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In particular, the stellarator does not generally guarantee confinement of particles on collisionless trajectories due to its lack of continuous symmetry, leading to large neoclassical transport \\citep{helanderTheoryPlasmaConfinement2014}. However, the use of numerical optimisation techniques has led to advanced stellarator designs with good confinement properties, culminating in the design and construction of the HSX \\citep{andersonHelicallySymmetricExperiment1995} and W7-X stellarators \\citep{beidlerPhysicsEngineeringDesign1990}.\n\nAlthough gradient-based optimisation algorithms are generally more efficient than gradient-free algorithms, because of the large number of parameters (e.g. to represent the plasma boundary) they can be prohibitively expensive computationally if the gradients are evaluated via finite-differences. A more efficient way of obtaining gradient information is provided by adjoint methods, which were recently introduced in the stellarator optimisation field and have already found widespread application \\citep{landremanComputingLocalSensitivity2018, paulAdjointMethodGradientbased2018, antonsenAdjointApproachCalculating2019, paulAdjointMethodNeoclassical2019, paulAdjointApproachCalculating2020, paulAdjointMethodsStellarator2020, paulGradientbasedOptimization3D2021, geraldiniAdjointMethodDetermining2021, giulianiSinglestageGradientbasedStellarator2020}.\n\nPrevious work \\citep{antonsenAdjointApproachCalculating2019, paulAdjointApproachCalculating2020, paulGradientbasedOptimization3D2021} applied adjoint methods to ideal magnetohydrostatic (MHS) equilibria, building in the assumption of integrability, i.e. the existence of a set of nested flux surfaces. However, three-dimensional magnetic fields are generally not integrable due to the lack of continuous symmetry. Moreover, singularities arise at rational surfaces for linearised ideal MHS equilibria, making the computation of derivatives challenging \\citep{paulGradientbasedOptimization3D2021}. To overcome these challenges, different equilibrium models can be considered, such as vacuum or force-free fields. We herein apply adjoint methods to vacuum magnetic fields, relinquishing the assumption of global integrability, and avoiding the singular behaviour of MHS equilibria. Modeling the plasma magnetic field as a vacuum field is justified in the limit of vanishing plasma current and $\\beta$, the ratio of thermal pressure to magnetic pressure. Vacuum fields are thus broadly relevant for stellarators configurations, which tend to operate at low $\\beta$ and low plasma current, as non-axisymmetric shaping of the coils is used to generate rotational transform. Moreover, optimised vacuum solutions can serve as useful starting points for the optimisation of finite-pressure equilibria \\citep{boozerCurlfreeMagneticFields2019}.\n\nWe shall consider two objective functions, one targeting a rotational transform value on the boundary, and another targeting quasisymmetry on the boundary. As a subset of the larger class of omnigenous fields \\citep{hallThreedimensionalEquilibriumAnisotropic1975}, for which particles are confined on collisionless trajectories, quasisymmetric fields \\citep{nuhrenbergQuasihelicallySymmetricToroidal1988} have attracted strong interest, notably leading to the designs of the HSX \\citep{andersonHelicallySymmetricExperiment1995} and NCSX \\citep{zarnstorffPhysicsCompactAdvanced2001} stellarators. Multiple formulations of quasisymmetry exist \\citep{helanderTheoryPlasmaConfinement2014, rodriguezNecessarySufficientConditions2020, burbyMathematicsQuasisymmetry2020}, all of which employ flux coordinates and therefore require the existence of nested flux surfaces. We propose a method of constructing approximate flux coordinates on an isolated flux surface, on which quasisymmetry can then be defined and optimised for. The existence of at least one isolated flux surface will be guaranteed, by imposing the boundary condition that the magnetic field be tangential on a prescribed boundary. Note that we will not consider whether this boundary condition can actually be realised with a set of coils, a task pursued by codes like FOCUS \\citep{zhuNewMethodDesign2018}.\n\nWith the exception of \\cite{landremanMagneticFieldsPrecise2021}, previous optimisation studies \\citep{nuhrenbergQuasihelicallySymmetricToroidal1988, kuNewClassesQuasihelically2011, drevlakESTELLQuasiToroidallySymmetric2013, baderStellaratorEquilibriaReactor2019, hennebergPropertiesNewQuasiaxisymmetric2019, hennebergImprovingFastparticleConfinement2020, landremanStellaratorOptimizationGood2021} targeted quasisymmetry by minimising the symmetry-breaking components of the magnetic field strength in Boozer coordinates, often for vacuum magnetic fields. The most widely-used solver, whether for vacuum fields or plasmas with finite pressure, is the VMEC code \\citep{hirshmanThreedimensionalFreeBoundary1986}, which notably assumes the existence of nested flux surfaces. \nWe will employ the SPEC code \\citep{hudsonComputationMultiregionRelaxed2012}, which does not build in this assumption. Furthermore, in contrast to most previous studies, we use a formulation of quasisymmetry that does not rely on a Boozer coordinate transformation, although it still enables the specification of a desired helicity of the magnetic field strength. \n\nPrevious studies have sought to optimise for quasisymmetry either on a single flux surface \\citep[e.g.][]{hennebergImprovingFastparticleConfinement2020}, or on multiple flux surfaces \\citep[e.g.][]{landremanMagneticFieldsPrecise2021} with the aim of approximating quasisymmetry in a finite volume. We will herein consider quasisymmetry on a single flux surface only. Away from a surface with exact quasisymmetry, the deviation from quasisymmetry will generally increase linearly in the flux difference \\citep{senguptaVacuumMagneticFields2021}. It will thus be of interest to extend the present work on vacuum fields to multi-region relaxed magnetohydrodynamic (MRxMHD) equilibria. In this model, the interfaces between force-free regions are flux surfaces, such that quasisymmetry can be optimised for on multiple flux surfaces.\n\nThis paper is structured as follows. We begin with a brief introduction to adjoint methods in \\S\\ref{sec:basics_adjoint_methods}. A method of constructing approximate flux coordinates on a single flux surface is introduced in \\S\\ref{sec:approximate_flux_coordinates}. The derived adjoint equations for vacuum fields are presented in \\S\\ref{sec:adjoint_formalism_vacuum_fields}, first for a simpler objective function targeting a given rotational transform value on the boundary in \\S\\ref{sec:iota_fom_shape_derivative}, then for one targeting quasisymmetry on the boundary with a given helicity in \\S\\ref{sec:QS_fom_shape_derivative}. The resulting shape gradients are evaluated numerically and benchmarked against finite-difference calculations.\n\n\\section{Basics of adjoint methods}\n\\label{sec:basics_adjoint_methods}\n\nWe are interested in obtaining derivative information for a functional $f(\\mathcal{S}, u(\\mathcal{S}))$, called hereafter the objective function. This functional depends on the surface $\\mathcal{S}$ explicitly and also implicitly through the solution $u(\\mathcal{S})$ to a partial differential equation (PDE) $\\mathcal{P}(\\mathcal{S},u) = 0$. Here, $\\mathcal{P}$ is a general operator and $u$ is member of a Hilbert space with associated inner product $\\langle \\cdot , \\cdot \\rangle$, taken in our case to be the surface integral $\\int_\\mathcal{S} \\diff S \\;(\\cdot)(\\cdot)$. \n\nConsider a displacement of the surface $\\mathcal{S}$ in the direction $\\delta\\mathbf{x}$ with magnitude $\\epsilon$, resulting in a perturbed surface $\\mathcal{S}_\\epsilon = \\{ \\mathbf{x}_0 + \\epsilon \\delta\\mathbf{x}(\\mathbf{x}_0) : \\mathbf{x}_0 \\in \\mathcal{S}\\}$. The shape derivative of an arbitrary function $g(\\mathcal{S})$ in the direction $\\delta\\mathbf{x}$ is now defined as\n\\begin{equation}\n \\delta g(\\mathcal{S})[\\delta\\mathbf{x}] = \\lim_{\\epsilon\\rightarrow 0} \\frac{g(\\mathcal{S_\\epsilon}) - g(\\mathcal{S})}{\\epsilon}.\n\\end{equation}\nIf $g$ depends only on the geometrical shape of the surface, the shape derivative $\\delta g[\\delta\\mathbf{x}]$ will be a function of only the normal component $\\ensuremath{\\delta\\boldsymbol{\\mathrm{x}}\\cdot \\normvec}$ of the displacement, as any tangential component of $\\delta\\mathbf{x}$ leaves the shape of $\\mathcal{S}$ unchanged to first order. Here, $\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}$ is a normal vector on $\\mathcal{S}$.\n\nTo compute derivatives of the objective function while enforcing the PDE constraint $\\mathcal{P}(\\mathcal{S},u) = 0$, the method of Lagrange multipliers is used. Consider the Lagrangian\n\\begin{equation}\n \\mathcal{L}(\\mathcal{S},u,q) = f(\\mathcal{S},u) + \\int_\\mathcal{S} \\diff S \\; q \\; \\mathcal{P}(\\mathcal{S},u),\n\\end{equation}\nwith the Lagrange multiplier $q$. Its shape derivative $\\delta\\mathcal{L}[\\delta\\mathbf{x}]$ contains explicit contributions in the perturbation $\\delta\\mathbf{x}$, as well as implicit contributions through $\\delta q[\\delta\\mathbf{x}]$ and $\\delta u[\\delta\\mathbf{x}]$. The implicit dependencies are removed by making $\\mathcal{L}$ stationary with respect to $\\delta q[\\delta\\mathbf{x}]$, which is equivalent to enforcing the original PDE $\\mathcal{P}(\\mathcal{S},u) = 0$, and $\\delta u[\\delta\\mathbf{x}]$, which leads to an adjoint PDE for $q$. \n\nIf $\\mathcal{L}$ is stationary with respect to both $\\delta u[\\delta\\mathbf{x}]$ and $\\delta q[\\delta\\mathbf{x}]$, the remaining explicit dependence of its shape derivative $\\delta\\mathcal{L}(\\mathcal{S},u,q)[\\delta\\mathbf{x}]$ is equal to the shape derivative of the figure of merit $\\delta f(\\mathcal{S},u(\\mathcal{S}))[\\delta\\mathbf{x}]$ with $u=u(\\mathcal{S})$ satisfying the PDE constraint, as shown in e.g. \\citet{paulAdjointMethodsStellarator2020}. The Hadamard-Zol\\'esio structure theorem \\citep{delfourShapesGeometries2011} further states that the remaining contribution to the Lagrangian's shape derivative, provided $\\mathcal{L}$ is sufficiently smooth, can be expressed as\n\\begin{equation}\n \\delta\\mathcal{L}(\\mathcal{S},u,q)[\\delta\\mathbf{x}] = \\int_\\mathcal{S} \\diff S \\;(\\ensuremath{\\delta\\boldsymbol{\\mathrm{x}}\\cdot \\normvec})\\; \\mathcal{G},\n\\end{equation}\nwhere $\\mathcal{G}$ is called the shape gradient, and can be interpreted as the local sensitivity of the objective function to perturbations of $\\mathcal{S}$.\n\nIn practice, the surface $\\mathcal{S}$ is typically represented by a finite set of parameters $\\Omega = \\{\\Omega_i,\\; i = 1, 2, \\dots N\\}$, e.g. Fourier coefficients $\\{R_{m,n}, Z_{m,n}\\}$, and the functional $f(\\mathcal{S},u(\\mathcal{S}))$ is approximated by a function $f(\\Omega,u(\\Omega))$. The derivative of $f(\\Omega,u(\\Omega))$ with respect to a parameter $\\Omega_i$ can be approximated as\n\\begin{equation}\n \\frac{\\partial f(\\Omega, u(\\Omega))}{\\partial \\Omega_i} = \\int_\\mathcal{S} \\diff S\\; \\frac{\\partial \\mathbf{x}}{\\partial \\Omega_i}\\cdot \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} \\; \\mathcal{G}. \\label{eq:parameter_derivatives_shape_grad}\n\\end{equation}\nThe adjoint method of evaluating the parameter derivatives required for optimisation or sensitivity analysis \\citep{paulAdjointMethodsStellarator2020} thus consists in computing $\\delta\\mathcal{L}[\\delta\\mathbf{x}]$ to obtain the adjoint PDE for $q$ and the shape gradient $\\mathcal{G}$, which is then used to evaluate the right-hand-side of \\eqref{eq:parameter_derivatives_shape_grad}.\n\nWhen evaluating the parameter derivatives numerically through \\eqref{eq:parameter_derivatives_shape_grad}, errors are introduced from the inexact solutions to the original and adjoint PDEs. Indeed, these PDEs are assumed to be exactly satisfied in the preceding derivation to remove the implicit dependencies of $\\delta\\mathcal{L}[\\delta\\mathbf{x}]$ on $\\delta u[\\delta\\mathbf{x}]$ and $\\delta q[\\delta\\mathbf{x}]$, and also typically when deriving an expression for the shape gradient $\\mathcal{G}$.\n\nThe formalism presented above can easily be generalised to multiple PDE constraints, and, for a closed $\\mathcal{S}$, to PDEs satisfied not on $\\mathcal{S}$ but in the volume enclosed by it. This will be done in \\S\\ref{sec:adjoint_formalism_vacuum_fields}, where both the Laplace equation for the vacuum field and the straight field line equation, respectively valid in the volume and on the boundary, will be enforced as constraints, with two corresponding adjoint variables.\n\n\\section{Evaluating approximate flux coordinates on an isolated flux surface}\n\\label{sec:approximate_flux_coordinates}\n\nThe existence of nested flux surfaces is commonly assumed in theoretical studies of magnetically confined plasmas, e.g. to formulate quasisymmetry. In particular, many formulas involve $\\nabla\\psi$, where the toroidal flux $\\psi$ is a global flux surface label. However, three-dimensional magnetic fields lacking a continuous symmetry are not generally integrable. It is desirable to generalise $\\nabla\\psi$ to the case of an isolated flux surface, i.e. a flux surface in whose neighbourhood the field is generally non-integrable.\n\nOn a flux surface $\\mathcal{S}$, the magnetic field's normal component vanishes by definition, i.e. $\\mathbf{B}\\cdot\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} = 0$ with $\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}$ the unit normal vector on $\\mathcal{S}$. The field line label $\\alpha$ on $\\mathcal{S}$ is defined through the straight field line equation $\\mathbf{B}\\cdot\\nabla_\\Upgamma\\alpha = 0$. Here, the tangential gradient $\\nabla_\\Upgamma$, defined in App.~\\ref{app:diff_operators_surfaces}, is the component of the 3D gradient tangential to the surface \\eqref{eq:def_tangential_gradient}. Note that in the integrable case, $\\nabla\\psi$ is normal to the flux surfaces, and the magnetic field satisfies $\\mathbf{B} = \\nabla\\psi\\times\\nabla\\alpha$.\n\nWe now define the generalisation $\\overline{\\nabla\\psi}$ on $\\mathcal{S}$ of the toroidal flux gradient $\\nabla\\psi$, by setting $\\overline{\\nabla\\psi}$ normal to $\\mathcal{S}$, and by requiring $\\mathbf{B} = \\overline{\\nabla\\psi}\\times\\nabla\\alpha$ to be satisfied on $\\mathcal{S}$. Squaring the latter equality and using $\\overline{\\nabla\\psi} = \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} |\\overline{\\nabla\\psi}|$ yields\n\\begin{equation}\n \\overline{\\nabla\\psi} = \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} \\; \\frac{B}{\\abs{\\nabla_\\Upgamma \\alpha}}, \\label{eq:def_nabla_tilde_psi}\n\\end{equation}\nwhere $B = \\abs{\\mathbf{B}}$ is the magnetic field strength. Note that $\\overline{\\nabla\\psi}$ is defined through \\eqref{eq:def_nabla_tilde_psi}, and should not be misinterpreted as the gradient of a scalar function.\n\nThe defining expression for $\\overline{\\nabla\\psi}$ \\eqref{eq:def_nabla_tilde_psi} can be evaluated on any flux surface without requiring nested flux surfaces in its neighbourhood, and will revert to $\\overline{\\nabla\\psi} = \\nabla\\psi$ when the field is integrable in the neighbourhood of that flux surface. In practice, one might couple objective functions relying on \\eqref{eq:def_nabla_tilde_psi} with a figure of merit targeting integrability, aiming for a final plasma shape for which the field is integrable, such that $\\overline{\\nabla\\psi} = \\nabla\\psi$ and the minimised objective function represents the physical quantity of interest.\n\n\\begin{figure}\n\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/abs_nabla_psi_VMEC.pdf}\n \\caption{}\n \\label{fig:abs_nabla_psi_VMEC}\n\\end{subfigure}%\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/abs_nabla_psi_SPEC.pdf}\n \\caption{}\n \\label{fig:abs_nabla_psi_SPEC}\n\\end{subfigure}\\\\\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/relative_diff_abs_nabla_psi.pdf}\n \\caption{}\n \\label{fig:relative_diff_abs_nabla_psi}\n\\end{subfigure}%\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/poincare_surfaces_VMEC_SPEC.pdf}\n \\caption{}\n \\label{fig:poincare_surfaces_VMEC_SPEC}\n\\end{subfigure}%\n\\caption{Comparison of (a) the toroidal flux gradient $\\abs{\\nabla\\psi}$ evaluated with VMEC with (b) the generalised toroidal flux gradient $|\\overline{\\nabla\\psi}|$ \\eqref{eq:def_nabla_tilde_psi} obtained in SPEC, for a $5$-period rotating ellipse case with half a rotation per field period, major radius at the ellipse centre $R_0 = 5$\\;m, and ellipse major and minor axes values of $2$\\;m and $1$\\;m, respectively. The relative difference between the two quantities is below $1\\%$, as shown in (c). A small difference is to be expected in this case, where integrability is well satisfied, as attested in (d) by the Poincar\\'e plot at toroidal angle $\\phi=0$ from the SPEC calculation, which agrees well with the flux surfaces computed by VMEC. All data generated in this paper can be obtained from \\cite{niesDataCodesPaper2021}.}\n\\label{fig:abs_nabla_psi}\n\\end{figure}\n\nThe generalised toroidal flux gradient \\eqref{eq:def_nabla_tilde_psi} is evaluated in Fig.~\\ref{fig:abs_nabla_psi_SPEC} for a rotating ellipse configuration computed with the SPEC code. It agrees excellently with the toroidal flux gradient evaluated by VMEC, which can be calculated directly due to the imposed nestedness of flux surfaces, shown in Fig.~\\ref{fig:abs_nabla_psi_VMEC}. The relative difference between the two is below a percentage point in this case, as shown in Fig.~\\ref{fig:relative_diff_abs_nabla_psi}. The difference is expected to be small when integrability is satisfied, which indeed seems to hold here, as attested by the absence of islands and chaotic regions in the SPEC Poincar\\'e plot shown in Fig.~\\ref{fig:poincare_surfaces_VMEC_SPEC}. Note that SPEC solves for a vacuum magnetic field, while VMEC computes an ideal MHS equilibrium with vanishing thermal pressure, and with a plasma current that is small but finite due to the constraint of integrability.\n\nThe generalised toroidal flux gradient $\\overline{\\nabla\\psi}$ can be applied generally in any situation where local flux coordinates need to be evaluated, e.g. in calculations of perpendicular transport or magnetohydrodynamic stability. Isolated flux surfaces occur e.g. in fixed-boundary equilibrium calculations, where the plasma outer boundary is constrained to be a flux surface as a boundary condition on the magnetic field, or at the interfaces of MRxMHD equilibria computed by e.g. SPEC \\citep{hudsonComputationMultiregionRelaxed2012} or BIEST \\citep{malhotraTaylorStatesStellarators2019}. In the following, we will employ \\eqref{eq:def_nabla_tilde_psi} specifically for a fixed-boundary vacuum field to formulate quasisymmetry on the boundary.\n\n\\section{Application of adjoint formalism to vacuum fields}\n\\label{sec:adjoint_formalism_vacuum_fields}\n\nConsider a vacuum magnetic field $\\mathbf{B}$ in a toroidal domain $\\mathcal{V}$ bounded by the surface $\\mathcal{S} = \\partial \\mathcal{V}$. As the vacuum magnetic field is curl-free, it can be expressed as $\\mathbf{B}=\\nabla\\Phi$, with the scalar potential $\\Phi$. Because we consider a simple torus $\\mathcal{V}$, the most general form for the scalar potential is $\\Phi=G (\\omega+\\phi)$, where $G$ is a constant, $\\omega$ is a single-valued function on $\\mathcal{S}$, and $\\phi$ is an arbitrary toroidal angle. By integrating the magnetic field along a toroidal loop around the torus, the constant $G$ is found to be proportional to the net external current through the `hole' of the torus.\n\nAs the magnetic field is divergence-less, the magnetic scalar potential satisfies the Laplace equation. The field's normal component is constrained to vanish on $\\mathcal{S}$ by imposing a Neumann boundary condition on the magnetic scalar potential. Further prescribing e.g. $G$, or the toroidal flux, guarantees a unique solution to Laplace's equation. We herein opt to hold the toroidal flux fixed, although the shape derivative $\\delta G[\\delta\\mathbf{x}]$ will not appear in this study due to our normalisation of the figure of merit for quasisymmetry \\eqref{eq:definition_fQS}. A different choice of normalisation would lead to an additional contribution proportional to $\\delta G[\\delta\\mathbf{x}]$ in the shape derivative of the Lagrangian.\n\nFor convenience, we define the normalised magnetic field $\\mathbf{\\breve{B}}$ as\n\\begin{equation}\n \\mathbf{\\breve{B}} \\equiv \\mathbf{B}\/G = \\nabla\\big(\\omega + \\phi\\big). \\label{eq:magnetic_field_scalar_pot}\n\\end{equation}\nLet us further assume the toroidal angle $\\phi$ to be the azimuthal angle in cylindrical coordinates, satisfying $\\Updelta\\phi=0$ in the domain of interest. We can thus write\n\\begin{subequations}\n\\begin{align}\n \\nabla\\cdot\\mathbf{\\breve{B}} = \\Updelta\\omega = 0 \\qquad\\qquad\\qquad\\mathrm{in}\\;&\\mathcal{V}, \\label{eq:vacuum_field_Laplace}\\\\\n \\mathbf{\\breve{B}}\\cdot\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} = \\nabla(\\omega+\\phi)\\cdot\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} = 0 \\qquad\\mathrm{on}\\; & \\mathcal{S} \\label{eq:vacuum_field_normal_BC},\n\\end{align}\n\\end{subequations}\nwith $\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}$ the normal unit vector on $\\mathcal{S}$. Furthermore, the shape derivative $\\delta\\omega[\\delta\\mathbf{x}]$ satisfies\n\\begin{subequations}\n\\begin{align}\n \\Updelta(\\delta\\omega[\\delta\\mathbf{x}]) = 0 \\qquad\\qquad\\mathrm{in}\\;&\\mathcal{V}, \\label{eq:vacuum_field_pert_Laplace}\\\\\n \\mathbf{\\breve{B}}\\cdot\\delta\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}[\\delta\\mathbf{x}] + \\nabla(\\delta\\omega[\\delta\\mathbf{x}])\\cdot\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} + (\\ensuremath{\\delta\\boldsymbol{\\mathrm{x}}\\cdot \\normvec})\\;\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla \\mathbf{\\breve{B}}\\cdot\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} = 0 \\quad\\mathrm{on}\\; & \\mathcal{S} \\label{eq:vacuum_field_pert_normal_BC},\n\\end{align}\n\\end{subequations}\nwhere the Laplace equation is obtained from noting the commutative property of shape and spatial derivatives, and the normal boundary condition on $\\delta\\omega$ was derived in e.g. \\citet[\\S 3.2]{sokolowskiIntroductionShapeOptimization1992}. The shape derivative of the normal vector $\\delta\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}[\\delta\\mathbf{x}]=-\\nabla_\\Upgamma(\\ensuremath{\\delta\\boldsymbol{\\mathrm{x}}\\cdot \\normvec})$ is derived in App.~\\ref{app:normal_extension_and_normal_vector_shape_derivative}.\n\nEvaluating the rotational transform and quasisymmetry figures of merit further requires the solution to the straight field line equation\n\\begin{equation}\n 0 = \\mathbf{B}\\cdot\\nabla_\\Upgamma\\alpha \\qquad\\mathrm{on}\\; \\mathcal{S}, \\label{eq:straight_field_line_eq}\n\\end{equation}\nwith the field line label\n\\begin{equation}\n \\alpha \\equiv \\theta-\\iota\\phi+\\lambda(\\theta,\\phi), \\label{eq:def_field_line_label_alpha}\n\\end{equation}\nwhere $\\theta$ is a general poloidal angle, $\\lambda$ is a single-valued function of $\\theta$ and $\\phi$, and $\\iota$ is a scalar. Note that both $\\lambda$ and $\\iota$ are defined on the boundary $\\mathcal{S}$ only, through \\eqref{eq:def_field_line_label_alpha}.\n\nLet us define the Lagrangian corresponding to an arbitrary objective function $f(\\mathcal{S},\\omega,\\iota,\\lambda)$,\n\\begin{equation}\n \\mathcal{L}(\\mathcal{S}, \\omega, q_\\omega, \\iota, \\lambda, q_\\alpha ) = f(\\mathcal{S},\\omega,\\iota,\\lambda) + \\mathcal{M}(\\mathcal{S}, \\omega, q_\\omega) + \\mathcal{N}(\\mathcal{S}, \\omega, \\iota, \\lambda, q_\\alpha), \\label{eq:lagrangian_general}\n\\end{equation}\nwith the weak form of the Laplace equation \\eqref{eq:vacuum_field_Laplace}\n\\begin{equation}\n \\mathcal{M}(\\mathcal{S}, \\omega, q_\\omega) = \\int_{\\mathcal{V}} \\diff V \\; q_\\omega \\Updelta \\omega, \\label{eq:weak_form_Laplace_vac}\n\\end{equation}\nand the weak form of the straight field line equation \\eqref{eq:straight_field_line_eq} normalised by $G$ \n\\begin{equation}\n \\mathcal{N}(\\mathcal{S}, \\omega, \\iota, \\lambda, q_\\alpha) = \\int_{\\mathcal{S}} \\diff S \\; q_\\alpha \\mathbf{\\breve{B}} \\cdot \\nabla_\\Upgamma \\alpha. \\label{eq:weak_form_straight_field_line_eq}\n\\end{equation}\nAs explained in \\S\\ref{sec:basics_adjoint_methods}, $q_\\omega$ and $q_\\alpha$ act as Lagrange multipliers: making the Lagrangian \\eqref{eq:lagrangian_general} stationary with respect to $\\delta q_\\omega[\\delta\\mathbf{x}]$ and $\\delta q_\\alpha[\\delta\\mathbf{x}]$ ensures that the Laplace \\eqref{eq:vacuum_field_Laplace} and straight field line \\eqref{eq:straight_field_line_eq} equations are satisfied, respectively. These trivial variations are omitted in the following under the assumption that \\eqref{eq:vacuum_field_Laplace} and \\eqref{eq:straight_field_line_eq} are satisfied, thus considering only the implicit dependencies of $\\delta\\mathcal{L}[\\delta\\mathbf{x}]$ on $\\delta\\omega[\\delta\\mathbf{x}]$, $\\delta\\iota[\\delta\\mathbf{x}]$, and $\\delta\\lambda[\\delta\\mathbf{x}]$ to obtain the adjoint equations for $q_\\omega$ and $q_\\alpha$.\n\nFirst, the shape derivative of $\\mathcal{M}$ \\eqref{eq:weak_form_Laplace_vac}, derived in App.~\\ref{app:derivation_laplace_eq_shape_derivative}, is\n\\begin{equation}\n \\delta\\mathcal{M}[\\delta\\mathbf{x}] = \\int_{\\mathcal{V}} \\diff V \\; \\delta\\omega[\\delta\\mathbf{x}] \\Updelta q_\\omega - \\int_{\\mathcal{S}} \\diff S \\; \\bigg[ \\delta \\omega[\\delta\\mathbf{x}] \\nabla q_\\omega \\cdot \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} - (\\ensuremath{\\delta\\boldsymbol{\\mathrm{x}}\\cdot \\normvec})\\; \\mathbf{\\breve{B}}\\cdot\\nabla q_\\omega \\bigg]. \\label{eq:variation_M_tot}\n\\end{equation}\n\nSecond, the shape derivative of $\\mathcal{N}$ \\eqref{eq:weak_form_straight_field_line_eq}, derived in App.~\\ref{app:derivation_straight_field_line_eq_shape_derivative}, is\n\\begin{align}\n \\delta\\mathcal{N}[\\delta\\mathbf{x}] = \\int_{\\mathcal{S}} & \\diff S\\; \\bigg[ - \\delta\\omega[\\delta\\mathbf{x}] \\;\\nabla_\\Upgamma \\cdot \\left(q_\\alpha \\nabla_\\Upgamma \\alpha\\right) - \\delta\\iota[\\delta\\mathbf{x}]\\; q_\\alpha \\mathbf{\\breve{B}} \\cdot \\nabla\\phi \\label{eq:variation_N_tot}\\\\\n \\nonumber & - \\delta\\lambda[\\delta\\mathbf{x}]\\; \\nabla_\\Upgamma \\cdot \\left( q_\\alpha \\mathbf{\\breve{B}} \\right) + (\\ensuremath{\\delta\\boldsymbol{\\mathrm{x}}\\cdot \\normvec}) q_\\alpha \\left( \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla\\mathbf{\\breve{B}}\\cdot\\nabla_\\Upgamma\\alpha - \\mathbf{\\breve{B}}\\cdot\\nabla\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla_\\Upgamma\\alpha \\right) \\bigg].\n\\end{align}\nThe tangential gradient $\\nabla_\\Upgamma(\\cdot)$ and tangential divergence $\\nabla_\\Upgamma\\cdot(\\cdot)$ operators are defined in App.~\\ref{app:diff_operators_surfaces}.\n\nWe now proceed by computing the shape derivatives of two objective functions, first targeting a given rotational transform value on $\\mathcal{S}$ (\\S\\ref{sec:iota_fom_shape_derivative}), and second targeting quasi-symmetry on $\\mathcal{S}$ with a given helicity value (\\S\\ref{sec:QS_fom_shape_derivative}). We will then be able to evaluate the shape derivative of the Lagrangian \\eqref{eq:lagrangian_general}, yielding the adjoint equations and shape gradient formulas. Numerical verification and example shape gradients are shown for each figure of merit.\n\n\\subsection{Rotational transform objective function}\n\\label{sec:iota_fom_shape_derivative}\n\nBefore evaluating the more complicated shape gradient for the quasisymmetry figure of merit in \\S\\ref{sec:QS_fom_shape_derivative}, we consider a simple figure of merit targeting a given target rotational transform $\\iota_T$ on the surface $\\mathcal{S}$. We thus define\n\\begin{equation}\n f_\\iota(\\iota) = \\frac{1}{2} ( \\iota - \\iota_T )^2 \\label{eq:f_iota},\n\\end{equation}\nwhere $\\iota$ is the rotational transform on $\\mathcal{S}$, obtained by solving the straight field line equation \\eqref{eq:straight_field_line_eq}. The shape derivative of $f_\\iota$ is simply\n\\begin{equation}\n \\delta f_\\iota [\\delta\\mathbf{x}] = \\delta \\iota[\\delta\\mathbf{x}] \\; ( \\iota - \\iota_T ). \\label{eq:variation_f_iota_tot}\n\\end{equation}\nBy combining \\eqref{eq:variation_M_tot}, \\eqref{eq:variation_N_tot}, and \\eqref{eq:variation_f_iota_tot}, we obtain the shape derivative of the Lagrangian $\\mathcal{L}_\\iota$ [\\eqref{eq:lagrangian_general} with $f=f_\\iota$],\n\\begin{align}\n \\delta\\mathcal{L}&_\\iota[\\delta\\mathbf{x}] = \\int_{\\mathcal{V}} \\diff V \\; \\delta\\omega[\\delta\\mathbf{x}] \\; \\Updelta q_\\omega - \\int_{\\mathcal{S}} \\diff S \\; \\delta \\omega [\\delta\\mathbf{x}] \\bigg[ \\nabla q_\\omega \\cdot \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} + \\nabla_\\Upgamma \\cdot \\Big(q_\\alpha \\nabla_\\Upgamma \\alpha \\Big) \\bigg] \\label{eq:variation_L_iota_tot}\\\\\n \\nonumber & - \\delta\\iota[\\delta\\mathbf{x}]\\; \\bigg[ \\int_{\\mathcal{S}} \\diff S \\; q_\\alpha \\mathbf{\\breve{B}} \\cdot \\nabla\\phi + ( \\iota - \\iota_T ) \\bigg] - \\int_{\\mathcal{S}} \\diff S \\;\\delta\\lambda[\\delta\\mathbf{x}]\\; \\nabla_\\Upgamma \\cdot \\Big( q_\\alpha \\mathbf{\\breve{B}} \\Big) \\\\\n \\nonumber & + \\int_{\\mathcal{S}} \\diff S \\;(\\ensuremath{\\delta\\boldsymbol{\\mathrm{x}}\\cdot \\normvec}) \\bigg[\\nabla q_\\omega \\cdot \\mathbf{\\breve{B}} + q_\\alpha \\left( \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla\\mathbf{\\breve{B}}\\cdot\\nabla_\\Upgamma\\alpha - \\mathbf{\\breve{B}}\\cdot\\nabla\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla_\\Upgamma\\alpha \\right) \\bigg] \\Bigg].\n\\end{align}\n\nFirst, we obtain the adjoint equation for $q_\\alpha$ by requiring the second line of \\eqref{eq:variation_L_iota_tot} to vanish,\n\\begin{subequations}\n\\begin{align}\n \\nabla_\\Upgamma \\cdot \\Big( q_\\alpha \\mathbf{\\breve{B}} \\Big) = 0 \\label{eq:iota_fom_adjoint_diff_eq_qsfl}, \\\\\n \\int_{\\mathcal{S}} \\diff S \\; q_\\alpha \\mathbf{\\breve{B}} \\cdot \\nabla\\phi + ( \\iota - \\iota_T ) = 0 \\label{eq:iota_fom_adjoint_integral_eq_qsfl},\n\\end{align}\n\\end{subequations}\nwith both equations defined on $\\mathcal{S}$. Using \\eqref{eq:surface_divergence_theorem}, the surface integral of \\eqref{eq:iota_fom_adjoint_diff_eq_qsfl} yields $0 = \\mathbf{\\breve{B}}\\cdot\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}$, which is consistent with the boundary condition on the magnetic field \\eqref{eq:vacuum_field_normal_BC}. The first equation \\eqref{eq:iota_fom_adjoint_diff_eq_qsfl} can be recast in the form of a magnetic differential equation $\\mathbf{B}\\cdot\\nabla q_\\alpha = - q_\\alpha\\nabla_\\Upgamma\\cdot\\mathbf{B}$, while the second equation \\eqref{eq:iota_fom_adjoint_integral_eq_qsfl} is an integral condition on $q_\\alpha$ that ensures uniqueness of the solution.\n\nSecond, the adjoint equation for $q_\\omega$ is obtained by requiring the first line of \\eqref{eq:variation_L_iota_tot} to vanish,\n\\begin{subequations}\n\\begin{align}\n \\Updelta q_\\omega = 0 \\qquad\\qquad\\qquad\\mathrm{in}\\;&\\mathcal{V}, \\label{eq:iota_fom_adjoint_eq_qomega}\\\\\n \\nabla q_\\omega\\cdot\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} = -\\nabla_\\Upgamma \\cdot \\Big(q_\\alpha \\nabla_\\Upgamma \\alpha \\Big) \\qquad\\mathrm{on}\\; & \\mathcal{S} \\label{eq:iota_fom_adjoint_eq_qomega_normal_BC}.\n\\end{align}\n\\end{subequations}\nLike the magnetic potential $\\omega$, the adjoint variable $q_\\omega$ satisfies the Laplace equation in $\\mathcal{V}$ \\eqref{eq:iota_fom_adjoint_eq_qomega}. However, contrary to $\\omega$, $q_\\omega$ has a non-zero normal boundary condition on $\\mathcal{S}$ \\eqref{eq:iota_fom_adjoint_eq_qomega_normal_BC}, which notably depends on the straight field line adjoint variable $q_\\alpha$. Equations \\eqref{eq:iota_fom_adjoint_eq_qomega_normal_BC} and \\eqref{eq:iota_fom_adjoint_eq_qomega} are consistent, as $\\int_\\mathcal{V} \\diff V\\; \\Updelta q_\\omega = \\int_\\mathcal{S} \\diff S\\; \\nabla q_\\omega\\cdot\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} = 0$, by \\eqref{eq:surface_divergence_theorem}.\n\nFinally, the remaining contribution from the last line of \\eqref{eq:variation_L_iota_tot} yields the shape gradient\n\\begin{equation}\n \\mathcal{G_\\iota} = \\frac{1}{G} \\Big[ \\mathbf{B}\\cdot \\nabla q_\\omega + q_\\alpha \\big( \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla\\mathbf{B} - \\mathbf{B}\\cdot\\nabla\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} \\big)\\cdot\\nabla_\\Upgamma \\alpha \\Big], \\label{eq:shape_gradient_iota_fom}\n\\end{equation}\nwith $\\delta\\mathcal{L}_\\iota[\\delta\\mathbf{x}] = \\int_{\\mathcal{S}} \\diff S \\;(\\ensuremath{\\delta\\boldsymbol{\\mathrm{x}}\\cdot \\normvec})\\;\\mathcal{G_\\iota}$.\n\nWe now calculate the shape gradient numerically and verify it against a finite-difference evaluation. The solutions to Laplace's equation for the vacuum magnetic field \\eqref{eq:vacuum_field_Laplace} and adjoint equation for $q_\\omega$ \\eqref{eq:iota_fom_adjoint_eq_qomega} are calculated with the SPEC code \\citep{hudsonComputationMultiregionRelaxed2012}, employing the new Zernike polynomial implementation \\citep{quCoordinateParameterisationSpectral2020}. In all results shown, the radial resolution $L_\\mathrm{rad}$ in SPEC is tied to the poloidal Fourier resolution $M_\\mathrm{pol}$ through $L_\\mathrm{rad} = M_\\mathrm{pol} + 4$. The solutions to the straight field line and $q_\\alpha$ adjoint equations are obtained with a Fourier-Galerkin spectral solver.\n\n\\begin{figure}\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/shape_grad_3d_analytic_iota_target.pdf}\n \\caption{}\n \\label{fig:shape_grad_iota_fom_adjoint}\n\\end{subfigure}%\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/shape_grad_3d_findiff_iota_target.pdf}\n \\caption{}\n \\label{fig:shape_grad_iota_fom_findiff}\n\\end{subfigure}\n\\\\\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/shape_grad_3d_relative_error_iota_target.pdf}\n \\caption{}\n \\label{fig:shape_grad_iota_fom_relative_error}\n\\end{subfigure}%\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/dfdomega_convergence_epsilon_rel_error_iota_fom.pdf}\n \\caption{}\n \\label{fig:convergence_iota_fom}\n\\end{subfigure}\n\\caption{Shape gradient for the rotational transform objective function with $\\iota_T = 1$, evaluated through (a) adjoint methods and (b) a forward finite-difference scheme with step size $\\epsilon_\\mathrm{FD}=10^{-7}$, for the example rotating ellipse case introduced in Fig.~\\ref{fig:abs_nabla_psi}, with Fourier resolution $(N_\\mathrm{tor}, M_\\mathrm{pol}) = (16,16)$. The relative error, defined as the absolute error normalised by the maximal absolute value of the adjoint shape gradient, is shown in (c). The convergence of the relative error in the parameter derivative \\eqref{eq:parameter_derivatives_shape_grad} for a random direction in $\\Omega$ is shown in (d) as a function of the step-size $\\epsilon_\\mathrm{FD}$ and Fourier resolution $(N_\\mathrm{tor}, M_\\mathrm{pol})$. The black dashed line indicates the linear scaling in $\\epsilon_\\mathrm{FD}$ expected from the employed forward finite-difference scheme.}\n\\label{fig:iota_fom_numerical_eval_and_convergence}\n\\end{figure}\n\nThe shape gradient \\eqref{eq:shape_gradient_iota_fom} is shown in Fig.~\\ref{fig:shape_grad_iota_fom_adjoint} for the example rotating ellipse case introduced in Fig.~\\ref{fig:abs_nabla_psi}. The localisation at the ellipse tips is unsurprising, as near-axis expansions show that ellipticity of the flux surfaces generates rotational transform \\citep{mercierEquilibriumStabilityToroidal1964}. This shape gradient $\\mathcal{G}_\\mathrm{adjoint}$ can be verified against the direct finite-difference evaluation $\\mathcal{G}_\\mathrm{FD}$ shown in Fig.~\\ref{fig:shape_grad_iota_fom_findiff}, obtained by evaluating the parameter derivatives $\\partial f\/\\partial \\Omega_i$ through finite-differences and inverting \\eqref{eq:parameter_derivatives_shape_grad}, see \\citet{landremanComputingLocalSensitivity2018}. On the scale of the figure, the two shape gradients seem identical. The relative error is shown in Fig.~\\ref{fig:shape_grad_iota_fom_relative_error} to be small, limited to $\\sim 2\\%$ at the ellipse tips, and exhibits oscillations typical of a truncated Fourier resolution. The relative error is here defined as the absolute error normalised by the $L^\\infty$-norm of $\\mathcal{G}_\\mathrm{adjoint}$, i.e. its maximum absolute value. This choice is preferable to e.g. the $L^2$-norm, as the shape gradient and the error thereof have small average values on the boundary compared to their large values at the ellipse tips, such that unreasonably high relative errors would result at these locations if using the $L^2$-norm as normalisation. \n\nFurthermore, we test convergence of the shape gradient by evaluating a parameter derivative \\eqref{eq:parameter_derivatives_shape_grad} for a random direction in $\\Omega$. The parameter derivative is evaluated both through the adjoint shape gradient and by a forward finite-difference scheme. The relative error is shown in Fig.~\\ref{fig:convergence_iota_fom} as a function of the finite-difference step size $\\epsilon_\\mathrm{FD}$ and the Fourier resolution, which is used both in SPEC and the Fourier-Galerkin spectral solver. As $\\epsilon_\\mathrm{FD}$ is reduced, the error initially decreases linearly with $\\epsilon_\\mathrm{FD}$, as expected from the employed forward finite-difference scheme, until it plateaus at a value governed by the finite Fourier or radial resolution. As mentioned in \\S\\ref{sec:basics_adjoint_methods}, errors in the adjoint shape gradient are introduced by the assumption that the constraint and adjoint PDEs are exactly satisfied. In practice, these PDEs are solved only approximately, limited by the finite Fourier and radial resolution, such that a reduction of the error with increasing resolution is to be expected.\n\n\\subsection{Quasisymmetry objective function}\n\\label{sec:QS_fom_shape_derivative}\n\nFor a general (non-vacuum) magnetic field with nested flux surfaces, quasisymmetry can be expressed as\n\\begin{equation}\n \\frac{\\mathbf{B}\\cdot\\nabla\\psi\\times\\nabla B}{\\mathbf{B}\\cdot\\nabla B} = -\\frac{MG + NI}{N-\\iota M}, \\label{eq:quasisymmetry_magnetic_field_condition}\n\\end{equation}\nwhere $I$ is the net toroidal plasma current and $N\/M$ is the helicity of the field strength in Boozer coordinates, see e.g. \\citet{helanderTheoryPlasmaConfinement2014}. For the vacuum field considered here, $I=0$. In the following, we will not consider quasi-poloidal symmetry, i.e. we will assume $M\\ne 0$. If desired, it would be straight-forward to extend the derived results to include the case $M=0$.\n\nFor magnetic fields with globally nested flux surfaces labelled by $\\psi$, \\eqref{eq:quasisymmetry_magnetic_field_condition} is defined globally. However, we are considering a generally non-integrable field, assuming only that the boundary $\\mathcal{S}$ is a flux surface. Using the generalised toroidal flux gradient defined in \\eqref{eq:def_nabla_tilde_psi}, we are able to define quasisymmetry on the isolated flux surface $\\mathcal{S}$, leading to the quasisymmetry (QS) objective function\n\\begin{equation}\n f_\\mathrm{QS}(\\mathcal{S}, \\omega, \\iota, \\lambda) = \\frac{1}{2} \\int_\\mathcal{S} \\diff S\\; v_\\mathrm{QS}^2(\\omega, \\iota, \\lambda), \\label{eq:definition_fQS}\n\\end{equation}\nwith\n\\begin{equation}\n v_\\mathrm{QS} = \\mathbf{\\breve{B}}\\cdot\\nabla \\breve{B} - \\mathbf{\\breve{B}}\\times\\frac{\\overline{\\nabla\\psi}}{G}\\cdot\\nabla \\breve{B} \\left( \\iota - N\/M\\right). \\label{eq:definition_vQS}\n\\end{equation}\nIf $f_\\mathrm{QS}=0$ and the field is integrable in the neighbourhood of $\\mathcal{S}$, \\eqref{eq:quasisymmetry_magnetic_field_condition} will be satisfied on $\\mathcal{S}$, i.e. the field is quasisymmetric on the boundary.\n\nThe shape derivative of $f_\\mathrm{QS}$ is derived in App.~\\ref{app:derivation_QS_fom_shape_derivative}, with the final expression given in \\eqref{eq:delta_fQS_tot}. Combined with the shape derivatives of $\\mathcal{M}$ \\eqref{eq:variation_M_tot} and $\\mathcal{N}$ \\eqref{eq:variation_N_tot}, the shape derivative of the Lagrangian \\eqref{eq:lagrangian_general} with the quasisymmetric figure of merit follows \\eqref{eq:variation_L_QS_tot}.\n\nRequiring the Lagrangian to be stationary with respect to variations in $\\iota$ and $\\lambda$, the first two lines of \\eqref{eq:variation_L_QS_tot} yield the adjoint equations for $q_\\alpha$,\n\\begin{subequations}\n\\begin{align}\n \\nabla_\\Upgamma \\cdot \\Big( q_\\alpha \\mathbf{\\breve{B}} \\Big) = -\\nabla_\\Upgamma\\cdot\\left[ \\nabla_\\Upgamma\\alpha \\left( v_\\mathrm{QS}\\; \\mathbf{\\breve{B}}\\times\\frac{\\overline{\\nabla\\psi}}{G}\\cdot\\nabla \\breve{B}\\; \\frac{\\iota - N\/M}{\\abs{\\nabla_\\Upgamma \\alpha}^2}\\right) \\right] \\label{eq:QS_fom_adjoint_diff_eq_qsfl}, \\\\\n 0=\\int_{\\mathcal{S}} \\diff S \\left\\{ q_\\alpha \\mathbf{\\breve{B}} \\cdot \\nabla\\phi + v_\\mathrm{QS}\\; \\mathbf{\\breve{B}}\\times\\frac{\\overline{\\nabla\\psi}}{G}\\cdot\\nabla \\breve{B} \\left[ \\frac{\\nabla_\\Upgamma\\alpha\\cdot\\nabla_\\Upgamma \\phi}{\\abs{\\nabla_\\Upgamma \\alpha}^2} (\\iota-N\/M) + 1 \\right] \\right\\} \\label{eq:QS_fom_adjoint_integral_eq_qsfl}.\n\\end{align}\n\\end{subequations}\nSimilarly to the rotational transform objective function case, $q_\\alpha$ satisfies a magnetic differential equation \\eqref{eq:QS_fom_adjoint_diff_eq_qsfl} on $\\mathcal{S}$, with integral condition \\eqref{eq:QS_fom_adjoint_integral_eq_qsfl}. By \\eqref{eq:surface_divergence_theorem}, the surface integral of \\eqref{eq:QS_fom_adjoint_diff_eq_qsfl} is consistent with the magnetic field's normal component vanishing on the boundary \\eqref{eq:vacuum_field_normal_BC}.\n\nFurthermore, requiring the Lagrangian to be stationary with respect to variations in $\\omega$, we obtain the adjoint equations for $q_\\omega$ from the third and fourth lines of \\eqref{eq:variation_L_QS_tot}\n\\begin{subequations}\n\\begin{align}\n \\Updelta q_\\omega & = 0 \\qquad\\qquad\\qquad\\mathrm{in}\\;\\mathcal{V}, \\label{eq:QS_fom_adjoint_eq_qomega}\\\\\n \\nabla q_\\omega \\cdot \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} &= -\\nabla_\\Upgamma \\cdot \\Bigg\\{ q_\\alpha \\nabla_\\Upgamma \\alpha + v_\\mathrm{QS} \\;\\nabla_\\Upgamma \\breve{B} - \\frac{\\mathbf{\\breve{B}}}{\\breve{B}} \\nabla_\\Upgamma \\cdot (v_\\mathrm{QS} \\; \\mathbf{\\breve{B}})\\label{eq:QS_fom_adjoint_eq_qomega_normal_BC} \\\\\n \\nonumber & + \\left(\\iota-N\/M\\right) \\left[ v_\\mathrm{QS} \\; \\frac{\\overline{\\nabla\\psi}}{G}\\times\\nabla_\\Upgamma\\breve{B} - \\mathbf{\\breve{B}}\\; \\nabla_\\Upgamma\\cdot\\left(\\frac{1}{\\breve{B}}v_\\mathrm{QS} \\mathbf{\\breve{B}}\\times\\frac{\\overline{\\nabla\\psi}}{G}\\right) \\right] \\Bigg\\} \\quad \\text{ on } \\mathcal{S}.\n\\end{align}\n\\end{subequations}\nAgain, $q_\\omega$ satisfies the Laplace equation in $\\mathcal{V}$ \\eqref{eq:QS_fom_adjoint_eq_qomega}, with a normal boundary condition on $\\mathcal{S}$ that is the tangential divergence of a vector tangential to the surface \\eqref{eq:QS_fom_adjoint_eq_qomega_normal_BC}. The boundary condition \\eqref{eq:QS_fom_adjoint_eq_qomega_normal_BC} is consistent with the Laplace equation, as $\\int_\\mathcal{V} \\diff V\\; \\Updelta q_\\omega = \\int_\\mathcal{S} \\diff S\\; \\nabla q_\\omega\\cdot\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} = 0$, by \\eqref{eq:surface_divergence_theorem}. \n\nFinally, we obtain the shape gradient from the last three lines of \\eqref{eq:variation_L_QS_tot},\n\\begin{align}\n \\mathcal{G}&_\\mathrm{QS} = - (\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla\\breve{B}) \\nabla_\\Upgamma\\cdot\\left( v_\\mathrm{QS} \\mathbf{\\breve{B}} \\right) - v_\\mathrm{QS}\\;\\left(\\mathbf{\\breve{B}}\\cdot\\nabla\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} - \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla\\mathbf{\\breve{B}} \\right)\\cdot\\nabla_\\Upgamma\\breve{B} \\label{eq:QS_fom_shape_gradient} \\\\\n \\nonumber & + \\left(\\iota-N\/M\\right)\\; \\frac{|\\overline{\\nabla\\psi}|}{G} \\; \\mathbf{\\breve{B}}\\times\\nabla\\breve{B}\\cdot \\left[ \\abs{\\nabla_\\Upgamma\\alpha} \\nabla_\\Upgamma\\left( \\frac{v_\\mathrm{QS}}{\\abs{\\nabla_\\Upgamma\\alpha}} \\right) + \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\; v_\\mathrm{QS}\\; \\left( \\frac{\\nabla_\\Upgamma\\alpha\\cdot\\nabla\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla_\\Upgamma\\alpha}{\\abs{\\nabla_\\Upgamma \\alpha}^2} -h \\right) \\right] \\\\\n \\nonumber & + \\mathbf{\\breve{B}}\\cdot\\nabla q_\\omega + q_\\alpha \\left( \\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}}\\cdot\\nabla\\mathbf{\\breve{B}} - \\mathbf{\\breve{B}}\\cdot\\nabla\\ensuremath{\\boldsymbol{\\hat{\\mathrm{n}}}} \\right)\\cdot\\nabla_\\Upgamma\\alpha + \\frac{h}{2} v_\\mathrm{QS}^2,\n\\end{align}\nwith $\\delta\\mathcal{L}_\\mathrm{QS}[\\delta\\mathbf{x}] = \\int_{\\mathcal{S}} \\diff S \\;(\\ensuremath{\\delta\\boldsymbol{\\mathrm{x}}\\cdot \\normvec})\\;\\mathcal{G_\\mathrm{QS}}$, and $h$ the summed curvature.\n\n\\begin{figure}\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/shape_grad_3d_analytic_quasisymm.pdf}\n \\caption{}\n \\label{fig:shape_grad_QS_fom_adjoint}\n\\end{subfigure}%\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/shape_grad_3d_findiff_quasisymm.pdf}\n \\caption{}\n \\label{fig:shape_grad_QS_fom_findiff}\n\\end{subfigure}\n\\\\\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/shape_grad_3d_relative_error_quasisymm.pdf}\n \\caption{}\n \\label{fig:shape_grad_QS_fom_relative error}\n\\end{subfigure}%\n\\begin{subfigure}{.5\\textwidth}\n \\centering\n \\includegraphics[width=1\\linewidth]{plots_pdf\/dfdomega_convergence_epsilon_rel_error_QS_fom.pdf}\n \\caption{}\n \\label{fig:convergence_QS_fom}\n\\end{subfigure}\n\\caption{Shape gradient for the quasisymmetry objective function with helicity $N\/M=5$, evaluated through (a) adjoint methods and (b) a forward finite-difference scheme with step size $\\epsilon_\\mathrm{FD}=10^{-9}$, for the example rotating ellipse case introduced in Fig.~\\ref{fig:abs_nabla_psi}, with Fourier resolution $(N_\\mathrm{tor}, M_\\mathrm{pol}) = (16,16)$. The relative error, defined as the absolute error normalised by the maximal absolute value of the adjoint shape gradient, is shown in (c). The convergence of the relative error in the parameter derivative \\eqref{eq:parameter_derivatives_shape_grad} for a random direction in $\\Omega$ is shown in (d) as a function of the step-size $\\epsilon_\\mathrm{FD}$ and Fourier resolution $(N_\\mathrm{tor}, M_\\mathrm{pol})$. The black dashed line indicates the linear scaling in $\\epsilon_\\mathrm{FD}$ expected from the employed forward finite-difference scheme.}\n\\label{fig:QS_fom_numerical_eval_and_convergence}\n\\end{figure}\n\nThe shape gradient \\eqref{eq:QS_fom_shape_gradient} for targeted quasi-helical symmetry with helicity $N\/M = 5$ is shown in Fig.~\\ref{fig:shape_grad_QS_fom_adjoint} for the example rotating ellipse case introduced in Fig.~\\ref{fig:abs_nabla_psi}. The shape gradient obtained through adjoint methods is verified against a finite-difference evaluation in Fig.~\\ref{fig:shape_grad_QS_fom_findiff}. The error is visibly small, as is attested by the small relative error of the shape gradient shown in Fig.~\\ref{fig:shape_grad_QS_fom_relative error}. Convergence of the relative error for a parameter derivative in a random direction in $\\Omega$, evaluated with the adjoint method and with a centered finite-difference scheme, is shown in Fig.