} without\napplying Eq.~(\\ref{eq:ratio}) and with a different interpretation of the\nadjoint spinors $\\bar{u}_r(\\vec{p}\\,)$ and $\\bar{v}_r(\\vec{p}\\,)$.\nIn the case of unstable fermions, Eqs.~(\\ref{eq:dirac}) and (\\ref{eq:diraca})\nplay an important role in the implementation of the\nAoki-Hioki-Kawabe-Konuma-Muta (AHKKM) \\cite{Aoki:1982ed} renormalization\nconditions in general theories with intergeneration mixing, as pointed out\nin Refs.~\\cite{Kniehl:2008cj,Kniehl:2014dra,Kniehl:2014gfa}.\n\nSince $p^\\mu$ transforms as a four-vector, the proof of Lorentz covariance of\nEq.~(\\ref{eq:dirac}) follows the same steps as the proof of the Lorentz\ncovariance of the Dirac equation in coordinate space (see, for example,\nchapter~2 in Ref.~\\cite{bjorken}).\nSpecifically, if $p^\\mu$ and $u_r(\\vec{p}\\,)$ are the four-momentum and the\nspinor in the Lorentz frame $O$ and $p^{\\prime\\mu}$ and\n$u_r^\\prime(\\vec{p}^{\\,\\prime})$ are those in the Lorenz frame $O^\\prime$, one\nexpresses, for example, the first equality in Eq.~(\\ref{eq:dirac}) in terms of\nthe $O^\\prime$ variables by means of the relations\n$p_\\mu=a_{\\phantom{\\nu}\\mu}^\\nu p_\\nu^\\prime$, where $a_{\\phantom{\\nu}\\mu}^\\nu$ are the\ncoefficients of the Lorentz transformation between the four-vectors $p_\\mu$ and\n$p_\\mu^\\prime$, and $u_r(\\vec{p}\\,)=S^{-1}u_r^\\prime(\\vec{p}^{\\,\\prime})$, where $S$\nis a matrix that satisfies the relations\n$a_{\\phantom{\\nu}\\mu}^\\nu S\\gamma^\\mu S^{-1}=\\gamma^\\nu$ and\n$S^{-1}=\\gamma^0S^\\dagger\\gamma^0$.\nThen the first equality in Eq.~(\\ref{eq:dirac}) becomes\n$(\\slashed{p}^\\prime-Mc)u_r^\\prime(\\vec{p}^{\\,\\prime})=0$, which demonstrates its\nLorentz covariance.\nCarrying out the Hermitian conjugation of the $O^\\prime$ Dirac equation and\nusing Eq.~(\\ref{eq:ratio}), one finds\n$\\bar{u}_r^\\prime(\\vec{p}^{\\,\\prime})(\\slashed{p}^\\prime-Mc)=0$, which shows the\nLorentz covariance of the corresponding adjoint Dirac equation,\nEq.~(\\ref{eq:diraca}).\n\nThe adjoint spinors with respect to $u_r(\\vec{p}\\,)$ and $v_r(\\vec{p}\\,)$ in\nEq.~(\\ref{eq:spinor}) are\n\\begin{equation}\n\\bar{u}_r(\\vec{p}\\,)=\\left(\\frac{p^0+Mc}{2Mc}\\right)^{1\/2}\\left(\n\\chi_r^\\dagger,-\\chi_r^\\dagger\\frac{\\vec{\\sigma}\\cdot\\vec{p}}{p^0+Mc}\\right),\n\\quad\n\\bar{v}_r(\\vec{p}\\,)=\\left(\\frac{p^0+Mc}{2Mc}\\right)^{1\/2}\\left(\n\\chi_r^{\\prime\\dagger}\\frac{\\vec{\\sigma}\\cdot\\vec{p}}{p^0+Mc},-\\chi_r^{\\prime\\dagger}\n\\right),\n\\label{eq:spinora}\n\\end{equation}\nwhere we have again applied Eq.~(\\ref{eq:ratio}) to eliminate $p_\\mu^*$ and\n$M^*$.\nIn particular, Eq.~(\\ref{eq:ratio}) implies that $[(p^0+Mc)\/(2Mc)]^{1\/2}$ and\n$\\vec{p}\/(p^0+Mc)$ are real.\n\nIt is interesting to note that the four generalized Dirac equations shown in\nEqs.~(\\ref{eq:dirac}) and (\\ref{eq:diraca}) as well as their spinor solutions\npresented in Eqs.~(\\ref{eq:spinor}) and (\\ref{eq:spinora}) can be obtained from\nthe corresponding ones for stable fermions, for $\\Gamma=0$, by simply\nsubstituting $m\\to M$ in their explicit mass dependencies and in the definition\nof $p^\\mu$ in Eqs.~(\\ref{eq:p}) and (\\ref{eq:u}).\n\nSince $M$ cancels in the ratios $(p^0+Mc)\/(2Mc)$ and $\\vec{p}\/(p^0+Mc)$, these\nfactors are the same as in the $\\Gamma=0$ case.\nIt hence follows that the spinor solutions in Eqs.~(\\ref{eq:spinor}) and\n(\\ref{eq:spinora}) satisfy the same normalization and completeness relations as\nin the case of stable fermions, which are given, for example, by Eqs.~(A.29)\nand (A.30) in Ref.~\\cite{mandl}.\nIn particular,\n\\begin{eqnarray}\n\\bar{u}_r(\\vec{p}\\,)u_s(\\vec{p}\\,)&=&-\\bar{v}_r(\\vec{p}\\,)v_s(\\vec{p}\\,)\n=\\delta_{rs},\n\\nonumber\\\\\n\\bar{u}_r(\\vec{p}\\,)v_s(\\vec{p}\\,)&=&-\\bar{v}_r(\\vec{p}\\,)u_s(\\vec{p}\\,)=0.\n\\end{eqnarray}\n\nIn some applications, the two-component spinors $\\chi_r$ and $\\chi_r^\\prime$ in\nEq.~(\\ref{eq:chi}) are replaced by helicity eigenstates $\\phi_s$ and\n$\\phi_s^\\prime$, respectively.\nBecause, in the case of unstable particles, $\\vec{p}$ is complex [cf.\\\nEqs.~(\\ref{eq:p}) and (\\ref{eq:u})], the usual helicity projection,\n\\begin{equation}\n\\frac{\\vec{p}}{|\\vec{p}\\,|}\\cdot\\frac{\\vec{\\sigma}}{2}\\phi_s=s\\phi_s,\n\\qquad\n\\frac{\\vec{p}}{|\\vec{p}\\,|}\\cdot\\frac{\\vec{\\sigma}}{2}\\phi_s^\\prime\n=-s\\phi_s^\\prime,\n\\label{eq:hel}\n\\end{equation}\nwhere $|\\vec{p}\\,|=(\\vec{p}^{\\,*}\\cdot\\vec{p}\\,)^{1\/2}$, leads to complex\neigenvalues $s=\\pm(\\vec{p}^{\\,2})^{1\/2}\/(2|\\vec{p}\\,|)$, as can be checked by\napplying the helicity projection operator twice.\nA consistent alternative is to define the helicity projection operator in\nEq.~(\\ref{eq:hel}) according to $\\vec{u}\/|\\vec{u}|\\cdot\\vec{\\sigma}\/2$, where\n$\\vec{u}=\\vec{p}\/M$ are the spatial components of the four-velocity $u$ given\nbelow Eq.~(\\ref{eq:u}), or, equivalently,\n$\\vec{v}\/|\\vec{v}|\\cdot\\vec{\\sigma}\/2$.\n\nIt is also instructive to calculate the probability current using the spinor\nsolutions in Eqs.~(\\ref{eq:spinor}) and (\\ref{eq:spinora}).\nWe find\n\\begin{equation}\nc\\bar{u}_r(\\vec{p}\\,)\\gamma^\\mu u_r(\\vec{p}\\,)=\nc\\bar{v}_r(\\vec{p}\\,)\\gamma^\\mu v_r(\\vec{p}\\,)=\\frac{p^\\mu}{M}=u^\\mu.\n\\end{equation}\nAs expected, these currents transform as four-vectors.\nIn particular, for $\\mu=0$ we have\n\\begin{equation}\ncu_r^\\dagger(\\vec{p}\\,)u_r(\\vec{p}\\,)=\ncv_r^\\dagger(\\vec{p}\\,)v_r(\\vec{p}\\,)=u^0=\\gamma c.\n\\label{eq:pro}\n\\end{equation}\nSince $cu_r^\\dagger(\\vec{p}\\,)u_r(\\vec{p}\\,)$ and\n$cv_r^\\dagger(\\vec{p}\\,)v_r(\\vec{p}\\,)$ are the probability densities, they\nshould be real and positive, consistent with Eq.~(\\ref{eq:pro}).\n\nAnother interesting application is to examine the space-time dependence of the\nprobability density.\nIncorporating the space-time factor $\\exp(-ip\\cdot x)$ of Eq.~(\\ref{eq:exp}) in\n$cu_r^\\dagger(\\vec{p}\\,)u_r(\\vec{p}\\,)$, we find that the latter is multiplied by\nan overall factor $\\exp[-\\Gamma\\gamma(c^2t-\\vec{v}\\cdot\\vec{x})]$, which\nimplies that the probability density for the positive-energy states decreases\nexponentially with time, reflecting the fermion's instability.\n\nIn summary, (i) we have proposed a simple definition of the (complex)\nfour-momentum of a free unstable spin-$1\/2$ particle [cf.\\ Eqs.~(\\ref{eq:p})\nand (\\ref{eq:u})] and shown that it indeed transforms as a four-vector,\n(ii) we have derived the generalized Dirac equations in momentum space [cf.\\\nEq.~(\\ref{eq:dirac})] and found explicit spinor solutions [cf.\\\nEq.~(\\ref{eq:spinor})],\n(iii) we have derived the generalized adjoint Dirac equations [cf.\\\nEq.~(\\ref{eq:diraca})] and spinors [cf.\\ Eq.~(\\ref{eq:spinora})] by Hermitian\nconjugation of Eqs.~(\\ref{eq:dirac}) and (\\ref{eq:spinor}), respectively,\ntaking into account the basic relation in Eq.~(\\ref{eq:ratio}),\n(iv) using the important fact that $p^\\mu$ transforms as a four-vector, we have\nshown how the proof of Lorentz covariance carries over to the generalized Dirac\nequations and their adjoints,\n(v) we have pointed out the very simple relation that exists, in our\nformulation, between the generalized Dirac equations and spinors and the\ncorresponding expressions for stable fermions,\n(vi) in particular, we have shown that our spinors and adjoint spinors satisfy\nthe same normalization and completeness relations as in the case of stable\nfermions,\n(vii) we have proposed a modified definition of the helicity projection\noperator for unstable fermions that leads to real eigenvalues,\n(viii) as an illustration, we have applied our spinors and adjoint spinors to\ncalculate the probability density and found that it satisfies the expected\ntheoretical properties,\nand (ix) we have discussed the behavior of the generalized Dirac equations\nunder time reversal.\nAs mentioned after Eq.~(\\ref{eq:diraca}), the four generalized Dirac equations\nin Eqs.~(\\ref{eq:dirac}) and (\\ref{eq:diraca}) play an important role in the\nimplementation of the AHKKM \\cite{Aoki:1982ed} renormalization conditions for\nunstable fermions in general theories with intergeneration mixing.\n\n\n\n\n\n\n\n\n\\begin{acknowledgments}\nWe thank the Werner-Heisenberg-Institut for the hospitality extended to us\nduring a visit when this paper was prepared.\nThis research was supported in part by the German Research Foundation through\nthe Collaborative Research Center No.~676 {\\it Particles, Strings and the\nEarly Universe---The Structure of Matter and Space Time}.\nThe work of A.S. was supported in part by the National Science Foundation \nthrough Grant No.\\ PHY--1316452. \n\\end{acknowledgments}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nMotion of a symmetric top can be studied by using either a cubic function or effective potential.\nThe cubic function is mostly used in works that utilize geometric techniques \\cite{Routh, Scarborough, MacMillan, ArnoldMaunder, Groesberg, JoseSaletan},\nand effective potential is mostly used in works considering physical parameters \\cite{Symon, McCauley, LandauLifshitz, MarionThornton, Taylor}.\nIn some other works, both the cubic function and effective potential are used \\cite{Goldstein, Arnold, Corinaldesi, MatznerShepley, Arya, Greiner, FowlesCassiday}.\n\nEffective potential shows different characteristics when one of the conserved angular momenta greater than the other one or equal to.\nOne can find different aspects of effective potential in the literature when magnitudes of the conserved angular momenta are equal to each other \\cite{Symon, Tanriverdi_abeql}.\nHowever, it is not studied when magnitudes of the conserved angular momenta are not equal to each other except in Greiner's work, \nand his study does not cover different possibilities related to the conserved angular momenta and the minimum of effective potential \\cite{Greiner}. \nStudying this topic helps understand the motion of a spinning heavy symmetric top,\nand in this study, we will study this case together with the relation between the minimum of effective potential and a constant derived from parameters of gyroscope and conserved angular momenta.\n\nIn section \\ref{frst}, we will give a quick overview of constants of motion and effective potential.\nIn section \\ref{scnd}, we will study effective potential when magnitudes of the conserved angular momenta are not equal to each other.\nThen, we will give a conclusion.\nIn the appendix, we will compare the cubic function with effective potential.\n\n\n\\section{Constants of motion and effective potential}\n\\label{frst}\n\nFor a spinning heavy symmetric top, Lagrangian is \\cite{Goldstein}\n\\begin{eqnarray}\n\tL&=&T-U \\nonumber \\\\ \n\t&=&\\frac{I_x}{2}(\\dot \\theta ^2 + \\dot \\phi ^2 \\sin^2 \\theta)+\\frac{I_z}{2}(\\dot \\psi+\\dot \\phi \\cos \\theta)^2-M g l \\cos \\theta, \n\t\\label{lagrngn}\n\\end{eqnarray}\nwhere $M$ is the mass of the symmetric top, $l$ is the distance from the center of mass to the fixed point, $I_x=I_y$ and $I_z$ are moments of inertia, $g$ is the gravitational acceleration, $\\theta$ is the angle between the stationary $z'$-axis and the body $z$-axis, $\\dot \\psi$ is the spin angular velocity, $\\dot \\phi$ is the precession angular velocity and $\\dot \\theta$ is the nutation angular velocity. \nThe domain of $\\theta$ is $[0,\\pi]$.\nFor a spinning symmetric top on the ground $\\theta$ should be smaller than $\\pi\/2$, and if $\\theta>\\pi\/2$, then the spinning top is suspended from the fixed point. \n\nThere are two conserved angular momenta which can be obtained from Lagrangian,\nand one can define two constants $a$ and $b$ by using these conserved angular momenta as \\cite{Goldstein}\n\\begin{eqnarray}\n\ta&=&\\frac{I_z}{I_x}(\\dot \\psi+\\dot \\phi \\cos \\theta), \\\\\n\tb&=&\\dot \\phi \\sin^2 \\theta + a \\cos \\theta,\n\\end{eqnarray}\nwhere $a=L_z\/I_x$ and $b=L_{z'}\/I_x$.\nHere, $L_z$ and $L_{z'}$ are conserved angular momenta in the body $z$ direction and stationary $z'$ direction, respectively.\n\nOne can define a constant from energy as \n\\begin{equation}\n\tE'=\\frac{I_x}{2} \\dot \\theta ^2 +\\frac{I_x}{2} \\dot \\phi^2 \\sin^2 \\theta + Mgl \\cos \\theta \\label{eprime},\n\\end{equation}\nand its relation with the energy is $E'=E-I_x^2 a^2\/(2 I_z)$.\n\nBy using change of variable $u=\\cos \\theta$, one can obtain the cubic function from \\eqref{eprime} as\\cite{Goldstein}\n\\begin{equation}\n\tf(u)=(\\alpha - \\beta u)(1-u^2)-(b-a u^2)\t\n\t\\label{cubicf}\n\\end{equation}\nwhich is equal to $\\dot u^2$,\nwhere $\\alpha=2 E'\/I_x$ and $\\beta=2 Mgl\/I_x$.\nThis cubic function can be used to find turning angles.\n\nFrom $E'=I_x \\dot \\theta ^2\/2+U_{eff}$ \\cite{LandauLifshitz}, it is possible to define an effective potential\n\\begin{equation}\n\tU_{eff}(\\theta)= \\frac{I_x}{2}\\frac{(b-a \\cos \\theta)^2}{\\sin^2 \\theta}+Mgl \\cos \\theta.\n\t\\label{ueff}\n\\end{equation}\n\nBy using the derivative of $U_{eff}$ with respect to $\\theta$\n\\begin{equation}\n\t\\frac{d U_{eff}(\\theta)}{d \\theta}= \\frac{I_x}{\\sin^3 \\theta} \\left[ (b-a \\cos \\theta)(a-b \\cos \\theta)- \\frac{Mgl}{I_x} \\sin^4 \\theta \\right],\n\t\\label{dueff}\n\\end{equation}\nit is possible to find the minimum of $U_{eff}$.\nThe factor $\\sin \\theta$ is equal to zero when $\\theta$ is equal to $0$ or $\\pi$, and effective potential goes to infinity at these angles.\nThe root of equation \\eqref{dueff} is between $0$ and $\\pi$, and it will be designated by $\\theta_r$ giving the minimum of effective potential,\nand it can be found numerically.\nThen, the form of effective potential is like a well. \nThe general structure of $U_{eff}$ together with $E'$ can be seen in figure \\ref{fig:ueffg}.\n\n\\begin{figure}[!h]\n\t\\begin{center}\n\t\t\\includegraphics[width=7cm]{ueff_gn2_d.pdf}\n\t\t\\caption{General structure of $U_{eff}(\\theta)$ and $E'$. $\\theta_{min}$ and $\\theta_{max}$ show turning angles, and $\\theta_r$ represents the angle where minimum of $U_{eff}$ occurs.\n\t\tCurve (red) shows $U_{eff}$, dashed (blue) line shows $E'$ and horizontal continious (black) line shows the minimum of $U_{eff}$.\n\t\t}\n\t\t\\label{fig:ueffg}\n\t\\end{center}\n\\end{figure}\n\nBy using equation \\eqref{dueff}, one can write \\cite{Goldstein}\n\\begin{equation}\n\t\\dot \\phi^2 \\cos \\theta- \\dot \\phi a+\\frac{Mgl}{I_x}=0.\n\\end{equation}\nThe root of this equation can also be used to obtain the minimum of $U_{eff}$.\nBy using the discriminant of this equation, \none can define a parameter $\\tilde a=\\sqrt{4 Mgl \/I_x}$ to make a disrimination between \"strong top\" (or fast top) where $a> \\tilde a$ and \"weak top\" (or slow top) where $a< \\tilde a$ \\cite{KleinSommerfeld, Tanriverdi_abdffrnt}. \n\nThe position of the minimum and the shape of $U_{eff}$ can be helpful in understanding the motion.\nIf $E'$ is equal to the minimum of $U_{eff}$ then the regular precession is observed.\nIf $E'$ is greater than the minimum of $U_{eff}$, like figure \\ref{fig:ueffg}, the intersection points of $E'$ and $U_{eff}$ give turning angles.\nAnd, symmetric top nutates between these two angles periodically.\nThere can be different types of motion, and some of these motions can be determined by using relations between $E'$ \\& $Mglb\/a$ and $a$ \\& $b$ when $|a|\\ne|b|$ \\cite{Tanriverdi_abdffrnt}. \n\n\n\\section{Effective potential}\n\\label{scnd}\n\nThe relation between $a$ and $b$ can affect effective potential.\nThere are three possible relation between $a$ and $b$: $|a|>|b|$, $|a|<|b|$ and $|a|=|b|$. \nWe will consider two different possibilities, $|a|>|b|$ and $|a|<|b|$, to study effective potential since the third one is studied previously, i.e. $|a|=|b|$ \\cite{Symon, Tanriverdi_abeql}.\nWe will give examples to studied cases, and for examples, the following constants will be used: $Mgl=0.068 \\,J$, $I_x=0.000228 \\,kg \\,m^{2}$ and $I_z=0.0000572 \\,kg \\,m^{2}$.\n\n\\subsection{Effective potential when $|a|>|b|$}\n\nIn this section, we will study the case when $|a|>|b|$.\nAfter factoring equation \\eqref{dueff}, it can be written as\n\\begin{equation}\n\t\\frac{d U_{eff}(\\theta)}{d \\theta}= \\frac{a^2 I_x}{ \\sin^3 \\theta} \\left[ (\\frac{b}{a}- \\cos \\theta)(1- \\frac{b}{a} \\cos \\theta)- \\frac{Mgl}{I_x a^2} \\sin^4 \\theta \\right].\n\t\\label{dueff3}\n\\end{equation}\nThe angle, making the terms in the parentheses zero, gives the minimum of effective potential.\nIf $|a|>|b|$, the second term in the parentheses is always negative, and then $b\/a-\\cos \\theta$ should also be positive for the root.\nTherefore, the inclination angle should satisfy $\\pi>\\theta>\\arccos b\/a$.\nIn the limit where $a$ goes to infinity, $\\theta_r$ goes to $\\arccos b\/a$.\nIn $a$ goes to zero limit, $b$ should also go to zero since $|a|>|b|$, then the first term goes to zero (see equation \\eqref{dueff}) and the second term should also go to zero for the root which is possible when $\\theta_r$ goes to $\\pi$.\nIf both $a$ and $b$ are negative or positive, $\\theta_r$ is between $\\pi\/2$ and $\\pi$ when $|a|$ is close to zero, and it is between $0$ and $\\pi\/2$ when $|a|$ and $|b|$ are great enough.\nIf only one of them is negative, then $\\theta_r$ is always greater than $\\pi\/2$.\n\nWhen $b=0$, in $|a|$ goes to infinity limit $\\theta_r$ goes to $\\pi\/2$, and $a$ goes to zero limit does not change and remains as $\\pi$.\n\nThese shows that $\\theta_r \\in (\\arccos b\/a,\\pi)$. \nIf $b\/a$ goes to $1$, then $\\arccos b\/a$ goes to $0$.\nTherefore, $\\theta_r$ can take values between $0$ and $\\pi$ depending on signs of $a$ and $b$, the ratio $b\/a$ and greatness of $a$ and $b$.\n\nNow, we will consider the change of $U_{eff_{min}}$ when $|a|>|b|$.\nWe have seen that as $|a|$ goes to zero, $\\theta_r$ goes to $\\pi$ .\nThen, it can be seen from equation \\eqref{ueff} that $U_{eff_{min}}$ goes to $-Mgl$ as $|a|$ goes to zero.\nAs $|a|$ goes to infinity $\\theta_r$ goes to $\\arccos b\/a$, then $U_{eff_{min}}$ goes to $Mgl b\/a$ from below.\nThen, $Mglb\/a$ is always grater than $U_{eff_{min}}$ when $|a|>|b|$.\n\n\\begin{figure}\n\t\\begin{center}\n\t\t\\subfigure[$U_{eff}$]{\n\t\t\t\\includegraphics[width=4.0cm]{ueff_a3.pdf}\n\t\t\n\t\t\t}\n\t\t\t\\subfigure[$\\theta_r$]{\n\t\t\t\t\\includegraphics[width=4.0cm]{loop_a_theta.pdf}\n\t\t\t\n\t\t\t\t}\n\t\t\t\t\\subfigure[$U_{eff_{min}}$]{\n\t\t\t\t\t\\includegraphics[width=4.0cm]{loop_a_umin.pdf}\n\t\t\t\t\n\t\t\t\t\t}\n\t\t\t\t\t\\caption{ $U_{eff}$, change of $\\theta_r$ with respect to $a$ and change of $U_{eff_{min}}$ with respect to $a$.\n\t\t\t\t\ta) Three different effective potential: $a=10 \\, rad \\,s^{-1}$ (green dashed-dotted curve), $a=30 \\, rad \\,s^{-1}$ (blue dashed curve) and $a=60 \\, rad \\,s^{-1}$ (red continious curve), and all of them satisfy $b\/a=0.5$. Black line shows $Mglb\/a$.\n\t\t\t\t\tb) Change of $\\theta_r$ with respect to $a$ for constant $b\/a=0.5$ ratio (red curve). Black line shows $\\arccos(b\/a)=1.05$. Vertical dotted line shows position of $\\tilde a$.\n\t\t\t\t\tc) Change of $U_{eff_{min}}$ with respect to $a$ for constant $b\/a=0.5$ ratio (red curve). Black line shows $Mglb\/a$. Vertical dotted line shows position of $\\tilde a$.\n\t\t\t\t\t}\n\t\t\t\t\t\\label{fig:ueffg5}\n\t\\end{center}\n\\end{figure}\n\nAs an example, we will consider that there is a constant ratio between $a$ and $b$: $b\/a=0.5$.\nIn figure \\ref{fig:ueffg5}(a), three different effective potentials for three different $a$ values are shown together with $Mglb\/a$.\nIn this figure, it can be seen that the form and magnitude of the minimum of $U_{eff}$ are changing as $a$ changes, and it can also be seen that $\\theta_r$ is also changing.\nIn figure \\ref{fig:ueffg5}(b), it can be seen that $\\theta_r$ takes very close values to $\\pi$ for very small values of $a$ and goes to $\\arccos 0.5=1.05 \\,rad$ as $a$ increases.\nIn figure \\ref{fig:ueffg5}(c), it can be seen that the minimum of $U_{eff}$ takes very close values to $-Mgl$ when $a$ is small, and it goes to $Mglb\/a$ as $a$ goes to infinity.\nThese are consistent with previous considerations.\n\nIt can be considered that there is a shift in the behaviour of $\\theta_r$ and $U_{eff_{min}}$ near $a=\\tilde a$. \nBut this shift is not sudden, and one can say that the usage $\\tilde a$ gives an approximate separation when $|a|>|b|$.\n\nIn some cases, $Mgl$ can be negative and there are some differences in effective potential in these cases.\nWhen $Mgl$ is negative, the second term in equation \\eqref{dueff3} becomes positive, and then $\\arccos b\/a> \\theta > 0$ for the root.\nIn the limit where $a$ goes to infinity, again $\\theta_r$ goes to $\\arccos b\/a$.\nIn $a$ goes to zero limit, $\\theta_r$ goes to $0$.\nThese show that the interval for the minimum of effective potential changed from $(\\arccos b\/a,\\pi)$ to $(0,\\arccos b\/a)$ when $Mgl$ changed sign from positive to negative.\nIf both $a$ and $b$ are negative or positive, $\\theta_r$ is between $0$ and $\\pi\/2$.\nIf only one of them is negative, then $\\theta_r$ can be greater than $\\pi\/2$ when $|a|$ is great enough.\nThe minimum of $U_{eff}$ goes to $-|Mgl|$ when $a$ goes to $0$, and it goes to $-|Mgl| b\/a$ when $a$ goes to infinity when $Mgl$ is negative.\n\n\\subsection{Effective potential when $|b|>|a|$}\n\nIn this section, we will study the case when $|b|>|a|$.\nAfter factoring equation \\eqref{dueff} in another way, it can be written as \n\\begin{equation}\n\t\\frac{d U_{eff}(\\theta)}{d \\theta}= \\frac{b^2 I_x}{\\sin^3 \\theta} \\left[ (1-\\frac{a}{b} \\cos \\theta)(\\frac{a}{b} - \\cos \\theta)- \\frac{Mgl}{I_x b^2} \\sin^4 \\theta \\right].\n\t\\label{dueff2}\n\\end{equation} \nSimilar to the previous case, the first term should be positive, and $a\/b- \\cos \\theta$ should be positive when $|b|>|a|$ for the root, and then $\\pi>\\theta>\\arccos a\/b$.\nIn $b$ goes to infinity limit, the second term in the parentheses goes to zero.\nThen, as $|b|$ goes to infinity, $\\theta_r$ should go to $\\arccos a\/b$.\nIn $b$ goes to zero limit, $\\theta_r$ goes to $\\pi$ which can be seen from equation \\eqref{dueff} similar to the previous section. \nThen, $\\theta_r$ goes to $\\pi$ when $b$ goes to zero, and it goes to $\\arccos a\/b$ when $|b|$ goes to infinity.\n\nWhen $a$ and $b$ are both positive or negative, as $|b|$ increases from zero to infinity, $\\theta_r$ decreases from $\\pi$ to $\\arccos a\/b<\\pi\/2$.\nIf only one of them is positive, then $\\theta_r$ is always greater than $\\pi\/2$ and shows a similar decrease to both positive or negative cases.\n\nWhen $a=0$, as $|b|$ goes to infinity $\\theta_r$ goes to $\\pi\/2$ and it goes to $\\pi$ as $|b|$ goes to $0$.\n\nSimilar to the previous case, $\\theta_r$ can take values between $0$ and $\\pi$ depending on signs of $a$ and $b$, the ratio $a\/b$ and greatness of $a$ and $b$.\n\nThe magnitude of the minimum of $U_{eff}$ changes with respect to $b$.\nIn $b$ goes to zero limit, $U_{eff_{min}}$ goes to $-Mgl$ since $\\theta_r$ goes to $\\pi$.\nIn $b$ goes to infinity limit, $\\theta_r$ goes to $\\arccos a\/b$, and then the minimum of $U_{eff}$ goes to infinity with $I_x b^2(1-(a\/b)^2)\/2$.\n\n\\begin{figure}[!h]\n\t\\begin{center}\n\t\t\\subfigure[$U_{eff}$]{\n\t\t\t\\includegraphics[width=4.2cm]{ueff_b3.pdf}\n\t\t\t\\label{fig:ueffg4a}\n\t\t\t}\n\t\t\t\\subfigure[$\\theta_r$]{\n\t\t\t\t\\includegraphics[width=4.2cm]{loop_b_theta.pdf}\n\t\t\t\t\\label{fig:ueffg4b}\n\t\t\t\t}\n\t\t\t\t\\subfigure[$U_{eff_{min}}$]{\n\t\t\t\t\t\\includegraphics[width=4.2cm]{loop_b_umin.pdf}\n\t\t\t\t\t\\label{fig:ueffg4c}\n\t\t\t\t\t}\n\t\t\t\t\t\\caption{ $U_{eff}$, change of $\\theta_r$ with respect to $b$ and change of $U_{eff_{min}}$ with respect to $b$.\n\t\t\t\t\ta) Three different effective potential: $b=10 \\, rad \\,s^{-1}$ (green dashed-dotted curve), $b=30 \\, rad \\,s^{-1}$ (blue dashed curve) and $b=60 \\, rad \\,s^{-1}$ (red continious curve) with $a\/b=0.5$. Black line shows $Mglb\/a$.\n\t\t\t\t\tb) Change of $\\theta_r$ with respect to $b$ for constant $a\/b=0.5$ ratio (red curve). Black line shows $\\arccos(a\/b)=1.05 \\,rad$. Vertical dotted line shows the position of $b=2 \\tilde a$.\n\t\t\t\t\tc) Change of $U_{eff_{min}}$ with respect to $b$ for constant $a\/b=0.5$ ratio (red curve). Black line shows $Mglb\/a$. Vertical dotted line shows position of $b=2 \\tilde a$. Dotted curve shows $I_x b^2(1-(a\/b)^2)\/2$.\n\t\t\t\t\t}\n\t\t\t\t\t\\label{fig:ueffg6}\n\t\\end{center}\n\\end{figure}\n\nFor examples, similar to the previous case, a constant ratio between $a$ and $b$ is considered: This time $a\/b=0.5$.\nIn figure \\ref{fig:ueffg6}(a), three different effective potentials for three different $b$ values are shown similar to the previous section.\nIn this figure, there are some similarities and differences from figure \\ref{fig:ueffg5}(a).\nOne can see that $\\theta_r$ is also different for different $b$ values similar to the previous section.\nIt can be seen that as $b$ takes different values, the form and magnitude of the minimum of $U_{eff}$ becomes different similar to previous case, and it can be greater than $Mglb\/a$, unlike the previous case.\nIn figure \\ref{fig:ueffg6}(b), it can be seen that for very small values of $b$, $\\theta_r$ is close to $\\pi$ and it goes to $\\arccos 0.5=1.05 \\,rad$ as $b$ increases.\nIn figure \\ref{fig:ueffg6}(c), it can be seen that the minimum of $U_{eff}$ is close to $-Mgl$ if $b$ is small, and it goes to infinity with $I_x b^2(1-(a\/b)^2)\/2$ as $b$ goes to infinity.\nThese are the expected results from the explanations given above.\n\nBy considering these results, it can be said that $Mglb\/a$ is not important differently from $|a|>|b|$ case.\nFrom figures \\ref{fig:ueffg6}(b) and \\ref{fig:ueffg6}(c), one can say that the shift in the behaviour of $\\theta_r$ and $U_{eff_{min}}$ does not take place around $a=\\tilde a$, and the usage of $\\tilde a$ for seperation is not suitable when $|b|>|a|$.\n\nWhen $Mgl$ is negative, the second term in equation \\eqref{dueff3} becomes positive, and then in this case, $a\/b-\\cos \\theta$ should be negative which is possible when $\\arccos a\/b> \\theta > 0$.\nIn the limit where $b$ goes to infinity, again $\\theta_r$ goes to $\\arccos a\/b$.\nIn $b$ goes to zero limit, $\\theta_r$ goes to $0$.\nSimilar to the previous case, the interval for the minimum of effective potential changed from $(\\arccos b\/a,\\pi)$ to $(0,\\arccos b\/a)$.\nIf both $a$ and $b$ are negative or positive, $\\theta_r$ is between $0$ and $\\pi\/2$.\nIf only one of them is negative, then $\\theta_r$ can be greater than $\\pi\/2$ when $|b|$ goes to infinity, and $\\theta_r$ goes to $0$ as $b$ goes to zero.\nWhen $a=0$, in $|b|$ goes to infinity limit $\\theta_r$ goes to $\\pi\/2$, and $|b|$ goes to zero limit does not change and remains as $0$.\nIf $Mgl$ is negative, the minimum of $U_{eff}$ goes to $-|Mgl|$ when $b$ goes to $0$, and it goes to infinity as $|b|$ goes to infinity.\n\n\n\\section{Conclusion}\n\nEffective potential can be helpful in understanding the motion of a symmetric top in different ways.\n$E'$ should be equal to or greater than the minimum of $U_{eff}$ for physical motions.\nBy using the limits given in section \\ref{scnd}, one can say that the regular precession takes place at greater angles when $a$ and $b$ are small, \nand as $a$ and $b$ increase, it takes place at smaller angles.\nTo observe regular precession smaller than $\\pi\/2$, $a$ and $b$ should have the same sign and have greater magnitudes.\nThe limiting angle when $|a|$ or $|b|$ goes to infinity can be found by using inverse cosine of $b\/a$ and $a\/b$ when $|a|>|b|$ and $|b|>|a|$, respectively.\nIf $E'$ is greater than the minimum of $U_{eff}$, then different types of motions can be seen \\cite{Tanriverdi_abdffrnt}.\nThese motion will take place closer angles to $\\theta_r$ when $E'$ is close to the minimum of $U_{eff}$,\nand by considering signs and magnitudes of $a$ and $b$ one can have an opinion on the angles where the motion takes place.\n\nIf $a$ and\/or $b$ are small, then there can be a high asymmetry in the form of $U_{eff}$.\nFrom the definitions of $U_{eff}$ and $E'$, one can say that $\\dot \\theta$ is propotional to the difference $E'-U_{eff}(\\theta)$ for a specific $\\theta$ value.\nTherefore, one can say that as $\\theta$ increases from $\\theta_{min}$ to $\\theta_r$, the change in $\\dot \\theta$ is gradual, and as $\\theta$ increases from $\\theta_{r}$ to $\\theta_{max}$, the change in $\\dot \\theta$ is more rapid when $a$ and\/or $b$ are small.\nAs $\\theta$ changes from $\\theta_{max}$ to $\\theta_{min}$, this change in $\\dot \\theta$ is firstly rapid and then gradual.\n\nIf $a$ and $b$ are great enough and the difference $E'-U_{eff_{min}}$ is small enough, then the asymmetry in $U_{eff}$ can be ignored.\nIn these cases, one can make an approximation and find an exact solution for this approximation \\cite{Goldstein, Arnold}.\nThis approximation works better when the asymmetry in $U_{eff}$ is least.\n\nWe have seen that comparison of $|a|$ with $\\tilde a$ can be used when $|a|>|b|$ for an approximate seperation, and it is not suitable when $|b|>|a|$.\nBut comparison between $|b|$ and $\\tilde a$ can be used when $|b|>|a|$, and if it is used, one should use a naming other than \"strong top\" or \"weak top\". \nWe should note that comparison of $|a|$ with $\\tilde a$ is very useful when $|a|=|b|$ \\cite{Tanriverdi_abeql}.\n\nAnother thing that should be taken into account is the relation between $Mglb\/a$ and $E'$ \\cite{Tanriverdi_abdffrnt}.\nThis study has shown that the minimum of $U_{eff}$ is always smaller than $Mglb\/a$ when $|a|>|b|$, which shows that one can always observe all possible motions when $|a|>|b|$.\nOn the other hand, $Mglb\/a$ can be greater than or smaller than the minimum of $U_{eff}$ when $|b|>|a|$.\n\nThese results show that effective potential has different advantages over the cubic function in understanding the motion of a spinning heavy symmetric top.\nHowever, the cubic function is still important since it is better for proofs.\n\n\n\\section{Appendix}\n\nThere is an alternative to effective potential: the cubic function given in equation \\eqref{cubicf}.\n\nHere, we will compare the cubic function with effective potential.\nThe cubic function is equal to $\\dot u^2$, and its roots give the points where $\\dot u=0$.\n$\\dot \\theta$ is equal to zero at two of these three points, and the third root is irrelevant to turning angles.\nThen, one can use the cubic function to obtain turning angles. \nIf these two roots are the same, i.e. double root, then one can also say that this case gives regular precession.\nThese turning angles can also be obtained from effective potential by using $E'=U_{eff}(\\theta)$.\nAnd, if $E'=U_{eff_{min}}$ then the regular precession is observed as explained above.\n\nOn the other hand, there is not any correspondence between the minimum of $U_{eff}$ and the maximum of $f(u)$.\nThe reason for this is the multiplication with $1-u^2$ during the change of variable.\nThen, $f(u)$ can not be used to make further analyses similar to $U_{eff}$, given above.\n\nWe will consider a case satisfying $\\alpha=575.1 \\, s^{-2}$, $a=10 \\, rad \\, s^{-1}$, $b=2 \\, rad \\, s^{-1}$ as an example.\nFor the symmetric top with previously given parameters, $\\beta$ becomes $ 596.5 \\, s^{-2}$.\n$U_{eff}$ and $f(u)$ can be seen in figure \\ref{fig:ueff_fu}.\nOne can see that $\\theta_{min}=1.83 \\, rad$ and $\\theta_{max}=2.57 \\, rad$ can be obtained from $\\arccos(u2)=1.83 \\, rad$ and $\\arccos(u1)=2.57 \\, rad$, respectively.\nOn the other hand, $\\theta_r=2.28 \\, rad$ can not be obtained from $\\arccos(u_m)=2.18$.\n\n\\begin{figure}[!h]\n\t\\begin{center}\n\t\t\\subfigure[$U_{eff}$]{\n\t\t\t\\includegraphics[width=5.5cm]{ueff_comp.pdf}\n\t\t\t}\n\t\t\t\\subfigure[$f(u)$]{\n\t\t\t\t\\includegraphics[width=5.5cm]{fu_comp.pdf}\n\t\t\t\t}\n\t\t\t\t\\caption{$U_{eff}$ and $f(u)$ when $\\alpha=575.1 \\, s^{-2}$, $\\beta=596.5 \\, s^{-2}$, $a=10 \\, rad \\, s^{-1}$ and $b=2 \\, rad \\, s^{-1}$.\n\t\t\t\ta) $U_{eff}$ continious (red) curve, $E'=-0.0150 \\, J$ dashed (blue) line, $\\theta_{min}=1.83 \\, rad$, $\\theta_{max}=2.57 \\, rad$, $\\theta_r=2.28 \\, rad$ and $U_{eff_{min}}=-0.0299 J$. \n\t\t\t\tb) $f(u)$ continious (red) curve, $u_1=-0.841$, $u_2=-0.258$, $u_3=1.05$, $u_m=-0.575$ and $f_{max}=81.6 \\, s^{-2}$.\n\t\t\t\t}\n\t\t\t\t\\label{fig:ueff_fu}\n\t\\end{center}\n\\end{figure}\n\nThese show that $f(u)$ can be used to obtain turning angles, however, it can not be used to obtain $\\theta_r$ where the minimum of $U_{eff}$ occurs.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nLet $\\mathbb{T}=\\mathbb{R}\/\\mathbb{Z}$ and identify it with $[0,1]$ in the usual way. For integers $d\\ge 2$, let $F$ be the $d$-adic Bernoulli map \n$$F: \\mathbb T\\rightarrow \\mathbb T,\\ t\\mapsto d\\cdot t\\mod 1.$$\nLet $f$ be a function (weight) on $\\mathbb{T}$. Consider the weighted transfer operator $L$ associated to $F$:\n\\begin{align*}\n(Lu)(t)\n&=\\frac1 d\\sum_{s\\in F^{-1}(t)} f(s)u(s)\\\\\n&=\\frac1 d\\sum_{i=0}^{d-1} f\\left(\\frac{t+i}{d}\\right)u\\left(\\frac{t+i}{d}\\right),\\ t\\in\\mathbb{T}\n\\end{align*}\nwhere $u$ is a function on $\\mathbb{T}$. Such operators are also called Ruelle (or Ruelle-Perron-Frobenius) operators. They can also be defined associated to more general maps and on more general spaces (cf. Hennion \\cite{Hennion1993} and Baladi \\cite{Baladi2000} for more background).\n\nIn this paper, we study spectral properties of $L$ as an operator acting on $C(\\mathbb{T})$, the space of continuous functions on $\\mathbb{T}$. When $f\\equiv 1$, this question has been extensively studied,\nespecially in the case $d=2$ (cf. Vep\\v{s}tas \\cite{V} and references therein). For more general weights $f$, there are Perron-Frobenius type theorems that describe spectral properties of $L$ (cf. \\cite[Theorem~1.5]{Baladi2000}). However, such theorems often require $f$ to be strictly positive, which is not met by the main examples we are interested in:\n$$(c)\\ f(t)=|\\cos(\\pi t)|^q \\hspace{1cm} (s)\\ f(t)=|\\sin(\\pi t)|^q$$\nwhere $q>0$. In Section \\ref{sec:quasicompact}, we develop Perron-Frobenius type theorems for transfer operators $L$ with such `degenerate' weights (more precisely, weights that have exactly one zero on $\\mathbb{T}$). The theorems are derived using notions of quasicompactness and Krein property, which we verify by exploiting the specific structure of the Bernoulli map $F$; see also Fan and Lau \\cite{FanLau1998} for similar treatments. As a corollary, we conclude that the operator $L$ satisfies classical Perron-Frobenius theorems in all cases of $d$ and $(c)\/(s)$, except for the case $d=2$ and $(c)$. \n\nIn Section \\ref{sec:special-weights}, we study in more detail the spectral properties of $L$ in the non-exceptional cases. When $q$ is an even integer, we obtain explicit computations of $\\rho(L)$ (the spectral radius of $L$) by reducing to a finite-dimensional problem. When $q$ is not an even integer, evaluating $\\rho(L)$ is more difficult. We derive in this case estimations of $\\rho(L)$, particularly for $d=3$ (note that when $d$ is odd, $(c)$ and $(s)$ are equivalent). As an application, we obtain asymptotic behavior of some integrals of the form\n$$I_n=\\int_{\\mathbb{T}} \\prod_{j=0}^{n-1} f(d^j t)dt,\\quad \\text{as } n\\rightarrow\\infty.$$\nIn particular, we extend a result of Strichartz \\cite{Strichartz1990} concerning the Fourier transform of the middle-third Cantor set. We also study geometric properties of the function $L1$, \nas well as asymptotic behavior of $\\rho(L)$ as $q\\rightarrow\\infty$. For the latter question it turns out that one needs to distinguish the case when $d$ is even and $f$ is given by $(s)$.\n\nIn Section \\ref{sec:binary}, we give a detailed account of the exceptional case $d=2$ and $(c)$. Using an explicit formula for the iterates $L^n 1$, we find the spectral radius and eigenfunctions of $L$ explicitly (see also Fan and Lau \\cite{FanLau1998} for related results), and obtain geometric properties of $L^n1$ for $n\\ge 1$ (especially for $q\\le 1$ and even $q$'s). The spectral problem in this case is closely related to the case $f\\equiv 1$ mentioned above, and has to do with the Hurwitz zeta functions.\n\nIn Section \\ref{sec:Lp}, we study the spectral problem on Lebesgue spaces. In Section \\ref{sec:application}, we give an application to Fourier multipliers.\n\n\\section{Quasicompact transfer operators}\\label{sec:quasicompact}\n\nLet $f:\\mathbb{R}\\to\\mathbb{R}$ be a continuous nonnegative 1-periodic function, and let $d\\ge 2$ be an integer.\nWe consider the transfer operator\n\\begin{equation}\\label{L:L}\n (Lu)(t)=\\frac1 d\\sum_{i=0}^{d-1} f\\left(\\frac{t+i}{d}\\right)u\\left(\\frac{t+i}{d}\\right) .\n\\end{equation}\nLet $\\mathbb{T}=[0,1]$ with $0$ and $1$ identified (circle). Let $C(\\mathbb{T})$ be the Banach space of continuous complex-valued functions on $\\mathbb{T}$\nendowed with the maximum norm $\\|\\cdot\\|_\\infty$.\nThen $L:C(\\mathbb{T})\\to C(\\mathbb{T})$ is a bounded linear operator. Moreover, $L$ is positive in the sense that $u\\ge 0$ implies $Lu\\ge 0$.\n\nDefine a map $F:\\mathbb{T}\\to \\mathbb{T}$ by $F(t)=d\\cdot t \\mod 1$. Then we can write\n\\[ (Lu)(t)=\\frac1d \\sum_{s\\in F^{-1}(t)} f(s)u(s), \\quad t\\in\\mathbb{T} .\\]\nFor each $n\\in\\mathbb{N}$, set\n\\[ f_n(t)=\\prod_{j=0}^{n-1} f\\big(F^j(t)\\big) .\\]\nThen\n\\begin{equation}\\label{L:Ln}\n(L^n u)(t)=\\frac{1}{d^n} \\sum_{s\\in F^{-n}(t)} f_n(s)u(s) .\n\\end{equation}\n\nLet $0<\\alpha\\le 1$. \nConsider the Banach space $\\H$ \nof H\\\"older continuous functions $u:\\mathbb{T}\\to \\mathbb{C}$ with the norm\n\\[ \\|u\\|_\\alpha=\\sup_{s\\neq t} \\frac{|u(s)-u(t)|}{|s-t|^\\alpha} + \\|u\\|_\\infty .\\]\nIf $f\\in \\H$ then $L_\\alpha u=Lu$ defines a bounded linear operator $L_\\alpha:\\H\\to \\H$.\n\nLet $T$ be a bounded linear operator on a Banach space $X$. We denote its spectral radius by $\\rho(T)$.\n$T$ is called quasicompact if there exists a compact operator $K$ on $X$ such that $\\rho(T-K)<\\rho(T)$.\nIf $T$ is quasicompact and $\\lambda\\in\\mathbb{C}$ is in the spectrum of $T$ with $|\\lambda|>\\rho(T-K)$, then $\\lambda$ is an eigenvalue of\n$T$.\n\nThe following theorem is proved in \\cite[pages 3--4]{Sm}. In \\cite[Proposition 1]{Sm} it is assumed that $f$ is positive while we assume here that $f$ is nonnegative. However positivity of $f$ is not used on pages 3--4 of \\cite{Sm}. See also \\cite{Hennion1993}.\n\n\\begin{thm}\\label{L:t1}\nLet $0\\le f\\in \\H$ for some $0<\\alpha\\le 1$.\nThen $\\rho(L)=\\rho(L_\\alpha)$. Furthermore, if $\\rho(L)>0$, then $L_\\alpha$ is quasicompact.\n\\end{thm}\n\nFor each $n\\in\\mathbb{N}$, set\n\\begin{equation}\\label{L:hn}\n h_n=L^n 1.\n \\end{equation}\nDefine\n\\begin{equation}\\label{L:rnRn}\n r_n=\\min_{t\\in\\mathbb{T}} h_n(t),\\quad R_n=\\max_{t\\in\\mathbb{T}} h_n(t) .\n\\end{equation}\nIt is easy to show that\n\\[ r_{m+n}\\ge r_mr_n,\\quad R_{m+n}\\le R_mR_n .\\]\nTherefore, the limits\n\\begin{equation}\\label{L:rR}\n r=\\lim_{n\\to\\infty} (r_n)^{1\/n} =\\sup_n\\ (r_n)^{1\/n},\\quad R=\\lim_{n\\to\\infty} (R_n)^{1\/n}=\\inf_n\\ (R_n)^{1\/n}\n\\end{equation}\nexist. In particular, for every $n\\in\\mathbb{N}$ we have\n\\begin{equation}\\label{L:est1}\n r_n^{1\/n}\\le r\\le R\\le R_n^{1\/n}.\n\\end{equation}\nMoreover, since $R_n=\\|L^n\\|_{C(\\mathbb{T})\\rightarrow C(\\mathbb{T})}$, by Gelfand's formula, $R=\\rho(L)$.\n\n\\begin{thm}\\label{L:t2}\nLet $w\\in C(\\mathbb{T})$ be a unit, that is, $w(t)>0$ for all $t\\in\\mathbb{T}$.\nThen\n\\begin{equation}\\label{L:est2}\n\\min_{t\\in\\mathbb{T}} \\frac{(Lw)(t)}{w(t)}\\le r\\le R\\le \\max_{t\\in\\mathbb{T}} \\frac{(Lw)(t)}{w(t)} .\n\\end{equation}\n\\end{thm}\n\\begin{proof}\nWe define a bounded linear operator $S$ on $C(\\mathbb{T})$ by\n\\[ (Su)(t)= \\frac{1}{w(t)} L(wu)(t),\\]\na sequence of functions\n\\[ \\tilde h_n= S^n 1,\\]\nand sequences of numbers\n\\[ \\tilde r_n =\\min_{t\\in\\mathbb{T}} \\tilde h_n(t),\\quad \\tilde R_n=\\max_{t\\in\\mathbb{T}} \\tilde h_n(t) .\\]\nSince $S$ is positive, we obtain for every $n\\in\\mathbb{N}$\n\\[\n (\\tilde r_n)^{1\/n}\\le \\lim_{n\\to\\infty} (\\tilde r_n)^{1\/n}=: \\tilde r\\le \\tilde R:= \\lim_{n\\to\\infty} (\\tilde R_n)^{1\/n}\\le (\\tilde R_n)^{1\/n} .\n\\]\nSince $w$ is a unite, there are constants $a,b>0$ such that\n\\[ a\\le w(t)\\le b\\]\nfor all $t\\in\\mathbb{T}$. This implies\n\\[ a h_n(t)=(L^na)(t) \\le w(t) \\tilde h_n(t) \\le (L^nb)(t)=b h_n(t) .\\]\nFrom this we obtain\n\\[ r_n\\le \\frac{b}{a} \\tilde r_n,\\quad \\tilde r_n\\le \\frac{b}{a} r_n,\\quad R_n\\le \\frac{b}{a} \\tilde R_n,\\quad \\tilde R_n\\le \\frac{b}{a} R_n .\\]\nThus\n\\[ r=\\tilde r,\\quad R=\\tilde R .\\]\nNow \\eqref{L:est2} follows from\n\\[ \\tilde r_1\\le \\tilde r=r\\le \\tilde R=R\\le \\tilde R_1.\\]\n\\end{proof}\n\nWe say that $L$ is a Krein operator if, for all $u\\in C(\\mathbb{T})$ such that $u(t)\\ge 0$ for all $t\\in\\mathbb{T}$ but $u(t_0)>0$ for at least one $t_0\\in\\mathbb{T}$, there is $n\\in\\mathbb{N}$ such that $L^nu$ is a unit. Note that $n$ may depend on $u$.\nIt is easy to show that a Krein operator carries units to units (cf. \\cite[Lemma 5.2]{A}).\nIt follows from \\eqref{L:Ln} that if $f(t)>0$ for all $t$ then $L$ is a Krein operator,\nAlso, if $f$ vanishes on an interval of positive length, then $L$ cannot be a Krein operator.\n\n\\begin{lemma}\\label{L:Krein1}\nSuppose $f$ has exactly one zero in $[0,1)$.\nIf $f_n$ has four zeros that form an arithmetic progression with step size $d^{-n}$, then $d=2$ and $f(\\frac12)=0$.\n\\end{lemma}\n\\begin{proof}\nLet $s_0+\\mathbb{Z}$ be the set of zeros of $f$.\nSuppose that $t_i=t+id^{-n}$ is a zero of $f_n$ for $i=0,1,2,3$.\nThen there exist integers $0\\le k_i\\le n-1$, and integers $j_i$ such that\n\\begin{equation}\\label{eq}\n t_i=d^{-k_i}(s_0+j_i),\\quad i=0,1,2,3.\n\\end{equation}\nWe will assume that $k_0=\\max\\{k_0,k_1,k_2,k_3\\}$ (the other cases are mentioned at the end of the proof.)\nClearly, $k_0>k_1$.\nSince we can replace $s_0$ by $s_0+j$ with any integer $j$, we will assume that $j_1=0$ in order to simplify the notation.\nFrom $t_1-t_0=t_2-t_1=d^{-n}$, we obtain\n\\[ d^{-k_1}s_0-d^{-k_0}(s_0+j_0)=d^{-n},\\quad d^{-k_2}(s_0+j_2) -d^{-k_1}s_0=d^{-n} .\\]\nEliminating $s_0$ from these equations, we find\n\\[ (1-j_0d^{n-k_0})(d^{k_0-k_1}-d^{k_0-k_2})= (1-j_2d^{n-k_2})(1-d^{k_0-k_1}) .\\]\nThis is an equation involving only integers. Since $n>k_2$, $k_0>k_1$, the right-hand side is not divisible by $d$.\nTherefore, we must have $k_0=k_2$. But this is impossible when $d>2$. So we must have $d=2$ and $k_0=k_2=n-1$.\n\nNow suppose that $d=2$ and $k_0=k_2=n-1$.\nWithout loss of generality we take $j_0=0$, $j_2=1$.\nSince $t_1-t_0=t_3-t_2=2^{-n}$ we obtain\n\\[ 2^{n-k_1}(s_0+j_1)=2s_0+1,\\quad 2^{n-k_3}(s_0+j_3)=2s_0+3 .\\]\nEliminating $s_0$ this gives\n\\[ (3-j_32^{n-k_3})(2^{n-k_1-1}-1)=(1-j_12^{n-k_1})(2^{n-k_3-1}-1) .\\]\nIt is clear that $k_10$ for $m d^{-n}\\le t\\le (m+4) d^{-n}$ for some integer $m$, $0\\le m\\le d^n-4$.\nWe claim that $(L^n u)(t)>0$ for all $t\\in\\mathbb{T}$. In fact, by Lemma \\ref{L:Krein1}, if $t\\in\\mathbb{T}$ then among the four points $t_i=d^{-n}(t+m+i)$, $i=0,1,2,3$,\nat least one satisfies $f_n(t_i)>0$. Then \\eqref{L:Ln} implies $(L^n u)(t)\\ge f_n(t_i)u(t_i)>0$.\n\\end{proof}\n\nIf $d=2$ and $f(\\frac12)=0$ then $L$ is not a Krein operator: If $u(0)=0$ then $(L^nu)(0)=0$ for all $n\\in\\mathbb{N}$.\n\n\\begin{thm}[See also \\cite{FanLau1998}]\\label{L:t3}\nSuppose that $L$ is a Krein operator. Then the following statements hold.\\\\\n(a) $R>0$.\\\\\n(b) If $Lv=\\lambda v$ with $v\\ne 0$ and $|\\lambda|=R$,\nthen there is a constant $\\theta\\in\\mathbb{R}$ such that $e^{-i\\theta} v$ is a unit. \\\\\n(c) $L$ has no eigenvalue $\\lambda$ on the circle $|\\lambda|=R$ except possibly $\\lambda=R$.\\\\\n(d) If $R$ is an eigenvalue of $L$, then its algebraic multiplicity is $1$.\\\\\n(e) If $0\\le f\\in \\H$, then $L_\\alpha$ is quasicompact, $r=R$, and $R$ is an eigenvalue of $L_\\alpha$ of algebraic multiplicity $1$ with a unit eigenfunction. \\end{thm}\n\\begin{proof}\n(a) Since $L$ is a Krein operator, $h_1=L1$ is a unit, so $00$ such that\n\\[ (L^nz)(t)=(Lw)(t)-R w(t)\\ge\\delta w(t),\\quad t\\in \\mathbb{T}.\\]\nApplying Theorem \\ref{L:t2}, we obtain the contradiction\n\\[ R+\\delta\\le \\min_{t\\in\\mathbb{T}} \\frac{(Lw)(t)}{w(t)}\\le R .\\]\nTherefore, $z=0$ and\n\\begin{equation}\\label{L:eq}\nL|v|=R|v|.\n\\end{equation}\nThen\n\\[ |L^n v|=R^n |v|=L^n|v|=w.\\]\nTherefore, $|v|$ is a unit.\nWe claim that there is a constant $\\theta\\in\\mathbb{R}$ such that $e^{-i\\theta} v(t)>0$ for all $t$.\nSuppose this is not true. Since $L|v|=|Lv|$, there is $n\\in\\mathbb{N}$ and $1\\le i0$ such that $\\delta u\\le w$.\nThen Theorem \\ref{L:t2} leads to the contradiction\n\\[ R+\\delta\\le \\min_{t\\in\\mathbb{T}} \\frac{(Lu)(t)}{u(t)}\\le R .\\]\nThis shows that the algebraic multiplicity of the eigenvalue $R$ is $1$.\n\n\\noindent (e)\nSince $\\rho(L)=R>0$, it follows from Theorem \\ref{L:t1} that $L_\\alpha$ is quasicompact. Then $L_\\alpha$ has an eigenvalue $\\lambda$ on the circle $|\\lambda|=R$.\nBy (c), $R$ is an eigenvalue of $L_\\alpha$. There is a corresponding unit eigenfunction.\nNow $r=R$ follows from Theorem \\ref{L:t2}.\n\\end{proof}\n\n\\begin{thm}[See also \\cite{FanLau1998}]\\label{L:t4}\nSuppose that $0\\le f\\in \\H$ and that $L$ is a Krein operator.\nLet $P$ be the spectral projection onto the eigenspace of $L_\\alpha$ corresponding to the eigenvalue $R$.\\\\\n(a) The sequence $R^{-n}L_\\alpha^n$ converges to $P$ as $n\\to\\infty$ with respect to the operator norm.\\\\\n(b) The sequence $R^{-n} h_n$ converges in $\\H$ to an eigenfunction of $L_\\alpha$ corresponding to the eigenvalue $R$.\n\\end{thm}\n\\begin{proof}\n(a)\nBy Theorem \\ref{L:t3}, $L_\\alpha$ is quasicompact and the eigenvalue $R$ of $L_\\alpha$ is an isolated point of its spectrum.\nTherefore, there exists the spectral projection $P$ onto the one-dimensional root subspace belonging to the eigenvalue $R$.\nThe Banach space $\\H$ is a direct sum of the subspaces $P\\H$ and $(1-P)\\H$. Both subspaces are invariant under $L_\\alpha$.\nOn $P\\H$, $L_\\alpha$ acts as $R$ times the identity.\nSet $S:=R^{-1}(1-P)L_\\alpha$. By Theorem \\ref{L:t3}, the spectral radius of $S$ is less than $1$ so $S^n$ converges to $0$ as $n\\to\\infty$ in the operator norm.\nWe have $R^{-n} L_\\alpha^n =S^n+ P$ which implies statement (a).\n\n\\noindent (b)\nWe have $R^{-n}h_n=R^{-n}L^n_\\alpha1\\to P1$ as $n\\to\\infty$. Since\n\\[1\\le R^{-n}R_n\\le \\|R^{-n} h_n\\|_\\alpha\\to \\|P1\\|_\\alpha,\\]\nwe have $P1\\ne0$.\n\\end{proof}\n\nWe consider now the following problem that was the original motivation for this paper.\nLet $f:\\mathbb{R}\\to\\mathbb{C}$ be a bounded measurable and 1-periodic function, and let $d\\ge 2$ be an integer. For $n\\in\\mathbb{N}$, define as before\n\\[ f_n(t)=\\prod_{j=0}^{n-1} f(d^j t) .\\]\nThe problem is to find the behavior of the sequence of integrals\n\\[ I_n(f)=\\int_0^1 f_n(t)\\,dt \\]\nas $n\\to\\infty$. In particular, we want to find $c(f)$ defined by\n\\begin{equation}\\label{L:c}\n c(f)=\\limsup_{n\\to\\infty} |I_n(f)|^{1\/n}.\n \\end{equation}\n\nThe sequence $I_n$ is related to the bounded linear operator\n\\begin{equation}\\label{T}\n (Tx)(t)=f(t)x(d\\cdot t)\n\\end{equation}\nwhich maps $L^2(\\mathbb{T})$ to itself. Note that\n\\[ f_n=T^n 1 \\]\nand\n\\begin{equation}\\label{Im}\n I_n(f) =\\langle T^n1,1\\rangle\n\\end{equation}\nwith the inner product $\\langle\\cdot,\\cdot\\rangle$ in $L^2(\\mathbb{T})$.\nIn particular, \n\\[ |I_n|\\le \\|T^n\\|\\]\nand\n\\begin{equation}\\label{L:eq1}\nc(f)\\le \\lim_{n\\to\\infty} \\|T^n\\|^{1\/n}=\\rho(T).\n\\end{equation}\n\nWe show that $c(f)$ is equal to the spectral radius of a transfer operator under suitable assumptions on $f$. See also \\cite{FanLau1998} for related results.\n\n\\begin{thm}\\label{A:t1}\nSuppose that $0\\le f\\in \\H$ for some $0<\\alpha\\le 1$, and that the transfer operator $L$ defined by \\eqref{L:L} is a Krein operator. Then $r=c(f)=R=\\rho(L)$, where\n$r,R$ are defined in \\eqref{L:rR}. Moreover, we can replace $\\limsup$ by $\\lim$ in definition \\eqref{L:c}.\n\\end{thm}\n\\begin{proof}\nThe adjoint $T^\\ast$ of $T$ agrees with the operator $L$ when considered as an operator on $L^2(\\mathbb{T})$.\nLet $h_n, r_n, R_n$ be defined by \\eqref{L:hn}, \\eqref{L:rnRn}.\nIt follows from \\eqref{Im} that\n\\[ I_n=\\langle 1, h_n\\rangle=\\int_0^1 h_n(t)\\,dt .\\]\nThus\n\\[r_n^{1\/n}\\le I_n^{1\/n}\\le R_n^{1\/n}.\\]\nBy Theorem \\ref{L:t3}(e), the sequences $r_n^{1\/n}$ and $R_n^{1\/n}$ converge to the same limit $r=R$.\nTherefore, the sequence $I_n^{1\/n}$ converges and we obtain $r=c(f)=R$.\n\\end{proof}\n\nUsing Theorem \\ref{A:t1} in connection with \\eqref{L:est1} or Theorem \\ref{L:t2} we can estimate $c(f)$. We will look at some examples in the next section.\n\nWe mention two special classes of functions $f$ for which $c(f)$ can be calculated explicitly.\n\n1)\nSuppose that $f$ is a step function such that $f(t)=f_i={\\rm const}$ for $\\frac{i-1}{d}\\le t< \\frac{i}{d}$, $i=1,\\cdots,d$.\nThen it is easy to show that\n\\[ I_n(f)= \\left(\\int_0^1 f(t)\\,dt\\right)^n.\\]\nTherefore,\n\\[ c(f)=\\left|\\int_0^1 f(t)\\,dt\\right| .\\]\nIf $f$ is any nonnegative bounded measurable $1$-periodic function, we may introduce\ntwo step function $g,h$ defined by\n\\begin{eqnarray*}\n g(t)&=&\\inf\\left \\{ f(s): \\frac{i-1}{d}\\le s<\\frac{i}{d}\\right \\}\n \\quad\\text{for $\\frac{i-1}{d}\\le t<\\frac{i}{d}$},\\\\\nh(t)&=&\\sup\\left \\{ f(s): \\frac{i-1}{d}\\le s<\\frac{i}{d}\\right \\}\n\\quad\\text{for $\\frac{i-1}{d}\\le t<\\frac{i}{d}$}.\n \\end{eqnarray*}\nThen we obtain the estimate\n\\begin{equation}\\label{ineq}\nI_n(g)\\le I_n(f)\\le I_n(h)\n\\end{equation}\nand so\n\\begin{equation}\\label{ineq1}\n\\int_0^1 g(t)\\,dt\\le c(f)\\le \\int_0^1 h(t)\\,dt .\n\\end{equation}\n\n2) Let $f$ be any bounded measurable $1$-periodic function with\nFourier expansion\n\\[ f(t)=\\sum_{k\\in\\mathbb{Z}} a_k e^{2\\pi i k t} .\\]\nWe represent the operator $T$ by an infinite matrix in the orthonormal basis $\\{e^{2\\pi ikt}\\}_{k\\in\\mathbb{Z}}$.\nThe matrix of $T$ is\n\\begin{equation}\\label{T:matrix}\n \\left( a_{k-d\\ell} \\right)_{k,\\ell \\in\\mathbb{Z}} .\n\\end{equation}\nIn this notation $k$ is the row index and $\\ell$ is the column index.\nIf we write\n\\[ f_n(t)=\\sum_{k\\in\\mathbb{Z}} a_{k,n} e^{2\\pi i kt},\\]\nthen we obtain the coefficients $a_{k,n+1}$ from $a_{k,n}$ by application of $T$, so\n\\[ a_{k,n+1}=\\sum_{\\ell\\in\\mathbb{Z}} a_{k-d\\ell}\\, a_{\\ell,n} .\\]\nNote that $I_n=a_{0,n}$.\n\nIn particular, suppose that $f(t)$ is a trigonometric polynomial of degree $N$, so\n\\[ f(t)=\\sum_{k=-N}^N a_k e^{2\\pi i k t} .\\]\nWe set\n\\[ K:=\\left\\lfloor \\frac{N-1}{d-1}\\right\\rfloor.\\]\nConsider the central $2K+1$ by $2K+1$ submatrix $B$ of $T$ consisting of rows $-K\\le k\\le K$ and columns $-K\\le \\ell\\le K$.\nNotice that all entries in the rows $-N\\le k\\le N$ outside the central submatrix vanish. Therefore, we obtain the recursion\n\\[ a_{k,n+1} =\\sum_{\\ell=-K}^K a_{k-d\\ell}\\,a_{\\ell,n} \\quad\\text{if $-K\\le k\\le K$.}\\]\nHence we can calculate $I_n=a_{0,n}$ by computing the powers of the matrix $B$.\nIt is clear that\n\\begin{equation}\\label{eq2}\n c(f)\\le \\rho(B)\n\\end{equation}\nbut it is not immediately clear whether we have equality in \\eqref{eq2}.\nIt depends on how the constant function $1$ is represented in a Jordan basis of $B$ (whether the basis vectors associated with largest eigenvalue of $B$ contribute to the expansion of $1$.)\n\nThe situation is clear if the matrix $B$ is nonnegative and primitive ($B^p$ is a positive matrix for some $p\\in\\mathbb{N}$.)\nThen the spectral radius of $B$ is a simple positive eigenvalue and we can use Theorem 8.5.1 in \\cite{HJ} to show that there is equality in \\eqref{eq2}.\nSuppose that $a_k>0$ for all $k=-N,-N+1,\\cdots,N$. Then all entries in the main diagonal, the subdiagonal and superdiagonal of $B$ are positive. Therefore, $B$ is primitive.\n\nIf we have symmetry $a_{-k}=a_k$ then we can replace the matrix $B$ by a $K+1$ by $K+1$ matrix $C$ whose entries are\n\\[ c_{i,0}=a_i, \\quad c_{i,j}=a_{i-dj}+a_{i+dj} \\quad\\text{if $0\\le i\\le K$, $1\\le j\\le K$.}\\]\nSee the next section for examples.\n\n\\section{The special cases $f(t)=|\\cos(\\pi t)|^q$ and $f(t)=|\\sin(\\pi t)|^q$}\\label{sec:special-weights}\n\nIn this section we consider the functions\n\\[f(t)=|\\cos(\\pi t)|^q,\\quad \\tilde f(t)=|\\sin(\\pi t)|^q\\quad \\text{where $q>0$}.\n\\]\nWe set\n\\[ c(q):=c(f),\\quad \\tilde c(q):=c(\\tilde f).\\]\nObviously, $0\\le c(q), \\tilde c(q)\\le 1$. Note that $f, \\tilde f\\in \\H$ with $\\alpha=\\min(q,1)$. By Lemma \\ref{L:Krein2}, $L$ is a Krein operator except when $f(t)=|\\cos(\\pi t)|^q$ and $d=2$.\nThis is an exceptional case that will be considered in the next section.\nExcept for this case we can apply Theorems \\ref{L:t3} and \\ref{A:t1}.\n\nIf $d$ is odd, then $I_n(f)=I_n(\\tilde f)$, and consequently $c(f)=c(\\tilde f)$. In fact, in this case the transfer operators weighted by $f$ and $\\tilde f$ are conjugate to each other.\n\n\\begin{thm}\\label{S:t1}\nThe functions $c(q)$ and $\\tilde c(q)$ are convex and nonincreasing in $q>0$. Moreover, \n\\[ \\lim_{q\\to\\infty} c(q)=\\frac1 d, \\quad\n\\lim_{q\\to\\infty} \\tilde c(q)=\\begin{cases} \\frac1d & \\text{if $d$ is odd,}\\\\ 0 & \\text{if $d$ is even,}\\end{cases}\n\\]\nand\n\\[ \\lim_{q\\to 0^+} c(q)= \\lim_{q\\to 0^+} \\tilde c(q)=1.\\]\n\\end{thm}\n\\begin{figure}[h]\n\\includegraphics[scale=0.4]{cq.png}\n\\caption{A graph of $c(q)$ for $00$. Hence $$\\lim_{q\\to\\infty} c(q)=d^{-1}.$$\n\nWhen treating $\\tilde c(q)$, we may assume that $d$ is even. The proof is similar to the preceding one. To determine the limit of $\\tilde c(q)$ as $q\\to\\infty$, we use $c(\\tilde f)\\le R_2^{1\/2}$.\n\nTo show the limits as $q\\to 0^+$, it suffices to show that\n\\[c(q)^{1\/q}\\ge 1\/2,\\quad \\tilde c(q)^{1\/q}\\ge 1\/2\\]\nfor all $q>0$. To this end,\nlet $g(t)=|\\cos(\\pi t)|$ (or $|\\sin(\\pi t)|$). By Jensen's inequality, we have\n$$I_n^{1\/q}=\\left (\\int_0^1 |g_n(t)|^q dt\\right )^{1\/q} \n\\ge \\exp\\left (\\int_0^1 \\ln |g_n(t)|dt\\right )$$\nfor all $q>0$. \nOn the other hand, since\n$$\\int_0^1 \\ln |\\cos(\\pi t)|dt=\\int_0^1 \\ln |\\sin(\\pi t)|dt=\\ln (1\/2),$$\nwe have\n$$\\int_0^1 \\ln |g_n(t)|dt=\\sum_{j=0}^{n-1} \\int_0^1 \\ln |g(d^j t)|dt=n\\ln (1\/2).$$\nThus, for any $q>0$,\n$$c(q)^{1\/q}\\ (\\text{or }\\tilde c(q)^{1\/q})=\\lim_{n\\to\\infty} I_n^{1\/(nq)}\\ge 1\/2.$$\nThis completes the proof.\n\\end{proof}\n\n\\subsection{The case $f(t)=\\cos^{2N}(\\pi t)$, $d=3$}\nFor $N\\in\\mathbb{N}$, consider the trigonometric polynomial\n\\[ f(t)=\\cos^{2N}(\\pi t). \\]\nThe degree of $f$ is $N$ and, for $-N\\le k\\le N$,\n\\[ a_k=2^{-2N} \\binom{2N}{k+N}>0 .\\]\nWe use the method 2) from Section \\ref{sec:quasicompact}.\nIf $N=1,2$, then $K=0$ and so $c(2)=\\frac12$ and $c(4)=\\frac38$ (note that this recovers a result of Strichartz \\cite{Strichartz1990}).\nIf $N=3$, then $K=1$ and\n\\[ C=\\frac1{64} \\left[\\begin{array}{rr} 20 & 2\\\\ 15 & 6 \\end{array}\\right]. \\]\nSo\n\\[ c(6)=\\rho(C)=\\frac1{64}(13+\\sqrt{79}) =0.342003\\cdots.\\]\nIf $N=4$, then $K=1$ and\n\\[ C=\\frac1{256} \\left[\\begin{array}{rr} 70 & 16\\\\ 56 & 29 \\end{array}\\right]. \\]\nIt follows that\n\\[ c(8)=\\rho(C)= \\frac1{512}(99+9\\sqrt{65})=0.335078\\cdots.\\]\nWhen $N\\ge 5$, the formulas for $c(2N)$ become more complicated, but $c(2N)$ is easy to compute numerically.\nFor example, we obtain\n\\[ c(10)=0.333691\\cdots.\\]\n\nThe same method can be used to determine $c(2N)$ for other values of $d$.\n\n\\subsection{The case $f(t)=|\\sin(\\pi t)|$, $d=3$}\\label{subsec:d=3}\nEven if $f$ is not a trigonometric polynomial, we can still use matrix methods to estimate $c(f)$.\nAs an example, consider\n\\begin{equation}\\label{S:fs}\n f(t)=|\\sin(\\pi t)|.\n\\end{equation}\nThe Fourier coefficients of $f$ are\n\\[ a_k=-\\frac{2}{\\pi} \\frac{1}{(2k-1)(2k+1)},\\ k\\in\\mathbb{Z} .\\]\nNote that $a_0>0$ but all other $a_k$ are negative.\nLet $N\\in\\mathbb{N}$. We estimate\n\\[ f(t)\\le \\sum_{k=-N}^N a_k e^{2\\pi i k t} +\\frac2\\pi \\sum_{k=N+1}^\\infty \\frac{2}{(2k-1)(2k+1)} \\]\nso we have\n\\[ f(t)\\le h(t) ,\\]\nwhere\n\\[ h(t)=\\frac2\\pi \\frac1{2N+1} + \\sum_{k=-N}^N a_k e^{2\\pi i k t} .\\]\nUsing\n\\[ c(f)\\le c(h)\\le \\rho(C) \\]\nwe get upper bounds for $c(f)=c(1)$ (when $d=3$):\n\n\\begin{center}\n\\begin{tabular}{r|c}\n$N$ & $\\rho(C)$ \\\\\n\\hline\n1 & 0.848826\\\\\n2 & 0.763943\\\\\n3 & 0.737463\\\\\n4 & 0.717381\\\\\n5 & 0.704696\\\\\n10& 0.678384\\\\\n20& 0.663593\\\\\n30& 0.658613\\\\\n50 & 0.654552\\\\\n100 & 0.651436\n\\end{tabular}\n\\end{center}\nAs far as we know the exact value of $c(1)$ is not known. We conjecture that\n$c(1)=0.648314\\cdots$.\n\nWe also obtain\n\\[ g(t)\\le f(t) \\]\nwhere\n\\[ g(t)= -\\frac2\\pi \\frac1{2N+1} + \\sum_{k=-N}^N a_k e^{2\\pi i k t} .\\]\nSince $a_0>0$ and all other $a_k<0$, we can easily show that $g(t)\\ge 0$.\nTherefore, we have\n\\[ c(g)\\le c(f) .\\]\nHowever, the trigonometric polynomial $g$ does not have positive coefficients, so we do not know whether $c(g)=\\rho(C)$. Therefore, we do not obtain lower bounds by this method.\nFor $N=100$, one would get $\\rho(C)=0.645194\\cdots $.\n\nSomewhat surprisingly, the functions $h_n$ associated with \\eqref{S:fs}\ncan be represented in a fairly explicit way.\nIf\n\\[ u(t)=\\cos\\left(\\pi as\\right),\\quad s=t-\\tfrac12, \\]\nthen\n\\[ (Lu)(t)= \\tfrac16 \\big(1+2\\cos\\tfrac\\pi3(1+a)\\big)\\cos\\tfrac\\pi3(1+a)s+\\tfrac16\\big(1+2\\cos\\tfrac\\pi3(1-a)\\big) \\cos\\tfrac\\pi3(1-a) s .\\]\nIterating this formula, we see that $h_n$ is a sum of $2^{n-1}$ many terms of the form\n\\[ A\\cos(\\pi a s) \\]\nwhere $A>0$ and $a=\\frac13\\pm\\frac19\\pm\\frac1{27}\\pm\\cdots\\pm \\frac{1}{3^n}\\in (0,\\tfrac12)$.\nIt follows that\n\\[ r_n=h_n(0),\\quad R_n=h_n(\\tfrac12).\\]\nBy \\eqref{L:rR},\n\\[ \\left(h_n(0)\\right)^{1\/n}\\le c(f)\\le \\left(h_n(\\tfrac12)\\right)^{1\/n} .\\]\nFor example, if $n=1$ we get the bounds\n\\[ \\frac13\\sqrt3\\le c(f)\\le \\frac23.\\]\n\nSince $00,\\quad 00$ if $s_10$, we have $(-1)^{k-1}h_1'(t)>0$ when $04$, then $\\frac{h_{n+1}(t)}{h_n(t)}$ attains its maximum at $t=\\frac12$ and its minimum at $t=0$.\n\\end{conj}\n\nIf we believe these conjectures then $c(f)$ would lie between $\\frac{r_{n+1}}{r_n}$ and $\\frac{R_{n+1}}{R_n}$ for every $n$.\nIn the case $d=3$ we get the following estimates for $c(1)$:\n\n\\begin{center}\n\\begin{tabular}{r|r|r}\n$n$ & lower bound & upper bound \\\\\n\\hline\n1 & 0.577350 & 0.666666 \\\\\n2 & 0.646564 & 0.656538 \\\\\n3 & 0.648297 & 0.648396\n\\end{tabular}\n\\end{center}\n\nWe see that these bounds are much better than those from Section \\ref{subsec:d=3}. Unfortunately, we used conjecture \\ref{S:conj2} but for small $n$ it can be proved by direct computation.\n\n\\section{The case $f(t)=|\\cos(\\pi t)|^q$, $d=2$}\\label{sec:binary}\n\nIn the exceptional case\n\\[ f(t)=|\\cos(\\pi t)|^q, \\quad d=2\\]\nwe can obtain more explicit computations. See also \\cite{FanLau1998} for related results.\n\n\\subsection{The integrals $I_n$}\nUsing the identity\n\\[\n\\prod_{j=0}^{n-1} \\cos(2^j t)=\\frac{\\sin(2^n t)}{2^n \\sin(t)} ,\n\\]\nwe can write\n\\begin{equation}\\label{1:fm}\nf_n(t)=\\frac{1}{2^{q n}} \\frac{|\\sin(\\pi 2^n t)|^q}{|\\sin(\\pi t)|^q}.\n\\end{equation}\nTherefore,\n\\begin{equation}\\label{1:fm-integral}\nI_n=\\frac{1}{2^{q n}} \\int_0^1 \\frac{|\\sin(\\pi 2^n t)|^q}{|\\sin(\\pi t)|^q}\\, dt=\\frac{1}{2^{q n-1}} \\int_0^{1\/2} \\frac{|\\sin(\\pi 2^n t)|^q}{|\\sin(\\pi t)|^q}\\, dt.\n\\end{equation}\n\n\\begin{thm}\\label{1:t}\nFor $d=2$, we have\n\\[ c(q)=\\lim_{n\\to\\infty} I_n^{1\/n}=\n\\begin{cases} \n2^{-q} & \\text{if $01$.}\n\\end{cases}\n\\]\n\\end{thm}\n\\begin{proof}\nSubstituting $u=2^n t$ in \\eqref{1:fm-integral}, we get\n\\[ I_n=\\frac{1}{2^{n-1}} \\int_0^{2^{n-1}} \\frac{|\\sin(\\pi u)|^q}{2^{q n}|\\sin(\\pi 2^{-n} u)|^q}\\,du .\\]\nUsing\n\\[ \\frac{2}{\\pi} t\\le \\sin t\\le t,\\quad 0\\le t\\le \\frac{\\pi}{2},\\]\nwe find\n\\begin{equation}\\label{1:ineq}\n\\frac{\\pi^{-q}}{2^{n-1}} \\int_0^{2^{n-1}} \\frac{|\\sin(\\pi u)|^q}{u^q}\\, du\\le I_n \\le \\frac{2^{-q}}{2^{n-1}} \\int_0^{2^{n-1}} \\frac{|\\sin(\\pi u)|^q}{u^q}\\, du .\n\\end{equation}\nIf $q>1$, the integral\n\\begin{equation}\\label{1:int}\n\\int_0^\\infty \\frac{|\\sin(\\pi u)|^q}{u^q}\\,du\n\\end{equation}\nconverges. Therefore, the statement of the theorem follows for $q>1$.\nIf $q=1$, the integral \\eqref{1:int} diverges and the integrals in \\eqref{1:ineq} behave like $\\ln(2^n)$. Since $n^{1\/n}$ converges to $1$ as $n\\to\\infty$,\nwe obtain the statement of the theorem when $q=1$. If $00$}\\]\nand\n\\[ R=\\begin{cases}\n2^{-q} & \\text{if $01$.}\n\\end{cases}\n\\]\n\\end{thm}\n\\begin{proof}\nUsing only the term with $k=0$ in \\eqref{2:hm}, we obtain, for $0\\le t\\le \\frac12$,\n\\[ h_n(t)\\ge \\frac{1}{2^n}\\frac{1}{2^{q n}}\\frac{|\\sin(\\pi t)|^q}{|\\sin(\\pi 2^{-n}t)|^q}\\ge \\frac{1}{2^n}\\frac{2^q}{\\pi ^q}.\\]\nThis inequality together with $h_n(0)=2^{-n}$ proves $r=\\frac12$.\nThe proof of the formula for $R$ is elaborated in Section \\ref{subsec:convergence}.\n\\end{proof}\n\n\\subsection{Eigenfunctions}\\label{subsec:eigenfunctions}\nLet $\\alpha=\\min\\{1,q\\}$.\nBy Theorem \\ref{L:t1}, the spectral radii of $L$ and $L_\\alpha$ agree, and $L_\\alpha$ is quasicompact.\nSince $L$ is also a positive operator, $\\lambda=R$ must be an eigenvalue, so there must exist a corresponding eigenfunction.\nBut $L$ is not a Krein operator (cf. \\cite{A}), so we do not know whether the eigenfunction is unique (up to a constant factor) \nor whether it is positive on $\\mathbb{T}$.\n\nWe want to find nontrivial solutions $u\\in C(\\mathbb{T})$ to the equation $Lu=\\lambda u$, particularly for $\\lambda=R$.\nInterestingly, we can find these eigenfunctions fairly explicitly.\nIn fact, if we substitute\n\\[ u(t)=|\\sin(\\pi t)|^q g(t) \\]\nin $Lu=\\lambda u$, we find\n\\begin{equation}\\label{3:ber}\n\\tfrac12 g\\left(\\tfrac12 t\\right)+\\tfrac12 g\\left(\\tfrac12 (t+1)\\right) =\\mu g(t)\\quad \\text{where }\\mu=2^q \\lambda .\n\\end{equation}\nNote that $g(t)$ will usually be continuous only on the open interval $(0,1)$.\nMuch is known about equation \\eqref{3:ber} (cf. \\cite{V}).\nClearly, $g(t)=1$ is a solution to \\eqref{3:ber} with $\\mu=1$. Therefore, $u(t)=|\\sin(\\pi t)|^q$ is an eigenfunction of $L$ corresponding\nto the eigenvalue $\\lambda=2^{-q}$. If $01$.\n\nUsing an idea from \\cite{V}, we find eigenfunctions corresponding to $\\lambda=R$ when $q>1$.\nFor $s>1$, consider the Hurwitz zeta function\n\\begin{equation}\\label{3:g}\n\\zeta(s,t)=\\sum_{k=0}^\\infty \\frac{1}{(t+k)^s},\\quad 00$ we fix $\\delta>0$ such that\n$$\\left |\\frac{x}{\\sin x}-1\\right |<\\varepsilon,\\quad 00$ for $n=1,\\cdots,N(q)$, where $N(q)$ satisfies $\\lim_{q\\rightarrow\\infty}N(q)=\\infty$.\n\\end{prop}\n\\begin{proof}\n\\noindent(a) In the case $n=0$, $h_0(t)\\equiv 1$, so the statement obviously holds with strict inequality replaced by equality. Assume that $h_{n-1}''(t)\\le 0$ for all $t\\in (0,1)$. We now show that $h_n''(t)<0$ for all $t\\in (0,1)$.\n\nBy definition, we have\n$$h_n(t)=\\frac{1}{2}\\Big|\\cos\\Big(\\pi \\frac{t}{2}\\Big)\\Big|^q h_{n-1}\\Big(\\frac{t}{2}\\Big)+\\frac{1}{2}\\Big|\\cos\\Big(\\pi \\frac{t+1}{2}\\Big)\\Big|^q h_{n-1}\\Big(\\frac{t+1}{2}\\Big).$$\nSince the second term equals the first term after the change of variable $t\\rightarrow 1-t$, it suffices to show $(fg)''(t)<0$ for all $t\\in (0,1\/2)$, where\n$$f(t)=|\\cos(\\pi t)|^q,\\quad g(t)=h_{n-1}(t).$$\nHowever, by the product rule,\n$$(fg)''=f''g + 2 f'g' + fg''.$$\nSince $q\\le 1$, we have $f''(t)<0$ for all $t\\in (0,1\/2)$. Also, by symmetry we have $g'(1\/2)=0$, and so the induction hypothesis implies $g'(t)\\ge 0$ for all $t\\in (0,1\/2)$. Combining these we get $f''g<0$, $f'g'\\le 0$, and $fg''\\le 0$, which gives $(fg)''(t)<0$ for all $t\\in (0,1\/2)$. This completes the proof by induction.\n\n\\noindent(b) The proof is similar to that of (a). Using the same notation, we observe that\n$$f''(t)=\\pi^2 q |\\cos(\\pi t)|^q \\Big(q|\\sin(\\pi t)|^2-1\\Big),\\quad 01\/4$ if $q<2$, and $t_q<1\/4$ if $q>2$; moreover,\n$$\\lim_{q\\rightarrow 1^+} t_q = 1\/2,\\quad\n\\lim_{q\\rightarrow \\infty} t_q = 0.$$\nIn order to determine the sign of\n\\begin{equation}\\label{eqn:2nd-derivative}\n(fg)''=f''g + 2 f'g' + fg'',\n\\end{equation}\nas before we want all the three terms to have the same sign.\n\nIn the case $n=1$, since $g\\equiv 1$, we have $(fg)''(t)=f''(t)<0$ for all $t\\in(0,2t_q)$, where $2t_q>1\/2$ and $2t_q\\rightarrow 1$ as $q\\rightarrow 1^+$. This implies $h_1''(t)<0$ for all $t\\in (1-2t_q,2t_q)$. By symmetry we have $h_1'(t)>0$ for all $t\\in (1-2t_q,1\/2)$. Now proceeding by induction, we see that, using \\eqref{eqn:2nd-derivative},\n$$h_n''(t)<0,\\quad t\\in (2^{n-1}(1-2t_q),1-2^{n-1}(1-2t_q))$$\nand\n$$h_n'(t)>0,\\quad t\\in (2^{n-1}(1-2t_q),1\/2).$$\nIn particular, if $q>1$ is sufficiently close to 1, we have $2^{n-1}(1-2t_q)<1\/2$ and thus $h_n''(1\/2)<0$, as desired.\n\nThe proof for (c) is similar.\n\\end{proof}\n\nWhen $q$ is an even integer, we can have more information.\n\n\\begin{prop}\nIf $q\\ge 4$ is an even integer, then\n$$h_\\infty(t):=\\pi^{-q}|\\sin(\\pi t)|^q G(q,t)$$\nsatisfies $h_\\infty''(1\/2)>0$; moreover, $h_\\infty'(t)<0$ for all $t\\in(0,1\/2)$.\n\\end{prop}\n\n\\begin{proof}\nBy \\eqref{3:G}, we have\n$$G(q,t)=\\frac{(-1)^{q-1}\\pi}{(q-1)!}\n\\left (\\frac{d}{dt}\\right )^{q-1}\\cot (\\pi t).$$\nLemma \\ref{lem:polynomial} below shows that, after simplification,\n$$h_\\infty(t)=\\frac{2}{(q-1)!} P_{q-1}(\\cos(\\pi t))$$\nwhere $P_{q-1}(x)$ is a polynomial consisting of the even powers $1, x^2, \\cdots, x^{q-2}$ and has positive coefficients. By direct computation, we then have\n$$h_\\infty'(t)=-\\frac{2\\pi}{(q-1)!}P_{q-1}'(\\cos(\\pi t)) \\sin(\\pi t)$$\nand\n$$h_\\infty''(1\/2)=\\frac{2\\pi^2}{(q-1)!} P_{q-1}''(0).$$\nThe desired conclusions now follow immediately from the properties of $P_{q-1}(x)$ mentioned above.\n\\end{proof}\n\n\\begin{lemma}\\label{lem:polynomial}\nFor all $n\\in\\mathbb{N}$, we have\n$$\\left (\\frac{d}{dt}\\right )^{n}\\cot t=(-1)^n\\frac{P_n(\\cos t)}{(\\sin t)^{n+1}}$$\nwhere $P_n(x)$ is a polynomial of degree $n-1$ whose coefficients are nonnegative integers. Moreover, when $n$ is odd, $P_n(x)$ consists of the even powers $1,x^2,\\cdots,x^{n-1}$; when $n$ is $even$, $P_n(x)$ consists of the odd powers $x,x^3,\\cdots,x^{n-1}$.\n\\end{lemma}\n\n\\begin{proof}\nIt is easy to see that $P_0(x)=x$ and $P_1(x)=1$. Moreover, by direct computation we have\n$$P_{n+1}(x)=(n+1)xP_{n}(x)+(1-x^2)P_{n}'(x).$$\nSuppose the statement holds for $P_n(x)$, i.e.\n$$P_n(x)=a_{n-1}x^{n-1}+\\sum_{j=0}^{n-2} a_j x^j$$\nwhere $a_{n-1}$ is a positive integer and the $a_j$'s ($j\\le n-2$) are nonnegative integers. Then\n\\begin{equation}\\label{eqn:coefficients}\nP_{n+1}(x)=2 a_{n-1} x^{n} + \\sum_{j=0}^{n-2} (n-j+1) a_j x^{j+1}+\\sum_{j=0}^{n-1} j a_j x^{j-1}.\n\\end{equation}\nTherefore $P_{n+1}(x)$ is a polynomial of degree $n$ whose coefficients are nonnegative integers. By induction, this completes the proof of the first part of the lemma.\n\nThe fact that $P_n(x)$ consists of either the even powers $1,x^2,\\cdots,x^{n-1}$ or the odd powers powers $x,x^3,\\cdots,x^{n-1}$ (depending on whether $n$ is odd or even) follows easily from the recursion formula \\eqref{eqn:coefficients} and induction.\n\\end{proof}\n\nWe believe that the $N(q)$'s the Proposition \\ref{prop:convexity} should not be present, but we have not been able to remove them. By examining $h_\\infty''(1\/2)$ in its dependence on $q$, we make the following conjecture, where $\\zeta(s)$ denotes the Riemann zeta function.\n\n\\begin{conj}\nThe function\n$$F(s)=2(s+1)(2^{s+2}-1)\\zeta(s+2)-2\\pi^2 (2^s-1)\\zeta(s),\\ 11$, we have\n$$\\rho_p (L_q)=\\big[\\rho (L_{q p'})\\big]^{1\/p'}.$$\n\\end{prop}\n\nSimilar as in Section \\ref{sec:binary}, in the special case $d=2$, we can find eigenfunctions of $L_q$ in $L^p(\\mathbb T)$ explicitly. We consider two different cases.\n\nCase 1: $q p'\\le 1$. In this case we have, by Theorem \\ref{2:t},\n$$\\rho (L_{q p'})=2^{-q p'},$$\nand so\n$$\\rho_p (L_q)=2^{-q}.$$\nSince $q\\le 1$, the spectral radius of $L_q$ on $L^p(\\mathbb T)$ coincides that on $C(\\mathbb T)$. In particular, we have the same eigenfunction\n$$u(t)=|\\sin(\\pi t)|^q\\in L^p(\\mathbb{T})$$\ncorresponding to the eigenvalue $\\lambda=2^{-q}$.\n\nCase 2: $q p'>1$. In this case we have\n$$\\rho (L_{q p'})=\\frac12,$$\nand so\n$$\\rho_p (L_q)=2^{-1\/p'}.$$\nNote that $1\/p'1$ and $G(s,t)=\\zeta(s,t)+\\zeta(s,1-t)$ \n\\footnote{More generally, one can take $G(s,t)$ to be linear combinations of $\\zeta(s,t)$ and $\\zeta(s,1-t)$.}\nis as in \\eqref{3:Gst}. Since\n$$\\zeta(s,t)\\sim t^{-s},\\quad \\text{as }t\\rightarrow 0^+,$$\nwe have that $u_s\\in L^p(\\mathbb T)$ if and only if $(s-q)p<1$, i.e.\n$$s1$ exactly when $q+\\frac{1}{p}>1$,\nwe can take\n$$s=q+\\frac{1}{p}-\\varepsilon$$\nfor sufficiently small $\\varepsilon>0$ to obtain an eigenfunction in $L^p(\\mathbb T)$ corresponding to the eigenvalue\n$$2^{-q+(s-1)}=2^{-1\/p'-\\varepsilon}.$$\nTherefore, as $\\varepsilon\\rightarrow 0$, $u_s(t)$ gives an `approximate' eigenfunction corresponding to $\\rho_p (L_q)=2^{-1\/p'}$.\nNote that when $\\varepsilon=0$, $u_s(t)$ gives an eigenfunction in the Lorentz space $L^{p,\\infty}(\\mathbb T)$.\n \n\\section{An application to Fourier multipliers}\\label{sec:application}\nIn this section, we present an application to some Bochner-Riesz type multipliers introduced by Mockenhaupt in \\cite[Section~4.3]{Mockenhaupt}. Let $E\\subset\\mathbb{R}$ be the middle-third Cantor set obtained from dissecting the interval $[-1\/2,1\/2]$, and let $\\mu$ be the Cantor measure on $E$. It is well known that\n$$\\dim E=\\alpha:=\\frac{\\log 2}{\\log 3}$$\nand that the Fourier transform of $\\mu$ is given by\n\\begin{align}\\label{fourier-cantor}\n\\hat{\\mu}(x)=\\int_{\\mathbb R} e^{-\\pi i x \\xi}d\\mu(\\xi)=\\prod_{j=1}^\\infty {\\cos(\\pi 3^{-j} x)}.\n\\end{align}\nLet $\\chi\\in C_c^\\infty(\\mathbb{R})$ be a bump function with $\\hat{\\chi}\\ge 0$. For $\\delta>0$, let\n\\begin{align*}\nm_\\delta(\\xi)\n=\\frac{\\chi(\\cdot)}{|\\cdot|^{\\alpha-\\delta}}*\\mu(\\xi)\n=\\int_\\mathbb{R} \\frac{\\chi(\\xi-\\eta)}{|\\xi-\\eta|^{\\alpha-\\delta}}d\\mu(\\eta).\n\\end{align*}\nNote that $m_\\delta$ defines a bounded function only when $\\delta>0$. In particular, $m_\\delta$ is an $L^2$-Fourier multiplier if and only if $\\delta>0$. \n\n\\begin{thm}\\label{multiplier}\n$m_\\delta$ is an $L^1$-Fourier multiplier if and only if\n$$\\delta>\\frac{\\log 2}{\\log 3}+\\frac{\\log c(1)}{\\log 3}=0.236\\cdots$$\nwhere $c(1)$ is as in Section \\ref{sec:special-weights} (with $d=3$).\n\\end{thm}\n\n\\begin{proof}\nRecall that an $L^p$-Fourier multiplier is a function $m(\\xi)$ such that\n\\begin{equation}\\label{eq:multiplier}\n\\|\\mathcal{F}^{-1}\\big(m(\\xi)\\hat f(\\xi)\\big)\\|_{L^p(\\mathbb{R})}\\le C \\|f\\|_{L^p(\\mathbb{R})}\n\\end{equation}\nholds for a constant $C$ independent of $f$, where $\\mathcal{F}^{-1}$ denotes the inverse Fourier transform. In the case $p=1$, this is equivalent to $\\widehat {m}$ being a finite measure. If $\\alpha-\\delta\\le 0$, it is easy to see that this is the case with $m=m_\\delta$. If $\\alpha-\\delta>0$, then we have\n$$\\widehat{m}_\\delta(x)=c\\cdot\\left (\\hat{\\chi}*{|\\cdot|^{\\alpha-\\delta-1}}\\right )(x)\\cdot \\hat{\\mu}(x)$$\nfor some constant $c$. Thus, ${m}_\\delta$ is an $L^1$-Fourier multiplier if and only if \n\\begin{align*}\n\\int_{\\mathbb{R}} |\\widehat m_\\delta(x)|dx\n&=\\int_{|x|\\le 3} |\\widehat m_\\delta(x)|dx + \\sum_{k=1}^\\infty \\int_{3^k<|x|\\le 3^{k+1}} |\\widehat m_\\delta(x)|dx\\\\\n&\\approx 1 + \\sum_{k=1}^\\infty 3^{(\\alpha-\\delta-1)k} \\int_{3^k}^{3^{k+1}} \\prod_{j=1}^\\infty |{\\cos(\\pi 3^{-j} x)}|dx\\\\\n&<\\infty\n\\end{align*}\nwhere we have used \n$$\\hat{\\chi}*{|\\cdot|^{\\alpha-\\delta-1}}(x)\\approx |x|^{\\alpha-\\delta-1},\\ \\text{as } |x|\\rightarrow\\infty$$\nand \\eqref{fourier-cantor}. On the other hand, notice that\n\\begin{align*}\n\\int_{3^k}^{3^{k+1}} \\prod_{j=1}^\\infty |{\\cos(\\pi 3^{-j} x)}|dx\n&=3^k \\int_{1}^{3} \\prod_{j=1}^\\infty |{\\cos(\\pi 3^{k-j} x)}|dx\\\\\n&=3^k \\int_{1}^{3} |\\hat \\mu(x)| \\prod_{j=0}^{k-1} |{\\cos(\\pi 3^{j} x)}| dx\\\\\n&\\approx 3^k \\int_{0}^{1} \\prod_{j=0}^{k-1} |{\\cos(\\pi 3^{j} x)}| dx\n\\end{align*}\nwhere in the last line we have used periodicity and the fact that $|\\hat\\mu(x)|$ is bounded below on the interval $[2,3]$. Now by Theorem \\ref{L:t4}(b), we know that\n$$\\int_{0}^{1} \\prod_{j=0}^{k-1} |{\\cos(\\pi 3^{j} x)}| dx\\approx c(1)^k.$$\nTherefore \n$$\\int_{\\mathbb{R}} |\\widehat m_\\delta(x)|dx<\\infty$$\nif and only if\n$$\\sum_{k=1}^\\infty 3^{(\\alpha-\\delta-1)k} 3^k c(1)^k<\\infty,$$\nwhich is equivalent to\n$$\\delta>\\frac{\\log 2}{\\log 3}+\\frac{\\log c(1)}{\\log 3}.$$\nThis completes the proof.\n\\end{proof}\n\nSince $m_\\delta$ is compactly supported, we can choose $f\\in L^p(\\mathbb{R})$ in \\eqref{eq:multiplier} such that $\\hat f\\equiv 1$ on the support of $m=m_\\delta$, and get $\\widehat m_\\delta\\in L^p(\\mathbb{R})$ as a necessary condition for $m_\\delta$ to be an $L^p$-Fourier multiplier. By the same argument as above, this leads us to\n$$\\delta>\\delta(p):=\\frac{\\log 2}{\\log 3}-1+\\frac{1}{p}+\\frac{\\log \\big(c(p)^{1\/p}\\big)}{\\log 3}.$$\n\n\\begin{figure}[h]\n\\includegraphics[scale=0.4]{deltap.png}\n\\caption{A graph of $\\delta(p)$ as a function of $1\/p\\in (0,1)$.}\n\\end{figure}\n\n\\bibliographystyle{abbrv}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzhwes b/data_all_eng_slimpj/shuffled/split2/finalzzhwes new file mode 100644 index 0000000000000000000000000000000000000000..daafe035dc4316561648e9cb72224d65850d4278 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzhwes @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nThe nature of dark matter and dark energy is one of the most important issues today in physics. There are strong observational evidences in astrophysics and cosmology for the existence of these two components of the cosmic energy budget, indicating that about $95\\%$ of the Universe is composed by dark matter (about $25\\%$) and by dark energy (about $70\\%$), but no direct detection has been reported until now. The usual candidates \\textbf{for} dark matter (neutralinos and axions, for example) and dark energy (cosmological constant, quintessence, etc.) lead to very robust scenarios, but at same time they must face theoretical and observational issues. For recent reviews on the subject, see for example \\cite{Padmanabhan:2002ji, Peebles:2002gy, Sahni:2004ai, Bertone:2004pz, Sahni:2006pa, Copeland:2006wr, Frieman:2008sn, Martin:2008qp, Caldwell:2009ix, Li:2011sd}.\n\nThe strongest issue is perhaps the one regarding dark energy as the vacuum expectation value of some quantum field, which would be a natural candidate, but whose correct theoretical value could be predicted only in the framework of a complete theory of quantum gravity, which still we do not possess. Nevertheless, it is possible, at least, to guess some of the features of this theory. In particular, the holographic principle \\cite{'tHooft:1993gx, Susskind:1994vu, Bousso:2002ju} may shed some light on the dark energy problem. According to this principle, in presence of gravity the number of the degrees of freedom of a local quantum system would be related to the area of its boundary, rather than to the volume of the system (as expected when gravity is absent). Following this \\textbf{idea}, in \\cite{Cohen:1998zx} the authors suggested an entanglement relation between the infrared and ultraviolet \\textbf{cutoffs} due to the limitation set by the formation of a black hole, which sets an upper bound for the vacuum energy. We can then interpret the ultraviolet cutoff as the vacuum density value, but still we need an ansatz for the infrared cutoff. As a candidate for such distance, in \\cite{Li:2004rb, Huang:2004ai} the authors propose and investigate the future event horizon, tested against type Ia supernovae data and cosmic microwave background anisotropies in \\cite{Zhang:2005hs, Zhang:2007sh}. We shall present more detail on this in Sec.~\\ref{Sec:HolDE}. \n\nAdding new components of dark energy to the whole energy budget in order to explain the current observation is a way, but not the only one. Since General Relativity has been thoroughly tested up to solar system scales, it may be possible that the Einstein-Hilbert action contain corrections on larger, cosmological, scales thereby candidating as possible explanation of the evolution of the universe. Such modifications should be, in principle, composed by higher order curvature invariant terms (such as $R^2$, $R_{\\mu\\nu}R^{\\mu\\nu}$, etc) but also by non-trivial coupling between matter or fields and geometry. See for example \\cite{Nojiri:2006ri, Nojiri:2010wj, Amendola:2006kh, Amendola:2006we, Capozziello:2007ec, Sotiriou:2008rp, DeFelice:2010aj} for some reviews on the subject (especially on $f(R)$ theory). It is also worth pointing out that these terms should naturally emerge as quantum corrections in the low energy effective action of quantum gravity or string theory \\cite{Buchbinder:1992rb}.\n\nIn this paper we connect these two approaches, considering a $f(R,T)$ theory of gravity, where $R$ is the Ricci scalar, whereas $T$ is the trace of the stress-energy momentum. This modified gravity theory has been recently introduced in \\cite{Harko:2011kv}, where the authors derived the field equations and considered several cases, relevant in cosmology and astrophysics. As for the former, $f(R,T)$ models have been constructed describing the transition from the matter dominated phase to the late times accelerated one \\cite{Houndjo:2011tu}. \n\nOur task here, is to find out which form the function $f(R,T)$ has to have in order to reproduce the same properties of the holographic dark energy proposed in \\cite{Li:2004rb}. To this purpose, we employ the same reconstruction scheme proposed and employed in \\cite{Capozziello:2005ku, Nojiri:2006gh, Nojiri:2006jy, Nojiri:2006be, Wu:2007tn}. For reference, in order to track the contribution of the $T$ part of the action in the reconstruction, we consider two special $f(R,T)$ models: in the first instance, we investigate the modification $R + 2f(T)$, i.e. the usual Einstein-Hilbert term plus a $f(T)$ correction. In the second instance we consider a $f(R)+\\lambda T$ theory, i.e. a $T$ correction to the renown $f(R)$ gravity. In both cases, we consider dark energy accompanied by a pressureless matter component (which would determine $T$).\n\nThe paper is organised as follows. In Sec.~\\ref{Sec:HolDE}, the equations of motion are established and the holographic dark energy introduced. In Sec.~\\ref{Sec:Simpl} and \\ref{Sec:ComplCase} the above mentioned cases are analysed. Finally, Sec.~\\ref{Sec:DiscConcl} is devoted to discussion and conclusions.\n\nWe use $8\\pi G = c = 1$ units and adopt the metric formalism, i.e. \\textbf{the variation of the action is considered with respect to the metric quantities.}\n\n\n\\section{$f(R,T)$ gravity and holographic Dark Energy}\\label{Sec:HolDE}\n\nIn \\cite{Harko:2011kv}, the following modification of \\textbf{Einstein's} theory is proposed:\n\\begin{equation}\\label{actionfRT}\n S = \\frac{1}{2}\\int f(R,T) \\sqrt{-g}\\;d^4x + \\int L_{\\rm m} \\sqrt{-g}\\;d^4x\\;,\n\\end{equation}\nwhere $f(R,T)$ is an arbitrary function of the Ricci scalar $R$ and of the trace $T$ of the energy-momentum tensor, defined as\n\\begin{equation}\n T_{\\mu\\nu} = -\\frac{2}{\\sqrt{-g}}\\frac{\\delta\\left(\\sqrt{-g}L_{\\rm m}\\right)}{\\delta g^{\\mu\\nu}}\\;,\n\\end{equation}\nwhere $L_{\\rm m}$ is the matter Lagrangian density. We assume the matter lagrangian to depend on the metric, so that\n\\begin{equation}\n T_{\\mu\\nu} = g_{\\mu\\nu}L_{\\rm m} - 2\\frac{\\partial L_{\\rm m}}{\\partial g^{\\mu\\nu}}\\;.\n\\end{equation}\nVarying action \\eqref{actionfRT} with respect to the metric $g^{\\mu\\nu}$, one obtains \\cite{Harko:2011kv}\n\\begin{equation}\\label{Eqmod}\n f_R(R,T) R_{\\mu\\nu} - \\frac{1}{2}f(R,T)g_{\\mu\\nu} + \\left(g_{\\mu\\nu}\\square - \\nabla_\\mu\\nabla_\\nu\\right)f_R(R,T) = T_{\\mu\\nu} - f_T(R,T)T_{\\mu\\nu} - f_T(R,T)\\Theta_{\\mu\\nu}\\;,\n\\end{equation}\nwhere the \\textbf{subscripts} $R$ or $T$ \\textbf{imply} derivation with respect that quantity and we have also defined\n\\begin{equation}\n \\Theta_{\\mu\\nu} \\equiv g^{\\alpha\\beta}\\frac{\\delta T_{\\alpha\\beta}}{\\delta g^{\\mu\\nu}}\\;.\n\\end{equation}\n\\textbf{Planning} a cosmological application, we assume matter to be described by a perfect fluid energy-momentum tensor\n\\begin{equation}\n T_{\\mu\\nu} = \\left(\\rho + p\\right)u_\\mu u_\\nu - p g_{\\mu\\nu}\\;,\n\\end{equation}\nand that $L_{\\rm m} = -p$, so that we have \n\\begin{equation}\n \\Theta_{\\mu\\nu} = -2T_{\\mu\\nu} - p g_{\\mu\\nu}\\;,\n\\end{equation}\nand Eq.~\\eqref{Eqmod} simplifies as\n\\begin{equation}\\label{Eqmod2}\n f_R(R,T) R_{\\mu\\nu} - \\frac{1}{2}f(R,T)g_{\\mu\\nu} + \\left(g_{\\mu\\nu}\\square - \\nabla_\\mu\\nabla_\\nu\\right)f_R(R,T) = T_{\\mu\\nu} + f_T(R,T)T_{\\mu\\nu} + p f_T(R,T) g_{\\mu\\nu}\\;.\n\\end{equation}\nIn order to compare \\textbf{it} with \\textbf{Einstein's}, we cast the above equation as follows:\n\\begin{eqnarray}\\label{Eqmod3}\n G_{\\mu\\nu} &=& \\frac{1 + f_T(R,T)}{f_R(R,T)}T_{\\mu\\nu} + \\frac{1}{f_R(R,T)}p f_T(R,T) g_{\\mu\\nu} - \\frac{1}{f_R(R,T)}\\left(g_{\\mu\\nu}\\square - \\nabla_\\mu\\nabla_\\nu\\right)f_R(R,T)\\nonumber\\\\ &+& \\frac{1}{2f_R(R,T)}f(R,T)g_{\\mu\\nu} - \\frac{1}{2}g_{\\mu\\nu} R\\;,\n\\end{eqnarray}\nwhere $G_{\\mu\\nu} \\equiv R_{\\mu\\nu} - Rg_{\\mu\\nu}\/2$ is the Einstein tensor. Now we can identify\n\\begin{equation}\\label{Effmatt}\n \\tilde{T}_{\\mu\\nu}^{(m)} = \\frac{1 + f_T(R,T)}{f_R(R,T)}T_{\\mu\\nu} + \\frac{1}{f_R(R,T)}p f_T(R,T) g_{\\mu\\nu}\\;,\n\\end{equation}\nas the \\textit{effective} matter energy-momentum tensor and\n\\begin{equation}\\label{Effgeom}\n \\tilde{T}_{\\mu\\nu}^{(geom)} = - \\frac{1}{f_R(R,T)}\\left(g_{\\mu\\nu}\\square - \\nabla_\\mu\\nabla_\\nu\\right)f_R(R,T) + \\frac{1}{2f_R(R,T)}f(R,T)g_{\\mu\\nu} - \\frac{1}{2}g_{\\mu\\nu} R\\;,\n\\end{equation}\nas the energy-momentum tensor of a ``geometric'' matter component. \n\nWe now assume a \\textbf{background} described by the Friedmann-Lema\\^{\\i}tre-Robertson-Walker metric\n\\begin{equation}\\label{RWmet}\n ds^2 = dt^2 - a(t)^2\\delta_{ij}dx^idx^j\\;, \n\\end{equation}\nwith spatially flat hypersurfaces, and find a form for the function $f(R,T)$ which is able to reconstruct \\textbf{holographic dark energy}. \n\n\\subsection{Holographic Dark Energy}\n\nAccording to the holographic principle \\cite{'tHooft:1993gx, Susskind:1994vu, Bousso:2002ju} an entanglement relation between the infrared (IR) and ultraviolet (UV) cut-offs of a quantum theory, due to the limitation set by the formation of a black hole, sets an upper bound for the vacuum energy \\cite{Cohen:1998zx}:\n\\begin{equation}\\label{vacen}\n \\rho_{\\rm v} = \\frac{3b^2}{L^2}\\;,\n\\end{equation}\nwhere $b$ is a free parameter and the IR (large scales) cutoff $L$ needs to be specified by an ansatz. We are interested in the one proposed in \\cite{Li:2004rb}:\n\\begin{equation}\\label{anshol}\n L = R_{\\rm h} = a\\int_t^\\infty \\frac{dt'}{a(t')} = a\\int_a^\\infty \\frac{d\\bar{a}}{H(\\bar{a})\\bar{a}^{2}}\\;,\n\\end{equation}\ni.e. the future event horizon, that is the distance covered by a photon from now until the remote future. Note that the very presence of a vacuum energy component makes the above integration finite. We consider a model composed by holographic dark energy plus ordinary pressureless matter, i.e.\n\\begin{equation}\\label{Friede}\n 3H^2 = \\rho_{\\rm v} + \\rho_{\\rm m} = \\rho_{\\rm v} + \\rho_{\\rm m0}(1 + z)^3\\;,\n\\end{equation}\nwhere $H \\equiv \\dot{a}\/a$ is the Hubble parameter and the dot denotes derivation with respect to the cosmic time. Introduce the critical energy density $\\rho_{\\rm cr} := 3H^2$\\textbf{, we define}\n\\begin{equation}\\label{oliver15}\n\\Omega_{\\rm v} := \\frac{\\rho_{\\rm v}}{\\rho_{\\rm cr}}=\\frac{b^2}{R^2_{\\rm h}H^2}\\;,\n\\end{equation}\nUsing Eqs.\\eqref{vacen} and \\eqref{anshol}, it is easy to show that \n\\begin{equation}\\label{oliver16}\n\\dot{R}_{\\rm h} = \\frac{b}{\\sqrt{\\Omega_{\\rm v}}} - 1\\;.\n\\end{equation}\nThe holographic dark energy density $\\rho_{\\rm v}$ evolves according to the conservation law\n\\begin{equation}\\label{oliver17}\n\\dot{\\rho}_{\\rm v} + 3H\\rho_{\\rm v}\\left(1 + w_{\\rm v}\\right) = 0\\;,\n\\end{equation}\nbecause in Eq.~\\eqref{Friede} we have implicitly assumed the matter component to conserve separately. Now, using Eqs.~\\eqref{vacen}, \\eqref{anshol} and \\eqref{oliver16}, one can find\n\\begin{equation}\\label{oliver18}\n\\dot{\\rho}_{\\rm v} = -\\frac{2}{R_{\\rm h}}\\left(\\frac{b}{\\sqrt{\\Omega_{\\rm v}}}-1\\right)\\rho_{\\rm v}\\,.\n\\end{equation}\nComparing \\eqref{oliver18} with \\eqref{oliver17} one can read off\n\\begin{equation}\\label{oliver19}\nw_{\\rm v} = -\\left(\\frac{1}{3} + \\frac{2\\sqrt{\\Omega_{\\rm v}}}{3b}\\right)\\,\\,.\n\\end{equation}\nMoreover, combining Eq.~\\eqref{anshol} with Eqs.~\\eqref{vacen} and \\eqref{Friede}, the evolution for $\\Omega_{\\rm v}$ is determined by the following equation:\n\\begin{equation}\\label{OmegavEvo}\n \\Omega_{\\rm v}' = -\\left(1 + \\frac{2\\sqrt{\\Omega_{\\rm v}}}{b}\\right)\\frac{1}{1 + z}\\Omega_{\\rm v}\\left(1 - \\Omega_{\\rm v}\\right)\\;,\n\\end{equation}\nwhere the prime denotes derivation with respect to the redshift $z$. Testing this model against type Ia supernovae and cosmic microwave background anisotropies, $b$ turns out to be constrained around unity, with the case $b < 1$ favoured \\cite{Zhang:2005hs, Zhang:2007sh}. Note that, from Eq.~\\eqref{oliver19}, $b < 1$ means that the universe will end up in a phantom phase. For more comprehensive analysis of holographic dark energy models, we refer the reader to \\cite{Pavon:2005yx, delCampo:2011jp}.\n\nIn the next section we investigate a simple case of reconstruction of $f(R,T)$.\n\n\\section{A simple case}\\label{Sec:Simpl}\n\nWe now consider a single perfect fluid model with density $\\rho$ and pressure $p$, together with the following ansatz (one of the first considered in \\cite{Harko:2011kv}):\n\\begin{equation}\n f(R,T) = R + 2f(T)\\;,\n\\end{equation}\ni.e. the action is given by the same Einstein-Hilbert one plus a function of $T$. This is a particularly interesting choice since, from Eqs.~\\eqref{Effmatt} and \\eqref{Effgeom}, we get\n\\begin{equation}\\label{Tmtil}\n \\tilde{T}_{\\mu\\nu} = \\left(1 + 2f_T\\right)T_{\\mu\\nu} + 2p f_T g_{\\mu\\nu} + f(T)g_{\\mu\\nu}\\;.\n\\end{equation}\nFor $p = 0$ one has $T = \\rho$ and, choosing $f(T) = \\lambda T$ one can construct a model with an effective cosmological constant \\cite{Poplawski:2006ey}. From Eq.~\\eqref{Tmtil} one can read off the effective energy density and pressure of the universe content:\n\\begin{eqnarray}\n\\label{rhotot} 3H^2 &=& \\rho_{\\rm eff} = \\left(1 + 2f_T\\right)\\rho + 2p f_T + f(T)\\;,\\\\\n\\label{ptot} -2\\dot{H} - 3H^2 &=& p_{\\rm eff} = p - f(T)\\;,\n\\end{eqnarray}\nand therefore a dark energy component may appear, even if we are considering a single perfect fluid model. From Eqs.~\\eqref{rhotot} and \\eqref{ptot}, it is clear that we can pick out a ``fictitious'' component, due to $f(T)$, described by\n\\begin{eqnarray}\n \\rho_{f} &=& 2f_T\\rho + 2p f_T + f(T)\\;,\\\\\n p_{f} &=& - f(T)\\;,\n\\end{eqnarray}\nand, provided $f$ positive, it may well describe a dark energy component, since its pressure is negative. In order to reconstruct the function $f$ starting from the holographic principle, we note that the equation of state parameter of the dark component induced by $f$ is\n\\begin{equation}\n w_{f} = -\\frac{f(T)}{2(\\rho + p)f_T + f(T)}\\;,\n\\end{equation}\nand we identify it with $w_{\\rm v}$, the one provided by the holographic dark energy in Eq.~\\eqref{oliver19}:\n\\begin{equation}\\label{EqfT}\n \\frac{f(T)}{2(\\rho + p)f_T + f(T)} = \\frac{1}{3} + \\frac{2\\sqrt{\\Omega_{\\rm v}}}{3b}\\;.\n\\end{equation}\nFor the standard model given in Eq.~\\eqref{Friede}, consider the fluid component to be pressureless matter, i.e. $p = 0$. We are left to solve the following system of equations:\n\\begin{eqnarray}\n\\label{fevo} \\rho\\frac{df(\\rho)}{d\\rho} &=& f(\\rho)\\frac{b - \\sqrt{\\Omega_{\\rm v}}}{b + 2\\sqrt{\\Omega_{\\rm v}}}\\;,\\\\\n\\label{Omevo} \\frac{d\\Omega_{\\rm v}}{d\\rho} &=& -\\frac{1}{3\\rho}\\left(1 + \\frac{2\\sqrt{\\Omega_{\\rm v}}}{b}\\right)\\Omega_{\\rm v}\\left(1 - \\Omega_{\\rm v}\\right)\\;,\n\\end{eqnarray}\nwhere we have used $T = \\rho$, because we are considering pressureless matter. In order to solve the above system, we have to fix some initial conditions. Clearly, $\\Omega_{\\rm v}(\\rho = \\rho_0) = 1 - \\Omega_{\\rm m0}$, and we choose $\\Omega_{\\rm m0} = 0.3$, accordingly with current cosmological observation. For the initial condition on $f$, from Eq.~\\eqref{rhotot} (with $p = 0$) we write\n\\begin{equation}\\label{friedeqf0}\n \\left[1 + 2f_{T}(\\rho_0)\\right]\\Omega_{\\rm m0} + \\frac{f(\\rho_0)}{3H_0^2} = 1\\;.\n\\end{equation}\nEvaluating Eq.~\\eqref{fevo} today and combining it with Eq.~\\eqref{friedeqf0} we find the following algebraic equation determining the initial condition on $f$:\n\\begin{equation}\\label{f0cond}\n f(\\rho_0)\\left(2\\frac{b - \\sqrt{\\Omega_{\\rm v0}}}{b + 2\\sqrt{\\Omega_{\\rm v0}}} + 1\\right) = 3H_0^2\\Omega_{\\rm v0}\\;.\n\\end{equation}\nAs we expected, when $\\Omega_{\\rm v0} = 0$, then $f(\\rho_0) = 0$ and Eq.~\\eqref{fevo} implies that $f$ is identically vanishing.\\\\\n\\begin{figure}[htbp]\n \\includegraphics[width=0.45\\columnwidth]{Fig1.eps}\\;\\includegraphics[width=0.45\\columnwidth]{Fig2.eps}\n\\caption{Left panel: evolution of $f(\\rho)$. Right panel: evolution of $\\Omega_{\\rm v}$. The cases considered are $b = 0.6, 0.8, 1.0, 1.2$ (solid black, dashed red, dot-dashed blue and dotted green, respectively). We have chosen as initial conditions in $\\Omega_{\\rm m0} = 0.3$ the values $\\Omega_{\\rm v0} = 1 - \\Omega_{\\rm m0} = 0.7$ and $f_0$ given by Eq.~\\eqref{f0cond}. Note that $f$ and $\\rho$ are normalised to $3H_0^2$. The vertical lines in the plots represent $\\rho = \\rho_0$, i.e. the present instant.}\n\\label{Fig1}\n\\end{figure}\\\\\nIn \\figurename{ \\ref{Fig1}} we plot the solution of the system of differential equations \\eqref{fevo} and \\eqref{Omevo}. Note that we normalise $f$ and $\\rho$ to $3H_0^2$. As expected from Eq.~\\eqref{fevo}, for large values of $\\rho$, i.e. far in the past, $f \\propto \\rho$ because the dark energy component is subdominant. The actual difference among the various choices of $b$ takes place at late times, for small values of $\\rho$. Again from inspection of Eq.~\\eqref{fevo}, we can see that for large values of $b$ the linear evolution $f \\propto \\rho$ is again solution. That is why in the left panel of \\figurename{ \\ref{Fig1}} the curve seems to ``straighten up'' for increasing $b$. \n\nIn \\figurename{ \\ref{Fig2}} we plot the same quantities, but as functions of the redshift, in order to make clearer their cosmological evolution.\n\\begin{figure}[htbp]\n \\includegraphics[width=0.45\\columnwidth]{Fig3.eps}\\;\\includegraphics[width=0.45\\columnwidth]{Fig4.eps}\n\\caption{Same as \\figurename{ \\ref{Fig1}}, but with the redshift $z$ as independent variable.}\n\\label{Fig2}\n\\end{figure}\n\nA final remark about the future evolution. It is clear from Eq.~\\eqref{fevo}, that when $\\Omega_{\\rm v} \\to 1$, in the remote future, the solution for $f$ gets the asymptotic form\n\\begin{equation}\n f(\\rho) \\propto \\rho^{\\frac{b - 1}{b + 2}}\\;.\n\\end{equation}\nTherefore, we would have a future singularity for $-2 < b < 1$, as it appears for the relevant cases of \\figurename{ \\ref{Fig1}} and \\figurename{ \\ref{Fig2}}. The special case $b = 1$ implies an asymptotically constant $f$.\n\nWe now turn our discussion on a more general case, where the curvature $R$ \\textbf{comes into} the action \\textbf{as} a function to be determined.\n\n\n\\section{A more complicated case}\\label{Sec:ComplCase}\n\nNow we turn our attention to the special case\n\\begin{equation}\\label{complcase}\n f(R,T) = f(R) + \\lambda T\\;,\n\\end{equation}\ni.e. a $T$-linear correction to the class of $f(R)$ theories. With the ansatz \\eqref{complcase} the matter content \\eqref{Effmatt} is ``corrected'' as follows:\n\\begin{equation}\\label{Effmattcomplcase}\n \\tilde{T}_{\\mu\\nu}^{(m)} = \\frac{1 + \\lambda}{f_R(R)}T_{\\mu\\nu} + \\frac{1}{f_R(R)}\\lambda p g_{\\mu\\nu}\\;,\n\\end{equation}\nwhereas the geometry induced stress-energy tensor is\n\\begin{equation}\\label{Effgeomcomplcase}\n \\tilde{T}_{\\mu\\nu}^{(geom)} = - \\frac{1}{f_R(R)}\\left(g_{\\mu\\nu}\\square - \\nabla_\\mu\\nabla_\\nu\\right)f_R(R) + \\frac{1}{2f_R(R)}[f(R) + \\lambda T]g_{\\mu\\nu} - \\frac{1}{2}g_{\\mu\\nu} R\\;.\n\\end{equation}\nOur aim is now to reconstruct the form of the $f(R)$ which is able to reproduce the holographic dark energy paradigm. We again consider a pressureless perfect fluid with density $\\rho$ and again assume metric \\eqref{RWmet}. From Eqs.~\\eqref{Effmattcomplcase} and \\eqref{Effgeomcomplcase} the effective density and pressure are the following:\n\\begin{eqnarray}\n\\label{rhovcomplcase} 3H^2 &=& \\rho_{\\rm eff} = \\frac{\\rho}{f_R} + \\frac{3\\lambda}{2f_R}\\rho - \\frac{R}{2} + \\frac{f}{2f_R} - 3H\\frac{\\dot{f}_R}{f_R}\\;,\\\\\n\\label{pvcomplcase} -2\\dot{H} - 3H^2 &=& p_{\\rm eff} = \\frac{1}{f_R}\\left(\\ddot{f}_R + 2H\\dot{f}_R\\right) - \\frac{f}{2f_R} - \\frac{\\lambda}{2f_R}\\rho + \\frac{R}{2}\\;.\n\\end{eqnarray}\nFrom Eq.~\\eqref{rhovcomplcase} it appears that the energy density of the perfect fluid is rescaled by a factor $1\/f_R$. Looking at Eq.~\\eqref{Friede}, we can extract a form for $\\rho_{\\rm v}$ in the following way:\n\\begin{equation}\\label{rhovcomplcase2} \n\\rho_{\\rm v} = \\frac{\\rho}{f_R} - \\rho + \\frac{3\\lambda}{2f_R}\\rho - \\frac{R}{2} + \\frac{f}{2f_R} - 3H\\frac{\\dot{f}_R}{f_R}\\;,\n\\end{equation}\nwhereas the form of $p_{\\rm v}$ is already given in Eq.~\\eqref{pvcomplcase}, since our fluid is pressureless. From Eqs.~\\eqref{pvcomplcase} and \\eqref{rhovcomplcase2} we can write the following differential equation for $f_R$:\n\\begin{equation}\\label{freqcompl}\n \\ddot{f}_R - H \\dot{f}_R - \\left[\\rho + \\rho_{\\rm v}\\left(1 + w_{\\rm v}\\right)\\right]f_R = -\\rho(1 + \\lambda)\\;.\n\\end{equation}\nNote, as a cross-check, that for $\\rho_{\\rm v} = \\lambda = 0$ the above equation simplifies to\n\\begin{equation}\\label{freqcompl2simpl}\n \\ddot{f}_R - H\\dot{f}_R - \\rho f_R = - \\rho\\;,\n\\end{equation}\nwhich possesses the particular solution $f_R = 1$, i.e. $f = R + \\Lambda$, the original Einstein-Hilbert action plus an integration ``cosmological'' constant. We expect this solution to be the only one, otherwise there would exist an alternative $f(R)$ theory which would behave exactly as general relativity. Let us speculate a bit more on this point. If $\\rho_{\\rm v} = \\lambda = 0$, i.e. for a pure Einstein-de Sitter universe, we have from Eq.~\\eqref{Friede}\n\\begin{equation}\n H = \\frac{2}{3t}\\;, \\qquad \\rho = \\rho_0\\frac{t^2_0}{t^2}\\;,\n\\end{equation}\nwhere $t_0$ is the present cosmic time (i.e. the age of the universe). Considering the homogeneous part of Eq.~\\eqref{freqcompl2simpl} and looking for a solution of the form $f_R \\propto t^n$, we find:\n\\begin{equation}\\label{freqcompl2simplhom}\n n(n - 1) - \\frac{2}{3}n - \\rho_0 t_0^2 = 0\\;,\n\\end{equation}\nwhich gives:\n\\begin{equation}\\label{freqcompl2simplhomsol}\n n_{1,2} = \\frac{5}{6} \\pm \\sqrt{\\frac{25}{36} + \\rho_0 t_0^2}\\;,\n\\end{equation}\nand the general solution of Eq.~\\eqref{freqcompl2simpl} can be written as:\n\\begin{equation}\\label{freqcompl2simplhomsol2}\n f_R = 1 + C_1\\;t^{n_1} + C_2\\;t^{n_2}\\;.\n\\end{equation}\nNow, the initial conditions we adopt here are $f_R(t_0) = 1$ and $\\dot{f}_R(t_0) = 0$. The reason is essentially not spoiling the agreement between general relativity and solar system tests, see \\cite{Capozziello:2005ku, Nojiri:2006gh, Nojiri:2006jy, Nojiri:2006be, Wu:2007tn}. However, we stress here that these two conditions also imply $C_1 = C_2 = 0$ and therefore restore the general relativity limit $f_R = 1$ when $\\rho_{\\rm v} = \\lambda = 0$. \n\nChanging the variable to the redshift and employing Eqs.~\\eqref{Friede} and \\eqref{OmegavEvo} one can recast Eq.~\\eqref{freqcompl} in the following compact form:\n\\begin{eqnarray}\\label{freqcompl2}\n (1 + z)^2f''_R + \\frac{1 + z}{2}\\left[7 - \\Omega_{\\rm v}\\left(1 + \\frac{2\\sqrt{\\Omega_{\\rm v}}}{b}\\right)\\right]f_R' - 3\\left[1 - \\Omega_{\\rm v}\\left(\\frac{1}{3} + \\frac{2\\sqrt{\\Omega_{\\rm v}}}{3b}\\right)\\right]f_R = \\nonumber\\\\ - 3(1 + \\lambda)(1 - \\Omega_{\\rm v})\\;,\n\\end{eqnarray}\nwhere again the prime denotes derivation with respect to the redshift $z$. The curvature $R$ can be easily found as\n\\begin{equation}\n R = -6(\\dot{H} + 2H^2) = -\\frac{3H_0^2\\Omega_{\\rm m0}(1 + z)^3}{1 - \\Omega_{\\rm v}}\\left[1 + \\Omega_{\\rm v}\\left(1 + \\frac{2\\sqrt{\\Omega_{\\rm v}}}{b}\\right)\\right]\\;.\n\\end{equation}\nThe initial conditions $f_R(t_0) = 1$ and $\\dot{f}_R(t_0) = 0$, translated to the redshift variable, are\n\\begin{eqnarray}\n \\left.\\frac{d^2f}{dz^2}\\right|_{z = 0} = \\left.\\frac{d^2R}{dz^2}\\right|_{z = 0}\\;, \\qquad \\left.\\frac{df}{dz}\\right|_{z = 0} = \\left.\\frac{dR}{dz}\\right|_{z = 0}\\;,\\\\\n\\end{eqnarray}\nFinally, the initial condition on $f$ can be extracted by Eq.~\\eqref{rhovcomplcase}, being that $\\rho_{\\rm v0} = 3H_0^2 - \\rho_{\\rm m0}$. Thus, we have\n\\begin{equation}\nf(z = 0) = R(z = 0) + 6H_0^2\\left(1 - \\Omega_{\\rm m0} - \\frac{3}{2}\\lambda\\Omega_{\\rm m0}\\right)\\;.\n\\end{equation}\nNote the correction due to the $\\lambda T$ term.\n\\begin{figure}[htbp]\n \\includegraphics[width=0.45\\columnwidth]{Fig5.eps}\\;\\includegraphics[width=0.45\\columnwidth]{Fig6.eps}\n\\caption{Evolution of $f$ as a function of the curvature $R$. Left panel: $\\lambda = 0$, i.e. we are considering a pure $f(R)$ theory, and $b = 0.6, 0.8, 1.0, 1.2$ (solid black, dashed red, dot-dashed blue and dotted green, respectively). Right panel: $b = 1.0$ and $\\lambda = -0.2, 0, 0.2, 0.4$ (solid black, dashed red, dot-dashed blue and dotted green, respectively. The curves appear superposed.). We have chosen as initial conditions in $\\Omega_{\\rm m0} = 0.3$ the values $\\Omega_{\\rm v0} = 1 - \\Omega_{\\rm m0} = 0.7$. Note that $f$ and $R$ are normalised to $3H_0^2$ and the redshift interval chosen is $0 < z < 10$.}\n\\label{Fig3}\n\\end{figure}\nIn \\figurename{ \\ref{Fig3}} we plot the solution for $f$. Note that we normalise $f$ and $R$ to $3H_0^2$. In the left panel we fix $\\lambda = 0$, i.e. we are actually considering a pure $f(R)$ theory, and vary $b$. In the right panel, on the other hand, we consider positive and negative values of $\\lambda$. As one may note, $\\lambda$ has a poor influence on the evolution of $f$. We could expect this from inspection of Eq.~\\eqref{freqcompl2}. Indeed, $\\lambda$ only enters the source term on the right hand side and therefore, when $\\Omega_{\\rm v}$ grows to unity, its impact on the evolution of $f$ is weak. On the other hand, larger values of $\\lambda$ may have a relevant effect at early times, determining the slope of $f$.\n\\begin{figure}[ht]\n \\includegraphics[width=0.45\\columnwidth]{Fig7.eps}\\;\\includegraphics[width=0.45\\columnwidth]{Fig8.eps}\n\\caption{Same as \\figurename{ \\ref{Fig3}}, but with $-0.9 < z < 1$. Note, in the left panel, that the evolution of $f$ starts from below for all the cases.}\n\\label{Fig4}\n\\end{figure}\n\\newpage\nIn \\figurename{ \\ref{Fig4}} and \\figurename{ \\ref{Fig5}} we display the future evolution of $f$, both as function of $R$ or of $z$. For the same reason stated above, the effect of $\\lambda$ is not relevant.\n\\begin{figure}[ht]\n \\includegraphics[width=0.45\\columnwidth]{Fig9.eps}\\;\\includegraphics[width=0.45\\columnwidth]{Fig10.eps}\n\\caption{Same as \\figurename{ \\ref{Fig4}}, but with $f$ as function of $z$.}\n\\label{Fig5}\n\\end{figure}\n\\newpage\nFinally, in \\figurename{ \\ref{Fig6}} we plot the solution for $\\Omega_{\\rm v}$. Again, its evolution appears to be independent of $\\lambda$.\n\\begin{figure}[ht]\n \\includegraphics[width=0.45\\columnwidth]{Fig11.eps}\\;\\includegraphics[width=0.45\\columnwidth]{Fig12.eps}\n\\caption{Evolution of $\\Omega_{\\rm v}$ as a function of the redshift. Left panel: $\\lambda = 0$, i.e. we are considering a pure $f(R)$ theory, and $b = 0.6, 0.8, 1.0, 1.2$ (black, dashed red, dash-dotted blue and dotted green, respectively). Right panel: $b = 1.0$ and $\\lambda = -0.2, 0, 0.2, 0.4$ (black, dashed red, dash-dotted blue and dotted green, respectively). Note that the curves are superposed. We have chosen as initial conditions in $\\Omega_{\\rm m0} = 0.3$ the values $\\Omega_{\\rm v0} = 1 - \\Omega_{\\rm m0} = 0.7$.}\n\\label{Fig6}\n\\end{figure}\n\n\\newpage\n\n\\section{Discussion and Conclusions}\\label{Sec:DiscConcl}\n\nIn this work we have investigated a description of holographic dark energy in terms of suitably reconstructed $f(R, T)$ gravity theories. The latter have been recently introduced as modifications of Einstein's theory possessing some interesting solutions \\textbf{which are} relevant in cosmology and astrophysics \\cite{Harko:2011kv}.\n\nWe have \\textbf{considered two special} types of models: $f(R,T) = R + 2f(T)$, i.e. a correction to the Einstein-Hilbert action depending on the matter content, and $f(R,T) = f(R) + \\lambda T$, i.e. a simple $T$-linear correction to the class of $f(R)$ theories. \n\nSince we have assumed the matter content to be a pressureless perfect fluid, then $T = \\rho$, i.e. the corrections assumed are directly dependent on the energy density of the universe content. We \\textbf{have} constructed differential equations for the function $f$ under investigation and numerically solved \\textbf{them}, physically specifying the required initial conditions. Our simple analysis shows that holographic dark energy models are contained in the larger class of $f(R,T)$ theories, at least considering a given background evolution of the universe. \n\nIt would be interesting to investigate how the evolution of matter perturbations would change, depending on the description of dark energy. We expect, in principle, different results when using holographic dark energy or its $f(R,T)$ reconstruction and therefore there is possibility for discriminating between the two descriptions. For example, it would be interesting to adapt the recently proposed scheme for perturbations in $f(R)$ theories \\cite{Bertacca:2011wu} to the broader $f(R,T)$ class. We leave this as a future work.\n\n\n\\section*{Acknowledgements}\n\nMJSH and OFP thank Professor S. D. Odintsov for useful comments and also CNPq (Brazil) for partial financial support. \n\n\n\\bibliographystyle{unsrt}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{Introduction}\n\nCOMPASS is a fixed target experiment on the CERN SPS that uses tertiary high energy and high intensity muon or hadron beams with the aim of studying the nucleon spin structure and hadron spectroscopy. It is taking data since 2002, around seven months per year, and has shutdown periods in between, in which operations of maintenance and preparation of the following data taking period take place. The experimental setup is described in detail in \\cite{COMPASS01}.\n\nThe detector devices and the experiment's environmental parameters are monitored and controlled using an experiment-wide DCS. This system must ensure a coherent, safe and efficient operation of the experiment, by providing clear and prompt information for the shift crew and detector experts in the COMPASS control room. Some complex subsystems of the experiment have dedicated stand-alone control systems. These systems communicate with the DCS, providing it the most relevant parameters. Since 2003, the COMPASS DCS has been an exclusive responsibility of the LIP-Lisbon group participating in the collaboration. This structure and organization is at contrast with the one of the big LHC experiments, which have a hierarchical structure, with distributed responsibilities~\\cite{ATLAS,CMS,ALICE,LHCb}.\n\nThe DCS provides a graphical user interface for the shift crew in the COMPASS control room and detector experts to have access to all the relevant parameters monitored, their state (normal or in alert - indicated visually, by use of a color code, and acoustically) and their history, and a straightforward way to change their state, their settings, and the thresholds that define their state of alert.\n\nThe architecture of the DCS is shown in Fig.~\\ref{DCSscheme}. In the following sections, its different layers are described in detail: the supervisory layer, the front-ends layer and the devices layer.\n\n\\begin{figure*}\n\\centering\n\\includegraphics[width=0.8 \\textwidth]{dcs-schema-3.eps}\n\\caption{The COMPASS Detector Control System architecture, comprising a devices layer, a front-ends layer and a supervision layer. The technologies used in each layer are indicated in the rightmost column.} \n\\label{DCSscheme}\n\\end{figure*}\n\n\\noindent\\section{\\uppercase{The Supervisory Layer}}\n\\label{Supervisory} \n\nOn the supervisory layer, all the data collected, managed and published by the different kinds of servers or made available in databases is gathered, analysed and displayed to the end user. This includes visual and sound alarms in case of states of alarm. It also provides archiving of the data in an external database. Settings and alarm limits are also managed by the system.\n\nPVSS-II\\footnote{Pro\\-zess\\-teue\\-rung und Pro\\-zess\\-vi\\-sua\\-li\\-sie\\-rung. See http:\/\/www.etm.com}, is the commercial SCADA system that was chosen by CERN to use in the LHC experiments, after a thorough evaluation process. Some of the aspects taken into account were: openess, scalability, cross-platform capability (i.e. to run in Windows and Linux) and long term support.\n\nThe COMPASS experiment has adopted PVSS early in its development phase and has been a benchmark for other experiments at CERN. In fact, before the LHC starting in 2010, COMPASS was the biggest experiment operating at CERN. Over the years, several versions of PVSS were used in COMPASS, that had been previously tested and validated both by CERN and by the COMPASS DCS group.\nThe installation of these patches during the data-taking periods requires a careful evaluation.\n\nThe JCOP Framework~\\cite{JCOP} is a CERN project to develop common software tools for High Energy Physics related equipment and operations, to be used with PVSS. It provides templates of datapoint types, panels, functions and mass configuration tools for different classes of equipments or functionalities, providing, for instance, tools for the management of priviledges, or for trending plots.\n\nThe objects provided by PVSS and the JCOP Framework have sometimes to be adapted to meet COMPASS' needs. In addition, other solutions had to be developed independently for non-supported custom devices. This includes the control of custom devices,\naccessed using their serial (RS232) interfaces or their web servers; \nor the monitoring of items from dedicated control systems, such as (EPICS, LabView, etc.\\footnote{See http:\/\/www.aps.anl.gov\/epics, http:\/\/www.ni.com\/labview}), which are made available by various means, including mySQL and Oracle databases. \n\nThe PVSS production system in use is both distributed and scattered. Historically, it started as a scattered project, meaning that it was constituted by a main PVSS project running in a Linux machine, and 3 associated PVSS projects running on Windows machines, that had PVSS processes running as OPC\\footnote{OLE (Object Linking and Embedding) for Process Control. See http:\/\/www.opcfoundation.org} clients (7 clients in total). As the DCS developed, the main project was split into two distributed projects, for performance reasons. \n\nPVSS works with objects called datapoints, which are structures, {\\it i.e.} they have a tree structure that can include branches and where the leaves are the the monitored and controlled parameters (and can have different types, such as floats, integers, booleans, strings, etc., or the corresponding array types). \n\nPresently, the project comprises over 20000 datapoints. Close to 17000 parameter values have alert handling, whereas almost 19000 parameters have their values archived. \n\nThe polling rates are adapted to the rate of variation of the parameters, and range from one value per parameter read every 1.5 seconds (for fast varying parameters, sensitive to the beam, such as high voltage channels' actual values) to 2 minutes (for slowly varying parameters, such as high voltage channels' settings, or detector positions). For any given type of equipment, the items are grouped in PVSS subscription data sets according to these rates.\n\nThe access to the PVSS project is made available upon login. There is a general user name for the shift crew, user names for each of the detector experts, and a username for guests. For each login, there is an authorizations policy associated: certain operations are restricted (such as switching on or off the high voltage channels for guest users), or specifically allowed (such as saving recipes or reference files of high voltage settings; see \ndetails later in this text).\n\nThe graphical user interface (UI) is the main mean for users to interact with the DCS. It is composed of multiple subpanels, organised in a hierarchical way, as can be seen in Fig.~\\ref{ui}. One can see, on the top, the alert table and, on the left, the buttons to access dedicated detector panels and, below them, a table with the summary status of the experiment. In the larger area of the panel, a synoptic view of the spectrometer is displayed. This area is also used for navigation in the subsystems controlled and to display the actual data.\n\n\nThe datapoints history is made available online. In fact, PVSS trending plots (namely values over time) are one of the more useful and more used features of the DCS. In the user interface panels, customised buttons are created for the items that have numerical values (generally, floating point), so that their history can be easily accessed by users. The JCOP Framework provides its own trending plot widget, that was further customised for COMPASS, to make it simple to use. Users can choose the time range they want to visualise, change the scales or zoom in or out the abscissa or the ordinate by using the mouse scroll button, or choosing a rectangular region for zooming in by simply selecting two opposite vertices of the region. \nTemplates can be created to allow, with one click, to see related parameters and adjust the ranges for each.\nIt is also possible for the users to include additional parameters to a predefined plot as well as to print the trends being displayed, or to save them to a file, not only as an image, but also in ascii format (comma separated values or CVS).\n\n\\begin{figure*}\n\\centering\n\\includegraphics[width=0.7 \\textwidth]{dcsUI.eps}\n\\caption{The graphical user interface (UI) of the COMPASS DCS. One can see, on the top, the alert panel and, on the left, the buttons to access dedicated detector panels and, below them, a table with the summary status of the experiment.}\n\\label{ui}\n\\end{figure*}\n\nOne of the most important functionalities of the DCS is the display of visual and audible alarms, when predefined conditions are met, namely, when parameter values beyond predefined thresholds are reached for datapoints with numerical values or when devices send alarm flags. The visual display of alarms follows a color code that indicates its severity. For the most relevant parameters, it is important to assure that the operator didn't fail to notice the alarm condition, even if it has disapeared in the meantime; in this case, it is requested that the alarms are acknowledged by the operator in the graphical user interface.\nUpon activation of an alarm, detector experts are warned by email or SMS of states of alert in their detectors. \n\nSince the DCS has both a relatively fast knowledge of the state of the parameters it monitors and the ability to send commands to the devices, it is used to ensure software-wise protections to several equipments.\nFor instance, some detectors have components that are sensitive to magnetic field gradients; thus, when a trip of one of the spectrometer magnets (SM1 or SM2) occurs, or when these are switched off with the detectors high voltage channels still switched on, the DCS issues a switch-off command, so that the time interval during which the gradient is felt is minimised.\nIn addition, for some detectors, the high voltage channels should only be switched on or off in pairs; hence, if a trip is detected in only one member of the pair, the DCS sends a switch-off command to the other member of the pair.\n\nFurthermore, some detectors have front-end cards that are refrigerated by a water circuit. When this circuit stops for some reason, the temperature of the cards increases and can reach a value above a predefined threshold. If this happens, the DCS issues a switch-off command to the low voltage power supplies that power them, thus preventing these sensitive and sophisticated cards to be damaged. A hardware interlock is activated at a higher temperature, but the recovery from the interlocked state implies access to the experimental area, and therefore represents greater beam time losses and is therefore to be avoided. \n\nA configurations database associated to the project was implemented. It is based in a JCOP Framework package, which was adapted for the COMPASS needs. It saves and retrieves data in an independent Oracle database. This database has two main purposes. On the one hand, it allows to save and to retrieve the so called ``recipes\", {\\it i.e.} sets of thresholds for alarms of groups of items. The recipes can be created, or its values committed in the PVSS project, using the DCS UI, provided that the user has the privileges to change the respective detector items.\n\nThe second important purpose of the configuration database is to store so cal\\-led ``con\\-fi\\-gu\\-ra\\-tions\", {\\it i.e.} the mapping of hardware names vs.\\ logical names ({\\it i.e.} PVSS datapoint element names and respective aliases), for snapshots of stable states of the PVSS project. These are used to keep track of changes of the aforementioned mapping. These changes can happen either because, for instance, a high voltage channel gets broken and the same part of the detector ({\\it i.e.} same alias) is then powered by a different channel ({\\it i.e.} different datapoint), or because channels are reused when switching between the muon and the hadron Physics program data-taking.\n\nFor storage of settings of high voltage channels (set voltage, maximum current allowed, ramp up speed, ramp down speed and trip time), ascii files are used, for convenience. Experts can access the files, edit them, and send the values to be used by the equipment. The shift crew can use these reference file to recover the normal state of the equipment in case of problems.\n\nOnly a subset of all the data that PVSS receives and manages is actually saved. For this to happen, the PVSS datapoints need to have an archiving policy defined. This is chosen according to the known changes of each datapoint and the relevance of its history. For instance, it may be useful to store the readings of a temperature every ten minutes or if the change with respect to the previous reading exceeds one degree. This smoothing condition, called dead-band, is adjusted for each datapoint group or even per datapoint, if needed. The generic rates of archiving range from one value for every $\\sim$40 seconds (corresponding to the beam supercycle time interval) for beam-related quantities, to one value for every half an hour (for the positions of detectors). \n\nCurrently, there are around $2\\times 10^9$ values stored ({\\it i.e.} around 300 GB of data, including indexes), comprising the project history since 2006.\nThe DCS historical data that had been saved in PVSS internal format (during its first years of operation) was copied to a CERN central Oracle database, and the new data produced was all stored in this database. This way of storing data has all the advantages of Oracle and makes their access independent of PVSS. The data is continuously replicated to a second database, to ensure that the access to the data never compromises the performance of the production database. The data can also be provided in other formats, such as ascii or ROOT~\\cite{ROOT} trees.\n\nThe DCS data is very important for studies of detector performances.\nSome particularly relevant parameters for offline Physics data analysis are regularly copied to the experiment's mySQL conditions database, using a cron job.\nThe history of alerts of all the datapoint elements that have alert handling is also saved and made available by PVSS. This includes the timestamps of their arrival and departure, and of eventual acknowledgements done, among other information.\n\nThe knowledge of malfunctioning of parts of the experiment relies substantially on the DCS, namely on the display of alarms. Hence, it is important to assure its integrity and availability, ideally, at all times. Some of the mechanisms used are heart-beats, watch-dogs, back-ups, a security policy and the issuing of regular ping commands.\n\nThe managers of PVSS's main project, which are independent processes running in Linux, may, for a number of reasons, either block or stop running. On the other hand, the servers -- either OPC, \\cite{DIM}, or other -- may stop delivering meaningful data. For this reason, for each manager in PVSS that acts as a client, a heart-beat item was created, that gives the timestamp of the last meaningful data it received.\n\nMoreover, to verify that PLCs\\footnote{Programmable logic controllers.} are sending meaningful data at all times, a mechanism of watchdogs is implemented. The OPC server marks as invalid values sent by the PLC in case the values of the items published for this purpose (which have, during normal operation, varying integer values), stop begin updated.\n\nThe communication with individual VME\\footnote{VERSAmodule Eurocard bus.} crates or power supplies is also monitored, by continuously checking for selected equipment items that the timestamp of the latest value read is more recent than a predefined time interval.\n\nTo ensure the integrity of the project if a software corruption occurs in the PVSS project and associated software, the data is copied every twenty-four hours to a central repository. Furthermore, local copies are periodically made. \n\nA thorough security policy is implemented. All the computers that integrate the DCS belong to a dedicated experimental domain, that communicates with the CERN network using dedicated gateways. All the PCs in use have firewalls implemented.\nIn addition, all the user interfaces, with the exception of the one in the control room that should be permanently accessible, have an auto-logout after one hour.\n\nThe project should be available in the network at all times, for instance to diagnose of eventual DCS problems remotely. For this to happen, the gateways of the COMPASS domain have to be switched on and accessible via the CERN network. To check that this is the case at all times, regular ping commands are issued (every fifteen minutes) from an external server and the response is monitored; a notification is sent to the DCS experts in case those gateways are not reachable. \n\n\\noindent\\section{\\uppercase{The Front-ends Layer}}\n\\label{FrontEnds}\n\nThe experiment devices that are monitored and controlled by the DCS are spread over nearly two hundred meters, including the spectrometer and the beam tunnel. To communicate with all the devices, different field buses and communication protocols are \nused, namely CAN\\footnote{Controller area network. ISO standard 11898, see e.g.\\ www.iso.org} bus (8 daisy-chains), CAENet (6 daisy-chains), ModBus\\footnote{See http:\/\/www.modbus.org}, Profibus\\footnote{See http:\/\/www.profibus.com} (4 daisy-chains) and Ethernet.The general baud rate used for monitoring in the COMPASS CAN buses is 125 kbaud ($\\simeq 34$ kbits\/s), which is the recommended baud rate for the length of the daisy-chains used. These field buses transmit the information about the measurements of sensors to the front-end PCs (and commands to actuators in the opposite direction). In the front-end PCs, standard PCI\\footnote{Peripheral Component Interconnect.} cards are installed to collect the information carried by the field buses. The data is transmitted to the supervisory layer using a server-client model. An exception to this model is the three-layer model which is used when a database is included as an intermediate between the server and PVSS. This happens for the monitoring of the calorimeters, beam and trigger rates, and part of the polarised target system.\n\nIn ad\\-di\\-tion, spe\\-cialised de\\-vi\\-ces are used as intermediates between the devices and some of the front-end PCs, namely ELMBs (Embedded Local Monitor Boards) and PLCs.\n\nThe ELMB, described in \\cite{ELMB}, is a multi-purpose multiplexed ADC with 64-analog input channels with 16 bit-resolution which was developed by the ATLAS experiment. The communication of the ELMBs with the front-end PCs is done with the CAN field bus, using the CANopen protocol. The ELMB was designed and tested to be radiation- and magnetic field tolerant: its tolerance ranges up to about 5 Gy and $3\\times 10^10$ neutrons\/cm$^2$ for a period of 10 years and to a magnetic field up to 1.5 T.\n\nThe PLCs (Programmable Logic Controllers) are stand-alone, very robust, reliable and relatively fast devices that allow, among other operations, to regulate flows of gases and their percentage in mixtures, according to predefined settings and tolerance intervals, as well as to regulate cryogenic systems. The measurement of gas flows or gas percentages in mixtures is provided by the PLCs by ModBus to the DCS front-end PCs. \n\nManufacturer's OPC servers are used when available and stable. This is the case for the modern CAEN equipment and for Iseg equipment. In order to communicate with PLCs, an OPC server from Schneider\\footnote{See http:\/\/www.schneider-electric.com}, is used. Moreover, an OPC server was developed at CERN to control relatively old Wiener equipment, as the one used in COMPASS. To communicate with ELMBs, a CANopen OPC server, described in \\cite{CANopen}, is used.\n\nThe Distributed Information Management system (DIM) was developed at CERN and allows the implementation of a server-client model of publishing of lists of items and their actual values. The SLiC\\footnote{See http:\/\/j2eeps.cern.ch\/wikis\/display\/EN\/SLiC} DIM server developed at CERN, allows the control of the six CAENet lines used for the older type of CAEN crates. Each server has different groups of items with individually tunable speed reading cycles, thus permitting the separation of fast reading cycles (comprising voltages, currents and channel status) with reading frequencies as low as 1 Hz, thus allowing a fast detection of high voltage trips and failures; and of slow cycles, used for the read-back of settings.\n\t\t\t\t\nThe DIM protocol is also used to monitor other systems, namely temperatures and disk occupancy of servers, and processes of data transfer from the DAQ machines to CASTOR. \n\nDIP\\footnote{See http:\/\/en-dep.web.cern.ch\/en-dep\/Groups\/ICE\/Services\/DIP} is a protocol developed at CERN, based on DIM, but allowing exclusively read-only parameters, which are, in practical terms, those related to the CERN infrastructure (such as beam line magnet currents and the last beam file loaded, the primary target head inserted, the parameters to allow the monitoring of the CEDAR detectors, and data relative to the liquid nitrogen supply).\n\nOne PLC from the polarized target system is monitored using the S7 driver provided by PVSS, thus avoiding the use of an OPC server.\n\nPVSS provides functions to access relational databases such as mySQL and Oracle. This allows the access of information from the experiment conditions database (a set of mySQL databases), such as the calorimeter calibration event amplitudes, the beam and trigger rates, and parameters related with the polarised target.\n\nThe high voltage system of some of the detectors (Micromegas and the so called Saclay Drift Chambers) have special requirements with regards to its monitoring, and thus have a dedicated control system based on EPICS. This system publishes the most relevant data, which is read by a specially developed PVSS API (Application Programming Interface).\n\nThe Profibus protocol is used to transmit the data coming from the PLCs that monitor the detector gas systems to the PC that runs the Schneidar OPC server.\n\nMoreover, the magnetic field of the SM2 is measured with an NMRmeter that comes with a serial interface which, by use of the Profibus protocol, allows the communication with a standard PC, where a custom c program reads the information transmited, writes it in an ascii file and thereby makes it available for a PVSS API that collects the values and writes them into a datapoint.\n\n\n\\noindent\\section{\\uppercase{The Devices Layer}}\n\\label{Devices}\n\nMany different types of devices need to be controlled or simply monitored by the DCS, from high and low voltage crates and VME crates, to gas systems, sensors of temperature, humidity, pressure and magnetic fields.\n\nCOMPASS uses CAEN crates of different models to power most of its high voltage channels and for part of its low voltage channels. About 20 CAEN crates of older models (with CAENet interface) are in use, and six crates of newer models (with Ethernet interface).\nSeventeen Iseg high voltage modules are also in use and integrated in the DCS by use of their CAN interfaces. \nIn addition, fourteen Wiener low voltage power supplies are controlled, of which four are of type UEP6000, eight of type PL6021 and two of type PL508L. Nineteen VME crates are integrated in the DCS, both of older models (power supplies of type UEP5021) and newer models (power supplies of type UEP6021), the former being the majority. Both the power supplies and the VMEs are controlled by use of their CAN interface.\n\nIn subsystems where PLCs are used, the DCS only monitors the values that are published by them. This happens for the detector gas systems, the CEDAR detectors, and for systems that have dedicated control systems, namely the cryogenic systems of the polarized target, liquid hydrogen target and cold silicon detectors, see \\cite{Cesar, Anibus}.\n\nA wide range of devices are monitored by use of the ELMBs.\n\nHundreds of sensors are installed to monitor specific components of detectors or the experimental hall environment. For temperature monitoring, PT100 sensors in a 4-wire configuration are extensively used, whose output is read using the ELMBs.\n\nSome of the low voltage power supplies used in the experiment only have an interface for monitoring channel voltages or currents by means of voltage signals proportional to the values to be monitored, which is also read by ELMBs. In such cases, a calibration formula is used in the configuration of the CANopen OPC server, to provide the conversion to the real values delivered by the channels.\n\nMoreover, two of the most important magnets of the experiment, SM1 and Bend6, have their magnetic field monitored by Hall probes, whose output signals are read by ELMBs.\n\nIn the case of the second dipole magnet of the experiment, an NMRmeter is used. The NMRmeter has a serial interface, which, by use of the Profibus protocol, allows the communication with a PC.\n\nA custom power switch is also controlled, by use of the web server and driver provided with the equipment.\n\nThe DCS has an indirect monitoring of the powering system plus read-out chain of the four calorimeters of the experiment, based on the calibration signals of a laser system (for ECAL1) or a LED system (for the remaining three calorimeters). A component of the DAQ system calculates a spill-average amplitude of the signal read-out by each of the $\\sim$4500 channels, see \\cite{Konopka}, and saves this information in a mySQL conditions database, that is subsequently accessed by a PVSS script. In the DCS, a reference is chosen by the detector experts; afterwards, the DCS calculates, for each beam spill (using a synchronization scheme with the DAQ), the state of alert of each channel, based on the relative difference of the actual amplitude of the calibration amplitudes with respect to the reference values. The conditions to indicate alarms in the main panel are specified for the total of channels with a given alert state.\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.4 \\textwidth]{ecal2.eps}\n\\caption{Panel for monitoring of the high voltage system of ECAL2, comprising around 3000 channels.}\n\\label{ECAL2}\n\\end{figure}\n\nThe main power supplies powering the electromagnetic calorimeters, their monitoring systems, and the subgroups distributor voltages are monitored by use of ELMBs.\n\nThe positions and movement of the electromagnetic calorimeters are controlled by CAMAC\\footnote{Computer Automated Measurement And Control.} modules, from whose readings the detector positions are calculated and read by the DCS.\n\nA recent integration in the main PVSS project is the monitoring of the most relevant parameters of the complex Polarized Target system. The communication with the devices required the usage of different protocols and front-end solutions: PLC S7, ModBus, DIP and ODBC (for MySQL and Oracle database connection). \n\nThe rates of the different triggers of the experiment, mo\\-ni\\-to\\-red online by the shift crew, are stored in a mySQL conditions database read by the DCS, which calculates rates normalised to the beam flux, and triggers alarms when those normalised rates fall outside predefined ranges.\n\nThe servers used in the DAQ run DIM servers to publish data related to internal temperatures, occupancy of their disks and status of important processes.\n\n\n\nThe beam line M2 belongs to CERN's infrastructure and thus is monitored and controlled by dedicated programs. The most important parameters, such as magnet currets, collimator positions, the primary target head or the beam file loaded are made available via DIP, thus providing alarms and and historical values of these parameters.\n\nMoreover, the CEDAR detectors (\\v{C}Erenkov Differential counters with Achromatic Ring focus), used in the hadron program of the experiment, are a responsability of CERN, and its relevant parameters are published using a DIP server. For the operation of these detectors, the density of the gas used must be within a predefined range. When this doesn't happen, the DCS displays a state of alarm, allowing the shift crew to start a procedure to refill the detectors. The high voltage system and the motors are also monitored.\n\n\\noindent\\section{\\uppercase{Conclusions}}\n\\label{Conclusions}\n\nThe DCS of the COMPASS experiment at CERN was presented in detail. This is a centralized system that displays to the end user in a homogeneised graphical user interface many different subsystems that use very different devices and thus require the use of a wide range of front-end solutions.\n\n\\section*{Acknowledgements}\n\nWe gratefully acknowledge the Controls group of CERN (IT\/CO and, later, EN\/ICE) and CERN's PhyDB for their constant and efficient support. This work was supported by FCT.\n\n\\bibliographystyle{IEEEtran}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\\begin{table}[t]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{llll}\n\\multicolumn{4}{c}{\\textbf{List of Abbreviations}} \\\\ \\hline\n\\rowcolor[HTML]{EFEFEF} \n\\textbf{AMI} & Advanced Metering Infrastructure & \\textbf{HMS} & Head-End Management Server \\\\\n\\textbf{CA} & Certificate Authority & \\textbf{MAC} & Medium Access Control \\\\\n\\rowcolor[HTML]{EFEFEF} \n\\textbf{CRL} & Certificate Revocation List & \\textbf{OCSP} & Online Certificate Status Protocol \\\\\n\\textbf{DHT} & Distributed Hash Table & \\textbf{NIST} & National Institute of Standards and Technology \\\\\n\\rowcolor[HTML]{EFEFEF} \n\\textbf{DMZ} & Demilitarized Zone & \\textbf{PKI} & Public Key Infrastructure \\\\\n\\textbf{HES} & Head-End System & \\textbf{RSA} & Rivest\u2013Shamir\u2013Adleman \n\\\\ \\hline\n\\end{tabular}}\n\\end{table}\n\n\nThe existing power grid is currently going through a major transformation to enhance its reliability, resiliency, and efficiency by enabling networks of intelligent electronic devices, distributed generators, and dispersed loads \\citep{farhangi2010path}, which is referred to as \\textit{Smart(er) Grid}. Advanced Metering Infrastructure (AMI) network is one of the renewed components of Smart Grid that helps to collect smart meter data using a two-way communication\\citep{saputro2012}. Smart meters and integrated Internet-of-Things (IoT) devices are typically connected via a wireless mesh network with a gateway (or access point) serving as a relay between the meters and the utility company.\n\nThe security requirements for the AMI network are not different from the conventional networks as confidentiality, authentication, message integrity, access control, and non-repudiation are all needed to secure the AMI. Confidentiality is required to prevent exposure of customer's private data to unauthorized parties while integrity is necessary to ensure that power readings are not changed for billing fraud. Furthermore, authentication is crucial to prevent any compromised smart meters communicating with other smart meters. On the other hand, the National Institute of Standards and Technology (NIST) urges to use Public Key Infrastructure (PKI) for providing the security requirements of AMI \\citep{nist2014}. As an example, companies such as Landis\\&Gyr and Silver Spring Networks already use PKI to provide security for millions of smart meters in the US\\citep{landisgyr}. In such a PKI, the public-keys for smart meters and utilities are stored in \\textit{certificates} which are issued by Certificate Authorities (CAs). The employment of PKI in AMI requires management of certificates which include the creation, renewal, distribution and revocation. In particular, the certificate revocation and its association with smart meters are critical.\n\\vspace{10pt}\n\\begin{thisnote}\n\\subsection{Problem Description and Existing Solutions}\nSeveral reasons necessitate revoking certificates, such as key compromise, certificate compromise, excluding malicious meters, renewing devices, etc. Besides, if there is a vulnerability in the algorithms or libraries that are used in certificate generation, a massive number of revocations may additionally occur. For instance, a recent discovery of a chip deficiency on RSA key generation caused revocation of more than 700K certificates of devices that deployed this specific chip \\citep{2017ccsnemec} and renowned heartbleed vulnerability caused the revocation of millions of certificates, immediately \\citep{durumeric2014matter}. Thus, to establish secure communication, a smart meter should check the status of the other smart meter's certificate against a certificate revocation list (CRL) that keeps all revoked certificates. Considering the large number of smart meters in an AMI and the fact that the expiration period can be even lifelong in particular applications \\citep{landisgyr}, the CRL size will be huge. Consequently, revocation management becomes a burden for the AMI infrastructure which is typically restricted in terms of bandwidth. This overhead is particularly critical since the reliability and efficiency of AMI data communication are crucial for the functionality of the Smart Grid. Considering the potential impact on the performance of AMI applications \\citep{mahmoud2015investigating}, handling the overhead of revocation management is essential.\n\nCertificate revocation management is commonly handled by utilizing CRL that is stored in the smart meter. The status of a smart meter is determined by checking whether its certificate is listed in the CRL or not. An alternative method would be to store the CRL in a remote server as in the case of Online certificate status protocols (OCSPs) \\citep{galperin2013x}\\citep{pettersen2013transport}. In OCSP, an online and interactive certificate status server stores revocation information. Thus, each time a query is sent to the server to check the status of the certificate. While OCSP-like approaches can be advantageous on Internet communications, employing them for AMI is not attractive since it will require access to a remote server for each time. In this regard, another alternative would be to use OCSP \\textit{stapling} \\citep{pettersen2013transport} where the smart meters query the OCSP server at certain intervals and obtain a signed timestamped OCSP response which\nis included (\"stapled\") in the certificate. Again, this approach also needs frequent access to a remote server. Moreover, the 'stapled' certificates should be downloaded frequently by smart meters to ensure security, and this will create additional traffic overhead on the AMI which affects applications such as demand response or outage management. \n\n\\subsection{Our Approach and Contributions}\nIn this paper, we propose a communication-efficient revocation or CRL mananegment scheme for AMI networks by using RSA accumulators\\citep{camenisch2002dynamic}. RSA accumulator is a cryptographic tool which is able to represent a set of values with a single accumulator value (i.e., digest a set into a single value). Also, it provides a mechanism to check whether an element is in the set or not which implicitly means that cryptographic accumulators can be used for efficient membership testing. Due to the attractiveness of size, in this paper, we adapt RSA accumulators for our needs by introducing several novel elements as following:\n\n\\begin{itemize}\n\\item An accumulator manager is introduced within the utility company (UC) that is tasked with collection of CRLs from CAs and accumulating these CRLs (i.e., revoked certificates' serial numbers) to a single accumulator value which will then be distributed to the smart meters. \n\\item We also introduce a non-revoked proof tuple for allowing a smart meter to check whether another meter's certificate is revoked without referring to the CRL file.\n\\item We defined additional entities within AMI and assign functions to them to govern an accumulator based revocation management.\n\\item We introduced several security countermeasures against possible attacks to a accumulator-based scheme.\n\\end{itemize}\n\nThe computation and communication related aspects of the proposed approach is assessed via simulations in ns3 network. In addition, we built an actual testbed using in-house smart meters to assess the performance realistically. We compared our approach with the other methods that use conventional CRL schemes and Bloom-filters \\citep{akkaya2014efficient}. The results show that the proposed approach significantly outperforms the other existing methods in terms of reducing the communication overhead that is measured with the completion time. The overhead in terms of computation is not major and can be handled in advance within the utility that will not impact the smart meters.\n\nThis paper is organized as follows: In the next two sections, we summarize the related work and the background. Section IV introduces the threat model. Section V presents the proposed approach with its features. Section VI and VII are dedicated to evaluation criteria and experimental validation. Section VIII analyzes the security of the approach. Section IX discusses the benefits and limitations. The paper is concluded in Section X. \n\n\\end{thisnote}\n\n\\label{intro}\n\n\\section{Related Work} \\label{section2}\n\n\\begin{thisnote}\nDue to increasing threats towards Smart Grid, there has been a number of efforts to adapt PKI for Smart Grid communication infrastructure. For instance, Metke et al.\\citep{metke2010security} surveyed the existing key security technologies in Smart Grid domain by mainly focusing on PKI. On the other hand, the study \\citep{mahmoud2013efficient} stressed the importance of revocation overhead of PKI in Smart Grid. Beyond directly related studies on the PKI and Smart Grid relation, we also focus on studies about cryptographic accumulators and membership management. In this section, we examine the relation between this study and previous studies and highlight major differences.\n\n\\subsection{Revocation Management in AMIs}\nThe studies \\citep{mahmoud2015investigating} investigated different revocation management aspects such as short-lived-certificate scheme, tamper-proof device scheme, Online Certificate Status Protocol (OCSP), conventional CRL, and compressed CRL. However, this study just hypothetically analyzed the applicability of existent revocation solutions for AMI. The first offered approach that focused on reducing the revocation management overhead for AMI was based on Bloom Filters \\citep{rabieh2015scalable}. They provided a Bloom Filters based scheme particularly to reduce the size CRL. \\end{thisnote} However, since Bloom Filters suffer from false positives, the approach\nrequires accessing the CA to check the validity of a certificate. Our proposed scheme, on the other hand, never\nrequires accessing a remote server and provides a better reduction\non CRL size. The study in \\citep{cebe2017efficient} use distributed hash tables (DHT) to reduce the CRL size again. Although this study provides a reduction in CRL size, it suffers from additional inter-meter communication overhead for accessing the CRL information. \n\\begin{thisnote}\nWe would like to note that a very preliminary version of this work was published in \\citep{cebe2018efficient}. In this work, we improved the various aspects of the previous one. First, we improved computation performance utilizing Euler's Theorem. Second, we extended our threat\nmodel to new attack types that were not considered in the conference version. In this regard, we changed our approach in several ways: We proposed to use an initial secret during accumulation. We then introduced a non-revoked proof concept that was not used before in any of the revocation works. This required major changes to the accumulation process which was not in \\citep{cebe2018efficient}. We finally proposed an extensive certificate verification protocol as countermeasures to the new threats. This also required proposing a new secure multi-level AMI architecture as opposed to the monolithic architecture used in \\citep{cebe2018efficient}. In addition, we added several new experiments with accumulator computation overhead under various assumptions.\n\\end{thisnote}\n\\subsection{Cryptographic Accumulators}\nBenalog and DeMare \\citep{benaloh1993one} first introduced cryptographic accumulators. After their first appearance, there have been studies \\citep{camenisch2002dynamic,reyzin2016efficient,baldimtsi2017accumulators} offering to use them for membership testing. However, these studies solely focused on building the cryptographic fundamentals of accumulators, and thus, omit application-specific issues and security features when deploying them. Besides, these studies are offering to use accumulators for membership testing by accumulating a valid list. Considering AMI, accumulation of valid smart meter's certificates to provide a revocation mechanism would constitute a significant overhead due to the fact that revocation frequency is less than that of creating new certificates {(i.e., no need to update the accumulator each time when a new smart meter is added to AMI)}. Furthermore, since the number of revoked certificates is also less than the number of valid certificates which affects the required computation time significantly\\citep{durumeric2014matter}. \nOur approach mitigates these drawbacks by addressing security and application-specific issues and offering to use CRLs instead of valid certificates.\n\n\n\\section{Preliminaries}\n\\label{section3}\n\\begin{thisnote}\nBefore explaining our approach we provide some cryptographic background of accumulators and its particular form as RSA accumulators. In addition, to help the reader grasp a general idea of revocation management through CRLs, we explain the CRL and delta-CRL notions.\n\\end{thisnote}\n\\subsection{Background on Cryptographic Accumulators}\nBenaloh and De Mare\\citep{benaloh1993one} introduced the cryptographic accumulator concept which is a one-way hash function with a special property of being \\emph{quasi-commutative}. A quasi-commutative function is a special function $\\mathcal{F}$ such that $y_0,y_1,y_2 \\in \\mathbb{Y}:$\n\\begin{equation}\n\\mathcal{F}(\\mathcal{F}(y_0,y_1),y_2)=\\mathcal{F}(\\mathcal{F}(y_0,y_2),y_1) \\label{quasi}\n\\end{equation}\nThe properties of this function can be summarized as follows: \\textit{1)} it is a one-way function, i.e., hard to invert; \\textit{2)} it is a hash function for obtaining a secure digest $\\mathcal{A}$ (i.e., accumulator value) where $\\mathcal{A} = \\mathcal{F}(\\mathcal{F}(\\mathcal{F}(y_0,y_1),y_2),...,y_n)$ for a set of values $\\{y_0,y_1,y_2, . . . , y_n\\} \\in \\mathbb{Y}$; \\textit{3)} it is a \\emph{quasi-commutative} hash function which is different from other well-known hash functions such that the accumulator value $\\mathcal{A}$ does not depend on the order of $y_i$ accumulations. \n\nThese properties allow cryptographic accumulators to be used for a condensed representation of a set of elements. In addition, since the resulting accumulated hashes of $y_i$ ($\\mathbb{Y}=\\{y_i;~0 In fact, it is double the amount of time as shown in Table 1. The authors should not shy away from pointing this fact, while stressing on the other hand the space saving of their proposed approach}\n\n\\added{Response: We thank the reviewer for the comments.We updated the text accordingly}\n\\begin{itemize}\n \\item We observe that our approach has comparable results with the local CRL method which requires a simple text search over complete \\textit{full CRL} file, \\textcolor{red}{yet providing huge space saving benefit which effects both distribution and storage overhead.}\n\\end{itemize}\n\n\\end{itemize}\n\n\\item \\textit{Comment 7: Page 8, section 5. D.2: While the results in Table 1 show that the accumulator approach has higher revocation check time, it is hard to get a sense of the significance of the values provided in the table. It is not clear from the text whether the tests were done on the smart meters (and in this case, the technical characteristics of the smart meters are not provided), or on another device.}\n\n\\added{Response: We clarified the experiment is accomplished on the smart meter by adding following text to the revised manuscript}\n\n\\begin{itemize}\n \\item Finally, we looked at the computational time overhead for checking whether a certificate is revoked or not based on the three approaches compared. \\textcolor{red}{ This is an important experiment to understand the computation overhead our approach on the smart meter, which is shown in Figure 3.a, considering the fact that it has limited resources.}\n\\end{itemize}\n\n\\item \\textit{Comment 8: -A small typo in page 1: \"we focus efficient handling of this issue in this paper.\" -> focus \"on\" efficient\u2026\n}\n\\added{Response: The typo was relieved.}\n\n\\par\\bigskip\\hrul\n\n\\section{Responses to Reviewer 4}\n\\item \\textit{Comment 1: In section 4, it presented a high-level design of the approach but computation details of all algorithms are not given. \n}\n\n\\added{Response: We now give references to the equitations which are introduced in Background section}\n\n\\item \\textit{ Comment 2: In section 3.A, the introduction of one-way accumulator is slightly different from the paper they cited where $y_0$ and $y_1$, $y_2$ comes from different domains. Similarly, in section 3.B, domains of g and other variables are not clearly claimed.. \n}\n\n\\added{Response: This is the same issues raised by Reviewer #1 in Comment #2 and Reviewer #3 in Comment #3. We resolved these issues accordingly}\n\n\\item \\textit{ Comment 3: Since the details of the algorithms and variables are incomplete, the security of the approach cannot be evaluated.\n}\n\n\\added{Response: The revised manuscript now contains additional computational details. Moreover, it also contains Threat Model and Security Analysis sections. }\n\n\\item \\textit{Comment 4. To improve efficiency, CRL is used instead of valid list in the approach. However, there is still possibility that certificates presented by attackers which are not in CRL end up to be invalid ones. Further explanation is needed.}\n\n\\added{Response: We think Section V.D explains enough the authentication process. Nevertheless, I have added following text to help relieving confusions.}\n\n\\begin{itemize}\n\\item When two meters communicate by sending\/receiving signed messages, the signatures in these messages need to be verified. To be able to start the verification process, a receiving device needs to use the public key (for signature verification) presented in the certificate sent to itself. To ensure that this certificate is not revoked, then it needs to initiate a process which we call as certificate verification protocol. Figure~\\ref{fig:mutual} shows an overview of this process.\nBasically, the receiving device checks the corresponding $nr_{proof}$ tuple's signature to ensure that it is produced by the UC. Once the signature is verified, it then checks whether the the serial number within the tuple is same as the serial number of the provided certificate (i.e., either EndDevice\\#1.cer). For additional security, it also checks the length of the $nw_1\\&nw_2$ to see whether it is equal to the first accumulation setup parameter $k$. Finally, by performing $RevocationCheck()$ function, it checks whether the provided $nr_{proof}$ is correct. If all these steps are successful, the end-device has successfully complete the certificate verification protocol. \\textcolor{red}{Note that, without carrying the $nr_{proof}$ a smart meter can not be authenticated even if it has a valid certificate.}\n\\end{itemize}\n\n\\item \\textit{Comment 5: The implementation in simulation network consist of 81 and 196 meters which is far less than the scale in real smart grid, thus the results may not be credibly applied.\n}\n\n\\added{Response: Thanks to the reviewer for pointing out the fact that the produced simulation environment not exactly represent the real AMI size. However, to enable a larger AMI simulation, we should create our simulation environment by using Parallel Programming MPI + OpenMP and some sort of the cluster hardware. We believe that it will be out of scope of this study since the results clearly shows the scalibility of our approach by providing a revocation check mechanism which is independent from the size of CRL. In addition, the results shows that }\n\n\\item \\textit{Comment 6: There are some typos in section 2 and section 3. For example, \"collusion\" in line 21, section 3.A might be \"collision\".\n}\n\n\\added{Response: The typo is resolved.}\n\n\\par\\bigskip\\hrul\n\n\\section{Responses to Reviewer 5}\n\\item \\textit{Comment 1: About the use of CRLs, why ask the smart meter to store CRLs? I think the CRLs should be maintained by CAs. Whenever a smart meter MA needs to verify the validity of another smart meter MB's certificate, it just needs to download the latest certificate of the CA who signs MB's certificate. That is, the smart meter does not need to store the CRLs. If this is the case, then the motivation of this paper is not strong enough.}\n\n\\added{In light of the reviewer's comment, we think that could not clearly give the motivation of the problem in the Introduction section. Thus, we added following paragraph to the Introduction section. We hope this will help to unravel the motivation of the problem more clearly.}\n\n\\begin{itemize}\n \\item \\textcolor{red}{An alternative method would be to store the CRL in a remote server where an online and interactive certificate status server stores the revocation information as in the case of Online Certificate Status Protocol(OCSP) \\cite{galperin2013x}. By this way, a query can be sent to the server to check the status of a certificate whenever two meters would like to communicate. While OCSP can be advantageous for systems where Internet connections are always on, employing it for AMI is not an attractive idea since it will require access to a remote server for each interaction. In this regard, another alternative would be to use OCSP \\textit{stapling} \\cite{pettersen2013transport} where the smart meters query the OCSP server at certain intervals and obtains a timestamped OCSP response which is directly signed by the CA. This response is included (i.e., \"stapled\") in the certificate as a proof that it is not revoked. However, again, this approach requires frequent remote access. Furthermore, in order to perform authentication properly, the \"stapled\" smart meters' certificates should be download frequently by all the smart meters even in case there is no revocation incident, which will create enormous traffic on the AMI network. \n}\n\\end{itemize}\n\n\\item \\textit{Comment 2: In Section II.B, the authors claimed that their approach mitigated drawbacks of existing accumulator schemes by addressing security and application specific issues and offering to use CRLs instead of valid certificates in sense that the size of CRLs is much smaller than that of valid certificates. However, if the accumulator outputs a constant size of data, then it does not matter whether you accumulate a valid list or accumulate the CRLs.}\n\n\\added{Response: The reviewer is right about accumulating either valid or non-valid certificates would not be different in terms of accmulator size. However, the computation overhead and the number of times to update the accumulator value would be different. To clarify this issue, we modified the revised manuscript as follow:}\n\n\\begin{itemize}\n\\item Considering AMI, accumulation of valid smart meter's certificates to provide a revocation mechanism\nwould constitute a significant overhead due to the fact that revocation frequency is less than that of creating new certificates \\textcolor{red}{(i.e., no need to update accumulator for each new smart meter addition to AMI)} and number of revoked certificates is also less than the number of valid certificates \\textcolor{red}{ (i.e in terms of computation time)}\\cite{durumeric2014matter}.\n\\end{itemize}\n\n\\item \\textit{Comment 3: The proposed approach relies on the modification of RSA accumulator for the AMI case. However, the modifications inherit existing schemes [10] and the second part reducing the complexity of accumulator computation by using Euler's Theorem is actually common knowledge and thus not new. So what are the design challenges?}\n\n\\added{Response: As the reviewer pointed out, we are adapting existing cryptographic methods for revocation management. However, the adaptation of the existing cryptographic operations implicitly carry a number of different challenges which we resolved in our approach. The first challenge as pointed out is reducing the computation overhead by using Euler's theorem. The second is related to the adaption of certificate serial numbers to RSA accumulator. RSA accumulator requires special type of input to proper cryptographic operations. We implemented the method in [21] to produce a proper input to RSA accumulator from certificate serial numbers. Since the security of the accumulator is critical, we proposed a network topology and related additional components. Fourth, as we described in our Threat Model, to mitigate a possible attack regarding the \\textit{freshness of accumulator values}, we introduced a random secret $r_k$ accumulation. Fifth, we introduced $nr_{proof}$ concept and revocation check mechanism which is described in Figure 2 to mitigate any \\textit{stolen non-witness attack}. Without these modification, current accumulation approach can only mitigate a \\textit{comprised certificate attack}. In addition, naive application of the accumulator concept to the AMI for revocation management would be lack of many practical aspects as well who will compute accumulator value, how accumulator values should be updated to ensure the security of the mechanism through existing components, etc,.}\n\n\\item \\textit{Comment 4: The proposed algorithms are vaguely described, so it is not clear how these algorithms work.\n}\n\n\\added{Response: We have added additional description to Background section and its relation with our algorithm such as which equation used in which function in the Proposed appraoch section}\n\\item \\textit{Comment 5: The security analysis of the proposed protocol is missing.\n}\n\n\\added{Response: Now the revised text contains both Threat Model and Security Analysis.}\n\n\\section*{Acknowledgement}\nThis material is based upon work supported by the Department of Energy under Award Number DE-OE0000779.\n\\vspace{2\\baselineskip}\n\n\\begin{comment}\n\\begin{wrapfigure}[5]{l}{0.20\\textwidth}\n\\vspace{-9pt}\n\\centering\n\\includegraphics[width=0.20\\textwidth]{mumin.jpg}\n\\vspace{-34pt}\n\\end{wrapfigure}\n\\noindent Mumin Cebe is a PhD student in the Department of Electrical and Computer Engineering at Florida International University. He works at the Advanced Wireless and Security Lab (ADWISE) which is lead by Prof. Kemal Akkaya. He conducts research in the areas of wireless networking and security\/privacy that relates to Internet-of-Things (IoT) and Cyber-physical Systems (CPS), particularly in Smart Grids and Vehicular Networks.\n\n\\vspace{2\\baselineskip}\n\n\\begin{wrapfigure}[6]{l}{0.20\\textwidth}\n\\vspace{-9pt}\n\\centering\n\\includegraphics[width=0.20\\textwidth]{Akkaya.jpg}\n\\vspace{-34pt}\n\\end{wrapfigure}\n\\noindent Kemal Akkaya is a professor in the Department of Electrical and Computer Engineering at Florida International University. His current research interests include security and privacy, internet-of-things, and cyber-physical systems. He is the area editor of Elsevier Ad Hoc Network Journal and serves on the editorial board of IEEE Communication Surveys and Tutorials. He has published over 120 papers in peer reviewed journal and conferences. He has received ``Top Cited'' article award from Elsevier.\n\n\\end{comment}\n\n\\section*{References}\n\n\n\\section{Responses to Reviewer 1}\n\n\\begin{itemize}\n\n\\item \\textit{Comment 1: The paper is interesting, motivation is clear, the manuscript is well organized, and the authors able to convey their intended message. The paper explains the results in a clear manner.}\n\n\\added{\\textbf{Response}: We would like to thank the reviewer for his\/her appreciating comments.}\n\n\\item \\textit{Comment 2: It would be nice to list the limitations of current work precisely. If possible, discuss possible ways to attacks on the proposed certification revocation mechanism.}\n\n\\added{\\textbf{Response}: We thank the reviewer for this comment. In light of this comment, we further expanded our manuscript with a ``Benefits and Limitations'' Section to demonstrate the limitations of our approach where we discuss a possible side attack utilizing those limitations along with it precautions. Please refer to Subsection 9.2 for corresponding changes in the revised manuscript. (Page 32-33)}\n\n\n\\end{itemize}\n\n\\par\\bigskip\\hrul\n\n\n\\section{Responses to Reviewer 2}\n\n\\begin{itemize}\n\\item \\textit{The paper addresses the challenge of distributing and storing the certificate revocation list of smart metering systems under consideration of security protection. To avoid space limitations the paper proposes the use of cryptographic accumulators which shall allow reducing the size of needed revocation information. POINTS FOR IMPROVEMENT:}\n\\end{itemize}\n\\added{\\textbf{Response}: Thank you for the kind feedback. We addressed those issues in the revised manuscript as detailed below:}\n\\bigskip\n\n\\begin{itemize}\n\\item \\textit{Comment 1: FIU: Please introduce all abbreviations first. In the abstract try to avoid abbreviations at all. \nIn the abstract, the reader does not need to know that this has been applied at the FIU as this is obvious from the author affiliations and can be clarified in the detailed sections of the paper. Keep the Abstract short.}\n\n\\added{\\textbf{Response}: We thank the reviewer for this comment. We edited the abstract and removed all abbreviations and made abstract more succinct in the revised manuscript. In addition, we added a ``List of Abbreviations'' table at the end of the second page. \n}\n\n\n\\item \\textit{Comment 2: It does not become obvious in the introduction what the contribution will be. Do lines 73-80 introduce a second contribution? Make the contribution(s) of the paper clear, list them explicitly.\nHow does the proposed solutions relate to the problem introduced? This does not become clear. It might be advisable to restructure the introduction with clear subsections, i.e. background and motivation, problem statement, contribution, outline.}\n\n\\added{\\textbf{Response}: Thank you for the valuable suggestion. In light of the reviewer's comments, we clarified our position by restructuring the Introduction completely and precisely described the problem, motivation and our contribution in different subtitles. Please refer to pages 2-5 in the revised manuscript.} \n\n\n\\item \\textit{Comment 3: The related work is in need of a brief introduction. The reader does not get to know which related work is explained, why this is related and how this relates to the remainder of the paper.}\n\n\\added{\\textbf{Response}: In light of the reviewer's comment, related work is revised by adding a brief introduction and revising some portions to better describe the relation of our work with the previous ones. Please refer to pages 5-7 in the revised manuscript.}\n\n\n\\item \\textit{Comment 4: In line 115 it is discussed that this work provides 60 more content than in previous work. While this is an important information for the editor, it is not for the reader. The reader needs to know how this work relates to the previous work. Why have things been changed etc.}\n\n\\added{\\textbf{Response}: We thank the reviewer for this comment. We removed that part from the manuscript and re-structured the related work accordingly to resolve raised issues. Please see pages 5-6 in the revised manuscript.}\n\n\\item \\textit{Comment 5: Section 3 is also missing a brief introduction. Please explain to the reader what will happen in this section, why this section is needed and where these foundations are taken from. (Page 7)}\n\n\\added{\\textbf{Response}:\nTo help the reader, we provided a brief introduction to the Section 3 in the revised manuscript.}\n\n\n\\item \\textit{Comment 6: Also for Section 4, explain to the reader why this section is needed and how it relates to the contribution of the paper. How do the 4-points of the treat model relate to the approach. Are they already part of the approach? In this case integrate them with the next section.}\n\n\\added{\\textbf{Response}: We thank the reviewer for the comment. We are sorry to see that our explanations in the paper created a confusion about our threat model that is highly coupled with the approach. Actually, this was not our intention. To clarify this, we have completely modified the threat model section in the revised manuscript. Basically, in the threat model, we are now providing a more generic model where an adversary can both compromise the devices and communication. In addition, we simplified the system model so that it does not also get into approach description. Please refer to pages 10-12 for these changes. }\n\n\\item \\textit{Comment 7: For Section 5.2 please explain what this section is doing.}\n\\added{\\textbf{Response}: \nIn light of the reviewer's comments, we have updated the section and put an introduction to frame our bottom-up approach in Section 5.2 page 13 of the revised manuscript.}\n\n\\item \\textit{Comment 8: Between Section 5 and 6 a Section explaining the evaluation setup is needed. Please make clear what should be evaluated, why this shall be evaluated, and how this will show that the approach is feasible }\n\n\\added{\\textbf{Response}: We agreed that a section about evaluation would give a broad idea to reader on our evaluation criteria. Thus, we have added the requested section named \\textit{``Evaluation of the Approach and its Objective''} in the revised manuscript. (Pages 19-20)}\n\n\n\\item \\textit{Comment 9: Section 6 only lists loose proposals why the approach supports the threats from section 4. It is in need of deeper argumentation to explain or show why this is feasible. }\n\n\\added{\\textbf{Response}: Thanks for this valuable comment. This feedback help us to realize missing points related to the security analysis of our work. Thus, we now provided a deeper analysis and discussion for security analysis in the revised manuscript. Please refer to the \\textit{''Security Analysis''} for revised version. (Pages 28-31)}\n\n\n\\item \\textit{Comment 10: A discussion section is missing. Particularly, a discussion section should summarize the findings, discuss the limitations\/TTV, and deduce insights that go beyond the concrete technique and contribute to the state of the art in general. }\n\n\\added{\\textbf{Response}: In light of reviewer's comments, we added a new section to summarize our findings and also pointed out potential limitations of the proposed approach. Please refer to ``Benefits and Limitations'' section of the revised manuscript. (Pages 31-33)}\n\n\\item \\textit{Comment 11: Line 49: revoked certificates serial numbers -> revoked certificates' serial numbers\n}\n\n\\added{\\textbf{Response}: Corrected accordingly.}\n\n\\item \\textit{Comment 12: Line 89: remove one \"aspects such as\"\n}\n\n\\added{\\textbf{Response}: Corrected accordingly.}\n\n\\end{itemize}\n\n\\newpage\n\\section{Responses to Reviewer 5}\n\n\\begin{itemize}\n\n\\item \\textit{Comment 1: The objectives and potential impact of the research is not clearly presented. It should be included as a separate section in \"5 Proposed approach\"}\n\n\\added{\\textbf{Response}: Thanks for this valuable comment. This feedback help us to realize missing points related to the main objective and potential impact. As a result, we made some changes and now clearly state our objective and potential impacts in two different sections. The objective of our approach along with evaluation criteria are given in Section 6 after Section 5 as a separate section (page 19). The potential impacts of study are given in Section 9.1 on pages 31-32 of the revised manuscript.}\n\n\n\n\\item \\textit{Comment 2: The experimental simulation should be compared with a real world experiment and potential differences should be pointed out. Please also explain what is the meaning of \"realistic results\" in the paragraph: \"Finally, for more realistic results, we built an IEEE 802.11s-based mesh\"}\n\n\\added{\\textbf{Response}: The reviewer is right in pointing out what realistic refers to as we did not provide details about it. Basically, we built an actual mesh network at FIU by using Raspberry PIs and IEEE 802.11 antennas. Therefore, we updated manuscript accordingly to clarify the goal behind building such a testbed environment on pages 21-22 of the revised manuscript.}\n\n\\item \\textit{Comment 3: A security analysis is presented in section 6 but these aspects need to be addressed extensively in section 7 as well.}\n\n\\added{\\textbf{Response}: In light of reviewer's comment, we extended the security analysis section and moved it after experiments to be able emphasize obtained results and their affects on security. Please refer to Section 8 in the revised manuscript (Pages 28-31). }\n\n\\item \\textit{Comment 4: The added value of the research in comparison to other methods is not clearly addressed in the conclusion section. Some aspects are addressed in each of sub-sections of section 7. A suggestion is to address these aspects in a structured manner, pointing out the advantages and potential disadvantages in a separate section before conclusions.}\n\n\\added{\\textbf{Response}: Thank you the reviewer to stressing this important shortfall of the manuscript. As a result, we introduced a new ``Benefits and Limitations'' section. Please refer pages 31-34 for details in the revised manuscript.}\n\n\\end{itemize}\n\\end{document}\n\n\n\\section{Responses to Reviewer 1}\n\n\\begin{itemize}\n\n\\item \\textit{Comment 1: All the reviewer comments were addressed in the revised manuscript.}\n\n\\added{\\textbf{Response}: We thank the reviewer again for his\/her feedback which helped us to significantly improve the paper.}\n\n\\item \\textit{Comment 2: would recommend authors to add \"how the scalability or any other issues needs to handle when applied to the real testbeds such as EPIC [1] or real city-scale AMI metering infrastructure? \"\n}\n\n\\added{\\textbf{Response}: We thank the reviewer informing us a real testbed which is available to researchers. In light of this comment, we further expanded our future work where we discuss possibility of using a real testbed to assess our approach. Please refer to Section 10 for corresponding changes in the revised manuscript. (Page 34)}\n\n\n\\end{itemize}\n\n\\par\\bigskip\\hrul\n\n\n\\section{Responses to Reviewer 2}\n\n\\begin{itemize}\n\\item \\textit{Comment 1: The manuscript has considerably been revised. A great effort has been made to address all reviewer comments. The paper is in a state where it can be accepted as is. }\n\n\\added{\\textbf{Response}: We would like to thank the reviewer for his\/her appreciating comments.\n}\n\n\n\\item \\textit{Comment 2: There is one minor remark the authors might want to reconsider. The introduction section got rather long, which is not a bad thing in case of this paper. However, it might be advisable to use subsection headings within the introduction to guide the reader a bit more }\n\n\\added{\\textbf{Response}: Thank you for the valuable suggestion. In light of the reviewer's comments, we edited the introduction by adding a proper subsections introduction. Please refer to pages 2-5 in the revised manuscript.} \n\n\\end{itemize}\n\n\\newpage\n\\section{Responses to Reviewer 5}\n\n\\begin{itemize}\n\n\\item \\textit{Comment 1: The authors have addressed the comments\"}\n\n\\added{\\textbf{Response}: Thanks again for valuable comments during revision process to help us to realize missing points of our manuscript.}\n\n\n\n\\end{itemize}\n\\end{document}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nTopological crystalline phases in non-interacting, clean structures have attracted a great deal of recent theoretical and experimental attention\\cite{Fu2011,Hsieh2012,ando2015topological,Hughes11,Turner2010,Turner2012}. \nFrom the discovery of helical edge states in $\\mathbb{Z}_2$ topological insulators\\cite{Kane04,bernevig2006quantum,konig2007quantum}, to surface Dirac cones protected by time-reversal or crystal symmetries\\cite{fukanemele,xia2009observation,Hsieh2012,Hourglass}, the experimental manifestations of band topology have come primarily through the exploration of surface states. \nThe recent theoretical prediction of higher-order topological insulators\\cite{hotis,Po2017,khalaf2018higher,benalcazar2017quantized} has triggered a wave of materials predictions\\cite{NaturePaper,bigmaterials,bigmaterials-china,ashvin-materials,xu2020high} and experimental efforts to observe their predicted gapped surfaces but gapless corners (in 2D) or hinges (in 3D). \nAt a theoretical level, topological band insulators can be classified by exploiting the constraints of symmetry, relating the topology of bands to the transformation properties of Bloch functions under crystal symmetries\\cite{Kruthoff2016,NaturePaper,Po2017,Fu2007,po2020symmetry,cano2020band,MTQC,watanabe2018structure,Zhang_TMCI_corep_RPB,Slager_magnetic_spacegroup_rep}. \nIn the simplest cases, the symmetry eigenvalues of occupied electronic wavefunctions at different crystal momenta in the Brillouin zone can be used to deduce the absence of an exponentially localized, position space description of the occupied states, and hence the presence of non-trivial topology. \nProgress along these lines has led to a full, predictive classification of topological band structures both with and without time-reversal symmetry. \nEssential to these efforts is the presence of discrete translation symmetry, which ensures that localized electronic functions are identical in each unit cell, and hence allows the symmetry properties of the system to be described as a function of momentum. \n\nAt the same time, the interplay between topological bands and symmetry-breaking order has started to attract a great deal of attention. \nIt has been argued theoretically that in topological systems with charge-density wave (CDW) order, the collective phason mode of the CDW may inherit topological properties from the Fermi sea, such as an induced axion coupling to electromagetic fields\\cite{wang2013chiral,you2016response,BurkovCDW,zyuzin2012weyl,maciejko2014weyl}. \nSignatures of this axion coupling have been recently experimentally detected in (TaSe$_4$)$_2$I\\cite{gooth2019evidence,shi2019charge}. \nAdditionally, the quantum anomalous Hall phase in the Dirac semimetal ZrTe$_5$ can be understood as originating from a magnetic-field induced CDW transition\\cite{tang2019three,qin2020theory,song2017instability,zhang2017transport}. \nBecause CDW order is in general incommensurate with the underlying lattice, a full understanding of the interplay between mean-field CDW order and band topology requires us to examine topology of incommensurately modulated electronic systems. \nSuch a study would also yield insights into topology in artificially modulated photonic\\cite{ozawa2018topological,ozawa2016synthetic}, metamaterial\\cite{grinberg2020robust}, and cold-atomic lattice systems\\cite{Ane1602685}, which have become a focus of recent research due to their tunability and experimental accessibility.\n\n\n\nNaively, the breaking of translational and point group symmetries implied by incommensurate modulation would seem to prohibit the application of symmetry-based tools which have been so successful in identifying and classifying topological crystalline systems. \nHowever, it is often possible to view the single-particle dynamics in an incommensurately modulated system as describing the behavior of a particle in a larger number of dimensions, the phase offsets of the incommensurate modulations playing the role of momenta in the extra ``synthetic'' dimensions. \nThe canonical example of this mapping is the 1D Harper (Aubry-Andr\\'{e}) model with incommensurate on-site potential. \nAs was shown some time ago\\cite{hofstadter-original}, the Hamiltonian for the Harper model is equivalent to the Hamiltonian for a 2D square-lattice system coupled to a background magnetic field\\cite{equivalence_Fibo_Harper_2012,Kraus_1D_QC_to_2D_QHE}. \nThe phase of the on-site modulation plays the role of the momentum in the second, synthetic dimension, while the wavevector of the modulation plays the role of the magnetic flux per plaquette in the 2D lattice. \nBands in the enhanced, 2D system can be characterized by a Chern number, which mandates the presence of gapless chiral modes at the edges of the system. \nReducing back to 1D, these two-dimensional edge states manifest as boundary states of a 1D wire which appear and disappear as a function of the phase of modulation, thus realizing a Thouless pump\\cite{Thouless_pump_original_paper,niu1984quantised,Topologically_quantized_current_PRR}. \n\\textcolor{black}{Recent studies also show that certain generalization of the 1D Harper model allows for the investigation of higher-order topological phases~\\cite{Zeng_generalized_AAH_model_HOTI_PRB}.} \n\n\nIn this work, we will extend the connection beyond 1D, to show how modulated systems in 2D and 3D can be related to topological crystalline phases in higher dimensions. \nWe will first review a general method for representing a modulated system as a higher dimensional system coupled to a background gauge field\\cite{RiceMele,Thouless_pump_original_paper,Kraus_1D_QC_to_2D_QHE,equivalence_Fibo_Harper_2012}.\nFor systems with negligible spin-orbit coupling and spin-independent modulation, the gauge field will be a $U(1)$ magnetic field; for spin-dependent modulations we will show that there can also be induced $SU(2)$ gauge fields. \nWe will exploit the fact that both $U(1)$ and $SU(2)$ gauge fields with constant field strength preserve inversion symmetry to show that 2D modulated systems can realize higher-order chiral ($U(1)$) and helical ($SU(2)$) topological phases in one extra synthetic dimension. \nWe show how the hinge states of these synthetic higher-order topological insulators (HOTIs) manifest as corner modes in 2D, with energies that can be tuned by changing the phase of the modulation. \nGoing further, we use the mapping to synthetic dimensions to bring order to the complex landscape of eigenstates of the modulated system, showing how the states can be interpreted as bulk and surface Landau level (LL) wavefunctions in synthetic dimensions. Finally, we also revisit a 3D minimal model for a Weyl semimetal (WSM) with (generally incommensurate) CDW order\\cite{dynamical_axion_insulator_BB}, and show how it realizes a 4D nodal line semimetal gapped into a phase with a non-trivial second Chern number. We will verify our conclusions with a combination of exact numerical results and approximate low-energy analytic calculations.\nWe will also exploit the fact that the phase of a (charge- or spin-) density wave (DW) order parameter can be shifted with an applied electromagnetic field, by exciting the (nominally gapless, but sometimes pinned) sliding mode\\cite{gruner1988dynamics}. \nThis will allow us to make predictions about topological pumping of boundary states in modulated structures, driven by the sliding mode of the DW.\nIn contrast to other recent proposals for topological pumping in synthetic dimensions, the coupling of the DW sliding mode to electromagnetic fields allows for tunability of synthetic dimensions in modulated structures. \nWe will comment on potential experimental realizations in condensed matter, photonic, and cold-atom systems throughout. \nThis work will enable new avenues for exploring higher-order topological phenomena which, with the exception of some promising results in Bismuth\\cite{hsu2019topology,nayak2019resolving,schindler2018higher}, have not been unambiguously identified in crystalline electronic systems.\n\n\nThe rest of the paper is organized as follows. \nIn Sec.~\\ref{sec_review_thouless_pump_Rice_Mele}, we review how the Thouless pump in a 1D Rice-Mele [Su-Schrieffer-Heeger (SSH)] chain is realized by the sliding of a CDW, and we review its connection to topology by promoting the model to a 2D $\\pi$-flux lattice. \nIn Sec.~\\ref{sec_Dimension_promotion} we next develop a general method to compute the $U(1)$ gauge fields that are coupled to a higher dimensional models promoted from a low-dimensional modulated system. \nIn Secs.~\\ref{sec_chiral_HOTI} and~\\ref{sec_helical_HOTI_sliding_modes}, we construct 2D modulated systems that can be promoted to 3D chiral and helical HOTIs coupled to $U(1)$ and $SU(2)$ gauge fields, respectively. \nWe demonstrate the pumping of corner modes by the sliding of DWs in these systems via numerical calculations of the energy spectra. \nWe examine the properties of wave functions in these 2D modulated systems by constructing low energy theories coupled to gauge fields in 3D. \nWe show how the evolution of bulk, edge, and corner states in 2D can be understood from the perspective of the low energy theory in 3D. \nIn Sec.~\\ref{sec:Weyl_CDW}, we turn to a model for a 3D WSM gapped by a CDW. We show that this model can be promoted to a 4D nodal line system gapped by a $U(1)$ gauge field. \nWe derive the corresponding low energy theory in 4D, and use it to explain both the existence of QAH surface states and the interpolation between topologically distinct QAH phases at the two inversion-symmetric values of the CDW phase in this 3D system.\nFinally, in Sec.~\\ref{sec:outlook}, we give an outlook as to how our work may extend the search of (higher-order) topological insulators in higher dimensions and enable simulations of $SU(2)$ gauge physics in higher dimensions. \nSome details of our models, further numerical results, and detailed derivations of the low energy theories are presented in the Supplementary Material (SM)\\cite{SM}. \n\nThroughout this paper, we use units where $\\hbar = c = |e| = 1$, and where the electron has charge $-|e| = -1$. \nFurthermore, the Einstein summation convention will not be used; whenever there is a summation over an index, we will write the summation explicitly.\n\n\n\n\\section{\\label{sec_review_thouless_pump_Rice_Mele}Review - Thouless Pump as Sliding Mode }\n\n\nIn this section, we review the CDW picture of the Rice-Mele (SSH) model\\cite{RiceMele,ssh1979}, and the interpretation of the Thouless pump\\cite{Thouless_pump_original_paper} as a CDW sliding mode. \nConsider the following Hamiltonian for a 1D chain\n\\begin{align}\n H_{\\text{Rice-Mele}} = \\sum_{n} & \\left( t + \\delta t (-1)^{n} \\cos{\\phi} \\right)c^{\\dagger}_{n+1}c_{n} + \\text{h.c.} \\nonumber\\\\\n & + \\sum_{n} (-1)^{n+1} \\Delta \\sin{\\phi} c^{\\dagger}_{n}c_{n}, \n \\label{eq:1DRiceMele}\n\\end{align}\nwhere $c^{\\dagger}_{n}$ is the creation operator for an electron at site $n$. \nThe nearest-neighbor hopping and on-site potential are modulated with periodicity $2$, and their relative strength is related to the phase $\\phi$ of the modulation. \nWe thus identify $\\phi$ as the phase of this CDW modulation. In this paper, we use the terms \"CDW sliding phase\" and \"phases of the mean-field CDW order parameter\" interchangeably to refer to $\\phi$. \nFor suitable choices of $t$, $\\delta t$ and $\\Delta$, the spectrum of this Hamiltonian is gapped for all $\\phi \\in [0,2\\pi)$. \nFocusing on the half-filled insulating ground state in this parameter regime, the occupied-band Wannier centers\\cite{Kohn59,Brouder2007,Marzari2012,NaturePaper,shockley1939surface} will be pumped by a length of one unit cell (two sites) as the phase $\\phi$ adiabatically slides from $0$ to $2\\pi$, leading to a quantized change of bulk polarization\\cite{xiao2010berry,Aris2014,RiceMele,ksv}. \nThis quantization has a topological origin: If we regard $\\phi$ as a crystal momentum along a second, synthetic dimension which we call $y$, Eq.~(\\ref{eq:1DRiceMele}) is equivalent to a 2D square lattice model with a $U(1)$ $\\pi$-flux (equivalent to half flux quantum $\\Phi_{0} = 2\\pi \\hbar \/ |e|$ where electron has charge $-|e|$) per plaquette, and with a fixed crystal momentum $k_{y}$ along $y$. \nThe quantized polarization change is then identified as the Chern number\\cite{tknn,niu1984quantised,niu1985quantized,Aris2014,bernevigbook} of the occupied bands in 2D.\nWe provide further details, including numerical verification of charge pumping, and the explicit construction of the dimensional promotion to 2D, in the SM\\cite{SM}.\n\n\nWe see from this example that promoting the dimension of a modulated system to a higher dimensional lattice coupled to gauge fields can help explain the topological origin of low-dimensional properties, including charge transport and boundary modes. \nA general method for dimensional promotion will thus be helpful in dealing with various topological modulated systems in more than 1D. \nIn what follows, we will show that the dimensional promotion approach can be extended to higher dimensions, and to cases where the modulation is incommensurate with the underlying lattice periodicity.\n\n\n\\section{\\label{sec_Dimension_promotion}Dimensional Promotion Procedure}\n\n\nIn this section, we will generalize the $1$D-to-$2$D dimensional promotion of the Rice-Mele chain to general dimensions. \nTo begin, let us consider a $d$-dimensional ($d$D) electronic model on a cubic lattice with $N$ mutually \nincommensurate on-site modulations\\cite{Earliest_dimension_promotion_superspace,Kraus_1D_QC_to_2D_QHE,equivalence_Fibo_Harper_2012,2D_QC_4D_QHE,time_periodic_1,time_periodic_2}, described by the Hamiltonian\n\\begin{equation}\n H_{\\text{low-dim}} = \\sum_{\\vec{n},\\vec{m}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m}} \\left[{H}_{\\vec{m}}\\right] {\\psi}_{\\vec{n}} + \\sum_{\\vec{n}} \\sum_{i=1}^{N}{\\psi}^{\\dagger}_{\\vec{n}} \\left[{V}^{(i)}_{\\vec{n}}\\right] {\\psi}_{\\vec{n}}.\\label{eq:h_low_dim}\n\\end{equation}\nHere both $\\vec{n} = (n_{1},\\cdots,n_{d})$ and $\\vec{m} = (m_{1},\\cdots,m_{d}) \\in \\mathbb{Z}^{d}$ are vectors in the $d$D cubic lattice, and ${\\psi}^{\\dagger}_{\\vec{n}}$ is the electron creation operator for an electron at position $\\vec{n}$ with a given set of spin and orbital degrees of freedom.\nWe denote by $\\left[{H}_{\\vec{m}}\\right]$ the hopping matrix connecting position $\\vec{n}$ to $\\vec{n}+\\vec{m}$, and by $\\left[{V}^{(i)}_{\\vec{n}}\\right]$ the matrix representing $i^{\\text{th}}$ modulated on-site energy at position $\\vec{n}$ ($i = 1 ,\\ldots, N$), with matrix indices encoding the spin and orbital dependence of the hopping\\footnote{throughout this work, we will use square brackets to denote matrices and matrix-valued functions}. \nNote that hermiticity of the Hamiltonian requires that $\\left[{H}_{\\vec{m}}\\right] = \\left[{H}_{-\\vec{m}} \\right]^\\dag$ {{and $\\left[{V}^{(i)}_{\\vec{n}}\\right]^{\\dagger}=\\left[{V}^{(i)}_{\\vec{n}}\\right]$}}. \nWe further assume that $\\left[{V}^{(i)}_{\\vec{n}}\\right] = \\left[f^{(i)}\\left(2\\pi\\vec{q}^{(i)}\\cdot\\vec{n} + \\phi^{(i)} \\right)\\right]$ with $\\left[f^{(i)}(x)\\right] = \\left[f^{(i)}(x+2\\pi)\\right]$, where $\\vec{q}^{(i)}$ is the $i^{\\text{th}}$ modulation wave vector and $\\phi^{(i)}$ is the sliding phase associated with the $i^{\\text{th}}$ modulation.\nFor the cubic system with unit lattice vectors we are discussing here, each component $q^{(i)}_{j}$, ($j = 1 ,\\ldots, d$) of $\\vec{q}^{(i)}$ is defined within $[0,1)$; that is, each $2\\pi \\vec{q}^{(i)}$ lies within the primitive Brillouin zone of the unmodulated system.\nSince each $\\left[{V}^{(i)}_{\\vec{n}}\\right]$ is a periodic function, they can be expanded in terms of Fourier series as\n\\begin{equation}\n \\left[{V}^{(i)}_{\\vec{n}}\\right] =\\sum_{p_{i}\\in \\mathbb{Z}} \\left[{V}^{(i)}_{p_{i}}\\right] e^{ip_{i}\\left( 2\\pi\\vec{q}^{(i)}\\cdot\\vec{n} + \\phi^{(i)} \\right)}, \\label{eq:expand_FT_V}\n\\end{equation}\nwhere $\\left[{V}^{(i)}_{p_{i}}\\right]$ is the matrix-valued ${p_{i}}^{\\text{th}}$ Fourier component of $\\left[{V}^{(i)}_{\\vec{n}} \\right]$. \nNote that $\\left[{V}^{(i)}_{p_{i}}\\right] = \\left[{V}^{(i)}_{-p_{i}} \\right]^{\\dagger}$ due to hermiticity of the Hamiltonian.\n\nTo perform the enhancement of dimensions, we first insert the expansion Eq.~($\\ref{eq:expand_FT_V}$) into the Hamiltonian Eq.~(\\ref{eq:h_low_dim}). \nWe then regard each $\\phi^{(i)}$ as the $i^{\\text{th}}$ crystal momentum $k^{i}$ along one of the additional $N$ synthetic dimensions. \nWe then promote the $d$D model to a $(d+N)$D space by summing over $\\vec{k} = (k^{1},\\cdots,k^{N}) \\in \\mathbb{T}^{N}$ (where $\\mathbb{T}^N$ denotes the $N$-dimensional torus), which yields the Hamiltonian in $(d+N)$D as\n\\begin{align}\n H_{\\text{high-dim}} &= \\sum_{\\vec{n},\\vec{m},\\vec{k}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\vec{k}} \\left[{H}_{\\vec{m}}\\right] \\psi_{\\vec{n},\\vec{k}} \\nonumber \\\\\n & + \\sum_{\\vec{n},\\vec{k},i,p_{i}} {\\psi}^{\\dagger}_{\\vec{n},\\vec{k}} \\left[{V}^{(i)}_{p_{i}}\\right] e^{ip_{i}k^{i}} e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\vec{n}}{\\psi}_{\\vec{n},\\vec{k}}. \\label{eq:d_n_Bloch}\n\\end{align}\nEach physically distinct configuration of $\\{\\phi^{(i)}\\}$ can be recovered by restricting the Hamiltonian Eq.~(\\ref{eq:d_n_Bloch}) to a single $\\vec{k}$-point. \nOnce we sum over $\\vec{k}$, however, we can reinterpret the Hamiltonian in a $(d+N)$D space. \nAs we will see below, adiabatic pumping of the phases $\\phi^{(i)}$ by an external field will allow us to explore dynamics in the full $d+N$ dimensional space. \n\nTo obtain the $(d+N)$D model in position-space, we perform an inverse Fourier transform of ${\\psi}^{\\dagger}_{\\vec{n},\\vec{k}}$, yielding\n\\begin{align}\n H_{\\text{high-dim}} &= \\sum_{\\vec{n},\\vec{m},\\vec{\\nu}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\vec{\\nu}} \\left[{H}_{\\vec{m}}\\right] \\psi_{\\vec{n},\\vec{\\nu}} \\nonumber \\\\\n & + \\sum_{\\vec{n},\\vec{\\nu},i,p_{i}} {\\psi}^{\\dagger}_{\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i}} \\left[{V}^{(i)}_{p_{i}}\\right] e^{i2\\pi p_{i}\\vec{q}^{(i)}\\cdot \\vec{n} }\\psi_{\\vec{n},\\vec{\\nu}}, \\label{eq:general_n_plus_d_model}\n\\end{align}\nwhere $\\vec{\\nu} = (\\nu_{1},\\cdots,\\nu_{N}) \\in \\mathbb{Z}^{N}$ and $\\hat{\\nu}_{i}$ is the unit vector along the $i^{\\text{th}}$ additional dimension, such that $\\vec{\\nu}-p_{i}\\hat{\\nu}_{i}= (\\nu_{1},\\cdots,\\nu_{i}-p_{i},\\cdots,\\nu_{N})$. \nEq.~(\\ref{eq:general_n_plus_d_model}) can be viewed as the Hamiltonian for a system on a $(d+N)$D {{cubic lattice}} whose lattice sites are located at $(\\vec{n},\\vec{\\nu}) = (n_{1},\\cdots,n_{d},\\nu_{1},\\cdots,\\nu_{N}) \\in \\mathbb{Z}^{d+N}$. \nThe system is coupled to a continuous $U(1)$ gauge field\n\\begin{align}\n \\vec{A} = (\\underbrace{\\vec{0}}_\\text{$d$D},\\underbrace{2\\pi \\vec{q}^{(1)}\\cdot \\vec{r},\\cdots,2\\pi \\vec{q}^{(N)}\\cdot \\vec{r}}_\\text{$N$D}) \\label{eq:expression_A}\n\\end{align}\nthrough a Peierls substitution\\cite{Peierls_substitution}, explaining the appearance of the phase factors multiplying $\\left[{V}^{(i)}_{p_{i}}\\right]$ in Eq.~(\\ref{eq:general_n_plus_d_model}). \nNote $\\vec{r} \\in \\mathbb{R}^{d}$ is a vector in the original $d$D space.\n\n\nAs the vector potential in Eq.~(\\ref{eq:expression_A}) is linear in position $\\vec{r}$, the antisymmetric field strength $F_{\\mu \\nu} = \\partial_{\\mu}A_{\\nu} - \\partial_{\\nu}A_{\\mu}$ is constant in space. \nIn particular, Eq.~(\\ref{eq:expression_A}) implies that the nonzero components of $F_{\\mu \\nu}$ are given by\n\\begin{align}\n F_{i,j+d} = \\partial_{i}A_{j+d} - \\partial_{j+d}A_{i} = \\partial_{i}A_{j+d} = 2\\pi q^{(j)}_{i}, \\label{eq:F_i_j_plus_d}\n\\end{align}\nwhere $i = 1 ,\\ldots, d$ and $j = 1 ,\\ldots, N$. \nDue to the antisymmetry of the field strength, $F_{i+d,j}$ with $i = 1 ,\\ldots, N$, $j = 1 ,\\ldots, d$ is also nonzero and given by $F_{i+d,j} = -2\\pi q^{(i)}_{j}$. \nTherefore the (nonzero) constant field strength is proportional to the magnitude of the modulation wave vectors.\n\n\n\nThis shows that that a $d$D modulated system with phase offset $\\{\\phi^{(i)}\\}$ is equivalent to the Bloch Hamiltonian (see Eq.~(\\ref{eq:d_n_Bloch})) of the promoted $(d+N)$D lattice model with fixed crystal momenta $\\vec{k}$, once we identify $\\phi^{(i)}$ as $k^{i}$.\nIn practice, the modulation $\\left[{V}^{(i)}_{\\vec{n}}\\right]$ can be induced by a set of DW modulations. \nThe phase offset $\\{\\phi^{(i)}\\}$ is then regarded as the phason degrees of freedom, namely the phase of the $i^{\\text{th}}$ mean-field DW order parameter. \nBy applying electric fields that depin the DWs and make them slide\\cite{gooth2019evidence,Zakphase,gruner1988dynamics}, we may sample the whole spectrum of the $(d+N)$D model. \nIn particular, and as we will explore in subsequent sections, non-trivial topology in the $(d+N)$D lattice model--which may support localized boundary states--will manifest in the response of the $d$D model to adiabatic sliding of the DW phase mode(s). \n\\textcolor{black}{We emphasize here that in our dimensional promotion procedure for a DW system, there are no emergent electric fields in the promoted $(d+N)$D space. The electric fields mentioned here are external and serve as a way to depin the DW in order to vary $\\{\\phi^{(i)}\\}$ adiabatically. This allows for the sampling of the entire spectrum of the $(d+N)$D model as a function of $\\{\\phi^{(i)}\\}$, namely the additional crystal momenta.}\n\n\nBefore we move on to consider the band topology of promoted lattice models, let us make a few general comments about our dimensional promotion procedure. \nFirst, note that the dimensional promotion procedure places no constraints on the modulation vectors $\\vec{q}^{(i)}$; in particular, they need not be commensurate with the underlying lattice. \nIn the case of incommensurate modulation, the dimensional promotion procedure allows us to write the $d$D incommensurate model in terms of a periodic $(d+N)$D model with an irrational $U(1)$ flux per plaquette. \nWe will see below how we can use this to explore the topology of systems with incommensurate modulation. \n\\textcolor{black}{We emphasize that the dimensional promotion procedure is independent of whether in the original $d$D space the system is infinite or finite. When we promote the dimension of a $d$D system to $(d+N)$D space, the $(d+N)$D system is inherently infinite along the additional $N$ dimensions, as it allows a Fourier transformation to obtain the Bloch Hamiltonian with fixed $N$ additional crystal momenta. From this viewpoint there are two ways to utilize the dimensional promotion procedure. If we promote the dimension of an infinite $d$D system, we will obtain an infinite $(d+N)$D system that allows us to discuss the non-trivial bulk topology in the promoted $(d+N)$D space. If we instead promote the dimension of a finite $d$D system, we will obtain a $(d+N)$D system which is finite along the original $d$ dimensions and infinite in the additional $N$ dimensions. This allows us to compute the energy spectrum to examine whether there are boundary states protected by the non-trivial bulk topology in $(d+N)$D space.}\n\n\nSecond, although here we consider only dimensional promotion of a $d$D cubic lattice model with only on-site modulations \\textcolor{black}{and all orbitals located at the lattice points labelled by $\\vec{n} \\in \\mathbb{Z}^{d}$} to a $(d+N)$D cubic lattice model, we may generalize our method to $d$D models with modulations in both on-site and hopping matrix elements, together with non-orthogonal lattice vectors \\textcolor{black}{and arbitrary orbital positions}. \nWe show how to systematically promote the dimensions of such $d$D models to $(d+N)$D and compute the corresponding $U(1)$ gauge fields in the SM\\cite{SM}. \nWe also give several examples in the SM\\cite{SM}, including the dimensional promotion of: (1) the 1D Rice-Mele chain in Sec.~\\ref{sec_review_thouless_pump_Rice_Mele} to a 2D square lattice with $\\pi$-flux, (2) 1D lattices with modulation in both on-site energies and hopping terms to 2D hexagonal lattices under a perpendicular magnetic field, and (3) 2D modulated systems with hexagonal lattice to 3D systems also with hexagonal lattices coupled to a $U(1)$ gauge field. \nThe $U(1)$ gauge fields will take a slightly different form from Eq.~(\\ref{eq:expression_A}) when we consider a system with non-orthogonal lattice vectors. \nHowever, the vector potentials will still be linear in $\\vec{r} \\in \\mathbb{R}^{d+N}$, and hence will still produce constant field strengths $F_{\\mu \\nu}$. \nFurthermore, note that although we considered for simplicity models where the electrons were localized to the origin of each unit cell, this is not essential for the application of our formalism.\n\n\nThird, we emphasize that no additional parameters are used in the above derivation. \nThe hopping matrices connecting $(\\vec{n},\\vec{\\nu})$ to $(\\vec{n}+\\vec{m},\\vec{\\nu})$ and $(\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i})$ are given by $\\left[{H}_{\\vec{m}}\\right]$ and $\\left[{V}^{(i)}_{p_{i}}\\right]$, respectively, in the $(d+N)$D model. \nThe phase $\\phi^{(i)}$ corresponds to the $i^{\\text{th}}$ crystal momentum along the $i^{\\text{th}}$ additional dimensions. \nFurther, the modulation wave vectors $\\vec{q}^{(i)}$ specify the strength of the $U(1)$ gauge field in $(d+N)$D, see Eq.~(\\ref{eq:expression_A}). \nNotice that the on-site modulations $\\left[{V}^{(i)}_{\\vec{n}}\\right]$ only lead to hopping parallel to $\\hat{\\nu}_{i}$ in $(d+N)$D. \nIf we also consider modulated hopping matrices in $d$D, upon dimensional promotion we will get hopping along $\\vec{m} + p_{i}\\hat{\\nu}_{i}$ in $(d+N)$D\\cite{equivalence_Fibo_Harper_2012,Oded_4D_CI_to_2D_HOTI}, which we show in the SM\\cite{SM}. \nNotice that the index $i$ is not summed over in $p_{i}\\hat{\\nu}_{i}$. \nRecall also that $\\vec{m}$ and $\\hat{\\nu}_{i}$ are vectors in the original $d$D and additional $N$D space, respectively. \nAn example that demonstrates this is the 1D Rice-Mele model\\cite{RiceMele} in Sec.~\\ref{sec_review_thouless_pump_Rice_Mele}. \nIn the SM\\cite{SM} we promote Eq.~(\\ref{eq:1DRiceMele}) to a 2D lattice with $\\pi$-flux per plaquette in which the electrons can hop along $\\hat{x}+\\hat{y}$ (where $\\hat{x}$ and $\\hat{y}$ are in the original 1D and additional 1D space, respectively).\n \nNext, our construction provides a way to compute the promoted $(d+N)$D model and the $U(1)$ gauge field to which it is coupled. \nAs a $U(1)$ gauge field breaks time-reversal-symmetry (TRS), this dimensional promotion procedure is suitable to investigate non-trivial topological phases in $(d+N)$D space without TRS. \nBelow we will also consider a dimensional promotion to $(d+N)$D space with an $SU(2)$ gauge field, which preserves TRS and allows us to explore non-trivial topological phases protected by TRS\\cite{Kane04,bernevig2006quantum,ryu2010topological,KitaevClassify}.\nIn order to construct a low dimensional modulated model equivalent to a higher dimensional lattice coupled to an $SU(2)$ gauge field, we adopt a top-down approach. \nWe will in Sec.~\\ref{sec_helical_HOTI_sliding_modes} present a 2D modulated model which is obtained from a 3D model coupled to one $SU(2)$ gauge field with a fixed crystal momentum.\n\n\nIn the following sections, we explore various 2D and 3D modulated systems that admit a dimensional promotion to a higher dimensional topological phases coupled to either $U(1)$ or $SU(2)$ gauge fields. \nWe will show how an analysis of the higher-dimensional models can shed light on the eigenstates and boundary state dynamics of incommensurate DWs.\n\n\n\n\\section{\\label{sec_chiral_HOTI}Chiral Higher-Order Topological Sliding Modes}\nIn this section, we will show how the dimensional promotion procedure can be used to realize 3D chiral HOTIs in 2D density wave (DW) materials. \nWe will first construct a Hamiltonian for an insulating 2D modulated system that is inversion-symmetric for special values of the DW sliding phase $\\phi$. \nThen, we will show how, after dimensional promotion, the Hamiltonian corresponds to a 3D inversion-symmetric chiral HOTI coupled to a $U(1)$ gauge field. \nWe will explore the connection between hinge states of the 3D system and corner states of the 2D system using a combination of numerical diagonalization and a 3D low-energy $\\vec{k}\\cdot\\vec{p}$ theory.\n\n\\subsection{Dimensionally Promoted Chiral Model}\n\nConsider the following 2D Hamiltonian for electrons on a square-lattice, with one modulated on-site potential $[V(\\vec{q},\\vec{n},\\phi)]$:\n\\begin{equation}\n\\begin{aligned}\nH = {} & \\sum_{\\vec{n}} \\psi^{\\dagger}_{\\vec{n}+\\hat{x}} [H_{+\\hat{x}}]\\psi_{\\vec{n}} + \\psi^{\\dagger}_{\\vec{n}+\\hat{y}} [H_{+\\hat{y}}]\\psi_{\\vec{n}} + \\text{h.c.}\\\\\n&+ \\sum_{\\vec{n}} \\psi^{\\dagger}_{\\vec{n}}\\left( [H_{\\text{on-site}}] + [V(\\vec{q},\\vec{n},\\phi)] \\right)\\psi_{\\vec{n}},\n\\end{aligned}\\label{chiral_modulated_2}\n\\end{equation}\nwhere the unmodulated hoppings and on-site energies are\n\\begin{align}\n & [H_{+\\hat{e}_{i}}] = \\frac{J_{i}}{2}\\tau_{z}\\sigma_{0} - \\frac{\\lambda_{i}}{2i}\\tau_{x}\\sigma_{i}, \\label{eq:chiral_xy_hopping}\\\\\n & [H_{\\text{on-site}}] = M \\tau_{z}\\sigma_{0}+\\tau_{0} \\vec{B_0} \\cdot \\vec{\\sigma} . \\label{eq:chiral_on_site}\n\\end{align}\nWe use $\\hat{e}_{i}$ to denote the unit vector along the $i^{\\text{th}}$ ($i = 1, 2$) direction. \nThe Pauli matrices $\\vec{\\tau} = (\\tau_{x},\\tau_{y},\\tau_{z})$ and $\\vec{\\sigma} = (\\sigma_{x},\\sigma_{y},\\sigma_{z})$ denote respectively orbital (for example $s$ and $p$ orbitals) and spin degrees of freedom. \nThis Hamiltonian is inversion-symmetric, with inversion symmetry represented by $\\tau_z$. \nFurthermore, when $\\vec{B}_0=0$ the model is also time-reversal (TR) symmetric, with the TR operator represented as $i\\sigma_y\\mathcal{K}$ (where $\\mathcal{K}$ is the complex conjugation operator).\n\nWe assume that both orbital degrees of freedom are located at the lattice sites.\nThe hopping matrices $[H_{+\\hat{e}_{i}}]$, and $M \\tau_{z}\\sigma_{0}$ give rise to, at low energy, four-component massive Dirac fermions, allowing us to access various topological phases\\cite{ryu2010topological,haldanemodel,bernevigbook}.\nPhysically, we can interpret $M \\tau_{z}\\sigma_{0}$ as the on-site energy difference for different orbitals, and $\\tau_{0}\\vec{B_0} \\cdot \\vec{\\sigma}$ as a ferromagnetic potential which splits the spin degeneracy of bands\\cite{wieder2018axion}. \nThe modulated on-site potential, which can arise from a density wave modulation, is\n\\begin{align}\n [V(\\vec{q},\\vec{n},\\phi)] =& J_{z}\\cos\\theta_{\\vec{q},\\vec{n},\\phi}\\tau_{z}\\sigma_{0} + \\lambda_{z}\\sin\\theta_{\\vec{q},\\vec{n},\\phi}\\tau_{x}\\sigma_{z}, \\label{eq:chiral_modulated_V}\n\\end{align}\nwhere $\\theta_{\\vec{q},\\vec{n},\\phi} = 2\\pi \\vec{q}\\cdot \\vec{n} + \\phi$, $\\vec{q} = (q_{x},q_{y})$ is the modulation wave vector in 2D, $\\vec{n} \\in \\mathbb{Z}^{2}$ is the lattice position, and $\\phi$ is the sliding phase. \nThe first term in Eq.~(\\ref{eq:chiral_modulated_V}) modulates the mass $M \\tau_{z}\\sigma_{0}$ in Eq.~(\\ref{eq:chiral_on_site}), while the second modulation denotes an on-site spin-orbit coupling between $s$ and $p$ orbitals. \nNote that the modulation $J_{z}\\cos\\theta_{\\vec{q},\\vec{n},\\phi}\\tau_{z}\\sigma_{0}$ is a TR-even charge ordering, while $\\lambda_{z}\\sin\\theta_{\\vec{q},\\vec{n},\\phi}\\tau_{x}\\sigma_{z}$ is a TR-odd spin ordering. \nTo see this, note that TR maps $(\\tau_{0},\\tau_{x},\\tau_{y},\\tau_{z}) \\to (\\tau_{0},\\tau_{x},-\\tau_{y},\\tau_{z})$ and $(\\sigma_{0},\\sigma_{x},\\sigma_{y},\\sigma_{z}) \\to (\\sigma_{0},-\\sigma_{x},-\\sigma_{y},-\\sigma_{z})$. \nIn addition, the modulations $J_{z}\\cos\\theta_{\\vec{q},\\vec{n},\\phi}\\tau_{z}\\sigma_{0}$ and $\\lambda_{z}\\sin\\theta_{\\vec{q},\\vec{n},\\phi}\\tau_{x}\\sigma_{z}$ are both inversion-symmetric when $\\phi=0$, $\\pi$.\n\n\nDenoting the third, synthetic dimension as $z$ and identifying $\\phi$ as the corresponding crystal momentum $k_{z}$, we may use our general procedure in Sec.~\\ref{sec_Dimension_promotion} to promote this 2D modulated system to a 3D lattice model. \nWe first expand the modulations in terms of Fourier series as\n\\begin{align}\n & J_{z}\\cos\\theta_{\\vec{q},\\vec{n},\\phi}\\tau_{z}\\sigma_{0} = \\frac{J_{z}}{2}\\left(e^{i\\theta_{\\vec{q},\\vec{n},\\phi}} + e^{-i\\theta_{\\vec{q},\\vec{n},\\phi}} \\right) \\tau_{z}\\sigma_{0}, \\label{eq:J_expand} \\\\\n & \\lambda_{z}\\sin\\theta_{\\vec{q},\\vec{n},\\phi}\\tau_{x}\\sigma_{z} = \\frac{\\lambda_{z}}{2i}\\left( e^{i\\theta_{\\vec{q},\\vec{n},\\phi}} - e^{-i \\theta_{\\vec{q},\\vec{n},\\phi}} \\right)\\tau_{x}\\sigma_{z}. \\label{eq:lambda_z_expand}\n\\end{align}\nAccording to Eqs.~(\\ref{eq:expand_FT_V}) and (\\ref{eq:general_n_plus_d_model}), the hopping along $+\\hat{z}$ can be identified with the terms associated with $e^{-i \\theta_{\\vec{q},\\vec{n},\\phi}}$ in Eqs.~(\\ref{eq:J_expand}) (\\ref{eq:lambda_z_expand}). \nTherefore, the hopping along $+\\hat{z}$ in the promoted 3D space reads\n\\begin{equation}\n[H_{+\\hat{z}}]=\\frac{J_{z}}{2}\\tau_{z}\\sigma_{0} -\\frac{\\lambda_{z}}{2i}\\tau_{x}\\sigma_{z}.\n\\end{equation} \nFrom Eq.~(\\ref{eq:expression_A}) we can also identify the vector potential in the promoted 3D space as\n\\begin{align}\n \\vec{A} = (0,0,2\\pi \\vec{q} \\cdot \\vec{r}) = (0,0,2\\pi q_{x} x + 2\\pi q_{y}y), \\label{A_U1}\n\\end{align}\nwhere $\\vec{r} = (x,y) \\in \\mathbb{R}^{2}$. \nTherefore, we have that the lattice Hamiltonian in the promoted 3D space is given by\n\\begin{widetext}\n\\begin{align}\n H = \\sum_{\\vec{n}} & \\left[ \\left( \\psi^{\\dagger}_{\\vec{n}+\\hat{x}} [H_{+\\hat{x}}]\\psi_{\\vec{n}} +\\psi^{\\dagger}_{\\vec{n}+\\hat{y}} [H_{+\\hat{y}}]\\psi_{\\vec{n}} + {\\psi}^{\\dagger}_{\\vec{n}+\\hat{z}} [H_{+\\hat{z}}] e^{-i2\\pi (q_{x}n_{x}+q_{y}n_{y})} {\\psi}_{\\vec{n}} + \\text{h.c.} \\right)+ \\psi^{\\dagger}_{\\vec{n}} [H_{\\text{on-site}}] \\psi_{\\vec{n}} \\right], \\label{eq:lattice_model_chiral_sliding}\n\\end{align}\n\\end{widetext}\nwhere the vector potential Eq.~(\\ref{eq:expression_A}) is coupled to the system through a Peierls substitution\\cite{Peierls_substitution}, and\n$[H_{+\\hat{x}}]$, $[H_{+\\hat{y}}]$, $[H_{+\\hat{z}}]$ and $[H_{\\text{on-site}}]$ are given by Eqs.~(\\ref{eq:chiral_xy_hopping}) and (\\ref{eq:chiral_on_site}), respectively. \nHereafter, we will set $J_{x} = J_{y} = J_{z} = J$ for simplicity. \nIf we Fourier transform Eq.~(\\ref{eq:lattice_model_chiral_sliding}) along $z$ and regard $k_{z}$ (the wavenumber along $z$) as the sliding phase $\\phi$, we can obtain the 2D modulated system in Eq.~(\\ref{chiral_modulated_2}). \n\nWe will now use Eq.~(\\ref{eq:lattice_model_chiral_sliding}) to analyze the topological properties of the higher-dimensional model, in order to infer the properties of the low-dimensional modulated system.\nThis approach can also be employed in other low-dimensional modulated systems provided the corresponding higher-dimensional models are constructed. \nFor $q_{x}=q_{y}=0$ and $\\vec{B_0}=0$, Eq.~(\\ref{eq:lattice_model_chiral_sliding}) describes a TR and inversion-symmetric insulator whose inversion operation is represented by $\\tau_{z}$ (note that inversion symmetry acts to flip the sign on the synthetic momentum $k_z$). \nWe can employ the theory of symmetry-based indicators of band topology~\\cite{khalaf,Po2017,song2017,xu2020high,MTQC,Wieder_spin_decoupled_helical_HOTI,dynamical_axion_insulator_BB,Kruthoff2016} to compute the $\\mathbb{Z}_4$ indicator\n\\begin{align}\n z_{4} = \\frac{1}{4}\\sum_{\\vec{k}_{a} \\in \\text{TRIMs}} \\left( n^{a}_{+} - n^{a}_{-} \\right) \\text{ mod }4,\n\\end{align}\nwhere $n^{a}_{+}$[$n^{a}_{-}$] is the number of positive[negative] parity eigenvalues in the valence band at the time-reversal invariant momentum (TRIM) $\\vec{k}_{a} $. \nWe find that for $|M\/J|>3$, $|M\/J|<1$, $13$, $|M\/J|<1$, $1<|M\/J| < 3$ we have $\\tilde{z}_{4} = 0$, $0$ and $2$. \nThe corresponding weak indices are all necessarily trivial.\nTherefore, for $1 < |M\/J| <3$ with $q_{x}=q_{y}=0$, the system described by Eq.~(\\ref{eq:lattice_model_chiral_sliding}) gives a strong TI with $\\vec{B_0}=0$ and a chiral HOTI (axion insulator)\\cite{MTQC} with $\\vec{B_0}\\ne0$, where the gapless surface states of the strong TI are gapped by the inversion-preserving ferromagnetic potential $\\tau_{0} \\vec{B_0} \\cdot \\vec{\\sigma}$. \nTherefore, Eq.~(\\ref{eq:lattice_model_chiral_sliding}) with $\\vec{q} \\ne 0$ describes an inversion-symmetric chiral HOTI\\cite{pozo2019quantization} coupled via a Peierls substitution to a 3D $U(1)$ gauge field given by the $\\vec{A}$ in Eq.~(\\ref{eq:expression_A}).\nThis $\\vec{A}$ produces a constant $U(1)$ magnetic field \n\\begin{align}\n \\nabla \\cross \\vec{A} = (2\\pi q_{y},-2\\pi q_{x},0), \\label{B_U1}\n\\end{align} \nwhich preserves the inversion symmetry represented by $\\tau_{z}$ in 3D, up to a gauge transformation (see SM\\cite{SM}). \nTherefore, for a suitable choice of parameters, as long as the $U(1)$ gauge field does not close the bulk gap in 3D, the insulating ground state will be in the same inversion symmetry-protected non-trivial chiral HOTI phase. \nThis implies that our model should exhibit the characteristic boundary modes of a chiral HOTI in 3D. \nIn particular, our promoted model will support odd numbers of sample-encircling chiral hinge modes in rod geometries which respect inversion symmetry~\\cite{pozo2019quantization,wieder2018axion,dynamical_axion_insulator_BB}.\n\n\\begin{figure}[t]\n\\hspace{-0.5cm}\n\\includegraphics[width=\\columnwidth]{fig_main_text_chiral_sliding_change_ticking_freq.pdf}\n\\caption{(a) $\\phi$-sliding spectrum of the chiral 2D model in Eq.~(\\ref{chiral_modulated_2}) with parameters given in the text. \n(b) Probability distribution of corner modes in the gap-crossing bands at $\\phi = 0.5\\pi$ and $E=-0.1368$. \n(c) $\\&$ (d) Probability distribution of edge and bulk modes at $\\phi = 0.9\\pi$ and $E = -0.2508$ and $E = 0.278$, respectively. \nThe darker (black) color in (b)--(d) implies higher probability density. \\textcolor{black}{(b), (c) and (d) correspond to the corner mode, edge-confined mode and bulk-confined mode discussed in Sec.~\\ref{sec:chiral_sliding_corner_modes}, \\ref{sec:chiral_sliding_confined_edge_modes} and \\ref{sec:chiral_sliding_confined_bulk_modes}, respectively.}\nIn (b), (c) and (d), the $x$- and $y$-coordinate both range from $-15 ,\\ldots, +15$.}\n\\label{fig:chiral_sliding_main_text}\n\\end{figure}\n\n\\subsection{\\label{sec:chiral_sliding_corner_modes}Corner states}\n\nRecalling that in our case the $\\hat{z}$ direction is conjugate to the phase $\\phi$ of the sliding mode (regarded as the crystal momentum $k_z$), it is natural for us to consider inversion-symmetric rod geometries which are finite in the $\\hat{x}$ and $\\hat{y}$ directions, and infinite in the $\\hat{z}$ direction.\nIn our 2D system, this corresponds to considering the properties of a finite system as a function of the phase $\\phi$.\nWe can thus compute the energy spectrum of our 2D system in an open geometry with size $L_{x} \\times L_{y}$ as a function of $\\phi$ to obtain the energy dispersion along $k_{z}$ in the promoted model. \nIn the following, we call this kind of calculation the {\\it $\\phi$-sliding spectrum}, since the variation of $\\phi$ can be obtained by electromagnetically exciting the sliding mode of the underlying DW.\nFig.~\\ref{fig:chiral_sliding_main_text} (a) shows the $\\phi$-sliding spectrum of Eq.~(\\ref{chiral_modulated_2}) with parameters $J = 1$, $M =2 $, $\\lambda_{i}= 1$, $(\\vec{B}_{0})_{i}= 0.5\/\\sqrt{3}$\\cite{pozo2019quantization}, and $\\vec{q} = (0,q_{y})$, where $q_{y} = 0.11957$ is comparable with the experimental CDW wave vectors in (TaSe$_4$)$_2$I\\cite{shi2019charge} and is incommensurate with the underlying 2D square lattice in Eq.~(\\ref{chiral_modulated_2}).\nThe system size is $31 \\times 31$. \nAs we can see the spectrum contains modes which, as a function of $\\phi$, traverse the bulk spectral gap. \nExamining the wave functions of these ``gap-crossing modes,'' we see that they are localized to the corners of our $2$D sample, as shown in Fig.~\\ref{fig:chiral_sliding_main_text} (b). \nThe gap-crossing modes with opposite slopes correspond to states at inversion-related corners; in our example one mode is localized at the corner $(x_{\\text{corner}},y_{\\text{corner}})=(L\/2,-L\/2)$ (Fig.~\\ref{fig:chiral_sliding_main_text} (b)) and the other at $(x_{\\text{corner}},y_{\\text{corner}})=(-L\/2,L\/2)$ where $L = 30$.\nIf we start in a half-filled insulating ground state (with Fermi level $E_{F}=0$), then as $\\phi$ slides from $0$ to $2\\pi$, we realize charge pumping as one corner mode merges into the occupied-state subspace while the inversion-related counterpart flows into the unoccupied state subspace. \nThe ground states at the two inversion-symmetric values $\\phi=0,\\pi$ differ in electron number by $1$, demonstrating a ''filling anomaly''\\cite{benalcazar2018quantization,wieder2020strong}. \\tabularnewline\nBecause these corner modes originate as hinge modes in the $3$D dimensionally promoted system (where, recall, $\\phi$ is the momentum $k_z$), their existence is mandated by the non-trivial higher-order topology of the model Eq.~(\\ref{eq:lattice_model_chiral_sliding}).\n\nBy analyzing the low energy theory of the 3D hinge modes, we will now derive the dynamics of the 2D corner modes as a function of $\\phi$. \nIn 3D, the corresponding low energy 1D hinge Hamiltonian\\cite{hasan2010colloquium,khalaf,hotis} with a chiral mode as a function of $k_{z}$ is given by\n\\begin{align}\n H_{\\text{hinge}} = \\xi v_{F} \\left( k_{z} + 2\\pi \\left( q_{x}x_{\\text{hinge}} + q_{y}y_{\\text{hinge}} \\right) \\right). \\label{eq:chiral_low_energy_hinge_H_1}\n\\end{align}\nWe have assumed that for the hinge along $z$ at position $(x_{\\text{hinge}},y_{\\text{hinge}})$ there is only one chiral mode with Fermi velocity $\\xi v_{F}$ where $v_{F} > 0$. \nWe have introduced $\\xi = \\pm 1$ to denote whether the chiral mode has positive or negative velocity. \nFollowing our dimensional promotion procedure, Eq.~(\\ref{eq:chiral_low_energy_hinge_H_1}) is then minimally coupled to a $U(1)$ gauge field in Eq.~(\\ref{A_U1}) through the Peierls substitution $k_{z} \\to k_{z} + 2\\pi (q_{x}x+q_{y}y)$, where $x = x_{\\text{hinge}}$ and $y = y_{\\text{hinge}}$ are fixed. \n\nTo map Eq.~(\\ref{eq:chiral_low_energy_hinge_H_1}) in 3D to the corner mode dispersion in 2D, it is helpful to first compute the $\\phi$-sliding spectrum for Eq.~(\\ref{chiral_modulated_2}) with $\\vec{q} = (0,0)$, as shown in Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (a). \nIf we identify $\\phi$ as $k_{z}$ in the hinge theory (modulo a constant offset that we will fix later), Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (a) is the $\\hat{z}$-directed rod band structure for Eq.~(\\ref{eq:lattice_model_chiral_sliding}) without coupling to any vector potential. \nAs we can see, there are linear dispersing hinge modes spanning the bulk gap, which cross each other at $k_{z} = \\pi$. \nThis will be used below in Eq.~(\\ref{eq:low_E_corner_chiral}) to complete the mapping from Eq.~(\\ref{eq:chiral_low_energy_hinge_H_1}) to 2D. \nFig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (a) will also serve as a reference calculation when we examine the response of the $\\phi$-sliding spectrum as we increase the magnitude of $\\vec{q}$, which will confirm our low energy analysis.\n\n\nWe now use Eq.~(\\ref{eq:chiral_low_energy_hinge_H_1}) to construct a low energy description of the corner modes in Fig.~\\ref{fig:chiral_sliding_main_text} (a) for Eq.~(\\ref{chiral_modulated_2}). \nUpon projecting from 3D to 2D, the fixed hinge mode position $(x_{\\text{hinge}},y_{\\text{hinge}})$ becomes the fixed corner mode position $(x_{\\text{corner}},y_{\\text{corner}})$, and the hinge modes become corner modes. \nSince the gap-crossing modes in the $q=0$ system shown in Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (a) intersect at $\\phi = \\pi$, we replace $k_{z}$ in the hinge theory by $\\Delta \\phi = \\phi - \\pi$. \nThus, we obtain an effective low energy description of the corner modes as\n\\begin{align}\n H_{\\text{corner}} = \\xi v_{F} \\cdot \\left(\\Delta \\phi+2\\pi \\left(q_{x}x_{\\text{corner}}+q_{y}y_{\\text{corner}}\\right)\\right).\n \\label{eq:low_E_corner_chiral} \n\\end{align}\nWe now verify Eq.~(\\ref{eq:low_E_corner_chiral}) by numerically computing the $\\phi$-sliding spectrum shown in Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (b) with same parameters as Fig.~\\ref{fig:chiral_sliding_main_text} (a) but with $q_{y}$ changed to $0.02$. This small value of $q_y$ gives a smooth modulation--and hence a low flux per plaquette in the dimensionally-promoted model--and is thus a suitable platform to examine the low energy theory with minimal coupling. \nWe observe gap-crossing modes with negative and positive slopes corresponding to corner modes at $(-L\/2,L\/2)$ and $(L\/2,-L\/2)$ where $L =30$, respectively. \nThese are shown in Figs.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (c) and (d) at $\\phi = 0.4\\pi$ and $1.6\\pi$, respectively. \nUsing Eq.~(\\ref{eq:low_E_corner_chiral}), we have the low energy descriptions for these two corner modes governed by the Hamiltonians\n\\begin{align}\n & H_{\\text{corner 1}} = -v_{F}\\left( \\Delta \\phi + \\pi q_{y}L \\right), \\label{eq:eff_corner_chiral_1} \\\\\n & H_{\\text{corner 2}} = +v_{F}\\left( \\Delta \\phi - \\pi q_{y}L \\right), \\label{eq:eff_corner_chiral_2}\n\\end{align}\nwhere we have used $q_{x} = 0$. \nThus, if we ramp up $q_{y}$ from $0$ to some non-zero value, we expect to see the corner mode dispersion shift along the $\\phi$-axis. \nThis is demonstrated in Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (b), which is to be compared with Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (a). \nIn fact, a careful examination of Figs.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (a) and (b) shows that the dispersions of the two corner modes shift in opposite directions as a function of $\\Delta\\phi$, as indicated in Eq.~(\\ref{eq:eff_corner_chiral_1}) and Eq.~(\\ref{eq:eff_corner_chiral_2}), with the shift given by $\\pi q_{y}L \\approx 0.6 \\pi$ for $q_{y} = 0.02$ and $L = 30$. \nWe thus see that the corner mode dispersion in Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (b) can be explained by Eq.~(\\ref{eq:low_E_corner_chiral}). \nThis demonstrates the origin of the corner modes in the 2D modulated system as higher dimensional hinge modes minimally coupled to a $U(1)$ gauge field. \nIf we consider larger $q_{y}$, such as in Fig.~\\ref{fig:chiral_sliding_main_text} where we have $q_{y} = 0.11957$, then the shift of the corner mode dispersion is predicted to be $\\pi q_{y} L \\approx 3.5871 \\pi$. This lies outside the first Brillouin zone and needs to be folded back into the range $\\phi = [0,2\\pi)$. \nThis occurs because, in passing from low energy continuum theory to a lattice model, the periodicity of $\\phi$--which in the promoted dimension is the continuous wavenumber $k_{z}$--is restored.\nAdditionally, note that Eq.~(\\ref{eq:low_E_corner_chiral}) implies that we may tune the range of $\\phi$ where the corner mode energies emerge from the bulk continuum by varying the periodicity of the modulation $\\sim 1\/|\\vec{q}|$. \nAs shown in Eq.~(\\ref{A_U1}) and Eq.~(\\ref{B_U1}), tuning $\\vec{q}$ is equivalent to changing the direction and strength of the $U(1)$ gauge field and the corresponding magnetic field in 3D.\n\n\n\\subsection{\\label{sec:chiral_sliding_confined_edge_modes}Edge states}\n Having accounted for the low energy description of the corner modes, we observe that in Fig.~\\ref{fig:chiral_sliding_main_text} (a), there are additional modes with {\\it flat dispersion}. \n These non-dispersing modes describe states confined either to the bulk or edge of the system, as shown in Figs.~\\ref{fig:chiral_sliding_main_text} (c) and (d). \n We now use low energy theories to demonstrate that these states originate from the $U(1)$ Landau quantization of the surface and bulk electrons in the promoted 3D chiral HOTI. \n We will revisit Figs.~\\ref{fig:chiral_sliding_main_text} (c) and (d) after we complete the low energy theory analysis using relatively small $q_{y}$.\n\n\n\nWe start with the edge-confined modes. \nSince a chiral HOTI can be obtained by gapping out the surface of a 3D inversion and TR-symmetric TI with a TR-breaking mass term, the generic surface theory reads\\cite{khalaf,wieder2018axion,vanderbiltaxion}\n\\begin{align}\n H_{\\text{surf}} = \\left( \\vec{p} \\times \\vec{\\sigma}' \\right)\\cdot \\hat{n} + m \\hat{n} \\cdot \\vec{\\sigma}',\n\\end{align}\nwhere $\\vec{\\sigma}'$ are Pauli matrices that act in the basis of low-energy surface states and which capture their spin and orbital texture, $\\vec{p}$ is the momentum operator, and $\\hat{n}$ is the surface normal vector. \nThe time-reversal operator in this surface theory is given by $\\mathcal{T} = i\\sigma_{y}'\\mathcal{K}$ such that $\\mathcal{T} \\vec{\\sigma}' \\mathcal{T}^{-1} = -\\vec{\\sigma}'$. \nThe momentum dependent term $\\left( \\vec{p} \\times \\vec{\\sigma}' \\right)\\cdot \\hat{n}$ describes a helical surface Dirac cone, while $m \\hat{n} \\cdot \\vec{\\sigma}'$ is the TR-breaking mass term. \nAs shown in Eq.~(\\ref{B_U1}), if $q_{x} = 0$, which is the case we consider in Fig.~\\ref{fig:chiral_sliding_main_text} and Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp}, we have that $\\nabla \\cross \\vec{A}$ is parallel to $\\hat{x}$. \nWe then consider a surface theory on the $yz$-plane coupled to a perpendicular magnetic field $B \\hat{x}$ generated by a Landau-gauge $U(1)$ gauge field $\\vec{A} = (0,0,By)$.\nThe corresponding surface Hamiltonian with the $U(1)$ gauge field reads ${{H_{\\text{surf}} = p_{y}\\sigma_{z}' - \\left(p_{z}+By \\right)\\sigma_{y}' + m\\sigma_{x}',}}$ \nwhere we have made a Peierls substitution such that $p_{z} \\to p_{z} + By$, and we have assumed that both $B$ and $m$ are positive. \nTo facilitate the derivation, we perform a basis transformation through a $-2\\pi\/3$ radian spin rotation $U$ along the $[1,1,1]$ axis such that $U^{\\dagger} (\\sigma_{x}',\\sigma_{y}',\\sigma_{z}') U = (\\sigma_{z}',\\sigma_{x}',\\sigma_{y}')$. \nThe transformed Hamiltonian then reads\n\\begin{align}\n H_{\\text{surf}} = p_{y}\\sigma_{y}' - \\left(p_{z}+By \\right)\\sigma_{x}' + m\\sigma_{z}'. \\label{eq:surf_H_chiral_HOTI_transformed}\n\\end{align}\nFourier transforming Eq.~(\\ref{eq:surf_H_chiral_HOTI_transformed}) to replace {{$p_{z}$}} by the wavenumber $k_{z}$, and defining a $k_{z}$-dependent ladder operator\n\\begin{align}\n & {{a^{\\dagger}_{k_{z}} = \\frac{1}{\\sqrt{2B}}\\left(\\left(k_{z}+By \\right)-ip_{y} \\right),}} \\label{eq:U1_ladder}\n\\end{align}\nwhere $[a_{k_{z}},a^{\\dagger}_{k_{z}}]=1$, we can rewrite Eq.~(\\ref{eq:surf_H_chiral_HOTI_transformed}) as\n\\begin{align}\n {{H_{\\text{surf}}(k_{z}) = \\begin{bmatrix}\n m & -\\sqrt{2B} a_{k_{z}} \\\\\n -\\sqrt{2B} a^{\\dagger}_{k_{z}} & -m\n \\end{bmatrix}.}}\n \\label{eq:chiral_reexpress_H_surf}\n\\end{align}\nWe can solve for the eigenstates and energy eigenvalues of Eq.~(\\ref{eq:chiral_reexpress_H_surf}) to find\n\\begin{widetext}\n\\begin{align}\n & {{\\psi^{-}_{k_{z},n=0} = e^{ik_{z}z} \\begin{bmatrix}\n 0 \\\\ \\ket{0,k_{z}}\n \\end{bmatrix},\\ E^{-}_{k_{z},n=0} = -m,}} \\nonumber \\\\\n & {{\\psi^{-}_{k_{z},n>0} = e^{ik_{z}z} \\begin{bmatrix}\n \\alpha_{-}(n) \\ket{n-1,k_{z}}\\\\ \\ket{n,k_{z}}\n \\end{bmatrix},\\ E^{-}_{k_{z},n>0} = - \\sqrt{m^{2} + 2Bn},}} \\nonumber \\\\\n & {{\\psi^{+}_{k_{z},n>0} = e^{ik_{z}z} \\begin{bmatrix}\n \\alpha_{+}(n) \\ket{n-1,k_{z}}\\\\ \\ket{n,k_{z}}\n \\end{bmatrix},\\ E^{+}_{k_{z},n>0} = + \\sqrt{m^{2} + 2Bn},}} \\nonumber \\\\\n & {{\\text{where } \\alpha_{\\pm}(n) = \\frac{-1}{\\sqrt{2Bn}}\\left(\\pm\\sqrt{m^{2}+2Bn}+m \\right).}} \\label{eq:U1_surface_EE_EV}\n\\end{align}\n\\end{widetext}\nHere $n$ is a non-negative integer labelling the $U(1)$ Landau levels (LLs), and $\\ket{n,k_{z}}$ is the $n^{\\text{th}}$ simple harmonic oscillator (SHO) eigenstate localized along $y$ defined by the $a^{\\dagger}_{k_{z}}$ in Eq.~(\\ref{eq:U1_ladder}). \nNotice that the energies $E^{-}_{k_{z},n=0}$, $E^{-}_{k_{z},n>0}$ and $E^{+}_{k_{z},n>0}$ of these LLs shown in Eq.~(\\ref{eq:U1_surface_EE_EV}) are all independent of {{$k_{z}$}}. \nAs before, we now construct the low energy description of the edge-confined modes in the 2D modulated system from the above low energy surface theory in Eq.~(\\ref{eq:surf_H_chiral_HOTI_transformed}). \nWe identify $k_{z}$ in the surface theory as $\\Delta \\phi = \\phi - \\pi$, since we have flat bands as a function of $\\phi$ in our 2D modulated system. \nWe also identify $B$ with $2\\pi q_{y}$ since in our examples of Fig.~\\ref{fig:chiral_sliding_main_text} (a) and Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (b), we have $q_{x} = 0$ and the corresponding vector potential is $\\vec{A} = (0,0,2\\pi q_{y}y)$. \nWhen we project down to the 2D model, the surface electrons correspond to states confined in the left and right edges, as shown in Fig.~\\ref{fig:chiral_sliding_main_text} (c) and Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (e)--(h). \nWe again use $q_{y} = 0.02$ to demonstrate the low energy theory.\n\n\n\n\\begin{figure*}[t]\n\\hspace{-0.5cm}\n\\includegraphics[width=\\linewidth]{main_text_chiral_sliding_low_energy_theory.pdf}\n\\caption{(a) $\\&$ (b) $\\phi$-sliding spectrum of the chiral 2D model with the same parameters as Fig.~\\ref{fig:chiral_sliding_main_text} (a) but with $q_{y} = 0$ and $0.02$, respectively. \n(c) $\\&$ (d) Probability distribution of the corner modes in (b) at $\\phi = 0.4 \\pi$ and $1.6 \\pi$ and both with $E = 0.0365$. \n(e)--(g) Average of the probability distribution for the doubly degenerate edge-confined modes in the flat bands in (b) at $\\phi=\\pi$. \nThe double degeneracy is due to the pair of opposite edges related by inversion symmetry. \n(e)--(g) are edge-confined modes at $\\phi = \\pi$ with energies $E=-0.4705$, $-0.2235$ and $0.3811$, which are marked orange, green and red respectively in (b). \nThe corresponding energy eigenvalues are $E^{-}_{k_{z}=0,n=1}$, $E^{-}_{k_{z}=0,n=0}$ and $E^{+}_{k_{z}=0,n=1}$ in Eq.~(\\ref{eq:U1_surface_EE_EV}). \n(h) Edge-confined mode at $\\phi = 0.9 \\pi$ with energy $E = -0.2235$ corresponding to $E^{-}_{k_{z}=-0.1\\pi,n=0}$ in Eq.~(\\ref{eq:U1_surface_EE_EV}). \nThe darker (black) color in (c)--(h) implies higher probability density. \nThe inset in (e)--(h) is the probability distribution integrated over all $x$ coordinates. \nIn (c)--(h), the $x$- and $y$-coordinate both range from $-15 ,\\ldots, +15$.}\n\\label{fig:chiral_sliding_low_energy_main_text_temp}\n\\end{figure*}\n\n\n\nWe now remark on the implications of our low-energy analysis. \nFirst, the spectrum in Eq.~(\\ref{eq:U1_surface_EE_EV}) breaks particle-hole symmetry as there is a $-m$ energy eigenvalue but no $+m$ energy eigenvalue. \nThis can be observed in Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (b), where there are no flat bands of edge-confined modes around $E \\approx +0.2$, which corresponds to $E = +m$. \nWe thus identify the flat bands in Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (b) marked by red, green and orange as $E^{+}_{k_{z},n=1}$, $E^{-}_{k_{z},n=0}$ and $E^{-}_{k_{z},n=1}$ in Eq.~(\\ref{eq:U1_surface_EE_EV}). \n\n\nSecond, from Eq.~(\\ref{eq:U1_surface_EE_EV}), the probability distributions for the states $\\psi^{-}_{k_{z},n=0}$ and $\\psi^{\\pm}_{k_{z},n=1}$ are given by\n\\begin{align}\n & {{|\\psi^{-}_{k_{z},n=0}|^2 \\propto\\left|\\varphi_{0,B}(y+k_{z}\/B) \\right|^{2}}} \\\\\n & {{|\\psi^{\\pm}_{k_{z},n=1}|^2 \\propto\\left| \\alpha_{\\pm}(1) \\right|^{2}\\left|\\varphi_{0,B}(y+k_{z}\/B) \\right|^{2} + \\left|\\varphi_{1,B}(y+k_{z}\/B) \\right|^{2}}}\n\\end{align}\nup to a normalization factor, where $\\varphi_{n,B}(y)$ is the $n^{\\text{th}}$ eigenstate of an SHO localized along {{$y$}}. \nNotice that we have indicated the explicit $B$-dependence on $\\varphi_{n,B}(y)$ since the cyclotron frequency and the localization of the wave function depend on the strength of magnetic field. \nThis implies that $\\psi^{-}_{k_{z},n=0}$ has a pure Gaussian distribution. Furthermore, we expect that $\\psi^{-}_{k_{z},n=1}$ is more characteristic of an SHO first excited state than $\\psi^{+}_{k_{z},n=1}$ since $\\left| \\alpha_{-}(1) \\right|^{2} = \\left( -\\sqrt{m^{2} + 2B} + m \\right)^{2}\/(2B) < \\left( \\sqrt{m^{2} + 2B} + m \\right)^{2}\/(2B) = \\left| \\alpha_{+}(1) \\right|^{2}$,\nas we have assumed both $B$ and $m$ are positive. \nFigs.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (e)--(g) show the 2D wave function probability distributions at $\\phi = \\pi$ for edge confined modes in different LLs in our lattice model, together with the insets showing the integrated wave function probability over all $x$-coordinates. \nWhile both Figs.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (e) and (g) corresponds to $n = 1$ LL, the former is at the negative energy branch and the latter is at the positive energy branch.\nTherefore Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (e) shows split peaks characteristic of the SHO first excited state, more so than Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (g). \nIn contrast, Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (f), corresponding to the $n=0$ LL wave function, shows the Gaussian probability distribution characteristic of the SHO ground state. \nWe see that the qualitative properties of the wave functions are all consistent with the low energy surface theory.\n\nThird, the definition of the ladder operator in Eq.~(\\ref{eq:U1_ladder}) implies that the center of the wave functions will be shifted by $-k_{z}\/B$ from $y = 0$. \nIdentifying $k_{z}$ in the low energy theory as $\\Delta \\phi = \\phi - \\pi$ and $B$ as $2\\pi q_{y}$, we deduce that the distance $l$ that the edge-confined mode gets shifted along $y$ in the lattice model will be $l = -\\Delta \\phi\/({2\\pi q_{y}})$. \nNotice that the edge-confined mode in Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (h) at $\\phi = 0.9\\pi$ ($\\Delta \\phi = -0.1\\pi$) is shifted by $\\approx +2.5$ lattice constants along $y$ comparing with Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (f), which is at $\\phi = \\pi$ ($\\Delta \\phi = 0$). \nThis is consistent with our prediction, as $l$ will be $+2.5$ when $\\Delta \\phi = -0.1\\pi$ and $q_{y} = 0.02$.\n\nFourth, although Eq.~(\\ref{eq:U1_surface_EE_EV}) predicts non-degenerate energy levels for a single surface with a perpendicular $U(1)$ magnetic field, in Fig.~\\ref{fig:chiral_sliding_main_text} (a) the flat band corresponding to the $E^{-}_{k_{z},n=0}$ level is highly degenerate. \nThis is due to zone-folding effects, similar to what we observed for the corner mode dispersion in Fig.~\\ref{fig:chiral_sliding_main_text} (a). \nAs the gap-crossing modes are shifted outside $\\phi = [0,2\\pi)$, they get folded back to $\\phi = [0,2\\pi)$ together with the flat bands connected to them. \nUp to the degeneracy due to zone folding, the universal feature is that the edge-confined modes appearing in our 2D chiral DW system originate from the projection of surface electrons in a chiral HOTI with $U(1)$ Landau quantization.\n\n\nBefore moving on, let us remark on the robustness of our low-energy predictions to perturbations of the model. \nIf we consider a more complicated modulated system with, for example, long-range and anisotropic hopping terms together with other on-site potentials, as long as the promoted 3D system still preserves inversion symmetry and the gap is not closed, the 3D system will still be in the same chiral HOTI phase. \nHowever, the low energy theories that we have constructed might be modified. \nFor example, the low energy theory at the surfaces, which we model with Eq.~(\\ref{eq:surf_H_chiral_HOTI_transformed}), may be modified as \n\\begin{align}\n H_{\\text{surf}} =& \\alpha_{y}p_{y}\\sigma_{y}' - \\alpha_{z}\\left(p_{z}+By \\right)\\sigma_{x}' + m\\sigma_{z}' + \\Delta \\sigma_{0}' \\nonumber \\\\\n & + \\mathcal{O}(p_{y}^{2},p_{z}^{2},p_{y}p_{z}).\\label{eq:higher_order_terms}\n\\end{align}\nDifferences between $\\alpha_{x}$ and $\\alpha_{y}$ can lead to an anisotropic gapped Dirac cone. \nA nonzero $\\Delta$ induces unequal masses in different subspace of $\\vec{\\sigma}'$ which can shift the entire energy spectrum. \n$\\mathcal{O}(p_{y}^{2},p_{z}^{2},p_{y}p_{z})$ represents higher-order terms in the low energy theory which might cause nonlinearity in the band dispersion in Eq.~(\\ref{eq:higher_order_terms}) without minimal coupling.\nBy the same reasoning, we might also have non-linear hinge mode energies with a quadratic momentum correction in Eq.~(\\ref{eq:chiral_low_energy_hinge_H_1}).\nAll of these additional terms will change the energetic feature of the system, such as energy spectra, Fermi velocities, together with the detailed form of the wave functions, which will be inevitably different from Eq.~(\\ref{eq:U1_surface_EE_EV}). \nNevertheless, the following features are universal: (1) There will be electrons confined to the surface that undergo $U(1)$ Landau quantization, and therefore there will be states that are confined along some directions. \nUpon projecting down to the 2D modulated system, we will still obtain edge-confined modes. \n(2) There will be (non-)linear hinge mode dispersion that will be shifted along $k_{z}$ due to the minimal coupling. \nTherefore the statement that we can tune the range of $\\phi$ where the gap-crossing corner modes appear by tuning the magnitude of the modulation wave vectors, will still hold. \nWe use the low energy theories Eq.~(\\ref{eq:chiral_low_energy_hinge_H_1}) and Eq.~(\\ref{eq:surf_H_chiral_HOTI_transformed}) since these allow us to uncover the relation between the states in the promoted dimension and those in the original low dimensional modulated system in an analytically tractable way.\n\n\n\\subsection{\\label{sec:chiral_sliding_confined_bulk_modes}Bulk states}\n\nThe above analysis on corner- and edge-confined modes shows that the corresponding higher dimensional description of our modulated system is a 3D chiral HOTI minimally coupled to a $U(1)$ gauge field. \nTo complete our analysis, we will now focus on the bulk states.\nAs expected, the low energy description of the bulk-confined modes, shown in Fig.~\\ref{fig:chiral_sliding_main_text} (d), will correspond to the low energy theory of bulk electrons in a 3D chiral HOTI minimally coupled to a $U(1)$ gauge field. \nWe start with the Bloch Hamiltonian of the promoted 3D chiral HOTI (Eq.~(\\ref{eq:lattice_model_chiral_sliding}) with $q_{x} = q_{y} = 0$) expanded around the $\\Gamma$ point\\cite{pozo2019quantization}, \n\\begin{align}\n H_{\\text{bulk}} = m_{\\text{bulk}} \\tau_{z}\\sigma_{0} + \\tau_{x}\\vec{p} \\cdot \\vec{\\sigma} + \\tau_{0} \\vec{M} \\cdot \\vec{\\sigma}. \\label{eq:3D_chiral_HOTO_bulk}\n\\end{align}\nWe have defined several parameters to make Eq.~(\\ref{eq:3D_chiral_HOTO_bulk}) compact for later convenience, and introduced $\\tau_{0} \\vec{M} \\cdot \\vec{\\sigma}$ where $\\vec{M} = (M,M,M)$ corresponding to the ferromagnetic potential in Eq.~(\\ref{eq:chiral_on_site}). \nWe now couple this $H_{\\text{bulk}}$ to $\\vec{A} = By \\hat{z}$, which is equivalent to Eq.~(\\ref{A_U1}) with $q_{x} = 0$. \nThis can be done via the minimal substitution $p_{z} \\to p_{z} + By$. \nFourier transforming along $x$ and $z$ to replace $p_{x}$ and $p_{z}$ by wavenumbers $k_{x}$ and $k_{z}$, and defining the $k_{z}$-dependent ladder operator as\n\\begin{align}\n a^{\\dagger}_{k_{z}} = \\frac{1}{\\sqrt{2B}}\\left( k_{z} + By - ip_{y} \\right), \\label{eq:3D_chiral_bulk_U1_ladder}\n\\end{align}\nwe can rewrite Eq.~(\\ref{eq:3D_chiral_HOTO_bulk}) coupled to $\\vec{A} = By \\hat{z}$ in terms of $a_{k_{z}}$ and $a^{\\dagger}_{k_{z}}$ as\n\\begin{widetext}\n\\begin{align}\n H_{\\text{bulk}}(k_{x},k_{z}) = m_{\\text{bulk}} \\tau_{z}\\sigma_{0} + \\tau_{x} \\begin{bmatrix}\n \\sqrt{\\frac{B}{2}} \\left(a_{k_{z}}+a^{\\dagger}_{k_{z}} \\right) & k_{x} -\\sqrt{\\frac{B}{2}} \\left(a_{k_{z}}-a^{\\dagger}_{k_{z}} \\right) \\\\\n k_{x} +\\sqrt{\\frac{B}{2}} \\left(a_{k_{z}}-a^{\\dagger}_{k_{z}} \\right) & -\\sqrt{\\frac{B}{2}} \\left(a_{k_{z}}+a^{\\dagger}_{k_{z}} \\right)\n \\end{bmatrix} + \\tau_{0} \\vec{M} \\cdot \\vec{\\sigma}. \\label{eq:3D_bulk_LL_Hamiltonian}\n\\end{align}\n\\end{widetext}\nWe have numerically shown in SM\\cite{SM} that the effective theory in Eq.~(\\ref{eq:3D_bulk_LL_Hamiltonian}) captures several properties of the flat bulk bands in Fig.~\\ref{fig:chiral_sliding_low_energy_main_text_temp} (b) with relatively small $q_{y} = 0.02$, such as energy asymmetry with respect to $E = 0$ and the confinement direction of the bulk states due to $U(1)$ Landau quantization.\n\nFrom the above analysis on corner-, edge-, and bulk-confined modes, we conclude that we can characterize this topological 2D modulated system with chiral sliding modes in terms of a 3D chiral HOTI coupled to a $U(1)$ gauge field. \nIn addition, such 2D modulated systems provide a platform to examine the properties of a 3D chiral HOTI, by sliding the DW order parameter $\\phi$.\n\n\n\n\\section{\\label{sec_helical_HOTI_sliding_modes}Helical Higher-Order Sliding Modes and $SU(2)$ Gauge Fields }\n\n\n\n\nNext, we will generalize our formalism to time-reversal invariant spinful systems. \nIn doing so, we will see that incommensurate modulations induce coupling to $SU(2)$ gauge fields in the dimensionally promoted models.\n$SU(2)$ gauge fields can be used to represent spin-orbit coupling\\cite{YiLi_SU2_Hofstadter}, which is ubiquitous in topological states of matter. \nFor example, $SU(2)$ gauge fields in 3D and 4D generates $SU(2)$ LLs that give rise to 3D TIs and 4D QHEs\\cite{YiLi_TI_SU2_LL,zhang2001four}. \nA non-Abelian $SU(2)$ Peierls phase in 2D and 3D lattices can also lead to 2D and 3D TIs\\cite{SU2_gauge_in_2D_Goldman,YiLi_SU2_Hofstadter}. \nIn addition, in response to a bulk $SU(2)$ gauge flux insertion, a 2D TI can bind various quasi-particle excitations such as spinons, holons and chargeons\\cite{qispincharge}. \nIn this section, we present a 2D modulated system that allows us to simulate a 3D helical HOTI coupled to an $SU(2)$ gauge field.\n\n\\begin{figure}[t]\n\\hspace{-0.5cm}\n\\includegraphics[width=\\columnwidth]{fig_main_text_helical_sliding_revised_change_ticking_freq.pdf}\n\\caption{(a) $\\phi$-sliding spectrum of the 2D helical model in Eq.~(\\ref{eq:H_helical_sliding}) with parameters given in the text. \n(b) Summation of the probability density of the doubly-degenerate corners modes at $\\phi = 0.4\\pi$ and $E=0.0146$. \nThe two corner modes at the same $\\phi$ are related to each other by the $\\mathcal{I}\\mathcal{T}$-symmetry, and hence they are localized at inversion-related corners and have opposite spins. \n(c) $\\&$ (d) Probability distribution of edge and bulk modes at $\\phi = 0.9\\pi$ and $E = -0.1459$ and $E = 0.5227$, respectively. \nThe darker (black) color in (b)--(d) implies higher probability density. \nIn (b), (c) and (d), the $x$- and $z$-coordinate both range from $-15 ,\\ldots, +15$.}\n\\label{fig:helical_sliding_main_text}\n\\end{figure}\n\n\\subsection{Dimensionally Promoted Helical Model}\n\nWe start by considering the following 2D Hamiltonian on a square lattice with one modulated on-site potential $[V(\\vec{q},\\vec{n},\\phi)]$:\n\\begin{equation}\n\\begin{aligned}\nH = {} & \\sum_{\\vec{n}} \\psi^{\\dagger}_{\\vec{n}+\\hat{x}} [H_{+\\hat{x}}]\\psi_{\\vec{n}} + \\psi^{\\dagger}_{\\vec{n}+\\hat{z}} [H_{+\\hat{z}}]\\psi_{\\vec{n}} + \\text{h.c.}\\\\\n&+ \\sum_{\\vec{n}} \\psi^{\\dagger}_{\\vec{n}}\\left( [H_{\\text{on-site}}] + [V(\\vec{q},\\vec{n},\\phi)] \\right)\\psi_{\\vec{n}}, \\label{eq:H_helical_sliding}\n\\end{aligned}\n\\end{equation}\nwhere the unmodulated couplings are are\n\\begin{align}\n & [H_{+\\hat{x}}] = \\frac{v_{x}}{2}\\tau_{z}\\mu_{0}\\sigma_{0} - \\frac{u_{x}}{2i}\\tau_{y}\\mu_{y}\\sigma_{0}, \\\\\n & [H_{+\\hat{z}}] = \\frac{v_{z}}{2}\\tau_{z}\\mu_{0}\\sigma_{0} - \\frac{u_{z}}{2i}\\tau_{x}\\mu_{0}\\sigma_{0}, \\\\\n & [H_{\\text{on-site}}] = m_{1}\\tau_{z}\\mu_{0}\\sigma_{0}+m_{2}\\tau_{z}\\mu_{x}\\sigma_{0} + m_{3}\\tau_{z}\\mu_{z}\\sigma_{0} \\nonumber \\\\ \n & +m_{v_{1}}\\tau_{0}\\mu_{z}\\sigma_{0} + m_{v_{2}}\\tau_{0}\\mu_{x}\\sigma_{0}. \\label{eq:helical_H_on_site}\n\\end{align}\nThe matrices $\\vec{\\tau}$, $\\vec{\\mu}$ and $\\vec{\\sigma}$, are Pauli matrices and denote the orbital, sub-lattice and spin degrees of\nfreedom, respectively. \nThe hopping matrices $[H_{+\\hat{x}}]$ and $[H_{+\\hat{z}}]$, together with the on-site potential $[H_{\\text{on-site}}]$ respect both inversion and TR symmetries. \nThe inversion and TR operations are represented by $\\tau_{z}$ and $i\\tau_{z}\\sigma_{y}\\mathcal{K}$, respectively\\cite{Wieder_spin_decoupled_helical_HOTI}. \nThese hoppings give rise to low energy four-component Dirac fermions in each spin subspace, realizing a topological critical point. \nThe modulated on-site energy is given by\n\\begin{eqnarray}\\nonumber\n[V(\\vec{q},\\vec{n},\\phi)]&=& v_{y}\\tau_{z}\\mu_{0}\\begin{bmatrix}\n \\cos\\theta^{+}_{\\vec{q},\\vec{n},\\phi} & 0 \\\\ 0 & \\cos\\theta^{-}_{\\vec{q},\\vec{n},\\phi}\n \\end{bmatrix}\\\\\n&& + v_{H} \\tau_{y}\\mu_{z}\\begin{bmatrix}\n \\sin\\theta^{+}_{\\vec{q},\\vec{n},\\phi} & 0 \\\\ 0 & \\sin\\theta^{-}_{\\vec{q},\\vec{n},\\phi}\n \\end{bmatrix},\n \\label{eq:helical_sliding_modulation_H}\n\\end{eqnarray}\nwhere $\\theta^{\\pm}_{\\vec{q},\\vec{n},\\phi} = 2\\pi \\vec{q}\\cdot \\vec{n}\\pm\\phi$, $\\vec{q} = (q_{x},q_{z})$ is the modulation wave vector in 2D, $\\vec{n} \\in \\mathbb{Z}^{2}$ is the lattice position, and $\\phi$ is the sliding phase.\nThe first term in Eq.~(\\ref{eq:helical_sliding_modulation_H}) modulates the mass $m_{1}\\tau_{z}\\mu_{0}\\sigma_{0}$ in Eq.~(\\ref{eq:helical_H_on_site}), which may represent unequal on-site energy for $s$ and $p$ orbitals, with forward ($-\\phi$) and backward ($+\\phi$) sliding phase in each spin subspace\\cite{SU2_gauge_in_2D_Goldman,SU2_gauge_2D_to_1D_Goldman}. \nThe second term in Eq.~(\\ref{eq:helical_sliding_modulation_H}) describes a modulation of the on-site energy which mixes $s$ and $p$ orbitals with unequal strength for different sublattices. \nSimilarly, we have forward and backward sliding phases in different spin subspaces for the second term. \nSince the modulation in Eq.~(\\ref{eq:helical_sliding_modulation_H}) has opposite phase offsets in each spin subspace, it may be induced from spin-orbit coupled spin ordering. \nThis modulation is TR- and inversion-symmetric only when $\\phi=0$, $\\pi$. \nNote, however, that the product of inversion and TR symmetry, which we will denote $\\mathcal{I}\\mathcal{T}$-symmetry, is preserved for all values of $\\phi$.\nIf we denote the third, synthetic dimension as $y$, this 2D model is equivalent to the inversion and TR symmetric 3D helical HOTI model of Ref.~\\onlinecite{Wieder_spin_decoupled_helical_HOTI}, coupled to an $SU(2)$ gauge field given by\n\\begin{align}\n \\vec{A} = (0,2\\pi (q_{x}x+q_{z}z)\\sigma_{z},0). \\label{SU2_A}\n\\end{align}\nThis matrix-valued $\\vec{A}$ produces a constant $SU(2)$ magnetic field\\cite{Estienne_2011} $\\vec{B} = \\vec{\\nabla} \\cross \\vec{A} - i \\vec{A} \\cross \\vec{A}$, determined from the field strength\\cite{eguchi1980gravitation} $F_{\\mu \\nu} = \\partial_{\\mu} A_{\\nu} - \\partial_{\\nu} A_{\\mu} - i \\left[ A_{\\mu},A_{\\nu}\\right]$, and given by\n\\begin{align}\n \\vec{B} = (-2\\pi q_{z}\\sigma_{z},0,2\\pi q_{x} \\sigma_{z} ). \\label{SU2_B}\n\\end{align}\nThis constant $SU(2)$ field strength preserves both inversion and TR symmetry in 3D, up to a spin-dependent gauge transformation (see SM\\cite{SM}). \nNotice that Eq.~(\\ref{SU2_B}) implies that the $SU(2)$ magnetic field in this example can be interpreted as a $U(1)$ magnetic field with opposite sign for spin-up and spin-down electrons\\cite{SU2_gauge_in_2D_Goldman,SU2_gauge_2D_to_1D_Goldman}. \nWe then expect that, for a suitable choice of parameters such that the $SU(2)$ gauge field does not close the bulk gap in 3D, the insulating ground-state will be in the same non-trivial helical HOTI phase as the model with $\\vec{q}=0$\\cite{khalaf,hotis,Po2017}.\nTherefore, in 3D, our promoted model will support an odd number of pairs of sample-encircling helical hinge modes respecting inversion and TR symmetries\\cite{khalaf,Wieder_spin_decoupled_helical_HOTI}.\nUpon projected back to 2D, the helical hinge modes in 3D become $\\mathcal{I}\\mathcal{T}$-related pairs of corner modes at the same $\\phi$ in the 2D modulated system. \nIn the SM\\cite{SM}, we give the form of the 3D dimensionally-promoted model in position-space. \n\n\\subsection{Calculation of the Spectrum}\n\nLet us now numerically verify these conclusions. \nFig.~\\ref{fig:helical_sliding_main_text} (a) shows the $\\phi$-sliding spectrum of Eq.~(\\ref{eq:H_helical_sliding}) with parameters $m_{1} = -3$, $m_{2} = 0.3$, $m_{3} = 0.2$, $m_{v_{1}} = -0.4$, $m_{v_{2}} = 0.2$, $v_{x}=v_{z}=u_{x}=u_{z} = 1$, $v_{y} = 2$, $v_{H} = 1.2$\\cite{Wieder_spin_decoupled_helical_HOTI}, and $\\vec{q} = (0,q_{z})$ where $q_{z} = 0.11957$ \\cite{shi2019charge}. \nThe system size is $31 \\times 31$. \nThere are doubly-degenerate pairs of states which cross the gap as a function of $\\phi$, where the degeneracy is protected by $\\mathcal{IT}$-symmetry. \nWe see from the wave functions that these are corner modes related by $\\mathcal{IT}$-symmetry, as shown in Fig.~\\ref{fig:helical_sliding_main_text} (b) for the branch with negative slope around $\\phi \\approx 0.4\\pi$. \nIn the other branch of doubly-degenerate gap-crossing states with positive slope, the corner modes are the inversion-symmetric counterpart (where recall that inversion symmetry leaves spin invariant) to those in Fig.~\\ref{fig:helical_sliding_main_text} (b). \nTherefore, as $\\phi$ slides from $0$ to $2\\pi$, this model realizes a $\\mathbb{Z}_{2}$ pump\\cite{fu2006time,teo2010topological} as one of the pairs of corner states will merge into the occupied state subspace (with Fermi level $E_{F}=0$) while the other pair will flow out. \nIn our specific examples, the two states in each $\\mathcal{I}\\mathcal{T}$-related pair at the same $\\phi$ are spin eigenstates and therefore in this case the $\\mathbb{Z}_2$ pump is a spin pump; our conclusions, however, hold even when spin is not conserved.\n\n\nAs mentioned earlier, and in analogy with our chiral HOTI model, the corner modes here are equivalent to hinge modes along $y$ in 3D. \nThe corresponding low energy theory for these corner modes is \n\\begin{align}\n H_{\\text{corner}}= v_{F} \\left( \\phi \\sigma_{z}' + 2\\pi \\left( q_{x}x_{\\text{corner}} + q_{z}z_{\\text{corner}} \\right) \\sigma_{0}' \\right), \\label{eq:H_helical_hinge_1}\n\\end{align}\nwhere $v_{F}$ is the group velocity of the hinge modes in 3D. \nWe use the Pauli matrices $\\vec{\\sigma}'$ to denote the effective basis where in each subspace the states have opposite spin together with some orbital and sub-lattice textures. \nWe have assumed without loss of generality that there is only one pair of helical hinge modes at the hinge along $y$ in the promoted 3D system. \nBy denoting $\\phi$ as $k_{y}$, which is the crystal momentum along $y$, we recognize Eq.~(\\ref{eq:H_helical_hinge_1}) as the hinge mode dispersion $H(k_{y}) = v_{F}k_{y}\\sigma_{z}'$ in 3D minimally coupled to an $SU(2)$ gauge field described by Eq.~(\\ref{SU2_A}).\nSimilar to Sec.~\\ref{sec_chiral_HOTI}, as we vary $\\vec{q}$--which is equivalent to changing the strength and (spatial) direction of the $SU(2)$ gauge field in Eqs.~(\\ref{SU2_A}) and (\\ref{SU2_B})--the dispersion of the spin-polarized corner modes will shift along the $\\phi$-axis. \nIn the SM\\cite{SM} we present a complete low energy theory analysis for the corner modes with the same structure as Sec.~\\ref{sec_chiral_HOTI}.\n\nIn addition, we show in Figs.~\\ref{fig:helical_sliding_main_text} (c) and (d) the probability density for the edge- and bulk-confined modes in the flat bands of Fig.~\\ref{fig:helical_sliding_main_text} (a). \nSimilar to the corner modes, these can be respectively understood in terms of 3D low energy surface and bulk theories minimally coupled to an $SU(2)$ gauge field, leading to an $SU(2)$ Landau quantization\\cite{YiLi_TI_SU2_LL,YiLi_SU2_Hofstadter}. \nThe relevant surface theory describes a time-reversed pair of Chern insulators. \nThe relevant bulk theory is the $\\vec{k}\\cdot\\vec{p}$ expansion around $\\Gamma$ of the promoted 3D helical HOTI Hamiltonian\\cite{Wieder_spin_decoupled_helical_HOTI}. \nWe provide further details in the SM\\cite{SM}.\nTogether with the corner mode analysis, we see that this topological 2D modulated system with helical sliding modes can be characterized by a 3D lattice model coupled to an $SU(2)$ gauge field. \nIn addition, we have shown how 2D modulated systems can provide a platform to examine $SU(2)$ gauge physics in higher dimensions, by sliding the phase $\\phi$ of the DW order parameter.\n\n\n\\section{\\label{sec:Weyl_CDW}Weyl-CDWs and 4D topological modes}\n\n\nAs a final demonstration of our dimensional promotion formalism and its utility to investigating physics in more than 3D, we consider the mean-field state of a correlated inversion-symmetric 3D Weyl semimetal with CDW distortion (Weyl-CDW)\\cite{dynamical_axion_insulator_BB,gooth2019evidence,shi2019charge,wang2013chiral,CDW_Weyl_Sehayek,CDW_in_Q1D_Cohn,Monopole_CDW_in_Weyl_Yi_Li,yu2020dynamical}. \nIt has been shown that such a system can realize various topological phases. \nDepending on the phase $\\phi$ of the CDW order parameter, the system can interpolate between quantum anomalous Hall (QAH) and {\\it obstructed} QAH (oQAH) phase\\cite{dynamical_axion_insulator_BB}. \nThis is due to the $\\pi$ mod $2\\pi$ axion angle difference $\\delta \\theta_{\\phi} = \\theta(\\phi = \\pi) - \\theta(\\phi = 0)$ for the system with $\\phi = 0$ and $\\phi = \\pi$, in the thermodynamic limit. \nPhysically, this leads to a Hall conductance difference\n\\begin{align}\n \\left|G_{xy}(\\phi = \\pi) - G_{xy}(\\phi = 0) \\right| = e^{2}\/h \\text{ mod } 2e^{2}\/h \\label{eq:main_G_xy_eqn}\n\\end{align}\nfor a semi-infinite slab [see also Eq.~(\\ref{eq:agnostic}) below, as well as Refs.~\\onlinecite{olsen2020gapless,2020_Axion_coupling_Vanderbilt}]. \nIn this section, we analyze a minimal model of a 3D inversion-symmetric magnetic Weyl-CDW system, which admits a dimensional promotion to 4D with a $U(1)$ gauge field. \nWe will explain the origin of the background QAH response and the interpolation between QAH and oQAH phases using the corresponding 4D theory. \nIn the following, we will denote a sample infinite along $x$ and $y$ with finite thickness $L_z$ along the $z$ direction as an $xy$-slab. \nSimilarly, we will use the term $y$-rod to denote a sample infinite along $y$ and finite along $x$ and $z$ with size $L_{x}\\times L_{z}$.\n\n\n\\subsection{3D Weyl-CDW Model and Dimensional Promotion}\n\nTo begin, we consider electrons on a 3D cubic-lattice with Hamiltonian $H = H_{0} + H_{CDW}(\\phi) $. \nHere $H_0$ is a periodic tight-binding Hamiltonian given by\n\\begin{align}\n H_0&=\\left(\\sum_{\\vec{n}}\\left[-it_x\\psi^\\dag_{\\vec{n}+\\hat{x}}\\sigma_x \\psi_{\\vec{n}}-it_y \\psi^\\dag_{\\vec{n}+\\hat{y}}\\sigma_y \\psi_{\\vec{n}}+t_z\\psi^\\dag_{\\vec{n}+\\hat{z}}\\sigma_z \\psi_{\\vec{n}}\\right]\\right. \\nonumber \\\\\n &+\\sum_{\\vec{n}}\\frac{m}{2}\\left(\\psi^\\dag_{\\vec{n}+\\hat{x}}\\sigma_z \\psi_{\\vec{n}} + \\psi^\\dag_{\\vec{n}+\\hat{y}}\\sigma_z \\psi_{\\vec{n}} -2 \\psi^\\dag_{\\vec{n}}\\sigma_z \\psi_{\\vec{n}} \\right) \\nonumber \\\\\n &\\left.-\\sum_{\\vec{n}} t_z \\left(\\cos (\\pi q)\\right) \\psi^\\dag_{\\vec{n}}\\sigma_z c_{\\vec{n}}\\right) +\\mathrm{h.c.}\n\\end{align}\nin position space. \nThe corresponding Bloch Hamiltonian is\n\\begin{align}\n &H_0(\\vec{k})= -2[t_x\\sin (k_x)\\sigma_x +t_y\\sin (k_y)\\sigma_y] \\nonumber \\\\\n & -m[2-\\cos (k_x) - \\cos (k_y)]\\sigma_{z} +2t_z[\\cos (k_z) -\\cos (\\pi q)]\\sigma_{z} \\label{eq:Bloch_H_0} , \n\\end{align}\nwith $m\/2 \\ge t_{x},-t_{y},t_{z} >0$. \nWe take for the on-site modulation\n\\begin{align}\n H_{CDW}(\\phi)&=2|\\Delta|\\sum_{\\vec{n}}\\cos(2\\pi q n_{z}+\\phi)\\psi^\\dag_{\\vec{n}}\\sigma_z \\psi_{\\vec{n}}.\n\\end{align}\nHere $2|\\Delta|$ is the strength of the CDW modulation, $2\\pi q$ is the magnitude of the modulation wave vector $2\\pi \\vec{q}=(0,0,2\\pi q)$ and $\\phi$ is the phase of CDW order parameter. \nWe again use $\\vec{\\sigma}$ to denote the Pauli matrices, which here index an orbital degree of freedom. \nThe inversion and TR operation are represented by $\\sigma_{z}$ and $\\mathcal{K}$, respectively (note that this is a model of spinless electrons).\nThe Hamiltonian $H_{0}$ then describes a TR-breaking, inversion-symmetric magnetic Weyl semimetal (WSM) with Weyl nodes at $\\vec{k} = (0,0,\\pm \\pi q)$, see Eq.~(\\ref{eq:Bloch_H_0})\\cite{mccormick2017minimal}.\nThe perturbation $H_{CDW}(\\phi)$ is the CDW modulation that couples these two Weyl nodes and opens a gap in the bulk spectrum~\\cite{dynamical_axion_insulator_BB}. \nNote that in this simple model, we have chosen the modulation wavevector to be exactly equal to the Weyl node separation vector for simplicity of analysis. \nEven though the bulk is gapped, the surface of this 3D Weyl-CDW is gapless, due to the presence of QAH surface states. \nIn Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} (a) we show the $\\phi$-sliding spectrum for a $y$-rod of $H_{0}+H_{CDW}(\\phi)$ at $k_{y} = 0$ with size $L_{x} \\times L_{z} = 25 \\times 25$, $t_{x} = - t_{y} = t_{z} = 1$, $m = 2$, $2|\\Delta|=0.75$ and $q = 1\/5$. \nThis corresponds to a commensurate Weyl-CDW system. \nThe mid-gap zero modes in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} (a) correspond to the QAH surface states. \nIn Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} (b) we show the probability distribution of the 10 zero modes at $\\phi = 0$. \nTogether with Wilson loop and Berry curvature calculation in the SM\\cite{SM}, we verify that the corresponding $xy$-slab with $L_{z} = 25$ carries a slab Hall conductance $ G_{xy}(\\phi = 0) = -5 e^{2}\/h$. \nWe can then identify the weak Chern number\\cite{qi2008topological,kohmoto1992diophantine,halperin1987possible,fukanemele} of the 3D periodic system with $5=25\/5$ unit cells (since $q= 1\/5$) as $\\nu_{z} = -1$.\n\n\n\\begin{figure*}[t]\n\\hspace{-0.5cm}\n\\includegraphics[width=\\linewidth]{Q_2pi_over_5_tx_1_ty_-1_tz_1_m_2_D_0_75_Lz_25.pdf}\n\\caption{(a) $\\phi$-sliding spectrum of the Weyl-CDW model in a $y$-rod geometry at $k_{y} = 0$ with size $L_{x}\\times L_{z} = 25 \\times 25$, $t_{x} = -t_{y} = t_{z} = 1$, $m = 2$, $2|\\Delta| = 0.75$ and $q = 1\/5$. \n(b) The average probability distribution of the 10 zero modes at $\\phi = 0$ in (a). \nThese zero modes correspond to QAH surface states. \n(c) The average probability distribution of the 5 non-trivial states at $\\vec{k} = \\Gamma$ of the $xy$-slab at $\\phi = 0$, which in total lead to $G_{xy}(\\phi = 0) = -5 e^{2}\/h$. \n(d) The average probability distribution of the 8 zero modes at $\\phi = \\pi$ in (a). \nThese zero modes correspond to QAH surface states. \n(e) The average probability distribution of the 4 non-trivial states at $\\vec{k} = \\Gamma$ of the $xy$-slab at $\\phi = \\pi$, which in total lead to $G_{xy}(\\phi = \\pi) = -4 e^{2}\/h$. \nThe darker (black) color in (b)--(e) implies higher probability density. \nIn (b) and (d), the $x$- and $z$-coordinate both range from $-12 ,\\ldots, +12$. \nIn (c) and (e), the $z$-coordinate ranges from $-12 ,\\ldots, +12$.}\n\\label{fig:temporary_3d_weyl_cdw_fig}\n\\end{figure*}\n\n\n\nAs in Sec.~\\ref{sec_chiral_HOTI} and~\\ref{sec_helical_HOTI_sliding_modes}, we identify $\\phi$ with the crystal momentum $k_{w}$ along a fourth, synthetic direction denoted by $w$. \nUsing the dimensional promotion procedure in Sec.~\\ref{sec_Dimension_promotion}, we can promote $H_{0} + H_{CDW}(\\phi)$ to a 4D nodal line system coupled to a $U(1)$ gauge field. \nIn the SM\\cite{SM} we give the explicit form of the promoted model in 4D position space. \nThe corresponding 4D nodal line system (with $q=0$) has a Bloch Hamiltonian\n\\begin{align}\n &H(\\vec{k})= -2[t_x\\sin (k_x)\\sigma_x +t_y\\sin (k_y)\\sigma_y] + 2|\\Delta|\\cos{(k_{w})}\\sigma_{z} \\nonumber \\\\\n & -m[2-\\cos (k_x) - \\cos (k_y)]\\sigma_{z} +2t_z[\\cos (k_z) -\\cos (\\pi q)]\\sigma_{z}. \n\\label{eq:ham0}\n\\end{align}\nThe spectrum of this Hamiltonian features nodal lines at $k_{x}=k_{y} = 0$ defined by the implicit equation\n\\begin{align}\n t_{z}\\cos{(k_{z})} + |\\Delta| \\cos{(k_{w})} = t_{z} \\cos{(\\pi q)}.\n\\end{align}\nAccording to Eq.~(\\ref{eq:expression_A}), we then couple this Hamiltonian to a 4D $U(1)$ gauge field given by\n\\begin{align}\n \\vec{A} = (0,0,0,2\\pi q z), \\label{eq:U1_4D}\n\\end{align}\nsince $2\\pi \\vec{q} = 2\\pi q \\hat{z}$ in this system. \nThis $\\vec{A}$ only produces non-zero field strength threading the $zw$ plane,\n\\begin{align}\n F_{zw} = -F_{wz}= \\partial_{z}A_{w} - \\partial_{w}A_{z}=2 \\pi q, \\label{eq:Fzw}\n\\end{align}\nwhere all other components of $F_{\\mu \\nu} = \\partial_{\\mu} A_{\\nu} - \\partial_{\\nu} A_{\\mu}$ are zero. \nWe are now in a position to reinterpret the existence of a background QAH response and QAH surface states when the bulk gap is opened due to the CDW. \nWe will see how these features emerge from the low energy approximation for this 4D system minimally coupled to Eq.~(\\ref{eq:U1_4D}).\n\n\\subsection{Low Energy Theory Analysis}\n\n\\begin{widetext}\n\nWe start from the 4D Bloch Hamiltonian in Eq.~(\\ref{eq:ham0}). \nExpanding around $\\vec{k} = \\vec{0}$, we have\n\\begin{align}\n H(\\vec{k}) \\approx -2[t_x k_{x} \\sigma_{x} +t_y k_{y} \\sigma_{y}] + 2t_{z} \\left( 1 - \\frac{k_{z}^{2}}{2} - \\cos (\\pi q) \\right)\\sigma_{z} + 2|\\Delta| \\left( 1 - \\frac{k_{w}^{2}}{2}\\right) \\sigma_{z}. \\label{eq:ham0_low_energy}\n\\end{align}\nThe nodal line in this low energy theory is an ellipse in the $k_{z}$-$k_{w}$ plane with $k_{x}=k_{y}=0$, defined by\n\\begin{align}\n t_{z}k_{z}^{2} + |\\Delta|k_{w}^{2} = 2t_{z}\\left[ 1 - \\cos{ (\\pi q)} \\right] + 2|\\Delta| > 0.\n\\end{align}\nReplacing the 4D wave vector $\\vec{k}=(k_{x},k_{y},k_{z},k_{w})$ by the 4D momentum operator $\\vec{p}=(p_{x},p_{y},p_{z},p_{w})$ using the so-called envelope function approximation\\cite{Envelope_function_approximation_1,Envelope_function_approximation_2,Envelope_function_approximation_3,Envelope_function_approximation_4,Envelope_function_approximation_5,Qi_Zhang_RMP,hasan2010colloquium,bernevig2006quantum}, the Hamiltonian governing the low energy dynamics reads\n\\begin{align}\n H = -2[t_x p_{x} \\sigma_{x} +t_y p_{y} \\sigma_{y}] + 2t_{z} \\left( 1 - \\frac{p_{z}^{2}}{2} - \\cos (\\pi q)\\right) \\sigma_{z} + 2|\\Delta| \\left( 1 - \\frac{p_{w}^{2}}{2}\\right) \\sigma_{z}. \\label{eq:ham0_low_energy_p}\n\\end{align}\nNext, let us minimally couple Eq.~(\\ref{eq:ham0_low_energy_p}) to a {{4D}} $U(1)$ gauge field $\\vec{A} = (0,0,0,2\\pi qz)$ via a Peierls substitution such that $p_{w} \\to p_{w} + 2\\pi q z$. \nEq.~(\\ref{eq:ham0_low_energy_p}) then becomes\n\\begin{align}\n H = -2[t_x p_{x} \\sigma_{x} +t_y p_{y} \\sigma_{y}] + 2 \\left( t_{z}\\left[ 1 - \\cos{(\\pi q)} \\right] + |\\Delta| \\right) \\sigma_{z} - \\left( t_{z}p_{z}^{2} +|\\Delta| \\left( p_{w} + 2 \\pi q z \\right)^{2} \\right) \\sigma_{z}, \\label{eq:after_4D_U1_couple}\n\\end{align}\nwhere we have assumed that the particle carries $-1$ charge. \nFourier transforming along $x$, $y$ and $w$, we may replace $p_{x}$, $p_{y}$ and $p_{w}$ by the corresponding wavenumbers $k_{x}$, $k_{y}$, $k_{w}$, such that\n\\begin{align}\n H(k_{x},k_{y},k_{w}) = -2[t_x k_{x} \\sigma_{x} +t_y k_{y} \\sigma_{y}] +2 \\left( t_{z}\\left[ 1 - \\cos{(\\pi q)} \\right] + |\\Delta| \\right) \\sigma_{z} - \\left( t_{z}p_{z}^{2} + |\\Delta| \\left( k_{w} + 2\\pi q z \\right)^{2} \\right) \\sigma_{z}. \\label{eq:FT1_4D_U1_LL}\n\\end{align}\n\\end{widetext}\n\nNotice that the coefficient of $\\sigma_z$ in the final term in the Hamiltonian,\n\\begin{align}\n t_{z}p_{z}^{2} + |\\Delta| \\left( k_{w} + 2 \\pi q z \\right)^{2}, \\label{eq:4D_U1_SHO_H}\n\\end{align}\nis an SHO Hamiltonian along $z$ which can be diagonalized as\n\\begin{align}\n 4 \\pi q \\sqrt{t_{z}|\\Delta|} \\left( n + \\frac{1}{2} \\right).\n\\end{align}\nHere $n$ is a non-negative integer and the eigenvalue of the number operator $ a^{\\dagger}_{k_{w},q} a_{k_{w},q}$ with\n\\begin{align}\n a^{\\dag}_{k_{w},q} = \\frac{1}{\\sqrt{ 4\\pi q}} \\left( \\frac{t_{z}}{|\\Delta|} \\right)^{\\frac{1}{4}}\\left[ \\left({\\frac{|\\Delta|}{t_{z}}}\\right)^{\\frac{1}{2}} \\left(k_{w}+ 2\\pi q z \\right) - ip_{z} \\right]. \\label{eq:4D_U1_LL_ladder}\n\\end{align}\nThe quantum number $n$ is the 4D $U(1)$ LL index. \nBy restricting to a subspace of the full Hilbert space with fixed $n$ and $k_{w}$, we see that the 4D low energy Hamiltonian Eq.~(\\ref{eq:after_4D_U1_couple}) may be decomposed into a direct sum of 2D low energy Chern insulators (CIs) in $xy$-plane parameterized by $n$ and $k_{w}$.\nThe Hamiltonian for these 2D CIs is given by\n\\begin{align}\n H_{\\text{2D CI}}(n,k_{w}) = -2[t_x p_{x} \\sigma_{x} +t_y p_{y} \\sigma_{y}] + 2 m \\sigma_{z}, \\label{eq:2D_CI_subspace}\n\\end{align}\nwhere\n\\begin{align}\n m = t_{z}\\left(1 - \\cos{(\\pi q)} \\right)+ |\\Delta| - 2\\pi q \\sqrt{t_{z}|\\Delta|}\\left( n + \\frac{1}{2} \\right). \\label{eq:mass_term_emerge_CIs}\n\\end{align}\nSince we have restricted to the subspace with fixed $n$ and $k_{w}$ in Eq.~(\\ref{eq:2D_CI_subspace}), according to Eq.~(\\ref{eq:4D_U1_SHO_H}) the wave function along $z$ and $w$ will be SHO eigenstates centered at $z = -k_{w} \/ (2\\pi q)$ multiplied by a plane wave $e^{ik_{w} w}$. \nNotice that the $k_{w}$-dependence of Eq.~(\\ref{eq:2D_CI_subspace}) is due to the integer $n$ in Eq.~(\\ref{eq:mass_term_emerge_CIs}) which is an eigenvalue of the number operator $a_{k_{w},q}^{\\dagger}a_{k_{w},q}$. \nTherefore, the eigenstates in the low energy approximation take the form of plane waves in $w$, and Chern insulator eigenstates as a function of $(x,y)$ localized at different constant-$z$ planes for different $k_{w}$.