~\\ref{fig:convergence_QS_fom}. Akin to the rotational transform figure of merit convergence study in Fig.~\\ref{fig:convergence_iota_fom}, the error decreases linearly with $\\epsilon_\\mathrm{FD}$ until it plateaus due to finite Fourier or radial resolution. While the lowest resolution of $(N_\\mathrm{tor}, M_\\mathrm{pol}) = (8,8)$ seemed reasonable for the rotational transform figure of merit in Fig.~\\ref{fig:convergence_iota_fom}, a higher resolution is clearly required for the quasisymmetry figure of merit. This could be due to the fact that higher derivatives of the magnetic field are involved in the shape gradient for quasisymmetry \\eqref{eq:QS_fom_shape_gradient} than in the one for rotational transform \\eqref{eq:shape_gradient_iota_fom}, through derivatives of $v_\\mathrm{QS}$. The resulting fine-scale structure of $\\mathcal{G}$ is harder to resolve with a truncated Fourier series. However, the relative errors in Figs.~\\ref{fig:convergence_iota_fom}~and~\\ref{fig:convergence_QS_fom} are similarly small at the highest Fourier resolutions employed.\n\n\\section{Conclusions}\n\nIn this work, we derived the adjoint equations and shape gradient for the rotational transform and quasisymmetry of a vacuum field on a surface. The shape gradients allow fast computation of derivatives with respect to the parameters that describe the geometry of the surface, which are used in optimisation and sensitivity analyses. For a boundary represented by $N$ parameters, the speed-up from the adjoint method is $O(N)$ compared to a finite-difference evaluation. \n\nThis should enable future use of codes such as SPEC \\citep{hudsonComputationMultiregionRelaxed2012} in optimisation calculations, which was hitherto neglected in favour of the more widely-used VMEC code \\citep{hirshmanThreedimensionalFreeBoundary1986}. Contrary to VMEC, SPEC does not rely on the assumption of nested flux surfaces and can therefore model stochastic and island regions. In practice, employing adjoint methods and computing derivatives of quantities arising from ideal MHS equilibria is challenging, as the linearised MHS equilibrium equations possess regular singular points at every rational surface that resonates with the perturbation. These challenges can be avoided by the use of alternative equilibrium models, such as force-free magnetic fields, or the vacuum fields considered in this work. The generality of the results presented herein would also allow for their implementation in other solvers such as BIEST \\citep{malhotraTaylorStatesStellarators2019}. It is left for future work to extend the vacuum field results presented herein to the more general force-free fields modeled by SPEC. Furthermore, the adjoint methods for vacuum fields introduced in this work could be fruitfully applied to other optimisation problems, e.g. in neoclassical transport calculations.\n\nIt is generally believed that exact quasisymmetry cannot be obtained exactly in a finite volume as near-axis expansions lead to an an overdetermined system of equations \\citep{garrenExistenceQuasihelicallySymmetric1991}, although that can be resolved by allowing for an anisotropic plasma pressure \\citep{rodriguezSolvingProblemOverdetermination2021a, rodriguezSolvingProblemOverdetermination2021}. Exact quasisymmetry on a surface is thought generally possible \\citep{garrenExistenceQuasihelicallySymmetric1991, plunkQuasiaxisymmetricMagneticFields2018}; and indeed, a vacuum solution near axisymmetry was recently found \\citep{senguptaVacuumMagneticFields2021}. The shape gradient for quasisymmetry derived in this work could be used to numerically probe the existence of quasisymmetric solutions on a surface that are not close to axisymmetry. For this purpose, the shape gradient for the rotational transform objective function \\eqref{eq:shape_gradient_iota_fom} could be used to avoid the axisymmetric solution at $\\iota = 0$, or also to avoid low order rationals. Furthermore, the shape gradients derived herein could be used to investigate if and how optimisation for quasisymmetry and for the rotational transform compete with each other. Finally, combining the derivatives of quasisymmetry and rotational transform with previously obtained derivatives of coil shapes \\citep{hudsonDifferentiatingShapeStellarator2018} and island size \\citep{geraldiniAdjointMethodDetermining2021} should, in principle, allow for the efficient search of a stellarator configuration with significant rotational transform, good integrability and neoclassical confinement at the boundary, realised by simple coils.\n\n\\begin{acknowledgments}\nThis work was supported by U.S. DOE DE-AC02-09CH11466, DE-SC0016072 and DE-AC02\u201376CH03073. A.B. acknowledges the generous support of the Simons Foundation.\n\\end{acknowledgments}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nKontsevich has associated certain characteristic classes to\nfinite-dimensional $L_\\infty$- or $A_\\infty$-algebras equipped with an\ninvariant inner product, \\cite{Kncsg, KFd}. These are expressed in\nterms of the homology of certain complexes spanned by graphs with some\nadditional structures. This construction is by now well-understood\nboth from the point of view of Lie algebra homology and topological\nconformal field theory; see, for example, \\cite{HaLacc}.\n\nIn this note, we explore a natural generalization of this construction\nto the case of \\emph{curved} algebras, introduced by Positselski in\n\\cite{Pnqdc}. It turns out that a complete description of these\nclasses, and of the homology of the associated graph complexes, is\npossible. We show that these are all obtainable from\none-dimensional algebras, and that these classes are zero for algebras\nwith zero curvature. This contrasts with the corresponding problem for\nconventional graph complexes, which is still widely open.\n\nAs explained by Kontsevich, these graph complexes can be viewed as\ncomputing the stable homology of Lie algebras of symplectic vector\nfields on a vector space $W$ (in the $A_\\infty$ case, one should\ntake \\emph{noncommutative} symplectic vector fields).\nThis motivates us to consider the\nstability maps. In this direction, we prove that the map from the Lie\nalgebra of symplectic vector fields on $W$ vanishing at the origin to\nthe homology of the Lie algebra of \\emph{all} vector fields on $W\n\\oplus \\CC \\cdot w$, where $w$ is an odd vector, is zero. Similarly,\nwe show the same for the Lie algebra of noncommutative\nsymplectic vector fields.\n\nThe precise relation to the previous result is as follows. Any cyclic\n$L_\\infty$-algebra structure on $V$ defines an unstable characteristic\nclass in the homology of the Lie algebra of symplectic vector fields\non the shifted vector space $W = \\Pi V$. As $\\dim V \\rightarrow\n\\infty$, the homology of this Lie algebra converges to the graph\nhomology (at least if $V$ is only growing in the purely even or purely\nodd direction: see Theorem \\ref{GFgraph} below), and the image of the\nunstable characteristic class under the stability maps gives, in the\nlimit, the aforementioned (stable) characteristic class. Hence, our\nresult above says that the unstable curved characteristic class of an\nalgebra with zero curvature already maps to zero under the first\nstability map $W:=\\Pi V \\into W \\oplus \\CC\\cdot w$.\n\n\nA related observation is the following: if $A$ is a curved\n(associative or Lie, or more generally $A_\\infty$- or $L_\\infty$-)\nalgebra with nonzero curvature, then $A$ is gauge equivalent (i.e.,\nhomotopy isomorphic) to the algebra with the same curvature and zero\nmultiplication, in a sense we will recall below. In the case of\n\\emph{cyclic} curved algebras, we also compute the gauge equivalence\nclasses, which are less trivial: nontrivially curved algebras are\ngauge equivalent to the direct sum of a curved algebra of dimension at\nmost two of a certain form (but having nontrivial multiplications in\ngeneral), with a zero algebra.\n\nThis observation hints at a triviality of\ncurved infinity-algebras from a homological point of view, at least when the cyclic structure is not considered. A similar\nresult on the triviality of the corresponding derived categories was\nobtained recently in \\cite{KLNnv}; another manifestation of this triviality principle is briefly discussed in the last section of this paper.\n\nFinally, we generalize these results to the operadic setting, i.e., to\ntypes of algebras other than associative and Lie algebras. In\nparticular, we can apply it to Poisson, Gerstenhaber, BV, permutation,\nand pre-Lie algebras. For the most part, the generalization is\nstraightforward, and we restrict ourselves with giving only an outline\nof arguments in this section. There is, however, one important aspect\nwhich is less visible in the special cases of commutative and ribbon\ngraphs: a curved graph complex associated with a cyclic (or even\nmodular) operad $\\mathcal O$ is quasi-isomorphic to a variant of the\n\\emph{deformation complex of a curved $\\mathcal O$-algebra on a\n one-dimensional space}. Therefore, this graph complex supports the\nstructure of a differential graded Lie algebra. This differential Lie\nalgebra, and its Chevalley-Eilenberg complex, appeared in various\nguises in the works of Zwiebach-Sen, Costello and\nHarrelson-Voronov-Zuniga on quantum master equation, \\cite{SZbias,\n Cospftft, HVZocms}.\n\\subsection{Notation and conventions}\\label{notsec}\nIn this paper we work in the category of $\\mathbb Z\/2$-graded vector\nspaces (also known as (super)vector spaces) over $\\CC$ although all\nresults continue to hold in the $\\mathbb Z$-graded context and when\n$\\CC$ is replaced by any field of characteristic zero. We will usually\nrefer to these graded vector spaces simply as ``spaces.'' The parity of a homogeneous vector $v$ in a space will be denoted by $|v|$. The adjective\n`differential graded' will mean `differential $\\mathbb Z\/2$-graded'\nand will be abbreviated as `dg'. A (commutative) differential graded\n(Lie) algebra will be abbreviated as\n(c)dg(l)a.\nAll of\nour unmarked tensors are understood to be taken over $\\CC$. For a $\\mathbb Z\/2$-graded vector space $V=V_0\\oplus V_1$ the symbol $\\Pi\n V$ will denote the \\emph{parity reversion} of $V$; thus $(\\Pi\n V)_0=V_1$ while $(\\Pi V)_1=V_0$.\n\n\n We will make use of the language of \\emph{formal}\\footnote{Here the\n word ``formal'' is understood in the sense of a formal\n neighborhood, which differs from the notion of ``formality'' in\n rational homotopy theory.} spaces and algebras (which exist since\n \\cite{Lefat} under the name \"linearly compact\"; see, e.g.,\n \\cite{HaLactha} for a recent treatment relevant to the present work,\n under the present name). A formal space is an inverse limit of\n finite-dimensional spaces. \n\n\n\n\n The functor of taking the linear dual establishes an\n anti-equivalence between the category of (discrete) vector spaces and that of\n formal vector spaces.\n\n It will always be clear from the context whether\n we work with formal or discrete vector spaces, and we will typically\n not mention this specifically later on; the tensor product of two formal spaces is understood to be their \\emph{completed} tensor product. Furthermore, the symbol $V$\n will be reserved for a discrete space, with its dual $V^*$ therefore\n a formal space.\n\n In particular, we will work with formal (c)dg(l)as. The main\n examples will be completed tensor and symmetric algebras on formal\n spaces $W$; these will be denoted by $\\hat{T}W$ and\n $\\hat{S}W$ respectively. Note that we will \\emph{never} consider the\n uncompleted $TW$ and $SW$ when $W$ is formal, and similarly never\n consider the completed $\\hat{T}V$ or $\\hat{S}V$ when $V$ is discrete,\n so as to stay in either the category of formal spaces or that of\n discrete spaces.\n\n\n The Lie algebras of continuous derivations of $\\hat{T}W$ and\n of $\\hat{S}W$ will be denoted by $\\Der(\\hat{T}W)$ and\n $\\Der(\\hat{S}W)$ respectively; we will also consider their Lie\n subalgebras $\\Der^0(\\hat{T}W)$ and $\\Der^0(\\hat{S}W)$ consisting of\n derivations having no constant terms.\n\nWe mention here two potential pitfalls present in this framework. Firstly,\nthe categories of discrete and formal vector spaces are not disjoint: the\nspaces which are both discrete and formal are precisely finite-dimensional\nspaces. And secondly, not every space is either discrete or formal; moreover\nsuch spaces arise as a result of some natural operations with discrete or\nformal spaces. For example if $U$ and $W$ are infinite-dimensional formal or\ndiscrete spaces then the vector space $\\Hom(U,W)$ is neither formal nor\ndiscrete. Similarly, the Lie algebras\n$\\Der(\\hat{T}V^*)$ and $\\Der({T}V)$ will be neither\nformal nor discrete if $V$ is infinite-dimensional.\n\nTherefore, to avoid possible confusion, we make the blanket assumption\nthat the $\\ZZ\/2$-graded vector space $V$ which appears throughout the\npaper, in addition to being discrete as above, is in fact\n\\emph{finite-dimensional}. This way all the objects we consider will\nlive in either the category of formal spaces or the category of\ndiscrete spaces (but not both: so each finite-dimensional space we\nconsider will be viewed in only one way). The price we pay is that\nsome of our results are not formulated in maximal generality; namely\nTheorem \\ref{curvtrivthm} and Claim \\ref{opclaim} do not need the\nspace $V$ to be finite-dimensional (although essentially none of the\nexposition needs to be modified to obtain this generalization).\n\n\n For a \\emph{formal} dgla $\\g$ its Chevalley-Eilenberg cohomological\n complex will be denoted by $\\CE^\\bullet(\\g)$. This is defined as\n follows (note that the definition \\emph{differs} from the usual one\n in where completions are taken, since $\\g$ is formal rather than\n discrete):\nthe underlying graded vector space of $\\CE^\\bullet(\\g)$ is $S\\Pi{\\mathfrak g}^*$ and the differential is given as a sum of two maps $d_{\\operatorname I}$ and $d_{\\CE}$. Here $d_{\\operatorname I}$ and $d_{\\CE}$ are both specified by their restriction onto $\\Pi{\\mathfrak g}^*$ and extended to the whole $S\\Pi{\\mathfrak g}^*$ by the Leibniz rule; further $d_{\\operatorname I}:\\Pi{\\mathfrak g}^*\\to \\Pi{\\mathfrak g}^*$ is the shift of the dual of the internal differential on ${\\mathfrak g}$ whereas $d_{\\CE}:\\Pi{\\mathfrak g}^*\\to S^2(\\Pi{\\mathfrak g}^*)$ is induced by the commutator map $[,]:{\\mathfrak g}\\otimes {\\mathfrak g} \\to {\\mathfrak g}$.\n\nThis is in general a $\\ZZ\/2$-graded complex. In the case that the\ndifferential $d$ is zero, it has an additional grading by\n\\emph{cohomological degree}, i.e., $S^i \\Pi\\mathfrak{ g}^*$ is in degree\n$i$. The corresponding homological complex is the linear dual:\n$\\CE_\\bullet(\\g)=(\\CE^\\bullet(\\g))^*$, which has the underlying formal space $\\hat S \\Pi \\mathfrak{g}$.\nNote that in some papers (e.g. \\cite{HaLacc}) the Chevalley-Eilenberg complex of a graded Lie algebra ${\\mathfrak g}$ is defined using the (in our\ncase completed) \\emph{exterior} algebra $\\hat \\Lambda {\\mathfrak g}$; this definition is equivalent to ours under a canonical isomorphism $\\hat S(\\Pi{\\mathfrak g})\\cong \\hat \\Lambda {\\mathfrak g}$ where\n\\[\\Pi g_1\\ldots\\Pi g_n\\mapsto (-1)^{|g_{n-1}|+2|g_{n-1}|+\\ldots+(n-1)|g_1|}g_1\\wedge\\ldots\\wedge g_n.\\]\n\n\n\n \\section{Gauge equivalence classes of curved (cyclic) $A_\\infty$- and\n $L_\\infty$-algebras}\n\\subsection{Curved $A_\\infty$- and $L_\\infty$- algebras}\nWe recall the definition of $A_\\infty$- and $L_\\infty$-algebras\nfollowing \\cite{HaLacc}, as well as their curved analogues; cf.~e.g.,\n\\cite{Nicbd} and references therein.\n\n\nA curved $A_\\infty$-algebra structure on a\n(finite-dimensional\\footnote{As in \\S \\ref{notsec}, $V$ is considered as\na discrete space throughout; in the present subsection\none could allow it to be infinite-dimensional and discrete.\nWe will not make further mention of this.}) space $V$ is\na continuous odd derivation $m$ of the formal dga $\\hat{T}\\Pi V^*$ and\na curved $L_\\infty$-algebra structure on $V$ is a continuous odd\nderivation $m$ of the formal cdga $\\hat{S}\\Pi V^*$; additionally $m$\nis required to square to zero in both cases. An ordinary (i.e. uncurved) $A_\\infty$-\nor $L_\\infty$-structure is specified by the requirement that $m$ have\nno constant term. The components $m_i:{T}^i\\Pi V\\to \\Pi V$ or\n${S}^i\\Pi V\\to \\Pi V$ of the dual of the restriction of $m$ to the\ntensor or symmetric powers $\\Pi V$ are the structure maps of the\ncorresponding $A_\\infty$- or $L_\\infty$-structure.\n\nWe note that sometimes it is convenient to use the more traditional\nway of writing the structure maps of an $A_\\infty$- or\n$L_\\infty$-algebra $V$ as ${T}^iV\\to V$ or ${\\Lambda}^iV\\to V$; these\nmaps will then be even or odd depending on whether $i$ is even or\nodd. To alleviate the notation we will still write $m_i$ for these\nmaps when the meaning is clear from the context.\n\nWe are interested in \\emph{gauge equivalence} classes of $A_\\infty$- or $L_\\infty$- structures on a fixed space $V$. In particular, this equivalence relation implies other relations found in the literature under the names of homotopy or quasi-isomorphism.\n\nNamely, a gauge equivalence between $(V,m)$ and $(V,m')$ is defined as\na derivation $\\xi \\in \\Der^0(\\hat{T}\\Pi V^*)$ or $\\xi \\in\n\\Der^0(\\hat{S} \\Pi V^*)$ which is even and satisfies $m' = e^{\\ad \\xi}\nm$.\\footnote{In the case that our ground field is not $\\CC$, $e^\\xi$\n still makes sense if we require additionally that $\\xi_1 = 0$ as\n well, i.e., $\\xi$ has no linear term. We can then modify the above\n by saying that a gauge equivalence is a composition of such an\n equivalence (for $\\xi_1=0$) with a linear isomorphism of $V$.} In\nparticular, such a gauge equivalence yields an isomorphism of dgas\n$e^\\xi: (\\hat{T} \\Pi V^*,m) \\iso (\\hat{T} \\Pi V^*, m')$ or cdgas\n$e^\\xi: (\\hat{S} \\Pi V^*,m) \\iso (\\hat{S} \\Pi V^*, m')$, and we usually\ndenote the gauge equivalence by $e^\\xi$.\n\\begin{theorem}\\label{curvtrivthm}\n If $(V, m)$ is a curved $A_\\infty$- or $L_\\infty$- algebra for which\n the curvature $m_0 = c \\in V_0$ is nonzero, then $m$ is gauge\n equivalent to the structure $m' = c$ with all higher multiplications\n $m'_i = 0$ for $i > 0$.\n\\end{theorem}\nRoughly, the above is saying that, when an (odd, noncommutative)\nformal vector field is nonzero evaluated at zero, then it is\nequivalent to a constant vector field up to (generally nonlinear) change of coordinates.\n\nAs a corollary of the theorem, it follows that any two\nnontrivially curved algebras with the same underlying graded vector space $V$\nare gauge equivalent.\n\\begin{proof}[Proof of Theorem \\ref{curvtrivthm}]\n We consider the $A_\\infty$ case; the $L_\\infty$ case is similar.\n\n Any $A_\\infty$-algebra structure $(V,m')$ with $m'_0=m_0=c$ can be\n viewed as a deformation of $(V,m_0)$. Indeed, let us introduce a\n formal parameter $\\hbar$; then $(V,m')$ is equivalent to the\n deformed structure $(V,m'_\\hbar)$, where $m'_\\hbar = m'_0 + \\sum_{i\n \\geq 1} \\hbar^i m'_i$. This yields an equivalence with deformed\n structures whose $i$-ary operations are homogeneous of degree $i$ in\n $\\hbar$.\n\n The structures of the form $(V,m')$ are governed by the dgla\n $\\Der(\\hat T \\Pi V^*, [,c])$, where $c \\in V$ is viewed as an odd\n constant derivation of $\\hat T \\Pi V^*$. Formal deformations (such\n as $(V, m'_\\hbar)$) are governed by the dgla $\\Der(\\hat T \\Pi V^*[[\\hbar]],\n [,c])$. Gauge equivalences $e^{\\xi}$ of Maurer-Cartan elements of\n $\\Der(\\hat T \\Pi V^*, [,c])$, which we can assume satisfy $\\xi_0 = 0\n = \\xi_1$ (so as to not change the curvature) are identified with\n gauge equivalences $e^{\\frac{1}{\\hbar}\\xi_\\hbar}$ (for $\\xi_\\hbar :=\n \\sum_{i} \\hbar^i \\xi_i$) of the corresponding Maurer-Cartan elements\n of $\\Der(\\hat T \\Pi V^*[[\\hbar]], [,c])$, which preserve the grading\n $|\\hbar|=1=|V^*|$.\n\n Since gauge equivalence classes of deformations of Maurer-Cartan\n elements are preserved under quasi-isomorphisms of dglas, it suffices\n to show that $\\Der(\\hat T \\Pi V^*, [,c])$ is acyclic. Let us write\n down this complex $C^\\bullet$ explicitly; note that $C^\\bullet$ is a version of\n the Hochschild complex in the curved setting.\n\n Set $C^i:=\\Hom((\\Pi V)^{\\otimes i}, \\Pi V)$ with the differential $d:C^i\\to\n C^{i-1}$ given by the formula, for $f\\in\n C^i$, and $x_1, \\ldots, x_{i-1} \\in \\Pi V$:\n\\[\ndf(x_1,\\ldots, x_{i-1})=\\sum_k(-1)^{|x_1|+\\cdots+|x_k|}f(x_1,\\ldots, x_{k},c,x_{k+1},\\ldots, x_i).\n\\]\nWe construct an explicit contracting homotopy. Choose an odd linear\nmap $\\epsilon:\\Pi V\\to\\CC$ such that $\\epsilon(c)=1$ (here, odd means\nthat $\\epsilon|_{\\Pi (V_1) = (\\Pi V)_0} = 0$). Define maps $s_i:C^i\\to\nC^{i+1}$ by the formula, for $f\\in C^i$:\n\\begin{equation} \\label{siref}\ns_if(x_1,\\ldots,x_{i+1})=\\epsilon(x_1)f(x_2,\\ldots, x_{i+1}).\n\\end{equation}\nThen,\n\\begin{equation}\nd s_i f(x_1,\\ldots,x_{i}) + s_{i-1} d f(x_1, \\ldots, x_{i}) = \\epsilon(c) f(x_1, \\ldots, x_{i}) = f(x_1, \\ldots, x_{i}). \\qedhere\n\\end{equation}\n\\end{proof}\n\\subsection{Cyclic algebras}\nWe now extend the results of the previous subsection to the case of\nalgebras with a cyclic inner product. By an \\emph{inner product} on a\n$\\ZZ\/2$-graded vector space $V$ we mean a nondegenerate symmetric\nbilinear form $( -, - ): V\\otimes V \\rightarrow \\CC$, where $V$ is\nrequired to be finite-dimensional. An \\emph{inner product space} is a\nspace equipped with an inner product.\n\\begin{definition} Let $(V,m)$ be a finite-dimensional $A_\\infty$- or\n $L_\\infty$-algebra. A cyclic inner product on $V$ is an inner\n product $( -, - ): V\\otimes V \\rightarrow \\CC$ for which the tensors\n $( m_i(v_1,\\ldots,v_i), v_{i+1})$ are invariant with respect to the\n signed cyclic permutations of arguments:\n\\[\n(\n m_i(v_1,\\ldots,v_i), v_{i+1})=\n (-1)^{i+|v_1|(|v_2|+\\ldots+|v_{i+1}|)}( m_i(v_{i+1},v_1\\ldots,v_{i-1}), v_{i})\n \\] We call an algebra $(V,m)$ a \\emph{cyclic} $A_\\infty$- or\n $L_\\infty$-algebra when it is equipped with such a cyclic inner\n product.\n\\end{definition}\nWe need to define a subspace of $\\Der(\\hat{T}\\Pi V^*)$ of\n\\emph{cyclic} derivations. To do so, first note that $\\Der(\\hat{T}\n\\Pi V^*) \\cong \\Hom(\\Pi V^*, \\hat{T}\\Pi V^*)$ via the restriction map.\nUsing the inner product, we can identify the latter with\n$(\\Pi V^* \\otimes\n\\hat{T}\\Pi V^*)$. Next, on any tensor power $(\\Pi V^*)^{\\otimes n}$, we\ncan define the (graded) cyclic permutation operator $\\sigma:\n(\\Pi V^*)^{\\otimes n} \\rightarrow (\\Pi V^*)^{\\otimes n}$. This extends to a\ncontinuous linear automorphism of $(\\Pi V^* \\otimes \\hat{T}\\Pi V^*)$, and\nyields a continuous linear automorphism of $\\Der(\\hat{T} \\Pi\nV^*)$.\n\\begin{definition}\\label{cycderdefn}\n Let $\\CDer(\\hat{T} \\Pi V^*)$ denote the space of derivations\n which are invariant under cyclic permutation: we call these\n \\emph{cyclic} derivations. Similarly, define $\\CDer^0(\\hat{T} \\Pi V^*)$ as those cyclic derivations with zero constant term (i.e., preserving the\naugmentation $\\hat{T}^{\\geq 1} \\Pi V^*$). Finally, define the spaces $\\CDer(\\hat{S} \\Pi V^*)$ and $\\CDer^0(\\hat{S} \\Pi V^*)$ analogously.\n\\end{definition}\nNote that $\\CDer(\\hat{S} \\Pi V^*)$ can be\nviewed as the subspace of $\\CDer(\\hat{T} \\Pi V^*)$ landing in symmetric\ntensors, and similarly for the version with zero constant term.\n\nGiven $V$ together with an inner product, cyclic curved\n$A_\\infty$-structures are the same as odd derivations $\\xi \\in\n\\CDer(\\hat{T} \\Pi V^*)$ which square to zero. Similarly,\nuncurved structures correspond to odd square-zero $\\xi \\in\n\\CDer(\\hat{T} \\Pi V^*)$, and the $L_\\infty$-versions are\nobtained by replacing $\\hat{T}$ with $\\hat{S}$. See, e.g.,\n\\cite{HaLactha} for details.\n\\begin{definition} A gauge equivalence of cyclic $A_\\infty$- or\n $L_\\infty$- structures $(V,m)$ and $(V,m')$ on a fixed inner product\n space $V$ is a map $e^\\xi$ where $\\xi \\in \\CDer^0(\\hat{T}\\Pi V^*)$\n or $\\xi \\in \\CDer^0(\\hat{S} \\Pi V^*)$ satisfies $m' = e^{\\ad \\xi}\n m$.\n\\end{definition}\n\\begin{remark}\n In the literature, the term \\emph{symplectic} is sometimes used\n instead of cyclic, the idea being that an inner product\n on $V$ is equivalent to a (constant) symplectic structure on $\\Pi\n V$, so that the cyclic derivations on $\\hat S \\Pi V^*$ or $\\hat T \\Pi V^*$ are the same\n as formal (possibly noncommutative) symplectic vector fields on $\\Pi V$; a cyclic gauge equivalence could then be interpreted as a (formal, noncommutative) symplectomorphism (preserving the $\\ZZ\/2$-grading).\nNote that it follows from this interpretation that\nthe cyclic derivations of $V$ form a Lie superalgebra, and the cyclic\ngauge equivalences a Lie group.\n\\end{remark}\n\\begin{theorem}\\label{cyccurvtrivthm}\\\n \\begin{enumerate}\n\\item[(a)] If $(V, m)$ is a curved $A_\\infty$-algebra with a\n cyclic inner product for which the curvature $m_0 = c \\in V_0$ is\n nonzero, and $c' \\in V$ any even element for which $(c, c') = 1$, then $(V,m)$ is gauge equivalent to the\n structure $m'$ with $m'_0 =c$, and higher multiplications\n\\begin{gather}\n m_{2i-1}' = 0, i \\geq 1, \\\\\n m_{2i}'(x_1, \\ldots, x_{2i}) = \\bigl(\\prod_{j=1}^{2i} (c', x_j) \\bigr) \\cdot (m_{2i}(c,c,\\ldots,c), c) \\cdot c'.\n\\end{gather}\n\\item[(b)] If $(V,m)$ is a curved $L_\\infty$-algebra with a cyclic inner product for which the curvature $m_0 = c \\in V_0$ is nonzero, then $(V,m)$ is gauge equivalent to the\n structure $m'$ with $m'_0 =c$, and higher multiplications $m'_i = 0$ zero for all $i \\geq 1$.\n\\end{enumerate}\n\\end{theorem}\nOne immediately deduces\n\\begin{corollary}\\\n\\begin{enumerate}\n\\item[(a)]\nTwo curved cyclic $A_\\infty$-algebra structures $(V,m)$ and $(V,m')$ on the same underlying inner product space $V$ with nonzero curvature $m_0, m_0'$\nare gauge equivalent if and only if\n\\begin{equation}\n(m_0, m_0) = (m_0', m_0'), \\quad (m_{2i}(m_0, \\ldots, m_0), m_0) = (m_{2i}'(m_0', \\ldots, m_0'), m_0'), \\forall i \\geq 1.\n\\end{equation}\n\\item[(b)] Two curved cyclic $L_\\infty$-algebra structures $(V,m)$ and\n $(V,m')$ on the same underlying inner product space $V$ with\n nonzero curvature $m_0, m_0'$ are gauge equivalent if and only if\n $(m_0, m_0) = (m_0', m_0')$.\n\\end{enumerate}\n\\end{corollary}\n\\begin{remark}\nAnother, perhaps intuitively more clear, way to understand this result is as follows. Consider the curved cyclic $A_\\infty$-algebra $V$ with curvature $c$ whose underlying inner product space is spanned by a single even vector $c$ with $(c,c)=1$ and higher products $m_{2i}(c,\\ldots,c)=t_ic$; $m_{2i+1}=0$ for $i=0,1,\\ldots$. Here $t_i$ are arbitrary numbers.\n\nConsider also the curved cyclic $A_\\infty$-algebra $V^\\prime$ whose underlying space is spanned by two even vectors $c$ and $c^\\prime$ with $(c,c^\\prime)=(c^\\prime,c)=1, (c,c)=(c^\\prime,c^\\prime)=0$. The $A_\\infty$-structure is given as\n$m_0 = c$ and, for $i > 0$, $m_{2i}(c,\\ldots, c)=t_ic^\\prime$, where $t_i$ are arbitrary, and all other higher products are zero.\n\nThen any cyclic curved $A_\\infty$-algebra with nonzero curvature is gauge equivalent to the direct sum of either $V$ or $V^\\prime$ with an $A_\\infty$-algebra having zero $A_\\infty$-structure.\n\\end{remark}\n\\begin{proof}[Proof of Theorem \\ref{cyccurvtrivthm}]\n (a) We modify the previous\n obstruction theory argument. Deformations of the algebra with $m_0=c$ and all\nhigher operations zero are governed by the subcomplex\n\\begin{equation}\\label{bbeq}\nB^\\bullet := \\CDer(\\hat{T}\\Pi V^*, [,c]) \\subset C^\\bullet\n\\end{equation}\nof the one\nconsidered in Theorem \\ref{curvtrivthm}.\n\nIn Lemma \\ref{qil} below, we show that $B^{\\bullet}$ is quasi-isomorphic to the subcomplex\n$B^{\\bullet}_0$ spanned by\n the cocycles\n\\begin{equation}\\label{ceq}\n\\epsilon^{i+1}(x_1, \\ldots, x_i) := \\epsilon(x_1) \\cdots \\epsilon(x_i) c', \\quad \\epsilon(v) := (c', v).\n\\end{equation}\nNote that, when $i$ is odd, $\\epsilon^{i+1} = 0$. Moreover, $B^{\\bullet}_0$\nis an abelian sub-dgla with zero differential.\n\nUsing the lemma, the result follows as in the proof of Theorem\n\\ref{curvtrivthm}. In more detail, the gauge equivalence classes of\nMaurer-Cartan elements of $B^\\bullet[[\\hbar]]$ and\n$B^\\bullet_0[[\\hbar]]$ which are zero modulo $\\hbar$ are\nidentified. Moreover, the formal Maurer-Cartan elements which are\nhomogeneous of the form $\\sum_{i \\geq 1} \\hbar^i m_i$ for $m_i \\in B^i$\nor $B^i_0$ are identified with actual Maurer-Cartan elements $\\sum_{i\n \\geq 1} m_i$ with zero constant term. Hence, the gauge equivalence\nclasses of cyclic curved $A_\\infty$- structures with $m_0 = c$ are\nidentified with gauge equivalence classes of Maurer-Cartan elements of\n$B^\\bullet_0$ with zero constant term. Since the latter is abelian\nwith zero differential, all elements are Maurer-Cartan, and define\ndistinct gauge equivalence classes.\n\nWe deduce that all curved $A_\\infty$-structures with $m_0 = c$ are gauge\nequivalent to one with all odd operations equal to zero, and even operations given by some multiple of the operation\n\\begin{equation}\n(x_1, \\ldots, x_{2i}) \\mapsto\n\\bigl(\\prod_{j=1}^{2i} (c', x_j) \\bigr) \\cdot c'.\n\\end{equation}\nIt remains to show that this multiple is $(m_{2i}(c,c,\\ldots,c), c)$. This follows by taking the component of the original operation in the direct summand $B^i_0 \\subseteq B^i = (B^i_0 \\oplus B^i_+)$, using the definition of $B^i_+$ in\n\\eqref{bipdfn} below.\n\n(b) We can apply the same argument as above, except now with\n$B:=\\CDer(\\hat{S}\\Pi V^*, [,c])$. The same argument as above applies\nand we deduce the same result, except that this time\n$m_{2i}(c,c,\\ldots,c) = 0$ for all $i \\geq 1$ by skew-symmetry of $m_{2i}$.\n\\end{proof}\n\n\\begin{lemma}\\label{qil} Keeping the assumptions and notation\n of the theorem, the complex $B^\\bullet$ decomposes as $B^\\bullet =\nB^\\bullet_0 \\oplus B^\\bullet_+$, where $B^\\bullet_+$ is acyclic. Moreover,\nthe inclusion $B^\\bullet_0 \\into B^\\bullet$ is a quasi-isomorphism of dglas.\n\\end{lemma}\n\\begin{proof}\nThe second statement follows from the first, since $B^\\bullet_0 \\into B^\\bullet$ is a dgla map.\n\nTo prove the first statement, we modify the contracting\n homotopy $s_i$ from Theorem \\ref{curvtrivthm} to act on $B^\\bullet$. The result will \\emph{not} be a contracting homotopy, but will instead accomplish the desired goal.\n\nDefine maps $s_i':B^i\\to B^{i+1}$ by\n\\begin{equation}\\label{sipfla1}\ns_i' f = \\sum_{j=0}^{i+1} \\sigma^j (s_i f),\n\\end{equation}\nwhere $\\sigma^j$ is the $j$-th power of the cyclic permutation defined above Definition \\ref{cycderdefn}.\nWe now compute $s_{i-1}' d + d s_i'$. Assume that $f \\in B^i$. In this\ncase, we may use the formula\n\\begin{multline}\\label{sipfla2}\ns_i' f(x_1,\\ldots,x_{i+1}) = \\sum_{j=1}^{i+1} (-1)^{|x_1|+\\ldots+|x_{j-1}|} \\epsilon(x_j) f(x_1, \\ldots, x_{j-1}, x_{j+1}, \\ldots, x_{i+1})\n\\\\\n+ (-1)^{|x_1| + \\ldots + |x_{i+1}|} (f(x_1, \\ldots, x_i), x_{i+1}) c'.\n\\end{multline}\nThen, we compute\n\\begin{multline}\\label{sipfla3}\n(s_{i-1}' d + d s_i')f(x_1, \\ldots, x_i) = (i+1) \\epsilon(c) f(x_1, \\ldots, x_i) \\\\ - \\bigl( \\sum_{j=1}^{i+1} \\epsilon(x_j) f(x_1, \\ldots, x_{j-1}, c, x_{j+1}, \\ldots, x_{i}) + (f(x_1, \\ldots, x_{i}), c)c' \\bigr).\n\\end{multline}\nThus, the operator on the RHS is contractible. Next, for $k \\leq i+1$, define subspaces\n\\begin{gather}\n B^i_k \\subset B^i, \\quad B^i_k = B^i \\cap \\bigl(\\CC[S_{i+1}] \\cdot \\bigl( (\\CC \\cdot \\epsilon)^{\\otimes (i+1-k)} \\otimes ((\\CC \\cdot c)^\\perp)^{\\otimes k} \\bigr) \\bigr), \\label{bipdfn0}\\\\\n B^i_+ := \\sum_{k =1}^{i+1} B^i_k. \\label{bipdfn}\n\\end{gather}\nNote that $B^\\bullet_+$ is a subcomplex, and $B^\\bullet = B^\\bullet_0\n\\oplus B^\\bullet_+$.\n\nWe claim that the RHS of \\eqref{sipfla3} acts as $k \\cdot \\Id$ on\n$B^i_k$ for all $k$. This follows directly. As a result, $s_{i-1}' d + d\ns_i'$ restricts to zero on $B^\\bullet_0$ and to an automorphism on\n$B^\\bullet_+$. This proves the lemma. \\qedhere\n\n\\end{proof}\n\\begin{remark}\n Lemma \\ref{qil} is equivalent to the statement that the cyclic\n (co)homology of any curved $A_\\infty$-algebra $V$ whose structure\n maps are all zero, except for $m_0$ (which is not zero), is\n isomorphic to the cyclic (co)homology of a one-dimensional\n $A_\\infty$-algebra $V_1$ having the same property. See\n \\cite{GJainfa, HaLactha} for the notion of cyclic (co)homology of\n $A_\\infty$-algebras. These (co)homologies could be computed in\n other ways from the above, for instance, with the help of Connes'\n exact sequence connecting Hochschild (co)homology with cyclic\n (co)homology (which appears in \\cite{GJainfa} in the curved\n setting), since Theorem \\ref{curvtrivthm} shows that the Hochschild\n (co)homologies of $V$ and $V_1$ are trivial.\n\\end{remark}\n\\section{Characteristic classes of curved algebras}\n\\subsection{The uncurved case}\\label{ccs}\nWe briefly recall Kontsevich's construction of characteristic classes\nof finite-dimensional $A_\\infty$- or $L_\\infty$-algebras with cyclic\ninner products.\nFirst, we recall the definition of certain graph complexes, for which\ncyclic $A_\\infty$- or $L_\\infty$-algebras will produce cycles.\n\\begin{definition}\n A graph is a tuple $(H,V,E, \\varphi_V, \\varphi_E)$ of sets $H, V, E$\n of \\emph{half-edges}, \\emph{vertices}, and \\emph{edges}, and\n surjective maps $\\varphi_V: H \\rightarrow V, \\varphi_E: H\n \\rightarrow E$ such that the fibers of $\\varphi_E$ all have\n cardinality two.\n\\end{definition}\nGiven $\\Gamma = (H, V, E, \\varphi_V, \\varphi_E)$, we will also write $H_\\Gamma = H, V_\\Gamma = V, E_\\Gamma = E, \\varphi_V^\\Gamma =\n\\varphi_V$, and $\\varphi_E^\\Gamma = \\varphi_E$.\n\\begin{definition}\n A ribbon graph is a graph together with a cyclic ordering on each\n fiber $\\varphi_V^{-1}(t)$.\n\\end{definition}\nIntuitively, one may think of the edges of ribbon graphs as slightly\nfattened, which explains the cyclic ordering at vertices.\n\nKontsevich's graph complexes have a basis of graphs of a certain type,\nwith differential taking a graph to the sum over all edges of the\ncontracted graph obtained by shrinking that edge to a point, together\nwith a sign. To make this precise requires the notion of\n\\emph{orientation}:\n\\begin{definition}\n An orientation on a graph is a choice of ordering of all the\n half-edges, ordering of all the vertices, and a sign $\\pm 1$, modulo\n the relation that applying a transposition to the ordering of either\n the half-edges or the vertices is the same as changing the sign.\n\\end{definition}\n\\begin{definition}\n An oriented graph is a graph equipped with an orientation. An\n isomorphism of oriented graphs is an isomorphism of graphs which\n preserves orientation. Similarly, the same definition applies replacing\n ``graph'' with ``ribbon graph.''\n\\end{definition}\nNext, given a graph $\\Gamma = (H,V,E, \\varphi_V, \\varphi_E)$ and an\nedge $e \\in E$ with endpoints $v_1, v_2 \\in V$ meeting halves $h_1,\nh_2 \\in H$, one defines the contracted graph $d_e(\\Gamma) = (H, V \/\n\\{v_1 = v_2\\}, E, \\varphi_V', \\varphi_E)$ by identifying the endpoints\n$v_1$ and $v_2$. If $\\Gamma$ is moreover a ribbon graph, with the\ncyclically ordered sets $\\varphi_V^{-1}(v_1) = (a_1, a_2, \\ldots, a_i\n= h_1)$ and $\\varphi_V^{-1}(v_2) = (b_1, b_2, \\ldots, b_j = h_2)$,\nthen the cyclic ordering of the half edges at the new vertex $v = v_1\n= v_2$ is defined as $(a_1, a_2, \\ldots, a_{i-1}, b_1, b_2,\n\\ldots, b_{j-1})$. Finally, if $\\Gamma$ is equipped with an\norientation, where the half-edges are ordered as $h_1, h_2, p_1,\n\\ldots, p_m$ and with vertices ordered by $v_1, v_2, w_1, \\ldots,\nw_{k}$, the new orientation is given by the ordering $p_1, \\ldots,\np_m$ and $v, w_1, \\ldots, w_{k}$ of vertices, without changing the\nsign.\n\nConsider the graded vector space with basis the isomorphism classes of\noriented graphs \\emph{whose vertices have valence $\\geq 2$}, modulo\nthe relation that a graph is negative its opposite orientation. The\ngrading is given by the number of vertices. Let $\\mathcal{G}$ be the\ncompletion of this graded vector space with respect to the number of\nedges (so \\emph{not} with respect to the defining grading on the\nvector space, which is by number of vertices). Similarly, define\n$\\mathcal{G}_r$ using ribbon graphs rather than graphs. In other\nwords, these are the spaces of possibly infinite linear combinations\nof isomorphism classes of oriented graphs which are not isomorphic to\nthe graph obtained by reversing the orientation.\n\nThen, it is a result of \\cite{Kncsg} that\n\\begin{equation}\nd(\\Gamma) := \\sum_{e \\in E_\\Gamma} d_e(\\Gamma)\n\\end{equation}\ndefines a differential on $\\mathcal{G}$ and $\\mathcal{G}_r$.\n\\begin{definition}\n Kontsevich's graph complex is defined as $(\\mathcal{G}, d)$, and his\n ribbon graph complex is defined as $(\\mathcal{G}_r, d)$.\n\\end{definition}\n\\begin{remark}\n We could alternatively have used the uncompleted graph complex\n above; however, the completed version is the one which naturally\n contains characteristic classes of $L_\\infty$- or\n $A_\\infty$-algebras. In particular, taking homology commutes with\n taking completion, for the following well-known reason: We can write the\n uncompleted graph complex as a direct sum of the subcomplexes of\n graphs of a fixed genus (i.e., first Betti number of the graph as a\n topological space). For each fixed genus, the completion with\n respect to number of edges is the same as the completion with\n respect to the grading, i.e., the number of vertices, since there\n are only finitely many graphs with a fixed genus and number of\n vertices. Hence, the completed graph complex is the same as the\n direct product of the completions of these subcomplexes with respect\n to their usual grading.\n\\end{remark}\nFinally, given a cyclic $A_\\infty$-algebra $V$, one constructs an\nelement of $\\mathcal{G}_r$, given by a sum\n\\begin{equation}\n[V]:=\\sum_{\\Gamma} \\frac{1}{|\\Aut(\\Gamma)|} c_\\Gamma(V) \\cdot \\Gamma,\n\\end{equation}\nwhere we sum over isomorphism classes of ribbon graphs $\\Gamma$ (with\ngroup of automorphisms $\\Aut(\\Gamma)$), and $c_\\Gamma$ is given as\nfollows. Equip $\\Gamma$ with an orientation. We will define\n$c_\\Gamma$ so that the opposite orientation would produce $-c_\\Gamma$.\nNamely, $c_\\Gamma$ is given by contracting the multiplications $m_i$\nof $V$ with the pairings $( -, -)$ according to the graph.\nIn more detail, consider\n\\begin{equation} \\label{multmaps} \\prod_{i=1}^n ( m(-), -\n ): \\bigotimes_{i=1}^n V^{\\otimes |\\phi_V^{-1}(v_i)|}\n \\rightarrow \\CC.\n\\end{equation}\nLet $h_1, \\ldots, h_{|H|}$ be the ordering of the half-edges and $v_1,\n\\ldots, v_{|V|}$ the ordering of the vertices defined by the\norientation, and assume that the sign is $1$. Let us pick\n\\emph{ciliations} of each of the vertices $v_1, \\ldots, v_{|V|}$,\nwhich means a linear ordering of the half-edges $\\varphi^{-1}_V(v_i)$\nmeeting each vertex $v_i$, compatible with the cyclic ordering given\nby the ribbon structure. Up to changing the sign, let us assume that\nthe ordering of the half-edges is $\\varphi^{-1}_V(v_1),\n\\varphi^{-1}_V(v_2), \\ldots, \\varphi^{-1}_V(v_{|V|})$. Let $f \\in V\n\\otimes V$ be the inverse to the pairing $(-,-): V \\otimes V\n\\rightarrow \\CC$. Finally, pick an arbitrary ordering of the edges\n$e_1, \\ldots, e_{|E|}$. Then, one applies \\eqref{multmaps} to the\nelement obtained by applying the signed permutation of components of\n$f^{\\otimes |E|} \\in V^{\\otimes |H|}$ which rearranges the half-edges\n$\\varphi_E^{-1}(e_1), \\ldots, \\varphi_E^{-1}(e_{|E|})$ into the\nordering $h_1, \\ldots, h_{|H|}$. One can check that the result does\nnot depend on the choices of orderings (but only depends on the\norientation of $\\Gamma$ by a sign, as mentioned above), and we let\n$c_\\Gamma(V)$ to be the result of this computation.\n\nIn a similar manner, one constructs from any cyclic $L_\\infty$-algebra $V$\nan element $[V]$ of $\\mathcal{G}$. Then, the following result\nis due to Kontsevich:\n\\begin{proposition}\\cite{KFd} If $V$ is a cyclic $A_\\infty$- or $L_\\infty$-algebra then $[V]$ is a cycle on\n $(\\mathcal{G}, d)$ or $(\\mathcal{G}_r, d)$ respectively.\n\\end{proposition}\n A direct proof of the proposition in the $A_\\infty$-case\n is contained, e.g., in \\cite{Igu}. More conceptually, one can view a\n cyclic $A_\\infty$-algebra $V$ as an algebra over ${\\mathsf\n F}\\underline{\\mathscr{A}\\textit{ss}}^{1}$, the Feynman transform of the $\\Det$-twisted naive\n modular closure of the cyclic operad $\\mathscr{A}\\textit{ss}$; see \\cite{GKmo} and\n \\cite{ChLadft} concerning these notions. Therefore, we obtain a map of\n modular operads ${\\mathsf F}\\underline{\\mathscr{A}\\textit{ss}}^1\\to\n \\mathscr{E}(V)$, where $\\mathscr{E}(V)$ is the modular endomorphism\n operad of $V$. The map between the vacuum parts of the corresponding\n operads\n\\[{\\mathsf F}\\underline{\\mathscr{A}\\textit{ss}}^1((0))\\to\n\\mathscr{E}(V)((0))\\cong \\CC\n\\]\nis precisely the characteristic class described above, and it follows that it does indeed give a cycle. One can prove the proposition similarly\nin the $L_\\infty$ case.\n\n\n\\subsection{Curved characteristic classes}\nThe preceding results have a natural generalization to the case of\ncurved algebras. We need to remove the valence $\\geq 2$ condition,\nhowever, and study the graph complexes $(\\widetilde{\\mathcal{G}}, d),\n(\\widetilde{\\mathcal{G}_r}, d)$ of formal linear combinations of\ngraphs and ribbon graphs where vertices are allowed to have valence\n$1$ (we will not allow valence-zero vertices, since they don't add\nanything of value). We can consider this to be the complex of ``graphs\nwith stubs,'' where a stub is an edge incident to a valence-one\nvertex. Note that the conventional graph complex $(\\mathcal{G}, d)$ is\na subcomplex of $(\\widetilde{\\mathcal{G}}, d)$ and similarly in the\nribbon case. Then, everything else goes through exactly as above, and\nwe obtain the following result.\n\\begin{proposition} Any curved cyclic $A_\\infty$- or\n $L_\\infty$-algebra $V$ induces a cycle $[V]$ on\n $(\\widetilde{\\mathcal{G}_r}, d)$ or on $(\\widetilde{\\mathcal{G}},\n d)$ respectively.\n\\end{proposition}\nThe curved graph homology could be expressed in terms of certain\nGelfand-Fuks type homology. Namely, let $W$ be a graded symplectic\nvector space and consider the graded formal Lie algebra $\\g(W)$ of formal\nsymplectic vector fields on $W$. Similarly consider the graded formal Lie\nalgebra $\\g_r(W)$ consisting of formal \\emph{noncommutative}\nsymplectic vector fields on $W$, i.e. the Lie algebra\n$\\CDer(\\Pi W)$. Taking the stable limit as the dimension\nof the even or odd part of $W$ goes to infinity, we arrive at\nfollowing result (which\nis a straightforward adaptation of \\cite[Theorem 1.1]{Kncsg}, up to technical\nproblems stemming from the lack of complete reducibility of finite-dimensional representations of simple Lie superalgebras). Let\n$\\CC^{m}$ denote the even space of dimension $m$ and $\\Pi \\CC^m$ the odd\nspace of dimension $m$. We equip $\\CC^{2m}$ with the standard symplectic\nform, and $\\Pi \\CC^m$ with the standard odd symplectic (i.e., orthogonal) form.\n\\begin{theorem}\\label{GFgraph}\n Let $W$ be a fixed inner product space.\n There are isomorphisms\n\\begin{equation}\n\\operatorname{H}_\\bullet\n (\\widetilde{\\mathcal{G}_r}) \\cong \\lim_{m \\rightarrow \\infty}\n \\HCE_\\bullet(\\g_r(W \\oplus \\CC^{2m})), \\quad \\operatorname{H}_\\bullet\n (\\widetilde{\\mathcal{G}_r}) \\cong \\lim_{m \\rightarrow \\infty}\n \\HCE_\\bullet(\\g_r(W \\oplus \\Pi \\CC^m))\n\\end{equation}\n between the stable Chevalley-Eilenberg\n homology of the Lie algebra $\\g_r$ and of the\n corresponding version of the curved graph complex.\nSimilarly, we have isomorphisms\n\\begin{equation}\nH_\\bullet(\\widetilde{\\mathcal{G}})\\cong \\lim_{m \\rightarrow \\infty}\n \\HCE_\\bullet(\\g(W \\oplus \\CC^{2m})), \\quad H_\\bullet(\\widetilde{\\mathcal{G}})\\cong \\lim_{m \\rightarrow \\infty}\n \\HCE_\\bullet(\\g(W \\oplus \\Pi \\CC^m)).\n\\end{equation}\n\\end{theorem}\n\\begin{remark}\nIt might be possible to further generalize this result\nto (certain) cases where both the even and the odd part have dimension going to\ninfinity, but that creates additional technical difficulties that we\nprefer to avoid (as we do not need such generality). In any case, they\nare the same difficulties that arise in the original uncurved setting\nof \\cite{Kncsg} (note that in \\emph{op.~cit.} only the even case is\nconsidered).\n\\end{remark}\n\\begin{proof}\n The corresponding result for the uncurved graph complex and vector\n fields vanishing at the origin was established by Kontsevich\n \\cite{Kncsg} and his proof carries over to the present context, up\n to some technical difficulties created by the fact that we are in\n the super context, where $W_0$ and $W_1$ can both be nonzero. Define\n a nonnegative grading on $\\CE^\\bullet(\\g_r(W))$ called\n \\emph{weight}, which is the sum of the homological grading and the\n degree of polynomial coefficients of the vector fields (this will\n correspond, on graphs, to the number of half-edges).\n\n The main tool is that the inclusion of subcomplexes\n\\begin{equation} \\label{invincleq}\n\\CE^\\bullet(\\g_r(W \\oplus \\CC^{2m}))^{\\mathfrak{osp}(W \\oplus\n \\CC^{2m})} \\into \\CE^\\bullet(\\g_r(W \\oplus \\CC^{2m}))\n\\end{equation}\nis asymptotically a quasi-isomorphism, and similarly replacing\n$\\CC^{2m}$ with $\\Pi \\CC^m$. By this, we mean that in weights $\\leq\nN$, there exists $M$ such that, if $m \\geq M$, then the inclusion is\nan isomorphism on homology in weights $\\leq N$.\n\nWe carry out the argument with $\\CC^{2m}$; replacing this by $\\Pi\n\\CC^m$ will not affect anything. Let $U_m := W \\oplus \\CC^{2m}$. First note that $\\mathfrak{osp}(U_m)\n\\subseteq \\g_r(U_m)$ is the subspace of\nlinear vector fields and hence acts trivially on the cohomology of\n$\\CE^\\bullet(\\g_r(U_m))$. Hence, the statement would\nfollow if it were true that $\\mathfrak{osp}(U_m)$ acted\ncompletely reducibly on $\\CE^\\bullet(\\g_r(U_m))$. This\nis not, in general, true (when $W$ is not purely even); however, it\nfollows from Lemma\n\\ref{superredlem} below that,\nfor each $N \\geq 0$, there exists $M$ so that $m \\geq M$\nimplies that the weight $\\leq N$ part of $\\CE^\\bullet(\\g_r(U_m))$ is completely reducible as an $\\mathfrak{osp}(U_m)$-representation. This is sufficient\nto deduce that \\eqref{invincleq} is a quasi-isomorphism in weights $\\leq N$.\n\n\nTo complete the proof, it remains to relate the invariant subcomplex\nto graphs. This part of the argument is nearly identical to\n\\emph{op.~cit.}, so we will be brief. Associated to each graph is an\n$\\mathfrak{osp}(W)$-invariant element of $\\CE_\\bullet(\\g_r(W))$, as\ndescribed in the previous subsection. However, the resulting map\n$\\mathcal{G}_r \\to \\CE_\\bullet(\\g_r(W))$ does not linearly extend to a\nmap of complexes. Instead, if we attach to the dual of a graph in\n$\\mathcal{G}_r^*$ a canonical element of\n$\\CE^\\bullet(\\g_r(W))^{\\mathfrak{osp}(W)}$, as explained in\n\\emph{op.~cit.}, one obtains a canonical map of complexes\n$(\\mathcal{G}_r^*, d^*) \\to \\CE^\\bullet(\\g_r(W))^{\\mathfrak{osp}(W)}$.