\nThis provides a four-dimensional interpretation of the layer construction of the Weyl-CDW presented in Refs.~\\onlinecite{dynamical_axion_insulator_BB,CDW_Weyl_Sehayek}.\n\nAs in a 3D nodal ring system with a perpendicular magnetic field~\\cite{Nodal_ring_perpendicular_B}, Eq.~(\\ref{eq:2D_CI_subspace}) can yield a gapped 4D bulk spectrum provided that $m \\ne 0$ $\\forall n \\ge 0$. \nThis insulating ground state will then carry non-trivial topology inherited from the nodal line system, since in Eq.~(\\ref{eq:2D_CI_subspace}) we found that the gapped 4D continuum theory is composed of low energy 2D CIs. \nWe then expect that there will be CI layers in the $xy$-plane of the corresponding 4D lattice model (see SM\\cite{SM}). \nThe CI layers will also be separated along $z$ by $2\\pi \/ (2\\pi q) = 1\/q$ for a fixed $k_{w}$, due to the $2\\pi$ periodicity of $k_{w}$ in the lattice model. \nIn our current example this separation is $5$ since $q =1\/5$. \nNotice that $k_{w}$ is now interpreted as the crystal momentum along the $4^{\\text{th}}$ dimension. \nTo connect these observation in 4D back to the physical 3D Weyl-CDW system with Hamiltonian $H_{0} + H_{CDW}(\\phi)$, we notice that each 3D Weyl-CDW system with a fixed $\\phi$ corresponds to the 4D theory with a fixed $k_{w}$. \nFocusing on the $xy$-slab with $\\phi = 0$ and thickness $L_{z} = 25$, in which $ G_{xy}(\\phi = 0) = -5 e^{2}\/h$, we show wavefunctions corresponding to the only $5$ layers of non-trivial CIs separated from each other by $5$ lattice constants along $z$ in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} (c). \nEach of these CI layers carries Chern number $C = -1$ and contributes one chiral edge mode along $y$ in the ${y}$-rod, shown in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} (b). \nIn the SM\\cite{SM} we provide technical details on identifying the non-trivial CI layers using hybrid Wannier function, Berry phases and Berry curvature calculations for an $xy$-slab. \nWe can thus regard the CDW-induced gap opening and the existence of background QAH response as the results of $U(1)$ Landau quantization in the 4D nodal line system. \n\n\nNext, we address the interpolation between the QAH phase at $\\phi = k_w=0$ and the oQAH phase at $\\phi = k_w=\\pi$ using the 4D theory. \nBefore we turn to the 4D low energy theory, we begin with the observation that in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} (a), the number of mid-gap zero modes corresponding to QAH surface states decreases by $2$ as $\\phi$ slides from $0$ to $\\pi$; one state is lowered into the valence band, while one state is elevated to the conduction band.\nThis is consistent with the change in Hall conductance Eq.~(\\ref{eq:main_G_xy_eqn}), which is derived in the thermodynamic limit where the 2D slab thickness $L_{z} \\to \\infty$ with infinitesimal but non-zero $2|\\Delta|$~\\cite{dynamical_axion_insulator_BB}. \nThe ambiguity modulo $2e^{2}\/h$ in the change of Hall conductance is due to the axion angle $\\theta$, which is only well-defined mod $2\\pi$, as shown below in Eq.~(\\ref{eq:agnostic}). \nTaking $L_{z} \\to \\infty$ with infinitesimal $2|\\Delta|$ ensures that the only effect the CDW modulation has is to open the gap at the Weyl points without inverting bands at other high-symmetry points in the 3D Brillouin zone. \nWe have also verified that our choice for the parameters in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} (a) is adiabatically connected to this condition by increasing $L_{z}$ and decreasing $2|\\Delta|$. \nThe slab Hall conductance $G_{xy}$ of the $xy$-slab contains both an extensive contribution from the bulk QAH phase through the weak Chern number $\\nu_{z}$, and an intensive contribution from axion angle $\\theta$, which collectively gives\\cite{vanderbiltaxion,2020_Axion_coupling_Vanderbilt}\n\\begin{align}\n G_{xy} = \\frac{e^2}{h}(\\nu_{z} l_{z} + \\theta \/ \\pi), \\label{eq:agnostic}\n\\end{align}\nwhere $l_{z}$ is the number of unit cells in the slab. \nIn our examples for $q = 1\/5$, $l_{z}$ will be given by $L_{z}\/5$. \nRecall also that as we slide $\\phi$ from $0$ to $\\pi$, the bulk gap of the 3D Weyl-CDW system never closes, hence the $\\nu_{z}$ is unchanged during the process. \nPutting this all together, we see that Eq.~(\\ref{eq:main_G_xy_eqn}) implies that there is a $\\pi$ mod $2\\pi$ change in the axion angle between $\\phi = 0$ and $\\phi = \\pi$. \nTo be more specific, in our current examples we have $G_{xy}(\\phi = 0) = -5 e^{2}\/h$ and $G_{xy}(\\phi = \\pi) = -4 e^{2}\/h$. \nThis quantized change of $G_{xy}$ or $\\theta$ can be explained again using the 4D low energy theory, as we now show. \n\n\nGoing back to the 4D low energy theory, Eq.~(\\ref{eq:4D_U1_SHO_H}) predicts that if we shift $k_{w}$ to $k_{w} + \\Delta k_{w}$, the corresponding CI layers described by the Hamiltonian in Eq.~(\\ref{eq:2D_CI_subspace})--which are localized around $z = -k_{w}\/(2\\pi q)$--will be shifted in the $z$ direction by $\\Delta z = -\\Delta k_{w} \/ (2\\pi q)$. \nConnecting this observation back to the physical 3D Weyl-CDW system, it implies that as we slide $\\phi$ from $0$ to $\\pi$, all the CI layers will be shifted by $\\Delta z = -\\pi \/ (2\\pi q) = -1\/(2q)$; for our choice of $q= 1\/5$ this gives a shift of $\\Delta z = -2.5$. \nWe demonstrate this numerically in Figs.~\\ref{fig:temporary_3d_weyl_cdw_fig} (d) and (e) which show the probability distribution of the $8$ QAH zero modes and the corresponding 4 non-trivial CI layers (with Chern number $C = -1$) for $\\phi = \\pi$. \nThe physical interpretation of Eq.~(\\ref{eq:main_G_xy_eqn}) is now clear: As we slide $\\phi$ from $0$ to $\\pi$, the non-trivial CI layers will be shifted by $\\Delta z = -2.5$ unit cells, all in the same direction. \nTherefore, the bottom non-trivial CI layer at $\\phi = 0$ and $z = -10$ depicted in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} (c) will be shifted outside the finite sample and hence will not appear when $\\phi=\\pi$. \nAt $\\phi = \\pi$, there will be only $4$ non-trivial CI layers remaining. \nThis leads to a change in the Hall conductance by $e^2\/h$, as indicated by Eq.~(\\ref{eq:main_G_xy_eqn}). \nSimultaneously, the number of QAH zero modes in the $y$-rod decreases by $2$ when we slide $\\phi$ from $0$ to $\\pi$. \nPhysically, these two QAH zero modes are pushed toward the boundary of the system, due to the shifting of the bottom non-trivial CI layer. \nTherefore, their energies will be pushed toward the bulk continuum, leading to the inevitable appearance of gap-crossing bands as shown in Fig~\\ref{fig:temporary_3d_weyl_cdw_fig} (a). \nNumerically, we have observed that in all of our examples (Figs.~\\ref{fig:temporary_3d_weyl_cdw_fig} and~\\ref{fig:temporary_3d_weyl_cdw_fig_2}), the zero modes in the band structure of the $y$-rod only appear at $k_{y} = 0$. \nTherefore, as far as the zero modes are concerned, we can focus on the energy spectrum of the $y$-rod at $k_{y}=0$, as in Figs.~\\ref{fig:temporary_3d_weyl_cdw_fig} (a) and \\ref{fig:temporary_3d_weyl_cdw_fig_2} (a). \nAnalytically, this can be understood from the Hamiltonian of the low energy Chern insulator Eq.~(\\ref{eq:2D_CI_subspace}) for each $n$ and $k_{w}$, which has zero energy edge modes only at $k_{y} = 0$\\cite{hasan2010colloquium,Qi_Zhang_RMP,bernevigbook,Jackiw_Rebbi}.\n\n\n\nTo summarize, we have shown that the identity Eq.~(\\ref{eq:main_G_xy_eqn}) can be regarded as a consequence of the $U(1)$ Landau quantization of a 4D nodal line system in which the localization centers along $z$ of the states are directly related to $k_{w}$.\nWe then identified $k_w$ with the sliding phase $\\phi$ through our dimensional promotion formalism in Sec.~\\ref{sec_Dimension_promotion}. \nThe change in conductance as a function of $\\phi$ can thus be regarded as a physical manifestation of the {\\it Chern number polarization}, which can alternatively be computed in terms of $z$-localized hybrid Wannier centers~\\cite{2020_Axion_coupling_Vanderbilt,zilberberg2018photonic,dynamical_axion_insulator_BB,yu2020dynamical,olsen2020gapless}.\n\n\\begin{figure*}[t]\n\\hspace{-0.5cm}\n\\includegraphics[width=\\linewidth]{Q_taupi_over_2_tx_1_ty_-1_tz_1_m_2_D_2_Lz_21.pdf}\n\\caption{(a) $\\phi$-sliding spectrum of the Weyl-CDW model in a $y$-rod geometry at $k_{y} = 0$ with size $L_{x}\\times L_{z} = 21 \\times 21$, $t_{x} = -t_{y} = t_{z} = 1$, $m = 2$, $2|\\Delta| = 2$ and $q = \\tau \/4$ where $\\tau = (1+\\sqrt{5})\/2$. \n(b) The average probability distribution of the 18 zero modes at $\\phi = 0$ in (a). \nThese zero modes correspond to QAH surface states. \n(c) The average probability distribution of the 9 non-trivial states at $\\vec{k} = \\Gamma$ of the $xy$-slab at $\\phi = 0$, which in total lead to $G_{xy}(\\phi = 0) = -9 e^{2}\/h$. \n(d) The average probability distribution of the 16 zero modes at $\\phi = \\pi$ in (a). \nThese zero modes correspond to QAH surface states. \n(e) The average probability distribution of the 8 non-trivial states at $\\vec{k} = \\Gamma$ of the $xy$-slab at $\\phi = \\pi$, which in total lead to $G_{xy}(\\phi = \\pi) = -8 e^{2}\/h$. \nThe darker (black) color in (b)--(e) implies higher probability density. \nIn (b) and (d), the $x$- and $z$-coordinate both range from $-10 ,\\ldots, +10$. \nIn (c) and (e), the $z$-coordinate ranges from $-10 ,\\ldots, +10$.}\n\\label{fig:temporary_3d_weyl_cdw_fig_2}\n\\end{figure*}\n\nHaving demonstrated the utility of our dimensional promotion formalism for a 3D Weyl-CDW system coupled to a commensurate CDW with $q = 1\/5$, we next explore the case of incommensurate modulations which are prevalent in nature\\cite{gruner1988dynamics}. \nIn particular, the experimentally intriguing Weyl-CDW (TaSe$_4$)$_2$I is incommensurate\\cite{shi2019charge,tasei_original,heeger_tasei,tournier2013electronic,zhang2020first,shi2019charge}. \nWe still consider $H_{0}+H_{CDW}(\\phi)$ with $t_{x}=-t_{y}=t_{z}=1$, $m=2$, $2|\\Delta|=2$. \nHowever, we now choose the modulation $q = \\tau \/4$ where $ \\tau = (1+\\sqrt{5})\/2$ is the golden ratio. \nFor an $xy$-slab we choose $L_{z} = 21$ and for $y$-rod we choose $L_{x} \\times L_{z} = 21\\times 21$. \nSince $q=\\tau\/4$ is an irrational number, the modulation $H_{CDW}(\\phi)$ is incommensurate with $H_{0}$. \nCrucially though, we can use our dimensional promotion procedure regardless of whether or not the modulation is commensurate with the underlying lattice. \nThe $U(1)$ gauge field to which the 4D nodal line system is coupled still takes the form in Eq.~(\\ref{eq:U1_4D}).\nThe main difference is that now, the 4D system has an irrational flux $2\\pi q = \\pi\\tau\/2$ per plaquette in the $zw$ plane. \nWe have verified that for the $xy$-slab we have $G_{xy}(\\phi = 0) = -9 e^{2}\/h$ and $G_{xy}(\\phi = \\pi) = -8 e^{2}\/h$, consistent with Eq.~(\\ref{eq:agnostic}). \nWe also show in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig_2} (a) the $\\phi$-sliding spectrum of the $y$-rod at $k_{y}=0$. \nWe see that there are $2$ fewer QAH zero modes at $\\phi = \\pi$ than at $\\phi = 0$. \nThe 18 and 16 QAH zero modes for $\\phi = 0$ and $\\phi = \\pi$ are shown in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig_2} (c) and (e), respectively. \nWe again identify 9 and 8 non-trivial states in the $xy$-slab at $\\vec{k} = \\Gamma$ for $\\phi = 0$ and $\\phi = \\pi$, and show their probability distributions in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig_2} (b) and (d), respectively. \nIn the SM\\cite{SM} we present the details of the numerical methods for identifying non-trivial states in the $xy$-slab. \nThe existence of non-zero QAH response and QAH zero modes can again be attributed to 4D $U(1)$ Landau quantization which gaps the 4D nodal line system, yielding a topologically non-trivial insulating ground state. \nIn particular, we also have $\\left| G_{xy}(\\phi = \\pi) - G_{xy}(\\phi = 0) \\right| = e^{2}\/h$ mod $2e^{2}\/h$. \nThis can again be understood from the shifting of non-trivial CI layers. \nIn this case, as $\\phi$ slides from $0$ to $\\pi$, all the non-trivial CI layers will be shifted downward by $\\Delta z = -\\pi \/ (2\\pi q) = -2 \/ \\tau \\approx -1.236$ lattice constants. \nThe non-trivial CI layer at the bottom ($z = -10$) of Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig_2} (b) will be shifted outside the finite size system and thus the absolute value of slab Hall conductance will be changed by $-1$. \nConsequently, the number of QAH zero modes in the $y$-rod at $k_{y}=0$ will be decreased by $2$. \nTherefore, together with the examples in Sec.~\\ref{sec_chiral_HOTI} and Sec.~\\ref{sec_helical_HOTI_sliding_modes}, we see that our dimensional promotion procedure provides a way to understand topological properties of systems with incommensurate modulations.\n\n\n\\subsection{Weyl-CDW and 4D Chern Number}\n\nWe can also understand the topological properties of the Weyl-CDW model from the perspective of 4D response theory. \nCombining the field strength in Eq.~(\\ref{eq:Fzw}) with our analysis of the Hall conductance above allows us to formulate a $(4+1)$D field-theoretical description of the QAH response in a 3D Weyl-CDW system. \nThe corresponding action is that of the $(4+1)$D Chern-Simon theory\\cite{qi2008topological,zhang2001four}\n\\begin{align}\n S = \\frac{C_{2}}{24 \\pi^{2}} \\sum_{\\mu\\nu\\lambda\\rho\\sigma}\\int d^{5}x \\epsilon^{\\mu \\nu \\lambda \\rho \\sigma} A_{\\mu} \\partial_{\\nu} A_{\\lambda} \\partial_{\\rho} A_{\\sigma}, \\label{eq:action}\n\\end{align}\nwhere $C_{2}$ is the second Chern number, $A_{\\mu}$ is the electromagnetic gauge potential and $\\epsilon^{\\mu \\nu \\lambda \\rho \\sigma}$ is the Levi-Civita symbol in $(4+1)$D.\nThe Greek indices here are taken to run over all $4+1$ dimensions. \nEq.~(\\ref{eq:action}) gives the electromagnetic response through\n\\begin{align}\n J^{\\mu} = \\frac{\\delta S}{\\delta A_{\\mu}} =\\frac{C_{2}}{32 \\pi^{2}} \\sum_{\\nu\\lambda\\rho\\sigma}\\epsilon^{\\mu \\nu \\lambda \\rho \\sigma} F_{\\nu \\lambda} F_{\\rho \\sigma},\n\\end{align}\nwhere $J^{\\mu}$ is the current density along the $\\mu$ direction. \nSince we have $F_{zw} = 2\\pi q$, an electric field $E^{y}$ along $y$ (implying $F_{ty} = E_{y}$) \nwill induce a Hall current density along $x$ through\n\\begin{align}\n J^{x} = q \\frac{C_{2}}{2\\pi} E^{y}. \\label{eq:Hall_current_x}\n\\end{align}\nIntegrating this along the $z$ direction, we find then that, with non-zero $C_{2}$, the Hall conductance $G_{xy}$ is proportional to $qL_z$. \nThis is consistent with Eq.~(\\ref{eq:agnostic}) and the the recent calculation\\cite{dynamical_axion_insulator_BB} showing that the Hall conductance $G_{xy}$ of a 3D Weyl-CDW system is given by \n\\begin{align}\n G_{xy} = \\left( |\\vec{Q}|L_{z}+2\\theta \\right) \\cdot e^{2} \/ (2\\pi h), \\label{eq:Eq_from_dynamical_axion_insulator_preprint}\n\\end{align}\nwhere $L_{z}$ is the thickness of the $xy$-slab, $\\vec{Q}$ is the CDW wave vector along $z$, which in our specific model system is $\\vec{Q} = 2\\pi q \\hat{z}$, and $\\theta$ is the bulk axion angle computed from the inversion-symmetric unit cell. \nAs we take the thermodynamic limit $L_{z} \\to \\infty$, the axion angle contribution to $G_{xy}$ becomes negligible and thus $G_{xy}$ can also be regarded as proportional to the magnitude of CDW wave vector, which is consistent with Eq.~(\\ref{eq:Hall_current_x}).\nTherefore, the field strength in Eq.~(\\ref{eq:Fzw}) indeed allows a sensible construction of higher dimensional continuum theory.\n\nTo see concretely that the 3D Weyl-CDW system indeed emulates a 4D system with non-zero $C_{2}$, we notice that for both examples in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} and Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig_2}, the system can be deformed into a limit where we have layers of decoupled Chern insulators localized along $z$. \nIn the decoupled-layer limit, for the commensurate case, for example, $q = 1\/5$, where we consider the single nontrivial band in each unit cell, this implies that $C_{2}$, which is defined through\\cite{2D_QC_4D_QHE,zilberberg2018photonic,ozawa2016synthetic,4D_QHE_ultracold_atom,qi2008topological}\n\\begin{align}\n C_{2} = \\frac{1}{4\\pi^{2}} \\int_{\\mathbb{T}^{4}} d^{4}k \\left( \\Omega_{xy}\\Omega_{zw} + \\Omega_{wx}\\Omega_{zy} + \\Omega_{zx} \\Omega_{yw} \\right)\n\\end{align}\nbecomes\n\\begin{align}\n C_{2} = \\frac{1}{4\\pi^{2}} \\left(\\int_{\\mathbb{T}^{2}} dk_{x}dk_{y} \\Omega_{xy}\\right)\\left(\\int_{\\mathbb{T}^{2}} dk_{z}dk_{w}\\Omega_{zw}\\right)\n\\end{align}\nin this limit, where $\\Omega_{\\mu \\nu}$ is the Abelian Berry curvature in the $k_{\\mu}$-$k_{\\nu}$ plane. \nFor both examples in Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} and Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig_2}, we have identified the weak Chern number $\\nu_{z} = -1$, implying that both systems have $\\frac{1}{2 \\pi} \\int_{\\mathbb{T}^{2}} dk_{x}dk_{y} \\Omega_{xy} = -1$. \nIn fact, for 3D Weyl-CDW system it have been shown that there will always be background QAH response in the $xy$ plane\\cite{dynamical_axion_insulator_BB}, implying that in the limit of decoupled Chern insulators we have $\\frac{1}{2 \\pi} \\int_{\\mathbb{T}^{2}} dk_{x}dk_{y} \\Omega_{xy} \\ne 0$. \nFurthermore, as we shift the CDW sliding phase $\\phi$, which is equivalent to shifting the momentum $k_{w}$, by $2\\pi$, all the Chern insulating layers will be shifted by $\\Delta z = -\\Delta k_{w}\/(2\\pi q) = -2\\pi \/(2\\pi q) = -1\/q$, implying a non-trivial Thouless charge pump along $z$. \nSpecifically, for Fig.~\\ref{fig:temporary_3d_weyl_cdw_fig} with $q = 1\/5$, all the Chern insulating layers will be shifted by $\\Delta z = -5$, which is equal to the unit cell length along $z$, implying $\\left| \\frac{1}{2 \\pi} \\int_{\\mathbb{T}^{2}} dk_{z}dk_{w}\\Omega_{zw} \\right|=1$. \nThe fact that the 3D Weyl-CDW system can be viewed as layers of Chern insulators\\cite{dynamical_axion_insulator_BB} and the expression $\\Delta z = -1\/q$ governing the charge pumping along $z$ as we vary $k_{w}$ by $2\\pi$ collectively predict a non-zero $\\frac{1}{2 \\pi} \\int_{\\mathbb{T}^{2}} dk_{z}dk_{w}\\Omega_{zw}$. \nTherefore, for a 3D Weyl-CDW system with QAH surface states\\cite{dynamical_axion_insulator_BB}, the corresponding 4D theory is described by a (4+1)D Chern-Simon theory in Eq.~(\\ref{eq:action}) with non-zero $C_{2}$. \nFurthermore, this result holds even as we deform away from the decoupled-layer limit, provided no energy gaps close. \nThus the 3D Weyl-CDW system serves as a platform to study higher-dimensional topological field theories.\n\n\nLet us conclude with two remarks. \nFirst, from the above analysis, we see that a 3D Weyl-CDW system with QAH surface states provides a platform to examine a 4D nodal line system gapped by a $U(1)$ gauge field and carries non-zero second Chern number $C_{2}$. \nSecondly, as opposed to Secs.~\\ref{sec_chiral_HOTI} and~\\ref{sec_helical_HOTI_sliding_modes} where we have in higher dimensions a gapped topological phase coupled to $U(1)$ or $SU(2)$ gauge fields, in the 4D model promoted from a 3D Weyl-CDW system it is precisely the coupling to a $U(1)$ gauge field that opens up a bulk gap, inducing emergent CI layers, QAH surface states and non-zero $C_{2}$.\n\n\n\\section{\\label{sec:outlook}Outlook}\n\n\nTo conclude, we have shown in Secs.~\\ref{sec_chiral_HOTI} and \\ref{sec_helical_HOTI_sliding_modes} that higher-order topology in 3D can be probed in 2D DW systems. \nFurthermore, we showed in Sec.~\\ref{sec:Weyl_CDW} how 3D Weyl-CDW systems with background QAH response can be used to study 4D topology. \nThe next and natural step is to identify 3D systems with modulations coexisting with hinge or corner modes. \nThis will be a platform for studying 4--or even higher--dimensional higher-order topology. \nOur dimensional promotion procedure in Sec.~\\ref{sec_Dimension_promotion} can also be used together with the topological classification based on crystalline symmetries\\cite{Po2017,khalaf2018higher,Chiu2016,NaturePaper} in the promoted dimensions, in order to explore topological crystalline phases in higher dimensions. \nWith suitably chosen modulated systems, we may either study (1) how topological crystalline insulators diagnosed by symmetry-based indicators\\cite{Po2017,comment,khalaf,ashvin-materials,po2020symmetry,watanabe2018structure} in the promoted dimensions respond to a background $U(1)$ or $SU(2)$ gauge fields, or (2) how topological semimetals\\cite{armitage2018weyl} in the promoted dimensions can be gapped by background $U(1)$ or $SU(2)$ gauge fields. \nWith the dimensional promotion procedure, we may also extend our studies of topological materials to those with space groups beyond 3D, known as superspace groups\\cite{superspace1,superspace2,superspace3,superspace4,superspace5}. \nTo extract the full information in higher dimension, a way to control the phase offset $\\{\\phi^{(i)} \\}$ experimentally is needed, and currently applying electromagnetic fields to depin the (charge- or spin-)density waves is one practical approach\\cite{gooth2019evidence,gruner1988dynamics}. \nIn addition, since we can systematically compute the background continuous gauge field coupled to the dimensionally-promoted model, we can again use low dimensional modulated systems to study the low energy dynamics in higher dimensions, by minimally coupling the low energy theory to the known continuous gauge fields as in Sec.~\\ref{sec:Weyl_CDW}. \nAs our dimensional promotion procedure can be carried out for both commensurate and incommensurate modulations, this approach can be used to study topological properties of system with quasi-periodic potentials~\\cite{Earliest_dimension_promotion_superspace,2D_QC_4D_QHE,zilberberg2018photonic,equivalence_Fibo_Harper_2012,Kraus_1D_QC_to_2D_QHE} where conventional band theory is not applicable. \nThe general procedure will be to promote the dimension of these quasi-periodic systems and examine the response of possible topological phase in higher dimensions to a gauge field producing an irrational flux per plaquette. \nThese techniques can be applied to analyze the DW phases in material systems of interest such as (TaSe$_4$)$_2$I\\cite{gooth2019evidence,shi2019charge,zhang2020first,tournier2013electronic} and ZrTe$_5$\\cite{tang2019three,qin2020theory,song2017instability,zhang2017transport}. \nThis can also lead to interesting studies on the higher-dimensional Hofstadter butterfly, complementing the recent studies of Refs.~\\onlinecite{Bernevig_fragile_LL,Herzog_Hof_topo}. \n\\textcolor{black}{Another interesting direction is to introduce dynamics to the DW modulation. This can happen, for example, when the phase offsets $\\{ \\phi^{(i)}\\}$ acquire non-adiabatic time-dependence and become $\\{ \\phi^{(i)}(t)\\}$. Previous studies have focused on promoting the dimension of a periodically-driven system to a Floquet lattice, which under certain conditions can lead to topologically-protected quantized energy pump~\\cite{time_periodic_1,time_periodic_2,Refael_Topo_freq_conversion_cavity_PRB}. We expect that richer phenomena in higher-dimensional space can be investigated when the DW modulations are not only periodic in real-space but also (1) periodic in time or (2) have general time-dependence.} Finally, we have shown in Sec.~\\ref{sec_helical_HOTI_sliding_modes} the simplest case of how $SU(2)$ gauge field physics may be studied through a 2D modulated system. \nRecently, the spin-orbit-coupled Hofstadter models induced by non-Abelian $SU(2)$ gauge fields have also been studied both in 2D\\cite{2D_SU2_butterfly_Joannopoulos} and 3D\\cite{3D_SU2_butterfly_Joannopoulos}, where Dirac points with up to 16-fold degeneracy and various topological insulating states were found. \nWe expect that 3D DW materials with different types of spin-orbit coupled modulations may enable simulation of various aspects of the physics of $SU(2)$ gauge fields in 4D or higher dimensions, including topological states and $SU(2)$ Hofstadter butterflies~\\cite{YiLi_SU2_Hofstadter,2D_SU2_butterfly_Joannopoulos,3D_SU2_butterfly_Joannopoulos}. \nWe hope that this work will lay the groundwork for the exciting future investigations mentioned above, and extend the search for exotic topological phases beyond 3D. \nIn particular, there are many possible defects that one can imagine in a spin-orbit coupled density wave order parameter, each of which may correspond to a non-trivial response to $SU(2)$ gauge field defects in the higher-dimensional system.\n\n\n\n\n\\begin{acknowledgments}\nThe authors would like to thank Y. Li and B. Wieder for fruitful discussions. \nThis work was supported by the Alfred P. Sloan Foundation, and by the National Science Foundation under grant DMR-1945058. \nNumerical computations made use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications (NCSA) and which is supported by funds from the University of Illinois at Urbana-Champaign. \nNumerical calculations in this work employed the open-source PythTB package\\cite{PythTB}.\n\\end{acknowledgments}\n\n\n\n\\section{\\label{sec:General_dimension_promotion}General dimensional promotion procedure}\n\nIn this section, we develop a general procedure to promote a $d$-dimensional ($d$D) modulated system to a $(d+N)$D lattice model coupled to a $U(1)$ gauge field. \nGeneralizing Sec.~III in the main text, here we allow for both on-site energy and hopping modulations, general (potentially non-cubic) lattice structures in the higher-dimensional model, as well as arbitrary orbital positions. \nSec.~\\ref{subsec:formalism} is devoted to the development of the method. \nSpecific examples are given in Sec.~\\ref{subsec:examples_general_dim_promotion}.\n\n\n\\subsection{\\label{subsec:formalism}Formalism}\n\n\\subsubsection{\\label{subsubsec:__dimensional_promotion}Dimensional promotion}\nSuppose we have a $d$D periodic electronic system with lattice vectors $\\{\\vec{a}_{1},\\cdots,\\vec{a}_{d} \\}$, which form a general $d$D Bravais lattice. \nNote that the $\\vec{a}_i$ need not be mutually orthogonal. \nWe add $N$ mutually incommensurate on-site and hopping modulations with modulation wave vectors $\\{\\vec{q}^{(1)},\\cdots,\\vec{q}^{(N)}\\}$, such that the Hamiltonian is given by\n\\begin{equation}\n H_{\\text{low-dim}} = \\sum_{\\vec{n},\\vec{m}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m}} \\left[H_{\\vec{m}}\\right] {\\psi}_{\\vec{n}} + {\\sum_{\\vec{n},\\vec{l}}} \\sum_{i=1}^{N} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l}} \\left[H_{\\vec{l},\\vec{n}}^{(i)} \\right] {\\psi}_{\\vec{n}} + \\sum_{\\vec{n}} \\sum_{i=1}^{N}{\\psi}^{\\dagger}_{\\vec{n}} \\left[ V^{(i)}_{\\vec{n}} \\right] {\\psi}_{\\vec{n}}. \\label{eq:H_low_general}\n\\end{equation}\nHere $\\vec{n} = (n_{1},\\cdots,n_{d})$, $\\vec{m} = (m_{1},\\cdots,m_{d})$ and $\\vec{l} = (l_{1},\\cdots,l_{d})\\in \\mathbb{Z}^{d}$ index lattice positions in reduced coordinates. \nFor example, the vector $\\vec{n}$ uniquely denotes the lattice site $\\sum_{j=1}^d n_j\\vec{a}_j$. \nThe multi-component operator ${\\psi}^{\\dagger}_{\\vec{n}}$ creates an electron at site $\\vec{n}$ with a given set of spin and orbital degrees of freedom.\nThe matrix $\\left[H_{\\vec{m}}\\right]$ is the unmodulated hopping matrix connecting lattice position $\\vec{n}$ to $\\vec{n}+\\vec{m}$, with matrix indices encoding the spin and orbital dependence of the hopping. \nSimilarly, $\\left[H_{\\vec{l},\\vec{n}}^{(i)} \\right]$ is the $i^{\\text{th}}$ ($i = 1 ,\\ldots, N$) modulated hopping matrix connecting lattice position $\\vec{n}$ to $\\vec{n}$+$\\vec{l}$, and $\\left[ V^{(i)}_{\\vec{n}} \\right]$ is the $i^{\\text{th}}$ ($i = 1 ,\\ldots, N$) modulated on-site energy at lattice position $\\vec{n}$. \n\nEach $\\vec{q}^{(i)}$ is associated with a set of modulated couplings $\\left[V^{(i)}_{\\vec{n}} \\right]$ and $\\left[ H^{(i)}_{\\vec{l},\\vec{n}} \\right]$. \nNote it is possible that for a given $\\vec{q}^{(i)}$, $\\left[ H^{(i)}_{\\vec{l},\\vec{n}} \\right]$ is only non-zero for some $\\vec{l}$.\nHere we sum over all $\\vec{l}$, and set $\\left[ H^{(i)}_{\\vec{l},\\vec{n}} \\right] =0$ if for a given $\\vec{q}^{(i)}$ there is no modulated hopping matrix with the given $\\vec{l}$. \nIn most practical cases of interest, $\\vec{l}$ will be restricted to a nearest-neighbor hopping. \nNote that in the situations considered in the main text we took $\\left[ H^{(i)}_{\\vec{l},\\vec{n}} \\right]=0$ for all $\\vec{l}$. \nAnalogous considerations let us analyze systems with modulated hoppings but no modulated on-site potentials by taking $[V_{\\vec{n}}^{(i)}]=0$.\n\nWe require $H_{\\text{low-dim}}$ to be Hermitian, such that\n\\begin{align}\n & \\left[ H_{-\\vec{m}} \\right]^{\\dagger} = \\left[H_{\\vec{m}} \\right], \\label{eq:Hermitian_1} \\\\\n & \\left[ H^{(i)}_{-\\vec{l},\\vec{n}+\\vec{l}} \\right]^{\\dagger} = \\left[ H^{(i)}_{\\vec{l},\\vec{n}} \\right], \\label{eq:Hermitian_2} \\\\\n & \\left[ V^{(i)}_{\\vec{n}} \\right]^{\\dagger} = \\left[ V^{(i)}_{\\vec{n}} \\right].\\label{eq:Hermitian_3} \n\\end{align}\nEq.~(\\ref{eq:Hermitian_2}) is another statement that the hopping process from $\\vec{n}$ to $\\vec{n}+\\vec{l}$ is the conjugate of the hopping process from $\\vec{n}+\\vec{l}$ to $\\vec{n}$. \nWe assume that the modulations are periodic with functional dependence\n\\begin{align}\n & \\left[ H_{\\vec{l},\\vec{n}}^{(i)} \\right] = \\left[ g^{(i)}_{\\vec{l}} \\left( 2\\pi \\vec{q}^{(i)} \\cdot \\left( n_{1}\\vec{a}_{1}+\\cdots+n_{d}\\vec{a}_{d} \\right) + \\phi^{(i)} \\right)\\right], \\label{eq:H_form_1} \\\\\n & \\left[ V^{(i)}_{\\vec{n}} \\right] = \\left[f^{(i)} \\left( 2\\pi \\vec{q}^{(i)} \\cdot \\left( n_{1}\\vec{a}_{1}+\\cdots+n_{d}\\vec{a}_{d} \\right) + \\phi^{(i)} \\right)\\right], \\label{eq:H_form_2}\n\\end{align}\nsuch that\n\\begin{align}\n & \\left[ g^{(i)}_{\\vec{l}} (x)\\right] = \\left[ g^{(i)}_{\\vec{l}} (x + 2\\pi)\\right], \\\\\n & \\left[f^{(i)} (x)\\right] = \\left[f^{(i)} (x+2\\pi)\\right].\n\\end{align}\nTherefore, we can expand $\\left[ H_{\\vec{l},\\vec{n}}^{(i)} \\right]$ and $\\left[ V^{(i)}_{\\vec{n}} \\right]$ in terms of Fourier components,\n\\begin{align}\n & \\left[ H_{\\vec{l},\\vec{n}}^{(i)} \\right] = \\sum_{p } \\left[ H_{\\vec{l},p}^{(i)} \\right] e^{i p \\left( 2\\pi \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) + \\phi^{(i)} \\right) }, \\label{eq:general_FT_1} \\\\\n & \\left[ V^{(i)}_{\\vec{n}} \\right] = \\sum_{p } \\left[ V^{(i)}_{p} \\right] e^{i p \\left( 2\\pi \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) + \\phi^{(i)} \\right) }, \\label{eq:general_FT_2}\n\\end{align}\nwhere $p \\in \\mathbb{Z}$. The matrices $\\left[ H_{\\vec{l},p}^{(i)} \\right]$ and $\\left[ V^{(i)}_{p} \\right]$ are the $p^{\\text{th}}$ (matrix-valued) Fourier components of $\\left[ H_{\\vec{l},\\vec{n}}^{(i)} \\right]$ and $\\left[ V^{(i)}_{\\vec{n}} \\right]$, respectively. \nEqs.~(\\ref{eq:Hermitian_2}) and (\\ref{eq:Hermitian_3}) also imply\n\\begin{align}\n & \\left[ H^{(i)}_{-\\vec{l},-p} \\right]^{\\dagger} e^{ip 2 \\pi \\vec{q}^{(i)} \\cdot \\sum_{j=1}^{d}l_{j}\\vec{a}_{j}} = \\left[ H^{(i)}_{\\vec{l},p} \\right], \\label{eq:Hermitian_4}\\\\\n & \\left[ V^{(i)}_{-p} \\right]^{\\dagger} = \\left[ V^{(i)}_{p} \\right]. \\label{eq:Hermitian_5}\n\\end{align}\n\nAlthough Eq.~(\\ref{eq:Hermitian_4}) involves several different indices, it will be crucial in forming our dimensionally-promoted Hamiltonian. \nAs such, it is illuminating to prove it as follows. \nStarting from Eq.~(\\ref{eq:general_FT_1}), we can obtain\n\\begin{align}\n \\left[ H_{-\\vec{l},\\vec{n}}^{(i)} \\right] = \\sum_{p } \\left[ H_{-\\vec{l},p}^{(i)} \\right] e^{i p \\left( 2\\pi \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) + \\phi^{(i)} \\right) }\n\\end{align}\nby changing $\\vec{l} \\to -\\vec{l}$. \nWe next shift $\\vec{n} \\to \\vec{n} + \\vec{l}$ such that\n\\begin{align}\n \\left[ H_{-\\vec{l},\\vec{n}+\\vec{l}}^{(i)} \\right] = \\sum_{p } \\left[ H_{-\\vec{l},p}^{(i)} \\right] e^{i p \\left( 2\\pi \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) + \\phi^{(i)} \\right) } e^{ip 2\\pi \\vec{q}^{(i)} \\cdot \\sum_{j=1}^{d}l_{j}\\vec{a}_{j}}. \\label{eq:general_before_Hermitian_1}\n\\end{align}\nTaking Hermitian conjugate of Eq.~(\\ref{eq:general_before_Hermitian_1}) and setting $p \\to -p$, we have\n\\begin{align}\n \\left[ H_{-\\vec{l},\\vec{n}+\\vec{l}}^{(i)} \\right]^{\\dagger} = \\sum_{p } \\left[ H_{-\\vec{l},-p}^{(i)} \\right]^{\\dagger} e^{i p \\left( 2\\pi \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) + \\phi^{(i)} \\right) } e^{ip 2\\pi \\vec{q}^{(i)} \\cdot \\sum_{j=1}^{d}l_{j}\\vec{a}_{j}}. \\label{eq:pf_1}\n\\end{align}\nIn order for Eq.~(\\ref{eq:pf_1}) being consistent with the Hermiticity constraint Eq.~(\\ref{eq:Hermitian_2}), we deduce that Eq.~(\\ref{eq:Hermitian_4}) must hold.\n\nTo continue with our development of the dimensional promotion procedure, we plug Eq.~(\\ref{eq:general_FT_1}) and Eq.~(\\ref{eq:general_FT_2}) into Eq.~(\\ref{eq:H_low_general}) such that Hamiltonian of the $d$D modulated system can be written as\n\\begin{align}\n H_{\\text{low-dim}} & = \\sum_{\\vec{n},\\vec{m}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m}} \\left[ H_{\\vec{m}}\\right] {\\psi}_{\\vec{n}} \\nonumber \\\\\n & + {\\sum_{\\vec{n},\\vec{l}}} \\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l}} \\left[H_{\\vec{l},p_{i}}^{(i)}\\right] e^{i p_{i} \\left( 2\\pi \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) + \\phi^{(i)} \\right) } {\\psi}_{\\vec{n}} \\nonumber \\\\\n & + \\sum_{\\vec{n}} \\sum_{i=1}^{N} \\sum_{p_{i}}{\\psi}^{\\dagger}_{\\vec{n}} \\left[ V^{(i)}_{p_{i}} \\right] e^{i p_{i} \\left( 2\\pi \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) + \\phi^{(i)} \\right) } {\\psi}_{\\vec{n}}. \\label{eq:H_low_general_2}\n\\end{align}\nNotice that for each $i$ (which indexes the modulation wave vectors) we sum over all $p_{i} \\in \\mathbb{Z}$. \nTo promote Eq.~(\\ref{eq:H_low_general_2}) to a $(d+N)$D space, let us introduce a set of additional lattice vectors $\\{\\vec{c}_{1},\\cdots,\\vec{c}_{N} \\}$ such that, together with $\\{\\vec{a}_{1},\\cdots,\\vec{a}_{d} \\}$, we have a linearly independent basis spanning a $(d+N)$D lattice. \nNotice that this $(d+N)$D lattice is not necessarily orthorhombic, since we do not require the lattice vectors $\\{\\vec{a}_{1},\\cdots,\\vec{a}_{d} \\}$ and $\\{\\vec{c}_{1},\\cdots,\\vec{c}_{N} \\}$ be pairwise orthogonal. \nNext, we introduce the corresponding reciprocal lattice vectors $\\{\\vec{g}_{1},\\cdots,\\vec{g}_{d}\\}$ and $\\{\\vec{G}_{1},\\cdots,\\vec{G}_{N}\\}$ in the $(d+N)$D reciprocal space such that\n\\begin{align}\n & \\vec{g}_{i} \\cdot \\vec{a}_{j} = 2 \\pi \\delta_{ij},\\ i = 1 ,\\ldots, d,\\ {\\text{and }}j = 1 ,\\ldots, d, \\label{eq:reciprocal_1} \\\\\n & \\vec{g}_{i} \\cdot \\vec{c}_{j} = 0,\\ i = 1 ,\\ldots, d,\\ {\\text{and }}j = 1 ,\\ldots, N, \\label{eq:reciprocal_2} \\\\\n & \\vec{G}_{i} \\cdot \\vec{a}_{j} = 0,\\ i = 1 ,\\ldots, N,\\ {\\text{and }}j = 1 ,\\ldots, d, \\label{eq:reciprocal_3} \\\\\n & \\vec{G}_{i} \\cdot \\vec{c}_{j} = 2\\pi \\delta_{ij},\\ i = 1 ,\\ldots, N,\\ {\\text{and }}j = 1 ,\\ldots, N. \\label{eq:reciprocal_4}\n\\end{align}\nWe can now identify $\\phi^{(i)} \\in [0,2\\pi)$ with $\\vec{k} \\cdot \\vec{c}_{i}$, which is $2\\pi$ times the coefficient of $\\vec{k}$ along $\\vec{G}_{i}$ (see Eq.~(\\ref{eq:reciprocal_4})); the periodicity of $\\phi^{(i)}$ is reflected in the periodicity of the $(d+N)$D Brillouin zone. \nWe then promote the $d$D model to a $(d+N)$D space by summing over $\\{ \\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N} \\} \\in \\mathbb{T}^{N}$, where $\\mathbb{T}^N$ denotes the $N$-torus represented as a parallelepiped with boundary spanned by $\\{\\vec{G}_{1},\\cdots,\\vec{G}_{N} \\}$, and opposite edges identified. \nWe further label the original creation and annihilation operators by additional symbols $\\{ \\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N} \\} \\in \\mathbb{T}^{N}$, such that\n\\begin{align}\n H_{\\text{high-dim}} =&\\sum_{\\vec{k}\\cdot\\vec{c}_{1},\\cdots,\\vec{k}\\cdot\\vec{c}_{N}} \\sum_{\\vec{n},\\vec{m}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N}} \\left[H_{\\vec{m}}\\right] \\psi_{\\vec{n},\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N}} \\nonumber \\\\\n & + \\sum_{\\vec{k}\\cdot\\vec{c}_{1},\\cdots,\\vec{k}\\cdot\\vec{c}_{N}} {\\sum_{\\vec{n},\\vec{l}}} \\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l},\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N}} \\left[H_{\\vec{l},p_{i}}^{(i)}\\right] e^{ip_{i}\\vec{k}\\cdot\\vec{c}_{i}} e^{i p_{i} 2\\pi \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) } {\\psi}_{\\vec{n},\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N}} \\nonumber \\\\\n & + \\sum_{\\vec{k}\\cdot\\vec{c}_{1},\\cdots,\\vec{k}\\cdot\\vec{c}_{N}}\\sum_{\\vec{n}} \\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n},\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N}} \\left[V^{(i)}_{p_{i}}\\right] e^{ip_{i}\\vec{k}\\cdot\\vec{c}_{i}} e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right)}{\\psi}_{\\vec{n},\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N}}, \\label{eq:non_ortho_3}\n\\end{align}\nwhere the summation of each $\\vec{k} \\cdot \\vec{c}_{i}$ is from $0$ to $2\\pi$. \nNotice that the summation over $\\{\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k} \\cdot \\vec{c}_{N} \\}$ is outside of $\\sum_{i=1}^{N} \\sum_{p_{i}} $ over all integer $p_{i}$ for each modulation $i = 1 ,\\ldots, N$. \nThis means that we are summing over $\\{\\vec{k}\\cdot \\vec{c}_{1},\\cdots, \\vec{k}\\cdot\\vec{c}_{N} \\}$ for each term in Eq.~(\\ref{eq:H_low_general_2}). \nAs mentioned in the main text, the physical motivation to sum over $\\{\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot\\vec{c}_{N} \\}$ is because a single set of $\\{\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot\\vec{c}_{N} \\}$ does not contain the full information of the lattice model in the promoted $(d+N)$D space. \nOnly when we consider all values of $\\{\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot\\vec{c}_{N} \\}$, can we obtain the exact form of the promoted lattice model, shown in Eq.~(\\ref{eq:non_ortho_4}) below. \nWe remind the readers that repeated indices are not implicitly summed.\n\nNote that ${\\psi}^{\\dagger}_{\\vec{n},\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N}}$ is the Fourier transform of $\\psi^{\\dagger}_{\\vec{n},\\vec{\\nu}}$ through\n\\begin{align}\n {\\psi}^{\\dagger}_{\\vec{n},\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N}} = \\frac{1}{\\sqrt{L}} \\sum_{\\vec{\\nu}} e^{i \\vec{k}\\cdot\\left( \\nu_{1}\\vec{c}_{1}+ \\cdots + \\nu_{N}\\vec{c}_{N} \\right)} \\psi^{\\dagger}_{\\vec{n},\\vec{\\nu}}, \\label{eq:FT_general}\n\\end{align}\nwhere $\\vec{\\nu} = (\\nu_{1},\\cdots,\\nu_{N}) \\in \\mathbb{Z}^{N}$ and $L$ is the size of the system in the additional dimensions (taken to infinity at the end of the calculation). We can thus take an inverse Fourier transform to find\n\\begin{align}\n H_{\\text{high-dim}} =& \\sum_{\\vec{n},\\vec{m},\\vec{\\nu}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\vec{\\nu}} \\left[ H_{\\vec{m}}\\right] \\psi_{\\vec{n},\\vec{\\nu}} \\nonumber \\\\\n & + {\\sum_{\\vec{n},\\vec{l},\\vec{\\nu}}} \\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i}} \\left[H_{\\vec{l},p_{i}}^{(i)}\\right] e^{i 2\\pi p_{i} \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) } {\\psi}_{\\vec{n},\\vec{\\nu}} \\nonumber \\\\\n & + \\sum_{\\vec{n},\\vec{\\nu}}\\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i}} \\left[V^{(i)}_{p_{i}}\\right] e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right)}{\\psi}_{\\vec{n},\\vec{\\nu}}, \\label{eq:non_ortho_4}\n\\end{align}\nwhere ${\\psi}^{\\dagger}_{\\vec{n},\\vec{\\nu}}$ denotes the electron creation operator for an electron at the lattice position $\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j}$, and ${\\psi}^{\\dagger}_{\\vec{n}+\\vec{l},\\vec{\\nu} - p_{i}\\hat{\\nu}_{i}}$ denotes the electron creation operator for an electron at the lattice position $\\sum_{j=1}^{d}(n_{j} + l_{j})\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} - p_{i} \\vec{c}_{i}$. \nNotice that a $d$D modulated system with phase offsets $\\{\\phi^{(1)},\\cdots,\\phi^{(N)}\\}$ is described by the Bloch Hamiltonian (see Eq.~(\\ref{eq:non_ortho_3})) of the promoted $(d+N)$D lattice model with fixed crystal momenta $\\{\\vec{k}\\cdot \\vec{c}_{1},\\cdots,\\vec{k}\\cdot \\vec{c}_{N} \\}$ via our identification of $\\vec{k} \\cdot \\vec{c}_{i}$ with $\\phi^{(i)}$. \nFor later convenience, we can also rewrite Eq.~(\\ref{eq:non_ortho_4}) as\n\\begin{align}\n H_{\\text{high-dim}} =& \\sum_{\\vec{n},\\vec{m},\\vec{\\nu}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\vec{\\nu}} \\left[H_{\\vec{m}}\\right] \\psi_{\\vec{n},\\vec{\\nu}} \\label{eq:non_ortho_5_1} \\\\\n & + {\\sum_{\\vec{n},\\vec{l},\\vec{\\nu}}} \\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i}} \\left[H_{\\vec{l},p_{i}}^{(i)}\\right]e^{-i \\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d} l_{j}\\vec{a}_{j} \\right) }e^{i 2\\pi p_{i} \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}\\left( n_{j} + \\frac{1}{2}l_{j} \\right)\\vec{a}_{j} \\right) } {\\psi}_{\\vec{n},\\vec{\\nu}} \\label{eq:non_ortho_5_2} \\\\\n & + \\sum_{\\vec{n},\\vec{\\nu}}\\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i}} \\left[V^{(i)}_{p_{i}}\\right] e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right)}{\\psi}_{\\vec{n},\\vec{\\nu}}, \\label{eq:non_ortho_5_3}\n\\end{align}\nwhere we have used\n\\begin{align}\n \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} = \\sum_{j=1}^{d}\\left( n_{j} + \\frac{1}{2}l_{j} \\right)\\vec{a}_{j} - \\sum_{j=1}^{d} \\frac{1}{2}l_{j} \\vec{a}_{j}.\n\\end{align}\nThis will prove useful when we go to identify the gauge fields appearing in the hopping matrix elements. \nWe remind the readers again that the product $p_{i}\\vec{q}^{(i)}$ in Eqs.~(\\ref{eq:non_ortho_5_1}--\\ref{eq:non_ortho_5_3}) does not imply a summation over $i$, rather it denotes the product of integer $p_{i}$ and the $i^{th}$ modulation wave vector $\\vec{q}^{(i)}$. \nEqs.~(\\ref{eq:non_ortho_5_1}--\\ref{eq:non_ortho_5_3}) can then be interpreted as a $(d+N)$D lattice model with Hamiltonian\n\\begin{align}\n H_{\\text{high-dim}} =& \\sum_{\\vec{n},\\vec{m},\\vec{\\nu}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\vec{\\nu}} \\left[H_{\\vec{m}}\\right] \\psi_{\\vec{n},\\vec{\\nu}} \\label{eq:non_ortho_6_1} \\\\\n & + {\\sum_{\\vec{n},\\vec{l},\\vec{\\nu}}} \\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i}} \\left[H_{\\vec{l},p_{i}}^{(i)}\\right]e^{-i \\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d} l_{j}\\vec{a}_{j} \\right) }{\\psi}_{\\vec{n},\\vec{\\nu}} \\label{eq:non_ortho_6_2} \\\\\n & + \\sum_{\\vec{n},\\vec{\\nu}}\\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i}} \\left[V^{(i)}_{p_{i}}\\right] {\\psi}_{\\vec{n},\\vec{\\nu}}, \\label{eq:non_ortho_6_3}\n\\end{align}\ncoupled through a Peierls substitution\\cite{Peierls_substitution} to a $U(1)$ gauge field \n\\begin{align}\n \\vec{A} = \\frac{1}{2\\pi} \\sum_{i=1}^{N} \\sum_{j=1}^{d} \\vec{G}_{i} \\left( \\vec{q}^{(i)}\\cdot \\left[ \\left( \\vec{r} \\cdot \\vec{g}_{j} \\right) \\vec{a}_{j} \\right] \\right), \\label{eq:non_ortho_A}\n\\end{align}\nwhere $\\vec{r} \\in \\mathbb{R}^{d+N}$. \nNotice that Eqs.~(\\ref{eq:non_ortho_6_1}--\\ref{eq:non_ortho_6_3}) represent the $(d+N)$D model {\\it without} $U(1)$ gauge fields.\nAlthough there are complex phases $e^{-i \\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d} l_{j}\\vec{a}_{j} \\right) }$ in Eq.~(\\ref{eq:non_ortho_6_2}), they do not depend on the reduced coordinates $(\\vec{n},\\vec{\\nu}) \\in \\mathbb{Z}^{d+N}$ in the dimensionally-promoted lattice and so may be regarded as inherent phase factors in the $(d+N)$D model without $U(1)$ gauge field. \nTo validate the identification Eq.~(\\ref{eq:non_ortho_A}), we compute the various Peierls phase factors in the next section. \n\n\\subsubsection{\\label{sec:computing_Peierls_phase_1}Computation of Peierls phases}\n\nLet us consider the terms in Eq.~(\\ref{eq:non_ortho_6_3}) where the electrons hop from \n\\begin{equation}\n\\vec{r}_{i} = \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j}\n\\end{equation}\nto \n\\begin{equation}\n \\vec{r}_{f} =\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} - p_{i} \\vec{c}_{i}. \n\\end{equation}\nThe Peierls phase can be computed from a straight line integral from $\\vec{r}_{i}$ to $\\vec{r}_{f} \\in$ $\\mathbb{R}^{d+N}$ through\n\\begin{align}\n \\exp{-i \\int_{\\vec{r}_{i}}^{\\vec{r}_{f}} d\\vec{r} \\cdot \\vec{A}},\n\\end{align}\nwhere we have worked in unit $\\hbar = c= |e| = 1$ and the electron has charge $-|e| = -1$. \nThe straight line connecting $\\vec{r}_{i}$ to $\\vec{r}_{f}$ is\n\\begin{align}\n \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} - p_{i} \\vec{c}_{i}t,\n\\end{align}\nwhere $ t\\in [0,1]$ and the corresponding infinitesimal displacement vector is $-p_{i}\\vec{c}_{i}dt$. \nThe line integral can then be computed as follows:\n\\begin{align}\n \\exp{-i \\int_{\\vec{r}_{i}}^{\\vec{r}_{f}} d\\vec{r} \\cdot \\vec{A}} & = \\exp{i \\int_{0}^{1}dt p_{i} \\vec{c}_{i} \\cdot \\frac{1}{2\\pi} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\left( \\sum_{l=1}^{d}n_{l}\\vec{a}_{l} + \\sum_{l=1}^{N}\\nu_{l}\\vec{c}_{l} - p_{i} \\vec{c}_{i}t \\right) \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_1_1} \\\\\n & = \\exp{i \\int_{0}^{1}dt p_{i} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\delta_{ij} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\left( \\sum_{l=1}^{d}n_{l}\\vec{a}_{l} \\right) \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_1_2} \\\\\n & = \\exp{i 2\\pi \\int_{0}^{1}dt p_{i} \\sum_{k=1}^{d} \\left( \\vec{q}^{(i)}\\cdot \\left[ \\sum_{l=1}^{d}n_{l}\\delta_{lk} \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_1_3} \\\\\n & = \\exp{i 2\\pi \\int_{0}^{1}dt p_{i} \\left( \\vec{q}^{(i)}\\cdot \\sum_{k=1}^{d} n_{k} \\vec{a}_{k} \\right) } \\label{eq:Peierls_1_4} \\\\\n & = \\exp{i 2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\sum_{k=1}^{d} n_{k} \\vec{a}_{k} }. \\label{eq:Peierls_1_5}\n\\end{align}\nWe used Eq.~(\\ref{eq:reciprocal_2}) and Eq.~(\\ref{eq:reciprocal_4}) in going from Eq.~(\\ref{eq:Peierls_1_1}) to Eq.~(\\ref{eq:Peierls_1_2}), Eq.~(\\ref{eq:reciprocal_1}) in going from Eq.~(\\ref{eq:Peierls_1_2}) to Eq.~(\\ref{eq:Peierls_1_3}) and then finally do an integral $\\int_{0}^{1}dt=1$ to obtain Eq.~(\\ref{eq:Peierls_1_5}). \nEq.~(\\ref{eq:Peierls_1_5}) is exactly the additional phase factor in Eq.~(\\ref{eq:non_ortho_5_3}) compared to Eq.~(\\ref{eq:non_ortho_6_3}). \n\n\nNext, consider Eq.~(\\ref{eq:non_ortho_6_2}) where the electrons hop from \n\\begin{equation}\n\\vec{r}_{i} = \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j}\n\\end{equation}\nto \n\\begin{equation}\n\\vec{r}_{f} =\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\sum_{j=1}^{d}l_{j}\\vec{a}_{j} - p_{i} \\vec{c}_{i}.\n\\end{equation} \nThe straight line connecting $\\vec{r}_{i}$ to $\\vec{r}_{f}$ is\n\\begin{align}\n \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\left( \\sum_{j=1}^{d}l_{j}\\vec{a}_{j} - p_{i} \\vec{c}_{i} \\right) t,\n\\end{align}\nwhere $ t= [0,1]$ and the corresponding infinitesimal displacement vector is $\\left( \\sum_{j=1}^{d}l_{j}\\vec{a}_{j} - p_{i} \\vec{c}_{i} \\right) dt$. \nThe line integral can then be computed as follows:\n\\begin{align}\n & \\exp{-i \\int_{\\vec{r}_{i}}^{\\vec{r}_{f}} d\\vec{r} \\cdot \\vec{A}} \\\\\n & = \\exp{-i \\int_{0}^{1}dt \\left( \\sum_{r=1}^{d}l_{r}\\vec{a}_{r} - p_{i} \\vec{c}_{i} \\right) \\cdot \\frac{1}{2\\pi} \\sum_{j,k} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\left( \\sum_{r=1}^{d}n_{r}\\vec{a}_{r} + \\sum_{r=1}^{N}\\nu_{r}\\vec{c}_{r} + \\left( \\sum_{r=1}^{d}l_{r}\\vec{a}_{r} - p_{i} \\vec{c}_{i} \\right) t \\right) \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_2_1} \\\\\n & = \\exp{i \\int_{0}^{1}dt p_{i} \\vec{c}_{i} \\cdot \\frac{1}{2\\pi} \\sum_{j,k} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\left( \\sum_{r=1}^{d}\\left( n_{r} + l_{r}t \\right)\\vec{a}_{r} \\right) \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_2_2} \\\\\n & = \\exp{i 2\\pi \\int_{0}^{1}dt p_{i} \\sum_{j,k} \\delta_{ij} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\sum_{r=1}^{d}\\left( n_{r} + l_{r}t \\right)\\delta_{rk} \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_2_3} \\\\\n & = \\exp{i 2\\pi \\int_{0}^{1}dt p_{i} \\sum_{k=1}^{d} \\left( \\vec{q}^{(i)}\\cdot \\left[ \\left( n_{k} + l_{k}t \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_2_4} \\\\\n & = \\exp{i 2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{k=1}^{d} \\left( n_{k} + \\frac{1}{2}l_{k} \\right) \\vec{a}_{k} \\right) }. \\label{eq:Peierls_2_5}\n\\end{align}\nWe used Eq.~(\\ref{eq:reciprocal_2}) and Eq.~(\\ref{eq:reciprocal_3}) in going from Eq.~(\\ref{eq:Peierls_2_1}) to Eq.~(\\ref{eq:Peierls_2_2}), Eq.~(\\ref{eq:reciprocal_1}) and Eq.~(\\ref{eq:reciprocal_4}) in going from Eq.~(\\ref{eq:Peierls_2_2}) to Eq.~(\\ref{eq:Peierls_2_3}) and then finally do integrals $\\int_{0}^{1}dt=1$ and $\\int_{0}^{1}tdt=\\frac{1}{2}$ to obtain Eq.~(\\ref{eq:Peierls_2_5}). \nEq.~(\\ref{eq:Peierls_2_5}) is exactly the additional phase factor in Eq.~(\\ref{eq:non_ortho_5_2}) compared to Eq.~(\\ref{eq:non_ortho_6_2}). \nCrucially, we see that our redefinition Eq.~(\\ref{eq:non_ortho_A}) accounts for the factor of $1\/2$ arising in the line integral of the vector potential.\n\n\nFinally, consider Eq.~(\\ref{eq:non_ortho_6_1}) where the electrons hop from \n\\begin{equation}\n\\vec{r}_{i} = \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j}\n\\end{equation}\nto\n\\begin{equation} \n \\vec{r}_{f} =\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\sum_{j=1}^{d}m_{j}\\vec{a}_{j}.\n\\end{equation}\nThe straight line connecting $\\vec{r}_{i}$ to $\\vec{r}_{f}$ is\n\\begin{align}\n \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\left( \\sum_{j=1}^{d}m_{j}\\vec{a}_{j} \\right) t,\n\\end{align}\nwhere $ t= [0, 1]$ and the corresponding infinitesimal displacement vector is $\\left( \\sum_{j=1}^{d}m_{j}\\vec{a}_{j} \\right) dt$. \nThe line integral can then be computed as follows:\n\\begin{align}\n & \\exp{-i \\int_{\\vec{r}_{i}}^{\\vec{r}_{f}} d\\vec{r} \\cdot \\vec{A}} \\\\\n & = \\exp{-i \\int_{0}^{1}dt \\left( \\sum_{r=1}^{d}m_{r}\\vec{a}_{r} \\right) \\cdot \\frac{1}{2\\pi} \\sum_{j,k} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\left( \\sum_{r=1}^{d}n_{r}\\vec{a}_{r} + \\sum_{r=1}^{N}\\nu_{r}\\vec{c}_{r} + \\left( \\sum_{r=1}^{d}m_{r}\\vec{a}_{r} \\right) t \\right) \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_3_1} \\\\\n & = 1. \\label{eq:Peierls_3_2}\n\\end{align}\nWe used Eq.~(\\ref{eq:reciprocal_3}) in going from Eq.~(\\ref{eq:Peierls_3_1}) to Eq.~(\\ref{eq:Peierls_3_2}). \nThis means that no additional phase factors arise if we compare Eq.~(\\ref{eq:non_ortho_5_1}) and Eq.~(\\ref{eq:non_ortho_6_1}). \nThus, we see that our dimensionally-promoted Hamiltonian is consistent with Eqs.~(\\ref{eq:non_ortho_6_1}--\\ref{eq:non_ortho_6_3}) coupled via a Peierls substitution to the vector potential Eq.~(\\ref{eq:non_ortho_A}).\n\n\\subsubsection{\\label{sec:remarks_consistency_check_and_summary}Remarks, consistency checks and summary}\n\nLet us briefly recap what we have developed. \nWe began with a $d$D system with modulated hoppings and on-site energies, together with lattice vectors $\\{\\vec{a}_{1},\\cdots,\\vec{a}_{d}\\}$. \nUpon dimensional promotion, we can choose {\\it any} linearly independent additional lattice vectors $\\{\\vec{c}_{1},\\cdots,\\vec{c}_{N}\\}$ which together with $\\{\\vec{a}_{1},\\cdots,\\vec{a}_{d}\\}$ span the promoted $(d+N)$D space. \nThe corresponding $U(1)$ gauge field is given in Eq.~(\\ref{eq:non_ortho_A}), which has linear dependence on $\\vec{r}$, the position vector in the promoted $(d+N)$D space. \nThis implies the field strength $F_{\\mu \\nu} = \\partial_{\\mu} A_{\\nu} - \\partial_{\\nu} A_{\\mu}$ is constant in space. \nIf we choose $\\{\\vec{a}_{1},\\cdots,\\vec{a}_{d},\\vec{c}_{1},\\cdots,\\vec{c}_{N} \\}$ to be orthogonal unit vectors in the $(d+N)$D Cartesian coordinates, and if the model only has modulated on-site energies, the general dimensional promotion procedure present here will reduce to the one present in Sec.~III of the main text.\n\nIn this more general construction in terms of non-orthogonal lattice vectors with modulated hopping terms, the mapping from the $d$D modulated system to the promoted $(d+N)$D lattice coupled to a $U(1)$ gauge field also requires no additional parameters. \nThe Hamiltonians before and after the dimensional promotion, together with the convention for the Fourier series expansions are summarized in Table~\\ref{tab:model_summary}. \nThe hopping matrices in $(d+N)$D space, and the corresponding Peierls phases are summarized in Table~\\ref{tab:hopping_1}. \nTo use Table~\\ref{tab:hopping_1} we multiply the hopping matrix entry and the Peierls phases to obtain the Hamiltonian matrix elements (with background $U(1)$ gauge fields) in the promoted $(d+N)$D space in Eqs.~(\\ref{eq:non_ortho_5_1}--\\ref{eq:non_ortho_5_3}). \nWe remind the readers that there are phase factors $e^{-i p_{i} \\pi \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d}l_{j}\\vec{a}_{j} \\right)}$ in the hopping matrix from $(\\vec{n},\\vec{\\nu})$ to $(\\vec{n}+\\vec{l},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i})$, as shown in Table~\\ref{tab:hopping_1}. \nThis means that these phase factors are included in the definition of the hopping matrices in the $(d+N)$D model, in addition to the Peierls phase factor. \nIn addition, we reemphasize that the phases $\\phi^{(i)}$ correspond to $\\vec{k}\\cdot \\vec{c}_{i}$ where $\\vec{k}$ is the crystal momentum $\\in \\mathbb{T}^{N}$ in the dimensionally-promoted Brillouin zone.\nThe modulation wave vectors $\\vec{q}^{(i)}$ enter the definition of the $(d+N)$D $U(1)$ gauge fields in Eq.~(\\ref{eq:non_ortho_A}).\n\n\\begin{table}[h]\n\\centering\n\\begin{tabular}{|c|c|}\n\\hline\nOriginal $d$D modulated system & Eq.~(\\ref{eq:H_low_general}) \\\\\n\\hline\nFourier expansion convention of on-site and hopping modulations & Eqs.~(\\ref{eq:general_FT_1}--\\ref{eq:general_FT_2})\\\\\n\\hline\nPromoted $(d+N)$D system with $U(1)$ gauge fields & Eqs.~(\\ref{eq:non_ortho_5_1}--\\ref{eq:non_ortho_5_3}) \\\\\n\\hline\nPromoted $(d+N)$D system without $U(1)$ gauge fields & Eqs.~(\\ref{eq:non_ortho_6_1}--\\ref{eq:non_ortho_6_3}) \\\\\n\\hline\n\\end{tabular}\n\\caption{Relevant equations in the general dimensional promotion formalism.}\n\\label{tab:model_summary}\n\\end{table}\n\n\\begin{table}[h]\n\\centering\n\\begin{tabular}{|c|c|c|}\n\\hline\nHopping from $(\\vec{n},\\vec{\\nu})$ to & Hopping matrices & Peierls phases \\\\\n\\hline\n\\hline\n$(\\vec{n}+\\vec{m},\\vec{\\nu})$ & $\\left[H_{\\vec{m}}\\right]$ & $1$ \\\\\n\\hline\n$(\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i})$ & $ \\left[V^{(i)}_{p_{i}}\\right] $ & $e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right)}$ \\\\\n\\hline\n$(\\vec{n}+\\vec{l},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i})$ & $\\left[H_{\\vec{l},p_{i}}^{(i)}\\right]e^{-i \\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d} l_{j}\\vec{a}_{j} \\right) }$ & $e^{i 2\\pi p_{i} \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}\\left( n_{j} + \\frac{1}{2}l_{j} \\right)\\vec{a}_{j} \\right) }$ \\\\\n\\hline\n\\end{tabular}\n\\caption{Hopping terms in the promoted $(d+N)$D model and the corresponding Peierls phases, expressed in terms of parameters from the $d$D modulated system in Eq.~(\\ref{eq:H_low_general}). \nNotice that $p_{i}\\hat{\\nu}_{i}$ does not imply a summation over $i$.}\n\\label{tab:hopping_1}\n\\end{table}\n\nBefore moving on, some additional remarks are in order. \nFirst, the vector potential in Eq.~(\\ref{eq:non_ortho_A}) satisfies\n\\begin{align}\n \\vec{A}\\left( \\vec{r} \\right) = \\vec{A}\\left( \\vec{r} + \\sum_{j=1}^{N} \\nu_{j} \\vec{c}_{j} \\right)\n\\end{align}\nfor any $\\{\\nu_{1},\\cdots,\\nu_{N} \\} \\in \\mathbb{Z}^{N}$, which is a direct consequence of Eq.~(\\ref{eq:reciprocal_2}), and corresponds to a generalized Landau gauge condition. \nThis allows us to recover our low dimensional modulated system, by Fourier transforming the higher-dimensional model Eq.~(\\ref{eq:non_ortho_4}) along $\\vec{k}$ in the subspace spanned by $\\{\\vec{G}_{1},\\cdots,\\vec{G}_{N} \\} \\in \\mathbb{T}^{N}$ and regarding $\\vec{k} \\cdot \\vec{c}_{i}$ as $\\phi^{(i)}$. \nAny fixed value of $\\{ \\phi^{(1)},\\cdots,\\phi^{(N)} \\}$ then describes a lower-dimensional modulated system with fixed phase offsets. \nIn other words, we can use a $d$D modulated system with controllable phase offsets $\\{ \\phi^{(1)},\\cdots,\\phi^{(N)} \\}$ to map out, by varying the values of $\\phi^{(i)}$, the whole spectrum of the promoted $(d+N)$D model coupled to a $U(1)$ gauge field. \nSecond, the constraints Eq.~(\\ref{eq:Hermitian_1}), Eq.~(\\ref{eq:Hermitian_4}) and Eq.~(\\ref{eq:Hermitian_5}) ensures that $H_{\\text{high-dim}}$ in Eq.~(\\ref{eq:non_ortho_4}) with Peierls phases, as well as Eqs.~(\\ref{eq:non_ortho_6_1}--\\ref{eq:non_ortho_6_3}) without Peierls phases are Hermitian. \nWe will make use of this in the subsequent examples and only list non-redundant matrix elements of the Hamiltonian. \nThird, we have assumed so far that for a given modulation $\\vec{q}^{(i)}$, the phase offsets of the on-site and hopping modulation are the same (see Eqs.~(\\ref{eq:H_form_1}) and (\\ref{eq:H_form_2})). \nHowever, it is possible for a system to develop incoherence between the modulation of the on-site energy and hopping terms. \nIn these cases, we can relax our requirement that all $\\vec{q}^{(i)}$ are mutually incommensurate, and regard the modulated hopping terms and on-site energy as being described by two distinct but numerically equal modulation wave vectors. \nThis will increase the number of additional dimensions necessary to represent the system.\nWe demonstrate how this situation can be handled using the 1D Rice-Mele chain later in Sec.~\\ref{sec:1D_RM_chain_incoherence}. \nFourth, in Refs.~\\onlinecite{LL_fragile_Lian_Biao,Herzog_Hof_topo}, it is shown that in general the line integral of the Peierls phase should be taken on a piecewise linear path along which the Wannier functions have greatest overlap, as opposed to the linear path we used here. \nIn our present situation, this means we are implicitly assuming that the total Hilbert space (occupied plus unoccupied states) for our dimensionally promoted model can be described by topologically trivial Wannier functions. \nThe topological properties of the Wannier functions in the promoted $(d+N)$D space are an interesting direction for future investigation, including investigating whether topologically non-trivial $(d+N)$D Wannier functions can imply any physical properties in the low dimensional modulated system. \nLastly, we emphasize that our interpretation of the promoted models in $(d+N)$D, given in Eq.~(\\ref{eq:non_ortho_4}) is not unique. \nOur interpretation is fixed by the requirement that the $U(1)$ gauge fields (Eq.~(\\ref{eq:non_ortho_A})) have a spatially constant field strength $F_{\\mu\\nu}$ in $(d+N)$D. \nTherefore, this allows us to easily construct the continuum theory describing low energy dynamics in $(d+N)$D, as we know the underlying $U(1)$ gauge field, written as a function of $\\vec{r} \\in \\mathbb{R}^{d+N}$. \nThis completes our description on how to compute the $U(1)$ gauge field in a $(d+N)$D lattice with non-orthogonal lattice vectors, and the corresponding lattice model in $(d+N)$D space.\n\n{\n\\subsubsection{Generalization to systems with arbitrary orbital positions in the original $d$D space}\n\nIn Sec.~\\ref{subsubsec:__dimensional_promotion}--\\ref{sec:remarks_consistency_check_and_summary}, we have implicitly chosen to promote the dimension of a $d$D modulated lattice model whose orbitals are located exactly at the $d$D lattice points $\\sum_{j=1}^{d}n_{j}\\vec{a}_{j}$ constructed from $\\{ \\vec{a}_{1},\\ldots,\\vec{a}_{d}\\}$. \nThis choice makes the emergence of the $U(1)$ gauge field and the identification of Peierls phases transparent. \nHowever, in many models of practical interest, not all of the orbitals are located at the $d$D lattice points. \nAs the computation of Peierls phases depends on the actual orbital positions, we now examine the effect of orbital positions in our dimensional promotion method.\n\nWe consider a $d$D modulated system whose $\\alpha^{\\text{th}}$ orbital is located at the position $\\vec{r}_{\\alpha}$ measured from the origin of the unit cell. \nWe will assume that $\\vec{r}_{\\alpha}$ can be written as a linear combination of the $d$D lattice vectors $\\{\\vec{a}_{1},\\ldots,\\vec{a}_{d}\\}$, namely\n\\begin{align}\n \\vec{r}_{\\alpha} = \\sum_{j=1}^{d} x^{j}_{\\alpha} \\vec{a}_{j}, \\label{eq:r_alpha_expansion}\n\\end{align}\nwhere $x^{j}_{\\alpha} \\in [0,1)$ denotes the fractional component of $\\vec{r}_{\\alpha}$ along $\\vec{a}_{j}$ (this excludes certain quasi-$d$-dimensional models). \nTherefore, the actual position of the $\\alpha^{\\text{th}}$ orbital in the unit cell labelled by $\\vec{n} \\in \\mathbb{Z}^{d}$ is $\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\vec{r}_{\\alpha}$. \nWhen all the orbitals are located at the lattice points $\\sum_{j=1}^{d}n_{j}\\vec{a}_{j}$, we will have $\\vec{r}_{\\alpha} = 0$ for all $\\alpha$. \nThe generic Hamiltonian for our $d$D system is still given by Eq.~(\\ref{eq:H_low_general}). \nFor later convenience, here we rewrite Eq.~(\\ref{eq:H_low_general}) with an explicit summation over the orbital components as\n\\begin{equation}\n H_{\\text{low-dim}} = \\sum_{\\alpha,\\beta}\\sum_{\\vec{n},\\vec{m}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\alpha} \\left[H_{\\vec{m}}\\right]_{\\alpha,\\beta} {\\psi}_{\\vec{n},\\beta} + \\sum_{\\alpha,\\beta}{\\sum_{\\vec{n},\\vec{l}}} \\sum_{i=1}^{N} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l},\\alpha} \\left[H_{\\vec{l},\\vec{n}}^{(i)} \\right]_{\\alpha,\\beta} {\\psi}_{\\vec{n},\\beta} + \\sum_{\\alpha,\\beta}\\sum_{\\vec{n}} \\sum_{i=1}^{N}{\\psi}^{\\dagger}_{\\vec{n},\\alpha} \\left[ V^{(i)}_{\\vec{n}} \\right]_{\\alpha,\\beta} {\\psi}_{\\vec{n},\\beta}, \\label{eq:H_low_general_alpha}\n\\end{equation}\nwhere ${\\psi}^{\\dagger}_{\\vec{n},\\alpha}$ and ${\\psi}_{\\vec{n},\\alpha}$ are the creation and annihilation operator of the $\\alpha^{\\text{th}}$ orbital at the unit cell labelled by $\\vec{n} \\in \\mathbb{Z}^{d}$. \nAs before, $\\left[H_{\\vec{m}}\\right]_{\\alpha,\\beta}$, $\\left[H_{\\vec{l},\\vec{n}}^{(i)} \\right]_{\\alpha,\\beta}$ and $\\left[ V^{(i)}_{\\vec{n}} \\right]_{\\alpha,\\beta}$ are the $(\\alpha,\\beta)$ entries for the unmodulated Hamiltonian $\\left[H_{\\vec{m}}\\right]$, the hopping modulations $\\left[H_{\\vec{l},\\vec{n}}^{(i)} \\right]$ and the on-site modulations $\\left[ V^{(i)}_{\\vec{n}} \\right]$, respectively. \nThe dimensional promotion procedure is identical to that in Sec.~\\ref{subsubsec:__dimensional_promotion} and thus we obtain the same $(d+N)$D model given by Eq.~(\\ref{eq:non_ortho_4}). \nAgain, for later convenience we rewrite Eq.~(\\ref{eq:non_ortho_4}) with an explicit summation over the orbital components as\n\\begin{align}\n H_{\\text{high-dim}} =& \\sum_{\\alpha,\\beta}\\sum_{\\vec{n},\\vec{m},\\vec{\\nu}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\vec{\\nu},\\alpha} \\left[ H_{\\vec{m}}\\right]_{\\alpha,\\beta} \\psi_{\\vec{n},\\vec{\\nu},\\beta} \\label{eq:H_high_dim_normal_hopping_alpha} \\\\\n & + \\sum_{\\alpha,\\beta}{\\sum_{\\vec{n},\\vec{l},\\vec{\\nu}}} \\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i},\\alpha} \\left[H_{\\vec{l},p_{i}}^{(i)}\\right]_{\\alpha,\\beta} e^{i 2\\pi p_{i} \\vec{q}^{(i)} \\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right) } {\\psi}_{\\vec{n},\\vec{\\nu},\\beta} \\label{eq:H_high_dim_hopping_modulation_alpha} \\\\\n & + \\sum_{\\alpha,\\beta}\\sum_{\\vec{n},\\vec{\\nu}}\\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i},\\alpha} \\left[V^{(i)}_{p_{i}}\\right]_{\\alpha,\\beta} e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} \\right)}{\\psi}_{\\vec{n},\\vec{\\nu},\\beta}. \\label{eq:H_high_dim_onsite_modulation_alpha}\n\\end{align}\nWe then take the actual position of the $\\alpha^{\\text{th}}$ orbital in the unit cell labelled by $(\\vec{n},\\vec{\\nu}) \\in (\\mathbb{Z}^{d},\\mathbb{Z}^{N})$ to be $\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\vec{r}_{\\alpha}$ where we have assumed that there are no components of $\\vec{r}_\\alpha$ along $\\vec{c}_{j}$ and Eq.~(\\ref{eq:r_alpha_expansion}) still holds. \nHowever, due to the generic position $\\vec{r}_{\\alpha}$ of the orbitals, the separation of phase factors in Eqs.~(\\ref{eq:H_high_dim_normal_hopping_alpha}--\\ref{eq:H_high_dim_onsite_modulation_alpha}) between periodic hopping and the Peierls phase due to the gauge field in Eq.~(\\ref{eq:non_ortho_A}) needs to be modified compared with those in Sec.~\\ref{sec:computing_Peierls_phase_1}.\n\nWe now compute the various Peierls phases associated with terms in Eqs.~(\\ref{eq:H_high_dim_normal_hopping_alpha}--\\ref{eq:H_high_dim_onsite_modulation_alpha}) due to the gauge field in Eq.~(\\ref{eq:non_ortho_A}). First, we consider the terms in Eq.~(\\ref{eq:H_high_dim_onsite_modulation_alpha}) where the electrons hop from \n\\begin{equation}\n\\vec{r}_{i} = \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\vec{r}_{\\beta}\n\\end{equation}\nto \n\\begin{equation}\n \\vec{r}_{f} =\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} - p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha}. \n\\end{equation}\nThe straight line connecting $\\vec{r}_{i}$ to $\\vec{r}_{f}$ is\n\\begin{align}\n \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\vec{r}_{\\beta} + \\left(- p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)t,\n\\end{align}\nwhere $ t\\in [0,1]$ and the corresponding infinitesimal displacement vector is $\\left(- p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)dt$. The corresponding Peierls phase with a line integral can then be computed as follows:\n\\begin{align}\n & \\exp{-i \\int_{\\vec{r}_{i}}^{\\vec{r}_{f}} d\\vec{r} \\cdot \\vec{A}} \\\\\n & = \\exp{-i \\int_{0}^{1}dt \\left(- p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right) \\cdot \\frac{1}{2\\pi} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\begin{bmatrix}\n \\sum_{l=1}^{d}n_{l}\\vec{a}_{l} + \\sum_{l=1}^{N}\\nu_{l}\\vec{c}_{l} + \\vec{r}_{\\beta} \\\\\n +\\left(- p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)t\n \\end{bmatrix}\n \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_1_1_alpha}\\\\\n & = \\exp{+i \\int_{0}^{1}dt p_{i} \\vec{c}_{i} \\cdot \\frac{1}{2\\pi} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left(\n \\left(\n \\sum_{l=1}^{d}n_{l}\\vec{a}_{l} + \\vec{r}_{\\beta} + \\left( \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)t\n \\right)\n \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_1_2_alpha} \\\\\n & = \\exp{+i \\int_{0}^{1}dt p_{i} \\vec{c}_{i} \\cdot \\frac{1}{2\\pi} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left(\n \\left(\n \\sum_{l=1}^{d}\\left( n_{l} + x_{\\beta}^{l} + t x_{\\alpha}^{l} - t x_{\\beta}^{l} \\right)\\vec{a}_{l} \n \\right)\n \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_1_3_alpha}\\\\\n & = \\exp{+i2\\pi \\int_{0}^{1}dt p_{i} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\delta_{ij} \\left( \\vec{q}^{(j)}\\cdot \\left[ \n \\sum_{l=1}^{d}\\left(n_{l} + x_{\\beta}^{l}+tx^{l}_{\\alpha} - t x^{l}_{\\beta}\\right)\\delta_{lk} \n \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_1_4_alpha} \\\\\n & = \\exp{+i2\\pi \\int_{0}^{1}dt p_{i} \\left( \\vec{q}^{(i)}\\cdot \\left[ \n \\sum_{l=1}^{d}\\left(n_{l} + x_{\\beta}^{l}+tx^{l}_{\\alpha} - t x^{l}_{\\beta}\\right)\n \\vec{a}_{l} \\right] \\right) } \\label{eq:Peierls_1_5_alpha} \\\\\n & = \\exp{+i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left[ \n \\sum_{l=1}^{d}\\left(n_{l} + x_{\\beta}^{l}+\\frac{1}{2}x^{l}_{\\alpha} - \\frac{1}{2} x^{l}_{\\beta}\\right)\n \\vec{a}_{l} \\right] }\\label{eq:Peierls_1_6_alpha} \\\\\n & = \\exp{+i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left[ \n \\sum_{l=1}^{d}\\left(n_{l} + \\frac{x^{l}_{\\alpha} + x^{l}_{\\beta}}{2} \\right)\n \\vec{a}_{l} \\right] } \\label{eq:Peierls_1_7_alpha} .\n\\end{align}\nTo go from Eq.~(\\ref{eq:Peierls_1_1_alpha}) to Eq.~(\\ref{eq:Peierls_1_2_alpha}) we have used Eq.~(\\ref{eq:reciprocal_2}) and the fact that $(\\vec{r}_{\\alpha} - \\vec{r}_{\\beta}) \\cdot \\vec{G}_{j} = 0$ because of Eqs.~(\\ref{eq:reciprocal_3}) and (\\ref{eq:r_alpha_expansion}). \nWe then use Eq.~(\\ref{eq:r_alpha_expansion}) to go from Eq.~(\\ref{eq:Peierls_1_2_alpha}) to Eq.~(\\ref{eq:Peierls_1_3_alpha}). \nTo go from Eq.~(\\ref{eq:Peierls_1_3_alpha}) to Eq.~(\\ref{eq:Peierls_1_4_alpha}), we have used Eqs.~(\\ref{eq:reciprocal_1}) and (\\ref{eq:reciprocal_4}). \nPerforming the summation over $j$ and $k$ we obtain Eq.~(\\ref{eq:Peierls_1_5_alpha}). \nUsing $\\int_{0}^{1}dt = 1$ and $\\int_{0}^{1}tdt = \\frac{1}{2}$ we arrive at Eqs.~(\\ref{eq:Peierls_1_6_alpha}--\\ref{eq:Peierls_1_7_alpha}).\n\nNext, let us consider the terms in Eq.~(\\ref{eq:H_high_dim_hopping_modulation_alpha}) where the electrons hop from \n\\begin{equation}\n\\vec{r}_{i} = \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\vec{r}_{\\beta}\n\\end{equation}\nto \n\\begin{equation}\n\\vec{r}_{f} =\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\sum_{j=1}^{d}l_{j}\\vec{a}_{j} - p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha}.\n\\end{equation}\nThe straight line connecting $\\vec{r}_{i}$ to $\\vec{r}_{f}$ is\n\\begin{align}\n \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\vec{r}_{\\beta} + \\left( \\sum_{j=1}^{d}l_{j}\\vec{a}_{j}- p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)t,\n\\end{align}\nwhere $ t\\in [0,1]$ and the corresponding infinitesimal displacement vector is $\\left( \\sum_{j=1}^{d}l_{j}\\vec{a}_{j}- p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)dt$. \nThe corresponding Peierls phase with a line integral can then be computed as follows:\n\\begin{align}\n & \\exp{-i \\int_{\\vec{r}_{i}}^{\\vec{r}_{f}} d\\vec{r} \\cdot \\vec{A}} \\\\\n & = \\exp{-i \\int_{0}^{1}dt \\left( \\sum_{r=1}^{d}l_{r}\\vec{a}_{r}- p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right) \\cdot \\frac{1}{2\\pi} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\begin{bmatrix}\n \\sum_{r=1}^{d}n_{r}\\vec{a}_{r} + \\sum_{r=1}^{N}\\nu_{r}\\vec{c}_{r} + \\vec{r}_{\\beta} \\\\\n + \\left( \\sum_{r=1}^{d}l_{r}\\vec{a}_{r}- p_{i} \\vec{c}_{i} + \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)t\n \\end{bmatrix}\n \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_2_1_alpha}\\\\\n & = \\exp{+i \\int_{0}^{1}dt p_{i} \\vec{c}_{i} \\cdot \\frac{1}{2\\pi} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\begin{bmatrix}\n \\sum_{r=1}^{d}n_{r}\\vec{a}_{r} + \\vec{r}_{\\beta} \\\\\n + \\left( \\sum_{r=1}^{d}l_{r}\\vec{a}_{r} + \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)t\n \\end{bmatrix}\n \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_2_2_alpha}\\\\\n & = \\exp{+i \\int_{0}^{1}dt p_{i} \\vec{c}_{i} \\cdot \\frac{1}{2\\pi} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\left( \\sum_{r=1}^{d}\\left( n_{r} + tl_{r} + x^{r}_{\\beta} + t x^{r}_{\\alpha} - t x^{r}_{\\beta} \\right)\\vec{a}_{r} \\right)\n \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_2_3_alpha}\\\\\n & = \\exp{+i2\\pi \\int_{0}^{1}dt p_{i} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\delta_{ij} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\sum_{r=1}^{d}\\left( n_{r} + tl_{r} + x^{r}_{\\beta} + t x^{r}_{\\alpha} - t x^{r}_{\\beta} \\right)\\delta_{rk} \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_2_4_alpha}\\\\\n & = \\exp{+i2\\pi \\int_{0}^{1}dt p_{i} \\vec{q}^{(i)}\\cdot \\left[ \\sum_{r=1}^{d}\\left( n_{r} + tl_{r} + x^{r}_{\\beta} + t x^{r}_{\\alpha} - t x^{r}_{\\beta} \\right) \\vec{a}_{r} \\right] } \\label{eq:Peierls_2_5_alpha}\\\\\n & = \\exp{+i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left[ \\sum_{r=1}^{d}\\left( n_{r} + \\frac{1}{2}l_{r} + x^{r}_{\\beta} + \\frac{1}{2} x^{r}_{\\alpha} - \\frac{1}{2} x^{r}_{\\beta} \\right) \\vec{a}_{r} \\right] } \\label{eq:Peierls_2_6_alpha}\\\\\n & = \\exp{+i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left[ \\sum_{r=1}^{d}\\left( n_{r} + \\frac{l_{r} +x^{r}_{\\alpha} + x^{r}_{\\beta} }{2} \\right) \\vec{a}_{r} \\right] }. \\label{eq:Peierls_2_7_alpha}\n\\end{align}\nTo go from Eq.~(\\ref{eq:Peierls_2_1_alpha}) to Eq.~(\\ref{eq:Peierls_2_2_alpha}) we have used Eqs.~(\\ref{eq:reciprocal_2}--\\ref{eq:reciprocal_3}), and again the fact that $(\\vec{r}_{\\alpha} - \\vec{r}_{\\beta}) \\cdot \\vec{G}_{j} = 0$ because of Eqs.~(\\ref{eq:reciprocal_3}) and (\\ref{eq:r_alpha_expansion}).\n We then use Eq.~(\\ref{eq:r_alpha_expansion}) to go from Eq.~(\\ref{eq:Peierls_2_2_alpha}) to Eq.~(\\ref{eq:Peierls_2_3_alpha}). \n To go from Eq.~(\\ref{eq:Peierls_2_3_alpha}) to Eq.~(\\ref{eq:Peierls_2_4_alpha}), we have used Eqs.~(\\ref{eq:reciprocal_1}) and (\\ref{eq:reciprocal_4}). \n Performing the summation over $j$ and $k$ we obtain Eq.~(\\ref{eq:Peierls_2_5_alpha}). \n Using $\\int_{0}^{1}dt = 1$ and $\\int_{0}^{1}tdt = \\frac{1}{2}$ we arrive at Eqs.~(\\ref{eq:Peierls_2_6_alpha}--\\ref{eq:Peierls_2_7_alpha}).\n\nFinally, let us consider the terms in Eq.~(\\ref{eq:H_high_dim_normal_hopping_alpha}) where the electrons hop from \n\\begin{equation}\n\\vec{r}_{i} = \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\vec{r}_{\\beta}\n\\end{equation}\nto \n\\begin{equation}\n\\vec{r}_{f} =\\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\sum_{j=1}^{d}m_{j}\\vec{a}_{j} + \\vec{r}_{\\alpha}.\n\\end{equation}\nThe straight line connecting $\\vec{r}_{i}$ to $\\vec{r}_{f}$ is\n\\begin{align}\n \\sum_{j=1}^{d}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{N}\\nu_{j}\\vec{c}_{j} + \\vec{r}_{\\beta} + \\left( \\sum_{j=1}^{d}m_{j}\\vec{a}_{j}+ \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)t,\n\\end{align}\nwhere $ t\\in [0,1]$ and the corresponding infinitesimal displacement vector is $\\left( \\sum_{j=1}^{d}m_{j}\\vec{a}_{j}+ \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)dt$. \nThe corresponding Peierls phase with a line integral can then be computed as follows:\n\\begin{align}\n & \\exp{-i \\int_{\\vec{r}_{i}}^{\\vec{r}_{f}} d\\vec{r} \\cdot \\vec{A}} \\\\\n & = \\exp{-i \\int_{0}^{1}dt \\left( \\sum_{r=1}^{d}m_{r}\\vec{a}_{r}+ \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right) \\cdot \\frac{1}{2\\pi} \\sum_{j=1}^{N} \\sum_{k=1}^{d} \\vec{G}_{j} \\left( \\vec{q}^{(j)}\\cdot \\left[ \\left( \\begin{bmatrix}\n \\sum_{r=1}^{d}n_{r}\\vec{a}_{r} + \\sum_{r=1}^{N}\\nu_{r}\\vec{c}_{r} + \\vec{r}_{\\beta} \\\\\n + \\left( \\sum_{r=1}^{d}m_{r}\\vec{a}_{r}+ \\vec{r}_{\\alpha} - \\vec{r}_{\\beta} \\right)t\n \\end{bmatrix}\n \\cdot \\vec{g}_{k} \\right) \\vec{a}_{k} \\right] \\right) } \\label{eq:Peierls_3_1_alpha}\\\\ \n & = 1. \\label{eq:Peierls_3_2_alpha}\n\\end{align}\nTo go from Eq.~(\\ref{eq:Peierls_3_1_alpha}) to Eq.~(\\ref{eq:Peierls_3_2_alpha}) we have used Eq.~(\\ref{eq:reciprocal_3}) and again the fact that $(\\vec{r}_{\\alpha} - \\vec{r}_{\\beta}) \\cdot \\vec{G}_{j} = 0$ because of Eqs.~(\\ref{eq:reciprocal_3}) and (\\ref{eq:r_alpha_expansion}).\n\n\nWith this knowledge of the Peierls phases in Eqs.~(\\ref{eq:Peierls_1_7_alpha}), (\\ref{eq:Peierls_2_7_alpha}), and (\\ref{eq:Peierls_3_2_alpha}), we can rewrite $H_{\\text{high-dim}}$ in Eqs.~(\\ref{eq:H_high_dim_normal_hopping_alpha}--\\ref{eq:H_high_dim_onsite_modulation_alpha}) as \n\\begin{align}\n H_{\\text{high-dim}} =& \\sum_{\\alpha,\\beta}\\sum_{\\vec{n},\\vec{m},\\vec{\\nu}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\vec{\\nu},\\alpha} \\left[ H_{\\vec{m}}\\right]_{\\alpha,\\beta} \\psi_{\\vec{n},\\vec{\\nu},\\beta} \\label{eq:H_high_dim_normal_hopping_alpha_split} \\\\\n & + \\sum_{\\alpha,\\beta}{\\sum_{\\vec{n},\\vec{l},\\vec{\\nu}}} \\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i},\\alpha} \\left[H_{\\vec{l},p_{i}}^{(i)}\\right]_{\\alpha,\\beta} e^{-i\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\\sum_{j=1}^{d}\\left( l_{j} +x^{j}_{\\alpha} + x^{j}_{\\beta} \\right) \\vec{a}_{j} \\right) } e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d}\\left( n_{j} + \\frac{l_{j} +x^{j}_{\\alpha} + x^{j}_{\\beta} }{2} \\right) \\vec{a}_{j} \\right) } {\\psi}_{\\vec{n},\\vec{\\nu},\\beta} \\label{eq:H_high_dim_hopping_modulation_alpha_split} \\\\\n & + \\sum_{\\alpha,\\beta}\\sum_{\\vec{n},\\vec{\\nu}}\\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i},\\alpha} \\left[V^{(i)}_{p_{i}}\\right]_{\\alpha,\\beta} e^{-i\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\n \\sum_{j=1}^{d}\\left( x^{j}_{\\alpha} + x^{j}_{\\beta} \\right)\n \\vec{a}_{j} \\right) } \n e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \n \\sum_{j=1}^{d}\\left(n_{j} + \\frac{x^{j}_{\\alpha} + x^{j}_{\\beta}}{2} \\right)\n \\vec{a}_{j} \\right) } \n {\\psi}_{\\vec{n},\\vec{\\nu},\\beta}. \\label{eq:H_high_dim_onsite_modulation_alpha_split}\n\\end{align}\nEqs.~(\\ref{eq:H_high_dim_normal_hopping_alpha_split}--\\ref{eq:H_high_dim_onsite_modulation_alpha_split}) can then be interpreted as a $(d+N)$D lattice model with Hamiltonian\n\\begin{align}\n H_{\\text{high-dim}} =& \\sum_{\\alpha,\\beta}\\sum_{\\vec{n},\\vec{m},\\vec{\\nu}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{m},\\vec{\\nu},\\alpha} \\left[ H_{\\vec{m}}\\right]_{\\alpha,\\beta} \\psi_{\\vec{n},\\vec{\\nu},\\beta} \\label{eq:H_high_dim_normal_hopping_alpha_no_gauge} \\\\\n & + \\sum_{\\alpha,\\beta}{\\sum_{\\vec{n},\\vec{l},\\vec{\\nu}}} \\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n}+\\vec{l},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i},\\alpha} \\left[H_{\\vec{l},p_{i}}^{(i)}\\right]_{\\alpha,\\beta} e^{-i\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\\sum_{j=1}^{d}\\left( l_{j} +x^{j}_{\\alpha} + x^{j}_{\\beta} \\right) \\vec{a}_{j} \\right) } {\\psi}_{\\vec{n},\\vec{\\nu},\\beta} \\label{eq:H_high_dim_hopping_modulation_alpha_no_gauge} \\\\\n & + \\sum_{\\alpha,\\beta}\\sum_{\\vec{n},\\vec{\\nu}}\\sum_{i=1}^{N} \\sum_{p_{i}} {\\psi}^{\\dagger}_{\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i},\\alpha} \\left[V^{(i)}_{p_{i}}\\right]_{\\alpha,\\beta} e^{-i\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\n \\sum_{j=1}^{d}\\left( x^{j}_{\\alpha} + x^{j}_{\\beta} \\right)\n \\vec{a}_{j} \\right) } \n {\\psi}_{\\vec{n},\\vec{\\nu},\\beta}, \\label{eq:H_high_dim_onsite_modulation_alpha_no_gauge}\n\\end{align}\nwhich is periodic with lattice vectors $\\{\\vec{a}_{1},\\ldots,\\vec{a}_{d},\\vec{c}_{1},\\ldots,\\vec{c}_{N} \\}$, coupled through a Peierls substitution\\cite{Peierls_substitution} to a $U(1)$ gauge field given in Eq.~(\\ref{eq:non_ortho_A}). \nNotice that we have regarded\n\\begin{align}\n e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d}\\left( n_{j} + \\frac{l_{j} +x^{j}_{\\alpha} + x^{j}_{\\beta} }{2} \\right) \\vec{a}_{j} \\right) } \n\\end{align}\nand\n\\begin{align}\n e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \n \\sum_{j=1}^{d}\\left(n_{j} + \\frac{x^{j}_{\\alpha} + x^{j}_{\\beta}}{2} \\right)\n \\vec{a}_{j} \\right) } \n\\end{align}\nin Eqs.~(\\ref{eq:H_high_dim_hopping_modulation_alpha_split}) and (\\ref{eq:H_high_dim_onsite_modulation_alpha_split}) as the Peierls phases (see Eqs.~(\\ref{eq:Peierls_2_7_alpha}) and (\\ref{eq:Peierls_1_7_alpha})) due to the $U(1)$ gauge field in Eq.~(\\ref{eq:non_ortho_A}). \nNotice that there are no Peierls phases induced from the gauge field in Eq.~(\\ref{eq:H_high_dim_normal_hopping_alpha_split}). \n\nWe have thus generalized our dimensional promotion method to $d$D modulated lattice models whose orbitals are not located at the $d$D lattice points $\\sum_{j=1}^{d}n_{j}\\vec{a}_{j}$. \nSimilar to Tables~\\ref{tab:model_summary} and \\ref{tab:hopping_1}, we have summarized the Hamiltonian before and after the dimensional promotion in Table~\\ref{tab:model_summary_general_orbital_positions}, and the hopping matrix elements together with the Peierls phases in Table~\\ref{tab:hopping_1_general_orbital_positions}. \nA notable feature is that now both the hopping matrix elements of the $(d+N)$D lattice model {\\it without} the gauge field and the Peierls phases due to the gauge field in Eq.~(\\ref{eq:non_ortho_A}) encode information of the orbital positions. \nWhen all the orbitals are located right at the lattice points we have $\\vec{r}_{\\alpha} = 0$ for all $\\alpha$, namely $x^{j}_{\\alpha}=0$ for all $j$ and $\\alpha$ in Eq.~(\\ref{eq:r_alpha_expansion}). \nIn such cases, Table~\\ref{tab:hopping_1_general_orbital_positions} effectively reduces to Table~\\ref{tab:hopping_1}.\n\n\n\n\\begin{table}[h]\n\\centering\n\\begin{tabular}{|c|c|}\n\\hline\nOriginal $d$D modulated system & Eq.~(\\ref{eq:H_low_general_alpha}) \\\\\n\\hline\nPromoted $(d+N)$D system with $U(1)$ gauge fields & Eqs.~(\\ref{eq:H_high_dim_normal_hopping_alpha_split}--\\ref{eq:H_high_dim_onsite_modulation_alpha_split}) \\\\\n\\hline\nPromoted $(d+N)$D system without $U(1)$ gauge fields & Eqs.~(\\ref{eq:H_high_dim_normal_hopping_alpha_no_gauge}--\\ref{eq:H_high_dim_onsite_modulation_alpha_no_gauge}) \\\\\n\\hline\n\\end{tabular}\n\\caption{Relevant equations in the general dimensional promotion formalism with arbitrary orbital positions.}\n\\label{tab:model_summary_general_orbital_positions}\n\\end{table}\n\n\\begin{table}[h]\n\\centering\n\\begin{tabular}{|c|c|c|}\n\\hline\nHopping from $\\beta^{\\text{th}}$ orbital in unit cell $(\\vec{n},\\vec{\\nu})$ to & Hopping matrix elements & Peierls phases \\\\\n\\hline\n\\hline\n$\\alpha^{\\text{th}}$ orbital in unit cell $(\\vec{n}+\\vec{m},\\vec{\\nu})$ & $\\left[H_{\\vec{m}}\\right]_{\\alpha,\\beta}$ & $1$ \\\\\n\\hline\n$\\alpha^{\\text{th}}$ orbital in unit cell $(\\vec{n},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i})$ & $ \\left[V^{(i)}_{p_{i}}\\right]_{\\alpha,\\beta} e^{-i\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d}\\left( x^{j}_{\\alpha} + x^{j}_{\\beta} \\right) \\vec{a}_{j} \\right) } $ & $e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d}\\left(n_{j} + \\frac{x^{j}_{\\alpha} + x^{j}_{\\beta}}{2} \\right) \\vec{a}_{j} \\right) } $ \\\\\n\\hline\n$\\alpha^{\\text{th}}$ orbital in unit cell $(\\vec{n}+\\vec{l},\\vec{\\nu}-p_{i}\\hat{\\nu}_{i})$ & $\\left[H_{\\vec{l},p_{i}}^{(i)}\\right]_{\\alpha,\\beta} e^{-i\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left(\\sum_{j=1}^{d}\\left( l_{j} +x^{j}_{\\alpha} + x^{j}_{\\beta} \\right) \\vec{a}_{j} \\right) } $ & $e^{i2\\pi p_{i} \\vec{q}^{(i)}\\cdot \\left( \\sum_{j=1}^{d}\\left( n_{j} + \\frac{l_{j} +x^{j}_{\\alpha} + x^{j}_{\\beta} }{2} \\right) \\vec{a}_{j} \\right) } $ \\\\\n\\hline\n\\end{tabular}\n\\caption{Hopping terms in the promoted $(d+N)$D model with arbitrary orbital positions and the corresponding Peierls phases, expressed in terms of parameters from the $d$D modulated system in Eq.~(\\ref{eq:H_low_general}). \nNotice that $p_{i}\\hat{\\nu}_{i}$ does not imply a summation over $i$.}\n\\label{tab:hopping_1_general_orbital_positions}\n\\end{table}\n\n}\n\n\\subsection{\\label{subsec:examples_general_dim_promotion}Examples}\n\nTo demonstrate our general construction, we will use Table~\\ref{tab:model_summary} and Table~\\ref{tab:hopping_1} to consider four examples: \n(1) promoting the 1D Rice-Mele chain to a 2D square lattice with $\\pi$-flux, \n(2) promoting the 1D Rice-Mele chain with incoherent phase offsets in on-site and hopping modulations to a 3D cubic lattice coupled to a $U(1)$ gauge field, \n(3) promoting a 1D modulated system to a 2D hexagonal lattice with a perpendicular magnetic field, and \n(4) promoting a 2D modulated system with hexagonal lattice to a 3D hexagonal lattice coupled to a $U(1)$ gauge field.\n\n\\subsubsection{\\label{sec:ex_1D_RM_chain} 1D Rice-Mele chain $\\to$ 2D square lattice with $\\pi$-flux}\n\nThe Hamiltonian of the 1D Rice-Mele\\cite{RiceMele} chain oriented along the $x$-axis is given by\n\\begin{align}\n H_{\\text{Rice-Mele}} = \\sum_{n} \\left( t + \\delta t (-1)^{n} \\cos{\\phi} \\right)\\psi^{\\dagger}_{n+1}\\psi_{n} + \\text{h.c.} + \\sum_{n} (-1)^{n+1} \\Delta \\sin{\\phi} \\psi^{\\dagger}_{n}\\psi_{n},\n \\label{eq:supp_Rice_Mele_1}\n\\end{align}\nwhere $\\psi^{\\dagger}_{n}$ is the creation operator for an electron at position $n$, and $\\text{h.c.}$ denotes the Hermitian conjugate of all terms before it. \nWe can rewrite Eq.~(\\ref{eq:supp_Rice_Mele_1}) as\n\\begin{align}\n H_{\\text{Rice-Mele}} & = \\sum_{n} \\left( t + \\delta t \\cos{(\\pi n + \\phi)} \\right)\\psi^{\\dagger}_{n+1}\\psi_{n} + \\text{h.c.} - \\sum_{n} \\Delta \\sin{(\\pi n + \\phi)} \\psi^{\\dagger}_{n}\\psi_{n} \n \\label{eq:supp_Rice_Mele_2} \\\\\n & = \\sum_{n} \\left( t + \\delta t \\cdot \\frac{e^{i\\left( \\pi n + \\phi\\right)} + e^{-i\\left( \\pi n + \\phi\\right)}}{2} \\right)\\psi^{\\dagger}_{n+1}\\psi_{n} + \\text{h.c.} - \\sum_{n} \\Delta \\cdot \\frac{e^{i\\left( \\pi n + \\phi\\right)} - e^{-i\\left( \\pi n + \\phi\\right)}}{2i} \\psi^{\\dagger}_{n}\\psi_{n} .\\label{eq:supp_Rice_Mele_3}\n\\end{align}\nThus we can see that 1D Rice-Mele chain is a 1D modulated system with modulation wave vector $q = 1\/2$ ($2\\pi q = \\pi$). \nWe now choose the second, synthetic lattice vector to be $\\hat{y}$. \nThe promote 2D system will then have\n\\begin{align}\n \\vec{a}_{1} = (1,0),\\ \\vec{c}_{1} = (0,1),\\ \\vec{g}_{1} = 2\\pi(1,0) \\text{ and } \\vec{G}_{1} = 2\\pi (0,1),\n\\end{align}\nin terms of the notation from Eqs.~(\\ref{eq:reciprocal_1}--\\ref{eq:reciprocal_4}). \nNotice that $\\vec{a}_{1}$ and $\\vec{c}_{1}$ describe a square lattice. \nUsing the Fourier expansion convention in Table~\\ref{tab:model_summary} and Table~\\ref{tab:hopping_1} with $\\vec{q} = (1\/2,0)$ and Eq.~(\\ref{eq:supp_Rice_Mele_3}), we obtain the hopping terms summarized in Table~\\ref{tab:hopping_2}. \nMultiplying the entries for hopping matrices and Peierls phases, the Hamiltonian for the promoted 2D model is\n\\begin{align}\n H_{\\text{2D}} = \\sum_{n,m} \\left( t\\psi^{\\dagger}_{n+1,m}\\psi_{n,m}- \\frac{i\\delta t}{2}e^{i\\pi \\left(n+\\frac{1}{2} \\right)} \\psi^{\\dagger}_{n+1,m-1}\\psi_{n,m}+ \\frac{i\\delta t}{2}e^{-i\\pi \\left(n+\\frac{1}{2} \\right)} \\psi^{\\dagger}_{n+1,m+1}\\psi_{n,m}- \\frac{i\\Delta}{2}e^{-i\\pi n} \\psi^{\\dagger}_{n,m+1}\\psi_{n,m} + \\text{h.c.} \\right), \\label{eq:1D_Rice_Mele_to_2D_2}\n\\end{align}\nwhere $\\psi^{\\dagger}_{n,m}$ is the creation operator for an electron at position $(n,m)$, and the vector potential to which this 2D model is coupled is \n\\begin{align}\n \\vec{A} = \\frac{1}{2\\pi} \\vec{G}_{1} \\left( \\vec{q}\\cdot \\left[ \\left( \\vec{r} \\cdot \\vec{g}_{1}\\right) \\vec{a}_{1} \\right] \\right) = (0,\\pi x).\n\\end{align}\nThis $\\vec{A}$ produces a perpendicular magnetic field $\\vec{B} = \\pi \\hat{z}$ in the promoted 2D system such that there is a $\\pi$-flux per plaquette. \n\n\\begin{table}[h]\n\\centering\n\\begin{tabular}{|c|c|c|}\n\\hline\nHopping from $(n,m)$ to & Hopping matrices & Peierls phases \\\\\n\\hline\n\\hline\n$(n+1,m)$ & $t$ & $1$ \\\\\n\\hline\n$(n,m+1)$ & $ \\Delta \/ (2i) = -i\\Delta \/ 2$ & $e^{-i \\pi n}$ \\\\\n\\hline \n$(n+1,m+1)$ & $(\\delta t\/ 2) \\cdot e^{i \\pi \/2} = i\\delta t\/ 2 $ & $e^{-i \\pi \\left( n + \\frac{1}{2} \\right)}$ \\\\\n\\hline\n$(n+1,m-1)$ & $(\\delta t\/ 2) \\cdot e^{-i \\pi \/2} = -i\\delta t\/ 2 $ & $e^{i \\pi \\left( n + \\frac{1}{2} \\right)}$ \\\\\n\\hline\n\\end{tabular}\n\\caption{Hopping matrices and the corresponding Peierls phases for the promoted $2$D model from a 1D Rice-Mele chain.\nThe hopping matrices along the opposite directions of those listed here are omitted and can be obtained through Hermitian conjugation.}\n\\label{tab:hopping_2}\n\\end{table}\n\n\\subsubsection{\\label{sec:1D_RM_chain_incoherence}1D Rice-Mele chain with phase offset incoherence $\\to$ 3D cubic lattice coupled to a $U(1)$ gauge field}\n\nConsider again a 1D Rice-Mele chain oriented along the $x$-axis as in Sec.~\\ref{sec:ex_1D_RM_chain}. \nNow, however, we assume that the phase offsets in the hopping and on-site modulation can be different.\nThe $1$D Hamiltonian then reads\n\\begin{align}\n H_{\\text{Rice-Mele}} = \\sum_{n} \\left( t + \\delta t (-1)^{n} \\cos{\\phi^{(1)}} \\right)\\psi^{\\dagger}_{n+1}\\psi_{n} + \\text{h.c.} + \\sum_{n} (-1)^{n+1} \\Delta \\sin{\\phi^{(2)}} \\psi^{\\dagger}_{n}\\psi_{n},\n \\label{eq:Rice_Mele_incoherence}\n\\end{align}\nwhere $\\psi^{\\dagger}_{n}$ is the creation operator for an electron at position $n$, and $\\text{h.c.}$ means the Hermitian conjugate of all terms before it. \nThis situation arises when we are able to tune the phase offsets of the on-site and hopping modulations independently. \nWe can write Eq.~(\\ref{eq:Rice_Mele_incoherence}) as\n\\begin{align}\n H_{\\text{Rice-Mele}} & = \\sum_{n} \\left( t + \\delta t \\cos{(\\pi n + \\phi^{(1)})} \\right)\\psi^{\\dagger}_{n+1}\\psi_{n} + \\text{h.c.} - \\sum_{n} \\Delta \\sin{(\\pi n + \\phi^{(2)})} \\psi^{\\dagger}_{n}\\psi_{n} \n \\label{eq:Rice_Mele_incoherence_2} \\\\\n & = \\sum_{n} \\left( t + \\delta t \\cdot \\frac{e^{i\\left( \\pi n + \\phi^{(1)}\\right)} + e^{-i\\left( \\pi n + \\phi^{(1)}\\right)}}{2} \\right)\\psi^{\\dagger}_{n+1}\\psi_{n} + \\text{h.c.} - \\sum_{n} \\Delta \\cdot \\frac{e^{i\\left( \\pi n + \\phi^{(2)}\\right)} - e^{-i\\left( \\pi n + \\phi^{(2)}\\right)}}{2i} \\psi^{\\dagger}_{n}\\psi_{n}. \\label{eq:Rice_Mele_incoherence_3}\n\\end{align}\nWe now choose the second and third synthetic lattice vectors to be $\\hat{y}$ and $\\hat{z}$. \nThe promote 3D system will then have\n\\begin{align}\n & \\vec{a}_{1} = (1,0,0),\\ \\vec{c}_{1} = (0,1,0),\\ \\vec{c}_{2} = (0,0,1) \\\\\n & \\vec{g}_{1} = 2\\pi(1,0,0),\\ \\vec{G}_{1} = 2\\pi(0,1,0),\\ \\vec{G}_{2} = 2\\pi(0,0,1),\n\\end{align}\nin terms of the notation from Eqs.~(\\ref{eq:reciprocal_1}--\\ref{eq:reciprocal_4}). \nNotice that $\\{\\vec{a}_{1},\\vec{c}_{1},\\vec{c}_{2} \\}$ describe a 3D cubic lattice. \nUsing the Fourier expansion convention in Table~\\ref{tab:model_summary} and Table~\\ref{tab:hopping_1} with $\\vec{q}^{(1)}= \\vec{q}^{(2)} = (1\/2,0,0)$ and Eq.~(\\ref{eq:Rice_Mele_incoherence_3}), we obtain the hopping terms in this case summarized in Table~\\ref{tab:hopping_2_incoherence}. \nMultiplying the entries for hopping matrices and Peierls phases, the Hamiltonian for the promoted 3D model is\n\\begin{equation}\n\\hspace*{-0cm}\n H_{\\text{3D}} = \\sum_{n,m,l} \\left( t\\psi^{\\dagger}_{n+1,m,l}\\psi_{n,m,l}- \\frac{i\\delta t}{2}e^{i\\pi \\left(n+\\frac{1}{2} \\right)} \\psi^{\\dagger}_{n+1,m-1,l}\\psi_{n,m,l}+ \\frac{i\\delta t}{2}e^{-i\\pi \\left(n+\\frac{1}{2} \\right)} \\psi^{\\dagger}_{n+1,m+1,l}\\psi_{n,m,l}- \\frac{i\\Delta}{2}e^{-i\\pi n} \\psi^{\\dagger}_{n,m,l+1}\\psi_{n,m,l} + \\text{h.c.} \\right),\n\\end{equation}\nwhere $\\psi^{\\dagger}_{n,m,l}$ is the creation operator for an electron at position $(n,m,l)$, and the vector potential to which this 3D model is coupled is \n\\begin{align}\n \\vec{A} = \\frac{1}{2\\pi} \\left( \\vec{G}_{1} \\left(\\vec{q}^{(1)}\\cdot\\left[ \\left( \\vec{r}\\cdot \\vec{g}_{1} \\right)\\vec{a}_{1} \\right] \\right) + \\vec{G}_{2} \\left(\\vec{q}^{(2)}\\cdot\\left[ \\left( \\vec{r}\\cdot \\vec{g}_{1} \\right)\\vec{a}_{1} \\right] \\right) \\right) = (0,\\pi x, \\pi x).\n\\end{align}\nThis $\\vec{A}$ produces a magnetic field $\\vec{B} = (0,-\\pi,\\pi)$ in the promoted 3D system such that there is a $\\pi$-flux threading through the plaquettes in $zx$- and $xy$-planes. \nIn contrast to Sec.~\\ref{sec:ex_1D_RM_chain} where the promoted 2D model is a Chern insulator (see Sec.~\\ref{sec:Thouless_pump_1D_Rice_Mele}), the 1D Rice-Mele chain with phase offset incoherence promotes to a 3D gapless model. \nWe can see this by noting that with $\\phi^{(1)} = \\pi \/2$ and $\\phi^{(2)} = 0$, Eq.~(\\ref{eq:Rice_Mele_incoherence}) becomes\n\\begin{align}\n \\sum_{n} \\left( t \\psi^{\\dagger}_{n+1}\\psi_{n} + \\text{h.c.} \\right),\n\\end{align}\nwhich is the Hamiltonian for the 1D Rice-Mele chain at the gapless critical point.\n\n\\begin{table}[h]\n\\centering\n\\begin{tabular}{|c|c|c|}\n\\hline\nHopping from $(n,m,l)$ to & Hopping matrices & Peierls phases \\\\\n\\hline\n\\hline\n$(n+1,m,l)$ & $t$ & $1$ \\\\\n\\hline\n$(n,m,l+1)$ & $ \\Delta \/ (2i) = -i\\Delta \/ 2$ & $e^{-i \\pi n}$ \\\\\n\\hline \n$(n+1,m+1,l)$ & $(\\delta t\/ 2) \\cdot e^{i \\pi \/2} = i\\delta t\/ 2 $ & $e^{-i \\pi \\left( n + \\frac{1}{2} \\right)}$ \\\\\n\\hline\n$(n+1,m-1,l)$ & $(\\delta t\/ 2) \\cdot e^{-i \\pi \/2} = -i\\delta t\/ 2 $ & $e^{i \\pi \\left( n + \\frac{1}{2} \\right)}$ \\\\\n\\hline\n\\end{tabular}\n\\caption{Hopping matrices and the corresponding Peierls phases for the promoted $3$D model for a $1$D Rice-Mele chain with an incoherence between the phase offsets of on-site and hopping modulations. \nThe hopping matrices along the opposite directions of those listed here are omitted and can be obtained through Hermitian conjugation.}\n\\label{tab:hopping_2_incoherence}\n\\end{table}\n\n\\subsubsection{\\label{sec:1D_to_2D_hexa}1D modulated system $\\to$ 2D hexagonal lattice under a perpendicular magnetic field}\n\nLet us now explore what happens when our dimensionally-promoted lattice vectors are non-orthogonal. \nConsider a $1$D modulated system with both on-site and nearest-neighbor hopping modulations, with Hamiltonian\n\\begin{align}\n H_{1D} = & \\sum_{n_{1}} \\left( \\psi^{\\dagger}_{n_{1}+1} [H_{\\vec{a}_{1}}] \\psi_{n_{1}} + \\psi^{\\dagger}_{n_{1}+1} e^{i \\left( 2\\pi \\vec{q} \\cdot \\left( n_{1} + \\frac{1}{2} \\right)\\vec{a}_{1} + \\phi \\right)} [H_{\\vec{a}_{1}-\\vec{a}_{2}}] \\psi_{n_{1}} + \\text{h.c.} \\right) \\label{eq:1D_hexagonal_1} \\\\\n & + \\sum_{n_{1}} \\left( \\psi^{\\dagger}_{n_{1}} [H_{0}] \\psi_{n_{1}} + \\psi^{\\dagger}_{n_{1}} [V_{n_{1}}] \\psi_{n_{1}} \\right), \\label{eq:1D_hexagonal_2}\n\\end{align}\nwhere $\\text{h.c.}$ denotes hermitian conjugation, and $\\psi^{\\dagger}_{n_{1}}$ is the creation operator for an electron at position $n_{1}\\vec{a}_{1}$. \nAdditionally, $[V_{n_{1}}]$ is a modulated on-site interaction which can be decomposed into\n\\begin{align}\n [V_{n_{1}}] = e^{-i \\left( 2\\pi \\vec{q} \\cdot n_{1}\\vec{a}_{1} + \\phi\\right)} [H_{\\vec{a}_{2}}] + e^{i \\left( 2\\pi \\vec{q} \\cdot n_{1}\\vec{a}_{1} + \\phi \\right)} [H_{\\vec{a}_{2}}]^{\\dagger}, \\label{eq:1D_hexagonal_3}\n\\end{align}\nand $\\vec{a}_{1} = (1,0)$ and $\\vec{q} = (q,0)$. \nWe also have a modulated nearest-neighbor hopping term from site $n_{1}$ to $n_{1} + 1$ given by the second term\n\\begin{equation}\ne^{i \\left( 2\\pi \\vec{q} \\cdot \\left( n_{1} + \\frac{1}{2} \\right)\\vec{a}_{1} + \\phi \\right)} [H_{\\vec{a}_{1}-\\vec{a}_{2}}]\n\\end{equation}\nin Eq.~(\\ref{eq:1D_hexagonal_1}). \nThe $\\vec{a}_{2}$ in Eq.~(\\ref{eq:1D_hexagonal_1}--\\ref{eq:1D_hexagonal_3}) will become useful when we promote the dimension: \nAt this stage, $\\vec{a}_{2}$ is an unspecified label, though we have anticipated that it will be identified with the synthetic direction in the promoted lattice. \n\nWe promote the dimension of this 1D modulated system to 2D and choose the second, synthetic lattice vector as $\\vec{a}_{2} = (1\/2, \\sqrt{3}\/2)$. \nThus, $\\{\\vec{a}_{1},\\vec{a}_{2} \\}$ forms a 2D hexagonal lattice. \nThe reciprocal lattice vectors in this promoted 2D space are then\n\\begin{align}\n & \\vec{g}_{1} = 2\\pi \\left( 1 , -\\frac{1}{\\sqrt{3}} \\right), \\\\\n & \\vec{g}_{2} = 2\\pi \\left( 0,\\frac{2}{\\sqrt{3}}\\right),\n\\end{align}\nwhere we bear in mind that, if compared with our previous notation from Eqs.~(\\ref{eq:reciprocal_1}--\\ref{eq:reciprocal_4}), we should identify $\\vec{a}_{2} \\leftrightarrow \\vec{c}_{1}$ and $\\vec{g}_{2} \\leftrightarrow \\vec{G}_{1}$. \nUsing the Fourier expansion convention in Table~\\ref{tab:model_summary} and Table~\\ref{tab:hopping_1} with $\\vec{q} = (q,0)$, and Eqs.~(\\ref{eq:1D_hexagonal_1}--\\ref{eq:1D_hexagonal_3}), we obtain the hopping terms in this case in Table~\\ref{tab:hopping_3}. \nMultiplying the entries for hopping matrices and Peierls phases, the Hamiltonian for the promoted 2D model is\n\\begin{align}\n H_{2D}= & \\sum_{n_{1},n_{2}} \\left( \\psi^{\\dagger}_{n_{1}+1,n_{2}} [H_{\\vec{a}_{1}}] \\psi_{n_{1},n_{2}} + \\psi^{\\dagger}_{n_{1},n_{2}+1} [H_{\\vec{a}_{2}}]e^{-i 2\\pi \\vec{q} \\cdot n_{1}\\vec{a}_{1}} \\psi_{n_{1},n_{2}} + \\psi^{\\dagger}_{n_{1}+1,n_{2}-1} [H_{\\vec{a}_{1}-\\vec{a}_{2}}] e^{i 2\\pi \\vec{q} \\cdot \\left( n_{1} + \\frac{1}{2} \\right)\\vec{a}_{1}} \\psi_{n_{1},n_{2}} + \\text{h.c.} \\right) \\\\\n & + \\sum_{n_{1},n_{2}} \\psi^{\\dagger}_{n_{1},n_{2}} [H_{0}] \\psi_{n_{1},n_{2}},\n\\end{align}\nwhere $\\psi^{\\dagger}_{n_{1},n_{2}}$ is the creation operator for an electron at position $n_{1}\\vec{a}_{1}+ n_{2}\\vec{a}_{2}$, and the vector potential to which this 2D model is coupled is \n\\begin{align}\n \\vec{A} = \\frac{1}{2\\pi} \\vec{g}_{2} \\left( \\vec{q}\\cdot \\left[ \\left( \\vec{r} \\cdot \\vec{g}_{1}\\right) \\vec{a}_{1} \\right] \\right) = \\left( 0 , \\frac{4\\pi q}{\\sqrt{3}} \\left( x - \\frac{y}{\\sqrt{3}} \\right) \\right).\n\\end{align}\nThis $\\vec{A}$ reproduces a perpendicular magnetic field $\\vec{B} = \\frac{4\\pi q}{\\sqrt{3}} \\hat{z}$ in the promoted 2D system. \nWe thus see that this 1D modulated system may be used to map out the Hofstadter spectrum of a hexagonal lattice with an irrational magnetic flux.\n\n\\begin{table}[h]\n\\centering\n\\begin{tabular}{|c|c|c|}\n\\hline\nHopping from $n_{1}\\vec{a}_{1} + n_{2}\\vec{a}_{2}$ to & Hopping matrices & Peierls phase \\\\\n\\hline\n\\hline\n$n_{1}\\vec{a}_{1} + n_{2}\\vec{a}_{2}$ & $[H_{0}]$ & $1$ \\\\\n\\hline\n$(n_{1}+1)\\vec{a}_{1} + n_{2}\\vec{a}_{2}$ & $[H_{\\vec{a}_{1}}]$ & $1$ \\\\\n\\hline\n$n_{1}\\vec{a}_{1} + (n_{2}+1)\\vec{a}_{2}$ & $[H_{\\vec{a}_{2}}]$ & $e^{-i 2\\pi \\vec{q} \\cdot n_{1}\\vec{a}_{1}}$ \\\\\n\\hline\n$(n_{1}+1)\\vec{a}_{1} + (n_{2}-1)\\vec{a}_{2}$ & $e^{i 2\\pi \\vec{q} \\cdot \\frac{1}{2}\\vec{a}_{1}} [H_{\\vec{a}_{1}-\\vec{a}_{2}}]e^{-i \\pi \\vec{q}\\cdot \\vec{a}_{1}} = [H_{\\vec{a}_{1}-\\vec{a}_{2}}]$ & $e^{i 2\\pi \\vec{q}\\cdot \\left( n_{1} + \\frac{1}{2} \\right)\\vec{a}_{1}}$\\\\\n\\hline\n\\end{tabular}\n\\caption{Hopping matrices and the corresponding Peierls phases for the promoted $2$D model for a 1D modulated chain described by Eqs.~(\\ref{eq:1D_hexagonal_1}--\\ref{eq:1D_hexagonal_3}). \nThe hopping matrices along the opposite directions for those listed here are omitted and can be obtained through Hermitian conjugate.}\n\\label{tab:hopping_3}\n\\end{table}\n\n\\subsubsection{2D hexagonal lattice $\\to$ 3D hexagonal lattice coupled to a $U(1)$ gauge field}\n\nConsider a 2D modulated system with one on-site modulation, hexagonal lattice and Hamiltonian\n\\begin{align}\n H_{2D}= \\sum_{\\vec{n},\\vec{m}} \\psi^{\\dagger}_{\\vec{n}+\\vec{m}} [H_{\\vec{m}}] \\psi_{\\vec{n}} + \\sum_{\\vec{n}} \\psi^{\\dagger}_{\\vec{n}} [V_{\\vec{n}}] \\psi_{\\vec{n}}, \\label{eq:2D_hexa_to_3D_hexa_1}\n\\end{align}\nwhere $\\vec{n} = (n_{1},n_{2})$, $\\vec{m} = (m_{1},m_{2})$, $\\vec{a}_{1} = \\left(1\/2,\\sqrt{3}\/2\\right)$, $\\vec{a}_{2} = \\left(-1\/2,\\sqrt{3}\/2\\right)$, and $\\psi^{\\dagger}_{\\vec{n}}$ is the creation operator for an electron at lattice position $n_{1} \\vec{a}_{1} + n_{2} \\vec{a}_{2}$.\nThe matrix $[H_{\\vec{m}}]$ describes hopping terms from position $n_{1} \\vec{a}_{1} + n_{2} \\vec{a}_{2}$ to $(n_{1}+m_{1}) \\vec{a}_{1} + (n_{2}+m_{2})\\vec{a}_{2}$ which can include long range hopping terms. \nThe on-site modulation can be expanded as \n\\begin{align}\n [V_{\\vec{n}}] = \\sum_{p} [V_{p}] e^{i p \\left( 2\\pi \\vec{q} \\cdot \\left(\\sum_{j=1}^{2}n_{j}\\vec{a}_{j} \\right) + \\phi \\right) }, \\label{eq:2D_hexa_to_3D_hexa_2}\n\\end{align}\nwhere $p \\in \\mathbb{Z}$, $[V_{p}]$ is the $p^{\\text{th}}$ Fourier coefficient of $[V_{\\vec{n}}]$ and $\\vec{q} = (q_{x},q_{y})$ is the modulation wave vector parallel to the 2D system. \nWe next promote the dimension of this 2D system to 3D and choose the third, synthetic lattice vector $\\vec{a}_{3}$ as $(0,0,1)$. \nThe lattice vectors in the promoted 3D space are then\n\\begin{align}\n \\vec{a}_{1} = \\left( \\frac{1}{2},\\frac{\\sqrt{3}}{2},0 \\right),\\ \\vec{a}_{2} = \\left( -\\frac{1}{2},\\frac{\\sqrt{3}}{2},0 \\right),\\ \\vec{a}_{3} = \\left( 0,0,1 \\right),\n\\end{align}\nwhich describes a 3D hexagonal lattice. \nThe corresponding reciprocal lattice vectors are\n\\begin{align}\n \\vec{g}_{1} = 2\\pi \\left( 1, \\frac{1}{\\sqrt{3}},0 \\right),\\ \\vec{g}_{2} = 2\\pi \\left( -1, \\frac{1}{\\sqrt{3}},0 \\right),\\ \\vec{g}_{3} = 2\\pi (0,0,1).\n\\end{align}\nAs in Sec.~\\ref{sec:1D_to_2D_hexa}, we bear in mind that in comparing with Eqs.~(\\ref{eq:reciprocal_1}--\\ref{eq:reciprocal_4}), we should identify $\\vec{a}_{3} \\leftrightarrow \\vec{c}_{1} $ and $\\vec{g}_{3} \\leftrightarrow \\vec{G}_{1}$. \nUsing the Fourier expansion convention in Table~\\ref{tab:model_summary} and Table~\\ref{tab:hopping_1} with $\\vec{q} = (q_{x},q_{y})$, and Eqs.~(\\ref{eq:2D_hexa_to_3D_hexa_1}--\\ref{eq:2D_hexa_to_3D_hexa_2}), we obtain the hopping terms given in Table~\\ref{tab:hopping_4}. \nMultiplying the entries for hopping matrices and Peierls phases, the Hamiltonian for the promoted 3D model is\n\\begin{align}\n H_{3D} = \\sum_{\\vec{n},\\vec{m}} \\psi^{\\dagger}_{\\vec{n} + \\vec{m}} [H_{\\vec{m}}] \\psi_{\\vec{n}} + \\sum_{\\vec{n},p} \\psi^{\\dagger}_{\\vec{n} - (0,0,p)} [V_{p}] e^{i 2\\pi p \\vec{q} \\cdot \\left( \\sum_{j=1}^{2}n_{j}\\vec{a}_{j} \\right)} \\psi_{\\vec{n}},\n\\end{align}\nwhere $\\vec{n} = (n_{1},n_{2},n_{3})$, $\\vec{m }= (m_{1},m_{2},0)$, $\\psi^{\\dagger}_{\\vec{n}}$ is the creation operator for an electron at lattice position $\\sum_{j=1}^{3}n_{j}\\vec{a}_{j}$, and the vector potential to which this 3D model is coupled is \n\\begin{align}\n \\vec{A} = \\frac{1}{2\\pi} \\vec{g}_{3} \\left( \\vec{q}\\cdot \\left[ \\left( \\vec{r} \\cdot \\vec{g}_{1}\\right) \\vec{a}_{1} + \\left( \\vec{r} \\cdot \\vec{g}_{2}\\right) \\vec{a}_{2} \\right] \\right) = \\left( 0,0,2\\pi\\left(q_{x}x + q_{y}y \\right) \\right).\n\\end{align}\nThis $\\vec{A}$ produces a magnetic field $\\vec{B} = \\left(2\\pi q_{y},-2\\pi q_{x},0 \\right)$ in the promoted 3D space.\n\n\n\\begin{table}[h]\n\\centering\n\\begin{tabular}{|c|c|c|}\n\\hline\nHopping from $\\sum_{j=1}^{3}n_{j}\\vec{a}_{j}$ to & Hopping matrices & Peierls phase \\\\\n\\hline\n\\hline\n$\\sum_{j=1}^{3}n_{j}\\vec{a}_{j} + \\sum_{j=1}^{2} m_{j}\\vec{a}_{j}$ & $[H_{\\vec{m}}]$ & $1$ \\\\\n\\hline\n$\\sum_{j=1}^{3}n_{j}\\vec{a}_{j} - p \\vec{a}_{3}$ & $[V_{p}]$ & $e^{i2\\pi p \\vec{q} \\cdot \\left(\\sum_{j=1}^{2}n_{j}\\vec{a}_{j} \\right)}$ \\\\\n\\hline\n\\end{tabular}\n\\caption{Hopping matrices and the corresponding Peierls phases for the promoted $3$D model from a 2D hexagonal lattice. \nThe hopping matrices along the opposite directions of those listed here are omitted and can be obtained through Hermitian conjugation.}\n\\label{tab:hopping_4}\n\\end{table}\n\n\n\n\\section{\\label{sec:Thouless_pump_1D_Rice_Mele}Thouless pump and dimensional promotion for 1D Rice-Mele model}\n\nHere we review how topological properties such as the Thouless pump\\cite{Thouless_pump_original_paper} in the 1D Rice-Mele model\\cite{RiceMele} (SSH chain\\cite{su1979solitons}) can be attributed to a 2D lattice model coupled to a $U(1)$ gauge field, via our dimensional promotion procedure. \nConsider the 1D Rice-Mele model\n\\begin{align}\n H_{\\text{Rice-Mele}} = \\sum_{n} \\left( t + \\delta t (-1)^{n} \\cos{\\phi} \\right)c^{\\dagger}_{n+1}c_{n} + \\text{h.c.} + \\sum_{n} (-1)^{n+1} \\Delta \\sin{\\phi} c^{\\dagger}_{n}c_{n},\n \\label{eq:supp_Rice_Mele}\n\\end{align}\nwhere $c^{\\dagger}_{n}$ is the creation operator for an electron at 1D sites $n \\in \\mathbb{Z}$, and $\\text{h.c.}$ means the Hermitian conjugate of all terms before it. \nThis Hamiltonian describes a $1$D chain with a twofold (Peierls) CDW distortion. \nSuppose we prepare the ground state (with Fermi level $E_{F}=0$) for $H_{\\text{Rice-Mele}}$ with $t = 1$, $\\delta t=-0.1$, $\\Delta = 0.5$ at $\\phi = 0$. \nBefore dimensional promotion, let us first review the properties of this 1D Hamiltonian. \nAs the phase $\\phi$ adiabatically changes from $0$ to $2\\pi$, the single valence band Wannier center\\cite{Kohn59,Brouder2007,Marzari2012,ksv,fu2006time} in the bulk is pumped by one unit cell (two lattice sites), as shown in Fig.~\\ref{Fig_Rice_Mele} (c). \nThe bulk polarization then changes by $(-1)\\times d = -d$. \nThis bulk polarization change is quantized\\cite{ksv,resta1994macroscopic} in units of the unit cell length $d=2$. \nThis is the classic realization of the Thouless pump\\cite{Thouless_pump_original_paper} in 1D. \nIn Fig.~\\ref{Fig_Rice_Mele} (a) we show the $\\phi$-sliding spectrum (defined in Sec.~IV of the main text) for a finite chain with size $100$ and positions $n = 1 ,\\ldots, 100$. \nWe see that in addition to the gapped bulk states, there are two linearly dispersing modes that cross the bulk gap. \nThis indicates that during the pumping process, localized charges at the right and left end (see Figs.~\\ref{Fig_Rice_Mele} (b) and (d)) of the chain flow out of and into the occupied state subspace, respectively. \nThis is the celebrated topological origin of the quantized polarization change in the bulk, and the existence of boundary states crossing the energy gap.\n\\begin{figure}[h]\n \\includegraphics[scale=0.4]{Fig_1D_Rice_Mele.pdf}\n \\caption{(a) $\\phi$-sliding spectrum of the Rice-Mele model with $t = 1$, $\\delta t=-0.1$ and $\\Delta = 0.5$. \n (c) The shifting of the valence band Wannier center (blue dots) as a function of $\\phi$. \n The vertical black lines denote the boundary of the unit cell, which has length $2$. \n The orange dots denote the positions of the tight-binding basis orbitals. \n The origin of the $x$-axis is placed at the middle of the bond with hopping $t - \\delta t \\cos{\\phi}$ where $t = 1$ and $\\delta t = -0.1$. \n In other words, the unit cell is formed by the sites $n=1$ and $n=2$ in Eq.~(\\ref{eq:supp_Rice_Mele}) and the origin is placed at the midpoint between these two sites. \n The inversion centers at $\\phi=0$ and $\\pi$ lie at integer values of $x$, and as such the Wannier centers at these two $\\phi$ are located either at the center of the unit cell ($\\phi=0$) or the boundaries between unit cells ($\\phi=\\pi)$\\cite{Aris2014}. \n Also, this unit cell choice is commensurate with the finite size system in (a), (b) and (d), where we choose $n = 1 ,\\ldots, 100$. \n (b) $\\&$ (d) Probability distribution of localized modes around the two ends at $\\phi = 0.9 \\pi$ and energy $E = -0.1545$ $\\&$ $+0.1545$. \n Notice that during the adiabatic pumping from $\\phi =0$ to $2\\pi$, it is the boundary state at the left [right] end, see (d) [(b)], that flows into [out of] the subspace of occupied states. \n This is consistent with (c) where the Wannier center flows toward the right and reappear at the left boundary of the unit cell at $\\phi = \\pi$.}\n \\label{Fig_Rice_Mele}\n\\end{figure}\n\nLet us now see how these properties emerge in our 2D dimensionally-promoted picture. \nIdentifying $\\phi$ as the crystal momentum $k_{y}$ along the second, synthetic dimension $y$, $H_{\\text{Rice-Mele}}$ is equivalent to the Bloch Hamiltonian of a 2D model (see Sec.~\\ref{sec:ex_1D_RM_chain} for the detailed derivation) \n\\begin{align}\n H_{\\text{2D}} = \\sum_{n,m} \\left( tc^{\\dagger}_{n+1,m}c_{n,m}- \\frac{i\\delta t}{2}e^{i\\pi \\left(n+\\frac{1}{2} \\right)} c^{\\dagger}_{n+1,m-1}c_{n,m}+ \\frac{i\\delta t}{2}e^{-i\\pi \\left(n+\\frac{1}{2} \\right)} c^{\\dagger}_{n+1,m+1}c_{n,m}- \\frac{i\\Delta}{2}e^{-in\\pi} c^{\\dagger}_{n,m+1}c_{n,m} \\right)+ \\text{h.c.}, \\label{eq:1D_Rice_Mele_to_2D_1}\n\\end{align}\nwith a fixed $k_{y}$. Here $c^{\\dagger}_{n,m}$ is the creation operator for an electron at 2D site $(n,m)$ on a square lattice. \nIf, during the adiabatic process, $H_{\\text{Rice-Mele}}$ is always gapped, the quantized polarization change is determined by the Chern number\\cite{tknn,niu1984quantised,niu1985quantized,Aris2014,fu2006time,bernevigbook} of the occupied bands of $H_{\\text{2D}}$. \nWe can thus characterize the topological properties shown in Fig.~\\ref{Fig_Rice_Mele} by a Chern number $C=1$. \nWe notice that in addition to hopping terms from site $(n,m)$ to $(n\\pm1,m)$ and $(n,m\\pm1)$, $H_{\\text{2D}}$ also has hopping terms going from site $(n,m)$ to $(n\\pm1,m+1)$ and $(n\\pm1,m- 1)$. \nThis is in contrast to the standard textbook correspondence between the SSH chain and a 2D Chern insulator, where a re-embedding of orbitals within the enlarged unit cell is typically used to obtain a 2D model with only perpendicular hoppings along the $\\hat{x}$ and $\\hat{y}$ directions.\nNote also that $H_{\\text{2D}}$ is a 2D lattice model coupled to a $U(1)$ gauge field $\\vec{A} = (0,\\pi x)$ through the Peierls substitution\\cite{Peierls_substitution} assuming the electron has charge $-1$. \nThis $\\vec{A}$ produces a uniform $U(1)$ magnetic field threading $\\pi$-flux per plaquette, since the magnetic field $\\vec{B} = \\vec{\\nabla} \\cross \\vec{A} = \\pi \\hat{z}$. \nReinserting factors of $\\hbar$ and $|e|$, this corresponds to half a flux quantum $\\Phi_{0} = 2\\pi \\hbar \/ |e|$ per plaquette. \nThe localized and mid-gap states in $H_{\\text{Rice-Mele}}$ are then identified as the chiral edge modes due to the quantum Hall effect in $H_{\\text{2D}}$.\n\n\\section{\\label{sec:promoted_lattice_model} Promoted lattice models}\n\nFor completeness, in this section we give the promoted lattice models in position space corresponding to the 2D modulated system with helical sliding modes and the 3D Weyl semimetal with mean-field charge-density waves (CDWs) in Secs.~V and VI of the main text, respectively.\n\n\\subsection{\\label{sec:promoted_helical_lattice_model}2D modulated system with helical sliding modes}\n\nThe promoted 3D lattice model\\cite{Wieder_spin_decoupled_helical_HOTI} for the 2D modulated system with helical sliding modes in the main text is\n\\begin{align}\n H = \\sum_{\\vec{n}} & \\left( \\psi^{\\dagger}_{\\vec{n}+\\hat{x}} [H_{+\\hat{x}}]\\psi_{\\vec{n}} + \\psi^{\\dagger}_{\\vec{n}-\\hat{x}} [H_{+\\hat{x}}]^{\\dagger}\\psi_{\\vec{n}} +\\psi^{\\dagger}_{\\vec{n}+\\hat{z}} [H_{+\\hat{z}}]\\psi_{\\vec{n}} + \\psi^{\\dagger}_{\\vec{n}-\\hat{z}} [H_{+\\hat{z}}]^{\\dagger}\\psi_{\\vec{n}}+ \\psi^{\\dagger}_{\\vec{n}} [H_{\\text{on-site}}] \\psi_{\\vec{n}} \\nonumber \\right. \\\\\n & \\left. + \\psi^{\\dagger}_{\\vec{n}+\\hat{y}} [H_{+\\hat{y}}] e^{-i\\text{A}_{\\vec{n}+\\hat{y},\\vec{n}}}\\psi_{\\vec{n}} + \\psi^{\\dagger}_{\\vec{n}-\\hat{y}} e^{i\\text{A}_{\\vec{n},\\vec{n}-\\hat{y}}}[H_{+\\hat{y}}]^{\\dagger} \\psi_{\\vec{n}} \\right) \\label{eq:lattice_model_helical_sliding} \n\\end{align}\nwith\n\\begin{align}\n & [H_{+\\hat{x}}] = \\frac{v_{x}}{2}\\tau_{z}\\mu_{0}\\sigma_{0} - \\frac{u_{x}}{2i}\\tau_{y}\\mu_{y}\\sigma_{0},\\\\\n & [H_{+\\hat{y}}] = \\frac{v_{y}}{2}\\tau_{z}\\mu_{0}\\sigma_{0} - \\frac{v_{H}}{2 i} \\tau_{y}\\mu_{z}\\sigma_{z},\\\\\n & [H_{+\\hat{z}}] = \\frac{v_{z}}{2}\\tau_{z}\\mu_{0}\\sigma_{0} - \\frac{u_{z}}{2i}\\tau_{x}\\mu_{0}\\sigma_{0}, \\\\\n & [H_{\\text{on-site}}] = m_{1}\\tau_{z}\\mu_{0}\\sigma_{0}+m_{2}\\tau_{z}\\mu_{x}\\sigma_{0} + m_{3}\\tau_{z}\\mu_{z}\\sigma_{0}+m_{v_{1}}\\tau_{0}\\mu_{z}\\sigma_{0} + m_{v_{2}}\\tau_{0}\\mu_{x}\\sigma_{0}.\n\\end{align}\nThe $SU(2)$ lattice gauge field is given by\n\\begin{align}\n \\text{A}_{\\vec{n}+\\hat{y},\\vec{n}} =2\\pi \\left( q_{x}n_{x}+q_{z}n_{z} \\right) \\tau_{0}\\mu_{0}\\sigma_{z}. \\label{eq:lattice_SU2_A}\n\\end{align}\nThe $SU(2)$ gauge field in the continuous coordinate representation is then (dropping the $\\tau_{0}\\mu_{0}$)\n\\begin{align}\n \\vec{A} = (0,2\\pi(q_{x}x+q_{z}z)\\sigma_{z},0). \\label{eq:A_SU2}\n\\end{align}\nThe corresponding $SU(2)$ magnetic field $\\vec{B} = \\vec{\\nabla} \\cross \\vec{A} - i \\vec{A} \\cross \\vec{A}$ is\\cite{Estienne_2011}\n\\begin{align}\n \\vec{B} = (-2\\pi q_{z}\\sigma_{z},0,2\\pi q_{x}\\sigma_{z}). \\label{eq:B_SU2_1}\n\\end{align}\nEq.~(\\ref{eq:lattice_model_helical_sliding}) describes a helical higher-order topological insulator (HOTI) coupled to a $SU(2)$ gauge field. \nIf we Fourier transform Eq.~(\\ref{eq:lattice_model_helical_sliding}) along $y$ and regard $k_{y}$ (wavenumber along $y$) as the sliding phase $\\phi$, we can obtain the 2D modulated system in the main text.\n\n\\subsection{\\label{sec:promoted_4D_lattice_model}3D Weyl semimetal with mean-field charge density waves}\n\nThe promoted 4D lattice model for the 3D Weyl semimetal with mean-field CDW order\\cite{dynamical_axion_insulator_BB} in the main text is\n\\begin{align}\n H&=\\left(\\sum_{\\vec{n}}\\left[-it_x\\psi^\\dag_{\\vec{n}+\\hat{x}}\\sigma_x \\psi_{\\vec{n}}-it_y \\psi^\\dag_{\\vec{n}+\\hat{y}}\\sigma_y \\psi_{\\vec{n}}+t_z\\psi^\\dag_{\\vec{n}+\\hat{z}}\\sigma_z \\psi_{\\vec{n}} + |\\Delta| e^{-i2 \\pi q n_{z}} \\psi^\\dag_{\\vec{n}+\\hat{w}} \\sigma_{z} \\psi_{\\vec{n}} \\right]\\right. \\nonumber \\\\\n & \\left. +\\sum_{\\vec{n}}\\frac{m}{2}\\left(\\psi^\\dag_{\\vec{n}+\\hat{x}}\\sigma_z \\psi_{\\vec{n}} + \\psi^\\dag_{\\vec{n}+\\hat{y}}\\sigma_z \\psi_{\\vec{n}} -2 \\psi^\\dag_{\\vec{n}}\\sigma_z \\psi_{\\vec{n}} \\right) -\\sum_{\\vec{n}} t_z \\left(\\cos (\\pi q)\\right) \\psi^\\dag_{\\vec{n}}\\sigma_z \\psi_{\\vec{n}}\\right) +\\mathrm{h.c.}, \\label{eq:lattice_model_4D_nodal_line}\n\\end{align}\nwhere the phase factors correspond to Peierls substitution of a 4D $U(1)$ gauge field\n\\begin{align}\n \\vec{A} = (0,0,0,2\\pi q z).\n\\end{align}\nThe only non-zero components of the $U(1)$ field strength $F_{\\mu \\nu} = \\partial_{\\mu} A_{\\nu} - \\partial_{\\nu} A_{\\mu}$ is\n\\begin{align}\n F_{zw} = -F_{wz} = \\partial_{z}A_{w} - \\partial_{w}A_{z}=2 \\pi q, \\label{eq:4D_U1_Fzw}\n\\end{align}\nwhich threads through the $zw$ plane. \nWithout coupling to a 4D $U(1)$ gauge field, the Bloch Hamiltonian of Eq.~(\\ref{eq:lattice_model_4D_nodal_line}) with $q=0$ is\n\\begin{align}\n H(\\vec{k})=& -2[t_x \\sin (k_x) \\sigma_x +t_y \\sin (k_y) \\sigma_y] +2t_z[\\cos (k_z) -\\cos (\\pi q)] \\sigma_{z} -m[2-\\cos (k_x) - \\cos (k_y)] \\sigma_{z} + 2|\\Delta|\\cos{(k_{w})}\\sigma_{z},\n\\label{eq:lattice_model_4D_nodal_line_bloch}\n\\end{align}\nwhich has a nodal line in the $k_{z}$-$k_{w}$ plane (with $k_{x}=k_{y}=0$) defined by\n\\begin{align}\n t_{z}\\cos(k_{z}) + |\\Delta| \\cos(k_{w}) = t_{z}\\cos{(\\pi q)}. \\label{eq:lattice_nodal_line}\n\\end{align}\nTherefore, Eq.~(\\ref{eq:lattice_model_4D_nodal_line}) describes a 4D nodal line system coupled to a 4D $U(1)$ gauge field where the corresponding field strength Eq.~(\\ref{eq:4D_U1_Fzw}) threads through the area enclosed by the nodal line defined in Eq.~(\\ref{eq:lattice_nodal_line}). \nIf we Fourier transform Eq.~(\\ref{eq:lattice_model_4D_nodal_line}) along $w$ and regard $k_{w}$ as the sliding phase $\\phi$, we obtain the model for a 3D Weyl semimetal with mean-field CDW order given in the main text.\n\n\\section{\\label{sec:inv_sym_gauge_tr}Inversion symmetry up to a gauge transformation}\n \nIn this section we will show that if a lattice has inversion symmetry, such as the 3D models promoted from our examples of 2D modulated systems with chiral and helical sliding modes, then upon coupling to a $U(1)$ or $SU(2)$ gauge field producing a constant magnetic field, the inversion symmetry is still preserved {\\it up to a gauge transformation}. \nWe will do this in details in the simplest case, which is a 2D square lattice coupled to a perpendicular magnetic field. \nWe will briefly mention the generalization to the 3D cases, which corresponds to the dimensional promotion from 2D modulated systems.\n\n\\subsection{2D system with inversion symmetry}\n\nLet us consider a 2D square lattice with only one degree of freedom within each unit cell labelled by $(n,m) \\in \\mathbb{Z}^{2}$\n\\begin{align}\n H = -t\\sum_{n,m} \\left( \\psi^{\\dagger}_{n+1,m}\\psi_{n,m} +\\psi^{\\dagger}_{n-1,m}\\psi_{n,m}+ e^{-i2\\pi bn}\\psi^{\\dagger}_{n,m+1}\\psi_{n,m}+ e^{+i2\\pi bn}\\psi^{\\dagger}_{n,m-1}\\psi_{n,m} \\right). \\label{eq:inv_sym_gauge_tr_1}\n\\end{align}\nEq.~(\\ref{eq:inv_sym_gauge_tr_1}) is coupled to a $U(1)$ gauge field $\\vec{A} = 2\\pi bx \\hat{y}$ through Peierls substitution where we have assumed the particle carries charge $-1$. \nThis $U(1)$ gauge field produces a constant perpendicular magnetic field $\\vec{B} = 2\\pi b \\hat{z}$. \nIf we turn off the $U(1)$ gauge field by setting $b = 0$, Eq.~(\\ref{eq:inv_sym_gauge_tr_1}) will have inversion symmetry, where the inversion operators are defined by \n\\begin{align}\n & \\mathcal{I}_{n_{c},m_{c}} \\psi^{\\dagger}_{n,m} \\mathcal{I}_{n_{c},m_{c}}^{-1} = \\psi^{\\dagger}_{2n_{c}-n,2m_{c}-m}, \\label{eq:inv_sym_gauge_tr_2} \\\\\n & \\mathcal{I}_{n_{c},m_{c}} \\psi_{n,m} \\mathcal{I}_{n_{c},m_{c}}^{-1} = \\psi_{2n_{c}-n,2m_{c}-m}. \\label{eq:inv_sym_gauge_tr_3}\n\\end{align}\nHere $\\mathcal{I}_{n_{c},m_{c}}$ is a unitary operator and $(n_{c},m_{c})$ is the inversion center (which can be any lattice point). We then have\n\\begin{align}\n \\mathcal{I}_{n_{c},m_{c}} H \\mathcal{I}_{n_{c},m_{c}}^{-1} = H\\ \\forall (n_{c},m_{c})\n\\end{align}\nprovided that the summation over $n$ and $m$ in Eq.~(\\ref{eq:inv_sym_gauge_tr_1}) goes from $- \\infty$ to $+\\infty$. \nWhen $b \\ne 0$, we must modify our unitary inversion operations in Eq.~(\\ref{eq:inv_sym_gauge_tr_2}) and Eq.~(\\ref{eq:inv_sym_gauge_tr_3}) to be\n\\begin{align}\n & \\mathcal{I}_{n_{c},m_{c}} \\psi^{\\dagger}_{n,m} \\mathcal{I}_{n_{c},m_{c}}^{-1} = \\psi^{\\dagger}_{2n_{c}-n,2m_{c}-m}e^{i4\\pi b n_{c}(m-m_{c})}, \\label{eq:inv_sym_gauge_tr_4} \\\\\n & \\mathcal{I}_{n_{c},m_{c}} \\psi_{n,m} \\mathcal{I}_{n_{c},m_{c}}^{-1} = \\psi_{2n_{c}-n,2m_{c}-m}e^{-i4\\pi b n_{c}(m-m_{c})}, \\label{eq:inv_sym_gauge_tr_5}\n\\end{align}\nwhich acts as inversion through the center $(n_{c},m_{c})$ together with a gauge transformation.\nWith these modified inversion operations, the following three identities can be proved: \n\\begin{align}\n & \\mathcal{I}_{n_{c},m_{c}} H \\mathcal{I}_{n_{c},m_{c}}^{-1} = H \\ \\forall (n_{c},m_{c}), \\label{eq:inv_sym_gauge_tr_6} \\\\\n & \\left( \\mathcal{I}_{n_{c},m_{c}} \\right)^{2} \\psi^{\\dagger}_{n,m} \\left( \\mathcal{I}_{n_{c},m_{c}}^{-1} \\right)^{2} = \\psi^{\\dagger}_{n,m}, \\label{eq:inv_sym_gauge_tr_7}\\\\\n & \\left( \\mathcal{I}_{n_{c},m_{c}} \\right)^{2} \\psi_{n,m} \\left( \\mathcal{I}_{n_{c},m_{c}}^{-1} \\right)^{2} = \\psi_{n,m}. \\label{eq:inv_sym_gauge_tr_8}\n\\end{align}\nWe prove Eq.~(\\ref{eq:inv_sym_gauge_tr_6}) as follows:\n\\begin{align}\n \\mathcal{I}_{n_{c},m_{c}} H \\mathcal{I}_{n_{c},m_{c}}^{-1} & = -t \\sum_{n,m} \\begin{bmatrix}\n \\psi^{\\dagger}_{2n_{c}-n-1,2m_{c}-m} e^{i 4\\pi b n_{c}(m-m_{c})} \\psi_{2n_{c}-n,2m_{c}-m} e^{-i 4\\pi b n_{c}(m-m_{c})} \\\\\n + \\psi^{\\dagger}_{2n_{c}-n+1,2m_{c}-m} e^{i 4\\pi b n_{c}(m-m_{c})} \\psi_{2n_{c}-n,2m_{c}-m} e^{-i 4\\pi b n_{c}(m-m_{c})} \\\\\n + e^{-i2\\pi b n } \\psi^{\\dagger}_{2n_{c}-n,2m_{c}-m-1}e^{i 4\\pi b n_{c}(m+1-m_{c})} \\psi_{2n_{c}-n,2m_{c}-m} e^{-i 4\\pi b n_{c}(m-m_{c})} \\\\\n + e^{+i2\\pi b n } \\psi^{\\dagger}_{2n_{c}-n,2m_{c}-m+1}e^{i 4\\pi b n_{c}(m-1-m_{c})} \\psi_{2n_{c}-n,2m_{c}-m} e^{-i 4\\pi b n_{c}(m-m_{c})}\n \\end{bmatrix} \\label{eq:pf_inv_1}\\\\\n & = -t \\sum_{n,m} \\begin{bmatrix}\n \\psi^{\\dagger}_{2n_{c}-n-1,2m_{c}-m} \\psi_{2n_{c}-n,2m_{c}-m} \\\\\n + \\psi^{\\dagger}_{2n_{c}-n+1,2m_{c}-m} \\psi_{2n_{c}-n,2m_{c}-m} \\\\\n + e^{-i2\\pi b n } \\psi^{\\dagger}_{2n_{c}-n,2m_{c}-m-1}e^{i 4\\pi b n_{c}} \\psi_{2n_{c}-n,2m_{c}-m} \\\\\n + e^{+i2\\pi b n } \\psi^{\\dagger}_{2n_{c}-n,2m_{c}-m+1}e^{-i 4\\pi b n_{c}} \\psi_{2n_{c}-n,2m_{c}-m} \n \\end{bmatrix} \\label{eq:pf_inv_2} \\\\\n & = -t \\sum_{n,m} \\begin{bmatrix}\n \\psi^{\\dagger}_{n-1,m} \\psi_{n,m} \\\\\n + \\psi^{\\dagger}_{n+1,m} \\psi_{n,m} \\\\\n + e^{-i2\\pi b (-n+2n_{c}) } \\psi^{\\dagger}_{n,m-1}e^{i 4\\pi b n_{c}} \\psi_{n,m} \\\\\n + e^{+i2\\pi b (-n+2n_{c}) } \\psi^{\\dagger}_{n,m+1}e^{-i 4\\pi b n_{c}} \\psi_{n,m} \n \\end{bmatrix} \\label{eq:pf_inv_3} \\\\\n & = -t \\sum_{n,m} \\begin{bmatrix}\n \\psi^{\\dagger}_{n-1,m} \\psi_{n,m} \\\\\n + \\psi^{\\dagger}_{n+1,m} \\psi_{n,m} \\\\\n + e^{+i2\\pi bn } \\psi^{\\dagger}_{n,m-1}\\psi_{n,m} \\\\\n + e^{-i2\\pi bn } \\psi^{\\dagger}_{n,m+1} \\psi_{n,m} \n \\end{bmatrix} \\label{eq:pf_inv_4} \\\\\n & = H. \\label{eq:pf_inv_5}\n\\end{align}\nIn Eq.~(\\ref{eq:pf_inv_1}) we apply the inversion operation to each of the creation and annihilation operators according to Eq.~(\\ref{eq:inv_sym_gauge_tr_4}) and Eq.~(\\ref{eq:inv_sym_gauge_tr_5}). \nThe first to fourth terms in Eq.~(\\ref{eq:pf_inv_1}) correspond to the (transformed) first to fourth terms in Eq.~(\\ref{eq:inv_sym_gauge_tr_1}). \nIn Eq.~(\\ref{eq:pf_inv_2}) we cancel out redundant exponential phase factors and reindex $n \\to -n + 2n_{c}$ and $m\\to -m+2m_{c}$ in Eq.~(\\ref{eq:pf_inv_3}). \nEq.~(\\ref{eq:pf_inv_4}) and Eq.~(\\ref{eq:pf_inv_5}) then show that the transformed Hamiltonian is the same. \nWe can also prove Eq.~(\\ref{eq:inv_sym_gauge_tr_7}) as follows:\n\\begin{align}\n \\left( \\mathcal{I}_{n_{c},m_{c}} \\right)^{2} \\psi^{\\dagger}_{n,m} \\left( \\mathcal{I}_{n_{c},m_{c}}^{-1} \\right)^{2} & = \\mathcal{I}_{n_{c},m_{c}} \\mathcal{I}_{n_{c},m_{c}} \\psi^{\\dagger}_{n,m} \\mathcal{I}_{n_{c},m_{c}}^{-1} \\mathcal{I}_{n_{c},m_{c}}^{-1} \\\\\n & = \\mathcal{I}_{n_{c},m_{c}} \\psi^{\\dagger}_{2n_{c}-n,2m_{c}-m}e^{i4\\pi b n_{c}(m-m_{c})} \\mathcal{I}_{n_{c},m_{c}}^{-1}\\\\\n & = \\psi^{\\dagger}_{2n_{c}-(2n_{c}-n),2m_{c}-(2m_{c}-m)} e^{i4\\pi b n_{c}((2m_{c}-m)-m_{c})} e^{i4\\pi b n_{c}(m-m_{c})}\\\\\n & = \\psi^{\\dagger}_{n,m} e^{i4\\pi b n_{c}(m_{c}-m)} e^{i4\\pi b n_{c}(m-m_{c})} \\\\\n & = \\psi^{\\dagger}_{n,m} .\n\\end{align}\nWe have used Eq.~(\\ref{eq:inv_sym_gauge_tr_4}) twice above, first acting on $\\psi^{\\dagger}_{n,m}$ and then on $\\psi^{\\dagger}_{2n_{c}-n,2m_{c}-m}$. \nEq.~(\\ref{eq:inv_sym_gauge_tr_8}) can also be proved in a similar way as follows:\n\\begin{align}\n \\left( \\mathcal{I}_{n_{c},m_{c}} \\right)^{2} \\psi_{n,m} \\left( \\mathcal{I}_{n_{c},m_{c}}^{-1} \\right)^{2} & = \\mathcal{I}_{n_{c},m_{c}} \\mathcal{I}_{n_{c},m_{c}} \\psi_{n,m} \\mathcal{I}_{n_{c},m_{c}}^{-1} \\mathcal{I}_{n_{c},m_{c}}^{-1} \\\\\n & = \\mathcal{I}_{n_{c},m_{c}} \\psi_{2n_{c}-n,2m_{c}-m}e^{-i4\\pi b n_{c}(m-m_{c})} \\mathcal{I}_{n_{c},m_{c}}^{-1}\\\\\n & = \\psi_{2n_{c}-(2n_{c}-n),2m_{c}-(2m_{c}-m)} e^{-i4\\pi b n_{c}((2m_{c}-m)-m_{c})} e^{-i4\\pi b n_{c}(m-m_{c})}\\\\\n & = \\psi_{n,m} e^{-i4\\pi b n_{c}(m_{c}-m)} e^{-i4\\pi b n_{c}(m-m_{c})} \\\\\n & = \\psi_{n,m} .\n\\end{align}\nEq.~(\\ref{eq:inv_sym_gauge_tr_6}) implies that if the $H$ in Eq.~(\\ref{eq:inv_sym_gauge_tr_1}) has non-zero $b$ with the summation over $n$ and $m$ going from $- \\infty$ to $+\\infty$, then $H$ is invariant under the inversion operation $\\mathcal{I}_{n_{c},m_{c}}$ defined by Eqs.~(\\ref{eq:inv_sym_gauge_tr_4}) and (\\ref{eq:inv_sym_gauge_tr_5}). \nAlso, since $\\psi^{\\dagger}_{n,m}$ and $\\psi_{n,m}$ denote the creation and annihilation operators for electronic states spanning the whole Hilbert space, Eq.~(\\ref{eq:inv_sym_gauge_tr_7}) and Eq.~(\\ref{eq:inv_sym_gauge_tr_8}) imply that $\\left( \\mathcal{I}_{n_{c},m_{c}} \\right)^{2}$ is the identity operation. \n\nTo complete the proof, we give a construction for the unitary operator $\\mathcal{I}_{n_c,m_c}$.\nThe matrix representation of $\\mathcal{I}_{n_{c},m_{c}}$ in Eq.~(\\ref{eq:inv_sym_gauge_tr_5}) for sites at $(n,m)$ and $(2n_{c}-n,2m_{c}-m)$ is given by\n\\begin{align}\n \\begin{bmatrix}\n 0 & e^{-i4\\pi b n_{c}(m-m_{c})} \\\\\n e^{-i4\\pi b n_{c}(m_{c}-m)} & 0\n \\end{bmatrix}, \\label{eq:mat_I_nc_mc}\n\\end{align}\nsince\n\\begin{align}\n & \\mathcal{I}_{n_{c},m_{c}} \\psi_{n,m} \\mathcal{I}_{n_{c},m_{c}}^{-1} = \\psi_{2n_{c}-n,2m_{c}-m}e^{-i4\\pi b n_{c}(m-m_{c})}, \\\\\n & \\mathcal{I}_{n_{c},m_{c}} \\psi_{2n_{c}-n,2m_{c}-m} \\mathcal{I}_{n_{c},m_{c}}^{-1} = \\psi_{n,m}e^{-i4\\pi b n_{c}(m_{c}-m)}.\n\\end{align}\nAs we can see Eq.~(\\ref{eq:mat_I_nc_mc}) is in fact a unitary matrix. \nTherefore, even though in this 2D lattice we have a constant perpendicular magnetic field which couples to the lattice through a Peierls substitution, there still exist unitary inversion operators with inversion centers at every lattice site which square to the identity and commute with the Hamiltonian.\n\n\\subsection{3D system with inversion symmetry}\n\nWe have shown that by defining unitary inversion operations up to a gauge transformation in Eq.~(\\ref{eq:inv_sym_gauge_tr_4}) and Eq.~(\\ref{eq:inv_sym_gauge_tr_5}), the inversion symmetry of Eq.~(\\ref{eq:inv_sym_gauge_tr_1}) is preserved. \nLet us now discuss how this extends to our $3$D promoted systems. \nSince our $3$D model of a chiral HOTI from the main text is also coupled to a constant $U(1)$ gauge field given by $\\vec{A} = (0,0,2\\pi(q_{x}x+q_{y}y))$, we can construct the proper unitary inversion operators squaring to the identity, given by\n\\begin{align}\n & \\mathcal{I}_{n_{x}^{c},n_{y}^{c},n_{z}^{c}} \\psi^{\\dagger}_{n_{x},n_{y},n_{z}} \\mathcal{I}_{n_{x}^{c},n_{y}^{c},n_{z}^{c}}^{-1} = \\psi^{\\dagger}_{2n_{x}^{c}-n_{x},2n_{y}^{c}-n_{y},2n_{z}^{c}-n_{z}} [\\mathcal{I}]^{-1} e^{i 4\\pi \\left( q_{x}n_{x}^{c} + q_{y}n_{y}^{c} \\right) \\left( n_{z} - n_{z}^{c} \\right)} , \\\\\n & \\mathcal{I}_{n_{x}^{c},n_{y}^{c},n_{z}^{c}} \\psi_{n_{x},n_{y},n_{z}} \\mathcal{I}_{n_{x}^{c},n_{y}^{c},n_{z}^{c}}^{-1} = [\\mathcal{I}] \\psi_{2n_{x}^{c}-n_{x},2n_{y}^{c}-n_{y},2n_{z}^{c}-n_{z}} e^{-i 4\\pi \\left( q_{x}n_{x}^{c} + q_{y}n_{y}^{c} \\right) \\left( n_{z} - n_{z}^{c} \\right)}.\n\\end{align}\nIn these expressions $\\psi^{\\dagger}_{n_{x},n_{y},n_{z}}$ is the 4-component creation operator for an electron at site $(n_{x},n_{y},n_{z})$, $(n_{x}^{c},n_{y}^{c},n_{z}^{c})$ is the inversion center at any lattice point, and $[\\mathcal{I}] = \\tau_{z}\\sigma_{0}$ is the unitary inversion matrix which also squares to the identity and acts on the degrees of freedom within a unit cell. \nFor the case of our helical model, recall that our example of Eq.~(\\ref{eq:lattice_model_helical_sliding}) in Sec.~\\ref{sec:promoted_helical_lattice_model} is spin-decoupled. \nThus the $SU(2)$ gauge field given by $\\vec{A} = (0,2\\pi (q_{x}x+q_{z}z)\\sigma_{z},0)$ to which the model is coupled acts effectively as oppositely oriented $U(1)$ magnetic fields for spin up and down electrons. \nWe can thus define the unitary inversion operations squaring to identity up to a spin-dependent ($SU(2)$) gauge transformation\n\\begin{align}\n & \\mathcal{I}_{n_{x}^{c},n_{y}^{c},n_{z}^{c}} \\psi^{\\dagger}_{n_{x},n_{y},n_{z},\\sigma} \\mathcal{I}_{n_{x}^{c},n_{y}^{c},n_{z}^{c}}^{-1} = \\psi^{\\dagger}_{2n_{x}^{c}-n_{x},2n_{y}^{c}-n_{y},2n_{z}^{c}-n_{z},\\sigma} [\\mathcal{I}]^{-1} e^{i 4\\sigma\\pi \\left( q_{x}n_{x}^{c} + q_{z}n_{z}^{c} \\right) \\left( n_{y} - n_{y}^{c} \\right)} , \\\\\n & \\mathcal{I}_{n_{x}^{c},n_{y}^{c},n_{z}^{c}} \\psi_{n_{x},n_{y},n_{z},\\sigma} \\mathcal{I}_{n_{x}^{c},n_{y}^{c},n_{z}^{c}}^{-1} = [\\mathcal{I}] \\psi_{2n_{x}^{c}-n_{x},2n_{y}^{c}-n_{y},2n_{z}^{c}-n_{z},\\sigma} e^{-i 4\\sigma\\pi \\left( q_{x}n_{x}^{c} + q_{z}n_{z}^{c} \\right) \\left( n_{y} - n_{y}^{c} \\right)},\n\\end{align}\nwhere $\\psi^{\\dagger}_{n_{x},n_{y},n_{z},\\sigma}$ is the 4-component creation operator for an electron at site $(n_{x},n_{y},n_{z})$ for a fixed spin $\\sigma = \\pm$ and $[\\mathcal{I}] = \\tau_{z}\\mu_{0}$. \nTherefore, the 3D chiral (helical) HOTI coupled to a $U(1)$ ($SU(2)$) gauge field in our examples preserves inversion symmetry.\n\n\n\\section{\\label{bigsec:chiral_sliding}Low energy bulk theory of chiral higher-order topological sliding modes}\n\nIn this section, we consider the low energy bulk theory of electrons in a 3D chiral HOTI minimally coupled to a $U(1)$ gauge field, shown in the main text and verify that it can capture several qualitative properties in numerical results of the lattice model calculation with $q_{y} = 0.02$.\n\nAs mentioned in the main text, the relevant low energy theory is \n\\begin{align}\n H_{\\text{bulk}} = m_{\\text{bulk}} \\tau_{z}\\sigma_{0} + \\tau_{x}\\vec{p} \\cdot \\vec{\\sigma} + \\tau_{0} \\vec{M} \\cdot \\vec{\\sigma}. \\label{eq:3D_chiral_HOTO_bulk}\n\\end{align}\nCoupling this $H_{\\text{bulk}}$ to a vector potential $\\vec{A} = By \\hat{z}$ such that $p_{z} \\to p_{z} + By$, and defining \n\\begin{align}\n a^{\\dagger}_{k_{z}} = \\frac{1}{\\sqrt{2B}}\\left( k_{z} + By - ip_{y} \\right), \\label{eq:3D_chiral_bulk_U1_ladder}\n\\end{align}\nwe can rewrite the Hamiltonian as \n\\begin{align}\n H_{\\text{bulk}}(k_{x},k_{z}) = m_{\\text{bulk}} \\tau_{z}\\sigma_{0} + \\tau_{x} \\begin{bmatrix}\n \\sqrt{\\frac{B}{2}} \\left(a_{k_{z}}+a^{\\dagger}_{k_{z}} \\right) & k_{x} -\\sqrt{\\frac{B}{2}} \\left(a_{k_{z}}-a^{\\dagger}_{k_{z}} \\right) \\\\\n k_{x} +\\sqrt{\\frac{B}{2}} \\left(a_{k_{z}}-a^{\\dagger}_{k_{z}} \\right) & -\\sqrt{\\frac{B}{2}} \\left(a_{k_{z}}+a^{\\dagger}_{k_{z}} \\right)\n \\end{bmatrix} + \\tau_{0} \\vec{M} \\cdot \\vec{\\sigma}. \\label{eq:3D_bulk_LL_Hamiltonian}\n\\end{align}\n\nFig.~\\ref{SM_chiral_bulk_low_energy} (b) shows the numerically computed energy spectrum $E_{k_{x},k_{z}}$ for Eq.~(\\ref{eq:3D_bulk_LL_Hamiltonian}) as a function of $k_{x}$. \nNote that $E_{k_{x},k_{z}}$ does not depend on $k_{z}$, giving rise to flat Landau levels (LLs) along $k_{z}$. \nWe now relate this low energy bulk theory to the $\\phi$-sliding spectrum of the 2D modulated system by identifying $k_{z}$ in Eq.~(\\ref{eq:3D_bulk_LL_Hamiltonian}) with $\\Delta \\phi = \\phi - \\pi$, as we have done in the main text. \nTo assist the following discussion, we also show again the $\\phi$-sliding spectrum of the 2D modulated system which can be promoted to a 3D chiral HOTI coupled to a $U(1)$ gauge field with $\\vec{q} = (0,q_{y})=(0,0.02)$ in Fig.~\\ref{SM_chiral_bulk_low_energy} (a). \nThe flat bands corresponding to bulk-confined modes in Figs.~\\ref{SM_chiral_bulk_low_energy} (c) and (d) are marked in green ($E = -0.5144$) and orange ($E = +0.4656$).\n\n\nFirst, the band edges for the valence and conduction in Fig.~\\ref{SM_chiral_bulk_low_energy} (b) are at energy $E = -0.5248$ and $+0.4661$.\nThis indicates that the particle-hole symmetry in the bulk of a 3D chiral HOTI is generally broken upon $U(1)$ Landau quantization.\nThis is also reflected in the energy eigenvalues of the bulk flat bands in the lattice model Fig.~\\ref{SM_chiral_bulk_low_energy} (a), with probability density shown in Figs.~\\ref{SM_chiral_bulk_low_energy} (c) and (d). \nWe see that the energies ($E = -0.5144$ for Fig.~\\ref{SM_chiral_bulk_low_energy} (c) and $E = +0.4656$ in Fig.~\\ref{SM_chiral_bulk_low_energy} (d)) of these states are not symmetric about 0. \n\n\nSecond, the Hamiltonian in Eq.~(\\ref{eq:3D_bulk_LL_Hamiltonian}) has eigenstates that are extended along $x$ and $z$ while confined along $y$. \nThe confinement along $y$ can be understood through the definition of the ladder operator in Eq.~(\\ref{eq:3D_chiral_bulk_U1_ladder}), which creates simple harmonic oscillator (SHO) states along $y$. \nProjecting the 3D eigenfunction to the 2D modulated system, only the probability distributions along $x$ and $y$ are physically meaningful. \nTherefore in 2D we expect to see flat bands along $k_{z}$, which is identified as $\\Delta \\phi = \\phi - \\pi$ in the low energy model. The wave functions with these energies are confined along $y$ while extended along $x$. \nThis is consistent with Figs.~\\ref{SM_chiral_bulk_low_energy} (a) and (c)--(d). \nThe above discussion shows that our low energy bulk theory in 3D does qualitatively explain the existence of bulk confined modes in 2D and their flat dispersion along the $\\phi$-axis.\n\n\n\\begin{figure}[h]\n\\includegraphics[scale=0.4]{SM_chiral_bulk_low_energy_add_horizontal_line_at_pm_0_5_new.pdf}\n\\caption{(a) $\\phi$-sliding spectrum of the 2D modulated system that can be promoted to a 3D chiral HOTI\\cite{pozo2019quantization} coupled to a $U(1)$ gauge field with $\\vec{q} = (0,q_{y})$ and $q_{y} = 0.02$. \n(b) Bulk Landau levels of Eq.~(\\ref{eq:3D_bulk_LL_Hamiltonian}) for the low energy model of a 3D chiral HOTI with $m_{\\text{bulk}} = 0.5$, $B = 0.2$ and $\\vec{M} = (0.03,0.03,0.03)$. \nThe band edges are given by $E = -0.5248$ and $0.4661$. \nThe orange, black and green horizontal lines correspond to energies $E = +0.5$, $0$ and $-0.5$, respectively. \n(c) $\\&$ (d) Probability distribution of bulk-confined modes in the flat bands of (a) at $\\phi = \\pi$ with energies $-0.5144$ and $0.4656$ marked by green and orange respectively.\nWe note that (c) and (d) share similar probability distributions, for example both are confined along $y$ and centered around the middle of the finite sample. This is consistent with the low energy theory prediction of energy-asymmetry in Eq.~(\\ref{eq:3D_bulk_LL_Hamiltonian}) and (b). The darker (black) color in (c)--(d) implies higher probability density. \nIn (c) and (d), the $x$- and $y$-coordinate both range from $-15, \\ldots, +15$.}\n\\label{SM_chiral_bulk_low_energy}\n\\end{figure}\n\n\n\\section{\\label{bigsec:helical_sliding}Low energy theory of helical higher-order topological sliding modes}\n\nIn this section, we construct various low energy theories for the dimensionally-promoted 3D helical HOTI coupled to an $SU(2)$ gauge field. \nWe use these low energy models to explain corner, edge and bulk states in the 2D modulated system with helical sliding modes.\n\n\\subsection{2D corner modes $\\leftrightarrow$ 3D hinge modes with $SU(2)$ gauge field}\n\nIn our promoted 3D model, different spin ($\\vec{\\sigma}$) subspaces are decoupled, see Sec.~\\ref{sec:promoted_helical_lattice_model}. \nTherefore, all eigenstates are spin-polarized.\nThe 2D spin-polarized corner modes are the projection of the helical hinge modes in the 3D lattice. \nIf we denote the third, synthetic dimension as $y$ and the corresponding crystal momentum as $k_{y}$, the low energy theory of the helical hinge mode will be\n\\begin{align}\n H_{\\text{hinge}}= v_{F} \\left( k_{y}\\sigma_{z}' + 2\\pi \\left( q_{x}x_{\\text{hinge}} + q_{z}z_{\\text{hinge}} \\right) \\sigma_{0}' \\right), \\label{3D_helical_hinge_mode_Hamiltonian}\n\\end{align}\nwhere we have minimally coupled the Hamiltonian $v_{F}k_{y}\\sigma_{z}'$ to a $SU(2)$ gauge field \n\\begin{align}\n \\vec{A} = (0,2\\pi (q_{x}x+q_{z}z)\\sigma_{z}',0). \\label{SU2_A}\n\\end{align}\nNotice that we denote our basis as $\\vec{\\sigma}'$. \nAlthough the eigenstates of $\\sigma'_z$ have opposite spins, they might contain non-trivial orbital and sub-lattice textures. \nAlso notice that we have $2\\pi \\left( q_{x}x_{\\text{hinge}} + q_{z}z_{\\text{hinge}} \\right) \\sigma_{0}'$ in Eq.~(\\ref{3D_helical_hinge_mode_Hamiltonian}) instead of $2\\pi \\left( q_{x}x_{\\text{hinge}} + q_{z}z_{\\text{hinge}} \\right) \\sigma_{z}'$. \nSince as we replace $k_{y}$ by $k_{y} + 2\\pi \\left( q_{x}x_{\\text{hinge}} + q_{z}z_{\\text{hinge}} \\right) \\sigma_{z}'$ through the minimal coupling, we will have \n\\begin{align}\n v_{F}k_{y}\\sigma_{z}' = v_{F}\\begin{bmatrix}\n k_{y} & 0 \\\\ \n 0 & -k_{y}\n \\end{bmatrix} & \\to v_{F}\\begin{bmatrix}\n \\left( k_{y} + 2\\pi \\left( q_{x}x_{\\text{hinge}} + q_{z}z_{\\text{hinge}} \\right) \\right) & 0 \\\\ \n 0 & -\\left( k_{y} - 2\\pi \\left( q_{x}x_{\\text{hinge}} + q_{z}z_{\\text{hinge}} \\right) \\right)\n \\end{bmatrix} \\\\\n & = v_{F}k_{y}\\sigma_{z}' + v_{F} \\cdot 2\\pi \\left( q_{x}x_{\\text{hinge}} + q_{z}z_{\\text{hinge}} \\right)\\sigma_{0}'.\n\\end{align}\nIn Eq.~(\\ref{3D_helical_hinge_mode_Hamiltonian}), we have assumed that: \n(1) there is only one pair of helical hinge modes along this hinge,\n(2) the magnitude of the group velocity is $v_{F}$, \n(3) the electron has charge $-1$, and \n(4) the fixed position of the hinge along $y$ is at $(x_{\\text{hinge}},z_{\\text{hinge}})$ which is set by the coordinate system. \nEq.~(\\ref{SU2_A}) again implies that the modulation wave vector $\\vec{q} = (q_{x},q_{z})$ enters the definition of the $SU(2)$ gauge field. \nAs we can see in Eq.~(\\ref{3D_helical_hinge_mode_Hamiltonian}), if we tune $\\vec{q}$, we are effectively shifting the hinge mode dispersion along the $k_{y}$-axis for spin up [down] electrons by an amount $-2\\pi \\left(q_{x}x_{\\text{hinge}}+q_{z}z_{\\text{hinge}}\\right)$ [$+2\\pi \\left(q_{x}x_{\\text{hinge}}+q_{z}z_{\\text{hinge}}\\right)$]. \nSince we use $q_{x}=0$ and $q_{z}\\ne0$ in the main text for helical sliding modes, the following discussion will focus on this case. \nThe generalization to other combinations of $q_{x}$ and $q_{z}$ follows the same procedure.\n\n\nTo connect the low energy theory of the $\\phi$-sliding spectrum to the shifting of spin-polarized corner mode dispersion, we identified $k_{y}$ in Eq.~(\\ref{3D_helical_hinge_mode_Hamiltonian}) as $\\phi$, since the center of the $\\phi$-sliding spectrum is at $\\phi = 0$, as shown in Fig.~\\ref{SM_Fig_5} (a) for $q_{z} = 0$. \nUpon projection to 2D, the position $(x_{\\text{hinge}},z_{\\text{hinge}})$ of the hinge along $y$ again becomes the position $(x_{\\text{corner}},z_{\\text{corner}})$ of the 2D corner. \nWe thus obtain\n\\begin{align}\n H_{\\text{corner}}= v_{F} \\left( \\phi \\sigma_{z}' + 2\\pi \\left( q_{x}x_{\\text{corner}} + q_{z}z_{\\text{corner}} \\right) \\sigma_{0}' \\right) \\label{eq:H_helical_hinge_1}\n\\end{align}\nfrom the main text. \nWe now examine this low energy theory through numerical simulations. \nWe will be focusing on how the doubly-degenerate gap-crossing bands corresponding to spin-polarized corner modes respond as we increase magnitude of $\\vec{q}$.\n\nWe show in Fig.~\\ref{SM_Fig_5} (b) the $\\phi$-sliding spectrum for $q_{z} = 0.02$. \nComparing Fig.~\\ref{SM_Fig_5} (b) with Fig.~\\ref{SM_Fig_5} (a), we see that as we turn on $q_{z}$, the two doubly-degenerate bands cross the gap with opposite slopes. \nTo explain this, we notice that each of the doubly-degenerate gap-crossing states corresponds to a localized pair of modes at inversion-related corners with opposite spins, as they are related by the $\\mathcal{I}\\mathcal{T}$-symmetry (see main text Sec.~V) and shown in Figs.~\\ref{SM_Fig_5} (c) and (d). \nIn our coordinate system, the corner modes are localized at positions $(x_{\\text{corner}},z_{\\text{corner}})$ = $\\pm(L\/2,L\/2)$, where $L=30$. \nThe corresponding 3D helical hinge mode dispersion relations are\n\\begin{align}\n & H_{\\text{hinge 1}}= v_{F} \\left( k_{y}\\sigma_{z}' - \\pi q_{z}L \\sigma_{0}' \\right), \\\\\n & H_{\\text{hinge 2}} = -v_{F} \\left( k_{y}\\sigma_{z}' + \\pi q_{z}L \\sigma_{0}' \\right),\n\\end{align}\nwhere inversion symmetry requires that the eigenstate of $H_{\\text{hinge 1}}$ and $H_{\\text{hinge 2}}$ with same spins have opposite group velocities. \nIdentifying $k_{y}$ as $\\phi$, we have that the Hamiltonians of the bands crossing the gap are given by\n\\begin{align}\n & H_{\\text{corner 1}}= v_{F} \\left( \\phi \\sigma_{z}' - \\pi q_{z}L \\sigma_{0}' \\right), \\label{eq:corner_1_SU2}\\\\\n & H_{\\text{corner 2}} = -v_{F} \\left( \\phi \\sigma_{z}' + \\pi q_{z}L \\sigma_{0}' \\right). \\label{eq:corner_2_SU2}\n\\end{align}\nWe then see that as we increase the $q_{z}$, the band in $H_{\\text{corner 1}}$ along $\\phi$ with slope $+v_{F}$ and spin $\\uparrow$ moves in the same direction as the band in $H_{\\text{corner 2}}$ with group velocity $+v_{F}$ and spin $\\downarrow$, which is consistent with Fig.~\\ref{SM_Fig_5} (b). \nIn fact, a detailed comparison between Figs.~\\ref{SM_Fig_5} (a) and (b) shows that the corner mode dispersion shifts along $\\phi$ by $\\pi q_{z}L \\approx 0.6\\pi $ for $q_{z} = 0.02$ and $L = 30$, implying that our low energy theory describes both the 2D corner modes and the corresponding 3D helical hinge modes. \nThis confirms that we can tune the range of $\\phi$ where the spin-polarized corner modes emerge from the bulk bands by modifying the periodicity of the modulation, as we have stated in the main text. \nSimilar to the chiral sliding modes in the main text, when $\\pi q_{z}L > 2\\pi$, the gap-crossing bands will be folded back within the range $\\phi = [0,2\\pi)$. \nThis happens in Fig. 3 (a) of the main text, where $q_{z} = 0.11957$.\n\n\\begin{figure}[h]\n \\includegraphics[scale=0.4]{SM_Fig_5_change_ticking_freq_add_color.pdf}\n \\caption{(a) $\\phi$-sliding spectrum of Eq.~(35) in the main text with parameters $m_{1} = -3$, $m_{2} = 0.3$, $m_{3} = 0.2$, $m_{v_{1}} = -0.4$, $m_{v_{2}} = 0.2$, $v_{x}=v_{z}=u_{x}=u_{z} = 1$, $v_{y} = 2$, $v_{H} = 1.2$ and $\\vec{q} = (0,q_{z})$ where $q_{z} = 0$. \n This reduces to the $y$-rod band structure of a 3D helical HOTI\\cite{Wieder_spin_decoupled_helical_HOTI} without $SU(2)$ gauge fields. \n Notice the Rashba-like shifting of the surface band away from $\\phi = 0$, near $E \\approx \\pm 0.2$. \n (b) $\\phi$-sliding spectrum with the same parameters as (a) but with $q_{z} = 0.02$. \n We identify the flat dispersion in (b) marked by orange, green and red as $E^{-}_{\\sigma',k_{y},n = 1}$, $E^{-}_{\\sigma',k_{y},n =0}$ and $E^{+}_{\\sigma',k_{y},n = 1}$ in Eq.~(\\ref{wvfn_top}), respectively. \n (c)$\\&$(d) Summation of probability distribution of the doubly-degenerate corners modes related to each other by the $\\mathcal{I}\\mathcal{T}$-symmetry at $\\phi = 0.6\\pi$ and $1.4\\pi$ with both energies equal to $-0.0124$. \n The two corner modes in each of (c) and (d) are localized at inversion-related corners and have opposite spins. \n The darker (black) color in (c)--(d) implies higher probability density. \n In (c) and (d), the $x$- and $z$-coordinate both range from $-15,\\ldots,+15$.}\n \\label{SM_Fig_5}\n\\end{figure}\n\n\\subsection{\\label{sec:surface_SU2}2D edge-confined modes $\\leftrightarrow$ 3D surface modes with $SU(2)$ gauge field}\n\nAs stated in the main text, we may understand the flat bands corresponding to edge-confined modes using a low energy theory in the promoted 3D lattice. \nIn this subsection we construct a surface theory minimally coupled to an $SU(2)$ gauge field to describe the projected 2D edge states in the modulated system.\n\nDue to the shifted band edge of the surface bands in the corresponding 3D helical HOTI (see Fig.~\\ref{SM_Fig_5} (a) and its caption), we may consider the surface theory by stacking a low energy Chern insulator with spin $\\uparrow$ and its time-reversal counterpart, with a relative shift in momentum space. \nThe low energy theory of a Chern insulator is given by $H_{\\text{Chern}}=\\vec{p}_{\\parallel}\\cdot \\vec{\\tau}_{\\parallel}' - m\\hat{n} \\cdot \\vec{\\tau}'$, where $\\vec{\\tau}'$ is the basis describing spin $\\uparrow$ electron with some orbital and sub-lattice textures, $\\parallel$ is the parallel component along the surface, $\\hat{n}$ is the surface normal vector and $m$ is the mass term. \nAs shown in Eq.~(\\ref{eq:B_SU2_1}), if $q_{x} = 0$, which is the case we consider in Fig.~\\ref{SM_Fig_6} below, we have $\\vec{B} = \\vec{\\nabla} \\cross \\vec{A} - i \\vec{A} \\cross \\vec{A}$ parallel to $\\hat{x}$. \nWe then consider a surface theory on the $yz$-plane whose surface normal is $\\hat{x}$. \nThe corresponding surface Hamiltonian without an $SU(2)$ gauge field reads $H_{\\text{surf}} = p_{y}\\tau_{y}'\\sigma_{0}'+p_{z}\\tau_{z}'\\sigma_{z}'+(\\Delta k_{y})\\tau_{y}'\\sigma_{z}'-m\\tau_{x}'\\sigma_{0}'$, where $\\vec{\\sigma}'$ again denotes the spin degrees of freedom, and $\\Delta k_{y}$ is a {\\it real constant} denoting the shift of low energy surface band minima from $\\vec{k}=\\Gamma$. \nTo facilitate the subsequent analysis, we perform a basis transformation through a $-2\\pi\/3$ radian rotation $U$ along the $[1,1,1]$ axis in the {\\it orbital} space $\\vec{\\tau}'$ such that $U^{\\dagger}(\\tau_{x}',\\tau_{y}',\\tau_{z}')U = (\\tau_{z}',\\tau_{x}',\\tau_{y}')$. \nThe transformed surface Hamiltonian then reads\n\\begin{align}\n H_{\\text{surf}} = p_{y}\\tau_{x}'\\sigma_{0}'+p_{z}\\tau_{y}'\\sigma_{z}'+(\\Delta k_{y})\\tau_{x}'\\sigma_{z}'-m\\tau_{z}'\\sigma_{0}'.\n \\label{eq:top_surf_SU2_helical_transformed}\n\\end{align}\nNotice that this surface theory is spin-decoupled. \nIn a general helical HOTI the spins are coupled. \nHowever, the general procedure will be the same: we first construct a low energy surface theory, and then couple it to the desired $SU(2)$ gauge field. \n\nWe now couple Eq.~(\\ref{eq:top_surf_SU2_helical_transformed}) to an $SU(2)$ gauge field $\\vec{A} = (0,Bz \\sigma_{z}',0)$ which produces a $SU(2)$ magnetic field $\\vec{B} = (-B\\sigma_{z}',0,0)$. \nTherefore opposite spins experience opposite magnetic fields. \nThe minimally-coupled surface theory is\n\\begin{align}\n H_{\\text{surf}} = \\begin{bmatrix}\n \\left(p_{y}+\\Delta k_{y} + Bz \\right)\\tau_{x}' + p_{z}\\tau_{y}' - m \\tau_{z}' & 0 \\\\ 0 & \\left(p_{y}-\\Delta k_{y} - Bz \\right)\\tau_{x}' - p_{z}\\tau_{y}' - m \\tau_{z}'\n \\end{bmatrix}, \\label{eq:helical_surface_before_rewrite}\n\\end{align}\nwhere we have assumed that both $B$ and $m$ are positive. \nFourier transforming to replace $p_{y}$ by the wavenumber $k_{y}$, and defining spin- and $k_{y}$-dependent ladder operators\n\\begin{align}\n a^{\\dagger}_{k_{y},\\uparrow} = \\frac{1}{\\sqrt{2B}}\\left(k_{y} + \\Delta k_{y} + Bz - ip_{z} \\right) \\text{ and } a^{\\dagger}_{k_{y},\\downarrow} = \\frac{1}{\\sqrt{2B}}\\left(k_{y} - \\Delta k_{y} - Bz + ip_{z} \\right), \\label{eq:top_ladders_SU2}\n\\end{align}\nwe can rewrite Eq.~(\\ref{eq:helical_surface_before_rewrite}) in each spin subspace ($\\sigma'=\\pm$) as\n\\begin{align}\n H_{\\text{surf}}(k_{y},\\sigma') = \\begin{bmatrix}\n -m & \\sqrt{2B} a_{k_{y},\\sigma'}^{\\dagger} \\\\\n \\sqrt{2B} a_{k_{y},\\sigma'} & m\n \\end{bmatrix}.\n\\end{align}\nWe can solve for the eigenstates and energies to find\n\\begin{align}\n & \\psi^{-}_{\\sigma',k_{y},n=0} = e^{ik_{y}y}\\begin{bmatrix}\n \\ket{0,k_{y},\\sigma'} \\\\ 0\n \\end{bmatrix},\\ E^{-}_{\\sigma',k_{y},n=0} = -m, \\nonumber \\\\\n & \\psi^{-}_{\\sigma',k_{y},n>0} = e^{ik_{y}y}\\begin{bmatrix}\n \\ket{n,k_{y},\\sigma'} \\\\ \\alpha_{-}(n)\\ket{n-1,k_{y},\\sigma'}\n \\end{bmatrix},\\ E^{-}_{\\sigma',k_{y},n>0} = -\\sqrt{m^{2} + 2Bn}, \\nonumber \\\\\n & \\psi^{+}_{\\sigma',k_{y},n>0} = e^{ik_{y}y}\\begin{bmatrix}\n \\ket{n,k_{y},\\sigma'} \\\\ \\alpha_{+}(n)\\ket{n-1,k_{y},\\sigma'}\n \\end{bmatrix},\\ E^{+}_{\\sigma',k_{y},n>0} = +\\sqrt{m^{2} + 2Bn}, \\nonumber \\\\\n & {\\color{black}{\\text{where }}} \\alpha_{\\pm}(n) = \\frac{1}{\\sqrt{2Bn}}\\left(\\pm \\sqrt{m^{2} + 2Bn} + m \\right). \\label{wvfn_top}\n\\end{align}\nHere $\\sigma' = \\pm$ is the spin quantum number, $k_{y}$ is the wavenumber along $y$, $n$ is an non-negative integer {\\color{black}{labelling}} the $SU(2)$ LLs, and $\\ket{n,k_{y},\\sigma'}$ is the $n^{\\text{th}}$ simple harmonic oscillator (SHO) eigenstate along $z$ defined using $a^{\\dagger}_{k_{y},\\sigma'}$. \nNotice that the coefficient $\\alpha_{\\pm}(n)$ does not depend on which spin sector we are considering. \nWe now connect the above surface theory to the flat bands and corresponding wavefunctions in the $\\phi$-sliding spectrum of the 2D modulated system by identifying $k_{y}$ as $\\phi$ and $B$ as $2\\pi q_{z}$. \nThis is because in our 3D promoted lattice Eq.~(\\ref{eq:lattice_model_helical_sliding}) and the numerical examples, we have $q_{x} = 0$ such that $\\vec{A} = (0,2\\pi q_{z}z\\sigma_{z}',0)$ produces an $SU(2)$ magnetic field with strength $2\\pi q_{z}$. \nWe will mainly focus on the two [one] flat bands in Fig.~\\ref{SM_Fig_5} (b) with negative [positive] energy closest to the zero. \nThese correspond to $E^{-}_{\\sigma',k_{y},n \\le 1}$ [$E^{+}_{\\sigma',k_{y},n=1}$].\n\nFirst, notice that the spectrum in Eq.~(\\ref{wvfn_top}) breaks particle-hole symmetry in both spin sectors, as there are no $+m$ energy eigenvalues. \nThis can be observed in Fig.~\\ref{SM_Fig_5} (b) where there are no flat bands of edge-confined modes around $E\\approx +0.2$ corresponding to $E = +m$. \nWe thus identify the flat bands in Fig.~\\ref{SM_Fig_5} (b) marked by orange, green and red as $E^{-}_{\\sigma',k_{y},n = 1}$, $E^{-}_{\\sigma',k_{y},n =0}$ and $E^{+}_{\\sigma',k_{y},n = 1}$ in Eq.~(\\ref{wvfn_top}), respectively.\n\nNext, the probability densities $|\\psi^{-}_{\\sigma',k_{y},n=0}|^2$ and $|\\psi^{\\pm}_{\\sigma',k_{y},n=1}|^2$ are respectively proportional to $\\left|\\varphi_{0,B}(z+(\\Delta k_{y} + \\sigma' k_{y})\/B) \\right|^{2}$ and $\\left| \\alpha_{\\pm}(1) \\right|^{2}\\left|\\varphi_{0,B}(z+(\\Delta k_{y} + \\sigma' k_{y})\/B) \\right|^{2} + \\left|\\varphi_{1,B}(z+(\\Delta k_{y} + \\sigma' k_{y})\/B) \\right|^{2}$, where $\\varphi_{n,B}(z)$ is the $n^{\\text{th}}$ SHO eigenstate along $z$. \nNotice that we have put explicit $B$-dependence on $\\varphi_{n,B}(z)$ since the cyclotron frequency and the localization of wave functions depend on the field strength.\nThis implies that: \n(1) the probability density computed from $\\psi^{-}_{\\sigma',k_{y},n=0}$ has a pure Gaussian distribution along $z$, and \n(2) $\\psi^{-}_{\\sigma',k_{y},n=1}$ has a larger contribution from the SHO first excited state than $\\psi^{+}_{\\sigma',k_{y},n=1}$, since $\\left| \\alpha_{-}(1) \\right|^{2} < \\left| \\alpha_{+}(1) \\right|^{2}$ and we have assumed both $B$ and $m$ are positive. \nFigs.~\\ref{SM_Fig_6} (a)--(c) show the 2D probability distribution at $\\phi = 0$ for edge-confined modes in different LLs together with the insets showing the probability integrated over non-negative $x$-coordinates. \nWhile both Figs.~\\ref{SM_Fig_6} (a) and (c) corresponds to $n = 1$ LL, Fig.~\\ref{SM_Fig_6} (a) is from the negative energy branch and Fig.~\\ref{SM_Fig_6} (c) is from the positive energy branch. \nTherefore Fig.~\\ref{SM_Fig_6} (a) is more characteristic of the SHO first excited state than Fig.~\\ref{SM_Fig_6} (c). \nIn contrast, Fig.~\\ref{SM_Fig_6} (b), being the $n=0$ LL wave function, shows a Gaussian probability distribution characteristic of the SHO ground state. \nWe notice that, if we restrict ourselves to a single edge, the wave function in Fig.~\\ref{SM_Fig_6} (a) contains a slightly asymmetric SHO first excited state. \nThis is due to the complicated on-site and hopping energies in our model, which break extraneous symmetries, such that the wave function in Fig.~\\ref{SM_Fig_6} (a) has nonzero penetration to bulk. \nThis can be compared with Fig.~\\ref{SM_Fig_6} (b) where the edge-confined modes are much more localized on the edges. \nThus, the surface theory present above should be recognized as an effective theory we use to extract out qualitative properties of wave functions, and it suffices to identify the SHO first excited state character of Fig.~\\ref{SM_Fig_6} (a) corresponding to $\\psi^{-}_{\\sigma',k_{y},n=1}$ with energy level $E^{-}_{\\sigma',k_{y},n = 1}$ in Eq.~(\\ref{wvfn_top}).\n\nThird, the definition of the ladder operator in Eq.~(\\ref{eq:top_ladders_SU2}) predicts that even when we have $\\phi = 0$ (which corresponds to $k_{y} = 0$) the center of the SHO eigenstate will be shifted from the center of the coordinate system by a distance $|\\Delta k_{y}\/ B|$ along $z$. \nRecall that $\\Delta k_{y}$ is the slight Rashba-like shift of the surface bands away from $\\Gamma$ (see Fig.~\\ref{SM_Fig_5} (a) and its caption). \nIn the 2D modulated system, this is equivalent to saying that the edge-confined modes at $\\phi = 0$ will not have wavefunctions centered at the middle of the edge along $z$. \nThe probability distribution at $\\phi = 0$ shown in Fig.~\\ref{SM_Fig_6} (a) to (c) confirms this, as no states are centered around the middle of the edges.\nGoing further, we also expect from Eq.~(\\ref{eq:top_ladders_SU2}) that the center of wave functions will be shifted along $z$ by $(-\\Delta k_{y} - \\sigma' k_{y})\/B$ for a given $k_{y}$. \nIdentifying $k_{y}$ as $\\phi$ and $B$ as $2\\pi q_{z}$, we deduce that the shifting of the edge-confined modes from the center of the edges is spin-dependent. \nThe center of each probability distribution is given by $l_{\\sigma'} = (-\\Delta k_{y} - \\sigma' \\phi)\/(2\\pi q_{z}) $. \nNotice that the edge-confined modes in Figs.~\\ref{SM_Fig_6} (d) and (e) are shifted by $\\approx \\pm 2.5$ lattice constants comparing with Fig.~\\ref{SM_Fig_6} (b). \nThis is because Figs.~\\ref{SM_Fig_6} (d) and (e) correspond to $\\Delta \\phi = \\phi - 0 = 0.1\\pi$, $q_{z} = 0.02$, and wave functions with opposite spins will be shifted in the opposite directions, according to the expression of $l_{\\sigma'}$ given above and $a^{\\dagger}_{k_{y},\\sigma'}$ in Eq.~(\\ref{eq:top_ladders_SU2}). \nTo be more precise, we estimate the center of the wave functions in Figs.~\\ref{SM_Fig_6} (b), (d) and (e) to be around $z \\approx 1.5$, $4$ and $-1$, respectively.\n\nFinally, similar to the chiral sliding modes in the 2D modulated system, we also have an additional degeneracies in the flat band states due to zone-folding (see Sec.~IV of the main text). \nUp to the degeneracy due to zone folding, the universal property we expect in these 2D helical sliding systems is that as we vary $\\phi$, which corresponds to sliding of density waves (DWs) with spin-orbit coupled interactions, there will be flat bands together with edge-confined modes that are projected from the surface of a helical HOTI with $SU(2)$ Landau quantization. \nWe have seen that the qualitative properties of the wave functions are all consistent with the low energy surface theory.\n\n\\begin{figure}[h]\n\\includegraphics[scale=0.4]{SM_helical_sliding_edge_modes.pdf}\n\\caption{(a)--(c) Average of the probability distribution for the four-fold degenerate edge-confined modes in the flat bands shown in Fig.~\\ref{SM_Fig_5} (b). \nThe four-fold degeneracy comes considering the two edges and two spins. \n(a)--(c) are edge-confined modes at $\\phi = 0$ with $E = -0.4227$, $-0.1711$ and $0.3675$ which are marked orange, green and red in Fig.~\\ref{SM_Fig_5} (b). \nThe corresponding energy levels are $E^{-}_{\\sigma^\\prime,k_{y}=0,n=1}$, $E^{-}_{\\sigma^\\prime,k_{y}=0,n=0}$ and $E^{+}_{\\sigma^\\prime,k_{y}=0,n=1}$in Eq.~(\\ref{wvfn_top}). \n(d)$\\&$(e) Edge modes at $\\phi = 0.1 \\pi$ with $E = -0.1711$ confined at the right edge.\nThey correspond to $E^{-}_{\\sigma^\\prime,k_{y}=0.1\\pi,n=0}$ with $\\sigma^\\prime = \\pm$. \nWe notice that in addition to (d) and (e) there are two other edge-confined modes with nearly identical energies localized on the left edge.\nThe darker (black) color in (a)--(e) implies higher probability density. \nThe inset in (a)--(e) is the probability distribution integrated over non-negative $x$-coordinates, from $x= 0,\\ldots, L\/2$ where $L=30$. \nIn (a)--(e), the $x$- and $z$-coordinate both range from $-15,\\ldots, +15$.}\n\\label{SM_Fig_6}\n\\end{figure}\n\n\n\\subsection{2D bulk-confined modes $\\leftrightarrow$ 3D bulk modes with $SU(2)$ gauge field}\n\nHaving accounted for the edge-confined modes in the 2D modulated system with helical sliding, we move on to consider the flat bands corresponding to bulk-confined modes. \nSimilar to Sec.~\\ref{sec:surface_SU2}, we will construct a low energy theory and couple it to an $SU(2)$ gauge field.\n\nWe consider the Bloch Hamiltonian of the promoted 3D helical HOTI around the $\\Gamma$ point (see Sec.~\\ref{sec:promoted_helical_lattice_model}), which is\n\\begin{align}\n H_{\\text{bulk}} = & m_{\\text{bulk}} \\tau_{z}\\mu_{0}\\sigma_{0} + m_{2} \\tau_{z}\\mu_{x}\\sigma_{0} + m_{3} \\tau_{z}\\mu_{z}\\sigma_{0} + m_{v_{1}}\\tau_{0}\\mu_{z}\\sigma_{0} + m_{v_{2}} \\tau_{0}\\mu_{x}\\sigma_{0} \\nonumber \\\\\n & + u_{x} p_{x} \\tau_{y}\\mu_{y}\\sigma_{0} + u_{z}p_{z}\\tau_{x}\\mu_{0}\\sigma_{0} + v_{H}p_{y} \\tau_{y}\\mu_{z}\\sigma_{z}. \\label{eq:3D_helical_HOTI_bulk}\n\\end{align}\nWe have redefined several parameters in the original model in Sec.~\\ref{sec:promoted_helical_lattice_model} for convenience. \nFor example, $m_{\\text{bulk}}$ is effectively $m_{1} +v_{x} + v_{y} + v_{z}$ from Eq.~(A1) \nin Ref.~\\onlinecite{Wieder_spin_decoupled_helical_HOTI}. \nWe now couple this $H_{\\text{bulk}}$ to the $SU(2)$ vector potential $\\vec{A} = Bz \\tau_{0}\\mu_{0}\\sigma_{z} \\hat{y}$, which is equivalent to Eq.~(\\ref{SU2_A}) with $q_{x} = 0$. \nTherefore, the term $v_{H}p_{y} \\tau_{y}\\mu_{z}\\sigma_{z}$ becomes\n\\begin{align}\n v_{H}p_{y} \\tau_{y}\\mu_{z}\\sigma_{z} \\to v_{H}\\tau_{y}\\mu_{z} \\begin{bmatrix}\n p_{y} + Bz & 0 \\\\ 0 & -(p_{y}-Bz)\n \\end{bmatrix},\n\\end{align}\nwhere the matrix acts in spin space. \nFourier transforming along $x$ and $y$ and defining the $k_{y}$- and spin($\\sigma = \\pm$)-dependent ladder operators\n\\begin{align}\n a^{\\dagger}_{\\sigma,k_{y}} = \\frac{1}{\\sqrt{2B}}\\left( \\sigma k_{y} + Bz - ip_{z} \\right), \\label{eq:ladder_bulk_helical_SU2}\n\\end{align}\nwe can rewrite Eq.~(\\ref{eq:3D_helical_HOTI_bulk}) coupled to $\\vec{A} = Bz \\tau_{0}\\mu_{0}\\sigma_{z} \\hat{y}$ in different spin sectors as\n\\begin{align}\n H_{\\sigma,k_{x},k_{y}} &= m_{\\text{bulk}} \\tau_{z}\\mu_{0} + m_{2} \\tau_{z}\\mu_{x} + m_{3}\\tau_{z}\\mu_{z} + + m_{v_{1}} \\tau_{0}\\mu_{z} + m_{v_{2}}\\tau_{0}\\mu_{x} \\nonumber \\\\\n & + u_{x}k_{x}\\tau_{y}\\mu_{y} + v_{H}\\sqrt{\\frac{B}{2}} \\left( a_{\\sigma,k_{y}} + a^{\\dagger}_{\\sigma,k_{y}} \\right)\\tau_{y}\\mu_{z} -i u_{z} \\sqrt{\\frac{B}{2}}\\left( a_{\\sigma,k_{y}} - a^{\\dagger}_{\\sigma,k_{y}} \\right)\\tau_{x}\\mu_{0}. \\label{eq:3D_bulk_SU2_LL_Hamiltonian}\n\\end{align}\nIn Fig.~\\ref{SM_Fig_7} (a) we show the numerically computed spectrum $E_{\\sigma,k_{x},k_{y}}$ as a function of $k_{x}$. \nNote that $E_{\\sigma,k_{x},k_{y}}$ does not depend on $\\sigma$ and $k_{y}$, giving rise to flat Landau levels as a function of $k_{y}$. \nWe relate this low energy bulk theory to the $\\phi$-sliding spectrum of the 2D modulated system by identifying $k_{y}$ in Eq.~(\\ref{eq:3D_bulk_SU2_LL_Hamiltonian}) with $\\phi$. \nWe now examine the consequences of this correspondence.\n\n\nFirst, examining the band edges for the valence and conduction bands in Fig.~\\ref{SM_Fig_7} (a) we notice that there is no particle-hole symmetry in the low energy spectrum of helical HOTI with $SU(2)$ Landau quantization.\nThis is also reflected in the energy eigenvalues and eigenstate probability distribution for the flat bands in Fig.~\\ref{SM_Fig_5} (b) corresponding to bulk-confined modes shown in Figs.~\\ref{SM_Fig_7} (b) and (c), which are in the bulk flat continuum directly below and above the edge flat dispersion marked in orange and red in Fig.~\\ref{SM_Fig_5} (b). \nThe figure caption of Fig.~\\ref{SM_Fig_7} gives the asymmetric energies of the two bulk states in Figs.~\\ref{SM_Fig_7} (b) and (c).\n\nSecond, we note that the Hamiltonian in Eq.~(\\ref{eq:3D_bulk_SU2_LL_Hamiltonian}) has eigenstates that are extended along $x$ and $y$ while confined along $z$. \nThe confinement along $z$ can also be understood through the definition of the spin-dependent ladder operators in Eq.~(\\ref{eq:ladder_bulk_helical_SU2}), which create SHO states along $z$. \nProjecting this 3D wave function to the 2D modulated system, only the probability distribution along $x$ and $z$ is preserved. \nTherefore in 2D we expect to see flat bands along $k_{y}$--which is identified as $\\phi$--and with corresponding wave functions confined along $z$ while extended along $x$. \nThis is consistent with Figs.~\\ref{SM_Fig_7} (b) and (c). \nThe above discussion shows that our low energy bulk theory in 3D qualitatively explains the existence of bulk confined modes in 2D. \nThe universal property of these types of 2D helical sliding systems, even with larger $|\\vec{q}|$ presented in the main text, is that there will be flat bands as we vary $\\phi$ with bulk-confined modes that are projected from the bulk of helical HOTI with $SU(2)$ Landau quantization.\n\n\n\\begin{figure}[h]\n \\includegraphics[scale=0.4]{SM_Fig_7_add_horizontal_pm_0_6_new.pdf}\n \\caption{(a) Bulk $SU(2)$ Landau levels of Eq.~(\\ref{eq:3D_bulk_SU2_LL_Hamiltonian}) for the low energy model of a 3D helical HOTI\\cite{Wieder_spin_decoupled_helical_HOTI} with $m_{\\text{bulk}} = 1$, $m_{2} = 0.3$, $m_{3} = 0.2$, $m_{v_{1}} = -0.4$, $m_{v_{2}} = 0.2$, $u_{x} = 1$, $v_{H}=1.2$, $u_{z} = 1$ and $B = 0.2$. \n The band edges occur at $E = -0.6618$ and $0.601$. \n The green, black and orange horizontal lines correspond to energy $+0.6$, $0$ and $-0.6$, respectively. \n Notice that there is in general no particle-hole symmetry in the bulk $SU(2)$ LL spectrum of a 3D helical HOTI. \n (b)$\\&$(c) Probability distribution of bulk-confined modes in the flat band of Fig.~\\ref{SM_Fig_5} (b) at $\\phi = 0$ with energy $-0.5393$ and $0.4835$ respectively with similar wave function confinement. \n These two modes are at the flat bulk continuum right below and above those flat bands for {\\it edge-confined} modes corresponding to $E^{-}_{\\sigma',k_{y},n=1}$ and $E^{+}_{\\sigma',k_{y},n=1}$ in Eq.~(\\ref{wvfn_top}), which we have marked in orange and red in Fig.~\\ref{SM_Fig_5} (b).\n We note that both (b) and (c) show modes confined along $z$, which is consistent with the ladder operators defined in Eq.~(\\ref{eq:ladder_bulk_helical_SU2}). \n However, the absolute value of energies for (b) and (c) are not same, indicating the spectrum is not particle-hole symmetric, and the minimal continuum model calculation in (a) captures this trend. \n The darker (black) color in (b)--(c) implies higher probability density. \n In (b) and (c), the $x$- and $z$-coordinate both range from $-15,\\ldots, +15$.}\n \\label{SM_Fig_7}\n\\end{figure}\n\n\n\n\\section{\\label{sec:numerical_method}Numerical methods to identify non-trivial Chern insulating layers in 3D Weyl-CDW systems}\n\n\nIn this section, we describe the numerical methods we used to identify layers (hybrid Wannier functions) and corresponding Bloch states in our 3D Weyl-CDW model. \nIn the following, we use the term $xy$-slab to denote a sample of 3D Weyl-CDW system infinite along the $x$ and $y$ directions and finite along $z$ with size $L_{z}$. \nIn addition, we use $y$-rod to denote a sample of 3D Weyl-CDW system infinite along $y$ and finite along $x$ and $z$ with size $L_{x} \\times L_{z}$. \nIn this section, we will present the numerical methods based on Berry phase, Berry curvature, and hybrid Wannier functions to identify the non-trivial bands in the $xy$-slab for $q = 1\/5$, $L_{z} = 25 $, $t_{x} = -t_{y} = t_{z} = 1$, $m=2$, $2|\\Delta|=0.75$ with $\\phi = 0$ and $\\phi = \\pi$ in the main text. \nThe numerical methods can also be applied to systems such as incommensurate CDWs with $q = \\tau \/4$ where $\\tau = (1+\\sqrt{5})\/2$ is the golden ratio (see main text).\n\nAs mentioned in the main text, we can understand the existence of quantum anomalous Hall (QAH) surface states by viewing the $xy$-slab as composed of a layered stack of Chern insulators (CIs). \nWhen we cut the $xy$-slab into the corresponding $y$-rod, those states in the $xy$-slab with non-zero Chern number will each contribute a number of chiral edge modes, depending on the Chern number of the bands. \nThese chiral edge modes collectively form the QAH surface states. The number of chiral edge modes in the $y$-rod on each edge is equal to the Chern number of occupied bands in the corresponding $xy$-slab. Specifically, the low energy theory in 4D predicts that topologically non-trivial bands in the $xy$-slab have a layered structure in position space, in which non-trivial bands will be localized along $z$ and separated by some amount of lattice constants\\cite{NicoDavidAXI2,dynamical_axion_insulator_BB}. \nIn the following, we will use a combination of Berry phase, Berry curvature and hybrid Wannier function calculations to identify those non-trivial bands in the $xy$-slab. \nThe goal is to find non-trivial bands whose probability distributions along $z$ match the probability distribution of chiral edge modes (or QAH surface states) in the $y$-rod {\\color{black}{shown in Figs.~4 and 5 in the main text}}.\n\nThe first step of our analysis is to compute the hybrid Wannier bands for the occupied states in the $xy$-slab. \nThis involves diagonalizing the position operator $z$ projected into the space of occupied bands at each $\\vec{k}$-point of the 2D Brillouin zone (BZ)\\cite{PythTB,Marzari2012}. \nNotice that, as we have finite size along $z$, the position operator $z$ is well-defined. \nWe thus obtain hybrid Wannier bands, which are the eigenstates of the projected $z$ operator as a function of $k_x$ and $k_y$. \nIn our calculation we find that for $q = 1\/5$, $t_{x}=-t_{y}=t_{z}=1$, $m = 2$, $2|\\Delta|=0.75$, $\\phi = 0$ and $L_{z} = 25$ the hybrid Wannier bands are non-degenerate.\nThis allows us to compute the Berry phase integrated along $k_{y}$ as a function of $k_{x}$ for each hybrid Wannier band, as shown in the first column of Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_0}. \nWe denote such Berry phases as $\\gamma^{m}_{\\vec{G}_{2}}(k_{x})$, where $m$ indexes the hybrid Wannier band, $\\vec{G}_{2}$ indicates that we are integrating along $k_{y}$, and $k_{x}$ denotes the functional dependence on $k_{x}$. \nDue to the non-degeneracy of the hybrid Wannier bands, the Berry phases $\\gamma^{m}_{\\vec{G}_{2}}(k_{x})$ coincide with the eigenvalues of the $\\hat{y}$-directed non-Abelian slab Berry phase (Wilson loop) $\\text{arg}(\\mathcal{W})$\\cite{wieder2018axion,Wilson_2,Yu11,Alexandradinata16}. \nFrom the negative winding of $\\gamma^{m}_{\\vec{G}_{2}}(k_{x})$ shown in the first column of Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_0}, we find that each of the $m = 2$, $7$, $12$, $17$ and $22$ hybrid Wannier bands (the first hybrid Wannier band is labelled by $m=0$) carries Chern number $C = -1$. \nImportantly, at $\\Gamma$ point, the hybrid Wannier functions for these 5 hybrid Wannier bands are localized at $z_\\Gamma \\approx -10$, $-5$, $0$, $5$ and $10$, see the second column of Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_0} and the second column of Table~\\ref{tab:hwfc_table}. \nThis is consistent with our deduction in the main text that the CIs will be separated by $1\/q$ along $z$, which is $5$ lattice constants in this case. \nThis also supports our identification that this system can be viewed as layered stack of CIs.\nThe discrepancy between $z_\\Gamma$ and $\\langle z\\rangle$ (reported in the third column in Table~\\ref{tab:hwfc_table}), the average of the hybrid Wannier center over all momenta, can be attributed to the fact that our present model has nonzero coupling along the $z$-direction, and hence nonzero coupling between CI layers. \n\nFor those hybrid Wannier bands with non-zero winding of the Berry phase, we compute the decomposition of hybrid Wannier functions in terms of Bloch states at $\\vec{k} = \\Gamma$: \n\\begin{align}\n\t\\psi^{\\text{hybrid}}_{m,\\Gamma} = \\sum_{n=1}^{N_{\\text{occ}}} c_{m,\\Gamma}^{n} \\psi^{\\text{Bloch}}_{n,\\Gamma}, \\label{eq:wvfn_decomposition}\n\\end{align}\nwhere $N_{\\text{occ}}$ is the number of occupied bands. \nThe values of $n$ for those non-zero $ c_{m,\\Gamma}^{n} $ then directly tell us the index of the topologically non-trivial Bloch bands (not hybrid Wannier bands) at $\\vec{k} = \\Gamma$. \nHereafter, we will use $m$ to denote hybrid Wannier band indices and $n$ to denote Bloch band indices.\nIn our specific examples, the decomposition of $\\psi^{\\text{hybrid}}_{m,\\Gamma}$ in the Bloch basis where $m$ is the index of non-trivial hybrid Wannier bands are given in the third column of Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_0}. \nWe can then identify that the Bloch band with index $n = 18,\\ldots, 22$ at $\\vec{k} = \\Gamma$ are non-trivial. \nImportantly, we notice that for the hybrid Wannier bands at $\\Gamma$ with $m \\ne 2$, $7$, $12$, $17$ and $22$, the decomposition coefficient $c^{n}_{m,\\Gamma}$ are zero for $n = 18,\\ldots, 22$, showing that the hybrid Wannier bands at $\\Gamma$ with $m = 2$, $7$, $12$, $17$ and $22$ are truly spanned only by Bloch bands with $n = 18,\\ldots, 22$. \nThese states contribute to the chiral QAH surface states. \nAlthough the band structure of $xy$-slab is complicated with various band entanglements, we can track the topologically non-trivial bands by starting from the non-trivial bands at $\\vec{k} = \\Gamma$. \nWhenever we encounter band crossing, we choose to proceed along the direction where there is no discontinuity of the bands. \nFor example, we can identify the non-trivial valence bands at $\\vec{k} = \\Gamma$ and $\\vec{k} = (0,\\pi) = Y $, as shown in Fig.~\\ref{slab_band_and_wvfn_Q_0.4pi_Lz_25_D_0.75_phi_0_and_pi} marked by orange and green respectively. \nWe also plot the probability distribution of these non-trivial states in Fig.~\\ref{slab_band_and_wvfn_Q_0.4pi_Lz_25_D_0.75_phi_0_and_pi}. \nThe probability distribution along $z$ for these non-trivial states are exactly the same as the QAH zero modes for the corresponding $y$-rod, which we have shown in the main text, and the hybrid Wannier function at $\\Gamma$ for $m = 2$, $7$, $12$, $17$ and $22$, shown in the middle column of Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_0}.\nFurthermore, we have computed the Berry curvature of each Bloch band (not hybrid Wannier band) around the $\\Gamma$ point of the $xy$-slab in Fig.~\\ref{Berry_curvature_around_Gamma_Q_0.4pi_Lz_25_D_0.75_phi_0}. \nThose non-trivial bands with band index $n = 18,\\ldots, 22$ show negative values of Berry curvature around $\\Gamma$, which is distinct from the other Bloch bands having positive values of Berry curvature around $\\Gamma$.\nThis negative Berry curvature contributes to the total Chern number $C = -5$ of occupied bands. \nThis is consistent with $G_{xy}(\\phi = 0) = -5 e^{2}\/h$, as we stated in the main text.\n\nThe above process can be also applied to the case with $\\phi = \\pi$. \nWe also identify the non-trivial bands for the $xy$-slab, and plot their probability distribution at $\\vec{k} = \\Gamma$ and $\\vec{k} = Y$ in Fig.~\\ref{slab_band_and_wvfn_Q_0.4pi_Lz_25_D_0.75_phi_0_and_pi}. \nAgain, the probability distribution along $z$ for these non-trivial states are exactly the same as the QAH zero modes for the corresponding $y$-rod, which we have shown in the main text, and the hybrid Wannier functions at $\\Gamma$ for $m = 4$, $9$, $15$ and $20$, shown in the second column of Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_pi}. \nThe similar analysis of Berry phase for hybrid Wannier bands, wave function decomposition of Eq.~(\\ref{eq:wvfn_decomposition}) and Berry curvature around $\\vec{k} = \\Gamma$ are also shown in Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_pi} and Fig.~\\ref{Berry_curvature_around_Gamma_Q_0.4pi_Lz_25_D_0.75_phi_pi}. \nImportantly, we can see that in the second column of Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_pi} and the fifth column of Table~\\ref{tab:hwfc_table}, the hybrid Wannier functions at $\\Gamma$ corresponding to the non-trivial bands are localized at $z_\\Gamma \\approx -7.5$, $-2.5$, $2.5$ and $7.5$. \nAs above, we attribute the discrepancy between $z_\\Gamma$ and $\\langle z \\rangle$ (the average of the hybrid Wannier center over the Brillouin zone, reported in column six of Table~\\ref{tab:hwfc_table}) to the nonvanishing interlayer coupling in our model. \nThis is again consistent with our stacked layer identification. \nAs mentioned in the main text, as we vary $\\phi$ by $\\pi$, each of the CIs will be shifted by $-\\Delta \\phi \/ (2\\pi q) = -\\pi\/(2\\pi q) = -1\/(2q)$, which in our current example is equal to $-2.5$ lattice constants. \nComparing this with the second column of Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_0}, which shows that the non-trivial CIs are at $z \\approx -10$, $-5$, $0$, $5$ and $10$, we can see that the stacked layers in Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_pi} can be identified as those in Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_0} with a shift of $-2.5$ lattice constant along $z$. \nWe notice that in this case, where we have used $q = 1\/5$, $t_{x}=-t_{y}=t_{z}=1$, $m = 2$, $2|\\Delta|=0.75$, $\\phi = \\pi$ and $L_{z} = 25$, the hybrid Wannier bands are nearly degenerate at $\\Gamma$ for $m = 9$, $10$ and $m = 14$, $15$, which result in a dramatic (though still continuous) change of $\\gamma^{m}_{\\vec{G}_{2}}(k_{x})$ for $m = 9$ and $15$ around $k_{x} = 0$, as shown in the first column of Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_pi}. \nNevertheless, we are able to separate the hybrid Wannier bands.\n\n\nWe conclude this section by making several remarks. \nFirst, since the above process does not involve computing the band structure in a 3D BZ, there is no problem in generalizing the above method to 3D Weyl-CDW systems with incommensurate density waves with modulation wave vector along $z$. \nWe have carried out the same numerical analysis for $q = \\tau \/ 4$ (where $\\tau = (1+\\sqrt{5})\/2$ is the golden ratio).\nThe results are shown in the main text in which we can identify non-trivial states corresponding to CI layers and these states have exactly the same probability distribution along $z$ as the QAH zero modes in the $y$-rod at $k_{y} = 0$. \nWe shall in here briefly summarize some numerical results for $q = \\tau \/ 4$, $t_{x} = -t_{y} = t_{z} = 1$, $m=2$, $2|\\Delta|=2$: \n(1) The $xy$-slab Hall conductances with $L_{z} = 21$ are $G_{xy}(\\phi = 0)=-9 e^{2}\/h$ and $G_{xy}(\\phi = \\pi)=-8 e^{2}\/h$\\cite{dynamical_axion_insulator_BB}. \n(2) There are 9 and 8 non-trivial hybrid Wannier bands in the $xy$-slab for $\\phi = 0$ and $\\pi$, respectively. \nEach of these non-trivial hybrid Wannier bands carries Chern number $C = -1$. \n(3) Similar to Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_0} and Fig.~\\ref{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_pi}, we have identified 9 and 8 non-trivial Bloch bands of the $xy$-slab using non-zero values of $c^{n}_{m,\\Gamma}$ in Eq.~(\\ref{eq:wvfn_decomposition}). \nThe Berry curvature of these 9 and 8 non-trivial Bloch bands around $\\Gamma$ are negative, which is distinct from other Bloch bands having positive values of Berry curvature around $\\Gamma$, and contribute to $G_{xy}(\\phi = 0)=-9 e^{2}\/h$ and $G_{xy}(\\phi = \\pi)=-8 e^{2}\/h$. \nThis is similar to the cases of $q = 1\/5$ in Fig.~\\ref{Berry_curvature_around_Gamma_Q_0.4pi_Lz_25_D_0.75_phi_0} and Fig.~\\ref{Berry_curvature_around_Gamma_Q_0.4pi_Lz_25_D_0.75_phi_pi}. \nSecondly, the above procedure to filter out non-trivial CI layers might break down when we have strong interlayer coupling, causing complicated hybridization between layer states. \nWe emphasize again that the hybrid Wannier center $\\left\\langle z \\right\\rangle$ averaged over the 2D Brillouin zone for a fixed non-trivial hybrid Wannier band can differ slightly from the hybrid Wannier center $z_{\\Gamma}$ at $\\vec{k} = (k_{x},k_{y})=(0,0)=\\Gamma$ where our low energy theory works. \nFor example, as shown in Table~\\ref{tab:hwfc_table}, for $\\phi = 0$, the hybrid Wannier band with index $m = 2$ has $z_{\\Gamma} = -9.9245$ while $\\left\\langle z\\right\\rangle = -9.9997 \\pm 0.0026$. \nThis can be viewed as the consequence of hybridization between layers. \nWe expect that if the layers are completely decoupled from each other, there will be no difference between $z_{\\Gamma}$ and $\\left\\langle z \\right\\rangle$. \nNotice that for $\\phi = 0$ the inversion center of the $xy$-slab is at $z = 0$, which pins both $z_{\\Gamma}$ and $\\left\\langle z \\right\\rangle$ to $z = 0$ for the $m=12$ hybrid Wannier band. \nThis is consistent with Refs.~\\onlinecite{song2017,MTQC}, where it was emphasized that the distinction between QAH and oQAH states is due to the presence or absence of a non-trivial CI layer exactly at the inversion center $\\left\\langle z \\right\\rangle = 0$, respectively. \nWe have also verified this for the incommensurate case of $q=\\tau \/4$ where $\\tau = (1+\\sqrt{5})\/2$, which indicates that the difference of a non-trivial CI layer at the inversion center $\\left\\langle z \\right\\rangle=0$ between QAH and oQAH states holds for generic CDW wave vectors. \nOur method can be viewed as a way to map a system with interlayer coupling to another system with decoupled CI layers.\n\n\\begin{table}[h]\n\\centering\n\\begin{tabular}{|c|c|c||c|c|c|}\n\\hline\n$m$ for $\\phi = 0$ & $z_{\\Gamma}$ & $\\left\\langle z\\right\\rangle$ & $m$ for $\\phi = \\pi$ & $z_{\\Gamma}$ & $\\left\\langle z\\right\\rangle$ \\\\\n\\hline\n2 & $-9.9245$ & $-9.9997 \\pm 0.0026$ & 4 & $-7.5467$ & $-7.9963 \\pm 0.0167$\\\\\n\\hline\n7 & $-4.9972$ & $-5.0 \\pm 0.0001$ & 9 & $-2.5023$ & $-2.9963 \\pm 0.0176$ \\\\\n\\hline\n12 & $0.0$ & $0.0 \\pm 0.0$ & 15 & $2.5023$ & $2.9963 \\pm 0.0176$ \\\\\n\\hline\n17 & $4.9972$ & $5.0 \\pm 0.0001$ & 20 & $7.5467$ & $7.9963 \\pm 0.0167$ \\\\\n\\hline\n22 & $9.9245$ & $9.9997 \\pm 0.0026$ & & & \\\\\n\\hline\n\\end{tabular}\n\\caption{Hybrid Wannier centers along $z$ for different hybrid Wannier bands with non-zero winding of $\\gamma^{m}_{\\vec{G}_{2}}(k_{x})$ and $q = 1\/5$.\n$m$ denotes the non-trivial hybrid Wannier band index. \nThe first and last three columns summarize the results for $\\phi = 0$ and $\\phi = \\pi$, respectively. \nThere are $5$ and $4$ non-trivial hybrid Wannier bands for $\\phi = 0$ and $\\pi$ with $m = 2$, $7$, $12$, $17$, $22$ and $m = 4$, $9$, $15$, $20$, respectively.\n$z_{\\Gamma}$ is the hybrid Wannier center along $z$ for the hybrid Wannier functions at $\\vec{k} = (k_{x},k_{y}) = (0,0) = \\Gamma$.\n$\\left\\langle z \\right\\rangle$ is the hybrid center along $z$ averaged over the 2D Brillouin zone with grid size $100 \\times 100$. \nWe also report the standard deviation of $\\left\\langle z \\right\\rangle$ in the same column.}\n\\label{tab:hwfc_table}\n\\end{table}\n\n\n\\begin{figure}[ht]\n \\includegraphics[width=\\columnwidth]{grid_100_hwf_winding_decomposition_orbitals_bloch_Q_0_4pi_Lz_25_D_0_75_phi_0.pdf}\n \\caption{Berry phase winding of non-trivial hybrid Wannier bands (first column), hybrid Wannier functions at $\\vec{k} = \\Gamma$ decomposed in terms of orbital (second column), and Bloch bases (third column) for an $xy$-slab of the Weyl-CDW model with $t_{x}=-t_{y}=t_{z}=1$, $m =2$, $2|\\Delta|=0.75$, $q = 1\/5$, $L_{z} =25$ and $\\phi = 0$. \nThe $z$-coordinate ranges from $-12, \\ldots, +12$. \nThere are 5 non-trivial hybrid Wannier bands for the occupied states, each of them displaying $-2\\pi$ winding of $\\gamma^{m}_{\\vec{G}_{2}}(k_{x})$ as $k_{x}$ goes from $0$ to $2\\pi$, such that the Chern number for each of the CI layers is $C = -1$. \nFrom the second column, we can deduce that the non-trivial states are localized at $z \\approx -10$, $-5$, $0$, $+5$, $+10$. \nFrom the third column, where we plot $|c^{n}_{m,\\Gamma}|^{2}$, we can deduce that the non-trivial Bloch band indices at $\\vec{k} = \\Gamma$ are $ n =18,\\ldots, 22$ (the first band is labelled by $n=0$).}\n \\label{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_0}\n\\end{figure}\n\n\n\n\n\\begin{figure}[ht]\n \\includegraphics[scale=0.4]{Berry_curvature_around_Gamma_Q_0_4pi_Lz_25_D_0_75_phi_0.pdf}\n \\caption{The Berry curvature around $\\vec{k} = \\Gamma$ for the 25 valance Bloch bands (not hybrid Wannier bands) of the Weyl-CDW model for an $xy$-slab with $t_{x}=-t_{y}=t_{z}=1$, $m =2$, $2|\\Delta|=0.75$, $q = 1\/5$, $L_{z} =25$ and $\\phi = 0$.\n All the figures have $k_{x}$ and $k_{y}$ in the range range $2\\pi [-0.003,0.003]$.\n We notice that the Bloch bands with indices $n = 18,\\ldots, 22$ have negative Berry curvature around $\\vec{k} = \\Gamma$ contributing to the total Chern number $C = -5$ of this $xy$-slab, leading to $G_{xy} = -5 e^{2}\/h$.}\n \\label{Berry_curvature_around_Gamma_Q_0.4pi_Lz_25_D_0.75_phi_0}\n\\end{figure}\n\n\\begin{figure}[ht]\n \\includegraphics[width=\\columnwidth]{grid_100_hwf_winding_decomposition_orbitals_bloch_Q_0_4pi_Lz_25_D_0_75_phi_pi.pdf}\n \\caption{Berry phase winding of non-trivial hybrid Wannier bands (first column), hybrid Wannier functions at $\\vec{k} = \\Gamma$ decomposed in terms of orbital (second column), and Bloch basis (third column) for an $xy$-slab of the Weyl-CDW model with $t_{x}=-t_{y}=t_{z}=1$, $m =2$, $2|\\Delta|=0.75$, $q = 1\/5$, $L_{z} =25$ and $\\phi = \\pi$.\nThe $z$-coordinate ranges from $-12, \\ldots, +12$. \nThere are 4 non-trivial hybrid Wannier bands for the occupied states, each of them displaying $-2\\pi$ winding of $\\gamma^{m}_{\\vec{G}_{2}}(k_{x})$ as $k_{x}$ goes from $0$ to $2\\pi$, such that the Chern number for each of the CI layers is $C = -1$. \nFrom the second column we can deduce that the non-trivial states are localized at $z \\approx -7.5$, $-2.5$, $+2.5$, $+7.5$. \nFrom the third column we can deduce that the non-trivial Bloch band indices at $\\vec{k} = \\Gamma$ are $n = 17$, $18$, $20$ and $21$ (the first band is labelled by $n=0$).}\n \\label{hwf_winding_decomposition_orbitals_bloch_Q_0.4pi_Lz_25_D_0.75_phi_pi}\n\\end{figure}\n\n\n\\begin{figure}[ht]\n \\includegraphics[scale=0.4]{Berry_curvature_around_Gamma_Q_0_4pi_Lz_25_D_0_75_phi_pi.pdf}\n \\caption{The Berry curvature around $\\vec{k} = \\Gamma$ for the 25 valance Bloch bands (not hybrid Wannier bands) for the $xy$-slab of the Weyl-CDW with $t_{x}=-t_{y}=t_{z}=1$, $m =2$, $2|\\Delta|=0.75$, $q = 1\/5$, $L_{z} =25$ and $\\phi = \\pi$. \n All the figures have $k_{x}$ and $k_{y}$ in range $2\\pi [-0.003,0.003]$. \n We notice that the Bloch bands with indices $n=17$, $18$, $20$ and $21$ have negative Berry curvature around $\\vec{k} = \\Gamma$ contributing to the total Chern number $C = -4$ of this $xy$-slab, leading to $G_{xy} = -4 e^{2}\/h$.}\n \\label{Berry_curvature_around_Gamma_Q_0.4pi_Lz_25_D_0.75_phi_pi}\n\\end{figure}\n\n\n\n\\begin{figure}[ht]\n \\includegraphics[scale=0.6]{slab_band_and_wvfn_Q_0_4pi_Lz_25_D_0_75_phi_0_and_pi.pdf}\n \\caption{The $xy$-slab valence band structure of the Weyl-CDW model plotted along the path $-Y \\to \\Gamma \\to Y$ with $t_{x}=-t_{y}=t_{z}=1$, $m =2$, $2|\\Delta|=0.75$, $q = 1\/5$, $L_{z} =25$, (a) $\\phi = 0$ and (b) $\\phi = \\pi$.\n The (a) 5 and (b) 4 non-trivial bands around $\\vec{k}=\\Gamma$ and $Y$ are marked by orange and green. \n The summation of the probability distribution for the (a) 5 and (b) 4 non-trivial states at $\\vec{k}=\\Gamma$ and $Y$ are also plotted on the right of each $xy$-slab band structure for (a) $\\phi = 0$ and (b) $\\pi$. \n The $z$-coordinate ranges from $-12,\\ldots, +12$. \n As the non-trivial states at $\\vec{k} = \\Gamma$ and $\\vec{k} =Y$ have exactly same probability distribution along $z$, they lie in the same Landau level index $n$ (see Sec.~VI of the main text) subspace. \n This also confirms that our identification of non-trivial bands is consistent.}\n \\label{slab_band_and_wvfn_Q_0.4pi_Lz_25_D_0.75_phi_0_and_pi}\n\\end{figure}\n\n\\clearpage\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nLet $g\\in L^2(\\Bbb R^d)$, and let $\\Lambda\\subset \\Bbb R^{2d}$ be a countable subset. We define the {\\it Gabor system} (also known as {\\it Weyl-Heisenberg system}) $\\mathcal G(g, \\Lambda)$ with respect to $g$ and $\\Lambda$ to be the collection of functions $\\pi(a, b)g$ defined by combined time and frequency shifts of $g$:\n$$\n\\pi(a,b)g(x)= M_{b}T_{a}g = e^{2\\pi i \\langle b, x\\rangle} g(x-a) \\quad (a,b)\\in \\Lambda .\n$$\n$\\Lambda$ is also known as {\\it time-frequency set} and\nthe frequency shift is also called modulation. We say $g$ is an {\\it orthonormal Gabor window function} with respect to $\\Lambda$, or simply a window function, if $\\mathcal G(g, \\Lambda)$ is an orthonormal basis for $L^2(\\Bbb R^d)$. See e.g. \n\\cite{OleBook} or \\cite{Groechenig-book}. \n\n\n\n\nWe call $\\Lambda$ {\\it separable} if it is of the form of $\\Lambda = {\\mathcal T}\\times \\Gamma$ for some countable subsets ${\\mathcal T}$ and $\\Gamma$ in ${\\mathbb R}^d$. Gabor systems have been introduced for the first time in 1946 by Gabor \\cite{Gabor46} and are now fundamental objects in applied and computational harmonic analysis. Moreover, for $\\mathcal G(g, \\Lambda)$ to be a Gabor orthonormal basis, the (Beurling) density of $\\Lambda$, denoted by dens$(\\Lambda)$, must be $1$ \\cite{RS}.\n\n\n\n\\medskip\n\n\nThe existence of a window function for a given lattice has been investigated for several special cases of $M$. The question of existence has been completely answered\nby Han and Wang \\cite{HanW1} for separable lattices of the form $\\Lambda= \\mathcal J\\times \\mathcal T$ with dens$(\\Lambda)=1$. They answered the question by showing the existence of a common fundamental domain for two different lattices. Later, the same authors partially answered the question for non-separable lattices (i.e. the lattices of not of the form of $\\mathcal J \\times \\mathcal T$) for special cases of matrix $M$ \\cite{HanW4}. Indeed, they proved that, when for example $M$ is a block triangular matrix, a window function $g$ exists and it can be chosen so that $|g|$ or $|\\hat g|$ is the scalar multiple of a characteristic function. They also showed the existence of a window function with compact support for rational matrices $M$.\n\n\\medskip\n\nGiven a subset $K\\subset \\Bbb R^d$,\nwe denote by $\\chi_K$ the indicator function of $K$ and by $|K|$ its Lebesgue measure.\nThe main focus of this paper is the following. Suppose that $\\Lambda = M({\\mathbb Z}^{2d})\\subset \\Bbb R^{2d}$ is a full lattice with Beurling density dens$(\\Lambda)= |\\det(M)|^{-1} =1$ and suppose that $\\mathcal G(|K|^{-1\/2} \\chi_K, \\Lambda)$ forms a Gabor orthonormal basis for $L^2(\\Bbb R^d)$. What can we say about the structure of $K$? This question is related to the study of spectral sets and translational tiles which we will call the {\\it Fuglede-Gabor Problem} later on.\n\n\\medskip\n\n\n\\begin{definition}[Spectral and tiling sets]\\label{Spectral and tiling sets} A Lebesgue measurable set\n $K\\subset \\Bbb R^d$ with positive and finite measure is a {\\it spectral set} in $\\Bbb R^d$ if there is a countable set $B\\subset \\Bbb R^d$ (not necessarily unique) such that\n exponentials $\\{e_b(x):= e^{2\\pi i \\langle b,x\\rangle} : b\\in B, x\\in K\\}$ constitute an orthogonal basis for $L^2(K)$, i.e., the exponentials are mutual orthogonal and complete in $L^2(K)$. In this case $B$ is called a {\\it spectrum} for $K$.\n\n \\medskip\n\n We say $K$ {\\it multi-tiles} $\\Bbb R^d$ by its translations if there\n is a countable set ${\\mathcal J}\\subset \\Bbb R^d$ and integer $N\\ge 1$ such that\n\n\\begin{equation}\\label{multitile-generic}\n\\sum_{t\\in{\\mathcal J}} \\chi_{K} (x+t)=N\\quad a.e. \\ x\\in \\Bbb R^d .\n\\end{equation}\nIf $N=1$, then $K$ {\\it tiles} ${\\mathbb R}^d$ and the set ${\\mathcal J}$ is called {\\it tiling set} for $K$ (For more details about multi-tiles, see e.g. \\cite{Kol}).\n \\end{definition}\n\n\\medskip\n\n Spectral sets have been studied extensively in the recent years and their study has been reduced to the study of tiling sets by the Fuglede Conjecture or Spectral Set Conjecture \\cite{Fug74} which asserts: {\\it A set $K\\subset \\Bbb R^d$ with positive and finite measure is a spectral set if and only if $K$ tiles $\\Bbb R^d$ by translations. }\n Fuglede proved the conjecture in his celebrated 1974 paper \\cite{Fug74} for the case when $K$ tiles by a lattice or $K$ has a spectrum which is a lattice. The Fuglede Conjecture led to considerable activity in the past three decades. In\n 2004, Tao \\cite{T04} disproved the Fuglede conjecture for dimension $5$ and higher, followed by Kolountzakis and Matolcsi\\rq{}s result \\cite{FMM,KM06} where they proved that the conjecture fails in dimensions $3$ and higher. For more recent results and historical comments see e.g. \\cite{BHM16,IMP17}.\n\n\n\\medskip\nSpectral sets and tiles appear naturally in the Gabor setting. Indeed, let $\\Lambda= \\mathcal J \\times \\mathcal T$ be a separable countable set (not necessarily a lattice) with dens$(\\Lambda)=1$ and let $\\Omega$ be a compact set in $\\Bbb R^d$ which tiles by $\\mathcal J$ and is spectral for $\\mathcal T$. For example, take $\\Omega = [0,1]^2$ and ${\\mathcal J} = {\\mathcal T} = {\\mathbb Z}^2$. Let $g$ be a function supported in $\\Omega$. Then an easy calculation shows that the Gabor system\n $\\mathcal G(g, \\Lambda)$ is an orthonormal basis if $|g(x)|=|\\Omega|^{-1\/2} \\chi_\\Omega(x)$\n (see also \\cite{LW03}, Lemma 3.1). We call such Gabor bases {\\it standard}. Liu and Wang \\cite{LW03} conjectured the converse of this result that for a compactly supported function $g$ and a countable separable set $\\Lambda= \\mathcal J \\times \\mathcal T$, if $\\mathcal G(g, \\Lambda)$ is an orthonormal basis for $L^2(\\Bbb R^d)$, then there is a compact set $\\Omega\\subset \\Bbb R^d$ such that\n $|g|$ is a constant multiple of $\\chi_\\Omega$, $\\Omega$ tiles by $\\mathcal J$ and is a spectral set for $\\mathcal T$. Liu and Wang proved their conjecture when the support of $g$ is an interval.\n Dutkay and the first listed author recently proved that the Liu and Wang\\rq{}s conjecture is affirmative if $g$ is non-negative \\cite[Theorem 1.8]{Dutkay-Lai}. \n\n\n \n\nHowever, the conjecture is still unsolved for general compactly supported $g$.\n\n\n\n\n\\iffalse\n\\begin{Dutkay-Lai}\\label{Dutkay-Lai}(\\cite{Dutkay-Lai}, Theorem 1.8 resp. Theorem 6.2) Suppose that $g$ is a non-negative function with bounded and measurable support $K$ in $\\Bbb R^d$. Suppose that $\\Lambda=\n \\mathcal J\\times \\mathcal T$ is a countable set with $D(\\Lambda)=1$. If $\\mathcal G(g, \\Lambda)$ is an orthonormal basis for $L^2(\\Bbb R^d)$, then the following hold.\n\\begin{itemize}\n\\item[(i)] $K$ tiles $\\Bbb R^d$ by translations by $\\mathcal J$.\n\\item[(ii)] $g= |K|^{-1\/2}\\chi_K$ a.e. on $K$.\n\\item[(iii)] $K$ is a spectral with respect to $\\mathcal T$.\n\\end{itemize}\n\\end{Dutkay-Lai}\n\n\n\nWe shall sketch the proof of Theorem A here. The completeness of the Gabor system $\\mathcal G(g, \\Lambda)$ proves that $\\cup_{j\\in \\mathcal J} K+j$ is a cover for $\\Bbb R^d$. The orthogonality implies that the collection of sets $\\{K+j: \\ j\\in \\mathcal J\\}$ are mutual disjoint up to a Lebesgue measure zero set, thus $K$ is a tiling set with respect to $\\mathcal J$. Further, the measure $d\\mu(x):=g(x)^2 dx$ has a spectrum by proving an equivalent statement that\n$$\\sum_{p\\in \\mathcal T} |\\widehat{\\mu} (x-p)|^2 =1.$$\nThis implies that the measure $\\mu$ is spectral, thus $|g|^2$ must be a constant multiple of a characteristic function by\nCorollary 1.4 of \\cite{Dutkay-Lai}. This forces $g= |K|^{-1\/2}\\chi_K$ a.e. on its support $K$, hence the conclusions of (ii) and (iii). The converse holds by a direct calculation.\n\\fi\n\n\n\n\nThe following problem links the study of window functions associated with Gabor orthonormal bases to the tiling and spectral properties of sets.\n\\medskip\n\n\\begin{problem}\\label{our conjecture1}(Fuglede-Gabor Problem)\nLet $K\\subset \\Bbb R^d$ be a measurable subset with positive and finite measure, and let $\\Lambda \\subset \\Bbb R^{2d}$ be a countable subset. If the Gabor family $\\mathcal G(|K|^{-1\/2}\\chi_K, \\Lambda)$ is an orthonormal basis for $L^2(\\Bbb R^d)$, then $K$ tiles and is a spectral set.\n \\end{problem}\n\n\n\nSince the indicator function of a set is non-negative, {\\it Problem \\ref{our conjecture1} is already affirmative if the time-frequency set is a separable countable sets} using the results of Dutkay and the first listed author.\n Therefore we only focus on the case when the time-frequency set is non-separable. Moreover, although the problem does not require any boundedness assumption of $K$, our interest will mainly be focused on the set $K$ being bounded. \n\n\n It is hard to speculate whether Problem \\ref{our conjecture1} is true or not in its full generality. But from the point of view of Fuglede's result for lattices, we still hope that the Fuglede-Gabor problem is true for non-separable lattices as well. Unfortunately, after our intensive study, we found out that, similar to many notoriously difficult problems in Gabor analysis (see e.g. \\cite{Groechenig-mystery}), \n the Fuglede-Gabor problem for lattices appears to be uneasy. This paper gives a partial answer towards the full solution together with some unexpected examples, as we explain below. \n\n\n\n\\noindent{\\bf Main Results of the paper.} Our main results will mostly be focused on the lower triangular block matrices since most matrices can be reduced to the lower triangular form:\n \\begin{equation}\\label{lower_block}\n \\Lambda = \\left(\n \\begin{array}{cc}\n A & O \\\\\n C & B \\\\\n \\end{array}\n \\right)({\\mathbb Z}^{2d}), \\ \\mbox{and} \\ |\\det(AB)|=1\n \\end{equation}\n (i.e. dens$(\\Lambda)=1$). We will use $B^{-t}$ to denote the inverse transpose of the matrix $B$. Our first general key lemma is as follows, it will serve as a key step for our further analysis.\n\n \\medskip\n\n \\begin{lemma}[Key Lemma]\\label{Th_union of FD} Let $\\Lambda = M({\\mathbb Z}^{2d})$ with $M$ an $2d\\times 2d$ invertible lower triangular block matrix of the form (\\ref{lower_block}).\nSuppose that ${\\mathcal G}\\left({|K|^{-1\/2}}\\chi_K,\\Lambda\\right)$ is a Gabor orthonormal basis. Then there exists an integer $N\\ge 1$ such that\n $$\n K = \\bigcup_{j=1}^N D_j=\\bigcup_{j=1}^N E_j\n $$\nwhere $D_j$\\rq{}s are fundamental domains of $B^{-t}(\\Bbb Z^d)$ and $E_j$\\rq{}s are almost disjoint fundamental domains of $A(\\Bbb Z^d)$ with $|D_i\\cap D_j| = 0$ and $|E_i\\cap E_j| =0$ for all $i\\ne j$. (i.e. K multi-tiles ${\\mathbb R}^d$ simultaneously by $A({\\mathbb Z}^d)$ and $B^{-t}(\\Bbb Z^d)$.)\n\\end{lemma}\n\n If we can prove that $N=1$, then $K$ will be a common fundamental domain for $A(\\Bbb Z^d)$ and $B^{-t}(\\Bbb Z^d)$ and this will imply that the Fuglede-Gabor problem holds. In particular, this is true when $A^tB$ is an integer matrix and $K$ is a bounded set, as our next result confirms.\n\n\\medskip\n\n\n\\begin{theorem}\\label{lower triangle} Let $K$ be a bounded measurable subset of $ \\Bbb R^d$ with positive measure, and let $\\Lambda\\subset \\Bbb R^{2d}$ be a lower triangular lattice in (\\ref{lower_block}). Suppose that\n ${\\mathcal G}(|K|^{-1\/2}\\chi_K, \\Lambda)$ is a Gabor orthonormal basis for $L^2(\\Bbb R^d)$ and $A^tB$ is an integer matrix. Then $K$ tiles and is spectral. More precisely, $K$ is a common fundamental domain for $A(\\Bbb Z^d)$ and $B^{-t}(\\Bbb Z^d)$, $K$ tiles by $A(\\Bbb Z^d)$ and is spectral with spectrum $B(\\Bbb Z^d)$.\n\\end{theorem}\n\n\n\n\\medskip\n\n\n\n\nAs a consequence of Theorem \\ref{lower triangle}, we resolve the Fuglede-Gabor Problem \\ref{our conjecture1} in dimension one for rational matrices and $K$ is bounded.\n\n\\vskip.124in\n\n\\begin{theorem}\\label{rational_dim1} Suppose that $K\\subset \\Bbb R$ is a bounded set with positive Lebesgue measure. Suppose that $\\Lambda$ is a rational lattice in $\\Bbb R^{2}$ with $dens(\\Lambda)=1$. If\n ${\\mathcal G}(|K|^{-1\/2}\\chi_K, \\Lambda)$ is a Gabor orthonormal basis for $L^2(\\Bbb R)$, then $K$ tiles and is spectral.\n\\end{theorem}\n\n\n\\medskip\n\nWe also have the following result for upper triangular block matrices using Theorem \\ref{lower triangle}.\n\n \\vskip.124in\n\n\\begin{theorem}\\label{UT} Suppose that $K\\subset \\Bbb R^d$ is a bounded set with positive Lebesgue measure. Supposes that $\\Lambda\\subset \\Bbb R^{2d}$ is a lattice such that\n $\\Lambda = \\left(\n \\begin{array}{cc}\n A & D \\\\\n O & B \\\\\n \\end{array}\n \\right)({\\mathbb Z}^{2d})$ with $dens(\\Lambda)=1$,\n $A^{-1}D$ symmetric rational matrix and $A^tB=I$. If\n ${\\mathcal G}(|K|^{-1\/2}\\chi_K, \\Lambda)$ is a Gabor orthonormal basis for $L^2(\\Bbb R^d)$, then $K$ tiles and is spectral.\n \\end{theorem}\n\n \\medskip\n\nTheorem \\ref{lower triangle} may also be consider as a converse of \\cite[Lemma 4.1]{HanW4}, which states that if $K$ is a common fundamental domain for the lattice $A({\\mathbb Z}^d)$ and $B^{-t}({\\mathbb Z}^d)$, then for any matrix $C$, the system ${\\mathcal G}(|K|^{-1\/2}\\chi_K,\\Lambda)$ is an Gabor orthonormal basis. Therefore one may naturally expect that $N=1$ in Theorem \\ref{Th_union of FD} is always the case. However, we will show that {\\it $N>1$ can actually happen with a suitable choice of $C$ if $A^tB$ is a rational matrix (see Example \\ref{mutli-tile K})}. This poses additional difficulty to solve the Fuglede-Gabor Problem for rational matrices in higher dimension, as we shall discuss it later. Finally, for a general matrix containing irrational entries, the answer to Fuglede-Gabor problem is completely open. We will discuss this in detail in Section \\ref{Open problems}.\n\n\n\n\n %\n\n\n\n \\medskip\n\n\n\n\n\n {\\bf Outline of the paper.} We organize the paper as follows: After some definitions and recalling some known and basic facts about lattices and Gabor analysis in Section \\ref{notations}, in Section \\ref{proof of Theorem 1.3} we prove Theorem \\ref{Th_union of FD}.\n The proof of Theorems \\ref{lower triangle} are \\ref{rational_dim1} are presented in\n Section \\ref{thm:lower triangle}. In Section \\ref{thm:rational_dim1 and UT} we prove Theorem \\ref{UT}. Section \\ref{Examples} is devoted to examples illustrating the possibility for $N> 1$.\n We conclude the paper with a series of open problems in Section \\ref{Open problems} both for rational and irrational lattices as well as the full generality of the Fuglede-Gabor Problem. In our exposition, we discover that a new notion of completeness, which we will call {\\it exponential completeness}, is crucial in studying the Fuglede-Gabor problem, we will give a short study in Appendix A. In Appendix B, we will show that the octagon will not produce any Gabor orthonormal basis using rational matrices. \n\n\n\n\n\n\\iffalse\n A classical example of a set in dimension $d$ which tiles and is spectral is the unit cube $[0,1]^d$. It is known that $[0,1]^d$ tiles $\\Bbb R^d$ with integer shifts with $\\Bbb Z^d$ and $\\{e_n(x):=e^{2\\pi in \\langle n, x\\rangle } : n\\in \\Bbb Z^d\\}$ is an orthonormal basis for $L^2([0,1]^d)$, also known as Fourier basis.\n It is also well-known that the Gabor system $\\mathcal G(\\chi_{[0,1]^d}, \\alpha\\Bbb Z^d\\times\\beta\\Bbb Z^d)$ is an orthonormal basis for $L^2(\\Bbb R^d)$ when $\\beta=\\alpha^{-1}$ (see e.g. \\cite{Groechenig-book}). This simple observation suggests that there is a link between the study of the Gabor orthonormal basis and the study of tiling and spectral properties. The connection was observed in the earliest by Liu and Wang \\cite{LW03} in 2003, who conjectured the following for separable time-frequency shifts $\\Lambda$:\n \\medskip\n\n \\fi\n\n\n \\iffalse\n The link between the study of the Gabor orthonormal basis and the study of tiling and spectral properties was observed in more generality by Liu and Wang \\cite{LW03} in 2003. They conjectured that:\n\n {\\bf Liu and Wang\\rq{}s Conjecture:} (\\cite{LW03}) {\\it Given a compactly supported function $g(\\neq 0) \\in L^2(\\Bbb R^d)$ with finite and positive measure support $\\Omega$ and given a separable countable set $\\Lambda=\\mathcal J\\times \\mathcal T \\subseteq \\Bbb R^{2d}$, the Gabor family $\\mathcal G(g, \\Lambda)$ is an orthonormal basis if and only if the following hold: \\\\\n (1) $\\Omega$ tiles $\\Bbb R^d$ with translations with tiling set $\\mathcal J$. \\\\\n (2) $|g|=|\\Omega|^{-1\/2} \\chi_\\Omega$. \\\\\n (3) $\\Omega$ is a spectral set with spectrum $\\mathcal T$.}\n\nWhen (1) and (3) hold, then for any function $g$ satisfying (2) the Gabor family $\\mathcal G(g, \\Lambda)$ constitutes an orthonormal basis for $L^2(\\Bbb R^d)$ (\\cite{LW03}, Lemma 3.1).\nThe other direction of the conjecture has not been solved in general but only in some special cases. Recently,\n Dutkay and the first listed author (\\cite{Dutkay-Lai}, Theorem 6.2), proved that if for the separable countable set $\\Lambda=\\mathcal J\\times \\mathcal T$, the Gabor system $\\mathcal G(g, \\Lambda)$ is an orthonormal basis for $L^2(\\Bbb R^d)$, and if the window function $g\\neq 0$ is non-negative on its support $K$, a bounded and measurable set, then $g$ is a constant multiple of characteristic function of $K$, and $K$ tiles by $\\mathcal J$ and is spectral in $\\Bbb R^d$ with respect to $\\mathcal T$. This result provides a partial affirmative answer to the Liu and Wang\\rq{}s Conjecture in separable case.\n\n \\fi\n\n\n\n\n\n \\section{Preliminaries}\\label{notations}\n\nIn this section, we will collect several basic definitions and results required for the rest of the paper. A {\\it full-rank lattice}\n $\\Lambda\\subset \\Bbb R^d$ is a discrete and countable subgroup of $\\Bbb R^{d}$ with compact quotient group $\\Bbb R^{d}\/\\Lambda$. A full-rank lattice in $\\Bbb R^d$ is given by $\\Lambda = {M}({\\mathbb Z}^{2d})$ for some\n $2d\\times 2d$ invertible matrix $M\\in GL(2d, \\Bbb R)$. The density of $\\Lambda$ is given by dens$(\\Lambda)=|\\det(M)|^{-1}$.\n\n Let $\\Lambda$ be a lattice in $\\Bbb R^d$.\n The {\\it dual lattice} of $\\Lambda$ is defined as\n $$\\Lambda^\\perp:= \\{ x\\in \\Bbb R^{2d} : \\ \\langle \\lambda, x\\rangle\\in \\Bbb Z, \\ \\forall \\lambda\\in \\Lambda\\} .$$\nA direct calculation shows that $\\Lambda^\\perp= M^{-t}(\\Bbb Z^{d})$.\n\nA fundamental domain of a lattice $\\Lambda$ is a measurable set $\\Omega$ in $\\Bbb R^d$ which contains distinct representatives (mod $\\Lambda$) in $\\Bbb R^d$, so that the any intersection of $\\\n\\Omega$ with any coset $x+\\Lambda$ has only one element. For the existence of a fundamental domain see \\cite[Theorem 1]{Feld-Green68}. It is also evident that $\\Omega$ tiles $\\Bbb R^d$ with translations by $\\Lambda$ and\nany other tiling set differs from $\\Omega$ at most for a zero measure set.\n\n\n\\noindent{\\bf 1. A reduction lemma.} For an invertible $d\\times d$ matrix\n $A$, the operator ${\\mathcal D}_A:L^2(\\Bbb R^d)\\to L^2(\\Bbb R^d)$ defined by\n $${\\mathcal D}_Ag(x):=|\\det(A)| ^{1\/2} g(Ax).$$\n %\n is unitary, i.e, ${\\mathcal D}_A$ is onto and isometry $\\|{\\mathcal D}_Ag\\|= \\|g\\|$. The following lemma follows from a simple computation and is in general known. We will omit the detail of the proof. \n\n\\vskip.124in\n\n \\begin{lemma}\\label{lemma 3-prim}\n Let ${\\Lambda} $ be a lattice such that\n %\n\\begin{align}\\label{block lattice}\n\\Lambda= \\left(\n \\begin{array}{cc}\n A & D\\\\\n C & B \\\\\n \\end{array}\n \\right)({\\mathbb Z}^{2d})\n \\end{align}\n where $A$ is an invertible $d\\times d$ matrix. Then\n$$\\mathcal G(g, \\Lambda) = {\\mathcal D}_{A^{-1}}\\mathcal G({\\mathcal D}_Ag, \\widetilde{\\Lambda})$$\n where $$\n\\widetilde{\\Lambda} = \\left(\n \\begin{array}{cc}\n I & A^{-1}D\\\\\n A^tC & A^tB \\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d}).\n $$\n\nConsequently, $\\mathcal G(g, \\Lambda) $ is a Gabor orthonormal basis if and only if $\\mathcal G({\\mathcal D}_Ag, \\widetilde{\\Lambda})$ is a Gabor orthonormal basis.\n \\end{lemma}\n\n\n\nIn Lemma \\ref{lemma 3-prim}, if we let\n $D=O$ and $g(x)=|K|^{-1\/2} \\chi_K$, then the conclusion of the lemma shows that ${\\mathcal G}(|K|^{-1\/2} \\chi_K, \\Lambda)$ is an orthonormal basis for $L^2(\\Bbb R^d)$ if and only if ${\\mathcal G}(|A^{-1}K|^{-1\/2} \\chi_{A^{-1}K}, \\widetilde{\\Lambda})$ is an orthonormal basis with\n$$\n\\widetilde{\\Lambda} = \\left(\n \\begin{array}{cc}\n I & O\\\\\n A^tC & A^tB \\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d}).\n$$\n We shall use this observation later.\n\n \\iffalse\n\\begin{proof}\nLet $g = |K|^{-1\/2} \\chi_K$. For each $\\lambda = (Am,Cm+Bn),\\lambda' =(Am',Cm'+Bn')\\in \\Lambda$. Note that by a change of variable $x \\rightarrow Ax$\n$$\n\\begin{aligned}\n\\langle\\pi(\\lambda)g,\\pi(\\lambda')g\\rangle =& |K|^{-1} \\int \\chi_K (x-Am)\\chi_K (x-Am') e^{-2\\pi i \\langle C(m-m')+B(n-n'),x\\rangle}dx\\\\\n=& |K|^{-1}\\int \\chi_K (x-Am)\\chi_K (x-Am') e^{-2\\pi i \\langle C(m-m')+B(n-n'),x\\rangle}dx\\\\\n=& |\\det A||K|^{-1}\\int \\chi_K (Ax-Am)\\chi_K (Ax-Am') e^{-2\\pi i \\langle C(m-m')+B(n-n'),Ax\\rangle}dx\\\\\n=& |A^{-1}K|^{-1} \\int \\chi_{A^{-1}K} (x-m)\\chi_{A^{-1}K} (x-m')e^{-2\\pi i \\langle A^tC(m-m')+A^tB(n-n'),x\\rangle}dx.\n\\end{aligned}\n$$\nThis shows that ${\\mathcal G}(|K|^{-1\/2} \\chi_K, \\Lambda)$ is mutually orthogonal if and only if ${\\mathcal G}(|A^{-1}K|^{-1\/2} \\chi_{A^{-1}K}, \\widetilde{\\Lambda})$ is mutually orthogonal. In a similar change of variable, we see that the Parseval identities of these Gabor system are actually equivalent. Hence, the statement of the lemma follows.\n\\end{proof}\n\\fi\n\n\\medskip\n\n\\noindent{\\bf 2. Orthogonality implies completeness.} The following proposition says that completeness automatically holds for a lattice of density one if we can establish the mutually orthogonality. \n\n\n\\medskip\n\n \\begin{proposition}\\label{complt}\n Let $g\\in L^2(\\Bbb R^d)$, $\\|g\\|=1$ and $\\Lambda\\subset \\Bbb R^{2d}$ be a lattice with density $\\mbox{dens}(\\Lambda)=1$. Assume that\n $\\mathcal G(g,\\Lambda)$ is an orthonormal set. Then $\\mathcal G(g,\\Lambda)$ is complete.\n \\end{proposition}\n\n\\medskip\n For the proof of Proposition \\ref{complt} we require the following lemma.\n Note that for a positive Borel measure $\\mu$,\n $$\n f\\ast\\mu (x) = \\int f(x-y)d\\mu(y),\n $$\n given that the integral is well-defined. If\n $\\mu = \\sum_{\\lambda\\in\\Lambda}\\delta_{\\lambda}$, then $\\chi_K\\ast\\mu = 1$ ($\\le 1$) if and only if $K$ tiles (packs) ${\\mathbb R}^d$ by $\\Lambda$\\footnote{Recall that $K$ packs ${\\mathbb R}^d$ by ${\\mathcal J}$ if $\\sum_{t\\in{\\mathcal J}}\\chi_{K}(x-t)\\le1$, a.e. $x\\in \\Bbb R^d$}. With this introduction we recall the following result.\n\n\\medskip\n\n \\begin{lemma}\\label{GLW}(\\cite[Theorem 2.1]{GLW})\n Suppose that $f,g\\in L^1({\\mathbb R}^d)$ are non-negative functions such that $\\int f(x) dx = \\int g(x) dx= 1$. Suppose that for positive Borel measure $\\mu$ on $\\Bbb R^d$\n$$\nf\\ast\\mu \\le 1 \\ \\mbox{and} \\ g\\ast\\mu\\le 1.\n$$\nThen $f\\ast\\mu = 1$ if and only if $ g\\ast \\mu= 1$.\n\\end{lemma}\n\n\n Given $f,g\\in L^2({\\mathbb R}^d)$, the {\\it short time Fourier transform} is defined by\n \\begin{align}\\label{STFT}\nV_gf(t,\\xi)=\\int f(x)\\overline{g(x-t)}e^{-2\\pi i \\langle\\xi,x\\rangle}dx, \\quad (t,\\xi)\\in \\Bbb R^{2d}\n \\end{align}\nand it is a continuous function on ${\\mathbb R}^{2d}$ \\cite{Groechenig-book}.\n\n\n \\begin{proof}[Proof of Proposition \\ref{complt}]\n The mutual orthogonality of $\\mathcal G(g, \\Lambda)$ implies the Bessel inequality of the system:\n\\begin{align}\\label{Bessel}\n\\sum_{(t,\\xi)\\in \\Lambda} |V_gf(t, \\xi)|^2\\leq \\|f\\|^2 \\ , \\quad \\forall f\\in L^2(\\Bbb R^d).\n\\end{align}\nLet $ s,\\xi \\in \\Bbb R^d$. The inequality (\\ref{Bessel}) for $e^{2\\pi i \\langle \\nu, x\\rangle} f(x-s)$ in the place of $f$ yields\n\\begin{align}\\notag\n\\sum_{(t,\\xi)\\in \\Lambda} |V_gf(t-s, \\xi-\\nu)|^2\\leq \\|f\\|^2 \\quad \\forall \\ (s, \\nu)\\in \\Bbb R^{2d}.\n\\end{align}\nHence, $|V_gf|^2\\ast \\delta_\\Lambda \\leq \\| f\\|^2$. Take $G:= \\| f\\|^{-2}|V_gf|^2$. Then $\\int_{\\Bbb R^{2d}} G(z) dz=1$ and $G\\ast \\delta_\\Lambda\\leq 1$. On the other hand, $\\Lambda$ is a lattice with density $1$. Let $\\Omega\\subset \\Bbb R^{2d}$ be any fundamental domain for $\\Lambda$. Then $|\\Omega|=1$ and it tiles $\\Bbb R^{2d}$ by $\\Lambda$. Therefore $\\chi_\\Omega\\ast \\delta_\\Lambda=1$. Now Lemma \\ref{GLW} implies that $G\\ast \\delta_\\Lambda =1$. But this is equivalent to the completeness of the system $\\mathcal G(g, \\Lambda)$ and we are done.\n \\end{proof}\n\n\\medskip\n\n\n\n\\noindent{\\bf 3. Some reduction to lower triangular block matrices.} The following result is due to Han and Wang which states that any invertible integer matrix can be converted into a lower triangular integer matrix. We will need it in later sections. An integer matrix $P$ is called {\\it unimodular} if $\\det P=1$.\n\n\n\\medskip\n\n\\begin{lemma}\\label{HanW1}(\\cite{HanW4}, Lemma 4.4) Let $M$ be an $d\\times d$ invertible integer matrix. Then there is an $d\\times d$ unimodular matrix $P$ such that $MP$ is a lower triangular integer matrix.\n\\end{lemma}\n\n\n\n\nAs a corollary of Lemma \\ref{HanW1} we can show that any rational matrix can be represented as a lower triangular rational matrix.\n\n\\medskip\n\n\\begin{corollary}\\label{M=N}\nLet $M$ be an $d\\times d$ invertible rational matrix. Then there is a lower triangular rational matrix $N$ such that $M(\\Bbb Z^d) = N(\\Bbb Z^d)$.\n\\end{corollary}\n\nHenceforth, we shall say matrix $M$ is {\\it equivalent} to $N$ if $M(\\Bbb Z^d) = N(\\Bbb Z^d)$. \n\n\n\\medskip\n\n\\noindent{\\bf 4. Exponential Completeness.}\\label{exponential completeness} Recall that a collection of functions $\\{\\varphi_n\\}$ is said to be {\\it complete} in $L^2(\\Omega)$ if $\\langle f, \\varphi_n\\rangle =0$ for all $n$ implies that $f=0$ a.e. on $L^2(\\Omega)$. Given $f\\in L^2({\\mathbb R}^d)$, the Fourier transform of $f$ is defined to be $\\widehat{f}(\\xi)= \\int_{{\\mathbb R}^d}f(x)e^{-2\\pi i \\langle\\xi, x\\rangle}dx$. In our study, we will need to following weaker notion of the completeness property. \n\n\\medskip\n \n\\begin{definition}\\label{exponential completeness} \n Let $\\Lambda$ be a countable set and let $\\Omega$ be a Lebesgue measurable set with positive finite measure. We say that the set of exponentials $\\{e^{2\\pi i \\langle\\lambda,x\\rangle}:\\lambda\\in\\Lambda\\}$ (or $\\Lambda$) is {\\it exponentially complete} for $L^2(\\Omega)$ if there does not exist any $\\xi\\in{\\mathbb R}^d$ such that \n\n $$\n \\widehat{\\chi_{\\Omega}}(\\lambda-\\xi)=\\int_{\\Omega} e^{2\\pi i \\langle \\xi ,x\\rangle} e^{-2\\pi i \\langle \\lambda, x\\rangle} dx =0, \\ \\forall \\lambda\\in\\Lambda.\n $$\n \\end{definition}\n\n\\medskip\n\n\n\\begin{remark}\\label{remark2}\nThroughout the paper, we will see that exponential completeness plays an important role in constructing Gabor orthonormal basis using non-separable lattices. If a countable set of exponentials is complete for $L^2(\\Omega)$, then it must be exponentially complete (otherwise $e^{2\\pi i \\langle \\xi,x\\rangle}$ will be orthogonal to all $e^{2\\pi i \\langle\\lambda,x\\rangle}$ contradicting completeness). However, the converse is not true.\n For example, the set of exponentials associated to the lattice $\\Lambda=\\sqrt{2}{\\mathbb Z}$ is exponentially complete in $L^2([0,1])$, but it is not complete in it (see Lemma \\ref{lemma 4}). In Appendix 1, we will give a short study about the exponential completeness for lattices in $L^2[0,1]^d$.\n\\end{remark}\n\n\n\n\n\\section{Proof of Lemma \\ref{Th_union of FD} - Union of fundamental domains}\\label{proof of Theorem 1.3} \n We now prove our Theorem \\ref{Th_union of FD}. It follows from two theorems in two separate fields. The first one is taken from the study of Fuglede's problems. It was first proved by Jorgensen and Pedersen \\cite[Theorem 6.2 (b)]{JoPe} and then Lagarias and Wang \\cite[Theorem 2.1]{LW97} gave a simpler proof.\n\n\\medskip\n\n\\begin{theorem}\\label{thJoPeLgWa}\nLet $\\Omega\\subset{\\mathbb R}^d$ be a Lebesgue measurable set with positive finite measure. Suppose $\\Gamma$ is a full-rank lattice such that $\\Gamma \\subseteq \\{\\xi: \\widehat{\\chi_{\\Omega}}(\\xi) = 0\\} \\cup \\{0\\}$. Then\n$\n\\Omega = \\bigcup_{j=1}^N D_j$, up to measure zero, where $D_j$ are fundamental domains for the dual lattice $\\Gamma^\\perp$, $|D_i\\cap D_j|=0, \\ i\\neq j$ ~ and $N=|\\Omega|\/|D_j|$.\n \\end{theorem}\n\n\n\n\\medskip\n\nGiven a lattice $\\Lambda=M(\\Bbb Z^{2d})$, the {\\it adjoint lattice} $\\Lambda^\\circ$ is a lattice such that\n $$J(\\Lambda^\\circ)= \\Lambda^\\perp $$\n where\n $J= \\left(\n \\begin{array}{cc}\n O & -I \\\\\n I & O \\\\\n \\end{array}\n \\right)$. In other words, $\\Lambda^\\circ= J^{-1}M^{-t}(\\Bbb Z^d)$.\n\nThe Ron-Shen duality theorem \\cite{RS-duality97} is well-known in Gabor analysis. It was first proved over symplectic lattices, it is known to be true over any lattice (see e.g \\cite[Theorem 2.3]{Groechenig-mystery} for a proof by Poisson Summation Formula). We will need the following version of duality theorem.\n\n\\medskip\n\n\\begin{theorem}\\label{thRS} ${\\mathcal G}(g,\\Lambda)$ is a Gabor orthonormal basis if and only if ${\\mathcal G}(g,\\Lambda^{\\circ})$ is a Gabor orthonormal basis.\n\\end{theorem}\n\n\\begin{proof}[Sketch of Proof.] This statement is well-known. Here we provide a simple proof based on \\cite[Theorem 2.3]{Groechenig-mystery} and Proposition \\ref{complt}. Since $(\\Lambda^{\\circ})^{\\circ} =\\Lambda$, both sides of the statements are symmetric and we just need to prove one side of the equivalence. Suppose that ${\\mathcal G}(g,\\Lambda)$ is a Gabor orthonormal basis. Then $g$ is the only dual window with the property that \n$$\n\\langle g, \\pi (\\mu)(g)\\rangle = 0 \\ \\forall \\mu\\in\\Lambda^{\\circ}\\setminus \\{0\\}\n$$\n(by \\cite[Theorem 2.3]{Groechenig-mystery}). This means that for all distinct $\\mu,\\mu'\\in\\Lambda^{\\circ}$, $\\langle \\pi(\\mu)g, \\pi (\\mu')(g)\\rangle = c \\langle g, \\pi (\\mu-\\mu')(g)\\rangle = 0$ ($c$ is some unimodular constant). Thus ${\\mathcal G}(g,\\Lambda^{\\circ})$ is mutually orthogonal. As ${\\mathcal G}(g,\\Lambda)$ is a Gabor orthonormal basis, $\\|g\\|=1$ and dens$(\\Lambda^{\\circ})$ = dens$(\\Lambda)$ =1, by Proposition \\ref{complt}, ${\\mathcal G}(g,\\Lambda^{\\circ})$ is complete and is thus an orthonormal basis.\n\\end{proof}\n\nFor a lower triangular lattice $\\Lambda= \\left(\n \\begin{array}{cc}\n A & O \\\\\n C & B \\\\\n \\end{array}\n \\right){\\mathbb Z}^{2d}$, the adjoint lattice $\\Lambda^\\circ$ is also a lower triangular and it can be calculated as follows:\n %\n$$\n\\Lambda^{\\circ} = \\left(\n \\begin{array}{cc}\n O & I \\\\\n -I & O \\\\\n \\end{array}\n \\right)\\left(\n \\begin{array}{cc}\n A^{-t} & -A^{-t}C^tB^{-t} \\\\\n O & B^{-t} \\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d}) = \\left(\n \\begin{array}{cc}\n O & B^{-t} \\\\\n -A^{-t} & A^{-t}C^tB^{-t} \\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d}).\n$$\n\nFrom the other hand we can write $$\\left(\n \\begin{array}{cc}\n O & B^{-t}\\\\\n -A^{-t}& A^{-t}C^tB^{-t} \\\\\n \\end{array}\n \\right) = \\left(\n \\begin{array}{cc}\n B^{-t} & O\\\\\n A^{-t}C^tB^{-t} & A^{-t} \\\\\n \\end{array}\n \\right)\n \\left(\n \\begin{array}{cc}\n O & I \\\\\n -I & O\\\\\n \\end{array}\n \\right)({\\mathbb Z}^{2d}).\n$$\nBut $\\left(\n \\begin{array}{cc}\n O & I \\\\\n -I & O\\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d})={\\mathbb Z}^{2d}$, therefore we have\n$$\n\\Lambda^{\\circ}= \\left(\n \\begin{array}{cc}\n B^{-t} & O\\\\\n A^{-t}C^tB^{-t} & A^{-t} \\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d}).\n$$\n\n\n\\begin{proof}[Proof of Theorem \\ref{Th_union of FD}]\nThe orthogonality of the Gabor system implies that\n \\begin{align}\\label{ortho}\n \\int_K e^{-2\\pi i \\langle Bn, x\\rangle} dx = 0 \\quad \\forall \\ n\\in \\Bbb Z^d\\setminus\\{0\\} .\n \\end{align}\n By Theorem \\ref{thJoPeLgWa}, (\\ref{ortho}) implies that $K$ can be written as\n $\n K = \\bigcup_{j=1}^N D_j,\n $\n where $D_j$ is a fundamental domain for $B^{-t}({\\mathbb Z}^d)$. On the other hand, by the duality Theorem \\ref{thRS}, ${\\mathcal G}({|K|^{-1\/2}}\\chi_K,\\Lambda^{\\circ})$ is a Gabor orthonormal basis, too. Similarly, the exponentials $\\{e^{2\\pi i \\langle A^{-t}n, x\\rangle}: n\\in {\\mathbb Z}^d\\}$ are mutually orthogonal in $L^2(K)$. Hence, by Theorem \\ref{thJoPeLgWa}, we have\n $\n K = \\bigcup_{j=1}^M E_j\n $\n where $E_j$\\rq{}s are fundamental domains for $A({\\mathbb Z}^d)$. Since $\\det(AB)=1$ we conclude that $|D_i|=|E_j|, \\forall \\ i, j$. Since $|K|<\\infty$, the latter forces that $M=N$, hence the proof of the theorem is completed.\n\\end{proof}\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Proof of Theorems \\ref{lower triangle} and \\ref{rational_dim1} - Lower triangular matrices}\\label{thm:lower triangle}\n\nTo prove Theorem \\ref{lower triangle}, first we shall apply some preliminary reductions to the theorem, as follows. Due to Lemma \\ref{lemma 3-prim} and the hypothesis of the theorem on the matrices $A$ and $B$, for the proof it is sufficient\n to assume that\n$\n\\Lambda = \\left(\n \\begin{array}{cc}\n I & O\\\\\n C & B\\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d}) ,\n $\n where $B$ is an invertible matrix with integer entries (since originally $A^tB$ has integer entries by the assumption of Theorem \\ref{lower triangle}). Notice by the density condition dens$(\\Lambda) = 1$, we have $|\\det(B)|=1$. Thus $B^{-1}$ is also an integral matrix with determinant 1 and we have\n $\\Bbb Z^{2d} = \\left(\n \\begin{array}{cc}\n I & O\\\\\n O & B^{-1}\\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d})$. Thus\n we can rewrite $\\Lambda$ as follows:\n $$\n\\Lambda = \\left(\n \\begin{array}{cc}\n I & O\\\\\n C & B\\\\\n \\end{array}\n \\right) \\left(\n \\begin{array}{cc}\n I & O\\\\\n O & B^{-1}\\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d}) = \\left(\n \\begin{array}{cc}\n I & O\\\\\n C & I\\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d}).\n $$\nTherefore to prove Theorem \\ref{lower triangle} it suffices to consider $\\Lambda = \\left(\n \\begin{array}{cc}\n I & O\\\\\n C & I\\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d})$. In this case if ${\\mathcal G}\\left(|K|^{-1\/2}\\chi_K,\\Lambda\\right)$ is an orthonormal basis, then by Theorem \\ref{Th_union of FD} we must have\n $\nK = \\bigcup_{j=1}^N E_j\n $\nwhere $E_j$\\rq{}s are disjoint fundamental domains of ${\\mathbb Z}^d$. In particular, $K$ is a multi-tile for $\\Bbb R^d$ with respect to ${\\mathbb Z}^d$, i.e.,\n\\begin{equation}\\label{multitile}\n\\sum_{k\\in{\\Bbb Z}^d} \\chi_K(x+k) = N \\ \\mbox{a.e.} \\ x\\in [0,1)^d.\n\\end{equation}\n Our goal is to show that $N=1$. For this, the following proposition will serve a key role.\n \\medskip\n\n\n\\begin{proposition}\\label{prop2} Suppose $K$ is a bounded set which multi-tiles $\\Bbb R^d$ with respect to $\\Bbb Z^d$ at level $N$, i.e. (\\ref{multitile}) holds. Suppose that $N>1$. Then there exists $m \\in{\\mathbb Z}^d$ such that\n\\begin{enumerate}\n\\item $K\\cap (K+m)$ has positive Lebesgue measure.\n\\item $ K\\cap (K+m)$ consists of distinct representative (mod ${\\mathbb Z}^d$).\n\\item $K\\cap (K+m)$ is a packing by ${\\mathbb Z}^d$, i.e.\n$$\n\\sum_{n\\in {\\mathbb Z}^d} \\chi_{K\\cap (K+m)} (x+n)\\le 1. \\ a.e.\n$$\n\\end{enumerate}\n\\end{proposition}\n \\begin{proof}\nPut $Q := [0,1)^d$. The identity (\\ref{multitile}) means that for a.e. $x\\in Q$, there are exactly $N$ integers $n_1,...,n_N$ such that $x+n_i\\in K$ $i=1,...,N$. Using this observation we will decompose $K$ as follows. For each $x\\in Q$, put\n$$\nK(x) := \\{k\\in \\Bbb Z^d: x+ k\\in K\\}.\n$$\nThen $|K(x)|=N$.\n Let $S \\subset{\\mathbb Z}^d$ and $|S| = N$. Define\n$$\nK_S = \\{x\\in Q: K(x) = S\\}.\n$$\nSince $K$ is a multi-tile of level $N$, we have\n$$\nK = \\bigcup_{|S|=N} (K_S+S), \\ \\mbox{and} \\ \\bigcup_{|S|=N} K_S = Q = [0,1)^d,\n$$\nwhere the union runs through all possible subsets $S\\in{\\mathbb Z}^d$ of cardinality $N$. Furthermore,\n $K_S \\cap K_{S'} =\\emptyset, \\forall S\\ne S'\n$, since there are exactly $N$ integers $n$ for which $x+n\\in K$. By the boundedness of $K$, there are only finitely many possible $S\\in{\\mathbb Z}^d$ with $|S|=N$ such that $|K_S|>0$. Thus, we can enumerate those $S$ as $S_1,...,S_r$ so that\n\\begin{equation}\\label{decomp}\nK = \\bigcup_{i=1}^r (K_{S_i}+S_i).\n\\end{equation}\n\n(Notice the decomposition (\\ref{decomp}) also holds for any multi-tile bounded set $K$ with respect to any lattice $\\Gamma$ in place of $\\Bbb Z^d$ and\n any bounded fundamental set of $\\Gamma$ in the place of $Q$.)\n\n\n\nWe order ${\\mathbb Z}^d$ by the natural lexicographical ordering. We then enumerate all possible elements in $S_i$, $1\\le i\\leq r$, by\n$$\n n_1^{S_i}<....1$. By Proposition \\ref{prop2}, there exists ${m}\\in{\\mathbb Z}^d$ such that $K\\cap (K+{m})$ has positive Lebesgue measure and $K\\cap (K+{ m})$ is a packing in $\\Bbb R^d$ by ${\\mathbb Z}^d$. Thus by Lemma \\ref{lemma 4}, the set $\\{e^{2\\pi i \\langle n,x\\rangle}: n\\in{\\mathbb Z}^d\\}$ is complete in $K\\cap (K+{m})$. By Remark \\ref{remark2}, ${\\mathbb Z}^d$ is exponentially complete in $L^2(K\\cap (K+{m}))$. Obviously, ${ m}=0$ cannot satisfy the packing property (3) in Proposition \\ref{prop2} since we have assumed $N>1$. Thus ${m}\\ne 0$. On the other hand, the orthogonality of the Gabor system implies that for any $n\\in \\Bbb Z^d$ we must have\n\\begin{align}\\label{eq}\n \\widehat{\\chi_{K\\cap (K+{m})}} (C{m}+n)= \\int_{\\Bbb R^d} \\chi_K(x)\\chi_{K}(x-{m}) e^{-2\\pi i \\langle C { m}, x\\rangle}e^{-2\\pi i \\langle n, x\\rangle}dx =0\n \\end{align}\n for all $n\\in {\\mathbb Z}^d$. \nThis contradicts the exponential completeness of ${\\mathbb Z}^d$. Therefore, the assumption $N>1$ cannot hold, and $K$ is thus a fundamental domain of ${\\mathbb Z}^d$. This completes the proof.\n\\end{proof}\n\n\n\\begin{remark} In proving Theorem \\ref{lower triangle}, once $\\Lambda$ is reduced to the lattice \n$${\\mathcal A}({\\mathbb Z}^{2d}), \\ \\mbox{where} \\ {\\mathcal A}= \\left(\n \\begin{array}{cc}\n I & O\\\\\n C & I \\\\\n \\end{array}\n \\right),\n$$\none may suspect that we can apply a metaplectic transformation to further reduce the lattice to the separable lattice ${\\mathbb Z}^{d}\\times {\\mathbb Z}^d$ and hence the problem is trivially solved. Unfortunately, this approach does not seem to work. To be precise, one can look up \\cite{Groechenig-book} for the metaplectic transformation in this case. We have that ${\\mathcal G}(|K|^{-1\/2}\\chi_K,{\\mathcal A}({\\mathbb Z}^{2d}))$ is a Gabor orthonormal basis if and only if ${\\mathcal G}( \\tilde{g},{\\mathbb Z}^{d}\\times{\\mathbb Z}^d)$ is a Gabor orthonormal basis, where \n$$\n\\tilde{g} = e^{-\\pi i\\langle x,Cx\\rangle}|K|^{-1\/2}\\chi_K.\n$$\nAlthough the lattice is separable, the window funciton $\\tilde{g}$ is now complex-valued, we cannot conclude that $K$ has no overlap in the time domain as it were the case when $C=O$. \n\n\\smallskip\n\nNonetheless, metaplectic transformation and symplectic matrices seems to provide some strong tools that may lead to a progess in the Fuglede-Gabor problem, readers may refer to \\cite{Folland,deGosson,Groechenig-book} for details about these tools.\n\\end{remark}\n\n\\medskip\n\n\n\n Theorem \\ref{rational_dim1} is now straightforward. We prove it here for the sake of completeness. \n\n\\begin{proof}[Proof of Theorem \\ref{rational_dim1}]\nLet $\\Lambda= M(\\Bbb Z^2)$ where $M=\\left(\n \\begin{array}{cc}\n a & d \\\\\n c & b \\\\\n \\end{array}\n \\right)$ is a rational matrix with $\\det M=1$. Let $q$ be the least common multiple of $a, b, c $ and $d$. Then we can write $\\Lambda$ as $\\Lambda= q^{-1} \\tilde M(\\Bbb Z^2)$ where $\\tilde M$ is an integer matrix. By Lemma \\ref{HanW1}, we can find a unimodular integer matrix $P$ such that $\\tilde M P$ is the lower triangular integer matrix. By the unimodularity of the matrix $P$ we have\n $q^{-1} \\tilde M(\\Bbb Z^2) = q^{-1} \\tilde M P(\\Bbb Z^2)$. Therefore, $\\Lambda = q^{-1} \\tilde M P(\\Bbb Z^2)$ and $q^{-1} \\tilde M P$ is a lower triangular rational matrix. We can therefore write\n $$\n \\Lambda = \\left(\n \\begin{array}{cc}\n \\alpha & 0 \\\\\n \\gamma & \\beta \\\\\n \\end{array}\n \\right)(\\mathbb Z^2)$$\n for some $\\alpha, \\beta, \\gamma\\in \\Bbb Q$.\n Notice that the density of $\\Lambda$ equals $1$, meaning that $\\alpha\\beta = 1$. Thus, all assumptions of Theorem \\ref{lower triangle} are satisfied. Hence, $K$ is a translational tile with tiling set $\\Bbb Z$ and is a spectral set. \n\\end{proof}\n\n\n \\section{ Proof of Theorem \\ref{UT} - Upper triangular matrices}\\label{thm:rational_dim1 and UT}\nWe will discuss a case of upper triangular matrices which can be converted into the lower one. Then we will use Theorem \\ref{lower triangle} to prove the theorem. First we need few lemmas.\n\\medskip\n\n\\begin{lemma}\\label{Gamma-D}\n Let $D$ be a $d\\times d$ rational matrix.\n Then there is an integer lattice $\\Gamma$ such that $D\\gamma\\in \\Bbb Z^d$ for $\\gamma\\in \\Gamma$.\n \\end{lemma}\n \\begin{proof}\n Define\n$$\n\\Gamma := \\{k\\in{\\mathbb Z}^d: Dk\\in{\\mathbb Z}^d\\}.\n$$\nIt is easy to check that $\\Gamma$ is a lattice contained in $\\Bbb Z^d$. Moreover, $\\Gamma$ contains $p\\Bbb Z^d$ where\n $p$ is the least common multiple of the denominators of entries of $D$. Thus, $\\Gamma$ is a full-rank lattice and has the form of\n$\n\\Gamma = M({\\mathbb Z}^d),\n $\nfor some invertible $d\\times d$ matrix $M$ with integer entries.\n\\end{proof}\nObserve that according to the Lemma \\ref{Gamma-D}, for any given rational matrix $D$, there is an integer $M$ such that $DM$ is integer.\n With this observation, we have the following result.\n\n \\medskip\n\n\\begin{lemma}\\label{complete residue classes} Let $D$ be a rational matrix, and let $\\Gamma$ and $M$ be given as in Lemma \\ref{Gamma-D} and $\\det M=n$.\nSuppose that $\\{\\gamma_1,\\cdots, \\gamma_n\\}$ be a complete representative (mod $M({\\mathbb Z}^d)$) in ${\\mathbb Z}^d$. If $D$ is symmetric, then $\\{M^tD\\gamma_1,\\cdots,$ $ M^tD\\gamma_n\\}$ is a complete representative (mod $M^t{\\mathbb Z}^d$) in ${\\mathbb Z}^d$.\n\\end{lemma}\n\\begin{proof} We saw above that by\n the structure of $M$ and $\\Gamma$, $DM$ is an integer matrix, therefore $DM(\\mathbb Z^d)\\subseteq{\\mathbb Z}^d$. We also have $(DM)^t{\\mathbb Z}^d\\subseteq{\\mathbb Z}^d$. This implies that\n$$M^tD({\\mathbb Z}^d) = (D^tM)^t({\\mathbb Z}^d )\\subseteq{\\mathbb Z}^d.$$\nThus, $M^tD\\gamma_i$ are all integer vectors for all $i = 1,\\cdots,n$.\n\n\\medskip\n\nNext, we show that $\\{M^tD\\gamma_1,...,M^tD\\gamma_n\\}$ consists of distinct representative (mod $M^t({\\mathbb Z}^d)$) in $\\Bbb Z^d$. Suppose that this is not the case. Then for some $i\\neq j$ we must have $M^tD\\gamma_i - M^tD\\gamma_j \\in M^t({\\mathbb Z}^d)$. This implies that $D\\gamma_i-D\\gamma_j\\in {\\mathbb Z}^d$, which means that $\\gamma_i$ and $\\gamma_j$ belong to the same representative class (mod $M({\\mathbb Z}^d)$) in ${\\mathbb Z}^d$ which is a contradiction.\n\n\\medskip\n\nFinally, we show that $\\{M^tD\\gamma_1,...M^tD\\gamma_n\\}$ is complete. This follows immediately by counting the number of\ncosets present in ${\\mathbb Z}^d\/M^t({\\mathbb Z}^d)$ which is $|\\det(M^t)| = |\\det(M)|= n$. Hence, $\\{M^tD\\gamma_1,...M^tD\\gamma_n\\}$ is complete.\n\\end{proof}\n\nIn the following constructive lemma we shall present a class of upper triangle lattice which can be converted into a lower triangular lattice.\n\n \\begin{lemma}\\label{a technical lemma}\n For $\\Lambda=\n \\left(\n \\begin{array}{cc}\n I & D \\\\\n O & I \\\\\n \\end{array}\n \\right)(\\Bbb Z^{2d})$ with $D$ rational and symmetric, there are integer matrices $E$ and $X$ such that\n $\\Lambda = \\left(\n \\begin{array}{cc}\n E^{-t} & O \\\\\n X & E\\\\\n \\end{array}\n \\right)({\\mathbb Z}^{2d})$.\n \\end{lemma}\n\n \\begin{proof}\n Let $\\{{\\bf e_i}: \\ i=1, \\cdots, d\\}$ denote the standard basis in $\\Bbb Z^d$. Associated to this $D$ let\n $\\Gamma$ and $M$ be given as in Lemma \\ref{Gamma-D}.\n Then by Lemma \\ref{complete residue classes}, for any $i$ there exists $z_i\\in \\Bbb Z^d$ and $\\gamma^i\\in \\{\\gamma_1, \\cdots, \\gamma_n\\}$ such that ${\\bf e_i}= M^tz_i + M^tD\\gamma^i$. Put $Z:=[z_1 \\cdots z_d]$\n and $X=[\\gamma_1\\cdots \\gamma_d]$. It is clear that $Z$ and $X$ are integer matrices.\n\n\n A direct calculation shows that $(Z+DX)(\\Bbb Z^d)= M^{-t}(\\Bbb Z^d)$.\n\n \\medskip\n\nPut $P:=\\left(\n \\begin{array}{cc}\n Z& -DM \\\\\n X & M\\\\\n \\end{array}\n \\right)$. Then $P$ is an integer matrix with $\\det P=1$ and $P(\\Bbb Z^{2d}) = \\Bbb Z^{2d}$. Recall that\n $DM$ is an integer matrix. So we can write\n $$\\Lambda=\n \\left(\n \\begin{array}{cc}\n I & D \\\\\n O & I \\\\\n \\end{array}\n \\right)(\\Bbb Z^{2d}) = \\left(\n \\begin{array}{cc}\n I & D \\\\\n O & I \\\\\n \\end{array}\n \\right)P(\\Bbb Z^{2d}) = \\left(\n \\begin{array}{cc}\n M^{-t} & O \\\\\n X & M\\\\\n \\end{array}\n \\right)(\\Bbb Z^{2d}). $$ Now take $E=M$, and we are done.\n\\end{proof}\n\\medskip\n\nAt this point we are ready to complete the proof of Theorem \\ref{UT}.\n\n\\begin{proof}[Proof of Theorem \\ref{UT}]\n Let $\\Lambda=\n \\left(\n \\begin{array}{cc}\n A & D \\\\\n O & B \\\\\n \\end{array}\n \\right)(\\Bbb Z^{2d})$ where $B= A^{-t}$ and $\\widetilde{D}: = A^{-1}D$ is rational and symmetric. By Lemma \\ref{lemma 3-prim}, the associated matrix can be reduced to a block matrix of the form $\\left(\n \\begin{array}{cc}\n I & \\widetilde{D} \\\\\n O & I \\\\\n \\end{array}\n \\right)$ where $\\widetilde{D}$ is rational and symmetric. Therefore, it is sufficient to prove the theorem for a lattice of the form\n $\\Lambda=\n \\left(\n \\begin{array}{cc}\n I &D \\\\\n O & I \\\\\n \\end{array}\n \\right)(\\Bbb Z^{2d})$ where $D$ is rational and symmetric. By Lemma \\ref{a technical lemma}, there are integer matrices $E$ and $X$ such that $\\Lambda= \\left(\n \\begin{array}{cc}\n E^{-t} & O \\\\\n X & E\\\\\n \\end{array}\n \\right)({\\mathbb Z}^{2d})$. The rest of the proof is now a conclusion of Theorem \\ref{lower triangle} and we have completed the proof.\n \\end{proof}\n\n \\medskip\n\n In \\cite{HanW4}, the authors proved that for any lattice $\\Lambda$ formed by an upper triangular matrix exists a window $g$ such that ${\\mathcal G}(g,\\Lambda)$ is a Gabor orthonormal basis and such $g$ satisfies $\\widehat{g} = \\chi_K$, where $K$ is the common fundamental domain for the diagonal block matrices $A$ and $B^{-t}$. However, their proof does not provide any constructive technique for producing a compactly supported window. The proof of Theorem \\ref{UT} provides a technique to construct a large class of examples of sets $K$ forming a Gabor orthonormal basis with respect to the lattice generated by upper triangular matrices. We explain this next.\n\n\\medskip\n\n\\begin{proposition}\\label{converse of UT} Assume that the lattice $\\Lambda$ and matrices $A$, $B$ and $D$ are given as in Theorem \\ref{UT} and satisfying\n the hypotheses of the theorem. Then there is a set $K$ such that ${\\mathcal G}(|K|^{-1\/2}\\chi_K, \\Lambda)$ is a Gabor orthonormal basis for $L^2(\\Bbb R^d)$. \n\\end{proposition}\n\n \\begin{proof}\n Let $A$, $B$ and $D$ be given. Notice that \n by Lemma \\ref{lemma 3-prim} and the hypotheses of the proposition we know that for any given set $K$, the system ${\\mathcal G}(|K|^{-1\/2}\\chi_K, \\Lambda)$ is an orthonormal basis if and only if ${\\mathcal G}(|A^{-1}(K)|^{-1\/2}\\chi_{A^{-1}(K)}, \\tilde \\Lambda)$ is an orthonormal basis where $\\tilde\\Lambda= \\left(\\begin{array}{cc} I & A^{-1}D\\\\ O & I\\end{array}\\right)(\\Bbb Z^{2d})$. And, by Lemma \\ref{a technical lemma} we also know that for $\\tilde\\Lambda$ there are integer matrices $X$ and $E$ such\n $$E(\\Bbb Z^d) = \\{n\\in \\Bbb Z^d: ~ A^{-1}Dn\\in \\Bbb Z^d\\}$$ \n and \n $\\widetilde\\Lambda= \\left(\\begin{array}{cc} {E}^{-t} & O\\\\ X & E \\end{array}\\right)(\\Bbb Z^{2d})$.\n By appealing to Lemma \\ref{lemma 3-prim} one more time,\n ${\\mathcal G}(|K|^{-1\/2}\\chi_K, \\Lambda)$ is an orthonormal basis if and only if\n %\n $${\\mathcal G}\\left(|K|^{-1\/2}\\chi_{K}, \\left(\\begin{array}{cc} A{E}^{-t} & O\\\\ A^{-t} X & A^{-t} E \\end{array}\\right)(\\Bbb Z^{2d})\\right)$$\n is an orthonormal basis. Now take $K$ as a fundamental domain of $AE^{-t}(\\Bbb Z^d)$ and we are done. \n \\end{proof}\n\n\\medskip\n\nThe following gives a more explicit example.\n\n\\begin{example}\\label{D not I} Let $A = \\left(\n \\begin{array}{cc}\n \\frac{1}{2} & 0\\\\\n 0 & 2 \\\\\n \\end{array}\n \\right)$, $B = \\left(\n \\begin{array}{cc}\n 2 & 0 \\\\\n 0 & \\frac{1}{2} \\\\\n \\end{array}\n \\right)$. Take $D= \\left(\n \\begin{array}{cc}\n 1 & 0\\\\\n 0 & \\frac{1}{3} \\\\\n \\end{array}\n \\right)$. Then $A^{-1}D$ is rational and symmetric. Let $\\Gamma= \\{n\\in \\Bbb Z^2: \\ A^{-1}Dn\\in \\Bbb Z^2\\}$. $\\Gamma$ is a full lattice and a simple calculation shows that $\\Gamma= E(\\Bbb Z^2)$ where $E= \\left(\n \\begin{array}{cc}\n 1& 0\\\\\n 0 & 6 \\\\\n \\end{array}\n \\right)$.\n Now let $K$ be any fundamental domain of the lattice $AE^{-t} ({\\mathbb Z}^2)= \\left(\n \\begin{array}{cc}\n 1\/2& 0\\\\\n 0 & 1\/3 \\\\\n \\end{array}\n \\right)(\\Bbb Z^2)$. Then by Proposition \\ref{converse of UT} the system\n $\\mathcal G(|K|^{-1\/2}\\chi_K, \\Lambda)$ is a Gabor orthonormal basis for $L^2(\\Bbb R^2)$ for the lower triangular lattice $\\Lambda$ with diagonal matrices $AE^{-t}$ and $A^{-t}E$ and any matrix $C$.\n \\end{example}\n\n\n\\iffalse\n \\begin{example}\\label{D=I} Let $A = \\left(\n \\begin{array}{cc}\n \\frac{1}{2} & 0\\\\\n 0 & 2 \\\\\n \\end{array}\n \\right)$, $B = \\left(\n \\begin{array}{cc}\n 2 & 0 \\\\\n 0 & \\frac{1}{2} \\\\\n \\end{array}\n \\right)$. Take $D=I$. Then $A^{-1}D=A^{-1}$ is rational and symmetric. Let $\\Gamma= \\{n\\in \\Bbb Z^2: \\ A^{-1}n\\in \\Bbb Z^2\\}$. $\\Gamma$ is a full lattice and we can see that $\\Gamma= E(\\Bbb Z^2)$ where $E= \\left(\n \\begin{array}{cc}\n 1& 0\\\\\n 0 & 2 \\\\\n \\end{array}\n \\right)$.\n Now take $K=[0,1\/2]\\times [0,1]$. Then $K$ is a fundamental domain for the lattice $AE^{-t} ({\\mathbb Z}^2)$\nand $\\mathcal G(|K|^{-1\/2}\\chi_K, \\Lambda)$ is a Gabor orthonormal basis for $L^2(\\Bbb R^2)$ by Proposition \\ref{converse of UT}.\n \\end{example}\n\\fi\n\n \\section{Examples}\\label{Examples}\nConsider $\\Lambda=L(\\Bbb Z^{2d})$ where $L= \\left(\\begin{array}{cc}\n A & O \\\\\n C & B \\\\\n \\end{array}\n \\right)$ is rational. In Theorem \\ref{lower triangle} we proved that for a set $K$ if $\\mathcal G(|K|^{-1\/2}\\chi_K, \\Lambda)$ is an orthonormal basis for $L^2(\\Bbb R^d)$ and $A^tB$ is an integer matrix, then $K$ is a fundamental domain of $A(\\Bbb Z^d)$. By Theorem \\ref{Th_union of FD} this means $N=1$. Thus $K$ tiles by $A({\\mathbb Z}^d)$ and is spectral with spectrum $B^{-t}(\\mathbb Z^d)$. In this section we will provide examples showing that $N>1$ can also happen in Theorem \\ref{Th_union of FD} if $A^tB$ is not an integer matrix and yet the system $\\mathcal G(|K|^{-1\/2}\\chi_K, \\Lambda)$ is an orthonormal basis for $L^2(\\Bbb R^d)$. \n\n\n\n\n\n\n \n \n \n %\n %\n \n\n\n\n\n \\medskip\n\n We are now ready to present our example of a set $K$ which is the union of fundamental domains of lattice $A(\\Bbb Z^d)$ and the union of fundamental domains of lattice $B^{-t}(\\Bbb Z^d)$, $\\chi_K$ is a window function for a possible Gabor orthonormal basis, and $A^tB$ is {\\it not an integer matrix}. \n\n\n\\begin{example}\\label{mutli-tile K}\n\nLet $K = [0,2]\\times [0,1]$ and\n$\\Lambda = \\left(\n \\begin{array}{cccc}\n I& O \\\\\n C & B \\\\\n \\end{array}\n \\right)({\\mathbb Z}^{4})$\nwhere\n $B=\n \\left(\n \\begin{array}{cc}\n 1\/2 & 0 \\\\\n 0& 2\n \\end{array}\n \\right)$ and $C= \\left(\n \\begin{array}{cc}\n c_{11}& c_{12} \\\\\n c_{21}& c_{22}\n \\end{array}\n \\right)$. The system $\\mathcal G(|K|^{-1\/2}\\chi_K, \\Lambda)$ is a Gabor orthonormal basis if\n $c_{21}$ is an odd number.\n \\end{example}\n\n \\begin{proof}\n Observe that\n\\begin{align}\\notag\n[0,2]\\times [0,1] &= [0,1]^2 + \\{(0,0), (1,0)\\} \\\\\\notag\n &= [0,2]\\times[0,1\/2]+ \\{(0,0), (0,1\/2)\\}.\n \\end{align}\n\nThis shows that $K$ is union of two fundamental domains of $\\Bbb Z^2$ and union of two fundamental domains of $B^{-t}(\\Bbb Z^2)$, respectively.\n\n Let $({m}, n) \\in {\\mathbb Z}^4$ with ${m},{ n} \\in{\\mathbb Z}^2$ and $({m},{ n})\\neq (0,0)$. Put\n %\n\\begin{align}\\notag\n\\mathcal I: = &\\int \\chi_{K}(x-{m})\\chi_{K}(x) e^{-2\\pi i \\langle C({m}), x\\rangle} e^{-2\\pi i \\langle B({n}), x\\rangle}dx\\\\\\notag\n=&\\int \\chi_{(K+{\\bf m})\\cap K}(x) e^{-2\\pi i \\langle C({ m}), x\\rangle} e^{-2\\pi i \\langle B({n}), x\\rangle}dx .\n\\end{align}\n Mutual orthogonality of the Gabor system will follow if we can show that $\\mathcal I=0$ for any $( m, n)\\neq (0,0)$. Note that\n$$\n(K+m)\\cap K \\simeq \\left\\{\n \\begin{array}{ll}\n K, & \\hbox{${m} = 0$;} \\\\\n {[0,1]}^2, & \\hbox{${m} = (\\pm 1,0)$;} \\\\\n \\emptyset , & \\hbox{otherwise.}\n \\end{array}\n \\right.\n$$\n\n(Here, we write $A\\simeq B$ if $A = B+k$ for some $k\\in{\\mathbb Z}^d$.)\n\n\n Since $K$ is a union of fundamental domains for $B^{-t}(\\Bbb Z^2)$, then if ${m}=0$, we automatically have $\\mathcal I=0. $\n\n\n If ${ m}= (\\pm 1,0)$, for any ${ n} = (n_{1},n_{2})$, $\\mathcal I$ is equal to the following integral up to a unimodular constant:\n\\begin{equation}\\label{eq6.3}\n\\begin{aligned}\n\\int_{[0,1]^2} e^{-2\\pi i \\langle C((\\pm 1, 0)^t), x\\rangle} e^{-2\\pi i \\langle B({n}), x\\rangle}dx=&\\int_{[0,1]}\\int_{[0,1]} e^{-2\\pi i (\\pm c_{11}x_1\\pm c_{21}x_2)} e^{-2\\pi i (\\frac{1}{2}n_{1}x_1+2n_{2}x_2)}dx_1dx_2 \\\\\n=& \\widehat{\\chi_{[0,1]}}(\\pm c_{11}+\\frac{1}{2} n_1) \\widehat{\\chi_{[0,1]}}(\\pm c_{21}+2 n_2).\n\\end{aligned}\n\\end{equation}\n It is obvious that the last line equals to zero for all $(n_1, n_2)$ only if $c_{21}$ is an odd number. For other cases of $m$ it is trivial that $\\mathcal I=0$. Thus the orthogonality is obtained and\n the completeness\n is a direct conclusion of Proposition \\ref{complt}.\n\\end{proof}\n\nWe notice that the previous example exploited the fact that $B({\\mathbb Z}^2)$ is exponentially incomplete for $L^2[0,1]^2$ in (\\ref{eq6.3}). The following example illustrates a case where, contrary to the previous example, the finite union of fundamental domains cannot form a Gabor orthonormal basis for any choice of matrix $C$.\n\n\\medskip\n\n \\begin{example}\\label{example2}\n Let $K=[0,2]^2$ and let $B$ be the matrix as in Example \\ref{mutli-tile K}. Then $K$ is union of $4$ fundamental domains of the lattice $\\Bbb Z^2$ and union of $4$ fundamental domains of $B^{-t}(\\Bbb Z^2)$. However, there is no matrix $C$ for which $\\chi_K$ is a window function yielding an orthonormal basis for the lower triangular lattice $\\Lambda=\\left(\\begin{array}{cc} I & O\\\\ C & B\\end{array}\\right)(\\Bbb Z^{4})$. \\end{example}\n\n \n\n\\begin{proof}\nThe fact that $K$ is union of fundamental domains of $\\Bbb Z^d$ and $B^{-t}(\\Bbb Z^d)$ is straight forward. In short,\n$$\n\\begin{aligned}\nK =& [0,1]^2 + \\{(0,0), (1,0), (0,1), (1,1)\\}\\\\\n =& [0,2]\\times [0,1\/2] + \\{(0,0), (0,1\/2), (0,1), (0,3\/2)\\}.\n\\end{aligned}\n$$\nSuppose that there exists a matrix $C$ such that $\\chi_K$ is a window function for the lower triangular lattice $\\Lambda=\\left(\\begin{array}{cc} I & O\\\\ C & B\\end{array}\\right)(\\Bbb Z^{4})$. Thus, for any $(0,0)\\neq (m, n)\\in \\Bbb Z^{4}$ we must have\n$$\n 0 = {\\mathcal I}: =\\int \\chi_{(K+{m})\\cap K}(x) e^{-2\\pi i \\langle C({ m}), x\\rangle} e^{-2\\pi i \\langle B({n}), x\\rangle}dx.\n$$\nFrom the other side, the only non-empty intersection sets $(K+ m)\\cap K$ are\n$$\n(K+m)\\cap K \\simeq \\left\\{\n \\begin{array}{ll}\n K, & \\hbox{if }{m}=0 \\\\\n {[0,1]\\times[0,2]}, & \\hbox{if } {m} = (\\pm1,0) \\\\\n {[0,2]\\times[0,1]}, & \\hbox{if } m = (0,\\pm1) \\\\\n {[0,1]}^2, & \\hbox{if } m = (\\pm1,\\pm1).\n \\end{array}\n\\right.\n$$\nFrom the last three intersections, we obtain that if ${\\mathcal I}=0$, then the following three equations must hold for all integer vectors $(n_1,n_2)$, respectively:\n\n\n \\begin{align}\\notag\n \\widehat{\\chi_{[0,1]}}(\\pm c_{11}+\\frac{1}{2} n_1) \\widehat{\\chi_{[0,2]}}(\\pm c_{21}+2 n_2) &=0 \\\\\\notag\n \\widehat{\\chi_{[0,2]}}(\\pm c_{12}+\\frac{1}{2}n_1) \\widehat{\\chi_{[0,1]}}(\\pm c_{22}+2 n_2)\n &=0\\\\\\notag\n \\widehat{\\chi_{[0,1]}}(\\pm c_{11}\\pm c_{12}+ \\frac{1}{2} n_1) \\widehat{\\chi_{[0,1]}}(\\pm c_{21}\\pm c_{22}+2 n_2) &=0\n \\end{align}\n\n\\medskip\n\nWe claim that if the first equation holds, then $\\widehat{\\chi_{[0,2]}}(\\pm c_{21}+2 n_2) =0$ for all integers $n_2$ and $c_{21}\\in 2{\\mathbb Z}+\\{\\frac{1}{2} ,1,\\frac{3}{2} \\}$.\n\n\\medskip\n\nTo justify the claim, suppose that there exists an integer $n_2$ such that $\\widehat{\\chi_{[0,2]}}(\\pm c_{21}+2 n_2) \\ne0$. Then $\\widehat{\\chi_{[0,1]}}(\\pm c_{11}+\\frac{1}{2} n_1)=0$ for all integers $n_1$. However, this would imply the existence of an exponentials $e^{-2\\pi i c_{11} x}$ such that it is orthogonal to all $\\{e^{2\\pi i (\\frac{1}{2} n) x}: n\\in{\\mathbb Z}\\}$ in $L^2[0,1]$. This is impossible since the exponentials set $\\{e^{2\\pi i (\\frac{1}{2} n) x}: n\\in{\\mathbb Z}\\}$ is complete in $L^2[0,1]$. Hence, we have only $\\widehat{\\chi_{[0,2]}}(\\pm c_{21}+2 n_2) =0$ for all integers $n_2$. Finally, since the zero set for $\\widehat{\\chi_{[0,2]}}$ is $\\frac{1}{2} {\\mathbb Z}$ except zero, then we must have $c_{21}\\in 2{\\mathbb Z}+\\{\\frac{1}{2} ,1, \\frac{3}{2} \\}$, as desired.\n\n\\medskip\n\nSimilarly, the second equation implies that $\\widehat{\\chi_{[0,1]}}(\\pm c_{22}+2 n_2) =0$ for all integers $n_2$ and thus $c_{22}\\in 2{\\mathbb Z}+1$ must be an odd integer.\n\n\\medskip\n\nThe third equation implies that\n\\begin{equation}\\label{eq5.4}\n\\widehat{\\chi_{[0,1]}}(\\pm c_{21}\\pm c_{22}+2 n_2) =0\n\\end{equation}\nfor any integers $n_2$. Now, we write $c_{21} = 2k_1+ \\frac{1}{2} j$, for some $j = 1,2,3$ and $c_{22} = 2k_2+1$. Then $\\pm c_{21}\\pm c_{22}\\in 2{\\mathbb Z}+ \\{\\frac{3}{2},2,\\frac{5}{2}\\}$. In the case of fraction, (\\ref{eq5.4}) cannot be zero. If $\\pm c_{21}\\pm c_{22} = 2m+2$, we take $n_2 = -m-1$. Then (\\ref{eq5.4}) will imply $\\widehat{\\chi_{[0,1]}}(0) = 1$, which is impossible. Thus, the third equation can never be zero. This implies that such $C$ does not exist.\n\\end{proof}\n\n\\medskip\nThe following example proves that the boundedness property for the set $K$ is necessary in Proposition \\ref{prop2}. \n\n \\begin{example}\\label{example3} Let $I_0 = [0,1)$ and $I_n = \\left[1-\\frac{1}{2^{n-1}},1-\\frac{1}{2^{n}}\\right)$ for $n\\ge1$. Define \n $$\n K = \\bigcup_{k\\in{\\mathbb Z}} (k+ I_{|k|}) \n $$\n The set $K$ is unbounded and we have the following. \n \n\\begin{enumerate}\\item $K$ multi-tiles ${\\mathbb R}$ by ${\\mathbb Z}$ at level 3. However, for any $m\\ne 0$, $K\\cap( K+m)$ is not a packing of ${\\mathbb R}$. Therefore, Proposition \\ref{prop2} does not hold if $K$ is unbounded.\n \\item Nonetheless, $K$ cannot form a Gabor orthonormal basis using lattices of the form $\n \\left(\n \\begin{array}{cc}\n 1 & 0 \\\\\n c& 1\n \\end{array}\n \\right) ({\\mathbb Z}^2)$. \\end{enumerate}\n\\end{example}\n\n\n\\begin{proof}\nThe fact that $K$ mult-tiles ${\\mathbb R}$ by ${\\mathbb Z}$ at level 3 follows from a direct observation, so we omit the details here. We note that for any $m\\ne 0$,\n$$\nK\\cap (K+m)\\supset I_{|m|} \\cup (I_{|m|}+m).\n$$\nHence, for all $x\\in I_{|m|}$, $\\sum_{n\\in{\\mathbb Z}} \\chi_{K\\cap(K+m)}(x+n)\\ge 2$. Therefore, it is never a packing for any $m\\ne 0$. To show the last statement, notice \n\n$$\nK\\cap (K+1) = \\left[0,\\frac12\\right)\\cup\\left[1,\\frac32\\right), \\ K\\cap (K+2) = \\left[\\frac12,\\frac34\\right)\\cup\\left[1,\\frac32\\right)\\cup\\left[\\frac52,\\frac{11}{4}\\right).\n$$\nSuppose that $K$ forms a Gabor orthonormal basis using some lattice of the form $\n \\left(\n \\begin{array}{cc}\n 1 & 0 \\\\\n c& 1\n \\end{array}\n \\right) ({\\mathbb Z}^2)$. Then $\\widehat{\\chi_{K\\cap(K+1)}}(c+m) = 0$ and $\\widehat{\\chi_{K\\cap(K+2)}}(c+m) = 0$ for all $m\\in{\\mathbb Z}$. In particular, \n $$\n \\widehat{\\chi_{K\\cap(K+1)}} (c+m) = (1+e^{2\\pi i (c+m)})\\widehat{\\chi_{[0,1\/2]}}(c+m) =0, \\forall m\\in{\\mathbb Z}.\n $$\nWe see that the only possibility of the above is that $c\\in\\frac12+{\\mathbb Z}$. We now consider \n$$\n\\widehat{\\chi_{K\\cap(K+2)}} (\\xi) = e^{2\\pi i \\frac12\\xi}\\left(1+e^{2\\pi i \\frac12\\xi}+e^{2\\pi i \\frac34\\xi}+e^{2\\pi i \\frac2\\xi}\\right)\\widehat{\\chi_{[0,1\/4)}}(\\xi).\n$$\nIf $c \\in\\frac12+{\\mathbb Z}$ and $\\widehat{\\chi_{K\\cap(K+2)}} (c) =0$, we must have \n$$\n0=1+e^{2\\pi i \\frac12c}+e^{2\\pi i \\frac34c}+e^{2\\pi i 2c} = 2+e^{2\\pi i \\frac12c}+e^{2\\pi i \\frac34c}.\n$$ \nThis forces $e^{2\\pi i \\frac12c} = e^{2\\pi i \\frac34c}=-1$. Thus, \n$$c\\in 2(1\/2+{\\mathbb Z})\\cap \\frac43(1\/2+{\\mathbb Z}) = (1+2{\\mathbb Z})\\cap \\left(\\frac23+\\frac43{\\mathbb Z}\\right)\\subset 1+2{\\mathbb Z}$$\nThis is a contradiction since $c\\in\\frac12+{\\mathbb Z}$ is never an integer. This completes the proof.\n \\end{proof}\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\\section{Discussions and Open problems}\\label{Open problems}\n\nThis paper investigates the Fuglede-Gabor Problem \\ref{our conjecture1} over the lattices. We believe that this problem should be true for all lattices. We solved the problem completely in dimension one when the lattice is rational and in higher dimensions when the lattice is integer. In what follows we shall explain how to resolve Problem \\ref{our conjecture1} in full generality for any lattices $\\Lambda = \\left(\n \\begin{array}{cc}\n A & D \\\\\n C & B \\\\\n \\end{array}\n \\right)({\\mathbb Z}^{2d})$. In fact,\n it is sufficient to solve the following two cases.\n\\begin{enumerate}\n\\item[(1)]\\label{item 1} {\\bf Rational case:} After converting a rational lattice into a lower triangular rational matrix by Corollary \\ref{M=N} and reducing the matrix where $A=I$ by Lemma \\ref{lemma 3-prim}, $\\Lambda$ is a lattice of the form of $ \\left(\n \\begin{array}{cc}\n I & O \\\\\n C & B \\\\\n \\end{array}\n \\right)({\\mathbb Z}^{2d})$, where $B$ and $C$ are $d\\times d$ rational matrices but $B$ is not necessarily an integral matrix. \n\n\n \\medskip\n\n \n\n\n\n\\item[(2)] {\\bf Irrational case:} After a reduction process by Lemma \\ref{lemma 3-prim}, $\\Lambda$ is a lattice of the form of \n$$\\left(\n \\begin{array}{cc}\n I & D\\\\\n C & B \\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d})$$ which contains irrational entries.\n\\end{enumerate}\n\n\n\\medskip\nIn what follows, we shall discuss these two cases in more details.\n\n\n\n\\subsection{Rational Case}\nLet $\\Lambda= \\left(\n\\begin{array}{cc}\nA & O\\\\\nC & B\n\\end{array}\\right)(\\Bbb Z^{2d})$ be a lower triangular rational matrix.\n Example \\ref{mutli-tile K} tells us that when $A^tB$ is a non-integer matrix, $K$ is not a fundamental domain for the lattices $A(\\Bbb Z^d)$ and $B^{-t}(\\Bbb Z^d)$ but the union of their fundamental domains. However, in Example \\ref{example2} we see that there exists no $C$ such that $K$, as the union of fundamental domains, is a set whose characteristic function generates a Gabor orthonormal basis associated to the given matrices $A$ and $B$. We predict that this failure is due to the number of decompositions of $K$ into fundamental domains of $B^{-t}(\\Bbb Z^d)$.\n In this concern and in relation to the examples illustrated in Section \\ref{Examples},\n we conjecture the following problem for the lattices\n$\\Lambda= \\left(\n \\begin{array}{cc}\n I & D\\\\\n C & B \\\\\n \\end{array}\n \\right) ({\\mathbb Z}^{2d})$.\n \\medskip\n\n\n\n \\medskip\n\n \\begin{conjecture}\\label{our conjecture} Let $K\\subset \\Bbb R^d$ and $B$ be a rational matrix with $\\det(B)=1$. Let\n $s$ be the least common multiple of the denominators of the matrix entries $(b_{ij})$.\n There is a matrix $C$ such that $\\mathcal G(|K|^{-1\/2}\\chi_K, \\Lambda)$ is a Gabor orthonormal basis for $L^2(\\Bbb R^d)$ if and only if $K$ tiles and\n $$K= \\bigcup_{i=1}^sE_i= \\bigcup_{i=1}^sF_i $$\n where $E_i$ and $F_i$ are\n fundamental domains of $\\Bbb Z^d$ and $B^{-t}(\\Bbb Z^d)$, respectively.\n \\end{conjecture}\n\nIt is obvious that the conjecture automatically holds when $B$ is an integer matrix which means $s=1$. Indeed, this is the result of Theorem \\ref{lower triangle}.\n\n\n\n\n\n\n \\medskip\n\n Observe that if $K$ is as in Conjecture \\ref{our conjecture}, then for any non-zero $n\\in \\Bbb Z^d$ we have $\\int_K e^{2\\pi i \\langle Bn, x\\rangle} dx=0$. And, there are only finitely many $m\\in \\Bbb Z^d$ such that $|K\\cap (K+m)|\\neq 0$, as $K$ is a finite union of fundamental domains of $\\Bbb Z^d$. To prove the orthogonality, one must first show the existence of a matrix $C$ such that for all $n\\in \\Bbb Z^d$\n\\begin{equation}\\label{eq6}\n\\int_{K\\cap K+m} e^{-2\\pi i \\langle Cm, x\\rangle} e^{-2\\pi i \\langle Bn, x\\rangle} dx=0,\n\\end{equation}\nwhich is equivalent to the exponential incompleteness of the lattices $B(\\Bbb Z^d)$ for $L^2(K\\cap K+m)$. It appears that we need to study the exponential completeness of the lattices over different domains. In fact, the following problem has not yet had a definite answer.\n \n \\medskip\n\n\\begin{problem} Given a set $K$ with positive and finite measure, classify all invertible $d\\times d$ matrices $B$ for which \n the exponents $\\{ e^{-2\\pi i \\langle Bn, x\\rangle} : n\\in \\Bbb Z^d\\}$ are exponentially complete in $L^2(K)$. \n\\end{problem}\n\n\\medskip\n\nProposition \\ref{prop_G} provides a sufficient condition for matrices $B$ when $K=[0,1]^d$. Unfortunately, the converse of the proposition does not hold in dimensions $d=2$ and higher, as the counterexample following Proposition \\ref{prop_G} shows. However, in Proposition \\ref{dimension one} we obtain a full characterization of exponential completeness for $[0,1]$ in dimension $d=1$ for integer lattices in $\\Bbb Z$. \n\n\n\\medskip \n\nAs mentioned earlier, the exponential completeness of a set does not imply the completeness of the set in general. For a recent developments in study of \n the completeness, frame and Riesz bases properties of exponentials we refer the reader to the paper by De Carli and her co-authors \\cite{De Carli}. \n\n\n\n \n\n\n\n\n\\subsection{Irrational Cases} The case for irrational lattices is more challenging and complicated. It appears that the lower and upper triangular case is asymmetric. We have seen that in Theorem \\ref{lower triangle}, the lower triangular block matrix $C$ is not involved in the statement. Thus, irrational entries are allowed for lower triangular matrices. On the other hand, Han and Wang \\cite{HanW4} implicitly conjectured the following problem in their paper \\cite{HanW1}:\n\n\\medskip\n\n {\\bf Han and Wang\\rq{}s Conjecture:} {\\it Let $\\Lambda = \\left(\n \\begin{array}{cc}\n 1 & \\alpha \\\\\n 0 & 1 \\\\\n \\end{array}\n \\right)({\\mathbb Z}^{2})$ where $\\alpha$ is irrational. Then there doesn't exist compactly supported window $g$ such that ${\\mathcal G}(g,\\Lambda)$ forms a Gabor orthonormal basis for $L^2({\\mathbb R})$.}\n\n \\medskip\n\n Observe that if $\\Lambda= \\left(\n \\begin{array}{cc}\n 1& \\alpha \\\\\n 0 & 1 \\\\\n \\end{array}\n \\right)$, with $\\alpha$ irrational, our method applied to construct Example \\ref{D not I} would not work for the existence of $K$ since in that case $\\Gamma=\\{0\\}$. This observation predicts that Han and Wang Conjecture \\cite{HanW1} might be true, although we do not have a proof for it now. However, a simple calculation shows that the function $\\chi_{[0,1]}$ can not be a window function for this lattice. \n\n \\medskip\n\n\n \n\n\\subsection{Full generality of Fuglede-Gabor Problem \\ref{our conjecture1}} \nIt is known that non-symmetric convex bodies as well as convex sets with a point of non-vanishing Gaussian curvature have no basis of exponentials and yet they do not tile \\cite{K99,IKT01}. Recently, similar results were also proved for Gabor bases. Indeed, the authors in \\cite{IM17} proved that in dimensions $d\\neq1$ (mod 4), convex sets with a point of non-vanishing Gaussian curvature cannot generate any Gabor orthonormal basis with respect to any countable time-frequency set $\\Lambda$. Also, the authors in \\cite{CL} proved that non-symmetric convex polytopes do not produce any Gabor orthonormal basis with respect to any countable time-frequency set. However, the result for non-symmetric convex domains is not known yet. The existent results predict that Fuglede-Gabor problem will still be true to some extent.\n\n\n \n\n\n\\medskip\n\n\n\n One may notice that there are also \n examples of spectral sets which do not tile by translations (see e.g.\n \\cite{T04}, \\cite{Matolcsi} and \\cite{KM06}). Therefore, by a result of Dutkay and the first listed author \\cite{Dutkay-Lai}, it is known that the indicator function of these sets can not serve as Gabor orthonormal window with resepct to any separable time-frequency set. However, they may still produce a Gabor orthonormal basis using some non-separable and countable time-frequency sets. \n This requires some input of new ideas.\n\n\n\\medskip\n\nFinally, it is known that octagon does not tile ${\\mathbb R}^2$ by translations, but it is a multi-tile by ${\\mathbb Z}^2$. The following example tells us that it does not form Gabor orthonormal basis using any lattices, confirming that the Fuglede-Gabor problem holds up to some extent.\n\n\\medskip\n\n\n\n\n\n \\begin{example}\\label{Octagon} Let ${\\mathcal O}_8\\subset \\Bbb R^2$ be the octagon symmetrically centred at the origin with integer vertices $\\{(\\pm 1, \\pm 2), (\\pm 2, \\pm 1)\\}$. ${\\mathcal O}_8$ multi-tiles $\\Bbb R^2$ with $\\Bbb Z^2$ and it is the union of $s=14$ fundamental domains of $\\Bbb Z^2$. Yet there doesn't exist any $\\Lambda= \\left(\n \\begin{array}{cc}\n I & O \\\\\n C & B \\\\\n \\end{array}\\right) (\\Bbb Z^4)$ with rational matrix $B$ for which $\\mathcal G(|{\\mathcal O}_8|^{-1\/2}\\chi_{{\\mathcal O}_8}, \\Lambda)$ forms an orthonormal basis for $L^2(\\Bbb R^2)$ .\n \\end{example}\n \n \n The proof of this example is involved and so it will be provided in the next section. \n \n \n\n\n \n \n \n \n\\iffalse\n \\begin{lemma}\nLet $K$ be a measurable set in $\\Bbb R^d$ with positive and finite measure. Let $\\Lambda$ be the lower triangular lattice with diagonal block matrices $A=I$ and $B$ and $C\\neq 0$.\n\n Assume that there is a non-zero $m\\in \\Bbb Z^2$ such that $K\\cap K+m$ is a packing by $B^{-t}(\\Bbb Z^d)$. Then $\\mathcal G(\\chi_K, \\Lambda)$ is not an orthogonal set.\n\\end{lemma}\n\n\\begin{proof}\nThe proof is a direct consequence of Lemma \\ref{lemma 4}.\n\\end{proof}\n\\fi\n\n\\section{Gabor orthonormal bases on Octagon and Proof of Example \\ref{Octagon}}\\label{Appendix Octagon}\n\nIn this appendix, we will prove that the octagon symmetrically centered at the origin with integer vertices $\\{(\\pm 1, \\pm 2), (\\pm 2, \\pm 1)\\}$ cannot admit any Gabor orthonormal basis with $\\Lambda= \\left(\n \\begin{array}{cc}\n I & O \\\\\n C & B \\\\\n \\end{array}\\right) (\\Bbb Z^4)$, where $B$ is a rational matrix. For this we need the following lemma.\n \n \n\\begin{lemma}\\label{lemma_shear}\nLet $M = \\left(\n \\begin{array}{cc}\n \\alpha & 0 \\\\\n 0 & 1\/\\alpha \\\\\n \\end{array}\\right)$ and $M_{\\beta} = \\left(\n \\begin{array}{cc}\n \\alpha & 0 \\\\\n \\beta & 1\/\\alpha \\\\\n \\end{array}\\right)$, $\\alpha\\neq 0$. Then $\\Omega$ is a multi-tile by $M_{\\beta}({\\mathbb Z}^2)$ if and only if $\\Omega$ is a multi-tile by $M({\\mathbb Z}^2)$.\n\\end{lemma}\n\n\\begin{proof}\nLet $\\alpha>0$ and put $Q_{\\alpha} = [0,\\alpha)\\times[0,1\/\\alpha)$. Then $Q_{\\alpha}$ is a fundamental domain for the lattice $M(\\Bbb Z^2)$. We claim that $Q_{\\alpha}$ is a fundamental domain for $M_{\\beta}(\\Bbb Z^2)$ for any $\\beta\\in\\Bbb R$. Indeed, this follows from a direct calculation: For almost every $(x,y)$, we have \n$$\n\\begin{aligned}\n\\sum_{m,n\\in{\\mathbb Z}}\\chi_{Q_{\\alpha}}(x-\\alpha m,y-\\beta m-n\/\\alpha) =& \\sum_{m\\in{\\mathbb Z}}\\chi_{[0,\\alpha)}(x-\\alpha m)\\left(\\sum_{n\\in{\\mathbb Z}}\\chi_{[0,1\/\\alpha)}(y-\\beta m-n\/\\alpha)\\right) \\\\\n=&\\sum_{m\\in{\\mathbb Z}}\\chi_{[0,\\alpha)}(x-\\alpha m)=1.\\\\\n\\end{aligned}\n$$\n \n\nLet $\\Omega$ be any set which is a tile by $M({\\mathbb Z}^2)$. Let $\\{E_{(u,v)} : (u,v)\\in \\Bbb Z^2\\}$ be a partition of $Q_{\\alpha}$ such that \n %\n$$\n\\Omega = \\bigcup_{(u,v)\\in{\\mathbb Z}^2} (E_{(u,v)}+(\\alpha u,v\/\\alpha)) .$$ \n \n \n Then set $\\Omega$ is a tile by $M_\\beta(\\Bbb Z^2)$. Indeed, \n$$\n\\begin{aligned}\n\\sum_{m,n\\in{\\mathbb Z}}\\chi_{\\Omega}(x-\\alpha m,y-\\beta m-n\/\\alpha)=&\\sum_{m,n\\in{\\mathbb Z}} \\sum_{(u,v)\\in{\\mathbb Z}^2}\\chi_{E_{(u,v)+(\\alpha u,v\/\\alpha)}} (x-\\alpha m,y-\\beta m-n\/\\alpha)\\\\\n=&\\sum_{(u,v)\\in{\\mathbb Z}^2}\\sum_{m,n\\in{\\mathbb Z}} \\chi_{E_{(u,v)}} (x-\\alpha (m+u),y-\\beta m-(n+v)\/\\alpha)\\\\\n=&\\sum_{(u,v)\\in{\\mathbb Z}^2}\\sum_{m,n\\in{\\mathbb Z}} \\chi_{E_{(u,v)}} (x-\\alpha m,y-\\beta m-n\/\\alpha)\\\\\n=&\\sum_{m,n\\in{\\mathbb Z}}\\sum_{(u,v)\\in{\\mathbb Z}^2} \\chi_{E_{(u,v)}} (x-\\alpha m,y-\\beta m-n\/\\alpha)\\\\\n=&\\sum_{m,n\\in{\\mathbb Z}} \\chi_{Q_{\\alpha}} (x-\\alpha m,y-\\beta m-n\/\\alpha)=1.\\\\\n\\end{aligned}\n$$\nNote that above we used an application of Fubini\\rq{}s theorem. \nNow let \n $\\Omega$ be a multi-tile with respect to the lattice $M(\\Bbb Z^2)$. Then $\\Omega = \\bigcup_{i=1}^N\\Omega_i$, where $\\Omega_i$ are tiles by $M({\\mathbb Z}^2)$. Thus, by the previous results above, each $\\Omega_i$ is a tile by $M_\\beta(\\Bbb Z^2)$, hence we have the following. \n$$ \n\\sum_{m,n\\in{\\mathbb Z}}\\chi_{\\Omega}(x-\\alpha m,y-\\beta m-n\/\\alpha)=\\sum_{i=1}^N\\sum_{m,n\\in{\\mathbb Z}}\\chi_{\\Omega_i}(x-\\alpha m,y-\\beta m-n\/\\alpha)=N.\n$$\n\n The converse can be obtained by a similar calculation. \n \n\\end{proof}\n\n \\begin{proof}[Proof of Example \\ref{Octagon}]\n Before we prove our claim, note that if the family\n ${\\mathcal G}(|{\\mathcal O}_8|^{-1\/2}\\chi_{{\\mathcal O}_8}, \\Lambda)$ is a Gabor basis and $B$ is an integer matrix, then according to Theorem \\ref{lower triangle} the set ${\\mathcal O}_8$ must tile which is impossible. Thus, we assume that $B$ has some non-integer rational entries. Let ${\\bf m}_0=(3,2)$. Then ${\\mathcal P}:=K\\cap (K+{\\bf m}_0)$, $K=\\mathcal O_8$, is the parallelogram with vertices\n $\\{(1,2), (2,1), (1,1), (2,0)\\}$. Hence,\n $$\n {\\mathcal P} = Q[0,1]^2+(1,1)^t, \\ \\mbox{where} \\ Q = \\left(\n \\begin{array}{cc}\n 1 & 0 \\\\\n -1 & 1 \\\\\n \\end{array}\\right).\n $$\n The Fourier transform of $\\chi_{\\mathcal P}$ at $\\xi = (\\xi_1,\\xi_2)$ is given by \n $$\n \\widehat{\\chi_{{\\mathcal P}}}(\\xi) = c ~ \\widehat{\\chi_{[0,1]^2}}(Q^{T}\\xi)\n $$\n where $c:=c(\\xi)$ is some unimodular constant.\n \n \n \\medskip\n \n \n By the mutual orthogonality of the element $( m_0, n)$ with $(0, 0)$ for all $ n\\in{\\mathbb Z}^2$, we obtain \n \n \\begin{align}\\label{integral-octagon}\n 0= I:= & \\int_{K\\cap (K+{m}_0)} e^{-2\\pi i \\langle C m_0, x\\rangle} e^{-2\\pi i \\langle Bn,x\\rangle} dx \n = c\\cdot\\widehat{\\chi_{[0,1]^2}}(Q^TCm_0+Q^TB n) . \n \\end{align}\n \n \n If $Cm_0=0$, by putting ${n}=0$, then one must have $c=0$, which is a contradiction . Otherwise, (\\ref{integral-octagon}) shows that $Q^TB({\\mathbb Z}^2)$ is exponentially incomplete for $L^2[0,1]^2$. By Theorem \\ref{Theorem_Appendix1} in the appendix, $Q^TB$ is equivalent to one of the following two forms.\n %\n %\n \\begin{align}\\label{matrices forms}\n \\left(\n \\begin{array}{cc}\n \\frac{1}{q'} & 0 \\\\\n r' & q' \\\\\n \\end{array}\\right) \\ \\ \\mbox{or} \\ \\ \\left(\n \\begin{array}{cc}\n p' & 0 \\\\\n \\frac{r''}{s''} & \\frac{1}{p'} \\\\\n \\end{array}\\right)\n \\end{align}\n for some integers $p',q', r'>1$, gcd of $r'$ and $q'$ is strictly greater than 1 and $(r'',s'')$ is relatively prime. \n \n \\medskip\n \n \n We now prove that $B$ also is equivalent to the same desired form in (\\ref{matrices forms}). We are going to establish the first case, the second case is similar. Recall that $Q = \\left(\n \\begin{array}{cc}\n 1& 0 \\\\\n -1 & 1 \\\\\n \\end{array}\\right)$. If $Q^TB\\tilde U = \\left(\n \\begin{array}{cc}\n \\frac{1}{q} & 0 \\\\\n r & q \\\\\n \\end{array}\\right)$ for some unimodular integer matrix $\\tilde U$, then $B\\tilde U= \\left(\n \\begin{array}{cc}\n \\frac{1+rq}{q} & q \\\\\n r & q \\\\\n \\end{array}\\right)$. Note that $1+rq$ and $q^2$ is relatively prime. Thus there exist co-prime integers $u,v$ such that $(1+rq)u+q^2v = 1$. Define $ U = \\left(\n \\begin{array}{cc}\n u & -q^2 \\\\\n v & 1+rq \\\\\n \\end{array}\\right)$. Then $\\det(U)=1$ and the lattice $B\\tilde U(\\mathbb Z^2) = B\\tilde UU({\\mathbb Z}^2)$. Note that \n $$\n B\\tilde UU = \\left(\n \\begin{array}{cc}\n \\frac{1+rq}{q} & q \\\\\n r & q \\\\\n \\end{array}\\right)\\left(\n \\begin{array}{cc}\n u & -q^2 \\\\\n v & 1+rq \\\\\n \\end{array}\\right) = \\left(\n \\begin{array}{cc}\n \\frac{1}{q} & 0 \\\\\n ru+vq & q\\\\\n \\end{array}\\right).\n $$ \n This shows that $B$ is equivalent to the desired form in (\\ref{matrices forms}). \n For the rest, we prove that the neither of these forms can form an Gabor orthonormal basis. By the Lemma \\ref{lemma_shear} and Theorem \\ref{Th_union of FD}, we just need to prove that the octagon ${\\mathcal O}_8$ is not a multi-tile for the matrices $\\left(\\begin{array}{cc}\n p& 0 \\\\\n 0& 1\/p \\\\\n \\end{array}\\right)$ and $\\left(\\begin{array}{cc}\n 1\/p& 0 \\\\\n 0 & p \\\\\n \\end{array}\\right)$ where $p>1$ is an integer. By the symmetry of the octagon, we just need to prove that ${\\mathcal O}_8$ is not a multi-tile for the first one. Let $B_p:=\\left(\\begin{array}{cc}\n p& 0 \\\\\n 0& 1\/p \\\\\n \\end{array}\\right)$. To prove this, we first note that an elementary calculation shows that the area of ${\\mathcal O}_8$ is 14. If it multi-tiles by $B_p({\\mathbb Z}^2)$, then for almost every $x\\in{\\mathbb R}^2$, the cardinality of the set $(x+B_p({\\mathbb Z}^2))\\cap {\\mathcal O}_8$ is $l=14$.