\nIn more detail, under the identification $\\mathcal{G}^*_r \\cong\n\\mathcal{G}_r$ using the basis of ribbon graphs (with fixed\norientations), this map sends each oriented ribbon graph $\\Gamma$ to\nthe corresponding functional which contracts elements of\n$\\CE_{|V_\\Gamma|}(\\g_r(W))$ using the symplectic form $W \\otimes\nW\\rightarrow \\CC$, similarly to the construction of \\S \\ref{ccs}. That is,\nwe view $\\CE_{|V_\\Gamma|}(\\g_r(W)) \\subset \\Hom(W, \\hat{T}(W))$ as a subspace\nof $\\hat{T}(W)$ using the symplectic form, and contract with a\npermutation of $\\omega^{\\otimes |E_\\Gamma|}$, where $\\omega \\in W\n\\otimes W$ is the inverse to the symplectic form, so that the copy of\n$\\omega$ corresponding to each edge contracts the corresponding pair\nof half-edges. For details on how to prove this indeed yields a map\nof complexes, see, e.g., \\cite[Theorem 4.10]{HaLacc}, and also\n\\cite{HamsaKt}.\n\nBy the fundamental theorems of invariant theory \\cite{Wftit} (see\nalso, e.g., \\cite{Howeftit}) and the super generalization found in\n\\cite{Seracit}, following the reasoning in the proof of Lemma\n\\ref{superredlem} below, it follows that this map of complexes is\nasymptotically an isomorphism: for every fixed $N \\geq 0$, there\nexists $M \\geq 0$ so that, when $\\dim W \\geq M$, this map is an\nisomorphism if we restrict to graphs with at most $N$ edges, and hence\nelements of $\\CE^\\bullet(\\g_r(W))^{\\mathfrak{osp}(W)}$ of weight $\\leq N$.\n\nThis construction goes through completely analogously for commutative\ngraphs and the Lie algebra $\\g$.\n\\end{proof}\nAs in the proof above, let $U_m := W \\oplus \\CC^{2m}$. Also define $U_m' := W \\oplus \\Pi \\CC^m$.\n\\begin{lemma}\\label{superredlem}\nFor all $N \\geq 0$, there exists $M \\geq 0$ such that, when $m \\geq M$, $U_m^{\\otimes N}$ and $(U_m')^{\\otimes N}$ are completely reducible $\\mathfrak{osp}(U_m)$- and $\\mathfrak{osp}(U_m')$-modules, respectively.\n\\end{lemma}\n\\begin{proof}\n We carry out the argument for $U_m$; the same applies for $U_m'$.\n Suppose that one has subrepresentations $0 \\neq \\tau_1 \\subsetneq\n \\tau_2 \\subseteq U_m^{\\otimes N}$. We wish to show that the\n inclusion $\\tau_1 \\into \\tau_2$ splits. By adjunction, we\n equivalently need to show that the composition $\\CC \\into \\tau_1^*\n \\otimes \\tau_1 \\into \\tau_1^* \\otimes \\tau_2$ splits. Since\n $U_m^{\\otimes N}$ is self-dual using the pairing, $\\tau_1^* \\otimes\n \\tau_2 \\subseteq U_m^{\\otimes 2N}$. This means that it suffices to\n show that the inclusion of the invariant part, $(U_m^{\\otimes\n 2N})^{\\mathfrak{osp}(U_m)} \\into U_m^{\\otimes 2N}$, splits. Let us\n replace $2N$ by $N$ for convenience.\n\n This last fact follows using the fundamental theorems of invariant\n theory. Namely, by \\cite{Seracit}, all of the $\\mathfrak{osp}(U_m)$-invariants in the tensor algebra $T(U_m)$ are tensor products of the\n pairing on $U_m$ with an invariant related to the determinant, whose\n tensor weight (i.e., number of tensor components) is a function of\n $m$ that goes to infinity when $m$ does. Hence, for large enough\n $m$, all of the invariants in weight $U_m^{\\otimes \\leq N}$ are\n spanned by tensor products of the pairing on $W$. By the classical\n second fundamental theorem of invariant theory \\cite{Wftit} (see\n also \\cite{Howeftit}), for large enough $m$, there are no nontrivial\n relations between these invariants. The same argument\n applied to $U_m^*$ shows that the coinvariants have the same\n description. Moreover, for large enough $m$, the invariants of\n $U_m^{\\otimes \\leq N}$ and invariants of $(U_m^*)^{\\otimes \\leq N}$\n have a perfect pairing. This implies that the composition\n $(U_m^{\\otimes \\leq N})^{\\mathfrak{osp}(U_m)} \\into U_m^{\\otimes N}\n \\onto (U_m^{\\otimes N})_{\\mathfrak{osp}(U_m)}$ is an isomorphism for\n large enough $m$.\n\\end{proof}\n\nUnlike the case for the usual graph homology, it is possible to give a\ncomplete calculation of the homology of the curved graph complex and\ncurved ribbon graph complex:\n\\begin{theorem}\n\\label{acythm}\\\n\\begin{enumerate}\n\\item[(a)]\nThe homology of the complex $(\\widetilde{\\mathcal{G}_r}, d)$ is identified with the space of\n formal linear combinations of graphs all of whose connected\n components are graphs whose vertices have valence one with the exception of at most a\n single vertex, which has odd valence.\n\\item[(b)]\nThe homology of the complex $(\\widetilde{\\mathcal{G}}, d)$ is identified with the space of\n formal linear combinations of graphs each of whose connected\n components is the connected graph with one edge and two vertices.\n\\end{enumerate}\n\\end{theorem}\nWe can reinterpret this theorem as follows. First, it is enough to\ncompute the homology of the subcomplex of connected nonempty graphs,\n$\\widetilde{\\mathcal{G}_{r,c}} \\subset \\widetilde{\\mathcal{G}_r}$ and\n$\\widetilde{\\mathcal{G}_c} \\subset \\widetilde{\\mathcal{G}}$. We call\nthese complexes the \\emph{connected} ribbon graph complex and the\nconnected graph complex. Note that $\\widetilde{\\mathcal{G}_r} \\cong\n\\Sym \\widetilde{\\mathcal{G}_{r,c}}$ and $\\widetilde{\\mathcal{G}} \\cong\n\\Sym \\widetilde{\\mathcal{G}_c}$. Then, the desired result is that the\nhomology of the former is identified with linear combinations of\nstar-shaped graphs with an odd number of edges and at most a single\nvertex of valence $\\geq 2$, and the latter is one-dimensional and\nspanned by the connected graph with two vertices and a single edge.\n\nIn other words, the theorem states that\n$\\widetilde{\\mathcal{G}_{r,c}}$ and $\\widetilde{\\mathcal{G}_c}$ are\nquasi-isomorphic to the subcomplexes spanned by connected graphs with\nat most a single vertex of valence $\\geq 2$. These complexes are\nidentical with the deformation complexes of Theorem \\ref{cyccurvtrivthm}\nfor the one-dimensional curved $A_\\infty$ and $L_\\infty$ algebras with\ncurvature $c$ satisfying $(c,c) = 1$, and all higher operations zero.\nThat is, they are the graded vector spaces of noncommutative\nsymplectic vector fields and ordinary symplectic vector fields on the\nodd one-dimensional symplectic vector space $\\CC \\cdot c$, equipped\nwith the differential $\\ad(\\frac{\\partial}{\\partial c})$ (which turns out to be zero).\n\\begin{remark}\n The stable homology of the Lie algebra of symplectic vector fields\n has been computed by Guillemin and Shnider in \\cite{GSssrc}, and thus,\n part (b) of the above theorem could be deduced from their\n calculation, taking into account Theorem \\ref{GFgraph}. However we\n include this result for completeness, and because the argument we\n use in part (a) essentially extends without change to this case.\n\\end{remark}\n\n\\begin{proof}[Proof of Theorem \\ref{acythm}]\n Call a graph a \\emph{line segment} if it is topologically a line\n segment, i.e., it is connected, and either it is a single vertex\nwith no edges, or all of its vertices have valence\n two except for two vertices, which have valence one. Given a\n connected graph $\\Gamma$, let us call a vertex $v \\in V_\\Gamma$\n \\emph{exterior} if it either has valence at most one, or one of the\n connected components of $\\Gamma \\setminus v$ is a line segment. Call\n all other vertices \\emph{interior}.\n\n We will make use of a filtration on the connected (ribbon) graph\n complex given by the number of interior vertices in the graph. We\n call this the \\emph{interior vertex filtration}. The associated\n graded complex is identified, as a vector space, with the (ribbon)\n graph complex, and with the differential which is almost the same,\n but only contracts edges incident to at least one exterior\n vertex. Moreover, the associated graded complexes $\\gr\n \\widetilde{\\mathcal{G}_{r,c}}$ and $\\gr \\widetilde{\\mathcal{G}_c}$\n are graded not only by number of interior vertices, but by the\n subgraph $\\Gamma_0 \\subseteq \\Gamma$ obtained by restricting to\n interior vertices and edges which are incident only to interior\n vertices (allowing here also the empty graph and the graph with a\n single vertex and no edges). It is clear that $\\Gamma_0$ is\n connected (possibly empty). Let $\\gr_{\\Gamma_0}\n \\widetilde{\\mathcal{G}_{r,c}}$ or $\\gr_{\\Gamma_0}\n \\widetilde{\\mathcal{G}_c}$ denote the resulting subcomplex graded by\n $\\Gamma_0$.\n\n (a) We claim that, when $\\Gamma_0$ contains at least one edge, then\n $\\gr_{\\Gamma_0} \\widetilde{\\mathcal{G}_{r,c}}$ is acyclic.\n\n Assume that $\\Gamma_0$ contains an edge. Pick a half-edge $h$ of\n $\\Gamma_0$, and let $v$ be the incident vertex. We construct from\n $v$ and $h$ a contracting homotopy $s$ on $\\gr_{\\Gamma_0}\n \\widetilde{\\mathcal{G}_{r,c}}$. Namely, for every oriented graph\n $\\Gamma$ whose subgraph on internal vertices is $\\Gamma_0$, let\n $s \\Gamma$ be the graph obtained from $\\Gamma$ by adding a new\n univalent vertex together with an edge connecting it to $v$. The\n resulting new half-edge incident to $v$ is, in the cyclic ordering\n at $v$, one half-edge counterclockwise away from $h$. Pick the\n orientation on the new graph $s \\Gamma$ so that $\\Gamma$ is one of\n the summands of $d(s \\Gamma)$.\n\n We claim that $sd + ds = \\Id$. It suffices to show that, for every\n oriented graph $\\Gamma$ as above, $sd(\\Gamma) + ds(\\Gamma)=\\Gamma$.\n In turn, it suffices to show that, for every edge $e \\in E_\\Gamma$,\n $s d_e \\Gamma = - d_e (s \\Gamma)$. This follows immediately from\n our definition of $s$.\n\n\n\nHence, we deduce that $\\gr_{\\Gamma_0} \\widetilde{\\mathcal{G}_{r,c}}$\nis acyclic, as claimed.\n\nNext, we compute $\\gr_{\\Gamma_0} \\widetilde{\\mathcal{G}_{r,c}}$ where\n$\\Gamma_0$ is either empty or is the graph with a single vertex and no edges.\nCall the first graph ``$\\emptyset$'' and the second one ``$\\pt$.''\n\nFirst, $\\gr_{\\pt} \\widetilde{\\mathcal{G}_{r,c}}$ consists of star-shaped\ngraphs with a single vertex of valence $\\geq 3$, with the usual graph\ndifferential except that we do not allow to contract an edge that\nwould result in a graph without a vertex of valence $\\geq 3$, i.e., an\nedge which is incident to a vertex of valence $1$ and a vertex of\nvalence $3$. Consider the filtration by the valence of\nthe interior vertex. The associated graded complexes have homology\nwhich is one-dimensional, concentrated in the part where all vertices\nbut one have valence $1$. Moreover, such graphs are actually zero\nwhen the node has even valence (because a cyclic symmetry can reverse\nthe orientation). Hence, the associated spectral sequence computing\n$H_*(\\gr_{\\pt} \\widetilde{\\mathcal{G}_{r,c}})$ collapses at the first\npage, and the resulting homology is spanned by the star-shaped graphs\nwith a single vertex of odd valence $\\geq 3$ and with all other\nvertices of valence $1$.\n\nNext, $\\gr_\\emptyset \\widetilde{\\mathcal{G}_{r,c}}$ is the subcomplex of line\nsegments, whose homology is one-dimensional and spanned by the line segment\nwith two vertices.\n\nWe deduce that the first page of the spectral sequence of the interior\nvertex filtration on $\\widetilde{\\mathcal{G}_{r,c}}$ is concentrated\nin the part with $\\leq 1$ interior vertices. The part in degree $1$\nis the span of star-shaped graphs with one vertex of odd valence $\\geq\n3$ and the other vertices of valence $1$, and the part in degree $0$\nis the span of the line segment with two vertices. Since all of these\ngraphs have only odd numbers of edges, it is clear that the spectral\nsequence collapses at the first page, and the graphs above span the\nhomology of $\\widetilde{\\mathcal{G}_{r,c}}$, as desired.\n\n(b) The same argument as above applies in this case, except that,\nsince our graphs no longer have cyclic orderings of half edges at\nvertices, we modify the construction of the contracting homotopy $s$\n(used to show that $\\gr_{\\Gamma_0} \\widetilde{\\mathcal{G}_{r,c}}$ is\nacyclic when $\\Gamma_0$ contains an edge) accordingly. Namely, we\nremove the condition that the new edge be next to $h$ in the\ncounterclockwise cyclic ordering of half edges at the vertex $v$.\nEverything else goes through without change, except that now the\nstar-shaped graphs with a single vertex of valence $\\geq 1$ are all\nzero except for the one with only two vertices. This implies the\ndesired result. \\qedhere\n\n\\end{proof}\nWe see that the complex of simply-connected graphs (which splits off\nas a direct summand) carries all of the homology of the complexes\n$(\\widetilde{\\mathcal{G}_r}, d)$ and $(\\widetilde{\\mathcal{G}}, d)$,\nand we obtain the following result.\n\\begin{corollary}\\label{curveduncurved}\n The inclusions of complexes $(\\mathcal{G}_r,\n d)\\subset(\\widetilde{\\mathcal{G}_r}, d)$ and $(\\mathcal{G},\n d)\\subset(\\widetilde{\\mathcal{G}}, d)$ induce the zero maps on\n homology.\n\\end{corollary}\n\\begin{remark}\n It is natural to ask whether the nontrivial homology classes in the\n curved graph complexes $\\widetilde{\\mathcal{G}_r}$ and\n $\\widetilde{\\mathcal{G}}$ are detected by curved $A_\\infty$- and\n $L_\\infty$-algebras. The answer is yes; in fact, they are detected by\n one-dimensional algebras. For the $A_\\infty$ case, let $\\Gamma(i)$\n be the star-shaped graph whose central vertex has valence $2i+1$ and\n $V(i)$ be the one-dimensional cyclic curved $A_\\infty$-algebra\n spanned by an even vector $c$ such that $( c,c)=1$, $m_0=c$,\n $m_{2i}(c,\\ldots,c)=c$, and all other $A_\\infty$-products are\n zero. Then it is easy to see that the cycle\n $[V(i)]\\in\\widetilde{\\mathcal{G}_r}$ is homologous to\n $\\pm\\frac{1}{2i+1}\\Gamma(i)$ (the sign depends on the choice of an\n orientation on $\\Gamma(i)$). Next, let $\\Gamma(0)$ be the connected\n graph with one edge and two vertices. Then, we may let $V(0)$ be\n the one-dimensional cyclic curved $A_\\infty$-algebra spanned by an\n even vector $c$ such that $m_0=c$, $( c, c ) = 1$, and all the\n higher $A_\\infty$-products are zero. Then,\n $[V(0)]\\in\\widetilde{\\mathcal{G}_r}$ is again homologous to\n $\\pm\\frac{1}{2}\\Gamma(0)$. The same statement holds for the\n $L_\\infty$-setting, if we now let $\\Gamma(0)$ be an ordinary (not\n ribbon) connected graph also with two vertices and one edge, and\n consider the one-dimensional curved cyclic $L_\\infty$-algebra,\n $V(0)$, with curvature $c$ satisfying $( c, c ) = 1$ and all higher\n operations equal to zero: $[V(0)]\\in\\widetilde{\\mathcal{G}_r}$ is\n homologous to $\\pm\\frac{1}{2}\\Gamma(0)$.\n\\end{remark}\n\\section{Homology of Lie algebras of vector fields and stability maps}\\label{unstabsec}\nIn order to refine Theorem \\ref{acythm}, we recall first a more\ngeneral way to view the construction of characteristic classes.\nLet $\\mathfrak{g}$ be a formal dgla.\nConsider a Maurer-Cartan element of $\\mathfrak{g}$, i.e., an element\n$x \\in \\Pi\\mathfrak{g}$ satisfying $dx + \\frac{1}{2} [x,x] = 0$.\nThen, $e^x = \\sum_{i \\geq 0}x^{i}\/i!$ defines a cycle in\n $\\CE_\\bullet(\\mathfrak{g})$, and hence a homology class of even\ntotal degree. In the situation where the differential $d$ on\n$\\mathfrak{g}$ is zero, using the homological degree $|S^{i}\n\\Pi\\mathfrak{g}| = i$, each element $x^{ i}$ itself is a cycle, and we\nobtain \\emph{unstable} characteristic classes $[x^{ i}] \\in\n\\CE_i(\\mathfrak{g})$.\n\nMoreover, if $\\mathfrak{h} \\subseteq \\mathfrak g_0$ is a pronilpotent\nLie subalgebra of the even part of $\\mathfrak{g}$, then there is\ndefined a notion of \\emph{gauge equivalence} of Maurer-Cartan elements\ncorresponding to the adjoint action of the Lie group of the Lie\nalgebra $\\mathfrak{h}$; then it follows that if two Maurer-Cartan\nelements are gauge equivalent by a gauge in $\\mathfrak{h}$, then their\ncharacteristic classes are homologous. (One can more generally take\n$\\mathfrak{h} \\subseteq \\mathfrak{g}_0$ to be a Lie subalgebra which\nis the Lie algebra of a pro-Lie group). This statement, as well as a\nmore detailed treatment of characteristic classes, can be found in,\ne.g., \\cite{Hamccms}.\n\nReturning to the situation of a (cyclic or curved) $L_\\infty$- or\n$A_\\infty$-algebra $V$, the corresponding element of the Lie algebra\n$\\Der^0(\\hat{S} (\\Pi V^*)), \\Der(\\hat{T}(\\Pi V^*))$, etc., defines a\ncanonical homology class.\n\nThe relationship to the aforementioned characteristic classes is\nKontsevich's result that the limit as the dimension of $V$ goes to\n$\\infty$ of the Lie homology of $\\CDer^0(\\hat{S} (\\Pi V^*))$ or\n$\\CDer^0(\\hat{T}(\\Pi V^*))$ is the completion (by number of edges) of\nthe homology of the graph complexes $(\\mathcal{G}, d)$. In the curved\nsituation the relevant result is Theorem \\ref{GFgraph}.\n\nFurthermore, given $V$, and any inner product space $W$,\n we can form the trivial extension $V \\oplus\nW$ where all multiplications with the second factor are zero. This\ninduces maps\n\\begin{gather} \\label{vplieeqn}\n\\varphi_{V,W}: \\CE_\\bullet(\\CDer(\\hat{S}(\\Pi V^*)))\n\\rightarrow \\CE_\\bullet(\\CDer(\\hat{S}(\\Pi (V \\oplus W)^*))), \\\\\n\\label{vpasseqn}\n\\varphi_{V,W}: \\CE_\\bullet(\\CDer(\\hat{T}(\\Pi V^*)))\n\\rightarrow \\CE_\\bullet(\\CDer(\\hat{T}(\\Pi (V \\oplus W)^*))),\n\\end{gather}\nand similarly the restrictions $\\varphi_{V,W}^0 :=\n\\varphi_{V,W}|_{\\CE_\\bullet(\\CDer^0(\\hat{S}(\\Pi V^*)))}$ or\n$\\varphi_{V,W}^0 := \\varphi_{V,W}|_{\\CE_\\bullet(\\CDer^0(\\hat{T}(\\Pi\n V^*)))}$. It is well known (and easy to check) that Kontsevich's\nconstruction (\\S \\ref{ccs}) is obtained from the above construction in\nthe limit: the image of the unstable characteristic cycle $\\sum_i\n\\xi^{i}\/i! \\in \\CE_\\bullet(\\CDer(\\hat{S}(\\Pi V^*)))$ under\n$\\varphi_{V,W}^0$ as $\\dim W_0 \\rightarrow \\infty$ (for fixed $W_1$)\nidentifies with the characteristic cycle on $(\\mathcal{G}, d)$ given\nin $\\S \\ref{ccs}$; similarly if we fix $W_0$ and let $\\dim W_1 \\to\n\\infty$. In the curved setting, by Theorem \\ref{acythm}, for each\nfixed degree $i$, if we fix $W_1$, then for large enough $\\dim W_0$,\n$\\varphi_{V,W}$ induces a projection on homology,\n$$\\HCE_i(\\CDer(\\hat{S}(\\Pi V^*)))\n\\onto \\begin{cases} \\CC, & \\text{if $i$ is even}, \\\\ 0, & \\text{if $i$\n is odd}.\\end{cases}$$\nThe same is true if we fix $W_0$ and consider\nlarge enough $\\dim W_1$. A similar result is true in the associative\nversion, where now we project onto the span of graphs whose connected\ncomponents are stars with odd valence as in Theorem \\ref{acythm}.\n\n Using Theorem \\ref{cyccurvtrivthm}, we can prove an unstable\n analogue of Corollary \\ref{curveduncurved}, which gives information\n about the maps $\\varphi_{V,W}$ for all $W$ with $W_0 \\neq 0$:\n\\begin{theorem}\\label{unscurveduncurved}\n If $V$ and $W$ are inner product spaces and $W_0 \\neq\n 0$, then the compositions\n\\begin{gather} \\label{lstabmaps}\n\\CE_i(\\CDer^0(\\hat S(\\Pi V^*))) \\to \\CE_i(\\CDer(\\hat S(\\Pi V^*))) \\mathop{\\to}^{\\varphi_{V,W}} \\CE_i(\\CDer(\\hat S(\\Pi (V \\oplus W)^*))), \\\\ \\label{astabmaps}\n\\CE_i(\\CDer^0(\\hat T(\\Pi V^*))) \\to \\CE_i(\\CDer(\\hat T(\\Pi V^*))) \\mathop{\\to}^{\\varphi_{V,W}} \\CE_i(\\CDer(\\hat T(\\Pi (V \\oplus W)^*)))\n\\end{gather}\nare zero on homology.\n\\end{theorem}\nBefore we prove the theorem, we first prove a lemma which may be\ninteresting in itself. Given an element $c \\in V$ and a $L_\\infty$- or\n$A_\\infty$-structure given by a Maurer-Cartan element $\\xi \\in\n\\CDer^0(\\hat S(\\Pi V^*))$ or $\\xi \\in \\CDer^0(\\hat T(\\Pi V^*))$, we\nsay that $c$ is \\emph{central} if $[c, \\xi] = 0$. In particular, for\nan ordinary Lie algebra, $c$ is an element satisfying $\\{c, v\\}=0$ for\nall $v \\in V$, and for an ordinary associative algebra, $c$ satisfies\n$c \\cdot v = v \\cdot c$ for all $v \\in V$.\n\\begin{lemma}\nSuppose that $V$ is an (uncurved) $L_\\infty$- (or $A_\\infty$-) algebra with a nonzero even central element $c \\in V_0$.\nThen, the image of the resulting (unstable) characteristic class of $V$ under the appropriate map,\n\\begin{gather}\n\\CE_\\bullet(\\CDer^0(\\hat S(\\Pi V^*))) \\to \\CE_\\bullet(\\CDer(\\hat S(\\Pi V^*))) \\text{ or }\\\\\n\\CE_\\bullet(\\CDer^0(\\hat T(\\Pi V^*))) \\to \\CE_\\bullet(\\CDer(\\hat T(\\Pi V^*))),\n\\end{gather}\nis zero on homology.\n\\end{lemma}\n\\begin{proof}\nWe first consider the $L_\\infty$ case. Let $\\mathfrak{g} :=\n \\CDer(\\hat{S}(\\Pi V^*))$. Let $\\xi\n \\in \\CDer^0(\\hat{S}(\\Pi V^*)) \\subseteq\n \\mathfrak{g}$ correspond to the $L_\\infty$-structure on $V$,\n i.e., $\\xi$ satisfies $[\\xi, \\xi] = 0$, and viewed as an element\nof $\\mathfrak{g}$, $\\xi(v)$ has no\n constant term for all $v \\in \\Pi V^*$.\n Let $\\ell$ denote the structure maps for the algebra\n $V$, with $\\ell_i: V^{\\otimes i} \\rightarrow V$ the $i$-th component.\n\n Now, consider the $L_\\infty$-structure $\\{\\ell^\\lambda_i\\}$ on $V$\n which is obtained by $\\ell^\\lambda_i := \\ell_i$ if $i \\geq 1$, and\n $\\ell_0^\\lambda = \\lambda c$ for $\\lambda \\in \\CC$. Since $c$ is\n central and even, these indeed define $L_\\infty$-structures.\n\n Then, by Theorem \\ref{cyccurvtrivthm}, $V$ equipped with\n $\\ell^\\lambda$ is gauge equivalent to an algebra with all higher\n multiplications equal to zero, and curvature equal to $\\lambda\n c$. This gauge equivalence is by vector fields with zero constant\n and linear term, which form a pronilpotent dgla. Hence, in the\n limit as $\\lambda \\rightarrow 0$, we see that the characteristic\n class of $(V, \\ell^\\lambda)$ becomes a boundary, i.e., the original\n characteristic class of $(V, \\ell)$ is a boundary in\n $\\CE_\\bullet(\\CDer(\\hat S(\\Pi V^*)))$, as desired.\n\n In the $A_\\infty$ case, the same argument applies: as before, one\n deforms the uncurved $A_\\infty$-structure $\\{m_i\\}$ to the curved\n structure $\\{m^\\lambda_i\\}$ with $m^\\lambda_i = m_i$ for $i \\geq 1$\n and $m^\\lambda_0 = \\lambda c$. The difference is, by Theorem\n \\ref{cyccurvtrivthm}, the resulting structure $(V, m^\\lambda)$ is\n gauge equivalent to the algebra described there, which does not have\n all higher operations zero. Call this algebra structure\n $\\{(m')^\\lambda_i\\}$. Even though these are nonzero for infinitely\n many $i$, it is still true that, as $\\lambda \\rightarrow 0$,\n $(m')^\\lambda_i \\rightarrow 0$, and so we still deduce that the\n original characteristic class was a boundary.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem \\ref{unscurveduncurved}]\n We treat only the $L_\\infty$ case, since the $A_\\infty$ case is\n identical. If $V$ is an uncurved $L_\\infty$-algebra, then $V \\oplus\n W$ is an $L_\\infty$-algebra with a nonzero central element, namely,\n any nonzero element of $W_0$. Hence, the image of the resulting\n characteristic class under \\eqref{lstabmaps} is zero.\n\nHowever, in general, not all homology classes of\n$\\CE_\\bullet(\\CDer^0(\\hat{S}(\\Pi V^*)))$ are obtainable in the above manner.\nTo fix this problem, we can consider nontrivial coefficients. Given\nany cdga $\\mathfrak{a}$, we may consider\n$\\mathfrak{a}$-linear $L_\\infty$-algebra structures on $U := V\n\\otimes_\\CC \\mathfrak{a}$, for a fixed finite-dimensional\nspace $V$. Denote $U^* := \\Hom_{\\mathfrak{a}}(U,\n\\mathfrak{a})$. Then, these algebra structures on $U$ are, by\ndefinition, Maurer-Cartan elements of\n$\\Der^0(\\hat{S}_{\\mathfrak{a}}(\\Pi U^*))$, i.e., odd elements $f$\nsatisfying $df + \\frac{1}{2} [f, f] = 0$, where $d$ is the\ndifferential induced by the differential on $\\mathfrak{a}$. If we fix\nan inner product on $V$, we obtain an induced $\\mathfrak{a}$-linear\ninner product on $U$ (of the form $U \\otimes_{\\mathfrak{a}} U\n\\rightarrow \\mathfrak{a}$), and cyclic $L_\\infty$-algebras of this\nform are given by elements of $\\CDer^0(\\hat{S}_{\\mathfrak{a}}(\\Pi\nU^*))$.\n\nAs above, we obtain an unstable characteristic class of\n$\\CE_\\bullet(\\CDer^0(\\hat{S}_{\\mathfrak{a}}(\\Pi U^*)))$. The above argument\nshows that the images of such classes under the tensor product of \\eqref{lstabmaps} with $\\Id_{\\mathfrak{a}}$ are boundaries.\n\nTo conclude the proof, we will use a universal example.\nQuite generally, if $\\mathfrak{h}$ is a formal dgla and\n$\\mathfrak{a}$ a cdga, then $\\CE^\\bullet(\\mathfrak{h}) =\nS\\Pi \\mathfrak{h}^*$ is naturally a cdga, and\nMaurer-Cartan elements of $\\mathfrak{h} \\otimes \\mathfrak{a}$\nidentify with cdga morphisms $\\CE^\\bullet(\\mathfrak{h})\n\\rightarrow \\mathfrak{a}$.\nIf we set $\\mathfrak{a}\n:= \\CE^\\bullet(\\mathfrak{h})$, then the identity map\n$\\Id_{\\CE^\\bullet(\\mathfrak{h})}$ yields a Maurer-Cartan element of\n$\\mathfrak{h} \\otimes \\mathfrak{a}$, and the resulting cycle $\\sum_i\n\\xi^{ i} \/ i! \\in \\widehat{\\CE_\\bullet(\\mathfrak{h}) \\otimes \\mathfrak{a}}\n\\cong \\End(\\CE^\\bullet(\\mathfrak{h}))$ is nothing but the identity map.\n\nApplying this construction to the case $\\mathfrak{h} =\n\\CDer^0(\\hat{S}(\\Pi V^*))$ and $\\mathfrak{a} =\n\\CE^\\bullet(\\CDer^0(\\hat{S}(\\Pi V^*)))$, we deduce from the above that\nthe $\\mathfrak{a}$-linear maps $\\widehat{\\varphi_{V,W} \\otimes\n \\Id_{\\mathfrak{a}}}$ send the cycle $\\xi$ corresponding to the\nidentity element of $\\End(\\CE^\\bullet(\\mathfrak{h}))$ to a\nboundary. This implies that \\eqref{lstabmaps} itself is zero on\nhomology, as desired.\n\\end{proof}\n\n\n\n\\section{Operadic generalization}\nIn this section we sketch an operadic generalization of our main\nresults, from the associative and Lie cases to more general\nsettings. As we will show in the following section, these include\nPoisson, Gerstenhaber, BV, permutation, and pre-Lie algebras: see\nExamples \\ref{pgexam}, \\ref{bvexam}, and \\ref{permexam} in the\nfollowing section.\n\nWe may think of curved (cyclic) $A_\\infty$- and $L_\\infty$-algebras as\narising from the following construction, which we think of\nheuristically as a type of ``Koszul duality'' between operads\ngoverning curved algebras and those governing unital algebras (we do\nnot attempt to make this description precise).\n\\begin{remark}\n For a somewhat related result, see \\cite{HiMickdt}, where resolutions\n for operads of unital algebras are constructed by defining a Koszul\n dual curved cooperad and performing a version of the cobar\n construction. Here, we will not make use of the notion of curved\n (co)operads defined in \\cite{HiMickdt}, and will only use ordinary (dg)\n operads.\n\\end{remark}\nLet $\\mathcal{O}$ be a (cyclic) dg operad \\cite{GKcoch}, which we\nassume to be unital with unit $I \\in \\mathcal{O}(1)$. Moreover, we\nwill assume throughout that each $\\mathcal{O}(i)$ is a\nfinite-dimensional $\\ZZ\/2$-graded vector space (this isn't really\nessential, but it makes dualization less technical, and includes all\noperads we have in mind. Properly speaking, we view $\\mathcal{O}(i)$\nas a formal space, and everything generalizes to the\ninfinite-dimensional formal setting.) Let $m_0 \\in \\mathcal{O}(0)$ be\nan element (corresponding to a ``0-ary'' operation). Recall that\nevery (cyclic) operad is an $\\SS$ ($\\SS_+$)-module, where an\n$\\SS$-module is defined as a collection $\\{V_m\\}_{m \\geq 0}$ of\n$S_m$-modules for all $m \\geq 0$, and an $\\SS_+$-module is an\n$\\SS$-module where each $V_m$ is actually a module over $S_{m+1}$ (the\nunderlying $\\SS$-module is obtained using the inclusion $S_m \\subseteq\nS_{m+1}$ of permutations fixing $m+1$).\n\nWe will now consider $\\mathcal{O}$ as a nonunital operad and\nperform the cobar construction \\cite{GiKa}. Namely, let $\\mathcal{O}^* := \\{ \\mathcal{O}(m)^* \\}$ be the dual\n$\\SS$ (or $\\SS_+$)-module. Let $C(\\mathcal{O})$ be the free operad\ngenerated by $\\Pi \\mathcal{O}^*$, equipped with a differential\n$d_{C(\\mathcal{O})}$ obtained as follows. For every $j \\geq 0$ and\nall $1 \\leq i \\leq k$, there is an operadic composition map\n\\begin{equation}\n\\circ^{k,j}_i: \\mathcal{O}(k) \\otimes \\mathcal{O}(j) \\rightarrow \\mathcal{O}(j+k-1),\n\\end{equation}\nwhich corresponds to plugging the element of $\\mathcal{O}(k)$ into the\n$i$-th input of the element of $\\mathcal{O}(j)$. Let\n$(\\circ^{k,j}_i)^*$ be the linear dual to the above map.\nThen, we\ndefine the differential $d_{C(\\mathcal{O})}$ to be the\nunique extension to a derivation of the operation\n\\begin{equation}\nd_{C(\\mathcal{O})}|_{\\mathcal{O}(i)^*} = d_{\\mathcal{O}}^* +\n\\bigoplus_{j,k} (\\circ^{k,j}_i)^*.\n\\end{equation}\nNow, in the case that $\\mathcal{O}$ is the ordinary (non-dg) operad\ngoverning \\emph{unital} associative or commutative algebras, then\n$C(\\mathcal{O})$ is a dg operad with the property that\ngraded (dg) algebras $V$ over $C(\\mathcal{O})$, equipped\nwith zero differential, are the same as curved $A_\\infty$- or\n$L_\\infty$-algebras. In this case, $m_0 \\in \\mathcal{O}(0)$ is the unit of the\nmultiplication, and the curvature of a graded algebra $V$ over $C(\\mathcal{O})$ is the image of $m_0^* \\in \\mathcal{O}(0)^*$ in $V$.\n\n\nNext, suppose that\n$\\mathcal{O}'$ is the suboperad of $\\mathcal{O}$ in positive arity,\ni.e., $\\mathcal{O}'(0) = 0$ and $\\mathcal{O}'(i) = \\mathcal{O}(i)$ for $i \\geq 1$.\n Further\nsuppose that $\\mathcal{O}'$ is a Koszul operad with augmentation\n$\\overline{\\mathcal{O}'}$ (i.e., $\\overline{\\mathcal{O}'}$ is\na suboperad such that $\\mathcal{O}'(1) = \\overline{\\mathcal{O}'}(1) \\oplus \\CC \\cdot \\Id$, and\n$\\overline{\\mathcal{O}'}(i) = \\mathcal{O}'(i)$ for $i \\neq 1$).\nThen, $C(\\overline{\\mathcal{O}'})$ yields a resolution of the\nKoszul dual operad $(\\mathcal{O}')^!$ (this last fact is equivalent to\nKoszulity; see \\cite{GiKa}). \\emph{Caution:} This operad $C(\\overline{\\mathcal{O}'})$ is sometimes called the cobar construction on the unital operad\n$\\mathcal{O}'$ itself, but\nhere we consider cobar constructions only on nonunital operads.\n\nWe think of an algebra over $C(\\mathcal{O})$ (with zero differential)\nas a certain type of \\emph{curved} version of an\n$((\\mathcal{O}')^!)_\\infty$-algebra, as opposed to an algebra over\n$C(\\overline{\\mathcal{O}'})$, which, in the case $\\mathcal{O}'$ is\nKoszul, is the same as an ordinary\n$((\\mathcal{O}')^!)_\\infty$-algebra. (More generally, given a dg\nspace $V$ with possibly nontrivial differential, one could view a\n$C(\\mathcal{O})$-algebra structure on it as a dg generalization of a\ncurved $((\\mathcal{O}')^!)_\\infty$-algebra; thus one could speak, for\ninstance, about a curved dg $A_\\infty$- or $L_\\infty$-algebra. This\nwould be a Maurer-Cartan element in the appropriate differential\ngraded Lie algebras of (formal, noncommutative) vector fields rather\nthan simply square-zero odd derivations.)\n\n\nLet us return to the general setting of an arbitrary (cyclic) operad\n$\\mathcal{O}$. The analogues of Theorems \\ref{curvtrivthm} and\n\\ref{cyccurvtrivthm} are then the following.\n\\begin{definition}\n A \\emph{weakly unital multiplication} $m_2 \\in \\mathcal{O}(2)$\n is an element such that the image of the maps\n\\begin{equation}\\label{wtm2map}\nm_2(- \\otimes \\Id), m_2(\\Id \\otimes -): \\mathcal{O}(0) \\rightarrow \\mathcal{O}(1)\n\\end{equation}\nlie in the one-dimensional space $\\CC \\cdot \\Id \\subseteq \\mathcal{O}(1)$, and such that the maps are not both zero.\n\\end{definition}\nIf $m_2$ is weakly unital, then\n\\begin{equation} \\label{m2ideq}\nm_2(- \\otimes \\Id) = m_2(\\Id \\otimes -).\n\\end{equation}\nIndeed, if $m_0 \\in \\mathcal{O}(0)$ is any element such that\n$m_2(m_0 \\otimes \\Id) = \\lambda \\cdot \\Id$ for some nonzero $\\lambda\n\\in \\CC$, then it follows that $m_2(m_0 \\otimes m_0) = \\lambda\\cdot m_0$,\nand hence that $m_2(\\Id \\otimes m_0) = \\lambda \\cdot \\Id$.\n\nTherefore, when $m_2$ is weakly unital, we define $\\widetilde{m_2}\n\\in \\mathcal{O}(0)^*$ to be the resulting map \\eqref{m2ideq}.\n\n\nNext, given a $C(\\mathcal{O})$ algebra $V$, the $0$-ary operations form a map $\\gamma: \\mathcal{O}(0)^* \\rightarrow V$, which we call the \\emph{$0$-ary structure\nmap}.\n\\begin{definition}\nGiven an operad $\\mathcal{O}$ with weakly unital multiplication $m_2$,\na $C(\\mathcal{O})$ algebra $V$ with $0$-ary structure map $\\gamma$\nis said to be\n\\emph{nontrivially curved with respect to $m_2$}\nif the element $\\gamma(\\widetilde{m_2})$ is nonzero.\n\\end{definition}\nWe call the element $\\gamma(\\widetilde{m_2})$ the\n\\emph{curvature} of $V$ (for the element $m_2$).\n\nThen, Theorems \\ref{curvtrivthm} and \\ref{cyccurvtrivthm}\ngeneralize as follows. Fix a graded vector space $V$ (with zero\ndifferential). If $\\mathcal{O}$ is any dg operad, we denote a\n$\\mathcal{O}$-algebra structure on $V$ as a pair $(V, \\phi)$ for the\nalgebra structure, where given any $o \\in \\mathcal{O}(k)$, $\\phi(o) \\in\n\\Hom_\\CC(V^{\\otimes k}, V)$.\n\\begin{claim} Fix a graded vector space $V$. \\label{opclaim}\n\\begin{enumerate}\n\\item[(i)] Let $\\mathcal{O}$ be an operad with a weakly unital\n multiplication $m_2$. Let $(V,\\phi)$ be a\n $C(\\mathcal{O})$-algebra structure with nonzero curvature $c\n \\in V$.\n Then, $(V,\\phi)$ is gauge equivalent to the algebra $(V,\\phi')$ with\n the same $0$-ary structure map $\\gamma$,\nbut with all\n higher multiplications equal to zero.\n\\item[(ii)] Let $\\mathcal{O}$ be a cyclic operad with a weakly unital\n multiplication $m_2$. Let $(V,\\phi)$ be a cyclic\n $C(\\mathcal{O})$-algebra structure, with nonzero curvature $c\n \\in V$. Let $c' \\in V$ be an element such that $(c', c) = 1$.\n Then, $(V,\\phi)$ is gauge equivalent to the algebra $(V,\\phi')$ with\n the same $0$-ary structure map $\\gamma$, but with higher\n multiplications of the form\n\\begin{equation}\\label{opclaim12eq}\n\\phi'(m)(v_1, \\ldots, v_i) = \\bigl(\\prod_{j=1}^i (c',v_j) \\bigr) \\cdot (\\phi(m)(c, c, \\ldots, c), c) \\cdot c'.\n\\end{equation}\n\\end{enumerate}\n\\end{claim}\nHere, gauge equivalence is defined as follows. For any\n$C(\\mathcal{O})$-algebra structure on $V$, one can form the formal dg\n$\\mathcal{O}$-algebra $\\hat{C}_{\\mathcal{O}}(V)$, which is defined as the completed\nfree $\\mathcal{O}$-algebra generated by $\\Pi V^*$:\n\\[\n\\hat{C}_{\\mathcal{O}}(V)=\\prod_{n=0}^\\infty {\\mathcal\n O}(n)\\otimes_{S_n}(\\Pi V^*)^{\\otimes n} ,\\] equipped with the\ndifferential $d_{\\hat{C}_{\\mathcal{O}}(V)}$ on\n$\\hat{C}_{\\mathcal{O}}(V)$ defined by the canonical linear map $\\Pi V^*\n\\rightarrow \\hat{C}_{\\mathcal{O}}(V)$, which is shifted dual to a\nrestriction of the structure maps $C(\\mathcal{O}) \\rightarrow\n\\Hom(V^{\\otimes n}, V)$.\n\nNext, assume for the moment that $V$ is equipped with the zero\n$C(\\mathcal{O})$-algebra structure, i.e.,\n$d_{\\hat{C}_{\\mathcal{O}}(V)} = 0$. We may then form the formal dgla\n$\\Der(\\hat{C}_{\\mathcal{O}}(V))$, where the differential is induced by\nthe differential on $V$ (and is zero in the case where $V$ was merely\na graded vector space, as in the setting of (non-dg) curved\n$A_\\infty$- or $L_\\infty$-algebras considered in earlier sections of\nthis paper). It is then a standard fact (see, e.g., \\cite[Proposition\n2.15]{GJohaii}, Proposition 2.15 where, however, this result is\nformulated in the language of coalgebras) that the above yields a\nbijection between square-zero odd derivations of\n$\\hat{C}_{\\mathcal{O}}(V)$ and $C(\\mathcal{O})$-structures on $V$.\nFinally, we define two $C(\\mathcal{O})$-structures on $V$ to be gauge\nequivalent if the corresponding differentials\n$d_{\\hat{C}_{\\mathcal{O}}(V)}$ and $d_{\\hat{C}_{\\mathcal{O}}(V)}'$ are\ngauge equivalent, i.e., that there exists an even derivation $\\xi \\in\n\\Der^0(\\hat{C}_{\\mathcal{O}}(V))$ with zero constant term such that\n$d_{\\hat{C}_{\\mathcal{O}}(V)}' = e^{\\ad \\xi}\nd_{\\hat{C}_{\\mathcal{O}}(V)}$.\n\nSimilarly, if $\\mathcal{O}$ is a cyclic operad, and $V$ is a cyclic\nalgebra over $\\mathcal{O}$ (i.e., an algebra equipped with a\nnondegenerate inner product compatible with the cyclic structure on\n$\\mathcal{O}$), then there is a natural $\\ZZ\/(m+1)$ action on the\nsubspace $\\Pi V^* \\otimes \\mathcal{O}(m) \\otimes_{S_m} (\\Pi V^*)^{\\otimes m}\n\\cong \\Hom(\\Pi V^*, \\hat{C}_{\\mathcal{O}}(V)) = \\Der(\\hat{C}_{\\mathcal{O}}(V))$\ncoming from the $S_{m+1}$-structure on $\\mathcal{O}(m)$ and the\ncompatible inner product on $V$. Then, we can define the \\emph{cyclic\n derivations}, $\\CDer(\\hat{C}_{\\mathcal{O}}(V))$, to be the subspace of\n$\\ZZ\/(m+1)$-invariants in each degree $m$. Then, a gauge equivalence\n$e^{\\ad\\xi}: d_{\\hat{C}_{\\mathcal{O}}(V)} \\iso d_{\\hat{C}_{\\mathcal{O}}(V)}'$ is\n\\emph{cyclic} if $\\xi$ is cyclic.\n\nFinally, we explain the main idea of the proof of the claim (without\ngoing into full detail). We explain only the second part, since it is\nmore involved. We first form the cyclic deformation complex for the\n$C(\\mathcal{O})$-structure on $V$\nstructure with all higher operations $C(\\mathcal{O})(i), i > 0$ acting as\nzero, and with $0$-ary structure map $\\gamma: C(\\mathcal{O})(0) = \\Pi\\mathcal{O}(0)^* \\to V$.\nThis complex is $\\CDer(\\hat{C}_{\\mathcal{O}}(V))$, equipped with the\ndifferential $\\ad \\xi$, where $\\xi \\in\n\\CDer(\\hat{C}_{\\mathcal{O}}(V))$ is the derivation obtained from $\\Pi\\gamma^*: \\Pi V^* \\to \\mathcal{O}(0)$, by the formula (for $o \\in \\mathcal{O}(n)$ and $f_1, \\ldots, f_n \\in \\Pi V^*$):\n\\begin{equation}\n\\xi(o \\otimes_{S_n} (f_1 \\otimes \\cdots \\otimes f_n)) = \\sum_{i=1}^n (o \\circ_i \\Pi\\gamma^*(f_i)) \\otimes_{S_{n-1}} (f_1 \\otimes \\cdots \\otimes \\hat f_i \\otimes \\cdots \\otimes f_n),\n\\end{equation}\nwhere $\\hat f_i$ denotes omitting $f_i$ from the tensor product.\n\nThen, we again show that this complex is quasi-isomorphic\nto the subcomplex spanned by cochains such that $\\mathcal{O}(i)$ acts by\nmultiples of the element\n\\begin{equation}\\label{ceq2}\n\\epsilon^{i+1}(x_1, \\ldots, x_i) := \\epsilon(x_1) \\cdots \\epsilon(x_i) c', \\quad \\epsilon(v) := (c', v),\n\\end{equation}\nand hence that the original algebra structure is gauge equivalent to\none where all $\\geq 1$-ary operations are multiples of the operation\n\\begin{equation}\n\\phi'(m)(v_1, \\ldots, v_i) = \\bigl(\\prod_{j=1}^i (c',v_j) \\bigr) \\cdot c'.\n\\end{equation}\nFinally, as before, we can see that this multiple is $(\\phi(m)(c, c, \\ldots, c), c)$.\n\nThe main technical step in the above is showing that the complex is\nquasi-isomorphic to the subcomplex spanned by cochains as in\n\\eqref{ceq2}. This amounts to a generalization of Lemma \\ref{qil}.\nThe main point is that the map $s_i'$ defined in \\eqref{sipfla1} can\nbe generalized to this setting, as follows. If $f = o\n\\otimes_{S_{n+1}} (f_1 \\otimes \\cdots \\otimes f_{n+1}) \\in\n\\mathcal{O}(n) \\otimes_{S_{n+1}} (\\Pi V^*)^{\\otimes (n+1)}$, viewed as\nan element of $\\CDer(\\hat{C}_{\\mathcal{O}}(V))$, then $s_n f \\in\n\\mathcal{O}(n+1) \\otimes_{S_{n+2}} (V^*)^{\\otimes(n+2)}$ is obtained\nas\n\\[\ns_n f = \\sum_{j=1}^{n+1} (o \\circ_j m_2) \\otimes_{S_{n+2}} (f_1 \\otimes \\cdots \\otimes f_{j-1} \\otimes \\epsilon \\otimes f_j \\otimes \\cdots \\otimes f_{n+1}).\n\\]\nThen, one obtains a similar result to \\eqref{sipfla3}, which gives the desired conclusion.\n\nNext, Theorem \\ref{acythm} generalizes as follows. Let $\\cO$ be a\ncyclic operad, and let $\\mathcal{G}_{\\mathcal{O}}$ be the graph\ncomplex constructed from the operad $\\mathcal{O}$. This complex is a\ngeneralization of Kontsevich's graph homology which has many\nequivalent definitions in the literature; one definition is via the\nFeynman transform construction of \\cite{GKmo}. Namely, consider the\nnaive $\\Det$-twisted modular closure $\\underline{\\mathcal O}^1$ of\n$\\mathcal O$ by considering all contraction operations to be zero and\nall parts of $\\mathcal{O}$ of genus $\\geq 1$ to be zero; see, e.g.,\n\\cite[\\S 2]{ChLadft} for this notion. Then form the Feynman transform\noperad $\\mathsf{F}\\underline{\\mathcal O}^1$. The $0$-ary part\n$\\mathsf{F}\\underline{\\mathcal O}^1((0))$ is the desired graph\ncomplex. As before, let $\\mathcal{G}_{\\mathcal{O},c} \\subseteq\n\\mathcal{G}_{\\mathcal{O}}$ be the subcomplex spanned by connected\nnonempty graphs; one has $\\mathcal{G}_{\\mathcal{O}} \\cong \\Sym\n\\mathcal{G}_{\\mathcal{O},c}$.\n\\begin{claim}\\label{opclaim2}\n The complex $\\mathcal{G}_{\\mathcal{O},c}$ is quasi-isomorphic to\n the quotient of the subcomplex spanned by graphs with at most one\n vertex of valence $\\geq 2$ by the span of line segments with three\n vertices whose central vertex is labeled by $\\Id$.\\footnote{Note\n that, if $\\mathcal{O}(0)$ is even one-dimensional, as in the\n preceding cases of $\\mathscr{C}\\!\\mathit{omm}$ or $\\mathscr{A}\\!\\mathit{ss}$, then these\n line segments with central vertex labeled by $\\Id$ are already\n zero. More generally, the span of these line segments is\n isomorphic to $\\wedge^2 \\cO(0)$, by considering the labels at the\n univalent vertices.}\n\\end{claim}\nIn other words, $\\mathcal{G}_{\\mathcal{O},c}$ is quasi-isomorphic to a\nquotient of the deformation complex of a certain canonical\ncyclic $C(\\mathcal{O})$-algebra (which we think of as a type of curved\n$((\\mathcal{O}')^!)_\\infty$-algebra), as follows.\\footnote{This\n interpretation requires that $\\mathcal{O}(0)$ be finite-dimensional,\n as we are assuming. For infinite-dimensional formal\n $\\mathcal{O}(0)$, while the claim above still holds, this\n interpretation is technically not available.} Let $V :=\n\\mathcal{O}(0)^*$. View $V$ as a $\\mathcal{O}$-algebra with all $\\geq\n1$-ary operations trivial, and with $0$-ary structure $\\Id:\n\\mathcal{O}(0)^* \\to \\mathcal{O}(0)^*$. Since all higher operations\nare trivial, any inner product on $V$ is cyclic; we can fix one but it\nwill not really affect anything. Then, the deformation complex of $V$\nas a $C(\\mathcal{O})$-algebra is\n$$\\CDer(\\hat{C}_{\\mathcal{O}}(V)) \\cong \\bigoplus_{m \\geq 0} \\mathcal{O}(m) \\otimes_{S_{m+1}} \\mathcal{O}(0),$$\nequipped with the differential $\\ad \\xi$ where $\\xi \\in\n\\CDer(\\hat{C}_{\\mathcal{O}}(V))$ is the element corresponding to $\\Id\n\\in V \\otimes \\mathcal{O}(0) \\cong \\Hom(V, V) \\subseteq\n\\Der(\\hat{C}_{\\mathcal{O}}(V))$. By the above,\n$\\mathcal{G}_{\\mathcal{O},c}$ is quasi-isomorphic to the quotient\nof this deformation complex by the subcomplex in arity $2$, $\\Id\n\\otimes_{S_2} \\mathcal{O}(0) \\subseteq \\bigoplus_{m \\geq 0}\n\\mathcal{O}(m) \\otimes_{S_{m+1}} \\mathcal{O}(0)$.\n\nThe proof of Claim \\ref{opclaim2} is a direct generalization of the\nproof of Theorem \\ref{acythm}. First, we generalize the notion of\ninterior vertex. Note that $\\mathcal{G}_{\\mathcal{O},c}$ is\nspanned by oriented graphs of the following form: the vertices are\nordered, and each $m$-valent vertex is labeled by an element of\n$\\cO(m-1)$. Next, the half-edges are also ordered. Many of these\ngraphs are equivalent: applying a permutation of the half-edges\nincident to a given vertex is set equal to applying the corresponding\npermutation to the element of $\\cO$ labeling that vertex; also,\napplying any permutation of the half-edges is set equal to multiplying\nby the sign of that permutation. Finally, applying a permutation to\nthe vertices is the same as multiplying by the sign of that\npermutation.\n\nThen, an interior vertex of a graph as above is a vertex which has\nvalence $\\geq 2$ and whose removal does not result in a graph one of\nwhose connected components consists only of univalent vertices and\nbivalent vertices labeled by elements of $\\CC \\cdot \\Id \\subseteq\n\\cO(1)$. As before, the number of interior vertices defines an\nincreasing filtration. Moreover, if we choose an arbitrary basis\n$\\{\\Gamma_i\\}$ of $\\mathcal{G}_{\\mathcal{O},c}$, then the\nassociated graded complex is graded by this basis, where\n$\\gr_{\\Gamma_i} \\mathcal{G}_{\\mathcal{O},c}$ is spanned by graphs\nwhose restriction to interior vertices yields $\\Gamma_i$ (note that\n$\\Gamma_i$ need not be a graph).\n\n\nThe main step is to generalize the construction of the contracting\nhomotopy which shows that $\\gr_{\\Gamma_0}\n\\mathcal{G}_{\\mathcal{O},c}$ is acyclic when $\\Gamma_0$ is a graph\ncontaining an edge. To do so, first fix an element $m_0 \\in \\cO(2)$\nsuch that $m_2 \\circ_1 m_0 = m_2 \\circ_2 m_0 = \\Id \\in \\cO(1)$. We\nchoose the basis $\\{\\Gamma_i\\}$ to consist of graphs. Let $\\Gamma_0$\nbe one such graph which contains an edge. Fix a half edge $h$\nof $\\Gamma_0$\nbased at a vertex $v$. Let us choose $v$ to be the last vertex in the\nordering of vertices, and $h$ to be the last half-edge in the ordering\nof half-edges based at $v$. Suppose $v$ is $m$-valent, and let the\nlabel of $v$ be $o_v \\in \\cO(m-1)$. Label the half-edges of $v$, in\norder, $h_1, \\ldots, h_m$, with $h = h_m$. The contracting homotopy\n$s$ then acts on any graph $\\Gamma \\in \\gr_{\\Gamma_0}\n\\mathcal{G}_{\\mathcal{O},c}$ by first adding a new half-edge\n$h_{m+1}$ to $v$ (last in the ordering at $v$). Then, the label $o_v$\nis replaced by $o_v \\circ_m m_2$, where $m_2 \\in \\cO(2)$ is the weakly\nunital multiplication. Finally, one adds a new univalent vertex $y$ to\n$\\Gamma$ (which becomes the last vertex), labels it by $m_0$, and\nattaches it to $h_{m+1}$. It is then straightforward to verify that\n$(sd + ds) \\Gamma = \\Gamma$, and hence $\\gr_{\\Gamma_0}\n\\mathcal{G}_{\\mathcal{O},c}$, is acyclic.\n\nFinally, we explain the appearance of the quotient by line segments\nwith three vertices whose central vertex is labeled by $\\Id$. Namely,\nthese are exactly the graphs with a single vertex of valence $\\geq 2$\nwhich, nonetheless, have no interior vertices. By a generalization of\nthe arguments in the proof of Theorem \\ref{acythm}, $\\gr_{\\pt}\n\\mathcal{G}_{\\mathcal{O},c}$ is quasi-isomorphic to the quotient of\nthe subcomplex spanned by star-shaped graphs with a single vertex of\nvalence $\\geq 2$ by the span of these graphs. On the other hand,\n$\\gr_{\\emptyset} \\mathcal{G}_{\\mathcal{O},c}$ is quasi-isomorphic\nto the subcomplex spanned by graphs with two vertices, each univalent.\nThus, the second page of the spectral sequence for the interior vertex\nfiltration yields, in degree one, the claimed quotient\ncomplex modulo the graphs with only two vertices, and in degree zero,\nthe span of graphs with only two vertices and one edge. The spectral\nsequence collapses at the third page to the homology of the whole\nquotient subcomplex stated in Claim \\ref{opclaim2}, which proves the\nresult. We omit further details.\n\nFinally, one can deduce an unstable version of Claim \\ref{opclaim2},\nanalogous to Theorem \\ref{unscurveduncurved}:\n\\begin{claim}\\label{opclaim3}\nThe composition\n\\begin{equation}\n\\CE_\\bullet(\\CDer^0(\\hat{C}_{\\mathcal{O}}(V))) \\to \\CE_\\bullet(\\CDer(\\hat{C}_{\\mathcal{O}}(V)))\n\\rightarrow \\CE_\\bullet(\\CDer(\\hat{C}_{\\mathcal{O}}(V \\oplus W)))\n\\end{equation}\nis zero on homology for any uncurved cyclic $C(\\mathcal{O})$-algebra $V$,\nwhere $W$ is an inner product space with $W_0 \\neq 0$.\n\\end{claim}\nWe omit the proof, which is obtained by combining the preceding\nmaterial with \\S \\ref{unstabsec}.\n\\section{Examples and further comments}\nIn this section we provide some remarks and examples regarding the\nmaterial of the previous section.\n\n\\subsection{Generalization to the modular case}\nHere we briefly sketch how the results of the previous section\ncan be extended from the\nsetting of cyclic operads to that of modular operads.\n\nAn analogous result to Claim \\ref{opclaim2} holds for the\n\\emph{twisted} version of the $\\mathcal O$-graph complex which\ncorresponds to the Feynman transform ${\\mathsf F}\\underline{\\mathcal\n O}((0))$ (as opposed to ${\\mathsf F}\\underline{\\mathcal O}^1((0))$)\nwhich is the usual version of the graph complex.\\footnote{We note that\n the operads ${\\mathsf F}\\underline{\\mathcal O}^d((n))$ are not,\n strictly speaking, modular operads in the sense of \\cite{GKmo},\n since the stability condition is not satisfied; however the\n construction of the Feynman transform still makes sense and this\n causes us no trouble. We will ignore this point henceforth.} For\nexample, in the commutative and ribbon graph case the difference\nbetween two types of graph complex lies in a different notion of\norientation: a twisted orientation corresponds to ordering edges of a\ngraph, as opposed to ordering the half edges and vertices.\n\nMore generally, one can replace cyclic operads $\\cO$ by arbitrary\n(twisted) modular operads. Then, if $\\cO$ admits a weakly unital\nmultiplication, one still obtains in the same manner the result that\nthe graph complex for $\\cO$ is quasi-isomorphic to the subcomplex\ndefined analogously to the above.