\n To obtain a contradiction \n consider the rectangle $R_p:=[0,1)\\times[0,1\/p)$ for $p>1$. It is a simple observation that $R_p$ can be covered $12p$ by translations of $\\mathcal O_8$ by the matrix $B_p$. This is a contradiction to the level of multi-tiling $l=14$, thus we have completed the proof.\n \n \n \\iffalse \n \n \n \\medskip\n \n If $p=2$, we consider the upper corner of triangle formed by the vertices $(1,2), (1,3\/2)$ and $(3\/2,3\/2)$ and denote it by $\\Delta$, then for any $x\\in \\Delta$, \n $$ \n x+\\left\\{(k,j\/2): k\\in\\{0,-2\\},j \\in\\{0,-1,-2,-3,-4,-5,-6,-7\\}\\right\\}\\in (x+B_2({\\mathbb Z^2}))\\cap {\\mathcal O}_8.\n$$\nThere are 16 elements and $\\Delta$ has positive measure. This shows that ${\\mathcal O}_8$ cannot be a multi-tile by $B_2({\\mathbb Z}^2)$.\n\n\\medskip\n\nIf $p\\ge3$, observe that the rectangle $[0,1)\\times[0,1\/p)$ is covered $12p$ times by $B_p(\\Bbb Z^2)$, and $p\\geq 3$. \n\n $ \\{(0,j\/p): -2p\\leq j\\leq 2p-1\\}$ exactely $4p$ times. Since $4$ does not divide $14$, thus we have completed the proof.\n \\fi \n\n\\end{proof}\n\n\n ","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzjics b/data_all_eng_slimpj/shuffled/split2/finalzzjics new file mode 100644 index 0000000000000000000000000000000000000000..de40b73883b4dc2954af6fdcda26fe9591e3c60e --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzjics @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\n$ {\\tt MergeSort} $ is one of the fundamental sorting algorithms that is being taught in undergraduate Computer Science curricula across the U.S. and elsewhere. Its worst-case performance, measured by the number of comparisons of keys performed while sorting them, is optimal for the class of algorithms that sort inductively\\footnote{Inductive sorting of $ n $ keys sorts a set of $ n-1 $ of those keys first, and then ``sorts-in'' the remaining $ n $-th key.} by comparisons of keys.\\footnote{In its standard form analyzed in this paper, $ {\\tt MergeSort} $ is not an inductive sorting algorithm. However, its worst-case performance, measured by the number of comparisons of keys performed while sorting them, is equal to the worst-case performance of the \\textit{binary insertion sort} first described by Steinhaus in \\cite{stein:matsnap} that is worst-case optimal in the class of inductive sorting algorithms that sort by comparisons of keys; see \\cite{knu:art} page 186.} Historically, it\\footnote{A bottom-up version of it, invented by John Neumann.} was the first sorting algorithm to run in $ O(n \\lg n) $ time\\footnote{In the worst case.}.\n\n\\medskip\n\nSo it seems only fitting to provide an exact formula for $ {\\tt MergeSort} $'s worst-case performance \\textit{and} derive it precisely.\nUnfortunately, many otherwise decent texts offer unnecessarily imprecise\\footnote{Notable exceptions in this category are \\cite{baavan:ana} and \\cite{sedfla:ana} that derive almost exact formulas, but see Section~\\ref{sec:oth} page~\\pageref{sec:oth} for a brief critique of the results and their proofs offered there.} variants of it, and some with quite convoluted, incomplete, or incorrect proofs. Due to these imperfections, the fact that the worst-case performance of $ {\\tt MergeSort} $\nis the same as that of another benchmark sorting algorithm, the \\textit{binary insertion sort} of \\cite{stein:matsnap}, has remained unnoticed\\footnote{Even in \\cite{knu:art}.}.\n\n\\medskip\n\n\nIn this paper, I present two outlines\\footnote{The detailed derivations can be found in \\cite{suc:worstmergeMS}.} of elementary yet precise and complete derivations of an exact formula\n \\[ W(n) = \\sum_{i=1} ^{n} \\lceil \\lg i \\rceil = n \\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} + 1 \\]\nfor the maximum number $ W(n) $ \\footnote{Elementary derivation of an exact formula for the \\textit{best}-case performance $ B(n) $ of $ {\\tt MergeSort} $, measured by the number of comparisons of keys performed while sorting them, has been done in \\cite{suc:bestmerg}; see Section~\\ref{sec:best} page~\\pageref{sec:best} of this paper.} of comparisons of keys performed by \\linebreak $ {\\tt MergeSort} $ on an $ n $-element array. The first of the two, due to its structural regularity, is well worth carefully studying in its own right. \n\n\\smallskip\n\nUnlike some other basic sorting algorithms\\footnote{For instance, $ {\\tt Heapsort} $; see \\cite{suc:wcheap} for a complete analysis of its worst-case behavior.} that run in $ O(n \\lg n) $ time, $ {\\tt MergeSort} $ exhibits a remarkably regular\\footnote{As revealed by Theorem~\\ref{thm:mersorbounds}, page~\\pageref{thm:mersorbounds}.} worst-case behavior, \nthe elegant simplicity of which\nhas been mostly lost on its rough analyses. In particular,\n$ W(n) $ is linear\\footnote{See Figure \\ref{fig:boundsMergeSort} page \\pageref{fig:boundsMergeSort}.} between the points $ n = 2^{\\lfloor \\lg n \\rfloor} $ and it linearly interpolates its own lower bound $ n \\lg n - n + 1 $ \\footnote{Given by the left-hand side of the inequality~\\eqref{eq:mersorbounds} page~\\pageref{eq:mersorbounds}.} between these points.\n\n\\bigskip\n\nWhat follows is a short version (SV) of a manuscript dated January 20, 2017, of the full version version \\cite{suc:worstmergeMS} of this paper that has been posted at: \\bigskip \\\\\n\\verb|http:\/\/csc.csudh.edu\/suchenek\/Papers\/Analysis_of_MergeSort.pdf| \\bigskip \\\\\nThe derivation of the worst case of $ {\\tt MergeSort} $ presented here is roughly the same\\footnote{Except for the present proof of Lemma~\\ref{thm:sumCeilLog} which I haven't been using in my class.} as the one I have been doing in my undergraduate Analysis of Algorithms class. \\ref{sec:slides} shows sample class notes from one of my lectures. \n\n\n\n\\section{Some Math prerequisites}\n\n\nA manuscript of the full version \\cite{suc:worstmergeMS} of this paper contains a clever derivation of a well-known\\footnote{See \\cite{knu:art}.} closed-form formula for\n$ \\sum_{i=1} ^{n} \\lceil \\lg i \\rceil $. It proves insightful in my worst-case analysis of $ {\\tt MergeSort} $ as its right-hand side will occur on page~\\pageref{eq:result} in the fundamental equality~\\eqref{eq:result} and serve as an instrument to derive the respective exact formula for $ {\\tt MergeSort} $'s worst-case behavior.\n\n\\begin{lem} \\label{thm:sumCeilLog}\nFor every integer $ n\\geq 1 $,\n\\begin{equation} \\label{eq:sumCeilLog} \n\\sum_{i=1} ^{n} \\lceil \\lg i \\rceil = \\sum _{y=0}^{\\lceil \\lg n \\rceil-1} ( n - 2^y) .\n\\end{equation}\n\\end{lem}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\\medskip\n\n\nFrom this one can easily conclude that:\n\n\n\\begin{cor} \\label{cor:sumCeilLog}\nFor every integer $ n\\geq 1 $,\n\\begin{equation} \\label{eq:sumCeilLog2} \n\\sum_{i=1} ^{n} \\lceil \\lg i \\rceil = n \\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} + 1 .\n\\end{equation}\n\\end{cor}\n\n\n\\section{$ {\\tt MergeSort} $ and its worst-case behavior $ W(n) $} \\label{sec:mergsor}\n\n\nA call to $ {\\tt MergeSort} $ inherits an $ n $-element array $ {\\tt A} $ of integers and sorts it non-decreasingly, following the steps described below.\n\n\\begin{algMergeSort} \\label{alg:mersor}\nTo \nsort an $ n $-element array $ {\\tt A} $ do:\n\n\\begin{enumerate}\n\\item \\label{alg:mersor:item1} If $ n \\leq 1 $ then return $ {\\tt A} $ to the caller,\n\\item \\label{alg:mersor:item2} If $ n \\geq 2 $ then\n\\begin{enumerate}\n\\item \\label{alg:mersor:item2:it1} pass the first $ \\lfloor \\frac{n}{2} \\rfloor $ elements of $ {\\tt A} $ to a recursive call to $ {\\tt MergeSort} $,\n\\item pass the last $ \\lceil \\frac{n}{2} \\rceil $ elements of $ {\\tt A} $ to another recursive call to $ {\\tt MergeSort} $,\n\\item \\label{alg:mersor:item2:it2} linearly merge, by means of a call to $ {\\tt Merge} $, the non-decreasingly sorted arrays that were returned from those calls onto one non-decreasingly sorted array $ {\\tt A}^{\\prime} $,\n\\item \\label{alg:mersor:item2:it3} return $ {\\tt A}^{\\prime} $ to the caller.\n \\end{enumerate} \n\\end{enumerate}\n\\end{algMergeSort}\n\n\n\nA Java code of \n$ {\\tt Merge} $ is shown on the Figure~\\ref{fig:Merge}.\\footnote{A Java code of \n$ {\\tt MergeSort} $ is shown in \\ref{sec:appCode} Figure~\\ref{fig:Sort} page~\\pageref{fig:Sort}.}\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.7\\linewidth]{.\/Merge1} \n\\includegraphics[width=0.7\\linewidth]{.\/Merge2}\n\\caption{A Java code of $ {\\tt Merge} $, based on a pseudo-code from \\cite{baa:ana}. Calls to $ {\\tt Boolean} $ method $ {\\tt Bcnt.incr()} $ count the number of comps for the purpose of experimental verification of the worst-case analysis of $ {\\tt MergeSort} $.}\n\\label{fig:Merge}\n\\end{figure}\n\n\\medskip\n\nA typical measure of the running time of $ {\\tt MergeSort} $ is the number of \\textit{comparisons of keys}, which for brevity I call \\textit{comps}, that it performs while sorting array $ {\\tt A} $. \n\n\\begin{df}\nThe worst-case running time \n\\[W(n)\\]\nof $ {\\tt MergeSort} $ is defined as the maximum number of comps it performs while sorting an array of $ n $ distinct\\footnote{This assumption is superfluous for the purpose of worst-case analysis as the mere presence of duplicates does not force $ {\\tt MergeSort} $ to perform more comps.} elements. \n\\end{df}\n\n\\label{pag:n>0}Clearly, if $ n=0 $ then $ W(n) = 0 $. From this point on, I am going to assume that $ n \\geq 1 $.\\footnote{This assumption turns out handy while using expression $ \\lg n $.}\n\n\n\\medskip\n\nSince no comps are performed outside $ {\\tt Merge} $, $ W(n) $ can be computed as the sum of numbers of comps performed by all calls to $ {\\tt Merge} $ during the execution of $ {\\tt MergeSort} $. The following classic results will be useful in my analysis.\n\n\n\\begin{theorem} \\label{thm:merge_n-1}\nThe maximum number of comps performed by $ {\\tt Merge} $ on two sorted list of total number $ n $ of elements is $ n-1 $.\n\\end{theorem}\n\\noindent \\textit{Proof} (constructive, with Java code that generates worst cases shown in the \\ref{sec:gen_worst_cases}) in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\\medskip\n\n\\label{pag:mergopt} Moreover, if the difference between the lengths of merged list is not larger than 1 then no algorithm that merges sorted lists by means of comps beats $ {\\tt Merge} $ in the worst case, that is, has a lower than $ n-1 $ maximum number of comps.\\footnote{Proof in \\cite{knu:art}, Sec. 5.3.2 page 198; the worst-case optimality of $ {\\tt Merge} $ ($ n-1 $ comps) was generalized in \\cite{stoyao:optmerge} over lists of lengths $ k $ and $ m $, with $ k \\leq m $, that satisfy $ 3k \\geq 2m-2 $.} This fact makes $ {\\tt MergeSort} $ optimal in the intersection of the class of sorting algorithms that sort by merging two sorted lists of lengths' difference not larger than 1 \\footnote{Or, by virtue of the above-quoted result from \\cite{stoyao:optmerge}, with the difference not larger than the half of the length of the shorter list plus 1.}\nwith the class of sorting algorithms that sort by comps.\n\n\\section{An easy yet precise derivation of $ W(n) $} \\label{sec:easy}\n\n$ {\\tt MergeSort} $ is a recursive algorithm. If $ n \\geq 2 $ then it spurs a cascade of two or more recursive calls to itself. A rudimentary analysis of the respective recursion tree $ T_n $, shown on Figure~\\ref{fig:rectre}, yields a neat derivation of the exact formula for the maximum number $ W(n) $ of comps that $ {\\tt MergeSort} $ performs on an $ n $-element array.\n\n\\begin{figure} [h]\n\\includegraphics[scale=.175]{RecTreeMergeSort_sketch3.jpg} \n\\caption{\\label{fig:rectre} A sketch of the recursion 2-tree $ T_n $ for $ {\\tt MergeSort} $ for a sufficiently large $ n $, with level numbers shown on the left and the numbers of nodes in the respective level shown on the right. The nodes correspond to calls to $ {\\tt MergeSort} $ and show sizes of (sub)arrays passed to those calls. The last non-empty level is $ h $. The empty levels (all those numbered $ > h $) are not shown. The root corresponds to the original call to $ {\\tt MergeSort} $. If a call that is represented by a node $ p $ executes further recursive calls to $ {\\tt MergeSort} $ then these calls are represented by the children of $ p $; otherwise $ p $ is a leaf. The wavy line \n$ \\photon $ \nrepresents a path in $ T_n $.}\n\\end{figure}\n\n\\medskip\n\n\n\n\n\n\nThe idea behind the derivation\nis strikingly simple. It is based on the observation\\footnote{Which I prove in \\cite{suc:worstmergeMS} as Theorem~4.6, page~14.} that for every $ k \\in \\mathbb{N} $, the maximum number $ C_k $ of comps performed at each level\\footnote{Empty or not.} $ k $ of $ T_n $ is given by this neat formula:\\footnote{It is a simplification of formulas used in derivation presented in \\cite{baavan:ana} and discussed in Section~\\ref{sec:oth} page~\\pageref{sec:oth}; in particular, it does not refer to the depth $ h $ of the decision tree $ T_n $.}\n\\begin{equation} \\label{eq:main_level_comps_formula}\nC_k = \\max \\{n - 2^k, 0\\} .\n\\end{equation}\nSince \n\\begin{equation} \\label{eq:hight_rec_tree}\nn - 2^k > 0 \\mbox{ if, and only if, } \\lceil \\lg n \\rceil - 1 \\geq k ,\n\\end{equation}\nthe Corollary~\\ref{cor:sumCeilLog} will allow me to conclude from \\eqref{eq:main_level_comps_formula} and \\eqref{eq:hight_rec_tree} the main result of this paper\\footnote{This is how I have been deriving it in my undergraduate Analysis of Algorithms class for some 15 years or so, now.}:\n\\begin{equation} \\label{eq:result}\nW(n) = \\sum \n_{k \\in \\mathbb{N}}\n C_k = \\sum _{k=0}^{\\lceil \\lg n \\rceil-1} (n - 2^k) = n\\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} + 1 = \\sum_{i=1} ^{n} \\lceil \\lg i \\rceil.\n\\end{equation}\n\n\n\n\n\\bigskip \n\nThe missing details\\footnote{Which I did not show in my Analysis of Algorithms class.} in the above sketch are in \\cite{suc:worstmergeMS}. Naturally, their only purpose is to prove the equality \\eqref{eq:main_level_comps_formula} for all $ k \\in \\mathbb{N} $,\nas the rest, shown in \\eqref{eq:result}, easily follows from it. \nIn particular, we get:\n\n\n\\begin{mth} \\label{thm:w-cMS}\nThe number $ W(n) $ of comparisons of keys that $ {\\tt MergeSort} $ performs in the worst case while sorting an $ n $-element array is\n\\begin{equation} \\label{eq:levComp2} \nW(n) = \\sum_{i=1} ^{n} \\lceil \\lg i \\rceil = n \\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} + 1 .\n\\end{equation}\n\\end{mth}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\n\n\n\\medskip\n\nFrom that we can conclude a usual rough characterization of $ W(n) $: \n\\[ W(n) \\leq n (\\lg n + 1) - 2^{ \\lg n } + 1 = n \\lg n + n - n + 1 = n \\lg n + 1\\]\nand\n\\[ W(n) \\geq n \\lg n - 2^{ \\lg n + 1 } + 1 = n \\lg n - 2n + 1. \\]\nTherefore,\n\\[ W(n) \\in \\Theta (n \\log n). \\]\n\n\n\n\\medskip\n\nThe occurrence of $ \\sum_{i=1} ^{n} \\lceil \\lg i \\rceil $ in \\eqref{eq:levComp2} allows to conclude that $ W(n) $ is exactly equal\\footnote{\\cite{knu:art} contains no mention of that fact.} to the number of comparisons of keys that the \\textit{binary insertion sort}, considered by H. Steinhaus in \\cite{stein:matsnap} and analyzed in \\cite{knu:art}, performs in the worst case. Since the \\textit{binary insertion sort} is known to be worst-case optimal\\footnote{With respect to the number of comparisons of keys performed.} in the class of algorithms that perform incremental sorting, $ {\\tt MergeSort} $ is worst-case optimal in that class\\footnote{Although it is not a member of that class.}, too. From this and from the observation at the end of Section~\\ref{sec:mergsor}, page~\\pageref{pag:mergopt}, I conclude that no algorithm that sorts by merging two sorted lists and only by means of comps is worst-case optimal in the class of algorithms that sort by means of comps as it must perform 8 comps in the worst case while sorting 5 elements\\footnote{They can be split in two: 1 plus 4, and follow the \\textit{binary insertion sort}, or 2 plus 3, and follow $ {\\tt MergeSort} $.}, while one can sort 5 elements by means of comps with no more than 7 comps. \n\n\n\\section{Close smooth bounds on $ W(n) $}\n\n\nOur formula for $ W(n) $ contains a function ceiling that is harder to analyze than arithmetic functions and their inverses. In this Section, I outline a derivation of close lower and upper bounds on $ W(n) $ that are expressible by simple arithmetic formulas. I show that these bounds are the closest to $ W(n) $ in the class of functions of the form $ n \\lg n + c n + 1 $, where $ c $ is a real constant.\n\\label{pag:epsilon} \nThe detailed derivation and missing proofs can be found in \\cite{suc:worstmergeMS}.\n\n\\medskip\n\nUsing the function $ \\varepsilon $ (analyzed briefly in \\cite{knu:art} and \\cite{suc:wcheap}), a form of which is shown on Figure~\\ref{fig:eps}, \ngiven by:\n\\begin{equation} \\label{eq:eps}\n\\varepsilon = 1 + \\theta\n- 2^{\\theta} \\mbox{ and } \\theta = \\lceil \\lg \\, n \\rceil - \\lg \\, n ,\n\\end{equation}\none can conclude\\footnote{\\label{foo:proofdelta}See \\cite{suc:wcheap}, Thm. 12.2 p. 94 for a proof.} that, for every $ n > 0 $,\n\\begin{equation} \\label{eq:eps_basic_formula}\n n \\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} = n(\\lg n + \\varepsilon - 1) ,\n\\end{equation}\nwhich yields\n\\begin{equation} \\label{eq:W_eps}\n W(n) = n( \\lg n+ \\varepsilon - 1) + 1 = n \\lg n + (\\varepsilon - 1) n + 1 . \n\\end{equation}\n\\begin{figure} [h]\n\\center\n\\includegraphics[scale=.34]{Alpha_epsilon-1.pdf} \n\\caption{\\label{fig:eps} Graph of $ \\varepsilon - 1 $ as a function of $ \\lg n $.}\n\\end{figure}\n\n\n\\begin{property} \\label{prop:delta}\nFunction $ \\varepsilon $ given by \\eqref{eq:eps} is a continuous function of $ n $ \non the set of reals $ > 0 $. It assumes the minimum $ 0 $ for every $ n = 2^{\\lfloor \\lg n \\rfloor} $ and the maximum \n\\begin{equation} \\label{eq:Erdos}\n\\delta = 1 - \\lg e + \\lg \\lg e \\approx 0.0860713320559342 ,\n\\footnote{The constant $ 1 - \\lg e + \\lg \\lg e $\nhas been known as the {\\em Erd\\\"{o}s constant} $ \\delta $. Erd\\\"{o}s used it around 1955 in order to establish an asymptotic upper bound for the number $ M(k) $ of different numbers in a multiplication table of size $ k \\times k $ by means of the following limit:\n\\[\\lim _{k \\rightarrow \\infty} \\frac{\\ln \\frac{k \\times k}{M(k)}}{\\ln \\ln (k \\times k)} = \\delta .\n\\]}\n\\end{equation}\nfor every\n\\begin{equation} \\label{eq:n_max_eps}\n n = 2^{\\lfloor \\ln n + \\lg\\lg e \\rfloor} \\ln 2 \n\\end{equation}\nand only such $ n $.\nThe function \n$ \\varepsilon $ restricted to integers never reaches the value $\\delta$. However, $\\delta$ is the \\textit{supremum} of $ \\varepsilon $ restricted to integers. \n\\end{property}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\nCharacterization \\eqref{eq:W_eps} and Property~\\ref{prop:delta} yield close smooth bounds of $ W(n) $. They are both of the form $ n \\lg n + c n + 1 $ and they sandwich tightly $ W(n) $ between each other. If one sees $ W(n) $ as an infinite polygon\\footnote{Which it is.}, its lower bound circumscribes it and its upper bound inscribes it.\n\n\\begin{theorem} \\label{thm:mersorbounds} \n$ W(n) $ is a continuous concave function, linear between the points $ n = 2^{\\lfloor \\lg n \\rfloor}$, that for every $ n > 0 $ satisfies this inequality:\n\\begin{equation} \\label{eq:mersorbounds} \n n \\lg n - n + 1 \\leq W(n) \\leq n \\lg n - (1-\\delta) n + 1 < n \\lg n - 0.913 n + 1 , \n\\end{equation}\nwith the left $ \\leq $ becoming $ = $ for every $ n = 2^{\\lfloor \\lg n \\rfloor} $ and the right $ \\leq $ becoming $ = $ for every $ n = 2^{\\lfloor \\lg n + \\lg \\lg e \\rfloor} \\ln 2 $, and only for such $ n $. Moreover, the graph of $ W(n) $ is tangent to the graph of $ n \\lg n - (1-\\delta) n + 1 $ at the points $ n = 2^{\\lfloor \\lg n + \\lg \\lg e \\rfloor} \\ln 2 $, and only at such points.\n\\end{theorem}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\t\\begin{figure} [h] \\center\n\\includegraphics[scale=.34]{Worst_Case_Mergesort_bounds4}\n\t\t\\caption{\\label{fig:boundsMergeSort} $ W(n) = n\\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} +1 $ (the middle line) and its bounds $ n \\lg n - n + 1 $ and $ n \\lg n - (1-\\delta) n + 1 $ $ \\approx $ $ n \\lg n - 0.913 n + 1 $, all three treated as functions of a positive real variable $ n $, plotted for $ n \\in [1,6] $. $ W(n) $ is linear between the points $ n = 2^{\\lfloor \\lg n \\rfloor} $ and it linearly interpolates its lower bound $ n \\lg n - n + 1 $ between these points. Its upper bound $ n \\lg n - (1-\\delta) n + 1 $ inscribes it and is tangent to it at the points $ n $ $ = $ $ 2^{\\lfloor \\lg n + \\lg\\lg e \\rfloor} \\ln 2 $.}\n\t\\end{figure}\n\t\n\t \\medskip\n\t \n\t \n\t \n\t The bounds given by (\\ref{eq:mersorbounds}) are really close\\footnote{The distance between them is less than $ \n\t \\delta n \\approx 0.0860713320559342 n $ for any positive integer $ n $.} to the exact value of $ W(n) $, as it is shown on Figure~\\ref{fig:boundsMergeSort} page~\\pageref{fig:boundsMergeSort}. The exact value $ n\\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} +1 $ is a continuous function (if $ n $ is interpreted as a real variable) despite that it incorporates discontinuous function \\textit{ceiling}. \n\t \n\t\\medskip\n\t\n\t\\begin{note}\n\t\\label{pag:interesting_interpolation} It seems interesting that $ W(n) = n\\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} +1 $ (whether $ n $ is interpreted as a real variable or an integer variable) is linear between points $ n = 2^{\\lfloor \\lg n \\rfloor} $ and linearly interpolates its own lower bound $ n \\lg n - n + 1 $ between these points.\n\t\\end{note}\n\t\n\t \n\t \\medskip\n\t\nFor $ n $ restricted to positive integers, the inequality \\eqref{eq:mersorbounds} can be slightly enhanced by replacing the $ \\leq $ symbol with $ < $, with the following result.\n\\begin{theorem} \\label{thm:minconst}\n$ 1-\\delta $ is the greatest constant $ c $ such that for every integer $ n \\geq 1 $, \n\\begin{equation} \\label{eq:minconst}\n W(n) < n \\lg n - c n + 1.\n\\end{equation}\n\\end{theorem}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\\medskip\n\nTheorem \\ref{thm:minconst} can be reformulated as follows.\n\n\n\\begin{cor} \\label{cor:minconstinf}\n\\begin{equation} \\label{eq:minconstinf}\n\\inf \\{c \\in \\mathbb{R} \\mid \\forall n \\in \\mathbb{N} \\setminus \\{0\\} , W(n) < n \\lg n - c n + 1 \\} = 1 - \\delta .\n\\end{equation}\n\\end{cor}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\nNo upper bound of $ W(n) $ that has a form $ n \\lg n - c n + 1 $ can coincide with $ W(n) $ at any integer $ n $, as the following fact ascertains.\n\n\\begin{cor} \\label{cor:minconst_neq}\nThere is no constant $ c $ such that for every integer $ n \\geq 1 $, \n\\begin{equation} \\label{eq:minconst_neq1}\n W(n) \\leq n \\lg n - c n + 1\n\\end{equation}\nand for some integer $ n \\geq 1 $, \n\\begin{equation} \\label{eq:minconst_neq2}\n W(n) = \\lg n - c n + 1.\n\\end{equation}\n\\end{cor}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\\medskip\n\nIn particular\\footnote{Note the $ \\leq $ symbol in \\eqref{eq:minconstinf2}.},\n\\begin{equation} \\label{eq:minconstinf2}\n\\inf \\{c \\in \\mathbb{R} \\mid \\forall n \\in \\mathbb{N} \\setminus \\{0\\}, W(n) \\leq n \\lg n - c n + 1 \\} = 1 - \\delta .\n\\end{equation}\n\n\\medskip\n\nMoreover, we can conclude from Theorem \\ref{thm:minconst} the following fact.\n\n\\begin{cor} \\label{cor:minconst}\n$ 1-\\delta $ is the greatest constant $ c $ such that for every integer $ n \\geq 1 $, \n\\begin{equation} \\label{eq:cor:minconst}\n W(n) \\leq \\lceil n \\lg n - c n \\rceil.\n\\end{equation}\n\\end{cor}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\n\n\\medskip\n\nSince for any integer $ n \\geq 1 $, $ W(n) $ is integer, the lower bound given by \\eqref{eq:mersorbounds}\nyields\n\\begin{equation} \\label{eq:mersorbounds2} \n \\lceil n \\lg n \\rceil - n + 1 \\leq W(n) \\leq \\lceil n \\lg n - 0.913 n \\rceil . \n\\end{equation}\nBy virtue of Corollary \\ref{cor:minconst}, for some integers $ n \\geq 1 $,\\footnote{For instance, for $ n=11 $.}\n\\begin{equation} \\label{eq:wrongbound}\nW(n) > \\lceil n \\lg n - 0.914 n \\rceil .\n\\end{equation}\nAlthough the bounds given by \\eqref{eq:mersorbounds2} \\footnote{Almost the same bounds were given in \\cite{baavan:ana}; see Section~\\ref{sec:oth} for more details on this.} are tighter than those given by \\eqref{eq:mersorbounds}, they nevertheless involve the discontinuous \\textit{ceiling} function, so that they may not be as easy to visualize or analyze as some differentiable functions, thus losing their advantage over the precise formula $ W(n) = n\\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} +1 $. Therefore, the bounds given by \\eqref{eq:mersorbounds} appear to have an analytic advantage over those given by \\eqref{eq:mersorbounds2}.\n\n\n\\section{Other properties of the recursion tree $ T_n $}\n\n\nThis sections contains some well-known auxiliary facts that I didn't need for the derivation of the exact formula for $ W(n) $ but am going to derive from the Main Lemma 4.1 of \\cite{suc:worstmergeMS} for the sake of a thoroughness of my analysis of the decision tree $ T_n $.\n\n\\begin{thm} \\label{thm:depthRectree}\nThe depth $ h $ of the recursion tree $ T_n $ is \n\\begin{equation} \\label{eq:height}\nh = \\lceil \\lg n \\rceil .\n\\end{equation}\n\\end{thm}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\\medskip\n\n\\begin{note}\nTheorem~\\ref{thm:depthRectree} allows for quick derivation of fairly close upper bound on the number of comps performed by $ {\\tt MergeSort} $ on an $ n $-element array. Since at each level of $ T_n $ less than $ n $ comparisons are performed by $ {\\tt Merge} $ and at level $ h $ no comps are performed, and there are $ h $ $ = $ $ \\lceil \\lg n \\rceil $ levels below level $ h $, the total number of comps is not larger than\n\\begin{equation} \\label{eq:UBapprox} \n(n-1)h = (n-1)(\\lceil \\lg n \\rceil) < (n-1) (\\lg n + 1) \\in O(n \\log n).\n\\end{equation}\n\\end{note}\n\n \\medskip\n\n\n\n\n\\medskip\n\nA \\textit{cut} of a tree $ T_n $ is a set $ \\Gamma $ of nodes of $ T $ such that every branch\\footnote{A maximal path.} in $ T_n $ has exactly one element in $ \\Gamma $.\n\n\\begin{thm} \\label{thm:cut}\nThe sum of values shown at the elements of any cut of $ T_n $ is $ n $.\n\\end{thm}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\n\n\n\\begin{thm} \\label{thm:sum_leaves}\nThe number of leaves in the recursion tree $ T_n $ is $ n $.\n\\end{thm}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\nThe following corollary provides some statistics about recursive calls to $ {\\tt MergeSort} $.\n\n\\begin{cor} \\label{note:calls_stats}\nFor every integer $ n > 0 $,\n\\begin{enumerate}\n \\renewcommand\\labelenumi{\\theenumi}\n \\renewcommand{\\theenumi}{(\\roman{enumi})}\n\\item \\label{note:calls_stats:1} $ T_n $ has $ 2n - 1 $ nodes.\n\n\\item \\label{note:calls_stats:2} The number or recursive calls spurred by $ {\\tt MergeSort} $ on any $ n $-element array is $ 2(n - 1) $.\n\n\\item \\label{note:calls_stats:3}\nThe sum $ S_n $ of all values shown in the recursion tree $ T_n $ on Figure~\\ref{fig:rectre} is equal to:\n\\begin{equation} \\label{eq:sum_nodes}\nS_n = n \\lceil \\lg n \\rceil - 2^{\\lceil \\lg n \\rceil} + 2n = n(\\lg n + \\varepsilon + 1).\n\\end{equation}\n\\item \\label{note:calls_stats:4}\nThe average size $ A_n $ of array passed to any recursive call to $ {\\tt MergeSort} $ while sorting an $ n $-element array is:\n\\begin{equation} \\label{eq:avg_nodes}\nA_n = \\frac{1}{2}(1+\\frac{1}{n-1}) (\\lg n + \\varepsilon) \\approx \\frac{1}{2} (\\lg n + \\varepsilon).\n\\end{equation}\n\\end{enumerate}\n\\end{cor}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\\medskip\n\n\n Here is a very insightful property. It states that $ {\\tt MergeSort} $ is splitting its input array fairly evenly\\footnote{The sizes of the sub-arrays passed to recursive calls at any non-empty level $ k $ of the decision tree $ T_n $ above the last non-empty level $ h $ are the same as the sizes of the elements of the maximally even partition of an $ n $-element set onto $ 2^k $ subsets.} so that at any level of the recursive tree, the difference between the lengths of the longest sub-array and the shortest sub-array is $ \\leq 1. $ This fact is the root cause of \ngood worst-case performance\nof $ {\\tt MergeSort} $.\n\n\\begin{property} \\label{pro:level} \nThe difference between values shown by any two nodes in the same level of of the recursion tree $ T_n $ is $ \\leq 1 $.\n\\end{property} \n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\\label{pag:merg_opti_for_mersor}Property \\ref{pro:level} has this important consequence that $ {\\tt Merge} $ is, by virtue of the observation on page~\\pageref{pag:mergopt} after the Theorem~\\ref{thm:merge_n-1} page~\\pageref{thm:merge_n-1}, worst-case comparison-optimal while merging any two sub-arrays of the same level of the recursion tree. Thus the worst-case of $ {\\tt MergeSort} $ cannot be improved just by replacing $ {\\tt Merge} $ with some tricky merging $ {\\tt X} $ as long as $ {\\tt X} $ merges by means of comparisons of keys.\n\n\\begin{cor} \\label{cor:merg_opti_for_mersor}\nReplacing {\\tt Merge} with any other method that merges sorted arrays by means of comps will not improve the worst-case performance of $ {\\tt MergeSort} $ measured with the number of comps while sorting an array.\n\\end{cor}\n\\begin{proof}\nProof follows from the above observation.\n\\end{proof}\n\n\n\\medskip\n\nSince a parent must show a larger value than any of its children, the Property~\\ref{pro:level} has also the following consequence.\n\\begin{cor} \\label{cor:last2}\nThe leaves in the recursion tree $ T_n $ can only reside at the last two non-empty levels of $ T_n $. \n\\end{cor}\n\\begin{proof}\nProof follows from the Property~\\ref{pro:level} as the above observation indicates.\n\\end{proof}\n\nAs a result, one can conclude\\footnote{Cf. \\cite{knu:art}, Sec. 5.3.1 Ex. 20 page 195.} that the recursion tree $ T_n $ has the miminum \\textit{internal} and \\textit{external path length}s among all binary trees on $ 2n - 1 $ nodes.\n\n\\medskip\n\nSince all nodes at the level $ h $ of the recursion tree $ T_n $ are leaves and show value $ 1 $, no node at level $ h-1 $ can show a value $ >2 $. Indeed, level $ h-1 $ may only contain leaves, that show value $ 1 $, and parents of nodes of level $ h $ that show value $ 1+1=2 $. This observation and the previous result allow for easy characterization of contents of the last two non-empty levels of tree $ T_n $.\n\n\n\\noindent \\begin{cor} \\label{cor:last2char}\nFor every $ n\\geq 2 $: \n\n\\begin{enumerate}\n \\renewcommand\\labelenumi{\\theenumi}\n \\renewcommand{\\theenumi}{(\\roman{enumi})}\n\\item \\label{cor:last2char:item1} there are $ 2^h - n $ leaves, all showing value $ 1 $, \nat the level $ h-1 $,\n\\item \\label{cor:last2char:item2} there are $ n - 2^{h-1} $ non-leaves, all showing value $ 2 $, \nat the level $ h-1 $,\nand\n\\item \\label{cor:last2char:item3} there are $ 2n - 2^{h} $ \\footnote{This value shows in the lower right corner of Figure~\\ref{fig:rectre} page~\\pageref{fig:rectre} of a sketch of the recursion tree $ T_n $; it was not need needed for the derivation of the main result \\eqref{eq:levComp2} page~\\pageref{eq:levComp2}, included for the sake of completeness only.} nodes, all leaves showing value $ 1 $, at the level $ h $\n\\end{enumerate}\n\\noindent of the recursion tree $ T_n $, where $ h $ is the depth\\footnote{The level number of the last non-empty level of $ T_n $.} of $ T_n $.\n\\end{cor}\n\\noindent \\textit{Proof} in \\cite{suc:worstmergeMS}.\n\\hfill $ \\Box $\n\n\\section{A derivation of $ W(n) $ without references to the recursion tree}\n\n\nIn order to formally prove Theorem~\\ref{thm:w-cMS} without any reference to the recursion tree, I use here the well-known\\footnote{For instance, derived in \\cite{baa:ana} and \\cite{baavan:ana}.} recurrence relation\n\\begin{equation} \\label{eq:recmergesort1} \nW(n) = W(\\lfloor \\frac{n}{2} \\rfloor) + W(\\lceil \\frac{n}{2} \\rceil) + n - 1 \\mbox{ if } n \\geq 2\n\\end{equation}\n\\begin{equation} \\label{eq:recmergesort2} \nW(1) = 0\n\\end{equation}\nthat easily follows from the description (Algorithm~\\ref{alg:mersor} page~\\pageref{alg:mersor}) of $ {\\tt MergeSort} $, steps~\\ref{alg:mersor:item2:it1}, \\ref{alg:mersor:item2:it2} and Theorem~\\ref{thm:merge_n-1}. I am going to prove, by direct inspection, that the function $ W(n) $ defined by (\\ref{eq:levComp2}) satisfies equations (\\ref{eq:recmergesort1}) and (\\ref{eq:recmergesort2}).\n\n\\medskip\n\nThe details of the proof are in \\cite{suc:worstmergeMS}.\n\n\\section{Other work} \\label{sec:oth}\n\nAlthough some variants of parts of the formula \\eqref{eq:levComp2} appear to have been known for quite some time now, even otherwise precise texts offer derivations that leave room for improvement. For instance, the recurrence relation for $ {\\tt MergeSort} $ analyzed in \\cite{sedfla:ana} asserts that the least number of comparisons of keys performed outside the recursive calls, if any, that suffice to sort an array of size $ n $ is $ n $ rather than $ n-1 $. This seemingly inconsequential variation results in a solution $ W(n) = \\sum_{i=1} ^{n-1} (\\lfloor \\lg i \\rfloor + 2)$ \\footnote{I saw $ W(n) = \\sum_{i=1} ^{n-1} \\lfloor \\lg i \\rfloor $ on slides that accompany \\cite{sedfla:ana}.} on page 2, Exercise 1.4, rather than the correct formula (\\ref{eq:result}) $ W(n) = \\sum_{i=1} ^{n} \\lceil \\lg i \\rceil $ derived in this paper. (Also, the relevant derivations presented in \\cite{sedfla:ana}, although quite clever, are not nearly as precise and elementary as those presented in this paper.) As a result, the fact that $ {\\tt MergeSort} $ performs exactly the same number of comparisons of keys as does another classic, \\textit{binary insertion sort}, considered by H. Steinhaus and analyzed in \\cite{knu:art}, remains unnoticed.\n\n\\bigskip\n\n\\bigskip\n\nPages 176 -- 177 of \\cite{baavan:ana} contain \nan early sketch of proof of \n\\begin{equation} \\label{eq:baavan_result}\nW(n) = n h - 2^{h} + 1,\n\\end{equation}\nwhere $ h $ is the depth of the recursion tree $ T_n $, with remarkably close\\footnote{Although not 100 percent correct.} bounds given by~\\eqref{eq:baavan_bound} page~\\pageref{eq:baavan_bound}. It is similar\\footnote{The idea behind the sketch of the derivation in \\cite{baavan:ana} was based on an observation that \\[W(n) = \\sum _{i = 0} ^{h-2} (n - 2^i) + \\frac{n - B}{2} , \\] where $ B $ was the number of leaves at the level $ h-1 $ of the decision tree $ T_n $; it was sketchily derived from the recursion tree shown on Figure~\\ref{fig:RecTreeBaaVan} and properties stated in the Corollary~\\ref{cor:last2char} page~\\pageref{cor:last2char} (with only a sketch of proof in \\cite{baavan:ana}) not needed for the derivation presented in Section~\\ref{sec:easy}.} to a simpler \nderivation based on the equality \\eqref{eq:main_level_comps_formula}, presented in this paper in Section~\\ref{sec:easy} and outlined in~\\eqref{eq:result} page~\\pageref{eq:result} (except for the $ \\sum_{i=1} ^{n} \\lceil \\lg i \\rceil $ part), which it predates by several years. \n\n\\medskip\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=1\\linewidth]{.\/RecTreeBaaVan}\n\\caption{A snapshot from \\cite{baavan:ana}, page 177, showing a decision tree for $ {\\tt MergeSort}$. \\textit{\\textbf{Note}}: This picture is copyrighted by \\textit{Addison Wesley Longman} (2000). It was reproduced here from \\cite{baavan:ana} for \\textit{criticism} and \\textit{comment} purposes only, and not for any other purpose, as prescribed by U.S. Code Tittle 17 Chapter 1 para 107 that established the ``fair use'' exception of copyrighted material. }\n\\label{fig:RecTreeBaaVan}\n\\end{figure}\n\n\nThe \\cite{baavan:ana}'s version of the decision tree $ T_n $ (Figure 4.14 page 177 of \\cite{baavan:ana}, shown here on Figure~\\ref{fig:RecTreeBaaVan}) was a re-use of a decision tree for the special case of $ n = 2^{\\lfloor \\lg n \\rfloor} $, with an ambiguous, if at all correct\\footnote{It may be interpreted as to imply that for any level $ k $, all the left-child sizes at level $ k $ are the same and all the right-child sizes at level $ k $ are the same, neither of which is a valid statement.}, comment in the caption that ``[w]henever a node size parameter is odd, the left child size parameter is rounded up\\footnote{Should be: \\textit{down}, according to \\eqref{eq:recmergesort1} page~\\pageref{eq:recmergesort1}.} and the right child size is rounded down\\footnote{Should be: \\textit{up}, according to \\eqref{eq:recmergesort1} page~\\pageref{eq:recmergesort1}.}.'' The proof of the fact, needed for the derivation in \\cite{baavan:ana}, that $ T_n $ had no leaves outside its last two levels (Corollary~\\ref{cor:last2} page~\\pageref{cor:last2}, not needed for the derivation presented in Section~\\ref{sec:easy}) was waved with a claim ``[w]e can\\footnote{This I do not doubt.} determine that [...]''\n\n\\medskip\n\n\nAlthough $ h $ was claimed in \\cite{baavan:ana} to be equal to $ \\lceil \\lg (n+1) \\rceil $~\\footnote{Which claim must have produced an incorrect formula $ n \\lceil \\lg (n+1) \\rceil - 2^{\\lceil \\lg (n+1) \\rceil} + 1 $ for $ W(n) $ and precluded concluding the neat characterization $ W(n) = \\sum_{i=1} ^{n} \\lceil \\lg i \\rceil $.} (and not to the correct $ \\lceil \\lg n \\rceil $ given by the equality~\\eqref{eq:height} page~\\pageref{eq:height}, a fact not needed for the derivation presented in Section~\\ref{sec:easy}), somehow the mostly correct conclusion\\footnote{Almost identical with \\eqref{eq:mersorbounds2} page~\\pageref{eq:mersorbounds2}, except for the constant $ 0.914 $.}\nwas inferred from it, however, with no details offered - except for a mention that a function $ \\alpha $ that satisfies $ h = \\lg n + \\lg \\alpha $, similar to function $ \\varepsilon $ shown on Figure~\\ref{fig:eps} page~\\pageref{fig:eps}, was used. It stated that (Theorem 4.6, page 177, in \\cite{baavan:ana}):\n\\begin{equation} \\label{eq:baavan_bound}\n \\lceil n \\lg n - n + 1 \\rceil \\leq W(n) \\leq \\lceil n \\lg n - 0.914 n \\rceil .\n\\end{equation}\n\t\t\nIt follows from \\eqref{eq:wrongbound} page \\pageref{eq:wrongbound} that the constant $ 0.914 $ that appears in \\eqref{eq:baavan_bound} is incorrect. It was a rounding error\\footnote{Of $ 1 - \\delta $, where $ \\delta $ is given by \\eqref{eq:Erdos} page~\\pageref{eq:Erdos}.}, I suppose, that produced a false upper bound\\footnote{For instance, if $ n = 11 $ then $ {\\tt MergeSort} $ performs 29 comparisons of keys while the value of the upper bound $ \\lceil n \\lg n - .914 n \\rceil $ given in \\cite{baavan:ana}, Theorem 4.6. p. 177, is 28; this is a significant error as 28 or less comps while sorting any 11-element array beats the \\textit{binary insertion sort} that requires $ \\sum_{i=1} ^{11} \\lceil \\lg i \\rceil = 29$ comps in the worst case.}. \n\n\\section{Best-case analysis of $ {\\tt MergeSort} $} \\label{sec:best}\n\nIt turns out that derivation of minimum number $ B(n) $ of comps performed by $ {\\tt MergeSort} $ on an $ n $-element array is a bit more tricky. A formula\n\\begin{equation} \\label{eq:best}\n \\frac{n}{2}(\\lfloor \\lg n \\rfloor + 1) - \\sum _{k=0} ^{\\lfloor \\lg n \\rfloor} 2^k \\mbox{\\textit{Zigzag}}\\,(\\frac{n}{2^{k+1}}),\n\\end{equation}\n where\n \\[ \\mbox{\\textit{Zigzag}} (x) = \\min (x - \\lfloor x \\rfloor, \\lceil x \\rceil - x), \\]\n has been derived and thoroughly analyzed in \\cite{suc:bestmerg}. It has been also demonstrated in \\cite{suc:bestmerg} that there is no closed-form formula for $ B(n) $.\n \n \\medskip\n \n Incidentally, as it was pointed out in \\cite{suc:bestmerg} $, B(n) $ is equal to the sum $ A(n,2) $ of bits in binary representations of all integers $ < n $.\n\n\n\\newpage\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nIt has been nearly a century since the first nuclear reaction was intentionally measured in the laboratory ($^{14}\\rm{N}(\\alpha,p)$ in 1919 by Rutherford~\\cite{Ruth35}) and a half century since the existence of the neutron star crust was proposed by Ruderman~\\cite{Rude68}. These milestones, and the later discovery of the first X-ray burst in the source 3U~1820-30~\\cite{grindlay75}, laid the foundation for a collaborative effort between nuclear physics and astrophysics to advance our understanding of dense matter. \nIt was soon postulated that X-ray bursts on accreting neutron stars arise from the unstable ignition of accreted hydrogen and helium material \\cite{Woos76,joss77}, an idea later supported by numerical models~\\cite{fujimoto81,ayasli1982,Woos04}. Burst models now serve as an important probe of nuclear reactions~\\cite{Cybu16} because nuclear burning in X-ray bursts proceeds through a combination of the triple-$\\alpha$ reaction, rapid proton-capture (rp)-process, and $\\alpha$-capture proton-emission ($\\alpha \\rm{p}$)-process \\cite{Wall81}. Moreover, burst models reveal the thermal structure of the neutron star's outer layers where the bursts originate. \n\nAs accreted material accumulates on the neutron star \nsurface it compresses underlying material to greater depths and higher mass densities. Before the ashes of hydrogen and helium burning join the crust, further nuclear burning takes place. For example, any $^4$He still present will be captured on heavier isotopes. Runaway $^{12}$C+$^{12}$C fusion powering superbursts is the most dramatic instance of burning in the ashes layer \\cite{cumming2001,Stro02}. These superbursts are a thousand times more energetic, a thousand times longer, and occur much deeper than the standard X-ray bursts. These rare events allow one to probe deeper layers of the stellar envelope.\n\nCooling neutron stars provide another avenue to explore the thermal properties of dense matter and the nuclear reactions therein. In transient systems, the neutron star accretes material in episodes lasting days to years (see Reference~\\cite{Dege15} for a summary) and the accretion-induced compression\nof the neutron star crust triggers non-equilibrium nuclear reactions~\\cite{bisnovatyi1979,Sato79} that heat the neutron star crust~\\cite{Haen90,Haen01}. When an accretion episode ends, the neutron star cools on observable timescales~\\cite{ushomirsky2001,rutledge2002} and the cooling trend probes the microphysics of the interior~\\cite{Shte07,Brow09}. In addition to constraints on the thermal properties of the outer layers, the observed cooling reveals the strength and location of nuclear reaction heating, and by extension the details of the nuclear reactions themselves. For example, observations of cooling neutron stars can be explained by models including \n$e^{-}$-capture heating~\\cite{Gupt07} and pycnonuclear fusion heating~\\cite{haensel2008} in the neutron star crust. Furthermore, crust cooling has the potential to reveal strong Urca cooling layers~\\cite{Scha14,Deib16} that are suspected to be present in crusts enriched by X-ray burst ashes~\\cite{Meis17}.\n\nExperimental nuclear data are critical physics input for neutron star models. Dense matter constraints derived from neutron star observations are therefore limited by the precision of nuclear physics input in addition to other systematics. \nThe reduction of nuclear physics uncertainties, \nas well as the development of more sophisticated neutron star models, will improve future observational constraints on dense matter. For example, current investigations of the critical reaction rates in X-ray bursts~\\cite{Cybu16,Scha17}, studies of key properties of nuclei in the accreted crust~\\cite{Deib16,Meis17}, and nuclear reaction network calculations of accreted crust compositions~\\cite{Gupt07,Scha14,Lau18}, all promise to improve the observational constraints on dense matter derived from accreting neutron stars. \n\nWe begin in section~\\ref{section:structure} by briefly outlining the neutron star structure. Sections~\\ref{subsection:pristine} and \\ref{section:accretion} describe the original composition of the neutron star crust and the accretion process which drives the system from equilibrium. In section~\\ref{section:production}, we discuss the nuclear burning that can occur on the surface of accreting neutron stars and the nuclei produced during the different possible burning regimes. In actively accreting neutron stars, the ashes of prior surface nuclear burning are compressed to greater depths by newly accreted material. We discuss in section~\\ref{section:interaction} the nuclear interactions involving the ashes that take place as the ambient mass density increases. In section~\\ref{section:impact}, we investigate the impact of interior nuclear interactions on observable neutron star phenomena. We summarize and discuss prospects for future work in section~\\ref{section:summary}. \n\n\\section{Neutron Star Structure} \\label{section:structure}\nA neutron star is born in the death of a massive star (from 8 and up to 50\\ $\\rm{M}_{\\odot}$ \\cite{Heger2003,Pejcha2015ApJ}, where $M_\\odot=1.99\\times 10^{33}\\,\\mathrm{g}$ is the mass of the Sun), when the lack of sufficient outward pressure from the core to balance the inward compression from gravity leads to a dramatic collapse. The iron-group nuclei in the progenitor core are rapidly transmuted by electron-captures and photodisintegration, transforming the core into a hot and extremely dense neutron-rich sphere~\\cite{Woos05,Burr86}. The collapse halts when the central density approaches the mass density of atomic nuclei, $\\rho_0=2.8\\times 10^{14}$~g~cm$^{-3}$ (baryon density $n_{0}=0.16~\\rm{fm}^{-3}$), and the neutron degeneracy pressure and the repulsion from the strong nuclear force bounce back the infalling matter. This can finally result in a core collapse supernova event, leaving a dense compact remnant -- a neutron star \n-- behind \\cite{Heger2003,Pejcha2015ApJ}. \n\nAs a consequence, neutron stars \ncontain $\\sim1\\textrm{--}2~\\rm{M}_{\\odot}$ in a sphere of radius $R\\sim10\\textrm{--}15 \\, \\mathrm{km}$~\\cite{steiner2010,steiner13} and have average mass densities of several $\\rho_0$. Almost all (99 per cent) of the mass is concentrated in the bulky core composed of uniform nuclear (or possibly more exotic) matter. The equation of state and even the composition of the neutron star core are unknown and their elucidation are some of the fundamental problems of neutron star astrophysics \\cite{HPY2007}. \n\nAt mass densities $\\rho\\lesssim 0.5\\, \\rho_0$ the uniform nuclear matter is unstable and arranges into \nnuclear clusters, which at the low densities are familiar albeit neutron-rich nuclei \\cite{Peth95}. These outer layers of the neutron star, though comprising only roughly one percent of the \nmass, provide the settings for all of the astronomical observables used to characterize accreting neutron stars,\nand are the primary subjects of the present review. \n\nMuch like a terrestrial planet, the neutron star outer layers consist, from the outside-in, of an atmosphere and liquid ocean (collectively referred to as the envelope), solid crust (outer and inner), and the mantle, roughly 1~km thick in total as schematically shown in figure~\\ref{figure:schematic}. \nIn contrast to planetary envelopes, matter in these layers is \nunder extreme conditions, for example, the enormous gravity $g=(GM\/R^2)(1+z)$, where $G$ is the gravitational constant, $M$ is the neutron star mass, $(1+z)\\equiv \\left(1-2GM\/(Rc^2)\\right)^{-1\/2}$ is the gravitational redshift at the neutron star surface, and $c$ is the speed of light. For a canonical neutron star of $M=1.4 M_\\odot$ and $R=10$~km, $g=2.44\\times 10^{14}$~cm~s$^{-2}$ and $1+z= 1.31$. Large gravity ensures that General Relativity effects are important when neutron star phenomena are studied. Since the outer layers are thin, the gravity and the redshift can be set constant and equal to the surface values. \n\nThroughout this paper we will use various physical quantities to specify the current position in the global structure of the envelope depending on the aspects of the problem discussed. The equation of state and composition of dense matter mainly depend on the mass density $\\rho$ or baryon number density $n_B$. However, these quantities may be discontinuous at composition changes (see below) and thus may not be the appropriate variables to follow the crustal structure. Instead, the pressure $P$ or the column density $y\\equiv P\/g$ are continuous and increase monotonically, and are convenient measures of the depth. The column density is especially useful when accretion phenomena are studied, since the total baryon mass above a layer with $y=\\mathrm{constant}$ is $\\Delta M_B(y)=4\\pi R^2 y$. Note that the gravitational mass of the same layer is smaller, $\\Delta M = \\Delta M_B\/(1+z)$, because of the gravitational binding energy.\nOne also distinguishes between the radial coordinate $r$ (so that the surface area is $4\\pi r^2$) and the proper depth measured by a local observer in the envelope $\\zeta=\\int_r^R (1+z(r))\\mathrm{d}r\\approx (1+z)(R-r)$. \n\n\n\\begin{figure} [ht]\n\\centering\n\\includegraphics[scale=0.7]{ns_structure}\n\\caption{Schematic of the outer layers of a neutron star.}\n\\label{figure:schematic}\n\\end{figure}\n\n\nThe outer neutron star envelope -- atmosphere and ocean -- contains a non-ideal plasma of electrons and ions, and except in the outermost layers, the plasma is fully ionized. There is usually no phase transition between the gaseous and liquid state, except for some special cases \\cite{Potekhin2014PhyU}. The atmosphere is distinguished as the region where the neutron star surface emission is formed \nand extends up to a density of \n$\\sim 10^{-4}-10^{6}~\\rm{g}~\\rm{cm}^{-3}$ \\cite{Potekhin2010PhyU}, depending on the physical conditions. Both the atmosphere and ocean may host convective processes, driven by gradients of temperature and chemical potentials~\\cite{Malone2011,Medin2011}. \nIn most of the envelope, electrons are strongly degenerate, relativistic (at $\\rho\\gg 10^6$~g~cm$^{-3}$), and provide the main contribution to the pressure. For ultrarelativistic electrons, their contribution to the pressure is $P_e=\\mu_e^4\/(12\\pi^2 \\hbar^3 c^3)$, where $\\mu_e$ is the electron chemical potential and $\\hbar$ is the reduced Planck constant. Thus $\\mu_e$ is directly related to the column depth $y$ (such that $\\mu_e\\propto y^{1\/4}$) and provides another useful measure of the depth. $\\mu_e$ is a particularly convenient coordinate to use when the crust composition \nand nuclear reactions are studied (sections~\\ref{subsection:pristine}, \\ref {section:interaction}). \nFor convenience, we give the relation between various measures of depth in the neutron star outer envelope and crust, where the dominant pressure is from degenerate electrons:\n\\begin{eqnarray}\ny&\\approx& 7.2\\times 10^9\\, \\mathrm{g}\\,\\mathrm{cm}^{-2} \\left(\\frac{\\mu_e} {1~\\mathrm{MeV}}\\right)^4\\frac{2.44\\times 10^{14}~\\mathrm{cm}~\\mathrm{s}^{-2}}{g},\\label{eq:y-mu}\\\\\n\\rho&\\approx&7.2\\times 10^6\\, \\mathrm{g}\\,\\mathrm{cm}^{-3} \\left(\\frac{\\mu_e} {1~\\mathrm{MeV}}\\right)^3\\frac{1}{Y_e},\\label{eq:rho-mu}\\\\\n\\zeta&\\approx&40~\\mathrm{m}\\, \\frac{\\mu_e} {1~\\mathrm{MeV}}\\frac{2.44\\times 10^{14}~\\mathrm{cm}~\\mathrm{s}^{-2}}{g} Y_e \\label{eq:z-mu},\n\\end{eqnarray}\nwhere $Y_e$ is the electron fraction, \nand in equation~(\\ref{eq:z-mu}) it is a depth-averaged quantity.\n\n\nThe properties of ions are determined by the ratio of their Coulomb energy to the thermal energy. For a plasma containing ions of one species with charge number $Z$, the plasma coupling parameter $\\Gamma=Z^2 e^2\/ (a k_\\mathrm{B} T)$, where $a=[3\/(4\\pi n_i)]^{1\/3}$ is the ion-sphere (Wigner-Seitz) radius, $n_i$ is the ion number density, $e$ is the elementary electric charge, and $k_\\mathrm{B}$ is the Boltzmann constant. When $\\Gamma\\gtrsim 1$, ion correlations are important so they form a non-ideal Coulomb liquid.\nIn the region where electrons are ultrarelativistic,\n\\begin{equation}\n\\Gamma\\approx 105\\, \\left(\\frac{Z}{26}\\right)^{5\/3} \\left(\\frac{T}{10^8~{\\rm K}}\\right)^{-1} \\frac{\\mu_e}{1~\\mathrm{MeV}}\\approx 361 \\left(\\frac{Z}{26}\\right)^{5\/3} \\left(\\frac{T}{10^8~{\\rm K}}\\right)^{-1} y_{12}^{1\/4},\n\\label{eq:Gamma}\n\\end{equation}\nwhere $y_{12}$ is the column depth measured in units of $10^{12}$~g~cm$^{-2}$ and the canonical value of $g$ is used. When there are multiple ion species $Z_j$, which is the case for the accreted crust, partial $\\Gamma_j$ are introduced, and an average $\\langle\\Gamma_j\\rangle$ can be used to describe the state of the whole mixture. An important \nquantity is the so-called impurity parameter which describes variance\nof the charge mixture around the mean charge $\\langle Z\\rangle$. It is defined as\n\\begin{equation}\nQ_{\\rm imp} \\equiv \\frac{1}{n_{\\rm ion}} \\sum_j n_j (Z_j - \\langle Z \\rangle)^2 \\ ,\n\\label{equation:Qimp}\n\\end{equation}\nwhere $n_{\\rm ion}$ is the total local number density of ions, and $n_j$ is the local number density of nuclei species $j$.\nThe impurity parameter is important when the transport properties such as thermal conductivity are discussed (see section~\\ref{section:impact}).\n\nAt a depth of several tens of meters, the ocean solidifies~\\cite{Rude68,Medi10}. The pure Coulomb plasma solidifies at about $\\Gamma\\approx175$ \\cite{Potekhin2000} \nand forms a perfect crystal with a body-centered cubic lattice. As follows from equation~(\\ref{eq:Gamma}), for a typical temperature $T=10^8$~K solidification occurs at $\\mu_e\\approx1.7$~MeV ($y\\approx6\\times 10^{10}$~g~cm$^{-2}$, $\\zeta\\approx 140$~m) for $Z=26$ (iron). This point corresponds to mass density $\\rho\\approx 7\\times 10^7$~g~cm$^{-3}$ and shifts deeper inside the neutron star for decreasing $Z$.\n\nThe solidification of the multi-component mixture is thought to occur at a similar order of magnitude of the average $\\langle \\Gamma \\rangle$ \\cite{Mcki16}. However, the exact structure of the multicomponent solid crust and solidification layer at the ocean-crust boundary are not well understood. There is strong theoretical and observational evidence that the solid mixture also arranges in a regular but impure crystalline structure (see sections \\ref{subsection:impact_inner} and \\ref{sec:cooling_transients}). \n\nA few-hundred meters deeper, the rising electron chemical potential results in $\\beta$-equilibrium nuclei closer to the neutron dripline, until ultimately the neutron separation energy for nuclei in the plasma becomes negative.\nAt this point it is energetically favorable for neutrons to drip out of nuclei and form a neutron gas~\\cite{Chamel2008LRR,HPY2007} \nmarking the upper boundary of the inner crust. This neutron drip occurs at densities $\\rho_{\\rm{nd}}\\sim 4-10 \\times 10^{11}$~g~cm$^{-3}$ \ndepending on the composition and the theoretical model used. For the accreted crust, generally, $\\rho_{\\rm{nd}}$ is on the high end of the aforementioned range.\nThus in the inner crust an ion lattice coexists with a gas of free neutrons which soon becomes degenerate and starts giving the dominant contribution to the pressure. At this point, equations~(\\ref{eq:y-mu})--(\\ref{eq:z-mu}) are now inapplicable.\n\nDue to the attractive component of the strong interaction, the degenerate neutron gas is subject to a pairing instability. It is believed that in the crust paring occurs in the singlet $^1S_0$ channel and the critical temperature $T_\\mathrm{cn}$ is density-dependent with the maximal value of order of $\\sim 5\\times 10^9$~K \\cite{Lombardo2001LNP}.\nThus a large portion of the crustal neutrons is thought to be superfluid, although the precise form of the $T_\\mathrm{cn}(\\rho)$ profile is very model-dependent. At large densities the nuclear interaction in the $^1S_0$ channel becomes repulsive and superfluidity ceases at the lower bound of the inner crust. However, in some models, $^1S_0$ superfluidity penetrates the core. We \ndiscuss the observable impact of neutron superfluidity in section~\\ref{sec:quasipers}.\n\nWhen the density increases further in the inner crust, groupings\nof protons and neutrons are no longer accurately considered as nuclei \nand can rather be called nuclear clusters. Eventually, the clusters become so large and closely spaced that the competition between the nuclear surface energy and Coulomb energy distorts the nuclei in into complex shapes called nuclear pasta \\cite{ravenhall1983}.\nMolecular dynamics simulations demonstrate that nuclear pasta can begin to appear near mass densities of $\\rho \\gtrsim 8\\times 10^{13} \\ \\mathrm{g\\,cm^{-3}}$ \\cite{hashimoto1984}.\nAs a consequence, this `mantle' layer can contain half the mass of the whole crust. \nNote that the pasta layer does not exist for all equations of state \\cite{Douchin2000PhLB,Oyamatsu2007PhRvC}. Finally, when proton clustering ceases at $\\rho\\gtrsim 0.5\\rho_0$, the transition to a liquid core occurs.\n\nThe structure and properties of the crust as described above can be modified by the presence of fast rotation or high magnetic fields. For instance, rotation deforms the crust, making it non-spherical. The presence of a strong magnetic field in the outer layers can result in the absence of the ocean and a first order phase transition between the thin gaseous atmosphere and the condensed neutron star surface. These effects are outside the scope of this paper (see, e.g., References~\\cite{Chamel2008LRR,Potekhin2014PhyU}). \n\n\n\n\nFor the upper reaches of the inner crust and above, these regions are composed of nuclei which can be made in current and near-future nuclear physics laboratories. As such, accreting neutron stars\nprovide a medium to explore many interesting multi-physics questions through astronomical observations and theory, as well as nuclear physics theory and experiment. The following sections will explore the dominant nuclear physics processes occurring in accreting neutron stars' \nouter layers and the astronomical observables these processes impact. But first, a brief discussion of the crust composition in the absence of accretion is beneficial in order to provide context for the dramatic transformation accretion induces.\n\n \\section{Pristine Crust Composition}\n \\label{subsection:pristine}\n \nThe extreme temperatures ($\\gtrsim 10^{11}$~K) achieved during the supernova collapse drive \nmatter in the unaccreted crust to its ground state in terms of nuclear and $\\beta$ equilibrium, as determined by the local environment conditions and nuclear masses~\\cite{Beth70,Buch71,Baym71a,Baym71b}. \nThis means that the unaccreted outer crust is stratified into several layers, each of which is comprised of a single species of nuclide. \nHere we discuss the cold-catalyzed crust, where matter is in its absolute ground state~\\cite{Baym71a}. \nIn principle, the composition may freeze-in at a higher temperature, modifying the equilibrium abundance distribution due to thermal fluctuations (see, e.g., Reference~\\cite{Gori11}).\n\nIn the outer crust, it is energetically favorable for nuclei to arrange themselves in a Coulomb lattice within an electron Fermi gas~\\cite{Rude68}. As such, beyond minimizing the usual liquid drop terms describing the nuclear mass in vacuum, the additional energy contributions from the lattice and the electron gas must be considered~\\cite{Buch71,Baym71a}. The dominant liquid drop terms include the volume energy, which can be modified due to compression by the dense environment; the surface energy, which generally favors large-$A$ nuclides;\nthe Coulomb energy, which favors low-$A$ nuclides; and the symmetry energy, which favors nuclides with neutron number $N=Z$. The lattice energy favors the existence of large-$A$ nuclei, providing a Coulomb energy of opposite sign to its liquid-drop partner. The resulting total energy per nucleon is\n$E_{\\mathrm{total}}\/A = E_{\\mathrm{nuclear}}\/A + E_{e^{-}\\mathrm{-gas}}\/A + E_{\\mathrm{lattice}}\/A$,\nwhose minimum defines the equilibrium nucleus. In practice, the equilibrium composition is solved for at a given pressure, which is a proper continuous variable. Therefore, it is generally the Gibbs free energy per nucleon that is minimized~\\cite{Baym71b,Haen01,Roca08}.\n \n \\begin{figure} [h]\n\\centering\n\\includegraphics[scale=0.5]{EquilibriumNucleusMuE.pdf}\n\\caption{Equilibrium composition for a cold-catalyzed neutron star outer crust calculated using a liquid drop model for nuclear masses (thick lines). To demonstrate the impact of shell effects, the equilibrium composition obtained using experimental masses when available and the BSk8 Skyrme model otherwise, as reported by Reference~\\cite{Rust06}, is also shown (thin lines). See figure~\\ref{figure:ECcrustcomposition} to compare to the accreted crust composition.}\n\\label{figure:pristinecomp}\n\\end{figure}\n \nAt relatively low density near the neutron star \nsurface, the nuclear energy term will dominate and the nucleus with the minimum mass per nucleon, $^{56}\\rm{Fe}$, is optimal. At deeper depths, the increasing electron chemical potential $\\mu_e$ creates an energetic incentive to lower the electron fraction, $Y_{e}=Z\/A$, favoring more neutron-rich nuclides, in competition with the nuclear symmetry energy which favors $Y_{e}=1\/2$. A decent estimate of the equilibrium nuclide at a given depth can be obtained by employing the semi-empirical mass formula for the nuclear binding energy in conjunction with the $e^{-}$-gas chemical potential and lattice energy, resulting in the total energy per nucleon\n\\begin{eqnarray}\n\\label{eqn:CrustE}\nE_{\\rm{total}}\\left(A,Z,\\mu_{e}\\right)\/A= &m_\\mathrm{p}c^{2}Y_{e}+m_\\mathrm{n}c^{2}\\left(1-Y_{e}\\right)-a_{v}+\\frac{a_{s}}{A^{1\/3}}+a_{c}A^{2\/3}Y_{e}^{2}\\\\\n&+a_{a}\\left(1-2Y_{e}\\right)^{2}+\\frac{3}{4}\nY_{e}\n\\mu_{e}-C_{\\ell}A^{2\/3}\nY_{e}^{5\/3}\\mu_{e},\\nonumber\n\\end{eqnarray}\nwhere $m_\\mathrm{p}$ and $m_\\mathrm{n}$ are the proton and neutron masses, respectively; $a_{v}$, $a_{s}$, $a_{c}$, and $a_{a}$ are the volume, surface, Coulomb, and asymmetry coefficients of the semi-empirical mass formula; \nand $C_{\\ell}$ is a (relatively small) constant scale factor for the Coulomb lattice energy~\\cite{Roca08}. This assumes an ultrarelativistic form for the electron Fermi gas contribution. \nThe equilibrium nuclide is found by minimizing equation~(\\ref{eqn:CrustE}) with respect to $Y_{e}$\nand separately with respect to $A^{1\/3}$, which gives $Z$ and $A$ at the minimum.\n \nTo demonstrate how liquid-drop nuclear binding, electron gas, and Coulomb lattice energy contributions impact the equilibrium nucleus, an approach similar to Reference~\\cite{Roca08} is followed here. We fit the nuclear binding contribution from equation~(\\ref{eqn:CrustE}) to the experimental masses of the 2012 Atomic Mass Evaluation~\\cite{Audi12}, though including an additional term for the nuclear pairing energy that goes as $i{a_{p}}{A^{-3\/2}}$~\\cite{Mart09}, where $i=+1,-1,$ or $0$ to enhance, penalize, or leave alone nuclear binding for even-even, odd-odd, and even-odd nuclides, respectively. For such a fit, $a_{v}$=15.302, $a_{s}$=16.518, $a_{c}$=0.687, $a_{a}$=22.243, and $a_{p}$=5.898, each with units of MeV. Using these parameters and $C_{\\ell}$=3.40665$\\times10^{-3}$ (for a body-centered cubic lattice, e.g. Reference~\\cite{Roca08}), simultaneous minimization of $A^{1\/3}$ and $Y_{e}$ results in the equilibrium nucleus trend shown with thick lines in figure~\\ref{figure:pristinecomp}.\n\nThe trend in composition for a liquid drop model is modified by the presence of nuclear shell closures present in more realistic nuclear mass models, as shown with thin lines in figure~\\ref{figure:pristinecomp}. \nThough models disagree on the detailed composition depending on which nuclear mass model is employed, generally, the equilibrium nuclide is near the $Z=28$ shell closure at shallow depths \n($n_B\\sim$$10^{-7}$~fm$^{-3}$, i.e. $\\sim$50 m), transitioning to $N=50$ nuclides ($n_B\\sim$$10^{-5}$~fm$^{-3}$, i.e. $\\sim$150 m), and finally resulting in $N=82$ nuclides prior to neutron-drip ($n_B\\sim$$10^{-4}$~fm$^{-3}$, i.e. $\\sim$300 m)~\\cite{Baym71b,Roca08,Pear11,Rust06}.\nNuclear mass measurements at radioactive ion beam facilities are working their way toward more exotic nuclides to resolve existing discrepancies in theoretical predictions, where experimental constraints are presently available down to a depth of $\\sim$200~m~\\cite{Wolf13}. \nBeyond this, theoretical estimates are necessary~\\cite{Utam17}.\n\\begin{figure}[ht]\n\\begin{center}\n\\begin{minipage}{0.4\\textwidth}\n\\includegraphics[width=\\textwidth]{inner.pdf}\n\\end{minipage}\n\\hspace{0.05\\textwidth}\n\\begin{minipage}{0.5\\textwidth}\n\\includegraphics[width=\\textwidth]{nuc_prof.pdf}\n\\end{minipage}\n\\end{center}\n\\caption{{\\it (a)}: Composition in the inner crust in the BSk21 model after Reference \\cite{Pote13}. Solid, dashed, and dash-dotted lines show charge number $Z$, mass number in the cluster $A$, and total mass number in the Wigner-Seitz cell $A'$, respectively. {\\it Right:} Neutron and proton number density distributions in the Wigner-Seitz cell. {\\it (b)}: nucleon density profiles near the top of the inner crust, at $\\mu_\\mathrm{e}=30$~MeV. Relevant nuclear quantities are given in the plot. {\\it (c)}: the same for $\\mu_\\mathrm{e}=70$~MeV, close to the nuclear pasta onset. }\\label{fig:inner}\n\\end{figure}\n\nBy contrast, the pristine inner crust composition is far more uncertain. The presence of the neutron gas makes describing the composition in terms of nuclei less adequate and, instead, it is more precise to use the concept of proton clusters~\\cite{Chamel2008LRR}. Example equilibrium ``nuclei\" are $^{1269}\\rm{Zr}$ and $^{633}\\rm{Ca}$~\\cite{Bald06}, where the total mass number per cluster is used to mark the isotopes. In this respect, one usually wants to distinguish between the total number of nucleons $A'$ per cluster and the number of nucleons inside the cluster $A$. Such a distinction is not a well-defined procedure and one usually takes the nucleon density far from the cluster center as the `gas' phase density.\nOne approach to describe this region is the classical approach employing a compressible liquid drop model~\\cite{Baym71a}. Conceptually, this technique is similar to the method described above for the outer crust, however, extra terms are included in the total energy to account for contributions from the neutron gas and modifications to the liquid-drop semi-empirical mass formula to account for nuclear compressibility. Instead, semi-classical approaches describe the inner crust using energy density functionals, e.g. via the Thomas-Fermi approximation~\\cite{Onsi08}. The most sophisticated and computationally-intensive approach is a quantum mechanical solution of the wave function for the cluster existing at each depth~\\cite{Bald06}. Each of the three approaches feature compositions dominated by proton shells $Z=20, 40,$ and $50$, though disagreement remains as to which shell the composition converges (see tables 3 and 4 of Reference~\\cite{Chamel2008LRR}). The example of the composition resulting from calculations in the BSk21 model \\cite{Pote13} is shown in the left panel of figure~\\ref{fig:inner} where we plot it until the density where the possible pasta phase would appear. In this model, the proton shell $Z=40$ is found everywhere in the inner crust. \nThe concept of a ``cluster\" as opposed to a nucleus is illustrated in the right panels in figure~\\ref{fig:inner} where we plot the nucleon density profiles in the same model for $\\mu_e=30$~MeV (panel (b)) and near the bottom of the crust $\\mu_e=70$~MeV (panel (c)). In the second case there is a significant free neutron fraction \n$Y_\\mathrm{n}\\approx 0.8$ and a broad neutron distribution is clearly seen. We refer the reader to References~\\cite{Chamel2008LRR,HPY2007} and more recent works \\cite{Sharma2015A&A,Pote13,Lim2017PhRvC,Pastore2017JPhG} for a detailed discussion of the inner crust composition in the absence of accretion. \n\n\n\n\\section{Accretion onto the Neutron Star Surface}\\label{section:accretion}\n\nThe neutron star \ncrust undergoes a significant transformation from the pristine state due to accretion.\nNeutron stars \nin binary systems with a lower-mass companion (low-mass X-ray binaries) can accrete material onto their surfaces through Roche-lobe overflow. Matter from the outer layers of the companion is transferred to an accretion disk surrounding the neutron star.\nThere it loses angular momentum and subsequently falls onto the neutron star. \nAccretion can proceed continuously and we refer to the system as a ``persistent X-ray source'', or can proceed episodically in ``transient'' sources. A transient outburst may have any duration from weeks or months to years (section~\\ref{sec:cooling_transients}). \nThe composition of the accreted material is that of the outer layers of the companion star, which is often assumed to be similar to that of the Sun with mass fractions of $0.739$\\,$^1$H, $0.247$\\,$^4$He, and the rest heavier isotopes \\cite{Lodders2010}. In Ultra-Compact X-ray Binaries~\\cite{intZand2007} with a binary period shorter than $80$ minutes, the companion star lacks a hydrogen-rich envelope, and the accretion composition is mostly $^4$He. The signatures of hydrogen and helium have been detected in the optical spectra of low-mass X-ray binaries, but it is challenging to determine the mass fractions precisely~\\cite{Nele06}.\n\nWhen the companion mass is higher than the mass of the compact object, the accretion via Roche-lobe overflow is not possible. However, the neutron star in the binary system with massive ($>10\\,M_\\odot$) early-type companion (high-mass X-ray binaries) can accrete matter from the stellar wind or Be-star decretion disk. Neutron stars in high-mass X-ray binaries usually have relatively large magnetic fields ($\\sim 10^{12}$~G) and show regular X-ray pulsations, while the magnetic fields of neutron stars in low-mass X-ray binaries are low ($10^{8}-10^{9}$~G). We do not consider high-mass X-ray binaries below and focus on low-mass X-ray binary sources, since the latter provide the main observational manifestations of nuclear processes during accretion. \n\n\nWhen an accreted nucleon finally settles at the neutron star surface, it releases $z\/(1+z)\\times m_u c^2\\approx 220$~MeV of the gravitational energy (as measured in a distant observer's frame), where $m_u$ is the nucleon mass unit \\cite{ShaprioTeukolsky1986}. Part of this energy is radiated from the accretion disk and the remaining part is assumed to be radiated away from the surface. Therefore the estimate for the total accretion luminosity (mostly in X-rays), as seen by the distant observer, is\n\\begin{equation}\nL_A^\\infty=\\frac{z}{1+z}\\dot{M} c^2,\n\\end{equation}\nwhere $\\dot{M}$ is the mass accretion rate as seen at infinity (the mass accretion rate as seen by the observer on the neutron star surface is $\\dot{M}(1+z)$). Thus a measurement of the persistent accretion flux in principle allows one to estimate $\\dot{M}$. This is usually done by assuming spherically-symmetric accretion and introducing the appropriate bolometric correction factor to convert from the observed X-ray luminosity $L_X^\\infty$ to bolometric luminosity $L_A^\\infty$. \nIt is customary to quantify $\\dot{M}$ in fractions of the Eddington mass accretion rate $\\dot{M}_\\mathrm{Edd}$ at which the radiation pressure of the emitted X-ray photons balances the gravitational pull on the infalling material. The critical Eddington luminosity is\n\\begin{equation}\\label{eq:Ledd}\nL^\\infty_\\mathrm{Edd}=\\frac{4\\pi G M c}{\\kappa_\\mathrm{es}}\/(1+z_\\mathrm{ph}),\n\\end{equation}\nwhere $\\kappa_{\\rm es}=0.2\\,(1+X)$~g$^{-1}$~cm$^2$, with $X$ being the hydrogen mass fraction in the infalling material, is the electron scattering opacity and $(1+z_\\mathrm{ph})$ is the gravitational redshift of the emission region (photosphere); this region \ncan be high above the neutron star surface. \nUsually the Eddington luminosity without the latter factor is used, which is the truly maximal limiting luminosity a distant observer can see \\cite{ShaprioTeukolsky1986}. Alternatively, one sometimes sets $z_\\mathrm{ph}=z$ in equation~(\\ref{eq:Ledd}). For a 10~km Newtonian neutron star (neglecting all General Relativity corrections) and a solar composition,\none obtains $\\dot{M}_\\mathrm{Edd}=1.72\\times 10^{-8}\\,M_\\odot$~yr$^{-1}$. \n\nDuring the time interval $\\Delta t$, a neutron star \naccretes $\\Delta M_B = \\dot{M}\\Delta t$ baryon mass. According to section~\\ref{section:structure}, the base of this slab will be at the column density $y=\\dot{M}\\Delta t\/(4\\pi R^2)$. When accretion continues, this layer is compressed to higher $y$. In this sense, by following the crust structure in section~\\ref{section:structure} \nwith increasing column depth we at the same time are following the journey\nof a given accreted fluid element deep through the crust, where it undergoes various nuclear transformations, as discussed in the following sections, to finally merge with the neutron star core. It is clear {\\it a fortiori} that the composition of the accreted layers shall be very different from the pristine crust composition discussed in section~\\ref{subsection:pristine}. A neutron star accreting steadily at $0.01\\dot{M}_\\mathrm{Edd}$, will replace its entire crust with the accreted crust in $10^8$~yr.\n\n\\section{Production of Nuclei on Neutron Star Surfaces} \\label{section:production}\n\nA variety of processes are responsible for nucleosynthesis on and near the surface of accreting neutron stars. \nThough the products of these burning processes, the ashes, are likely unable to escape the neutron star\ngravitational potential and contribute to the cosmic abundances~\\cite{Wall81}, they \nhave a significant impact on the accreted crust thermal and compositional structure. Here we discuss the primary mechanisms through which nuclei are produced in and on accreting neutron stars. \n\n\n\\begin{figure} [ht]\n\\centering\n\\includegraphics[scale=0.8]{AshComparison2.pdf}\n\\caption{Ash abundances predicted for stable surface burning at near-Eddington acretion rate \n(black-lined unfilled histogram), superbursts (gray-filled histogram), and Type-I X-ray bursts for a nominal reaction rate library (red-filled translucent histogram) and, to highlight the impact of nuclear physics uncertainties, for the $^{59}\\rm{Cu}(p,\\gamma)$ rate reduced by a factor of 100 (red-lined unfilled histogram). Calculation details are described in Reference~\\cite{Meis17}. General ash properties are listed in Table~\\ref{table:AvgZAQTable}.\\label{figure:ashes}}\n\\end{figure}\n \n \\subsection{Ash Production from Type I X-ray Bursts and Other Hydrogen\/Helium Burning Regimes}\n The most prominent departure from the single-species-per-depth composition of pristine crusts is realized for accreting neutron stars \nexhibiting Type I X-ray bursts (see figure~\\ref{figure:ashes})~\\cite{Meis17}. Here we describe the key features of X-ray burst nucleosynthesis, focusing on helium-ignited hydrogen and helium burning bursts, as this case requires the most extensive nuclear physics input as compared to other burning regimes. For completeness, we first summarize the key observational and theoretical aspects of X-ray bursts before discussing the nuclear physics sensitivities in detail (see also the recent review \\cite{GallowayKeek2017}).\n \n\n\\subsubsection{Observation} \n\\label{sec:burst_observation}\n \\hfill\\\\\n \nOver $100$ X-ray bursting systems are currently known in our Galaxy, including both persistent and transient sources\\footnote{\\url{https:\/\/personal.sron.nl\/~jeanz\/bursterlist.html}}; figure~\\ref{figure:LCobs} shows examples of bursting systems. For many systems the mass accretion rate is observed to be time variable, and the bursting behavior changes accordingly, such that for a specific source different kinds of bursts may be observed over time. X-ray bursts were first reported in 1974 from observations with the Astronomical Netherlands Satellite (ANS; \\cite{grindlay75}). At present several thousands of bursts have been observed with a wide variety of durations, recurrence times, and energetics \\cite{Galloway2008catalog}. Notable instruments include the {\\it European X-ray Observatory Satellite} (EXOSAT; active from 1983 to 1986) and the {\\it BeppoSAX Wide-Field Cameras} (WFCs; active from 1996 to 2002),\nwhich observed large burst samples from a range of sources, and demonstrated how the bursting behavior changes as a function of the accretion rate \\cite{Paradijs1988,Cornelisse2003}. The majority of bursts are observed at accretion rates $\\dot{M}\\simeq 0.01-1.00\\ \\dot{M}_\\mathrm{Edd}$, with bursts lasting tens-to-hundreds of seconds and recurring every few hours-to-days. Typically, the burst rate increases with $\\dot{M}$: bursts repeat faster when their fuel is replenished more rapidly. Around $0.1-0.3\\ \\dot{M}_\\mathrm{Edd}$, however, the burst rate reaches a maximum, and decreases at higher $\\dot{M}$. For most systems, no bursts are observed near $\\dot{M}_\\mathrm{Edd}$. In that case, it is thought that the accreted material burns \nin a stable way, \nunlike a thermonuclear runaway that would produce a burst.\nNote that we do not consider Type-II X-ray bursts, which generally have much shorter recurrence times (tens of seconds to minutes)\nand $\\sim$100 times greater time-averaged luminosities than Type-I bursts~\\cite{Lewi93}. While Type-I bursts are attributed to nuclear burning (see section~\\ref{sec:burst_theory}), Type-II bursts are attributed to impulses of accretion onto the neutron star~\\cite{Hoffman1978}.\n\n\\begin{figure} [h]\n\\centering\n\\includegraphics[scale=0.8]{LCobs.pdf}\n\\caption{Bolometric luminosity light curves observed with the RXTE\/PCA\nfor sources 4U~1820$-$303, SAX~J1808.4$-$3658, GS~1826$-$24 (panel (a)), and 4U~1636$-$536 (panel (b)), reproduced from Reference~\\cite{Gall17}. These bursts are thought to be pure-He Type-I, He-rich Type-I, H-rich Type-I, and a superburst, respectively. \nNote that the precursor burst for 4U~1636$-$536~\\cite{Stro02} is not visible in panel (b) due to the coarse time binning.\nThe gap in the data for panel (b) is due to occultation by the Earth. Data are courtesy of the Multi-Instrument Burst Archive (MINBAR): \\url{https:\/\/burst.sci.monash.edu\/minbar\/}.\n\\label{figure:LCobs}}\n\\end{figure}\n\nThe most detailed burst observations have been performed with the Proportional Counter Array (PCA) on-board the {\\it Rossi X-ray Timing Explorer} (RXTE; active from 1995 to 2012) \\cite{Strohmayer2006,Galloway2008catalog}. With its large collecting area, RXTE detected detailed burst light curves that can be compared to nucleosynthesis models \\cite{Hege07}. For example, the contribution of the rp-process to the burst tail has been quantified for a large sample \\cite{Zand17}, and the burst rise is significantly shaped by flame spreading across the neutron star surface \\cite{Maurer2008}. Recently, a \nreference sample of RXTE bursts has been created for different fuel compositions (mixed hydrogen and helium, pure helium, and carbon) as a benchmark for nucleosynthesis calculations \\cite{Gall17}.\n\nIn many astrophysical sites, for example novae and supernovae, the ash composition can be inferred from spectral lines or edges from the debris of the explosion. Unfortunately this is not the case for most X-ray bursts. The strong surface gravity binds the ashes to the neutron star, \nand they are quickly covered by newly accreted material. An exception is a small group of bursts with strong photospheric radius expansion.\nAt the peak of these powerful bursts, the luminosity reaches the Eddington limit. Potentially, the upper layers of the star are blown off, and the burst ashes are exposed for a few seconds. Observations of such bursts hint at the presence of spectral edges, but the data quality has been insufficient to identify the ions involved \\cite{Zand2013,Barr15,Kaja17}. Another possibility is that the ashes could be ejected in the photospheric radius expansion wind~\\cite{Wein06}.\n\nRXTE has ceased operations, but at present, similar high quality burst observations are performed with the {\\it Nuclear Spectroscopic Telescope Array} (NuSTAR \\cite{Harrison2013}), ASTROSAT \\cite{Verdhan2017} and the {\\it Neutron Star Interior Composition Explorer} (NICER \\cite{Gendreau2012NICER}).\nThe latter is sensitive in a lower energy band, which will allow for a more accurate separation of the X-ray flux from nuclear burning and from the surrounding environment.\n\n \\subsubsection{Theory} \n \\label{sec:burst_theory}\n \\hfill\\\\\nThe first X-ray burst observations occurred just after the publication of a study that predicted shell flashes from neutron stars\n\\cite{Hansen1975}, such that the thermonuclear nature was quickly established \\cite{Woos76,Maraschi1977,Lamb1978}. Simple one-zone ignition models were used to map out the different burning regimes as a function of $\\dot{M}$ \\cite{fujimoto81} (see also \\cite{Fushiki1987ApJ,Nara03}), and one-dimensional multi-zone models with increasingly sophisticated nuclear networks have been used to simulate the nuclear burning during bursts in great detail (e.g., \\cite{joss77,Wall81,Taam1996,Woos04,Fisker2008,Jose10}). Many of the observed features of bursts and other burning modes are reproduced, including a burst rate that increases with $\\dot{M}$ and stable burning at high $\\dot{M}$. For those systems that accrete a mixture containing both hydrogen and helium, the burning of both is investigated separately. Unstable hydrogen burning via the CNO cycle produces bursts at ignition temperatures of $T\\lesssim0.7\\times 10^8\\ \\mathrm{K}$. These conditions are reached at the lowest accretion rates, $\\dot{M}\\lesssim 0.004\\dot{M}_\\mathrm{Edd}$. If hydrogen burning ignites helium as well, a mixed H\/He burst results. However, for $\\dot{M}\\gtrsim0.001\\dot{M}_\\mathrm{Edd}$ this is not the case: helium continues to pile up during several (relatively weak) hydrogen bursts until a powerful helium burst ignites. The weak hydrogen bursts have not been identified in observations. \nAt these low accretion rates, there is sufficient time for gravitational settling to separate the ions in the accreted composition, which may have an effect on the boundaries of these regimes \\cite{Peng2007}.\n\nFor $\\dot{M}\\gtrsim0.004\\dot{M}_\\mathrm{Edd}$, hydrogen burns to helium via the ``hot'' or ``$\\beta$-limited'' CNO cycle, where the burning time scale is set by the combined half lives of $^{14}$O and $^{15}$O \\cite{Wall81}. This burning rate is independent of temperature, hence hydrogen burning cannot run away, and instead proceeds stably. Bursts in this regime are, therefore, produced by unstable helium burning, starting with runaway $3\\alpha$ burning. For $\\dot{M}\\lesssim0.1\\dot{M}_\\mathrm{Edd}$ there is sufficient time to burn all hydrogen prior to the helium runaway. The burst then ignites in a layer where all hydrogen has burned to helium: a so-called pure-helium burst. Runaway $3\\alpha$ burning of helium to carbon raises the temperature and enables a series of $\\alpha$-captures on carbon, creating a chain of $\\alpha$ elements up to calcium. Interestingly, when $T\\gtrsim 1$~GK, $(\\alpha,\\rm{p})$ reactions take place which create a small amount of protons \\cite{Wein06}. This expands the accessible reactions with proton-captures, even though no hydrogen was present at ignition. The by-pass of $^{12}\\mathrm{C}(\\alpha,\\gamma)$ by the faster $^{12}\\mathrm{C}(\\rm{p},\\gamma)$, has been suggested as the explanation for observed bursts with exceptionally short rise times of $\\sim 1$\\,ms \\cite{Wein06,Zand2014a}.\n\nAt mass accretion rates in excess of $0.1\\dot{M}_\\mathrm{Edd}$, some hydrogen remains when helium burning ignites, and again\na mixed hydrogen\/helium burst results. In the presence of hydrogen, the $3\\alpha$ runaway is more complicated. Helium burns to carbon, which boosts the burning of hydrogen in the $\\beta$CNO cycle. In turn, the CNO-cycle burning increases the helium abundance as well as the temperature, which further boosts $3\\alpha$. This interplay continues until at a temperature of $5\\times 10^8$~K breakout from the CNO cycle via $^{15} \\mathrm{O}(\\alpha,\\gamma)$ and via $^{18} \\mathrm{Ne}(\\alpha,\\rm{p})$ at $6\\times 10^8$~K becomes efficient~\\cite{Wies86}. The break-out reactions open the door for two long reaction chains. First the $\\alpha \\rm{p}$-process: a series of $(\\alpha,\\rm{p})(\\rm{p},\\gamma)$ reactions. These are fast reactions that mostly take place at the burst onset. This process feeds into the rp-process: a series of $(\\rm{p},\\gamma)$ reactions and $\\beta^+$-decays. The proton captures are typically fast, and the timescale of the rp-process is set by the half lives of the decays. Therefore, the initial part of the reaction chain goes fast, until the first slow decay is reached at $^{30}$S \\cite{Woos04,Fisker2008} within roughly a second. \nA small amount of the material in high-temperature regions enables this waiting point to be bypassed, but the reaction sequence nonetheless stalls at $^{56}\\rm{Ni}$~\\cite{Fisker2008}. The rest of the rp-process burning takes place in the tail of the X-ray burst, where it powers the light curve. The length of the reaction chain depends on the amount of hydrogen that is still present at this time and on the maximum temperature that is reached. The end of the rp-process lies at the closed SnSbTe cycle \\cite{Scha01,Jose10}, but for typical burst conditions the composition of ashes peaks at lighter elements near Ge and Ga \\cite{Woos04}.\n\nThe rp-process can follow multiple branches in the nuclear reaction network. \nWhich branch is dominant depends on the reaction rates, which are often only weakly constrained by theoretical calculations (e.g., \\cite{Raus00}). Experimental constraints on the rates are crucial for accurate predictions of the ash composition. Changes to the reaction path that substantially change the ashes often also produce observable changes in the X-ray light curve (e.g., \\cite{Heger2005}). Large numerical studies identify which reactions are most important in this respect and require new nuclear physics experiments to improve their accuracy \\cite{Pari13,Cybu16,Scha17}.\n\nBoth the pure-helium and mixed hydrogen\/helium bursts as described by theory have been observed. Furthermore, theory predicts that helium burning becomes stable at high mass accretion rates $\\dot{M}\\geq\\dot{M}_\\mathrm{Edd}$.\nObservations see stable burning already at a three times lower rate. Moreover, the observed reduced burst rate is not predicted by theory. Possibly a hot crust \\cite{Keek2009,Zamfir2014} or mixing processes \\cite{Piro2007,Keek2009,Cave17} in the envelope stabilize nuclear burning already at lower accretion rates. In addition, the reaction rates of the CNO breakout reactions influence the stability transition \\cite{Fisker2006,Keek14}. A further observational constraint is that hydrogen and helium burning must produce sufficient carbon to power the rare superbursts (Section \\ref{sec:superbursts}). Whereas burst models do not create enough carbon, stable burning and shallow heating may be of importance here \\cite{Stev14,Reic17}. \n\nHere we discussed one-dimensional models, which resolve the neutron star \nenvelope in the radial direction. They employ an approximation for turbulent mixing, which hints at the fact that convective mixing of ashes and fresh fuel has an important effect \non the burst ignition and the resulting ash composition (``compositional inertia\" \\cite{Woos04}). In recent years 2D and 3D hydrodynamics models have been created to study convection at the burst onset \\cite{Fryx1982,Zingale2001,Malone2011,Malone2014,Zingale2015} and flame spreading across the stellar surface \\cite{Spit2002,Cavecchi2013,Cavecchi2015,Cavecchi2016}. The computational demands on such simulations are substantial, and only small approximate nuclear networks are employed that lack the $\\alpha \\rm{p}$- and rp-processes.\n\n \\subsubsection{Nuclear Sensitivities and Recent Uncertainty Reduction Efforts}\\label{sec:rpprocess}\n \\hfill\\\\\n The nuclear reaction sequence powering Type-I X-ray bursts involves more than a thousand reactions on over three-hundred nuclei, posing a daunting experimental challenge at first glance. Fortunately, not all nuclei and their associated reaction rates are of equal importance. Model calculations of X-ray burst light curves and nucleosynthesis have played and continue to play a critical role in identifying nuclear physics uncertainties that are of the highest priority. The key nuclear physics uncertainties associated with Type-I X-ray bursts are summarized here. Since X-ray burst nucleosynthesis is covered extensively elsewhere~\\cite{Lewi93,Stro03,Scha06,Pari13,Jose16}, we will focus on past highlights and important results from roughly the past decade.\n \n Detailed calculations of the rp-process demonstrate that, of the large number of reactions present, only a handful play a significant role in X-ray bursts~\\cite{Scha98,Wies00,Woos04}. Broadly speaking, these reactions can be categorized into groups (1) ignition\/breakout, (2) branch points, (3) waiting points, and (4) cycles. For a rough orientation on the nuclear chart, (1) and (2) are generally located within the HCNO and $\\alpha \\rm{p}$-process portions of figure~\\ref{figure:RPprocess}, respectively, whereas (3) and (4) are generally located within the rp-process reaction sequence.\n \n \n\\begin{figure} [h]\n\\centering\n\\includegraphics[scale=0.6]{RPprocess.png}\n\\caption{Nuclear reaction sequence powering Type I X-ray bursts~\\cite{Scha01} with colored lines indicating rates driving particular parts of the X-ray burst light-curve~\\cite{Lewi93}.\n\\label{figure:RPprocess}}\n\\end{figure}\n \n \n Group (1) reactions are responsible for triggering the thermonuclear runaway that initiates bursts. Namely, these are reaction rates that connect and break-out of the CNO cycles via $\\alpha$-capture~\\cite{Wies99}. $\\alpha$-capture enables material to flow along the valley of $\\beta$-stability in the nuclear landscape and avoid unbound nuclides that easily photodisintegrate. The important $\\alpha$-captures occur on nuclides with large equilibrium abundances in the $\\beta$-limited CNO cycles, i.e. those whose destruction while in the cycle happens via the relatively slow $\\beta^{+}$-decay \n process~\\cite{Wies99}. \n \n Group (2) reactions occur at nuclides whose competing destruction rates are of a similar order of magnitude, meaning the details of the competition determine the subsequent reaction network flow. Branch points are especially prevalent during the burst rise, when $(\\alpha,\\rm{p})$ and $(\\rm{p},\\gamma)$ reactions on relatively low-$Z$ nuclides compete~\\cite{Fisk04}. These reactions are all located below $^{40}\\rm{Ca}$, as the Coulomb barrier is too large at and above this $Z$ for $\\alpha$-capture to be competitive.\n \n Group (3) reactions are nuclides without a swift destruction process, which temporarily brings most energy generation to a stand-still until a sufficient quantity of material has been transmuted beyond that point. Generally, a waiting-point nucleus has a low or negative proton-capture $Q$-value $Q_{\\rm{p},\\gamma}$, meaning the photodisintegration reaction \n is competitive with the corresponding radiative captures. \n Therefore, the rp-process flow is funneled into much slower $\\beta^{+}$-decay \n or $e^{-}$-capture decay, while a small percentage of the flow bypasses the waiting-point via proton-capture on the equilibrium abundance of the first proton-capture daughter~\\cite{Gorr95,Scha98}. The waiting-point nuclide location and equilibrium abundance are defined by the local proton and photon densities, $n_{\\rm{p}}$ and $n_{\\gamma}$, respectively, and the environment temperature $T$. Waiting points occur when $Q_{\\rm{p},\\gamma}$\n is such that the photodisintegration rate $\\lambda_{\\gamma,\\rm{p}}$ is related to the radiative proton-capture rate $\\lambda_{\\rm{p},\\gamma}$ via\n \\begin{equation}\n \\label{eqn:equilibrium}\n \\frac{\\lambda_{\\gamma,\\rm{p}}}{\\lambda_{\\rm{p},\\gamma}}=\\frac{n_{\\rm{p}}}{n_{\\gamma}}\\left(\\frac{\\mu_\\mathrm{red} c^{2}}{k_\\mathrm{B}T}\\right)^{3\/2}\\mathrm{exp}{\\left(-\\frac{Q_{\\rm{p},\\gamma}}{k_\\mathrm{B}T}\\right)}\\gtrsim1,\\\\\n \\end{equation}\n where \n $\\mu_\\mathrm{red}$ is the reduced mass of the $(\\rm{p},\\gamma)$ reaction, which, for most cases of relevance, $\\mu_\\mathrm{red}\\approx1$.\n From the Planck distribution, $n_{\\gamma}=\\pi\\left(k_{B}T\\right)^{3}\/(13c^{3}\\hbar^{3})$. %\n Using typical rp-process conditions, $\\rho\\approx10^{6}$~g~cm$^{-3}$, $X(\\mathrm{H})\\approx0.7$, and $T\\approx1$~GK, relation~(\\ref{eqn:equilibrium}) is satisfied for $Q_{\\mathrm{p},\\gamma}\\lesssim$1~MeV.\n \nGroup (4) refers to reaction sequences that operate collectively like a single-nucleus waiting point~\\cite{VanW94}. The most well known of which is probably the SnSbTe cycle which marks the rp-process end-point~\\cite{Scha01}. In these cases, $(\\rm{p},\\alpha)$ and $(\\rm{p},\\gamma)$ reactions compete, where the former continues the cycle and the latter enables the matter flow to break free.\n \nEarly X-ray burst calculations explored the impact of reaction rates on features of the burst light-curve and ashes. For instance, $(\\rm{p},\\gamma)$ reactions beyond $^{56}\\rm{Ni}$ strongly impact the late time light curve in single-zone calculations~\\cite{Hana83}, as \nconfirmed later through calculations with higher-fidelity nuclear physics input~\\cite{Koik99} and multiple zones~\\cite{Woos04}. These results prompted efforts to improve nuclear physics inputs, such \nas implementing Hauser-Feshbach calculations and empirically-based direct capture and resonant rates computed for \nastrophysical calculations~\\cite{VanW94}.\n \nThe impact of nuclear physics uncertainties on X-ray burst ashes has been explored by several groups. \nEarly \nstudies \ntested different reaction rate~\\cite{Koik99} and mass~\\cite{Clem03} libraries. Due to limited computing power, early multi-zone work was restricted to modifying half-lives to approximate the behavior of waiting-points~\\cite{Woos04}. Until recently, detailed sensitivity studies investigating individual reaction rates were necessarily limited to post-processing studies, which lack feedback between nuclear energy generation and environmental conditions, but enabled exploring the impact of rates for a variety of astrophysical conditions~\\cite{Pari08,Pari09}. To date only one X-ray burst sensitivity study has been performed for the whole rp-process with feedback between energy generation and the environment~\\cite{Cybu16}. Even in this case, multi-zone reaction rate variations were limited to cases highlighted as important by a single-zone study, as a train of a dozen or so bursts used to assess rate sensitivity required \na week or more to calculate (see Reference~\\cite{GallowayKeek2017} for a discussion of burst models with different dimensionality). The \nfirst single-zone mass sensitivity study with coupled energy generation and hydrodynamics was only \nrecently performed~\\cite{Scha17}. \nNote that progress in high-precision mass measurements (e.g. References within~\\cite{Kank17}) means that \nonly a handful of masses with insufficient precision remain. Half-life sensitivities need not be investigated, as almost all relevant half-lives are experimentally well-constrained~\\cite{Scha06,Scha17}. That said, uncertainties remain in the modifications to terrestrial half-lives that are required to account for high-temperature and high-density effects, such as thermal population of excited states, inhibition of bound state electron capture, and continuum electron capture~\\cite{Full1982,Oda1994,Prue2003,Lau18b}.\n \n A cautionary point regarding what the ``most important'' nuclear sensitivities are is that this will depend on the astrophysical conditions. For instance, it is known that the rp-process reaction network path depends on metallicity~\\cite{Jose10} and very different bursts result from models with varied accretion rates~\\cite{Woos04,Lamp16}. \n Additionally, at least one model~\\cite{Davi11} has found discrepant sensitivity with respect to other models~\\cite{Fisker2006,Cybu16} for $^{15}\\rm{O}(\\alpha,\\gamma)$, indicating more cross-code comparisons (e.g., \\cite{Meis17,Meis18}) are needed.\n \n Efforts to remove or reduce the most critical nuclear physics uncertainties have primarily consisted of indirect measurements to constrain nuclear reaction rates. These include measurements of nuclear masses, spectroscopy of low-lying nuclear excited states, and determinations of statistical properties for more highly excited compound nuclei. While direct measurements are preferable, the necessary radioactive beams at astrophysically relevant energies and sufficient intensities are not yet available. \n \n\nWe continue with a \nbrief summary of recent uncertainty reduction efforts for relevant rp-process reaction rates grouped by reaction categories defined above.\n\n\\paragraph{Ignition\/Breakout: } Experimental efforts have primarily focused on the two key break-out rates, $^{18}\\rm{Ne}(\\alpha,p)$ and $^{15}\\rm{O}(\\alpha,\\gamma)$, and the $^{14}\\rm{O}(\\alpha,p)$ rate which links the CNO cycles on the way to breakout. $^{14}\\rm{O}(\\alpha,p)$ has most recently been summarized in References~\\cite{Alma12b} and \\cite{Hu14}. Stated briefly, this rate may be known to sufficient precision. However, further experimental investigations are merited in the $\\sim6-7$~MeV excited state energy region of the $^{18}\\rm{Ne}$ compound nucleus to confirm important resonance information. Furthermore, theoretical confirmation is required to assess the impact of $^{14}\\rm{O}(\\alpha,p)$ in more sophisticated X-ray burst calculations. $^{15}\\rm{O}(\\alpha,\\gamma)$ has most recently been evaluated in Reference~\\cite{Davi11}, though the most recent experimental results come from References~\\cite{Tan07,Tan09,Lund16,Wred17}. For this case the uncertainty stems from the unknown resonance strength for capture into the $\\sim 4$~MeV excited state of $^{19}\\rm{Ne}$. The small $\\alpha$-branching ratio dominates the rate uncertainty and has thus far evaded direct measurement. An example of this rate's impact on the X-ray burst light curve is shown in figure~\\ref{figure:modelLC}. The state of $^{18}\\rm{Ne}(\\alpha,p)$ is summarized in Reference~\\cite{Mohr14}, relying primarily on complementary measurements from References~\\cite{Mati09,Salt12,He13,Zhan14}. Further progress for this rate would require experimental resonance strength determinations in the $\\sim9-11$~MeV excitation energy region in $^{22}\\rm{Mg}$. A less significant but nonetheless notable development is the recent set of experimental constraints placed on $^{19}\\rm{Ne}(p,\\gamma)$, which connects $^{15}\\rm{O}(\\alpha,\\gamma)$ to the rp-process, including the first direct measurement of capture onto a radioactive ion excited state~\\cite{Wilk17,Bela16}.\n \n \n\\begin{figure} [h]\n\\centering\n\\includegraphics[scale=0.7]{RateVariationExample.pdf}\n\\caption{Averaged X-ray burst light curves and asymmetric 1-$\\sigma$ uncertainties corresponding to sequences of $>$10 bursts, for multi-zone X-ray burst calculations performed with MESA~\\cite{Paxt11,Paxt13,Paxt15}. Calculations employ a solar accreted composition at $\\dot{M}$=0.17$\\dot{M}_\\mathrm{Edd}$ onto an 11.2~km neutron star with $0.1$~MeV per accreted nucleon of base heating, where the distance and redshift of GS~1826$-$24 reported in Reference~\\cite{Gall17} are used to convert luminosity (from the calculation results) to flux. Results are shown using the ReacLibV2.2 \nrate library~\\cite{Cybu10} (gray band), with a reduced $^{15}\\rm{O}(\\alpha,\\gamma)$ rate (blue band), and a reduced $^{59}\\rm{Cu}(p,\\gamma)$ rate (red band). All other calculation details are as described in Reference~\\cite{Meis17}.\n\\label{figure:modelLC}}\n\\end{figure}\n \n \\paragraph{Branch Points: } Rates in the $\\alpha \\rm{p}$-process of particular importance are those involving nuclides where destruction via $(\\alpha,\\rm{p})$ is competitive with the $(\\rm{p},\\gamma)$ or $\\beta^{+}$-decay alternatives. Chief among these are $^{26}\\rm{Si}(\\alpha,\\rm{p})$, $^{30}\\rm{S}(\\alpha,\\rm{p})$ and its competitor $^{30}\\rm{S}(\\rm{p},\\gamma)$, and $^{34}\\rm{Ar}(\\alpha,\\rm{p})$. A single measurement is the source of experimental constraints for $^{26}\\rm{Si}(\\alpha,\\rm{p})$, providing \n level energies but largely lacking certain spin assignments and $\\alpha$-widths~\\cite{Alma12a}. However, the relevant level densities are large enough that a statistical reaction rate approach may be adequate. $^{30}\\rm{S}(\\alpha,\\rm{p})$ has been the subject of more investigations, including direct measurements of the time-reversed reaction~\\cite{Deib11} and spectroscopic studies of the compound nucleus $^{34}\\rm{Ar}$~\\cite{Obri09,Kahl18}. As with the previous case, absent a direct measurement of the forward reaction,\n $\\alpha$-widths are the source of the main experimental uncertainty. The competing reaction $^{30}\\rm{S}(\\rm{p},\\gamma)$ involves lower-lying excitation energies in the compound nucleus $^{31}\\rm{Cl}$ and has thus been more amenable to spectroscopic constraints, resulting in a relatively well-constrained reaction rate~\\cite{Wred09,Lang14b}. In the past few years $^{34}\\rm{Ar}(\\alpha,\\rm{p})$ has been the subject of a series of complementary direct and indirect measurements. These include a spectroscopic measurement using the $(\\rm{p},t)$ mechanism~\\cite{Long17}, a direct measurement above the Gamow-window~\\cite{Schm17}, and another announced work on spectroscopy of the compound nucleus using $\\rm{p},\\rm{p}'$~\\cite{Laue16}.\nA comprehensive analysis of these works will likely vastly improve the situation regarding this branch-point nucleus.\n \n\\paragraph{Waiting Points: }\nThe nuclides which satisfy\nrelation~(\\ref{eqn:equilibrium})\nand have several-second half-lives\nare $^{56}$Ni, $^{64}$Ge, $^{68}$Se, $^{72}$Kr, and $^{100}$Sn. For these cases, $Q_{\\rm{p},\\gamma}$ for the waiting-point nucleus is essential to determine the equilibrium abundance of the proton-capture daughter. $Q_{\\rm{p},\\gamma}$ for the first proton-capture daughter and the structure of the second proton-capture daughter are equally important in order to determine the flow of material through the waiting-point. For waiting-points away from the proton drip-line, the possibility of material flowing around the waiting-point also needs to be investigated. $^{56}$Ni falls into the latter category, where experimental constraints suggest a strong waiting-point~\\cite{Lang14}, but \nit is possible this waiting-point could be bypassed \nat high temperatures and densities~\\cite{Ong17,Valv18}. $^{64}$Ge has been declared to not be a waiting-point~\\cite{Wing93,Tu11}, but this claim was likely premature. The large remaining uncertainty in the nuclear masses of $^{65}$As and $^{66}$Se leave open the possibility for a strong waiting-point, as do uncertain properties of relevant resonances for the $^{64}\\rm{Ge}(\\rm{p},\\gamma)^{65}\\rm{As}(\\rm{p},\\gamma)^{66}\\rm{Se}$ sequence~\\cite{Lam16}. Independent measurements have determined a rather negative proton-separation energy of $^{69}\\rm{Br}$, resulting in $<$13\\% of material flowing through proton-capture on $^{68}$Se, solidifying $^{68}$Se as a strong waiting-point~\\cite{Blan95,Pfaf96,Roge11,DelS14}. Similar recently completed measurements will soon resolve the waiting-point status of $^{72}$Kr, but results are not yet published~\\cite{Roge17}.\nStudies of $^{100}\\rm{Sn}$ are limited to a confirmation of its long half-life~\\cite{Bazi08}, but the proximity of this reaction to the rp-process end-point limits its importance for all but the most energetic X-ray bursts~\\cite{Scha01}.\n\n\\begin{figure} [h]\n\\centering\n\\includegraphics[scale=0.8]{NiCuZnGaCyclesV2.png}\n\\caption{Portion of the rp-process reaction sequence featuring the NiCu and ZnGa reaction cycles. $^{59}\\rm{Cu}(p,\\gamma)$, $^{61}\\rm{Ga}(p,\\gamma)$, and $^{63}\\rm{Ga}(p,\\gamma)$ (red arrows) significantly affect the reaction flow in this region.\n\\label{figure:cycles}}\n\\end{figure}\n\n\\paragraph{Cycles: }\nFor cases where the $\\alpha$ emission threshold is lower than the proton threshold, $(\\rm{p},\\gamma)$ and $(\\rm{p},\\alpha)$ reactions can compete~\\cite{Remb97}. In the vicinity of $Z=28-31$, just beyond $^{56}\\rm{Ni}$ in the rp-process path, this leads to the NiCu and ZnGa cycles~\\cite{VanW94}. For these cycles, radiative proton-capture onto $^{59}\\rm{Cu}$, $^{61}\\rm{Ga}$, and $^{63}\\rm{Ga}$ are of particular importance, as indicated in figure~\\ref{figure:cycles}. Proton-capture onto $^{59}\\rm{Cu}$ either returns the cycle to $^{56}\\rm{Ni}$ or breaks-out via $^{59}\\rm{Cu}(\\rm{p},\\gamma)$, depending on the $(\\rm{p},\\gamma)\/(\\rm{p},\\alpha)$ rate ratio. Once out of the NiCu cycle, the several minute half-life of $^{60}\\rm{Zn}$ stalls the rp-process unless it is bypassed by proton-capture. The $\\sim250$~keV $Q$-value for $^{60}\\rm{Zn}(p,\\gamma)^{61}\\rm{Ga}$ enables an equilibrium abundance of $^{61}\\rm{Ga}$ to be built-up, which can then bypass $^{60}\\rm{Zn}(\\beta^{+})$ via $^{61}\\rm{Ga}(\\rm{p},\\gamma)$. $^{63}\\rm{Ga}$ plays the analogous role in the ZnGa cycle to the role of $^{59}\\rm{Cu}$ in the NiCu cycle. At present these rates are primarily determined by theoretical estimates~\\cite{Fisk01,Raus00} since the distance from the valley of $\\beta$-stability makes even indirect measurements challenging.\n\n\\paragraph{}\nThe majority of rp-process reaction rates lack any experimental constraints beyond nuclear masses and rough structure details. In their stead, the most common approach is to employ shell model rates when available (and applicable), particularly in the mid-mass region~\\cite{Fisk01}, and statistical model rates otherwise, e.g.~\\cite{Raus00}. A commonly used reaction library which follows this strategy is the JINA-CEE ReacLib database~\\cite{Cybu10}.\n\nHowever, rapid experimental progress is anticipated in the near future. Reaccelerated beams at the Facility for Rare Isotope Beams studied with devices such as the JENSA gas-jet target~\\cite{Bard16} and SECAR recoil separator~\\cite{Berg17} will enable direct measurements of several interesting reaction rates at astrophysically relevant energies. Nonetheless, indirect measurements will continue to guide direct measurement studies and to cover the large number of cases for which direct measurements will still not be possible. Newer techniques, such as the $\\beta$-Oslo method~\\cite{Spyr14}, are poised to significantly grow the ranks of experimentally-constrained reaction rates for nuclides far off stability. \n\nThough only discussed tangentially here, much of the discussion above also pertains to nuclosynthesis during stable burning on the neutron star \nsurface. For near Eddington and super-Eddington accretion rates, nuclear reaction sequences often resemble the rp-process~\\cite{Scha99,Stev14,Keek16,Meis17}, resulting in burst-like ash distributions (see figure~\\ref{figure:ashes}). However, the detailed abundance distribution depends on the astrophysical conditions, and so a wide variety of ash abundances can be produced in stable burning.\nFor instance, recently a regime of stable burning has been proposed around $0.1\\dot{M}_\\mathrm{Edd}$, with carbon-rich ashes \\cite{Keek16}.\nThe exact reactions of interest for these processes will depend on the hydrogen\/helium composition\nof the accreted material and the accretion rate\n~\\cite{Nara03,Keek14}. \n \n \\subsection{X-ray Superbursts:}\n\\label{sec:superbursts}\n\n \\subsubsection{Observation}\n \\label{sssec:SuperburstObs}\n \\hfill\\\\\nSuperbursts were first observed in 1996 with the BeppoSAX\/WFCs \nand RXTE~\\cite{Cornelisse2000,Stro02}. To date, 26 superbursts have been detected from 15 neutron stars \\cite{Zand17}. Superbursts reach a peak flux that is similar to normal bursts, but their decay lasts several hours, and the total emitted energy is $\\sim 10^3$ times larger than for hydrogen\/helium flashes \nwhich justifies the ``super'' designation \\cite{wijnands2001}.\nThe X-ray observatories that detected superbursts are in low Earth orbits of $\\sim 90$ minutes, which is shorter than the typical superburst duration. Therefore, superburst observations are interrupted by data gaps of up to $30$ minutes, when the view of the X-ray source is blocked by the Earth (see figure~\\ref{figure:LCobs}, bottom panel).\nOften the start of the superburst falls in a data gap, and it is challenging to accurately determine the burst's properties and confirm its superburst nature, so it is instead referred to as a ``superburst candidate\"~\\cite{Altamirano2012}. \nMost superbursts have been observed with wide-field or all-sky instruments, which produce data of modest quality. Only in two cases have detailed observations been performed with RXTE\/PCA \\cite{Stro02,Strohmayer2002a}.\n\nThe observed superburst light curves are fit with numerical models of cooling envelopes to determine the ignition conditions. From the tail of the light curve the ignition column depth is measured, \nand the fluence constrains the energetics of the fuel \\cite{2004CummingMacBeth,Cumming2006}. The inferred parameters suggest that unstable carbon burning in the neutron star ocean ignites superbursts \\cite{cumming2001,Stro02}. Ignition column\ndepths are found to be in the range of $y_\\mathrm{ign}\\simeq(0.2-3)\\times 10^{12}\\ \\mathrm{g\\ cm^{-2}}$, and the fuel energetics are equivalent to a carbon mass fraction of $\\sim 20\\%$ \\cite{Cumming2006,Zand17}. The rise of the light curve is shaped by the temperature profile left behind by the carbon flame (measured only once \\cite{Keek16}).\n\nAll superbursting sources also exhibit normal (short) bursts. Most have mass accretion rates in the range of $0.1-0.3\\ \\dot{M}_\\mathrm{Edd}$, where the burst rate drops and a substantial fraction of the accreted hydrogen\/helium burns in a stable manner \\cite{Zand2004}. Both explosive and stable hydrogen\/helium burning may be necessary for superbursts. As the normal bursts do not produce sufficient carbon, stable burning is likely needed to create the carbon fuel (e.g., \\cite{Stev14,Keek16}; Section~\\ref{sec:burst_theory}). However, the bursts produce heavy isotopes (iron group or heavier), which increase the opacity, allowing the base of the ocean to reach the temperature required for runaway carbon fusion \\cite{cumming2001}.\n\nBecause of their long recurrence times (typically a year) \\cite{Zand17}, superbursts are rare, and each new observation brings new insight. \n\n \\subsubsection{Theory}\n \\hfill\\\\\nThe nuclear processes that power superbursts show a resemblance to those in models of Type Ia supernovae. The runaway is initiated by $^{12}$C+$^{12}$C burning. One of the dominant channels is $\\mathrm{^{12}C(^{12}C,\\alpha)^{20}Ne}$. A fraction of the $\\alpha$ particles capture on carbon to form oxygen, enabling the follow-up reactions $^{12}$C+$^{16}$O and $^{16}$O+$^{16}$O. The ashes of these reactions are rich in $^{28}$Si. At sufficiently high temperatures ($T\\gtrsim 10^9$~K) photodisintegration of silicon occurs (``silicon melting''). A large number of $\\alpha$ particles is \ncreated, the majority of which are quickly captured again to form a composition that is dominated by $^{56}$Ni. The precise ash composition is set by the nuclear statistical equilibrium of photodisintegration and capture reactions. Any isotopes in the hydrogen\/helium ashes substantially heavier than nickel will also photodisintegrate, and this can contribute up to 50\\% of the superburst energy~\\cite{Scha03}. From the carbon runaway, all these burning processes take less than a second to complete. Afterwards, as the layer cools, electron captures transform $^{56}$Ni into $^{56}$Fe, making $A=56$ the primary component of the ashes as seen in figure~\\ref{figure:ashes}. \n\nSimilar to the hydrogen\/helium bursts (section~\\ref{sec:burst_theory}), one-dimensional multi-zone models are employed to study superburst ignition and the various burning processes. Self-consistent simulations should model hundreds of hydrogen\/helium flashes to build up a carbon layer and produce a superburst, but this approach suffers from two problems. First, it is computationally expensive, and studies with reduced rp-process networks are likely not accurate (compare Reference~\\cite{Taam1996} to Reference~\\cite{Woos04}). Second, as noted in section~\\ref{sssec:SuperburstObs}, current models of hydrogen\/helium burning do not produce enough carbon. \nTherefore, superburst models typically directly accrete a carbon-rich fuel \nto study \nignition conditions \\cite{Keek11}. Shortly before ignition, the accretion composition can be switched to hydrogen\/helium-rich material to study the effect of a superburst on the atmosphere \\cite{Keek2012}. Importantly, the heated ocean after a superburst quenches short bursts in the atmosphere: for several days hydrogen and helium burning becomes stable.\n\nNo multi-dimensional models have yet been created for superbursts. One-dimensional simulations show, however, that the hydrodynamics of the carbon flame is important for shaping the observable X-ray light curve. Initially, a convective region forms around the ignition depth, until \nheating by thermonuclear burning in one zone becomes faster than cooling by convection.\nA local runaway ensues, burning all carbon in that zone. The burning time scale becomes shorter than the sound-crossing time scale, raising the question whether the flame will spread as a detonation \\cite{Weinberg2006sb,Weinberg2007}, or whether convection can spread the flame to lower depths as a deflagration. Either of these processes can generate a shock that produces a short precursor burst at the start of the superburst light curve \\cite{Keek2012}. \nThe carbon flame leaves behind a temperature profile in the envelope, which shapes the observable light curve. It encodes information about the ignition depth, fuel energetics, and the flame spreading (e.g., whether the flame reached the surface or stalled) \\cite{2004CummingMacBeth,Cumming2006,Keek16}.\n\nThe ignition depths inferred from observations are smaller than those predicted by theory \\cite{Keek11}, leading to questions about carbon fusion as the process that powers superbursts.\nThe problem of carbon ignition became more pressing with the discovery of superbursts from transient sources \\cite{Keek08,Altamirano2012,Chenevez2011ATel,Serino2016}. The neutron star \nenvelope heats up during accretion, but in \nmost of these cases accretion was active for mere days or weeks, which was thought to be insufficient to reach the temperature needed for runaway carbon fusion \\cite{Keek08}. Therefore, stronger heating of the base of the crust is required on the relatively short time scales of the transient accretion events. This may be related to the shallow heat source inferred for \nquasi-persistent transients\nwhen they cool after accretion ceases (section~\\ref{sec:cooling_transients}). Simulations confirm that the ignition depth is reduced with the addition of extra heating \\cite{Keek11}. Interestingly, additional shallow heating has also been proposed for enhancing carbon production during stable burning~\\cite{Reic17}.\n\n\n \\subsubsection{Nuclear Sensitivities and Recent Uncertainty Reduction Efforts}\n \\label{sssec:superburstnuc}\n \\hfill\\\\\n The several gigakelvin temperature achieved in superbursts is more than sufficient to drive material to nuclear statistical equilibrium~\\cite{Scha03}.\n As such, the final abundance distribution is largely determined by the nuclear masses, environment temperature, and environment \n $Y_{e}$. In principle the abundance distribution will be modified by light charged-particle capture after nuclear statistical equilibrium freeze-out, but low proton and $\\alpha$ abundance predictions result in a negligible modification~\\cite{Scha03}. \nSince the majority of the relevant nuclear masses are well known, uncertainties which may affect $Y_{e}$ are of interest. Namely, these are the reactions modifying the ashes of the stable burning and X-ray bursts \nalready discussed above.\n\nThough inconsequential in terms of the resulting crust composition, the $^{12}$C$+$$^{12}$C fusion rate is nonetheless of interest due to its role in triggering superburst ignition~\\cite{Coop09}. The relatively low energy of interest for the large Coulomb barrier involved (see section~\\ref{sec:pycno})\nhave made advances in this area extremely challenging~\\cite{Cost09}. Only very recently have direct \nlaboratory measurements pushed into the astrophysical energy region\n~\\cite{Buch15,Jian18}. Of particular interest is the possible existence of a resonance in $^{12}$C$+$$^{12}$C at a center of mass energy of $\\sim$1.5~MeV~\\cite{Coop09}. This resonance has been posited to explain the discrepancy between the observed and predicted superburst ignition depth, much in the way the Hoyle state was predicted to explain cosmic carbon production~\\cite{Hoyl54}.\nHowever, present experimental constraints are contradictory and rely on theoretical extrapolations~\\cite{Buch15b}.\n\n\\section{Interaction of Nuclei Within the neutron star Ocean and Crust} \\label{section:interaction}\n\nThe ashes of surface burning experience compression under the pressure of the continuously infalling material (section~\\ref{section:accretion}). As they move to higher mass densities (and greater electron chemical potentials), they undergo a variety of non-equilibrium reactions.\nThese reactions, primarily $e^{-}$-captures, cause the compressed nuclei to become increasingly neutron-rich. Near the neutron drip point, the reaction mechanisms operating in the crust transitions from $e^{-}$-capture reactions (with only $\\nu$ and $\\gamma$ emission) \nto neutron emission reactions \\cite{Gupt07,Gupt08,Lau18}. The presence of pycnonuclear (density-driven) fusion reactions~\\cite{Came59,Harr64,Salp69} was also found in the inner crust~\\cite{Sato79}. These reactions\ndirectly modify the crust composition, but more importantly, they can drastically alter the thermal structure of the crust, impacting thermonuclear processes on and near the surface \nand related observables~(\\cite{Mira90,Keek11,steiner2012,Scha14,Deib16,Meis17}; section~\\ref{section:impact}). \n\n\nNuclear reactions deposit heat in the neutron star \ncrust during an accretion episode~\\cite{Haen90,Haen03,haensel2008} and an accurate accounting of crustal heating during active accretion is required to match the neutron star's \nsurface temperature when accretion ends~\\cite{Brow09}. The impact of $e^{-}$-captures on heating was substantially increased when it was realized that nuclear de-excitations following electron captures increase heat deposition~\\cite{Gupt07,Gupt08,Zhan15}. Experimental investigations along these lines include References~\\cite{Estr11,Meis15}. \n\nNon-equilibrium reactions may also cool the neutron star \ncrust. Urca cooling\\textrm{---} cycles of $e^{-}$-capture and $\\beta^{-}$-decay that\ngenerate neutrino emission\\textrm{---} was introduced for white dwarf stars~\\cite{Gamo40,Gamo41}. \nOnly recently was it realized that the finite temperature of the neutron star ocean and crust allows sufficient phase-space for Urca cooling to exist~\\cite{Gupt07,Scha14}. \nShortly after, this was limited to odd-A nuclides~\\cite{Meis15} and efforts have been made to employ more experimentally-based nuclear data and identify the presence of Urca cooling in the neutron star's \nouter layers \nbased on observations of neutron star crustal cooling~\\cite{Deib16,Meis17}. \n\n \n \\subsection{$e^{-}$-Capture Reactions}\n \\label{ssec:ECrxns}\nThe first transmutation ashes experience as they are buried by accretion of additional material is a sequence of $e^{-}$-captures.\nThe rising electron chemical potential \n$\\mu_e$ with increased depth makes it energetically possible for $e^{-}$-capture to proceed for nuclei with steadily more negative $e^{-}$-capture $Q$-values, $Q_{\\rm{EC}}$. Once $\\mu_e>|Q_{\\rm{EC}}|$, $e^{-}$-capture immediately proceeds.\nThe end result is that the crust becomes steadily more neutron-rich with increasing depth\nas shown in figure~\\ref{figure:ECcrustcomposition}. Surface values of $\\langle Z\\rangle$, $\\langle A\\rangle$, and $Q_{\\rm{imp}}$ for the ashes shown in figure~\\ref{figure:ashes} are listed in table~\\ref{table:AvgZAQTable}.\n\n \n\\begin{figure} [h]\n\\centering\n\\includegraphics[scale=0.65]{CrustCompositionFromECMuE.pdf}\n\\caption{Evolution of the average proton number $\\langle Z\\rangle$, average neutron number $\\langle N\\rangle$, and impurity $Q_\\mathrm{imp}$ (see equation~\\ref{equation:Qimp}) \ndue to $e^{-}$-capture for X-ray burst and superburst ashes (see figure~\\ref{figure:ashes}), using experimental masses when available~\\cite{Audi12} and the WS3 global mass model otherwise~\\cite{Wang10}. The superburst impurity is multiplied by 10 for visibility. \nThis plot does not account for neutron emission and fusion reactions (sections \\ref{sec:nuc_emission} and \\ref{sec:pycno}).\nSee figure~\\ref{figure:pristinecomp} to compare to the pristine crust composition.\n\\label{figure:ECcrustcomposition}}\n\\end{figure}\n \n \\begin{table}\n\\caption{Properties on the neutron star surface of the ashes shown in figure~\\ref{figure:ashes}.}\n\\begin{center}\n\\begin{tabular}{c c c c }\n\\hline\nQuantity & X-ray Burst & Superburst & Stable Burning \\\\\n\\hline\n$\\langle Z\\rangle$ & 24 & 25 & 26 \\\\\n$\\langle A\\rangle$ & 52 & 55 & 60 \\\\\n$Q_{\\mathrm{imp}}$ & 69 & 3 & 101 \\\\\n\\hline\n \\label{table:AvgZAQTable}\n\\end{tabular}\n\\end{center}\n\\end{table}\n\n\nThe interesting pattern in the impurity shown for X-ray burst and superburst ashes in figure~\\ref{figure:ECcrustcomposition} deserves some discussion. The overall decline in impurity with increasing depth is due to the fact that higher-$Z$ elements near stability undergo $e^{-}$-capture at lower $\\mu_e$ relative to their lower-$Z$ counterparts because the nuclear mass surface is more shallow near the valley of $\\beta$-stability at high $A$.\nThe jumps in impurity are due to $e^{-}$-captures on the few isotopes which are the most abundant (see figure~\\ref{figure:ashes}). For superbursts, the dominant species are $A=52,54$, and $56$, while for X-ray bursts $A=60,64$, and $68$ dominate the composition with significant but less influential contributions from $A=28$ and $32$. This can be confirmed \nby a comparison of the location for jumps in the impurity in figure~\\ref{figure:ECcrustcomposition} and $|Q_{\\rm{EC}}|$ for neutron-rich isotopes of the aforementioned isobars.\n\n \\begin{figure} [h]\n\\centering\n\\includegraphics[scale=0.6]{ECschematic.png}\n\\caption{Scenarios for $e^{-}$-capture in the accreted crust at a depth indicated by the electron chemical potential $\\mu_{e}=|Q_{\\rm{EC}}|$, resulting in heating (a and b), which happen in steps indicated by the encircled numbers, or cooling (c and d), which are cyclic.\nFor $e^{-}$-capture on an odd-$A$ nucleus, a subsequent $e^{-}$-capture cannot occur until the nucleus has been buried to a larger $\\mu_{e}$. Therefore whether heating (a) or cooling (c) occurs depends on whether $\\beta^{-}$-decay from the first $e^{-}$-capture daughter is favorable. For $e^{-}$-capture on an even-$A$ nucleus, odd-even mass staggering generally enables an immediate subsequent $e^{-}$-capture, resulting in heating (b). However, special circumstances, namely a low-lying (relative to $k_\\mathrm{B}T$) low-$J^{\\pi}$ isomer in the odd-odd daughter of the first $e^{-}$-capture, can enable cooling via $e^{-}$-capture-$\\beta^{-}$-cycling to proceed instead (d). \n\\label{figure:ECschematic}}\n\\end{figure}\n\n\n$e^{-}$-capture reactions are the richest in terms of potential for experimental nuclear physics constraints with present and near-future facilities. Depending on the nuclear masses and structure of the nuclei involved in an $e^{-}$-capture reaction sequence,\none of four scenarios can take place once $\\mu_e\\approx|Q_{\\rm{EC}}|$, as depicted in figure~\\ref{figure:ECschematic}. For these cases, the two-step $e^{-}$-captures will generally create local heat sources~\\cite{Gupt07}, whereas the $e^{-}$-captures that can be reversed via $\\beta^{-}$-decays can create local cooling sources~\\cite{Scha14}. The heating predominantly comes from radiative de-excitation of states populated in $e^{-}$-capture, whereas cooling is due to neutrinos produced in the $e^{-}$-capture and $\\beta^{-}$-decay escaping the neutron star \ncrust. \n\nThe majority of $e^{-}$-capture heating comes from the scenario depicted in panel (b) of figure~\\ref{figure:ECschematic}, where odd-even mass staggering makes it energetically favorable for an $e^{-}$-capture on an even-even nucleus to be immediately followed by $e^{-}$-capture on the odd-odd daughter of the first reaction. For this case, $\\mu_e\\approx|Q_{\\rm{EC}}(Z,A)|>|Q_{\\rm{EC}}(Z-1,A)|$. The first $e^{-}$-capture happens near threshold (unless there is a large change in $J^{\\pi}$ required for a ground-state to ground-state transition), \nso little heating or cooling is achieved. In the second $e^{-}$-capture, roughly one-quarter of the surplus energy will be deposited into the crust~\\cite{Haen03}. \nUsually the second $e^{-}$-capture proceeds into an excited state of energy $E_\\mathrm{xs}$, and all of the de-excitation energy \nis deposited into the crust, resulting in the lion's share of the heating.\nThe energy deposited for a two-step $e^{-}$-capture starting on isotope $Z,A$ at a depth $\\mu_e=|Q_{\\rm{EC}}(Z,A)|$ is \n\\begin{equation}\nE_{\\rm{heat}}=\\eta\\left(|Q_{\\rm{EC}}(Z,A)|-|Q_{\\rm{EC}}(Z-1,A)|-E_\\mathrm{xs}\\right)+E_\\mathrm{xs},\n\\label{equation:NucHeating}\n\\end{equation}\nwith $1\/6\\lesssim\\eta\\lesssim1\/4$, where the exact pre-factor $\\eta$ requires an explicit calculation of $e^{-}$-capture to excited states~\\cite{Haen03,Gupt07}.\nIn the case depicted in figure~\\ref{figure:ECschematic}a, $\\mu_e$ is not large enough for the second e$^-$-capture to occur immediately. However, the $\\beta^{-}$-decay from the first $e^{-}$-capture daughter is much slower than the accretion timescale. Therefore the second $e^{-}$-capture happens only after the chemical potential has increased enough for the next $e^{-}$-capture to proceed. Little heating is produced in this case.\nEvidently the nuclear masses and low-lying excited states of neutron-rich isobars of nuclides predicted to be abundant in the ashes of surface burning processes are of significant interest for $e^{-}$-capture heating in the accreted neutron star outer crust. \nThe amount of heating produced by $e^{-}$-capture reactions in the outer crust depends on the ashes composition. For X-ray burst ashes, the total heating is on the order of $0.1-0.15$~MeV\/u, or about $10^{35}$~erg~s$^{-1}$ at Eddington accretion rate \\cite{Gupt07}. Notice that this is more than five times larger than the earlier estimates for a single-component composition \\cite{Haen90} which missed the role of excited states shown in figure~\\ref{figure:ECschematic}b. Significant heating mostly results from predominant nuclides with $X(A)\\gtrsim 10\\%$. It was found that when the heating through excited states is included, the simple single-component model give similar $e^{-}$-capture heating as compared to results of more complete multi-component reaction networks \\cite{haensel2008}.\n\n$e^{-}$-capture cooling via neutrino-emission primarily occurs from scenario (c) in figure~\\ref{figure:ECschematic}, where the odd-even mass staggering eliminates the possibility of an immediate subsequent $e^{-}$-capture following $e^{-}$-capture on an odd-$A$ nucleus. Instead, a $\\beta^{-}$-decay may proceed (as opposed to case (a), where we assumed a much slower rate for the weak transition), \nresulting in an $e^{-}$-capture\/$\\beta^{-}$-decay cycle known as an Urca process. Nominally such a scenario is possible for an even-even nucleus, however a specific set of circumstances is required. \nNamely, as depicted in scenario (d) of figure~\\ref{figure:ECschematic}, a low $J$ ($\\sim0$$-$1) state with $E_\\mathrm{xs}\\sim k_\\mathrm{B}T$ must be present for an odd-odd nucleus with a high $J$ ($\\gtrsim2$) ground state. The only promising candidate so far for scenario (d) has been ruled-out~\\cite{Meis15}.\n\nThe strength of Urca cooling is quantified by the luminosity of neutrinos produced by the pair of nuclides involved in the $e^{-}$-capture\/$\\beta^{-}$-decay Urca cycle. In its essence, the neutrino luminosity is determined by the quantity of nuclides in an Urca pair, which is \nset by the isobaric abundance and the Urca shell thickness, multiplied by the Urca cycle rate,\nwhich in turn is determined by the weak transition rate. The shell thickness is proportional to the temperature $T$, since the Urca cycle can operate within the window $\\mu_e\\approx|Q_{\\rm{EC}}|\\pm k_\\mathrm{B}T$, and inversely proportional to the local gravity $g$,\nas follows from differentiating equation~(\\ref{eq:z-mu})~\\cite{Scha14}. The weak transition rates for the Urca cycle are \nmostly determined by the integral over the momentum phase-space~\\cite{Beth47}. \nDue to the electron-degeneracy, the phase-space is limited to the small \nthermal window around $E_{e^{-}}\\approx \\mu_e$, which is several $k_\\mathrm{B}T$ wide~\\cite{Tsur70}.\n\nThe above considerations result~\\cite{Tsur70,Deib16} in a neutrino cooling luminosity\n\\begin{equation}\n\\label{equation:Lnu}\nL_{\\nu}(Z,A) \\approx L_{34}(Z,A)\\times10^{34}\\,{\\rm{erg~s}}{}^{-1}X(A)T_{9}^{5}\\left(\\frac{g_{14}}{2}\\right)^{-1}\\left(\\frac{R}{10~\\mathrm{km}}\\right)^{2}. \\\\\n\\end{equation}\nHere $X(A)$ is the mass-fraction of the $e^{-}$-capture parent nucleus in the composition, $T_{9}$ is the temperature of the Urca shell in units of $10^{9} \\, \\mathrm{K}$, \nand $g_{14}\\equiv g\/(10^{14}~\\rm{cm}~\\rm{s}^{-2})$.\nThe intrinsic cooling strength of the Urca pair, $L_{34}(Z,A)$, is given by\n\\begin{equation}\n\\label{equation:L34}\nL_{34}(Z,A)\\approx 0.87\\left(\\frac{10^{6}~{\\rm{s}}}{ft}\\right)\\left(\\frac{56}{A}\\right)\\left(\\frac{Q_{\\rm{EC}}(Z,A)}{4~{\\rm{MeV}}}\\right)^{5}\\left(\\frac{\\langle F\\rangle^{*}}{0.5}\\right), \\\\\n\\end{equation}\nwhere $\\langle F\\rangle^{*}\\equiv\\langle F\\rangle^{+}\\langle\nF\\rangle^{-}\/(\\langle F\\rangle^{+}+\\langle F\\rangle^{-})$, the Coulomb factor $\\langle F\\rangle^{\\pm}\\approx2\\pi\\alpha_f\nZ\/|1-\\exp(\\mp2\\pi\\alpha_f Z)|$, and $\\alpha_f\\approx1\/137$ is the\nfine-structure constant. $ft$ is the comparative half-life, which should be implemented as $ft=({ft_{e^{-}\\rm{-capture}}+ft_{\\beta^{-}}})\/{2}$, since the degeneracy of the parent state impacts the transition rate. However, $ft$ from the $e^{-}$-capture and $\\beta^{-}$-decay generally agree within a factor of a few~\\cite{Paxt15e}, so using an estimate for one or the other transitions results in a negligible uncertainty compared to other contributions.\n\nThe cyclic nature of the $e^{-}$-capture\/$\\beta^{-}$-decay enables the energy release from cooling to exceed energy deposition from the inherently one-way $e^{-}$-capture-only cases by more than an order of magnitude~\\cite{Meis15}. We stress that this is \nplausible when the crust temperature is of the order of $1~$GK, given the $T^{5}$ dependence of equation (\\ref{equation:Lnu}). For a typical outer crust $Q_{\\rm{EC}}\\approx12$~MeV and a not unreasonable estimate of ${\\rm log}_{10}(ft)\\approx5$, \nequation (\\ref{equation:L34}) results in $L_{34}\\approx10^{3}$. Similar $L_{34}$ values \nare obtained for $Q_{\\rm{EC}}\\approx8$~MeV and ${\\rm log}_{10}(ft)\\approx4$ or $Q_{\\rm{EC}}\\approx15$~MeV and ${\\rm log}_{10}(ft)\\approx5.7$.\nFor such nuclei, $L_{\\nu}$ will be significant for $X(A)\\gtrsim1$\\%~\\cite{Meis17} (compare with the $e^-$-capture heating estimate above). \nTherefore, it is critical for model calculations of near-surface nuclear burning to follow abundance evolutions even for relatively low-abundance nuclides. This requires multi-species and multizone nuclear reaction networks with precise nuclear physics input~\\cite{Cybu16}. \nFurthermore, it is worth noting that cooling by $e^{-}$-capture\/$\\beta^{-}$-decay cycling does not require continued accretion, whereas $e^{-}$-capture heating only occurs while accretion is active.\n\n\nFrom equations~(\\ref{equation:Lnu}) and (\\ref{equation:L34}), it is evident that nuclear masses and low-lying nuclear structure of neutron-rich isobars of abundant surface-burning ashes are required to determine the strength of Urca cooling in accreted neutron star crusts. Ash abundances (discussed in detail in section~\\ref{section:production}) are also critical, as $L_{\\nu}(Z,A)$ scales linearly with the abundance of the Urca pair $X(A)$. Nuclear masses impact the location and strength of Urca cooling since $Q_{\\rm{EC}}$ is \nrelated to atomic mass excesses $\\rm{ME}$ via $Q_{\\rm{EC}}=\\rm{ME}(Z,A)-\\rm{ME}(Z-1,A)$. Low-lying structure of Urca nuclides is key as $ft$ is directly related to the change in spin $\\Delta J$ and parity $\\Delta\\pi$ required for a weak transition to occur~\\cite{Sing98}.\n\nIn the absence of direct measurements, nuclear masses are estimated using global mass models. Popular choices for crust model calculations include the Finite Range Droplet Model (FRDM)~\\cite{Moll12}, one of the many Hartree-Fock-Bogoliubov variations (e.g. HFB-21~\\cite{Gori10}), and one of the Weisz\\\"{a}cker-Skyrme variations (e.g. WS3~\\cite{Wang10}). Globally, the trend in the odd-even mass staggering for increasing neutron-richness is of particular interest. Large odd-even mass-differences along an isobar will result in greater $e^{-}$-capture heating, but will eliminate the possibility of Urca cooling for even-$A$ nuclides~\\cite{Meis15}. Due to this effect, the largest crustal heating is predicted for FRDM, whereas HFB-21 \nresults in the most significant cooling~\\cite{Scha14}. Nonetheless, the sensitivity of $L_{34}(Z,A)$ to $Q_{\\rm{EC}}(Z,A)$ highlights the need for experimental constraints. Recently, such constraints have been the province of time-of-flight mass measurements, as this technique is able to access the most exotic nuclides as compared to alternatives~\\cite{Meis13}. Already such measurements have constrained the heating for some of the strongest heating sources~\\cite{Estr11,Meis16} and ruled-out Urca cooling for $^{56}\\rm{Ti}\\leftrightarrow^{56}\\rm{Sc}$~\\cite{Meis15}, which was once thought to be the strongest cooling source~\\cite{Scha14}.\n\nPresently, experimental data for $ft$ of weak transitions between neutron-rich nuclides are limited.\nFor several cases near to stability, $ft$ is available for ground-state to ground-state transitions and ground-state to excited-state (which would be $e^{-}$-capture parent excited state to $e^{-}$-capture daughter ground-state) from $\\beta^{-}$-decay measurements. Developments are ongoing to enable $(d,^{2}\\rm{He})$ charge-exchange measurements in inverse kinematics with radioactive ion beams, which would provide $ft$ for $e^{-}$-capture parent ground-state to $e^{-}$-capture daughter ground and excited states. However, the latter technique requires a radioactive beam rate of $\\sim10^{6}$~Hz (e.g. Ref.~\\cite{Noji15}), limiting the applicable distance from stability. For the large number of remaining cases lacking direct $ft$ measurements, one must resort to theoretical calculations or estimates derived from data-based systematics~\\cite{Scha14,Deib16,Meis17}.\n\nAmong theoretical estimates, $ft$ from shell-model calculations tend to compare most favorably to experiment~\\cite{Cole12}; however, the absence of large-scale calculations results in limited coverage of the nuclear chart~\\cite{Sull16}. As such, predictions from quasi-random phase-approximation (QRPA) calculations have frequently been adopted instead~\\cite{Gupt07,Scha14}. A more recent, alternative procedure has been to employ $ft$-values consistent with systematics based on the change in spin and parity $\\Delta J^{\\Delta\\pi}$ for a weak transition~\\cite{Deib16,Meis17}. Such a compilation is available in Reference~\\cite{Sing98}. This \nmeans that improved predictions can be provided by spectroscopic measurements, which determine $E_{xs}$ and $J^{\\pi}$ for low-lying states of interest. This is advantageous since spectroscopic measurements require far fewer statistics than measurements of $ft$ via $\\beta^{-}$-decay. \nTherefore constraints can be provided for more exotic nuclides.\n\nGiven the dearth of available data, it is clear that many additional direct and indirect experimental constraints on $ft$ are necessary. In particular, measurements are critical for neutron-rich isobars of odd-$A$ nuclides predicted to have surface-burning ash abundances on the percent-level. Current estimates implicate $^{33}\\rm{Al}\\leftrightarrow^{33}\\rm{Mg}$ and $^{55}\\rm{Sc}\\leftrightarrow^{55}\\rm{Ca}$ as the most significant Urca pairs\nfor X-ray burst and superburst ashes, respectively~\\cite{Meis17}. \n\n\n \\subsection{Neutron Emission}\\label{sec:nuc_emission}\n \nWith increasing $\\mu_e$, $e^{-}$-captures shift the nuclear composition along isobaric chains towards the neutron drip line where the neutron separation energy $S_\\mathrm{n}$ becomes negative. In that case, $(e^{-},x\\mathrm{n})$ reactions \n(here $x$ means the number of neutrons emitted)\nlead to the buildup of a free neutron abundance. In fact, neutron emission in this process can occur before the neutron drip line if excited states with $E_\\mathrm{xs}> S_\\mathrm{n}$ \nare populated during electron captures \\cite{Gupt08}, as shown in case (b) in figure~\\ref{figure:ECschematic} (at the second $e^{-}$-capture in double capture, or if the first allowed transition at the first step is to the excited state). After the neutron drip line, some neutron emission can proceed in rapid sequences called superthreshold electron-capture cascades (SEC) \\cite{Gupt08}. In these sequences the products of $(e^{-}, x\\mathrm{n})$ reactions can be highly unstable to more $e^{-}$-captures which can occur in a cascade faster than neutron captures.\nThis situation can repeat many times resulting in a diverse reaction sequence with large neutron exchange between isotopic chains. SEC are set by a competition between the $e^{-}$-capture and $(\\mathrm{n},\\gamma)-(\\gamma, \\mathrm{n})$ rates. Note that neutron capture reactions strongly depend on the \nneutron degeneracy and, since radiative processes are involved, can be enhanced by plasma physics effects \\cite{Shternin2009PhRvD,Shternin2012PhRvC}. Some free neutrons can appear (to be immediately recaptured by the most abundant nuclei) from $(e^{-},x\\mathrm{n})$ reactions as early as densities of $\\mu_e\\gtrsim 14$~MeV, but do not significantly alter the reaction sequences~\\cite{Lau18}. The SEC processes are only found in multicomponent reaction networks \\cite{Gupt08,Lau18} and do not appear in simplified one-species treatments \\cite{Haen90,Haen03,haensel2008}. Neutron emission processes are an important source of heat since emitted neutrons are rapidly thermalized by mutual collisions. The presence of SEC shifts this type of heating to shallower depths \\cite{Gupt08} than found in one-component models.\n\n\n\\subsection{Pycnonuclear Reactions}\\label{sec:pycno}\n\nFusion reactions become significant when the reaction rate is faster than the rate at which matter is buried to a depth where another $e^{-}$-capture and\/or neutron emission is\/are energetically favorable~\\cite{Sato79,Haen90}. As nuclei are charged, \nfusion reactions require tunneling through the Coulomb barrier. In dense stellar matter the probability of the barrier penetration, and so the rates of fusion reactions, are strongly affected by the environmental conditions. Only when ions are in the weak coupling regime, $T\\gtrsim T_\\mathrm{l}$, where $T_\\mathrm{l}$ corresponds to $\\Gamma=1$ (see section~\\ref{section:structure}), the standard thermonuclear burning (regime I in figure~\\ref{fig:pycno}a) operates. In this regime, the reaction rate is determined solely by the temperature and reacting nuclide abundances. In neutron star crusts most fusion reactions operate in the opposite, pycnonuclear limit, where the possibility to overcome the Coulomb barrier is driven by zero-point oscillations of the ions around their equilibrium positions. The rates of the pycnonuclear reactions are subject to huge uncertainties. In order to discuss those we first briefly address fusion in the familiar thermonuclear regime.\n\n\n\\begin{figure}[ht]\n\\begin{center}\n\\begin{minipage}[t]{0.45\\textwidth}\n\\includegraphics[width=\\textwidth]{Cpycno.pdf}\n\\end{minipage}\n\\hspace{0.05\\textwidth}\n\\begin{minipage}[t]{0.45\\textwidth}\n\\includegraphics[width=\\textwidth]{MgNepycno.pdf}\n\\end{minipage}\n\\end{center}\n\\caption{Fusion burning in dense stellar matter. {\\it (a)}: Phase diagram for carbon burning adapted from \\cite{Chugunov2007PhRvD}. Black solid lines show relevant temperatures (marked in the plot) separating different burning regimes (I--V). The dash-dotted line shows solidification temperature in a classical liquid ($\\Gamma=175$). \nUpper and lower red solid curves show the loci of the burning time $\\tau_\\mathrm{burn}=1\\,\\mathrm{s}$ and $10^{10}\\,\\mathrm{yr}$, respectively. Thin dashed lines bracket the uncertainty in calculations as discussed in the text. Dotted lines represent the pure thermonuclear regime neglecting all medium effects. Values of $\\mu_e$ are shown on the upper horizontal scale. {\\it (b)}: Pycnonuclear reaction rates for $^{34}$Ne fusion (upper, red curves) and $^{40}$Mg fusion (lower, blue curves) in the neutron star crust assuming respective one-species composition and body-centered cubic lattice. Solid lines give the optimal reaction rates, while thin dashed lines bracket the theoretical uncertainties in Coulomb barrier penetration calculations. Symbols (open circles for $^{34}$Ne and open triangles for $^{40}$Mg) indicate in each case the depths where $\\tau_\\mathrm{burn}=1$~yr, 1~d, and 1~s. }\\label{fig:pycno}\n\\end{figure}\n\n\nThe key nuclear physics quantity for estimating a fusion reaction rate is the astrophysical $S$-factor~\\cite{Mart09} \nrelated to the reaction cross section $\\sigma(E)$ for charged-particle reactions by $\\sigma(E)= S(E) E^{-1}\\,\\rm{exp}\\left(-2\\pi\\eta\\right)$, where $E$ is the center of mass energy for the reaction,\n$\\eta=\\alpha_f Z_{1}Z_{2}\\sqrt{{\\mu_{\\rm{red}}c^{2}}\/{(2E)}}$, is the Sommerfeld parameter, \n$Z_{i}$ and $\\mu_{\\rm{red}}c^{2}$ are the nuclear charges and reduced mass for the fusing nuclei, respectively. The advantage of introducing the $S$-factor is that it is a relatively smooth function of energy, where the main part of the strong energy-dependence resulting from the Coulomb tunneling probability is factored out by $\\rm{exp}\\left(-2\\pi\\eta\\right)$. For a structureless (i.e. non-resonant) reaction, which most of the relevant reactions here are thought to be (though see the remark in section~\\ref{sssec:superburstnuc} $^{12}$C$+$$^{12}$C), \nthe counterbalance between the exponential increase in tunneling probability towards higher energies and the exponential decrease of the number of energetic nuclei results in that only nuclei in the vicinity of the \nGamow peak energy $E_\\mathrm{pk}$ in the tail of the thermal distribution actually fuse. The Gamow peak energy is given by\n\\begin{equation}\\label{eq:Gamow_th}\nE_\\mathrm{pk}=\\left(\\frac{\\pi^2 \\mu_\\mathrm{red} Z_1^2 Z_2^2 e^4 k_\\mathrm{B}^2 T^2}{2\\hbar^2}\\right)^{1\/3}\\approx 0.5\\,\\mathrm{MeV}\\ \\left(\\frac{A}{12}\\right)^{1\/3} \\left(\\frac{Z}{6}\\right)^{4\/3} \\left(\\frac{T}{10^8\\,\\mathrm{K}}\\right)^{2\/3},\n\\end{equation}\nwhere the second equality is for the fusion of like nuclei. Under the assumption of weak $S(E)$ dependence, the thermonuclear rate becomes \n\\begin{equation}\\label{eq:fus_rate_therm}\n\\lambda_\\mathrm{th}= 4\\frac{n_i^2}{2} \\sqrt{\\frac{2 E_\\mathrm{pk}}{3\\mu_\\mathrm{red}}}\\frac{S(E_\\mathrm{pk})}{k_\\mathrm{B} T} \\mathrm{exp}\\left(-3E_\\mathrm{pk}\/(k_\\mathrm{B} T)\\right).\n\\end{equation}\n\nDirect measurements of fusion between two neutron-rich nuclides is not possible, as this would require high-intensity low-energy beam-beam collisions between radioactive nuclides and no such facility exists. As such, experimental studies focus on fusion involving stable nuclides, which are useful in terms of benchmarking theoretical models~\\cite{Bass77,Yako06a,Bear10}. Nonetheless, measurements of $S(E)$ for more systems are welcome, particularly in light of the fact that, while theoretical predictions and experimental results agree for fusion reactions involving some neutron-rich nuclides~\\cite{Carn14}, intriguing discrepancies remain for other cases~\\cite{Rudo12,Stei14,Sing17}.\n\nThe energies relevant for neutron stars are smaller than the current experimental measurements reach. \nHence, determinations of the $S$-factor are either performed by extrapolations from fits to data measured at much higher energies or by theoretical calculations based on tunneling through a barrier corresponding to a theoretical nuclear potential~\\cite{Gasq05,Yako06a,Yako06b,Bear10,Horo08}. Theoretical \nstudies amount to calculating the tunneling probability for some angular momentum transfer $\\ell$, as represented by the so called transmission coefficient $T_{\\ell}$ ($\\leq1$). The cross section results from the semi-classical relation $\\sigma(E)=\\pi\\mbox{\\makebox[-0.5ex][l]{$\\lambda$} \\raisebox{0.7ex}[0pt][0pt]{--}}^{2}\\Sigma_{\\ell=0}^{\\ell,\\rm{max}}(2\\ell+1)T_{\\ell}$,\nwhere $\\mbox{\\makebox[-0.5ex][l]{$\\lambda$} \\raisebox{0.7ex}[0pt][0pt]{--}}$ is the reduced de Broglie wavelength. $\\ell_{\\rm{max}}={R_{\\rm{max}}}\/{\\mbox{\\makebox[-0.5ex][l]{$\\lambda$} \\raisebox{0.7ex}[0pt][0pt]{--}}}$ corresponds to the angular momentum at which the impact parameter is equal to the sum of the participating nuclear\nradii $R_{\\rm{max}}=r_{0}(A_{\\rm{1}}^{1\/3}+A_{\\rm{2}}^{1\/3}$) ($r_{0}\\sim1.2$~fm), though generally the few lowest $\\ell$ provide the dominant contributions~\\cite{Gasq05}. \nA large set \nof astrophysical $S$-factors relevant to neutron star studies was collected recently in Reference \\cite{Afanasjev2012PhRvC}, where an economical analytical parameterization was proposed.\n\nUsing $S$-factors from Reference~\\cite{Afanasjev2012PhRvC}, in figure~\\ref{fig:pycno}a we plot with dotted lines the locations in $T-\\rho$ plane where the thermonuclear burning time $\\tau_\\mathrm{burn}\\equiv n_\\mathrm{i}\/\\lambda_\\mathrm{th}$ is equal to $10^{10}$~yr and $1$~s for the $^{12}$C+$^{12}$C reaction in a one-species carbon plasma. However, at large densities and\/or low temperatures (when $\\Gamma\\gtrsim 1$), these rates become irrelevant and the effects of ion correlations start to play the dominant role. The respective lines of $\\tau_\\mathrm{burn}=\\mathrm{constant}$ are shown with solid red curves and are clearly very different from the dotted lines. One finds five regimes of nuclear burning (figure~\\ref{fig:pycno}a) \\cite{Salp69}. In the regime of strong ion coupling $\\Gamma\\gtrsim 1$, screening of the Coulomb interaction eases barrier penetration enhancing the reaction rates as seen in figure~\\ref{fig:pycno}a. This burning regime II, called thermonuclear with strong plasma screening, operates until quantum effects in ion motion become important at $T\\lesssim T_\\mathrm{pi}$. Here $T_\\mathrm{pi}$ is the \nion plasma temperature (for a one-species plasma)\n\\begin{equation}\\label{eq:Tpi}\n T_\\mathrm{pi}\\equiv \\frac{\\hbar \\omega_\\mathrm{pi}}{k_\\mathrm{B}} = \\frac{\\hbar}{k_\\mathrm{B}} \\sqrt{\\frac{4 \\pi Z^2 e^2 n_i}{A m_u}}\\approx1.9\\times 10^9\\,\\mathrm{K}\\ \\sqrt{\\frac{Z}{A}}\\left(\\frac{\\mu_e}{20\\,\\mathrm{MeV}}\\right)^{3\/2},\n\\end{equation}\nwhere $\\omega_\\mathrm{pi}$ is the ion plasma frequency.\nThe pycnonuclear regime V, already mentioned above, operates for the lowest temperatures $T\\lesssim T_\\mathrm{q}=0.5\\, T_\\mathrm{pi}\/\\ln\\left(T_\\mathrm{l}\/T_\\mathrm{pi}\\right)$. The remaining regimes are the thermally enhanced pycnonuclear regime IV ($0.5T_\\mathrm{pi}\\gtrsim T\\gtrsim T_\\mathrm{q}$) and the thermopycnonuclear regime III ($T_\\mathrm{pi}\\gtrsim T\\gtrsim 0.5T_\\mathrm{pi}$), which is the most uncertain. We do not discuss intermediate regimes II--IV here, see detailed discussions in References \\cite{Gasques2005PhRvC,Yako06a,Chugunov2007PhRvD,Pote12,Chugunov2009PhRvC}.\n\nWe now briefly describe the pycnonuclear regime mainly following the discussion in References \\cite{Gasques2005PhRvC,Yako06a}. More details can be found there and in References \\cite{Salp69,Yako06b,Afanasjev2012PhRvC,Chugunov2007PhRvD}. For simplicity we first address the single-species composition and then discuss the more relevant case of mixtures. \nThe rate in the pycnonuclear regime $T\\lesssim T_\\mathrm{q}$ is temperature-independent. In analogy to equation (\\ref{eq:fus_rate_therm}) it can be written\n\\begin{equation}\\label{eq:fus_rate_pycno}\n\\lambda_\\mathrm{pyc} =C_\\mathrm{pyc} \\frac{n_i^2}{2} S(E_\\mathrm{pk}) \\frac{\\hbar}{m_i Z^2 e^2}\\, \\xi^\\beta \\exp\\left(-\\alpha \\xi \\right),\n\\end{equation}\nwhere the typical interaction energy is $E_\\mathrm{pk}\\sim \\hbar \\omega_\\mathrm{pi}$ [cf. equation~(\\ref{eq:Gamow_th})]. Constants $C_\\mathrm{pyc}$, $\\alpha\\sim 0.7$, and $\\beta$ depend on the model for the Coulomb barrier penetration via zero-point vibrations, and the parameter \n$\\xi=d^2\/r_\\mathrm{rms}^2$ characterizes the relative amplitude of these vibrations. Here $r_\\mathrm{rms}^2=\\hbar\/(2\\mu_\\mathrm{red}\\omega_\\mathrm{pi})$ is the mean-square displacement of the oscillating ion and $d$ is the equilibrium distance between the closest reacting neighbors. It is assumed that ions in the one-species plasma form a body-centered cubic lattice. Then $d=(3\\pi^2)^{1\/6} a$, where $a$ is the ion-sphere radius defined in section~\\ref{section:structure}. Putting numbers in,\n\\begin{equation}\n\\xi\\approx 0.2 A^{1\/2} Z^{7\/6} \\left(\\frac{\\mu_e}{20\\,\\mathrm{MeV}}\\right)^{-1\/2}.\n\\end{equation}\nUnder conditions relevant for the neutron star crust, $\\xi$ is large and gradually decreases with depth. For pure $^{12}$C at $\\mu_e=20$~MeV, $\\xi=5.7$, and for $^{34}$Ne, the first species to fuse in the one-component model of Reference \\cite{Haen90}, $\\xi=17$ at the same depth. The pre-exponential factor in equation (\\ref{eq:fus_rate_pycno}) is usually large so the pycnonuclear reactions start to be important at $\\xi\\gg1 $. Such reaction rate is very sensitive to variations in $\\xi$ and in the exponential prefactor $\\alpha$. Different models of tunneling resulting in small variations in $\\alpha$ transform to huge (several orders of magnitude) variations in $\\lambda_\\mathrm{pyc}$ \\cite{Gasq05}. If the type of the crystalline lattice is other than body-centered cubic, the value of $d$ can change producing huge variations again (see the MCP case below). To illustrate these uncertainties, in figure~\\ref{fig:pycno}b we plot the zero-temperature pycnonuclear reaction rates as a function of $\\mu_e$ for two potentially important pycnonuclear reactions for the neutron star inner crust, $^{34}$Ne+$^{34}$Ne \\cite{Haen90} and $^{40}$Mg+$^{40}$Mg, using $S$-factors from Reference~\\cite{Afanasjev2012PhRvC}. Solid lines are the `optimal' rates, while thinner dashed lines correspond to maximal and minimal rates, see Reference~\\cite{Gasq05} for an extensive discussion. Triplets of open symbols on each rate curve give depths where $\\tau_\\mathrm{burn}=1\\,\\mathrm{yr},\\,1\\,\\mathrm{d},\\,\\mathrm{and}\\, 1\\,\\mathrm{s}$. Note, that we use $\\mu_e$ instead of $\\rho$ here, since in the inner crust the latter depends on the amount of free neutrons and hence on the equation of state. As seen, the uncertainty in the rate can be as large as ten orders of magnitude. Therefore, depending on a model, a given pycnonuclear reaction can start at various points, can be delayed and start after the accretion ceased \\cite{Yako06b}, or does not operate at all. \nThe same uncertainties are shown with dashed lines in the left panel in figure~\\ref{fig:pycno}a for carbon burning where all regimes are considered. It is clear that the theoretical uncertainty increases with regime number.\n\nThe situation is even more complicated in a multi-species plasma \\cite{Yako06a}. \nThe most important point is the availability of close neighbors. In the most popular uniform mixing model, the mean equilibrium distance is taken to be the same as in the one-component model. Then the rates are calculated in a similar way as in the one-component plasma (with proper renormalization of the model parameters, and this procedure is also not very certain \\cite{Yako06a}). \nIt is unclear, however, if the formalism applicable for a one-component body-centered cubic lattice is equally good for potentially amorphous structures, or for less abundant chaotic impurities (if they have smaller charges they may start to fuse first). One can expect more frequent close encounters for this case than in a regular lattice, increasing the reaction rate. On the other hand, consider a regular lattice of two intermittent species. The closest distance $d$ for two nuclei of the same species will be a factor of 1.155 larger than in the one-component or uniformly mixed case, greatly suppressing the pycnonuclear rate due to exponential behavior in $d^2$ \\cite{Yako06a}. Additional complications arise when the structural changes caused during the course of pycnonuclear burning are considered \\cite{Salp69}. When two nuclei in a lattice fuse, this clearly creates a defect. What will happen next, how the lattice will react to a number of such defects and in what respect this can affect the burning rates, is basically unexplored.\n\nNuclear physics enters the pycnonuclear reaction rate (equation (\\ref{eq:fus_rate_pycno})) through the astrophysical factor $S(E_\\mathrm{pk})\\approx S(0)$. As the pycnonuclear reactions are thought to occur in the inner crust of the neutron star, \nthe presence of free neutrons provides another potential source of uncertainty. It is not unreasonable to assume that the neutron gas can alter the properties of the Coulomb barrier, making it lower and\/or thicker. It is impossible to study this experimentally. In Reference~\\cite{Afanasjev2012PhRvC}, the authors addressed this problem by introducing phenomenological modifications of the barrier shape. They found, taking $^{34}$Ne as an example, that even slight perturbations of the barrier shape lead to another ten orders of magnitude variations in the $S$-factor at small energies and, as a consequence, in the reaction rate. \n\nThe onset of pycnonuclear burning is thus strongly model-dependent. The lighter nuclei can burn even in the ocean \\cite{Horo08}. However the main pycnonuclear fusion is expected to occur in the inner crust \\cite{Haen90,haensel2008,steiner2012}. The importance of the pycnonuclear burning is that it is the main deep crustal heating source. For instance, in a one-species-per-depth model of \\cite{Haen90,Haen03,haensel2008}, the first pycnonuclear reaction ($^{34}$Ne+$^{34}$Ne) releases about 0.5~MeV\/u, or $25-35$ percent of the total heating. In some cases, similar to superthreshold electron capture cascades discussed in the previous section, the product of the pycnonuclear fusion may become immediately unstable to a cascade of electron capture reactions which drives it back to the initial nucleus by a chain of neutron emission reactions. In other words, one of the nuclei acts as a catalyst for the conversion of the other one into free neutrons \\cite{steiner2012,Lau18}. Note, that like superthreshold electron capture cascades this scenario is only possible when multi-component reaction networks are employed. \n\nAt first glance, the strong variation in the properties of pycnonuclear burning make impossible any conclusions about the nuclear transformations and heat release in the inner crust. However, this is not the case. As it has been shown \\cite{haensel2008}, the total heat released in the crust is remarkably independent of the particular sequence of nuclear reactions an accreted element encounters. Indeed, close to the crust-core boundary the accreted crust composition is expected to merge with the cold-catalyzed one. This means that the energy released in the accreted crust is determined by the heat reservoir the initial ashes have with respect to the ground (cold-catalyzed) state. During compression, this extra energy is released in one way or another, but the net result is the same \\cite{haensel2008}. The total heat release in the accreted crust is found to be about $2$~MeV\/u. Looking from the other side, the total heating is governed by the properties of the cold-catalyzed state. Reference~\\cite{steiner2012} analyzed several equations of state in the inner crust and found that some of them have lower ground states predicting twice the heating that is assumed in traditional models. Unfortunately, the pycnonuclear uncertainties strongly impact the heating sources' distribution, which may considerably affect the crustal thermal state during and after an accretion outburst. \n\n\n\\section{Observable Impact of Nuclei in the neutron star Ocean and Crust} \\label{section:impact}\n\n \\subsection{Thermal Timescale of the Accreted Crust }\\label{subsection:impact_inner}\n\nAs shown above, nuclear transformations during accretion modify the composition of the crust and are the source of non-equilibrium heating\/cooling processes. Below we discuss the importance of these processes for observable systems -- cooling X-ray transients and for ignition conditions and lightcurves of X-ray superbursts. To begin, we consider the heat propagation in the crust.\n\nThe thermal diffusion timescale \nbetween a column depth $y$ \nand the surface is %\n\\cite{Henyey1969ApJ}\n\\begin{equation}\\label{eq:tau_diff}\n\\tau = \\frac{1+z}{4}\\left[\\int_0^y \\left(\\frac{c_P}{\\rho K}\\right)^{1\/2} \\mathrm{d } y' \\right]^2,\n\\end{equation}\nwhere $c_P$ is the specific heat capacity per unit mass\\footnote{Under neutron star crust conditions, the heat capacity at constant pressure $c_P$ and heat capacity at constant volume $c_V$ are the same, $c_P\\approx c_V$ \\cite{HPY2007}.} and $K$ is the thermal conductivity. The heat capacity in the ocean and outer crust is set by ions, except for the outer ocean at high temperatures, where the electrons dominate. In the inner crust, free neutrons dominate the heat capacity unless they are superfluid (see section~\\ref{section:structure}). In the superfluid regions, the neutron contribution is suppressed and $c_P$ is determined by ions and electrons. \n\nDegenerate electrons provide the dominant contribution to the thermal conductivity and it is mainly set by electron-ion scattering:\n\\begin{equation}\\label{eq:kappa_e}\nK=K_e=\\frac{\\pi^2 c^2 k_\\mathrm{B}^2 T n_e}{3 \\mu_e \\nu_{\\rm ei}},\n\\end{equation}\nwhere $\\nu_{\\rm ei}$ is the effective electron-ion collision frequency. The character of the electron-ion collisions depends strongly on the phase state of the ionic system. In the multicomponent liquid ocean, under the linear mixing rule approximation (e.g., \\cite{pote99})\n\\begin{equation}\\label{eq:nuei_liq}\n\\nu_{\\rm ei} = \\frac{4\\alpha_f^2}{3\\pi} \\frac{\\mu_{\\rm e}}{\\hbar} \\frac{\\langle Z^2 \\Lambda_{\\rm ei}\\rangle }{\\langle Z\\rangle}, \n\\end{equation}\nwhere $\\Lambda_{\\rm ei}\\sim 1$ \nis the Coulomb logarithm of the species in mixture. For estimates, the mean ion model with $\\langle Z^2 \\Lambda_{\\rm ei}\\rangle\\approx \\langle Z \\rangle^2$ can be used \\cite{pote99}. Consider, for example, thermal wave propagation in the hot ocean, where the heat capacity is set by electrons, $c_P=\\pi^2 \\langle Z\\rangle k_\\mathrm{B}^2 T\/(\\langle A \\rangle m_u \\mu_e)$. The integrand in equation~(\\ref{eq:tau_diff}) is then temperature-independent and behaves like $y^{-5\/8}$ (neglecting the composition dependence on $y$). Thus the resulting estimate is\n\\begin{equation}\\label{eq:time_ocean}\n\\tau \\approx 16.5\\ {\\rm hr} \\left(\\frac{y}{10^{12}~\\mathrm{g}~\\mathrm{cm}^{-2}}\\right)^{3\/4}\n\\left(\\frac{g_{14}}{2.44}\\right)^{-5\/4} \\frac{\\langle Z\\rangle \\langle Z^2 \\Lambda_{\\rm ei}\\rangle}{26^3} \\left(\\frac{56}{\\langle A\\rangle}\\right)^2\n\\end{equation}\nfor $1+z=1.31$.\nThis gives \nan order of magnitude estimate of the timescale for superburst decay \\cite{cumming2001,2004CummingMacBeth,Stro02}.\n\nThe situation is different in the crust. If a perfect crystal is formed (as expected for the pristine crust, section~\\ref{subsection:pristine}), the thermal conductivity is set by electron-phonon scattering. The corresponding collision frequency in the classical limit ($T\\gtrsim 0.2 T_\\mathrm{pi}$) in the single-phonon approximation is \\cite{Yakovlev1980,Schmitt17}\n\\begin{equation}\\label{eq:nuei_phon}\n\\nu_{\\rm ei}=\\nu_{\\rm e-ph}\\approx 13\\;\\alpha_f \\frac{k_\\mathrm{B}T}{\\hbar}.\n\\end{equation}\nNote that this expression neglects various corrections (e.g., \\cite{pote99}) but is sufficient for the qualitative discussion here. Clearly, since $\\nu_{\\rm e-ph}\\propto T$, it is much smaller than $\\nu_{\\rm e i}$ in the liquid phase [equation~(\\ref{eq:nuei_liq})]; accordingly, the thermal conductivity is high. The reason is that the elastic (Bragg) part of the scattering off the ordered crystalline lattice leads to renormalization of the electron states to Bloch waves, and as such does not lead to dissipation.\n\nConsider for example the diffusion timescale of the crystalline outer crust. This is relevant for studies of crustal cooling in young ($\\lesssim 100$~yr) neutron stars \n(e.g., \\cite{Gnedin2001MNRAS,Shternin2008AstL}) and \nin the so-called quasi-persistent transients (section \\ref{sec:quasipers}). \nFor a classical crystal,\nthe ion heat capacity is $c_P=3 k_\\mathrm{B}\/(A m_u)$ and from equation~(\\ref{eq:tau_diff}) one obtains \\cite{Brow09}\n\\begin{equation}\\label{eq:time_crust}\n\\tau\\approx 40\\ \\mathrm{days}\\times y_{14}^{3\/4} \\left(\\frac{g_{14}}{2.44}\\right)^{-5\/4} \\left(\\frac{56}{\\langle A \\rangle}\\right)^2 \\frac{\\langle Z \\rangle}{26},\n\\end{equation}\nwhere now $y_{14}$ is the column density measured in units of $10^{14}~\\mathrm{g}~\\mathrm{cm}^{-2}$ and again $1+z=1.31$. The pure crust cools relatively fast. However, if weakly correlated impurities are present, they contribute to the charge fluctuations that electrons scatter off. It is customary to write the total conductivity using Mathiessen's rule $\\nu_{\\rm ei}=\\nu_{\\rm e-ph}+\\nu_{\\rm e-imp}$, where $\\nu_{\\rm e-imp}$ is given by equation~(\\ref{eq:nuei_liq}) with the substitution $\\langle Z^2 \\Lambda_{\\rm ei}\\rangle \\to Q_\\mathrm{imp} \\Lambda_\\mathrm{imp}$, where $\\Lambda_\\mathrm{imp}\\sim 1$ is the Coulomb logarithm for uncorrelated impurities scattering. \nComparing $\\nu_{\\rm e-imp}$ with $\\nu_{\\rm e-ph}$ we find that the former dominates if\n\\begin{equation}\nQ_\\mathrm{imp} \\Lambda_\\mathrm{imp}\\gtrsim 31\\ \\frac{\\langle Z \\rangle}{26} \\frac{30\\ \\mathrm{MeV}}{\\mu_e}\\, \\frac{T}{10^8~\\mathrm{K}}.\n\\end{equation}\nThus, the impurity scattering becomes more important in the inner crust (large $\\mu_e$) at low temperatures (since impurity scattering is elastic and thus temperature-independent). Moreover, at $T\\lesssim 0.2 T_\\mathrm{pi}$, the electron-phonon scattering is further suppressed by quantum effects roughly as $\\propto T\/(0.2 T_\\mathrm{pi})$ (e.g., \\cite{Schmitt17}), making the impurity contribution dominant even for a ``small'' impurity parameter $Q_\\mathrm{imp}\\sim 1$. These estimates show that large impurity content would increase the thermal diffusion time. \n\nThe large impurity parameter of the ashes from most surface burning processes (see figure~\\ref{figure:ECcrustcomposition} and table~\\ref{table:AvgZAQTable}) raises doubts about the applicability of the simple prescription of the pure crystal with uncorrelated impurities to the multicomponent mixture of the accreted crust. Most of the recent molecular dynamics studies suggest that crystallization occurs even for large $Q_{\\rm imp}$ in a way that ions of large charge occupy lattice sites (of a body-centered cubic lattice), while smaller charge nuclei are found in interstitial regions \\cite{Horo09,Horowitz2007PhRvE,Hughto2011PhRvE,Horowitz2009PhRvE}. The appearance of the Bragg structure in scattering is clearly seen in simulations. In addition, it was found that the impurities are actually correlated. Calculations of the thermal conductivity \\cite{Horowitz2007PhRvE,Horo09,Rogg16} have shown that the uncorrelated impurities limit underestimates the impurity contribution. Recently, the authors of Reference~\\cite{Rogg16} used the Path Integral Monte Carlo approach to find that the electron-impurity scattering can be adequately approximated by $\\nu_\\mathrm{e-imp}$, where the {\\it effective} impurity parameter $\\tilde{Q}_\\mathrm{imp}=L(\\Gamma) Q_\\mathrm{imp}$ is used in place of $Q_\\mathrm{imp}$. \nAccording to~\\cite{Rogg16}, \nthe correction factor $L(\\Gamma)$ is about $1.5-2$ at $\\Gamma\\sim 300-500$ and increases to $3-4$ at lower temperatures (higher densities) where $\\Gamma\\sim 10^{4}$. \n\nFinally, \nif the mixture is so diverse that no crystal is formed and the solid phase is in an amorphous state, then no regular lattice exists and equation~(\\ref{eq:nuei_liq}) is applicable as well (e.g., \\cite{Daligault2009}). This is thought to be a lower limit for the thermal conductivity and results in much longer diffusion timescales than given by equation~(\\ref{eq:time_crust}) (e.g., \\cite{Brow00}). Nevertheless, it has been shown that diffusion in a multicomponent Coulomb plasma is sufficiently fast to relax an initially amorphous structure to a crystalline state \\cite{Hughto2011PhRvE}.\n\nThe presence of a pasta layer at the base of the inner crust (section~\\ref{section:structure}) with anisotropic nuclear clusters can drastically change the thermal behavior of the inner crust. Transport properties of the pasta were studied by several authors during the last decade (e.g., \\cite{horowitz2008,Schn16,NandiSchramm2018ApJ,Yakovlev2015MNRAS}), but a consistent picture has not been developed yet. It is expected that transport in the pasta phase is anisotropic, with conductivity along one of the symmetry directions being much more effective than along another \\cite{Yakovlev2015MNRAS,Schn16}. The resulting thermal conductivity will depend on the orientation of the pasta domains in the star. If these domains are oriented chaotically, or the lower-conduction axis is aligned with the radius, the conductivity can be significantly reduced. The pasta phase is thought to have a regular structure within one domain, but can contain topological defects that can act like impurities \\cite{Schn16}. This can make pasta a highly resistive phase. Taking into account these complications, one usually describes thermal conductivity in the pasta phase by introducing the phenomenological (usually large) impurity parameter $Q_\\mathrm{imp,\\mathrm{pasta}}$. Note that some studies do not find reduced conductivity in pasta (e.g., \\cite{NandiSchramm2018ApJ}).\n\nAn additional uncertainty that affects thermal relaxation in the inner crust is contained in the heat capacity $c_P$. If neutrons are superfluid (section \\ref{section:structure}), they do not contribute to the heat capacity. However, in regions where neutrons are normal, they \ncomprise the dominant contribution to $c_P$. As follows from equation~(\\ref{eq:tau_diff}), the presence of normal neutrons will delay thermal relaxation; additionally, they can store more heat. Thus, the results are sensitive to the profile of the neutron singlet $^1$S$_0$ pairing gap. This is especially important if the pairing gap does not penetrate the core and closes at lower mass densities than the crust-core transition (e.g., \\cite{gandolfi2008}). In this case a layer with a high abundance of normal neutrons would be present at the base of the inner crust. In the regions where normal neutrons exist, the neutron thermal conductivity $K_\\mathrm{n}$ may play a role. It was found, however, that generally $K_{\\rm n} < K_e$ throughout \nthe inner crust~\\cite{Bisnovaty1982,Deib17}. Still, the neutron thermal conductivity can be important near the crust-core interface and is worthy of future study \\cite{Deib17}.\n\n\n\\subsection{Cooling Transients}\n\\label{sec:cooling_transients}\n\nAccreting neutron star \ntransients in low-mass X-ray binaries (section~\\ref{section:accretion}) are\nbright ($L_\\mathrm{X}\\sim 10^{36}-10^{39}$~erg~s$^{-1}$) \nX-ray sources during accretion outbursts (see table 1 of Reference~\\cite{Dege15} for a summary). \n\nAs described in section~\\ref{section:interaction}, non-equilibrium \nreactions \ndeposit $\\approx 1 \\textrm{--} 2 \\, \\mathrm{MeV}$ per accreted nucleon in the neutron star \ncrust during an accretion episode. When an accretion outburst ends, the X-ray luminosity drops by several orders of magnitude, the neutron star \nenters quiescence, and the fainter ($L_\\mathrm{X}\\sim 10^{31}-10^{34}$~erg~s$^{-1}$) \nthermal emission from the now cooling surface can be measured by sensitive X-ray observatories. The subsequent evolution depends on the duration of the accretion episode. One distinguishes normal transients, where accretion lasts for several weeks, and \nquasi-persistent transients, where accretion can proceed for years or even decades. A recent detailed discussion of the properties of cooling transients can be found in Reference~\\cite{Wijnands2017JApA}. \nHere we briefly describe the properties of transients necessary to understand the nuclear physics impact on these objects.\n\n\\subsubsection{Normal transients}\nBefore the start of an accretion episode, the neutron star is isothermal inside (except a thin outer \nheat blanketing envelope). Energy release from nuclear reactions during accretion breaks this equilibrium and heats the crust creating an inward and outward energy flux from the heating regions. During a relatively short accretion outburst, the crust is not heated strongly and quickly relaxes to thermal equilibrium with the core after accretion ceases. In this process, a fraction $f\\lesssim 1$ of the total heat release enters the neutron star core, typically $\\approx 90\\,\\%$~\\cite{Brow00}, and a smaller fraction $1-f$ of heat diffuses toward the surface and is radiated away during crustal cooling and by increased neutrino emission from the heating regions. Many cycles of accretion\/quiescence result in secular heating of the whole star with the secular heating rate (as measured by a distant observer) of $L_\\mathrm{heat}^\\infty=fQ \\langle \\dot{M} \\Delta t\\rangle\/(m_u t_\\mathrm{rec} (1+z))$,\nwhere $Q$ is the deep crustal heating power, $\\langle\\dot{M} \\Delta t\\rangle$ is the mass accreted during an outburst, on average, and $t_\\mathrm{rec}$ is the average recurrence time. \nThis heating is enough to balance the energy loss due to surface emission $L_\\gamma^\\infty$ and neutrino cooling $L_\\nu^\\infty$ from the bulk. If the transient accretion behavior persists for a long time, the neutron star is found in a steady state set by the condition $L_\\mathrm{heat}^\\infty=L_\\nu^\\infty+L_\\gamma^\\infty$, with the temperature much higher than expected for a passively cooling neutron star of comparable age \\cite{BBR1998}. This steady state point is thus determined by the (unknown) neutrino emission rate from the core. For higher rates, the steady state will be reached at lower temperatures (and thus surface luminosities) for a given $L_\\mathrm{heat}^\\infty$. Measurements of the quiescent luminosity with sensitive X-ray observatories\nthus in principle allow one to constrain the neutrino emission mechanisms operating in the neutron star core \\cite{Yakovlev2004ARA&A,Wijnands2013,Wijnands2017JApA}, as well as the core heat capacity~\\cite{Cumm17}.\nAt the moment, the strongest constraint comes from the coldest (in quiescence) \ntransient in the observed sample, SAX~J1808.4$-$3658 \\cite{Heinke2009ApJ}, whose conditions suggest that the powerful direct Urca neutrino emission process is operating in the core of the neutron star in this binary. In addition, the need for a direct Urca process in about 1 percent of the core was recently proposed to explain the low inferred core temperature in the quasi-persistent transient (section \\ref{sec:quasipers}) MXB~1659$-$29 \\cite{Brown2018arXiv}. However, this case is more uncertain as the accretion duty cycle is not known.\n\nThe main nuclear input here is the \npower of the deep crustal heating $Q$; however, a robust inference is plagued by other uncertainties such as distance measurements, stability of accretion duty cycles, and composition of the \nheat blanketing envelope of the neutron star. The latter strongly affects the relation between the measured surface temperature and the internal temperature \nwhich determines\n$L_\\nu^\\infty$. \nThis uncertainty does not apply for hot and weakly accreting systems where $L_\\gamma^\\infty\\gg L_\\nu^\\infty$ so that the steady state is determined by the directly measurable neutron star surface emission (e.g., IGR~J00291+5934, \\cite{Wijnands2017JApA}). Such systems in principle have a potential to constrain $Q$, but the observational uncertainties are still large. \n\n\n\\subsubsection{Quasi-persistent transients}\\label{sec:quasipers}\nDuring the long accretion outbursts in quasi-persistent transients,\nthe heat deposited in the crust is so large that it brings the crust out of thermal equilibrium with the core~\\cite{Page13}. When an accretion outburst ends, the neutron star\nenters quiescence, \nthe crust cools toward thermal equilibrium with the core, and the neutron star's \nsurface thermal emission powers an X-ray light curve~\\cite{ushomirsky2001,rutledge2002}. The shapes of quiescent cooling curves depend on \nthe thermal structure of the neutron star's \nouter layers at the \nbeginning of quiescence. Confronting thermal evolution models of the neutron star's \nouter layers \nwith observations leads to constraints on crustal properties such as thermal conductivity and specific heat of dense matter, as discussed in section~\\ref{subsection:impact_inner}. \n\nActive studies of crust cooling in quasi-persistent transients started about two decades ago when the first source KS~1731$-$260 ended a $\\gtrsim 12.5$~yr outburst and was observed in quiescence with Chandra \\cite{wijnands2001c,rutledge2002}. The continuous monitoring of this source in quiescence revealed the crust cooling towards thermal equilibrium with the core on a $\\sim$yr timescale \\cite{cackett2006}. This relatively short cooling timescale \nsuggests a high thermal conductivity in the crust consistent with an ordered lattice \ncontaminated by a small impurity content \\cite{cackett2006,Shte07,Brow09}. An amorphous crust with low thermal conductivity, which would have a much longer thermal time (section~\\ref{subsection:impact_inner}), contradicts observations.\n\nSince 2001, quiescent cooling curves have been observed in several low-mass X-ray binaries and provide important data on the thermal evolution of the neutron star outer layers across accretion regimes and \ngravities \\cite{Wijnands2017JApA}. Interestingly, crustal cooling is observed now not only in the quasi-persistent sources with long $>1$~yr outbursts, but also for several `ordinary' transients with shorter accretion outbursts. The results of these observations and crustal light curve modeling are extensively reviewed elsewhere (e.g., \\cite{Wijnands2017JApA}). Here we summarize only the main points:\n\n\\paragraph{The crust is pure:} The analyses of all cooling sources generally suggest that a high thermal conductivity and hence a low effective impurity parameter $\\tilde{Q}_{\\rm imp}\\lesssim 4-7$ throughout the crust is favored (see, however, Reference~\\cite{Degenaar2014}). Taking into account the results of Reference~\\cite{Rogg16}, this indicates even smaller actual impurity content $Q_{\\rm imp}\\lesssim 1-3$. By contrast, the distribution of ashes produced from X-ray bursts and stable surface burning can have $Q_{\\mathrm{imp}} \\sim 70-100$ (see figure~\\ref{figure:ECcrustcomposition} and table~\\ref{table:AvgZAQTable}) suggesting that a purification mechanism exists in the crust. \nWe illustrate the impact of the impurity parameter on cooling curves in figure~\\ref{figure:NScool_Qimp}. This crust cooling model uses the accreted crust composition from Reference~\\cite{Haen90} and contains nuclear heating of $Q = 0.3 \\, \\mathrm{MeV \\ u^{-1}}$ in the outer crust, and deep crustal heating of $Q = 1.5 \\, \\mathrm{MeV \\ u^{-1}}$ in the inner crust. As can be seen in figure~~\\ref{figure:NScool_Qimp}, large values of $Q_{\\rm imp}$ lower the thermal conductivity of the inner crust and increase the thermal time. Therefore, it takes longer for heat to diffuse out of the inner crust during quiescence and for the crust to reestablish thermal equilibrium with the core.\n\nThe estimates obtained in this way are more relevant for the inner crust where the impact of impurities is more pronounced (section~\\ref{subsection:impact_inner}). In addition, the constraints on the outer crust properties are contaminated by the mysterious shallow heating source (discussed momentarily). \nThe impurity parameter in most models and in our illustrative calculations is set constant. Reference~\\cite{Page13} allowed for a variable $Q_{\\rm imp}$ with a higher value in the outer crust, lower value in the inner crust, and a smooth transition between them. This allowed the authors \nto explain the observations of the source XTE~J1701$-$462; the adopted model is also consistent with observations of other transients. \n\n\\paragraph{Neutron superfluidity is favored:} The intermediate part of the transient cooling curve is usually fit better when neutron superfluidity is taken into account (e.g., \\cite{Shte07,Brow09}), which lowers the heat capacity in the inner crust causing it to store less heat and cool more rapidly (section~\\ref{subsection:impact_inner}). \n\n\\paragraph{Extra shallow heating is required:} It has been found that the standard heat release from deep crustal heating models is usually insufficient to heat the crust to the high effective temperatures observed at the early phase of quiescence \\cite{Brow09,Turl15}. Although the uncertainties in the nuclear physics may vary the heat deposited in the inner crust~\\cite{steiner2012}, analyses of quiescent light curve shapes demonstrate that the additional heating source must be located at densities $\\rho_\\mathrm{shallow}\\lesssim 10^{11}$~g~cm$^{-3}$. For a standard model of deep crustal heating, the strength of this additional source is found to be on the $Q_\\mathrm{shallow}\\sim 1 \\, \\mathrm{MeV}$ level. However, this estimate is model-dependent, varies from source to source, and may vary from one outburst to another in the same source~\\cite{Turl15,Pari17}. \n\n\\paragraph{Nuclear pasta is favored:} It was found that the cooling curve of MXB~1659$-$29 is better explained if a low-conducting region is included in the inner crust, that can be attributed to the pasta layer \\cite{Horo15,Deib17}. We illustrate the impact of the presence of pasta in figure~\\ref{figure:NScool_pasta}, where the thermal conductivity in the pasta is modeled by a large impurity parameter $Q_{\\mathrm{imp},\\mathrm{pasta}}$ for $\\rho>8\\times10^{13}$~g~cm$^{-3}$ and the model uses the $^1$S$_0$ neutron singlet pairing gap from Reference~\\cite{gandolfi2008}. \nThe presence of the heat insulator in the pasta layer may explain the continuous cooling observed in MXB~1659$-$29 after 11 yr in quiescence \\cite{Cackett2013ApJ}. If the nuclear pasta layer has a low thermal conductivity, some regions of the inner crust may remain above the neutron superfluid critical temperature depending on the choice for the pairing gap \\cite{Deib17}. As discussed in section~\\ref{subsection:impact_inner}, there may be normal neutrons present in the pasta layer and the heat stored by normal neutrons will impact the late-time cooling behavior of neutron star \ntransients which remain in quiescence for more than $ > 1000 \\, \\mathrm{days}$~\\cite{Deib17}. Note, however, that the temperature drop observed in MXB~1659$-$29 may not be caused by continuous crustal cooling, but could be a result of an increase in the absorption column density prior to the last observation \\cite{Cackett2013ApJ}.\n\n\\paragraph{}\nIt is important to note that strong constraints on the composition of the crust are currently difficult to obtain because the shape of the cooling light curve is degenerate with other neutron star \nparameters, such as the \ngravity and the core temperature, which are unknown a priori. Also, accurate accounting for a variable accretion rate during a long outburst can be important \\cite{Ootes2016MNRAS}.\nFinally, the observations, especially of the early stages of crustal cooling, may be contaminated by residual accretion.\n\n \n\n\\begin{figure}\n\\centering\n\\includegraphics[scale=1.2]{NScooling_Qimp.pdf}\n\\caption{Crust cooling models, showing the temperature decline of a neutron star's surface in quiescence,\nemploying various choices of $Q_{\\mathrm{imp}}$ for the entire crust. This crust cooling model uses a neutron star mass of $M= 1.4 \\, \\mathrm{M}_{\\odot}$, neutron star radius of $R = 12 \\, \\mathrm{km}$, and a core temperature of $T_{\\rm core} = 3 \\times 10^{7} \\, \\mathrm{K}$.\n\\label{figure:NScool_Qimp}}\n\\end{figure}\n\n\\begin{figure}\n\\centering\n\\includegraphics[scale=1.2]{NScooling_pasta.pdf}\n\\caption{Crust cooling models, showing the temperature decline of a neutron star's surface in quiescence, employing various choices of $Q_{\\mathrm{imp}}$ for the crust and the pasta layer at $\\rho \\gtrsim 8 \\times 10^{13} \\, \\mathrm{g \\ cm^{-3}}$. This crust cooling model uses a neutron star mass of $M= 1.4 \\, \\mathrm{M}_{\\odot}$, neutron star radius of $R = 12 \\, \\mathrm{km}$, and a core temperature of $T_{\\rm core} = 3 \\times 10^{7} \\, \\mathrm{K}$. \\label{figure:NScool_pasta}}\n\\end{figure}\n\nThe hottest neutron star \ntransient MAXI~J0556$-$332 \\cite{matsumura2011,Homa11,sugi13,homan14} is an excellent test bed for observational signatures of Urca cooling nuclei pairs \\cite{Deib15} because of the strong ($T^5$) temperature dependence of Urca neutrino cooling (see equation~(\\ref{equation:Lnu})). The inferred surface temperature at the onset of quiescence is nearly twice as large as the next hottest transient observed~\\cite{homan14}, and it is the only transient thought to have a hot enough crust the Urca cooling could produce an appreciable $L_{\\nu}$. It was found that MAXI~J0556$-$332 likely does not have Urca cooling operating in its crust because the quiescent cooling trend would follow a different behavior than what is observed \\cite{Deib15}.\n\nThe authors of Reference~\\cite{Meis17} reexamined the quiescent cooling of MAXI~J0556$-$332 and self-consistently added Urca cooling nuclei to the ocean and crust composition of a thermal relaxation model. \nThey found that Urca cooling would be\napparent for X-ray burst or superburst ashes, but not for the ashes of stable burning. Interestingly, stable burning ashes produce percent-level $X(A)$ for odd-$A$ nuclides in the $70\\lesssim$$A$$\\lesssim80$ range, but these isobars appear to lack significant Urca pairs due to their relatively large $ft$~\\cite{Scha14,Meis17}, see equations~(\\ref{equation:Lnu})-- (\\ref{equation:L34}). Because of the large ocean and crust temperature in MAXI~J0556$-$332, it is indeed likely that stable burning has occurred \nin the outer layers. As such, the absence of an Urca cooling signature in the MAXI~J0556$-$332 light curve, as shown in figure~\\ref{figure:urcaLC}, is consistent with model calculations. Further consistency checks will require the remaining nuclear physics uncertainties, particularly $ft$-values (section~\\ref{ssec:ECrxns}) of abundant nuclides, to be determined experimentally. Spectroscopy studies at the limits of experimental accessibility are underway for this purpose.\n\n \\begin{figure} [h]\n\\centering\n\\includegraphics[scale=0.5]{MAXIlc.png}\n\\caption{Light curve of MAXI J0556$-$332 after the end of an accretion outburst~\\cite{homan14} compared to {\\tt dStar}~\\cite{Brow15} model calculations with (dashed line) and without (solid line) Urca cooling expected for $^{33}\\rm{Al}\\leftrightarrow^{33}\\rm{Mg}$~\\cite{Meis17}. Models employ $Q_{\\rm{shallow}}=8$~MeV per accreted nucleon, $Q_{\\rm{imp}}=1$ throughout the crust, and an outburst duration and accretion rate that match observations~\\cite{homan14}. The inset shows the residuals to the no-cooling calculation.\\label{figure:urcaLC}}\n\\end{figure}\n\nWell-constrained estimates of Urca cooling strengths, coupled with models of cooling transient light curves, enable surface nuclear burning to be constrained over vast timescales \nfor neutron stars \nwith high-temperature crusts. By analyzing cooling transient light curves for signatures from Urca cooling, the composition of the accreted neutron star crust\ncan be constrained by determining the presence or absence of Urca pairs~\\cite{Meis17}. As the composition is determined by\npast nucleosynthetic activity on the neutron star \nsurface, this method provides a theoretical\napproach to constrain surface nuclear burning on accreting neutron stars \nover the past millenia.\n\n \n\\subsection{X-ray Superbursts}\\label{subsection:obs_superbursts}\nRecall that X-ray superbursts are thermonuclear runaways triggered by carbon ignition in the accreted neutron star \nocean (section~\\ref{sec:superbursts}). \nTheir long duration is set by the thermal time scale of deep neutron star ocean layers, indicating an ignition column depth of $10^{11}-10^{12}\\ \\mathrm{g\\ cm^{-2}}$. This depth is relatively close to the outer crust. Therefore, the occurrence of superbursts provides a measure of the thermal properties at the crust-ocean interface as a function of depth. This is illustrated in figure~\\ref{figure:NStemp} where the dashed line shows temperature required to ignite carbon at a given column depth. Its position is sensitive to the $^{12}$C$+$$^{12}$C reaction rate, and enhancements in this rate are of particular interest, as they may help to explain the discrepancy between carbon ignition depths inferred from observations and required in simulations (section~\\ref{sssec:SuperburstObs}).\nFurthermore, superbursts quantify the carbon content of the hydrogen\/helium burning ashes, which cannot be inferred directly from observations of short bursts (section \\ref{sec:burst_observation}).\n\n \\begin{figure}\n\\centering\n\\includegraphics[scale=0.6]{CrustEquilibriumTemperature2.png}\n\\caption{Steady-state ocean temperature as a function of column depth (solid line) for a neutron star with $\\dot{M}=0.3~\\dot{M}_{\\rm{Edd}}$, $M=1.4~M_{\\odot}$, $R=10$ km, and\n$T_{\\rm{core}}=3\\times10^{7}$ K. A shallow heating of 5~MeV per nucleon is assumed, resulting in $\\sim$0.25~MeV per accreted nucleon in the ocean. The dashed black line indicates the temperature and density at which ignition of unstable\ncarbon burning in a mixed iron-carbon ocean with $X_{\\rm{Fe}}=0.8$ and $X_{\\rm{C}}=0.2$ will ensue. \nThe open arrows indicate the regions where $e^{-}$-capture heating\/cooling and pycnonuclear heating are active, where the region of pycnonuclear heating extends to depths beyond the figure extent.\n(Adapted from Reference~\\cite{Deib16}).\n\\label{figure:NStemp}}\n\\end{figure}\n\nThe thermal profile around the ignition layer is set by the heating sources in the ocean and the crust.\nThe impact of the nuclear reactions discussed in section~\\ref{section:interaction} \nis highlighted in figure~\\ref{figure:NStemp}, where the locations for $e^{-}$-capture and pycnonuclear reactions are shown. \nHeat from these reactions, which is typically produced at depths below carbon ignition,\ndiffuses upwards, raising the ocean temperature and enabling carbon to ignite at some depth~\\cite{Brow04,Keek11}.\nThis heat is insufficient, though, to raise the ocean temperature to the level required to explain observations. The deep crustal heating uncertainty of $\\sim 2$~MeV\/u, discussed above \\cite{steiner2012}, modifies the heating in the `pycno' region just beyond the right bound of figure~\\ref{figure:NStemp}. \nThis heat is mainly conducted inwards to the core due to high conductivity and provides insufficient impact on the ignition layer temperature. The temperature profile shown by a solid line in figure~\\ref{figure:NStemp}, which crosses the ignition curve (dashed line) at reasonable densities, is obtained by adding a strong ($\\sim 5$~MeV\/u) shallow heat source at $y_\\mathrm{shallow}\\sim 10^{15}$~g~cm$^{-2}$ \\cite{Deib16}.\nThe need for such a shallow heat source is in line with the findings for the quasi-persistent transients discussed in section~\\ref{sec:quasipers}. Some heating can also come from compositionally-driven convection proposed in the ocean~\\cite{Medin2011} (see section~\\ref{sec:pure}). \n\nAdditional complications come from the \nUrca pairs that can be present \nin the neutron star's ocean and \ncrust.\nThey form a thermal barrier between heat released deeper in the crust and the carbon ignition layer. The strong temperature sensitivity of the neutrino luminosity from Urca cooling (see equation~(\\ref{equation:Lnu})) means that even \nstrong deep crustal heating will not raise the ocean temperature if such a barrier exists. \n\nA recent study investigated the interplay between heating and cooling reactions in the accreted ocean and crust and the carbon ignition depth~\\cite{Deib16}.\nThe authors self-consistently calculated the Urca cooling nuclei pairs \\cite{Scha14} one would expect \nfrom the compressed \nashes of Type I X-ray bursts and superbursts \\cite{Scha99,Scha03,Keek08}. Urca cooling layers were then implemented into a superburst ignition model \\cite{Pote12} and it was found that Urca cooling in the neutron star's \ncrust lowers the ocean's steady state temperature during an accretion outburst, while the ocean Urca pairs produce little effect. \nAs a consequence, superburst ignition occurs deeper than it would otherwise (see dashed curve in figure~\\ref{figure:NStemp}).\n\nThe highest mass-fraction odd-$A$ Urca nuclides produced in superbursts are of interest for this scenario, i.e. $A=53,55$, and $57$ (see figure~\\ref{figure:ashes}), assuming recurring superbursts would erase the signature of X-ray burst ashes. Of these, $A=55$ is by far the most significant due to the predicted percent-level abundance. Theoretical estimates vary largely \nfor $L_{\\nu}$ for $A=55$ nuclides, where the primary uncertainty comes from the assumptions used to determine the values of ${\\rm log}_{10}(ft)$ for the $^{55}$Ti$\\rightarrow^{55}$Sc$\\rightarrow^{55}$Ca $e^{-}$-capture sequence~\\cite{Scha14,Deib16}. As such, measurements are required to resolve this issue. Should ${\\rm log}_{10}(ft)\\lesssim5$ be found for either of these transitions, this would imply that the corresponding \nthermal barrier \nprevents a significant heat flow from deep crustal heating \nto the ocean. \nThis would deepen the mystery of the superburst ignition problem. Furthermore, the presence of such an Urca layer would \nlimit any shallow heating source, discussed above, to locations above existing Urca layers.\n\n\\subsection{Accreting neutron stars as Sources of Gravitational Waves}\\label{sec:gw}\nThe first detection of the gravitational wave transient GW~150914 from a double black hole coalescence with LIGO in 2015 \\cite{Abbott2016PhRvLGW150914} has started the era of gravitational wave astronomy. Only two years after the first detection, a double neutron star \nmerger GW~170817 was reported \\cite{Abbott2017PhRvLGW170817} opening a new window for neutron star \nastrophysics and providing exciting new insights into the physics of these objects.\n\nNot only binary neutron stars \ncan emit\ngravitational waves. In fact, fast spinning solitary neutron stars \nhave long been considered as possible {\\it persistent} \ngravitational wave sources if they possess some degree of asymmetry; for a recent review see Reference~\\cite{Glampedakis2017arXiv}. This can result from the certain types of oscillation modes possibly excited in the star, or because of the presence of static density inhomogeneities (``mountains'') that the neutron star crust can hold \\cite{Glampedakis2017arXiv}. Let us focus on the latter case. If the mountains result in neutron star ellipticity $\\varepsilon$, a star rotating with a frequency $\\nu$ would emit \ngravitational waves mainly with a frequency $f=2\\nu$ and the energy loss is\n\\begin{equation}\\label{eq:E_gw}\n\\dot{E}_\\mathrm{GW}=2\\pi\\nu N_\\mathrm{GW} = - \\frac{32}{5} \\frac{G(2\\pi \\nu)^6 (I\\varepsilon)^2}{c^5},\n\\end{equation}\nwhere $I$ is the neutron star moment of inertia and $N_\\mathrm{GW}$ is the associated braking torque. Thus, the emission of \ngravitational waves results in effective spin down which strongly scales with frequency. The maximal ellipticity that crustal mountains can produce is quite large $\\varepsilon_\\mathrm{max}\\sim (0.1-1)\\times10^{-4}\\, \\sigma_\\mathrm{max}$ \\cite{Johnson2013,Ushomirsky2000MNRAS,Haskell2006MNRAS} where \n$\\sigma_\\mathrm{max}\\lesssim 0.1$ is the maximal strain \\cite{Horowitz2009PhRvL,Chugunov2010MNRAS} the crust can sustain.\nThe largest mountains could produce a detectable signal. However no \ngravitational wave emission has been detected yet from known pulsars and current upper limits on ellipticity with advanced LIGO are of the order of $10^{-7}$ with a minimum value of few of $10^{-8}$ \\cite{Abbott2017ApJaLIGO}. \n\nMountains can be built up in accreting neutron stars \nonce some asymmetry in the accretion process is assumed. One of the proposed mechanisms is directly related to nuclear reactions in the crust (for other possibilities such as ``magnetic'' mountains see, e.g., Reference~\\cite{Glampedakis2017arXiv}). The idea is that if lateral thermal or compositional gradients are present in the crust, this radially shifts the positions of the electron capture layers and hence the associated density jumps \\cite{Bildsten1998ApJL,Ushomirsky2000MNRAS}. The physics of the temperature sensitivity of the $e^{-}$-capture layers is based on the observation that at sufficiently high temperatures $T\\gtrsim 2\\times 10^{8}$~K the $e^{-}$-capture rates become faster than the accretion timescale considerably before the corresponding threshold. Calculations show that in fact, most $e^{-}$-capture transitions occur subthreshold at $|Q_\\mathrm{EC}|-\\mu_e=\\Upsilon k_\\mathrm{B} T$, where $\\Upsilon\\approx 10-20$ and depends logarithmically on $T$, comparative half-life $ft$, \nthreshold $Q_\\mathrm{EC}$, and local accretion rate \n$\\dot{m}\\equiv \\dot{M}\/(4\\pi R^2)$ \\cite{Bildsten1998ApJL,Ushomirsky2000MNRAS,Bild98}. \nThe value of $\\Upsilon$ gives a measure of the thermal sensitivity of the position of the $e^{-}$-capture layer. The radial amplitude of the layer variation is then $\\Delta \\zeta \\approx \\Upsilon Y_e k_\\mathrm{B} \\delta T\/(m_u g) $, where $\\delta T$ is the amplitude of lateral temperature variations \\cite{Ushomirsky2000MNRAS}. If these variations are quadrupole, the resulting ellipticity is proportional to $\\Delta \\zeta \\Delta\\rho$, where in the outer crust the density jump at a given $e^{-}$-capture layer is $\\Delta \\rho\/\\rho= 2\/Z$. Based on this, the authors of Reference~\\cite{Ushomirsky2000MNRAS} obtain the following fiducial estimate\n\\begin{equation}\\label{eq:ellipt_fid}\n\\varepsilon \\approx 1.7\\times 10^{-9}\\, \\frac{\\Upsilon}{Z} \\frac{\\delta T}{10^7\\,\\mathrm{K}} \\frac{2.44}{g_{14}}\\, \\left(\\frac{R}{10~\\mathrm{km}}\\right)^4 \\,I_{45}^{-1}\\left(\\frac{|Q_\\mathrm{EC}|}{30\\,\\mathrm{MeV}}\\right)^3,\n\\end{equation}\nwhere $I_{45}$ is the neutron star moment of inertia in units of $10^{45}$~g~cm$^{2}$.\nThis estimate is strictly speaking applicable for the outer crust, but numerical calculations give similar orders of magnitude also when the reactions in the inner crust are considered (there the capture layers are thicker and their structure is modified since degenerate neutrons dominate the pressure). In addition, equation~(\\ref{eq:ellipt_fid}) does not account for the elastic response of the crust which in fact can result in an order of magnitude smaller values \\cite{Ushomirsky2000MNRAS}.\nSimilar $\\Delta \\zeta$ can be provided by compositional gradients instead of thermal ones, if, for instance, the ashes that are compressing are not symmetrically distributed. The source of the temperature asymmetry can stem from the asymmetry of deep crustal heating. Therefore the largest gradients can be expected from the regions \nof the largest heat release -- i.e. in the inner crust where pycnonuclear reaction or superthreshold electron-capture cascades operate. Notice that too large of gradients would result in flux variations that are currently constrained at the level of $\\delta T\/T\\lesssim 0.1$ \\cite{Haskell2015MNRAS}. \n\nCould nuclear mountains be detected with gravitational observatories? When accretion ceases, the thermal gradients and thus mountains are thought to be erased on the thermal timescale (section~\\ref{subsection:impact_inner}) \\cite{Bildsten1998ApJL,Haskell2015MNRAS}. Thus, although the mountains in the inner crust can be sustained longer, the chance of the gravitational wave detection from transient sources is small. However, for the persistent accretion sources the situation is more optimistic with the future instrumentation \\cite{Haskell2015MNRAS}. There is also a possibility that the mountains are frozen in the crust and build up incrementally. However, this can lead to too large of a spin-down rate due to gravitational wave emission which contradicts observations for at least some sources \\cite{PatrunoWatts2012}. See Reference~\\cite{Haskell2015MNRAS} for a detailed discussion.\n\nEven if they are undetected by gravitational wave observatories, crustal mountains have important astrophysical implications. Initially the gravitational emission from accreting neutron stars \nwas\ninvoked as a mechanism for limiting the neutron star spin-up due to accretion \\cite{Bildsten1998ApJL}. The fastest rotator among the low-mass X-ray binaries is 4U~1608$-$52 with a frequency measured from bursts oscillations of 619~Hz \\cite{Hartman2003HEAD} and the fastest radio millisecond pulsar spins at 716~Hz \\cite{Hessels2006Sci} while basically one expects that the accretion torque can spin a star up until the stability limiting frequency $\\sim 1$~kHz \\cite{HPY2007}. The strong frequency dependence of gravitational wave spin-down torque in equation~(\\ref{eq:E_gw}) thus provides a natural explanation of the cutoff existence. Estimating the accretion torque as $N_a=\\dot{M}\\sqrt{ G M R}$, we get the ellipticity which allows gravitational wave torque to balance $N_a$: \n\\begin{equation}\\label{eq:ellip_a}\n\\varepsilon_a\\approx 8\\times 10^{-9} I_{45}^{-1} \\left(\\frac{600\\,\\mathrm{Hz}}{\\nu}\\right)^{5\/2} \\frac{\\dot{M}}{10^{-9}\\, M_\\odot\\, \\mathrm{yr}^{-1}}\n\\end{equation}\nfor $M=1.4 M_\\odot$ and $R=10$~km.\nIn fact, the accretion torque at high frequencies may be smaller, so the estimate from equation~(\\ref{eq:ellip_a}) is the upper limit (see Reference~\\cite{Patruno2017ApJ} for further discussion). This estimate is generally consistent with equation~(\\ref{eq:ellipt_fid}) if the mountains are built in the inner crust (note also that equation~(\\ref{eq:ellipt_fid}) is for a single capture layer so many asymmetric capture layers can combine in larger mountains). \nAlthough it is not clear now that \ngravitational wave emission is even needed for the explanation of the spin-up limit and, instead, \nthe physics of accretion possibly plays the major role \\cite{Patruno2017ApJ,D'Angelo2017MNRAS}, the bimodal distribution of the low-mass X-ray binaries over spin frequencies reveals a sharp spike clustering around $600$~Hz which is hard to explain without assuming that gravitational wave \nemission becomes important around this frequency \\cite{Patruno2017ApJ}.\n\nAnother piece of evidence came recently from observations of the spin down of the transient millisecond pulsar PSR~J1023$+$0038 which showed a transition between radiopulsar and low-mass X-ray binary stages. According to Reference~\\cite{Haskell2017PhRvL},\nthe PSR~J1023$+$0038 spin-down rate is faster in the low-mass X-ray binary state by $\\dot{\\nu}_\\mathrm{diff}=-6.428\\times 10^{-16}$~Hz~s$^{-1}$. This could be the first evidence that a mountain is built up during accretion and the \ngravitational wave torque provides additional spin down. The required ellipticity (estimated from equation~(\\ref{eq:E_gw})) is about $5\\times10^{-10}$, in line with the distortions nuclear mountains can provide according to equation~(\\ref{eq:ellipt_fid}). If this is indeed the case, then the gradual decrease of the spin-down rate on thermal timescales is expected in the radiopulsar stage. On the other hand, if continuous monitoring of X-ray oscillations during the low-mass X-ray binary stage reveals a spin-down rate increase, this could indicate mountain(s) build-up and the nuclear mountain mechanism can be tested.\n\n \n\\section{Summary and Outlook} \\label{section:summary}\n\nRemarkable progress has been made in the 50 years since the neutron star \ncrust was first proposed. We now know the outer layers of accreting neutron stars \nhost a variety of nuclear phenomena, many of which can be studied in terrestrial laboratory experiments. Decades of efforts with X-ray telescopes have yielded a number of complementary observations which, with insight provided by astrophysics model calculations, have enabled \nthe construction of the tomographic picture of the accreted neutron star \nouter layers that we have today. These advances and current efforts in observation, experiment, and theory have been \ndiscussed\nin the previous sections. Here we summarize major remaining challenges and planned efforts to address them.\n\n\\subsection{What is Made In Surface Burning Processes?}\nSurface burning processes provide the seeds for crust processes, determining major features of the crust composition. Therefore they indicate which nuclear reaction heating\/cooling processes are relevant for a particular neutron star. \nHowever, major uncertainties remain as to the ashes produced in various burning regimes. \n Models struggle to reproduce observational signatures of surface burning processes when employing the inferred environment conditions.\nFor example, among H-rich H\/He-burning X-ray bursters, only GS~1826$-$24 has had its light curve successfully modeled and for that case models need a higher accretion rate than is inferred from observations (possibly due to a high inclination angle of this source)~\\cite{Hege07,Meis18}.\n\nType-I X-ray burst ash compositions from model calculations are sensitive to a large number of poorly constrained reaction rates~(section \\ref{sec:rpprocess}). \nRates of particular interest are those that lead to global changes in $\\langle Z\\rangle$, which would alter the depth at which pycnonuclear fusion could proceed~\\cite{Wijn13}, and rates affecting odd-$A$ abundances, which may result in significant Urca cooling in the crust~\\cite{Meis17}.\nWhile some studies have \ninvestigated which rates these may be~\\cite{Pari08,Pari09,Cybu16,Scha17}, a relatively small range of astrophysical conditions has been explored in self-consistent calculations. In principle the rates which impact superburst ash production, namely rates operating after freeze-out from nuclear statistical equilibrium, would be of interest, but computational challenges have made sensitivity studies prohibitively \nexpensive.\n\nThe range in which different burning regimes operate is also not clear. One challenge facing the study of Type I X-ray bursts is a discrepancy between theory and observations for the boundary between stable and unstable nuclear burning (section~\\ref{sec:burst_theory}). Observational surveys of thousands of Type I X-ray bursts find a peak burst rate near a mass accretion rate of $\\dot{M} \\approx 0.3 \\, \\dot{M}_{\\rm Edd}$~\\cite{Paradijs1988,Cornelisse2003,galloway08} after which the burst rate decreases with increasing $\\dot{M}$. Numerical models, however, predict an increase in burst rate with increasing $\\dot{M}$. Furthermore, studies of stable and explosive burning fail to produce adequate amounts of carbon needed to ignite superbursts \nseen in some objects exhibiting Type-I bursts~\\cite{Stev14}.\n\n\\subsection{What are the Major Heating\/Cooling Sources in the Accreted Crust?}\nThe thermal structure of the crust influences the transitions between burning regimes, the depth at which fuel is ignited for unstable burning, and the rate of cooling after accretion turns off. Therefore identifying and quantifying the most significant heat sources and heat sinks is paramount to constructing accurate models of near-surface phenomena on accreting neutron stars.\nWhile the general features of nuclei that make them susceptible to generating substantial heating or cooling are known, identifying specific nuclei is presently \nproblematic (see section~\\ref{section:interaction}).\n\nThe masses, low-lying structure, and weak transition rates for neutron-rich nuclides with $A\\lesssim100$ lack sufficient constraints to definitively quantify the presence and strengths of $e^{-}$-capture heating and cooling. For pycnonuclear fusion, the properties of the nuclear potential for low-$Z$ nuclides far from stability needs further study to establish whether observed enhancement in fusion rates, e.g. Reference~\\cite{Sing17}, are the exception or the rule. The myriad of theoretical uncertainties associated with estimating pycnonuclear rates (section~\\ref{sec:pycno}) need to be reconciled if the region of deep crustal heating is to be determined. More modeling efforts for crust composition evolution need to be coupled to surface-burning models for the same objects in order to remove degrees of freedom and more rigorously test our understanding of heating and cooling reactions in the crust.\n\nA related and even \nless certain issue is the identity of the shallow heating mechanism required in model-observation comparisons for nearly all accreting neutron star \nobservables. Heating beyond the scale of nuclear physics uncertainties (as we understand them) appears to be necessary to reproduce observed properties of some Type-I X-ray bursts~\\cite{Keek17},\ncooling transients~\\cite{Turl15,Deib15}, and possibly superbursts~\\cite{Keek11,Reic17}. Better constraints on reaction-based heating and establishing the presence or absence of Urca cooling layers will be key to restricting the strength and location, and therefore mechanism, of shallow heating in accreting neutron stars. Furthermore, a proper accounting of all possible heat sources needs to be done~\\cite{Fattoyev2017arXiv}.\n\n\\subsection{How does the Crust Become So Pure?}\\label{sec:pure}\nInferences of the accreted crust thermal conductivity from observed cooling indicate that \nminimal crust impurities are allowed (section ~\\ref{sec:cooling_transients}).\nHowever, Type-I X-ray burst\nand stable burning ashes have more than 10 times larger \n$Q_{\\rm{imp}}$ (table~\\ref{table:AvgZAQTable} and figure~\\ref{figure:ashes}). \nModels suggest that phase separation in the ocean and crust can be crucial for \nformation of a purified crust. \nMolecular dynamics simulations\n\\cite{Horowitz2007PhRvE,Horowitz2007PhRvE,Schneider2012PhRvE,Hughto2012PhRvE,Capl17} and semi-analytical calculations \\cite{Medi10,Mcki16} show \nthat at the crystallization interface in most cases the solid phase is enriched in heavier (higher $Z$) elements, while lighter elements remain in the liquid phase. In some cases, however, (when the ashes average charge is low) the inverse situation was found \\cite{Mcki16,Capl17}. \n\nThe consequences of phase separation are not entirely clear. It was proposed that enrichment of the ocean with light elements will \nresult in compositionally-driven convection~\\cite{Medin2011,Medin2014ApJ,Medin2015ApJ}.\nHowever once a steady state is achieved, the composition of the freezing solid should match on average the composition of matter entering\nthe top of the ocean \\cite{Medin2011}. \nAlternatively, the accumulation of light nuclei in the ocean eventually results in their solidification so that alternating crystalline layers or crystalline domains of different composition can form, reducing considerably $Q_{\\mathrm{imp}}$ in each layer or domain \\cite{Capl17}\n\nAnother possibility is that nuclear reactions can purify the ashes as they sink deep in the crust (section~\\ref{section:interaction}). \nRecall that the observational constraints on impurities are more relevant for the inner crustal regions (section~\\ref{sec:cooling_transients}). Pycnonuclear reactions burn lighter nuclei before the neutron drip point and neutron emission reactions around neutron drip simplify the composition towards a few closed-shell nuclei. Interestingly, this results in lowering the impurity for rp-process ashes, but on the contrary increases the diversity of species if one starts from a pure one-element composition \\cite{Gupt08,Lau18,steiner2012}. Furthermore, very recent network calculations that follow the \nevolution of ashes beyond neutron drip indicate that these multicomponent configurations are quickly destroyed and all matter at $\\rho\\gtrsim 1.5\\times 10^{12}$~g~cm$^{-3}$ is converted into a single nucleus and a neutron gas, regardless of the initial surface abundances~\\cite{Lau18}. The only exception is the case when heavy nuclei ($A\\ge 106$) are present in the initial ashes. In this case the $N=82$ nuclei are locked and still present in the inner crust; the impurity parameter remains high in this case \\cite{Lau18}.\n\nWe also note, that binary (or multinary) crystalline structures can form, for instance, if two species dominate the composition in a certain proportion \\cite{Chamel2017JPhCS}. \nThen the impurity parameter can formally be high (depending on the charges in the mixture) but the pure crystalline structure will lead to a high conductivity. Indeed, the compositions dominated by two or few species in similar fractions are found at some point in the reaction network evolved to the inner crust \\cite{Lau18}.\n\nClearly, a solid understanding of the accreted crust structure and composition does not exist yet. This would require time-dependent calculations which couple models \nof the structural changes in the crust (e.g. molecular dynamics) \nto a nuclear reaction network, following the reaction sequence all the way to the crust-core transition, including all relevant nuclear transitions. \n\n\\begin{figure} [h]\n\\centering\n\\includegraphics[scale=0.6]{ReAProductionRP.pdf}\n\\caption{Predicted production rates in particles per second, \nas indicated by the color,\nfor ReA3 beams at the Facility for Rare Isotope Beams~\\cite{Boll11} compared to the rp-process path~\\cite{Scha06}. See Reference~\\cite{Meis16b} for a similar figure with anticipated fast beam rates.\n\\label{figure:reaproduction}}\n\\end{figure}\n\n\\subsection{What is Being Done to Solve These Problems?}\nOngoing efforts in experiment, observation, and theory are constantly advancing the frontier of what is known about the nuclear processes occurring in accreted neutron star \ncrusts and their observable impacts. Recent and near-future advances in instrumentation and the open-source software movement promise to accelerate the pace of progress.\n\nX-ray observations, on which neutron star \nstudies rely, are \nperformed with\na number of relatively new and advanced telescopes. These include stalwarts like the {Neil Gehrels Swift Observatory}~\\cite{swift}, INTEGRAL~\\cite{integral}, the Chandra X-ray observatory~\\cite{chandra}, XMM-Newton~\\cite{newton}, \nand { MAXI}~\\cite{maxi} and newcomers {ASTROSAT}~\\cite{astrosat}, {NUSTAR}~\\cite{nustar}, and {NICER}~\\cite{nicer}. The problems discussed here would benefit from additional observational data of Type-I X-ray bursts, superbursts, and cooling transients. For instance, more regular bursters like GS~1826$-$24 would provide extra tests for models that successfully reproduce observations~\\cite{Hege07,Meis18,Gall17}. Superbursts and cooling transients have small populations, making our conclusions about these phenomena susceptible to small sample biases. Another cooling transient with a hot crust like MAXI~J0556$-$332 would provide \na new \ntest for the presence of Urca cooling in the accreted crust. The holy grail from this perspective would be a source with a variable accretion rate that exhibits Type-I X-ray bursts and\/or superbursts prior to an extended accretion outburst at high (near Eddington) accretion rate. Further progress will also require next-generation telescopes, e.g. the planned missions eXTP~\\cite{extp} and Strobe-X~\\cite{strobex}, in order to enable high-precision observations that will obviate the need for light-curve averaging and possibly allow time-resolved spectroscopy.\n\nNuclear physics experimental efforts are beginning to benefit from newly-developed techniques and will soon benefit from next-generation facilities. Improved constraints on masses, structure, and reaction rates are required from nearly dripline to dripline on the nuclear chart for $A\\lesssim100$. A consequence of the large number of nuclei involved is that many useful indirect measurements are left to be done at presently existing stable and radioactive ion beam facilities. The reach of these facilities is being expanded by studies leveraging recently developed capabilities such as in-ring reactions~\\cite{Mei15}, the $\\beta$-Oslo technique~\\cite{Spyr14}, phase-imaging penning trap mass measurements~\\cite{Elis15}, and ion beams produced in projectile fragmentation that have been stopped and re-accelerated to astrophysical energies coupled to windowless gaseous targets~\\cite{Bard16}. Nonetheless, the next-generation facilities FRIB~\\cite{frib} and FAIR~\\cite{fair} will provide unprecedented access to nuclei participating in surface burning and crust reactions (see figure~\\ref{figure:reaproduction}).\n\nMost aspects of the accreted neutron star \natmosphere, ocean, and crust have been modeled, including various regimes of nuclear burning, phase separation, and exotic reaction processes. Some of the most valuable ongoing and near-future efforts are and will be those which seek to combine the major aspects of several models, e.g. time-dependent accretion, phase separation, and a nuclear reaction network. More rigorous tests of crust models could be achieved by engaging in consistent multi-observable modeling for sources such as KS~1731$-$260 which exhibits Type-I X-ray burst, superburst, and cooling transient phases. Aside from reproducing observables, more parameter studies assessing the sensitivity of models to nuclear processes are desired to move beyond the small set of conditions that have been explored thus far~\\cite{Pari08,Pari09,Cybu16,Scha17}. Open-source astrophysics model codes, such as {\\tt MESA}~\\cite{Paxt11} and {\\tt dStar}~\\cite{Brow15} are invaluable resources for these studies by enabling more members of the nuclear astrophysics community to contribute.\n\nIt \ngoes without saying that efforts in observation, experiment, and theory will benefit tremendously from cross-discipline collaboration. In this regard the nuclear astrophysics community\nis performing exceedingly well. Continuing and strengthening these bonds (through focal points such as the Joint Institute for Nuclear Astrophysics) will be key to solving the many remaining issues of nuclear physics in the outer layers of accreting neutron stars and the affected astronomical observables.\n\n\\section*{Acknowledgements}\nThe authors thank M. Prakash, H. Schatz, and D.~G. Yakovlev for useful discussions. Z.~M. was supported by the U.S. Department of Energy under grant No. DE-FG02-88ER40387. P.~S. was supported by the BASIS Foundation.\nThis work was supported in part by the U.S. National Science Foundation under grant No. PHY-1430152 (Joint Institute for Nuclear Astrophysics--Center for the Evolution of the Elements).\n\n\n\\section*{References}\n\\bibliographystyle{iopart-num}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nIn this paper we focus on parametric estimation of interacting particle system of the form \n\\begin{equation}\n\\begin{cases}\n d X_t^{\\theta,i, N} = b \\big(\\theta_1, X_t^{\\theta,i, N}, \\mu_t^{\\theta, N} \\big) dt + a \\big( \\theta_2, X_t^{\\theta,i, N}, \\mu_t^{\\theta, N} \\big) d W_t^i, \\qquad i = 1, ... , N, \\quad t\\in [0, T], \\\\[1.5 ex]\n \\mathcal{L} \\big( X_0^{{\\theta,} 1, N}, ... , X_0^{{\\theta,} N, N} \\big) : = \\mu_0 \\times ... \\times \\mu_0.\n\\end{cases}\n \\label{eq: model}\n\\end{equation}\nHere the unknown parameter $\\theta\n:= (\\theta_1, \\theta_2)$ belongs to the set $\\Theta := \\Theta_1 \\times \\Theta_2$, where $\\Theta_j\\subset \\mathbb{R}^{p_j}${, $j=1,2$,} are compact {and convex} sets; we set \n$p: = p_1 + p_2$. The processes $(W^i_t)_{t \\in [0, T]}$, $i=1,\\dots,N$, are independent $\\mathbb{R}$-valued Brownian motions, independent of the initial {value $(X^{\\theta,1,N}_0, \\dots, X^{\\theta,N,N}_0\n$ of the system}\nand $\\mu_t^{\\theta, N}$ is the empirical measure of the system at time $t$, i.e.\\\n\\[\n\\mu_t^{\\theta, N} := \\frac{1}{N} \\sum_{i = 1}^N \\delta_{X_t^{\\theta, i, N}}.\n\\]\nThe model coefficients are functions $b: \\Theta_1 \\times \\mathbb{R} \\times \\mathcal{P}_2 \\rightarrow \\mathbb{R}$ and $a: \\Theta_2 \\times \\mathbb{R} \\times \\mathcal{P}_2 \\rightarrow \\mathbb{R}$, where $\\mathcal{P}_2$ denotes the set of probability measures on $\\mathbb{R}$ with a finite second moment, endowed with the Wasserstein 2-metric\n\\begin{equation}\nW_2(\\mu, \\nu) := \\Big( \\inf_{m \\in \\Gamma (\\mu, \\nu)} \\int_{\\mathbb{R}^2} |x - y|^2 m(dx, dy) \\Big)^{\\frac 1 2},\n\\label{eq: wass}\n\\end{equation}\nand $\\Gamma(\\mu, \\nu)$ denotes the set of probability measures on $\\mathbb{R}^2$ with marginals $\\mu$ and $\\nu$. The underlying observations\nare\n$\n\\big(X_{t_{j,n}}^{\\theta,i,N}\\big)_{j= 1, \\dots, n}^{i=1, \\ldots, N}, $$\nwhere \n$t_{j,n} := {T j\/n}$ and $\\Delta_n := {T\/n}$ is the discretization\n{step}.\nWe assume \nthat the time horizon $T$ is fixed, and\n${N,n \\to \\infty}$.\n\nThe interacting particle system is naturally associated to its\nmean field equation as $N \\rightarrow \\infty$. The latter is described by the 1-dimensional McKean-Vlasov SDE\n\\begin{equation}\nd \\bar{X}_t^{\\theta} = b \\big(\\theta_1, \\bar{X}_t^{\\theta}, \\bar{\\mu}_t^{\\theta} \\big) dt + a \\big(\\theta_2, \\bar{X}_t^{\\theta}, \\bar{\\mu}_t^{\\theta} \\big) d W_t, \\quad t\\in [0, T],\n\\label{eq: McK}\n\\end{equation}\nwhere $\\bar{\\mu}_t^{\\theta}$ is the law of $\\bar{X}_t^{\\theta}$ and $(W_t)_{t \\in [0, T]}$ is a standard Brownian motion{, independent of the initial value $\\bar X^\\theta_0$ having the law $\\bar \\mu_0^\\theta := \\mu_0$}.\nThis equation is non-linear in the sense of McKean, see e.g.\\ \\cite{60imp,61imp,79imp}. It means, in particular, that the coefficients {depend not only on the current state but also on the current distribution of the solution.}\nIt is well known that, under appropriate assumptions on the coefficients $a$ and $b$, it is possible to obtain a phenomenon commonly named \\textit{propagation of chaos} (see e.g.\\ \\cite{79imp}). It implies that the empirical law $\\mu_t^{\\theta, N}$ weakly converges to $\\bar{\\mu}_t^{\\theta}$ as $N \\rightarrow \\infty$. The McKean-Vlasov SDE in \\eqref{eq: McK} links to a non-linear non-local partial differential equation on the space of probability measures (see e.g.\\ \\cite{Cat08}), which naturally arises in several applications in statistical physics. Indeed, stochastic systems of interacting particles and the associated McKean non-linear Markov processes have been introduced in 1966 in \\cite{60imp} starting from statistical physics, to model the dynamics of plasma. Their importance has increased in time, and a huge number of probabilistic tools have been progressively developed in this context (see \\cite{Cat08,Fer97,Mal01,Mel96}, just to name a few). \n\n\nOn the other hand, however, statistical inference in this framework remained out of reach for many years (except for the early work of Kasonga in \\cite{Kas90}), mainly as microscopic particle systems derived from statistical physics are not directly observable. Later on, McKean-Vlasov models found applications in several other fields, in which the data is observable. Nowadays, these models are used in finance (smile calibration in \\cite{smile}; systemic risk in \\cite{Fou13}) as well as social sciences (opinion dynamics in \\cite{Cha17}) or mean-field games (see e.g.\\ \\cite{Car19, Zanella, Giesecke}). Moreover, some applications in neuroscience and population dynamics can be found respectively in \\cite{Bal12} and \\cite{Mog99}. At the same time, the interest in analysis of statistical models related to PDEs has gradually increased. A clear illustration of that is provided by the works on nonparametric Bayes and uncertainty quantification for inverse problems, as in \\cite{AbrNic,Nickl1,Nickl2}. \n\n\nMotivated by the increasing interest in statistical inference for McKean-Vlasov processes, we aim at estimating jointly the parameters ${\\theta_1, \\theta_2}$ starting from the discrete observations of the interacting particle systems \\eqref{eq: model} over a fixed time interval $[0, T]$. \nDespite recent interest in the study of the McKean-Vlasov SDEs, the problem of parameter estimation for this class has received relatively little attention. In \\cite{Wen} the authors established asymptotic consistency and normality of the maximum likelihood estimator for a class of McKean-Vlasov SDEs with constant\n{diffusion coefficient}, based on the continuous observation of the trajectory. This has been extended to the path dependent case in \\cite{Liu}. The mean field regime has been firstly considered by Kasonga in \\cite{Kas90}, who studied a system of interacting diffusion processes depending linearly in the drift coefficient on some unknown parameter. Starting from continuous observation of the system over a fixed time interval $[0, T]$, he showed that the MLE is consistent and asymptotically normal as $N \\rightarrow \\infty$. This has been extended in \\cite{Sharrock} to the case where the parametrisation is not linear, while Bishwal \\cite{Bis} extended it to the case where only discrete observations of the system are available and the parameter to be estimated is a function of time. In \\cite{Giesecke} the authors develop an asymptotic inference approach based on the approximation of the likelihood function for mean-fields models of large interacting financial systems. Moreover, Chen \\cite{24 imp} has established the optimal convergence rate for the MLE in the large $N$ and large $T$ case. Even in this work the drift coefficient is linear and the diffusion coefficient is constant. \n\n\nLet us also mention the works \\cite{GenLar1, GenLar2}, where parametric inference for a particular class of nonlinear self-stabilizing SDEs is studied, starting from continuous observation of the non-linear diffusion. Some different asymptotic regimes are considered, such as the small noise and the long time horizon. The problem of the semiparametric estimation of the drift coefficient starting from the observation of the particle system at time $T$, for $T \\rightarrow \\infty$ is studied in \\cite{Vyt}, while \\cite{Marc} considers non-parametric estimation of the drift term in a McKean-Vlasov SDE, based on the continuous observation of the associated interacting particle system over a fixed time horizon. \n\nNone of these works, however, consider the problem of the joint estimation of the drift and\n{diffusion} coefficients. Moreover, not only we are not aware of any work about parameter estimation for interacting particle system where the\n{diffusion} coefficient can depend on the solution and on the law of the solution itself, but in the majority of the above mentioned work the\n{diffusion coefficient} is directly assumed to be constant. We consider a more general model, as in \\eqref{eq: model}, motivated by several applications in which the\n{diffusion coefficient} depends on the law. For example, this is the case in mathematical finance for the calibration of local and stochastic volatility models, with applications connected to the Dupire's local volatility function (see \\cite{Bos,Gyo,Lac}). Moreover, they are used to capture the diversity of a financial market, as in \\cite{Alm}. \n\nWe underline that the joint estimation of the two parameters introduces some significant difficulties: since the drift and\nthe {diffusion coefficient}\nparameters are not estimated at the same rate, we have to deal with asymptotic properties in two different regimes. Another challenge comes from the fact that both coefficients depend on the empirical law of the process. This introduces some complexity compared to the case where $a$ is constant. \n\nA natural approach to estimation of unknown parameters in our context would be to use a maximum likelihood\nestimation. However, the likelihood function based on the discrete sample is not tractable in this setting, since\nit depends on the transition densities of the process, which are not explicitly known. To overcome this difficulty several methods have been developed, in the case of high frequency estimation for discretely observed classical SDEs. A widely-used method is to consider a pseudo likelihood\nfunction, for instance based on the high frequency approximation of the dynamic of the process\nby the dynamic of the Euler scheme, see for example \\cite{FloZmi,Kes97,Yos92}. \n\nOur statistical analysis is based upon minimisation of a contrast function, which is similar in spirit to the methods \\cite{FloZmi, Kes97,Yos92} that have been proposed in the setting of classical SDEs.\nThe main result of the paper is the consistency and asymptotic normality of the resulting estimator, which is showed by using a central limit theorem for martingale difference triangular arrays. The convergence rates for estimation of the two parameters are different, which leads us to the study of the asymptotic properties of the contrast function in two different asymptotic schemes.\nWe emphasize that our inference is made on the time horizon $[0, T]$ with $T$ being fixed. It is well known that it is impossible to estimate the drift parameter of a classical SDE on a finite time horizon. However, due to increasing number of particles, we are able to consistently estimate the drift even when $T$ is fixed.\n\n\nThe outline of the paper is as follows. In Section \\ref{s: ass} we present the estimation approach,\nlist the required assumptions and demonstrate some examples. Section \\ref{s: main} is devoted to main results of the paper, which include consistency and asymptotic normality of the estimator. In Section \\ref{s: tec} we provide the technical lemmas we will use in order to show our main results. The proofs of the main results are collected in Section \\ref{s: proof main} while the technical results are shown in Section \\ref{s: proof technical}.\n\n\n\n\\subsection*{Notation}\n\nThroughout the paper all positive constants are denoted by $C$ or $C_q$ if they depend on an external parameter $q$. \n{All vectors are row vectors, $\\| \\cdot \\|$ denotes the Euclidean norm for vectors\n}\n{ We write $f(\\theta) = f(\\theta_1,\\theta_2)$ for $\\thet\n= (\\theta_\n,\\theta_\n)$.}\n{For $r=0,1,\\dots$, we denote by $C^{r}(X;\\mathbb{R})$ the set of\n$r$ times continuously differentiable functions $f: X \\to \\mathbb{R}$. We denote by $\\partial_x f$ the partial derivative of a function $f(x,y,\\dots)$ with respect to $x$. We denote by $\\nabla_{\\theta_j} f$ the vector $(\\partial_{\\theta_{j,1}} f, \\dots, \\partial_{\\theta_{j,p_j}} f)$, $j=1,2$, and $\\nabla_\\theta f = (\\nabla_{\\theta_1} f, \\nabla_{\\theta_2} f)$.}\nWe say that a function $f:\\mathbb{R} \\times \\mathcal{P}_l \\to \\mathbb{R}$ has \\textit{polynomial growth} if \n\\begin{align} \\label{eq: pol growth}\n|f(x,\\mu)| \\le C (1 +|x|^k +W_2^l(\\mu,\\delta_0) )\n\\end{align}\nfor some {$k,l =0,1,\\dots$ and all $(x,\\mu)\\in \\mathbb{R}\\times \\mathcal{P}_l$, where $\\mathcal{P}_l$ denotes the set of probability measures with a finite $l$-th absolute moment}. In the sequel, we suppress the dependence \nof several objects on the true parameter $\\theta_0$. In particular, we write $\\P := \\P^{\\theta_0}$, $\\mathbb{E} := \\mathbb{E}^{\\theta_0}$, \n$X_t^{i, N}:=X_t^{\\theta_0, i, N}$, $\\bar X_t := \\bar X^{\\theta_0}_t$, $\\mu_t^N:=\\mu_t^{\\theta_0, N}$ and \n$\\bar \\mu_t := \\bar \\mu^{\\theta_0}_t$.\nFurthermore, we denote by $\\xrightarrow{\\mathbb{P}}$, $\\xrightarrow{\\mathcal{L}}$, \n$\\xrightarrow{L^p}$ the convergence in probability, in law, in $L^p$ respectively. We also denote by $c(\\theta_2,x,\\mu)$ the value $a^2(\\theta_2,x,\\mu)$. \n\n\n\n\n\n\n\\section{Minimal contrast estimator, assumptions and examples}{\\label{s: ass}}\n\nWe aim at estimating the unknown parameter $\\theta_0 \n= (\\theta_{0,1}, \\theta_{0,2}) \n\\in \\Theta^{\\circ}$ given equidistant discrete observations of the system introduced in \\eqref{eq: model}.\nWe study the asymptotic regime $N,n\\to \\infty$. \n\nThe estimator we propose is based upon a contrast function, which originates from the Gaussian quasi-likelihood. \nStarting from discrete observations of the model there are difficulties due to the fact that the transition density of the process is unknown. A common way to overcome this issue is to base the inference on a discretization of the continuous likelihood (see for example \\cite{Gen90}, \\cite{Kes97} and \\cite{Yos92} where classic SDEs are considered). \nThis motivates us to consider the following contrast function:\n\\begin{align}\nS^N_n (\\theta)\n:= \\sum_{i=1}^N \\sum_{j=1}^n \\Bigg\\{ &\\frac{\\big(X^{i,N}_{t_{j,n}} - X^{i,N}_{t_{j-1,n}} - \\Delta_n b\\big(\\theta_1, X^{i,N}_{t_{j-1,n}}, \\mu^N_{t_{j-1,n}}\\big)\\big)^2}{\\Delta_n c \\big(\\theta_2,X^{i,N}_{t_{j-1,n}}, \\mu^N_{t_{j-1,n}}\\big)}\n+\\log c \\big(\\theta_2, X^{i,N}_{t_{j-1,n}}, \\mu^N_{t_{j-1,n}}\\big) \\Bigg\\}.\n\\label{eq:def contrast}\n\\end{align}\n{for $\\theta = (\\theta_1,\\theta_2)$.} The estimator $\\hat{\\theta}_n^N = (\\theta^N_{n,1},\\theta^N_{n,2})$ of $\\theta_0$\nis obtained as \n\\begin{equation*}\n\\hat{\\theta}_n^N \\in \\mathop{\\mathrm{arg\\,min}}_{\\theta \\in \\Theta} S_n^N(\\theta).\n\\end{equation*}\nIn order to study the asymptotic properties of $\\hat{\\theta}_n^N$\nwe need to introduce a set of conditions. \nThe first four conditions are regularity assumptions on the coefficients $a$ and $b$.\n\\begin{assumption} \\label{as1}\n(\\textit{Boundedness of moments})\n\\textit{For all $k \\ge 1$,}\n$$\n\\int_{\\mathbb{R}} |x|^k \\mu_0 (d x) \\le C_k.\n$$\n\\end{assumption} \n\n\\begin{assumption} \\label{as2}\n(\\textit{Lipschitz condition}) \\textit{The drift and\n{diffusion} coefficients are Lipschitz continuous {in $(x,\\mu)$}, i.e.\\ {for all $\\theta$ there exists $C$ such that} for all $(x,\\mu), (y,\\nu) \\in \\mathbb{R} \\times {\\cal P}_2$,\n$$|b(\\theta_1, x, \\mu) - b(\\theta_1, y, \\nu)| + |a(\\theta_2, x, \\mu) - a(\\theta_2, y, \\nu)| \\le C ( |x - y| + W_2(\\mu, \\nu) ).$$\n}\n\\end{assumption}\n\\begin{assumption} \\label{as3}\n(\\textit{Regularity of the diffusion coefficient})\n\\textit{The diffusion coefficient is uniformly bounded away from $0$:\n$$\\inf_{(\\theta_2, x, \\mu) \\in \\Theta_2 \\times \\mathbb{R} \\times {\\cal P}_2} c(\\theta_2, x, \\mu) >0.\n$$}\n\\end{assumption}\n\\begin{assumption} \\label{as4}\n(\\textit{Regularity of the derivatives}) \\textit{(I) {For all $(x,\\mu)$}, the functions $b(\\cdot, x,\\mu)$,\n$a(\\cdot, x,\\mu)$ are in $C^3(\\Theta_1;\\mathbb{R})$,\n$C^3(\\Theta_2;\\mathbb{R})$ respectively. Furthermore, all their {partial} derivatives up to order three have polynomial growth, in the sense\nof \\eqref{eq: pol growth},\n uniformly in $\\theta$.\n \\\\\n(II) The {first and} second order derivatives in $\\theta$ are locally Lipschitz in $(x,\\mu)$ with polynomial weights, i.e.\\ {for all $\\theta$ there exists $C >0$, $k,l =0,1,\\dots$ such that for all $r_1 + r_2 =1, 2$, $h_1, h_2 = 1, ... , p_1$, $\\tilde{h}_1, \\tilde{h}_2 = 1, ... , p_2$,\n$(x,\\mu), (y,\\nu) \\in \\mathbb{R} \\times {\\cal P}_2$,}\n\\begin{align*}\n&\\big|\\partial_{\\theta_{1,h_1}}^{r_1} \\partial_{\\theta_{1,h_2}}^{r_2} b (\\theta_1, x, \\mu)- \\partial_{\\theta_{1,h_1}}^{r_1} \\partial_{\\theta_{1,h_2}}^{r_2} b (\\theta_1, y, \\nu) \\big| + \\big|\\partial_{\\theta_{2,\\tilde{h}_1}}^{r_1} \\partial_{\\theta_{2,\\tilde{h}_2}}^{r_2} a (\\theta_2, x, \\mu)-\\partial_{\\theta_{2,\\tilde{h}_1}}^{r_1} \\partial_{\\theta_{2,\\tilde{h}_2}}^{r_2} a (\\theta_2, y, \\nu) \\big|\n\\\\%[1.5 ex]\n&\\qquad \\le C (|x-y| + W_2 (\\mu,\\nu)) \\big( 1 + |x|^k + |y|^k + W_2^l(\\mu,\\delta_0) + W_2^l(\\nu,\\delta_0) \\big).\n\\end{align*}\n} \n\\end{assumption}\n\n\n\\begin{remark} \\rm\n(i) It is possible to relax assumption \\ref{as2} on the drift coefficient to allow for a locally Lipschitz condition in $x$ with polynomial weights, cf.\\ \\cite[Assumption 2.1]{DosReis}. In this setting \nthe boundedness of moments shown in our Lemma \\ref{l: moments} can be replaced by \\cite[Theorem 3.3]{AAP} and the propagation of chaos needed in order to prove Lemma \\ref{l: Riemann} would follow from \\cite[Proposition 3.1]{DosReis}. As a consequence the main results of this paper still hold. \\\\ \\\\\n(ii) \n\\ref{as4}(I) \nis sufficient to show consistency of the estimator $\\hat{\\theta}_n^N$. We require the additional condition (II) of \\ref{as4} to prove the asymptotic normality. \\qed\n\\end{remark}\n\n\\noindent\nWe now state an assumption on the identifiability of the model\nand some further conditions that are required to prove the asymptotic normality. For this purpose we define the functions $I : \\Theta \\to \\mathbb{R}$, $J : \\Theta_2 \\to \\mathbb{R}$ as\n\\begin{align}\\label{def:I}\nI (\\theta)\n&:= \\int_0^{{T}} \\int_{\\mathbb{R}} \\frac{\\left(b(\\theta_1,x, \\bar \\mu_t) - b(\\theta_{0,1}x, \\bar \\mu_t)\\right)^2}{c(\\theta_2,x, \\bar \\mu_t)} \\bar \\mu_t (d x) d t,\\\\[1.5 ex] \\label{def:J}\nJ (\\theta_2) &:= \\int_0^{{T}} \\int_{\\mathbb{R}} \\Big( \\frac{c(\\theta_{0,2},x, \\bar \\mu_t)}{c (\\theta_2,x, \\bar \\mu_t)} + \\log c (\\theta_2,x, \\bar \\mu_t) \\Big) \\bar \\mu_t (d x) d t.\n\\end{align}\nThe next set of conditions are the following assumptions.\n\\begin{assumption} \\label{as5}\n \\textit{(Identifiability)} \\textit{The functions $I, J$ defined above satisfy that for every $\\varepsilon > 0$,\n$$\n\\inf_{\\theta \\in \\Theta: \\|\\theta_1-\\theta_{0,1}\\|\\ge \\varepsilon} I(\\theta)>0\\qquad \\mbox{and } \\inf_{\\theta_2 \\in \\Theta_2: \\|\\theta_2-\\theta_{0,2}\\|\\ge \\varepsilon} (J(\\theta_2)-J(\\theta_{0,2}))>0.\n$$}\n\\end{assumption}\n\\begin{assumption}\\label{as6}\n (\\textit{Invertibility}) \\textit{We define a $p \\times p$ block diagonal matrix \n$\\Sigma(\\theta_0) {:}=\n\\operatorname{diag}(\\Sigma^{(1)}(\\theta_0),\\Sigma^{(2)}(\\theta_0))$ whose main-diagonal blocks \n$\\Sigma^{(j)}(\\theta_0) = (\\Sigma^{(j)}_{kl}(\\theta_0))$\nare defined via\n \\begin{align*}\n\\Sigma^{(j)}_{kl} (\\theta_0) {:}= \\begin{cases}\n\\displaystyle 2 \\int_0^{{T}} \\int_\\mathbb{R} \\frac{\\partial_{\\theta_{1, k}} b(\\theta_{0, 1}, x, \\bar{\\mu}_s) \\, \\partial_{\\theta_{1, l}} b(\\theta_{0, 1}, x, \\bar{\\mu}_s)}{c(\\theta_{0, 2}, x, \\bar{\\mu}_s)} \\bar{\\mu}_s(dx) ds, \\qquad &j=1, \\, k, l = 1, \\dots, p_1,\n\\\\\n\\displaystyle \\int_0^{{T}} \\int_\\mathbb{R} \\frac{\\partial_{\\theta_{2, k}}c(\\theta_{0, 2}, x, \\bar{\\mu}_s) \\, \\partial_{\\theta_{2, l}}c(\\theta_{0, 2}, x, \\bar{\\mu}_s)}{c^2(\\theta_{0, 2}, x, \\bar{\\mu}_s)} \\bar{\\mu}_s(dx) ds, \\qquad &j=2, \\, k,l = 1,\\dots, p_2.\n\\end{cases}\n\\end{align*}\nWe assume that $\\operatorname{det}(\\Sigma^{{(j)}} (\\theta_0)) \\neq 0${, $j=1,2$.\n}\n}\n\\end{assumption}\n\n\n \n\n\\begin{assumption} \\label{as7}\n \\textit{\n\n \n \n (\\textit{Integral condition on the diffusion coefficient}) {At $\\theta_{0,2}$ for all $(x,\\mu)$ t}he diffusion coefficient takes the form \n $$\n a(\\theta_{{0,}2}, x, \\mu) {:} = \\Tilde{a}\n \\Big(\n x, \\int_{\\mathbb{R}} \n (x, y) \\mu(dy)\\Big)\n $$\n {for some functions \n\n\n\n $\\tilde a, K \\in C^2(\\mathbb{R}^2; \\mathbb{R})$,\n which satisfy $|\\partial^{r_1}_x \\partial_y^{r_2} \\tilde \n (x,y)| + |\\partial^{r_1}_x \\partial^{r_2}_y \n (x,y)| \\le C (1+|x|^{k}+|y|^l)$ for some $k,l = 0,1,\\dots$ and all $r_1+r_2\n 1,2$, \n $(x,y) \n \\in \\mathbb{R}^2\n $.\n }\n }\n\n\\end{assumption}\n\n\\noindent\nAssumptions \\ref{as1}-\\ref{as5} are required to prove consistency of our estimator, while additional conditions \\ref{as6}-\\ref{as7} are need to obtain the central limit theorem. We also remark that, in the case where the unknown parameter $\\theta$ appears only in the drift coefficient, there is no need to add a further assumption on the derivatives of the diffusion coefficient to estimate it, even if the diffusion coefficient still depends on the law of the process. \n\n\\begin{example} \\rm\nA number of interacting particle models (and associated mean field equations) have been analyzed in the literature. We highlight a few here to illustrate the scope of our paper. \\\\\nWe start by considering some examples where the diffusion coefficient is a constant on a compact set that does not include the origin. This case has several applications (see (i) and (ii)). After that, some more general examples are presented. \\\\\n\\\\\n{\n(i) The Kuramoto model is the most classical model for synchronization phenomena in large populations of coupled oscillators such as a clapping crowd, a population of fireflies or a system of neurons (see Section 5.2 of \\cite{Review prop} and references therein). Let $N$ oscillators be defined by $N$ angles $X_t^{i,N}$, $i=1,\\dots,N$ (defined modulo $2 \\pi$, in this way they can actually be considered as elements of the circle), evolving in $t \\in [0,T]$ according to}\n$$d X_t^{i, N}=-\\frac{\\theta_{0, 1}}{N}\\sum_{j=1}^N \\sin \\big(X_t^{i, N} - X_t^{j, N} \\big) d t + \\theta_{0,2}d W^{{i}}_t.$$\n{This variant of the model satisfies our assumptions.\\\\ \n\\\\\n(ii) A popular model for opinion dynamics (see e.g.\\ \\cite{Cha17,65imp}) takes the form\n$$d X_t^{i, N}=-\\frac{1}{N}\\sum_{j=1}^N \\varphi_{\\theta_{0, 1}} \\big( \\big| X_t^{i, N} - X_t^{j, N} \\big| \\big) \\big( X_t^{i, N} - X_t^{j, N} \\big) d t + \\theta_{0,2}d W^{{i}}_t \n$$\nfor $i=1,\\dots,N$, $t \\in [0,T]$, where $\\varphi_{\\theta_{0,1}} (x) := \\theta_{0,1,1} \\mathds{1}_{[0,\\theta_{0,1,2}]} (x)$, $x \\in \\mathbb{R}$, is the influence function which acts on the ``difference of opinions'' between agents. To have our regularity assumptions hold true in practice we can replace the function $\\varphi_{\\theta_{0,1}}$ by its infinitely differentiable approximation as it is done in Section 5.2 of \\cite{Sharrock}. In \\cite{Sharrock} we also note that the proxy of $\\varphi_{\\theta_{0,1}}$ depends non-linearly on the parameter $\\theta_{0,1,2}$.\\\\\n\\\\\n(iii) Another example is\n$$\nd X^{i,N}_t = \\Big( \\theta_{0,1,1} + \\frac{\\theta_{0,1,2}}{N} \\sum_{j=1}^N X^{j,N}_t - \\theta_{0,1,3} X^{i,N}_t \\Big) d t + \\theta_{0,2} \\sqrt{ 1+\\big( X^{i,N}_t \\big)^2 } d W^i_t\n$$\nfor $i=1,\\dots,N$, $t \\in [0,T]$. We note that in the case $\\theta_{0,1,2}=0$ the interacting particle system reduces to $N$ independent samples of a special case of the Pearson diffusion, which has applications in finance, see \\cite{For08} and references therein.\\\\\n\\\\\n(iv) We consider the dynamic of the system\n$$\nd X^{i,N}_t = \\Big( \\theta_{0,1,1} + \\frac{\\theta_{0,1,2}}{N} \\sum_{j=1}^N X^{j,N}_t - \\theta_{0,1,3} X^{i,N}_t \\Big) d t + \\Big( \\theta_{0,2,1} + \\theta_{0,2,2} \\sqrt{ \\frac{1}{N} \\sum_{j=1}^N \\big( X^{j,N}_t \\big)^2 } \\Big) d W^i_t\n$$\nfor $i=1,\\dots,N$ in $t\\in [0,T]$, where both the coefficients $b$ and $a$ depend on the law argument. We remark that the mean field limit of the above interacting particle system is a time-inhomogeneous Ornstein-Uhlenbeck process. See \\cite{Kas9\n} for the case\n$\\theta_{0,1,1} = \\theta_{0,2,2} = 0$. }\n\n\\end{example}\n\n\n\n\n\n\n\n\n\n\n\\section{Main results}{\\label{s: main}}\nOur main results demonstrate the consistency and the asymptotic normality of the estimator $\\hat{\\theta}_n^N$. \n\n\\begin{theorem}{(Consistency)}\nAssume that \\ref{as1}-\\ref{as5} hold, with only condition (I) in \\ref{as4}. Then the estimator $\\hat{\\theta}_n^N$ is consistent in probability:\n$$\\hat{\\theta}_n^N \\xrightarrow{\\mathbb{P}} \\theta_0 \\quad \\mbox{as } n, N \\rightarrow \\infty.$$\n\\label{th: consistency}\n\\end{theorem}\n\\noindent\nIn order to obtain the asymptotic normality of our estimator we need to add an assumption on the relation between the rates $N$ and $\\Delta_n$. In particular, we require that $N{\\Delta_n} \\rightarrow 0$ as $N, n \\rightarrow \\infty$.\n\n\\begin{theorem}{(Asymptotic normality)}\nAssume that \\ref{as1}-\\ref{as7} hold. If $N {\\Delta_n} \\rightarrow 0$ then \n$$\\big(\\sqrt{N}(\\hat{\\theta}_{n,1}^N - \\theta_{0,1}), \\sqrt{N\n\/ {\\Delta_n}}(\\hat{\\theta}_{n,2}^N - \\theta_{0,2}) \\big)\n\\xrightarrow{\\mathcal{L}} {\\cal N}\\big(0, 2 ( \\Sigma(\\theta_0) )^{-1}\\big) \\quad \\mbox{as } n, N\\rightarrow\\infty,$$\nwhere \n$$ 2 ( \\Sigma(\\theta_0) )^{-1} {:}= \n2 \\operatorname{diag} \\big( ( \\Sigma^{(1)}(\\theta_0) )^{-1}, ( \\Sigma^{(2)}(\\theta_0) )^{-1} \\big)$$\nwith $\\Sigma^{(j)} (\\theta_0)${, $j=1,2$,} being defined in \\ref{as6}. \n\n\\label{th: normality}\n\\end{theorem}\n\n\n\\noindent\nAs common in the literature on contrast function based methods, understanding the asymptotic behaviour of $S^N_n (\\theta_1,\\theta_2)$ and its derivatives is key to obtain the statements of Theorems \\ref{th: consistency} and \\ref{th: normality}. In particular, we show that, under proper normalisation, the first derivative of \n$S^N_n (\\theta_1,\\theta_2)$ \nconverges to a Gaussian law with mean $0$ and covariance matrix $2 \\Sigma(\\theta_0)$ (see Proposition \\ref{p: norm L}), while the second derivative converges in probability to the matrix $\\Sigma(\\theta_0)$ defined in \n\\ref{as6} (see Proposition \\ref{p: second derivatives contrast}). These results lead to the statement of Theorem \\ref{th: normality}. \n\nThe condition on relation between the two convergence rates have been discussed in details in the framework of classical SDEs. Therein only the discrete trajectory of one particle is observed with discretization step $\\Delta_n\\to 0$ and terminal time $T:=n\\Delta_n \\to \\infty$. In \\cite{FloZmi} the corresponding condition was $T \\Delta_n = n \\Delta_n^2 \\rightarrow 0$\nas $n\\to \\infty$, which has been later improved to $n \\Delta_n^3 \\rightarrow 0$ in \\cite{Yos92} thanks to a correction introduced in the contrast function. Finally, Kessler \\cite{Kes97} proposed a contrast function based on a Gaussian approximation of the transition density, which allowed him to consider a weaker condition $n \\Delta_n^p \\rightarrow 0$ for an arbitrary integer $p$. Similar developments have been made in the setting of classical SDEs with jumps in \\cite{Sjs,Joint,GLM,Shi}. \n\nOne may wonder if it possible to weaken the condition on the discretization step in the setting of particle systems. On the one hand, the condition $N {\\Delta}_n \\rightarrow 0$ is needed in order to approximate the derivative of the contrast function via a triangular array of martingale increments, as it is the case for classical SDEs. In this step higher order approximation similar to \\cite{Kes97} can potentially help to obtain a weaker condition. \nOn the other hand, our condition on the discretization step is also required due to correlation between particles and higher order approximation does not seem to solve this issue. Thus, we leave this investigation for future research. \n\nA recent paper \\cite{Hof2} establishes the \\textit{LAN property} for\ndrift estimation in $d$-dimensional McKean-Vlasov models under continuous observations and with diffusion {coefficient \nbeing a function of $(t,\\bar X_t)$ {only}. The authors show that the Fisher information matrix is given as\n$$\\Bigg(\\int_0^T \\int_{\\mathbb{R}^d} \\partial_{\\theta_k} (c^{- \\frac{1}{2}} b) (\\theta, {t}, x, \\bar \\mu_t)^{{\\top}} \\partial_{\\theta_l} (c^{- \\frac{1}{2}} b) (\\theta, {t}, x, \\bar \\mu_t) \\bar \\mu_t (dx) dt \\Bigg)_{1 \\le k, l \\le p}$$\n(cf.\\ \\cite{Sharrock} where the diffusion coefficient is {an identity matrix}). This is consistent with our Theorem \\ref{th: normality} when restricted to drift estimation. In other words, our drift estimator is asymptotically efficient. When considering joint estimation of the drift and diffusion coefficients, the LAN property has not yet been shown, although the results of Gobet \\cite{Gobet 2002} in the classical diffusion setting give some hope. \n\n\n\n\n\n\n\n\n\n\n\\section{Technical lemmas}{\\label{s: tec}}\nBefore proving the main statistical results stated in previous section, we need to introduce some additional notations and to state some lemmas which will be useful in the sequel. \n\nDefine $\\mathcal{F}_t^N := \\sigma \\{(W_u^k)_{u \\in [0,t]}, \\, X_0^\nk, N}; \\, k= 1, ... , N \\}$ and $\\mathbb{E}_t [\\cdot] := \\mathbb{E}[\\cdot | \\mathcal{F}^N_t]$. For a set $(Y^{i,N}_{t,n})$ of random variables and $\\delta \\ge 0$, the notation \n$$\nY^{i,N}_{t,n} = R_t^i (\\Delta_n^\\delta)\n$$ \nmeans that $Y^{i,N}_{t,n}$ is $\\mathcal{F}_t^N$-measurable and the set $(Y_{t,n}^{i,N}\/\\Delta_n^\\delta)$ is \nbounded in $L^q$ for all $q \\ge 1$, uniformly in $t, i, n, N$. That is \\\n$$\n\\mathbb{E} \\big[ \\big|Y_{t,n}^{i,N}\/\\Delta_n^\\delta \\big|^q \\big]^{1\/q} \\le C_q\n$$ \nfor all $t, i, n, N$, $q \\ge 1$. \n\n\n\n\nWe will repeatedly use some moment inequality gathered in the following lemma. \n\\begin{lemma}\nAssume \\ref{as1}-\\ref{as2}. Then, for all $p \\ge 1$, $0 \\le s < t \\le T$ such that $t-s \\le 1$, $i \\in \\{ 1, ..., N \\}$, $N \\in \\mathbb{N}$, the following\nhold true.\n\\begin{enumerate}\n \\item $\\sup_{t \\in [0,T]} \\mathbb{E}[|X_t^\n i, N}|^p] < C$,\n \n \n \n \n \n moreover, $\\sup_{t \\in [0,T]} \\mathbb{E}[W_p^q(\\mu_t^\n N}, \\delta_0)] < C$ for $p \\le q$.\n \\item\n $\\mathbb{E}[|X_t^\n i, N} - X_s^\n i, N}|^p] \\le C (t - s)^{\\frac{p}{2}}$.\n \\item\n $\\mathbb{E}_s [|X_t^\n i, N} - X_s^\n i, N}|^p\n ] \n \\le C (t - s)^\\frac{p}{2}R_s^i\n 1)$. \n \\item\n $\\mathbb{E}[W_2^p(\\mu_t^\n N}, \\mu_s^\n N})] \\le C (t - s)^{\\frac{p}{2}}$.\n \\item\n $\\mathbb{E}_s [W_2^p(\\mu_t^\n N}, \\mu_s^\n N}) |\n ] \\le C (t - s)^{\\frac{p}{2}}R_s \n 1)$.\n\\end{enumerate}\n\\label{l: moments}\n\\end{lemma}\nThe asymptotic properties of the estimator are deduced by the asymptotic behaviour of our contrast function. To study it, the following lemma will be useful. \n\n\n\\begin{lemma}\nAssume \\ref{as1}-\\ref{as2}. \nLet $f : \\mathbb{R} \\times {\\cal P}_l \\to \\mathbb{R}$ satisfy for some $C>0$, $k,l =0,1,\\dots$ and all $(x,\\mu), (y,\\nu) \\in \\mathbb{R} \\times {\\cal P}_l$,\n\\begin{equation}\\label{cond:Riemann}\n|f(x,\\mu)-f(y,\\nu)| \\le C (|x-y| + W_2(\\mu,\\nu))(1 + |x|^k + |y|^k + W_l^l(\\mu,\\delta_0) + W_l^l(\\nu,\\delta_0))\n\\end{equation}\nMoreover, let\nthe mapping $(x,t) \\mapsto f(x,\\bar \\m\n_t)$ be integrable with respect to $\\bar \\m\n_t (d x) d t$ over $\\mathbb{R} \\times [0,T]$.\nThen \n$$\n\\frac{\\Delta_n}{N} \\sum_{i=1}^N \\sum_{j=1}^n f(X^\ni,N}_{t_{j-1,n}},\\mu^\nN}_{t_{j-1,n}}) \\xrightarrow{\\mathbb{P}} \\int_0^T \\int_{\\mathbb{R}} f(x,\\bar \\m\n_t) \\bar \\m\n_t (d x) d t \\quad \\text{as } n,N \\to \\infty.\n$$\n\\label{l: Riemann}\n\\end{lemma}\n\n\nOur main results heavily rely on the study of derivatives of our contrast function and so on the moments of its numerator. We recall that, in the sequel, we will denote by $c(\\theta_2, x, \\mu)$ the\nvalue $a^2(\\theta_2, x, \\mu)$.\n\n\\begin{lemma}\nAssume \\ref{as1}-\\ref{as2}.\nThen, the following hold true. \n\\begin{enumerate}\n \\item If also \\ref{as7} is satisfied, then\\\\ \n $\\mathbb{E}_{t_{j,n}} [(X_{t_{j+1,n}}^\n i, N} - X_{t_{j,n}}^\n i, N} - \\Delta_n b(\\theta_{0,1\n , X_{t_{j,n}}^\n i, N}, \\mu_{t_{j,n}}^\n N}) )^2] = \\Delta_{n} c(\\theta_{0,2\n , X_{t_{j,n}}^\n i, N},\\mu_{t_{j,n}}^\n N}) + R_{t_{j,n}}^i\n \\Delta_{n}^{2}).$\n \\item $\\mathbb{E}_{t_{j,n}} [(X_{t_{{j+1},n}}^\n i, N} - X_{t_{j,n}}^\n i, N} - \\Delta_n b(\\theta_{0,1}, X_{t_{j,n}}^\n i, N}, \\mu_{t_{j,n}}^\n N}) )^4] = 3 \\Delta_{n}^2 c^2(\\theta_{0, 2}\n , X_{t_{j,n}}^{i, N},\\mu_{t_{j,n}}^\n N}) + R_{t_{j,n}}^i\n \\Delta_{n}^{\\frac{5}{2}}).$\n \\item $|\\mathbb{E}_{t_{j,n}} [ X_{t_{j+1,n}}^\n i, N} - X_{t_{j,n}}^\n i, N} - \\Delta_n b(\\theta_{0,1}, X_{t_{j,n}}^\n i, N}, \\mu_{t_{j,n}}^\n N}) ] |\n = R_{t_{j,n}}^i\n \\Delta_{n}^{\\frac{3}{2}}).$\n \n \n\\end{enumerate}\n\\label{l: conditional expectation}\n\\end{lemma}\nThe proof of the lemmas stated in this section can be found in Section \\ref{s: proof technical}.\n\n\n\\section{Proofs}{\\label{s: proof main}}\n\t\n\\subsection{Consistency}\t\t\n\t\n\n\nLet us prove the (asymptotic) consistency of $\\hat \\theta^N_n = (\\hat \\theta^N_{n,1},\\hat \\theta^N_{n,2})$ component-wise. Our approach is similar to that taken in the proof of \\cite[Theorem 5.7]{van98}. \nIn particular, we consider a criterion function $\\theta \\mapsto S^N_n (\\theta)$ \nas a random element taking values in $(C(\\Theta;\\mathbb{R}), \\| \\cdot \\|_\\infty)$. The uniform convergence of criterion functions is proved in the following lemma. \n\n\n\\begin{lemma}\\label{lemma:cns}\nAssume \\ref{as1}-\\ref{as3}, {\\ref{as4}(I)}, \\ref{as5}. {Then a}s $N,n \\to \\infty$,\n\\begin{align}\\label{lim:con2}\n&\\sup_{(\\theta_1,\\theta_2) \\in \\Theta} \\Big| \\frac{\\Delta_n}{N} S^N_n (\\theta_1,\\theta_2) - J (\\theta_2) \\Big| \\xrightarrow{\\mathbb{P}} 0,\\\\ \\label{lim:con1}\n&\\sup_{(\\theta_1,\\theta_2) \\in \\Theta} \\Big| \\frac{1}{N} (S^N_n (\\theta_1,\\theta_2)-S^N_n (\\theta_{0, 1},\\theta_2)) - I (\\theta_1,\\theta_2) \\Big| \\xrightarrow{\\mathbb{P}} 0, \n\\end{align}\nwhere the functions $I, J$ are defined in \\eqref{def:I}, \\eqref{def:J} respectively.\n\\end{lemma}\n\n\n\n\n\\begin{proof}\nIt suffices to show the following steps:\n\\begin{enumerate}\n\\item $\\frac{\\Delta_n}{N} S^N_n (\\theta_1,\\theta_2) \\xrightarrow{\\mathbb{P}} J (\\theta_2)$ for every $(\\theta_1,\\theta_2) \\in \\Theta$.\n\\item The sequence $(\\theta_1,\\theta_2) \\mapsto \\frac{\\Delta_n}{N} S^N_n(\\theta_1, \\theta_2)$ is tight in $(C(\\Theta;\\mathbb{R}), \\| \\cdot \\|_\\infty)$.\n\\item $\\frac{1}{N} (S^N_n (\\theta_1,\\theta_2)-S^N_n (\\theta_{0,1},\\theta_2)) \\xrightarrow{\\mathbb{P}} I (\\theta_1,\\theta_2)$ for every $(\\theta_1,\\theta_2) \\in \\Theta$,\n\\item The sequence $(\\theta_1,\\theta_2) \\mapsto \\frac{1}{N} (S^N_n (\\theta_1,\\theta_2)-S^N_n (\\theta_{0,1},\\theta_2))$ is tight in $(C(\\Theta;\\mathbb{R}), \\| \\cdot\\|_\\infty)$.\n\\end{enumerate}\n\n\n\n\\noindent\nLet us omit the notation for dependence on $N,n$, in particular, write $X^i_t$ for $X^{i,N}_t$, $\\mu_t$ for $\\mu^N_t$, $t_j$ for $t_{j,n}$.\nDenote $f(\\cdot, X^i_t, \\mu_t)$ by $f^i_t (\\cdot)$ for a function $f$, for example equal to\n$h$ or $g$ defined as\n\\begin{equation}\\label{def:gd}\nh (\\theta, x,\\mu) = \\frac\n(b(\\theta_{0,1}, x, \\mu) - b(\\theta_1, x, \\mu))^2}{c (\\theta_2, x, \\mu)}, \\qquad g(\\theta, x, \\mu) = \\frac\nb(\\theta_{0,1}, x, \\mu) - b(\\theta_1, x, \\mu)}{c (\\theta_2, x, \\mu)\n\\end{equation}\nfor all $\\theta = (\\theta_1,\\theta_2) \\in \\Theta_1 \\times \\Theta_2 = \\Theta$, $x \\in \\mathbb{R}$, $\\mu \\in {\\cal P}_2$. \\\\\n\n\\noindent \n$\\bullet$ Step 3. We start proving that for every $\\theta = (\\theta_1,\\theta_2) \\in \\Theta_1 \\times \\Theta_2 = \\Theta$,\n$$\n\\frac{1}{N} (S^N_n(\\theta_1,\\theta_2) - S^N_n(\\theta_{0,1},\\theta_2)) \\xrightarrow{\\P} I(\\theta) = \\int_0^{T} \\int_{\\mathbb{R}} h(\\theta, x,\\bar \\mu_t) \\bar \\mu_t (d x) d t.\n$$\nLet us first decompose the left hand side as a sum of a main term and remainder.\nWe have\n$$\nS^N_n(\\theta_1,\\theta_2) = \\sum_{i=1}^N \\sum_{j=1}^n \\frac{({H}^i_j + \\Delta_n (b^i_{t_{j-1}}(\\theta_{0,1}) - b^i_{t_{j-1}}(\\theta_1))\n)^2}{\\Delta_n c^i_{t_{j-1}} (\\theta_2) } + (\\log c)^i_{t_{j-1}}(\\theta_2),\n$$\nwhere\n${H}^i_j = X^i_{t_{j-1}} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}}(\\theta_{0,1})$\nfor all $i, j$. We decompose\n\\begin{equation}\\label{cfdeComp}\n\\frac{1}{N} (S^N_n(\\theta_1,\\theta_2) - S^N_n(\\theta_{0,1},\\theta_2)) = I^N_n (\\theta) + 2 \\rho^N_n (\\theta),\n\\end{equation}\nwhere\n\\begin{equation}\\label{def:INnrhoNn}\nI^N_n (\\theta) = \\frac{\\Delta_n}{N} \\sum_{i=1}^N \\sum_{j=1}^n\nh^i_{t_{j-1}}(\\theta), \n\\qquad \\rho^N_n (\\theta) = \\frac{1}{N} \\sum_{i=1}^N \\sum_{j=1}^n\ng^i_{t_{j-1}} (\\theta) {H}^i_j.\n\\end{equation}\nThen\n$$\nI^N_n (\\theta) \\xrightarrow{\\P} I(\\theta)\n$$\nfollows from Lemma~\\ref{l: Riemann} if the function $h(\\theta,\\cdot)$\nis locally Lipschitz with polynomial growth. To check this assumption we note\nthat the functions $\nb(\\theta_{0,1},\\cdot) - b(\\theta_1,\\cdot)$, $a(\\theta_2,\\cdot)$ are Lipschitz continuous and have linear growth by \\ref{as2}. We also recall that $\\inf_{x,\\mu} c (\\theta_2,x,\\mu) > 0$ by \\ref{as3}. Hence, $h(\\theta,\\cdot)$\nsatisfies the assumption of Lemma~\\ref{l: Riemann}.\n\n\n\n\nIt remains to show that \n\\begin{equation}\\label{lim:rhoNn}\n\\rho^N_n (\\theta) \\xrightarrow{\\P} 0.\n\\end{equation}\nWith \n${H}^i_j = B^i_j + A^i_j$, where\n$$\nB^i_j = \\int_{t_{j-1}}^{t_j} (b^i_s(\\theta_{0,1}) - b^i_{t_{j-1}} (\\theta_{0,1})) d s, \\qquad A^i_j = \\int_{t_{j-1}}^{t_j} a^i_s (\\theta_{0,2}) d W^i_s,\n$$\nfor all $i,j$, let us further decompose\n\\begin{equation}\\label{rhoNndeComp}\n\\rho^N_n (\\theta) = \\rho^N_{n,1} (\\theta) + \\rho^N_{n,2} (\\theta),\n\\end{equation}\nwhere\n$$\n\\rho^N_{n,1} (\\theta)= \\frac{1}{N} \\sum_{i=1}^N \\sum_{j=1}^n g^i_{t_{j-1}}(\\theta) B^i_j, \\qquad\n\\rho^N_{n,2} (\\theta) = \\frac{1}{N} \\sum_{i=1}^N \\sum_{j=1}^n g_{t_{j-1}}^i (\\theta) A^i_j.\n$$\nIt is enough to show that\n\\begin{equation}\\label{lim:rhoNnp}\n\\rho^N_{n,k} (\\theta) \\xrightarrow{L^k} 0, \\qquad k=1,2.\n\\end{equation}\nFirst, let us show \\eqref{lim:rhoNnp} in case $k=2$. Note that for all $i_1 = i_2$ and $j_1 \\neq j_2$, \n\\begin{equation}\\label{covgA}\n\\mathbb{E} [ g^{i_1}_{t_{j_1-1}}(\\theta) A^{i_1}_{j_1} g^{i_2}_{t_{j_2-1}}(\\theta) A^{i_2}_{j_2} ] = 0\n\\end{equation}\nfollows from $\\mathbb{E}_{t_{j_1-1}} [A^{i_1}_{j_1}] = 0$, whereas independence of Brownian motions implies \\eqref{covgA} for all $i_1 \\neq i_2$ and $j_1, j_2$. We conclude that\n\\begin{equation}\\label{varrhoNn2}\n\\mathbb{E} [(\\rho^N_{n,2} (\\theta))^2] = \\frac{1}{N^2} \\sum_{i=1}^N \\sum_{j=1}^n \\mathbb{E} [ (g^i_{t_{j-1}}(\\theta) A^i_j)^2 ]. \n\\end{equation}\nNext, the It\\^o isometry gives \n$$\n\\mathbb{E} [ (g^i_{t_{j-1}}(\\theta) A^i_j)^2 ] = \\int_{t_{j-1}}^{t_j} \\mathbb{E} [ (g^2)^i_{t_{j-1}}(\\theta) c^i_s(\\theta_{0,2}) ] d s\n$$\nwhere $\\mathbb{E} [ (g^2)^i_{t_{j-1}}(\\theta) c^i_s(\\theta_{0,2}) ] = O(1)$ \nuniformly in $t_{j-1} \\le s \\le t_j, j,i$ thanks to $\\inf_{x,\\mu}c(\\theta_2,x,\\mu)>0$ by \\ref{as3}, linear growth of $a(\\theta_{0,2},\\cdot)$, $b(\\theta_1,\\cdot)$\nby \\ref{as2}\nand moment bounds in Lemma~\\ref{l: moments}(1). We conclude that $\\mathbb{E} [ (g^i_{t_{j-1}}(\\theta) A^i_j)^2 ] = O(\\Delta_n)$ uniformly in $i,j$, which in turn implies \n$$\n\\mathbb{E} [(\\rho^N_{n,2}(\\theta))^2] = O \\big(N^{-1} \\big).\n$$\nFinally, let us show \\eqref{lim:rhoNnp} in case $k=1$. For this purpose, use\n$$\n\\mathbb{E}[ |g^i_{t_{j-1}}(\\theta) B^i_j| ] \\le \\int_{t_{j-1}}^{t_j} \\mathbb{E} [|g^i_{t_{j-1}}(\\theta) (b^i_s(\\theta_{0,1}) - b^i_{t_{j-1}}(\\theta_{0,1}))|] d s\n$$ \nand then the Cauchy\u2013Schwarz inequality.\nNote $\\mathbb{E} [ (g^2)^i_{t_{j-1}} (\\theta) ] = O(1)$ uniformly in $j,i$ follows in the same way\nas \nabove. Lipschitz continuity of $b(\\theta_{0,1},\\cdot)$ by \\ref{as2} and moment bounds in Lemma~\\ref{l: moments}(2) and (4)\nimply $\\mathbb{E} [ (b^i_s(\\theta_{0,1}) - b^i_{t_{j-1}}(\\theta_{0,1}))^2 ] = O(\\Delta_n)$ uniformly in $t_{j-1} \\le s \\le t_j, j, i$. We conclude that \n$$\n\\mathbb{E} [|\\rho^N_{n,1}|] = O(\\Delta_n^{\\frac{1}{2}}).\n$$ \nThis completes the proof of Step 3. \\\\\n\n\\noindent\n$\\bullet$\nStep 4. \nRecall the decomposition \\eqref{cfdeComp}, \\eqref{rhoNndeComp}.\nIt is enough to show tightness of \n$$\n\\theta \\mapsto I^N_n (\\theta), \\qquad\n\\theta \\mapsto \\rho^N_{n,k} (\\theta), \\qquad k=1,2.\n$$\nOur approach to showing tightness of both sequences are based upon \\cite[Theorem 14.5]{Kall}. We need to show that for all $N,n$:\n\\begin{equation}\\label{ineq:tight1}\n\\mathbb{E} \\big[ \\sup_{\\theta} \\| \\nabla_\\theta I^N_n (\\theta) \\| \\big] \\le C, \\qquad \\mathbb{E} \\big[ \\sup_{\\theta} \\| \\nabla_\\theta \\rho^N_{n,1} (\\theta) \\| \\big] \\le C.\n\\end{equation}\nThe above bounds follow if for all $N,n$, and $i,j,t_{j-1} \\le s \\le t_j$,\n\\begin{equation}\\label{ineq:tight10}\n\\mathbb{E} \\big[ \\sup_\\theta \\| \\nabla_\\theta h^i_{t_{j-1}}(\\theta) \\| \\big] \\le C,\n\\qquad \n\\mathbb{E} \\big[ | b^i_s(\\theta_{0,1}) | \\sup_\\theta \\|\\nabla_\\theta g^i_{t_{j-1}}(\\theta) \\| \\big] \\le C,\n\\end{equation}\nwhere $h,g : \\Theta \\times \\mathbb{R} \\times {\\cal P}_2\\to \\mathbb{R}$ are defined by \\eqref{def:gd}. In $\\nabla_{\\theta_k} h, \\nabla_{\\theta_k} g$, $k=1,2$,\nwe note $\\nabla_{\\theta_1} (b(\\theta_{0,1},\\cdot) - b(\\theta_1,\\cdot)) = - \\nabla_{\\theta_1} b(\\theta_1,\\cdot)$. Moreover, by the mean value theorem, $|b(\\theta_{0,1},\\cdot) - b(\\theta_1,\\cdot)| \\le C \\sup_{\\theta_1} \\|\\nabla_{\\theta_1} b(\\theta_1,\\cdot)\\|$ for all $\\theta_1 \\in \\Theta_1$, since $\\Theta_1$ is convex, bounded.\nAdditionally using $\\inf_{\\theta_2,x,\\mu} {c}(\\theta_2,x,\\mu) >0$ by \\ref{as3}, we get\n$$\n\\|\\nabla_{\\theta_1} g(\\theta,\\cdot)\\| \\le C \\sup_{\\theta_1} \\|\\nabla_{\\theta_1} b(\\theta_1,\\cdot) \\|, \\qquad \\|\\nabla_{\\theta_2} g (\\theta,\\cdot)\\| \\le C \\sup_{\\theta_1} \\| \\nabla_{\\theta_1} b(\\theta_1,\\cdot) \\| \\sup_{\\theta_2} \\|\\nabla_{\\theta_2} a(\\theta_2,\\cdot)\\|,\n$$ \nand\n$$\n\\|\\nabla_{\\theta_1} h (\\theta,\\cdot)\\| \\le C \\sup_{\\theta_1} \\|\\nabla_{\\theta_1} b(\\theta_1,\\cdot) \\|^2, \\qquad \\|\\nabla_{\\theta_2} h (\\theta,\\cdot)\\| \\le C \\sup_{\\theta_1} \\| \\nabla_{\\theta_1} b(\\theta_1,\\cdot)\\|^2 \\sup_{\\theta_2} \\|\\nabla_{\\theta_2} a(\\theta_2,\\cdot)\\|\n$$\nfor all $\\theta$. We have the polynomial growth of $\\sup_{\\theta_1} \\|\\nabla_{\\theta_1}b(\\theta_1,\\cdot)\\|$, $\\sup_{\\theta_2} \\|\\nabla_{\\theta_2} a(\\theta_2,\\cdot)\\|$ thanks to assumption \\ref{as4} and linear growth of $b(\\theta_{0,1},\\cdot)$ thanks to \\ref{as2}. \nThe Cauchy-Schwarz inequality and\nmoment bounds in Lemma~\\ref{l: moments}(1) yield \\eqref{ineq:tight10} and so \\eqref{ineq:tight1}. \n\n\nFollowing the approach of \\cite[Theorem 20 in Appendix 1]{IbrHas}, we want to show that for all $N,n$ and $\\theta, \\theta' \\in \\Theta$,\n$$\n\\mathbb{E} [ | \\rho^N_{n,2}(\\theta) |^2 ] \\le C, \\qquad \\mathbb{E} [ |\\rho^N_{n,2}(\\theta) - \\rho^N_{n,2}(\\theta')|^2 ] \\le C \\| \\theta - \\theta' \\|^2_2.\n$$\nWe note that the second relation implies the first one because $\\rho^N_{n,2}(\\theta) = 0$ with $\\theta_1 = \\theta_{0,1}$ and $\\Theta_2$ is bounded. \nIn the same way as in \\eqref{varrhoNn2} we get \n$$\n\\mathbb{E} [ |\\rho^N_{n,2}(\\theta)-\\rho^N_{n,2}(\\theta') |^2 ] = \\frac{1}{N^2} \\sum_{i=1}^N \\sum_{j=1}^n \\mathbb{E} [ | (g^i_{t_{j-1}}(\\theta) - g^i_{t_{j-1}} (\\theta')) A^i_j |^2 ],\n$$\nwhere the It{\\^o} isometry gives\n$$\n\\mathbb{E} [ | (g^i_{t_{j-1}}(\\theta)-g^i_{t_{j-1}}(\\theta')) A^i_j |^2 ] = \\int_{t_{j-1}}^{t_j} \\mathbb{E} [ (g^i_{t_{j-1}}(\\theta)-g^i_{t_{j-1}}(\\theta'))^2 c^i_s (\\theta_{0,2}) ] d s. \n$$\nBy the mean value theorem, \n$$\n|g(\\theta,\\cdot)-g(\\theta',\\cdot)| \\le \\| \\theta - \\theta' \\| \\sup_{\\theta} \\| \\nabla_\\theta g(\\theta,\\cdot) \\| \n$$\nsince $\\Theta$ is convex. Then \n$$\n\\mathbb{E} \\big[ \\sup_\\theta \\| \\nabla_\\theta g^i_{t_{j-1}} (\\theta) \\|^2 c^i_s (\\theta_{0,2}) \\big] \\le C\n$$ \nfor all $t_{j-1} \\le s \\le t_j, j, i$ and $N,n$ follows in a similar way as the second bound in \\eqref{ineq:tight10} does using, in addition, linear growth of $a(\\theta_{0,2},\\cdot)$, which follows from its Lipschitz continuity by \\ref{as2}. \\\\\n\n\n\\noindent\n$\\bullet$\nStep 1. \nWe want to prove that for every $\\theta \\in \\Theta$,\n\\begin{equation}\\label{lim:intJ}\n\\frac{\\Delta_n}{N} S^N_n (\\theta) \\xrightarrow{\\P} J (\\theta_2) = \\int_0^{{T}} \\int_{\\mathbb{R}} f (\\theta_2,x,\\bar \\mu_t) \\bar \\mu_t(d x) d t,\n\\end{equation}\nwhere \n$$\nf (\\theta_2, x, \\mu) = \\frac{c (\\theta_{0,2},x,\\mu)}{c (\\theta_2,x,\\mu)} + \\log c (\\theta_2,x,\\mu)\n$$\nfor every $(\\theta_2,x,\\mu) \\in \\Theta_2 \\times \\mathbb{R} \\times {\\cal P}_2$. For this purpose, in $\\Delta_n S^N_n(\\theta)$ let us decompose every term as \n\\begin{equation}\\label{def:rij}\n\\frac{(X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}} (\\theta_1))^2}{c^i_{t_{j-1}} (\\theta_2)} + \\Delta_n (\\log c)^i_{t_{j-1}} (\\theta_2) = \\Delta_n f^i_{t_{j-1}}(\\theta_2)+ r^i_j.