\n\nSimilarly, we can generalize all the constructions of the preceding\nsection from the cyclic to the modular setting. Let $\\cO$ be an\narbitrary (twisted) modular operad $\\cO$. In this case, one replaces\n$C(\\cO)$ (used in the cyclic case above) by the Feynman transform\n(twisted) modular operad, ${\\mathsf F}\\mathcal O$, and modular\nalgebras over this operad are then thought of as curved algebras. The\nconstructions, results (Claims \\ref{opclaim}.(ii) and \\ref{opclaim3}),\nand proofs carry over to this setting. In the special case where one\nhas a cyclic operad and considers it $\\Det$-twisted modular using the\nnaive $\\Det$-twisted modular closure, one recovers the above results.\nAnother example concerns so-called quantum $A_\\infty$-algebras: Let\n$\\mathcal{O}'$ be the $\\Det$-twisted modular closure of\n$\\mathscr{A}\\!ss$ such that ${\\mathsf F}\\mathcal O'$-algebras are\nso-called quantum $A_\\infty$-algebras (see for instance \\cite[Example\n5.2]{ChLafdmm} for an explanation of this notion). Then, if we let\n$\\mathcal{O} = \\mathcal{O}' \\oplus \\CC[0]$ be the unital version\n(adding a $0$-ary operation providing a unit for the associative\nmultiplication), then ${\\mathsf F}\\mathcal O$ defines a notion of\ncurved quantum $A_\\infty$-algebras. As in the cyclic $A_\\infty$ case,\none sees that nontrivially curved quantum $A_\\infty$-algebra\nstructures on a fixed vector space are gauge equivalent to those where\nall the operations of positive arity are of the form\n\\eqref{opclaim12eq}, and in particular all land in a fixed\none-dimensional vector space. We refrain from making precise\nstatements.\n\n\\subsection{Examples}\nHere we provide examples of the preceding constructions for Poisson, Gerstenhaber, BV, and permutation (or pre-Lie) algebras.\n\\begin{example}\\label{pgexam} Consider the case of Poisson algebras.\n It is natural to consider unital Poisson algebras, where here a unit\n $f$ is an element satisfying $\\{1, f\\} = \\{f, 1\\} = 0$ for all $f$,\n whereas $1 \\cdot f = f \\cdot 1 = f$. Let $\\mathcal{O}$ be the operad\n governing unital Poisson algebras, i.e., $\\mathcal{O} =\n u\\mathscr{P}\\!\\textit{oiss} = \\mathscr{P}\\!\\textit{oiss} \\oplus\n \\CC[0]$ where $\\mathscr{P}\\!\\textit{oiss}$ is the Poisson operad and\n $\\CC[0]$ is the one-dimensional vector space concentrated in degree\n zero, which has zero compositions with the bracket of $\\mathcal{O}$,\n and composition $m_2(m_0 \\otimes \\Id) = \\Id = m_2(\\Id \\otimes m_0)$\n with the commutative multiplication, i.e.,\n $\\mathscr{P}\\!\\textit{oiss} \\supset u\\mathscr{C}\\!\\textit{omm}$.\n Then, by the above, one can think of $C(\\mathcal{O})$-algebras as\n curved Poisson-infinity algebras (since Poisson is Koszul self-dual,\n as in the associative case), and it follows from Claim\n \\ref{opclaim}.(i) that nontrivially curved Poisson-infinity structures\n on a vector space are all gauge equivalent.\n\n Similarly, the Gerstenhaber operad $\\mathscr{G}\\!\\textit{erst}$ is\n Koszul and dual to its suspension $\\mathfrak{s}\n \\mathscr{G}\\!\\textit{erst}$; here the suspension is defined by\n tensoring by the endomorphism operad of the one-dimensional odd\n vector space (as a $\\ZZ\/2$-graded $\\SS$-module, this means that one\n applies $\\Pi$ to the even-ary part). As before, one can consider\n unital $\\mathscr{G}\\!\\textit{erst}$ algebras,\n $u\\mathscr{G}\\!\\textit{erst}$, and similarly its suspension $\\mathfrak{s}(u\n \\mathscr{G} \\! \\textit{erst})$. Let $\\cO$ be this latter operad,\n which we can also think of as $u\n \\mathscr{G}\\!\\textit{erst}^!$. Here, the unit is odd. We define\n curved $\\mathscr{G}\\!\\textit{erst}_\\infty$ algebras as algebras over\n $C(\\cO)$, where now the curvature is odd, rather than even. From\n Claim \\ref{opclaim}.(i), we deduce that any two nontrivially curved\n Gerstenhaber-infinity algebra structures on the same graded vector\n space are gauge equivalent.\n\n In the case of the Poisson operad (but not for the Gerstenhaber operad),\n in fact $\\mathscr{P}\\!\\textit{oiss}$ is cyclic, and hence one obtains\n the notion of cyclic curved Poisson-infinity algebras. Claim\n \\ref{opclaim}.(ii) then implies that all nontrivially curved cyclic\n Poisson-infinity structures on a vector space are gauge equivalent\n to those for which all operations of positive arity are of the form\n \\eqref{opclaim12eq}, and in particular all land in a fixed\n one-dimensional vector space.\n\n Moreover, in fact one can compute the associated (unital Poisson)\n graph homology. By Claim \\ref{opclaim2}, the associated graph\n complex is quasi-isomorphic to the subcomplex spanned by graphs\n whose vertices are all univalent except for at most one. Since the\n Poisson operad is the associated graded operad of the associative\n operad, and in particular has the same $\\SS$-module structure, one\n sees that this subcomplex has zero differential, and is spanned by\n the star-shaped graphs with central vertex of odd valence. That is,\n the graph homology for the unital Poisson operad is isomorphic to\n the homology of the graph complex $\\widetilde{\\mathcal{G}_r}$ for the unital\n associative operad.\n\\end{example}\n\\begin{example}\\label{bvexam} The BV operad $\\BV$ \\cite{Getbva} is the homology operad of the operad of framed little discs; it is known to be cyclic. Its algebras, called $\\BV$-algebras, are Gerstenhaber algebras together with an odd operator $\\Delta$ which is a differential operator of second order with respect to the commutative multiplication and a derivation of the odd Lie bracket. Let $\\overline{\\BV}$, as before, denote the augmentation ideal of the operad $\\BV$.\n The $C(\\overline{\\BV})$-graph complex computes, according to\n \\cite{Giafl2d}, the homology of the classifying space of diffeomorphism\n groups of 3-dimensional oriented handlebodies. Although the operad\n $\\BV$ is not defined by (homogeneous) quadratic relations, in\n \\cite{GTVhbva} it was shown to be Koszul in a more general sense,\n and its Koszul dual, $\\BV^!$ was described, and shown to be\n quasi-isomorphic to $C(\\overline{\\BV})$.\n\n A unital $\\BV$-algebra is a $\\BV$-algebra with a unit with respect\n to the commutative multiplication and such that the value of\n $\\Delta$ on the unit is zero.\n\n Consider the operad ${\\mathcal O} =u\\BV$ governing unital\n $\\BV$-algebras; then we can view $C(\\mathcal O)$ as the operad\n governing curved $\\BV^!_\\infty$-algebras. It follows that all\n nontrivially curved $\\BV^!_\\infty$-algebras are gauge equivalent.\nFurthermore, according to Claim\n \\ref{opclaim}.(ii) all nontrivially curved cyclic $\\BV^!_\\infty$-algebras are gauge equivalent\n to those for which all operations of positive arity are of the form\n \\eqref{opclaim12eq}.\n\n Finally, by Claim \\ref{opclaim2}, the graph complex for $\\mathcal O$\n is quasi-isomorphic to the subcomplex with at most one vertex of\n valence $\\geq 2$.\n\\end{example}\n\\begin{example}\\label{permexam} Consider the case of (right) pre-Lie\n algebras, i.e., those with a single operation $\\star$ satisfying the\n relation\n\\begin{equation}\nx \\star (y \\star z) - (x \\star y) \\star z = x \\star (z \\star y) - (x \\star z) \\star y.\n\\end{equation}\nFor such algebras, it makes perfect sense to define the notion of a unit, $1$, such that\n\\begin{equation} \\label{prelieunit}\n1 \\star x = x = x \\star 1.\n\\end{equation}\nWe thus obtain an operad $\\mathcal{O} = u \\mathscr{P}\\!\\textit{re-Lie}\n= \\mathscr{P}\\!\\textit{re-Lie} \\oplus \\CC[0]$ governing \\emph{unital}\npre-Lie algebras, where now the compositions with $\\CC[0]$ are given\nby \\eqref{prelieunit}, or more precisely, $m_2(m_0 \\otimes \\Id) = \\Id\n= m_2(\\Id \\otimes m_0)$ where $m_0 = 1 \\in \\CC[0]$ and $m_2 \\in\n\\mathscr{P}\\!\\textit{re-Lie}[2]$ is the multiplication operation\n$\\star$. By the above procedure, we then obtain a type of curved\nalgebra, namely algebras over $C(\\mathcal{O})$. We will call these\n \\emph{curved (right)\n $\\mathscr{P}\\!\\textit{erm}_{\\infty}$-algebras}, since the operad\n(right) $\\mathscr{P}\\!\\textit{erm}$ is Koszul dual to (right)\n$\\mathscr{P}\\!\\textit{re-Lie}$. In particular, curved\n $\\mathscr{P}\\!\\textit{erm}_\\infty$ algebras with zero curvature\n ($m_0=0$, i.e., the corresponding derivation of $C_{\\mathcal{O}}(V)$\n has zero constant term) are the same as ordinary\n $\\mathscr{P}\\!\\textit{erm}_\\infty$ algebras. Let us recall here\n that ordinary permutation algebras are algebras with an operation\n $\\circ$ satisfying the relation\n\\begin{equation}\nx \\circ (z \\circ y) = x \\circ (y \\circ z) = (x \\circ y) \\circ z,\n\\end{equation}\ni.e., associative algebras additionally satisfying the first equality\nabove. The operad $\\mathscr{P}\\!\\textit{erm}$ is the one whose algebras are\npermutation algebras.\n\nFrom the above results, we deduce that any two curved\n$\\mathscr{P}\\!\\textit{erm}_\\infty$-algebra structures on $V$ with nonzero curvature\nare gauge-equivalent.\n\nNote that, strictly speaking the operad $\\mathscr{P}\\!\\textit{re-Lie}$ is not cyclic, it is\nanticyclic \\cite[p. 9]{GKcoch}; however we can ignore this difference; any anticyclic operad gives rise to a cyclic one via the operadic suspension. Therefore, we obtain the corresponding result on the classification of nontrivially curved (anti)cyclic $\\mathscr{P}\\!\\textit{erm}_\\infty$-algebras, and on\n$u\\mathscr{P}\\!\\textit{re-Lie}$-graph homology.\n\\end{example}\n\\subsection{On the cobar construction of unital operads}\nFinally, we remark that there is a certain subtlety associated with\ntaking cobar-constructions of unital operads (such as an operad with a\nweakly unital multiplication), as we do. Let $\\mathcal O$ be\nsuch an operad. Then it is easy to see that for any $n\\geq 0$ the\ncomplex $C{\\mathcal O}(n)$ is contractible. However it does not follow\nthat any $C\\mathcal O$-algebra is gauge equivalent to a trivial\none. Indeed, one can take $\\mathcal O$ to be the operad $u{\\mathscr\n A}\\!\\textit{ss}$ or $u{\\mathscr C}\\!\\textit{omm}$ governing unital\nassociative or unital commutative algebras; then $C\\mathcal\nO$-algebras (on a graded vector space $V$ with zero differential) will\nbe curved $A_\\infty$- or $L_\\infty$-algebras, respectively, and there is\nno reason for an arbitrary curved $A_\\infty$- or $L_\\infty$-algebra to\nbe gauge equivalent to a trivial one. In fact, even if we let\n$\\mathcal O$ be the operad ${\\mathscr A}\\!\\textit{ss}$ or ${\\mathscr\n C}\\!\\textit{omm}$, and take the bar construction of it (i.e., of the\nwhole unital operad, rather than just the augmentation ideal as one\nusually does in these cases), we again obtain that $C \\mathcal\nO$-algebras will be ordinary $A_\\infty$- or $L_\\infty$-algebras, with\nthe usual (nontrivial) gauge equivalence relation, even though $C\n\\mathcal{O}$ remains acyclic.\n\nThe explanation of this apparent paradox is that the operad $C\\mathcal\nO$ is \\emph{not cofibrant}; see \\cite{BMahto} for this notion. An algebra\nover $C\\mathcal O$ is a map from $C\\mathcal O$ to an endomorphism\noperad of a dg vector space and this map is not necessarily homotopic\nto zero even though $C\\mathcal O$ is acyclic. A similar phenomenon\noccurs when considering a cobar-construction $(T\\Pi V^*,d)$ for a\nunital associative algebra $V$; dg maps from $T\\Pi V^*$ to the field\n$\\CC$ are the Maurer-Cartan elements in $V$, i.e. the odd elements\n$v\\in V$ for which $dv+v^2=0$. Such elements need not be gauge\nequivalent to zero despite $(T\\Pi V^*,d)$ being acyclic; again,\nprecisely because $(T\\Pi V^*,d)$ is not a cofibrant dga.\n\nFurthermore, given a cyclic operad $\\mathcal O$ as above we can form its $\\Det^d$-modular closure $\\overline{\\mathcal O}^d$ and its naive $\\Det^d$-modular closure $\\underline{\\mathcal O}^d$. Then the same reasoning\nshows that the complexes ${\\mathsf F}\\overline{\\mathcal O}^d((n))$ and ${\\mathsf F}\\underline{\\mathcal O}^d((n))$ are acyclic\nfor $n>0$; here ${\\mathsf F}{\\mathcal O}$ is the Feynman transform of\n$\\mathcal O$.\n\nNext, the complexes ${\\mathsf F}\\underline{u{\\mathscr C}\\!\\textit{omm}}^1((0))$ and ${\\mathsf\n F}\\underline{u{\\mathscr A}\\!\\textit{ss}}^1((0))$ are\nnothing but our graph complexes $\\widetilde{\\mathcal G}$ and\n$\\widetilde{\\mathcal G}_r$. For $n > 0$, ${\\mathsf F}\\underline{u{\\mathscr C}\\!\\textit{omm}}^1((n))$ and ${\\mathsf\n F}\\underline{u{\\mathscr A}\\!\\textit{ss}}^1((n))$ are similar except they are\nspanned by graphs which are additionally equipped with $n$ external\nlabeled edges (legs) which are not allowed to be contracted. When legs\nare present, these graph complexes are therefore acyclic. However,\nthe vacuum (legless) part ${\\mathsf F}\\overline{\\mathcal O}^d((n))$ and ${\\mathsf F}\\underline{\\mathcal O}^d((n))$ of the\nFeynman transform need not be acyclic, as our results demonstrate.\n\n\n\n\\vskip 10 pt\n\\noindent\n\\bibliographystyle{amsalpha}\n\\def\\cprime{$'$}\n\\providecommand{\\bysame}{\\leavevmode\\hbox to3em{\\hrulefill}\\thinspace}\n\\providecommand{\\MR}{\\relax\\ifhmode\\unskip\\space\\fi MR }\n\\providecommand{\\MRhref}[2]\n \\href{http:\/\/www.ams.org\/mathscinet-getitem?mr=#1}{#2}\n}\n\\providecommand{\\href}[2]{#2}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\tPerovskite photovoltaic solar cells (PPSC) have reached efficiency comparable to those of the best crystalline silicon (c-Si) devices within only a few years \\cite{nrel.gov}, thanks to the tremendous electrical and optical properties of the metal-halide hybrid perovskites materials \\cite{Snaith2013,jeon2015,Tress2017}. For single junction PPSC, the best performance yet demonstrated reach or even overpass a short-circuit current density $J_{sc}$ of 25.8 $mA.cm^{-2}$, an open circuit voltage $V_{oc}$ of 1.179 $V$, and a fill factor $FF$ of 0.846, leading to power conversion efficiency $PCE$ of 25.7\\% \\cite{nrel.gov}. These achievements are already quite close to the theoretically expected maximum values, especially for the $J_{sc}$, which is typically of 27.83 $mA.cm^{-2}$ under AM1.5G illumination, given that the band gap energy is estimated in their studies to 1.53 $eV$. Furthermore, tandem PPSC made of at least two different perovskite materials appears promising bust also challenging and less mature. In the case of the most common 2 terminal (2T) configuration, a net performance increase could have been recently demonstrated compared to single junction PPSC \\cite{lin_all-perovskite_2022}. \n\t\n\tAs for every optoelectronic device, light management (LM) in PPSC is of primary importance to reach the best performance. As schematized in Figure \\ref{fig:Interplays}, the LM is expected to impact three key performance parameters. First, thanks to the LM, impinging sunlight can be efficiently collected and then absorbed leading to a high $J_{sc}$. Then the LM can help to enhance the $V_{oc}$ thanks to the so-called photon recycling (PR)\\cite{miller2012strong}, provided a strong external luminescence \\cite{Ross1967} and considering the semiconductor exhibits weak non radiative recombinations. Finally, as recently proposed, the energy yield (EY) tends to substitute to the two previous criteria usually considered under standard test conditions (STC). For the latter, requested studies, however, remain complex since they intend to couple illumination models and angular-dependant optical, thermal and electrical models of the cell for one year \\cite{Peters2018}. However, simply considering the incidence angle in the evaluation of absorption and thus $J_{sc}$ is a first step towards EY improvement.\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\includegraphics[scale=0.6]{Fig\/InterPlays.png}\n\t\t\\caption{The three main correlated aspects that governed Light Management.}\n\t\t\\label{fig:Interplays}\n\t\\end{figure}\n\t\n\t\n\tAs illustrated in Figure \\ref{fig:Interplays}, the LM in PPSC results from three interdependent considerations: the photonic engineering (PE) at the wavelength scale, the material choices, and the nanofabrication processes. Practically, the last two directly define the PPSC architecture.\n\t\n\tIn brief, still regardless of the envisaged semiconductor, the LM can invoke several kinds of PE. Most frequently, it results in anti-reflection (AR) over the whole absorbed spectral range, leading to a larger $J_{sc}$ as well as possibly larger EY if this the AR effect also occurs over a wide angular domain. Moreover, beyond the AR effects, light trapping (LT) mechanisms can be involved at the band edge of the perovskite material, also leading to a larger $J_{sc}$ \\cite{Bermel2007,Zhou2008}, whereas enhanced PR thanks to absorption suppression just above the band gap could enhance $V_{oc}$ \\cite{Sandhu2013}, still provided previously mentioned conditions. This well established PE is reminded in more details in section \\ref{light_management}. the LM could even be extended to the infrared domain to possibly limit the parasitic sub band gap absorption and enhance the thermal radiation, so as to decrease the operating temperature \\cite{dumoulin_radiative_2021}. Finally, for the specific case of 2T tandem PPSC, the LM is required to ensure at the same time absorption optimization and current matching between the two cells. \n\t\n\tIn the specific case of metal-halide perovskites, intrinsic properties make these materials particularly relevant for LT, compared to the standard case of c-Si solar cells. First, metal-halide perovskites are direct band gap semiconductors, the light is then more easily absorbed in a submicron thick layer, where LT and possibly PR can occur at their band edge, as detailed later. Second, their refractive indices are lower than that of c-Si, thus, AR effect is easier to obtain over the spectral range of interest. Lastly, the ability to recycle photons has been observed\/shown in the specific case of lead iodide perovskite material \\cite{Pazos-Outon2016}.\n\t\n\tOther key properties of metal-halide perovskites, mainly electrical and with regards to aging, have direct consequences on the typical architecture of the PPSC. The diffusion lengths of the carriers in the perovskites imply both contacts should be in the vicinity of the photocarrier generation, thereby covering both sides of the cell {\\cite{yang2019enhancing}}. Additionally, one of these contacts should obviously be transparent to the sunlight. The double heterojunction architecture is widespread, leading to the use of additional intermediate layers, the electron\/hole transport layers (ETL\/HTL). Then, the back metallic contact, ideally made of noble metal, can thus act as a back mirror. In any case, a careful optical design is required to keep low parasitic absorption in these surrounding layers. Moreover, an effective encapsulation should be added to prevent from the degradation of the perovskite. It should be carefully chosen considering the impact of its optical properties. \n\t\n\tLet us finally envisage the wide panel of low-cost elaboration techniques and strategies enabling the structuration of the perovskite thin film at various scales, from the sub-micron to the over-micron one. First PPSC mainly relied on a mesoporous architecture, in which the perovskite infiltrated a scaffold. Then, progress in the elaboration techniques of the perovskite in liquid or vapor phases basically results in a better microcrystallization, and thus larger diffusion lengths. Simple planar architectures have therefore replaced mesoporous ones \\cite{Liu2013,Liu2014b,Zhao2019}; thanks to these far larger diffusion lengths, current planar PPSC overpass the performances of the best mesoporous PPSC. \n\t\n\tFollowing these huge modifications, the LM has evolved: if the mesoporous architecture could have been optimized for efficient LM thanks to the scaffold (typically made of TiO$_2$) to enhance the $J_{sc}$, most of the planar cells demonstrated in the last few years (see e.g. supporting information of ref \\cite{Xu2020}) exhibit performances approaching the limits, up to about 90\\% of the maximum achievable $J_{sc}$, mostly by simply taking care of AR.\n\t\n\tHowever, refined the LM still enables additional improvements of the performances towards the limits. For example, reduced optical losses thanks to LT especially at the band edge helps to gain the last missing $mA\/cm^2$, as detailed in the following. In addition, the $V_{oc}$ could be improved thanks to an accurate treatment of the photocarrier recombination mechanisms and the associated rates.\n\t\n\tA few review papers discussed on various demonstrations of LM for PPSC at the time of their writing \\cite{Zhang2018,Zhang2019}. In this paper, we focus on PE mainly in corrugated dielectric, without any additional metallic material inside or at the vicinity of the perovskite material. Indeed, if using metal can induce LT due to plasmonics effect, other phenomena such parasitic absorption can frequently balance this effect \\cite{SiavashMoakhar2020}.\n\t\n\tAfter a brief presentation of the main PPSC architectures and material choices that impact LM, and summarizing the PE, we propose to further analyze various published structures relying on a wave optics approach. Thus, by unraveling the various mechanisms involved, we are able to compare the various proposed strategies to deduce guidelines for further optimization. These guidelines are finally illustrated thanks to simulations of the optical properties of a few case studies.\n\t\n\t\\section{Key PPSC materials and architectures}\n\t\n\tThe LM in the PPSC directly derive from their architecture, i.e. a stack of layers of various materials and thus optical indices, each layer having a thickness of the order of a few tenth of wavelengths, and with possible structurations from small to large scales compared to the wavelength. The main properties of the perovskite materials impacting LM have already been discussed. However, comparisons will appear difficult and need to be treated carefully, due to discrepancies between the structures investigated, as detailed hereafter. \n\t\n\t\\subsection{Patterning of the PPSC}\n\t\\label{possible_architecture}\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\includegraphics[width=\\linewidth]{Fig\/Architecture.png}\n\t\t\\caption{Various typical architectures of complete PPSC obtained using nanofabrication. a. Almost flat (possible nanoscale roughness) b. patterned perovskite using \"bottom-up\" approach, i.e. structuration of the ETL\/HTL, then overcoating with perovskite; c. patterned perovskite using \"top down\" approach, i.e. perovskite coating then structuration and overcoating. d. Flat perovskite but patterned surrounding layers and e. Patterned substrate coated with conformal stack. All envisaged patterned can be either periodic or aperiodic.}\n\t\t\\label{fig:Architecture}\n\t\\end{figure}\n\t\n\tLet us present an overview of the main patterning processes and deriving architectures that have been proposed in the literature.\n\tDue to the low diffusion lengths in the first fabricated metal-halide hybrid perovskites, first PPSC used mesoporous architectures to limit recombination. Indeed, TiO$_2$ scaffolds were infiltrated with the perovskite material, enabling a more efficient collection of the carriers, and, in some cases, LM \\cite{Lee2015,Lin2016,Ramos2016}. Nevertheless, as already mentioned in the introduction, most promising approaches relies nowadays in planar architectures. \n\t\n\tIn the following, layers will be considered as almost flat provided their roughness is at a sub-wavelength, nanometre scale, especially thanks to a good crystallinity in the case of the perovskite. \n\t\n\tHowever, a 2D structuration can be introduced in the PPSC stack, notably for single junction PPSC. In this frame, the perovskite layer itself can be one of the patterned layers, or only surrounding layers are structured, and the perovskite can be considered as flat. \n\tThe various envisaged technical solutions within each of the two strategies are sketched in Figure \\ref{fig:Architecture} b-e.\n\n\tPatterning of the perovskite layer can be achieved by various approaches. Such a structuration can first be generated by depositing this active layer on top of patterned layers, for example the ETL\/HTL standing underneath (\"bottom-up\" like approach, Figure \\ref{fig:Architecture} b.); the pattern can be either random \\cite{Xu2020,Huang2016,Pascoe2016}, or periodic \\cite{Paetzold2015,Abdelraouf2016,Wang2018,Kim2019}. Alternatively, the perovskite layer itself can be directly patterned \\textit{after its deposition} (\"top-down\" like approach, Figure \\ref{fig:Architecture} c.), again either in a random \\cite{Wang2018a,Wang2018b} or periodic way \\cite{Schmager2019,Schmager2019b}. \n\t\n\tAnother approach is to realize flat perovskite layers and to corrugate other adjacent layers (Figure \\ref{fig:Architecture} d.), such as the encapsulation layer \\cite{Nguyen2016,Li2019,kim_light_2021}, transport layers \\cite{kang2016b,Qarony2018,wang_coordinating_2021}, possibly associated with the metal back contact \\cite{Wei2017,Zhang2018b}, or only the front contacts \\cite{Hossain2020,Tockhorn2020}, and finally a glass substrate covering layer \\cite{Tavakoli2015,Dudem2016,Jost2017,Peer2017,doi:10.1002\/adom.201900018,Thangavel2020}.\n\t\n\tFinally, the front substrate or the back contact layer can be patterned within a broad range of sizes, from the hundreds of nanometers to almost the millimeter; then the other layers of the stack are conformally deposited, leading to a fully structured cell \\cite{Qarony2018,Wang2016b,Du2016,Shi2017,Soldera2018,Tooghi2020a,Wang2019,Soldera2020,Haque2020} (Figure \\ref{fig:Architecture} e.). It is noticeable that among these numerous papers, only two \\cite{Tockhorn2020,Soldera2020} are dealing with experimental results. Indeed, spin coating of perovskite on a patterned substrate remains challenging. The former mesoporous architecture has recently been revisited under the form of opal like structuring \\cite{Lobet2020}.\n\t\n\t\\subsection{Influence of the choice of Materials}\n\t\n\t\\subsubsection{Perovskite medium}\n\tThe choice of the absorbing material for PPSC results from multiple constraints. It should first exhibit an optimal band gap energy, in the range 1.1 - 1.4 $eV$\\cite{shockley1961detailed}, as well as optimal electrical properties and stability. Then, various deposition processes can be envisaged for a given metal halide perovskite. It may mainly lead to various grain sizes of the material during the crystallization process. For sizes of the order of the wavelength, this can affect LM. \n\t\n\tMethylammonium lead iodide perovskite material (CH$_3$NH$_3$PbI$_3$, MAPI) has been widely considered in the early stages of PPSC development, thanks to its rather simple chemical composition, and despite $E_g$ slightly above the optimal range. \n\tIt is even noticeable that different $E_g$ are reported for MAPI typically from 1.55 to 1.6 $eV$ (see e.g. \\cite{jiang2015}). This implies a more than 7\\% uncertainty on the maximum achievable $J_{sc}$, that is already of the order of the achievable gain using LM as reported in the following. Thus, the quantitative comparison between different studies, even implying the same material within the meaning of the chemical composition, is rather delicate. Differences of aging of the perovskite could also impact the comparison. Finally, various dispersion models can be used to fit the dielectric function \\cite{Li2020b}, leading to differences that also impact the optical indices that are used for simulations. \n\t\n\tFor all these reasons, it has to be emphasized that, in the following, only the relative impact of LM could be put into perspective, but the various given performances cannot be accurately compared. Finally, if MAPI can still be used as a typical case study, other materials have been developed in the past few years. They are more stable and exhibit a slightly lower $E_g$, mainly thanks to the substitution of MA by FA (Formamidinium, (NH$_2$)$_2$CH), possibly with Cs, as can be seen later.\n\t\n\t\\subsubsection{Transport and contact layer materials}\n\tFor the transport layers, mainly polymer materials are used, as well as thin layers of inorganic materials such as TiO$_2$. It is noticeable than materials such as those based on C$_{60}$ fullerene, acting as ETL, exhibit significant absorption at the shortest wavelengths of the solar spectrum, whereas polymers used as HTL mainly absorb at larger wavelengths, close to and above the band gap of the perovskite. Then, there is often a compromise to be found between suitable band levels, high electrical conductivity, and barrier to possible migrations, and low parasitic absorption, even if the thicknesses of these transport layers remain limited.\n\t\n\t\\section{Photonic engineering}\n\t\\label{Photonic concepts description}\n\t\n\t\\begin{figure}[b]\n\t\t\\begin{center}\n\t\t\t\\includegraphics[width=\\linewidth]{Fig\/LightManagement.png}\n\t\t\t\\caption{Main Light Management strategies a. Without peculiar LM, reflectance can be important. b. Using a flat structure, or a patterned that can be considered as homogeneous (e.g. given its subwavelength dimensions), LM can lead to a rather broadband AR effect as well as to Fabry Perot modes that can enhance the absorption. c. Using a random texturation, LM can results from scattering, possibly Lambertian, that can also enhance the absorption. d. Using a periodic or strongly correlated patterned, in addition to AR and Fabry Perot effects, some Light Trapping can occur, drastically enhancing the absorption especially at band edge of perovskite.}\n\t\t\t\\label{fig:LightManagement}\n\t\t\\end{center}\n\t\\end{figure}\n\t\n\tLet us now summarize the various kinds of PE, mostly at the wavelength scale, that can be envisaged to improve LM in both single-junction and 2T multi-junction PPSC, and their impact on geometrical requirements in the PPSC. They are sketched in Figure \\ref{fig:LightManagement}. Most of the strategies derive from those already developed for other kinds of direct or even indirect band gap material used in thin film \\cite{massiot2020progress}.\n\t\n\tIn this frame, LM results from various light-structure interaction mechanisms. These mechanisms occur mainly either in the vertical direction (i.e. orthogonal to the stack) or in the directions of the patterns. Depending on the scale of the structure with respect to the wavelength, three main models can be used: first, at sub-wavelength scales, an effective medium; then, at wavelength scale, wave optics, or, at larger scales, geometrical optics.\n\t\n\t\\subsection{Photonic engineering in multilayer unpatterned stack}\n\t\\label{photonic concepts multilayer}\n\tIt is well known that geometrical optics are mainly suitable when the dimensions are far larger than the wavelength. Thus, it cannot accurately describe the interference effects that occur within the thickness of the numerous thin films that compose the cell. In contrast, the optical indices of medium textured at subwavelength scales can be homogenized in a approximated way using Effective Medium Theories such as the Bruggeman model. Then, descriptions involving wave optics are the most rigorous, and wave optics mechanisms might also be the most promising ones.\n\t\n\tLet us first briefly recall the two basic mechanisms able to enhance light harvesting in a flat stack, i.e. without the assistance of any patterning. In a rather high index, highly absorbing material, a properly designed stack enables to drastically reduce the impedance mismatch with the surrounding environment, whereas in a finite thickness absorbing layer, especially with a low absorption, it is rather a Fabry Perot like approach that can be used, leading to highly enhanced but narrow band absorption. \n\t\n\tWithin a typical PPSC stack, having a metallic contact that can act has a back mirror, these two approaches can be invoked. Schematically, at short wavelength, the large extinction coefficient of the perovskite enables the single pass absorption in the perovskite film, but AR effect is required. At larger wavelengths close to band gap, where the absorption drops, multiple passes are required, the second approach is well suited (Figure \\ref{fig:LightManagement} b.). \n\t\n\tHowever, the exact nature of the layers, and thus their optical indices and thicknesses are restricted by other constraints such as energy band levels, electrical resistance, risk of shunting, or even diffusion barriers for some species. Consequently, parasitic optical absorption has to be carefully studied. Moreover, broadband enhancement of the absorption under normal incidence is unlikely to be obtained using such simple architectures. \n\t\n\t\\subsection{Photonic engineering in multilayer, patterned stack}\n\t\\label{photonic_concepts_multilayer_patterned}\n\tIn addition to the continuum of propagative waves, light can be confined into the discrete set of in-plane, transverse guided modes existing in the stack. It is noticeable that the in-plane wave vector of any of these guided modes is larger than those of the free space modes. Thus these modes cannot be simply coupled from the free space in a perfectly flat stack, without any periodic or aperiodic corrugation. Then, the various kinds of in-plane structurations - having dimensions from the wavelength scale up to two orders larger - all induce diffraction. Whereas the impinging light lies around the normal incidence and has thus negligible in-plane wave vector $k_{in\\parallel}$, the light inside the device is diffracted. Indeed, the electromagnetic field can be expanded over a set of plane waves thanks to the in-plane spatial frequencies induced in the medium by the structuration. The reflected waves also experience diffraction accordingly. Considering the various kinds of structurations and thus of the resulting spatial frequencies, three main cases can be envisaged:\n\t\n\t\\begin{enumerate}[label=\\roman*)]\n\t\t\\item A random structuration tends to induce isotropic diffraction, so Lambertian scattering. Moreover, this scattering does not depend on the wavelength within the absorbed spectral range. It can typically lead to broadband AR effect (Figure \\ref{fig:LightManagement} c.). However the absorption enhancement remains limited. Indeed, given the already large absorption (except at the band edge) of the perovskite, and the back reflection induced by the metallic contact, it will be at most of the order of $4n^2$ with an index lower than other materials, in particular inorganic ones \\cite{Yablonovitch1982}.\n\t\t\\item A correlated disordered structuration that enhances a specific set of spatial frequencies, leads to a spectral dependent absorption enhancement. A careful choice of the sizes of the patterns is required to enhance the absorption in the desired spectral range \\cite{Vynck2012nmat}.\n\t\t\\item As a particular case from the previous case, periodic, so-called photonic crystal (PC) structurations can be envisaged. Most of them consists in a square or triangular lattice a simple pattern such as pillars or holes with a vertical profile, or smoother pyramidal, or even parabolic profiles. They mainly lead to discrete modal properties. A few complex patterns arranged in a periodic way have been explored \\cite{martins2013deterministic,Oskooi2014,Bozzola2014,ding2016design,Li2020}, enabling an absorption enhancement in a targeted spectral range thanks to a larger local density of modes. \n\t\\end{enumerate}\n\t\n\t\n\tIn any case, the diffraction efficiency, i.e. the amount of light that is effectively diffracted, and that is thus intended to be absorbed, strongly depends on the pattern of the structuration, including the optical index contrast, as well as on the diffraction order $p$. Whereas, as already mentioned, for scattering, the diffraction efficiency hardly depends on the direction, for diffraction by periodic structures, it is generally larger for the first diffraction order.\n\t\n\tIn this frame, it is noticeable that a LT phenomenon rigorously occurs when the impinging light is coupled thanks to, at least, strongly correlated structure, ideally periodic patterns, having respectively a correlation length or a period $\\Lambda$, within at least one guided mode of the stack (Figure \\ref{fig:LightManagement} d.). According to a perturbation approach \\cite{CamarilloAbad2020}, in a 1D case for sake of illustration without losing generality, a phase matching condition can be written under the form:\n\t\n\t\\begin{equation}\n\t\t\\beta_m=k_{in\\parallel}+p \\frac{2\\pi}{\\Lambda}\n\t\t\\label{QGM}\n\t\\end{equation}\n\tWhere $\\beta_m$ is the in-plane wave number of the $m^{th}$ guided mode of the stack. Due to the time reversal symmetry, the light coupled into the guided mode, if not fully absorbed, can be decoupled after a certain length. These modes are thus so-called \"quasi-guided mode\" (QGM).\n\t\n\tSuch modes can drastically enhance the light path inside the waveguide layer. Thus, if the in-coupled QGM is mainly confined in the perovskite layer rather than in the surrounding layers, the useful absorption is enhanced. Moreover, it also depends on the absorption; more precisely, the absorption is optimized at critical coupling, i.e. when an equilibrium is reached between diffraction efficiency and absorption. Consequently, the spectral bandwidth of the LT is set since the quality factor of the mode at the optimal absorption is of the order of $n\/2\\kappa$, $n,\\kappa$ being respectively the real and imaginary parts of the complex optical index \\cite{Park2009}.\n\t\n\tSuch a resonant LT appears promising to enhance the low absorption close to the band gap of the perovskite, provided it is not reduced elsewhere at shorter wavelengths. More precisely, within the width of the resonance, the absorption can be significantly larger than the previously mentioned broadband limit \\cite{Yu2010}. It could also be used in tandem PPSC mainly to optimize the $J_{sc}$, especially provided LT occurs in a guided mode specifically confined in one of the perovskite layers. As already discussed, for the other spatial frequencies that do not lead to LT, patterning can still result in a rather broadband AR effect, in addition to the one resulting from the design of the stack. Further analysis of all these effects can be found elsewhere \\cite{Zanotto2010,Brongersma2014}. \n\t\n\tIn addition to these PE approaches that contribute at first order to enhance the absorption and so the $J_{sc}$, other strategies could be used to enhance the $V_{oc}$, using a PR mechanism. In a simplified way, PR is supposed to induce at the $V_{oc}$ operating point a high density of photons into the absorbing materials, and so to give a chance to electrons-holes pairs to recombine radiatively rather than in a non radiative way. Typically, this requires at first order to inhibit the radiation, and consequently the absorption, in the luminescence spectral domain of the semiconductor. Thus, enhancing simultaneously the $J_{sc}$ using LT and the $V_{oc}$ using PR is a compromise \\cite{Sandhu2013}.\n\t\n\tIn any case, it has to be emphasized that all these effects should ideally target the perovskite layer only and not enhance the parasitic absorption in the other layers.\n\t\n\t\\subsection{Main geometrical requirements on patterns for Light Trapping}\n\t\n\tAs mentioned previously, 2D in-plane structurations are able to diffract the impinging sunlight under a quasi-normal incidence into the large number of modes of the PPSC, possibly including the guided modes.\n\tIndeed, the architectures of both single and multi-junction PPSC reviewed in the section \\ref{possible_architecture} can be schematized as one or several sub-micron thick high index perovskite layers lying between two lower indices ETL and HTL and possibly also lower index layers in the multi-junction case. The whole is lying on medium index TCO on a low index glass substrate and coated on top with metal.\n\tSuch structures exhibit several guided modes in both TE and TM polarization states.\n\tLet us first make the assumption of weak corrugation that doesn't change significantly the effective indices of the stack of active layers lying on a substrate. The largest effective index modes - typically 2.2 - are mainly confined in the perovskite, whereas the other modes, whose effective indices are below 1.9, can be confined in the whole stack of layers.\n\tIn this frame, for normal incident light ($k_{in\/\/}=0$) and for the highest efficient coupling at order $p=1$, equation (\\ref{QGM}) can be rewritten under the form:\n\t\\begin{equation}\n\t\t\\beta_m=\\frac{2\\pi N_{eff,m}}{\\lambda}=\\frac{2\\pi}{\\Lambda}\n\t\t\\label{phase_matching_equation}\n\t\\end{equation}\n\twhere $\\Lambda$ is the period, and could even be a characteristic length in correlated patterns. This 1D case can easily be extended to 2D patterns. \n\t\n\tThus, to be able to efficiently couple into the fundamental guided mode, which is mainly confined into the perovskite, at wavelengths corresponding to its band edge, e.g. 750 $nm$, $\\Lambda$ should be around 340 $nm$, whereas it should be larger than 400 $nm$ for higher order guided modes. This results in a pseudo-guided mode, as discussed at the end of section \\ref{photonic_concepts_multilayer_patterned}. At shorter wavelengths, such structurations can also couple light into the various low effective index modes (including Fabry Perot like modes) of the stack, that may also result in a broadband AR effect. \n\tThis typically implies that all the micron scale or even larger patterns in any of the layers of the stack are rather unlikely to couple the impinging light into guided modes in the low absorption domain, where such a coupling is of main interest. Such patterns only induce coupling into the Fabry Perot like modes of the stack and\/or diffraction at high orders with a limited efficiency.\n\t\n\t\\section{Examples of reported light management strategies in PPSC}\n\t\n\tWe report here on some studies to illustrate concrete LM strategies. For any case, the chosen perovskite material and its thickness are of primary importance. First of all, planar PPSC as envisaged in the section \\ref{possible_architecture} for their architecture and in section \\ref{photonic concepts multilayer} for the corresponding PE are discussed, putting mainly into evidence the importance of the thicknesses on LM. Then, results on in-plane patterned PPSC are reviewed, with focuses on the involved PE.\n\t\n\t\\subsection{Optimization of the thicknesses in planar PPSC}\n\t\n\tMost studies related to thickness optimization only focus on that of the perovskite layer, and report mainly on the derived useful absorption, as detailed just below. However, the whole stack has been considered in a few papers, with a view to investigate the parasitic absorption, as discussed later.\n\t\n\t\\subsubsection{Choice of the perovskite thickness in single junction PPSC}\n\t\n\tAs already mentioned, the optimal thickness of the sole perovskite layer is chosen as a compromise between maximizing optical absorption and keeping low bulk recombination of the carriers. In addition to the analysis of a few seminal cases of single junction PPSC, we present a synthesis of several studies, mainly experimental, that report on the effect of the perovskite layer thickness of planar single junction PPSC, first and mainly made of MAPI, then of other lead-halide perovskites. The detailed performances of the representative cells are summarized in Table \\ref{tableau 1} and, when available, the EQE are plotted in Figure \\ref{fig:EQE_tableau_1} in a spectral range limited from 400 to 800 $nm$ for sake of simple comparison.\\\\\n\t\n\t\\paragraph{MAPI}\n\t\n\tOne of the first high efficiency planar PPSC \\cite{Liu2013} which is made of an about 300 $nm$ thick MAPICl layer exhibited a $J_{sc}$ of 21.5 $mA.cm^{-2}$. In this seminal paper, M. Liu \\textit{et al.} already mentioned the possibility of an optimum thickness due to the balance between absorption and recombination. It was shown later by Y. Da \\textit{et al.} \\cite{Da2018} that this cell suffered from optical losses that represented 11.3\\% of the energy losses, and especially more than 20\\% of the achievable $J_{sc}$. This was mainly related to reflectance and parasitic absorption in the FTO. This thus emphasizes the importance of minimizing these effects by a careful design of the stack.\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\includegraphics[width=\\linewidth]{Fig\/T1_final.png}\n\t\t\\caption{EQE of various PPSC also reported in Table \\ref{tableau 1} \\cite{Liu2014b,Momblona2014,Liu2014,Liu2019b,Correa-Baena2016,jeong_pseudo-halide_2021,Ball2015,Lin2015}.}\n\t\t\\label{fig:EQE_tableau_1}\n\t\\end{figure}\n\t\n\tThen, the first high-efficiency PPSC, solution processed at room temperature, proposed by D. Liu \\textit{et al.} \\cite{Liu2014b} was made of an about 300 $nm$ thick MAPI layer. A $J_{sc}$ of 20.4 $mA.cm^{-2}$ was obtained, so about 5 $mA.cm^{-2}$ below the maximum achievable $J_{sc}$ given a typical $E_g$ of MAPI. The EQE (see Figure \\ref{fig:EQE_tableau_1}) was rather flat and remained lower than 0.8. The deficit could be due to both optical losses (too low absorption in the perovskite) and electrical losses (too high recombination). \n\t\n\tC. Momblona \\textit{et al.} \\cite{Momblona2014} conducted an experimental study on PPSC made of MAPI deposited by thermal evaporation, especially by increasing its thickness from 160 to 900 $nm$. If the $J_{sc}$ increased rather monotonously with the perovskite layer thickness, it never exceeded 20.4 $mA.cm^{-2}$. The main parameter that decreased with increasing perovskite layer thickness was the fill factor. As a result, the cell owning optimal performance was obtained for a perovskite layer thickness around 300 $nm$. More precisely, whereas the $J_{sc}$ slightly increases, both the $V_{oc}$ and the $FF$ significantly decreased at thicknesses larger than 300 $nm$. At these early ages of PPSC, the corresponding optimal PCE was 12.7\\%, with a $J_{sc}$ of 18.8 $mA.cm^{-2}$. Then, leading further investigations on the $J_{sc}$ deficit, it could be derived from the EQE (see Figure \\ref{fig:EQE_tableau_1}) that optical losses in almost 300 $nm$ thick perovskite were important at both short wavelengths and from 600 to 700 $nm$, compared to the 900 $nm$ thick perovskite layer that fully absorbs. The enhanced absorption in the range 700 to 750 $nm$ is likely due to a cavity effect. Thus, even if such a thickness of about 300 $nm$ appears to be optimal under the PCE point of view, it remains too thin to avoid optical losses. In addition, even with such thin absorbing layers, such cells still suffers from electrical losses. \n\t\n\tIn a similar way, D. Liu \\textit{et al.} \\cite{Liu2014} also then studied PPSC consisting in a ITO\/ZnO\/MAPI\/P3HT\/Ag stack, in which MAPI was obtained using thermal evaporation or solution processing. Again, in spite of a larger absorption and limited short-circuit for thicker films, a limited diffusion length led to an optimum thickness of the MAPI layer of about 330 $nm$ for the solution processed PPSC. It is noticeable that the corresponding EQE (see Figure \\ref{fig:EQE_tableau_1}) was rather low and flat. This could be related to scattering due to sub-micron crystallinity that mitigates cavity effects at larger wavelengths.\n\t\n\tMore recently, Y. Liu \\textit{et al.} \\cite{Liu2019b}, still using MAPI, similarly concluded that there were more power loss in PPSC when the thickness of the MAPI layer was either less or more than about 300 $nm$. They noticed that the grain size increased with the thickness, in favor of a larger thickness. However, it was noticeable that, at wavelengths larger than 650 $nm$, the EQE of the 303 $nm$ thick MAPI PPSC (see Figure \\ref{fig:EQE_tableau_1}) was lower than the one of the 564 $nm$ thick MAPI PPSC. This is related to too low absorption in this spectral range. \\\\\n\t\n\t\n\t\\paragraph{Other lead-halide perovskites}\n\t\n\tAs an alternative, J.P. Correa-Baena \\textit{et al.