\n\\end{equation}\nWe can decompose $r^i_j$ further with \n\\begin{equation}\\label{eq:incrX}\nX^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}} (\\theta_1) = B^i_j (\\theta_1) + A^i_j, \n\\end{equation}\nwhere \n\\begin{equation}\\label{eq:incrXBA}\nB^i_j (\\theta_1) = \\int_{t_{j-1}}^{t_j} b^i_s(\\theta_{0,1}) d s - \\Delta_n b^i_{t_{j-1}} (\\theta_1), \\qquad A^i_j = \\int_{t_{j-1}}^{t_j} a^i_s (\\theta_{0,2}) d W^i_s,\n\\end{equation}\nnote\n$$\n\\mathbb{E}_{t_{j-1}} [ (A^i_j)^2 ] = \\int^{t_j}_{t_{j-1}} c^i_s (\\theta_{0,2}) d s\n$$\nWe get\n\\begin{equation}\\label{def:rijk}\nr^i_j = \\sum_{k=0}^2 r^i_{j,k}, \\qquad \\text{where } r^i_{j,k} = \\frac{H^i_{j,k}}{c^i_{t_{j-1}} (\\theta_2)}, \\qquad k=0,1,2,\n\\end{equation}\nand \n\\begin{align*}\n&H^i_{j,2} = (A^i_j)^2 - \\mathbb{E}_{t_{j-1}} [ (A^i_j)^2 ],\\qquad H^i_{j,1} = 2 A^i_j B^i_j(\\theta_1) + (B^i_j (\\theta_1))^2,\\\\\n&\\qquad H^i_{j,0} = \\mathbb{E}_{t_{j-1}} [ (A^i_j)^2 ] - \\Delta_n c^i_{t_{j-1}} (\\theta_{0,2}). \n\\end{align*}\nOur proof of \\eqref{lim:intJ} consists of the following steps: \n\\begin{equation}\n\\frac{\\Delta_n}{N} \\sum_{i=1}^N \\sum_{j=1}^n f^i_{t_{j-1}} (\\theta_2) \\xrightarrow{\\P} J(\\theta_2), \\qquad \\frac{1}{N} \\sum_{i=1}^N \\sum_{j=1}^n r^i_{j,k} \\xrightarrow{L^1} 0, \\quad k=0,1,2.\\label{cns:vol}\n\\end{equation}\nLet us start from the convergence in \\eqref{cns:vol} for $k=2$. It is enough to show that \n$\\sup_i \\mathbb{E} [ ( \\sum_j r^i_{j,2} )^2] = o(1)$. \nWe note that $\\mathbb{E} [ r^i_{j_1,2} r^i_{j_2,2} ] = 0$, $j_1 \\neq j_2$, since $\\mathbb{E}_{t_{j-1}} [ r^i_{j,2}] = 0$. We are left to show that $\\sup_i \\sum_j \\mathbb{E} [ (r^i_{j,2})^2 ] = o(1)$.\nThanks to assumption \\ref{as3}\nit reduces to showing $\\sup_i \\sum_j \\mathbb{E} [ (H^i_{j,2})^2 ] = o(1)$,\nwhere $\\mathbb{E}_{t_{j-1}} [ (H^i_{j,2})^2 ] = \\mathbb{E}_{t_{j-1}} [ (A^i_j)^4 ] - (\\mathbb{E}_{t_{j-1}} [ (A^i_j)^2 ])^2$ leads to $\\mathbb{E} [ (H^i_{j,2})^2 ] \\le \\mathbb{E} [ (A^i_j)^4 ]$ for all $i,j$. Furthermore, by the Burkholder-Davis-Gundy inequality and Jensen's inequality,\n\\begin{equation}\\label{ineq:A4}\n\\mathbb{E} [ (A^i_j)^4 ] \\le C \\mathbb{E} \\Big[ \\Big( \\int_{t_{j-1}}^{t_j} c^i_s (\\theta_{0,2}) d s \\Big)^2 \\Big] \\le C \\Delta_n \\int_{t_{j-1}}^{t_j} \\mathbb{E} [ (c^2)^i_s (\\theta_{0,2}) ] d s = O(\\Delta_n^2)\n\\end{equation}\nuniformly in $i,j$, where the last relation follows thanks to linear growth of $a(\\theta_{0,2},\\cdot)$ by \\ref{as2} and moment bounds in Lemma~\\ref{l: moments}(1).\nWe conclude that $\\sup_{i,j} \\mathbb{E} [(R^i_{j,2})^2] = O (\\Delta_n^2)$.\n\nWe now turn to the convergence in \\eqref{cns:vol} for $k=1$. It is enough to show that $n \\sup_{i,j} \\mathbb{E} [ | r^i_{j,1} | ] = o(1)$. Assumption \n\\ref{as3}\nimplies $\\mathbb{E} [|r^i_{j,1}|] \\le C \\mathbb{E} [ |H^i_{j,1}| ]$ for all $i,j$, where $\\sup_{i,j} \\mathbb{E} [ ( A^i_j )^2 ] = O (\\Delta_n)$ follows from \\eqref{ineq:A4}. Moreover, by Jensen's inequality,\n$$\n\\mathbb{E} [ (B^i_j (\\theta_1))^2 ] \n\\le 2 \\Delta_n \\int_{t_{j-1}}^{t_j} \\mathbb{E} [ ( b^i_s (\\theta_{0,1}) )^2 ] d s + 2 \\Delta_n^2 \\mathbb{E} [ (b^i_{t_{j-1}}(\\theta_1))^2 ] = O(\\Delta_n^2)\n$$\nuniformly in $i,j$, where the last relation follows thanks to linear growth of $b(\\theta_1,\\cdot)$ for every $\\theta_1$ by \\ref{as2} and moment bounds in Lemma~\\ref{l: moments}(1).\nWe conclude that\n$\\sup_{i,j} \\mathbb{E} [ |r^i_{j,1}| ] = O (\\Delta_n^{\\frac 3 2})$.\n \nNext, we consider the convergence in \\eqref{cns:vol} for $k=0$. It is enough to show that $n \\sup_{i,j} \\mathbb{E} [ | r^i_{j,0} | ] = o(1)$. Assumption \n\\ref{as3} \nimplies $\\mathbb{E} [| r^i_{j,0} |] \\le C \\mathbb{E} [ |H^i_{j,0}| ]$, where\n$$\n\\mathbb{E} [ | H^i_{j,0}| ] \\le \\int^{t_j}_{t_{j-1}} \\mathbb{E} [ | c^i_s (\\theta_{0,2}) - c^i_{t_{j-1}} (\\theta_{0,2}) | ] d s.\n$$\nLipschitz continuity of $a(\\theta_{0,2},\\cdot)$ and Lemma~\\ref{l: moments}(2) and (4) imply $\\mathbb{E} [ (a^i_s (\\theta_{0,2}) - a^i_{t_{j-1}} (\\theta_{0,2}))^2 ] = O(\\Delta_n)$ uniformly in $t_{j-1} \\le s \\le t_j,j,i$. \nFinally, linear growth of $a(\\theta_{0,2},\\cdot)$ and moment bounds in Lemma~\\ref{l: moments}(1) guarantee $\\mathbb{E} [ (a^i_s (\\theta_{0,2}) + a^i_{t_{j-1}} (\\theta_{0,2}))^2 ] = O(1)$ uniformly in $t_{j-1} \\le s \\le t_j, j, i$. We conclude\nby Cauchy-Schwarz inequality that $\\mathbb{E} [|c^i_s (\\theta_{0,2}) - c^i_{t_{j-1}} (\\theta_{0,2})|] = O(\\Delta_n^{\\frac{1}{2}})$ uniformly in $t_{j-1} \\le s \\le t_j, j,i$, whence\n$\\sup_{i,j} \\mathbb{E} [|r^i_{j,0}|] = O (\\Delta_n^{\\frac 3 2})$. \n\nThe first relation in \\eqref{cns:vol} follows from Lemma \\ref{l: Riemann} if the function $f(\\theta_2,\\cdot)$ is locally Lipschitz with polynomial growth. To check this assumption, use \n$\n|\\log y_1 - \\log y_2| \\le |y_1-y_2|\/ \\min(y_1, y_2) \n$\nfor $y_1,y_2>0$ and assumption \\ref{as3}. Note $b(\\theta_1,\\cdot)$, $a(\\theta_2,\\cdot)$ are Lipschitz continuous and have linear growth by \\ref{as2}. Hence, the function $f (\\theta_2,\\cdot)$ satisfies the assumption of Lemma~\\ref{l: Riemann}. \\\\\n\\noindent\n$\\bullet$\nStep 2. We want to prove that the sequence $\\frac{\\Delta_n}{N} S^N_n(\\theta)$ in $(C(\\Theta;\\mathbb{R}), \\| \\cdot \\|_\\infty)$ is tight. So we have to show that for all $N,n$,\n$$\n\\frac{\\Delta_n}{N} \\mathbb{E} \\Big[\\sup_\\theta {\\sum_{k=1}^2} \\| \\nabla_{\\theta_k} S^N_n (\\theta) \\| \\Big] \\le C.\n$$\nWe have \n$$ \n\\nabla_{\\theta_k} S^N_n (\\theta)=\\sum_{i=1}^N \\sum_{j=1}^n \\zeta_{j,k}^i (\\theta), \\qquad k =1,2,\n$$\nwhere\n\\begin{align*}\n\\zeta_{j,1}^i (\\theta) &= - \\frac{2(X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j - 1}}(\\theta_1) )}{c^i_{t_{j-1}}(\\theta_2)} \\nabla_{\\theta_1} b^i_{t_{j - 1}}(\\theta_1),\\\\ \n\\zeta_{j,2}^i (\\theta) &= - \\frac{(X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j - 1}}(\\theta_1) )^2}{ \\Delta_n (c^2)^i_{t_{j - 1}}(\\theta_2)} \\nabla_{\\theta_2} c^i_{t_{j - 1}}(\\theta_2) + \\frac{1}{c^i_{t_{j - 1}} (\\theta_2)} \\nabla_{\\theta_2} c^i_{t_{j - 1}}(\\theta_2).\n\\end{align*}\nIt suffices to show that for all $N,n$ and $i,j$,\n\\begin{equation}\\label{ineq:Esupxi}\n\\mathbb{E} \\big[ \\sup_\\theta \\|\\zeta_{j,k}^i (\\theta)\\| \\big] \\le C, \\qquad k =1,2. \n\\end{equation}\nUsing \\ref{as3} and the Cauchy-Schwarz inequality, we get \n\\begin{align*}\n\\mathbb{E} \\big[ \\sup_\\theta \\|\\zeta_{j,1}^i (\\theta)\\|\\big] &\\le C \\big(\\mathbb{E} \\big[ \\sup_{\\theta_1} |X^i_{t_j}-X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}}(\\theta_1)|^2 \\big] \\big)^{\\frac{1}{2}} \\big( \\mathbb{E} \\big[ \\sup_{\\theta_1} \\| \\nabla_{\\theta_1} b^i_{t_{j-1}}(\\theta_1) \\|^2 \\big] \\big)^{\\frac 1 2},\\\\\n\\mathbb{E} \\big[ \\sup_\\theta \\|\\zeta_{j,2}^i (\\theta)\\|\\big] &\\le \\frac{C}{\\Delta_n} \\big( \\mathbb{E} \\big[ \\sup_{\\theta_1} |X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}} (\\theta_1)|^4 \\big] \\big)^{\\frac{1}{2}} \\big( \\mathbb{E} \\big[ \\sup_{\\theta_2} \\| \\nabla_{\\theta_2} a^i_{t_{j - 1}}(\\theta_2) \\|^2 \\big] \\big)^{\\frac{1}{2}} \\\\\n&\\qquad + C \\mathbb{E} \\big[ \\sup_{\\theta_2} \\| \\nabla_{\\theta_2} a^i_{t_{j - 1}} (\\theta_2) \\| \\big].\n\\end{align*}\nWe use polynomial growth of $\\sup_{\\theta_1}\\| \\nabla_{\\theta_1} b(\\theta_1,\\cdot)\\|$, $\\sup_{\\theta_2} \\|\\nabla_{\\theta_2} a(\\theta_2,\\cdot)\\|$ and moment bounds in Lemma~\\ref{l: moments}(1). Moreover, Lemma~\\ref{l: moments}(2) gives $\\sup_{i,j} \\mathbb{E} [|X^i_{t_j} - X^i_{t_{j-1}}|^4] = O(\\Delta_n^2)$. Finally, $b(\\theta_{0,1},\\cdot)$ has a linear growth and the mean value theorem implies \n$b(\\theta_1,\\cdot) - b(\\theta_{0,1},\\cdot) = \\int_0^1 \\nabla_{\\theta_1} b(\\theta_{0,1} + (\\theta_1-\\theta_{0,1})u,\\cdot) d u \\cdot (\\theta_1-\\theta_{0,1})$ \nfor all $\\theta_1$ in $\\Theta_1$, where $\\Theta_1$ is convex, bounded and we recall that $\\sup_{\\theta_1}\\| \\nabla_{\\theta_1} b(\\theta_1,\\cdot)\\|$ has polynomial growth. The moment bounds in Lemma~\\ref{l: moments}(1) imply $\\mathbb{E} [\\sup_{\\theta_1} |b^i_{t_{j-1}}(\\theta_1)|^4] \\le C$, completing the proof of \\eqref{ineq:Esupxi}.\n\\end{proof}\n\n\\subsubsection{Proof of Theorem \\ref{th: consistency}}\n\\begin{proof}\nAssumption \\ref{as5} implies that for every $\\varepsilon>0$ there exists $\\eta>0$ such that $J(\\theta_2) - J(\\theta_{0,2}) > \\eta$\nfor every $\\theta_2$ with $\\|\\theta_2 - \\theta_{0,2}\\| \\ge \\varepsilon$. Thus $\\{ \\|\\hat \\theta^N_{n,2}-\\theta_{0,2}\\| \\ge \\varepsilon \\}\\subseteq \\{J(\\hat \\theta^N_{n,2}) - J(\\theta_{0,2}) > \\eta\\}$. The probability of the latter event converges to $0$ in view of \n$$\nJ (\\hat \\theta^N_{n,2}) - J (\\theta_{0,2}) = J^N_{n,0} + J^N_{n,1},\n$$\nwhere \nthe definition of $\\hat \\theta^N_n$ and \\eqref{lim:con2} imply respectively\n\\begin{align*}\nJ^N_{n,0} &:= \\frac{\\Delta_n}{N} (S^N_n(\\hat \\theta^N_{n,1}, \\hat \\theta^N_{n,2}) - S^N_n(\\hat \\theta^N_{n,1}, \\theta_{0,2})) \\le 0,\\\\\nJ^N_{n,1} &:= J (\\hat \\theta^N_{n,2}) - J (\\theta_{0,2}) - J^N_{n,0} \\le 2 \\sup_{(\\theta_1,\\theta_2) \\in \\Theta} \\Big| \\frac{\\Delta_n}{N} S^N_n(\\theta_1,\\theta_2)-J(\\theta_2) \\Big| = o_\\P(1).\n\\end{align*}\nConsistency of $\\hat \\theta^N_{n,1}$ follows in a similar way. \nAssumption \\ref{as5} implies that for every $\\varepsilon>0$ there exists $\\eta >0$ such that $I(\\theta_1,\\theta_2) > \\eta$ for every $(\\theta_1,\\theta_2)$ with $\\|\\theta_1-\\theta_{0,1}\\| \\ge \\varepsilon$. Thus $\\{\\|\\hat \\theta^N_{n,1}-\\theta_{0,1}\\| \\ge \\varepsilon\\}\\subseteq \\{I(\\hat \\theta^N_{n,1}, \\hat \\theta^N_{n,2}) > \\eta \\}$. The probability of the latter event converges to $0$ because \n$$\nI(\\hat \\theta^N_{n,1}, \\hat \\theta^N_{n,2}) = I^N_{n,0} + I^N_{n,1},\n$$\nwhere the definition of $\\hat \\theta^N_n$ and \\eqref{lim:con1} imply respectively\n\\begin{align*}\nI^N_{n,0} &:= \\frac{1}{N} (S^N_n (\\hat \\theta^N_{n,1},\\hat \\theta^N_{n,2}) - S^N_n(\\theta_{0,1},\\hat \\theta^N_{n,2})) \\le 0,\\\\\nI^N_{n,1} &:= I(\\hat \\theta^N_{n,1},\\hat \\theta^N_{n,2}) - I^N_{n,0}\n= o_\\P(1).\n\\end{align*}\n\\end{proof}\n\n\n\n\n\n\n\n\n\\subsection{Asymptotic normality}\n\nThe proof of the asymptotic normality of our estimator is obtained following a classical route. It consists in proving the asymptotic normality of the first derivative of the contrast function \\eqref{eq:def contrast} (see for example \\cite[Section5a]{GenJac93}). We introduce in particular the appropriate normalization matrix\n$$M_n^N := \\operatorname{diag} \\Big( \\underbrace{\\frac{1}{\\sqrt{N}}, \\dots , \\frac{1}{\\sqrt{N}}}_{\\text{$p_1$ times}}, \\underbrace{\\sqrt{\\frac{\\Delta_n}{N}}, \\dots , \\sqrt{\\frac{\\Delta_n}{N}}}_{\\text{$p_2$ times}} \\Big).\n$$\nThe proof of Theorem \\ref{th: normality} is based on the following proposition.\n\\begin{proposition}\nAssume\n\\ref{as1}-\\ref{as4}(I) and \n(II),\n\\ref{as7}. \nIf $N\\Delta_n \\to 0$ then\nas $N,n \\rightarrow \\infty$,\n$\n\\nabla_\\theta S^N_n(\\theta_0) {M^N_n}\n\\xrightarrow{\\mathcal{L}} {\\cal N}(0, 2 \\Sigma (\\theta_0)),\n$$\nwhere \n{ $\\Sigma (\\theta_0)$ is a $p \\times p$ matrix defined in \\ref{as6}}.\n\n\n\n\\label{p: norm L}\n\\end{proposition}\n\\noindent\nWe observe that, as $\\nabla_\\theta S_n^N (\\hat{\\theta}_n^N) = 0$, by Taylor's formula we obtain\n\\begin{equation}\n{(\\hat \\theta^N_n - \\theta_0)} \\int_0^1 \\nabla^2_\\theta S_n^N (\\theta_0 + s (\\hat{\\theta}_n^N - \\theta_0)) ds \n\\, = - \\nabla_\\theta S_n^N (\\theta_0).\n\\label{eq: taylor}\n\\end{equation}\nMultiplying the equation \\eqref{eq: taylor} by $M^N_n$,\nwe obtain\n\\begin{equation}\n{(\\hat \\theta^N_n - \\theta_0) (M^N_n)^{-1}} \\int_0^1 \\Sigma_n^N (\\theta_0 + s (\\hat{\\theta}_n^N - \\theta_0)) d s\n= -\n\\nabla_\\theta S^N_n(\\theta_0) {M^N_n},\n\\label{eq: Taylor2}\n\\end{equation}\nwhere \n$$\n\\Sigma^N_n (\\theta) := M_n^N \\nabla^2_\\theta S_n^N (\\theta) M_n^N = \\begin{pmatrix}\n\\Sigma_n^{N,(1)} (\\theta) & \\Sigma_n^{N,(12)} (\\theta) \\\\\n\\Sigma_n^{N,(21)} (\\theta) & \\Sigma_n^{N,(2)} (\\theta)\n\\end{pmatrix}\n$$\nwith\n\\begin{equation*}\n\\begin{aligned}[c]\n\\Sigma_n^{N,(1)} (\\theta) &= (1\/N) \\nabla^2_{\\theta_1} S_n^N {(\\theta)},\\\\\n\\Sigma_n^{N,(21)}{(\\theta)} &= (\\sqrt{\\Delta_n}\/N) \\nabla_{\\theta_2} \\nabla_{\\theta_1} S_n^N (\\theta), \n\\end{aligned}\n\\qquad\n\\begin{aligned}[c]\n\\Sigma_n^{N,(12)} {(\\theta)} &= (\\sqrt{\\Delta_n}\/N) \\nabla_{\\theta_1} \\nabla_{\\theta_2} S_n^N (\\theta),\\\\ \\Sigma_n^{N,(2)}{(\\theta)}&= (\\Delta_n\/N) \\nabla^2_{\\theta_2} S_n^N (\\theta).\n\\end{aligned}\n\\end{equation*}\nThe analysis of the second derivatives of the contrast function is gathered in the following proposition, which will be proven at the end of this section. \n\\begin{proposition}\nAssume \\ref{as1}-\\ref{as5} \nwith both (I) and \n(II) in \\ref{as4}.\nThen\nas $N,n \\to \\infty$,\n\\begin{enumerate}\n\\item $\\Sigma_n^N (\\theta_0) \\xrightarrow{\\mathbb{P}} \\Sigma (\\theta_0)$,\n\\item $\\sup_{s \\in [0,1]} \\| \\Sigma^N_n (\\theta_0 + s (\\hat \\theta^N_n -\\theta_0)) - \\Sigma^N_n (\\theta_0) \\| \\xrightarrow{\\mathbb{P}} 0$, where $\\| \\cdot \\|$ refers to the operator norm on the space of $p \\times p$ matrices induced by the Euclidean norm for vectors.\n\\end{enumerate}\n\\label{p: second derivatives contrast}\n\\end{proposition}\n\n\\noindent\nBy Proposition \\ref{p: second derivatives contrast} assumption \\ref{as6} implies that the probability that\n$\n\\int_0^1 \\Sigma_n^N (\\theta_0 + s (\\hat{\\theta}_n^N - \\theta_0)) d s\n$\nis invertible tends to 1. Applying its inverse to the equation \\eqref{eq: Taylor2},\nby Proposition~\\ref{p: norm L} and the continuous mapping theorem, we get \n$$\n{ \\big( \\sqrt{N} (\\hat{\\theta}_{n,1}^N - \\theta_{0, 1} ), \\sqrt{N\/\\Delta_n} (\\hat{\\theta}_{n,2}^N - \\theta_{0, 2}) \\big) }\n=\n(\\hat \\theta^N_n -\\theta_0 ) {(M^N_n)^{-1} } \\xrightarrow{{\\cal L}} \n{\\cal N} \\big(0, 2 ( \\Sigma(\\theta_0) )^{-1} \\big). $$\n\n\n\n\n\\subsection{Proof of Proposition \\ref{p: norm L}}\n\\begin{proof}\nAs in the proof of consistency, we omit the notation for dependence on $N,n$. In particular, we write $X^i_t$ for $X^{i,N}_t$, $\\mu_t$ for $\\mu^N_t$, $t_j$ for $t_{j,n}$. Denote by $f^i_{t_{j-1}}(\\theta)$ the values of $f(\\theta, X^i_{t_{j-1}}, \\mu_{t_{j-1}})$.\nWe note that $-\n\\nabla_{\\theta} S^N_n (\\theta) {M^N_n}$ consists of\n$- \\partial_{\\theta_{1, h}} S_n^N(\\theta)\/\\sqrt{N} =:\\sum_{j = 1}^n \\xi_{j,h}^{(1)} (\\theta)$ and $- \\sqrt{\\Delta_n\/N} \\partial_{\\theta_{2, \\Tilde{h}}} S_n^N(\\theta) =: \\sum_{j = 1}^n {\\xi}_{j,\\Tilde{h}}^{(2)} (\\theta)$,\nwhere\n\\begin{align*}\n\\xi_{j,h}^{(1)} (\\theta) &{:}= \\frac{1}{\\sqrt{N}} \\sum_{i = 1}^N 2 \\frac{\\partial_{\\theta_{1, h}} b^i_{t_{j - 1}}(\\theta_1)}{c^i_{t_{j - 1}}(\\theta_2)} (X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j - 1}}(\\theta_1) ),\\\\\n\\xi_{j,\\Tilde{h}}^{(2)} (\\theta) &{:}= \\sqrt{\\frac{\\Delta_n}{N}} \\sum_{i = 1}^N \\frac{\\partial_{\\theta_{2, \\Tilde{h}}} c^i_{t_{j - 1}}(\\theta_2)}{ \\Delta_n (c^i_{t_{j - 1}}(\\theta_2))^2} (X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j - 1}}(\\theta_1) )^2 - \\frac{\\partial_{\\theta_{2, \\Tilde{h}}} c^i_{t_{j - 1}}(\\theta_2) }{c^i_{t_{j - 1}}(\\theta_2) }\n\\end{align*}\nfor $h = 1, \\dots, p_1$, $\\Tilde{h} = 1, \\dots, p_2$.\nTo prove the asymptotic normality of \n\n\\nabla_\\theta S^N_n (\\theta_0){M^N_n}$ we want to use a central limit theorem for martingale difference arrays,\nin accordance with Theorems 3.2 and 3.4 of \\cite{HalHey80}.\nApproximation of $\n\\nabla_\\theta S^N_n (\\theta_0){M^N_n}$ by a martingale array follows from \n\\begin{equation}\n\\sum_{j = 1}^n \\mathbb{E}_{t_{j - 1}}[\\xi^{(1)}_{j,h} (\\theta_0)] \\xrightarrow{\\mathbb{P}} 0, \\qquad \\sum_{j = 1}^n \\mathbb{E}_{t_{j - 1}}[\\xi^{(2)}_{j,\\Tilde{h}} (\\theta_0)] \\xrightarrow{\\mathbb{P}} 0\n\\label{e: mtg array}\n\\end{equation}\nfor $h = 1, \\dots, p_1$, $\\Tilde{h} = 1, \\dots, p_2$.\nMoreover, application of the central limit theorem requires that for some $r > 0$ the following convergences hold:\n\\begin{align}\n &\\sum_{j = 1}^n \\mathbb{E}_{t_{j - 1}}[\\xi^{(1)}_{j,h_1} (\\theta_0)\\xi^{(1)}_{j,h_2} (\\theta_0)] \\xrightarrow{\\mathbb{P}} 4 \\int_0^{{T}} \\int_{\\mathbb{R}} \\frac{\\partial_{\\theta_{1, h_1}} b(\\theta_{0, 1}, x, \\bar{\\mu}_t)\\partial_{\\theta_{1, h_2}} b(\\theta_{0, 1}, x, \\bar{\\mu}_t)}{c(\\theta_{0, 2}, x, \\bar{\\mu}_t)} \\bar{\\mu}_t(dx) dt,\n\\label{e: norm theta1}\\\\\n &\\sum_{j = 1}^n \\mathbb{E}_{t_{j - 1}}[{\\xi}^{(2)}_{j,\\Tilde{h}_1} (\\theta_0){\\xi}^{(2)}_{j,\\Tilde{h}_2} (\\theta_0)] \\xrightarrow{\\mathbb{P}} 2 \\int_0^{{T}} \\int_{\\mathbb{R}} \\frac{\\partial_{\\theta_{2, \\Tilde{h}_1}} c(\\theta_{0, 2}, x, \\bar{\\mu}_t)\\partial_{\\theta_{2, \\Tilde{h}_2}} c(\\theta_{0, 2}, x, \\bar{\\mu}_t)}{c^2(\\theta_{0, 2}, x, \\bar{\\mu}_t)} \\bar{\\mu}_t(dx) dt,\n\\label{e: norm theta2}\\\\\n &\\sum_{j = 1}^n \\mathbb{E}_{t_{j - 1}}[{\\xi}^{(1)}_{j,h} (\\theta_0) \\xi^{(2)}_{j,\\Tilde{h}} (\\theta_0)] \\xrightarrow{\\mathbb{P}} 0,\n \\label{e: norm mixed}\\\\\n &\\sum_{j = 1}^n \\mathbb{E}_{t_{j - 1}}[|{\\xi}^{(1)}_{j,h} (\\theta_0)|^{2 + r}] \\xrightarrow{\\mathbb{P}} 0, \\qquad \\sum_{j = 1}^n \\mathbb{E}_{t_{j - 1}}[|{\\xi}^{(2)}_{j,\\Tilde{h}} (\\theta_0)|^{2 + r}] \\xrightarrow{\\mathbb{P}} 0,\n\\label{e: negl theta}\n\\end{align}\nwhere $h,h_1,h_2 = 1,\\dots,p_1$, $\\tilde h, \\tilde h_1, \\tilde h_2 = 1,\\dots,p_2.$\\\\\n\n\\noindent\n$\\bullet$ Proof of \\eqref{e: mtg array}. \\\\\nAssumptions \\ref{as3} and \\ref{as4}(I) imply that $F^i_{j,h} := 2 \\partial_{\\theta_{1, h}} b^i_{t_{j-1}}(\\theta_{0,1})(c^i_{t_{j - 1}}(\\theta_{0, 2}))^{-1}$ satisfies $|F^i_{j,h}|\\le C (1 + |X_{t_{j - 1}}^i|^{k_1} + W_2^{l_1} (\\mu_{t_{j - 1}}, \\delta_0))$. Hence, from Lemma \\ref{l: moments}(1) it is easy to see that $F^i_{j,h} = R^i_{t_{j - 1}}(1)$.\nIf $N \\Delta_{{n}} \\to 0$ then Lemma \n\\ref{l: conditional expectation}(3) implies \n\\begin{align*}\n\\sum_{j=1}^n \\mathbb{E}_{t_{j - 1}}[\\xi^{(1)}_{j,h} (\\theta_0)]\n&= \\frac{1}{N^{\\frac 1 2}} \\sum_{i=1}^N \\sum_{j=1}^n R^i_{t_{j - 1}}(1) R^i_{t_{j - 1}}(\\Delta_n^{\\frac 3 2}) \\xrightarrow{L^1} 0\n\\end{align*}\nand so the convergence in probability.\nIn a similar way, using Lemma \\ref{l: conditional expectation}(1), we obtain \n\\begin{align*}\n \\sum_{j=1}^n \\mathbb{E}_{t_{j - 1}}[{\\xi}^{(2)}_{j,\\Tilde{h}} (\\theta_0)] & = \\Big( \\frac{\\Delta_n}{N} \\Big)^{\\frac 1 2} \\sum_{i=1}^N \n \\sum_{j=1}^n \\Delta_n^{-1} R^i_{t_{j-1}}(1)\n (\\Delta_n c^i_{t_{j - 1}}(\\theta_{0, 2}) + R^i_{t_{j - 1}}(\\Delta_n^2) - \\Delta_n c^i_{t_{j - 1}}(\\theta_{0, 2}))\n \\\\\n& = \\Big( \\frac{\\Delta_n}{N} \\Big)^{\\frac 1 2} \\sum_{i=1}^N \n\\sum_{j=1}^n R^i_{t_{j - 1}}(\\Delta_n), \n\\end{align*}\nwhich converges to $0$ in $L^1$ and so in probability if $N \\Delta_n \\to 0$.\\\\\n\n\\noindent\n$\\bullet$ Proof of \\eqref{e: norm theta1}. \\\\\nWe have\n\\begin{equation}\\label{eq: start norm theta1}\n\\mathbb{E}_{t_{j-1}} [\\xi_{j,h_1}^{(1)} (\\theta_0)\\xi_{j,h_2}^{(1)} (\\theta_0)] = \\frac{1}{N} \\sum_{i_1, i_2=1}^N \\mathbb{E}_{t_{j-1}} [(A^{i_1}_j + B^{i_1}_j)(A^{i_2}_j + B^{i_2}_j)] F^{i_1}_{j,h_1} F^{i_2}_{j,h_2}, \\end{equation}\nwhere \n$$\nF^i_{j,h} := 2 \\frac{\\partial_{\\theta_{1, h}} b^{i}_{t_{j-1}}(\\theta_{0,1})} {c^{i}_{t_{j-1}}(\\theta_{0,2})} = R^{i}_{t_{j-1}}(1),\n$$\nand\n\\begin{equation}\\label{def: BAnorm}\nB^{i}_j := \\int_{t_{j-1}}^{t_j} (b^{i}_s (\\theta_{0,1}) - b^{i}_{t_{j-1}}(\\theta_{0,1})) d s, \\qquad A^{i}_j := \\int_{t_{j-1}}^{t_j} a_s^{i}(\\theta_{0,2}) d W^{i}_s.\n\\end{equation}\nWe have $\\mathbb{E}_{t_{j-1}} [(B^{i}_j)^2] = R^{i}_{t_{j-1}}(\\Delta_n^3)$ and $\\mathbb{E}_{t_{j-1}} [(A^{i}_j)^2] = R^{i}_{t_{j-1}}(\\Delta_n)$, whereas\nif $i_1 \\neq i_2$ then $\\mathbb{E}_{t_{j-1}} [A^{i_1}_j A^{i_2}_j] = 0$ because of the independence of Brownian motions. \nHence, by the Cauchy-Schwarz inequality, \n$$\n\\mathbb{E}_{t_{j-1}} [(A^{i_1}_j + B^{i_1}_j)(A^{i_2}_j + B^{i_2}_j)] = \\mathbb{E}_{t_{j-1}}[(A^{i_1}_j)^2] \\mathbf{1} (i_1=i_2) + R_{t_{j-1}}^{i_1,i_2} (\\Delta_n^2).\n$$ \nWe get\n$$\n\\sum_{j=1}^n \\mathbb{E}_{t_{j-1}} [\\xi_{j,h_1}^{(1)} (\\theta_0)\\xi_{j,h_2}^{(1)} (\\theta_0)] = \\frac{1}{N} \\sum_{j=1}^n \\sum_{i=1}^N \\mathbb{E}_{t_{j-1}} [(A^i_j)^2] F^i_{j,h_1} F^i_{j,h_2} + \\frac{1}{N} \\sum_{j=1}^n \\sum_{i_1{,} i_2=1}^N R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^2),\n$$\nwhere the last sum converges to $0$ in $L^1$ and so in probability if $N \\Delta_n \\to 0$. We can therefore focus on the first sum. We decompose the term $\\mathbb{E}_{t_{j-1}}[(A^i_j)^2]$ into $\\Delta_n c^i_{t_{j-1}}(\\theta_{0,2})$\nand \n$$\n\\mathbb{E}_{t_{j-1}} [(A^i_j)^2] - \\Delta_n c^i_{t_{j-1}}(\\theta_{0,2}) = \\int_{t_{j-1}}^{t_j} \\mathbb{E}_{t_{j-1}} [c^i_s(\\theta_{0,2}) - c^i_{t_{j-1}}(\\theta_{0,2})] d s = R^i_{t_{j-1}} (\\Delta_n^{\\frac 3 2})\n$$\nThe result follows from $\\Delta_n \\to 0$ and application of Lemma \\ref{l: Riemann}.\\\\\n\n\\noindent\n$\\bullet$ Proof of \\eqref{e: negl theta}, first convergence. \\\\\nWe want to show \\eqref{e: negl theta} with $r=2$. We use the same notation as in \\eqref{eq: start norm theta1} and consider the terms\n\\begin{equation}\\label{eq:AB4}\n\\mathbb{E}_{t_{j-1}} [(A^{i_1}_j + B^{i_1}_j)(A^{i_2}_j + B^{i_2}_j)(A^{i_3}_j+B^{i_3}_j)(A^{i_4}_j +B^{i_4}_j)] F^{i_1}_{j,h} F^{i_2}_{j,h} F^{i_3}_{j,h} F^{i_4}_{j,h}. \n\\end{equation}\nWe have $F^i_j = R^i_{t_{j-1}}(1)$, moreover, $\\mathbb{E}_{t_{j-1}} [(A^{i}_j)^4] = R_{t_{j-1}}^{i} (\\Delta_n^2)$, $\\mathbb{E}_{t_{j-1}}[(B^{i}_j)^4] = R_{t_{j-1}}^{i} (\\Delta_n^6)$ and so\n$\\mathbb{E}_{t_{j-1}} [(A^{i}_j + B^{i}_j)^4] = R^{i}_{t_{j-1}}(\\Delta_n^2)$. Application of the Cauchy-Schwarz inequality shows that the term in \\eqref{eq:AB4} is also $R^{i_1,i_2,i_3,i_4}_{t_{j-1}}(\\Delta_n^2)$.\nIn case where $i_1, i_2, i_3, i_4$ are pairwise distinct we decompose $A^i_j$ into \n\\begin{equation}\\label{def: A12norm}\nA^{i}_{j,2} := \\int_{t_{j-1}}^{t_j} (a^{i}_s(\\theta_{0,2}) - a^{i}_{t_{j-1}}(\\theta_{0,2})) d W^{i}_s,\\qquad\nA^{i}_{j,1} := \\int_{t_{j-1}}^{t_j} a^{i}_{t_{j-1}} (\\theta_{0,2}) d W^{i}_s,\n\\end{equation}\nwhich satisfy $\\mathbb{E}_{t_{j-1}} [(A^i_{j,k})^4] = R^i_{t_{j-1}}(\\Delta_n^{2k})$, $k=1,2$. In particular the independence of the Brownian motions implies \n$$\n\\mathbb{E}_{t_{j-1}} [A^{i_1}_{j,1} A^{i_2}_{j,1} A^{i_3}_{j,1} A^{i_4}_{j,k}] F^{i_1}_{j,h} F^{i_2}_{j,h} F^{i_3}_{j,h} F^{i_4}_{j,h} = 0\n$$ \nfor $k=1,2$. The\nterm {converging to $0$ at the slowest rate} in \\eqref{eq:AB4} is then, up to a permutation of the indices $i_1,i_2,i_3,i_4$, \n$$\n\\mathbb{E}_{t_{j-1}} [A^{i_1}_{j,1} A^{i_2}_{j,1} A^{i_3}_{j,2} A^{i_4}_{j,2} + A^{i_1}_{j,1} A^{i_2}_{j,1} A^{i_3}_{j,1} B^{i_4}_j] F^{i_1}_{j,h} F^{i_2}_{j,h} F^{i_3}_{j,h} F^{i_4}_{j,h} = R^{i_1,i_2,i_3,i_4}_{t_{j-1}} (\\Delta_n^3).\n$$\nWe get\n\\begin{equation}\\label{eq:negl1}\n\\sum_{j=1}^n \\mathbb{E}_{t_{j-1}} [(\\xi_{j,h}^{(1)}(\\theta_0))^4] = \\frac{1}{N^2} \\sum_{j=1}^n \\Big( \\sum_{i \\in I} R^i_{t_{j-1}} (\\Delta_n^3) + \\sum_{i \\in I^c} R^i_{t_{j-1}} (\\Delta_n^2) \\Big),\n\\end{equation}\nwhere $I$ denotes a set of all $i = (i_1,i_2,i_3,i_4) \\in \\{1,\\dots,N\\}^4$ such that $i_1, i_2, i_3, i_4$ are pairwise distinct. We note that $\\operatorname{card}(I) = O(N^4)$ and $\\operatorname{card}(I^c) = O(N^3)$. We conclude that \\eqref{eq:negl1} converges to $0$ in $L^1$ and so in probability if $N \\Delta_n \\to 0$.\\\\\n\n\\noindent\n$\\bullet$ Proof of \\eqref{e: norm theta2}. \\\\\nWe rewrite the left hand side of \\eqref{e: norm theta2} as\n\\begin{equation}\n\\frac{\\Delta_n}{N} \\sum_{j=1}^n \\sum_{i_1, i_2=1}^N \\Delta_n^{-2} C^{i_1}_{j,\\Tilde{h}_1} C^{i_2}_{j,\\Tilde{h}_2} \\mathbb{E}_{t_{j-1}} [ D^{i_1}_j D^{i_2}_j],\\label{eq: norm theta2 start}\n\\end{equation}\nwhere\n$$\nC^i_{j,\\Tilde{h}} := \\frac{\\partial_{\\theta_{2, \\Tilde{h}}} c^{i}_{t_{j-1}} (\\theta_{0,2})}{(c^{i}_{t_{j-1}}(\\theta_{0,2}))^2} = R^{i}_{t_{j-1}}(1), \\qquad D^{i}_j := (X^{i}_{t_j}-X^{i}_{t_{j-1}}-\\Delta_n b^{i}_{t_{j-1}}(\\theta_{0,1}))^2 - \\Delta_n c^{i}_{t_{j-1}}(\\theta_{0,2}).\n$$\nWe consider the term $\\mathbb{E}_{t_{j-1}} [D^{i_1}_j D^{i_2}_j]$ in \\eqref{eq: norm theta2 start}. By Lemma \\ref{l: conditional expectation}(1)\nit equals\n\\begin{align}\\label{eq:cexpected22}\n&\\mathbb{E}_{t_{j-1}} [ (X^{i_1}_{t_j} - X^{i_1}_{t_{j-1}} - \\Delta_n b^{i_1}_{t_{j-1}}(\\theta_{0,1}))^2 (X^{i_2}_{t_j} - X^{i_2}_{t_{j-1}} - \\Delta_n b^{i_2}_{t_{j-1}}(\\theta_{0,1}))^2]\\\\\n&\\qquad- \\Delta_n c^{i_1}_{t_{j-1}} (\\theta_{0,2}) \\Delta_n c^{i_2}_{t_{j-1}} (\\theta_{0,2}) + R^{i_1,i_2}_{t_{j-1}}(\\Delta_n^3).\\nonumber\n\\end{align}\nIf $i_1=i_2$ then Lemma \\ref{l: conditional expectation}(2) implies\n$$\n\\mathbb{E}_{t_{j-1}} [ (X^{i}_{t_j} - X^{i}_{t_{j-1}} - \\Delta_n b^{i}_{t_{j-1}}(\\theta_{0,1}))^4] = 3 \\Delta_n^{2} (c^{i}_{t_{j-1}}(\\theta_{0,2}))^2 + R^{i}_{t_{j-1}} (\\Delta_n^{\\frac 5 2}),\n$$\nwhence \n\\begin{equation}\\label{eq:condD2}\n\\mathbb{E}_{t_{j-1}}[(D^{i}_j)^2] = 2 \\Delta_n^2 (c^{i}_{t_{j-1}}(\\theta_{0,2}))^2 + R^{i}_{t_{j-1}} (\\Delta_n^{\\frac 5 2}).\n\\end{equation}\nIf $i_1 \\neq i_2$ then to deal with the term in \\eqref{eq:cexpected22} we decompose\n$$\nX^{i}_{t_j} - X^{i}_{t_{j-1}} - \\Delta_n b^{i}_{t_{j-1}}(\\theta_{0,1}) = A^i_{j,1} + A^i_{j,2} + B^i_j\n$$\nas in \\eqref{def: BAnorm}, \\eqref{def: A12norm}, where\n$\\mathbb{E}_{t_{j-1}} [(A^{i}_{j,k})^{4}] = R^{i}_{t_{j-1}}(\\Delta_n^{2k})$, $k=1,2$, and $\\mathbb{E}_{t_{j-1}} [(B^{i}_j)^4] = R^{i}_{t_{j-1}}(\\Delta_n^6)$.\nWe note that \n$$ \n\\mathbb{E}_{t_{j-1}}[(A^{i_1}_{j,1})^2 (A^{i_2}_{j,1})^2] = \\Delta_n c^{i_1}_j (\\theta_{0,2}) \\Delta_n c^{i_2}_j (\\theta_{0,2}). \n$$ \nMoreover, we have\n\n\\begin{align*}\n\\mathbb{E}_{t_{j-1}} [ (A^{i_1}_{j,1})^2 A^{i_2}_{j,1} A^{i_2}_{j,2} ] \n= \nc^{i_1}_{t_{j-1}} (\\theta_{0,2}) a^{i_2}_{t_{j-1}} (\\theta_{0,2}) \nV^{i_1,i_2}_j,\n\\end{align*}\nwhere independence of Brownian motions together with It\\^o isometry implies \n\\begin{align}\nV^{i_1,i_2}_j &:= \\mathbb{E}_{t_{j-1}} \\Big[ (W^{i_1}_{t_j}-W^{i_1}_{t_{j-1}})^2 \\int_{t_{j-1}}^{t_j} d W^{i_2}_s \\int_{t_{j-1}}^{t_j} (a^{i_2}_s(\\theta_{0,2}) - a^{i_2}_{t_{j-1}}(\\theta_{0,2})) d W^{i_2}_s \\Big]\\nonumber\\\\\n&= \\int_{t_{j-1}}^{t_j} \\mathbb{E}_{t_{j-1}} [ (W^{i_1}_{t_j}-W^{i_1}_{t_{j-1}})^2 (a^{i_2}_t (\\theta_{0,2}) - a^{i_2}_{t_{j-1}}(\\theta_{0,2}))] d t.\\label{eq:ri1i2j}\n\\end{align}\nAssumption \\ref{as7} allows us to apply It\\^o's lemma to $a_t^{i_2}(\\theta_{0,2})$. We get that the conditional expectation in \\eqref{eq:ri1i2j} equals\n\n\\begin{align*}\n&\\mathbb{E}_{t_{j-1}} \\Big[ (W^{i_1}_{t_j}-W^{i_1}_{t_{j-1}})^2 \\int_{t_{j-1}}^t \\sum_{k{=1}}^{{N}} \\Big( b^k_s (\\theta_{0,1}) \\partial_{x_k} a^{i_2}_s (\\theta_{0,2}) + \\frac{1}{2} c^k_s (\\theta_{0,2}) \\partial^2_{x_k} a^{i_2}_s (\\theta_{0,2}) \\Big) d s \\Big] \\\\\n&\\qquad+ \\mathbb{E}_{t_{j-1}} \\Big[ (W^{i_1}_{t_j}-W^{i_1}_{t_{j-1}})^2 \\int_{t_{j-1}}^t \\sum_{k{=1}}^{{N}} a^{k}_s (\\theta_{0,2}) \\partial_{x_k} a^{i_2}_s (\\theta_{0,2}) d W_s^k \\Big].\n\\end{align*}\nThe first term is clearly a $R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^2)$ function. Regarding the second one, for $k \\neq i_1$, the independence of the Brownian motions makes it directly equal to $0$. For $k = i_1$, instead, we have \n$$\n\\mathbb{E}_{t_{j-1}} \\Big[ (W^{i_1}_{t_j}-W^{i_1}_{t_{j-1}})^2 \\int_{t_{j-1}}^t a^{i_1}_s (\\theta_{0,2}) \\partial_{x_{i_1}} a^{i_2}_s (\\theta_{0,2}) d W_s^{i_1} \\Big],\n$$\nwhere under \\ref{as7} we obtain \n$$\n\\partial_{x_{i_1}} a^{i_2}_s (\\theta_{0,2}) := \\partial_y \\tilde \n\\Big( X^{i_2}_s, \\frac{1}{N} \\sum_{l{=1}}^{{N}} \n(X^{i_2}_s,X^l_s) \\Big) \\frac{1}{N} \\partial_y \n(X^{i_2}_s,X^{i_1}_s)\n$$\nwith\n$\\partial_y \\tilde \n$, $\\partial_y \n$ having\npolynomial growth.\nUsing the Cauchy-Schwarz inequality, it follows that the above quantity is upper bounded by \n\\begin{align*}\n&\\Big( 3\\Delta_n^2 \\mathbb{E}_{t_{j-1}} \\Big[\\Big( \\int_{t_{j-1}}^t a^{i_1}_s (\\theta_{0,2}) \\partial_{x_{i_1}} a^{i_2}_s (\\theta_{0,2}) d W_s^{i_1}\\Big)^2 \\Big] \\Big)^\\frac{1}{2} \\\\\n&\\qquad = \\Big( 3 \\Delta_n^2\n\\int_{t_{j-1}}^t \\mathbb{E}_{t_{j-1}} [ ( a^{i_1}_s (\\theta_{0,2}) \\partial_{x_{i_1}} a^{i_2}_s (\\theta_{0,2}) )^2 ] ds \\Big)^\\frac{1}{2} \n= \\frac{1}{N} R^{i_1, i_2}_{t_{j-1}} (\\Delta_n^\\frac{3}{2}).\n\\end{align*}\nIt implies\n\\begin{equation}\\label{eq:ito}\n\\mathbb{E}_{t_{j-1}}[(A^{i_1}_{j,1})^2 A^{i_2}_{j,1} A^{i_2}_{j,2}] = R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^3) + \\frac{1}{N} R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^\\frac{5}{2}).\n\\end{equation}\nWe conclude that\n\\begin{align*}\n&\\mathbb{E}_{t_{j-1}} [ (X^{i_1}_{t_j} - X^{i_1}_{t_{j-1}} - \\Delta_n b^{i_1}_{t_{j-1}}(\\theta_{0,1}))^2 (X^{i_2}_{t_j} - X^{i_2}_{t_{j-1}} - \\Delta_n b^{i_2}_{t_{j-1}}(\\theta_{0,1}))^2]\\\\ \n&\\qquad = \\Delta_n c^{i_1}_j (\\theta_{0,2}) \\Delta_n c^{i_2}_j (\\theta_{0,2}) + R^{i_1,i_2}_{t_{j-1}}(\\Delta_n^3) + \\frac{1}{N} R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^\\frac{5}{2}), \n\\end{align*}\nwhence \n\\begin{equation}\\label{eq:condD12}\n\\mathbb{E}_{t_{j-1}}[D^{i_1}_j D^{i_2}_j] = R^{i_1,i_2}_{t_{j-1}}(\\Delta_n^3) + \\frac{1}{N} R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^\\frac{5}{2}) \n\\end{equation}\nif $i_1 \\neq i_2$. Finally, we plug \\eqref{eq:condD2}, \\eqref{eq:condD12} back into \\eqref{eq: norm theta2 start}, where \nuse of the conditions $N \\Delta_n \\to 0$, $\\Delta_n \\to 0$ and Lemma \\ref{l: Riemann} completes the proof of the convergence in \\eqref{e: norm theta2}.\\\\\n\n\n\\noindent\n$\\bullet$ Proof of \\eqref{e: negl theta}, second convergence. \\\\\nWe prove it for $r = 2$. We use the same notation as in \\eqref{eq: norm theta2 start} and rewrite the left hand side of \\eqref{e: negl theta} as \n\\begin{equation}\\label{eq:sumCD}\n\\frac{\\Delta_n^2}{N^2} \\sum_{j=1}^n \\sum_{i_1, i_2, i_3, i_4=1}^N \\Delta_n^{-4} C^{i_1}_{j,\\Tilde{h}} C^{i_2}_{j,\\Tilde{h}} C^{i_3}_{j,\\Tilde{h}} C^{i_4}_{j,\\Tilde{h}} \\mathbb{E}_{t_{j - 1}} [D_j^{i_1} D_j^{i_2} D_j^{i_3} D_j^{i_4}]. \\end{equation}\nWe have $\\mathbb{E}_{t_{j-1}}[(D^{i}_j)^4] = R^{i}_{t_{j-1}}(\\Delta_n^{4})$ and $\\operatorname{card}(I^c) = O(N^3)$, where $I$ denotes a set of all $i=(i_1,i_2,i_3,i_4) \\in \\{1,\\dots,N\\}^4$ such that $i_1,i_2,i_3,i_4$ are pairwise distinct. In \\eqref{eq:sumCD} the sum over $i \\in I^c$ converges to $0$ in $L^1$ and so in probability since $N\\Delta_n \\to 0$. In case $i \\in I$ we use the decomposition \n\\begin{align*}\nD^i_j &= (A^i_{j,1} + A^i_{j,2} + B^i_j)^2 - \\Delta_n c^i_{t_{j-1}}(\\theta_{0,2})\\\\\n&= (A^i_{j,2}+B^i_j)(2 A^i_{j,1}+A^i_{j,2} + B^i_j) + (A^i_{j,1})^2 - \\Delta_n c^i_{t_{j-1}} (\\theta_{0,2}).\n\\end{align*}\nWe note that\n\\begin{equation*}\n\\begin{gathered}\n\\mathbb{E}_{t_{j-1}} [((A^i_{j,1})^2- \\Delta_n c^i_{t_{j-1}}(\\theta_{0,2}))^4] = R^i_{t_{j-1}}(\\Delta_n^4),\\\\\n\\mathbb{E}_{t_{j-1}} [(A^i_{j,k})^8] = R^i_{t_{j-1}} (\\Delta_n^{4k}), \\ k=1,2, \\qquad\n\\mathbb{E}_{t_{j-1}} [(B^i_j)^8] = R^i_{t_{j-1}} (\\Delta_n^{12}). \n\\end{gathered}\n\\end{equation*}\nMoreover, because of the independence of Brownian motions, we have\n\\begin{align*}\n\\mathbb{E}_{t_{j-1}} \\Big[ \\prod_{k=1}^4 ((A^{i_k}_{j,1})^2- \\Delta_n c^{i_k}_{t_{j-1}}(\\theta_{0,2})) \\Big] = 0\n\\end{align*}\nand in a similar manner as in \\eqref{eq:ito} under \\ref{as7} we have\n\\begin{align*}\n&\\mathbb{E}_{t_{j-1}} \\Big[ A^{i_1}_{j,2} A^{i_1}_{j,1} \\prod_{k=2}^4 ( (A^{i_k}_{j,1})^2 - \\Delta_n c^{i_k}_{t_{j-1}} (\\theta_{0,2}) ) \\Big]\\\\ \n&\\qquad = a^{i_1}_{t_{j-1}} (\\theta_{0,2}) \\prod_{k=2}^4 c^{i_{k}}_{t_{j-1}} (\\theta_{0,2}) \\int_{t_{j-1}}^{t_j} \\mathbb{E}_{t_{j-1}} \\Big[ (a^{i_1}_s (\\theta_{0,2}) - a^{i_1}_{t_{j-1}} (\\theta_{0,2})) \\prod_{l=2}^4 ((W^{i_{l}}_{t_j} - W^{i_{l}}_{t_{j-1}})^2 - \\Delta_n) \\Big] d s\\\\ \n&\\qquad= R_{t_{j-1}}^{i_1,i_2,i_3,i_4}(\\Delta_n^5) + \\frac{1}{N} R_{t_{j-1}}^{i_1,i_2,i_3,i_4} (\\Delta_n^{\\frac{9}{2}}),\n\\end{align*}\nwhence it follows\n$$\n\\mathbb{E}_{t_{j-1}} [D^{i_1}_j D^{i_2}_j D^{i_3}_j D^{i_4}_j] = R^{i_1,i_2,i_3,i_4}_{t_{j-1}} (\\Delta_n^5) + \\frac{1}{N} R_{t_{j-1}}^{i_1,i_2,i_3,i_4} (\\Delta_n^{\\frac{9}{2}}).\n$$\n We recall that $\\operatorname{card}(I) = O(N^4)$. Since $N\\Delta_n \\to 0$, $\\Delta_n \\to 0$, the sum over $i \\in I$ in \\eqref{eq:sumCD} converges to $0$ in $L^1$ and so in probability.\\\\\n\n\\noindent\n$\\bullet$ Proof of \\eqref{e: norm mixed}. \\\\\nWe rewrite the left hand side of \\eqref{e: norm mixed} as\n\\begin{equation}\\label{eq:mixed}\n\\frac{\\Delta_n^{\\frac{1}{2}}}{N} \\sum_{j=1}^n \\sum_{i_1,i_2=1}^N \\mathbb{E}_{t_{j-1}} [ (A^{i_1}_{j,1}+A^{i_1}_{j,2}+B^{i_1}_j) D^{i_2}_j ] \\Delta_n^{-1} C^{i_2}_{j,\\Tilde{h}} F^{i_1}_{j,h},\n\\end{equation}\nwhere\n\\begin{align*}\nD^{i}_j\n&= (A^{i}_{j,2} +B^{i}_j)(2 A^{i}_{j,1} + A^{i}_{j,2} +B^{i}_j) + (A^{i}_{j,1})^2 - \\Delta_n c^{i}_{t_{j-1}} (\\theta_{0,2})\n\\end{align*}\nwith the notations introduced above. We recall that \n$F^i_{j,h} = R^{i}_{t_{j-1}}(1)$,\n$C^i_{j,\\Tilde{h}} = R^{i}_{t_{j-1}}(1)$, \n$\\mathbb{E}_{t_{j-1}} [(B^{i}_j)^4] = R^{i}_{t_{j-1}}(\\Delta_n^6)$, $\\mathbb{E}_{t_{j-1}}[(A^{i}_{j,k})^4] = R^{i}_{t_{j-1}}(\\Delta_n^{2k})$, $k=1,2$, and so $\\mathbb{E}_{t_{j-1}}[((A^{i}_{j,1})^2 - \\Delta_n c^{i}_{t_{j-1}} (\\theta_{0,2}))^2] = R^{i}_{t_{j-1}}(\\Delta_n^2)$. We note that\n$$\n\\mathbb{E}_{t_{j-1}} [A^{i_1}_{j,1}((A^{i_2}_{j,1})^2 - \\Delta_n c^{i_2}_{t_{j-1}}(\\theta_{0,2}))] = 0\n$$\nfor all $i_1,i_2$. This is a consequence of the independence of the Brownian motions for $i_1 \\neq i_2$, while for $i_1 = i_2$ it derives from the fact that the odd moments are centered. Hence, in case $i_1=i_2 = i$ the term $\\mathbb{E}_{t_{j-1}} [(A^{i}_{j,1})^2 A^{i}_{j,2}]$ makes the main contribution to\n$$\n\\mathbb{E}_{t_{j-1}} [ (A^{i}_{j,1}+A^{i}_{j,2}+B^{i}_j) D^{i}_j ] = R^{i}_{t_{j-1}}(\\Delta_n^2).\n$$\nNow we can see that the sum over $i_1=i_2$ in \\eqref{eq:mixed} converges to $0$ in $L^1$ and so in probability. In case $i_1 \\neq i_2$ we have\n$$\n\\mathbb{E}_{t_{j-1}} [A^{i_1}_{j,2}((A^{i_2}_{j,1})^2 - \\Delta_n c^{i_2}_{t_{j-1}}(\\theta_{0,2}))] = 0. \n$$\nMoreover,\n\\begin{align*}\n\\mathbb{E}_{t_{j-1}} [A^{i_1}_{j,1} A^{i_2}_{j,1} A^{i_2}_{j,2}]\n= a^{i_1}_{t_{j-1}} (\\theta_{0,2}) a^{i_2}_{t_{j-1}} (\\theta_{0,2}) \\int_{t_{j-1}}^{t_j} \\mathbb{E}_{t_{j-1}} [(W^{i_1}_{t_j} - W^{i_1}_{t_{j-1}}) (a^{i_2}_s (\\theta_{0,2}) - a^{i_2}_{t_{j-1}} (\\theta_{0,2}) ] ds.\n\\end{align*}\nThe application of It\\^o's lemma to $a_s^{i_2}(\\theta_{0,2})$ under \\ref{as7} similarly as in the proof of \\eqref{eq:ito} provides \n$$\\mathbb{E}_{t_{j-1}} [A^{i_1}_{j,1} A^{i_2}_{j,1} A^{i_2}_{j,2}] = R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^{\\frac 5 2}) + \\frac{1}{N} R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^{2}). $$\nWe conclude that\n$$\n\\mathbb{E}_{t_{j-1}} [(A^{i_1}_{j,1}+A^{i_1}_{j,2}+B^{i_1}_j) D^{i_2}_j] = R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^{\\frac 5 2}) + \\frac{1}{N} R^{i_1,i_2}_{t_{j-1}} (\\Delta_n^{2}).\n$$\nin case $i_1 \\neq i_2$. Hence, the sum over $i_1 \\neq i_2$ in \\eqref{eq:mixed} converges to $0$ in $L^1$ and so in probability when $N \\Delta_n \\to 0$, $\\Delta_n \\to 0$. \nThis concludes the proof of the asymptotic normality of $\n\\nabla_{\\theta}S^N_n (\\theta_0) {M^N_n}$. \n\\end{proof}\n\n\\subsection{Proof of Proposition \\ref{p: second derivatives contrast}}\n\\begin{proof}\nThe proof relies on the computation of the second derivatives of the contrast function. We have that, for any $k,l = 1, ... , p_1$,\n\\begin{align*}\n\\partial_{\\theta_{1, k}} \\partial_{\\theta_{1, l}} S_n^N (\\theta) = 2 \\sum_{i = 1}^N \\sum_{j = 1}^n \\Big\\{ \\Delta_n &\\frac{\\partial_{\\theta_{1, k}} b^i_{t_{j - 1}}(\\theta_1) \\partial_{\\theta_{1, l}} b^i_{t_{j - 1}}(\\theta_1)}{c^i_{t_{j - 1}}(\\theta_2)}\\\\ \n- &\\frac{\\partial_{\\theta_{1, k}} \\partial_{\\theta_{1, l}} b^i_{t_{j - 1}}(\\theta_1)}{c^i_{t_{j - 1}}(\\theta_2)} (X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j - 1}}(\\theta_1) )\\Big\\}, \n\\end{align*}\nwhere the last factor can further be decomposed into $\\Delta_n (b^i_{t_{j-1}}(\\theta_{0,1}) - b^i_{t_{j-1}}(\\theta_1))$ and $X^i_{t_{j}} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}}(\\theta_{0,1})$. We can see that $\\partial_{\\theta_{1, k}} \\partial_{\\theta_{1, l}} S_n^N (\\theta)\/N$ converges to\n\\begin{align}\\label{def:B11}\n\\Sigma^{(1)}_{kl} (\\theta) := 2 \\int_0^1 \\int_{\\mathbb{R}} \\Big\\{ &\\frac{\\partial_{\\theta_{1, k}} b(\\theta_1, x,\\bar \\mu_t) \\partial_{\\theta_{1, l}} b(\\theta_1, x,\\bar \\mu_t)}{c(\\theta_2, x,\\bar \\mu_t)} \\\\\n- &\\frac{\\partial_{\\theta_{1, k}} \\partial_{\\theta_{1, l}} b(\\theta_1, x,\\bar \\mu_t)}{c(\\theta_2, x,\\bar \\mu_t)} (b(\\theta_{0,1}, x,\\bar \\mu_t) - b(\\theta_1, x,\\bar \\mu_t)) \\Big\\} \\bar \\mu_t (d x) d t \\nonumber\n\\end{align}\nuniformly in $\\theta$ in probability. Indeed, the proof follows along the lines of the proof of \\eqref{lim:con2}. We refer to Steps 3, 4 of the proof of Lemma \\ref{lemma:cns}, where in \\eqref{def:INnrhoNn} in $I^N_n(\\theta)$, $\\rho^N_n (\\theta)$ it is enough to replace the functions $h(\\theta,\\cdot)$ {and} $g(\\theta,\\cdot)$ with the integrand of \\eqref{def:B11} and $\\partial_{\\theta_{1, k}} \\partial_{\\theta_{1, l}} b (\\theta_1,\\cdot) \/c(\\theta_2,\\cdot)$ respectively, and to check them for the respective conditions. We note that both functions have polynomial growth. Moreover, the integrand in \\eqref{def:B11} is locally Lipschitz continuous, which allows us to apply Lemma~\\ref{l: Riemann} and yields the convergence in probability of the sequence $\\partial_{\\theta_{1,k}} \\partial_{\\theta_{1, l}} S_n^N (\\theta)\/N$ for every $\\theta$. To get tightness in $(C(\\Theta{; \\mathbb{R}}), \\| \\cdot\\|_\\infty)$, we use that uniformly in $\\theta$ the partial derivatives with respect to $\\theta_{i',j'}$, $j'=1,\\dots,p_{i'}$, $i'=1,2$, of the two functions have polynomial growth.\n\nIn the same way as above we get that for any $k = 1, ... , p_1$, $l = 1, ... , p_2$, once multiplied by $\\sqrt{\\Delta_n}\/N$, \n$$\n\\partial_{\\theta_{1,k}} \\partial_{\\theta_{2,l}} S_n^N (\\theta) = 2 \\sum_{i = 1}^N \\sum_{j = 1}^n \\frac{\\partial_{\\theta_{1,k}} b^i_{t_{j - 1}}(\\theta_1) \\partial_{\\theta_{2,l}} c^i_{t_{j - 1}}(\\theta_2)}{(c^i_{t_{j - 1}}(\\theta_2))^2} (X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j - 1}}(\\theta_1) ),\n$$\nconverges to $\n0$ uniformly in $\\theta$ in probability. \n\nFinally, we have that for any $k,l = 1,\\dots, p_2$, \n\\begin{align*}\n\\partial_{\\theta_{2,k}} \\partial_{\\theta_{2,l}} S^N_n (\\theta) = \\sum_{i=1}^N \\sum_{j=1}^n &\\Big\\{\n\\frac{\\partial_{\\theta_{2,k}} \\partial_{\\theta_{2,l}} c^i_{t_{j-1}}(\\theta_2) c^i_{t_{j-1}}(\\theta_2) - \\partial_{\\theta_{2,k}} c^i_{t_{j-1}}(\\theta_2) \\partial_{\\theta_{2,l}} c^i_{t_{j-1}}(\\theta_2)}{(c^i_{t_{j-1}}(\\theta_2))^2}\\\\\n&+ \\frac{2 \\partial_{\\theta_{2,k}} c^i_{t_{j-1}}(\\theta_2)\\partial_{\\theta_{2,l}} c^i_{t_{j-1}}(\\theta_2)-\\partial_{\\theta_{2,k}} \\partial_{\\theta_{2,l}} c^i_{t_{j-1}}(\\theta_2)c^i_{t_{j-1}}(\\theta_2)}{\\Delta_n (c^i_{t_{j-1}}(\\theta_2))^3}\\\\ \n&\\times (X^i_{t_j}-X^i_{t_{j-1}}- \\Delta_n b^i_{t_{j-1}}(\\theta_1))^2\n\\Big\\},\n\\end{align*}\nwhere the last factor can further be decomposed into $(X^i_{t_j}-X^i_{t_{j-1}}- \\Delta_n b^i_{t_{j-1}}(\\theta_1))^2 - \\Delta_n c^i_{t_{j-1}}(\\theta_{0,2})$ and $\\Delta_n c^i_{t_{j-1}}(\\theta_{0,2})$. We note that $ (\\Delta_n\/N) \\partial_{\\theta_{2,k}} \\partial_{\\theta_{2,l}} S^N_n(\\theta)$ converges to\n\\begin{align*}\n\\Sigma^{(2)}_{kl} (\\theta) := \\int_0^T \\int_{\\mathbb{R}} &\\Big\\{ \\frac{\\partial_{\\theta_{2,k}} \\partial_{\\theta_{2,l}} c(\\theta_2, x,\\bar \\mu_t)c(\\theta_2, x,\\bar \\mu_t)-\\partial_{\\theta_{2,k}} c(\\theta_2, x,\\bar \\mu_t)\\partial_{\\theta_{2,l}} c(\\theta_2, x,\\bar \\mu_t)}{c(\\theta_2, x,\\bar \\mu_t)^2}\\\\\n&+ \\frac{2 \\partial_{\\theta_{2,k}} c(\\theta_2, x,\\bar \\mu_t)\\partial_{\\theta_{2,l}} c(\\theta_2, x,\\bar \\mu_t) - \\partial_{\\theta_{2,k}} \\partial_{\\theta_{2,l}} c(\\theta_2, x,\\bar \\mu_t)c(\\theta_2, x,\\bar \\mu_t)}{c(\\theta_2, x,\\bar \\mu_t)^3}\\\\ &\\times c(\\theta_{0,2}, x,\\bar \\mu_t) \\Big\\} \\bar \\mu_t (d x) d t\n\\end{align*}\nuniformly in $\\theta$ in probability. We will prove the uniform in $\\theta$ convergence to the second term of \n\\Sigma^{(2)}_{kl}(\\theta)$ only: \n\\begin{equation}\\label{lim:B221}\n\\sum_{j=1}^n \\chi^N_{n,j} (\\theta) \\xrightarrow{\\P} \\tilde\n\\Sigma^{(2)}_{kl} (\\theta) := \\int_0^T \\int_{\\mathbb{R}} \\tilde f (\\theta_2, x,\\bar \\mu_t) c(\\theta_{0,2}, x,\\bar\\mu_t) \\bar \\mu_t (d x) d t, \n\\end{equation}\nwhere \n$$\n\\chi^N_{n,j}(\\theta) = \\frac{1}{N} \\sum_{i=1}^N \\tilde f^i_{t_{j-1}} (\\theta_2) (X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}}(\\theta_1))^2\n$$\nand function $\\tilde f : \\Theta_2 \\times \\mathbb{R} \\times {\\cal P} \\to \\mathbb{R}$ is given by $(2 (\\partial_{\\theta_{2,k}} c) (\\partial_{\\theta_{2,l}} c) - (\\partial_{\\theta_{2,k}} \\partial_{\\theta_{2,l}} c) c) \/ c^3$.\nFor every $\\theta$ the convergence in \\eqref{lim:B221} follows from\n$$\n\\sum_{j=1}^n \\mathbb{E}_{t_{j-1}} [\\chi^N_{n,j}(\\theta)] \\xrightarrow{\\P} \\tilde\n\\Sigma^{(2)}_{kl} (\\theta), \\qquad \\sum_{j=1}^n \\mathbb{E}_{t_{j-1}}[(\\chi^N_{n,j}(\\theta))^2] \\xrightarrow{\\P} 0\n$$\nby \\cite[Lemma 9]{GenJac93}. Indeed, the above relations hold, because by Lemma \\ref{l: conditional expectation}(1), \n$$\n\\mathbb{E}_{t_{j-1}} [\\chi^N_{n,j} (\\theta)] = \\frac{1}{N} \\sum_{i=1}^N \\tilde f^i_{t_{j-1}} (\\theta_2) (\\Delta_n c^i_{t_{j-1}} (\\theta_{0,2}) + R^i_{t_{j-1}}(\\Delta_n^{3\/2})),\n$$ \nby Jensen's inequality and Lemma \\ref{l: conditional expectation}(2),\n$$\n\\mathbb{E}_{t_{j-1}} [(\\chi^N_{n,j}(\\theta))^2] \\le \\frac{1}{N} \\sum_{i=1}^N (\\tilde f^i_{t_{j-1}}(\\theta_2))^2 R^i_{t_{j-1}}(\\Delta_n^2),\n$$\nby polynomial growth of $\\partial^{i'}_{\\theta_{2,j'}} c (\\theta_2,\\cdot)$, $i'=0,1,2$, $j'=1,\\dots,p_2$, \\ref{as3} and Point 1.\\ of Lemma~\\ref{l: moments},\n$$\n(\\tilde f^i_{t_{j-1}}(\\theta_2))^2 = R^i_{t_{j-1}}(1)\n$$\nThe tightness in $(C(\\Theta{;\\mathbb{R}}), \\| \\cdot \\|_\\infty)$ follows from \n$\\mathbb{E} [ \\sup_\\theta \\|\\nabla_\\theta \\sum_{j{=1}}^{{n}} \\chi^N_{n,j}(\\theta) \\| ] = O(1)$. Indeed, we have \n\\begin{align*}\n\\nabla_{\\theta_1} \\chi^N_{n,j} (\\theta) &= - 2 \\frac{\\Delta_n}{N} \\sum_{i=1}^N \\nabla_{\\theta_1} b^i_{t_{j-1}} (\\theta_1) \\tilde f^i_{t_{j-1}} (\\theta_2) (X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}}(\\theta_1) ),\\\\\n\\nabla_{\\theta_2} \\chi^N_{n,j} (\\theta) &= \\frac{1}{N} \\sum_{i=1}^N \\nabla_{\\theta_2} \\tilde f^i_{t_{j-1}}(\\theta_2) (X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}}(\\theta_1))^2,\n\\end{align*}\nwhere by polynomial growth of $\\sup_{\\theta_1} \\|\\nabla_{\\theta_1} b (\\theta_1,\\cdot)\\|$, $\\sup_{\\theta_2} |\\partial_{\\theta_{2,j'}}^{i'} c (\\theta_2,\\cdot)|$, $i'=0,1,2,3$, $j'=1,\\dots,p_2$, and \\ref{as3},\n$$\n\\sup_\\theta \\|\\nabla_{\\theta_{1}} b^i_{t_{j-1}}(\\theta_1) \\tilde f^i_{t_{j-1}} (\\theta_2) \\| = R^i_{t_{j-1}} (1), \\qquad \\sup_{\\theta_2} \\| \\nabla_{\\theta_2} \\tilde f^i_{t_{j-1}}(\\theta_2)\\| = R^i_{t_{j-1}} (1)\n$$\nand \n$$\n\\sup_{\\theta_1} |X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}}(\\theta_1)| \\le |X^i_{t_j}-X^i_{t_{j-1}}| + \\Delta_n \\sup_{\\theta_1} |b^i_{t_{j-1}}(\\theta_1)|\n$$\nwith $\\sup_{\\theta_1} |b^i_{t_{j-1}}(\\theta_1)| = R^i_{t_{j-1}}(1)$. Finally, we have\n$\\mathbb{E} [|X^i_{t_j}-X^i_{t_{j-1}}|^4] \\le C \\Delta_n^2$ uniformly in $i,j$ and $N,n$ by Lemma~\\ref{l: moments}(2).\n\nWe conclude that the matrix \n$\\Sigma^N_n (\\theta)$ converges to $\\Sigma(\\theta) =\n\\operatorname{diag}(\\Sigma^{(1)}(\\theta),\\Sigma^{(2)}(\\theta))$ uniformly in $\\theta$ and so at $\\theta = \\theta_0$ in probability. Hence,\n\\begin{align*}\n\\| \\Sigma^N_n (\\theta_0 + s (\\hat \\theta^N_n - \\theta_0)) - \\Sigma^N_n (\\theta_0) \\| \n&\\le o_\\P (1) + \\| \\Sigma(\\theta_0 + s (\\hat \\theta^N_n - \\theta_0)) - \\Sigma(\\theta_0) \\|,\n\\end{align*}\nwhere the uniform convergence in probability (in $s$) of the last term to $0$ follows from continuity of $\\Sigma (\\theta)$ at $\\theta = \\theta_0$ and consistency of the estimator sequence $\\hat \\theta^N_n$. \n\\end{proof}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Proof of technical results}{\\label{s: proof technical}}\n\n\\subsection{Proof of Lemma \\ref{l: moments}}\n\n\\begin{proof} \n\\noindent\nProof of Lemma \\ref{l: moments}(1). \\\\\nWe have, for any $i = 1, \\dots , N$, $0 \\le t \\le T$, $p \\ge 2$, \n\\begin{align*}\n\\mathbb{E}[|X_t^i|^p] & \\le \\mathbb{E} \\Big[ \\Big|X_0^i + \\int_0^t b_u^i(\\theta_{0,1}) du + \\int_0^t a_u^i(\\theta_{0,2}) dW_u^i \\Big|^p \\Big] \\\\\n& \\le C \\Big( \\mathbb{E}[|X_0^i|^p] + t^{p - 1} \\int_0^t \\mathbb{E}[|b_u^i(\\theta_{0,1})|^p] d u + t^{\\frac{p}{2} - 1} \\int_0^t \\mathbb{E}[|a_u^i(\\theta_{0,2})|^p] d u \\Big),\n\\end{align*}\nwhere we have used the Burkholder-Davis-Gundy and Jensen inequalities. We observe that, as a consequence of the lipschitzianity gathered in \\ref{as2}, for the true value of the parameter both coefficients are upper bounded by $C(1 + |X_u^i| + W_2(\\mu_u, \\delta_0))$.\nDue to \nJensen's inequality, we have \n$$\\mathbb{E}[W_2^p(\\mu_u, \\delta_0)] \\le \\frac{1}{N} \\sum_{j = 1}^N \\mathbb{E} [ |X_u^j|^p ] \n= \\mathbb{E}[|X_u^i|^p].\n$$\nThe last identity follows from the fact that the particles are equally distributed.\nWe obtain\n\\begin{equation}{\\label{eq: end for p larger than 2}}\n\\mathbb{E}[|X_t^i|^p] \\le C \\Big( \\mathbb{E}[|X_0^i|^p] + (t^{p - 1} + t^{\\frac{p}{2} - 1} ) \\Big( t + 2\\int_0^t \\mathbb{E}[|X_u^i|^p] du \\Big)\n\\Big).\n\\end{equation}\nWe infer by Gronwall's lemma that \n$$\n\\mathbb{E}[|X_t^i|^p] \\le \nC( \\mathbb{E}[|X_0^i|^p] + {T^{p}} + {T^{\\frac{p}{2}}} ) \\exp ( {C' (T^{p} + T^{\\frac{p}{2}})} ).\n$$\nAs the constants do not depend on $t \\le T$ and $\\mathbb{E}[|X_0^i|^p] < \\infty$ by \\ref{as1}, we have the wanted result for $p \\ge 2$. \nThen, by a Jensen argument and the boundedness of the moments for $p \\ge 2$, it follows the result also for $p< 2$.\n\\\\\n\n\\noindent\nProof of Lemma \\ref{l: moments}(2). \\\\\nWe have for any $0 \\le s < t \\le T$, $p \\ge 2$,\n\\begin{align*}\n\\mathbb{E}[|X_t^i - X_s^i|^p] & = \\mathbb{E} \\Big[ \\Big|\\int_s^t b_u^i(\\theta_{0,1}) du + \\int_s^t a_u^i(\\theta_{0,2}) dW_u^i \\Big|^p \\Big] \\\\\n& \\le C \\Big( (t-s)^{p - 1}\\int_s^t \\mathbb{E}[|b^i_u(\\theta_{0,1})|^p] du + (t - s)^{\\frac{p}{2}-1} \\int_s^t \\mathbb{E}[|a^i_u(\\theta_{0,2})|^p] ds \\Big),\n\\end{align*}\nwhere we have used the Jensen and Burkholder-Davis-Gundy inequalities. Because of \\eqref{eq: pol growth} and the just shown Lemma \\ref{l: moments}(1), the result follows letting $t-s \\le 1$. \\\\\n\n\\noindent\nProof of Lemma \\ref{l: moments}(3). \\\\\nAccording to the definition of $R^i_s(1)$, we want to evaluate the $L^q$ norm of $\\mathbb{E}_s [|X^i_t-X^i_s|^p]$.\nFor any $0 \\le s < t \\le T$ such that $t-s\\le 1$ and $p \\ge 2$, $q \\ge 1$, \n$$\n\\mathbb{E} \\big[ \\big| \\mathbb{E}_s [ | X^i_t-X^i_s|^p ] \\big|^q \\big]^{\\frac 1 q} \\le \\mathbb{E} [ |X^i_t-X^i_s|^{pq} ]^{\\frac 1 q} \\le C (t-s)^{\\frac{p}{2}}\n$$\nfollows by conditional Jensen's inequality and Lemma \\ref{l: moments}(2).\n\\\\\n\n\\noindent\nProof of Lemma \\ref{l: moments}(4).\\\\ \nThis is a straightforward consequence of\n\\begin{equation}\\label{eq: bound W2}\nW_2^p (\\mu_t,\\mu_s) \\le \\Big( \\frac{1}{N} \\sum_{j=1}^N |X^j_t-X^j_s|^2 \\Big)^{\\frac{p}{2}} \\le \\frac{1}{N} \\sum_{j=1}^N |X^j_t - X^j_s|^p\n\\end{equation}\nby Jensen's inequality for any $0 \\le s < t \\le T$ such that $t-s \\le 1$, $p \\ge 2$ and Lemma \\ref{l: moments}(2).\\\\\n\n\\noindent\nProof of Lemma \\ref{l: moments}(5).\\\\ \nIt follows directly from \\eqref{eq: bound W2}, where we use Minkowski's inequality as follows:\n$$\n\\mathbb{E} \\big[ \\big| \\mathbb{E}_s [ W^p_2 (\\mu_t,\\mu_s) ] \\big|^q \\big]^{\\frac{1}{q}} \\le \\frac{1}{N} \\sum_{j=1}^N \\mathbb{E} \\big[ \\big| \\mathbb{E}_s [ |X^j_t-X^j_s|^{pq} ] \\big| \\big]^{\\frac{1}{q}}, \n$$\nand then Lemma \\ref{l: moments}(3).\n\\end{proof}\n\n\n\\subsection{Proof of Lemma \\ref{l: Riemann}}\n\\begin{proof} \n\\noindent \nStep 1. We prove that \n$$\n\\frac{\\Delta_n}{N} \\sum_{i=1}^N \\sum_{j=1}^n f(X^{i,N}_{t_{j{-1,n}}}, \\mu^N_{t_{j{-1,n}}}) - \\frac{1}{N} \\sum_{i=1}^N \\int_0^T f(X^{i,N}_s, \\mu^N_s) d s \\xrightarrow{L^1} 0.\n$$\nHere we note $\\Delta_n = t_{j,n}-t_{j-1,n}$ and decompose the above integral into integrals over $[t_{j-1,n},t_{j,n})$. We can see that the above convergence\nfollows from \n$$\n\\sum_{j=1}^n \\int_{t_{j-1,n}}^{t_{j,n}} \\mathbb{E} [ | f(X^{i,N}_{t_{j-1,n}}, \\mu^N_{t_{j-1,n}}) - f(X^{i,N}_s, \\mu^N_s) | ] d s \\to 0, \\qquad N,n \\to \\infty,\n$$\nfor fixed $i$,\nwhich in turn follows using\nthe condition \\eqref{cond:Riemann}, Cauchy-Schwarz inequality and moment bounds in Lemma \\ref{l: moments}(1), (2) and (4). In particular, $\\mathbb{E} [|X^{i,N}_{t_{j-1,n}} - X^{i,N}_s|^2] \\le C \\Delta_n$ for all $t_{j-1,n} \\le s \\le t_{j,n}$, $j$ and $n,N$. \\\\\n\n\\noindent\nStep 2. Next, let us prove that \n$$\n\\frac{1}{N} \\sum_{i=1}^N \\int_{{0}}^{{T}} f(X^{i,N}_s, \\mu^N_s) d s - \\frac{1}{N} \\sum_{i=1}^N \\int_0^T f(\\bar X^i_s, \\bar \\mu_s) d s \\xrightarrow{L^1} 0, \\qquad N \\to \\infty,\n$$\n{where each $(\\bar X^i_t)_{t\\in [0,T]}$ satisfies \\eqref{eq: McK} with $(W_t)_{t \\in [0,T]} = (W^i_t)_{t \\in [0,T]}$ and $\\bar X^i_0 = X^{i,N}_0$.}\nIt suffices to prove\n$$\n\\int_0^T \\mathbb{E} [ | f(X^{i,N}_s, \\mu^N_s ) - f(\\bar X^i_s, \\bar \\mu_s ) |] d s \\to 0,\n$$\nwhere $i$ is fixed and the integral is over a bounded interval.\nFor this purpose, let us use again the condition \\eqref{cond:Riemann} and the Cauchy-Schwarz inequality. Following the same arguments as in the proof of Lemma \\ref{l: moments}(1) and Gronwall lemma, it is easy to show that for all $p>0$ there exists $ C_p > 0$ such that for all $s, i, N$ it holds $\\mathbb{E} [|\\bar X^i_s|^p ] < C_p$.\n Moreover we have\n$$\n\\mathbb{E} [ |X^{i,N}_s - \\bar X^i_s|^2 ] \\le \\frac{C}{\\sqrt{N}}\n$$ \nfor all $0 \\le s \\le {T}$ and $i, N$, thanks to Theorem 3.20 in \\cite{Review prop}, based on Theorem 1 of \\cite{145 review}. We remark that, from the boundedness of the moments, the quantity $q$ appearing in the statement of Theorem 3.20 in \\cite{Review prop} is larger than $4$. Hence, the rate $N^{- (q - 2)\/q}$ is negligible compared to $N^{- 1\/2}$.\nThe propagation of chaos stated above implies\n$$\n\\mathbb{E} [ W^2_2 (\\mu^N_s, \\bar \\mu_s) ] \\le \\frac{C}{\\sqrt{N}}.\n$$ \nIndeed, to get the last relation, we introduce the empirical measure $\\bar \\mu^N_s = N^{-1} \\sum_{i=1}^N \\delta_{\\bar X^i_s}$ of the independent particle system at time $s$ and use the triangle inequality for $W_2$. Then\n$$\n\\mathbb{E} [ W_2^2 (\\mu^N_s, \\bar \\mu^N_s) ] \\le \\frac{1}{N} \\sum_{i=1}^N \\mathbb{E} [|X^{i,N}_s-\\bar X^i_s|^2]\n\\le \\frac{C}{\\sqrt{N}},\n$$\nwhereas Theorem~1 of {\\cite{145 review}} implies\n$$\n\\mathbb{E} [W^2_2(\\bar \\mu^N_s,\\bar \\mu_s)] \\le \\frac{C}{\\sqrt{N}}.\n$$\n\n\\noindent\nStep 3. Finally, the law of large numbers gives \n$$\n\\frac{1}{N} \\sum_{i=1}^N \\int_0^T f(\\bar X^i_s, \\bar \\mu_s) d s \\xrightarrow{\\P} \\mathbb{E} \\Big[ \\int_0^T f(\\bar X_s, \\bar \\mu_s) d s \\Big], \\qquad N \\to \\infty.\n$$\n\\end{proof}\n\n\n\\subsection{Proof of Lemma \\ref{l: conditional expectation}}\n\\begin{proof}\nWe use the same notation as before. \\\\ \n\\\\\nProof of Lemma \\ref{l: conditional expectation}(2). We decompose $X^i_{t_j} - X^i_{t_{j-1}} - \\Delta_n b^i_{t_{j-1}}(\\theta_{0,1})$ into $A^i_{j,1}$ and $H^i_{j,2} := A^i_{j,2} + B^i_j$, where\n\\begin{equation}\n\\begin{gathered}\nA^i_{j,1} := \\int^{t_j}_{t_{j-1}} a^i_{t_{j-1}}(\\theta_{0,2}) d W^i_s, \\qquad \nA^i_{j,2} := \\int^{t_j}_{t_{j-1}} (a^i_s(\\theta_{0,2})-a^i_{t_{j-1}}(\\theta_{0,2})) d W^i_s,\\\\\nB^i_j := \\int^{t_j}_{t_{j-1}} (b^i_s(\\theta_{0,1})-b^i_{t_{j-1}}(\\theta_{0,1})) d s,\n\\end{gathered}\\label{eq: dynamics X}\n\\end{equation}\nare the same as in {\\eqref{def: BAnorm}, \\eqref{def: A12norm}}. \n\nFirstly, we will show that for any $p \\ge 2$,\n\\begin{equation}\\label{eq: bound R}\n\\mathbb{E} [|H^i_{j,2}|^p] \\le C \\Delta_n^p.\n\\end{equation}\nUsing Jensen's inequality and Lipschitz continuity of $b(\\theta_1,\\cdot)$ we get\n\\begin{align}\n\\mathbb{E} [|B^i_j|^p] & \\le \\mathbb{E} \\Big[ \\Delta_n^{p - 1} \\int_{t_{j-1}}^{t_j} |b^i_{s} (\\theta_{0,1})- b^i_{t_{j - 1}} (\\theta_{0,1})|^p d s \\Big] \\nonumber \\\\\n& \\le C \\Delta_n^{p - 1} \\int_{t_{j-1}}^{t_j} ( \\mathbb{E} [|X_s^i - X_{t_{j - 1}}^i|^p] + \\mathbb{E} [W_2^p(\\mu_s, \\mu_{t_{j - 1}})] ) ds \\nonumber \\\\\n& \\le C \\Delta_n^{p-1} \\int_{t_{j-1}}^{t_j} (s-t_{j-1})^{\\frac p 2} d s = C \\Delta_n^{\\frac{3}{2}p},\\label{ineq: bound Bp} \n\\end{align}\nwhere the last inequality follows from Lemma \\ref{l: moments}(2) and (4). \nFurther use of the Burkholder-Davis-Gundy and Jensen inequalities gives \n\\begin{align}\n\\mathbb{E} [ |A^i_{j,2}|^p] &\\le C \\mathbb{E} \\Big[ \\Big( \\int_{t_{j-1}}^{t_j} | a^i_s (\\theta_{0,2})-a^i_{t_{j-1}}(\\theta_{0,2}) |^2 d s \\Big)^{\\frac p 2} \\Big]\\nonumber\\\\ \n&\\le C \\Delta_n^{\\frac{p}{2}-1} \\int_{t_{j-1}}^{t_j} \\mathbb{E} [ | a^i_s (\\theta_{0,2})-a^i_{t_{j-1}}(\\theta_{0,2}) |^p ] d s\\nonumber\\\\\n&\\le C \\Delta_n^p,\\label{e: bound A2}\n\\end{align}\nwhere the last inequality follows from Lipschitz continuity of $a(\\theta_2,\\cdot)$ and Lemma~\\ref{l: moments}(2) and (4) as so does \\eqref{ineq: bound Bp}. Hence, we have\nshown \\eqref{eq: bound R}.\n\nNext, we have\n\\begin{equation}\\label{e: bound A1}\n\\mathbb{E} [|A^i_{j,1}|^p] = C \\Delta_{{n}}^{\\frac{p}{2}} \\mathbb{E} [ |a^i_{t_{j-1}} (\\theta_{0,2})|^p ] \\le C \\Delta_n^{\\frac p 2}\n\\end{equation} \nsince we know the absolute moments of a centered normal distribution and have linear growth of $a(\\theta_{0,2},\\cdot)$, moment bounds in Lemma \\ref{l: moments}(1). In particular, we note\n$$ \n\\mathbb{E}_{t_{j-1}}[(A^i_{j,1})^4] = 3 \\Delta_n^2 (c^2)^i_{t_{j-1}}(\\theta_{0,2}).\n$$\nFinally, we have\n\\begin{equation}\\label{eq:X4}\n\\mathbb{E}_{t_{j - 1}}[(X^i_{t_j} - X^i_{t_{j - 1}} - \\Delta_n b^i_{t_{j - 1}}(\\theta_{0,1}))^4]\n= 3 \\Delta_n^2 (c^2)^i_{t_{j-1}}(\\theta_{0,2}) + \\sum_{k = 0}^3 \\binom{4}{k} \\mathbb{E}_{t_{j - 1}} [ ( A^i_{j,1} )^k (H^i_{j,2})^{4-k} ].\n\\end{equation}\nFor any $k = 0,1,2,3$ and $q \\ge 1$, using Jensen's inequality for conditional expectation, we get\n\\begin{align*}\n\\mathbb{E} \\big[ \\big| \\mathbb{E}_{t_{j-1}} [ (A^i_{j,1})^k (H^i_{j,2})^{4-k} ] \\big|^q \\big] &\\le \\mathbb{E} \\big[ \\big| (A^i_{j,1})^k (H^i_{j,2})^{4-k} \\big|^q \\big] \n\\le C \\Delta_n^{(4-\\frac{k}{2})q},\n\\end{align*}\nwhere the last inequality follows from \\eqref{e: bound A2}, \\eqref{eq: bound R} using Cauchy-Schwarz inequality. Hence, the\nterm {converging to $0$ in $L^q$ at the slowest rate}\nis the one for which $k= 3$. \nWe therefore obtain that the remaining sum on the right hand side of \\eqref{eq:X4} is an $R^{{i}}_{t_{j - 1}}(\\Delta_n^{\\frac{5}{2}})$ function.\\\\\n\\\\\nProof of Lemma \\ref{l: conditional expectation}(3). This follows directly from \\eqref{ineq: bound Bp} by decomposing the dynamics of $X^i$ as in \\eqref{eq: dynamics X} and remarking that the stochastic integral is centered. \\\\ \\\\\nProof of Lemma \\ref{l: conditional expectation}(1). We decompose\n$X^i_{t_j} - X^i_{t_{j - 1}} - \\Delta_n b^i_{t_{j - 1}}(\\theta_{0,1})$ into \n$$\nA^i_j := A^i_{j,1} + A^i_{j,2} = \\int_{t_{j-1}}^{t_j} a^i_{s} (\\theta_{0,2}) dW^i_s,\n$$\nand $B^i_j$ satisfying respectively $\\mathbb{E} [|A^i_j|^{2p}] \\le C \\Delta_n^{p}$ and $\\mathbb{E} [|B^i_j|^{2p}] \\le C \\Delta_n^{3p}$, whence $\\mathbb{E} [ |A^i_j B^i_j|^p ] \\le C \\Delta_n^{2p}$ for any $p \\ge 1$, see \\eqref{ineq: bound Bp}-\\eqref{e: bound A1}. We conclude that\n\\begin{align*}\n\\mathbb{E}_{t_{j - 1}}[(X^i_{t_j} - X^i_{t_{j - 1}} - \\Delta_n b^i_{t_{j - 1}}(\\theta_{0,1}))^2]\n= \\int_{t_{j-1}}^{t_j} \\mathbb{E}_{t_{j - 1}}[c^i_{s} (\\theta_{0,2})] ds + R^{{i}}_{t_{j-1}}(\\Delta_n^2). \n\\end{align*}\nWe are left to show that we can replace $\\mathbb{E}_{t_{j - 1}}[c^i_{s} (\\theta_{0,2})]$ with $c^i_{t_{j-1}} (\\theta_{0,2})$ and that the remaining integral is an $R^{{i}}_{t_{j-1}}(\\Delta_n^2)$ function.\n\nUnder A7 we have that for any $i$, \n$$\n(x_1,\\dots,x_N) \\mapsto c \\Big(\\theta_{0,2}, x_i,\\frac{1}{N}\\sum_{j=1}^N \\delta_{x_j} \\Big) = {\\tilde a^2\n} \n\\Big( x_i, \\frac{1}{N} \\sum_{j=1}^N \n(x_i,x_j) \\Big) =: g^i(x_1,\\dots,x_N)\n$$\nis a\n{twice continuously differentiable} function\nfrom $\\mathbb{R}^N$ to $\\mathbb{R}$.\nGiven a vector \n$(X^1_s,\\dots,X^N_s)_{s \\in [0,T]}$ of processes, we denote\n$$\n(\\partial^l_{x_k} c)^i_s (\\theta_{0,2}) := \\partial^l_{x_k} g^i(X^1_s, \\dots, X^N_s).\n$$ \nWe apply the multidimensional It\\^o's formula to $g^i(X^1_s,\\dots,X^N_s) = c^i_s (\\theta_{0,2})$\nas follows: \n\\begin{align*}\nc^i_s(\\theta_{0,2}) - c^i_{t_{j-1}} (\\theta_{0,2}) \n= \\sum_{k=1}^N \\int_{t_{j-1}}^s \\Big( &(\\partial_{x_k} c)^i_u (\\theta_{0,2}) b^k_u (\\theta_{0,1}) + \\frac{1}{2} (\\partial^2_{x_k} c)^i_u (\\theta_{0,2}) c^k_u (\\theta_{0,2}) \\Big) d u\\\\\n+ \\sum_{k=1}^N \\int_{t_{j-1}}^s &(\\partial_{x_k} c)^i_u (\\theta_{0,2}) a^k_u (\\theta_{0,2}) d W^k_u.\n\\end{align*}\nSince the driving $(W^1_u, \\dots, W^N_u)_{u \\in[t_{j-1},s]}$ is independent of ${\\cal F}^{{N}}_{t_{j-1}}$, it follows that\n\\begin{align}\n\\mathbb{E}_{t_{j-1}} [&c^i_s(\\theta_{0,2})] - c^i_{t_{j-1}} (\\theta_{0,2}) \\nonumber\\\\ = \\mathbb{E}_{t_{j-1}} \\Big[ \\sum_{k=1}^N \\int_{t_{j-1}}^s \\Big( (\\partial_{x_k} &c)^i_u (\\theta_{0,2}) b^k_u (\\theta_{0,1})\n + \\frac{1}{2} (\\partial^2_{x_k} c)^i_u (\\theta_{0,2}) c^k_u (\\theta_{0,2}) \\Big) d u \\Big].\\label{eq:a2remainder}\n\\end{align}\nTo conclude, we need to bound {each} $(\\partial^l_{x_k} c)^i_u (\\theta_{0,2})$, $l=1,2$. To do that, we rely on the assumption about the dependence of the diffusion coefficient on the convolution with a probability measure gathered in \\ref{as7}.\nTo compute the derivatives with respect to $x_k$ we need to consider two different cases, depending on whether $k \\neq i$ or $k = i$. When $k \\neq i$ we have $(\\partial_{x_k} c)^i_u (\\theta_{0,2}) = 2\na^i_u (\\theta_{0,2}) (\\partial_{x_k} a)^i_u (\\theta_{0,2})$, where\n\\begin{equation}\\label{eq:partialaki}\n(\\partial_{x_k} a)^i_u (\\theta_{0,2}) := \\partial_y \\tilde \n\\Big( X^i_u, \\frac{1}{N} \\sum_{j=1}^N \n(X^i_u, X^j_u) \\Big) \\frac{1}{N} \\partial_y \n(X^i_u,X^k_u),\n\\end{equation}\nwhile for $k = i$ we have $(\\partial_{x_i} c)^i_u (\\theta_{0,2}) = 2 a^i_u (\\theta_{0,2}) (\\partial_{x_i} a)^i_u (\\theta_{0,2})$, where\n\\begin{align*}\n(\\partial_{x_i} a)^i_u (\\theta_{0,2}) := \\partial_x \\tilde \n\\Big( X^i_u, \\frac{1}{N} \\sum_{j=1}^N &\n(X^i_u, X^j_u) \\Big) + \\partial_y \\tilde \n\\Big( X^i_u, \\frac{1}{N} \\sum_{j=1}^N \n(X^i_u, X^j_u) \\Big)\\\\\n\\times \\Big( \\frac{1}{N} \\sum_{j=1}^N \\partial_x &\n(X^i_u,X^j_u) + \\frac{1}{N} \\partial_y \n(X^i_u,X^i_u) \\Big).\n\\end{align*}\nFrom polynomial growth of the $l$-th order partial derivatives of\n$K, \\tilde a$ for $l = 0, 1$, that of $b(\\theta_{0,1},\\cdot)$, moment bounds in Lemma~\\ref{l: moments}{(1)} applying Jensen's inequality it follows that \n$\\sum_{k=1}^N (\\partial_{x_k} c)^i_u (\\theta_{0,2}) b^k_u (\\theta_{0,1})$ is bounded in $L^p$ for any $p\\ge 1$ uniformly in $u,i$. We proceed similarly to compute $(\\partial_{x_k}^2 c)^i_u(\\theta_{0,2})$. Then from polynomial growth of the $l$-th order partial derivatives of\n$K, \\tilde a$ for $l=0,1,2$, moment bounds in Lemma~\\ref{l: moments}(1) applying Jensen's inequality it follows that\n$\\sum_{{k}=1}^N (\\partial^2_{x_k} c)^i_u (\\theta_{0,2}) c^k_u (\\theta_{0,2})$ is bounded in $L^p$ for any $p \\ge 1$ uniformly in $u,i$.\nFor any $p \\ge 1$, $t_{j-1} \\le s \\le t_j$, repeatedly applying Jensen's inequality to \\eqref{eq:a2remainder} we get\n$$\n\\mathbb{E} \\big[ \\big| \\mathbb{E}_{t_{j-1}} [c^i_s (\\theta_{0,2})] - c^i_{t_{j-1}}(\\theta_{0,2}) \\big|^p \\big] \\le C (s-t_{j-1})^p,\n$$\nwhence \n$$\n\\mathbb{E} \\Big[ \\Big| \\int_{t_{j-1}}^{t_j} \\big( \\mathbb{E}_{t_{j-1}} [c^i_s (\\theta_{0,2})] - c^i_{t_{j-1}} (\\theta_{0,2}) \\big) d s \\Big|^p \\Big] \\le C \\Delta_n^{2p}.\n$$\nwhich completes the proof.\n\\end{proof}\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction} \nA standard approach in noise time series simulation is based on Carson's theorem \\cite{carson31}. In fact, the superposition of randomly delayed pulses of a definite shape $f(t)$ with arbitrary coefficients a$_k$ gives rise to a pulse train \n\\begin{equation}\nn(t)=\\sum_{k} a_k f(t-t_k)\n\\label{series}\n\\end{equation}\nwhose power spectrum $N(\\omega)$ can be expressed in terms of the $f(t)$ power spectrum $P_f(\\omega)$ according to\n\\begin{equation}\nN(\\omega)=\\alpha P_f(\\omega) = \\alpha {|\\widehat F(\\omega)|^2}\n\\label{carson_law}\n\\end{equation}\nwhere $\\alpha$ is a proper normalizing constant and $\\widehat F(\\omega)$ is the Fourier transform of f(t). The delays $t_{k}$'s are distributed according to Poisson statistics and $f(t)$ is usually required to have a zero mean value so that $n(t)$ averages to zero. It can be shown that this result is independent of the density function used for the random amplitudes $a_k$, if the variance of such distribution is fixed by the choice of the normalizing constant $\\alpha$.\\\\\n\nThe basic idea of the method consists then in searching a proper expression for $f(t)$ so that the generated time series $n(t)$ have the desired power spectrum $N(\\omega)$. In other terms one is faced with the problem of inverting eq.~(\\ref{carson_law}) to obtain the expression of $f(t)$ given $P_f(\\omega)$. Then, $n(t)$ can be built iteratively according to eq.~(\\ref{series}) with a proper choice of the random parameters $a_k$ and $t_k$.\\\\\n\nThis approach can be quite difficult whenever the desired spectrum is different from a simple power law and analytical approaches to the determination of $f(t)$ are usually not straight-forward. In fact, the desired noise spectrum often shows complex behaviours in the measured frequency range. An example of such a common situation is shown in fig.~\\ref{menotre_spectra} (referring to the output of a few hundred microgram bolometric detector) where we have a complex forest of microphonic lines overimposed to a smoothly varying distribution. The anti-aliasing filter cut frequency is also apparent by the quick drop of the power spectrum in the 100-200 Hz region.\\\\\n\nThe method proposed here consists in the determination of an $f(t)$ which exactly matches a given noise power spectrum. This is essentially based on the choice of a particular (out of an infinite number of possibilities) function $f(t)$ which satisfies eq.~(\\ref{carson_law}) and can be applied both to the case in which $N(\\omega)$ is known analytically or experimentally measured.\\\\\n\nLet us begin with the discrete case of a sampled sequence $f[k]$. If we consider a finite number L of samples and assume ergodicity, then the power spectrum can be approximated by an ensemble average over a sufficiently large number of finite sequences $f_L[k]$ of fixed length L according to\n\\begin{equation}\nP_f[k] \\simeq <|\\mathcal{F}(f_{L}[k])|^2>\n\\label{powerspectrum}\n\\end{equation}\nwhere $\\mathcal{F}$ stands for the Discrete Fourier Transform (DFT) operator.\nSince the sequence elements $f_L[k]$ are supposed to be real, the corresponding power spectrum is a real even function and only half of its values are independent. In fact all the informations concerning the phases are lost in the quadrature and this is the reason why the inversion of eq.~(\\ref{powerspectrum}) does not admit a unique solution $f_L[k]$.\\\\ \nThe simplest solution to eq.~(\\ref{powerspectrum}) is obtained by deleting the ensemble average and inverting the resulting expression. Phases can be then randomly added assuming a flat distribution. The result represents the core of this work and can be summarized as follows:\n\\begin{equation}\nF[k] \\equiv \\sqrt{N[k]}\\cdot e^{i\\theta_k}\n\\label{met01}\n\\end{equation}\n\\begin{equation}\nf[k]=\\mathcal{F}^{-1}(F[k])\n\\label{met02}\n\\end{equation}\n\nwhere $N[k]$ is the power spectrum according to which we want to generate our time series (known a priori) and $\\theta_k$ are random numbers uniformely distributed between $0$ and $2\\pi$. In order to guarantee the condition of reality of $f[k]$ upon inverse DFT the sYmmetry constraint $F[k]=F^*[-k]$ must be imposed.\\\\\n\nNoise time series can now be built following eq.~(\\ref{series}), simply generating the delays $t_k$ according a Poisson distribution:\n\\begin{equation}\nt_k = t_{k-1} -\\ln(1-R)\/\\lambda\n\\label{delays}\n\\end{equation}\nwhere R is a random number with a uniform distribution and $\\lambda$, which represents the overlap rate of the signals $f[k]$, is the only free parameter of the method and is usually adjusted to improve the quality of the simulated time series.\\\\ \n\nAs stated above, the choice of the normalization term $\\alpha$ partially fixes the arbitraryness in the choice of the amplitudes density function by setting its variance. This can be better understood by following some of the steps in the derivation of the Carson's theorem. Actually, by substituting eq.~(\\ref{series}) into the the power spectrum definition we obtain: \n\\begin{eqnarray}\nN(\\omega) = \\left< \\left| \\mathcal{F} \\left[\\sum_{k} a_k f(t-t_k)\\right] \\right|^2 \\right> = \\nonumber \\\\\n=\\left< \\left| F(\\omega) \\right|^2 \\cdot \\sum_{ij} a_i a_j e^{i\\omega(t_j-t_i)} \\right>\n\\label{car01}\n\\end{eqnarray}\nThe last sum can then be separated into two different terms, one for $i=j$, the other for $i\\neq j$. It can be shown that the latter is proportional to $F(0)$ and therefore vanishes since we required $f(t)$ to average to zero. We obtain therefore:\\\\\n\\begin{equation}\nN(\\omega) = \\left< \\left| F(\\omega)\\right|^2\\right> \\left<\\sum_{k=1}^{L} |a_k|^2\\right>\n\\label{car02}\n\\end{equation}\n\n\\begin{figure}[htb]\n\\includegraphics[width=.4\\textwidth]{noisemenotre-comp.eps}\n\\caption{Superposition between a simulated (black) and measured (red) bolometric noise power spectrum obtained with a bolometric detector of O(mg) mass~\\cite{ale03}. The simulated spectrum was obtained averaging 50 finite time series. Error bars are the average standard deviations of the simulated spectra. The distribution of the $\\chi^2$\/d.o.f. for a set of simulated spectra is shown in the inset.}\n\\label{menotre_spectra}\n\\end{figure}\n\n\\begin{figure}[htb]\n\\includegraphics[width=.4\\textwidth]{noisefet-comp.eps}\n\\caption{Superposition between a simulated (black) and measured (red) jfet noise power spectrum. The simulated spectrum was obtained averaging 50 finite time series. Error bars are the average standard deviations of the simulated spectra. The distribution of the $\\chi^2$\/d.o.f. for a set of simulated spectra is shown in the inset.}\n\\label{fet_spectra}\n\\end{figure}\n\n\\begin{figure}[htb]\n\\includegraphics[width=.4\\textwidth]{noisemenotre-ratio.eps}\n\\caption{Ratio between the simulated and measured bolometric noise power spectra shown in Fig.\\ref{menotre_spectra}.} \n\\label{bolo_ratio}\n\\end{figure}\n\n\\begin{figure}[htb]\n\\includegraphics[width=.4\\textwidth]{noisefet-time-real2.eps}\n\\includegraphics[width=.4\\textwidth]{noisefet-time-sim.eps}\n\\caption{Samples of simulated (top) and a real (bottom) noise time series of a jfet}\n\\label{timedomain}\n\\end{figure}\n\n\\begin{figure}[htb]\n\\includegraphics[width=.4\\textwidth]{noisefet-ratio2.eps}\n\\caption{Ratio between a simulated and measured jfet noise power spectra shown in Fig.\\ref{fet_spectra}.}\n\\label{fet_ratio}\n\\end{figure}\n\n\\begin{figure}[htb]\n\\includegraphics[width=.4\\textwidth]{sqrt.eps}\n\\caption{Average normalized standard deviation (dots) of the simulated power spectra as a function of the number of simulated time series used for the average process. The black line joins simulated points while the red one is an inverse square root law shown for reference.}\n\\label{chisquare}\n\\end{figure}\n\nThe averaged sum on the right hand part can be thought as the average of the squared amplitude of each single pulse times their average number $M$ of occurencies. As a result we can express the average power spectrum as: \\\\\n\\begin{equation}\nN(\\omega) = \\left< \\left| F(\\omega)\\right|^2\\right> M<\\left|a\\right|^2>\n\\label{nps_fin}\n\\end{equation}\n\nBy comparing eq.~(\\ref{nps_fin}) and eq.~(\\ref{carson_law}) we can finally obtain the relation between the average rate $\\lambda$, the normalization constant $\\alpha$ and the amplitudes variance $|a|^2$ :\n\\begin{equation}\n\\left|a\\right|^2=\\frac {\\alpha} {M}=\\frac{\\alpha}{\\lambda T}\n\\label{rate}\n\\end{equation}\nwhere $T$ is the finite length of the time series. Any arbitrary distribution with a variance given by eq.~(\\ref{rate}) can now be used to generate the random amplitudes $a_k$ and the method is fully defined.\\par\nWe can therefore summarize the most relevant steps for the method implementation as follows:\n\\\\\\\\\\\\\n\\begin{itemize}\n\\item Given the desired noise power spectrum $N(\\omega)$ select the basis function f(t) according to eqs.~(\\ref{met01}) and (\\ref{met02}). Then eq.~(\\ref{carson_law}) fixes the constant $\\alpha$ \n\\item Generate a set of increasing delays $t_k$ according to a Poisson distribution with average rate $\\lambda$. \n\\item Generate the random amplitudes $a_k$ according to an arbitrary distribution with variance fixed by eq.(\\ref{rate}). \n\\item Shift $f(t)$ by $t_k$ imposing a periodicity constraint $f(t)=f(t+T)$ and multiply the result by $a_k$.\n\\item Sum iteratively over $t_k$ while $\\sum t_k1$ and $k=|E(G)|-|V (G)|$.\n\t\n\\end{conjecture}\n\nWe prove the following theorem about homology of $\\Delta^{G}_{U}$ settling the conjecture in the case of $|{U}|>1$ and for $U= \\emptyset$ we provide a counterexample at the end of this section. \n\n\\begin{theorem} \\label{maintheorem}\nFor $|U|>1$, $\\tilde{H}_{k}(\\Delta^{G}_{U};\\mathbb{R}) \\cong 0$ unless $k=|E(G)|-|V(G)|$. \n\n\\end{theorem}\n\n\nOur approach to prove Theorem~\\ref{maintheorem} is inspired by Grinberg's proof of Elser's conjecture for the case $k=1$ (i.e., $\\mathrm{els}_1(G)=0$) using discrete Morse theory \\cite{Grinberg}. For $F \\subseteq E(G)$, an $F$-path is a path consisting of edges from $F$. Now given a vertex $v\\in V(G)$, we define $\\mathrm{Shade}_v (F)$ as follows. \n\n\\[ \\mathrm{Shade}_v (F):=\\{ e \\in E(G) : \\text{ There is a } F \\text{-path from an endpoint of } e \\text { to } v \\}. \\]\n\nWe consider the following abstract simplicial complex as defined in \\cite{Grinberg} \n\n\\[ \\mathcal{A}_U = \\{ F \\subseteq E(G) : \\mathrm{Shade}_v(F) \\subsetneq E(G) \\text{ for some } v \\in U \\}. \\]\n\n\nIt can be shown that $\\mathcal{A}_U $ is the Alexander dual of $\\Delta^{G}_{U}$. Grinberg showed that $\\mathcal{A}_U $ is collapsible when $|U|=1$ by producing an acyclic matching (also known as gradient vector field) with no unmatched simplices. By Alexander duality, it follows that in the case of $|U|=1$, $\\tilde{H}_i(\\Delta^{G}_{U}) \\cong \\tilde{H}^{|E(G)|-i-3}(\\mathcal{A}_U) \\cong \\tilde{H}_{|E(G)|-i-3}(\\mathcal{A}_U) \\cong 0 $. Therefore, $\\Delta^{G}_{U}$ has homology of a point. \\\\\n\n\nWe observe that $\\mathcal{A}_U = \\cup_{x \\in U} \\mathcal{A}_x$. We extend Grinberg's matching to $\\mathcal{A}_U $ when $|U|>1$ and show that $\\mathcal{A}_U $ can be given an acyclic matching with unmatched simplices in the dimension $k=|V(G)|-3$. Again by Alexander's duality we conclude that, $\\Delta^{G}_{U}$ has homology concentrated in a single homological degree $|E(G)|-|V(G)|$.\\\\\n\nHowever, we note that the Conjecture~\\ref{conj} as stated is not true for the other case $U= \\emptyset$. For example, for any cycle $C_n$ with $n \\ge 3$, we note that the connected vertex covers of $C_n$ are (i) any path of length $n-2$ (i.e., a path with $n-2$ edges), (ii) any path of length $n-1$, and (iii) $C_n$ itself. Then it can be verified that the nuclei complex, $\\Delta^{C_n}_{\\emptyset}$ is homotopy equivalent to the circle and thus, $\\tilde{H}_{1}(\\Delta^{C_n}_{\\emptyset};\\mathbb{R}) \\neq 0$.\\\\\n\n\n\n\n\\textbf{Acknowledgments} The authors would like to thank Goutam Mukherjee for helpful discussions.\n\n\n\n\\section{Preliminaries}\n\n\n\\subsection{Graph theoretic notation}\n\n\\begin{itemize}\n\n\\item A (simple undirected) graph $G$ is an ordered pair $(V(G), E(G))$, where $V(G)$ is a finite set and $E(G) \\subseteq \\{ S \\subseteq V(G): \\ |S|=2 \\}$. Elements of $V(G)$ are called vertices and elements of $E(G)$ are called edges. \\\\\n\n\\item We call a vertex $y$ a \\emph{neighbor} of $x$ in $G$ if $x$ and $y$ are adjacent, i.e., $\\{ x,y \\} \\in E(G)$. \\\\\n\n\\item A \\emph{leaf} in $G$ is a vertex with only one neighbor.\\\\\n\n\\item A \\emph{leaf-edge} denotes an edge which is incident to (i.e., contains) a leaf.\\\\\n\n\\item For a connected graph $G$, an edge $e$ is called a \\emph{bridge} if the graph obtained after deleting $e$ is disconnected.\\\\\n\n\\item \\emph{Edge-induced subgraph} : Let $\\sigma \\subseteq E(G)$. We define the subgraph induced by $\\sigma$, denoted by $G_\\sigma$ as follows\n \\[ V(G_{\\sigma})= \\{ x \\in V(G) : x \\in e \\text{ for some } e \\in \\sigma \\} \\text{ and }\nE(G_{\\sigma})= \\sigma.\\]\n\nWe use $V_{\\sigma}$ to denote $V(G_{\\sigma})$ for brevity.\\\\\n\n\\item \\emph{Vertex cover (of edges)} of $G$: $X \\subseteq V(G) \\text{ such that for all } e \\in E(G), e\\cap X \\neq \\emptyset$.\n\n\n\\end{itemize}\n\n\\subsection{Basics of discrete Morse Theory}\n\nIn the case of smooth Morse theory, existence of a Morse function is equivalent to the existence of a gradient vector field. Similarly, existence of a \\emph{discrete Morse function} on a simplicial complex is equivalent to the existence of a \\emph{discrete gradient vector field}. First we recall the notion of a \\emph{discrete vector field} or \\emph{matching} on a simplicial complex. Let $K$ be a simplicial complex. \n\n\\begin{definition}[Discrete vector field \/ matching]\n\t\n\tA discrete vector field $V$ on $K$ is a collection of pairs $\\{\\alpha^{(p)} < \\beta^{(p+1)}\\}$ of simplices of $K$ such that each simplex is in at most one pair of $V$.\n\t\n\\end{definition} \n\nPictorially, given a discrete vector field on $K$, we can assign arrows on $K$ such that for a pair $\\{\\alpha^{(p)} < \\beta^{(p+1)}\\}$ the tail of the arrow lies in $\\beta^{(p+1)}$ and the head of the arrow lies in $\\alpha^{(p)}$. A gradient vector field is a discrete vector field with some special properties about this arrows. \n\n\n\\begin{definition}[Gradient vector field]\n\tA discrete vector field is called a gradient vector field if given any simplex $\\alpha$ in $K$, it satisfies exactly one the following.\n\t\n\t\n\t\\begin{enumerate}\n\t\t\\item $\\alpha$ is the tail of exactly one arrow.\n\t\t\n\t\t\\item $\\alpha$ is the head of exactly one arrow.\n\t\t\n\t\t\\item $\\alpha$ is neither the head nor the tail of an arrow.\n\t\\end{enumerate}\n\n\\end{definition}\n\n\n\n\\begin{figure}[htbp] \n\t\\centering\n\t\\def12cm{8cm}\n\t\\input{gradientfield.pdf_tex}\n\t\\caption{An example of a discrete gradient vector field.}\\label{gradfield}\n\\end{figure}\n\n\nAccording to this notion of discrete Morse functions in terms of discrete gradient fields, a simplex is \\emph{critical} if and only if it is neither the tail nor the head of any arrow. For instance, in Figure \\ref{gradfield}, $e_2$ is a critical $1$-simplex while $e_1$ is not critical. The criteria when a discrete vector field is a gradient vector field is more straight forward once we have the notion of a \\emph{$V$-path}.\n\n\n\\begin{definition}\n\tGiven a discrete vector field $V$ on a simplicial complex $K$, a $V$-path is a sequence of simplices $$\\alpha_0^{(p)}, \\beta_0^{(p+1)}, \\alpha_1^{(p)}, \\beta_1^{(p+1)},\\cdots,\\beta_r^{(p+1)}, \\alpha_{r+1}^{(p)}$$ such that for each $i \\in \\{0,1, \\cdots, r\\}$, $\\{\\alpha_i < \\beta_i \\} \\in V$ an $\\beta_i > \\alpha_{i+1} \\neq \\alpha_i$.\n\n\t \n\\end{definition}\n\n\\noindent \n\nWe say a path is a non-trivial \\emph{closed path} if $r \\geq 0$ and $\\alpha_0 = \\alpha_{r+1}$. It can be shown that a gradient vector field can not have a non-trivial closed $V$-path (i.e., a cycle). Moreover, the other direction is also true.\n\n\\begin{theorem}[Forman \\cite{Forman}] \\label{v-path criteria}\n\tA discrete vector field $V$ is the gradient vector field of a discrete Morse function if and only if there are no non-trivial closed $V$-path.\n\\end{theorem} \n\nIn other words we say a discrete vector field $V$ is gradient vector field if the corresponding matching is acyclic (i.e., it contains no closed $V$-path).\\\\\n \n\\noindent The notion of gradient vector field is closely related to the notion of \\emph{simplicial collapse}. A simplex $\\alpha$ is called a face of a simplex $\\beta$ if $\\alpha \\subsetneq \\beta$. A simplex which is a face of exactly one simplex is called a \\emph{free face}. For example, in Figure \\ref{gradfield}, $v_1$, $e_1$ and $e_2$ are free faces while $v_2$ is not. Whenever we have a free face $\\alpha$ of a simplex $\\beta$ in a simplicial complex $K$, we can remove $\\alpha$ and $\\beta$ (but keeping faces of $\\beta$ other than $\\alpha$ unperturbed) from $K$ by a deformation retraction. This is known as \\emph{elementary collapse}. If $K \\setminus \\{ \\alpha, \\beta\\}$ is obtained from $K$ by a elementary collapse, we can extend any gradient vector field of $V$ of $K \\setminus \\{ \\alpha, \\beta\\}$ to $K$ by adding the pair $(\\alpha,\\beta) $ to $V$.\n\n\n\n\\begin{figure}\n\t\\def12cm{12cm}\n\t\\input{collapse.pdf_tex}\n\t\\caption{A sequence of elementary collapses.} \\label{collapse}\n\\end{figure}\n\nIf one can go from a simplicial complex $K_1$ to another simplicial complex $K_2$ via a sequence of elementary collapses, then we say $K_1$ collapses to $K_2$. If a gradient vector field on a simplicial complex $K$ does not have any critical faces of dimension greater than $0$, then $K$ collapses to a point. We also say $K$ is collapsible. \\\\\n\nNow, we state the fundamental theorem of discrete Morse theory.\n\n\n\n\\begin{theorem}[Forman \\cite{Forman}] \\label{forth}\nSuppose $K$ is a simplicial complex with a discrete Morse function. Then $K$ is homotopy equivalent to a CW complex with exactly one cell of dimension $p$ for each critical simplex of dimension $p$.\n\\end{theorem}\n\n\n\nWe also need the following crucial fact from discrete Morse theory which follows from the theorem above.\n\n\\begin{theorem}[Sphere theorem, \\cite{J}] If $K$ has only critical faces of dimension $d$ ($\\geq 1$), then $K$ is homotopy equivalent to a wedge of $d$-spheres. \n\t\n\\end{theorem}\n\n\n\\subsection{Collapsibility of $\\mathcal{A}_x$}\n\nWe consider the case $U= \\{x \\}$. Grinberg produced an acyclic matching on $\\mathcal{A}_x$ with no critical simplices (here we adopt the convention that if only one $0$-simplex is unpaired in some matching, we are allowed to pair it with $\\emptyset$). Then, we can conclude by Forman's theorem \\cite{Forman} that $\\mathcal{A}_x$ is collapsible.\\\\\n\nWe briefly explain the matching $\\Phi_0$ constructed in Grinberg's proof. We fix an arbitrary ordering in $E(G)$. For any set $F \\in \\mathcal{A}_x $ define, $\\sigma(F)$ to be the minimum edge in $E(G) \\setminus \\mathrm{Shade}_x(F)$ (this set is non-empty by definition). If $ \\sigma(F) \\notin F$, we pair off $F$ with $F \\cup \\sigma(F)$. This gives an acyclic matching in $\\mathcal{A}_x $ with no critical simplices \\cite{Grinberg}. Thus we have the following theorem. \n\n\\begin{theorem} \\cite{Grinberg}\nFor any $x\\in V(G)$, $\\mathcal{A}_x$ is collapsible.\n\\end{theorem}\n\n\n\n \n\n\\section{Proof of the main theorem}\n\n\n\\begin{proof}[Proof of Theorem \\ref{maintheorem}]\n\nLet $E(G)=\\{ e_1, e_2,\\cdots, e_m \\}$. We fix the ordering $ e_1 < e_2 < \\cdots < e_m $ on $E(G)$. The proof is by induction. For the base case, let $U=\\{x,y\\}.$ Clearly, $\\mathcal{A}_U = \\mathcal{A}_x \\cup \\mathcal{A}_y.$ We consider the acyclic matching $\\Phi_0$ on $\\mathcal{A}_x$ with no critical simplices as mentioned before. Our aim is to extend this matching $\\Phi_0$ to the whole simplicial complex $\\mathcal{A}_U.$ In order to achieve our goal we proceed recursively. We divide this recursive process into several steps. Note that we want to define an acyclic matching on the simplices of $\\mathcal{A}_y \\setminus\\mathcal{A}_x.$ First, we make the following observation to characterize the simplices in $\\mathcal{A}_y \\setminus\\mathcal{A}_x$. \n\n\\begin{obs} \n Let $\\sigma \\subseteq E(G)$ and $\\sigma \\neq \\emptyset$. Then, $ \\sigma \\in \\mathcal{A}_y \\setminus \\mathcal{A}_x$ if and only if the following conditions hold \n \\begin{enumerate}\n \\item $G_\\sigma$ is a connected subgraph of $G$,\n \\item $V_\\sigma$ is a vertex cover of $G$,\n \\item $x \\in V_\\sigma$ and $y \\notin V_\\sigma$.\n \n \\end{enumerate}\nAlso $\\sigma= \\emptyset \\in \\mathcal{A}_y \\setminus\\mathcal{A}_x$ only if $G$ is a star graph with $x$ as the central vertex.\n\\end{obs}\n\n\nNow, we proceed to our initial step of the recursive process. \n\n\n\n \\textbf{Step 1:} Consider those simplices $\\sigma$ in $\\mathcal{A}_y \\setminus\\mathcal{A}_x,$ such that, $e_1 \\in \\sigma$ and one of the following holds. \n \\begin{enumerate}\n \\item $e_1$ is a part of a cycle (i.e., not a bridge) in $G_\\sigma$ (see Fig. \\ref{sigma1}).\n \\item $e_1$ is a leaf-edge in $G_\\sigma$ such that \n \\begin{enumerate}\n \\item $e_1$ doesn't contain $x$ \\emph{as a leaf} in $G_\\sigma$ \\\\ and\n \\item If $z \\in V(G) \\setminus V_\\sigma$, then $e_1$ doesn't contain any neighbor of $z$ \\emph{as a leaf} in $G_\\sigma$ (see Fig. \\ref{sigma2}). \n\\end{enumerate} \n\\end{enumerate} \n\n\n\n\\begin{figure}[!ht]\n\\begin{minipage}{0.45\\textwidth}\n\\[\\begin{tikzpicture}[scale=1.5]\n\t\\node at (-0.3,0) {$x$};\n\t\\node at (2.3,0) {$y$};\n\t\n\t\\node at (0.5,0.2) {$e_1$};\n\t\\node at (1.2,-0.5) {$e_2$};\n\t\\node at (0.2,-0.5) {$e_3$};\n\t\n\t\\node[vertex] (x) at (0,0) {};\n\t\\node[vertex] (w) at (1,0) {};\n\t\\node[vertex] (z) at (1,-1) {};\n\t\n\t\\node[redvert] (y) at (2,0) {};\n\n\t\\path [line width=1pt]\n\t\t(x) edge (w)\n\t\t(x) edge (z)\n\t\t(w) edge (z)\n\t ; \n\n\t\\path [red,dashed,line width=1pt]\n\t\t(w) edge (y)\n\t\t(z) edge (y)\n\t; \n\n\\end{tikzpicture}\\]\n\\caption{ $\\sigma = \\{e_1,e_2,e_3\\}$; $e_1$ is a part of a cycle in $G_\\sigma$.} \\label{sigma1}\n\\end{minipage}\n\\hfill\n\\begin{minipage}{0.45\\textwidth}\n\t\\[\\begin{tikzpicture}[scale=1.5]\n\t\\node at (-0.3,0) {$x$};\n\t\\node at (1.3,1) {$y$};\n\t\\node at (1.3,-1) {$z$};\n\t\n\t\\node at (1.5,0.2) {$e_1$};\n\t\\node at (0.5,0.2) {$e_2$};\n\t\n\t\\node[vertex] (x) at (0,0) {};\n\t\\node[vertex] (w1) at (1,0) {};\n\t\\node[vertex] (w2) at (2,0) {};\n\t\n\t\\node[redvert] (y) at (1,) {};\n\t\\node[redvert] (z) at (1,-1) {};\n\t\n\t\\path [line width=1pt]\n\t(x) edge (w1)\n\t(w2) edge (w1)\n\t; \n\t\n\t\\path [red,dashed,line width=1pt]\n\t(x) edge (y)\n\t(x) edge (z)\n\t(w1) edge (y)\n\t(w1) edge (z)\n\t; \n\t\n\t\n\t\\end{tikzpicture}\\]\n\t\\caption{ $\\sigma = \\{e_1, e_2\\}$; $e_1$ is a leaf-edge in $G_\\sigma$ and $e_1$ doesn't contain any neighbor of $y$ or $z$ as a leaf.} \\label{sigma2}\n\t\\end{minipage}\n\n\n\\end{figure}\n\n\n\n\n\n\nWe note that in this case $\\sigma \\setminus \\{ e_1 \\} \\in \\mathcal{A}_y \\setminus \\mathcal{A}_x.$ We pair off $\\sigma$ with $\\sigma \\setminus \\{e_1\\}.$ We mention an exceptional pairing in the special case of $ \\{e_1 \\} \\in \\mathcal{A}_y \\setminus \\mathcal{A}_x$ as well as $\\emptyset \\in \\mathcal{A}_y \\setminus \\mathcal{A}_x$ ( as mentioned before possible only if $G$ is a star graph with $x$ as the central vertex). In that case we pair off $ \\{e_1 \\}$ with $\\emptyset$.\\\\ \n\nWe observe that conversely for any $\\sigma \\in \\mathcal{A}_y \\setminus \\mathcal{A}_x$ with $e_1 \\notin \\sigma$ is uniquely paired with $\\sigma \\cup \\{ e_1 \\}$ whenever $\\sigma \\cup \\{ e_1 \\} \\in \\mathcal{A}_y \\setminus \\mathcal{A}_x$. Therefore, this extension of $\\Phi_0$ is well defined and is denoted by $\\Phi_1.$\n\n\n\n\n\n \n\\textbf{Step 2:} The set of all unpaired simplices after step $1$ is denoted by $\\mathcal{C}_1.$ Note that $\\mathcal{C}_1$ consists of simplices $\\tau$ in $\\mathcal{A}_y \\setminus\\mathcal{A}_x,$ such that, either $e_1 \\notin \\tau$ (possible only if $y \\in e_1$) or $e_1 \\in \\tau$ and one of the following holds.\n\n \\begin{enumerate}\n \\item $e_1$ is a bridge but not a leaf-edge in $G_\\tau$ (see Fig. \\ref{sigma3}).\n \\item $e_1$ is a leaf-edge in $G_\\tau$ such that,\n \\begin{enumerate}\n \\item $e_1$ contains $x$ as a leaf in $G_\\tau$ (see Fig. \\ref{sigma4}, \\ref{sigma5})\\\\ or \n \\item there exists $z \\in V(G) \\setminus V_\\tau$ such that $e_1$ contains a neighbor of $z$ as a leaf in $G_\\tau$ (see Fig. \\ref{sigma5}, \\ref{sigma6}). \n\n\\end{enumerate}\n\\end{enumerate} \n\n\n\\begin{figure}[!ht]\n\n\\begin{minipage}{0.45\\textwidth}\n\t\\[\\begin{tikzpicture}[scale=1.5]\n\t\\node at (-0.3,0) {$x$};\n\t\\node at (4.3,0) {$y$};\n\t\n\t\\node at (0.5,0.2) {$e_2$};\n\t\\node at (1.2,-0.5) {$e_5$};\n\t\\node at (0.2,-0.5) {$e_4$};\n\t\\node at (2,-0.5) {$e_1$};\n\t\\node at (2.7,-0.5) {$e_3$};\n\t\n\t\\node[vertex] (x) at (0,0) {};\n\t\\node[vertex] (w1) at (1,0) {};\n\t\\node[vertex] (w2) at (2.5,-1) {};\n\t\\node[vertex] (w3) at (2.5,0) {};\t\n\t\\node[vertex] (z) at (1,-1) {};\n\t\n\t\\node[redvert] (y) at (3.5,0) {};\n\t\n\t\\path [line width=1pt]\n\t(x) edge (w)\n\t(x) edge (z)\n\t(w1) edge (z)\n\t(w1) edge (w2)\n\t(w3) edge (w2)\n\t; \n\t\n\t\\path [red,dashed,line width=1pt]\n\t(w3) edge (y)\n\t(w2) edge (z)\n\t(w3) edge (w1)\n\t; \n\t\n\t\n\t\\end{tikzpicture}\\]\n\t\\caption{ $\\sigma = \\{e_1,\\ldots,e_5\\}$; $e_1$ is a bridge in $G_\\sigma$, but not a leaf-edge.}\\label{sigma3}\n\\end{minipage}\n\\hfill\n\\begin{minipage}{0.45\\textwidth}\n\t\t\\[\\begin{tikzpicture}[scale=1.5]\n\t\\node at (-0.3,0) {$x$};\n\t\\node at (2.3,0) {$y$};\n\t\n\t\\node at (0.5,0.2) {$e_1$};\n\t\\node at (1.2,-0.5) {$e_2$};\n\t\n\t\\node[vertex] (x) at (0,0) {};\n\t\\node[vertex] (w) at (1,0) {};\n\t\\node[vertex] (z) at (1,-1) {};\n\t\n\t\\node[redvert] (y) at (2,0) {};\n\t\n\t\\path [line width=1pt]\n\t(x) edge (w)\n\t(w) edge (z)\n\t; \n\t\n\t\\path [red,dashed,line width=1pt]\n\t(w) edge (y)\n\t(x) edge (z)\n\t; \n\t\n\t\n\t\\end{tikzpicture}\\]\n\t\\caption{ $\\sigma = \\{e_1,e_2\\}$; $e_1$ is a leaf-edge in $G_\\sigma$ and $e_1$ contains $x$ as a leaf in $G_\\sigma$.}\\label{sigma4}\n\t\\end{minipage}\n\t\n\\end{figure}\n\n\n\\begin{figure}[!ht]\n\t\n\\begin{minipage}{0.45\\textwidth}\n\t\\[\\begin{tikzpicture}[scale=1.5]\n\t\\node at (-0.3,0) {$x$};\n\t\\node at (2.3,0) {$y$};\n\t\\node at (1.3,-1) {$z$};\n\t\n\t\\node at (0.5,0.2) {$e_1$};\n\t\n\t\\node[vertex] (x) at (0,0) {};\n\t\\node[vertex] (w) at (1,0) {};\n\t\n\t\\node[redvert] (y) at (2,0) {};\n\t\\node[redvert] (z) at (1,-1) {};\n\t\n\t\\path [line width=1pt]\n\t(x) edge (w)\n\t; \n\t\n\t\\path [red,dashed,line width=1pt]\n\t(w) edge (y)\n\t(x) edge (z)\n\t(w) edge (z)\n\t; \n\t\n\t\n\t\\end{tikzpicture}\\]\n\t\\caption{ $\\sigma = \\{e_1\\}$; $e_1$ is a leaf-edge in $G_\\sigma$ and $e_1$ contains $x$ as a leaf in $G_\\sigma$. Moreover, $e_1$ also contains neighbors of $y$ and $z$ as leaves.}\\label{sigma5}\n\t\\end{minipage}\n\\hfill\n\\begin{minipage}{0.45\\textwidth}\n\t\\[\\begin{tikzpicture}[scale=1.5]\n\\node at (-0.3,0) {$x$};\n\\node at (3.3,0) {$y$};\n\\node at (1.3,-1) {$z$};\n\n\\node at (1.5,0.2) {$e_1$};\n\\node at (0.5,0.2) {$e_2$};\n\n\\node[vertex] (x) at (0,0) {};\n\\node[vertex] (w1) at (1,0) {};\n\\node[vertex] (w2) at (2,0) {};\n\n\\node[redvert] (y) at (3,0) {};\n\\node[redvert] (z) at (1,-1) {};\n\n\\path [line width=1pt]\n(x) edge (w1)\n(w2) edge (w1)\n; \n\n\\path [red,dashed,line width=1pt]\n(w2) edge (y)\n(x) edge (z)\n(w1) edge (z)\n(w2) edge (z)\n; \n\\end{tikzpicture}\\]\n\\caption{ $\\sigma = \\{e_1, e_2\\}$; $e_1$ is a leaf-edge in $G_\\sigma$ and $e_1$ contains (some) neighbors of $y$ and $z$ as leaves.} \\label{sigma6}\n\\end{minipage}\t\n\t\n\\end{figure}\n\nSimilarly as before, we consider those simplices $\\tau$ in $\\mathcal{C}_1$ such that, $e_2$ is in $\\tau$ and either a part of a cycle or a leaf-edge in $G_\\tau$ such that $e_2$ doesn't contain $x$ as a leaf, and if $z \\in V(G) \\setminus V_\\tau$ then $e_2$ doesn't contain any neighbor of $z$ as a leaf in $G_\\tau$. We pair off $\\tau$ with $\\tau \\setminus \\{e_2\\}.$ This extended matching is denoted by $\\Phi_2.$\n\n As before, in the special case of $ \\{e_2 \\} \\in \\mathcal{C}_1$ and $\\{e_2 \\}, \\emptyset \\in \\mathcal{A}_y \\setminus \\mathcal{A}_x$, we pair off $ \\{e_2 \\}$ with $\\emptyset$.\n\n\\textbf{Step 3:} The set of all unpaired simplices after step $2$ is denoted by $\\mathcal{C}_2.$ Note that $\\mathcal{C}_2$ consists of simplices $\\eta$ in $\\mathcal{C}_1$ if\none of the following four conditions holds: \n\n\n \\begin{enumerate}\n\\item $e_2 \\in \\eta$ is a bridge but not a leaf-edge in $G_\\eta$. \n\n\\item $e_2 \\in \\eta$ is a leaf-edge in $G_\\eta$ such that, \n\n \\begin{enumerate}\n \\item $e_2$ contains $x$ as a leaf in $G_{\\eta}$ \\\\ or\n \\item there exists $z \\in V(G) \\setminus V_\\eta$ such that $e_2$ contains a neighbor of $z$ as a leaf in $G_\\eta$. \n\n\\end{enumerate}\n\\item $e_2 \\notin \\eta$ and $y \\in e_2.$\n\\item $e_2 \\notin \\eta, y \\notin e_2$ and $\\eta \\cup \\{e_2\\}$ contains a cycle containing $e_1.$\n\n\\end{enumerate}\n\nNow we proceed as before, i.e., consider those simplices $\\eta$ in $\\mathcal{C}_2$ such that, $e_3$ is in $\\eta$ and either a part of a cycle or a leaf-edge in $G_\\eta$ such that $e_3$ doesn't contain $x$ as a leaf, and if $z \\in V(G) \\setminus V_\\eta$ then $e_3$ doesn't contain any neighbor of $z$ as a leaf in $G_\\eta$. We pair off $\\eta$ with $\\eta \\setminus \\{e_3\\}.$ This extended matching is denoted by $\\Phi_3.$\n\nProceeding this way, we end up with a matching $\\Phi$. To verify that $\\Phi$ is acyclic, we first observe that at the $i$-th step, we pair a simplex $\\Sigma$ with $\\Sigma \\setminus e_i$ only if $\\mathrm{Shade}_x(\\Sigma)=\\mathrm{Shade}_x(\\Sigma \\setminus e_i)=E(G) $. More generally, we make the following observation.\n\n\\begin{obs} \\label{pair}\n\nWe pair off $\\Sigma \\in \\mathcal{A}_y \\setminus \\mathcal{A}_x$ with $\\Sigma \\setminus e$ only if $e$ is the minimal edge in $\\Sigma$ such that $\\mathrm{Shade}_x(\\Sigma \\setminus e)=E(G) $. \n\n\\end{obs} \n\nWe also make the following observations regarding a unpaired simplex $\\Sigma$ in this matching $\\Phi$.\n\n\\begin{obs}\n\nIf a simplex $\\Sigma \\in \\mathcal{A}_y \\setminus \\mathcal{A}_x $ is unpaired in $\\Phi$, then the following properties hold.\n\\begin{enumerate}\n\t\\item $G_\\Sigma$ is a spanning tree of $G \\setminus \\{ y \\}$. Therefore, it is a $(|V(G)|-3)$-simplex (i.e., it has $|V(G)|-2$ edges).\n\t\\item For any $e_j \\notin \\Sigma$ with $y \\notin e_j$, the unique cycle in $G_{\\Sigma \\cup e_j}$ has an edge $e_i$ such that $e_if(j)$ which contradicts the fact that $f(1)$ is the minimum. Therefore, the matching $\\Phi$ is an acyclic extension of the acyclic matching $\\Phi_0$ for $\\mathcal{A}_x$. \n\n\nNow, let $U_n= \\{ x_1,\\cdots,x_n \\}$. We observe that $\\mathcal{A}_{U_n}=\\mathcal{A}_{U_{n-1}} \\cup (\\mathcal{A}_{x_n} \\setminus \\mathcal{A}_{U_{n-1}})$. Proceeding inductively, we can construct an acyclic matching of $\\mathcal{A}_U$ where $|U|>1$ that has critical simplices at dimension $|V(G)|-3$. \n \nBy Theorem \\ref{forth}, we can conclude that $\\mathcal{A}_U$ is homotopy equivalent to a wedge of spheres of dimension $|V(G)|-3$. Since, $\\Delta^{G}_{U}$ is the Alexander dual of $\\mathcal{A}_U$, it follows by Alexander duality that $\\tilde{H}_k(\\Delta^{G}_{U}) \\cong \\tilde{H}^{|E(G)|-k-3}(\\mathcal{A}_U) \\cong \\tilde{H}_{|E(G)|-k-3}(\\mathcal{A}_U) \\cong 0 $ unless $|E(G)|-k-3=|V(G)|-3$. Therefore, $\\tilde{H}_{k}(\\Delta^{G}_{U};\\mathbb{R}) \\cong 0$ unless $k=|E(G)|-|V(G)|$.\n\n\\end{proof}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzkgvq b/data_all_eng_slimpj/shuffled/split2/finalzzkgvq new file mode 100644 index 0000000000000000000000000000000000000000..5535e9e298161719064a24ddfce728b35202154e --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzkgvq @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\\label{sec:intro}\nCircinus~X-1 exhibits a dramatic 16.55-d cycle of X-ray flaring which\nis believed to be the result of enhanced mass transfer occurring near\nperiastron of a highly eccentric binary orbit (\\cite{kaluzienski76};\n\\cite{murdin80}). Although the identification of the orbital period\nseems to be secure, there is very little direct evidence concerning\nthe masses of the two components of the binary and the other orbital\nparameters. Nonetheless, the compact component is thought to be a\nweakly magnetized neutron star on the basis of three type~I X-ray\nbursts seen with {\\it EXOSAT\\,}\\ (\\cite{tennant86}). Further type~I bursts\nhave not been observed from Cir X-1 since the {\\it EXOSAT\\,}\\ discovery,\npossibly because the source intensity has been higher during\nsubsequent observations. No coherent pulsations, which would be\nexpected to be present if the compact star is a strongly magnetized\nneutron star, have been detected (\\cite{dower82}; \\cite{vaughan94}).\n\nQuasi-periodic oscillations (QPOs) were seen at 1.4~Hz, 5--20~Hz,\nand 100--200~Hz in {\\it EXOSAT\\,}\\ observations of Cir~X-1 in a bright state\n(\\cite{tennant87}; \\cite{tennant88}), but other observations at lower\nintensity showed no such QPOs (\\cite{oosterbroek95}). Based on these\ndata, it was suggested that Cir~X-1 is an atoll-source low-mass X-ray\nbinary (LMXB) that can uniquely reach the Eddington accretion rate and\nexhibit normal\/flaring branch QPOs (NBOs\/FBOs) at 5--20~Hz\n(\\cite{oosterbroek95}; \\cite{klis94}). Similar QPOs were observed in\n{\\it Ginga\\,}\\ observations of Cir~X-1 (\\cite{makino93}).\n\nObservations with the All-Sky Monitor (ASM) and Proportional Counter\nArray (PCA) on the {\\em Rossi X-ray Timing Explorer} ({\\it RXTE\\,}) have\nshown that since early 1996 (from the beginning of {\\it RXTE\\,}\\ monitoring)\nthe baseline intensity level of Cir~X-1 has remained bright\n($\\sim$1~Crab, 2--12~keV; $\\sim$1060~$\\mu$Jy at 5.2~keV) at all phases\nof the 16.55-d cycle, with both dips and flares associated with phase\nzero of the cycle (Shirey et~al.\\ 1996\\nocite{shirey96},\n1998\\nocite{shirey98:feb97}, hereafter Papers I \\& II;\n\\cite{shirey98:phd}). During non-flaring phases (intensity $\\approx$\n1~Crab) of a cycle in 1996 March, the centroid frequency of a narrow\nQPO was observed to evolve between 1.3~Hz and 12~Hz and was strongly\ncorrelated with the cutoff frequency of low-frequency noise and with\nthe centroid frequency of a broad peak ranging from 20--100~Hz, at\n$\\sim$13 times the frequency of the lower frequency QPO\n(\\cite{shirey96})\\@. During portions of a more active cycle in 1997\nFebruary--March, a similar narrow QPO evolved between 6.8--32~Hz,\nwhile during other portions of the cycle, a broad QPO was observed at\n4~Hz (\\cite{shirey98:feb97})\\@. By correlating the timing properties\nwith fragmented branches in hardness-intensity diagrams, we identified\nhorizontal, normal, and flaring branches. Thus, we interpreted the\nnarrow 1.3--32~Hz QPOs in Cir~X-1, including the 5--20~Hz QPOs\nobserved with {\\it EXOSAT\\,}, as horizontal-branch oscillations (HBOs), and\nthe broad 4~Hz QPOs as NBOs, thus calling into question the earlier\ninterpretation of the QPOs in Cir~X-1.\n\nIn this paper we present results from a 10-day set of high-efficiency\n{\\it RXTE\\,}\\ observations, including 56\\% coverage for 7~d starting 1.5~d\nbefore phase zero. Portions of these data show a complete Z-source\ntrack for Cir~X-1. These results confirm our previous interpretation\nbased on incomplete and shifted spectral tracks constructed from\nshorter and more widely spaced observations. We show how both the\nFourier power spectra and the energy spectra evolve along the various\nspectral tracks. We explore possible models for the energy spectrum\nand parameterize the evolution by fitting the data at various points\nalong the hardness-intensity track to a model consisting of a\nmulti-temperature ``disk blackbody'' and a higher-temperature\nisothermal blackbody. \n\n\\section{Observations} \n\nThe PCA light curves and a hardness ratio (``broad color'') for the\n1997 June observations are shown in Figure~\\ref{fig:june97_10d}. These\ndata show only moderate variability for the first day of\nobservations. The source entered a phase of significant dipping during\nthe half day before phase zero (day 611.5), based on the radio\nephemeris (\\cite{stewart91}). The hardness ratio shows that dramatic\nspectral evolution, both hardening and softening, occurs during these\ndips.\n\nBy phase zero the main dipping episode ends, and the transition to the\nflaring state begins. While the intensity increases by more than a\nfactor of three in the lowest energy band (2--6.3~keV), the intensity\nbetween 6.3~keV and 13~keV does not climb at all, and above 13~keV it\ndecreases by a factor of about 10 over the first 1.5~days following\nphase zero. This anti-correlation of the low and high-energy intensity\nduring the transition results in a decreased hardness ratio after\nphase zero, as is observed in {\\it RXTE\\,}\\ ASM data (Papers\nI\\nocite{shirey96} \\& II\\nocite{shirey98:feb97};\n\\cite{shirey98:phd}). \n\nAfter a relatively smooth transition toward high total intensity\n(2--18~keV) during the first day following phase zero, the intensity\nbecomes highly variable (i.e., the ``active'' or ``flaring'' state)\nfor the remaining 7 days of the observation. The intensity in the hard\nband remains much lower than before phase zero but shows strong\nvariability with a peak value that gradually increases from about 24 to\n36 counts~s$^{-1}$~PCU$^{-1}$. Based on the results in\n\\cite{shirey98:feb97} and those discussed below, we can identify\nshort-term variability within restricted limits as motion along\nbranches in hardness-intensity diagrams and the gradual evolution of\nthe limits as shifting of the branches.\n\n\n\\section{Complete Track in Hardness-intensity and Color-color Diagrams}\n\nThe color-color and hardness-intensity diagrams (CDs\/HIDs) for all\ndata in Figure~\\ref{fig:june97_10d} are shown in\nFigure~\\ref{fig:june97_cchid_all}. Only data from three of the five\nPCA detectors, PCUs 0, 1, and 2, were included in the diagrams, since\nPCUs 3 and 4 were not operating during portions of the observations.\nThese data cover a significant portion (10~d) of an entire 16.55~d\ncycle. Data from before day 612.5 generally had soft color ${\\stackrel{>}{_\\sim}}$\n1.5 and broad color ${\\stackrel{>}{_\\sim}}$ 0.325, while the data after day 612.5\ngenerally fell below those values.\n\nThe dips seen in Figure~\\ref{fig:june97_10d} appear as prominent but\nless dense tracks with two sharp bends in the CD (initially toward the\nright of the main arc-shaped locus) and one sharp bend in the HID\n($I<2.3$~kcounts s$^{-1}$ PCU$^{-1}$). We show in another paper\n(Shirey, Levine, \\& Bradt 1999\\nocite{shirey99:dips}) that tracks with\nthese shapes are due to a variably absorbed bright spectral component\nplus an unobscured faint component. Brandt et~al.\\\n(1996\\nocite{brandt96}) used a similar model to explain the spectral\nchanges during an intensity transition of Cir~X-1 observed with\n{\\it ASCA\\,}\\@. Having identified absorption dip signatures, we now focus on\nnon-dip spectral behavior, which is presumably more directly related\nto the mechanisms of X-ray production.\n\nMost of the data in Figure~\\ref{fig:june97_cchid_all} fall along a\nsingle arc-shaped locus in the CD and a more complicated structure in\nthe HID\\@. We use spectral bands with lower energies than those used\nfor the 1997 February--March observations in \\cite{shirey98:feb97};\nthis enhances the branch structure in the CD\\@. When the diagrams for\nthe two observations are constructed in the same manner, with the same\nhardness ratios and only three PCUs rather than five, the data from\nthese two cycles cover approximately the same extent in both\ndiagrams. The high-efficiency coverage of the current observations\nresulted in tracks that are more complete than the tracks of the\nearlier observations. Unpublished data from several other cycles\nobserved with the PCA also fall in similar regions of the diagrams as\nthe data in Figure~\\ref{fig:june97_cchid_all}.\n\nThe detailed structure within the overall locus of CD\/HID points is\nrevealed in CD\/HID plots of data divided into shorter time segments\n(${\\stackrel{<}{_\\sim}}$12~h). In particular, four time segments labelled ``A'',\n``B'', ``C'', and ``D'' in Figure~\\ref{fig:june97_10d} have been\nselected for further study. The intensity from time segments A and B\n(days 609.93--610.16 and 610.66--610.90 respectively) shows minimal\nvariability in each energy channel, and thus these time segments\nproduce small CD\/HID clusters whose locations are indicated in\nFigure~\\ref{fig:june97_cchid_all}.\nThe source was more active during time segments C and D (days\n612.625--613.125 and 616.075--616.600 respectively), and the CD\/HID\ntracks from these times show several connected branches. Enlarged\nviews of the CD and HID for these time segments are shown in\nFigure~\\ref{fig:june97_cchid_13_17}. The full ranges of the diagrams\nof Figure~\\ref{fig:june97_cchid_13_17} are indicated by dashed\nrectangular boxes in Figure~\\ref{fig:june97_cchid_all}.\nTracks of other time segments generally each resemble some portion of\nthe entire pattern shown in Figure~\\ref{fig:june97_cchid_13_17}, but\noften with a shifted position in the diagrams. \n\nThe data from segment~C in Figure~\\ref{fig:june97_cchid_13_17} include\nsome absorption dips, which result in tracks moving off the right side\nof the CD and the left side of the HID (and far beyond the limits of\nthe plot in both cases). The timing and spectral data associated with\nthese absorption dips will be omitted from analysis of the HID\nbranches.\n\nThe HID patterns reveal the shape of the full ``Z'' track as\npreviously inferred from the fragmented tracks in\n\\cite{shirey98:feb97}\\@. Time segments C and D both show a horizontal\nbranch, a normal branch, and a flaring branch which turns above the\nnormal branch. In addition, segment~C exhibits a long nearly-vertical\nextension on the left end of the HB, while for segment~D, there is\nonly a small hint of an upward turn at the left end of the HB\\@. The\nHIDs show significant shifts of the HB and upper NB between the time\nsegments C and D, which were separated by several days.\n\nThe branches in the CDs of Figure~\\ref{fig:june97_cchid_13_17} are\nless well-separated than those in the HIDs. This was also the case for\nthe diagrams in \\cite{shirey98:feb97}\\@. However, the flaring branch\nclearly turns above the normal branch in the lower left part of the\ncurrent CDs, and the upturned left extension of the HID horizontal\nbranch of segment~C is marked by an increase in the slope in the upper\nright part of the associated CD.\n\nThe HID for segment~C is similar to that derived from\n{\\it RXTE\\,}\\ PCA observations of the Z~source Cyg~X-2 (\\cite{smale98}). The\nCyg~X-2 HID also shows a very prominent vertical extension of the\nHB\\@. A similar upturned HB was reported in {\\it Ginga\\,}\\ and {\\it EXOSAT\\,}\\\nobservations of GX~5$-$1 (\\cite{lewin92:gx5-1};\n\\cite{kuulkers94:gx5-1}).\nThe upturned flaring branch is similar to the flaring branch observed\nin the CD for the Z~source GX~349+2 in recent {\\it RXTE\\,}\\ PCA observations\n(\\cite{zhang98}).\n\nThe HID track for time segment~C was divided into 20 regions which\nwere used to group data for further timing and spectral analysis. The\n20 regions have been numbered as shown in\nFigure~\\ref{fig:june97_hid20reg}, with numbers increasing from the\nvertical HB, through the NB, to the FB\\@. We show below that region~6\ndoes not adhere to the otherwise monotonic variation of\nspectral\/temporal characteristics with region number. This region may\nbe an indication of an upward-shifted horizontal portion of the HB.\n\nDetails of the temporal variability of the intensity, hardness ratio,\nand HID region numbers for time segment~C are shown in\nFigure~\\ref{fig:june97_lc_hr_reg}. During this half-day segment, the\nsource generally moves from lower to higher region number as the\nobservation progresses. Thus, the time series can be divided into four\nsub-segments which predominantly correspond to each portion of the HID\ntrack: the vertical and horizontal portions of the HB, the NB, and the\nFB\\@. Absorption dips occur in all but the flaring branch during this\nparticular data set; these are easily identified by brief intensity\ndips coupled with pronounced increases in broad color. No region\nnumber was assigned to most data points from dips since the HID\nregions in Figure~\\ref{fig:june97_hid20reg} were selected to avoid\ndips.\n\nThe light curves of the different branches\n(Fig.~\\ref{fig:june97_lc_hr_reg}) exhibit the following\ncharacteristics, excluding the behavior associated with the dips.\nWhen the source is on the vertical portion of the HB, the intensity\nevolves relatively smoothly, with a slight increase in the 2--6.3~keV\nband and a decrease of almost a factor of two in the 13--18~keV\nband. On the horizontal portion of the HB, the source shows a\nsubstantial increase in soft intensity and on average shows relatively\nsteady hard intensity. On the normal branch, the intensity is high in\nthe soft band while decreasing and highly variable in the hard band.\nThe NB\/FB transition occurs at lower intensity in all bands compared\nto most of the NB\\@. The flaring branch itself is then produced by\nhigh-variability ``mini-flares'' or bursts above the NB\/FB apex level\n(region~17).\n\nAlthough the HID regions were defined such that obvious absorption\ntracks were avoided, one brief dip, at day 612.98, occurred from\nregion 12 on the normal branch and placed a few points artificially\nacross regions 9, 7, and 5. These points are easily identified in\nFigure~\\ref{fig:june97_lc_hr_reg} (bottom plot) and are thus not\nincluded in subsequent timing and spectral analysis.\n\nLikewise, the highest mini-flares on the flaring branch actually\nextend beyond region~20 and cross regions 8 and 9. In fact a few such\npoints can even be seen above region~10 in\nFigure~\\ref{fig:june97_hid20reg}. The FB points that fell into HB\nregions can also be clearly identified as points with region numbers\nof 8 or 9 in the FB portion of\nFigure~\\ref{fig:june97_lc_hr_reg}. These are not included in\nsubsequent timing and spectral analysis.\n\n\\section{Evolution of the Power Density Spectrum}\n\nFourier power density spectra (PDSs) were computed for each 16~s of\ntime segment~C\\@. Each transform used $2^{16}$ 244-$\\mu$s\n($2^{-12}$~s) time bins and covered the full 2--32~keV energy range.\nThe expected Poisson level, i.e., the level of white noise due to\ncounting statistics, was estimated taking into account the effects of\ndeadtime~(\\cite{morgan97}; \\cite{zhang95}; \\cite{zhang96}) and\nsubtracted from each PDS; this method tends to slightly underestimate\nthe actual Poisson level. For each of the 20 HID regions defined in\nFigure~\\ref{fig:june97_hid20reg}, an average PDS was calculated from\nthe power spectra corresponding to points in that region. The average\nPDSs were then logarithmically rebinned and are shown in\nFigure~\\ref{fig:june97_20pds}.\n\nThe general features of the power spectra are similar to those\nobserved in previous PCA observations (see Papers I\\nocite{shirey96}\n\\& II\\nocite{shirey98:feb97}). In \\cite{shirey98:feb97}, based on\nfeatures of the power spectra associated with fragmented spectral\ntracks, we identified many of these features with those of a Z-source,\nand we discussed the properties of the QPOs on the horizontal and\nnormal branches. In the current observations, the characteristics of\nthe time variability are seen to evolve smoothly during a single 12-h\nobservation (segment~C). The narrow QPO is observed to evolve from\n12~Hz in region~1 to 25~Hz in region~7 as the HID location moves down\nthe vertical extension of the HB (region 6 may be a shifted version of\n8). Across the horizontal portion of the HB (regions 8--11), the\nnarrow QPO fades into a knee close to 30~Hz, while the broad QPO\ngradually rises near 4~Hz. The broad QPO is present near 4~Hz along\nthe normal branch (regions 12--16). It is most prominently peaked in\nthe middle of the branch and weak at the bottom of the branch\n(region~17). On the flaring branch (regions 18--20), no QPOs are\npresent and the power spectrum shows only strong very low frequency\nnoise.\n\nThe PDS properties of the horizontal portion of the HB are somewhat\nsimilar to those of the upper normal branch of some Z sources, namely\nno significant evolution of the HBO frequency and weak NBOs. However,\nwe will continue to refer to this branch as part of the the HB since\nother Z sources also show both vertical and horizontal portions of the\nHB (see above).\n\n\\section{Evolution of the Energy Spectrum}\n\\label{sec:june97_spec_qual}\n\nThe Standard2 data mode of the PCA instrument produces 129-channel\nenergy spectra every 16~s. A parallel background file was constructed\nusing the ``pcabackest'' program\\footnote[1]{The background model was\ndefined in three files provided by the PCA instrument team at\nNASA\/GSFC: \\nl {\\tt pca\\_bkgd\\_q6\\_e03v01.mdl}, {\\tt\npca\\_bkgd\\_xray\\_e03v02.mdl}, and {\\tt pca\\_bkgd\\_activ\\_e03v03.mdl}.}\nprovided with the FTOOLS analysis package (version 4.0). Average\npulse-height spectra (and background spectra) were constructed for\neach of the 20 HID regions, separately for each of the five\nPCUs. Version 2.2.1 response matrices were used in the analysis of\nthese spectra. A 1\\% systematic error estimate was added in\nquadrature to the estimated statistical error (1~$\\sigma$) for each\nchannel of the spectra to account for calibration uncertainties.\nAlthough the instrument response matrix is imperfectly known, we can\nsafely assume that any spectral features that vary during the 12-hours\nspanned by time segment~C are due to evolution of the source spectrum.\nRepresentative spectra from the hard, bright, and soft extremes\n(regions 1, 11, and 17, respectively) of the evolution along the HID\ntrack are shown in Figure~\\ref{fig:june97_spec_branches}.\n\nThe evolution of the spectrum may be studied by inter-comparison of\nratios of pulse-height spectra from each region to that of a reference\nspectrum, from region~11 (Fig.~\\ref{fig:june97_pha_ratio}).\nThe spectrum is hardest in region~1, at the top of the vertical\nextension of the horizontal branch. Motion down the branch\n(softening, regions~1--7) corresponds to pivoting of the spectrum\nabout $\\sim$7~keV, i.e., increasing intensity below $\\sim$7~keV and\ndecreasing intensity above that energy.\nMotion to the right across the horizontal portion of the HB\n(regions~8--11) corresponds to continued increasing low-energy\nintensity with modest softening, but with a nearly constant spectrum\nabove 12 keV\\@. Note that in the hardness ratios of\nFigure~\\ref{fig:june97_hid20reg} the high-energy channel (6.3--13 keV)\nis dominated by photons near the lower bound of the interval.\n\nWhen the source moves down the normal branch (regions 11--17), the\nflux generally decreases across the entire 2.5--25~keV band but\ndecreases most significantly at high energy from region 11 to 15 (thus\nfurther softening). Moving down the NB, the spectrum gradually\ndevelops a dip or step above $\\sim$9~keV and a peak slightly above\n10~keV.\nMotion up the FB (regions 18--20) is produced by increasing intensity\nat intermediate energies with a relatively constant spectrum at low\nenergy, thus hardening. The peaked feature near 10~keV becomes more\nprominent moving up the FB and will be discussed below.\n\n\\section{Selection of Spectral Models}\n\\label{sec:june97_spec_models}\n\nSpectral forms (e.g., blackbody emission, a power law, etc.) for use\nin fitting the spectra from the HID regions were explored by first\napplying them to high-quality spectra from time segments A and B (see\nFig.~\\ref{fig:june97_10d}). Variability in both of these segments was\nlimited to less than 10\\% in all energy bands between 2.5--18~keV. We\nthus constructed a single (averaged) pulse-height spectrum, known\nhereafter as spectrum~A or spectrum~B, for the entire 17--19~ks of\neach segment. Errors in these spectra are dominated by the 1\\%\nsystematics at all energies up to $\\sim$20~keV.\n\nThe timing properties measured throughout data sets A and B indicate\nthat time segment~A falls on the vertical portion of the HB (strong\nnarrow QPO at 8.4--11.5~Hz) and time segment~B falls near the HB\/NB\napex (weak narrow QPO above 30~Hz and\/or the broad 4~Hz QPO). The\nlocations of time segments A and B in\nFigure~\\ref{fig:june97_cchid_all} indicate that the branches are\nsignificantly shifted relative to those of segments C and D.\n\nRemillard et~al.\\ (1998\\nocite{remillard98}) studied version 2.2.1 PCA\nresponse matrices using Crab nebula data and found that the response\nmodel was most accurate for PCUs 0, 1, and 4 (of the five PCA\ndetectors) and for energies between 2.5~keV and 25~keV\\@. Thus, in\nfitting spectra we only include data from PCUs 0, 1, and 4 and from\nenergy channels corresponding to 2.5--25 keV\\@. Spectra from each of\nthese detectors are fit separately. Fit parameters reported are the\naverage values for PCUs 0, 1, and, when appropriate, 4. Errors are\nconservatively estimated as the entire range encompassed by the 90\\%\nconfidence intervals from each of the detectors. PCU~4 consistently\ngives lower normalizations for fitted spectral components, so fit\nparameters from that detector are not included when computing the\naverage normalizations and flux values and their errors. Spectrum~B\nwas not constructed for PCU~4 since that detector was turned off\nduring part of time segment~B.\n\nInterstellar photoelectric absorption was included in all models. The\nabsorption model used solar abundances (\\cite{anders82}) and\ncross-sections given by Morrison \\& McCammon (1983\\nocite{morrison83}).\n\nSeveral single-component models were fit to spectra~A and\nB\\@. Blackbody and power-law models fit very poorly in both cases, as\ndid a multi-temperature ``disk blackbody'' spectrum (\\cite{mitsuda84};\n\\cite{makishima86}; model ``diskbb'' in XSPEC), with reduced $\\chi^2$\n($\\chi^2_r$) values of 22--545. A thermal bremsstrahlung model\nprovided a better fit to spectrum~B ($\\chi^2_r=4.0$), but fit\nspectrum~A poorly ($\\chi^2_r=34$).\nA relatively good fit was achieved for both spectra with a modified\nbremsstrahlung model (see Table~\\ref{tab:june97_fitsAB}) which\nincludes the effects of Compton scattering of bremsstrahlung photons\nto higher energy in an optically thick plasma cloud (\\cite{compls};\nmodel ``compLS'' in XSPEC).\n\nA number of two-component models were also fit to these two spectra.\nA model using a disk blackbody and power law did not fit well\n($\\chi^2_r$=3--5), mainly because a single power-law slope does\nnot adequately describe the spectrum at high energy.\nA blackbody with $kT {\\stackrel{>}{_\\sim}}$ 2~keV is often included in models of the\nhard X-ray emission of LMXBs thought to contain a neutron star, where\nemission from or near the surface might produce high-temperature\nblackbody emission with a small effective area.\nTwo blackbodies ($kT \\sim$ 1.1~keV and 2.2~keV) fit moderately well\n(see Table~\\ref{tab:june97_fitsAB}), but required negligible\ninterstellar absorption. The low absorption is inconsistent with\nprevious measurements from {\\it ASCA\\,}\\ and {\\it ROSAT\\,}\\ (both sensitive below\n2~keV where the absorption is most easily constrained) which were used\nto estimate the interstellar column density to be\n$N_H=$(1.8--2.4)$\\times 10^{22}$~cm$^{-2}$\n(\\cite{brandt96}; \\cite{predehl95}).\n\nTwo models commonly used to fit Z-source energy spectra are the\n``Western model'' and the ``Eastern model'' (\\cite{hasinger90};\n\\cite{asai94}). The Western model consists of blackbody emission from\nthe hot surface of the neutron star or from a boundary between the\naccretion disk and surface, plus a Boltzmann-Wien component due to\nunsaturated Comptonization of soft photons by hot electrons\n(\\cite{white86}; Schulz, Hasinger, \\& Tr\\\"{u}mper\n1989\\nocite{schulz89}; Langmeier, Hasinger, \\& Tr\\\"{u}mper\n1990\\nocite{langmeier90}; \\cite{schulz93}). The Eastern model also\nincludes blackbody radiation emitted from or near the surface, plus\nemission from a multi-temperature accretion disk (\\cite{mitsuda84};\n\\cite{hoshi91}; \\cite{hirano95}).\n\nThe Western model, a power law with a high-energy exponential cutoff\nplus a blackbody, fit well (see Table~\\ref{tab:june97_fitsAB}), but\nthe best-fitting high-energy cutoff energy ($E_{cut}\\approx1.7$~keV)\nwas so low relative to the PCA bandpass (${\\stackrel{>}{_\\sim}} 2$~keV) that the\npower law photon index was not well constrained.\nThe Eastern model, a disk blackbody with temperatures at the inner\nedge of the disk of 1.5--1.8~keV, plus a $\\sim$2~keV blackbody, fit\nspectra A \\& B quite well (see Table~\\ref{tab:june97_fitsAB} and\nFig.~\\ref{fig:june97_specA_diskbb_bb}) and gave absorption column\ndensities roughly consistent with the {\\it ASCA\\,}\\ and {\\it ROSAT\\,}\\ values.\nAlthough a number of other two-component models also produce similar\nquality fits, the Eastern model is used below to provide a physically\nmotivated parameterization of the spectra from the HID regions.\n\nThe Eastern model fit to spectrum~A\n(Fig.~\\ref{fig:june97_specA_diskbb_bb}) shows peaked residuals at\n6--7~keV, suggesting the presence of an emission line, probably iron\nK$\\alpha$. Very similar residuals appear in most of the fits discussed\nabove for both spectra A and B\\@. Addition of a Gaussian line to the\nmodels does in fact improve the fits in almost all cases; however, the\nbest-fitting line often has an extremely large Gaussian width\n($\\sigma>1$~keV). The energy resolution of the PCA is about 1~keV FWHM\nat 6~keV; thus it is difficult to place reliable constraints on\nparameters such as the centroid and width of a narrow component. We\nhave not included an emission line in the fits reported in\nTable~\\ref{tab:june97_fitsAB}. The presence of an emission line near\n6.4~keV is discussed in more detail in Shirey et~al.\\\n(1999\\nocite{shirey99:dips}) in conjunction with spectra of absorption\ndips, which show the line more prominently.\n\n\\section{Fits to Spectra from 20 HID Regions}\n\nA disk blackbody plus isothermal blackbody model was fit to the\naverage spectrum for each of the 20 HID regions. Representative fits\nand residuals are shown in Figure~\\ref{fig:june97_spec_hidfits}. The\nresulting fit parameters are listed in Table~\\ref{tab:june97_fitsHID}\nand plotted versus HID region number in\nFigure~\\ref{fig:june97_fit_params}. The distance to Cir~X-1 has been\nestimated to be about 6--10~kpc (\\cite{stewart91}; \\cite{goss77}), so\nwe adopt a value of 8~kpc in converting blackbody and disk blackbody\nnormalizations to radii.\n\nAlthough both spectra A and B are fit well by the Eastern model, the\nreduced chi-squared values in Table~\\ref{tab:june97_fitsHID} indicate\nthat none of the fits for the 20 regional spectra are formally\nacceptable. The fit results must therefore be regarded as an\napproximate description of the spectrum and its evolution. We\nemphasize that here, as in any case, caution is advised in drawing\nphysical conclusions from the best-fit model parameters.\n\nThe spectra along the horizontal branch (regions 1--11) were all fit\nrelatively well. The residuals for these fits (see\nFig.~\\ref{fig:june97_spec_hidfits}) are similar in structure to those\nfor spectra A and B above. Thus they also suggest the presence of an\nemission line from iron. These spectra all show column densities of\n1.8--2.3$\\times10^{22}$~cm$^{-2}$, consistent with the {\\it ASCA\\,}\\ and\n{\\it ROSAT\\,}\\ values discussed above. \nThe temperature of the $\\sim$2.0~keV blackbody is relatively stable on\nthe HB\\@ (see Fig.~\\ref{fig:june97_fit_params}). The temperature of\nthe inner disk decreases from region 1 to region 5 (down the vertical\nportion of the branch) and then stabilizes at $\\sim$1.3 keV\\@. The\ncooling is at least in part responsible for the pivoting of the\nspectrum on the vertical portion of the HB.\n\nThe inner radius of the disk blackbody component, times a factor of\norder unity involving the inclination angle of the disk, increases\nfrom 19 to 33~km, while the radius of the blackbody remains\nbetween 3~and 4~km. These size scales are consistent with the\nhypothesis that these components arise from emission close to a\nneutron star. In this model, an increasing inner radius of the\naccretion disk is the most significant factor in producing the HB\ntrack; however, one should use caution in interpreting this as an\nactual physical radius. The inclination angle of the disk in Cir~X-1\nis unknown but might be high since absorption dips are observed.\n\nFrom region~1 to 11, the total 2.5--25~keV flux increases\nmonotonically, with the exception of region~6, from\n2.89$\\times10^{-8}$ to 4.35$\\times10^{-8}$~erg~cm$^{-2}$~s$^{-1}$,\ncorresponding to 1.2--1.8 times the Eddington luminosity limit for a\n1.4~${\\rm M}_\\odot$\\ neutron star at 8~kpc.\n\nAlong the normal branch, the quality of the fits decrease from\nregion~12 to region~17, as indicated by increasing $\\chi^2_r$ values\n(see Table~\\ref{tab:june97_fitsHID}). The absorption column density\ngradually decreases by a factor of two, but this may be related to the\ndecreasing fit quality.\nThe inner radius and temperature of the disk blackbody change only\nslightly on the normal branch. In contrast, the $\\sim$2~keV blackbody\nbegins to fade on the upper portion of the normal branch (regions\n12--14), as indicated by a decreasing radius for the emission area.\nThe fading blackbody is illustrated in\nFigure~\\ref{fig:june97_photon_spec}, which shows the modeled incident\nspectra and both model components for several spectral fits. By the\nmiddle of the normal branch, the $\\sim$2~keV blackbody has faded\nentirely and fits have lower $\\chi^2_r$ values without it. Thus, the\nblackbody is omitted from the fits for regions 15--20. The residuals\nbelow $\\sim$6~keV continue to appear similar to those on the HB (see\nFig.~\\ref{fig:june97_spec_hidfits}). The peak at $\\sim$6.5~keV\nbecomes broader and more complicated than on the HB\\@, and the dip and\npeak above 8~keV become more prominent.\n\nOn the flaring branch (regions 18--20), the fit quality decreases\nfurther, accompanied by very low values for the absorption column\ndensity. A number of other spectral models were fit to the HID-region\nspectra, and all failed to satisfactorally fit the spectra from the\nlower portion of the HID track (region number 14 and greater). A\nsignificant contribution to the high $\\chi^2$ values on the flaring\nbranch is due to the feature near 10~keV. Addition of a Gaussian line\nor an absorption edge at 9--11~keV does improve the fits somewhat, but\nthese components cannot account for all the residuals near that\nenergy. A combination of a line {\\em and} an edge near 10~keV can\nadequately fit the residuals, but such features are difficult to\njustify physically at that energy. Even hydrogen-like iron can be\nruled out as a possible cause due to the high energy of the feature.\nMany X-ray pulsars show cyclotron absorption features at high\nenergy. Inclusion of a cyclotron absorption component in the spectral\nmodel results in a fit similar in quality to that of an absorption\nedge. However, such features require magnetic fields of $\\sim\n10^{12}$~G, which would be expected to result in strong pulsations\nrather than Z or atoll behavior.\n\n\n\\section{Discussion}\n\nOur spectral and timing analysis of the current observations shows\nclear evidence for Z~source behavior in Cir~X-1. This is significant\nbecause Cir~X-1 was reported to exhibit atoll source behavior at lower\nintensity (\\cite{oosterbroek95}). Earlier {\\it RXTE\\,}\\ observations, each\nlasting about two hours and separated by about two days, showed\nfragments of one or two spectral branches in hardness-intensity\ndiagrams (\\cite{shirey98:feb97})\\@. In the much more extensive\nobservations presented in this paper, we have found longer 12-hour\nsegments (time intervals C and D) which clearly exhibit all three\nbranches of a Z~source. We have demonstrated that these complete Z\ntracks shift in the HID, confirming the behavior we inferred from the\nfragmented tracks of the previous observations.\n\nThe current data also allow us to demonstrate how the timing\nproperties evolve along the complete HID track of Cir~X-1 and confirm\nour original identification, in \\cite{shirey98:feb97}\\@, of horizontal\nand normal branch QPOs. Fourier power spectra for different regions of\nthe complete HID track show continuous evolution from the narrow QPO\n(increasing in frequency from 12~Hz to 30~Hz in the current\nobservations) on the horizontal branch, to the broad 4~Hz QPO on the\nnormal branch, to only very low frequency noise on the flaring branch.\nProperties of the fast timing characteristics associated with spectral\nbranches in Cir~X-1 were discussed in \\cite{shirey98:feb97}\\@. For\nthe remainder of this discussion we focus on the properties of the\nenergy spectrum.\n\nWe tried fitting energy spectra of Cir~X-1 with various simple models.\nThe spectra for time intervals A and B were well fit using the Western\nand Eastern models (see discussion below), but no simple spectral form\nwas found that fit the range of spectra seen during time interval~C\\@.\nWe have not attempted to go beyond simple parameterized spectral\nmodels, e.g., by computing model spectra based on the \"unified model\"\nof Lamb and collaborators (\\cite{lamb89}; Psaltis, Lamb, \\& Miller\n1995\\nocite{psaltis95}; \\cite{psaltis98}), which was proposed to\nexplain the X-ray spectra and rapid variability of Z~sources. Such\nsophisticated models may be necessary to correctly interpret the\nspectral changes in Cir~X-1 and other Z~sources.\n\nThe fits of spectra A and B with the Western model yielded a\ncutoff energy of $\\sim$1.7 keV for the Comptonized (Boltzmann-Wien)\ncomponent. The cutoff energy in GX 5-1 was found to be 1--3~keV\n(\\cite{asai94}), similar to our results for Cir~X-1, but was found\nto be higher, 4--6 keV, in Cyg~X-2 (\\cite{hasinger90}). We did not\nuse the Western model in parameterizing evolution associated with the\n``Z'' track because the cutoff energy in Cir~X-1 is low relative to\nthe PCA bandpass, resulting in a poorly constrained power law index\nand absorption column density.\n\nParameters for the Eastern model were more well-constrained (see\nTable~\\ref{tab:june97_fitsAB}), and thus this model was used to\nparameterize the spectral variations associated with the\nhardness-intensity track. In this model, motion along the HB is\nmainly associated with an increasing inner radius of the disk\n(increasing disk blackbody normalization) but also by a decreasing\ninner disk temperature. This would imply that, as the luminosity\nincreases across the HB, the inner edge of the disk is pushed further\naway from the surface. It is not clear how this is related to the\nincreasing QPO frequency, which would typically be expected to require\na {\\em decreasing} radius if the QPOs were related to Keplerian motion\nat the inner edge of the disk, e.g., through the magnetospheric beat\nfrequency model (\\cite{alpar85}; \\cite{lamb85}). \n\nFits of the Eastern model to energy spectra from the normal branch\nindicate that the $\\sim$2~keV blackbody gradually fades away, leaving\nonly the disk blackbody. This is similar to the result obtained when\nthe Eastern model was fit to the spectrum of Cyg~X-2, where the\nblackbody luminosity decreases from the HB to the FB\n(\\cite{hasinger90}). Furthermore, the FB of GX~5$-$1 is characterized\nby intensity dips which in the Eastern model can be explained by\ndisappearance of the blackbody component, suggesting that accretion\nflow onto the neutron-star surface is interrupted (\\cite{mitsuda84}).\n\nOn the lower NB, a feature in the spectrum develops above 10~keV. This\nfeature becomes more prominent on the flaring branch. A very similar\nline-like feature at $\\sim$10~keV was reported in {\\it Ginga\\,}\\ observations\nof the Z~source GX~5$-$1 (\\cite{asai94}). In GX~5$-$1, as in Cir~X-1,\nthe feature was present on the lower NB and stronger on the FB; we\nthus suggest that these features of the two sources may be of similar\norigin. Asai et~al.\\ showed that a peak near 11~keV occurs in the\ncorrelation coefficients of the time-series data of different energy\nbands versus the 1.7--4.0 keV band. This demonstrates a temporal\ncharacter in the narrow band at 11~keV different than that at adjacent\nenergies. In turn, this gives assurance that the line-like feature at\nthat energy is not the result of the continuum model used to fit the\nspectrum but is intrinsic to the source.\nWe carried out similar cross-correlation analysis for each of the 20\nHID regions in our study. The cross-correlation results, relative to\nthe 2.5--2.9~keV band, from three representative regions are shown in\nFigure~\\ref{fig:june97_cross_corr}. We find a clear peak in the\ncross-correlation coefficient at about 11~keV in regions 19 and 20 of\nthe FB\\@, further confirming the similarity of the spectral features\nin Cir X-1 and GX~5$-$1. Regions 16 and 17 of the lower NB show an\nabrupt drop in the cross-correlation coefficient above 8--10~keV. The\ncross-correlation coefficients on the HB and upper NB, where the line-\nor edge-like 10~keV spectral feature is absent or weak, generally show\nmuch less remarkable behavior.\n\nAsai et al.\\ found that that the spectral feature near 10~keV in\nGX~5$-$1 was better fit with a Gaussian line rather than, e.g., an\nabsorption edge. However, as we mentioned above, such a feature\ncannot be produced by even hydrogen-like iron. Asai et~al.\\ discuss\nseveral of the mechanisms that could possibly produce a line at\n$\\sim$10~keV. For example, emission from a heavy element such as Ni\ncould produce the line, but is unlikely since iron should be far more\nabundant than the heavier elements. Alternately, a line could be\nblue-shifted due to motion in a relativistic jet or rotation in the\naccretion disk, but extreme conditions would be required to boost the\nenergy up to $\\sim$10~keV and one might expect a red-shifted line to\nalso be observed in the X-ray spectrum. As mentioned above, we find\nthat in Cir~X-1 an emission line component alone is insufficient to\nproduce the observed structure of the feature. Both line-like and\nedge-like components may be required to explain this unusual spectral\nfeature at high energy.\n\nCir X-1 exhibits a number of unusual features in its Z-source behavior\nin addition to the 10~keV spectral feature. Its HBOs are observed at\nfrequencies as low as 1.3~Hz (\\cite{shirey96}), an order of magnitude\nlower than those of other Z~sources. The highest frequency reached by\nHBOs in Cir X-1, at the HB\/NB apex, is 30--32~Hz\n(\\cite{shirey98:feb97}), about a factor of two below the extreme HBO\nfrequency in other Z sources. Power spectra of Cir~X-1 show a broad\nhigh-frequency peak, centered at 20--200~Hz, which shifts in frequency\nmaintaining an almost constant ratio with the HBO frequency\n(\\cite{shirey96}; \\cite{tennant87}). Cir X-1 shows a long vertical\nextension of the HB\\@. The entire HID track shows very large\ncolor\/intensity shifts, possibly associated with the presumed\neccentric 16.55-d binary orbit (\\cite{shirey98:feb97}). Atoll\nbehavior has been reported at lower intensity (\\cite{oosterbroek95}).\nIt is likely that some or many of these unusual features are related\nby some physical property of the system, indicating that Cir~X-1 may\nprovide important constraints on models of low-mass X-ray binaries.\n\n\\acknowledgements \n\nWe would like to acknowledge the {\\it RXTE\\,}\\ teams at MIT and GSFC for\ntheir support. In particular we thank E.~Morgan, R.~Remillard,\nW.~Cui, and D.~Chakrabarty for useful discussions related to this\nwork. We thank D.~Psaltis for useful discussions regarding Z-source\nspectral models. We also appreciate the detailed comments and\nsuggestions provided by the referee. Support for this work was\nprovided through NASA Contract NAS5-30612.\n\n\n\\newpage\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{...}\n\n\nAn important source of photons in nuclear collisions are ``prompt photons''. They are produced in initial hard parton collisions, through two mechanisms\\footnote{This division into two mechanisms is useful conceptually, although it can only be done rigorously at leading order.}\n: \n\t(i) processes such as Compton scattering ($q g \\to q \\gamma$) \n\tand quark annihilation ($q \\bar{q} \\to g \\gamma$), \n\twhere a photon is a direct final state of the hard parton interaction; and\n\t(ii) fragmentation, where a hard parton-parton collision (e.g. $q \\bar{q} \\to q \\bar{q}$) is followed by a photon being radiated from one of the final state partons.\n \n\nPrompt photon production in proton-proton (p+p) collisions has been calculated in perturbation theory at next-to-leading order in $\\alpha_s$;\nagreement with p+p data is good\nin a wide range of center-of-mass energies~\\cite{Aurenche:2006vj}. \nPrompt photons remain an important source of direct photons in heavy ion collisions. They are generally said\nto scale with the number of binary nucleon collisions, up to modest corrections from isospin and nuclear effects on the parton distribution functions; this statement is supported by the good agreement of high $p_T^\\gamma$ photon spectra ($\\gtrsim 5-10$~GeV) in heavy ion measurements with binary-scaled prompt photon calculations (or p+p photon data).\nAt low $p_T^\\gamma$, the production of prompt photon in heavy ion collisions is more complex.\nThe fragmentation component\nof prompt photons, subdominant at high $p_T^\\gamma$, is the dominant source of prompt photons at low $p_T^\\gamma$. These fragmentation photons are affected by parton energy loss.\nMoreover prompt photons are not the only source of low $p_T^\\gamma$ direct photons in heavy ion collisions: additional photons originate from (i) blackbody radiation produced by the hot expanding plasma (``thermal photons''), and (ii) ``jet-medium photons'' produced in parton-plasma interactions (interactions which also lead to parton energy loss) \\cite{Turbide:2007mi}. \nMeasurements indicate that the low $p_T^\\gamma$ direct photon spectra measured in heavy ion collisions is considerably larger than binary-scaled photon spectra measurements from proton-proton collisions. This suggests that thermal and jet-medium photons more than compensate for the suppression of prompt photons resulting from parton energy loss. Additional simulations are still required to confirm this scenario\\footnote{Almost all recent comparisons with data rely on prompt photon calculations that neglect the effect of energy loss.\nUp-to-date calculations of jet-medium photons and of prompt photons that account for parton energy loss will be essential to clarify the status of model-to-data comparisons.\n}.\n\n\n\nRecent work by the PHENIX Collaboration brought the role of prompt photons back to the front of the discussion~\\cite{Adare:2018wgc}. They observed that the \\emph{centrality dependence} of the direct photon multiplicity is consistent with binary collisions scaling:\n\\begin{equation}\n\\left. d N_{direct}\/d y \\right|_{p_T^\\gamma>p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma}\n\\approx N_{binary}^{\\alpha} K\\left(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma \\right) \n\\label{eq:phenix_scaling}\n\\end{equation}\nwhere the number of binary collisions $N_{binary}$ is a \ncalculated\nfrom the Monte-Carlo Glauber model and the exponent $\\alpha$ was found to be consistent with $1$. The normalization factor $K(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma )$ depends on the center-of-mass energy $\\sqrt{s_{NN}}$, and the multiplicity cutoff $p_T^\\gamma$. However the centrality dependence itself of the multiplicity appears not to depend on the cutoff $p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma$ (i.e. $\\alpha\\approx 1$ independent of $p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma$).\nThe same conclusions had already been obtained for one center-of-mass energy in Ref.~\\cite{Adare:2014fwh}.\n\nIn what follows we provide a theoretical perspective on this binary-scaling from the point of view of thermal photons, medium-modified prompt photons and jet-medium photons.\n\n\n\\paragraph{Medium-modified prompt photons and jet-medium photons}\nThe multiplicity of sufficiently high $p_T^\\gamma$ prompt and jet-medium photons in heavy ion collisions can be written schematically as\n\\begin{align}\n\\left. d N_{j}\/d y \\right|_{p_T^\\gamma>p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma} =\n\\frac{N_{binary}}{\\sigma^{inel}_{NN }} & \\int \\frac{d \\phi}{2 \\pi} \\int_{p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma} d p_T p_T \\left[ \\sum_{a, b, c} f_{a\/A} \\left(x_a,Q\\right) \n\\otimes f_{b\/A}\\left(x_b,Q \\right) \\otimes \\, d\\hat{\\sigma}_{a b \\to c \\gamma} \\right. \\nonumber \\\\\n& \\left. +\\sum_{a, b, c, d} f_{a\/A} \\left(x_a,Q\\right) \n\\otimes f_{b\/A}\\left(x_b,Q \\right) \\otimes \\, d\\hat{\\sigma}_{a b \\to c d} \\otimes D^M_{\\gamma\/c} \\left(z_c,Q\\right)\n\\right]\n\\label{eq:prompt_jetmedium}\n\\end{align}\nwhere $f(x,Q)$ are nuclear parton distribution;\n$d\\hat{\\sigma}$ are perturbative parton cross-sections; and $\\otimes$ represents a convolution over the kinematic variables. All effects from the quark-gluon plasma --- parton energy loss and jet-medium photon production --- are absorbed into a medium-modified fragmentation function $D^M_{\\gamma\/c} \\left(z_c,Q\\right)$. At a fixed collision energy, the centrality dependence of Eq.~\\ref{eq:prompt_jetmedium} originates from the $N_{binary}$ pre-factor as well as from $D^M_{\\gamma\/c} \\left(z_c,Q\\right)$. This medium-modified fragmentation function is assumed to encode the same non-perturbative fragmentation into photons as the vacuum function. However, the perturbative sector of the vacuum fragmentation function is modified to account for the presence of a medium: it includes\\footnote{We are not aware of numerical studies of photons that include both medium-modified high and low-virtuality showering. Simulations that include only the latter have been performed previously, however (e.g. Ref.~\\cite{Turbide:2005fk}).} (i) perturbative DGLAP-like photon emission between virtuality $Q$ and a lower virtuality $Q_0$; and (ii) perturbative low-virtuality showering, which simultaneously leads to medium-induced photon emission and medium-induced parton energy loss. In vacuum, only the DGLAP-like photon emission is present.\nThe final non-perturbative fragmentation into photons is performed after this perturbative showering. Unlike vacuum fragmentation functions, the medium-modified $D^M_{\\gamma\/c} \\left(z_c,Q\\right)$ is not universal: it must be evaluated dynamically to account for the exact profile of the medium.\n\n\nTypically the spectra of prompt and jet-medium photons fall off rapidly with $p_T^\\gamma$ and the photon multiplicity is dominated by the smallest $p_T^\\gamma$ regions of the integrand ($p_T^\\gamma \\sim p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma$).\nThis presents a challenge for Eq.~\\ref{eq:prompt_jetmedium}.\nIntrinsically in collinear perturbative calculations, there is a lower scale $Q_0 \\sim 1-2$~GeV separating the perturbative and non-perturbative sectors. In Eq.~\\ref{eq:prompt_jetmedium}, the scale $Q>Q_0$ is expected to be of the order of the photon transverse momentum: $Q\\sim p_T^\\gamma$. This implies that multiplicities evaluated with small $p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma\\sim Q_0$ may be dominated by $p_T^\\gamma$ regions where the perturbative calculations are the least reliable. Careful numerical simulations of medium-modified prompt and jet-medium photons will be essential to clarify the situation. Alternative approaches to calculate low momentum jet and photon production may also be necessary (see e.g. Ref.~\\cite{Hattori:2016jix} and references therein).\n\n\\paragraph{Thermal photons}\nThermal photons are calculated by convoluting a thermal emission rate $E d^3 \\Gamma\/d^3 p$ with a spacetime profile of the plasma obtained from hydrodynamics:\n\\begin{equation}\n\\left. d N_{th}\/d y \\right|_{p_T^\\gamma>p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma} =\n\\int d^4X \\int \\frac{d \\phi}{2 \\pi} \\int_{p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma} d p_T p_T \\left [E \\frac{d^3 \\Gamma}{d^3 p}\\left(P \\cdot u(X),T(X),\\pi^{\\mu\\nu}(X),\\Pi(X)\\right) \\right]\n\\label{eq:thermal}\n\\end{equation}\nwhere $T$, $u$, $\\pi^{\\mu\\nu}(X)$,$\\Pi(X)$ are the temperature, flow velocity, shear tensor and bulk pressure profiles of the plasma, and $P$ is the photon four-momentum.\n\n\\begin{figure}[t]\n\t\\includegraphics[width=0.38\\linewidth]{thermal_pTcuts_sqrts200_Nbin}\n\t\\hspace{2.cm}\n\t\\includegraphics[width=0.39\\linewidth]{thermal_pTcuts_sqrts5020_Nbin}\n\t\\caption{Thermal photon multiplicity as a function of the number of binary nucleon collisions, obtained by varying the centrality for (a) Au-Au at $\\sqrt{s_{NN}}=200$~GeV, and (b) Pb-Pb at $\\sqrt{s_{NN}}=5020$~GeV.}\n\t\\label{fig:thermal_vs_bin}\n\\end{figure}\n\nFigure~\\ref{fig:thermal_vs_bin} shows the power law scaling of the photon multiplicity as a function of $N_{binary}$\n, for Au-Au collisions at $\\sqrt{s_{NN}}=200$~GeV and Pb-Pb collisions at $\\sqrt{s_{NN}}=5020$~GeV. Three different multiplicity cutoff $p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma$ are shown: 0.5, 1 and 1.5~GeV. The results can be fitted reasonably well with a linear function; the slopes\nare indicated on the figures. Based on Figure~\\ref{fig:thermal_vs_bin}, we can write the thermal photon multiplicity as:\n\\begin{equation}\n\\left. d N_{th}\/d y \\right|_{p_T^\\gamma>p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma}\n\\approx N_{binary}^{\\alpha\\left(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma\\right)} M\\left(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma \\right) \n\\label{eq:thermal_Nbin_scaling}\n\\end{equation}\nwhere $M(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma ) $ is a function that can be tabulated, and $\\alpha(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma)$ is an exponent for $N_{binary}$ that is approximately $1.1-1.3$. This exponent increases as the cutoff $p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma$ is increased. The exact numerical values of $\\alpha(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma)$ depend on the details of the hydrodynamic simulation (initial conditions, viscosities, \\ldots) as well as the photon emission rates; this exact dependence will need to be studied in greater details\\footnote{In particular, we believe an observation made in Ref.~\\cite{Shen:2013vja} --- that an increase in the photon emission rate at low temperatures leads to smaller slopes for the centrality dependence --- may not as general as previously thought.}.\nNevertheless, the $p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma$ \\emph{dependence} of these values is a robust prediction: there is no expected scenario where the thermal photon multiplicity is independent from this cutoff.\n\n\\paragraph{Discussion \\& Outlook}\n\n\n \n\nAny discussion of the multiplicity scaling must be made with other photon observables in mind, in particular the photon momentum anisotropy $v_2$. Thermal photons generally have a large $v_2$ at low $p_T^\\gamma$, while medium-modified prompt and jet-medium are generally understood to have a small $v_2$~\\cite{Turbide:2005bz}. The measured photon $v_2$ is large, and is interpreted as favoring thermal photons as the dominant source of low $p_T^\\gamma$ direct photons.\n\n\nSumming thermal, prompt and jet-medium photons, we write the direct photon multiplicity:\n\\begin{equation}\n\\left. d N_{direct}\/d y \\right|_{p_T^\\gamma>p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma}\n\\approx \\left[ \n\\left. d N_{j}\/d y \\right|_{p_T^\\gamma>p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma}\n+ N_{binary}^{\\alpha\\left(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma\\right)} M\\left(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma \\right) \\right]\n\\end{equation}\n\nInevitably the centrality dependence of this prediction depends on the relative size of thermal photons as opposed to that of the medium-modified prompt photons and jet-medium photons. Given we do not currently have calculations of the latter two sources in a setting consistent with our thermal photon calculations, we limit our discussion to asymptotic scenarios.\n\nThe first limit is a multiplicity dominated by jet-medium and medium-modified prompt photons. To simultaneously describe the measured photon $v_2$, these jet-medium and prompt photons need to have a large $v_2$. This scenario is arguably not supported by older calculations~\\cite{Turbide:2005bz}. Whether calculations that include recent developments in hydrodynamics simulations (e.g. fluctuating initial conditions, initial flow and bulk viscosity) would produce a significantly larger momentum anisotropy for these photons will need to be studied numerically.\n\nIf thermal photons dominate the multiplicity, the centrality dependence of the photon multiplicity should be close to $N_{binary}^{\\alpha}$ with $\\alpha=\\alpha(\\sqrt{s_{NN}},p_{T, \\textrm{\\footnotesize cutoff}}^\\gamma)$ discussed above. Importantly measurements could rule out this possibility with improved constraints on the cutoff \\emph{independence} of the $\\alpha$ exponent.\n\nGiven that intermediate scenarios are also possible,\nadditional measurements and calculations are essential to determine if the observed centrality scaling can be explained by our current understanding of photon production, or if there is a new direct photon puzzle.\n\n\\paragraph{Acknowledgements} We are grateful to the ALICE and PHENIX photon working groups for insightful discussions. This work was supported by the U.S. Department of Energy\nunder Award Numbers DE-FG02-05ER41367 (JFP) and DE-SC0013460 (CS), and by the Natural Sciences and Engineering Research Council of Canada. SM acknowledges funding from The Fonds de recherche du Qu\\'ebec - Nature et technologies \n(FRQ-NT) \nthrough the Programme de Bourses d'Excellence pour \\'Etudiants \\'Etrangers.\nComputations were made \non the supercomputer Guillimin, managed by Calcul Qu\\'ebec and Compute Canada and funded by the Canada Foundation for Innovation (CFI), Minist\\`ere de l'\\'Economie, des Sciences et de l'Innovation du Qu\\'ebec (MESI) and FRQ-NT. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\\bibliographystyle{JHEP}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec:intro}\n\nA minor of a graph $G$ is a graph obtained from $G$ by a succession of\nedge deletions, edge contractions and vertex deletions.\nAll graphs we consider are simple, i.e. without loops or multiple edges.\nThe following theorem of Mader~\\cite{mader1} bounds the number of edges in a\n$K_r$-minor free graph.\n\\begin{thm}[Mader, 1968,~\\cite{mader1}]\nFor $3 \\leq r \\leq 7$, any $K_r$-minor free graph $G$ on $n\\ge r$\nvertices has at most $(r-2)n - {{r-1}\\choose{2}}$ edges.\n\\end{thm}\nNote that since $|E(G)| = \\frac{1}{2} \\sum_{u \\in V(G)}\\,\\deg(u)$,\nthis theorem implies that every $K_r$-minor\nfree graph $G$, for $3 \\leq r \\leq 7$, is such that $\\delta(G)\\le\n2r-5$, where $\\delta(G)$ denotes the minimum degree of $G$.\nThis property will be of importance in the following. We are\ninterested in a sufficient condition for a graph to\nadmit a complete graph as a minor, dealing with the minimum number\nof triangles each edge belongs to. Nevo~\\cite{nevo1} already studied\nthis problem for small cliques. In the following, we assume\nthat every graph has at least one edge.\n\\begin{thm}[Nevo, 2007,~\\cite{nevo1}]\nFor $3 \\leq r \\leq 5$, any $K_r$-minor free graph $G$ has an edge that\nbelongs to at most $r - 3$ triangles.\n\\label{th:nevoleq6}\n\\end{thm}\nHe also gave a weaker version for $K_6$-minor free graphs.\n\\begin{thm}[Nevo, 2007,~\\cite{nevo1}]\nAny $K_6$-minor free graph $G$ has an edge that belongs to at\nmost $r - 3$ triangles, or is a clique-sum over $K_r$, $r \\leq 4$.\n\\label{th:nevok6}\n\\end{thm}\n\nNevo has conjectured that Theorem \\ref{th:nevok6} can be extended to\nthe case of $K_7$-minor free graphs. We improve\nTheorems~\\ref{th:nevoleq6} and \\ref{th:nevok6} in the following way.\n\n\\begin{thm}\\label{th:krtri}\nFor $3 \\leq r \\leq 7$, any $K_r$-minor free graph $G$ has\nan edge $uv$ such that $\\deg(u) \\leq 2r-5$ and $uv$ belongs to at\nmost $r-3$ triangles.\n\\end{thm}\n\nIn particular, this answers Nevo's conjecture about $K_7$-minor free graphs.\nAs pointed out by Nevo, Theorem~\\ref{th:nevok6} cannot be further extended\nto $K_8$-minor free graphs as such, since $K_{2,2,2,2,2}$\nis a $K_8$-minor free graph whose every edge belongs to $6$\ntriangles. Actually, one can obtain $K_8$-minor free graphs whose every edge\nbelongs to $6$ triangles by gluing copies of $K_{2,2,2,2,2}$ on cliques of\nany $K_8$-minor free graph. It is interesting to notice that $K_{2,2,2,2,2}$\nappears in a Mader-like theorem for $K_8$-minor free graphs~\\cite{jorg1}.\n\\begin{thm}[J\u00f8rgensen, 1994,~\\cite{jorg1}]\nEvery graph on $n \\geq 8$ vertices and at least $6n - 20$ edges either\nhas a $K_8$-minor, or is a $(K_{2,2,2,2,2}, 5)$-cockade (i.e. any\ngraph obtained from copies of $K_{2,2,2,2,2}$ by 5-clique sums).\n\\label{th:jorg}\n\\end{thm}\n\nAlthough Theorem~\\ref{th:krtri} cannot be extended to $K_8$-minor free\ngraphs, some similar conclusions can be reached by considering stronger\nhypotheses. By increasing the minimum degree of the graph or\nexcluding $K_{2,2,2,2,2}$ as an induced subgraph, we have the following\nthree theorems.\n\\begin{thm}\nAny $K_8$-minor free graph $G$ with $\\delta(G)=11$ has an edge $uv$\nsuch that $u$ has degree 11 and $uv$ belongs to at most $5$\ntriangles.\n\\label{th:k8triweak}\n\\end{thm}\n\n\\begin{thm}\nAny $K_8$-minor free graph $G$ with $\\delta(G)\\ge 9$ has an edge that\nbelongs to at most $5$ triangles.\n\\label{th:k8-deg9}\n\\end{thm}\n\n\\begin{thm}\nAny $K_8$-minor free graph $G$ with no $K_{2,2,2,2,2}$ as induced\nsubgraph has an edge that belongs to at most $5$ triangles.\n\\label{th:k8tri}\n\\end{thm}\n\nWe investigate applications of the previous results in the\nrest of the paper.\nIn Section~\\ref{sec:moytri}, we relax the hypothesis into a\nmore global condition on the overall number of triangles in\nthe graph. In particular, we prove that, for $3 \\leq k \\leq 7$\n(resp. $k=8$), if a graph has $m\\ge 1$ edges and at least\n$\\frac{k-3}{2}m$ triangles, then it has a $K_k$-minor (resp.\na $K_8$- or a $K_{2,2,2,2,2}$-minor).\nIn Section~\\ref{sec:stress}, we show applications to stress freeness\nof graphs, and settle some open problems of Nevo~\\cite{nevo1}.\nFinally, we show some applications to graph coloration\nin Section~\\ref{sec:double} and Section~\\ref{sec:coloration}.\nIn the former section, we show an application to double-critical\n$k$-chromatic graphs which settle a special case of a conjecture\nof Kawarabayashi, Toft and Pedersen~\\cite{kpt10}.\nIn the latter section, motivated by Hadwiger's conjecture,\nwe show that every $K_7$-minor free graph is $8$-colorable\nand that every $K_8$-minor free graph is $10$-colorable.\n\n\n\\section{Proof of Theorem~\\ref{th:krtri} for $r\\le 6$ : A slight improvement of Nevo's theorem}\\label{sec:proofk6}\n\nFirst note that the cases $r=3$ or $4$ are trivial. The case $r=5$ is\nalso quite immediate, but we need a few definitions to prove it. A\n\\emph{separation} of a graph $G$ is a pair $(A,B)$ of subsets of\n$V(G)$ such that $A \\cup B = V(G)$, $A\\setminus B \\neq \\emptyset$,\n$B\\setminus A \\neq \\emptyset$, and no edge has one end in $A\n\\backslash B$ and the other in $B \\backslash A$. The \\emph{order} of a\nseparation is $|A \\cap B|$. A separation of order $k$ will be denoted\nas a $k$-separation, and a separation of order at most $k$ as a $(\\leq\nk)$-separation. Given a vertex set $X\\subseteq V(G)$ (eventually $X$\nis a singleton) the sets $N(X)$ and $N[X]$ are respectively defined by\n$\\{y\\in V(G)\\setminus X \\ |\\ \\exists x\\in X\\ {\\rm s.t.}\\ xy\\in E(G)\\}$\nand $X\\cup N(X)$.\n\nLet us prove the case $r=5$. Consider any $K_5$-minor free graph\n$G$. According to Wagner's characterization of $K_5$-minor free\ngraphs~\\cite{w37}, $G$ is either the Wagner graph, a 4-connected\nplanar graph, or has a $(\\le 3)$-separation $(A,B)$ such that $H=G[A]$\nis either the Wagner graph or a 4-connected planar graph. If $G$ or\n$H$ is the Wagner graph, as this graph has only degree 3 vertices and\nno triangle, we are done. If $G$ (resp. $H$) is a 4-connected planar\ngraph, Euler's formula implies that there is a vertex $v$\nof degree at most 5 in $V(G)$ (resp. in $A \\setminus B$). One can then observe\nthat, any edge around $v$ belongs to at most 2 triangles, as otherwise there would be\na separating triangle in $G$ (resp. $H$), contradicting its\n4-connectivity.\n\nLet us now focus on the case $r=6$ of Theorem~\\ref{th:krtri}.\nConsider by contradiction a $K_6$-minor free graph $G$ with at least\none edge, and such that every edge incident to a vertex of degree at\nmost $7$, belongs to at least $4$ triangles. By Mader's theorem, we\nhave that $\\delta(G)\\le 7$. We start by studying the properties of\n$G[N(u)]$, for the vertices $u$ of degree at most 7. First, it is\nclear that $G[N(u)]$ is $K_5$-minor free because otherwise there would\nbe a $K_6$-minor in $G$, contradicting the hypothesis.\n\n\\begin{lem}\n$\\delta(G)\\ge 6$, and for any vertex $u$ of degree at most 7, $\\delta(G[N(u)])\\ge 4$.\n\\label{lem:mindeg6}\n\\end{lem}\n\n\\begin{proof}\nFor any vertex $u$ of degree at most 7, and any vertex of $v\\in N(u)$\nthe edge $uv$ belongs to at least 4 triangles. The third vertex of\neach triangle clearly belongs to $N(u)$ and is adjacent to $v$. Thus\n$v$ has degree at least $4$ in $G[N(u)]$.\n\nSince for any vertex $u$ of degree at most $7$ we have\n$\\delta(G[N(u)])\\ge 4$, $|N(u)|\\ge 5$ (i.e. $\\deg(u)\\ge 5$).\nFurthermore if there was a vertex $u$ of degree 5, as\n$\\delta(G[N(u)])\\ge 4$, the graph $G[N(u)]$ would be isomorphic to\n$K_5$, contradicting the fact that $G[N(u)]$ is $K_5$-minor free. Thus\n$\\delta(G)\\ge 6$.\n\\end{proof}\n\nAs observed by Nevo (Proposition 3.3,~\\cite{nevo1}), since\n$|N(u)| \\leq 7$, $\\delta(G[N(u)]) \\geq 4$ and $N(u)$ is $K_5$-minor\nfree, then $G[N(u)]$ is $4$-connected. Note that by Wagner's\ncharacterization of $K_5$-minor free graphs, every $4$-connected\n$K_5$-minor free is planar. Chen and Kanevsky~\\cite{ck1} proved that\nevery $4$-connected graph can be obtained from $K_5$ and the\ndouble-axle wheel $W_4^2$ by operations involving vertex splitting and\nedge addition. Their result implies that the only two possibilities\nfor $G[N(u)]$ are the double-axle wheels on $4$ and $5$ vertices\ndepicted in Figure~\\ref{fig:doublewheel}. Note that theses two graphs\nhave $3|N(u)| - 6$ edges, and hence are maximal $K_5$-minor free (by\nMader's theorem).\n\n\\begin{figure}[h]\n\\centering\n\\subfigure{\n\\includegraphics[scale=1]{doubleaxlewheel4.eps}\n}\n\\subfigure{\n\\includegraphics[scale=1]{doubleaxlewheel5.eps}\n}\n\n\\caption{The double-axle wheel on $4$ and $5$ vertices.}\n\\label{fig:doublewheel}\n\\end{figure}\n\nWe need the following lemmas on the neighborhood of the vertices with\nsmall degree.\n\n\\begin{lem}\nFor any vertex $u$ of degree at most 7, every vertex $v\\in N(u)$ has a\nneighbor in $G\\setminus N[u]$.\n\\label{lem:voisink6}\n\\end{lem}\n\\begin{proof}\nRecall that $G[N(u)]$ is a double-axle wheel. Note that in a\ndouble-axle wheel, every vertex has degree at most 5, and every edge\nbelongs to exactly 2 triangles. Thus, every vertex of $N(u)$ has\ndegree at most 6 in $G[N[u]]$, and every edge of $G[N(u)]$ belongs to\nexactly 3 triangles in $G[N[u]]$. This implies that any vertex $v\\in\nN(u)$ has either degree $>8$ in $G$, and thus at least 2 neighbors in\n$G\\setminus N[u]$, or that any of its incident edges $vw$ in $G[N(u)]$\nis contained in a fourth triangle $vwx$, with $x\\in G\\setminus N[u]$.\n\\end{proof}\n\n\\begin{lem}\nFor any vertex $u$ of degree at most $7$, and any connected component\n$C$ of $G\\setminus N[u]$, the graph $G[N(C)]$ is a clique on at most 3\nvertices.\n\\label{lem:voisin2k6}\n\\end{lem}\n\\begin{proof}\nAs $G[N(u)]$ has no clique on more than 3 vertices, let us show that\n$N(C)$ does not contain two non-adjacent vertices , say $v_1$ and\n$v_2$. There exists a path from $v_1$ to $v_2$ with inner vertices in\n$C$. Since $G[N(u)]$ is maximal $K_5$-minor free, this path together\nwith $G[N[u]]$ induces a $K_6$ minor in $G$, a contradiction.\n\\end{proof}\n\n\\begin{lem}\nFor any vertex $u$ of degree at most $7$, and any connected component\n$C$ of $G\\setminus N[u]$, there exists a vertex $u'\\in C$ of degree at\nmost 7 in $G$.\n\\label{lem:existsk6}\n\\end{lem}\n\\begin{proof}\nSuppose for contradiction that every vertex of $C$ has degree at least\n8 in $G$. Note that by definition, every vertex in $N(C)$ has a\nneighbor in $C$. Thus, as by Lemma~\\ref{lem:voisin2k6} $G[N(C)]$ is a\nclique on $k\\le 3$ vertices, the vertices in $N(C)$ have degree at\nleast $k$ in $G[N[C]]$. Thus the number of edges of $G[N[C]]$ is at\nleast\n\\[|E(G[N[C]])| \\geq \\frac{1}{2}(8|C| + k^2) > 4(|C|+k) - 10\\]\nand by Mader's theorem, there is a $K_6$-minor in $G[N[C]]$, a\ncontradiction.\n\\end{proof}\n\n\nNow choose a vertex $u$ of degree at most 7 and a connected component\n$C$ of $G\\setminus N[u]$, in such a way that $|C|$ is minimum. By\nLemma~\\ref{lem:existsk6}, $C$ has a vertex $v$ of degree at most 7.\n\nLet $C_u$ be the connected component of $G\\setminus N[v]$ that\ncontains $u$, and let $x\\in N(v)\\setminus N(C_u)$. By\nLemma~\\ref{lem:voisink6}, there is a connected component $C'$ of\n$G\\setminus N[v]$ such that $x\\in N(C')$.\n\nAs $N[u]\\subset N[C_u]$, it is clear that $G[C'\\cup\\{x,v\\}]$ is a\nconnected subgraph of $G\\setminus N[u]$. We thus have that\n$C'\\subsetneq C$ and thus that $|C'|<|C|$, contradicting the choice of\n$u$ and $C$. This concludes the proof of the case $r=6$ of\nTheorem~\\ref{th:krtri}.\n\n\n\\section{Proof of Theorem~\\ref{th:krtri} for $r=7$ : the case of $5$ triangles}\\label{sec:proofk7}\n\nConsider by contradiction a $K_7$-minor free graph $G$ with at least\non edge, and such that every edge incident to a vertex of degree at\nmost $9$ belongs to at least $5$ triangles. By Mader's theorem,\n$|E(G)| \\leq 5|V(G)| - 15$, hence there are vertices $u$ such that\n$\\deg(u) \\leq 9$.\n\nWe start by studying the properties of $G[N(u)]$, for any vertex $u$\nof degree at most 9. First, it is clear that $G[N(u)]$ is $K_6$-minor\nfree because otherwise there would be a $K_7$-minor in $G$,\ncontradicting the hypothesis.\n\n\\begin{lem}\n$\\delta(G)\\ge 7$, and for any vertex $u$ of degree at most 9, $\\delta(G[N(u)])\\ge 5$.\n\\label{lem:mindeg7}\n\\end{lem}\n\n\\begin{proof}\nFor any vertex $u$ of degree at most 9, and any vertex of $v\\in N(u)$\nthe edge $uv$ belongs to at least 5 triangles. The third vertex of\neach triangle clearly belongs to $N(u)$ and is adjacent to $v$. Thus\n$v$ has degree at least $5$ in $G[N(u)]$.\n\nSince for any vertex $u$ of degree at most $9$ we have\n$\\delta(G[N(u)])\\ge 5$, $|N(u)|\\ge 6$ (i.e. $\\deg(u)\\ge 6$).\nFurthermore if there was a vertex $u$ of degree 6, as\n$\\delta(G[N(u)])\\ge 5$, the graph $G[N(u)]$ would be isomorphic to\n$K_6$, contradicting the fact that $G[N(u)]$ is $K_6$-minor free. Thus\n$\\delta(G)\\ge 7$.\n\\end{proof}\n\nThere is no appropriate theorem (contrarily to the previous case) to\ngenerate all possible neighbourhoods of the small degree vertices.\nInstead, we use a computer to generate all graphs with at most $9$\nvertices and minimum degree at least 5. Then we refine (by computer)\nour list of graphs, by removing the ones having a $K_6$-minor. At the\nend, we end up with a list of $22$ graphs. A difference with the\nprevious case is that not all the $22$ graphs are maximal $K_6$-minor\nfree graphs. We deduce two of the following lemmas from the study of\n$N(u)$ by computer~\\cite{ag1}.\n\n\\begin{lem}\nFor any vertex $u$ of degree at most $9$, any connected component $C$\nof $G\\setminus N[u]$ is such that $|N(C)| = k \\le 5$ and $|E(N(C))|\\ge\n{k \\choose 2} -3$ (i.e. $G[N[C]]$ has at most 3 non-edges).\n\\label{lem:compk7}\n\\end{lem}\n\n\\begin{proof}\nAs any connected component $C$ could be contracted into a single\nvertex, we prove the lemma by attaching a new vertex to all possible\ncombinations of $k$ vertices of $N[u]$ (as we know that $N(u)$ induces\none of the 22 graphs generated above), for any $k\\le 6$, and check\nwhen it induces a $K_7$-minor.\n\\end{proof}\n\nThis allows us to prove the following equivalent of\nLemma~\\ref{lem:existsk6}.\n\\begin{lem}\nFor any vertex $u$ of degree at most $9$, any connected component $C$\nof $G\\setminus N[u]$ has a vertex $u'$ of degree at most 9 in $G$.\n\\label{lem:existsk7}\n\\end{lem}\n\n\\begin{proof}\nLet $u$ be a vertex of $G$ of degree at most $9$ and let $C$ be a\nconnected component of $G\\setminus N[u]$ which vertices have degree at\nleast 10 in $G$. Note that by definition every vertex of $N(C)$ has at\nleast one neighbor in $C$. Lemma~\\ref{lem:compk7} implies that\n$|N(C)|=k \\leq 5$ and that $G[N(C)]$ has at most 3 non-edges. Thus,\ncontracting a conveniently choosen edge between $u$ and $N(C)$, one\nobtains that $G[N(C)]$ has at most 1 non-edge. After this\ncontraction, we have:\n\\begin{align*}\n|E(N[C])| &\\geq \\frac{1}{2} \\Big[ 10|C| + k(k-1) - 2 + k \\Big] \\\\\n&= 5|C| + \\frac{k^2}{2} -1 > 5(|C|+k) - 15 .\n\\end{align*}\nThis contradicts the fact that $G[N[C]]$ is $K_7$-minor free, and thus\nconcludes the proof of the lemma.\n\\end{proof}\n\n\\begin{lem}\nFor any vertex $u$ of degree at most $9$, at most one vertex $v$ of\n$N(u)$ is such that $N(v) \\subseteq N[u]$.\n\\label{lem:numberk7}\n\\end {lem}\n\n\\begin{proof}\nFor every such vertex $v$, as $\\deg(v)\\le \\deg(u)\\le 9$, the edges\nadjacent to $v$ with both ends in $N(u)$ belong to at least $5$\ntriangles in $G$ (i.e. belong to at least $4$ triangles in\n$G[N(u)]$). We checked that for every graph in the list at most one\nsuch vertex satisfies this condition.\n\\end{proof}\n\nThis allows us to prove the following lemma.\n\\begin{lem}\nFor any vertex $u$ of degree at most $9$ and any connected component\n$C$ of $G\\setminus N[u]$, there exists a connected component $C'$ of\n$G\\setminus N[u]$ such that $N(C')\\setminus N(C) \\neq \\emptyset$.\n\\label{lem:numberk7bis}\n\\end {lem}\n\\begin{proof}\nAs $\\deg(u)\\ge 7$ (by Lemma~\\ref{lem:mindeg7}) and $|N(C)|\\le 5$ (by\nLemma~\\ref{lem:compk7}), there are at least 2 vertices in\n$N(u)\\setminus N(C)$. By Lemma~\\ref{lem:numberk7}, one of these 2\nvertices has a neighbor $x$ out of $N[u]$. Thus the component of\n$G\\setminus N[u]$ containing $x$ fulfills the requirements of the\nlemma.\n\\end{proof}\n\nNow choose a vertex $u$ of degree at most 9 and a connected component\n$C$ of $G\\setminus N[u]$, in such a way that $|C|$ is minimum. By\nLemma~\\ref{lem:existsk7}, $C$ has a vertex $v$ of degree at most 9.\nLet $C_u$ be the connected component of $G\\setminus N[v]$ that\ncontains $u$. By Lemma~\\ref{lem:numberk7bis} there exists a connected\ncomponent $C'$ of $G\\setminus N[v]$ such that $N(C')\\setminus N(C_u)\n\\neq \\emptyset$, and let $x\\in N(C')\\setminus N(C_u)$. As\n$N[u]\\subset N[C_u]$, it is clear that $G[C'\\cup\\{x,v\\}]$ is a\nconnected subgraph of $G\\setminus N[u]$. We thus have that\n$C'\\subsetneq C$ and thus that $|C'|<|C|$, contradicting the choice of\n$u$ and $C$. This concludes the proof of case $r=7$ of\nTheorem~\\ref{th:krtri}\n\n\\section{Proof of Theorem~\\ref{th:k8triweak}, \\ref{th:k8-deg9} and \\ref{th:k8tri} : the case of $6$ triangles}\\label{sec:proofk8}\n\nAs in the previous sections, we will consider vertices of small degree\n(and their neighborhoods) in $K_8$-minor free graphs. We thus need the\nfollowing technical lemma that has been proven by computer~\\cite{ag1}.\n\n\\begin{lem}\nEvery $K_7$-minors free graph $H$ distinct from $K_{2,2,2,2}$,\n$K_{3,3,3}$ and $\\overline{P_{10}}$ (the complement of the Petersen\ngraph), and such that $8\\le |V(H)| \\le 11$ and $\\delta(H)\\ge 6$,\nverifies:\n\\begin{itemize}\n\\item $H$ is 5-connected.\n\\item $H$ has at most one vertex $v$ such that each of its\n incident edges belongs to 5 triangles.\n\\item For any subset $Y\\subsetneq V(H)$ of size 7, the graph obtained\n from $H$ by adding two vertices $x$ and $y$ such that $N(x)=V(H)$\n and $N(y)=Y$, has a $K_8$-minor.\n\\end{itemize}\n\\label{lem:compk8}\n\\end{lem}\nNote that the second property also holds for $K_{2,2,2,2}$,\n$K_{3,3,3}$ and $\\overline{P_{10}}$. Actually any edge of these 3\ngraphs belongs to less than 5 triangles.\n\nBy Theorem~\\ref{th:jorg}, any $K_8$-minor free graph has minimum\ndegree at most 11. Theorem~\\ref{th:k8triweak} considers the case\nwhere the minimum degree is exactly 11. It will be used in\nSection~\\ref{sec:coloration} to color $K_8$-minor free graphs.\n\n\\begin{proofof}{Theorem~\\ref{th:k8triweak}}\nWe prove this using the same technique as in\nSection~\\ref{sec:proofk7}. Consider by contradiction a $K_8$-minor\nfree graph $G$ with $\\delta(G)=11$, and such that every edge adjacent\nto a degree 11 vertex belongs to at least $6$ triangles. We start by\nstudying the properties of $G[N(u)]$, for any degree 11 vertex $u$.\nFirst, it is clear that $G[N(u)]$ is $K_7$-minor free because\notherwise there would be a $K_8$-minor in $G$, contradicting the\nhypothesis.\n\n\\begin{lem}\nFor any degree 11 vertex $u$, $\\delta(G[N(u)])\\ge 6$.\n\\label{lem:mindegk8}\n\\end{lem}\n\\begin{proof}\nFor any degree 11 vertex $u$ and any vertex of $v\\in N(u)$,\nthe edge $uv$ belongs to at least 6 triangles. The third vertex of\neach triangle clearly belongs to $N(u)$ and is adjacent to $v$. Thus\n$v$ has degree at least $6$ in $G[N(u)]$.\n\\end{proof}\n\n\\begin{lem}\nFor any degree 11 vertex $u$, any connected component $C$\nof $G\\setminus N[u]$ has a vertex $u'$ of degree at most 11 in $G$.\n\\label{lem:existsk8}\n\\end{lem}\n\\begin{proof}\nLet $u$ be a degree 11 vertex of $G$ and let $C$ be any connected\ncomponent of $G\\setminus N[u]$ which vertices have degree at least 12\nin $G$. Lemma~\\ref{lem:compk8} implies that $G[N(u)]$ is 5-connected\nand that $|N(C)|=k \\leq 6$. Thus the lemma holds by considering the\ngraph $G[N[u]\\cup C]$ in the following Lemma~\\ref{lem:existsk8bis}.\n\\end{proof}\n\n\\begin{lem}\nA graph $H$ with a degree 11 vertex $u\\in V(H)$ and such that:\n\\begin{itemize}\n\\item[(A)] $H[N(u)]$ is 5-connected,\n\\item[(B)] $\\delta(H[N(u)])\\ge 6$,\n\\item[(C)] the set $C=V(H)\\setminus N[u]$ is non-empty, and all its vertices have degree at least 12, and\n\\item[(D)] the set $N(C) \\subseteq N(u)$ has size $k\\le 6$,\n\\end{itemize}\nhas a $K_8$-minor.\n\\label{lem:existsk8bis}\n\\end{lem}\n\\begin{proof}\nConsider a minimal counter-example $H$, that is a $K_8$-minor free\ngraph $H$ fulfilling conditions (A), (B) (C) and (D), and minimizing\n$|V(H)|$. Note that by definition every vertex of $N(C) \\subseteq\nN(u)$ has at least one neighbor in $C$. Let us prove that actually\nevery vertex of $N(C)$ has at least 2 neighbors in $C$. If $x\\in\nN(C)$ has only one neighbor $y$ in $C$, contract the edge $xy$ and\ndenote $G'$ the obtained graph. It is clear that $G'$ is $K_8$-minor\nfree, and fulfills conditions (A), (B) and (D). Moreover, $C\\setminus\n\\{y\\}$ is non-empty as it contains at least 6 vertices of $N(y)\\cap C$\n(as $\\deg(y)\\ge 12$ and $|N(C)|=k\\le 6$), and every vertex of\n$C\\setminus \\{y\\}$ has degree at least 12 in $H'$ as none of these\nvertices are adjacent to $x$ in $H$. So $G'$ also fulfills condition\n(C), and this contradicts the minimality of $G$. Thus every vertex of\n$N(C)$ has at least 2 neighbors in $C$.\n\nOne can easily see that every $(K_{2,2,2,2,2}, 5)$-cockade has at\nleast 10 degree 8 vertices. Thus the graph $H[N[C]]$, and any graph\nobtained from $H[N[C]]$ by adding edges, cannot be a\n$(K_{2,2,2,2,2}, 5)$-cockade as it has at most $6$ vertices of\ndegree $8$. Thus as $H[N[C]]$ has at least $\\frac{1}{2}(12|C| + 2k)$\nedges and as this is at least $6(|C|+k)-20$ for $k \\leq 4$, by\nTheorem~\\ref{th:jorg} we have that $5\\le k\\le 6$.\n\nNow suppose that $k = 5,6$. Let $v_1$ and $v_2$ be two vertices of\nsmallest degree in $H[N(C)]$. Denote $\\delta_1$ and $\\delta_2$ their\nrespective degree in $H[N(C)]$. Note that if $k=6$ then $\\delta_1\\ge\n1$ as $v_1$ has at least $6$ neighbors in $N(u)$ and as there are only\n$5$ vertices in $N(u)\\setminus N(C)$. By contracting the edge $uv_1$,\nwe have $k - 1 -\\delta_1$ additionnal edges in $H[N[C]]$. Moreover\nsince $H[N(u)]$ is $5$-connected and since $|N(C)| \\leq 6$, for every\nvertex $x\\neq v_2$ of $N(C)$ we have $|N(C)\\setminus\\{x,v_2\\}|=4$ and\nthus the graph $H[N(u)]\\setminus (N(C)\\setminus\\{x,v_2\\})$ is\nconnected. Thus, iteratively contracting all the edges between $v_2$\nand $N(u)\\setminus N(C)$ we add at least $k - 2 - \\delta_2$ edges in\n$H[N[C]]$ (as we have potentially already added the edge $v_1v_2$ in\nthe previous step). The number of edges in the obtained graph is at\nleast\n\\[\\frac{1}{2}[(\\delta_1 +2) + (\\delta_2\n +2)(k-1)) + 12|C|] + (k - 1 -\\delta_1) + (k - 2 -\\delta_2)\\]\nwhich is more than $6(|C|+k)-20$ (as $k\\le 6$ and as if $k=6$ then\n$\\delta_1 \\ge 1$). Thus this graph has a $K_8$-minor, and so does $H$.\nThis completes the proof of the lemma.\n\\end{proof}\n\n\n\\begin{lem}\nFor any degree 11 vertex $u$ and any connected component\n$C$ of $G\\setminus N[u]$, there exists a connected component $C'$ of\n$G\\setminus N[u]$ such that $N(C')\\setminus N(C) \\neq \\emptyset$.\n\\label{lem:numberk8bis}\n\\end {lem}\n\\begin{proof}\nAs $\\deg(u)=11$ and $|N(C)|\\le 6$ (by Lemma~\\ref{lem:compk8}), there\nare at least 5 vertices in $N(u)\\setminus N(C)$. As $\\delta(G)=11$\none can easily derive from Lemma~\\ref{lem:compk8} that one (actually,\nat least 4) of these vertices has a neighbor $x$ out of $N[u]$. Thus\nthe component of $G\\setminus N[u]$ containing $x$ fulfills the\nrequirements of the lemma.\n\\end{proof}\n\nNow choose a degree 11 vertex $u$ and a connected component $C$ of\n$G\\setminus N[u]$, in such a way that $|C|$ is minimum. By\nLemma~\\ref{lem:existsk8}, $C$ has a degree 11 vertex $v$. Let $C_u$\nbe the connected component of $G\\setminus N[v]$ that contains $u$. By\nLemma~\\ref{lem:numberk8bis} there exists a connected component $C'$ of\n$G\\setminus N[v]$ such that $N(C')\\setminus N(C_u) \\neq \\emptyset$,\nand let $x\\in N(C')\\setminus N(C_u)$. As $N[u]\\subset N[C_u]$, it is\nclear that $G[C'\\cup\\{x,v\\}]$ is a connected subgraph of $G\\setminus\nN[u]$. We thus have that $C'\\subsetneq C$ and thus that $|C'|<|C|$,\ncontradicting the choice of $u$ and $C$. This concludes the proof of\nTheorem~\\ref{th:k8triweak}\n\\end{proofof}\n\nlet us now prove Theorem~\\ref{th:k8tri}. Given a counter-exemple $G$\nof Theorem~\\ref{th:k8tri}, note that adding a vertex $s$ to $G$,\nadjacent to a single vertex of $G$, one obtains a counter-exemple of\nthe following theorem, thus Theorem~\\ref{th:k8tri} is a corollary of\nthe following theorem.\n\n\\begin{thm}\nConsider a connected $K_8$-minor free graph $G$ with a vertex $s$ of\ndegree at most 7 and such that $N[s] \\subsetneq V(G)$. If every edge\n$e \\in E(G) \\setminus E(G[N[s]])$ belongs to at least 6 triangles, then\n$G$ contains an induced $K_{2,2,2,2,2}$.\n\\label{th:k8triwiths}\n\\end{thm}\nNote that as $K_{2,2,2,2,2}$ is maximal $K_8$-minor free, any\n$K_8$-minor free graph $G$ containing a copy of $K_{2,2,2,2,2}=G[X]$,\nfor some vertex set $X\\subseteq V(G)$, is such that any connected\ncomponent $C$ of $G\\setminus X$ verifies that $N(C)$ induces a clique\nin $G[X]$.\n\\begin{proof}\nConsider a connected $K_8$-minor free graph $G$ with a vertex $s$ of\ndegree at most 7 such that $N[s] \\subsetneq V(G)$, such that $G$ does not\ncontain an induced $K_{2,2,2,2,2}$, and such that every\nedge $e \\in E(G) \\setminus E(G[N[s]])$ belongs to at least 6\ntriangles. Assume also that $G$ minimizes the number of vertices.\nThis property implies that $G\\setminus N[s]$ is connected. Indeed,\notherwise one could delete one of the connected components in\n$G\\setminus N[s]$ and obtain a smaller counter-example. The graph $G$\nis almost 8-connected as observed in the following lemma.\n\\begin{lem}\nFor any separation $(A,B)$ of $G$ (denote $S = A \\cap B$), we have\neither:\n\\begin{itemize}\n\\item $|S| \\geq 8$, or\n\\item $s \\notin S$ and $A\\setminus B = \\{s\\}$ (i.e. $B = V(G) \\setminus\n \\{s\\}$), or\n\\item $s \\in S$ and $|S| \\geq 6$.\n\\end{itemize}\n\\label{lem:connectk8withs}\n\\end{lem}\n\\begin{proof}\nSuppose there exists a separation $(A,B)$ contradicting the lemma.\nNote that $|S| < 8$ and let us assume that $s\\in A$.\n\nConsider first the case where $s\\notin S = A\\cap B$, that is the case\nwhere $\\{s\\} \\subsetneq A\\setminus B$. Assume that among all such\ncounter-examples, $(A,B)$ minimizes $|S|$. In this case, if the\nconnected component of $A\\setminus B$ containing $s$ has more vertices\nthen, contracting this component into $s$, one obtains a proper minor\n$G'$ of $G$ such that $N[s]\\subsetneq V(G')$ (as $B\\setminus A \\neq\n\\emptyset$) and such that every edge not in $E(N[s])$ belongs to $6$\ntriangles. This would contradict the minimality of $G$, and we thus\nassume the existence of a component $C_0=\\{s\\}$ in $G\\setminus B$. As\n$\\{s\\} \\subsetneq A\\setminus B$, let $C_1\\neq \\{s\\}$ be some connected\ncomponent of $G\\setminus B$. Let also $C_2$ be some component of\n$G\\setminus A$. Note that for any of these components $C_i$,\n$N(C)\\subsetneq S$. Otherwise one could contract (if needed) all the\ncomponent into a single vertex $s'$ and the graph induced by\n$\\{s\\}\\cup N[C_1]$ or by $\\{s\\}\\cup N[C_2]$ (a proper minor of $G$)\nwould be a smaller counter-example. Note now that $S = N(s)\\cup\nN(C_i)$ for $i=1$ or 2. Indeed, otherwise the separation $(N[s]\\cup\nN[C_i], V(G)\\setminus (C_i\\cup \\{s\\}))$ would be a counter-example\ncontradicting the minimality of $S$. Finally note that\n$N(C_2)\\not\\subseteq N(C_1)$, as otherwise contracting $C_1$ into a\nsingle vertex $s'$ and considering the graph induced by\n$C_2\\cup\\{s'\\}$ one would obtain a smaller counter-example. Thus there\nexists a vertex $x\\in N(s)\\cap N(C_2)$ such that $x\\notin\nN(C_1)$. Contracting the edge $xs$ and contracting the whole component\n$C_2$ into $x$, and considering the graph induced by $C_1\\cup \\{x\\}$\none obtains a smaller counter-example (where $x$ plays the role of\n$s$).\n \nConsider now the case where $s\\in S=A\\cap B$ and note that $|S|< 6$.\nAssume that among all the separations containing $s$, $(A,B)$\nminimizes $|S|$. Note that every connected component $C$ of $G\n\\setminus S$ is such that $s\\in N(C)$. Indeed, we have seen above that\notherwise $C$ would be such that $|N(C)|\\ge 8$ (by considering the\nseparation $(V(G)\\setminus C,N[C])$ and noting that $\\{s\\}\\subsetneq\nV(G)\\setminus N[C]$), and this would contradict the fact that $|S|<\n6$. This implies that every connected component $C$ of $G \\setminus S$\nis such that $N(C)=S$. Otherwise $(V(G)\\setminus C, N[C])$ would be a\nseparation containing $s$ contradicting the minimality of $S$. We can\nassume without loss of generality that $s$ has at most as many\nneighbors in $B\\setminus A$ than in $A\\setminus B$. In particular,\nsince $\\deg(s)\\le 7$, $s$ has at most 3 neighbors in $B\\setminus\nA$. Note that $B \\not\\subseteq N[s]$ as otherwise $G \\setminus\n(B\\setminus A)$ would be a smaller counter-example. Thus there is an\nedge in $G[B\\setminus N[s]$ that belongs to at least 6 triangles, and\nthus $|B|\\ge 9$ ($s$ and the 6 triangles). Thus contracting every\ncomponent of $A \\setminus B$ on $s$, results in a proper minor $G'$\nof $G$ such that $\\deg(s) \\leq 7$ (at most 4 in $S$ and 3 in\n$B\\setminus A$), such that $N[s]\\subsetneq V(G')$ (as\n$|V(G')|=|B|\\ge 9$), and such that every edge not in $E(N[s])$\nbelongs to $6$ triangles, contradicting the minimality of $G$. This\nconcludes the proof of the lemma.\n\\end{proof}\n\nBy Theorem~\\ref{th:jorg} $G$ has at most $6n-20$ edges, and thus there\nare several vertices in $G$ with degree at most 11. Let us prove that\nthere are such vertices out of $N[s]$.\n\n\\begin{lem}\nThere are at least 2 vertices in $V(G) \\setminus N[s]$ with degree at\nmost 11.\n\\label{lem:2small_ink8}\n\\end{lem}\n\\begin{proof}\nAssume for contradiction that every vertex of $V(G) \\setminus N[s]$\nbut one, say $x$, has degree at least 12, and recall that such vertex\nhas degree at least 8. Note that every vertex $v\\in N(s)$ has a\nneighbor in $V(G) \\setminus N[s]$, as otherwise $G\\setminus v$ would\nbe a smaller counter-exemple. Thus every vertex $v\\in N(s)$ has an\nincident edge that belongs to at least 6 triangles (without using the\nedge $sv$), which implies that $\\deg(v)\\ge 8$. This implies that the\nnumber of edges in $G$ verifies :\n\\[ 12n-42 \\ge 2|E(G)| = \\sum_{v\\in V(G)} \\deg(v) \\ge 8 + k + 8k + 12(n-k-2) \\] \nwhere $k=\\deg(s)$. This implies that $3k\\ge 26$ which contradicts\nthe fact that $k=\\deg(s) \\le 7$. This concludes the proof of the\nlemma.\n\\end{proof}\n\nAs for any vertex $u\\in V(G)\\setminus N[s]$ each of its incident edges\nbelongs to 6 triangles, the graph $G[N(u)]$ has minimum degree at\nleast 6. As $G$ does not contain $K_8$ as subgraph, this also implies\nthat $\\deg(u)\\ge 8$. So there are at least two vertices in\n$V(G)\\setminus N[s]$ with degree between 8 and 11. The next lemma\ntells us more on the neighborhood of these small degree vertices.\n\\begin{lem}\nFor every vertex $u \\in V(G) \\setminus N[s]$ with degree at most 11 in\n$G$, $G[N(u)]$ is isomorphic to $K_{2,2,2,2}$, $K_{3,3,3}$ or\n$\\overline{P_{10}}$.\n\\label{lem:NminDeg}\n\\end{lem}\n\\begin{proof}\nLet $u$ be any vertex of $V(G) \\setminus N[s]$ with degree at most 11\nin $G$. As observed earlier $8\\le \\deg(u)\\le 11$ and\n$\\delta(G[N(u)])\\ge 6$. Assume for contradiction that $N(u)$, is not\nisomorphic to $K_{2,2,2,2}$, $K_{3,3,3}$ or $\\overline{P_{10}}$. Note\nthat $|N(u)\\cap N(s)| \\le 6$, as otherwise Lemma~\\ref{lem:compk8}\nwould contradict the $K_8$-minor freeness of $G$.\n\nBy Lemma~\\ref{lem:compk8} one of the (at least two) vertices in\n$N(u)\\setminus N(s)$, say $x$, has an incident edge in $G[N(u)]$ that\nbelongs to at most 5 triangles in $G[N[u]]$. Thus the sixth triangle\ncontaining this edge goes through a vertex $v$ of $V(G) \\setminus\n(N[u] \\cup \\{s\\})$.\n\nLemma~\\ref{lem:connectk8withs} implies that the connected component\n$C$ of $v$ in $V(G) \\setminus N[u]$ is such that $N(C)\\ge 8$. The\ngraph obtained by contracting $C$ into a single vertex has a\n$K_8$-minor (by Lemma~\\ref{lem:compk8}), a contradiction.\n\\end{proof}\n\nA $K_3$-minor rooted at $\\{a, b, c\\}$, or a $\\{a, b, c\\}$-minor, is a\n$K_3$-minor in which you can contract edges incident to $a$, $b$ or\n$c$, to obtain a $K_3$ with vertex set $\\{a, b, c\\}$. For the rest of\nthe proof we need the following characterization of rooted $K_3$-minor.\n\n\\begin{thm}[D. R. Wood and S. Linusson, Lemma 5 of~\\cite{wl1}]\nFor distinct vertices a, b, c in a graph G, either:\n\\begin{itemize}\n\\item $G$ contains an $\\{a, b, c\\}$-minor, or\n\\item for some vertex $v \\in V(G)$ at most one of $a, b, c$ are in each component of $G \\setminus v$.\n\\end{itemize}\n\\label{th:rootedtri}\n\\end{thm}\n\n\\begin{lem}\nFor every vertex $u \\in V(G) \\setminus N[s]$ with degree at most 11 in\n$G$, the graph $G[N(u)]$ is not isomorphic to $K_{3,3,3}$.\n\\label{lem:noK333}\n\\end{lem}\n\\begin{proof}\nObserve that adding two vertex disjoint edges or three edges of a\ntriangle in $K_{3,3,3}$ yields a $K_7$-minor. Now assume for\ncontradiction that there exists some vertex $u\\in V(G) \\setminus N[s]$\nsuch that $G[N(u)]$ is isomorphic to $K_{3,3,3}$.\n\nAs the set $N(u) \\setminus N[s]$ is non-empty (it has size at least\n$9-7$) and as every vertex $v$ in $N(u) \\setminus N[s]$ has degree at\nleast $8$, and thus has a neighbor out of $N[u]$, $G \\setminus N[u]$\nhas a connected component $C\\neq \\{s\\}$. By\nLemma~\\ref{lem:connectk8withs} $|N(C)| \\geq 8$.\n\nIf $G \\setminus N[u]$ has another connected component $C'$ such that\n$|N(C')| \\geq 6$, one can create two vertex disjoint edges in\n$K_{3,3,3}$ by contracting two vertex disjoint paths with non-adjacent\nends in $N(u)$, one living in each component. This would contradict\nthe $K_8$-minor freeness of $G$. Thus if there is a component $C'$,\nwe should have $C'=\\{s\\}$ and $\\deg(s)\\le 5$, as by\nLemma~\\ref{lem:connectk8withs} a component $C'\\neq \\{s\\}$ would be\nsuch that $|N(C')| \\geq 8$. In the following we consider the graph\n$G' = G[N[u] \\cup C]$ (which is $G$ or $G \\setminus s$).\n\nLet $\\{a_1,a_2,a_3\\}$, $\\{b_1,b_2,b_3\\}$ and $\\{c_1,c_2,c_3\\}$ be the\nthree disjoint stables of $N(u) = K_{3,3,3}$. Without loss of\ngenerality we can assume that $\\{a_1,a_2,a_3\\}\\subset N(C)$, and that\n$a_1\\notin N(s)$. As the edges of $N(u)$ incident to $a_1$ belong to\nat least 6 triangles, $a_1$ has at least two neighbors in $G'\n\\setminus N[u]$. By Theorem~\\ref{th:rootedtri} (applied to\n$\\{a_1,a_2,a_3\\}$ in the graph $G''= G'\\setminus\n\\{u,b_1,b_2,b_3,c_1,c_2,c_3\\}$), there is a vertex $v \\in V(G'')$ such\nthat at most one of $a_1, a_2, a_3$ are in each component of $G''\n\\setminus v$. Note that since $a_1$, $a_2$ and $a_3\\in N(C)$, all the\nsets $C\\cup\\{a_i,a_j\\}$ induce a connected graph, and thus $v\\neq\na_1$, $a_2$ or $a_3$. Equivalently we have that $v\\in V(G') \\setminus\nN[u]$. Hence $G'' \\setminus \\{v\\}$ contains at least $3$ components\n$C_1$, $C_2$ and $C_3$ with $a_i \\in C_i$, for $1 \\leq i \\leq\n3$. Since $a_1$ has at least two neighbors in $G' \\setminus N[u]$, one\nof them is distinct from $v$ and we can define $C'_1$ as a connected\ncomponent of $C_1\\setminus \\{a_1\\}$. Note that by construction\n$N(C'_1) \\subset N(u)\\cup\\{v\\}$. Since $C'_1\\neq\\{s\\}$ (as $a_1\\notin\nN(s)$) and as we might have $v=s$, Lemma~\\ref{lem:connectk8withs}\nimplies that $N(C'_1)\\ge 6$ (including $v$ and $a_1$). So $C'_1$ has\nat least 4 neighbors in $\\{b_1,b_2,b_3,c_1,c_2,c_3\\}$ and there is a\npath with interior vertices in $C'_1$ between two vertices $b_i$ and\n$b_j$, or between two vertices $c_i$ and $c_j$. Furthermore, there is\na path with interior vertices in $C_2 \\cup \\{v\\} \\cup C_3$ between the\nvertices $a_2$ and $a_3$. This contradicts the $K_8$-minor freeness of\n$G$, and thus concludes the proof of the lemma.\n\\end{proof}\n\n\\begin{lem}\nFor every vertex $u \\in V(G) \\setminus N[s]$ with degree at most 11 in\n$G$, the graph $G[N(u)]$ is not isomorphic to $K_{2,2,2,2}$.\n\\label{lem:noK2222}\n\\end{lem}\n\\begin{proof}\nAssume for contradiction that there exists some vertex $u\\in V(G)\n\\setminus N[s]$ such that $G[N(u)]$ is isomorphic to\n$K_{2,2,2,2}$. One can check that adding two edges in $K_{2,2,2,2}$\ncreates a $K_7$-minor. Thus as $G$ is $K_8$-minor free it should not\nbe possible to add (by edge contractions) two new edges in $N(u)$.\n\n\\begin{claim}\nA vertex $v\\in V(G) \\setminus N[u]$ has at most six neighbors in\n$N(u)$.\n\\label{claim:k8-v6neighbors}\n\\end{claim}\n\\begin{proof}\nIf there was a vertex $v$ with 8 neighbors in $N(u)$, $N[u]\\cup \\{v\\}$\nwould induce a $K_{2,2,2,2,2}$, a contradiction to the definition of\n$G$. We thus assume for contradiction that there is a vertex $v$ with\nexactly 7 neighbors in $N(u)$. Note that eventually $v=s$. Let us\ndenote $x$ the only vertex in $N(u)\\setminus N(v)$. Note that among\nthe 4 non-edges of $G[N(u)]$, only one cannot be created by\ncontracting an edge incident to $v$. So if there is a path whose ends\nare non-adjacent in $N(u)$ and whose inner vertices belong to\n$V(G)\\setminus (N[u]\\cup \\{v\\})$, then we have a $K_8$-minor, a\ncontradiction. There is clearly such path if $s\\neq v$ and if $s$ has\n5 neighbors in $N(u)$, we thus have that either $s=v$ or $s$ has at\nmost 4 neighbors in $N(u)$. Both cases imply that some edge $xy$\n(incident to $x$) does not belong to $G[N[s]]$, and thus $xy$ belongs\nto at least 6 triangles. As $xy$ belongs to only 5 triangles in\n$G[N[u]]$, this implies the existence of a vertex $w\\in V(G) \\setminus\nN[u]$ adjacent to $x$ such that $w\\neq s, v$. Let $C$ be the connected\ncomponent of $w$ in $G\\setminus (N[u]\\cup \\{v\\})$. As $C\\neq \\{s\\}$,\nLemma~\\ref{lem:connectk8withs} implies that $N(C)$ has size at least\n6. Thus $C$ has at least 5 neighbors in $N(u)$ and one can link two\nnon-adjacent vertices of $N(u)$ by a path going through $C$, a\ncontradiction.\n\\end{proof}\n\nBy Lemma~\\ref{lem:2small_ink8} there exists another vertex $u'\\in V(G)\n\\setminus N[s]$ such that $\\deg(u')\\le 11$. By Lemma~\\ref{lem:NminDeg}\nand Lemma~\\ref{lem:noK333}, $G[N(u')]$ is isomorphic to $K_{2,2,2,2}$\nor $\\overline{P_{10}}$.\n\n\\begin{claim}\nThe vertices $u$ and $u'$ are non-adjacent.\n\\end{claim}\n\\begin{proof}\nWe assume for contradiction that $u$ and $u'$ are adjacent and we\nfirst consider the case where $G[N(u')]$ is isomorphic to\n$K_{2,2,2,2}$. In this case, as $u'$ has allready 7 neighbors in\n$N[u]$, $u'$ has a exactly one neighbor $v$ in $G\\setminus N[u]$. As\n$v$ has 7 neighbors in $N(u')$, we have that $|N(u)\\cap N(v)|\\ge 7$, a\ncontradiction to Claim~\\ref{claim:k8-v6neighbors}.\n\nIf $G[N(u')]$ is isomorphic to $\\overline{P_{10}}$, this implies that\n$G[N(u)\\cap N(u')]$ is isomorphic to $\\overline{C_6}$ (the complement\nof the 6-cycle). This is not compatible with $G[N(u)]$ being\nisomorphic to $K_{2,2,2,2}$, as this in turn implies that $G[N(u)\\cap\nN(v)]$ is isomorphic to $K_{2,2,2}$.\n\\end{proof}\n\nAs by Lemma~\\ref{lem:connectk8withs} there is no $(\\le 5)$-separator\n$(A,B)$ with $u\\in A\\setminus B$ and $u'\\in B\\setminus A$, Menger's\nTheorem implies the existence of 6 vertex disjoint paths between $u$\nand $u'$. These paths induces $6$ disjoint paths $P_1 \\ldots P_6$\nbetween $N(u)$ and $N(u')$. Note that every vertex in $N(u) \\cap\nN(u')$ can be seen as a path of length $0$.\n\nTherefore, since $N(u)$ is isomorphic to $K_{2,2,2,2}$, there are two\nnon-edges $a_1a_2$ and $a_3a_4$ of $G[N(u)]$ such that each $a_i$ is\nthe end of the path $P_i$. We denote by $b_i$, $1 \\leq i \\leq 4$ the\nend in $N(u')$ of the path $P_i$. Note that if $a_i \\in N(u) \\cap\nN(u')$ then $a_i = b_i$. Moreover we can suppose that the choice of\n$a_1a_2$ and $a_3a_4$ maximizes the size of $\\{a_1,a_2,a_3,a_4\\} \\cap\nN(u')$. Since $N(u)$ is isomorphic to $K_{2,2,2,2}$ and since $|N(u)\n\\cap N(u')| \\leq 6$ (by Claim~\\ref{claim:k8-v6neighbors}), there are\nat most two vertices in $N(u) \\cap N(u')$ distinct from $a_1,a_2,a_3$,\nand $a_4$. Let $X= (N(u) \\cap N(u')) \\setminus \\{a_1,a_2,a_3,a_4\\}$.\n\nSince both $K_{2,2,2,2}$ and $\\overline{P_{10}}$ are $6$-connected\nthen $N[u']$ is $7$-connected and so $G[N[u'] \\setminus X]$ is\n$5$-connected. Moreover $G[N[u'] \\setminus X]$ has too many edges to\nbe planar. Indeed, it has $9-|X|$ vertices and at least $32-7|X|$\nedges, which is more than $3(9-|X|)-6$ for $0 \\le |X| \\le 2$. We now\nneed the following theorem of Robertson and Seymour about vertex\ndisjoint pairs of paths.\n\n\\begin{thm}[Robertson and Seymour~\\cite{rs1}]\n\\label{th:disjointpath}\nLet $v_1 , \\ldots, v_k$ be distinct vertices of a graph $H$. Then either\n\\begin{itemize}\n\\item[(i)] there are disjoint paths of $H$ with ends $p_1$ $p_2$ and\n $q_1$ $q_2$ respectively, so that $p_1$, $q_1$, $p_2$, $q_2$ occur\n in the sequence $v_1, \\ldots, v_k$ in order, or\n\\item[(ii)] there is a $(\\le 3)$-separation $(A,B)$ of $H$ with $v_1,\n \\ldots, v_k \\in A$ and $|B \\setminus A| \\geq 2$, or\n\\item[(iii)] $H$ can be drawn in a disc with $v_1 , \\ldots, v_k$ on\n the boundary in order.\n\\end{itemize}\n\\end{thm}\n\nApplying this theorem to the graph $G[N[u'] \\setminus X]$ with\n$(v_1,\\ldots v_k) = (b_1,b_3,b_2,b_4)$ one obtains that there are two\nvertex disjoint paths in $N[u'] \\setminus X$, a path $P_{1,2}$ between\n$b_1$ and $b_2$, and a path $P_{3,4}$ between $b_3$ and $b_4$. Theses\npaths are disjoint from $N[u]$ by construction, except possibly at\ntheir ends. Finally, since the paths $P_i$, for $1 \\leq i \\leq 4$,\nconstructed above are disjoint from $N[u]$ and from $N[u'] \\setminus\nX$, except at their ends, there exists two disjoint paths respectively\nlinking $a_1$ with $a_2$ (through $P_1$, $P_{1,2}$ and $P_2$), and\n$a_3$ with $a_4$ (through $P_3$, $P_{3,4}$ and $P_4$). This\ncontradicts the $K_8$-minor freeness of $G$ and thus concludes the\nproof of the lemma.\n\\end{proof}\n\n\nBy Lemma~\\ref{lem:2small_ink8} there exists at least two vertices\n$u$ and $u'\\in V(G) \\setminus N[s]$ with degree at most $11$. By\nLemma~\\ref{lem:NminDeg}, Lemma~\\ref{lem:noK333}, and\nLemma~\\ref{lem:noK2222}, both $G[N(u)]$ and $G[N(u')]$ are isomorphic\nto $\\overline{P_{10}}$. The two graphs induced by $N[u]$ and $N[u']$\nare close to a $K_8$-minor as observed in the following claim.\n\\begin{claim}\n\\label{cm:2edgeP10}\nIn $\\overline{P_{10}}$, adding two edges $ab$ $cd$, such that $ab$,\n$bc$ and $cd \\notin E(\\overline{P_{10}})$, creates a $K_7$-minor.\nFurthermore adding three edges $e_1$ $e_2$ and $e_3$, such that $e_1\n\\cap e_2 \\cap e_3 =\\emptyset$ in $\\overline{P_{10}}$, creates a\n$K_7$-minor.\n\\end{claim}\n\\begin{proof}\nOne can easily check the accuracy of the first statement, by noting\nthat adding any such pair of edges $ab$ and $cd$, yields the same\ngraph, and by noting that adding the edges $u_1u_2$ and $u_3u_4$ in\n$\\overline{P_{10}}$ (notations come from Figure~\\ref{fig:P10}) the\npartition $\\{\\{0,2\\},\\{1\\},\\{3\\},\\{4\\},\\{5\\}, \\{6,7\\}, \\{8,9\\}\\}$\ninduces a $K_7$-minor.\n\nFor the second statement, we can assume that the three added edges are\nsuch that they pairwise do not correspond to the first statement.\nWithout loss of generality, assume that one of the three edges is\n$u_0u_5$, and note that the other added edges are distinct from\n$u_1u_2$, $u_1u_6$, $u_3u_4$, $u_4u_9$, $u_2u_7$, $u_7u_9$, $u_3u_8$\nand $u_6u_8$. Consider the case where one of the other added edges is\nincident to $u_0u_5$. By symmetry one can assume that this edge is\n$u_0u_1$, but this implies that the third added edge is distinct from\n$u_0u_4$ (as the three edges would intersect), and from $u_3u_4$,\n$u_6u_9$, $u_5u_7$ and $u_5u_8$ (by the first statement). There is\nthus no remaining candidate for the third edge.\nThis implies that it is sufficient to consider the case where the\nedges $u_0u_5$, $u_2u_3$ and $u_6u_9$ are added in\n$\\overline{P_{10}}$. In this case the partition $\\{\\{1\\},\\{ 4\\},\\{\n7\\},\\{ 8\\},\\{ 0,5\\},\\{ 6,9\\},\\{ 2,3\\} \\}$ induces a $K_7$-minor.\n\\end{proof}\n\\begin{figure}[h]\n\\centering\n\\includegraphics{petersen.eps}\n\\caption{The Petersen graph $P_{10}$.}\n\\label{fig:P10}\n\\end{figure}\n\nLet us list the induced subgraphs of $\\overline{P_{10}}$ of size 6.\n\\begin{claim}\n\\label{cm:k8-P10-6subgraphs}\nThere are exactly 6 distinct induced subgraphs of size 6 in\n$\\overline{P_{10}}$, including $K_{2,2,2}$. The complements of these\ngraphs are represented in Figure~\\ref{fig:P10-6subgraphs}.\nFurthermore note that every induced subgraphs of $\\overline{P_{10}}$\nof size at least 7, has a subgraph of size 6 distinct from\n$K_{2,2,2}$.\n\\end{claim}\nWe do not prove the claim here as one can easily check its accuracy.\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=390px]{p10-sub.eps}\n\\caption{The complements of the subgraphs of $\\overline{P_{10}}$ of\n size 6 (i.e. the subgraphs of $P_{10}$ of size 6).}\n\\label{fig:P10-6subgraphs}\n\\end{figure}\n\n\\begin{lem}\nThe vertices of $N(u)\\setminus N(s)$ (resp. of $N(u')\\setminus N(s)$)\nhave degree at least 12. Thus in particular, $u$ and $u'$ are\nnon-adjacent.\n\\label{lem:k8-P10-deg12}\n\\end{lem}\n\\begin{proof}\nWe assume for contradiction that $u$ has a neighbor $v$ of degree at\nmost 11. By Lemma~\\ref{lem:NminDeg}, Lemma~\\ref{lem:noK2222}, and\nLemma~\\ref{lem:noK333}, the graph $G[N(v)]$ is isomorphic to\n$\\overline{P_{10}}$.\n\nAssume $v=u_0$ in Figure~\\ref{fig:P10}. Since $N(u_0) \\supset\n\\{u,u_2,u_3,u_6,u_7,u_8,u_9\\}$, the adjacencies in\n$G[\\{u,u_2,u_3,u_6,u_7,u_8,u_9,\\}]$ allow us to denote $u$ by $u'_0$,\nand denote $u'_1$, $u'_4$ and $u'_5$ the vertices in $N(u_0)\\setminus\nN[u]$, in such a way that these indices again correspond to\nFigure~\\ref{fig:P10}. It is now easy to see that contracting one edge\nin each of the paths $(u_2,u'_4,u_7)$ and $(u_6,u'_5,u_9)$ creates the\nedges $u_2u_7$ and $u_6u_9$ in $G[N[u]]$ and thus yields a $K_8$-minor\n(by Claim~\\ref{cm:2edgeP10} as $u_7u_9$ is a non-edge of\n$\\overline{P_{10}}$), a contradiction.\n\\end{proof}\n\nThe vertices $u$ and $u'$ are non-adjacent, however they can share\nneighbors. Let us prove that they cannot share more than 7 neighbors.\n\\begin{lem}\n$|N(u) \\cap N(u')|\\le 7$.\n\\label{lem:k8-P10->=7common}\n\\end{lem}\n\\begin{proof}\nAssume for contradiction that $|N(u) \\cap N(u')|\\ge 8$, that is\nequivalently that $|N(u) \\setminus N(u')|\\le 2$ and $|N(u') \\setminus\nN(u)|\\le 2$. Note that as $\\deg(s)\\le 7$ the set $(N(u) \\cap N(u'))\n\\setminus N(s)$ is non-empty, and denote $x$ one of its vertices. By\nLemma~\\ref{lem:k8-P10-deg12}, this vertex $x$ as degree at least $12$.\nAs it has exactly 6 neighbors in $N(u)$, at most 2 neighbors in $N(u')\n\\setminus N(u)$, and as it is adjacent to both $u$ and $u'$, $x$ has\nat least two neighbors in $V(G) \\setminus (N[u] \\cup N[u'])$. Thus\nthere exists a component $C\\neq\\{s\\}$ in $G \\setminus (N[u] \\cup\nN[u'])$. As $C\\neq\\{s\\}$ and $N(C)\\subseteq N(u)\\cup N(u')$,\nLemma~\\ref{lem:connectk8withs} implies that $|N(C)|\\ge 8$. Therefore,\nas $|N(u') \\setminus N(u)|\\le 2$, $|N(C) \\cap N(u)| \\ge 6$ and there\nexist a path $P$ with inner vertices in $C$ and with non-adjacent ends\nin $N(u)$ (by Claim~\\ref{cm:k8-P10-6subgraphs}). Let us denote $x$ and\n$y$ the ends of $P$. As $|N(u) \\cap N(u')|\\ge 8$ and by\nClaim~\\ref{cm:k8-P10-6subgraphs}, there exists a vertex $z\\in N(u)\n\\cap N(u')$ such that $z\\neq x$ or $y$, and such that contracting the\nedge $zu'$ creates at least two edges in $N(u)$. As these three added\nedges ($xy$ and the edges adjacent to $z$) do not intersect,\nClaim~\\ref{cm:2edgeP10} implies that there is a $K_8$-minor, a\ncontradiction.\n\\end{proof}\n\nAs by Lemma~\\ref{lem:connectk8withs} there is no $(\\le 5)$-separator\n$(A,B)$ with $u\\in A\\setminus B$ and $u'\\in B\\setminus A$, Menger's\nTheorem implies the existence of 6 vertex disjoint paths $P_1 \\ldots\nP_6$ between $u$ and $u'$.\nBy minimizing the total length of these paths we can assume that\neach vertex in $N(u)\\cap N(u')$ corresponds to one of these paths,\nand that any of these paths intersect $N(u)$ (resp. $N(u')$) in\nonly one vertex. Contracting the inner edges (those non-incident to\n$u$ or $u'$) of these paths, and considering the graph induced by\n$N[u]\\cup N[u']$ one obtains a graph $H$ such that:\n\\begin{itemize}\n\\item $u$ and $u'$ are nonadjacent and $|N_H(u)\\cap N_H(u')| = 6$ or\n $7$.\n\\item $\\deg_H(u)=10$, and $H[N(u)]$ contains $\\overline{P_{10}}$ as a\n subgraph.\n\\item $\\deg_H(u')=10$, and $H[N(u')]$ contains $\\overline{P_{10}}$ as\n a subgraph.\n\\end{itemize}\n\nIf the graph induced by $N_H(u)\\cap N_H(u')$ is isomorphic to\n$K_{2,2,2}$, then one can assume without loss of generality that\n$N(u)=\\{u_0,\\ldots,u_9\\}$ and that $N(u')=\\{u_0,u'_1, u_2,u_3,\nu'_4,u_5,u_6, u'_7,u'_8,u_9\\}$, where the indices correspond to\nFigure~\\ref{fig:P10}. Now observe that contracting the edge $u_0u'$,\nthe path $(u_6,u'_7,u'_8)$, and the path $(u_2,u'_4,u'_1)$,\nrespectively create the edges $u_0u_5$, $u_6u_9$, and $u_2u_3$. This\nimplies by Claim~\\ref{cm:2edgeP10} that $N[u]$ contains a $K_8$-minor,\na contradiction. We can thus assume by\nClaim~\\ref{cm:k8-P10-6subgraphs} that the complement of $N_H(u)\\cap\nN_H(u')$ contains a path $(a,b,c,d)$. As $\\overline{P_{10}}$ is\n6-connected, the graph induced by $\\{a,b\\}\\cup (N_H(u')\\setminus\nN(u))$ is connected, and thus contains a path from $a$ to $b$. By\nClaim~\\ref{cm:2edgeP10}, this path with the path $(c,u',d)$, imply\nthat $H$ (which is a minor of $G$) contains a $K_8$-minor, a\ncontradiction. Thus there is no counter-example $G$, and this\nconcludes the proof of the theorem.\n\\end{proof}\n\nThe proof Theorem~\\ref{th:k8-deg9} is very similar. To do this one can\nprove the following variant of Theorem~\\ref{th:k8triwiths}.\n\\begin{thm}\nConsider a connected $K_8$-minor free graph $G$ with a vertex $s$ of\ndegree at most 7, such that $N[s] \\subsetneq V(G)$ and such that\n$\\min_{v\\in V(G)\\setminus N[s]}\\ge 9$. Then $G$ has an edge\n$e \\in E(G) \\setminus E(G[N[s]])$ that belongs to at most 5 triangles.\n\\end{thm}\nThe proof of this theorem is as the proof of\nTheorem~\\ref{th:k8triwiths}, except that one does not need to consider\nthe case where some vertex $u$ is such that $N(u)$ induces a\n$K_{2,2,2,2}$.\n\n\\section{Global density of triangles}\\label{sec:moytri}\n\nIn this section, we investigate the relation between the number of\ntriangles and the number of edge of a graph. Denotes by $\\rho =\n\\frac{t}{m}$ the ratio between the number of triangles $t$ and the\nnumber of edges $m$ of a graph $G$. For each $k$, what is the minimum\nnumber $f(k)$ such that for all graph $G$ with $\\rho \\ge f(k)$, $G$\ncontains a $K_k$ minor ?\n\nIt is easy to notice that 2-trees on $n \\ge 2$ vertices have exactly\n$1 + 2(n-2)$ edges and $n-2$ triangles. Furthermore, for $k \\ge 3$\none can notice that $k$-trees on $n \\ge k$ vertices have exactly\n$\\frac{k(k-1)}{2} + k(n-k)$ edges and $\\frac{k(k-1)(k-2)}{6} +\n(n-k)\\frac{k(k-1)}{2}$ triangles. Thus any $k$-tree, for $k \\ge 2$,\nverifies\n\\[ t = \\frac{k-1}{2} m -\\frac{1}{2}{{k+1}\\choose{3}} .\\]\nSince $k$-trees are $K_{k+2}$-minor free, for all $k \\ge 4$ there\nexists $K_k$-minor free graphs with $\\frac{k-3}{2} m\n-\\frac{1}{2}{{k-1}\\choose{3}}$ triangles.\n\nWe deduce that for all $k \\ge 4$, $f(k) \\geq \\frac{k-3}{2}$. Indeed\nfor every $\\epsilon > 0$, there exists a number $m$ and a $K_k$-minor\nfree graph with $m$ edges such that $\\frac{k-3}{2} - \\epsilon \\leq\n\\rho < \\frac{k-3}{2}$. In fact, for $4 \\le k \\le 7$, the following\ntheorem proves that this lower bound is best possible, so we have\n$f(k) = \\frac{k-3}{2}$.\n\n\\begin{thm}\nFor $4 \\le k \\le 7$ (resp. $k = 8$), every graph with $m \\ge 1$ edges\nand $t \\ge m(k-3)\/2$ triangles has a $K_k$-minor (resp. a $K_8$- or a\n$K_{2,2,2,2,2}$-minor).\n\\label{thm:global}\n\\end{thm}\n\n\\begin{proof}\nConsider by contradiction, a non-trivial $K_k$-minor free\n(resp. $K_8$- and $K_{2,2,2,2,2}$-minor free) graph $G$ with $t\n\\ge m(k-3)\/2$ triangles. Among the possible graphs $G$, consider one\nthat minimizes $m$ (given that $m \\ge 1$).\n\nGiven any edge $uv \\in E(G)$ let $H_{uv} = G[N(u) \\cap N(v)]$ and\ndenote $n'$ and $m'$ its number of vertices and edges respectively.\nContracting $uv$ yields a proper minor of $G$, with exactly $1 + n'$\nedges less, and with at most $n' + m'$ triangles less. Thus by\nminimality of $G$, for every edge $uv$\n\\[ n' + m' > \\frac{k-3}{2} (1 + n') \\]\nwhich implies that\n\\[ m' > \\frac{k-3}{2} + \\frac{k-5}{2} n' .\\]\nOn the other hand we have that $\\frac{n'(n' - 1)}{2} \\ge m'$, and this\nimplies that $n'$ should verify $(n' + 1)(n' + 3 - k) > 0$, that is\nthat $n' \\ge k - 2$. In other words, every edge $uv$ of $G$ belongs to\nat least $k-2$ triangles. By Theorems~\\ref{th:krtri},\n(resp. Theorem~\\ref{th:k8tri}), this contradicts the $K_k$-minor\nfreeness (resp. $K_8$- and $K_{2,2,2,2,2}$-minor freeness) of $G$.\n\\end{proof}\n\n\n\\section{Application to stress freeness of graphs}\\label{sec:stress}\n\nThe motivation of this application is a problem that arises from the\nstudy of tension and compression forces applied on frameworks in the\nEuclidian space $\\mathbb{R}^d$. A $d$-framework is a graph $G=(V,E)$\nand an embedding $\\rho$ of $G$ in $\\mathbb{R}^d$. The reader should\nthink of a framework as an actual physical system where edges are\neither straight bars or cables and vertices are articulated joints. A\n\\textit{stress} on a framework $(G,\\rho)$ is a function $\\omega:\nE(G)\\, \\rightarrow\\; \\mathbb{R}$ such that $\\forall v \\in V$,\n\\[\\underset{\\{u,v\\}\\in E}{\\sum}\\:\\omega(\\{u,v\\})(\\rho(v) - \\rho(u)) = 0 .\\]\nStress corresponds to some notion of equilibrium for the associated\nphysical system. Each vertex is affected by tension and compression\nforces created by the bars and cables. $\\omega(\\{u,v\\})$ can be\nthought of as the magnitude of such force per unit length, with\n$\\omega(\\{u,v\\}) < 0$ for a cable tension and $\\omega(\\{u,v\\}) > 0$\nfor a bar compression. A stress is a state of the system where these\nforces cancel each other at every vertex. We can see that every\nframework admits a \\textit{trivial} stress where $\\omega$ is\nidentically zero. A $d$-framework admitting only the trivial stress\nis called \\textit{$d$-stress free}.\n\nTo make this notion independent of the embedding of $G$, the following\nwas introduced. A graph $G$ is \\textit{generically $d$-stress free}\nif the set of all $d$-stress free embeddings of $G$ in $\\mathbb{R}^d$\nis open and dense in the set of all its embeddings (i.e. every\nstressed embedding of $G$ is arbitrary close to a stress free\nembedding).\n\nThis notion has been first used on graphs coming from $1$-skeletons of\n$3$-dimensional polytopes\n\\cite{cauchy-13,maxwell-64,cw-93,whiteley-84}, which are planar by\nSteiniz's theorem. Gluck generalized the results on $3$-dimensional\npolytopes to the whole class of planar graphs.\n\\begin{thm}[Gluck, 1975,~\\cite{gluck-75}]\nPlanar graphs are generically $3$-stress free.\n\\label{th:gluckstress}\n\\end{thm}\nNevo proved that we can generalize Theorem~\\ref{th:gluckstress} for\n$K_5$-minor free graphs, and extended the result as follows.\n\n\\begin{thm}[Nevo, 2007,~\\cite{nevo1}]\nFor $2 \\leq r \\leq 6$, every $K_r$-minor free graph is\ngenerically $(r-2)$-stress free.\n\\label{th:nevostress}\n\\end{thm}\n\nHe conjectured this to hold also for $r = 7$ and noticed that the\ngraph $K_{2,2,2,2,2}$ is an obstruction for the case $r=8$. Indeed,\n$K_{2,2,2,2,2}$ is $K_8$-minor free and has too many edges to be\ngenerically $6$-stress free (a generically $\\ell$-stress free graph\nhas at most $\\ell n - {\\ell+1 \\choose 2}$ edges~\\cite{nevo1}). We\nanswer positively to Nevo's conjecture and we give a variant for the\ngenerically $6$-stress freeness.\n\n\\begin{thm}\nEvery $K_7$-minor free graph (resp. $K_8$- and $K_{2,2,2,2,2}$-minor\nfree graph) is generically $5$-stress free (resp. $6$-stress free).\n\\label{th:genstressk78}\n\\end{thm}\n\nThe following result of Whiteley~\\cite{whiteley-89} is used to derive\nTheorem~\\ref{th:genstressk78}.\n\\begin{thm}[Whiteley, 1989,~\\cite{whiteley-89}]\nLet $G'$ be obtained from a graph $G$ by contracting an edge $\\{u,v\\}$.\nIf $u$, $v$ have at most $d - 1$ common neighbors and $G'$ is generically\n$d$-stress free, then $G$ is generically $d$-stress free.\n\\label{th:whitcontract}\n\\end{thm}\n\nNow, we prove Theorem~\\ref{th:genstressk78}.\n\\begin{proof}\nAssume that $G$ is a $K_7$-minor free graph (resp. a $K_8$- and\n$K_{2,2,2,2,2}$-minor free graph). Without loss of generality, we can\nalso assume that $G$ is connected. Now, contract edges belonging to\nat most $4$ (resp. 5) triangles as long as it is possible and we\ndenotes by $G'$ the graph obtained. Note that by construction, every\nedge of $G'$ belongs to 5 (resp. 6) triangles. Note also that $G'$ is\na minor of $G$, and is thus $K_7$-minor free (resp. $K_8$- and\n$K_{2,2,2,2,2}$-minor free). Theorem~\\ref{th:krtri}\n(resp. Theorem~\\ref{th:k8tri}) thus implies that $G'$ is the trivial\ngraph without any edge and with one vertex. This graph is trivially\ngenerically $5$-stress free (resp. $6$-stress free), and so by\nTheorem~\\ref{th:whitcontract}, $G$ also is generically $5$-stress free\n(resp. $6$-stress free).\n\\end{proof}\n\nWe denote by $\\mu(G)$ the Colin de Verdi\u00e8re parameter of a graph $G$. A\nresult of Colin de Verdi\u00e8re~\\cite{cdv1} is that a graph $G$ is planar\nif and only if $\\mu(G) \\leq 3$. Lov\\'asz and Schrijver~\\cite{ls1}\nproved that $G$ is linklessy embeddable if and only if $\\mu(G) \\leq\n4$. Nevo conjectured that the following holds.\n\n\\begin{conj}[Nevo, 2007,~\\cite{nevo1}]\nLet $G$ be a graph and let $k$ be a positive integer. If $\\mu(G) \\leq k$\nthen $G$ is generically $k$-stress free.\n\\label{conj:munevo}\n\\end{conj}\n\nThis conjecture holds for the cases $k = 5$ and $k = 6$ as a\nconsequence of Theorem~\\ref{th:genstressk78}.\n\n\\begin{cor}\nIf $G$ is a graph such that $\\mu(G) \\leq 5$ (resp. $\\mu(G) \\leq 6$) then\n$G$ is generically $5$-stress free (resp. $6$-stress free).\n\\end{cor}\n\n\\begin{proof}\nNote that $\\mu(K_r) = r - 1$ and that if the complement of an\n$n$-vertex graph $G$ is a linear forest, then $\\mu(G) \\geq n -\n3$~\\cite{lsv1}. So we have that $\\mu(K_7) = 6$, $\\mu(K_8) = 7$,\nand $\\mu(K_{2,2,2,2,2}) \\geq 7$.\n\nAs the parameter $\\mu$ is minor-monotone~\\cite{cdv1}, the graph $K_7$\n(resp. $K_8$ and $K_{2,2,2,2,2}$) is an excluded minor for the class\nof graphs defined by $\\mu(G) \\leq 5$ (resp. $\\mu(G) \\leq 6$). Hence by\nTheorem~\\ref{th:genstressk78}, these graphs are generically $5$-stress\nfree (resp. $6$-stress free).\n\\end{proof}\n\n\n\\section{Application to double-critical $k$-chromatic graphs}\\label{sec:double}\n\nA connected $k$-chromatic graph is said to be double-critical is for\nall edge $uv$ of $G$, $\\chi(G \\setminus\\{u,v\\}) = \\chi(G) - 2$. It is\nclear that the clique $K_k$ is such a graph. The following\nconjecture, known has the Double-Critical Graph Conjecture, due to\nErd\u0151s and Lov\u00e1sz states that they are the only ones.\n\n\\begin{conj}[Erd\u0151s and Lov\u00e1sz, 1968,~\\cite{e68}]\nIf $G$ is a double-critical $k$-chromatic graph, then $G$ is\nisomorphic to $K_k$.\n\\label{conj:dcgraph}\n\\end{conj}\n\nThis conjecture has been proved for $k \\leq 5$ but remains open for $k\n\\geq 6$. Kawarabayashi, Pedersen and Toft have formulated a relaxed\nversion of both Conjecture~\\ref{conj:dcgraph} and the Hadwiger's\nconjecture, called the Double-Critical Hadwiger Conjecture.\n\n\\begin{conj}[Kawarabayashi, Pedersen, and Toft, 2010,~\\cite{kpt10}]\nIf $G$ is a double-critical $k$-chromatic graph, then $G$ contains a\n$K_k$-minor.\n\\label{conj:dchadwiger}\n\\end{conj}\n\nThe same authors proved this conjecture for $k \\leq 7$~\\cite{kpt10},\nbut the case $k = 8$ is left as an open problem. Pedersen proved that\nevery $8$-chromatic double-critical contains $K_8^-$ as a\nminor~\\cite{ped11}. Below we prove that the conjecture also holds for\n$k=8$.\n\nThe following proposition lists some interesting properties about\n$k$-chromatic double-critical graphs :\n\\begin{prop}[Kawarabayashi, Pedersen, and Toft, 2010,~\\cite{kpt10}]\nLet $G \\neq K_k$ be a double-critical $k$-chromatic graph, then\n\\begin{itemize}\n\\item The graph $G$ does not contains $K_{k-1}$ as a subgraph,\n\\item The graph $G$ has minimum degree at least $k + 1$,\n\\item For all edges $uv \\in E(G)$ and all $(k-2)$-coloring of $G - u -\n v$, the set of common neighbors of $u$ and $v$ in $G$ contains\n vertices from every color class.\n\\end{itemize}\n\\label{prop:kpt}\n\\end{prop}\n\nIn particular, the last item implies that every edge belongs to at\nleast $k - 2$ triangles.\n\n\\begin{thm}\nEvery double-critical $k$-chromatic graph, for $k\\le 8$, contains\n$K_k$ as a minor.\n\\end{thm}\n\\begin{proof}\nConsider for contradiction a $K_k$-minor free graph $G$ that is\ndouble-critical $k$-chromatic. By the second item of\nProposition~\\ref{prop:kpt}, $\\delta(G)\\ge k+1$. By\nTheorem~\\ref{th:krtri} and Theorem~\\ref{th:k8-deg9}, this graph\nhas an edge that belongs to at most $k-3$ triangles. This\ncontradicts the last item of Proposition~\\ref{prop:kpt}.\n\\end{proof}\n\nLet us now give an alternative proof of the case $k=8$ that does not\nneed Theorem~\\ref{th:k8-deg9}, but uses Theorem~\\ref{th:k8tri}\ninstead. This might be usefull to prove the next case of\nConjecture~\\ref{conj:dchadwiger}.\n\nConsider for contradiction a $K_8$-minor free graph $G$ that is\ndouble-critical $8$-chromatic. By Theorem~\\ref{th:k8tri} this graph\nhas an edge that belongs to at most $5$ triangles or contains\n$K_{2,2,2,2,2}$ as an induced subgraph. By Proposition~\\ref{prop:kpt}\nevery edge of $G$ belongs to at least 6 triangles, thus $G$ contains\n$K_{2,2,2,2,2}$ as an induced subgraph. Let us denote $K\\subseteq\nV(G)$ the vertex set of a copy of $K_{2,2,2,2,2}$ in $G$. As\n$K_{2,2,2,2,2}$ is maximal $K_8$-minor free, any connected component\n$C$ of $G\\setminus K$ is such that $N(C)\\subset K$ induces a clique.\nAs $G$ is double-critical $8$-chromatic, there exists a $6$-coloring\nof $G[N[C]]$, and a $6$-coloring of $G\\setminus C$. As these two\ngraphs intersect on a clique one can combine their colorings and thus\nobtain a 6-coloring of $G$, a contradiction.\n\n\n\\section{Application for coloration of $K_d$-minor free graphs}\\label{sec:coloration}\n\nHadwiger's conjecture says that every $t$-chromatic graph $G$\n(i.e. $\\chi(G) =t$) contains $K_t$ has a minor. This conjecture has\nbeen proved for $t \\leq 6$, where the case $t = 5$ is equivalent to\nthe Four Color Theorem by Wagner's structure theorem of $K_5$-minor\nfree graph, and the case $t = 6$ has been proved by Robertson, Seymour\nand Thomas~\\cite{rst1}. The conjecture remains open for $t \\geq 7$.\nFor $t = 7$ (resp. $t = 8$) the conjecture asks $K_7$-minor free graphs\n(resp. $K_8$-minor free graphs) to be $6$-colorable (resp. $7$-colorable).\nUsing Claim~\\ref{claim-alpha-Nv} and the $9$-degeneracy\n(resp. $11$-degeneracy) of these graphs, one can prove that they are\n$9$-colorable (resp. 11-colorable). We improve these bounds by one.\n\nA graph $G$ is said to be \\emph{$t$-minor-critical} if $\\chi(G) = t$\nand $\\chi(H) < t$ whenever $H$ is a strict minor of $G$. Hadwiger's\nconjecture can thus be reformulated as follows : Every\n$t$-minor-critical graph contains $K_t$ has a minor. In the following\n$\\alpha(S)$ means $\\alpha(G[S])$,the independence number of\n$G[S]$. The following is a folklore claim, here for completeness.\n\n\\begin{claim}\n\\label{claim-alpha-Nv}\nGiven a $k$-minor critical graph $G$, for every vertex $v\\in V(G)$ we\nhave that $\\deg(v) + 2 - \\alpha(N(v)) \\ge k$.\n\\end{claim}\n\n\\begin{proof}\nGiven a vertex $v$ and a stable set $S$ of $N(v)$, consider the graph\n$G'$ obtained from $G$ by contracting the edges between $v$ and\n$S$. Since $G'$ is a strict minor of $G$ it is\n$(k-1)$-colorable. Given such coloring of $G'$, one can $(k-1)$-color\n$G\\setminus\\{v\\}$ in such a way that all the vertices of $S$ have the\nsame color assigned. In this coloring at most $\\deg(v) +1 - |S|$\ncolors are used in $N(v)$, thus $\\deg(v) +2 - |S|$ colors\nare sufficient to color $G$, and thus $\\deg(v) + 2 - \\alpha(N(v)) \\ge k$.\n\\end{proof}\n\nA \\emph{split graph} is a graph which vertices can be partionned into\none set inducing a clique, and one set inducing an independent set.\nThese graphs are the graphs that do not contain $C_4$, $C_5$ or $2K_2$\nas induced subgraphs~\\cite{fh1}.\n\n\\begin{claim}\n\\label{claim-no-split}\nGiven a $k$-minor critical graph $G$, every separator $(A,B)$ of $G$\nis such that $G[A \\cap B]$ is not a split graph (i.e $G[A \\cap B]$\ncontains $C_4$, $C_5$ or $2K_2$ as an induced subgraph).\n\\end{claim}\n\n\\begin{proof}\nAssume by contradiction that there exists such separator $(A',B')$.\nThis implies the existence of a separator $(A,B)$ such that $S = A\\cap\nB \\subseteq A'\\cap B'$ , and such that each $G[A\\setminus S]$ and\n$G[B\\setminus S]$ have a connected component, $C_{A}$ and $C_{B}$ such\nthat $N(C_{A})=N(C_{B})=S$. Note that $G[S]$ is a split graph and let\n$I$ be one of its maximum independent sets and let $K=S \\setminus I$\nbe a clique. Let $G_A$ and $G_B$ be the graphs respectively obtained\nfrom $G[A]$ and $G[B]$ by identifying the vertices of $I$ into a\nsingle vertex $i$. By maximality of $I$, in both graphs the vertex set\n$K\\cup \\{i\\}$ induces a clique. Furthermore, these graphs are strict\nminors of $G$ as the identification of the vertices in $I$ can be done\nby contracting edges incident to $C_B$ or $C_A$ respectively. Thus,\nthese graphs are $(k-1)$-colorable and these colorings imply the\nexistence of compatible $(k-1)$-colorings of $G[A]$ and $G[B]$, since\nin both colorings the vertices of $I$ use the same color, and each\nvertex of $K$ uses a distinct color. This yields in a $(k-1)$-coloring\nof $G$, a contradiction.\n\\end{proof}\n\n\\begin{thm}\n$K_7$-minor free graphs are $8$-colorable.\n$K_8$-minor free graphs are $10$-colorable.\n\\end{thm}\n\n\\begin{proof}\nConsider by contradiction that there is a $K_7$-minor free graph $G$\nnon-8-colorable (resp. a $K_8$-minor free graph $G$ non-10-colorable).\nThis graph is chosen such that $|E(G)|$ is minimal, this graph is\nthus 9-minor-critical (resp. 11-minor-critical).\n\nFor any vertex $v$, since $\\alpha(N(v))$ is at least 1,\nClaim~\\ref{claim-alpha-Nv} implies that $\\deg(v) > 7$ (resp. $\\deg(v)\n> 9$). If $\\deg(v) = 8$ (resp. $\\deg(v) = 10$), since $G$ is\n$K_7$-minor free (resp. $K_8$-minor free), we have $\\alpha(N(v)) \\ge\n2$, contradicting Claim~\\ref{claim-alpha-Nv}. Finally if $\\deg(v) = 9$\n(resp. $\\deg(v) = 11$), Claim~\\ref{claim-alpha-Nv} implies that\n$3>\\alpha(N(v))$, and since $N(v)$ cannot be a clique, $\\alpha(N(v)) =\n2$. Thus with Mader's theorem we have that $\\delta(G)= 9$\n(resp. $\\delta(G)= 11$), and that for every vertex $v$ of degree 9\n(resp. of degree 11), $\\alpha(N(v)) = 2$. By Theorem~\\ref{th:krtri}\n(resp. Theorem~\\ref{th:k8triweak}), we consider a vertex $u$ of degree\n9 (resp. 11) such that there is an edge $uv$ which belongs to at most\n$4$ (resp. $5$) triangles. Let $H = G[N(u)]$, and recall that\n$\\alpha(H) = 2$.\n\n\\begin{claim}\n\\label{claim-no-K5}\nThe graph $H = G[N(u)]$ does not contain a $K_5$ (resp. a $K_6$).\n\\end{claim}\n\n\\begin{proof}\nAssume by contradiction that $H$ contains a $K_t$ with vertices\n$x_1,\\ldots,x_t$, for $t=5$ (resp. for $t=6$). Assume first that the\ngraph induced by $Y = N(u) \\setminus \\{x_1,\\ldots,x_t\\}$ is\nconnected. Since $\\delta(G) \\ge 9$ every vertex $x_i$ has a neighbor\nin $Y$ or a neighbor $w_i$ in $G \\setminus N[u]$. In the latter case,\ndenote $C_i$ the connected component of $w_i$ in $G\\setminus N[u]$.\nSince by Claim~\\ref{claim-no-split} (for the partition $(N[C_i], V(G)\n\\setminus C_i)$) $N(C_i)$ intersects $Y$, one can contract $Y\\cup\n(V(G) \\setminus N[u])$ into a single vertex and form a $K_{t+2}$\ntogether with vertices $u,x_1,\\ldots,x_t$, a contradiction.\n\nAssume now that the graph induced by $Y$ is not connected and let\n$y_1, y_2 \\in Y$ be non-adjacent vertices. Since $G$ is $(2t-1)$-minor\ncritical, consider a $(2t-2)$-coloring of the graph $G'$ obtained from\n$G$ by contracting $uy_1$ and $uy_2$. This coloring implies the\nexistence of a $(2t-2)$-coloring $c$ of $G\\setminus u$ such that\n$c(y_1) = c(y_2)$. As this coloring does not extends to $G$, the\n$2t-1$ vertices in $N(u)$ use all the $(2t-2)$ colors. This implies\nthat the colors used for the $x_i$ are used only once in $N(u)$, and\nthat there exists a vertex $z \\in Y$ which color is used only once in\n$N(u)$. Assume $c(x_i)=i$ and $c(z)=7$. Given two colors $a$, $b$ and\na vertex $v$ colored $a$, the \\emph{$(a,b)$-component of $v$} is the\nthe connected component of $v$ in the graph induced by $a$- or\n$b$-colored vertices. For any $1\\le i \\le t$, suppose we switch colors\nin the $(i,7)$-component of $z$. As this cannot lead to a coloring\nwhich does not use all the colors in $N(u)$, there exists a\n$(7,i)$-bicolored path from $z$ to $x_i$. This is impossible as\ncontracting these paths on $z$ would lead to a $K_{t+2}$ (with vertex\nset $\\{u,z,x_1,\\ldots,x_t\\}$). This concludes the proof of the claim.\n\\end{proof}\n\nLet $v$ be a vertex of $H$ with minimum degree in $H$. By the choice\nof $u$ and Theorem~\\ref{th:krtri} (resp. Theorem~\\ref{th:k8triweak}),\n$\\deg_{H}(v)\\le 4$ (resp. $\\deg_{H}(v)\\le 5$).\n\n\\begin{claim}\n$\\delta(H) = \\deg_H(v) = 4$ (resp. $\\delta(H) = \\deg_H(v) = 5$).\n\\end{claim}\n\\begin{proof}\nSince $\\alpha(H)=2$, the non-neighbors of $v$ in $H$ form a\nclique. Furthermore since $H$ does not contain a $K_5$ (resp. a $K_6$)\nwe have that $9 - 1 - \\deg_{H}(v) < 5$ (resp. that $11 - 1 -\n\\deg_{H}(v) < 6$), and hence $\\deg_{H}(v) = 4$ (resp. $\\deg_{H}(v) =\n5$).\n\\end{proof}\n\nLet $y_1,\\ldots,y_t$ with $t = 4$ (resp. $t = 5$) be the neighbors of\n$v$ in $H$, and let $K$ be the $t$-clique formed by its non-neigbors.\nBy Claim~\\ref{claim-no-K5} we can assume that $y_1$ and $y_2$ are\nnon-adjacent. Note that since $\\alpha(G[N(u)]) = 2$ every vertex of\n$K$ is adjacent to $y_1$ or $y_2$. Since $G$ is $(2t+1)$-minor\ncritical, consider a $2t$-coloring of the graph $G'$ obtained from $G$\nby contracting $uy_1$ and $uy_2$. This coloring implies the existence\nof a $2t$-coloring $c$ of $G \\setminus u$ such that $c(y_1) = c(y_2)$. As\nthis coloring does not extends to $G$, the $2t + 1$ vertices in $N(u)$\nuse all the $2t$ colors. In particular, the colors used by $K$ (say\n$1,\\ldots t$) and $y_3$ (say 6) are thus used only once in $N(u)$.\nFor any $1\\le i\\le t$, suppose we switch colors in the $(i,6)$-component\nof $y_3$. As this cannot lead to a coloring which does not use all the\ncolors in $N(u)$, there exists a $(i,6)$-bicolored path from $y_3$ to\nthe $i$-colored vertex of $K$. This is impossible as contracting these\npaths on $y_3$, and contracting the edges $vy_1$ and $vy_2$ on $v$\nwould lead to a $K_{t+2}$ with vertex set $\\{u,v,y_3\\} \\cup K$. This\nconcludes the proof of the theorem.\n\\end{proof}\n\n\n\\section{Conclusion}\\label{sec:concl}\n\nTheorem~\\ref{thm:global} gives a sufficient condition for a graph to\nhave a $K_k$-minor. We wonder whether this condition is stronger than\nMader's Theorem : Is there a graph $G$ with a $K_k$-minor, for $4\\le\nk\\le 7$, that has $m\\le (k-2)n - {k-1 \\choose 2}$ edges and $t\\ge\nm(k-3)\/2$ triangles ?\n\nWe believe that our work can be extended to the next case. Song and\nThomas~\\cite{st1} proved a Mader-like theorem, similar to\nTheorem~\\ref{th:jorg} in the case of $K_9$-minor free graphs.\n\n\\begin{thm}[Song and Thomas, 2006,~\\cite{st1}]\nEvery graph on $n \\geq 9$ vertices and at least $7n - 27$ edges either\nhas a $K_9$-minor or is a $(K_{1,2,2,2,2,2}, 6)$-cockade or is isomorphic to\n$K_{2,2,2,3,3}$.\n\\end{thm}\n\nNote that $K_{2,2,2,3,3}$ has edges that belong to exactly $6$ triangles\nand contains $K_{2,2,2,2,2,1}$ as a minor. We conjecture that we\ncan extend our main theorem as follows.\n\\begin{conj}\nLet $G$ a graph such that every edge belongs to at least $7$ triangles\nthen either $G$ has a $K_9$-minor or contains $K_{1,2,2,2,2,2}$\nas an induced subgraph.\n\\label{conj:extendk9}\n\\end{conj}\n\n\nProving this conjecture would have several consequences.\nThis would extend Theorem~\\ref{thm:global}\nas follows : Every graph $G$ with $m\\ge 1$ edges and $t\\ge 3m$ triangles\nhas a $K_9$ or $K_{1,2,2,2,2,2}$-minor.\nIt would also imply Conjecture~\\ref{conj:munevo} for the case $k = 7$,\ni.e. $\\mu(G)\\le 7$ implies that $G$ is generically $7$-stress free.\nFinally, it would imply Conjecture~\\ref{conj:dchadwiger} for $k = 9$,\ni.e. double-critical $9$-chromatic graphs have a $K_9$-minor.\nWe also conjecture that the following holds. In particular, it would\nimply that $K_9$-minor free graphs are $12$-colorable (using the same\narguments as in Section \\ref{sec:coloration}).\n\\begin{conj}\nAny $K_9$-minor free graph $G$ with $\\delta(G)=13$ has an edge $uv$\nsuch that $u$ has degree $13$ and $uv$ belongs to at most $6$\ntriangles.\n\\label{conj:extendk9weak}\n\\end{conj}\n\nWe also believe that these structural properties on graph with edges\nbelonging to many triangles can actually be extended to\nmatroids. Graph minors can be studied in the more general context of\nmatroid minors~\\cite{o1}. A triangle is then a circuit of size\n$3$. Contrary to graphs, the case when every element of the matroid\nbelongs to $3$ triangles is already intricate. There are three\nwell-known matroids for which each element belongs to $3$ triangles :\nthe Fano matroid $F_7$, the uniform matroid $\\mathcal{U}_{2,4}$, and\nthe graphical matroid $\\mathcal{M}(K_5)$. We conjecture that the\nfollowing holds.\n\n\\begin{conj}\nLet $\\mathcal{M}$ be a matroid where each element is contained in $3$\ntriangles, then $\\mathcal{M}$ admits $\\mathcal{M}(K_5)$, $F_7$ or\n$\\mathcal{U}_{2,4}$ as a minor.\n\\end{conj}\n\n\\bibliographystyle{plain}\n\\nocite{*}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nManipulating the molecular rotational degrees of freedom in gas phase by means of laser fields remains a very attractive topic in quantum control \\cite{reviewQC1,reviewQC2} with a wide range of applications in photochemistry extending from chemical reactivity \\cite{Warren1993, Stapelfeldt2003} to nanoscale design \\cite{Seideman1997,Stapelfeldt1997}, stereochemistry \\cite{Rakitzis2004}, surface processing \\cite{Seideman1997,reuter2008}, catalysis \\cite{Bulthuis1991}, and attosecond molecular dynamics \\cite{Krausz2009}. Such phenomena play also a role in quantum computing \\cite{shapiro2003} and high-order harmonic generation \\cite{Atabek2003,Ramakrishna2007,Ramakrishna2010,houzet2013}. In this setting, molecular alignment and orientation can be identified as crucial prerequisites before exploring more complex control scenarios \\cite{reviewseideman,Stapelfeldt2003}. The alignment process \\cite{Friedrich1995} is by now a well-established concept from both the experimental and theoretical points of view with recent extensions ranging from the deflection of aligned molecules \\cite{gershnabel2010}, the introduction of planar alignment \\cite{hoque2011}, the control of molecular unidirectional motion \\cite{korech2013,steinitz2014,karras2015}, the study of molecular superrotors \\cite{korobenko2016,korobenko2014,milner2015} or the analysis of dissipation effects due to molecular collisions \\cite{ramakrishna2005,viellard1,viellard2,milner2014}. The description and the control of molecular orientation are not currently at the same degree of improvement, in particular from the experimental point of view. Molecular orientation was achieved in the adiabatic regime \\cite{Goban2008,Ghafur2009,Filsinger2009,Takei2016,Muramatsu2009,Holmegaard2009}. If no static field is used, a rapid turn-off of the laser field allows to get orientation under field-free conditions. At low temperature, a very high degree of orientation can be obtained using such control strategies and a molecular quantum-state selection \\cite{Filsinger2009,Takei2016,Muramatsu2009,Holmegaard2009}. Control schemes in the sudden regime, where the duration of the control field is short with respect to the rotational period, have been also developed \\cite{averbukh2001,daems2005,dion2001,dion2005,ortigoso2012,Atabek2003,sugny2005,sugny2014,Lapert2012,machholm2001,henriksen1999,prasad2013,shu2013,Tehini08,zhang,Tehini2012,wu,Kanai2001}.\nAmong other techniques, we can mention the interaction of the molecule with a terahertz (THz) laser pulse and the $(\\omega,2\\omega)$- scheme. Note that a larger degree of orientation can be achieved with the two-color mechanisms through ionization depletion \\cite{Spanner2012}. In this second regime, molecular orientation has been recently addressed experimentally for linear molecules using only a THz field \\cite{Nelson2011} or its combination with a laser field \\cite{Kitano2011}, and in \\cite{Znakovskaya2009,Kraus2014} for an excitation process with two-color laser fields.\n\nIn this work, we complement the previous experimental and theoretical works on the orientation dynamics produced by THz fields by exploring the orientation at high temperature (typically room temperature) of a symmetric top molecule, CH$_3$I. This molecule is a good candidate to achieve a high degree of orientation at room temperature due to its large permanent dipole moment and its relatively small rotational constant \\cite{Lapert2012}. The THz pulses are obtained from the excitation of a plasma by a two-color femtosecond laser field \\cite{Cook2000}, while the detection process is based on the free-induction decay (FID) emitted by the molecular sample after the THz excitation \\cite{hardprl,hard1991,hard1994,bigourd2008}. We extend the previous studies on the subject by considering the case of a symmetric top molecule at room temperature. We show that a noticeable degree of orientation can be reached. A theoretical description of the propagation of a THz field in the sample shows that the FID is not proportional to the degree of orientation but to its time derivative. A complete analytical derivation of this result is given in this paper. Note that this dependency has been already mentioned in \\cite{Nelson2011}. The relation between the FID and the degree of orientation allows us to quantitatively compare the experimental observations with the numerical simulations. A very good match is found for the first two orientation revivals. This agreement has been improved by accounting for the centrifugal distorsion and the relaxation effects in the computations \\cite{ramakrishna2005,viellard1,viellard2}. We also use this theoretical description to explore the influence of the laser parameters on the orientation dynamics.\n\nThe paper is organized as follows. The experimental setup is described in Sec.~\\ref{sec3}, with a special emphasis on the generation of THz pulses and on the detection process. We show in Sec.~\\ref{sec2p} that the FID is given at first order by the time derivative of the degree of orientation. The model system is introduced in Sec.~\\ref{sec2}. The numerical and the experimental results are discussed in Sec.~\\ref{sec4}. Conclusions and prospective views are given in Sec.~\\ref{sec5}.\n\n\\section{Experimental setup for producing molecular orientation \\label{sec3}}\nThe experimental set-up for producing and measuring molecular orientation is shown in Fig.\\ref{Set_Up}. It is based on a THz pulsed source and an electro-optical sampling device for the detection. The THz pulses are produced through plasma generation in gases with two-color femtosecond pulses \\cite{Cook2000,Kress2004,Kim2007,Kim2008,Vvedenskii2014}. A fraction of the output of a chirped pulse amplifier (CPA, 800 nm wavelength, 100 fs pulse duration, 7.5 mJ energy, 100 Hz repetition rate) is focused in dry nitrogen. A type I phase matching $\\beta$-Barium Borate (BBO) crystal is inserted on the beam path at a given distance from the focusing point to produce the second harmonic at 400 nm. The generation of THz pulses is optimized by adjusting the phase matching angle of the frequency doubling crystal and its longitudinal position using a micrometric stage. The THz source provides pulses of typically few hundred femtoseconds pulse duration with only one cycle (see Fig.~\\ref{fig2} below for details) and covers a spectral range from 0 to 4 THz, with a maximum at about 1.5 THz. After collimation by an off-axis parabolic mirror, the two incoming beams at 800 and 400 nm are filtered out by means of a 2-mm thick plate from polytetrafluoroethylene and a thin black polyethylene sheet. The THz pulse is then focused in a cell (40 mm optical pathway) containing the sample at room temperature. The CH$_{3}$I sample (\\textit{Sigma Aldrich}, batch reference \\emph{BCBK 1300 V}, reagent grade chemicals) is initially stored in the liquid phase and vaporized by expansion into the cell under vacuum, just before the experiment so as to avoid water pollution of the sample.\nThe THz electric field in the gas sample is less than 100 kVcm$^{-1}$. The typical pressure used in this work is around 0.2-0.3 bar (\\textit{e.g.} below the saturation vapour pressure of CH$_{3}$I). For a good transmission of the THz beam through the gas cell, the windows are in polymethylpentene polymer (TPX, 38.1 mm diameter, 2.0 mm thickness). The transmitted THz radiation is collected from the sample by another off-axis parabolic mirror and sent to the detection device. This latter is based on an electro-optical sampling of the THz pulse shape in the time domain \\cite{Winnewisser1997,Cai1998,Gallot1999,Planken2001}. The weak probe beam (typically $\\approx 5$ nJ) derived from the output of the CPA is focused and spatially overlapped with the THz radiation in a ZnTe (110) crystal. The polarization of the probe beam is modified through the Pockels effect induced by the THz beam (electro-optic detection process). This allows us to sample the THz electric field by changing the time delay between the two pulses with a motorized delay line stage. The change of the polarization state of the probe beam is measured from the combination of a quarter wave plate (QWP), a Wollaston Prism (WP), and two head-to-tail connected photodiodes so that the difference of their signals is directly obtained (see Fig.~\\ref{Set_Up}). The difference signal is then amplified and sent to a lock-in amplifier synchronized with the laser repetition rate. The quarter wave plate is oriented so as to get a circular polarization without THz pulse and equivalent signals for the two photodiodes. The THz pathway is included in a box continuously purged (relative humidity $\\lesssim$ 7 $\\%$) with dry nitrogen to avoid absorption by water vapor.\n\\begin{figure*} [ht]\n \\centering\n \\includegraphics[width=12cm]{fig1}\n \\caption{Experimental set-up. CPA: Chirped Pulse Amplifier (100 Hz repetition rate, $\\tau_L \\approx 100~\\textrm{fs}$, $\\lambda_0 = 800~\\textrm{nm}$, $E_{100~\\textrm{Hz}} \\approx 7.5~\\textrm{mJ}$), QWP: Quarter Wave Plate, HWP: Half Wave Plate, P: Polarizer, L: Lens, BBO: Beta Barium Borate crystal, Filter: see the text, M: off-axis parabolic Mirror, BS: indium tin oxide Beam Splitter, ZnTe: electro-optical crystal, DL: motorized Delay Line stage, WP: Wollaston Prism, PhD1 and PhD2: balanced photodiodes, A: integrated amplifier, PB: purged box.}\n \\label{Set_Up}\n\\end{figure*}\nThe signal $\\Delta{V}$ recorded by the lock-in amplifier is proportional to $\\Delta{I}$, the difference between the intensities measured by the two head-to-tail connected photodiodes. $\\Delta{I}$ is given by \\cite{Planken2001}:\n\\begin{equation}\\label{firsteq}\n\\Delta{V}\\propto\\Delta{I}=I_{\\textrm{probe}} \\omega n^{3} {E(t) } r_{41} \\frac{L}{{c}},\n\\end{equation}\nwhere $I_{\\textrm{probe}}$ is the probe intensity, $\\omega$ the probe angular frequency, $n$ the refraction index at the probe frequency, $E(t)$ the THz electric field, $r_{41}$ the electro-optic coefficient of the ZnTe crystal (Pockels effect, $r_{41}$=4 pm~V$^{-1}$), $L$ the crystal length (200 $\\mu$m), and $c$ the speed of light in vacuum. The typical ratio $\\frac{{\\Delta V_{\\textrm{THz}} }}{{2V_{\\textrm{PhD}} }}=\\frac{{\\Delta{I}}}{I_{\\textrm{probe}}}$ measured in the experiment is $\\cong$ 1 - 5 $\\%$, $\\Delta V_{\\textrm{THz}}$ being the signal produced by the THz pulse and $V_{\\textrm{PhD}}$ the signal delivered by each photodiode. The electric field $E(t)$ can be directly evaluated by using Eq.~(\\ref{firsteq}) and taking into account the transmission and reflection coefficients of the different optical components. The estimated electric field in the gas sample is typically within the range 6~-~30 kV~cm$^{-1}$. Note that $E(t)$ in Eq.~(\\ref{firsteq}) is the total electric field including the transmitted THz pulse $E_{0}(x,t)$ and the FID electric field, as discussed below. A typical recording of the electro-optical sampling signal is depicted in Fig.~\\ref{Typical experimental signal}. It exhibits the transmitted THz pulse at zero delay and the two first orientational revivals at 67 and 134 ps. The goal of Sec.~\\ref{sec2p} is to interpret this experimental trace in terms of orientation efficiency.\n\\begin{figure} [ht]\n\\centering\n\\includegraphics[width=10cm]{fig2}\n\\caption{Experimental electro-optical sampling signal as a function of the delay between probe and THz pulses in CH$_{3}$I. The pressure in the gas cell was 0.26 bar. The transmitted THz pulse around zero delay and two first orientational revivals at 67 and 134 ps are shown.}\n\\label{Typical experimental signal}\n\\end{figure}\n\\section{Propagation of a THz field in a gaseous sample \\label{sec2p}}\nThis section is aimed at describing the production of FID and its propagation in a gaseous sample. We follow here the formalism and the approximations used in \\cite{hard1991,hard1994,hardprl,bigourd2008}.\n\nTo be more concrete, we consider a THz pulse linearly polarized along the $z$- direction and propagating along the $x$- one. This field is of the form $E_{0}(0, t)$ at $x = 0$.\nThis THz field experiences an instantaneous dipole $d(t)$ along the $z$- direction which satisfies:\n\\begin{equation}\nd(t) = \\mu_{0} \\langle\\cos\\theta\\rangle(t),\n\\end{equation}\nwhere $\\mu_0$ is the permanent dipole moment, $\\mu_0=1.6406$~D for the CH$_{3}$I molecule \\cite{gachi1989}, and $\\theta$ stands for the angle between the molecular axis defined by the C-I bond and the field polarization direction. The induced dipole $d(t)$ generates a contribution to the THz field which also propagates within the sample.\nWe introduce the polarization $p(\\omega)$ of the sample given in the frequency domain by:\n\\begin{equation}\np(\\omega) =\\frac{N}{V}d(\\omega)\n\\end{equation}\nwhere $N$ is the number of molecules and $V$ the corresponding volume. This polarization is also related to the electric field through the susceptibility parameter $\\chi(\\omega)$:\n\\begin{equation}\np(\\omega) = \\epsilon_{0}\\chi(\\omega)E(\\omega)\n\\end{equation}\nwhere $\\varepsilon_0$ is the vacuum permittivity. The spectral distribution of the field $E(\\omega)$ is defined as:\n\\begin{equation}\nE(\\omega) = \\frac{1}{2\\pi} \\int_{-\\infty}^{+\\infty} E_0(0, t) e^{i\\omega t} dt,\n\\end{equation}\nwhich leads to:\n\\begin{equation}\n\\chi(\\omega) = \\frac{Nd(\\omega)}{\\epsilon_{0}V E(\\omega)}.\n\\end{equation}\nIn a linear approximation framework where $\\chi$ does not depend on the amplitude of the field, the resolution of the Maxwell equations gives that the propagation of the field can be written as follows \\cite{hard1991,hard1994}:\n\\begin{equation}\nE(x, t) = \\int_{-\\infty}^{+\\infty} E(\\omega)e^{i[k(\\omega)x-\\omega t]}d\\omega,\n\\end{equation}\nwith $k(\\omega) = n(\\omega)\\omega\/c$, $n^{2} = 1+\\chi(\\omega)$. In the case of a dilute medium, the complex refractive index is given by $n(\\omega) \\simeq 1+\\frac{\\chi(\\omega)}{2}$. Expanding the exponential term $\\exp[\\frac{i\\omega x\\chi}{2c}]$ in Taylor series up to the second order, we arrive at:\n\\begin{eqnarray*}\nE(x, t)\n&= \\int_{-\\infty}^{+\\infty} E(\\omega)e^{i( \\omega \\frac{x}{c}-\\omega t)}d\\omega \\\\\n&+ \\int_{-\\infty}^{+\\infty}E(\\omega)e^{i( \\omega\\frac{x}{c}-\\omega t)}i\\frac{\\omega x}{c}\\frac{\\chi}{2}d\\omega ,\n\\end{eqnarray*}\nwhich leads to:\n\\begin{equation}\nE(x, t) = E_{0}(x, t) + \\int_{-\\infty}^{+\\infty} E(\\omega)e^{i( \\omega\\frac{x}{c}-\\omega t)}i \\frac{\\omega x}{c} \\frac{Nd(\\omega)}{2\\epsilon_{0}V E(0, \\omega)} d\\omega\n\\end{equation}\nwhere $E_{0}(x, t)$ is the initial THz pulse.\nWe then get:\n\\begin{equation}\nE(x,t) = E_{0}(x,t) - \\frac{xN}{2c\\epsilon_{0}V}\\frac{d}{dt}d(t-\\frac{x}{c}) ,\n\\end{equation}\nwhere we have used the fact that the time derivative of a Fourier transform ($FT$) of a function $f$ is given by: $FT[\\frac{d}{dt}f(t)] = -i\\omega FT[f(t)]$. The THz electric field can finally be written as follows\n\\begin{equation}\nE(x,t) = E_{0}(x,t) - \\alpha(x) \\frac{d}{dt} \\langle \\cos\\theta\\rangle(t-\\frac{x}{c})\n\\label{eq:A10}\n\\end{equation}\nwhere $\\alpha(x)$ is a positive scalar factor depending upon the propagation coordinate $x$. The second term of the right hand side of Eq. (\\ref{eq:A10}) is the FID electric field emitted by the molecules of the sample. This contribution is used in the detection process as described above.\n\nFrom the experimental point of view, we point out that the FID manifests itself as recurrent THz echos launched by the molecular sample after its interaction with the THz pulse. It originates from transient orientation revivals of molecules inducing, under field-free conditions, a non-zero dipole. Another way of interpreting this phenomenon is to consider that the THz pulse experiences spectral shaping during its propagation. Absorption related to $J\\to J+1$ transitions produces periodic holes in the spectrum with a spectral separation in angular frequency $\\Delta \\omega= 4 \\pi c B_e$, with $B_e$ the rotational constant (neglecting the centrifugal distortion). A periodic modulation in the frequency domain leads to periodic replica in the time domain every $\\Delta t=2\\pi\\Delta \\omega$. We emphasize that the first term of Eq.~(\\ref{eq:A10}) does not reflect the absorption experienced by the incident pulse because of the use of first order expansion.\n\n\\section{Model system \\label{sec2}}\nWe describe in this section the model used in the numerical computations to study the control of molecular orientation of the symmetric top molecule of methyl-iodide CH$_{3}$I. We consider the rotational dynamics of this molecular system, which is assumed to be in its ground vibronic state, in interaction with a linear polarized THz field. The Hamiltonian of the system can be written as:\n\\begin{equation}\n\tH(t) = H_{0} + H_{\\textrm{int}}(t),\n\t\\label{eq:01}\n\\end{equation}\nwhere $H_0$ and $H_{\\textrm{int}}$ describe respectively the field-free Hamiltonian and the interaction with the laser field.\nThe Hamiltonian of the molecular system is given by \\cite{zare}:\n\\begin{equation}\n\tH_{0} = B_{e}J^2 + (A_{e} - B_{e})J_{Z}^{2} - D_{J}J^{4} - D_{JK}J^2J_{Z}^{2} - D_{K}J_{Z}^{4}\n\t\\label{eq:02}\n\\end{equation}\nwhere $J$ is the angular momentum operator and $J_{Z}$ the component of $J$ along the body-fixed $Z$- axis defined by the C-I bond. The energy eigenvalues $E_{JK}$ of the operator $H_{0}$ in the Wigner basis $|JKM\\rangle$ for a prolate symmetric top can be expressed as follows:\n\\begin{eqnarray}\n\tE_{JK}\n\t&= B_{e}J(J+1) + (A_{e} - B_{e})K^{2} - D_{J}J^{2}(J+1)^{2}\\nonumber \\\\\n\t& - D_{JK}J(J+1)K^{2} - D_{K}K^{4},\n\\label{eq:03}\n\\end{eqnarray}\nwhere $A_{e}$ and $B_{e}$ are the rotational constants. The states $|JKM\\rangle$ are the eigenstates of the square of the angular momentum operator $J^2$ and of its projections, $J_Z$ and $J_z$ on the body-fixed $Z$- axis and space-fixed $z$- axis, respectively \\cite{zare}. The molecular parameters of the CH$_{3}$I molecule used in the numerical computations are given in Tab.~\\ref{tab:01} \\cite{Carocci1998}.\n\\begin{table}[!htp]\n\\centering\n\\begin{tabular}{p{3cm} p{3cm}}\n\t\\hline\n\tParameters & Values in cm$^{-1}$ \\\\ \\hline\\hline\n\t$B_{e}$ & $0.25098$ \\\\\n\t$A_{e}$ & $5.173949$ \\\\\n\t$D_{J}$ & $2.1040012\\times 10^{-7}$ \\\\\n\t$D_{JK}$ & $3.2944780\\times 10^{-6}$ \\\\\n\t$D_{K}$ & $8.7632195\\times 10^{-5}$ \\\\ \\hline\n\\end{tabular}\n\\caption{Values of the rotational and of the centrifugal constants of the CH$_{3}$I molecule used in the numerical computations.}\n\\label{tab:01}\n\\end{table}\nThe interaction between the molecular system and the external electromagnetic field reads:\n\\begin{equation}\n\tH_{\\textrm{int}}(t) = -\\mu_{0} E(t) \\cos\\theta,\n\t\\label{eq:04}\n\\end{equation}\nwhere the function $E(t)$ represents here the amplitude of the THz electric field. We neglect in this paper the effect of the polarizability components since the maximum intensity of the electric field remains moderate. The units used are atomic units unless otherwise specified.\n\nAt room temperature, the system is described by a density matrix $\\rho(t)$ whose dynamics is\ngoverned by the Liouville-von Neumann equation \\cite{Shapiro2012}:\n\\begin{equation}\n\ti\\frac{\\partial{\\rho(t)}}{\\partial{t}} = [H(t),\\rho(t)],\n\t\\label{eq:05}\n\\end{equation}\nwhere the initial condition $\\rho(0)$ is given by the canonical density operator at thermal equilibrium\n\\begin{equation}\n\t\\rho (0) = \\frac{1}{Z} \\sum_{J=0}^{\\infty}\\sum_{M,K=-J}^{J} e^{-E_{JK}\/(k_{B}T)} |JKM\\rangle \\langle JKM|,\n\t\\label{eq:06}\n\\end{equation}\nwhere $Z = \\sum_{J=0}^{\\infty}\\sum_{M,K=-J}^{J} e^{-E_{JK}\/(k_{B}T)}$ is the partition function, with $T$ the temperature fixed to $T=298$~K and $k_{B}$ the Boltzmann constant.\n\nThe degree of orientation of the molecular system is given by the expectation value:\n\\begin{equation}\n\t\\langle\\cos\\theta\\rangle (t) = \\textrm{tr}[\\rho(t) \\cos\\theta].\n\t\\label{eq:07}\n\\end{equation}\nFurthermore, in order to simulate more realistic experimental conditions (see Sec.~\\ref{sec4}), we add to the model system the dissipative effects due to molecular collisions \\cite{Seideman2005,viellard1}. To limit the complexity of the numerical computations, we consider in this paper the effective approach proposed in \\cite{Seideman2005} to account for coherence relaxation described by the time $T_2\/P$, where $P$ is the pressure of the sample. We approximate the decoherence by an exponential decay such that the final degree of orientation is given by:\n\\begin{equation}\n\\langle\\cos\\theta\\rangle (t) = \\langle \\cos\\theta\\rangle _{\\textrm{ND}} e^{-\\frac{tP}{T_{2}}},\n\\end{equation}\nwhere the non-dissipative orientation, $\\langle \\cos\\theta\\rangle _{\\textrm{ND}}$, is computed from the Liouville-von Neumann equation (\\ref{eq:05}).\n\n\\section{Numerical and experimental results \\label{sec4}}\nIn this paragraph, we first investigate theoretically the degree of orientation that can be achieved with a THz laser pulse in the experimental conditions of the set-up. The Hilbert space is spanned by the Wigner's functions $|J,K,M\\rangle$, with $0\\leq J$, $-J\\leq K\\leq J$ and $-J\\leq M\\leq J$. $M$ and $K$ being good quantum numbers, the Hamiltonian of the system only depends on the angle $\\theta$. Numerically, we consider a finite dimensional Hilbert space with $J\\leq J_{\\textrm{max}}$, $J_{\\textrm{max}}=90$. From a physical point of view, this reduction can be justified by the fact that the THz excitation only transfers a finite amount of energy to the system, which thus stays in a finite dimensional subspace.\n\nThe control pulse can be approximated by a set of Hermite polynomials as follows:\n\\begin{equation}\\label{eqherm}\nE_0(t)\n= \\frac{E_1}{2} e^{ -\\frac{t^2}{2\\sigma^2}} [ -3 D_{3} \\hat{H}_{2}(\\frac{t}{\\sigma}) + D_{1} \\hat{H}_{0}(\\frac{t}{\\sigma})]\n\\end{equation}\nwhere $D_{n}=(2^{n}n!\\pi^{1\/2})^{-\\frac{1}{2}}$, and $\\hat{H}_{n}(t)$ stands for the Hermite polynomials of order $n$, $\\hat{H}_2(t)=4 t^2-2$ and $\\hat{H}_0(t)=1$.\nThe parameter $\\sigma$ is given by the relation $16\\log(2)\\sigma^2 = \\tau^2$ with $\\tau = 1$~ps. The reasonable match between the theoretical THz pulse and the experimental one shown in Fig.~\\ref{fig2} justifies the choice made for the electric field in Eq.~(\\ref{eqherm}). Note that the peak-to-peak amplitude is experimentally of 9.4~kV~cm$^{-1}$ in this case.\n\\begin{figure} [ht]\n\t\t\\centering\n\t\t\\includegraphics[width=8cm]{fig3}\n\t\t\\caption{(Color online) Experimental (red or dark gray) and theoretical (black), given by Eq.~(\\ref{eqherm}), THz pulses at delay zero. The amplitude $E_1$ has been adjusted to get the best match between the two pulses.}\n\t\t\\label{fig2}\n\\end{figure}\n\nWe start the analysis of the dynamics by giving a global picture of the time evolution of the molecular orientation as displayed in Fig.~\\ref{fig3}, where the relaxation effects are taken into account with $T_{2}= 23$~ps~atm \\cite{Hennequin1987,Roberts1968}. The pressure of the cell is $P=0.35~\\textrm{bar}$. Here, we fix the amplitude $E_1$ to 100 kV~cm$^{-1}$, which is the maximum experimental available amplitude. Revivals are observed at times multiples of the rotational period. The maximum of orientation is respectively of the order of $5\\times 10^{-4}$ and $2\\times 10^{-4}$ for the first and second revivals. Note that this maximum is larger than $10^{-3}$ in the non-dissipative case.\n\\begin{figure} [ht]\n\t\t\\centering\n\t\t\\includegraphics[width=8cm]{fig4}\n\t\t\\caption{(Color online) Numerical orientation dynamics of the CH$_{3}$I molecule after an excitation with a $1~\\textrm{ps}$ pulse centered at $t=0~\\textrm{ps}$. The small insert displays the numerical evolution of the maximum degree of orientation at delay zero (blue, circle) and for the first (red, rectangle) and second (yellow, diamond) revivals as a function of the amplitude $E_1$ of the THz field.}\\label{fig3}\n\\end{figure}\nIn Fig.~\\ref{fig3}, we also study how the amplitude of the initial THz field affects the behavior of the orientation dynamics. Due to the low intensity of the field, we observe that the maximum values of each transient evolve linearly with respect to the amplitude. The effect of the pulse duration on the degree of orientation is displayed in Fig.~\\ref{fig4new}. More precisely, we consider the role of the parameter $\\tau$ as defined in Eq.~(\\ref{eqherm}). Note that the overall structure of the field is not modified by this parameter, the field is only compressed or extended in time. This modification corresponds to the currently available shaping techniques of THz pulses. In Fig.~\\ref{fig4new}, we observe a nonlinear behavior of the degree of orientation, which is maximum for the two revivals for a value of $\\tau$ of the order of 2 ps.\n\\begin{figure} [ht]\n\t\t\\centering\n\t\t\\includegraphics[width=8cm]{fig5}\n\t\t\\caption{(Color online) Maximum of $|\\langle\\cos\\theta\\rangle |$ for the delay zero (blue-circle) and the first (orange-square) and the second (yellow-diamond) revivals as a function of the parameter $\\tau$ of the pulse given by Eq.~(\\ref{eqherm}). In the experiment, the parameter $\\tau$ is of the order of 1 ps.}\\label{fig4new}\n\\end{figure}\n\nAs shown in Sec.~\\ref{sec2p}, the detection process is sensitive to the time derivative of the degree of orientation, i.e. $\\frac{d[\\langle\\cos\\theta\\rangle]}{dt}$ and not directly to the orientation. In Fig.~\\ref{var_1tr}, we study the effect of the amplitude of the field on the first revival. We observe that the shape of the transient does not change when the amplitude is varied. Similar results are obtained for the second revival.\n\n\n\\begin{figure} [ht]\n\t\t\\centering\n\t\t\\includegraphics[width=8cm]{fig6}\n\t\t\\caption{(Color online) Numerical evolution of the time derivative of $\\langle \\cos\\theta\\rangle$ of the first revival as a function of the peak amplitude of the electric field which is set to 20, 40, 60, 80 and 100 kV~cm$^{-1}$. The $\\alpha$ parameter is set to 1~kV~cm$^{-1}$~ps.}\n\t\t\\label{var_1tr}\n\\end{figure}\nAfter this complete theoretical description of the orientation dynamics, the goal is now to make a full comparison of the experimental and theoretical results. A scaling factor and a shift parameter along the vertical axis are determined to get the best match between the two sets of data. As shown in Fig.~\\ref{fig:Figs}, the numerical simulation reproduces quite well the experimental signal for the two first transients. The signal is due to a large number of molecules within the volume of the sample, each molecule being excited by a field of different amplitude. The fact that the shape of the revivals does not depend on the intensity of the electric field explains the good agreement observed in Fig.~\\ref{fig:Figs}. The signal is too weak after the second revival to pursue this comparison.\n\\begin{figure}[htp]\n\\centering\n\\includegraphics[scale=0.6]{fig7}\n\\caption{(Color online) Time derivative of the degree of molecular orientation of the (a) first and (b) second revivals. The red lines (light gray) represent the experimental data while the black ones correspond to the numerical results. A filtered experimental signal is plotted in blue (dark gray) to ease the comparison with the simulated dynamics. The parameter $\\alpha$ is set to 5.75~kV~cm$^{-1}$~ps.}\n\\label{fig:Figs}\n\\end{figure}\n\n\\section{Conclusion \\label{sec5}}\nIn this article we have investigated the orientation of a symmetric top molecule, namely CH$_{3}$I. We have shown the efficiency of the full-optical ultrafast set-up resolved in time to generate THz pulses and to produce molecular orientation. We provide a detailed description of the experimental set-up used and a complete numerical study of the corresponding dynamics. The analysis of the detection process shows that the degree of orientation is indirectly measured via the time derivative of the expectation value of $\\cos\\theta$. The theoretical model reproduces accurately the experimental results up to the second orientation revival. Additional numerical simulations reveal that the orientation dynamics induced by this THz pulse is qualitatively similar for the linear molecule, OCS. It will be interesting to consider also asymmetric top molecules which have a more complex and non-periodic field-free evolution than linear or symmetric top molecules.\n\nThe results of this work can be viewed as an important step forward for the control of molecular orientation. The good match between theory and experiment will allow us to explore the efficiency of more complex strategies using for instance a pre-alignment by a laser field. Such approaches are necessary to increase the degree of orientation and reach efficiencies where molecular orientation could be useful in practice.\\\\ \\\\\n\n\n\n\\noindent\\textbf{ACKNOWLEDGMENT}\\\\\nD. Sugny acknowledges the support from the ANR-DFG research programs Explosys (ANR-14-CE35-0013-01) and Coqs (ANR-15-CE30-0023-01). This work was supported by the Conseil R\\'egional de Bourgogne under the Photcom Pari program as well as the Labex ACTION program (ANR-11-LABX-01-01) and the CoConiCs program (Contract No. ANR-13-BS08-0013). This work has been done with the support of the Technical University of Munich, Institute for Advanced\nStudy, funded by the German Excellence Initiative and the European Union Seventh Framework Programme under grant agreement 291763.\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nIt is generally agreed that two-dimensional field-theory models may provide an excellent and rich framework to test ideas in gauge theories. In fact, the interest in studying these models is basically connected to the possibility of obtaining exact solutions, which are believed to be shared by their more realistic counterparts in four dimensions.\nOf these, the Schwinger model, also known as Quantum Electrodynamics in $(1+1)$-space-time dimensions, or ${QED}_2$\\cite{Schwinger:1962tn,Coleman:1975pw} has probably enjoyed the greatest popularity due to some special features that it possesses. For example, the energy spectrum contains a massive mode in spite of the gauge invariance of the original Lagrangian, the charge is screened and confinement is enforced by the explicit occurrence of a rising Coulomb potential. To our mind, these special features represent the essential ingredients of a mechanism by which one hopes to understand the phenomenon of quark-binding into physical hadrons. These issues were first analyzed in ${QED}_2$ in Refs. \\cite{Schwinger:1962tn,Schwinger:1962tp, Casher:1973uf,Coleman:1975pw}.\n\nUnfortunately, against this suggestive two-dimensional perspective, it seems to us that a convincing analytical proof of color confinement in quantum chromodynamics (QCD) still eludes us. The root of the problem is well known: while asymptotic freedom is a well established property of the perturbative dynamics of QCD, the transition to infrared slavery is problematic because of non-perturbative effects that dominate in the large distance limit of the theory. Once this ?large distance limit? is defined in terms of some phenomenological scale of distance, the immediate problem is that of identifying the dynamical variables that operate in that limit. A hint about the nature of those hidden dynamical variables comes from the phenomenological bag models of hadrons: the partial success of those models indicate that, in the large distance limit of QCD, the spatial extension of hadrons and the bag degrees of freedom must somehow be included among those new dynamical variables. It is clear that, in order to speak meaningfully of a ?QCD-solution? of the confinement problem, one would expect that such variables should arise from the very dynamics of QCD and control the mechanisms of color confinement \\cite{Luscher:1978rn}. \nThis is where the extrapolation of results from two to four spacetime dimensions may play a significant role in the understanding of the confinement mechanism in QCD. For instance, the correspondence between the colorless topological sector of QCD and the zero-charge sector of ${QED}_2$ was noted long ago in Ref.\\cite{Aurilia:1979dw} but never fully exploited; The extrapolation from two to four dimensions, at least for the bosonized version of the Schwinger model, was considered in \\cite{ Aurilia:1980jz} while a general \"gauge mixing mechanism for the generation of mass\" was proposed in \\cite{Aurilia:1981xg}. \n\nMotivated by these observations, the general purpose of the present discussion is to communicate a deeper understanding of the physical content of the $(3+1)$-dimensional generalization of the Schwinger model. The many avenues of research that are open to us were outlined in a research proposal by the authors \\cite{Aurilia:2015qia}. However, it seems clear that the first line of inquiry is to explore in more detail the role of the Abelian $3$-form field among the physical observables of the model. It has long been known that this $3$-form field does not support any propagating degree of freedom, its sole physical effect consisting of a static interaction between two probe charges.This remarkable property is entirely analogous to the two dimensional case where in ${QED}_2$ there are no \"photons\" associated with the electromagnetic field \\cite{Aurilia:1979dw}. Then, if the Schwinger model has any relevance in the issue of confinement in four dimensions, then the static potential induced by the Abelian $3$-form field must also exhibit the same behavior found in the two-dimensional case. We find that this reasonable expectation is fully supported by the explicit calculation of the interaction energy between two external test charges.\n\nA second objective of this work is to elucidate the remarkable interplay between guge invariance and the appearance of mass in the physical spectrum of the Schwinger model. With hindsight, the emergence of this massive mode can be traced back directly to the dimensionality of the coupling constant in ${QED}_2$ which sets a mass scale in the model. Evidently this is not the case in ${QED}_4$ but a similar phenomenon takes place, at least in the bosonized version of the Schwinger model in $(3+1)$ dimensions. We illustrate how this same generalization of the S-model basically amounts to a Stueckelberg-like formulation of a massive gauge theory characterized by the mixing between a $U(1)$ potential and an Abelian $3$-form field.\n\nOur work is organized according to the following outline: in Section II, we recall the salient features of dualization in terms of two simple Lagrangian systems and show their equivalence to different representations of a massive Proca field. In Section III, using a path-integral approach, we compute the interaction energy, and hence the analytic form of the static potential in the bosonized version of the Scwinger model in four spacetime dimensions. Finally, some Concluding Remarks are cast in Sec. IV. \n\nThroughout the following discussion, the signature of the metric is ($+1,-1,-1,-1$).\n\n\\section{Dualization, gauge invariance and mass generation}\n\nLet us start our considerations by recalling that the study of duality symmetry in gauge theories has been of considerable importance in \norder to provide an equivalent description of physical phenomena by distinct theories. As well-known, duality refers to a physical equivalence \nbetween two field theories which formulated in terms of different dynamical variables \\cite{Hjelmeland:1997eg}. \n\nIn order to put our discussion into context, we also recall that the dualization of Stueckelberg-like massive gauge theories \nand $ B \\wedge F$ models follows from a general $p$ dualization of interacting theories in $d$ spacetime \ndimensions \\cite{Ansoldi:1999wi,Smailagic:1999qw,Ansoldi:2000qs,Smailagic:2000hr,Smailagic:2001ch}. \nIn particular, in the case of $(3+1)$ dimensions, the following $ B \\wedge F$ models are found:\n\\begin{equation}\n{\\cal L}^{(1)} = - \\frac{1}{4}F_{\\mu \\nu }^2\\left( A \\right) + \\frac{1}{{12}}H_{\\mu \\nu \\rho }^2\\left( B \\right) \n+ \\frac{m}{{24}}{\\varepsilon ^{\\mu \\nu \\rho \\sigma }}{B_{\\mu \\nu }}{\\partial _{[\\rho }}{A_{\\sigma ]}}, \\label{Dual05}\n\\end{equation}\n\n\\begin{equation}\n{\\cal L}^{(2)} = - \\frac{1}{4}H_{\\mu \\nu }^2\\left( B \\right) + \\frac{1}{{12}}F_{\\mu \\nu \\rho }^2\\left( A \\right) \n+ \\frac{m}{{24}}{\\varepsilon ^{\\mu \\nu \\rho \\sigma }}{B_\\mu }{\\partial _{[\\nu }}{A_{\\rho \\sigma ]}}, \\label{Dual10}\n\\end{equation}\n\n\\begin{equation}\n{\\cal L}^{(3)} = \\frac{1}{2}{\\left( {{\\partial _\\mu }\\varphi } \\right)^2} - \\frac{1}{{48}}F_{\\mu \\nu \\rho \\sigma }^2\\left( A \\right) \n+ \\frac{m}{{24}}{\\varepsilon ^{\\mu \\nu \\rho \\sigma }}\\varphi {\\partial _{[\\mu }}{A_{\\nu \\rho \\sigma ]}}. \\label{Dual15}\n\\end{equation}\n\nAt this point, it is instructive to make a brief re-examination of equations (\\ref{Dual05}) and (\\ref{Dual10}). For this purpose, we observe \nthat the Lagrangian density (\\ref{Dual05}) may be rewritten as\n\\begin{equation}\n{{\\cal L}^{\\left( 1 \\right)}} = - \\frac{1}{4}F_{\\mu \\nu }^2 - \\frac{1}{2}{\\tilde H_\\sigma }{\\tilde H^\\sigma } \n- \\frac{m}{6}{\\tilde H^\\sigma }{A_\\sigma }, \\label{Dual25}\n\\end{equation}\nwhere we have made use of ${\\tilde H^\\mu } = \n{\\raise0.5ex\\hbox{$\\scriptstyle 1$}\\kern-0.1em\/\\kern-0.15em\\lower0.25ex\\hbox{$\\scriptstyle 2$}}{\\varepsilon ^{\\mu \\nu \\lambda \\rho }}{\\partial _\\nu }{B_{\\lambda \\rho }}$.\n\nNext, in order to eliminate the dual-field $H^{\\sigma}$ care must be taken, for it satisfies the constraint \n${\\partial _\\mu }{\\tilde H^\\mu } = 0$ (Bianchi identity). Thus, to take into account the constraint, we shall introduce a Lagrange \nmultiplier $\\chi$. In such a case, the corresponding effective Lagrangian density (\\ref{Dual25}) reads \n\\begin{equation}\n{{\\cal L}^{\\left( 1 \\right)}} = - \\frac{1}{4}F_{\\mu \\nu }^2 - \\frac{1}{2}{\\tilde H_\\sigma }{\\tilde H^\\sigma }\n- \\frac{m}{6}{\\tilde H^\\sigma }{A_\\sigma } + \\chi {\\partial _\\sigma }{\\tilde H^\\sigma } .\\label{Dual30}\n\\end{equation}\nBy defining ${Z_\\sigma } \\equiv {A_\\sigma } + \\frac{6}{m}{\\partial _\\sigma }\\chi$, with $ {Z_{\\mu \\nu }} \n= {F_{\\mu \\nu }}$, we readily verify that\n\\begin{equation}\n{{\\cal L}^{\\left( 1 \\right)}} = - \\frac{1}{4}Z_{\\mu \\nu }^2 - \\frac{1}{2}{\\tilde H_\\sigma }{\\tilde H^\\sigma } \n- \\frac{m}{6}{\\tilde H^\\sigma }{Z_\\sigma }.\\label{Dual35} \n\\end{equation}\nBy a further definition of the fields, ${W_\\sigma } \\equiv {\\tilde H_\\sigma } + \\frac{m}{6}{Z_\\sigma }$, we find that the Lagrangian \ndensity (\\ref{Dual05}) can be brought to the form\n\\begin{equation}\n{{\\cal L}^{\\left( 1 \\right)}} = - \\frac{1}{4}Z_{\\mu \\nu }^2 + \\frac{1}{2}{\\mu ^2}Z_\\mu ^2, \\label{Dual40}\n\\end{equation}\nwith ${\\mu ^2} \\equiv {\\raise0.5ex\\hbox{$\\scriptstyle {{m^2}}$}\n\t\\kern-0.1em\/\\kern-0.15em\n\t\\lower0.25ex\\hbox{$\\scriptstyle {36}$}}$. We immediately see that the Lagrangian density (\\ref{Dual40}) exhibits a Proca-type mass term.\n\nWe now turn our attention to the Lagrangian density (\\ref{Dual10}). It is convenient to rewrite this equation in the alternative form \n\\begin{equation}\n{{\\cal L}^{\\left( 2 \\right)}} = \\frac{1}{4}\\tilde H_{\\mu \\nu }^2 + \\frac{1}{{12}}F_{\\mu \\nu \\rho }^2 \n+ \\frac{m}{{24}}{\\tilde H^{\\rho \\sigma }}{A_{\\rho \\sigma }}, \\label{Dual45}\n\\end{equation}\nwhere ${\\tilde H^{\\mu \\nu }} = {\\raise0.5ex\\hbox{$\\scriptstyle 1$}\n\t\\kern-0.1em\/\\kern-0.15em\n\t\\lower0.25ex\\hbox{$\\scriptstyle 2$}}{\\varepsilon ^{\\mu \\nu \\lambda \\rho }}{H_{\\lambda \\rho }}$.\n\nIt is worthy to notice that the $B^{\\mu}$- field appears only through ${\\tilde H^{\\mu\\nu}}$.\nAgain, in order to eliminate the dual-field $\\tilde H^{\\mu\\nu}$ care must be taken, for it satisfies the \nconstraint ${\\partial _\\mu }{\\tilde H^{\\mu\\nu} } = 0$. As before, we shall introduce a Lagrange multiplier $\\chi_{\\nu}$. \nIt gives rise to the following Lagrangian density,\n\\begin{equation}\n{{\\cal L}^{\\left( 2 \\right)}} = \\frac{1}{4}\\tilde H_{\\mu \\nu }^2 + \\frac{1}{{12}}F_{\\mu \\nu \\rho }^2 \n+ \\frac{m}{{24}}{\\tilde H^{\\mu \\nu }}{A_{\\mu \\nu }} - \\frac{1}{2}{\\tilde H^{\\mu \\nu }}{\\chi _{\\mu \\nu }},\\label{Dual50}\n\\end{equation}\nwhere ${\\chi _{\\mu \\nu }} = {\\partial _\\mu }{\\chi _\\nu } - {\\partial _\\nu }{\\chi _\\mu }$.\nNow, letting ${Z_{\\mu \\nu }} = {A_{\\mu \\nu }} - \\frac{{12}}{m}{\\chi _{\\mu \\nu }}$, we obtain\n\\begin{equation}\n{{\\cal L}^{\\left( 2 \\right)}} = \\frac{1}{4}\\tilde H_{\\mu \\nu }^2 + \\frac{1}{{12}}F_{\\mu \\nu \\rho }^2 \n+ \\frac{m}{{24}}{{\\tilde H}^{\\mu \\nu }}{Z_{\\mu \\nu }}. \\label{Dual55}\n\\end{equation}\nIt should be further noted that, by defining ${W_{\\mu \\nu }} = {{\\tilde H}_{\\mu \\nu }} \n+ \\frac{m}{{12}}{Z_{\\mu \\nu }}$, equation (\\ref{Dual55}) reduces to \n\\begin{equation}\n{{\\cal L}^{\\left( 2 \\right)}} = \\frac{1}{{12}}F_{\\mu \\nu \\rho }^2 - \\frac{1}{2}{\\mu ^2}Z_{\\mu \\nu }^2, \\label{Dual60} \n\\end{equation}\nwhere we have written, ${\\mu ^2} = \\frac{{{m^2}}}{{288}}$, and \n$F_{\\mu \\nu \\rho }^2 = Z_{\\mu \\nu \\rho }^2$. Thus ${{\\cal L}^{\\left( 2 \\right)}}$ describes a massive field of spin $1$, exactly a \nProca equation, although ${Z_{\\mu \\nu }} \\in \\left[ {\\left( {1,0} \\right) \\oplus \\left( {0,1} \\right)} \\right]$. Actually, a massive\nrank-two skew-symmetric tensor field is, on-shell, equivalent to a Proca field.\n\nIn short, equations (\\ref{Dual05}) and (\\ref{Dual10}) are equivalent; both of these equations describe a Proca field.\n\nConsidering, finally, equation (\\ref{Dual15}), we find that this model reduces to a massless Schwinger model in $(3+1)$ dimensions, \nas we shall indicate it below.\n\n\\section{Interaction energy}\n\nInspired by the preceding observation, we shall now consider the $(3+1)$-dimensional generalization of the Schwinger model, as originally \nintroduced in Ref.\\cite{Aurilia:1979dw}. As we have already noticed, we will work out the static potential for this $(3+1)$ generalization, \nvia a path-integral approach. To this end, we consider the bosonized form of the Schwinger model in D=$(3+1)$, that is,\n\\begin{equation}\n{\\cal L} = \\frac{1}{2}{\\left( {{\\partial _\\mu }\\phi } \\right)^2} + \\frac{1}{2}m_\\phi ^2{\\phi ^2} \n+ \\frac{g}{{6\\sqrt \\pi }}{\\partial _\\mu }\\phi\\ {\\varepsilon ^{\\mu \\nu \\rho \\sigma }}{A_{\\nu \\rho \\sigma }} \n- \\frac{1}{{48}}F_{\\mu \\nu \\rho \\sigma }^2, \\label{Scwhin3-05}\n\\end{equation}\nwhere $g$ is a coupling constant and $m_\\phi$ refers to the mass of the scalar field $\\phi$.\n\nWe readily verify that when, ${m_\\phi } \\to 0$,\nequation (\\ref{Scwhin3-05}) reduces to equation (\\ref{Dual15}).\n\nAccording to usual procedure, integrating out the $\\phi$ field induces an effective theory for the $A_{\\nu \\rho \\sigma }$ field. \nIt is now important to recall that the $ A_{\\nu \\rho \\sigma }$ field can also be written as ${A_{\\nu \\rho \\sigma }} \n= {\\varepsilon _{\\nu \\rho \\sigma \\lambda }}{\\partial ^\\lambda }\\xi$ \\cite{Aurilia:2004cb, Aurilia:2004fz}, \nwhere $\\xi$ refers to an another scalar field. This then leads to the following effective theory for the model under consideration:\n\\begin{equation}\n{\\cal L} = \\frac{1}{2}\\left[ {\\xi \\ \\Delta \\left( {1 + \\frac{{{\\raise0.7ex\\hbox{${{g^2}}$} \\!\\mathord{\\left\/\n\t\t\t\t\t\t{\\vphantom {{{g^2}} \\pi }}\\right.\\kern-\\nulldelimiterspace}\n\t\t\t\t\t\\!\\lower0.7ex\\hbox{$\\pi $}}}}{{\\left( {\\Delta - m_\\phi ^2} \\right)}}} \\right)\\Delta \\ \\xi } \\right], \\label{Scwhin3-10}\n\\end{equation}\nwhere $\\Delta = {\\partial _\\mu }{\\partial ^\\mu }$.\n\nWe are now ready to compute the interaction energy between static pointlike sources. We start off our analysis by writing down the functional \ngenerator of the Green's functions, that is,\n\\begin{equation}\nZ\\left[ J \\right] = \\exp \\left( { - \\frac{i}{2}\\int {{d^4}x{d^4}yJ(x)D(x,y)J(y)} } \\right), \\label{Scwhin3-15}\n\\end{equation}\nwhere, $D(x,y) = \\int {\\frac{{{d^4}k}}{{{{\\left( {2\\pi } \\right)}^4}}}D(k){e^{ - ikx}}}$, is the propagator. In this case, the corresponding \npropagator is given by\n\\begin{equation}\nD(k) = \\left( {1 - \\frac{{m_\\phi ^2}}{{{{\\cal M}^2}}}} \\right)\\frac{1}{{{k^2}\\left( {{k^2} + {{\\cal M}^2}} \\right)}} \n+ \\frac{{m_\\phi ^2}}{{{{\\cal M}^2}}}\\frac{1}{{{k^4}}}, \\label{Scwhin3-20}\n\\end{equation}\nwhere ${{\\cal M}^2} = m_\\phi ^2 - {\\raise0.5ex\\hbox{$\\scriptstyle {{g^2}}$}\\kern-0.1em\/\\kern-0.15em\n\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}}$.\n\nBy means of expression $Z = {e^{iW\\left[ J \\right]}}$ and employing Eq. (\\ref{Scwhin3-15}), ${W\\left[ J \\right]}$ takes the form \n\\begin{eqnarray}\nW\\left[ J \\right] &=& - \\frac{1}{2}\\int {\\frac{{{d^4}k}}{{{{\\left( {2\\pi } \\right)}^4}}}} {J^ * }\\left( k \\right)\n\\frac{{\\left( {1 - \\frac{{m_\\phi ^2}}{{{{\\cal M}^2}}}} \\right)}}{{{k^2}\\left( {{k^2} + {{\\cal M}^2}} \\right)}}\nJ\\left( k \\right) \\nonumber\\\\\n&-& \\frac{1}{2}\\int {\\frac{{{d^4}k}}{{{{\\left( {2\\pi } \\right)}^4}}}} {J^ * }\\left( k \\right)\\frac{{m_\\phi ^2}}{{{{\\cal M}^2}}}\n\\frac{1}{{{k^4}}}J\\left( k \\right).\n\\label{Scwhin3-25}\n\\end{eqnarray}\n\nNext, for $J({\\bf x}) = \\left[ {Q{\\delta ^{\\left( 3 \\right)}}\\left( {{\\bf x} - {{\\bf x}^{\\left( 1 \\right)}}} \\right) \n\t+ {Q^ \\prime }{\\delta ^{\\left( 3 \\right)}}\\left( {{\\bf x} - {{\\bf x}^{\\left( 2 \\right)}}} \\right)} \\right]$, we obtain that the interaction \nenergy of the system is given by\n\\begin{eqnarray}\nV &=& - Q{Q^ * }\\int {\\frac{{{d^3}k}}{{{{\\left( {2\\pi } \\right)}^3}}}} \n\\frac{{\\left( {\\frac{{{\\raise0.5ex\\hbox{$\\scriptstyle {{g^2}}$}\n\t\t\t\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}}}}{{{\\raise0.5ex\\hbox{$\\scriptstyle {{g^2}}$}\n\t\t\t\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}} - m_\\phi ^2}}} \\right)}}{{\\left( {{{\\bf k}^2} + {\\raise0.5ex\\hbox{$\\scriptstyle {{g^2}}$}\n\t\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}} - m_\\phi ^2} \\right)}}\n{e^{i{\\bf k} \\cdot {\\bf r}}} \\nonumber\\\\\n&+& Q{Q^ * }\\int {\\frac{{{d^3}k}}{{{{\\left( {2\\pi } \\right)}^3}}}} \\left( {\\frac{{m_\\phi ^2}}{{{\\raise0.5ex\\hbox{$\\scriptstyle {{g^2}}$}\n\t\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}} - m_\\phi ^2}}} \\right)\\frac{1}{{{{\\bf k}^4}}}{e^{i{\\bf k} \\cdot {\\bf r}}}, \\label{Scwhin3-30}\n\\end{eqnarray}\nwhere ${\\bf r} = {{\\bf x}^{\\left( 1 \\right)}} - {{\\bf x}^{\\left( 2 \\right)}}$.\n\nThis, together with ${Q^ \\prime }=-Q$, yields finally\n\\begin{eqnarray}\nV &=& \\frac{{{Q^2}}}{{4\\pi }}\\frac{{{\\raise0.5ex\\hbox{$\\scriptstyle {{g^2}}$}\n\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}}}}{{{{\\left( {{\\raise0.5ex\\hbox{$\\scriptstyle {{g^2}}$}\n\t\t\t\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}} - m_\\phi ^2} \\right)}^2}}}\\frac{1}{L}\\left( {1 - {e^{ - \\sqrt {{\\raise0.5ex\\hbox{$\\scriptstyle {{g^2}}$}\n\t\t\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}} - m_\\phi ^2} L}}} \\right) \\nonumber\\\\\n&+& \\frac{{{Q^2}}}{{4\\pi }}\\frac{{m_\\phi ^2}}{{2\\left( {{\\raise0.5ex\\hbox{$\\scriptstyle {{g^2}}$}\n\t\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}} - m_\\phi ^2} \\right)}}L, \\label{Scwhin3-35}\n\\end{eqnarray}\nwhere $L = |{\\bf r}|$. One immediately sees that the above static potential profile is analogous to that encountered in the two-dimensional \nSchwinger model. Incidentally, in order to put our discussion into context it is useful to summarize the relevant aspects of the \ntwo-dimensional Schwinger model. In such a case, we begin by recalling the bosonized form of the model under consideration \\cite{Gross:1995bp}:\n\\begin{eqnarray}\n{\\cal L} &=& - \\frac{1}{4}F_{\\mu \\nu }^2 + \\frac{1}{2}{\\left( {{\\partial _\\mu }\\phi } \\right)^2} \n- \\frac{e}{{2\\sqrt \\pi }}{\\varepsilon ^{\\mu \\nu }}{F_{\\mu \\nu }}\\phi \\nonumber\\\\\n&+&m\\sum \\left( {\\cos \\left( {2\\pi \\phi + \\theta } \\right) - 1} \\right), \\label{Scwhin3-40}\n\\end{eqnarray}\nwhere $\\sum = \\left( {\\frac{e}{{2{\\pi ^{{\\raise0.5ex\\hbox{$\\scriptstyle 3$}\n\t\t\t\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle 2$}}}}}}} \\right){e^{{\\gamma _E}}}$ with ${\\gamma _E}$ the Euler-Mascheroni constant and $\\theta$ refers to \nthe $\\theta$-vacuum. \n\nConsequently, by using the gauge-invariant but path-dependent variables formalism which provides a physically-based alternative to the Wilson \nloop approach \\cite{Gaete:1999iy, Gaete:2001wh}, the static potential reduces to\n\\begin{equation}\nV = \\frac{{{Q^2}}}{2}\\frac{{\\sqrt \\pi }}{e}\\left( {1 - {e^{ - \\frac{e}{{\\sqrt \\pi }}L}}} \\right), \\label{Scwhin3-45}\n\\end{equation}\nfor the massless case. On the other hand, for the massive case ($\\theta=0$), the static potential then becomes\n\\begin{equation}\nV = \\frac{{{Q^2}}}{{2\\lambda }}\\left( {1 + \\frac{{4\\pi m\\sum }}{{{\\lambda ^2}}}} \\right)\\left( {1 - {e^{ - \\lambda L}}} \\right) \n+ \\frac{{{q^2}}}{2}\\left( {1 - \\frac{{{\\raise0.5ex\\hbox{$\\scriptstyle {{e^2}}$}\n\t\t\t\t\\kern-0.1em\/\\kern-0.15em\n\t\t\t\t\\lower0.25ex\\hbox{$\\scriptstyle \\pi $}}}}{{{\\lambda ^2}}}} \\right)L, \\label{Scwhin3-50}\n\\end{equation}\nwhere ${\\lambda ^2} = \\frac{{{e^2}}}{\\pi } + 4\\pi m\\sum$. The above results clearly show that the $(3+1)$-D generalization of the Schwinger \nmodel is structurally identical to the $(1+1)$-D Schwinger model.\n\nIn this perspective it is worth recalling that there is an alternative way of obtaining the Lagrangian density (\\ref{Scwhin3-10}), \nwhich provides a complementary view into the physics of confinement. In fact, we refer to a theory of antisymmetric tensor fields that \nresults from the condensation of topological defects as a consequence of the Julia-Toulouse mechanism. \nMore precisely, the Julia-Toulouse mechanism is a condensation process dual to the Higgs mechanism proposed in \\cite{Quevedo:1996uu}. \nThis mechanism describes phenomenologically the electromagnetic behavior of antisymmetric tensors in the presence of magnetic-branes \n(topological defects) that eventually condensate due to thermal and quantum fluctuations. Using this phenomenology we have discussed \nin \\cite {Gaete:2004dn,Gaete:2005am} the dynamics of the extended charges (p-branes) inside the new vacuum provided by the condensate. \nActually, in \\cite {Gaete:2004dn} we have considered the topological defects coupled both longitudinally and \ntransversally to two different tensor potentials, $A_p$ and $B_q$, such that $p+q+2=D$, where $D=d+1$ space-time dimensions.\n\nWe skip all the technical details and refer to \\cite{Gaete:2004dn} for them. Thus, after the condensation, the Lagrangian density turns out to be\n\\begin{eqnarray}\n{\\cal L} &=& \\frac{{{{\\left( { - 1} \\right)}^q}}}{{2\\left( {q + 1} \\right)!}}{\\left[ {{H_{q + 1}}\\left( {{B_q}} \\right)} \\right]^2} \n+ e{B_q}{\\varepsilon ^{q,\\alpha ,p + 1}}{\\partial _\\alpha }{\\Lambda _{p + 1}} \\nonumber\\\\\n&+& \\frac{{{{\\left( { - 1} \\right)}^{p + 1}}}}{{2\\left( {p + 2} \\right)!}}{\\left[ {{F_{p + 2}}\\left( {{\\Lambda _{p + 1}}} \\right)} \\right]^2} \n\\nonumber\\\\\n&+& \\frac{{{{\\left( { - 1} \\right)}^{p + 1}}\\left( {p + 1} \\right)!}}{2}{m^2}\\Lambda _{p + 1}^2, \\label{Scwhin3-55}\n\\end{eqnarray}\nshowing a $B$$\\wedge$$F$ type of coupling between the $B_q$ potential with the tensor $\\Lambda_{p+1}$ carrying the degrees of freedom of the \ncondensate. Following our earlier procedure \\cite{Gaete:2004dn}, the effective theory that results from integrating out the fields representing \nthe vacuum condensate, is given by\n\\begin{equation}\n{\\cal L} = \\frac{{{{\\left( { - 1} \\right)}^{q + 1}}}}{{2\\left( {q + 1} \\right)!}}{H_{q + 1}}\\left( {{B_q}} \\right)\n\\left( {1 + \\frac{{{e^2}}}{{\\Delta - {m^2}}}} \\right){H^{q + 1}}\\left( {{B_q}} \\right). \\nonumber\\\\\n\\label{Scwhin3-60}\n\\end{equation} \nHence we see that this expression with $p=-1$ and $q=3$ becomes\n\\begin{equation}\n{\\cal L} = \\frac{1}{{2 \\times 4!}}{F_{\\mu \\nu \\rho \\lambda }}\\left( A \\right)\\left( {1 + \\frac{{{e^2}}}{{\\Delta - {m^2}}}} \\right)\n{F^{\\mu \\nu \\rho \\lambda }}\\left( A \\right). \\label{Scwhin3-65}\n\\end{equation}\nIt is straightforward to verify that Eq. (\\ref{Scwhin3-65}) reduces to Eq. (\\ref{Scwhin3-10}).\n\nIn this way, we establish a new connection among different effective theories. It must be clear from this discussion that the above connections \nare of interest from the point of view of providing unifications among diverse models as well as exploiting their equivalence in explicit \ncalculations. \n\n\n\\section{Concluding Remarks}\n\nFinally, the point we wish to emphasize is that there are two generic features that are common in the four-dimensional case and their \nupper\/lower extensions, as we shall show below. First, the existence of a linear potential, leading to the confinement of static charges. \nThe second point is related to the correspondence among diverse effective theories. To see this, it should be noted that by using the \nmethodology illustrated in \\cite{ Smailagic:1999qw}, we have that one of the $ B \\wedge F$ models in $(4+1)$ dimensions is given by the \nmixing between a $U(1)$ potential and an Abelian $3$-form field by means of a topological mass term, that is,\n\\begin{eqnarray}\n{\\cal L}^{\\left( {4 + 1} \\right)} &=& - \\frac{1}{4}{F_{\\mu \\nu }}\\left( A \\right){F^{\\mu \\nu }}\\left( A \\right) \n+ \\alpha {H_{\\mu \\nu \\kappa \\lambda }}\\left( C \\right){H^{\\mu \\nu \\kappa \\lambda }}\\left( C \\right) \\nonumber\\\\\n&+& \\beta {\\varepsilon ^{\\mu \\nu \\kappa \\lambda \\rho }}{A_\\mu }{\\partial _\\nu }{C_{\\kappa \\lambda \\rho }}, \\label{CR-05}\n\\end{eqnarray} \nwith $\\alpha = - \\frac{1}{{48}}$ and $ \\beta = \\frac{\\sigma }{6}$, where the parameter $\\beta$ has mass dimension. This model was considered \nin \\cite{Cocuroci:2013bga}, and the main motivation to consider this model is based on the possible connection with dark energy.\n\nHowever, we shall start from the five-dimensional spacetime model\n\\begin{eqnarray}\n{\\cal L}^{\\left( {4 + 1} \\right)} &=& - \\frac{1}{4}{F_{\\hat \\mu \\hat \\nu }}{F^{\\hat \\mu \\hat \\nu }} \n+ \\alpha {H_{\\hat \\mu \\hat \\nu \\hat \\kappa \\hat \\lambda }}{H^{\\hat \\mu \\hat \\nu \\hat \\kappa \\hat \\lambda }} \\nonumber\\\\\n&+& \\beta {\\varepsilon ^{\\hat \\mu \\hat \\nu \\hat \\kappa \\hat \\lambda \\hat \\rho }}{A_\\mu }{\\partial _\\nu }{C_{\\hat \\kappa \\hat \\lambda \\hat \\rho }} + \\frac{1}{{12}}m_C^2{C_{\\hat \\mu \\hat \\nu \\hat \\rho }}{C^{\\hat \\mu \\hat \\nu \\hat \\rho }}, \\nonumber\\\\\n\\label{CR-10}\n\\end{eqnarray}\nwith the additional presence of a mass term $m_C$ for the Abelian $3$-form field; this explicit mass term makes a difference: if it were not introduced, the model\ncould be reduced to nothing but a Proca-type model in $(4+1)$ dimensions. Next, we perform its dimensional reduction along the \nlines of \\cite{Cocuroci:2013bga,Gaete:2012yu}: \n${A^{\\hat \\mu }} \\to \\left( {{A^{\\bar \\mu }},{A^4}} \\right)$, ${A^4} = \\phi$, ${\\partial _4}\\left( {everything} \\right) = 0$, \n${C^{\\hat \\mu \\hat \\nu \\hat \\kappa }} = \\left( {{C^{\\bar \\mu \\bar \\nu \\bar \\kappa }},{C^{\\bar \\mu \\bar \\nu 4}}} \\right)$ \nand ${C^{\\bar \\mu \\bar \\nu 4}} = {B^{\\bar \\mu \\bar \\nu }}$. Carrying out this prescription in equation (\\ref{CR-10}), we then obtain\n\\begin{eqnarray} \n{{\\cal L}^{\\left( {3 + 1} \\right)}} &=& - \\frac{1}{4}{F_{\\bar \\mu \\bar \\nu }}{F^{\\mu \\nu }} \n+ \\frac{1}{2}{\\left( {{\\partial _{\\bar \\mu} }\\phi } \\right)^2} + \\alpha {H_{\\bar \\mu \\bar \\nu \\bar \\kappa \\bar \\lambda }}\n{H^{\\bar \\mu \\bar \\nu \\bar \\kappa \\bar \\lambda }} \\nonumber\\\\\n&-& 4\\alpha {G_{\\bar \\mu \\bar \\nu \\bar \\kappa }}{G^{\\bar \\mu \\bar \\nu \\bar \\kappa }} - 3\\beta {\\varepsilon ^{4 \\bar \\mu \\bar \\nu \\bar \\kappa \n\t\t\\bar \\lambda }}{A_{\\bar \\mu} }{\\partial _{\\bar \\nu} }{B_{\\bar \\kappa \\bar \\lambda }} \\nonumber\\\\\n&-& \\beta {\\varepsilon ^{4 \\bar \\nu \\bar \\kappa \\bar \\lambda \\bar \\rho }}\\phi {\\partial _{\\bar \\nu} }{C_{\\bar \\kappa \\bar \\lambda \\bar \\rho }} \n+ \\frac{{m_C^2}}{{12}}{C_{\\bar \\mu \\bar \\nu \\bar \\rho }}{C^{\\bar \\mu \\bar \\nu \\bar \\rho }} \\nonumber\\\\\n&-& \\frac{{m_C^2}}{4}{B_{\\bar \\mu \\bar \\nu }}{B^{\\bar \\mu \\bar \\nu }}, \\label{CR-15}\n\\end{eqnarray}\nwhere $\\bar \\mu ,\\bar \\nu ,\\bar \\kappa ,\\bar \\lambda ,\\bar \\rho = 0,1,2,3$. Making use of an additional dimensional reduction, that is, \n$ {A^{\\bar \\mu }} \\to \\left( {{A^\\mu },{A^3}} \\right)$, ${\\partial _3}\\left( {everything} \\right) = 0$, ${B^{\\bar \\mu \\bar \\nu }} \n= \\left( {{B^{\\mu \\nu }},{C^{\\mu }}} \\right)$\n\\begin{eqnarray}\n{{\\cal L}^{\\left( {2 + 1} \\right)}} &=& - \\frac{1}{4}{F_{\\mu \\nu }}{F^{\\mu \\nu }} + 12\\alpha {G_{\\mu \\nu }}{G^{\\mu \\nu }} \\nonumber\\\\\n&-& 6\\beta {\\varepsilon ^{\\mu \\nu \\kappa }}{A_\\mu }{\\partial _\\nu }{C_\\kappa } \n+ \\frac{{m_C^2}}{2}{C_\\mu }{C^\\mu}, \\label{CR-20}\n\\end{eqnarray}\nwhere ${G_{\\mu \\nu }} = {\\partial _\\mu }{C_\\nu } - {\\partial _\\nu }{C_\\mu }$. Next, after performing the integration over $C_{\\mu}$, \nthe induced effective Lagrangian density is given by\n\\begin{equation}\n{{\\cal L}^{\\left( {2 + 1} \\right)}} = - \\frac{1}{4}{F_{\\mu \\nu }}\\left( {1 + \\frac{\\sigma }{{\\left( {\\Delta + m_C^2} \\right)}}} \\right)\n{F^{\\mu \\nu }}. \\label{CR-25}\n\\end{equation}\nAgain, by applying the gauge-invariant formalism, the corresponding static potential for two opposite charges located at ${\\bf y}$ and \n${\\bf y}\\prime$ turns out to be\n\\begin{equation}\nV = - \\frac{{{q^2}}}{{2\\pi }}{K_0}\\left( {ML} \\right) + \\frac{{{q^2}m_C^2}}{{4M}}L, \\label {CR-30}\n\\end{equation}\nwhere $L = |{\\bf y} - {{\\bf y}^ \\prime }|$ and ${M^2} = {\\sigma ^2} + m_C^2$. In summary, then: this potential displays the conventional \nscreening part, encoded in the Bessel function, and the linear confining potential. As expected, confinement disappears whenever ${m_C} \\to 0$\nand also in the case $m_{C}$ is non-trivial, but much smaller than the topological mass parameter, $\\sigma$.\n\n\nA final consideration we would like to raise concerns the presence of some sort \nof fundamental mechanism that endows one of the gauge potentials, the $p$- or the\n$(p+1)$-form, with a Proca-type mass term: if only the usual field-strength squared and\nthe topological mass terms are present, a field reshuffling is always possible to be done \nand one of the gauge potentials can be integrated over yielding, at the end, a Proca-like \n$p$-form or $(p+1)$-form massive model; exactly like we have worked out for the Lagrangians\n(\\ref{Dual05}) and (\\ref{Dual10}). However, if a more fundamental mechanism is at work\n(like the Higgs mechanism, for example) that gives an explicit (non-topological) mass term\nto one of the gauge fields, then the simple equivalence to a $p$-form\nProca field is no longer true and a confining contribution to the static interparticle potential\nshows. We would like to conclude our work by pointing out the relationship between the\ngeneration of a non-topological mass and the confinig profile of the interparticle potential.\n\n\\section{Acknowledgments}\n\nOne of us (P. G.) wishes to thank the Abdus Salam ICTP for hospitality, the Field Theory Group of the COSMO\/CBPF for the pleasant visit with the PCI-BEV\/MCTIC support. P. G. was partially supported by Fondecyt (Chile) grant 1130426 and by Proyecto Basal FB0821.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzkyfi b/data_all_eng_slimpj/shuffled/split2/finalzzkyfi new file mode 100644 index 0000000000000000000000000000000000000000..ad493c36635cf9bd8789700be8a658b69432741b --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzkyfi @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\\section{Introduction}\nQuantum mechanics is one of the most transformative physical theories of the 20th century. However, while the evolution of the quantum wave function is deterministically described by Sch\\\"odinger's equation, the outcome of a measurement is probabilistic, given by Born's rule. Despite recent progress \\cite{Ozawa_Bayes_1997,Zurek_Darwinism_2009,Shrapnel_Born_2018}, there is no consensus on how to reconcile these two viewpoints, as illustrated by the measurement paradox \\cite{Schroedinger_Cat_1935}. There are two conceptually distinct approaches: either the interpretative postulates must be modified \\cite{Adler_Quantum_2009,Zeh_Measurement_1970,Everett_Relative_1957,Heisenberg_Prinzipien_1930,Bohm_Hidden_1952}, or quantum mechanics approximates a deeper theory yet to be discovered. The later approach gives rise to collapse models \\cite{Bassi_Review_2012,Bassi_Gravitational_2017}, postulating a stochastic nonlinear modification to Schr\\\"odinger's equation. Irrespective of whether they successfully allow the reconciliation of quantum evolution and measurement theory, these collapse models are considered the only mathematically consistent, phenomenological modifications against which quantum theory can be tested \\cite{Adler_Quantum_2009,Ferialdi_Dissipative_2012}.\n\nThe most universal and well studied collapse model is Continuous Spontaneous Localization (CSL) \\cite{Pearle_Localization_1989,Ghirardi_Markov_1990},\nwhich serves as a framework to describe a variety of collapse mechanisms \\cite{Bassi_Review_2012,Bassi_Gravitational_2017,Ghirardi_gravity_1990,Adler_Nonwhite_2007,Adler_Nonwhite_2008,Smirne_Dissipative_2014,Smirne_Dissipative_2015,Bassi_Breaking_2010}.\nIn CSL, a collapse noise field is introduced which couples nonlinearly to the local mass density. In its simplest form this noise is white, and the model has two parameters --- the collapse rate $\\lambda_c$, which determines the interaction strength with the collapse noise field, and the correlation length $r_c$, which determines the spatial resolution of the collapse process \\cite{Ghirardi_Markov_1990,Adler_Quantum_2009}.\nThe correlation length is expected to be $\\sim100$~nm \\cite{Adler_Bounds_2006}, since the behaviour of larger systems is generally adequately described by classical theories, whereas quantum mechanics appears to apply on smaller scales.\nRefined dissipative and coloured models introduce two additional parameters, associating a temperature and high-frequency cut-off to the collapse noise field to ensure energy conservation and permit an identifiable physical origin of collapse \\cite{Adler_Nonwhite_2007,Adler_Nonwhite_2008,Smirne_Dissipative_2014,Smirne_Dissipative_2015,Bassi_Breaking_2010}. \nBased on the assumption that the origin is of cosmological nature, and thermalised to the photon-, neutrino-, or gravitational wave background,\nthe high-frequency cut-off is estimated to occur at $\\Omega_{\\rm csl}\/2\\pi\\sim 10^{10}-10^{11}$~Hz \\cite{Bassi_Breaking_2010}.\n\nTo date, the most stringent unambiguous upper bounds on the collapse rate at the expected correlation length are based on mechanical resonators, with signatures of spontaneous collapse expected to manifest as an anomalous temperature increase.\nHowever, the suggested lower bounds to the collapse-induced heating are lower than one phonon per day \\cite{Adler_Bounds_2006,Bassi_Breaking_2010,Ghirardi_Unified_1986}.\nThe challenge of resolving these exquisitely small collapse signatures over a large thermal noise background has precluded conclusive tests of CSL, and has also introduced significant challenges for data interpretation \\cite{Vinante_Improved_2017}.\nEven were these issues resolved, quantum backaction heating \\cite{Khalili_backaction_2012,Nimmrichter_Optomechanical_2014} would remain orders of magnitude larger than the predicted collapse signatures.\nMoreover, with micron- \\cite{Vinante_Cantilever_2015,Vinante_Improved_2017} to meter-sizes \\cite{Carlesso_Gravitational_2016,Helou_LISA_2017}, the resonators employed to-date are larger than the anticipated correlation length and have frequencies far below the expected high-frequency cut-off. As such they are unable to provide insight into the physical origin of collapse \\cite{Adler_Nonwhite_2007,Adler_Nonwhite_2008,Smirne_Dissipative_2014,Smirne_Dissipative_2015,Bassi_Breaking_2010}.\n\n \\begin{figure}[h!]\n \\includegraphics{Fig_Setup_nc_1.pdf}\n\\caption{{\\bf Illustration of protocol.}\nTop left: array of optomechanical cavities.\nTop right: nonlinear pair production from a signal and pump photon (frequency $\\omega_{\\rm pump}$).\nBottom: Energy level diagram for scattering of a probe photon with a phonon.\n$n_b$: phonon number.\n}\n \\label{Fig_Setup}\n \\end{figure}\n\nIn this work we propose to test collapse theories with high frequency nanomechanical resonators. This offers the advantages of miniaturisation to match the expected collapse correlation length, resonance frequency around the high-frequency cut-off, and the abilities to both exponentially suppress thermal phonons via passive cryogenic cooling and apply quantum measurement techniques to improve performance.\nTo assess the approach, we develop a specific experimental implementation that makes use of phonon counting in a nanoscale mechanical resonator. \nOur proposal includes new mitigation strategies for optical, thermal, electrical and quantum back-action noise that, for the first time, provide a way to bring each of these noise sources below the expected lower bounds for collapse-induced heating. We conclude that with challenging but plausible improvements in the state-of-the-art our approach could conclusively test CSL, closing the gap between measured upper bounds and predicted lower bounds on the collapse rate, and could also potentially identify the physical mechanism underlying the collapse process.\nThis provides an experimental pathway to answer one of the longest standing questions in physics, and \nalso opens up possibilities for laboratory tests of astrophysical models of dark matter \\cite{Riedel_undetectable_2013,Riedel_collisions_2017}, \nand other exotic particles \\cite{Riedel_diffusion_2015}.\n\n\n\n\\section*{Results}\n\n\\subsection*{Basic protocol}\n\n \n \nOur protocol is illustrated in Fig. \\ref{Fig_Setup}, and is based on a gigahertz nanomechanical resonator, or array thereof,\nwithin a millikelvin environment.\nAs opposed to standard optomechanical measurement, consisting of an optical cavity linearly coupled to a mechanical resonator \\cite{Aspelmeyer_Review_2014}, we propose to perform phonon counting in a three-mode optomechanical system where two optical modes\nare coupled via a mechanical resonator with resonance frequency $\\Omega$.\nThis allows collapse signatures to be spectrally distinguished from most noise sources.\nOne mode, the {\\it probe mode}, is excited by a continuous weak laser at its resonance frequency $\\omega_p$.\nIn the ideal case, the other, the {\\it signal mode} at frequency $\\omega_s=\\omega_p+\\Omega$, is only excited by resonant anti-Stokes Raman scattering between collapse induced phonons and probe photons.\nA single-photon readout scheme minimises both absorption heating \\cite{Meenehan_millikelvin_2014} and quantum back-action heating.\nSignal photons are spectrally separated from probe photons by a filter cavity, while dark counts are suppressed by nonlinearly downconverting signal photons to pairs and performing coincidence detection.\n\n\n\nAs a concrete example, we consider using a three-mode photonic-phononic crystal optomechanical system, such as proposed in \\cite{Chang_Array_2011,BasiriEsfahani_Phonon_2012,Ludwig_TwoMode_2012,Safavi_Naeini_traveling_2011}. \nWe choose most parameters based on those achieved in \\cite{MacCabe_Ultralong_2019}, with a mechanical resonance frequency $\\Omega\/2\\pi=5.3$~GHz, a mechanical damping rate $\\Gamma\/2\\pi=108$~mHz, an effective mass $m_{\\rm eff}=136$~fg, and thermalisation to the base temperature of a dilution refrigerator ($T=10$~mK).\nWe use the theoretical scattering-limited intrinsic decay rate of $\\kappa_{p,0}=\\kappa_{s,0}=2\\pi\\cdot 9.2$~MHz calculated for these devices \\cite{Ren_Crystal_2019} for both optical modes, where the subscripts `$p$' and `$s$' distinguish the probe and signal mode throughout \\cite{Aspelmeyer_Review_2014}.\nFinally, we assume a tenfold improved single-photon optomechanical coupling rate of $g_0\/2\\pi=11.5$~MHz, as predicted to be feasible with optimized designs \\cite{Matheny_coupling_2018}.\n\n\n\n\n\\subsection*{Phonon flux induced by CSL}\n\nThe CSL phonon flux is $\\dot n_c= \\lambda_c D$, where $D$ is a geometrical factor that quantifies the susceptibility of the resonator to spontaneous collapse.\nThe requirement that CSL should resolve the measurement problem introduces lower bounds on $\\lambda_c$, and therefore on the phonon flux. \nAdler proposed $\\lambda_c\\geq 10^{-8\\pm 2}$~s$^{-1}$ from the postulate that collapse should account for latent image formation in photography \\cite{Adler_Bounds_2006}, while Bassi {\\it et al.} proposed $\\lambda_c\\geq 10^{-10\\pm 2}$~s$^{-1}$ from the presumption that collapse should occur in the human eye \\cite{Bassi_Breaking_2010}.\nWe estimate $D=5.1\\cdot 10^{5}$ for our proposed device \\cite{Vinante_Cantilever_2015} (see Supplemental Material \\cite{Supp}), which combined with these bounds implies minimum CSL induced phonon fluxes of $\\dot n_c=5.1\\cdot10^{-3\\pm 2}$~s$^{-1}$ and $\\dot n_c=5.1\\cdot10^{-5\\pm 2}$~s$^{-1}$, respectively. \n\n\n\n\n\\subsection*{Optomechanical dynamics and conversion efficiency}\n\nIf the oscillator is initially in its ground state,\nwith one photon in the probe mode, a phonon introduced by spontaneous collapse\nprepares the state $\\ket{n_b n_p n_s}=\\ket{110}$, where $n_b$ is the phonon number in the mechanical resonator, while $n_p$ and $n_s$ are the photon numbers in probe- and signal-mode, respectively. \nThe optomechanical conversion efficiency $\\eta_{\\rm om}$ for this state to emit a signal photon at frequency $\\omega_s$\nis obtained by numerically solving the Born-Markov master equation taking into account that $\\Gamma\\ll\\kappa_p,\\kappa_s$ (see Methods).\nWe choose the external probe decay rate $\\kappa_{p,\\rm ex}\/2\\pi=2.2$~MHz \\cite{Aspelmeyer_Review_2014}, allowing operation at the threshold of strong coupling with $g_0\\approx\\kappa_p$. \nThis is advantageous for efficient conversion of collapse-induced phonons to signal photons and ensures low occupancy, minimising noise, as discussed later.\nWe choose the signal mode to be significantly overcoupled ($\\kappa_{s,\\rm ex}\/2\\pi=0.7\\kappa_{s}=21$~MHz) in a trade-off between optimising the conversion efficiency and suppressing noise from direct occupancy of the signal mode (see later).\nTogether, these external decay rates result in relatively high conversion efficiency of $\\eta_{\\rm om}=0.32$.\n\n \\subsection*{Noise sources}\n\n\\begin{figure}[!htbp]\n\t \\includegraphics{Fig_heating_nc_1.pdf}\n\\caption{{\\bf Heating rates of a $Q=\\Omega\/\\Gamma=10^7$ silica sphere resonator vs. mechanical frequency and sphere diameter}. Red traces: heating due to coupling to the thermal environment at temperatures 300,1 and 0.01 K. Gray shaded: Lower bounds on CSL heating rates for a sphere, according to Adler \\cite{Adler_Bounds_2006}, Bassi {\\it et al.} \\cite{Bassi_Breaking_2010} and GRW \\cite{Ghirardi_Unified_1986}, assuming the fundamental mechanical breathing mode frequency $\\Omega=c\/R$, with sphere radius $R$ and speed of sound $c=3000$ m\/s. CSL heating rates drop once the resonator becomes smaller than the noise correlation length, which is set to $r_c=10^{-7}$~m. Green: lower bound on heating rate predicted from classical channel gravity \\cite{Kafri_Classical_2014,Khosla_Classical_2018,Altamirano__Pairwise_2018}. At high frequencies and low temperatures, collapse signatures exceed the thermal heating. Blue shaded: proposed range of $\\Omega_{\\rm csl}$.}\n \\label{Fig_heating} \n\\end{figure}\n\nFour classes of noise can potentially imitate a collapse signal: thermal phonons, probe photons that leak through the system, phonons introduced by the measurement process, and detector dark counts.\nPhotons that leak through the system can be efficiently filtered using a standard laser stabilisation reference cavity \\cite{Kessler_Laser_2011}\n(see Table \\ref{tab:noise comparison} and Methods), and will not be considered further here.\n\n\n\n\n{\\it Thermal phonons.}\n A collapse signature is resolvable in a thermal noise background if $\\dot n_c\/\\dot n_{\\rm th}>1$, where $\\dot n_{\\rm th}=\\Gamma (e^{\\hbar \\Omega\/k_BT}-1)^{-1}$ is the thermal phonon flux.\nThis gives a minimum testable collapse rate $\\lambda_{c,\\rm th}=\\dot n_{\\rm th}\/D$. \nExisting experiments have operated with comparatively low frequency oscillators in the high temperature limit, $k_BT \\gg \\hbar\\Omega$ \\cite{Vinante_Cantilever_2015,Vinante_Improved_2017,Carlesso_Gravitational_2016,Helou_LISA_2017}, with thermal phonon flux significantly larger than Bassi {\\it et al.}'s lower bound,\nand have sought to resolve small collapse signatures on top of this large thermal noise background.\nA significant advantage of our approach is that miniaturisation and cryogenic cooling allow access to the regime where $k_BT \\ll \\hbar\\Omega$.\nThe average thermal phonon occupation is then exponentially suppressed due to Bose-statistics, $\\dot{n}_{\\rm th} \\approx \\Gamma e^{-\\frac{\\hbar\\Omega}{k_ BT}}$.\nFig. \\ref{Fig_heating} shows this exponential suppression as a function of resonator size, and in comparison to the CSL signal, for the simple example of the fundamental breathing mode of a silica sphere (see Supplemental Material \\cite{Supp} for calculation).\nAs can be seen, for gigahertz resonators at millikelvin temperatures the exponential suppression allows thermal phonon fluxes beneath both Adler and Bassi {\\it et al.}'s lower bounds.\nFor the proposed photonic crystal device, we find $\\lambda_{c,\\rm th}=1.2\\cdot 10^{-17}$~s$^{-1}$,\nalso well beneath both bounds.\nThis provides the potential for\nunambiguous tests of collapse models.\n\n \\begin{figure}[!htbp]\n\\includegraphics{Fig_Energy_nc_1.pdf}\n\\caption{{\\bf Signal pathways due to measurement-induced phonons.}\na) Phonon created after direct excitation of signal mode.\nb) Phonon due to counter-rotating transition.\nc) Two phonons created by counter-rotating transition followed by resonant transition. \n}\n\\label{Fig_Energy}\n \\end{figure}\n\n{\\it Measurement-induced phonons.}\nPhonons introduced by the optomechanical measurement can imitate collapse signatures. These phonons are created by non-resonant scattering processes between the signal and probe modes, the three lowest order of which are represented in Fig. \\ref{Fig_Energy}.\nWe calculate the probability of phonon occupancy due to these processes numerically by solving the Born-Markov master equation (see Methods).\nWe find that each of these processes is suppressed by the square of the resolved sideband ratio $\\Omega\/\\kappa_p$, with predicted phonon occupancies shown in Fig. \\ref{figSOM} (a) and (b).\nPhotoabsorptive heating can also introduce phonons. \nHowever, it only adds a negligible contribution to the measurement-induced phonon occupancy (see Table \\ref{tab:noise comparison} and Methods).\n\n \n\n\n\\begin{figure}[!htbp]\n\t\\includegraphics{Fig_OM_nc_2.pdf}%\n\n\n\n\\caption{{\\bf Numerical calculations of noise magnitude.}\na) Blue (orange): occupancy of density matrix elements containing one (two) phonon(s);\ndashed blue: contribution from direct signal mode excitation.\nFast oscillations on timescale $\\Omega^{-1}$ correspond to the counter-rotating transition $\\ket{010}\\leftrightarrow\\ket{101}$; \nslow oscillations to the resonant process $\\ket{101}\\leftrightarrow\\ket{210}$, with period $g_0^{-1}=\\kappa_p^{-1}$. \nDotted lines: asymptotic values for $\\kappa_p^{-1},\\kappa_s^{-1}\\ll t\\ll\\Gamma^{-1}$.\nb) Same as (a) for $t\\sim\\Gamma^{-1}\\gg\\kappa_p^{-1}, \\kappa_s^{-1}$. \nc) Cumulative probability $p_{\\rm om}(t)$ of a probe photon creating a photon at frequency $\\omega_s$. \nDotted line: asymptotic value for $t\\gg\\Gamma^{-1}$.\n}\n\\label{figSOM}\n\\end{figure} \n \nA measurement-induced phonon can only be converted to a collapse-imitating photon at frequency $\\omega_s$ if it scatters with a second photon entering the probe mode within the lifetime $\\Gamma^{-1}$ of the mechanical resonator (see Fig. \\ref{Fig_Energy}).\nIt is therefore possible to suppress these photons by operating with a low average photon occupancy $\\bar n_p$. Here, we choose the photon occupancy so that the probability of a photon entering the probe mode during one mechanical oscillation lifetime is $\\eta_p = \\bar n_p \\kappa_p\/\\Gamma\\sim$~1\\%.\nThis reduces the rate of measurement-induced photons by a factor of a hundred.\nThe cumulative probability of a probe photon generating a phonon, and a second probe photon then causing emission of a photon at frequency $\\omega_s$, is shown in Fig. \\ref{figSOM} (c).\nThe asymptotic probability is $p_{\\rm om}(t\\rightarrow \\infty)=8.4\\cdot 10^{-8}$ (see Supplemental Material \\cite{Supp}).\n\n\n{\\it Coincidence dark counts.}\n Detecting collapse induced phonons at the predicted rate of less than one per day necessitates very high suppression of photon dark counts, which typically occur at hertz to kilohertz rates.\nOne way to achieve this is to nonlinearly downconvert signal photons to pairs (bottom right inset, Fig. \\ref{Fig_Setup}) \nusing a bright pump beam in a third-order nonlinear medium.\nIt has been shown that this process can convert single photons to pairs with near-unit efficiency $\\eta_\\chi$ \\cite{Langford_Conversion_2011} (see Supplemental Material \\cite{Supp}). \nA signal is recorded only if a coincidence detection event is registered.\nThe coincidence dark count rate is suppressed as the square of the single-detector dark count rate $R_{d,1}$, $R_{d,2}=R_{d,1}^2 \\cdot \\tau_c$, where $\\tau_c$ is the coincidence timing resolution. \nFor commercially available photon counters with $R_{d,1}=3.5$~s$^{-1}$ and $\\tau_c=30$~ps \\cite{Photonspot_private}, we predict $R_{d,2}\\sim 3.7\\cdot10^{-10}$~s$^{-1}$.\n\n \n \\subsection*{Minimum testable collapse rate}\n \n For $r_c=10^{-7}$~m, the rate of coincidence counts attributed to collapse is $R_c=\\lambda_c D \\eta=5.5\\cdot 10^{2}\\lambda_c$, where the efficiency $\\eta=\\eta_p\\eta_{\\rm om}\\eta_{\\chi}\\eta_d\\eta_{\\rm f}=1.1\\cdot10^{-3}$ quantifies the fraction of phonons in the mechanical resonator that result in a coincidence count, $\\eta_{\\chi}=0.95$, and $\\eta_d=0.64$ is the coincidence detection efficiency (see Supplemental Material \\cite{Supp}).\nThis rate must exceed the sum of the noise rates, setting the limit to the minimum observable collapse rate $\\lambda_c$.\nFor optomechanically induced phonons, probe photons leaking through the system, and thermal phonons, $R_{\\rm om}=\\kappa_{p,\\rm ex} \\bar n_pp_{\\rm om}(t\\rightarrow \\infty)\\eta_f\\eta_\\chi\\eta_d$, $R_{\\rm phot}=\\kappa_{p,\\rm ex} \\bar n_pp_f\\eta_\\chi\\eta_d$, and $R_{\\rm th}=\\eta\\dot n_{\\rm th}$, respectively, where $\\eta_f=0.56$ is the transduction efficiency through the filter and $p_f=3.5\\cdot 10^{-10}$ the probability of a probe photon leaking through the filter (see Supplemental Material \\cite{Supp}).\nThe numerical values and corresponding minimum testable collapse rates are given in Table \\ref{tab:noise comparison} (see also Supplemental Material \\cite{Supp}).\nOptomechanical measurement-induced phonons and coincidence dark counts set comparable limits on $\\lambda_c$, with negligible contributions from leaked probe photons, photoabsorption, and thermally excited phonons. \nThe minimum testable collapse rate limited by all noise sources is $\\lambda_c=\\sum_i\\lambda_{\\rm c,i}=1.0\\cdot10^{-12}$~s$^{-1}$, sufficient to test both Bassi {\\it et al.}'s and Adler's proposals.\n\n\\begin{table}\n\\center\n\\begin{tabular}{c | c | c | c} \n\nnoise type & scaling & signal rate~[s$^{-1}$] &$\\lambda_{c,\\rm min}$~[s$^{-1}$] \\\\\n\n\\hhline{=|=|=|=}\n\\rule{0pt}{9pt} collapse noise & $\\eta l^2\\rho^2x_0^2$\\cite{Supp} & $5.5\\cdot 10^{2}\\lambda_c$ & --- \\\\\n\\hline\n\\rule{0pt}{12pt} thermal & $e^{-\\frac{\\hbar\\Omega}{k_BT}}$ & $6.7 \\cdot 10^{-15}$ & $1.2\\cdot 10^{-17}$ \\\\\n\\hline\n\\rule{0pt}{10pt} optom. phonons & $\\eta_p\\kappa^2\/\\Omega^2$ & $1.9 \\cdot 10^{-10}$ & $3.5 \\cdot 10^{-13}$ \\\\\n\\hline\n\\rule{0pt}{10pt} abs. heating & $l\/(m^{\\frac{1}{4}}\\Omega)$ \\cite{Supp} & $1.4 \\cdot 10^{-14}$ & $ 2.6 \\cdot 10^{-17}$ \\\\ %\n\\hline\n\\rule{0pt}{10pt} probe photons & see \\cite{Supp}& $1.4\\cdot 10^{-12}$ &$2.6\\cdot 10^{-15}$\\\\\n\\hline\n\\rule{0pt}{9pt} dark counts & $R_{d,1}^2 \\cdot \\tau_c\/N$ &$3.7\\cdot 10^{-10}$ & $ 6.7\\cdot 10^{-13}\/N$ \\\\\n\\hline\n\\rule{0pt}{9pt} all noise & --- & $5.6\\cdot 10^{-10} $ & $ 1.0\\cdot 10^{-12}$ \\\\\n\\hline\n\\end{tabular}\n\\caption{{\\bf Comparison of noise sources and respective testable CSL parameter $\\lambda_c$.} $m$ is the oscillator mass, $l$ its linear size, $\\rho$ its density, and $x_0$ its zero point motion.}\n\\label{tab:noise comparison}\n\\end{table}\n\n\n\n \\subsection*{Signal rate and measurement time}\nThe predicted average time required to observe one signal due to CSL-collapse is $t_{\\rm meas}=(\\lambda_c D\\eta)^{-1}$. \nFully probing both Adler's and Bassi {\\it et al.}'s proposals with a single optomechanical resonator, including their respective uncertainties, would require $t_{\\rm meas}>57$~years.\nFabricating an array of $N$ optomechanical cavities on a silicon wafer \\cite{Bekker_tuning_2018,Xu_cascaded_2006,GilSantos_cascaded_2016,Zhang_synchronisation_2011} (see Fig. \\ref{Fig_Setup}), coupled to a single filter cavity, nonlinear medium and detector (or a small number of such elements)\ncould significantly reduce this time to $t_{\\rm meas}^{(N)}=t_{\\rm meas}\/N$, and also the dark count-limited testable collapse rate to $\\lambda_{c,\\rm det}^{(N)}=\\lambda_{c,\\rm det}\/N$.\nWe estimate that $N\\sim10^{4}$ may be feasible (see Supplemental Material \\cite{Supp}), essentially eliminating detector dark counts as a limit, and allowing a reduction of the measurement time to about two days.\n\n\n \\subsection*{Feasibility and alternative parameter regimes}\nThe only optomechanical parameters that must be improved from the current state-of-the-art \\cite{MacCabe_Ultralong_2019} to realise our protocol, are a reduced optical linewidth (by a factor of $\\sim50$), as predicted by theoretical modelling based on the device realized in \\cite{Ren_Crystal_2019}, and an enhanced single-photon coupling rate (by a factor of $\\sim10$), based on theoretical modelling in \\cite{Matheny_coupling_2018}. \nAlternatively, effective enhanced single-photon coupling could be achieved by coupling to a qubit or other highly nonlinear system, e.g. as demonstrated in \\cite{Pirkkalainen_hybrid_2013}.\nGiven the trajectory of the field,\nwe estimate these requirements to be likely achievable in the intermediate future. \nNevertheless, it is also useful to consider alternative realizations of the method.\n\n \n\n{\\it Quadratic coupling.}\nWhile here we consider phonon-counting via an optomechanical Raman interaction,\nin principle the method could be implemented with any low-noise phonon-counting method applied to a high-frequency oscillator \\cite{Oconnell_quantum_2010,Dellantonio_nondemolition_2018,Sletten_Fock_2019,Cohen_counting_2014,ArrangoizArriola_nanomechanical_2019}. \nOne promising approach may be quantum non-demolition measurement of phonon number using non-linear optomechanics \\cite{Thompson_strong_2008}. \nIn the regime of quadratic optomechanical coupling and resolved mechanical sidebands \\cite{Aspelmeyer_Review_2014}, a collapse-induced phonon imparts a frequency shift $2\\bar n_{\\rm cav}^{1\/2}g_0^{(2)}$ on the optical resonance at frequency $\\omega$, where $\\bar n_{\\rm cav}=\\langle a^\\dagger a \\rangle$ is the average intracavity photon number with $a$ the annihilation operator for the optical cavity field, and $g_0^{(2)}$ the zero-point quadratic coupling rate \\cite{Thompson_strong_2008,Paraiso_Squared_2015,Hauer_nondemolition_2018}.\nThe shift is detectable if it is larger than the significant noise sources, which are random fluctuations in the probe frequency, absorption heating, and quantum-backaction from spurious linear coupling.\n\nConsidering Bassi {\\it et al.}'s proposed mechanism \\cite{Bassi_Breaking_2010}, taking $\\bar n_{\\rm cav}=10^2$ and assuming that the probe is shot noise limited, we find that a zero-point quadratic coupling rate of $g_0^{(2)}\\gtrsim3.5\\sqrt{\\kappa\\Gamma}\\bar n_{\\rm cav}^{-3\/2}\\gtrsim 2\\pi\\cdot 28$~Hz would be sufficient for\nthe weakest possible collapse signal to exceed the probe frequency noise using the photonic-phononic crystal considered in the protocol above \\cite{MacCabe_Ultralong_2019}\n(see Methods).\nThis is well within experimentally achieved values in optomechanical photonic crystals (e.g. $g_0^{(2)}\/2\\pi=245$~Hz in \\cite{Paraiso_Squared_2015}).\n\nPerhaps the most significant challenge in this approach would be to engineer a strong suppression of linear optomechanical coupling, so that the phonon flux due to quantum back-action does not exceed the predicted CSL signature. \nIf using standard architectures, there is a fundamental limit to this suppression of linear coupling \\cite{Miao_Limit_2009}. Hence, either a different architecture would have to be employed \\cite{Kaviani_paddle_2015,Dellantonio_nondemolition_2018,Hauer_nondemolition_2018}, or the substantially more stringent condition $g_0^{(2)}\\geq\\kappa$ would have to be realized.\nThe phonon flux due to quantum back-action is given by $\\dot{n}_{\\rm ba}=4g_0^2\\bar n_{\\rm cav}\/\\kappa=4g^2\/\\kappa$. \nTo resolve a potential CSL signature, $\\dot{n}_{\\rm c}$ must be greater than $\\dot{n}_{\\rm ba}$.\nAs a result, the linear optomechanical coupling would need to be suppressed to $g\\leq\\sqrt{\\lambda_c D \\kappa \/4}$. To test $\\lambda_c=10^{-12}$, we find the condition $g\/2\\pi\\lesssim 10^{-1}$~Hz, about seven orders of magnitude lower than typical linear coupling rates in photonic-phononic crystal structures \\cite{MacCabe_Ultralong_2019}.\nWhile some architectures may in principle allow for vanishing linear coupling $g$, achieving the required suppression in practice may be challenging \\cite{Dellantonio_nondemolition_2018,Kaviani_paddle_2015,Anetsberger_Near-Field_2009}.\n\nIn continuous operation, with currently available technology \\cite{MacCabe_Ultralong_2019}, absorption heating would exceed the expected heating from collapse by about seven orders of magnitude.\nEven with this very large heating in the continuous domain, it may be possible to resolve the problem by operating in a pulsed regime, so that each optomechanical measurement process is completed in a timescale much shorter than the time required for absorption events to create phonons.\nIn this case, the measurements would need to be sufficiently temporally spaced to allow for phonons to fully dissipate.\n\n\n \n\n\n\\subsection*{Discussion}\n\n\\begin{figure}[!htbp]\n \\includegraphics{Fig_CSL_nc_2.pdf}\n\\caption{{\\bf Parameter diagram for CSL-model.}\nExcluded upper bounds: gravitational wave detectors (yellow shaded); cold atoms (gray shaded); microcantilevers (dashed blue line); KDTL-interferometry (dashed black); Excluded for simple CSL only: neutron stars (dashed black) and X-ray (dotted black).\nProposed lower bounds: Adler (vertical blue bars and dotted blue line) and Bassi {\\it et al.} (vertical black bar).\nRed: predicted testable parameter space using our protocol.\n}\n\n\n\\label{figCSL}\n\\label{stats}\n\\end{figure}\n\n\nFig. \\ref{figCSL} compares the predicted upper bound on the collapse rate $\\lambda_c$ from our protocol to those of existing experiments,\ntogether with Adler's and Bassi {\\it et al.}'s lower bounds, and their uncertainties.\nExisting upper bounds are provided by the motional stability of gravitational wave interferometers \\cite{Carlesso_Gravitational_2016,Helou_LISA_2017,Nobakht_Unraveling_2018} (yellow region); the thermalisation of ultracold cantilevers \\cite{Vinante_Cantilever_2015,Vinante_Improved_2017} (blue outlined region); Kapitza-Dirac-Talbot-Lau (KDTL)-Interferometry \\cite{Fein_25kDa_2019,Nimmrichter_Matterwave_2011,Toros_Colored_2017,Feldmann_Parameter_2012} (dashed black); spontaneous X-ray emission from Germanium \\cite{Curceanu_Xray_2016,Curceanu_LNGS_2020,Fu_Radiation_1997} (dotted black) and the observed temperature of neutron stars \\cite{Adler_Fermi_2019,Stace_Neutron_2019} (dashed black), which are valid however only for white noise CSL; and cold atom interference \\cite{Toros_Colored_2017} (gray region), though we note the controversy \\cite{StamperKurn_Comment_2016} on the actual size of the superposition reported in \\cite{Kovachy_Superposition_2015}.\n\nThe red shaded region in Fig. \\ref{figCSL} could be tested by our protocol\nas discussed above. In the case of white-noise collapse, the protocol could for the first time fully test Bassi {\\it et al.}'s proposal. \nIf collapse noise has one of the proposed physical origins \\cite{Bassi_Breaking_2010,Smirne_Dissipative_2014,Smirne_Dissipative_2015}, the envisaged protocol would also for the first time probe Adler's prediction, which is in this case not tested by X-ray emission (black dotted line in Fig. \\ref{figCSL}).\nThe resonance frequency is close to the frequency range in which a drastic frequency-dependent reduction of the collapse noise stemming from a physical origin is expected \\cite{Bassi_Breaking_2010}. \nTo identify the physical origin of collapse, and to differentiate between collapse-induced signal and technical noise, we suggest employing a number of mechanical resonators of slightly different frequencies, or one frequency-tunable resonator \\cite{Pfeifer_tunable_2016}, at frequencies around $\\Omega\/2\\pi\\sim10$~GHz, such as reported in \\cite{Ren_Crystal_2019}.\n\nWe also evaluate the capability of the protocol to constrain parameters in gravitational collapse models.\nWhile for the Di\\'osi-Penrose model \\cite{Diosi_Gravitation_1984,Diosi_Models_1989,Penrose_Reduction_1996} we find that it cannot exceed existing bounds, for the classical channel gravity model in a typical parameter range \\cite{Altamirano__Pairwise_2018,Kafri_Classical_2014,Khosla_Classical_2018} we predict about a one order-of-magnitude stronger bound than previously achieved \\cite{tbp} (see Supplemental Material \\cite{Supp}).\n\n\nIn summary, we have proposed the concept of testing quantum linearity using high-frequency mechanical oscillators. \nThis offers the advantages of thermal noise suppression to well below expected collapse signatures, and the potential for identification of the physical origin of collapse. \nAs a possible implementation we suggest a protocol based on a dual-cavity high-frequency optomechanical device passively ground-state-cooled and operating in the strong coupling regime. This design, combined with nonlinear optical techniques to reduce dark counts, is predicted to allow measurement of the minuscule phonon-flux generated by collapse-induced heating. While challenging, the protocol has the potential to conclusively test CSL, and thus whether collapse mechanisms can be invoked to resolve the measurement paradox. Unlike previous proposals and experiments, it is designed to allow for identification of the physical noise field underlying CSL, and for differentiation between excess technical noise and signatures of collapse.\n \n\n\n\\section*{Methods} \n\n\\subsection*{Born-Markov master equation} \n\nTo model the dynamics of the three-mode optomechanical system we employ the Born-Markov framework for open quantum systems \\cite{Nunnenkamp_Single_2011,Liu_Strong_2013,BasiriEsfahani_control_2016}.\n The interaction picture Hamiltonian for our system is \\cite{BasiriEsfahani_Phonon_2012,BasiriEsfahani_control_2016,Chang_Array_2011}\n\\begin{equation}\nH_{\\rm int}=\\hbar g_0(b^{\\dagger}e^{-i\\Omega t}+be^{i\\Omega t})(a_p^{\\dagger}a_se^{i\\Omega t}+a_pa_s^{\\dagger}e^{-i\\Omega t})+\\hbar\\sqrt{\\kappa_{p,\\rm ex}}(a_p^{\\dagger}a_{\\rm in}+a_{\\rm in}^{\\dagger}a_p),\n\\end{equation}\nwhere $b$, $a_p$ and $a_s$ are annihilation operators for the mechanical mode and optical modes, respectively, and $a_{\\rm in}$ is the coherent input field. The first term describes the mechanically mediated cross-coupling of the optical modes, while the second term describes the coherent excitation \\cite{Aspelmeyer_Review_2014}. In the parameter regime of this work where $g_0\\ll \\Omega$ and $\\Gamma \\ll \\kappa_p,\\kappa_s,g_0$, the dynamics of the system can be described by the\nBorn-Markov master equation as \\cite{Nunnenkamp_Single_2011,Liu_Strong_2013}\n\\begin{equation}\n\\frac{d\\hat \\rho}{dt}=-\\frac{i}{\\hbar}\\big[H_{\\rm int},\\hat \\rho\\big] + \\kappa_p \\mathcal{D}[a_p]\\hat \\rho + \\kappa_s \\mathcal{D}[a_s]\\hat \\rho + \\Gamma(1+\\bar{n}_{\\rm th}) \\mathcal{D}[b]\\hat \\rho + (\\Gamma \\bar{n}_{\\rm th} + \\dot n_c)\\mathcal{D}[b^{\\dagger}]\\hat \\rho,\n\\label{eq: master_equation}\n\\end{equation}\nwhere $\\hat \\rho$ is the density matrix, $\\bar n_{\\rm th}$ the mechanical mean thermal occupancy, \nand $\\mathcal{D}$ the dissipating superoperator, $\\mathcal{D}[A]\\hat \\rho=A\\hat \\rho A^{\\dagger}-\\frac{1}{2}(A^{\\dagger}A\\hat\\rho+\\hat\\rho A^{\\dagger}A)$. \nA weak phonon flux due to spontaneous collapse is described by $\\dot n_c=\\lambda_c D$, independent of its origin.\nThis allows us to model the conversion of a signal phonon to a signal photon, as well as creation of noise phonons introduced by measurement (see Supplemental Material \\cite{Supp}).\n\n\n\n\n\\subsection*{Negligible sources of noise}\n\n{\\it Probe photons leaking though the system.}\n Probe photons passing directly from the laser through the optomechanical system, without a scattering event, could in principle imitate a signal, obfuscating collapse signatures.\nWe find that, using a standard laser stabilisation reference cavity as a filter \\cite{Kessler_Laser_2011}, this noise can be suppressed well below both Adler's and Bassi {\\it et al.}'s lower bounds.\nSimilarly, if a photon is created in an optomechanical conversion process and subsequently outcoupled into the signal mode, due to energy conservation it either remains at frequency $\\omega_p$, or has a frequency reduced by integer multiples $n$ of the mechanical resonance frequency, $\\omega_p-n\\Omega$. In both cases, this noise is doubly suppressed --- first by the suppression of the direct occupation pathway, and second by the filter. \nThis makes probe photons that leak through the system a negligible source of noise (see Supplemental Material \\cite{Supp} for details).\n\n{\\it Optical absorption heating.}\nTo estimate the phonon occupancy due to optical absorption,\nwe use the model for absorption heating in silicon optomechanical crystals outlined in \\cite{Meenehan_millikelvin_2014,Ren_Crystal_2019}.\nPhotoabsorption creates an electronic excitation, which is then transferred to terahertz-frequency phonons. While radiating from the resonator to the environment with a geometry- and material-dependent rate $\\gamma_{\\rm THz}$, they also couple to lower energy phonons with a generally longer timescale, potentially exciting the mechanical resonator. \nIn \\cite{Meenehan_millikelvin_2014,Ren_Crystal_2019}, the average phonon number $\\bar n_b$ is related to the average intracavity photon number $\\bar n_{\\rm cav}$ via $\\bar n_b\\propto \\bar n_{\\rm cav}^{1\/3}$. \nWe expect this relationship to break down when the time between photoabsorption events is long enough for the generated heat to fully dissipate, $\\bar n_{\\rm cav} \\cdot \\gamma_{\\rm THz}\/\\kappa \\lesssim1$, where $\\kappa$ is the loaded optical decay rate, as any discrete photon absorption event is expected to create a fixed amount of heat.\nIn this case $\\bar n_{\\rm cav}$ determines the frequency of these events, but not the magnitude of dissipated heat.\nWe compute the average phonon number excited by of one probe photon in the mechanical resonator, $\\bar n_{\\gamma}$, due to photoabsorption for time $t_{\\rm abs}$, at which the oscillator is in thermal equilibrium with the material, but not yet with the environment, $\\Gamma^{-1}\\gg t_{\\rm abs} \\gg \\gamma_{\\rm THz}^{-1}$.\nFor the proposed setup we find $p_{\\rm abs}(t\\rightarrow\\infty)=6.1\\cdot 10^{-12}$ and $R_{\\rm abs}=1.4\\cdot10^{-14}$~s$^{-1}$ (see Supplemental Material \\cite{Supp} for calculation details).\n\n\\subsection*{Measurement-induced phonons.}\nA probe photon can create a noise phonon by coupling directly into the signal mode instead of the probe mode (Fig. \\ref{Fig_Energy} (a)). \nThis process is suppressed by the square of the resolved-sideband ratio $\\Omega\/\\kappa_s$.\nThe corresponding occupancy is calculated by numerically solving the Born-Markov master equation (see Methods and Supplemental Material \\cite{Supp}) and shown by the dashed blue line in Fig. \\ref{figSOM} (a).\n\nA photon that does enter the probe mode, corresponding to the state $\\ket{ n_b n_p n_s}=\\ket{010}$, can introduce noise by undergoing the non-resonant phonon-creating transition $\\ket{010}\\rightarrow\\ket{101}$ (see Fig. \\ref{Fig_Energy} (b)).\nThe resulting state can also resonantly transition to a two-phonon state, $\\ket{101}\\rightarrow\\ket{210}$, as shown in Fig. \\ref{Fig_Energy} (c).\nSimilarly to above, noise phonons from this process are suppressed by $\\sim(\\Omega\/\\kappa_p)^2$.\nPredicted phonon occupancies are shown in Fig. \\ref{figSOM} (a) and (b).\n\n\\subsection*{Zero-point quadratic coupling rate required to fully test Bassi et al.'s lower bound}\n\nThe linearised quadratic part of the optomechanical interaction Hamiltonian is $H_{\\rm int}^{(2)}=\\bar n_{\\rm cav}^{1\/2}g_0^{(2)}(a^{\\dagger}+a)(2b^{\\dagger}b+b^{\\dagger}b^{\\dagger}+bb)$ \\cite{Hauer_nondemolition_2018}. \nThe term proportional to $b^{\\dagger}b$ yields a per-phonon optical resonance frequency shift of $2\\bar n_{\\rm cav}^{1\/2}g_0^{(2)}$, which is the signature of a collapse-induced phonon.\nA random fluctuation $\\delta \\omega$ in the frequency of the probe can imitate a signal if it is larger or equal to this frequency shift, and sustained over a time comparable to the phonon lifetime $\\Gamma^{-1}$. \nFor shot noise limited probe, the probability of a fluctuation larger than $2\\bar n_{\\rm cav}^{1\/2}g_0^{(2)}$ is given by an error function of a Gaussian distribution\n\\begin{equation}\np(\\delta \\omega)=\\bigg(\\frac{1}{\\sqrt{2\\pi \\sigma^2}}\\int^\\infty_{\\delta \\omega}e^{-\\omega^2\/2 \\sigma^2}d\\omega\\bigg),\n\\label{eq:Gauss}\n\\end{equation}\nwith standard deviation of\n$\\sigma\\approx\\kappa\/\\sqrt{N}$, where $N$ is\nthe number of photons interacting with a phonon within the mechanical lifetime $\\Gamma^{-1}$, and is related to the average intracavity photon number via $N=\\bar n_{\\rm cav} \\cdot \\kappa\/\\Gamma$ for a continuous measurement. \nThe rate of spurious signals due to such fluctuations is $R_{\\delta\\omega}=\\Gamma p(\\delta\\omega)$.\nTo test a collapse-induced phonon flux of $\\dot n_{\\rm c}$, we require $\\dot n_{\\rm c}\\geq R_{\\rm \\delta\\omega}$.\nFrom Eq. \\ref{eq:Gauss} we find that, to fully exclude Bassi {\\it et. al's} lower bound using the photonic-phononic crystal considered in the protocol above \\cite{MacCabe_Ultralong_2019}, requires $\\bar n_{\\rm cav}^{1\/2}g_0^{(2)}\\gtrsim 3.5\\sigma$.\nAssuming an average intracavity photon number of $\\bar n_{\\rm cav}=10^2$, with $\\kappa\/2\\pi=575$~MHz \\cite{MacCabe_Ultralong_2019}, leads to the condition $g_0^{(2)}\\gtrsim3.5\\sqrt{\\kappa\\Gamma}\\bar n_{\\rm cav}^{-3\/2}\\gtrsim 2\\pi\\cdot 28$~Hz.\n\nThe term proportional to $b^{\\dagger}b^{\\dagger}a$ converts a probe photon to two phonons, potentially imitating a collapse-signature. \nHowever, the shift induced by two phonons is $4\\bar n_{\\rm cav}^{1\/2}g_0^{(2)}$ and can be clearly distinguished from the collapse-induced shift caused by one phonon.\nTherefore, two-phonon creation can only imitate a collapse signal if it coincides with a frequency fluctuation of the probe mode $-\\delta \\omega\\geq2 n_{\\rm cav}^{1\/2}g_0^{(2)}$, sustained at least over the two-phonon lifetime $(\\sqrt2\\Gamma)^{-1}$. The low probability of such a fluctuation, together with suppression on the order of $(2\\Omega\/\\kappa)^2$ due to the non-resonant nature of the interaction, make this source of noise negligible.\n\n\n\\section*{Data availability}\nThe data that support the findings of this study are available within the paper and its Supplemental Material.\nCodes for the numerical simulations are available on request from the corresponding author.\n\n\\section*{Author contributions}\nWPB provided overall leadership for the project. SF and WPB conceptualized the idea. SF, SB, MZ and KK developed the theoretical model. SF and SB performed numerical simulations. All co-authors contributed in the development of the manuscript which was drafted by SF and WPB.\n\n\n\n\\section*{Acknowledgements}\nThe authors thank Gerard Milburn, Nathan McMahon and James Bennett for helpful discussions, and Nicolas Mauranyapin for preparing Figure 1. We also acknowledge funding by Australian Research Council grants (EQUS, CE170100009, DE180101443) and the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 663830.\n\n\n\n\\section*{Additional information}\n\n\n{\\bf Competing interests:} The Authors declare no competing interests.\n\n\n\n\\newpage\n\\footnotesize \\renewcommand{\\refname}{\\vspace*{-30pt}} \n\\bibliographystyle{apsrev4-2} \n\n\\section*{Supplementary Note 1. Signal rates from spontaneous collapse}\n\\label{sec:collapserates}\nWhile there exists a plethora of collapse models \\cite{Bassi_Review_2012,Bassi_Gravitational_2017}, they can all be formulated in terms of a stochastic nonlinear modification to Schr\\\"odinger's equation of the general form \\cite{Ferialdi_Dissipative_2012,Adler_stochastic_2001}: \n\\begin{equation}\n\\frac{d\\Psi}{dt} = \\bigg[-\\frac{i}{\\hbar}H + \\sqrt{\\lambda}A\\xi(t)-\\frac{\\lambda}{2}(A^{\\dagger}A+A^2) \\bigg]\\Psi,\n\\label{stochastic} \n\\end{equation}\nwhere the operator $H$ is related to the standard Hamiltonian of the system, $\\xi(t)$ is defined in terms of an increment $dW(t)$ of a stochastic Wiener process $W(t)$ through\n$\\xi(t) dt = dW(t)$; \n$\\lambda$ is a coupling that sets the strength of the collapse and $A$ is the reduction operator, which is specific to the particular realization of the collapse mechanism. The requirements of norm-preservation of the state evolution and {of} no-superluminal signalling make these collapse models the only mathematically consistent, phenomenological modifications against which quantum theory can be tested in this context \\cite{Ferialdi_Dissipative_2012}.\n\n\nIn the following we give expressions for the \ndecoherence rates due to two commonly studied collapse mechanisms. Collapse models \ndiffer in the properties and nature of the mechanisms purported to cause the collapse and can thus be classified according to the basis in which decoherence occurs, the mathematical properties of the stochastic mechanism and whether the mechanism has a quantum mechanical origin, or is due to a modification to Sch\\\"odinger's equation from a deeper-level theory. In the models discussed here decoherence acts in the position basis with Gaussian correlations in space, see \\cite{Bassi_Review_2012} and references therein. \nA generic expression for the diffusion term $\\sqrt{\\lambda}A\\xi(t)dt$ in these models is $\\sqrt{\\lambda}\\int d\\vec x \\left(\\rho(\\vec x)-\\langle{\\rho(x)}\\rangle\\right) dW(\\vec x,t)$ with $\\rho(\\vec x)$ being the mass density, $W(\\vec x,t)$ the ensemble of Wiener processes (for different points $\\vec x$ in space) which can in general be correlated. The noise correlation length $r_c$ and the coupling rate $\\lambda$ to the noise field are the model parameters. The mass density typically takes the form $\\rho(\\vec x) = \\sum_jm_j\\int dy G(y-x)\\psi_j^\\dagger(y)\\psi_j(y)$ with $G(y)$ a ``smearing function'' and $\\psi_j^\\dagger(y), \\psi_j(y)$ creation and annihilation operators of the particle with mass $m_j$. \nThe stochastic Sch\\\"odinger equation Eq. \\eqref{stochastic} and the above form of the diffusion term result in a master equation for the density operator $\\hat\\rho$ where the off-diagonal elements $\\vec x', \\vec x''$ evolve as $\\frac{\\partial}{\\partial t}\\bra{\\vec x'} \\hat\\rho \\ket{\\vec x''} = -\\tilde{D}( x', x'')\\bra{\\vec x'} \\hat\\rho \\ket{\\vec x''}$ where \n $\\tilde{D}(\\vec x', \\vec x'') = \\sum_{i,j}\\frac{\\lambda}{2}\\left[G(\\vec x_i' -\\vec x_j')+G(\\vec x_i'' -\\vec x_j'')-2G(\\vec x_i' - \\vec x_j'')\\right]$\nand describes the rate at which the spatial coherence is suppressed. Taking as an example the typically used Gaussian smearing function of the mass density $G(\\vec x)=(4\\pi r^2_c)^{-3\/2}e^{-\\vec x^2\/4r_c^2}$, the decoherence rate reads $\\tilde{D}(\\vec x', \\vec x'')=\\lambda\\cdot(4\\pi r^2_c)^{-3\/2}\\cdot(1-e^{-|\\vec x'-\\vec x''|^2\/4 r^2_c})$. We note that this is exactly the one-particle decoherence rate of the Ghirardi-Rimini-Weber (GRW) \\cite{Ghirardi_Unified_1986} and in the Continuous Spontaneous Localization (CSL) models \\cite{Pearle_Localization_1989,Ghirardi_Markov_1990}. Furthermore, for small superpositions sizes (as compared to the correlation length $r_c$) $|\\vec x'-\\vec x''|\\ll r_c$ the decoherence rate becomes approximately $\\tilde{D}(\\vec x', \\vec x'')\\propto |\\vec x'-\\vec x''|^2\/4 r^2_c$. In this limit one obtains as expected that the collapse rate increases with the size of superposition and the corresponding Lindblad term in the master equation takes a simple form $\\propto - [\\vec x, [\\vec x, \\hat\\rho]]$. \n\nPosition-basis decoherence yields localization of macroscopic superposition states. The same Lindblad term $-\\tilde{D}[\\vec x,[\\vec x, \\hat\\rho]]$ results in momentum diffusion of the system (this can be seen as a direct consequence of the Heisenberg Uncertainty \nPrinciple and the fact that the above decoherence process can equivalently be seen as a reduction of the position uncertainty of the system). In turn, the momentum diffusion can be interpreted as heating with the collapse-induced heating rate being directly proportional to the decoherence rate $\\tilde{D}$. \nThis allows testing the collapse models also by monitoring spontaneous heating of even isolated systems.\nFor a quantum oscillator the heating leads to an increase of the phonon number expectation value. \nThe equivalent Lindblad term reads $-\\tilde{D}(b)[ b,[ b, \\hat\\rho]]$, with $\\tilde{D}(b)=x_0^2\\tilde{D}(\\vec x', \\vec x'')$, where $x_0$ is the zero-point motion (or equivalently, for spatial superpositions of massive particles, the superposition size $\\Delta x$). \n\n\nThe most studied collapse model is the\nCSL model \\cite{Pearle_Localization_1989,Ghirardi_Markov_1990}.\nIt considers second-quantized (albeit non-relativistic) indistinguishable particles where the collapse occurs in the particle number (Fock) basis. The key consequence of the indistinguishability of the particles is that for multi-particle systems the model predicts quadratic dependence of the decoherence rate on the number of particles that are within the cutoff distance $r_c$. For a comparison, the GRW model postulates discrete in time collapse events of the wave-function of individual (and also distinguishable) particles in the positions basis which yields linear dependence of the decoherence rate on the particle number \\cite{Ghirardi_Unified_1986}.\nThe stochastic process in the CSL model is introduced in terms of a time-dependent Wiener noise at each point in space, coupling to mass-density smeared over some length scale $r_c$. As CSL is linear in the coupling rate $\\lambda_c$ of matter to the collapse noise field, we define a dimensionless decoherence operator $D$ by $\\tilde{D}_{\\rm csl}(b)=\\lambda_c D$.\nFor an oscillator with mass density $\\rho(\\vec x)$ and direction of motion along the $z$-axis, the decoherence operator in the CSL-model reads \\cite{Nimmrichter_Optomechanical_2014,Vinante_Cantilever_2015}\n\n\\begin{equation}\nD_{\\rm csl}=\\frac{(4\\pi)^{3\/2} r_c^3 x_0^2}{u^2}\\int \\frac{d^3\\vec{k}}{(2\\pi)^3}k^2_z e^{-k^2r_c^2}\\vert\\tilde{\\rho}(\\vec{k})\\vert^2,\n\\end{equation}\nwhere $\\tilde{\\rho}(\\vec{k})=\\int d^3r\\rho(\\vec{r})e^{-i\\vec{k}\\cdot\\vec{x}}$ is the Fourier transform of the mass density and $u=1.66\\cdot 10^{-27}$~kg is the atomic mass unit\n\nCSL in its original form predicts infinite energy increase as time goes to infinity \\cite{Ghirardi_Markov_1990,Bassi_Dynamical_2003,Bassi_energy_2005}. This problem can be solved by postulating a finite temperature of the noise \\cite{Smirne_Dissipative_2015}. Furthermore, the model assumes a white noise spectrum, which cannot be identified with any physical origin \\cite{Ferialdi_Dissipative_2012}. In order to generalise the model to become compatible with relativity and with observations, the noise field should have a more general, i.e.~non-white spectrum. In such a case, however, the model becomes non-Markovian~\\cite{Adler_Nonwhite_2007, Adler_Nonwhite_2008}. While such models are difficult to study in full generality, it has been demonstrated~\\cite{Adler_Nonwhite_2007, Adler_Nonwhite_2008} that to lowest order in $\\lambda$ the qualitative features of the model are the same as for the white-noise model. A common assumption that helps to lift ambiguities in defining the model is that the field underlying the collapse process has a cosmological origin. This allows one to introduce a high-frequency cutoff of the order of $\\Omega_{\\rm csl}\/2\\pi\\approx 10^{10}-10^{11}$ Hz \\cite{Bassi_Breaking_2010,Smirne_Dissipative_2015} which ensure the collapse rate is essentially as in a white-noise CSL model, but which changes the relaxation behaviour: in the coloured-noise model the system in the limit of long times thermalises to the temperature of the noise field, while in the white-noise model the system energy keeps growing with time \\cite{Pearle_Collapse_1996}.\n\n$D$ can be calculated analytically for simple geometries of composite test-systems \\cite{Nimmrichter_Optomechanical_2014,Vinante_Cantilever_2015}.\nFor a sphere of radius $R$ the decoherence operator reads\n\n\\begin{equation}\nD_{\\rm sphere}=\\frac{14\\pi^2 R^2 r_c^2\\rho^2 x_0^2}{3 u^2} \\big(1-\\frac{2r_c^2}{R^2}+e^{-R^2\/r_c^2}\\big(1+\\frac{2r_c^2}{R^2}\\big)\\big),\n\\end{equation} \nand for the case of a cuboid with constant density $\\rho$ and sidelengths $L_1$, $L_2$ and $L_3$, where $L_3$ is the direction of motion \n\\begin{equation}\nD_{\\rm cuboid}= \\frac{32 r_c^4\\rho^2 x_0^2}{u^2}\\big(1-e^{-\\frac{L_3^2}{4r_c^2}}\\big) \\big(e^{-\\frac{L_2^2}{4r_c^2}}-\\frac{\\sqrt{\\pi}L_2}{2r_c}{\\rm Erf}(\\frac{L_2}{2r_c}) -1\\big) \\big(e^{-\\frac{L_1^2}{4r_c^2}}-\\frac{\\sqrt{\\pi}L_1}{2r_c}{\\rm Erf}(\\frac{L_1}{2r_c}) -1\\big).\n\\label{eq:dcuboid}\n\\end{equation}\nFor our proposed experiment, we estimate length, height and width of the photonic crystal beam to $L_1,L_2,L_3=1.21,0.22,0.22~\\mu$m, respectively, reproducing the effective motional mass of the relevant mechanical mode of 136~fg \\cite{MacCabe_Ultralong_2019}, where we used the density of silicon, $\\rho=2.33\\cdot10^{3}$~kg\/m$^3$. We choose the cuboid approximation for the modeshape following \\cite{Vinante_Cantilever_2015}. From Eq. \\eqref{eq:dcuboid} we find $D=5.1\\cdot10^{5}$. \n\n\nFor the models where the collapse is assumed to have a gravitational origin, two main types of theories can be distinguished: where decoherence arises due to an intrinsic uncertainty in the local value of the gravitational field \\cite{Diosi_Gravitation_1984,Diosi_Models_1989} or, equivalently, gravitational self-interaction \\cite{Penrose_Reduction_1996}; and where decoherence is a consequence of the assumption that gravity is fundamentally a classical channel \\cite{Kafri_Classical_2014,Khosla_Classical_2018,Altamirano_Pairwise_2018}. In both cases for small superposition size, the resulting effect has the same general form as the corresponding regime of the CSL and GRW models: For an oscillator it is proportional to the square of the zero point motion, for spatial superpositions of massive particles the effect is proportional the square of the superposition size. Gravity-based decoherence has also been described within the framework of CSL in \\cite{Ghirardi_gravity_1990}.\n\nIn the Di\\'osi-Penrose (DP) model the decoherence rate is quantified by gravitational potential evaluated between superposed amplitudes of the system: \n$\\frac{G}{2\\hbar}[U(XX)+U(YY)-2U(XY)]$, where $U(XY)=-G\\int d^3r\\int d^3r'\\frac{\\rho_X(\\vec x)\\rho_Y(\\vec x')}{|\\vec x-\\vec x'|}$ is the gravitational interaction between mass-densities $\\rho_X, \\rho_Y$ associated with the superposed configurations $X, Y$ \\cite{Diosi_bulk_2014}, with $G=6.67\\cdot10^{-11}$~m$^3$kg$^{-1}$s$^{-2}$ being Newton's gravitational constant. For point particles the above expression gives divergent decoherence rate and thus a short-distance cutoff $r_{\\rm DP}$ is needed. The decoherence operator reads \\cite{Nimmrichter_Optomechanical_2014}\n\n\\begin{equation}\n\\tilde{D}_{\\rm DP}=\\frac{x_0^2 G}{6\\sqrt{\\pi}\\hbar}\\big(\\frac{a}{r_{\\rm DP}}\\big)m\\rho(\\vec x),\n\\label{eq:DPheating}\n\\end{equation}\nwhere $a$ is the lattice constant of the composite object.\nComparing the heating rate expected from Eq. \\eqref{eq:DPheating} to measurement-induced spurious phonons, which constitute the strongest noise source in our proposed experiment (see main text), we find that short-distance cutoffs up to $r_{\\rm DP}\\approx 3.9$~fm can be excluded. For a discussion of experimental tests of classical channel gravity \\cite{Kafri_Classical_2014,Khosla_Classical_2018,Altamirano_Pairwise_2018}, see \\cite{tbp}.\n\n\n\n\n\n\n\n\\section*{Supplementary Note 2. Calculation details: efficiency and noise levels of the optomechanical system}\n\\label{sec:optomechanics}\nThe optomechanical system is probed by a strongly attenuated coherent source, such as described in \\cite{Kessler_Laser_2011}, which can be stabilized to a sub-Hz linewidth $\\kappa_L$. Because $\\kappa_L$ is much smaller than $\\kappa_s$, $\\kappa_p$, and $\\kappa_f$, which are the linewidths of the {\\it probe mode}, the {\\it signal mode}, and the filter cavity, respectively, the field $a_{\\rm in}$ of the incoming probe laser is well approximated with a $\\delta$ - function: $a_{\\rm in}(\\omega)=a_{\\rm in}\\delta(\\omega_L)$, where $\\omega_{\\rm L}$ is the laser frequency. We set $\\omega_L=\\omega_p$ to maximize coupling into the probe mode $a_p$. \nThe mechanical decay rate $\\Gamma$, as well as the collapse rate are slow compared to timescales of the optomechanical interaction: $D,\\Gamma \\ll g_0,\\kappa_{p\/s}$, where $g_0$ is the single photon optomechanical coupling rate \\cite{Aspelmeyer_Review_2014}. \nIn this limit, the conversion dynamics can be modelled by the reduced master equation:\n\\begin{equation}\n\\frac{d\\hat \\rho}{dt}=-\\frac{i}{\\hbar}\\big[H_{\\rm int},\\hat\\rho\\big] +\\kappa_p \\mathcal{D}[a_p]\\hat\\rho + \\kappa_s \\mathcal{D}[a_s]\\hat\\rho,\n\\label{eq:reducedME}\n\\end{equation}\nwhere $\\hat \\rho$ is the density matrix, the operators $a_p$ and $a_s$ correspond to annihilation operators for the optical probe- and signal-modes, respectively, and $H_{\\rm int}$ is the interaction Hamiltonian given in the main text.\n\n{\\it Resonant anti-Stokes scattering.} \nIf a phonon is introduced into the ground-state cooled oscillator, and a probe pulse is incident within the lifetime of the mechanical excitation, the system is in the initial state $\\ket{ n_b n_p n_s}=\\ket{110}$. \nThe optomechanical conversion efficiency $\\eta_{\\rm om}$ is the probability of one photon in the probe mode $a_p$ scattering with one phonon in the mechanical resonator, creating a photon in the signal mode $a_s$ ($\\ket{110}\\rightarrow\\ket{001}$) via a anti-Stokes Raman process, and this photon being outcoupled to create a signal photon at frequency $\\omega_s$. \n$\\eta_{\\rm om}$ is obtained by numerically solving Eq. (\\ref{eq:reducedME}) and time-integrating over the emission from the signal mode, $\\eta_{\\rm om}=\\kappa_{s,\\rm ex}\\int_0^{\\infty}\\langle a_s^{\\dagger}(t)a_s(t)\\rangle dt$, where $\\kappa_{s,\\rm ex}=\\kappa_s-\\kappa_{s,0}$ is the external decay rate of the signal mode due to coupling, with $\\kappa_{s,0}$ and $\\kappa_s$ its intrinsic and loaded decay rates, respectively.\nSupplementary Figure \\ref{fig_hOM} (a) shows $\\eta_{\\rm om}$ as a function of the effective coupling strength $g_0\/\\kappa_p$ for critical coupling of the probe mode $\\kappa_{p,0}=\\kappa_{p,\\rm ex}$ and equal intrinsic couplings $\\kappa_{s,0}=\\kappa_{p,0}$, for critically ($\\kappa_{s,0}=\\kappa_{s,\\rm ex}$) and overcoupled ($\\kappa_{s,\\rm ex}\/\\kappa_p=2$ and $4$) signal mode.\nThe efficiency $\\eta_{\\rm om}$ is sensitive to the ratio $g_0\/\\kappa_p$, as $g_0$ sets the rate for anti-Stokes scattering, and $\\kappa_p$ sets the rate for the competing optical decay.\nFurthermore, it depends on the ratio $\\kappa_{s,\\rm ex}\/\\kappa_p$ as higher values favour outcoupling of the signal photon.\nFor our proposed experiment, $g_0=\\kappa_p$ and $\\kappa_{s,\\rm ex}\/\\kappa_p=1.8$.\n\n\n \n\n \\begin{figure}[h!]\n\n \\includegraphics[width=\\textwidth]{Fig_Supp_hOM5.pdf}\n\\caption{Efficiencies of optomechanical transitions.\na) Cumulative probability for the output of a signal photon due to anti-Stokes scattering from the initial state $\\ket{ n_bn_p n_s}=\\ket{110}$ as a function of $g_0\/\\kappa_p$, for $\\kappa_{s,\\rm ext}\/\\kappa_p=1,2,$ and $4$.\nb) Efficiency of spurious Stokes scattering, corresponding to the phonon number occupancy expectation value $\\eta_{\\rm Stokes}=\\langle b^{\\dagger}(t)b(t)\\rangle$ after an incident photon in the signal mode, for times $\\Gamma^{-1}\\gg t \\gg \\kappa_p^{-1},\\kappa_s^{-1}$.\nc) Cumulative probability for the output of a signal photon due to anti-Stokes scattering from the two-phonon state $\\ket{n_b n_p n_s}=\\ket{210}$.\n}\n\n\\label{fig_hOM}\n\\end{figure}\n\n\nThe probability of a phonon in the mechanical resonator translating to a coincidence count, imitating a signal, is given by $\\eta=\\eta_p\\eta_{\\rm om}\\eta_{\\rm f}\\eta_{\\chi}\\eta_d=1.1\\cdot10^{-3}$, where $\\eta_p$ is the probability of a photon entering the probe mode during the mechanical excitation lifetime, $\\eta_f=0.56$ is the transduction efficiency through the filter for a signal photon at frequency $\\omega_s$, $\\eta_{\\chi}=0.95$ and $\\eta_d=0.64$ are downconversion and coincidence detection efficiencies, respectively. These efficiencies are analysed in more detail in the following paragraphs. As the rate of phonons created in the resonator due to spontaneous collapse is given by the collapse rate $\\lambda_cD$, the rate of registered collapse signatures is $R_c=\\lambda_cD\\eta=5.5\\cdot 10^{2}\\lambda_c$.\n\n{\\it Probe field occupancy.}\nThe average number of photons encountered by one phonon is given by\n\\begin{equation}\n\\bar n_{\\rm ph}=\\int_0^\\infty e^{-\\Gamma t}\\kappa_p\\bar n_p dt,\n\\label{eq:etap}\n\\end{equation}\nwhere $\\bar n_p$ is the average photon occupancy of the probe mode and $e^{-\\Gamma t}$ is the phonon occupancy at time $t$ after one phonon is created in the mechanical resonator at time $t=0$. \nIn the limit $\\bar n_p \\ll \\Gamma\/\\kappa_p$, Eq. (\\ref{eq:etap}) also quantifies the probability $\\eta_p$ of a phonon in the mechanical resonator encountering a probe photon, $\\eta_p=\\bar n_{\\rm ph}$. \nFurthermore, in this limit, $\\eta_p$ asymptotes to $\\eta_p=\\bar n_p\\kappa_p\/\\Gamma$. \nIn our protocol, the probe laser power is adjusted so that $\\eta_p=0.01$, corresponding to an average of 0.01 photons per mechanical oscillator lifetime.\nIn the steady state, the average intracavity photon number is given by \n$\\bar n_p=4\\kappa_{p,\\rm ex}\\bar n_{\\rm in}\/\\kappa_p^2$ \\cite{Aspelmeyer_Review_2014},\nand hence, to achieve a given $\\eta_p$ the input field occupancy is adjusted to $\\bar n_{\\rm in}(\\eta_p)=(\\eta_p\\Gamma\\kappa_p)\/(4\\kappa_{p,\\rm ex})$. \nBecause the signal is proportional to $\\eta_p$, and the noise background from measurement-induced noise phonons is proportional to $\\eta_p^2$, the input field occupancy gives a handle to lower the noise at the cost of longer measurement time, or vice versa.\n\n{\\it Transmission through the filter cavity.} \nThe efficiency $\\eta_f$ of the signal passing through the filter, assuming equal input- and output coupling strengths $\\kappa_{f,\\rm ex}$, is given by \\cite{Hecht_Optics_2002}\n\\begin{equation}\n\\eta_f=\\big(1-\\frac{\\kappa_{f,0}}{\\kappa_f}\\big)^2,\n\\end{equation}\nwhere $\\kappa_{f,0}$ is the intrinsic filter linewidth and $\\kappa_f=\\kappa_{f,0}+\\kappa_{f,\\rm in}+\\kappa_{f,\\rm out}$ is the loaded filter linewidth, with $\\kappa_{f,\\rm in}$ and $\\kappa_{f,\\rm out}$ the in- and output coupling, respectively.\nUsing a typical a laser stabilisation filter cavity \\cite{Kessler_Laser_2011}, $\\kappa_{f,0}=30$~kHz, and overcoupling both at the input and output $\\kappa_{f,\\rm in}=\\kappa_{f,\\rm out}=1.5\\cdot\\kappa_{f,0}$, a transmission efficiency of $\\eta_f=0.56$ is achieved.\n\n{\\it Nonlinear downconversion.} \nAfter separation from probe light at frequency $\\omega_p$, signal photons at frequency $\\omega_s$ are downconverted to photon pairs in a nonlinear medium.\nIn order to minimize detector dark counts, we propose a nonlinear conversion process to convert signal photons to pairs. A bright classical pump beam with electric field amplitude $E$ is coupled into a medium exhibiting a third order $\\chi^{(3)}$ optical nonlinearity. This yields an effective second order interaction \n$H_{\\chi,\\rm eff}=\\gamma E a_{f,\\rm out}d_1^{\\dagger}d_2^{\\dagger}+\\gamma^* E a_{f,\\rm out}^{\\dagger}d_1d_2$, where $\\gamma$ is the nonlinear coupling strength \\cite{Langford_Conversion_2011}, $a_{f,\\rm out}$ is the mode of the signal transmitted through cavity and filter, and $d_{1\/2}$ are the modes coupled to the detectors. \nGiven the input state $\\ket{n_{f,\\rm out}n_{d1}n_{d2}}=\\ket{100}$, the time evolution in the nonlinear medium is \\cite{Langford_Conversion_2011}\n\\begin{equation}\n\\ket{\\Psi(t)}=\\cos{(\\abs{\\gamma}t\/\\hbar)}\\ket{100}+i \\sin{(\\abs{\\gamma}t\/\\hbar)}\\ket{011}.\n\\end{equation}\n \nBy setting the length of the nonlinear medium to $L=\\frac{1}{2}\\pi\\hbar c_n\\abs{\\gamma}^{-1}$, where $c_n$ is the speed of sound in the medium, the output state is $\\ket{\\Psi(t_{\\rm final})}=\\ket{011}$, corresponding to a photon in each detector mode $d_{1\/2}$.\nIt has been shown that this conversion can be performed with near-unit efficiency \\cite{Langford_Conversion_2011}, hence we assume $\\eta_\\chi=0.95$.\n\n{\\it Coincidence detection.} \nAssuming a detection efficiency of $80\\%$ for a single detector \\cite{Photonspot_private}, the efficiency for coincidence detection is $\\eta_d=(0.80)^2=0.64$. \nThe coincidence dark count rate is $R_{\\rm coincidence}=R_{\\rm d}^2 \\cdot \\tau_c$, where $R_{\\rm d}$ is the dark count rate of a single detector and $\\tau_c$ is the coincidence timing resolution (time jitter). \nThis allows a suppression of dark counts with the square of the single-detector dark count rate. For $R_{\\rm d}=3.5$~Hz and $\\tau_c=30$~ps \\cite{Photonspot_private}, the predicted coincidence dark count rate is $R_{\\rm coincidence}=3.7\\cdot10^{-10}$~s$^{-1}$.\n \n{\\it Probe photons leaking through the system.}\nThere are two ways in which probe photons can leak through the system and potentially imitate a collapse signature.\nFirstly, optomechanical conversion processes can create photons at frequency $\\omega_p$, or at a frequency reduced by multiple integers $n$ of the mechanical resonance frequency, $\\omega_p-n\\Omega$.\nDiscussions of these processes are included in the following paragraphs on noise phonons.\nThe amplitudes of these processes are negligible due to strong suppression (see main text).\nSecondly, measurement noise is introduced by probe photons transmitted through the filter cavity and downconverted to a pair of photons in the nonlinear medium, imitating a signal. \nThe probability of a probe photon with detuning $\\Delta=\\omega_s-\\omega_p=\\Omega$ transmitting through both the near-critically coupled optomechanical system and a subsequent filter cavity of linewidth $\\kappa_f$ and free spectral range $\\omega_{\\rm fsr}$ is given by \\cite{Hecht_Optics_2002}\n\n\\begin{equation}\np_f(\\Omega)=\\eta_f\\big[ 1 + (\\frac{4}{\\kappa_f})^2 \\cdot {\\rm sin}^2{(\\frac{\\pi\\Delta}{\\omega_{\\rm fsr}})}\\big]^{-1}.\n\\label{eqFilter}\n\\end{equation}\nFor the frequency of the proposed mechanical resonator of $\\Omega\/2\\pi=5.3$~GHz \\cite{Chan_Nanomechanical_2011}, a finesse of $3.16\\cdot10^{5}$ as in a standard laser stabilisation reference cavity \\cite{Kessler_Laser_2011} and a cavity length $L$ of one centimeter, we find $\\omega_{\\rm fsr}\/2\\pi=c\/2L=15$~GHz and $p_f=3.5\\cdot 10^{-10}$.\nA noise photon is then downconverted to a photon pair and registered as a coincidence count with efficiency $\\eta_\\chi\\eta_d$. \nAs the rate of incoming probe photons coupled into the probe mode in the steady state is $4\\kappa_{p,\\rm ex}\\bar n_{\\rm in}\/\\kappa_{p}=\\bar n_p\\kappa_p=\\eta_p\\Gamma$, the rate of coincidence counts due to noise photons, imitating a signal, is \n\\begin{equation}\nR_{\\rm phot}=\\eta_p\\Gamma p_f \\eta_\\chi\\eta_d=1.4\\cdot 10^{-12}\\,{\\rm s}^{-1}.\n\\label{eqRf}\n\\end{equation}\n\n\n{\\it Noise phonons from direct occupation of the signal mode.}\nA photon from the coherent probe laser can create a phonon by coupling into the signal mode $a_s$ instead of the probe mode $a_p$, which results in a direct occupation of the signal mode $\\ket{ n_b n_p n_s}=\\ket{001}$, as shown in Fig. 3 (a) in the main text. \nThis process is suppressed due to the small spatiotemporal overlap $\\Theta$ of the signal mode with the coherent laser beam, $\\Theta=\\kappa_s^2\/(\\kappa_s^2+\\Omega^2)\\approx(\\kappa_s\/\\Omega)^2=3.2\\cdot 10^{-5}$, where $\\kappa_s\/2\\pi=30$~MHz is the loaded decay rate of the signal mode.\nIf the photon is outcoupled, it has a frequency of $\\omega_p$ due to energy conservation and can be efficiently filtered from a signal at frequency $\\omega_s$.\nHowever, a scattering process to the probe mode can create a noise phonon in mode $b$, imitating a decoherence-signature.\nThe efficiency of this Stokes scattering process is given by the probability of the initial state $\\ket{001}$ causing the mechanical oscillator to be in the excited state, which equals the phonon occupancy after optical decay, $\\eta_{\\rm Stokes}=\\langle b^{\\dagger}(t)b(t)\\rangle$, for times $t\\gg\\kappa_p^{-1},\\kappa_{s}^{-1}$.\n$\\eta_{\\rm Stokes}$ is shown as a function of $g_0\/\\kappa_p$, for $\\kappa_{s,\\rm ex}\/\\kappa_p =1,2,$ and $4$ in Supplementary Figure \\ref{fig_hOM} (b).\nHigher values of $\\kappa_{s,\\rm ex}$ correspond to lower probabilities for this type of noise, as the decay process of rate $\\kappa_{s,\\rm ex}$ competes with the Stokes scattering of rate $g_0$.\nFor the proposed experiment with $\\kappa_{s,\\rm ex}\/\\kappa_p=1.8$ we find $\\eta_{\\rm Stokes}=0.17$.\nThe probability of phonon creation, due to this process, \nat time $t_0$ after incidence of the probe photon is $p_{\\rm direct}=(\\kappa_p\/\\kappa_{p,\\rm ex})\\cdot\\Theta\\eta_{\\rm Stokes}=2.7\\cdot10^{-5}$.\n\n\n{\\it Noise phonons from counterrotating optomechanical processes.}\nAnother mechanism for noise phonon creation is given by probe photons anti-Stokes scattering into the signal mode, corresponding to the resonantly suppressed (counterrotating) transition $b^{\\dagger}a_p a_s^{\\dagger}e^{-2i\\Omega t}\\ket{010}\\rightarrow\\ket{101}$. \nThe resulting state contains a photon in the signal mode as well as a phonon in the mechanical resonator, which could in principle both imitate a signal. \nHowever, the outcoupled photon has a frequency of $\\omega_p-\\Omega$ and can, therefore, be filtered from the signal at frequency $\\omega_s$.\nThe probability of phonon creation due to this process, caused by a single incident probe photon, is obtained by numerically solving Eq. (\\ref{eq:reducedME}), is $p_{\\rm counterrot,1}=1.7\\cdot 10^{-5}$, where $p_{\\rm counterrot,1}=\\bra{001}\\hat \\rho(t\\gg\\kappa_p^{-1},\\kappa_s^{-1}) \\ket{100}+\\bra{101}\\hat \\rho(t\\gg\\kappa_p^{-1},\\kappa_s^{-1}) \\ket{101}$, with $\\hat \\rho(t)$ the density matrix initialized in the state $\\hat\\rho(t=0)=\\ket{010}\\bra{010}$ (see Fig. 4 (a) and (b) in the main text).\nFurther, the state $\\ket{101}$ can resonantly transition to a state with two phonons in the mechanical resonator and one photon in the probe mode: $b^{\\dagger}a_p^{\\dagger}a_s\\ket{101}\\rightarrow\\ket{210}$.\nThe probability of one incident probe photon preparing the system in this two-phonon state $p_{\\rm counterrot,2}=$\\mbox{$\\bra{002}\\hat \\rho(t\\gg\\kappa_{p}^{-1},\\kappa_s^{-1}) \\ket{200}$}$+\\linebreak$\\mbox{$\\bra{012}\\hat \\rho(t\\gg\\kappa_p^{-1},\\kappa_s^{-1}) \\ket{210}$} (with $\\hat\\rho(t=0)=\\ket{010}\\bra{010}$) numerically obtained by solving Eq. (\\ref{eq:reducedME}) (see Fig. 4 (b) in the main text), finding $p_{\\rm counterrot,2}=1.6\\cdot 10^{-6}$.\nWe find that the occupancies $p_{\\rm counterrot,1}$ and $p_{\\rm counterrot,2}$ scale with $(\\kappa_p\/\\Omega)^2$ in the limit $g_0=\\kappa_p$, with higher order transitions suppressed by $(\\kappa_p\/\\Omega)^4$ and therefore negligible.\n\n\n{\\it Scattering of noise phonons to signal photons.}\nA spurious signal photon at frequency $\\omega_s$ is created if a noise phonon scatters with a second photon entering the probe mode within the lifetime of the mechanical excitation. \nFor timescales of the mechanical excitation lifetime, $t \\sim \\Gamma^{-1}\\gg \\kappa_{p}^{-1},\\kappa_{s}^{-1}$, after incidence of a probe photon, it is convenient to define the `occupancy probabilities' of the one- and two-phonon states from the optomechanical processes described above: $p_{n_b=1}(t_0)=p_{\\rm direct}+p_{\\rm counterrot,1}$ and $p_{n_b=2}(t_0)=p_{\\rm counterrot,2}$, where $t_0$ is long compared to timescales of the optical decay, so that all optomechanical conversions have concluded, but short compared to the mechanical decay ($\\Gamma^{-1}\\gg t_0\\gg\\kappa_{p}^{-1},\\kappa_{s}^{-1}$). The time dynamics of these one- and two phonon occupancy probabilities are described by\n\\begin{equation}\np_{n_b=1}(t)=n_{n_b=1}(t_0) e^{-\\Gamma t}+2p_{n_b=2}(t_0)\\big( e^{-\\Gamma t}-e^{-2\\Gamma t} \\big)\n\\end{equation} \nand \n\\begin{equation}\np_{n_b=2}(t) =p_{n_b=2}(t_0) e^{-2\\Gamma t},\n\\end{equation}\nrespectively.\nThe probability of these measurement-induced phononic states encountering a second probe photon is given by $\\eta_p\\Gamma\\int_0^\\infty p_{n_b=1}(t) dt$ and $\\eta_p\\Gamma\\int_0^\\infty p_{n_b=2}(t) dt$, respectively. The conversion efficiency of the one- and two-phonon state to a spurious photon outcoupled from the cavity is then given by $\\eta_{\\rm om}$ and $ \\eta_{\\rm om,2}$, respectively, where latter is numerically calculated, and plotted as a function of $g_0\/\\kappa_p$ for different values of $\\kappa_{s,\\rm ex}\/\\kappa_p =1,2,$ and $4$, as shown in Supplementary Figure \\ref{fig_hOM} (c).\nThe resulting probability of a noise phonon of frequency $\\omega_s$ coupled out of the cavity, due to optomechanical processes, potentially imitating a signal, is\n\\begin{equation}\np_{\\rm om}(t)= \\eta_p\\Gamma\\int_0^t p_{n_b=1}(t)\\eta_{\\rm om}+ p_{n_b=2}(t)\\eta_{\\rm om,2} dt,\n\\label{eq:pom} \n\\end{equation}\nas shown in the main text in Fig. 4 (c). \nWe find the asymptotic value $p_{\\rm om}(t\\rightarrow\\infty)=8.4\\cdot 10^{-8}$.\nIn analogy to Eq. (\\ref{eqRf}), the rate of coincidence counts due to noise is given by $R_{\\rm om}=\\eta_p\\Gamma p_{\\rm om}(t\\rightarrow\\infty)\\eta_f \\eta_\\chi\\eta_d=1.9\\cdot 10^{-10}$~s$^{-1}$.\n\n\n\n{\\it Noise phonons due to photoabsorptive heating.}\nThe number of noise phonons originating from absorption heating is estimated following \\cite{Meenehan_millikelvin_2014,Ren_Crystal_2019}.\nWe compute the average phonon number excited by one probe photon in the mechanical resonator, $\\bar n_{\\rm abs,1}$, due to photoabsorption for time $t_{\\rm abs}$, at which the oscillator is in thermal equilibrium with the material, but not yet with the environment ( $\\Gamma^{-1}\\gg t_{\\rm abs} \\gg \\gamma_{\\rm THz}^{-1}$, with $\\gamma_{\\rm THz}$ the rate at which THz-frequency phonons radiate to the environment, see also Methods).\nWe approximate $\\bar{n}_{\\rm abs,1}$ by extrapolating from \\cite{MacCabe_Ultralong_2019,Ren_Crystal_2019}.\nIn this case, an average intracavity photon number $ \\bar n_{\\rm cav}=1$ yields an average phonon number of $\\bar n_{\\rm abs}=10$ in the mechanical resonator.\n$\\bar n_{\\rm cav}=1$ is equivalent to $\\kappa\/\\Gamma$ photons passing through the cavity within the lifetime $\\Gamma^{-1}$ of the mechanical resonator, creating in total 10 phonons.\nThe intrinsic optical decay is limited by photon absorption, $\\kappa_0\\approx\\kappa_{\\rm abs}$ and $\\gamma_{\\rm THz} \\approx \\kappa_0\/2\\pi$, within the errors given.\nTherefore, from one photon we expect the induced occupancy $\\bar{n}_{\\rm abs,1}=10\\frac{\\Gamma}{\\kappa}=10\\cdot\\frac{108\\ \\rm mHz\/2\\pi}{575\\ \\rm MHz\/2\\pi}\\approx 1.9\\cdot 10^{-9}$.\n\n\nAs with the optomechanical heating rates, a spurious signal will only occur if the phonons resulting from absorption heating interact with another probe photon. In analogy to Eq. (\\ref{eq:pom}), the probability of a probe photon creating a spurious signal photon at frequency $\\omega_s$ due to absorption heating is \n\\begin{equation}\np_{\\rm abs}(t)=\\eta_p\\Gamma \\int_0^t\\bar n_{\\rm abs,1} e^{-\\Gamma t'}\\eta_{\\rm om} dt'.\n\\label{eq:pabs}\n\\end{equation}\nFor the proposed setup we find $p_{\\rm abs}(t\\rightarrow\\infty)=6.1\\cdot 10^{-12}$ and $R_{\\rm abs}=1.4\\cdot10^{-14}$~s$^{-1}$.\n\nFor the quadratic coupling approach, $\\bar n_{\\rm cav}=10^2$ (see main text), and the relation $\\bar n_{\\rm abs} \\propto \\bar n_{\\rm cav}^{1\/3}$ holds \\cite{Meenehan_millikelvin_2014,Ren_Crystal_2019}, and we find an average intracavity phonon number of $\\bar n_{\\rm abs} \\approx 5$. This corresponds to a noise phonon rate of $\\dot n=\\Gamma\\bar n_{\\rm abs} \\approx 3$~s$^{-1}$, close to seven orders of magnitude higher than the lowest possible phonon flux expected from Bassi {\\it et al.}'s proposal of $\\dot n_c=5.1\\cdot10^{-7}$~s$^{-1}$.\n\n\n\n{\\it Multiplexing.}\nA photonic-phononic crystal, including suspension and phononic shield, requires an area of about 1000 $\\mu$m$^2$ \\cite{MacCabe_Ultralong_2019}. Thus it would be conceivable to fabricate a high number of them on a 4-inch-wafer with an area of $\\sim 2\\cdot 10^{9}$ $\\mu$m$^2$. We assume $N\\sim10^{4}$, allowing more than 99$\\%$ of the wafer to be reserved for waveguide coupling, fabrication tolerances, etc. In principle, all these devices to may be connected to the same filter cavity, nonlinear medium, and detector pair, or to a small number of such elements. \n\n\n\n\n\\section*{References}\n\n\\footnotesize \\renewcommand{\\refname}{\\vspace*{-30pt}} \n\\bibliographystyle{apsrev4-2}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nComputational fluid dynamics (CFD) is a branch of fluid mechanics that deals with numerically solving and analyzing fluid flow problems such as those found in aerodynamics, geology, biology, etc. CFD simulations are known for their high computational requirements, memory usage, and run times. Because of this, there is an ever growing body of work using simulation data to create reduced order models or surrogate models that can be evaluated with significantly less resources. Towards this end, we develop a neural network approach that both compresses the computation time and memory usage of fluid simulations.\n\nWe investigate fluid simulations that contain complex time dependent turbulence. Simulations of this form are difficult because they require fluid solver to have high resolution and small times steps. Never the less, they frequently occur in nature and are an important area of study. Motivated by need for these simulations and the recent success of neural network based models in related areas \\cite{tompson2016accelerating} \\cite{guo2016convolutional} \\cite{yang2016data}, we choice this setting to test our model.\n\nThe most popular approach to modeling fluid flow is with the Navier stokes equation. The solution to this partial differential equation gives the flow velocity field for a given domain. Recently, a new method for simulating fluid flow has emerged named the Lattice Boltzmann Method (LBM). It is derived from the Boltzmann equation and grew out of Lattice Gas Automaton (LGA) in the late 80s \\cite{mcnamara1988use}. The main advantages of the LBM are its ability to run on complex geometries, its scalability to parallel architectures (particularly GPUs) and applicability to complex flows that contain phenomena such as heat transfer and chemical reactions. Our method is centered around this method of simulating flow.\n\nLat-Net works by compressing the state of a simulation while learning the dynamics of the simulation on this compressed form. The model can be broken up into three pieces, an encoder, compression mapping, and decoder. The encoder compresses both the state of the simulation as well as the given boundary conditions. The compression mapping learns the dynamics on the compressed state that correspond to the dynamics in the fluid simulation. The decoder decompresses the compressed state allowing either the whole simulation state or desired pieces to be extracted.\n\nWe focus the content of this paper on LBM fluid simulations because this is the most popular use of the LBM, however, this method of simulation is known to be able to solve a large set of partial differential equations \\cite{galindolattice}. In fact, LBM can simulate many physical systems of interest such as Electromagnetism, Plasma, Multiphase flow, Schr\u00f6dinger equation etc. \\cite{mendoza2010three} \\cite{kim2008wavelet} \\cite{zhong2006lattice} \\cite{shan1993lattice}. With this in mind, we keep our method general and show evidence our method works equally well on Electromagnetic simulations. However, because the dominate use of LBM is on fluid flow problems we center discussion on this subject.\n\nOur work has the following contributions.\n\\begin{itemize}\n \\item It allows for simulations to be generated with less memory then the original flow solver. There is a crucial need for such methods because memory requirements grow cubic to grid size in 3D simulations. In practice, this quickly results in the need for large GPU clusters \\cite{onodera2013large} \\cite{xian2011multi}.\n \\item Once our model is trained, it can be used to generate significantly larger simulations. This allows the model to learn from a training set of small simulations and then generate simulations as much as 16 times bigger with little effect in accuracy.\n \\item Our method is directly applicable to a variety of physics simulations, not just fluid flow. We show this with our electromagnetic example and note that the changes to our model are trivial.\n\\end{itemize}\n\n\\section{Related Work}\n\nRecently, there have been several papers applying neural networks to fluid flow problems. Guo etc. \\cite{guo2016convolutional} proposed to use a neural network to learn a mapping from boundary conditions to steady state flow. Most related to our own work, Yang etc. \\cite{yang2016data} and Tompson etc. \\cite{tompson2016accelerating} use a neural network to solve the Poisson equation in order to accelerate Eulerian fluid simulations. The key difference between this and Lat-Net is its ability to compress the memory usage and the generality of our method to other physics simulations.\n\nThere has also been an increasing body of work applying neural networks to other physics modeling problems. For example, neural networks have been readily adopted in many chemistry applications such as predicting molecular properties from descriptors, protein contact prediction and computational material design \\cite{goh2017deep}. Very recently, neural networks have been applied to quantum mechanics problems as seen in Mills etc. \\cite{mills2017deep} and Giuseppe etc. \\cite{carleo2017solving} where neural networks are used to approximate solutions to the Schr\u00f6dinger equation. In high energy Physics, Paganini etc. \\cite{2017arXiv170502355P} uses a generative adversarial networks (GAN)\\cite{goodfellow2014generative} to model electromagnetic showers in a longitudinally segmented calorimeter. Many of these applications are relatively recent and indicate a resurgence of interest in applications of neural networks to modeling physics.\n\nReduced order Modeling is an area of research that focuses on techniques to reduce the dimensionality and computational complexity of mathematical models. A Reduced order model (ROM) is constructed from high-fidelity simulations and can subsequently be used to generate simulations for lower computation. The most popular ROM method for fluid dynamics is Galerikin projection \\cite{rowley2004model} \\cite{barone2009reduced}. This method uses Proper Orthogonal Decomposition to reduce the dimensionality of flow simulations and then finds the dynamics on this reduced space. There are other methods that build on this such as reduced basis methods and balanced truncation \\cite{veroy2005certified} \\cite{rowley2005model}. While these approaches are centered around the Navier stokes equation and thus not directly comparable to our own, we note that the compression mapping present in these methods is typically quite simple. Given the recent success neural networks have had in creating well structured encodings (such as Variational Autoencoders \\cite{kingma2013auto} \\cite{watter2015embed}), we feel our approach is well justified.\n\n\\section{Deep Neural Networks for Compressed Lattice Boltzmann}\n\nIn this section, we present our model for compressing Lattice Boltzmann simulations.\n\n\\subsection{Review: The Lattice Boltzmann Method}\n\n\\begin{figure}[!t]\n\\centering\n\\subfigure{\\includegraphics[scale=0.19]{.\/figs\/lattice_boltzmann.pdf}}\n\\caption{Illustration of the Lattice Boltzmann update steps}\n\\label{lattice_boltzmann}\n\\end{figure}\n\nIn a Lattice Boltzmann simulation, the domain is discretized into an equal sized Cartesian grid. Each cell of this grid contains a velocity distribution function $f_i$ that describes the velocity of flow at that point. $f_i$ has values ranging over $i$ that correspond to the $\\{ \\vec{c}_i \\}$ directions of flow. In our 2 dimensional simulations, there are 9 such directions (D2Q9 scheme). A figure showing this grid structure is seen in \\ref{lattice_boltzmann}. From the distribution function $f_i$, one can calculate the density ($p$) and velocity ($\\vec{u}$) of the fluid flow with the following equations.\n\n\\begin{equation}\n p = \\sum_i f_i \\qquad\\text{and}\\qquad \\vec{u} = \\sum_i \\vec{c}_i f_i \n\\end{equation}\n\nThe lattice states are updated with two separate steps, the collision step and the streaming step. The collision step mimics the flow interacting with itself and is updated in the following way,\n\n\\begin{equation}\n f^t_i(x, t + \\delta_t) = f_i(x,t) + \\frac{1}{\\tau} (f_i^{eq} - f_i)\n\\end{equation}\n\nwhere $\\tau$ is the relaxation constant and $f_i^{eq}$ is the flow equilibrium. For our simulations, we use the $f_i^{eq}$ from the Lattice Bhatnagar-Gros-Krook (LBGK) scheme \\cite{guo2013lattice}. After the collision step is applied, the flow propagates to adjacent cells following the streaming step. \n\n\\begin{equation}\n f_i(x + c_i, t + \\delta_t) = f^t_i(x,t + \\delta_t)\n\\end{equation}\n\nThis step will contain bounce back if one of the adjacent cells is a boundary. Figure \\ref{lattice_boltzmann} illustrates these steps for the 2 dimensional case.\n\nIt is interesting to note the simplicity of this method and its similarity to convolutional neural networks. In fact, if we treat the lattice state $f$ as a tensor of size ($n_x$,$n_y$,9), as we do for the remainder of this paper, the streaming operator can be mimicked with a 3 by 3 convolution and the collision step can be performed with a 1 by 1 convolution (D2Q9). This offers a unique way to interpret our method. In some sense, we are taking a large convolutional neural network and compressing it onto a much smaller and more memory efficient network. With this mental picture in mind, we now describe our approach.\n\n\\subsection{Proposed Architecture}\n\nFigure \\ref{fig_1} shows a sketch of the model. The figure can be understood by following the arrows starting from the flow state $f_t$ and the boundary $b$. We treat $f_t$ as a tensor with shape ($n_x,n_y,9$) for the 2D case and ($n_x,n_y,n_z,15$) for the 3D case. The boundary is treated as a binary tensor of shape ($n_x,n_y,1$) and ($n_x,n_y,n_z,1$) with the value being 1 if the cell is solid. Bellow we walk through each step of our method.\n\nFirst, we compress both the state of the fluid simulation $f_t$ and the boundary conditions $b$ using two separate neural networks $\\phi_{enc}$ and $\\phi'_{enc}$ respectively. The result from $\\phi_{enc}$ is a compressed representation of the flow $g_t$ and the result of $\\phi'_{enc}$ are two tensors $b_{mul}$ and $b_{add}$ of equal size to $g_t$. These three tensors represent the entirety of the compressed state of the simulation.\n\nIn a Lattice Boltzmann solver, the boundary conditions are used at each time-step to add bounce back to the streaming step. In a similar way, our model applies the compressed boundary to the compressed state every time-step. We do this in the following way,\n\\begin{equation}\n g_t = (g_t \\odot b_{mul}) + b_{add}\n\\end{equation}\nThis method proved extremely successful at keeping the boundary information firmly planted through the duration of the simulation. This method of applying boundary conditions was inspired by \\cite{vondrick2016generating} where they use a similar method to combine foreground and background information in video prediction. After the boundary is applied to $g_t$, we can run the state through another neural network to emulate the dynamics, i.e. $\\phi_{comp}:g_{t} \\rightarrow g_{t+1}$. Each step of $\\phi_{comp}$ is equivalent to $n$ time-steps of the Lattice Boltzmann solver. For example, in the 2 dimensional simulation, each step of $\\phi_{comp}$ mimics 120 steps of the Lattice Boltzmann solver. Once $g_t$ is computed, we can extract out the generated state of the simulation with a decoder network $\\phi_{dec}$. \n\n\\begin{figure}[!t]\n\\centering\n\\subfigure{\\includegraphics[scale=0.28]{.\/figs\/fig_1.png}}\n\\caption{Illustration of the Lat-Net architecture}\n\\label{fig_1}\n\\end{figure}\n\n\\subsection{Network Implementation Details}\n\nWe implement 2 networks trained on 2 dimensional and 3 dimensional lattice simulations. The encoder, compression mapping and decoder pieces of the 2D network are each a series of 3 by 3 residual blocks with the sequences 4x(down res-res)-res, 4x(res), and 3x(transpose conv-res-res)-transpose conv, respectively \\cite{he2016deep}. For the 3D network, the sequences are 2x(down res)-res, 3x(res), and (transpose conv-res-transpose conv) where 3 by 3 by 3 convolutions are used. The down residual blocks are created by changing the first convolution to have kernel size 4 by 4, stride 2 and double the filter size. The up sampling is achieved with transpose convolutions of kernel size 4 by 4, stride 2 and half the filter size. For the last residual block on the 3D network, the filter size is halved once again.\n\nAs mentioned above, each network is kept entirely convolutional. Fluid flow is inherently spatially correlated so using convlutional layers allows this spatial information to be preserved. Keeping the network convolutional also allows different input sizes to be used. This is how our model is able to train on small simulations and then generate larger simulations.\n\nResidual connections have been used in many deep learning architectures with much success. Adding residual connections allows for much deeper networks to be trained, often resulting in improved results \\cite{he2016deep}. When training our model, it is necessary to unroll the compression network over several time-steps. This has the same effect as making the network deeper. For this reason, it seems logical to take advantage of this network architecture. We have seen that removing these residual connections results in much slower convergence and worse accuracy.\n\n\\subsection{Training Details}\n\nLat-Net is trained by unrolling the network and comparing the generated flow with the true. Our loss function is Mean Squared Error (MSE) with Image Gradient Difference Loss (GDL) \\cite{mathieu2015deep}. The GDL is multiplied by $\\lambda_{GDL}$ and then added to the MES. In all our experiments, $\\lambda_{GDL}$ is set to $0.2$. Removing the GDL tended to produce less accurate models. Lat-Net is unrolled 5 time-steps and then trained with the Adam optimizer \\cite{kingma2014adam}.\n\n\\section{Experiments}\n\nIn this section, we describe our experiments testing Lat-Net on a variety of problems. Our experiments are designed to test our models ability to generate large simulations as well as its transferability to new boundary geometries. We also explore computation time and working memory usage. Finally, we briefly show results applying this method to electromagnetic simulations.\n\n\\subsection{Dataset Generation}\nIn order to train and test our model, we generate sets of fluid and electromagnetic simulations. All simulations were generated with the MechSys library \\cite{mechsys}.\n\nThe train set for the 2D fluid simulations consists of 50 runs of grid size 256 by 256 and 9 directional flows in the lattice Boltzmann solver (D2Q9 scheme)\\cite{guo2013lattice}. The simulations use periodic boundary conditions on top and bottom as well as uniform inlet flow and outlet flow of 0.04 from the left and right. 8 Objects are placed randomly with height and width sizes ranging from 140 to 20 cells. The test set for the 2 dimensional simulations consists of 10 runs of size 256 by 256, and 5 runs of size 1024 by 1024 with the same boundary conditions and object densities. We also generate a test set of size 256 by 512 with vehicle cross sections as objects. There are 28 cross sections used ranging from trucks to minivans. For all 2 dimensional simulations, the ratio of network steps to Lattice Boltzmann steps is 1 to 120.\n\nThe train set for the 3D fluid simulations consists of 50 runs of grid size 40 by 40 by 160 and 15 directional flows in the lattice Boltzmann solver (D3Q15 scheme)\\cite{guo2013lattice}. Similar to the 2D simulations, periodic boundary conditions are used with same inlet and outlet flow. 4 spheres are randomly placed with height and width 24. The reason different object geometries and sizes were not explored was due to the fact that smaller objects or objects with complex geometries tended to have too course a resolution for the lattice Boltzmann solver and larger objects required too large a simulation size. The test set comprises 10 runs of 40 by 40 by 160 and 5 runs of 160 by 160 by 160 simulations with the same object density. The ratio of network steps to Lattice Boltzmann steps is 1 to 60.\n\nThe train set for the electromagnetic simulations are grid size 256 by 256 with periodic boundaries. An electromagnetic wave is initialized in the top of the simulations and proceeds to interact with randomly placed objects of different dielectric constants. When the wave hits these objects, the reflection and refraction phenomenon is seen. The test set consists of simulations of size 512 by 512 with the same object density.\n\n\\subsection{Generating Simulations}\n\n\\begin{figure}[!t]\n\\centering\n\\subfigure{\\includegraphics[scale=0.302]{.\/figs\/256x256_2d_flow_image.png}}\n\\subfigure{\\includegraphics[scale=0.302]{.\/figs\/1024x1024_2d_flow_image.png}}\n\\subfigure{\\includegraphics[scale=0.302]{.\/figs\/160x160x160_3d_flow_image.png}}\n\\caption{A visual comparison of flows generated by Lat-Net and the Lattice Boltzmann method. Each figure shows the Generated, True, and Difference of the flow for various time-steps.}\n\\label{2d_image_plot}\n\\end{figure}\n\n\\begin{figure}[!t]\n\\centering\n\\subfigure{\\includegraphics[scale=0.280]{.\/figs\/256x256_2d_error_plot.png}}\n\\subfigure{\\includegraphics[scale=0.280]{.\/figs\/1024x1024_2d_error_plot.png}}\n\\subfigure{\\includegraphics[scale=0.280]{.\/figs\/40x40x160_3d_error_plot.png}}\n\\subfigure{\\includegraphics[scale=0.280]{.\/figs\/160x160x160_3d_error_plot.png}}\n\\caption{ Comparison plot of the flows generated by Lat-Net and the Lattice Boltzmann method. Each plot shows the average mean squared error of the true and generated generated along with the average divergence of the velocity vector field for both simulations. The standard deviation is also displayed. In addition, the calculated values for drag and flux are displayed for a single simulation run. For the 1024 by 1024 simulation, the flow produced by the Lattice Boltzmann solver tended to produce instabilities resulting in the chaotic divergence observed.}\n\\label{2d_error_plot}\n\\end{figure}\n\n\\begin{figure}[!t]\n\\centering\n\\subfigure{\\includegraphics[scale=0.74]{.\/figs\/car_2d_flow_image.png}}\n\\subfigure{\\includegraphics[scale=0.28]{.\/figs\/256x512_2d_error_plot.png}}\n\\caption{Comparison of generated flows for the vehicle cross section dataset. The images show the generated and true flow at step 100 for 3 different cars in the dataset. The plot shows the same values as described in \\ref{2d_error_plot}.}\n\\label{car_dataset}\n\\end{figure}\n\n\nA key component of our model is its ability to generate larger simulations then those trained on. To test its effectiveness in doing so, we compare the accuracy of generated 2D and 3D simulations to ground truth simulations.\n\nComparing the accuracy of our simulations require some consideration. The naive approach is to compare the MSE between the generated and true simulation at various time-steps. The problem with this approach is that fluid flow is a chaotic dynamical system and small perturbations in flow quickly compound leading to dramatic differences at latter times. For this reason, we compare a variety of metrics in evaluating the generated flows accuracy. Similar to \\cite{tompson2016accelerating}, we compare the divergence of the generated and true velocity vector field to test our models stability. We also compare the computed values of drag and flux. Flow simulations are often run to calculate such values so comparing this is a strong indicator of our models real world applicability. The drag is calculated directly from the lattice state via the momentum transfer method \\cite{guo2013lattice}. The flux value is the average of the flux in each non-boundary cell. These values can be used to calculate important quantities such as the drag coefficient and Reynolds number. Lastly, we visually inspect the produced flow to check for instabilities and blurring effects.\n\nIn figure \\ref{2d_error_plot}, we see the predicted values for different grid sizes in 2D and 3D simulations. In the 2D simulations, Lat-Net is able to effectively transfer to larger domain sizes with very similar calculated values of drag and flux. The generated flow also maintains its stability even after hundreds of steps. In the 3D simulation we see that, while our model predicts realistic values for the 40x40x160 simulation, it tends to have a slight bias in the direction of flow that manifests itself in the 160x160x160 simulation.\n\nWhen visually inspecting our produced flows (figure \\ref{2d_image_plot}), we see a slight blurring effect but overall similar structure in the 2D flows. We attribute this blur effect to the dimensionality reduction and use of MSE. This can possibly be overcome with the use of generative adversarial network \\cite{goodfellow2014generative} where the loss is derived from a discriminator network. Another solution may be to craft a loss function that takes advantage of the statistical properties of flow \\cite{kim2008wavelet}. We leave these pursuits to future work.\n\nThere is a distinct difference in the generated and true flow for the 3D 160x160x160 simulation. While the generated flow appears accurate close to the objects, in regions far between objects the network tends to underestimate the flow velocity. We believe this is due to these types of regions not being present in the train set and is the probable cause for the biases seen in the drag and flux. As mentioned above, our 3D train set is limited due to memory constraints and so developing a diverse train set to overcome this proved difficult.\n\nThe boundaries used in the above evaluation are drawn from the same distribution as the train set. This motivates the question of how our model performs on drastically different geometries. To test this, we apply our model to predicting flow around vehicle cross sections. Surprisingly, even though are model is only trained on flows around simple shapes (ovals and rectangles) it can effectively generalize to this distinctly different domain. In figure \\ref{car_dataset}, we see the predicted flows are quite similar but with the same blurring effect. Calculating the same values as above, we see the flow is stable and produces similar drag and flux.\n\n\\subsection{Computation and Memory Compression}\n\n\\begin{table}[]\n\\small\n\\caption{Computation Time of Network} \\label{compute_times}\n\\centering\n\\begin{tabular}{|c|cccccc|}\n\\hline\nSimulation & Comp. Size & Comp. Mapping & Full State & Plane & Line & Point \\\\ \\hline\n(1024, 1024, 9) & (64, 64, 128) & 2.7 ms & 36.2 ms & NA & 6.7 ms & 6.6 ms \\\\\n(160, 160, 160, 15) & (40, 40, 40, 64) & 23.1 ms & 272.1 ms & 38.2 ms & 25.6 ms & 24.1 ms \n\\\\ \\hline\n\\end{tabular}\n\\label{computation_table}\n\\end{table}\n\n\nIn this section, we investigate the computational speed-up of our model. The standard performance metric for Lattice Boltzmann Codes is Million Lattice Updates per Second (MLUPs). This metric is calculated by the following equation,\n\\begin{equation}\n MLUP = \\frac{n_x \\times n_y \\times n_z \\times 10^{-6}}{Compute \\ Time}\n\\end{equation}\n where $n_x$, $n_y$, and $n_z$ are the dimensions of the simulation. For 3 dimensional simulations like the ones seen in this paper, a speed of 1,200 MLUPS can be achieved with a Nvidia K20 GPU and single precision floats \\cite{januszewski2014sailfish}. We use this as our benchmark value to compare against.\n\nThe computation time and memory usage of the encoder can be neglected because this is a one time cost for the simulation. In addition, if the simulation is started with uniformly initialized flow as seen in our experiments, the computation to compress the flow is extremely redundant and can easily be optimized.\n\nAs seen in table \\ref{computation_table}, the computation time of the compression mapping is 23.1 ms for a 3D simulation of grid size 160 by 160 by 160. Because each step of the compression mapping is equivalent to 60 Lattice Boltzmann steps, this equates to 10,600 MLUPS and a roughly 9x speed increase (a similar speed-up is seen with the 2D simulation). This does not give a complete picture though. Once the compressed states have been generated, the flow must be extracted with the decoder. Unfortunately, this requires considerable amounts of computation and memory because it involves applying convolutions to the full state size. Fortunately, there are ways around this. In many applications of CFD, it is not necessary to to have the full state information of the flow at each time-step. For example, calculating the drag only requires integrating over the surface of the object. By using the convolutional nature of the decoder, we can extract specif pieces of the flow without needing to compute the full state. In table \\ref{computation_table}, we show computation times for extracting flow information of a plane, line and single point. While these computations can still be somewhat expensive, they do not necessarily need to be performed at every time-step and require very little working memory. \n\nUnfortunately, there are some measurements that do require the full state information to compute such as the average flux seen in our tests. Our method is currently unable to handle these without requiring high run-times and large working memory. A possible solution is training a separate neural network that takes in the compressed state and predicts the desired measurement. This would negate the need to extract out the full state and keep memory usage low. We leave this and similar ideas for future work.\n\nWhile Lat-Net compresses the simulation state size by more then an order of magnitude, the working memory requirements for the compression network must be considered. A typical GPU based Lattice Boltzmann solver requires around 1.5 times as much working GPU memory as the memory size of the lattice \\cite{januszewski2014sailfish}. For example, the maximum sized D3Q15 lattice that can fit on a GPU with 8 Gigabytes is $446^3$. We have observed that in our implementation of Lat-Net, the maximum 3D compression network we can run with an 8 Gigabyte GPU corresponds to a lattice size of $672^3$. This represents a 3.4x efficiency gain in working memory usage. While this is certainly a nontrivial gain, we feel that further improvements can be realized with a more memory efficient implementation of the compression mapping.\n\n\n\\subsection{Electromagnetic Results}\n\nFinally, we illustrate the generality of our method by applying it to electromagnetic simulations. The same neural network architecture is used as in the 2D flow simulations with the only difference being the filter size on the compression is half of that in the flow network. The loss is kept identical however the lattice values are scaled up by a factor of 10 so they are on the same range as the flow lattice values. In figure \\ref{em_dataset}, we see very similar waves formed with the same reflection and refraction.\n\n\\begin{figure}[!t]\n\\centering\n\\subfigure{\\includegraphics[scale=0.28]{.\/figs\/512x512_2d_em_image.png}}\n\\subfigure{\\includegraphics[scale=0.14]{.\/figs\/512x512_2d_em_error_plot.png}}\n\\caption{ Comparison of generated Electromagnetic fields. The images show the true and generated magnetic field a various time-steps. The reflection and refraction phenomena can clearly be seen in both. The plot shows the mean squared error of the true and generated simulation.}\n\\label{em_dataset}\n\\end{figure}\n\n\n\\section{Conclusion}\n\nFluid Simulations are incredibly important for a variety of tasks however they are extremely computation and memory intensive. In this work, we have developed a unique method to overcome this using deep neural networks. We have demonstrated it is capable of accurately reconstructing a variety of simulations under different conditions with significantly less computation and memory. We have also shown that our method can be readily applied to other physics simulations such as electromagnetic simulations. While our method has proved successful on the problems in this paper, there is still significant room for improvement. A loss function that either takes into account the statistical nature of the flow or uses recent advances in GANs could produce shaper, more realistic flow. Training a network to extract desired measurements from the compressed state such as average flux would overcome the current memory limitation for such a task. We leave these and other improvements for future work.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\chapter{Light-cone Co-ordinates}\\label{appendixA}\nTo perform the light-cone expansion one relate the meson's 4-momentum $P_\\mu$, polarisation vector $e^{(\\lambda)}$ and the coordinate $x_\\mu$ to two light-like vectors $p_\\mu$ and $z_\\mu$. We have the usual relations\n\\begin{equation}\np^2=0, \\hspace{1in} z^2=0\\,,\n\\end{equation}\nand\n\\begin{equation}\nP^2=m_{K^*}^2, \\hspace{1in} e^{(\\lambda)}\\cdot e^{(\\lambda)} =-1,\\hspace{1in} P\\cdot e^{(\\lambda)} =0,\n\\end{equation}\nso that the limit $m_{K^*}^2\\to 0$ gives $p \\to P$ and $x^2 \\to 0$ gives $z \\to x$. From this it follows that\n\\begin{eqnarray}\\label{lccoords1}\n z_\\mu &=& x_\\mu-P_\\mu\\,\\frac{1}{m_{K^*}^2}\\left[x\\cdot P -\\sqrt{(x\\cdot P)^2-x^2m^2_{K^*}}\\,\\right]\n= x_\\mu\\left[1-\\frac{x^2m_{{K^*}}^2}{4(z\\cdot p)^2}\\right]\n-\\frac{1}{2}p_\\mu\\,\\frac{x^2}{p\\cdot z}+ \\mbox{\\cal O}(x^4)\\,,\\nonumber\\\\\np_\\mu &=& P_\\mu-\\frac{1}{2}\\,z_\\mu\\, \\frac{m^2_{K^*}}{p\\cdot z}\\,.\n\\end{eqnarray}\nThe meson's polarization vector $e^{(\\lambda)}$ can be decomposed into projections onto the two light-like vectors and the orthogonal plane\n\\begin{eqnarray}\\label{lccoords2}\n e^{(\\lambda)}_\\mu &=& \\frac{e^{(\\lambda)}z}{p\\cdot z}\\, p_\\mu + \\frac{e^{(\\lambda)} p}{p\\cdot z}\\, z_\\mu +\n e^{(\\lambda)}_{\\perp\\mu} = \\frac{e^{(\\lambda)} z}{p\\cdot z}\\left( p_\\mu -\\frac{m^2_{K^*}}{2p\\cdot z}\\, z_\\mu \\right)+e^{(\\lambda)}_{\\perp\\mu}\\,,\\nonumber\\\\\n&=&(e^{(\\lambda)} \\cdot x)\\frac{P_\\mu (x\\cdot P)-x_\\mu m^2_{K^*}}{(x\\cdot P)^2 -x^2 m^2_{K^*}}+ e^{(\\lambda)}_{\\perp\\mu}\\,.\n\\end{eqnarray}\nWe also need the projector $g_{\\mu\\nu}^\\perp$ onto the directions orthogonal to $p$ and $z$\n\\begin{equation}\ng^\\perp_{\\mu\\nu} = g_{\\mu\\nu} -\\frac{1}{p\\cdot z}(p_\\mu z_\\nu+ p_\\nu z_\\mu)\\,.\n\\end{equation}\nSome useful scalar products are\n\\begin{eqnarray}\nz\\cdot P = z\\cdot p &=& \\sqrt{(x \\cdot P)^2 - x^2 m^2_{K^*}}\\,,\\nonumber\\\\\np \\cdot e^{(\\lambda)}&=& -\\frac{m^2_{K^*}}{2 pz} z \\cdot e^{(\\lambda)}\\,,\\nonumber\\\\\nz \\cdot e^{(\\lambda)}&=&x \\cdot e^{(\\lambda)}\\,.\n\\end{eqnarray}\nWill use the notations\n\\begin{equation}\\label{note1}\na_z\\equiv a_\\mu z^\\mu, \\qquad b_p\\equiv b_\\mu p^\\mu,\\qquad \\slash{c}\\equiv \\gamma_\\mu c^\\mu,\\qquad d_\\mu^\\perp\\equiv g_{\\mu \\nu}^\\perp d^\\nu,\n\\end{equation}\nfor arbitrary Lorentz vectors $a_\\mu$, $b_\\mu$, $c_\\mu$ and $d_\\mu$ and \n\\begin{equation}\nx^\\mu = x_- n^\\mu + x_+ \\bar{n}^\\mu +x^\\mu_\\perp\\,,\n\\end{equation}\nfor null unit vectors $n^2=\\bar{n}^2=0$ and $n \\cdot \\bar{n} =1$. The following notation is also used:\\begin{equation}\na_+=a\\cdot z\\,,\\qquad a_- =\\frac{a\\cdot p}{p\\cdot z}\\,,\\qquad a^{\\perp}_\\mu\n= a_\\mu - \\frac{a_- p_\\mu}{p\\cdot z}-a_+ z_\\mu\\,.\n\\end{equation}\n\n\n\n\n\\chapter{Useful formulas for sum rule determinations}\\label{appendixB}\n\\section{Loop Integrals}\nHere we summarise the loop integrals needed for calculating the twist-3 correlation\nfunctions in Chapter~\\ref{chapter4_det}. At one loop, one has ($z^2=0$)\\cite{Ball:2003sc}\n\\begin{eqnarray}\n\\int \\left[d^L k\\right] e^{i f_k k\\cdot z} \\,\\frac{(k\\cdot z)^n}{(k^2)^a\n ((k-p)^2)^b} & = & (-1)^{a+b} \\left(-p^2\\right)^{D\/2-a-b} (p\\cdot z)^n\n \\,\\frac{\\Gamma(a+b-D\/2)}{\\Gamma(a)\\Gamma(b)}\n\\nonumber\\\\\n&& \\times\\int_0^1 dw\\,\n e^{i(1-w) f_k p\\cdot z}\\, w^{D\/2-1-b} (1-w)^{D\/2+n-1-a}\\,,\\nonumber\\\\ \\label{E.1}\n\\end{eqnarray}\nwhere the integration measure is defined as $d^D k = i\/(4\\pi)^2 \\left[d^L k\\right]$ and $f_k$ is an arbitrary numerical factor, which in the cases considered in Chapter~\\ref{chapter4_det} is either $v$ or $\\bar v$. One also needs the integral\n\\begin{eqnarray}\n\\lefteqn{\\int \\left[d^L l\\right] e^{i f_l l\\cdot z} \\,\\frac{(l\\cdot p)(l\\cdot z)^j}{(l^2)^c\n ((l-k)^2)^d}}\n\\nonumber\\\\\n& = & (-1)^{\\frac{D-4}{2}} \\left(k^2\\right)^{D\/2-c-d} (k\\cdot p) (k\\cdot z)^j\n \\,\\frac{\\Gamma(c+d-D\/2)}{\\Gamma(c)\\Gamma(d)}\\int_0^1 du\\,\n e^{i(1-u) f_l k\\cdot z}\\, u^{D\/2-1-d} (1-u)^{D\/2+j-c}\n\\nonumber\\\\\n&&{}+(-1)^{\\frac{D-4}{2}} \\left(k^2\\right)^{D\/2+1-c-d} (p\\cdot z)(k\\cdot z)^{j-1}\n \\,\\frac{\\Gamma(c+d-D\/2-1)}{2\\Gamma(c)\\Gamma(d)}\n\\nonumber\\\\\n&&{}\\times\\int_0^1 du\\,\n e^{i(1-u) f_l k\\cdot z}\\, u^{D\/2-d} (1-u)^{D\/2-1+j-c}\\left( j + i f_l\n (1-u) (k\\cdot z)\\right)\\,.\\label{E.2}\n\\end{eqnarray}\nTwo-loop integrals are obtained by combining the above one-loop integrals.\n\n\\section{Borel Subtraction}\nTo derive the sum rules from $\\widetilde{\\pi}_{3;V}^\\parallel$, $\\pi_{3;V}^\\parallel$ and $\\pi_{3;V}^\\perp$ we use the relation\n\\begin{equation}\n\\frac{1}{\\pi}\\textrm{Im}_s \\left[-q^2-i0\\right]^\\alpha=\\frac{s^\\alpha}{\\Gamma(-\\alpha)\\Gamma(1+\\alpha)}\\Theta(s)\\,,\n\\end{equation}\nwhere $s=-q^2$, to find the imaginary part. Using the following notation for the Borelisation and continuum subtraction procedure\n \\begin{equation}\n\\hat{\\mathcal{B}}_{sub}\\left[X\\right]=\\int^{s_0}_0ds\\,e^{-s\/M^2}\\frac{1}{\\pi} \\textrm{Im}_s X\\,,\n\\end{equation}\nand the definition of the Borel transform (\\ref{borel1}) allows one to write the required results as\n\\begin{eqnarray}\n\\qquad\\hat{\\mathcal{B}}_{sub}\\left[\\frac{1}{(q^2)^\\alpha}\\right]&=&\\frac{(-1)^\\alpha}{(\\alpha-1)! (M^2)^{\\alpha-1}} \\,,\\qquad\n\\hat{\\mathcal{B}}_{sub}\\left[\\ln(-q^2)\\right]=-M^2+\\int^\\infty_{s_0}ds\\,e^{-s\/M^2}\\,,\\nonumber\\\\\n\\hat{\\mathcal{B}}_{sub}\\left[q^2\\ln(-q^2)\\right]&=&-M^4+\\int^\\infty_{s_0}ds\\,e^{-s\/M^2}s\\,,\\nonumber\\\\\n\\hat{\\mathcal{B}}_{sub}\\left[\\frac{\\ln(-q^2)}{q^2}\\right]&=&\\gamma_{E}-\\ln M^2+\\int^\\infty_{s_0}ds\\,e^{-s\/M^2}\\frac{1}{s}\\,,\\nonumber\\\\\n\\hat{\\mathcal{B}}_{sub}\\left[\\frac{\\ln(-q^2)}{q^4}\\right]&=&\\frac{1}{M^2}\\left(1-\\gamma_E +\\ln M^2\\right)+\\int^\\infty_{s_0}ds\\,e^{-s\/M^2}\\frac{1}{s^2}\\,,\\nonumber\\\\\n\\hat{\\mathcal{B}}_{sub}\\left[\\ln(-q^2)^2\\right]&=&2M^2\\left(\\gamma_E-\\ln M^2\\right) +2\\int^\\infty_{s_0}ds\\,e^{-s\/M^2}\\ln s\\,,\n\\end{eqnarray}\nwhere $\\gamma_E$ is Euler's constant. \n\n\\section{Input Parameters}\nFor the twist-2 and twist-3 DA parameter sum rule determinations of Chapter~\\ref{chapter4_det} we use the following input parameters:\n\\begin{table}[ht]\n\\renewcommand{\\arraystretch}{1.3}\n\\addtolength{\\arraycolsep}{3pt}\n$$\n\\begin{array}{|r@{\\:=\\:}l||r@{\\:=\\:}l|}\n\\hline\n\\langle \\bar q q\\rangle & (-0.24\\pm0.01)^3\\,\\mbox{GeV}^3 & \\langle \\bar s s\\rangle & (1-\\delta_3)\\,\\langle \\bar q q\\rangle\\\\\n\\langle \\bar q \\sigma g_sG q\\rangle & m_0^2\\,\\langle \\bar q q\\rangle & \\langle \\bar s \\sigma g_sG s\\rangle & (1-\\delta_5)\\langle \\bar q \\sigma g_sG q\\rangle\\\\[6pt]\n\\displaystyle \\left\\langle \\frac{\\alpha_s}{\\pi}\\,G^2\\right\\rangle & (0.012\\pm 0.003)\\, \n{\\rm GeV}^4 & \\multicolumn{2}{l|}{}\\\\[6pt]\\hline\n\\multicolumn{4}{|c|}{m_0^2 = (0.8\\pm 0.1)\\,{\\rm GeV}^2~,\\qquad \\delta_3\n = 0.2\\pm 0.2, \\qquad \\delta_5 = 0.2\\pm 0.2}\\\\\\hline\n\\multicolumn{4}{|c|}{\\overline{m}_s(2\\,\\mbox{GeV}) = (100\\pm\n20)\\,\\mbox{MeV}\\qquad\\longleftrightarrow\\qquad\\overline{m}_s(1\\,\\mbox{GeV})\n= (133\\pm 27)\\,\\mbox{MeV}}\\\\\n\\multicolumn{4}{|c|}{\\overline{m}_q(\\mu) = \\overline{m}_s(\\mu)\/R\\,,\n \\qquad R = 24.6\\pm 1.2}\\\\\\hline\n\\multicolumn{4}{|c|}{\\alpha_s(M_Z) = 0.1176\\pm 0.002 ~\\longleftrightarrow~ \n\\alpha_s(1\\,\\mbox{GeV}) = 0.497\\pm 0.005}\\\\\\hline\n\\end{array}\n$$\n\\renewcommand{\\arraystretch}{1}\n\\addtolength{\\arraycolsep}{-3pt}\n\\caption[Summary of input parameters for Chapter 4.]{\\small Input parameters for sum rules at the renormalisation scale $\\mu=1\\,$GeV. The value of $m_s$ is obtained from unquenched lattice calculations with $N_f=2$ flavours as summarised in \\cite{mslatt}, which agrees with the results from QCD sum rule calculations \\cite{jamin}. $\\overline{m}_q$ is taken from chiral perturbation theory \\cite{chPT}. $\\alpha_s(M_Z)$ is the PDG average \\cite{Yao:2006px}.}\n\\label{QCDSRinput}\n\\end{table}\n\nTo evaluate the sum rules for the three-particle twist-3 DA parameters we use the following values of the continuum threshold $s_0$\n\\begin{eqnarray}\ns_0^\\parallel (\\rho) &=& (1.3\\pm 0.3)\\,{\\rm GeV}^2\\,,\\quad \ns_0^\\parallel (K^*) = (1.3\\pm 0.3)\\,{\\rm GeV}^2\\,,\\quad\ns_0^\\parallel (\\phi) = (1.4\\pm 0.3)\\,{\\rm GeV}^2\\,,\\quad \\nonumber\\\\\ns_0^\\perp (\\rho) &=& (1.5\\pm 0.3)\\,{\\rm GeV}^2\\,,\\quad \ns_0^\\perp (K^*) = (1.6\\pm 0.3)\\,{\\rm GeV}^2\\,,\\quad\ns_0^\\perp (\\phi) = (1.7\\pm 0.3)\\,{\\rm GeV}^2\\,. \\nonumber\\\\\n\\end{eqnarray}\nThe threshold for the $\\rho$ channel is from \\cite{Shifman:1978bz}. \n\n\n\\chapter*{Introduction}\\label{chapter0_intro}\\addcontentsline{toc}{chapter}{\\protect\\numberline{Introduction\\hspace{-96pt}}} \nOne only has to ask the question ``why?'' a handful of times before one reaches the answer ``I don't know'', regardless of the topic considered and regardless of the person asked. It is safe to say, however, almost all questions of the structure of matter at the smallest of distances leads one directly to, or at least through, the field of modern particle physics. The beginnings of our understanding of the physical world harks back to the dawn of scientific reasoning in the ancient world; logic and reasoning were applied with the aim of describing the behaviour of physical systems in terms of simple universal axioms, a philosophy which still holds strong today. Through experimentation and the language of mathematics the scientific method has driven back the edge of ignorance to frontiers unimaginable to those physicists of 100 years ago, let alone the natural philosophers of millennia ago. The present ``coal face'' is known as the Standard Model \\cite{weak, QCD} which describes three of the four known forces of nature -- electromagnetism, and the weak and strong nuclear forces -- in one unifying framework. \n\nFrustratingly, the Standard Model does not explain many of the things which it encompasses; it does not provide an origin for CP violation but only gives a parameterisation, nor does it explain why there are three generations of quarks and leptons, or their hierarchy of masses. All attempts to bring gravity into the fold have so far failed, however, whatever theory lies beyond must yield the Standard Model as some limiting case.\n\nThe Standard Model has been scrutinised relentlessly since its inception. Remarkably, nearly without fail it has held its ground over the entire breadth of its theoretical reach and so the task of finding new ways to probe its structure requires ever more the creativity and ingenuity of both theorists and experimentalists alike. Novel experimental signatures, against which to pit theory, must be used to maximum potential. From a theoretical standpoint there are still many challenges to be met, especially in preparation for the next generation of collider experiments now just round the corner. Particularly, the control and reduction of the theoretical uncertainty of Standard Model predictions is of paramount importance as only then can one hope to be in a position to discern signs of new physics from that of the Standard Model background.\n\nSome of its most challenging tests of the Standard Model fall in the field of heavy-flavour physics, within which $B$ physics has proven itself to be rich and fertile. Today it is an area of high activity with many success stories, including the recent measurement of the $B_s^0$-$\\bar{B}_s^0$ mass difference $\\Delta m_s$ at the Tevatron \\cite{Abulencia:2006ze}. Moreover, two dedicated ``$B$-factories'', Belle at KEK \\cite{:2000cg} and \\textsc{BaBar} at SLAC \\cite{Aubert:2001tu}, have measured a range of observables, such as branching fractions and CP asymmetries, of a vast number of $B$ decay modes. Looking to the future, the $B$ physics community eagerly await the forthcoming LHCb experiment, and beyond that so-called ``superflavour factories'' \\cite{superB} have been championed with the aim of probing rare $B$ decays to extract CP violation parameters to much higher levels of accuracy. It is imperative to find tests of the Standard Model which may be observed in these up-and-coming experiments \\cite{Gershon:2006mt} and promising modes include the rare decays $B\\to V \\gamma$ and $B\\to K \\mu^+\\mu^-$. \n\nThe strict pattern of CP violation of the Standard Model finds its origin in the Cabbibo-Kobayashi-Maskawa (CKM) matrix \\cite{Cabibbo:1963yz,Kobayashi:1973fv}. CP violation was discovered in $B$ physics via the decay mode $B_d^0\\to J\/\\psi K_S^0$ and found to be large, in contrast to $K$ decays where the violation is tiny. The possible largeness of CP violation in $B$ decays offers promising ways to detect new physics indirectly via CP violating observables testing the CKM paradigm. \n\nTheoretically, central to the description of $B$ decays is the disentanglement of the weak decay process from strong interaction effects leading to a low-energy effective Hamiltonian in which the physics at a scale $\\mathcal{O}(M_W)$ is well under control. Achieving this goal for the wide range of $B$ decays of interest has only been possible through huge calculational effort; the availability in the literature of Wilson coefficients at next-to-leading-order, and in some cases next-to-next-to-leading-order, is testament to this. Furthermore, the theoretical description of the matrix elements of effective $B$ decay operators has been hugely improved through QCD factorisation methods. We discuss and make use of one such framework, namely that introduced by Beneke, Buchalla, Neubert and Sachrajda \\cite{Beneke:1999br, Beneke:2000ry,Beneke:2001ev}. The so-called BBNS approach showed, to leading-order in a $1\/m_b$ expansion, that the $\\alpha_s$ corrections beyond naive-factorisation of a large class of non-leptonic $B$ decay matrix elements are calculable in terms of $B$ transition form factors and meson light-cone distribution amplitudes. Armed with the corresponding amplitudes the phenomenologist may construct observables, such as branching ratios, CP asymmetries and isospin symmetries, which may then be compared to experiment. The predictive power of the QCD factorisation framework is jeopardised by a poor understanding of both these non-perturbative QCD quantities and the impact of the generally unknown power-suppressed contributions $\\mathcal{O}(1\/m_b)$; this in part motivates the work of this thesis.\n\nIn this thesis we investigate $\\rm SU(3)_F$-breaking effects in vector meson distribution amplitudes which are crucial in differentiating between the particles $\\rho$, $K^*$ and $\\phi$. The leading non-perturbative DA parameters are determined via the method of QCD sum rules introduced by Shifman, Vainshtein and Zakharov \\cite{Shifman:1978bz, Shifman:1978by, Shifman:1978bx}. The method provides a prescription for the systematic calculation of non-perturbative QCD parameters, albeit with an irreducible error $\\sim 20-30\\%$, and constitutes an extremely useful theoretical tool. \n\nThe sum rule results have a direct application in the QCD factorisation description of $B$ decays to $\\rho$, $K^*$ and $\\phi$ mesons. In particular, radiative $B$ decays to vector mesons $B\\to V\\gamma$, are an excellent example of a process potentially sensitive to new physics contributions, as at leading order the decays are mediated at loop level in the Standard Model. We perform a phenomenological analysis of these decays using the QCD factorisation framework of Bosch and Buchalla \\cite{Bosch:2001gv,Bosch:2002bw} including leading power-suppressed corrections for which the updated non-perturbative distribution amplitude parameters find use. The impact of the power-suppressed corrections on the key decay observables is discussed and leads to a better understanding of the theoretical uncertainty of the QCD factorisation predictions. \n\nAlso, we calculate important contributions to the $B\\to\\eta^{(\\prime)}$ transition form factors via a variant sum rule approach, known as light-cone sum rules, for which distribution amplitudes play a crucial role. The result of the analysis elucidates a major source of theoretical uncertainty of the $B\\to \\eta^{(\\prime)}$ form factor. The result impacts $B\\to K^* \\eta^{(\\prime)}$, for example, where the experimental data and QCD factorisation predictions of the branching ratios are inconsistent.\n\nThe thesis is structured as follows: \n\\begin{itemize}\n\\item{Chapter~\\ref{chapter1_basics} introduces some of the fundamentals of the Standard Model and its application to $B$ physics. We define the QCD Lagrangian and the CKM matrix, introduce CP violation in Standard Model $B$ decays, and briefly discuss the structure of the $\\Delta B =1$ weak effective Hamiltonian. }\n\\item{Chapter~\\ref{chapter2_DAs} covers the definitions of the light-cone distribution amplitudes of the light vector mesons $\\rho$, $K^*$ and $\\phi$. We determine their structure up to twist-3 accuracy and using the conformal expansion and QCD equations of motion express the distribution amplitudes in terms of a finite set of non-perturbative parameters. We extend previous determinations in order to fully differentiate between the three particles by including all G-parity violating contributions and $\\rm{SU}(3)_F$-breaking effects. }\n\\item{Chapter~\\ref{chapter3_SR} discusses the QCD sum rule method and its extension light-cone sum rules. The methods allow, amongst other things, the determination of the non-perturbative distribution amplitude parameters and transition form factors respectively, and are very widely applicable in and beyond $B$ physics.}\n\\item{In Chapter~\\ref{chapter4_det} we apply QCD sum rules to determine the leading non-perturbative distribution parameters defined in Chapter~\\ref{chapter2_DAs}. Consistency requires the inclusion of all G-parity violating contributions and $\\rm{SU}(3)_F$-breaking effects to the sum rules, and we extend previous determinations by including higher-order strange quark mass effects and $\\mathcal{O}(\\alpha_s)$ contributions to the quark condensates. We analyse the resulting sum rules and provide updated numerical results for all parameters. The results of this section find immediate application in QCD factorisation and light-cone sum rule descriptions of processes involving these vector mesons.}\n\\item{In Chapter~\\ref{chapter5_eta} we calculate the gluonic flavour-singlet contribution to the semileptonic $B\\to \\eta^{(\\prime)}$ transition form factor in the framework of light-cone sum rules. In doing so we discuss pseudoscalar meson and two-gluon distribution amplitudes. The new contribution is combined with the previous determination of the quark contribution, to complete the theoretical treatment of these form factors. The $\\eta^{(\\prime)}$ system is complicated due to large mixing effects via the $\\rm U(1)_A$ anomaly. We introduce the phenomenological framework of $\\eta$-$\\eta^{\\prime}$ mixing and connect it to the form factor calculation in a consistent manner. The results of this chapter find immediate application in the QCD factorisation description of $B\\to \\eta^{(\\prime)}$ transitions, which in turn, in principle, allow a determination of the CKM matrix element $|V_{ub}|$ from $B\\to\\eta^{(\\prime)} l \\nu$.}\n\\item{Chapter~\\ref{chapter6_QCDF} introduces the framework of QCD factorisation, which is an important application of meson distribution amplitudes and transition form factors. We briefly discuss the BBNS approach and then go on to discuss the leading contributions to QCD factorisation in the context of $B\\to V \\gamma$ decays.}\n\\item{In Chapter~\\ref{chapter7_rad} we investigate the impact of the relevant, power-suppressed contributions to $B\\to V \\gamma$ beyond the QCD factorisation formula. We include long-distance photon emission from weak annihilation diagrams and soft gluon emission from quark loops. The non-perturbative distribution amplitude parameters determined in Chapter~\\ref{chapter4_det} find use in a light-cone sum rule estimation of the latter. The key observables are the branching ratios, isospin asymmetries and the indirect time-dependent CP asymmetry $S(V\\gamma)$ which, as has been know for some time, forms the basis of a ``null test'' of the Standard Model. Assuming no new physics contributions, we extract the ratio of CKM matrix parameters $\\left|V_{td}\/V_{td}\\right|$ to a competitive degree of accuracy.}\n\\item{We summarise and conclude in Chapter~\\ref{chapter8_conc}.}\n\\end{itemize}\nThe material of Chapters~\\ref{chapter2_DAs} and \\ref{chapter4_det} follows Ref.~\\cite{Ball:2007rt} and the material of Chapters~\\ref{chapter5_eta} and \\ref{chapter7_rad} follows Refs.~\\cite{Ball:2007hb} and \\cite{Ball:2006eu}, respectively. Some of the more bulky equations, and material not necessary in the general flow of reading the thesis, are given in two appendices. \n\\chapter{Fundamentals Of $B$ Physics}\\label{chapter1_basics}\nIn this chapter we begin with the basics of the Standard Model and then go on to discuss two concepts which are central to the investigations of $B$ physics, and those of this thesis:\n\\begin{itemize}\n\\item{CP violation in the flavour sector, which follows a strict pattern in the Standard Model and can readily be sensitive to new physics;}\n\\item{the $\\Delta B=1$ effective weak Hamiltonian, which we briefly discuss as it is the starting point of many phenomenological studies in $B$ physics.}\n\\end{itemize}\n\n\n\\section{The Standard Model}\nThe \\textit{Standard Model} (SM) \\cite{weak,QCD} is a model of great scope and predictive power. Despite its successes, however, we know it to be incomplete; for example, the recent discovery of neutrino oscillation and the lack of conclusive evidence for the Higgs particle providing two areas of intense theoretical and experimental effort. The SM describes three of the four known fundamental forces of nature; the strong force, the weak force and electromagnetism. \\textit{Quantum Chromodynamics} (QCD) is a Yang-Mills gauge theory based on the gauge group $\\rm SU(3)$ and describes the fundamental interactions of the strong interaction as interactions between quarks and gluons \\cite{Yang:1954ek,Gell-Mann:1964nj,FGM,Fritzsch:1973pi}. The basic QCD Lagrangian is\n\\begin{equation}\n\\mathcal{L}_{\\rm QCD}=\\sum_q \\bar{q}^i \\left(i \\gamma_\\mu \\left(D^\\mu\\right)_{ij}-m_q \\delta_{ij}\\right)q^j -\\frac{1}{4} G^a_{\\mu\\nu} G^{a \\mu\\nu}\\,,\n\\label{basics_eq1}\n\\end{equation}\nwith\n\\begin{equation}\n(D_\\mu)_{ij} = \\delta_{ij} \\partial_\\mu -i g_s (t^a)_{ij} A^a_\\mu\\,, \\qquad G^a_{\\mu\\nu}=\\partial_\\mu A_\\nu^a-\\partial_\\nu A_\\mu^a + g_s f^{abc}A^b_\\mu A^c_\\nu\\,,\n\\label{basics_eq2}\n\\end{equation}\nwhere the sum is over all quark flavours $q$, $i,j=\\{1,2,3\\}$ are colour indices, the $t^d$ are the $3\\times3$ colour matrices with $d=\\{1,\\dots,8\\}$ and $f^{abc}$ are the structure constants. $G^a_{\\mu\\nu}$ is the gluonic field strength tensor, and $A^a_\\mu$ is the gluon field. We will make use of the notation $(G_{\\mu\\nu})_{ij}=G^a_{\\mu\\nu} (t^a)_{ij}$ and the relation $g_s^2=4 \\pi \\alpha_s$ (and $e^2=4 \\pi \\alpha_{\\rm QED}$). The Lagrangian can alternatively be defined with the replacement $g_s\\to-g_s$ and the sign convention matters for the applications in Chapters~\\ref{chapter4_det} and \\ref{chapter7_rad}.\n\nThe non-Abelian nature of QCD leads to the possibility of gluon self-interaction and the celebrated \\textit{asymptotic freedom} property of QCD \\cite{FGM,Fritzsch:1973pi,Politzer:1973fx,Gross:1973id1}. The coupling tends to zero, giving a theory of free quarks, at asymptotically high energy. On the other hand, at low energy, or large distances, the coupling increases. At energies for which $\\alpha_s\\gtrsim1$ perturbation theory is not applicable, and one has to resort to \\textit{non-perturbative} methods to determine the effects of QCD. Despite the simplicity of the QCD Lagrangian (\\ref{basics_eq1}) an accurate determination of non-perturbative QCD from first principles, and hence \\textit{confinement}, poses a major challenge. One such method, based on ideas of Wilson \\cite{Wilson:1974sk}, is that of Lattice QCD, which aims to calculate the QCD action computationally on a grid of discretised spacetime points. An altogether different, and less rigourous, method is that of QCD sum rules, which encodes non-perturbative effects in terms of non-vanishing vacuum expectation values of operators with the quantum numbers of the vacuum. This method is central to the work in this thesis, and shall be discussed in Chapter~\\ref{chapter3_SR}.\n\nThe electroweak force is the unification of the weak nuclear force and electromagnetism given by the \\textit{Glashow-Salam-Weinberg model}. The model is based on the gauge group $\\rm SU(2)_L \\otimes U(1)_Y$, which is broken by \\textit{spontaneous symmetry breaking} to yield $\\rm U(1)_Q$ - the gauge group corresponding to \\textit{Quantum Elecrodynamics} (QED). The weak interaction is mediated by three massive gauge bosons $W^\\pm$ and $Z^0$ and occurs between quarks and leptons. The quarks and leptons are arranged, within the three generations, into left-handed doublets and right-handed singlets under $\\text{SU(2)}_{\\rm L}$\n\\begin{eqnarray}\n Q_{\\rm L}= \\left(\\begin{array}{c} \n U\\\\ \n D \\\\ \n \\end{array}\\right)_{\\rm L}\\,,\\quad\n E_{\\rm L}= \\left(\\begin{array}{c} \n \\nu_l \\\\ \n l^- \\\\ \n \\end{array}\\right)_{\\rm L} \\,;\\quad\n U_{\\rm R}\\,,D_{\\rm R}\\,,l^-_{\\rm R}\\,,\n \\label{basics_eq5}\n\\end{eqnarray}\nwhere the \\textit{weak eigenstates} $U=\\{u,c,t\\}$, $D=\\{d,s,b\\}$ and $l^-=\\{e^-,\\mu^-,\\tau^-\\}$ are the up-type quarks, down-type quarks and charged leptons respectively. The subscript L (R) represents the left (right)-handed projectors $q_{\\rm L (R)}=\\frac{1}{2}(1\\mp \\gamma_5)q$ which reflect the chiral nature of the weak interaction. The neutrinos are massless in the SM, and the right handed neutrino does not exist. The electroweak interactions of the quarks are described by the following Lagrangian, which consists of a \\textit{charged current} ($CC$) and a \\textit{neutral current} ($NC$)\n\\begin{eqnarray}\n \\mathcal{L}^{\\textrm{ew}} &=& \\mathcal{L}_{CC} +\n \\mathcal{L}_{NC}\\,,\\nonumber \\\\\n &=& \\frac{g}{\\sqrt{2}}\\left[J_\\mu^+W^{+\\mu} + J_\\mu^-W^{-\\mu}\\right]\\,,\\nonumber \\\\\n & +&\n e\\,\\left[J^{\\textrm{em}}_\\mu A^\\mu \\right]+ \\frac{g}{\\cos{\\theta_W}}\\left[ \\left(J^3_\\mu - \\sin^2{\\theta_W}J^{em}_\\mu\\right) Z^\\mu \\right]\\,.\n\\end{eqnarray}\nThe neutral current part of the Lagrangian is made up of the electromagnetic current $J^{\\textrm{em}}_\\mu$ and neutral weak current $J^3_\\mu$:\n\\begin{equation}\n J^{\\textrm{em}}_\\mu = Q_U\\,\\bar{U}_{\\rm L}\\gamma_\\mu U_{\\rm L}+Q_D\\,\\bar{D}_{\\rm L}\\gamma_\\mu D_{\\rm L}\\,, \\qquad\n J^3_\\mu= \\frac{1}{2}(\\bar{U}_{\\rm L} \\gamma_\\mu U_{\\rm L}-\\bar{D}_{\\rm L}\\gamma_\\mu D_{\\rm L})\\,,\n\\end{equation}\nwhere $Q_{U(D)}=2\/3\\,(-1\/3)$ is the electric charge of the $U$ $(D)$ quarks, $\\theta_W$ is the weak mixing angle and $g$ is the electroweak coupling related to the electromagnetic coupling by $e=g \\sin \\theta_W$. Rotating to the basis of \\textit{mass eigenstates} modifies the charged current in the quark sector to\n\\begin{equation}\n J^+_\\mu = \\bar{U}_{\\rm L}^m \\gamma_\\mu \\,\\hat{V}_{\\rm{CKM}}\\, D_{\\rm L}^m\\,,\n\\end{equation}\nwhere $\\hat{V}_{\\mathrm{CKM}}$ is the \\textit{Cabbibo-Kobayashi-Maskawa} matrix \\cite{Cabibbo:1963yz,Kobayashi:1973fv} and the superscript $m$ denotes mass eigenstates. The CKM matrix is $3\\times3$ (for three quark generations), unitary, and its off-diagonal entries allow for transitions between the quark generations. There are no flavour-changing neutral-currents (FCNC) at tree-level in the SM as the neutral currents $ J^{\\textrm{em}}_\\mu$ and $J^3_\\mu$ are invariant under the transformation to the mass eigenbasis, which is known as the \\textit{Glashow-Iliopoulos-Maiani (GIM) mechanism} \\cite{Glashow:1970gm}. The entries of the CKM matrix are written as\n\\begin{equation}\n \\hat{V}_{\\mathrm{CKM}} = \\left(\\begin{array}{ccc}\n V_{ud} & V_{us} & V_{ub} \\\\\n V_{cd} & V_{cs} & V_{cb} \\\\\n V_{td} & V_{ts} & V_{tb} \n \\end{array}\\right)\\,,\n\\label{basics_eq6}\n\\end{equation}\nand are fundamental parameters of the SM that have to be determined from experiment. Evidently, the matrix has $n^2=9$ parameters $n (n-1)\/2=3$ of which are rotation angles due to its unitarity. The six quark fields in Eq.~(\\ref{basics_eq5}) can be re-phased, up to an overall phase, leaving the Lagrangian invariant and therefore $9-5-3=1$ phase remains giving rise to complex entries -- complex coupling constants. This is the origin of \\textit{CP violation} in the quark sector of the weak interaction. The leptonic sector is described by an analogous mixing matrix which, in the absence of neutrino masses, is given by the unit matrix because all phases can be rotated away.\n\nThe CKM matrix (\\ref{basics_eq6}) is often parameterised to incorporate the constraints of unitarity.\\footnote{The ``standard'' parameterisation of the CKM matrix is in terms of the three mixing angles $\\theta_{ij}$ $(i,j=1,2,3)$ and the CP violating phase $\\delta$ \\cite{Yao:2006px}.} A very useful and convenient parameterisation is the \\textit{Wolfenstein parameterisation} \\cite{Wolfenstein:1983yz} which, along with unitarity, incorporates the experimental observations $|V_{us}|\\ll 1$, $|V_{cb}|\\sim|V_{us}|^2$ and $|V_{ub}| \\ll |V_{cb}|$. It is an expansion in $\\lambda=|V_{us}| \\approx 0.22$, and as such is only approximately unitary at a given order in $\\lambda$:\n\\begin{equation}\n \\hat{V}_\\mathrm{CKM}=\\left(\n \\begin{array}{ccc} \n 1-\\frac{\\lambda^2}{2} & \\lambda & A\\lambda^3 (\\rho-i\\eta) \\\\\n -\\lambda & 1-\\frac{\\lambda^2}{2} & A\\lambda^2 \\\\\n A \\lambda^3 (1-\\rho-i\\eta) & -A\\lambda^2 & 1\n \\end{array}\\right)+\\mathcal{O}(\\lambda^4)\\,.\n \\label{basics_eq7}\n\\end{equation}\nThe matrix is given in terms of the four parameters ($A,\\lambda,\\rho,\\eta$); $A$ and $\\rho^2+\\eta^2$ are order unity and the hierarchy of sizes of elements can be infered from the powers of $\\lambda$. The smallness of $V_{cb}$ and $V_{ub}$ are responsible for the relatively long lifetime of $B$ mesons (and baryons), which facilitates their experimental detection. The unitarity of the CKM matrix gives six equations that equal zero and can be represented as triangles in the complex plane. The most widely used of these relations in $B$ physics is\n\\begin{equation}\nV_{ud}V_{ub}^* + V_{cd}V_{cb}^* + V_{td}V_{tb}^* = 0\\,,\n\\label{basics_eq8}\n\\end{equation}\nwhich is invariant under phase transformations and is an observable. The above relation is divided by $V_{cd}V_{cb}^*$ to give a triangle in the complex plane with a base of unit length and upper apex at the point $(\\bar\\rho,\\bar\\eta)$\\footnote{The following rescaling proves convenient to the definition of the UT: $\\rho\\to\\bar\\rho=\\rho\\,(1-\\lambda^2\/2)$ and $\\eta\\to\\bar\\eta=\\eta\\,(1-\\lambda^2\/2)$.} known as \\textit{The Unitary Triangle} (UT), see Figs.~\\ref{basics_fig1} and \\ref{basics_fig2}. The sides of the UT are given by\n\\begin{eqnarray}\nR_b &\\equiv& \\frac{|V_{ud}V_{ub}^*|}{|V_{cd}V_{cb}^*|} = \\sqrt{\\bar\\rho^2+ \\bar\\eta^2}=\n\\left(1-\\frac{\\lambda^2}{2}\\right)\\frac{1}{\\lambda}\n\\left|\\frac{V_{ub}}{V_{cb}}\\right|\\,,\\label{basics_eq88}\\\\ \nR_t &\\equiv& \\frac{|V_{td}V_{tb}^*|}{|V_{cd}V_{cb}^*|} = \\sqrt{(1-\\bar\\rho)^2+ \\bar\\eta^2}\\,.\n\\label{basics_eq9}\n\\end{eqnarray}\nThe angles are given by\n\\begin{equation}\n\\alpha\\equiv\\arg\\left(-V_{td}V_{ub}V^*_{tb}V_{ud}^*\\right)\\,,\\quad\\beta\\equiv\\arg\\left(-V_{cd}V_{tb}V^*_{cb}V_{td}^*\\right)\\,,\\quad\\gamma\\equiv\\arg\\left(-V_{ud}V_{cb}V^*_{ub}V_{cd}^*\\right)\\,.\n\\end{equation}\nThe (over) determination of the sides and angles of the UT is a major quest in understanding the SM. To achieve this goal one must construct decay observables, which can then be matched to experimental results in order to extract values for the desired CKM (or equivalently UT) parameters. Such observables include branching ratios, which may appear simply proportional to a CKM matrix element, and CP asymmetries, which encode the effects of the SM predictions of CP violation, and can also be measured experimentally.\n\\begin{figure}[h]\n$$ \\epsfxsize=0.5\\textwidth\\epsffile{UT.eps}$$\n\\caption[The Unitary Triangle.]{\\small The Unitary Triangle. The determination of the sides $R_b$ and $R_t$ and the angles $\\alpha$, $\\beta$ and $\\gamma$ lead to stringent tests of the Standard Model.} \n\\label{basics_fig1}\n$$ \\epsfxsize=0.5\\textwidth\\epsffile{CKMfitterUT.eps}$$\n\\caption[Constraints on the angles and sides of the Unitarity Triangle.]{\\small Constraints on the angles $\\alpha$, $\\beta$, and $\\gamma$ and sides $R_b$ and $R_t$ of the Unitarity Triangle as imposed from numerous experimental sources. Complied by the CKM fitter group \\cite{global}.} \n\\label{basics_fig2}\n\\end{figure}\n\n\\section{CP Violation In $B$ Decays}\nDoes the CKM matrix (\\ref{basics_eq6}) account for the CP violation observed in nature? Examining CP violation in $B$ decays allows one to probe the structure of the CKM matrix and is a very promising way to detect the effects of new physics, which many not be expressed through other decay observables. Consequently, the CP properties of FCNC processes, which are characterised by their potential sensitivity to new physics effects, have been under intense theoretical and experimental investigation for many years. Prime examples of such processes include $B^0$-$\\bar B^0$ mixing (see for example Ref.~\\cite{Ball:2006xx}) and radiative $B$ decays, see Chapter~\\ref{chapter7_rad}.\n\nThe idea that the weak interaction may violate parity was first suggested many years ago by Lee and Yang \\cite{Lee:1956qn}, and quickly confirmed in the $\\beta$ decay of $^{60}$Co by Wu \\textit{et al.} \\cite{Wu:1957my}. The violation of the combined CP symmetry was first observed in the context of $K$ decays in 1964 \\cite{Christenson:1964fg} and it was not until 2001 that it was first observed outside the $K$ system in $B^0_d \\to J\/ \\psi \\,K^0_S$ decays \\cite{Aubert:2001nu,Abe:2001xe}; in both cases the CKM paradigm was upheld. Recently discoveries in $B$ physics include the measurement by CDF of the mass difference $\\Delta m_s$ \\cite{Abulencia:2006ze}. Some of the most important sources of information about the UT from $B$ physics include: the determination of $\\sin 2 \\beta$ from the ``gold-plated'' decay $B\\to J\/\\psi \\,K_S$; the extraction of $\\alpha$ from non-leptonic $B$ decays such as $B \\to \\pi\\pi$; the extraction of $|V_{td}|\/|V_{ts}|$ from $B$ mixing and radiative $B$ decays, such as $B\\to V \\gamma$; and the determination of $|V_{ub}|$ from $B\\to\\pi l\\nu$.\n\nThe $B^0_q$-$\\bar B^0_q$ systems, where $q=\\{d,s\\}$, exhibit the phenomenon of particle-antiparticle mixing, which, in the SM is mediated by so-called \\textit{box diagrams} whose amplitudes are $\\sim G_F^2$ and therefore very small. We do not go into any detail about the theory of neutral state mixing and we restrict ourselves to only the formulas required in this thesis; for more information see Refs.~\\cite{Buras:1998ra,CPV}. State mixing causes, for example, an initially pure beam of $B^0$ mesons to evolve into a time-dependent linear combination of $B^0$ and $\\bar B^0$ mesons. There are four main quantities that describe the $B^0_q$-$\\bar B^0_q$ system and its decays: the width difference $\\Delta \\Gamma_q$, the mass difference $\\Delta m_q$, the CP violating mixing phase $\\phi_q$ and $\\lambda_f$ (not to be confused with the Wolfenstein CKM parameter $\\lambda\\approx 0.22$). One begins by writing the heavy (H) or light (L) eigenstates of evolution in terms of the flavour states:\n\\begin{equation}\n\\ket{B_{\\rm H}}=p \\ket{B^0}-q \\ket{\\bar{B}^0}\\,,\\qquad \\ket{B_{\\rm L}}=p \\ket{B^0}+q \\ket{\\bar{B}^0}\\,,\n\\label{basics_eq10}\n\\end{equation}\nwith $|p|^2+|q|^2=1$. The ratio $q\/p$ is given in terms of the $B^0_q$-$\\bar B^0_q$ mixing matrix $M_{12}^q$, by\n\\begin{equation}\n\\left.\\frac{q}{p}\\right|_{q} = \\sqrt{\\frac{(M_{12}^{q})^*}{M_{12}^{q}}} = e^{-i\\phi_{q}}\\,,\n\\label{basics_eq11}\n\\end{equation}\nunder the condition $\\Delta \\Gamma_q \\ll \\Delta m_q$. Experimentally, there is no evidence for \\textit{mixing-indiced} CP violation in the $B^0_q$-$\\bar B^0_q$ systems, i.e. $\\left|q\/p\\right|_{d,s}\\approx 1$ \\cite{Barberio:2007cr}. The CP violating mixing phase is given by $ \\phi_q={\\rm arg}\\left[M_{12}^q\\right]$ which in the SM and the Wolfenstein parametrisation of the CKM matrix can be written in terms of the UT angles as\n\\begin{equation}\n\\phi_d \\equiv {\\rm arg}[(V_{td}^* V_{tb})^2] = 2 \\beta\\,,\\qquad \n\\phi_s \\equiv {\\rm arg}[(V_{ts}^* V_{tb})^2] = -2 \\lambda\n\\left|\\frac{V_{ub}}{V_{cb}}\\right| \\sin\\gamma\\,.\n\\label{basics_eq12}\n\\end{equation}\nBesides mixing-induced CP violation there also exists \\textit{direct} and \\textit{indirect} CP violation for $B$ and $\\bar B$ decays to a common CP eigenstate $f$. The corresponding time-dependent CP asymmetry is given by\n\\begin{eqnarray}\nA_{CP}(t) &=& \\frac{\\Gamma(\\bar B^0_q(t)\\to f) - \\Gamma( B^0_q(t)\\to \\bar f)}{\\Gamma(\\bar B^0_q(t)\\to f) + \\Gamma( B^0_q(t)\\to \\bar f)} \\nonumber\\\\\n&=&\\underbrace{S(f)}_{\\rm indirect} \\sin(\\Delta m_q\\, t )-\\underbrace{C(f)}_{\\rm direct}\\cos(\\Delta m_q\\, t)\\,,\n\\label{basics_eq13}\n\\end{eqnarray}\nwhere we have neglected the width difference $\\Delta\\Gamma_q=2 {\\rm Re}\\left[M^q_{12} \\Gamma^{q*}_{12}\\right]\/|M^q_{12}|$. The oscillation frequency is set by the mass difference between the heavy and light states\n\\begin{equation}\n\\Delta m_q = m_H^q-m_L^q=2|M_{12}^{q}|\\,,\n\\label{basics_eq14}\n\\end{equation}\nand the current world averages are \\cite{Barberio:2007cr}: \n\\begin{equation}\n\\Delta m_d =0.507\\pm 0.004 \\,{\\rm ps}^{-1}\\,,\\qquad\\Delta m_s=17.77\\pm \\overbrace{0.10}^{stat.}\\pm \\overbrace{0.07}^{sys.} {\\rm ps}^{-1}\\,.\n\\label{basics_eq15}\n\\end{equation}\nFinally, if we define the observable quantity \n\\begin{equation}\n\\lambda_f =\\frac{q}{p} \\frac{\\bar A}{A}\\,,\n\\label{basics_eq16}\n\\end{equation}\nwhere $A$ denotes the decay amplitude, then the two CP asymmetries can be written as\n\\begin{equation}\nC(f)=\\frac{1-|\\lambda_f|^2}{1+|\\lambda_f|^2}\\,,\\qquad S(f)=\\frac{2 \\,{\\rm Im}\\left[ \\lambda_f\\right]}{1+|\\lambda_f|^2}\\,.\n\\label{basics_eq17}\n\\end{equation}\n\n\n\\section{Effective Field Theories Of Weak Decays}\\label{basics_eftowd}\nA very widely used tool in the theoretical description of $B$ decay processes is the framework of \\textit{effective field theories} \\cite{Gilman:1979bc,Buras:1998ra}. The framework simplifies the dynamics of the weak decay by relying on an \\textit{operator product expansion} (OPE) \\cite{Wilson:1969zs} of the weak vertices to separate the short and long distance physics. The OPE yields a concise \\textit{effective Hamiltonian} $\\mathcal{H}^{eff}$ built from a set of local effective operators $Q_i$ multiplied by renormalisation-scale dependent perturbatively calculable \\textit{Wilson coefficient functions} $C_i(\\mu)$:\n\\begin{equation}\n\\left<\\mathcal{H}\\right> \\stackrel{\\rm OPE}{\\longrightarrow}\\left<\\mathcal{H}^{eff}\\right> \\sim \\sum_i C_i(\\mu) \\left+\\mathcal{O}(k^2\/M_W^2)\\,,\\\n\\label{basics_eq18}\n\\end{equation}\nwhere $k$ is the momentum flowing through the $W$ boson propagator. The separation of energy scales stems naturally from the fact that the weak decay of the $B$ meson is governed by physics originating at well separated scales: $m_t,\\,M_W\\gg m_{b,c}\\gg \\Lambda_{\\rm QCD} \\gg m_{u,d,s}$. It is the interplay of weak and strong effects that complicates the treatment of these decays, and must be dealt with appropriately. By taking into account radiative corrections to tree-level and penguin diagrams, ultimately one obtains the effective Hamiltonian in terms of the set of all relevant local operators, which is closed under renormalisation. The full $\\Delta B=1$ effective Hamiltonian is, for a final state containing a $D$ quark\n\\begin{equation}\n\\mathcal{H}^{eff}=\\frac{G_f}{\\sqrt{2}}\\sum_{U=u,\\,c}\\lambda_U^{(D)} \\left[C_1 Q_1^U+C_2 Q_2^U+C_{7\\gamma} Q_{7\\gamma}+C_{8g} Q_{8g}+\\sum_{i=3,\\dots,10} C_i Q_i\\right]\\,,\n\\label{basics_eq20}\n\\end{equation}\nwhere make use of the standard short-hand notation for the product of CKM matrix elements $\\lambda_U^{(D)}\\equiv V^*_{UD} V_{Ub}$. The form of Eq.~(\\ref{basics_eq20}) is chosen by assuming the unitarity of the CKM matrix (\\ref{basics_eq8}) to explicitly remove the dependence of the top quark CKM matrix elements which originate from penguin loops. The effective operators are\n\\begin{eqnarray}\n\\lefteqn{\\bf{Current-Current\\footnote{The literature is not consistent concerning the labelling of the two operators $Q_{1,2}$ and one should be aware that the practice of swapping of these two operators is commonplace. We use the convention that the larger Wilson coefficient belongs to $Q_2$; that is, $Q_1$ is the new operator.}:}}\\hspace{4cm}\\nonumber\\\\\nQ^U_1 &=& (\\bar D_i U_j)_{V-A}(\\bar U_j b_i)_{V-A}\\,,\n\\qquad Q^U_2 = (\\bar DU)_{V-A}(\\bar Ub)_{V-A}\\,,\\nonumber\\\\\n\\lefteqn{\\rm\\bf{QCD~Penguin:}}\\hspace{4cm}\\nonumber\\\\\nQ_3 &=& (\\bar Db)_{V-A} \\sum_q (\\bar qq)_{V-A}\\,,\n\\qquad Q_4 = (\\bar D_i b_j)_{V-A} \\sum_q (\\bar q_j q_i)_{V-A}\\,,\\nonumber\\\\\nQ_5 &=& (\\bar Db)_{V-A} \\sum_q (\\bar qq)_{V+A}\\,, \n\\qquad Q_6 = (\\bar D_i b_j)_{V-A} \\sum_q (\\bar q_j q_i)_{V+A}\\,,\\nonumber\\\\\n\\lefteqn{\\rm\\bf{Electroweak~Penguin:}}\\hspace{4cm}\\nonumber\\\\\nQ_7 &=& (\\bar Db)_{V-A} \\sum_q \\frac{3}{2} e_q (\\bar qq)_{V+A}\\,,\n\\qquad Q_8 = (\\bar D_i b_j)_{V-A} \\sum_q \\frac{3}{2} e_q (\\bar q_j q_i)_{V+A}\\,,\\nonumber\\\\\nQ_9 &=& (\\bar Db)_{V-A} \\sum_q \\frac{3}{2} e_q (\\bar qq)_{V-A}\\,, \n\\qquad Q_{10} = (\\bar D_i b_j)_{V-A} \\sum_q \\frac{3}{2} e_q (\\bar q_j q_i)_{V-A}\\,,\\nonumber\\\\\n\\lefteqn{\\rm\\bf{Electromagnetic~Dipole:}}\\hspace{4cm}\\nonumber\\\\\nQ_{7\\gamma} &=& \\frac{e}{8\\pi^2}m_b\\, \n \\bar D\\sigma^{\\mu\\nu}(1+\\gamma_5)F_{\\mu\\nu}\\,b\n + \\frac{e}{8\\pi^2}m_D\\, \n \\bar D\\sigma^{\\mu\\nu}(1-\\gamma_5)F_{\\mu\\nu}\\,b\\,, \\nonumber\\\\\n\\lefteqn{\\rm\\bf{Chromomagnetic~Dipole:}}\\hspace{4cm}\\nonumber\\\\\nQ_{8g} &=& \\frac{g_s}{8\\pi^2}m_b\\, \n \\bar D\\sigma^{\\mu\\nu}(1+\\gamma_5)G_{\\mu\\nu}\\, b\n + \\frac{g_s}{8\\pi^2}m_D\\, \n \\bar D\\sigma^{\\mu\\nu}(1-\\gamma_5)G_{\\mu\\nu}\\, b\\,,\n \\label{basics_eq21}\n\\end{eqnarray}\nwhere $e_q$ is the electric charge of the quark $q$ in units of $|e|$ and $F_{\\mu\\nu}$ is the photonic field strength tensor. The Wilson coefficients entering the effective Hamiltonian are essentially effective coupling constants of the local effective operators. One can view the renormalisation of the matrix elements as an equivalent renormalisation of their Wilson coefficients. One makes use of renormalisation-group techniques to sum the potentially large logarithms $\\sim \\ln M_W^2\/\\mu^2$ that appear naturally in the evolution from weak scales $\\mathcal{O}(M_W)$ to hadronic scales, such as $\\mu\\sim m_b$. The operators (\\ref{basics_eq21}) mix with each other under evolution and from the renormalisation-scale invariance of $\\mathcal{H}^{eff}$ one finds\n\\begin{equation}\n\\mu \\frac{d}{d \\mu} C_i (\\mu)=\\gamma_{ji}(\\mu)\\, C_j(\\mu)\\,, \n\\label{basics_eq22}\n\\end{equation}\nwhere $\\hat \\gamma$ is the \\textit{anomalous dimension} matrix, which can be given as an expansion in the strong coupling via the renormalisation constant $\\hat Z$\n\\begin{equation}\n\\gamma_{ji}(\\mu)= Z^{-1}_{ik}\\frac{d Z_{kj}}{d \\ln \\mu}\\,,\\qquad \\hat{\\gamma}=\\left(\\frac{\\alpha_s}{4\\pi}\\right)\\hat{\\gamma}^{(0)}+\\left(\\frac{\\alpha_s}{4\\pi}\\right)^2\\hat{\\gamma}^{(1)}+\\mathcal{O}(\\alpha_s^3)\\,.\n\\label{basics_eq23}\n\\end{equation}\nSolving Eq.~(\\ref{basics_eq22}) yields the evolution of the Wilson coefficients via the evolution matrix $\\hat U(\\mu,\\mu_0)$\n\\begin{equation}\n C_i(\\mu)= U_{ij}(\\mu,\\mu_0)\\, C_j(\\mu_0)\\,,\\qquad\\hat{U}(\\mu,\\mu_0)=\\exp \\int^{g(\\mu)}_{g(\\mu_0)}dg^\\prime\\,\\frac{\\hat{\\gamma}^{T}(g^\\prime)}{\\beta(g^\\prime)}\\,,\n\\label{basics_eq24}\n\\end{equation}\nwhere $\\beta(g)$ is the QCD $\\beta$-function. To leading order one has\n\\begin{equation}\n\\hat{U}^{\\rm LO}(\\mu,\\mu_0)=\\left(\\frac{\\alpha_s(\\mu_0)}{\\alpha_s(\\mu)}\\right)^{\\frac{\\hat{\\gamma}^{(0)T}}{2 \\beta_0}}= \\hat V\\left[\\left(\\frac{\\alpha_s(\\mu_0)}{\\alpha_s(\\mu)}\\right)^{\\frac{\\overrightarrow{\\gamma}^{\\left(0\\right)}}{2 \\beta_0}}\\right]_D\\hat V^{-1}\\,,\n\\label{basics_eq25}\n\\end{equation}\nwhere $V$ is the matrix that diagonalises $\\hat{\\gamma}^{(0)T}$ and $\\overrightarrow{\\gamma}^{\\left(0\\right)}$ is a vector of the eigenvalues of the leading order anomalous dimension matrix $\\hat{\\gamma}^{(0)}=\\hat V\\hat{\\gamma}^{(0)T}_D\\hat V^{-1}$. At NLO we have\n\\begin{equation}\nC_i(\\mu)=C_i^{(0)}(\\mu)+\\frac{\\alpha_s(\\mu)}{4\\pi}C_i^{(1)}(\\mu)\\,,\n\\label{basics_eq26}\n\\end{equation}\nand the evolution is a bit more complicated:\n\\begin{equation}\n\\hat{U}^{\\rm NLO}(\\mu,\\mu_0)=\\left[1+\\frac{\\alpha_s(\\mu)}{4\\pi}\\hat{J}\\right]\\hat{U}^{\\rm LO}(\\mu,\\mu_0)\\left[1-\\frac{\\alpha_s(\\mu_0)}{4\\pi}\\hat{J}\\right]\\,,\n\\label{basics_eq27}\n\\end{equation}\nwith\n\\begin{equation}\n\\hat{J}=V \\hat{S}V^{-1}\\,,\\qquad S_{ij}=\\delta_{ij}\\gamma_{i}^{(0)}\\frac{\\beta_1}{2\\beta_0^2}-\\frac{G_{ij}}{2\\beta_0+\\gamma_i^{(0)}-\\gamma_j^{(0)}}\\,,\\qquad\\hat{G}=V^{-1} \\hat{\\gamma}^{(1)T}V\\,.\n\\label{basics_eq28}\n\\end{equation}\nTo NLO the required $\\beta$-function coefficients are $\\beta_1=\\frac{34}{3}N_c^2-\\frac{10}{3}N_c N_f -2 C_F N_f$ and $\\beta_0=\\frac{11}{3}N_c -\\frac{2}{3} N_f$ with $N_f$ is the number of active flavours, $C_F=(N_c^2-1)\/(2N_c)$ and $N_c$ the number of colours. Care must be taken in evolving through ``thresholds'' where the number of active flavours $N_f$ changes; the evolution must then be taken in stages, as a change in $N_f$ changes the $\\beta$-function coefficients and the anomalous dimension matrices. If there is a flavour threshold $\\mu_{\\rm th}$ between $\\mu_0$ and $\\mu$, which changes the number of active flavours from $N_f$ to $N_f+1$, then one has to make the replacement\n\\begin{equation}\n\\hat U(\\mu,\\mu_0)\\to \\left.\\hat U(\\mu,\\mu_{\\rm th})\\right|_{N_f +1}\\cdot \\left.\\hat U(\\mu_{\\rm th},\\mu_0) \\right|_{N_f}.\n\\label{basics_eq29}\n\\end{equation} \nThe effective Hamiltonian, combined with the renormalisation-group improvement of the perturbative series forms an exceptionally powerful framework. The matrix elements of the local operators $\\left$ are the subject of QCD factorisation theorems, such as that discussed in Chapter~\\ref{chapter6_QCDF}, which allow the calculation of $B$ decay amplitudes. From these amplitudes one can construct observables such as branching fractions, CP asymmetries, and isospin asymmetries which can be investigated phenomenologically.\n\\chapter{Vector Meson Light-Cone Distribution Amplitudes}\\label{chapter2_DAs}\nIn this chapter we discuss light vector meson light-cone distribution amplitudes and via the (approximate) conformal symmetry of QCD present expressions for the distribution amplitudes up to twist-3. The method introduces a set of non-perturbative parameters which is reduced in size by invoking the QCD equations of motion to relate the two-particle twist-3 distribution amplitudes to the three-particle twist-3 and two-particle twist-2 distribution amplitudes. In our analysis we include all $\\rm SU(3)_F$-breaking effects and G-parity violating terms thus allowing one to fully differentiate between $\\rho$, $K^*$ and $\\phi$ mesons. Moreover, a non-zero quark mass induces a mixing between twist-2 and twist-3 parameters under a change of renormalisation scale $\\mu$. To simplify notation we explicitly consider the $K^*$ meson, with quark composition $s\\bar{q}$ where $q=\\{u,d\\}$.\\footnote{The notation in this thesis, $K^*$ being a $(s\\bar q)$ bound state, is in contrast to the standard labelling, according to which $K^{*0}=(d\\bar s)$ and $\\bar K^{*0}=(s\\bar d)$. This is the standard notation used for light-cone distribution amplitudes where $K^*$ always contains an $s$ quark, and $\\bar K^*$ an $\\bar s$ quark. This distinction is relevant because of a sign change of G-odd matrix elements under $(s\\bar q)\\leftrightarrow (q\\bar s)$. This notation also applies to calculations of form factors and other matrix elements which involve light-cone distribution amplitudes.}\n\nThere are two main applications of meson distribution amplitudes that motivate their study:\n\\begin{itemize}\n\\item{they are directly applicable to the theoretical description of exclusive decay processes via QCD factorisation theorems, which require the distribution amplitudes as a non-perturbative input, see Chapter~\\ref{chapter6_QCDF}.}\n\\item{they are also applicable to the determination of transition form factors from the light-cone sum rule approach and as such are indirectly applicable to the same QCD factorisation theorems for which the transition form factors are also required, see Chapters~\\ref{chapter3_SR} and \\ref{chapter5_eta}.}\n\\end{itemize}\nIn Chapter~\\ref{chapter4_det} we calculate, from QCD sum rules, numerical values for the leading twist-2 and twist-3 distribution amplitude parameters defined here. Standard notations used, such as the light-cone coordinates, are given in Appendix~\\ref{appendixA}. The material covered in this chapter partially follows that of Ref.~\\cite{Ball:2007rt}.\n\n\\section{Introduction}\nHadronic light-cone distribution amplitudes (DAs) of light mesons were first discussed in the ground-breaking papers of Brodsky, Lepage, and others, see Refs.~\\cite{Chernyak:1977fk, Chernyak:1980dk, Lepage:1980fj, Efremov:1979qk, Efremov:1978rn,Chernyak:1977as, Lepage:1979zb,Chernyak:1980dj} and play an essential role in the QCD description of hard exclusive processes \\cite{Chernyak:1981zz,Brodsky:1989pv}. The amplitudes that describe such processes factorise in the asymptotic limit $Q^2\\sim 1\/x^2 \\to \\infty$ -- where $Q^2$ is the momentum transfer and $x$ the transverse separation of the partons -- and are dominated by contributions from near the light-cone. The factorisation is given by the convolution of a hard-scattering kernel, calculable in perturbation theory, and process-independent, universal, non-perturbative DAs. \n\nThe study of hadronic DAs has a long history. The simplest and first to be investigated were the twist-2 DA of the $\\pi$ \\cite{Lepage:1980fj,Efremov:1979qk,Chernyak:1977as,Lepage:1979zb}. Higher twist DAs of the $\\pi$, alongside those of the other pseudoscalar mesons followed \\cite{Ball:1998je}. For vector mesons, the leading-twist DAs of the $\\rho$ were first investigated by Chernyak and Zhitnitsky in Ref.~\\cite{Chernyak:1983ej} and later in Refs.~\\cite{Ali:1993vd, Ball:1996tb}. The formalism of higher twist-3 and twist-4 contributions, including meson mass corrections, was investigated by Ball \\textit{et al.} in Refs.~\\cite{Ball:1998sk,Ball:1998ff,Ball:2006wn,Ball:2007zt}. \n\nThe DAs of the $K^*$ ($K$) differ to those of the $\\rho$ ($\\pi$) due to the non-zero strange quark mass which yields $\\rm SU(3)_F$-breaking and G-parity violating corrections from a number of different sources.\\footnote{Perfect $\\rm SU(3)_F$ symmetry is realised for equal $u,d,$ and $s$ quark masses.} The study of the various contributions span many publications:\n\\begin{itemize}\n\\item{explicit quark mass corrections to DAs and evolution equations are generated by the QCD equations of motion (EOM) and only affect higher twist DAs. The contributions for vector mesons were calculated in Ref.~\\cite{Ball:1998sk} up to twist-3, and those to the evolution equations for vector mesons in Ref.~\\cite{Ball:2007rt} and flavour-octet pseudoscalar mesons Ref.~\\cite{Ball:2006wn}.}\n\\item{G-parity violating contributions, which are proportional to $m_s-m_q$ and hence vanish for equal quark masses, i.e. for $\\rho$ and $\\phi$, were investigated for twist-2 DAs in Refs.~\\cite{Chernyak:1983ej,Ball:1998sk,Ball:2003sc,Braun:2004vf,Ball:2005vx,Ball:2006fz} and for twist-3 DAs in Ref.~\\cite{Ball:2007rt}.}\n\\item{$\\rm SU(3)_F$-breaking of non-perturbative hadronic parameters entering the DAs.\nThe effects for the twist-2 parameters are known from\nRefs.~\\cite{Chernyak:1983ej,Ball:1998sk,Ball:2003sc}, twist-3 from Ref.~\\cite{Ball:2007rt} and twist-4 from Ref.~\\cite{Ball:2007zt}. The twist-3 vector meson parameters are discussed in Chapter~\\ref{chapter4_det} where we include all these effects in a determination of numerical values using QCD sum rules.}\n\\end{itemize}\nThe objects which define the DAs are vacuum-to-meson matrix elements of non-local operators at strictly light-like separations $z^2=0$ \\cite{Chernyak:1983ej}. Two examples we shall encounter are\n\\begin{eqnarray}\n\\bra{0}\\bar{q}(z)\\Gamma [z,-z] s(-z) \\ket{K^*(p,\\lambda)}\\,,\\qquad \\bra{0}\\bar{q}(z) [z,v z] g_s G_{\\mu \\nu}(vz)\\Gamma [v z,-z] s(-z) \\ket{K^*(p,\\lambda)}\\,,\\nonumber\\\\\n\\label{das_eq1}\n\\end{eqnarray}\nwhere $\\Gamma$ is a general Dirac matrix, $\\lambda=\\{\\parallel,\\perp\\}$ is the polarisation of the $K^*$ meson and the quark fields are taken at symmetric separation for simplicity.\\footnote{The Dirac matrices $\\Gamma =\\{ \\sigma_{\\mu \\nu},\\,i \\gamma_5,\\, \\bf 1\\}$ give rise to so-called \\textit{chiral-odd} distributions because they are chirality-violating. Likewise, distributions generated from $\\Gamma =\\{ \\gamma_{\\mu},\\,\\gamma_{\\mu} \\gamma_5\\}$ are \\textit{chiral-even}.} The first (second) matrix element above corresponds to a two- (three-) particle Fock state. To render the matrix element gauge invariant the path-ordered gauge factor is included\n\\begin{equation}\n[x,y]=\\textrm{P}\\, \\exp \\left[ i g_s \\int^1_0 dt\\, (x-y)_\\mu A^\\mu (t x+(1-t)y)\\right].\n\\end{equation}\nFor convenience we work in the \\textit{fixed-point} gauge\\footnote{also known as the \\textit{Fock-Schwinger} gauge.}\n\\begin{equation}\n(x-x_0)^\\mu A_\\mu^a (x)=0\\,,\n\\label{das_eq2}\n\\end{equation}\nand by choosing $x_0=0$ we have $[x,-x]=1$. The gauge factor will be implied unless otherwise stated. The DAs are dimensionless functions of the collinear momentum fractions of a fixed number of constituents within a meson, at zero transverse separation. For two-particle DAs the constituent strange quark and antiquark ($\\bar{q}$) share $u$ and $\\bar{u}=1-u$ of the meson momentum $p$ respectively. For three-particle DAs we have $\\underline{\\alpha} = (\\alpha_1, \\alpha_2, \\alpha_3 )$ corresponding to the momentum fractions carried by the strange quark, antiquark ($\\bar{q}$) and gluon, respectively. For a minimum number of constituents, the DAs are related to the \\textit{Bethe-Salpeter wavefunction} $\\phi_{BS}$ by integration over the transverse momenta\n\\begin{equation}\n\\phi(u, \\mu) \\sim \\int^{|k_{\\perp}|<\\mu}d^2 k_{\\perp}\\,\\phi_{BS}(u,k_{\\perp})\\,,\n\\label{das_eq3}\n\\end{equation}\nwhere $\\mu$ is the renormalisation scale. The price to pay for integrating out $k_{\\perp}$ below $\\mu$ is a renormalisation-scale dependence of the DAs governed by renormalisation-group equations. The DAs have to be evaluated at the scale $\\mu^2 \\sim x^{-2}$ i.e. of the order of the deviation from the light-cone \\cite{Balitsky:1987bk}.\n\nNon-local operators that appear at finite $Q^2$ or mass scales are expanded near the light-cone $x^2 \\neq 0$ as an OPE in terms of the renormalised non-local operators on the light-cone - the \\textit{light-cone expansion} \\cite{Balitsky:1987bk}.\\footnote{The expansion is facilitated by using light-cone coordinates which are given in Appendix~\\ref{appendixA}.} After taking matrix elements the resulting Lorentz-invariant amplitudes are matched to the definitions of the DAs with the coefficient functions of the expansion taken at tree-level, to leading logarithmic accuracy. \n\nThe structure of vector meson DAs follows the same pattern as the nucleon structure functions and can be classified in the same way \\cite{Jaffe:1991ra}. They are described by separate DAs for each polarisation and thus there are more vector meson DAs than pseudoscalar DAs. \n\nLastly, we briefly mention some other DAs. Flavour-singlet pseudoscalar meson DAs\nare complicated by the $\\rm U(1)_A$ anomaly of QCD and are discussed in Chapter~\\ref{chapter5_eta} in the context of the $B \\to \\eta^{(\\prime)}$ transition form factor \\cite{Ball:2007hb}. Much work has been done concerning the DAs of heavy mesons, such as the $B$ meson \\cite{Szczepaniak:1990dt ,Braun:2003wx}; indeed, the DAs of $B$ mesons enter the QCD factorisation framework of radiative and non-leptonic $B$ decays, as discussed in Chapter~\\ref{chapter6_QCDF}, and a variant light-cone sum rule method devised in Ref.~\\cite{Khodjamirian:2006st}. There also exist DAs of the photon which describe its ``soft'' hadronic components, along with the usual ``hard'' electromagnetic components \\cite{Ball:2002ps}. The photonic DAs can be important in, for example, $B \\to V \\gamma$ decays \\cite{Ball:2006eu} as investigated in Chapter~\\ref{chapter7_rad}, and $B \\to \\gamma e \\nu$ \\cite{Descotes-Genon:2002mw,Ball:2003fq}. Finally, the field of baryon DAs is also active and many of the tools and concepts we cover in this thesis find application there, see for example Ref.~\\cite{Braun:2006hn} for a review.\n\n\\section{The Conformal Expansion}\nThe standard determination of meson DAs proceeds by making use of the conformal symmetry of massless QCD at tree-level. The conformal expansion is analogous to the partial wave expansion of wave functions in quantum mechanics in spherical harmonics $\\psi(r,\\theta,\\phi) \\to R(r) \\sum_{m,l} Y^l_m (\\theta,\\phi)$. The expansion uncovers a simple multiplicative renormalisation at leading-order, and as such different partial waves, with different \\textit{conformal spin}, do not mix under a change of renormalisation scale. At next-to-leading-order this is not the case, because strictly speaking the conformal symmetry of a quantum theory requires its $\\beta$ function to vanish. Proximity to the conformal limit in QCD is therefore governed by the value of the strong coupling constant, becoming true as $\\alpha_s \\to 0$ and we pass to the free theory.\\footnote{It must be noted that mass terms break the conformal expansion immediately at the classical level. This does not upset the conformal expansion, however. See Ref.~\\cite{Braun:2003rp} for details.} Using the QCD equations of motion we can elucidate this mixing order-by-order in the conformal expansion. \n\nThe application of conformal symmetry to exclusive processes has recieved a lot of attention in the literature, see Refs.~\\cite{Brodsky:1980ny,Brodsky:1985ve,Ohrndorf:1981qv,Braun:1989iv,Makeenko:1980bh}. The main benefit of the conformal expansion is the systematic separation of the longitudinal and transverse degrees of freedom in meson DAs. The former correspond to the longitudinal momentum fractions and is given by irreducible representations of the relevant symmetry group, SL(2,$\\mathbb R$). The latter are integrated out to yield a renormalisation-scale dependence of the DAs, described by renormalisation-group equations. Here we focus on the most important points, see Ref.~\\cite{Braun:2003rp} for a detailed review. \n\n\\subsection{Conformal Group}\nThe conformal group is defined as all transformations that change only the scale of the metric and as such preserve angles and leave the light-cone invariant $g_{\\mu\\nu}^\\prime(x^\\prime)=\\omega(x) g_{\\mu\\nu}(x)$; the spacetime interval $ds^2=g_{\\mu\\nu}(x) \\,dx_\\mu dx_\\nu$ is conserved up to scaling. These transformations form a generalisation of the Poincar\\'e group. The full conformal algebra in 4 dimensions includes fifteen generators\n\\begin{eqnarray}\n\\textbf{P}_\\mu &\\to& \\rm 4~ Translations, \\nonumber\\\\\n\\textbf{M}_{\\mu \\nu}&\\to& \\rm 6 ~Lorentz ~rotations, \\nonumber\\\\\n\\textbf{D} &\\to& \\rm 1 ~Dilatation,\\nonumber\\\\\n\\textbf{K}_\\mu &\\to& \\rm 4 ~Special ~conformal ~translations.\n\\label{das_eq4}\n\\end{eqnarray}\nOur hadronic picture is of partons moving collinearly in, say the $p_{\\mu}$ direction, existing near the light-cone. We therefore restrict the \\textit{fundamental fields} of the conformal group to the light-cone\n$\\Phi(x) \\to \\Phi(\\alpha z)$, where $\\alpha $ is a real number, and we assume fields to be eigenstates of the spin operator \n\\begin{equation}\n\\Sigma^{\\mu\\nu} \\psi = \\frac{i}{2}\\sigma^{\\mu\\nu} \\psi\\,,\n\\label{das_eq5}\n\\end{equation}\nso as to have a fixed Lorentz-spin projection $s$ in the $z_{\\mu}$ (``plus'') direction $\\Sigma_{+-} \\Phi(\\alpha z) = s \\,\\Phi(\\alpha z)$. For leading-twist operators this is automatically satisfied and for higher-twist operators projections are used to separate different spin states, as we shall discuss shortly. The full conformal symmetry (\\ref{das_eq4}) is now modified and it turns out that the resulting group of transformations form the special linear group SL(2,$\\mathbb R$), or so-called \\textit{collinear conformal group}, given by just four generators. They are written in standard form by constructing the following linear combinations\n\\begin{eqnarray}\n\\textbf{L}_+= \\textbf{L}_1+i \\textbf{L}_2=-i \\textbf{P}_+\\,,& \\qquad& \\textbf{L}_-= \\textbf{L}_1-i \\textbf{L}_2= \\frac{i}{2} \\textbf{K}_-\\,,\\nonumber\\\\\n\\textbf{L}_0= \\frac{i}{2}(\\textbf{D}+\\textbf{M}_{+-})\\,,& \\qquad& \\textbf{E}=\\frac{i}{2}(\\textbf{D}-\\textbf{M}_{+-})\\,.\n\\end{eqnarray}\nwhich leads to the familiar relations\n\\begin{equation}\n[\\textbf{L}_0,\\,\\textbf{L}_\\mp]=\\mp \\textbf{L}_\\mp\\,, \\qquad [\\textbf{L}_-,\\,\\textbf{L}_+]=-2 \\textbf{L}_0\\,.\n\\end{equation}\nThe operators act on the fundamental fields as\n\\begin{eqnarray}\n\\left[\\textbf{L}_+,\\Phi(\\alpha z) \\right]&=&-\\partial_{\\alpha} \\Phi(\\alpha n)\\,,\n\\label{das_eq6}\\\\\n\\left[\\textbf{L}_{-},\\Phi(\\alpha z)\\right]&=&(\\alpha^2 \\partial_{\\alpha} +2 j \\alpha)\\Phi(\\alpha n)\\,,\n\\label{lower}\\\\\n\\left[\\textbf{L}_0,\\Phi(\\alpha z) \\right]&=&(\\alpha \\partial_{\\alpha}+j) \\Phi(\\alpha n)\\,,\n\\label{das_eq7}\\\\\n\\left[\\textbf{E},\\Phi(\\alpha z)\\right]&=&\\frac{1}{2} (l-s) \\Phi(\\alpha n)\\,,\n\\label{das_eq8}\n\\end{eqnarray}\nwhere $t=l-s$ is the \\textit{twist},\\footnote{strictly it is the \\textit{collinear twist} which is defined as ``dimension minus spin projection on the positive direction''. There also exists \\textit{geometric twist} which is defined as ``dimension minus spin''.} $l$ is the canonical mass dimension,\\footnote{For example, $l=3\/2$ for quarks and $l=2$ for gluons.} $s$ the Lorentz-spin projection, and $j=\\frac{1}{2}(l+s)$ the \\textit{conformal spin} of the field $\\Phi$. The conformal spin specifies the representation of the collinear conformal group. The operator $\\textbf{E}$ commutes with all $\\textbf{L}_i$ and therefore twist is a good quantum number for each conformal field. The Casimir operator commutes with all $\\textbf{L}_i$ and is given by\n\\begin{equation}\n\\sum_{i=0,1,2}[\\textbf{L}_i,[\\textbf{L}_i,\\Phi(\\alpha z)]]=j(j-1)\\Phi(\\alpha z) = \\textbf{L}^2 \\Phi(\\alpha z)\\,.\n\\end{equation}\nAt the origin of the light-cone $\\alpha=0$ and the field $\\Phi(0)$ is killed by the lowering operator $\\textbf{L}_-$ and as such has the minimum spin projection $j_{\\rm min}$ of states of conformal spin $j$. One can define a \\textit{conformal operator} $\\mathbb{O}_n=\\Phi(0)$ by requiring that it transforms just as the fundamental field, Eqs.~(\\ref{lower} - \\ref{das_eq8}), and is killed by the lowering operator $\\textbf{L}_-$. The raising operator $\\textbf{L}_+$ can be repeatedly applied to $\\Phi(0)$ to give\n\\begin{equation}\n\\mathbb{O}_{n,n+k}=\\underbrace{\\left[ \\textbf{L}_+,...,\\left[\\textbf{L}_+,\\left[\\textbf{L}_+\\right.\\right.\\right.}_{k},\\left.\\left.\\left.\\Phi(0)\\right]\\right]\\right]=\\left(-i \\partial_+\\right)^k \\mathbb{O}_n\\,,\n\\end{equation}\nwhere $\\mathbb{O}_{n,n}=\\mathbb{O}_n$ and the subscript $n$ defines the \\textit{conformal tower} of states, of conformal spin $j_{\\textrm{min}} \\equiv \\Pi^{\\rm{OPE}}\\,,\n\\label{sr_eq4}\n\\end{equation}\nwhere the non-perturbative long distance effects of QCD are encoded in the condensates $\\left< O_i\\right>$ and the short-distance effects are included in the Wilson coefficient functions $C_i$ which are calculable in perturbation theory. Both the condensates and their coefficients are in general renormalisation scale dependent. Perturbative corrections to the condensates are calculated when necessary. The perturbation theory contribution to Eq.~(\\ref{sr_eq4}) has $D=0$ and corresponds to the unit operator $\\left=\\bf{1}$. The condensates play the role of power-corrections and are suppressed by inverse powers of the hard scale as $(Q^2)^{-D\/2}$. In the asymptotic limit $Q^2\\to\\infty$ only the unit operator survives, corresponding to asymptotic freedom. \n\n\\subsection{Condensates}\nThe condensates represent the effects of non-perturbative QCD and they cannot be determined from first principles due to the unknown nature of the QCD vacuum. The determination of the condensates is an industry in itself. The light quark condensate $\\bra{0} \\bar{q} q\\ket{0}$ has been known for a long time \\cite{Gell-Mann:1968rz} and it drives the breakdown of the chiral symmetry of the light quarks $q=\\{u,d\\}$ and its value can be extracted from experiment:\n\\begin{equation}\nm_\\pi^2 f_\\pi^2 \\approx-(m_u+m_d)\\left<\\bar q q\\right>\\,,\n\\end{equation}\nwhere we use the notation $\\left\\equiv\\bra{0}O_i\\ket{0}$. To define other condensates, one notes that the only vacuum expectation values of operators that can survive are those which are Lorentz invariant, spin zero, colour and flavour-singlets i.e. possess the quantum numbers of the vacuum. The complete set of condensates $\\left$ that contribute with $D\\le 6$ are\n\\begin{eqnarray}\n\\underbrace{\\left<\\textbf{1}\\right>}_{D=0}\\,, \\hspace{1cm}&\\underbrace{m_q\\left< \\bar{q} q\\right>}_{D=4}\\,,& \\hspace{1cm} \\underbrace{\\left<\\frac{\\alpha_s}{\\pi}G^2\\right>}_{D=4}\\,,\\nonumber \\\\\n\\underbrace{m_q\\left< \\bar{q}\\sigma g_s G q \\right>}_{D=6}\\,, \\hspace{1cm}&\\underbrace{\\left<\\bar{q} \\Gamma_1q\\,\\bar{q}\\Gamma_2 q\\right>}_{D=6}\\,,&\\hspace{1cm} \\underbrace{\\left}_{D=6}\\,,\\nonumber\n\\end{eqnarray}\nwhere $q=\\{u,d,s\\}$ is a light quark spinor and all indices are contracted.\\footnote{The heavy quarks $c$, $b$ and $t$ do not form condensates because they are too massive to interact non-perturbatively with the QCD vacuum.} We assume isospin symmetry for $q=\\{u,d\\}$ and one must differentiate $q=s$ when $\\rm SU(3)_F$-breaking effects are taken into account. Higher dimensional condensates $D> 6$ are not very well determined and generally unknown. If required, however, they can be estimated by employing the \\textit{vacuum saturation hypothesis} whereby the operator fields are simply split to form products of known condensates; for example, the quark-antiquark $D=6$ operator can be simplified to the product of two $\\bar{q} q$ operators \\cite{PT:84,Shifman:1978by}. In practice, the OPE is truncated to a given order, and is usually justified by the stability of the resulting sum rule. The series, Eq.~(\\ref{sr_eq4}), is then given in terms of a limited number of condensates allowing sum rules to be written in terms of a small set of parameters incorporating the general features of non-perturbative QCD, while retaining its predictive power.\n\nThe procedure works in reverse, of course, where the values of condensates are deduced from sum rules for which the hadronic parameters are known from other methods; two-point correlation functions featuring $\\bar{b} \\gamma_\\mu b$ or $\\bar{c} \\gamma_\\mu c$ currents correspond to the $\\Upsilon$ and $J\/\\Psi$ resonances respectively, of which the decay constants and masses are known. Values for the condensates are given, along with other input parameters, in Appendix~\\ref{appendixB}. Uncertainties in the values of the condensates and other input parameters constitute part of the reducible theoretical uncertainty of the sum rule approach. \n\n\\subsection{Dispersion Relation}\nTo proceed we need to relate the result of the OPE to a second representation of the correlation function which is obtained in terms of the spectrum of hadronic states in the physical region $q^2>0$. This is done via a \\textit{dispersion relation}, which is derived from the analytic properties of the correlation function as follows. The function $\\Pi(q^2)$ is analytic in all $q^2$ except on the real axis starting at a pole corresponding to the ground state particle. At higher energy higher mass excited states and a continuum of many-particle states also feature. The higher mass resonances give poles above the ground state, the details of which depend on the physical spectrum of particles which possess the correct quantum numbers to couple to $\\Pi$. The continuum of many-particle states, correspond to a continuous cut, see Fig.~\\ref{sr_fig1}.\n\\begin{figure}[h]\n$$ \\epsfxsize=0.5\\textwidth\\epsffile{analytic.eps}$$\n \\caption[The spectral density function in the complex plane.]{\\small The general features of a spectral density function $\\rho^{\\rm had}(s)$ in the complex plane. The blob represents the pole due to the ground-state, the cross possible poles due to higher mass resonances, and the thick line the cut due to the continuum of multi-particle states. The dotted line is the integration contour.} \n \\label{sr_fig1}\n\\end{figure}\n\nUsing Cauchy's formula we can write\n\\begin{equation}\n\\Pi(q^2) =\\frac{1}{2 \\pi i} \\oint\\limits_{|z|=R} dz\\, \\frac{\\Pi(z)}{z-q^2}+\\frac{1}{2 \\pi i} \\int^R_0 dz\\, \\frac{\\Pi(z+i \\epsilon)-\\Pi(z- i \\epsilon)}{z-q^2}\\,,\n\\end{equation}\nwhere the region of integration is split into the parts just above and below the positive real axis and the circle of radius $R$. Provided that the correlation function vanishes at least as quickly as $q^{-2}$ as $|q^2| \\sim R \\to \\infty$ then the integral over the circle at radius $R$ goes to zero.\\footnote{If $\\Pi$ does not vanish quickly enough we subtract the first few terms in its Taylor expansion as required. We shall see that this does not matter in the end, due to the Borel transformation.} The remaining integral can be simplified using the fact that below the first pole at $q^2=s_{\\rm min}$, $\\Pi(q^2)$ is real and above this point, according to the Schwarz reflection principle, $\\Pi(z+i \\epsilon)-\\Pi(z- i \\epsilon)= 2 i\\, \\textrm{Im}\\,\\Pi(z+ i \\epsilon)$. Hence\n\\begin{equation}\\label{disp}\n\\Pi(q^2) = \\int_{s_{\\rm min}}^{\\infty} ds\\, \\frac{\\rho(s)}{s-q^2- i \\epsilon}\\,,\n\\end{equation}\nwhere the function $\\rho(s)=\\frac{1}{\\pi} \\textrm{Im}\\, \\Pi(s)$ is the \\textit{spectral density} and describes the physical particle spectrum as a function of energy $s$. \n\n\\subsection{Unitarity Relation}\nAs we have seen, for large negative $q^2$ our correlation function is dominated by short-distance physics. As $q^2$ becomes more positive the separation of the quarks increases. For large enough positive values of $q^2$ long-distance QCD interactions become more important and the correlation function then describes the creation of hadrons, which is the basis of its second representation. As discussed in the last section, $\\Pi$ uncovers a very complicated spectrum of states for $q^2>0$. We describe this situation by using the \\textit{unitarity relation}, which allows in insertion of a complete set of states into the correlation function\n\\begin{equation}\\label{complete}\n\\textbf{1}= \\sum_{n} \\int d \\Omega_n\\, \\ket{n(p)}\\bra{n(p)}\\,,\n\\end{equation}\nwhere $d \\Omega_n$ includes all phase-space factors and momentum conservation and the sum runs over all possible particles and polarisations, starting from the ground state $M$ of mass $m_M$. Inserting (\\ref{complete}) between the currents of our original correlation function (\\ref{sr_eq1}) yields an expression which we can relate to the hadronic spectral density\n\\begin{equation}\\label{unitarity}\n \\Pi^{\\rm had}(q^2) = \\int \\frac{d^4 p}{(2 \\pi)^4} \\frac{1}{m^2_{M}-p^2} \\int d^4 x \\,e^{i q \\cdot x} \\bra{0} J_{1}(x) \\ket{M(p)}\\bra{M(p)}J_{2}(0) \\ket{0} + \\dots\\,,\n\\end{equation}\nwhere the dots denote higher mass states which contribute to the continuum. We are usually interested in the ground state, and can insert the expressions for the matrix elements on the right hand side. The local matrix elements considered here can be used to extract vacuum-meson decay constants, for example. Using the unitarity relation (\\ref{unitarity}) one can single out the ground state $M$ by comparing it to (\\ref{disp}) and writing the hadronic spectral density as:\n\\begin{equation}\\label{gs}\n\\rho^{\\rm had}(s)=f_{M} \\,\\delta(s-m_{M}^2)+\\rho^{\\textrm{cont}}(s),\n\\end{equation}\nwhere $f_{M}$ is directly related to the matrix elements of the currents $J_1$ and $J_2$ in Eq.~(\\ref{unitarity}). For example, one could choose $J_1=J_2^\\dagger= \\bar q \\gamma_{z} s$ to extract $(f^\\parallel_{K^*})^2$ c.f. Eq.~(\\ref{das_eq22}). The exact form of the spectral density beyond the ground state is mostly unknown and the higher mass states and continuum contributions are usually lumped together in one function $\\rho^{\\textrm{cont}}(s)$. If the next highest particle above the ground state occurs at an energy not very much higher than $m_{M}$ then it is possible to explicitly include this particle as another delta-function term, analogously to the ground state. This procedure was used, for example, while investigating the leading-twist $K^*$ and $\\rho$ DA parameters for which the relevant correlators couple to the $K_1$ and $b_1$ resonances respectively \\cite{Ball:1996tb,Ball:2005vx}. \n\n\n\\subsection{Quark-Hadron Duality}\nIt is possible to write the result of the OPE as a dispersion relation, with spectral density $\\rho^{\\textrm{OPE}}(s)$. As $\\rho^{\\textrm{cont}}(s)$ is mostly unknown we replace it by $\\rho^{\\textrm{OPE}}(s)$ above a certain energy $s_0$\n\\begin{equation}\\label{qhd}\n\\rho^{\\textrm{cont}}(s) \\to \\rho^{\\textrm{OPE}}(s)\\,\\Theta(s-s_0)\\,.\n\\end{equation}\nThis assumption relies on the validity of the hadronic representation being approximated by the partonic representation at higher energies. Inserting Eqs.~(\\ref{gs}) and (\\ref{qhd}) into Eq.~(\\ref{disp}) one finds\n\\begin{equation}\n\\Pi^{\\rm had}(q^2)=\\frac{f_M}{m_M^2-q^2}+\\int_{s_{0}}^{\\infty} ds\\, \\frac{\\rho^{\\rm OPE}(s)}{s-q^2- i \\epsilon}\\,.\n\\end{equation}\nNow the assumption is not so strict because we only require a duality between the integrated spectral densities, not the spectral densities themselves. This is called \\textit{semi-global quark-hadron duality}. The parameter $s_0$ is called the \\textit{continuum threshold} and its value is specific for each particle spectrum being roughly equal to the energy of the next highest resonance above the ground state: $s_0 \\sim (m_M + \\Delta)^2$ where $\\Delta \\sim \\mathcal{O}(\\Lambda_{\\textrm{QCD}})$. Ultimately it must be determined from the sum rule itself by requiring the numerical value of the determined quantity to be largely insensitive to its variation and this introduces the first source of systematic uncertainty to the sum rule method. We are now in a position to equate both representations\n\\begin{equation}\n\\Pi^{\\rm{had}}=\\Pi^{\\rm{OPE}}\\,, \n\\end{equation}\nto derive our sought after sum rule, however, before we do so, there is one last procedure to discuss, which greatly improves the behaviour of the sum rule.\n\n\n\\subsection{Borel Transformation And The Sum Rule}\nThe sum rule can be improved by suppressing the continuum contribution, which we have assumed to be well described by $ \\rho^{\\textrm{OPE}}(s>s_0)$ and the possible detrimental impact of this assumption is thus reduced. We do this by performing a \\textit{Borel transformation} to both sides of the sum rule. The transformation is obtained by applying the operator\n\\begin{equation}\\label{borel1}\n\\mathcal{\\hat{B}} = \n\\lim_{\\stackrel{-q^2,n \\to \\infty}{-q^2\/n=M^2}}\\frac{(-q^2)^{(n+1)}}{n!}\\left(\\frac{d\\phantom{q^2}}{dq^2}\\right)^{n+1},\n\\end{equation}\nwhich takes a function of $q^2$ and produces a new function of the \\textit{Borel parameter} $M^2$. One frequently encountered example is\n\\begin{equation}\n\\mathcal{\\hat{B}} \\frac{1}{(m^2-q^2)^k}= \\frac{1}{(k-1)!}\\frac{e^{-m^2\/M^2}}{(M^2)^{k}}\\,,\n\\end{equation}\nproviding an exponential suppression of the unknown continuum contributions, and a suppression of the power-corrections by factorials thus reducing the impact of neglected higher dimensional condensates. Also, as $\\mathcal{\\hat{B}} (q^2)^k=0$, any subtraction terms introduced to Eq.~(\\ref{disp}), which can only appear as polynomials in $q^2$, are killed off. The Borel transformation improves the stability and accuracy of the sum rule.\n\nThe Borel parameter $M^2$ is the second and last sum rule specific parameter to be introduced; along with $s_0$ it is required to impact very little, when varied, on the numerical value of the quantity being determined. The variation of $M^2$ changes the relative impact of the power-corrections and perturbation theory contributions. In evaluating sum rules one looks for a \\textit{Borel window} which is usually in the range $1\\,\\rm GeV^2\\leqslant M^2\\leqslant 2 \\,\\rm GeV^2$ for a typical mesonic DA parameter. The sum rule should be reliable if a weak dependence (a plateau) is found, the contribution from the continuum is small, and there are no unnatural numerical cancellations.\n\nWe now equate Eqs.~(\\ref{disp}) and (\\ref{sr_eq4}) to reach the sum rule\n\\begin{equation}\\label{sr1}\nf_M\\,e^{-m_M^2\/M^2}= \\int^{s_0}_0 ds\\,e^{-s\/M^2} \\,\\rho^{\\textrm{OPE}}(s)\\,,\n\\end{equation}\nwhere the hadronic quantity $f_M$ is given as a function of the universal non-perturbative condensates, the perturbative short-distance coefficients as calculated from QCD, and the sum rule parameters $s_0$ and $M^2$. The sum rule is saturated by the ground state and higher mass states are suppressed. As the correlation function (\\ref{sr_eq1}) does not depend on the renormalisation scale, the $\\mu$ dependence of the condensates, when multiplied by their coefficient functions, must cancel in the sum of (\\ref{sr_eq4}). The sum is always truncated, however, and the residual $\\mu$ dependence will be a source of theoretical uncertainty.\n\n\\subsection{Non-local Formalism}\nOne way to gain access to parameters higher in conformal spin is to calculate sum rules involving operators which are related to moments of DAs\n\\begin{equation}\n\\bra{0}\\bar{q}(0) (\\stackrel{\\leftrightarrow}{D} \\cdot z)^k \\Gamma s(0)\\ket{V}\\sim \\int^1_0 du\\, (2u-1)^k \\phi(u)\\equiv \\left<\\xi^k\\right>\\,.\n\\end{equation}\nFor the $K^*$ for example the first few moments of both the leading-twist DAs are $\\left<\\xi^0\\right>=1$, $\\left<\\xi^1\\right>=\\frac{3}{5} a_1(K^*)$, $\\left<\\xi^2\\right>=\\frac{1}{35}(7+12 a_2(K^*))$ and $\\left<\\xi^3\\right>=\\frac{1}{105}(27 a_1(K^*)+20 a_3(K^*))$. A more elegant method, enabling the DA parameters to be extracted individually, relies on calculating a correlator of two currents, one of which is non-local, with fields at light-like separations ($z^2=0$) \\cite{Ball:2003sc}. Consider the following\n\\begin{equation}\n\\Pi(q\\cdot z) = i \\int d^4 x \\,e^{i q \\cdot x} \\bra{0} T J(x) \\bar{s}(0) \\gamma_{z} q(z) \\ket{0}\\,,\n\\label{nonlocalCF}\n\\end{equation}\nwhere $J(x)$ is local, and the non-local current yields the leading-twist DA (\\ref{das_eq16}). The sum rule (\\ref{sr1}) then reads\n\\begin{equation}\\label{sr2}\nf_J f^\\parallel_{K^*}\\,e^{-m_M^2\/M^2}\\int^1_0 du\\,e^{-i \\bar{u}q\\cdot z} \\phi_{2;K^*}^\\parallel= \\int^{s_0}_0 ds\\,e^{-s\/M^2} \\,\\int^1_0 du\\,e^{-i \\bar{u}q\\cdot z}\\rho^{\\textrm{OPE}}(s,u)\\,.\n\\end{equation}\nThe integration over $u$ on the right hand side naturally arises via the Feynman parameterisation used in the calculation. At this point one can exploit the orthogonality of the Gegenbauer polynomials by replacing the exponential weight function $e^{-i\\xi q\\cdot z} \\to C_n^{3\/2}(\\xi)$ on both sides to project out $a_n^\\parallel(K^*)$ via Eqs.~(\\ref{das_eq15}) and (\\ref{das_eq17}). \n\\begin{figure}[h]\n$$ \\epsfxsize=0.3\\textwidth\\epsffile{generalnonlocal.eps}$$\n \\caption[A generic diagram for a non-local sum rule.]{\\small A generic non-local diagram. The dotted line denotes the path ordered gauge factor $[z,-z]$ between the two quark fields. The momentum $q$ is injected at point $y$ - the vertex on the right hand side.} \n \\label{sr_fig2}\n\\end{figure}\nIn Fig.~\\ref{sr_fig2} we show the leading diagram of the non-local correlation function (\\ref{nonlocalCF}). The dotted line denotes the path ordered gauge factor $[z,-z]$ between the two quark fields. The non-local formalism allows, in principle, an extraction of parameters of arbitrary order $n$. In practice, however, only the parameters of the lowest few orders $n$ are accessible due to instability of the resulting sum rules. One finds that the power-corrections in $\\rho^{\\textrm{OPE}}$ grow with positive powers of $n$ compared to the perturbative contribution. For high enough $n$ this behaviour upsets the hierarchy of contributions to the OPE, where non-perturbative terms are expected to be moderately sized corrections to the leading term. Hence the method is justified for low-order coefficients $n\\leq2$ where the non-perturbative coefficients describe the general features of the DA. It breaks down for higher-order coefficients $n>3$ because the local vacuum condensates appear with $\\delta$-functions which cannot accommodate the information needed to describe the more detailed shape of the DA, see Refs.~\\cite{Ball:1997rj,Ball:2003sc}. \n\n\\section{QCD Sum Rules On The Light-Cone}\\label{LCSR}\nA modification of the QCD sum rule method known as QCD sum rules on the light-cone, or \\textit{light-cone sum rules} (LCSRs) \\cite{Balitsky:1989ry,Braun:1988qv,Chernyak:1990ag}, was developed to overcome difficulties encountered when calculating transition and electromagnetic form factors in the SVZ method.\\footnote{The term ``light-cone sum rules\" first appears in Ref.~\\cite{Ball:1991bs}.} The problems are related to the asymptotic scaling behaviour of the form factors in the heavy-quark limit $m_b \\to \\infty$. LCSRs rely on the use of DAs as their universal non-perturbative hadronic input and lead to the correct asymptotic behaviour in the heavy-quark limit. The DAs represent a partial re-summation of the operators appearing in the condensates and appear ordered in contributions of increasing twist \\cite{Ball:1997rj}. We can view LCSRs as a marriage of the SVZ technique and the theory of hard exclusive processes \\cite{Chernyak:1977fk,Lepage:1980fj,Efremov:1979qk,Chernyak:1977as}. In the case of the ``heavy-to-light'' $B\\to M$ transition form factors, LCSRs have been applied successfully to pseudoscalar transition form factors \\cite{oldpseudo,Ball:1998tj,Ball:2001fp,Ball:2004ye} and vector transition form factors \\cite{Ball:1998kk,Ball:2004rg}. \n\nFor LCSRs to become competitive with the SVZ sum rules, a good knowledge of higher-twist DAs is required. This motivates the determination of the non-perturbative DA parameters via SVZ sum rules and via LCSR, the DAs themselves to determine other non-perturbative parameters, such as transition form factors. As with SVZ sum rules, the starting point of LCSRs is with a suitable correlation function. For the extraction of $B\\to M$ transition form factors we require a two-point correlator, this time sandwiched between the vacuum and the meson state $M$, which is the example considered in this section. One employs much of the same methodology as in the last section, although now one requires the correlation function to be expanded in an OPE on the light-cone. In doing so one finds that the correlation function factorises and can be written in terms of a convolution of hard scattering kernels and the universal non-perturbative DAs of the light-meson. To that end, consider a correlation function of two quark currents taken between the vacuum and an on-shell meson $M$\n\\begin{equation}\\label{CFLCSR}\n\\Pi(q,p_B)=i\\int d^4x \\,e^{i q\\cdot x} \\bra{M(p)}T J_1(x) j_B^{\\dagger}(0)\\ket{0}\\,,\n\\end{equation}\nwhere $j_B = m_b\\, \\bar{q}i \\gamma_5 b$ is the \\textit{interpolating current} of the $B$ meson which defines the $B$ meson decay constant\n\\begin{equation}\n\\bra{B(p_B)}j_B\\ket{0}=m^2_B f_B\\,.\n\\label{bdecayconstant}\n\\end{equation}\nThe current $J_1(x)$ is chosen to project out the form factor of interest. The momentum $q$ is injected into the weak vertex, $p_B$ is the momentum of the $B$ meson and $p$ is the momentum of $M$ with $q+p=p_B$. The correlation function is dominated by light-like distances for virtualities\n\\begin{equation}\nm_b^2 - p_B^2 \\geq \\mathcal{O}(\\Lambda_{\\rm QCD} m_b)\\,, \\qquad m_b^2 - q^2 \\geq \\mathcal{O}(\\Lambda_{\\rm QCD} m_b)\\,,\n\\end{equation}\nwhich ensures the slow variation of the exponential in Eq.~(\\ref{CFLCSR}) and its suitability for an expansion around the light-cone. The light-cone expansion results in the transverse and ``minus'' degrees of freedom being integrated out, leaving the longitudinal momenta of the partons as the relevant degrees of freedom. As a result a cutoff $\\mu$ is introduced below which the transverse momenta are included in the resulting light-mesons DAs. The contributions from momenta above this cutoff are calculable in perturbation theory. The procedure yields the \\textit{collinear factorisation} of the correlation function\n\\begin{equation}\\label{LCSR:fac}\n\\Pi(q^2,p_B^2)=\\sum_n \\int_0^1 \\,du\\, T^{(n)}(u,q^2,p_B^2,\\mu)\\,\\phi_{n;M}(u,\\mu)\\,,\n\\end{equation}\nwhere $u$ $(1-u)$ denotes the momentum fraction of the outgoing quark (antiquark) and the sum is over all twist and possible polarisation contributions. The scale dependence of the hard scattering kernels $T^{(n)}$ must cancel that of the DAs $\\phi_{n;M}$. The factorisation formula has to be verified by direct calculation and a proof to all orders in $\\alpha_s$ does not exist. The verification relies on the cancelation of divergences, of which there are two types: the IR and UV singluarities arising from loop calculations and so-called soft singularities which appear when the convolution over $u$ does not converge at the end-point regions ($u \\sim 0~\\textrm{or}~1$) i.e. when one of the quarks is soft. In terms of kinematics there are two main contributing processes: the hard-scattering mechanism and the soft contribution or Feynman mechanism. Both mechanisms are included in the LCSR approach for which there are no soft divergences and the IR\/UV divergences can be treated in dimensional regularisation. \n\nOne can write the result of the light-cone expansion (\\ref{LCSR:fac}) as a dispersion relation in $p_B^2$\n\\begin{equation}\n\\Pi^{\\rm LC} (p_B^2, q^2) = \\int_{m_b^2}^{\\infty} ds\\,\\frac{\\rho^{\\rm LC} (s, q^2)}{s-p_B^2}\\,.\n\\end{equation}\nTaking the imaginary part, to obtain $\\rho^{\\rm LC} $, is straight forward after integration over the momentum fraction $u$ is performed. The correlation function has a cut in $p_B^2$ starting at $m_b^2$ over the physical region. One now matches this calculation to the hadronic representation of the correlation function, which can also be written as a dispersion relation\n\\begin{equation}\n\\Pi^{\\rm had} (p_B^2, q^2) = \\int_{m_B^2}^{\\infty} ds\\,\\frac{\\rho^{\\rm had} (s, q^2)}{s-p_B^2}\\,,\n\\end{equation}\nwhere the physical spectral density is given by the ground state $B$ meson plus higher mass states forming a continuum as\n\\begin{equation}\n\\rho^{\\rm had} (s, q^2)= F_M\\, \\delta(s-m_B^2) + \\rho^{\\rm LC } (s, q^2) \\,\\Theta(s-s_0)\\,.\n\\end{equation}\nThe quantity $F_M$ will contain the form factor we require. We perform the Borel transformation to arrive at the LCSR\n\\begin{equation}\nF_M\\, e^{-m_B^2\/M^2} =\\int_{m_b^2}^{s_0} ds \\, e^{-s\/M^2} \\rho^{\\rm LC} (s, q^2)\\,.\n\\end{equation}\nTo extract the form factor we need to find a sets of parameters $M^2$ and $s_0$ such that the form factor is largely insensitive to their variation. As with SVZ sum rules, there is no rigourous way to do this and so the procedure introduces the irreducible source of uncertainty to the method. \n\n\\section{Example Calculation - The Gluon Condensate}\\label{example}\nHere we present an example calculation within the SVZ sum rule framework. The result of the calculation is used in the sum rule for the G-even $K$ meson three-particle twist-3 DA parameter $f_{3K}$, see Ref.~\\cite{Ball:2006wn}. We calculate the $\\alpha_s$ correction to the gluon condensate $\\left< \\frac{\\alpha_s}{\\pi} G^2 \\right>$ which proceeds from the following local correlation function\n\\begin{equation}\\label{cf2}\n\\Pi^{(G^2)} = i \\int d^4 y \\, e^{i q\\cdot y}\\bra{0} T \\bar q(0) \\sigma^{\\mu z} g_sG_{\\mu z}(0) s(0) \\bar s(y) \n\\sigma^{\\nu z} g_sG_{\\nu z}(y) q(y)\\ket{0}\\,,\n\\end{equation}\nfor which the leading-order contribution vanishes. A convienient way of extracting the gluon condensate is to make use of the \\textit{back-ground field technique} in which the fixed-point gauge allows the Taylor expansion of quark and gluon fields to be written in a gauge-covariant form, see Ref.~\\cite{Novikov:1983gd} for details. The gluon field in the QCD Lagrangian (\\ref{basics_eq1}) is split into ``quantum'' and ``classical'' (background) fields\n\\begin{equation}\\label{split}\nA^a_\\mu\\to a^a_\\mu+\\mathcal{A}^a_\\mu\\,,\n\\end{equation}\nwhere the background field $\\mathcal{A}^a_\\mu$ is taken in the fixed-point gauge at $x_0=0$. The quantum field $a^a_\\mu$ is taken to be in the Feynman gauge, thus requiring the gauge fixing term ($\\xi=1$)\n\\begin{equation}\n\\mathcal{L}^{\\rm fix}=-\\frac{1}{2}(\\partial^\\mu a_\\mu^a+g_s f^{abc}\\mathcal{A}^{b \\mu} a_\\mu^c)^2\\,,\n\\end{equation}\nto be added to the QCD Lagrangian. The quantum field propagates perturbatively and we may use the standard expression\n\\begin{equation}\n\\begC1{a^a_\\mu}\\conC{(x)\\,}\\endC1{a^b_\\nu}(y)=i \\delta^{ab}\\int \\frac{d^4 l}{(2 \\pi)^4}D_{\\mu\\nu}(l)e^{-i l\\cdot (x-y)}\\,,\\qquad D_{\\mu\\nu}(l)=\\frac{-g_{\\mu\\nu}}{l^2},\n\\end{equation}\nThe background field does not propagate perturbatively, and is the field that goes to form the condensate; it represents the low-energy, long distance modes of the gluon field that probe the non-perturbative structure of the QCD vacuum. The fixed-point gauge condition allows $\\mathcal{A}_\\mu^a(x)$ to be expressed in terms of the gluonic field strength tensor as\n\\begin{equation}\n\\mathcal{A}_\\mu^a(x)=\\sum^\\infty_{n=0}\\frac{1}{n!(n+2)} x^\\omega x^{\\omega_1}... x^{\\omega_n}\\left[ D_{\\omega_1}(0),\\left[D_{\\omega_2}(0),\\left[...\\left[D_{\\omega_n}(0),G_{\\omega\\mu}^a(0)\\right]...\\right]\\right]\\right]\\,,\n\\end{equation}\nand translating to momentum space one finds\n\\begin{equation}\\label{condfield}\n\\mathcal{A}_\\mu^a(k)=-\\frac{i}{2} G^a_{\\omega\\mu}(0)\\left[(2\\pi)^4 \\delta^{(4)}(k)\\right]\\frac{\\partial\\phantom{ k^\\omega}}{\\partial k^\\omega}+\\dots\\,,\n\\end{equation}\nwhere we only require the first term; higher order terms give rise to higher dimensional condensates which we do not consider. As we have to introduce two condensate gluons to construct $\\left$ we introduce two auxiliary vacuum momenta $k$ and $k^\\prime$ for which the fixed-point $x_0=0$ is a sink. After integration over coordinates these momenta appear in the quark and gluon propagators. The two corresponding derivatives are then taken, and the vacuum momenta set to zero. The following expression proves very useful in managing derivatives of quark propagators\n\\begin{equation}\\label{quarkderiv}\n\\frac{\\partial}{\\partial p_\\mu} S^{(q)}(p)=-S^{(q)}(p)\\gamma^\\mu S^{(q)}(p)\\,,\\qquad S^{(q)}(p)=\\frac{\\slash{p}+m_q}{p^2-m_q^2}\\,,\n\\end{equation}\nfor arbitrary quark flavour $q$. The gluon condensate is finally extracted using\n\\begin{equation}\\label{vacav}\nG^a_{\\omega\\mu}(0)G^b_{\\omega^{\\prime}\\nu}(0)=\\frac{1}{D(D-1)}\\frac{\\delta^{ab}}{N_c^2-1}\\left(g_{\\omega\\omega^{\\prime}}g_{\\mu \\nu}-g_{\\omega\\nu}g_{\\omega^{\\prime}\\mu}\\right)\\left\\,,\n\\end{equation}\nwhere $D$ is the spacetime dimension. Due to Eq.~(\\ref{split}) the expansion of $\\mathcal{L}_{\\rm QCD}$ yields ``interaction'' terms in which background fields are radiated from the propagating gluons at single or double vertices, both of which contribute to the $\\mathcal{O}(\\alpha_s)$ correction to the gluon condensate. These vertices are shown in Fig.~\\ref{sr_fig3} and the corresponding terms are \n\\begin{eqnarray}\\label{glueint}\n \\mathcal{L}^{\\mathcal{A}aa}_{int}&=&-\\frac{1}{2}g_s f^{abc}\\left[\\left(\\partial^\\mu\\mathcal{A}^{a\\nu}-\\partial^\\nu\\mathcal{A}^{a\\mu}\\right)a_\\mu^b a_\\nu^c\\right.\\nonumber\\\\\n &+&\\left.(\\partial^\\mu a^{a\\nu}-\\partial^\\nu a^{a\\mu})(\\mathcal{A}^b_\\mu a^c_\\nu+a^b_\\mu\\mathcal{A}^c_\\nu)+2(\\partial_\\mu a^{a\\mu})\\mathcal{A}^{b\\nu}a_\\nu^c\\right]\\,,\\nonumber\\\\\n \\mathcal{L}^{\\mathcal{AA}aa}_{int}&=&-\\frac{1}{2} g_s^2 f^{abc} f^{ade}\\left[\\mathcal{A}^b_\\mu \\mathcal{A}^{d\\mu} a_\\nu^e a^{c\\nu} +\\mathcal{A}^b_\\mu a^{d\\mu} \\mathcal{A}^e_\\nu a^{c\\nu}+\\mathcal{A}^b_\\mu a^{c\\mu} \\mathcal{A}^d_\\nu a^{e\\nu}\\right]\\,,\n\\end{eqnarray}\nwhere terms which vanish eventually via Eq.~(\\ref{vacav}) due to $f^{abc}\\delta^{bc}=0$ are omitted.\n\\begin{figure}[h]\n$$ \\epsfxsize=0.8\\textwidth\\epsffile{gluonvertices.eps}$$\n\\caption[Interactions of the background field $\\mathcal{A}^a_\\mu$ with the quantum field $a^a_\\mu$.]{\\small The interactions of the background field $\\mathcal{A}^a_\\mu$ (denoted by a cross) with the quantum field $a^a_\\mu$ corresponding to $\\mathcal{L}^{\\mathcal{A}aa}_{int}$ and $\\mathcal{L}^{\\mathcal{AA}aa}_{int}$ respectively.} \n\\label{sr_fig3}\n\\end{figure}\nContributions also stem directly from the gluonic field strength tensors in Eq.~(\\ref{cf2}) which give rise to gluon emission of either one or two fields from the vertices at co-ordinates $0$ and $y$. Due to the gauge condition there is no ``left-right'' symmetry and all diagrams with two gluons, of which at least one is a condensate gluon, emerging from the vertex at $x=0$ vanish due to $A_\\mu(0)=0$. Diagrams with two condensate gluons at point $y$, which originate from the non-linear part of the gluonic field strength tensor, also give zero due to $f^{abc}\\delta^{bc}=0$. There is an ``up-down'' symmetry where diagrams related by a reflection in the central horizontal axis are equal. Overall we find there to be 10 distinct non-zero diagrams to be calculated which are shown in Fig.~\\ref{sr_fig4}. \n\\begin{figure}[h]\n$$ \\epsfxsize=0.7\\textwidth\\epsffile{cond1.eps}$$\n$$\\epsfxsize=0.5\\textwidth\\epsffile{cond2.eps}$$\n\\caption[Diagrams contributing to the gluon condensate at $\\mathcal{O}(\\alpha_s)$.]{\\small The diagrams contributing to the gluon condensate at $\\mathcal{O}(\\alpha_s)$ for the SVZ sum rule of the $K$ twist-3 DA parameter $f_{3K}$ -- see Ref.~\\cite{Ball:2006wn}. For each diagram the fixed-point $x_0=0$ is at the left most vertex and the right most is at $y$.} \n\\label{sr_fig4}\n\\end{figure}\n\nSome of the diagrams are divergent, however, all divergences cancel in the sum of all diagrams.\\footnote{We use dimensional regularisation and the $\\overline{MS}$ renormalisation scheme throughout this thesis.} For an explicit example consider the last diagram in the second line of Fig.~\\ref{sr_fig4}. It is evident that we require $\\mathcal{L}^{\\mathcal{A}aa}_{int}$ to be contracted in all possible ways with quantum fields originating from the linear part of the gluonic field strength tensors at points $0$ and $y$. This, multiplied by the condensate field originating from the quark loop yields the gluonic part of the calculation\n\\begin{equation}\\label{allcon}\n\\sim\\mathcal{A}^d_\\delta(v) \\left.(\\partial_\\mu a^a_z(0)-\\partial_z a^a_\\mu(0))\\, \\mathcal{L}^{\\mathcal{A}aa}_{int}(w) \\,(\\partial_\\nu a^b_z(y)-\\partial_z a^b_\\nu(y))\\right|_{\\rm all\\,contractions}\\,,\n\\end{equation}\nwhich is eventually given in momentum space by (omitting Lorentz indices)\n\\begin{equation}\\label{gluonicpart}\n\\sim \\frac{\\partial}{\\partial k}\\frac{\\partial}{\\partial k^\\prime}\\frac{f(l,k^\\prime)}{l^2 (l-k^\\prime)^2} \\left\\,,\n\\end{equation}\nwhere the condensate gluon within $\\mathcal{L}^{\\mathcal{A}aa}_{int}(w)$ is expressed by Eq.~(\\ref{condfield}) with momentum $k^\\prime$ and $f(l,k^\\prime)$ is a function of the loop momentum $l$ and the vacuum momentum $k^\\prime$. The quark loop yields a usual trace \n\\begin{equation}\\label{quarkpart}\n\\sim \\frac{\\textrm{tr}\\left[(\\slash{p}+\\slash{q}-\\slash{l})\\sigma^{\\mu z}(\\slash{p}+\\slash{k})\\gamma^\\delta\\slash{p}\\,\\sigma^{\\nu z}\\right]}{(p+q-l)^2(p+k)^2 p^2}\\,,\n\\end{equation}\nand after multiplying together Eqs.~(\\ref{gluonicpart}) and (\\ref{quarkpart}), performing the derivatives in $k$ and $k^\\prime$ and integrating over the momenta $p$ and $l$ we find\n\\begin{equation}\n\\Pi^{(G^2)}_{\\rm example} = \\frac{1}{384}\\frac{\\alpha_s}{\\pi} \\left\\langle\\frac{\\alpha_s}{\\pi}\\,G^2\\right\\rangle \\frac{(q\\cdot z)^4}{q^2}\\,.\n\\end{equation}\nIn this way we can include all the other diagrams shown in Fig.~\\ref{sr_fig4} to obtain the contribution to the sum rule\n\\begin{equation}\n\\Pi^{(G^2)} = \n-\\frac{89}{5184}\\,\\frac{\\alpha_s}{\\pi}\\,\\left\\langle\\frac{\\alpha_s}{\\pi}\\,G^2\\right\\rangle \\,\\frac{(q \\cdot z)^4}{q^2}\\,,\n\\label{last}\n\\end{equation}\nwhich differs from the result obtained in Ref.~\\cite{Zhitnitsky:1985dd}; the logarithmic term is not reproduced:\n\\begin{equation}\n\\sim \\log \\left(\\frac{M^2}{\\mu^2}\\right) \\left< \\frac{\\alpha_s}{\\pi} \\,G^2 \\right>\\,.\n\\label{least}\n\\end{equation}\n\n\\chapter{The Determination Of Vector Meson Twist-2 And Twist-3 Parameters}\\label{chapter4_det}\nIn this chapter we determine the leading twist-2 and twist-3 two- and three-particle vector meson DA parameters using the non-local modification of SVZ sum rules. The parameters are defined in Chapter~\\ref{chapter2_DAs} and the sum rule method is outlined in Chapter~\\ref{chapter3_SR}. We express the relevant correlation functions, via the OPE, in terms of the perturbative and condensate contributions. Key to the analysis is the inclusion of all G-parity and $\\rm SU(3)_F$-breaking effects which, as discussed in Chapter~\\ref{chapter2_DAs}, come from a variety of sources, and allow a consistent determination of the parameters for the $\\rho$, $K^*$, and $\\phi$. Motivation for the present analysis comes from various sources, including:\n\\begin{itemize}\n\\item{values for the decay constants and leading-twist DA Gegenbauer moments are required as input for QCD factorisation frameworks which provide a systematic method for the calculation of $B$ decay matrix elements. We discuss one such framework in Chapter~\\ref{chapter6_QCDF}.}\n\\item{Twist-2 and twist-3 DAs provide the leading non-perturbative input within the method of LCSR, as discussed in Chapter~\\ref{chapter3_SR}, and as such are applied to many problems in heavy-flavour physics, such as the calculation of $B$ transition form factors and the estimation of $B$ decay matrix elements including power-suppressed contributions to QCD factorisation frameworks, see Chapter~\\ref{chapter7_rad}.}\n\\item{A full determination of the twist-3 DA parameters, including $\\rm SU(3)_{F}$-breaking and G-parity violating effects, and the inclusion of $\\mathcal{O}(\\alpha_s)$ and $\\mathcal{O}(m_s^2)$ corrections to the quark condensate contributions to the twist-2 DA parameter sum rules are new to the present analysis, allowing $a_2^{\\parallel,\\perp}(\\phi)$ to be determined, for the first time, to the same accuracy as $a_2^{\\parallel,\\perp}(\\rho,K^*)$.}\n\\end{itemize}\nAll input parameters for the sum rules, and useful formulas, such as those required to take the imaginary parts of intermediate results, and various relevant integrals, are given in Appendix~\\ref{appendixB}. In performing the calculations we find Refs.~\\cite{PT:84,Borodulin:1995xd} very useful. The material covered in this chapter partially follows that of Ref.~\\cite{Ball:2007rt}.\n\n\\section{Twist-2}\nIn this section we focus on the determination of the twist-2 DA Gegenbauer coefficients $a_{2}^{\\parallel,\\perp}$ defined by Eqs.~(\\ref{das_eq16}) and (\\ref{das_eq19}). The sum rules for $f_{K^*}^{\\parallel,\\perp}$, including $\\rm SU(3)_F$-breaking corrections, were calculated in Refs.~\\cite{Govaerts:1986ua,Ball:2005vx,Ball:2006fz}. Those for the G-parity violating $a_1^{\\parallel,\\perp}(K^*)$ in Refs.\\cite{Ball:2005vx,Ball:2006fz} and those for $a_2^{\\parallel,\\perp}(K^*)$ in \\cite{Ball:2003sc} apart from perturbative terms in $m_s^2$ and the $\\mathcal{O}(\\alpha_s)$ and $\\mathcal{O}(m_s^2)$ corrections to the quark condensate, which are new to the present analysis. Motivation for including these corrections is found by examining the individual contributions to the sum rules for $a_2^{\\parallel,\\perp}(K^*)$ given in Ref.~\\cite{Ball:2003sc}. They are found to be dominated by $\\left<\\bar s s\\right>$ as we can see from the following explicit break down of contributions:\n\\begin{eqnarray}\na_2^\\parallel(K^*)&=&\\overbrace{0.05}^{\\textrm{PT}}+\\overbrace{0.08}^{\\left<\\frac{\\alpha_s}{\\pi} G^2\\right>}+\\overbrace{0.11}^{\\left<\\bar s g_s \\sigma G s\\right>}+\\overbrace{0.04}^{\\left<\\bar q q\\right>^2}-\\overbrace{0.16}^{\\left<\\bar s s\\right>}+\\overbrace{0.02}^{\\left<\\bar s s\\right>^2}-\\overbrace{0.05}^{\\left<\\bar q q\\right>\\left<\\bar s s\\right>}\\nonumber\\\\\na_2^\\perp(K^*)&=&0.06+\\,\\,0.10\\,\\,+\\,\\,\\,0.25\\,\\,\\,+0.03-0.27+0.02\\,-0\\,,\n\\end{eqnarray}\nfor the reference point $s_0=1.2\\,\\textrm{GeV}^2$, $M^2=1\\,\\textrm{GeV}^2$ and $\\mu=1\\,\\textrm{GeV}$. Moreover, for the $\\phi$ the impact of a finite strange quark mass may be even more pronounced with respect to perturbation theory and the gluon condensate. \n\nFirstly, we give an overview of the calculation of the $\\mathcal{O}(\\alpha_s)$ and $\\mathcal{O}(m_{s}^2)$ corrections to the quark condensate $\\left<\\bar s s\\right>$; the calculations for $\\left<\\bar q q\\right>$ are analogous. We only need extract terms proportional to $m_s$ as the contributions proportional to $m_q$ are identical; we can find the contributions for $\\phi$ by simply replacing $\\left<\\bar q q\\right>\\to\\left<\\bar s s\\right>$ and doubling the terms in $m_{s} \\left<\\bar s s\\right>$, $m_{s} \\left<\\bar q q\\right>$ and $m_{s} \\left<\\bar s g_s G s\\right>$. Contributions for $\\rho$ are found by setting $m_s\\to0$. Secondly, we go on to analyse the sum rules for $a_2^{\\parallel,\\perp}(\\phi)$. We end this section by presenting the results.\n\n\\subsection{Calculation}\nFor both polarisations we begin from the diagonal correlation function\n\\begin{equation}\n\\Pi_{2;K^*}(q\\cdot z) = i \\int d^4y \\,e^{-iq\\cdot y} \\bra{0} T \\bar q(y) \\Gamma s(y) \\bar s(0) \\Gamma [0,z]q(z) \\ket{0}\\,\n\\label{C.0}\n\\end{equation}\nwhere $\\Gamma^{\\parallel}=\\gamma_z$ and $\\Gamma^{\\perp}=\\sigma_{\\mu z}$. For the longitudinal parameters the sum rule is exactly that given by Eq.~(\\ref{sr2}) with $f_J\\to f^\\parallel_{K^*}$ and for the transverse parameters the sum rule is analogous. Both polarisations have the same projections onto the DA parameters\n\\begin{eqnarray}\n\\left(f_{K^*}\\right)^2e^{-m_{K^*}^2\/M^2}\\left[1\\right]\n & = & \\int_0^{s_0}ds\\, e^{-s\/M^2} \\int^1_0 du\\,\\left[1\\right]\n \\frac{1}{\\pi}\\, {\\rm Im}_{s}\n\\pi_{2;K^*}(u)\\,,\\label{C0}\n\\\\\n\\left(f_{K^*}\\right)^2 e^{-m_{K^*}^2\/M^2}\n \\,\\left[\\frac{9}{5}\\,a_{1}(K^*)\\right]\n& = & \\int_0^{s_0}ds\\, e^{-s\/M^2} \\int^1_0 du\\,\n \\left[ 3 \\xi \\right]\\frac{1}{\\pi}\\, {\\rm Im}_{s}\n \\pi_{2;K^*}(u)\\,,\n\\nonumber\\\\\n\\left(f_{K^*}\\right)^2 e^{-m_{K^*}^2\/M^2}\n \\,\\left[\\frac{18}{7}\\,a_{2}(K^*)\\right]\n& = & \\int_0^{s_0}ds\\, e^{-s\/M^2} \\int^1_0 du\\,\n \\left[ \\frac{1}{2}( 15 \\xi^2-3)\\right]\\frac{1}{\\pi}\\, {\\rm Im}_{s}\n \\pi_{2;K^*}(u)\\,, \\nonumber\n\\end{eqnarray}\nwhere ${\\rm Im}_s$ denotes taking the imaginary part with respect to $s$. The fact that we are dealing with non-local correlation functions means that we do not integrate over the co-ordinate $z$. The resulting residual exponential function remains throughout the calculation and can contribute to the momentum integrals yielding powers of $i c (q\\cdot z)$, where $c$ is a constant. Ultimately the exponential functions can be cast into the ``canonical form'' set by the exponential appearing in front of the leading-twist DA i.e. $e^{- i \\bar{u}q\\cdot z}$ -- see Eq.~(\\ref{sr2}). \n\\subsection*{Quark Condensate}\n\\begin{figure}[h]\n$$ \\epsfxsize=0.25\\textwidth\\epsffile{tw2_quark0_sr.eps}$$\n\\caption[Diagram contributing to the quark condensate $\\left<\\bar s s \\right>$ at leading-order.]{\\small The leading-order diagram contributing to the quark condensate $\\left<\\bar s s \\right>$.} \n\\label{det_fig1}\n\\end{figure}\nThe tree-level diagram is shown in Fig.~\\ref{det_fig1}. To extract the quark condensates to $\\mathcal{O}(m_s^2)$ we use the following expansion of the quark fields (for general quark flavour $q$)\n\\begin{eqnarray}\n\\bra{0}\\!:\\! \\bar q^i_\\alpha(x_1)\\, q^j_\\beta(x_2)\\!:\\!\\ket{0}&=&\\delta^{ij}\\frac{\\left<\\bar q q\\right>}{12}\\left\\{\\delta_{\\beta\\alpha}\\left(1-\\frac{\\Delta^2}{2D}m_q^2\\right)\\right.\\nonumber\\\\\n&-&\\left.m_q\\frac{i }{D}(\\gamma_\\lambda)_{\\beta\\alpha}\\Delta^\\lambda\\left(1-\\frac{\\Delta^2 }{2(2+D)}m_q^2\\right)\\right\\}\\,,\n\\label{quarkextract}\n\\end{eqnarray}\nwhere $\\Delta_\\mu=(x_2-x_1)_\\mu$ and $i,j$ are colour and $\\alpha,\\beta$ spinor indices. One can deal with the co-ordinate $\\Delta_\\mu$ by trading it, via partial integration (PI), for a derivative of the trace that arises from the quark loop. A convenient way to do so is via an auxiliary momentum $Q$\n\\begin{equation}\n\\Delta_\\kappa \\stackrel{\\textrm{PI}}{\\longrightarrow} ie^{-i\\Delta\\cdot Q}\\frac{\\partial}{\\partial Q_\\kappa}\\Big|_{Q\\to 0}\\,.\n\\label{deriv1}\n\\end{equation}\n\\begin{figure}[h]\n$$ \\epsfxsize=0.8\\textwidth\\epsffile{tw2_quark1_sr.eps}$$\n\\caption[Diagrams contributing to the quark condensate $\\left<\\bar s s \\right>$ at $\\mathcal{O}(\\alpha_s)$.]{\\small Diagrams contributing to the quark condensate $\\left<\\bar s s \\right>$ at $\\mathcal{O}(\\alpha_s)$. The crossed circle $\\otimes$ depicts the emission of a gluon from the non-local gauge factor -- see Eq.~(\\ref{emissiongf}). The corresponding diagrams for $\\left<\\bar q q\\right>$ are identical but reflected top to bottom.} \n\\label{det_fig2}\n\\end{figure}\nDiagrams for the $\\mathcal{O}(\\alpha_s)$ corrections to the strange quark condensate are shown in Fig.~\\ref{det_fig2}. Importantly there are contributions from the gauge-factor which need to be included\n\\begin{equation}\n[0,z]=\\textrm{P} \\exp{\\left\\{-i g_s \\int^1_0 dt\\,z^\\mu A_\\mu(\\bar{t}z)\\right\\}}=1-i g_s \\int^1_0 dt\\,z^\\mu A_\\mu(\\bar{t}z)+\\dots\\,.\n\\label{emissiongf}\n\\end{equation}\nCalculating $\\mathcal{O}(\\alpha_s)$ corrections leads to divergent diagrams and the dependence of the condensate on the spacetime dimension $D$ leads to $\\mathcal{O}(\\epsilon)$ contributions at tree level, that then cause finite counter-terms upon renormalisation. Also, the derivative with respect to $Q_\\kappa$ in Eq.~(\\ref{deriv1}) yields $\\gamma_\\kappa$ in the trace via Eq.~(\\ref{quarkderiv}) which can also give a finite counter-term. This happens for the vertex correction diagrams.\n\n\n\\subsection{Evaluation of The Sum Rules}\nThe new quark condensate contributions are added to the results presented in the literature, see Refs.~\\cite{Ball:2005vx,Ball:2007rt}. For $f_{K^*}^{\\parallel,\\perp}$ the sum rules read\n\\begin{eqnarray}\n\\lefteqn{(f_{K^*}^\\parallel)^2 e^{-m_{K^*}^2\/M^2} = \n\\frac{1}{4\\pi^2}\\int\\limits_{m_s^2}^{s_0}\nds\\,e^{-s\/M^2} \\,\\frac{(s-m_s^2)^2 (s+2m_s^2)}{s^3} +\n\\frac{\\alpha_s}{\\pi}\\, \\frac{M^2}{4\\pi^2}\\left( 1 -\ne^{-s_0\/M^2}\\right)}\\nonumber\\\\[5pt]\n&&{} +\\frac{m_s\\langle \\bar s s\\rangle}{M^2}\\left(1 +\n\\frac{m_s^2}{3M^2} - \n\\frac{13}{9}\\,\\frac{\\alpha_s}{\\pi}\\right)+\n\\frac{4}{3}\\,\\frac{\\alpha_s}{\\pi} \\, \\frac{m_s\\langle \\bar q\n q\\rangle}{M^2}+\\frac{1}{12M^2}\\,\n\\langle\\frac{\\alpha_s}{\\pi}\\,G^2\\rangle \\left(1+\\frac{1}{3}\\frac{m_s^2}{M^2}\\right)\\label{tw2sr1}\n\\nonumber\\\\[5pt]\n&&{} -\\frac{16\\pi\\alpha_s}{9M^4}\\,\n\\langle \\bar q q\\rangle\\langle \\bar s s\\rangle +\n\\frac{16\\pi\\alpha_s}{81M^4}\\,\\left( \\langle \\bar q q\\rangle^2 +\n\\langle \\bar s s\\rangle^2 \\right),\\label{eq:fKP}\\\\ \\nonumber \\\\\n\\lefteqn{(f_{K^*}^\\perp)^2 e^{-m_{K^*}^2\/M^2}\n= \\frac{1}{4\\pi^2}\\int\\limits_{m_s^2}^{s_0}\nds\\,e^{-s\/M^2} \\,\\frac{(s-m_s^2)^2 (s+2m_s^2)}{s^3} }\\nonumber\\\\[5pt]\n&&{}+ \\frac{1}{4\\pi^2}\\int\\limits_{0}^{s_0}\nds\\,e^{-s\/M^2} \\,\\frac{\\alpha_s}{\\pi}\\left( \\frac{7}{9} +\n\\frac{2}{3}\\,\\ln \\frac{s}{\\mu^2}\\right) \n-\\frac{1}{12M^2}\\,\\langle\\frac{\\alpha_s}{\\pi}\\,G^2\\rangle \n\\nonumber\\\\[5pt]\n&&{}\n\\times\\left\\{ 1-\\frac{2m_s^2}{M^2}\\left( \\frac{7}{6}-\\gamma_E + {\\rm Ei}\\left(\n-\\frac{s_0}{M^2}\\right) - \\ln\\,\\frac{\\mu^2}{M^2} +\n\\frac{M^2}{s_0}\\left( 1 - \\frac{M^2}{s_0}\\right) e^{-s_0\/M^2}\n\\right)\\right\\} \\nonumber\\\\[5pt]\n&&{} +\\frac{m_s\\langle \\bar s\n s\\rangle}{M^2}\\left\\{1+\\frac{m_s^2}{3M^2}+\n\\frac{\\alpha_s}{\\pi}\\left(-\\frac{22}{9} + \\frac{2}{3}\n\\left[ 1-\\gamma_E + \\ln\\,\\frac{M^2}{\\mu^2} +\n \\frac{M^2}{s_0}\\,e^{-s_0\/M^2} + {\\rm Ei}\\left(-\\frac{s_0}{M^2}\\right)\\right]\n\\right)\\right\\}\\nonumber\\\\[5pt]\n&&{} \n-\\frac{1}{3M^4}\\,m_s\\langle \\bar s\\sigma gGs\\rangle -\n\\frac{32\\pi\\alpha_s}{81M^4}\\,\\left( \\langle \\bar q q\\rangle^2 +\n\\langle \\bar s s\\rangle^2 \\right)\\,,\\label{eq:fKT}\n\\end{eqnarray}\nand for $a_2^{\\parallel,\\perp}(K^*)$\n\\begin{eqnarray}\n\\lefteqn{\na_2^\\parallel(K^*) (f_{K^*}^\\parallel)^2 e^{-m_{K^*}^2\/M^2} = }\\nonumber\\\\\n&&{}\\frac{7}{4\\pi^2}\\,m_s^4\\int\\limits_{m_s^2}^{s_0}\nds\\,e^{-s\/M^2} \\,\\frac{(s-m_s^2)^2(2m_s^2-s)}{s^5} +\n\\frac{7}{72\\pi^2}\\,\\frac{\\alpha_s}{\\pi} \\,M^2 (1-e^{-s_0\/M^2})\n+\\frac{7}{36M^2}\\,\\left\\langle\\frac{\\alpha_s}{\\pi}\\,G^2\\right\\rangle \n\\nonumber\\\\[5pt]\n&&{}+\\frac{7}{3}\\,\\frac{m_s\\langle \\bar s\n s\\rangle}{M^2}\\left\\{1+ \n\\frac{\\alpha_s}{\\pi}\\left[ -\\frac{184}{27} +\n\\frac{25}{18} \\left(1-\\gamma_E + \\ln\\,\\frac{M^2}{\\mu^2} +\n\\frac{M^2}{s_0}\\,e^{-s_0\/M^2} + {\\rm Ei}\\left(-\\frac{s_0}{M^2}\\right)\n\\right)\\right]\\right\\} \\nonumber\\\\[5pt]\n&&{}+\\frac{49}{27}\\,\\frac{\\alpha_s}{\\pi} \\, \\frac{m_s\\langle \\bar q\n q\\rangle}{M^2} - \\frac{35}{18}\\, \n\\frac{m_s\\langle\\bar s \\sigma g Gs\\rangle}{M^4}\n+\\frac{224\\pi\\alpha_s}{81M^4}\\,\\left( \\langle \\bar q q\\rangle^2 +\n\\langle \\bar s s\\rangle^2 \\right) -\n\\frac{112\\pi\\alpha_s}{27M^4}\\,\\langle \\bar q q\\rangle \\langle \\bar s\ns\\rangle\\,,\\label{tw2sr3}\\\\ \\nonumber \\\\[5pt]\n\\lefteqn{\na_2^\\perp(K^*) (f_{K^*}^\\perp)^2 e^{-m_{K^*}^2\/M^2} = }\\nonumber\\\\[5pt]\n&&{}\\frac{7}{4\\pi^2}\\,m_s^4\\int\\limits_{m_s^2}^{s_0}\nds\\,e^{-s\/M^2} \\,\\frac{(s-m_s^2)^2(2m_s^2-s)}{s^5} +\n\\frac{7}{90\\pi^2}\\,\\frac{\\alpha_s}{\\pi} \\,M^2 (1-e^{-s_0\/M^2})\n+\\frac{7}{54M^2}\\,\\left\\langle\\frac{\\alpha_s}{\\pi}\\,G^2\\right\\rangle \n\\nonumber\\\\[5pt]\n&&{}+\\frac{7}{3}\\,\\frac{m_s\\langle \\bar s\n s\\rangle}{M^2}\\left\\{1+ \n\\frac{\\alpha_s}{\\pi}\\left[ -\\frac{206}{27} +\n\\frac{16}{9} \\left(1-\\gamma_E + \\ln\\,\\frac{M^2}{\\mu^2} +\n\\frac{M^2}{s_0}\\,e^{-s_0\/M^2} + {\\rm Ei}\\left(-\\frac{s_0}{M^2}\\right)\n\\right)\\right]\\right\\} \\nonumber\\\\[5pt]\n&& - \\frac{49}{18}\\, \n\\frac{m_s\\langle\\bar s \\sigma g Gs\\rangle}{M^4}\n+\\frac{112\\pi\\alpha_s}{81M^4}\\,\\left( \\langle \\bar q q\\rangle^2 +\n\\langle \\bar s s\\rangle^2 \\right)\\,.\\label{tw2sr4}\n\\end{eqnarray}\nTo obtain the sum rules for $f_{\\phi}^{\\parallel,\\perp}$ and\n$a_2^{\\parallel,\\perp}(\\phi)$, one has to substitute $\\langle \\bar qq\\rangle\\to \\langle\n\\bar s s\\rangle$ and to double the terms in $m_s\\langle\n\\bar s s\\rangle$, $m_s\\langle \\bar q q\\rangle$ and $m_s \\langle \\bar s\n\\sigma g G s \\rangle$, and replace the perturbative contribution by\n\\begin{eqnarray}\n\\mbox{for $(f_{\\phi}^{\\parallel,\\perp})^2$:~~}&&\n \\frac{1}{4\\pi^2}\\int_{4m_s^2}^{s_0} ds \\,e^{-s\/M^2} \\frac{(s+2 m_s^2)\n \\sqrt{ 1-4 m_s^2\/s}}{s}\\,,\\nonumber\\\\\n\\mbox{for $a_2^{\\parallel,\\perp}(\\phi)(f_{\\phi}^{\\parallel,\\perp})^2$:~~}&&\n -\\frac{7}{2\\pi^2}\\int_{4m_s^2}^{s_0} ds \\,e^{-s\/M^2} \\frac{m_s^4\n \\sqrt{ 1-4 m_s^2\/s}}{s^2}\\,.\n\\end{eqnarray}\nWe have derived sum rules for the decay constants $f^{\\parallel,\\perp}_V$, however, numerical values can be extracted from experiment for the longitudinal decay constants. The perpendicular decay constants, on the other hand, must be determined from non-perturbative methods; results are available from Lattice QCD calculations and previous QCD sum rule determinations. A detailed discussion of the latest numerical values of the decay constants can be found in Ref.~\\cite{Ball:2006eu} from which we just quote the following\n\\begin{eqnarray}\nf_{\\phi}^\\parallel=(215\\pm5)\\,\\textrm{MeV}\\,,\\qquad f_{\\phi}^\\perp =(186\\pm9)\\,\\textrm{MeV}\\,,\n\\label{decayresults}\n\\end{eqnarray}\nwhere $f_{\\phi}^\\parallel$ is an experimental result, and $f_{\\phi}^\\perp$ is from Lattice QCD \\cite{Becirevic:2003pn}. We can compare these results to the sum rules of Eqs.~(\\ref{eq:fKP}) and (\\ref{eq:fKT}) which are plotted in the upper row of Fig.~\\ref{aaa}. The sum rule determinations of $a_2^{\\parallel,\\perp}(\\phi)$ are plotted in the lower row. \n\\begin{figure}[h]\n$$ \\epsfxsize=0.5\\textwidth\\epsffile{fparaphi.eps}\\quad \\epsfxsize=0.5\\textwidth\\epsffile{fperpphi.eps}$$\n$$ \\epsfxsize=0.5\\textwidth\\epsffile{a2paraphi.eps}\\quad \\epsfxsize=0.5\\textwidth\\epsffile{a2perpphi.eps}$$\n \\caption[The hadronic parameters $f^{\\parallel,\\perp}_\\phi$ and $a_2^{\\parallel,\\perp}(\\phi)$ as a function of $M^2$.]{\\small The decay constants $f^\\parallel_\\phi$ (upper left) and $f^\\perp_\\phi$ (upper right) and the Gegenbauer coefficients $a_2^\\parallel(\\phi)$ (lower left) and $a_2^\\perp(\\phi)$ (lower right) plotted as a function of $M^2$. The continuum thresholds are $s_0^\\parallel =1.85\\pm 0.05\\,{\\rm GeV}^2$ and $s_0^\\perp =1.40\\pm 0.05\\,{\\rm GeV}^2$ -- see text. Solid line: central input parameters of Tab.~\\ref{QCDSRinput}. Dashed lines: variation due to the uncertainties of $m_s$ and the gluon condensate. All quantities are evaluated at $\\mu=1\\,{\\rm GeV}$.} \n \\label{aaa}\n\\end{figure}\n\nIn all the plots the dashed line and shaded region represent the central value and uncertainty of the parameter in question. To evaluate the sum rules we use the input parameters of Tab.~\\ref{QCDSRinput}. For the continuum threshold we note that for the sum rule determination of $f_{K^*}^\\parallel$ in Ref.~\\cite{Ball:2005vx} it is taken to be $s_0^\\parallel(K^*)=1.7\\,{\\rm GeV}^2$, and we expect for $\\phi$ it to be slightly larger. Indeed, by taking $s_0^\\parallel(\\phi)=1.85\\pm 0.05\\,{\\rm GeV}^2$ we find a stable plateau and excellent agreement with the experimental result for $f_{\\phi}^{\\parallel}$ (upper left plot). Likewise, guided by $s_0^\\perp(K^*)=1.3\\,{\\rm GeV}^2$ \\cite{Ball:2005vx} we find $s_0^\\perp(\\phi)=1.40\\pm 0.05\\,{\\rm GeV}^2$ yields a result consistent with that from Lattice QCD (upper right plot). We use these thresholds in evaluating the sum rules for $a_2^{\\parallel,\\perp}(\\phi)$ and also replace the decay constants by their sum rules, which helps reduce dependence on the Borel parameters. The results are plotted for $a_2^{\\parallel}(\\phi)$ (lower left plot) and $a_2^{\\perp}(\\phi)$ (lower right plot). It is found that the longitudinal parameters exhibit a stronger continuum threshold dependence, which is reflected in the larger uncertainty of the determined value of $a_2^\\parallel(\\phi)$. The sum rule determinations of the other particle DA parameters follow analogously and all the numerical results are given in Tab.~\\ref{det_tab1}.\n\\begin{table}[h]\n\\renewcommand{\\arraystretch}{1.3}\n\\addtolength{\\arraycolsep}{3pt}\n$$\n\\begin{array}{| l || c | c || l | l || c | c |}\n\\hline\n& \\multicolumn{2}{c||}{\\rho} & \\multicolumn{2}{c||}{K^*} & \n\\multicolumn{2}{c|}{\\phi}\\\\\n\\cline{2-7}\n& \\mu = 1\\,{\\rm GeV} & \\mu = 2\\,{\\rm GeV} & \\mu = 1\\,{\\rm GeV} & \\mu =\n2\\,{\\rm GeV} & \\mu = 1\\,{\\rm GeV} & \\mu = 2\\,{\\rm GeV}\\\\\n\\hline\na_1^\\parallel & 0 & 0 & \\phantom{-}0.03(2) & \\phantom{-}0.02(2) & 0 & 0 \n\\\\\na_1^\\perp & 0 & 0 & \\phantom{-}0.04(3) & \\phantom{-}0.03(3) & 0 & 0\n\\\\\na_2^\\parallel & 0.15(7) & 0.10(5) & \\phantom{-}0.11(9) & \n\\phantom{-}0.08(6) & 0.18(8) & 0.13(6)\n\\\\\na_2^\\perp & 0.14(6) & 0.11(5) & \\phantom{-}0.10(8) &\n\\phantom{-}0.08(6) & 0.14(7) & 0.11(5)\n\\\\\\hline\n\\end{array}\n$$\n\\renewcommand{\\arraystretch}{1}\n\\addtolength{\\arraycolsep}{-3pt}\n\\caption[Results for the leading twist-2 distribution amplitude parameters.]{\\small Results for the twist-2 hadronic DA parameters at the scale $\\mu= 1\\,{\\rm GeV}$ and scaled up to $\\mu= 2\\,{\\rm GeV}$\nusing the evolution equations (\\ref{evo}). Note that $a_1^{\\parallel,\\perp}({K^*})$ refers to a $(s\\bar q)$ bound state; for a $(q\\bar s)$ state it changes sign.}\n\\label{det_tab1}\n\\end{table}\n\n\\section{Twist-3}\nIn this section we determine the twist-3 three-particle parameters of the DAs $\\Phi_{3;K^*}^\\perp$, $\\Phi_{3;K^*}^\\parallel$ and $\\widetilde\\Phi_{3;K^*}^\\parallel$ as defined by Eq.~(\\ref{das_eq27}). Previous determinations of these parameters are rather few and far between, thus motivating the present analysis. The chiral-even $\\rho$ parameters $\\zeta^\\parallel_{3\\rho}$, $\\omega^\\parallel_{3\\rho}$, and $\\widetilde{\\omega}^\\parallel_{3\\rho}$ were obtained in Ref.~\\cite{Zhitnitsky:1985dd}, and $\\omega^\\perp_{3\\rho}$ was obtained in Ref.~\\cite{Ball:1998sk}. We make a comparison with these results in Section~\\ref{section_evaluation}.\n\nFirstly, we outline the calculation of the three functions $\\pi_{3;K^*}$ which all proceed in a similar manner, and secondly we explicitly discuss the sum rules for $\\widetilde\\Phi_{3;K^*}^\\parallel$ and present the results. In the diagrams that follow, $q$ is the upper line and $s$ is the lower line.\n\n\\subsection{Calculation}\nEach DA is accessed via a correlation function featuring its defining current. The chiral-even twist-3 parameters $\\zeta_{3K^*}^\\parallel$, $\\widetilde\\omega_{3K^*}^\\parallel$, $\\widetilde\\lambda_{3K^*}^\\parallel$\ncan be determined from\n\\begin{equation}\n\\widetilde\\Pi^\\parallel_{3;K^*}(v,q\\cdot z) = \\frac{i g_{\\alpha\\mu}^{\\perp}}{(q\\cdot z)^2 (2-D)} \\int d^4y \\,e^{-iq\\cdot y} \\bra{0} T \\bar q(z) g_s\n\\widetilde G^{\\alpha z} (vz) \\gamma_z \\gamma_5 s(0) \\bar s(y)\n\\gamma^\\mu q(y) \\ket{0}\\,,\n\\label{C.1}\n\\end{equation}\nwhere the definition of $g^\\perp_{\\mu\\nu}$ is given in Appendix~\\ref{appendixA}.\\footnote{We also make use of the relation $\\gamma_\\mu\\gamma_5=\\frac{i}{6}\\epsilon_{\\mu\\lambda\\nu\\pi}\\gamma^\\lambda\\gamma^\\nu\\gamma^\\pi$ defined in $D$ dimensions.} The parameters $\\kappa_{3K^*}^\\parallel$, $\\omega_{3K^*}^\\parallel$ and $\\lambda_{3K^*}^\\parallel$ can be obtained from the correlation\nfunction $\\Pi_{3;K^*}^\\parallel$ obtained from $\\widetilde\\Pi^\\parallel_{3;K^*}$ by making the replacement\n\\begin{equation}\ng_s\\widetilde G_{\\alpha z} \\gamma_z\\gamma_5 \\to g_s G_{\\alpha z} i \\gamma_z\\,.\n\\end{equation}\nLastly for the chiral-odd operator \n\\begin{equation}\n\\Pi^\\perp_{3;K^*}(v,q\\cdot z)= \\frac{1}{ (q\\cdot z)^3 }\\int d^4y e^{-iq\\cdot y} \\bra{0}T \\bar q(z) \\sigma_{z\\mu} g_s\nG_{z\\mu}(vz) s(0) \\bar s(y) \\sigma_{qz} q(y) \\ket{0}\\,.\n\\label{C.3} \n\\end{equation}\nAll three correlation functions $\\Pi$ can be written as\n\\begin{equation}\n\\Pi_{3;K^*}(v,q\\cdot z) = \\int {\\cal D}\\underline{\\alpha}\\,e^{-i q\\cdot z (\\alpha_2 + v \\alpha_3)}\\pi_{3;K^*}(\\underline{\\alpha})\\,,\n\\label{C.4}\n\\end{equation}\nwhere the exponential function is due to the fact that we keep the correlation functions non-local. The calculation proceeds for each correlation function analogously. Considering Eq.~(\\ref{C.1}) for instance, firstly we express it in terms of hadronic contributions\n\\begin{equation}\n\\widetilde{\\Pi}_{3;K^*}^\\parallel(v,q\\cdot z) =\n\\frac{(f_{K^*}^\\parallel)^2\n m_{K^*}^2}{m_{K^*}^2-q^2} \\int{\\cal\n D}(\\underline{\\alpha})\\,e^{-iq\\cdot z(\\alpha_2+v\\alpha_3)}\\,\n\\widetilde{\\Phi}_{3;K^*}^ \\parallel(\\underline{\\alpha}) + \\dots\\, ,\n\\label{C.5}\n\\end{equation}\nwhere the dots denote contributions from higher-mass states. To derive the sum rule we tread down a well worn path; express Eq.~(\\ref{C.4}) as a dispersion relation and equate to\nEq.~(\\ref{C.5}), subtract the continuum contribution for $s>s_0$, perform the Borel transformation and project out the desired DA parameter by substitution of the relevant polynomial. The three hadronic parameters $\\zeta_{3K^*}^\\parallel$, $\\widetilde\\omega_{3K^*}^\\parallel$, $\\widetilde\\lambda_{3K^*}^\\parallel$ are projected out like so:\n\\begin{eqnarray}\n\\left(f_{K^*}^ \\parallel\\right)^2 m_{K^*}^2 e^{-m_{K^*}^2\/M^2}\n \\left[ \\zeta_{3K^*}^\\parallel\\right] \n& = & \\int_0^{s_0}ds\\, e^{-s\/M^2} \\int {\\cal D}\\underline{\\alpha}\\,\n \\left[1\\right] \\frac{1}{\\pi}\\, {\\rm Im}_{s}\n \\widetilde{\\pi}^\\parallel_{3;K^*}(\\underline{\\alpha})\\,,\n\\\\\n\\left(f_{K^*}^ \\parallel\\right)^2 m_{K^*}^2 e^{-m_{K^*}^2\/M^2}\n \\, \\left[\\frac{1}{14}\\,\\widetilde\\lambda_{3K^*}^\\parallel\\right] \n& = & \\int_0^{s_0}ds\\, e^{-s\/M^2} \\int {\\cal D}\\underline{\\alpha}\\,\n \\left[ \\alpha_1-\\alpha_2\\right] \\frac{1}{\\pi}\\, {\\rm Im}_{s}\n \\widetilde{\\pi}_{3;K^*}^\\parallel(\\underline{\\alpha})\\,,\n\\nonumber\\\\\n\\left(f_{K^*}^ \\parallel\\right)^2 m_{K^*}^2 e^{-m_{K^*}^2\/M^2}\n \\, \\left[\\frac{3}{28}\\,\\widetilde\\omega_{3K^*}^\\parallel\\right] \n& = & \\int_0^{s_0}ds\\, e^{-s\/M^2} \\int {\\cal D}\\underline{\\alpha}\\,\n \\left[\\alpha_3-\\frac{3}{7}\\right] \\frac{1}{\\pi}\\, {\\rm Im}_{s}\n \\widetilde{\\pi}_{3;K^*}^\\parallel(\\underline{\\alpha})\\,.\\nonumber\n \\label{C.6}\n\\end{eqnarray}\nThe formulas for the other parameters are analogous. In calculating the functions $\\pi_{3;K^*}$ we keep explicit mass corrections $\\mathcal{O}(m_s^2,m_q^2,m_s m_q)$ and all operators up to $D=6$ except the triple gluon condensate $\\left$ which is expected to yield a negligible contribution. By retaining all mass terms the resulting formulas for $\\pi_{3;K^*}$ can be used to derive sum rules for all the DA parameters for $K^*$, $\\rho$ and $\\phi$ by setting $m_q=0$, $m_q= m_s=0$ and $m_q= m_s$ respectively. For $\\rho$ and $\\phi$ expressions for the three-particle twist-3 DAs are analogous to Eq.~(\\ref{das_eq27}), except that the G-parity violating parameters $\\kappa$ and $\\lambda$ vanish.\n\n\\subsection*{Perturbation Theory}\nThe perturbation theory calculation is given by the two diagrams shown in Fig.~\\ref{a}. \n\\begin{figure}[h]\n$$ \\epsfxsize=0.4\\textwidth\\epsffile{pt_sr.eps}$$\n \\caption[Diagrams contributing to perturbation theory.]{\\small Diagrams contributing to perturbation theory.} \n \\label{a}\n\\end{figure}\nAs an example, consider the first diagram, which up to an overall factor can be written generally as\n\\begin{eqnarray}\n\\Pi^{(\\rm{PT}_1)}&=&g_s^2\\int\\frac{d^D p}{(4 \\pi)^D}\\int\\frac{d^D l}{(4 \\pi)^D}\\,\\textrm{Tr}[\\Gamma_1 S^{(s)}(\\slash{p}+\\slash{q})\\Gamma_2 S^{(q)}(\\slash{p})\\gamma^{\\beta}S^{(q)}(\\slash{p}+\\slash{l})] \\nonumber\\\\\n&&\\cdot\\,[l_\\mu D_{\\nu\\beta}(l)-l_\\nu D_{\\mu\\beta}(l)]\\,e^{iz \\cdot(l \\bar{v}+p)}\\,.\\label{part1}\n\\end{eqnarray}\nwhere the Dirac matrices $\\Gamma_{1,2}$ depend on the correlation function. In performing the two successive integrations over $l$ and $p$, Feynman parameterisation leads to shifting the variables $l\\to l-p\\bar x$ and $p\\to p-q\\bar y$ respectively. Each time the exponential in (\\ref{part1}) is also shifted. In expanding the part of the exponential that contributes to the integral, for example, for $l$ we have $e^{i l \\cdot z \\bar{v}}=1+i (l \\cdot z)\\bar{v} +\\dots$, only the first two terms contribute; higher order terms are killed off either via $z^2=0$ or because integrals with odd numbers of open indices, for example $l^{\\mu_1}l^{\\mu_2}l^{\\mu_3}$, in the numerator vanish due to symmetry. After the integrations any terms $(\\mathcal{T})$ including factors of $i( q \\cdot z)\\bar{v}$ are dealt with by trading them for derivatives of $\\mathcal{T}$ by using partial integration of the final exponential\n\\begin{equation}\ni (q\\cdot z) \\bar{v}=\\frac{1}{\\bar{y}}\\frac{\\partial}{\\partial\\bar{x}}e^{-i q \\cdot z \\bar{y}(1-\\bar{x}\\bar{v})} \\qquad\\Rightarrow\\qquad (q\\cdot z) \\bar{v}\\, \\mathcal{T} \\stackrel{\\textrm{PI}}{\\longrightarrow} \\frac{i}{\\bar{y}}\\frac{\\partial }{\\partial\\bar{x}}\\mathcal{T} \\,,\\label{PI}\n\\end{equation}\nwhere surface terms do not contribute as they vanish for $x=\\{1,0\\}$. The exponential can be matched to the ``canonical form'' by writing\n\\begin{equation}\n\\int^1_0dx\\,\\int^1_0dy\\, e^{-i q \\cdot z \\bar{y}(1-\\bar{x}\\bar{v})} = \\int^1_0dx\\,\\int^1_0dy\\, \\int {\\cal D}\\underline{\\alpha}\\, \\delta(\\alpha_1-y)\\delta(\\alpha_2-\\bar{x}\\bar{y})\\delta(\\alpha_3- x\\bar{y})\\,e^{-iq\\cdot z(\\bar\\alpha_1-\\bar{v}\\alpha_3)}\\,.\n\\end{equation}\nPerforming the $x$ and $y$ integration of the whole expression gives the desired result\n\\begin{equation}\n\\int {\\cal D}\\underline{\\alpha}\\, e^{-iq\\cdot z(\\alpha_2+v\\alpha_3)}\\pi^{(\\textrm{PT}_1)}(\\underline{\\alpha})\\,.\n\\end{equation}\nThe second diagram follows analogously. Both diagrams are divergent and need to be renormalised separately. We find finite counter terms which are proportional to the quark masses. \n\n\\subsection*{Gluon Condensate}\nThe leading order contribution to the gluon condensate $\\left<\\frac{\\alpha_s}{\\pi} G^2\\right>$ is found using the background field method as outlined in Section~\\ref{example}. There are only two diagrams contributing as depicted in Fig.~\\ref{b}. One vacuum momentum $k$, from the gluon attached to the quark line, is introduced and hence one derivative is taken. As the gluon emerging from the non-local vertex $G(vz)$ carries no momentum these diagrams are proportional to $\\delta(\\alpha_3)$ and the remaining momentum fractions are related by $1-\\alpha_1=\\alpha_2$; the identification of the momentum fractions with the Feynman parameters is therefore straightforward. \n\\begin{figure}[h]\n$$ \\epsfxsize=0.4\\textwidth\\epsffile{g2_sr.eps}$$\n \\caption[Diagrams contributing to the gluon condensate $\\left<\\frac{\\alpha_s}{\\pi} G^2\\right>$.]{\\small Diagrams contributing to the gluon condensate $\\left<\\frac{\\alpha_s}{\\pi} G^2\\right>$.}\n \\label{b} \n\\end{figure}\nThe calculation requires the integration over one momentum $p$ and the result can simply be written unexpanded in the quark masses. \n\n\n\\subsection*{Mixed Condensate}\nThe mixed condensates $\\left<\\bar q \\sigma g_s G q \\right>$ and $\\left<\\bar s \\sigma g_s G s \\right>$ originate from the diagrams shown in Fig.~\\ref{f}. \n\\begin{figure}[h]\n$$ \\epsfxsize=0.4\\textwidth\\epsffile{mixed_sr.eps}$$\n \\caption[Diagrams contributing to the mixed condensates $\\left<\\bar q \\sigma g_s G q \\right>$ and $\\left<\\bar s \\sigma g_s G s \\right>$.]{\\small Diagrams contributing to the mixed condensates $\\left<\\bar q \\sigma g_s G q \\right>$ and $\\left<\\bar s \\sigma g_s G s \\right>$.}\n \\label{f} \n\\end{figure}\nTo extract the mixed condensates one uses the first non-local term in the expansion $(D=4)$ \\cite{PT:84}\n\\begin{eqnarray}\n\\lefteqn{\\bra{0}\\!:\\!\\bar{q}^i_\\alpha(x_1) g_s (G_{\\mu\\nu})_{ij}(y)q^j_\\beta(x_2)\\!:\\!\\ket{0}=}\\hspace{1.2in}\\\\\n&&\\delta^{ij}\\left[\\frac{\\left<\\bar{q} g_s \\sigma G q\\right>}{144}\\left\\{\\sigma_{\\mu\\nu}+\\frac{m_q}{2}\\left[\\Delta_\\mu \\gamma_\\nu-\\Delta_\\nu \\gamma_\\mu -i(\\Delta^\\lambda \\gamma_\\lambda)\\sigma_{\\mu\\nu}\\right]\\right\\}\\right.\\nonumber\\\\\n&&\\left.+g_s^2 \\left<6\\right>\\left\\{\\frac{i}{288}(x_2^\\xi \\sigma_{\\mu\\nu}\\gamma_\\xi-x_1^\\xi \\gamma_\\xi \\sigma_{\\mu\\nu})-\\frac{1}{216}(y_\\mu\\gamma_\\nu-y_\\nu\\gamma_\\mu)\\right\\}\\right]_{\\beta\\alpha}\\,. \\nonumber\n\\label{mixed}\n\\end{eqnarray}\nThe first $\\sigma_{\\mu\\nu}$ does not contribute, but the term $\\sim m_q$ does. The $\\Delta_\\mu$s can be expressed as derivatives of the trace via partial integration which is dealt with simply by using Eq.~(\\ref{quarkderiv}). Along with the condensate gluon, the quark condensate lines carry no momentum. There is therefore no loop integration to perform and the results are proportional to $\\delta(\\alpha_3)\\delta(\\alpha_{1,2})$.\n\n\\subsection*{Quark Condensates}\nThe diagrams of Fig.~\\ref{c} generate the condensates $m_{q,s}\\left<\\bar q q\\right>$ and $m_{q,s}\\left<\\bar s s \\right>$. \n\\begin{figure}[h]\n$$ \\epsfxsize=0.8\\textwidth\\epsffile{quark1_sr.eps}$$\n \\caption[Diagrams contributing to the quark condensates $\\left<\\bar q q\\right>$ and $\\left<\\bar s s \\right>$.]{\\small Diagrams contributing to the quark condensates $\\left<\\bar q q\\right>$ and $\\left<\\bar s s \\right>$.}\n \\label{c} \n\\end{figure}\nWe do not consider $\\mathcal{O}(m_{q,s}^2)$ corrections, which are however of dimension six, as they are very well suppressed with respect to the other contributions. To extract all $\\mathcal{O}(m_{q,s})$ mass corrections the first non-local term in the expansion of the quark fields, given by Eq.~(\\ref{quarkextract}), is needed. There is one loop momentum to integrate over and one finds contributions from the exponential which can be dealt with via partial integration in the same way as with the perturbation theory calculation, see Eq.~(\\ref{PI}). The results are proportional to $\\delta(\\alpha_{1,2})$. The diagrams in Fig.~\\ref{d} generate the condensate $\\left<\\bar q q\\right>\\left<\\bar s s \\right>$ which is already of dimension six, so we do not require mass corrections. \n\\begin{figure}[h]\n$$ \\epsfxsize=0.4\\textwidth\\epsffile{quark2_sr.eps}$$\n \\caption[Diagrams contributing to the quark condensate $\\left<\\bar q q\\right>\\left<\\bar s s \\right>$.]{\\small Diagrams contributing to the quark condensate $\\left<\\bar q q\\right>\\left<\\bar s s \\right>$.}\\label{d} \n\\end{figure}\nThe two diagrams are of equal magnitude and cancel, however only for $\\widetilde{\\pi}^{\\parallel}_{3;K^{*}}$ they add. There is no loop integral to perform and the result is proportional to $\\delta(\\alpha_1)\\delta(\\alpha_2)$. The four quark condensate is simplified via the vacuum saturation hypothesis (VSH) \\cite{PT:84,Shifman:1978by}\n\\begin{equation}\n\\bra{0}\\!:\\!\\bar{q}_\\alpha^i (x_1) q_\\beta^j(x_2)\\bar{s}_\\gamma^k (x_3) s_\\delta^l(x_4)\\!:\\!\\ket{0} \\stackrel{\\textrm{VSH}}{\\longrightarrow}\\bra{0}\\!:\\!\\bar{q}_\\alpha^i (x_1) q_\\beta^j(x_2)\\!:\\!\\ket{0}\\bra{0}\\!:\\!\\bar{s}_\\gamma^k (x_3) s_\\delta^l(x_4)\\!:\\!\\ket{0}\\,.\n\\end{equation}\nThe diagrams in Fig.~\\ref{e} generate the condensates $\\left<\\bar q q\\right>^2$ and $\\left<\\bar s s \\right>^2$. They stem from the operator $\\left<6\\right>$ appearing in the expansion of the mixed condensate, Eq.~(\\ref{mixed}), which simplifies as\n\\begin{equation}\n \\left<6\\right>=\\langle\\bar q \\gamma_\\kappa t^a q \\sum_{u,d,s}\\bar{q} \\gamma^\\kappa t^a q\\rangle\\stackrel{\\textrm{VSH}}{\\longrightarrow} -\\frac{4}{9}\\left<\\bar q q\\right>^2\\,;\n \\end{equation}\nthus at higher order the mixed condensate also contributes to the quark condensates. The light-like co-ordinate of the gluonic field strength tensor $v z_\\mu$ simplifies the resulting trace via $z^2=0$ from Eq.~(\\ref{mixed}) and the other co-ordinates are dealt with as before. \n \\begin{figure}[h]\n$$ \\epsfxsize=0.4\\textwidth\\epsffile{quark3_sr.eps}$$\n \\caption[Diagrams contributing to the quark condensates $\\left<\\bar q q\\right>^2$ and $\\left<\\bar s s \\right>^2$.]{\\small Diagrams contributing to the quark condensates $\\left<\\bar q q\\right>^2$ and $\\left<\\bar s s \\right>^2$ from the expansion of the mixed condensate -- see Eq.~(\\ref{mixed}).}\n \\label{e} \n\\end{figure}\n\\subsection*{Results}\nFor the functions $\\pi_{3;K^*}$, given by Eq.~(\\ref{C.6}), we find (dropping all terms that vanish upon taking the imaginary part):\n\\begin{eqnarray}\n\\pi^{\\perp }_{3;K^{*}}\\left(\\underline{\\alpha}\\right)\n&=&\n\\frac{\\alpha_{s}}{2\\pi^{3}}\\ln\\frac{-q^2}{\\mu^{2}}\n\\left[q^2\\alpha_{1}\\alpha_{2}\\alpha_{3}\\left(\\frac{1}{\\bar{\\alpha}_{2}}-\n\\frac{1}{\\bar{\\alpha}_{1}}\\right)\\right.\n\\nonumber \\\\\n&+&m_{s}m_{q}\\frac{\\alpha_{3}^{2}}{\\bar{\\alpha}_{1}\\bar{\\alpha}_{2}}\n\\left[\\bar{\\alpha}_{2}\\left(\\ln\\frac{\\alpha_{2}\\alpha_{3}}{\\bar{\\alpha}_{1}}+\n\\frac{1}{2}\\ln\\frac{-q^2}{\\mu^{2}}\\right)-\\left\\{\\alpha_{1}\n\\leftrightarrow\\alpha_{2}\\right\\}\\right]\n\\nonumber \\\\\n&+&m_{s}^{2}\\left\\{-\\alpha_{2}\\alpha_{3}\\left(\\frac{1}{\\bar{\\alpha}_{2}}-\n\\frac{1}{\\bar{\\alpha}_{1}}\\right)-\\frac{\\alpha_{2}\\alpha_{3}^{2}}{\n\\bar{\\alpha}_{2}^{2}}\\left(\\ln\\frac{\\alpha_{1}\\alpha_{3}}{\\bar{\\alpha}_{2}}+\n\\frac{1}{2}\\ln\\frac{-q^2}{\\mu^{2}}\\right)\\right\\}\n-m_{q}^{2}\\left\\{\\alpha_{1}\\leftrightarrow\\alpha_{2}\\right\\}] \n\\nonumber \\\\\n&+&\\frac{1}{12}\\langle\\frac{\\alpha_{s}}{\\pi}G^{2}\\rangle\n\\frac{\\alpha_{1}\\alpha_{2}\\left(\\alpha_{1}-\\alpha_{2}\\right)\\delta\n\\left(\\alpha_{3}\\right)}{\\alpha_{1}m_{q}^{2}+\\alpha_{2}m_{s}^{2}-\n\\alpha_{1}\\alpha_{2}q^2}\n\\nonumber \\\\\n&+&\\frac{2}{3q^2}\\frac{\\alpha_{s}}{\\pi}\\left\\{\\right.\n\\frac{\\bar{\\alpha}_{3}}{2}\\left(1+\\alpha_{3}\\right)\\left(m_{q}\n\\langle\\bar{q}q\\rangle\\delta\\!\\left(\\alpha_{2}\\right)-m_{s}\\langle\\bar{s}s\n\\rangle\\delta\\!\\left(\\alpha_{1}\\right)\\right) \n\\nonumber \\\\\n&+&\\alpha_{3}\\left[1+\\alpha_{3}\\left(1+\\ln\\left(\\alpha_{3}\n\\bar{\\alpha}_{3}\\right)+\\ln\\frac{-q^2}{\\mu^{2}}\\right)\\right]\\left(m_{s}\n\\langle\\bar{q}q\\rangle\\delta\\!\\left(\\alpha_{2}\\right)-m_{q}\\langle\\bar{s}s\n\\rangle\\delta\\!\\left(\\alpha_{1}\\right)\\right)\\left.\\right\\} \n\\nonumber \\\\\n&+&\\frac{1}{6q^4}\\delta\\!\\left(\\alpha_{3}\\right)\\left\\{m_{q}\\langle\\bar{q}\n\\sigma g_s G q\\rangle\\delta\\!\\left(\\alpha_{2}\\right)-m_{s}\\langle\\bar{s}\n\\sigma g_s G s\\rangle\\delta\\!\\left(\\alpha_{1}\\right)\\right\\}\n\\nonumber \\\\\n&+&\\frac{16}{27q^4} \\pi \\alpha_{s} \\delta\\!\\left( \\alpha_{3}\\right) \n\\left\\{\\langle\\bar{q}\nq\\rangle^{2}\\delta\\!\\left(\\alpha_{2}\\right)-\\langle\\bar{s}s\\rangle^{2}\n\\delta\\!\\left(\\alpha_{1}\\right)\\right\\},\\label{corresult1}\n\\end{eqnarray}\n\\begin{eqnarray}\n\\pi^{\\parallel}_{3;K^{*}}\\left(\\underline{\\alpha}\\right)\n&=&\n\\frac{\\alpha_{s}}{4\\pi^{3}}\\ln\\frac{-q^2}{\\mu^{2}}\\left[\\right.q^2\n\\alpha_{1}\\alpha_{2}\\alpha_{3}\\left(\\frac{1}{\\bar{\\alpha}_{2}}-\n\\frac{1}{\\bar{\\alpha}_{1}}\\right)\n\\nonumber \\\\\n&+&m_{s}m_{q}\\frac{\\alpha_{3}^{2}}{\\bar{\\alpha}_{1}\\bar{\\alpha}_{2}}\n\\left\\{\\bar{\\alpha}_{2}\\left(\\ln\\frac{\\alpha_{2}\\alpha_{3}}{\\bar{\\alpha}_{1}}+\n\\frac{1}{2}\\ln\\frac{-q^2}{\\mu^{2}}\\right)-\\left\\{\\alpha_{1}\n\\leftrightarrow\\alpha_{2}\\right\\}\\right\\}\n\\nonumber \\\\\n&+&m_{s}^{2}\\left\\{-\\alpha_{2}\\alpha_{3}\\left(\\frac{1}{\\bar{\\alpha}_{2}}-\n\\frac{1}{\\bar{\\alpha}_{1}}\\right)-\\frac{\\alpha_{2}\\alpha_{3}^{2}}{\n\\bar{\\alpha}_{2}^{2}}\\left(\\ln\\frac{\\alpha_{1}\\alpha_{3}}{\\bar{\\alpha}_{2}}+\n\\frac{1}{2}\\ln\\frac{-q^2}{\\mu^{2}}\\right)\\right\\}\n-m_{q}^{2}\\left\\{\\alpha_{1}\\leftrightarrow\\alpha_{2}\\right\\}\\left.