} \\cite{Correa-Baena2016} used multi-cations perovskites materials; these were recently under the spotlight, especially considering their better stability \\cite{jeon2015compositional}. Using FA$_{0.83}$MA$_{0.17}$Pb(I$_{0.83}$Br$_{0.17}$)$_3$ together with mesoporous TiO$_2$, the thickness that maximises the PCE was found to be at least 480 $nm$. The corresponding $J_{sc}$ was almost 24.4 $mA.cm^{-2}$. Considering the band gap of this material, reported to be about 1.63 eV \\cite{jacobsson2016exploration}, this is already the maximum achievable value. The EQE (see Figure \\ref{fig:EQE_tableau_1}) is noticeably high (also thanks to the an IQE of almost 100\\%) and flat at large wavelength. This could be related to the mesoporous TiO$_2$ that induces some scattering effect. \n\t\n\tUnlike the previous papers, M. Rai \\textit{et al.} \\cite{Rai2020} focused on the $V_{oc}$ deficit related to non-radiative recombination as a function of the thickness, expressed in terms of molar concentration of the precursor solution. This time, the studied perovskite was Cs$_{0.2}$FA$_{0.8}$Pb(I$_{0.85}$Br$_{0.15}$)$_3$, whose band gap has been found to be 1.62 $eV$, so close to the one of MAPI. They noticed as well that the $V_{oc}$ decreases with the thickness, whereas the $J_{sc}$ increases. As for the previously mentioned paper, the grain size increases with the thickness; the molar concentration of the precursor solution that maximizes PCE correspond to thickness of the perovskite layer of about 400 $nm$, so 30\\% larger than the one usually obtained using MAPI. This might be due to a larger diffusion length of the photocarriers for this perovskite.\n\t\n\tK.B. Nine \\textit{et al.} \\cite{nine_performance_2020} simulated optically and electrically the effect of the thickness of several FACsPI layers, leading to a larger optimal thickness, of almost 600 $nm$, so twice the thickness usually found using MAPI. Indeed, better electrical properties of this perovskite material, especially carrier mobility, allow such a thicker layer. \\\\\n\t\n\t\\paragraph{Synthesis}\n\t\n\t\n\t\n\t\\begin{table}[!h]\n\t\t\\begin{tabular}{@{}lllllll@{}}\n\t\t\t\\hline\n\t\t\tMaterial & Thickness ($nm$) & $J_{sc} (mA.cm^{-2})$ & $V_{oc} (V)$ & $FF$ & PCE (\\%) & Ref \\\\\n\t\t\t\\hline\n\t\t\tMAPICl & 330 & 21.5 & 1.07 & 0.67 & 15.4 & \\cite{Liu2013} \\\\ \n\t\t\tMAPI & 300 & 20.4 & 1.03 & 0.749 & 15.7 & \\cite{Liu2014b} \\\\ \n\t\t\tMAPI & 285 & 18.8 & 1.07 & 0.63 & 12.7 & \\cite{Momblona2014} \\\\\n\t\t\tMAPI (solution process) & 330 & 17 & 0.94 & 0.62 & 11.8 & \\cite{Liu2014} \\\\\n\t\t\tMAPI & 303 & 21.3 & 1.07 & 0.715 & 18.4 & \\cite{Liu2019b} \\\\\n\t\t\tFAMAPBrI & 480 & 24 & 1.14 & 0.75 & 20.8 & \\cite{Correa-Baena2016} \\\\\n\t\t\tFAPI & 600 & 26.35 & 1.189 & 0.817 & 25.59 & \\cite{jeong_pseudo-halide_2021}\\\\\n\t\t\tMAPI & 492 & 21.56 & nr & nr & nr & \\cite{Ball2015}\\\\\n\t\t\tMAPI & 350 & 21.9 & 1.05 & 0.72 & 16.5 & \\cite{Lin2015} \\\\ \n\t\t\t\\hline\n\t\t\\end{tabular}\n\t\t\\caption{Experimental performances of planar PPSC with optimized perovskite thickness (nr: not reported)}\n\t\t\\label{tableau 1}\n\t\\end{table}\n\t\n\tIt is noticeable that these studies tend to an optimal thickness of about 300 $nm$ when using MAPI. This thickness could lead to state-of-the-art PCE of J. Li \\textit{et al.} \\cite{LI2018331}, with noticeably high $J_{sc}$ of 24.1 $mA.cm^{-2}$, mainly thanks to an ETL including graphdiyne, which improves electrical properties. Then, FAPI is the perovskite used for the best up-to-date single junction PPSC \\cite{jeong_pseudo-halide_2021} which performance were mentioned in the introduction, with a thickness of about 600 $nm$, even if, among numerous refinements of the architecture, the rough, about 50 $nm$ thick TiO$_2$ layer already may help to trap the light. \n\t\n\tIt appears that choosing a thickness smaller than the one maximizing the $J_{sc}$ could help to keep low resistance, so high FF and PCE. Then, with such a $J_{sc}$ around 90\\% of the maximum achievable $J_{sc}$, LM can optimize the absorption in the perovskite, especially at photon energies close to the band gap, where the absorption starts to decrease.\n\t\n\tIn any case, it is of high interest to understand the reasons why large variations of the internal perovskite absorption spectrum have been obtained for similar thicknesses of the same perovskite material (MAPI), as can be noticed through the cases 2 to 5 of Table \\ref{tableau 1} and the corresponding EQE plotted in Figure \\ref{fig:EQE_tableau_1}. They might be due to differences in the surroundings layers (thicknesses, indices) or in the perovskite itself, like microcrystallinity that can induce scattering. These phenomena will be described in the following, starting with the analysis of the effect of the surroundings layers in a unpatterned stack. \n\t\n\t\\subsubsection{Single junction unpatterned PPSC stack analysis}\n\tIf there can be an optimal thickness for the perovskite layer, the effect of the other layers, especially on the absorption and reflectance, should also be analyzed. Here, we review a selected set of publications focusing on this issue.\n\t\n\tJ.M. Ball \\textit{et al.} \\cite{Ball2015} studied planar PPSC using an optical model based on the transfer-matrix formalism with experimentally determined complex refractive index data. They focused on a typical stack made of FTO\/TiO$_2$\/MAPI\/SPIRO-OMeTAD\/Au. Under the assumption of $E_g = 1.56 eV$, the detailed analysis revealed that for a calculated $J_{sc}$ of 21.56 $mA.cm^{-2}$, parasitic absorption induces a lack of about 1.72 $mA.cm^{-2}$, and reflection losses corresponding to a $J_{sc}$ decrease of 1.36 $mA.cm^{-2}$, so a total of more than 10\\% of the collected current. On the other hand, the IQE losses can be estimated to another 10\\%. Moreover, most of the optical losses take place in the 420 $nm$ thick FTO. This underlines the importance of taking care of all the layers of the stack.\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/lin_cavite.png}\n\t\t\t\\caption{Optical-field distribution for four wavelengths: for $\\lambda < 500 nm$ the optical-field distribution follows the Beer\u2013Lambert law and no optical field reaches to the back electrode as a result of the high absorption coefficient. In such cases the absorption is saturated and no optical interference occurs. For $\\lambda > 500 nm$ the optical field is governed by low-finesse cavity interference.}\n\t\t\\end{subfigure}\n\t\t\\hfill\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]\n\t\t\t{Fig\/lin_eqe.png}\n\t\t\t\\caption{EQE spectra of devices with different MAPI-layer thicknesses. For $\\lambda < 500 nm$ there is minimal influence of the film thickness, but for $\\lambda < 500 nm$ the EQE is strongly thickness dependent because of the optical interference (Fabry Perot like modes)}\n\t\t\\end{subfigure}\n\t\t\\caption{PPSC properties as a function of the MAPI layer thickness \\cite{Lin2015}. Reproduced with permission}\n\t\t\\label{fig:lin eqe}\n\t\\end{figure}\n\t\n\tQ. Lin \\textit{et al.} \\cite{Lin2015} deeply investigated the properties of PPSC made of MAPI. Thanks to their measurement of the optical indices of the MAPI and of the other materials, they simulated the properties of PPSC, assuming an IQE of 100\\%. They identified the two absorption regimes of a flat PPSC, previously discussed in section \\ref{photonic concepts multilayer}, namely the single pass and the cavity regime. Thus, thanks to optical cavity effects at wavelengths larger than 500 $nm$, the noticeably high EQE of their cell (see Figure \\ref{fig:EQE_tableau_1}) appeared to be optimized for a thickness of about 370 $nm$ (see Figure \\ref{fig:lin eqe}). The experimental data noticeably confirmed the simulation study, since a cell having a 350 $nm$ thick MAPI layer led to the best properties, $J_{sc}=21.9 mA.cm^{-2}$, $V_{oc}=1.05 V$, $FF=0.72$ and $PCE=16.5\\%$. Given the resulting limited thickness of the MAPI, the obtained $J_{sc}$ was 87\\% of the maximum achievable value. These remarkable results were also obtained thanks to the optimization of the p and n type interlayers that were used to optimize the heterojunctions. It was especially shown that the PCDTBT p type interlayer has to be as thin as possible ( $\\sim 5 nm$), because of its absorption, even if it enabled a nice crystallinity of the MAPI. Otherwise, given the almost 100\\% IQE, most of the 4 to 5 $mA.cm^{-2}$ decrease of the photocurrent (depending on the exact value of the band gap energy of the considered MAPI) corresponds to optical losses, which could be either parasitic absorption or reflectance.\n\t\n\tM. Van Eerden \\textit{et al.} \\cite{VanEerden2017} analyzed the optical losses of 350 $nm$ thick MAPI layers PPSC. Within the considered cell, most of the losses are related to reflectance (equivalent to 4.2 $mA.cm^{-2}$), whereas the parasitic absorption remains limited to the half, and despite a subwavelength roughness (typically from 10 to 50 nm RMS) at both ITO\/TiO$_2$ and MAPI\/SPIRO-OMeTAD interfaces. \n\t\n\t\\subsubsection{Analysis of unpatterned, up-to-date record 2T tandem PPSC}\n\t\\label{tandem_plan}\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\begin{subfigure}[b]{0.3\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/xiao_tandem_schema.png}\n\t\t\t\\caption{Stack of materials }\n\t\t\t\\label{fig:xiao tandem schema}\n\t\t\\end{subfigure}\n\t\t\\hfill\n\t\t\\begin{subfigure}[b]{0.3\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/xiao_tandem_jsc.png}\n\t\t\t\\caption{Simulated $J_{sc}$ as function of wide-band gap and narrow-band gap perovskite layer thicknesses.}\n\t\t\t\\label{fig:xiao tandem jsc }\n\t\t\\end{subfigure}\n\t\t\\hfill\n\t\t\\begin{subfigure}[b]{0.3\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/xiao_tandem_eqe.png}\n\t\t\t\\caption{EQE of both subcells.}\n\t\t\t\\label{fig:xiao tandem eqe }\n\t\t\\end{subfigure}\n\t\t\\caption{Best up-to-date 2T tandem, all perovskite PPSC \\cite{xiao_all-perovskite_2020}. Reproduced with permission.} \n\t\\end{figure}\n\t\n\tWhen compared to other tandem architectures, the 2T case leads to possibly limited optical losses, but such cells require an equilibrium of the current densities delivered by the two subcells. Among recent results \\cite{wang_prospects_2021}, K. Xiao \\textit{et al.} \\cite{xiao_all-perovskite_2020} have deeply investigated 2T tandem PPSC (see Figure \\ref{fig:xiao tandem schema}) both numerically and experimentally. They have shown using optical simulations that the reachable $J_{sc}$, which is indeed the smallest $J_{sc}$ delivered by each of the subcell, strongly depends on the thicknesses of each perovskite layer (see Figure \\ref{fig:xiao tandem jsc }), provided that thicknesses of the other layers have been set taking into account both electrical and optical properties (low parasitic absorption). Indeed, it appears that the top perovskite has to be thick enough to absorb enough light, but that a too thick layer also absorbs too much light, and finally that the overall $J_{sc}$ is limited by the bottom sub cell. The thickness of the perovskite bottom sub cell has to be large enough, more than 1.1 $\\mu m$.\n\tWhen fabricated on a small surface, such a cell exhibits even a slightly larger $J_{sc}$ than simulated, likely due to the too pessimistic predicted absorption. Even if this result remains a record at the time of its publication, its EQE (see Figure \\ref{fig:xiao tandem eqe }) reveals some non-idealities. Indeed, in addition to the relatively low values of the EQE plateaus, there is an overlap of the absorption domains of the two perovskite materials between 500 and 700 $nm$. This means that some undesirable thermalization still occurs below 700 $nm$ in the bottom sub cell. \n\t\n\t\\subsection{In-plane structuration for light management in single junction PPSC}\n\t\\label{light_management}\n\t\n\tAt this stage, it appears that even the best reported cells exhibit an EQE that can still be improved using PE at the wavelength scale, even if most of them already benefit from the roughness of the microcrystalline perovskite.\n\t\n\tIn this section, we will highlight important criteria that should be met to ensure a fair demonstration of LM. The report on selected studies is organized as a function of the various architectures as envisaged in section \\ref{possible_architecture}.\n\t\n\t\\subsubsection{General criteria for fair LM demonstration}\n\tThe impact of LM on the performance of a solar cell can be demonstrated by comparing a patterned device with an unpatterned reference. In case of superficial structurations or with the addition of specific LM layers, the absorption improvement appears generally obvious compared to the same unpatterned stack, or, even more favorable, to the unpatterned stack without the additional flat LM layer. Moreover, integrating LM layer could lead to a better charge collection, by decreasing the carrier path; the specific LM effect would then be difficult to distinguish. Therefore, great care should be taken in the definition of a reference structure or device. In particular, the volume of perovskite materials should be as close as possible in both structures. Additionally, the net enhancement of EQE, efficiency or even yield value can be accurately estimated only if the unpatterned reference has been optimized first. These conditions should be met in order to discuss LM for PPSC performance optimization. \n\t\n\t\\subsubsection{Periodic patterning for resonant LT}\n\t\\paragraph{Superficial structuration and light management layers}\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/peer_bismwas_MLA_schema.png}\n\t\t\t\\caption{Schematic of the PPSC.}\n\t\t\t\\label{fig:peer bismwas_schema}\n\t\t\\end{subfigure}\n\t\t\\hfill\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/peer_bismwas_absorption.png}\n\t\t\t\\caption{Simulated absorption spectrum for microlens array of period $~700 nm$ and height $800 nm$. The absorption of flat solar cell is overlaid for comparison.}\n\t\t\t\\label{fig:peer bismwas_absorption}\n\t\t\\end{subfigure}\n\t\t\\caption{PPSC with microlens array on air-glass side \\cite{Peer2017}. Reproduced with permission.}\n\t\\end{figure}\n\t\n\tA. Peer \\textit{et al.} \\cite{Peer2017} simulated planar PPSC with a stack including a 400 $nm$ thick MAPI layer. The cell lay on top of a 700 $\\mu m$ thick glass substrate. A micro lens array was then added on the top face of the glass substrate, by imprinting a polymer such as polystyrene (see Figure \\ref{fig:peer bismwas_schema}). Each micro lens of the triangular lattice had a smooth profile close to truncated pyramids. For an aspect ratio period\/height of the micro lenses close to 1, and a period of 700 $nm$, optimized gain of 6.3\\% of the $J_{sc}$ was reported compared to the flat reference, whose EQE noticeably shown dips likely due to cavity effects in the cell (see Figure \\ref{fig:peer bismwas_absorption}). Then, according to the EQE of the cell coupled to the micro lens array, the overall resulting broadband enhancement of the absorption is due at lower wavelength to AR effect, and to LT at the band edge. The optimal period of the pattern is rather large, in agreement with a low effective index of the coupled guided mode. It means that LT occurs in the mode that is hardly guided in the perovskite, but that has a nonnegligible overlap with the pattern. This overlap remains anyway limited, as revealed by the high quality factor of the resonance. Therefore, the absorption reaches almost 1, so is close to critical coupling conditions.\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/wei_2017_adv_Energy_mat_afm.png}\n\t\t\t\\caption{SEM (left) and AFM (right) images of the spin-coated layer (top) of grating patterned layer (middle) and moth-eye patterned layer (bottom).}\n\t\t\t\\label{fig:wei 2017 adv Energy mat afm} \n\t\t\\end{subfigure}\n\t\t\\hfill\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/wei_2017_adv_Energy_mat_EQE2.png}\n\t\t\t\\caption{External quantum efficiency (EQE) spectra of PPSC, and relative enhancement obtained by dividing the spectra of grating and moth-eye patterned devices by that of flat one.}\n\t\t\t\\label{fig:wei 2017 adv Energy mat perf}\n\t\t\\end{subfigure}\n\t\t\\caption{PPSC with bio inspired PCBM HTL \\cite{Wei2017}. Reproduced with permission.}\n\t\\end{figure}\n\t\n\tJ. Wei \\textit{et al.} \\cite{Wei2017} fabricated bioinspired back electrodes by imprinting the HTL made of PCBM before the conformal deposition of the Bphen\/Ag back contact. Patterns were either a periodic sinusoidal 1D grating or a uniform 2D moth-eye structure (see Figure \\ref{fig:wei 2017 adv Energy mat afm}), with a typical pitch of about 600 $nm$. It has then been experimentally shown (see Figure \\ref{fig:wei 2017 adv Energy mat perf}) that the EQE of the 240 $nm$ thick MAPICl PPSC in increased by 10\\% up to 40\\% at the band edge, mainly thanks to the 2D moth eye pattern. The overall $J_{sc}$ increased by 14.3\\%, mainly due to absorption increase, but a lower series resistance was also measured for patterned cells, which led to a slight increase of the IQE. As shown by there FDTD simulations of periodic structures, this is due light diffraction into the guided modes, rather than plasmonic effects. It can be confirmed by Fourier Analysis of the top view of the moth eye pattern that a significant part of the spatial frequencies lies in the optimal range for LT (see Figure SI-\\ref{fig:Supplemental Fourier Analysis Wei}).\n\t\n\t\n\t\\begin{figure}[!h]\n\t\t\\centering\n\t\t\\includegraphics[width=\\linewidth]{Fig\/T2_final.png}\n\t\t\\caption{EQE of the patterned PPSC (solid line) and of their planar references (dashed line) (left) and EQE enhancement ((right) of reported simulated $J_{sc}$ enhancements of Table \\ref{tableau pattern mapi}, mainly resulting from Light Management, for MAPI single junction PPSC, with any kind of in-plane pattern (additional layers, structuration of the perovskite or conformal perovskite) and whatever the main identified photonic concept \\cite{Peer2017,Wei2017,kim_light_2021,Schmager2019,Du2016,Hossain2020,Qarony2018}}\n\t\t\\label{fig:EQE_table_2}\n\t\\end{figure}\n\t\n\t\n\tH. Kim \\textit{et al.} \\cite{kim_light_2021} simulated nanosphere arrays of TiO$_2$ conformally coated with silica on a standard MAPI PPSC stack, with a MAPI thickness limited to 100 $nm$. Provided suitable spacing and nanosphere diameter, the $J_{sc}$ could jump from 14 to 18.7 $mA.cm^{-2}$. Authors invoked the Mie scattering effect of the array to explain the enhancement, but it appears that the FEM simulation has likely been done using periodic boundary conditions, and thus that a LT effect occurs at several wavelengths, such as 705 or 790 $nm$ for those at the band edge of MAPI. This is in line with the larger EQE enhancement close to the band edge, as can be seen in Figure \\ref{fig:EQE_table_2}. It remains that the overall $J_{sc}$ is anyway limited due to the unusually low thickness. \\\\\n\t\n\t\\paragraph{Structuration of the perovskite} \n\t\n\t\\begin{figure}[!h]\n\t\t\\centering\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/schmager_solmat_schema.png}\n\t\t\t\\caption{Layer stack of the simulated patterned PPSC, along with associated layer thicknesses. Three configurations of the active layer are simulated (1) a planar reference, (2) a cylindrical indention into the perovskite layer of variable depth; and (3) a hole geometry which corresponds to the maximum cylindrical indention.}\n\t\t\t\\label{fig:schmager solmat schema}\n\t\t\\end{subfigure}\n\t\t\\hfill\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/schmager_solmat_simul.png}\n\t\t\t\\caption{Absorptance in the perovskite layer of the hole pattern and the 120 $nm$ deep indentation compared to the corresponding planar reference and the theoretical Yablonovitch limit for two different initial perovskite layer thicknesses of (left) 200 $nm$ and (middle) 300 $nm$. $J_{sc}$ for all data are compared (right). The displayed nanophotonic patterns employ a geometrical $ff=0.4$ and a period of 380 $nm$.}\n\t\t\t\\label{fig:schmager solmat simul}\n\t\t\\end{subfigure}\n\t\t\\caption{Various 2D in plane patterns of the MAPI layer in a PPSC \\cite{Schmager2019}. Reproduced with permission}\n\t\\end{figure}\n\t\n\tR. Schmager \\textit{et al.} \\cite{Schmager2019} simulated a classical MAPI PPSC (see Figure \\ref{fig:schmager solmat schema}). Compared to the planar configuration, etching of the MAPI layer was envisaged, thanks to a square lattice of holes, filled with Spiro-OMeTAD, with various etching depths. To avoid any short circuit, an optimized partial etching of 120 $nm$ led to a 5.6\\% increase of the $J_{sc}$, compared to the initially flat 300 $nm$ thick MAPI layer, provided an equivalent volume of perovskite. It results from a LT at the band edge, but without any sharp resonance, as can be observed on the spectral response of other patterns (see Figure \\ref{fig:schmager solmat simul}).\n\t\n\t\\begin{figure}[!h]\n\t\t\\centering\n\t\t\\includegraphics[width=\\linewidth]{Fig\/schmager_solmat_carac.png}\n\t\t\\caption{External quantum efficiency and reflectance spectra of the planar and nanoimprinted perovskite solar cells. The nanoimprinted perovskite layer has a period of 480 $nm$. The nanoimprinted perovskite solar cell shows enhanced absorption and current generation close to the band gap. For energies below the band gap (wavelength larger than 790 $nm$) discrete peaks are visible in the reflectance measurement. \\cite{Schmager2019b}. Reproduced with permission.}\n\t\t\\label{fig:schmager solmat carac}\n\t\\end{figure}\n\t\n\t\n\t\\begin{figure}[!h]\n\t\t\\centering\n\t\t\\includegraphics[width=\\linewidth]{Fig\/T3_final.png}\n\t\t\\caption{EQE of the patterned PPSC (solid line) and of their planar references (dashed line) (left) and EQE enhancement ((right) of Table \\ref{tableau pattern exp}, mainly resulting from Light Management, for single junction PPSC, with any kind of in-plane pattern (additional layers, structuration of the perovskite or conformal perovskite) and whatever the main identified photonic concept \\cite{Schmager2019b,Tockhorn2020,Paetzold2015,Dudem2016,Jost2017,Zhang2018b,Pascoe2016}}\n\t\t\\label{fig:EQE_table_3}\n\t\\end{figure}\n\t\n\tThe same group \\cite{Schmager2019b} also realized nanoimprinted PPSC made of Cs$_{0.1}$(FA$_{0.83}$MA$_{0.17}$)$_{0.9}$Pb(I$_{0.83}$Br$_{0.17}$)$_3$. Their experimental results show a relative improvement of 2\\% of the PCE compared to the planar reference for the complete, gold coated solar cell. This was obtained thanks to an increase of the $J_{sc}$ from 19.1 to 19.4 $mA.cm^{-2}$ and noticeably identical $V_{oc}$ and $FF$.The EQE of the patterned cell was improved at wavelengths larger than 680 $nm$. A coupling to a quasi-guided mode is observed in the perovskite band edge (see Figure \\ref{fig:schmager solmat carac}), leading to a significant EQE enhancement (see Figure \\ref{fig:EQE_table_3}). \\\\\n\t\n\t\\paragraph{Textured substrate}\n\t\n\t\\begin{figure}[!h]\n\t\t\\centering\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/du_john_schema.png}\n\t\t\t\\caption{Three-dimensional perspective and two-dimensional side view of the inverted vertical cone perovskite unit cell.}\n\t\t\t\\label{fig:du john schema}\n\t\t\\end{subfigure}\n\t\t\\hfill\n\t\t\\begin{subfigure}[b]{0.45\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/du_john_simul.png}\n\t\t\t\\caption{Simulated absorption and reflection spectra of the optimized (period $a = 600 nm$, radius $R = 380 nm$) inverted cone PC and planar PPSC. The MAPI thickness of the planar cell is 180 $nm$, corresponding to the \"equivalent bulk thickness\" of MAPI in the inverted-cone PC device.}\n\t\t\t\\label{fig:du john simul}\n\t\t\\end{subfigure}\n\t\t\\caption{MAPI PPSC patterned with inverted cones\\cite{Du2016}. Reproduced with permission.}\n\t\\end{figure}\n\t\n\tQ. G. Du \\textit{et al.} \\cite{Du2016} simulated a strongly corrugated MAPI layer. It was obtained thanks to cones realized in ITO coated glass substrate with an intermediate conformal PEDOT layer lying on ITO (see Figure \\ref{fig:du john schema}). Thanks to optimized period and radius, a 15-17\\% improvement was expected in the PCE compared to the best flat cells of equivalent volume (see Figure \\ref{fig:du john simul}). The optimal period of about 400 $nm$ fits well the required period for LT into the guided modes, given the fact that the effective index of the modes is lower than our examples, due to the strong corrugation of the MAPI filled with low index material. AR and resonant light trapping are thus simultaneously obtained.\n\t\n\t\\subsubsection{Periodic structures for non resonant LM: anti-reflection and scattering}\n\t\n\t\\paragraph{Superficial structuration and light management layers}\n\t\n\tM. I. Hossain \\textit{et al.} \\cite{Hossain2020} proposed to coat the MAPBI like perovskite, having a thickness from 100 to 400 $nm$, with zinc oxide. More precisely, a first thick, lightly doped ZnO ensured the top contact, then it could be covered by texturations patterned either as pyramids or as non-resonant metasurfaces. The period was set to 800 $nm$. Accordingly to the mechanism typically occurring using such a period, no sharp resonance enhances the absorption at the band-edge, only an AR like effect occurs. Both kinds of patterns lead to simulated equivalent $J_{sc}$ enhancements of about 3 $mA.cm^{-2}$ whatever the thickness of the perovskite between 100 and 400 $nm$, so a relative enhancement roughly from 25\\% to 12\\% within the same range, but only 10\\% for the thickness 300 $nm$.\n\t\n\tP. Tockhorn \\textit{et al.} \\cite{Tockhorn2020} simulated and fabricated a 550 $nm$ thick PPSC made of a mixed cation, mixed halide Cs$_{0.05}$(FA$_{0.83}$MA$_{0.17}$)$_{0.95}$Pb(I$_{0.83}$Br$_{0.17}$) perovskite material on various periodically patterned glass substrates, in p-i-n configuration. The top side of the glass substrates was coated with low index, NaF thin film. Compared to the planar reference, patterned structures could exhibit a $J_{sc}$ up to 1 $mA\/cm^2$ larger. This results from a broadband enhancement of the EQE, including at the band edge, mainly attributed to AR effect, as verified by simulations where the volume of perovskite has been kept constant. This is in line with the flat EQE enhancement, as can be seen in Figure \\ref{fig:EQE_table_3} The resulting PCE reaches 19.7\\%, 1\\% absolute above the one of the planar reference.\\\\\n\t\n\t\\paragraph{Structuration of the perovskite} \n\t\n\tU. W. Paetzold \\textit{et al.} \\cite{Paetzold2015} proposed to pattern the front ITO electrode with a square lattice of pillars. The ETL was then corrugated, as well as the about 320 $nm$ thick MAPICl layer that planarized the stack, before the HTL and Al back contact deposition. They noticed increased absorption and EQE as the lattice period decreased, up to the smallest value envisaged, i.e. 500 $nm$. This led to an increase ofthe $J_{sc}$ by 5\\%. Broadband enhancements were observed, at wavelengths shorter than the band edge of the material, as well as a limited LT at the band edge, according to the EQE enhancement plotted in Figure \\ref{fig:EQE_table_3}. This is likely due to the lack of large enough spatial frequencies to couple the quasi-guided modes of the structure, given a period slightly above the optimal range. In addition, there might be a slight change in the volume of perovskite between the patterned cells compared to the flat reference.\\\\\n\t\n\t\\paragraph{Textured substrate}\n\t\n\tW. Qarony \\textit{et al.} \\cite{Qarony2018} calculated the EQE for three various configurations of PPSC using the same volume of perovskite and including moth eye periodical patterns with a period of about 150 $nm$, typically leading to scattering. The fist one had only a pattern at the top air \/ ZnO interface (thus is more comparable to former structures), but the two others considered a conformally patterned stack, either on a patterned Al substrate, or on a patterned NiO\/ITO layer. The patterned Al substrate clearly led to a lower EQE due to additional parasitic absorption. Moreover, a slightly lower $J_{sc}$ was obtained with the conformal stack on the flat Al substrate compared to the top patterned stack. Even this last case exhibits a low EQE enhancement (see Figure \\ref{fig:EQE_table_3}).\n\t\n\t\\subsubsection{Aperiodic patterning for LM}\n\t\n\t\\paragraph{Superficial structuration and light management layers}\n\tB. Dudem \\textit{et al.} \\cite{Dudem2016} proposed a multifunctional inverted micro-structured pyramidal Polydimethylsiloxane (PDMS) AR layer for enhancing the device efficiency, through AR and self cleaning. The MAPI layer was 340 $nm$ thick. The sizes of the pyramid were in the range from 1 to 10 $\\mu m$, therefore too large for efficient diffraction. Compared to flat PDMS, the $J_{sc}$ increases by 0.38 $mA.cm^{-2}$ up to 21.25 $mA.cm^{-2}$, corresponding to a limited AR effect, as confirmed by the flat EQE enhancement (see Figure \\ref{fig:EQE_table_3}).\n\t\n\tRather similarly, M. Jo\\v{s}t \\textit{et al.} \\cite{Jost2017} fabricated a so-called light management foil on the glass substrate of a planar 270 $nm$ thick MAPI cell, which led to a limited EQE enhancement (see Figure \\ref{fig:EQE_table_3}). At first, the thick glass substrate prevents from a strong overlap between any guided mode into the perovskite and the foil. Moreover, whatever the glass thickness, the lack of LT is also related to the far too low spatial frequencies resulting from the texturation. Indeed, thanks to available top view of the foil, a Fourier transform analysis reveals (see Figure SI-\\ref{fig:Supplemental Fourier Analysis Jost}) that most of Fourier's components lie below 7 $\\mu m^{-1}$, whereas the optimum $\\beta_m$ should be in the range 18 - 34 $\\mu m^{-1}$ at first order of diffraction, and even larger for other orders.\n\t\n\tOn the other side of stack, H. Zhang \\textit{et al.} \\cite{Zhang2018b} measured and modelled the impact of the roughness of the back mirror on the back scattering of MAPICl PPSC. The reference was made of a perovskite layer with a significant roughness after crystallization, coated with a thick Spiro layer that planarizes, then a flat gold mirror. With a thinner HTL, the gold mirror replicated the roughness of the perovskite layer leading to light back scattering. The PCE increased from 19.3\\% to 19.8\\%, mainly thanks to the $J_{sc}$ increase from 22.7 to 23.6 $mA.cm^{2}$. It is noticeable that the obtained EQE enhancement is the largest at the band edge. This could be related to the grain size that appears to be in the range 200 - 500 $nm$, so including the optimal range for efficient LT.\\\\\n\t\n\t\\paragraph{Structuration of the perovskite}\n\t\n\tA. R. Pascoe \\textit{et al.} \\cite{Pascoe2016} proposed textured MAPI at the scale of several hundreds of nanometers thanks to gas crystallization. It enhanced the EQE at wavelengths larger than 550 $nm$ (see Figure \\ref{fig:EQE_table_3}), thanks to an induced so-called scattering. Accordingly, the averaged $J_{sc}$ increased from 21.3 for planar references having an about 300 $nm$ thick MAPI layer to 22.1 $mA.cm^{2}$ for patterned samples of comparable volume. As previously, the noticeably large EQE enhancement close to the band edge can be related to the typical grain size of the order of 500 $nm$.\n\t\n\t\\subsubsection{Synthesis}\n\tIn the various previously described studies reporting on $J_{sc}$ enhancements thanks to LM, it can be noticed that most of the possible architectures envisaged in the section \\ref{possible_architecture} have been considered, using all the PE described in section \\ref{Photonic concepts description}. Tables \\ref{tableau pattern mapi} and \\ref{tableau pattern exp} summarize the $J_{sc}$ enhancements reported in the various previously described studies.\n\t\n\tFrom Table \\ref{tableau pattern mapi}, focusing on simulations of periodic patterning of MAPI based single junction PPSC, it comes out that LT can indeed significantly increase the $J_{sc}$ for very thin perovskite layers. The enhancement is more limited at a thickness of about 300 $nm$. It remains that an accurate comparison of the $J_{sc}$ enhancements is not possible, since performance enhancement strongly depends on the choice of the unpatterned reference, and especially its optimization in terms of LM as discussed previously. However, the EQE enhancements (Figure \\ref{fig:EQE_table_2}) can clearly reach larger values thanks to LT than for AR.\n\t\n\tFrom Table \\ref{tableau pattern exp}, focusing on experimental results, using various perovskite materials, the $J_{sc}$ enhancements are of the same order as the simulated ones, again with the same precautions as above. As for the simulated PPSC, the EQE enhancements (Figure \\ref{fig:EQE_table_3}) also reach larger values using patterns at the scale of the wavelength in the material. Moreover, the periodic case lead to the largest enhancement.\n\t\n\t\\begin{table}[h]\n\t\t\\caption{Reported simulated $J_{sc}$ enhancements mainly resulting from Light Management, for MAPI single junction PPSC, with any kind of in-plane pattern (additional layers, structuration of the perovskite or conformal perovskite) and whatever the main identified PE}\n\t\t\\label{tableau pattern mapi}\n\t\t\\begin{tabular}{@{}lllll@{}}\n\t\t\t\\hline\n\t\t\tPerov.thick. ($nm$ ) & $J_{sc}$ enhanc. (\\%) & Pattern. type & Photonic concept & Ref \\\\\n\t\t\t\\hline\n\t\t\t400 & 6.3 & periodic add. patterned layer & Broadband AR and LT & \\cite{Peer2017} \\\\ \n\t\t\t120 & 31.7 & periodic add. patterned layer & Broadband AR and LT & \\cite{kim_light_2021} \\\\ \n\t\t\t300 & 5.6 & periodic struct. of the perovskite & Broadband AR and LT & \\cite{Schmager2019} \\\\\n\t\t\t180 & 17 & periodic conformal perovskite & Broadband AR and LT & \\cite{Du2016} \\\\\n\t\t\t300 & 10 & periodic add. patterned layer & Broadband AR & \\cite{Hossain2020} \\\\\n\t\t\t300 & 10 & periodic conformal perovskite & Broadband AR & \\cite{Qarony2018}\\\\ \n\t\t\t\\hline\n\t\t\\end{tabular}\n\t\\end{table}\n\t\n\t\\begin{table}[!h]\n\t\t\\caption{Reported experimental $J_{sc}$ enhancements mainly resulting from Light Management, for single junction PPSC, with any kind of in-plane pattern, and whatever the main identified PE}\n\t\t\\begin{tabular}{@{}lllllll@{}}\n\t\t\n\t\t\t\\hline\n\t\t\tMaterial & Perov.thick. ($nm$) & $J_{sc}$ enhanc. (\\%) & Pattern. type & Photonic concept & Ref \\\\\n\t\t\t\\hline\n\t\t\tMAPI & 240 & 14.3 & periodic add. patterned layer & Broadband AR and LT & \\cite{Wei2017} \\\\\n\t\t\tCsFAMAPBI & 370 & 2 & periodic struct. of the perovskite & Broadband AR and LT & \\cite{Schmager2019b} \\\\ \n\t\t\tCsFAMAPBI & 550 & 6.3 & periodic, struct. of the ETL & Broadband AR & \\cite{Tockhorn2020} \\\\ \n\t\t\tMAPICl & 320 & 5 & periodic struct. of the perovskite & Broadband AR & \\cite{Paetzold2015} \\\\\n\t\t\tMAPI & 340 & 1.8 & aperiodic add. patterned layer & Broadband AR & \\cite{Dudem2016} \\\\\n\t\t\tMAPI & 270 & 4.8 & aperiodic add. patterned layer & Broadband AR & \\cite{Jost2017} \\\\\n\t\t\tMAPICl & 300 & 4 & aperiodic add. patterned layer & Broadband AR & \\cite{Zhang2018b} \\\\\n\t\t\tMAPI & 300 & 3 & aperiodic substrate corrugation & Broadband AR & \\cite{Pascoe2016}\\\\\n\t\t\t\\hline\n\t\t\\end{tabular} \\label{tableau pattern exp}\n\t\\end{table}\n\t\n\t\n\t\\subsection{LM for PR}\n\t\n\tLM for $V_{oc}$ enhancement thanks to PR in PPSC is discussed in a limited number of publications.\n\t\n\tS. Nanz \\textit{et al.} \\cite{Nanz2019} investigated mainly theoretically the effect of various kinds of LM strategies on the PR, for multilayer stacks that are part of PPSC, mainly without the HTL and metallic contact. They were thus able to derive an upper limit $\\Delta V_{oc}$ for each case, under the assumption of pure radiative recombination. According to the summarized principle reminded previously, the $\\Delta V_{oc}$ resulting from PR in a patterned multilayer was in between the values obtained with the Lambertian multilayer and the rigorously planar multilayer. Indeed, the Lambertian multilayer, as it consists in a better absorber, also radiates the luminescence, whereas, for targeted thicknesses, luminescence can be partly guided, leading to recycling. Then, the quasi-guided mode of patterned multilayer led to enhanced PR compared to the Lambertian multilayer, and also an enhanced $J_{sc}$ compared to the flat multilayer.\n\tHowever, A. Bowman \\textit{et al.} \\cite{Bowman2020} showed that using a more realistic model including recombination, PR was rather unlikely to occur at maximum peak power even if the cell only interacts with a limited solid angle. In this context, it thus appears more promising to increase the absorption and thus the extraction, to the detriment of recycling.\n\t\n\t\\section{Simulations and Perspective}\n\t\n\tAs shown in the previous section, a limited number of studies, mainly focused on $J_{sc}$ enhancements, evidenced a LM effect in PPSC. This synthesis also illustrates that due to a lack of common references, different kinds of PE can hardly be compared, so as the most promising architectures for AR and light trapping. Therefore, we propose to simulate some of the promising patterns, to be able to compare their performances using typical materials and architecture of a PPSC.\n\t\n\t\\subsection{Methodology}\n\t\n\tThe Rigorous Coupled Wave Analysis \\cite{moharam1981rigorous} suits well for the simulation of the stacks periodically patterned under plane wave illumination. We have used the $S^4$ code \\cite{LIU20122233} available in the Solcore package \\cite{Alonso-Alvarez2018}. The derived $J_{sc}$ are obtained using AM1.5G spectrum \\cite{Gueymard1995} provided an IQE of 1.\n\t\n\tOptical indices used are from S. Manzoor \\textit{et al.} \\cite{Manzoor2018} for MAPI, from K.R. McIntosh \\textit{et al.} \\cite{McIntosh2009} for PMMA, and from J.M. Ball \\textit{et al.} \\cite{Ball2015} for all other materials: Sodalime Glass as a substrate, ITO, TiO$_2$, doped Spiro-OMeTAD (as HTL material), and gold. Thicknesses are set to 100 $nm$ for top contact, 20 $nm$ for ETL, and 300 $nm$ for Au.\n\t\n\tThe ETL is supposed to be a thin TiO$_2$ layer, dense and flat, to avoid scattering, and to limit parasitic absorption compared to other envisaged organic materials.\n\t\\subsection{Optimization of the key layers thicknesses in various planar single-junction PPSC}\n\t\n\tLet us first consider a planar PPSC on an infinitely thick substrate, illuminated through this substrate under normal incidence (see Figure \\ref{fig:perspective_single_junction}). The thicknesses of the MAPI layer $th_{MAPI}$ and of the HTL $th_{HTL}$ are supposed to vary in the ranges 300 - 700 $nm$ and 200 - 400 $nm$ respectively. As can be seen in Figure SI-\\ref{fig:jsc_thicknesses}, the $J_{sc}$ of such a cell increases with $th_{MAPI}$. Moreover, for a given $th_{MAPI}$, the $th_{HTL}$ has a non-negligible influence in the $J_{sc}$; e.g. for the smallest MAPI thickness of the considered range, the $J_{sc}$ can be increased by more than 0.5 $mA.cm^{-2}$, up to about 21.74 $mA.cm^{-2}$.\n\t\n\tIt remains that the low $J_{sc}$ for the thinnest considered MAPI is due to lower absorption at long wavelengths, as can be seen in Figure SI- \\ref{fig:absorption_spectra}, for a given thickness of HTL of 240 $nm$.\n\t\n\tGiven the possible identified advantages of using a thinner MAPI layer, mainly for electrical properties, its thickness will be set to 300 $nm$ in the following.\n\tAn AR PMMA layer is then coated on the glass substrate, which thickness is set to 1 $mm$ (see Figure \\ref{fig:perspective_single_junction}), with a negligible roughness. For the chosen $th_{MAPI}$, the coupled influence of PMMA thickness, $th_{PMMA}$, and $th_{HTL}$ is studied. According to Figure SI- \\ref{fig:jsc_thicknesses_pmma}, a significantly increased $J_{sc}$, up to about 21.92 $mA.cm^{-2}$, can be obtained for $th_{PMMA} = 360\\ nm$ together with $th_{HTL} = 250\\ nm$. This last structure will be then used as a planar but optimized reference for fair estimation of the impact of PE in the following. The corresponding spectrum is drawn in Figure \\ref{fig:A_tous}.\n\t\n\t\\subsection{Introduction of various 2D PC to enhance the current density}\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\includegraphics[width=\\linewidth]{Fig\/perspective_single_junction.png}\n\t\t\\caption{Various configurations of single junction PPSC simulated in the perspective. (a) planer PPSC on an infinite glass substrate, to study the effect of MAPI and Spiro-OmeTAD thicknesses (see results in Figure SI-\\ref{fig:jsc_thicknesses}), (b) Planar reference having a 300 $nm$ thick MAPI layer, and various PMMA and Spiro-Ometad thicknesses to maximise absorption (see results in Figure SI-\\ref{fig:jsc_thicknesses_pmma}), (c) Cross section of a 2D square lattice of cylindrical holes in MAPI, to study the effect of the corresponding PC parameters (see results in Figures SI-\\ref{fig:TOTAL_absorption_Jsc_Jo_fn_period_etch_depth40_PC_perovskite} and \\ref{fig:A_tous}), (d) Cross section of a 2D square lattice of cylindrical holes in PMMA, to study the effect of the corresponding PC parameters (see results in Figures SI-\\ref{fig:TOTAL_absorption_Jsc_Jo_fn_period_etch_depth40_PC_pmma} and \\ref{fig:A_tous}), (e) Cross section of two 2D distinct square lattices of cylindrical holes in PMMA and MAPI, to study the effect of PC parameters (see results in Figures SI-\\ref{fig:scan_N_Nprime} and \\ref{fig:A_tous}) }\n\t\t\\label{fig:perspective_single_junction}\n\t\\end{figure}\n\t\n\t\n\tAs already discussed, to further increase the absorption and thus the $J_{sc}$, the most efficient strategies should be to couple the impinging light into the guided modes thanks to properly designed patterns. In the following, we envisage 2D square lattices of cylindrical patterns. Each resulting 2D PC owns typically three parameters that can be optimized: i) its period $P$, ii) its filling fraction ($ff$) which is the ratio between the hole surface and the period surface, and iii) its thickness $t$. \n\tThese 2D PC can be located either (see Figure \\ref{fig:perspective_single_junction}):\n\t\\begin{itemize}\n\t\t\\item in the MAPI layer, made of holes in the MAPI layer filled with HTL material; for a fair comparison, the volume of MAPI material is the same as the planar PPSC, so its total thickness changes. Moreover, $t < th_{MAPI}$ to prevent from short circuits between HTL and ETL. A $t_{HTL}=250 nm$ thick slab of HTL is kept for planarization; \n\t\t\\item in the top PMMA layer, patterned in a PC of air holes, with $t = th_{HTL}$, in favor of the diffraction efficiency, given the low index of the PMMA;\n\t\t\\item simultaneously at the two previous locations, but each PC has its own set of parameters.\n\t\\end{itemize}\n\t\n\tIn the following studies, all the PC parameters are scanned over realistic ranges. The step for $P$ and $t$ is 5 $nm$, whereas only 3 $ff$ have been envisaged: 0.3, 0.4 and 0.5; these appear to be the most realistic values compatible with a large area patterning at a reasonable cost. \n\t\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\includegraphics[width=\\textwidth]{Fig\/A_tous_cascade.png}\n\t\t\\caption{Simulated absorption spectra for various 1 mm thick glass substrate PPSC with maximized short circuit current density: the planar PPSC (Figure \\ref{fig:perspective_single_junction} (b)), the PPSC with a 2D PC into the MAPI (Figure \\ref{fig:perspective_single_junction} (c)), the PPSC with a 2D PC in the PMMA (Figure \\ref{fig:perspective_single_junction} (d)), and the PPSC with both 2D PC (Figure \\ref{fig:perspective_single_junction} (e)). Small amplitude, high spectral resolution Fabry Perot resonances take place in the $1 mm$ thick glass substrate.}\n\t\t\\label{fig:A_tous}\n\t\\end{figure}\n\t\n\t\n\t\\subsubsection{Single junction: 2D PC in the perovskite layer}\n\tWith respect to the best planar PPSC coated with PMMA, an increase of almost 1 $mA.cm^{-2}$, leading to a $J_{sc}$ about 22.88 $mA.cm^{-2}$ is obtained thanks to a PC into the MAPI (see Figure SI- \\ref{fig:TOTAL_absorption_Jsc_Jo_fn_period_etch_depth40_PC_perovskite}), with $P = 400\\ nm$, $ff= 0.4$ and $t= 110\\ nm$. It has been checked that $ff$ of 0.3 and 0.5 lead to lower current densities than the optimal one, but still larger than the reference. The corresponding spectra in Figure \\ref{fig:A_tous} reveal that the improvement is mainly due to a larger band edge absorption, so LT, as confirmed by the absorption enhancement in the same Figure.\n\t\n\tIt is noticeable that other periods, smaller than 550 $nm$, can lead to more limited $J_{sc}$ enhancements. However, it has been checked that using periods around 5 and 10 $\\mu m \\pm 0.5$ lead to a $J_{sc}$ lower than 21.85 $mA.cm^{-2}$. It confirms that such periods, far larger than the sub-micron optimal one, do not enable an efficient diffraction and thus do not lead to any $J_{sc}$ enhancement, when compared to an optimized planar reference. \n\t\n\t\\subsubsection{Single junction: 2D PC in the PMMA covering layer}\n\t\n\tWith respect to the best planar PPSC coated with PMMA, an increase of almost 0.9 $mA.cm^{-2}$, leading to a $J_{sc}$ about 22.75 $mA.cm^{-2}$, is obtained thanks to a PC in the PMMA layer. The 2D PC parameters are $P = 665\\ nm$, $ff = 0.4$ and $t= 615\\ nm$ (see Figure SI-\\ref{fig:TOTAL_absorption_Jsc_Jo_fn_period_etch_depth40_PC_pmma}). It has been checked that $ff$ of 0.3 and 0.5 lead to lower current densities. It can be noticed on the corresponding spectra in Figure \\ref{fig:A_tous} that the improvement is due to both a AR effect and limited LT at band edge absorption, since it implies low effective index guided modes that are the only able to interact, weakly, with the pattern on top of the thick substrate. The enhancement remains lower than the one induced by the 2D PC in the perovskite layer. \n\tAgain, it has been checked that periods far larger than the optimal one, typically around around 5 and 10 $\\mu m \\pm 0.5$ lead to a $J_{sc}$ lower than 22 $mA.cm^{-2}$, so to a limited enhancement, because of a reduced diffraction efficiency.\n\t\n\t\\subsubsection{Single junction: Combination of the two 2D PC}\n\t\n\tGiven the previous enhancements, a structure that combines the two 2D PC, one in the PMMA and the second at the MAPI\/HTL interface, can be envisaged. It is noticeable that RCWA method implies that the period of such a combined architecture is a integer $N_{PMMA}$ times the period of the 2D PC in the PMMA ($P_{PMMA}$), and another integer $N_{MAPI}$ times the pitch of the 2D PC in the MAPI ($P_{MAPI}$); other PC parameters ($ff_{PMMA}$, $t_{PMMA}$ on the one hand, $ff_{MAPI}$, $t_{MAPI}$ on the other hand) can differ (see Figure \\ref{fig:perspective_single_junction}).\n\tFor the sake of illustration of a possible further $J_{sc}$ enhancement, it has been chosen to set the parameters of the 2D PC at the MAPI\/HTL as for the optimized cell with a flat PMMA layer, i.e. $P_{MAPI} = 400\\ nm$, $ff_{MAPI} = 0.4$, $t_{MAPI} = 110\\ nm$, as well as $ff_{PMMA} = 0.5$ and $t_{PMMA} = 405\\ nm$, among the thinnest most favorable values of the 2D PC in PMMA associated with planar MAPI. Then, $N_{PMMA}$ is scanned from 5 to 8 and $N_{MAPI}$ from 9 to 15, given the fact that $P_{PMMA}$ is typically larger $P_{MAPI}$ according to the previous studies. The simulated $J_{sc}$ displayed in Figure SI-\\ref{fig:scan_N_Nprime} shows that a limited increase, of about 0.26 $mA.cm^{-2}$, up to 23.14 $mA.cm^{-2}$, is possible provided $P_{PMMA} = 530\\ nm$. It can be seen in Figure \\ref{fig:A_tous} that such $J_{sc}$ enhancement results from both LT and AR effects compared to the planar reference. If the full space of various possible PC parameters has not been scanned, and thus the previous parameters not fully optimized, the interest of such a combination is yet demonstrated.