\\right]\n\\nonumber \\\\\n&+&\\frac{1}{24}\\langle\\frac{\\alpha_{s}}{\\pi}G^{2}\\rangle\\frac{\\alpha_{1}\n\\alpha_{2}\\left(\\alpha_{1}-\\alpha_{2}\\right)\\delta\\!\\left(\\alpha_{3}\\right)}{\n\\alpha_{2}m_{s}^{2}+\\alpha_{1}m_{q}^{2}-\\alpha_{1}\\alpha_{2}q^2}\n\\nonumber\\\\\n&+&\\frac{1}{3q^2}\\frac{\\alpha_{s}}{\\pi}\\left\\{\\right.\\frac{\n\\bar{\\alpha}_{3}}{2}\\left(1+\\alpha_{3}\\right)\\left(m_{q}\\langle\\bar{q}q\n\\rangle\\delta\\!\\left(\\alpha_{2}\\right)-m_{s}\\langle\\bar{s}s\\rangle\\delta\n\\left(\\alpha_{1}\\right)\\right) \n\\nonumber \\\\\n&+&\\alpha_{3}\\left[1+\\alpha_{3}\\left(\\ln\\left(\\alpha_{3}\\bar{\\alpha}_{3}\n\\right)+\\ln\\frac{-q^2}{\\mu^{2}}\\right)\\right]\\left(m_{s}\\langle\\bar{q}q\n\\rangle\\delta\\!\\left(\\alpha_{2}\\right)-m_{q}\\langle\\bar{s}s\\rangle\\delta\n\\left(\\alpha_{1}\\right)\\right)\\left.\\right\\}\n\\nonumber \\\\\n&+&\\frac{1}{12 q^4}\\delta\\!\\left(\\alpha_{3}\\right)\\left\\{\nm_{q}\\langle\\bar{q}\n\\sigma g_s G q\\rangle\\delta\\!\\left(\\alpha_{2}\\right)-m_{s}\\langle\\bar{s}\n\\sigma g_s G s\\rangle\\delta\\!\\left(\\alpha_{1}\\right)\\right\\}\n\\nonumber \\\\\n&+&\\frac{8}{27 q^4}\\alpha_{s}\\pi \\delta\\!\\left( \\alpha_{3}\\right)\n\\left(\\langle\\bar{q}q\\rangle^{2}\\delta\\!\\left(\\alpha_{2}\\right) -\n\\langle\\bar{s}s\\rangle^{2}\\delta\\!\\left(\\alpha_{1}\\right) \\right),\\label{corresult2}\n\\end{eqnarray}\n\\begin{eqnarray}\n\\widetilde{\\pi}^{\\parallel}_{3;K^{*}}\\left(\\underline{\\alpha}\\right)\n&=&\\frac{\\alpha_{s}}{4\\pi^{3}}\\ln\\frac{-q^2}{\\mu^{2}}\\left[\\right.-\nq^2\\alpha_{1}\\alpha_{2}\\alpha_{3}\\left(\\frac{1}{\\bar{\\alpha}_{1}}+\n\\frac{1}{\\bar{\\alpha}_{2}}\\right)\n\\nonumber \\\\\n&+&m_{s}m_{q}\\frac{\\alpha_{3}^{2}}{\\bar{\\alpha}_{1}\\bar{\\alpha}_{2}}\n\\left\\{\\bar{\\alpha}_{1}\\left(\\ln\\frac{\\alpha_{1}\\alpha_{3}}{\\bar{\\alpha}_{2}}-\n\\frac{1}{2}\\ln\\frac{-q^2}{\\mu^{2}}\\right)+\\left\\{\\alpha_{1}\n\\leftrightarrow\\alpha_{2}\\right\\}\\right\\}\n\\nonumber \\\\\n&+&m_{s}^{2}\\left\\{\\alpha_{2}\\alpha_{3}\\left(\\frac{1}{\\bar{\\alpha}_{1}}+\n\\frac{1}{\\bar{\\alpha}_{2}}\\right)+\\frac{\\alpha_{2}\\alpha_{3}^{2}}{\n\\bar{\\alpha}_{2}^{2}}\\left(\\ln\\frac{\\alpha_{1}\\alpha_{3}}{\\bar{\\alpha}_{2}}+\n\\frac{1}{2}\\ln\\frac{-q^2}{\\mu^{2}}\\right)\\right\\}\n+m_{q}^{2}\\left\\{\\alpha_{1}\\leftrightarrow\\alpha_{2}\\right\\}\\left.\\right]\n\\nonumber \\\\\n&+&\\frac{1}{24}\\langle\\frac{\\alpha_{s}}{\\pi}G^{2}\\rangle\\frac{\\alpha_{1}\n\\alpha_{2}\\delta\\!\\left(\\alpha_{3}\\right)}{\\alpha_{2}m_{s}^{2}+\\alpha_{1}\nm_{q}^{2}-\\alpha_{1}\\alpha_{2}q^2}\n\\nonumber\\\\\n&+&\\frac{1}{3q^2}\\frac{\\alpha_{s}}{\\pi}\\left\\{\\right.\\frac{\n\\bar{\\alpha}_{3}^{2}}{2}\\left(m_{s}\\langle\\bar{s}s\\rangle\\delta\\!\\left(\n\\alpha_{1}\\right)+m_{q}\\langle\\bar{q}q\\rangle\\delta\\!\\left(\\alpha_{2}\\right)\n\\right) \n\\nonumber \\\\\n&+&\\alpha_{3}\\left[1-\\alpha_{3}\\left(2+\\ln\\left(\\alpha_{3}\\bar{\\alpha}_{3}\n\\right)+\\ln\\frac{-q^2}{\\mu^{2}}\\right)\\right]\\left(m_{s}\\langle\\bar{q}q\n\\rangle\\delta\\!\\left(\\alpha_{2}\\right)+m_{q}\\langle\\bar{s}s\\rangle\\delta\n\\left(\\alpha_{1}\\right)\\right)\\left.\\right\\}\n\\nonumber \\\\\n&+&\\frac{1}{12 q^4}\\delta\\!\\left(\\alpha_{3}\\right)\\left\\{ m_{q}\\langle\n\\bar{q}\\sigma g_s\nGq\\rangle\\delta\\!\\left(\\alpha_{2}\\right)+m_{s}\\langle\\bar{s}\n\\sigma g_s G s\\rangle\\delta\\!\\left(\\alpha_{1}\\right)\\right\\}\n\\nonumber \\\\\n&+&\\frac{8}{27 q^4}\\alpha_{s}\\pi \\delta\\!\\left( \\alpha_{3}\\right)\n\\left(\\langle\\bar{q}q\\rangle^{2}\\delta\\!\\left(\\alpha_{2}\\right)+\n\\langle\\bar{s}s\\rangle^{2}\\delta\\!\\left(\\alpha_{1}\\right) \\right)\n\\nonumber\\\\\n&+&\\frac{2}{3 q^4}\\alpha_{s}\\pi \\delta\\!\\left( \\alpha_{1}\\right) \n\\delta\\!\\left( \\alpha_{2}\\right)\\langle\\bar{q}q\\rangle\\langle\\bar{s}s\n\\rangle.\\label{corresult3}\n\\end{eqnarray}\n\n\n\\subsection{Evaluation of The Sum Rules}\\label{section_evaluation}\nIn the following we consider $\\widetilde{\\pi}_{3;K^*}^\\parallel$; the sum rules for the other DA parameters and particles $\\rho$ and $\\phi$ follow similarly. The values of the input parameters and the continuum thresholds used for all sum rules are given in Appendix~\\ref{appendixB}. \n\nOne subtlety must be noted: upon integration over $\\alpha_i$ and subsequent\nexpansion in powers of the quark masses, the gluon condensate contribution yields\nterms in $m_{q,s}^2 \\ln (m_{q,s}^2\/(-q^2))$, which are long-distance\neffects and must not appear in the short-distance OPE of the correlation functions of Eqs~(\\ref{C.1}) and (\\ref{C.3}). The appearance of these \nlogarithmic terms is due to the fact that the expressions of Eqs.~(\\ref{corresult1}-\\ref{corresult3}) are\nobtained using Wick's theorem which implies that the condensates are normal-ordered: \n$\\langle O \\rangle =\\bra{0}\\!:\\!O\\!:\\! \\ket{0}$ \\cite{logms}. Rewriting the OPE in terms of\nnon-normal-ordered operators, all infrared sensitive terms can be\nabsorbed into the corresponding condensates. Indeed, using, \n\\begin{equation}\n\\bra{0}\\bar s g_s G s \\ket{0} = \\bra{0}\\!:\\!\\bar s g_s G s\\!:\\!\\ket{0} +\n\\frac{m_s}{2}\\, \\ln\\,\\frac{m_s^2}{\\mu^2} \\,\\bra{0}\\! :\\!\n\\frac{\\alpha_s}{\\pi}\\, G^2\\!:\\!\\ket{0}\\,,\n\\end{equation}\nand the corresponding formula for $q$ quarks, all terms in $\\ln m_{q,s}^2$ can be absorbed into the mixed quark-quark-gluon condensate and the resulting short-distance coefficients\ncan be expanded in powers of $m_{q,s}^2$. \n\\begin{figure}[h]\n$$\n\\epsfxsize=0.6\\textwidth\\epsffile{ltileven.eps}\n$$\n$$\n\\epsfxsize=0.6\\textwidth\\epsffile{otileven.eps}\n$$\n$$\n\\epsfxsize=0.6\\textwidth\\epsffile{zeven.eps}\n$$\n\\caption[Hadronic parameters of $\\widetilde\\Phi_{3;K^*}^\\parallel$ as functions of $M^2$.]{\\small Hadronic parameters of the twist-3 distribution amplitude $\\widetilde\\Phi_{3;K^*}^\\parallel$ as functions of $M^2$. Upper: $\\widetilde\\lambda_{3K^*}^\\parallel$, middle: $\\widetilde\\omega_{3K^*}^\\parallel$, and lower: $\\zeta_{3K^*}^\\parallel$. The solid curve is for central input values for $\\mu=1\\,{\\rm GeV}$ and outer curves take into consideration their uncertainties -- see Tab.~\\ref{QCDSRinput}. Horizontal dashed line is the extracted DA parameter value and shaded region its uncertainty -- see Tab.~\\ref{det_tab2}.}\n \\label{g} \n\\end{figure}\n\nIn Fig.~\\ref{g} we plot the sum rules for $\\widetilde\\lambda_{3K^*}^\\parallel$, $\\widetilde\\omega_{3K^*}^\\parallel$ and $\\zeta_{3K^*}^\\parallel$, given by Eqs.~(\\ref{C.6}), which are evaluated for the central input parameters of Tab.~\\ref{QCDSRinput} and at a scale $\\mu=1\\,{\\rm GeV}$. The parameters unfortunately exhibit very strong $M^2$ dependence, which leads to increased uncertainty of their values; we do not find a stable plateau in the region $ 1\\,\\textrm{GeV}^2\\leqslant M^2\\leqslant 2.5\\,\\textrm{GeV}^2$. On the other hand, there is only a very small $s_0$ dependence $\\approx 1 \\%$ over the range $s_0^\\parallel (K^*) = (1.3\\pm 0.3)\\,{\\rm GeV}^2$. The curves flatten at high $M^2$ which is expected, as the power corrections become negligible compared to the perturbative contribution.\\footnote{The quark condensates survive as $M^2\\to\\infty$ as $\\hat{\\mathcal{B}}\\left[q^{-2}\\right]=-1$ but perturbation theory $\\sim M^4$ -- see Appendix~\\ref{appendixB}.} The sum rules for the other parameters and particles show the same general behaviour which is fairly typical of non-diagonal correlation functions. If one were to use diagonal correlation functions then it is possible that the sum rules would be better behaved and thus the uncertainties would be reduced somewhat. The calculation of diagonal correlation functions of three-particle operators, as we saw with the gluon condensate in Chapter~\\ref{chapter3_SR}, is rather more involved, especially when calculating radiative corrections, which may very well be necessary in this case. \n\nAll the numerical results, including the uncertainties from the variation of $M^2$, $s_0$, and input parameters, are given in Tab.~\\ref{det_tab2}. The results are presented at the scale $\\mu= 1\\,{\\rm GeV}$ and scaled up to $\\mu= 2\\,{\\rm GeV}$, \nusing the evolution equations, Eq.~(\\ref{evo}). The only previous determination for comparison is for the chiral-even $\\rho$ parameters, $\\zeta^\\parallel_{3\\rho}(1\\,\\rm{GeV})=0.033\\pm0.003$, $\\omega^\\parallel_{3\\rho}(1\\,\\rm{GeV})=0.2$, and \n$\\widetilde{\\omega}^\\parallel_{3\\rho}(1\\,\\rm{GeV})=-0.1$ \\cite{Zhitnitsky:1985dd}\nand $\\omega^\\perp_{3\\rho}(1\\,\\rm{GeV})=0.3\\pm0.3$ \\cite{Ball:1998sk}. These results agree with ours, although we consider the uncertainty of $\\zeta^\\parallel_{3\\rho}$ to be optimistic.\n\n\\begin{table}[h]\n\\renewcommand{\\arraystretch}{1.3}\n\\addtolength{\\arraycolsep}{3pt}\n$$\n\\begin{array}{| l || c | c || l | l || c | c |}\n\\hline\n& \\multicolumn{2}{c||}{\\rho} & \\multicolumn{2}{c||}{K^*} & \n\\multicolumn{2}{c|}{\\phi}\\\\\n\\cline{2-7}\n& \\mu = 1\\,{\\rm GeV} & \\mu = 2\\,{\\rm GeV} & \\mu = 1\\,{\\rm GeV} & \\mu =\n2\\,{\\rm GeV} & \\mu = 1\\,{\\rm GeV} & \\mu = 2\\,{\\rm GeV}\\\\\n\\hline\n\\zeta_{3V}^\\parallel \n& 0.030(10) & 0.020(9) & \\phantom{-}0.023(8) & \\phantom{-}0.015(6) & \n0.024(8) & 0.017(6)\n\\\\\n\\widetilde\\lambda_{3V}^\\parallel \n& 0 & 0 & \\phantom{-}0.035(15)& \\phantom{-}0.017(8) & 0 & 0\n\\\\\n\\widetilde\\omega_{3V}^\\parallel \n& -0.09(3) & -0.04(2) & -0.07(3) & -0.03(2) & -0.045(15) & -0.022(8)\n\\\\\n\\kappa_{3V}^\\parallel \n& 0 & 0 & \\phantom{-}0.000(1) & -0.001(2) & 0 & 0\n\\\\\n\\omega_{3V}^\\parallel \n& 0.15(5) & 0.09(3) & \\phantom{-}0.10(4) & \\phantom{-}0.06(3) &\n0.09(3) & 0.06(2)\n\\\\\n\\lambda_{3V}^\\parallel \n& 0 & 0 & -0.008(4) & -0.004(2) & 0 & 0\n\\\\\n\\kappa_{3V}^\\perp \n& 0 & 0 & \\phantom{-}0.003(3) & -0.001(2) & 0 & 0 \n\\\\\n\\omega_{3V}^\\perp \n& 0.55(25) & 0.37(19) & \\phantom{-}0.3(1) & \\phantom{-}0.2(1) & \n0.20(8) & 0.15(7)\n\\\\\n\\lambda_{3V}^\\perp \n& 0 & 0 & -0.025(20) & -0.015(10) & 0 & 0\\\\\\hline \n\\end{array}\n$$\n\\renewcommand{\\arraystretch}{1}\n\\addtolength{\\arraycolsep}{-3pt}\n\\caption[Results for the leading twist-3 distribution amplitude parameters.]{\\small Results for the leading three-particle twist-3 hadronic parameters of the DAs of Eq.~(\\ref{das_eq27}). The results are presented at the scale $\\mu= 1\\,{\\rm GeV}$ and scaled up to $\\mu= 2\\,{\\rm GeV}$ using the evolution equations (\\ref{evo}). The sign of the parameters corresponds to the sign convention for the strong coupling defined by the covariant derivative $D_\\mu = \\partial_\\mu - i g_s A^a_\\mu t^a$; they change sign if $g_s$ is fixed by $D_\\mu = \\partial_\\mu + i g_s A^a_\\mu t^a$.}\n\\label{det_tab2}\n\\end{table}\n\nIn Fig.~\\ref{graphs} we plot the two-particle twist-3 DAs as defined by Eqs.~(\\ref{das_eq30}- \\ref{das_eq33}). G-parity violating effects cause the small asymmetry of the $K^*$ curves. The effects of $\\rm SU(3)_F$-breaking are larger and cause the pronounced difference between $\\phi_{3}^\\parallel$ and $\\phi_{3}^\\perp$ for the $\\rho$ and $\\phi$. We notice in particular the end-point behaviour of the DAs is greatly modified. The fact that both $\\phi_{3;\\rho}^{\\parallel\\perp}$ and $\\phi_{3;K^*}^{\\parallel\\perp}$ diverge as $u\\to1$ and $\\phi_{3;\\rho}^{\\parallel\\perp}$ for $u\\to0$ is in itself not a problem. It is only the leading-twist DA that can be considered a probability distribution and likewise there is no cause for concern that $\\phi_{3;\\rho}^{\\parallel}$ takes negative values. Moreover, in practical calculations we are only interested in convolutions of the DAs with hard scattering kernels, which are generally finite. If not, this signals a problem with the hard scattering kernel, rather than the DA, as happens with end-point divergences within the QCD factorisation framework for non-leptonic $B$ decays, see Chapter~\\ref{chapter6_QCDF}. \n\\begin{figure}[t]\n$$\n\\epsfxsize=0.5\\textwidth\\epsffile{fig1a.eps}\\quad\n\\epsfxsize=0.5\\textwidth\\epsffile{fig1b.eps}\n$$\n\\caption[The distribution amplitudes $\\phi^\\parallel_{3;V}$ and $\\psi^\\parallel_{3;V}$ as a function of $u$.]{\\small Left: $\\phi^\\parallel_{3}$ as a function of $u$ for the central values of hadronic parameters, for $\\mu=1\\,$GeV. Red line: $\\phi_{3;\\rho}^\\parallel$, green: $\\phi_{3;K^*}^\\parallel$, blue: $\\phi_{3;\\phi}^\\parallel$. Right: same for $\\psi^\\parallel_{3}$.}\n$$\n\\epsfxsize=0.5\\textwidth\\epsffile{fig2a.eps}\\quad\n\\epsfxsize=0.5\\textwidth\\epsffile{fig2b.eps}\n$$\n\\caption[The distribution amplitudes $\\phi^\\perp_{3;V}$ and $\\psi^\\perp_{3;V}$ as a function of $u$.]{\\small Left: $\\phi^\\perp_{3}$ as a function of $u$ for the central values of hadronic parameters, for $\\mu=1\\,$GeV. Red line: $\\phi_{3;\\rho}^\\perp$, green: $\\phi_{3;K^*}^\\perp$, blue: $\\phi_{3;\\phi}^\\perp$. Right: same for $\\psi^\\perp_{3}$.}\n \\label{graphs}\n\\end{figure}\n\n\\chapter{ $B \\to \\eta^{(\\prime)}$ Form Factors in QCD}\\label{chapter5_eta}\nIn this chapter we discuss the semileptonic $B\\to\\eta^{(\\prime)}$ form factors $f_+^{B\\to\\eta^{(\\prime)}}$ in the LCSR approach. The previous LCSR determination of the $B\\to\\eta^{(\\prime)}$ form factors presented in Ref.~\\cite{Ball:2004ye} is completed by calculating the gluonic contribution, the mechanism for which involves the annihilation of the $B$ meson to two gluons. The $\\eta^{(\\prime)}$ particles undergo pronounced mixing with each other due to the $\\rm U(1)_A$ anomaly of QCD and the $\\eta$-$\\eta^{\\prime}$ system, after many years of investigation, has succumbed to the phenomenologically motivated mixing scheme proposed by Feldmann, Kroll and Stech \\cite{Feldmann:1998vh,Feldmann:1998sh}. The consideration of this mixing scheme is central to the correct description of the $B\\to\\eta^{(\\prime)}$ form factors.\n\nMotivation to complete the calculation of $f_+^{B\\to\\eta^{(\\prime)}}$ comes from a variety of sources, with probably the most prominent being:\n\\begin{itemize}\n\\item{the flavour-singlet contributions to the QCD factorisation framework to be discussed in Chapter~\\ref{chapter6_QCDF} were added by Beneke and Neubert in Ref.~\\cite{Beneke:2002jn}. It is found that the branching ratios of $B \\to \\eta^{\\prime} (V,P)$ are very sensitive to $f_+^{B\\to\\eta^{(\\prime)}}$ as the leading-order annihilation diagrams can be interpreted as a gluon contribution to the $B \\to \\eta^{(\\prime)}$ form factors \\cite{Beneke:2003zv}. Therefore a consistent estimation of the annihilation diagrams necessitates the inclusion of the gluonic contributions to the form factor.}\n\\item{There exists a ``tension'' in the determinations of $|V_{ub}|$ from inclusive semileptonic decays $B\\to X_u l\\nu$ and their exclusive counterparts, namely from $B\\to\\pi l \\nu$. The former have led to larger values than the latter, and the reason for the discrepancy is unclear. $B\\to \\eta^{(\\prime)}$ transitions are at leading order a $b\\to u$ transition and so sensitive to $|V_{ub}|$ which can, in principle, be extracted from $B\\to \\eta^{(\\prime)} l \\nu$. An improved calculation of $f_+^{B\\to\\eta^{(\\prime)}}$ would reduce the theoretical uncertainty of the result.}\n\\item{Finally, the observation that exclusive $B\\to \\eta^{\\prime} K$ and inclusive $B\\to \\eta^{\\prime} X$ decays have shown unexpectedly large branching ratios with respect to $B\\to\\pi$ transitions, for example, is an unresolved issue which an improved calculation of $f_+^{B\\to\\eta^{(\\prime)}}$ may help clarify.}\n\\end{itemize}\nWe begin by introducing the $\\eta^{(\\prime)}$ system and define two closely related $\\eta$-$\\eta^{\\prime}$ mixing schemes. We then discuss the calculation of the flavour-singlet contribution to the form factor before lastly we discuss the results of the LCSR analysis, the framework for which was covered in Chapter~\\ref{chapter3_SR}. The material presented in this chapter follows that of Ref.~\\cite{Ball:2007hb}.\n\n\\section{The $\\eta$-$\\eta^{\\prime}$ System}\nThe approximate chiral symmetry of light quarks $u,d$ and $s$ in QCD seems to be broken by Nature to reveal the pseudoscalar mesons $(\\pi^0,\\pi^+,\\pi^-, K^+ ,K^-, K^0 ,\\bar{K}^0, \\eta)$ as the corresponding octet of Goldstone bosons (all massless in the \\textit{chiral limit} $m_{u,d,s}\\to 0$) of the broken $\\rm SU(3)\\otimes SU(3)$ symmetry. There is another symmetry of the QCD Lagrangian (\\ref{basics_eq1}); a global $\\rm U(1)_A$ symmetry which exists at the classical level in the chiral limit. Due to non-vanishing quark masses, the broken $\\rm U(1)_A$ symmetry creates a Goldstone boson, but such a light particle does not appear in the physical spectrum and this embodies the \\textit{$\\rm U(1)_A$ problem}. At the quantum level, however, the $\\rm U(1)_A$ symmetry in the massless limit is broken due to the QCD anomaly and so was not present in the first place; thus a ninth state, the $\\eta^{\\prime}$, exists as a singlet and only becomes massless in the chiral limit \\textit{and} as $N_c\\to\\infty$, causing the effects of anomaly to vanish. The situation is complicated by instanton effects, but was ultimately resolved by 't Hooft with the same conclusion \\cite{Hooft:1976up,Hooft:1986nc}. It has been known for a while that the $\\rm U(1)_A$ anomaly plays a decisive role in the $\\eta^{(\\prime)}$ system with the $\\eta^{\\prime}$ consisting of a large gluonic component \\cite{Witten:1978bc,Ball:1995zv}. The large mass of the $\\eta^{\\prime}$ is mostly generated by the anomaly and $\\rm SU(3)_F$-breaking effects.\\footnote{The particles $\\eta^{(\\prime)}$ have masses $m_{\\eta}=547.51 \\pm 0.18 ~\\textrm{MeV}$ and $m_{\\eta^{\\prime}}=957.78\\pm 0.14 ~\\textrm{MeV}$ and quantum numbers $J^{PC}=0^{-+}$ \\cite{Yao:2006px}.}\n\nThe $\\eta$-$\\eta^{\\prime}$ system has been of considerable interest for a number of years \\cite{Fritzsch:1976qc,Isgur:1976qg,Novikov:1979ux}. Vast simplifications can be made in studying the low-energy particle spectrum of QCD by employing \\textit{Chiral Perturbation Theory} (ChPT) which is an effective theory in which the heavy quarks are integrated out and the dynamically relevant light quarks remain at a scale $\\mu\\sim\\Lambda_{\\rm QCD}$ after an expansion in powers of energies, momenta and quark masses. Alongside the $1\/N_c$ expansion, ChPT is the method of choice for analysing the light pseudoscalar mesons.\\footnote{Another interesting approach to understanding the $\\eta^{(\\prime)}$ system was given in Ref.~\\cite{Katz:2007tf}.} We do not discuss ChPT in any detail although we do quote a few of its constraints; for more details see for example \\cite{Weinberg:1978kz,Gasser:1984gg,Leutwyler:2001hn} \n\nConcerning $\\eta$-$\\eta^{\\prime}$ mixing, ChPT requires a description in terms of two mixing angles beyond leading-order \\cite{Leutwyler:1997yr,Feldmann:1997vc}. How this is implemented in practice has caused some confusion in the past but a consistent picture has emerged \\cite{Feldmann:1998vh,Feldmann:1998sh}. Key to the phenomenological picture of the $\\eta$-$\\eta^{\\prime}$ system is the understanding that the main contributions to the mixing are due to the $\\rm U(1)_A$ anomaly of QCD, and so-called \\textit{OZI-rule violating} processes. Named after Okubo, Zweig and Iizuka the OZI-rule states that strong interaction processes that must proceed via the annihilation of all initial state quarks to gluons are suppressed \\cite{Okubo:1963fa,Iizuka:1966fk,Zweig:1964wu}. In Fig.~\\ref{eta_ozi} we show the unsuppressed process $\\phi \\to K^+ K^-$ (left) alongside the suppressed process $\\phi \\to \\pi^+\\pi^-\\pi^0$ (right) for which the rule was originally formulated. Such processes are shown to be $\\mathcal{O}(1\/N_c)$ in a $1\/N_c$ expansion and phenomenologically they are found to be small $\\approx 10\\%$; they can be safely neglected, leaving the $\\rm U(1)_A$ anomaly as the only mixing mechanism. For the mixing schemes we discuss in the next section, this assumption has been confronted with experimental data and holds to the expected accuracy. \n\\begin{figure}[h]\n$$ \\epsfxsize=0.6\\textwidth\\epsffile{ozi.eps}$$\n\\caption[Examples of an OZI-rule suppressed and allowed strong decays.]{\\small Examples of strong interaction decays. Left: $\\phi \\to K^+ K^-$, right: $\\phi \\to \\pi^+\\pi^-\\pi^0$. The former occurs preferentially over the latter due to the fact that the annihilation of the $\\phi$ requires all gluons to be hard, yielding a suppression via a small $\\alpha_s$ which need not be the case for the first decay. This forms the basis of the OZI-rule.}\n\\label{eta_ozi}\n\\end{figure}\n\nA schematic picture of the $\\rm U(1)_A$ anomaly at work for $B\\to\\eta^{(\\prime)}$ is shown in Fig.~\\ref{eta_u1a}., where the flavour-singlet contribution is defined as the amplitude for producing either a quark-antiquark pair in a singlet state which does not contain the $B$'s spectator quark, or two gluons, which then hadronise into an $\\eta^{(\\prime)}$.\n\\begin{figure}[h]\n$$ \\epsfxsize=0.3\\textwidth\\epsffile{eta_schem.eps}$$\n\\caption[$B\\to \\eta^{(\\prime)}$ via the $\\rm U(1)_A$ anomaly.]{\\small $B\\to \\eta^{(\\prime)}$ via the $\\rm U(1)_A$ anomaly. The $b\\to u$ transition allows for an annihilation of the $B$ meson's quarks to two gluons, thus probing the gluonic content of $\\eta^{(\\prime)}$.}\n\\label{eta_u1a}\n\\end{figure}\n\nWhat about mixing between other pseudoscalar mesons? In $\\eta$ - $\\eta^{\\prime}$ - $\\pi^{0}$ mixing the gluonic component present in the $\\pi^0$ is found to be at the level of a few percent and so can be neglected \\cite{Feldmann:1998sh,Feldmann:1999uf,Kroll:2004rs}. There also exists a $c \\bar{c}$ component to $\\eta^{(\\prime)}$ ($\\eta_{c}$) which is considered in Ref.~\\cite{Feldmann:1998vh} and found to be small with the conclusion that it is not the solution to the abnormally large $B \\to K \\eta^{\\prime}$ branching ratio. Sometimes other particles are included as possible glueball candidates produced via OZI-rule suppressed processes in $J\/\\psi$ decay, see for example Refs.~\\cite{Ball:1995zv,Li:2007ky}. Although it is unclear whether pseudoscalar mesons contain pure glueball properties, Ref.~\\cite{Kroll:2003yi} concludes that it is unlikely. Thus the $\\eta$-$\\eta^{\\prime}$ system stands out on its own. \n\nPhenomenologically, the semileptonic decay $B \\to \\eta^{(\\prime)} l \\nu_l$ can be used to determine the size of the CKM matrix element $|V_{ub}|$ from the spectrum\n\\begin{equation}\\label{eq:spectrum}\n\\frac{d\\Gamma}{dq^2}(B \\to P l \\nu_l) = \\frac{G_F^2 |V_{ub}|^2}{\n192\\pi^3m_B^3}\\lambda^{3\/2}_P(q^2) |f^P_+(q^2)|^2 \\,,\n\\end{equation}\nwhere $P=\\{\\eta,\\eta^{\\prime}\\}$ and $\\lambda_P(x) = (m_B^2+m_{P}^2-x)^2-4m_B^2m_{P}^2$. Alternatively,\nas we shall see, the ratio of branching ratios ${\\cal B}(B\\to\\eta^{\\prime}\n\\ell\\nu)\/{\\cal B}(B\\to \\eta\\ell\\nu)$ can be used to constrain the\ngluonic Gegenbauer moment $B_2^g$.\n\n\\section{State Mixing}\nThe first step in describing $\\eta$-$\\eta^{\\prime}$ mixing is to decompose the two physical states $\\ket{\\eta^{(\\prime)}}$ into other, more convenient orthogonal states. As proposed in Refs.~\\cite{Feldmann:1998vh,Feldmann:1998sh} one can proceed in two ways; either by employing the singlet-octet scheme (SO) or the quark-flavour scheme (QF). The SO axial-vector currents are respectively\n\\begin{equation}\n J^{0}_{\\mu 5}=\\frac{1}{\\sqrt{3}}\\left(\\bar u \\gamma_\\mu\\gamma_5 u+\\bar d \\gamma_\\mu\\gamma_5 d +\\bar s \\gamma_\\mu\\gamma_5 s\\right)\\,,\\quad J^{8}_{\\mu 5}=\\frac{1}{\\sqrt{6}}\\left(\\bar u \\gamma_\\mu\\gamma_5 u+\\bar d \\gamma_\\mu\\gamma_5 d -2\\bar s \\gamma_\\mu\\gamma_5 s\\right)\\,,\n\\end{equation}\nand their couplings are given by\n\\begin{equation}\n\\bra{0} J^{i}_{\\mu 5}\\ket{P(p)} = i f^{i}_{P} p_{\\mu} \\qquad (i=0,8)\\,,\n\\label{SOdc}\n\\end{equation}\nwhere $J^{8}_{\\mu 5}$ denotes the $\\rm SU(3)_F$-octet and $J^{0}_{\\mu 5}$ the $\\rm SU(3)_F$-singlet axial-vector current. The four quantities are related to the decay constants of a pure singlet or octet state $\\ket{\\eta_i}$ by two mixing angles $\\theta_i$\n\\begin{equation}\n\\left(\n\\begin{array}{cc}\nf_\\eta ^8 & f_\\eta^0 \\\\\nf_{\\eta^{\\prime}} ^8 & f_{\\eta^{\\prime}}^0\n\\end{array}\\right)\n= \n\\left(\n\\begin{array}{cc}\n\\cos\\theta_8 & -\\sin\\theta_0 \\\\\n\\sin\\theta_8 & \\phantom{-}\\cos\\theta_0 \n\\end{array}\\right)\n\\left(\n\\begin{array}{cc}\nf_8 & 0 \\\\\n0 & f_0\n\\end{array}\n\\right).\n\\label{corr}\n\\end{equation}\nEvidently $\\rm SU(3)_F$-breaking effects cause $\\theta_i\\neq 0$ and $f_8\\neq f_\\pi$, and as such the SO scheme is very natural. In fact, at leading-order in ChPT an expansion in quark masses and $1\/N_{c}$ gives \\cite{Leutwyler:1997yr}\n\\begin{equation}\n\\sin (\\theta_{0}-\\theta_{8})=\\frac{2 \\sqrt{2} (f_{K}^{2}-f_{\\pi}^{2})}{4f_{K}^{2}-f_{\\pi}^{2}}+\\dots\\,,\n\\label{su3}\n\\end{equation}\nwhere the dots denote neglected higher-order terms which are required to match phenomenology \\cite{Kaiser:1998ds}. The impact of the U(1)$_{\\rm A}$ anomaly is plainly localised in $f_0$ via the divergence of the singlet current $J_{\\mu 5}^0$ which can be written\n\\begin{equation}\n\\partial^\\mu J^{a}_{\\mu 5} = 2\\,\\bar{q} \\left[ t^a \\hat{m} i \\gamma_5 \\right]q + \\delta^{a 0}\\,\\frac{\\alpha_{s}}{4 \\pi} G \\widetilde{G}\\,,\n\\label{anomaly}\n\\end{equation} \nwhere $a=\\{0,1,\\dots,8\\}$, $\\textrm{Tr}[t^a t^b ] = \\frac{1}{2}\\delta^{a b}$, $t^0 = \\textbf{1}\/\\sqrt{3}$ and the mass matrix $\\hat{m}=\\textrm{diag}[m_u,m_d,m_s]$. The SO scheme diagonalises the renormalisation-scale dependence of parameters; $f_8$ and $\\theta_i$ are\nscale-independent, whereas $f_0$ renormalises multiplicatively\n\\begin{equation}\n\\mu\\,\\frac{d f_0}{d \\mu} = - N_f \\left(\\frac{\\alpha_s}{\\pi}\\right)^2 f_0 + O(\\alpha_s^3)\\,.\n\\label{scaledep}\n\\end{equation}\nIn the QF mixing scheme, on the other hand, the basic axial-vector currents are\n\\begin{equation}\nJ^q_{\\mu 5} = \\frac{1}{\\sqrt{2}} \\left(\\bar u \\gamma_\\mu \\gamma_5 u + \n\\bar d \\gamma_\\mu \\gamma_5 d \\right),\\qquad\nJ^s_{\\mu 5} = \\bar s \\gamma_\\mu \\gamma_5 s\\,,\n\\end{equation}\nand the corresponding couplings are\n\\begin{equation}\\label{6}\n\\bra{0} J^r_{\\mu 5}\\ket{P(p)} = i f_P^r p_\\mu \\quad (r=q,s)\\,.\n\\end{equation}\nThe mixing is analogous to (\\ref{corr}) with\n\\begin{equation}\n\\left(\n\\begin{array}{cc}\nf_\\eta ^q & f_\\eta^s \\\\\nf_{\\eta^{\\prime}} ^q & f_{\\eta^{\\prime}}^s \n\\end{array}\\right)\n= \n\\left(\n\\begin{array}{cc}\n\\cos\\phi_q & -\\sin\\phi_s \\\\\n\\sin\\phi_q & \\phantom{-}\\cos\\phi_s \n\\end{array}\\right)\n\\left(\n\\begin{array}{cc}\nf_q & 0 \\\\\n0 & f_s\n\\end{array}\n\\right).\n\\label{corr2}\n\\end{equation}\nBoth quark flavour states $\\ket{\\eta_{q,s}}$ have vanishing vacuum-particle matrix elements with the opposite currents\n\\begin{equation}\n\\bra{0}J^s_{\\mu 5}\\ket{\\eta_q}=\\bra{0}J^q_{\\mu 5}\\ket{\\eta_s}=0\\,,\n\\end{equation}\nwhich is an assumption that has been tested. It is in part motivated by the observation of near ideal mixing in vector and tensor mesons. It implies that the mixing of states is the same as that of the decay constants and moreover leads to the diagonalisation of the mass matrix, which we come back to shortly. This hypothesis does not hold for the SO basis. It is found by Refs.~\\cite{Feldmann:1997vc,Feldmann:1999uf} that the difference between the two mixing angles of the QF scheme $\\phi_q-\\phi_s$ is generated by OZI-rule suppressed processes and is not caused by SU(3)$_{\\rm F}$-breaking effects, as for the SO scheme (\\ref{su3}). While the numerical values of $\\theta_i$ differ largely, with typical values $\\theta_8\\approx -20^\\circ$ and $\\theta_0\\approx - 5^\\circ$, one finds $\\phi_s-\\phi_q\\, \\lesssim\\, 5^\\circ$, with $\\phi_q\\approx\n\\phi_s \\approx 40^\\circ$ \\cite{Feldmann:1998vh,Feldmann:1998sh,Feldmann:1997vc}. This observation led the authors of Refs.~\\cite{Feldmann:1998vh,Feldmann:1998sh} to suggest the QF scheme as an approximation to\ndescribe $\\eta$-$\\eta^{\\prime}$ mixing, based on neglecting the difference $\\phi_q-\\phi_s$ (and all other OZI-breaking effects):\n\\begin{equation}\n\\phi\\equiv \\phi_{q,s},\\qquad \\phi_q-\\phi_s\\equiv 0\\,.\n\\end{equation}\nThe state mixing is then given by\n\\begin{equation}\n\\left(\n\\begin{array}{c}\n\\ket{\\eta} \\\\ \\ket{\\eta^{\\prime}}\n\\end{array}\n\\right) \n= \n\\left(\n\\begin{array}{ll}\n\\cos\\phi & -\\sin\\phi\\\\\n\\sin\\phi & \\phantom{-}\\cos\\phi\n\\end{array}\n\\right)\n\\left(\n\\begin{array}{c}\n\\ket{\\eta_q}\\\\ \\ket{\\eta_s}\n\\end{array}\n\\right)\\,.\n\\label{8}\n\\end{equation}\nThe re\\-nor\\-ma\\-li\\-sa\\-tion-scale dependence of $f_0$ given by Eq.~(\\ref{scaledep}) is not reproduced as it is induced precisely by neglected OZI-breaking terms \\cite{Feldmann:1999uf}. Numerically, this is not a problem as the scale-dependence of $f_0$ is a two-loop effect. In the case of non-local matrix elements, the DAs, this lack of scale dependence of the QF scheme is somewhat problematic. We come back to this point in the next section.\n\nReturning to the diagonalisation of the mass matrix; from Eq.~(\\ref{SOdc}) one finds the quadratic diagonal mass matrix, for example\n\\begin{equation}\\label{div}\n\\bra{0} \\partial^{\\mu}J^{s}_{\\mu 5}\\ket{\\eta (p)} = M^{2}_{\\eta} f^{s}_{\\eta}\\,,\n\\end{equation}\nwhich, via Eq.~(\\ref{anomaly}), gives the mass matrix in QF basis\n\\begin{equation}\n\\mathcal{M}_{\\rm QF}^2=\\left(\n\\begin{array}{cc}\nm_{qq}^2 +\\frac{\\sqrt{2}}{f_q}\\bra{0}\\frac{\\alpha_s}{4\\pi}G\\widetilde{G}\\ket{\\eta_q} & \\frac{1}{f_s}\\bra{0}\\frac{\\alpha_s}{4\\pi}G\\widetilde{G}\\ket{\\eta_q} \\\\\n\\frac{\\sqrt{2}}{f_q}\\bra{0}\\frac{\\alpha_s}{4\\pi}G\\widetilde{G}\\ket{\\eta_s}&m_{ss}^2+\\frac{1}{f_s}\\bra{0}\\frac{\\alpha_s}{4\\pi}G\\widetilde{G}\\ket{\\eta_s}\n\\end{array}\\right)\\,,\n\\label{mass}\n\\end{equation}\nwith the short-hand notation\n\\begin{equation}\nm_{qq}^2=\\frac{\\sqrt{2}}{f_q}\\bra{0}m_u \\bar{u}i\\gamma_5 u+m_d \\bar{d} i \\gamma_5 d\\ket{\\eta_q}\\,,\\qquad m_{ss}^2=\\frac{2}{f_s}\\bra{0}m_s \\bar{s} i \\gamma_5 s\\ket{\\eta_s}\\,.\n\\end{equation}\nFrom Eq.~(\\ref{mass}) the crucial impact of the anomaly, as the only term in the off-diagonal elements, is evident. To first order in $\\rm SU(3)_F$-breaking, the decay constants and quantities $m_{qq,ss}^2$ are fixed giving the theoretical estimate\n\\begin{eqnarray}\nf_q=f_\\pi\\,,&\\quad& f_s =\\sqrt{2 f_K^2-f^2_\\pi}\\,,\\nonumber\\\\\n m_{qq}^2=M_\\pi^2\\,,&\\quad& m_{ss}^2=2 M_K^2-M_\\pi^2\\,,\n\\end{eqnarray}\nwhich also leads to a fixed value of $\\phi$; there is no free parameter left and thus the QF scheme is totally determined \\cite{Feldmann:1998vh}. We do not work in this limit, however, and take numerical values of the decay constants and mixing angle from phenomenology. Given enough data to fix all independent parameters, there is no reason to prefer the QF over the SO scheme. The QF scheme is beneficial when considering DAs as the SO scheme leads to a proliferation of unknown parameters. For this reason we decide to use the QF scheme for the analysis. Its basic parameters have been determined as \\cite{Feldmann:1998vh,Feldmann:1998sh}\n\\begin{equation}\nf_q = (1.07\\pm 0.02)f_\\pi,\\qquad f_s = (1.34\\pm\n0.06)f_\\pi\\,,\\qquad\n\\phi = 39.3^\\circ\\pm 1.0^\\circ\\,.\n\\end{equation}\nThis can be translated into values for the SO parameters as\n\\begin{eqnarray}\nf_8 & = & \\sqrt{\\frac{1}{3}\\,f_q^2 + \\frac{2}{3} f_s^2} = (1.26\\pm\n0.04) f_\\pi\\,,\\nonumber\\\\\nf_0 &=& \\sqrt{\\frac{2}{3}\\,f_q^2 + \\frac{1}{3} f_s^2} = (1.17\\pm\n0.03) f_\\pi\\,,\\nonumber\\\\\n\\theta_8 & = & \\phi-{\\rm arctan}[\\sqrt{2} f_s\/f_q] = (-21.2 \\pm\n1.6)^\\circ\\,,\\nonumber\\\\\n\\theta_0 &=& \\phi-{\\rm arctan}[\\sqrt{2} f_q\/f_s] = (-9.2 \\pm\n1.7)^\\circ\\,,\n\\end{eqnarray}\nNote that in the QF scheme $f_{q,s}$ are scale-independent parameters, and so is $f_0$ as obtained from the above relations. The SO decay constants are related to those of the QF scheme by a change of basis\n\\begin{equation}\\label{11}\n\\left(\n\\begin{array}{cc}\nf_\\eta ^8 & f_\\eta^0 \\\\\nf_{\\eta^{\\prime}} ^8 & f_{\\eta^{\\prime}}^0 \n\\end{array}\\right)\n= \n\\left(\n\\begin{array}{cc}\n\\cos\\phi & -\\sin\\phi \\\\\n\\sin\\phi & \\phantom{-}\\cos\\phi \n\\end{array}\\right)\n\\left(\n\\begin{array}{cc}\nf_q & 0 \\\\\n0 & f_s\n\\end{array}\\right)\n\\left(\n\\begin{array}{cc}\n\\phantom{-}\\sqrt{\\frac{1}{3}} & \\sqrt{\\frac{2}{3}}\\\\\n-\\sqrt{\\frac{2}{3}} & \\sqrt{\\frac{1}{3}}\n\\end{array}\\right).\n\\end{equation}\nThe last matrix originates from the ideal mixing angle $\\theta_{\\textrm{ideal}}=\\arctan{\\sqrt{2}}$ which rotates from the QF basis to the SO basis.\n\n\\section{Pseudoscalar Meson Distribution Amplitudes}\nAs discussed in Chapter~\\ref{chapter3_SR}, the method of LCSRs relies on the non-perturbative universal light-cone DAs; specifically here we require pseudoscalar meson DAs including the two-gluon DA. At leading-twist both these DAs contribute and indeed mix with each other under renormalisation. The quark-antiquark DAs are extensions of the matrix elements given by Eqs.~(\\ref{SOdc}) and (\\ref{6}) to those of non-local operators on the light-cone. Pseudoscalar mesons' quark-antiquark DAs have been investigated previously in Refs.~\\cite{Ball:1998je,Ball:2006wn,Braun:1989iv}. The two-gluon DAs of leading and higher twist have been investigated in Ref.~\\cite{AP03}. In this analysis we only include the effects of the leading-twist two-gluon DA, which is justified as its effects turn out to be fairly small and higher-twist DAs are estimated to have even smaller impact. Following Ref.~\\cite{Kroll:2002nt}, the twist-2 two-quark DAs of $\\eta^{(\\prime)}$ are defined as\n\\begin{equation}\n\\bra{0} \\bar\\Psi(z) {\\cal C}^i\\gamma_z \\gamma_5 [z,-z] \\Psi(-z) \\ket{P(p)} = i (p\\cdot z) f_P^i \\int_0^1 du\\, e^{i \\xi p \\cdot z} \\phi_{2;P}^i(u) \\,.\n\\end{equation}\n$\\phi_{2;P}^i(u)$ is the twist-2 DA of the meson $P$ with respect to the current whose flavour content is given by ${\\cal C}^i$, with $\\Psi = (u,d,s)$ the triplet of light-quark fields in flavour space. For the SO currents, one has ${\\cal C}^0 = \\mbox{\\boldmath $1$}\/\\sqrt{3}$ and ${\\cal C}^8 = \\sqrt{2}\\, t^8$, while for the QF currents ${\\cal C}^q = (\\sqrt{2} {\\cal C}^0 + {\\cal C}^8)\/\\sqrt{3}$ and ${\\cal C}^s = ({\\cal C}^0 - \\sqrt{2} {\\cal C}^8)\/\\sqrt{3}$. Due to the positive G-parity of $\\eta$ and $\\eta^{\\prime}$, the two-quark DAs are symmetric under $u\\leftrightarrow 1-u$, and hence all odd Gegenbauer moments vanish:\n\\begin{equation}\n\\phi_{2;P}^i(u) = \\phi_{2;P}^i(1-u)\\,,\n\\end{equation}\nand the DAs are expanded in terms of Gegenbauer polynomials in exactly the same way as for the vector mesons\n\\begin{equation}\n\\phi_{2;P}^i(u) = 6 u (1-u) \\left( 1 + \\sum_{n=2,4,\\dots} a_n^{P,i}(\\mu)\nC^{3\/2}_n(\\xi) \\right) \\,\\quad (i=1,8,q,s)\\,,\n\\label{expansion}\n\\end{equation}\nwhere $a_n^{P,i}$ are the quark Gegenbauer moments. The gluonic twist-2 DA is defined as\\footnote{This definition refers to the ``$\\sigma$-rescaled'' DA $\\phi^\\sigma_g$ in Ref.~\\cite{Kroll:2002nt} with $\\sigma = \\sqrt{3}\/C_F$. It agrees with that used in Refs.~\\cite{AP03,Charng:2006zj}, which means that we can use their results for the two-gluon Gegenbauer moment\n$B^g_2$ without rescaling.}\n\\begin{equation}\n\\bra{0} G_{\\mu z}(z) [z,-z] \\widetilde G^{\\mu z}(-z) \\ket{P(p)} = \\frac{1}{2}\\,(p\\cdot z)^2 \\frac{C_F}{\\sqrt{3}} f_P^0 \\int_0^1 du\\, e^{i\\xi p\\cdot z} \\psi_{2;P}^g(u)\\,.\n\\end{equation}\nIn order to perform the calculation of the correlation function defined in the next section, we also need the matrix element of the meson $P$ over two gluon fields. Dropping the gauge factor $[z,-z]$ one has\n\\begin{equation}\n\\bra{0} A^a_\\alpha(z) A^b_\\beta(-z)\\ket{P(p)} =\n\\frac{1}{4}\\,\\epsilon_{\\alpha\\beta\\rho\\sigma} \\,\\frac{z^\\rho\n p^\\sigma}{p\\cdot z} \\,\\frac{C_F}{\\sqrt{3}}\\, f_P^0 \\,\\frac{\\delta^{ab}}{8}\n\\int_0^1 du\\, e^{i\\xi p\\cdot z}\\,\\frac{\\psi_{2;P}^g(u)}{u(1-u)}\\,.\n\\label{gluefields}\n\\end{equation}\nThe two-gluon asymptotic DA is $u^{2j-1}(1-u)^{2j-1}$ with $j=3\/2$ the lowest conformal spin of the operator $G_{\\mu z}$ and the expansion goes in terms of Gegenbauer polynomials $C^{5\/2}_n$, see Eq.~(\\ref{basics_eq18}). One can show that $\\psi_{2;P}^g$ is antisymmetric:\n\\begin{equation}\n\\psi_{2;P}^g(u) = - \\psi_{2:P}^g(1-u)\\,,\n\\end{equation}\nand in particular $\\int_0^1 du\\, \\psi_{2;P}^g(u) = 0$ and the local twist-2 matrix element $\\bra{0} G_{\\mu z} \\widetilde G^{\\mu z}\\ket{P}$ vanishes. The non-vanishing coupling $\\bra{0}G_{\\alpha\\beta} \\widetilde G^{\\alpha\\beta}\\ket{P}$ induced by the U(1)$_{\\rm A}$ anomaly is a twist-4 effect. The corresponding matrix elements are discussed in Refs.~\\cite{Feldmann:1998vh,Feldmann:1998sh} and are given, in the QF scheme, by:\n\\begin{eqnarray}\n\\bra{0}\\frac{\\alpha_s}{4\\pi} G\\widetilde{G} \\ket{\\eta_q} & = & \nf_s (m_\\eta^2-m_{\\eta^{\\prime}}^2) \\sin\\phi \\cos\\phi\\,,\\nonumber\\\\\n\\bra{0}\\frac{ \\alpha_s}{4\\pi} G\\widetilde{G} \\ket{\\eta_s} & = & \nf_q (m_\\eta^2-m_{\\eta^{\\prime}}^2)\/\\sqrt{2} \\sin\\phi \\cos\\phi\\,.\n\\label{extra}\n\\end{eqnarray}\nIn taking the ratios of both sides of the above relations one can see that $\\rm SU(3)_F$-breaking in the decay constants $f_q\/f_s$ is driven by the anomaly. There are no twist-3 two-gluon DAs and the remaining twist-4 DAs also have vanishing normalisation \\cite{AP03}. The conformal expansion of the twist-2 two-gluon DA reads\n\\begin{equation}\n\\psi_{2;P}^g(u,\\mu) = u^2 (1-u)^2 \\sum_{n=2,4,\\dots} B^{P,g}_n(\\mu)\nC^{5\/2}_{n-1}(\\xi)\\,,\n\\end{equation}\nwith the gluonic Gegenbauer moments $B^{P,g}_n$. In this analysis, we truncate both $\\phi^i_{2;P}$ and $\\psi^g_{2;P}$ at $n=2$. An estimate of the effect of higher Gegenbauer moments in $\\phi_{2;\\pi}$ on the $B\\to\\pi$ form factor $f_+^\\pi$ has been given in Ref.~\\cite{Ball:2005ei}, based on a\ncertain class of models for the full DA beyond conformal expansion. The effect of neglecting $a_{n\\geq 4}^\\pi$ was found to be very small $\\approx 2\\%$ hence we expect the truncation error from neglecing $B^g_{n\\geq 4}$ to be of similar size.\n\n$\\phi_{2;P}^0$ and $\\psi_{2;P}^g$ mix upon a change of scale $\\mu$ and as discussed in Refs.~\\cite{Baier:1981pm,Kroll:2002nt} this amounts to a mixing of $a_2^{P,0}$ and $B^{P,g}_2$, resulting in the renormalisation-group equation to LO accuracy\n\\begin{equation}\n\\mu\\,\\frac{d}{d\\mu}\\left(\\begin{array}{c} a_2^0\\\\ B^g_2\n\\end{array}\\right)\n=\n-\\frac{\\alpha_s}{4\\pi} \\left(\\begin{array}{cc} \\displaystyle\\frac{100}{9} &\n\\displaystyle -\\frac{10}{81}\\\\\\vphantom{\\displaystyle\\frac{100}{9}}\n -36 & 22\\end{array}\\right)\n\\left(\\begin{array}{c} a_2^0\\\\ B^g_2\n\\end{array}\\right),\n\\label{20}\n\\end{equation}\nwhere for simplicity we have dropped the superscript $P$. The solution for $a_2^0$ reads\n\\begin{eqnarray}\na_2^0(\\mu^2) & = & \\left[ \\left(\\frac{1}{2} -\n \\frac{49}{2\\sqrt{2761}}\\right) L^{\\gamma_2^+\/(2\\beta_0)} + \n\\left(\\frac{1}{2} +\n \\frac{49}{2\\sqrt{2761}}\\right) L^{\\gamma_2^-\/(2\\beta_0)}\\right]\n a_2^0(\\mu_0^2) \\nonumber\\\\\n&&{}+ \\frac{5}{9\\sqrt{2761}}\\left[L^{\\gamma_2^-\/(2\\beta_0)}-\nL^{\\gamma_2^+\/(2\\beta_0)}\\right] B_2^g(\\mu_0^2)\n\\label{21}\n\\end{eqnarray}\nwith the anomalous dimensions $\\gamma_2^\\pm = (149\\pm \\sqrt{2761})\/9$. The octet Gegenbauer moment does not have another DA with which it can mix and so its evolution is simpler\n\\begin{equation}\na_2^8(\\mu^2) = L^{50\/(9\\beta_0)} a_2^8(\\mu_0^2)\\,.\n\\label{22}\n\\end{equation}\nThe mixing amongst the DAs complicates matters; as the scale dependence of the decay constants is lost in the QF scheme, one expects to have to lose scale dependence in the DAs too, and we must be careful to be consistent. The verification of the anomalous dimensions in Eq.~(\\ref{20}) from the singlet and octet parts of the form factor calculations is a crucial test of the LCSR analysis. For this reason, we discuss the implications of mixing on the twist-2 DA parameters, and only briefly cover higher-twist quark DAs which are included in the octet part; for a detailed discussion one is referred to Ref.~\\cite{Ball:2007hb}. Following Ref.~\\cite{Kroll:2002nt}, for the DAs introduced by Eq.~(\\ref{expansion}) we have, in terms of the quark valence Fock states $\\ket{q\\bar q}$ and $\\ket{s\\bar s}$\n\\begin{equation}\n\\ket{\\eta_q} \\sim \\phi_2^q (u) \\ket{q\\bar q} + \\phi_2^{\\rm\n OZI}(u) \\ket{s\\bar s}\\,,\\quad\n\\ket{\\eta_s} \\sim \\phi_2^{\\rm OZI} (u) \\ket{q\\bar q} + \n\\phi_2^s(u) \\ket{s\\bar s}\\,,\n\\end{equation}\nwhere $q\\bar q$ is shorthand for $(u\\bar u + d\\bar d)\/\\sqrt{2}$ and \n\\begin{equation}\n\\phi_2^q = \\frac{1}{3}\\,(\\phi_2^8 + 2\\phi_2^0)\\,,\\quad\n\\phi_2^s = \\frac{1}{3}\\,(2\\phi_2^8 + \\phi_2^0)\\,,\\quad\n\\phi_2^{\\rm OZI} = \\frac{\\sqrt{2}}{3} (\\phi_2^0-\\phi_2^8)\\,.\n\\label{darel}\n\\end{equation}\nIn the QF scheme, the ``wrong-flavour'' DA $\\phi_2^{\\rm OZI}$, which is generated by OZI-violating interactions, is set to 0. Once this is done at a certain scale, however, the different evolution of $a_n^0$ and $a_n^8$ will generate a non-zero $\\phi_2^{\\rm OZI}$ already to LO accuracy. A consistent implementation of the QF scheme hence requires one to either set $a_n^{0,8}\\equiv 0$ and also $B^g_n\\equiv 0$, or to set $a_n^8\\equiv a_n^0$ and neglect the different \nscale-dependence of these parameters. The induced non-zero DA $\\phi_2^{\\rm OZI}$ is numerically very small for the scales relevant for our calculation, $\\mu=1\\,$GeV and $2.4\\,$GeV.\\footnote{$2.4\\,$GeV is a typical scale in the calculation of form factors from LCSRs: $\\mu=\\sqrt{m_B^2-m_b^2}$ is chosen as an intermediate scale between $m_b$ and the typical hadronic scale $1\\,{\\rm GeV}$.} The left panel of Fig.~\\ref{eta_fig2} shows a plot of $\\Delta=100\\,| (a^0_2(\\mu)-a^8_2(\\mu))\/a^0_2(\\mu)|$ as a function of scale $\\mu$, according to Eqs.~(\\ref{21}) and (\\ref{22}), for $a_2^8(1\\,{\\rm GeV})\\equiv a_2^0(1\\,{\\rm GeV})$ and $B_2^g=0$. We see that $\\Delta$ is less than $0.25\\,\\%$ over the range $1\\,\\textrm{GeV}<\\mu<2.4\\,\\textrm{GeV}$. Choosing $a_2^8(1\\,{\\rm GeV})=0.25\\pm0.15$, guided by our knowledge of twist-2 DAs of the $\\pi$; we have $a_2^8(2.4\\,{\\rm GeV}) = 0.171$ from Eq.~(\\ref{22}), and $a_2^0(2.4\\,{\\rm GeV}) = 0.171$ for $B^g_2=0$, from Eq.~(\\ref{21}). Evidently, the impact of the different anomalous dimensions of $a_2^0$ and $a_2^8$ is negligible.\n\\begin{figure}[h]\n$$\\epsfxsize=0.45\\textwidth\\epsffile{scaling1.eps}\\qquad \\qquad\\epsfxsize=0.45\\textwidth\\epsffile{scaling2.eps}$$\n\\caption[Scale dependence of the twist-2 distribution amplitude parameters.]{\\small Left: $\\Delta=100\\,| (a^0_2(\\mu)-a^8_2(\\mu))\/a^0_2(\\mu)|$ as a function of scale $\\mu$, according to Eqs.~(\\ref{21}) and (\\ref{22}) with $B_2^g=0$. Right: dependence of $a^0_2(2.4\\,{\\rm GeV})$ on $B^g_2(1\\,{\\rm GeV})$ for $a^0_2(1\\,{\\rm GeV})=0.25$ according to Eq.\\,(\\ref{21})}\n\\label{eta_fig2}\n\\end{figure}\nAlso, the evolution of $a_2^0$ is not hugely different to that of $a_2^8$, for a wide range of values of $B^2_g$. The right panel of Fig.~\\ref{eta_fig2} shows the evolution of the singlet Gegenbauer moment $a_2^0$ from $\\mu=1\\,{\\rm GeV}$ - $2.4\\, {\\rm GeV}$, from Eq.~(\\ref{21}), for the range of gluon Gegenbauer moments $|B_2^g(1\\,{\\rm GeV})|<20$, which is a \\textit{very} conservative estimated range, as discussed below. The mixing of $B_2^g$ into $a_2^0$ is up to $20\\%$ for $B_2^g=20$ and $40\\%$ for $B_2^g=-20$.\n\nFrom the conclusions of the above discussion we are justified in implementing the QF scheme for DAs as follows: we set $\\phi_2^0\\equiv \\phi_2^8$ at the scale $\\mu=1\\,$GeV, which, by virtue of Eq.~(\\ref{darel}), implies $\\phi_2^q\\equiv\\phi_2^s$ at the same scale. We then evolve $a_2$ according to the scaling-law for the octet Gegenbauer moment (\\ref{22}).\\footnote{This is equivalent to imposing the QF-scheme relation $a_2^0=a_2^8$ as the scale $\\mu=2.4\\,$GeV and defining $B^g_2$ as $B^g_2(2.4\\,{\\rm GeV})$.} We also set $\\psi_{2;\\eta}^g=\\psi_{2;\\eta^{\\prime}}^g$; again any SU(3)$_{\\rm F}$-breaking of this relation is expected to have only very small impact on $f_+^{B\\to\\eta^{(\\prime)}}$. The twist-2 parameters used in our calculation are then reduced to two: $a_2$ and $B^g_2$. \n\nConcerning numerical values, we assume that the bulk of SU(3)$_{\\rm F}$-breaking effects is described by the decay constants via $f_q\\neq f_\\pi$, and that SU(3)$_{\\rm F}$-breaking in Gegenbauer moments is sub-leading \\cite{Ball:2006wn}. Sum rules for $a_2^\\pi$ and $a_2^q$ would essentially be the same, with $f_\\pi\\neq f_q$ driving the SU(3)$_{\\rm F}$-breaking and any small differences in $s_0$ and $M^2$ being negligible. This motivates setting $a_2^q = a_2^\\pi$, with $a_2^\\pi(1\\,{\\rm GeV}) = 0.25\\pm 0.15$ as an average over a large number of calculations and fits to experimental data \\cite{Ball:2006wn}. \n\nFor $B_2^g$, however, no direct calculation is available. Results from fits to data have been obtained from the $\\eta^{\\prime}\\gamma$ transition form factor, yielding $B_2^g(1\\,{\\rm GeV}) = 9\\pm 12$ \\cite{Kroll:2002nt}, and the combined analysis of this form factor and the inclusive decay $\\Upsilon(1S)\\to \\eta^{\\prime} X$ yielding $B_2^g(1.4\\,{\\rm GeV}) = 4.6\\pm 2.5$ \\cite{AP03}. Caution must be taken when considering these results as they are highly correlated with the simultaneous determination of $a_2^0$ and $a_2^8$ from the same data, yielding $a_2^0(1\\,{\\rm GeV}) = -0.08\\pm 0.04$, $a_2^8(1\\,{\\rm GeV}) =\n-0.04\\pm 0.04$ and $a_2^0(1.4\\,{\\rm GeV}) = a_2^8(1.4\\,{\\rm GeV}) = -0.054\\pm 0.029$, respectively. The same analysis, applied to the $\\pi\\gamma$ form factor, returns $a_2^\\pi (1\\,{\\rm GeV}) =-0.06\\pm 0.03$ \\cite{vogt}. These results are not really compatible with those from the direct calculation of $a_2^\\pi$ from Lattice QCD and QCD sum rules; in particular the sign of $a_2^\\pi$ is unambiguously fixed as being positive. A possible reason for this discrepancy is the neglection of higher-order terms in the light-cone expansion and that, in addition, as one of the photons in the process is nearly real with virtuality $q^2\\approx 0$, one also has to take into account long-distance photon interactions, of order $1 \\sqrt{q^2}$, as discussed in Ref.~\\cite{rady}. For this reason, we assume the very conservative range $B_2^g(2.4\\,{\\rm GeV}) = 0\\pm20$ in the analysis.\n\nAs far as higher-twist quark DAs are concerned, we only need those involving currents with flavour content $\\bar q q = (\\bar u u + \\bar d d)\/\\sqrt{2}$. In line with the implementation of the QF scheme\nfor twist-2 DAs, we include SU(3)$_{\\rm F}$-breaking only via the decay constants. The precise definitions of all twist-3 and 4 DAs, as well as up-to-date numerical values of the $\\pi$'s hadronic parameters can be found in Ref.~\\cite{Ball:2006wn}. A discussion of the correct treatment of these DAs\nwithin LCSR, as modified to describe $\\eta^{(\\prime)}$, can be found in Ref.~\\cite{Ball:2007hb}. \n\n\\section{Calculation}\nWe define the $B\\to P$ form factors analogously to those of other pseudoscalar mesons as \\cite{Ball:2004ye}\n\\begin{equation}\n\\bra{P(p)} \\bar u \\gamma_\\mu b \\ket{B(p+q)} = \\left\\{\n(2p+q)_\\mu - \\frac{m_B^2-m_P^2}{q^2}\\,q_\\mu\\right\\} \\frac{f_+^P(q^2)}{\\sqrt{2}} + \\frac{m_B^2-m_P^2}{q^2}\\,q_\\mu\\,\\frac{f_0^P(q^2)}{\\sqrt{2}}\\,.\n\\label{FF}\n\\end{equation}\nwhere the factor of $1\/\\sqrt{2}$ on the right-hand side is to ensure that in the SU(3)$_{\\rm F}$ symmetry limit, without $\\eta$-$\\eta^{\\prime}$ mixing, $f_+^\\eta = f_+^\\pi$. For semileptonic decays $B\\to \\eta^{(\\prime)} l \\nu_l$ the form factor $f_0^P$ appears proportional to $q^2\\approx m_l^2$ which is negligible for light leptons $l=\\{e,\\mu\\}$ for which only $f_+^P$ is required. Using the LCSR method outlined in Chapter~\\ref{chapter3_SR} we extract the semileptonic form factor $f_{+}^P$ from the following correlation function\n\\begin{eqnarray}\n\\Pi^P_{\\mu}(p,q) &=& i\\int d^4x\\,e^{i q\\cdot x} \\bra{P(p)} T [\\bar u \\gamma_\\mu b](x) j_B^{\\dagger}(0)\\ket{0}\\\\\n&=& \\Pi_+^P(q^2,p_B^2) (2p+q)_\\mu + \\dots\\,,\\nonumber\n\\label{eq:corr}\n\\end{eqnarray}\nwhere $j_B= m_b \\bar u i\\gamma_5 b$ is the interpolating current for the $B$ meson and $p_B^2=(p+q)^2$ its virtuality. In calculating the correlation function, we use Eq.~(\\ref{8}) which relates the physical states $\\ket{\\eta^{(\\prime)}}$ and the QF basis states $\\ket{\\eta_{q,s}}$ so that\n\\begin{equation}\n\\Pi^\\eta_\\mu = \\frac{1}{\\sqrt{2}}\\left(\\Pi^q_{\\mu} \\cos\\phi - \\Pi^s_{\\mu} \\sin\\phi\\right),\\quad\n\\Pi^{\\eta^{\\prime}}_\\mu = \\frac{1}{\\sqrt{2}}\\left(\\Pi^q_{\\mu} \\sin\\phi + \\Pi^s_{\\mu} \\cos\\phi\\right)\\,.\n\\label{32}\n\\end{equation}\nThe interpolating current $\\bar u \\gamma_\\mu b$ only probes the $\\bar u u $ quark component of the $\\eta^{(\\prime)}$ so $\\Pi^s_\\mu$ vanishes to leading order in $\\alpha_s$ and at $O(\\alpha_s)$ is due only to gluonic Fock states of the meson. $\\Pi^q_\\mu$, on the other hand, receives contributions from both quark and gluon states. The final LCSR for $f_+^P$ then reads\n\\begin{equation}\\label{35}\ne^{-m_B^2\/M^2}\\,m_B^2 f_B\\, \\frac{f_+^P(q^2)}{\\sqrt{2}} = \\int_{m_b^2}^{s_0}\nds\\,e^{-s\/M^2}\\, \\frac{1}{\\pi}\\,{\\rm Im}_s\\,\\Pi^P_+(s,q^2)\\,,\n\\end{equation}\nwith the usual sum rule specific parameters $M^2$, the Borel parameter, and $s_0$, the continuum threshold.\n\\subsection*{Quark Contribution}\nThe quark contributions follow from the studies already undertaken for the $\\pi$, for more details see Ref.~\\cite{Ball:2004ye}. We briefly cover the general features of the calculation to put the singlet contribution in context. The leading quark contributions to $\\Pi_+^P$ originate from the diagrams of Fig.~\\ref{eta_fig1}, where first order $\\mathcal{O}(\\alpha_s)$ corrections are shown. The external quarks have momentum fractions $u p$ and $(1-u)p$ and are on-shell; $p^2=m_P^2$.\n\\begin{figure}[h]\n$$ \\epsfxsize=0.85\\textwidth\\epsffile{eta_diags1.eps}$$\n\\caption[The quark contributions to $f_+^{\\eta^{(\\prime)}}(q^2)$ to $\\mathcal{O}(\\alpha_s)$.]{\\small The quark-antiquark contributions to the semileptonic $B\\to\\eta^{(\\prime)}$ form factors $f_+^{\\eta^{(\\prime)}}(q^2)$ from light-cone sum rules. The top left diagram is the leading one, the others are $\\mathcal{O}(\\alpha_s)$. The double line corresponds to the $b$ quark and the dashed lines the injection of the weak vertex momentum $q$, and the momentum of the $B$ meson $p_B$.} \n\\label{eta_fig1}\n\\end{figure}\nThe two-particle DAs are projected out by using the general spinor decomposition of quark fields\n\\begin{eqnarray}\n\\bar{q}_a q_b^\\prime &=& \\frac{1}{4} (\\textbf{1})_{ba}(\\bar q q^\\prime)-\\frac{1}{4} (i \\gamma_5)_{ba}(\\bar q i \\gamma_5 q^\\prime)+\\frac{1}{4} (\\gamma_\\mu)_{ba}(\\bar q \\gamma^\\mu q^\\prime)-\\frac{1}{4} (\\gamma_\\mu \\gamma_5)_{ba}(\\bar q\\gamma^\\mu \\gamma_5 q^\\prime)\\nonumber\\\\\n&-&\\frac{1}{8} (i \\sigma_{\\mu\\nu}\\gamma_5)_{ba}(\\bar q i \\sigma^{\\mu\\nu} \\gamma_5 q^\\prime)\\,.\n\\end{eqnarray}\nThe vacuum-meson matrix elements of each term above either vanish or yield a DA depending on the quantum numbers of the meson in question. For pseudoscalar mesons the leading-twist contribution comes from $\\gamma_\\mu\\gamma_5$, whereas $i\\gamma_5$ and $i\\sigma_{\\mu\\nu}\\gamma_5$ give two-particle twist-3 contributions, and although the two-particle twist-3 contributions appear in the sum rules as formally $1\/m_b$, they are \\textit{chirally enhanced} by numerically large factors \\cite{Ball:1998je} and so are included in typical LCSR analyses \\cite{Ball:2004ye}. Three-particle twist-3 and two- and three-particle twist-4 DAs are also included; all twist-2 and -3 contributions include $O(\\alpha_s)$ corrections twist-4 contributions are to tree level accuracy. The corresponding expressions yield $\\Pi^q_+$, with the replacement $f_\\pi\\to f_q$. \n\n\\subsection*{Gluonic Contribution}\nIn order to obtain the gluonic contribution to $\\Pi_+^P$, one needs to calculate the diagrams shown in Fig.~\\ref{eta_diags}. The last diagram is divergent and the other two are finite.\n\\begin{figure}[h]\n$$ \\epsfxsize=0.85\\textwidth\\epsffile{eta.eps}$$\n\\caption[The leading diagrams for the flavour-singlet contribution to $f_+^{\\eta^{(\\prime)}}(q^2)$.]{\\small The leading diagrams for the flavour-singlet contribution to the semileptonic $B\\to\\eta^{(\\prime)}$ form factors from light-cone sum rules. The double line corresponds to the $b$ quark. The dashed lines the injection of weak vertex momentum $q$, and momentum of the $B$ meson interpolating current $p_B$.} \n\\label{eta_diags}\n\\end{figure}\nThe gluon fields are introduced in the standard way\n\\begin{eqnarray}\n\\left.\\Pi^{P}_{\\mu}\\right|_{\\textrm{gluon}}=i\\int d^4[x,w,y] \\, e^{i q\\cdot x} \\bra{P(p)} T [\\bar{u}\\gamma_{\\mu} b](x) [m_b \\bar{b} i\\gamma_5 u](0) \\, S \\mathcal{L}^{q_1}_{g}(w)\\mathcal{L}^{q_2}_{g}(y)\\ket{0}\\,,\\nonumber\n\\end{eqnarray}\nwith the usual interaction Lagrangian $\\mathcal{L}^{q_{i}}_{g}(x) = ig_{s} [\\bar{q_i} \\gamma^{\\alpha} A_{\\alpha}^a t^a q_i](x)$ with $q_{i} = \\{u,b\\}$ and the statistical factor $S$ takes values $1$ if $q_1\\neq q_2$ and $1\/2$ if $q_1=q_2$. The integral is over each co-ordinate separately. To extract the gluon contribution we need the projection onto the twist-2 two-gluon DA, which can be read off Eq.~(\\ref{gluefields}), which amounts to the following replacement of the gluon fields (up to the numerical factor)\n\\begin{equation}\nA_{\\alpha}^a(w) A_{\\beta}^b(y) \\stackrel{\\textrm{twist-2}}{\\longrightarrow}\\delta^{ab} \\epsilon_{\\alpha \\beta \\rho\\sigma} \\,\\frac{\\tilde{z}^\\rho p^\\sigma }{p \\cdot \\tilde{z}}\\,\\,\\int^1_0 du\\,\\frac{\\psi^g_{2;P}(u)}{u \\bar{u}} \\,e^{i p \\cdot (u w + \\bar{u} y)}\\,,\n\\end{equation}\nwhere the separation $\\tilde{z}$ is light-like i.e. $\\tilde{z}^2 = (w-y)^2=0$. Via partial integration we can simplify the resulting expression for $\\left.\\Pi^{P}_{+}\\right|_{\\textrm{gluon}}$; the co-ordinate $\\tilde{z}$ is traded for a derivative of the hard scattering kernel with respect to the momentum of one of the emitted gluons; and the dot product $1\/ (p\\cdot \\tilde{z})$ can be traded for an integral with respect to the DA momentum fraction. As the boundary terms vanish due to the leading-twist gluon DA being antisymmetric, the calculation takes a rather simple form:\n\\begin{equation}\n\\left.\\Pi^{P}_{+}\\right|_{\\textrm{gluon}}=\\left.\\int^1_0 du \\, \\left[\\frac{\\partial \\,T_{\\mu }^\\rho(u p) }{\\partial(u p)^\\rho}\\right] \\int^u_0 dv \\,\\frac{\\psi_{2;P}^g(v)}{v\\bar{v}}\\right|_{p_\\mu \\to \\frac{1}{2},\\,q_\\mu \\to 0}\\,,\n\\end{equation}\nwhere $T_{\\mu }^\\rho(u p) $ is the hard scattering kernel. Both the gluonic and quark contributions are renormalisation scale dependent. The relevant term concerning the quark Gegenbauer moment $a_2$ is\n\\begin{equation}\n\\Pi^q_+ \\sim 18 f_q a_2 \\left( 1 + \\frac{\\alpha_s}{4\\pi}\n\\,\\frac{50}{9}\\,\\ln\\,\\frac{\\mu^2}{m_b^2}\\right)F(p_B^2,q^2)\\,,\n\\label{33}\n\\end{equation}\nwhere $F(p_B^2,q^2)$ is a function of $p_B^2$ and $q^2$. The logarithmic terms in the convolution of the gluonic diagrams of Fig.~\\ref{eta_diags} with $\\psi_{2;P}^g$ are\n\\begin{equation}\n\\Pi^P_+ \\sim -\\frac{10}{9\\sqrt{3}}\\,\\frac{\\alpha_s}{4\\pi}\\, B_2^g f^0_P\n\\ln\\,\\frac{\\mu^2}{m_b^2}\\, F(p_B^2,q^2) \\,.\n\\end{equation}\nBy expressing $f_q$ via Eq.~(\\ref{11}) in terms of $f^0_\\eta$ and $f^0_{\\eta^{\\prime}}$, respectively, and inserting Eq.~(\\ref{33}) into Eq.~(\\ref{32}), one verifies that the renormalisation-group equation, Eq.~(\\ref{20}), is fulfilled. The twist-2 two-gluon contribution to the correlation functions $\\Pi_+^P$, Eq.~(\\ref{32}), is given in terms of a spectral density as\n\\begin{equation}\n\\left.\\Pi_{+}^{P}\\right|_{\\textrm{gluon}} = \\int_{m_b^2}^\\infty ds\\,\\frac{\\rho^P_{\\textrm{gluon}}(s)}{s-p_B^2}\n\\end{equation}\nwith the result being\n\\begin{eqnarray}\n\\rho^P_{\\textrm{gluon}}(s) \n& = & B_2^g \\alpha_s f_0^P m_b\\, \\frac{5}{36\\sqrt{3}}\\, \\frac{m_b^2-s}{(s-q^2)^5} \\, \\left\\{ 59 m_b^6 + 21 q^6 - 63 q^4 s - 19 q^2 s^2 + 2 s^3\\right. \\nonumber\\\\\n&& \\hspace*{3cm}\\left. + \\,m_b^2 s (164 q^2 + 13 s) - m_b^4 (82 q^2 + 95s)\\right\\}\n\\nonumber\\\\\n&& {} + B_2^g \\alpha_s f_0^P m_b\\, \\frac{5}{6 \\sqrt{3}}\\, \\frac{(m_b^2-q^2)(s-m_b^2)}{(s-q^2)^5} \\,\\{ 5 m_b^4 + q^4 + 3 q^2 s + s^2 - 5m_b^2 (q^2+s)\\} \\nonumber\\\\\n&& \\hspace*{3cm} \\times\\left\\{ 2 \\ln\\,\\frac{s-m_b^2}{m_b^2} - \\ln\\,\\frac{\\mu^2}{m_b^2} \\right\\}.\n\\end{eqnarray}\n\n\\section{Discussion}\nFor the evaluation of the LCSR, Eq.~(\\ref{35}), as with any sum rule, optimum values of $M^2$ and $s_0$ need to be found. The standard procedure \\cite{Ball:2004ye} is to replace $f_B$ by its sum rule, derived via SVZ sum rules, thus reducing the dependence of the LCSR on $m_b$ for which we use the one-loop pole mass $m_b=4.80\\pm0.05\\,\\rm{GeV}$ \\cite{Colangelo:2000dp}. From the $f_B$ sum rule the optimum threshold parameter $s_0=34.2 \\pm 0.7\\,{\\rm GeV}^2$ is found, and this value is taken over to the LCSR. As mentioned before $\\mu=2.4\\,{\\rm GeV}$ is chosen as an intermediate scale between $m_b$ and $1\\,{\\rm GeV}$. The Borel parameter is taken to be $M^2>6\\,{\\rm GeV}^2$ and is varied in the range $6\\,{\\rm GeV}^25$,\ncan be distinguished from the OZI-breaking parameter\n$|a_2^\\eta-a_2^{\\eta^{\\prime}}|$, once an accurate experimental value of\n$R_{\\eta\\eta^{\\prime}}$ is available, but that for smallish $B^g_2$ and\nunknown $|a_2^\\eta-a_2^{\\eta^{\\prime}}|$ only mutual constraints on these\nparameters can be extracted from the data. In this case also twist-4 gluonic DAs can become important. \n\n\n\n\n\\chapter{QCD Factorisation}\\label{chapter6_QCDF}\nIn this chapter we discuss the framework of QCD factorisation which was introduced in the context of exclusive two-body non-leptonic $B$ decays by Beneke, Buchalla, Neubert and Sachrajda in Refs.~\\cite{Beneke:1999br, Beneke:2000ry}. We shall refer to the the original implementation of the framework as the BBNS approach. We also focus on its application to the radiative $B$ decays $B \\to V \\gamma$, as presented by Bosch and Buchalla in Refs.~\\cite{Bosch:2001gv,Bosch:2002bw}. \n\nQCD factorisation allows a rigourous determination of the $B$ decay matrix elements of the weak effective Hamiltonian (\\ref{basics_eq20}) to leading order in the heavy-quark limit of QCD $m_b\\gg \\Lambda_{\\rm QCD}$, and yields a neat factorisation formula. It relies on the factorisation of hadronic matrix elements into universal non-perturbative hadronic parameters, given by transition form factors and meson light-cone DAs, and process dependent hard-scattering kernels, calculable in perturbation theory. The validity of the QCD factorisation formula, to all orders in $\\alpha_s$, and the impact of generally unknown power corrections, formally suppressed by powers of $1\/m_b$, must be addressed case by case. The introduction of the QCD factorisation framework has made more discerning phenomenological studies of exclusive $B$ decays possible whereby key observables, such as branching ratios, CP and isospin asymmetries, can be calculated and confronted with experimental data.\n\nThe dependence of the factorisation formula on meson DAs, either directly or via LCSR calculations of the transition form factors, greatly motivates their study, with their better determination reducing the theoretical uncertainty of the QCD factorisation predictions, and aiding the quest to discover new physics effects from decay observables. \n\nWe begin with a short introduction, in the context of $B\\to M_1 M_2$ decays, of the general features of QCD factorisation, and in particular, discuss the appearance of meson DAs. We then discuss the framework as applied to the radiative $B$ decays $B \\to V \\gamma$. We postpone all discussions of phenomenology to Chapter~\\ref{chapter7_rad} in which we perform an analysis of the decays $B_{u,d} \\to (\\rho, \\omega, K^*)\\gamma$ and $B_{s} \\to (\\bar{K}^*,\\phi)\\gamma$ using QCD factorisation, augmented by the inclusion of the dominant power-suppressed corrections.\n\n\\section{Introduction}\n\\textit{QCD factorisation} (QCDF) \\cite{Beneke:1999br, Beneke:2000ry} was introduced in the context of the ``heavy-to-light'' decays $B \\to \\pi\\pi$ where the factorisation of the relevant QCD matrix elements was shown to apply, to leading order in a $1\/m_b$ expansion, to a large class of non-leptonic $B$ decays. Consequently, QCDF has opened up the rich and varied landscape of $B$ decays to a more complete quantitative analysis. The existence of factorisation in non-leptonic decays is non-trivial and complicated by the possible gluonic interactions amongst the initial and final states. Conversly, leptonic and semi-leptonic decays factorise much more easily into the product of a quark current and a leptonic current, which cannot interact via gluon exchange.\n\nPhenomenologically, QCDF has been remarkably successful, especially given the range of processes for which the method holds. After its introduction, it was swiftly generalised to encompass $\\pi K$ final states \\cite{Beneke:2001ev}, pseudoscalar-vector final states \\cite{Beneke:2003zv} and vector-vector meson final states \\cite{Kagan:2004uw}. The gluonic flavour-singlet contributions to $B \\to K^{(*)} \\eta^{(\\prime)} $ decays were added by Ref.~\\cite{Beneke:2002jn}. To date, the framework has been extended to many other processes, including for example, (double) radiative $B$ decays $B \\to \\gamma (\\gamma, V)$ \\cite{Bosch:2002bv,Bosch:2002bw} and $B \\to \\gamma l \\nu$ \\cite{Descotes-Genon:2002mw}. Also, other factorisation frameworks have since been developed and applied to the same problems:\n\\begin{itemize}\n\\item{\\textit{Soft Collinear Effective Theory} (SCET) \\cite{SCET1, SCET2, SCET3, SCET4} makes a careful distinction between a hierarchy of ``hard'' $(m_b)$, ``hard-collinear'' ($\\sqrt{\\Lambda_{\\rm QCD} m_b}$) and ``collinear'' ($\\Lambda_{\\rm QCD}$) scales via contributions of internal quark and gluon lines. Details of the differences between the SCET and BBNS approaches to QCD factorisation can be found in Refs.~\\cite{Bauer:2004tj,Beneke:2004bn,Bauer:2005wb}.}\n\\item{The \\textit{Perturbative QCD} (pQCD) approach \\cite{PQCD}, which yields a factorisation formula that depends on the mesons' transverse momenta.}\n\\item{The method of LCSRs, although having existed before the advent of QCDF, was applied to $B \\to \\pi \\pi$, both to the matrix elements which exhibit factorisation and also a class of power corrections, providing some useful complementary insights, see Refs.~\\cite{Khodjamirian:2000mi, Khodjamirian:2005wn}.}\n\\end{itemize}\nWe now go on to discuss the general features of QCDF. \n\\section{General Structure}\nConsider the case of non-leptonic decays where the $B$ meson decays into two mesons. The simplest way of dealing with the resulting matrix elements is to employ \\textit{naive factorisation} \\cite{Fakirov:1977ta,Cabibbo:1977zv}. Simply put, naive factorisation splits each local operator $Q_i$ of the effective Hamiltonian into two colour-singlet currents, whose matrix elements are proportional to a decay constant and a transition form factor respectively. For example, consider the four-quark operator $Q_2^U =(\\bar{D} U)_{\\rm V-A}(\\bar U b)_{\\rm V-A}$ then\n \\begin{equation}\n\\bra{M_1 M_2}(\\bar{D} U)_{\\rm V-A}(\\bar U b)_{\\rm V-A}\\ket{B} \\,\\stackrel{\\rm{NF}}{\\longrightarrow}\\,\\underbrace{\\bra{M_2}(\\bar{D} U)_{\\rm V-A}\\ket{0}}_{f_{M_2}}\\underbrace{\\bra{M_1}(\\bar U b)_{\\rm V-A}\\ket{B}}_{F^{B \\to M_1}}\\,.\n\\label{qcdf_1}\n\\end{equation}\nThe motivation for factorising in this way comes from the \\textit{colour transparency} argument \\cite{Bjorken:1988kk}. It follows that a major shortcoming of naive factorisation is that it assumes the exchange of gluons of virtualites $\\mu \\lesssim m_b$ to be negligible and hence rescattering between the decay products is not considered; there is then no mechanism for the generation of strong phase effects between different amplitudes. Also, the matrix elements (\\ref{qcdf_1}) do not display the correct renormalisation-scale dependence.\n\nThe framework of QCDF allows the calculation of $\\mathcal{O}(\\alpha_s)$ corrections to naive factorisation, which occur at scales $\\mu\\lesssim m_b$. It is constructed by observing the cancelation of infrared (IR) and collinear divergences, via consistent power-counting arguments, allowing the use of perturbation theory to describe the hard-gluon exchanges. The resulting intuitive factorisation formula thus presents a massive simplification of the long-distance QCD effects, with QCDF recovering naive factorisation in the limit $m_b\\to \\infty$. In terms of two-body non-leptonic $B$ decays to light pseudoscalar mesons $B\\to M_1 M_2$ the factorisation formula, as presented in Ref.~\\cite{Beneke:1999br}, reads schematically as\n\\begin{eqnarray}\n\\bra{M_1 M_2} Q_i\\ket{B}&=& F^{B \\to M_1}\\int^1_0 du\\,T^{I}_i (u)\\, \\phi_{2;M_2}(u) + (M_1 \\leftrightarrow M_2)\\nonumber \\\\\n&+&\\int^1_0 d\\xi\\,du\\,dv\\,T^{II}_i (\\xi,u,v)\\,\\phi_B(\\xi) \\,\\phi_{2;M_1}(v)\\,\\phi_{2;M_2}(u) \\nonumber \\\\\n&+&\\mathcal{O}(\\Lambda_{\\rm QCD}\/m_b)\\,\n\\label{qcdf_2}\n\\end{eqnarray}\nwhere $F^{B \\to M_1}$ is the relevant form factor, $T^{I,II}_i$ are the hard-scattering kernels, $\\phi_{B}$ is one of the leading-twist DAs of the $B$ meson and $\\phi_{2;P}$ the leading-twist DA of the final state meson $P$, and the $Q_i$ are the operators of the effective Hamiltonian. The matrix elements are given as the convolution of the universal DAs and the process dependent hard-scattering kernels, with respect to the meson momentum fractions. Since the transition form factor and the DAs are real functions, all strong phases are generated by the hard-scattering kernels and are suppressed by powers of $\\alpha_s$. Factorisation has be proven to one-loop for ``light-light'' final states and two-loop for ``heavy-light'' final states \\cite{Beneke:2000ry}. It has be proven to all orders in $\\alpha_s$ for $B\\to D \\pi $ using SCET \\cite{SCET2}.\n\nThe ability of QCDF to accurately describe $B$ decay processes is limited by two main considerations; firstly, by the nature of the factorisation formula itself, which is valid up to power corrections $\\mathcal{O}(1\/m_b)$ and to a given order in $\\alpha_s$; and secondly by uncertainties of the necessary input parameters, such as the DAs, the transition form factors, the strange quark mass, the $B$ meson decay constant $f_B$ etc. Whether a discrepancy between experiment and QCDF predictions can be put down to new physics, or not, requires an estimation of neglected power corrections; certainly the $b$ quark mass is not asymptotically large $m_b\\sim 5 \\,{\\rm GeV}$ and power corrections are therefore expected to feature at the level of $\\mathcal{O}(\\Lambda_{\\rm QCD}\/m_b) \\sim 10\\%$. The size and nature of power corrections can be probed via phenomenology, however, the task is not straight forward; even the initial focus of the approach, the decays $B \\to \\pi (K,\\pi)$, which stands as a crucial test, has not been resolved satisfactorily, see for example Ref.~\\cite{Feldmann:2004mg} and Refs.~\\cite{Fleischer:2005vz,Fleischer:2007wd}. Better determined input parameters will nevertheless shed light, case by case, on whether power corrections are important, and the QCDF predictions must be used to determine or constrain CKM matrix elements (UT angles), or detect signs of new physics, with that in mind. \n\n\n\\section{Light-Cone Distribution Amplitudes}\nTo leading-order in the heavy-quark limit the leading-twist final state meson DAs contribute to the factorisation formula and can be safely truncated after the second Gegenbauer moment $a_2$. For pseudoscalar meson final states the two-particle twist-3 DAs come with large normalisation factors $r^P_\\chi$ and are said to be \\textit{chirally enhanced}, and are therefore included even though they are formally $1\/m_b$ suppressed. The vector mesons do not have the same large normalisation factors but their two-particle twist-3 DAs are included in the BBNS approach for consistency. For a pseudoscalar or vector meson, with valence quark content $\\bar q q^{\\prime}$, the normalisation factors are respectively\n\\begin{equation}\nr_\\chi^P(\\mu) = \\frac{2 m_P^2}{m_b(\\mu)(m_q+m_{q^\\prime})(\\mu)} \\sim\\frac{\\Lambda_{\\rm QCD}}{m_b}\\,,\\qquad r^V_\\chi(\\mu)=\\frac{2 m_V}{m_b(\\mu)}\\frac{f_V^\\perp(\\mu)}{f_V^\\parallel}\\,.\n\\label{qcdf_3}\n\\end{equation}\nThree-particle twist-3 DAs are neglected because they do not come with large normalisations. The inclusion of the chirally enhanced DAs leads to end-point divergences from the convolutions of the two-particle twist-3 pseudoscalar DAs with the corresponding hard-scattering kernels originating from both the hard-spectator scattering and annihilation contributions. The resulting divergent integrals signal the breakdown of factorisation and are parameterised by two universal unknown parameters $X_{H,A}$, introducing a source of theoretical uncertainty to the BBNS approach \\cite{Beneke:1999br}. \n\nAt leading-twist the $B$ meson is described by two DAs, only one of which is required as input for Eq.~(\\ref{qcdf_2}) and appears in the hard-spectator diagrams contributing to $T^{II}_i$. The DAs of the $B$ mesons are complicated by the fact that the momentum of the meson is shared in a highly antisymmetric way: the $b$ quark has most of it. The $B$ meson DAs are given, at leading-order in $1\/m_b$, by\n\\begin{equation}\n\\bra{0}\\bar{q}_\\alpha(0) b_{\\beta}(z)\\ket{B(p_B)}=i \\frac{f_B}{4} \\left[(\\slash{p}_B+m_b) \\gamma^5\\right]_{\\beta\\gamma}\\int^1_0 d\\xi\\,e^{-i\\xi (p_B)_+ z_-}\\left[\\Phi_{B1}(\\xi)+\\slash{n}_- \\Phi_{B2}(\\xi)\\right]_{\\gamma \\alpha}\\,,\n\\label{qcdf_4}\n\\end{equation}\nwith the decay constant $f_B$ given by Eq.~(\\ref{bdecayconstant}). With a careful choice of $n_-=(1,0,0,-1)$ only the following normalisation conditions are required\n\\begin{equation}\n\\int^1_0d\\xi\\, \\Phi_{B1}(\\xi)=1\\,,\\qquad\\int^1_0d\\xi\\, \\Phi_{B2}(\\xi)=0\\,,\n\\label{qcdf_5}\n\\end{equation}\nalong with the first inverse moment of $ \\Phi_{B1}$ which is parameterised as\n\\begin{equation}\n\\int^1_0 d\\xi\\,\\frac{\\Phi_{B1}(\\xi)}{\\xi}\\equiv \\frac{m_B}{\\lambda_B}\\,,\n\\label{qcdf_6}\n\\end{equation}\nand the numerical value of $\\lambda_B$ is a source of uncertainty in the QCDF framework for both $B\\to M_1 M_2$ and $B\\to V \\gamma$. We now discuss the radiative decays $B\\to V \\gamma$ within QCDF.\n\n\\section{Radiative $B$ decays to Vector Mesons}\\label{qcdf_rad}\nWe consider the leading contributions to the $B\\to V \\gamma$ QCDF factorisation formula as of Refs.~\\cite{Bosch:2001gv,Bosch:2002bw,Bosch:2004nd,Beneke:2001at} in which a model independent framework is presented. Contributions that are power-suppressed by one power of $1\/m_b$ or more \\textit{and} are $\\mathcal{O}(\\alpha_s)$ are not considered. At the quark level the decays are $b\\to D \\gamma$ transitions, where $D=\\{s,d\\}$. If otherwise not stated, in the following we refer to $\\bar{B}\\to V\\gamma$ decays where $\\bar{B}$ ($V$) denotes a $b \\bar{q}$ ($D \\bar{q}$) bound state. For $B\\to V \\gamma$ decays the matrix element of each relevant local operator in the effective Hamiltonian factorises as\n\\begin{equation}\n\\bra{V\\gamma}Q_i\\ket{B}=e^* \\cdot \\left[ T_1^{B\\to V} (0) \\,T^{I}_i+\\int^1_0 d\\xi du\\,T^{II}_i(\\xi,u)\\phi_B(\\xi) \\phi_{2;V}^{\\perp}(u)\\right]+\\mathcal{O}(1\/m_b)\\,,\n\\label{qcdf_7}\n\\end{equation}\nwhere $e_\\mu$ is the photon polarisation vector and $T_1^{B \\to V}(0)$ is the relevant form factor. $\\phi_{2;V}^{\\perp}$ the leading-twist DA of the perpendicularly polarised final state vector meson (\\ref{das_eq19}); contributions from $\\phi^{\\parallel}_{2;V}$ are power-suppressed in the heavy-quark limit. Problems of end-point divergences are not encountered in $B\\to V\\gamma$ decays and the twist-3 vector meson DA does not feature -- the $B$ meson DAs (\\ref{qcdf_6}) do however. The factorisation formula is accurate up to corrections suppressed by powers of $1\/m_b$, as shown, and was proven to hold to all orders in $\\alpha_s$ in SCET \\cite{Becher:2005fg}. The form factor $T_1^{B \\to V}(0)$ has been calculated, for example, from LCSR in Ref.~\\cite{Ball:2004rg}. \n\nThe $B\\to V \\gamma$ decay produces either left- or right-handed photons, which therefore constitute, in principle, two separate observable processes. In practise the direct measurement of the photon's helicity is very difficult; indirectly, however, it can be accessed by measurement of the time-dependent CP asymmetry in $\\bar B^0\\to V^0\\gamma$, which vanishes if one of them is absent, see Chapter~\\ref{chapter7_rad}. We define the two amplitudes as\n\\begin{equation}\n\\bar{\\cal A}_{L(R)} = {\\cal A}(\\bar B\\to V\\gamma_{L(R)})\\,, \\qquad\n{\\cal A}_{L(R)} = {\\cal A}(B\\to \\bar V \\gamma_{L(R)})\\,.\n\\label{qcdf_8}\n\\end{equation}\nFor ($B$) $\\bar B$ decays the production of the (left-) right-handed photon is suppressed by $1\/m_b$ with respect to the opposite helicity. The decays are dominated by the electromagnetic dipole operator $Q_{7\\gamma}$, and as such are penguin mediated and so loop-suppressed. The operators $Q_{7\\gamma}^{L(R)}$ are given by\n\\begin{equation}\nQ_{7\\gamma}^{L(R)} = \\frac{e}{8\\pi^2}\\, m_b \\bar D \\sigma_{\\mu\\nu}\\left(1 \\pm \\gamma_5\\right)b F^{\\mu\\nu}\\,,\n\\label{qcdf_9}\n\\end{equation} \nand generate left- (right-) handed photons. Their matrix elements can be parameterised in terms of the form factor $T_1^{B\\to V}$ as\n\\begin{eqnarray}\n\\lefteqn{\\bra{V(p,\\eta) \\gamma_{L(R)}(q,e)} Q_{7\\gamma}^{L(R)} \\ket{\\bar\nB}}\\hspace*{1cm}\\nonumber\\\\\n&=& -\\frac{e}{2\\pi^2}\\, m_b T_1^{B\\to V}(0) \\left[\n\\epsilon^{\\mu\\nu\\rho\\sigma} e_\\mu^* \\eta_\\nu^* p_\\rho q_\\sigma \\pm i\n\\{ (e^* \\cdot \\eta^*) (p \\cdot q) - (e^* \\cdot p)(\\eta^* \\cdot q)\\}\\right]\n\\nonumber\\\\\n&\\equiv& -\\frac{e}{2\\pi^2}\\, m_b T_1^{B\\to V}(0) S_{L(R)}\\,,\n\\label{qcdf_10}\n\\end{eqnarray}\nwhere $S_{L,R}$ are the helicity amplitudes corresponding to left- and right-handed photons, respectively, and $e_\\mu$ $(\\eta_\\mu)$ is the polarisation four-vector of the photon (vector meson). The leading-order diagram is given in Fig.~\\ref{qcdf_fig1} which is also the leading diagram for the form factor $T_1^{B\\to V}$.\n\\begin{figure}[h]\n$$\\epsfxsize=0.2\\textwidth\\epsffile{rad_leading.eps}$$\n\\caption[The leading contribution to $B \\to V\\gamma$.]{\\small The leading contribution to $B \\to V\\gamma$ due to the electromagnetic dipole operator $Q_{7\\gamma}$. }\\label{qcdf_fig1}\n\\end{figure}\nThe factorisation formula (\\ref{qcdf_10}) is therefore trivial to leading order in $\\alpha_s$ and the heavy-quark limit; the matrix element given by the standard form factor, the scattering kernel $T^I_7$ by a purely kinematical function and $T^{II}_7$ does not feature. The electroweak penguin operators $Q_{7,\\dots,10}$ appear at higher-order and safely neglected in the analysis. All other operators begin to contribute at $\\mathcal{O}(\\alpha_s)$. The hard-vertex corrections contribute to $T^I_i$ yielding functions of $m_{u,c}^2\/m_b^2$ and originate from penguin contractions of the operators $Q_{1,\\dots,6}$ and the chromomagnetic operator $Q_{8g}$ as shown in Fig.~\\ref{qcdf_fig2}. \n\\begin{figure}[h]\n$$\\epsfxsize=0.5\\textwidth\\epsffile{rad_vertexcorr.eps}$$\n\\caption[Contributions to the hard-scattering kernel $T^{I}_i$ for $B\\to V \\gamma$ decays.]{\\small Penguin contractions of $Q_{1,\\dots,6}$ (top line) and the chromomagnetic dipole operator $Q_8$ (bottom line) contributing to the hard-vertex corrections of $T^{I}_i$ at $\\mathcal{O}(\\alpha_s)$. Crosses denote possible photon emission vertices.}\n\\label{qcdf_fig2}\n\\end{figure}\nThe hard-spectator scattering diagrams of Fig.~\\ref{qcdf_fig3}, in which the spectator quark of the $B$ meson participates, contribute to $T^{II}_i$ and involve the same operators as the hard-vertex corrections. The hard-gluon exchange probes the momentum distribution of the $B$ and vector mesons and so requires the introduction of the mesons' light-cone DAs, as suggested by the factorisation formula; it is in these contributions that the $B$ meson DA parameter $\\lambda_B$ and decay constants $f_B$ and $f_V^\\perp$ appear. \n\\begin{figure}[h]\n$$\\epsfxsize=0.5\\textwidth\\epsffile{rad_spec.eps}$$\n\\caption[Contributions to the hard-scattering kernel $T^{II}_i$ for $B\\to V \\gamma$ decays.]{\\small Penguin contractions of $Q_{1,\\dots,6}$ (left) and the chromomagnetic dipole operator $Q_{8g}$ (right) contributing to the hard-scattering kernel $T^{II}_i$ at $\\mathcal{O}(\\alpha_s)$. Crosses denote possible photon emission vertices at leading order. Photon emission from the other quark lines power-suppressed. Photon emission from the final state meson for $Q_{8g}$ breaks factorisation.}\n\\label{qcdf_fig3}\n\\end{figure}\nAlso, the dominant power-suppressed \\textit{weak annihilation} (WA) contributions, shown in Fig.~\\ref{qcdf_fig4}, are calculable in the QCDF approach, and involve the operators $Q_{1,\\dots,6}$. WA contributions are\n$O(1\/m_b)$; photon emission from the $b$ quark and the quarks in the vector meson is further suppressed and $O(1\/m_b^2)$ -- unless the weak interaction operator is $Q_{5,6}$, which can be Fierz transformed into $(\\bar D (1+\\gamma_5) q) (\\bar q (1-\\gamma_5) b)$ and picks up an additional factor $m_B$ from the projection onto the $B$ meson DA thus resulting in this contribution being $O(1\/m_b)$. Consequently, due to the large Wilson coefficients $C_{1,2}$ these contributions are sizeable and important phenomenologically, see Chapter~\\ref{chapter7_rad}. \n\\begin{figure}[h]\n$$\\epsfxsize=0.2\\textwidth\\epsffile{rad_ann.eps}$$\n\\caption[Weak annihilation contributions to $B\\to V \\gamma$.]{\\small Weak annihilation contributions, which are suppressed by one power of $1\/m_b$. Crosses denote possible photon emission vertices at leading order. The dominant mechanism for $Q_{1,\\dots,4}$ is the emission of the photon from the light quark in the $B$ meson and for $Q_{5,6}$ it is the emission from the final state vector meson quarks. Other possible emissions are either vanishing or more strongly suppressed.}\n\\label{qcdf_fig4}\n\\end{figure}\n\nThe decay amplitude is then given by\n\\begin{equation}\n\\mathcal{A}(\\bar{B}\\to V \\gamma_{L(R)})=\\frac{G_F}{\\sqrt{2}}\\left(\\lambda_u^D a_{7 L(R)}^u(V)+ \\lambda_c^D a_{7 L(R)}^c(V)\\right)\\bra{V\\gamma_{L(R)}}Q_{7\\gamma}^{L(R)}\\ket{\\bar{B}}\\,,\n\\label{qcdf_11}\n\\end{equation}\nwhere the left-handed coefficients are given, to leading order in QCDF, by\n\\begin{equation}\na_{7L}^{U,{\\rm QCDF}}(V)=C_7+\\mathcal{O}(\\alpha_s,1\/m_b)\\,,\n\\label{qcdf_12}\n\\end{equation} \nand the right-handed parameters, for a $b\\to D$ transition, by \\cite{Ball:2006cv}\n\\begin{equation}\na_{7R}^{U,{\\rm QCDF}} = C_7\\,\\frac{m_D}{m_b}+\\mathcal{O}(1\/m_b,\\alpha_s\/m_b)\\,.\n\\label{qcdf_13}\n\\end{equation}\nExplicit expressions for the $\\mathcal{O}(\\alpha_s)$ corrections to the left-handed coefficients can be found in Refs.~\\cite{Bosch:2001gv,Bosch:2002bw} and will be considered in Chapter~\\ref{chapter7_rad}, alongside the dominant power-suppressed corrections.\n\\chapter{$B \\to V \\gamma$ Beyond QCD Factorisation}\\label{chapter7_rad}\nIn this chapter we perform a phenomenological analysis of the exclusive radiative $B$ decays to vector mesons. We make use of the QCDF framework outlined in Chapter~\\ref{chapter6_QCDF} and investigate the impact of the leading power-corrections on the branching ratios, CP asymmetries and isospin asymmetries for all $b\\to D$ transitions; $B_{u,d} \\to (\\rho, \\omega, K^*)\\gamma$ and $B_{s} \\to (\\phi, \\bar{K}^*)\\gamma$. Weak annihilation effects, although power-suppressed, are calculable in QCDF, and are included for all decay modes in this analysis. The other power-suppressed contributions ``beyond QCDF'' considered are; soft photon emission from the soft $B$ spectator quark \\cite{Ball:2003fq}; and long-distance contributions from heavy quark loops \\cite{Ball:2006cv} and light quark loops \\cite{Ball:2006eu} which have been estimated from LCSR. The estimation of the light quark loop contribution is new to the present analysis. Whereas the branching ratios are generally dominated by the leading contributions, and power-suppressed contributions play a minor role, the same cannot be said for the CP and isospin asymmetries for which the impact of power-corrections is in fact crucial.\n\nThe motivation to study radiative $B$ decays stems from a variety of sources:\n\\begin{itemize}\n\\item{as loop-induced, penguin mediated decays, they allow the extraction of the CKM matrix element $|V_{t,(d,s)}|$ complimentarily to the determination from $B$ mixing and also that from the SM UT analysis based on the tree-level observables $|V_{ub}\/V_{cb}|$ and the angle $\\gamma$.}\n\\item{They are sensitive to new physics contributions, which may occur within the penguin loops, with the time-dependent CP asymmetry a very promising avenue of investigation. They are also subject to large short-distance QCD corrections, which now approach next-to-next-to-leading-order accuracy, see Refs.~\\cite{misiak,NNLObsgamma}.}\n\\item{The decay rates are of order $G_F^2 \\alpha_{\\rm QED}$ and are enhanced with respect to other loop-induced non-radiative rare decays which are of order $G_F^2 \\alpha_{\\rm QED}^2$. Also, the $b\\to s$ modes are CKM-favoured. Consequently there exist good experimental results for the exclusive branching ratios; $B\\to K^* \\gamma$ is known to 5\\%, but the $b\\to d$ transitions are not so well known.}\n\\end{itemize}\n\nAs discussed in Chapter~\\ref{chapter6_QCDF}, the QCDF framework for $B\\to V\\gamma$ relies on the leading-twist vector meson DA $\\phi_{2;V}^\\perp$. Moreover, the LCSR calculations of the form factors $T_1^{B\\to V}$ and the parameters entering expressions for the soft-quark contributions rely also on the higher-twist DAs of the vector mesons and thus we find immediate use for the results of the twist-2 and twist-3 DA parameters of Chapter~\\ref{chapter4_det}, as presented in Tab.~\\ref{det_tab1} and Tab.~\\ref{det_tab2}.\\footnote{The analysis presented in Ref.~\\cite{Ball:2006eu} used preliminary input for the DA parameters, values for which were later finalised in Ref.~\\cite{Ball:2007rt}. The conclusions and numerics of the analysis are unaffected, due somewhat to the large errors attributed to the soft quark loop calculations in which the twist-3 DA parameters feature.}\n\nWe begin with an introduction, and then go on to discuss the power-suppressed contributions and investigate their impact on the decay observables. We extract the CKM parameter $|V_{t,d}\/V_{ts}|$ from the branching ratio results, assuming no new physics contributions, and discuss possible new physics contributions to the CP and isospin asymmetries. The material covered in this chapter follows that of Ref.~\\cite{Ball:2006eu}.\n\n\\section{Introduction}\n$B \\to V \\gamma$ decays are a very rich and promising probe of flavour physics. Both the inclusive decay $B\\to X_s \\gamma$ and the exclusive decays $B\\to (K^*,\\rho)\\gamma$ have been under scrutiny for many years, see for example Refs.~\\cite{Neubert:2002ku,Kagan:1998bh}. The experimental results for $B\\to (\\rho,\\omega,K^*)\\gamma$ are shown in Tab.~\\ref{rad_tab1}. For $B_s\\to\\phi\\gamma$ only an upper bound ${\\cal B}(B_s\\to\\phi\\gamma)<120\\times 10^{-6}$ exists and no experimental information is available for $B_s\\to \\bar K^*\\gamma$ \\cite{Yao:2006px}. \n\\begin{table}[h]\n\\renewcommand{\\arraystretch}{1.4}\\addtolength{\\arraycolsep}{3pt}\n$$\n\\begin{array}{l||c|c||l|c}\n\\hline\n{\\cal B} \\times 10^6 & \\mbox{{\\sc BaBar} \\cite{babar_rad}} & \\mbox{Belle\n \\cite{belle_rad}} & {\\cal B} \\times 10^6 & \\mbox{HFAG \\cite{Barberio:2007cr}}\n\\\\\\hline\nB\\to (\\rho,\\omega)\\gamma & 1.25^{+0.25}_{-0.24}\\pm 0.09 &\n 1.32^{+0.34}_{-0.31}{}^{+0.10}_{-0.09} \n &\nB^+\\to K^{*+}\\gamma & 40.3\\pm 2.6\n \n\\\\\nB^+\\to \\rho^+\\gamma & 1.10^{+0.37}_{-0.33}\\pm 0.09 &\n 0.55^{+0.42}_{-0.36}{}^{+0.09}_{-0.08} \n \n &\nB^0\\to K^{*0}\\gamma & 40.1\\pm 2.0\n\\\\\nB^0\\to\\rho^0\\gamma & 0.79^{+0.22}_{-0.20}\\pm0.06 &\n 1.25^{+0.37}_{-0.33}{}^{+0.07}_{-0.06} \n \n\\\\\nB^0\\to\\omega\\gamma & <0.78 &\n 0.96^{+0.34}_{-0.27}{}^{+0.05}_{-0.10}\n \n\\\\\\hline\n\\end{array}\n$$\n\\caption[Experimental branching ratios of exclusive $b\\to (d,s)\\gamma$\n transitions.]{\\small Experimental branching ratios of exclusive $b\\to (d,s)\\gamma$\n transitions. All entries are CP averaged. The first error is statistical, the second\n systematic. $B\\to (\\rho,\\omega)\\gamma$ is the CP average of the\n isospin average over $\\rho$ and $\\omega$ channels:\\\\ $\\overline{\\cal B}(B\\to\n (\\rho,\\omega)\\gamma) = \\frac{1}{2} \\left\\{ \\overline{\\cal B}(B^\\pm\\to \\rho^\\pm\\gamma) +\n \\frac{\\tau_{B^\\pm}}{\\tau_{B^0}} \\left[ \\overline{\\cal B}(B^0\\to \n \\rho^0\\gamma) + \\overline{\\cal B}(B^0\\to \\omega \\gamma)\\right]\\right\\}$.}\n \\label{rad_tab1}\n\\end{table}\n\nIn the SM the decays are flavour-changing-neutral-current (FCNC) $b\\to D\\gamma$ transitions, mediated by penguin diagrams; they are therefore loop-suppressed and potentially very sensitive to new physics. To determine the relative sizes of contributions to the decays one must consider the following points:\n\\begin{itemize}\n\\item{the leading term is loop-suppressed $\\sim 1\/(4 \\pi)^2$ and proportional to $C_7\\sim-0.3$.}\n\\item{Evidently from Eq.~(\\ref{qcdf_11}) for each mode there are two amplitudes proportional to different CKM factors $\\lambda^{(D)}_{u,c}$. For $b\\to d$ transitions both $\\lambda^{(d)}_u$ and $\\lambda^{(d)}_c$ are $\\sim\\lambda^3$, however, for $b\\to s$ transitions $\\lambda^{(s)}_u\\sim\\lambda^4$ and $\\lambda^{(s)}_c\\sim\\lambda^2$; there is a relative CKM suppression of the up-quark contribution.}\n\\item{Power suppressed corrections from WA are formally $\\sim 1\/m_b$ although come with large Wilson coefficients $C_1\\sim-0.3$ and $C_2\\sim 1$ and are not loop suppressed. The WA contributions drive the isospin asymmetries.}\n\\item{The production of ``wrong'' helicity photons is suppressed by $m_D\/m_b$ (\\ref{qcdf_13}). The interplay of both helicity amplitudes generates the time-dependent CP asymmetries, which are small in the SM due to this suppression.}\n\\end{itemize}\n\n\\section{Wilson Coefficients}\nConsiderable effort has gone into calculating the Wilson coefficients to NLO accuracy. Using the expressions for the NLO anomalous dimension matrices available in the literature we employ the renormalisation techniques of Eqs.~(\\ref{basics_eq22}-\\ref{basics_eq29}) to calculate the Wilson coefficients at the required scales. Numerical values of all the NLO Wilson coefficients $C_i$ used in the analysis are given in Tab.~\\ref{rad_tab2}. The situation is complicated by the fact that the QCDF results of Ref.~\\cite{Bosch:2002bw} are given in terms of two bases. The first, the so-called BBL basis named after the authors of Ref.~\\cite{Buchalla:1995vs}, is that of Eqs.~(\\ref{basics_eq20}) and (\\ref{basics_eq21}) except with $Q_1$ and $Q_2$ exchanged with respect to the basis of Ref.~\\cite{Bosch:2001gv}. The second is the so-called CMM basis of Ref.~\\cite{munz, buras}. The two bases differ except for $Q_{7(8)}^{\\rm BBL}=Q_{7(8)}^{\\rm CMM}$. Following Ref.~\\cite{Bosch:2002bw}, the CMM set is used for calculating hard-vertex corrections to the QCDF formulas and the BBL set at the lower scale $\\mu_h\\sim \\sqrt{\\Lambda_{h} \\,\\mu}$ (with $\\lambda_h\\sim 0.5\\,{\\rm GeV}$ and $\\mu= \\mathcal{O}(m_b)$) is used to calculate hard-spectator corrections. Power corrections are calculated from the BBL set at scale $m_b$. \n\nNLO accuracy is mandatory only for $C_7$, as it is for this term only that the hadronic matrix element is also known to NLO accuracy. We evaluate all $\\mathcal{O}(\\alpha_s)$ and power-suppressed corrections using both LO and NLO scaling for Wilson coefficients and hadronic matrix elements and include the resulting scale dependence in the theoretical uncertainty.\n\\begin{table}[h]\n\\renewcommand{\\arraystretch}{1.3}\n\\addtolength{\\arraycolsep}{2pt}\n$$\n\\begin{array}{c|c|c|c|c|c|c}\nC^{\\rm CMM}_1(m_b) & C^{\\rm CMM}_2(m_b) & C^{\\rm CMM}_3(m_b) & \nC^{\\rm CMM}_4(m_b) & C^{\\rm CMM}_5(m_b) & C^{\\rm CMM}_6(m_b) &\nC^{\\rm CMM}_7(m_b)\n\\\\\\hline\n-0.322 & 1.009 & -0.005 & -0.087 & 0.0004 & -0.001 & -0.309 \n\\\\\\hline\\hline\nC^{\\rm BBL}_1(m_b) & C^{\\rm BBL}_2(m_b) & C^{\\rm BBL}_3(m_b) & \nC^{\\rm BBL}_4(m_b) & C^{\\rm BBL}_5(m_b) & C^{\\rm BBL}_6(m_b) \n& C^{\\rm CMM}_8(m_b) \n\\\\\\hline\n-0.189 & 1.081 & 0.014 & -0.036 & 0.009 & -0.042 & -0.170\n\\\\\\hline\\hline\nC^{\\rm BBL}_1(\\mu_h) & C^{\\rm BBL}_2(\\mu_h) & C^{\\rm BBL}_3(\\mu_h) & \nC^{\\rm BBL}_4(\\mu_h) & C^{\\rm BBL}_5(\\mu_h) & C^{\\rm BBL}_6(\\mu_h) &\nC^{\\rm CMM}_8(\\mu_h)\n\\\\\\hline\n-0.288 & 1.133 & 0.021 & -0.051 & 0.010 & -0.065 & -0.191\n\\end{array}\n$$\n\\caption[Numerical values of the next-to-leading-order Wilson coefficients.]{\\small NLO Wilson coefficients to be used in the analysis, at the scales $m_b=4.2\\,$GeV and $\\mu_h=2.2\\,$GeV. The coefficients labelled BBL correspond to the operator basis of Ref.~\\cite{Buchalla:1995vs} and given in Eq.~(\\ref{basics_eq21}), whereas CMM denotes the basis of Ref.~\\cite{munz}. We use $\\alpha_s(m_Z) = 0.1176$ \\cite{Yao:2006px} and ${m}_t({m}_t) = 163.6\\,$GeV \\cite{mt}. Note that $C_1^{\\rm BBL}$ and $C_2^{\\rm BBL}$ are exchanged with respect to the basis of Ref.~\\cite{Bosch:2001gv} and that $C_{7(8)}^{\\rm BBL}=C_{7(8)}^{\\rm CMM}$. Following Ref.~\\cite{ Bosch:2002bw}, the CMM set is used for calculating hard-vertex corrections to the QCDF formulas and the BBL set at the lower scale $\\mu_h$ is used to calculate hard-spectator corrections. The BBL set at scale $m_b$ is used for the calculation of power-corrections.}\n\\label{rad_tab2}\n\\end{table}\n\n\n\n\\section{Leading and Power Suppressed Contributions}\nIt proves convenient to split to the coefficients in Eq.~(\\ref{qcdf_11}) into three contributions which we will investigate separately:\n\\begin{eqnarray}\na_{7L}^U( V) &=& a_{7L}^{U,{\\rm QCDF}}( V) + a_{7L}^{U,{\\rm ann}}( V) + a_{7L}^{U,{\\rm soft}}( V)+\\dots\\,,\\nonumber\\\\\na_{7R}^U( V) &=& a_{7R}^{U,{\\rm QCDF}}( V) + a_{7R}^{U,{\\rm ann}}(V)+ a_{7R}^{U,{\\rm soft}}( V)+\\dots\\,,\n\\label{asplit}\n\\end{eqnarray} \nwhere the leading term in the $1\/m_b$ expansion is given by Eq.~(\\ref{qcdf_12}) and all other terms are suppressed by at least one power of $m_b$. The dots denote terms of higher order in $\\alpha_s$ and further $1\/m_b$ corrections to QCDF, most of which are incalculable. We only include those power-suppressed terms that are either numerically large or relevant for isospin and CP asymmetries. \n\n\n\n\\subsection{Leading Contributions}\nThe diagrams giving the leading QCDF contributions are given in Chapter~\\ref{chapter6_QCDF}. It turns out that, at the level of two decimal places, all $a_{7L}^{c,{\\rm QCDF}}$ are equal and so are\n$a_{7L}^{u,{\\rm QCDF}}$.\\footnote{Explicit formulas for $a_{7L}^{U,{\\rm QCDF}}$, complete to $\\mathcal{O}(\\alpha_s)$, can be found in Ref.~\\cite{Bosch:2002bw}.} For central values of the input parameters of Tab.~\\ref{rad_tab8} we obtain\n\\begin{eqnarray}\na^{c,{\\rm QCDF}}_{7L}(V) & = & -\\overbrace{(0.41+0.03i)}^{\\shortstack{{\\rm \\footnotesize Vertex}\\\\{\\rm \\footnotesize Corrections}}} - \n\\overbrace{(0.01+0.01i)}^{\\shortstack{{\\rm \\footnotesize Hard-Spectator}\\\\{\\rm \\footnotesize Corrections}}}\\,,\\nonumber\\\\\na^{u,{\\rm QCDF}}_{7L}(V) & = & -(0.45+0.07i) + (0.02-0i)\\,.\n\\label{10}\n\\end{eqnarray}\nThe size of the hard-spectator corrections is set by the factor\n\\begin{equation}\nh_V = \\frac{2 \\pi^2}{9}\\,\\frac{f_B f_V^\\perp}{m_B \nT_1^{B\\to V}(0)\\lambda_B}\\,.\n\\end{equation}\nFor $B_s$ decays one has to set $f_B\\to f_{B_s}$ and correspondingly for the other $B$ meson parameters. We estimate the value of $\\lambda_{B_s}$, the first inverse moment of the twist-2 $B$-meson light-cone DA, from $\\lambda_{B_d}$ by a simple scaling argument:\n\\begin{equation}\n\\frac{m_{B_s}}{\\lambda_{B_s}}\\,(\\Lambda_{\\rm QCD}+m_s) = \n\\frac{m_{B_q}}{\\lambda_{B_q}}\\,\\Lambda_{\\rm QCD}\\,,\n\\label{rad_bs}\n\\end{equation}\nwhich follows from the assumption that the $B_q$ DA peaks at the spectator momentum $k_+ = \\Lambda_{\\rm QCD}$, whereas that of $B_s$ peaks at $\\Lambda_{\\rm QCD}+m_s$. Its numerical value is given, along with all the other input parameters, in Tab.~\\ref{rad_tab8}.\n\n\n\n\\subsection{Weak Annihilation}\n $a_{7L}^{U,{\\rm ann}}$ encodes the $\\mathcal{O}(1\/m_b)$ contribution of the WA diagram of Fig.~\\ref{rad_fig1}(a) which drives the isospin asymmetries and has been calculated in QCDF in Ref.~\\cite{Bosch:2002bw} with $\\alpha_s$ corrections given in Ref.~\\cite{Kagan:2001zk} for $\\rho$ and $K^*$ and in Ref.~\\cite{Bosch:2004nd} for $\\omega$. WA receives contributions from the current-current operator $Q_2^u$, which for $b\\to s$ transitions is doubly CKM suppressed, and QCD penguin operators $Q_{3,\\dots,6}$, which are not CKM suppressed. Formulas for $a_{7L}^{U,{\\rm ann}}(\\rho,K^*)$ in QCDF can be found in Refs.~\\cite{Bosch:2002bw,Bosch:2004nd}; in this approximation, there is no contribution to $a_{7R}^{U,{\\rm ann}}$. \n\\begin{figure}[h]\n$$\\epsfxsize=0.6\\textwidth\\epsffile{rad_power.eps}$$\n\\caption[Diagrams for weak annihilation and soft-gluon emission from a quark loop.]{\\small (a) Weak annihilation diagram where photon emission from the $B$ meson spectator quark is power-suppressed. The crosses denote possible photon emission vertices for $Q_{5,6}$ only. (b) soft-gluon emission from a quark loop, where there is also a second diagram in which the gluon is picked up by the $B$ meson.}\n\\label{rad_fig1}\n\\end{figure}\n\nPreliminary results for the $\\mathcal{O}(\\alpha_s)$ corrections to WA in $B\\to\\rho \\gamma$ were presented in Ref.~\\cite{chamonix}. In QCDF, the $a_{7L}^{U,{\\rm ann}}$ are expressed in terms of the hadronic quantities\n\\begin{equation}\nb^V = \\frac{2\\pi^2}{T_1^{B\\to V}(0)} \\,\\frac{f_B m_V f_V}{m_B m_b\n \\lambda_B}\\,, \\qquad\nd^V_{v} = -\\frac{4\\pi^2}{T_1^{B\\to V}(0)} \\,\\frac{f_B f_V^\\perp}{m_B\n m_b} \\,\\int_0^1 dv\\,\\frac{\\phi_{2;V}^\\perp(v)}{v}\n\\end{equation}\nand $d^V_{\\bar v}$, obtained by replacing $1\/v\\to 1\/\\bar v$ in the integrand; $\\phi_{2;V}^\\perp$ is the twist-2 DA of a transversely polarised vector meson, (\\ref{das_eq19}). Numerically, one finds, for instance for the $\\rho$, $b^\\rho = 0.22$ and $d^\\rho = -0.59$, at the scale $\\mu = 4.2\\,$GeV. As $T_1\\sim 1\/m_b^{3\/2}$ and $f_B\\sim m_b^{-1\/2}$ in the heavy-quark limit, these terms are $\\mathcal{O}(1\/m_b)$, but not numerically small because of the tree-enhancement factors of $\\pi^2$.\n\\begin{table}\n\\renewcommand{\\arraystretch}{1.3}\n\\addtolength{\\arraycolsep}{3pt}\n$$\n\\begin{array}{l||c|c|c|c|c}\n\\mbox{WA} & \nB^-\\to K^{*-} & \\bar B^0\\to K^{*0} & B\\to (\\rho,\\omega) & B_s\\to\\phi & \nB_s\\to \\bar K^*\\\\\\hline\n\\mbox{induced by} & \\mbox{C (and P)} & \\mbox{P} & \\mbox{C and P} &\n\\mbox{P} & \\mbox{P}\\\\\n\\mbox{CKM} & \\lambda^2 \\mbox{~(and 1)} & 1 & 1 & 1 & 1\n\\end{array}\n$$\n\\caption[Parametric size of the weak annihilation contributions.]{\\small Parametric size of WA contributions to $B\\to V\\gamma$. C denotes the charged-current operators $Q_{1,2}$, P the penguin operators $Q_{3,\\dots,6}$; their Wilson coefficients are small -- see Tab.~\\ref{rad_tab2}. CKM denotes the order in the Wolfenstein parameter $\\lambda$ with respect to the dominant amplitude induced by $Q_7$.}\n\\label{rad_tab3}\n\\end{table}\n\nFor $\\omega$, $\\bar K^*$ and $\\phi$ we obtain\n\\begin{eqnarray}\n\\left. a_{7L}^{u,{\\rm ann}}(\\omega)\\right|_{\\rm QCDF}\n & = & Q_d b^\\omega (a_1 + 2 (a_3+a_5)\n+ a_4) + Q_d (d^\\omega_v + d^\\omega_{\\bar v}) a_6\\,,\\nonumber\\\\\n\\left. a_{7L}^{c,{\\rm ann}}(\\omega)\\right|_{\\rm QCDF} \n& = & Q_d b^\\omega (2 (a_3+a_5)\n+ a_4) + Q_d (d^\\omega_v + d^\\omega_{\\bar v}) a_6\\,,\\nonumber\\\\\n\\left. a_{7L}^{U,{\\rm ann}}(\\phi)\\right|_{\\rm QCDF} \n& = & Q_s b^\\phi (a_3+a_5) + \n Q_s (d^\\phi_v + d^\\phi_{\\bar v}) a_6\\,,\\nonumber\\\\\n\\left. a_{7L}^{U,{\\rm ann}}(\\bar K^*)\\right|_{\\rm QCDF} \n& = & Q_s b^{\\bar K^*} a_4 + \n Q_s (d^{\\bar K^*}_v Q_d\/Q_s + d^{\\bar K^*}_{\\bar v}) a_6\\,,\n\\label{15}\n\\end{eqnarray}\nwith $a_1 = C_1+C_2\/3$, $a_3 = C_3+C_4\/3$, $a_4 = C_4+C_3\/3$, $a_5 = C_5+C_6\/3$, $a_6 = C_6+C_5\/3$.\\footnote{Note that $a_1\\leftrightarrow a_2$ as compared to \\cite{Bosch:2002bw} as in our operator basis (i.e.\\ the BBL basis) $Q_1$ and $Q_2$ are exchanged.} The expressions for $\\phi$ and $\\bar K^*$ are new; for $\\omega$, we do not agree with \\cite{Bosch:2004nd}. Apart from for $\\rho$ and $\\omega$, all the WA coefficients are numerically small and do not change the branching ratio significantly; the terms in $a_6$, however, are relevant for the isospin asymmetries. \n\nIn Tab.~\\ref{rad_tab3} we show the relative weights of these diagrams in terms of CKM factors and Wilson coefficients. The numerically largest contribution occurs for $B^\\pm\\to \\rho^\\pm\\gamma$: it comes with the large combination of Wilson coefficients $C_2+C_1\/3=1.02$ and is not CKM suppressed. For $B^0\\to (\\rho^0,\\omega)\\gamma$ it comes with the factor $C_1+C_2\/3 = 0.17$\ninstead and an additional suppression factor $1\/2$ from the electric charge of the spectator quark ($d$ instead of $u$). For all other decays, WA is suppressed by small (penguin) Wilson coefficients. Apart from $B\\to(\\rho,\\omega)\\gamma$, WA is not relevant so much for the total values of $a_{7L}$, but rather for isospin breaking, which is set by photon emission from the spectator quark. WA is the only mechanism to contribute to isospin asymmetries at tree-level; see Ref.~\\cite{Kagan:2001zk} for $\\mathcal{O}(\\alpha_s)$ contributions.\n\nIn view of the large size of $a_{7L}^{u,{\\rm ann}}(\\rho)$ it is appropriate to have a look at further corrections. The most obvious ones are $\\mathcal{O}(\\alpha_s)$ corrections to the QCDF expressions, shown in Fig.~\\ref{rad_fig2}.\n\\begin{figure}\n$$\\epsfxsize=\\textwidth\\epsffile{rad_fig2.eps}$$\n\\caption[Example radiative corrections to \nweak annihilation.]{\\small Example radiative corrections to \nweak annihilation. The corrections to the $B$ vertex in (a) are known \n\\cite{bellnu,Descotes-Genon:2002mw} and those to the $V$ vertex in (b) are included in $f_V$. \nFor the non-factorisable corrections in (c) only preliminary results\nare available \\cite{chamonix}.}\n\\label{rad_fig2}\n\\end{figure}\nAs it turns out, the corrections to the $B$ vertex in Fig.~\\ref{rad_fig2}(a) are known: they also enter the decay $B\\to\\gamma \\ell\\nu$ and were calculated in Ref.~\\cite{bellnu,Descotes-Genon:2002mw}. Numerically, they are at the level of 10\\%. Fig.~\\ref{rad_fig2}(b) shows the vertex corrections to the $V$ vertex, which are actually included in the decay constant $f_V$. For the non-factorisable corrections shown in Fig.~\\ref{rad_fig2}(c) preliminary results have been reported in Ref.~\\cite{chamonix} according to which these corrections are of a size similar to the $B$ vertex corrections. \n\nIn Ref.~\\cite{Kagan:2001zk} also another class of $1\/m_b$ corrections to $B\\to K^*\\gamma$ was calculated, namely $\\mathcal{O}(\\alpha_s)$ corrections to the isospin asymmetry in this decay. As these corrections break factorisation (require an infra-red cut-off in the momentum distribution of the valence quarks in the $K^*$ meson) and are numerically small, we do not include them in our analysis. \n\n\\subsection{Long-Distance Photon Emission}\nAnother class of corrections is suppressed by one power of $m_b$ with respect to the QCDF contributions and is due to long-distance photon emission from the soft $B$ spectator quark. A first calculation of this effect was attempted in Ref.~\\cite{WA} and was corrected and extended in Ref.~\\cite{Ball:2003fq}. The long-distance photon emission from a soft-quark line requires the inclusion of higher-twist terms in the expansion of the quark propagator in a photon background field, beyond the leading-twist (perturbative) contribution; a comprehensive discussion of this topic can be found in Ref.~\\cite{Ball:2002ps}. The quantity calculated in Ref.~\\cite{Ball:2003fq} is\n\\begin{eqnarray}\n\\lefteqn{\n\\bra{\\rho^-(p)\\gamma(q)} (\\bar d u)_{V-A} (\\bar u b)_{V-A}\\ket{B^-(p+q)} =}\\hspace*{3cm}\\nonumber\\\\\n& = & e\\,\\frac{m_\\rho f_\\rho}{m_B} \\eta^*_\\mu\n\\left\\{F_V \\epsilon^{\\mu\\nu\\rho\\sigma} e^*_\\nu p_{\\rho}\n q_\\sigma - i F_A [e^{*\\mu} (p \\cdot q) - q^\\mu\n (e^* \\cdot p)]\\right\\}\\nonumber\\\\\n& = & -e \\,\\frac{m_\\rho f_\\rho}{m_B} \\left\\{ \\frac{1}{2}\\,\n F_V (S_L+S_R) + \\frac{1}{2}\\, F_A (S_L-S_R)\\right\\}\n \\label{problem}\n\\end{eqnarray}\nin terms of the photon-helicity amplitudes $S_{L,R}$.\\footnote{Eq.~(\\ref{problem}) differs from the one given in \\cite{Ball:2003fq} by an overall sign, which is due to the different convention used in \\cite{Ball:2003fq} (and in \\cite{Ball:2002ps}) for the covariant derivative: $D_\\mu = \\partial_\\mu\n- i e Q_f A_\\mu$ instead of $D_\\mu = \\partial_\\mu + i e Q_f A_\\mu$ as in this analysis.}\nIn QCDF, $F_{A,V}$ are given by $Q_u f_B\/\\lambda_B$ and induce a term $Q_u a_2 b^\\rho$ in $a_{7L}^{u,{\\rm ann}}(\\rho^-)$. The long-distance photon contribution to $F_{V,A}$ was found to be \\cite{Ball:2003fq}\n\\begin{equation}\nF^{\\rm soft}_A = -0.07\\pm 0.02 \\equiv Q_u G_A\\,,\\qquad \nF^{\\rm soft}_V = -0.09\\pm\n0.02 \\equiv Q_u G_V\\,.\n\\label{Fsoft}\n\\end{equation}\nwith $G_A+G_V = -0.24\\pm 0.06$ and $G_V-G_A = -0.030\\pm 0.015$.\\footnote{Again, there is a relative sign with respect to the results in \\cite{Ball:2003fq}. This comes from the fact that the product $e F_{A,V}^{\\rm soft}$ is independent of the sign convention for $e$, and as we have changed the overall sign of (\\ref{problem}) with respect to \\cite{Ball:2003fq}, we also have to change the sign of $F_{A,V}^{\\rm soft}$. Stated differently: the relative sign between $F_{A,V}^{\\rm soft}$ and $F_{A,V}^{\\rm hard}$ in \\cite{Ball:2003fq} is wrong because of a mismatch in sign conventions for $e$ in the covariant derivative.}\nIn order to obtain concise expressions for $a_{7L(R)}^{U,{\\rm ann}}$, it proves convenient to define one more hadronic quantity:\n\\begin{equation}\ng^\\rho_{L,R} = \\frac{\\pi^2}{T_1^\\rho}\\,\\frac{m_\\rho f_\\rho}{m_b m_B}\\,\n(G_V\\pm G_A)\n\\end{equation}\nand correspondingly for other mesons. $g_L$ is $\\mathcal{O}(1\/m_b^2)$ as $G_V+G_A$ has the same power scaling in $m_b$ as $T_1$, i.e.\\ $\\sim m_b^{-3\/2}$, as one can read off from the explicit expressions in \\cite{WA}. The difference $G_V-G_A$, on the other hand, is a twist-3 effect due to three-particle light-cone DAs of the photon and is suppressed by one more power of $m_b$, i.e.\\ $g_R\\sim\n1\/m_b^3$. This quantity will enter the CP asymmetry. Our final expressions for $a_{7L(R)}^{U,{\\rm ann}}$ then read:\n\\begin{eqnarray}\na_{7L}^{U,{\\rm ann}}(V) & = & \\left. a_{7L}^{U,{\\rm\n ann}}(V)\\right|_{\\rm QCDF} (b^V\\to b^V + g^V_L)\\,,\\nonumber\\\\\na_{7R}^{U,{\\rm ann}}(V) & = & \\left. a_{7L}^{U,{\\rm\n ann}}(V)\\right|_{\\rm QCDF} (b^V\\to g^V_R, d^V\\to 0)\\,.\n \\label{20A}\n\\end{eqnarray}\nNumerically, one has $g^{\\rho}_L\/b^\\rho = -0.3$, so these corrections, despite being suppressed by one more power in $1\/m_b$, are not small numerically and larger than the known $\\mathcal{O}(\\alpha_s)$\ncorrections to QCDF from $B\\to\\gamma\\ell\\nu$. Based on this, we feel justified in including these long-distance corrections in our analysis, while dropping the radiative ones of Figs.~\\ref{rad_fig2}(a) and (c). For central values of the input parameters we find the following numerical values for the various WA and long-distance photon contributions, including in particular those to which $Q_{1,2}$ contribute (with no Cabibbo suppression):\n\\begin{eqnarray}\na_{7L}^{c,{\\rm ann}}(K^{*0}) &=& -0.013-0.001\\, {\\rm LD}\\,,\n\\qquad a_{7L}^{c,{\\rm ann}}(K^{*-}) = 0.004+0.001\\, {\\rm\n LD}\\,,\\nonumber\\\\\na_{7L}^{u,{\\rm ann}}(\\rho^0) &=& -0.001-0.004\\, {\\rm LD}\\,,\n \\qquad ~~ a_{7L}^{u,{\\rm ann}}(\\rho^-) = 0.149-0.043\\, {\\rm\n LD}\\,,\\nonumber\\\\\n a_{7L}^{u,{\\rm ann}}(\\omega) &=& -0.024+0.003\\, {\\rm LD}\\,.\n \\label{LDcont}\n\\end{eqnarray}\nThe contribution from the long-distance photon emission is labelled ``LD'' (LD$\\to 1$ at the end). \nThe unexpectedly small $a_{7L}^{u,{\\rm ann}}(\\rho^0)$ is due to a numerical cancellation between the charged-current and penguin-operator contributions. Comparing these results with those from QCDF,\nEq.~(\\ref{10}), it is evident that WA is, as expected, largely irrelevant for the branching ratios, except for $B^\\pm\\to \\rho^\\pm\\gamma$.\n\n\n\\subsection{Soft Quark Loops}\n$a_{7L(R)}^{U,{\\rm soft}}$ encodes soft-gluon emission from a (light or heavy quark) loop as shown in Fig.~\\ref{rad_fig1}(b). Soft-gluon emission from a charm loop was first considered in Ref.~\\cite{KRSW97} as a potentially relevant long-distance contribution to the branching ratio of $B\\to K^*\\gamma$, however, the same diagram also contributes dominantly to the time-dependent CP asymmetry in $B^0\\to K^{*0}\\gamma$ \\cite{grin05}. As for $a_{7R}^U$, the dominant contributions to $a_{7R}^c(K^*)$ were calculated in Ref.~\\cite{Ball:2006cv} and new to this analysis is their generalisation to the other vector mesons and the inclusion of contributions from light-quark loops. Motivation to include light quark loops stems from the fact that they are doubly CKM-suppressed for $b\\to s\\gamma$ transitions, but not for $b\\to d\\gamma$, for which they are on an equal footing as the heavy quark loops. The quark loop contributions are suppressed by one power of $m_b$ with respect to $a_{7L}^{U,{\\rm QCDF}}$, but they also induce a right-handed photon amplitude which is of the same order in $1\/m_b$ as $a_{7R}^{U,{\\rm QCDF}}$ (\\ref{qcdf_13}), and this amplitude induces the time-dependent CP asymmetry. The asymmetry is expected to be very small in the SM and $\\propto m_D\/m_b$ due to the chiral suppression of the leading transition (\\ref{qcdf_13}), but could be drastically enhanced by new physics contributions -- thus constituting an excellent ``null test'' of the SM \\cite{Gershon:2006mt,Ball:2006cv}. It was noticed in Refs.~\\cite{grin04,grin05} that the chiral suppression is relaxed by emission of a gluon from the quark loop and contributes dominantly to the time-dependent CP asymmetry in $B^0\\to K^{*0}\\gamma$, which motivates the inclusion of these contributions. The task of the present analysis, however, is not so much to calculate these contributions to high accuracy, but to exclude the possibility of {\\em large} contributions to the CP asymmetry. With this in mind, the theoretical uncertainties of the results are very generously estimated --- which is somewhat unavoidable due to the current uncertainties of the relevant hadronic input parameters.\n\nPotentially the most important contribution to the soft-gluon emission diagram in Fig.~\\ref{rad_fig1}(b) \ncomes from the charged-current operator $Q_2^U$ with the large Wilson coefficient $C_2\\sim 1$; it vanishes for $Q_1^U$ by gauge invariance. In addition, the penguin operators $Q_{3,4,6}$ give a non-zero contribution. Details of the derivation of $a_7^{U,{\\rm soft}}$ can be found in Ref.~\\cite{Ball:2006eu} in which the following expression is obtained:\n\\begin{eqnarray}\na_{7L(R)}^{U,{\\rm soft}}(V) & = & \\frac{\\pi^2}{m_b T_1^{B\\to V}(0)} \\left\\{ Q_U C_2 (l_U\\pm \\tilde l_U)(V) + Q_D C_3 (l_D\\pm \\tilde l_D)(V)\\right.\\nonumber\\\\\n&&\\left. + \\sum_q Q_q (C_4-C_6) (l_q\\pm \\tilde l_q)(V)\\right\\}.\n\\label{24}\n\\end{eqnarray}\nHere the sum over $q$ runs over all five active quarks $u,d,s,c,b$. The contribution from $Q_5$ is proportional to $m_D$ and hence helicity suppressed and neglected. Assuming $\\rm SU(3)_F$-flavour symmetry for the light quark loops, one has $l_u=l_d=l_s$, and ditto for $\\tilde l_{u,d,s}$, which causes a cancellation of these contributions in the last term of Eq.~(\\ref{24}). ${\\rm SU(3)_F}$-breaking effects are estimated to be around 10\\% \\cite{Ball:2006eu}. The parameters $l_c(K^*)$ and $\\tilde{l}_c(K^*)$ were first calculated from three-point sum rules in Ref.~\\cite{KRSW97} and were re-calculated in the more suitable method of LCSR via a local OPE in Ref.~\\cite{Ball:2006cv}. The analysis therein as been updated and extended to $l_b,\\, \\tilde{l}_b$ and the other particles $\\rho,\\,\\omega ,\\,\\bar{K}^*,\\,\\phi$ for the present analysis \\cite{Ball:2006eu}. The results for $l_c$ and $\\tilde l_c$ are given in the upper table of Tab.~\\ref{rad_tab5}. Those for $l_b$ and $\\tilde l_b$ are obtained as\n\\begin{equation}\nl_b = \\frac{m_c^2}{m_b^2}\\, l_c\\,,\\qquad \\tilde l_b = \\frac{m_c^2}{m_b^2}\\, \\tilde l_c\\,.\n\\end{equation}\nFor light-quark loops the photon is almost at threshold and the local OPE does not apply. In Ref.~\\cite{Ball:2006eu} a method was developed for calculating these contributions via LCSRs. Similar to the method of Ref.~\\cite{Khodjamirian:2000mi} used for the calculation of soft-gluon contributions to $B\\to\\pi\\pi$, a dispersion relation approach is used to connect the off-shell matrix element to the physical regime $q^2=0$. The results are presented in the lower table of Tab.~\\ref{rad_tab5}.\n\\begin{table}[h]\n\\renewcommand{\\arraystretch}{1.3}\n\\addtolength{\\arraycolsep}{3pt}\n$$\n\\begin{array}{l ||r|r|r|r}\n& \\multicolumn{1}{c|}{l_c} & \\multicolumn{1}{c|}{\\tilde l_c} \n& \\multicolumn{1}{c|}{l_c-\\tilde l_c} & \\multicolumn{1}{c}{l_c +\n \\tilde l_c} \\\\\n \\hline\n B \\to K^* & -355 \\pm 280 & -596 \\pm 520 & 242 \\pm 370 & -952 \\pm 800 \\\\\n B \\to (\\rho,\\omega) & -382 \\pm 300 & -502\\pm 430 & 120 \\pm 390 & \n-884\\pm 660 \\\\\n B_s \\to \\bar K^* & -347\\pm 260 & -342\\pm 400 & -4\\pm 300 & -689\\pm 600 \\\\\n B_s \\to \\phi & -312 \\pm 240 & -618 \\pm 500 & 306 \\pm 320 & -930 \\pm\n750 \n\\end{array}\n$$\\\\\n\\renewcommand{\\arraystretch}{1.3}\n\\addtolength{\\arraycolsep}{3pt}\n$$\n\\begin{array}{l ||r|r|r|r}\n& \\multicolumn{1}{c|}{l_u} & \\multicolumn{1}{c|}{\\tilde l_u} & \n\\multicolumn{1}{c|}{l_u-\\tilde l_u} & \\multicolumn{1}{c}{l_u + \\tilde l_u} \\\\\n \\hline\n B \\to K^* & 536 \\pm 70\\% & 635 \\pm 70\\% & -99 \\pm 300 & 1172 \\pm 70 \\% \\\\\n B \\to (\\rho,\\omega) & 827 \\pm 70\\% & 828\\pm 70\\% & -1\\pm 300&\n 1655\\pm 70\\% \\\\\n B_s \\to \\bar K^* & 454\\pm 70\\% & 572\\pm 70\\% & -118\\pm 300 & 1025\\pm\n70\\% \\\\\n B_s \\to \\phi & 156\\pm 70\\% & 737\\pm 70\\% & -581\\pm 300 & 893\\pm 70\\% \\\\\n\\end{array}\n$$\n\\caption[Soft-gluon contributions from $c$-quark and $u$-quark loops in units KeV. ]{\\small Soft-gluon contributions from $c$-quark (upper table) and $u$-quark (lower table) loops in units KeV. The quantities $l_{c,u}$ and $\\tilde l_{c,u}$ are defined in Ref.~\\cite{Ball:2006eu}. We assume equal parameters for $\\rho$ and $\\omega$. $l_b$ is obtained as $l_b = l_c m_c^2\/m_b^2$ and correspondingly for $\\tilde l_b$. The uncertainty for $l_u-\\tilde l_u$ is given in absolute numbers because of cancellations. In the $\\rm SU(3)_F$-flavour limit assumed in this calculation one has $l_u = l_d = l_s \\equiv l_q$}\n\\label{rad_tab5}\n\\end{table}\n\n\\section{Phenomenological Results}\nIn this section we combine the different contributions to the factorisation coefficients $a_{7L(R)}^U$ and give results for the observables, namely the branching ratios, the isospin asymmetries and the time-dependent CP asymmetries. \n\\subsection{Branching Ratios}\\label{rad_brs}\nThe (non-CP-averaged) branching ratio of the $b\\to D\\gamma$ decay \n$\\bar B\\to V\\gamma$ is given by\n\\begin{eqnarray}\n{\\cal B}(\\bar B\\to V\\gamma) & = & \\frac{\\tau_B}{c_V^2}\\,\n\\frac{G_F^2\\alpha_{\\rm QED} m_B^3 m_b^2}{32 \\pi^4} \\left(1-\\frac{m_V^2}{m_B^2}\\right)^3 \\left[T_1^{B\\to V}(0)\\right]^2\\nonumber\\\\\n&&\\times \\left\\{ \\left| \\sum_{U=u,c} \\lambda_U^{(D)} a_{7L}^U(V)\\right|^2 + \\left| \\sum_{U=u,c} \\lambda_U^{(D)}\n a_{7R}^U(V)\\right|^2\\right\\}\n \\label{BR}\n\\end{eqnarray}\nwith the isospin factors $c_{\\rho^\\pm,K^*,\\phi}=1$ and $c_{\\rho^0,\\omega} = \\sqrt{2}$. The branching ratio for the CP-conjugated channel $B\\to \\bar V\\gamma$ ($\\bar b\\to \\bar D\\gamma$ at parton level) is obtained by replacing $\\lambda_U^{(D)}\\to (\\lambda_U^{(D)})^*$. With the input parameters from Tab.~\\ref{rad_tab8} and the lifetimes given in Tab.~\\ref{rad_tab9} we find the following CP-averaged branching ratios for $B\\to K^*\\gamma$, making explicit various sources of uncertainty:\n\\begin{eqnarray}\n\\overline{\\cal B}(B^- \\to K^{*-}\\gamma) & = & (53.3\\pm \\overbrace{13.5}^{T_1}\n\\pm \\overbrace{4.8}^{\\mu}\n\\pm \\overbrace{1.8}^{V_{cb}}\\pm \\overbrace{1.9}^{l_{u,c}} \\pm \\overbrace{1.3}^{\\mbox{other}})\\times\n10^{-6}\\nonumber\\\\\n& =& (53.3\\pm \\underbrace{13.5}_{T_1}\\pm 5.8)\\times 10^{-6}\\,,\n\\nonumber\\\\\n\\overline{\\cal B}(\\bar B^0 \\to K^{*0}\\gamma) & = & (54.2\\pm \\overbrace{13.2}^{T_1}\n\\pm \\overbrace{6.0}^{\\mu}\n\\pm \\overbrace{1.8}^{V_{cb}}\\pm \\overbrace{1.8}^{l_{u,c}} \\pm \\overbrace{1.4}^{\\mbox{other}})\\times\n10^{-6}\\nonumber\\\\\n& = & (54.2\\pm \\underbrace{13.2}_{T_1}\\pm 6.7)\\times 10^{-6}\\,.\n\\label{50}\n\\end{eqnarray}\nWe have added all individual uncertainties in quadrature, except for that induced by the form factor. The uncertainty in $\\mu$ is that induced by the renormalisation-scale dependence, with $\\mu= m_b(m_b)\\pm 1\\,$GeV. ``Other'' sources of uncertainty include the dependence on the parameters in Tab.~\\ref{rad_tab7}, on the size of LD WA contributions and the replacement of NLO by LO Wilson coefficients. The above results agree, within errors, with the experimental ones given in Tab.~\\ref{rad_tab1}, within the large theoretical uncertainty induced by the form factor.\n\nAs the uncertainties of all form factors in Tab.~\\ref{rad_tab8} are of roughly the same size, one might conclude that the predictions for all branching ratios will carry uncertainties similar to those in\n(\\ref{50}). This is, however, not the case: the accuracy of the theoretical predictions can be improved by making use of the fact that the {\\em ratio} of form factors is known much better than the individual form factors themselves. The reason is that the values given in Tab.~\\ref{rad_tab8}, which were calculated using the same method, LCSRs, and with a common set of input parameters, include common systematic uncertainties (dependence on $f_B$, $m_b$ etc.) which partially cancel in the ratio. In Ref.~\\cite{Ball:2006nr} the ratio of the $K^*$ and $\\rho$ form factors was found to be\n\\begin{equation}\n\\xi_\\rho \\equiv \\frac{T_1^{B\\to K^*}(0)}{T_1^{B\\to \\rho}(0)} = 1.17\\pm\n0.09\\,.\n\\label{xirho}\n\\end{equation}\nThe uncertainty is by a factor 2 smaller than if we had calculated $\\xi_\\rho$\nfrom the entries in Tab.~\\ref{rad_tab8}; analogously for $\\omega$ one finds\n\\begin{equation}\n\\xi_\\omega \\equiv \\frac{T_1^{B\\to K^*}(0)}{T_1^{B\\to \\omega}(0)} = 1.30\\pm\n0.10\\,.\n\\label{xiomega}\n\\end{equation}\nThe difference between $\\xi_\\rho$ and $\\xi_\\omega$ is mainly due to the difference between $f_\\omega^\\perp$ and $f_\\rho^\\perp$. For the $B_s$ form factors, we also need the ratio of decay constants $f_{B_s}\/f_{B_d}$. The status of $f_B$ from Lattice QCD was reviewed in Ref.~\\cite{Onogi}; the present state-of-the-art calculations are unquenched with $N_f=2+1$ active flavours \\cite{unquenchedfB}, whose average is $f_{B_s}\/f_{B_d}=1.23\\pm 0.07$. Again, this ratio is fully consistent with that quoted in Tab.~\\ref{rad_tab8}, but has a smaller uncertainty. One then finds the following ratios for $B_s$ form factors:\n\\begin{equation}\n\\xi_\\phi \\equiv \\frac{T_1^{B\\to K^*}(0)}{T_1^{B_s\\to\\phi}(0)} = 1.01\\pm 0.13\n\\,,\\qquad\n\\xi_{\\bar K^*} \\equiv \\frac{T_1^{B\\to K^*}(0)}{T_1^{B_s\\to\\bar K^*}(0)} \n= 1.09\\pm 0.09\\,.\n\\label{xis}\n\\end{equation}\nThe uncertainty of $\\xi_{\\bar K^*}$ is smaller than that of $\\xi_\\phi$ because the input parameters for $K^*$ and $\\bar K^*$ are the same (except for G-odd parameters like $a_1^\\perp$) and cancel in the ratio; the uncertainty is dominated by that of $f_{B_s}\/f_{B_d}$. To benefit from this reduced theoretical uncertainty in predicting branching ratios, one has to calculate ratios of branching ratios, which mainly depend on $\\xi_V$ and only mildly on $T_1$ itself: in addition to the overall normalisation, $T_1$ also enters hard-spectator interactions and power-suppressed corrections, whose size is set by hadronic quantities $\\propto 1\/T_1$. As these corrections are subleading (in $\\alpha_s$ or $1\/m_b$), however, a small shift in $T_1$ has only very minor impact on the branching ratios. The absolute scale for the branching ratios is set by the CP- and isospin-averaged branching ratio with the smallest experimental uncertainty, i.e.\\ $B\\to K^*\\gamma$; from Tab.~\\ref{rad_tab1}, one finds:\n\\begin{equation}\n\\overline{\\cal B}(B\\to K^*\\gamma) = \\frac{1}{2}\\left\\{ \\overline{\\cal\n B}(B^\\pm \\to K^{*\\pm}\\gamma) + \\frac{\\tau_{B^\\pm}}{\\tau_{B^0}}\\, \n \\overline{\\cal B}(\\bar B^0 \\to K^{*0}\\gamma)\\right\\} = (41.6\\pm\n 1.7)\\times 10^{-6}\\,.\n \\label{x}\n\\end{equation}\nThat is, we obtain a theoretical prediction for $\\overline{\\cal B}(B\\to\nV\\gamma)$ as \n\\begin{equation}\n\\left.\\overline{\\cal B}(B\\to V\\gamma)\\right|_{\\rm th}= \\left[ \\frac{\\overline{\\cal B}(B\\to V\\gamma)}{\n\\overline{\\cal B}(B\\to K^*\\gamma)}\\right]_{{\\rm th}} \\, \\left.\n\\overline{\\cal B}(B\\to K^*\\gamma)\\right|_{\\rm exp}\\,,\n\\end{equation}\nwhere $\\left[\\dots\\right]_{\\rm th}$ depends mainly on $\\xi_V$ and only in subleading terms on the individual form factors $T_1^{B\\to K^*}$ and $T_1^{B\\to V}$. It is obvious that, except for these subleading terms, this procedure is equivalent to extracting an {\\em effective form factor} $\\left.T_1^{B\\to K^*}(0)\\right|_{\\rm eff}$ from $B\\to K^*\\gamma$ and using $\\left.T_1^{B\\to V}(0)\\right|_{\\rm eff} = \\left.T_1^{B\\to K^*}(0)\\right|_{\\rm eff}\/\\xi_{V}$ for calculating the branching ratios for $B\\to V\\gamma$. From (\\ref{x}) we find\n\\begin{equation}\n\\left. T_1^{B\\to K^*}(0)\\right|_{\\rm eff} = 0.267\\pm \\overbrace{0.017}^{{\\rm th}} \\pm\n\\overbrace{0.006}^{{\\rm exp}} = 0.267\\pm 0.018\\,,\n\\end{equation}\nwhere the theoretical uncertainty follows from the second uncertainty given in (\\ref{50}). Eqs.~(\\ref{xirho}), (\\ref{xiomega}) and (\\ref{xis}) then yield\n\\begin{eqnarray}\n\\left. T_1^{B\\to \\rho}(0)\\right|_{\\rm eff} &=& 0.228 \\pm 0.023\\,, \\qquad\n\\left. T_1^{B\\to \\omega}(0)\\right|_{\\rm eff} = 0.205 \\pm\n0.021\\,,\\nonumber\\\\\n\\left. T_1^{B_s\\to \\bar K^*}(0)\\right|_{\\rm eff} &=& 0.245 \\pm\n0.024\\,, \n\\qquad\n\\left. T_1^{B_s\\to \\phi}(0)\\right|_{\\rm eff} = 0.260 \\pm\n0.036\\,.\n\\label{56}\n\\end{eqnarray}\nNote that all effective form factors agree, within errors, with the results from LCSRs given in Tab.~\\ref{rad_tab8}, which confirms the results obtained from this method; the crucial point, however, is that the uncertainties are reduced by a factor of 2 (except for $T_1^{B_s\\to \\phi}$). We would like to stress that the motivation for this procedure is to achieve a reduction of the theoretical uncertainty of the\npredicted branching fractions in $B\\to (\\rho,\\omega)\\gamma$ and $B_s$ decays. The effective form factors do {\\em not} constitute a new and independent theoretical determination, but are derived from the experimental results for $B\\to K^*\\gamma$ under the following assumptions:\n\\begin{itemize}\n\\item there is no new physics in $B\\to K^*\\gamma$;\\footnote{Which is motivated by the\n results from inclusive $B\\to X_s \\gamma$ decays \\cite{misiak}.}\n\\item QCDF is valid with no systematic uncertainties;\n\\item LCSRs can reliably predict the ratio of form factors at zero\n momentum transfer.\n\\end{itemize}\n From (\\ref{BR}) and (\\ref{56}), we then predict the following CP-averaged branching ratios:\n\\begin{eqnarray}\n\\overline{\\cal B}(B^-\\to \\rho^-\\gamma) & = & (1.16\\pm \\overbrace{0.22}^{T_1}\\pm \\overbrace{0.13}^{\\rm Other})\\times 10^{-6}\\,, \\nonumber\\\\\n\\overline{\\cal B}(B^0\\to \\rho^0\\gamma) & = & (0.55\\pm 0.11\\pm \n0.07)\\times 10^{-6}\\,, \\nonumber\\\\\n\\overline{\\cal B}(B^0\\to \\omega\\gamma) & = & (0.44\\pm 0.09 \\\n\\pm 0.05)\\times 10^{-6}\\,,\\nonumber\\\\\n\\overline{\\cal B}(B_s\\to \\bar K^*\\gamma) & = & (1.26\\pm 0.25\\pm \n0.18)\\times 10^{-6}\\,, \\nonumber\\\\\n\\overline{\\cal B}(B_s\\to \\phi\\gamma) & = & (39.4\\pm 10.7 \\ \\pm 5.3)\\times 10^{-6}\\,,\n\\label{57}\n\\end{eqnarray}\nwhere the first uncertainty is induced by the effective form factors and the second includes the variation of all inputs from Tab.~\\ref{rad_tab8} except for the angle $\\gamma$ of the UT, which is fixed at\n$\\gamma=53^\\circ$.\\footnote{The value of the UT angle\n$\\gamma$ in Tab.~\\ref{rad_tab8} comes from Belle's \nDalitz-plot analysis of the CP asymmetry in $B^-\\to (K_S^0 \\pi^+\\pi^-)\nK^-$, with $K_S^0 \\pi^+\\pi^-$ \\cite{Bellegamma} being a three-body final state common\nto both $D^0$ and $\\bar D^0$. Other determinations all come with theoretical uncertainties and\/or possible contamination by unresolved new physics, so we take this result as a reference point.} The total uncertainty in each channel is $\\sim 20\\%$, except for $B_s\\to \\phi\\gamma$, where it is 30\\%. The results for $\\rho$ and $\\omega$ agree very well with those of {\\sc BaBar}, Tab.~\\ref{rad_tab1}, but less so with the Belle results, although present experimental and theoretical uncertainties preclude a firm conclusion. Our prediction for $B_s\\to \\phi\\gamma$ is well below the current experimental bound $120\\times 10^{-6}$ \\cite{Yao:2006px}. A branching ratio of the size given in (\\ref{57}) implies that $\\mathcal{O}(10^3)$ $B_s\\to\\phi\\gamma$ events will be seen within the first few years of the LHC.\n\nIn Tab.~\\ref{rad_tab6} we detail the contributions of individual terms to the branching ratios. In all cases ${\\cal B}$ is dominated by the QCDF contribution, with WA most relevant for $B^-\\to \\rho^-\\gamma$. This is expected as WA enters with the large Wilson coefficient $C_2\\sim 1$. The effect is extenuated by long-distance (LD) photon emission, which itself is compensated by soft-gluon emission. The other channels follow a similar pattern, although the size of the effects is smaller.\n\\begin{table}[h]\n\\renewcommand{\\arraystretch}{1.3}\n\\addtolength{\\arraycolsep}{3pt}\n$$\n\\begin{array}{l||c|c|c|c}\n\\mathcal{B}\\times 10^{6}& \\mbox{QCDF} & \\mbox{+ WA (no LD)} & \\mbox{+ WA (inc.\\ LD)}& \n\\mbox{+ soft gluons}\\\\\\hline\nB^-\\to \\rho^-\\gamma & 1.05&1.17 & 1.11&1.16\n\\\\\nB^0\\to \\rho^0\\gamma & 0.49&0.53& 0.53&0.55\n\\\\\nB^0\\to \\omega\\gamma & 0.40& 0.42&0.42 &0.44\n\\\\\nB^-\\to K^{*-}\\gamma &39.7&38.4& 38.3&39.4\n\\\\\nB^0\\to K^{*0}\\gamma & 37.1&39.7 &39.9 &41.0\n\\\\\nB_s^0\\to \\bar K^{*0}\\gamma & 1.12& 1.22& 1.23&1.26\n\\\\\nB_s^0\\to \\phi\\gamma &34.6 & 38.2& 38.3&39.4\n\\end{array}\n$$\n\\caption[Contributions to CP-averaged branching ratios.]{\\small Contributions to CP-averaged branching ratios, using effective form factors and central values of all other input parameters, Tab.~\\ref{rad_tab8} (in particular $\\gamma=53^\\circ$). LD stands for long-distance photon-emission contributions. Each column labelled ``+X'' includes the contributions listed in the previous column plus the contribution induced by X. The entries in the last column are our total central values.}\n \\label{rad_tab6}\n\\end{table}\n\\begin{figure}[h]\n$$ \\epsfxsize=0.48\\textwidth\\epsffile{fig7a.eps}\\quad\\epsfxsize=0.48\\textwidth\\epsffile{fig7b.eps}$$\n$$ \\epsfxsize=0.48\\textwidth\\epsffile{fig7c.eps}$$\n \\caption[CP-averaged branching ratios of $B\\to(\\rho,\\omega)\\gamma$ as function of angle $\\gamma$.]{\\small CP-averaged branching ratios of $B\\to(\\rho,\\omega)\\gamma$ as function of UT angle $\\gamma$, using the effective form factors and central values of other input parameters. (a): $B^\\pm \\to \\rho^\\pm\\gamma$, (b): $B^0\\to \\rho^0\\gamma$, (c): $B^0\\to\\omega\\gamma$. The boxes indicate the 1$\\sigma$ experimental results from {\\sc BaBar} \\cite{babar_rad}, Tab.~\\ref{rad_tab1}. Note that the resulting value of $\\gamma$ from the average of all three channels is $\\gamma = (61.0^{+13.5}_{-16.0}({\\rm exp})^{+8.9}_{-9.2})^\\circ$ -- see Section~\\ref{ckmextract}.} \n \\label{rad_fig3}\n\\end{figure}\n\nWe would like to close this section by making explicit the dependence of the three $B\\to (\\rho,\\omega)\\gamma$ branching ratios on $\\gamma$. In Fig.~\\ref{rad_fig3} we plot these branching ratios, for central values of the input parameters, as functions of $\\gamma$. We also indicate the present experimental results from {\\sc BaBar} \\cite{babar_rad}, Tab.~\\ref{rad_tab1}, within their 1$\\sigma$ uncertainty. \n\n\\subsection{Isospin Asymmetries}\\label{isosec}\nThe asymmetries $A_I(\\rho)$, $A_I(K^*)$, and $A(\\rho,\\omega) $ are given by\n\\begin{eqnarray}\nA(\\rho,\\omega) & = & \\frac{\\overline{\\Gamma}(B^0\\to \\omega \\gamma)}{\\overline{\\Gamma}(B^0\\to \\rho^0 \\gamma)}-1\\,, \n \\label{rad_arw} \\\\\nA_{I}(\\rho) & = & \\frac{2\\overline{\\Gamma}(\\bar B^0\\to \\rho^0 \\gamma)}{\\overline{\\Gamma}(\\bar B^\\pm\\to \\rho^\\pm \\gamma)} - 1\\,,\n \\label{rad_air}\\\\\nA_{I}(K^*) & = & \\frac{\\overline{\\Gamma}(\\bar B^0\\to K^{*0} \\gamma) - \\overline{\\Gamma}(B^\\pm\\to \n K^{*\\pm} \\gamma)}{\\overline{\\Gamma}(\\bar B^0\\to K^{*0} \\gamma) + \\overline{\\Gamma}(B^\\pm\\to \n K^{*\\pm} \\gamma)}\\,,\n \\label{rad_aik}\n\\end{eqnarray}\nwhere the partial decay rates are CP-averaged. Let us first discuss $A(\\rho,\\omega)$ and $A_{I}(\\rho)$ which are relevant for the experimental determination of $\\overline{\\cal B}(B\\to(\\rho,\\omega)\\gamma)$, which in turn is used for the determination of $|V_{td}\/V_{ts}|$ (or $\\gamma$), see Section~\\ref{ckmextract}. The present experimental statistics for $b\\to d\\gamma$ transitions is rather low, so the experimental value of $\\overline{\\cal B}(B\\to(\\rho,\\omega)\\gamma)$ is obtained under the explicit assumption of perfect symmetry, i.e.\\ $\\overline{\\Gamma}(B^\\pm\\to \\rho^\\pm \\gamma) = 2 \\overline{\\Gamma}(B^0\\to \\rho^0 \\gamma) = 2 \\overline{\\Gamma}(B^0\\to \\omega \\gamma)$. In reality, the symmetry between $\\rho^0$ and $\\omega$ is broken by different values of the form factors, and isospin symmetry between neutral and charged $\\rho$ is broken by photon emission from the spectator quark, the dominant mechanism of which is WA. From the formulas for individual branching ratios, Eq.~(\\ref{BR}), and the various contributions to the factorisation coefficients $a_{7L(R)}^U$, we find\n\\begin{equation}\nA(\\rho,\\omega) = -0.20\\pm \\overbrace{0.09}^{{\\rm th.}}\\,.\n\\label{eq:AIrw}\n\\end{equation}\nThe uncertainty is dominated by that of the form factor ratio $T_1^{B\\to\\omega}(0)\/T_1^{B\\to\\rho}(0)=0.90\\pm 0.05$.\\footnote{Note that this result is dominated by the ratio of decay constants given in Tab.~\\ref{rad_tab8} and discussed in Ref.~\\cite{Ball:2006eu}. The experimental results entering these averages have a large spread which may cast a shadow of doubt on the averaged final branching ratios for $(\\rho^0,\\omega)\\to e^+ e^-$ quoted by PDG \\cite{Yao:2006px}.} The dependence on all other input parameters is marginal. The result (\\ref{eq:AIrw}) is not compatible with the strict isospin limit $A(\\rho,\\omega) =0$. $A_{I}(\\rho)$, on the other hand, is very sensitive to $\\gamma$, whereas the form factors drop out. It is driven by the WA contribution and, in the QCDF framework, vanishes if WA is set to zero. In Fig.~\\ref{rad_fig4}(a) we plot $A_{I}(\\rho)$ as function of $\\gamma$, including the theoretical uncertainties. \n\\begin{table}\n\\renewcommand{\\arraystretch}{1.3}\n\\addtolength{\\arraycolsep}{3pt}\n$$\n\\begin{array}{c||c|c|c|c}\n\\gamma & 40^\\circ & 50^\\circ & 60^\\circ & 70^\\circ\\\\\\hline\nA_I(\\rho) & -(5.3\\pm 6.9)\\% & (0.4\\pm 5.3)\\% & (5.7\\pm 3.9)\\% &\n(10.5\\pm 2.7)\\%\n\\end{array}\n$$\n\\caption[Isospin asymmetry $A_I(\\rho)$ for different values of $\\gamma$.]{\\small Isospin asymmetry $A_I(\\rho)$ for different values of $\\gamma$.}\n \\label{rad_tab7}\n\\end{table}\nAs suggested by the findings of Ref.~\\cite{chamonix}, these results are not expected to change considerably upon inclusion of the non-factorisable radiative corrections of Fig.~\\ref{rad_fig2}(c). In Tab.~\\ref{rad_tab7}, we give the corresponding results for several values of $\\gamma$, together with the theoretical uncertainty. Our result agrees very well with that obtained by the {\\sc BaBar} collaboration: $A_I(\\rho)_{\\rm BaBar} = 0.56\\pm 0.66$ \\cite{babar_rad}.\n\n$A_I(K^*)$ was first discussed in Ref.~\\cite{Kagan:2001zk}, including power-suppressed $\\mathcal{O}(\\alpha_s)$ corrections which unfortunately violate QCDF, i.e.\\ are divergent. It is for this reason that we decide to drop these corrections and include only leading-order terms in $\\alpha_s$. We then find\n\\begin{eqnarray}\nA_I(K^*) &=& (5.4\\pm \\overbrace{1.0}^{\\mu} \\pm \\overbrace{0.6}^{{\\rm NLO}\\leftrightarrow{\\rm\n LO}} \\pm \\overbrace{0.6}^{f_B} \\pm \\overbrace{0.6}^{{\\rm other}})\\%\\nonumber\\\\\n&=& (5.4\\pm 1.4)\\%\\,,\n\\label{62}\n\\end{eqnarray}\nwhere ${\\rm NLO}\\leftrightarrow{\\rm LO}$ denotes the uncertainty induced by switching from NLO to LO accuracy in the Wilson coefficients and ``other'' summarises all other sources of theoretical uncertainty. \nAs can be inferred from the entries in Tab.~\\ref{rad_tab1}, the present experimental result is $A_I(K^*)_{\\rm exp}=(3.2\\pm 4.1)\\%$. In Ref.~\\cite{Kagan:2001zk} it was pointed out that $A_I(K^*)$\nis very sensitive to the values of the Wilson coefficients $C_{5,6}^{\\rm BBL}$ in the combination $a_6\\equiv C_{5}^{\\rm BBL}+C_6^{\\rm BBL}\/3$. In the SM, varying the renormalisation scale as $\\mu=m_b(m_b)\\pm 1\\,{\\rm GeV}$ and switching between LO and NLO accuracy for the Wilson coefficients, one has $a_6= -0.039\\pm 0.008$, which actually induces the bulk of the uncertainty in Eq.~(\\ref{62}). In Fig.~\\ref{rad_fig4}(b) we plot $A_I(K^*)$ as function of $a_6\/a_6^{\\rm SM}$, with $a_6^{\\rm SM}=-0.039$.\n\\begin{figure}[tb]\n$$\\epsfxsize=0.45\\textwidth\\epsffile{fig8a.eps}\\quad\n \\epsfxsize=0.45\\textwidth\\epsffile{fig8b.eps}$$\n\\caption[$A_I(\\rho)$ as function of the angle $\\gamma$ and $A_I(K^*)$ as function of $r\\equiv a_6\/a_6^{\\rm SM}$.]{\\small Left panel: isospin asymmetry $A_I(\\rho)$, Eq.~(\\ref{rad_air}), as function of the UT angle $\\gamma$. Solid line: central values of input parameters; dashed lines: theoretical uncertainty. Right panel: $A_I(K^*)$, Eq.~(\\ref{rad_aik}), in percent, as function of the ratio $r\\equiv a_6\/a_6^{\\rm SM}$ of the combination of penguin Wilson coefficients $a_6\\equiv C_6+C_5\/3$. Solid line: central value of input parameters, dashed lines: theoretical uncertainty. The box indicates the present experimental uncertainty and the straight black lines the theory uncertainty in $r$.}\n \\label{rad_fig4}\n\\end{figure}\nThe figure clearly indicates that, although there is presently no\ndiscrepancy between theoretical prediction and experimental result,\na reduction of the experimental uncertainty\nof $A_I(K^*)$ may well reveal some footprints of new physics\nin this observable.\n\n\n\\subsection{CP Asymmetries}\\label{cpsec}\nThe time-dependent CP asymmetry in $\\bar B^0\\to V^0\\gamma$ is given analogously to Eq.~(\\ref{basics_eq13}) as\n\\begin{equation}\nA_{CP}(t) = S(V\\gamma) \\sin(\\Delta m_D\\, t ) - C(V\\gamma) \n\\cos(\\Delta m_D\\, t)\\,.\n\\label{rad_cpa}\n\\end{equation}\nThe above equation is technically only valid for $\\Delta \\Gamma =0$ and while this is a good assumption for $B^0_d$ decays, it is not so for $B_s^0$ decays. Although Eq.~(\\ref{rad_cpa}) can easily be adapted to non-zero $\\Delta \\Gamma_s$ we refrain from doing so: the whole point in calculating the CP asymmetry is not so much to give precise predictions for $S$ and $C$, but rather to exclude the possibility of large corrections to the naive expectation $S\\sim m_D\/m_b$. With this is mind, small corrections from a non-zero $\\Delta\\Gamma_s$ are irrelevant. The time-dependent CP asymmetries are given in terms of the left- and right-handed photon amplitudes (\\ref{qcdf_8}) by\n\\begin{equation}\nS(V\\gamma) \n = \\frac{2 \\,{\\rm Im}\\,\\left(\\frac{q}{p}({\\cal A}_L^* \\bar{\\cal A}_L + \n {\\cal A}_R^* \\bar{\\cal A}_R)\\right)}{\n |{\\cal A}_L|^2 + |{\\cal A}_R|^2 + |\\bar{\\cal A}_L|^2 + |\\bar{\\cal\n A}_R|^2}\\,,\n\\quad\nC(V\\gamma) = \\frac{|{\\cal A}_L|^2 + |{\\cal A}_R|^2 - |\\bar{\\cal A}_L|^2 - \n |\\bar{\\cal A}_R|^2}{\n |{\\cal A}_L|^2 + |{\\cal A}_R|^2 + |\\bar{\\cal A}_L|^2 + |\\bar{\\cal\n A}_R|^2}\\,.\n\\label{54}\n\\end{equation}\nWith ${\\cal A}_{L,R}$ and $\\bar{\\cal A}_{L,R}$ as given in (\\ref{qcdf_11}). The indirect CP asymmetry $S(V\\gamma)$ relies on the interference of both left- and right-helicity amplitudes and vanishes if one of them is absent; it thus probes indirectly the photon helicity. The direct CP asymmetry\n$C(V\\gamma)$ is less sensitive to $\\bar{\\cal A}_R$, but very sensitive to the strong phase of $\\bar{\\cal A}_L$ and vanishes if the radiative corrections to $a_{7L}^{U,{\\rm QCDF}}$, Eq.~(\\ref{10}), are\n neglected. As the accuracy of the prediction of strong phases in QCDF is subject to discussion, and in any case $C(V\\gamma)$ is less sensitive to new physics than $S(V\\gamma)$, we shall\n not consider direct CP asymmetries in this analysis.\n\nLet us briefly discuss the reason for the expected smallness of $S$. In the process $b\\to D\\gamma$, in the SM, the emitted photon is predominantly left-handed in $b$, and right-handed in $\\bar b$ decays. This is due to the fact that the dominant contribution to the amplitude comes from the chiral-odd dipole operator $Q_7$. As only left-handed quarks participate in the weak interaction, an effective operator of this type necessitates, in the SM, a helicity flip on one of the external quark lines, which results in a factor $m_b$ (and a left-handed photon) in $b_R\\to D_L\\gamma_L$ and a factor $m_D$ (and a right-handed photon) in $b_L\\to D_R\\gamma_R$. Hence, the emission of right-handed photons is suppressed by a factor $m_D\/m_b$, which leads to the QCDF prediction (\\ref{qcdf_13}) for $a_{7R}^U$. The interesting point is not the smallness of the CP asymmetry {\\em per se}, but the fact that the helicity suppression can easily be alleviated in a large number of new physics scenarios where the spin flip occurs on an internal line, resulting in a factor $m_i\/m_b$ instead of $m_D\/m_b$. A prime example is left-right symmetric models \\cite{LRS}, whose impact on the photon polarisation was discussed in\nRefs.~\\cite{alt, grin04,grin05}. These models also come in a supersymmetric version whose effect on $b\\to s\\gamma$ was investigated in Ref.~\\cite{frank}. Supersymmetry with no left-right symmetry can also provide large contributions to $b\\to D\\gamma_R$, see Ref.~\\cite{susy} for recent studies. Other\npotential sources of large effects are warped extra dimensions \\cite{warped} or anomalous right-handed top couplings \\cite{anomalous}. Unless the amplitude for $b\\to D\\gamma_R$ is of the same order as the SM prediction for $b\\to D \\gamma_L$, or the enhancement of $b\\to D \\gamma_R$ goes along with a suppression of $b\\to D \\gamma_L$, the impact on the branching ratio is small, as the two helicity amplitudes add incoherently. This implies there can be a substantial contribution of new physics to $b\\to D\\gamma$ escaping detection when only branching ratios are measured.\n\nWe can calculate $S$ directly from (\\ref{54}) and obtain, making explicit the contributions from different sources:\n\\begin{equation}\n\\renewcommand{\\arraystretch}{1.3}\n\\begin{array}[b]{rll}\nS(\\rho\\gamma) = \n& \\phantom{-}(\\overbrace{~0.01~}^{m_D\/m_b}+\\overbrace{~0.02~}^{\\rm\n LD~WA}+\\overbrace{~0.20~}^{\\shortstack{\\footnotesize{soft} \\\\ \\footnotesize{gluons}}}~\\pm~ 1.6)\\%\n& = \\phantom{-}(0.2\\pm 1.6)\\%\\,,\\\\\nS(\\omega\\gamma) =\n& \\phantom{-}(0.01-0.08+0.22\\pm 1.7)\\% \n& = \\phantom{-}(0.1\\pm 1.7)\\%\\,,\\\\\nS(K^*\\gamma) =\n& -(2.9-0+0.6\\pm 1.6)\\%\n& = -(2.3\\pm 1.6)\\%\\,,\\\\\nS(\\bar K^*\\gamma) =\n& \\phantom{-}(0.12+0.03+0.11\\pm 1.3)\\%\n& = \\phantom{-}(0.3\\pm 1.3)\\%\\,,\\\\\nS(\\phi\\gamma) =\n& \\phantom{-}(0+0+5.3\\pm8.2)\\times 10^{-2}\\,\\%\n& = \\phantom{-}(0.1\\pm 0.1)\\%\\,.\n\\label{eq:SVgamma}\n\\end{array}\n\\end{equation}\nIncluding only the helicity-suppressed contribution, one expects, for $B\\to K^*\\gamma$, neglecting the doubly Cabibbo suppressed amplitude in $\\lambda_u^{(s)}$\n\\begin{equation}\n\\left.S(K^*\\gamma)\\right|_{\\mbox{\\footnotesize no soft gluons}} \n = -2\\, \\frac{m_s}{m_b}\\,\\sin\\,\\phi_d\\approx -2.7\\%\\,.\\label{75}\n\\end{equation}\nFor $B_s\\to\\phi\\gamma$, one expects the CP asymmetry to vanish if the decay amplitude is proportional to $\\lambda_t^{(s)}$, which, at tree-level, precludes any contributions of type $\\sin(\\phi_s) m_s\/m_b$ and also any contribution from WA. This is because the mixing angle $\\phi_s$ is given by ${\\rm\n arg}[(\\lambda_t^{(s)})^2]$, Eq.~(\\ref{basics_eq12}), and the interference of amplitudes in (\\ref{54}) also yields a factor $(\\lambda_t^{(s)})^2$, if the individual amplitudes are proportional to $\\lambda_t^{(s)}$ or \n$(\\lambda_t^{(s)})^*$, respectively; this is indeed the case for the helicity-suppressed term $m_s\/m_b$ induced by the operator $Q_7$ and the WA contributions to $a_{7R}^U(\\phi)$,\nEqs.~(\\ref{15}) and (\\ref{20A}), so that the phases cancel in (\\ref{54}).\n\n\nThe actual results in (\\ref{eq:SVgamma}) disagree with the above\nexpectations because of the contributions from soft-gluon emission,\nwhich enter $a_{7R}^U$. Moreover, for $S(\\phi\\gamma)$ this is because the\nsoft-gluon emission from quark loops is different for $u$ and $c$ loops so that $a_{7R}^c\\neq a_{7R}^u$ and hence $\\bar {\\cal A}_{R}$ (${\\cal A}_{L}$) is not proportional to $\\lambda_t^{(s)}$ ($(\\lambda_t^{(s)})^*$). Note that a substantial enhancement of $S(\\phi\\gamma)$ by new physics requires\nnot only an enhancement of $|\\bar{\\cal A}_R|$ (and $|{\\cal\n A}_L|$), but also the presence of a large phase in (\\ref{54}); \nthis could be either\na large $B_s$ mixing phase which will also manifest itself in\na sizable CP violation in, for instance, $B_s\\to J\/\\psi \\phi$, see\nRef.~\\cite{BF06,Ball:2006xx}; or it could be a new weak phase in $\\bar{\\cal A}_{R}$\n(and ${\\cal A}_L$); or it could be a non-zero strong phase in\none of the $a_{7R}^{c,u}$ coefficients. Based on the light quark loop results there is not much scope for\na large phase in $a_{7R}^{u}$ (whose contribution is, in addition,\ndoubly Cabibbo suppressed), but the situation could be different for\n$a_{7R}^{c,{\\rm soft}}$, where only the\nleading-order term in a $1\/m_c$ expansion is included, which does not carry a\ncomplex phase \\cite{Ball:2006eu}. It is not excluded that a\nresummation of higher-order terms in this expansion will generate a\nnon-negligible strong phase --- which is not really relevant for our\nresults in Eq.~(\\ref{eq:SVgamma}), but could be relevant for the\ninterpretation of any new physics to be found in that observable. For\n$S(K^*\\gamma)$, on the other hand, no new phases are required, and\nany enhancement of $ |\\bar{\\cal A}_R|$ (and $|{\\cal A}_L|$) by new physics will\nresult in a larger value of $S(K^*\\gamma)$.\n\nFor all $S$ except $S(K^*\\gamma)$,\nthe uncertainty is entirely dominated by that of the soft-gluon emission\nterms $l_{u,c}-\\tilde l_{u,c}$, whose uncertainties have been doubled with\nrespect to those given in Tab.~\\ref{rad_tab5}. The smallness of\n$S((\\rho,\\omega)\\gamma)$ is due to the fact that the helicity\nfactor is given by $m_d\/m_b$ (we use $m_{u,d}\/m_s = 1\/24.4$ from\nChPT). For $\\bar K^*$,\nthe suppression from the small mixing\nangle is relieved by the fact that both weak amplitudes in\n$\\lambda_U^{(d)}$ contribute\nso that the CP asymmetry is comparable\nwith that of $\\rho$ and $\\omega$. Despite the generous uncertainties, it is\nobvious that none of these CP symmetries is larger than\n4\\% in the SM, which makes these observables very interesting\nfor new physics searches. The present experimental result from the $B$\nfactories, $S(K^*\\gamma)=-0.28\\pm 0.26$ \\cite{Barberio:2007cr}, certainly encourages\nthe hope that new physics may manifest itself in that\nobservable. While a measurement of the $b\\to d$ CP asymmetries is\nprobably very difficult even at a super-flavour factory,\n$S(K^*\\gamma)$ is a promising observable for $B$ factories \\cite{superB}, but\nnot for the LHC.\\footnote{$K^*$ has to be traced via its decay into a CP\neigenstate, i.e.\\ $K_S\\pi^0$. Neutrals in the final state are not\nreally LHC's favourites.} $B_s\\to \\phi(\\to K^+K^-)\\gamma$, on the\nother hand, will be studied in detail at the LHC, and in particular at\nLHCb, and any largely enhanced value of $S(\\phi\\gamma)$ \nwill be measured within the first years of running. \n\n\\section{Extraction Of CKM Parameters}\\label{ckmextract}\n\nLet us now turn to the determination of CKM parameters from the\nbranching ratios determined in Section~\\ref{rad_brs}. In this context, two particularly interesting\nobservables are \n\\begin{equation}\\label{58}\nR_{\\rho\/\\omega}\\equiv\\frac{\\overline{\\cal B}(B\\to (\\rho,\\omega)\\gamma)}{\n\\overline{\\cal B}(B\\to K^*\\gamma)}\\,,\\qquad\nR_{\\rho}\\equiv\\frac{\\overline{\\cal B}(B\\to \\rho\\gamma)}{\n\\overline{\\cal B}(B\\to K^*\\gamma)}\\,,\\qquad\n\\end{equation}\ngiven in terms of the CP- and isospin-averaged branching ratios of\n$B\\to(\\rho,\\omega)\\gamma$ and $B\\to \\rho\\gamma$, respectively, \nand $B\\to K^*\\gamma$ decays. $R_{\\rho\/\\omega}$\nhas been measured by both {\\sc BaBar} and Belle \\cite{babar_rad,belle_rad}, a first\nvalue of $R_\\rho$ has been given by {\\sc BaBar} \\cite{babar_rad}. \nThe experimental determinations\nactually assume exact isospin symmetry, i.e.\\\n$\\overline{\\Gamma}(B^\\pm\\to \\rho^\\pm\\gamma) \\equiv\n2 \\overline{\\Gamma}(B^0\\to \\rho^0\\gamma)$, and also\n$\\overline{\\Gamma}(B^0\\to \\rho^0\\gamma) \\equiv \n\\overline{\\Gamma}(B^0\\to \\omega\\gamma)$; and as we have seen in Section~\\ref{isosec}, these relations are not in fact exact. Hence, the present\nexperimental results for $R_{\\rho\/\\omega}$ are theory-contaminated. \nAs the isospin asymmetry between the charged and neutral $\\rho$ decay \nrates turns out to be smaller than the asymmetry\n between $\\rho^0$ and $\\omega$, \nit would actually be preferable, from an experimental point of view,\nto drop the $\\omega$ channel and\nmeasure $R_\\rho$ instead of $R_{\\rho\/\\omega}$, as done in the most\nrecent {\\sc BaBar} analysis on that topic \\cite{babar_rad}. \nWe will give numerical results and\ntheory uncertainties for both $R_{\\rho\/\\omega}$ and $R_{\\rho}$.\n\nOne parametrisation of $R_{\\rho\/\\omega}$ often quoted, \nin particular in experimental papers, is\n\\begin{equation}\\label{Brat}\nR_{\\rho\/\\omega} = \\left|\\frac{V_{td}}{V_{ts}}\\right|^2\n\\left(\\frac{1-m_{\\rho}^2\/m_B^2}{1-m_{K^*}^2\/m_B^2}\\right)^3\n\\frac{1}{\\xi^2_{\\rho}} \\left [ 1 + \\Delta R\\right],\n\\end{equation}\nwith $\\Delta R=0.1\\pm 0.1$ \\cite{BVga1} and again assuming isospin\nsymmetry for $\\rho$ and $\\omega$. This parametrisation creates the impression\nthat $\\Delta R$ is a quantity completely unrelated to\nand with a fixed value independent of\n$|V_{td}\/V_{ts}|$. We would like to point out here that this \nimpression is {\\em wrong}: $\\Delta R$ contains both QCD\n(factorisable and non-factorisable) effects and such from weak\ninteractions. In Ref.~\\cite{Ball:2006nr} $\\Delta R$ is expressed in\nterms of the factorisation coefficients $a_{7L}^U$, assuming isospin\nsymmetry for $\\rho^0$ and $\\omega$, as\n\\begin{eqnarray}\n1+\\Delta R & = & \\left|\n \\frac{a_{7L}^c(\\rho)}{a_{7L}^c(K^*)}\\right|^2 \\left( 1 +\n {\\rm Re}\\,(\\delta a_\\pm + \\delta a_0) \\left[\\frac{R_b^2 - R_b\n \\cos\\gamma}{1-2 R_b \\cos\\gamma + R_b^2}\\right]\\right.\\nonumber\\\\\n& & \\left. + \\frac{1}{2}\\left( |\\delta a_\\pm|^2 + |\\delta a_0|^2\\right)\n \\left\\{ \\frac{R_b^2}{1-2 R_b \\cos\\gamma + R_b^2}\\right\\} \\right)\n\\label{delR}\n\\end{eqnarray}\nwith $\\delta a_{0,\\pm}=\na_{7L}^u(\\rho^{0,\\pm})\/a_{7L}^c(\\rho^{0,\\pm})-1$. Eq.~(\\ref{delR}) shows explicitly that $\\Delta R$ depends both on QCD\n ($\\delta a_{\\pm,0}$) and CKM parameters ($R_b,\\gamma$).\nThe point we would like to make is that the calculation of $\\Delta R$\nrequires input values for $R_b$ and $\\gamma$. Once these parameters\n(and the Wolfenstein parameter $\\lambda$)\nare fixed, however, $|V_{td}\/V_{ts}|$ is also fixed and given by\n\\begin{equation}\\label{61}\n\\left|\\frac{V_{td}}{V_{ts}}\\right| = \\lambda \\sqrt{1-2 R_b \\cos\\gamma\n + R_b^2} \\left[ 1 + \\frac{1}{2}\\,( 1 - 2 R_b \\cos\\gamma) \\lambda^2 +\n \\mathcal{O}(\\lambda^4)\\right]\\,.\n\\end{equation} \nHence, as $|V_{td}\/V_{ts}|$ and $(R_b,\\gamma)$ are not independent\nof each other, \nit is {\\em impossible} to extract $|V_{td}\/V_{ts}|$ from (\\ref{Brat})\nwith a fixed value of $\\Delta R$. Of course $R_{\\rho\/\\omega}$ and $R_\\rho$ of (\\ref{58}) \n{\\em can} be used to extract information\nabout CKM parameters, but in order to do so one has to settle for a set\nof truly independent parameters. Based on (\\ref{61}), one can\nexchange, say, $\\gamma$ for $|V_{td}\/V_{ts}|$.\\footnote{Strictly speaking, (\\ref{61}) only\n fixes $\\cos\\gamma$ as function of $|V_{td}\/V_{ts}|$, leaving\n a twofold degeneracy of $\\gamma$. Eq.~(\\ref{delR}), however, only\n depends on $\\cos\\gamma$, so that indeed one can unambiguously \nreplace $\\gamma$ by $|V_{td}\/V_{ts}|$.} So we can either consider $R_V$ as a\nfunction of the CKM parameters $R_b$ and $\\gamma$ (let us call this\nthe $\\gamma$ set of parameters) or as a function of $R_b$ and\n$|V_{td}\/V_{ts}|$ (to be called the $|V_{tx}|$ set). Using the\n$\\gamma$ set, a measurement of $R_V(\\gamma,R_b)$ allows a\ndetermination of $\\gamma$, whereas \n$R_V(|V_{td}\/V_{ts}|,R_b)$ allows the\ndetermination of $|V_{td}\/V_{ts}|$. \nIn either case, the simple quadratic relation\n(\\ref{Brat}) between $R_V$ and $|V_{td}\/V_{ts}|$ becomes more\ncomplicated.\n\n\n In Figs.~\\ref{rad_fig5} and \\ref{rad_fig6} we plot the resulting values of $|V_{td}\/V_{ts}|^2$ and $\\gamma$, respectively, as a function of $R_V$. Although the curve in Fig.~\\ref{rad_fig5}(a) looks like a straight line, as naively expected from (\\ref{Brat}), this is not exactly the case, because of the dependence of $\\Delta R$ on $|V_{td}\/V_{ts}|$. In Fig.~\\ref{rad_fig5}(b) we plot $\\Delta R$ for the $|V_{tx}|$ set of parameters. The dependence of $\\Delta R$ on $|V_{td}\/V_{ts}|$ is rather strong. Apparently indeed $\\Delta R=0.1\\pm 0.1$ in the expected range $0.16<|V_{td}\/V_{ts}|<0.24$, but this estimate does not reflect the true theoretical uncertainty which is indicated by the dashed lines in the figure. \n\\begin{figure}\n$$\n\\epsfxsize=0.45\\textwidth\\epsffile{rad_fig4a.eps}\\qquad\\epsfxsize=0.45\\textwidth\\epsffile{rad_fig4b.eps}\n$$\n\\caption[$|V_{td}\/V_{ts}|^2$ as function of $R_{\\rho\/\\omega}$ and $\\Delta R$ as function of $|V_{td}\/V_{ts}|$.]{\\small Left panel: $|V_{td}\/V_{ts}|^2$ as function of $R_{\\rho\/\\omega}$, Eq.~(\\ref{58}), in the $|V_{tx}|$ basis -- see text. Solid line: central values. Dash-dotted lines: theoretical uncertainty induced by $\\xi_\\rho = 1.17\\pm 0.09$, (\\ref{xirho}). Dashed lines: other theoretical uncertainties, including those induced by $|V_{ub}|$, $|V_{cb}|$ and the hadronic parameters of Tab.~\\ref{rad_tab8}. Right panel: $\\Delta R$ from Eq.~(\\ref{delR}) as function of\n $|V_{td}\/V_{ts}|$ in the $|V_{tx}|$ basis. Solid line: central values. Dashed lines: theoretical uncertainty.}\\label{rad_fig5} \n $$\\epsfxsize=0.45\\textwidth\\epsffile{rad_fig5.eps}$$\n\\caption[The UT angle $\\gamma$ as function of $R_{\\rho\/\\omega}$.]{\\small The UTangle $\\gamma$ as function of $R_{\\rho\/\\omega}$ in the $\\gamma$ set of CKM parameters. Solid lines: central values of input parameters. Dash-dotted lines: theoretical uncertainty induced by $\\xi_\\rho = 1.17\\pm 0.09$. Dashed lines: other theoretical uncertainties.}\\label{rad_fig6}\n$$\\epsfxsize=0.45\\textwidth\\epsffile{rad_fig6.eps}$$\n\\caption[Central values of $R_{\\rho\/\\omega}$ and $R_{\\rho}$ as functions of $|V_{td}\/V_{ts}|$]{\\small Central values of $R_{\\rho\/\\omega}$ (solid line) and $R_{\\rho}$ (dash-dotted line) as functions of $|V_{td}\/V_{ts}|$.}\\label{rad_fig7}\n\\end{figure}\n\nIt is now basically a matter of choice whether to use\n$R_{\\rho\/\\omega}$ to determine $|V_{td}\/V_{ts}|$ or $\\gamma$. Once one of\nthese parameters is known, the other one follows from Eq.~(\\ref{61}). In\nFig.~\\ref{rad_fig6} we plot $\\gamma$ as a function of\n$R_{\\rho\/\\omega}$, together with the theoretical uncertainties. In\nFig.~\\ref{rad_fig7} we also compare the central values of\n$R_{\\rho\/\\omega}$ with those of $R_{\\rho}$, as a function of\n$|V_{td}\/V_{ts}|$. Although the difference is small, $R_{\\rho}$ is\nexpected to be larger than $R_{\\rho\/\\omega}$. $R_{\\rho\/\\omega}$ and $R_{\\rho}$ are dominated by the uncertainties of $\\xi_{\\rho}$ and as discussed in Ref.~\\cite{Ball:2006nr}, a reduction of this uncertainty would require a reduction of the uncertainty of the transverse decay constants $f_V^\\perp$ of $\\rho$ and $K^*$. With the most recent results from {\\sc BaBar}, $R_{\\rho\/\\omega} = 0.030\\pm 0.006$ \\cite{babar_rad}, and from Belle, $R_{\\rho\/\\omega} = 0.032\\pm 0.008$ \\cite{belle_rad}, we then find\n\\begin{equation}\n\\renewcommand{\\arraystretch}{1.3}\n\\begin{array}[b]{l@{\\quad}l@{\\quad\\leftrightarrow\\quad}l}\n\\mbox{{\\sc BaBar}:} & \\displaystyle\n\\left|\\frac{V_{td}}{V_{ts}}\\right| = 0.199\\overbrace{^{+0.022}_{-0.025}}^{{\\rm exp}}\\pm\n\\overbrace{0.014}^{{\\rm th}} &\\displaystyle\n\\gamma = (61.0\\overbrace{^{+13.5}_{-16.0}}^{{\\rm exp}}\\overbrace{^{+8.9}_{-9.3}}^{\n{\\rm th}})^\\circ\\,,\\\\[10pt]\n\\mbox{Belle:} & \\displaystyle\n\\left|\\frac{V_{td}}{V_{ts}}\\right| = 0.207\\,^{+0.028}_{-0.033}\\,\n^{+0.014}_{-0.015} &\\displaystyle\n\\gamma = (65.7\\,^{+17.3}_{-20.7}\\,^{+8.9}_{-9.2})^\\circ\\,.\n\\end{array}\n\\label{63}\n\\end{equation}\nThese numbers compare well with the Belle result \\cite{Bellegamma} \nfrom tree-level processes, $\\gamma=(53\\pm 20)^\\circ$, quoted in\nTab.~\\ref{rad_tab8}, and results from global fits\n\\cite{global}. We also would like to point out that the above\ndetermination of $\\gamma$ is actually a determination of\n$\\cos\\gamma$, via Eq.~(\\ref{61}), and implies, in principle, a twofold\ndegeneracy $\\gamma\\leftrightarrow 2\\pi-\\gamma$. This is in contrast to the\ndetermination from $B\\to D^{(*)} K^{(*)}$ in \\cite{Bellegamma}, which\ncarries a twofold degeneracy\n$\\gamma \\leftrightarrow \\pi+\\gamma$. Obviously these two\ndeterminations taken together remove the degeneracy and \nselect $\\gamma\\approx 55^\\circ<180^\\circ$. If \n$\\gamma\\approx 55^\\circ+180^\\circ$ instead, one would have \n$|V_{td}\/V_{ts}|\\approx 0.29$ from\n(\\ref{61}), which is definitely ruled out by data. Hence, the result\n(\\ref{63}) confirms the SM interpretation of $\\gamma$ from \nthe tree-level CP asymmetries in $B\\to D^{(*)} K^{(*)}$.\n\n\\begin{table}[ht]\n$$\n\\begin{array}{c|c|c}\n\\tau_{B^0} & \\tau_{B^\\pm}\/\\tau_{B^0} & \\tau_{B_s^0}\/\\tau_{B^0}\n\\\\\\hline\n1.530(9)\\,{\\rm ps} & 1.071(9) & 0.958(39)\n\\end{array}\n$$\n\\caption[$B$ lifetimes from HFAG.]{\\small $B$ lifetimes from HFAG \\cite{Barberio:2007cr}.}\n\\label{rad_tab9}\n\\addtolength{\\arraycolsep}{3pt}\n\\renewcommand{\\arraystretch}{1.3}\n$$\n\\begin{array}{c|c|c|c|c|c}\\hline\\hline\n\\multicolumn{6}{c}{\\mbox{CKM parameters and couplings}}\\\\\\hline\n\\lambda \\mbox{~\\cite{Yao:2006px}} & |V_{cb}| \\mbox{~\\cite{inclmoments}} & \n|V_{ub}| & \\gamma \\mbox{~\\cite{Bellegamma}} & \\alpha_s(m_Z)\n\\mbox{~\\cite{Yao:2006px}} & \\alpha_{\\rm QED}\\\\\\hline\n0.227(1) & 42.0(7)\\times 10^{-3} & 4.0(7)\\times\n10^{-3} & (53\\pm 20)^\\circ & 0.1176(20) & 1\/137\\\\\\hline\\hline\n\\multicolumn{6}{c}{\\mbox{B parameters}}\\\\\\hline\nf_{B_q}\\mbox{~\\cite{Onogi}} & f_{B_s}\\mbox{~\\cite{Onogi}} &\n \\lambda_{B_q}(\\mu_h) \\mbox{~\\cite{Ball:2006nr}} & \\lambda_{B_s}(\\mu_h)\n& \\mu_h \\\\\\hline\n200(25)\\,{\\rm MeV} & 240(30)\\,{\\rm MeV} & 0.51(12)\\,{\\rm GeV} &\n0.6(2)\\,{\\rm GeV} & 2.2\\,{\\rm GeV}\\\\\\hline\\hline\n\\multicolumn{6}{c}{\\mbox{$\\rho$ parameters}}\\\\\\hline\nf_{\\rho} & f_{\\rho}^\\perp & a_1^\\perp({\\rho}) &\na_2^\\perp({\\rho}) & T_1^{B\\to\\rho}(0)\\\\\\hline\n216(3)\\,{\\rm MeV} & 165(9)\\,{\\rm MeV} & 0 & 0.14(6) & 0.27(4)\n\\\\\\hline\\hline\n\\multicolumn{6}{c}{\\mbox{$\\omega$ parameters}}\\\\\\hline\nf_{\\omega} & f_{\\omega}^\\perp & a_1^\\perp({\\omega}) &\na_2^\\perp({\\omega}) & T_1^{B\\to\\omega}(0)\\\\\\hline\n187(5)\\,{\\rm MeV} & 151(9)\\,{\\rm MeV} & 0 & 0.15(7) & 0.25(4)\n\\\\\\hline\\hline\n\\multicolumn{6}{c}{\\mbox{$K^*$ parameters}}\\\\\\hline\nf_{K^*} & f_{K^*}^\\perp & a_1^\\perp({K^*})\\mbox{~\\cite{Ball:2005vx}} & \na_2^\\perp({K^*}) & T_1^{B_q\\to K^*}(0) & \nT_1^{B_s\\to \\bar K^*}(0)\\\\\\hline\n220(5)\\,{\\rm MeV} & 185(10)\\,{\\rm MeV} & 0.04(3) & 0.15(10) &\n0.31(4) & 0.29(4)\\\\\\hline\\hline\n\\multicolumn{6}{c}{\\mbox{$\\phi$ parameters}}\\\\\\hline\nf_{\\phi} & f_{\\phi}^\\perp & a_1^\\perp({\\phi}) &\na_2^\\perp({\\phi}) & T_1^{B_s\\to\\phi}(0)\\\\\\hline\n215(5)\\,{\\rm MeV} & 186(9)\\,{\\rm MeV} & 0 & 0.2(2) & 0.31(4) & \\\\\\hline\\hline\n\\multicolumn{6}{c}{\\mbox{quark masses}}\\\\\\hline\n\\multicolumn{2}{c|}{m_s(2\\,{\\rm GeV})\\mbox{~\\cite{ms}}} \n& m_b(m_b)\\mbox{~\\cite{inclmoments}} & \nm_c(m_c)\\mbox{~\\cite{czakon}} & \\multicolumn{2}{c}{\nm_t(m_t)\\mbox{~\\cite{mt}}}\\\\\\hline\n\\multicolumn{2}{c|}{100(20)\\,{\\rm MeV}} \n& 4.20(4)\\,{\\rm GeV} & 1.30(2)\\,{\\rm GeV} & \n\\multicolumn{2}{c}{163.6(2.0)\\,{\\rm GeV}} \\\\\\hline\\hline\n\\end{array}\n$$\n\\caption[Summary of input parameters.]{\\small Summary of input parameters. The value of $|V_{ub}|$ is an average over inclusive and exclusive determinations and the result from UTangles Refs.~\\cite{Barberio:2007cr,global,Vub}. None of our results is very sensitive to $|V_{ub}|$. For an explanation of our choice of the value of the UT angle $\\gamma$, see text. $\\lambda_{B_s}$ is obtained from $\\lambda_{B_q}$, see Eq.~(\\ref{rad_bs}). The vector meson decay constants $f_V$, $f_V^\\perp$ are discussed in Ref.~\\cite{Ball:2006eu}; the values of the Gegenbauer moments $a_i^\\perp$ are compiled from various sources \\cite{Ball:2006nr,Ball:1996tb,Ball:1998sk,Ball:2003sc} and include only small ${\\rm SU(3)_F}$-breaking, in line with the findings for pseudoscalar mesons \\cite{Ball:2006wn}. The form factors $T_1$ are \nupdates of previous LCSR results \\cite{Ball:2004rg}, including the updated values of the decay constants\n $f_{\\rho,\\omega,\\phi}$ and of $a_1^\\perp({K^*})$ \\cite{Ball:2005vx,Ball:2006fz}. \n All scale-dependent quantities are given at the scale $\\mu=1\\,$GeV unless stated otherwise.}\n\\label{rad_tab8}\n\\end{table}\n\\chapter{Summary and Conclusions}\\label{chapter8_conc}\nThis thesis has consisted of three main analyses centred on the investigations and determinations of meson light-cone distribution amplitudes. We have seen how the determinations of decay observables in $B$ decays are reliant on the sound understanding of both theoretical and experimental uncertainties with the work presented in this thesis striving towards the former. To summarise:\n\nWe began, in Chapter~\\ref{chapter1_basics}, with a brief introduction defining the QCD Lagrangian, discussing CP violation and the $\\Delta B =1$ effective Hamiltonian.\n\nIn Chapter~\\ref{chapter2_DAs} we investigated the structure of vector mesons distribution amplitudes to twist-3 accuracy. We included all $\\rm SU(3)_F$-breaking and G-parity violating effects. The QCD equations of motion were implemented to unpick the interwoven relations between the distribution amplitudes ultimately expressing the two-particle twist-3 distribution amplitudes in terms of the three-particle twist-3 and two-particle twist-2 distribution amplitudes. The equations of motion result in integral equations which are readily solved order-by-order in conformal spin and to the order considered all the distribution amplitudes are then expressed by a small number of non-perturbative parameters. Finite quark mass effects appear in the equation of motion and therefore impact the two-particle twist-3 distribution amplitudes (\\ref{das_eq31}-\\ref{das_eq33}). Such effects also cause mixing between the twist-3 hadronic parameters under renormalisation scale evolution, see Eq.~(\\ref{das_eq37}).\n\nIn Chapter~\\ref{chapter3_SR} we discussed the methods of QCD sum rules (the SVZ method) and QCD sum rules on the light-cone. We outlined the procedures with example correlation functions and ended the chapter with an example calculation of the $\\alpha_s$ corrections to the gluon condensate contribution to a $K$ meson sum rule. The calculation made use of the background field technique and served to illustrate the calculation of radiative corrections to -- and extraction of -- vacuum condensates in the SVZ method. The result of the calculation is in conflict with that in the literature, see Eqs.~(\\ref{last}) and (\\ref{least}).\n\n\nIn Chapter~\\ref{chapter4_det} we determined the leading hadronic parameters defined in Chapter~\\ref{chapter2_DAs} via SVZ sum rules. We calculated the three-particle twist-3 parameters to NLO in conformal spin, also including all G-parity violating terms and finite strange quark mass effects. The determination of the twist-3 parameters is new for $K^*$ and $\\phi$. The results for the $\\rho$ agree within uncertainties with previous determinations and are presented in Tabs.~\\ref{det_tab1} and \\ref{det_tab2}. We also calculate $\\mathcal{O}(\\alpha_s)$ and $\\mathcal{O}(m_s^2)$ corrections to the quark condensate for the sum rules for $a_n^{\\parallel,\\perp}(V)$, which for $n=2$ is the first non-trivial Gegenbauer coefficient of the G-even particles $\\rho$ and $\\phi$. We add this contribution to the existing sum rules taken from the literature and update the value of $a_2^{\\parallel,\\perp}(\\phi)$ which we find to be consistent with that found for $K^*$ and $\\rho$; $a^\\perp_{1,2}(V)=a^\\parallel_{1,2}(V)$ within uncertainties. The results find direct application in QCD factorisation descriptions of $B\\to V$ decays, and the light-cone sum rule analyses of $B\\to V$ transition form factors.\n\nIn Chapter~\\ref{chapter5_eta} we calculated the form factors of $B\\to \\eta^{\\prime}$\nsemileptonic transitions from light-cone sum rules,\nincluding the gluonic singlet contributions. We built upon the previous light-cone sum rule determination of the $B\\to \\eta$ form factor by casting the calculation consistently within the phenomenologically motivated $\\eta$-$\\eta^{\\prime}$ mixing scheme of Refs.~\\cite{Feldmann:1998vh,Feldmann:1998sh}. We found that, as\nexpected, these contributions are more relevant for $f_+^{\\eta^{\\prime}}$ than\nfor $f_+^\\eta$ and can amount up to 20\\% in the former, \ndepending on the only poorly\nconstrained leading Gegenbauer moment $B^g_2$ of the gluonic twist-2\ndistribution amplitude of $\\eta^{\\prime}$. The numerical results, with each contribution listed separately, are given by Eqs.~(\\ref{fp1}) and (\\ref{fp0}). Consequently, it seems unlikely that the large exclusive $B\\to \\eta^{\\prime} K$ and inclusive $B\\to \\eta^{\\prime} X$ branching ratios can be explained by a large $B^g_2$, as it would have to assume a very extreme value. We also found that the form factors\nare sensitive to the values of the twist-2 two-quark Gegenbauer\nmoments $a_2^{\\eta,\\eta^{\\prime}}$ which, given the uncertainty of independent\ndeterminations, we have set equal to $a_2^\\pi$, see Fig.\\ref{eta_diags3}.\n\nThe ratio\nof branching ratios ${\\cal B}(B\\to\\eta^{\\prime} e\\nu)\/{\\cal B}(B\\to\\eta e\\nu)$\nis sensitive to both $a_2$ and $B^g_2$ and may be used to constrain\nthese parameters, once it is measured with sufficient accuracy, see Fig.~\\ref{eta_fig8}. The\nextraction of $|V_{ub}|$ from these semileptonic decays, in particular\n$B\\to\\eta e\\nu$, with negligible singlet contribution, although\npossible in principle, at the moment is obscured by the lack of\nknowledge of $a_2$. We\nwould also like to stress that, in the framework of the quark-flavour\nmixing scheme for the $\\eta$-$\\eta^{\\prime}$ system as used in this analysis,\n$B\\to \\eta^{\\prime}$ transitions probe only the $\\eta_q$ component of these\nparticles. The $\\eta_s$ component could be probed directly for\ninstance in the $b\\to s$ penguin transition $B_s\\to \\eta^{\\prime}\n\\ell^+\\ell^-$, although such a measurement would also be sensitive\nto new physics in the penguin diagrams.\n\nIn Chapter~\\ref{chapter6_QCDF} we discussed the QCD factorisation (QCDF) approach of Refs.~\\cite{Beneke:1999br, Beneke:2000ry} and its application to the radiative $B$ decays $B \\to V \\gamma$ of Refs.~\\cite{Bosch:2001gv,Bosch:2002bw}. We discussed the appearance of distribution amplitudes in the factorisation formulas and focused on the leading contributions to the $B\\to V \\gamma$ decays.\n\n\nIn Chapter~\\ref{chapter7_rad} we performed a phenomenological analysis of the radiative $B$ decays to vector mesons $B\\to V \\gamma$, using the framework discussed in Chapter~\\ref{chapter6_QCDF}. We investigated the most relevant power-suppressed corrections to the QCDF predictions for the radiative decays $B_{u,d} \\to (\\rho, \\omega, K^*)\\gamma$ and $B_{s} \\to (\\phi, \\bar{K}^*)\\gamma$. We use the QCDF framework presented in Refs.~\\cite{Bosch:2001gv,Bosch:2002bw} in which we find use for the twist-2 DA parameters determined in Chapter~\\ref{chapter4_det}. Besides the leading QCDF contributions we included long-distance photon emission and soft-gluon mission from quark loops. These effects, although formally $\\sim 1\/m_b$ with respect to the leading contributions, augment the QCDF predictions for the branching ratios, CP and isospin asymmetries. \n\nThe impact of the power-suppressed corrections on the branching ratios is found to be very small, with the exception of the weak annihilation contributions to $B^\\pm\\to \\rho^\\pm \\gamma$ which are large due to a large combination of Wilson coefficients $C_2+C_1\/3=1.02$ and no CKM-suppression. Moreover, long-distance photon emission also impacts most here, see Eq.~(\\ref{LDcont}). An explicit break down of the results are given in Tab.~\\ref{rad_tab6}. \n\nThe isospin asymmetries $A(\\rho,\\omega)$, $A_I(\\rho)$ and $A_I(K^*)$ are driven by weak annihilation and long-distance photon emission contributions. We found a non-zero asymmetry $A(\\rho,\\omega)=-0.20\\pm0.09$ which suggests the explicit assumption of perfect symmetry, i.e.\\ $\\overline{\\Gamma}(B^\\pm\\to \\rho^\\pm \\gamma) = 2 \\overline{\\Gamma}(B^0\\to \\rho^0 \\gamma) = 2 \\overline{\\Gamma}(B^0\\to \\omega \\gamma)$ used to obtain the experimental value of $\\overline{\\cal B}(B\\to(\\rho,\\omega)\\gamma)$ is not so well justified. We found $A_I(\\rho)$ to depend strongly on the UT angle $\\gamma$, as shown in Tab.~\\ref{rad_tab7}. With our central value of $\\gamma=53^\\circ$ (see Tab~\\ref{rad_tab8}) our result agrees very well with the {\\sc BaBar} result $A_I(\\rho)_{\\rm BaBar} = 0.56\\pm 0.66$ \\cite{babar_rad}. For $A_I(K^*)$ we found a result consistent with the experimental result $A_I(K^*)_{\\rm exp}=(3.2\\pm4.1)\\%$ and, via its sensitivity to the Wilson coefficient combination $C_5+C_6\/3$ conclude that a reduction in the experimental uncertainty may uncover signs of new physics contributing to these Wilson coefficients, see Fig.~\\ref{rad_fig4}.\n\nThe indirect CP asymmetries $S(V\\gamma)$ are caused by the interference between the amplitudes describing the production of left and right-handed photons, see Eqs.~(\\ref{qcdf_8}) and (\\ref{54}). The right-handed amplitude is suppressed by $m_D\/m_b$ with respect to the left-handed one for $\\bar B =b \\bar q$ decays (and vice versa for $B$ decays). Due to this natural suppression in the SM we expect the CP asymmetries to be small, and this suppression can be relieved by many new physics senarios. We investigated the soft-gluon effects arising from soft heavy and soft quark loops. The calculation of these contributions makes use of the three-particle twist-3 DA parameters determined in Chapter~\\ref{chapter4_det}. They contribute to both the left and right-handed amplitudes, and so may also relieve to SM suppression. We found that although they do divert the results from the values naively expected, there is no scope for a large enhancement due to these power-suppressed contributions. The results are given in Eq.~(\\ref{eq:SVgamma}).\n\nFinally, using the most recent results from {\\sc BaBar} and Belle, we extracted the CKM parameter ratio $|V_{td}\/V_{ts}|$ and equivalently the UT angle $\\gamma$ from the ratio of branching ratios $R_{\\rho\/\\omega}$. The results are \n\\begin{equation}\n\\renewcommand{\\arraystretch}{1.3}\n\\begin{array}[b]{l@{\\quad}l@{\\quad\\leftrightarrow\\quad}l}\n\\mbox{{\\sc BaBar}:} & \\displaystyle\n\\left|\\frac{V_{td}}{V_{ts}}\\right| = 0.199\\overbrace{^{+0.022}_{-0.025}}^{{\\rm exp}}\\pm\n\\overbrace{0.014}^{{\\rm th}} &\\displaystyle\n\\gamma = (61.0\\overbrace{^{+13.5}_{-16.0}}^{{\\rm exp}}\\overbrace{^{+8.9}_{-9.3}}^{\n{\\rm th}})^\\circ\\,,\\\\[10pt]\n\\mbox{Belle:} & \\displaystyle\n\\left|\\frac{V_{td}}{V_{ts}}\\right| = 0.207\\,^{+0.028}_{-0.033}\\,\n^{+0.014}_{-0.015} &\\displaystyle\n\\gamma = (65.7\\,^{+17.3}_{-20.7}\\,^{+8.9}_{-9.2})^\\circ\\,.\n\\end{array}\n\\end{equation}\nand agree well with the Belle result $\\gamma=(53\\pm20)^\\circ$ obtained from tree-level processes, and results from global fits \\cite{global}. The result confirms the SM interpretation of $\\gamma$ from \nthe tree-level CP asymmetries in $B\\to D^{(*)} K^{(*)}$.\n\n\\chapter*{Acknowledgements}\n\nFirst and foremost, I would like to thank my supervisor Patricia Ball for all her help and guidance over the last three years. It has been a great opportunity to work with her, and a fantastic learning experience. I must also thank Roman Zwicky for always finding the time to quell my confusions, and with whom it was a pleasure to collaborate. Also, I thank Angelique Talbot for all her friendly discussions, and I wish Aoife Bharucha all the best with her future projects.\n\nI also thank my office mates Ciaran Williams, Karina Williams, Kemal Ozeren, Martyn Gigg and Stefan Hoeche, and the many other friends who have made my time in Durham and the IPPP so enjoyable.\n\nTo those whose support cannot be appreciated enough; I must thank my parents. I thank my brother too for all the discussions and debates we had over coffee, and finally, I must also thank my grandparents. \n\nThis work was supported by a PPARC studentship which is gratefully acknowledged.\n\n\\chapter*{Declaration}\nI declare that no material presented in this thesis has previously been submitted\nfor a degree at this or any other university. The research described in this thesis has been carried out in collaboration with Prof.~Patricia Ball and Dr.~Roman Zwicky and has been published as follows:\n\\begin{itemize}\n \\item{``$B \\to V \\gamma$ beyond QCD factorisation,''\\newline\n P.~Ball, G.~W.~Jones and R.~Zwicky, Phys.\\ Rev.\\ D {\\bf 75} (2007) 054004,\\newline [arXiv:hep-ph\/0612081].}\n\n \\item{``Twist-3 distribution amplitudes of $K^*$ and $\\phi$ mesons,''\\newline\nP.~Ball and G.~W.~Jones, JHEP {\\bf 03} (2007) 069, \\newline [arXiv:hep-ph\/0702100].}\n \n \\item{``$B \\to \\eta^{(\\prime)}$ Form Factors in QCD,''\\newline\nP.~Ball and G.~W.~Jones, JHEP {\\bf 08} (2007) 025, \\newline arXiv:0706.3628 [hep-ph].}\n \n\\end{itemize}\nThe copyright of this thesis rests with the author. No quotation from it should be published without their prior written consent and information derived from it should be acknowledged. \n\\begin{flushright}\ngarethwarrenjones@gmail.com\n\\end{flushright}\n\\tableofcontents\n\\listoffigures\n\\listoftables\n\\input{chapter0_intro.tex}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nLet $K$ be a complete discretely valued field of mixed characteristic with perfect residue field $k$. Fix a separable closure of $\\overline{K}$ of $K$ and let $G_K$ be the absolute Galois group of $K$. The study of stable lattices in crystalline representations of $G_K$ plays an important role in number theory. For example, in many modularity lifting results, one wants to understand liftings of mod $p$ representations of the Galois group of a number field $F$ to Galois representations over $\\mathbb Z_p$-lattices with nice properties when restricted to the Galois groups of $F_v$ for all places $v$ of $F$. And a reasonable property at places over $p$ is that the representation of the Galois group of the local field is crystalline. There are various theories about characterizing $G_K$-stable lattices in crystalline representations, for example, theory of strongly divisible lattices of Breuil(cf. \\cite{BreuilIntegral}), Wach modules(cf. \\cite{Wach96} and \\cite{Berger}), Kisin modules(cf. \\cite{KisinFcrystal}), Kisin-Ren's theory(cf. \\cite{KisinRen}) and the theory of $(\\varphi, \\widehat{G})$-modules(cf. \\cite{liu-notelattice}). The theories above state that one can describe lattices in crystalline representations using certain linear algebraic data over certain commutative rings $A$. \n\nIn a recent work of Bhatt-Scholze\\cite{BS2021Fcrystals}, they give a different characterization of the category of lattices in crystalline representations. To explain their result, let $\\O_K$ be the ring of integers in $K$, and they consider the absolute prismatic site $(\\O_K)_{{\\mathlarger{\\mathbbl{\\Delta}}}}$, which is defined as the opposite category of all bounded prisms over $\\O_K$ and equipped with the faithfully flat topology. Let $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$ be the structure sheaf over $(\\O_K)_{{\\mathlarger{\\mathbbl{\\Delta}}}}$, and $\\mathcal{I}_{{\\mathlarger{\\mathbbl{\\Delta}}}} \\subset \\O_{\\mathlarger{\\mathbbl{\\Delta}}}$ be the ideal sheaf of the Hodge-Tate divisor, then $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$ carries a $\\varphi$-action coming from the $\\delta$-structures. A prismatic $F$-crystal in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules over $(\\O_K)_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ is defined as a crystal $\\mathfrak{M}_{\\mathlarger{\\mathbbl{\\Delta}}}$ over $(\\O_K)_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules together with an isomorphism $(\\varphi^\\ast \\mathfrak{M}_{\\mathlarger{\\mathbbl{\\Delta}}})[1\/\\mathcal{I}_{{\\mathlarger{\\mathbbl{\\Delta}}}}] \\simeq \\mathfrak{M}_{\\mathlarger{\\mathbbl{\\Delta}}}[1\/\\mathcal{I}_{{\\mathlarger{\\mathbbl{\\Delta}}}}]$. The main result in \\cite{BS2021Fcrystals} is the following:\n\n\\begin{theorem}\\label{thm-intro-main-1}(\\cite[Theorem 1.2]{BS2021Fcrystals} and Theorem~\\ref{Thm-main-1})\nThere is an equivalence of the category of prismatic $F$-crystals in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules over $(\\O_K)_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ and the category of Galois stable lattices in crystalline representations of $G_K$.\n\\end{theorem}\n\nTo relate the result of Bhatt-Scholze with previous works of characterizing lattices in crystalline representations using linear algebraic data, one should first realize the base rings $A$ used in those theories as certain prisms $(A,I)$ over $\\O_K$. Then one should expect that evaluating the prismatic $F$-crystals on $(A,I)$ should recover the corresponding theory. For example, in the theory of Kisin \\cite{KisinFcrystal}, he uses the base ring $A=\\mathfrak{S}:=W(k)[\\![u]\\!]$ with $\\delta(u)=0$, and if one fixes a uniformizer $\\varpi$ of $\\O_K$ which is a zero of an Eisenstein polynomial $E \\in W(k)[u]$, then it is well-known that $(A,(E))$ is the so-called Breuil-Kisin prism which is inside $(\\O_K)_{{\\mathlarger{\\mathbbl{\\Delta}}}}$. And Kisin was able to attach any lattice $T$ in a crystalline representation of $G_K$ a finite free $A$-module together with an isomorphism $(\\varphi^\\ast \\mathfrak{M})[1\/E] \\simeq \\mathfrak{M}[1\/E]$. Now, if $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ is the prismatic $F$-crystal attaching to $T$ under Theorem~\\ref{thm-intro-main-1}, then Bhatt-Scholze show that the evaluation of $\\mathfrak{M}_{\\mathlarger{\\mathbbl{\\Delta}}}$ on $(A,(E))$ recovers Kisin's theory (cf. Theorem 1.3 of $loc.cit.$). \n\nThe first question answered in this paper is whether and how one can recover the theory of $(\\varphi,\\hat{G})$-modules from the prismatic $F$-crystals characterization of Bhatt-Scholze. The category of $(\\varphi,\\hat{G})$-modules, roughly speaking, consisting of pairs $((\\mathfrak{M},\\varphi_{\\mathfrak{M}}),\\hat{G})$, where $(\\mathfrak{M},\\varphi_{\\mathfrak{M}})$ is a Kisin module, and $\\hat{G}$ is a $G_K$-action on $\\mathfrak{M}\\otimes_{\\mathfrak{S},\\varphi} \\widehat {\\mathcal R}$ that commutes with $\\varphi_{\\mathfrak{M}}$ and satisfying some additional properties. Here $\\widehat {\\mathcal R}$ is a subring of $\\Ainf$ that is stable under $\\varphi$ and $G_K$, where $\\Ainf=W(\\O_{\\overline{K}}^\\flat)$ introduced by Fontaine, and there is a surjection $\\theta: \\Ainf:=W(\\O_{\\overline{K}}^\\flat) \\to \\widehat{\\O_{\\overline{K}}}$. However, the period ring $\\widehat {\\mathcal R}$ introduced by Liu is not known to be $p$-adically complete or not, and it is even harder to determine whether it can be shown up as a prism. So in order to relate the theory of $(\\varphi,\\hat{G})$-modules with the category of prismatic $F$-crystals of Bhatt-Scholze, we develop a theory of prismatic $(\\varphi,\\hat{G})$-modules, in which theory the ring $\\widehat {\\mathcal R}$ is replaced by $A^{(2)}_{\\st}$, a subring of $\\Ainf$ constructed as certain prismatic envelope in \\S \\ref{subsec-Ast}. \n\nThe first result of this paper is about the theory of prismatic $(\\varphi,\\hat{G})$-modules. We can show similar to the classical $(\\varphi,\\hat{G})$-module theory, there is an equivalence between the category of prismatic $(\\varphi,\\hat{G})$-modules and lattices in semi-stable representations of $G_K$. Moreover, $(A^{(2)}_{\\st},(E))$ is indeed a prism in $(\\O_K)_{{\\mathlarger{\\mathbbl{\\Delta}}}}$, it admits a map $(A,(E)) \\to (A^{(2)}_{\\st},(E))$ of prisms, and carries an action of $G_K$. For a $G_K$-stable lattice $T$ in a crystalline representation, if $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ is the prismatic $F$-crystal attaches to $T$, then evaluating $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ on the morphism $(A,(E)) \\to (A^{(2)}_{\\st},(E))$ recovers the prismatic $(\\varphi,\\hat{G})$-module attaches to $T$. We can also show the map $A^{(2)}_{\\st} \\to \\Ainf \\xrightarrow{\\varphi} \\Ainf$ factor through $\\widehat {\\mathcal R}$, so the theory of prismatic $(\\varphi,\\hat{G})$-modules recovers the classical theory. The ring $A^{(2)}_{\\st}$ is simpler than $\\widehat {\\mathcal R}$ in many ways, although it is still very complicated and non-noetherian, it is more explicitly described and is $p$-adic complete. In particular, our new theory can be used to fix the gap \\cite{liu-Fontaine} indicated by \\cite[Appendix B]{gao2021breuilkisin}.\n\nThe second attempt made in this paper is to provide a new approach to the equivalence between the category of prismatic $F$-crystals and the category of lattices in crystalline representation established by Bhatt and Scholze as in Theorem~\\ref{thm-intro-main-1}. That is, using the known equivalence between lattices in semi-stable representations and prismatic $(\\varphi, \\hat G)$-modules, we will establish a functor from the category of prismatic $(\\varphi, \\hat G)$-modules that correspond to crystalline representations to prismatic $F$-crystals, and show this functor is an equivalence. \n\nTo be more precise, let $T$ be a $G_K$-stable lattice in a crystalline representation with positive Hodge-Tate weights, let $(A, E)$ be the Breuil-Kisin prism, and let $(A^{(2)},(E))$ (resp. $(A^{(3)},(E))$) be the self-product (self-triple-product) of $(A, (E))$ in $(\\O_K)_{\\mathlarger{\\mathbbl{\\Delta}}}$. Then evaluating prismatic $F$-crystals on the diagram $(A,(E)) \\xrightarrow{i_1} (A^{(2)},(E)) \\xleftarrow{i_2} (A,(E))$ induces an equivalence of the category of prismatic $F$-crystals and Kisin modules with descent data, that is pairs $((\\mathfrak{M},\\varphi_{\\mathfrak{M}}),f)$ where $(\\mathfrak{M},\\varphi_{\\mathfrak{M}})$ is a Kisin module and \n$$\nf: \\mathfrak{M}\\otimes_{\\mathfrak{S},i_1} A^{(2)} \\simeq \\mathfrak{M}\\otimes_{\\mathfrak{S},i_2} A^{(2)}\n$$\nis an isomorphism of $A^{(2)}$-modules that is compatible with $\\varphi$ and satisfies cocycle condition over $A^{(3)}$. Using this, to establish an equivalence between prismatic $(\\varphi, \\hat G)$-modules that correspond to crystalline representations and prismatic $F$-crystals, it remains to find certain correspondence between the $\\hat{G}$-action and the descent isomorphism $f$. We will show the descent isomorphism can be obtained by taking the $G_K$-action of the $(\\varphi,\\widehat{G})$-module at a specific element. To be more precise, fix a Kummer tower $K _\\infty = \\bigcup_{n = 1}^\\infty K (\\varpi _n )$ used in the theory of Kisin, where $\\{\\varpi _n\\}_{n}$ is a compatible system of $p^n$-th roots of $\\varpi_0=\\varpi$, and let $L$ be the normalization of $K _\\infty$ inside $\\overline{K}$. Choose $\\tau\\in \\hat{G}:=\\Gal(L\/K)$ satisfying $\\tau(\\varpi_n)=\\zeta_{p^n}\\varpi_n$ such that $\\{\\zeta_{p^n}\\}$ is a compatible system of primitive $p^n$-th roots of $1$, then our slogan is that the descent isomorphism corresponds to the $\\tilde{\\tau}$-action on the Kisin module $\\mathfrak{M}$ inside $T^{\\vee}\\otimes \\Ainf$ where $\\tilde{\\tau}\\in G_K$ is any lifting of $\\tau$ under the quotient map $G_K \\to \\widehat{G}$. \n\nTo sketch our idea, first we have the maps $u\\mapsto [{\\varpi}^\\flat]$ and $u\\mapsto [{\\tau}({\\varpi}^\\flat)]$ defines two morphisms of $(A,(E))$ to $(\\Ainf,\\Ker\\theta)$. By the universal property of $(A^{(2)},(E))$, these two maps induce a morphism $(A^{(2)},(E)) \\to (\\Ainf,\\Ker\\theta)$. We can show this map is injective, and the embedding factors through $A^{(2)}_{\\st}$, which is the base ring used in our prismatic $(\\varphi, \\hat G)$-module theory. That is, we have a chain of subrings $A\\subset A^{(2)} \\subset A^{(2)}_{\\st}$ of $\\Ainf$, such that $\\tilde{\\tau}(A)$ is also contained in $A^{(2)}$. We can show a prismatic $(\\varphi, \\hat G)$-module corresponds to a crystalline representation if and only if the coefficients of the $\\tilde{\\tau}$-action on $\\mathfrak{M}$ in $T^{\\vee}\\otimes \\Ainf$ lie inside $A^{(2)}$. And once this is proved, the $\\tilde{\\tau}$-action will induce an isomorphism:\n$$\nf_{\\tau}: \\mathfrak{M}\\otimes_{\\mathfrak{S},\\tau} A^{(2)} \\simeq \\mathfrak{M}\\otimes_{\\mathfrak{S}} A^{(2)}.\n$$\nWe will see $f_{\\tau}$ gives the descent isomorphism. As a result, we give a new proof for Theorem~\\ref{thm-intro-main-1}.\n\nAn advantage of our approach is that our new method can be easily generalized to the semi-stable representations cases. It turns out that the prism $(A^{(2)}_{\\st},(E))$ is isomorphic to the self-coproduct of $(A,(E))$ in the category of logarithmic prisms over $\\O_K$ defined by Koshikawa\\cite{Koshikawa2021log-prism}. Using the equivalence between prismatic $(\\varphi, \\hat G)$-modules and lattices in semi-stable representations of $G_K$. we will show in \\S\\ref{sec-logprismandsemistablereps} the following generalization of Theorem~\\ref{thm-intro-main-1} for semi-stable representations. \n\n\\begin{theorem}\\label{thm-intro-log-main}(Theorem~\\ref{thm-log-main-1})\nThere is an equivalence of the category of prismatic $F$-crystals in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules over $(\\O_K)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ and the category of Galois stable lattices in semi-stable representations of $G_K$.\n\\end{theorem}\n\nAnother interesting and natural question one can ask is whether Theorem~\\ref{thm-intro-main-1} and Theorem~\\ref{thm-intro-log-main} can accommodate more general base rings. Motivated by our strategy, it seems to us that the answer should be affirmative if a suitable theory of $(\\varphi, \\hat G)$-module can accommodate more general base rings, for example, if the base ring $R$ is a complete DVR with \\emph{imperfect} residue field that admits a finite $p$-basis. We are working on such direction and hopefully will report our progress in the future. So part of our paper, for example, \\S~\\ref{sec-ring-strcuture} do allow specific general base rings. \n\n\\subsection*{Acknowledgments} It is our pleasure to thank Hui Gao, Wansu Kim, Teruhisa Koshikawa, Zeyu Liu, Yong Suk Moon, Peter Scholze, Koji Shimizu, Yupeng Wang, Zhiyou Wu and Min Yu for comments and conversations during the preparation of this paper. \n\n\\section{Ring Structures on certain prismatic envelope} \\label{sec-ring-strcuture}\n\nRecall that $K$ is a completed discrete valuation field in mix characteristic $(0 , p)$ with ring of integers of $\\O_K$ and prefect residue field $k$. Write $W= W(k)$. Let $\\varpi\\in \\O_K$ be a uniformizer and $E= E(u)\\in W[u]$ be the Eisenstein polynomial of $\\varpi$. \nLet $\\mathbb C_p$ be the $p$-adic completion of $\\overline{K}$, and $\\O_{\\mathbb C_p}$ be the ring of integers. Let $R_0$ be a $W(k)$-algebra which admits Frobenius lift $\\varphi : R_0 \\to R_0 $. Set $R: = R_0 \\otimes_{W(k)}\\O_K$. We make the following assumptions for $R_0$ and $R$: \n\\begin{enumerate}\n\\item Both $R_0$ and $R$ are $p$-adically complete integral domains, and $R_0 \/ p R_0= R\/ \\varpi R$ is an integral domain; \n \\item Let $\\Breve{R}_0 =W\\langle t _1, \\dots , t _m \\rangle $. $R_0$ is a $\\Breve{R}_0 $-\\emph{formally \\'etale} algebra with $p$-adic topology; \n\\item $\\breve{R}_0$ admits a Frobenius lift such that $\\breve{R_0} \\to R_0$ defined in (2) is $\\varphi$-equivalent. \n\n\\item The $k$-algebra $R_0 \/ p R_0$ has finite $p$-basis in the sense of \\cite[Definition 1.1.1]{deJong}.\n\\end{enumerate}\nOur main example is $R_0= \\breve R_0 = W(k). $ We will not use the finite $p$-basis assumption until \\S4. The following are other examples of $R_0$: \n\\begin{example}\\label{Eg-1} \n\\begin{enumerate}\n \\item $R_0 = W(k) \\langle t _1^{\\pm 1} , \\dots , t _m ^{\\pm 1}\\rangle$ with $\\varphi (t_j) = t ^p_j$\n\\item $ R_ 0 = W(k) [\\![t]\\!]$ with $\\varphi (t) = t^p$ or $(1+t)^p -1 $.\n\\item $ R_0$ is an unramified complete DVR with imperfect field $\\kappa$ with finite $p$-basis. See \\S\\ref{subsec-baserings} for more discussions. \n\\end{enumerate}\n\\end{example} \n\nWe reserve $\\gamma_i(\\cdot)$ to denote $i$-th divided power. \n\\subsection{Construction of \\texorpdfstring{$A^{(2)}$}{A(2)}} \\label{subsrc-construct-A2}\nLet $A=\\mathfrak{S}=R_0[\\![u]\\!]$ and extend $\\varphi : A \\to A$ by $\\varphi (u)= u^p$. It is well-known that $(A, E)$ is a prism and we can define a surjection $\\theta: A \\to R$ via $u\\mapsto \\varpi$. We have $\\Ker\\theta = (E(u))$. Let $\\breve A := \\breve R_0 [\\![u]\\!]$ and define $\\varphi$ and $\\breve \\theta: \\breve A \\to \\breve R : = \\O_K \\otimes _W \\breve R_0$ similarly. \nWe set \\[ A ^{\\ho 2}: = A [\\![y -x, s_1 - t_1, \\dots , s_m - t_m]\\!], \\ A^{\\ho 3}: = A[\\![ y -x, w-x , \\{ s_i - t _i , r_i - t_i\\}_{j= 1, \\dots , m}]\\!].\\] \nNote that $A ^{\\ho 2}$ (resp. $ A^{\\ho 3}$) is $\\breve A \\otimes_{\\mathbb Z_p} \\breve A $(resp. $\\breve A \\otimes_{\\mathbb Z_p} \\breve A \\otimes_{\\mathbb Z_p} \\breve A$)-algebra by $ u \\otimes 1 \\mapsto x$, \n$1\\otimes u \\mapsto y$ and $1 \\otimes t_i \\mapsto s_i$ (resp. $1\\otimes 1 \\otimes u \\mapsto w$ and $1 \\otimes 1 \\otimes t_i \\mapsto r_i$). So in this way, we can extend Frobenius $\\varphi$ of $A$, which is compatible with that on $\\breve A$ to $A ^{\\ho 2}$ and $A^{\\ho 3}$. \nSet $J ^{(2)}= (E, y -x , \\{s_i- t _i \\}_{i = 1, \\dots, m} )\\subset A^{\\ho 2}$ and $J ^{(3)} = (E, y-x , w-x , \\{s_i-t_i , r_i - t_i\\}_{i = 1, \\dots , m}) \\subset A ^{\\ho 3}.$ Clearly, we have $A^{\\ho i}\/ J ^{(i)}\\simeq R$ for $i = 2, 3$. And we have $A^{\\ho 2}\/(p,E)$ (resp. $A^{\\ho 3}\/(p,E)$) is a formal power series ring over the variables $\\bar{y}-\\bar{x}, \\{\\bar{s}_i-\\bar{t}_i\\}_{i = 1, \\dots, m}$ (resp. $\\bar{y}-\\bar{x} , \\bar{w}-\\bar{x} , \\{\\bar{s}_i-\\bar{t}_i , \\bar{r}_i - \\bar{t}_i\\}_{i = 1, \\dots , m}$), so $(A,(E)) \\to (A^{\\ho i}, J ^{(i)})$ satisfies the requirements of in \\cite[Prop. 3.13]{BS19}, and we can construct the prismatic envelope with respect to this map, which will be denoted by $A^{(i)}$. More precisely, $A^{(i)}\\simeq A^{\\ho i}\\left \\{\\frac{J ^{(i)}}{E}\\right\\}_\\delta^{\\wedge}$, here $\\{\\cdot\\}_\\delta^{\\wedge}$ means freely adjoining elements in the category of $(p, E(u))$-completed $\\delta$-$A$-algebras. We will see $A^{(i)}$, $i = 2 ,3$ are the self product and triple product of $A$ in category $X_{\\mathlarger{\\mathbbl{\\Delta}}}$ in \\S \\ref{subsec-pris-crystal}. \n\n\\subsection{The ring \\texorpdfstring{$A^{(2)}_{\\max}$}{A2max}} Now we set $t_0 = x$, $s_0 = y$ and \n\\[ z_j = \\frac{s_i - t _i}{E} \\textnormal{ and } z_0 = z= \\frac{y -x }{E}= \\frac{s_0 - t_0}{E}. \\]\nNote that $A^{(i)}$ are $A$-algebras via $u \\mapsto x$.\n\\begin{definition}\nLet $\\Omax$ be the $p$-adic completion of the $A$-subalgebra of $A[\\frac{1}{p}]$ generated by $p^{-1}E$. And let $A_{\\max}^{(2)}$ be the $p$-adic completion of the $A$-subalgebra of $ A [z_j , \\frac{1}{p}; j = 0 , \\dots , m ]$ generated by $p^{-1}E$ and $\\{\\gamma_i(z_j)\\}_{i\\geq 1, j = 0 , \\dots , m}$. \n\\end{definition}\nWe first note that $A^{(2)}_{\\max}$ is an $A^{\\widehat \\otimes 2}$-algebra via $ (s_j - t_j ) = E z_j, j =0 ,\\dots, m$. Write $\\iota: A^{\\ho 2} \\to A^{(2)}_{\\max}$ for the structure map. \nBy construction, it is easy to see that $A^{(2)} _{\\max}\\subset R_0[\\frac 1 p] [\\![ E, z_j, j = 0, \\dots , m]\\!]$. In particular, $A^{(2)}_{\\max}$ is a domain and \nany element $b\\in A^{(2)}_{\\max}$ can be \\emph{uniquely} written as \n$\\sum\\limits_{i_0= 0}^\\infty\\cdots \\sum\\limits_{i_m= 0 }^\\infty b_{i_1 , \\dots, i _m} \\prod\\limits_{j= 0}^m\\gamma_{i_j} (z_{j})$ with $b _{i_0 , \\dots , i_m} \\in \\Omax$ and $b_{i_0, \\dots , i_m}\\to 0$ $p$-adically when $i_0 + \\cdots+ i_m \\to \\infty$. \nOur next aim is to define $\\varphi$ on $A^{(2)}_{\\max}$. For this, we need a little preparation. \n\\begin{lemma}\\label{lem-Omax}\n$c: = \\frac{\\varphi(E)}{p}\\in \\Omax$ and $ c^{-1} \\in \\Omax$. \n\\end{lemma}\n\\begin{proof}\nWe have $A$ is a $\\delta$-ring, and $E$ is a distinguished element, so in particular\n$$\n\\varphi(E)\/p=c_0+E^p\/p\n$$\nwhere $c_0=\\delta(E)\\in A^\\times$. So $c = \\varphi(E)\/p\\in \\Omax$, and \n$c ^{-1} = c_0 ^{-1} \\sum\\limits_{i = 0}^\\infty \\frac {(- c_0^{-1} E^p ) ^i }{p ^i }\\in \\Omax. $\n\\end{proof}\n\nNow we define $\\varphi(z)=\\varphi(z_0)= \\frac{y^p-x^p}{\\varphi(E)}$ and $\\varphi (z_j) = \\frac{\\varphi(s_j) - \\varphi(t_j) }{\\varphi (E)}$. Since \n\\begin{IEEEeqnarray*}{+rCl+x*}\n\\varphi(z)=\\frac{y^p-x ^p}{\\varphi(E)}=c^{-1}\\frac{y^p-x^p}{p}=c^{-1}\\frac{(x+ Ez)^p-x^p}{p} &= & c^{-1}\\sum_{i=1}^px^{p-i}(Ez)^i\\binom{p}{i}\/p\\\\\n&=& c^{-1}\\sum_{i=1}^{p}a_iz^i,\n\\end{IEEEeqnarray*}\nwhere $a_i\\in W(k)[\\![x]\\!][\\frac{E^p}{p}]\\subset \\Omax\\subset A^{(2)}_{\\max}$ and $c$ is a unit in $\\Omax$, we have $ \\varphi (z) \\in A^{(2)}_{\\max}$.\nThen \n$$\n \\gamma_n(\\varphi(z))=\\frac{\\varphi(z)^n}{n!}=\\frac{z^n}{n!}(c^{-1}\\sum_{i=1}^{p}a_iz^{i-1})^n\n$$\nis in $A^{(2)}_{\\max}.$ The argument for $\\varphi (z_j)$ for $j >1$ need a little more details. Note that $\\varphi (t_j) = t_j ^p + p \\delta (t_j)$ with $\\delta(t_j) \\in \\breve R_0$ by our assumptions. It is clear that $\\delta(s_j)-\\delta (t_j) = (s_j - t_j) \\lambda_j$ with $\\lambda_j \\in A ^{\\ho 2}$. Using that $(s_j -t_j) = E z_j$, so\n\\begin{equation}\\label{eqn-special-shape}\n\\varphi (z_j) = c^{-1} (\\frac{s^p _j - t^p_j}{p} + E z_j \\lambda_j) \n\\end{equation}\nThe same argument as that for $\\varphi (z_0)$ also shows that $\\gamma_n (z_j)\\in A^{(2)}_{\\max}$, for $j =1 , \\dots , m$. \n\nSince any element $b\\in A^{(2)}_{\\max}$ can be uniquely written as $\\sum\\limits_{i_0= 0}^\\infty\\cdots \\sum\\limits_{i_m= 0 }^\\infty b_{i_1 , \\dots, i _m} \\prod\\limits_{j= 0}^m\\gamma_{i_j} (z_{j})$ with $b _{i_0 , \\dots , i_m} \\in \\Omax$ and $b_{i_0, \\dots , i_m}\\to 0$ $p$-adically when $i_0 + \\cdots+ i_m \\to \\infty$, this allows to extend Frobenius map $\\varphi $ on $ A$ to a \\emph{ring} map $\\varphi: A^{(2)}_{\\max} \\to A^{(2)}_{\\max}$ by sending $u \\mapsto u ^p$, $z \\mapsto \\frac{y ^p -x^p}{\\varphi(E)}$, $\\varphi (z_j) = \\frac{\\varphi(s_j) - \\varphi(t_j)}{\\varphi (E)}$, and $\\gamma_i (z_j) \\mapsto \\gamma_i (\\varphi (z_j))$ as the above.\n\n\\begin{remark}\\label{rem-not-Frob-lift} The ring map $\\varphi: A^{(2)}_{\\max}\\to A^{(2)}_{\\max}$ is \\emph{not} a Frobenius lift of $A^{(2)} _{\\max}\/ p$ because $\\varphi (E\/p)- (E\/p) ^p \\not \\in p A^{(2)}_{\\max}$. In particular, $A^{(2)}_{\\max}$ is not a $\\delta$-ring. \n\\end{remark}\n\nRecall that $A^{(2)}_{\\max}$ is an $A^{\\ho 2}$-algebra via map $\\iota : A^{\\ho 2}\\to A^{(2)}_{\\max}$. The above construction of Frobenius $\\varphi$ on $A^{(2)} _{\\max}$ is obviously compatible with $\\iota$. \n\nOur next goal is to show that $\\iota$ induces a map $A^{(2)}\\to A^{(2)}_{\\max}$ so that $A^{(2)} $ is a subring of $A^{(2)}_{\\max}$ which is compatible with $\\varphi$-structures and filtration. We need a little preparation. \nWrite $\\mathfrak z_{n}= \\delta^n (z)$ with $\\delta_0(z)= z= \\mathfrak z_0$, and $A_0 = W(k) [\\![u]\\!]$. \n\\begin{lemma}\\label{lem-delta-n} $$\\delta^n (Ez) = b_n \\mathfrak z_{n} + \\sum_{i = 0} ^p a^{(n)}_i \\mathfrak z_{n -1} ^i. $$\nwhere $a^{(n)}_i \\in A_0 [\\mathfrak z_0, \\dots, \\mathfrak z_{n -2}]$ so that $a^{(n)}_p\\in A_0 ^\\times$ and for $0 \\leq i \\leq p-1$ each monomials of $a^{(n)}_i $ contains a factor $\\mathfrak z_{j}^p$ for some $ 0 \\leq j\\leq n -2$. Furthermore, $b_{n+1 } =p\\delta (b_n ) + b^p_n $ and $b_1 = p \\delta (E) + E^p$. \n\\end{lemma}\n\\begin{proof} Given $f \\in A_0[x_1 , \\dots , x_m]$, if each monomials of $f$ contains $x_j^l$ for some $j$ and $l \\geq p$ then we call $f$ \\emph{good}. For example, $f= x_1^p x_2 + 2 x_ 1x_2^{p +3}.$ So we need to show that $a^{(n)}_i\\in A_0[\\mathfrak z_0, \\dots, \\mathfrak z_{n -2}]$ is good. Before making induction on $n$, we discuss some properties of good polynomial. It is clear that the set of good polynomials is closed under addition and multiplications. Note that \n\\begin{equation}\\label{eqn-delta}\n\\delta(\\mathfrak z_{l}^i)= \\frac 1 p (\\varphi (\\mathfrak z_{l}^i) - \\mathfrak z_{l} ^{p i})= \\frac 1 p \\big( (p\\mathfrak z_{l +1} + \\mathfrak z_{l } ^p)^i - \\mathfrak z_{l}^{p i}\\big) = \\sum \\limits_{j = 1 }^{i} \\binom{i}{j}(p^{j -1} \\mathfrak z_{l }^{p(i-j)} ) \\mathfrak z_{l+1} ^{j }. \n\\end{equation}\nIn particular, given an $f\\in A_0[\\mathfrak z_0 , \\dots , \\mathfrak z_{m}]$, $\\delta(\\mathfrak z_{m}^p f)= f^p \\delta (\\mathfrak z_{m} ^p) + \\mathfrak z_{m}^{p ^2}\\delta (f) + p \\delta (\\mathfrak z_{m} ^p)\\delta (f)$ is a good polynomial in $A[\\mathfrak z_0 , \\dots, \\mathfrak z_{m+1}]$. Using the fact that \n$\\delta (a+b)=\\delta(a) + \\delta (b) + F(a, b)$ where $F(X, Y) = \\frac 1 p ( X^p + Y^p - (X+Y)^p) = - \\sum\\limits_{i = 1}^{p-1} \\binom{p}{i}\/p X^i Y^{p-i}$, together with the above argument of $\\delta(\\mathfrak z_l^p f)$, it is not hard to show that if $g\\in A_0[\\mathfrak z_0 , \\dots , \\mathfrak z_{m}]$ is good then $\\delta (g) \\in A_0[\\mathfrak z_0 , \\dots , \\mathfrak z_{m}, \\mathfrak z_{m +1}]$ is also good. \n\nNow we make induction on $n$. When $n =1$, we have \n$$\\delta (Ez)= E^p \\mathfrak z_{1} + z^p \\delta(E) + p \\delta (E) \\mathfrak z_{1}= (p \\delta (E) + E^p) \\mathfrak z_{1} + \\delta(E) z^p.$$ \nThen $b_1 = p \\delta (E) + E^p $, $a^{(1)}_p= \\delta(E)\\in A_0 ^\\times$ and $a^{(1)}_i = 0$ for $1 \\leq i \\leq p-1$ are required. Now assume the formula is correct for $n$, then \n$$\\delta ^{n +1} (Ez) = \\delta (b_n \\mathfrak z_{n} + \\sum_{i = 0} ^p a^{(n)}_i \\mathfrak z_{n -1} ^i) = \\delta (b_n \\mathfrak z_{n}) + \\delta (\\sum_{i = 0} ^p a^{(n)}_i \\mathfrak z_{n -1} ^i)) + F(b_n \\mathfrak z_{n}, \\sum_{i = 0} ^p a^{(n)}_i \\mathfrak z_{n -1} ^i)), $$\nClearly, $ F(b_n \\mathfrak z_{n}, \\sum\\limits_{i = 0} ^p a^{(n)}_i \\mathfrak z_{n -1} ^i)) = \\sum\\limits_{j = 1} ^{p-1} \\tilde a^{(n)}_j \\mathfrak z_{n} ^j$ with $\\tilde a^{(n)}_j$ being good. An easy induction shows that \n$\\delta (\\sum\\limits_{i = 0} ^p a^{(n)}_i \\mathfrak z_{n -1} ^i) = \\sum\\limits_{i = 0} ^p \\delta (a^{(n)}_i \\mathfrak z_{n -1} ^i) + f$ with $f \\in A_0[\\mathfrak z_0, \\dots, \\mathfrak z_{n -1}]$ being good. Since \n$\\delta (a^{(n)}_i \\mathfrak z_{n -1} ^i)= (a^{(n)}_i)^p \\delta (\\mathfrak z_{n -1}^i) + (\\mathfrak z_{n-1}^{pi})\\delta (a_i ^{(n)}) + p \\delta (\\mathfrak z_{n -1}^i ) \\delta (a_i^{(n)})$, by using formula of $\\delta(\\mathfrak z_{n-1}^i)$ in \\eqref{eqn-delta} and that $a^{(n)}_i$ is good implies that \n$\\delta (a_i^{(n)}) $ is also good, we conclude that for $0 \\leq i \\leq p-1$, $$\\sum\\limits_{i = 0} ^{p-1} \\delta (a^{(n)}_i \\mathfrak z_{n -1} ^i) = \\sum_{i =0}^{p-1} \\alpha_i \\mathfrak z_{n} ^i$$ with $\\alpha_i \\in A_0 [\\mathfrak z_0, \\dots, \\mathfrak z_{n-1}]$ being good polynomials. Using that $a _p^{(n)} \\in A_0 ^\\times$, we compute that $\\delta (a_p^{(n)}\\mathfrak z^p_{n-1}) = \\sum \\limits_{i = 0}^p \\beta_i \\mathfrak z_{n}^i$ with $\\beta_p \\in p A_0$ and $\\beta_j\\in A_0 [\\mathfrak z_0 , \\dots , \\mathfrak z_{n-1}]$ being good for $1\\leq j \\leq p-1$. \n Now we only need to analyze $\\delta (b_n \\mathfrak z_{n})$, which is \n $\\delta (b_n ) \\mathfrak z_{n}^p + b_n^p \\mathfrak z_{n+1} + p \\delta (b_n)\\mathfrak z_{n+1}$. So $b_{n+1} = p \\delta(b_n) + b_n ^p$ and $a_p^{(n+1)} = \\delta (b_n) + \\beta_p$. Since $\\delta(b_n) \\in A_0^\\times$, we see that $a_p^{(n+1)}= \\delta(b_n) + \\beta_p \\in A_0^\\times$ as required. \n\\end{proof}\n\nLet $\\widetilde A^{(2)} := A^{\\ho 2} [z_j]_\\delta= A^{\\ho2} [\\delta ^n (z_j), n \\geq 0, j =0 , \\dots , m]$ and natural map $\\alpha : \\widetilde A^{(2)} \\to \\widetilde A^{(2)} [\\frac 1 p]$ (we do not know $\\alpha$ is injective at this moment). \n\\begin{lemma}\\label{lem:gamma(z)-polynomial-in-E\/p}\nFor $i\\geq 0$ and $j=0,1,\\ldots,d$, there exists $f_{ij}(X) \\in \\widetilde A^{(2)} [X]$ such that, as elements of $\\widetilde A^{(2)}[\\frac 1 p] $ via $\\alpha: \\widetilde A^{(2)} \\to \\widetilde A^{(2)} [\\frac 1 p]$, \n\\[\n\\gamma_i(z_j) = f_{ij}\\Bigl(\\frac{E}{p}\\Bigr).\n\\]\n\\end{lemma}\n\\begin{proof}\nWrite $z = z_{j}$ for simplicity, and let $\\tilde{\\gamma}(z)= \\frac{z^p}{p}$ and $\\tilde{\\gamma}^n = \\underbrace{\\tilde{\\gamma} \\circ \\tilde{\\gamma} \\cdots \\circ \\tilde{\\gamma}}_n$. It suffices to show that for each $n \\geq 1$, we have $\\tilde{\\gamma}^n(z) = f_n(\\frac{E}{p})$ inside $\\widetilde A^{(2)}[\\frac 1 p]$ for some $f_n(X) \\in \\widetilde A^{(2)} [X]$. For an element $x \\in A[\\delta^i(z)]_{i \\geq 0}$, we say that $x$ has \\emph{$\\delta$-order $\\leq n$} if $x\\in \\sum_{0\\leq j\\leq n}A[\\{\\delta^i(z)\\}_{0 \\leq i \\leq n }] \\delta^j(z)$, namely, if $x$ can be written as a sum of monomials such that each term is divisible by $\\delta^j(z)$ for some $0 \\leq j \\leq n$. \n\nWe claim that the following two equations hold for each $n \\geq 1$:\n\\begin{enumerate}\n\\item We have\n\\begin{equation} \\label{eq:delta(z)}\n \\delta^n(z) = \\nu_n \\tilde{\\gamma}^n(z)+P_n\\Bigl(\\frac{E}{p}\\Bigr)+\\frac{E^p}{p}d_n\\delta^n(z)\n\\end{equation}\nfor some $\\nu_n \\in A^{\\times}$, $d_n \\in A$, and $P_n(X) \\in (A[\\delta^i(z)]_{i \\geq 0})[X]$ such that each coefficient of $P_n(X)$ has $\\delta$-order $\\leq n-1$. \n\n\\item We have\n\\begin{equation} \\label{eq:gamma(delta(z))}\n \\tilde{\\gamma}(\\delta^{n-1}(z)) = \\mu_{n-1}\\tilde{\\gamma}^n(z)+Q_{n-1}\\Bigl(\\frac{E}{p}\\Bigr)\n\\end{equation}\nfor some $\\mu_{n-1} \\in A^{\\times}$ and $Q_{n-1}(X) \\in (A[\\delta^i(z)]_{i \\geq 0})[X]$ such that each coefficient of $Q_{n-1}(X)$ has $\\delta$-order $\\leq n-1$.\n\\end{enumerate}\n\nWe prove claims (1) and (2) by induction. For $n = 1$, since\n\\[\n\\delta(Ez) = z^p\\delta(E)+(p\\delta(E)+E^p)\\delta(z)\n\\]\nand $\\delta(E) \\in \\mathfrak{S}^{\\times}$, we have\n\\[\n\\delta(z) = -\\tilde{\\gamma}(z)+\\delta(E)^{-1}\\frac{\\delta(Ez)}{p}-\\delta(E)^{-1}\\frac{E^p}{p}\\delta(z). \n\\]\nBy easy induction, we also have $\\delta^i(Ez) \\in (Ez)A$ for each $i \\geq 1$. So claim (1) holds. Claim (2) holds for $n = 1$ trivially with $Q_0(X) = 0$.\n\nSuppose that claims (1) and (2) hold for $1 \\leq n \\leq m$. We will verify claims (1) and (2) for $n = m+1$. We first consider claim (2). Since each coefficient of $P_m(X)$ has $\\delta$-order $\\leq m-1$, $\\frac{E^p}{p}=p^{p-1}\\bigl(\\frac{E}{p}\\bigr)^p$, and Equations \\eqref{eq:delta(z)} and \\eqref{eq:gamma(delta(z))} hold for $1\\leq n \\leq m$, applying $\\tilde{\\gamma}(\\cdot)$ to Equation \\eqref{eq:delta(z)} for $n = m$ yields\n\\[\n\\tilde{\\gamma}(\\delta^m(z)) = \\nu_m^p \\tilde{\\gamma}^{m+1}(z)+Q_m\\Bigl(\\frac{E}{p}\\Bigr)\n\\]\nfor some $Q_m(X) \\in (\\mathfrak{S}[\\delta^i(z)]_{i \\geq 0})[X]$ such that each coefficient of $Q_m(X)$ has $\\delta$-order $\\leq m$. This proves the claim (2) for $n = m+1$.\n\n\nWe now consider claim (1) for $n = m+1$. By the above Lemma for $n = m+1$ and that $b_n= p\\alpha_n +\\beta_n E^p$\nfor some $\\alpha_n \\in A^{\\times}$ and $\\beta_n \\in A$ (via an easy induction on $n$), we have \n\\[\n\\alpha_{m+1}\\delta^{m+1}(z) = \\frac{\\delta^{m+1}(Ez)}{p}-\\beta_{m+1}\\frac{E^p}{p}\\delta^{m+1}(z)-a_p^{(m+1)}\\tilde{\\gamma}(\\delta^m(z))-\\frac{1}{p}\\sum_{j=0}^{p-1} a_j^{(m+1)}(\\delta^{m}(z))^j.\n\\]\nAs noted above, we have $\\delta^{m+1}(Ez) \\in (Ez)A$. Furthermore, by the condition on $a_j^{(m+1)}$, the last term $\\frac{1}{p}\\sum_{j=0}^{p-1} a_j^{(m+1)}(\\delta^{m}(z))^j$ is a linear combination of terms involving $\\tilde{\\gamma}(\\delta^l(z))=\\frac{1}{p}(\\delta^l(z))^p$ for some $0\\leq l\\leq m-1$.\nThus, by applying Equations~\\eqref{eq:delta(z)} and \\eqref{eq:gamma(delta(z))} for $1 \\leq n \\leq m$, we see that claim (1) also holds for $n = m+1$ with $\\nu_{m+1} = -\\alpha_{m+1}^{-1}a_p^{(m+1)}\\mu_m$ and $d_{m+1} = -\\alpha_{m+1}^{-1}\\beta_{m+1}$.\nThis completes the induction and prove the lemma .\n\\end{proof}\n\n\\begin{remark}\nIn the above proof, by equation~\\eqref{eq:gamma(delta(z))}, we even have for each $i,j\\geq 0$, $\\gamma_i(\\delta^j(z))=f(\\frac{E}{p})$ for some $f\\in \\widetilde A^{(2)} [X]$. \n\\end{remark}\n\nAn easy induction by \\eqref{eq:delta(z)} implies that $\\alpha (\\delta^n (z) ) \\in A^{\\ho 2}[\\{\\gamma_i (z_j )\\}_{i \\geq 0 , j =1 , \\dots , m}, \\frac E p]\\subsetA^{(2)}_{\\max}$, which satisfies equations in Lemma \\ref{lem-delta-n} by replacing \n$\\mathfrak z_n$ by $\\alpha (\\delta^n (z))$ inside $A^{(2)}_{\\max}$. It is clear that $\\iota$ is still Frobenius compatible (because both $A ^{\\ho 2}$ and $A^{(2)}_{\\max}$ are domains). Since $E = p \\frac E p$, $\\iota$ is a continuous for $(p , E)$-topology on $\\widetilde A^{(2)} $ and $p$-topology on $A^{(2)}_{\\max}$. Finally, we construct a ring map $\\iota: A^{(2)} \\to A^{(2)}_{\\max}$ so that $\\iota$ is compatible with Frobenius. \n\nOur next goal is to show that $\\iota$ is injective. Define ${\\textnormal{Fil}} ^i A^{(2)}_{\\max}[\\frac 1 p]: = E^i A^{(2)}_{\\max}[\\frac 1 p]$. For any subring $B \\subset A^{(2)}_{\\max}[\\frac 1 p]$, set \n$${\\textnormal{Fil}} ^i B : = B \\cap {\\textnormal{Fil}} ^i A^{(2)}_{\\max}[\\frac 1 p]= B \\cap E^i A^{(2)}_{\\max}[\\frac 1 p].$$\nLet $D_z$ be the $p$-adic completion of $R [\\gamma_i (z_j), i \\geq 0; j = 0, \\dots , m]$. \n\\begin{proposition} \\label{prop-key-property}\n\\begin{enumerate}\n \\item $\\widetilde A^{(2)}\/ E = R [\\gamma_i (z_j ), i \\geq 0; j = 0, \\dots , m]$.\n \\item $ A^{(2)}\/ E \\simeq D_z$.\n \\item $\\iota$ is injective.\n \\item ${\\textnormal{Fil}} ^1 A^{(2)}= E A ^{(2)}$.\n \\item $A^{(i)}$ are flat over $A$ for $i = 2, 3$.\n\\end{enumerate}\n\\end{proposition}\n\\begin{proof}\n(1) By definition, $\\widetilde A^{(2)} = A ^{\\ho 2}[ z^{(n)}_j , n\\geq 0; j = 0 , \\dots, m ]\/ J $ where $\\mod J $ is equivalent the following relations (note that $z_0 = z$): $E z= (x-y), E z_j = s_j - t_j, \\delta (z^{(n)}_j)= z^{(n+1)}_{j}, \\delta ^n (Ez) = \\delta^n (y -x) , \\delta ^n (Ez_j)= \\delta ^n (s_j-t _j). $ Since $\\delta (x-y)= \\frac{(x^p - y ^p)- (x-y)^p}{p}$ and $\\delta(s_j - t_j) = \\frac{\\varphi (s_j - t_j)- (s_j - t_j)^p}{p}$, \nit is easy to prove by induction that $\\delta^n (x-y)$ and $\\delta^n (s_j - t_j)$ always contains a factor $(x-y)$, $s_j - t_j$ and hence $\\delta^n (x-y), \\delta(s_j - t_j)\\equiv 0 \\mod E$. Therefore $\\delta ^n (Ez_j) \\equiv 0 \\mod E$. By Lemma \\ref{lem-delta-n}, we see that \n$$p \\mu_n z^{(n )}_{j} = -\\sum_{i = 0} ^p \\overline{a^{(n)}_i} (z^{(n -1)}_j) ^i \\mod E \\text{ and } pz^{(1)}_j = z_j^p \\mod E $$\nwhere $\\overline {a^{(n)}_i} = a^{(n)}_i \\mod E$ and $\\mu_n = \\frac{\\delta (b_n)}{p}\\mod E \\in \\O_K^\\times$. Using that $a_p^{(n)} \\in A_0^\\times$, and $a_i^{(n)}, 1 \\leq i \\leq p-1$ are good in the sense that they contains factor of $(z^{(l)}_j) ^p$ for some $l = 0 , \\dots, n-2$, we easily see by induction that \n$\\widetilde A^{(2)} \/E = R [\\widetilde \\gamma^n (z_j ), n \\geq 0; j = 0 , \\dots , m ]$. But it is well-known that $R [\\widetilde \\gamma^n (z_j), n \\geq 0; j = 0 , \\dots , m ] = R [\\gamma_n (z_j), n \\geq 0; j = 0 , \\dots , m]. $\n\nNow we show that the natural map $\\iota: \\widetilde A^{(2)}\\to A^{(2)}_{\\max}[\\frac 1 p]$ induced by $\\alpha (\\delta ^n(z_j))$ is injective. Note that $\\widetilde A^{(2)} $ is the direct limit of $\\widetilde A^{(2)}_n : = A ^{\\hat \\otimes 2}[\\{ \\delta^i (z_j)\\}_{i = 1 , \\dots , n, j = 0 , \\dots , m}]$. A similar argument similar as above show that $\\widetilde A^{(2)}_n \/ E $ injects to $A^{(2)}_{\\max}[\\frac 1 p]\/E = D_z [\\frac 1 p]$. \nSince $\\widetilde A^{(2)}_n$ is $E$-separate and $A^{(2)}_{\\max}$ is a domain, this implies that $\\widetilde A^{(2)}_n$ injects to $A^{(2)}_{\\max}[\\frac 1 p]$. So \n$\\widetilde A^{(2)}$ injects to $A^{(2)}_{\\max}$ via $\\iota$. \n\n(2) Since $A^{(2)}$ is $(p, E)$-completion of $\\widetilde A^{(2)}$ \\footnote{Indeed, $A^{(2)}$ is \\emph{derived} $(p, E)$-completion. Since $\\widetilde A^{(2)}\/ E$ is $\\mathbb Z_p$-flat, then derived completion coincides with the classical completion, which is used here.}, we have a natural map from $\\bar \\iota: A^{(2)}\/E \\to D_z$. The surjectivity of $\\bar \\iota$ is straightforward as $A^{(2)}$ is also $p$-complete. To see injectivity, given an sequence $f_n $ so that $f_{n +1}- f _n \\in (p , E)^n \\widetilde A^{(2)} $ and $ f_n = E g_n$ for all $n$, we have to show that $g_n$ is a convergent sequence in $A^{(2)}$. Since $E (g_{n +1} - g_n) = \\sum_{i = 0} ^n p ^i E ^{n - i} h_i$ with $h _i \\in \\widetilde A^{(2)} $. Then $ E|p ^n h_n $. Since $\\widetilde A^{(2)} \/E$ has no $p$-torsion,\nwe have $E | h_n $ and write $h_n = E h'_n $. Since $\\widetilde A^{(2)} $ is a domain as it is inside the fraction field of $A ^{\\ho 2}$, we see that \n$ g_{n +1}- g_n = p ^n h'_n + \\sum\\limits_{i = 0} ^{n -1} p ^i E^{n - i - 1} h_i $. Hence $g_n$ converges in $ A^{(2)}$ as required. \n \n(3) It is clear that $A^{(2)}_{\\max}[\\frac 1 p]\/ E \\simeq D_z [\\frac 1 p]$. So the map $\\iota \\mod E(u)$ induces an injection $D_z \\hookrightarrow D_z [\\frac 1 p]$. So for any $x\\in \\Ker (\\iota)$, we see that $x= Ea$ for some $a \\in A^{(2)}$. As $A^{(2)}_{\\max}$ is $E$-torsion free and $A^{(2)}$ is $E$-complete, we see that $x= 0$ as required. \n \n(4) By the definition of ${\\textnormal{Fil}} ^1 A^{(2)} $, we see that $E A ^{(2)} \\subset {\\textnormal{Fil}} ^1 A ^{(2)}$ and $ A^{(2)} \/ {\\textnormal{Fil}} ^1 A^{(2)} $ injects to $A^{(2)}_{\\max}[\\frac 1 p ]\/E= D_z[\\frac 1 p]$. But we have seen that $A^{(2)}\/E = D_z$ injects to $D_z$. Then ${\\textnormal{Fil}} ^1 A^{(2)} = E A^{(2)}$. \n\n(5) Both $A^{(2)}$ and $A^{(3)}$ are obtained by the construction of \\cite[Proposition 3.13]{BS19}, which implies that they are $(p,E)$-complete flat over $A$. Since $A$ is Noetherian, by \\cite[Tag 0912]{stacks-project}, we have both $A^{(2)}$ and $A^{(3)}$ are $A$-flat.\n\\end{proof}\n\\begin{corollary}\\label{cor-filtration-shape}\n\\begin{enumerate}\n \\item ${\\textnormal{Fil}} ^i A ^{(2)} = E^i A^{(2)}. $\n \\item $A^{(i)}$ are bounded prisms for $i = 2, 3$. \n\\end{enumerate}\n\\end{corollary} \n\\begin{proof}These follow that $A^{(2)} \/ E A^{(2)} \\simeq D_z$ which is $\\mathbb Z_p$-flat. For (2), we have $A^{(2)}$ and $A^{(3)}$ are $(p,E)$-complete flat over $A$, so boundedness follows from (2) in \\cite[Lemma 3.7]{BS19}.\n\\end{proof}\n\n\\begin{lemma}\\label{lem-subring} $A^{(2)}$ is a closed subset inside $A^{(2)}_{\\max}$. \n\\end{lemma}\n\\begin{proof} We need to show the following statement: Given $ x \\in \\widetilde A^{(2)} $, if $x= p ^n y$ with $y \\in A^{(2)}_{\\max}$ then $x = \\sum\\limits_{i = 0}^n p ^{n-i} E^i x_i$ with $x_i \\in \\widetilde A^{(2)} . $ Indeed, since $A^{(2)}\/E \\simeq A^{(2)}_{\\max}\/ {{\\textnormal{Fil}} ^1}$, there exists $x_0, w_1 \\in \\widetilde A^{(2)} $ so that $x= p ^n x_0 + E w_1$. Then $Ew_1 \\in p ^n A^{(2)}_{\\max}$. Write $ E w_1= p ^n \\sum \\limits_{i =0} ^\\infty\\sum\\limits_{j =0}^m f_{ij} \\gamma_i (z_j)$, we see that $f_{ij}= \\sum_{l \\geq 1} a_{ijl} \\frac{E^l}{p^l}\\in {\\textnormal{Fil}} ^1 \\Omax$. So it is easy to see that $p ^n E^{-1}f_{ij} \\in p ^{n -1} \\Omax$ and then \n$w_1 = p ^{n -1} x_1 $ with $x_1 \\in A^{(2)}_{\\max}$. Then we may repeat the above argument to $w_1$, and finally $x= \\sum\\limits_{i = 0}^n p ^{n-i} E^i x_i$ with $x_i \\in \\widetilde A^{(2)}$ as required. \n\\end{proof}\n\nNow we realize $A^{(2)}$ as a subring of $A^{(2)}_{\\max}$ via $\\iota$. We need to introduce some auxiliary rings. By the description of elements in $A^{(2)}_{\\max}$, we define ${\\widetilde S}_0$ be the subring of $A^{(2)}_{\\max}$ as follow\n$$\n\\widetilde{S} := A^{(2)} [\\![ \\frac {E ^p}{p} ]\\!] := \\{ \\sum_{i \\geq 0} a_i (\\frac{E^p}{p})^i \\mid a_i\\in A^{(2)} \\}.\n$$\nAnd when $p=2$, we define \n$\n\\widehat{S} := A^{(2)} [\\![\\frac{E^4}{2}]\\!]\n$ simiarly. \nWe will have $\\widehat{S} \\subset \\widetilde{S} \\subset A^{(2)}_{\\max}$. Viewing $\\widetilde S$ and $\\widehat{S}$ as subrings of $A^{(2)}_{\\max}$, we give them the filtration induced from $A^{(2)}_{\\max}$. The following lemma is crucial for later applications and we thank Yong Suk Moon for many useful comments to improve many details in the proof. \n\n\\begin{lemma}\\label{lem-auxiliaryrings}\nFix $h \\in \\mathbb N$, then we have\n\\begin{enumerate}\n \\item We have $\\varphi(A^{(2)}_{\\max}) \\subset \\widetilde{S} \\subset A^{(2)}_{\\max}$, and when $p=2$, we have $\\varphi(\\widetilde{S}) \\subset \\widehat{S} \\subset \\widetilde{S}$;\n \\item $x \\in {\\textnormal{Fil}} ^h \\widetilde S $ if and only if $x$ can be written as\n $$\n x = \\sum\\limits_{i \\geq h } a_i \\frac {E ^i}{p ^{\\lfloor \\frac i p\\rfloor}} \n $$\n with $a_i\\in A^{(2)}$. \n \\item when $p>2$, there is a $h_0>h$ such that $\\varphi ({\\textnormal{Fil}} ^m \\widetilde S ) \\subset A ^{(2)} + E^h{\\textnormal{Fil}}^{m +1} \\widetilde S$ for all $m > h_0$;\n \\item when $p=2$, then $x \\in {\\textnormal{Fil}} ^h \\widehat S $ if and only if $x$ can be written as\n $$\n x = \\sum\\limits_{i \\geq h } a_i \\frac {E ^i}{2 ^{\\lfloor \\frac i 4\\rfloor}} \n $$\n with $a_i\\in A^{(2)}$;\n \\item when $p=2$, there is a $h_0>h$ such that $\\varphi ({\\textnormal{Fil}} ^m \\widehat{S} ) \\subset A ^{(2)} + E^h{\\textnormal{Fil}}^{m +1} \\widehat{S} $ for all $m > h_0$.\n\\end{enumerate}\n\\end{lemma}\n\\begin{proof}\nFor $(1)$, any $a\\in A^{(2)}_{\\max}$, we can write \n$$\na = \\sum _{i_0 = 0}^\\infty \\cdots \\sum_{i _m = 0}^\\infty \\sum_{l = 0}^\\infty a_{i_0 , \\dots , i_m, l } \\left (\\frac E p\\right ) ^l \\prod_{j = 0}^m \\gamma_{i_j} (z_j)\n$$\nwhere $a_{i_0 , \\dots , i_m, l}\\in A$ and $a_{i_0 , \\dots , i_m, l}\\to 0$ $p$-adically when $\\sum_j i _j + l \\to \\infty$. Thanks for Lemma \\ref{lem:gamma(z)-polynomial-in-E\/p}, we see that $b_{i_0 , \\dots , i_m , l}: = \\varphi \\left (\\left (\\frac E p\\right ) ^l \\prod_{j = 0}^m \\gamma_{i_j} (z_j) \\right)\\in \\widetilde S$. So $\\varphi (a) = \\sum a_{i_0 , \\dots , i_m , l} b_{i_0 , \\dots , i_m , l}$ converges in $\\widetilde S$. \n\nFor the claim in $(1)$ for $p=2$, we have $\\varphi(\\frac{E^2}{2})=(E^2+2b')^2\/2= \\frac{E^4}{2} + 2b$ for some $b,b'\\in A$. And for $a = \\sum_{i \\geq 0} a_i (\\frac{E^p}{p})^i \\in \\widetilde{S}$, we have \n$$\n\\varphi(a) = \\sum_{i \\geq 0} \\varphi(a_i) (\\frac{\\varphi(E^2)}{2})^i= \\sum_{i \\geq 0} \\varphi(a_i) \\sum_{j=0}^{i} c_{ij}(2b)^{i -j} (\\frac{E^4}{2})^j = \\sum_{j \\geq 0} \\left (\\sum_{i=j}^{\\infty} \\varphi(a_i)c_{ij} (2b)^{i-j} \\right ) (\\frac{E^4}{2})^j\n$$\nfor some $c_{ij} \\in \\mathbb Z$. So we have $\\varphi(a) \\in \\widehat{S}$.\n\nFor $(2)$, the if part is trivial. For the other direction, any $x \\in {\\textnormal{Fil}} ^h \\widetilde S $, we have \n$$\nx = \\sum\\limits_{i \\geq 0 } a_i \\frac {E^{i}}{p^{\\lfloor \\frac i p \\rfloor}} \n$$\nas element in $\\widetilde S$. And if we also have $x \\in {\\textnormal{Fil}} ^h A^{(2)}_{\\max}[\\frac 1 p ] = E^h A^{(2)}_{\\max}[\\frac 1 p]$, this implies for $\\tilde a_0=\\sum\\limits_{0\\leq i \\leq h} a_i \\frac {E^{i}}{p^{\\lfloor \\frac i p \\rfloor}}$ is in ${\\textnormal{Fil}} ^h A^{(2)} [\\frac 1 p]$. This implies $p^{\\lfloor \\frac h p\\rfloor}\\tilde a_0 \\in {\\textnormal{Fil}} ^h A^{(2)}= E^h A^{(2)}$. That is $\\tilde a_0={p^{-\\lfloor \\frac h p\\rfloor}}{E^h} b$ for some $b \\in A^{(2)}$. So $x$ is of the given form. The proof for $(4)$ is similar.\n\nFor $(3)$, we have by $(2)$, $x \\in {\\textnormal{Fil}} ^m \\widetilde S$, $x$ can be written as\n$$\nx = \\sum\\limits_{i \\geq m } a_i \\frac {E ^i}{p ^{\\lfloor \\frac i p\\rfloor}}.\n$$\nAnd use the fact $\\varphi(E)=E^p+pb$ for some $b \\in A^{(2)}$, we have \n$$\n\\varphi(x) = \\sum\\limits_{i \\geq m } \\varphi(a_i) \\sum_{j=0}^{i} \\frac {c_{ij}E^{p(i-j)} p^j}{p ^{\\lfloor \\frac i p\\rfloor}} = \\sum_{i \\geq m} \\sum_{ j \\geq \\lfloor \\frac i p\\rfloor}^{i} \\frac {b_{ij}E^{p(i-j)} p^j}{p ^{\\lfloor \\frac i p\\rfloor}} + \\sum_{i \\geq m} \\sum_{0 \\leq j < \\lfloor \\frac i p\\rfloor} E^h\\frac {b_{ij}E^{p(i-j)-h} p^j}{p ^{\\lfloor \\frac i p\\rfloor}}\n$$ \nwith $b_{ij} \\in A^{(2)}$.\n\nIn particular, we have $\\sum_{i \\geq m} \\sum_{ j \\geq \\lfloor \\frac i p\\rfloor}^{i} \\frac {b_{ij}E^{p(i-j)} p^j}{p ^{\\lfloor \\frac i p\\rfloor}}$ is inside $A^{(2)}$. To prove $(3)$, it is amount to find $h_0$ such that whenever $m>h_0$, $i \\geq m$ and $0 \\leq j < \\lfloor \\frac i p\\rfloor$, we have \n$$\n\\sum_{i \\geq m} \\sum_{0 \\leq j < \\lfloor \\frac i p\\rfloor} \\frac {b_{ij}E^{p(i-j)-h} p^j}{p ^{\\lfloor \\frac i p\\rfloor}} \\in {\\textnormal{Fil}}^{m +1} \\widetilde S.\n$$\nThe claim follows if we can find $h_0 > h$ such that $\\frac {E^{p(i-j)-h} p^j}{p ^{\\lfloor \\frac i p\\rfloor}} \\in \\widetilde S$ and $p (i-j)- h \\geq m +1$ for all $m>h_0$, $i \\geq m$ and $0 \\leq j < \\lfloor \\frac i p\\rfloor$. That is $\\lfloor\\frac{p (i -j)-h}{p} \\rfloor +j \\geq \\lfloor \\frac i p \\rfloor $ and $p(i-j)-h\\geq m+1$ for all $i,j,m$ in this range. And solve this we have it is enough to choose $h_0 > \\max \\{h, \\frac{p(h+1)+1}{p(p-2)}\\}$, which is valid for $p>2$.\n\nStatement in $(5)$ is similar to $(3)$. Any $x \\in {\\textnormal{Fil}} ^m \\widehat S$, $x$ can be written as\n$$\nx = \\sum\\limits_{i \\geq m } a_i \\frac {E ^i}{2^{\\lfloor \\frac i 4\\rfloor}}.\n$$\nWe have $\\varphi(E)=E^2+2b$ for some $b \\in A^{(2)}$, so \n$$\n\\varphi(x) = \\sum\\limits_{i \\geq m } \\varphi(a_i) \\sum_{j=0}^{i} \\frac {c_{ij}E^{2(i-j)} 2^j}{2 ^{\\lfloor \\frac i 4\\rfloor}} = \\sum_{i \\geq m} \\sum_{ j \\geq \\lfloor \\frac i 4\\rfloor}^{i} \\frac {b_{ij}E^{2(i-j)} 2^j}{2 ^{\\lfloor \\frac i 4\\rfloor}} + \\sum_{i \\geq m} \\sum_{0 \\leq j < \\lfloor \\frac i 4\\rfloor} E^h\\frac {b_{ij}E^{2(i-j)-h} 2^j}{2 ^{\\lfloor \\frac i 4\\rfloor}}.\n$$ \nSimilar to the argument in $(3)$, it is amount to find $h_0$ such that whenever $m>h_0$, $i \\geq m$ and $0 \\leq j < \\lfloor \\frac i 4\\rfloor$, we have $\\lfloor (i -j)-\\frac h 2 \\rfloor + j \\geq \\lfloor \\frac i 4 \\rfloor $ and $2(i-j)-h \\geq m +1$. It is enough to choose $h_0 > 2(h+2)$. \n\\end{proof}\n\n\nIf $A$ is a ring then we denote by ${\\rm M} _d (A)$ the set of $d\\times d$-matrices with entries in $A$.\n\n\\begin{proposition}\\label{prop-desecnt} Let $Y \\in {\\rm M}_d (A^{(2)}_{\\max})$ so that $E^ h Y = B \\varphi (Y) C$ with $B$ and $C$ in ${\\rm M}_d (A ^{(2)})$ then $Y$ is in ${\\rm M}_d (A ^{(2)}[\\frac 1 p])$. \n\\end{proposition}\n\\begin{proof} First, we claim that there is a constant $s$ only depends on $h$, such that the entries of $p^s Y$ is in $\\widetilde S$. By $(1)$ of Lemma~\\ref{lem-auxiliaryrings}, entries of $E ^ h Y$ are in $\\widetilde S$. So for each entry $a$ of $Y$, we can write $E^h a = \\sum \\limits_{i = 0}^\\infty a_i \\frac{ E^{pi}}{p^i}$ with $a_i \\in A ^{(2)}$. It is clear that $E^h p ^h a = a' + E^h \\sum\\limits_{i \\geq h} a_j \\frac {E^{pi-h }}{p ^i} $ so that $a' \\in A^{(2)}$. Therefore, $a' \\in {\\textnormal{Fil}}^h A^{(2)} = E^h A ^{(2)}$ by Corollary \\ref{cor-filtration-shape}. So write $a' = E^h b$, we have $ p ^h a = b' + \\sum\\limits_{i \\geq h} a_j \\frac {E^{pi-h }}{p ^i}$. In particular, we see that $p ^{2h} a \\in \\widetilde S$, this proves our claim. When $p=2$, then we may repeat the above argument and we can assume $p^s Y$ is in ${\\rm M} _d (\\widehat{S})$.\n\nLet $R=\\widetilde S$ when $p>2$ and $R=\\widehat{S}$ when $p=2$, then we may assume $Y$ is inside ${\\rm M} _d (R)$. Then we claim there is another constant $r$ only depends on $h$, such that for each entry $a$ of $Y$, there is a sequence $\\{b_i\\}_{i\\geq 1}$ in $A^{(2)}$ such that we have $a - \\sum\\limits_{i = 0} ^m b _i E^ i \\in {\\textnormal{Fil}} ^{m +1} R$. Note that once this is known, we will have $\\sum\\limits_{i = 0} ^m b _i E^ i$ converges to an element $b$ in $A^{(2)}$, and $a-b=0$ since it is in ${\\textnormal{Fil}} ^{m} R$ for all $m\\in \\mathbb N$. \n\nSo it remains to show our claim. When $p>2$, let $h_0$ be the integer in $(3)$ of Lemma~\\ref{lem-auxiliaryrings}, then it is easy to show there is a constant $r$ only depends on $h_0$ (so only on $h$) and sequence $\\{b_i\\}_{i=1}^{h_0}$ such that for each entry $a$ of $Y':=p^rY$, we have\n$$\na - \\sum\\limits_{i = 0} ^{h_0} b _i E^ i \\in {\\textnormal{Fil}} ^{h_0 +1} R.\n$$\nNow we show our claim by induction, assume for each entry $a$ in $Y'$, there is a sequence $\\{b_i\\}_{i=1}^{m}$ such that,\n$$\na - \\sum\\limits_{i = 0} ^{m} b _i E^ i \\in {\\textnormal{Fil}} ^{m +1} R.\n$$\nfor some $m \\geq h_0$. So we can write $Y'$ as\n$$\n\\sum_{i = 0}^m Y_i E^i + Z_{m+1}, \n$$\nwith $Y_i \\in {\\rm M}_d (A^{(2)})$ and $Z_{m+1} \\in {\\rm M}_d ({\\textnormal{Fil}} ^{m +1} R)$. Writing $X_{m}= \\sum_{i = 0}^m Y_i E^i$, then $E^h Y' = B \\varphi (Y') C$ implies \n$$\nE^hZ_{m+1} = B\\varphi(X_m)C -E^hX_m + B\\varphi(Z_{m+1})C.\n$$\nBy $(3)$ in Lemma~\\ref{lem-auxiliaryrings}, we have $B\\varphi(Z_{m+1})C = A_{m+1} + E^h B_{m+1}$, with $A_{m+1} \\in {\\rm M}_d (A^{(2)})$ and $B_{m+1} \\in {\\rm M}_d ({\\textnormal{Fil}} ^{m +2} R)$. One can check $B\\varphi(X_m)C -E^hX_m + A_{m+1} \\in {\\rm M}_d ({\\textnormal{Fil}}^{h +m +1 } A^{(2)} )$, so $B\\varphi(X_m)C -E^hX_m + A_{m+1}=E^{h+m+1} Y_{m+1}$ with $Y_{m+1} \\in {\\rm M}_d (A^{(2)})$. And we have $Y - \\sum_{i = 0}^{m+1} Y_i E^i = B_{m+1} \\in {\\rm M}_d ({\\textnormal{Fil}} ^{m +2}R)$ as required.\n\nAt last when $p=2$. We know we can assume $Y$ is inside ${\\rm M}_d (\\widehat{S})$. Then repeat the above arguments by replacing $(3)$ in Lemma~\\ref{lem-auxiliaryrings} with $(5)$, we can also prove our claim.\n\\end{proof}\n\n\\subsection{The ring \\texorpdfstring{$A^{(2)}_{\\st}$}{A(2)st}}\\label{subsec-Ast} We assume that $R = \\O _K$ in the following two subsections. \nFor our later use for semi-stable representations, we construct $A^{(2)}_{\\st}$ as the following: Define $\\varphi$ on $W(k)[\\![x, \\mathfrak y]\\!]$ by $\\varphi(x)= x^p$ and $\\varphi (\\mathfrak y ) = (1+\\mathfrak y)^p -1$ and set $w = \\frac{\\mathfrak y}{ E}$. Set $ A^{(2)}_{\\st}: = W(k)[\\![x,\\mathfrak y ]\\!]\\{w\\}_\\delta^\\wedge$ where $\\wedge$ means $(p, E)$-completion. Similarly, we define $A^{(3)}_{\\st}=W(k)[\\![x,\\mathfrak y, \\mathfrak z ]\\!]\\{\\frac{\\mathfrak y}{E},\\frac{\\mathfrak z}{E}\\}^\\wedge_{\\delta}$, with the $\\delta$-structure on $W(k)[\\![x,\\mathfrak y, \\mathfrak z ]\\!]$ given by $\\delta(x)=0$, $\\varphi(\\mathfrak y)=(\\mathfrak y+1)^p-1$ and $\\varphi(\\mathfrak z)=(\\mathfrak z+1)^p-1$. Define $A^{(2)}_{\\st,\\max}$ to be the $p$-adic completion of $W(k)[\\![x, \\mathfrak y]\\!][w, \\frac E p , \\gamma_i (w), i \\geq 0].$ It is clear that for any $f \\in A^{(2)}_{\\st ,\\max}$ can be written uniquely $a = \\sum\\limits_{i= 0}^\\infty f_i \\gamma_i (w) $ with $f_i \\in \\Omax$ and $f_i \\to 0$ $p$-adically. For any subring $B\\subset A^{(2)}_{\\st , \\max}[\\frac 1 p]$, we set ${\\textnormal{Fil}} ^i B : = B \\cap E^i A^{(2)}_{\\st , \\max}[\\frac 1 p]$ and $D_w $ the $p$-adic completion of $\\O_K [\\gamma_i(w), i \\geq 0]$. \n\nIt turns out that $A^{(2)}$ and $A^{(2)}_{\\st}$ share almost the same properties by replacing $z$ with $w$. \nSo we summarize all these properties in the following: \n\\begin{proposition}\\label{prop-Ast-properties}\n\\begin{enumerate}\n \\item One can extend Froebnius from $A$ to $A^{(2)}_{\\st, \\max}$. \n \\item There exists an embedding $\\iota : A^{(2)}_{\\st} \\hookrightarrow A^{(2)}_{\\st , \\max}$ so that $\\iota$ commutes with Frobenius. \n \\item $A^{(2)}_{\\st} \\cap E ^ i A^{(2)}_{\\st , \\max}[\\frac 1 p] = E A^{(2)} _{\\st}$. \n \\item $A ^{(2)}_{\\st}\/E \\simeq D_w = A ^{(2)}_{\\st , \\max}\/ {\\textnormal{Fil}} ^1 A^{(2)}_{\\st , \\max}.$\n \\item $A^{(2)}_{\\st}$ is closed in $A^{(2)}_{\\st, \\max}$. \n \\item $A^{(2)}_{\\st}$ and $A^{(3)}_{\\st}$ are flat over $A$, and in particular they are bounded. \n \\item Proposition \\ref{prop-desecnt} holds by replacing $A^{(2)}_{\\max}$ and $A^{(2)}$ by \n $A^{(2)}_{\\st} $ and $A^{(2)}_{\\st, \\max}$ respectively. \n\\end{enumerate}\n\n\\end{proposition}\n\\begin{proof}\nAll previous proof applies by noting the following difference \n$$\\varphi(w)= \\varphi ( \\frac {\\mathfrak y}{E}) = c ^{-1} \\frac {1}{p} \\sum_{i =1}^p \\binom{p}{i} \\mathfrak y ^i= c ^{-1 }\\sum _{i =1}^{p-1} \\mathfrak y ^i\\binom{p}{i}\/ p + c^{-1}\\frac{E^p w^p}{p}. $$\nAlso $\\delta (\\mathfrak y) = \\sum\\limits _{i =1}^{p-1} \\mathfrak y ^i\\binom{p}{i}\/ p$ always contains $\\mathfrak y$-factor and this is a key input for the analogy of Lemma \\ref{lem:gamma(z)-polynomial-in-E\/p}. \n\nFor the boundedness of $A^{(3)}_{\\st}$, we have \n$$ \nW(k)[\\![x,\\mathfrak y, \\mathfrak z]\\!]\/(p,E)\\simeq (\\O_K\/p)[\\![\\bar{\\mathfrak y}, \\bar{\\mathfrak z}]\\!]\n$$ so $\\{\\mathfrak y, \\mathfrak z\\}$ form a $(p,E)$-complete regular sequence, and by \\cite[Proposition 3.13]{BS19}, $A^{(3)}_{\\st}$ is also $A$-flat, and this implies $A^{(3)}_{\\st}$ is bounded by (2) in Lemma 3.7 of $loc.cit.$. \n\\end{proof}\n\nNote that $A^{\\ho 2}= W(k) [\\![x, y]\\!]\\subset W(k)[\\![x,\n \\mathfrak y]\\!]$ via $y = x(\\mathfrak y +1)$ or equivalently $\\mathfrak y = \\frac y x -1 $. It is clear that this inclusion is a map of $\\delta$-rings. By the universal property of prismatic envelope to construct $A^{(2)}$, the inclusion induces a map of prisms $\\alpha: A^{(2)}\\to A^{(2)}_{\\st}$. Since $z= xw$, we easily see that $A^{(2)}_{\\max} \\subset A^{(2)}_{\\st, \\max}$. So $A^{(2)}\\subset A^{(2)} _{\\st}$ via $\\alpha$. We will see that $A^{(2)}$ (resp. $A^{(2)}_{\\st}$) is the self product of $A$ in category $X_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ (resp. $(X, M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\text{log}}}$) in \n \\S \\ref{subsec-pris-crystal} and \\S \\ref{sec-logprismandsemistablereps}. Then the existence of $\\alpha: A^{(2)} \\to A^{(2)}_{\\st}$ can be explained by the universal property of self product. See \\S\\ref{sec-logprismandsemistablereps} for details. \n\nTo simplify our notation, let $B^{(2)}_{\\st}$ (resp. $B^{(3)}_{\\st}$, $B^{(2)}$, $B ^{(3)}$) be the $p$-adic completion of $ {A^{(2)}_{\\st}} [\\frac 1 E]$ (resp. $A^{(3)}_{\\st}[\\frac 1 E]$, $A^{(2)} [\\frac 1 E]$, $A^{(3)}[\\frac 1 E]$). \n\\begin{lemma}\\label{lem-intersection} \\begin{enumerate}\n\\item $ A^{(i)}_{\\st} \\subset B^{(i)}_{\\st}\\subset B^{(i)}_{\\st}[\\frac 1 p]$ and $ A^{(i)} \\subset B ^{(i)} \\subset B ^{(i)}[\\frac 1 p]$ for $i = 2, 3$. \n \\item $B^{(2)}_{\\st} \\cap {A^{(2)}_{\\st}} [\\frac 1 p] = A^{(2)}_{\\st}$ and $B ^{(2)} \\cap {A^{(2)}} [\\frac 1 p] = A^{(2)}$. \n\\end{enumerate}\n\\end{lemma}\n\\begin{proof} Here we only prove the case $A^{(2)}$ while the proofs for $A^{(2)}_{\\st}$, $A^{(3)}$ and $A^{(3)}_{\\st}$ are almost the same. \n\nBy Proposition \\ref{prop-key-property}, $A^{(2)}$ is a subring of $A^{(2)} _{\\max}\\subset K_0 [\\![x, z]\\!]$. So $A^{(2)}$ and hence $A^{(2)} [\\frac 1 E]$\nis an integral domain. Then $B^{(2)} $ has no $p$-torsion: Assume that $x \\in B^{(2)}$ so that $p x = 0 $. Suppose that $x_n \\in A^{(2)} [\\frac 1 E]$ so that $x \\equiv x _n \\mod p ^n$. Then $p x_n \\equiv 0 \\mod p ^n A^{(2)} [\\frac 1 E]$. Since $A^{(2)} [\\frac 1 E]$ is domain, $x_{n}\\equiv 0 \\mod p ^{n -1}$. Hence $x = 0 $. As $B^{(2)}$ has no $p$-torsion, we see that $B^{(2)}\\subset B^{(2)}[\\frac 1 p]$. \nTo see the natural map $A^{(2)} \\to B^{(2)}$ is injective, it suffices to show that $A^{(2)} \/ p A^{(2)} $ injects to $ A^{(2)} \/ pA^{(2)} [\\frac 1 u]= B ^{(2)}\/ pB^{(2)}$. Clearly, this is equivalent to that $A^{(2)} \/ pA^{(2)}$ has no $u$-torsion. Note that $A^{(2)}$ is obtained by taking prismatic envelope of $A^{\\ho 2}= W(k)[\\![x, z]\\!]$ for the ideal $ I = (z)$. As mentioned before, we can apply \\cite[Prop. 3.13]{BS19} to our situation. So $A^{(2)}$ is flat over $A$ and hence $A^{(2)} \/ p A^{(2)}$ has no $u$-torsion as desired. \n\n\nNow we can regard $B^{(2)}$ and $A^{(2)} [\\frac 1 p]$ as subrings of $B^{(2)}[\\frac 1 p]$. In particular, $ B^{(2)} \\cap A^{(2)} [\\frac 1 p ]$ makes sense and contains $A^{(2)}$. For any $x\\in B^{(2)} \\cap A^{(2)} [\\frac 1 p ]$, if $x\\not \\in A^{(2)}$ but $p x \\in A^{(2)}$. Then the image of $y = px $ inside $A^{(2)}\/ p A^{(2)}$ is nonzero but the image of $y$ in $B ^{(2)}\/ p B ^{(2)}$ is zero. This contradicts to that $A^{(2)} \/ p A^{(2)} $ injects to $B^{(2)}\/ p B^{(2)}$. So such $x$ can not exist and we have $B ^{(2)} \\cap {A^{(2)}} [\\frac 1 p] = A^{(2)}$ as required. \\end{proof}\n\n\nBy \\cite[Lem. 3.9]{BS19}, any prism $(B, J)$ admits its perfection $(B,J)_{\\perf}=(B_{\\perf}, JB _{\\perf})$. \n\\begin{remark}\nIn \\cite{BS19}, the underlying $\\delta$-ring of $(B,J)_{\\perf}$ is denoted by $(B_{\\infty},JB_{\\infty})$, and $B_{\\perf}$ is defined as the direct perfection of $B$ in the category of $\\delta$-rings. In this paper, we write $B_{\\perf}$ as the $(p,J)$-adic completion of $\\mathrm{colim}_{\\varphi} B$, which also coincides with the derived $(p, I)$-completion of $\\mathrm{colim}_{\\varphi} B$ (cf. Lemma 3.9 of $loc.cit.$).\n\\end{remark}\n\n\\begin{lemma}\\label{lem-perfisflat}\nWe have $(A^{(2)})_{\\perf}$ and $(A^{(2)}_{\\st})_{\\perf}$ are $A$-flat.\n\\end{lemma}\n\\begin{proof} We have seen that $A^{(2)}$ is $A$-flat via $i_1$. And it is easy to see $\\varphi$ on $A$ is flat. Since $i_1$ is a $\\delta$-map, so we have $\\varphi^n\\circ i_1 =i_1 \\circ \\varphi^n$ which is flat. So $\\mathrm{colim}_\\varphi A^{(2)}$ is flat over $A$. In particular, we will have $A_{\\perf}$ is $(p , E)$-complete flat over $A$. Now since $A$ is Noetherian, by \\cite[Tag 0912]{stacks-project}, we have $(A^{(2)})_{\\perf}$ is $A$-flat. The proof for $(A^{(2)}_{\\st})_{\\perf}$ is the same. \n\\end{proof}\n\n\\subsection{Embedding \\texorpdfstring{$A^{(2)}$}{A(2)} and \\texorpdfstring{$A^{(2)}_{\\st}$}{A(2)st} to \\texorpdfstring{$\\Ainf$}{Ainf}}\\label{subsec-embedding}\nLet $\\Ainf=W(\\O_{\\mathbb C_p}^\\flat)$, then there is a surjection $\\theta: \\Ainf \\to \\O_{\\mathbb C_p}$ and $\\Ker\\theta=(E)$. And let $\\BdR^+$ be the $\\ker\\theta$-adic completion of $\\Ainf[\\frac{1}{p}]$.\n\n\\begin{definition}\nLet $\\A_{\\max}$ be the $p$-adic completion of the $\\Ainf$-subalgebra of $\\BdR^+$ generated by $E\/p$. \n\\end{definition}\nIt can be easily seen that $\\varphi(E\/p):=\\varphi(E)\/p\\in A_{\\mathrm{cris}}\\subset \\A_{\\max}$ is well-defined and it extends the Frobenius structure on $\\Ainf$ to an endomorphism on $\\Amax$.\n\nLet $\\{\\varpi_n\\}_{n\\geq 0}$ be a compatible system of $p^n$-th roots of $\\varpi_0=\\varpi$ and $\\{\\zeta_n\\}_{n\\geq 0}$ be a compatible system of $p^n$-th roots of 1. Write $\\varpi^\\flat : = \\{\\varpi_n\\}_{n\\geq 0}, \\zeta^\\flat : = \\{\\zeta_n\\}_{n\\geq 0}\\in \\O_{\\mathbb C_p}^\\flat$ and let $u=[\\varpi^\\flat ]$, $\\epsilon=[\\zeta^\\flat]$, $v=\\epsilon u$ and $\\mu=\\epsilon-1$ be elements inside $\\Ainf$. We can regard $W(k)[\\![x,y]\\!]$ as a subring of $\\Ainf$ via $x \\mapsto u$ and $y\\mapsto v$. Consider $z' = \\frac{u-v}{E}\\in \\Ainf [\\frac 1 E]$. Since $u -v = u (\\epsilon -1)$ is clearly inside $\\Ker (\\theta )$ and $ \\Ker (\\theta) = E \\Ainf$, we conclude that $ z' \\in \\Ainf$. Hence we have a natural map (of $\\delta$-rings) $\\iota_A : \\widetildeA^{(2)} \\to \\Ainf$ via $z\\mapsto z'$, which naturally extends to $\\iota _A : A^{(2)}\\to \\Ainf$ because $(p, E)$-topology of $A^{(2)}$ matches with the weak topology of $\\Ainf$. Similarly, we have map of $\\delta$-rings $\\iota_{\\st}: A^{(2)}_{\\st} \\to A_{\\inf}$ via $ x \\mapsto u$ and $\\mathfrak y \\mapsto \\epsilon-1$ and $w\\mapsto \\frac{\\epsilon-1}{E}$. \n\\begin{remark}\\label{rem-embedding-depend} Once we know that $A^{(2)}$ is self-product of $A$ inside $X_{\\mathlarger{\\mathbbl{\\Delta}}}$ with $X= \\Spf (\\O_K)$ as explained in \\S \\ref{subsec-pris-crystal}. The map $\\iota_A$ can be constructed as the following: First we fix an embedding $A\\to \\Ainf$ by sending $x \\mapsto u = [\\varpi^\\flat]$. Then $A\\to \\Ainf$ by $x \\to v= \\epsilon u$ is another map of prisms. By universal property of $A^{(2)}$, these two maps extends to a map $\\iota_A : A^{(2)} \\to \\Ainf$. Clearly, the map $\\iota_A : A^{(2)} \\to \\Ainf$ depends on choice of ${\\varpi}^\\flat = (\\varpi_n)_{n \\geq 0}$ and ${\\zeta}^\\flat = (\\zeta_n)_{n \\geq 0}$. Also $\\iota_A$ is a special case of $\\iota^{(2)}_\\gamma$ defined by \\eqref{equ-diagram-prisms} in \\S\\ref{subsec-pris-crystal-proof}. Indeed if $\\gamma ([w^\\flat])= [\\zeta^\\flat][w ^\\flat]$ then $\\iota_A = \\iota^{(2)}_{\\gamma}$. Similarly comment also applies to $\\iota_{\\st}$. \n\\end{remark}\n\n\n\\begin{proposition}\nThere is a unique embedding \n$\n\\begin{tikzcd}\nA_{\\max}^{(2)} \\arrow[r, hookrightarrow] & \\A_{\\max}\n\\end{tikzcd}\n$ such that\n$$\n\\begin{tikzcd}\nW(k)[\\![x,y]\\!] \\arrow[r, hookrightarrow]\\arrow[d, hookrightarrow] & \\Ainf \\arrow[d, hookrightarrow] & \\\\\nA_{\\max}^{(2)} \\arrow[r, hookrightarrow] & \\A_{\\max} \\arrow[r, hookrightarrow] & \\BdR^+\n\\end{tikzcd}\n$$\ncommutes. Furthermore, ${\\textnormal{Fil}} ^ i \\BdR^+ \\cap A^{(2)}_{\\max}= {\\textnormal{Fil}} ^i A^{(2)}_{\\max}$. The same result holds when $A^{(2)}_{\\max}$ is replaced by $A^{(2)}_{\\st , \\max}$. \n\\end{proposition}\n\\begin{proof} In the following, we only treat the case of $A^{(2)}_{\\st , \\max}$ while the proof of $A^{(2)}_{ \\max}$ is the same by noting that $z= u w$ in $A_{\\inf}$. \n\nThe uniqueness is clear. To show the existence of the embedding, it is enough to show $\\gamma_i(w)\\in\\Amax$ for all $i\\geq 1$. \n\nIt is a well-known fact that $\\Amax$ is isomorphic to the $p$-adic completion of $\\Ainf[\\frac{u^e}{p}]$, and $\\Amax[1\/p]$ is a Banach $\\mathbb Q_p$-algebra, which is the completion of $\\Ainf[1\/p]$ under the norm $\\lvert \\cdot \\rvert_{p^{-1}}$ such that\n$$\n\\lvert x \\rvert_{p^{-1}} = \\sup_{n} \\{p^{-n}\\lvert x_n \\rvert_{\\O_{C}^\\flat}\\}\n$$\nwhere $x=\\sum_{n\\gg 0} [x_n]p^n \\in \\Ainf[1\/p]$. And we have for $x\\in \\Amax[1\/p]$, $x\\in \\Amax$ if and only if $\\lvert x \\rvert_{p^{-1}} \\leq 1$. Moreover $\\lvert \\cdot \\rvert_{p^{-1}}$ is multiplicative. So now it is enough to show for $x=\\gamma_i(w)$ considered as an element inside $ \\Amax[1\/p]$, we have $\\lvert x^{p-1} \\rvert_{p^{-1}}\\leq 1$. To show this, we have by \\cite[Proposition 3.17]{BMS1}, $\\xi:=\\mu\/\\varphi^{-1}(\\mu)$ is a generator of $\\Ker\\theta$ with $\\mu = \\epsilon-1$. In particular, $w=\\mu\/E = a \\varphi^{-1} (\\mu) \\in \\Ainf$ with $a\\in \\Ainf^\\times$. \nAnd we can check $\\overline{w}^{p-1} = c \\overline{u}^e$ inside $\\O_C^\\flat=\\Ainf\/p\\Ainf$, with $c$ a unit. So $w^{p-1}=au^e+bp$ with $a,b\\in \\Ainf$, and\n$$\nx^{p-1}=\\frac{(au^e+bp)^i}{(i!)^{p-1}}.\n$$\nUsing the fact $v_p(i!)< \\frac{i}{p-1}$, one can show each term in the binomial expansion on the right hand side of the equation has $\\lvert \\cdot \\rvert_{p^{-1}}$-norm less or equal to $1$, so in particular, $\\lvert x^{p-1} \\rvert_{p^{-1}}\\leq 1$.\n\nTo prove that ${\\textnormal{Fil}} ^i \\BdR^+ \\cap A ^{(2)}_{\\st, \\max} = {\\textnormal{Fil}} ^i A ^{(2)}_{\\st , \\max}$, it suffices to show that $E \\BdR ^+ \\cap A ^{(2)}_{\\st, \\max}[\\frac 1 p] = E A ^{(2)}_{\\st , \\max}[\\frac 1 p]$. By Proposition \\ref{prop-key-property}, we reduces to prove that the map $$\\theta : D_w= A ^{(2)}_{\\st , \\max}[\\frac 1 p ]\/E \\to \\BdR^+ \/ E= \\mathbb C_p$$ is injective. Let $f(w) = \\sum_{i \\geq 0} a_i \\gamma_i (w)\\in \\Ker \\theta$ with $a_i \\in \\O_K$ limits to $0$ $p$-adically. Then $f(w_0) = 0$ with $w_0: = \\theta (w)= \\theta (\\frac{\\epsilon-1}{E}) \\in \\mathbb C_p$. Note $v_p (w_0)\\geq \\frac{1}{p-1}$ because it is well-known $ \\frac{\\epsilon-1}{\\varphi^{-1}(\\epsilon) -1}$ is another generator of kernel $\\theta: A_{\\inf} \\to \\O_{\\mathbb C_p}$ and then \n$v_p (w_0) = v_p (\\theta (\\varphi^{-1} (\\epsilon)-1))= \\frac{1}{p-1}$. \nSince we are aiming to show that $f= 0$, without loss of generality, we can assume that $K$ contains $p_1= \\sqrt[p-1]{p}$. Note that \n$v_p (i !)\\leq \\frac{1}{p-1}$, we conclude that $\\frac{w_0}{p_1}$ is a root of $f(p_1 w)$ which is in $\\O_K\\langle w\\rangle$. By Weierstrass preparation theorem, $w_0$ is algebraic over $K$ unless $f=0$. By Lemma below, $w_0: = \\theta (w) \\in \\mathbb C_p$ is transcendental over $K$ and hence $f= 0$. \n\\end{proof}\n\\begin{lemma}\\label{lem-transcendental}\n$w_0 = \\theta (\\frac{\\epsilon-1}{E})$ is transcendental over $K$. \n\\end{lemma}\n\\begin{proof}\nIf $w_0$ is contained in an algebraic extension $L$ over $K$, we define $L_{0,\\infty}=\\bigcup_n L(\\varpi_n)$. For $g\\in G_{L_{0,\\infty}}$, we will have \n$$\n\\theta(g(\\frac{\\epsilon-1}{E}))=g(w_0)=w_0=\\theta(\\frac{\\epsilon-1}{E}).\n$$\nSince $G_{L_{0,\\infty}}$ fix $E$, $\\theta(\\frac{g(\\epsilon-1)-(\\epsilon-1)}{E})=0$. This implies $g(\\epsilon-1)-(\\epsilon-1)\\in {\\textnormal{Fil}}^2\\BdR^+$. Recall for $t=\\log \\epsilon$, $t-(\\epsilon-1)\\in {\\textnormal{Fil}}^2\\BdR^+$, so we have $g(t)-t \\in {\\textnormal{Fil}}^2\\BdR^+$. But this can't be true. Since $L_{0,\\infty}$ can only contain finitely many $p^n$-th roots of $1$, for $g\\in G_{L_{0,\\infty}}$, $g(t)=c(g)t$ satisfying $c(g) \\in \\mathbb Q_p$ and $c(g)\\neq 1$. This implies $g(t)-t = (c(g)-1)t \\in {\\textnormal{Fil}}^1\\BdR^+ \\setminus {\\textnormal{Fil}}^2\\BdR^+$.\n\\end{proof}\n\n\\begin{corollary}\\label{cor-inj}\nThe natural maps $\\iota _ A : A ^{(2)} \\to A_{\\inf}$ and $\\iota_{\\st} : A^{(2)}_{\\st} \\to A_{\\inf}$ are injective. \n\\end{corollary}\n\nTo summarize, we have the following commutative diagram of rings inside $\\BdR^+$:\n$$\n\\begin{tikzcd}\nA^{(2)} \\arrow[d, hookrightarrow] \\arrow[r, hookrightarrow] & A^{(2)}_{\\st}\\arrow[r, hookrightarrow] \\arrow[d, hookrightarrow] & \\Ainf \\arrow[d, hookrightarrow] \\\\\nA^{(2)}_{\\max} \\arrow[r, hookrightarrow] & A^{(2)}_{\\st, \\max} \\arrow[r, hookrightarrow] & \\Amax. \n\\end{tikzcd}\n$$\n\n \\section{Application to semi-stable Galois representations}\nIn this section, we assume that $R= \\O_K$. We explain how to use the period ring $A^{(2)}$ and $A^{(2)}_{\\st}$ to understand lattices in crystalline and semi-stable representations. Roughly speaking, we are going to use $A^{(2)}$ and $A^{(2)}_{\\st}$ to replace $\\widehat{\\mathcal R}$ in the theory of $(\\varphi , \\hat G)$-modules developed in \\cite{liu-notelattice}. \n\nLet $K _\\infty =\\bigcup_{n = 1}^\\infty K (\\varpi _n )$, $G_\\infty: = {\\rm Gal } (\\overline K \/ K_\\infty)$ and $G_K: = {\\rm Gal } (\\overline K \/ K)$. Recall that $A= \\mathfrak S = W(k)[\\![u]\\!]$. Let $S $ be the $p$-adic completion of $ W(k) [\\![u , \\frac{E^i}{i !}, i \\geq 1]\\!]$, which is the PD envelope of $W(k)[u]$ for the ideal $(E)$. It is clear that $S\\subset \\Omax$. We define $\\varphi$ and ${\\textnormal{Fil}} ^i$ on $S$ induced that from those on $\\Omax$, in particular, ${\\textnormal{Fil}} ^i S = S \\cap E ^i \\Omax[\\frac 1 p]$. Note that $A$ embeds to $\\Ainf$ via $u \\mapsto [\\varpi^\\flat ]$ is not stable under $G_K$-action but only on $G_\\infty$-action. For any $g \\in G_K$, define ${\\underline{\\varepsilon}} (g)= \\frac{g(u)}{u}$. It is clear that ${\\underline{\\varepsilon}} (g) = \\epsilon ^{a(g)}$ with $a(g) \\in \\mathbb Z_p$. We define \\emph{two} differential operators $N_S$ and $\\nabla_S$ on $S$ by $N_S(f) = \\frac{d f}{du}u$ and $\\nabla_S (f) = \\frac{ df }{du}$. We need $\\nabla_S$ to treat crystalline representations. \n\n\\subsection{Kisin module attached to semi-stable representation}\\label{subsec-Kisin-st} Fix $h \\geq 0$, \na \\emph{Kisin module of height $h$} is a finite free $A$-module $\\mathfrak{M} $ with a semi-linear endomorphism $\\varphi_{\\mathfrak{M}}: \\mathfrak{M} \\to \\mathfrak{M}$ so that $\\coker (1 \\otimes \\varphi_\\mathfrak{M})$ is killed by $E^h$, where $1 \\otimes \\varphi_{\\mathfrak{M}} : \\mathfrak{M} ^* : = A \\otimes _{\\varphi, A}\\mathfrak{M} \\to \\mathfrak{M} $ is linearization of $\\varphi_\\mathfrak{M}$. Note here we are using classical setting of Kisin modules used in \\cite{liu-notelattice} but it is good enough for this paper. The following summarizes the results on Kisin modules attached to $G_K$-stable $\\mathbb Z_p$-lattices in semi-stable representations. The details and proofs of these facts can be found in \\cite{liu-notelattice}. \n\nLet $T$ be a $G_K$-stable $\\mathbb Z_p$-lattice inside a semi-stable representation $V$ of $G_K$ with Hodge-Tate weights in $\\{0, \\dots , h\\}$. Let $D: = D^*_{\\st} (V)= \\mathrm{Hom}_{\\mathbb Q_p, G_K} (V , B_{\\st})$ be the filtered $(\\varphi , N)$-module attached to $V$ and $D_K : = K \\otimes_{K_0} D$. Then there exists a unique Kisin module $\\mathfrak{M} : = \\mathfrak{M} (T) $ of height $h$ attached to $T$ so that \n\\begin{enumerate}\n \\item $\\mathrm{Hom}_{\\varphi , A} (\\mathfrak{M} , \\Ainf)\\simeq T|_{G_\\infty}$. \n \\item There exists an $S$-linear isomorphism \n $$\\iota_S : S [\\frac 1 p] \\otimes _{\\varphi, A}\\mathfrak{M} \\simeq D \\otimes _{W(k)} S $$ so that $\\iota_S$ is compatible with $\\varphi$ on the both sides. \n\\item $\\iota_S$ also induces an isomorphism ${\\textnormal{Fil}}^h (S [\\frac 1 p] \\otimes _{\\varphi, A}\\mathfrak{M}) \\simeq {\\textnormal{Fil}} ^h (D\\otimes_{W(k)} S) $. The filtration on the both sides are defined as following: \n\\[{\\textnormal{Fil}}^h (S [\\frac 1 p] \\otimes _{\\varphi, A}\\mathfrak{M}): =\\left \\{ x \\in S [\\frac 1 p] \\otimes _{\\varphi, A}\\mathfrak{M}| (1\\otimes \\varphi_\\mathfrak{M} (x)) \\in {\\textnormal{Fil}} ^h S[\\frac 1 p ] \\otimes_A \\mathfrak{M} \\right \\}. \\] \nTo define \nfiltration on ${\\mathcal D} : = S \\otimes _ {W(k)} D$, we first extend the monodromy operator $N_{\\mathcal D}$ (resp. $\\nabla_{\\mathcal D}$) on $D$ to ${\\mathcal D}$ by $N_{{\\mathcal D}}= 1 \\otimes N_D + N_S \\otimes 1$ (resp. $\\nabla_{\\mathcal D} = 1 \\otimes N_D + \\nabla_S \\otimes 1$). Then we define ${\\textnormal{Fil}} ^i {\\mathcal D}$ by induction: set ${\\textnormal{Fil}}^0 {\\mathcal D} = {\\mathcal D}$ and \n\\[ {\\textnormal{Fil}} ^i{\\mathcal D}: = \\{x \\in {\\mathcal D}| N_{{\\mathcal D}}(x) \\in {\\textnormal{Fil}}^{i-1}{\\mathcal D}, f_\\varpi (x) \\in {\\textnormal{Fil}} ^i D_K\\}\\]\nwhere $f_\\varpi : {\\mathcal D} \\to D_K$ is induced by $S\\to \\O_K$ via $u \\mapsto \\varpi. $\n\\end{enumerate}\n\\begin{remark}[Griffith transversality]\\label{rem-GT} From the construction of ${\\textnormal{Fil}} ^i {\\mathcal D}$, we see that $N_{{\\mathcal D}} ({\\textnormal{Fil}} ^i{\\mathcal D}) \\subset {\\textnormal{Fil}}^{i -1}{\\mathcal D}$. This property is called Griffith transversality. \n\nWe only use $\\nabla_{\\mathcal D}$ when $N_D = 0$, that is, when $V$ is crystalline. In this case, it is clear that $N_{\\mathcal D} = u \\nabla_{{\\mathcal D}}$. So it is clear that $\\nabla_{\\mathcal D} ({\\textnormal{Fil}} ^i{\\mathcal D}) \\subset {\\textnormal{Fil}} ^{i-1}{\\mathcal D}$. \n\\end{remark}\nFor ease of notations, we will write $N = N_{\\mathcal D}$ and $\\nabla = \\nabla_{\\mathcal D}$ in the following. \nLet $T^\\vee: = \\mathrm{Hom}_{\\mathbb Z_p} (T , \\mathbb Z_p)$ and $V ^\\vee : = T^\\vee \\otimes_{\\mathbb Z_p}\\mathbb Q_p$ denote the dual representations. \nThen there exists an $A_{\\inf}$-linear injection \n\\begin{equation}\\label{eqn-iota-A}\n\\iota_\\mathfrak{M}: A_{\\inf} \\otimes _A \\mathfrak{M} \\to T ^\\vee \\otimes_{\\mathbb Z_p} \\Ainf, \n\\end{equation}\nwhich is compatible with $G_\\infty$-actions ($G_\\infty$ acts on $\\mathfrak{M}$ trivially) and $\\varphi$ on both sides. Applying $S \\otimes_{\\varphi, A}$ and using $\\iota _S: =S \\otimes_{\\varphi, A} \\iota_\\mathfrak{M} $, we obtain the following commutative diagram \n$$\n\\xymatrix@C=5em{ \\Acris[\\frac 1 p] \\otimes_{\\varphi , A}\\mathfrak{M} \\ar[d]_\\wr ^{\\Acris \\otimes_S \\iota _S} \\ar[r] ^{S \\otimes_{\\varphi, A} \\iota _\\mathfrak{M}} & V^\\vee \\otimes_{\\mathbb Z_p} \\Acris \\ar@{=}[d]\\\\ \\Acris \\otimes_{W(k)} D \\ar[r]^{\\alpha} & V^\\vee \\otimes _{\\mathbb Z_p} \\Acris}\n$$\nwhere the second row $\\alpha$ is built by the classical comparison $$B_{\\st} \\otimes_{K_0} D^*_{\\st}(V) \\simeq V^\\vee \\otimes_{\\mathbb Q_p} B_{\\st}, $$\nand $\\alpha$ is $G_K$-stable on the both sides. The left side of $\\alpha$ is defined by \n\\[ \\forall x \\in D, \\forall g \\in G_K, g (x) = \\sum_{i = 0}^\\infty N^i (x) \\gamma_i (\\log ({\\underline{\\varepsilon}}(g))) \\]\nTherefore, if we regard $\\mathfrak{M}^* : = A \\otimes_{\\varphi, A} \\mathfrak{M}$ as an $A$-submodule of \n$ V^\\vee \\otimes_{\\mathbb Z_p} \\Acris$ via injection $\\iota^* : = S \\otimes_{\\varphi, A} \\iota_A$, one can show that: \n\\begin{equation}\\label{eqn-G-action}\n\\forall g \\in G_K, x \\in \\mathfrak{M}^*, g(x) = \\sum_{i = 0}^\\infty N_{\\mathcal D}^i (x) \\gamma_i (\\log ({\\underline{\\varepsilon}}(g))). \n\\end{equation}\n When $V$ is crystalline, or equivalently, $N_D = 0$, we have (\\cite[\\S8.1]{LL2021comparison})\n \\begin{equation}\\label{eqn-G-action-2}\n\\forall g \\in G_K, x \\in \\mathfrak{M}^*, g(x) = \\sum_{i = 0}^\\infty \\nabla_{\\mathcal D}^i (x) \\gamma_i (u {\\underline{\\varepsilon}}(g)). \n\\end{equation}\n\n\\subsection{Descent of the \\texorpdfstring{$G_K$}{GK}-action}\\label{subsec-G-image}\n\nLet us first discuss the $G_K$-action on $\\mathfrak{M} \\subset T ^\\vee \\otimes_{\\mathbb Z_p}\\Ainf$ via $\\iota_\\mathfrak{M}$ in \\eqref{eqn-iota-A} in more details. \nWe select an $A$-basis $e_1, \\dots , e_d$ of $\\mathfrak{M}$ so that $\\varphi (e_1, \\dots, e_d)= (e_1, \\dots , e_d )\\mathfrak A$ with $\\mathfrak A \\in {\\rm M}_d (A)$. Then there exists a matrix $B\\in {\\rm M}_d (A)$ so that $\\mathfrak A B = B \\mathfrak A = E^h I_d$. For any $g \\in G_K, $ let $X_g$ be the matrix so that \n\\[ g (e_1, \\dots , e_d) = (e_1, \\dots , e_d) X_g. \\]\nIn this section, we are interested in where the entries of $X_g$ locates. \n\\begin{theorem}\\label{Thm-1}The entries of $X_g$ are in $A^{(2)}_{\\st}$. If $V$ is crystalline and $g(u)- u = Ez$ then $X_g \\in {\\rm M}_{d} (A^{(2)}). $\n\\end{theorem}\n\nFirst, it is well-known that $ W (\\mathbb C_p^\\flat) \\otimes_{\\Ainf} \\iota_\\mathfrak{M} $ is an isomorphism. So $X_g\\in {\\rm M}_d (W (\\mathbb C_p^\\flat))$. Since $G_K$-actions and $\\varphi$-commutes, we have $$ \\mathfrak A \\varphi(X_g)= X_g g (\\mathfrak A) .$$ \n Define $${\\textnormal{Fil}} ^h \\mathfrak{M} ^* : = \\{ x \\in \\mathfrak{M}^* | (1 \\otimes \\varphi_{\\mathfrak{M}}) (x) \\in E^h \\mathfrak{M}\\}. $$\nSince $\\mathfrak{M}$ has $E$-height $h$, it is easy to show that ${\\textnormal{Fil}} ^h \\mathfrak{M}^*$ is a finite free $A$-module and ${\\textnormal{Fil}}^h {\\mathcal D}$ is generated by ${\\textnormal{Fil}} ^h \\mathfrak{M}^*$. \n\nTo be more precise, let $\\{e^*_i : =1 \\otimes e_i, i =1 , \\dots , d\\}$ be an $A$-basis of $\\mathfrak{M}^*$. It is easy to check that $(\\alpha_1, \\dots , \\alpha_d)= (e_1^* , \\dots , e_d^*) B$ is an $A$-basis of ${\\textnormal{Fil}} ^h \\mathfrak{M}^*$, and it is also an $S[\\frac 1 p]$-basis of ${\\textnormal{Fil}} ^h {\\mathcal D}$. \nSo for any $g \\in G_K$, we have $g (\\alpha_j) = \\sum\\limits_{i = 0}^\\infty N ^i (\\alpha_j) \\gamma _i (\\log ({\\underline{\\varepsilon}} (g))) $. By Griffith transversality in Remark \\ref{rem-GT}: \n$N ({\\textnormal{Fil}} ^i {\\mathcal D})\\subset {\\textnormal{Fil}}^{i-1}{\\mathcal D}, $\n we have, \n \\begin{equation}\\label{eqn-action-g}\n g (\\alpha_j)= \\sum_{i = 0}^h N ^i (\\alpha_j) E^i \\gamma _i (\\frac {\\log ({\\underline{\\varepsilon}} (g))}{E}) + \\sum_{i > h}^\\infty N^i (\\alpha_j) \\gamma_i(E) (\\frac{\\log ({\\underline{\\varepsilon}} (g))}{E})^i. \n \\end{equation}\nSince $N^i (\\alpha_j) E^i \\in {\\textnormal{Fil}} ^h {\\mathcal D}$, $\\gamma_i (E)$ in $\\Omax$ and $w ^n \\to 0$ inside $A^{(2)}_{\\st, \\max}$, we see that $g (\\alpha_1, \\dots , \\alpha_d) = (\\alpha_1 , \\dots , \\alpha_d) Y_g $ with $Y_g \\in A^{(2)}_{\\st , \\max}[\\frac 1 p ]. $\n\nIn the case that $V$ is crystalline, using \\eqref{eqn-G-action-2}, we have \n$$g (\\alpha_j)= \\sum_{i = 0}^h \\nabla ^i (\\alpha_j) E^i \\gamma _i (\\frac {u{\\underline{\\varepsilon}} (g)}{E}) + \\sum_{i > h}^\\infty \\nabla^i (\\alpha_j) \\gamma_i(E) (\\frac{u{\\underline{\\varepsilon}} (g)}{E})^i $$\n\\emph{If $g $ is chosen so that $ g (u)-u = Ez$} then, a similar argument can shows that $g (\\alpha_1, \\dots , \\alpha_d) = (\\alpha_1 , \\dots , \\alpha_d) Y^\\nabla_g $ with $Y^\\nabla_g \\in A^{(2)}_{\\max}[\\frac 1 p]. $\n\nNow $g (e_1^*, \\dots , e_d^*) = (e_1^*, \\dots, e_d ^*) \\varphi (X_g)$. Using similar arguments, we see that $\\varphi(X_g)$'s entry are in $A^{(2)}_{\\st , \\max}[\\frac 1 p ]$ and $A^{(2)}_{\\max}[\\frac 1 p]$ respectively. Since $(\\alpha_1 , \\dots , \\alpha_d) = (e_1^*, \\dots, e_d^*)B$, we conclude that $$ \\varphi(X_g) g (B) = B Y_g.$$ \nUsing the formula that $\\mathfrak A \\varphi (X_g) = X_g g (\\mathfrak A)$ and $\\mathfrak A B = B \\mathfrak A = E^h I_d$, we conclude that \n$Y_g = (\\frac{g (E)}{E}) ^h X_g$. Write $r= \\frac{g (E)}{E}$. We claim that $r$ is a unit in $A^{(2)}_{\\st}$. Indeed, \n$\\frac{g(E)}{E}= \\frac{E (u \\epsilon ^{a(g)})}{E(u)}= \\sum\\limits_{i =0}^e E^{(i)} (u) \\frac{ u ^i (\\epsilon^{a(g)}-1)^i}{E i !}$ is again inside $A_{\\st}^{(2)}$, where $E^{(i)}$ means the $i$-th derivative of $E$. And it is easy to show $g(E)$ is also a distinguished element $A_{\\st}^{(2)}$, so by \\cite[Lemma 2.24]{BS19}, $r$ is a unit. Similarly, when $g(u)-u=Ez$, we will have $r=\\frac{g (E)}{E}\\in (A^{(2)})^\\times$. Hence \n\\begin{equation}\\label{eqn-key-eqn}\nE^h X_g = r ^{-h} \\mathfrak A \\varphi (X_g) g (B).\n\\end{equation}\n\n\n\nNow we can apply Proposition \\ref{prop-desecnt} and Proposition \\ref{prop-Ast-properties} (5) to the above formula, we conclude that for $g\\in G_K$ (resp. $g\\in G_K$ such that $g(u)-u=Ez$ and $V$ is crystalline), we have $X_g$ has entries in $A^{(2)}_{\\st}[\\frac 1 p]$ (resp. $A^{(2)}[\\frac 1 p]$). \n\nTo complete the proof of Theorem \\ref{Thm-1}, it suffices to show that entries $X_g$ are in $A^{(2)}_{\\st}$ (resp. $A^{(2)}$). Unfortunately, the proof to remove ``$\\frac 1 p$\" is much harder, which needs \\S \\ref{subsec-phi-tau} and \\S \\ref{subsec-pris-crystal-proof}. For the remaining of this subsection, we only show that the proof of Theorem \\ref{Thm-1} can be reduced to the case that $g = \\tilde \\tau$ for a special selected $\\tilde \\tau \\in G_K$. \n\nLet $L = \\bigcup\\limits_{n =1}^\\infty K_{\\infty} (\\zeta_{p ^n})$, $K_{1^\\infty}: = \\bigcup_{n =1}^\\infty K (\\zeta_{p ^n})$, $\\hat G : = \\Gal(L \/K) $ and $H_K : = \\Gal (L \/ K _\\infty)$. \nIf $p > 2$ then it is known that $\\hat G \\simeq \\Gal (L\/ K_{1 ^\\infty}) \\rtimes H_K $ with \n$\\Gal (L\/ K_{1 ^\\infty}) \\simeq \\mathbb Z_p$. Let $\\tau$ be a topological generator of $\\Gal (L\/ K_{1 ^\\infty}) $. We have $\\tau (u) = \\epsilon^a u$ with $a\\in \\mathbb Z_p ^\\times$. Without loss of generality, we may assume that $\\tau (u) = \\epsilon u$. If $p=2$ then we can still select $\\tau \\in \\hat G $ so that $\\tau (u)= \\epsilon u$ and $\\tau, H_K$ topologically generate $\\hat G$. Pick $\\tilde \\tau \\in G_K$ a lift of $\\tau$. Clearly, we have $\\tilde \\tau (u ) - u = E z$. \n\n\\begin{proposition}\\label{thm-1prime} \nFor $g = \\tilde \\tau, $ the entries of $X_g$ are in $A^{(2)}_{\\st}$, and if further $V$ is crystalline, then $X_g \\in {\\rm M}_{d} (A^{(2)}).$\n\\end{proposition}\n\\begin{lemma}\\label{lem-equivalenceofthm}\nProposition ~\\ref{thm-1prime} is equivalent to Theorem~\\ref{Thm-1}.\n\\end{lemma}\n\\begin{proof}\nSince $\\hat{G}$ is topologically generated by $\\tau$ and $H_K$. So $G_K$ is topologically generated by $G_\\infty$ and $\\tilde{\\tau}$. And we have $\\tau(u)-u=(\\epsilon-1)u=Ez$. Now if $X_{\\tilde{\\tau}}$ has coefficient in $A^{(2)}_{\\st}$ and $X_g=I_d$ for all $g\\in G_\\infty$ then to show that $X_g \\in A^{(2)}_{\\st}$ for all $g \\in G_K$, it suffices to show that $X_{\\tilde \\tau^{p ^n }}$ converges to $I_d$ inside ${\\rm M}_d (A^{(2)}_{\\st})$. Since $A^{(2)}_{\\st}$ is closed in $A^{(2)}_{\\st, \\max}$ by Proposition \\ref{prop-Ast-properties} (5), it suffices to show that $X_{\\tilde \\tau^{p ^n}}$ converges inside $A^{(2)}_{\\st, \\max}$. Since $X_g= (\\frac{E}{g (E)})^r Y_g$ and $Y_g$ is defined by \\eqref{eqn-action-g}, we easily check that $X_{\\tilde \\tau^{p ^n}}$ converges to $I_d$ in $A^{(2)}_{\\st,\\max}$ by using that ${\\underline{\\varepsilon}}(\\tilde \\tau^{p ^n})$ converges to $0$ in $(p , \\epsilon-1)$-topology. The proof for the crystalline case is similar by replacing $A^{(2)} _{\\st}$ with $A^{(2)}$.\n\\end{proof}\nSo it remains to prove Proposition ~\\ref{thm-1prime} to complete the proof of Theorem~\\ref{Thm-1}. We will prove Proposition~\\ref{thm-1prime} in \\S\\ref{subsec-pris-crystal-proof}. Briefly speaking, for $g = \\tilde \\tau$, we have shown that the linearization of the $g$-action defines a $\\varphi$-equivariant isomorphism:\n$$\nf_g: \\mathfrak{M}\\otimes_{A,\\iota_g} A_{\\st}^{(2)}[\\frac{1}{p}] \\simeq \\mathfrak{M}\\otimes_{A} A_{\\st}^{(2)}[\\frac{1}{p}]\n$$\nof $A_{\\st}^{(2)}[\\frac{1}{p}]$-modules, and since $g(u)- u = Ez$ and $V$ is crystalline, $f_g$ defines a $\\varphi$-equivariant isomorphism:\n$$\nf_g: \\mathfrak{M}\\otimes_{A,\\iota_g} A^{(2)}[\\frac{1}{p}] \\simeq \\mathfrak{M}\\otimes_{A} A^{(2)}[\\frac{1}{p}]\n$$\nof $A^{(2)}[\\frac{1}{p}]$-modules. Here $\\iota_g: A \\to A^{(2)}_{\\st}$ (resp. $\\iota_g: A \\to A^{(2)})$) is defined by $u \\to g(u)$. On the other hand, by \\cite[Theorem 5.6]{wu2021galois}, we will see the $g$-action on $T^\\vee \\otimes W(\\mathbb C_p^\\flat)$ also descent to a $\\varphi$-equivariant morphism $c$ of $B^{(2)}$-modules, and recall that $B^{(2)}$ the is $p$-adic completion of $A^{(2)} [\\frac 1 E]$. Then by comparing $c$ and $f_g$ using the technique developed in \\S\\ref{subsec-phi-tau}, we will deduce Proposition~\\ref{thm-1prime} from Lemma~\\ref{lem-intersection}.\n \n\\begin{remark}\\label{rem-inputofWu} Our original strategy to prove Theorem \\ref{Thm-1} is to show $A^{(2)}_{\\st} [\\frac 1 p] \\cap W ({\\mathbb C^\\flat_p}) = A^{(2)}_{\\st}$ (resp. $A^{(2)} [\\frac 1 p] \\cap W (\\O^\\flat_{\\mathbb C_p}) = A^{(2)}$). This is equivalent to that $ A^{(2)}\/ p , A^{(2)}_{\\st}\/ p$ injects in $\\mathbb C_p^\\flat$. Unfortunately, it does not work out though we can show $ \\widetilde A^{(2)}\/ p , \\widetilde {A^{(2)}_{\\st}}\/ p$ injects in $\\mathbb C_p^\\flat$.\n\n\\end{remark}\n\n\n\\subsection{Relation to \\texorpdfstring{$(\\varphi, \\hat G)$}{(phi,Ghat)}-modules}\\label{subsec-phiGhatmodules} In this subsection, we show that the base ring $\\widehat{{\\mathcal R}}$ for the theory of $(\\varphi, \\hat G)$-modules can be replaced by $A^{(2)}_{\\st}$. To this end, this builds a new theory of $(\\varphi, \\hat G)$-modules with new base ring $A^{(2)}_{\\st}$. Since the idea of this new theory is almost the same as that of the old one, We will use \\emph{classical} to indicate we are using the theory over $\\widehat {\\mathcal R}$. For example, when we say classical $(\\varphi, \\hat G)$-module, it means a $(\\varphi , \\hat G)$-module over $\\widehat {\\mathcal R}$. \nRecall $L = \\bigcup\\limits_{n =1}^\\infty K_{\\infty} (\\zeta_{p ^n})$, $\\hat G : = \\Gal(L \/K) $ and $H_K : = \\Gal (L \/ K _\\infty)$. Let $\\mathfrak m $ be the maximal ideal of $\\O_{\\mathbb C_p}^\\flat$ and set $I_+ = W(\\mathfrak m)$ so that $\\Ainf\/ I _+ = W(\\bar k)$. For any subring $B\\subset \\Ainf$ set $ I_+ B = B \\cap I_+$. Let $t = \\log \\epsilon$, $t ^{(i)} = t ^{r(i)} \\gamma_{\\tilde q(i)}(\\frac{t ^{p-1}}{p})$ where $ i = (p-1) \\tilde q (i) + r(i)$ with $ 0 \\leq r(i)}[rd]^{i_{2, n}} & A^{\\ho 2} \/ (p , J^{(2)} ) \\\\ {\\breve A} \\ar[u]\\ar[r]^-{\\breve i_{2, n} } & A^{\\ho 2} \/ (p , J ^{(2)})^n\\ar[u]} $$\nHere $\\breve i_{2, n}= \\breve i_2 \\mod (p , J ^{(2)})^n$ and $\\overline{i}_2$ is induced by $A \\to A\/(p, E) \\simeq A^{\\ho 2}\/ (p , J ^{(2)}) $. Since $\\breve i_2 (u) = y = x+ (y -x)$ and $\\breve i_2 (t_i) = s_i = t _i + (s_i - t_i)$, we see that the above (outer) diagram commutes. Since $A$ is formally \\'etale over $\\breve A $ by $(p, u)$-adic topology, we conclude that there exists a unique map $i_{2, n} : A \\to A^{\\ho 2} \/ (p , J^{(2)})^n$ so that the above diagram commutes. Since $A ^{\\ho 2}$ is $(p, J ^{(2)} )$-complete, there uniquely exists $i _2 : A \\to A^{\\ho 2}$ which extends $\\breve i_2$. To see $i_2$ is compatible with $\\delta$-structures. it suffices to show that $\\varphi \\circ i _2 = i_2 \\circ \\varphi$. But both of $\\varphi \\circ i _2$ and $ i_2 \\circ \\varphi$ extend $ \\breve A \\overset \\varphi \\to \\breve A \\to A^{\\ho 2}$. Again by formally \\'etaleness of $A$ over $\\breve A$, we see that $\\varphi \\circ i _2 = i_2 \\circ \\varphi$. Hence we obtain a map $ 1 \\otimes i_2: A \\otimes _{\\mathbb Z_p }A \\to A^{\\ho 2}$. Define $\\theta^{\\otimes 2}: A\\otimes_{\\mathbb Z_p} A \\to R$ via $\\theta^{\\otimes 2} (a \\otimes b)= \\theta (a) \\theta (b)$. By the construction of $i _2$, we have the following commutative diagram\n\\[ \\xymatrix{ A \\otimes _{\\mathbb Z_p} A \\ar[r] ^{1 \\otimes i _2} \\ar[d]^{\\theta ^{\\otimes 2 }} & A ^{\\ho 2}\\ar[d] \\\\ R \\ar[r]^- \\sim & A^{\\ho 2}\/ J ^{(2)} }\\]\nLet $\\widehat {A ^{\\otimes 2}} $ be the $(p , \\ker (\\theta ^{\\otimes 2}))$-completion of $A^{\\otimes 2}: = A \\otimes_{\\mathbb Z_p} A$. \nHence $1 \\otimes i_2$ induces a map $\\hat i_2$ from the $\\widehat {A ^{\\otimes 2}}$ to $ A ^{\\ho 2}$ because $A ^{\\ho 2}$ is clearly $(p , J ^{(2)})$-complete. To treat $A^{\\ho 3}$, we construct $ i _3: A \\to A ^{\\ho 3}$ by extending $\\breve i _3: A \\to A^{\\ho 3}$ by sending $u \\mapsto w $ and $t _j \\mapsto r_j$. The same method shows that $i_3$ is compatible with $\\delta$-structure and we obtain a map $1 \\otimes i _2 \\otimes i_3 : A^{\\otimes 3} \\to A^{\\ho 3}$ with $A ^{\\otimes 3}: A \\otimes_{\\mathbb Z_p} A \\otimes_{\\mathbb Z_p}A$. Similarly, we obtain a natural map $\\hat i _3 : \\widehat {A ^{\\otimes 3}} \\to A ^{\\ho 3} $. \n\\begin{lemma} For $s= 2, 3$, $\\hat i_s : \\widehat {A ^{\\otimes s}} \\to A ^{\\ho s}$ are isomorphisms. \n\\end{lemma}\n\\begin{proof}\nWe need to construct an inverse of $\\hat i_s$. We only show for $\\hat i _2$ and the proof for $\\hat i _3$ is the same. \nLet $g: A^{\\ho 2} \\to \\widehat {A ^{\\otimes 2}}$ be the $A$-linear map by sending $y -x \\mapsto 1 \\otimes u - u \\otimes 1 $ and $s_j - t _j \\mapsto 1 \\otimes t_j - t_j \\otimes 1$. Clearly $g$ is well-defined because $ 1 \\otimes u - u \\otimes 1$ and $1 \\otimes t_j - t_j \\otimes 1$ are in $\\Ker (\\theta ^{\\otimes 2})$. Since $i_2 (u) = y$ and $i_2 (t_j) = s_j$, $\\hat i _2 \\circ g $ is identity on $A ^{\\ho 2}$. Now it suffices to show that $h : = g \\circ \\hat i_2 $ is identity. Write $K = (p , \\Ker (\\theta ^{\\otimes 2}))$. Note that we have map $ A \\otimes_{\\mathbb Z_p} \\breve A \\to \\widehat {A ^{\\otimes 2}} \\overset h \\to \\widehat {A ^{\\otimes 2}}$ induced by $h $ which we still call it $\\breve h $. \nNow we have the following commutative diagram \n$$\\xymatrix@C=55pt{ A\\otimes_{\\mathbb Z_p} A \\ar[r] ^-{\\mod K }\\ar@{-->}[rd]^{\\mod K^n}_{h_{ n} } & (A\\otimes_{\\mathbb Z_p} A ) \/ K \\\\ A \\otimes_{\\mathbb Z_p}{\\breve A} \\ar[u]\\ar[r]^-{\\breve h \\mod K^n } & (A \\otimes_{\\mathbb Z_p} A )\/ K ^n\\ar[u]}, $$\nwhere $h_n$ is induced by $h \\mod K^n$. \nWe see that both $ h_n $ and $\\mod K^n$ on the dashed arrow can make the diagram commute. Then by the formal \\'etaleness of $A$ over $\\breve A$, we conclude that $h_n = \\mod K^n$ and $h$ is the identity map. \n\\end{proof}\n\\begin{proposition}\\label{prop-selfproduct} $A^{(2)}$ and $A^{(3)}$ is self-product and triple product of $A$ in $X_{\\mathlarger{\\mathbbl{\\Delta}}}$. \n\\end{proposition}\n\\begin{proof} \nIn the following, we only treat the case of $A^{(2)}$ while the proof for $A^{(3)}$ is the same. \nWe need to prove that for any $B = (B,J)$ in $X_{\\mathlarger{\\mathbbl{\\Delta}}}$, \n$$\n\\mathrm{Hom}_{X_{\\mathlarger{\\mathbbl{\\Delta}}}^{\\mathrm{opp}}}(A^{(2)},B)=\\mathrm{Hom}_{X_{\\mathlarger{\\mathbbl{\\Delta}}}^{\\mathrm{opp}}}(A, B ) \\times \\mathrm{Hom}_{X_{\\mathlarger{\\mathbbl{\\Delta}}}^{\\mathrm{opp}}}(A,B).\n$$\nBy the above lemma, we have natural maps $A \\otimes_{\\mathbb Z_p} A \\to \\widehat{A^{\\otimes 2}} \\simeq A^{\\ho 2}$. Combined with natural map $A^{\\ho 2}\\to A^{(2)}$ as $A^{(2)}$ is the prismatic envelope of $ A^{\\ho 2}$ for the ideal $J^{(2)}$, we have map $\\alpha : A \\otimes_{\\mathbb Z_p} A \\to A^{(2)}$ which is compatible with $\\delta$-structures. Then $\\alpha$ induces map $$\n\\beta: \\mathrm{Hom}_{X_{\\mathlarger{\\mathbbl{\\Delta}}}^{\\mathrm{opp}}}(A^{(2)},B)\\to \\mathrm{Hom}_{X_{\\mathlarger{\\mathbbl{\\Delta}}}^{\\mathrm{opp}}}(A, B ) \\times \\mathrm{Hom}_{X_{\\mathlarger{\\mathbbl{\\Delta}}}^{\\mathrm{opp}}}(A,B).\n$$\nTo prove the surjectivity of $\\beta$, given $f_i \\in \\mathrm{Hom}_{X_{\\mathlarger{\\mathbbl{\\Delta}}}}(A, B )$ for $i = 1,2$, we obtain a map $f _1 \\otimes f _2: A \\otimes_{\\mathbb Z_p} A \\to B$. It is clear that $(f_1 \\otimes f_2) (\\Ker (\\theta ^{\\otimes 2})) \\subset J$. Since $B$ is $(p, J)$-derived complete, $f \\otimes f_2$ extends to a map $ f _1 \\ho f_2 : \\widehat{A ^{\\otimes 2}}\\simeq A ^{\\ho 2} \\to B$ which is compatible with $\\delta$-structures, Hence $f_1 \\ho f _2$ is a morphism of $\\delta$-algebra. Finally, by the universal properties of prismatic envelope, $f _1 \\ho f_2 $ extends to a map of prisms $ f_1 \\ho_{{\\mathlarger{\\mathbbl{\\Delta}}}} f_2: A^{(2)} \\to B$ as required. \n\nFinally, we need to show that $\\beta$ is injective. It suffices to show that $A$-algebra structure map $i _1 : A\\to A^{(2)} $ and $i'_2: A \\overset{i_2}{\\to } A^{\\ho 2} \\to A^{(2)}$ both are injective. \nSince all rings here are $(p, E)$-complete integral domains, it suffices to check that $i_1 , i_2' \\mod (p, E)$ are injective. By Proposition \\ref{prop-key-property}, we see that $i_1 \\mod (p, E)$ is $R\/ pR \\to R\/pR [\\{\\gamma _i (z_j)\\}] $, so it is injective. By the construction $i'_2$ and $i_2$, we see that $i'_2 \\mod (p, E)$ is the same as $A\/(p, E) \\to A ^{\\ho 2}\/ (p , J ^{(2)}) \\to A^{(2)} \/(p, E)$, which is same as $R\/ pR \\to R\/pR [\\{\\gamma _i (z_j)\\}] $. So it is injective. \n\\end{proof}\n\n\\begin{remark}\\label{rem-Astprelog}\n When $R=\\O_K$ is a complete DVR with perfect residue field $k$, we know a priori, the self-product $A^{(2)}$ of $(A,(E))$ in $X_{\\mathlarger{\\mathbbl{\\Delta}}}$ can be constructed as the prismatic envelope of $(A,(E))\\to (B,I)$, where $B$ is the $(p,E(u),E(v))$-adic completion of $W(k)[\\![u]\\!] \\otimes_{\\mathbb Z_p} W(k)[\\![v]\\!]$ and $I$ is the kernel of the map:\n $$\n B \\to A\/(E)\\otimes_{R} A\/(E)=R.\n $$\n On the other hand, $W(k)$ is formally \\'etale over $\\mathbb Z_p$ for the $p$-adic topology, so for all $(C,J)\\in X_{\\mathlarger{\\mathbbl{\\Delta}}}$, the map $W(k)\\to R \\to C\/J$ lifts uniquely to a map $W(k) \\to C$. In particular, for all $(C,J)\\in X_{\\mathlarger{\\mathbbl{\\Delta}}}$, $C$ has a natural $W(k)$-algebra structure. So when we construct the self-product, we can also consider $A^{(2)}$ as the prismatic envelope of $(A,(E))\\to (C,J)$, where $C$ is the $(p,E(u),E(v))$-adic completion of $A\\otimes_{W(k)} A$ and $J$ is the kernel of the map:\n $$\n C \\to A\/(E)\\otimes_{R} A\/(E)=R.\n $$\n We have $C\\simeq W(k)[\\![u,v]\\!]$, $J=(E(u),u-v)$ and $A^{(2)}=W(k)[\\![u,v]\\!]\\{\\frac{u-v}{E}\\}^\\wedge_\\delta$.\n\\end{remark}\n\n\\begin{definition}\\label{def-Fcrystal}\n\\begin{enumerate}\n \\item \nA prismatic crystal over $X_{\\mathlarger{\\mathbbl{\\Delta}}}$ in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules (resp. $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}[1\/I]^\\wedge_p$-modules) is a finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-module (resp. $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}[1\/I]^\\wedge_p$-module) $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ such that for all morphisms $f: (A, I) \\to (B, J)$ of prisms, it induces an isomorphism:\n$$\nf^\\ast \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}},A} := \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((A,I))\\otimes_A B \\simeq \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}},B} := \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((B,J))\n$$\n$$\n(resp.\\quad f^\\ast \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}},A} := \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((A,I))\\otimes_{A[1\/I]^\\wedge_p} B[1\/I]^\\wedge_p \\simeq \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}},B} := \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((B,J))).\n$$\n\n\\item A prismatic $F$-crystal over $X_{\\mathlarger{\\mathbbl{\\Delta}}}$ of height $h$ in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules is a prismatic crystal $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules together with a $\\varphi_{\\O_{\\mathlarger{\\mathbbl{\\Delta}}}}$-semilinear endomorphism $\\varphi_{\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}}$ of the $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-module $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}: \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}} \\to \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ such that the cokernel of the linearization $\\varphi^\\ast \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}} \\to \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ is killed by $\\mathcal{I}^h$.