\n\t\n\t\\subsection{2T tandem}\n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\includegraphics[scale=0.7]{Fig\/figures_perspective_tandem.png}\n\t\t\\caption{Various configurations of 2T tandem PPSC simulated in the perspective. (i) Planar ref on an infinite glass substrate with varying perovskite materials thicknesses, to reach and equilibrium of $J_{sc}$. (ii) Cross section of a 2D square lattice of ITO pillars in 1.77-eV perovskite, to enhance $J_{sc, 1.77eV-perovskite}$ (iii) Cross section of a 2D square lattice of ITO pillars in the 1.77-eV perovskite, and of dots of 1.22-eV perovskite in HTL. Inspired by the configuration proposed by K. Xiao \\textit{et al.} \\cite{xiao_all-perovskite_2020}}\n\t\t\\label{fig:figures perspective tandem}\n\t\\end{figure}\n\t\n\tA 2T tandem PPSC generally exhibits thermalization, as the one previously described in section \\ref{tandem_plan}. Thus, we will consider this example as a case study for possible improvement. In the following simulations, the optical indices they provided for the considered materials are used. The planar stack (see Figure \\ref{fig:figures perspective tandem}), considered as a reference, is close to the one shown in Figure \\ref{fig:xiao tandem schema}. Oppositely to our previous studies, the glass substrate is again supposed to be infinitely thick to avoid the additional interferences in the substrate. Within the stack, the layer thicknesses (except perovskites) have been set to realistic values such as $t_{ITO}=100\\ nm$, $t_{NiO}=t_{NVPB}= 10\\ nm$ (simplified version of a mixed material ETL), $t_{C60} = 10\\ nm$ for both HTL, as well as $t_{PEDOT-PSS} = 10\\ nm$ and $t_{Cu}=100\\ nm$. As justified later, the SnO$_2$ layer, with $t_{SnO_2}=100\\ nm$, acts as an optical spacer (the 1 $nm$ thick Au layer has thus been neglect).\n\t\n\tSetting the thicknesses of both perovskite layers to $t_{1.77\\ eV\\ PK} = 400\\ nm$ (also the maximum in the considered range) and $t_{1.22\\ eV\\ PK} = 880\\ nm$ (in the range 800 - 1200 $nm$) leads to the highest $J_{sc}$ of 16.25 $mA.cm^{-2}$, that appears to be limited by the 1.77 $eV$ perovskite subcell.\n\tThis value is slightly larger than the one obtained by K. Xiao \\textit{et al.}. Moreover, it is obtained in our case for different perovskite thicknesses, due to a mismatch between the thicknesses of the charge transport layers we have chosen and author's choices. However, our derived absorbance spectra for both sub cells (see Figure \\ref{fig:A_tous_tandem}) still exhibits a thermalization effect. Simply increasing $t_{1.77\\ eV\\ PK}$ could be at the expense of the charges collection. \n\t\n\tIn this frame, the possible enhancement of the $J_{sc,1.77\\ eV\\ PK}$ thanks to a 2D PC at the ITO \/ 1.77 $eV$ perovskite layer interface is studied. The 2D PC consists of a square lattice of ITO pillars, coated with conformal ETL and then with a 1.77 $eV$ perovskite layer that planarizes the corresponding subcell, while keeping an equivalent volume of perovskite as in the planar tandem cell. \n\t\n\t\\begin{figure}[h]\n\t\t\\centering\n\t\t\\begin{subfigure}[b]{0.65\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/tandem.png}\n\t\t\t\\caption{Simulated absorption spectra of the various envisaged 2T tandem PPSC.}\n\t\t\t\n\t\t\\end{subfigure}\n\t\t\\hfill\n\t\t\\begin{subfigure}[b]{0.25\\textwidth}\n\t\t\t\\centering\n\t\t\t\\includegraphics[width=\\textwidth]{Fig\/tandem_zoom.png}\n\t\t\t\\caption{Zoom on LT in 1.77-eV perovskite material}\n\t\t\t\n\t\t\\end{subfigure}\n\t\t\\caption{2T tandem PPSC simulated in the perspective, Inspired by the configuration proposed by K. Xiao \\textit{et al.} \\cite{xiao_all-perovskite_2020}.}\n\t\t\\label{fig:A_tous_tandem}\n\t\\end{figure}\n\t\n\tA significant enhancement of $J_{sc,1.77\\ eV PK}$, up to 17.92 $mA.cm^{-2}$, has been found for $P=325\\ nm$, $ff=0.5$ (here defined as the ITO filling fraction) and $t=160\\ nm$.\n\tThe reason is twofold (see spectra in Figure \\ref{fig:A_tous_tandem}): a broadband AR as well as a limited LT at the band edge of the 1.77 $eV$ perovskite material that slightly reduces the thermalization. LT specifically occurs in one of the absorbing materials, to the detriment of the other one. It results from a guided mode mainly confined in the perovskite of interest, with a limited overlapping in the other perovskite, especially thanks to the rather large, 100 $nm$ SnO$_2$ optical spacer. Indeed, this layer can prevent evanescent coupling between quasi guided modes of the perovskites. Additionally, it could protect the 1.77 $eV$ perovskite material during the fabrication, but the junction between the two subcells might then also be less efficient. \n\t\n\tMoreover, this first step leads to a strong $J_{sc}$ disequilibrium between the two sub cells, since $J_{sc,1.22\\ eV\\ PK}$ even slightly decreases to 15.7 $mA.cm^{-2}$, compared to the planar reference. To increase in a second step $J_{sc,1.22\\ eV\\ PK}$, this low-Eg perovskite layer can also be patterned rather than simply increasing its already large thickness.\n\t\n\tStarting from the last structure, a second 2D PC is introduced at the 1.22 $eV$ perovskite material \/ HTL interface. To target a LT at larger wavelength, close to the band edge of this perovskite, its period has to be increased. However, given the constraint induced by the boundary conditions of the simulation, we simply chose a supercell having a period twice the one of the single PC device, with one pattern in the 1.22 $eV$ perovskite material, and two patterns in the 1.77 $eV$ perovskite material (see Figure \\ref{fig:figures perspective tandem}), while keeping $ff$ and thicknesses constant. Moreover, again, the volume of this perovskite is kept constant, HTL acting as a planariser, with a minimum thickness of 10 $nm$ (with a larger volume as in the planar alternative). \n\t\n\tIt appears that $J_{sc,1.77\\ eV PK}$ remains unchanged, whereas $J_{sc,1.22\\ eV\\ PK}$ is increased up to 16.2 $mA.cm^{-2}$, reducing, but not cancelling the disequilibrium. As expected, this results from LT at the band edge in the 1.22 $eV$ perovskite material (see Figure \\ref{fig:A_tous_tandem}). \n\tThis last result appears as a proof of concept to integrate two PC in order to induce LT, as well as possibly AR, in two different absorbing layers of a given stack, provided each one exhibits guided modes without overlapping. If not yet optimized, it is noticeable that, providing the first subcell is planarized, the two PC could practically have independent parameters, offering additional degrees of freedom to reduce disequilibrium. Given the required spacer, such a concept could be also applied to other kind of three or four terminal multijunctions cells. \n\t\n\t\\section{Conclusion - Perspectives}\n\t\n\tLight management for PV solar cells was mainly intended to increase the $J_{sc}$, simply thanks to a larger absorption. When using metal-halide perovskite materials, LM is shown to result from an interaction between the various material choices, the patterning processes and the PE. Moreover, demonstrating sole LM needs to meet some rigorous criteria to get rid of mixed electrical, material and photonic effects.\n\t\n\tThe investigations presented in this review have demonstrated that the easiest $J_{sc}$ enhancement is indeed the most frequently envisaged effect, especially for single junction cells, compared to the still highly challenging $V_{oc}$ enhancement, and the immature studies of the EY due to the lack of studies at the module scale. As demonstrated by several authors, even thicknesses optimization within flat PPSC can enhance absorption thanks to Fabry Perot effects while taking care of the electrical constraints. To further increase the absorption at the perovskite band edge, an in-plane pattern, at various scales, can be introduced in one of the layers. However, patterns at the wavelength scale appeared as the most efficient. In any case, the $J_{sc}$ enhancement compared to already optimized flat reference remained limited to a few percent. As regards the photonic regimes, the broadband AR effect was the most frequently observed, but only LT was able to significantly enhance the absorption at the band edge, as confirmed by our own simulations. In addition, we confirmed that direct patterning of the perovskite leads to more efficient LT than an additional layer on top of the substrate. Finally, in 2T tandem cells, we showed that one PC in each subcell can enhance each of the $J_{sc}$ separately.\n\t\n\tBeyond, some of the strategies developed for these opaque, single junction PPSC can be adjusted for other applications of hybrid perovskites for sun light harvesting. Indeed, accurately tuned spectral absorption is required in semitransparent single junction. Moreover, the same concepts can be tweaked to tailor reflectivity spectrum for perovskite-based color printing devices \\cite{Gholipour2017,Gao2018,Fan2019,Yoo2021} and engineering absorption management in full-color perovskite detectors \\cite{Hossain2020b,Qarony2020}.\n\t\n\tThis work is even part of far larger context. Indeed, other studies are ongoing concerning materials, which need to be more stable, and processes. In this frame, it can be noticed that record cells might not share the same encapsulation strategies as more realistic, large surface cells and modules \\cite{Wang2021b}, both for aging and safety reasons \\cite{wu_evolution_2021, wu_main_2021}. All perovskite tandem cells are even more challenging on the fabrication point of view \\cite{Zheng2020}, but are also very promising since they combine all the potential of perovskite based cells and modules with high yields.\n\t\n\tFinally, according to the reciprocity relation between absorption and emission \\cite{Rau2007}, light extraction strategies in perovskite LED \\cite{Gholipour2017,Wang:17,C7NR01631J}, as well as resonances of high quality factor in perovskite-based laser \\cite{Chen2016b,Pourdavoud2017,Qin2020} can use similar concepts to those derived in this work. Even LM in 2T tandem cells could be mimicked in white LEDs obtained by stacking several emitting materials. For LEDs, a high light extraction efficiency is a even more the key of good efficiency.\n\t\n\t\\textit{Acknowledgement:}\n\t\n\tRCWA simulations were performed on the Newton computer cluster facilities operated by PMCS2I at Ecole Centrale de Lyon and on the CNRS\/IN2P3 Computing Center in Lyon. R. M. L. acknowledges project EMIPERO (ANR-18-CE24-0016).\n\t\n\t\n\n\t","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{INTRODUCTION}\n\nCubeSat space telescopes are a growing asset in astronomical research\\cite{Shkolnik_2018,douglas2019cubesats}. Their standardized format and compact volume enables the rapid development of high-impact mission concepts above the earth's atmosphere. Flight-tested commercial microelectromechanical systems (MEMS) have further enhanced CubeSat telescopes by providing dynamic pointing and aberration control on-orbit (DeMi, NODE) \\cite{Yenchesky2019OptomechanicalDA,Clements2016}. The expanding capacity of CubeSats for astronomical research invites the development of novel technologies for their optical systems. However, the resolution in CubeSat optics is fundamentally limited by the clear aperture permitted by the CubeSat volume. The rayleigh criterion tells us that resolution ($\\Delta \\theta$) is inversely related to to the entrance pupil diameter ($D$) of the optical system.\n\n\\begin{equation}\n \\Delta \\theta = 1.22 \\frac{\\lambda}{D}\n\\end{equation}\n\nReflective CubeSat objectives traditionally require mounting hardware that limits the available entrance pupil diameter. Single-point diamond turned (SPDT) mirrors can mitigate this limitation by enabling the manufacturer to machine the mounting hardware directly into the rear of the mirror substrate, eliminating the need for mounting hardware around the edge of the mirror and enhancing the nominal throughput and resolution. This also grants the objective a considerable degree of athermalization by eliminating the difference in the coefficient of thermal expansion between the mirror and mounting hardware\\cite{Zhang:17}. SPDT surfaces are typically used in longer wavelengths (MWIR, LWIR) due to the midspatial frequency errors left by tooling marks. However, recent developments in optical polishing\\cite{Jeon:17} have introduced Magnetorheological Finishing (MRF) to the fabrication of SPDT surfaces, reducing the dominant midspatial frequencies considerably and therefore extending their use into the optical. Athermal CubeSat objectives composed of low surface roughness mirrors with a large entrance pupil invites the design of high-performance optical CubeSat payloads to make the next generation of research and technology development in space more accessible. The Versatile CubeSat Telescope (VCT) concept is a prototypical fore-optic that boasts compatibility with a variety of research payloads. The goal of the VCT is to develop a flexible, large aperture telescope that can be replicated at low cost for easy adaptability to future research payloads and technology demonstrations. \n\n\\section{OPTICAL DESIGN - ON-AXIS V.S. OFF-AXIS IMPLEMENTATIONS}\nThe design of the VCT began with two realizations of a CubeSat telescope that could take advantage of a large primary mirror. The first, an on-axis telescope with a more classical Ritchey-Chretien objective outfitted with a plano-convex aspheric collimator. The second, an all-reflective off-axis Ritchey-Chretien solution with a freeform collimator. We baselined a 95mm entrance pupil diameter with a 20$\\%$ obscuration for the on-axis telescope, and scaled the entrance pupil diameter of the off-axis telescope to be equivalent in collecting area. A high pupil magnification is required for both designs to image the primary mirror onto the small MEMS FSM clear aperture (5mm). The specifications and system layouts are shown in table \\ref{tab:specs} and figure \\ref{fig:layout} respectively.\n\n\\begin{table}[H]\n \\centering\n \\begin{tabular}{c c}\n \\hline\n Specification & Value \\\\\n \\hline\n Entrance Pupil Diameter & \\textbf{95 mm} \\\\\n Exit Pupil Diameter & 5 mm \\\\\n Half Field of View & 0.2$^{\\circ}$ \\\\ \n Secondary Obscuration & \\textbf{20$\\%$} \\\\\n Central Field Strehl & $>$0.99 \\\\\n Average Field Strehl & $>$ 0.80 \\\\\n OTA Packaging Volume & $<$ 2U \\\\\n \\hline\n \\\\\n \\end{tabular}\n \\qquad\n \\begin{tabular}{c c}\n \\hline\n Specification & Value \\\\\n \\hline\n Entrance Pupil Diameter & \\textbf{88 mm} \\\\\n Exit Pupil Diameter & 5 mm \\\\\n Half Fiel of View & 0.2$^{\\circ}$ \\\\ \n Secondary Obscuration & \\textbf{0$\\%$} \\\\\n Central Field Strehl & $>$0.99 \\\\\n Average Field Strehl & $>$ 0.80 \\\\\n OTA Packaging Volume & $<$ 2U \\\\\n \\hline\n \\\\\n \\end{tabular}\n \\caption{Optical design specifications for the (left) On-Axis and (right) Off-Axis telescope designs. All specifications are similar except for those in bold, which are set such that the apertures of each design are equal in throughput.}\n \\label{tab:specs}\n\\end{table}\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.9\\textwidth]{vct_drawings.png}\n \\caption{System layouts of the (left) On-Axis Telescope and the (right) Off-Axis Telescope designs. Each has three optical elements, two fast-steering mirrors (FSM) for fine pointing control, and a volume dedicated to a research payload.}\n \\label{fig:layout}\n\\end{figure}\n\n Aspheric surfaces were necessary for both telescopes to achieve a high pupil magnification (19x and 17.5x for the on- and off-axis designs respectively) while maintaining a well-corrected field of view. While this slightly complicates the metrology of the mirrors it did not add any additional cost because the mirrors were already going to be diamond-turned. Both telescopes utilized a Ritchey-Chretien objective that forms an image near the primary mirror, which is collimated by an aspheric element to produce an external exit pupil. The size of the exit pupil was chosen to match the clear aperture of a MEMS fast-steering mirror (FSM)\\cite{memsfsm,Serra21} to correct for spacecraft pointing and jitter errors while in orbit. The external pupil also serves as a convenient interface for any instrument suite that would be included on the VCT (e.g. spectrographs, cameras, lasers). The concept for the on-axis design was to make a well-established telescope format work given the pupil magnification and field of view specifications. The aspheric surfaces granted the design a well-corrected field of view in a compact format suitable for a CubeSat (2U). However, the design has a secondary obscuration which limits the throughput of the system and adds diffraction features to the image. The primary goal for the off-axis design was to offer a viable alternative to the classical on-axis design that would not suffer from undesirable diffraction effects brought upon by the secondary obscuration. Unfortuantely, shifting the primary parabolic mirror off-axis comes at the cost of more field-dependent aberration that limits the system's field of view. To accommodate this we selected the tertiary mirror of the off-axis design to be freeform. Before optimization of the freeform surface, the rotational symmetry of the system was broken by tilting the secondary and tertiary mirrors about their foci to make the system free of linear astigmatism. A linear-astigmatism free three mirror system (LAF-TMS) has been shown by Park et al\\cite{park_development_2020} to be an excellent starting point for freeform designs by using the mirror tilt angles and inter-mirror distances (Eq \\ref{eq:laf}) to mitigate a dominating aberration early in the design process.\n\n\\begin{equation}\n \\frac{l_2^\\prime}{l_2}\\frac{l_3^\\prime}{l_3}tan{i_1}+\\left(1+\\frac{l_2^\\prime}{l_2}\\right)\\frac{l_3^\\prime}{l_3}tan{i_2+}\\left(1+\\frac{l_3^\\prime}{l_3}\\right)tan{i_3=0}\n \\label{eq:laf}\n\\end{equation}\n\nIn this equation, parameters i$_{1,2,3}$ are the angles of incidence of the optical axis ray on surfaces 1,2, and 3, while $l_{2,3}$ and ${l^{\\prime}}_{2,3}$ are the front and rear focal lengths the secondary and tertiary mirror. Once the baseline LAF-TMS design had been implemented, the third mirror was changed to a freeform XY-Polynomial surface where coefficients were optimized that maintained the bilateral symmetry of the optical system. The mirrors were shifted off-axis in the $\\hat{y}$ direction, as was the field bias (+0.2$^{o}$). Consequently the freeform surface was constrained to be plane-symmetric about the $\\hat{y}-\\hat{z}$ plane by solely optimizing coefficients of even order in $\\hat{x}$. The resultant design is nearly diffraction-limited across the biased field of view.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.8\\textwidth]{strhvsfov.png}\n \\caption{Maps of strehl ratio v.s. field of view for the (left) On-Axis and (right) Off-Axis VCT. The symmetry of the optical system mirrors the performance quite readily, with the on-axis design having a rotationally symmetric field performance whereas the off-axis design exhibits plane-symmetric performance.}\n \\label{fig:strvfov}\n\\end{figure}\n\n\\section{SENSITIVITY ANALYSIS}\n \nThe misalignment sensitivities of the two designs were explored in Zemax OpticStudio (ZOS) by iteratively perturbing each optic in six degrees of freedom using the Python ZOS-API. Our as-built performance goal for both systems was an average Strehl ratio $>$ 0.7 across the field of view to maintain reasonably diffraction-limited performance. The $0\\%$, $70\\%$, and $100\\%$ field of view in $\\pm \\hat{x}$ and $\\pm \\hat{y}$ were considered in the average calculation, considerably biasing the sensitivity analysis toward the edge of the field of view where performance is expected to degrade faster. This results in a more pessimistic analysis of misalignment sensitivity. The sensitivity analysis fixes the primary mirror while M2 and L3\/M3 were perturbed to better understand how misalignments with respect to the primary would impact the optical performance over all fields of view.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=\\textwidth]{qkdsens.png}\n \\caption{Sensitivity to misalignment curves of the On-Axis (Solid Red) and Off-Axis (Dashed Blue) designs for their respective secondary (top) and tertiary (bottom) optic. The on-axis system is less sensitive for X\/Y decenter of both elements, X\/Y\/Z tilt of M2, and X\/Z tilt of M3.}\n \\label{fig:my_label}\n\\end{figure}\n\nFigure 2 shows that in most cases the on-axis design was less sensitive to misalignment in six degrees of freedom. This result is consistent with the complexity of each system. The off-axis design employs off-axis conics and a freeform surface, both of which will be sensitive to misalignment due to the higher mirror slopes than the on-axis design. In applications where the secondary obscuration is tolerable, the on-axis design would be a better choice for applications where cost and misalignment risk are limiting factors.\n\n\\section{POLARIZATION ANALYSIS}\nMany spaceborne optical payloads have a degree of sensitivity to polarization effects. From laser experiments in support of quantum communications\\cite{Serra21} to polarimetry of atmospheric ice\\cite{Hart20}, there is a clear need to characterize the response of cubesat payloads to a vector electric field. To offer support for polarization-sensitive research payloads a comprehensive polarization ray trace (PRT) analysis was conducted for each realization of the VCT. PRT is a ray-based approach to vector field calculations enabled by determining the local fresnel reflection coefficients that alter the transmission and phase of the electric field at each surface in the optical system. These effects are traced from the entrance pupil to the exit pupil of the optical system to determine the cumulative polarization behavior of the instrument. The proposed VCT designs use fast primary mirrors to achieve the high pupil magnification, so some polarization aberration is expected. However mirror substrates and coatings can be selected to mitigate the impact of polarization aberration for a given application. \n\nA PRT tool was written using the ZOS-API to create maps of diattenuation and retardance at the exit pupil of the system. The ZOS-API tool creates a batch raytrace and propagates it from the entrance pupil of the optical system to the surface under investigation. The output of the raytrace gives surface normal vectors in addition to exit ray vectors which are used to calculate the incident ray vectors and angle of incidence. Diattenuation ($D$) and retardance ($\\delta$) data is produced from the Zemax coating files and ray angles of incidence to generate the surface and pupil maps shown in figure \\ref{fig:polmaps_al}.\n\n\\begin{equation}\n D = \\frac{|r_p|^{2} - |r_p|^{2}}{|r_s|^{2} + |r_p|^{2}};\\phantom{flapjackfacts}\\delta = |\\phi_{p} - \\phi_{s}|\n \\label{eq:diat}\n\\end{equation}\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=\\textwidth]{polplots.png}\n \\caption{Polarization pupil maps assuming bare aluminum mirrors and uncoated N-BK7 lens surfaces. (Top) Diattenuation and (Bottom) retardance maps of the (left) On-Axis and (right) Off-axis versatile cubesat telescopes. These data were computed at $\\lambda$ = 780nm. At this wavelength the Off-Axis Aluminum VCT sees the greatest diattenuation, and the Off-Axis Gold sees the greatest retardance. This indicates that the off-axis design would introduce more challenges to polarization-sensitive payloads than the on-axis design would.}\n \\label{fig:polmaps_al}\n\\end{figure}\n\nThe polarization pupil map permits analysis of the optimal material and configuration for a given polarization-dependent application. Figure \\ref{fig:polmaps_al} demonstrates that aluminum is more diattenuating than gold at $\\lambda=780nm$, and the off-axis configuration performs worse than the on-axis configuration due to the higher angles of incidence on the mirror surfaces. \n\n\\section{MECHANICAL DESIGN}\n\nThe VCT must be designed to deliver a high optical wavefront when operational on-orbit, yet also robust enough to survive the rigors of launch and orbital injection separation. Achieving both goals in a compact package that affords straight-forward assembly and alignment is quite challenging. Another important aspect of the design must also consider the mass contribution of the structural elements as this can be costly when considering launch requirements and on-orbit maneuvering. With the prevalence of small satellite design ( $<$ 12U), development of a telescope that fits within a 2U volume with a mass less than 2 kg would present a low-cost solution that enables rapid prototyping of research payloads. For a prototype realization of the VCT, the optical system design called for SPDT Aluminum primary and secondary mirrors. All mechanical structural elements were also chosen to be aluminum to match the thermal behavior of the mounting structure to the optical elements. The collimating lens, which locates the instrument pupil, could also be easily mounted to the OTA using a common aluminum barrel structure with simple shims for precise alignment. \n\nThe first goal in the mechanical design of the telescope Optical Tube Assembly (OTA) is to support the optical elements with minimal effect due to expected thermal perturbations that would be encountered on-orbit. These effects would include both rigid body misalignment and wavefront aberrations caused by subtle flexing of the optical elements. In order to bound the problem, the first design decisions centered on overall sizing to fit within the small-sat form factor. The primary-secondary despace tolerance is very tight, so a truss structure was designed to properly space the two mirrors. The truss structure presents a good strength to weight ratio and is commonly used in ground-based telescope designs. For this space-based application, the truss structure would not allow for simple and easy shrouding of the optical path and thus would likely require additional structural pieces to support stray light baffling elements and thermal mitigation elements (i.e., mylar blankets). Therefore, an early design decision was made to investigate a tube structure that would serve as both the metering structure that could maintain the mirror separation tolerance, transverse misalignment tolerance, and provide an in-place baffle that could be customized for the particular optical application. A significant benefit of the tube design is the mass savings realized by the ability to select a very thin tube wall thickness that still retains the desired structural performance for all loading cases. This tube would also allow for easy installation of thermal mitigation measures and targeted design of thermal conduction paths that further ensure optical performance stability while on-orbit.\n\nThe next major goal in the mechanical design considers the manufacturability of the system. Considering its small size, the OTA manufacturability includes the fabrication processes for all structural components, as well as thoughtfully planned assembly and alignment processes that ensure the optical performance requirements can be met. The aluminum mirrors provide a unique opportunity to meet the need for an assembly that is easy to integrate and align. The primary mirror optical surface is thus applied directly to the structural component (primary mirror) which has appropriate design elements that consider thermal performance and mounting points for both the metering tube and the interface to the downstream optical system via a \\emph{hex plate} interface. Clearly defining the rear of the primary mirror blank as the OTA support location allows for simple design of flexural components that will not propagate errors from other systems into the OTA, or vice-versa. Similarly, the secondary mirror surface is applied directly to a head-ring structure that also considers thermal performance and mounting points to the metering tube, but also minimizes the size of support strut spiders that are common in the on-axis design. Considering the small size of the OTA, the one-piece secondary mirror\/head-ring simplifies its alignment features by moving them out to the tube diameter, rather than trying to squeeze them into the shadow of the secondary mirror on the entrance aperture.\n\nThe final goal of the mechanical design considers the interface of the OTA to the instrument and the spacecraft. Most terrestrial optical system applications utilize a breadboard style support that allows for many mounting possibilities for both the OTA and any downstream instrument opto-mechanical elements, and can be purposefully designed to incorporate spacecraft mounting options that will isolate the payload from the spacecraft both thermally and dynamically as necessary. The hex plate provides all of these interfaces in a single machined aluminum component. The connection of the OTA to the hex plate is of particular importance for this discussion. As mentioned earlier, the OTA must remain dynamically and thermally isolated from the other spacecraft or instrument components to preserve the telescope optical performance. A Finite Element Model (FEM) was created using the aluminum tube structure OTA, with both structural and optical components represented, and a Finite Element Analysis (FEA) was performed to determine the model behavior with both structurally dynamic boundary conditions and also varying thermal environmental conditions. The FEM is shown in figure \\ref{fig:vct_fea}.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.55\\textwidth]{vct_fea.png}\n \\caption{The FEM of the VCT OTA highlighting the hex plate, flexure, and tube design.}\n \\label{fig:vct_fea}\n\\end{figure}\n\nA simple three flexure design was selected to mount the OTA to the hex plate. The flexures were positioned tangentially to the primary mirror blank to better accommodate thermal deformations that could arise at the hex plate. The flexure material properties were varied to determine the best trade of optical path stability, launch survivability, manufacturability, and cost. The materials considered were Aluminum (Al 6061-T6) and Titanium (Ti-6Al-4v). For the structural analysis the hex plate was fixed at three points on its outer edge simulating spacecraft support, and MAC loads (100g accelerations in three orthogonal directions) were applied to the model. Various flexure throat cross sectional areas were considered, and the best design was chosen based on the resulting maximum stresses revealed in the FEA. The results for each material selection under this loading are shown in table \\ref{tab:macload}. \n\n\\begin{table}[H]\n \\centering\n \\begin{tabular}{c c c}\n \\hline\n Load Case Direction & Max Flexure Stress [Al] & Max Flexure Stress [Ti] \\\\\n \\hline\n 100g $\\hat{x}$ & 103ksi & 112ksi \\\\\n 100g $\\hat{y}$ & 98ksi & 103ksi \\\\ \n 100g $\\hat{z}$ & 42ksi & 46ksi \\\\\n \\hline\n \\end{tabular}\n \\caption{MAC loading in three orthogonal directions and the corresponding maximum flexure stress for Aluminum and Titanium flexures. The Ti flexures were chosen due to their higher maximum stress.}\n \\label{tab:macload}\n\\end{table}\n\nAn additional analytical consideration for the structure is the modal behavior of the OTA. The modal FEA reveals the lowest expected resonant frequency of the OTA under the prescribed mounting conditions for given material selections and flexure design choices. The table below shows the first three modal FEA results for the same conditions discussed above. These results indicate there would be little chance of exciting resonance in the OTA structure when considering expected launch loads. And similarly, the telescope itself would not likely impart damaging resonance to any other spacecraft systems or partner satellites if launched as a secondary payload ride-share.\n\n\\begin{table}[H]\n \\centering\n \\begin{tabular}{c c c}\n \\hline\n Al Flexures & Ti Flexures \\\\\n \\hline\n 130Hz & 156Hz \\\\\n 130Hz & 156Hz \\\\\n 363Hz & 434Hz \\\\\n \\hline\n \\end{tabular}\n \\caption{Modal behavior of the OTA for Al and Ti flexures for the cases described in \\ref{tab:macload}. Based on the results from the FEA for both materials, the Titanium flexure design was selected based on the extra margin provided on mass and stress.}\n \\label{tab:my_label}\n\\end{table}\n\n Verification of the selected flexure design needed to also consider the thermal behavior of the system. For ease of creating the thermal FEM, the boundary conditions for the structure were preserved from the structural FEA and a 27.8\u00b0C differential in temperature was applied to the hex plate. This set of conditions would bring into focus the thermal isolation of the OTA from the hex plate. The results from this thermal analysis were normalized to show an expected mirror sag of 10nm P-V for each 1.0\u00b0C of temperature differential. The results (shown below) also indicate an axisymmetric deformation of the OTA as would be expected.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.55\\textwidth]{fea_results.PNG}\n \\caption{Finite Element results of the hexplate-primary mirror temperature differential. The flexure design results in $\\approx$ 10nm of trefoil surface sag per degree Celsius.}\n \\label{fig:fea_results}\n\\end{figure}\n\nOverall, an iterative analysis process was implemented to determine the final telescope structure geometry. The results from dynamic survivability, modal, and thermal analyses were considered for each element. Part of the FEM definition involves application of material properties that also allow for the consideration of mass as a secondary factor in selection of the final geometry. Resulting optical performance effects are then analyzed in the Zemax OpticStudio STAR module for each iteration by exporting the three-dimensional nodal parameters from the FEA results. Other geometry selections made during the design process were more straightforward than the flexural interface between the OTA and the hex plate. For instance, given the axisymmetric behavior for all analyses, the tube geometry selections were made without direct optical analyses as the major performance effect would be in focus change due to varying thermal conditions. For this, common engineering knowledge can be applied to bias the focus alignment for all optical elements prior to integration with an instrument for the given expected on-orbit operational conditions (for this project +\/-1\u00b0C). Similarly, the hex plate design only needed to preserve the axisymmetric geometry approximation when considering its overall shape and interface definition. \nWhile not discussed in detail earlier, mass of each components was considered as an important mechanical design decision. An overall maximum allowable mass budget of 2 kg was allotted to the design and did not present a problem during the iterative analyses. The final telescope design mass is expected to be less than 1 kg once all hardware is integrated. The only major design decision based on mass reduction involved light-weighting the primary mirror blank by cutting pockets into the structure and analyzing these pockets for their structural effect on mirror surface figure. \n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.6\\textwidth]{3dprintlw.PNG}\n \\caption{3D Prints of the primary mirror (left) and hex plate (right) demonstrating the lightweighting scheme used to achieve the mass requirement of $< 2kg$}\n \\label{fig:my_label}\n\\end{figure}\n\n\\section{STOP EVALUATION - MERGING FEA WITH SEQUENTIAL RAYTRACING}\n\nNumerous loading conditions were studied through FEA software to ensure the overall survival of the VCT payload, however, FEA analysis by itself could not determine how the deformed elements would impact the system's optical performance. Further studies of the wavefront deformation were necessary to confirm that the system would be able to function properly in its environment of operation. Typically, this kind of analysis would require the deformed surface to be decomposed into hundreds of polynomial terms to accurately model the shape of the surface, with no guarantee of the raytracing software being able to support the number of terms needed. However, through use of OpticStudio's STOP (Structural, Thermal, and Optical Performance) analysis feature \\emph{STAR} (Structural, Thermal, Analysis and Results) evaluation of the system's performance in various loading scenarios was made quick and efficient. STAR allows for the data from FEA software to be loaded directly into OpticStudio and onto select optical elements. By using the position and displacement of each node in a 6 column (x, y, z, dx, dy, dz) format STAR accurately deforms the surface shape, allowing for the deformed wavefront to be observed under various loading conditions. For the purposes of our design study we primarily looked at the surface deformation on the primary mirror caused by rotation about the optical axis.\n\nIn figure \\ref{fig:star} the change of the optical system's wavefront and PSF are shown after applying a -237 arcsecond rotation about the optical axis. This causes clear degradation in the system's overall optical performance. This optical analysis is critical for an iterative design process between optical and mechanical design teams. Quick optical performance analysis through STAR allows for the mechanical team to adjust the structures supporting the optical surfaces where necessary, ensuring performance specifications are met in realistic structural and thermal scenarios. \n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=\\textwidth]{starinv.png}\n \\caption{Influence of STAR on the wavefront (left) and the PSF (right). A force tangent to the primary mirror was applied in FEA and the resultant surface deformation was loaded into the OpticStudio raytrace model. This permits evaluation of performance degradation as a function of structural and thermal deformation.}\n \\label{fig:star}\n\\end{figure}\n\n\n\\section{Laboratory Prototype Status}\n\nThe next step for the development of the VCT is the construction of a laboratory prototype of the on-axis aluminum VCT. Mirrors for the on-axis VCT have been fabricated by Hanbat national university and are shown in figure \\ref{fig:fab_mirrors}. \n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.7\\textwidth]{mirror_img.PNG}\n \\caption{The fabricated (left) primary and (right) secondary of the on-axis VCT design manufactured by Hanbat National University in South Korea.}\n \\label{fig:fab_mirrors}\n\\end{figure}\n\nThe primary and secondary mirror have arrived at the University of Arizona and are awaiting measurement and assembly so that they can be mounted into the OTA described in section 5 for system-level tests. The UArizona Space Astrophysics lab test facilities include a TVAC chamber and Hexapod that can be used for thermal vaccum and jitter tests respectively. Details of the comprehensive characterization and assembly of the Versatile Cubesat Telescope prototype will be published in the future.\n\n\\section{Conclusion}\n\nWe present diffraction-limited on- and off-axis designs for a Versatile CubeSat Telescope that fits within a 2U volume at low cost. Comprehensive analysis of the sensitivity to misalignment and polarization were considered, and a stable mechanical housing was created for the on-axis VCT. The VCT is a well-characterized high-performance design that can be adapted to a variety of space-borne research payloads. Future iterations of the VCT will expand upon the development of MEMS devices in space (e.g. DeMi\\cite{Morgan21}) by replacing the FSM with a Deformable Mirror for a higher degree of active wavefront control. Closed loop thermal simulations will be conducted to demonstrate the behavior of the VCT in response to a dynamic thermal environment.\n\n\\section{Acknowledgements}\nWe thank Zemax for the early access to their STAR analysis feature. This research made use of community-developed core Python packages, including: Numpy\\cite{numpy}, Matplotlib \\cite{matplotlib}, SciPy \\cite{jones_scipy_2001}, Astropy \\cite{the_astropy_collaboration_astropy_2013}, and Jupyter, IPython Interactive Computing architecture \\cite{perez_ipython_2007,kluyver_jupyter_2016}. Portions of this work were supported by the Arizona Board of Regents Technology Research Initiative Fund (TRIF).\n\n\n\\section{INTRODUCTION}\n\nCubeSat space telescopes are a growing asset in astronomical research\\cite{Shkolnik_2018,douglas2019cubesats}. Their standardized format and compact volume enables the rapid development of high-impact mission concepts above the earth's atmosphere. Flight-tested commercial microelectromechanical systems (MEMS) have further enhanced CubeSat telescopes by providing dynamic pointing and aberration control on-orbit (DeMi, NODE) \\cite{Yenchesky2019OptomechanicalDA,Clements2016}. The expanding capacity of CubeSats for astronomical research invites the development of novel technologies for their optical systems. However, the resolution in CubeSat optics is fundamentally limited by the clear aperture permitted by the CubeSat volume. The rayleigh criterion tells us that resolution ($\\Delta \\theta$) is inversely related to to the entrance pupil diameter ($D$) of the optical system.\n\n\\begin{equation}\n \\Delta \\theta = 1.22 \\frac{\\lambda}{D}\n\\end{equation}\n\nReflective CubeSat objectives traditionally require mounting hardware that limits the available entrance pupil diameter. Single-point diamond turned (SPDT) mirrors can mitigate this limitation by enabling the manufacturer to machine the mounting hardware directly into the rear of the mirror substrate, eliminating the need for mounting hardware around the edge of the mirror and enhancing the nominal throughput and resolution. This also grants the objective a considerable degree of athermalization by eliminating the difference in the coefficient of thermal expansion between the mirror and mounting hardware\\cite{Zhang:17}. SPDT surfaces are typically used in longer wavelengths (MWIR, LWIR) due to the midspatial frequency errors left by tooling marks. However, recent developments in optical polishing\\cite{Jeon:17} have introduced Magnetorheological Finishing (MRF) to the fabrication of SPDT surfaces, reducing the dominant midspatial frequencies considerably and therefore extending their use into the optical. Athermal CubeSat objectives composed of low surface roughness mirrors with a large entrance pupil invites the design of high-performance optical CubeSat payloads to make the next generation of research and technology development in space more accessible. The Versatile CubeSat Telescope (VCT) concept is a prototypical fore-optic that boasts compatibility with a variety of research payloads. The goal of the VCT is to develop a flexible, large aperture telescope that can be replicated at low cost for easy adaptability to future research payloads and technology demonstrations. \n\n\\section{OPTICAL DESIGN - ON-AXIS V.S. OFF-AXIS IMPLEMENTATIONS}\nThe design of the VCT began with two realizations of a CubeSat telescope that could take advantage of a large primary mirror. The first, an on-axis telescope with a more classical Ritchey-Chretien objective outfitted with a plano-convex aspheric collimator. The second, an all-reflective off-axis Ritchey-Chretien solution with a freeform collimator. We baselined a 95mm entrance pupil diameter with a 20$\\%$ obscuration for the on-axis telescope, and scaled the entrance pupil diameter of the off-axis telescope to be equivalent in collecting area. A high pupil magnification is required for both designs to image the primary mirror onto the small MEMS FSM clear aperture (5mm). The specifications and system layouts are shown in table \\ref{tab:specs} and figure \\ref{fig:layout} respectively.\n\n\\begin{table}[H]\n \\centering\n \\begin{tabular}{c c}\n \\hline\n Specification & Value \\\\\n \\hline\n Entrance Pupil Diameter & \\textbf{95 mm} \\\\\n Exit Pupil Diameter & 5 mm \\\\\n Half Field of View & 0.2$^{\\circ}$ \\\\ \n Secondary Obscuration & \\textbf{20$\\%$} \\\\\n Central Field Strehl & $>$0.99 \\\\\n Average Field Strehl & $>$ 0.80 \\\\\n OTA Packaging Volume & $<$ 2U \\\\\n \\hline\n \\\\\n \\end{tabular}\n \\qquad\n \\begin{tabular}{c c}\n \\hline\n Specification & Value \\\\\n \\hline\n Entrance Pupil Diameter & \\textbf{88 mm} \\\\\n Exit Pupil Diameter & 5 mm \\\\\n Half Fiel of View & 0.2$^{\\circ}$ \\\\ \n Secondary Obscuration & \\textbf{0$\\%$} \\\\\n Central Field Strehl & $>$0.99 \\\\\n Average Field Strehl & $>$ 0.80 \\\\\n OTA Packaging Volume & $<$ 2U \\\\\n \\hline\n \\\\\n \\end{tabular}\n \\caption{Optical design specifications for the (left) On-Axis and (right) Off-Axis telescope designs. All specifications are similar except for those in bold, which are set such that the apertures of each design are equal in throughput.}\n \\label{tab:specs}\n\\end{table}\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.9\\textwidth]{vct_drawings.png}\n \\caption{System layouts of the (left) On-Axis Telescope and the (right) Off-Axis Telescope designs. Each has three optical elements, two fast-steering mirrors (FSM) for fine pointing control, and a volume dedicated to a research payload.}\n \\label{fig:layout}\n\\end{figure}\n\n Aspheric surfaces were necessary for both telescopes to achieve a high pupil magnification (19x and 17.5x for the on- and off-axis designs respectively) while maintaining a well-corrected field of view. While this slightly complicates the metrology of the mirrors it did not add any additional cost because the mirrors were already going to be diamond-turned. Both telescopes utilized a Ritchey-Chretien objective that forms an image near the primary mirror, which is collimated by an aspheric element to produce an external exit pupil. The size of the exit pupil was chosen to match the clear aperture of a MEMS fast-steering mirror (FSM)\\cite{memsfsm,Serra21} to correct for spacecraft pointing and jitter errors while in orbit. The external pupil also serves as a convenient interface for any instrument suite that would be included on the VCT (e.g. spectrographs, cameras, lasers). The concept for the on-axis design was to make a well-established telescope format work given the pupil magnification and field of view specifications. The aspheric surfaces granted the design a well-corrected field of view in a compact format suitable for a CubeSat (2U). However, the design has a secondary obscuration which limits the throughput of the system and adds diffraction features to the image. The primary goal for the off-axis design was to offer a viable alternative to the classical on-axis design that would not suffer from undesirable diffraction effects brought upon by the secondary obscuration. Unfortuantely, shifting the primary parabolic mirror off-axis comes at the cost of more field-dependent aberration that limits the system's field of view. To accommodate this we selected the tertiary mirror of the off-axis design to be freeform. Before optimization of the freeform surface, the rotational symmetry of the system was broken by tilting the secondary and tertiary mirrors about their foci to make the system free of linear astigmatism. A linear-astigmatism free three mirror system (LAF-TMS) has been shown by Park et al\\cite{park_development_2020} to be an excellent starting point for freeform designs by using the mirror tilt angles and inter-mirror distances (Eq \\ref{eq:laf}) to mitigate a dominating aberration early in the design process.\n\n\\begin{equation}\n \\frac{l_2^\\prime}{l_2}\\frac{l_3^\\prime}{l_3}tan{i_1}+\\left(1+\\frac{l_2^\\prime}{l_2}\\right)\\frac{l_3^\\prime}{l_3}tan{i_2+}\\left(1+\\frac{l_3^\\prime}{l_3}\\right)tan{i_3=0}\n \\label{eq:laf}\n\\end{equation}\n\nIn this equation, parameters i$_{1,2,3}$ are the angles of incidence of the optical axis ray on surfaces 1,2, and 3, while $l_{2,3}$ and ${l^{\\prime}}_{2,3}$ are the front and rear focal lengths the secondary and tertiary mirror. Once the baseline LAF-TMS design had been implemented, the third mirror was changed to a freeform XY-Polynomial surface where coefficients were optimized that maintained the bilateral symmetry of the optical system. The mirrors were shifted off-axis in the $\\hat{y}$ direction, as was the field bias (+0.2$^{o}$). Consequently the freeform surface was constrained to be plane-symmetric about the $\\hat{y}-\\hat{z}$ plane by solely optimizing coefficients of even order in $\\hat{x}$. The resultant design is nearly diffraction-limited across the biased field of view.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.8\\textwidth]{strhvsfov.png}\n \\caption{Maps of strehl ratio v.s. field of view for the (left) On-Axis and (right) Off-Axis VCT. The symmetry of the optical system mirrors the performance quite readily, with the on-axis design having a rotationally symmetric field performance whereas the off-axis design exhibits plane-symmetric performance.}\n \\label{fig:strvfov}\n\\end{figure}\n\n\\section{SENSITIVITY ANALYSIS}\n \nThe misalignment sensitivities of the two designs were explored in Zemax OpticStudio (ZOS) by iteratively perturbing each optic in six degrees of freedom using the Python ZOS-API. Our as-built performance goal for both systems was an average Strehl ratio $>$ 0.7 across the field of view to maintain reasonably diffraction-limited performance. The $0\\%$, $70\\%$, and $100\\%$ field of view in $\\pm \\hat{x}$ and $\\pm \\hat{y}$ were considered in the average calculation, considerably biasing the sensitivity analysis toward the edge of the field of view where performance is expected to degrade faster. This results in a more pessimistic analysis of misalignment sensitivity. The sensitivity analysis fixes the primary mirror while M2 and L3\/M3 were perturbed to better understand how misalignments with respect to the primary would impact the optical performance over all fields of view.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=\\textwidth]{qkdsens.png}\n \\caption{Sensitivity to misalignment curves of the On-Axis (Solid Red) and Off-Axis (Dashed Blue) designs for their respective secondary (top) and tertiary (bottom) optic. The on-axis system is less sensitive for X\/Y decenter of both elements, X\/Y\/Z tilt of M2, and X\/Z tilt of M3.}\n \\label{fig:my_label}\n\\end{figure}\n\nFigure 2 shows that in most cases the on-axis design was less sensitive to misalignment in six degrees of freedom. This result is consistent with the complexity of each system. The off-axis design employs off-axis conics and a freeform surface, both of which will be sensitive to misalignment due to the higher mirror slopes than the on-axis design. In applications where the secondary obscuration is tolerable, the on-axis design would be a better choice for applications where cost and misalignment risk are limiting factors.\n\n\\section{POLARIZATION ANALYSIS}\nMany spaceborne optical payloads have a degree of sensitivity to polarization effects. From laser experiments in support of quantum communications\\cite{Serra21} to polarimetry of atmospheric ice\\cite{Hart20}, there is a clear need to characterize the response of cubesat payloads to a vector electric field. To offer support for polarization-sensitive research payloads a comprehensive polarization ray trace (PRT) analysis was conducted for each realization of the VCT. PRT is a ray-based approach to vector field calculations enabled by determining the local fresnel reflection coefficients that alter the transmission and phase of the electric field at each surface in the optical system. These effects are traced from the entrance pupil to the exit pupil of the optical system to determine the cumulative polarization behavior of the instrument. The proposed VCT designs use fast primary mirrors to achieve the high pupil magnification, so some polarization aberration is expected. However mirror substrates and coatings can be selected to mitigate the impact of polarization aberration for a given application. \n\nA PRT tool was written using the ZOS-API to create maps of diattenuation and retardance at the exit pupil of the system. The ZOS-API tool creates a batch raytrace and propagates it from the entrance pupil of the optical system to the surface under investigation. The output of the raytrace gives surface normal vectors in addition to exit ray vectors which are used to calculate the incident ray vectors and angle of incidence. Diattenuation ($D$) and retardance ($\\delta$) data is produced from the Zemax coating files and ray angles of incidence to generate the surface and pupil maps shown in figure \\ref{fig:polmaps_al}.\n\n\\begin{equation}\n D = \\frac{|r_p|^{2} - |r_p|^{2}}{|r_s|^{2} + |r_p|^{2}};\\phantom{flapjackfacts}\\delta = |\\phi_{p} - \\phi_{s}|\n \\label{eq:diat}\n\\end{equation}\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=\\textwidth]{polplots.png}\n \\caption{Polarization pupil maps assuming bare aluminum mirrors and uncoated N-BK7 lens surfaces. (Top) Diattenuation and (Bottom) retardance maps of the (left) On-Axis and (right) Off-axis versatile cubesat telescopes. These data were computed at $\\lambda$ = 780nm. At this wavelength the Off-Axis Aluminum VCT sees the greatest diattenuation, and the Off-Axis Gold sees the greatest retardance. This indicates that the off-axis design would introduce more challenges to polarization-sensitive payloads than the on-axis design would.}\n \\label{fig:polmaps_al}\n\\end{figure}\n\nThe polarization pupil map permits analysis of the optimal material and configuration for a given polarization-dependent application. Figure \\ref{fig:polmaps_al} demonstrates that aluminum is more diattenuating than gold at $\\lambda=780nm$, and the off-axis configuration performs worse than the on-axis configuration due to the higher angles of incidence on the mirror surfaces. \n\n\\section{MECHANICAL DESIGN}\n\nThe VCT must be designed to deliver a high optical wavefront when operational on-orbit, yet also robust enough to survive the rigors of launch and orbital injection separation. Achieving both goals in a compact package that affords straight-forward assembly and alignment is quite challenging. Another important aspect of the design must also consider the mass contribution of the structural elements as this can be costly when considering launch requirements and on-orbit maneuvering. With the prevalence of small satellite design ( $<$ 12U), development of a telescope that fits within a 2U volume with a mass less than 2 kg would present a low-cost solution that enables rapid prototyping of research payloads. For a prototype realization of the VCT, the optical system design called for SPDT Aluminum primary and secondary mirrors. All mechanical structural elements were also chosen to be aluminum to match the thermal behavior of the mounting structure to the optical elements. The collimating lens, which locates the instrument pupil, could also be easily mounted to the OTA using a common aluminum barrel structure with simple shims for precise alignment. \n\nThe first goal in the mechanical design of the telescope Optical Tube Assembly (OTA) is to support the optical elements with minimal effect due to expected thermal perturbations that would be encountered on-orbit. These effects would include both rigid body misalignment and wavefront aberrations caused by subtle flexing of the optical elements. In order to bound the problem, the first design decisions centered on overall sizing to fit within the small-sat form factor. The primary-secondary despace tolerance is very tight, so a truss structure was designed to properly space the two mirrors. The truss structure presents a good strength to weight ratio and is commonly used in ground-based telescope designs. For this space-based application, the truss structure would not allow for simple and easy shrouding of the optical path and thus would likely require additional structural pieces to support stray light baffling elements and thermal mitigation elements (i.e., mylar blankets). Therefore, an early design decision was made to investigate a tube structure that would serve as both the metering structure that could maintain the mirror separation tolerance, transverse misalignment tolerance, and provide an in-place baffle that could be customized for the particular optical application. A significant benefit of the tube design is the mass savings realized by the ability to select a very thin tube wall thickness that still retains the desired structural performance for all loading cases. This tube would also allow for easy installation of thermal mitigation measures and targeted design of thermal conduction paths that further ensure optical performance stability while on-orbit.\n\nThe next major goal in the mechanical design considers the manufacturability of the system. Considering its small size, the OTA manufacturability includes the fabrication processes for all structural components, as well as thoughtfully planned assembly and alignment processes that ensure the optical performance requirements can be met. The aluminum mirrors provide a unique opportunity to meet the need for an assembly that is easy to integrate and align. The primary mirror optical surface is thus applied directly to the structural component (primary mirror) which has appropriate design elements that consider thermal performance and mounting points for both the metering tube and the interface to the downstream optical system via a \\emph{hex plate} interface. Clearly defining the rear of the primary mirror blank as the OTA support location allows for simple design of flexural components that will not propagate errors from other systems into the OTA, or vice-versa. Similarly, the secondary mirror surface is applied directly to a head-ring structure that also considers thermal performance and mounting points to the metering tube, but also minimizes the size of support strut spiders that are common in the on-axis design. Considering the small size of the OTA, the one-piece secondary mirror\/head-ring simplifies its alignment features by moving them out to the tube diameter, rather than trying to squeeze them into the shadow of the secondary mirror on the entrance aperture.\n\nThe final goal of the mechanical design considers the interface of the OTA to the instrument and the spacecraft. Most terrestrial optical system applications utilize a breadboard style support that allows for many mounting possibilities for both the OTA and any downstream instrument opto-mechanical elements, and can be purposefully designed to incorporate spacecraft mounting options that will isolate the payload from the spacecraft both thermally and dynamically as necessary. The hex plate provides all of these interfaces in a single machined aluminum component. The connection of the OTA to the hex plate is of particular importance for this discussion. As mentioned earlier, the OTA must remain dynamically and thermally isolated from the other spacecraft or instrument components to preserve the telescope optical performance. A Finite Element Model (FEM) was created using the aluminum tube structure OTA, with both structural and optical components represented, and a Finite Element Analysis (FEA) was performed to determine the model behavior with both structurally dynamic boundary conditions and also varying thermal environmental conditions. The FEM is shown in figure \\ref{fig:vct_fea}.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.55\\textwidth]{vct_fea.png}\n \\caption{The FEM of the VCT OTA highlighting the hex plate, flexure, and tube design.}\n \\label{fig:vct_fea}\n\\end{figure}\n\nA simple three flexure design was selected to mount the OTA to the hex plate. The flexures were positioned tangentially to the primary mirror blank to better accommodate thermal deformations that could arise at the hex plate. The flexure material properties were varied to determine the best trade of optical path stability, launch survivability, manufacturability, and cost. The materials considered were Aluminum (Al 6061-T6) and Titanium (Ti-6Al-4v). For the structural analysis the hex plate was fixed at three points on its outer edge simulating spacecraft support, and MAC loads (100g accelerations in three orthogonal directions) were applied to the model. Various flexure throat cross sectional areas were considered, and the best design was chosen based on the resulting maximum stresses revealed in the FEA. The results for each material selection under this loading are shown in table \\ref{tab:macload}. \n\n\\begin{table}[H]\n \\centering\n \\begin{tabular}{c c c}\n \\hline\n Load Case Direction & Max Flexure Stress [Al] & Max Flexure Stress [Ti] \\\\\n \\hline\n 100g $\\hat{x}$ & 103ksi & 112ksi \\\\\n 100g $\\hat{y}$ & 98ksi & 103ksi \\\\ \n 100g $\\hat{z}$ & 42ksi & 46ksi \\\\\n \\hline\n \\end{tabular}\n \\caption{MAC loading in three orthogonal directions and the corresponding maximum flexure stress for Aluminum and Titanium flexures. The Ti flexures were chosen due to their higher maximum stress.}\n \\label{tab:macload}\n\\end{table}\n\nAn additional analytical consideration for the structure is the modal behavior of the OTA. The modal FEA reveals the lowest expected resonant frequency of the OTA under the prescribed mounting conditions for given material selections and flexure design choices. The table below shows the first three modal FEA results for the same conditions discussed above. These results indicate there would be little chance of exciting resonance in the OTA structure when considering expected launch loads. And similarly, the telescope itself would not likely impart damaging resonance to any other spacecraft systems or partner satellites if launched as a secondary payload ride-share.\n\n\\begin{table}[H]\n \\centering\n \\begin{tabular}{c c c}\n \\hline\n Al Flexures & Ti Flexures \\\\\n \\hline\n 130Hz & 156Hz \\\\\n 130Hz & 156Hz \\\\\n 363Hz & 434Hz \\\\\n \\hline\n \\end{tabular}\n \\caption{Modal behavior of the OTA for Al and Ti flexures for the cases described in \\ref{tab:macload}. Based on the results from the FEA for both materials, the Titanium flexure design was selected based on the extra margin provided on mass and stress.}\n \\label{tab:my_label}\n\\end{table}\n\n Verification of the selected flexure design needed to also consider the thermal behavior of the system. For ease of creating the thermal FEM, the boundary conditions for the structure were preserved from the structural FEA and a 27.8\u00b0C differential in temperature was applied to the hex plate. This set of conditions would bring into focus the thermal isolation of the OTA from the hex plate. The results from this thermal analysis were normalized to show an expected mirror sag of 10nm P-V for each 1.0\u00b0C of temperature differential. The results (shown below) also indicate an axisymmetric deformation of the OTA as would be expected.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.55\\textwidth]{fea_results.PNG}\n \\caption{Finite Element results of the hexplate-primary mirror temperature differential. The flexure design results in $\\approx$ 10nm of trefoil surface sag per degree Celsius.}\n \\label{fig:fea_results}\n\\end{figure}\n\nOverall, an iterative analysis process was implemented to determine the final telescope structure geometry. The results from dynamic survivability, modal, and thermal analyses were considered for each element. Part of the FEM definition involves application of material properties that also allow for the consideration of mass as a secondary factor in selection of the final geometry. Resulting optical performance effects are then analyzed in the Zemax OpticStudio STAR module for each iteration by exporting the three-dimensional nodal parameters from the FEA results. Other geometry selections made during the design process were more straightforward than the flexural interface between the OTA and the hex plate. For instance, given the axisymmetric behavior for all analyses, the tube geometry selections were made without direct optical analyses as the major performance effect would be in focus change due to varying thermal conditions. For this, common engineering knowledge can be applied to bias the focus alignment for all optical elements prior to integration with an instrument for the given expected on-orbit operational conditions (for this project +\/-1\u00b0C). Similarly, the hex plate design only needed to preserve the axisymmetric geometry approximation when considering its overall shape and interface definition. \nWhile not discussed in detail earlier, mass of each components was considered as an important mechanical design decision. An overall maximum allowable mass budget of 2 kg was allotted to the design and did not present a problem during the iterative analyses. The final telescope design mass is expected to be less than 1 kg once all hardware is integrated. The only major design decision based on mass reduction involved light-weighting the primary mirror blank by cutting pockets into the structure and analyzing these pockets for their structural effect on mirror surface figure. \n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.6\\textwidth]{3dprintlw.PNG}\n \\caption{3D Prints of the primary mirror (left) and hex plate (right) demonstrating the lightweighting scheme used to achieve the mass requirement of $< 2kg$}\n \\label{fig:my_label}\n\\end{figure}\n\n\\section{STOP EVALUATION - MERGING FEA WITH SEQUENTIAL RAYTRACING}\n\nNumerous loading conditions were studied through FEA software to ensure the overall survival of the VCT payload, however, FEA analysis by itself could not determine how the deformed elements would impact the system's optical performance. Further studies of the wavefront deformation were necessary to confirm that the system would be able to function properly in its environment of operation. Typically, this kind of analysis would require the deformed surface to be decomposed into hundreds of polynomial terms to accurately model the shape of the surface, with no guarantee of the raytracing software being able to support the number of terms needed. However, through use of OpticStudio's STOP (Structural, Thermal, and Optical Performance) analysis feature \\emph{STAR} (Structural, Thermal, Analysis and Results) evaluation of the system's performance in various loading scenarios was made quick and efficient. STAR allows for the data from FEA software to be loaded directly into OpticStudio and onto select optical elements. By using the position and displacement of each node in a 6 column (x, y, z, dx, dy, dz) format STAR accurately deforms the surface shape, allowing for the deformed wavefront to be observed under various loading conditions. For the purposes of our design study we primarily looked at the surface deformation on the primary mirror caused by rotation about the optical axis.\n\nIn figure \\ref{fig:star} the change of the optical system's wavefront and PSF are shown after applying a -237 arcsecond rotation about the optical axis. This causes clear degradation in the system's overall optical performance. This optical analysis is critical for an iterative design process between optical and mechanical design teams. Quick optical performance analysis through STAR allows for the mechanical team to adjust the structures supporting the optical surfaces where necessary, ensuring performance specifications are met in realistic structural and thermal scenarios. \n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=\\textwidth]{starinv.png}\n \\caption{Influence of STAR on the wavefront (left) and the PSF (right). A force tangent to the primary mirror was applied in FEA and the resultant surface deformation was loaded into the OpticStudio raytrace model. This permits evaluation of performance degradation as a function of structural and thermal deformation.}\n \\label{fig:star}\n\\end{figure}\n\n\n\\section{Laboratory Prototype Status}\n\nThe next step for the development of the VCT is the construction of a laboratory prototype of the on-axis aluminum VCT. Mirrors for the on-axis VCT have been fabricated by Hanbat national university and are shown in figure \\ref{fig:fab_mirrors}. \n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[width=0.7\\textwidth]{mirror_img.PNG}\n \\caption{The fabricated (left) primary and (right) secondary of the on-axis VCT design manufactured by Hanbat National University in South Korea.}\n \\label{fig:fab_mirrors}\n\\end{figure}\n\nThe primary and secondary mirror have arrived at the University of Arizona and are awaiting measurement and assembly so that they can be mounted into the OTA described in section 5 for system-level tests. The UArizona Space Astrophysics lab test facilities include a TVAC chamber and Hexapod that can be used for thermal vaccum and jitter tests respectively. Details of the comprehensive characterization and assembly of the Versatile Cubesat Telescope prototype will be published in the future.\n\n\\section{Conclusion}\n\nWe present diffraction-limited on- and off-axis designs for a Versatile CubeSat Telescope that fits within a 2U volume at low cost. Comprehensive analysis of the sensitivity to misalignment and polarization were considered, and a stable mechanical housing was created for the on-axis VCT. The VCT is a well-characterized high-performance design that can be adapted to a variety of space-borne research payloads. Future iterations of the VCT will expand upon the development of MEMS devices in space (e.g. DeMi\\cite{Morgan21}) by replacing the FSM with a Deformable Mirror for a higher degree of active wavefront control. Closed loop thermal simulations will be conducted to demonstrate the behavior of the VCT in response to a dynamic thermal environment.\n\n\\section{Acknowledgements}\nWe thank Zemax for the early access to their STAR analysis feature. This research made use of community-developed core Python packages, including: Numpy\\cite{numpy}, Matplotlib \\cite{matplotlib}, SciPy \\cite{jones_scipy_2001}, Astropy \\cite{the_astropy_collaboration_astropy_2013}, and Jupyter, IPython Interactive Computing architecture \\cite{perez_ipython_2007,kluyver_jupyter_2016}. Portions of this work were supported by the Arizona Board of Regents Technology Research Initiative Fund (TRIF).\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec:intro}\nCardiac magnetic resonance imaging is a valuable tool for myocardial structure, function, and tissue assessment, providing essential information for clinical diagnosis and treatment decisions in cardiovascular disease. Using standard segmented sequences in which data acquisition is segmented over multiple heart beats, good image quality can be obtained in patients with regular cardiac rhythm and good breath-holding ability; however, image quality can be degraded by motion artifacts when scanning patients with arrhythmia or poor breath-hold compliance. In comparison to segmented acquisitions, single-shot techniques can be applied for rapid image acquisition of a whole slice within a single shot, greatly reducing the scan time. Due to the short acquisition duration of single-shot techniques (typically < 200 ms), artifacts from intra-shot motion are negligible, therefore such methods tend to be robust against cardiac and breathing motion. However, this motion robustness comes at the expense of lower spatial resolution and signal to noise ratio (SNR). An example of the benefit of single-shot over segmented Late Gadolinium Enhanced (LGE) imaging in a patient who could not breath-hold is shown in \\ref{fig:fig1}. Recent techniques proposed to enhance the SNR of single-shot methods by motion correcting and then averaging multiple single-shot images acquired in free-breathing \\cite{Kellman2005}. While this technique shows good results with low acceleration factors, it may not provide optimal image quality for higher undersampling, introducing blurring and undersampling artifacts, mainly due to higher weight given to the regularization. Batchelor et al. \\cite{Batchelor2005} proposed a first generalized reconstruction framework for motion compensation. The method allows arbitrary motion to be compensated by solving a general matrix inversion problem. This technique, however, requires an adequate knowledge of the displacement fields. The recent GRICS method \\cite{Odille2008a} extended this work by jointly estimating the motion and the recovered image, however, it relied on a motion model provided by external sensors (e.g. ECG, respiratory belt).\n\nIn this work, we sought to develop an efficient motion correction implementation suitable for reconstructing a high-resolution, high-SNR image from multiple accelerated single-shot images. The proposed method combines the benefits of using a hybrid self-navigated sampling scheme (see Fig. \\ref{fig:fig1}) with a joint reconstruction framework. In the image reconstruction step, a highly efficient feature-preserving regularization scheme (Beltrami) is proposed for recovering sharp details. We show that the proposed method is robust to high acceleration factors and yields results with efficient noise reduction and better overall image quality at a low computational cost.\n\n\n\\section{Theory}\n\n\\subsection{General Motion Compensation Framework}\n\nMotion compensation techniques aim to solve the following inverse problem: find an underlying image $\\rho$ free of motion artifacts, given derived measurements $s$ through the system $E$, affected by noise $\\nu: s = E\\rho + \\nu$. Where $E$ is the encoding matrix, generally composed of a sampling operator $\\xi$, a Fourier transform $F$, coil sensitivity maps $\\sigma$ (in case of parallel imaging reconstruction), and a motion warping operator $W$ describing a non-rigid deformation for each shot. Here $\\rho \\in \\mathbb{C}^{n_x \\times n_y}$ and $s \\in \\mathbb{C}^{n_x \\times n_y\/acc \\times n_r \\times n_c}$ are complex data with $n_c$ the number of receiver coils, $n_r$ the number of repetitions and $acc$ the acceleration factor. In this work, the acquired data s represents the k-space data from multiple single-shot images and is generally corrupted by noise. A typical approach to solve this problem is to minimize the squared difference as assessed by the Euclidean norm. However, this problem is generally ill-posed (e.g. due to undersampling and noise), leading to non-uniqueness of the solution, if it exists. Thus, regularity constraints on the unknown solution $\\rho$ have to be considered. Furthermore, motion should also be considered as unknown. The general joint optimization framework is then defined as\n\n\\begin{equation}\n\\label{eq:eq1}\n\\rho = \\argmin\\limits_{\\left( \\rho, \\vec{u}\\right)} \\lbrace \\Vert s - E\\left(\\vec{u}\\right) \\rho \\Vert_2^2 + \\lambda \\phi \\left( \\rho \\right) \\rbrace \\text{ where } E\\left( \\vec{u}\\right) = \\xi F \\sigma W \\left( \\vec{u}\\right)\n\\end{equation}\n\nHere $\\vec{u}$ represents the displacement fields, $\\phi$ is the chosen regularization function and $\\lambda > 0$ is the corresponding regularization parameter. The optimization problem in Equation \\ref{eq:eq1} is solved in four steps: (i) we first use the k-space center of the single-shot images to extract a self-navigation signal and to cluster the raw data into a reduced number of respiratory bins \\cite{Usman2013}; (ii) we reconstruct the images from each bin independently using a Beltrami-regularized SENSE (B-SENSE) reconstruction; (iii) then an estimate of the motion is obtained using a non-rigid registration (minimization of Equation \\ref{eq:eq1} with regards to $\\vec{u}$ \\cite{Odille2014}) and (iv) a high resolution\/SNR image is generated using the proposed motion-compensated reconstruction process (minimization with regards to $\\rho$). A general description of the method is shown in Fig. \\ref{fig:fig2}.\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=0.91\\textwidth]{Fig1.png}\n\\caption{(Left) Comparison between 2D segmented LGE (top) and 2D single-shot LGE (bottom) on a 77-year-old patient with breath-holding difficulties. The segmented LGE has higher resolution than the single-shot LGE but shows severe motion artefacts. (Right) Proposed hybrid k-space acquisition scheme including motion-calibration data (fully sampled center) and undersampled periphery, aimed to combine the resolution of segmented LGE with the motion-robustness of single-shot LGE.}\n\\label{fig:fig1}\n\\end{figure}\n\n\n\\subsection{Beltrami-Regularized SENSE}\n\nIn our framework, a respiratory signal is extracted from the motion calibration data itself. This pre-processing step is achieved by stacking the low resolution images along the time dimension. Singular value decomposition is then applied to the stack and the first left-singular vector is used as a good approximation of the true respiratory signal. A specific respiratory phase is then assigned to each acquired shot, as explained in \\cite{Usman2013}. This binning strategy would split the data into fewer motion states $n_b \\left( < n_r\\right)$ with negligible respiration motion and lower undersampling in each of them. Images from each respiratory bin $\\left( \\rho_i \\right)_{i = 1,\\dots,n_b}$ are then individually reconstructed by solving\n\n\\begin{equation}\n\\label{eq:eq2}\n\\rho_i = \\argmin\\limits_{\\rho} \\lbrace \\Vert s_i - E_i \\rho \\Vert_2^2 + \\lambda \\sqrt{1+\\beta^2 \\vert \\nabla \\rho \\vert^2} \\rbrace\n\\end{equation}\n\nThe first term in Equation \\ref{eq:eq2} is a data fidelity term that aims to minimize the difference between the reconstructed image and the acquired data. The Beltrami regularization $\\sqrt{1+\\beta^2 \\vert \\nabla \\rho \\vert^2}$ has been introduced in the field of string theory for physics and has shown high potential in several imaging problems, including image denoising and enhancement \\cite{Polyakov1981} and super-resolution reconstruction \\cite{Odille2015}. In particular, the metric can be chosen such that the Beltrami energy corresponds to an arbitrary interpolation between Gaussian diffusion $\\beta \\to 0$ and total variation (TV) \\cite{Rudin1992a} regularization $\\beta \\gg 1$. In \\cite{Zosso2014}, the authors showed that Beltrami regularization is able to maintain the advantage of TV (edges preserving, noise reduction) as well as reducing the effect of staircasing. B-SENSE is very similar to compressed sensing SENSE (CS-SENSE) methods presented by other authors \\cite{Liang2008}, where here Beltrami is making the image sparse in the gradient domain. Even though this suggests that B-SENSE has a close relationship with the compressed sensing (CS) theory, it is, however, not CS as defined by Cand\\`es et al. \\cite{Candes2006}, especially due to the pseudo-random undersampling pattern used here (i.e. a uniform random pattern is used in \\cite{Candes2006}). We propose to solve Equation \\ref{eq:eq2} by adopting a primal-dual projected gradient approach \\cite{Chan1999} with the potential to converge faster than the classic primal gradient-descent \\cite{Zosso2014}. Respiratory motion estimation is then accomplished using independent non-rigid registration of the images reconstructed from each respiratory bin. Here we use an iterative framework validated in a large patient database for myocardial $T_2$ mapping \\cite{Odille2014}, which is based on minimizing the sum-of-squared differences of the pixel intensities within a multi-resolution Gauss-Newton scheme.\n\n\n\\subsection{Motion Compensated Reconstruction with Preserved-Features}\n\nThis section presents the final step for solving the motion compensated problem in Equation \\ref{eq:eq1}. The aim of the method is to reconstruct the high resolution, high SNR image $\\rho$ from the acquired raw data $s = \\left( s_i \\right)_{i = 1,\\dots,n_b}$. For the motion compensated reconstruction, we solve the following optimization problem, with the acquisition model now including the estimated motion fields:\n\n\\begin{equation}\n\\label{eq:eq3}\n\\rho = \\argmin\\limits_{\\rho} \\lbrace \\Vert s - E\\left(\\vec{u}\\right) \\rho \\Vert_2^2 + \\lambda \\sqrt{1+\\beta^2 \\vert \\nabla \\rho \\vert^2} \\rbrace\n\\end{equation}\n\nAs in the previous section, we use a primal-dual projected gradient approach, employing the Beltrami energy as regularity prior \\cite{Zosso2014}. Note that regularization is always preferred in motion compensated reconstruction due to the ill-conditioning induced by the motion operators, as shown in \\cite{Atkinson2003}.\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=\\textwidth]{Fig2.png}\n\\caption{Schematic illustration of the proposed reconstruction, including the non-rigid motion extraction. Acquisition is performed using complementary trajectories, leading to uniform samplings in the phase encoding direction, which allows for an optimal combination of the k-spaces according to their positions in the breathing signal. The motion model, initialized by registering the images from each respiratory bin, is incorporated into the reconstruction process.}\n\\label{fig:fig2}\n\\end{figure}\n\n\\section{Material and Methods}\n\nThe proposed reconstruction algorithm was applied and validated with different experiments using Matlab (The MathWorks, Natick, MA) on a PC with Intel Xeon 3.3 GHz CPU and 64GB ram. The experiments were performed on 3T MR750w and 1.5T MR450w systems (GE Healthcare, WI, USA).\n\n\n\\begin{table}\n \\centering\n \\caption{Parameters used for the different experiments. The acquisition matrix size was 192 x 256.}\n \\label{tab:table1}\n \\begin{tabular}{cccccccc}\n \\toprule\n & \\#repetition & \\#calib & \\#periphery & acc & acc shot & acc shot & NEX\\\\\n & $n_r$ & lines & lines & peri & pre-bin & post-bin & sequence\\\\\n \\midrule\n Simulation 1 & 4 & 32 & 48 & 3.3 & 2.4 & - & 1.67\\\\\n Simulation 2 & 4 & 32 & 32 & 5 & 3 & - & 1.33\\\\\n Phantom & 6 & 32 & 48 & 3.3 & 2.4 & - & 2.5\\\\\n In vivo & 15 & 17 & 43 & 4.1 & 3.2 & 1.1 (5 bins) & 4.7\\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\n\n\\subsection{Offline Simulation on Synthetic Data}\n\nIn order to perform a realistic simulation, we first created a synthetic dataset based on actual LGE patient images. In one patient with suspected cardiovascular disease, four repetitions of a cardiac-gated, inversion recovery prepared, single-shot LGE scan were acquired in free-breathing 10 minutes after Gadolinium injection. Cardiac images were obtained with a spoiled fast gradient echo sequence and the following parameters: matrix size 192 x 256, in-plane spatial resolution 1.52 mm x 1.52 mm in short axis with slice thickness = 8 mm, readout flip angle = 20 degrees, echo time (TE) = 2.02 ms, mid-diastolic trigger delay, pulse repetition time (TR) = 4.43 ms and SENSE factor = 2 with partial Fourier. Synthetic k-space data were created by the application of synthetic coil sensitivity maps (with Gaussian profiles) to the LGE images, Fourier transformation and undersampling in the phase encoding direction. A full sampling of the central k-space area (17 lines) was used and the peripheral area was undersampled with a Golden Step Cartesian trajectory \\cite{Derbyshire2011} with an acceleration factor $R = 3.3$. Spacing between samples was proportional to the Golden ratio ($p = 0.618$). This trajectory enables an irregular but almost uniform distribution of the acquired data for any arbitrary number of repetitions, leading to incoherent aliasing (Fig. \\ref{fig:fig1}, right). The motion-free image was reconstructed using our reconstruction and compared to a motion correction method similar to that proposed by Kellman et al. \\cite{Kellman2005}, where the motion-free image is recovered by averaging the registered images obtained after the B-SENSE reconstruction step. We call this prior method reconstruction-registration-average (RRA).\n\n\\subsection{Phantom Imaging}\n\nSingle-shot pulse sequences were used to acquire phantom images with a 26-channel cardiac coil. The sequence was modified to take into account the same Golden ratio sampling as in our offline simulation experiment. The protocol was applied to acquire phantom images with a resolution of 1.48 mm x 1.48 mm. A translational motion was imposed to the table to mimic respiratory motion.\n\n\n\\subsection{In Vivo Validation Experiment with Self-Navigation}\n\nIn vivo cardiac datasets from two healthy adult volunteers were acquired on a 1.5T scanner using a 32-channel cardiac coil. A multi-shot slice (15 shots) of a free-breathing, cardiac-gated, spoiled fast gradient echo sequence (without inversion-recovery preparation) was collected with the following parameters: TE = 2.10 ms, TR = 4.52 ms, 8 mm slice thickness, FOV = 253 mm x 338 mm, matrix size 192 x 256 and 1.32 mm x 1.32 mm in-plane resolution, diastolic trigger delay. A fully sampled reference was acquired additionally in breath-hold for visual comparison. Each shot consists of 60 k-space lines: the central k-space was fully sampled with 17 lines and the periphery (43 lines) was undersampled, leading to a global acceleration factor of 3.2. An estimate of the respiratory signal was extracted using the proposed self-navigated technique, and was subsequently used to separate the acquired data into five respiratory bins. An overview of the parameters used in this study is given in Table \\ref{tab:table1}.\n\n\\section{Results}\n\nThe time needed to run the motion-compensated reconstruction for 15 shots of matrix size equal to 192 x 256 with 32-channel cardiac coils was about 1 min 35 s, including the time to compute the motion between shots.\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=0.9\\textwidth]{Fig3.png}\n\\caption{Cardiac short-axis reconstruction of a synthetic dataset generated from 4 single-shot LGE acquisitions in free-breathing on a 37-year-old patient with acceleration factors $r = 3.3$ and $r = 5$. a) One reconstruction using a classic SENSE (192 x 256), b) Sum-of-Squares (all repetitions), c) Reconstruction-Registration-Average, d) proposed reconstruction.}\n\\label{fig:fig3}\n\\end{figure}\n\n\\subsection{Offline Simulation on Synthetic Data}\n\nExample reconstruction results on the simulated data generated from a patient with nonischemic cardiomyopathy are shown in Fig. \\ref{fig:fig3}. One can see a spatially blurred result with a standard reconstruction-registration-average (RRA) method. The proposed reconstruction exhibits significant quality improvements over each method with an acceleration factor $r = 3.3$ while reconstructing sharper edges (arrows) and small structures. For higher acceleration factors the performance of our method is much better compared to RRA, both in terms of reconstruction accuracy and image quality.\n\n\n\\subsection{Phantom Imaging}\n\nSimilar results can be observed in phantom experiments (Fig. \\ref{fig:fig4}). Comparisons with a classic Tikhonov reconstruction are shown in Fig. \\ref{fig:fig4}. The results present the reconstructed phantom motion experiments where here the motion has been applied with the table. The sum-of-squares reconstruction (Fig. \\ref{fig:fig4}, left) clearly exhibits the effect of motion. As in the previous experiment, the RRA method exhibits blurry results (due to the undersampling), although providing a motion-corrected denoised image. A visual improvement can be noticed when applying a motion compensated reconstruction with Tikhonov regularization. The latter method performs well but is, however, unable to recover sharp edges and some residual artifacts can still be seen on the recovered image. The use of a fast primal-dual algorithm combined with Beltrami regularization makes the proposed reconstruction robust with better performance in terms of image quality, reduced artifacts and sharpness (Fig. \\ref{fig:fig4}, Bel).\n\n\n\\begin{figure}[h!]\n\\includegraphics[width=\\textwidth]{Fig4.png}\n\\caption{Reconstructions on a phantom using two different regularization methods with acceleration factor 3.3. Six single-shot repetitions have been acquired. From left to right: Sum-of-Squares (SoS), Reconstruction-Registration-Average (RRA), Tikhonov, Beltrami, Reference.}\n\\label{fig:fig4}\n\\end{figure}\n\n\\subsection{In Vivo Validation Experiment with Self-Navigation}\n\nFigure \\ref{fig:fig5} shows an example of the proposed fast and automatic self-navigated binning method on 150 consecutive slices of liver SPGR acquisition. The temporal rate was 400 ms, corresponding to a total acquisition duration of 1 min. The extracted respiratory signal (red) shows good agreement with the respiratory belt placed on the subject's thorax (blue), with a coefficient of determination $R^2 = 0.76$. Raw data acquired in similar motion states can be clustered into a reduced number of motion states, thereby improving the quality of images from which to extract motion in free-breathing without the need for navigators or external sensors.\nShort-axis images of the myocardium of a healthy subject without Gadolinium injection and without inversion recovery preparation are shown in Fig. \\ref{fig:fig6}. Both cardiac structures (myocardium wall, papillary muscles) and non-cardiac structures (blood vessels) are very well recovered with our reconstruction. The method yields significant sharpening of the myocardium wall and papillary muscles. However, due to the relatively high-undersampling, the RRA method is unable to recover a good quality image, exhibiting blurry structures and losing some of the details in the image such as blood vessels (arrows). This particularity is also seen in Fig. \\ref{fig:fig6} d where a specific intensity profile is plotted. The sharpness of the edges on our motion-corrected reconstruction is confirmed as well as the fidelity to the breath-hold acquisition.\n\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=0.6\\textwidth]{Fig5.png}\n\\caption{Data binning step of the proposed self-navigated signal obtained from 150 consecutive 2D fast spoiled gradient echo acquisitions in free-breathing liver imaging.}\n\\label{fig:fig5}\n\\end{figure}\n\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=\\textwidth]{Fig6.png}\n\\caption{Data binning step of the proposed self-navigated signal obtained from 150 consecutive 2D fast spoiled gradient echo acquisitions in free-breathing liver imaging.}\n\\label{fig:fig6}\n\\end{figure}\n\n\\section{Discussion and Conclusion}\n\nWe introduced a new free-breathing single-shot LGE pipeline, including an optimized sampling and the associated joint reconstruction and motion correction algorithm designed for fast and robust cardiac imaging. By incorporating the estimated motion into the reconstruction process, we increased the robustness of the model and exhibited good quality images.\n\nIn this study, we used a fast and automatic self-navigated binning strategy that aims to cluster the acquired raw data into similar motion states. While preliminary results have shown improved image quality and better motion estimation, additional optimization of number of bins and number of repetitions is still required to maintain an optimal tradeoff between reconstruction quality, reconstruction time and accuracy of motion estimates. The motion corrected images show better visual quality than classic reconstructions but appear less sharp than corresponding breath-held acquisitions, especially for high accelerations. Possible explanations are the inaccuracies in motion estimates or other effects related to MR physics, such as spin history or changes in $B_0$ and $B_1$ inhomogeneities induced by breathing.\n\nOne interesting application of the proposed motion correction model is for high-resolution 3D isotropic LGE single-shot imaging of the heart, such as the one proposed recently in \\cite{Dzyubachyk2015} for myocardial scar assessment. This will allow for the reconstruction of 3D isotropic motion corrected volumes by keeping the advantages of a 2D acquisition (high tissue and vessel contrast, short acquisition time), e.g. using super-resolution techniques \\cite{Odille2015}. Other applications, such as abdominal imaging \\cite{Buerger2013} and coronary vessel imaging \\cite{Cruz2016}, are being investigated.\n\nA limitation to the method is that potential through-plane motion cannot be corrected, although it remains small compared to the slice thickness. To overcome this problem, one could consider weighting the images according to the motion amplitude compared to the target image or acquiring 3D slab instead of 2D slice data and applying motion compensation. The preliminary results presented in this work should be confirmed with further patient studies.\n\nThe feasibility of the proposed reconstruction has been evaluated in simulation, phantom and volunteer experiments. The method has been shown to allow non-rigid motion correction while efficiently recovering features, thanks to the Beltrami regularization scheme. The conventional segmented LGE acquisition is limited by the maximum breath-hold time, which limits the signal-to-noise ratio and\/or spatial resolution. This limitation is overcome by the presented free breathing approach. Ultimately, this method could enable accurate motion corrected reconstruction of single-shot images with higher spatial resolution and a higher signal-to-noise ratio compared to conventional segmented methods, with the potential to offer high-quality LGE imaging in challenging patients.\n\n\n\\subsection*{Acknowledgments}\nThe authors thank Mayo Clinic (Rochester, MN), Advanced Cardiovascular Imaging (New York, NY) and Morriston Hospital (Swansea, UK) for providing some of the imaging data. This publication was supported by the European Commission, through Grant Number 605162. The content is solely the responsibility of the authors and does not necessarily represent the official views of the EU.\n\n\n\\bibliographystyle{splncs}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzarjz b/data_all_eng_slimpj/shuffled/split2/finalzzarjz new file mode 100644 index 0000000000000000000000000000000000000000..22d7be912d9a8f2d2e882e6617ecf3d45fd89134 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzarjz @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nConsider the linear transport equation in $1$D \n\\begin{equation}\\label{eq:transport}\n\\partial_t f + v \\partial_x f - \\partial_x \\Phi \\partial_v f = 0,\n\\end{equation}\nfor an unknown function $f: [0,\\infty) \\times \\mathbb R_x \\times \\mathbb R_v \\to \\mathbb R_{\\geq 0}$\nwith a smooth external confining potential $\\Phi:\\mathbb R \\to \\mathbb R$. \n\nThe following is the main result of this note:\n\\begin{theorem}\\label{thm:main}\nLet $\\epsilon>0$ and $\\Phi(x) = \\frac {x^2}2 + \\frac{\\epsilon x^4}2$. Consider the unique solution $f$ to \\eqref{eq:transport} with initial data $f \\restriction_{t=0} = f_0$ such that \n\\begin{itemize}\n\\item $f_0: \\mathbb R_x \\times \\mathbb R_v \\to \\mathbb R_{\\geq 0}$ is smooth, and\n\\item there exists $c_s >0$ such that $\\mathrm{supp}(f_0) \\subseteq \\{ (x,v): c_s \\leq \\frac{v^2}2 + \\Phi(x) \\leq c_s^{-1} \\}$.\n\\end{itemize}\n\nThen, for $\\epsilon$ sufficiently small, there exists $C>0$ depending on $\\epsilon$ and $c_s$ such that the following estimate holds:\n$$\\sup_{x\\in \\mathbb{R}} |\\partial_t \\varphi|(t,x) \\leq C \\langle t\\rangle^{-2} \\sup_{(x,v) \\in \\mathbb R\\times \\mathbb R} \\sum_{|\\alpha|+ |\\beta| \\leq 2} |\\partial_x^\\alpha \\partial_v^\\beta f_0|(x,v),$$\nwhere $\\varphi$ is defined by\n\\begin{equation}\\label{eq:phi}\n\\partial_{xx}^2\\varphi(t,x) = \\int_{\\mathbb{R}} f(t,x,v)\\, \\mathrm{d} v,\\quad \\varphi(t,0) = \\partial_x \\varphi(t,0) = 0.\n\\end{equation}\n\\end{theorem}\n\nA few remarks of the theorem are in order.\n\n\\begin{remark}[Nonlinear Vlasov--Poisson system]\nThe reason that we are particularly concerned with $\\partial_t \\varphi$ is that it appears to be the quantity relevant for the stability of the\nzero solution for the nonlinear Vlasov--Poisson system in $1$D; see Section~\\ref{sec:VP}.\n\nIt should be noted that $\\varphi$ itself is not expect to decay to $0$ (since $\\int_{\\mathbb{R}} f\\, \\mathrm{d} v \\geq 0$). Thus the decay for $\\partial_t \\varphi$ can be viewed as a measure of the rate that $\\varphi$ approaches the limit $\\lim_{t\\to +\\infty} \\varphi(t,x)$.\n\n\\end{remark}\n\n\\begin{remark}[Derivatives of $\\partial_t\\varphi$]\nFor the applications on the Vlasov--Poisson system, one may also wish to obtain estimates for the derivatives of $\\partial_t \\varphi$. It is easy to extend our methods to obtain\n$$|\\partial_x \\partial_t\\varphi |\\lesssim \\langle t\\rangle^{-1},\\quad |\\partial_x^2 \\partial_t \\varphi|\\lesssim 1.$$\nNotice that these decay rates, at least by themselves, do not seem sufficient for a global nonlinear result.