\n\\end{enumerate}\n\n\\end{definition}\n\n\\begin{proposition}\\label{prop-cover-final-object}\nIf the sheaf represented by $(B,I)$ in $\\Shv(X_{\\mathlarger{\\mathbbl{\\Delta}}})$ covers the final object $\\ast$ in $\\Shv(X_{\\mathlarger{\\mathbbl{\\Delta}}})$, i.e., for any $(C,J)$ in $X_{\\mathlarger{\\mathbbl{\\Delta}}}$, there is a $(P, J)$ lies over $(B,I)$ and covers $(C,J)$. Also assume that the self-coproduct $B^{(2)}$ and self-triple-coproduct $B ^{(3)}$ of $(B,I)$ are inside $X_{{\\mathlarger{\\mathbbl{\\Delta}}}}$, i.e., they are bounded. Then a prismatic crystal $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ over $X$ in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules is the same as a finite projective module $\\mathfrak{M}$ over $B$ together with a descent data $\\psi: \\mathfrak{M}\\otimes_{i_1,B} B^{(2)}\\simeq \\mathfrak{M}\\otimes_{i_2,B} B^{(2)}$ satisfies the cocycle condition. Here $i _j : B \\to B^{(2)}$ $(j=1,2)$ are the two natural maps.\n\\end{proposition}\n\n\\begin{proof}\nFirst let $\\mathfrak{M}$ be a prismatic crystal in finite projective modules. Define $\\mathfrak{M}= \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((B,I))$, and the descent data comes from the crystal property:\n$$\n\\psi:\\mathfrak{M}\\otimes_{i_1,B} B^{(2)}\\simeq \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((B^{(2)},I)) \\simeq \\mathfrak{M}\\otimes_{i_2,B} B^{(2)}.\n$$\nNow given $(\\mathfrak{M}, \\psi)$, then for any $(C,J)$ in $X_{\\mathlarger{\\mathbbl{\\Delta}}}$, we need to construct a finite projective module over $C$. We choose the $(P, J)$ as in the assumption, let $\\mathfrak{M}_P=\\mathfrak{M} \\otimes_B P$, and consider the following diagram:\n$$\n\\begin{tikzcd}\nC \\arrow[r] & P \\arrow[rr,\"f_1\"] & & P^{(2)}_C \\\\\n & B \\arrow[u] \\arrow[r,\"i_1\"] & B^{(2)} \\arrow[ur,\"f\"] & \\\\\n & & B \\arrow[u,\"i_2\"] \\arrow[r] & P \\arrow[uu,\"f_2\"] \\\\\n & & & C \\arrow[u]\n\\end{tikzcd}\n$$\nHere $(P^{(2)}_C,J)$ is the self-coproduct of $(P,J)$ in the category of prisms over $(C,J)$, and the existence of $(P^{(2)}_C,J)$ is from \\cite[Corollary 3.12]{BS19}, where they also show that $P^{(2)}_C$ is the derived $(p, J)$-completion of $P\\otimes^\\mathbb L_C P$ and $(P^{(2)}_C,J)$ is bounded. As a bounded prism over $(C,J)$, $(P^{(2)}_C,J)$ is naturally inside $X_{\\mathlarger{\\mathbbl{\\Delta}}}$, so $f$ exists by the universal property of $B^{(2)}$. So if we take the base change of $\\psi$ along $f$, we get \n$$\nf^\\ast\\psi: (\\mathfrak{M}\\otimes_{i_1,B} B^{(2)})\\otimes_{B^{(2)},f}P^{(2)}_C \\simeq (\\mathfrak{M}\\otimes_{i_2,B} B^{(2)})\\otimes_{B^{(2)},f}P^{(2)}_C\n$$\nwhich is the same as an isomorphism:\n$$\n\\psi_C: \\mathfrak{M}_{P}\\otimes_{P,f_1}P^{(2)}_C \\simeq \\mathfrak{M}_{P}\\otimes_{P,f_2}P^{(2)}_C.\n$$\nSimilar arguments will show $\\psi_C$ satisfies the cocycle condition. And $\\mathfrak{M}_{P}$ descents to a finite projective module over $C$ by \\cite[Proposition A.12]{ALB}.\n\\end{proof}\n\n\\begin{remark}\nWe want to note that the structures of finite nonempty coproducts in the category of bounded prisms over a prism $(A,I)$ is much simpler compared with the structure of finite nonempty products in the category $(R\/A)_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ (cf. \\cite[Lecture V, Corollary 5.2]{BhaNotes18}).\n\\end{remark}\n\n\\begin{lemma}\\label{lem-AEcoversfinal}\nThe prism $(A,(E))$ defined in \\S\\ref{subsrc-construct-A2} covers the final object $\\ast$ in $\\Shv(X_{\\mathlarger{\\mathbbl{\\Delta}}})$ in the sense of Proposition~\\ref{prop-cover-final-object}. And $A^{(2)}$ and $A^{(3)}$ are bounded.\n\\end{lemma}\n\\begin{proof}\nThe proof is similar to \\cite[Lemma 5.2.8]{ALB}, we need to show for $R$ defined as in \\S\\ref{subsrc-construct-A2}, there exists a quasi-syntomic perfectoid cover of $R$. We will construct this perfectoid cover similar to \\cite[\\S 7.1]{Kim12}.\n\nFirst recall we have $R=\\O_K\\otimes_{W}R_0$, and we fix a compatible system $\\{\\varpi_n\\}_{n\\geq 0}$ of $p^n$-th roots of a uniformizer $\\varpi_0$ of $\\O_K$ inside $E$. Let $\\widehat K_\\infty$ be the $p$-adic completion of $\\cup_n K(\\varpi_n)$, we know $\\widehat K_\\infty$ is perfectoid. Use $\\overline{R}_0[\\![u]\\!]$ to denote $A\/(p)=R\/(\\varpi)=R_0\/(p)[\\![u]\\!]$, and let $\\overline{R}_0[\\![u]\\!]_{\\rm perf}^\\wedge$ to be the $u$-adic completion of the direct perfection of $\\overline{R}_0[\\![u]\\!]$, it can be checked directly that $(\\overline{R}_0[\\![u]\\!]_{\\rm perf}^\\wedge[1\/u],\\overline{R}_0[\\![u]\\!]_{\\rm perf}^\\wedge)$ is a perfectoid affinoid $\\widehat K_\\infty^\\flat$-algebra, by tilt equivalence, there is a corresponded perfectoid affinoid $\\widehat K_\\infty$-algebra. More explicitly, let $\\tilde{R}_\\infty = W(\\overline{R}_0[\\![u]\\!]_{\\rm perf}^\\wedge)\\otimes_{W(\\O_{\\widehat K_\\infty}^\\flat),\\theta} \\O_{\\widehat K_\\infty}$. Then $\\tilde{R}_\\infty$ is naturally an $R$-algebra, and we claim it is a quasi-syntomic cover of $R$.\n\nTo show this, by \\cite[\\S 7.1.2]{Kim12}, we have\n$$\n\\tilde{R}_\\infty = (R_0\\widehat {\\otimes}_{W}\\O_{\\widehat K_\\infty})\\widehat {\\otimes}_{\\mathbb Z_p} \\mathbb Z_p\\langle T_i^{-p^\\infty}\\rangle\n$$\nwhere $T_i \\in R_0$ is any lift of a $p$-basis of $R_0\/(p)$. We have $\\O_K\\to \\O_{\\widehat K_\\infty}$ is a quasi-syntomic cover so by (2) of \\cite[Lemma 4.16]{BMS2}, $R \\to R_0\\widehat {\\otimes}_{W}\\O_{\\widehat K_\\infty}$ is also a quasi-syntomic cover. And we have $S=\\mathbb Z_p\\langle T_i^{-p^\\infty}\\rangle$ is a quasi-syntomic ring, this can be seen by constructing a perfectoid quasi-syntomic covering of it, so by Lemma 4.34 of $loc.cit.$, we have the complex $\\mathbb{L}_{S\/\\mathbb Z_p} \\in D(S)$ has $p$-complete Tor amplitude in $[-1,0]$. In particular, $\\mathbb Z_p \\to \\mathbb Z_p\\langle T_i^{-p^\\infty}\\rangle$ is also a quasi-syntomic cover, so applying (1) in Lemma 4.16 of $loc. cit.$, $R \\to \\tilde{R}_\\infty$ is a quasi-syntomic perfectoid cover.\n\nThe boundedness of $A^{(2)}$ and $A ^{(3)}$ is from (2) in Corollary~\\ref{cor-filtration-shape}.\n\\end{proof}\n\n\\begin{corollary}\\label{cor-crystal-descentdata}\nAssume the the base $X=\\Spf(R)$ satisfies the condition in \\S\\ref{sec-ring-strcuture}, and let $A$, $A^{(2)}$ and $A^{(3)}$ be defined as in \\S\\ref{subsrc-construct-A2}, then a prismatic $F$-crystal $(\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}, \\varphi_{\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}})$ in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules of height $h$ over $X$ is the same as a Kisin module $(\\mathfrak{M},\\varphi_\\mathfrak{M})$ of height $h$ over $A$ with a descent datum\n$$\nf: \\mathfrak{M} \\otimes_{A,i_1} A^{(2)} \\simeq \\mathfrak{M} \\otimes_{A,i_2} A^{(2)}\n$$\nthat compatible with the $\\varphi$-structure and satisfies the cocycle condition over $A^{(3)}$.\n\\end{corollary}\n\n\n\\begin{theorem}(\\cite[Theorem 1.2]{BS2021Fcrystals})\\label{Thm-main-1} \nLet $T$ be a crystalline representation of $G_K$ over a $\\mathbb Z_p$-lattice of Hodge-Tate weights in $[0,h]$, then there is a prismatic $F$-crystal $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}(T)$ over $X_{\\mathlarger{\\mathbbl{\\Delta}}}$ of height $h$ over $X$ such that $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((A,E))$ is the Kisin module associated to $T$. Moreover, the association of $T\\mapsto \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}(T)$ induces an equivalence of the above two categories. \n\\end{theorem}\nWe will prove this theorem in \\S \\ref{subsec-pris-crystal-proof}. \n\n\\begin{remark}\nTheorem~\\ref{Thm-main-1} was first established by Bhatt-Scholze in \\cite[Theorem 1.2]{BS2021Fcrystals}. The harder direction of \\cite[Theorem 1.2]{BS2021Fcrystals} is to show for all $\\mathbb Z_p$-lattices inside crystalline representations of $G_K$, one can attach a prismatic $F$-crystal. Using the theory of $(\\varphi,\\hat{G})$-modules, we have shown in \\S\\ref{subsec-G-image}, given a crystalline representation of $G_K$ over a $\\mathbb Z_p$-lattices $T$, we can attach a Kisin module $\\mathfrak{M}$ and a descent data\\footnote{Strictly speaking, \\S\\ref{subsec-G-image} only constructs an isomorphism but have not checked that it satisfies cocycle condition, which will be proved in \\S \\ref{subsec-pris-crystal-proof}.} \n$$\nf_{\\tilde{\\tau}}: \\mathfrak{M}\\otimes_{A,i_1} A^{(2)}[\\frac{1}{p}] \\simeq \\mathfrak{M}\\otimes_{A,i_2} A^{(2)}[\\frac{1}{p}]\n$$\ncomes from the $\\tau$-action. We just show this is a $\\varphi$-equivariant isomorphism, and we need to show it gives rise to a descent data over $A^{(2)}$. As we have mentioned in Remark~\\ref{rem-inputofWu}, we can not find a direct ring theoretic proof of this. Our idea is to use result of \\cite{wu2021galois} or \\cite[Corollary 3.7]{BS2021Fcrystals}: the underlying Galois representation $T$ gives a descent data over $A^{(2)}[\\frac{1}{E}]^\\wedge_p$. To finish our proof, we need to compare this descent data with $f_{\\tilde{\\tau}}$ over $A^{(2)}[\\frac{1}{E}]^\\wedge_p[\\frac{1}{p}]$. This lead us to develop a ``prismatic\" $(\\varphi,\\tau)$-module theory in the next subsection, where we will have Lemma~\\ref{lem-evaluation-1} and Lemma~\\ref{lem-evaluation-2} to help us compare descent data over $A^{(2)}[\\frac{1}{E}]^\\wedge_p$ and $A^{(2)}[\\frac{1}{E}]^\\wedge_p[\\frac{1}{p}]$ via an evaluation map to $W(\\O_{\\hat{L}}^\\flat)$.\n\\end{remark}\n\n\n\\subsection{\\texorpdfstring{$(\\varphi,\\tau)$}{(phi,tau)}-modules and prismatic \\texorpdfstring{$F$}{F}-crystals}\\label{subsec-phi-tau} In this subsection, we make some preparations to prove Proposition \\ref{thm-1prime} and Theorem \\ref{Thm-main-1}. So we restrict to the case that $R=\\O_K$ is a complete DVR with perfect residue field. \n\\begin{definition}\nAn \\'etale $\\varphi$-module over $A[1\/E]^\\wedge_p$ is a pair $(\\mathcal M, \\varphi_\\mathcal M)$ such that $\\mathcal M$ is a finite free module over $A[1\/E]^\\wedge_p$, and $\\varphi_\\mathcal M$ is an isomorphism\n$$\n\\varphi_\\mathcal M: \\varphi^\\ast \\mathcal M: = A[1\/E]^\\wedge_p\\otimes_{\\varphi , A[1\/E]^\\wedge_p} \\mathcal M \\simeq \\mathcal M\n$$\nof $A[1\/E]^\\wedge_p$-modules. And we define an \\'etale $\\varphi$-module over $A[1\/E]^\\wedge_p[1\/p]$ to be a $\\varphi$-module over $A[1\/E]^\\wedge_p[1\/p]$ such that it is obtained from an \\'etale $\\varphi$-module over $A[1\/E]^\\wedge_p$ by base change.\n\nAn \\'etale $\\varphi$-module over $A[1\/E]^\\wedge_p$ (resp. $A[1\/E]^\\wedge_p[1\/p]$) with descent data is a triple $(\\mathcal M, \\varphi_\\mathcal M, c)$, such that $(\\mathcal M, \\varphi_\\mathcal M)$ is an \\'etale $\\varphi$-module over $A[1\/E]^\\wedge_p$ (resp. $A[1\/E]^\\wedge_p[1\/p]$), and $c$ is an isomorphism\n$$\nc: \\mathcal M \\otimes_{A[1\/E]^\\wedge_p,i_1} B^{(2)} \\simeq \\mathcal M \\otimes_{A[1\/E]^\\wedge_p,i_2} B^{(2)}\n$$\n$$\n(\\text{resp. }c: \\mathcal M \\otimes_{A[1\/E]^\\wedge_p[1\/p],i_1} B^{(2)}[1\/p] \\simeq \\mathcal M \\otimes_{A[1\/E]^\\wedge_p[1\/p],i_2} B^{(2)}[1\/p])\n$$\nthat compatible with the $\\varphi$-structure and satisfies the cocycle condition over $B^{(3)}$ (resp. $B^{(3)}[\\frac 1 p]$). Here for $j=1,2$, $i_j: A[1\/E]^\\wedge_p \\to B^{(2)}$ is the map induced from $i_j: (A,(E)) \\to (A^{(2)},(E))$. \n\\end{definition}\n\n\\begin{remark}\\label{rmk-Wuandevaluation}\nIt is the main result in \\cite{wu2021galois} and \\cite[\\S2]{BS2021Fcrystals} that there is an equivalence of the category of lattices in representations of $G_K$ and the category of prismatic $F$-crystals in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}[1\/I]^\\wedge_p$-modules over $\\O_K$. Also by \\cite[Proposition 2.7]{BS2021Fcrystals}, one can show prismatic $F$-crystals in finite locally free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}[1\/I]^\\wedge_p$-modules is the same as \\'etale $\\varphi$-modules over $A[1\/E]^\\wedge_p$ with descent data. \n\\end{remark}\n\n\nThe aim of this subsection is to use the ideas in \\cite{wu2021galois} and \\cite[\\S 5.5]{KedlayaLiu-relativeII} show that \\'etale $\\varphi$-modules over $A[1\/E]^\\wedge_p$ (resp. $A[1\/E]^\\wedge_p[1\/p]$) with descent data are equivalence to $\\mathrm{Rep}_{\\Z_p}(G_K)$ (resp. $\\mathrm{Rep}_{\\Q_p}(G_K)$). More importantly, for all $\\gamma \\in \\hat{G}$, we will construct an evaluation at $\\gamma$ map\n$$\ne_\\gamma: B^{(2)} \\to W(\\hat{L}^\\flat)\n$$\nand use it to study $\\varphi$-equivariant morphisms between finite free $B^{(2)}$ and $B^{(2)}[1\/p]$-modules. We will see the evaluation at $\\tau$ map will play a crucial role in our proof of Proposition~\\ref{thm-1prime} and the Theorem~\\ref{Thm-main-1} below.\n\n\nRecall in \\S\\ref{subsec-phiGhatmodules}, we define $L = \\bigcup\\limits_{n =1}^\\infty K_{\\infty} (\\zeta_{p ^n})$, $\\hat G : = \\Gal(L \/K) $ and $H_K : = \\Gal (L \/ K _\\infty)$. Moreover, we define $\\widehat K_{1^\\infty}$ to be the $p$-adic completion of $\\cup_{n\\geq 0} K(\\zeta_{p^n})$, and we let $\\hat{L}$ to be the $p$-adic completion of $L$. It is clear that $A[1\/E]_p^\\wedge\\subset W(\\hat L ^\\flat)^{H_K}$.\nRecall the following definition and theorem in \\cite{Caruso-phitau}:\n\n\\begin{theorem}\\label{thm-caruso}\nAn \\'etale $(\\varphi,\\tau)$-module is a triple $(\\mathcal M, \\varphi_{\\mathcal M}, \\hat{G})$ where\n\\begin{itemize}\n \\item $(\\mathcal M, \\varphi_{\\mathcal M})$ is an \\'etale $\\varphi$-module over $A[1\/E]^\\wedge_p$;\n \\item $\\hat{G}$ is a continuous $W(\\hat{L}^\\flat)$-semi-linear $\\hat{G}$-action on $\\hat{\\mathcal M}:=W(\\hat{L}^\\flat)\\otimes_{A[1\/E]^\\wedge_p}\\mathcal M$, and $\\hat{G}$ commutes with $\\varphi_{\\mathcal M}$;\n \\item regarding $\\mathcal M$ as an $A[1\/E]^\\wedge_p$-submodule of $\\hat{\\mathcal M}$, we have $\\mathcal M\\subset \\hat{\\mathcal M}^{H_K}$.\n\\end{itemize} \nThen there is an anti-equivalence of the category of \\'etale $(\\varphi,\\tau)$-modules and $\\mathrm{Rep}_{\\Z_p}(G_K)$, such that if $T$ corresponds to $(\\mathcal M, \\varphi_{\\mathcal M}, \\hat{G})$, then\n$$\nT^\\vee = (\\hat{\\mathcal M}\\otimes_{W(\\hat{L}^\\flat)}W(\\mathbb C_p^\\flat))^{\\varphi=1}.\n$$\n\\end{theorem}\n\nOne of the basic facts used in the theory of \\'etale $(\\varphi,\\tau)$-modules developed in \\cite{Caruso-phitau} is that $\\Gal(\\hat{L}\/\\widehat K_{1^\\infty})\\simeq \\mathbb Z_p$, and we write $\\tau$ to be a topological generator of $\\Gal(\\hat{L}\/K_{1^\\infty})$ determined by $\\tau(\\varpi_n)=\\zeta_{p^n}\\varpi_n$ as the discussion before Corollary \\ref{cor-crystalline}. Also $\\hat{G}$ is topologically generated by $\\tau$ and $H_K$, so in particular, the $\\hat{G}$-action on $\\hat{\\mathcal M}$ is determined by the action of $\\tau$ on $\\mathcal M$ inside $\\hat{\\mathcal M}$. As discussed before, we will provides a direct correspondence of the category of \\'etale $(\\varphi,\\tau)$-modules and the category of \\'etale $\\varphi$-modules over $A[1\/E]^\\wedge_p$ with descent data. Moreover, we will construct an evaluation at $\\tau$ map:\n$$\ne_\\tau: B^{(2)} \\to W(\\hat{L}^\\flat),\n$$\nand show that the $\\tau$-action on $\\mathcal M$ inside $\\hat{\\mathcal M}$ is given by the base change of the descent data along $e_\\tau$. \n\n\\begin{remark}\nIn \\cite[Theorem 5.2]{wu2021galois}, they prove a similar equivalence but for \\'etale $(\\varphi,\\Gamma)$-modules. The theory of \\'etale $(\\varphi,\\Gamma)$-module is defined for the cyclotomic tower $K_{1^\\infty}$ over $K$ while the theory of \\'etale $(\\varphi,\\tau)$-modules is defined using the Kummer tower $K_{\\infty}$. We will use a lot of ideas and results developed in \\cite{wu2021galois} when proving our claims in this subsection. The main difficulty in our situation is that the Kummer tower $K_\\infty$ is not a Galois tower over $K$. To deal with this, we have to use the idea in \\cite[\\S 5.5]{KedlayaLiu-relativeII}. Roughly speaking, we will take the Galois closure $L$ of $K_\\infty$, then prove results over $\\hat{L}$, then descent back to $K_\\infty$ using the fact $\\widehat K_\\infty=\\hat{L}^{H_K}$. \n\nOne should be able to construct the evaluation map in the content of \\cite{wu2021galois} the same way as we define in this subsection. This map will give a more direct correspondence of the descent data and the $\\Gamma$-actions on \\'etale $(\\varphi,\\Gamma)$-modules.\n\\end{remark}\n\nBy \\cite[Lem 3.9]{BS19}, any prism $(B , J)$ admits a map into its perfection $(B_{\\perf}, JB_{\\perf})$. The following theorem (\\cite[Thm 3.10]{BS19}) is the key to understand perfect prisms. \n\\begin{theorem}\\label{thm-perfectprismandperfectoidring}\n$(A,I)\\to A\/I$ induces an equivalence of the category of perfect prisms over $\\O_K$ with the category of integral perfectoid rings over $\\O_K$.\n\\end{theorem}\n \nLet $(A,(E))$ be the Breuil-Kisin prism defined in \\S\\ref{subsrc-construct-A2}, we have\n\\begin{lemma}\\label{lem-perfectionofA} $A_{\\perf}\\simeq W(\\O_{\\widehat K_\\infty}^\\flat)$.\n\\end{lemma}\n\\begin{proof}\nExactly the same as the proof of \\cite[Lemma 2.17]{wu2021galois}\n\\end{proof}\n\n\\begin{lemma}\nLet $\\Perfd_K$ be the category of perfectoid $K$-algebras, then $\\Perfd_{K}$ admits finite non-empty coproducts. \n\\end{lemma}\n\\begin{proof}\nLet $R$ and $S$ be two perfectoid $K$-algebras, it follows from \\cite[Corollary 3.6.18]{KedlayaLiu-relativeI} that the uniform completion $(R\\otimes_K S)^u$ of the tensor product $(R\\otimes_K S)$ is again a perfectoid $K$-algebra, and it is easy to show this is the coproduct of $R$ and $S$ in the category of perfectoid $K$-algebras.\n\\end{proof}\n\nFor $i\\in \\mathbb N_{>0}$, let $(A^{(i)},(E))$ (resp. $(\\Ainf(\\O_{\\hat{L}})^{(i)},(E))$) denote the $i$-th self-coproduct of $(A,(E))$ (resp. $(\\Ainf(\\O_{\\hat{L}}),(E))$) in the category of prisms over $\\O_K$, where $\\Ainf(\\O_{\\hat{L}}):=W(\\O_{\\hat{L}}^\\flat)$. The following is a description of $(A^{(i)})_{\\perf}[1\/E]^\\wedge_p$ and $(\\Ainf(\\O_{\\hat{L}})^{(i)})_{\\perf}[1\/E]^\\wedge_p$. \n\n\\begin{lemma}\\label{lem-Aiperf}\nLet $\\widehat K_\\infty^{(i)}$ (resp. $\\hat{L}^{(i)}$) be the $i$-th self-coproduct of $\\widehat K_\\infty$ (resp. $\\hat{L}$) in $\\Perfd_K$, then $(A^{(i)})_{\\perf}[1\/E]^\\wedge_p \\simeq W((\\widehat K_\\infty^{(i)})^\\flat)$ (resp. $(\\Ainf(\\O_{\\hat{L}})^{(i)})_{\\perf}[1\/E]^\\wedge_p \\simeq W((\\hat{L}^{(i)})^\\flat)$).\n\\end{lemma}\n\\begin{proof}\nWe will only prove the lemma for $(A^{(i)})_{\\perf}[1\/E]^\\wedge_p$, and the case for $(\\Ainf(\\O_{\\hat{L}})^{(i)})_{\\perf}[1\/E]^\\wedge_p $ is similar.\n\nWe use similar arguments as in \\cite[Lemma 5.3]{wu2021galois}. Fix $i$, first we can show $(A^{(i)})_{\\perf}$ is the $i$-th self-coproduct of $(A_{\\perf}, (E))$ in the category of perfect prisms over $\\O_K$, i.e. $(A^{(i)})_{\\perf}=(A_{\\perf})^{(i)}_{\\perf}$. By Theorem~\\ref{thm-perfectprismandperfectoidring}, Lemma~\\ref{lem-perfectionofA} and \\cite[Proposition 2.15]{wu2021galois}, if we let $S=(A^{(i)})_{\\perf}\/E$, then $S[1\/p]$ is the $i$-th self-coproduct of $\\widehat K_\\infty$ in the category of perfectoid $K$-algebras. Now we have \n$$\n(A^{(i)})_{\\perf}[1\/E]^\\wedge_p\\simeq W(S^\\flat)[1\/[\\varpi^\\flat ]]^\\wedge_p=W(S^\\flat[1\/\\varpi^\\flat ])=W((S[1\/p])^\\flat)\\simeq W((\\widehat K_\\infty^{(i)})^\\flat).\n$$\n\\end{proof}\n\n\\begin{remark}\\label{rem-diamonds}\nThere is another way to view $\\widehat K_\\infty^{(i)}$ in terms of diamonds over $\\mathrm{Spd}(K,\\O_K)$ which is used in the proof of \\cite[Lemma 5.3]{wu2021galois}, that there exist a ring of integral elements $\\widehat K_\\infty^{(i),+}$ in $\\widehat K_\\infty^{(i)}$, such that we have \n\\begin{equation}\\label{eq-diamondKi}\n \\Spa(\\widehat K_\\infty^{(i)},\\widehat K_\\infty^{(i),+})^\\diamond \\simeq \\underbrace{\\Spa(\\widehat K_\\infty,\\widehat K_\\infty^{+})^\\diamond \\times_{\\mathrm{Spd}(K,\\O_K)} \\ldots \\times_{\\mathrm{Spd}(K,\\O_K)}\\Spa(\\widehat K_\\infty,\\widehat K_\\infty^{+})^\\diamond}_{i\\text{-copies of } \\Spa(K_\\infty,K_\\infty^{+})^\\diamond}.\n\\end{equation}\nAnd similar results hold for $\\hat{L}$. Using this description and the fact that functor from perfectoid spaces over $\\Spa(K, \\O_K)$ to diamonds over $\\mathrm{Spd}(K, \\O_K)$ is an equivalence, we have $\\hat{L}^{(i)}$ has a natural action of $\\hat{G}^i$ coming from the action on the diamond spectrum. Since $\\hat{L}^{H_K}=\\widehat K_\\infty$, we have\n$$\n\\Spa(\\widehat K_\\infty^{(i)},\\widehat K_\\infty^{(i),+})^\\diamond \\simeq \\left (\\Spa(\\hat{L},\\O_{\\hat{L}})^\\diamond \\times \\ldots \\times_{\\mathrm{Spd}(K,\\O_K)}\\Spa(\\hat{L},\\O_{\\hat{L}})^\\diamond \\right )^{H_K^i} \\simeq (\\Spa(\\hat{L}^{(i)},\\hat{L}^{(i),+})^\\diamond )^{H_K^i}.\n$$\nThat is, $(\\hat{L}^{(i)})^{H_K^i}=\\widehat K_\\infty^{(i)}$.\n\\end{remark}\n\nNow we use ideas in \\cite{wu2021galois} and \\cite[\\S 5.5]{KedlayaLiu-relativeII} to study \\'etale $\\varphi$-modules over $A[1\/E]^\\wedge_p$ with descent data. We will show this category is the same as generalized $(\\varphi,\\Gamma)$-modules in the work of Kedlaya-Liu. The following is a quick review of Example 5.5.6 and 5.5.7 in \\cite{KedlayaLiu-relativeII}.\n\nFirstly, one has $\\hat{L}^{(i)}\\simeq \\Cont(\\hat{G}^{i-1}, \\hat{L})$, here $\\Cont$ means the set of continuous functions. One can see this fact from the proof of \\cite[Theorem 5.6]{wu2021galois}. When $i=2$, we choose the two canonical maps $i_1,i_2:\\hat{L} \\to \\hat{L}^{(2)}$, corresponds to $j_1,j_2: \\hat{L} \\to \\Cont(\\hat{G}, \\hat{L})$ given by \n\\begin{equation}\\label{eq-j1j2}\n j_1(x): \\gamma \\mapsto \\gamma (x) \\quad \\text{ and } \\quad j_2(x): \\gamma \\mapsto x.\n\\end{equation}\n\nFrom Remark~\\ref{rem-diamonds}, there is a natural action of $\\hat{G}^2$ on $\\hat{L}^{(2)}$. One can check this corresponds to the $\\hat{G}^2$-action on $\\Cont(\\hat{G},\\hat{L})$ given by:\n$$\n(\\sigma_1,\\sigma_2)(f)(\\gamma)=\\sigma_2 f(\\sigma_2^{-1}\\gamma\\sigma_1).\n$$\n\n\\begin{remark}\nWe interchange the roles of $j_1$ and $j_2$ comparing with the isomorphism defined in \\cite[Example 5.5.6]{KedlayaLiu-relativeII}, so the $\\hat{G}^2$-action is different from that in Example 5.5.7 of $loc. cit.$, we will see this definition is more convenient when relating the descent data with the semilinear group actions. \n\\end{remark}\n\nOne can show $\\Cont(\\hat{G},-)$ commutes with tilting and the Witt vector functor, as been discussed in \\cite[Lemma 5.3]{wu2021galois}, so in particular, we have \n$$\nW((\\hat{L}^{(i)})^\\flat) \\simeq \\Cont(\\hat{G}^{i-1}, W(\\hat{L}^\\flat)).\n$$\nFor $i=2$, we still use $j_1$ and $j_2$ to represent the two canonical maps from $W(\\hat{L}^\\flat)$ to $\\Cont(\\hat{G}, W(\\hat{L}^\\flat))$ that comes from \\eqref{eq-j1j2}. The above isomorphism also is compatible with the action of $\\hat{G}^2$, so we have\n\\begin{equation}\\label{eq-K(2)andhatGaction}\nW((\\widehat K_\\infty^{(2)})^\\flat) \\simeq \\Cont(\\hat{G}, W(\\hat{L}^\\flat))^{H_K^2}\n\\end{equation}\nWe prove the following lemma for our later use.\n\nNow let $\\mathcal M$ be an \\'etale $\\varphi$-module over $W(\\widehat K_{\\infty}^\\flat)$ with a descent data: \n$$\n\\psi: \\mathcal M \\otimes_{W(\\widehat K_\\infty^\\flat),j_1} W((\\widehat K_\\infty^{(2)})^\\flat) \\simeq \\mathcal M \\otimes_{W(\\widehat K_\\infty^\\flat),j_2} W((\\widehat K_\\infty^{(2)})^\\flat)\n$$\nas \\'etale $\\varphi$-modules over $W((\\widehat K_\\infty^{(2)})^\\flat)$ and satisfies cocycle condition over $W((\\widehat K_\\infty^{(3)})^\\flat)$. Using \\eqref{eq-K(2)andhatGaction}, we have $\\psi$ is the same as a descent data:\n\\begin{equation}\\label{eq-descentdata-1}\n\\hat{\\psi}: {\\mathcal M} \\otimes_{W(\\widehat K_\\infty^\\flat),j_1} \\Cont(\\hat{G}, W(\\hat{L}^\\flat))^{H_K^2} \\simeq {\\mathcal M} \\otimes_{W(\\widehat K_\\infty^\\flat),j_2} \\Cont(\\hat{G}, W(\\hat{L}^\\flat))^{H_K^2}.\n\\end{equation}\n\nFor each $\\gamma \\in \\hat{G}$, we have an evaluation map $\\tilde{e}_\\gamma: \\Cont(\\hat{G}, W(\\hat{L}^\\flat)) \\to W(\\hat{L}^\\flat)$ given by evaluating at $\\gamma$. Using \\eqref{eq-j1j2}, one can check $\\tilde{e}_\\gamma \\circ j_2: W(\\widehat K_\\infty^\\flat) \\to W(\\hat{L}^\\flat)$ is given by the natural embedding and $\\tilde{e}_\\gamma \\circ j_1: W(\\widehat K_\\infty^\\flat) \\to W(\\hat{L}^\\flat)$ is given by $x\\mapsto \\gamma(x)$. So for each $\\gamma \\in \\hat{G}$, if we tensor \\eqref{eq-descentdata-1} against the evaluation map $\\tilde{e}_\\gamma$, we get an isomorphism:\n$$\n\\psi_\\gamma: {\\mathcal M}\\otimes_{W(\\widehat K_\\infty^\\flat),\\gamma} W(\\hat{L}^\\flat) \\simeq {\\mathcal M}\\otimes_{W(\\widehat K_\\infty^\\flat)} W(\\hat{L}^\\flat). \n$$\nAnd similar to the classical Galois descent theory, the cocycle condition for $\\psi$ implies $\\{\\psi_\\gamma\\}_\\gamma$ satisfies \n$$\n\\psi_{\\sigma \\gamma} = \\psi_\\sigma \\circ \\sigma^\\ast \\psi_\\gamma.\n$$\nHence $\\{\\psi_\\gamma\\}_\\gamma$ defines a continuous semilinear action of $\\hat{G}$ on $\\hat{\\mathcal M}:=\\mathcal M\\otimes_{W(\\widehat K_\\infty^\\flat)} W(\\hat{L}^\\flat)$. One can check for $\\gamma \\in H_K$, we have the composition\n$$\nW(\\widehat K_\\infty^\\flat) \\xrightarrow{j_k} W((\\widehat K_\\infty^{(2)})^\\flat) \\to \\Cont(\\hat{G}, W(\\hat{L}^\\flat)) \\xrightarrow{\\tilde{e}_\\gamma} W(\\hat{L}^\\flat)\n$$\nis the natural embedding $W(\\widehat K_\\infty^\\flat) \\hookrightarrow W(\\hat{L}^\\flat)$ for $k=1,2$. And using the cocycle condition, one can show $\\psi_\\gamma=\\id$ for $\\gamma \\in H_K$, so in particular, $\\mathcal M \\subset \\hat{\\mathcal M}^{H_K}$. Conversely, given a semilinear action of $\\hat{G}$ on $\\hat{\\mathcal M}$ such that $\\mathcal M \\subset \\hat{\\mathcal M}^{H_K}$, $\\{\\psi_\\gamma\\}_\\gamma$ defines a descent data $\\psi$ over $\\Cont(\\hat{G}, W(\\hat{L}^\\flat))^{H_K^{2}}$ if and only if the semilinear action is continuous. In summary, we have\n\n\\begin{theorem}\\label{thm-evaluation-1}\n\\begin{enumerate}\n \\item The category of \\'etale $\\varphi$-modules over $A[1\/E]^\\wedge_p$ with descent data over $A^{(2)}[1\/E]^\\wedge_p$ is equivalent to the category of \\'etale $(\\varphi,\\tau)$-modules over $A[1\/E]^\\wedge_p$;\n \\item Given a descent data $f$ of an \\'etale $\\varphi$-module $\\mathcal M$ over $A[1\/E]^\\wedge_p$, and $\\gamma\\in \\hat{G}$, we can define the evaluation $f_\\gamma$ of $f$ at $\\gamma$, defined by the base change of $f$ along \n$$\ne_{\\gamma}: A^{(2)}[1\/E]^\\wedge_p \\to (A^{(2)})_{\\perf}[1\/E]^\\wedge_p \\xrightarrow{\\tilde{e}_\\gamma} W(\\hat{L}^\\flat),\n$$\nwhich defines an isomorphism:\n$$\nf_\\gamma: \\mathcal M \\otimes_{A[1\/E]^\\wedge_p,\\tilde{\\iota}_\\gamma} W(\\hat{L}^\\flat) \\simeq \\mathcal M \\otimes_{A[1\/E]^\\wedge_p} W(\\hat{L}^\\flat)\n$$\nwhere $\\tilde{\\iota}_\\gamma: A[1\/E]^\\wedge_p \\to W(\\hat{L}^\\flat) \\xrightarrow{\\gamma} W(\\hat{L}^\\flat)$. Suppose that $(\\mathcal M,f)$ corresponds to a $\\mathbb Z_p$-representation $T$ of $G_K$, then $f_\\gamma$ corresponds to the semilinear action of $\\gamma$ on $\\mathcal M$ inside $\\mathcal M \\otimes_{A[1\/E]^\\wedge_p} W(\\mathbb{C}_p^\\flat)\\simeq T^\\vee \\otimes W(\\mathbb{C}_p^\\flat)$. Moreover, two descent data $f , g$ are equal if and only if $f_{\\tau} = g_{\\tau}$.\n\\end{enumerate} \n\\end{theorem}\n\\begin{proof} The discussion above the theorem establishes the equivalence between the category of \\'etale $\\varphi$-modules over $A_{\\perf}[1\/E]^\\wedge_p$ with descent data over $(A^{(2)})_{\\perf}[1\/E]^\\wedge_p$ is equivalent to the category of \\'etale $(\\varphi,\\tau)$-modules over $A[1\/E]^\\wedge_p$. Now (1) follows \\cite[Theorem 4.6]{wu2021galois} which shows that the category of \\'etale $\\varphi$-modules over $B[\\frac 1 I]^\\wedge_p$ is equivalent to the category of \\'etale $\\varphi$-modules over $B_{\\perf}[\\frac 1 I]^\\wedge_{p}$ for bounded prism $(B, I)$ satisfying $\\varphi (I) \\mod p$ is generated by a non-zero\ndivisor in $B\/p$. Then it just remains to prove the last statement in (2). Actually one can check (2) by chasing all the functors used in (1), and use the fact that for any \\'etale $(\\varphi,\\tau)$-module, the $\\hat{G}$-action on $\\hat{\\mathcal M}$ is determined by the $\\tau$-action on $\\mathcal M$. However, this can also been seen directly from the following lemma.\n\\end{proof}\n\n\\begin{lemma}\\label{lem-evaluation-1}\nGiven two finite free \\'etale $\\varphi$-modules $\\mathcal M,\\mathcal{N}$ over $A^{(2)}[1\/E]^\\wedge_p$ and two morphisms $f, g: \\mathcal M \\to \\mathcal{N}$ of \\'etale $\\varphi$-modules over $A^{(2)}[1\/E]^\\wedge_p$. Let $f_\\tau,g_\\tau$ be the base changes of $f,g$ along the map \n$$\ne_{\\tau}: A^{(2)}[1\/E]^\\wedge_p \\to (A^{(2)})_{\\perf}[1\/E]^\\wedge_p \\simeq \\Cont\\Big(\\hat{G}, W\\big((\\hat{L}^{(2)})^\\flat\\big)\\Big)^{H_K^{2}} \\xrightarrow{\\tilde{e}_\\tau} W(\\hat{L}^\\flat).\n$$\nThen $f=g$ if and only if $f_\\tau=g_\\tau$.\n\\end{lemma}\n\n\\begin{proof}\nWe take the natural base change of $f$ and $g$ along $A^{(2)}[1\/E]^\\wedge_p \\to (A^{(2)})_{\\perf}[1\/E]^\\wedge_p$, we get two morphisms $\\psi$ and $\\psi'$ between \\'etale $\\varphi$-modules over $(A^{(2)})_{\\perf}[1\/E]^\\wedge_p$. Since the base change functor between \\'etale $\\varphi$-modules over $A^{(2)}[1\/E]^\\wedge_p$ and $(A^{(2)})_{\\perf}[1\/E]^\\wedge_p$ is an equivalence of categories, it reduces to show that $\\psi=\\psi'$ if and only if their base change along \n$$\n\\tilde{e}_{\\tau}: (A^{(2)})_{\\perf}[1\/E]^\\wedge_p \\simeq \\Cont\\Big(\\hat{G}, W\\big((\\hat{L}^{(2)})^\\flat\\big)\\Big)^{H_K^{2}} \\xrightarrow{} W(\\hat{L}^\\flat)\n$$\nis equal. Since $\\mathcal M$ and $\\mathcal{N}$ are finite free, it is enough to show the evaluation map:\n$$\n\\tilde{e}_{\\tau}: \\Cont\\Big(\\hat{G}, W\\big((\\hat{L}^{(2)})^\\flat\\big)\\Big)^{H_K^{2}} \\to W\\big((\\hat{L}^{(2)})^\\flat\\big)\n$$\nis injective. Suppose $h\\in \\Cont\\Big(\\hat{G}, W\\big((\\hat{L}^{(2)})^\\flat\\big)\\Big)^{H_K^{2}}$ satisfies $h(\\tau)=0$, then \n$$\n(\\sigma_1,\\sigma_2)(h)(\\tau)=\\sigma_2 h(\\sigma_2^{-1}\\tau\\sigma_1)=0\n$$\nfor $(\\sigma_1,\\sigma_2)\\in H_K^{2}$. Since $\\hat{G}$ is topologically generated by $H_K$ and $\\tau$, we get $h\\equiv 0$.\n\\end{proof}\n\nNow we give the $\\mathbb Q$-isogeny versions of Theorem \\ref{thm-evaluation-1} and Lemma \\ref{lem-evaluation-1}. \nRecall that the \\'etale $(\\varphi,\\tau)$-modules over $A[1\/E]^\\wedge_p[\\frac{1}{p}]$ is equivalent to the category of $\\mathbb Q_p$-representations of $G_K$, and recall the following definition of \\'etale $(\\varphi,\\tau)$-modules over $B[1\/J]^\\wedge_p[\\frac{1}{p}]$ for a prism $(B,J)\\in X_{\\mathlarger{\\mathbbl{\\Delta}}}$.\n\n\\begin{definition}\\label{def-etalephimodule-2}\nAn (globally) \\'etale $\\varphi$-module $\\mathcal M$ over $B[1\/J]^\\wedge_p[\\frac{1}{p}]$ is a (finite projective) $\\varphi$-module over $B[1\/J]^\\wedge_p[\\frac{1}{p}]$ that arises by base extension from an \\'etale $\\varphi$-module $B[1\/J]^\\wedge_p$.\n\\end{definition}\n\nFrom this definition, we immediately deduce the following result from \\cite[Theorem 4.6]{wu2021galois}\n\\begin{proposition}\nFor any prism $(B,J)\\in X_{\\mathlarger{\\mathbbl{\\Delta}}}$ satisfying $\\varphi (J) \\mod p$ is generated by a non-zero\ndivisor in $B\/p$, the base change functor defined by $B[1\/J]^\\wedge_p[\\frac{1}{p}]\\to B_{\\perf} [1\/J]^\\wedge_p[\\frac{1}{p}]$ induces an equivalence between the category of \\'etale $\\varphi$-modules over $B[1\/J]^\\wedge_p[\\frac{1}{p}]$ and the category of \\'etale $\\varphi$-modules over $B_{\\perf}[1\/J]^\\wedge_p[\\frac{1}{p}]$.\n\\end{proposition}\n\nAnd similar to Theorem~\\ref{thm-evaluation-1} and Lemma~\\ref{lem-evaluation-1}, we have\n\\begin{theorem}\\label{thm-evaluation-2}\nThe category of \\'etale $\\varphi$-modules over $A[1\/E]^\\wedge_p[\\frac{1}{p}]$ with descent data over $A^{(2)}[1\/E]^\\wedge_p[\\frac{1}{p}]$ is equivalent to the category of \\'etale $(\\varphi,\\tau)$-modules over $A[1\/E]^\\wedge_p[\\frac{1}{p}]$. Moreover, \n$$\n\\Cont\\big(\\hat{G}, W(\\hat{L}^\\flat)[\\frac{1}{p}]\\big)^{H_K^{2}} \\simeq W(\\widehat K_\\infty^{(2)})^\\flat[\\frac{1}{p}]. \n$$\nFor $\\gamma\\in \\hat{G}$, we can define the evaluation map\n$$\n\\tilde{e}_\\gamma: \\Cont\\big(\\hat{G}, W(\\hat{L}^\\flat)[\\frac{1}{p}]\\big) \\to W(\\hat{L}^\\flat)[\\frac{1}{p}].\n$$\nAnd given a descent data $f$ of an \\'etale $\\varphi$-module $\\mathcal M$ over $A[1\/E]^\\wedge_p[\\frac{1}{p}]$, and $\\gamma\\in \\hat{G}$, we can define the evaluation $f_\\gamma$ of $f$ at $\\gamma$, defined by the base change of $f$ along \n$$\ne_\\gamma: A^{(2)}[1\/E]^\\wedge_p[\\frac{1}{p}] \\to (A^{(2)})_{\\perf}[1\/E]^\\wedge_p[\\frac{1}{p}] \\xrightarrow{\\tilde{e}_\\gamma} W(\\hat{L}^\\flat)[\\frac{1}{p}],\n$$\nwhich defines an isomorphism:\n$$\n\\mathcal M \\otimes_{A[1\/E]^\\wedge_p[1\/p],\\tilde{\\iota}_\\gamma} W(\\hat{L}^\\flat)[\\frac{1}{p}] \\simeq \\mathcal M \\otimes_{A[1\/E]^\\wedge_p[1\/p]} W(\\hat{L}^\\flat)[\\frac{1}{p}]\n$$\nwhere $\\tilde{\\iota}_\\gamma: A[1\/E]^\\wedge_p[\\frac{1}{p}] \\to W(\\hat{L}^\\flat)[\\frac{1}{p}] \\xrightarrow{\\gamma} W(\\hat{L}^\\flat)[\\frac{1}{p}]$. If $(\\mathcal M,f)$ corresponds to a $\\mathbb Q_p$-representation $V$ of $G_K$, then $f_\\gamma$ corresponds to the semilinear action of $\\gamma$ on $\\mathcal M $ inside $ V^\\vee \\otimes W(\\mathbb{C}_p^\\flat)[1\/p]$. Moreover, two descent data $f , g$ are equal if and only if $f_{\\tau} = g_{\\tau}$.\n\\end{theorem}\n\n\\begin{lemma}\\label{lem-evaluation-2}\nGiven two finite free \\'etale $\\varphi$-modules $\\mathcal M,\\mathcal{N}$ over $A^{(2)}[1\/E]^\\wedge_p[\\frac{1}{p}]$ and two morphisms $f, g: \\mathcal M \\to \\mathcal{N}$ of \\'etale $\\varphi$-modules over $A^{(2)}[1\/E]^\\wedge_p[\\frac{1}{p}]$. Let $f_\\tau,g_\\tau$ be the base changes of $f,g$ along the map \n$$\ne_\\tau:A^{(2)}[1\/E]^\\wedge_p[\\frac{1}{p}] \\to (A^{(2)})_{\\perf}[1\/E]^\\wedge_p[\\frac{1}{p}] \\simeq \\Cont\\Big(\\hat{G}, W\\big((\\hat{L}^{(2)})^\\flat[\\frac{1}{p}]\\big)\\Big)^{H_K^{2}} \\xrightarrow{\\tilde{e}_\\tau} W(\\hat{L}^\\flat)[\\frac{1}{p}].\n$$\nThen $f=g$ if and only if $f_\\tau=g_\\tau$.\n\\end{lemma}\n\n\\begin{proof}\nThe proofs are exactly the same as the proof of Theorem~\\ref{thm-evaluation-1} and Lemma~\\ref{lem-evaluation-1}, plus the following fact that\n$$\n\\Cont\\big(\\hat{G}, W(\\hat{L}^\\flat)[\\frac{1}{p}]\\big) = \\Cont\\big(\\hat{G}, W(\\hat{L}^\\flat)\\big)[\\frac{1}{p}],\n$$\nwhich can be shown by the compactness of $\\hat{G}$. \n\\end{proof}\n\n\\subsection{Proofs of Proposition~\\ref{thm-1prime} and Theorem \\ref{Thm-main-1}}\\label{subsec-pris-crystal-proof} We keep the assumption that $R=\\O_K$ is a mixed characteristic complete DVR with perfect residue field in this subsection, and keep our notations in \\S 2.1.\n\nLet us first prove Proposition~\\ref{thm-1prime} using Lemma~\\ref{lem-intersection} and results in \\S\\ref{subsec-phi-tau}. First, we give a different interpretation of the ``evaluation map\":\n$$\ne_\\gamma: A^{(2)}[1\/E]^\\wedge_p \\to (A^{(2)})_{\\perf}[1\/E]^\\wedge_p \\simeq \\Cont\\Big(\\hat{G}, W\\big((\\hat{L}^{(2)})^\\flat\\big)\\Big)^{H_K^{2}} \\xrightarrow{\\tilde{e}_\\gamma} W(\\hat{L}^\\flat)\n$$\nin Theorem~\\ref{thm-evaluation-1} when restricted on $A^{(2)}$ . Recall that we fix a compatible system $\\{\\varpi_n\\}_n$ of $p^n$-th roots of a uniformizer $\\varpi \\in \\O_K$, this defines a map of prisms $\\iota: (A,(E)) \\to (\\Ainf,(E))$ maps $u$ to $[{\\varpi}^\\flat ]$, and given a $\\gamma \\in G_K$, we define $\\iota_{\\gamma}$ to be the composition of $\\iota$ with $\\gamma: (\\Ainf,(E)) \\to (\\Ainf,(E))$ where the second map is defined as $a \\mapsto \\gamma(a)$. Since $(E)\\subset \\Ainf$ is equal to $\\Ker(\\theta)$ and $\\theta$ is $G_K$-equivariant, $\\gamma$ is a well-defined map of $\\delta$-pairs. By the universal property of $A^{(2)}$, we can define a map of prisms $\\iota_{\\gamma}^{(2)} : (A^{(2)},(E)) \\to (\\Ainf,(E))$ so that the following diagram commutes: \n\\begin{equation}\\label{equ-diagram-prisms}\n\\begin{tikzcd}\n(A,(E)) \\arrow[rr,\"i_1\"] \\arrow[ddrr,\"\\iota_\\gamma\",swap] & & (A^{(2)}, (E) )\\arrow[dd,\"\\iota^{(2)}_\\gamma\"] & & (A,(E)) \\arrow[ll,\"i_2\",swap] \\arrow[ddll,\"\\iota\"]\\\\\n& & & &\\\\\n& & (\\Ainf, (E)) & &\n\\end{tikzcd} \n\\end{equation}\nWe have $\\iota^{(2)}_{\\gamma}$ induces a morphism $\\tilde{\\iota}^{(2)}_{\\gamma}: A^{(2)}[1\/E]^\\wedge_p \\to W(\\mathbb C_p^\\flat)$. We claim for all $\\gamma \\in G_K$, $\\tilde{\\iota}^{(2)}_{\\gamma}$ is the same as the \n$$\nA^{(2)}[1\/E]^\\wedge_p \\to (A^{(2)})_{\\perf}[1\/E]^\\wedge_p \\simeq \\Cont\\Big(\\hat{G}, W\\big((\\hat{L}^{(2)})^\\flat\\big)\\Big)^{H_K^{2}} \\xrightarrow{\\tilde{e}_\\gamma} W(\\hat{L}^\\flat) \\hookrightarrow W(\\mathbb C_p^\\flat).\n$$\nTo see this, by the universal property of direct perfection, we have \\eqref{equ-diagram-prisms} factorizes as:\n$$\n\\begin{tikzcd}\n(A,(E)) \\arrow[d]\\arrow[rr,\"i_1\"] & & (A^{(2)}, (E) )\\arrow[d] & & (A,(E)) \\arrow[d] \\arrow[ll,\"i_2\",swap]\\\\\n(A_{\\perf},(E)) \\arrow[rr,\"i'_1\"] \\arrow[ddrr,\"\\iota'_\\gamma\",swap] & & ((A^{(2)})_{\\perf}, (E) )\\arrow[dd,\"\\iota'^{(2)}_\\gamma\"] & & (A_{\\perf},(E)) \\arrow[ll,\"i'_2\",swap] \\arrow[ddll,\"\\iota'\"]\\\\\n& & & &\\\\\n& & (\\Ainf, (E)) & &\n\\end{tikzcd} \n$$\nSo $\\tilde{\\iota}^{(2)}_\\gamma$ has a factorization\n$$\nA^{(2)}[1\/E]^\\wedge_p \\to (A^{(2)})_{\\perf}[1\/E]^\\wedge_p \\to W(\\mathbb C_p^\\flat).\n$$\nWe just need to check $\\iota'^{(2)}_\\tau$ induces the evaluation map \n$$\n(A^{(2)})_{\\perf}[1\/E]^\\wedge_p \\simeq \\Cont\\Big(\\hat{G}, W\\big((\\hat{L}^{(2)})^\\flat\\big)\\Big)^{H_K^{2}} \\xrightarrow{\\tilde{e}_\\tau} W(\\hat{L}^\\flat) \\xhookrightarrow{} W(\\mathbb{C}_p^\\flat).\n$$\nAnd this follows from the isomorphism of $(A^{(2)})_{\\perf}[1\/E]^\\wedge_p\\simeq W((K^{(2)}_\\infty)^\\flat)$, then one check directly for $j_1,j_2$ defined in \\eqref{eq-j1j2}, $\\tilde{e}_\\gamma\\circ j_1: A_{\\perf}[1\/E]^\\wedge_p \\to W(\\hat{L}^\\flat)$ is equal to the map induced from $\\iota'_\\gamma$ and $\\tilde{e}_\\gamma\\circ j_2: A_{\\perf}[1\/E]^\\wedge_p \\to W(\\hat{L}^\\flat)$ is equal to the map induced from $\\iota'$. In particular, we have a commutative diagram:\n\\begin{equation}\\label{eq-iotaandevaluation}\n\\begin{tikzcd}\nA^{(2)} \\arrow[d, hook] \\arrow[rrr, \"\\iota^{(2)}_\\gamma\"] &&& \\Ainf \\arrow[d, hook]\\\\\nA^{(2)}[1\/E]^\\wedge_p \\arrow[r] & (A^{(2)})_{\\perf}[1\/E]^\\wedge_p \\arrow[r,\"\\tilde{e}_\\gamma\",hook] & W(\\hat{L}^\\flat) \\arrow[r,hook] & W(\\mathbb C_p^\\flat).\n\\end{tikzcd} \n\\end{equation}\nNow we can prove Proposition~\\ref{thm-1prime}.\n\n\\begin{proof}[Proof of Proposition~\\ref{thm-1prime}]\nFirst we pick $\\gamma=\\tilde{\\tau}$ that is a preimage of $\\tau$ under the map $G_K \\to \\hat{G}$, we have $\\gamma(u)-u=Ez$ and $\\iota^{(2)}_{\\gamma}$ defined as above is the embedding defined in \\S\\ref{subsec-embedding} by Remark~\\ref{rem-embedding-depend}. In particular, composing the embedding $A^{(2)} \\hookrightarrow \\Ainf$ defined in \\S\\ref{subsec-embedding} with $\\Ainf \\hookrightarrow W(\\mathbb C_p^\\flat)$, one get the evaluation map \n$$\n(A^{(2)})_{\\perf}[1\/E]^\\wedge_p \\simeq \\Cont\\Big(\\hat{G}, W\\big((\\hat{L}^{(2)})^\\flat\\big)\\Big)^{H_K^{2}} \\xrightarrow{\\tilde{e}_\\tau} W(\\hat{L}^\\flat) \\xhookrightarrow{} W(\\mathbb{C}_p^\\flat).\n$$\nrestricted on $A^{(2)}$.\n\nKeep the notations as in \\S\\ref{subsec-G-image}, and let $\\mathcal M_{\\Ainf}=W(\\mathbb C_p^\\flat)\\otimes_A \\mathfrak{M}$ and $\\mathcal M_A \\simeq \\mathfrak{M}\\otimes_A A[1\/E]^\\wedge_p$. By Theorem~\\ref{thm-evaluation-1} and Theorem~\\ref{thm-caruso}, recall we use $B^{(2)}= A^{(2)} [\\frac 1 E]^\\wedge_p$ and $B^{(2)}_{\\st}= A^{(2)}_{\\st} [\\frac 1 E]^\\wedge_p$ to simplify our notations, we have there is a descent data \n$$\nc: \\mathcal M_A \\otimes _{A[1\/E]^\\wedge_p, \\tilde{i}_1} B^{(2)} \\to \\mathcal M_A \\otimes_{A[1\/E]^\\wedge_p, \\tilde{i}_2} B^{(2)} \n$$\nof $\\mathcal M_A$ over $B^{(2)}$ that corresponds to the representation $T$. And the semilinear action of $\\gamma=\\tilde{\\tau}$ on $\\mathcal M_{\\Ainf}$ is given by the evaluation $c_\\tau$, that is, we have the linearization of the $\\tilde{\\tau}$-action is defined by\n$$\nc_\\tau: W(\\mathbb C_p^\\flat) \\otimes_{\\tilde{\\iota}_\\gamma,A[1\/E]^\\wedge_p} \\mathcal M_A \\simeq W(\\mathbb C_p^\\flat) \\otimes_{\\tilde{\\iota} , A[1\/E]^\\wedge_p} \\mathcal M_A.\n$$\nBy base change $c$ along $B^{(2)} \\to B^{(2)}[\\frac{1}{p}]$, we get a $B^{(2)}[\\frac{1}{p}]$-linear $\\varphi$-equivariant morphism:\n$$\nc': \\mathcal M_A \\otimes _{A[1\/E]^\\wedge_p, \\tilde{i}_1} B^{(2)}[\\frac{1}{p}] \\to \\mathcal M_A \\otimes_{A[1\/E]^\\wedge_p, \\tilde{i}_2} B^{(2)}[\\frac{1}{p}]. \n$$\nOn the other hand, from the discussions after Proposition~\\ref{thm-1prime}, $\\tilde{\\tau}$-action also defines a $\\varphi$-equivariant morphism \n$$\nf_{\\tilde{\\tau}}: \\mathfrak{M}\\otimes_{A,\\iota_{\\tilde{\\tau}}} A_{\\st}^{(2)}[\\frac{1}{p}] \\simeq \\mathfrak{M}\\otimes_{A} A_{\\st}^{(2)}[\\frac{1}{p}].\n$$\nWe will see in Proposition~\\ref{prop-descentBsttoB2} below that $f_{\\tilde{\\tau}}$ actually descents to a $B^{(2)}[1\/p]$-linear morphism. Assuming this fact, then if we base change $f_{\\tilde{\\tau}}$ along $A^{(2)}[\\frac{1}{p}] \\to W(\\mathbb C_p^\\flat)[\\frac{1}{p}]$, we will have $f_{\\tilde{\\tau}}\\otimes W(\\mathbb C_p^\\flat)[\\frac{1}{p}]=c_\\tau$ since the way we define $f_{\\tilde{\\tau}}$ is by taking the ${\\tilde{\\tau}}$-action. From the discussion at the beginning of the proof and Lemma~\\ref{lem-evaluation-2}, we have $f_{\\tilde{\\tau}}=c'$ as a $B^{(2)}[\\frac{1}{p}]$-linear isomorphism between $\\mathcal M_A \\otimes _{A[1\/E]^\\wedge_p, \\tilde{i}_1} B^{(2)}[\\frac{1}{p}] $ and $ \\mathcal M_A \\otimes_{A[1\/E]^\\wedge_p, \\tilde{i}_2} B^{(2)}[\\frac{1}{p}]$.\n\nWe fix a basis $\\{e_i\\}$ of $\\mathfrak{M}$, for $j=1,2$ let $\\{e^j_i\\}$ be the basis of $\\mathcal M_A \\otimes _{A, \\tilde{i'}_j} B^{(2)}[\\frac{1}{p}] $ defined by $e^j_i=e_i\\otimes 1$ and the tensor is via $A \\to A[1\/E]^\\wedge_p \\xrightarrow{\\tilde{i}_j} B^{(2)}[1\/p]$. So we can interpret $f_{\\tilde{\\tau}}=c'$ as matrix using this two basis, this matrix is $X_{\\tilde{\\tau}}$ from this definition, so it has coefficients inside $A_{\\st}^{(2)}[\\frac{1}{p}]$ by the discussion before \nProposition~\\ref{thm-1prime}. On the other hand, $X_{\\tilde{\\tau}}$ has coefficients in $B^{(2)}\\subset B_{\\st}^{(2)}$ since $c'$ is defined by the $B^{(2)}$-linear map $c$. So by Lemma~\\ref{lem-intersection}, we have $X_{\\tilde{\\tau}}$ has coefficients inside $A_{\\st}^{(2)}$. The same argument shows when $T$ is crystalline, then $X_{\\tilde{\\tau}}$ has coefficients inside $A^{(2)}$.\n\\end{proof}\n\n\\begin{proposition}\\label{prop-descentBsttoB2}\nBase change along $B^{(2)} \\to A^{(2)}_{\\st}[1\/E]^\\wedge_p$ defines an equivalence of categories of \\'etale $\\varphi$-modules over $B^{(2)}$ and $A^{(2)}_{\\st}[1\/E]^\\wedge_p$ and an equivalence of categories of \\'etale $\\varphi$-modules over $B^{(2)}[1\/p]$ and $A^{(2)}_{\\st}[1\/E]^\\wedge_p[1\/p]$.\n\\end{proposition}\n\\begin{proof}\nBy \\cite[Theorem 4.6]{wu2021galois}, we just need to show the same result after perfections, we will show $(A^{(2)})_{\\perf}= (A^{(2)}_{\\st})_{\\perf}$ in Lemma~\\ref{lem-perfectionofA2andAst2} using the logarithmic prismatic site.\n\\end{proof}\n\nNow, let us prove Theorem~\\ref{Thm-main-1} by first producing a functor $\\mathcal T$ from prismatic $F$-crystals in finite $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-modules to lattices inside a crystalline representation. For prism $A$, we use $i_k: A \\to A^{(2)}$ or $A^{(3)}$ for natural map from $A$ to $k$-th factor of $A^{(2)}$ or $A^{(3)}$. The notation $i_{kl}: A ^{(2)} \\to A ^{(3)}$ has the similar meaning. \n\nBy Corollary \\ref{cor-crystal-descentdata}, given a prismatic $F$-crystal ${\\mathfrak{M}}_{\\mathlarger{\\mathbbl{\\Delta}}}$, we obtain a Kisin module $(\\mathfrak{M} , \\varphi _{\\mathfrak{M}})$ of height $h$ together with descent data\n$f: \\mathfrak{M} \\otimes _{A, i_1} A^{(2)} \\to \\mathfrak{M} \\otimes_{A, i_2}A^{(2)} $ so that $f$ satisfies the following cocycle condition $ i _{13} \\otimes f = (i _{23} \\otimes f) \\circ (i _{12} \\otimes f ) $, where $i_{kl} \\otimes f$ is the base change of $f$ along $i_{kl}$, and $f$ also compatible with the $\\varphi$-structure on the both sides of $f$. Note that the existence of $f$ follows from the crystal property of $\\mathfrak{M}_{\\mathlarger{\\mathbbl{\\Delta}}}$: \n\\begin{equation}\\label{eqn-cocyclefromcrystal}\nf: \\mathfrak{M} \\otimes _{A, i_1} A^{(2)} \\simeq \\mathfrak{M}_{\\mathlarger{\\mathbbl{\\Delta}}}((A^{(2)},(E))) \\simeq \\mathfrak{M} \\otimes_{A, i_2}A^{(2)} \n\\end{equation}\n\nWe let $\\mathcal M=\\mathfrak{M}\\otimes_A A[1\/E]^\\wedge_p$ and $c=f\\otimes_{A^{(2)}} B^{(2)}$, then $(\\mathcal M,c)$ is an \\'etale $\\varphi$-module with descent data, which corresponds to a $\\mathbb Z_p$-representation of $G_K$. Moreover the semilinear action of $G_K$ on $\\mathfrak{M}\\otimes_A W(\\mathbb{C}_p^\\flat)$ comes from $\\{c_\\gamma\\}_{\\gamma\\in G_K}$ using the evaluation maps. If we define \n$$\nf_\\gamma: \\Ainf \\otimes_{\\iota_\\gamma,A} \\mathfrak{M} \\to \\Ainf \\otimes_{\\iota , A} \\mathfrak{M}\n$$\nas the base change of $f$ along $\\iota_{\\gamma}^{(2)}$, then by \\eqref{eq-iotaandevaluation}, we have $c_\\gamma=f_\\gamma$. The $G_K$-semilinear action commutes with $\\varphi$ as $f$ does. For any $\\gamma \\in G_K$, we have $\\gamma (A) \\subset W(k)[\\![u , \\epsilon-1]\\!] \\subset A^{(2)}_{\\st} \\subset \\Ainf$. Therefore, the $G_K$-action on the $\\Ainf \\otimes_A \\mathfrak{M}$ defined the above factors through $A^{(2)}_{\\st} \\otimes_A \\mathfrak{M}$. We claim that $G_K$-action on $\\widehat \\mathfrak{M} : = A^{(2)}_{\\st}\\otimes_A \\mathfrak{M}$ defines a $(\\varphi, \\hat G)$-module which corresponds to a crystalline representation.\n\nFirst, for $\\gamma \\in G_\\infty$, $\\gamma(A) = A$ in $\\Ainf$, we conclude $\\iota^{(2)}_\\gamma : A^{(2)} \\to \\Ainf$ satisfies $\\iota^{(2)}_{\\gamma}\\circ i_1=\\iota^{(2)}_{\\gamma}\\circ i_2$. In particular, for any $\\gamma \\in G_\\infty$ and $j=1,2$, using \\eqref{eqn-cocyclefromcrystal} and the crystal property of $\\mathfrak{M}_{\\mathlarger{\\mathbbl{\\Delta}}}$, $f_\\gamma$ comes from the base change of \\eqref{eqn-cocyclefromcrystal} along $\\iota^{(2)}_\\gamma : A^{(2)} \\to \\Ainf$, in particular, we have \n$$\nf_\\gamma: \\mathfrak{M} \\otimes _{A, \\iota^{(2)}_{\\gamma}\\circ i_1} \\Ainf \\simeq \\mathfrak{M}_{\\mathlarger{\\mathbbl{\\Delta}}}((\\Ainf,\\Ker\\theta)) \\simeq \\mathfrak{M} \\otimes_{A, \\iota^{(2)}_{\\gamma}\\circ i_2}\\Ainf. \n$$\nSince $\\iota^{(2)}_{\\gamma}\\circ i_1=\\iota^{(2)}_{\\gamma}\\circ i_2$, we have $f_\\gamma={\\rm id}$ which means $\\mathfrak{M} \\subset (\\widehat \\mathfrak{M}) ^{G_\\infty}$. Similarly, $G_K$ acts on $\\widehat \\mathfrak{M}\/ I_+$ corresponds the base change of $f$ along \n$$A^{(2)} \\xrightarrow{\\iota^{(2)}_\\gamma} \\Ainf \\to W(\\bar{k}) $$\nwhere the last arrow is the reduction modulo $W(\\mathfrak m)$ ($\\mathfrak m$ is the maximal ideal of $\\O_{\\mathbb C_p}^\\flat$). One can check for all $\\gamma\\in G_K$ and $j=1,2$, we have \n$$\nA \\xrightarrow{i_j} A^{(2)} \\xrightarrow{\\iota^{(2)}_\\gamma} \\Ainf \\to W(\\bar{k})\n$$\nare all equal to $A \\to W(k) \\hookrightarrow W(\\overline{k})$ with the first arrow given by $u \\mapsto 0$. The above map induces a morphism of prisms $(A,(E)) \\to (W(k),(p))$, then using \\eqref{eqn-cocyclefromcrystal} and the crystal condition of $\\mathfrak{M}_{\\mathlarger{\\mathbbl{\\Delta}}}$, we can similarly prove that $G_K$ acts on $\\widehat \\mathfrak{M}\/ I_+$-trivially, so $(\\mathfrak{M}, \\varphi_{\\mathfrak{M}}, G_K)$ is a $(\\varphi, \\hat G)$-module. Furthermore, $\\widehat T (\\widehat \\mathfrak{M})$ is crystalline by Corollary \\ref{cor-crystalline} and Theorem~\\ref{Thm-1}. \n\n\\begin{remark}\nIn \\S\\ref{sec-logprismandsemistablereps}, we will consider a category consisting of modules with descent data, and similar arguments about the triviality of the Galois actions can be shown directly using the cocycle condition of the descent data. We summarize this fact in the following easy fact.\n\\end{remark}\n\\begin{lemma}\nLet $q:(A^{(2)},(E)) \\to (B,J)$ be a map of prisms satisfying $q\\circ i_1 =q\\circ i_2$, then for any descent data $f$ over $A^{(2)}$, the base change of $f$ along $q$ is the identity map.\n\\end{lemma}\n\nTo show the fully faithfulness of this functor, first let $(\\mathfrak{M}, f)$, $(\\mathfrak{M}', f')$ be two Kisin modules with descent data $f , f'$ respectively. Suppose that there exists a map $ \\alpha: \\mathcal T ((\\mathfrak{M} , f)) \\to \\mathcal T ((\\mathfrak{M}' , f'))$ as lattices of crystalline representations, then from our construction of $\\mathcal{T}$ and Theorem~\\ref{thm-2}, $\\alpha$ is induced from a map $\\hat{\\alpha}: (\\mathfrak{M} , \\varphi_{\\mathfrak{M}}, \\hat G_{\\mathfrak{M}}) \\to (\\mathfrak{M}' , \\varphi _{\\mathfrak{M}'}, \\hat G_{\\mathfrak{M}'})$ between $(\\varphi, \\hat G)$-modules. The faithfulness of $\\mathcal{T}$ follows the fact that $A \\to A[1\/E]^\\wedge_p$ induces a fully faithful functor between Kisin modules over $A$ and \\'etale $\\varphi$-modules over $A[1\/E]^\\wedge_p$ from \\cite[Proposition 2.1.12]{KisinFcrystal}. On the other hand, $\\hat{\\alpha}$ gives morphisms $\\hat{\\alpha}_1: \\mathfrak{M} \\otimes_{A,i_1} A^{(2)} \\to \\mathfrak{M}' \\otimes_{A,i_1}A^{(2)}$ and $\\hat{\\alpha}_2: \\mathfrak{M} \\otimes_{A,i_2} A^{(2)} \\to \\mathfrak{M}' \\otimes_{A,i_2}A^{(2)}$. If we view $A$ and $A^{(2)}$ as subrings of $\\Ainf$ using diagram \\eqref{equ-diagram-prisms}, then the following diagram commutes by the fact that $\\hat{\\alpha}: \\widehat{\\mathfrak{M}} \\to \\widehat{\\mathfrak{M}'}$ is compatible with $\\tau$-action.\n$$\n\\begin{tikzcd}\n\\mathfrak{M} \\otimes_{A,i_1} A^{(2)} \\arrow[r,\"f\"] \\arrow[d,\"\\hat{\\alpha}_1\"] & \\mathfrak{M} \\otimes_{A,i_2} A^{(2)} \\arrow[d,\"\\hat{\\alpha}_2\"] \\\\\n\\mathfrak{M}' \\otimes_{A,i_1} A^{(2)} \\arrow[r,\"f'\"] & \\mathfrak{M}' \\otimes_{A,i_2} A^{(2)} \\\\\n\\end{tikzcd}\n$$\nThus we produces a morphism between $(\\mathfrak{M}, f)$ and $(\\mathfrak{M}', f')$, i.e. $\\mathcal{T}$ is also full. \n\nIt remains to show the functor $\\mathcal{T}$ is essential surjective. Given a lattice $T$ in a crystalline representation of $G_K$, let $\\mathfrak{M}$ be the corresponded Kisin module, it suffices to construct a descent data of $\\mathfrak{M}$ over $A^{(2)}$. We have shown in our proof of Proposition~\\ref{thm-1prime} that if we view $A^{(2)}$ as a subring of $\\Ainf$ via $\\iota^{(2)}_{\\tilde{\\tau}}$, then $X_{\\tilde{\\tau}}$ defines a $\\varphi$-equivariant isomorphism $f: \\mathfrak{M}\\otimes_{A,i_1} A^{(2)} \\simeq \\mathfrak{M}\\otimes_{A,i_2} A^{(2)}$ of $A^{(2)}$-modules. We also show the base change of $f$ along $A^{(2)} \\to B^{(2)}$ is equal to the descent data $c$ of the \\'etale $\\varphi$-module $\\mathcal M_A=\\mathfrak{M}\\otimes_A A[1\/E]^\\wedge_p$ that corresponds to $G_K$-action on $T$. In particular, $c: \\mathfrak{M}\\otimes_{A,i_1} B^{(2)} \\simeq \\mathfrak{M}\\otimes_{A,i_2} B^{(2)}$ satisfies the cocycle condition. By Lemma \\ref{lem-intersection}, $A^{(2)}$ (resp. $A^{(3)}$) injects into $B^{(2)}$ (resp. $B^{(3)}$), so we have $f$ also satisfies the cocycle condition. In particular, $(\\mathfrak{M},f)$ together produce a primatic $F$-crystals in finite free $\\O_{\\mathlarger{\\mathbbl{\\Delta}}}$-module by Corollary~\\ref{cor-crystal-descentdata}.\n\n\\begin{remark}\nGiven an \\'etale $\\varphi$-module $(\\mathcal M_A,\\varphi_{\\mathcal M_A}, c)$ over $A[1\/E]^\\wedge_p$ with descent datum $c$, we call $(\\mathcal M_A,\\varphi_{\\mathcal M_A}, c)$ is \\emph{of finite $E$-height} if $\\mathcal M_A$ is of finite $E$-height, i.e., if there is a finite free Kisin module $(\\mathfrak{M},\\varphi_{\\mathfrak{M}})$ of finite height and defined over $A$ such that $\\mathfrak{M}\\otimes_A A[1\/E]^\\wedge_p \\simeq \\mathcal M_A$ as $\\varphi$-modules. Since $(\\mathcal M_A, \\varphi_{\\mathcal M_A})$ is the \\'etale $\\varphi$-module for $T|_{G_\\infty}$, our definition of finite $E$-height is compatible with the one given by Kisin under the equivalence in (1) of Theorem~\\ref{thm-evaluation-1}. \n\nWe expect same arguments in the proof of Proposition~\\ref{thm-1prime} will be used to study representations of finite $E$-height. Similar result has been studied using the theory of $(\\varphi,\\tau)$-modules by Caruso. For example, in the proof of \\cite[Lemma 2.23]{Caruso-phitau}, Caruso shows for representations of finite $E$-height, the $\\tau$-actions descents to $\\mathfrak{S}_{u\\text{-np},\\tau}$, which is a subring of $\\Ainf$ closely related to $\\tilde{\\iota}^{(2)}_{\\tilde\\tau}(B^{(2)})\\cap \\Ainf$, where $\\tilde{\\tau}$ is a preimage of $\\tau$ in $G_K$. \n\\end{remark}\n\n\\begin{remark}\nWe can also establish the compatibility of our Theorem~\\ref{Thm-main-1}, the theory of Kisin and \\cite[Theorem 1.2]{BS2021Fcrystals}. Given a lattice $T$ in a crystalline representation of $G_K$ with non-negative Hodge-Tate weight, and let $\\mathfrak{M}$ be the Kisin module corresponds to $T$ in \\cite{KisinFcrystal}, and let $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ (reso. $\\mathfrak{M}'_{{\\mathlarger{\\mathbbl{\\Delta}}}}$) be the prismatic $F$-crystal corresponds to $T^\\vee$ under \\cite[Theorem 1.2]{BS2021Fcrystals} (resp. $T$ under Theorem~\\ref{Thm-main-1}). Note that we need to take $T^\\vee$ since in the work of Bhatt-Scholze, the equivalence is covariant. By our construction of $\\mathfrak{M}'_{{\\mathlarger{\\mathbbl{\\Delta}}}}$, we have $\\mathfrak{M}'_{{\\mathlarger{\\mathbbl{\\Delta}}}}((A,(E)))\\simeq \\mathfrak{M}$. By \\cite[Remark 7.11]{BS2021Fcrystals}, $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((A,(E)))\\simeq \\mathfrak{M}$. Next we need to show the descent data over $A^{(2)}$ constructed respectively are the same. By Corollary~\\ref{cor-inj}, we just need to show they are the same as descent data of \\'etale $\\varphi$-modules over $A^{(2)}[1\/E]^\\wedge_p$, but they are the same by our $\\tau$-evaluation criteria in Lemma~\\ref{lem-evaluation-1}. \n\\end{remark}\n\n\n\\section{Logarithmic prismatic \\texorpdfstring{$F$}{F}-crystals and semi-stable representations}\\label{sec-logprismandsemistablereps}\nIn this section, we will propose a possible generalization of Theorem~\\ref{Thm-main-1} to semi-stable representations using the absolute logarithmic prismatic site. The main reference of this subsection is \\cite{Koshikawa2021log-prism}. We will restrict ourselves to the base ring $R=\\O_K$, a complete DVR with perfect residue field. And we give $R$ the log structure associated to the prelog structure $\\alpha: \\mathbb N \\to R$ such that $\\alpha(1)=\\varpi$ is a uniformizer in $R$, i.e., let $D=\\{\\varpi=0\\}$, then the log structure on $X=\\Spf(R)$ is defined by \n$$\nM_X=M_D \\hookrightarrow \\O_X \\text{ where } M_D(U):=\\{f\\in \\O_X(U) \\,|\\, f|_{U\\backslash D}\\in \\O^\\times(U\\backslash D) \\}.\n$$\nLet us introduce the absolute logarithmic site over $(X,M_X)$.\n\\begin{definition}\\cite[Definition 2.2 and Definition 3.3]{Koshikawa2021log-prism}\n\\begin{enumerate}\n \\item A $\\delta_{\\log}$-ring is a tuple $(A,\\delta, \\alpha:M\\to A, \\delta_{\\log}:M\\to A)$, where $(A,\\delta)$ is a $\\delta$-pair and $\\alpha$ is a prelog-structure on $A$. And $\\delta_{\\log}$ satisfies:\n \\begin{itemize}\n \\item $\\delta_{\\log}(e)=0$,\n \\item $\\delta(\\alpha(m))=\\alpha(m)^p\\deltalog(m)$,\n \\item $\\deltalog(mn)=\\deltalog(m)+\\deltalog(n)+p\\deltalog(m)\\deltalog(n)$\n for all $m,n\\in M$. And we will simply denote it by $(A,M)$ if this is no confusion. Morphisms are morphisms of $\\delta$-pairs that compatible with the perlog structure and $\\deltalog$-stucture.\n \\end{itemize}\n \\item A $\\delta_{\\log}$-triple is $(A,I,M)$ such that $(A,I)$ is a $\\delta$-pair and $(A,M)$ is a $\\delta_{\\log}$-ring.\n \\item A $\\delta_{\\log}$-triple $(A,I,M)$ is a prelog prism if $(A,I)$ is a prism, and it is bounded if $(A,I)$ is bounded.\n \\item A bounded prelog prism is a log prism if it is $(p, I )$-adically log-affine (cf. \\cite[Definition 3.3]{Koshikawa2021log-prism}). \n \\item A bounded (pre)log prism is integral if $M$ is an integral monoid.\n \\item A $\\delta_{\\log}$-triple $(A,I,M)$ is said to be over $(R,\\mathbb N)$ if $A\/I$ is an $R$-algebra and there is a map $M\\to \\mathbb N$ of monoids such that the following diagram commutes.\n $$\n \\begin{tikzcd}\n M \\arrow[rr] \\arrow[d] & & A \\arrow[d] \\\\\n \\mathbb N \\arrow[r] & R \\arrow[r] & A\/I\n \\end{tikzcd}\n $$\n All $\\delta_{\\log}$-triples over $(R,\\mathbb N)$ form a category. Similarly, we can define the category of prelog prisms over $(R,\\mathbb N)$ and the category of bounded log prisms over $(R,\\mathbb N)^a$.\n\\end{enumerate}\n\\end{definition}\n\n\\begin{remark}\nIf $A$ is an integral domain, or more general if $\\alpha(M)$ consists of non-zero divisors, then $\\deltalog$ is uniquely determined by $\\delta$ if exists. In particular, morphisms between such $\\delta_{\\log}$-rings are just morphisms of $\\delta$-rings.\n\\end{remark}\n\n\\begin{remark}\nNote that in this paper, for a $\\delta$-pair $(A,I)$, we always assume $A$ is $(p,I)$-adic complete, but in \\cite{Koshikawa2021log-prism}, non-$(p,I)$-adic completed $\\delta_{\\log}$-triples are also been studied. By Lemma 2.10 of loc.cit., we can always take the $(p,I)$-adic completions of the $\\delta$-pair $(A,I)$ and the $\\delta_{\\log}$-structure will be inherited. \n\\end{remark}\n\n\\begin{proposition}\\cite[Corollary 2.15]{Koshikawa2021log-prism}\nGiven a bounded prelog prism $(A,I,M)$, one can associate it with a log prism\n$$\n(A,I,M)^a=(A,I,M^a)\n$$\n\\end{proposition}\n\\begin{remark}\nWhen we deal with log prisms in this paper, we will always take it as the log prism associated with some prelog prism. And by the above proposition, we know taking the associated log prism does not change the underlying $\\delta$-pair. Moreover, it is a general fact that $(A,I,M)^a$ is integral if $(A,I,M)$ is a integral.\n\\end{remark}\n\n\\begin{definition}\\label{def-logprism}\nThe absolute logarithmic prismatic site $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ is the opposite of the category whose objects are \n\\begin{enumerate}\n \\item bounded log prisms $(A,I,M_A)$ with \\textit{integral} log structure,\n \\item maps of formal schemes $f_A: \\Spf(A\/IA) \\to X$,\n \\item the map $f_A$ satisfies\n$$\n(\\Spf(A\/IA),f_A^\\ast M_X) \\to (\\Spf(A),M_A)^a\n$$\ndefines an exact closed immersion of log formal schemes.\n\\end{enumerate}\nA morphism $(A,I,M_A) \\to (B,I,M_B)$ is a cover if and only if $A \\to B$ is $(p,I)$-complete faithfully flat and the pullback induces an isomorphism on log structure. We define the structure sheaf $\\O_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ on $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ by $(A,I,M_A) \\mapsto A$.\n\\end{definition}\n\nThere is a variant of the about definition that we will also use in this subsection, we define $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}^{\\perf}$ be the full subcategory of $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ whose objects are $(A,I,M_A)$ with $A$ perfect.\n\n\\begin{remark}\nOur definition of the absolute logarithmic prismatic site is different from \\cite[Definition 4.1]{Koshikawa2021log-prism}. First, we need to consider the absolute prismatic site, not the relative one. Furthermore, we use the $(p,I)$-complete faithfully flat topology compared with the $(p,I)$-complete \\'etale topology. Also we require the log-structures to be integral. \n\\end{remark}\n\n\\begin{proposition}\\label{prop-logprismisasite}\n$(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ forms a site.\n\\end{proposition}\n\\begin{proof}\nSimilar to \\cite[Corollary 3.12]{BS19}, we need to show for a given diagram \n$$\n\\begin{tikzcd}\n(C,I,M_C) & (A,I,M_A) \\arrow[l,\"c\",swap] \\arrow[r,\"b\"] & (B,I,M_B)\n\\end{tikzcd}\n$$\nin $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ such that $b$ is a cover, then the pushout of $b$ along $c$ is a covering. From the argument in $loc. cit.$, we known for the underlying prisms, the pushout of $b$ along $c$ is the $(p,I)$-completed tensor product $D=C\\widehat {\\otimes}_A B$, and $(D,I)$ is a bounded prism covers $(C,I)$ in the $(p,I)$-complete faithful flat topology. And we give $D$ the log structure $M_D$ defined by viewing $\\Spf(D)$ as the fiber product via \\cite[Proposition 2.1.2]{Ogus_logbook}, then $(C,M_C)\\to (D,M_D)$ is strict morphism by Remark 2.1.3 of $loc.cit.$, so in particular, $M_D$ is integral since $M_C$ is. For the same reason, \n$$\n(\\Spf(D\/ID),f_D^\\ast M_X) \\to (\\Spf(D),M_D)^a\n$$ is strict since it is the base change of a strict morphism. It is an exact closed immersion since pushout of a surjective map of monoids is again surjective.\n\\end{proof}\n\n\\begin{example}\\cite[Example 3.4]{Koshikawa2021log-prism}\\label{exa-logprism}\n\\begin{enumerate}\n \\item Let $(A,(E))$ be the Breuil-Kisin prism, then we can define a perlog structure to $(A,(E))$ given by $\\mathbb N \\to A; n\\mapsto u^n$, one have $(A,(E),\\mathbb N)^a$ is in $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$, where (3) in Definition~\\ref{def-logprism} follows from the prelog structures $\\mathbb N \\to R \\to A\/(E)$ and $\\mathbb N \\to A \\to A\/(E)$ induce the same log structure.\n \\item For any prism $(B,J)$ over $(A,(E))$, it has a natural prelog structure $\\mathbb N \\to A \\to B$, and similar to $(1)$, $(B,J,\\mathbb N)^a$ is in $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$.\n \\item A special case of (2) is that $(B,J)=(A_{\\perf},(E))$, the perfection of $(A,(E))$. One has the prelog structure in (2) can be directly defined as $1\\mapsto [\\varpi^\\flat]$. And $(A,(E),\\mathbb N)^a \\to(B,J,\\mathbb N)^a$ is a covering of log prisms in $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$.\n \\end{enumerate}\n\\end{example}\n\nActually, all logarithmic structures of log prisms in $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ is the log structure associated to a prelog structure defined by $\\mathbb N$. We thank Teruhisa Koshikawa for letting us know the following lemma.\n\n\\begin{lemma}\\label{lem-Nchart}\nFor any log prism $(B,J,M_B)$ inside $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$, $(B,M_B)^a$ admits a chart $\\mathbb N \\to B$ defined by $n \\mapsto u_B^n$ for some $u_B\\in B$ satisfying $u_B \\equiv \\varpi \\mod J$. \n\\end{lemma}\n\\begin{proof}\nFor any log prism $(B,J,M_B)$ inside $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$, we have \n$$\n(\\Spf(B\/J),f_B^\\ast M_X) \\to (\\Spf(B),M_B)^a\n$$\ndefines an exact closed immersion of log formal schemes. So by the proof of \\cite[Proposition 3.7]{Koshikawa2021log-prism}, if we let $N^a_{B\/J}:=\\Gamma(\\Spf(B\/J),\\underline{\\mathbb N}^a)$ for the prelog structure $\\mathbb N \\to \\O_K \\to B\/J$ induced from the given prelog structure on $\\O_K$, then the fiber product $M_B \\times_{N^a_{B\/J}} \\mathbb N$ is a chart for $(B,M_B)^a$. Moreover, since we assume $M_B$ to be integral, we have $(\\Spf(B\/J),f_B^\\ast M_X) \\to (\\Spf(B),M_B)^a$ is a log thickening with ideal $J$ in the sense of \\cite[Definition 2.1.1.]{Ogus_logbook}, and one can show $M_B \\times_{N^a_{B\/J}} \\mathbb N \\simeq \\mathbb N \\times (1+J)$. Now $(1+J)^\\times =(1+J)$, so \n$$\n\\mathbb N \\to \\mathbb N \\times (1+J) \\simeq M_B \\times_{N^a_{B\/J}} \\mathbb N \\to B\n$$\nis also a chart for $(B,M_B)^a$. And the prelog structure given by $n \\mapsto u_B^n$ for some $u_B\\in B$ satisfying the image of $u_B$ in $B\/J$ coincides with the image of $\\varpi$ under $\\O_K \\to B\/J$.\n\\end{proof}\n\nIn the rest of this subsection, we will try to generalize results we proved in \\S\\ref{subsec-pris-crystal}-\\S\\ref{subsec-pris-crystal-proof} for the logarithmic prismatic site. \n\n\\begin{lemma}\\label{lem-log-nonemptyproduct}\n\\begin{enumerate}\n \\item For $(A,I_A,M_A)^a, (B,I_B,M_B)^a\\in (X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ such that $M_A,M_B$ are integral and $(A,M_A)\\to (A\/I_A,\\mathbb N)$ and $(B,M_B)\\to (B\/I_B,\\mathbb N)$ are exact surjective, there is a prelog prism $(C,I_C,M_C)$ with integral log structure that is universal in the sense that the diagram \n $$\n \\begin{tikzcd}\n (A,I_A,M_A) \\arrow[r] & (C,I_C,M_C) & (B,I_B,M_B) \\arrow[l] \n \\end{tikzcd}\n $$\n is initial in the category of diagrams \n $$\n \\begin{tikzcd}\n (A,I_A,M_A) \\arrow[r] & (D,I_D,M_D) & (B,I_B,M_B) \\arrow[l] \n \\end{tikzcd}\n $$\n of prelog prisms over $(R,\\mathbb N)$, and $(D,M_D)\\to (D\/I_D,\\mathbb N)$ is an exact surjective.\n \\item If $(C,I_C)$ in (1) is bounded, then $(C,I_C,M_C)^a$ is the product of $(A,I_A,M_A)^a$ and $(B,I_B,M_B)^a$ inside $ (X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$.\n \\item If $(A,I_A,M_A)^a, (B,I_B,M_B)^a$ in (1) are in $ (X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}^{\\perf}$, and let $(C_{\\perf},I_C)$ be the perfection of $(C,I_C)$ defined in (1). Let $(C_{\\perf},I_C,M_C)$ be the prelog prism with prelog structure induced from $C$. Then $(C_{\\perf},I_C,M_C)^a$ is the product of $(A,I_A,M_A)^a$ and $(B,I_B,M_B)^a$ in $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}^{\\perf}$. \n\\end{enumerate}\n\\end{lemma}\n\\begin{proof}\nLet $(A,I_A,M_A),(B, I_B, M_B)\\in (X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$, define $C_0$ to be the $(p,I_A,I_B)$-adic completion of $A\\otimes_{W(k)}B$ and let $J$ be the kernel of\n$$\nC_0 \\to A\/I_A \\widehat {\\otimes}_R B\/I_B.\n$$\nThen $(C_0,J,M_A\\times M_B)$ is a $\\deltalog$-triple over $(A,I_A,M_A)$. And we have $(C_0,J,M_A\\times M_B) \\to (C_0\/J,\\mathbb N)$ is surjective. Then we can apply \\cite[Proposition 3.6]{Koshikawa2021log-prism} to get a universal prelog prism $(C,I_C,M_C)$ over $(A,I_A,M_A)$ and $(B, I_B, M_B)$ and satisfies $(C,M_C)\\to (C\/J,\\mathbb N)$ is exact surjective. Just recall in the proof of \\cite[Proposition 3.6]{Koshikawa2021log-prism}, we first construct a $\\deltalog$-triple $(C',J',M_C')$ which is universal in the sense that it is a $\\deltalog$-triple over both $(A,I_A,M_A)$ and $(B, I_B, M_B)$ satisfying $C'\/J'$ is over $A\/I_A$ and $B\/I_B$ as $R$-algebra and $(C',M_C')\\to (C'\/J',\\mathbb N)$ is exact surjective. Then we take the prismatic envelope with respect to $(A,I_A) \\to (C',J')$ to get $(C,I_C)$. Then we can check such $(C,I_C,M_C)$ satisfies the universal property. For (2), when $(C,I_C)$ is bounded, the fact that $(C,I_C,M_C)^a$ is the product of $(A,I_A,M_A)^a$ and $(B,I_B,M_B)^a$ inside $ (X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ follows from Proposition 3.7 of $loc.cit.$. For (3), we have $(C_{\\perf},I_C)$ is automatic bounded, and one can check $(C_{\\perf},I_C)$ is universal using exactly the same proof of Proposition 3.7 of $loc.cit.$.\n\\end{proof}\n\nWe thank Koji Shimizu for the following lemma on $A^{(2)}_{\\st}$. \n\n\\begin{lemma}\\label{lem-Ast2islogprism}\nLet $(A,I,\\mathbb N)^a$ be the Breuil-Kisin prism defined in $(1)$ of Example~\\ref{exa-logprism}, then the self-product (resp. self-triple product) of $(A,I,\\mathbb N)^a$ in $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ exist. Moreover, if we let $(A^{\\langle 2 \\rangle},I,M^2)^a$ (resp. $(A^{\\langle 3 \\rangle},I,M^3)^a$) be self-product (resp. self-triple product) of $(A,I,\\mathbb N)^a$, then $A^{\\langle i \\rangle}\\simeq A^{(i)}_{\\st}$ for $i=2,3$.\n\\end{lemma}\n\\begin{proof}\nBy our construction in Lemma~\\ref{lem-log-nonemptyproduct}, $(A^{\\langle 2 \\rangle},I,M)$ is the prelog prismatic envelope $(C,I_C,M_C)$ with respect to\n $$\n (A,(E),\\mathbb N) \\to (C_0,J,\\mathbb N^2) \\text{ and } (C_0\/J,\\mathbb N^2)\\to (R,\\mathbb N)\n $$\n where $C_0=W[\\![u,v]\\!]$, $J=(E(u),u-v)$ with the prelog structure given by $\\beta: (1,0)\\mapsto u, (0,1)\\mapsto v$. The prelog prismatic envelope is constructed using the technique of exactification: consider $\\pi: (C_0,\\mathbb N^2)\\to (R=C\/J,\\mathbb N)$ where the map between log structures is given by $\\pi_{\\log}: \\mathbb N\\times \\mathbb N \\to \\mathbb N;(m,n)\\mapsto m+n$, here $\\pi_{\\log}$ is surjective but not exact, so to constructsthe exactification of $\\pi: (C,\\mathbb N^2)\\to (R,\\mathbb N)$ (cf. \\cite[Construction 2.18]{Koshikawa2021log-prism}), first we have the exactification of $\\pi_{\\log}$ is \n $$\n \\alpha: M^2 \\to \\mathbb N \\quad \\text{ given by } \\quad (m,n) \\mapsto m+n,\n $$\n where $M^2=\\{(m,n)\\in \\mathbb Z\\times \\mathbb Z \\,|\\, m+n\\in \\mathbb N \\}$. Since $M^2$ is generated by $(-1,1)$, $(1,-1)$, $(0,1)$ and $(1,0)$, one has the exactification of $\\pi$ is \n $$\n \\Big( W(k)[\\![u,v]\\!]\\big[\\frac{v}{u},\\frac{u}{v}\\big]^\\wedge_{(p,J')}, J',M^2; \\alpha: (1,0)\\mapsto {u}, (0,1)\\mapsto v, (1,-1)\\mapsto \\frac{u}{v}, (-1,1)\\mapsto \\frac{v}{u} \\Big)\n $$\n where $J':=\\ker(W(k)[\\![u,v]\\!]\\big[\\frac{v}{u},\\frac{u}{v}\\big] \\to R)$. \n \n We have the $(p,J')$-adic completion of $W(k)[\\![u,v]\\!]\\big[\\frac{v}{u},\\frac{u}{v}\\big]$ is $W(k)[\\![u,\\frac{v}{u}-1]\\!]$. Then take prismatic envelope of \n $\n (A,(E))\\to (W(k)[\\![u,\\frac{v}{u}-1]\\!], (E,\\frac{v}{u}-1)).\n $ One can check \n $$ W(k)[\\![u,\\frac{v}{u}-1]\\!]\\big\\{\\frac{v\/u-1}{E(u)}\\big\\}^\\wedge_\\delta \\simeq A_{\\st}^{(2)}$$ \n directly from the definition of $A_{\\st}^{(2)}$.\n \n Similarly, we can show $A^{\\langle 3 \\rangle}\\simeq A^{(3)}_{\\st}$ which is also bounded. \n\\end{proof}\n\nThe following is one of our key observations.\n\\begin{lemma}\\label{lem-perfectionofA2andAst2}\nWe have $(A^{\\langle 2 \\rangle})_{\\perf} \\simeq (A^{(2)})_{\\perf}$.\n\\end{lemma}\n\\begin{proof}\nLet $u_1,u_2$ be the image of $u$ under the two natural maps $i_{j}: A_{\\perf} \\to (A^{(2)})_{\\perf}$ for $j=1,2$. We claim that $u_2\/u_1$ is inside $(A^{(2)})_{\\perf}$.\n\nFirstly, we have already shown $A_{\\perf}\\simeq W(\\widehat{\\O}_{K_\\infty}^\\flat)$ and $u=[\\varpi^\\flat]$, here $\\varpi^\\flat=(\\varpi_n)$ with $\\{\\varpi_n\\}_{n\\geq 0}$ being a compatible system of $p^n$-th roots of $\\varpi$ inside $\\O_{\\widehat{K}_\\infty}$, and $(\\varpi_n) \\in \\O_{\\widehat K_\\infty}^\\flat$ via the identification $\\O_{\\widehat K_\\infty}^\\flat \\simeq \\lim_{x \\mapsto x^p} \\O_{\\widehat K_\\infty}$. Let $S=(A^{(2)})_{\\perf}\/(E)$, this is an integral perfectoid ring over $\\O_K$ in the sense of \\cite{BMS1}. We have $S^\\flat\\simeq (A^{(2)})_{\\perf}\/(p)$. For $j=1,2$, define $\\varpi_j^\\flat=u_j \\mod (p) \\in S^\\flat$, then we have $u_j=[\\varpi_j^\\flat]$ for $j=1,2$.\n\nRecall in \\S~\\ref{subsrc-construct-A2}, we have $z = \\frac{y -x}{E(x)}$ in $A^{(2)}$. Since $ E(x) \\equiv x^e \\mod p$, we have $ x (1 + x^{e-1} z) \\equiv y \\mod p$. If we denote $\\iota : A^{(2)} \\to (A^{(2)})_{\\perf} $ the natural map, then $\\iota(x)=u_1$ and $\\iota(y)=u_2$ in our definition, and $u _1 (1 + u_1^{e-1} \\iota(z)) \\equiv u _2 \\mod p$ inside $S^\\flat=A^{(2)}_{\\perf}\/(p)$. This is the same as $\\varpi_1^\\flat \\mu = \\varpi_2^\\flat$ with $\\mu = (1 + u_1^{e-1} \\iota(z)) \\mod p$ in $S ^\\flat$. So we have $ [\\mu] u _1 = [\\mu] [\\varpi_1^\\flat] = [\\varpi_2^\\flat]= u_2$, which proves our claim.\n\n\nNow by symmetry, $u_1\/u_2$ is also inside $(A^{(2)})_{\\perf}$, so $u_1\/u_2$ is a unit in $(A^{(2)})_{\\perf}$. So we can give $(A^{(2)})_{\\perf}$ a prelog structure\n$$\n\\alpha: M^2 \\to (A^{(2)})_{\\perf} \\text{ with } (1,-1)\\mapsto \\frac{u_1}{u_2}, (-1,1)\\mapsto \\frac{u_2}{u_1}, (1,0)\\mapsto {u_1}, (0,1)\\mapsto {u_2}\n$$\nwith the monoid $M^2$ defined as in the proof of Lemma~\\ref{lem-Ast2islogprism}, then $((A^{(2)})_{\\perf},(E),M^2)^a$ is in $X_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}^{\\perf}$. \n\nOne can check the maps $i_1,i_2: (A,(E)) \\to (A^{(2)},(E)) \\to ((A^{(2)})_{\\perf},(E))$ induce $i_1,i_2: (A_{\\perf},(E),\\mathbb N) \\to ((A^{(2)})_{\\perf},(E),M^2)$ of prelog prisms. So by Lemma~\\ref{lem-Ast2islogprism}, there is a unique map $(A^{\\langle 2 \\rangle},I,M^2)\\to ((A^{(2)})_{\\perf},(E),M^2)$, which factors through $((A^{\\langle 2 \\rangle})_{\\perf},(E),M^2)$. So it induces a map $((A^{\\langle 2 \\rangle})_{\\perf},(E),M^2) \\to ((A^{(2)})_{\\perf},(E),M^2)$ inside $X_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}^{\\perf}$. On the other hand, by the universal property of $A^{(2)}$, we know there is a map $(A^{(2)})_{\\perf} \\to (A^{\\langle 2 \\rangle})_{\\perf}$ fits into the coproduct diagram in $X_{{\\mathlarger{\\mathbbl{\\Delta}}}}^{\\perf}$, which is the full subcategory of $X_{\\mathlarger{\\mathbbl{\\Delta}}}$ containing perfect prisms.\n\nOne can check the composition $\\eta: ((A^{(2)})_{\\perf},(E)) \\to ((A^{\\langle 2 \\rangle})_{\\perf},(E)) \\to ((A^{(2)})_{\\perf},(E))$ satisfies $\\eta\\circ i_j= i_j \\circ \\eta$ for $i_1,i_2:(A_{\\perf},(E))\\to ((A^{(2)})_{\\perf},(E))$. Such a map is unique inside $X_{{\\mathlarger{\\mathbbl{\\Delta}}}}^{\\perf}$, so $\\eta=\\id_{((A^{(2)})_{\\perf},(E))}$. \n\nOn the other hand, the composition \n$$\n\\eta': ((A^{\\langle 2 \\rangle})_{\\perf},(E),M^2)^a \\to ((A^{(2)})_{\\perf},(E),M^2)^a \\to ((A^{\\langle 2 \\rangle})_{\\perf},(E),M^2)^a\n$$ satisfies $\\eta\\circ i'_j= i'_j \\circ \\eta$ for $i'_1,i'_2:(A_{\\perf},(E),\\mathbb N)^a\\to ((A^{\\langle 2 \\rangle})_{\\perf},(E),M^2)^a$ induced from $i'_1,i'_2:(A,(E),\\mathbb N)\\to (A^{\\langle 2\\rangle},(E),M^2)$. Such map is also unique inside $X_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}^{\\perf}$, so $\\eta'=\\id_{((A^{\\langle 2 \\rangle})_{\\perf},(E),M^2)^a}$. So in particular we have $(A^{\\langle 2 \\rangle})_{\\perf}\\simeq (A^{(2)})_{\\perf}$.\n\\end{proof}\n\n\n\\begin{theorem}\\label{thm-log-phitau}\nThe category of \\'etale $\\varphi$-module over $A[1\/E]^\\wedge_p$ with a descent data over $A_{\\st}^{(2)}[1\/E]^\\wedge_p$ is equivalent to the category of lattice in representations of $G_K$. Moreover, for all $\\gamma\\in\\hat{G}$, we can define the evaluation map\n$$\ne_\\gamma: A_{\\st}^{(2)}[1\/E]^\\wedge_p \\to W(\\hat{L}^\\flat)\n$$\nsuch that Lemma~\\ref{lem-evaluation-1} is still valid. Moreover, the $\\mathbb Q$-isogeney version of this theorem also holds.\n\\end{theorem}\n\n\\begin{remark}\nThe above theorem should be related to the \\'etale comparison theorem in the log prismatic settings, which has not been studied in \\cite{Koshikawa2021log-prism} yet.\n\\end{remark}\n\nMoreover, we have a log version of Lemma~\\ref{lem-AEcoversfinal} also holds. We thank Teruhisa Koshikawa for hints of the following result.\n\n\\begin{proposition}\\label{prop-logcoverfinalobject}\nThe sheaf represented by $(A,(E),\\mathbb N)^a$ covers the final object $\\ast$ in in $\\Shv((X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}})$.\n\\end{proposition}\n\\begin{proof}\nFor any log prism $(B,J,M_B)$, by Lemma~\\ref{lem-Nchart}, we can assume $(B,J,M_B)^a=(B,J,\\mathbb N)^a$, with prelog structure defined by $n \\mapsto u_B^n$ with $u_B \\equiv \\varpi \\mod J$.\n\nUsing deformation theory, we have there is a unique $W(k)$-algebra structure for $B$, and we define $C=B[\\![u]\\!][\\frac{u_B}{u},\\frac{u}{u_B}]\\{\\frac{u_B\/u-1}{J}\\}^\\wedge_\\delta$, where the completion is taken for the $(p,J)$-adic topology. Similar to the proof of Lemma~\\ref{lem-Ast2islogprism}, we have $(C,JC,\\mathbb N)^a$ is the product of $(A,(E),\\mathbb N)^a$ and $(B,J,\\mathbb N)^a$ inside $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$. Moreover, we have $B \\to C$ is $(p, J)$-complete flat by \\cite[Proposition 3.13]{BS19}. It remains to show that $(B,J) \\to (C,J)$ is a covering, i.e., $B \\to C$ is $(p, J)$-complete faithfully flat. Let \n$$\nC^{nc}:=B[\\![u]\\!][\\frac{u_B}{u},\\frac{u}{u_B}]\\{\\frac{u_B\/u-1}{J}\\}_{\\delta}\n$$\nbe the non-complete version of $C$ that we have the $(p,J)$-adic completion of $C^{nc}$ is $C$. Now we just need to show the flat ring map $B\/(p,J) \\to C\/(p,J)=C^{nc}\/(p,J)$ is also faithful. \n\nWe claim that $C\/ (p , J)$ is free over $B \/ (p , J)$. One has $JC=E(u)C$, and $(p , J)= (p , E) = (p , J , E)$ in $C$. So $C\/ (p , J)=C^{nc}\/(p,J)$ is equal to \n\\[ B [\\![u ]\\!][\\frac{u_B}{u}, \\frac{u}{u _B}][\\delta^i(z), i \\geq 0 ]\/ \\left (p , J , E , Ez = \\frac{u_B}{u }-1 , \\delta^i (\\frac{u_B}{u }-1))= \\delta^i (Ez), i \\geq 1 \\right ).\\]\nAfter modulo $(p,J)$, the above is the direct limit of\n\\[B \/ (p , J)[\\delta ^i (z)]\/ \\left (\\delta^i (\\frac{u_B}{u }-1))= \\delta^i (Ez) \\mod (p, E , J) \\right )\\]\nfor $i\\geq 0$.\n\nNow we use Lemma \\ref{lem-delta-n} to compute $\\delta^i (\\frac{u_B}{u }-1)= \\delta^i (Ez) \\mod (p, E , J)$. We keep the notations in Lemma \\ref{lem-delta-n}, by induction, we have $b_n = 0 \\mod (p , E)$. Using that $a_p^{(j)}\\in A_0^\\times$, $\\delta^i (\\frac{u_B}{u }-1)= \\delta^i (Ez) \\mod (p, E , J)$ gives a relation $ (z_{i -1}) ^ p = \\sum\\limits_{j= 0}^{p -1} \\tilde a_j^{(i)} (z_{i-1}) ^j$ where $z_i = \\mathfrak z_i \\mod (p , J , E)$ and $\\tilde a_j^{(i)} \\in B \/ (p , J)[z_0, z_1, \\dots, z_{i-2}]$. In summary, we have \n$$C\/ (p , J) = B\/(p, J)[z_i, i \\geq 0]\\Bigg\/ \\left ((z_{i}) ^ p - \\sum\\limits_{j= 0}^{p -1} \\tilde a_j^{(i)} (z_{i}) ^j, i\\geq 1 \\right )$$\nwhich is free over $B \/(p, J)$. \n\\end{proof}\n\n\\begin{definition}\\label{def-logFcrystal}\n\\begin{enumerate}\n \\item \nA prismatic crystal over $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ in finite locally free $\\O_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$-modules is a finite locally free $\\O_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$-module $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ such that for all morphisms $f: (A, I,M_A) \\to (B, J,M_B)$ of log prisms, it induces an isomorphism:\n$$\nf^\\ast \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}},A} := \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((A,I,M_A))\\otimes_A B \\simeq \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}},B} := \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}((B,J,M_B))\n$$\n\n\\item A prismatic $F$-crystal over $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ of height $h$ (in finite locally free $\\O_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$-modules) is a prismatic crystal $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ in finite locally free $\\O_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$-modules together with a $\\varphi_{\\O_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}}$-semilinear endomorphism $\\varphi_{\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}}$ of the $\\O_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$-module $\\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}: \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}} \\to \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ such that the cokernel of the linearization $\\varphi^\\ast \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}} \\to \\mathfrak{M}_{{\\mathlarger{\\mathbbl{\\Delta}}}}$ is killed by $\\mathcal{I}_{\\mathlarger{\\mathbbl{\\Delta}}}^h$.\n\n\\end{enumerate}\n\\end{definition}\n\nIn particular, with help of Theorem~\\ref{thm-log-phitau} and Proposition~\\ref{prop-logcoverfinalobject}, a direct translation of proofs in \\S\\ref{subsec-pris-crystal-proof} with $A^{(2)}$ replaced by $A^{(2)}_{\\st}$ shows the following theorem.\n\\begin{theorem}\\label{thm-log-main-1}\nThe category of prismatic $F$-crystals over $(X,M_X)_{{\\mathlarger{\\mathbbl{\\Delta}}}_{\\log}}$ of height $h$ is equivalent to the category of lattices in semi-stable representations of $G_K$ with Hodge-Tate weights between $0$ and $h$.\n\\end{theorem}\n\n\\section{Some discussions on base rings}\\label{subsec-baserings} In this section, we show that our base ring assumed at the beginning of \\S \\ref{sec-ring-strcuture} covers many situations of base rings used in \\cite{Kim12} and \\cite{Brinon}. \n\nLet $K$ be complete DVR with perfect residue field $k$, and let $K_0=W[\\frac{1}{p}]$ with $W=W(k)$, fix a uniformizer $\\varpi\\in \\O_K$ and $E(u)\\in W[u]$ a minimal polynomial of $\\varpi$ over $K_0$. Let $R$ be a normal domain and satisfies that $R$ is a $p$-complete flat $\\O_K$-algebra that is complete with respect to $J$-adic topology, for an ideal $J=(\\varpi, {t_1},\\ldots,{t_d})$ of $R$ containing $\\varpi$. We also assume $\\overline{R}=R\/(\\varpi)$ is a finite generated $k$-algebra with \\emph{finite $p$-basis} discussed in \\cite[\\S 1.1]{deJong}.\n\n\\begin{lemma}[\\cite{Kim12} Lemma 2.3.1 and lemma 2.3.4]\\label{Kim-lemma}\n\\begin{enumerate}\n \\item In the above setting, there is a $p$-adic formally smooth flat $W$-algebra $R_0$ equipped with a Frobenius lift $\\varphi_0$ such that $\\overline{R}: = R_0\/(p)$. Moreover let $J_0$ be the preimage of $\\overline{J}$ inside $R_0$, then $R_0$ is $J_0$-adically complete, and under this topology, $R_0$ is formally smooth. \n \\item $R_0\/(p)\\xrightarrow{\\sim}R\/(\\varpi)$ lifts to a $W$-algebra morphism $R_0 \\to R$ and the induced $\\O_K$-algebra morphism $\\O_K\\otimes_W R_0 \\to R$ is an isomorphism. Moreover this isomorphism is continuous with respect to the $J_0$-adic topology.\n\\end{enumerate}\n\\end{lemma}\n\nLet $(R_0, \\varphi_{R_0})$ denote a flat $W$-lift of $R\/(\\varpi)$ obtained from the above lemma. And we will have $J_0=(p, t_1, \\ldots, t_d)\\in R_0$, and we write $\\overline{J}=(\\overline{t_1},\\ldots, \\overline{t_d})\\subset \\overline{R}$. \n\n\\begin{definition}\\label{RAE}\nLet $R_0$ be a $p$-complete $\\mathbb Z_p$-algebra, we say $R_0$ satisfies the ``refined almost \u00e9talenes\" assumption, or simply RAE assumption, if $\\hat{\\Omega}_{R_0}=\\oplus_{i=1}^m R_0 dT_i$ with $T_i\\in R_0^\\times$. Where $\\hat{\\Omega}_{R_0}$ is the module of of $p$-adically continuous K\\\"ahler differentials.\n\\end{definition}\nThe following are examples of $R_0$ and $R$ which satisfy assumptions of Lemma \\ref{Kim-lemma} and RAE assumption. \n\\begin{example}\n\\begin{enumerate}\n \\item If $R\/(\\varpi)$ is a completed noetherian regular local ring with residue field $k$, then Cohen structure theorem implies\n $R\/(\\varpi)=k[\\![\\overline{x_1},\\ldots,\\overline{x_d}]\\!]$. In this case, $R_0=W[\\![x_1,\\ldots, x_d]\\!]$ and $J_0=(p,x_1,\\ldots, x_d)$. Then $R=W[\\![x_1,\\ldots, x_d]\\!][u]\/E$, with $E\\in W[u]$ is a Eisenstein polynomial.\n \\item Let $R_0 = W(k) \\langle t _1^{\\pm 1} , \\dots , t _m ^{\\pm 1}\\rangle$ and $J_0=(p)$, in this example, $\\overline{R}=k[\\overline{t}_1^{\\pm 1} , \\dots , \\overline{t}_m ^{\\pm 1}]$ is not local.\n \\item An unramified complete DVR $(R_0 , p)$ with residue field $k$ so that $[k : k ^p]<\\infty$. \n \\item Note the the Frobenius liftings in Lemma ~\\ref{Kim-lemma} is not unique. In (2) we can choose $\\varphi_{R_0}(t_i)=t_i^p$. In (1), we can choose the $\\varphi_{R_0}(x_i)=x_i^p$ or $\\varphi_{R_0}(x_i)=(x_i+1)^p-1$.\n\\end{enumerate}\n\\end{example}\nLet $R_0$ be $p$-complete algebra which satisfies the RAE assumption, Set $\\breve R_0 = W\\langle t_1 , \\dots , t_m \\rangle$ and $f : \\breve R_0 \\to R_0$ by sending $t_i$ to $T_i$. \n\\begin{proposition}\\label{prop-fetale} Assume that $R_0$ is a $p$-complete integral domain which admits finite $p$-basis and satisfies RAE assumption. \nThen $f$ is formally \\'etale $p$-adically. \n\\end{proposition}\n\\begin{proof} We thanks for Wansu Kim providing the following proof. By standard technique using \\cite[Ch.III, Corollaire 2.1.3.3]{Illusie1} (e.g., see the proof in \\cite[Lem. 2.3.1]{Kim12}), it suffices to show that the cotangent complex \n$\\mathbb L_{R_0 \/ \\breve R_0}$ is acyclic. Since both $R_0$ and $\\breve R_0$ are $\\mathbb Z_p$-flat, it suffice to show that $\\mathbb L_{R_1 \/ \\breve R_1}$ is acyclic where $R_1 = R_ 0 \/ p R_0$ and $\\breve R_1 = \\breve R_0 \/ p \\breve R_0$. Since $R_0$ has finite $p$-basis, by \\cite[Lem. 1.1.2]{deJong}, $\\mathbb L_{R_1 \/k}\\simeq \\Omega_{R_1\/k}$. Note that maps $k \\to \\breve R_1 \\to R_1$ induces a fiber sequence \n\\[ \\mathbb L_{\\breve R_1 \/k}\\otimes^{\\mathbb L}_{\\breve R_1} R_1 \\to \\mathbb L _{R_1 \/ k} \\to \\mathbb L_{R_1 \/ \\breve R_1}\\]\nSince that $ \\mathbb L_{\\breve R_1 \/k} \\simeq \\Omega_{\\breve R_1\/k}$ and $\\Omega_{\\breve R_1\/k}\\simeq \\Omega_{R_1\/k}$ by RAE condition, we conclude that $\\mathbb L_{R_1\/ \\breve R_1}= 0$ as required. \n\\end{proof}\nLet us end with a discussion about our base rings and the base rings used in \\cite{Brinon}. As explained in the beginning of \\cite[Chap. 2]{Brinon}, his base ring $R_0$ in \\cite{Brinon} is obtained from $W\\langle t_1^{\\pm 1}, \\ldots, t_m^{\\pm 1}\\rangle$ by a finite number of iterations of certain operations and is also assumed to satisfy certain properties. By Prop. 2.0.2 \\emph{loc. cit.}, we see that $R_0$ has finite $p$-basis and satisfies RAE assumption. 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\\newbox\\ncinttbox\n\t\\setbox0=\\hbox{$-$}\n\t\\setbox2=\\hbox{$\\displaystyle\\int$}\n\t\\setbox\\ncintdbox=\\hbox{\\rlap{\\hbox\n\t\tto \\wd2{\\hskip-.125em \\box2\\relax\\hfil}}\\box0\\kern.1em}\n\t\\setbox0=\\hbox{$\\vcenter{\\hrule width 4pt}$}\n\t\\setbox2=\\hbox{$\\textstyle\\int$}\n\t\\setbox\\ncinttbox=\\hbox{\\rlap{\\hbox\n\t\tto \\wd2{\\hskip-.175em \\box2\\relax\\hfil}}\\box0\\kern.1em}\n\\newcommand{\\ncint}{\\mathop{\\mathchoice{\\copy\\ncintdbox}%\n\t\t\t\t\t{\\copy\\ncinttbox}{\\copy\\ncinttbox}%\n\t\t\t\t\t{\\copy\\ncinttbox}}\\nolimits}\n\n\\newcommand{\\commentA}[1]{\\textcolor{red}{\\textsf{A: #1}}}\n\\newcommand{\\commentJ}[1]{\\textcolor{blue}{\\textsf{J: #1}}}\n\\newcommand{\\commentS}[1]{\\textcolor{green}{\\textsf{S: #1}}}\n\n\\newcommand{\\raisebox{-2pt}{$\\overset{\\cdotp\\,\\,\\cdotp}{\\frown}$}}{\\raisebox{-2pt}{$\\overset{\\cdotp\\,\\,\\cdotp}{\\frown}$}}\n\\newcommand{\\raisebox{-2pt}{$\\overset{\\cdotp\\,\\,\\cdotp}{\\smile}$}}{\\raisebox{-2pt}{$\\overset{\\cdotp\\,\\,\\cdotp}{\\smile}$}}\n\n\n\\hyphenation{geo-me-try ma-ni-fold ma-ni-folds pro-duct pro-ducts}\n\n\n\n\\begin{document}\n\n\\maketitle\n\n\\vspace{-2pc}\n\n\n\n\\begin{abstract}\nLet $B$ be a $C^{*}$-algebra, $X$ a Hilbert $C^{*}$-module over $B$ and $M,N\\subset X$ a pair of complemented submodules. We prove the $C^{*}$-module version of von Neumann's alternating projections theorem: the sequence $(P_{N}P_{M})^{n}$ is Cauchy in the $*$-strong module topology if and only if $M\\cap N$ is the complement of $\\overline{M^{\\perp}+N^{\\perp}}$. In this case, the $*$-strong limit of $(P_{M}P_{N})^{n}$ is the orthogonal projection onto $M\\cap N$. We use this result and the local-global principle to show that the cosine of the Friedrichs angle $c(M,N)$ between any pair of complemented submodules $M,N\\subset X$ is well-defined and that $c(M,N)<1$ if and only if $M\\cap N$ is complemented and $M+N$ is closed. \n\\end{abstract}\n\n{\\bf Keywords:}\n{\\small two projections, von Neumann's alternating projection theorem, Friedrichs angle, Hilbert $C^{*}$-module, local-global principle.}\n\n{\\bf MSC2020:} {\\small 46L08, 47A46}\n\n\\parskip=6pt\n\\parindent=0pt\n\\allowdisplaybreaks\n\\section*{Introduction}\n\nIn this note we offer a new and general approach to the two projection problem in Hilbert $C^{*}$-modules.\nAs an application we extend and improve upon several of the main results in the recent work of \\cite{Luo} by giving new proofs that allow for the removal of a key hypothesis. \n\nBriefly, we begin by proving the Hilbert $C^{*}$-module version of von Neumann's alternating projections theorem, which computes the projection onto $M\\cap N$ for a concordant pair of complemented submodules $M,N$ (see below). We then proceed to use this result to define the Friedrichs angle between an arbitrary pair of complemented submodules. The angle is realised as a function on the space of representations of the coefficient algebra of the module. The properties of the Friedrichs angle give necessary and sufficient conditions for the sum and intersection of two complemented submodules to again be complemented.\nWe now give a little more detail on these results. \n\nGiven two closed subspaces $M,N$ of a Hilbert space $H$ there is an orthogonal direct sum decomposition\n\\begin{equation}\n\\label{directsum}\nH=(M\\cap N)\\oplus \\overline{(M^{\\perp}+N^{\\perp})}.\n\\end{equation}\nA fundamental result of von Neumann, the \\emph{method of alternating projections}, states that the projection $P_{M\\cap N}$ onto $M\\cap N$ can be obtained as the $*$-strong limit\n\\[P_{M\\cap N}=s-\\lim_{n\\to\\infty} (P_{M}P_{N})^{n}=s-\\lim_{n\\to\\infty} (P_{N}P_{M})^{n}.\\]\nThe (cosine) of the \\emph{Friedrichs angle between $M$ and $N$} is the quantity \n$$\nc(M,N):=\\|P_{M}P_{N}-P_{M\\cap N}\\|,\n$$ \nand the subspace $M^{\\perp}+N^{\\perp}$ is closed if and only if $c(M,N)<1$.\n\nIn this paper we consider a pair $(M,N)$ of complemented submodules of a Hilbert $C^{*}$-module $X$ over a $C^{*}$-algebra $B$. It is well-known that closed submodules of Hilbert $C^*$-modules need not be orthogonally complemented. This one technical constraint necessitates the discussion of adjointable endomorphisms and regular (unbounded) operators for these modules, \\cite{FL,Lance}. \n\nThe complementability issue does not arise for finite dimensional vector spaces of course, but already in the case of finite rank, locally trivial vector bundles on compact Hausdorff base spaces we see examples \nof non-complementability of intersections. Classically the issue gives rise to the notion of a strict homomorphism of vector bundles \\cite[Section 1.3]{Atiyah}, and we relate the vector bundle situation to the complementability problem in Remarks \\ref{eg:VB-unstrict}, \\ref{discontangle} and \\ref{Atiyah} below.\n\nIn Theorem \\ref{vN} we show that the pair $(M,N)$ induces a direct sum decomposition like \\eqref{directsum} of the Hilbert $C^*$-module $X$ if and only if von Neumann's theorem on alternating projections is valid for this pair of submodules. We call such pairs \\emph{concordant} and characterise them in terms of their Hilbert space localisations in Theorem \\ref{locharm}. Our results have implications for the Hilbert module version of the two projection problem, \\cite{Luo}. The Hilbert space version first gained prominence in the work of Halmos \\cite{H}, and has since had numerous incarnations and applications: for a recent survey see \\cite{BS}.\n\nIn \\cite{Luo}, the Friedrichs angle between complemented submodules has been defined under the constraint that $M\\cap N$ is complemented. In Section \\ref{sec:angle} of this note we remove this hypothesis and extend the definition of the Friedrichs angle to arbitrary pairs of complemented submodules via the local-global principle of \\cite{Pierrot}. We interpret the Friedrichs angle as a function on the space $\\widehat{B}$ of irreducible representations of $B$ and prove that $c(M,N)=c(M^{\\perp},N^{\\perp})$. We deduce that $c(M,N)<1$ if and only if the sequence $(P_{N}P_{M})^{n}$ is Cauchy for the operator norm if and only if $M\\cap N$ is complemented and $M^{\\perp}+N^{\\perp}$ is closed.\n\n{\\bf Notation.} For a Hilbert $C^{*}$-module $X$ over a $C^{*}$-algebra $B$ we denote by $\\End^{*}_{B}(X)$ the unital $C^{*}$-algebra of adjointable operators on $X$ and by $\\mathbb{K}(X)\\subset \\End^{*}_{B}(X)$ the ideal of compact operators. The symbols $\\otimes^{\\textnormal{alg}}_{B}, \\widehat{\\otimes}_{B}$ and $\\otimes_{B}$ denote the balanced algebraic, projective and $C^{*}$-module tensor products, respectively.\n\n{\\bf Acknowledgements.} We thank Marcel de Jeu for helpful conversations and Michael Frank for valuable correspondence.\n\\section{Concordant submodules}\nLet $X$ be a Hilbert $C^{*}$-module over the $C^{*}$-algebra $B$.\nGiven two complemented submodules $M$ and $N$ of $X$, we write $P_{M},P_{N}$ respectively for the corresponding projections in $\\End^{*}_{B}(X)$. The intersection $M\\cap N$ is a closed submodule of $X$, and there is an inclusion\n\\[M^{\\perp}+N^{\\perp}\\subset (M\\cap N)^{\\perp}.\\]\nThe submodule $M^{\\perp}+N^{\\perp}$ need not be closed, but since $(M\\cap N)^{\\perp}$ is closed,\n\\[\\overline{M^{\\perp}+N^{\\perp}}\\subset (M\\cap N)^{\\perp},\\]\nas well. In case $X$ is a Hilbert space there is an equality (see (\\cite[Theorem 4.6.4]{Dbook})\n\\begin{equation}\n\\label{concordant}\n \\overline{M^{\\perp}+N^{\\perp}}= (M\\cap N)^{\\perp},\n\\end{equation} \nand thus the projections $P_{M\\cap N}$ and $P_{\\overline{M^{\\perp}+N^{\\perp}}}$ exist and satisfy $1-P_{M\\cap N}=P_{\\overline{M^{\\perp}+N^{\\perp}}}$. \n\nIn general, the projections do not exist unless the submodules are complemented. To our knowledge, it is an open question whether the intersection of complemented submodules is again complemented. In \\cite[Section 3]{Luo} it was shown that even in case all the projections exist, \\eqref{concordant} need not not hold (see Remark \\ref{LMXcounter} below).\n\\begin{defn}\nLet $M$ and $N$ be complemented submodules of a Hilbert $C^{*}$-module $X$. The pair $(M,N)$ is \\emph{concordant} if $X=(M\\cap N)\\oplus\\overline{(M^{\\perp}+N^{\\perp})}$. If the pair $(M,N)$ is not concordant, we say it is \\emph{discordant}. \n\\end{defn}\nThe pair $(M,N)$ is concordant if their intersection $M\\cap N$ is complemented and its complement is $\\overline{M^{\\perp}+N^{\\perp}}$.\n\\begin{rmk}\\label{LMXcounter} The pair $(M,N)$ being concordant is strictly stronger than the requirement that $M\\cap N$ be complemented. In \\cite[Section 3]{Luo} it is shown that for $X=B=C([0,\\frac{\\pi}{2}], M_{2}(\\mathbb{C}))$, the submodules\n\\[M=\\textnormal{Ran } \\begin{pmatrix} 1 & 0 \\\\ 0 & 0 \\end{pmatrix},\\quad N= \\textnormal{Ran } \\begin{pmatrix} \\cos^{2}t & \\sin t \\cos t \\\\ \\sin t \\cos t &\\sin^{2}t\\end{pmatrix},\\]\nsatisfy $M\\cap N =0$, which is complemented, whereas $\\overline{M^{\\perp}+N^{\\perp}}\\neq X$ so $(M,N)$ is not concordant.\n\\end{rmk}\n\n\\begin{rmk} \nNote that $(M,N)$ is \\emph{harmonious} in the sense of \\cite[Definition 4.1]{Luo} if each of the submodules\n\\[\n\\overline{M+N},\\,\\, \\overline{M+N^{\\perp}},\\,\\,\\overline{M^{\\perp}+N},\\,\\, \\overline{M^{\\perp}+N^{\\perp}} \n\\]\nis complemented. In this case the respective complements are\n\\[\nM^{\\perp}\\cap N^{\\perp},\\,\\,M^{\\perp}\\cap N,\\,\\, M+N^{\\perp},\\,\\, M\\cap N,\n\\]\nas explained in the discussion after \\cite[Definition 4.1]{Luo}. Thus $(M,N)$ is harmonious if and only if each of the pairs $(M,N)$, $(M,N^{\\perp})$, $(M^{\\perp},N)$ and $(M^{\\perp},N^{\\perp})$ is concordant.\n \\end{rmk}\n \\begin{rmk} If $M+N$ is closed, then by \\cite[Proposition 4.6]{LSX} $M^{\\perp}+N^{\\perp}$ is closed and $X=(M\\cap N)\\oplus (M^{\\perp}+N^{\\perp})$. In particular, $M+N$ is closed if and only if $M^{\\perp}+N^{\\perp}$ is closed and in this case both $(M,N)$ and $(M^{\\perp},N^{\\perp})$ are concordant (see Proposition 3.10 below).\n \\end{rmk}\n \\begin{rmk}\n \\label{rmkuniversal} \n In \\cite{RS} it was shown that the universal $C^{*}$-algebra $C^{*}(p,q)$ generated by two projections $p$ and $q$ admits the following concrete model\n\\[\nC^{*}(p,q)\\simeq\\left\\{A(t)\\in C([0,\\pi\/2], M_2(\\mathbb{C})): A(0)\\ \\mbox{and}\\ A(\\pi\/2)\\ \\mbox{diagonal}\\right\\},\n\\]\nwith the isomorphism is determined by\n\\[\np\\mapsto P:=\\begin{pmatrix} 1 & 0 \\\\ 0 & 0 \\end{pmatrix}, \\quad q\\mapsto Q\n:=\\begin{pmatrix} \\cos^{2}t & \\sin t \\cos t \\\\ \\sin t \\cos t &\\sin^{2}t\\end{pmatrix}.\n\\]\nFrom this point of view, the counterexample of \\cite[Section 3]{Luo} discussed in Remark \\ref{LMXcounter} above arises from the universal example. This shows that specific properties such as being concordant or harmonious hold in some representations of $C^{*}(p,q)$, but not in all of them.\n\\end{rmk}\n\nWe will now characterise concordant pairs by looking at their Hilbert space localisations.\n\n\nLet $\\pi:B\\to \\mathbb{B}(H_{\\pi})$ be a representation of $B$ on the Hilbert space $H_{\\pi}$ and write $X_{\\pi}:=X\\otimes_{B}H_{\\pi}$. There is a representation\n\\begin{equation}\n\\widehat{\\pi}:\\End^{*}_{B}(X)\\to \\mathbb{B}(X_{\\pi}),\\quad T\\mapsto T\\otimes 1.\n\\label{eq:bob}\n\\end{equation}\nWrite $M_{\\pi}:=M\\otimes_{B}H_{\\pi}\\subset X\\otimes_{B}H_{\\pi}$, and similarly for $N$. Then $M_{\\pi}$ and $N_{\\pi}$ are closed subspaces of the Hilbert space $X_{\\pi}$ and we have $P_{M_{\\pi}}:=\\widehat{\\pi}(P_{M})=P_{M}\\otimes 1$, as well as $P_{N_{\\pi}}:=\\widehat{\\pi}(P_{N})=P_{N}\\otimes 1$. Since the subspace $M_{\\pi}\\cap N_{\\pi}$ is closed, there is a projection $P_{M_{\\pi}\\cap N_{\\pi}}\\in\\mathbb{B}(X_{\\pi})$ that projects onto $M_{\\pi}\\cap N_{\\pi}$. In general, the equality $M_{\\pi}\\cap N_{\\pi}=(M\\cap N)_{\\pi}$ need not hold, even if $M\\cap N$ is complemented. \nWe recall the following fact.\n\\begin{prop}[Local-global principle for complemented submodules \\cite{Pierrot}]\n\\label{locglob} \nLet $\\Omega\\subset X$ be a closed submodule. Then $\\Omega$ is complemented if and only if for every irreducible representation $\\pi:B\\to \\mathbb{B}(H_{\\pi})$ there is an equality $(\\Omega_{\\pi})^{\\perp}=(\\Omega^{\\perp})_{\\pi}$.\n\\end{prop}\n\\begin{proof}\nBy \\cite[Corollaire 1.17]{Pierrot}, we have that $X=\\Omega\\oplus \\Omega^{\\perp}$ if and only if for every irreducible representation $\\pi:B\\to \\mathbb{B}(H_{\\pi})$ there is an equality\n\\[\nX_{\\pi}=X\\otimes_{B}H_{\\pi}=(\\Omega\\oplus \\Omega^{\\perp})\\otimes_{B}H_{\\pi}=\\Omega\\otimes_{B}H_{\\pi}\\oplus \\Omega^{\\perp}\\otimes_{B}H_{\\pi}=\\Omega_{\\pi}\\oplus (\\Omega^{\\perp})_{\\pi}.\n\\]\nSince $(\\Omega^{\\perp})_{\\pi}\\subset (\\Omega_{\\pi})^{\\perp}$, this holds if and only if $(\\Omega^{\\perp})_{\\pi}=(\\Omega_{\\pi})^{\\perp}$.\n\\end{proof}\nA weaker form of this result was proved independently, though several years later, in \\cite{KL12}. There, the local side of the equivalence involved \\emph{all} representations of the $C^{*}$-algebra $B$. The two results are equivalent because the proof of the implication $\\Rightarrow$ in Proposition \\ref{locglob} holds verbatim for an arbitrary representation of the $C^{*}$-algebra $B$, see \\cite{KL17}. We will use both instances of the result.\n\\begin{lemma}\n\\label{inclusion} \nLet $X$ be a Hilbert $C^{*}$-module over $B$, $M,N$ complemented submodules and $\\pi:B\\to\\mathbb{B}(H_{\\pi})$ a representation. Then there is an equality of closed subspaces\n\\[\n\\overline{(M_{\\pi})^{\\perp}+(N_{\\pi})^{\\perp}}=\\big(\\,\\overline{M^{\\perp}+N^{\\perp}}\\,\\big)_{\\pi}.\n\\]\n\\end{lemma}\n\\begin{proof}\nThe inclusion of subspaces\n\\begin{align*}\n(M^{\\perp})_{\\pi}+(N^{\\perp})_{\\pi}\\subset \\Big(\\overline{M^{\\perp}+N^{\\perp}}\\Big)_{\\pi}\n\\end{align*}\nshows that we have an inclusion of closed linear subspaces\n\\begin{equation*}\n\\overline{(M^{\\perp})_{\\pi}+(N^{\\perp})_{\\pi}}\\subset \\left(\\overline{M^{\\perp}+N^{\\perp}}\\right)_{\\pi}.\\end{equation*} \n The subspace $(M^{\\perp}+N^{\\perp})\\otimes^{\\textnormal{alg}}_{B}H_{\\pi}$ is dense in $(\\,\\overline{M^{\\perp}+N^{\\perp}}\\,)_{\\pi}$ and since\n \\[\n (M^{\\perp}+N^{\\perp})\\otimes^{\\textnormal{alg}}_{B}H_{\\pi}\\subset(M^{\\perp})_{\\pi}+(N^{\\perp})_{\\pi}\\subset \\overline{(M^{\\perp})_{\\pi}+(N^{\\perp})_{\\pi}}\\subset \\left(\\overline{M^{\\perp}+N^{\\perp}}\\right)_{\\pi},\\]\n it follows that $\\overline{(M^{\\perp})_{\\pi}+(N^{\\perp})_{\\pi}}=\\left(\\overline{M^{\\perp}+N^{\\perp}}\\right)_{\\pi}$. Since $M$ and $N$ are complemented we have $(M_{\\pi})^{\\perp}=(M^{\\perp})_{\\pi}$ and $(N_{\\pi})^{\\perp}=(N^{\\perp})_{\\pi}$ and thus $\\overline{(M_{\\pi})^{\\perp}+(N_{\\pi})^{\\perp}}=\\left(\\overline{M^{\\perp}+N^{\\perp}}\\right)_{\\pi}$.\n\\end{proof}\n\\begin{thm}\n\\label{locharm}\nLet $X$ be a Hilbert $C^{*}$-module over $B$ and $M$ and $N$ complemented submodules. Then \nthe pair $(M,N)$ is concordant if and only if for every irreducible \nrepresentation $\\pi:B\\to \\mathbb{B}(H_{\\pi})$ there is an equality of closed subspaces $M_{\\pi}\\cap N_{\\pi}=(M\\cap N)_{\\pi}$.\n\\end{thm}\n\\begin{proof} \nSuppose that $M$ and $N$ are concordant so that\n\\[\nX=(M\\cap N)\\oplus \\left(\\overline{M^{\\perp}+N^{\\perp}}\\right).\n\\]\nTherefore Proposition \\ref{locglob} and Lemma \\ref{inclusion} give\n \\[\n ((M\\cap N)_{\\pi})^{\\perp}=((M\\cap N)^{\\perp})_{\\pi}=(\\overline{M^{\\perp}+N^{\\perp}})_{\\pi}=\\overline{(M_{\\pi})^{\\perp}+(N_{\\pi})^{\\perp}}.\n \\] \n\nTaking orthogonal complements we find \n$(M\\cap N)_{\\pi}=\\Big(\\overline{(M_{\\pi})^{\\perp}+(N_{\\pi})^{\\perp}}\\Big)^{\\perp}=M_{\\pi}\\cap N_{\\pi}$.\n\nConversely, suppose that $M_{\\pi}\\cap N_{\\pi}=(M\\cap N)_{\\pi}$ for all irreducible \nrepresentations $\\pi$. \nBy Lemma \\ref{inclusion} and Equation \\eqref{concordant} we have \n\\[\n(\\overline{M^{\\perp}+N^{\\perp}})_{\\pi}= \\overline{(M_{\\pi})^{\\perp}+(N_{\\pi})^{\\perp}}=(M_{\\pi}\\cap N_{\\pi})^{\\perp},\n\\] \n\nand we deduce that\n\\begin{align*}\n(M\\cap N)_{\\pi}\\oplus (\\overline{M^{\\perp}+N^{\\perp}})_{\\pi}&= (M_{\\pi}\\cap N_{\\pi})\\oplus (M_{\\pi}\\cap N_{\\pi})^{\\perp}=X_{\\pi}.\n\\end{align*}\n\nBy Proposition \\ref{locglob} we conclude that $X=(M\\cap N)\\oplus \\overline{M^{\\perp}+N^{\\perp}}.$\n\\end{proof}\nIn line with the local-global principle, Proposition \\ref{locglob}, we obtain the same result when we consider all representations of the base algebra $B$.\n\\begin{corl}\n\\label{locharmcor}\nLet $X$ Hilbert $C^{*}$-module over $B$ and $M$ and $N$ complemented submodules. Then $(M,N)$ is concordant if and only if for every representation $\\pi:B\\to \\mathbb{B}(H_{\\pi})$ there is an equality of closed subspaces $M_{\\pi}\\cap N_{\\pi}=(M\\cap N)_{\\pi}$.\n\\end{corl}\n\\begin{proof} \nThe proof of $\\Rightarrow$ in Theorem \\ref{locharm} shows that $M_{\\pi}\\cap N_{\\pi}=(M\\cap N)_{\\pi}$ for every representation whenever $(M,N)$ is concordant.\n\\end{proof}\n\\begin{rmk}\n\\label{eg:VB-unstrict}\nConsider $B=C([0,\\frac{\\pi}{2}])$, $X=C([0,\\frac{\\pi}{2}], \\mathbb{C}^{2})$ and consider the submodules\n\\[\nM=\\textnormal{Ran }\\!\\!\\begin{pmatrix} 1 & 0 \\\\ 0 & 0 \\end{pmatrix},\\quad N\n= \\textnormal{Ran }\\!\\!\\begin{pmatrix} \\cos^{2}t & \\sin t \\cos t \\\\ \\sin t \\cos t &\\sin^{2}t\\end{pmatrix}.\n\\] We have $M\\cap N=0$ and for the irreducible representations given by $t\\in[0,\\pi\/2]$ we have\n\\[\nM_t\\cap N_t=\\begin{cases} 0 & t=0\\\\ \\C & t\\neq 0,\\end{cases}\n\\]\nso $(M,N)$ is discordant by Theorem \\ref{locharm}.\n\\end{rmk}\n\n\n\\section{Von Neumann's theorem of alternating projections}\nLet $P,Q\\in \\End^{*}_{B}(X)$ be projections\n\\[\nP^{*}=P^{2}=P,\\quad Q^{*}=Q^{2}=Q.\n\\]\nThe submodules $\\textnormal{Ran } P$ and $\\textnormal{Ran } Q$ are complemented in $X$, and every complemented submodule is the range of an adjointable projection. As noted before, it is an open question whether the intersection $\\Omega:=\\textnormal{Ran } P\\cap \\textnormal{Ran } Q$, which is a closed submodule, is complemented. In case $B=\\mathbb{C}$ and $X$ is a Hilbert space this is true and thus there is a projection $P_{\\Omega}$ with $\\textnormal{Ran } P_{\\Omega}=\\Omega$. For $n\\geq 0$, write\n\\[\n(P,Q)_{n}:=\\cdots PQPQ,\\quad \\textnormal{the product of exactly } n \\,\\,\\textnormal{alternating factors ending in } Q.\n\\]\nVon Neumann proved the following well-known theorem.\n\\begin{thm}[{\\cite[Lemma 22]{vN}}]\n\\label{vN} \nLet $H$ be a Hilbert space, $M,N\\subset H$ closed subspaces and $\\Omega:=M\\cap N$. Let $P=P_{M}$ and $Q=P_{N}$ be the orthogonal projections onto $M$ and $N$ respectively. The orthogonal projection $P_{\\Omega}$ onto $\\Omega$ can be obtained as the strong limit of any of the sequences\n\\begin{equation}\n\\label{alternatingseq}\n(PQ)^{n},\\quad (QP)^{n}, \\quad (P,Q)_{n},\\quad (Q,P)_{n},\n\\end{equation}\nor any of their subsequences. \n\\end{thm}\nIn a Hilbert $C^{*}$-module $X$, the analogue of the $*$-strong topology is defined by the seminorms\n\\[\n\\|T\\|_{x}:=\\max\\{\\|Tx\\|,\\|T^{*}x\\|\\},\\quad x\\in X,\n\\]\nand we refer to this topology as the $*$-\\emph{strong module topology}.\nOn bounded sets the $*$-strong module topology coincides with the \n\\emph{strict topology} on $\\End_{B}^{*}(X)$ relative to the ideal $\\mathbb{K}(X)$, \\cite[Proposition 5.5.9]{Troitsky}.\nThe following fact is well-known.\n\\begin{lemma}\\label{complete}\nThe $*$-strong module topology is complete on bounded sets.\n\\end{lemma}\n\\begin{proof} Let $T_{n}\\in\\End^{*}_{B}(X)$ be a sequence that is Cauchy for the seminorms $\\|\\cdot \\|_{x}$, $x\\in X$. By the Uniform Boundedness Principle, the operators \\[Tx:=\\lim_{n\\to\\infty} T_{n}x, \\quad \\textnormal{and} \\quad T^{*}x:=\\lim_{n\\to\\infty} T^{*}_{n}x,\\] are well-defined, bounded and mutually adjoint.\n\\end{proof}\n\\begin{lemma}\\label{reductionlemma} Let $P,Q\\in\\End^{*}_{B}(X)$ be projections. Then $(PQ)^{n}$ and $(QP)^{n}$ are $*$-strongly Cauchy if and only if $(PQP)^{n}$ and $(QPQ)^{n}$ are $*$-strongly Cauchy if and only if $(P,Q)_{n}$ and $(Q,P)_{n}$ (as defined in \\eqref{alternatingseq}) are $*$-strongly Cauchy. The same statement holds for the norm topology.\n\\end{lemma}\n\\begin{proof} \nSince\n\\[\n(P,Q)_{n}=\\left\\{\\begin{matrix} (PQ)^{\\frac{n}{2}} & n\\quad \\textnormal{even}\\\\ (QPQ)^{\\frac{n-1}{2}} & n\\quad \\textnormal{odd},\\end{matrix}\\right.\\quad (Q,P)_{n}=\\left\\{\\begin{matrix} (QP)^{\\frac{n}{2}} & n\\quad \\textnormal{even}\\\\ (PQP)^{\\frac{n-1}{2}} & n\\quad \\textnormal{odd},\\end{matrix}\\right. \n\\]\nit suffices to prove that $(PQ)^{n}$ and $(QP)^{n}$ are $*$-strongly Cauchy if and only $(PQP)^{n}$ and $(QPQ)^{n}$ are $*$-strongly Cauchy. The same holds for the norm topology.\n\nAny projection $P$ satisfies $\\langle Px,Px\\rangle\\leq \\langle x,x\\rangle$ and $Q(PQ)^{n}=(QPQ)^{n}$ so that\n\\begin{align}\n\\label{sandwich}\n\\langle (PQ)^{n}x,(PQ)^{n}x\\rangle&=\\langle (Q(PQ)^{n}+(1-Q)(PQ)^{n})x,(Q(PQ)^{n}+(1-Q)(PQ)^{n})x\\rangle\\nonumber\\\\\n&\\geq \\langle (QPQ)^{n}x,(QPQ)^{n}x\\rangle\\nonumber\\\\\n&=\\langle (P(QPQ)^{n}+(1-P)(QPQ)^{n})x,(P(QPQ)^{n}+(1-P)(QPQ)^{n})x\\rangle\\nonumber\\\\\n&\\geq \\langle (PQ)^{n+1}x,(PQ)^{n+1}x\\rangle.\n\\end{align}\nNow for $m>n$ we have \n\\[\n(PQ)^{n}-(PQ)^{m}=(PQ)^{n}(1-(PQ)^{m-n}),\\quad ((QPQ)^{n}-(QPQ)^{m})=(QPQ)^{n}(1-(QPQ)^{m-n}),\n\\]\nwhich, together with \\eqref{sandwich} gives\n\\begin{align*}\n\\langle ((PQ)^{n}-(PQ)^{m})x ,((PQ)^{n}&-(PQ)^{m})x\\rangle =\n\\langle (PQ)^{n}(1-(PQ)^{m-n})x,(PQ)^{n}(1-(PQ)^{m-n})x\\rangle \\\\ &\\geq \\langle (QPQ)^{n}(1-(PQ)^{m-n})x,(QPQ)^{n}(1-(PQ)^{m-n})x\\rangle\\\\\n&= \\langle ((QPQ)^{n}-(QPQ)^{m})x,((QPQ)^{n}-(QPQ)^{m})x\\rangle\\\\\n&= \\langle (QPQ)^{n}(1-(QPQ)^{m-n})x,(QPQ)^{n}(1-(QPQ)^{m-n})x\\rangle\\\\\n&\\geq \\langle (PQ)^{n+1}(1-(QPQ)^{m-n})x,(PQ)^{n+1}(1-(QPQ)^{m-n})x\\rangle\\\\\n&\\geq \\langle( (PQ)^{n+1}-(PQ)^{m+1})x,((PQ)^{n+1}-(PQ)^{m+1})x\\rangle.\n\\end{align*}\nThis proves that $(PQ)^{n}$ is pointwise Cauchy if and only if $(QPQ)^{n}$ is pointwise Cauchy. Thus $(PQ)^{n}$ and $(QP)^{n}$ are both $*$-strongly Cauchy if and only if $(PQP)^{n}$ and $(QPQ)^{n}$ are both $*$-strongly Cauchy. The statements for the norm topology follow from the same inequalities. This completes the proof.\n\\end{proof}\n\\begin{prop} \n\\label{Cauchy} \nSuppose that $(PQ)^{n}$ is $*$-strongly Cauchy. Then so are $(QP)^{n}$, $(PQP)^{n}$, $(QPQ)^{n}$, $(Q,P)_{n}$ and $(P,Q)_{n}$. The $*$-strong limits of each of these sequences is a projection $P_{\\Omega}$ with range $\\Omega:=\\textnormal{Ran } P \\cap \\textnormal{Ran } Q$. In particular $\\Omega$ is complemented.\n\\end{prop}\n\\begin{proof} \nSince $((PQ)^{n})^{*}=(QP)^{n}$, the first statement follows from Lemma \\ref{reductionlemma}. \nWe will prove that $s-\\lim_{n\\to\\infty} (PQP)^n=s-\\lim_{n\\to \\infty} (QPQ)^{n}$ and that this operator is a projection $P_{\\Omega}$ with range $\\Omega$. It then follows that $\\Omega$ is complemented and that \n\\[\nP_{\\Omega}=s-\\lim_{n\\to\\infty}(P,Q)_{n}=s-\\lim_{n\\to\\infty}(Q,P)_{n},\n\\]\nsince $(PQP)^{n}$ is a subsequence of $(Q,P)_{n}$ and $(QPQ)^{n}$ is a subsequence of $(P,Q)_{n}$. Then $(PQ)^{n}$ and $(QP)^{n}$ are subsequences of $(P,Q)_{n}$ and $(Q,P)_{n}$, respectively it follows that\n\\[\nP_{\\Omega}=s-\\lim_{n\\to\\infty}(PQ)^{n}=s-\\lim_{n\\to\\infty}(QP)^{n},\n\\]\nas well.\n\nBy Lemma \\ref{complete} the $*$-strong limit $\\tilde{P}:=\\lim (PQP)^{n}$ exists, is self-adjoint and $\\|\\tilde{P}\\|\\leq 1$. To prove that $\\tilde{P}$ is a projection let $x\\in X$ and $\\varepsilon >0$. Choose $N$ such that for all $k\\geq N$ we have\n\\[\n\\|\\tilde{P}x-(PQP)^{k}x\\|<\\varepsilon.\n\\]\nNow consider\n\\begin{align*}\n\\|\\tilde{P}^{2}x-\\tilde{P}x\\|\n&=\\|\\tilde{P}(PQP)^{k}x-\\tilde{P}x\\|+ \\|\\tilde{P}(\\tilde{P}-(PQP)^{k})x\\|\\\\\n&\\leq \\|\\tilde{P}(PQP)^{k}x-\\tilde{P}x\\|+ \\|(\\tilde{P}-(PQP)^{k})x\\|\\\\\n&< \\|\\tilde{P}(PQP)^{k}x-\\tilde{P}x\\|+ \\varepsilon\\\\\n&=\\lim_{n\\to\\infty}\\|(PQP)^{n+k}x-\\tilde{P}x\\|+\\varepsilon=\\varepsilon,\n\\end{align*}\nand as $\\varepsilon$ was arbitrary, it follows that $\\tilde{P}^{2}x=\\tilde{P}x$.\n\nTo prove that $\\textnormal{Ran }\\tilde{P}=\\Omega$, first observe that if $x\\in \\Omega$ then $$x=Px=Qx=PQPx,$$ so $\\tilde{P}x=x$ and $\\Omega\\subset \\textnormal{Ran } \\tilde{P}$. \n\nFor the reverse inclusion we will show that $\\tilde{P}=P\\tilde{P}=Q\\tilde{P}$. The equalities \n\\[\nP\\tilde{P}x=\\tilde{P}x,\\quad \\textnormal{and}\\quad PQ\\tilde{P}x=\\tilde{P}x,\n\\] \nhold by construction. Now for any $x\\in X$ we have\n\\[\n\\langle Px, Px\\rangle\\leq \\langle x,x\\rangle,\\quad\\langle Qx, Qx\\rangle\\leq \\langle x,x\\rangle,\n\\]\n from which we deduce that\n \\[\n \\langle\\tilde{P}x,\\tilde{P}x\\rangle=\\langle PQ\\tilde{P}x,PQ\\tilde{P}x\\rangle\\leq \\langle Q\\tilde{P}x,Q\\tilde{P}x\\rangle\\leq \\langle\\tilde{P}x,\\tilde{P}x\\rangle.\n \\]\nTherefore $\\langle Q\\tilde{P}x,Q\\tilde{P}x\\rangle= \\langle\\tilde{P}x,\\tilde{P}x\\rangle$ and $\\langle (1-Q)\\tilde{P}x,(1-Q)\\tilde{P}x\\rangle=0$. It follows that $(1-Q)\\tilde{P}x=0$ so $Q\\tilde{P}x=\\tilde{P}x$. This shows that $Q\\tilde{P}=\\tilde{P}$ and thus $\\textnormal{Ran } \\tilde{P}\\subset\\Omega$. Therefore $\\Omega$ is complemented and $P_{\\Omega}=\\tilde{P}=s-\\lim (PQP)^{n}$ in the $*$-strong module topology. By exhanging the r\\^oles of $P$ and $Q$, we find that $P_{\\Omega}=s-\\lim (QPQ)^{n}$ as well.\n\\end{proof}\nIn order to address the appropriate converse to Proposition \\ref{Cauchy}, we need a description of the Banach space dual $X^{*}:=\\mathbb{B}(X,\\mathbb{C})$ of bounded linear functionals on a Hilbert $C^{*}$-module $X$. To this end we first recall the \\emph{dual or conjugate $C^{*}$-module}. \n\nThe space of compact operators $\\mathbb{K}(X,B)$ from $X$ to $B$ is a left $B$-module via $(b\\cdot K)(x):=bK(x)$ and carries a natural left $B\\simeq \\mathbb{K}(B,B)$ valued inner product $\\langle K, L\\rangle:=KL^{*}$. The \\emph{conjugate module} $\\overline{X}$ is defined to be the set $X$ with the conjugate $\\mathbb{C}$-vector space structure, and we write elements of $\\overline{X}$ as $\\overline{x}$ with $x\\in X$. The left $B$-module structure and inner product\n\\[b\\cdot \\overline{x}:=\\overline{xb^{*}},\\quad \\langle \\overline{x},\\overline{y}\\rangle:=\\langle x,y\\rangle.\\]\nThese left Hilbert $C^{*}$-modules over $B$ are isomorphic, by the following well-known theorem \\cite[page 13]{Lance}.\n\\begin{prop}[Riesz-Fr\\'echet theorem for Hilbert $C^{*}$-modules]\nThe map \n\\[\nT:\\overline{X}\\rightarrow \\mathbb{K}(X,B),\\quad \\overline{x}\\mapsto T_{x},\\quad T_{x}(y):=\\langle x,y\\rangle, \\quad x,y\\in X, \n\\]\nis a unitary isomorphism of left Hilbert $C^{*}$-modules over $B$.\n\\end{prop}\nThe dual Banach space of the $C^{*}$-algebra $B$, $B^{*}:=\\mathbb{B}(B,\\mathbb{C})$, is a right Banach $B$-module via\n\\[\n(\\varphi\\cdot b) (a):=\\varphi(ba),\\quad a,b\\in B.\n\\]\nLastly, for a right Banach $B$-module $V$ and a left Banach $B$-module $W$, we denote by $V\\widehat{\\otimes}_{B}W$ the balanced Banach space projective tensor product of $V$ and $W$. We are now ready to recall a result of Schweizer, \\cite[Proposition 3.1]{Schweizer}, giving a complete description of the dual Banach space $X^{*}$ of the module $X$.\n\\begin{prop}\\label{Schweizer} \nLet $X$ be a Hilbert $C^{*}$-module, $\\overline{X}:=\\mathbb{K}(X,B)$ the conjugate module and $X^{*}=\\mathbb{B}(X,\\mathbb{C})$ the dual Banach space of $X$. The map $\\psi:B^{*}{\\otimes}_{B}^{\\textnormal{alg}}\\overline{X}\\to X^{*}$ given by\n\\[\n\\psi(\\phi\\otimes\\overline{y})(x):=\\phi(\\langle y,x\\rangle), \\quad \\phi\\in B^{*},\\quad x,y\\in X,\n\\]\nextends to an isometric isomorphism $B^{*}\\widehat{\\otimes}_{B}\\overline{X}\\to X^{*}$ of Banach spaces. \n\\end{prop}\nFor a Banach space $W$, the \\emph{weak topology} on $W$ is the locally convex topology defined by the seminorms $\\|w\\|_{\\varphi}:=\\|\\varphi(w)\\|$. In general the weak topology is \\emph{not} complete, that is, weak Cauchy sequences need not have a weak limit in $X$. However, we do have the following fundamental result for weakly convergent sequences. \n\\begin{thm}[{\\cite[Chap II, Section 38]{RSz}}]\n\\label{Mazur}\nLet $W$ be a Banach space and $C\\subset W$ a convex set. Then the weak closure of $C$ coincides with the norm closure of $C$. In particular, if $w_j\\to w$ in the weak topology, then there exists a sequence of convex combinations $y_j:=\\sum_{k=j}^{n_{j}} t_j w_j$ such that $\\|y_j-w\\|\\to 0$.\n\\end{thm}\nIn the sequel we will freely use the following computational tool.\n\\begin{lemma}\n\\label{powers} \nLet $P,Q\\in\\End^{*}_{B}(X)$ be projections such that $\\Omega:=\\textnormal{Ran } P\\cap \\textnormal{Ran } Q$ is complemented. Then for all $k\\geq 1$ we have\n\\begin{align*}\n(PQ-P_{\\Omega})^{k}&=(PQP)^{k}-P_{\\Omega},\\quad (QP-P_{\\Omega})^{k}=(QPQ)^{k}-P_{\\Omega},\\\\\n(PQP-P_{\\Omega})^{k}&=(PQP)^{k}-P_{\\Omega},\\quad (QPQ-P_{\\Omega})^{k}=(QPQ)^{k}-P_{\\Omega}.\n\\end{align*}\n\\end{lemma}\n\\begin{proof}\nThe statement holds for $k=1$. Since $P_{\\Omega}=P_{\\Omega}P=PP_{\\Omega}=P_{\\Omega} Q=QP_{\\Omega}$ we have \n\\[\n(PQ)^{k+1}-P_{\\Omega}=(PQ-P_{\\Omega})((PQ)^{k}-P_{\\Omega}),\n\\quad (QP)^{k+1}-P_{\\Omega}=(QP-P_{\\Omega})((QP)^{k}-P_{\\Omega}),\n\\]\nand\n\\[(PQP)^{k+1}-P_{\\Omega}=P((QP)^{k+1}-P_{\\Omega}),\\quad (QPQ)^{k+1}-P_{\\Omega}=Q((PQ)^{k+1}-P_{\\Omega}),\\]\nso the result follows by induction on $k$.\n\\end{proof}\nWe are now ready to prove our main theorem.\n\n\\begin{thm}\n\\label{complementedalternating} \nLet $M,N$ be complemented submodules of a Hilbert $C^{*}$-module $X$. Then $(M,N)$ is a concordant pair if and only if the sequence $(P_{N}P_{M})^{n}$ is Cauchy in the $*$-strong module topology on $\\End^{*}_{B}(X)$.\n\\end{thm}\n\\begin{proof} \nWe write $P=P_{M}$, $Q=P_{N}$ and $\\Omega:=M\\cap N$.\n\n\n$\\Leftarrow$ In Proposition \\ref{Cauchy} it was proved that $\\Omega$ is complemented and $\\lim (PQ)^{n}x=P_{\\Omega}x$. Now if $\\pi:B\\to\\mathbb{B}(H_{\\pi})$ is an irreducible representation then\n\\begin{align*}\nP_{M_{\\pi}\\cap N_{\\pi}}(x\\otimes h)&=\\lim_{n\\to\\infty}(P_{M_{\\pi}}P_{N_{\\pi}})^{n}(x\\otimes h)=\\lim_{n\\to\\infty}\\widehat{\\pi}(P_{M}P_{N})^{n}(x\\otimes h)\\\\\n&=\\lim_{n\\to\\infty}((PQ)^{n}x)\\otimes h=P_{\\Omega}x\\otimes h=\\widehat{\\pi}(P_{\\Omega})(x\\otimes h),\n\\end{align*}\nso $P_{M_{\\pi}\\cap N_{\\pi}}=\\widehat{\\pi}(P_{\\Omega})$ and thus $\\Omega_{\\pi}=M_{\\pi}\\cap N_{\\pi}$, so $(M,N)$ is concordant by Theorem \\ref{locharm}.\n\nFor the converse, assume that $(M,N)$ is concordant and write $P_{\\Omega}$ for the projection onto $\\Omega$. By Lemma \\ref{reductionlemma} it suffices to prove that $(PQP)^{n}x\\to P_{\\Omega} x$ and $(QPQ)^{n}x\\to P_{\\Omega} x$ for all $x\\in X$. \n\nWe first prove that $(PQP)^{n}x$ converges to $P_{\\Omega}x$ in the weak topology on $X$. To this end observe that since $\\|(PQP)^{n}\\|\\leq \\|PQP\\|^{n}\\leq 1$ the sequence $(PQP)^{n}x$ is bounded in norm. Therefore, by Proposition \\ref{Schweizer} it suffices to show that $(\\phi\\otimes \\overline{y}) ( (PQP)^{n}x)\\to (\\phi\\otimes \\overline{y})(P_{\\Omega}x)$ for all $\\phi\\in B^{*}$ and $y\\in X$, as such functionals generate the weak topology. Since every functional on the $C^{*}$-algebra $B$ is a linear combination of four states (see \\cite{T}), we may restrict ourselves to states $\\sigma\\in B^{*}$. In the universal representation $H_{u}$ of $B$, every state $\\sigma$ arises as a vector state associated to a unit vector $h_{\\sigma}\\in H_{u}$. Denote by $\\pi_{\\sigma}$ the GNS-representation associated to the state $\\sigma$. Then by Theorem \\ref{vN} we find\n\\begin{align*}\n(\\sigma\\otimes \\overline{y})((PQP)^{n}x)&=\\sigma(\\langle y, (PQP^{n})x\\rangle )=\\langle h_{\\sigma}, \\langle y, (PQP)^{n}x\\rangle h_{\\sigma}\\rangle\\\\\n&=\\langle y\\otimes h_{\\sigma}, (PQP)^{n}x\\otimes h_{\\sigma}\\rangle \\to \\langle y\\otimes h_{\\sigma}, P_{\\Omega_{\\sigma}} (x\\otimes h_{\\sigma})\\rangle.\n\\end{align*}\nBy Corollary \\ref{locharmcor}, $M_{\\pi_\\sigma}\\cap N_{\\pi_\\sigma}=\\Omega_{\\pi_\\sigma}$ so $P_{\\Omega_{\\sigma}}=P_{\\Omega}\\otimes 1=\\widehat{\\pi}_{\\sigma}(P_{\\Omega})$,\nand\n$(PQP)^{n}x\\otimes h_{\\sigma}\\to P_{\\Omega} x\\otimes h_{\\sigma}$ in the Hilbert space $X\\otimes_{B} H_{u}$. Therefore $(PQP)^{n}x\\to P_{\\Omega}x$ weakly in $X$. \n\nBy Theorem \\ref{Mazur}, there is a sequence of convex combinations $y_{k}=\\sum_{i=k}^{n_{k}} t_{i}(PQP)^{i}x$ such that $y_k\\to P_{\\Omega}x$ in norm in $X$. Since for all $n$ we have\n\\[\nP_{\\Omega}(PQP)^{n}=(PQP)^{n}P_{\\Omega}=P_{\\Omega},\\quad (PQP)^{m}\\leq (PQP)^{n},\\quad m\\geq n,\n\\]\nwe can estimate\n\\begin{align*}\n\\langle (y_{k}-P_{\\Omega})x, (y_{k}-P_{\\Omega})x\\rangle&=\n\\left\\langle \\left(\\sum_{i=k}^{n_{k}} t_{i}(PQP)^{i}-P_{\\Omega}\\right)x , \\left(\\sum_{i=k}^{n_{k}} t_{i}(PQP)^{i}-P_{\\Omega}\\right)x\\right\\rangle\\\\ \n&=\\left\\langle \\left(\\sum_{i=k}^{n_{k}} t_{i}(PQP)^{i}-P_{\\Omega}\\right)^{2}x , x\\right\\rangle\\\\\n&=\\left\\langle \\left(\\sum_{i,j=k}^{n_{k}}t_{i}t_{j}(PQP)^{i+j}-P_{\\Omega}\\right)x, x\\right\\rangle \\\\\n&\\geq \\left\\langle \\left(\\sum_{i,j=k}^{n_k}t_{i}t_{j}(PQP)^{2n_{k}}-P_{\\Omega}\\right)x, x\\right\\rangle \\\\\n&=\\langle ((PQP)^{2n_k}-P_{\\Omega})x, x\\rangle \\\\%-\\langle P_{\\Omega}x, P_{\\Omega}\\rangle\n&=\\langle ((PQP)^{n_k}-P_{\\Omega})x, ((PQP)^{n_k}-P_{\\Omega})x\\rangle,\n\\end{align*}\nwhere the last step follows using Lemma \\ref{powers}. \nTherefore it follows that the subsequence $(PQP)^{n_{k}}$ is such that for all $x\\in X$ we have norm convergence $(PQP)^{n_{k}}x\\to P_{\\Omega}x$ as $k\\to \\infty$. Since for any $m\\geq n$ we have \n\\[\n\\langle ((PQP)^{n}-P_{\\Omega})x, ((PQP)^{n}-P_{\\Omega})x\\rangle \\geq\\langle( (PQP)^{m}-P_{\\Omega})x,( (PQP)^{m}-P_{\\Omega})x\\rangle,\n\\]\nwe find that \n\\begin{align*}\n\\|((PQP)^{n}-P_{\\Omega})x\\|\\geq \\|((PQP)^{m}-P_{\\Omega})x\\|.\n\\end{align*}\nThus it follows that $\\lim_{n\\to\\infty} \\|((PQP)^{n}-P_{\\Omega})x\\|\\to 0$. By swapping the r\\^oles of $P$ and $Q$ we find that $\\lim_{n\\to\\infty} \\|((QPQ)^{n}-P_{\\Omega})x\\|\\to 0$ as well. This completes the proof.\n\\end{proof}\n\n\\section{Angle, sum and intersection}\n\\label{sec:angle}\nWe now consider the applications of our main result to various problems concerning pairs of complemented submodules of Hilbert $C^*$-modules.\n\\subsection{The Friedrichs angle between complemented submodules}\nIn \\cite{Luo}, the following definition for the Friedrichs angle between complemented submodules was given, which we now recall. Let $M,N\\subset X$ be complemented submodules such that $M\\cap N$ is complemented and write $P_{M},P_{N}$ and $P_{M\\cap N}$ respectively for the corresponding projections. The quantity\n\\begin{equation}\n\\label{moduleangle}\nc(M,N):=\\|P_{M}P_{N}(1-P_{M\\cap N})\\|=\\|P_{M}P_{N}-P_{M\\cap N}\\|,\n\\end{equation}\nis called the (cosine of the) \\emph{Friedrichs angle between $M$ and $N$}. \n\nFor the above definition, the existence of the projection $P_{\\Omega}$ seems necessary. This is undesirable and ideally the angle should be an invariant associated to any pair $(M,N)$ of complemented submodules. We propose the following generalisation, based on Hilbert space localisation. \n\\begin{defn} \nLet $M,N\\subset X$ be complemented submodules. Let $\\pi:B\\to \\mathbb{B}(H_{\\pi})$ be a representation of $B$ on $H_{\\pi}$. The quantity\n\\begin{equation}\\label{localangle}\nc_{\\pi}(M,N):=c(M_{\\pi},N_{\\pi})=\\|P_{M_{\\pi}}P_{N_{\\pi}}(1-P_{M_{\\pi}\\cap N_{\\pi}})\\|=\\|P_{M_{\\pi}}P_{N_{\\pi}}-P_{M_{\\pi}\\cap N_{\\pi}}\\|,\n\\end{equation}\nis called the (cosine of the) \\emph{local Friedrichs angle between $M$ and $N$ at $\\pi$}. \n\\end{defn}\n\n\\begin{prop}\n\\label{independent} Suppose that $\\pi:B\\to \\mathbb{B}(H_{\\pi})$ is faithful. Then\n\\begin{enumerate}\n\\item If $(M,N)$ is concordant, then $c_{\\pi}(M,N)=c(M,N)$;\n\\item If $(M,N)$ is discordant, then $c_{\\pi}(M,N)=1$.\n\\end{enumerate}\nIn particular the (cosine of the) local Friedrich angle $c_{\\pi}(M,N)$ is independent of the choice of faithful representation $\\pi$.\n\\end{prop}\n\\begin{proof} Suppose $(M,N)$ is concordant, so that by Corollary \\ref{locharmcor}, $M_{\\pi}\\cap N_{\\pi}=(M\\cap N)_{\\pi}$ and $P_{M_{\\pi}\\cap N_{\\pi}}=\\widehat{\\pi}(P_{M\\cap N})$. \n Since $\\pi$ is faithful, the representation $\\End^{*}_{B}(X)\\to \\mathbb{B}(X_{\\pi})$ is faithful and hence isometric. Therefore \n\\[c_{\\pi}(M,N)=\\|\\widehat{\\pi}(P_{N}P_{M}-P_{M\\cap N})\\|=\\|P_{N}P_{M}-P_{M\\cap N}\\|=c(M,N),\\]\nwhich proves {\\em1}.\n\nClearly $0\\leq c_{\\pi}(M,N)\\leq 1$, so suppose that $c_{\\pi}(M,N)<1$ and write $P=P_{M}$ and $Q=P_{N}$. We will show that the sequence $(PQ)^{n}$ is Cauchy for the norm topology. Then by Theorem \\ref{complementedalternating}, $(M,N)$ is concordant, which proves {\\em2}. So for $m\\geq n$ recall the representation $\\widehat{\\pi}$ from Equation \\eqref{eq:bob} and consider\n\\begin{align*}\n\\|(PQ)^{n}-(PQ)^{m}\\|&=\\|\\widehat{\\pi}((PQ)^{n}-(PQ)^{m})\\|\\\\\n&\\leq \\|\\widehat{\\pi}(PQ)^{n}-P_{M_{\\pi}\\cap N_{\\pi}}\\|+\\|\\widehat{\\pi}(PQ)^{m}-P_{M_{\\pi}\\cap N_{\\pi}}\\|\\\\\n&=\\|(P_{M_\\pi}P_{N_\\pi})^{n}-P_{M_{\\pi}\\cap N_{\\pi}}\\|+\\|(P_{M_\\pi}P_{N_\\pi})^{m}-P_{M_{\\pi}\\cap N_{\\pi}}\\|\\\\\n&=\\|(P_{M_\\pi}P_{N_\\pi}-P_{M_{\\pi}\\cap N_{\\pi}})^{n}\\|+\\|(P_{M_\\pi}P_{N_\\pi}-P_{M_{\\pi}\\cap N_{\\pi}})^{m}\\|\\quad(\\textnormal{by Lemma \\ref{powers}})\\\\\n&\\leq \\|P_{M_\\pi}P_{N_\\pi}-P_{M_{\\pi}\\cap N_{\\pi}}\\|^{n}+\\|P_{M_\\pi}P_{N_\\pi}-P_{M_{\\pi}\\cap N_{\\pi}}\\|^{m}\\\\\n&=c_{\\pi}(M,N)^{n}+c_{\\pi}(M,N)^{m}\\to 0,\n\\end{align*}\nsince $c_{\\pi}(M,N)<1$. This completes the proof.\n\\end{proof}\nWe denote by $\\widehat{B}$ the space of unitary equivalence classes of irreducible representations of the $C^{*}$-algebra $B$, by $\\mathcal{P}(B)$ the pure state space of $B$ and by $\\pi_{\\sigma}$ the $GNS$-representation associated to the state $\\sigma$. We can view the local Friedrichs angles as a function $\\widehat{B}\\to [0,1]$ and via the composition $\\mathcal{P}(B)\\to \\widehat{B}$, also as a function on $\\mathcal{P}(B)$.\n\\begin{corl} \\label{localglobalangle} The Friedrichs angle \\eqref{moduleangle} and the local Friedrichs angles \\eqref{localangle} are related by $c(M,N)=\\sup_{\\pi\\in\\widehat{B}}c_{\\pi}(M,N)=\\sup_{\\sigma\\in\\mathcal{P}(B)}c_{\\pi_{\\sigma}}(M,N)$.\n\\end{corl}\n\\begin{proof}\nThe representations $\\widehat{H}=\\bigoplus_{\\pi\\in\\widehat{B}}H_{\\pi}$ and $H_{\\mathcal{P}}:=\\bigoplus_{\\sigma\\in\\mathcal{P}(B)}H_{\\pi_{\\sigma}}$ are faithful.\n\\end{proof}\nIn view of Proposition \\ref{independent} and Corollary \\ref{localglobalangle}, we \\emph{define} the Friedrichs angle between an arbitrary pair of complemented submodules to be $c(M,N):=c_{\\pi}(M,N)$, with $\\pi$ faithful. \nIt was shown in \\cite{Luo} that \n\\begin{equation}\n\\label{Deutschangle}\nc(M,N)=c(M^{\\perp},N^{\\perp}),\n\\end{equation}\nprovided that $M\\cap N$ and $M^{\\perp}\\cap N^{\\perp}$ are complemented. In particular, the equality holds for any pair of subspaces of a Hilbert space, \\cite[Theorem 2.16]{D}. \nWe will now show that the equality \\eqref{Deutschangle} holds for an arbitrary pair of complemented submodules. This gives an extension, and a different proof, of \\cite[Theorem 5.12]{Luo}.\n\\begin{thm}\n\\label{anglesymmetry}\nLet $X$ be a Hilbert $C^{*}$-module and $M,N\\subset X$ complemented submodules. Then $c(M,N)$ is well-defined and $c(M,N)=c(M^{\\perp},N^{\\perp})$.\n\\end{thm}\n\\begin{proof}\nFor any representation $\\pi:B\\to\\mathbb{B}(H_{\\pi})$ there is an equality of submodules $(M_{\\pi})^{\\perp}=(M^{\\perp})_{\\pi}$ whenever $M$ is complemented. Moreover Equation \\eqref{Deutschangle} holds for the subspaces $M_{\\pi},N_{\\pi}$ of the Hilbert space $X_{\\pi}$. Thus by Proposition \\ref{independent} we have\n\\begin{align*}\nc(M,N)&=c_{\\pi}(M,N)=c(M_{\\pi},N_{\\pi})\\\\ &=c((M_{\\pi})^{\\perp},(N_{\\pi})^{\\perp})=c((M^{\\perp})_{\\pi},(N^{\\perp})_{\\pi})\\\\&=c_{\\pi}(M^{\\perp},N^{\\perp})=c(M^{\\perp},N^{\\perp}),\\end{align*}\nas claimed.\n\\end{proof}\nNow we further analyse the properties of the local Friedrichs angles as a function on $\\widehat{B}$.\n\\begin{prop}\n\\label{continuity}\nSuppose $(M,N)$ is concordant. Then the map\n\\[\\widehat{B} \\to [0,1],\\quad\\pi\\mapsto c_{\\pi}(M,N),\\]\nis lower semi-continuous. If $X$ is full and $\\widehat{B}$ is Hausdorff, $\\pi\\mapsto c_{\\pi}(M,N)$ is continuous.\n\\end{prop}\n\\begin{proof} Let $J:=\\left\\langle B,B\\right\\rangle$ and $\\widehat{B}\\to\\widehat{J}$ the restriction map, which is continuous. The $C^{*}$-algebras $J$ and $\\mathbb{K}(X)$ are Morita equivalent, so by the Rieffel correspondence \\cite{RieffelInd} the map $\\pi\\mapsto\\widehat{\\pi}$ is a homeomorphism $\\widehat{J} \\to \\widehat{\\mathbb{K}(X)}$. Since $\\mathbb{K}(X)\\subset \\End^{*}_{B}(X)$ is an essential ideal, there is a continuous inclusion $\\widehat{\\mathbb{K}(X)}\\to \\widehat{\\End^{*}_{B}(X)}$, see \\cite[Section 2]{Dauns}. When $(M,N)$ is concordant the map $\\pi\\mapsto c_{\\pi}(M,N)$ can be written as a composition\n\\[\\pi\\mapsto\\widehat{\\pi}\\mapsto \\|\\widehat{\\pi}(P_{M}P_{N}-P_{M\\cap N})\\|,\\]\nand is thus lower semicontinuous by \\cite[Lemma A.30]{RW}. For $X$ full and $\\widehat{B}$ Hausdorff, continuity follows by \\cite[Lemma 5.2]{RW}.\n\\end{proof}\n\\begin{corl} \nSuppose $X$ is full, $B$ is unital, $\\widehat{B}$ is Hausdorff and $(M,N)$ is concordant. Then $c(M,N)<1$ if and only if $c_{\\pi}(M,N)<1$ for every irreducible representation $\\pi$.\n\\end{corl}\n\\begin{proof} \nSince $\\widehat{B}$ is compact Hausdorff and the Friedrichs angle is continuous, \nthe pointwise estimate $c_{\\pi}(M,N)<1$ implies that $c(M,N)=\\sup_{\\pi\\in\\widehat{B}} c_{\\pi}(M,N) <1$.\n\\end{proof}\n\\begin{rmk}\n\\label{discontangle}\nIn Proposition \\ref{continuity}, the condition that $(M,N)$ be concordant cannot be relaxed. Consider $B=C([0,\\frac{\\pi}{2}])$, $X=C([0,\\frac{\\pi}{2}], \\mathbb{C}^{2})$ and consider the submodules\n\\[\nM=\\textnormal{Ran } \\begin{pmatrix} 1 & 0 \\\\ 0 & 0 \\end{pmatrix},\\quad N\n= \\textnormal{Ran } \\begin{pmatrix} \\cos^{2}t & \\sin t \\cos t \\\\ \\sin t \\cos t &\\sin^{2}t\\end{pmatrix}.\n\\]\nFor $t\\in [0,\\pi\/2]$ we write $c_{t}(M,N)$ for the Friedrichs angle at $t$. For $0500$ using eq. (\\ref{wave}) with $\\alpha=1$, $A_k=1$ and $B_k=0$ as \\be\nQ(\\tau,\\rho_{bound})=\\sqrt{\\frac{2}{\\pi}}\\frac{\\sin(\\tau)}{\\tau}j_{l}(\\rho_{bound})\\label{boundary}\\ee\nSimilarly, the initial conditions set at $\\tau_i=1$ are: \\be\nQ(\\tau_i,\\rho)= \\sqrt{\\frac{2}{\\pi}}\\frac{\\sin(\\tau_i)}{\\tau_i}j_{l}(\\rho)\\label{initial1}\\ee\nand \\be \\frac{\\partial Q(\\tau_i,\\rho)}{\\partial\n\\tau}=\\frac{\\partial}{\\partial \\tau}[\\sqrt{\\frac{2}{\\pi}}\\frac{\\sin(\\tau)}{\\tau}j_{l}(\\rho)]|_{\\tau=\\tau_i}\\label{initial2}\\ee\nIn addition to the test of the validity of numerical solution\npresented in Fig. \\ref{fig1} we have performed other tests\nincluding the verification of the independence of the numerical\nsolution from the location of the boundary for $\\rho_{bound}>200$.\n\n\n\nWe have solved the partial differential equation (\\ref{qpdeconf})\nfor various values of $l$ with results that are qualitatively\nsimilar. For definiteness we present in Fig. \\ref{fig2} the\nsolution corresponding to $l=6$ for $R_s=5$ superposed with the\ncorresponding solution for $R_s=0$ in order to identify the new\nfeatures introduced in the evolution of the GW by the presence of\nthe point mass. \n\nThere are three main features to observe in Fig.\n\\ref{fig2}. First, the waves are practically identical far away\nfrom the point mass as expected. Second, there is a time delay for\nthe wave in the presence and in the vicinity of the point mass\n(Fig. 2b upper right). Third, the amplitude of the wave in the\npresence and in the vicinity of the mass increases (compare Fig.\n2b (upper right) with Fig. 2c (lower left)).\n\nThe main effect of the expansion is to reduce the amplitude of the\ngravitational wave by a factor proportional to the scale factor in the absence of the\nmass. This is shown in Fig. \\ref{fig3} which shows that the amplitude multiplied by the\nscale factor remains constant in the absence of the mass (blue oscillating line has\nconstant amplitude) for the particular time dependence of the scale factor considered\n($a(\\tau)\\sim \\tau$). In the presence of the mass however, the decrease of the amplitude due to\nthe expansion is less efficient (red line) and the product of the amplitude times the\nscale factor increases slowly with time.\n\nThe gravitational wave time evolution shown in Fig. \\ref{fig3} corresponds to $\\rho=7.9$ (closest\nmaximum amplitude to the mass for $l=6$) for $R_s=5$ (red dashed\nline) and is superposed with the corresponding evolution for $R_s=0$\n(blue continuous line). This plot demonstrates the relative\n(linear) increase of the amplitude with time, as well as the\nincreased period of the wave in the presence of the mass. It also \ndemonstrates (as discussed above) the well known fact that the wave amplitude in the\nabsence of the mass ($R_s=0$) is inversely proportional to the\nscale factor (the blue wave has a constant amplitude).\n\n\\begin{figure}[!t]\n\\centering\n\\vspace{0cm}\\rotatebox{0}{\\vspace{0cm}\\hspace{0cm}\\resizebox{0.49\\textwidth}{!}{\\includegraphics{fig2.eps}}}\n\\caption{ The time evolution of the first spatial maximum (at\n$\\rho=7.9$) of the partial spherical wave with $l=6$, $R_s=5$\n(red line) in comparison with the corresponding free solution\n($R_s=0$, blue continuous line) at the same spatial point. The\nfree wave reaches its maximum first (Fig. 2b) while the wave in\nthe presence of the point mass shows a delay in reaching its\nmaximum (Fig. 2c) due to gravitational redshift. The wave in the\npresence of the mass has an amplitude that increases with time as\nindicated with the dashed red line that in tangent to the\ngravitational wave maxima. As expected, the product $a(\\tau) Q(\\tau)$ is constant for the free wave in an expanding background. } \\label{fig3}\n\\end{figure}\n\nThe effects of the gravitational time delay on the evolution of\nthe wave may also be demonstrated by plotting the power spectrum\nobtained by a Fourier series expansion of the evolving in\nconformal time numerical solution at $\\rho=7.9$ in harmonic waves.\n\nThe finite time interval power spectrum may be defined through the expansion\n\\be\nQ(\\tau,\\rho)=\\frac{a_0}{2}+\\sum \\limits_{i=1}^n(a_n\n\\cos(n\\tau)+b_n\\sin(n\\tau))\\label{coef}\\ee\nas\n\\be P(n)\\equiv \\log\\sqrt{a_n^2+b_n^2}\\label{spectrum}\\ee\n\nWe used a time interval of approximately two\ncomplete oscillations which corresponds to a time interval\n$\\tau \\in [1,20]$ ($\\tau_i=1, \\tau_{max}=20$ as shown in Fig. \\ref{fig4}).\n\nAs shown in Fig. \\ref{fig4} the presence of the mass (red continuous line)\nleads to an increase of the amplitude of low harmonics and\ndecrease of the amplitude of higher harmonics which is consistent\nwith the effects of gravitational time delay. The exact\nform of the spectrum clearly depends on the time interval\nconsidered, however the qualitative feature of higher amplitudes\nfor lower frequencies persists for all time intervals. This\nfeature is more prominent for lower values of $\\rho$.\n\n\\begin{figure}[!b]\n\\centering\n\\vspace{0cm}\\rotatebox{0}{\\vspace{0cm}\\hspace{0cm}\\resizebox{0.49\\textwidth}{!}{\\includegraphics{fig3.eps}}}\n\\caption{The time power spectra of the gravitational wave in the\npresence (red line) and in the absence (blue line) of the mass. Notice that\nlower frequencies have a higher amplitude for the wave in the\npresence of the mass as expected due to the gravitational time\ndelay.} \\label{fig4}\n\\end{figure}\n\nIn accordance with eq. (\\ref{periodincrease}) the increase of the\nperiod of the wave at a given distance from the mass is\nproportional to the mass in the weak field approximation. This is\nconsistent with our numerical solution as shown in Fig. \\ref{fig5} where we\nshow the relative increase of the period of the wave $\\Delta\nT\/T_0$ at given distances $\\rho$ \nfrom the mass ($\\rho=7.9$ and $\\rho=15.89$) for various values the parameter $R_s$ (points in\nplot). In order to evaluate the relative change of the period $\\Delta\nT\/T_0$ we use the time evolution of the wave perturbation as shown in Fig. \\ref{fig3} to obtain the period of the wave in the\npresence of the mass and the corresponding period in the absence of the mass. Superposed in Fig. \\ref{fig5} \nis the best fit straight line in each case. As\nis theoretically expected there is a linear relationship in\naccordance with eq. (\\ref{periodincrease}). The correlation\ncoefficients of the points with the corresponding best fit straight line are\nequal to $0.99$ indicating an excellent quality of fit.\n\nThe theoretically predicted slope is $\\frac{1}{2a\\rho}$ where the scale factor can\nbe taken as approximately constant and equal to its average value\nduring the wave period used to evaluate $\\Delta\nT\/T_0$.\n\n\\begin{figure}[!t]\n\\centering\n\\vspace{0cm}\\rotatebox{0}{\\vspace{0cm}\\hspace{0cm}\\resizebox{0.49\\textwidth}{!}{\\includegraphics{fig5.eps}}}\n\\caption{The relative difference of the wave periods $\\Delta\nT\/T_0$, where $T$ is the period in the presence of mass and $T_0$\nis the period in the absence of mass, as a function of $R_s$. It\nis clear that as the value of the variable $\\rho$ increases, the\nstatistical slope of the curve decreases. This is an anticipated\nresult due to theoretical slope of the curve $\\frac{1}{2a\\rho}$.}\n\\label{fig5}\n\\end{figure}\n\n\nIn order to estimate the theoretical value of the scale factor, we\ncalculate the mean value $\\bar{a}(\\tau)$, in the time interval\n$\\tau_{1}-\\tau_{2}$ of a single period, through the formula: \\be\n\\bar{a}(\\tau)=\\frac{1}{\\tau_{2}-\\tau_{1}}\\int_{\\tau_1}^{\\tau_2}a(\\tau)d\\tau=\\frac{\\tau_2+\\tau_1}{2}\\label{bara}\\ee\n The observed\ndeviations by about $20\\%$ between theoretically expected slope\nand numerically obtained can be attributed to the approximations\nwe have made which include, the weak field assumption ($R_s \\ll\na\\rho$ while in the cases considered $\\frac{R_s}{a\\rho} \\leq\n0.1$), the assumed constant scale factor for\nthe evaluation of the slope etc. As shown in Figs 2 and 3 the amplitude of the wave also increases\nas the point mass is approached. A quantitative estimate of this\neffect is shown in Fig. \\ref{fig6} where we show the ratio of the\namplitudes of the waves $A\/A_0$ in the presence of a mass ($A$)\nand in the absence of the mass ($A_0$) for various values of the\nparameter $R_s$, when $\\rho=7.9$ and $\\rho=15.89$. The best fit\nstraight line is also superposed on the points showing that a\nlinear relationship between $A\/A_0$ and $R_s$ is a good\napproximation.\n\\begin{figure}[!b]\n\\centering\n\\vspace{0cm}\\rotatebox{0}{\\vspace{0cm}\\hspace{0cm}\\resizebox{0.49\\textwidth}{!}{\\includegraphics{fig6.eps}}}\n\\caption{The ratio of the amplitudes of the waves $A\/A_0$ in the\npresence of a mass ($A$) and in the absence of the mass ($A_0$) as a function of\nthe parameter $R_s$, when $\\rho=7.9$ and $\\rho=15.89$.}\n\\label{fig6}\n\\end{figure}\nThe amplitude increases up to $10\\%$ when $\\rho=7.9$ and $R_s=10$,\nwhile for $\\rho=15.89$ and the same value of $R_s$, the increase is about $5\\%$. Thus the\namplitude increase appears to vary inversely proportional with\n$\\rho$ which is consistent with the fact that the GW gains energy\nas it enters regions of space with higher curvature.\n\n\n\\section{Conclusion}\n\\label{sec:Section 4}\n\nThe effects of a point mass on a GW evolving in an expanding\nuniverse are determined by the mass $M$ and the physical distance\n$a \\rho$ of the wave from the mass through the expression\n$\\frac{R_s}{a\\rho}\\equiv\\frac{2GM}{a\\rho}$. In the context of a\nperturbative weak field analysis we have demonstrated that a point\nmass tends to increase the amplitude and the period of the GW\nlinearly with respect to $\\frac{R_s}{a\\rho}$. This result is\nconsistent with expectations based on gravitational time delay and\nenergy considerations.\n\nEven though our numerical results were presented for the special\ncase of a radiation dominated cosmological background ($a(\\tau)\\sim \\tau$) and a\nspecific multipole component of the wave ($l=6$) we have checked that their qualitative features persist for\nall multipole components and cosmological backgrounds provided that\nthe weak field condition (\\ref{smallrs}) is respected. Thus, even though we have considered specific spherical waves in this analysis, we anticipate that our\nresults can also describe a plane wave when expressed as a superposition of spherical\nwaves.\n\nThe time slicing we considered corresponds to the coordinate\ntime of the particular metric we used. This coordinate time is particularly interesting\nand generic as it corresponds to the proper time of a static observer located far away\nfrom the point mass or in the absence of the point mass. This is the standard cosmic\nobserver whose observations are consistent with the cosmological principle. Clearly a \ndifferent choice of time slicing would correspond to a different observer and would lead to a different metric and thus different\nresults.\n\nFrom the results shown in Fig. \\ref{fig5} and Fig. \\ref{fig6}, we conclude that $T=T_0(1+\\mu R_s)$ and $A=A_0(1+\\nu R_s)$ where $\\mu$ and $\\nu$ are the slopes of the curves which are approximately equal. Thus we have demonstrated that the energy density of GWs which is proportional to $\\omega^2 A^2$ has a weak dependence on $R_s$ in the context of our weak field approximation as long as the slopes $\\mu$ and $\\nu$ are approximately equal.\n\nOur result has interesting implications for the calculation of the\nRGW spectrum which currently assumes\n\\cite{Koh:2009cy,Corda:2009bx,Grishchuk:2007uz,Zhang:2008zx,Gogoberidze:2007an,Buonanno:1996xc}\na smooth homogeneous cosmological background and ignores the\npresence of mass concentrations which as shown in the present\nanalysis would tend to modify both the magnitude and the shape of\nthis spectrum. A proper stochastic analysis including the effects\nof mass concentrations on the relic GW spectrum is therefore an\ninteresting extension of the present work.\n\nA distortion of the RGW spectrum is expected due to the presence\nof point masses on various scales due to the increase of each mode amplitude and\ndecrease of each mode frequency. The effect will be stronger in regions of higher\nmass concentrations. On scales larger than the galactic scales the role of the point\nmass could be played by a galaxy while on scales of the solar system the role of the point mass could be played by a planet. \nIn the solar system the effect is expected to be rather weak and beyond the sensitivity of current experiments. \n\n\nAn additional interesting extension could be the drop of the weak\nfield approximation and the use of the full McVittie metric\n\\cite{McVittie:1933zz} for the study of GW evolution in an\nexpanding background and in the vicinity of a black hole allowing\nfor strong gravitational field.\n\nEven though our numerical analysis has been well tested and\nprovides detailed quantitative information on the GW evolution\nin the presence of expansion and a point mass, an analytical\nperturbative solution describing this evolution would provide\nfurther physical insight and appears to be a tractable useful\nextension of the present work.\n\n\\textbf{Numerical Analysis Files}: The mathematica files used for the production of the figures, as well as the figures may be downloaded from \\href{https:\/\/drive.google.com\/open?id=0B7rg6X3QljQXck5YSmQ5Rl9HeUU}{here} or upon request from the authors.\n\n\\section*{Acknowledgements}\nD. Papadopoulos would like to thank the Department of Physics of\nthe University of Ioannina for hospitality during the period when\npart of this work was in progress. We also thank K. Kleidis for useful comments.\n\n\\raggedleft\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec1.1}\n\n\\begin{figure}\n\\vspace{1cm}\n\\includegraphics [width=0.5\\linewidth]{WSFig1.eps}\n\\caption{Number of papers on the subject ``anomalous diffusion'' published\nper year according to the ISI Web of Science, Thomson-Reuters. \nNotice the ``phase transition''-like rise during 1990.}\n\\label{Fig1}\n\\end{figure}\n\nAnomalous diffusion becomes an increasingly popular subject with the number of papers\npublished per year growing fast over last twenty years after a distinct\nrise occurred in 1990 \n(cf. Fig. \\ref{Fig1}). Since then, \nit spreads from such typical physical applications as\ncharge transport in disordered solids and hot plasmas to biophysical\napplications and even quantitative finance \n\\cite{Shlesinger1,Scher,Bouchaud,Hughes,Shlesinger,\nMetzler,West,West2,Zaslavsky,Saxton,Seisenberg,Tolic,GH04,Golding,Wilhelm,\nAmblard,Szymanski,Kou,Mizuno,Reuveni}. \n\nThere are several sufficiently generic \nphysical mechanisms and accompanying theoretical approaches to describe the complexity\nof anomalous transport processes. One approach is intrinsically based on the physical\npicture of a stochastic time clock \n\\cite{Shlesinger1,Scher,Hughes,Shlesinger,Metzler,Sokolov1}. It models random \nsojourns of a travelling\nparticle in trapping domains of a disordered solid, e.g. due to energy disorder.\nAfter a random time spent in some spatially located trapping domain the particle jumps \nto a neighboring domain, or maybe farther, and such a jumping process\ncontinues in time. The jump directions and their\nlengths are not correlated from jump to jump and the next clock period\nis not correlated with those passed (semi-Markovian assumption \n\\footnote{This does not exclude infinite range memory\nleading to a weak ergodicity breaking \\cite{Barkai}.}). Such a random\nclock is completely characterized by the probability density of clock periods \n$\\psi(\\tau)$.\nIn a given time interval $t$ there will\nbe a random number of jumps $n$, or \nstochastic clock \nperiods completed. However, if the mean clock \nperiod $\\langle \\tau\\rangle$ exists,\nthe probability distribution $p(n,t)$ of ``ticking'' $n$ times within the \nobserver time window $t$ becomes for large $n$ a very sharp function \\cite{Hughes} around \n$n^*(t)=t\/\\langle \\tau\\rangle$, as characterized by the \nrelative dispersion, $\\langle \\delta n^2(t) \\rangle^{1\/2}\/\n\\langle n(t) \\rangle$ (see Appendix A). \nIn the continuum medium \napproximation the trapping domains shrink to points. \nThen, $\\langle \\tau \\rangle$ can be made arbitrarily small and $n$ becomes \nquasi-continuous variable for a finite $t$.\nCorrespondingly, the probability density of intrinsic time, \n$\\tau(t)=n(t)\\tau_{\\rm sc}$,\nwhere $\\tau_{\\rm sc}$ is a time-scaling parameter, an intrinsic clock \ntime unit equal to the\nduration of time period for the regular clock, \nassumes a delta-function,\n$p(\\tau)=\\delta(\\tau-t)$, and the stochastic clock is not different\nfrom the regular one.\n\nThe situation changes dramatically if the mean of\nsojourns does not exist, or better to say, it exceeds largely a \ntypical time required to\ndiffuse across the physical medium of a finite size. \nThen, $p(n,t)$ is not a sharp\nfunction around the mean number $\\langle n(t)\\rangle$.\nThis is the case, for example, when $\\psi(\\tau)$ possesses\na long tail, $\\psi(\\tau)\\propto \n(\\tau\/\\tau_{\\rm sc})^{-1-\\alpha}$ for $\\tau\/\\tau_{\\rm sc}\n\\to \\infty$ with $0<\\alpha<1$.\nThis implies a divergent mean $\\langle \\tau\\rangle \\to\\infty$, and\ndiverging higher moments as well.\nNevertheless, $\\langle n(t)\\rangle \\propto (t\/\\tau_{\\rm sc})^{\\alpha}$ \nexists for any finite $t$ and it scales \nsublinearly with the physical time $t$ (see in Appendix A). In terms of \nthe one-sided Levy distribution density ${\\cal L}_{\\alpha}(z)$, \n$p(n,t)=(t\/\\tau_{\\rm sc}){\\cal L}_{\\alpha}(n^{-1\/\\alpha}\nt\/\\tau_{\\rm sc})\/(\\alpha n^{1\/\\alpha+1})$. \n\nConsider now an ensemble of particles.\nUntil time $t$, each particle \nhas accomplished an {\\it individual} number of intrinsic time periods corresponding\nto the intrinsic time $\\tau(t)$ which becomes a random\nvariable broadly distributed: all the particles have their own history, \nmaintaining individuality \nand avoiding the fate of self-averaging even in the strict limit $t\\to\\infty$. \nOnly an additional ensemble averaging\nsmears out this principal randomness\\cite{Sokolov1,He,Lubelski}. \nThe {\\it unbiased} diffusion becomes anomalously slow and nonergodic with the spatial \nvariance of a {\\it cloud} of particles \ngrowing sublinearly, \n$\\langle \\delta x^2(t)\\rangle \\propto \\langle n(t)\\rangle \\propto \nt^{\\alpha}$. \nSuch a nonergodic approach\nto subdiffusion seems appropriate for disordered solids, e.g. thin amorphous \nfilms \\cite{Scher,Hughes}, with a more recent example \nprovided by ${\\rm Ti O_2}$ nanocrystalline electrodes in \nthe Gr\\\"atzel's\nphotovoltaic cell elements \\cite{Nelson}. For charged carriers within \nsuch media one can create\na potential energy profile $U(x)$ by applying an electrical field of spatially \ndistributed fixed charges and a static external electrical field. Then in the continuum\napproximation subdiffusion can be described by the fractional Fokker-Planck\nequation (FFPE) \\cite{Metzler,Metzler99,Barkai01}\n\\begin{eqnarray}\\label{FFPE1}\n \\frac{\\partial}{\\partial t} P(x,t) = \\sideset{_0}{_t}{\\mathop{\\hat\nD}^{1-\\alpha}} \\left [- \\frac{\\partial}{\\partial x} \n\\frac{f(x)}{\\eta _\\alpha} + \\kappa _\\alpha \\frac{\\partial ^2}{\\partial x^2}\n\\right ] P(x, t) \\,, \n\\end{eqnarray}\nwhere $f(x)=-d U(x)\/dx$ is the force, \n$\\eta _\\alpha$ is the fractional friction coefficient related to the\nfractional subdiffusion coefficient $\\kappa_{\\alpha}$ by the generalized Einstein\nrelation, $\\eta_{\\alpha}=k_BT\/\\kappa_{\\alpha}$, at temperature $T$ and \n\\begin{equation} \\label{RL}\n\\sideset{_{t_0}}{_t}{\\mathop{\\hat D}^{\\gamma}} P (x, t) =\\frac{1}{\n\\Gamma(1-\\gamma)} \\frac{\\partial}{\\partial t} \\int_{t_0}^{t}\n\\mathrm{d} t' \\, \\frac{P(x, t')}{(t-t')^{\\gamma}} \\, ,\n\\end{equation}\nis the Riemann-Liouville operator of the fractional derivative \n\\cite{Mainardi}, where $0<\\gamma<1$ and $\\Gamma(x)$ is the gamma-function.\nThe FFPE (\\ref{FFPE1}) can be derived within the above continuous time random walk \n(CTRW) framework. It can\nalso be written in the form using the \nCaputo fractional derivative\n\\cite{Mainardi}\n\\begin{eqnarray}\n\\sideset{_{t_0}}{_{*}}{\\mathop{D}^{\\gamma}}P(x,t):=\\frac{1}{\\Gamma(1-\\gamma)}\n\\int_{t_0}^t dt' \\frac{\\partial P(x,t')\/\\partial t'}{(t-t')^\\gamma}\n\\end{eqnarray}\nacting on the left hand side, yielding~\\cite{GH06}\n\\begin{eqnarray}\\label{FFPE2}\n\\sideset{_{0}}{_{*}}{\\mathop{D}^{\\alpha}}P(x,t)= \n\\kappa_{\\alpha} \\frac{\\partial }{\\partial\nx} \\left ( e^{-\\beta U(x)} \\frac{\\partial}{\\partial x} \\, e^{\\beta\nU(x)} P(x,t) \\right )=-\\frac{\\partial J(x,t)}{\\partial x} \\,,\n\\end{eqnarray}\nin the transport form.\nHere, $\\beta=1\/(k_BT)$ is inverse temperature and \n\\begin{eqnarray}\nJ(x,t)=-\\kappa_{\\alpha}\ne^{-\\beta U(x)} \\frac{\\partial}{\\partial x} \\, e^{\\beta\nU(x)} P(x,t)\n\\end{eqnarray}\nis the subdiffusive flux.\n It should be emphasized that a non-Markovian Fokker-Planck equation never defines\nthe corresponding non-Markovian process completely \\cite{HT77,Grabert}. \nIt allows to find merely the \nsingle-time, conditional, and double-time probability densities, but never the \nmulti-time probability densities.\nHowever, the FFPE dynamics can be nicely \nsimulated from the underlying CTRW with the nearest neighbors jumps only \n\\cite{GH06,H06}. \n \nA quite different approach to subdiffusion is associated with \nthe fractional Brownian motion \\cite{Mandelbrot}.\nHere, the principal issue is the long-range anticorrelations in the particle \ndisplacements, positive increments follow with a greater probability by negative \nincrements and {\\it vice versa}\\cite{Goychuk09,Jeon}, which can reflect e.g. the phenomenon of viscoelasticity in complex \nglass-forming liquids above, but close to the glass transition. The Brownian \nparticle\nis temporally trapped in a trap (cage effect) by an elastic force with spring constant\n$G(t)$ which decays to zero\nin time releasing the particle. Let's assume that the motion starts\nat $t_0$, $v(t)=\\dot x(t)=0$ for $t0$ departing just from different \nstanding points \\footnote{$\\eta(t)$ can also be negative, e.g. accounting for\nthe hydrodynamic memory or in the case\nof superdiffusion. Therefore, the memory-friction interpretation is, \nin fact, more general.}. \nThe anticorrelations in the particle's displacements are due to the\nelastic restoring force component. \n\nIn complex media, the memory function $G(t)$ is better described\nby a sum of exponentials reflecting a viscoelastic response with multiple time scales. \nMoreover, \nin 1936 A. Gemant \\cite{Gemant} found that some viscoelastic\nbodies are better described by a $G(t)$ relaxing in accordance with a\npower law, $G(t)\\propto t^{-\\alpha}$, rather than a single-exponential \nand introduced\na fractional integro-differential in the viscoelasticity theory. Using the notion of\nfractional Caputo derivative \nsuch a visco-elastic force can be short-handed, written as \n\\begin{equation}\\label{model2}\n F_{\\rm v-el}(t)=-\\eta_{\\alpha}\n \\sideset{_{t_0}}{_{*}}{\\mathop{D}^{\\alpha}} x(t)\\;.\n\\end{equation}\nIndeed, such and similar viscoelastic responses are measured \\cite{Wilhelm,Mizuno,Mason} \nusing the microrheology\nmethods \\cite{rheo}. The Brownian motion never stops and the frictional \nloss of energy must be compensated on average by the energy gain\nprovided by a zero-mean random force of environment so that at the thermal equilibrium \nthe equipartition theorem holds, in accordance with\nthe classical fluctuation-dissipation theorem. \nWithin the considered model of a linear \nmemory-friction such a force must be Gaussian \\cite{Reimann} \n(but not necessarily so beyond the linear friction model). \nAs a result, the Brownian motion\nof a particle of mass $m$ is described by the Fractional Langevin Equation (FLE)\n\\cite{Lutz,Coffey,Goychuk07a,Burov} \n\\begin{equation}\\label{fle}\nm\\ddot x+ \\eta_{\\alpha}\n\\sideset{_{0}}{_{*}}{\\mathop{D}^{\\alpha}} x(t)=f(x)+\\xi(t) \\;,\n\\end{equation}\n(from now on we fix $t_0=0$) which is a particular case of the celebrated \nGeneralized Langevin Equation (GLE) \\cite{Kubo1,Kubo2,Zwanzig,HTB90,WeissBook}\n\\begin{equation}\\label{gle}\nm\\ddot x+\\int_{0}^{t}\\eta(t-t')\\dot x(t')dt'=\nf(x)+\\xi(t) \\;,\n\\end{equation}\nwith the memory kernel $\\eta(t)=\\eta_{\\alpha}t^{-\\alpha}\/\\Gamma(1-\\alpha)$ \nand the noise autocorrelation \nfunction obeying the fluctuation\ndissipation relation \n\\begin{equation}\\label{fdt} \\langle\n\\xi(t)\\xi(t')\\rangle=k_BT\\eta(|t-t'|) \\;.\n\\end{equation}\nSuch a GLE can be derived also from a Hamiltonian model for a \nparticle bilinearly \ncoupled with coupling constants $c_i$\nto a thermal bath of harmonic oscillators with masses $m_{i}$ and frequencies\n$\\omega_i$, $H_{B,\\rm int}(p_i,q_i,x)=(1\/2)\\sum_i \\{p_i^2\/m_i+m_i\\omega_i^2\n[q_i-c_i x\/(m_i\\omega_i^2)]^2\\}$.\nThe total effect of the bath oscillators,\nwhich are initially canonically distributed with $H_{B,\\rm int}$ \nat temperature $T$ and fixed $x=x(0)$, \nis characterized\nby the bath spectral density\n\\begin{equation}\nJ(\\omega) = \\frac{\\pi}{2} \\sum_i\n\\frac{c_i^2}{m_{i} \\omega_{i}} \\delta(\\omega-\\omega_i).\n\\label{J}\n\\end{equation}\nThe memory kernel is $\\eta(t)=(2\/\\pi)\\int_0^{\\infty}J(\\omega)\\cos(\\omega t)d\\omega$ \nin terms of $J(\\omega)$ and the subdiffusive FLE corresponds to a \nsub-Ohmic, or fracton thermal bath \nwith $J(\\omega)=\\eta_{\\alpha}\\sin(\\pi\\alpha\/2)\n\\omega^{\\alpha}$ \\cite{WeissBook}. \nWithout frequency cutoffs such a model presents a \nclear idealization.\nThere always exists a highest frequency of the thermal bath and this leads to \na small time regularization of the memory kernel, i.e. a short-time cutoff. \nPhysically, this takes into account the medium's granularity beyond the continuum\napproximation. Moreover, in the case of a finite-size medium \nthere always exists also a smallest frequency of the medium's oscillators corresponding\nto the inverse size of the medium. These facts become especially clear if one evaluates\nthe spectral density of low-frequency ``fracton'' oscillators in proteins, \nsee in Refs. \\cite{Reuveni}.\nThis leads also to a cutoff at large times \nin the memory kernel and the dynamics can be\nsubdiffusive on the time scale smaller than the corresponding memory cutoff.\nThe latter one can be but prominently large which makes the considered \nidealization relevant. Important is also the result that an overdamped\nFLE description of subdiffusion can be derived \nfrom a broad class of phenomenological continuum elastic \nmodels \\cite{Taloni}. \n\n\n\nIn the inertialess limit with $m\\to 0$, \none can conceive the idea that FFPE (\\ref{FFPE1},\n\\ref{FFPE2})\nis the fractional Fokker-Planck equation corresponding to the FLE (\\ref{fle}). \nThis idea is but wrong \\cite{Goychuk07a}. \nThe non-Markovian Fokker-Planck equation (NMFPE) which corresponds\nto the GLE (with arbitrary kernel) \\cite{Adelman,Hanggi78,Hynes} and \nto the FLE, in particular\\cite{Goychuk07a}, \nis a different one. Presently, its explicit form\nis known only for constant or\nlinear forces $f(x)$\\cite{Adelman,Hanggi78,Hynes}. \nThis resulting NMFPE \nhas the form of \nFokker-Planck equation with \ntime-dependent kinetic coefficients. This time-dependence\nis not universal and it heavily depends on the form of potential. In turn, \nthe Langevin equation which corresponds to the above FFPE is known and it has \nthe form of a Langevin equation which is local in the stochastic time $\\tau(t)$\nand describes thus a doubly stochastic process \\cite{DoublyStoch}.\nHere lies also the profound mathematical difference between these two approaches\nto subdiffusion. The physical differences are also immense. In particular, the \nGLE and FLE\napproaches are asymptotically mostly ergodic\nas they are not based on the concept of \nfractal stochastic time\nwith divergent mean period and the mean residence time in a finite spatial\ndomain remains finite. \nBefore we discuss the striking differences in more detail, \nlet us start from some apparent, but misleading similarities.\n\n \n\\section{Free subdiffusion and constant bias}\n\nFree subdiffusion, as well as diffusion biased by a constant force $F$ can readily be \nsolved in both approaches using the method \nof Laplace-transform. First one finds the Laplace-transform of the mean \nensemble-averaged displacement $\\langle \\delta x(t)\\rangle $, and of the position \nvariance\n$\\langle \\delta x^2(t)\\rangle= \\langle x^2(t)\\rangle-\n\\langle x(t)\\rangle^2$, starting from a delta-peaked distribution at $x=0$ and \n$t_0=0$. Then, one transforms back to the time domain. This gives\\cite{Metzler} \n\\begin{eqnarray} \\label{mean}\n\\langle \\delta x(t)\\rangle=\\mu_{\\alpha} F t^{\\alpha}\/\\Gamma(1+\\alpha)\n\\end{eqnarray}\nand\n\\begin{eqnarray} \\label{variance}\n\\langle \\delta x^2(t)\\rangle=2\\kappa_{\\alpha}t^{\\alpha}\/\\Gamma(1+\\alpha)\n\\end{eqnarray}\nwith the generalized mobility $\\mu_{\\alpha}=1\/\\eta_{\\alpha}$\nrelated to the subdiffusion coefficient at $F=0$ by the generalized Einstein \nrelation $\\mu_{\\alpha}=\\kappa_{\\alpha}\/(k_BT)$. \nWithin the FLE approach these results are valid in the strict inertialess \nlimit $m\\to 0$. Furthermore, the Eq. (\\ref{variance}) is still valid then for \n{\\it arbitrary} $F\\neq 0$. However, within the FFPE approach the Eq. (\\ref{variance})\nis valid {\\it only} for $F=0$, which is the first striking difference, see also below. \nFurthermore, both results are also valid\nasymptotically, $t\\to\\infty$, within the FLE for a finite $m\\neq 0$.\n\n Generally, the GLE results\ncan be obtained for arbitrary memory kernel $\\eta(t)$. Assuming the particles\nbeing initially Maxwellian distributed, i.e. thermalized with\nthermal r.m.s. velocities \n\\begin{eqnarray}\nv_T=\\sqrt{k_BT\/m}, \n\\end{eqnarray}\none can\nobtain for the Laplace-transformed stationary velocity (fluctuation) \nautocorrelation function (VACF)\n$K_v(\\tau)=\\langle \\delta v(t+\\tau)\\delta v(t)\\rangle$, \n$\\delta v(t)=v(t)-\\langle v(t)\\rangle$,\n\\begin{eqnarray}\n\\tilde K_v(s)=\\frac{k_BT}{ms+\\tilde \\eta(s)},\n\\end{eqnarray} \nwhere $\\tilde \\eta(s)$ is the Laplace-transform of $\\eta(t)$.\nThis is a well-known result which was obtained first by Kubo\\cite{Kubo1,Kubo2} \nin the Fourier space.\nFor the FLE with $\\tilde \\eta(s)=\\eta_{\\alpha}s^{\\alpha-1}$ it \nyields by the inversion to the time-domain \\cite{Lutz}\n\\begin{eqnarray}\\label{vfle}\nK_v(\\tau)=v_T^2 E_{2-\\alpha}\n[-(\\tau\/\\tau_{v})^{2-\\alpha}]\n\\end{eqnarray}\nwith $\\tau_{v}=(m\/\\eta_{\\alpha})^{1\/(2-\\alpha)}$ being the anomalous \nvelocity relaxation time constant. In (\\ref{vfle}), \n$E_{\\gamma}(z)$ is the Mittag-Leffler\nfunction, $E_{\\gamma}(z)=\\sum_{n=0}^{\\infty}z^n\/\\Gamma(n\\gamma+1)$ \\cite{Metzler}.\nFor $0<\\alpha<1$, $K_v(\\tau)$ is initially positive reflecting \nballistic persistence due to\ninertial effects\nand then becomes negative (anti-persistence due to decaying elastic cage force). \nIn the limit $m\\to 0$, the VACF undergoes a jump starting from \n$v_T^2$ at $\\tau=0$ and\nthen becoming negative, \n$K_v(\\tau)\\propto -1\/\\tau^{2-\\alpha}$ for $\\tau>0$, corresponding\nto purely anti-persistent motion. The position variance is given by the \ndoubly-integrated VACF. Its Laplace-transform therefore reads,\n\\begin{eqnarray}\\label{x2}\n\\langle \\widetilde{\\delta x^2(s)}\\rangle =\\frac{2k_BT}{s^2[ms+\\tilde \\eta(s)]}.\n\\end{eqnarray}\nMoreover, \n\\begin{eqnarray}\n\\langle \\widetilde{\\delta x(s)}\\rangle =\\frac{F}{s^2[ms+\\tilde \\eta(s)]},\n\\end{eqnarray}\nfor arbitrary kernel, which can also be easily shown from the GLE,\n and therefore \\footnote{For nonequilibrium\ninitial preparations this result holds asymptotically in any asymptotic \nergodic case, \nincluding the FLE dynamics. \nThe relaxation to the asymptotic regime, or aging, can be but very\nslow \\cite{Pottier,Deng} which is a general feature of subdiffusive GLE dynamics \nalso in periodic potentials.} \n\\begin{eqnarray}\\label{normal}\n\\frac{\\langle \\delta x(t) \\rangle}{\n\\langle \\delta x^2(t) \\rangle}=\\frac{F}{2k_BT} \\;\n\\end{eqnarray}\nfor the thermally equilibrium initial preparation.\nFor the FLE with a finite $m$ the inversion of Eq. (\\ref{x2}) to the time domain\nyields\\cite{Lutz}, \n\\begin{eqnarray}\n\\langle \\delta x^2(t) \\rangle=2v_T^2t^2 E_{2-\\alpha,3}\n[-(t\/\\tau_{v})^{2-\\alpha}], \n\\end{eqnarray}\nwhere $E_{\\gamma,\\beta}(z)=\n\\sum_{n=0}^{\\infty}z^n\/\\Gamma(n\\gamma+\\beta)$ is the generalized Mittag-Leffler\nfunction. One recovers Eq. (\\ref{variance}) in the limit $m\\to 0$. \n \n\nHowever, for the subdiffusive CTRW\nand FFPE dynamics the behavior of the ensemble-averaged variance is very different \nfrom Eq. (\\ref{variance}) under a non-zero bias\n$F\\neq 0$. Then, the Eq. (\\ref{variance}) is not valid anymore. \nThis fact is ultimately related to the properties of the stochastic\nclock. The point is that starting from a CTRW picture it is easy \nto show (see Appendix A) \nthat the growing ensemble-averaged \nvariance $\\langle \\delta x^2(t)\\rangle$ depends in the asymmetric case\n(the probabilities to jump left and right are different) not only on the\nmean number $\\langle n(t)\\rangle$ of the stochastic clock periods passed, \nbut also on their variance $\\langle \\delta n^2(t)\\rangle$. For $\\alpha=1$\n(regular clock), $\\langle \\delta n^2(t)\\rangle$=0. However, for\n$0<\\alpha<1$, $\\langle \\delta n^2(t)\\rangle \\propto t^{2\\alpha}$ and this\ndramatically changes the character of anomalous CTRW and FFPE diffusion in the\npresence of bias. It becomes asymptotically \n$\\langle \\delta x^2(t)\\rangle\\propto F^2 t^{2\\alpha}$, while \n$\\langle \\delta x(t)\\rangle\\propto F t^{\\alpha}$. Notice that for $1\/2<\\alpha<1$\nthe subdiffusion at $F=0$ transforms into superdiffusion for $F\\neq 0$, i.e. a cloud\nof particles spreads out anomalously fast relative to its center of mass. \nThis yields a remarkable\nscaling for the ensemble-averaged quantities\n\\begin{eqnarray} \\label{anomal}\n\\lim_{t \\to \\infty} \\frac{\\langle \\delta x^2(t) \\rangle}{\n\\langle \\delta x(t) \\rangle^2} = \n\\lim_{t \\to \\infty} \\frac{\\langle \\delta n^2(t) \\rangle}{\n\\langle n(t) \\rangle^2}=\\frac{2\n\\Gamma ^2(\\alpha + 1)}{\\Gamma(2 \\alpha + 1)} - 1 \\, .\n\\end{eqnarray}\nThis scaling, which was observed first in Refs. \\cite{Shlesinger1,Scher} for a CTRW\nsubdiffusion in the absence of any additional potential $U(x)$, has been shown to be \n{\\it universal} within the FFPE description \nalso for arbitrary tilted washboard\npotentials and temperature \\cite{GH06,H06}. Recently, this astounding fact has been\nrelated to the universal fluctuations\nof anomalous mobility and weak ergodicity breaking \\cite{Sokolov}. \nUltimately, this is just \nthe property of the stochastic clock and it reflects the scaling between the variance\nand the mean number of stochastic periods passed within the external observed time $t$.\nSurprisingly, the viscoelastic GLE subdiffusion also exhibits a {\\it universal}\nasymptotical scaling in tilted washboard potentials. In the $t\\to\\infty $ limit\nit is the {\\it same} as in Eq. (\\ref{normal}).\nAstonishingly, it works both for a vanishingly small $F$, and for an arbitrary\nstrong bias. Moreover, both the diffusion and drift in the\ntilted washboard potentials\ndo not depend {\\it asymptotically} on the amplitude and the form of the periodic potential \nin the case of GLE subdiffusion and are given by \nEqs. (\\ref{variance}) and (\\ref{mean}), correspondingly. \nThis again is very much different from the FFPE case, where Eq. (\\ref{normal})\ncan be used only to calculate the anomalous flux response at a \nvanishingly small $F$ from the equilibrium $\\langle \\delta x^2(t) \\rangle_{F=0}$\nat $F=0$. Also, given $\\langle \\delta x(t)\\rangle$ at $F\\neq 0$\none can calculate $\\langle \\delta x^2(t) \\rangle_{F=0}$ using Eq. (\\ref{normal}) \nand the corresponding \nsubdiffusion coefficient in periodic potentials in the limit $F\\to 0$,\nfor details see in the work\\cite{H07} and below. \n\n\n\\section{Other similarities}\n \nOne more similarity emerges for the relaxation of mean fluctuation from\nequilibrium in harmonic potentials, $U(x)=k x^2\/2$. Then, both the FFPE\napproach and the FLE approach (in the limit $m\\to 0$) yield the same\nrelaxation law \\cite{Kou,Goychuk07a,Metzler99}, \n$\\langle \\delta x(t)\\rangle = \n\\langle \\delta x(0)\\rangle E_{\\alpha}[-(t\/\\tau_r)^{\\alpha}]$ with the\nultraslow position relaxation time constant $\\tau_r=(\\eta_{\\alpha}\/k)^{1\/\\alpha}$. \nAsymptotically, this relaxation follows a power-law, \n$\\langle \\delta x(t)\\rangle \\propto t^{-\\alpha}$. \n\nThe asymptotic \ndistributions of the residence times within a half-infinite spatial \ndomain (or the first \nreturn times to the origin in the infinite domain)\nin the case of free subdiffusion\nare also similar, following the same scaling law \\cite{Ding,Taloni,GH04} \n$\\Psi(\\tau) \\propto 1\/\\tau^{2-\\alpha\/2}$.\nHowever, here the similarities end. The asymptotics for a finite-size domain\ncannot be same. In particular, the mean residence time \nin any finite-size domain within the subdiffusive GLE description is finite \n\\cite{Goychuk09},\nwhereas within the FFPE description is not, except for the case of \ninjection of diffusing particles on the normal radiative boundary, where they\ncan be immediately absorbed\\cite{GH04}. Moreover, the GLE (for arbitrary $\\eta(t)$, \nincluding FLE) describe a Gaussian process for constant and linear forces $f(x)$\n\\footnote{This is just by the linearity of the transformation from the\nGaussian noise $\\xi(t)$ to the stochastic process $x(t)$ as\ndescribed by Eqs. (\\ref{fle}) and (\\ref{gle}).}, whereas the FFPE does not correspond\nto a Gaussian process in these cases, see\nin Ref.\\cite{Metzler}. \n\n \n\\section{Diffusion and transport in washboard potentials}\n\nLet us proceed with the case of washboard potentials, where the differences\nbetween the two discussed approaches to subdiffusion become particularly transparent.\nWe consider the tilted potential $U(x)=V(x)-xF$, where $V(x+L)=V(x)$ is a periodic\npotential with the spatial period $L$.\n\n\\subsection{FFPE dynamics} \n\nIn this case, one can find exact analytical results for the ensemble-averaged\nnonlinear mobility $\\mu_{\\alpha}(F)$ using Eq. (\\ref{mean}) asymptotically\nalso in washboard potentials. First, one finds the exact analytical expression\nfor the ensemble-averaged subvelocity $v_{\\alpha}(F)=\\mu_{\\alpha}(F)F$.\nThe FFPE in the form (\\ref{FFPE2}) is more convenient for this purpose. \nIndeed, it has the form of a fractional-time continuity \nequation with the flux $J(x)$. For the sake of\ngenerality we consider its further generalization with a spatially-dependent\nsubdiffusion coefficient $\\kappa_{\\alpha}(x)$, \n\\begin{eqnarray}\nJ(x,t)=-\\kappa_{\\alpha}(x)\ne^{-\\beta U(x)} \\frac{\\partial}{\\partial x} \\, e^{\\beta\nU(x)} P(x,t)\n\\end{eqnarray}\nwhich is assumed to be periodic with the same period $\\kappa_{\\alpha}(x+L)\n=\\kappa_{\\alpha}(x)$, and the generalized Einstein relation\nis fullfield locally at any $x$, $\\kappa_{\\alpha}(x)=k_BT\/\\eta_{\\alpha}(x)$. \nWe proceed similarly to the case of normal \ndiffusion \\cite{Stratonovich,ReimannRev,HM09},\n$\\alpha=1$. A spatial period averaged density $\\hat P(x,t)=\\sum_{k=-M}^{M} P(x+kL,t)\/(2M+1)$\nshould attain a steady-state regime (corresponding to a non-equilibrium\nsteady state for $F\\neq 0$) in the limit $M\\to\\infty$, $t\\to\\infty$ \nand that becomes periodic with the period $L$, $\\hat P_{\\rm st}(x+L)=\\hat P_{\\rm st}(x)$.\nThe corresponding subdiffusive flux $\\hat J(x)$, defined with $\\hat P_{\\rm st}(x)$, \nbecomes a constant $J_\\alpha$ in the steady state: \n\\begin{eqnarray}\\label{1}\nJ_\\alpha=-\\kappa_{\\alpha}(x)\ne^{-\\beta U(x)} \\frac{d}{d x} \\, e^{\\beta\nU(x)} \\hat P_{\\rm st}(x)\\;.\n\\end{eqnarray}\nThen, the dynamics of the averaged mean displacement follows as \n\\begin{eqnarray}\\label{aux}\n\\sideset{_{0}}{_{*}}{\\mathop{D}^{\\alpha}}\\langle x(t)\\rangle =LJ_\\alpha\\;,\n\\end{eqnarray}\nwhich can be shown akin to the normal diffusion case\\cite{ReimannRev}. The appearance of the fractional\nCaputo time derivative in the lhs of Eq. (\\ref{aux}) is the only mathematical\ndifference as compared with the normal diffusion case.\nThe solution of (\\ref{aux}) yields for the mean excursion\n\\begin{eqnarray}\n\\langle x(t)\\rangle = v_{\\alpha}^{({\\rm wb})}(F) t^{\\alpha}\/\\Gamma(1+\\alpha)\\;,\n\\end{eqnarray}\nwith $v_{\\alpha}^{({\\rm wb})}(F)=LJ_\\alpha $ \nbeing the subvelocity in the washboard potential. \n\n\nOne finds $J_{\\alpha}$ and $v_{\\alpha}^{({\\rm wb})}(F)$ by multiplying \nEq. (\\ref{1}) with\n$e^{\\beta U(x)}\/\\kappa_{\\alpha}(x)$ and integrating the result within one\nspatial period. Taking into account the spatial periodicity of \n$V(x)$ and $\\kappa_{\\alpha}(x)$ this yields:\n\\begin{eqnarray}\\label{2}\nJ_\\alpha\\int_y^{y+L}\\frac{e^{\\beta U(x)}}{\\kappa_{\\alpha}(x)}dx&& = -e^{\\beta\nU(y+L)} \\hat P_{\\rm st}(y+L)+e^{\\beta\nU(y)} \\hat P_{\\rm st}(y)\\nonumber \\\\\n&& =(1-e^{-\\beta FL})e^{\\beta U(y)}\\hat P_{\\rm st}(y) \\;.\n\\end{eqnarray}\nNext, multiplying (\\ref{2}) with $e^{-\\beta U(y)}$, integrating over $y$ within\n$[0,L]$, and using the normalization $\\int_{0}^L\\hat P_{\\rm st}(y)dy=1$ one finds \nthe main result\n\\begin{eqnarray}\\label{result}\nv_\\alpha^{({\\rm wb})}(F)=\\frac{(1-e^{-\\beta FL})L}{\n\\int_0^L e^{-\\beta U(y)}dy\\int_y^{y+L}\n\\frac{e^{\\beta U(x)}}{\\kappa_{\\alpha}(x)}dx}\\;.\n\\end{eqnarray}\nAccordingly, the nonlinear anomalous mobility is $\\mu^{(\\rm wb)}_{\\alpha}(F)=\nv_\\alpha^{({\\rm wb})}(F)\/F$. This presents a further generalization of\nthe result for subvelocity in Refs. \\cite{GH06,H06} \nto a spatially-dependent subdiffusion coefficient $\\kappa_{\\alpha}(x)$. \nThe subdiffusion coefficient in the \nunbiased washboard potential for $F=0$ can also be found using\nthe generalized Einstein relation $\\kappa^{(\\rm wb)}_{\\alpha}(F=0)=k_BT \n\\mu^{(\\rm wb)}_{\\alpha}(F=0)$. It reads,\n\\begin{eqnarray}\\label{result2}\n\\kappa_\\alpha^{({\\rm wb})}(F=0)=\\frac{L^2}{\n\\int_0^L e^{-\\beta V(y)}dy\\int_0^{L}\n\\frac{e^{\\beta V(x)}}{\\kappa_{\\alpha}(x)}dx}\\;,\n\\end{eqnarray}\nand for $\\kappa_{\\alpha}=const$ this is the result of the work\\cite{H07}.\nFor constant $\\kappa_{\\alpha}$ and a number of different potentials $V(x)$, \ntemperatures $T$\nand biasing forces $F$, \nthese two general results were beautifully confirmed by numerical simulations\nof the underlying CTRW \\cite{GH06,H06,H07} on a lattice from which the FFPE in the form \n(\\ref{FFPE2}) was derived in the work\\cite{GH06}. These simulations also confirmed \nthe universality of the scaling relation (\\ref{anomal}) within the FFPE approach. \nSurprisingly, it remains invariant\nalso in the presence of a driving which is periodic in time, \nin the biased case $F\\neq 0$ \\cite{H09}, featuring thus the universality class of\nsubdiffusion governed by a stochastic clock with divergent mean period\nand characterized by the only parameter $\\alpha$. The above $v_{\\alpha}^{(\\rm wb)}$\nis the ensemble-averaged result. The subvelocities of individual particles\nremain randomly distributed in the limit $t\\to\\infty$ and \nthey follow a universal subvelocity distribution which reflects the distribution of\nrandom individual time of travelling particles, as it has been clarified in\nRef. \\cite{Sokolov}. Both the weak ergodicity breaking and the universal fluctuations of\nanomalous mobility within the FFPE approach are \nultimately related to this remarkable\nproperty of the stochastic time.\n\n\\subsection{GLE dynamics in periodic potentials}\n\nThe GLE subdiffusion distinctly differs in the physical mechanism and\nthis leads to quite different results for washboard potentials\\cite{Goychuk09,G10}. \nFirst of\nall, it is {\\it asymptotically} ergodic and self-averaging over a single\ntrajectory yields a quite definite non-random result \\cite{Goychuk09}. \nNo additional\nensemble averaging is required. Moreover, it turns out that both the\nparticle anomalous mobility $\\mu^{(\\rm wb)}_{\\alpha}$ and the\nsubdiffusion coefficient $\\kappa^{(\\rm wb)}_{\\alpha}$ do not\ndepend {\\it asymptotically} neither on the potential $V(x)$, nor on the\nbias $F$ being universal and the same as for biased GLE subdiffusion\nin the absence of periodic potential, obeying the generalized Einstein\nrelation. The transition to this asymptotic regime is, however, very\nslow and it strongly depends on the amplitude of the periodic\npotential $V_0$ and the temperature $T$. Because of this slowness of the \ntransient aging, this asymptotic regime\nwill not necessarily be relevant on a finite time scale \nfor anomalous transport in finite-size systems. This is especially so if the periodic\npotential amplitude exceeds the thermal energy by many times. However, this\nremarkable property features the very mechanism of the GLE subdiffusion, which\nis based on the long-range velocity and displacement correlations and not on\ndiverging mean residence time within a potential well, in clear contrast to\nthe CTRW subdiffusion with independent increments. \nIt outlines a quite different universality class\nof subdiffusion. This is the long-range anti-persistence\nwhich limits asymptotically the GLE subdiffusion and transport processes in the \nwashboard potentials. Since the mean residence time in a potential well \nis finite \\cite{Goychuk09}, a coarse graining over the potential period, \nwhich makes the sojourns in the\ntrapping potential wells irrelevant, becomes {\\it asymptotically} possible.\nIn fact, upon increasing the potential height the escape kinetics out \nof a potential well (being asymptotically stretched-exponential) becomes ever closer\nto the normal exponential kinetics \\cite{Goychuk09}, where it becomes described \nby the non-Markovian rate theory\\cite{HTB90,HM82}. \nThis does not mean, however, that the diffusion spreading over many spatial periods \nbecomes normal. As a matter of fact, in the unbiased periodic potentials the\ndiffusion cannot become faster than the free subdiffusion and this is a reason why \nthe asymptotic limit of free subdiffusion is attained. A signature of this \nuniversality has been revealed theoretically for quantum transport in sinusoidal\npotentials for the case of sub-Ohmic thermal bath which classically corresponds\nto the considered case of fractional sub-diffusive friction. Technically this\nwas done by\nusing two different approaches, one perturbative\\cite{Chen} and one non-perturbative\nbased on a quantum duality transformation between the quantum dissipative washboard\ndynamics coupled to a sub-Ohmic bath and a quantum dissipative tight-binding dynamics\ncoupled to a super-Ohmic bath\\cite{WeissBook}. In the quantum case, there are \nalso tunneling\nprocesses which are accounted for. Our numerical results for the classical\nBrownian dynamics indicate, however, that this feature is purely \nclassical and, moreover, it is universal, i.e. is beyond the particular case of\nsinusoidal potentials\\cite{G10}. \nIt is {\\it not} caused by the quantum-mechanical effects.\n\nOur numerical simulation approach is also insightful and it can be considered\nas an independent theoretical route to model anomalous diffusion and transport processes.\nThe idea is to approximate the non-Markovian GLE dynamics with a power-law kernel\nby a {\\it finite-dimensional} Markovian dynamics of a sufficiently high \ndimensionality $D$ \\cite{Marchesoni,Kupferman,Goychuk09,Siegle}. Here,\n\"sufficient\" means the following: having subdiffusion \nextending over $r$ time-decades one finds a $D$-dimensional Markovian dynamics\nwhose projection on the $(x,v)$ plane approximates the GLE dynamics over the required\ntime range within the\naccuracy of stochastic simulations, as it can be checked for the cases where\nan exact solution of the GLE dynamics is available \n(free or biased subdiffusion, subdiffusion\nin harmonic potentials). Increasing $D$ one can cover larger $r$ of experimental\ninterest and the embedding\ndimension $D$ turns out to be finite \nto arrive at the asymptotic results\nvalid for the strict power law kernel. Needless to say that the practically\nobserved cases of anomalous diffusion hardly extend over more than 6 time decades\n(typically several only) which underpins the practical value of our approach.\n \nWe expand the power law kernel into a sum of exponentials\n\\begin{eqnarray}\\label{expansion}\n\\eta(t)=\\frac{\\eta_{\\alpha}}{\\Gamma(1-\\alpha)}C_{\\alpha}(b)\n\\sum_{i=1}^{N}\\nu_i^\\alpha \\exp(-\\nu_i t)\n\\end{eqnarray}\nobeying a fractal scaling with $\\nu_i=\\nu_0\/b^{i-1}$, where $b>1$ is a scaling \nparameter, $\\nu_0>0$ is high-frequency (short-time) cutoff corresponding to\nthe fastest time scale $\\tau_0=1\/\\nu_0$ in the hierarchy of the \nrelaxation time constants,\n$\\tau_i=\\tau_0b^{i-1}$, of viscoelastic memory kernel. \n$C_{\\alpha}(b)$ is a numerical constant to provide\na best fit to $\\eta(t)=\\eta_{\\alpha}t^{-\\alpha}\/\\Gamma(1-\\alpha)$ in the\ninterval $[\\tau_0,\\tau_0 b^{N-1}]$. In the theory of anomalous relaxation\nsimilar expansions are well-known \\cite{Hughes,Palmer}. In the present context, \nthe approach corresponds to an approximation of the\nfractional Gaussian noise by a sum of uncorrelated \nOrnstein-Uhlenbeck (OU) noises, \n$\\xi(t)=\\sum_{i=1}^{N}\\zeta_i(t)$, with \nautocorrelation functions, $\\langle\\zeta_i(t)\\zeta_j(t')\\rangle=k_BT \\kappa_i\n\\delta_{ij}\\exp(-\\nu_i|t-t'|)$. This idea is also known in the theory of\n$1\/f$ noise \\cite{Weissman}. \nFor $t>\\tau_0 b^{N-1}$ the tail of (\\ref{expansion}) is \nexponential and the diffusion becomes normal for $t\\gg \\tau_0 b^{N-1}$.\nHowever, by increasing $N$ one can enlarge the corresponding time scale\nand even make it practically irrelevant.\nThe subdiffusion can be modelled in this way over $r=N\\log_{10} b-2$ time decades\nand the corresponding embedding dimension, $D=N+2$, can be rather small. Such fits\nare known to exhibit logarithmic oscillations superimposed on the power law \\cite{Hughes}.\nHowever, their amplitude can be made negligibly small if to choose $b$\nsufficiently small, e.g. for $b=2$ they become already barely detectable. \nNevertheless, even the decade scaling with $b=10$\nsuffices to arrive at excellent (within the statistical errors of Monte Carlo\nsimulations) approximation of the FLE dynamics by a {\\it finite-dimensional} Markovian\ndynamics over a huge range of time scales.\nWeak logarithmic sensitivity of $r$ to $b$ and linear dependence on $N$ allows one \nto improve the quality of\nMarkovian embedding at a moderate computational price. The choice of \nMarkovian embedding which corresponds to (\\ref{expansion}) is not \nunique\\cite{Kupferman,Siegle}. \nA particular one is the following \\cite{Goychuk09}: \n\\begin{eqnarray}\\label{embedding1}\n\\dot x&=& v\\;,\\nonumber \\\\\nm\\dot v & =& f(x,t)+\n\\sum_{i=1}^{N}u_i(t) \\;,\\nonumber \\\\\n\\dot u_i& = &-k_i v-\\nu_iu_i+\\sqrt{2\\nu_i k_i k_BT}\\xi_i(t) \\;,\n \\end{eqnarray}\nwhere $k_i=C_\\alpha(b)\\eta_\\alpha\\nu_i^\\alpha\/\\Gamma(1-\\alpha)>0$ and \n$\\xi_i(t)$ are independent unbiased white Gaussian noise sources, \n$\\langle \\xi_i(t)\\xi_j(t')\\rangle=\\delta_{ij}\\delta(t-t')$. \nIndeed, integrating out the auxiliary force variables $u_i$ in Eq. (\\ref{embedding1})\nit follows that the resulting dynamics is equivalent to\nthe GLE (\\ref{gle}), (\\ref{fdt}) with the kernel (\\ref{expansion}), provided that $u_i(0)$ are unbiased\nrandom Gaussian variables with variances $\\langle u_i^2(0)\\rangle=k_ik_BT$.\nThe latter condition ensures the stationarity of $\\xi(t)$ in the GLE (\\ref{gle}),\nas well as validity of the FDR (\\ref{fdt}) {\\it for all times}.\nUsing different non-thermal preparations of $u_i(0)$ one can study the influence\nof {\\it initial} non-stationarity of the noise $\\xi(t)$ in the GLE \non the Brownian dynamics\\cite{Siegle}. In this aspect, our approach is even more flexible and \nmore general than the standard GLE approach.\n\nThe auxiliary variables $u_i$\ncan be interpreted as elastic forces, $u_i=-k_i(x-x_i)$, exerted \nby some overdamped particles \nwith positions $x_i$, which are coupled to the central Brownian particle with elastic\nspring constants $k_i$ and are subjected to viscous friction with frictional\nconstants $\\eta_i=k_i\/\\nu_i=C_{\\alpha}(b)\n\\eta_{\\alpha}\\tau_i^{1-\\alpha}\/\\Gamma(1-\\alpha)$ \nand the thermal random forces of environment. This corresponds to\nmotion of $N+1$ particles in a potential $U(x,\\{ x_i\\})=U(x,t)+(1\/2)\n\\sum_{i=1}^N k_i(x-x_i)^2$. The Brownian particle is massive (inertial effects\nare generally included), all other ``particles'' are overdamped \n(massless, $m_i\\to 0$). For example, one can imagine \nthat some coordination spheres\nof the viscoelastic environment stick to the Brownian particle and are co-moving.\nTheir influence can be effectively represented by $N$ ``quasi-particles''. \nIn this insightful physical interpretation, \nour embedding scheme is equivalent to: \n\\begin{eqnarray}\\label{embedding2}\nm\\ddot x & =& f(x,t)-\n\\sum_{i=1}^{N}k_i(x-x_i) \\;,\\nonumber \\\\\n\\eta_i\\dot x_i& = &k_i (x-x_i)+\\sqrt{2\\eta_ik_BT}\\xi_i(t) \\;.\n \\end{eqnarray}\nIt worth to notice that in this approach the mass of the Brownian particle\nand therefore the inertial effects are important. In order to perform an overdamped\nlimit $m\\to 0$, one has to include the viscous frictional force $-\\eta_0 \\dot x$ \nacting directly on \nthe particle and the corresponding random force. Then, in the limit $m\\to 0$,\none obtains\n\\begin{eqnarray}\\label{embedding3}\n\\eta_0 \\dot x & =& f(x,t)-\n\\sum_{i=1}^{N}k_i(x-x_i)+ \\sqrt{2\\eta_0k_BT}\\xi_0(t) \\;,\\nonumber \\\\\n\\eta_i\\dot x_i& = &k_i (x-x_i)+\\sqrt{2\\eta_ik_BT}\\xi_i(t) \\;,\n \\end{eqnarray} \nwhere $\\xi_0(t)$ is a zero-mean Gaussian random force of unit intensity which is not\ncorrelated with the set $\\{ \\xi_i(t) \\}$. \nHowever, it was noticed \\cite{Burov} that the inertial effects are\ngenerally important for the subdiffusive GLE dynamics and therefore we take them\ninto account. Of course, here emerges one more difference with the alternative \ndescription of subdiffusion within the FFPE (\\ref{FFPE1}), (\\ref{FFPE2}). \n \n\\begin{figure}\n\\includegraphics [width=0.5\\linewidth]{WSFig2.eps}\n\\caption{Anomalous diffusion in the potential $U(x)=-V_0\\sin(2\\pi x\/L)-Fx$ for various $V_0$\nand $F$ at $T=0.1$ for $\\alpha=0.5$. Notice an excellent agreement (differences\npractically cannot be detected in this plot) \nof simulations\nwith the exact FLE result, $\\langle \\delta x^2(t)\\rangle\n=2v_T^2t^2E_{2-\\alpha,3}[-(t\/\\tau_v)^{2-\\alpha}] $, \nin the absence of periodic potential $V_0=0$. Scaling: time in \n$\\tau_v=(m\/\\eta_{\\alpha})^{1\/(2-\\alpha)}$, distance in $L$, energy in \n$m(L\/\\tau_v)^2$, force in $mL\/\\tau_v^2$ and temperature in \n$mL^2\/(\\tau_v^2k_B)$.}\n\\label{Fig2}\n\\end{figure} \n \nA proper fractal scaling of coefficients $k_i$ and $\\eta_i$ with $i$ (see above) \nallows one to \nmodel viscoelastic subdiffusion\nover arbitrary time scales of the experimental interest. \nOne can numerically solve these stochastic differential equations (\\ref{embedding1})\ne.g. with a standard stochastic Heun method\\cite{Gard} (second order Runge-Kutta method)\nas done in Refs. \\cite{Goychuk09,G10}. \nAn example of such simulations is given in Figs. \\ref{Fig2}, \n\\ref{Fig3} for $\\alpha=0.5$, $\\nu_0=100$, $b=10$, $C_{\\alpha}(b)=1.3$, \n$N=12$ and $k_B T=0.1$. The following scaling is used: \ntime in the units of $\\tau_v$ \\footnote{This is a natural \nscaling of the velocity autocorrelation function in time. \nOther scalings are but also possible\\cite{Goychuk09,G10}. \nThey are more suitable to consider dynamical regimes close to overdamped.},\ndistance in the units of $L$. All the energy units are then scaled in \n$\\Delta E=m(L\/\\tau_v)^2$ and the force units in $mL\/\\tau_v^2$.\nStochastic Heun method is used to integrate Eq. (\\ref{embedding1}) with\na time step $\\Delta t=(1-5)\\cdot 10^{-3}$ until $t_{\\rm max}=2\\cdot 10^5$ \nand $n=10^4$ trajectories are used for\nthe ensemble averaging. Stochastic numerics are compared against the exact\nresults for the free subdiffusion and for the mean displacement \nunder a constant biasing force. The agreement is excellent. The considered\nparticular embedding\nstill works as an approximation to the FLE dynamics until $t=10^8$. If one needs to\ndescribe subdiffusion on an even longer time scale one can increase $N$. If one needs\na better precision of approximation one can make $b$ smaller. \nInitially all the particles are localized at the origin, $x=0$, with the velocities\nthermally distributed at the temperature $T$. \nFor the time span \n$t\\lesssim\\tau_v$ the motion\nis always ballistically persistent (superdiffusion). This reflects the\ninertia of the Brownian particle. It assumes the subdiffusive \ncharacter for $t\\gg \\tau_v$, when the VACF\nis negative. The presence of a periodic potential\n$V(x)=-V_0\\sin(2\\pi x\/L)$ dramatically changes\nboth subdiffusion, $\\langle x^2(t)\\rangle-\\langle x(t)\\rangle^2$,\nas well as subdiffusive transport, $\\langle x(t)\\rangle$, on intermediate time\nscale. However, the long time asymptotics\nof free or biased subdiffusion are gradually attained. \nThe initial behavior still within one\npotential well remains ballistic. One can \nconclude that both subdiffusion and subdiffusive transport are indeed asymptotically\ninsensitive to the presence of periodic potential within the GLE approach. \nThis finding is in\na striking contrast with the FFPE approach. \nHowever, the transient to\nthis asymptotic regime can be very slow, depending on the amplitude of the periodic\npotential and temperature. \n\nAn interesting phenomenon is also accelerated subdiffusion occurring on \nan intermediate time scale in tilted washboard potentials, as compared with the \nfree subdiffusion. It can be detected in Fig. \\ref{Fig2} for a strong yet subcritical\nbias $F=10$, see Fig.\\ref{Fig2} for details.}\n\\label{Fig3}\n\\end{figure}\n\n\\section{Summary and conclusions}\n\nWith this Chapter, we reviewed and scrutinized \ntwo different approaches, the FFPE approach and the FLE approach, to anomalously \nslow diffusion and transport in nonlinear force fields with a focus\non applications in tilted periodic \npotentials. In spite of some similarities in the case of constant or linear forcings\nit was shown that the nonlinear dynamics radically differ, obeying\nasymptotically two different universality classes. A first one reflects the\nuniversal fluctuations of intrinsic time clock and is closely tight\nto a weak ergodicity breaking. \nIn contrast, within the GLE and FLE approach the long-range \nantipersistence of the velocity and position fluctuations renders the asymptotic dynamics\nergodic. One approach seems more appropriate for the disordered solids,\nor glass-forming liquids below the glass-forming \ntransition, as characterized by the nonergodic \nglass phase. Another one seems more appropriate for the regime \nabove but close to the glass\ntransition, or for crowded viscoelastic environments like cytosols in biological\ncells. We have left out further pronounced differences between the \nFFPE and FLE approaches in the\ncase of time-dependent fields\\cite{SK06,HPRL07,G07,H09,West2}. \nWe are confident that \nour results not only shed light on the origin of profound differences, but also\nwill stimulate a further development of both approaches to subdiffusion, and\npossibly other interrelationships emerging in random potentials.\n\n\n\n\n\\acknowledgements\n\nWe would like to thank E. Heinsalu, M. Patriarca, G. Schmid, and P. Siegle for\na very fruitful collaboration on anomalous transport in washboard potentials. \nThis work was supported by the Deutsche Forschungsmeinschaft, grant \nNo. GO 2052\/1-1 (I.G.) and through Nanosystems Initiative Munich (P.H.).\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Theory for heat flow, thermal response, and heat production during EDL buildup}\nThe classical understanding of the EDL is in terms of an ideally polarizable, flat electrode whose charge is screened via a compact, salt-concentration independent Stern or Helmholtz layer within \\aa ngstr\\\"{o}ms of the surface, together with a Gouy-Chapman layer that extends into the solution on the scale of the salt-concentration dependent Debye length. Although the charging behavior of the water-immersed porous carbon electrodes of the main text is poorly described by Gouy-Chapman theory, \nit does aid the interpretation of the experiments of the main text. In particular, we will derive explicit expressions for the isothermal heat flow $\\mathbb{Q}$ (Appendix~\\ref{heatflow}) and the reversible heat production $\\Pi_{\\rm rev}$ (Appendix~\\ref{heatproduction}). A Gouy-Chapman approximation for the adiabatic temperature rise $\\Delta T_{\\rm adiab}$ (Appendix~\\ref{sec_adiabatictemperaturerise}) was previously derived in the supplemental material of Ref.~\\cite{janssen2017reversible}. To set the stage, we first review the known Gouy-Chapman expressions for the grand potential energy cost of EDL formation.\n\n\\subsection{Gouy-Chapman grand potential}\nWe consider a single electrode of area $A$ and an adjacent 1:1 electrolyte solution with a dielectric constant $\\varepsilon$, at temperature $T$ and at a bulk salt concentration $\\rho_{\\mathrm{s}}$. \nAt a finite electrode potential $\\Psi$, the surface obtains a surface charge density $\\sigma=Q\/(Ae)$, with $e$ the elementary charge, such that $\\sigma$ has the dimension of inverse area. To screen the surface charge, an inhomogeneous charge density profile develops in the electrolyte phase, characterized by the cationic and anionic density profiles $\\rho_{+}(z)$ and $\\rho_{-}(z)$, respectively. Here, the $z$-coordinate is defined as the direction out of the electrode into the solution, which runs from $z=0$ at the electrode to $z=\\infty$ far into the bulk, where the local electrostatic potential $\\psi(z)$ is zero and the ion densities take their bulk values $\\rho_{+}(\\infty)=\\rho_{-}(\\infty)=\\rho_{\\mathrm{s}}$.\nThe ionic density profiles can be obtained via classical density functional theory from an auxiliary functional $\\Omega_{\\mathcal{V}}$\nconsisting of an ideal-gas term and a mean-field Coulomb interaction term,\n\\begin{eqnarray}\\label{eq:Ftotal}\n&& \\frac{\\Omega_{\\mathcal{V}}[\\rho_{+},\\rho_{-}]}{Ak_{\\rm B}T} = \\int_{0}^{\\infty}\\bigg\\{\\sum_{\\alpha=\\pm}\\rho_{\\alpha}(z) \\Big[ \\ln\\big(\\rho_{\\alpha}(z)\\Lambda_{\\alpha}^3\\big) - 1-\\beta\\mu_{\\alpha} \\Big] \\nonumber\\\\\n&&\\hspace{3.5cm}+ \\frac{1}{2}q(z) \\phi(z) \\bigg\\}dz, \n\\end{eqnarray}\nwith $k_{B}$ Boltzmann's constant, $\\Lambda_{\\alpha}$ the thermal wavelength, $\\mu_{\\alpha}$ the ionic chemical potential, $\\phi(z)=e \\psi(z)\/k_{\\rm B}T$ the local dimensionless electrostatic potential, and $q(z)=\\sigma\\delta(z)+\\rho_{+}(z)-\\rho_{-}(z)$ the local unit charge density. \nThe ion density profiles follow from the Euler-Lagrange equation $\\delta \\Omega\/\\delta \\rho_{\\pm}=0$ and amount to $ \\rho_{\\pm}(z)=\\rho_{\\mathrm{s}} \\exp{[\\mp\\phi(z)]}$ after setting the chemical potential to $\\mu_{\\pm}=k_{\\rm B}T\\ln \\rho_{\\mathrm{s}} \\Lambda_{\\pm}^{3}$. Inserting $ \\rho_{\\pm}(z)$ into the Poisson equation, $\\phi''(z) = -4\\pi {\\lambda_{\\mathrm{B}}} q(z)$, results in the Poisson-Boltzmann equation.\nThe main result of Gouy-Chapman theory is the analytic solution to that equation: \n\\begin{align} \\label{eq:10}\n\\phi=4 \\tanh^{-1}\\left(\\exp\\left[-\\lambda_{\\mathrm{D}}^{-1}z\\right] \\tanh{\\frac{\\Phi}{4}}\\right),\n\\end{align}\nwith $\\Phi=e\\Psi\/k_{\\rm B}T$ the dimensionless surface potential. Moreover, $\\lambda_{\\mathrm{D}}=(8\\pi\\lambda_{\\mathrm{B}}\\rho_{\\mathrm{s}})^{-1\/2}$ is the Debye length, in terms of the Bjerrum length $\\lambda_{\\mathrm{B}}=e^2\/(4\\pi \\varepsilon_{0} \\varepsilon k_{\\rm B}T)$, and the vacuum permittivity $\\varepsilon_{0}$. From Eq.~(\\ref{eq:10}), various other quantities can be derived, for instance the relation between the surface charge and potential\n\\begin{equation} \\label{eq:gouy_chapman}\n\\sigma=\\bar{\\sigma} \\sinh\\frac{\\Phi}{2},\n\\end{equation}\nwith $\\bar{\\sigma}=4\\rho_{\\mathrm{s}}\\lambda_{\\mathrm{D}}$ the crossover surface charge density.\n\n\nAccording to density functional theory, we find the grand potential $\\Omega(T,V_{\\mathrm{el}},\\mu_{+},\\mu_{-},Q)= \\Omega_{\\mathcal{V}}[\\rho_{+},\\rho_{-}]$ (with $V_{\\mathrm{el}}$ the electrolyte volume) by evaluating the auxiliary functional at the equilibrium ion concentration profiles. Within Gouy-Chapman theory this yields\n\\begin{widetext}\n\\begin{align}\\label{eq:equilibriumgrandpotential}\n \\Omega^{\\mathrm{GC}} &= \\underbrace{2 A \\rhosk_{\\rm B}T\\int_{0}^{\\infty}dz\\left\\{ \\phi(z) \\sinh \\phi(z)-\\cosh\\phi(z)+1\\right\\}}_{ \\displaystyle\\equiv \\Omega^{\\mathrm{GC}}_{\\mathrm{ent}}}+ \\underbrace{\\frac{Ak_{\\rm B}T}{2}\\int_{0}^{\\infty}dz\\left[ q(z)\\phi(z)\\right]}_{ \\displaystyle\\equiv \\Omega^{\\mathrm{GC}}_{\\mathrm{el}}},\n\\end{align}\n\\end{widetext}\nwhere we subtracted the bulk grand potential $-pV_{\\mathrm{el}}$ of the electrolyte with osmotic pressure $p=2\\rhosk_{\\rm B}T$. The term $\\Omega^{\\mathrm{GC}}_{\\mathrm{ent}}$, the integrand of which goes to zero in the bulk, is associated with the excess entropy of the double layer. Both integrals in Eq.~(\\ref{eq:equilibriumgrandpotential}) were solved in Ref.~\\cite{overbeek1990role} by employing the Gouy-Chapman solution Eq.~(\\ref{eq:10}). The resulting expression for the entropic contribution reads\n\\begin{align}\\label{eq:omegaent}\n\\Omega_{\\mathrm{ent}}^{\\mathrm{GC}}&= A\\bar{\\sigma} k_{\\rm B}T\\left[\\Phi \\sinh\\frac{\\Phi}{2} -3 \\cosh\\frac{\\Phi}{2}+3\\right],\n\\end{align}\nand, likewise\n\\begin{align}\\label{eq:omegael}\n\\Omega^{\\mathrm{GC}}_{\\mathrm{el}}&=A\\bar{\\sigma} k_{\\rm B}T\\left[\\cosh{\\frac{\\Phi}{2}}-1\\right],\n\\end{align}\nwas found for the electrostatic contribution.\n\n\\subsection{Isothermal heat flow}\\label{heatflow}\nThe amount of heat that is required to flow into a capacitor during isothermal ($dT=0$) charging at temperature $T$ from an uncharged state to a charged state with charge $Q$ and potential $\\Psi$ was derived in Ref.~\\cite{hartel2015heat}:\n\\begin{equation}\\label{eq:thermodynamicidentity}\n\\mathbb{Q}= -T\\int_{0}^{Q}\\left(\\frac{\\partial \\Psi(Q',T)}{\\partial T}\\right)_{Q'}dQ'.\n\\end{equation}\nThe coulometric experiment determines temperature dependence of the equilibrium charge, $\\left(\\partial Q\/\\partial T\\right)_{\\Psi}$, which is related to the temperature dependence of the equilibrium potential via a cyclic reciprocity relation \n\\begin{equation}\\label{eq_experimentalthermalvoltagerise}\n\\left(\\frac{\\partial \\Psi}{\\partial T}\\right)_{Q}=-\\left(\\frac{\\partial \\Psi}{\\partial Q}\\right)_{T}\\left(\\frac{\\partial Q}{\\partial T}\\right)_{\\Psi}.\n\\end{equation}\nHence, we can equivalently express the isothermal heat flow as\n\\begin{equation}\\label{eq:isothermalheatflow2}\n\\mathbb{Q}= T\\int_{0}^{\\Psi}\\left(\\frac{\\partial Q(\\Psi',T)}{\\partial T}\\right)_{\\Psi'}d \\Psi'.\n\\end{equation}\nIn the coulometric experiment of the main text we found $\\left(\\partial Q\/\\partial T\\right)_{\\Psi}=\\alpha\\Psi$, with $\\alpha=-4.0\\pm0.1$~mC~V$^{-1}$~K$^{-1}$. Inserting this into the above equation we find $\\mathbb{Q}=\\alpha T\\Psi^2\/2$: the result stated in the main text.\n \nWithin Gouy-Chapman the heat flow reads [after inserting Eq.~(\\ref{eq:gouy_chapman})]\n\\begin{align}\\label{eq:gouychapmanisothermalheat}\n\\mathbb{Q}^{\\rm GC}\n&=-2Ak_{\\rm B}T\\int_{0}^{\\sigma }\\frac{\\partial }{\\partial T}\\left[T\\sinh^{-1} \\frac{\\sigma'}{\\bar{\\sigma}}\\right]d\\sigma'\\nonumber\\\\\n&= -2Ak_{\\rm B}T\\bigg[\\sigma\\sinh^{-1} \\frac{\\sigma}{\\bar{\\sigma}}-\\sqrt{\\bar{\\sigma}^{2}+\\sigma^{2}}+\\bar{\\sigma}\\bigg],\n\\end{align}\nwhere we ignored the temperature dependence of $\\bar{\\sigma}$.\nIn terms of the dimensionless surface potential $\\Phi$, and comparing to Eqs.~(\\ref{eq:omegaent}) and (\\ref{eq:omegael}), we find\n\\begin{align}\\label{eq:}\n\\mathbb{Q}^{\\rm GC}=& -A\\bar{\\sigma}k_{\\rm B}T \\bigg[2-2\\cosh\\frac{\\Phi}{2}+\\Phi\\sinh\\frac{\\Phi}{2}\\bigg]\\nonumber\\\\\n=&-\\Omega_{\\mathrm{ent}}^{\\mathrm{GC}}.\n\\end{align}\nThermodynamically, this equivalence is understood as follows. The differential $d\\Omega=-SdT+\\Psi dQ$ of the grand potential (at fixed $\\mu_{\\pm}$ and $V_{\\mathrm{el}}$) of the electric double layer capacitor\nimplies that the isothermal grand potential change during charging is equal to (minus) the performed work, $ \\Omega=-W=\\int\\Psi dQ$.\nMeanwhile, the isothermal heat Eq.~(\\ref{eq:thermodynamicidentity}) is equal and opposite, $\\mathbb{Q}=-\\int \\Psi dQ$, {\\it if} the charging behavior is such that $\\left(\\partial \\Psi\/\\partial T\\right)_{Q}=\\Psi\/T$, which is precisely what we assumed in this section by neglecting $\\partial \\bar{\\sigma}\/\\partial T$ in Eq.~(\\ref{eq:gouychapmanisothermalheat}). This implies moreover that the internal energy change is zero for the Gouy-Chapman model. In general, more advanced EDL models will have a nonzero internal energy change upon charging, in which case the heat flow is not equal to the grand potential change. \n\n\\subsection{Adiabatic temperature rise}\\label{sec_adiabatictemperaturerise}\nSlowly charging a thermally insulated electric double layer capacitor leads to a temperature rise $\\Delta T_{\\rm adiab}$, which is the capacitive analogue of the temperature increase upon compressing a gas in a thermally insulated container \\cite{Janssen:2014aa}. Here, $\\Delta T_{\\rm adiab}$ is found by integrating the relation \n\\begin{equation}\\label{eq:adiabatic_Tincrease}\ndT=\\frac{T}{\\mathbb{C}_{p}}\\left(\\frac{\\partial Q(\\Psi,T)}{\\partial T}\\right)_{\\Psi}d \\Psi,\n\\end{equation}\nthat follows from the total differential of $S(T,\\Psi)$, together with the $dS=0$ condition on isentropic processes. Using once more $\\left(\\partial Q\/\\partial T\\right)_{\\Psi}=\\alpha\\Psi$ (with $\\alpha=-4.0\\pm0.1$~mC~V$^{-1}$~K$^{-1}$) as obtained with the coulometric experiment of the main text, we find a small adiabatic temperature rise,\n\\begin{equation}\n\\Delta T_{\\rm adiab}=\\frac{\\alpha T\\Psi^2}{2\\mathbb{C}_{p}},\n\\end{equation}\nupon increasing the potential from 0 to $\\Psi$, with $T$ the initial temperature at the uncharged state.\n\n\n\n\n\\subsection{Reversible heat production}\\label{heatproduction}\nOverbeek pointed out that the work performed by the electric field on ions in a solution during quasistatic EDL buildup equals the entropic contribution to the grand potential $\\Omega_{\\mathrm{ent}}$ \\cite{overbeek1990role}. Meanwhile, the source term ${\\bf I}\\cdot{\\bf E}$ appearing in the heat equation~\\eqref{eq:microheatequation} is associated precisely with the power delivered locally by the electric field for out of equilibrium settings. This suggests the following relation\n\\begin{equation} \\label{eq:3}\n\\Pi_{\\rm rev}^{\\mathrm{ch}}\\equiv\\lim_{\\mathcal{T}\\to\\infty}\\int_{0}^{\\mathcal{T}} dt\\int_{\\mathcal{V}_{\\rm el}} d{\\bf x}~{\\bf I}\\cdot{\\bf E}=\\Omega_{\\mathrm{ent}},\n\\end{equation}\nwhere $\\mathcal{T}$ is the duration of the slow charging process.\nTo test Eq.~(\\ref{eq:3}), we consider a very slow charging process of an electrode from an uncharged state, to some final state at charge $Q$. We study the electrode as described in the introduction of this section, and assume that the instantaneous density profiles are given as in Gouy-Chapman theory [Eq.~(\\ref{eq:10})] and moreover that the temperature $T(z,t)$ is homogeneous throughout the electrolyte volume at each time $t$. Under these conditions, the integrand of Eq.~(\\ref{eq:3}) simplifies to $IE$, with $E=-\\partial_{z}\\psi$ the local electric field, and $I=eJ_{\\mathrm{\\mathrm{ch}}}=e(J_{+}-J_{-})$ the ionic current density (in A m$^{-2}$). \nThe time integral in Eq.~(\\ref{eq:3}) can be transformed into an integral over the unit surface charge \\mbox{density $\\sigma$}\n\\begin{align} \\label{eq:4}\n\\Pi_{\\rm rev}^{\\mathrm{ch, GC}}=-Ak_{\\rm B}T\\int_{0}^{\\sigma} \\left[ \\int_0^\\infty \\frac{J_\\mathrm{\\mathrm{ch}}}{J_\\text{tot}}\\> \\frac{\\partial \\phi}{\\partial z} \\> dz \\right] d\\sigma',\n\\end{align}\nwhere we implemented \n\\begin{equation} \\label{eq:5}\n\\frac{\\partial \\sigma}{\\partial t}=J_\\text{tot},\n\\end{equation}\nwith $J_{\\text{tot}}=J_{\\mathrm{\\mathrm{ch}}}+J_{\\mathrm{M}}$ the total current density, which is equal to the electronic current entering the electrode. Here, $J_{\\text{tot}}$, invariant with position in a Cartesian one-dimensional geometry, includes the Maxwell or displacement current $J_{\\mathrm{M}}=-\\varepsilon_{0}\\varepsilon\\partial_{t}\\partial_{z}\\psi$, which is proportional to the variation of the electric field in time \\cite{van2010diffuse}. As the Maxwell current vanishes in the charge neutral bulk, $J_\\text{tot}$ is equal to the ionic current $J_{\\mathrm{ch}}$ far from the electrode. For capacitive charging of the electrode, the ionic current $J_{\\mathrm{ch}}$ is zero at the electrode surface and increases with $z$ to reach $J_\\text{tot}$ outside the EDL. Thus, in general $J_{\\mathrm{ch}}$ is a fraction of $J_\\text{tot}$, i.e., $0 dz \\right] d\\sigma' \\nonumber\\\\\n&=-2 \\rho_{\\mathrm{s}} \\int_{0}^{\\sigma} \\left[\\int_0^\\infty \\phi \\cosh{\\phi}\\frac{\\partial \\phi}{\\partial \\Phi}\\frac{\\partial \\Phi}{\\partial \\sigma} \\> dz \\right] d\\sigma' \\nonumber\\\\\n&=-2 \\rho_{\\mathrm{s}} \\int_{0}^{\\Phi} \\left[\\int_0^\\infty \\phi \\cosh{\\phi}\\frac{\\partial \\phi}{\\partial \\Phi} \\> dz \\right] d\\Phi' \\nonumber\\\\\n&=-2 \\rho_{\\mathrm{s}} \\int_{0}^{\\Phi} \\left[\\int_{\\Phi'}^0 \\phi \\cosh{\\phi}\\frac{\\partial \\phi}{\\partial \\Phi} \\frac{\\partial z}{\\partial \\phi} \\> d\\phi \\right] d\\Phi' ,\n\\end{align}\nwhere in the second to last step we took $\\left(\\partial \\Phi\/\\partial \\sigma\\right)$ out of the spatial integral since it does not depend on~$z$. \nThe integrand of Eq.~(\\ref{eq:13}) can be simplified considerably, in particular by converting the relation $\\phi(\\Phi,z)$ [Eq.~(\\ref{eq:10})] into $\\Phi(\\phi,z)$. This gives the following term that can be taken out of $\\phi-$integral in Eq.~(\\ref{eq:13}),\n\\begin{align}\n\\frac{\\partial \\phi}{\\partial \\Phi} \\frac{\\partial z}{\\partial \\phi}\n&=\\left(\\frac{\\partial \\Phi}{\\partial z}\\right)^{-1}\n=\\frac{\\lambda_{\\mathrm{D}}}{4}\\frac{\\left[1-\\exp\\left[2z\/\\lambda_{\\mathrm{D}}\\right] \\tanh^{2}\\frac{\\phi}{4}\\right]}{\\exp\\left[z\/\\lambda_{\\mathrm{D}}\\right] \\tanh\\frac{\\phi}{4}}\\nonumber\\\\\n&=\\frac{\\lambda_{\\mathrm{D}}}{4}\\frac{\\left[1-\\tanh^{2}\\frac{\\Phi}{4}\\right]}{\\tanh\\frac{\\Phi}{4}}\n=\\frac{\\lambda_{\\mathrm{D}}}{2\\sinh\\frac{\\Phi}{2}},\n\\end{align}\nwhere, going to second line, we eliminated $z$ via Eq.~(\\ref{eq:10}). We can now evaluate Eq.~(\\ref{eq:13}), \n\\begin{align}\n\\frac{\\Pi_{\\rm rev}^{\\mathrm{ch, GC}}}{Ak_{\\rm B}T}&=-\\rho_{\\mathrm{s}}\\lambda_{\\mathrm{D}} \\int_{0}^{\\Phi}\\frac{1}{\\sinh\\frac{\\Phi'}{2}}\\left[\\int_{\\Phi'}^{0}\\phi\\cosh\\phi \\> d\\phi\\right] \\> d\\Phi'\\nonumber\\\\\n&=-\\rho_{\\mathrm{s}} \\lambda_{\\mathrm{D}}\\int_{0}^{\\Phi}\\frac{\\cosh\\Phi'-\\Phi'\\sinh\\Phi' -1}{\\sinh\\frac{\\Phi'}{2}}d\\Phi'\\nonumber\\\\\n&=-2\\rho_{\\mathrm{s}} \\lambda_{\\mathrm{D}}\\int_{0}^{\\Phi}\\left[\\sinh\\frac{\\Phi'}{2}-\\Phi'\\cosh\\frac{\\Phi'}{2}\\right] d\\Phi'\\nonumber\\\\\n&=\\bar{\\sigma} \\left[3-3\\cosh\\frac{\\Phi}{2}+\\Phi\\sinh\\frac{\\Phi}{2}\\right] .\n\\end{align}\nComparing with Eq.~(\\ref{eq:omegaent}), we see indeed that \\mbox{$\\Pi_{\\rm rev}^{\\mathrm{ch, GC}}=\\Omega_{\\mathrm{ent}}^{\\mathrm{GC}}$}: precisely the hypothesis we set out to test. \n\n\n\\section{The heat transfer coefficient $K$}\\label{sec:calibration}\nThe heat transfer coefficient $K$ appearing in Eq.~\\eqref{eq:heatequation} was determined by two calibration methods. \n\n\\subsection{Calibration with heating elements}\\label{sec:alternativecalorimetry}\nExperiments were carried out with the same cell as described in the main text, but with the electrodes and current collectors replaced by heating elements (positioned at the same respective locations as the electrodes) consisting of resistive wire glued as a flat spiral onto a glass disk (total resistance: 413~$\\Omega$). \nMeasurements were conducted with the cell filled with water and with the cell filled with air. Depending on the current $I$ [see Fig.~\\ref{fig_sup2}(a)] of up to 10~mA, different voltages $\\Psi_{\\rm R}$ [Fig.~\\ref{fig_sup2}(b)] over the resistive wire were measured, and different plateau values $\\Delta T_{\\rm max}$ of the temperature difference $\\Delta T$ [Fig.~\\ref{fig_sup2}(c)] between the cell and the thermostatic bath were reached. \n\n\\begin{figure}\n\\def0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}{0.45\\textwidth}{\n \\providecommand\\color[2][]{%\n \\errmessage{(Inkscape) Color is used for the text in Inkscape, but the package 'color.sty' is not loaded}%\n \\renewcommand\\color[2][]{}%\n }%\n \\providecommand\\transparent[1]{%\n \\errmessage{(Inkscape) Transparency is used (non-zero) for the text in Inkscape, but the package 'transparent.sty' is not loaded}%\n \\renewcommand\\transparent[1]{}%\n }%\n \\providecommand\\rotatebox[2]{#2}%\n \\ifx0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\setlength{\\unitlength}{515.84695303bp}%\n \\ifx\\svgscale\\undefined%\n \\relax%\n \\else%\n \\setlength{\\unitlength}{\\unitlength * \\real{\\svgscale}}%\n \\fi%\n \\else%\n \\setlength{\\unitlength}{0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}}%\n \\fi%\n \\global\\let0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\global\\let\\svgscale\\undefined%\n \\makeatother%\n \\begin{picture}(1,0.83)%\n \\put(0,0.05){\\includegraphics[width=\\unitlength]{Fig_sup2.pdf}}%\n \\put(0.92,0.77){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{(a)}}}%\n \\put(0.92,0.54){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{(b)}}}%\n \\put(0.92,0.31){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{(c)}}}%\n \\put(-0.000,0.43){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{\\begin{rotate}{90} $\\displaystyle \\Psi_{\\mathrm{R}}$~[V]\\end{rotate}}}}%\n \\put(0.00,0.67){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{\\begin{rotate}{90} $\\displaystyle I$~[A]\\end{rotate}}}}%\n \\put(0.0,0.15){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{\\begin{rotate}{90} $\\displaystyle \\Delta T$~[K]\\end{rotate}}}}%\n \\put(0.5,0.0){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{ $t$~[s]}}}%\n \\end{picture}%\n }\n\\caption{\\label{fig_sup2}%\nIn the calibration with the heating elements, the current $I$ (a) was cyclically switched on and off to different values, each lasting 400 s. The voltage $\\Psi_{\\rm R}$ (b) over the resistive wire, and temperature difference $\\Delta T$ (c) between the cell and the thermostatic bath were measured simultaneously. Shown are data for the air-filled cell configuration.}\n\\end{figure}\n\nThe occurrence of $\\Delta T$-plateaus indicates that a steady state is reached where Joule heating is balanced by heat flowing out the cell. From Eq.~\\eqref{eq:heatequation} it then follows that the heat transfer coefficient $K$ can be determined as the slope of a $\\Delta T_{\\rm max}$-versus-$I\\Psi_{\\mathrm{R}}$ plot. In Fig.~\\ref{fig_sup3}(a) we see that $\\Delta T_{\\rm max}$ is proportional to the heating power $I\\Psi_{\\mathrm{R}}$. We find $K = 0.174\\pm0.001$~J~s$^{-1}$~K$^{-1}$ and $K = 0.184\\pm0.001$~J~s$^{-1}$~K$^{-1}$ for the air-filled and water-filled cell, respectively.\n\n\\begin{figure}\n\\def0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}{0.49\\textwidth}{\n \\providecommand\\color[2][]{%\n \\errmessage{(Inkscape) Color is used for the text in Inkscape, but the package 'color.sty' is not loaded}%\n \\renewcommand\\color[2][]{}%\n }%\n \\providecommand\\transparent[1]{%\n \\errmessage{(Inkscape) Transparency is used (non-zero) for the text in Inkscape, but the package 'transparent.sty' is not loaded}%\n \\renewcommand\\transparent[1]{}%\n }%\n \\providecommand\\rotatebox[2]{#2}%\n \\ifx0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\setlength{\\unitlength}{610.97107821bp}%\n \\ifx\\svgscale\\undefined%\n \\relax%\n \\else%\n \\setlength{\\unitlength}{\\unitlength * \\real{\\svgscale}}%\n \\fi%\n \\else%\n \\setlength{\\unitlength}{0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}}%\n \\fi%\n \\global\\let0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\global\\let\\svgscale\\undefined%\n \\makeatother%\n \\begin{picture}(1,0.46)%\n \\put(0,0.032){\\includegraphics[width=\\unitlength]{Fig_sup3.pdf}}%\n \\put(0.18,0.00){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{$\\Delta T_{\\rm max}$ [K]}}}%\n \\put(0.67,0.00){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{$\\int\\Delta T dt$ [K s]}}}%\n \\put(0.55,0.17){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{\\begin{rotate}{90} $\\int\\>I\\Psi_{\\mathrm{R}}\\>dt$ [J]\\end{rotate}}}}%\n \\put(0.025,0.15){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{\\begin{rotate}{90} $I\\Psi_{\\mathrm{R}}$ [mJ s$^{-1}$]\\end{rotate}}}}%\n \\put(0.16,0.375){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{water}}}%\n \\put(0.16,0.33){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{air}}}%\n \\put(0.0,0.45){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{(a)}}}%\n \\put(0.49,0.45){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{(b)}}}%\n \\end{picture}%\n }\n\\caption{\\label{fig_sup3}%\nThe heat transfer coefficient $K$ is determined both from the plateau value $\\Delta T_{\\rm max}$ (a), as well as from the time-integrated temperature peaks $\\int\\Delta T dt$ (b), both for a water-filled cell (blue circles) and an air-filled cell (green triangles). Dotted lines indicate linear fits through these data.}\n\\end{figure}\n\nTaking a time integral over Eq.~\\eqref{eq:heatequation}, its left hand side vanishes for any time interval $t_{a}I\\Psi_{\\mathrm{R}}\\>dt$ versus $\\int_{t_{a}}^{t_{b}}\\>\\Delta T\\>dt$ [Fig.~\\ref{fig_sup3}(b)]. \nWe found $K = 0.173\\pm0.001$~J~s$^{-1}$~K$^{-1}$ for the air-filled cell and $K = 0.179\\pm0.001$~J~s$^{-1}$~K$^{-1}$ for the water-filled cell.\nClearly, these values agree well with those determined from Fig.~\\ref{fig_sup3}(a); we think that the second method is more reliable because it does not require a determination of the time at which $\\Delta T_{\\rm max}$ is reached.\n\nOnce the applied current was switched off, $\\Delta T$ decreased exponentially with time [see Figs.~\\ref{fig_sup2}(c) and \\ref{fig_sup2b}]; from Eq.~\\eqref{eq:heatequation} follows $\\Delta T(t) = \\Delta T_{0} \\exp(-t\/\\tau)$, where $\\Delta T_{0}$ is the starting value of $\\Delta T$, $t$ is the time that passed since the current was switched off, and $\\tau = \\mathbb{C}_{p}\/K$ is the thermal time constant, with $\\mathbb{C}_{p}$ the heat capacity of the cell. From the measured time scales $\\tau = 34.0\\pm0.2$~s for the air-filled cell and $\\tau = 61.1\\pm0.3$~s for the water-filled cell, we calculate heat capacities of $\\mathbb{C}_{p} = K\\tau = 6.1\\pm0.1$~J~K$^{-1}$ and $10.9\\pm0.1$~J~K$^{-1}$, respectively. The difference between the two $\\mathbb{C}_{p}$ values agrees within~5\\% with a cell volume of approximately 1.1 mL and the specific heat capacity of water, 4.2~J~K$^{-1}$~g$^{-1}$. This consistency supports the validity of the heat quantities obtained. \n\\begin{figure}\n\\def0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}{0.46\\textwidth}\n \\providecommand\\color[2][]{%\n \\errmessage{(Inkscape) Color is used for the text in Inkscape, but the package 'color.sty' is not loaded}%\n \\renewcommand\\color[2][]{}%\n }%\n \\providecommand\\transparent[1]{%\n \\errmessage{(Inkscape) Transparency is used (non-zero) for the text in Inkscape, but the package 'transparent.sty' is not loaded}%\n \\renewcommand\\transparent[1]{}%\n }%\n \\providecommand\\rotatebox[2]{#2}%\n \\ifx0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\setlength{\\unitlength}{543.16897759bp}%\n \\ifx\\svgscale\\undefined%\n \\relax%\n \\else%\n \\setlength{\\unitlength}{\\unitlength * \\real{\\svgscale}}%\n \\fi%\n \\else%\n \\setlength{\\unitlength}{0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}}%\n \\fi%\n \\global\\let0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\global\\let\\svgscale\\undefined%\n \\makeatother%\n \\begin{picture}(1,0.63)%\n \\put(0.04,0.02){\\includegraphics[width=\\unitlength]{Fig_sup2b.pdf}}%\n \\put(0.7,0.53){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{water}}}%\n \\put(0.7,0.46){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{air}}}%\n \\put(0.03,0.25){\\color[rgb]{0,0,0}\\rotatebox{89.7025396}{\\makebox(0,0)[lb]{\\smash{$\\ln\\left[\\Delta T(t)\/\\Delta T_{0}\\right]$}}}}%\n \\put(0.55,0.00){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{t [s]}}}%\n \\end{picture}%\n\\caption{\\label{fig_sup2b}%\nThe heat capacity $\\mathbb{C}_{p}$ of the cell is determined from the exponential decay of $\\Delta T$ after switching off the current $I$. Dotted lines indicate linear fits through these data.}\n\\end{figure}\n\nIn the main text we determined the adiabatic temperature rise $\\Delta T_{\\rm adiab}$ associated with charging of thermally insulated porous carbon electrodes via two independent routes. We note that both routes used $\\mathbb{C}_{p}=10.9\\pm0.1$~J~K$^{-1}$ as determined above, that is, with the heating elements. This means that the true heat capacity of the cell might differ slightly since it contains carbon electrodes instead of resistive wires. \n\n\\subsection{Calibration with porous carbon}\\label{sec:carboncalibration}\nThe heat transfer coefficient $K$ can also be determined using the Joule heat that is generated during EDL formation in experiments with porous carbon electrodes. Employing the same arguments as presented in Appendix~\\ref{sec:alternativecalorimetry}, a time integral over Eq.~\\eqref{eq:heatequation} will leave its left hand side zero for any time interval that starts and ends at $\\Delta T=\\SI{0}{\\degreeCelsius}$.\nIn particular, for a full cycle of charging and discharging as shown in Fig.~\\ref{fig3} we have\n\\begin{align}\\label{eq:Kdetermination}\nK\\int_{t_{0}}^{t_{2}}\\Delta T dt&=\\Pi_{\\rm irr}^{\\mathrm{ch}}+\\Pi_{\\rm irr}^{\\mathrm{dis}}\\equiv \\Pi_{\\rm irr},\n\\end{align} \nwhere we used Eq.~\\eqref{eq:productionsplitting} and the fact that the reversible heat $\\Pi_{\\rm rev}^{\\mathrm{dis}}$ that is produced upon discharging will exactly cancel the reversible heat $\\Pi_{\\rm rev}^{\\mathrm{ch}}$ produced upon charging ($\\Pi_{\\rm rev}^{\\mathrm{ch}}=-\\Pi_{\\rm rev}^{\\mathrm{dis}}$). \nThus, the only heat production appearing in Eq.~(\\ref{eq:Kdetermination}) is Joule heat dissipated from the resistive parts of the system, \nwhich is exothermic regardless of the opposite signs of voltages and currents during charging and discharging.\nTo calculate this Joule heat, we time-integrate the dissipated power $\\dot{\\Pi}_{\\rm irr}=I(t)\\Psi_{\\mathrm{R}}(t)$ over the two time intervals. \nHere, $\\Psi_{\\mathrm{R}}(t)=\\Psi-\\Psi_{\\mathrm{EDL}}(t)$ is the voltage drop across the resistive elements that dissipate heat, \nwhich we can access experimentally if we assume a voltage drop $\\Psi_{\\mathrm{EDL}}(t) = Q(t)\/C$ over the electric double layer (EDL), with $Q(t)$ the charge in the EDL and the capacitance $C=\\lim_{t\\to\\infty}Q(t)\/\\Psi$ is assumed constant. \nInterestingly, we find that equal amounts of Joule heat are produced during charging ($\\Pi_{\\rm irr}^{\\mathrm{ch}}$) and discharging ($\\Pi_{\\rm irr}^{\\mathrm{dis}}$), despite the difference in $I(t)$ and $\\Psi_{\\mathrm{R}}(t)$ during these processes. The heat transfer coefficient $K$ can now be determined from Fig.~\\ref{fig_sup1}, where we plot $\\Pi_{\\rm irr}=\\int_{t_{0}}^{t_{2}} I(t)\\Psi_{\\mathrm{R}}(t)dt$ versus $\\int_{t_{0}}^{t_{2}}\\Delta T dt$, obtained for different salt concentrations $\\rho_{\\mathrm{s}}$ and different voltage steps $\\Psi$. A straight line is observed, whose slope corresponds to $K = 0.239\\pm 0.003$~J K$^{-1}$~s$^{-1}$. Considering only the data at $\\rho_{\\mathrm{s}}=1$~M, the linear fit has a slope $K = 0.232\\pm 0.005$~J~K$^{-1}$~s$^{-1}$, hence, $K$ depends very weakly on the salt concentration and can be regarded constant. \n\n\\begin{figure}\n\\def0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}{0.45\\textwidth}{ \n \\providecommand\\color[2][]{%\n \\errmessage{(Inkscape) Color is used for the text in Inkscape, but the package 'color.sty' is not loaded}%\n \\renewcommand\\color[2][]{}%\n }%\n \\providecommand\\transparent[1]{%\n \\errmessage{(Inkscape) Transparency is used (non-zero) for the text in Inkscape, but the package 'transparent.sty' is not loaded}%\n \\renewcommand\\transparent[1]{}%\n }%\n \\providecommand\\rotatebox[2]{#2}%\n \\ifx0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\setlength{\\unitlength}{543.40262003bp}%\n \\ifx\\svgscale\\undefined%\n \\relax%\n \\else%\n \\setlength{\\unitlength}{\\unitlength * \\real{\\svgscale}}%\n \\fi%\n \\else%\n \\setlength{\\unitlength}{0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}}%\n \\fi%\n \\global\\let0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\global\\let\\svgscale\\undefined%\n \\makeatother%\n \\begin{picture}(1,0.73)%\n \\put(-0.02,0.04){\\includegraphics[width=\\unitlength]{Fig_sup1.pdf}}%\n \\put(-0.0000,0.2){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{\\begin{rotate}{90}$\\displaystyle \\Pi_{\\mathrm{irr}}=\\int_{t_{0}}^{t_{2}} I\\Psi_{\\mathrm{R}}\\>dt$ [J]\\end{rotate}}}}%\n \\put(0.4,0.0){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{$\\displaystyle\\int_{t_{0}}^{t_{2}}\\Delta T dt$~[K~s]}}}%\n \\put(0.14,0.585){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{$\\rho_{\\mathrm{s}}$ [mM]}}}%\n \\end{picture\n}\n\\caption{\\label{fig_sup1}%\nIn the calibration with porous carbon electrodes, the heat transfer coefficient $K^{\\mathrm{c.e.}} = 0.239\\pm 0.003$~J K$^{-1}$~s$^{-1}$ is determined from the slope of the parametric plot of time-integrated $\\Delta T$ peaks against the Joule heat $\\Pi_{\\mathrm{irr}}$ for several salt concentrations, indicated with different colors (see legend). Different symbols belong to different duplicate measurements at various cell potentials up to 1 V.}\n\\end{figure} \n\n\n\n\n\\subsection{Discussion}\\label{sec:discussion}\nIn the presence of carbon electrodes (c.e.), the heat transfer coefficient $K^{\\mathrm{c.e.}}=0.239$~J~K$^{-1}$~s$^{-1}$ is a bit higher than in the case of the 413~$\\Omega$ heating elements (h.e.), where we found $K^{\\mathrm{h.e.}} = 0.179$~J~s$^{-1}$~K$^{-1}$. Both experiments have their pros and cons, which we now discuss.\n\n\n\\textbf{Materials:} the calibration with the porous carbon has the benefit that it does not alter the cell setup. The different materials in the experiment with the resistive wires might affect its $K$-value. \n\n\\textbf{Joule heat determination:}\nBoth calibration methods use the Joule heat produced over a time interval that starts and ends at $\\Delta T=\\SI{0}{\\degreeCelsius}$. While the resistive wire has a precisely-known resistance, allowing for accurate Joule heat determination, the Joule heat in the porous carbon experiments is prone to larger uncertainty, since we assumed a constant capacitance in its determination. Relaxing this assumption is not straightforward without additional assumptions. \n\n\\textbf{Geometry:}\nIn both calibration methods, reversible heat can be found only after deducing $K$ from \\mbox{$\\Delta T$-signals}, which is done via Eq.~\\eqref{eq:heatequation} with an (approximately) known amount of Joule heat. We note that this equation relies on the assumption that the temperature is uniform throughout the electrochemical cell ($\\mathcal{V}_{\\rm s}$ and $\\mathcal{V}_{\\rm el}$), for which the Biot number Bi, comparing heat conduction within the cell to the heat loss to the environment, is commonly used as a benchmark. \nUnfortunately, even when the requirement of small Bi is satisfied (for our cell we estimate Bi~$\\approx0.2$, a bit higher than Bi~$\\approx0.1$ which is deemed sufficiently low \\cite{incropera2007fundamentals}), the assumption of a uniform temperature cannot be correct for far-out-of-equilibrium charging, since heat is generated quickly at specific locations within the cell.\n\nThese considerations have the following repercussions on our experiments.\nIn both calibration methods, the temperature probe was placed halfway between the two electrodes and heating elements, respectively. Meanwhile, the Joule heat is generated at different locations in the two calibration methods. In the heating elements experiment, Joule heat is generated in a resistive wire positioned at the exact same location as the electrodes in the porous carbon experiment; hence, the location of reversible heat production.\nMeanwhile, the weak salt-concentration dependence of the total resistance of the electrochemical cell (that was mentioned in the main text) indicates that in the porous carbon experiment, Joule heat is probably produced at the electrical contacts [see Fig.~\\ref{fig1}(a)], which is further away from the temperature probe than in the heating elements experiment.\nIn line with our experimental findings, we note that a unit amount of Joule heat, generated at increasingly large separations from the temperature probe, leads to ever smaller $\\Delta T$-signals, resulting thereby into ever higher $K$-values. Clearly, employing the $K^{\\mathrm{c.e.}}$--- calibrated with Joule heat generated at the electrical contacts--- in a calculation determining the reversible heat generated at a location closer to temperature probe, will overestimate its value. For the main text, we therefore chose to prioritize $K^{\\mathrm{h.e.}}$.\n\nThe above calibration uncertainty would not have been present if the cell had had a uniform internal temperature at each instance. \nTo accomplish that, instead of a sudden step, one should \\textit{slowly} apply the external potential, such that the cell can equilibrate internally while being heated. Such experiments with the current cell were unsuccessful because heat flowed out of the cell too quickly; the measured temperature differences were too small compared to the equilibrium temperature fluctuations to separate signal from noise. \nHence, resolving the calibration uncertainty poses an experimental challenge, which will require the development of a better insulated and an even more sensitive calorimetric setup. \n\nAll above-mentioned reservations aside, the obtained heat transfer coefficients $K^{\\mathrm{c.e.}}$ and $K^{\\mathrm{h.e.}}$ still only differ by approximately $30\\%$.\n\n\\section{Time scales of current and temperature-difference decay}\\label{sec:app_timescales}\nFigure~\\ref{fig3}(b) and (c) exhibit a decaying current and temperature difference that we now discuss in more detail.\n\nIn general, electric double layer capacitors (EDLCs) are known to display a richer charging dynamics than conventional RC-circuits because both the capacitance and electrical resistance of an EDLC depend on its charging state. For instance, model calculations on a parallel plate EDLC (plate separation $L$) showed that\nsurface charge is screened on a time scale $\\lambda_{\\mathrm{D}} L\/D$ (with $D$ the diffusion constant), but corrections also introduce other time scales: $\\lambda_{S}L\/D$, $\\lambda_{S}\\lambda_{\\mathrm{D}}\/D$, $\\lambda_{\\mathrm{D}}^{2}\/D$ (with $\\lambda_{S}$ the thickness of a Stern layer) \\cite{bazant2004diffuse}. At later times ($L^2\/D$) salt diffusion takes over.\n\nThe electrodes of this Letter, made from porous carbon, have a hierarchical structure in which more length scales must come into play. One can therefore expect to find an even richer charging dynamics. As such, it comes as no surprise that data of the left panel of Fig.~\\ref{fig3}(b) --- redrawn in Fig.~\\ref{fig_sup5} with blue circles on linear (a) and log-linear (b) scale--- \n confirm the picture sketched above of the absence of a single time scale to the decay of the current; clearly, the data is poorly fitted by a single decaying exponential (red line) $I_{\\#1}(t)=I_{1}\\exp[-t\/\\tau_{1}]$ with $I_{1}=0.080$ A and $\\tau_{1}=73$ s. Adding a second exponential $I_{\\#2}(t)=I_{1}\\exp[-t\/\\tau_{1}]+I_{2}\\exp[-t\/\\tau_{2}]$ with $I_{0}=0.071$ A, $I_{1}=0.028$ A, $\\tau_{0}=31$~s, and $\\tau_{0}=171$~s) fits the data better (blue line) at short times but still fails at long times. \n\n\\begin{figure}\n\\def0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}{0.49\\textwidth}{\n \\providecommand\\color[2][]{%\n \\errmessage{(Inkscape) Color is used for the text in Inkscape, but the package 'color.sty' is not loaded}%\n \\renewcommand\\color[2][]{}%\n }%\n \\providecommand\\transparent[1]{%\n \\errmessage{(Inkscape) Transparency is used (non-zero) for the text in Inkscape, but the package 'transparent.sty' is not loaded}%\n \\renewcommand\\transparent[1]{}%\n }%\n \\providecommand\\rotatebox[2]{#2}%\n \\ifx0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\setlength{\\unitlength}{611.42745019bp}%\n \\ifx\\svgscale\\undefined%\n \\relax%\n \\else%\n \\setlength{\\unitlength}{\\unitlength * \\real{\\svgscale}}%\n \\fi%\n \\else%\n \\setlength{\\unitlength}{0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}}%\n \\fi%\n \\global\\let0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\global\\let\\svgscale\\undefined%\n \\makeatother%\n \\begin{picture}(1,0.48)%\n \\put(0,0){\\includegraphics[width=\\unitlength]{Fig_sup4.pdf}}%\n \\put(0.01,0.23101035){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{\\begin{rotate}{90} $I$ [A]\\end{rotate}}}}%\n \\put(0.23253116,-0.02){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{t [s]}}}%\n \\put(0.73177572,-0.02){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{t [s]}}}%\n \\put(0.0,0.45){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{(a)}}}%\n \\put(0.5,0.45){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{(b)}}}%\n \\end{picture}%\n}\n\\caption{\\label{fig_sup5}%\n The current relaxation data of the left panel of Fig.~\\ref{fig3}(b) replotted on linear (a) and log-linear scale (b) with blue circles (only one tenth of the data is shown). Fits $I_{\\#1}(t)$ (red line) and $I_{\\#2}(t)$ (blue line) as introduced in the text are shown as well.}\n\\end{figure}\n\n\n\n\n\n\\begin{figure}\n\\def0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}{0.49\\textwidth}{\n \\providecommand\\color[2][]{%\n \\errmessage{(Inkscape) Color is used for the text in Inkscape, but the package 'color.sty' is not loaded}%\n \\renewcommand\\color[2][]{}%\n }%\n \\providecommand\\transparent[1]{%\n \\errmessage{(Inkscape) Transparency is used (non-zero) for the text in Inkscape, but the package 'transparent.sty' is not loaded}%\n \\renewcommand\\transparent[1]{}%\n }%\n \\providecommand\\rotatebox[2]{#2}%\n \\ifx0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\setlength{\\unitlength}{606.60228725bp}%\n \\ifx\\svgscale\\undefined%\n \\relax%\n \\else%\n \\setlength{\\unitlength}{\\unitlength * \\real{\\svgscale}}%\n \\fi%\n \\else%\n \\setlength{\\unitlength}{0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}}%\n \\fi%\n \\global\\let0.45\\textwidth}{ \\input{Fig_sup1.pdf_tex}}\\undefined%\n \\global\\let\\svgscale\\undefined%\n \\makeatother%\n \\begin{picture}(1,0.48)%\n \\put(0,0){\\includegraphics[width=\\unitlength]{Fig_sup5.pdf}}%\n \\put(0.01,0.2){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{\\begin{rotate}{90} $\\Delta T$ [K]\\end{rotate}}}}%\n \\put(0.19785768,-0.02){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{t [s]}}}%\n \\put(0.70107342,-0.02){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{t [s]}}}%\n \\put(0.0,0.45){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{(a)}}}%\n \\put(0.5,0.45){\\color[rgb]{0,0,0}\\makebox(0,0)[lb]{\\smash{(b)}}}%\n \\end{picture}%\n}\n\\caption{\\label{fig_sup6}%\n The thermal response data of the left panel of Fig.~\\ref{fig3}(c) replotted on linear (a) and log-linear scale (b) with black circles.\n\n Fits to these data are obtained with Eq.~(\\ref{eq.sol}) for different assumptions about $\\dot{\\Pi}_{\\rm tot}$: Joule heat only (red line), reversible heat only (blue line), and both Joule and reversible heat (black line).}\n\\end{figure}\n\nIn Fig.~\\ref{fig_sup6} we reproduce (with black circles) the data of the left panel of Fig.~\\ref{fig3}(c) on linear (a) and log-linear (b) scale. To model these data, we note that Eq.~\\eqref{eq:heatequation} allows for the following general solution\n\\begin{equation}\\label{eq.sol}\n\\Delta T(t)=\\exp[-t\/\\tau]\\left[\\int^{t}_{0}\\exp[t'\/\\tau]\\frac{\\dot{\\Pi}_{\\rm tot}(t')}{\\mathbb{C}_{p}}dt' \\right],\n\\end{equation}\nwith $\\dot{\\Pi}_{\\rm tot}(t)$ the heating rate and $\\tau=\\mathbb{C}_{p}\/K=61.1\\pm0.3$~s the thermal time constant as determined in the previous section. Clearly, the time scales of thermal response to charging are dictated both by $\\tau$ and by the time scales with which $\\dot{\\Pi}_{\\rm tot}$ decays. In the main text we argued that $\\dot{\\Pi}_{\\rm tot}$ contains Joule heat contributions ($\\sim I^2$) and reversible contributions ($\\sim I$):\nwe consider the following general form $\\dot{\\Pi}_{\\rm tot}(t)=\\left[\\zeta_{\\rm irr}I(t)^2+\\zeta_{\\rm rev}I(t)\\right]$ where $\\zeta_{\\rm irr}$ and $\\zeta_{\\rm rev}$ are parameters of dimension $\\Omega$ and $V$, respectively.\nFrom the above discussion we know that the current decay is characterized by multiple time scales. However, to proceed, we approximate $I(t)$ with the fit found above: $I(t)=\\Theta(t) I_{\\#2}(t)$, with $\\Theta(t) $ a Heaviside step function. Equation (\\ref{eq.sol}) can now be solved analytically: a lengthy expression with two free parameters $\\zeta_{\\rm irr}$ and $\\zeta_{\\rm rev}$.\n\nTo start, we consider the case of only Joule heat by setting $\\zeta_{\\rm rev}=0$ V. The best fit (red line) is then found for $\\zeta_{\\rm irr}=1.1$~$\\Omega$. Conversely, considering the case of only reversible heat (setting $\\zeta_{\\rm irr}=0$~$\\Omega$) leads to a best fit (blue) at $\\zeta_{\\rm rev}=0.051$~V. The short-time $\\Delta T$ response is decently described by the case of only Joule heat, while the case of only reversible heat performs poorly in that region.\nFigure~\\ref{fig_sup6}(b) shows that both choices do not reproduce the long-time relaxation accurately: they relax either too rapidly (only Joule heat) or too slowly (only reversible heat).\n\n\nAs can be expected on the basis of Ref.~\\cite{janssen2017reversible} and this Letter, the physical situation is that of reversible and irreversible heat {\\it simultaneous} at play.\nIndeed, the best fit through the data in Fig.~\\ref{fig_sup6} is achieved for $\\zeta_{\\rm irr}=0.76$~$\\Omega$ and $\\zeta_{\\rm rev}=0.016$ V; hence, a mixture of reversible and reversible contributions. We conclude that, in order to reproduce the long-time relaxation correctly, it seems necessary to account for both reversible and irreversible heat. \n\n\n\nSo far, we only considered $\\zeta_{\\rm rev\/irr}$ to be time-independent. However, for the slit geometry of Ref.~\\cite{janssen2017reversible}, $\\zeta_{\\rm rev}$ takes the form $\\zeta_{\\rm rev}=k_{\\rm B}T (\\partial_{z}q)\/[e (\\rho_{+}+\\rho_{-})]$; hence, there is no reason to believe $\\zeta_{\\rm rev}$ is truly time independent. Relaxing this assumption could lead to an even better reproduction of the measured temperature data. \n\n\n\n\\end{appendix}\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n \\label{sec:sec1}\n\nThe aim of weather forecasting is to give a good prediction of the\nfuture states of the atmosphere on the basis of present observations\nand mathematical models describing the dynamics (physical behaviour)\nof the atmosphere. \nThese models consist of sets of non-linear partial differential\nequations which have only numerical solutions. The problem with these\nnumerical weather prediction models is that the solutions highly\ndepend on the initial conditions which are always in a way or in\nanother not fully accurate. A\npossible solution to address this problem is to run the model with different\ninitial conditions and produce ensembles of forecasts. With the help\nof ensembles one can estimate the distribution of future weather\nvariables which leads us to probabilistic weather forecasting \\citep{gr}.\nThe ensemble\nprediction method was proposed by \\citet{leith} and since its first\noperational implementation \\citep{btmp,tk} it became a widely used\ntechnique all over the world. However, despite e.g. the ensemble\nmean gives a better estimate of a meteorological quantity than most or\nall of the ensemble members, the ensemble is usually under-dispersive\nand in this way, uncalibrated. This phenomena was observed at several\noperational ensemble prediction systems, for an overview see\ne.g. \\citet{bhtp}. \n\nThe Bayesian model averaging (BMA) method for\npost-processing ensembles in order to calibrate them was introduced by\n\\citet{rgbp}. The basic idea of BMA is that to each ensemble member\nforecast corresponds a conditional probability\ndensity function (PDF) that can be interpreted as the\nconditional PDF of the future weather quantity provided the considered\nforecast is the best one. Then the BMA predictive PDF of the future\nweather quantity is the weighted sum of the individual PDFs\ncorresponding to the ensemble members and the weights are based on the\nrelative performances of the ensemble members during a given training period.\nIn \\citet{rgbp} the BMA\nmethod was successfully applied to obtain 48 hour forecasts of surface\ntemperature and sea level pressure in the North American Pacific\nNorthwest based on the 5 members of the University of Washington\nMesoscale Ensemble \\citep{gm}. These weather quantities can be\nmodeled by normal distributions, so the predictive PDF is a Gaussian mixture.\nLater \\citet{srgf} developed a discrete-continuous BMA model for precipitation\nforecasting, where the discrete part corresponds to the event of no\nprecipitation, while the cubic root of the precipitation amount\n(if it is positive) is modeled by a gamma distribution. In\n\\citet{sgr10} the BMA method was used for wind speed forecasting and \nthe component PDFs follow gamma distribution. Finally, using von Mises\ndistribution to model angular data \n\\citet{bgrgg} introduced a BMA scheme to predict surface wind\ndirection.\n\nIn the present work we apply the BMA method for calibrating ensemble\nforecasts of wind speed produced by the operational Limited\nArea Model Ensemble \nPrediction System (LAMEPS) of the Hungarian Meteorological Service\n(HMS) called ALADIN-HUNEPS \\citep{hagel, horanyi}. ALADIN-HUNEPS\ncovers a large part of Continental Europe with a horizontal resolution\nof 12 km and it is obtained by dynamical downscaling (by the ALADIN\nlimited area model) of the global\nARPEGE based PEARP system of M\\'et\\'eo France \\citep{hkkr,dljn}. The\nensemble consists of 11 members, 10 initialized from perturbed initial\nconditions and one control member from the unperturbed analysis. This\nconstruction implies that the ensemble contains groups of exchangeable\nforecasts (the ensemble members cannot be distinguished), so for\npost-processing one has to use the modification of \nBMA as suggested by \\citet{frg}. \n\n\n\n\\section{Data}\n \\label{sec:sec2}\n\nAs it was mentioned in the Introduction, BMA post-processing\nof ensemble predictions was applied for wind speed data obtained from\nthe HMS. The \ndata file contains 11 member ensembles (10 forecasts started from perturbed\ninitial conditions and one control) of 42 hour \nforecasts for 10 meter wind speed (given in m\/s)\nfor 10 major cities in \nHungary (Miskolc, Szombathely, Gy\\H or, Budapest, Debrecen, Ny\\'\\i regyh\\'aza,\nNagykanizsa, P\\'ecs, Kecskem\\'et, Szeged) produced by the\nALADIN-HUNEPS system of the HMS, together with the corresponding\nvalidating observations, for the period between October 1, 2010 and March 25,\n2011. The forecasts are initialized at 18 UTC, the startup speed of\nthe anemometers measuring the validating observations is $0.1$\nm\/s. The data set is fairly complete, since there are only two days\n(18.10.2010 and 15.02.2011) when three ensemble members are missing\nfor all sites and one day (20.11.2010) when no forecasts are available. \n\n\\begin{figure}[t]\n\\begin{center}\n\\leavevmode\n\\epsfig{file=verrankn.eps,height=9cm, angle=-90}\n\\caption{Verification rank histogram of the 11-member ALADIN-HUNEPS\n ensemble. Period: October 1, 2010 -- March 25, 2011.} \n\\label{fig:fig1}\n\\end{center}\n\\end{figure}\nFigure \\ref{fig:fig1} shows the verification rank histogram of the raw\nensemble, that is the histogram of ranks of validating\nobservations with respect to the corresponding ensemble\nforecasts. This histogram is \nfar from the desired uniform distribution, in most of the cases the\nensemble members either underestimate, or overestimate the validating\nobservations (the ensemble range contains the observed wind speed only\nin $61.21\\%$ of the cases). Hence, the ensemble is under-dispersive\nand in this way it is uncalibrated.\n\n\n\n\\section{The model and diagnostics}\n \\label{sec:sec3}\n\n\n\nTo obtain a probabilistic forecast of wind speed the modification of \nBMA gamma model of \\citet{sgr10} for ensembles with\nexchangeable members \\citep{frg} was used. The first idea is to have two\nexchangeable groups: one contains \nthe control denoted by \\ $f_c$, \\ the other one the 10 ensemble members\ncorresponding to the different perturbed initial conditions which are denoted by\n\\ $f_{\\ell,1},\\ldots ,f_{\\ell,10}$, \\ respectively. \\ In this way we assume\nthat the probability density function (PDF) of the forecasted\nwind speed \\ $x$ \\ equals: \n\\begin{align}\n \\label{eq:eq3.1}\np(x | f_c,f_{\\ell,1},\\ldots ,\nf_{\\ell,10};b_0,b_1,c_0,c_1)=&\\, \\omega\ng(x;f_c,b_0,b_1,c_0,c_1) \\\\ &+ \\frac \n{1-\\omega}{10} \\sum_{j=1}^{10} g(x;f_{\\ell,j},b_0,b_1,c_0,c_1), \\nonumber \n\\end{align} \nwhere \\ $\\omega\\in [0,1]$, \\ and \\ $g$ \\\nis the conditional PDF corresponding to the ensemble members.\nAs we are working with wind speed data, \n\\ $g(x;f,b_0,b_1,c_0,c_1)$ \\ is a gamma PDF with mean\n\\ $b_0+b_1 f$ \\ and standard deviation \\ $c_0+c_1 f$. \\ Here we restrict both\nthe mean and the standard deviation parameters to constant values for all\nensemble members, which reduces the number of parameters and\nsimplifies calculations. \nMean parameters \\ $b_0,\\ b_1$ \\ are estimated with\nthe help of linear regression, while weight \\ $\\omega$ \\ and standard deviation\nparameters \\ $c_0, \\ c_1$, \\ by maximum likelihood method, using training\ndata consisting of ensemble members and verifying observations from\nthe preceding \\ $n$ \\ days (training period). In order to handle the problem\nthat the wind speed values under 0.1 m\/s are considered to be zero, the\nmaximum likelihood (ML) method for gamma distributions suggested by\n\\citet{wilks1} is \napplied, while the maximum of the likelihood\nfunction is found with the help of EM algorithm \\citep{mclk}. For more\ndetails see \\citet{sgr10,frg}. Once the estimated \nparameters for a given day are available, one can\nuse either the mean or the median of the predictive PDF\n\\eqref{eq:eq3.1} as a point forecast. \n\n\n\\begin{figure}[t]\n\\begin{center}\n\\leavevmode\n\\epsfig{file=plumedebn.eps,height=10cm}\n\\caption{Plume diagram of ensemble forecast of 10 meter wind speed\n for Debrecen initialized at 18 UTC, 22.10.2010.} \n\\label{fig:fig2}\n\\end{center}\n\\end{figure}\nBased on a more careful look on the ensemble members there are some\ndifferences in the generation of the ten \nexchangeable ensemble members. To obtain them only five perturbations\nare calculated and then they are added to (odd numbered members) and\nsubtracted from (even numbered members) the unperturbed initial\nconditions \\citep{horanyi}. Figure \\ref{fig:fig2} shows the plume\ndiagram of ensemble forecast of 10 meter wind speed\nfor Debrecen initialized at 18 UTC, 22.10.2010. (solid line: control;\ndotted line: odd numbered members, dashed line: even numbered\nmembers). This diagram clearly illustrates that the behaviour of\nensemble member groups \\ $\\{f_{\\ell,1}, \\ \nf_{\\ell,3}, \\ f_{\\ell,5}, \\ f_{\\ell,7}, \\ f_{\\ell,9}\\}$ \\ and \\ $\\{f_{\\ell,2}, \\\nf_{\\ell,4}, \\ f_{\\ell,6}, \\ f_{\\ell,8}, \\ f_{\\ell,10}\\}$ \\ really differ\nfrom each other. Therefore, in this way one can also consider a model with three\nexchangeable groups: control, odd numbered exchangeable members and even\nnumbered exchangeable members. This idea leads to the following PDF\nof the forecasted wind speed \\ $x$:\n\\begin{align}\n \\label{eq:eq3.2}\nq(x | f_c,f_{\\ell,1},\\ldots ,\nf_{\\ell,10};&\\,b_0,b_1,c_0,c_1)= \\omega_c\ng(x;f_c,b_0,b_1,c_0,c_1) \\\\ &+ \n\\sum_{j=1}^{5} \\big(\\omega_o g(x;f_{\\ell,2j-1},b_0,b_1,c_0,c_1)+ \n\\omega_e g(x;f_{\\ell,2j},b_0,b_1,c_0,c_1)\\big), \\nonumber \n\\end{align} \nwhere for weights \\ $\\omega_c,\\omega_o,\\omega_e\\in[0,1]$ \\ we have \\\n$\\omega_c+5\\omega_o+5\\omega_e=1$, \\ while PDF \\ $g$ \\ and parameters \\\n$b_0,b_1,c_0,c_1$ \\ are the same as for the model\n\\eqref{eq:eq3.1}. Obviously, both the weights and the parameters \ncan be estimated in the same way as before. \n \n\n\\begin{figure}[t!]\n\\begin{center}\n\\leavevmode\n\\hbox{\n\\epsfig{file=winddebpdf1g2n.eps,height=8cm, angle=-90} \\quad\n\\epsfig{file=winddebpdf2g2n.eps,height=8cm, angle=-90}}\n\n\\centerline{\\hbox to 9 truecm {\\scriptsize (a) \\hfill (b)}}\n\n\\hbox{\n\\epsfig{file=winddebpdf1g3n.eps,height=8cm, angle=-90} \\quad\n\\epsfig{file=winddebpdf2g3n.eps,height=8cm, angle=-90}}\n\n\\centerline{\\hbox to 9 truecm {\\scriptsize (c) \\hfill (d)}}\n\\caption{Ensemble BMA PDFs (overall: thick black line; control: red\n line; sum of exchangeable members on (a) and (b): light blue line;\n on (c) and (d): green (odd members) and blue (even members) lines),\n ensemble members (circles with the same colours as the corresponding\n PDFs), ensemble BMA median forecasts (vertical black line),\n verifying observations (vertical orange line) and the first and last\ndeciles (vertical dashed lines) for wind speed in Debrecen for\n models \\eqref{eq:eq3.1}: (a) 30.12.2010, (b) 17.03.2011; and\n \\eqref{eq:eq3.2}: (c) 30.12.2010, (d) 17.03.2011.} \n\\label{fig:fig3}\n\\end{center}\n\\end{figure}\nAs an illustration we consider the data and forecasts for \nDebrecen for two\ndifferent dates 30.12.2010 and 17.03.2011 for models \\eqref{eq:eq3.1}\nand \\eqref{eq:eq3.2}.\nFigures \\ref{fig:fig3}a and \\ref{fig:fig3}b show the PDFs of\nthe two groups in model \\eqref{eq:eq3.1}, the overall \nPDFs, the median forecasts,\nthe verifying observations, the first and last\ndeciles and the ensemble members. The same functions and\nquantities can be seen on Figures \\ref{fig:fig3}c and \\ref{fig:fig3}d,\nwhere besides the overall PDF we have three component PDFs and three\ngroups of ensemble members. On 30.12.2010 the spread of the ensemble\nmembers is quite fair and the ensemble range contains the validating\nobservation (3.2 m\/s). In this case the ensemble mean (3.5697 m\/s)\noverestimates, while BMA median forecasts corresponding to the two- and\nthree-group models (3.2876 m\/s and 3.2194 m\/s, respectively) are\npretty close to the true wind speed. A different situation is\nillustrated on Figures \\ref{fig:fig3}b and \\ref{fig:fig3}d, where the\nspread of the ensemble is even higher, but all\nensemble members underestimate the validating observation (6.1\nm\/s). Obviously, the same holds for the ensemble mean (3.2323 m\/s) and\ndue to the bias correction the BMA median forecasts corresponding to\nmodels \\eqref{eq:eq3.1} and \n\\eqref{eq:eq3.2} also give bad results (3.3409 m\/s and 3.0849 m\/s,\nrespectively).\n\n\\begin{figure}[t!]\n\\begin{center}\n\\leavevmode\n\\epsfig{file=avcov2gn.eps,height=15cm, angle=-90}\n\\caption{Average widths and coverages of $66.7\\,\\%$ and $90\\,\\%$\n BMA prediction intervals \n corresponding to two-group model \\eqref{eq:eq3.1} for various training period\n lengths.} \n\\label{fig:fig4}\n\\end{center}\n\\end{figure}\nTo check the performance of probabilistic forecasts based on\nmodels \\eqref{eq:eq3.1} and \\eqref{eq:eq3.2} and the\ncorresponding point forecasts, as a reference we use the ensemble mean\nand the ensemble median. We compare the mean absolute errors (MAE) and\nthe root mean square errors (RMSE) of\nthese point forecasts and also the mean continuous ranked probability scores\n(CRPS) \\citep{wilks2,grjasa} and the coverages and average widths of\n$66.7\\,\\%$ and $90\\,\\%$ prediction intervals of the BMA predictive probability\ndistributions and of the raw ensemble. We remark that for MAE and RMSE\nthe optimal point forecasts are the median and the mean, respectively\n\\citep{gneiting11, pinhag}. Further, given a cumulative distribution\nfunction (CDF) \\ $F(y)$ \\ and a real number \\ $x$, \\ the CRPS is defined as\n\\begin{equation*}\n\\crps\\big(F,x\\big):=\\int_{-\\infty}^{\\infty}\\big (F(y)-{\\mathbbm \n 1}_{\\{y \\geq x\\}}\\big )^2{\\mathrm d}y.\n\\end{equation*}\nThe mean CRPS of a probability forecast is the average of the CRPS values\nof the predictive CDFs and corresponding validating observations taken\nover all locations and time points considered. For the raw ensemble\nthe empirical CDF of the ensemble replaces the predictive CDF. The\ncoverage of a \\ $(1-\\alpha)100 \\,\\%, \\ \\alpha \\in (0,1),$ \\ prediction\ninterval is the proportion \nof validating observations located between the lower and upper \\\n$\\alpha\/2$ \\ quantiles of the predictive distribution. For a\ncalibrated predictive PDF this value should be around \\ $(1-\\alpha)100\n\\,\\%$. \n\n\n\\section{Results}\n \\label{sec:sec4}\n\n\\begin{figure}[t!]\n\\begin{center}\n\\leavevmode\n\\epsfig{file=crpsmae2gn.eps,height=15cm, angle=-90}\n\\caption{CRPS of BMA predictive distribution, MAE values of BMA median and RMSE\n values of BMA mean forecasts\n corresponding to two-group model \\eqref{eq:eq3.1} for various training period\n lengths.} \n\\label{fig:fig5}\n\\end{center}\n\\end{figure}\nData analysis provided below was performed with the help of the {\\tt\n ensembleBMA} package of R \\citep{frgs,frgsb}. As a first step the\nlength of the \nappropriate training period was determined, then the performances\nof the BMA post-processed ensemble forecasts corresponding to models\n\\eqref{eq:eq3.1} and \\eqref{eq:eq3.2} were analyzed. \n\n\\subsection{Training period}\n \\label{sec:sub4.1}\n\n\nAccording to the results of e.g. \\citet{rgbp} to determine the length\nof the training period to be used we compare the MAE values\nof BMA median forecasts, the RMSE values of BMA mean forecasts, the\nCRPS values of BMA predictive \ndistributions and the coverages and average\nwidths of $90\\,\\%$ and $66.7\\,\\%$ BMA prediction intervals for\ntraining periods of \nlength \\ $10,11, \\ldots, 60$ \\ calendar days. In order to ensure\nthe comparability of \nthe results we consider verification results from 02.12.2010 to\n25.03.2011 (114 days).\n\n\\begin{figure}[t!]\n\\begin{center}\n\\leavevmode\n\\epsfig{file=avcov3gn.eps,height=15cm, angle=-90}\n\\caption{Average widths and coverages of $66.7\\,\\%$ and $90\\,\\%$\n BMA prediction intervals \n corresponding to three-group model \\eqref{eq:eq3.2} for various\n training period \n lengths.} \n\\label{fig:fig6}\n\\end{center}\n\\end{figure}\nConsider first the two-group model \\eqref{eq:eq3.1}. On Figure\n\\ref{fig:fig4} the \naverage widths and coverages of $66.7\\,\\%$ and $90\\,\\%$ BMA prediction\nintervals are plotted against the length of the training period. The\naverage widths of the prediction intervals show an \nincreasing trend, so shorter training periods yield sharper forecasts.\nCoverages of $66.7\\,\\%$ and $90\\,\\%$ prediction intervals are not monotonously\nincreasing, too. For short training periods the coverage of the\n$66.7\\,\\%$ prediction interval oscillates around the\ncorrect $66.7\\,\\%$, but for training periods not shorter than 17 days it\nstays above this level. The coverage of the $90\\,\\%$ prediction\ninterval stabilizes above the correct $90\\,\\%$ for training periods\nlonger than 24 days. Hence, to have calibrated forecasts, one should\nchoose a training period not shorter than 25 days.\n\n\\begin{figure}[t!]\n\\begin{center}\n\\leavevmode\n\\epsfig{file=crpsmae3gn.eps,height=15cm, angle=-90}\n\\caption{CRPS of BMA predictive distribution, MAE values of BMA median and RMSE\n values of BMA mean forecasts\n corresponding to three-group model \\eqref{eq:eq3.2} for various\n training period lengths.} \n\\label{fig:fig7}\n\\end{center}\n\\end{figure}\nFigure \\ref{fig:fig5} shows\nCRPS values of BMA predictive distribution, MAE values of BMA\nmedian forecasts and RMSE values of BMA mean forecasts as functions of\nthe training period length. CRPS and RMSE both take their minima at 28\ndays, the corresponding values are $0.7388$ and $1.3675$,\nrespectively. MAE\ntakes its minimum of $1.0472$ at 30 days, while the second smallest value\n($1.0476$) is obtained with a training period of length 28 days.\nThis means that for model\n\\eqref{eq:eq3.1} a 28 days training period seems to be reasonable and\ntraining periods longer than 30 days cannot be taken into consideration.\n\nSimilar conclusions can be drawn from Figures \\ref{fig:fig6} and\n\\ref{fig:fig7} for the three-group model \\eqref{eq:eq3.2}. In this case the\n$66.7\\,\\%$ and $90\\,\\%$ prediction intervals are slightly narrower\nthan the corresponding intervals of model \\eqref{eq:eq3.1}, their\ncoverages stabilize above the correct\n$66.7\\,\\%$ and $90\\,\\%$ for training periods longer than 17 and 24\ndays, respectively. CRPS and MAE plotted on Figure \\ref{fig:fig7} both\nreach their minima of $0.7372$ and $1.0452$, respectively, at 30 days,\nwhile values $0.7376$ and $1.0456$ corresponding to training period of\nlength 28 days are both the fourth smallest ones. RMSE takes its\nminimum of $1.3632$ at 27 days, and increases afterwards. The fourth\nsmallest value ($1.3644$) again corresponds to 28 days, while the RMSE\ncorresponding to 30 days is significantly larger ($1.3664$). Moreover,\n$66.7\\,\\%$ and $90\\,\\%$ prediction intervals corresponding to 28 days\nare sharper than the appropriate prediction intervals calculated using training\nperiod of length 30 days ($2.5813$ and $4.4340$ vs. $2.5831$ and\n$4.4378$). Hence, we suggest the use of a training period of length 28\ndays for both BMA models.\n\n\n\n\\subsection{Predictions using BMA post-processing}\n\\label{sec:sub4.2}\n\nAccording to the results of the previous subsection, to test the performance\nof BMA post-processing on the 11 member ALADIN-HUNEPS ensemble we use a\ntraining period of 28 calendar days. In this way ensemble members,\nvalidating observations and BMA models are available for 146 calendar\ndays (on 20.11.2010 all ensemble members are missing). \n\n\\begin{figure}[t!]\n\\begin{center}\n\\leavevmode\n\\epsfig{file=pitsn.eps,height=15cm, angle=-90}\n\\caption{PIT histograms for BMA post-processed forecasts using two-group\n \\eqref{eq:eq3.1} and three-group \\eqref{eq:eq3.2} models.} \n\\label{fig:fig8}\n\\end{center}\n\\end{figure}\n\nFirst we check the calibration of BMA post-processed forecasts with the\nhelp of probability integral transform (PIT) histograms. The PIT is\nthe value of the BMA predictive cumulative distribution evaluated at\nthe verifying observations \\citep{frg}. The closer the histogram to the\nuniform distribution, the better is the calibration. On Figure\n\\ref{fig:fig8} the PIT histograms corresponding to two- and\nthree-group BMA models\n\\eqref{eq:eq3.1} and \\eqref{eq:eq3.2} are displayed. Compared to the\nverification rank histogram of the raw ensemble (see Figure\n\\ref{fig:fig1}) one can observe a large improvement with the use of\ncalibration. However, these PIT histograms are still not perfect,\ne.g. Kolmogorov-Smirnov test rejects uniformity both for the two- and\nfor the three-group model. The corresponding $p$-values are $0.0222$\nand $0.0187$, respectively, so the PIT of the two-group model is\nslightly better.\n\n\n\n\\begin{table}[b]\n\\begin{center}\n\\begin{tabular}{|l|c|c|c|c|c|} \\hline\n\\multicolumn{1}{|c|}{}&\n\\multicolumn{2}{|c|}{Coverage ($\\%$)}&\n\\multicolumn{2}{|c|}{Average Width}\\\\ \\cline{2-5}\nInterval&$66.7\\,\\%$ interval&$90.0\\,\\%$ interval&$66.7\\,\\%$\ninterval&$90.0\\,\\%$ interval\\\\ \\hline \nRaw ensemble&$38.70$&$55.14$&$1.4388$&$2.2001$\n\\\\ \nBMA model\n\\eqref{eq:eq3.1}&$68.08$&$90.34$&$2.6359$&$4.5297$\n\\\\ \nBMA model\n\\eqref{eq:eq3.2}&$68.36$&$90.21$&$2.6153$&$4.4931$\n\\\\ \n\\hline \n\\end{tabular} \n\\caption{Coverage and average width of prediction intervals.} \\label{tab:tab1}\n\\end{center}\n\\end{table}\nTable \\ref{tab:tab1} gives the coverages and average widths of\n$66.7\\,\\%$ and $90.0\\,\\%$ prediction intervals calculated using models\n\\eqref{eq:eq3.1} and \\eqref{eq:eq3.2}, and the corresponding measures calculated\nfrom the raw ensembles. In the latter case the ensemble of forecasts\ncorresponding to a given location and time is considered as a\nstatistical sample. The BMA prediction intervals calculated from\nboth models are approximately twice as wide, as the corresponding\nintervals of the raw ensemble. This comes from the small dispersion of\nthe raw ensemble, see the verification rank histogram of Figure\n\\ref{fig:fig1}. Concerning calibration one can observe that the\ncoverages of both \nBMA prediction intervals are rather close to the right coverages,\nwhile the coverages of the prediction intervals calculated from the\nraw ensemble are quite poor. This also shows that BMA post-processing\nhighly improves calibration. Further, BMA model \\eqref{eq:eq3.2}\nyields slightly sharper predictions but there is no big difference\nbetween the coverages of the two BMA models.\n\n\n\\begin{table}[t]\n\\begin{center}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|} \\hline\n\\multicolumn{1}{|c|}{}&\n\\multicolumn{1}{|c|}{Mean CRPS}&\n\\multicolumn{2}{|c|}{MAE}&\n\\multicolumn{2}{|c|}{RMSE}\\\\ \\cline{3-6}\n&&median&mean&median&mean\n\\\\ \\hline \nRaw ensemble&$0.8599$&$1.1215$&$1.1090$&$1.4634$&$1.4440$\n\\\\ \nBMA model\n\\eqref{eq:eq3.1}&$0.7577$&$1.0678$&$1.0763$&$1.4213$&$1.4067$\n\\\\ \nBMA model\n\\eqref{eq:eq3.2}&$0.7556$&$1.0643$&$1.0749$&$1.4153$&$1.4018$\n\\\\\\hline \n\\end{tabular} \n\\caption{Mean CRPS of probabilistic, MAE and RMSE of\ndeterministic forecasts.} \\label{tab:tab2}\n\\end{center}\n\\end{table}\n\n\n\n\\begin{figure}[t]\n\\begin{center}\n\\leavevmode\n\\epsfig{file=weightsn.eps,height=15cm, angle=-90}\n\\caption{BMA weights of two-group\n \\eqref{eq:eq3.1} and three-group \\eqref{eq:eq3.2} models.} \n\\label{fig:fig9}\n\\end{center}\n\\end{figure}\nOn Table \\ref{tab:tab2} the verification results of the model\nfit are given. Verification measures of probabilistic forecasts and point\nforecasts calculated using BMA models \\eqref{eq:eq3.1} and\n\\eqref{eq:eq3.2} are compared to the corresponding measures calculated\nfor the raw ensemble. Examining these results one can clearly observe\nthe advantage of BMA post-processing which resulted a significant\ndecrease in all verification scores. Further, the BMA median forecasts\nyield slightly lower MAE values than the BMA mean forecasts for both\nmodels, while in the case of RMSE values the situation is just the\nopposite, which is a perfect illustration of the theoretical results\nof \\cite{gneiting11} about the optimality of these verification scores. \nFinally, model \\eqref{eq:eq3.2} distinguishing three exchangeable\ngroups of ensemble forecasts slightly outperforms model \\eqref{eq:eq3.1}.\n\n\nFigure \\ref{fig:fig9} shows the BMA weights corresponding to models\n\\eqref{eq:eq3.1} and \\eqref{eq:eq3.2}. Examining the behaviour of \nweight \\ $\\omega$ \n\\ of the control member of the ensemble in the two-group model\n\\eqref{eq:eq3.1}, one can \nobserve that in $84.56\\,\\%$ of the cases there is a real mixture of\ngamma distributions. The values of \\ $\\omega$ \\ which are close to $1$\ncorrespond to a\ncontinuous time interval 17.11.2010 -- 09.12.2010, when the control\nmember of the ensemble gives much\nbetter forecasts than the ten exchangeable ensemble members. This can\nclearly be seen from Table \\ref{tab:tab3} where the MAE and RMSE\nvalues of the particular ensemble members are given for the above\nmentioned period. In all of these 23 subsequent days \\ $\\omega>0.995$ \\\nbut on 20.11.2010, when \\ $\\omega=0.9873$. \\ However, as it was\nmentioned earlier, on this particular day all ensemble \nforecasts are missing from the data set. The situation is quite\ndifferent in the case of the three-group model \\eqref{eq:eq3.2}, where\nthe weight \\ $\\omega_c$ \\ of the control is close to $1$ (greater than\n$0.98$) only on 7 days, so in the remaining cases ($95.30\\,\\%$) a\nreal mixture of gamma distributions present. Further, observe that\nthere are 55 days \\ ($36.91\\,\\%$) \\ when all BMA weights\nare positive, the even numbered exchangeable members have nearly zero weights \\\n(less than $0.001$) \\ in 45 cases \\ ($30.20\\,\\%$) \\ at the beginning of the\nconsidered time period, while the odd numbered exchangeable members\nare almost zero in 53 cases \\ ($35.57\\,\\%$), \\ mainly at the end of\nit.\n\n \n\\begin{table}[b]\n\\begin{center}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|c|} \\hline\n\\multicolumn{1}{|c|}{}&\n\\multicolumn{1}{|c|}{Control}&\n\\multicolumn{10}{|c|}{Exchangeable members}\n\\\\ \\cline{2-12}\n&$f_c$&$f_{\\ell,1}$&$f_{\\ell,2}$&$f_{\\ell,3}$&$f_{\\ell,4}$&$f_{\\ell,5}$&$f_{\\ell,6}$&$\nf_{\\ell,7}$&$f_{\\ell,8}$&$f_{\\ell,9}$&$f_{\\ell,10}$ \\\\ \\hline\nMAE&$1.32$&$1.60$&$1.46$&$1.52$&$1.68$&$1.51$&$1.49$&$1.56$&$\n1.42$&$1.41$&$1.65$\\\\\nRMSE&$1.69$&$2.16$&$1.86$&$1.96$&$2.26$&$1.92$&$1.95$&$2.05$&$\n1.89$&$1.81$&$2.23$\\\\ \\hline\n\\end{tabular} \n\\caption{MAE and RMSE of the control and exchangeable ensemble\n forecasts for the period 17.11.2010 -- 09.12.2010.} \\label{tab:tab3}\n\\end{center}\n\\end{table}\n\n\\begin{figure}[t]\n\\begin{center}\n\\leavevmode\n\\epsfig{file=parsn.eps,height=16.5cm, angle=-90}\n\\caption{Parameters of two-group model\n \\eqref{eq:eq3.1} and differences in standard deviation parameters\n between three- and two-group models.} \n\\label{fig:fig10}\n\\end{center}\n\\end{figure}\nFinally, on Figure \\ref{fig:fig10} common bias parameters \\ $b_0, \\ b_1$ \\\nof both BMA models investigated and standard deviation parameters \\\n$c_0, \\ c_1$ \\ \nof the two-group model \\eqref{eq:eq3.1} are plotted, together with the\ndifferences in standard deviation parameters of three- and two-group\nmodels. Bias parameters are rather\nstable, the relative standard deviations of \\ $b_0$ \\ and \\ $b_1$ \\\nare $25.44\\,\\%$ and $9.97\\,\\%$, respectively. Hence, the BMA mean\nforecast of a particular day is mainly determined by the corresponding\nensemble forecasts. The standard deviation \nparameters show more variability, for \\ $c_0$ \\\nand \\ $c_1$ \\ the relative standard deviations are equal to\n$23.41\\,\\%$ and $41.27\\,\\%$ for \nmodel \\eqref{eq:eq3.1}, and $22.64\\,\\%$ and $36.73\\,\\%$ for\nmodel \\eqref{eq:eq3.2}.\n\n\n\n\\section{Conclusions}\n \\label{sec:sec5}\n\nIn the present study the BMA ensemble post-processing method is applied \nfor the 11 member ALADIN-HUNEPS ensemble of the HMS to obtain 42 hour\npredictions for 10 meter wind speed. Two different BMA models are\ninvestigated, one assumes two groups of exchangeable members (control\nand forecasts from perturbed initial conditions), while the other\nconsiders three (control and forecasts from perturbed initial\nconditions with positive and negative perturbations). For both models\na 28 days training period is suggested. The comparison of the\nraw ensemble and of the probabilistic forecasts shows that the mean\nCRPS values \nof BMA post-processed forecasts are considerably lower than the mean\nCRPS of the raw ensemble. Further, the MAE and RMSE values of BMA point\nforecasts (median and mean) are also lower than the MAEs and RMSEs of the\nensemble median and of the ensemble mean. The calibrations of BMA\nforecasts are nearly perfect, the coverages of $66.7\\,\\%$ and $90.0\\,\\%$\nprediction intervals are very close to the right values. The\nthree-group BMA model slightly outperforms the two-group one and in\nalmost all cases yields a real mixture of gamma distributions.\n\nIn this way one can conclude that BMA post-processing of ensemble\nforecasts of wind speed data of the HMS significantly improves the\nprecision and calibration of the forecasts, its operational application\nis worth considering.\n\n\n\n\n\\bigskip\n\\noindent\n{\\bf Acknowledgments.} \\ \\ Research was supported by \nthe Hungarian Scientific Research Fund under Grants No. OTKA\nT079128\/2009 and OTKA NK101680 and by the T\\'AMOP-4.2.2.C-11\/1\/KONV-2012-0001\nproject. The project has been supported by the European Union,\nco-financed by the European Social Fund. The authors are indebted to Tilmann\nGneiting for his useful suggestions and remarks and to M\\'at\\'e Mile and \nMih\\'aly Sz\\H ucs from the HMS for providing the data.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzmkjv b/data_all_eng_slimpj/shuffled/split2/finalzzmkjv new file mode 100644 index 0000000000000000000000000000000000000000..9f7b73f8d1f8774651d9a195ea56546f898426a0 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzmkjv @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nRecently, several intriguing threads relating derived categories and\narithmetic geometry have emerged and motivated general structure questions \nfor $k$-linear triangulated categories for arbitrary\nfields $k$. Such exploration has yielded many nice applications as well as \nfurther enticing problems, see as a sampling \\cite{AKW, AAGZ, ADPZ, HT, Honigs, LiebMaulSnow}. \nMeanwhile over $\\mathbb{C}$, structural results for derived\ncategories seem to have deep implications for the underlying birational geometry, e.g.\n\\cite{AT, ABB, BB, BMMSS, KuznetsovCubic4fold, Vial}. Taking these together,\nderived categories become an important invariant for studying birational\ngeometry over a general field \\cite{AB}. A further benefit of this\nnoncommutative approach is direct utility for solving problems in algebraic $K$-theory,\nfor example \\cite{MerkPan}.\n\nWith such tantalizing ties, one would like a fertile testing ground for\nquestions. In this paper, we begin a systematic study of one such area:\nderived categories of arithmetic toric varieties. This area has the\nfollowing nice features:\n\n\\begin{itemize}\n \\item rationality issues are deep in general but tractable in examples,\n \\item robust tools already exist to investigate derived categories over\nthe separable closure,\n \\item and specific questions are often amenable to computational\nexperimentation.\n\\end{itemize}\n\nOne of the best tools for understanding a derived category is an\n\\emph{exceptional collection} consisting of \\emph{exceptional objects}.\nAs originally conceived in \\cite{Beilinson}, an exceptional object of a $k$-linear derived category is one whose endomorphism algebra\nis isomorphic to the base field $k$.\nWhen $k$ is not algebraically closed, this definition is too restrictive and\ninstead we use the existing notion: an object of $\\mathsf{D^b}(X)$ is \\emph{exceptional} if its endomorphism algebra is a division algebra. \nDetails are discussed in Section~\\ref{sect:galdesc} below.\n\nWe illustrate this more general notion for two arithmetic toric\nvarieties.\nThe real conic $X = \\{x^2 + y^2 + z^2 = 0\\} \\subset \\mathbb{P}^2_{{\\mathbb R}}$\nhas an exceptional collection $\\{ \\O, \\mathcal{F} \\}$, where\n$\\text{End}(\\mathcal{F})$ is isomorphic to the quaternion algebra\n$\\mathbb{H}$.\nOver ${\\mathbb C}$, we have $X_{\\mathbb C} \\simeq {\\mathbb P}^1_{\\mathbb C}$ and $\\mathcal{F}\\otimes_{{\\mathbb R}} {\\mathbb C} \\simeq \\O(1)^{\\oplus 2}$.\nAs another example, consider the Weil restriction $Y$ of ${\\mathbb P}^1_{\\mathbb C}$ over\n${\\mathbb R}$ (``${\\mathbb P}^1({\\mathbb C})$ viewed as an ${\\mathbb R}$-variety'').\nHere $Y$ has an exceptional collection $\\{ \\O, \\mathcal{G},\n\\mathcal{H}\\}$ where $\\text{End}(\\mathcal{G}) \\simeq {\\mathbb C}$ and\n$\\text{End}(\\mathcal{H})\n\\simeq {\\mathbb R}$.\nOver ${\\mathbb C}$, we have $Y\\otimes_{{\\mathbb R}} {\\mathbb C} \\simeq {\\mathbb P}^1 \\times {\\mathbb P}^1$\nwith\n$\\mathcal{G} \\otimes_{{\\mathbb R}} {\\mathbb C}\n\\simeq \\O(1,0) \\oplus \\O(0,1)$ and $\\mathcal{H} \\otimes_{{\\mathbb R}} {\\mathbb C}\\simeq \\O(1,1)$,\nwhere $\\O(i, j) = \\pi_1^*\\O(i) \\otimes \\pi_2 ^*\\O(j)$.\n\nA central question for derived categories of arithmetic toric varieties is the following: \n\n\\begin{question}\\label{quest:main}\n Let $X$ be a smooth projective toric variety over an arbitrary field. Does $X$ admit a full exceptional collection? If so, does $X$ possess a full exceptional collection of sheaves?\n\\end{question}\n \nOver an algebraically-closed field of characteristic zero, there is always a full exceptional collection of objects \\cite{Kawamata,Kawamata2} while the question of a full exceptional collection of sheaves is due to Orlov. \nMaking allowances for different language,\nthe question is also known to have a positive answer for\nSeveri-Brauer varieties~\\cite{AB,Bernardara},\nminimal toric surfaces~\\cite{BSS},\nand smooth projective toric varieties with absolute Picard rank at most\n$2$~\\cite{Yan}.\n\nIn this article, we provide further evidence for a positive answer to\nQuestion~\\ref{quest:main},\ntreating cases with low dimension or a high degree of symmetry.\n\n\\begin{thm} \\label{thm:examples}\n The following possess full exceptional collections of sheaves:\n \\begin{itemize}\n \\item smooth toric surfaces (Proposition~\\ref{prop:surface}),\n \\item smooth toric Fano 3-folds (Proposition~\\ref{prop:3fold}), \n \\item all forms of 43 of the 124 smooth split toric Fano 4-folds (Section~\\ref{sect:4fold}),\n \\item all forms of centrally symmetric toric Fano varieties (Corollary~\\ref{cor:centsym}), and\n \\item and all forms in characteristic zero of toric varieties corresponding to Weyl fans of root systems of type $A$ (Proposition~\\ref{prop:X(An)excpcoll}).\n \\end{itemize}\n\\end{thm}\n\nOur results leverage extant work in the algebraically closed case\nsuch as \\cite{Uehara} for 3-folds and\\cite{Prabhu} for 4-folds. We use Castravet and Tevelev's recently discovered exceptional collection for $X(A_n)$ \\cite{CT}. \nFor the centrally symmetric toric Fano varieties (which are\nproducts of ``generalized del Pezzo varieties'' and projective lines\n\\cite{VosKly}), we use an explicit exceptional\ncollection (see also \\cite{BDMdP}) closely related to the one found in \\cite{CT}.\nUp to a twist by a line bundle, the authors had independently\ndiscovered the exact same collection!\nThis suggests that symmetry imposes strong conditions on the\npossible exceptional collections, which paradoxically makes them easier to find.\n\nTo study arithmetic exceptional collections, we establish\nan effective Galois descent result for general exceptional collections.\nThis applies to general varieties, not just toric ones.\n\n\\begin{thm}[Theorem~\\ref{thm:descblocks}, Lemma~\\ref{lem:Galconverse}]\n Let $X$ be a $k$-scheme and $L\/k$ a $G$-Galois extension. Then $X_L$ admits a full (resp. strong) $G$-stable exceptional collection of objects of $\\mathsf{D^b}(X_L)$ (resp. sheaves, resp. vector bundles) if and only if $X$ admits a full (resp. strong) exceptional collection of objects of $\\mathsf{D^b}(X)$ (resp. sheaves, resp. vector bundles).\n\\end{thm}\n\nWe highlight one corollary of a positive answer to\nQuestion~\\ref{quest:main}. Arithmetic toric varieties are also studied\nin \\cite{MerkPan}, which focused on computing their algebraic $K$-groups\nvia decompositions in a certain noncommutative motivic category of\n$K_0$-correspondences. They showed that for an arithmetic toric\n$k$-variety $X$ with $G = \\text{Gal}(k^s\/k)$, the group $K_0(X_{k^s})$\nis a direct summand of a \\emph{permutation $G$-module} (there exists a\n${\\mathbb Z}$-basis permuted by $G$).\n\n\\begin{question}[Merkurjev-Panin \\cite{MerkPan}]\\label{quest:MP}\nLet $X$ be an arithmetic toric variety over $k$ and $G = \\operatorname{Gal}(k^s\/k)$. Is $K_0(X_{k^s})$ always a permutation $G$-module?\n\\end{question}\n\nQuestion~\\ref{quest:main} can be viewed as a categorification of Question~\\ref{quest:MP} as any such exceptional collection over $k$ immediately gives a permutation basis. \n\nIn order to show that every toric variety has a full exceptional collection\nover ${\\mathbb C}$, the main tool used in \\cite{Kawamata,Kawamata2} was the\nminimal model program (MMP) in birational geometry.\nThe basic building blocks are toric stacks with Picard rank one,\nwhich always have full strong exceptional collections of line bundles.\nIndeed, runs of the MMP can be leveraged to effectively produce\nexceptional collections \\cite{BFK}.\n\nOver a non-closed field, one hopes to use the Galois-equivariant MMP,\nbut the situation is more complicated.\nThe most basic building blocks in this framework are those varieties $X$\nwhich have $\\rho^G = \\text{rank} (\\text{Pic}(X)^G )= 1$.\nBased on the results above and the hope of using the MMP in the\narithmetic situation, we ask the following\nquestion in the vein of \\cite{King,BH,CM-RFrob}:\n\n\\begin{question}\\label{quest:invpicrank1}\nLet $X$ be a smooth toric $k$-variety and $L\/k$ a $G$-Galois splitting\nfield. If $\\operatorname{Pic}(X_L)^G$ is of rank 1, does $X_L$ possess\na full strong $G$-stable exceptional collection consisting of line\nbundles?\n\\end{question}\n\n\\subsection*{Acknowledgements}\n\nThe first author was partially supported by NSF DMS-1501813. He would\nalso like to thank the Institute for Advanced Study for providing a\nwonderful research environment. These ideas were developed during his\nmembership. The first author benefited from discussions with Alicia\nLamarche.\nThe second author was partially supported by NSA grant\nH98230-16-1-0309.\nThe third author would like to thank the Hausdorff Institute for their\nhospitality and lively research environment during the \\emph{K-theory\nand related fields} trimester program. A large portion of this\nmanuscript was drafted during his time in Bonn.\nAll authors also thank Fei Xie for pointing out that, due to an editing\nerror, in a previous version of this paper,\nProposition~\\ref{prop:surface} stated that all smooth toric surfaces\nhave strong collections of vector bundles instead of a not-necessarily\nstrong collection of sheaves as was proven.\nThe authors would also like to thank an anonymous referee for useful\ncomments.\n\n\n\\subsection*{Organization}\n\nSection~\\ref{sect:galdesc} treats Galois descent of exceptional\ncollections consisting of objects on (possibly non-toric) varieties. In\nSection~\\ref{section:toric}, we recall appropriate definitions of\narithmetic toric varieties and establish additional descent results\nwhich are specific to toric varieties. In\nSection~\\ref{section:minimal}, we consider a range of examples. We begin by treating toric surfaces, followed by toric Fano 3-folds. For toric Fano 4-folds, we give partial results. We conclude by investigating the class of centrally symmetric toric Fano varieties, including the generalized del Pezzo varieties, and handling toric varieties associated to root systems of type $A$. \n\n\n\\subsection*{Notation} Throughout, $k$ denotes an arbitrary field and\n$k^s$ a separable closure. A \\emph{variety} is a geometrically integral\nseparated scheme of finite type over $k$. All our schemes will be\nquasi-compact and quasi-separated. For a $k$-scheme $X$ and field\nextension $L\/k$, we write $X_L : = X \\times _{\\operatorname{Spec} k} \\operatorname{Spec} L$. If $A$\nis a $k$-algebra, we write $A_L = A\\otimes _{k} L$.\nWe use $\\mathsf{D^b}(X)$ to denote the bounded derived category\n$\\mathsf{D^b}(\\text{Coh}(X))$. For an $\\O_X$-algebra $A$, we write\n$\\mathsf{D^b}(A)$ for the bounded derived category of complexes of\n$A$-modules which are coherent $\\O_X$-modules.\n\n\n\\section{Galois descent and exceptional collections}\\label{sect:galdesc}\n\nIn this section we develop Galois descent for exceptional collections (in a generalized sense). We begin by recalling some definitions and conventions concerning structure theory of derived categories of schemes. We then give our main descent results for $G$-stable exceptional collections (Theorem~\\ref{thm:descblocks}). We complete the section by collecting a few useful consequences to be used in the sequel.\n\n\n\\subsection{Exceptional collections}\n\nWe give some conventions for semiorthogonal decompositions of derived categories and in particular exceptional collections. Such collections have been widely studied over algebraically closed fields but have recently been treated in more generality \\cite{AAGZ, AB, ABB, Bernardara, BSS, Elagin, Xie, Yan}. We refer the reader to Remarks~\\ref{rem:descSOD} and \\ref{rem:elagin} for added details on some of these results. \n\nFor a triangulated category $\\mathsf{T}$, we use the standard notation $\\text{Ext}^n_{\\mathsf{T}}(A, B) = \\hom _{\\mathsf{T}} (A, B[n])$. For objects $A, B $ of $\\mathsf{D^b}(X)$, we use $\\text{End}_X(A)$ and $\\text{Ext}_X^n(A, B)$ to denote $\\text{End}_{\\mathsf{D^b}(X)}(A)$ and $\\text{Ext}^n_{\\mathsf{D^b}(X)}(A, B)$, respectively.\n\n\\begin{defn}[see \\cite{BK}]\nLet $\\mathsf{T}$ be a triangulated category. A full triangulated subcategory of $\\mathsf{T}$ is \\emph{admissible} if its inclusion functor admits left and right adjoints. A \\emph{semiorthogonal decomposition} of $\\mathsf{T}$ is a sequence of admissible subcategories $\\mathsf{C}_1, ..., \\mathsf{C}_s$ such that \n\\begin{enumerate}\n\\item $\\hom _{\\mathsf{T}}(A_i, A_j) = 0$ for all $A_i \\in \\operatorname{Ob} (\\mathsf{C}_i)$, $A_j \\in \\operatorname{Ob} (\\mathsf{C}_j)$ whenever $i > j$.\n\\item For each object $T$ of $\\mathsf{T},$ there is a sequence of morphisms $0 = T_s \\to \\cdots \\to T_0 = T$ such that the cone of $T_i \\to T_{i-1}$ is an object of $\\mathsf{C}_i$ for all $i = 1,..., s$.\n\\end{enumerate}\nWe use $\\mathsf{T} = \\langle \\mathsf{C}_1,..., \\mathsf{C}_s\\rangle$ to denote such a decomposition.\n\\end{defn}\n\nParticularly nice examples of semiorthogonal decompositions are given by exceptional collections, the study of which goes back to Beilinson \\cite{Beilinson}.\n\n\\begin{defn}\\label{def:exceptional}\nLet $\\mathsf{T}$ be a $k$-linear triangulated category. An object $E$ in $\\mathsf{T}$ is \\emph{exceptional} if the following conditions hold:\n\\begin{enumerate}\n\\item $\\text{End}_{\\mathsf{T}}(E)$ is a division $k$-algebra.\n\\item $\\text{Ext}^n_{\\mathsf{T}}(E, E) = 0$ for $n \\neq 0$.\n\\end{enumerate}\nA totally ordered set $\\mathsf{E} = \\{E_1, ..., E_s\\}$ of exceptional objects is an \\emph{exceptional collection} if $\\text{Ext}^n_{\\mathsf{T}}(E_i, E_j) = 0$ for all integers $n$ whenever $i >j$. An exceptional collection is $\\emph{full}$ if it generates $\\mathsf{T}$, i.e., the smallest thick subcategory of $\\mathsf{T}$ containing $\\mathsf{E}$ is all of $\\mathsf{D^b}(X)$. An exceptional collection is \\emph{strong} if $\\text{Ext}^n_{\\mathsf{T}}(E_i, E_j) = 0$ whenever $n \\neq 0$. An \\emph{exceptional block} is an exceptional collection $\\mathsf{E} = \\{ E_1, ..., E_s\\}$ such that $\\text{Ext}^n_{\\mathsf{T}}(E_i, E_j) = 0$ for every $n$ whenever $i \\neq j$. Given an exceptional collection $\\mathsf{E} = \\{E_1, ..., E_s\\}$, we denote by $\\langle \\mathsf{E} \\rangle$ the category generated by the objects $E_i$.\n\\end{defn}\n\n\\begin{rem}\nOur notion of exceptional object generalizes the classical one, where item $(1)$ of Definition~\\ref{def:exceptional} is replaced by: $\\text{End}_{\\mathsf{T}}(E) = k$ \\cite[$\\S$2]{Bondal}. Over algebraically or separably closed fields, these definitions agree. Over non-closed fields, the classical definition is too restrictive to allow for the use of interesting arithmetic invariants in the study of exceptional collections on twisted forms, e.g., Brauer classes.\n\\end{rem}\n\n\\begin{prop}[Thm. 3.2 \\cite{Bondal}]\\label{prop:exctosod}\nLet $X$ be a $k$-scheme with exceptional collection $\\{E_1,..., E_s\\}$. If $\\mathscr{E}_i $ is the category generated by $E_i$, there is a semiorthogonal decomposition $\\mathsf{D^b}(X) = \\langle \\mathscr{E}_1,..., \\mathscr{E}_s, \\mathsf{A} \\rangle$, where $\\mathsf{A}$ is the full subcategory with objects $A$ such that $\\operatorname{Hom}_X(A, E_i) = 0$ for all $i$.\n\\end{prop}\n\n\\begin{rem}\n The reference assumes smoothness and projectivity but the conclusion is independent of this. Note further that if $X$ admits a full exceptional collection then it is automatically smooth and proper by \\cite[Propositions 3.30 and 3.31]{OrlovNCstuff}.\n\\end{rem}\n\nThe existence of an exceptional collection on a scheme $X$ provides a means of studying derived geometry of $X$ in purely algebraic terms. Indeed, in such a situation, one may identify an ``underlying\" $k$-algebra which is derived equivalent to $X$. For exceptional blocks, one obtains a similar but slightly stronger fact.\n\n\\begin{prop}[Thm. 6.2 \\cite{Bondal}]\\label{thm:tilting}\nLet $X$ be a smooth projective $k$-scheme and let $\\{E_1,..., E_n\\}$ be a full strong exceptional collection on $\\mathsf{D^b}(X)$. Let $\\mathcal{E} = \\bigoplus E_i$ and $A = \\operatorname{End}(\\mathcal{E})$. Then $\\mathsf{R} \\hom _{\\mathsf{D^b}(X)}(\\mathcal{E}, -) : \\mathsf{D^b}(X) \\to \\mathsf{D^b}(A)$ is a $k$-linear equivalence.\n\\end{prop}\n\n\\begin{prop}\nIf $\\mathsf{E} = \\{E_1,..., E_s\\}$ is an exceptional block with $\\operatorname{End}(E_i) = D_i$, there is a $k$-algebra isomorphism $\\operatorname{End}(\\bigoplus E_i) \\simeq D_1 \\times \\cdots \\times D_s$, and hence a $k$-linear equivalence $\\langle \\mathsf{E} \\rangle \\simeq \\mathsf{D^b}(D_1 \\times \\cdots \\times D_n)$.\n\\end{prop}\n\nThe object $\\mathcal{E} = \\oplus E_i$ of Proposition~\\ref{thm:tilting} is usually called a \\emph{tilting object}. If each $E_i$ is a sheaf (resp. vector bundle), then $E$ is called a \\emph{tilting sheaf} (resp. \\emph{tilting bundle}). Until recently, the theory of tilting objects has served as the main tool for extending the study of exceptional collections to non-closed fields. The results above show that any exceptional collection gives rise to both a tilting object and a semiorthogonal decomposition, and thus the admission of such a collection is a particularly special property of a given triangulated category. Our aim in the following subsection is to extend descent results for semiorthogonal decompositions and tilting objects to (our more general notion of) exceptional collections. We give a formal definition of tilting object for completeness.\n\n\\begin{defn}\nA \\emph{tilting object} for a $k$-scheme $X$ is an object $\\mathcal{E}$ of $\\mathsf{D^b}(X)$ which satisfies the following conditions:\n\\begin{enumerate}\n\\item $\\text{Ext}_X ^n (\\mathcal{E}, \\mathcal{E}) = 0$ for $n > 0$.\n\n\\item $\\mathcal{E}$ generates $\\mathsf{D^b}(X)$.\n\\end{enumerate}\n\\end{defn}\n\n\\begin{rem}[$K$-theory and motivic decompositions]\n\nExceptional collections have a particularly interesting manifestation in the realm of noncommutative motives. Indeed, an exceptional collection $\\{E_1,..., E_s\\}$ on a smooth projective variety $X$ yields a decomposition $U(X) \\simeq \\bigoplus _i U(D_i)$ of its corresponding universal additive invariant \\cite[$\\S$2.3]{Tabuada}, where $D_i = \\text{End}(E_i)$. This defines a motivic decomposition by viewing $X$ as an object in the Merkurjev-Panin category of $K$-motives \\cite{MerkPan} or Kontsevich's category of noncommutative Chow motives \\cite[Thm. 6.10]{Tabuada2} via its associated dg-category of perfect complexes.\n\nOne nice consequence is that this decomposition is detected by algebraic $K$-groups \\cite[Prop. 1.10]{AB} in addition to a slew of other additive invariants in the sense of Tabuada \\cite[$\\S$2.2]{Tabuada}. Such invariants include algebraic $K$-theory with coefficients, homotopy $K$-theory, \\'{e}tale $K$-theory, (topological) Hochschild homology, and (topological) cyclic homology.\n\\end{rem}\n\n\n\\subsection{Galois descent} We develop Galois descent for exceptional\ncollections consisting of objects in the derived category\n$\\mathsf{D^b}(X)$ of a (smooth projective) variety $X$. Throughout this\nsection, pushforward and pullback functors are understood to be derived.\nFor a $k$-scheme $X$ and finite Galois extension $L\/k$, any element $g\n\\in \\text{Gal}(L\/k)$ defines a morphism of $k$-schemes $g: X_L \\to X_L$\nwhich in turn defines the functor $g^*: \\mathsf{D^b}(X_L) \\to \\mathsf{D^b}(X_L)$.\n\n\\begin{defn}\nLet $X$ be a scheme with an action of a group $G$. A $G$-\\emph{stable\nexceptional collection} on $X$ is an exceptional collection $\\mathsf{E}\n= \\{E_1, ..., E_s\\}$ of objects in $\\mathsf{D^b}(X)$ such that\nfor all $g \\in G$ and $1 \\leq i \\leq s$ there exists $E \\in \\mathsf{E}$\nsuch that $g^*E_i \\simeq E$.\nWe say a $G$-stable exceptional collection $\\mathsf{E}$\nis a \\emph{$G$-orbit} if, for every pair of objects\n$E,E' \\in \\mathsf{E}$, there exists a $g \\in G$ such that\n$g^*E \\simeq E'$.\n\\end{defn}\n\n\\begin{rem}\\label{rem:invariant}\nA simple example of a $G$-stable exceptional collection is a\n$G$-\\emph{invariant} exceptional collection, i.e., an exceptional\ncollection $\\{E_1,..., E_s\\}$ such that $g^*E_i \\simeq E_i$ for all $1 \\leq i \\leq s$. It is often the case that toric varieties admit exceptional collections consisting of line bundles. If it is also the case that a group $G$ acts trivially on $\\text{Pic}(X)$, such a collection is automatically $G$-invariant, and hence $G$-stable (see Lemma~\\ref{lem:picinv}).\n\\end{rem}\n\n\\begin{lem}\\label{lem:collectiontoblocks}\nAny $G$-stable exceptional collection may be written as a collection of $G$-stable exceptional blocks (after possibly reordering).\n\\end{lem}\n\n\\begin{proof}\nThe decomposition of a $G$-stable exceptional collection into its $G$-orbits gives the desired exceptional blocks. Let $\\mathsf{E}$ be a $G$-stable exceptional collection and for elements $E, E' \\in \\mathsf{E}$, we write $E \\leadsto E'$ if $\\text{Ext}^n(E, E') \\neq 0$ for some $n$.\n\n Let $\\mathsf{A} \\subset \\mathsf{E}$ be a $G$-orbit. To see that\n$\\mathsf{A}$ is an exceptional block, suppose that $E \\leadsto E'$ for\n$E, E' \\in \\mathsf{A}$. Since $\\mathsf{A}$ is a $G$-orbit, $E' \\simeq g^*\nE$ for some $g \\in G$. Thus, $E \\leadsto g^* E$, and acting again by\n$g$, we have $g^*E \\leadsto (g^2)^*E$. Since $A$ is finite, we have $E\n\\leadsto g^*E \\leadsto \\cdots \\leadsto (g^s)^*E \\leadsto E$ for some\npositive integer $s$. Thus,\nthere is no ordering of the elements of $\\mathsf{A}$\nsuch that they form a subset of an exceptional collection --- a\ncontradiction.\n\nIf $\\mathsf{B}$ is another $G$-orbit (distinct from $\\mathsf{A}$), we\nwould like to see that these blocks can be ordered to form an\nexceptional collection. We claim that for any $E \\in \\mathsf{A}$ and $F\n\\in \\mathsf{B}$, one has $E \\leadsto F$ only if \n$\\mathsf{A}$ precedes\n$\\mathsf{B}$ in the collection $\\mathsf{E}$ (i.e., $\\text{Ext}^n(B, A) =\n0$ for all $n$ and all $A \\in \\mathsf{A}$, $B \\in \\mathsf{B}$).\nTo see this, assume that $E \\leadsto\nF$ and $F \\leadsto E'$ for some $E' \\in \\mathsf{A}.$ As $\\mathsf{A}$ is\na $G$-orbit, $E' \\simeq g^*E$ for some $g \\in G$. Hence, just as above,\nwe have a sequence $E \\leadsto F \\leadsto g^*E \\leadsto g^* F\n\\leadsto \\cdots \\leadsto (g^s)^* F \\leadsto E.$ Thus, there is no\nordering of the elements of $\\mathsf{A}$ and $\\mathsf{B}$ which forms an\nexceptional collection, contradicting the exceptionality of\n$\\mathsf{E}$.\n\\end{proof}\n\n\\begin{lem}\\label{lem:galconj}\nLet $X$ be a Noetherian $k$-scheme, $L\/k$ a finite Galois extension with group $G$, and $\\pi: X_L \\to X$ the natural projection map. For any object $M $ in $\\mathsf{D^b}(X_L)$ there is a natural isomorphism $\\displaystyle \\pi^* \\pi_*( M) \\simeq \\bigoplus _{g\\in G} g^*M.$ \n\\end{lem}\n\n\\begin{proof}\n\nAs $\\pi$ is flat and affine, every coherent sheaf on $X$ is acyclic for $\\pi^\\ast$ and every coherent sheaf on $X_L$ is acyclic for $\\pi_\\ast$. Hence, the derived functors coincide with the application of $\\pi^\\ast$ or $\\pi_\\ast$ component-wise to a complex. Thus, it suffices to establish a natural isomorphism at the level of coherent sheaves. \n\nFor any object $M$ of $\\text{Coh}(X_L)$, we have $ \\pi_*M \\simeq \\pi_*\ng^* M $, and adjunction yields a natural transformation $ \\pi^* \\pi_*\n\\to g^*$. Summing over all $g \\in G$ provides the transformation\n$\\alpha: \\pi^* \\pi_* \\to \\oplus g^* $. We show this is an\nisomorphism.\n\nIt suffices to check that $\\alpha$ is an isomorphism on any affine patch, $\\operatorname{Spec} R$, of $X$. Passing to modules, we abuse notation and let $M$ be a finitely-generated module over $R_L = R \\otimes_k L$. Choose a presentation of $M$\n\\begin{displaymath}\n R_L^{\\oplus m} \\to R_L^{\\oplus n} \\to M \\to 0\n\\end{displaymath}\nand evaluate $\\alpha$ on the sequence to get the commutative diagram \n\\begin{center}\n \\begin{tikzpicture}\n \\node at (-4,0.75) (pr1) {$R^{\\oplus m} \\otimes_k \\left( L \\otimes_k L \\right)$};\n \\node at (-4,-0.75) (gr1) {$R^{\\oplus m} \\otimes_k \\left( \\oplus_g \\Gamma_g(L) \\right)$};\n \\node at (0,0.75) (pr0) {$R^{\\oplus n} \\otimes_k \\left( L \\otimes_k L\\right) $};\n \\node at (0,-0.75) (gr0) {$R^{\\oplus m} \\otimes_k \\left( \\oplus_g \\Gamma_g(L)\\right) $};\n \\node at (3,0.75) (pm) {$M \\otimes_R R_L$};\n \\node at (3,-0.75) (gm) {$\\oplus_g g^\\ast M$};\n \\node at (5,0.75) (p0) {$0$};\n \\node at (5,-0.75) (g0) {$0$};\n \\draw[->] (pr1) -- (pr0);\n \\draw[->] (pr0) -- (pm);\n \\draw[->] (pm) -- (p0);\n \\draw[->] (gm) -- (g0);\n \\draw[->] (gr1) -- (gr0);\n \\draw[->] (gr0) -- (gm);\n \\draw[->] (pr1) -- node[left] {$\\alpha_{R^{\\oplus m}}$} (gr1);\n \\draw[->] (pr0) -- node[left] {$\\alpha_{R^{\\oplus n}}$} (gr0);\n \\draw[->] (pm) -- node[left] {$\\alpha_M$} (gm);\n \\end{tikzpicture}\n\\end{center}\nwhere $\\Gamma_g(L)$ denotes the graph of $g$ in $L \\otimes_k L$. The left and middle maps are isomorphisms, so the right map must also be an isomorphism. \n\\end{proof}\n\n\\begin{prop}[Descent for orbits]\\label{prop:objblockdescent}\nLet $X$ be a $k$-scheme, $L\/k$ a finite $G$-Galois extension,\nand $\\pi: X_L \\to X$ the natural projection map. If $\\mathsf{E} = \\{E_1,\n\\ldots, E_s\\}$ is a $G$-orbit forming an exceptional collection on $X_L$,\nand if $E$ is any element of $\\mathsf{E}$, then there is an exceptional\nobject $F$ in $\\mathsf{D^b}(X)$ such that $\\pi_*E \\simeq F^{\\oplus m}$\nand $\\pi^*F$ generates the category $ \\langle \\mathsf{E} \\rangle$.\n\\end{prop}\n\n\\begin{proof}\nBy Lemma~\\ref{lem:collectiontoblocks}, exceptional $G$-orbits are\ncompletely orthogonal (and by definition carry a transitive action of\n$G$), which will be used throughout the proof. Fix an element $E \\in\n\\mathsf{E}$, so that $E = E_i$ for some $i$. Lemma~\\ref{lem:galconj}\ngives $$\\pi^*\\pi_*E \\simeq \\bigoplus _{g \\in G} g^*E$$ We claim that\n$\\text{End}(\\pi_*E)$ is a matrix algebra over a division algebra, and\nprove this by first showing that it is semisimple. Indeed, using\n$\\text{End}_X(M) \\otimes _k L \\simeq \\text{End}_{X_L} (\\pi^*M)$ for any $M\n\\in \\mathsf{D^b}(X)$ \\cite[Rem. 2.1]{AB}, we have\n\\[ \\text{End}_X(\\pi_*E) \\otimes _k L \\simeq\n\\text{End}_{X_L}(\\pi^* \\pi_* E) \\simeq\n\\text{End}_{X_L}\\left(\\bigoplus _{ g\\in G} g^*E\\right).\n\\] Each $g^*E$ is exceptional so that\n$\\text{End}_{X_L}(g^*E) =: D_g$ is a division algebra for each element $g\n\\in G$. Let $H \\leq G$ be the subgroup consisting of elements $h$\nsatisfying $h^*E \\simeq E$. For any system of coset representatives $g\n\\in G\/H$, we have $\\text{End}_X(\\pi_*E)_L \\simeq \\prod _{g \\in\nG\/H}M_m(D_g)$, where $m = |H|$. This product of matrix algebras over\ndivision algebras is semisimple, i.e., the Jacobson radical\n$\\text{rad}(\\text{End}_X(\\pi_*E)_L) = 0$. We then have $0 =\n\\text{rad}(\\text{End}_X(\\pi_*E)_L) = \\text{rad}(\\text{End}_X(\\pi_*E))_L$\nby ~\\cite[Thm.~1]{Amitsur}, and hence $\\text{rad}(\\text{End}_X(\\pi_*E))\n= 0$. Thus, $\\text{End}_X(\\pi_*E)$ is semisimple and so must also\nbe a product of matrix algebras over division algebras by the\nArtin-Wedderburn Theorem. \n\nLet $Z$ be the center of $\\text{End}_X(\\pi_*E)$ and $Z_L$ the center of $\\text{End}_X(\\pi_*E)_L$.\nNote that $Z$ is an \\'{e}tale $k$-algebra, and to show that $\\text{End}(\\pi_*E)$ is a matrix algebra, it suffices to show that $Z$ has no zero divisors, and is thus a field. There is an embedding $Z \\hookrightarrow Z_L = \\prod_{g \\in G\/H} L_g$, where $L_g$ is the center of the division algebra $D_g$. The transitive action of $G$ on $\\{E_1,..., E_s\\}$ implies that $G$ acts transitively on a basis of $Z_L$, so that $Z = (Z_L)^G$ has no zero divisors.\n\nWe produce the object $F$ using the identification $\\text{End}_X(\\pi_*E)\n\\simeq M_n(D)$, where $D$ is a division algebra.\nLet $e_i = e_{ii}$ denote the usual idempotent matrices, so that\n$\\{e_i\\}$ is a complete set of primitive orthogonal idempotents.\nNotice that $F_i:= \\operatorname{Im} (e_i)$ is a simple $\\text{End}_X(\\pi_*E)$-submodule of $\\pi_*E$ for each $i$, and hence $F_i \\simeq F_j$ for each $i, j$, and $\\text{End}_X(F_i) \\simeq D$. Define $F = \\operatorname{Im} (e_1) \\subset \\pi_*E$, included as a direct summand. We note that $\\pi_*E \\simeq \\bigoplus F_i \\simeq F^{\\oplus n}$. \n\nWe now show that $F$ is an exceptional object on $X$. As stated above, $\\text{End}_X(F)$ is a division algebra, so it suffices to show that $\\text{Ext}^n_X(F, F) = 0$ for $ n \\neq 0$. Using Lemma~\\ref{lem:galconj} and $(\\pi^*, \\pi_*)$-adjunction, we have $$\\text{Ext}^n_X(\\pi_*E, \\pi_*E) = \\bigoplus _{g\\in G} \\text{Ext}^n _{X_L}(g^*E, E).$$ For $n \\neq 0$, each summand of the right-hand side is 0, which follows from the mutual orthogonality of the exceptional block $\\mathsf{E}$ (when $g^*E \\not\\simeq E$) and from exceptionality of $E$ (when $g^*E \\simeq E$). Since $F$ is a direct summand of $\\pi_*E$, it follows that $\\text{Ext}^n_X(F, F)$ is a summand of $\\text{Ext}^n_X(\\pi_*E, \\pi_*E) = 0$.\n\nLastly, we show that $\\pi^*F$ generates the category $\\langle \\mathsf{E}\n\\rangle$. Since $ F^{\\oplus m} \\simeq \\pi_*E$, extending scalars to $L$\ngives $ (\\pi^*F)^{\\oplus m} = \\pi^*(F^{\\oplus m}) \\simeq \\pi^*\\pi_*E\n\\simeq \\bigoplus g^*E$. Thus, $$\\langle \\pi^*F \\rangle = \\langle (\\pi ^*F )^{\\oplus m} \\rangle = \\langle \\bigoplus g^* E \\rangle = \\langle g^*E \\rangle _{g \\in G} = \\langle \\mathsf{E} \\rangle.$$\\end{proof}\n\n\n\\begin{rem}\\label{rem:descSOD}\nProposition~\\ref{prop:objblockdescent} provides a very specific case of descent\nfor triangulated categories, the main advantage of which is that it\nallows one to identify a specific exceptional object that base extends\nto the given orbit.\nMoreover, a $G$-orbit which forms an exceptional collection consisting of vector bundles (resp. sheaves) descends to an exceptional collection consisting of vector bundles (resp. sheaves). Compare to the following descent result for semiorthogonal decompositions, which generalizes \\cite[Cor. 2.15]{Toen}. Although this result is useful for descending semiorthogonal decompositions, it does not identify exceptional objects.\n\n\\begin{prop}[Prop. 2.12, \\cite{AB}]\\label{prop:ABdesc}\nLet $\\mathsf{T}$ be a $k$-linear triangulated category such that $\\mathsf{T}_{k^s}$ is $k^s$-equivalent to $\\mathsf{D^b}(k^s, (k^s)^n)$. Then there exists an \\'{e}tale algebra $K$ of degree $n$ over $k$, an Azumaya algebra $A$ over $K$, and a $k$-linear equivalence $\\mathsf{T} \\simeq \\mathsf{D^b}(K\/k, A)$.\n\\end{prop}\n\n\\noindent Let $X$, $\\mathsf{E}$, and $F$ be as in Proposition~\\ref{prop:objblockdescent}, and note that taking $\\mathsf{T} = \\langle F \\rangle$, we have $\\mathsf{T}_{k^s} = \\langle \\pi^* F \\rangle_{k^s} = \\langle \\mathsf{E} \\rangle _{k^s}$. Since $\\mathsf{E} = \\{ g^*E\\}_{g \\in G}$ is a full exceptional collection for $\\langle \\mathsf{E} \\rangle$, the bundle $\\mathcal{E} : = \\oplus (g^*E)_{k^s}$ is a tilting object for $\\langle \\mathsf{E}\\rangle_{k^s}$. This defines an equivalence $$ \\mathsf{T}_{k^s} \\simeq \\langle \\mathsf{E} \\rangle_{k^s} \\simeq \\mathsf{D^b}(k^s, \\text{End}(\\mathcal{E})) = \\mathsf{D^b}(k^s, (k^s)^n).$$ Proposition~\\ref{prop:ABdesc} yields an \\'{e}tale extension $K\/k$, an Azumaya $K$-algebra $A$, and an equivalence $\\mathsf{T} \\simeq \\mathsf{D^b}(K\/k, A)$. In this case, since $\\mathsf{T} = \\langle F \\rangle$, we see that $A = \\text{End}_X(F)$ is an Azumaya algebra over its center $Z$ (using the notation found in the proof of Proposition~\\ref{prop:objblockdescent}), which is simply a field extension of $k$.\n\\end{rem}\n\n\\begin{thm}[Descent for stable collections]\\label{thm:descblocks}\nLet $X$ be a $k$-scheme, $L\/k$ a finite $G$-Galois extension, and $\\pi: X_L \\to X$ the natural projection map. If $X_L$ admits a full $G$-stable exceptional collection $\\mathsf{E}$ of objects of $\\mathsf{D^b}(X_L)$, then $X$ admits a full exceptional collection $\\mathsf{F}$ of objects of $\\mathsf{D^b}(X)$. If $\\mathsf{E}$ is strong, so is $\\mathsf{F}$. If the elements of $\\mathsf{E}$ are vector bundles (resp. sheaves), the elements of $\\mathsf{F}$ are vector bundles (resp. sheaves).\n\\end{thm}\n\n\\begin{proof}\n\nBy Lemma~\\ref{lem:collectiontoblocks}, we may write $\\mathsf{E} =\n\\{\\mathsf{E}^1,..., \\mathsf{E}^s\\}$ as a collection of $G$-stable\nblocks, where each block is given by a $G$-orbit.\nProposition~\\ref{prop:objblockdescent} then associates to each block\n$\\mathsf{E}^i$ an exceptional object $F_i$ on $X$, and we show that\n$\\mathsf{F} = \\{F_1,..., F_s\\}$ is a full exceptional collection on $X$.\nWe first show that $\\text{Ext}^n_{X}(F_i, F_j) = 0$ for all $n$ whenever\n$i > j$. Let $E^i$ and $E^j$ be elements of the collections\n$\\mathsf{E}^i$ and $\\mathsf{E}^j$, respectively. We then have\n\\begin{equation}\n\\text{Ext}^n_X(\\pi_*E^i, \\pi_*E^j) \\simeq\n\\bigoplus _{g\\in G} \\text{Ext}^n _{X_L}(g^*E^i, E^j).\n\\label{eq:1}\n\\end{equation}\nSince\n$E^i$ and $E^j$ are elements of the exceptional collection $\\mathsf{E}$\nand $i < j$, each summand is 0 for all $n$, so that\n$\\text{Ext}_X^n(\\pi_*E^i, \\pi_*E^j) = 0$ for all $n$. The objects $F_i$\nand $F_j$ are direct summands of $\\pi_*E^i$ and $\\pi_*E^j$,\nrespectively, and it follows that $\\text{Ext}^n_X(F_i, F_j) = 0$ for all\n$n$.\n\nBy Proposition~\\ref{prop:exctosod}, the exceptional collection $\\{ F_1,\n\\ldots, F_s\\}$ yields a semiorthogonal decomposition\n\\[\n\\mathsf{D^b}(X) = \\langle \\mathscr{F}_1, \\ldots, \\mathscr{F}_s, \\mathsf{A}\\rangle,\n\\]\nwhere\n$\\mathscr{F}_i = \\langle F_i \\rangle$ and $\\mathsf{A}$ is the full\nsubcategory of objects $A$ with $\\text{Hom}_{\\mathsf{D^b}(X)}(A, F_i) =\n0$ for all $i$. In particular, the subcategories $\\mathscr{F}_i$ are\nadmissible. Extending scalars to $L$, we have $(\\mathscr{F}_i)_L =\n\\langle \\mathsf{E}^i\\rangle$, as both categories are generated by\n$\\pi^*F$ by Proposition~\\ref{prop:objblockdescent}. The exceptional collection\n$\\mathsf{E} =\\{\\mathsf{E}^1,\\ldots, \\mathsf{E}^s \\}$ is full, hence we have\na semiorthogonal decomposition $$ \\mathsf{D^b}(X_L) = \\langle\n(\\mathscr{F}_1)_L, \\ldots, (\\mathscr{F}_s)_L \\rangle.$$ Since our\nadmissible subcategories $\\mathscr{F}_i$ base extend to a semiorthogonal\ndecomposition, \\cite[Lem. 2.9]{ABB} gives a semiorthogonal decomposition\n$\\mathsf{D^b}(X) = \\langle \\mathscr{F}_1, \\ldots, \\mathscr{F}_s \\rangle$.\nIn particular, the collection $\\{F_1, \\ldots, F_s\\}$ generates\n$\\mathsf{D^b}(X)$, so this collection is full.\n\nIf $\\mathsf{E}$ is strong, the right side of \\eqref{eq:1} vanishes for $i \\neq j$ (and any $n$). It follows exactly as above that $\\text{Ext}^n_{X}(F_i, F_j) = 0$ for all $n$ when $i \\neq j$, so that $\\mathsf{F}$ is strong.\n\\end{proof}\n\n\\begin{rem}\\label{rem:elagin}\nSimilar descent results for collections of sheaves are given by Elagin in the algebraically closed case (i.e., $k = \\bar{k}$) using the framework of equivariant exceptional collections in equivariant derived categories \\cite{Elagin}. Indeed, for a variety $X$ with an action of a finite group $G$ and a $G$-invariant exceptional collection (see Remark~\\ref{rem:invariant}) consisting of sheaves, this descent result is given in terms of $\\alpha$-twisted representations of $G$ (see Theorem 2.2 of loc. cit.). For a $G$-stable exceptional collection consisting of sheaves, results are in terms of coinduced twisted representations of $G$ (see Theorem 2.3 of loc. cit.).\n\\end{rem}\n\n\\begin{lem} \\label{lem:Galconverse}\nLet $X$ be a $k$-scheme and $L\/k$ a finite $G$-Galois extension. If $X$ admits an exceptional collection, then $X_L$ admits a $G$-stable exceptional collection.\n\\end{lem}\n\n\\begin{proof}\n Let $E_1,\\ldots,E_s$ be the exceptional collection on $X$ and consider $\\pi^\\ast E_1, \\ldots, \\pi^\\ast E_s$ on $X_L$. To compute morphisms, we note that \n \\begin{displaymath}\n \\operatorname{Hom}_{X_L} (\\pi^\\ast E_i , \\pi^\\ast E_j ) = \\operatorname{Hom}_X( E_i, \\pi_\\ast \\pi^\\ast E_j) = \\operatorname{Hom}_X(E_i, E_j \\otimes_k L) = \\operatorname{Hom}_X(E_i,E_j) \\otimes_k L.\n \\end{displaymath}\n This vanishes if $j > i$. Let $A_i = \\operatorname{Hom}_X(E_i, E_i)$. We can split $A_i \\otimes_k L$ as a product of matrix algebras over division algebras $A_{i,j} = M_{N_{i,j}}(D_{i,j})$ and correspondingly decompose\n \\begin{displaymath}\n \\pi^\\ast E_i = \\bigoplus F_{i,j}^{N_{i,j}}\n \\end{displaymath}\n with \n \\begin{displaymath}\n \\operatorname{Hom}_{X_L}( F_{i,j} , F_{i,j} ) = D_{i,j}.\n \\end{displaymath}\n Note that $F_{i,j}$ and $F_{i,j^\\prime}$ are orthogonal for $j \\not = j^\\prime$. Thus, we have an exceptional collection.\n\\end{proof}\n\n\\begin{lem}\\label{lem:picinv}\nLet $X$ be a $k$-scheme and $L\/k$ a finite extension with Galois group $G$. If $G$ acts trivially on $\\operatorname{Pic}(X_L)$ and $X_L$ admits an exceptional collection of line bundles, then $X$ admits an exceptional collection of vector bundles.\n\\end{lem}\n\n\\begin{proof}\n The collection on $X_L$ is automatically $G$-stable pointwise. Hence we can apply Theorem~\\ref{thm:descblocks}. \n\\end{proof}\n\n\\begin{rem}\n Note that while we may start with a collection of line bundles,\nthe descended collection may not consist only of line bundles.\nAn example of this is the real conic discussed in the introduction.\n\\end{rem}\n\n\\begin{lem}\\label{lem:blowup}\nLet $X$ be a smooth $k$-variety and $L\/k$ a $G$-Galois extension. Let $Y_1, ..., Y_s$ be a $G$-orbit of smooth transversal subvarieties of $X_L$. Let $Y_I = \\cap_{i \\in I} Y_i$ and let $H_I$ be the normalizer of $Y_I$. If each $Y_I$ admits a full $H_I$-stable exceptional collection, then $\\tilde{X}$ admits an exceptional collection, where $\\tilde{X}_L$ is the iterated blow up of $X_L$ at the $Y_i$ (in any order).\n\\end{lem}\n\n\\begin{proof}\n This is an iterated application of Orlov's Theorem, see \\cite[Lemma 7.2]{CT}.\n\\end{proof}\n\n\n\n\\section{Arithmetic toric varieties}\\label{section:toric}\n\nWe introduce toric varieties over arbitrary fields. Such varieties, also known as \\emph{arithmetic toric varieties}, have been treated in \\cite{Duncan, ELFST, MerkPan, VosKly}. \n\\begin{defn}\n\nA \\emph{torus} (over $k$) is an algebraic group $T$ (over $k$) such that $T _{k^s} \\simeq \\mathbb{G}_{m} ^n$. A torus is \\emph{split} if $T \\simeq \\mathbb{G}_{m} ^n$. A field extension $L\/k$ satisfying $T_L \\simeq \\mathbb{G}_{m} ^n$ is called a \\emph{splitting field} of the torus $T$. Any torus admits a finite Galois splitting field. \n\\end{defn}\n\n\\begin{defn}\nGiven a torus $T$, a \\emph{toric} $T$-\\emph{variety} is a normal variety with a faithful $T$-action and a dense open $T$-orbit. A toric $T$-variety is \\emph{split} if $T$ is a split torus. A \\emph{splitting field} of a toric $T$-variety is a splitting field of $T$. A variety is a \\emph{toric variety} if it is a toric $T$-variety for some torus $T$.\n\\end{defn}\n\n\n\\begin{defn}\nLet $X$ be a toric $T$-variety whose dense open $T$-orbit contains a $k$-rational point. Then we say $X$ is \\emph{neutral} \\cite{Duncan} (or a \\emph{toric} $T$-\\emph{model} \\cite{MerkPan}).\nAn orbit of a split torus always has a $k$-point, so a split toric\nvariety is neutral; but the converse is not true in general.\n\\end{defn}\n\n\\begin{rem}\nIn what follows, we will use the term \\emph{toric variety} to mean toric $T$-variety for some fixed torus $T$, even though such a variety may have a toric structure for various tori. In fact, the choice of torus does not affect our analysis of toric varieties given below, and we refer interested readers to \\cite{Duncan} for such considerations.\n\nRecall that a $k$-\\emph{form} of a $k$-variety $X$ is a $k$-variety $X'$ such that $X_L \\simeq X_L '$ for some field extension $L\/k$. Any $k$-form of a toric variety is a toric variety \\cite{Duncan}.\n\\end{rem}\n\n\n\\subsection{The split case} Let us begin by recalling some facts concerning toric varieties with $T \\simeq \\mathbb{G}_{m} ^n$ (e.g., when $k = {\\mathbb C}$ or $k= k^s$), which are studied in terms of combinatorial data, e.g., lattices, cones, fans. Good references for toric varieties over ${\\mathbb C}$ include \\cite{Fulton, CLS}, and many results hold generally in the split case.\n\nLet $N$ be a finitely generated free abelian group of rank $n$ and $M = \\hom (N, {\\mathbb Z})$. A subsemigroup $\\sigma \\subset N_{{\\mathbb R}}$ is a \\emph{cone} if ($\\sigma ^{\\vee})^{\\vee} = \\sigma$, where $\\sigma ^{\\vee} = \\{ u \\in M \\mid u(v) \\geq 0 \\text{ for all } v \\in \\sigma\\}$. A subsemigroup $\\tau$ is a \\emph{face} of $\\sigma$ if it is of the form $\\tau = \\{v \\in \\sigma \\mid u(v) = 0 \\text{ for all } u \\in S \\}$ for some $S \\subseteq \\sigma ^{\\vee}$. A cone $\\sigma$ is \\emph{pointed} if 0 is a face of $\\sigma$, and in this case $\\sigma^{\\vee}$ generates $M_{{\\mathbb R}}$. Given a pointed cone $\\sigma$, we associate the affine $k$-scheme $U_{\\sigma} = \\operatorname{Spec} k[\\sigma ^{\\vee}]$, and for any face $\\tau \\subset \\sigma$ the induced map $U_{\\tau} \\hookrightarrow U_{\\sigma}$ is an open embedding.\n\nA \\emph{fan} $\\Sigma \\subset N_{{\\mathbb R}}$ is a finite collection of pointed cones such that (1) any face of a cone in $\\Sigma$ is a cone in $\\Sigma$ and (2) the intersection of any two cones in $\\Sigma$ is a face of each. To any fan $\\Sigma$ we associate a $k$-variety $X_{\\Sigma}$ which is obtained by gluing the affine schemes $U_{\\sigma}$ along common subschemes $U _{\\tau}$ corresponding to faces.\n\nOn the other hand, beginning with a split torus $T \\simeq \\mathbb{G}_{m} ^n$ and toric $T$-variety $X$ with fixed embedding $T \\hookrightarrow X$, we recover $M$ as the character lattice $\\hom (T, \\mathbb{G}_{m})$ of $T$ and $N$ as the cocharacter lattice $\\hom (\\mathbb{G}_{m}, T)$. The association $\\Sigma \\mapsto X_{\\Sigma}$ defines a bijective correspondence between fans $ \\Sigma \\subset N_{{\\mathbb R}}$ and toric $T$-varieties $X$ (we remind the reader that here we assume $T$ is a split torus; in general, fans $\\Sigma$ admitting an action by $\\text{Gal}(k^s\/k)$ are in bijection with neutral toric $T$-varieties).\n\nLet $\\Sigma(\\ell)$ denote the collection of cones in $\\Sigma$ of dimension $\\ell$. Let $\\text{Div}_T(X)$ denote the free abelian group generated by the \\emph{rays} of $\\Sigma$, i.e., elements of $\\Sigma (1)$. By the Orbit-Cone Correspondence \\cite[Thm. 3.2.6]{CLS}, $\\text{Div}_T(X)$ is isomorphic to the group of $T$-invariant Weil divisors of $X$. For $X$ a (split) smooth projective toric variety, we have natural identifications $\\text{Pic}(X) = \\text{Pic} (X_{k^s}) = \\text{Cl}(X_{k^s}) = \\text{Cl}(X)$ which yield an exact sequence $$0 \\to M \\to \\text{Div}_T(X) \\to \\text{Pic}(X) \\to 0.$$ In particular, if $X$ is of dimension $n$ and $m$ is the number of rays in $\\Sigma$, the Picard rank of $X$ is $\\rho = m-n$.\n\n\\begin{defn}\nA variety $X$ is \\emph{Fano} (resp. \\emph{weak Fano}) if its anticanoncial class $-K_X$ is ample (resp. nef and big). If $X$ is a normal variety, a Cartier $D$ divisor on $X$ is \\emph{nef} (``numerically effective\" or ``numerically eventually free\") if $D \\cdot C \\geq 0$ for every irreducible curve $C \\subset X$. A divisor $D$ is \\emph{very ample} if $D$ is base point free and $\\varphi_D : X \\to \\mathbb{P}(\\Gamma(X, \\O_X(D))^{\\vee})$ is an embedding. A divisor $D$ is \\emph{ample} if $\\ell D$ is very ample for some $\\ell \\in {\\mathbb Z}^+$. A line bundle $\\O_X(D)$ is nef or (very) ample if the corresponding divisor $D$ is nef or (very) ample. A Cartier divisor is \\emph{numerically trivial} if $D\\cdot C =0$ for every irreducible complete curve $C \\subset X$. Let $N^1(X)$ be the quotient group of Cartier divisors by the subgroup of numerically trivial divisors. The \\emph{nef cone} $\\text{Nef}(X)$ is the cone in $N^1(X)$ generated by the nef divisors, and the \\emph{anti-nef cone} is the cone $-\\text{Nef}(X) \\subset N^1(X)$. A line bundle $\\O_X(D)$ is nef (ample) if $D$ is nef (ample). \n\\end{defn}\n\n\\begin{prop}\\label{prop:nef}\nA Cartier divisor $D$ on a split proper toric variety $X$ is nef (resp. ample) if and only if $D\\cdot C \\geq 0$ (resp. $D\\cdot C > 0$) for all torus-invariant integral curves $C \\subset X$. \n\\end{prop}\n\n\\begin{proof}\nWhen $k$ is algebraically closed, these are Theorems 3.1 and 3.2 of \\cite{Mustata}. One can see that the arguments remain valid in the split case more generally.\n\\end{proof}\n\n\n\\subsection{The not necessarily split case}\n\nHere we provide a ``black box''\nfor producing exceptional collections on arbitrary forms of toric\nvarieties by identifying certain special exceptional collections on\na \\emph{split} toric variety.\nThis reduces an arithmetic question to a completely geometric question.\n\nWe begin by reviewing how to obtain arbitrary forms of toric varieties\nfrom the split case\n(see, for example, \\cite{Vos82Projective,ELFST}).\nLet $T$ be the split torus of a split smooth projective toric variety\n$X$ with fan $\\Sigma$ in the space $N \\otimes {\\mathbb R}$ associated to the\nlattice $N$.\nLet $\\operatorname{Aut}(\\Sigma)$ denote the subgroup of elements $g \\in \\operatorname{GL}(N)$\nsuch that $g(\\sigma) \\in \\Sigma$ for every cone $\\sigma \\in \\Sigma$.\nThere is a natural inclusion of $T \\rtimes \\operatorname{Aut}(\\Sigma)$\ninto $\\operatorname{Aut}(X)$ as the subgroup leaving the open orbit $T$-invariant.\n\nLet $k^s$ be the separable closure of $k$.\nThe Galois cohomology set $H^1(k^s\/k,\\operatorname{Aut}(X)(k^s))$ is in bijective\ncorrespondence with the $k$-forms of $X$.\nThe natural map\n\\[ H^1(k^s\/k, T(k^s) \\rtimes \\operatorname{Aut}(\\Sigma)) \\to H^1(k^s\/k, \\operatorname{Aut}(X)(L)) \\]\nin Galois cohomology is surjective;\nthe failure of this map to be a bijection amounts to the fact that there\nmay be several non-isomorphic toric variety structures\non the same variety (see \\cite{Duncan} for more details).\n\nSuppose $X'={}^\\gamma X$ is a twisted form of a split toric variety\nfor a cocycle $\\gamma$ representing a class in\n$H^1(k^s\/k, T(k^s) \\rtimes \\operatorname{Aut}(\\Sigma))$.\nThere is a ``factorization'' $X'={}^\\alpha({}^\\beta X)$\nwhere $\\beta$ represents a class in $H^1(k^s\/k, \\operatorname{Aut}(\\Sigma)$\nand $\\alpha$ represents a class in $H^1(k^s\/k, ({}^\\beta T)(k^s) )$.\nInformally, $\\beta$ changes the torus that acts on $X$,\nwhile $\\alpha$ changes the torsor of the open orbit in $X$.\n\nSuppose $X$ is a toric $T$-variety.\nWe say that an object $E \\in \\mathsf{D^b}(X)$ is \\emph{$T$-equivariant}\nif $E$ is in the image of the forgetful functor from\n$\\mathsf{D^b}(\\operatorname{Coh}_T(X))$\n(see \\S{2}~of~\\cite{BFK2}).\nIn particular, this implies that $t^\\ast E \\simeq E$ for all $t \\in\nT(k)$.\n\n\\begin{prop} \\label{prop:blackbox}\nLet $X$ be a split toric $T$-variety over a field $k$ and let $\\Sigma$\nbe the associated fan. Suppose that $X$ admits an\n$\\operatorname{Aut}(\\Sigma)$-stable full exceptional collection $\\mathsf{E}$\nsuch that each object is $T$-equivariant.\nThen any $k$-form $X'$ of $X$ admits a full exceptional collection\n$\\mathsf{E'}$.\nMoreover, $\\mathsf{E'}$ is strong (resp. consists of vector bundles,\nconsists of sheaves) as soon as $\\mathsf{E}$ is strong\n(resp. consists of vector bundles, consists of sheaves).\n\\end{prop}\n\n\\begin{proof}\nBy Lemma~\\ref{lem:Galconverse}, there exists a $G$-stable\nexceptional collection $\\mathsf{F}$ on $X_L$.\nFrom the proof of that lemma, the objects $F$ of $\\mathsf{F}$ are direct\nsummands of $\\pi^\\ast E$ for each object $E \\in \\mathsf{E}$,\nwhere each isomorphism class of simple direct summand is represented\nby exactly one $F$.\nSince $\\mathsf{E}$ is $\\operatorname{Aut}(\\Sigma)$-stable and each object\nis $T$-equivariant, we may conclude that $\\mathsf{F}$ is\n$(T(L) \\rtimes \\operatorname{Aut}(\\Sigma)) \\rtimes G$-stable.\n\nLet $X'$ be a $k$-form of $X$; there exists a finite Galois\nextension $L\/k$ with Galois group $G$ such that $X'_L \\simeq X_L$.\nFrom Theorem~5.1~of~\\cite{Duncan}, the natural map\n\\[ H^1(L\/k, T(L) \\rtimes \\operatorname{Aut}(\\Sigma)) \\to H^1(L\/k, \\operatorname{Aut}(X)(L)) \\]\nin Galois cohomology is surjective.\nThus, we may assume that $X' ={}^cX$ is the \\emph{twist}\nby a cocycle $c: G \\to T(L) \\rtimes \\operatorname{Aut}(\\Sigma)$.\nRecall that the cocycle condition is that\n$c(gh)=c(g){}^gc(h)$ for all $g,h \\in G$\nwhere ${}^gc(h)$ denotes the Galois action of $g$ on $T(L) \\rtimes \\operatorname{Aut}(\\Sigma)$.\n\nIdentifying $X_L=X'_L$, twisting gives $\\sigma'(g) = c(g) \\sigma(g)$\nwhere $\\sigma$ is the action of $G$ induced from $X$ and\n$\\sigma'$ is induced from $X'$.\nThe punchline is that the action $\\sigma'$ factors through the image of\n$(T(L) \\rtimes \\operatorname{Aut}(\\Sigma)) \\rtimes G$ described above.\nThus the exceptional collection $\\mathsf{F}$ is $G$-stable for the $X'$\naction as well.\nThe proposition now follows by Theorem~\\ref{thm:descblocks}.\n\\end{proof}\n\n\\begin{cor} \\label{cor:toricLB}\nLet $X$ be a split toric $T$-variety over a field $k$ and let $\\Sigma$\nbe the associated fan.\nIf $X$ admits an $\\operatorname{Aut}(\\Sigma)$-stable full (strong) exceptional collection of\nline bundles, then every $k$-form of $X$ admits a full (strong) exceptional\ncollection of vector bundles.\n\\end{cor}\n\n\\begin{proof}\nRecall that every line bundle is isomorphic to a $T$-equivariant line\nbundle by standard results on toric varieties.\nThe corollary now follows by Proposition~\\ref{prop:blackbox}.\n\\end{proof}\n\n\\begin{lem}\\label{lem:flips}\n Let $X$ and $Y$ be smooth projective toric varieties over $k$. Let $G = \\operatorname{Gal}(k^s\/k)$. Assume we have a $K$-positive toric flip $X \\dashrightarrow Y$ such that over $k^{s}$ the flipping loci $F_i$ are disjoint and permuted by $G$. Let $H_i$ be the normalizer of $F_i$. If $X_L$ admits a full $G$-stable exceptional collection and $Y_i$ admits a full $H_i$-stable exceptional collection, then $Y$ admits a full exceptional collection.\n\\end{lem}\n\n\\begin{proof}\n Passing to $k^{s}$ we are free to use \\cite{BFK} giving semi-orthogonal decompositions for the flip over each $Y_i$. Since the $Y_i$ are disjoint, we can concatenate these collections to get a $G$-stable collection. \n\\end{proof}\n\n\\subsection{Products of toric varieties}\n\nRecall that, given groups $G,H$ along with a homomorphism\n$\\rho : H \\hookrightarrow S_n$,\nthe \\emph{wreath product} $G \\wr H$ is the group $G^n \\rtimes H$\nwhere $H$ acts on $G^n$ by permuting the copies of $G$.\nWe say a toric variety $X$ is \\emph{indecomposable} if it cannot be written as\na product $X_1 \\times X_2$ where $X_1$ and $X_2$ are\npositive-dimensional toric varieties.\n\n\\begin{lem}\\label{lem:autprod}\nSuppose $Z= X_1^{n_1} \\times \\cdots \\times X_r^{n_r}$ is a product of\nproper split toric varieties $X_1, \\ldots, X_r$, where $X_i \\not\\simeq X_j$\nfor $i \\ne j$ and each $X_i$ is indecomposable.\nThen\n\\[ \\operatorname{Aut}(\\Sigma) \\simeq (\\operatorname{Aut}({\\Sigma_1}) \\wr S_{n_1}) \\times \\cdots \\times\n(\\operatorname{Aut}({\\Sigma_r}) \\wr S_{n_r}) , \\]\nwhere $\\Sigma$ is the fan of $Z$ and $\\Sigma_1, \\ldots, \\Sigma_r$ are\nthe fans of $X_1, \\ldots, X_r$.\n\\end{lem}\n\n\\begin{proof}\nFirst, consider $Z=X_1 \\times X_2$ where\n$X_1,X_2$ are proper split toric varieties.\nLet $N$ (resp. $N_1, N_2$) be the cocharacter lattice and\n$\\Sigma$ (resp. $\\Sigma_1, \\Sigma_2$) be the fan of\n$Z$ (resp. $X_1, X_2$).\nHere $N = N_1 \\oplus N_2$ and $\\Sigma$ is the set of cones\nof the form $\\sigma_1 \\times \\sigma_2$ where $\\sigma_1 \\in \\Sigma_1$\nand $\\sigma_2 \\in \\Sigma_2$.\nThe faces of a cone $\\sigma_1 \\times \\sigma_2$ are precisely\nthe cones of the form $\\sigma_1' \\times \\sigma_2'$ where\n$\\sigma_1'$ is a face of $\\sigma_1$ and $\\sigma_2'$ is a face of\n$\\sigma_2$.\nThe fan $\\Sigma_1$ can be canonically identified with the subfan of\n$\\Sigma$ via the bijection $\\sigma \\mapsto \\sigma \\times \\{0\\}$.\n\nNow, suppose also that $Z= Y \\times W$ is a product of proper split\ntoric varieties where $Y$ is indecomposable. Let $\\Sigma_Y$ be the fan\nof $Y$, which we can canonically identify with a subfan of $\\Sigma_Z$.\nEvery cone of $Y$ is of the form $\\sigma_1 \\times \\sigma_2$ where\n$\\sigma_1 \\in \\Sigma_1$ and $\\sigma_2 \\in \\Sigma_2$.\nSince fans are closed under taking faces, $\\sigma_1 \\times \\{0\\}$\nand $\\{0\\} \\times \\sigma_2$ are also cones in $\\Sigma_Y$.\nThus every cone in $\\Sigma_Y$ is a product of cones in the intersections\n$\\Sigma_Y \\cap \\Sigma_1$ and $\\Sigma_Y \\cap \\Sigma_2$.\n\nIn particular, since $X$ is proper, we have that the space\n$N_Y \\otimes {\\mathbb R}$ is the direct sum of\n$(N_Y \\otimes {\\mathbb R}) \\cap (N_1 \\otimes {\\mathbb R})$ and\n$(N_Y \\otimes {\\mathbb R}) \\cap (N_2 \\otimes {\\mathbb R})$,\nand $\\Sigma_Y$ is a product of the fans $\\Sigma_Y \\cap \\Sigma_1$\nand $\\Sigma_Y \\cap \\Sigma_2$.\nSince $Y$ is indecomposable, one of these fans is indecomposable\nand $\\Sigma_Y$ must be a subfan of either $\\Sigma_1$ or $\\Sigma_2$.\n\nReturning to the general case, we conclude that the decomposition\n$\\Sigma= \\Sigma_1^{n_1} \\times \\cdots \\times \\Sigma_r^{n_r}$\nis unique up to ordering.\nThe description of the automorphism group is immediate.\n\\end{proof}\n\n\\begin{lem}\\label{lem:stabprod}\nLet $Z$ be a proper toric $k$-variety with splitting field $L\/k$.\nSuppose $Z_L = \\prod_{i=1}^n X_i$ where each $X_i$ is an indecomposable\nsplit proper toric $L$-variety admitting a full (strong)\n$\\operatorname{Aut}(\\Sigma_i)$-stable exceptional collection of line bundles,\nwhere $\\Sigma_i$ is the fan of $X_i$.\nThen $Z$ has a full (strong) exceptional collection of vector bundles.\n\\end{lem}\n\n\\begin{proof}\nIt is a well known that the exterior product collection is an\nexceptional collection.\nFor each isomorphism class among the $X_i$ fix a full (strong)\n$\\operatorname{Aut}(\\Sigma_{X_i})$-stable exceptional collection of line bundles.\nThis ensures that the exterior product collection is\nstable under the action of\n$(\\operatorname{Aut}(\\Sigma_{X_1}) \\wr S_{a_1}) \\times \\cdots \\times (\\operatorname{Aut}(\\Sigma_{X_r})\n\\wr S_{a_r})$.\nSince this group is $\\operatorname{Aut}(\\Sigma)$ by Lemma~\\ref{lem:autprod},\nthe exterior product collection descends by Corollary~\\ref{cor:toricLB}.\n\\end{proof}\n\n\n\\section{Low dimension or high symmetry}\\label{section:minimal}\n\nWe provide exceptional collections for smooth toric surfaces, Fano 3-folds,\nsome Fano 4-folds, centrally-symmetric toric varieties, and\ntoric varieties corresponding to root systems of type $A$. \n\n\n\\subsection{Surfaces}\\label{sect:surfaces}\nHere we prove that every toric surface has a full exceptional\ncollection.\nWe begin by recalling the (classical) minimal model program for surfaces\nover non-closed fields.\n\nSuppose $f : X \\to X'$ is a birational morphism of smooth projective\nsurfaces over a field $k$.\nIf $k$ is separably closed, then by Proposition~5~of\\cite{Coombes}\nthe morphism factors into a sequence\n\\[\nX= X_0 \\to X_1 \\to \\cdots \\to X_r = X'\n\\]\nwhere each morphism $X_i \\to X_{i+1}$ is the blowup of a point on\n$X_{i+1}$.\nOver a non-closed field $k$, we can factor $f : X \\to X'$ into a\nsequence where each morphism $X_i \\to X_{i+1}$ is defined over $k$\nand is a blowup of a\n(necessarily finite) Galois orbit of $k^s$-points on $X_{i+1}$.\n\nBlowing up a point produces an exceptional curve: a smooth rational\ncurve with self-intersection $-1$. By Castelnuovo's contractibility\ncriterion, such a curve can always be obtained as the result of a blow-up.\nIf one finds a skew Galois orbit of such curves on $X$, then there\nexists a birational morphism $f : X \\to X'$ contracting these curves.\nRepetition of this procedure eventually terminates.\n\n\\begin{defn}\nA \\emph{minimal surface} $X$ is a smooth projective surface over a field $k$\nsuch that every birational morphism $X \\to X'$ to a smooth projective\nsurface $X'$ is an isomorphism.\n\\end{defn}\n\nAny smooth projective surface can be obtained by iteratively blowing up\nGalois orbits of separable points starting from a minimal model.\nA toric variety is geometrically rational.\nMinimal geometrically rational surfaces were classified by\nManin~\\cite{Manin} and Iskovskikh~\\cite{Iskovskikh}.\nOne checks that the toric surfaces in their collection are the\nfollowing (see also a direct proof in~\\cite{Xie}):\n\n\\begin{lem}\\label{lem:classification}\nA minimal smooth projective toric surface\nis a $k^s\/k$-form of one of the following:\n\\begin{enumerate}\n\\item $\\mathbb{P}^2$, $\\operatorname{Aut}(\\Sigma) = S_3$.\n\\item ${\\mathbb P}^1 \\times {\\mathbb P}^1$, $\\operatorname{Aut}(\\Sigma) = D_8$.\n\\item $\\mathbb{F}_{a} = \\operatorname{Proj}(\\O_{{\\mathbb P}^1} \\oplus \\O_{{\\mathbb P}^1} (a))$,\n$a \\geq 2$, $\\operatorname{Aut}(\\Sigma) = C_2$.\n\\item $\\mathsf{dP}_6 = $ del Pezzo surface of degree 6, $\\operatorname{Aut}(\\Sigma) = D_{12}$.\n\\end{enumerate}\n\\end{lem}\n\n\\begin{proof}\nA minimal geometrically rational surface is either a del Pezzo surface\nor has a conic bundle structure \\cite{Manin,Iskovskikh}.\nOver the separable closure, a del Pezzo surface is either\n${\\mathbb P}^1 \\times {\\mathbb P}^1$ or a blow up of ${\\mathbb P}^2$ at up to $8$ points in\ngeneral position.\nBlowing up only one or two points never results in a minimal surface, and\nno more than three points can be simultaneously torus invariant and\nin general position. Thus every del Pezzo surface is a $k^s\/k$-form\nof ${\\mathbb P}^2$, ${\\mathbb P}^1 \\times {\\mathbb P}^1$ or $\\mathsf{dP}_6$.\nOver the separable closure, a conic bundle structure has at most $2$\nsingular fibers since their images must be torus invariant points on the\nbase ${\\mathbb P}^1$.\nA minimal conic bundle with at most two singular fibers over the\nseparable closure must be either a del Pezzo surface or a minimal ruled\nsurface.\n\\end{proof}\n\n\\begin{figure}\n \\begin{center}\n \\begin{tikzpicture}\n [scale=.4, vertex\/.style={circle,draw=black!100,fill=black!100, inner sep=0.5pt,minimum size=0.5mm}]\n \\filldraw[fill=black!20!white,draw=white!100]\n (-5,5) -- (5,5) -- (5,-5) -- (-5,-5) -- (-5,5);\n \\draw (0,0) -- (5,0);\n \\draw (0,0) -- (0,5);\n \\draw (-5,-5) -- (0,0);\n \\foreach \\x in {-5,-4,...,5}\n \\foreach \\y in {-5,-4,...,5}\n {\n \\node[vertex] at (\\x,\\y) {};\n }\n\\node at (0,-6,0) {\\text{$\\mathbb{P}^2$}};\n \\end{tikzpicture}\n\\hspace{1cm}\n \\begin{tikzpicture}\n [scale=.4, vertex\/.style={circle,draw=black!100,fill=black!100, inner sep=0.5pt,minimum size=0.5mm}]\n \\filldraw[fill=black!20!white,draw=white!100]\n (-5,5) -- (5,5) -- (5,-5) -- (-5,-5) -- (-5,5);\n \\draw (0,0) -- (5,0);\n \\draw (0,0) -- (0,5);\n \\draw (0,0) -- (0,-5);\n \\draw (-5,0) -- (0,0);\n \\foreach \\x in {-5,-4,...,5}\n \\foreach \\y in {-5,-4,...,5}\n {\n \\node[vertex] at (\\x,\\y) {};\n }\n\\node at (0,-6,0) {\\text{${\\mathbb P}^1 \\times {\\mathbb P}^1$}};\n \\end{tikzpicture}\n\n\\vspace{0.5cm}\n\n\n \\begin{tikzpicture}\n [scale=.4, vertex\/.style={circle,draw=black!100,fill=black!100, inner sep=0.5pt,minimum size=0.5mm}]\n \\filldraw[fill=black!20!white,draw=white!100]\n (-5,5) -- (5,5) -- (5,-5) -- (-5,-5) -- (-5,5);\n \\draw (0,0) -- (5,0);\n \\draw (0,0) -- (0,5);\n \\draw (0,0) -- (0,-5);\n \\draw (-2.5,5) -- (0,0);\n \\foreach \\x in {-5,-4,...,5}\n \\foreach \\y in {-5,-4,...,5}\n {\n \\node[vertex] at (\\x,\\y) {};\n }\n\\node at (0,-6,0) {\\text{$\\mathbb{F}_a$}};\n \\end{tikzpicture}\n\\hspace{1cm}\n \\begin{tikzpicture}\n [scale=.4, vertex\/.style={circle,draw=black!100,fill=black!100, inner sep=0.5pt,minimum size=0.5mm}]\n \\filldraw[fill=black!20!white,draw=white!100]\n (-5,5) -- (5,5) -- (5,-5) -- (-5,-5) -- (-5,5);\n \\draw (0,0) -- (5,0);\n \\draw (0,0) -- (0,5);\n \\draw (0,0) -- (0,-5);\n \\draw (0,0) -- (-5,0);\n \\draw (5,5) -- (0,0);\n \\draw (-5,-5) -- (0,0);\n \\foreach \\x in {-5,-4,...,5}\n \\foreach \\y in {-5,-4,...,5}\n {\n \\node[vertex] at (\\x,\\y) {};\n }\n\\node at (0,-6,0) {\\text{$\\mathsf{dP}_6$ }};\n \\end{tikzpicture}\n\\vspace{-.3cm}\n\\caption{Fans for minimal toric surfaces}\\label{fig:fans}\n\\end{center}\n\\end{figure}\n\nHere we exhibit full strong exceptional collections consisting of\n$G$-stable blocks for each minimal toric surface exhibited above\n(none of these collections are original).\nThe fans associated to the split forms of these surfaces are given in Figure~\\ref{fig:fans}. In each case, we fix a torus $T$ which gives $X$ the structure of a toric $T$-surface. As remarked above, this gives a homomorphism $G \\to \\text{Aut}(\\Sigma)$ as well as an action of $G$ on $\\text{Pic}(X_L)$, where $L$ is a splitting field of $T$, $G = \\text{Gal}(L\/k)$, and $\\Sigma$ is the fan corresponding to the split toric surface $X_L$. We produce $G$-stable exceptional collections in each case by exhibiting $\\text{Aut}(\\Sigma)$-stable collections.\n\n\\begin{ex}\\label{ex:p2}\nLet $X$ be a toric $T$-surface whose split form is $\\mathbb{P}^2$ with\n$\\text{Aut}(\\Sigma) = S_3$. The $ S_3$-action on\n$\\text{Pic}(\\mathbb{P}^2) = {\\mathbb Z}$ is clearly trivial, so that the\nexceptional collection $\\{ \\O, \\O(1), \\O(2)\\}$, given in\n\\cite{Beilinson} yields a full strong $\\text{Aut}(\\Sigma)$-stable\nexceptional collection. By Corollary~\\ref{cor:toricLB},\n$X$ admits a full strong exceptional collection.\n\\end{ex}\n\n\\begin{ex}\\label{ex:p1p1}\nLet $X$ be a toric surface whose split form is ${\\mathbb P}^1 \\times {\\mathbb P}^1$ with $\\text{Aut}(\\Sigma) = D_8$, and consider the natural projections $p_1, p_2: {\\mathbb P}^1 \\times {\\mathbb P}^1 \\to {\\mathbb P}^1$. Let $\\O(p, q) = p_1 ^*\\O (p) \\otimes p_2^* \\O (q)$. By \\cite{KvichanskyNogin}, the collection $\\{\\O, \\O(1, 0), \\O(0, 1), \\O(1,1) \\}$ on ${\\mathbb P}^1 \\times {\\mathbb P}^1$ is exceptional since $\\{ \\O, \\O(1)\\} $ is an exceptional collection for ${\\mathbb P}^1$. The $ D_8$-action preserves this collection, with orbits given by the blocks $\\mathsf{E}^0 = \\{ \\O \\}$, $\\mathsf{E}^1 = \\{ \\O(1, 0), \\O(0,1) \\} $, and $\\mathsf{E}^2= \\{ \\O(1,1)\\}$. In particular, this collection above is $\\text{Aut}(\\Sigma)$-stable, and Corollary~\\ref{cor:toricLB} yields an exceptional collection on $X$.\n\\end{ex}\n\n\\begin{ex}\nLet $X$ be a toric surface whose split form is the Hirzebruch surface\n$\\mathbb{F}_a$; here $\\text{Aut}(\\Sigma) = C_2$.\nLet $e_1, e_2$ be the standard basis for ${\\mathbb Z}^2$.\nAs in \\cite[Ex. 4.1.8]{CLS},\nlet $u_1 =-e_1 + ae_2$, $u_2 = e_2$, $u_3 = e_1$, and $u_4\n= -e_2$ be the generators of $\\Sigma(1)$ with corresponding toric\ndivisors $D_i$. The Picard group of $\\mathbb{F}_a$ is freely generated\nby $\\{D_1, D_2\\}$ and $D_1$ is linearly equivalent to $D_3$.\nThe only nontrivial fan automorphism $\\sigma$ takes $e_1 \\mapsto -e_1+ae_2$\nand $e_2 \\mapsto e_2$.\nThus $\\sigma$ leaves $D_2,D_4$ fixed and interchanges $D_1$ and $D_3$.\nWe conclude the action of $C_2$ on\n$\\text{Pic}(\\mathbb{F}_a)$ is trivial, and thus, any exceptional\ncollection is necessarily $G$-stable (see Lemma~\\ref{lem:picinv}).\nAn exceptional collection for $\\mathbb{F}_a$ is given by $\\{\\O, \\O(D_3),\n\\O(D_4), \\O(D_3 + D_4)\\}$ \\cite{KvichanskyNogin}.\nCorollary~\\ref{cor:toricLB} then gives an exceptional collection on $X$.\n\\end{ex}\n\n\\begin{ex}\\label{ex:dp6}\nLet $X$ be a toric surface whose split form is $\\mathsf{dP}_6$; here\n$\\text{Aut}(\\Sigma) = D_{12}$. Viewing $\\mathsf{dP}_6$ as the blowup of\n$\\mathbb{P} ^2$ at 3 non-colinear points, let $H$ be the pullback of the\nhyperplane divisor on $\\mathbb{P}^2$ and $E_i$ the exceptional divisors, $i =\n1, 2, 3$. As shown in \\cite[Prop. 6.2(ii)]{King}, the collection\n$$\\{\\O, \\O(H - E_1), \\O(H - E_2), \\O(H - E_3), \\O(H), \\O(2H - (E_1 + E_2\n+ E_3)) \\}$$ gives an exceptional collection for $\\mathsf{dP}_6$,\nwhich is $\\text{Aut}(\\Sigma)$-stable.\n\nLet us rephrase this in the notation of \\cite{BSS}.\nThere are two morphisms $\\mathsf{dP}_6 \\to \\mathbb{P}^2$ realizing\n$\\mathsf{dP}_2$ as a blowup of $\\mathbb{P}^2$, and we denote the collection of\nall six exceptional divisors by $L_i$ and $M_i$, with $i = 1, 2, 3$.\nLet $H$ and $H'$ denote the pullbacks of the hyperplane divisors on\n$\\mathbb{P}^2$ under the maps contracting $M_i$ and $L_i$, respectively, where\nwe identify $H$ with the divisor given in King's collection above (and\nthus we also identify $E_i$ with $M_i$).\nThen $H = L_1 + M_2 + M_3$, and it follows that $$2H - (E_1 + E_2 + E_3)\n= L_1 + L_2 + M_3 = H'$$ using the relation $L_i + M_j = L_j + M_i$.\nFurthermore, one checks that $ H - E_1 = L_2 + M_3$, $H-E_2 = L_1 +\nM_3$, and $H- E_3 = L_1 + M_2$.\nAs described in \\cite[$\\S$2]{BSS}, the element $\\sigma$ in $S_3 \\times\nC_2 = D_{12}$ which cyclically permutes the six lines $L_i, M_i$ also\nsatisfies $\\sigma (H) = H'$ and $\\sigma^2(H) = H$.\nWe arrange the exceptional collection above into blocks $\\mathsf{E}^0 =\n\\{\\O \\}$, $\\mathsf{E}^1 = \\{ \\O(H - E_1), \\O(H - E_2), \\O(H - E_3)\\}$\nand $\\mathsf{E}^2 = \\{\\O(H), \\O(2H - (E_1 + E_2 + E_3))\\}$.\nIn particular, the exceptional collection given above is\n$\\text{Aut}(\\Sigma)$-stable, and by Corollary~\\ref{cor:toricLB} we have an exceptional\ncollection on $X$.\n\\end{ex}\n\n\\begin{prop}\\label{prop:surface}\nEvery toric surface admits a full exceptional collection of sheaves.\n\\end{prop}\n\n\\begin{proof}\nThere is a sequence of blowups $X = X_0 \\to \\cdots \\to X_s = X'$ where\n$X'$ is minimal, so must be one of the varieties given in\nLemma~\\ref{lem:classification}.\nBy Examples~\\ref{ex:p2}-\\ref{ex:dp6},\n$X'$ admits a full strong exceptional collection of vector bundles, and\nthus $X'_L$ admits a $G$-stable exceptional collection. By\nLemma~\\ref{lem:blowup}, $X_L$ admits a $G$-stable exceptional\ncollection.\n\\end{proof}\n\n\\begin{rem}\nThe authors would like to thank F.~Xie for pointing out a mistake\nin the statement of a previous version of\nProposition~\\ref{prop:surface}.\nXie also discusses exceptional collections of toric surfaces in\n\\cite{Xie}, although her definition of exceptional object is not the\nsame as ours. \nIn the second arXiv version of that paper, Xie sketched in Remark~8.8\nhow one might construct an exceptional collection for toric surfaces.\nAfter the authors posted a preliminary version of this paper to the\narXiv, Xie updated her preprint with Corollary~8.8, which proves the analog of\nthe above proposition for collections of vector bundles but using her notion of exceptional collection.\n\\end{rem}\n\n\n\\subsection{The toric Frobenius and toric Fano 3-folds}\\label{sect:3fold}\nIn Table~\\ref{tab:3-folds} we present the classification of smooth toric Fano 3-folds given in \\cite{Batyrev, Watanabe}, adopting Batyrev's enumeration. For each $X = X_{\\Sigma}$, we record the following invariants:\n\\begin{itemize}\n\n\\item $\\sigma(1) = | \\Sigma (1) |$ is the number of rays of $\\Sigma$ \\cite{BT}.\n\n\\item $k_0$ is the rank of the Grothendieck group $K_0(X)$, which coincides with the number of maximal cones in the fan $\\Sigma$ \\cite{BT}.\n\n\\item $\\operatorname{Aut}(\\Sigma) $ is the automorphism group of the (lattice $N$ which preserves the) fan $\\Sigma$ corresponding to $X$.\n\n\\item $\\rho$ is the Picard rank of $X$ \\cite{Watanabe}.\n\n\\item $\\rho ^G$ is the $\\operatorname{Aut}(\\Sigma)$-invariant Picard rank of $X$,\ni.e., the rank of $\\text{Pic}(X)^{\\operatorname{Aut}(\\Sigma)}$.\n\n\\item $\\mathfrak{fr} = | \\mathsf{Frob}(X) |$ is the number of\nisomorphism classes of line bundles produced by the push forward of the structure sheaf under the Frobenius morphism \\cite{BT, Uehara}.\n\n\\item $ \\mathfrak{fr}^- = |\\mathsf{Frob}(X) \\cap -\\text{Nef}(X)|$ is the\nnumber of isomorphism classes of line bundles in $\\mathsf{Frob}(X)$ which lie in the anti-nef cone of $X$ \\cite{Uehara}.\n\\end{itemize}\n\n\\begin{table}\n\\begin{center}\n\\begin{tabular}{lrlccccccc}\n\\toprule\n & &Toric Fano 3-fold $X$ & $\\sigma(1)$ &$k_0$ & $\\operatorname{Aut}(\\Sigma)$ & $\\rho$ & $\\rho^G$ & $\\mathfrak{fr}$ & $\\mathfrak{fr}^- $\\\\\n\\midrule\n\n& 1. & $\\mathbb{P}^3$ & 4 & 4 & $S_4$ & 1 & 1 & 4 & 4 \\\\\n\n&2. &$\\mathbb{P}_{\\mathbb{P}^2}(\\O \\oplus \\O(2))$ & 5 & 6 & $S_3$ & 2 & 2 & 7 & 6 \\\\\n\n &3. &$\\mathbb{P}_{\\mathbb{P}^2}(\\O \\oplus \\O(1))$ & 5 & 6 & $S_3$ & 2 & 2 & 6 & 6\\\\\n \n&4.& $\\mathbb{P}_{\\mathbb{P}^1}(\\O \\oplus \\O \\oplus \\O(1))$ & 5 & 6 & $C_2 \\times C_2$ & 2 & 2 & 6 & 6\\\\\n\n &5. & $\\mathbb{P}^2\\times \\mathbb{P}^1$ & 5 & 6 & $D_{12}$ & 2 & 2 & 6 & 6\\\\\n \n &6. &$\\mathbb{P}_{\\mathbb{P}^1\\times \\mathbb{P}^1}(\\O\\oplus \\O(1,1))$ & 6 & 8 & $D_8$ & 3 & 2 & 8 & 8\\\\\n \n &7. &$\\mathbb{P}_{\\mathsf{dP}_8}(\\O\\oplus \\O(l))$, $l^2=1$ on $\\mathsf{dP}_8$ & 6 & 8 & $D_8$ & 3 & 3 & 8 & 8 \\\\\n\n&8. &$\\mathbb{P}^1\\times \\mathbb{P}^1 \\times \\mathbb{P}^1$ & 6 & 8 & $C_2 \\times S_4$ & 3 & 1 & 8 & 8\\\\\n\n &9. &$\\mathsf{dP}_8\\times \\mathbb{P}^1 $ & 6 & 8 &$ C_2 \\times C_2$ & 3 & 3 & 8& 8 \\\\\n \n&10. &$\\mathbb{P}_{\\mathbb{P}^1\\times\\mathbb{P}^1}(\\O\\otimes \\O(1,-1))$& 6 & 8 & $D_8$ & 3 & 2 & 8 & 8\\\\\n\n &11. & $\\text{Bl}_{\\mathbb{P}^1}(\\mathbb{P}_{\\mathbb{P}^2}(\\O \\oplus \\O(1)))$& 6 & 8 & $C_2$ & 3 & 3 & 9 & 8 \\\\\n \n &12. & $\\text{Bl}_{\\mathbb{P}^1}(\\mathbb{P}^2\\times \\mathbb{P}^1)$& 6 & 8 & $C_2$ & 3 & 3 & 8 & 8 \\\\\n\n& 13. & $\\mathsf{dP}_7-$bundle over $\\mathbb{P}^1$ & 7 & 10 & $C_2$ & 4 & 4 & 10 & 10 \\\\\n\n & 14. & $\\mathsf{dP}_7-$bundle over $\\mathbb{P}^1$ & 7 & 10 & $C_2 \\times C_2$ & 4 & 3 & 10 & 10\\\\\n\n& 15. & $\\mathsf{dP}_7\\times \\mathbb{P}^1$& 7 & 10 & $C_2 \\times C_2$ & 4 & 3 & 10 & 10\\\\\n\n& 16. & $\\mathsf{dP}_7-$bundle over $\\mathbb{P}^1$& 7 & 10 & $C_2$ & 4 & 4 & 10 & 10\\\\\n\n & 17. &$\\mathsf{dP}_6\\times \\mathbb{P}^1$& 8 & 12 & $C_2 \\times C_2 \\times S_3$ & 5 & 2 & 12 & 12\\\\\n\n & 18. & $\\mathsf{dP}_6-$bundle over $\\mathbb{P}^1$& 8 & 12 & $C_2 \\times C_2$ & 5 & 4 & 12 & 12\\\\\n\\bottomrule\n\\end{tabular}\n\\vspace{.2cm}\n\\caption{Toric Fano 3-folds}\\label{tab:3-folds}\n\\end{center}\n\\end{table}\n\n\\subsubsection{Toric Frobenius}\\label{section:frob}\n\nLet $X$ be a split toric variety of dimension $n$ with fixed torus embedding $T \\hookrightarrow X$ and take $\\ell \\in {\\mathbb Z}^+$. Define the $\\ell^{\\text{th}}$ Frobenius map on $T = \\mathbb{G}_{m}^n$ to be $(x_1,..., x_n) \\mapsto (x_1^{\\ell},..., x_n^{\\ell})$. The unique extension to $X$ will be denoted $F_{\\ell}$ and called the \\emph{$\\ell^{th}$ Frobenius morphism}. Alternatively, if $\\Sigma \\subset N$ is the fan associated to $X$, define a lattice $N' = \\frac{1}{\\ell} N$. The inclusion $N \\subset N'$, which sends a cone in $N_{{\\mathbb R}}$ to the cone with the same support in $N'_{{\\mathbb R}}$, induces a finite surjective morphism which is precisely the $\\ell^{\\text{th}}$ Frobenius morphism $F_{\\ell}: X \\to X.$ \n\nThe sheaf $(F_{\\ell})_*(\\O_X)$ splits into line bundles and Thomsen\nprovides an algorithm for computing its direct summands \\cite{Thomsen}.\nWe let $\\mathsf{Frob}(X)$ denote the union of all isomorphism classes of line bundles arising as direct summands of $(F_{\\ell})_*(\\O_X)$ as $\\ell$ varies over ${\\mathbb Z}^+$. Note that $\\mathsf{Frob}(X)$ is a finite set. \n\n\\begin{conj}[Bondal \\cite{Bondal2}]\\label{conj:Bondal}\nIf $X$ is a smooth proper toric variety then the collection $\\mathsf{Frob}(X)$ generates $\\mathsf{D^b}(X)$.\n\\end{conj}\n\nFor a toric variety $X$ in which Bondal's Conjecture is true, we will\nsay that \\emph{the Frobenius generates the derived category of} $X$.\nIn loc. cit., Bondal proves that if all summands of $\\mathsf{Frob}(X)$ are nef, one actually gets a full strong exceptional collection, so that Conjecture~\\ref{conj:Bondal} is true in this case. He also notes his arguments work for all but two (isomorphism classes of) toric Fano threefolds. To cover all toric Fano threefolds, \nUehara noticed that discarding line bundles which do\nnot lie in the set $-\\text{Nef}(X)$ yields a full strong\nexceptional collection \\cite{Uehara}.\n\n\\begin{lem}\\label{lem:FrobNef}\nLet $X$ be a toric variety over $k$ with splitting field $L$.\nSuppose $\\mathsf{E}$ is a full (strong) exceptional collection for\n$\\mathsf{D^b}(X_L)$\nwhere either $\\mathsf{E} = \\operatorname{\\mathsf{Frob}}(X_L)$ or\n$\\mathsf{E} = \\operatorname{\\mathsf{Frob}}(X_L) \\cap - \\operatorname{Nef}(X_L)$.\nThen there exists a full (strong) exceptional collection for\n$\\mathsf{D^b}(X)$.\n\\end{lem}\n\n\\begin{proof}\nBoth $\\operatorname{\\mathsf{Frob}}(X_L)$ and $\\text{Nef}(X_L)$ are\ncanonical constructions and thus are $\\text{Aut}(X_L)$-stable.\nIn particular, $\\mathsf{E}$ is $\\text{Aut}(\\Sigma)$-stable\nand so Corollary~\\ref{cor:toricLB} applies.\n\\end{proof}\n\n\\begin{prop}\\label{prop:3fold}\nLet $X$ be a smooth projective toric Fano 3-fold over a field $k$. Then $X$ admits a full strong exceptional collection consisting of vector bundles.\n\\end{prop}\n\n\\begin{proof}\nLet $X_L$ be the associated split toric Fano 3-fold. The main result of \\cite{Uehara} guarantees that the set $\\mathsf{E} = \\mathsf{Frob}(X_L) \\cap - \\text{Nef}(X_L)$ defines a full strong exceptional collection on $X$. Lemma~\\ref{lem:FrobNef} completes the proof.\n\\end{proof}\n\n\n\\subsection{Toric Fano 4-folds}\\label{sect:4fold}\n\nThere are 124 split smooth toric Fano 4-folds,\nwhich were first classified in \\cite{Batyrev}\n(a missing case was added in~\\cite{Sato}).\nIn \\cite{Prabhu}, Prabhu-Naik exhibits full strong exceptional\ncollections for all 124 of these 4-folds.\nHowever, it is not clear that these collections are\n$\\operatorname{Aut}(\\Sigma)$-stable, so they do not necessarily lead to full strong\nexceptional collections in the arithmetic case.\n\nAll collections obtained\nusing Method 1 of loc. cit. produce $\\operatorname{Aut}(\\Sigma)$-stable collections (note that\nthis is precisely the method used in \\cite{Uehara} for toric Fano\n3-folds, and we will refer to this as the \\emph{Bondal-Uehara Method}).\nTogether with Lemmas~\\ref{lem:stabprod} and \\ref{lem:FrobNef}, this gives stable exceptional collections for 43 of the 124 smooth toric Fano 4-folds. However, there are examples when the\nBondal-Uehara Method fails to produce an exceptional collection.\nIn this case, all is not lost (see Section~\\ref{sec:centsym}).\n\nMore precisely, the varieties (61), (62), (63), (64), (77), (105),\n(107), (108), (110), (122), and (123) of \\cite{Prabhu} are shown to have\nexceptional collections using the Bondal-Uehara Method.\nHence, they admit exceptional collections which are $\\operatorname{Aut}(\\Sigma)$-stable\nand thus provide exceptional collections for the arithmetic forms. Secondly, for the varieties (109), (114), and (115), the set\n$\\mathsf{Frob}(X)$ is a full exceptional collection, which is $G$-stable\nby Lemma~\\ref{lem:FrobNef}. Lastly, Lemma~\\ref{lem:stabprod} guarantees the existence of exceptional collections on products. Hence, the following varieties also admit stable exceptional collections: (0), (4), (9), (17), (24), (25), (26), (27), (45), (52), (53), (54), (55), (56), (58), (67), (73), (88), (90), (92),\n(93), (97), (103), (111), (112), (113), (118), (119), (120).\n\n\n\n\\subsection{Centrally symmetric toric Fano varieties} \\label{sec:centsym}\n\nPolytopes with the highest degree of symmetry are the \\emph{centrally\nsymmetric} polytopes, i.e., $-P = P$.\nThe smooth split toric varieties $X$ whose anti-canonical polytope is full-dimensional and centrally symmetric were classified in \\cite{VosKly}. It was shown that any such variety (which we refer to as a \\emph{centrally symmetric toric Fano varieties}) is isomorphic to a product of projective lines and \\emph{generalized del Pezzo varieties} $V_n$ of dimension $n = 2m$. Note that $V_2 = \\mathsf{dP}_6$ and $V_4$ is the missing (116)\nfrom the list in Section~\\ref{sect:4fold} (this is (118) in the enumeration found in \\cite{Batyrev}). The goal of this section is to exhibit full stable exceptional collections on $V_n$, which in turn yields stable exceptional collections for any centrally symmetric toric Fano variety, in light of Lemma~\\ref{lem:stabprod}.\n\nIn \\cite[Theorem 6.6]{CT}, Castravet and Tevelev found $\\operatorname{Aut}(\\Sigma)$-stable full\nstrong exceptional collections for the varieties $V_n$.\nThe authors of this paper had independently discovered the same\nexceptional collection (up to a twist by a line bundle).\nNevertheless, the perspective here may be of independent interest, so we\nsketch the argument. The authors give a more detailed analysis in \\cite{BDMdP}.\n\nThe variety $V_{n}$ with $n= 2m$ has rays given by\n$$\\begin{array}{rl}\ne_1 &= (1,0,\\cdots,0)\\\\\ne_2 &= (0,1,\\cdots,0)\\\\\n&\\vdots\\\\\ne_n &= (0,0,\\cdots,1)\\\\\ne_{n+1} &= (-1,-1,\\cdots,-1)\\\\\n\\end{array}\n\\hspace{.4cm}\n\\begin{array}{rl}\n\\bar{e}_1 &= (-1,0,\\cdots,0)\\\\\n\\bar{e}_2 &= (0,-1,\\cdots,0)\\\\\n&\\vdots\\\\\n\\bar{e}_n &= (0,0,\\cdots,-1)\\\\\n\\bar{e}_{n+1} &= (1,1,\\cdots,1)\n\\end{array}$$ and whose maximal cones are given as follows. Among the rays $e_1,..., e_{n+1}$, omit a single $e_i$. From the remaining $n = 2m$ rays, choose $\\frac n 2$ of them and take their antipodes \\cite[Proof of Thm. 5]{VosKly}. Note that $V_2 = \\mathsf{dP}_6$ (whose fan is given in Figure~\\ref{fig:fans}). The number of maximal cones $c(n)$ of $V_n$ is given by\n\\[\nc(n)=\\frac{(n+1)!}{(\\frac n 2)!^2} = \\frac{(2m +1)! }{m!^2}\\ .\n\\]\nThere's a natural action of $S_{n+1} \\times C_2$, where $S_{n+1}$\npermutes $e_1,\\ldots,e_{n+1}$ and $\\bar{e}_1\\ldots\\bar{e}_{n+1}$ in the\nobvious way. The $C_2$-action is simply the antipodal map on the\ncocharacter lattice --- we will refer to it as ``the involution.''\nClearly, the involution interchanges $e_i$ and $\\bar{e_i}$. \n\nThe variety $V_n$ is of importance in birational geometry due its\nappearance in the factorization of the standard Cremona transformation\nof $\\mathbb{P}^n$. In fact, as is well-known, $V_n$ can be explicitly obtained\nfrom $\\mathbb{P}^n$ as follows. First blow up the torus fixed points, then\nflip the (strict transforms) of the lines through these points, then\nflip the (strict transforms) of planes through these points, \\ldots, up\nuntil, and not including, the half-dimensional linear subspaces. The\nresulting variety is $V_n$. For more, see \\cite{Casagrande}. \n\nSince $V_n$ and the blow up of $\\mathbb{P}^n$ at its torus fixed points are\nisomorphic in codimension $1$, they have isomorphic Picard groups. We\nuse a basis $H,E_1, \\ldots, E_{n+1}$ for $\\operatorname{Pic}(V_n)$,\nwhich correspond to the hyperplane section and the\nexceptional divisors of the blown up $\\mathbb{P}^n$.\nWe have\n\\[\n[e_i] = E_i, \\quad\n[\\bar{e}_i] = (H-\\sum_{j=1}^{n+1}E_j)+E_j\n\\]\nwhere $S_{n+1}$ permutes the $E_i$ leaving $H$ fixed, and the involution\nis represented by the following matrix\n\\[\n\\begin{pmatrix}\nn & 1 & 1 & \\cdots & 1 \\\\\n1-n & 0 & -1 & \\cdots & -1 \\\\\n1-n & -1 & 0 & \\cdots & -1 \\\\\n\\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n1-n & -1 & -1 & \\cdots & 0 \\\\\n\\end{pmatrix} \\ .\n\\]\n\nFor each $c \\in {\\mathbb Z}$ and $J \\subset \\{1, \\ldots, n+1\\}$, define \n\\[\n F_{c,J} := c\\left(\\sum_{i=1}^{n+1}E_i - H\\right) - \\sum_{j \\in J} E_j .\n\\]\nNote that the involution takes $F_{c,J}$ to $F_{|J|-c,J}$. Then, \n\n\\begin{prop} \\label{prop:Vn}\n The set of $F_{c,J}$ with\n \\begin{enumerate}\n \\item $\\displaystyle{|J|-\\frac{n}{4} \\le c \\le \\frac{n}{4}}$, or\n \\item $\\displaystyle{\\frac{n+2}{4} \\le c \\le |J| - \\frac{n+2}{4}}$.\n \\end{enumerate}\nform a full strong $(S_{n+1} \\times C_2)$-stable exceptional collection\non $V_n$ under any ordering of the\nblocks such that $|J|$ is (non-strictly) decreasing.\n\\end{prop}\n\n\\begin{sproof}\nThis collection is the same as that of \\cite[Theorem 6.6]{CT} up to a\ntwist by a line bundle.\nThus, we only sketch an argument here (expanded in \\cite{BDMdP}).\nOne checks that the description of ``forbidden cones'' given by\nBorisov and Hua in \\cite{BH} shows that relevant cohomology groups\nvanish --- this shows that it is a strong exceptional collection.\nTo prove generation, one considers the series of flips required to reach\n$\\mathbb{P}^n$ blown up at $n+1$ points.\nUsing the description of the semi-orthogonal decompositions in\n\\cite{BFK}, the line bundles can be shown to generate the necessary\nadmissible subcategories of each intermediate birational model.\n\\end{sproof}\n\nSince any centrally symmetric toric Fano variety is a product of projective lines and the varieties $V_n$, Lemma~\\ref{lem:stabprod} yields the following:\n\n\\begin{cor}\\label{cor:centsym}\nAny form of a centrally symmetric toric Fano variety admits a full\nstrong exceptional collection consisting of vector bundles.\n\\end{cor}\n\n\\subsection{Toric varieties from the Weyl fans of type A} \\label{sec:X(An)}\n\nOne method for identifying toric varieties with large symmetry groups is to start with root systems. Let $R$ be a root system in a Euclidean space $E$. The ${\\mathbb Z}$-lattice generated by $R$ is denoted $M(R)$, while its dual in $E^\\vee$ is denoted by $N(R)$. For every set $S$ of simple roots in $E$, we have the dual cone corresponding to a closed Weyl chamber\n\\begin{displaymath}\n \\sigma_S := \\{ f \\in E^\\vee \\mid \\langle f, \\alpha \\rangle \\geq 0 \\ , \\ \\forall \\alpha \\in S\\}.\n\\end{displaymath}\nThe cones $\\sigma_S$ are the maximal cones for a fan $\\Sigma_R$ in $E^\\vee$. We denote the associated toric variety by $X(R)$. Recall that an automorphism of $R$ is an element of $\\operatorname{GL}(E)$ preserving $R$. Let $W(R)$ be the Weyl group and $\\Gamma(R)$ the symmetry group of the Dynkin diagram of $R$. It is well-known that\n\\begin{displaymath}\n \\text{Aut}(R) \\simeq W(R) \\rtimes \\Gamma(R).\n\\end{displaymath}\nAny automorphism of $R$ induces an action on the fan $\\Sigma(R)$, which yields a homomorphism $\\phi: \\text{Aut}(R) \\to \\text{Aut}(\\Sigma(R))$.\n\n\\begin{lem} \\label{lem:X(R)FanAut}\n The map $\\phi: \\operatorname{Aut}(R) \\to \\operatorname{Aut}(\\Sigma(R))$ is an isomorphism.\n\\end{lem}\n\n\\begin{proof}\n First note that the set $R$ can be reconstructed from $\\Sigma(R)$ by taking the union of the extremal rays generating the dual cones $\\sigma_S^\\vee$ for all $\\sigma_S$. Thus any symmetry of the fan induces a symmetry of $R$. This gives the inverse map to $\\phi$.\n\\end{proof}\n\nHere we focus on the case $R = A_n$. In \\cite{LosevManin}, the authors showed that $X(A_n)$ is a moduli space of rational curves with $(n+1)$ marked points and $2$ poles. Another useful proof appeared in \\cite{BatyrevBlume}. \n\nUsing this perspective, Castravet and Tevelev exhibited an exceptional\ncollection on $X(A_n)$ that is stable under the action of permuting the\nmarked points and flipping the poles, i.e., an $(S_{n+1} \\rtimes\nC_2)$-stable collection. Here we demonstrate that Castravet and\nTevelev's exceptional collection satisfies the conditions of\nProposition~\\ref{prop:blackbox} and hence descends to an exceptional\ncollection on any form of $X(A_n)$ (in characteristic $0$). \n\nTo do this requires a bit of translating divisors and actions from the moduli-theoretic language to the toric language. We recall the moduli-theoretic languge. \n\n\\begin{defn}\n Let $N$ be a set of order $n$. A \\emph{chain of polar} ${\\mathbb P}^1$'s is a $\\left(\\{0,\\infty\\} \\cup N\\right)$-marked linear nodal chain of $\\mathbb{P}^1$'s with $0$ on the left tail and $\\infty$ on the right tail. A chain of polar ${\\mathbb P}^1$'s is \\emph{stable} if \n \\begin{enumerate}\n \\item marked points do not coincide with nodes,\n \\item only $N$-marked points are allowed to coincide,\n \\item each component of the chain has at least three special points (nodes or marked points). \n \\end{enumerate}\n We write $LM_N$ for the corresponding moduli space. We also use $LM_n$ depending on the context. Note that the universal curve over $LM_n$ is isomorphic to $LM_{n+1}$.\n\\end{defn}\n\n\n\\begin{thm}\\label{thm:univcurve}\n The toric variety $X(A_{n-1})$ is isomorphic to $LM_n$. Moreover, if we fix an embedding $A_{n-1} \\to A_n$, the corresponding map $X(A_n) \\to X(A_{n-1})$ is the universal curve.\nMoreover, $X(A_n) \\to X(A_{n-1})$ is a toric morphism.\n\\end{thm}\n\n\\begin{proof}\nThis is \\cite[Theorem 2.6.3]{LosevManin}. See also \\cite[Theorem 3.19]{BatyrevBlume}. \nThe map is consequently toric by \\cite[Proposition~1.4]{BatyrevBlume}.\n\\end{proof}\n\nUnder this isomorphism, the closures of the torus orbits on $X(A_n)$ have the\nfollowing moduli-theoretic description.\nFix a partition $N_1 \\sqcup N_2 = N$ and let $\\delta_{N_1}$ denote the\ndivisor parametrizing polar chains of length exactly $2$ having the\nfirst marked by $N_1$ and the last marked by $N_2$.\nFor a partition with more parts, $N_1 \\sqcup N_2 \\sqcup \\cdots \\sqcup N_t = N$,\none has the locus $Z_{N_1,\\dots,N_t}$ parametrizing polar chains of length exactly $t$,\nwhere the $i$-th ${\\mathbb P}^1$ is marked by $N_i$.\nThese loci are precisely the proper torus orbit closures on $X(A_n)$.\n\nNote that each loci is a complete intersection\n\\begin{displaymath}\n Z_{N_1,\\dots,N_t} := \\delta_{N_1} \\cap \\delta_{N_1 \\cup N_2} \\cap \\cdots \\cap \\delta_{N_1 \\cup \\cdots \\cup N_{t-1}}. \n\\end{displaymath}\nMoreover, we have an isomorphism \n\\begin{displaymath}\n Z_{N_1,\\dots,N_t} \\simeq LM_{N_1} \\times LM_{N_2} \\times \\cdots \\times LM_{N_t}\n\\end{displaymath}\nwhere the left node of each ${\\mathbb P}^1$ is marked with $0$ and the right node is marked with $\\infty$.\nThus, we have toric morphisms \n\\begin{displaymath}\n i_{N_1,\\ldots,N_t} : LM_{N_1} \\times LM_{N_2} \\times \\cdots \\times LM_{N_t} \\to LM_N \\ .\n\\end{displaymath} \nAlso, for each subset $K \\subset N$, we get a forgetful map $\\pi_K: LM_N\n\\to LM_K$, which is a toric morphism since it is a composition of maps\nfrom Theorem~\\ref{thm:univcurve}.\n\nRecall there is a set of line bundles $\\mathbb{G}_N$ on $LM_N$ \\cite[Definition 1.5]{CT}, and one generates a larger set $\\mathsf{H}_N$ of sheaves via\n\\begin{displaymath}\n \\mathsf{H}_N := \\left \\lbrace \\left(i_{N_1,\\ldots,N_t}\\right)_*\n(G_{l_1} \\boxtimes \\ldots \\boxtimes G_{l_t})\n\\mid \\forall N_1 \\cup \\cdots \\cup N_t = N \\ , \\ G_{l_j}\n\\in \\mathbb{G}_{N_j} \\right \\rbrace,\n\\end{displaymath}\nwhere $i_{N_1,\\ldots,N_t}: Z_{N_1, \\ldots, N_t} \\hookrightarrow LM_N$ is the inclusion. \n\n\\begin{thm}\\label{thm:CTexcp}\n There is an ordering on the set \n \\begin{displaymath}\n \\mathsf{CT}_N :=\n\\mathsf{H}_N \\cup \\left(\n\\bigcup_{K \\subsetneq N} \\{ \\pi_K^\\ast E \\mid \\ E \\in \\mathsf{H}_K \\} \\right)\n\\cup \\{ \\mathcal{O} \\}\n \\end{displaymath}\nmaking it into an $(S_N \\rtimes C_2)$-stable exceptional collection under permutations of the two sets of markings.\n\\end{thm}\n\n\\begin{proof}\n This is \\cite[Proposition 1.5]{CT}.\n\\end{proof}\n\n\\begin{prop}\\label{prop:CTaut=rootAut}\n The action of $S_{n+1} \\rtimes C_2$ given by permuting the two sets of marked points corresponds to the action of $\\operatorname{Aut}(A_n)$ on $X(A_n)$. \n\\end{prop}\n\n\\begin{proof}\n We use the standard presentation of the root system for $A_n$ as $e_i - e_j$ for $1 \\leq i < j \\leq n+1$ and follow \\cite[Construction 3.6]{BatyrevBlume}. The embedding $A_n \\hookrightarrow A_{n+1}$ gives the universal curve $X(A_{n+1}) \\to X(A_n)$. For $i \\in \\{1,\\ldots,n\\}$, we take the $(n+1)$ projections $A_{n+1} \\to A_n$, whose kernels are generated by $e_i - e_{n+1}$ for $1 \\leq i \\leq n+1$. These give sections $s_i : X(A_n) \\to X(A_{n+1})$. Finally, for the polar sections, we have the dual vector $v_{n+2}$. The vectors $v_{n+2}$ and $-v_{n+2}$ give toric invariant divisors which are isomorphic to $X(A_n)$ \\cite[Proposition 1.9]{BatyrevBlume}. The isomorphisms give the other sections $s_0$ and $s_{\\infty}$. \n \n The Weyl group is the permutation group of the $e_i$, and hence of the $e_i - e_{n+2}$. In particular, it permutes the $s_i$. The outer involution acts on the fan by negation and thus exchanges the cone corresponding to $v_{n+2}$ with the cone corresponding to $-v_{n+2}$.\n\\end{proof}\n\n\\begin{cor}\\label{cor:CTisstable}\n The set $\\mathsf{CT}_N$ is $\\operatorname{Aut}(\\Sigma(A_n))$-stable. \n\\end{cor}\n\n\\begin{proof}\n This is an immediate corollary of Lemma~\\ref{lem:X(R)FanAut} and Proposition~\\ref{prop:CTaut=rootAut}. \n\\end{proof}\n\n\\begin{prop}\\label{prop:CTistoric}\nEach object in the collection $\\mathsf{CT}_N$ is torus-equivariant. \n\\end{prop}\n\n\\begin{proof}\nLine bundles are always isomorphic to torus-equivariant line bundles,\nso all objects in $\\mathbb{G}_N$ are torus-equivariant.\nThere is a canonical equivariant structure on tensor products and on\npullbacks by equivariant morphisms (see \\S{2}~of~\\cite{BFK2});\nthus each object $G_1 \\boxtimes \\ldots \\boxtimes G_n$\nis torus-equivariant for $G_{l_j} \\in \\mathbb{G}_{N_j}$.\nLet $i : Z \\to X$ be shorthand for some map $i_{N_1,\\ldots,N_t}$.\nThere is a splitting of tori $T = S \\times S'$ where $Z$ is an $S$-toric\nvariety and $S'$ acts trivially on $i(Z)$.\nLet $\\psi : T \\to S$ denote the projection. \nWe have a composition of functors\n\\[\n\\mathsf{D^b}(\\operatorname{Coh}_S Z) \\to\n\\mathsf{D^b}(\\operatorname{Coh}_T Z) \\to\n\\mathsf{D^b}(\\operatorname{Coh}_T X)\n\\]\nwhere the first map is the functor $\\operatorname{Res}_\\psi$\n(\\S{2.9}~of~\\cite{BFK2}) and the second map is the $T$-equivariant\npushforward (\\S{2.5}~of~\\cite{BFK2}).\nThis composition reduces to the ordinary pushforward\n$i_\\ast : \\mathsf{D^b}(Z) \\to \\mathsf{D^b}(X)$ when the equivariant\nstructure is forgotten. We conclude that each object of\n$\\mathsf{H}_K$ is torus-equivariant\nand the result follows.\n\\end{proof}\n\nWe now prove the main result of this section:\n\n\\begin{prop}\\label{prop:X(An)excpcoll}\n Let $k$ be a field of characteristic zero and $X$ a form of $X(A_n)$ over $k$. Then $X$ admits a full exceptional collection of sheaves. \n\\end{prop}\n\n\\begin{proof}\n Combining Theorem~\\ref{thm:CTexcp}, Corollary~\\ref{cor:CTisstable}, and\nProposition~\\ref{prop:CTistoric} allows us to appeal to\nProposition~\\ref{prop:blackbox} and conclude that $\\mathsf{CT}_N$\ndescends to an exceptional collection of sheaves on $X$. \n\\end{proof}\n\n\\begin{rem}\n To remove the characteristic zero assumption one needs to extend generation results of \\cite{CT} to nonzero characteristic. This could conceivably be done by reversing the flow of reasoning in \\cite{CT}, using the fact we know the collections for $V_n$ in any characteristic. We do not pursue this. \n\\end{rem}\n\n\n\n\\bibliographystyle{alpha}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{} \n\nGalaxy bimodality, described by the red sequence and blue cloud, has been central to our understanding of galaxy evolution since the turn of this century \\citep[e.g.,][]{2004ApJ...600..681B,2004ApJ...608..752B,2009ARA&A..47..159B}. Passive galaxies follow a narrow color-magnitude relation while star-forming galaxies in the blue cloud have a broader range of optical colors, resulting from a range of stellar populations, star formation rates and dust obscuration. Although star-forming galaxies are diverse, they do fall along well-established correlations with mass, including the mass-metallicity relation \\citep[e.g.,][]{2004ApJ...613..898T}, star formation rate versus mass \\citep[e.g.,][]{2007ApJ...660L..43N} and declining dust content with decreasing mass \\citep[e.g.,][]{2006ApJ...639..157W}. These relations manifest themselves in broadband photometry, albeit outside the optical wavelength range, as illustrated by the dependence of infrared colors on galaxy type \\citep[e.g.,][]{2011ApJ...735..112J}.\n\nThe far-ultraviolet and mid-infrared are both star formation rate tracers, with the former tracing massive stars while the latter traces blackbody emission from warm dust. While far-ultraviolet luminosity is directly proportional to star formation rate, for $\\lesssim L^*$ galaxies mid-infrared luminosity is proportional to star formation rate to the power of $\\sim 1.3$ \\citep[e.g.,][]{2015AA...584A..87C,2017ApJ...847..136B}, which is a consequence of dust content varying with galaxy mass. We thus expect a blue sequence to be present in ultraviolet-infrared color-magnitude diagrams.\n\nTo measure the ultraviolet-infrared color-magnitude relation, we use the local galaxy sample of \\citet{2014ApJS..212...18B, 2017ApJ...847..136B} and their multiwavelength matched aperture photometry (in AB magnitudes). We limit the sample to galaxies with $m_{W2}-m_{W3}>-0.5$, which excludes passive galaxies from the \\citet{2014ApJS..212...18B} sample, and we remove active galactic nuclei with the emission line ratio criterion of \\citet{2003MNRAS.346.1055K}. To correct the GALEX $FUV$ photometry for internal dust obscuration we use $A_{FUV} \\propto (M_{FUV} - M_{NUV})$, leaving the constant as a free parameter that we use to minimize the scatter of the color-magnitude relation. \n\nIn Figure~\\ref{fig:thefigure} we present the ultraviolet-infrared color-magnitude plot of $z\\sim 0$ star-forming galaxies, using the $FUV$, $NUV$, and WISE $W3$ photometry. We find the best relation is produced when $A_{FUV} = 2.6 (M_{FUV} - M_{NUV})$, which is shallower than the dust extinction relation of \\citet{2011ApJ...741..124H}, where $A_{FUV} = (3.83\\pm 0.48) [M_{FUV} - M_{NUV} - (0.022\\pm 0.024)]$. As a cross check of our results, in Figure~\\ref{fig:thefigure} we also plot photometry of $z<0.05$ GAMA galaxies \\citep{2016MNRAS.460..765W} with WISE $m_{W2}-m_{W3}>-0.5$, and we find good agreement although GAMA spans a smaller range of $M_{W3}$ than \\citet{2014ApJS..212...18B, 2017ApJ...847..136B}. We also observe similar relations when we replace WISE $W3$ with WISE $W4$ or {\\it Spitzer} $24~{\\rm \\mu m}$, albeit with more scatter. \n\n\\begin{figure}\n\\begin{center}\n\\includegraphics[width=0.49\\textwidth,angle=0]{blueseqplot.pdf}\n\\includegraphics[width=0.49\\textwidth,angle=0]{gH.pdf}\n\\caption{The ultraviolet-infrared (left) and $g-H$ (right) color-magnitude relations for star-forming galaxies. Some of the outliers in the $g-H$ are labelled, and these are often merging galaxies rather than spirals. As the \\citet{2014ApJS..212...18B,2017ApJ...847..136B} sample deliberately selected galaxies to span parameter space, it shows more scatter than the magnitude limited GAMA sample. \\label{fig:thefigure}}\n\\end{center}\n\\end{figure}\n\nThe best-fit color-magnitude relation is given by \n\\begin{equation}\nM_{W3} = -14.8 - 2.1 \\times \\left[ 2.6 (M_{FUV} - M_{NUV}) - M_{W3} \\right].\n\\end{equation}\nUsing the \\citet{2014ApJS..212...18B, 2017ApJ...847..136B} sample, we find the $1\\sigma$ scatter of $M_{W3}$ about the relation is $\\sigma_{W3} =1.6~{\\rm mag}$. If ultraviolet - infrared color was used as a distance indicator then the 68\\% scatter of the distance would be a factor of $\\sim 2$. \n\nWe note color-magnitude relations for blue galaxies have been identified previously, including the median optical color of blue galaxies varying with magnitude \\citep{2004ApJ...600..681B}. Furthermore, \\citet{1982ApJ...257..527T} identified a tight $B-H$ color-magnitude relation for spiral galaxies, and in the right panel of Figure~\\ref{fig:thefigure} we reproduce this relation for star-forming galaxies using SDSS $g$ and 2MASS $H$-band photometry. \\citet{1982ApJ...257..527T} attributed this relation to specific star formation rate, chemical abundances and\/or initial mass function varying with mass. Interestingly, we do see some outliers in the $g-H$ versus $M_H$ diagram, including merging starbursts. These outliers are not unexpected, given $g$ and $H$ trace different galaxy properties, whereas the ultraviolet and mid-infrared are both (primarily) star formation rate tracers. \n\nIn this note we have identified and characterized the ultraviolet-infrared color-magnitude relation of star-forming galaxies. The ultraviolet to mid-infrared flux ratios of star-forming galaxies span over two orders of magnitude and show a clear dependence on absolute magnitude from $M_{W3}\\sim -13$ to $M_{W3}\\sim -25$, which may present problems for models of galaxy spectral energy distributions that have been largely verified on $\\sim L^*$ galaxies. The color-magnitude relation of star-forming galaxies illustrates the (broadband) spectral diversity of star-forming galaxies that results from established correlations between the physical properties and mass, including the mass-metallicity relation.\n \n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction and Background}\n\\label{sec:introduction}\n\nConventional dynamic random-access memory (DRAM)\nscaling has reached a critical tipping point as the miniaturization of the DRAM cell has plateaued in recent years. Feature size scaling below the 20 $\\text{nm}$ technology node is met with numerous challenges such as\nshorter retention times, higher leakage currents, and increased fault rates~\\cite{park2015technology}.\nSolutions to address these concerns include improved DRAM fault detection and recovery~\\cite{wang2017improving}, as well as architectural techniques to enhance DRAM scaling~\\cite{kim2015architectural}. \n\nA promising solution to the memory scaling problem is to realize the main memory system using non-volatile technologies~\\cite{mutlu2015main}. \nExamples of emerging non-volatile memories (NVMs) include spin-transfer torque magnetoresistive random-access memory (STT-MRAM), ferroelectric random-access memory (FeRAM), resistive random-access memory (ReRAM), and phase-change memory (PCM).\nInterest in the commercial application of such NVMs has increased significantly. For instance, Intel's current line of 3D XPoint memory systems utilize PCM-based NVM technology~\\cite{wyrwas2017proton}, and IBM and Everspin's solid-state drive comes with STT-MRAM write caches~\\cite{everspin}. While NVMs offer attractive features, such as high density, low leakage, and non-volatile data retention, they also suffer from poor endurance and high access latency in their current implementation.\n\nMemory security has come under more scrutiny over the years.\nThis is because of attacks such as \\textit{Spectre}~\\cite{kocher2019spectre} and \\textit{Meltdown}~\\cite{lipp2018meltdown}, which targets the side-channels associated with speculative execution and out-of-order execution, respectively, have exposed severe vulnerabilities in a wide array of currently deployed processors and their memory architectures. \nIn the case of NVMs, data remanence after power-down\npresents a severe threat to data confidentiality, as attackers aiming to steal private data can do so easily by mounting cold-boot attacks~\\cite{halderman2009lest} or other removal attacks like stealing the memory module (DIMM)~\\cite{young2015deuce}. \nMoreover, magnetic memories like STT-MRAM are highly sensitive to stray magnetic fields. \nAs such, magnetic field-based attacks~\\cite{jang2015self} can be used to corrupt the stored data or compromise the memory's functional integrity, resulting in a denial-of-service (DoS) attack. \nHence, such security vulnerabilities pose a significant impediment to the pervasive and large-scale proliferation of NVMs in the memory industry. \n\n\\subsection{Related work in Memory Security}\nPrior works on securing NVMs have focused mainly on memory encryption schemes, which are necessary to prevent attackers from exploiting data remanence in the off-state. \nChhabra \\textit{et al.} proposed an incremental encryption scheme~\\cite{chhabra2011nvmm} for NVMs where only inert memory pages, which have not been accessed for several clock cycles, are encrypted selectively. \nThe working set of the memory (which is in current use) is in plaintext and, hence, incurs no encryption overhead on access. Such a selective encryption ensures that the majority of the main memory content (but not all) remains encrypted at all times, without overly compromising the performance. \nHowever, it requires dedicated hardware, inert page prediction, and scheduling for its implementation. \nA sneak-path encryption (SPE) scheme was demonstrated for memristor-based NVMs in~\\cite{kannan2014secure}, wherein sneak paths in the memristor crossbar array are exploited to apply encryption pulses to change the resistances of the memory cells, and hence, encrypt the stored data. \n\nIn~\\cite{young2015deuce}, the authors proposed DEUCE, a dual counter encryption for PCM memories, which significantly reduces the number of modified bits per writeback, to improve performance and lifetime of the memory. \nThis scheme aims to mitigate the impact of the avalanche effect~\\cite{mandal2012performance} occurring during memory encryption, by re-encrypting and writing back only the modified words during any write operation. Swami~\\textit{et al.} took this concept forward and proposed SECRET~\\cite{swami2016secret}, a smart encryption scheme for NVMs, which integrates word-level re-encryption and zero-based partial writes to reduce memory write operations. They also demonstrate write optimization through the use of\n``energy masks'' (i.e., bit templates XORed with ciphertext to obtain lower energy dissipation)\nin the encryption XOR logic, which minimizes the bit flips in the encryption process, thereby reducing the total write energy.\nAn advanced counter-mode encryption (ACME) was presented in~\\cite{swami2018acme}, which utilizes the write leveling architecture inherent in PCM memories, to perform counter-write leveling. \nACME helps to avoid \\textit{Rowhammer}-type attacks by preventing the counter associated with any single cache line from overflowing.\n\nThe impact of contactless tampering on STT-MRAMs using external magnetic fields was highlighted in~\\cite{jang2015self}. \nUsing micromagnetic simulations, the authors of~\\cite{jang2015self}\nshowed how magnetic field-based attacks could corrupt the contents of STT-MRAM cells. Techniques to protect against contactless attacks proposed in~\\cite{jang2015self} included (i) an on-chip sensor to detect magnetic field-based incursions, and (ii) error correction modules to compensate cell failures arising due to magnetic field attacks. However, these techniques incur large energy and area penalties due to the additional hardware imposed by the magnetic field sensor and the error correction scheme.\n\n\\subsection{Contributions}\nIn this paper, we present an alternative to conventional NVMs such as STT-MRAM and PCM, in the form of \\textit{SMART: A Secure Magnetoelectric Antiferromagnet-Based Tamper-Proof Non-Volatile Memory}. \nSMART memory leverages the room-temperature linear magnetoelectric (ME) effect in antiferromagnets (AFMs) like chromia~\\cite{rado1961observation}, which can be switched solely using voltage pulses, without the use of electric currents, leading to ultra-low energy ($\\sim$ pico-Joules) operation. \nFurther, the intrinsic dynamics of AFMs is typically in the terahertz regime ($\\sim 10^{12}$ Hz)~\\cite{khymyn2017antiferromagnetic}, which could enable picosecond time-scale reversal of the AFM domain. \nIn addition to its energy and latency benefits, SMART memory offers a significant advancement in terms of secure and tamper-proof data storage. \nFor example, AFMs do not exhibit a magnetic signature since they do not have a net external magnetic moment, unlike ferromagnets (FM). \nHence, the SMART memory cannot be probed or switched with external magnetic fields, unlike the way STT-MRAMs can. \nThis, in turn, eliminates the possibility of magnetic field attacks undermining data integrity or aiming to induce DoS. \nTo address the post-shutdown data remanence of SMART memory, we demonstrate an in-memory encryption scheme employing ME-AFM transistor-based controlled-NOT (CNOT) logic. \nWe discuss the resilience of the SMART memory against attacks aiming to undermine data confidentiality and data fidelity, in both powered-on and powered-off states. \nThe main contributions of this work can be summarized as follows:\n\n\\begin{enumerate}\n \n\\item We discuss the design of SMART, a secure ME-AFM-based NVM and implement its SPICE circuit model to simulate the memory performance. \n \n\\item We demonstrate the resilience of SMART memory against magnetic field and temperature attacks, which can affect other NVMs like STT-MRAM. We explore the implications of various side-channel attacks on the SMART memory.\n \n\\item We present an in-memory encryption scheme with ME-AFM transistor-based CNOT gates, called \\textit{Memcryption}, to protect the data stored in SMART memory against cold-boot and stolen DIMM attacks, while incurring low encryption latency overheads.\nWe like to mention here that \\textit{Memcryption} is specifically tailored for the ME-AFMRAM, not for a generic NVM. Also, it does not secure the memory system against \\textit{bus snooping} attacks; such attacks are beyond the scope of this work.\n\n\\end{enumerate}\n\nIn the next section, we describe the modeling, implementation and benchmarking of the proposed ME-AFM memory both at cell- and array-level, before proceeding to evaluate its security properties in Section~\\ref{sec:security}.\n\n\\section{Device model and functionality}\n\\label{Modeling}\n\\subsection{The magnetoelectric effect}\nThe linear ME effect~\\cite{agyei1990linear} represents the coupling between applied magnetic field and induced polarization or between applied electric field and induced magnetization in non-centrosymmetric crystals like chromia ($\\text{Cr}_2\\text{O}_3$). Compared to the STT-based magnetization reversal of FMs requiring electric currents on the order of $\\sim10^6$ A\/cm$^2$ and incurring associated Joule heating, the ME effect provides an energy-efficient, all-electrical switching of the roughness-insensitive boundary magnetization of chromia~\\cite{echtenkamp2013electric}. Additionally, chromia is an AFM; hence, the net bulk magnetic moment\n(i.e., the difference of the sublattice magnetization vectors) vanishes and becomes imperceptible externally. \nHowever, the boundary magnetization is strongly coupled to the AFM order parameter. That is, the electrical switching of the AFM order results in reversal of the boundary magnetization~\\cite{wu2011imaging}, which is used to encode the information in ME-AFM memories.\n\nThe uncompensated surface moments at the (0001) surface of chromia result in an equilibrium boundary magnetization, which could be in one of the two oppositely aligned,\ndegenerate domain states. \nThe degeneracy between the domains is lifted through ME annealing, which allows the preferential selection of one of the states~\\cite{he2010robust}. That is, the ME annealing polarizes the surface and results in\na single-domain surface moment. \nIsothermal switching between these single domain states using an electric field $E$ and a small, symmetry-breaking\nDC magnetic field $H$ has been demonstrated experimentally~\\cite{he2010robust, fallarino2015magnetic}. \nThe critical condition for such ME switching is that the magnitude of the $E\\cdot H$ product must exceed the ME threshold energy barrier, which was shown experimentally to be as low as $\\approx$ 1 J\/m$^3$~\\cite{brown1969domain, martin1966antiferromagnetic}.\n\n\\subsection{ME-AFMRAM : Working principle}\nThe chromia-based ME-AFMRAM, which is at the heart of our SMART memory, is shown in Fig.~\\ref{fig:AFMRAM}. Experimentally demonstrated by Kosub \\textit{et al.}~\\cite{kosub2017purely}, the ME-AFMRAM has a bottom gate electrode (Platinum gate in the figure) for applying the gate voltage $V_G$ and providing the necessary electric field to write data into the memory. A small, symmetry-breaking magnetic field ($\\approx$ 30 mT) is provided by the stray field of a permanent magnet. A positive voltage $V_G$ will orient the bulk order and, hence, put the surface magnetization in one domain (with surface moments pointing up), whereas a negative voltage will result in the surface magnetization relaxing to the opposite domain (with surface moments pointing down). These two states correspond to binary levels `1' ($V_G > 0$) and `0' ($V_G < 0$), respectively. A gate voltage of 0 V corresponds to the `hold' mode of the memory cell. Note that the cell serves as non-volatile memory in all gate-voltage ranges, not only for $V_G = 0$.\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[scale=0.28]{figures\/AFMRAM.pdf}\n\\caption{Chromia-based magnetoelectric antiferromagnetic random-access memory. Data (1\/0) is written by applying a voltage ($+\/-$) to the bottom gate electrode. Read-out is achieved using an anomalous Hall bar electrode placed on top, by applying a Hall bias.}\n\\label{fig:AFMRAM}\n\\end{figure}\n\nThe read-out is achieved using an anomalous Hall (AH) bar electrode setup, which discerns the boundary magnetization of chromia by sensing the proximity effect-induced magnetization in the nearby Platinum (Pt) electrode, thereby producing a \nproportional Hall voltage $V_{\\text{xy}}$ (or $V_{\\text{AHE}}$)~\\cite{kosub2015all}. Traditionally, the order parameter of AFMs is read-out via an exchange bias arrangement~\\cite{toyoki2015magnetoelectric} in another FM attached adjacently to the AFM surface. However, the exchange bias and the FM's hysteresis increase the coercive voltage required to overcome the ME barrier and, hence, impact the write energy negatively. To avoid this effect, Kosub \\textit{et al.}~\\cite{kosub2017purely} proposed the use of an exclusively ME-AFM setup with an AH read-out of the surface magnetization, thereby eliminating the need for an FM.\nAt the time of writing this paper, a complete physical understanding of the read-out mechanism for the boundary magnetization in chromia is lacking. While the authors in~\\cite{kosub2017purely} have considered an AH-based read-out in their device, recent experiments by C. Binek's group at the University of Nebraska-Lincoln have revealed the contribution of spin-Hall magnetoresistance (SMR) to the read-out signal, which is currently being investigated.\nHowever, note that the magnitude of the signal levels is the same in both cases (AH versus SMR) and also the circuit models developed would remain the same, though with different input parameters. \nFor the purposes of this paper, we consider that the read-out signal is due to the AH effect in the proximal heavy metal, as also discussed in prior experimental work.\n\n\\subsection{Performance modeling}\nThe ME reversal mechanism in chromia can be classified broadly into two categories, depending on the size of the film compared to the characteristic domain-wall (DW) width. For chromia, the typical DW width $\\lambda =\\sqrt{A\/\\mathcal{K}}\\sim$ 50-100 nm, where $A$ is the exchange stiffness constant and $\\mathcal{K}$ is the uniaxial anisotropy energy~\\cite{belashchenko2016magnetoelectric}. \nIf the sample is much smaller than the DW width, the sample reverses via coherent rotation upon application of the ME pressure. For sample dimension comparable to the DW width, ME reversal occurs via DW nucleation and propagation, which is an incoherent switching process.\nFor both coherent rotation and DW propagation, the reversal could be thermally activated for applied ME pressure lower than the energy barrier between the stable domain states. Otherwise, the domain reversal proceeds in the `flow' regime~\\cite{parthasarathy2019dynamics}.\nME-AFMRAM devices currently fabricated have dimensions in the $\\mu$m range, rendering DW propagation the favorable ME reversal mechanism. To characterize the functionality and performance of chromia ME-AFMRAM, we develop circuit models that represent DW-based reversal in both the thermally activated and the flow regimes. We also provide perspectives and future\npotential concerning dimensional scaling of the device, which could enable ultra-fast, coherent, rotation-based reversal. \n\n\\subsubsection{DW reversal of chromia ME-AFMRAM}\nConsider a chromia sample, where the applied ME pressure creates a pressure difference of $\\mathcal{F} =|2\\alpha_{\\text{ME}} E H|$ between the two domains. Here, $\\alpha_\\mathrm{ME}$ is the linear ME coefficient.\n\nIf $\\mathcal{F}> \\mathcal{F}_d$ (i.e., for DW de-pinning pressure), the DW propagates as a viscous flow with velocity given as~\\cite{parthasarathy2019dynamics}\n$$\\nu_{\\text{flow}} = \\frac{\\alpha_{\\text{G}}\\gamma \\lambda}{\\alpha_+\\xi^2}\\Big( \\frac{\\mathcal{F}-\\mathcal{F}_{\\text{d}}}{M_{\\text{s}}} \\Big),$$\nwhere $\\alpha_{\\text{G}}$ is the Gilbert damping constant, $\\gamma$ is the gyromagnetic ratio of electron, $M_{\\text{s}}$ is the sublattice saturation magnetization, and $\\xi=\\frac{\\alpha_{\\text{ME}}E}{\\mu_0 M_{\\text{s}}}$. \nFor a mean free path of $l$ of the DW, the time-scale of ME reversal due to viscous DW propagation is $\\tau_{\\text{flow}}=l\/\\nu_{\\text{flow}}$.\n\nIf $\\mathcal{F}<\\mathcal{F}_{\\text{d}}$, the DW undergoes thermal creep to overcome the de-pinning barrier, with a time-scale~\\cite{parthasarathy2019dynamics}\n$$\\tau_{\\text{creep}}=\\sqrt{\\frac{\\sigma \\mathcal{S}^3}{kT}}\\Big(\\frac{\\mathcal{F}_{\\text{d}}-\\mathcal{F}}{2\\pi\\epsilon}\\Big)\\exp\\Big[{\\frac{\\mathcal{S}^2(\\mathcal{F}_{\\text{d}}-\\mathcal{F})^2}{4\\pi kT\\epsilon}\\Big]},$$\nwhere $kT$ is the thermal energy (25 meV at 300 K), $\\epsilon$, $\\sigma$, and $\\mathcal{S}$\nare the energy, areal density, and surface area, respectively, of the DW. The DW de-pinning pressure is determined by the DW energy, its surface area, and the radius of the non-magnetic de-pinning center.\n\nTo write `1' (`0') into the memory cell, a positive (negative) electric field, $E_\\mathrm{app}$, with a magnitude greater than the critical electric field, $E_\\mathrm{crit}$, is required, in order to meet the DW propagation criteria of $\\mathcal{F}>\\mathcal{F}_d$. In this case, the time to write data into the memory is equal to $\\tau_\\mathrm{flow}$. When $E_\\mathrm{app}$ is less than $E_\\mathrm{crit}$ (i.e., $\\mathcal{F}<\\mathcal{F}_d$), the memory cell is in the hold mode and the retention time is specified by $\\tau_\\mathrm{creep}$. For typical parameters of chromia, we find $\\tau_\\mathrm{creep}\\gg \\tau_\\mathrm{flow}$, which ensures that the memory cell is thermally stable when it is not accessed. Here, the stability of the cell is determined by $\\tau_{\\text{creep}}$, since longer data retention requires the time constant in the hold mode to be larger. The retention time of the cell can be further improved by enlarging the cell dimensions.\n\n\\begin{figure*}[ht]\n\\centering\n\\includegraphics[scale=0.19]{figures\/chromia_circuit_final.pdf}\n\\caption{Equivalent circuit for the chromia ME-AFMRAM cell. $I_{\\text{int}}$, derived from the bit line, writes data on to the node $V_{\\text{ME}}$. The time constant of the write operation is $\\tau_{\\text{flow}}$ ($\\tau_{\\text{creep}}$) if the applied voltage is greater (smaller) than the critical voltage. Read-out is achieved through an AH setup, modeled with a voltage-controlled voltage source. $\\text{C}_\\text{EL}$ is the electrostatic capacitance of the chromia dielectric.}\n\\label{fig:chromia_RC}\n\\end{figure*}\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[scale=0.37]{figures\/MERAM_transient.pdf}\n\\caption{Transient simulations showing write operations on the chromia ME-AFMRAM cell. Note that for writing a `1' the write pulse is positive, and for writing a `0' the write pulse is negative. In this simulation, a series of `1's (0.3 V) and `0's (-0.3 V) are being written to the cell, and then finally `0' is retained once Write Enable is switched off.}\n\\label{fig:AFMRAM_timing}\n\\end{figure}\n\nWe construct a SPICE circuit model to functionally capture the ME reversal dynamics of chromia. The time constant for reversal of the magnetization of chromia due to an applied ME pressure is represented as $R_{\\text{eq}}\\times C_{\\text{eq}}$. Without loss of generality, the circuit model uses $R_{\\text{eq}} = 1$ $\\Omega$, while $C_{\\text{eq}}$ is either $\\tau_{\\text{flow}}$ or $\\tau_{\\text{creep}}$. \nTo construct the full ME-AFMRAM cell, we combine the RC model of the ME response of chromia with the peripheral read\/write circuitry in \\textit{Cadence Virtuoso} using the 15-nm CMOS FreePDK technology. Figure~\\ref{fig:chromia_RC} shows the equivalent circuit of the ME-AFMRAM cell. \nThe write pulse, used to charge the chromia dielectric and switch its\nmagnetization $M$, is provided through the current source $I_{\\text{int}}$ (derived from the bit line) in the write setup. \nFor parameters of chromia listed in Table~\\ref{tab:params}, \n$C_{\\text{flow}}=\\tau_{\\text{flow}}\\sim0.223$ nF, $C_{\\text{creep}}=\\tau_{\\text{creep}}\\sim1$ mF, and $V_{\\text{crit}}= 0.2$ V. For $|V_G| > 0.2$ V, $V_{\\text{ME}}$ tracks $V_G$ and data is written into the cell after a write access latency of $\\tau_\\mathrm{flow}$. \nWhen $|V_G| = 0$ V, data is retained for a time interval of $\\tau_{\\text{creep}}$. Since $\\tau_{\\text{creep}}$ is very large, the response in retention\/creep mode is extremely slow as compared to write\/flow mode. The transient response of the ME-AFMRAM cell is shown in Fig.~\\ref{fig:AFMRAM_timing}, to highlight the write operation. The write latency of the ME-AFMRAM cell is obtained as $\\sim 0.63$ ns, and the energy-per-bit for one write operation is $\\sim 0.063$ pJ, including the energy required to charge the electrostatic capacitance of chromia.\nGiven relative dielectric permittivity of 11 and dimensions noted in Table~\\ref{tab:params}, the electrostatic capacitance of chromia is calculated as $5.8$ aF.\n\n\\subsubsection{Anomalous Hall read-out}\nTo evaluate the read cycle, we set the signals WE to 0 and RE to 1 in Fig.~\\ref{fig:chromia_RC}. The read setup is designed to sense the boundary magnetization of chromia through an AH arrangement, which transduces the magnetization into a voltage signal. This transduction process is modeled using a voltage-controlled voltage source\n(VCVS). Typically, a heavy metal such as Pt is used to sense the proximity effect-induced moment from the coupled chromia layer~\\cite{kosub2017purely}.\n\nThe AH voltage sensed from the Hall bar arrangement is given as~\\cite{griffiths2017anomalous}\n$$V_{\\text{AHE}}=\\Big(\\frac{\\mu_0R_{\\text{s}}}{t_{\\text{Hall}}}I_{\\text{Hall}}\\Big)M_{\\text{z}},$$\nwhere $\\mu_0$ is the vacuum permeability, $R_{\\text{s}}$ is the AH coefficient, $I_{\\text{Hall}}$ is the Hall bias current, $t_{\\text{Hall}}$ is the thickness of the Hall layer and $M_{\\text{z}}$ is the proximity effect-induced magnetization. In the case of Pt\/Cr$_2$O$_3$, $R_{\\text{s}}$ is only about $\\sim5$ p$\\Omega$m\/T for $t_{\\text{Pt}}=10$ nm and $T=300$ K~\\cite{meyer2015anomalous}. This results in an AH signal $V_{\\text{AHE}}\\sim$ 0.3 $\\mu$V, considering a Hall bias of 2 mA and a magnetoelectric node voltage $V_{\\text{ME}}=0.3$ V. The Hall signal can be raised to $\\sim$ 1 $\\mu$V by increasing $V_{\\text{app}}$ to 1 V, and further enhanced by applying a larger Hall bias. However, doing so would negatively impact the energy consumed in the read operation. Sensing such a low $\\mu$V-range AH signal would require sophisticated instrumentation sense amplifiers that are area- and power-prohibitive\n(e.g., 2.5 mm$^2$ area and $\\sim$mW-range power~\\cite{witte2008current}).\n\nThis problem can be addressed by exploring other material systems with much higher interfacial spin-orbit coupling (SOC), resulting in larger AH coefficients. \nIn~\\cite{zhang2014effective}, a Pt\/Co\/Pt tri-layer is shown to exhibit $R_{\\text{s}}\\sim7.3\\times 10^{-10}$ $\\Omega$m\/T at 300 K for $t_{\\text{Co}}\\sim$ 10 nm, resulting in $V_{\\text{AHE}}\\sim$ 43.8 $\\mu$V at a Hall bias of 2 mA and $V_{\\text{ME}}=0.3$ V. \nMagnetic semiconductors like EuTiO$_3$ possess higher $R_{\\text{s}}\\sim$ $8\\times 10^{-9}$ $\\Omega$m\/T for $t_{\\text{EuTiO}_3}=$ 25 nm~\\cite{takahashi2018anomalous}. However, AH signals in such samples have been detected only at very low temperatures, of 2K, at which the ME effect in Cr$_2$O$_3$ vanishes.\n \nThe Hall signal could be improved in a topological insulators (TI) due to the presence of high SOC-enhanced surface states. \nFor example, the Bi$_2$Se$_3$\/LaCoO$_3$ stack considered in~\\cite{zhu2018proximity} demonstrates $R_{\\text{s}}$ as high as $\\sim1.59$ $\\mu\\Omega$m\/T at 100 K for $t_{\\text{Bi}_2\\text{Se}_3}\\sim$ 20 nm. \nThis results in a substantial improvement in the AH signal generated (i.e., $\\sim47.7$ mV). \nThe AH effect in the Bi$_2$Se$_3$\/LaCoO$_3$ interface is ascribed to the exchange coupling between the Bi$_2$Se$_3$ layer and the ferromagnetic LaCoO$_3$ layer via the proximity effect, and is enhanced by the high interfacial SOC. \nSimilarly, the (BiSb)$_2$Te$_3$\/TIG system considered in~\\cite{tang2017above} achieves a mV-range AH signal, though much closer to room temperature.\nA comparison of $R_{\\text{s}}\/t$ in various material systems is illustrated in Fig.~\\ref{fig:R_AHE}. \nAs can be inferred, TIs are an ideal material candidate to implement the AH read-out layer with Cr$_2$O$_3$ due to the potential of a $\\sim$mV-range AH signal, which can be easily read-out using a normal current latch sense amplifier~\\cite{kobayashi1993current}, i.e., without the need for sophisticated sensing equipment.\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[scale=0.33]{figures\/R_AHE_new.pdf}\n\\caption{Comparison of the AH coefficient per unit thickness and AH signal magnitude in different material systems. The AH signal $V_{\\text{AHE}}$ is calculated for a Hall bias of 2 mA and a magnetoelectric node voltage $V_{\\text{ME}}\\sim$ 0.3 V. TIs with high interfacial SOC exhibit greater AH coefficients and can generate large AH signals, capable of being detected by conventional current sense amplifiers.}\n\\label{fig:R_AHE}\n\\end{figure}\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[scale=0.37]{figures\/MERAM_cell_comparison.pdf}\n\\caption{Benchmarking the ME-AFMRAM cell considered in this work against current state-of-the-art ME-AFMRAM technology, and trends in other emerging non-volatile storage devices from~\\cite{wong2016stanford}. Some important data points in this plot, representing the advances in various NVMs, include~\\cite{jan2012high,gajek2012spin,liu2010ultrafast} for STT-MRAM, \\cite{aratani2007novel,lin2010novel,vianello2012sb} for CBRAM, \\cite{zhao2014ultrathin,sekar2014technology,goux2014role} for RRAM, and~\\cite{matsui2006ta2o5,kim2010high,xiong2013self} for PCM, respectively.\nThe future potential of ME-AFMRAM lies in achieving ultra-fast, coherent rotation-based reversal (sub-100 ps write delay and fJ write energy) through a combination of dimensional scaling and strain-augmentation.}\n\\label{fig:cell_comparison}\n\\end{figure}\n\n\\subsubsection{Coherent rotation-based reversal}\nThe $\\sim$ns-range write latency of the ME-AFMRAM cell can be improved drastically if the chromia order can be switched through coherent rotation. In this case, the entire chromia sample undergoes reversal homogeneously, rather than following the incoherent DW propagation. For $\\mathcal{F}_{d} > 4\\mathcal{K}$, the order parameter switches via damping of gyromagnetic precessions~\\cite{parthasarathy2019dynamics}. However, if $\\mathcal{F}_{d} < 4\\mathcal{K}$, magnetization could switch due to thermal activation.\nHere, the switching time is exponentially dependent on the energy barrier of the sample.\nIn any case,\nit is thermal activation that leads to retention errors.\n\nTo realize coherent rotation in chromia, the applied ME pressure must exceed $4\\mathcal{K} = 2.92\\times 10^4$ J\/m$^3$. For a magnetic field of 0.5 T and $\\alpha_\\mathrm{ME} = 3.1$ ps\/m, the electric field required for coherent rotation is $1.18 \\times 10^{10}$ V\/m. Unfortunately, such a high electric field could lead to dielectric breakdown of chromia, given that the breakdown strength of chromia is $\\sim 2\\times 10^8$ V\/m~\\cite{sun2017local}.\nA potential solution to this challenge is to reduce the effective anisotropy of the sample such that the required threshold electric field scales down. This can be achieved through a variety of techniques, including substitutional alloying and the application of mechanical strain~\\cite{mu2019influence}. It is estimated that the write latency of a strain-augmented ME-AFMRAM cell can reach as low as a few 10's of ps. A comparison of the current state-of-the-art in ME-AFMRAM technology and its future potential versus trends in other emerging storage devices is presented in Fig.~\\ref{fig:cell_comparison}.\n\n\\subsubsection{Material and geometrical parameters of the chromia ME-AFMRAM cell}\n\nThe simulation parameters used in our SPICE models for the chromia ME-AFMRAM are listed in the following Table~\\ref{tab:params}.\n\n\\begin{table}[ht]\n\\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{1mm}\n\\renewcommand{\\arraystretch}{1.3}\n\\begin{tabular}{*{3}{c}}\n\\hline\n\\textbf{Parameter} & \\textbf{Value} & \\textbf{Ref.} \\\\\n\\hline\nSaturation magnetization of Cr$_2$O$_3$, $M_{\\text{s}}$ & $2.6\\times 10^5$ A\/m & \\cite{artman1965magnetic} \\\\ \\hline\nMagnetoelectric coefficient of Cr$_2$O$_3$, $\\alpha_{\\text{ME}}$ & $3.1\\times 10^{-12}$ s\/m & \\cite{hehl2008relativistic}\\\\ \\hline\nUniaxial anisotropy energy of Cr$_2$O$_3$, $\\mathcal{K}$ & $7300$ J\/m$^3$ & \\cite{foner1963high} \\\\ \\hline\nGilbert damping constant of Cr$_2$O$_3$, $\\alpha_{\\text{G}}$ & $2\\times 10^{-4}$ & \\cite{belashchenko2016magnetoelectric}\\\\ \\hline\nThreshold ME pressure to depin DW, $\\mathcal{F}_{\\text{d}}$ & $25$ J\/m$^3$ & \\cite{parthasarathy2019dynamics} \\\\ \\hline\nApplied magnetic field, $H_{\\text{app}}$ & $0.5$ T & \\\\ \\hline\nApplied voltage, $V_{\\text{G}}$ & $0.3$ V & \\\\ \\hline\nLength of cell, $l$ & $60$ nm & \\\\ \\hline\nWidth of cell, $w$ & $60$ nm & \\\\ \\hline\nThickness of cell, $t$ & $10$ nm & \\\\ \\hline\nTemperature, $T$ & $292$ K & \\\\ \\hline\n$\\tau_\\mathrm{creep}$ (@ $\\mathcal{F} = 0$) & $\\sim1$ ms & \\\\ \\hline\n$\\tau_\\mathrm{flow}$ (@ $\\mathcal{F} = 74.2$ J\/m$^3$) & $\\sim0.22$ ns & \\\\ \\hline\n\\end{tabular}\n\\caption{Simulation parameters considered for the ME-AFMRAM cell.}\n\\label{tab:params}\n\\end{table}\n\n\\begin{figure*}[ht]\n\\centering\n\\includegraphics[scale=0.6]{figures\/AFMRAM_organization.pdf}\n\\caption{64KB ME-AFMRAM organization with 4$\\times$1 banks, 2$\\times$1 mats, 4$\\times$2 sub-arrays, and 128$\\times$64 bit cell arrays. Here, the word length is 128 bit. The memory organization is leveraged from~\\cite{dong2012nvsim}.}\n\\label{fig:MERAM_organization}\n\\end{figure*}\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[width=\\columnwidth]{figures\/AFMRAM_array.pdf}\n\\caption{Construction of the ME-AFMRAM cell array used in the memory architecture.\nThe signals BL$_{\\text{i,in}}$ serve to write data into the cells when Write Enable (WE) is on, and signals BL$_{\\text{i,out}}$ serve to read data from the cells when Read Enable (RE) is on.}\n\\label{fig:MERAM_array}\n\\end{figure}\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[scale=0.43]{figures\/comparison_table.pdf}\n\\captionof{table}{Performance comparison of various memory technologies, from~\\cite{yang2013memristive, micron_nandflash, everspin_feram, chang2016resistance, kent2015new}. \nThe write and read latencies for ME-AFMRAM (DW model) are quoted for a 64KB memory with a 128-bit word line, simulated using NVSim~\\cite{dong2012nvsim}. \nThe energy-per-bit metric is for a single bit write onto a cell.}\n\\label{tab:Comparison_table}\n\\end{figure}\n\n\\subsection{ME-AFMRAM array}\n\nTo evaluate the system-level performance of ME-AFMRAM in the context of existing memory technologies, we simulate a 64KB DW-based ME-AFMRAM chip on NVSim, a standard tool for estimating the performance metrics of emerging NVMs~\\cite{dong2012nvsim}. \nThe organization of this 64KB memory, as leveraged from~\\cite{dong2012nvsim}, is shown in Fig.~\\ref{fig:MERAM_organization}. \nThe internal architecture of the ME-AFMRAM cell array, along with the peripheral decoders, drivers and sense amplifiers, constructed at the 15-nm CMOS node, is highlighted in Fig.~\\ref{fig:MERAM_array}. \nThe total write latency of the 64KB ME-AFMRAM, including the parasitics and peripheral latency (133.9 ps) and the dominant cell switching time ($\\sim$630 ps), is obtained as 763.9 ps from NVSim~\\cite{dong2012nvsim}. \nThe write latency can be improved by an order of magnitude via coherent rotation of the order parameter. \nThe total read latency of the chip, obtained from NVSim~\\cite{dong2012nvsim}, is $\\sim$2.3 ns. \nThis includes contributions from the sense amplifier (1.45 ns), bit-line parasitics (3.5 ps), decoders and other peripherals ($\\sim$150 ps), and the dominant AH measurement delay in the Bi$_2$Se$_3$ layer ($\\sim$0.7 ns)~\\cite{kikuchi2016anomalous}. \nState-of-the-art pulsed AH measurement schemes like~\\cite{kikuchi2016anomalous} are capable of operating in the GHz regime.\n\nThe output bit-line sensing can be achieved using a conventional current latch amplifier if a large-SOC material such as a TI is used to generate an AH signal in the range of tens of mV.\nThe read\/write endurance of the ME-AFMRAM is expected to be similar to that of STT-MRAM. A comparison of the performance metrics of the ME-AFMRAM with other memory technologies at the chip-level is presented in Table~\\ref{tab:Comparison_table}. It can be seen that the ME-AFMRAM offers some competitive advantages over other NVMs as well as over conventional memory systems.\n\n\n\\section{Application as Secure Memory}\n\\label{sec:security}\n\nAfter conducting cell- and array-level modeling and benchmarking of the chromia-based ME-AFMRAM, we continue with the implementation of the proposed SMART memory using the ME-AFMRAM.\n\n\\subsection{Threat model}\n\nFirst, we discuss the threat model, defining the strengths and capabilities of attackers, as well as the objectives and consequences of a successful attack. Most but not all attack scenarios presented here are\nspecific to NVMs.\n\n\\begin{itemize}\n\n\\item Attackers can launch cold-boot attacks~\\cite{halderman2009lest}.\nDuring power-down, there is some latency after the power-down sequence initiates until the moment when memory contents are completely secured. An attacker might use this gap to read out memory contents. To circumvent such attacks, memory encryption is typically employed~\\cite{chhabra2011nvmm,swami2018acme}.\n\n\\item Attackers could leverage properties like sensitivity to magnetic fields and temperature fluctuations to corrupt the data or induce a DoS~\\cite{jang2015self}. \nThey may forcibly write specific data patterns \nto memory, which accelerates aging and \ncauses memory failures.\n\n\\item With access to failure analysis equipment, attackers can also resort to advanced invasive attacks. \nThe majority of such attacks\ntarget at the back-end-of-line (BEOL), approaching from the top-most metal layer, which is also referred to as front-side attacks. \nVarious countermeasures have been proposed to protect the front-side, which include protective meshes, shields, and sensors~\\cite{lee19_shield,weiner18}.\nIn any case, \\textit{bus snooping} attacks are considered beyond the scope of this work.\n\n\\item Power-dissipation signatures when reading\/writing `0' and `1' within the NVM can be exploited for side-channel attacks to infer the data, through techniques like differential power analysis (DPA)~\\cite{kocher1999differential} and correlation power analysis (CPA)~\\cite{brier2004correlation}.\n\n\\end{itemize}\n\n\\subsection{Magnetic field and temperature attacks}\n\\label{sec:Mag}\n\nSTT-MRAMs have FM-based MTJs as their basic building blocks. FMs possess a macroscopic magnetization (or magnetic signature) that can be probed or inferred with using an external magnetic field. \nHence, magnetic fields can be used to infer or tamper with the stored data or even cause malfunctions in STT-MRAMs~\\cite{jang2015self}.\nStray magnetic fields as small as 10 mT could cause an unintended bit flip in STT-MRAM cells. Figure~\\ref{fig:STTMRAM} shows the magnetic field-induced bit flip in a representative FM, obtained by solving the Landau-Lifshitz-Gilbert equation for the FM dynamics~\\cite{ament2016solving}.\n\n\\begin{figure}[ht]\n\\centering\n\\subfigure[Trajectory for magnetic field-induced switching of a FM.]{%\n\\label{fig:STTMRAM_traj}%\n\\includegraphics[scale=0.18]{figures\/STTMRAM_traj.pdf}}%\n\\hspace{1ex}\n\\subfigure[Components for magnetic field-induced switching of a FM.]{%\n\\label{fig:STTMRAM_switching}%\n\\includegraphics[scale=0.23]{figures\/STTMRAM_switching.pdf}}%\n\\caption{The FMs in an STT-MRAM can be switched easily using external magnetic fields.}\n\\label{fig:STTMRAM}\n\\end{figure}\n\nAFMs, on the other hand, exhibit no external magnetic signature since their equal and opposite sublattice moments cancel each other out. \nHence, the bulk order parameter cannot be affected by external magnetic fields. \nTo switch the bulk order, staggered fields (opposite sign on opposite sublattices) must be applied on both the sublattice moments, as illustrated in Fig.~\\ref{fig:AFMRAM_field} inset. \nHowever, an external, homogeneous magnetic field is unable to provide such a staggered field arrangement, and hence, ends up canting the sublattice moments in a way wherein the torque due to the external field is exactly balanced by the exchange torque exerted by one sublattice moment on the other~\\cite{baltz2018antiferromagnetic}. \nSince external magnetic fields are unable to reorient the AFM order parameter, the SMART ME-AFMRAM is expected to be resistant to magnetic field attacks described in~\\cite{jang2015self}. \nWe note that switching the ME-AFM surface magnetization state using a combination of $E$ and $H$ fields would require an exact knowledge of the write cycles and the prior state of the surface, as well as\nmeans to control the electric field explicitly, which is to be concealed from an attacker. \n\nWith regards to temperature fluctuation-based attacks, an adversary might increase the ambient temperature of the ME-AFMRAM in an attempt to alter the stored data. \nNote that the N\\'{e}el temperature of pure chromia is 308 K~\\cite{shi2009magnetism}, above which the AFM ordering is destroyed. Hence, the attacker may corrupt the memory by heating it above the N\\'{e}el temperature. \nTo counter this, we consider Boron-doped chromia, whose N\\'{e}el temperature is demonstrated experimentally to be $\\sim400$ K~\\cite{street2014increasing}. \nHence, Boron-doped chromia can increase the resilience of SMART memory against temperature fluctuations. \nThat is because such larger temperature fluctuations (above 400 K) are easier to detect, and countermeasures like interception of such attacks become more feasible.\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[scale=0.3]{figures\/AFM_field_combined.pdf}\n\\caption{The application of a magnetic field is unable to switch the AFM order parameter, even when increasing the field magnitude. \nInset: (a) an external, homogeneous magnetic field may cant the sublattice moments, but it is incapable of rotating the AFM order; \n(b) staggering fields on the sublattice moments produce staggered tangential torques, which can reorient the AFM order.}\n\\label{fig:AFMRAM_field}\n\\end{figure}\n\n\\subsection{Data confidentiality attacks}\\label{Encrypt}\nAs with all NVMs, data remanence in the SMART memory could be exploited by attackers to steal sensitive information. The most effective countermeasure against such data confidentiality attacks, including cold-boot and stolen memory-modules attacks, is to encrypt the data using a secure encryption scheme before storing it in the memory. Advanced memory encryption techniques like counter mode encryption (CME) use block ciphers such as Advanced Encryption Standard (AES) to encrypt a seed using a secret key, in order to generate a one-time pad (OTP). \nThe seed for each write on a memory line consists of a secret key, the line address, and a counter value associated with that line, which is incremented with each subsequent write to the same line. Hence, the generated OTP is unique for each line address, and also for each write operation to the same address. \nThe OTP is then XOR-ed with the plaintext to obtain the ciphertext, which is stored in the non-volatile main memory.\nNote that the secret key used in the AES core is considered inaccessible to the attacker.\n\nDirectly applying XOR-based CME scheme to the SMART memory would result in large encryption overheads. This is because the CME scheme is tailored for NVMs like PCM and STT-MRAM, whose write time is on the order of $\\sim$ns. The access latency of ME-AFMRAM is sub-ns for DW-based propagation and few 10's of ps for coherent rotation. A general encryption scheme for SMART memory, switching either via DW propagation or coherent rotation, must be such that the overall memory access latency is not adversely affected. Existing encryption solutions based on CMOS XOR gates with 10's of ps delay are rendered ineffective as their encryption time is comparable to the memory write time, resulting in idle clock cycles.\n\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[scale=0.3]{figures\/Memcryption_scheme.pdf}\n\\caption{(a) CME uses AES to generate an OTP, using the memory line address, a counter, and a secret key. The encryption and decryption is performed outside the non-volatile main memory (NVMM).\n(b) \\textit{Memcryption} uses a secret key and the line address as seed for AES, to generate an encryption pulse. \nThat pulse is used to control the bitwise operation of CNOT gates, and is embedded in the data path within the NVMM.}\n\\label{fig:Memcryption_scheme}\n\\end{figure}\n\n\\begin{figure*}[ht]\n\\centering\n\\includegraphics[scale=0.42]{figures\/Memcryption.pdf}\n\\caption{SMART memory architecture with \\textit{Memcryption}.\nThe CNOT layer for decryption is not shown for simplicity.}\n\\label{fig:Memcryption}\n\\end{figure*}\n\nHere, we propose to use in-memory encryption, or \\textit{Memcryption}, using bitwise CNOT (i.e., controlled-NOT) gates constructed from ME-AFM-based logic. \nBy tying the encryption pulse to the control signals of CNOT gates, one can achieve such \\textit{Memcryption}.\nSpin devices like the ME-AFM transistor~\\cite{dowben2018towards} are able to implement polymorphic logic gates, which can provide inverting or non-inverting functionality based on a control signal~\\cite{patnaik2018advancing,patnaik2019spin}. \nHence, the ME-AFM transistor is used to realize the CNOT gate. Further, the ME-AFM transistor is shown to exhibit delays as small as $\\sim10$ ps, which is substantially faster than CMOS XOR gates and compatible with the SMART memory write-times.\nSuch homogeneity in the technology and materials by using only ME-AFM for both the memory cells and the CNOT gates will ease the fabrication. \nIn \\textit{Memcryption}, we embed ME-AFM transistor-based CNOT gates directly in the data path connected to the memory array; hence, the encryption is in-memory, as opposed to prior works using a separate encryption block. \nThis integration of encryption and memory array is not detrimental to the memory density since ME-AFM transistors have a footprint that is substantially smaller than that of CMOS XOR gates.\nFigure~\\ref{fig:Memcryption_scheme} contrasts our \\textit{Memcryption} scheme with prior CME techniques. \n\nThe SMART memory architecture with \\textit{Memcryption} is shown in Fig.~\\ref{fig:Memcryption}.\nA trusted 128-bit key, provided and stored within a secure processing module (SPM) along with the processor, is concatenated with the memory address and used as seed for AES.\nThe AES core, which is to be integrated on the NVM chip,\\footnote{Heterogeneous spin-CMOS integration is not prohibitive since the underlying AFM technology is compatible with \nCMOS processes in the BEOL. \nIn general, hybrid spin-CMOS designs have been explored in prior works~\\cite{yogendra2015domain}.} thus produces an encryption pulse whose bits are used as the control bits for the CNOT gates of the in-memory encryption layer.\nDepending on the control bits, the encryption layer flips bits selectively in the plaintext before performing a memory-write. \nDuring decryption, the same encryption pulse is generated again and used to perform bitwise CNOT operations on the ciphertext (read from memory), to obtain the plaintext.\n\nA comparison of the \\textit{Memcryption} scheme versus CME (when also applied to ME-AFMRAM) is presented in Table~\\ref{overhead_comparison}. \nThe array considered is a 128-bit ME-AFMRAM, while the AES and CMOS peripherals are synthesized using the 15nm \\textit{NanGate} technology.\nWe observe that Memcryption with SMART memory \nhas a better encryption latency than CME, which utilizes regular CMOS XORs.\nWe also note that \\textit{Memcryption} helps reduce the encryption latency but is similar to CME with respect to the energy overheads. \nThat is because energy dissipation is dominated by the AES core in any case.\nWe also reiterate that \\textit{Memcryption} is tailored specifically as a memory-side scheme \nfor ME-AFMRAM, to achieve low encryption latency, owing to the homogeneous delays of the memory array and the encryption layer. \nHowever, it may not serve well as an efficient implementation for any generic NVM.\n\nWith regards to the reliability and lifetime of the ME-AFMRAM used to construct the SMART memory, its endurance is comparable to that of STT-MRAM. \nHowever, it also suffers from the same errors that plague the STT-MRAM, i.e., faults in the peripheral CMOS circuitry including the access transistors~\\cite{chintaluri2016analysis}. \nTo address these faults and ensure the correctness of the stored data, standard error correction techniques for NVMs~\\cite{swami2017reliable} like the error correction pointer (ECP) and other advanced schemes based on ECP, including ``Pay-As-You-Go''~\\cite{qureshi2011pay} and ``Zombie memory''~\\cite{azevedo2013zombie}, can be implemented memory-side and integrated on the ME-AFMRAM array. \nThe ECP memory can be realized using homogeneous spintronics technology, including the STT-MRAM or the ME-AFMRAM itself, or by leveraging heterogeneous spin-CMOS integration.\n\n\\begin{table}[ht]\n\\centering\n\\footnotesize\n\\renewcommand{\\arraystretch}{1.3}\n\\begin{tabular}{*{3}{c}}\n\\hline\n\\textbf{Encryption technique}\n& \\textbf{Latency} & \\textbf{Energy} \\\\\n\\hline\nCME~\\cite{chhabra2009making} & 299.23 ps (2.99$\\times$) & 17.371 pJ \\\\ \\hline\nMemcryption & 273.46 ps (2.73$\\times$) & 17.370 pJ \\\\ \\hline\n\\end{tabular}\n\\caption{Comparison for latency and energy when applying the CME and \\textit{Memcryption} schemes to a 128-bit ME-AFMRAM array. The baseline latency for the unencrypted array is $\\sim 100$ ps.}\n\\label{overhead_comparison}\n\\end{table}\n\n\\subsection{Power side-channel attacks}\n\\label{Power}\n\nAsymmetric read\/write characteristics in NVMs like STT-MRAM make them susceptible to side-channel attacks which exploit the different signatures incurred when reading\/writing `1's \nand `0's bits. STT-MRAMs employ MTJs with a fixed FM reference layer, \nwith another free layer either oriented parallel or anti-parallel to that reference layer. Depending on the relative orientation of these two layers,\nthe MTJ falls into a low or high resistance state; the low or high state corresponds to logic `0' or logic `1' state, respectively. Hence, the currents drawn for read\/write operations are different depending on reading\/writing a `0' or a `1'. \nThus, an attacker could attach a resistor in a voltage-divider configuration with\nthe MTJ cell, monitor the voltage drops across that resistor, and perform DPA to recover the data being written to or read from the cell. In fact, such an attack was showcased against an STT-MRAM-based cache in~\\cite{khan2017side}.\n\nFor the SMART memory, recall that writing is achieved using electrical fields, not currents. Further, the electric-field magnitude required for writing `0's and `1's is equivalent; see write voltage and polarization voltage traces in Fig.~\\ref{fig:AFMRAM_timing}. This is because there is no reference layer or tunneling magnetoresistance in the ME-AFMRAM, which would cause asymmetricity. As for the read operation, the proximity effect-induced moment in the Pt electrode is slightly different for reading `0' or `1'. \nHowever, this imbalance in the Hall signals can be compensated for by introducing appropriate offsets in the Hall measurement setup, as demonstrated in~\\cite{kosub2017purely}. Hence, the SMART memory can achieve symmetric signatures for both read and write and for both `0$\\rightarrow$1' and `1$\\rightarrow$0' \ntransitions, thus thwarting any DPA-based power side-channel attacks.\n\n\\subsection{Photonic side-channel and \nbackside attacks}\n\\label{Photonic}\n\nLeveraging the photonic side-channel (PSC) to circumvent the security guarantees provided by cryptographic algorithms like AES and RSA has been demonstrated recently~\\cite{ferrigno2008aes,schlosser2013simple}. Simple Photonic Emission Analysis (SPEA) or Differential Photonic Emission Analysis (DPEA) can be carried out using photo-emission equipment available for similar cost as that of power-analysis equipment. The essence of the PSC is to observe photo-emissions emanating for switching of CMOS transistors.\nFor SRAM- or DRAM-based memories, this emission can then be correlated with the data being programmed into the memory. In~\\cite{ferrigno2008aes}, the PSC was found to originate when kinetic energy gained by charge carriers in the transistor channel is transferred to photons, which are visible through photo-detectors. In~\\cite{schlosser2013simple}, the authors leveraged this \ninformation to perform a side-channel attack, ultimately recovering the full AES key. Modern-day chips use several metal layers, which interfere with the emission of photons from the frontside of any integrated circuit (IC); therefore, a natural direction is to observe the photon emission from the backside of ICs. \n\nWhile CMOS-based memory technologies like SRAM and DRAM are prone to such PSC attacks, the SMART memory is AFM-based and involves no photonic emissions emanating from transistor channels. Data read-out in the SMART memory can only be accomplished through an AH measurement setup. Further, even if an advanced attacker is able to isolate the SMART memory cell and gain access to the AH setup from the frontside, they would only be able to recover the encrypted ciphertext (as described in Sec.~\\ref{Encrypt}).\n\n\\section{Conclusion}\n\\label{sec:conclusion}\n\nIn this paper, we present \\textit{SMART: A Secure Magnetoelectric Antiferromagnet $\\!$-Based Tamper-Proof Non-Volatile Memory}, by utilizing the unique properties of ME-AFMs. \nThe ME-AFMRAM, which is at the core of the SMART memory, has an access latency of sub-1 ns (for DW-based switching) down to only 10's of ps (for coherent rotation switching) with an energy-per-bit of $\\sim$ 0.13 pJ. \nBesides its superior performance as compared to prior NVMs like STT-MRAM and PCM, the SMART memory exhibits no sensitivity to external magnetic fields, which makes it resilient to magnetic field-based data tampering and denial of memory service attacks that commonly plague other ferromagnets-based NVMs. \nTo solve the security vulnerability of data remanence (after power-down) in the SMART memory, we demonstrate a new encryption technique called \\textit{Memcryption}. \nThis scheme employs emerging ME-AFM-based logic to implement a CNOT-centric in-memory encryption, which is particularly tailored to reduce the encryption and decryption latency in the SMART memory. \nFurther, symmetric read and write signatures for `0' and `1' bits render prominent side-channel attacks like the differential power attack futile against the SMART memory. \nAdvanced photonic side-channel attacks, which are powerful threats against any CMOS IC by observing all internal transistor activity from the frontside or backside, are ineffective against the SMART memory due to the fundamentally different switching mechanism as well as the proposed \\textit{Memcryption} safeguard.\n\n\\bibliographystyle{IEEEtran}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{Introduction}\nLet $C$ be a smooth complex curve of genus $g>1$,\ndenote by $C^2$ the cartesian product of\n$C$ with itself, by $C^{(2)}$ the \nsecond symmetric product of $C$ \nand by $\\pi\\colon C^2\\to C^{(2)}$ the\ndouble cover $(p,q)\\mapsto p+q$.\nLet $\\Delta$ be the image\nof the diagonal via $\\pi$ and $K$ be\na canonical divisor of $C^{(2)}$.\nIn this paper we are interested\nin the finite generation of the {\\em extended\ncanonical ring}:\n\\[\n R(\\Delta,K)\n \\, :=\\,\n \\bigoplus_{(a,b)\\in{\\mathbb Z}^2}H^0(C^{(2)},a\\Delta+bK).\n\\]\nIt is not difficult to show that finite generation\nis equivalent to ask that the two-dimensional \ncone $\\Nef(\\Delta,K)$ consisting of classes \nof nef divisors within the rational vector space \nspanned by the classes of $\\Delta$ and $K$ \nmust be generated by two semiample classes\n(see proof of Theorem~\\ref{teo-1}).\nThe Kouvidakis conjecture~\\cite{CK} states that,\nfor the very general curve, if $\\Nef(\\Delta,K)$ is \nclosed then $g$ must be a square, \ni.e. $g = (k-1)^2$ for $k>1$ integer. \nWe thus focus our attention to\nthe case of square genus and assume $C$\nto be a complete intersection of a \nquadric $Q$ with a degree $k>2$ hypersurface.\nWe denote by $\\eta_C$ the class of the\ndifference of the two $g_k^1$ of $C$\ninduced by the two rulings of $Q$, which\nis trivial if $Q$ is a cone.\nOur first result is the following.\n\\begin{introthm}\n\\label{teo-1}\nLet $C\\subseteq{\\mathbb P}^3$ be a smooth complete \nintersection of a quadric with a degree $k>2$\nsurface. Then the following are equivalent.\n\\begin{enumerate}\n\\item\n$R(\\Delta,K)$ is finitely generated.\n\\item\n$\\eta_C$ is torsion non-trivial.\n\\end{enumerate}\nMoreover if both the above conditions are satisfied\nthen $\\eta_C$ has order at least $k$.\n\\end{introthm}\nLet $\\mathcal F_k$ be the open subset \nof the Hilbert scheme of curves of bi-degree $(k,k)$ \nof ${\\mathbb P}^1\\times{\\mathbb P}^1$ consisting of smooth curves\nand let $\\mathcal F_k^{\\rm tor}\\subseteq\\mathcal F_k$\nbe the subset consisting of curves $C$ such that\nthe class $\\eta_C$ of the difference between the\ntwo $g_k^1$ is torsion.\nOur second theorem is the following.\n\\begin{introthm}\n\\label{teo-2}\nThe locus $\\mathcal F_k^{\\rm tor}$ is \na countable union of subvarieties of complex dimension \n$\\geq 4k-1$ and the set of subvarieties of\ndimension $4k-1$ is dense in $\\mathcal F_k$\nin the analytic topology.\n\\end{introthm}\nThe paper is organized as follows.\nIn Section~\\ref{sym}, after recalling some basic facts\nabout the symmetric product of a curve,\nwe prove Theorem~\\ref{teo-1}. In Section~\\ref{grid-fam}\nwe introduce the grid family consisting of \ncurves of bi-degree $(k,k)$ on a smooth quadric\nwhich pass through a complete intersection\nof type $(k,0),(0,k)$. We show that the\ngrid family is exactly the subvariety \nof $\\mathcal F_k^{\\rm tor}$ corresponding to \ntorsion of order $k$ and \nhas the expected dimension $4k-1$.\nSection~\\ref{density} is devoted to the proof of Theorem~\n\\ref{teo-2}. In Section~\\ref{hyp} we prove a density\ntheorem for hyperelliptic curves, providing\nan alternative proof for Theorem~\\ref{teo-2}\nin case $g=4$ (see Corollary~\\ref{cor}). \nThis result has an independent \ninterest and is proved in the spirit of Griffiths \ncomputations of the infinitesimal invariant~\\cite{Gr}.\nFinally, in Section~\\ref{exa} \nwe consider examples\nof curves $C$ with $\\eta_C$ torsion.\n\nIn all the paper we work over the field of complex \nnumbers except for Section~\\ref{sym} (see Remark~\\ref{ch}).\n\n\n\\section{The second symmetric product}\n\\label{sym}\nLet $C$ be a smooth projective \ncurve of genus $g>1$ defined over an algebraically \nclosed field ${\\mathbb K}$ of characteristic $0$.\n\\begin{proposition}\\label{pic}\nThe diagonal embedding \n$\\imath\\colon C\\to C^{(2)}$\ninduces an isomorphism \n$\\imath^*\\colon \\Pic^0(C^{(2)})\\to\n\\Pic^0(C)$ of abelian varieties.\n\\end{proposition}\n\\begin{proof}\nTo prove the statement we explicitly construct\nthe inverse map of $\\imath^*$. Given a point\n$p\\in C$ let $H_p$ be the curve of $C^{(2)}$\nwhich is the image of $\\{p\\}\\times C$ via $\\pi$.\nDefine the map ${\\rm Div}(C)\\to {\\rm Div}(C^{(2)})$ by\n$\\sum_in_ip_i\\mapsto\\sum_i n_iH_{p_i}$ and\nobserve that it maps principal divisors to principal \ndivisors. The induced map of Picard groups \nrestricts to a homomorphism $\\Pic^0(C)\\to\n\\Pic^0(C^{(2)})$ which is easily seen to be a \nright inverse of $\\imath^*$.\nSince the two abelian varieties\n$\\Pic^0(C)$ and $\\Pic^0(C^{(2)})$ have \nthe same dimension we conclude that\n$\\imath^*$ is an isomorphism.\n\\end{proof}\nObserve that $\\Delta$ is the branch divisor\nof the double cover $\\pi$ and thus \nits class is divisible by $2$ in $\\Pic(C^{(2)})$.\nMoreover the following linear equivalences\n\\[\n \\Delta|_\\Delta\n \\sim\n -2K_\\Delta\n \\qquad\n \\qquad\n K|_\\Delta\n \\sim\n 3K_\\Delta\n\\]\ncan be proved by passing to $C^2$ \nand calculating the restriction of $K_{C^2}$\nto the diagonal.\nBy the Riemann-Hurwitz formula we get the equalities\n$2(2g-2)^2 = K_{C^2}^2 = 2 (K + \\frac{\\Delta}{2})^2$ \nfrom which we deduce the following \n\\begin{equation}\n \\label{intersections}\n K^2 = (g-1)(4g-9)\n \\qquad\n K\\cdot\\Delta = 6(g-1)\n \\qquad\n \\Delta^2 = -4(g-1).\n\\end{equation}\nIn particular the classes of $\\Delta$ and $K$ are \nindependent in the N\\'eron-Severi group of ${C^{(2)}}$.\nWe let $\\langle \\Delta, K\\rangle$ be the rational \nvector subspace of $\\Pic({C^{(2)}})\\otimes_{\\mathbb Z}{\\mathbb Q}$\ngenerated by the classes of $\\Delta$ and $K$\nand form the following cone\n\\[\n {\\rm Nef}(\\Delta,K)\n =\n \\{D\\in\\langle \\Delta, K\\rangle\\, :\\, D\\text{ is nef}\\}.\n\\]\nThis cone is related to the Kouvidakis conjecture\nwhich predicts which ones are the extremal rays of \n${\\rm Nef}(\\Delta,K)$. In case the genus is a square,\ni.e. $g = (k-1)^2$, the conjecture is known to be\ntrue~\\cite{CK} for a very general curve $C$ and it \nholds as well if $C$ has an irreducible $g_k^1$,\nthat is the curve~\\eqref{eq:gamma} defined below\nis irreducible.\nIn this case the extremal rays of ${\\rm Nef}(\\Delta,K)$ \nare spanned by the classes of $2K+3\\Delta$ and \n$2K+(5-2k)\\Delta$.\n\n\\begin{proposition}\n\\label{ray-1}\nLet $C$ be a smooth curve of genus at least\ntwo. Then the divisor \n$2K+3\\Delta$ of $C^{(2)}$ is semiample.\n\\end{proposition}\n\\begin{proof}\nObserve that the divisor $2K+3\\Delta$\nis big since $K$ is ample and $\\Delta$\nis effective. Moreover it is nef since \n$(2K+3\\Delta)\\cdot\\Delta = 0$.\nWe have an exact sequence of sheaves\n\\[\n \\xymatrix@1{\n 0\\ar[r]\n &\n {\\mathcal O}_{C^{(2)}}(2K+2\\Delta)\\ar[r]\n &\n {\\mathcal O}_{C^{(2)}}(2K+3\\Delta)\\ar[r]\n &\n {\\mathcal O}_\\Delta\\ar[r]\n &\n 0.\n }\n\\]\nSince $2K+2\\Delta = N + K+\\frac{1}{2}\\Delta$,\nwith $N=\\frac{1}{2}(2K+3\\Delta)$ nef and big,\nthen by the Kawamata-Viehweg vanishing\ntheorem and the long exact sequence in \ncohomology of the above sequence we \nconclude that $\\Delta$ is not contained \nin the base locus of $|2K+3\\Delta|$.\nThe statement follows by the ampleness \nof $K$ and the Zariski-Fujita theorem\n~\\cite[Remark 2.1.32]{La}.\n\\end{proof}\n\n\nAssume now that $C$ is a smooth curve\nof genus $g = (k-1)^2>1$ which admits a \n$g_k^1$ and define the following curve \nof $C^{(2)}$:\n\\begin{equation}\n\\label{eq:gamma}\n \\Gamma \\, :=\\, \\{p+q : g_k^1-p-q\\geq 0\\}.\n\\end{equation}\nIt can be easily proved that $\\Gamma$ is irreducible \nif the $g_k^1$ does not contain a $g_r^1$ with $r1$. \nThen ${\\mathcal O}_F(F)$ is torsion non-trivial.\n\\end{lemma}\n\n\\begin{proof}\nWe first show that $\\mathcal L={\\mathcal O}_S(F)$ is not trivial.\nConsider the closure of the graph of $f$ \nin $S\\times \\mathbb P^1$, let \n$\\bar S$ be its minimal resolution and \n$\\bar f:\\bar S\\to \\mathbb P^1$ \nbe the fibration given by the projection \nto the second factor. \nAssume by contradiction that $F\\sim 0$ \nin $S$. Thus $F\\sim \\alpha F'$ in $\\bar S$,\nwhere $F'$ is a divisor with support contained \nin $\\bar S-S=\\bar f^{-1}(\\infty)$ and $\\alpha$ \nis a positive integer. \nSince $h^0(\\bar S, nF)=2$ with $n>1$, then \n$h^0(\\bar S, F)=1$, giving a contradiction.\n\n\nThe line bundle $\\mathcal L$ thus defines a\nnon-trivial \\'etale cyclic covering \n$\\eta\\colon S'\\to S$.\nBy taking the Stein factorization of $f\\circ\\eta$\nwe get a commutative diagram\n\\[\n \\xymatrix{\n S'\\ar[r]^-\\eta\\ar[d]^-{f'} & S\\ar[d]^-f\\\\\n B\\ar[r]^-\\nu & {\\mathbb C},\n }\n\\]\nwhere $f'$ is a morphism with connected \nfibers and $\\nu$ is a finite map.\nIf $\\mathcal L|_{F}={\\mathcal O}_F(F)$ is trivial, then \n$\\nu$ is an\n\\'etale covering of $\\mathbb {\\mathbb C}$, \nsince the restriction of $\\mathcal L$ \nto any fiber of $f$ is trivial.\nThus $\\nu$ is the trivial covering and $B$ \nhas $n$ connected components,\na contradiction since $\\eta$ is non-trivial.\n\nSince $\\mathcal L^{\\otimes n}$ is trivial,\nthen clearly its restriction to $F$ is trivial.\nThis concludes the proof. \n\\end{proof}\n\n\n\\begin{lemma}\\label{triv}\nLet $C$ be a non-hyperelliptic \ncurve of genus $g = (k-1)^2>1$ \nwhich is complete intersection \nof a quadric cone with a degree $k$\nsurface of ${\\mathbb P}^3$. Then the divisor\n$\\Gamma$ of $C^{(2)}$, \ncorresponding to the $g_k^1$ \nof $C$ defined by the ruling of \nthe cone, is not semiample.\n\\end{lemma}\n\\begin{proof}\nWe first show that the line bundle\n${\\mathcal O}_\\Gamma(\\Gamma)$ is trivial.\nIndeed let $Q_t\\subseteq{\\mathbb P}^3\\times\n\\mathbb A^1$ be a family of quadrics\nwhose central fiber $Q_0$ is the cone\ncontaining $C$ and whose general fiber \nis a smooth quadric. Let $\\mathcal D$\nbe a divisor of ${\\mathbb P}^3\\times\\mathbb A^1$\nwhich cuts out on the general fiber $Q_t$\na smooth curve $C_t$ of type $(k,k)$ with two\nsimple $g_k^1$ and $C$ on $Q_0$.\nThe family $\\mathcal C\\to\\mathbb A^1$\nof curves $C_t$ gives a family \n$\\mathcal C^{(2)}\\to\\mathbb A^1$\nwhose general fiber is $C_t^{(2)}$.\nOn any such fiber there are two \ncurves $\\Gamma_t$, $\\Gamma_t'$ \ncorresponding to the two $g_k^1$\non $C_t$. The line bundle \n${\\mathcal O}_{\\Gamma_t}(\\Gamma_t')$ is\ntrivial, by Lemma~\\ref{gamma},\nand its limit is ${\\mathcal O}_\\Gamma(\\Gamma)$,\nwhich proves the claim.\n\nAssume now, by contradiction, that \n$\\Gamma$ is semiample. \nSince $\\Gamma^2=0$,\na multiple $n\\Gamma$ defines a morphism\n$f\\colon C^{(2)}\\to B$, where $B$ is a curve. Moreover,\nafter possibly normalizing, we can assume $B$ \nto be smooth. Now, let $f=\\nu\\circ\\varphi$ be \nthe Stein factorization of $f$, where \n$\\varphi\\colon C^{(2)}\\to Y$ is a morphism with\nconnected fibers. By Lemma~\\ref{covering}\nand the fact that ${\\mathcal O}_\\Gamma(\\Gamma)$ is\ntrivial we deduce that $\\Gamma$ is a union \nof fibers of $\\varphi$. Moreover \nboth the hypotheses of Lemma~\\ref{fib} are satisfied, \nthus $Y$ must be a smooth rational curve.\nLet $H$ be the curve of\n$C^{(2)}$ which is the image of the curve \n$\\{p\\}\\times C$ via $\\pi$.\nThe equality $\\Gamma\\cdot H = k-1$ implies\nthat $\\varphi|_H$ is a covering of $Y$ whose degree \n$d$ divides $k-1$. \nThus $C\\cong H$ would admit two maps to \n${\\mathbb P}^1$ of degrees $k$ and $d$, respectively.\nBeing the degrees coprime, the curve $C$ \nwould be birational to a curve of bi-degree $(k,d)$\nof ${\\mathbb P}^1\\times{\\mathbb P}^1$, whose genus is smaller\nthan $g$, a contradiction.\n\\end{proof}\n\n \\begin{remark}\\label{rem1}\n If $D$ is a prime divisor on a projective \n surface $X$ such that $|D|$\n has dimension $0$ then $D$ is semiample \n if and only if $\\dim |nD| > 0$ for some $n>1$.\n Indeed the ``only if'' part is obvious, while \n the other implication follows from the fact\n that the fixed divisor of $|nD|$ is $mD$ \n for some $m0$\nwhose class $w$ lies in the interior of the cone \n\n\n\\begin{minipage}{0.5\\textwidth}\n\\[\n \\bigcap_{i=2}^r{\\rm cone}(w_1,w_i)\n \\cap {\\rm cone}(w_1,[K])\n \\]\n\\end{minipage\n\\begin{minipage}{0.5\\textwidth}\n\\begin{center}\n \\begin{tikzpicture}[scale=0.65]\n \\draw[-,thick] (0,0) -- (2,0) node[below]{$w_r$};\n \\draw[-,thick] (0,0) -- (-2.2,1.1) node[left] {$w_1$};\n \\draw[-,color=blue] (0,0) -- (0,1.8); \n \\node[right] at (-0.6,2.2){$[K]$};\n \\foreach \\x\/\\y in {-1\/1.5,-1.5\/1.3,-0.5\/1.7} \\draw[-,color=blue] (0,0) to (\\x,\\y);\n \\foreach \\x\/\\y in {1\/1.5,1.5\/1.3, 1.8\/0.8} \\draw[-,color=blue] (0,0) to (\\x,\\y);\n \\fill[black] (0,0) circle (2pt);\n \\fill[black] (2,0) circle (2pt);\n \\fill[black] (-2.2,1.1) circle (2pt);\n \\fill[blue] (-1,1.5) circle (2pt);\n \\fill[blue] (-1.5,1.3) circle (2pt);\n \\fill[blue] (-0.5,1.7) circle (2pt);\n \\fill[blue] (1,1.5) circle (2pt);\n \\fill[blue] (1.5,1.3) circle (2pt);\n \\fill[blue] (1.8,0.8) circle (2pt);\n \\fill[blue] (0,1.8) circle (2pt);\n\\node[above] at (-1,1.5) {};\n \\node[above, red] at (-1.7,1.3) {$w$};\n \\node[above] at (-1,1.5) {$w_2$};\n \\node[above] at (1.7,1.3) {$w_i$};\n\\end{tikzpicture}\n\\end{center}\n\\end{minipage}\n\n\\noindent If $\\Gamma$ is not semiample, then\nany section of $R_w = H^0(C^{(2)},D)$\nis divisible by $f_1$, a contradiction.\nThus we showed that $\\Gamma$ is semiample\nand as a consequence of Lemma~\\ref{triv}\nthe curve $C$ has two $g_k^1$. We denote\nthe corresponding curves of $C^{(2)}$ by \n$\\Gamma$ and $\\Gamma'$.\nA multiple of $\\Gamma$\ndefines a morphism $f\\colon C^{(2)}\\to B$\nonto a smooth curve $B$ whose Stein factorization is \nthe following \n\\[\n \\xymatrix{\n C^{(2)}\\ar[r]^{\\varphi}\\ar[rd]_{f} & Y\\ar[d]\\\\\n & B.\n }\n\\]\nTwo fibers of $\\varphi$ are $n\\Gamma$ and $m\\Gamma'$\nfor some positive rational numbers $n,m$. Since\n$\\Gamma$ is numerically equivalent to $\\Gamma'$ \nby Lemma~\\ref{gamma}, then $n=m$. \nMoreover by Lemma~\\ref{fib} the curve $Y$\nis rational, so that $n\\Gamma$ is linearly equivalent\nto $n\\Gamma'$. This implies that the class\nof $\\Gamma-\\Gamma'$ is torsion non-trivial\nin $\\Pic^0(C^{(2)})$\nand thus the same holds for \n\\begin{align*}\n 2\\eta_C \n & =\n \\text{ramification of $g_k^1$ - ramification of ${g_k^1}'$}\\\\\n & =\n \\imath^*(\\Gamma-\\Gamma').\n\\end{align*}\nWe now show that $(ii)\\Rightarrow (i)$ holds.\nFirst of all observe that if $\\eta_C$ is torsion\nnon-trivial, then the same holds for\n$\\Gamma-\\Gamma'$ by the above equalities\nand the fact that $\\imath^*$ is an isomorphism.\nIn this case $n\\Gamma\\sim n\\Gamma'$ for\nsome positive integer $n$ and this implies\nthat $|n\\Gamma|$ is base point free, being\n$\\Gamma^2=0$. Thus $\\Gamma$ is semiample.\nHence, if $L\\subseteq{\\mathbb Z}^2$ is the submonoid\ngenerated by the integer points of the cone \n${\\rm cone}(2K+3\\Delta,2K+(5-2k)\\Delta)$,\nthe following subalgebra \n\\[\n S := \\bigoplus_{(a,b)\\in L}H^0(C^{(2)},a\\Delta+bK)\n\\]\nof $R$ is finitely generated \nby~\\cite[Lemma 4.3.3.4]{ADHL}.\nThe homogeneous elements of $R$ \nwhich do not belong to $S$ are sections\nof Riemann-Roch spaces \n$H^0(C^{(2)},D)$ with $D\\cdot\\Delta < 0$.\nHence any such section is divisible by\nthe generator $f_\\Delta$ of $R$ which is a defining\nsection for $\\Delta$. Thus we conclude that \n$R$ is generated by $S$ and $f_\\Delta$\nand the statement follows.\n\\end{proof}\n\n\n \n\\begin{remark}\n\\label{ch}\nOver an algebraically closed field ${\\mathbb K}$ of positive \ncharacteristic the statement of Theorem~\\ref{teo-1}\nmust be modified as follows: the algebra\n$R(\\Delta,K)$ is finitely generated if and \nonly if $\\eta_C$ is torsion. Indeed if $\\eta_C$ \nis trivial, then ${\\mathcal O}_\\Gamma(\\Gamma)$ is trivial\nas well as shown in the proof of Lemma~\\ref{triv},\nthus $\\Gamma$ is semiample by~\\cite[Theorem 0.2]{Ke}.\nMoreover, if ${\\mathbb K}$ is the algebraic closure\nof a finite field, then $\\eta_C$ is always\ntorsion and thus $R(\\Delta,K)$ is always \nfinitely generated.\n\nThe conclusion of Lemma~\\ref{covering}\nis no longer true in positive characteristic. Indeed in\nthis case the algebraic fundamental group of the\naffine line ${\\mathbb K}$ is not trivial and thus there is no\ncontradiction. For example, in characteristic $p>0$,\nif $C$ is the curve of ${\\mathbb K}^2$ defined by the equation \n$x_1^p-x_1=x_2$, then the projection onto the second\nfactor defines a non-trivial \\'etale covering of ${\\mathbb K}$.\n\\end{remark}\n\n\\begin{remark}\nWe observe that the locus of smooth curves of genus\n$(k-1)^2 > 1$ which admit two $g_k^1$ has one component\nof maximal dimension which consists of curves of \ntype $(k,k)$ on a smooth quadric $Q$. \nIndeed by~\\cite{AC} the only\nother component which could have bigger dimension \nwould consist of curves $C$ admitting an involution. \nA parameter count shows that for our curves this \ncomponent has smaller dimension.\n\nMoreover any smooth curve $C$ of type\n$(k,k)$ on $Q$ admits exactly two $g_k^1$.\nIndeed let $S = \\{p_1,\\dots,p_k\\}$ be a set of\n$k$ distinct points with $p_1+\\dots+p_k\\in g_k^1$. \nBy the Riemann-Roch theorem $S$ is in Cayley-Bacharach \nconfiguration with respect to the curves of \ntype $(k-2,k-2)$. It follows that all the \npoints of $S$ are collinear. Indeed, let\n$\\ell$ be the line through the first two points\n$p_1,p_2$, let $H$ be a general hyperplane \nwhich contains $\\ell$ and let $q\\in S\\setminus\\{p_1,p_2\\}$.\nTake a union $\\Lambda$ of $k-3$\nhyperplanes through all the points of \n$S\\setminus\\{p_1,p_2,q\\}$ and such that\n$q\\notin\\Lambda$.\nThen $H\\cup \\Lambda$ cuts out on $Q$ \na curve of type\n$(k-2,k-2)$ which, by the Cayley-Bacharach\nconfiguration, must pass through $q$.\nHence $q\\in H$ and by the generality\nassumption on $H$ we deduce $q\\in\\ell$.\n\\end{remark}\n\n\n\n\\section{The grid family}\n\\label{grid-fam}\n\nIn this section we study families of curves of type $(k,k)$\non a smooth quadric $Q = {\\mathbb P}^1\\times{\\mathbb P}^1$\nsuch that the class of the difference of the two\n$g_k^1$ induced by the two rulings is \n$n$-torsion. We prove that $n\\geq k$\nand construct the family with $n=k$.\n\n\n\\begin{definition}\n\\label{grid}\nGiven two effective \ndivisors $L_1$ and $L_2$ of $Q$ of type\n$(k,0)$ and $(0,k)$ respectively, the {\\em grid \nlinear system} defined by $L_1$ and $L_2$ is \nthe linear system of curves of $Q$ of \nbi-degree $(k,k)$ which pass through the \ncomplete intersection $L_1\\cap L_2$.\nThe {\\em grid family} \n\\[\n \\mathcal G_k\n \\subseteq\\mathcal F_k\n\\]\nis the family \nof all curves of type $(k,k)$ which belong to some \ngrid linear system.\n\\end{definition}\nObserve that if $C$ is a smooth curve in $\\mathcal G_k$\nand $\\eta_C\\in \\Pic^0(C)$ is the class of the difference of the \ntwo $g_k^1$ cut out by the two rulings then $\\eta_C^{\\otimes k}$\nis trivial. This justifies the inclusion\n$\\mathcal G_k\\subseteq\\mathcal F_k^{\\rm tor}$.\nAny curve $C$ in $\\mathcal G_k$\nadmits an equation of the form\n\\begin{equation}\n\\label{equ-fam}\n f_1(x_0,x_1)g_2(y_0,y_1)+g_1(x_0,x_1)f_2(y_0,y_1)\n =\n 0,\n\\end{equation}\nwhere $f_1, f_2, g_1$ and $g_2$ are homogeneous\npolynomials of degree $k$. Indeed it is enough to\nprove the claim for a curve $C$ in a grid family\nwhere both $L_1$ and $L_2$ consist of\ndistinct lines and then conclude by specialization \nthat the same holds for any\ncurve $C$ of $\\mathcal G_k$.\nLet $h=0$ be an equation for $C$ and\nlet $f_i = 0$ be an equation for $L_i$, for $i=1,2$.\nBy the equality\n\\[\n V(h,f_1) = V(f_1,f_2),\n\\]\nthe fact that the two ideals $(h,f_1)$ and $(f_1,f_2)$ are both\nradical and saturated with respect to the\nirrelevant ideal $(x_0,x_1)\\cap (y_0,y_1)$ of $Q$,\nwe deduce that the equality $(h,f_1) = (f_1,f_2)$\nholds. The claim follows since $h$ has bi-degree $(k,k)$.\nObserve that the general element of $\\mathcal G_k$ is\nirreducible and smooth.\n\n\\begin{proposition}\nThe {\\em grid family} $\\mathcal G_k$\nhas dimension $4k-1$.\n\\end{proposition}\n\\begin{proof}\n\nThe projectivization of the set of homogeneous polynomials of\nbidegree $(k,k)$ of type $f(x_0,x_1)g(y_0,y_1)$ can be identified \nwith the image $\\mathcal S$ of the Segre embedding of \n${\\mathbb P}^k\\times{\\mathbb P}^k\\to{\\mathbb P}^N$,\nwhere $N={k^2+2k}$.\nThus, a curve in $\\mathcal G_k$ can be identified \nwith a point of the $1$-secant variety ${\\rm Sec}(\\mathcal S)$\nof $\\mathcal S$.\nBy~\\cite[Theorem 1.4]{CS} the dimension of\n${\\rm Sec}(\\mathcal S)$ is $4k-1$ and the \nstatement follows.\n\\end{proof}\n\n\n\\begin{lemma}\n\\label{composed}\nLet $C\\in\\mathcal F_k$ and let $D$ be a divisor\nof $C$ cut out by one of the two rulings. Then,\nfor any $1\\leq n\\leq k-1$, the linear system $|nD|$ \nis composed with the pencil $|D|$.\n\\end{lemma}\n\\begin{proof}\nTo prove the statement it is enough to show that\n$h^0(C,nD) = n+1$ for $1\\leq n\\leq k-1$. \nLet $Q = {\\mathbb P}^1\\times{\\mathbb P}^1$. Without loss of generality\nwe can assume that $D$ is cut out by the first ruling\nof $Q$, so that we have the following exact sequence \nof sheaves\n\\[\n \\xymatrix@1{\n 0\\ar[r] \n &\n {\\mathcal O}_Q(-k+n,-k)\\ar[r]\n &\n {\\mathcal O}_Q(n,0)\\ar[r]\n &\n {\\mathcal O}_C(nD)\\ar[r]\n &\n 0.\n }\n\\]\nTaking cohomology, using the vanishing of the higher\ncohomology groups of the middle sheaf, the Serre's \nduality theorem and the hypothesis on $n$ we deduce \nthe following equalities: \n\\[\nh^1(C,nD) = h^0(Q,{\\mathcal O}_Q(k-n-2,k-2))= (k-n-1)(k-1).\n\\]\nBy the adjunction formula $C$ has genus $(k-1)^2$.\nThus by the above and the Riemann-Roch formula \nwe conclude\n\\[\n h^0(C,nD)\n =\n nk+1-(k-1)^2+(k-n-1)(k-1)\n =\n n+1\n\\]\nand the statement follows.\n\\end{proof}\n\n\\begin{proposition}\n\\label{torsion}\nLet $C$ be a smooth curve of type\n$(k,k)$ on $Q$ and let $\\eta_C$\nbe the class of the difference of the two $g_k^1$. \nThen $\\eta_C$ has order $\\geq k$ and the \nfollowing are equivalent:\n\\begin{enumerate}\n\\item\n$\\eta_C$ has order $k$;\n\\item\nthe curve $C$ belongs to the grid family $\\mathcal G_k$.\n\\end{enumerate}\n\\end{proposition}\n\\begin{proof}\n\nWe first show that $\\eta_C$ has order $\\geq k$.\nLet $D_1$ be a divisor in the first $g_k^1$ and let \n$D_2$ be a divisor in the second $g_k^1$. \nLet $n 1$, where $\\mathcal U$\nis simply connected of dimension $2g-1$.\nWe denote by $j\\in\\Aut(\\mathcal C)$ the hyperelliptic\ninvolution and by\n$\\mathcal J\\to\\mathcal U$ the jacobian\nfamily. We consider the Abel-Jacobi map \n\\[\n \\nu\\colon \\mathcal C \\to \\mathcal J,\\quad \n x\\mapsto\\int_{j(x)}^x.\n\\]\nIf we take $\\pi^\\ast \\mathcal J\\to \\mathcal C$ the pull-back \nof the Jacobian family on $\\mathcal C,$ \nwe may consider $\\nu$ as a normal function.\nAs in Section~\\ref{density} we consider a $\\mathcal C^\\infty$\ntrivialization of the jacobian family\n$\\mathcal J\\cong \\mathcal U\\times \\mathbb T$,\nwhere $\\mathbb T\\cong J(C)$, to construct a map\n\\[\n \\gamma\\colon\\mathcal C\\to\\mathbb T.\n\\]\n\\begin{theorem}\n\\label{diff}\nIf $p\\in C$ is not a Weierstrass point, then the\ndifferential of $\\gamma$ at $p$ is surjective.\n\\end{theorem}\n\nOur strategy is as follows. For any holomorphic\nform $\\omega\\in H^0(C,\\Omega_C)$ we show that\nthere is a curve $r(t)$ in $\\mathcal C$ such that\n$r(0) = p$ and $d\\gamma_p(r'(0))\\cdot\\omega$\nis non-zero. To this aim we produce $r(t)$\naccordingly to the order $n$ of vanishing of $w$ \nat $p$. Since the divisor ${\\rm div}(w)$ is $j$-invariant, \nthen it is natural to consider $D = p+j(p)$.\nWe thus have a filtration \n\\begin{equation}\n\\label{filt}\n H^0(C,\\Omega_C)\n =\n L^0\\supseteq L^1\\supseteq...\\supseteq L^{g-1}\\supseteq L^g=0,\n\\end{equation}\nwhere $L^k$ is the Riemann-Roch space \n$H^0(C,\\Omega_C(-kD))$. Given $\\omega\\in L^k\n\\setminus L^{k+1}$, with $k>0$, we construct $\\zeta = \\partial(f)\n\\in H^1(C,T_C)^j$ as in Subsection~\\ref{def}, where $f\\in\nH^0(Z,{\\mathcal O}_Z)$. The one-dimensional family\n\\[\n \\mathcal C_\\zeta\\to\\Delta\n\\]\ndefined by $\\zeta$, plus a choice of a smooth\nsection through the point $p$ in the family, defines a curve \n$r(t)$ in $\\mathcal C$. We show that the family\nis equipped with a $\\mathcal C^\\infty$\n $1$-form $\\Theta$ such that the restriction \nof the $(1,0)$-part $\\Theta^{1,0}$\nto the central fiber $C$\nadmits a local expansion at $p$ of the\nform $w + \\tilde f(z)dt + o(t)$, where $z$ is a coordinate\nin $C$, $\\tilde f|_Z=f$ and $t\\in\\Delta$. We finally prove \n\\[\n d\\gamma_p(r'(0))\\cdot\\omega\n =\n \\lim_{t\\to 0}\\frac{1}{t}\\left(\\int_{\\Gamma_t}\\Theta_t-\\int_{\\Gamma_0}\\omega\\right)\n =\n 2f(p)\\neq 0,\n\\]\nwhere $\\Gamma_t$ is a path between $r(t)$ \nand $j(r(t))$. When $k=0$ we choose $r(t)$\nto be a path within the central fiber $C$,\nwe write $\\omega$ locally as $h(z)dz$\nand prove the equality\n\\[\n d\\gamma_p(r'(0))\\cdot\\omega\n =\n \\lim_{t\\to 0}\\frac{1}{t}\\int_{p}^{r(t)}\\omega\n =\n h(0)\\neq 0.\n\\]\n\n\n\\subsection{Deformation of curves}\n\\label{def}\nWe recall first a result on deformation and on \nextension of line bundles \nwhich will be applied to hyperelliptic curves (see also~\\cite{CP} and~\\cite{R}).\nLet $C$ be a smooth curve of genus $g>1$ and \nlet $T_C$ and $\\Omega_C$ be respectively the holomorphic \ntangent bundle and the canonical line bundle of $C$. \nFix a non trivial $\\omega\\in H^0(C,\\Omega_C)$ and \nlet $Z$ be the canonical divisor associated to $\\omega.$\nThe form $\\omega$ defines the following exact sequence\n\\[\n\\xymatrix@1{\n0\\ar[r] & T_C\\ar[r]^{\\omega}& {\\mathcal O}_C\\ar[r]& {\\mathcal O}_Z\\ar[r]& 0.\n}\n\\]\nPassing to the long exact sequence in cohomology \nwe obtain\n\\[\n\\xymatrix@C=20pt{\n0\\ar[r]& {\\mathbb C}\\cong H^0(C,{\\mathcal O}_C)\\ar[r]& \nH^0(Z,{\\mathcal O}_Z)\\ar[r]^\\partial& H^1(C,T_C)\n\\ar[r]^{\\omega}& H^1(C,{\\mathcal O}_C).\n}\n\\]\nGiven an element $f\\in H^0(Z,{\\mathcal O}_Z)$\nits image $\\zeta=\\partial(f)$ defines an\nextension of ${\\mathcal O}_C$ by $T_C$ via the\nisomorphism $H^1(C,T_C)\\cong \n{\\rm Ext}^1({\\mathcal O}_C,T_C)$\n\\[\n\\xymatrix@1{\n0\\ar[r] & T_C \\ar[r] & E_{\\zeta}\\ar[r] & {\\mathcal O}_C\\ar[r] & 0.\n}\n\\]\nTaking tensor product with $\\Omega_C$ \nand recalling that $T_C$ is dual with $\\Omega_C$\nwe get the following exact sequence\n\\begin{equation} \\label{zeta}\n\\xymatrix@1{\n0\\ar[r] &{\\mathcal O}_C\\ar[r] & E_{\\zeta}\\otimes \\Omega_C\\ar[r] &\n \\Omega_C\\ar[r]& 0,\n }\n \\end{equation}\nwhich passing to the long exact sequence in cohomology\ngives the following sequence \nwhose coboundary \nis the cup product with $\\zeta$:\n\\[\n \\xymatrix@C=20pt{\n0\\ar[r] & {\\mathbb C}\\cong H^0(C,{\\mathcal O}_C)\\ar[r]&\n H^0(C,E_\\zeta\\otimes \\Omega_C)\\ar[r]^-k & \n H^0(C,\\Omega_C)\\ar[r]^-{\\zeta}\\ar[r]&\n H^1(C,{\\mathcal O}_C).\n }\n\\]\nSince $\\zeta\\in\\ker(\\omega)$, or equivalently\nthe cup product $\\zeta\\cdot \\omega$ vanishes,\nthere exists an element $\\Omega\\in \nH^0(C,E_\\zeta\\otimes \\Omega_C)$ such that \n$k(\\Omega)=\\omega$.\nNow we consider the commutative diagram\n\n\\[\n \\xymatrix@1{\n & 0\\ar[d] & 0\\ar[d] & H^0(C,{\\mathcal O}_C)\\ar[d]^-\\zeta\\\\\n 0\\ar[r]\\ar[d]\n & H^0(C,{\\mathcal O}_C)\\ar[r]\\ar[d] \n & H^0(Z,{\\mathcal O}_Z)\\ar[d]^-\\rho\\ar[r]^-\\partial \n & H^1(C,T_C)\\ar[d] \\\\\n 0\\ar[r]\\ar[d]\n & H^0(C,E_\\zeta\\otimes \\Omega_C)\\ar[r]^-r\\ar[d]^-k\n & H^0(Z,E_\\zeta\\otimes \\Omega_C|_Z)\\ar[d]\\ar[r]\n & H^1(C,E_\\zeta)\\ar[d]\\\\\n H^0(C,{\\mathcal O}_C)\\ar[r]^-\\omega\n & H^0(C,\\Omega_C)\\ar[r]\n & H^0(Z,\\Omega_C|_Z)\\ar[r]\n & H^1(C,{\\mathcal O}_C)\n }\n\\]\n \\vspace{0.2cm}\n \nThe restriction of the lifting $\\Omega$ to $Z$ \ngives an element $r(\\Omega)\\in H^0(Z,E_\\zeta\\otimes \\Omega_C|_Z)$ \nthat by construction is in the image of the map\n$\\rho$, that is $\\Omega=\\rho(g)$ \nfor some $g\\in H^0(Z,{\\mathcal O}_Z)$.\nA diagram chase proves indeed that\n$\\partial (g)=\\zeta$ holds.\nSince $\\ker(\\partial)$ is isomorphic to ${\\mathbb C}$,\nwe conclude that $f$ equals $g$ up to a constant.\nThis means that we can realize the function $f$ \nby a unique suitable lifting $\\Omega$ of $\\omega.$\nWe collect the discussion in the following lemma.\n\n\\begin{proposition}\\label{coord}\nLet $\\omega$ be a non-zero element in \n$H^0(C,\\Omega_C)$ and let $Z$ be the \ndivisor of $\\omega$. \nThen for any $f\\in H^0(Z,{\\mathcal O}_Z)$ \nthere is a unique \n$\\Omega\\in H^0(C,E_{\\zeta}\\otimes \\Omega_C)$ \nsuch that $r(\\Omega)=\\rho (f)$. \n\\end{proposition}\n\nWhen we interpret $H^1(C,T_C)$ \nas the space of first order deformations of $C$,\nso that $\\zeta$ corresponds to a family\n\\[\n\\mathcal C_\\zeta\\to {\\rm Spec}\\, {\\mathbb C}[\\varepsilon],\n\\]\nthe isomorphism $H^1(C,T_C)\\to\n{\\rm Ext}^1({\\mathcal O}_C,\\Omega_C)$\ngives the identifications \n$E_{\\zeta}\\cong T_{\\mathcal C}|_{C}$ \nand $E_{\\zeta}\\otimes \\Omega_C\\cong\\Omega_{\\mathcal C}|_{C}.$ \nTherefore the sequence~\\eqref{zeta}\nis the cotangent sequence of the first order deformation.\nIn coordinates we may write \n\\begin{equation}\n\\label{Omega}\n \\omega= h(z)dz,\\qquad \\Omega=h(z)dz+\\tilde f(z)dt,\n\\end{equation}\nwhere $dt$ is the global section \nof the cotangent $\\Omega_{\\mathcal C}|_{C}$ \nand $f=\\tilde f|_Z$ is a section of $H^0(Z,{\\mathcal O}_Z)$ \nsuch that $r(\\Omega)=\\rho (f)$.\n\n\n\\subsection{The normal function}\nIn this subsection we will specialize the previous\nconstruction to hyperelliptic families.\nFirst of all, given a hyperelliptic curve $C$ of genus $g>1$,\nwe consider the $j$-invariant subspace\n\\[\n H^1(C,T_C)^{j}\n \\subseteq\n H^1(C,T_C),\n\\]\nwhich corresponds to the directions that are preserved\nby the hyperelliptic involution $j$.\nSince $j$ acts on $H^0(Z,{\\mathcal O}_Z)$\nas $f\\mapsto f\\circ j$\nand it acts as $-1$ on $H^0(C,\\Omega_C)$, \nthen $\\partial(f) = \\zeta$ is $j$-invariant if and only if\n$f\\circ j$ equals $-f$ up to a constant.\n\nLet $p$ be a non-Weierstrass point of $C$ and\n$\\omega\\in H^0(C,\\Omega_C)$\nbe a holomorphic form which vanishes \nwith order $k>0$ at $p$,\nthat is $\\omega\\in L^k\\setminus L^{k+1}$.\nWe now take $f_{\\omega}\\in H^0(Z,{\\mathcal O}_Z)$, where $Z \n= k(p+j(p)) + Z' = {\\rm div}(\\omega)$,\nsuch that\n\\[\n f_{\\omega}(p)=1,\\quad f_{\\omega}(j(p))=-1,\\quad f_{\\omega}(p')=0 \\text{ for } p'\\in Z'.\n\\]\nGiven $\\zeta_{\\omega}=\\partial(f_{\\omega})$,\nby the previous remark\nwe have $j(\\zeta_{\\omega})=\\zeta_{\\omega}.$ \nThus there exists a smooth family of hyperelliptic curves\n\\begin{equation}\n \\pi\\colon \\mathcal C_\\omega\\to \\Delta \\label{curvamod},\n\\end{equation} \n where $\\Delta\\subset \\mathcal U$ is a disk, such that \n$\\pi^{-1}(0)=C$ and such that \nthe Kodaira-Spencer \nclass of the family is $\\zeta_{\\omega}$. \nLet $\\Omega_{\\omega}$ be the section of \n$E_{\\zeta_{\\omega}}\\otimes \\Omega_C$ \nassociated to $f_{\\omega}$ as in Lemma ~\\ref{coord}.\n By means of a trivialization of \n the family we can construct a closed \n differential $1$-form $\\Theta$\n on $\\mathcal C_{\\omega}$ which is invariant \n with respect to the involution $j$ and\n such that the restriction of the $(1,0)$-part\n $\\Theta^{(1,0)}$ to the central fiber equals \n $\\Omega_{\\omega}$. \nIn local coordinates we can write \n \\[\n \\Theta(z,t)^{(1,0)}=\\Omega_{\\omega}+ o(t)=\\omega+f_{\\omega}(z)dt+o(t).\n \\]\n\nWe also assume to have a holomorphic section $r$ \nof $\\pi_{\\omega}$ such that $r(0)=p$ and define $r'= j(r).$\nMoreover, we fix a differentiable map\n\\[\n\\Gamma(t,s):\\Delta\\times [0,1]\\to \\mathcal C_{\\omega}\n\\] \nsuch that $\\Gamma(t,s)\\in \\pi^{-1}(t)$,\n $\\Gamma(t,0)=r'(t)$ and $\\Gamma(t,1)=r(t).$ \n Observe that $\\Gamma$ is a family of sections \n connecting $r'$ and $r$.\n We define the function\n\\[\n g\\colon\\Delta\\to {\\mathbb C},\\quad \n t\\mapsto \n \\int_{\\Gamma_t} \\Theta_{t}.\n\\]\n Following Griffiths~\\cite[(6.6)]{Gr} and using the fact that \n the Gauss-Manin connection vanishes on $\\Theta$,\n we have that the derivative of $g$ at $0$ \n equals\n$\nd\\gamma_p(r'(0))\\cdot \\omega.\n$\n \n\n\\begin{proof}[Proof of Theorem~\\ref{diff}]\nWith the previous notation, \ngiven any $\\omega\\in H^0(C,\\Omega_C)$ \nvanishing of order $k>0$ at $p$, \nwe consider the family $\\pi_{\\omega}$, \nwith its sections $r=r_{\\omega}$ and $r'=j(r)$, \nand $\\Theta$ the corresponding differential form on \n$\\mathcal C_{\\omega}$.\nBy the previous remark we have that \n\\[\nd\\gamma_p(r'(0))\\cdot \\omega=g'(0).\n\\]\nWe now compute the latter term:\n\\[\ng(t)-g(0)=\\int_{\\Gamma_t} \\Theta_{t}-\\int_{\\Gamma_0} \n\\Theta_{0}=\\int_{\\Gamma_t} \\Theta_{t}-\\int_{\\Gamma_0} \\omega.\n\\]\nWe call $r_t$ and $r'_t$ \nthe arcs $r([0,t])$ and $r'([0,t])$ respectively.\nSince $\\Theta$ is closed, then $\\Gamma^\\ast(\\Theta)$ is\nexact and we have \n$0= \\int_{r'_t}\\Theta_t+\\int_{\\Gamma_t}\\Theta_t - \\int_{r_t}\\Theta_t-\\int_{\\Gamma_0}\\Theta_t$, \nhence\n\\[\ng(t)-g(0)= \\int_{r_t}\\Theta_t- \\int_{r'_t}\\Theta_t=2\\int_{r_t}\\Theta_t,\n\\] \nwhere the last equality is due to the fact \nthat $j^\\ast(\\Theta)=-\\Theta.$\nFinally, since $\\Theta = \\omega+f_{\\omega}(z)dt + o(t)$,\nby the fundamental theorem of calculus \nwe get\n\\[\n \\lim_{t\\to 0}\\frac{1}{t}\\int_{r_t}\\Theta_t\n =\n \\lim_{t\\to 0}\\frac{1}{t}\\int_{r_t}\\Theta_t^{(1,0)}\n =\n f_{\\omega}(p)\\not=0.\n\\]\nThus $g'(0)\\not=0$.\nIf $k=0$, that is $\\omega$ does not vanish \nat $p$, we will choose a loop $r(t)$ in $C$ with\n$r(0)=p$ \nand we will compute the derivative \nof the Abel-Jacobi map ${\\rm AJ}$ on $C$. \nFirst note that if we take a \nWeierstrass point $q$ of $C$ \nwe have \n\\[\n{\\rm AJ}(r(t)-j(r(t)))=2{\\rm AJ}(r(t)-q).\n\\]\nTake a coordinate\n$z$ centered at $p$ \nsuch that $\\omega(z)=h(z)dz$ with $h(0)\\neq 0.$ \nFix a loop $r(t)$ such that $z(r(t))=t$, then\n\\[\n\\lim_{t\\to0}\\frac{1}{t} \\int_{q}^{r(t)}\\omega= \n\\lim_{t\\to 0}\\frac{1}{t}\\int_0^th(z)dz=h(0)\n\\] \nand we complete our result.\n\\end{proof}\n\n\\begin{corollary}\n\\label{cor}\nThe locus of curves $C$ in $\\mathcal M_4$ \nsuch that $\\eta_C$ is a non-trivial torsion point is a \ncountable union of subvarieties \nof complex dimension $\\geq 5$ \nand the set of subvarieties of\ndimension $5$ is dense in $\\mathcal M_4$\nin the analytic topology.\n\\end{corollary}\n\n\\begin{proof}\nLet $\\mathcal U$ be an open subset of $\\mathcal M_4$\nwhich intersects the hyperelliptic locus and let $\\tilde{\\mathcal U}$ \nbe the moduli space of pairs $(C,g_3^1)$,\nwhere $[C]\\in \\mathcal U$. \nLet $\\imath\\colon\\mathcal H\\to\\tilde{\\mathcal U}$ be the \nsubvariety containing pairs where $C$ is hyperelliptic. \nGiven a universal family \n$\\pi: \\mathcal C\\to \\tilde{\\mathcal U}$,\nwe construct the following commutative diagram\n \\[\n \\xymatrix{\n \\imath^*\\mathcal C\\ar[rrr]^-{(C,p)\\mapsto [j(p)-p]}\\ar[d]\n &&& \\mathcal J\\ar@{=}[d]\\ar[rd]\\\\\n \\mathcal C\\ar[d]^-\\pi\\ar[rrr]^-{(C,g_3^1)\\mapsto \\eta_C} \n &&& \\mathcal J\\ar[r]^-{\\varphi} \n & \\tilde{\\mathcal U}\\times\n \\mathbb T \\ar[d]^-{{\\rm pr}_2}\\\\\n \\tilde{\\mathcal U}\n \\ar[rrrr]^-{[C]\\mapsto{\\rm pr}_2(\\varphi(\\eta_C))}\n &&&& \\mathbb T\n }\n\\]\nwhere $\\eta_C={g'}_3^1-g_3^1=K_C-2g_3^1$ and $\\varphi$ is a $\\mathcal C^\\infty$-trivialization\n(defined after possibly shrinking $\\mathcal U$).\nThe commutativity of the top square comes from\nthe fact that on a hyperelliptic curve $C$ we have\n$g_3^1 = p + g_2^1$ and $K_C - 2g_3^1 = j(p)-p$,\nwhere $j\\in{\\rm Aut}(C)$ is the hyperelliptic involution.\nBy Theorem~\\ref{diff} the differential \nof the map $\\gamma\\colon\\imath^*\\mathcal C\\to \\mathbb T$ \nobtained composing the maps in the diagram \nis surjective at any point $p$ which is not Weierstrass. \nThis implies that the map $\\eta: \\tilde{\\mathcal U}\\to \\mathbb T$ \nis locally a submersion at any point corresponding to a hyperelliptic \ncurve, in particular its image contains an open subset of $\\mathbb T$.\nWe thus conclude as in the last part of the \nproof of Theorem~\\ref{teo-2} given in section~\\ref{density}.\n\\end{proof}\n\n\n\\section{Examples}\n\\label{exa}\n\nIn this section we will provide further examples \nof curves having two $g_k^1$'s whose \ndifference is a torsion element in the Jacobian.\nIn particular we will show how to use automorphism\ngroups to construct new examples (see\nExample~\\ref{exa:new}).\n\n\n\\begin{proposition}\\label{auto}\nLet $C$ be a curve in $\\mathcal F_k$.\nIf $G$ is an automorphism group of $C$\nof order $n$ which preserves each \n$g_k^1$ of $C$ and such that $C\/G$ \nhas genus zero, then the order of \n$\\eta_C$ divides $n$.\n\\end{proposition}\n\\begin{proof}\nLet $\\pi:C\\to C\/G\\cong {\\mathbb P}^1$ be \nthe quotient morphism, let \n$D=p_1+p_2+\\dots+p_k$ be an \nelement of the first $g_k^1$\nand let $q_i=\\pi(p_i)$. Then\nthe following linear equivalences\nhold\n\\[\n nD\n \\sim\n \\sum_{\\sigma\\in G} \\sigma^*(D)\n =\n \\pi^*(q_1)+\\pi^*(q_2)+\\dots+\\pi^*(q_k)\n \\sim\n kF, \n\\]\nwhere $F$ is a fiber of $\\pi$ and the first equivalence \nis due to the fact that $G$ preserves the \nlinear series $g_k^1$.\nSince the same property holds for an element\n$D'$ of the second $g_k^1$, the linear equivalence\n$nD\\sim nD'$ follows.\n\\end{proof}\n\n \n\\begin{example}\nLet $\\sigma$ be the order $k$ automorphism\nof ${\\mathbb P}^1\\times{\\mathbb P}^1$ defined by\n\\[\n \\sigma(x_0,x_1,y_0,y_1)=(\\zeta_k x_0,x_1, y_0,y_1),\n\\]\nwhere $\\zeta_k$ is a primitive $k$-th root of unity.\nWe now show that a curve $C\\in\\mathcal F_k$ \nwhich is $\\sigma$-invariant\nadmits an equation of the form\n\\[\n x_0^kg_2(y_0,y_1)+x_1^kf_2(y_0,y_1)=0,\n\\]\nwhere $f_2, g_2$ are homogeneous of degree \n$k$ in $y_0,y_1$. In particular the quotient\n$C\/\\langle\\sigma\\rangle$ \nhas genus zero. Thus $C\\in\\mathcal F_k^{\\rm tor}$\nby either Proposition~\\ref{auto} or Proposition~\\ref{torsion}.\nThe automorphism $\\sigma$ preserves \neach ruling of the quadric and acts identically \non one of the two rulings.\nConsider a point in $\\mathcal H_1\\cap\\mathcal H_2$,\nwith the notation in the proof of Proposition~\\ref{torsion}, \nwhich corresponds to a $\\sigma$-invariant grid.\nThe lines of the grid which belong to the first ruling \nare defined by either \n$x_0^k-x_1^k=0$ or $x_0^k=0$.\nAn equation of $C$ in such coordinates \nis then of the form\n\\[\n (x_0^k-\\mu x_1^k)h_1+(x_0^k+\\lambda x_1^k)h_2\n =\n x_0^k(h_1+h_2)+x_1^k(\\lambda h_2-\\mu h_1)\n =\n 0,\n\\]\nfor $\\mu\\in \\{0,1\\}$, $\\lambda\\in {\\mathbb C}$ and $h_1,h_2$ \nhomogeneous of degree three in $y_0,y_1$.\n\\end{example}\n\n\\begin{example}\n\\label{exa:new}\nThe moduli space of non-hyperelliptic \ncurves $C$ of genus four having an order five \nautomorphism $\\sigma$ such that \n$C\/\\langle\\sigma\\rangle$ \nhas genus zero is a 1-dimensional subvariety \nof $\\mathcal F_3^{\\rm tor}$.\nMoreover, any such $C$ is isomorphic to a \ncurve in the following family \n\\[\n x_0x_1^2y_1^3\n +\\alpha x_0^2x_1y_0^3\n +\\beta x_0^3y_0y_1^2\n +\\gamma x_1^3y_0^2y_1=0,\n\\]\nwhere $\\sigma(x_0,x_1,y_0,y_1)\n=(\\zeta_5x_0,x_1,\\zeta_5^3y_0,y_1)$ \nand $\\zeta_5$ is a primitive fifth root of unity.\nThe family contains curves which pass through the points \nof a grid of type $(5,5)$, for example the curve with\n\\[\n\\alpha=-\\zeta_5,\\ \\beta=\\zeta_5^3+\\zeta_5^2+\\zeta_5,\\ \\gamma=\\zeta_5^2+\\zeta_5. \n\\]\nHowever, the general element of the family is not \nof grilled type.\nThis means that if $D_i$ is a divisor of the $i$-th $g_3^1$ \nand $\\mathcal H_i\\subseteq |5D_1|\\cong{\\mathbb P}^{11}$ \nis the projectivization of the fifth symmetric \npower of $H^0(C,D_i)$, then the intersection\n$\\mathcal H_1\\cap\\mathcal H_2$ is empty.\nFor example this holds for the curve with $\\alpha=-1, \\beta=\\gamma=1$.\nThe statements for both curves can be checked \nby means of the Magma~\\cite{Magma} program\navailable here~\\url{http:\/\/www2.udec.cl\/~alaface\/software\/semiample\/aut}.\n\\end{example}\n\n\n\n\\begin{bibdiv}\n\\begin{biblist}\n\n\\bib{AC}{article}{\n author={Arbarello, Enrico},\n author={Cornalba, Maurizio},\n title={Footnotes to a paper of Beniamino Segre: ``On the modules of\n polygonal curves and on a complement to the Riemann existence theorem''\n (Italian) [Math. Ann. {\\bf 100} (1928), 537--551;\\ Jbuch {\\bf 54}, 685]},\n note={The number of $g^{1}_{d}$'s on a general $d$-gonal curve, and\n the unirationality of the Hurwitz spaces of $4$-gonal and $5$-gonal\n curves},\n journal={Math. Ann.},\n volume={256},\n date={1981},\n number={3},\n pages={341--362},\n issn={0025-5831},\n review={\\MR{626954 (83d:14016)}},\n doi={10.1007\/BF01679702},\n}\n\n\\bib{ADHL}{book}{\n AUTHOR = {Arzhantsev, Ivan},\n AUTHOR = {Derenthal, Ulrich},\n AUTHOR = {Hausen, J\\\"urgen},\n AUTHOR = {Laface, Antonio},\n TITLE = {Cox rings},\n series={Cambridge Studies in Advanced Mathematics},\n volume={144},\n publisher={Cambridge University Press, Cambridge},\n date={2014},\n pages={530},\n isbn={9781107024625},\n}\n\n\n\\bib{BHPV}{book}{\n author={Barth, Wolf P.},\n author={Hulek, Klaus},\n author={Peters, Chris A. M.},\n author={Van de Ven, Antonius},\n title={Compact complex surfaces},\n series={Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge. A\n Series of Modern Surveys in Mathematics [Results in Mathematics and\n Related Areas. 3rd Series. A Series of Modern Surveys in Mathematics]},\n volume={4},\n edition={2},\n publisher={Springer-Verlag, Berlin},\n date={2004},\n pages={xii+436},\n isbn={3-540-00832-2},\n review={\\MR{2030225 (2004m:14070)}},\n doi={10.1007\/978-3-642-57739-0},\n}\n\n\\bib{Magma}{article}{\n AUTHOR = {Bosma, Wieb},\n AUTHOR = {Cannon, John},\n AUTHOR = {Playoust, Catherine},\n TITLE = {The {M}agma algebra system. {I}. {T}he user language},\n NOTE = {Computational algebra and number theory (London, 1993)},\n JOURNAL = {J. Symbolic Comput.},\n VOLUME = {24},\n YEAR = {1997},\n NUMBER = {3-4},\n PAGES = {235--265}\n}\n\n\n\\bib{CK}{article}{\n author={Ciliberto, Ciro},\n author={Kouvidakis, Alexis},\n title={On the symmetric product of a curve with general moduli},\n journal={Geom. Dedicata},\n volume={78},\n date={1999},\n number={3},\n pages={327--343},\n issn={0046-5755},\n review={\\MR{1725369 (2001e:14005)}},\n doi={10.1023\/A:1005280023724},\n}\n\n\\bib{CP}{article}{\n author={Collino, Alberto},\n author={Pirola, Gian Pietro},\n title={The Griffiths infinitesimal invariant for a curve in its Jacobian},\n journal={Duke Math. J.},\n volume={78},\n date={1995},\n number={1},\n pages={59--88},\n issn={0012-7094},\n review={\\MR{1328752 (96f:14009)}},\n doi={10.1215\/S0012-7094-95-07804-1},\n}\n\n\\bib{CPP}{article}{\n author={Colombo, E.},\n author={Pirola, G. P.},\n author={Previato, E.},\n title={Density of elliptic solitons},\n journal={J. Reine Angew. Math.},\n volume={451},\n date={1994},\n pages={161--169},\n issn={0075-4102},\n review={\\MR{1277298 (95e:58079)}},\n}\n\n\\bib{CS}{article}{\n author={Cox, David},\n author={Sidman, Jessica},\n title={Secant varieties of toric varieties},\n journal={J. Pure Appl. Algebra},\n volume={209},\n date={2007},\n number={3},\n pages={651--669},\n issn={0022-4049},\n review={\\MR{2298847 (2008i:14077)}},\n doi={10.1016\/j.jpaa.2006.07.008},\n}\n\n\\bib{Gr}{article}{\n author={Griffiths, Phillip A.},\n title={Infinitesimal variations of Hodge structure. III. Determinantal\n varieties and the infinitesimal invariant of normal functions},\n journal={Compositio Math.},\n volume={50},\n date={1983},\n number={2-3},\n pages={267--324},\n issn={0010-437X},\n review={\\MR{720290 (86e:32026c)}},\n}\n\n\\bib{GH}{book}{\n author={Griffiths, Phillip},\n author={Harris, Joseph},\n title={Principles of algebraic geometry},\n series={Wiley Classics Library},\n note={Reprint of the 1978 original},\n publisher={John Wiley \\& Sons, Inc., New York},\n date={1994},\n pages={xiv+813},\n isbn={0-471-05059-8},\n review={\\MR{1288523 (95d:14001)}},\n doi={10.1002\/9781118032527},\n}\n\n\\bib{Ke}{article}{\n author={Keel, Se{\\'a}n},\n title={Basepoint freeness for nef and big line bundles in positive\n characteristic},\n journal={Ann. of Math. (2)},\n volume={149},\n date={1999},\n number={1},\n pages={253--286},\n issn={0003-486X},\n review={\\MR{1680559 (2000j:14011)}},\n doi={10.2307\/121025},\n}\n\n\\bib{K}{article}{ \n AUTHOR = {Kond{\\=o}, Shigeyuki},\n TITLE = {The moduli space of curves of genus 4 and {D}eligne-{M}ostow's\n complex reflection groups},\n BOOKTITLE = {Algebraic geometry 2000, {A}zumino ({H}otaka)},\n SERIES = {Adv. Stud. Pure Math.},\n VOLUME = {36},\n PAGES = {383--400},\n PUBLISHER = {Math. Soc. Japan},\n ADDRESS = {Tokyo},\n YEAR = {2002},\n MRCLASS = {14H15 (14D07 14H45 14J28 32S40 33C80)},\n MRNUMBER = {1971521 (2004h:14033)},\nMRREVIEWER = {I. Dolgachev},\n}\n\n\\bib{La}{book}{\n author={Lazarsfeld, Robert},\n title={Positivity in algebraic geometry. I},\n series={Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge. A\n Series of Modern Surveys in Mathematics [Results in Mathematics and\n Related Areas. 3rd Series. A Series of Modern Surveys in Mathematics]},\n volume={48},\n note={Classical setting: line bundles and linear series},\n publisher={Springer-Verlag, Berlin},\n date={2004},\n pages={xviii+387},\n isbn={3-540-22533-1},\n review={\\MR{2095471 (2005k:14001a)}},\n doi={10.1007\/978-3-642-18808-4},\n}\n\n\\bib{R}{article}{\n author={Raviolo, Emanuele},\n title={A note on Griffiths infinitesimal invariant for curves},\n journal={Ann. Mat. Pura Appl. (4)},\n volume={193},\n date={2014},\n number={2},\n pages={551--559},\n issn={0373-3114},\n review={\\MR{3180933}},\n doi={10.1007\/s10231-012-0290-x},\n}\n\n\n \\end{biblist}\n \\end{bibdiv}\n\n\n\\end{document}","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\n\\IEEEPARstart{W}{e} study the problem of extracting a prescribed number of random bits by reading the smallest possible number of symbols from imperfect stochastic processes. For perfect stochastic processes, including processes with known accurate distributions or perfect biased coins, this problem has been well studied. It dates back to von Neumann [9] who considered the problem of generating random bits from a biased coin with unknown probability. Recently, in \\cite{Zhou12_Streaming}, we improved von Neumann's scheme and introduced an algorithm that generates `random bit streams' from biased coins, uses bounded space and runs in expected linear time. This algorithm can generate a prescribed number of random bits with an asymptotically optimal efficiency. On the other hand, efficient algorithms have also been developed for extracting randomness from any known stochastic process (whose distribution is given). In \\cite{Knuth1976}, Knuth and Yao presented a simple procedure for generating sequences with\narbitrary probability distributions from an unbiased coin (the probability of H and T is $\\frac{1}{2}$). In \\cite{Abrahams1996}, Abrahams considered\na source of biased coin whose distribution is an integer power of a noninteger.\nHan and Hoshi \\cite{Han1997} studied the general problem and proposed an interval algorithm that generates a prescribed number of random bits from any known stochastic process and achieves the information-theoretic upper bound on efficiency. However, in practice, sources of stochastic processes have inherent correlations and are affected by measurement's noise, hence, they are not perfect. Existing algorithms for extracting randomness from perfect stochastic processes cannot work for imperfect stochastic processes, where uncertainty exists.\n\nTo extract randomness from an imperfect stochastic process, one approach is to apply a seeded or seedless extractor to a sequence generated by the process that contains a sufficient amount of randomness, and we call this approach as a fixed-length extractor for stochastic processes since all the possible input sequences have the same fixed length. Efficient constructions of seeded or seedless extractors have been extensively studied in last two decades, and it shows that the number of random bits extracted by them can approach the source's min-entropy asymptotically \\cite{Dvir08, Nis96, Sha02,Rao2007,Kamp11}. Although fixed-length extractors can generate random bits with good quality from imperfect stochastic processes,\ntheir efficiencies are not close to the optimality. Here, we define the \\emph{efficiency} of an extractor for stochastic processes as the asymptotic ratio\nbetween the number of extracted random bits and the entropy of its input sequence (the entropy of its input sequence is proportional to the expected input length if the stochastic process is stationary ergodic), which is upper bounded by $1$ since the process of extracting randomness does not increase entropy. Based on this definition, we can conclude that the efficiency of a fixed-length extractor is upper bounded by the ratio between the min-entropy and the entropy of the input sequence, which is usually several times smaller than $1$. So fixed-length extractors are not very efficient in extracting randomness from stochastic processes. The intuition is that, in order to minimize the expected number of symbols read from an imperfect stochastic process, the length of the input sequence should be adaptive, not being fixed.\n\nThe concept of min-entropy and entropy are defined as follows.\n\\begin{Definition}\nGiven a random source $X$ on $\\{0,1\\}^n$, the \\emph{min-entropy} of $X$ is defined as\n$$H_{\\min}(X)=\\min_{x\\in \\{0,1\\}^n } \\log \\frac{1}{P[X=x]}.$$\nThe \\emph{entropy} of $X$ is defined as\n$$H(X)=\\sum_{x\\in \\{0,1\\}^n } P[X=x] \\log \\frac{1}{P[X=x]}.$$\n\\end{Definition}\n\nThe following example is constructed for comparing entropy with min-entropy for a simple random variable.\n\\begin{Example}\nLet $X$ be a random variable such that $P[X=0]=0.9$ and $P[X=1]=0.1$, then\n$H_{\\min}(X)=0.152$ and $H(X)=0.469$. In this case, the entropy of $X$ is about three times its min-entropy.\\hfill\n\t$\\Box$\n\\end{Example}\n\nIn this paper, we focus on the notion and constructions of variable-length extractors (short for variable-to-fixed length extractors), namely, extractors with variable input length and fixed output length. (Note that the interval algorithm proposed by Han and Hoshi \\cite{Han1997} and the streaming algorithm proposed by us \\cite{Zhou12_Streaming} are special cases of variable-length extractors). Our goal is to extract a prescribed number of random bits in the sense of statistical distance while minimizing the expected input cost, measured by the entropy of the input sequence (whose length is variable). To make this precise, we let\n$d(\\mathcal{R},\\mathcal{M})$ be the difference between two known stochastic processes $\\mathcal{R}$ and $\\mathcal{M}$, defined by\n$$d(\\mathcal{R},\\mathcal{M})=\\limsup_{n\\rightarrow\\infty} \\max_{x\\in \\{0,1\\}^n} \\frac{\\log_2\\frac{P_{\\mathcal{R}}(x)}{P_{\\mathcal{M}}(x)}}{\\log_2\\frac{1}{P_{\\mathcal{M}}(x)}},$$\nwhere $P_{\\mathcal{R}}(x)$ is the probability of generating $x$ from $\\mathcal{R}$ when the sequence length is $|x|$, and $P_{\\mathcal{M}}(x)$ is the probability of generating $x$ from $\\mathcal{M}$ when the sequence length is $|x|$.\n\nA few models of imperfect stochastic processes are introduced and investigated, including,\n\\begin{itemize}\n \\item Let $\\mathcal{M}$ be a known stochastic process, we consider an arbitrary stochastic process $\\mathcal{R}$ such that $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$ for a constant $\\beta$.\n \\item We consider $\\mathcal{R}$ as an arbitrary stochastic process such that\n $\\min_{\\mathcal{M}\\in \\mathcal{G}_{s.e.}}d(\\mathcal{R},\\mathcal{M})\\leq \\beta$ for\n a constant $\\beta$, where $\\mathcal{G}_{s.e.}$ denotes the set consisting of all stationary ergodic processes.\n\\end{itemize}\n\nGenerally, given a real slight-unpredictable source $\\mathcal{R}$, it is not easy to estimate the exact value of $d(\\mathcal{R},\\mathcal{M})$ for\na stochastic process $M$. But its upper bound, i.e., $\\beta$, can be easily obtained.\nThe parameter\n$\\beta$ describes how unpredictable the real source $\\mathcal{R}$ is, so we call it the \\emph{uncertainty} of $\\mathcal{R}$.\nWe prove that it is impossible to construct an extractor that achieves efficiency strictly larger than $1-\\beta$\nfor all the possible sources $\\mathcal{R}$ with uncertainty $\\beta$. Then we introduce several constructions of variable-length extractors, and show that their efficiencies can reach $\\eta \\geq 1-\\beta$; that is, the constructions are asymptotically optimal. The proposed variable-length extractors have two benefits: (i) they are generalizations of algorithms for perfect sources to address general imperfect sources; and (ii) they bridge the gap between min-entropy and entropy on efficiency.\n\nThe following example is constructed to compare the performances of a variable-length extractor and\na fixed-length extractor when extracting randomness from a slightly-unpredictable independent process.\n\\begin{Example} Consider an independent process $x_1x_2x_3...$ such that $P[x_i=1]\\in [0.9, 0.91]$, then it can be obtained that $\\beta\\leq 0.0315$.\nFor this source, a variable-length extractor can generate random bits with efficiency at least $1-\\beta=0.9685$ that is very close to the upper bound $1$. In comparison, fixed-length extractors can only reach the efficiency at most $0.3117$.\n\\end{Example}\n\n\nThe remainder of this paper is organized as follows. Section \\ref{sec_pre} presents background and related results.\nIn Section \\ref{var_sec_efficiency}, we demonstrate that one cannot construct a variable-length extractor with efficiency strictly larger than $1-\\beta$ when the source has uncertainty $\\beta$. Then we focus on the seeded constructions of variable-length extractors, namely, we use a small number of additional truly random bits as the seed (catalyst).\nThree different constructions are provided and analyzed in Section \\ref{var_section_tech1}, Section \\ref{var_section_tech2} and Section \\ref{var_section_tech4}\nseparately. All these constructions have efficiencies lower bounded by $1-\\beta$, implying their optimality.\nFinally, we discuss seedless constructions of variable-length extractors for some types of random sources in Section \\ref{sec_randomnessextraction}, followed by the concluding remarks.\n\n\n\\section{Preliminaries}\n\\label{sec_pre}\n\n\\subsection{Statistical Distance}\n\n\\emph{Statistical Distance} is used in computer science to measure the difference between two distributions. Let $X$ and $Y$ be two random sequences with range $\\{0,1\\}^m$, then the statistical distance between $X$ and $Y$ is defined as\n$$\\|X-Y\\|=\\max_{T:\\{0,1\\}^m\\rightarrow \\{0,1\\}} |P[T(X)=1]-P[T(Y)=1]|$$\nover a boolean function $T$.\nWe say that $X$ and $Y$ are $\\epsilon$-close if $\\|X-Y\\|\\leq \\epsilon$. According to this definition, we can also write\n$$\\|X-Y\\|=\\frac{1}{2}\\sum_{x\\in \\{0,1\\}^m}|P[X=x]-P[Y=x]|\\leq \\epsilon.$$\nIt is equivalent to the former expression.\n\nLet $U_m$ denote the uniform distribution on $\\{0,1\\}^m$. In order to let a sequence $Y$ to be able to take place of the truly random bits in a randomized application, we let $Y$ be $\\epsilon$-close to $U_m$, where $\\epsilon$ is small enough. In this case,\nthe extra probability error introduced by this replacement is at most $\\epsilon$.\nIn this paper, we want to extract $m$ almost-random bits such that they form a sequence $\\epsilon$-close to the uniform distribution $U_m$ on $\\{0,1\\}^m$ with specified small $\\epsilon>0$, i.e.,\n$$\\|Y-U_m\\|\\leq \\epsilon.$$\n\n\\subsection{Seeded Extractors}\n\nIn 1990, Zuckerman introduced a general model of weak random sources, called $k$-sources, namely whose min-entropy is at least $k$ \\cite{Zuc90}.\nIt was shown that given a source on $\\{0,1\\}^n$ with min-entropy $k0$, and all positive integers $n, k$ and all $\\epsilon>0$, there is an explicit construction of a $(k,\\epsilon)$ extractor $E: \\{0,1\\}^n\\times \\{0,1\\}^d\\rightarrow\\{0,1\\}^m$ with $d\\leq \\log n+ O(\\log(k\/\\epsilon))$ and $m\\geq (1-\\alpha)k$.\n\\end{Lemma}\n\nThe above result implies that given any source $X \\in \\{0,1\\}^n$ with min-entropy $k$, if $\\geq (1+\\alpha)m$ with $\\alpha>0$, we can always construct\na seeded extractor to generates a random sequence $Y\\in \\{0,1\\}^m$ that is $\\epsilon$-close to the uniform distribution. In this case,\nthe seed length $d\\leq \\log n + O(\\log(k\/\\epsilon))$ depends on the input length $n$ and the parameter $\\epsilon$.\n\n\\subsection{Seedless Extractors}\n\nIn the last decade, the concept of seedless (deterministic) extractors has attracted renewed interests, motivated by the reduction of the computational complexity for simulating probabilistic algorithms as well as some requirements in cryptography \\cite{Dodis00}. Several specific classes of sources have been studied, including independent sources, which can be divided into several independent parts containing certain amount of randomness \\cite{Barak06, Rao2007, Raz2005}; bit-fixing sources, where some bits in a binary sequence are truly random and the remaining bits are fixed \\cite{Cohen89, Gabizon06, Kamp06}; samplable sources, where the source is generated by a process that has a bounded amount of computational resources like space \\cite{Kamp11, Trevisan00}. For example, suppose that we have multiple independent sources with the same length $n$. It is known how to extract from two sources when\nthe min-entropy in each is $\\geq 0.5n$ \\cite{Raz2005} or slightly less than $0.5n$ \\cite{Bou05}, how to extract from $O(1\/\\gamma)$ sources if the min-entropy in each is at least $n^\\gamma$ \\cite{Rao06}. All these constructions have exponentially small error, and they are able to extract $\\Theta(k)$ random bits.\n\nBoth seeded extractors and seedless extractors described above have fixed input length, fixed seed length ($d=0$ for seedless extractors) and fixed output length. So we call them fixed-length extractors. To apply fixed-length extractors in extracting randomness from a stochastic process, it needs to first read a sequence of fixed length, whose min-entropy is strictly larger than the number of random bits that we need to generate. Fixed-length extractors can generate random bits of good quality from imperfect stochastic processes,\nbut they usually consume more incoming symbols than what are necessarily required. To increase information efficiency,\nwe let the length of input sequences be adaptive, hence, we have the concept of `variable-length extractors'.\n\n\\subsection{Variable-Length Extractors}\n\nA variable-length extractor is an extractor with variable input length and fixed output length. When applying a variable-length extractor to\na stochastic process, it reads incoming symbols one by one until the whole incoming sequence meets certain criterion, then it maps the incoming sequence into a binary sequence of fixed length as the output. Depending on the sources, the construction may require a small number of additional truly random bits as the seed. Hence, we have seeded variable-length extractors and seedless variable-length extractors.\n\nA seeded variable-length extractor is a function,\n$$V_E: S_p\\times\\{0,1\\}^d\\rightarrow\\{0,1\\}^m,$$\nsuch that given a real source $\\mathcal{R}$, the output sequence is $\\epsilon$-close to\nthe uniform distribution $U_m$. Here, $S_p$ is the set consisting of all possible input sequences, called the input set. It is complete and prefix-free. The input sequence is compete, that means, any infinite sequence has a prefix in the set; so when reading symbols from any source, we can always meet a sequence in the set. Then we stop reading and map this sequence into a binary sequence of length $m$. Being prefix-free is not very necessary; it ensures that all the sequences in $S_p$ are possible to read.\n\nA general procedure of extracting randomness by using variable-length extractors can be divided into three steps:\n\n\\begin{enumerate}\n \\item Determining an input set $S_p$ such that its min-entropy based on the real source $\\mathcal{R}$ is at least $k$, namely,\n$$\\min_{x\\in S_p}\\log_2\\frac{1}{P_\\mathcal{R}(x)}\\leq k,$$\nwhere $k\\geq (1+\\alpha)m$ for any $\\alpha>0$.\n \\item We construct an injective function\n$$V:S_p\\rightarrow \\{0,1\\}^n,$$\nto map the sequences in $S_p$ into binary sequences of length $m$. We read symbols from the source $\\mathcal{R}$ one by one until\nthe current incoming sequence matches one in $S_p$. This incoming sequence is then mapped to a binary sequence of length $n$ based on function $V$. As a result, we get a random sequence $Z$ with length $n$ and min-entropy $k$ (since $V$ is injective).\n \\item Since $k=(1+\\alpha)$ with an $\\alpha>0$, according to Lemma \\ref{lemma_seededextractor}, we can always find a seeded extractor,\n $$E:\\{0,1\\}^n \\times\\{0,1\\}^d\\rightarrow\\{0,1\\}^m$$\n that can extract $m$ almost-random bits from a source with min-entropy $k$. By applying this seeded extractor $E$ to the sequence $Z$,\n we get a random sequence of length $m$ that is $\\epsilon$-close to the uniform distribution $U_m$. Here, the seed length $d\\leq \\log n + O(\\log(k\/\\epsilon))$.\n\\end{enumerate}\n\nWe can see that the construction of a variable-length extractor is a cascade of\na function $V$ and a seeded extractor $E$, i.e.,\n$$V_E=E\\bigotimes V.$$\n\nNote that our requirement is to extract a sequence of $m$ almost-random bits that is $\\epsilon$-close to the uniform distribution $U_m$. The key of constructing variable-length extractors is to find the input set $S_p$ with min-entropy $k$, even the distribution of the real source $\\mathcal{R}$ is slightly unpredictable, such that the expected length of the sequences in $S_p$ is minimized. For stationary ergodic processes, minimizing the expected length is equivalent to minimizing the entropy of the sequences in $S_p$ asymptotically (this will be discussed in this section).\n\nFor some specific types of sources, including independent sources and samplable sources, by applying the ideas in \\cite{Rao2007} and \\cite{Kamp11} we can remove\nthe requirement of truly random bits without degrading the asymptotic performance. As a result, we have seedless variable-length extractors.\nFor example, if the source $\\mathcal{R}$ is an independent process, we can first apply the method in \\cite{Rao2007} to extract $d$ almost-random bits from the first $\\Theta(\\log \\frac{m}{\\epsilon})$ bits, and then use them as the seed of a seeded variable-length extractor to extract randomness from the rest of the process. The detailed discussions will be given in Section \\ref{sec_randomnessextraction}.\n\n\\section{Efficiency and Uncertainty}\n\\label{var_sec_efficiency}\n\n\\subsection{Efficiency}\n\nTo consider the performance of an extractor, we define its \\emph{efficiency} as\nthe asymptotical ratio between the output length and the total entropy of all its inputs. So the efficiency of an extractor can be written as\n$$\\eta=\\lim_{m\\rightarrow\\infty} \\frac{m}{H_{\\mathcal{R}}(X_m)+d},$$\nsuch that the output sequence is $\\epsilon$-close to the uniform distribution $U_m$\non $\\{0,1\\}^m$, where $\\epsilon$ is small, $d$ is the seed length, $m$ is the output length, and $H_{\\mathcal{R}}(X_m)$ is the entropy of the input sequence $X_m$ with range on $S_p$.\nIn our constructions, $d\\leq \\log n + O(\\log(m\/\\epsilon))$, which is ignorable compared to $H_{\\mathcal{R}}(X_m)$ when $m\\rightarrow\\infty$. Hence,\nwe can write\n$$\\eta=\\lim_{m\\rightarrow\\infty} \\frac{m}{H_{\\mathcal{R}}(X_m)}.$$\nIn the definition, we use the entropy of the input sequence rather than the expected input length, because the source that we considered may not be stationary ergodic.\nIt needs to mention that, in seeded constructions, the value of $d$ is also an important parameter although it is much smaller than $m$. The problem of minimizing the seed length $d$ can be studied separately from minimizing the entropy of the input sequence, and it will be addressed in this paper.\n\nFirst, we demonstrate that if a distribution is $\\epsilon$-close to the uniform distribution $U_m$, then\nthe entropy of this distribution is asymptotically $m$ for any $\\epsilon<1$.\n\n\\begin{Lemma}\\label{var_lemma2}\nLet $X$ be a random sequence on $\\{0,1\\}^m$ that is $\\epsilon$-close to the uniform distribution $U_m$, then\n$$ m- \\log_2\\frac{1}{1-\\epsilon}\\leq H(X)\\leq m.$$\n\\end{Lemma}\n\n\\proof Since there are totally $2^m$ possible assignments for $X$, it is easy to get $H(X)\\leq m$.\nSo we only need to prove that\n$$H(X)\\geq m- \\log_2\\frac{1}{1-\\epsilon}.$$\n\nLet $p(x)$ denote $P[X=x]$ for $x\\in \\{0,1\\}^m$.\nSince $X$ is $\\epsilon$-close to the uniform distribution $U_m$, we have\n$$\\frac{1}{2} \\sum_{x\\in \\{0,1\\}^m} \\|p(x)-2^{-m}\\|\\leq \\epsilon.$$\n\nThen the lower bound of $H(X)$ can be written as\n$$\\min_{p} \\sum_{x\\in \\{0,1\\}^m} p(x) \\log_2\\frac{1}{p(x)}$$\nsubject to\n$$p(x)\\geq 0, \\forall x\\in \\{0,1\\}^m;$$\n$$\\sum_{x\\in\\{0,1\\}^m}p(x)=1;$$\n$$\\sum_{x\\in \\{0,1\\}^m} \\|p(x)-2^{-m}\\|\\leq 2\\epsilon.$$\n\nObviously, the optimal solution of the above problem happens at\n$$\\sum_{x\\in \\{0,1\\}^m} \\|p(x)-2^{-m}\\|=2\\epsilon.$$\n\nTo solve the problem based on Lagrange Multipliers, we let\n$$\\lambda(p)= \\sum_{x\\in \\{0,1\\}^m}\\ p(x) \\log_2\\frac{1}{p(x)}+\\lambda_1(\\sum_{x\\in\\{0,1\\}^m}p(x)-1)$$\n$$+\\lambda_2 (\\sum_{x\\in \\{0,1\\}^m} \\|p(x)-2^{-m}\\|-2\\epsilon).$$\n\nIf $p(x)\\geq 0$ with $x\\in\\{0,1\\}^m$ is a solution of the above question, then\n$$\\frac{\\partial \\lambda }{\\partial(p(x))}=0,$$\ni.e.,\n$$ \\left\\{ \\begin{array}{cc}\n \\frac{\\ln p(x)+1}{\\ln 2}+ \\lambda_1+\\lambda_2=0 & \\textrm{ if } 2^{-m}\\leq p(x)\n \\leq 1, \\\\\n \\frac{\\ln p(x)+1}{\\ln 2}+ \\lambda_1-\\lambda_2=0 & \\textrm{ if } 0\\leq p(x)\\leq 2^{-m}.\n \\end{array}\n\\right.$$\n\nSo there exists two constants $a$ and $b$ with $0\\leq a\\leq 2^{-m}\\leq b\\leq 1$, such that,\n$$ \\left\\{ \\begin{array}{cc}\n p(x)=a & \\textrm{ if } 2^{-m}\\leq p(x)\\leq 1, \\\\\n p(x)=b & \\textrm{ if } 0\\leq p(x)\\leq 2^{-m}.\n \\end{array}\n\\right.$$\n\nAssume that there are $t$ assignments of $x$ with $p(x)=a$, then there are $2^m-t$ assignments of $x$ with $p(x)=b$.\nHence, the problem is converted to the one over $a,b,t$, i.e.,\n$$\\min_{a,b,t} t a\\log \\frac{1}{a} +(2^m-t) b\\log \\frac{1}{b},$$\nsubject to\n$$0\\leq t\\leq 2^m;$$\n\\begin{equation}ta +(2^m-t)b=1;\\label{var_equ_entropy1}\\end{equation}\n\\begin{equation}t(2^{-m}-a)+(2^m-t)(b-2^{-m})=2\\epsilon.\\label{var_equ_entropy2}\\end{equation}\nFrom Equ.~(\\ref{var_equ_entropy1}) and (\\ref{var_equ_entropy2}), we get\n$$a=2^{-m}-\\frac{\\epsilon}{t}, \\quad b=2^{-m}+\\frac{\\epsilon}{2^m-t}.$$\n\nSo the question is finding the optimal $t$ that minimizes\n$$-t(2^{-m}-\\frac{\\epsilon}{t})\\log_2 (2^{-m}-\\frac{\\epsilon}{t})$$\n$$-(2^m-t)(2^{-m}+\\frac{\\epsilon}{2^m-t})\\log_2(2^{-m}+\\frac{\\epsilon}{2^m-t}),$$\nsubject to\n$$0\\leq t\\leq \\frac{\\epsilon}{2^{-m}}.$$\n\nThe optimal solution is $t^*=\\frac{\\epsilon}{2^{-m}}$. In this case, the entropy of $X$ is\n$$H(X)= \\log (2^m-t)=m-\\log_2\\frac{1}{1-\\epsilon},$$\nwhich is the lower bound.\n\nThis completes the proof.\n\\hfill\\IEEEQED\\vspace{0.1in}\n\nIn the following lemma, we show that for any extractor, its efficiency is upper bounded by $1$. The reason is that the amount of information, i.e., entropy, does not increase\nduring the process of randomness extraction.\n\n\\begin{Lemma}\\label{lemma_efficiency1}\nFor any extractor with seed length $d$ and output length $m$, if $d=o(m)$, its efficiency $\\eta\\leq 1$.\n\\end{Lemma}\n\n\\proof We consider fixed-length extractors as a special case of variable-length extractors, and consider seedless extractors as a special case of seeded extractors when $d=0$. So our proof only focus on seeded variable-length extractors.\n\nA main observation is that for any extractor, the entropy of its output sequence is bounded\nby the entropy of the input sequence plus the entropy of the seed, since the process of extracting randomness cannot create new randomness.\n\nFor the output sequence, denoted by $Y$, it is $\\epsilon$-close to the uniform distribution $U_m$. According to\nLemma \\ref{var_lemma2}, its entropy is $$H_{\\mathcal{R}}(Y)\\geq m-\\log_2\\frac{1}{1-\\epsilon}.$$\n\nThe total entropy of the inputs is $H_{\\mathcal{R}}(X_m)+d$. Hence,\n$$H_{\\mathcal{R}}(Y)\\leq H_{\\mathcal{R}}(X_m)+d.$$\n\nAs a result, the efficiency of the extractor is\n$$\\eta=\\lim_{m\\rightarrow\\infty} \\frac{m}{H_{\\mathcal{R}}(X_m)}=\\lim_{m\\rightarrow\\infty}\\frac{H_{\\mathcal{R}}(Y)}{H_{\\mathcal{R}}(X_m)+d}\\leq 1.$$\n\nThis completes the proof.\n\\hfill\\IEEEQED\\vspace{0.1in}\n\nIf $\\mathcal{R}$ is a stationary ergodic process, we define its entropy rate as\n$$h(\\mathcal{R})=\\lim_{l\\rightarrow\\infty}\\frac{H(X^l)}{l},$$\nwhere $X^l$ is a random sequence of length $l$ generated from the source $\\mathcal{R}$.\nIn this case, the entropy of the input sequence on $S_p$ is proportional to the expected input length.\n\n\\begin{Lemma} \\label{var_theorem_stionaryergodiclength} Given a stationary ergodic source $\\mathcal{R}$, let $X_m$ be the input sequence of a variable-length extractor that has an output length $m$. Then\n$$\\lim_{m\\rightarrow\\infty} \\frac{H_{\\mathcal{R}}(X_m)}{E_{\\mathcal{R}}[|X_m|]}=h(\\mathcal{R}),$$\nwhere $E_{\\mathcal{R}}[|X_m|]$ is the expected input length.\n\\end{Lemma}\n\n\\proof $X_m$ is a random sequence from $S_p$ based on the distribution of $\\mathcal{R}$.\nLet $l_1$ be the minimum length of the sequences in $S_p$, as $m\\rightarrow\\infty$, $l_1\\rightarrow\\infty$.\nNow, we define $$l_i=l_1+(i-1)\\log l_1 \\textrm{ for all }i\\geq 1.$$ Based on them, we divide all the sequences in $S_p$ into subsets\n$$S_i=\\{x|x\\in S_p, l_i\\leq |x|\\leq l_{i+1}-1\\}$$\nfor $i\\geq 1$.\n\nLet $p_i=P_{\\mathcal{R}}(X_m\\in S_i)$, then\n$$H_{\\mathcal{R}}(X_m)\n \\geq \\sum_{i}[(\\sum_{j>i} p_j)H_{\\mathcal{R}}(X_{l_{i-1}+1}^{l_i}|X_{1}^{l_{i-1}}, |X_m|\\geq l_i)],$$\nwhere $l_0=0$, $\\sum_{j>i} p_j$ is the probability that $|X_m|\\geq l_i$, and\n$X_a^b$ is a sequence of $X_m$ from the $a$th element to the $b$th element.\n\nSince $X_m$ is generated from a stationary ergodic process, and $l_i-{l_{i-1}}\\rightarrow\\infty$ as $m\\rightarrow\\infty$, we can get\n$$H_{\\mathcal{R}}(X_{l_{i-1}+1}^{l_i}|X_{1}^{l_{i-1}}, |X_m|\\geq l_i)\\rightarrow (l_i-l_{i-1})h(\\mathcal{R}).$$\n\nAs a result, as $l_1\\rightarrow\\infty$, we have\n\\begin{eqnarray*}\nH_{\\mathcal{R}}(X_m)&\\geq& (1-\\epsilon) \\sum_{i}(\\sum_{j>i} p_j) (l_i-l_{i-1})h(\\mathcal{R})\\\\\n&=&(1-\\epsilon)\\sum_i p_i l_i h(\\mathcal{R}),\n\\end{eqnarray*}\nfor an arbitrary $\\epsilon>0$.\n\nAlso considering the other direction, we can get\nthat as $l_1\\rightarrow\\infty$,\n\\begin{eqnarray*}\n H_{\\mathcal{R}}(X_m)\n&\\leq &(1+\\epsilon)\\sum_i p_i l_{i+1} h(\\mathcal{R})\\\\\n&=& (1+\\epsilon)\\sum_i p_i (l_i+\\log l_1) h(\\mathcal{R}),\n\\end{eqnarray*}\nfor an arbitrary $\\epsilon>0$.\n\nFor the expected input length, i.e., $E_{\\mathcal{R}}[|X_m|]$, it is easy to show that\n$$\\sum_i p_i l_i\\leq E_{\\mathcal{R}}[|X_m| ] \\leq \\sum_i p_i l_{i+1}=\\sum_i p_i( l_i+\\log l_1) .$$\n\nSo as $m\\rightarrow \\infty$, i.e., $l_1\\rightarrow\\infty$, it yields\n$$\\lim_{m\\rightarrow\\infty} \\frac{H_{\\mathcal{R}}(X_m)}{E_\\mathcal{R}[|X_m|]}=\\lim_{m\\rightarrow\\infty}\\frac{\\sum_i p_i l_i h(\\mathcal{R})}{\\sum_i p_i l_i}$$\n$$=h(\\mathcal{R}).$$\n\nThis completes the proof.\n\\hfill\\IEEEQED\\vspace{0.1in}\n\n\\subsection{Sources and Uncertainty}\n\nGiven a source $\\mathcal{R}$, if its distribution is known, we say that this source is a known stochastic process, and its uncertainty is $0$. In this paper,\nwe mainly focus on those imperfect processes whose distributions are slightly unpredictable due to many factors like\nthe existence of external adversaries.\n\nFirst, given two known stochastic processes $\\mathcal{R}$ and $\\mathcal{M}$, we let\n$d(\\mathcal{R},\\mathcal{M})$ be the difference between $\\mathcal{R}$ and $\\mathcal{M}$. Here, we define $d(\\mathcal{R},\\mathcal{M})$ as\n$$d(\\mathcal{R},\\mathcal{M})=\\limsup_{n\\rightarrow\\infty} \\max_{x\\in \\{0,1\\}^n} \\frac{\\log_2\\frac{P_{\\mathcal{R}}(x)}{P_{\\mathcal{M}}(x)}}{\\log_2\\frac{1}{P_{\\mathcal{M}}(x)}},$$\nwhere $P_{\\mathcal{R}}(x)$ is the probability of generating $x$ from $\\mathcal{R}$ when the sequence length is $|x|$, and $P_{\\mathcal{M}}(x)$ is the probability of generating $x$ from $\\mathcal{M}$ when the sequence length is $|x|$. Although there are some existing ways such as normalized Kullback-Leibler divergence to measure the difference between two sources, with them it is not easy to estimate the uncertainty of a source and it is\nnot easy to analyze the performances of constructed variable-length extractors.\n\nIn the rest of this paper, we investigate a few models of unpredictable sources. Most natural source can be well described in those ways.\n\n\\begin{enumerate}\n \\item The source $\\mathcal{R}$ is an arbitrary stochastic process such that $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$ for a constant $\\beta\\in [0,1]$ and\n a known stochastic process $\\mathcal{M}$.\n \\item $\\mathcal{R}$ is an arbitrary stochastic process such that there exists a stationary ergodic process $\\mathcal{M}$ (whose distribution is unknown)\nand $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$; that is, $\\min_{\\mathcal{M}\\in \\mathcal{G}_{s.e.}}d(\\mathcal{R},\\mathcal{M})\\leq \\beta$, where $\\mathcal{G}_{s.e.}$ denotes the set consisting of all stationary ergodic processes.\n\\end{enumerate}\n\nIn both the models, we call $\\beta$ as the \\emph{uncertainty} of the source $\\mathcal{R}$. In the real world,\n$\\beta$ can be easily estimated without knowing the distribution of the processes.\nIt just reflects how unpredictable the real source $\\mathcal{R}$ is.\n\nTo construct variable-length extractors, we only care about the possible input sequences, namely, those in $S_p$. Hence, for the case\nof finite length, $d_p(\\mathcal{R},\\mathcal{M})$ is a more important parameter for us, defined by\n$$d_p(\\mathcal{R},\\mathcal{M})= \\max_{x\\in S_p} \\frac{\\log_2\\frac{P_{\\mathcal{R}}(x)}{P_{\\mathcal{M}}(x)}}{\\log_2\\frac{1}{P_{\\mathcal{M}}(x)}},$$\n\nAs the number of required random bits $m$ increases, $d_p(\\mathcal{R},\\mathcal{M})$ quickly converge to $d(\\mathcal{R}, \\mathcal{M})$.\nAnd we can write\n$$d_p(\\mathcal{R},\\mathcal{M})=d(\\mathcal{R}, \\mathcal{M})+\\epsilon_p$$\nfor a very small constant $\\epsilon_p$. As $m\\rightarrow\\infty$, $\\epsilon_p\\rightarrow 0$. In this case, the upper bound of $d_p(\\mathcal{R},\\mathcal{M})$ or\n$\\min_{\\mathcal{M}\\in \\mathcal{G}_{s.e.}}d_p(\\mathcal{R},\\mathcal{M})$ is\n$$\\beta_p=\\beta+\\epsilon_p.$$\n\n\\begin{Example} Let $x_1x_2...\\in \\{0,1\\}^*$ be a sequence generated from an independent source $\\mathcal{R}$ such that $$\\forall i\\geq 1, P[x_i=1]\\in [0.8,0.82].$$\nIf we let $\\mathcal{M}$ be a biased coin with probability $0.8132$, then\n$$\\beta=\\max_{\\textrm{possible }\\mathcal{R}} d(\\mathcal{R},\\mathcal{M})$$\n$$=\\max(\\frac{\\log_2 \\frac{0.2}{0.1868}}{\\log_2 \\frac{1}{0.1868}}, \\frac{\\log_2 \\frac{0.82}{0.8132}}{\\log_2 \\frac{1}{0.8132}})=0.0405.$$ \\hfill $\\Box$\n\\end{Example}\n\nAccording to our definition, $d(\\mathcal{M},\\mathcal{R})\\leq \\beta$ if and only if\n$$P_{\\mathcal{R}}(x)\\leq P_{\\mathcal{M}}(x)^{1-\\beta}$$\nfor all $x\\in\\{0,1\\}^\\infty$ with $|x|\\rightarrow\\infty$. This is a condition that is very easy to be satisfied by many natural stochastic processes for a small $\\beta$.\n\n\\begin{Lemma} If $d(\\mathcal{R},\\mathcal{M})\\rightarrow 0$, we have\n$$P_{\\mathcal{R}}(x)\\rightarrow P_{\\mathcal{M}}(x) $$\nfor all $x\\in \\{0,1\\}^*$.\n\\end{Lemma}\n\n\\subsection{Efficiency and Uncertainty}\n\nIn this subsection, we investigate the relation between the efficiency and uncertainty. We show that given\na stochastic process $\\mathcal{R}$ with uncertainty $\\beta$, as described in the previous subsection, one cannot construct a variable-length extractor with efficiency strictly larger than $1-\\beta$ for all the possibilities of $\\mathcal{R}$.\n\nLet us first consider a simple example: let $X$ be a random sequence with the uniform distribution on $\\{0,1\\}^n$ and let $Y$\nbe an arbitrary random sequence on $\\{0,1\\}^n$ such that $$\\frac{\\log_2 \\frac{ P[Y=x]}{P[X=x]}}{\\log_2\\frac{1}{P[X=x]}}\\leq \\beta, \\forall x\\in \\{0,1\\}^n.$$\nNow, we show that from the source $Y$, one cannot construct an extractor with efficiency strictly larger than $1-\\beta$.\nTo see this, we consider an extractor $f$ with output length $m$, and a source $Y$ with\n$$P[Y=y]\\in \\{0, 2^{-n(1-\\beta)}\\}, \\forall y\\in \\{0,1\\}^n.$$\nFor this a source $Y$, its entropy is $H(Y)=n(1-\\beta)$. In order to make sure the output sequence of $f$, denoted by $Z$,\nis $\\epsilon$-close to $U_m$, it has\n$$\\lim_{m\\rightarrow\\infty} \\frac{m}{n(1-\\beta)}\\leq \\lim_{m\\rightarrow\\infty} \\frac{H(Z)+\\log_2\\frac{1}{1-\\epsilon}}{H(Y)}\\leq 1.$$\nSo we cannot generate more than $n(1-\\beta)$ random bits asymptotically. In this case, if we apply the seeded extractor $f$\nto the random sequence $X$, which is a possibility of $Y$, then the efficiency is\n$$\\eta=\\lim_{m\\rightarrow\\infty}\\frac{m}{H(X)}=\\lim_{m\\rightarrow\\infty}\\frac{m}{n}\\leq 1-\\beta.$$\nSo there does not exist a seeded extractor that can extract randomness from an arbitrary $Y$ and its efficiency is strictly larger than $1-\\beta$.\nHere, $\\beta$ is the uncertainty of the source.\n\n\n\\begin{Theorem} \\label{var_theorem_lowerbound1} Let $\\mathcal{M}$ be a known stochastic process, and $\\mathcal{R}$ be an arbitrary stochastic process such that\n$d(\\mathcal{R},\\mathcal{M})\\leq \\beta$, then one cannot construct a variable-length extractor whose efficiency\nis strictly larger than $1-\\beta$ for all possible $\\mathcal{R}$.\n\\end{Theorem}\n\n\\proof Let $f$ be a variable-length extractor whose input sequence is a random sequence $X_m$ on $S_p$ and\nits output sequence is a random sequence $Y$ on $\\{0,1\\}^m$. Assume that as $m\\rightarrow\\infty$, $f$ can extract from an arbitrary $\\mathcal{R}$ such that the output sequence $Y$ is $\\epsilon$-close to $U_m$.\n\nLet $h=H_{\\mathcal{M}}(X_m)$ be the entropy of the input sequence based on the distribution of $\\mathcal{M}$,\nthen we want to show that there exists a process $\\mathcal{R}$ such that $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$ and\n$H_{\\mathcal{R}}(X_m)\\leq h(1-\\beta)$ as $m\\rightarrow\\infty$.\n\nTo find such a process $\\mathcal{R}$, we order all the elements in $S_p$ as\n$x_1, x_2, x_3, ...$\nsuch that\n$$P_{\\mathcal{M}}(x_1)\\geq P_{\\mathcal{M}}(x_2)\\geq P_{\\mathcal{M}}(x_3)\\geq ...$$\n\nThen we divide all these elements into groups,\n$$\\{x_1, x_2, ..., x_{i_1}\\}, \\{x_{i_1+1},x_{i_1+2},...,x_{i_2}\\},...$$\nsuch that the total probability of the elements in each group is almost the probability of its first element to the power of $1-\\beta$, i.e.,\n$$0\\leq P_{\\mathcal{M}}(x_{i_j+1})^{1-\\beta}-\\sum_{k=i_j+1}^{i_{j+1}} P_{\\mathcal{M}}(x_k)\\leq P_{\\mathcal{M}}(x_{i_j+1}),$$ for all $j\\geq 0$,\nwhere $i_0=0$.\n\nLet $A=\\{x_{1}, x_{i_1+1}, x_{i_2+1}, ...\\}$ be the set consisting of the first elements of all the groups.\nNow, we consider a possibility of $\\mathcal{R}$ in the following way: for all $x\\in \\{x_{1}, x_{i_1+1}, x_{i_2+1}, ...\\}$,\nits probability is\n$$P_{\\mathcal{R}}(x)=\\sum_{k=i_j+1}^{i_{j+1}} P_{\\mathcal{M}}(x_k), \\textrm{ if } x=x_{i_j+1};$$\nFor all $x\\in S_p\/A=S_p\/\\{x_{1}, x_{i_1+1}, x_{i_2+1}, ...\\}$, its probability is\n$$P_{\\mathcal{R}}(x)=0.$$\n\nFor this source $\\mathcal{R}$, the entropy of the input sequence is\n$$H_{\\mathcal{R}}(X_m)=\\sum_{x\\in S_p} P_{\\mathcal{R}}(x) \\log_2 \\frac{1}{P_{\\mathcal{R}}(x)}.$$\n\nAs $m\\rightarrow\\infty$, we have\n\\begin{eqnarray*}\n&&H_{\\mathcal{R}}(X_m)\\\\\n &= & \\sum_{x\\in A} P_{\\mathcal{R}}(x) \\log_2 \\frac{1}{P_{\\mathcal{R}}(x)} \\\\\n &\\rightarrow& (1-\\beta) \\sum_{x\\in A} P_{\\mathcal{R}}(x) \\log_2 \\frac{1}{P_{\\mathcal{M}}(x)}\\\\\n&=& (1-\\beta) \\sum_{j\\geq 0} \\sum_{k=i_j+1}^{i_{j+1}} P_{\\mathcal{M}}(x_k) \\log_2 \\frac{1}{P_{\\mathcal{M}}(x_{i_j+1})}\\\\\n&\\leq & (1-\\beta) \\sum_{j\\geq 0} \\sum_{k=i_j+1}^{i_{j+1}} P_{\\mathcal{M}}(x_k) \\log_2 \\frac{1}{P_{\\mathcal{M}}(x_k)}\\\\\n&=& (1-\\beta) H_{\\mathcal{M}}(X_m)\\\\\n&=& (1-\\beta)h.\n\\end{eqnarray*}\n\nAccording to Lemma \\ref{var_lemma2}, as $m\\rightarrow\\infty$, $\\frac{m}{H_{\\mathcal{R}}(Y)}\\rightarrow 1$.\nFurthermore, we can get $$\\lim_{m\\rightarrow\\infty}\\frac{H_{\\mathcal{R}}(Y)}{H_{\\mathcal{R}}(X_m)} \\leq 1, $$\nit implies that\n$$\\lim_{m\\rightarrow\\infty} \\frac{m}{(1-\\beta)h}\\leq 1,$$\notherwise, the output sequence cannot be $\\epsilon$-close to the uniform distribution $U_m$.\n\nIf we apply the extractor $f$ to the source $\\mathcal{M}$, which is also a possibility for $\\mathcal{R}$, then its efficiency is\n$$\\eta=\\lim_{m\\rightarrow\\infty}\\frac{m}{h}\\leq 1-\\beta.$$\n\nSo it is impossible to construct a variable-length extractor with efficiency strictly larger than $1-\\beta$ for all the possibilities of the source $\\mathcal{R}$. This completes the proof.\n\\hfill\\IEEEQED\\vspace{0.1in}\n\nWith the same proof, we can also get the following theorem.\n\n\\begin{Theorem} Let $\\mathcal{R}$ be an arbitrary stochastic process such that\n$d(\\mathcal{R},\\mathcal{M})\\leq \\beta$ for a stationary ergodic process $\\mathcal{M}$ with unknown distribution,\n, then one cannot construct a variable-length extractor whose efficiency\nis strictly larger than $1-\\beta$ for all possible $\\mathcal{R}$.\n\\end{Theorem}\n\nThe above theorems show that one cannot construct an extractor whose efficiency is strictly larger than $1-\\beta$ for all the possible source $\\mathcal{R}$. Here, $\\beta$ is an important parameter that measures the uncertainty of a real source $\\mathcal{R}$, either to\na known process or to the nearest stationary ergodic process.\nIn the next a few sections, we will present a few constructions for efficiently extracting randomness from the sources described in this section.\nWe show that their efficiency $\\eta$ satisfies\n$$1-\\beta\\leq \\eta\\leq 1.$$\nThat means the bound $1-\\beta$ is actually achievable and the constructions proposed in this paper are asymptotically optimal on efficiency.\n\n\n\\section{Construction I: Approximated by Known Processes}\n\\label{var_section_tech1}\n\nIn this section, we consider those sources which can be approximated by a known stochastic process $\\mathcal{M}$, namely, an arbitrary process\n$\\mathcal{R}$ with $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$ for a known process $\\mathcal{M}$.\nWe say that a stochastic process $\\mathcal{M}$ is known if its distribution is given, i.e., $P_{\\mathcal{M}}(x)$ can be easily calculated for any $x\\in \\{0,1\\}^*$.\nNote that this process $\\mathcal{M}$ is not necessary to be stationary or ergodic. For instance, $\\mathcal{M}$ can be an independent process\n$z_1z_2...\\in \\{0,1\\}^*$ such that $$\\forall i\\geq1, P_{\\mathcal{M}}(z_i=1)= \\frac{1+sin(i\/10)}{2}.$$\n\n\\subsection{Construction}\n\nOur goal is to extract randomness from an imperfect random source $\\mathcal{R}$. The problem is that\nwe do not know the exact distribution of $\\mathcal{R}$, but we know that it can be approximated by a known process $\\mathcal{M}$.\nSo we can use the distribution of $\\mathcal{M}$ to estimate the distribution of $\\mathcal{R}$. As a result, we have the following procedure to extract $m$ almost-random bits.\n\nThe idea of the procedure is first producing a random sequence of length $n$ and min-entropy $k=m(1+\\alpha)$ with $\\alpha>0$, from which\nwe can further obtain a sequence $\\epsilon$-close to the uniform distribution $U_m$ by applying a $(k,\\epsilon)$ seeded extractor.\nAccording to the results of seeded extractors, this constant $\\alpha>0$ can be arbitrarily small.\n\n\\begin{Construction}\\label{const:1}\nAssume the real source $\\mathcal{R}$ is an arbitrary stochastic process such that $d(\\mathcal{R}, \\mathcal{M})\\leq \\beta$\nfor a known process $\\mathcal{M}$. Then we extract $m$ almost-random bits from $\\mathcal{R}$ based on the following procedure.\n\n\\begin{enumerate}\n \\item Read input bits one by one from $\\mathcal{R}$ until we get an input sequence $x\\in \\{0,1\\}^*$ such that\n $$\\log_2 \\frac{1}{P_{\\mathcal{M}}(x)}\\geq \\frac{k}{1-\\beta_p},$$\n where $\\beta_p=\\beta+\\epsilon_p$ with $\\epsilon_p>0$ and $k=m(1+\\alpha)$ with $\\alpha>0$. The small constant $\\epsilon_p$ has value depending on the input set $S_p$; as\n $m\\rightarrow\\infty$, $\\epsilon_p\\rightarrow 0$. The constant $\\alpha$ can be arbitrarily small.\n \\item Let $n$ be the maximum length of all the possible input sequences, then\n {$$n=\\arg\\min_{l}\\{l\\in \\mathbb{N}|\\forall y\\in \\{0,1\\}^l, $$\n $$\\log_2\\frac{1}{P_\\mathcal{M}(y)}\\geq \\frac{k}{1-\\beta_p}\\}.$$}\n If $|x|0$, according to Lemma \\ref{lemma_seededextractor},\nwe can construct a seeded extractor that applies to the sequence $Z$ and generates a binary sequence $\\epsilon$-close to the uniform distribution $U_m$.\n\nThis completes the proof.\n\\hfill\\IEEEQED\\vspace{0.1in}\n\n\\subsection{Efficiency Analysis}\n\nNow, we study the efficiency of Construction \\ref{const:1}. According to our definition, given\na construction, its efficiency is\n$$\\eta=\\lim_{m\\rightarrow\\infty}\\frac{m}{H_{\\mathcal{R}}(X_m)}.$$\n\n\\begin{Theorem}\\label{var_theorem1_2} Given a real source $\\mathcal{R}$ and a known process $\\mathcal{M}$ such that $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$, then the efficiency of Construction \\ref{const:1} is\n$$1-\\beta\\leq \\eta\\leq 1.$$\n\\end{Theorem}\n\n\\proof Since $\\eta$ is always upper bounded by $1$, we only need to show that $\\eta\\geq 1-\\beta$.\n\n According to Lemma \\ref{lemma_seededextractor}, as $m\\rightarrow \\infty$,\nwe have\n$$\\lim_{m\\rightarrow\\infty}\\frac{k}{m}=1.$$\n\nNow, let us consider the number of elements in $S_p$, i.e., $|S_p|$. To calculate $|S_p|$, we let\n$$S_p'=\\{x[1:|x|-1] |x\\in S_p\\},$$\nwhere $x[1:|x|-1]$ is the prefix of $x$ of length $|x|-1$,\nthen for all $y\\in S_p'$, $$\\log_2\\frac{1}{P_\\mathcal{M}(y)}\\leq \\frac{k}{1-\\beta_p}.$$ Hence,\n$$\\log_2|S_p'|\\leq \\frac{k}{1-\\beta_p}.$$\n\nIt is easy to see that $|S_p|\\leq 2|S_p'|$, so\n$$\\log_2|S_p|\\leq \\frac{k}{1-\\beta_p}+1.$$\n\nLet $X_m$ be the input sequence, then\n$$\\lim_{k\\rightarrow\\infty}\\frac{H_{\\mathcal{R}}(X_m)}{k}\\leq \\lim_{k\\rightarrow\\infty}\\frac{\\log_2|S_p|}{k}$$\n$$\\leq \\lim_{k\\rightarrow\\infty}\\frac{1}{1-\\beta_p}=\\frac{1}{1-\\beta}.$$\n\nFinally, it yields\n$$\\eta=\\lim_{m\\rightarrow\\infty}\\frac{m}{H_{\\mathcal{R}}(X_m)}\\geq 1-\\beta.$$\n\nThis completes the proof.\\hfill\\IEEEQED\\vspace{0.1in}\n\nWe see that the efficiency of the above construction is between $1-\\beta$ and $1$. As shown in Theorem \\ref{var_theorem_lowerbound1}, the\ngap $\\beta$, introduced by the uncertainty of the real source $\\mathcal{R}$, cannot be smaller. Our construction is asymptotically optimal in the sense that we cannot find a variable-length extractor with efficiency definitely larger than $1-\\beta$.\n\n\\begin{Corollary} Given a real source $\\mathcal{R}$ and a known process $\\mathcal{M}$ such that $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$, then as $\\beta\\rightarrow 0$, the efficiency of Construction \\ref{const:1} is\n$$\\eta\\rightarrow 1.$$\n\\end{Corollary}\n\nIn this case, the efficiency of the construction can achieve Shannon's limit.\n\nIf $\\mathcal{R}$ is a stationary ergodic process, we can also get the following result.\n\n\\begin{Corollary} Given a stationary ergodic process$\\mathcal{R}$ and a known process $\\mathcal{M}$ such that $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$, for the expected input length of Construction \\ref{const:1}, we have\n$$\\frac{1}{h(\\mathcal{R})}\\leq \\lim_{m\\rightarrow\\infty} \\frac{E[|X_m|]}{m}\\leq \\frac{1}{(1-\\beta)h(\\mathcal{R})},$$\nwhere $h(\\mathcal{R})$ is the entropy rate of the source $\\mathcal{R}$.\n\\end{Corollary}\n\n\\proof This conclusion is immediate following Lemma \\ref{var_theorem_stionaryergodiclength} and Theorem \\ref{var_theorem1_2}.\n\\hfill\n\\IEEEQED\\vspace{0.1in}\n\n\\section{Construction II: Approximately Biased Coins}\n\\label{var_section_tech2}\n\nIn this section, we use a general ideal model such as a biased coin or a Markov chain to approximate the real source $\\mathcal{R}$.\nHere, we do not care about the specific parameters of the ideal model. The reason is, in some cases, the source $\\mathcal{R}$ is\nvery close to an ideal source but we cannot (or do not want to) estimate the parameters accurately.\nAs a result, we introduce a construction by exploring the characters of biased coins or Markov chains. For simplicity,\nwe only discuss the case that\nthe ideal model is a biased coin, and the same idea can be generalized when the ideal model is a Markov chain. Specifically,\nlet $\\mathcal{G}_{b.c.}$ denote the set consisting of all the models of biased coins with different probabilities, and we\nconsider $\\mathcal{R}$ as an arbitrary stochastic process such that\n$$\\min_{\\mathcal{M}\\in \\mathcal{G}_{b.c.}} d(\\mathcal{R},\\mathcal{M}) \\leq \\beta.$$\n\n\\subsection{Construction}\n\nThe idea of the construction is similar as Construction \\ref{const:1}, i.e., we first produce a random sequence of length $n$ and with min-entropy $k=m(1+\\alpha)$ for $\\alpha>0$, from which\nwe can further obtain a sequence $\\epsilon$-close to the uniform distribution $U_m$ by applying a $(k,\\epsilon)$ seeded extractor.\n\n\\begin{Construction}\\label{const:2}\nAssume the real source $\\mathcal{R}$ is an arbitrary stochastic process such that $$\\min_{\\mathcal{M}\\in \\mathcal{G}_{b.c.}} d(\\mathcal{R},\\mathcal{M}) \\leq \\beta$$ for a constant $\\beta$. Then we extract $m$ almost-random bits from $\\mathcal{R}$ based on the following procedure.\n\\begin{enumerate}\n\\item Read input bits one by one from $\\mathcal{R}$ until we get an input sequence $x\\in \\{0,1\\}^*$ such that\n $$\\log_2 {\\nchoosek{k_0+k_1}{ \\max(1,\\min(k_0,k_1))}}\\geq \\frac{k}{1-\\beta_p},$$\n where $k_0$ is the number of zeros in $x$, $k_1$ is the number of ones in $x$,\n $\\beta_p=\\beta+\\epsilon_p$ with $\\epsilon_p>0$ and $k=m(1+\\alpha)$ with $\\alpha>0$. The small constant $\\epsilon_p$ has value depending on the input set $S_p$; as\n $m\\rightarrow\\infty$, $\\epsilon_p\\rightarrow 0$. The constant $\\alpha$ can be arbitrarily small.\n\\item Since the input sequence $x$ can be very long, we map it into a sequence $z$ of fixed length $n$ such that\n$$z=[I_{(k_0\\geq k_1)}, \\min(k_0,k_1), r(x)],$$\nwhere $I_{(k_0\\geq k_1)}=1$ if and only if $k_0\\geq k_1$, and $r(x)$ is the rank of $x$ among all the permutations of $x$ with respect to the lexicographic order.\nSince $x$ is randomly generated, the above procedure leads us to a random sequence $Z$ of length $n$.\n\\item Applying a $(k,\\epsilon)$ extractor to $Z$ yields a random sequence of length $m$ that is $\\epsilon$-close to $U_m$.\\hfill$\\Box$\n\\end{enumerate}\n\\end{Construction}\n\nTo see that the construction above works, we need to show that the random sequence $Z$ obtained after the second step has min-entropy at least $k$, and its length $n$ is well bounded.\n\n\\begin{Lemma}\n Given a source $\\mathcal{R}$ with $\\min_{\\mathcal{M}\\in \\mathcal{G}_{b.c.}} d(\\mathcal{R},\\mathcal{M}) \\leq \\beta$, Construction \\ref{const:2} yields a random sequence $Z$ with length\n $$n\\leq 1+\\lceil\\log_2(\\frac{k}{1-\\beta_p}+1)\\rceil+ \\lceil\\frac{2k}{1-\\beta_p}\\rceil.$$\n\\end{Lemma}\n\\proof\n1) $I_{(k_0\\geq k_1)}$ can be represented as $1$ bit.\n\n2) Without loss of generality, we assume $k_0\\leq k_1$. According to our construction,\n$$ \\log_2 {\\nchoosek{k_0+k_1-1}{ k_0-1}}< \\frac{k}{1-\\beta_p} \\textrm{ for } k_0>1,$$\nand\n$$ \\log_2 {\\nchoosek{k_1}{1}} < \\frac{k}{1-\\beta_p} \\textrm{ for } k_0=0 \\textrm{ or } k_0=1.$$\n\nThen\n\\begin{eqnarray*}\nk_0-1&\\leq &\\log_2 {\\nchoosek{2k_0-1}{ k_0-1}}\\\\\n&\\leq& \\log_2 {\\nchoosek{k_0+k_1-1}{k_0-1}}\\\\\n&<& \\frac{k}{1-\\beta_p}.\n\\end{eqnarray*}\n\nSo $\\min(k_0, k_1)$ can be represented as\n$\\lceil\\log_2 (\\frac{k}{1-\\beta_p} +1 )\\rceil$ bits.\n\n3) Let us consider the number of permutations of $x$, denoted by $N(x)$.\nIf $k_0>1$, then\n\\begin{eqnarray*}\n\\log_2 N(x)&=&\\log_2 {\\nchoosek{k_0+k_1}{ k_0}}\\\\\n&\\leq &\\log_2 {\\nchoosek{k_0+k_1-1}{ k_0-1}} +\\log_2\\frac{k_0+k_1}{k_0}\\\\\n&\\leq &\\frac{k}{1-\\beta_p} +\\log_2\\frac{k_0+k_1}{k_0}.\n\\end{eqnarray*}\n\nIf $k_0=1$, then\n$$\\log_2 N(x) \\leq \\log_2 {\\nchoosek{k_1 }{1}} +\\log_2 \\frac{k_1+1}{k_1}.$$\n\nIf $k_0=0$, then\n$$\\log_2 N(x)=0.$$\n\nBased on the analysis above, we can get $$\\log_2 N(x)\\leq \\frac{2k}{1-\\beta_p}.$$\n\nHence, $r(x)$ can be represented as $\\lceil\\frac{2k}{1-\\beta_p}\\rceil$ bits.\n\nThis completes the proof.\n\\hfill\\IEEEQED\\vspace{0.1in}\n\nLet $\\mathbf{1}^a$ denote the all-one vector of length $a$, then we get the following result.\n\\begin{Theorem}\n Construction \\ref{const:2} generates a random sequence of length $m$ that is $\\epsilon$-close to $U_m$ if\n $P_{\\mathcal{R}}(\\mathbf{1}^a)\\leq 2^{-k}, P_{\\mathcal{R}}(\\mathbf{0}^a)\\leq 2^{-k}$ for $a=2^{\\lfloor\\frac{k}{1-\\beta_p}\\rfloor}$.\n\\end{Theorem}\n\n\\proof Since the mapping in the second step is injective, it will not affect the min-entropy; we only need to prove that the input sequence has min-entropy $k$, i.e.,\n$$\\log_2 \\frac{1}{P_{\\mathcal{R}}(x)}\\geq k, \\forall x\\in S_p,$$\nwhere $S_p$ is the set consisting of all the possible input sequences.\n\nIt is not hard to see that if $\\min(k_0,k_1)\\geq 1$,\n$$P_{\\mathcal{M}}(x)\\leq \\frac{1}{{\\nchoosek{k_0+k_1}{ k_0}}},$$\nwhich leads to\n$$\\log_2 \\frac{1}{P_{\\mathcal{M}}(x)}\\geq \\frac{k}{1-\\beta_p}.$$\n\nFurthermore, based on the definition of $d_p(\\mathcal{R},\\mathcal{M})$, we can get if $\\min(k_0,k_1)\\geq 1$,\n$$\\log_2 \\frac{1}{P_{\\mathcal{R}}(x)}\\geq k.$$\n\nIf $\\min(k_0,k_1)=0$, according to the condition in the lemma, we can also have the same result.\n\nSince $k=m(1+\\alpha)$ with $\\alpha>0$, according to Lemma \\ref{lemma_seededextractor},\nwe can construct a seeded extractor that applies to the sequence $Z$ and generates a binary sequence $\\epsilon$-close to the uniform distribution $U_m$.\n\nThis completes the proof.\n\\hfill\\IEEEQED\\vspace{0.1in}\n\nActually, the idea above can be easily generalized if $\\mathcal{M}$ is a Markov chain that best approximates the real source $\\mathcal{R}$.\nThe idea follows the main lemma in \\cite{Zhou_Markov} that shows how to generate random bits with optimal efficiency from an arbitrary Markov chain.\n\n\\subsection{Efficiency Analysis}\n\nFor the efficiency of the construction, we can get the same bounds as Construction \\ref{const:1}.\n\n\\begin{Theorem}\nGiven an arbitrary source $\\mathcal{R}$ such that $$\\min_{\\mathcal{M}\\in \\mathcal{G}_{b.c.}}d (\\mathcal{R},\\mathcal{M}) \\leq \\beta,$$\nif there exists a model $\\mathcal{M}\\in \\mathcal{G}_{b.c.}$ with probability $p\\leq \\frac{1}{2}$ of being $1$ or $0$ and\n$$p>\\sqrt{d(\\mathcal{R},\\mathcal{M})\\log_2\\frac{1}{p}\\frac{\\ln 2}{2}},$$ then the efficiency of Construction \\ref{const:2} is\n$$1-\\beta \\leq \\eta\\leq 1.$$\n\\end{Theorem}\n\n\\proof Let $N_{k_0, k_1}$ denote the number of input sequences with $k_0$ zeros and $k_1$ ones in $S_p$, and let $p_{k_0,k_1}$ be the probability based on $\\mathcal{R}$ of generating such a sequence. Let us define\n$$A=\\{(k_0,k_1)| N_{k_0,k_1}>0\\},$$\nthen we can get\n$$\nH_{\\mathcal{R}}(X_m)\\leq H(\\{p_{k_0,k_1}|(k_0,k_1)\\in A\\})$$\n$$+\\sum_{(k_0,k_1)\\in A}p_{k_0,k_1}\\log_2 N_{k_0,k_1}.\n$$\n\nAccording to the proof in the above theorem, $\\min(k_0,k_1)\\leq \\frac{k}{1-\\beta_p}+1$. So there are totally at most $2(\\frac{k}{1-\\beta_p}+1)$ available pairs of $(k_0, k_1)$. Hence\n$$H(\\{p_{k_0,k_1}|(k_0,k_1)\\in A\\})\\leq \\log_2(2+(\\frac{k}{1-\\beta_p}+1))=o(k).$$\n\nNow, we write $n=k_0+k_1$. According to our method, if $\\min(k_0,k_1)\\geq 1$,\n$$\\nchoosek{k_0+k_1}{\\min(k_0,k_1)}\\geq 2^{\\frac{k}{1-\\beta_p}},$$\n$$\\nchoosek{k_0+k_1-1}{\\min(k_0,k_1)-1}<2^{\\frac{k}{1-\\beta_p}}.$$\n\nHence, given $n$, we get an upper bound for $\\min(k_0,k_1)$, which is\n\\begin{equation}\\label{var_equ_lemma1_1}\nt_n=\\max\\{i\\in\\{0,1,...,n\\}| {\\nchoosek{n-1}{ i-1}}<2^{\\frac{k}{1-\\beta_p}}\\}.\n\\end{equation}\n\nNote that if $\\nchoosek{n-1}{\\frac{n}{2}-1}\\geq 2^{\\frac{k}{1-\\beta_p}}$, then $t_n$ is a nondecreasing\nfunction of $n$. Using the Stirling bounds on factorials yields\n$$ \\lim_{n\\rightarrow \\infty }\\frac{1}{n}\\log_2 {\\nchoosek{n}{ \\rho n}}=H(\\rho),$$\nwhere $H$ is the binary entropy function. Hence, following (\\ref{var_equ_lemma1_1}), we can get\n\\begin{equation}\\label{var_equ_lemma1_3} \\lim_{n\\rightarrow \\infty} H(\\frac{t_n}{n})=\\lim_{n\\rightarrow\\infty}\\frac{k}{(1-\\beta_p) n}.\\end{equation}\n\nLet $P_n$ denote the probability of having an input sequence of length at least $n$ based on the distribution of $\\mathcal{R}$. In this case,\n$P_n$ is a nonincreasing function of $n$. Let $Q_n$ denote the probability of having an input sequence of length at least $n$ based on the distribution of $\\mathcal{M}\\in \\mathcal{G}_{b.c.}$\nwhose probability is $p\\leq \\frac{1}{2}$. Since for all binary sequence $x\\in \\{0,1\\}^n$,\n$$\\log_2 \\frac{1}{P_\\mathcal{M}(x)}\\leq n\\log_2\\frac{1}{p},$$\nwe can get\n$$\\log_2\\frac{P_{\\mathcal{R}(x)}}{P_\\mathcal{M}(x)}\\leq d n\\log_2 \\frac{1}{p},$$\nwhere $d=d_p(\\mathcal{R},\\mathcal{M})$.\n\nSince\n$P_n=\\sum_{x\\in S} P_{\\mathcal{R}}(x)$ and $Q_n=\\sum_{x\\in S}\nP_{\\mathcal{M}}(x)$ for some $S\\subset \\{0,1\\}^n$, it is not hard to prove that\n\\begin{equation}\\label{equ_lemma1_13}\\log_2\\frac{P_n}{Q_n}\\leq d n \\log_2\\frac{1}{p}.\\end{equation}\n\nAccording to Hoeffding's inequality, we can get\n\\begin{eqnarray*}\nQ_n&\\leq &2P[k_1\\leq t_n]\\\\\n&\\leq& 2P[\\frac{k_1}{n}-p \\leq \\frac{t_n}{n}-p] \\\\\n&\\leq& 2e^{-2n(p-\\frac{t_n}{n})^2}.\n\\end{eqnarray*}\n\nHence\n\\begin{equation}\\label{equ_lemma1_14}P_n \\leq 2^{- d n\\log_2 p} Q_n \\leq 2 e^{-\\log_2 p \\ln 2 \\cdot d n -2 n (p-\\frac{t_n}{n})^2}.\\end{equation}\n\nFrom this inequality, we see that $P_n\\rightarrow 0$ as $n\\rightarrow 0$ if\n\\begin{equation}\\label{equ_lemma1_15}-d \\log_2 p \\ln 2 -2 (p-\\frac{t_n}{n})^2 <0.\\end{equation}\n\nBased on (\\ref{var_equ_lemma1_3}) and (\\ref{equ_lemma1_15}), we can get that $P_n\\rightarrow 0$ as $n\\rightarrow 0$ if\n$$\\frac{n}{k}\\geq \\frac{1}{(1-\\beta_p)H(p-\\sqrt{d\\log_2\\frac{1}{p}\\frac{\\ln 2}{2}})}.$$\n\nNow, let $a=\\frac{1+\\epsilon}{(1-\\beta_p)H(p-\\sqrt{d\\log_2\\frac{1}{p}\\frac{\\ln 2}{2}})}$ with $\\epsilon>0$, we can write\n\\begin{eqnarray*}\nH_{\\mathcal{R}}(X_m) &\\leq &o(k)+ \\sum_{k_0,k_1: k_0+k_1\\geq a k} p_{k_0,k_1}\\log_2 N_{k_0,k_1}\\\\\n&&+ \\sum_{k_0,k_1: k_0+k_1\\sqrt{d(\\mathcal{R},\\mathcal{M})\\log_2\\frac{1}{p}\\frac{\\ln 2}{2}},$$ then for the expected input length of Construction \\ref{const:2}, we have\n$$\\frac{1}{h(\\mathcal{R})}\\leq \\lim_{m\\rightarrow\\infty} \\frac{E[|X_m|]}{m}\\leq \\frac{1}{(1-\\beta)h(\\mathcal{R})},$$\nwhere $h(\\mathcal{R})$ is the entropy rate of $\\mathcal{R}$.\n\\end{Corollary}\n\n\n\n\n\n\\section{Construction III: Approximately Stationary Ergodic Processes}\n\\label{var_section_tech4}\n\n\nIn this section, we consider imperfect sources that are approximately stationary and ergodic.\nHere, we let $\\mathcal{R}$ be an arbitrary stochastic process such that $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$ for a stationary ergodic process $\\mathcal{M}$.\nFor these sources, universal data compression can be used to `purify' input sequences, i.e., shortening their lengths while maintaining their entropies.\nIn \\cite{Visweswariah98}, Visweswariah, Kulkarni and Verd\\'{u} showed that optimal variable-length source codes asymptotically achieve optimal variable-length random bits generation in the sense of normalized divergence. Although\ntheir work only focused on ideal stationary ergodic processes and generates `weaker' random bits, it motivates us to combine\nuniversal compression with fixed-length extractors for efficiently generating random bits from noisy stochastic processes.\nIn this section, we will first introduce Lempel-Ziv code and then present its application in constructing variable-length extractors.\n\n\\subsection{Construction}\n\nLempel-Ziv code is a universal data compression scheme introduced by Ziv and Lempel \\cite{Ziv78}, which is simple to implement and can achieve the asymptotically optimal rate\nfor stationary ergodic sources. The idea of Lempel-Ziv code is to parse the source sequence into strings that have not appeared so far, as demonstrated by the following example.\n\n\\begin{Example} Assume the input is $010111001110000...$, then we parse it as strings\n$$0,1,01,11,00,111,000,...$$\nwhere each string is the shortest string that never appear before. That means all its prefixes have occurred earlier.\n\nLet $c(n)$ be the number of strings obtained by parsing a sequence of length $n$. For each string, we\ndescribe its location with $\\log c(n)$ bits. Given a string of length $l$, it can described by (1)\nthe location of its prefix of length $l-1$, and (2) its last bit. Hence, the code for the above sequence is\n{ $$(000,0), (000,1), (001,1), (010,1), (001,0), (100,1), (101,0),...$$}\nwhere the first number in each pair indicates the prefix location and the second number is\nthe last bit of the string.\n\\end{Example}\n\\vspace{-0.25cm}\n\\hfill$\\Box$\n\nTypically, Lempel-Ziv is applied to an input sequence of fixed length. Here, we are interested in\nLempel-Ziv code with fixed output length and variable input length. As a result, we can apply\na single fixed-length extractor to the output of Lempel-Ziv code for extracting randomness.\nIn our algorithm, we read raw bits one by one from an imperfect source until the length of the output\nof a Lempel-Ziv code reaches a certain length.\nIn another word,\nthe number of strings after parsing is a predetermined number $c$. For example, if\nthe source is $1011010100010...$ and $c=4$, then after reading $6$ bits, we can parse them into\n$1, 0, 11, 01$. Now, we get an output sequence\n$(000,1), (000,0), (001, 1), (010, 1)$, which can be used as the input of a fixed-length extractor.\nWe call this Lempel-Ziv code as a variable-length Lempel-Ziv code.\n\nLet $Z$ be a random sequence obtained based on variable-length Lempel-Ziv code such that its length is\n$$|Z|=(\\log c+1) c,$$\nfor a predetermined $c$. Then $Z$ is very close to truly random bits in the term of min-entropy if the source $\\mathcal{R}$ is stationary ergodic.\nAs a result, we have the following construction for variable-length extractors.\n\n\\begin{Construction}\\label{const:3}\nAssume the real source is $\\mathcal{R}$ and there exists a stationary ergodic process $\\mathcal{M}$ such that $d(\\mathcal{R},M)\\leq \\beta$.\nThen we extract $m$ almost random bits from $\\mathcal{R}$ based on the following procedure.\n\\begin{enumerate}\n\\item Read input bits one by one based on the variable-length Lempel-Ziv code until we get an output sequence $Z$ whose length reaches $$n=\\frac{k}{1-\\beta_p}(1+\\varepsilon),$$\n where $\\varepsilon>0$ is a small constant indicating the performance gap between the case of finite-length and that of infinite-length\n for Lempel-Ziv code; as $m\\rightarrow\\infty$, we have $\\varepsilon\\rightarrow 0$. Similar as above,\n $\\beta_p=\\beta+\\epsilon_p$ with $\\epsilon_p>0$ and $k=m(1+\\alpha)$ with $\\alpha>0$. The small constant $\\epsilon_p$ has value depending on the input set $S_p$; as\n $m\\rightarrow\\infty$, $\\epsilon_p\\rightarrow 0$. The constant $\\alpha$ can be arbitrarily small.\n Then we get a random sequence $Z$ of length $n$ and with min-entropy $k$.\n\\item Applying a $(k,\\epsilon)$ extractor to $Z$ yields a random sequence of length $m$ that is $\\epsilon$-close to $U_m$.\\hfill$\\Box$\n\\end{enumerate}\n\\end{Construction}\n\nWe show that the min-entropy of $Z$ is at least $k$ as $m\\rightarrow\\infty$. If $m$ is not very large, by adjusting the parameter $\\varepsilon$,\nwe can make the min-entropy of $Z$ be at least $k$.\nSo we can continue to apply\nan efficient fixed-length extractor to `purify' the resulting sequence. Finally, we can get $m$\nrandom bits that satisfy our requirements on quality in the sense of statistical distance.\n\n\\begin{Theorem}\\label{theorem_3_1}\nWhen $m\\rightarrow\\infty$, Construction \\ref{const:3} generates a random sequence of length $m$ that is $\\epsilon$-close to $U_m$.\n\\end{Theorem}\n\n\\proof Let $x$ be an input sequence. According to theorem 12.10.1 in \\cite{Cover2006}, for the stationary ergodic process $\\mathcal{M}$,\nwe can get\n$$\\frac{1}{|x|}\\log_2 \\frac{1}{ P_{\\mathcal{M}}(x)}\\geq \\frac{c}{|x|}\\log_2 c -\\frac{c}{|x|} H(U,V),$$\nwhere $$\\frac{c}{|x|}H(U,V)\\rightarrow 0 \\textrm{ as } |x|\\rightarrow 0.$$\n\nAs a result, if $k=\\Theta(n)$,\n\\begin{eqnarray*}\n\\lim_{k\\rightarrow\\infty}\\frac{1}{k}\\log_2 \\frac{1}{ P_{\\mathcal{R}}(x)}&\\geq& \\lim_{k\\rightarrow\\infty}(1-\\beta_p)\\frac{1}{k}\\log_2\\frac{1}{P_\\mathcal{M}(x)}\\\\\n& \\geq &\\lim_{k\\rightarrow\\infty} \\frac{(1-\\beta_p)c\\log_2 c}{k} \\\\\n&=& \\lim_{k\\rightarrow \\infty}\\frac{(1-\\beta_p)n}{k}\\\\\n&=& \\lim_{k\\rightarrow\\infty} 1+\\varepsilon\\\\\n&=& 1.\n\\end{eqnarray*}\n\nFinally, we can get that\n$$\\lim_{k\\rightarrow\\infty} \\frac{H_{\\min}(Z)}{k}=\\lim_{k\\rightarrow\\infty} \\frac{H_{\\min}(X_m)}{k}\\geq 1.$$\nThis implies that as $m\\rightarrow \\infty$, i.e., $k\\rightarrow \\infty$, the min-entropy of $Z$ is at least $k$.\n\n\nSince $k=m(1+\\alpha)$ for an $\\alpha>0$, we can continue to apply a $(k,\\epsilon)$ extractor to extract $m$ almost-random bits from $Z$.\n\\hfill\\IEEEQED\\vspace{0.1in}\n\n\\subsection{Efficiency Analysis}\n\nNow, we study the efficiency of the construction based on variable-length Lempel-Ziv codes.\n\n\\begin{Theorem}\nGiven a real source $\\mathcal{R}$ such that there exists a stationary ergodic process $\\mathcal{M}$ with $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$, then the efficiency of Construction \\ref{const:3} is \\vspace{-0.05cm}\n$$1-\\beta\\leq \\eta\\leq 1.$$\n\\end{Theorem}\n\n\\proof Similar as above, we only need to prove that $\\eta\\geq 1-\\beta$.\n\nSince there are at most $n=2^{c(\\log_2c+1)}$ distinct input sequences,\ntheir entropy\n$$H_{\\mathcal{R}}(X_m)\\leq c(\\log_2 c+1)=n.$$\n\nAccording to the proof in Theorem \\ref{theorem_3_1}, we have that the random sequence $Z$ has min-entropy at least $k$, and it satisfies\n$$\\lim_{m\\rightarrow\\infty}\\frac{n}{k}=\\frac{1}{1-\\beta}.$$\n\nBased on the construction of seeded extractors, we can also get\n$$\\lim_{m\\rightarrow\\infty}\\frac{m}{k}=1.$$\n\nAs a result,\n$$ \\eta= \\lim_{m\\rightarrow\\infty}\\frac{m}{H_{\\mathcal{R}}(X_m)} \\geq 1-\\beta.$$\nThis completes the proof.\n\\hfill\\IEEEQED\\vspace{0.1in}\n\nAlthough Construction \\ref{const:3} has the same efficiency as the other constructions, when $m$ is not large,\nit is less efficient than the other constructions because the Lempel-Ziv code does not always have the best performance when the input sequence is not long.\nBut its advantage is that it can manage more general sources without accurate estimations. In the above theorem, the gap $\\beta$ represents how far the source $\\mathcal{R}$ is from being stationary ergodic. In general, the efficiency loss introduced by the uncertainty of sources\nis a part that cannot be avoid.\n\n\\begin{Corollary} Given a real source $\\mathcal{R}$ such that there exists a stationary ergodic model $\\mathcal{M}$ with $d(\\mathcal{R},\\mathcal{M})\\leq \\beta$, then as $\\beta\\rightarrow 0$, the efficiency of Construction \\ref{const:3} is\n$$\\eta\\rightarrow 1.$$\n\\end{Corollary}\n\nIt shows that as $\\beta\\rightarrow 0$, Construction \\ref{const:3} reaches the Shannon's limit on efficiency.\n\n\\begin{Corollary} Given a stationary ergodic source $\\mathcal{R}$ (assume we do not know that it is stationary ergodic), for the expected input length of Construction \\ref{const:3}, we have\n$$\\frac{1}{h(\\mathcal{R})}\\leq \\lim_{m\\rightarrow\\infty} \\frac{E[|X_m|]}{m}\\leq \\frac{1}{(1-\\beta)h(\\mathcal{R})},$$\nwhere $h(\\mathcal{R})$ is the entropy rate of $\\mathcal{R}$.\n\\end{Corollary}\n\n\n\\section{Seedless Constructions}\n\\label{sec_randomnessextraction}\n\nTo simulate seeded constructions of variable-length extractors in randomized applications, we have to enumerate all possible assignments of the seed, hence,\nthe computational complexity will be increased significantly. In real applications, we prefer seedless constructions rather than seeded constructions. It motivates us to study the seedless constructions of variable-length extractors in this section.\n\n\\subsection{An Independent Source}\n\nLet us first consider a simple independent source described in the introduction. This type of sources have been widely studied in seedless constructions of fixed-length extractors.\n\n\\begin{Example} Let $x_1x_2...\\in\\{0,1\\}^*$ be an independent sequence generated from a source $\\mathcal{R}$ such that\n$$P[x_i=1]\\in [0.9,0.91] \\quad \\forall i\\geq i.$$\n\\end{Example}\n\\vspace{-0.4cm} \\hfill$\\Box$\n\\vspace{0.2cm}\n\nWe see that the existing methods for generating random bits from ideal sources (like biased coins or Markov chains) cannot be applied here, since\nthe probability of each bit is slightly unpredictable. Some seedless extractors have been developed for extracting randomness from such sources.\nIn particular, there exists seedless extractors which are able to extract as many as $H_{\\min}(X)$ random bits from a independent random sequence $X$ asymptotically. In order to extract $m$ random bits in the above example, it needs to read $\\frac{m}{\\log_2\\frac{1}{0.91}}$ input bits as\n$m\\rightarrow\\infty$. In this case, the entropy of the input sequence is in $$[H(0.9)\\frac{m}{\\log_2\\frac{1}{0.91}}, H(0.91)\\frac{m}{\\log_2\\frac{1}{0.91}}].$$ From which, we can get the efficiency of an optimal fixed-length extractor, which is\n$$\\eta_{fixed}\\in [0.2901, 0.3117],$$\ni.e., about only $0.3$ of the input entropy is used for generating random bits, which is far from optimal\n\nIn the above example, we let $\\mathcal{M}$ be a biased coin model with probability $p=0.9072$. In this case,\n$$\\beta\\leq d(\\mathcal{R},\\mathcal{M})=0.0315.$$\nAccording to the constructions in the previous sections, there exists seeded variable-length extractors such that\ntheir efficiencies are\n$$\\eta_{variable}\\in [1-\\beta, 1]\\subseteq [0.9685,1],$$\nwhich are near Shannon's limit.\n\nBased on the fact that the source is independent, we can eliminate the requirement of truly random bits as the seed, hence, we have seedless\nvariable-length extractors.\nTo construct a seedless variable-length extractor, we first apply a seedless fixed-length extractor $E_1$ (which may not be very efficient)\nto extract a random sequence of length $d$ from input bits.\nUsing this random sequence as the seed, we continue to apply a seeded variable-length extractors $E_2$ to\nextract $m$ almost-random bits from extra input bits. So seedless variable-length extractors can be constructed as cascades of seedless fixed-length extractors and seeded variable-length extractors. Since the input length of $E_1$ is much shorter (it is ignorable) than the input length\nof $E_2$, the efficiency of the resulting seedless extractor, i.e., $E=E_2\\bigotimes E_1$, is dominated by the efficiency of $E_2$.\nSo the efficiency of the seedless extractor $E$ is in $[0.9685,1]$,\nwhich is very close to the optimality.\n\nThis example demonstrates a simple construction of seedless variable-length extractors for independent sources, and it shows\nthe significant performance gain of variable-length extractors compared to fixed-length extractors.\n\n\\subsection{Generalized Sources}\n\n\nHere we consider a generalization of independent processes. Given a system, we use $\\lambda_i$ denote the complete system status at time $i$.\nFor example, in a system that generates thermal noise, the system status can include the value of the noise signal, the temperature, the environmental effects, etc. Usually, the evolution of such a system has a Markov property, namely,\n$$P[\\lambda_{i+1},\\lambda_{i+2},...|\\lambda_i, \\lambda_{i-1},..., \\lambda_1]=P[\\lambda_{i+1},\\lambda_{i+2},...|\\lambda_i],$$\nfor all $i\\geq 1$. Let $X=x_1x_2...\\in \\{0,1\\}^n$ be the binary sequence generated from this system, then for any $1< k