\n\\end{remark}\n\n\\begin{remark}[Phase mixing and the choice of $\\Phi$]\nThe result in Theorem~\\ref{thm:main} can be interpreted as a quantitative phase-mixing statement. It is well-known that for \n$$\\Phi(x) = \\frac {x^2}2,$$\nthe solution to \\eqref{eq:transport} does \\underline{not} undergo phase mixing (see chapter 3 in \\cite{BinTre_2011}). It is therefore important that we added the $\\frac{\\epsilon x^4}2$ term in the definition of the potential.\n\nOn the other hand, there are other choices of $\\Phi$ for which analogues of Theorem~\\ref{thm:main} hold. We expect that as long as $\\Phi$ is even and satisfies the non-degeneracy condition of \\cite{pRoS2020}, then a similar decay estimate holds. The particular example we used is only chosen for concreteness.\n\\end{remark}\n\n\\begin{remark}[Method of proof]\nIt is well-known that the linear transport equation \\eqref{eq:transport} can be written in action-angle variables, say $(Q, K)$, in which case \\eqref{eq:transport} takes the form\n\\begin{equation}\\label{eq:trivial}\n\\part_t f - c({K})\\part_{{Q}} f=0.\n\\end{equation}\n\nWhen $c'(K)$ is bounded away from $0$, phase mixing in the sense that $f$ converges \\emph{weakly} to a limit can be obtained after solving \\eqref{eq:trivial} with a Fourier series in $Q$; see \\cite{pRoS2020}. The point here is that $\\varphi$ is a (weighted) integral of $f$ over a region of phase space that is most conveniently defined with respect to the $(x,v)$ (as opposed to the action-angle) variables.\n\nWe quantify the strong convergence of $\\varphi_t\\to 0$ by finding an appropriate commuting vector field $Y$ that is adapted to the action-angle variables. The fact that $\\varphi$ is naturally defined as an integral over $v$ in $(x,v)$ coordinates makes it tricky to prove decay using this vector field. Furthermore, we are only able to prove $1\/\\jap{t}^2$ decay; this is for instance in contrast to the decay of the density for the free transport equation on a torus.\n\\end{remark}\n\n\\medskip\n\n\n\\subsection{Related result}\n\n\\subsection*{Linear phase mixing results}\nIn the particular context of Theorem~\\ref{thm:main}, decay of $\\partial_t\\varphi$, but without a quantitative rate, can be inferred from the work \\cite{pRoS2020}.\n\nThere are many linear phase mixing result, the simplest setting for this is the linear free transport equation. This is well-known; see for instance notes \\cite{villani2010landau} by Villani.\n\nOne of the most influential work on phase mixing is the groundbreaking paper \\cite{Landau1946} of Landau wherein he proposes a linear mechanism for damping for plasmas that does not involve dispersion or change in entropy. In the case of $\\mathbb T^d$, this is even understood in a nonlinear setting; see the section on nonlinear results below. The situation is more subtle in $\\mathbb R^d$, see \\cite{jBnMcM2020}, \\cite{rGjS1994}, \\cite{rGjS1995} and \\cite{dHKttNfR2020}.\n\nSee also \\cite{jBfW2020}, \\cite{faou2021linear} and \\cite{iT2017} for linear results on related models. In particular, we note that \\cite{faou2021linear} also rely on action-angle variables in their analysis.\n\n\\subsection*{Relation with other phase-mixing problems with integrable underlying dynamics}\nAs pointed out in \\cite{pRoS2020}, phase space mixing is relevant for the dynamics of kinetic models in many physical phenomena from stellar systems and dark matter halos to mixing of relativistic gas surrounding a black hole. See \\cite{dominguez2017description} for related discussions on dark matter halos. We also refer the interested reader to \\cite{BinTre_2011} for further background and discussions of phase mixing in other models, including the stability of galaxies.\n\nWe hope that the present work would also be a model problem and aid in understanding more complicated systems such as those described in \\cite{pRoS2020}. One particularly interesting problem is the stability of the Schwarzschild solution to the Einstein--Vlasov system in spherical symmetry. \n\n\\subsection*{Nonlinear phase mixing results}\nNonlinear Landau damping for Vlasov--Poisson on $\\mathbb{T}^d$ was first proven in analytic regularity by Mouhot--Villani in their landmark paper \\cite{cMcV2011}. Since then their work has been extended and simplified in \\cite{jBnMcM2016} and \\cite{eGtNiR2020a}.\n\nSee also other nonlinear results, e.g.~in \\cite{jB2017}, \\cite{jBnMcM2018}, \\cite{chaturvedi2021vlasov}, \\cite{faou2016landau}, \\cite{dHKttNfR2019}, \\cite{bY2016}.\n\n\n\n\\subsection*{Collisional problems with confining potentials}\n\nConfining potentials for kinetic equations have been well-studied, particularly for collisional models. Linear stability results can be found in \\cite{carrapatoso2021special}, \\cite{dolbeault2009hypocoercivity}, \\cite{dolbeault2015hypocoercivity}, \\cite{duan2011hypocoercivity} and \\cite{duan2012hypocoercivity}.\n\nIn this connection, it would also be of interest to understand how phase mixing effects (studied in the present paper) interact with collisional effects (cf.~\\cite{jB2017}, \\cite{chaturvedi2021vlasov}, \\cite{iT2017}.)\n\n\n\\section{The Vlasov--Poisson system}\\label{sec:VP}\n\nThe motivation of our result is the Vlasov--Poisson system:\n\\begin{equation}\\label{eq:VP}\n\\begin{cases}\n\\part_t f+v\\part_x f-(\\part_x \\Phi+\\part_x \\varphi)\\part_v f=0, \\\\\n-\\part^2_x \\varphi=\\int_{\\mathbb{R}} f\\d v.\n\\end{cases}\n\\end{equation}\n\nNote that \\eqref{eq:VP} can be rewritten as \n\\begin{equation}\\label{e.VP}\n\\part_t f+\\{H,f\\}=0,\n\\end{equation}\nwhere $H$ is the Hamiltonian given by \n\\begin{equation}\\label{eq:nonlin-hamiltonian}\nH(x,v)=\\frac{v^2}{2}+\\Phi(x)+\\varphi(t,x).\n\\end{equation}\n\nNotice that $f \\equiv 0$ is a solution to \\eqref{eq:VP}, and the transport equation \\eqref{eq:transport} is \nthe linearization of \\eqref{eq:VP} near the zero solution.\n\nOne cannot hope that the term $\\partial_x \\varphi$ in the nonlinear term decays as $t\\to +\\infty$. \n(This can be seen by noting that $\\int_{\\mathbb{R}} f\\d v \\geq 0$ pointwise.) At best one can hope \nthat $\\partial_x \\varphi$ converges to some (non-trivial) limiting profile as $t\\to +\\infty$. For $f$ satisfying \nthe linear equation \\eqref{eq:transport}, such convergence (without a quantitative rate) has been shown\nin \\cite{pRoS2020}.\n\nIn anticipation of the nonlinear problem, it is important to understand the quantitative convergence. Since\n$\\partial_x\\varphi$ does not converge to $0$, it is natural to understand the decay rate of $\\partial_t \\partial_x \\varphi$.\n\nAs a first step to understand \\eqref{eq:VP}, we look at the linearized problem \\eqref{eq:transport} around the zero solution and prove that we get integrable decay for $\\varphi_t$ in the linearized dynamics.\n\n\\begin{remark}\nNote that the Poisson's equation above reads\n$$-\\part^2_x \\varphi=\\rho.$$\nIn particular, $\\varphi$ is only defined up to a harmonic function, i.e.~a linear function a $x$. \nIn Theorem~\\ref{thm:main}, we remove this ambiguity by setting $\\varphi(0) = (\\partial_x \\varphi)(0) = 0$. \nNotice that other normalization, e.g., $\\varphi(-\\infty) = (\\partial_x \\varphi)(-\\infty)=0$ would not change the function $\\varphi_t = \\partial_t \\varphi$.\n\\end{remark}\n\n\\section{The action-angle variables}\\label{sec:action}\n\n\\subsection{First change of variables}\nFrom now on we will consider the Hamiltonian $$H=\\frac{v^2}{2}+\\Phi(x).$$ This is the Hamiltonian for the equations \\eqref{eq:transport}, which is also \\eqref{eq:nonlin-hamiltonian} without the $\\varphi$ (the self-interaction term). As an intermediate step to getting the action-angle variables we use the change of coordinates \n\\begin{align*}\n(t,x,v)\\mapsto (t,\\chi,H) &\\hspace{5 em}\\text{when $x>0$}\\\\\n(t,x,v)\\mapsto (t,\\pi-\\chi,H) &\\hspace{5 em}\\text{when $x\\leq 0$},\n\\end{align*}\nwhere $\\chi:=\\arcsin\\left(\\frac{v}{\\sqrt{2H}}\\right).$\\\\\nFirst we check if the change of variables is well defined by calculating the Jacobian for $x>0$,\n\\begin{equation*}\nJ=\\begin{pmatrix}\n\\part_t t& \\part_x t&\\part_v t\\\\\n\\part_t H& \\part_x H&\\part_v H\\\\\n\\part_t \\chi& \\part_x \\chi&\\part_v \\chi\n\\end{pmatrix}\n=\n\\begin{pmatrix}\n1&0&0\\\\\n0&\\Phi_x&v\\\\\n0&-\\frac{v}{2H}\\cdot\\frac{\\Phi_x}{\\sqrt{\\Phi}}&\\frac{\\sqrt{\\Phi}}{H}\n\\end{pmatrix}\n\\end{equation*}\nNow \n\\begin{align*}\n\\det(J)&=\\frac{\\Phi_x}{\\sqrt{\\Phi}}\\frac{(\\Phi+v^2\/2)}{H}\\\\\n&=\\frac{\\Phi_x}{\\sqrt{\\Phi}}.\n\\end{align*}\nSimilarly for $x\\leq 0$, \\begin{align*}\n\\det(J)&= - \\frac{\\Phi_x}{\\sqrt{\\Phi}}.\n\\end{align*}\nHence, \\begin{align*}\n\\det(J)\n&=\\sign{x}\\frac{\\Phi_x}{\\sqrt{\\Phi}}.\n\\end{align*}\nNext by chain rule and using that $H$ is independent of $t$, we get\n\n\\begin{align*}\n\\part_x&=\\sign{x}\\part_x \\chi\\part_{\\chi}+\\part_x H\\part_H\\\\\n&=-\\frac{v}{2H}\\cdot\\frac{\\Phi_x}{\\sqrt{\\Phi}}\\part_{\\chi}+\\Phi_x\\part_H,\n\\end{align*}\n\\begin{align*}\n\\part_v&=\\sign{x}\\part_v \\chi\\part_{\\chi}+\\part_v H\\part_H\\\\\n&=\\frac{\\sqrt{\\Phi}}{H}\\part_{\\chi}+v\\part_H.\n\\end{align*}\nPluggin this in \\eref{VP}, we get the equation\n\\begin{equation}\\label{e.VP_chi_H_lin}\n\\part_t f-\\sign{x}\\frac{\\Phi_x}{\\sqrt{\\Phi}}\\part_\\chi f=0.\n\\end{equation}\n\n\n\\subsection{Second change of variables} The coefficient in front of $\\part_\\chi f$ in \\eqref{e.VP_chi_H_lin} depends on both $\\chi$ and $H$. To take care of this, we reparametrize $\\chi$ (in a manner depending on $H$). More precisely, for a fixed $H$, we define $Q(\\chi,H)$ such that \n$$\\frac{\\d Q}{\\d \\chi}=\\frac{c(H)}{a(\\chi,H)},\\quad Q(0,H)=0,$$\nwhere $a(\\chi,H)=\\sign{x}\\frac{\\Phi_x(x)}{\\sqrt{\\Phi(x)}}$ such that $x=x(\\chi,H).$ To fix $c(H)$, we require that for every $H$, \n\\begin{equation}\\label{eq:C.def}\n2\\pi=\\int_0^{2\\pi}\\d Q=c(H)\\int_0^{2\\pi}\\frac{1}{a(\\chi,H)}\\d \\chi.\n\\end{equation}\nNow we define the change of variables, $(\\chi,H)\\mapsto (Q,K)$ where $K=H$. Then note,\n$$a(\\chi,H)\\partial_\\chi=c(H)\\part_Q$$\nand $$\\part_{H}=\\part_{K}+\\frac{\\part Q}{\\part H}\\part_Q.$$\nThus in these coordinates, we can rewrite \\eref{VP_chi_H_lin} as \n\\begin{equation}\\label{e.VP_Q_H_lin}\n\\part_t f-c({K})\\part_Q f=0.\n\\end{equation}\n\n\nFurther, the Jacobian is \n\\begin{equation*}\n\\begin{pmatrix}\n\\part_H K&\\part_\\chi K\\\\\n\\part_H Q& \\part_\\chi Q\n\\end{pmatrix}\n=\n\\begin{pmatrix}\n1&0\\\\\n\\part_H Q&\\frac{c(H)}{a(\\chi,H)}\n\\end{pmatrix}.\n\\end{equation*}\nNote that the determinant is $\\frac{c(H)}{a(\\chi,H)}$. Further, since \n\\begin{align*}\na(\\chi,H)&=\\sign{x}\\frac{\\Phi_x(x)}{\\sqrt{\\Phi(x)}}\\\\\n&=\\sqrt{2}\\frac{1+2\\varepsilon x^2}{\\sqrt{1+\\varepsilon x^2}},\n\\end{align*}\n\twe have that $a(\\chi,H) \\approx 1$ when $x$ is in a compact subset of $\\mathbb R$. As a result the determinant is bounded away from zero. For more details see Lemma~\\ref{lem:c-prime-bound}. \n\n\n\n\\section{The commuting vector field}\nWe first define the vector field $$Y=tc'(H)\\partial_Q-\\part_K.$$ In this section we prove that this vector field commutes with the transport operator as in \\eqref{e.VP_Q_H_lin} and that $|c'(H)|>0.$\n\\subsection{Commutation property}\n\nThe following commutation formula is an easy computation and thus we leave out the details.\n\\begin{lemma}\\label{lem:commutation}\nLet $Y = t c'(H) \\partial_Q - \\partial_{K}$. Then\n$$[\\partial_t - c(H) \\partial_Q, Y] = 0.$$\n\\end{lemma}\n\nThe following is an easy consequence of Lemma~\\ref{lem:commutation}:\n\\begin{lemma}\\label{lem:infinity-est}\nLet $f$ be a solution to \\eqref{e.VP_Q_H_lin} with initial data satisfying assumptions of Theorem~\\ref{thm:main}. Then\n$$\\sup_{(t,Q,K) \\in [0,\\infty)\\times \\mathbb T^1 \\times [c_s, c_s^{-1}]}\\hspace{.1em}\\sum_{\\ell\\leq 2} |Y^{\\ell} f|(t,Q,K) \\lesssim \\sup_{(x,v) \\in \\mathbb R\\times \\mathbb R} \\hspace{.1em}\\sum_{|\\alpha|+ |\\beta| \\leq 2} |\\partial_x^\\alpha \\partial_v^\\beta f_0|(x,v).$$\n\\end{lemma}\n\\begin{proof}\nBy Lemma~\\ref{lem:commutation}, we have that $Y^\\ell f$ satisfies the transport equation \\eqref{e.VP_Q_H_lin} for any $\\ell \\in \\mathbb N \\cap \\{0\\}$. Hence we get the estimate\n$$\\sup_{(t,Q,K) \\in [0,\\infty)\\times \\mathbb T^1 \\times [c_s, c_s^{-1}]}\\hspace{.1em}\\sum_{\\ell\\leq 2} |Y^{\\ell} f|(t,Q,K)\\lesssim \\sup_{(Q,K) \\in \\mathbb T^1 \\times [c_s, c_s^{-1}]}\\hspace{.1em}\\sum_{\\ell \\leq 2} |\\part_K^{\\ell} f_0|(Q,K).$$\nSince the change of variables $(Q,K)\\to (x,v)$ is well-defined and bounded away from zero, we get the required result.\n\\end{proof}\n\\subsection{Positivity of $|c'(K)|$}\nWe prove that $|c'(K)|$ is uniformly bounded below on the support of $f$. This plays a key role in the next section ensuring phase mixing.\n\\begin{lemma}\\label{lem:c-prime-bound}\nFor every $c_s <+\\infty$, there exists $\\epsilon_0>0$ such that whenever $\\epsilon \\in (0,\\epsilon_0]$, there is a small constant $\\delta >0$ (depending on $c_s$ and $\\epsilon$) such that \n$$\\inf_{K \\in [c_s, c_s^{-1}]} |c'(K)| = \\inf_{H \\in [c_s, c_s^{-1}]} |c'(H)| \\geq \\delta.$$\n\\end{lemma}\n\\begin{proof}\nBy definition of $c(H)$, we have\n$$\\frac{2\\pi}{c(H)}=\\int_0^{2\\pi}\\left|\\frac{\\sqrt{\\Phi}}{\\Phi'}\\right|\\d \\chi,$$\nso that using $\\Phi=\\frac{x^2}{2}+\\frac{\\epsilon}{2} x^4$, we obtain\n$$\\frac{2\\pi}{c(H)}=\\int_0^{2\\pi}\\frac{\\sqrt{1+\\epsilon x^2}}{\\sqrt{2}(1+2\\epsilon x^2)}\\d \\chi.$$\n\nNotice that for $H \\in [c_s, c_s^{-1}]$, $|x|$ is bounded. It follows that $c(H) \\approx 1$. Therefore, to prove strict positivity of $|c'(H)|$, it suffices to prove positivity of $\\left|\\frac{c'(H)}{c^2(H)}\\right|.$ Note that \n\\begin{equation}\\label{eq:c'\/c}\n\\begin{split}\n\\frac{-2\\pi c'(H)}{c^2(H)}&=\\frac 1{\\sqrt 2}\\int_0^{2\\pi}\\part_H\\left(\\frac{\\sqrt{1+\\epsilon x^2}}{1+2\\epsilon x^2}\\right)\\d \\chi\\\\\n&=\\frac 1{\\sqrt 2}\\int_0^{2\\pi}\\part_H x\\left[\\frac{\\epsilon x}{\\sqrt{1+\\epsilon x^2}(1+2\\epsilon x^2)}-\\frac{4\\epsilon x\\sqrt{1+\\epsilon x^2}}{(1+2\\epsilon x^2)^2}\\right]\\d \\chi\\\\\n&=\\frac 1{\\sqrt 2}\\int_0^{2\\pi}\\part_H x\\left[\\frac{-3\\epsilon x-2\\epsilon^2x^3}{\\sqrt{1+\\epsilon x^2}(1+2\\epsilon x^2)^2}\\right]\\d \\chi.\n\\end{split}\n\\end{equation}\n\nNow we calculate $\\part_H x.$ First we use the equation, $H=\\frac{v^2}{2}+\\Phi(x).$ Precisely, we have \n$$1=v\\part_H v+\\Phi'(x) \\part_H x.$$ Thus \n\\begin{equation}\\label{eq:dxH}\n\\part_H x=\\frac{1-v\\part_H v}{\\Phi'}.\n\\end{equation}\nNext we use that $\\frac{v}{\\sqrt{2H}}=\\sin\\chi,$\n$$0=\\frac{\\part_H v}{\\sqrt{2H}}-\\frac{v}{(2H)^{\\frac{3}{2}}}.$$\nThus $\\part_H v=\\frac{v}{2H}.$ Plugging this into \\eqref{eq:dxH}, we get that \n\\begin{equation}\\label{eq:dxH.final}\n\\part_H x=\\frac{1-\\frac{v^2}{2H}}{\\Phi'}=\\frac{\\cos^2 \\chi}{\\Phi'(x)} = \\frac{\\cos^2\\chi}{x+2\\epsilon x^3},\n\\end{equation}\nwhere in the last equality we used $\\Phi=\\frac{x^2+\\epsilon x^4}{2}$.\n\nPlugging \\eqref{eq:dxH.final} back into \\eqref{eq:c'\/c}, we get that\n\\begin{align*}\n\\frac{-2\\pi c'(H)}{c^2(H)}&=\\int_0^{2\\pi}\\frac{\\cos^2\\chi}{\\sqrt{2} x(1+2\\epsilon x^2)}\\left[\\frac{-x(3\\epsilon +2\\epsilon^2x^2)}{\\sqrt{1+\\epsilon x^2}(1+2\\epsilon x^2)^2}\\right]\\d \\chi\\\\\n&=-\\int_0^{2\\pi}{\\cos^2\\chi}\\left[\\frac{(3\\epsilon +2\\epsilon^2x^2)}{\\sqrt{2(1+\\epsilon x^2)}(1+2\\epsilon x^2)^3}\\right]\\d \\chi.\n\\end{align*}\nFinally note that since $|x|$ is bounded on the region of interest, after choosing $\\epsilon_0$ sufficiently small, we have\n$$\\frac{(3\\epsilon +2\\epsilon^2x^2)}{\\sqrt{2(1+\\epsilon x^2)}(1+2\\epsilon x^2)^3}\\approx \\epsilon,$$ \nand thus $|c'(H)|>\\delta$. \\qedhere\n\\end{proof}\n\n\n\n\n\n\n\n\\section{Decay for $\\varphi_t$}\nIn this section we finally prove the decay for $\\varphi_t$ (recall Theorem~\\ref{thm:main}). \n\nTo keep the notation lean, we will often suppress the explicit dependence on $t$.\n\\begin{lemma}\\label{lem:phi_t}\nFor $f$ satisfying the assumptions of Theorem~\\ref{thm:main}, and $\\varphi$ defined as in \\eqref{eq:phi}, we have the following formula\n$$\\varphi_t(x')=\\int_0^{x'}\\int_{\\mathbb{R}}v[f(y,v)-f(0,v)]\\d v\\d y.$$\n\\end{lemma}\n\\begin{proof}\nBy the continuity equation (following directly from \\eqref{eq:transport}), we have that $$\\rho_t=\\int_{\\mathbb{R}}v \\part_x f\\d v.$$\nThus $$-\\part^2_x \\varphi_t=\\rho_t=\\int_{\\mathbb{R}}v \\partial_x f\\d v.$$\nSolving the Laplace's equation (with boundary conditions \\eqref{eq:phi}), we get\n$$\\varphi_t(x')=\\int_0^{x'}\\int_0^y \\int_{\\mathbb{R}}v\\part_x f(z,v)\\d v\\d z\\d y.$$\nIntegrating by parts in $z$, we get\n$$\\varphi_t(x')=\\int_0^{x'}\\int_{\\mathbb{R}}v[f(y,v)-f(0,v)]\\d v\\d y.$$\n\\end{proof}\n\nIn view of Lemma~\\ref{lem:phi_t}, it suffices to bound $\\int_0^{x'}\\int_{\\mathbb{R}} v f(0,v) \\d v\\d y$ and $\\int_0^{x'}\\int_{\\mathbb{R}} v f(y,v) \\d v\\d y$, which will be achieved in the next two subsections respectively.\n\n\\subsection{Decay for the term involving $f(0,v)$}\nWe first prove decay for $\\int_{\\mathbb{R}} v f(0,v)\\d v.$ {Before proving the main estimate in Proposition~\\ref{prop:term-x=0}, we first prove a lemma.}\n\n\\begin{lemma}\\label{lem:Q.at.pi.2}\nThe level set $\\{x = 0\\}$ corresponds to the level sets $\\{ Q = \\frac \\pi 2 \\} \\cap \\{ Q = -\\frac \\pi 2\\} \\cup \\{(x,v) = (0,0)\\}$. \n\\end{lemma}\n\\begin{proof}\nFirst note that level set $\\{x = 0\\}$ corresponds to the level sets $\\{ \\chi = \\frac \\pi 2 \\} \\cap \\{ \\chi = -\\frac \\pi 2\\} \\cup \\{(x,v) = (0,0)\\}$. This is because when $x = 0$, $\\Phi(x)= 0 $, and thus by definition (when $v \\neq 0$) $\\chi:=\\arcsin\\left(\\frac{v}{\\sqrt{2H}}\\right) = \\arcsin (\\pm 1) = \\pm \\frac \\pi 2$.\n\nIt thus remains to show that\n\\begin{equation}\\label{eq:chi.Q.pi.2}\n\\chi = \\pm \\frac \\pi 2 \\iff Q = \\pm \\frac \\pi 2.\n\\end{equation}\n\nFix $H$, then since $a(\\chi,H)=\\lvert\\frac{\\Phi_x}{\\sqrt{\\Phi}}\\rvert$ is independent of $v$, we have\n$$c(H)\\int_0^{\\pi} \\frac{1}{a(\\chi,H)}\\d \\chi=c(H)\\int_{\\pi}^{2\\pi} \\frac{1}{a(\\chi,H)}\\d \\chi.$$\nFurther, by the evenness of $\\Phi$, we have\n$$c(H)\\int_0^{\\pi\/2} \\frac{1}{a(\\chi,H)}\\d \\chi=c(H)\\int_{\\pi\/2}^{\\pi} \\frac{1}{a(\\chi,H)}\\d \\chi.$$\nFinally, since we have by construction, $$c(H)\\int_0^{2\\pi} \\frac{1}{a(\\chi,H)}\\d \\chi=2\\pi,$$ we have that $$Q(\\chi=\\pi\/2,H)=c(H)\\int_0^{\\pi\/2} \\frac{1}{a(\\chi,H)}\\d \\chi=\\pi\/2.$$\nSimilarly, $Q(\\chi=-\\pi\/2,H)=-\\pi\/2.$ Combining these, we obtain \\eqref{eq:chi.Q.pi.2}. \\qedhere \n\\end{proof}\n\n\\begin{proposition}\\label{prop:term-x=0}\nFor $f$ satisfying the assumptions of Theorem~\\ref{thm:main}, we have the following estimate:\n$$\\Big| \\int_{\\mathbb{R}} v f(0,v)\\d v \\Big| \\lesssim \\langle t \\rangle^{-2} \\sup_{(x,v) \\in \\mathbb R\\times \\mathbb R} \\hspace{.1em}\\sum_{|\\alpha|+ |\\beta| \\leq 2} |\\partial_x^\\alpha \\partial_v^\\beta f_0|(x,v).$$\n\\end{proposition}\n\\begin{proof}\nThe transport equation preserves $L^\\infty$ bounds so that by the support properties, we obviously have\n$$\\Big| \\int_{\\mathbb{R}} v f(0,v)\\d v \\Big| \\lesssim \\sup_{(x,v) \\in \\mathbb R\\times \\mathbb R} \\hspace{.1em} | f_0|(x,v).$$\nIn other words, it suffices to prove the desired bound with $t^{-2}$ instead of $\\langle t \\rangle^{-2}$.\n\nNow note that $$\\int_{\\mathbb{R}} v f(0,v)\\d v=\\int_0^\\infty v [f(0,v)-f(0,-v)]\\d v.$$\nFor clarity of notation, we let $$\\bar{f}(t,Q,K) = f(t,x,v).$$ Now writing in the $(K,Q)$ variables, and using Lemma~\\ref{lem:Q.at.pi.2} together with the fact that $K = H = \\frac {v^2}2$ when $x=0$, we have\n$$\\int_{\\mathbb{R}} v f(0,v)\\d v=\\int_0^\\infty v [f(0,v)-f(0,-v)]\\d v=\\int_0^\\infty [\\bar f(\\pi\/2,K)-\\bar f(-\\pi\/2,K)]\\d { K}.$$\nBy the fundamental theorem of calculus, we have\n$$\\int_0^\\infty [\\bar f(\\pi\/2,K)- \\bar f(-\\pi\/2,K)]\\d { K}=\\int_{-\\pi\/2}^{\\pi\/2}\\int_0^\\infty \\part_Q \\bar f(Q,K)\\d {K}\\d Q.$$\nNext, the Cauchy--Schwarz inequality implies\n\\begin{align*}\n\\int_{-\\pi\/2}^{\\pi\/2}\\int_0^\\infty \\part_Q \\bar f(Q,K)\\d {K}\\d Q&=\\sqrt{\\pi}\\left(\\int_{-\\pi\/2}^{\\pi\/2}\\left(\\int_0^\\infty \\part_Q \\bar f(Q,K)\\d {K}\\right)^2\\d Q\\right)^{\\frac{1}{2}}\\\\\n&\\lesssim \\left(\\int_{0}^{2\\pi}\\left(\\int_0^\\infty \\part_Q \\bar f(Q,K)\\d {K}\\right)^2\\d Q\\right)^{\\frac{1}{2}}.\n\\end{align*}\nNow using Poincare's inequality we get that for any $\\ell\\geq 2$\n\\begin{equation}\\label{eq:easy.term.after.Poincare}\n\\left(\\int_{0}^{2\\pi}\\left(\\int_0^\\infty \\part_Q \\bar f(Q,K)\\d {K}\\right)^2\\d Q\\right)^{\\frac{1}{2}}\\lesssim \\left(\\int_{0}^{2\\pi}\\left(\\int_0^\\infty \\part^{\\ell}_Q \\bar f(Q,K)\\d {K}\\right)^2\\d Q\\right)^{\\frac{1}{2}}.\n\\end{equation}\n\nNow take $\\ell = 2$. We write $\\part_Q=\\frac{1}{c'(K)t}(Y+\\part_{K})$ so that \n\\begin{equation*}\n\\begin{split}\n&\\: \\left(\\int_{0}^{2\\pi}\\left(\\int_0^\\infty \\part^2_Q \\bar f(Q,K)\\d {K}\\right)^2\\d Q\\right)^{\\frac{1}{2}} \\\\\n= &\\: \\left(\\int_{0}^{2\\pi}\\left(\\int_0^\\infty \\frac{1}{|c'(K)|^2 t^2} ( Y^2 \\bar f+ 2\\part_{K} Y \\bar f+ \\partial^2_K \\bar f) (Q,K)\\d {K}\\right)^2\\d Q\\right)^{\\frac{1}{2}} \\\\\n\\lesssim &\\: \\frac 1{t^2} \\left(\\int_{0}^{2\\pi}\\left(\\int_0^\\infty (\\sum_{k=0}^2 |Y^k \\bar f|) (Q,K)\\d {K}\\right)^2\\d Q\\right)^{\\frac{1}{2}}.\n\\end{split}\n\\end{equation*}\nwhere in the last step we have integrated by parts in $K$ and bounded $\\frac 1{|c'(K)|}$, $\\frac{|c''(K)|}{|c'(K)|}$, etc.~using Lemma~\\ref{lem:c-prime-bound} and the smoothness of $c$.\n\nFinally, since $f(t,Q,K)$ is non-zero for $c_s\\leq K\\leq c_s^{-1}$ and $Q\\in [0,2\\pi]$, we can take supremum in $K$ and $Q$ followed by Lemma~\\ref{lem:infinity-est} to get the required result.\\qedhere\n\\end{proof}\n\n\n\\begin{remark}\nNotice that since we can take any $\\ell \\geq 2$ in \\eqref{eq:easy.term.after.Poincare}, we can write each $\\part_Q=\\frac{1}{c'(H)t}(Y+\\part_{K})$ and integrate by parts in $K$ many times to show that the term in Proposition~\\ref{prop:term-x=0} in fact decays faster than any inverse polynomial (depending on smoothness of $f$)! \n\nIn other words, the decay rate that we obtain in Theorem~\\ref{thm:main} is instead limited by the term treated in Proposition~\\ref{prop:bulk-term} below.\n\\end{remark}\n\n\\subsection{Decay for the term involving $f(y,v)$} \n\n{We now turn to the other term in Lemma~\\ref{lem:phi_t}. Before we obtain the main estimate in Proposition~\\ref{prop:bulk-term}, we first prove two simple lemmas.}\n\\begin{lemma}\\label{lem:Jacobian}\nUnder the change of variables $(x,v)\\mapsto (Q,K)$ as in Section~\\ref{sec:action}, the volume form transforms as follows:\n$$\\d v \\d x = c(K) \\,\\d Q\\d{K}.$$\n\\end{lemma}\n\\begin{proof}\nThe Jacobian determinant for the change of variables $(x,v)\\mapsto (\\chi,H)$ is $a(\\chi,H)=\\lvert\\frac{\\Phi_x}{\\sqrt{\\Phi}}\\rvert$. Further the Jacobian determinant for the change of variables $(\\chi,H)\\to (Q,K)$ is $\\frac{c(H)}{a(\\chi,H)}$ and hence the Jacobian determinant for $(x,v)\\to (Q,K)$ is $c(H) = c(K)$. \\qedhere\n\\end{proof}\n\n\\begin{lemma}\\label{lem:f-to-g}\nLet $\\bar{f}(Q,K) = f(x,v)$ as above. There exists a function $\\bar{g}(Q,K)$ such that\n\\begin{equation}\\label{eq:f-to-g}\n\\partial_Q^2 \\bar{g} = \\partial_Q \\bar{f}\n\\end{equation}\nand\n\\begin{equation}\\label{eq:g-bound}\n\\max_{\\ell\\leq 2} \\sup_{K} \\norm{Y^{\\ell} \\bar{g}}_{L^2_Q} \\lesssim \\max_{\\ell\\leq 2} \\sup_{Q,K} |Y^{\\ell} \\bar{f}|.\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nWe use the Fourier series of $f$ in $Q$ to get that $$\\bar{f}(Q,K)=\\sum_{k=-\\infty}^{k=\\infty} \\widehat{\\bar{f}}_k(K)e^{ikQ}.$$\nNow we define $$\\bar{g}(Q,K):=\\sum_{k\\in\\mathbb{Z}\\backslash\\{0\\}} \\frac{1}{ik}\\widehat{\\bar{f}}_k(K)e^{ikQ}.$$ Then we see that $\\partial^2_Q \\bar{g}=\\partial_Q \\bar{f}.$\n\nUsing Plancheral's theorem we can easily see that\n$$\\max_{\\ell\\leq 2} \\sup_{K} \\norm{Y^{\\ell} \\bar{g}}_{L^2_Q} \\lesssim \\max_{\\ell\\leq 2} \\sup_{K} \\norm{Y^{\\ell} \\bar{f}}_{L^2_Q}.$$\nFinally, the result follows by taking supremum in $Q$. \\qedhere\n\\end{proof}\n\n\\begin{proposition}\\label{prop:bulk-term}\nFor $f$ satisfying the assumptions of Theorem~\\ref{thm:main}, we have the following estimate:\n$$\\int_0^{x'}\\int_{\\mathbb{R}}vf(t,y,v)\\d v\\d y\\lesssim \\jap{t}^{-2} {\\sup_{(x,v) \\in \\mathbb R\\times \\mathbb R} }\\hspace{.1em} \\sum_{|\\alpha|+ |\\beta| \\leq 2} |\\partial_x^\\alpha \\partial_v^\\beta f_0|(x,v).$$\n\\end{proposition}\n\\begin{proof}\n{As in the proof of Proposition~\\ref{prop:term-x=0}, boundedness is obvious and thus it suffices to prove an estimate with $\\langle t\\rangle^{-2}$ replaced by $t^{-2}$.}\n\nWe first note that\n$$\\int_0^{x'}\\int_{\\mathbb{R}}vf(y,v)\\d v\\d y=\\int_0^{x'}\\int_0^\\infty v[f(y,v)-f(y,-v)]\\d v\\d y.$$\nAgain let $$\\bar{f}(t,Q,K) = f(t,x,v).$$\nNext we use the change of variables $(x,v)\\mapsto (Q,K)$, that $v=\\sqrt{2H}\\sin \\chi$ and Lemma~\\ref{lem:Jacobian} followed by the fundamental theorem of calculus to obtain\n\\begin{align*}\n&\\: \\int_0^{x'}\\int_0^\\infty v[f(y,v)-f(y,-v)]\\d v\\d y\\\\\n= &\\: \\int_0^{\\Phi(x')}\\int_0^{\\pi\/2} c(K)\\sqrt{2K}S(Q,K)[\\bar f(Q,K)-\\bar f(-Q,K)]\\d Q\\d {K}\\\\\n&+\\int_{\\Phi(x')}^\\infty\\int_{\\mathfrak{Q}_K}^{\\pi\/2} c(K)\\sqrt{2K}S(Q,K)[\\bar f(Q,K)-\\bar f(-Q,K)]\\d Q\\d {K}\\\\\n=&\\:\\int_0^{\\Phi(x')}\\int_0^{\\pi\/2}\\int_{-Q}^{Q} c(K)\\sqrt{2K}S(Q,K)\\part_Q \\bar f(Q',K)\\d {Q'}\\d Q\\d {K}\\\\\n&+\\int_{\\Phi(x')}^\\infty\\int_{\\mathfrak{Q}_{K}}^{\\pi\/2}\\int_{-Q}^{Q} c(K)\\sqrt{2K}S(Q,K)\\part_Q \\bar f(Q',K)\\d {Q'}\\d Q\\d {K}\\\\\n=: &\\: T_1+T_2,\n\\end{align*}\nwhere we have define\n\\begin{itemize}\n\\item $S(Q,K):=\\sin\\chi$, and \n\\item $\\mathfrak{Q}_K$ to be the angle in $(Q,K)$ coordinates corresponding to angle $\\arccos\\left(\\frac{\\Phi(x')}{H}\\right)$ in $(\\chi,H)$ coordinates.\n\\end{itemize}\n\nNow using Fubini's theorem, we have \n$$T_1=\\int_{-\\pi\/2}^{\\pi\/2}\\int_0^{\\Phi(x')}\\left(\\int^{\\pi\/2}_{|Q'|} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}\\part_Q \\bar f(Q',K)\\d {K}\\d {Q'}$$\nand \n\\begin{align*}\nT_2&=\\int_{-\\pi\/2}^{\\pi\/2}\\int_{\\Phi(x')}^{\\mathfrak{H}_{Q'}}\\left(\\int_{|Q'|}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}\\part_Q \\bar f(Q',K)\\d {K}\\d {Q'}\\\\\n&+\\int_{-\\pi\/2}^{\\pi\/2}\\int_{\\mathfrak{H}_{Q'}}^\\infty\\left(\\int_{\\mathfrak{Q}_{K}}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}\\part_Q \\bar f(Q',K)\\d {K}\\d {Q'},\n\\end{align*}\nwhere $\\mathfrak{H}_{Q'}$ is such that $\\left(\\arccos\\left(\\frac{\\Phi(x')}{\\mathfrak{H}_{Q'}}\\right),\\mathfrak{H}_{Q'}\\right)$ in $(\\chi,H)$ coordinates gets mapped to $(|Q'|,\\mathfrak{H}_{Q'})$ in $(Q,K)$ coordinates \n(such an $\\mathfrak{H}_{Q'}$ exists because $\\chi=\\arccos\\left(\\frac{\\Phi(x')}{H}\\right)$ increases as $H$ does and $Q$ is monotone\\footnote{Since $a(\\chi,H)>0$, we have that $Q$ is monotonically increasing as a function of $\\chi$ and vice-versa.} in $\\chi$.)\n\nPutting the above together we get,\n\\begin{align*}\nT_1+T_2=&\\int_{-\\pi\/2}^{\\pi\/2}\\int_{0}^{\\mathfrak{H}_{Q'}}\\left(\\int_{|Q'|}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}\\part_Q \\bar f(Q',K)\\d {K}\\d {Q'}\\\\\n&+\\int_{-\\pi\/2}^{\\pi\/2}\\int_{\\mathfrak{H}_{Q'}}^\\infty\\left(\\int_{\\mathfrak{Q}_{K}}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}\\part_Q \\bar f(Q',K)\\d {K}\\d {Q'}.\n\\end{align*}\nNow we use \\eqref{eq:f-to-g} from Lemma~\\ref{lem:f-to-g} and that $\\part_Q=\\frac{1}{c'({K})t}(Y+\\part_{K})$ to get that\n\\begin{align*}\nT_1+T_2=&t^{-1}\\int_{-\\pi\/2}^{\\pi\/2}\\int_{0}^{\\mathfrak{H}_{Q'}}\\frac{1}{c'({K})}\\left(\\int_{|Q'|}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K} (Y+\\part_{K})\\part_Q \\bar{g}(Q',K)\\d {K}\\d {Q'}\\\\\n&+t^{-1}\\int_{-\\pi\/2}^{\\pi\/2}\\int_{\\mathfrak{H}_{Q'}}^\\infty\\frac{1}{c'({K})}\\left(\\int_{\\mathfrak{Q}_{K}}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}(Y+\\part_{K})\\part_Q \\bar{g}(Q',K)\\d {K}\\d {Q'}.\n\\end{align*}\n\nNext we integrate by parts in $K$. Since $\\mathfrak{Q}_{\\mathfrak{H}_{Q'}}=|Q'|$, we see that the boundary terms exactly cancel! Hence,\n\\begin{align*}\n&t^{-1}\\int_{-\\pi\/2}^{\\pi\/2}\\int_{0}^{\\mathfrak{H}_{Q'}}\\frac{1}{c'(K)}\\left(\\int_{|Q'|}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K} \\part_{K}\\part_Q \\bar{g}(Q',K)\\d {K}\\d {Q'}\\\\\n&+t^{-1}\\int_{-\\pi\/2}^{\\pi\/2}\\int_{\\mathfrak{H}_{Q'}}^\\infty\\frac{1}{c'(K)}\\left(\\int_{\\mathfrak{Q}_{K}}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}\\part_{K}\\part_Q \\bar{g}(Q',K)\\d {K}\\d {Q'}\\\\\n&\\quad=-t^{-1}\\int_{-\\pi\/2}^{\\pi\/2}\\int_{0}^{\\mathfrak{H}_{Q'}}\\part_K\\left(\\frac{1}{c'(K)}\\left(\\int_{|Q'|}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}\\right)\\part_Q \\bar{g}(Q',K)\\d {K}\\d {Q'}\\\\\n&\\quad -t^{-1}\\int_{-\\pi\/2}^{\\pi\/2}\\int_{\\mathfrak{H}_{Q'}}^\\infty\\part_K\\left(\\frac{1}{c'(K)}\\left(\\int_{\\mathfrak{Q}_{K}}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}\\right)\\part_Q \\bar{g}(Q',K)\\d {K}\\d {Q'}.\n\\end{align*}\n\nSince there is no boundary term we can integrate by parts after writing $\\part_Q=\\frac{1}{c'({K})t}(Y+\\part_{K})$ once more. Next note that that $\\bar{g}(Q,K)$ is nonzero only for $K \\in [c_s,c_{s}^{-1}]$ and that derivatives of $\\frac{c(K)}{c'(K)}$ is bounded as $|c'(K)|\\geq \\delta$ by Lemma~\\ref{lem:c-prime-bound}. Futher, $S(Q,K)=\\sin\\chi$ is smooth as a function of $K$. Thus $$\\sum_{\\ell\\leq 2} \\partial_K^\\ell \\left(\\frac{1}{c'(K)}\\left(\\int_{|Q'|}^{\\pi\/2} S(Q,K)\\d Q\\right)c(K)\\sqrt{2K}\\right)\\lesssim 1.$$\n\nBy Cauchy--Schwarz in $Q'$ and $K$, we get that\n$$T_1+T_2\\lesssim \\sum_{\\ell\\leq 2}\\sup_{K}\\norm{Y^\\ell \\bar{g}}_{L^2_Q }.$$\nFinally, an application of \\eqref{eq:g-bound} from Lemma~\\ref{lem:f-to-g} followed by Lemma~\\ref{lem:infinity-est} gives us the required bound. \\qedhere\n\n\\end{proof}\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nThe proof follows by using Lemma~\\ref{lem:phi_t} and combining the estimates from Proposition~\\ref{prop:term-x=0} and Proposition~\\ref{prop:bulk-term}. \n\\end{proof}\n\\bibliographystyle{plain}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction and outline}\n\\label{sec:intro}\n\nThe goal of this paper is to understand the energy levels of near-extremal charged black holes in D dimensions from the perspective of the Euclidean gravitational path integral. The description of such black holes simplifies due to their $AdS_2 \\times X_{D-2}$ near-horizon geometry, where $X_{D-2}$ is a compact space, specifying the geometry of the horizon. \n\n\nThe spectrum of near-extremal black holes has been a source of confusion over the years.\\footnote{By near-extremal we mean large charge black holes with a small but non-zero temperature ($T\\ll r_0^{-1}$, where $r_0$ is the horizon radius at extremality). } The first manifestation of this was pointed out by Preskill et al \\cite{Preskill:1991tb} (see also \\cite{Maldacena:1998uz} and \\cite{Page:2000dk}). The thermodynamics derived from a semiclassical evaluation of the gravitational path integral gives a temperature-dependent mass above extremality $E = M-M_0$ as $E \\sim 2\\pi^2 \\Phi_r T^2 $, where $M_0$ is the extremal mass. A similar analysis gives the semiclassical entropy $S=S_0 + 4\\pi^2 \\Phi_r T$, where $S_0=A_0\/(4G_N)$ is proportional to the extremal area $A_0$. This behavior is universal, but the precise value of the parameter $\\Phi_r$ depends on the details of the model. Because of this scaling, it was noticed in \\cite{Preskill:1991tb} that the thermodynamic description of near-extremal black holes breaks down at low enough temperatures, $T\\lesssim 1\/\\Phi_r$. At such temperatures, the naive semiclassical analysis suggests that emitting even a single Hawking quanta is sufficient to change the black hole's temperature by a large amount.\\footnote{An equivalent issue was pointed out in \\cite{Maldacena:1998uz} due to the dependence of $\\Phi_r$ with Newton constant $G_N$ in particular models.} String theory microstate counting examples \\cite{Maldacena:1996ds} indicate the resolution of this issue is the presence of a mass gap of order $E_{gap}\\sim 1\/\\Phi_r$ in the black hole spectrum.\\footnote{In this paper, we will focus on scales of order $\\sim 1\/\\Phi_r$ also captured by the gravity description. Of course, at a completely different regime of even lower temperatures $T \\sim e^{-S_0}$, we expect to find a discrete spectrum with spacing of order $e^{-S_0}$. This scale requires a UV completion of the gravitational theory.} However, its origin from the gravitational description has, so far, been elusive.\n\nA second manifestation of the gap problem is the question of the extremal black hole degeneracy. If the gravity description somehow breaks down at the energy scale $E \\sim 1\/\\Phi_r$ then it is possible that the black hole entropy $S_0$, obtained by a semiclassical computation, does not correctly capture the degeneracy of the extremal black hole; rather, $e^{S_0}$ could instead capture the number of extremal and near-extremal states within a $\\sim 1\/\\Phi_r$ energy interval. Nevertheless, while some authors claimed the extremal entropy vanishes \\cite{Hawking:1994ii}, string theory examples that preserve sufficient supersymmetry showed that the degeneracy at extremality (assumed to be captured by an index) matches with the Bekenstein-Hawking area $S_0$ \\cite{Strominger:1996sh}. \n\nIn \\cite{Iliesiu:2020qvm} these questions have been revisited using lessons from SYK \\cite{kitaevTalks, Maldacena:2016hyu} and Jackiw-Teitelboim (JT) gravity \\cite{Teitelboim:1983ux, Jackiw:1984je, Almheiri:2014cka, Jensen:2016pah, Maldacena:2016upp, Engelsoy:2016xyb,Iliesiu:2019lfc, Kapec:2019ecr}. The procedure used to compute the higher dimensional Euclidean path integral for non-supersymmetric black holes is the following. First, we separate the full near extremal geometry into the near-horizon $AdS_2 \\times X_{D-2}$ throat (where the interesting physics takes place) and the far away region, which we take to be \nflat. The second step is to recast the $D$-dimensional theory on the throat as a 2D theory on $AdS_2$. It was argued in \\cite{Iliesiu:2020qvm} that the temperature-dependence of the Euclidean path integral near-extremality is solely captured by a JT gravity theory (composed of fluctuations around the $AdS_2$ metric and a dilaton which captures the size of $X_{D-2}$), a dimensional reduction of $D$-dimensional gauge fields to 2D, and a gauge field originating from isometries of the horizon $X_{D-2}$. This reduction was considered classically in \\cite{Sachdev:2015efa, Almheiri:2016fws, Anninos:2017cnw, Turiaci:2017zwd, Nayak:2018qej, Moitra:2018jqs, Hadar:2018izi, Castro:2018ffi, Larsen:2018cts, Moitra:2019bub, Sachdev:2019bjn, Hong:2019tsx, Castro:2019crn, Charles:2019tiu,Larsen:2020lhg} but the new feature of \\cite{Iliesiu:2020qvm} is the explanation that quantum effects at low temperatures are also captured by this simple theory. It can be shown that JT gravity exactly reduces to a boundary mode, the Schwarzian theory, which lives in the boundary of the throat (shown in figure \\ref{fig:4DnearBH}), and describes the breaking of the emergent $SL(2,\\mathbb{R})$ symmetry in the throat. In this theory, one can compute the path integral exactly and extract the near-extremal spectrum.\\footnote{Integrating out the massive KK modes and other fields have the only possible effect of introducing temperature-independent logarithmic corrections to $S_0$ previously computed in \\cite{Banerjee:2010qc, Banerjee:2011jp, Sen:2011ba, Sen:2012cj, Iliesiu:2020qvm} and thus such modes will not affect the computation of the density of states.}\n\n\\begin{figure}[t!]\n\\begin{center}\n \\begin{tikzpicture}[scale=1]\n \\pgftext{\\includegraphics[scale=0.6]{nearextBHWiggleCropped.png}} at (0,0);\n \\draw (-5.5,3.5) node {\\small Asymptotic flat region};\n \\draw (-3.9,-1) node[text width=5cm,align=center] {\\small Near-horizon boundary \\\\ $\\tau \\rightarrow f(\\tau)$ JT mode};\n \\draw (-2.3,-3.5) node[text width=10cm,align=center] {\\small Horizon};\n \\draw (3,-2.3) node[text width=5cm,align=center] {\\small Near AdS$_2$ region\\\\ Supported by a constant $U(1)$ flux};\n \n \\draw (4.5,1.5) node[text width=5cm,align=center] {\\small Near extremal black hole\\\\ Charge $Q \\sim $ Mass};\n \\end{tikzpicture}\n \\caption{\\label{fig:4DnearBH} The near extremal 4D black hole. A similar picture applies to near-extremal rotating BTZ black hole in AdS$_3$.}\n \\end{center}\n \\end{figure}\n \nWith this new perspective, the authors of \\cite{Iliesiu:2020qvm} addressed the puzzling questions about the thermodynamics of non-supersymmetric near-extremal Reissner-Nordstr\\\"om black holes. Regarding the first confusion pointed out above, the scale $T\\sim 1\/\\Phi_r$ identified by \\cite{Preskill:1991tb} was found to be the scale at which quantum effects in the gravitational path integral become large, and the Schwarzian description becomes strongly coupled. This effect signals that the breaking of the $SL(2, \\mathbb R)$ conformal symmetry in the throat becomes important \\cite{Almheiri:2016fws}. However, in contrast with string theory proposals \\cite{Maldacena:1996ds}, it was found that for non-supersymmetric black holes, there is no gap at the scale $E\\sim 1\/\\Phi_r$ and the gravitational path integral predicts a density of states that goes smoothly to zero as $E\\to0$. For convenience, this density of states is reproduced here in figure \\ref{fig:plot-of-rho} in the left column. Regarding the second confusion about the ground state degeneracy of the extremal black hole, \\cite{Iliesiu:2020qvm} also predicts the entropy of the extremal black hole to be much smaller than the naive prediction given by the area of the extremal horizon. A similar conclusion was obtained for near-extremal rotating black holes in 3D gravity \\cite{Ghosh:2019rcj,Maxfield:2020ale}.\n \n \n \nThe results of \\cite{Iliesiu:2020qvm} thus show a very different spectrum of near-extremal black holes compared to results obtained from microscopic counting: string theory predicts that the ground state degeneracy of extremal black holes should agree with Bekenstein-Hawking extremal area \\cite{Strominger:1996sh} and that the value of the mass gap $E_{gap}\\sim 1\/\\Phi_r$ can be extrapolated from the weak coupling regime \\cite{Maldacena:1996ds}. The purpose of this paper is to resolve this puzzle. The solution is that most examples in string theory involve supergravity and the extremal black hole preserves some supersymmetries. In the cases studied in this paper, the temperature dependence of the Euclidean path integral is captured by a supersymmetric generalization of JT gravity, which describes the breaking of an emergent superconformal symmetry.\n\n\n In this paper, we will discuss the case of near-BPS near-extremal charged black holes in two setups. The first is in four dimensions ($D=4$), where such objects are described classically by the Reissner-Nordstr\\\"om solution with a $AdS_2 \\times S^2$ throat. We will consider such black holes in 4D ungauged $\\cN=2$ supergravity in asymptotically flat space \\cite{Freedman:1976aw}. The situation is depicted in Figure~\\ref{fig:4DnearBH}. In our conventions $M_0=Q\/\\sqrt{G_N}$, proportional to the charge of the black hole, and $\\Phi_r= \\sqrt{G_N}\\hspace{0.1cm} Q^3$. Such a 4D theory has the right ingredients for SUSY preserving extremal black holes \\cite{Gibbons:1982fy,Tod:1983pm,Ferrara:1995ih}. The BPS nature of such gravitational solutions allows one to identify them with corresponding string theory constructions, for example \\cite{Maldacena:1997de, Ooguri:2004zv,Simons:2004nm,Denef:2007vg}. The second setup we will consider is a near-extremal rotating black hole in $(4,4)$ supergravity in $AdS_3$, with a near-horizon geometry $AdS_2 \\times S^1$. This system is relevant for comparison with the D1\/D5 system \\cite{Strominger:1996sh}. Even though some questions about the spectrum can be addressed using solely the $(4,4)$ super-Virasoro algebra in this particular case thanks to the presence of an $AdS_3$ throat, we will be able to show in section \\ref{sec:ads3} that the spectrum matches with the one derived from JT gravity. The Virasoro analysis obviously does not generalize to other cases, for example the Reissner-Nordstr\\\"om black hole mentioned above, but the JT analysis does and is universal as we explain below.\n\n \nThe main feature in 4D $\\cN=2$ supergravity and $(4,4)$ supergravity in $AdS_3$, compared to Einstein gravity, is the fact that the emerging symmetry in the throat becomes the superconformal group $PSU(1,1|2) \\supset SL(2,\\mathbb{R})\\times SU(2)$. In 4D, this includes the $AdS_2$ conformal symmetry and the $S^2$ isometries as bosonic subgroups \\cite{Kallosh:1997qw, Boonstra:1998yu,Claus:1998yw,Michelson:1999kn}. In $AdS_3$ the $SU(2)$ arises from a 3D gauge field. In the examples from string theory there is a second $SU(2)$ gauge field coming from the isometries of an extra $S^3$ factor. This second $SU(2)$ charge can be turned on without breaking supersymmetry, and corresponds to the black hole of \\cite{Breckenridge:1996is} which has rotation along the $S^3$. We find in both cases the relevant 2D mode in the throat controlling the temperature dependence of the partition function is given by $\\cN=4$ super-JT gravity, which describes the breaking of $PSU(1,1|2)$ and becomes strongly coupled at low temperatures. We will first define this theory and solve it exactly to extract the temperature dependence of the partition function, and from it the black hole spectrum. In order to do this, we will show that $\\cN=4$ super-JT gravity is equivalent to a $\\cN=4$ super-Schwarzian theory, which we introduce in this paper. We solve this theory using either path integral localization or canonical quantization.\\footnote{Previous attempts of defining the $\\mathcal{N}=4$ super-Schwarzian are \\cite{Aoyama:2018lfc, Aoyama:2018voj, Galajinsky:2020hsy} however, explicit formulae for all the components of the $\\mathcal{N}=4$ super-Schwarzian were not presented. Furthermore, to our knowledge, no previous quantization attempt has been made. }\n \n\\begin{figure}[t!]\n\\begin{center}\n \\begin{tikzpicture}[scale=0.65]\n \\pgftext{\\includegraphics[scale=0.46]{dosem.pdf}} at (0,0);\n \\draw (-5.5,-0.4) node {$e^{S_0}$};\n \\draw (-5.5,2.75) node {\\small $\\rho(E)$};\n \\draw (-1.7,-3.2) node {\\small $\\sim 1\/\\Phi_r$};\n \\draw (4.55,-3) node {\\small $E$};\n \\draw (-1,-4.5) node {\\small (a) Einstein gravity, $J=0$};\n \\end{tikzpicture}\n \\hspace{0.5cm}\n \\begin{tikzpicture}[scale=0.65]\n \\pgftext{\\includegraphics[scale=0.51]{dossugra.pdf}} at (0,0);\n \\draw (-5.7,-0.4) node {$e^{S_0}$};\n \\draw (-5.8,2.75) node {\\small $\\rho(E)$};\n \\draw (-1.7,-3.2) node {\\small $E_{gap}= \\frac{1}{8 \\Phi_r}$};\n \\draw (5,-3) node {\\small $E$};\n \\draw (-0.5,-4.5) node {\\small (b) SUGRA, $J=0$};\n \\end{tikzpicture}\n \\\\\n \\vspace{0.8cm}\n \\begin{tikzpicture}[scale=0.65]\n \\pgftext{\\includegraphics[scale=0.46]{dosj1.pdf}} at (0,0);\n \\draw (-5.5,-0.4) node {$e^{S_0}$};\n \\draw (-5.5,2.75) node {\\small $\\rho(E)$};\n \\draw (-1.7,-3.2) node {\\small $J(J+1)\/2\\Phi_r$};\n \\draw (4.55,-3) node {\\small $E$};\n \\draw (-1,-4.5) node {\\small (c) Einstein gravity, $J\\neq0$};\n \\end{tikzpicture}\n \\hspace{0.5cm}\n \\begin{tikzpicture}[scale=0.65]\n \\pgftext{\\includegraphics[scale=0.51]{dosj1susy.pdf}} at (0,0);\n \\draw (-5.7,-0.4) node {$e^{S_0}$};\n \\draw (-5.8,2.75) node {\\small $\\rho(E)$};\n \\draw (-1.7,-3.2) node {\\small $J^2\/2\\Phi_r$};\n \\draw (5,-3) node {\\small $E$};\n \\draw (-0.5,-4.5) node {\\small (d) SUGRA, $J\\neq0$};\n \\end{tikzpicture}\n \\caption{\\label{fig:plot-of-rho} Schematic shape of the black hole spectrum at fixed $SU(2)$ charge $J$ as a function of energy above extremality $E$. In 4D $J$ is angular momentum while in $AdS_3$ it is the $SU(2)$ charge (one of the two rotations along extra $S^3$ in string theory) that breaks supersymmetry. We show the semiclassical answer (red dashed) and the solution including quantum effects (purple). (a) Answer for Einstein gravity. We see there is no gap at scale $E\\sim 1\/\\Phi_r$ and the extremal entropy goes to zero. (b) Answer for supergravity (either $\\cN=2$ in 4D or $\\cN=(4,4)$ in 3D). We find a gap at the scale $E_{gap}=\\frac{1}{8\\Phi_r}$ and a number $e^{S_0}$ of extremal states, consistent with string theory expectations. (c) Einstein gravity spectrum for $J\\neq 0$. (d) Supergravity spectrum for $J\\neq 0$, the jumps indicate contributions from different supermultiplets $\\mathbf{J}, \\mathbf{J+1\/2}$ and $\\mathbf{J+1}$.}\n \\end{center}\n \\end{figure}\n\nTo contrast the results found in this paper, we show in Figure~\\ref{fig:plot-of-rho} the spectrum of near-extremal black holes derived from Einstein gravity in 4D coupled to a Maxwell field in the left panel. As previously mentioned, we see a small extremal entropy and a lack of a gap. In the right column, we show the main result of this paper, the density of states for near-extremal black holes in 4D $\\cN=2$ supergravity and $(4,4)$ supergravity in asymptotically $AdS_3$. Through a computation of the Euclidean path integral, independent of the UV completion of the theory, our analysis verifies and predicts the following results:\n\\begin{itemize}\n\\item We find that extremal BPS states exhibit an exact degeneracy. This degeneracy is given by the Bekenstein-Hawking horizon area, which is consistent with extremal microstate countings in string theory \\cite{Strominger:1996sh}. This is not true for extremal non-BPS states with $J\\neq 0$.\n\n\\item We observe the presence of a mass gap $E_{gap}=1\/(8\\Phi_r)$. In the context of $(4,4)$ supergravity in asymptotically $AdS_3$ and the D1\/D5 system, we verify the mass gap estimated at weak coupling from long strings \\cite{Maldacena:1996ds}. Here, this prediction is also expanded to black holes in 4D $\\cN=2$ supergravity. \n\n\\item We find that the extremal states are solely bosonic, implying the Witten index from a microscopic model coincides with the black hole degeneracy (For previous arguments see for example \\cite{Sen:2009vz, Mandal:2010cj, Benini:2015eyy}). This implies that the typical counting of microstates in string theory using an index, such as \\cite{Strominger:1996sh}, is correct.\n\n\\item A previous attempt to obtain the gap from a gravitational perspective was studied by Maldacena and Strominger \\cite{Maldacena:1997ih}. Their argument is only correct for black holes in AdS$_3$, and not for Reissner-Nordstr\\\"om for example. Our analysis that takes into account the breaking of $PSU(1,1|2)$ can in principle be applied to any system with this pattern of symmetry breaking and applies to situations without an $AdS_3$ factor in the throat.\n\n\\item In combination with the prior non-supersymmetric results from \\cite{Iliesiu:2020qvm}, we conclude that these features found in string theory examples heavily rely on supersymmetry and are special to supergravity. \n\\end{itemize}\n\n\n\nWithout jumping into all the details, we will give a summary of the technical results derived below. The $\\cN=4$ super-Schwarzian theory describing the spectrum of supersymmetric black holes, is given by the following action\n\\beq\n\\label{eq:intro-N=4-Schw-action}\nI_{\\cN=4} =-\\Phi_r \\int d\\tau \\left[ \\text{Sch}(f,\\tau) + {\\rm Tr} \\left( g^{-1} \\partial_\\tau g\\right)^2 + ({\\rm fermions}) \\right],\n\\eeq\nwhere the Schwarzian derivative is defined as \n\\beq\n\\text{Sch}(f,\\tau) \\equiv \\frac{\\partial_\\tau^3 f}{\\partial_\\tau f} - \\frac{3}2 \\left(\\frac{\\partial_\\tau^2 f}{\\partial_\\tau f} \\right)^2.\n\\eeq\nIn the action \\eqref{eq:intro-N=4-Schw-action}, $f(\\tau)$ is a reparametrization mode, $g(\\tau)$ is a $SU(2)$ element, and we ignore the terms involving fermionic fields $\\eta(\\tau)$ and $\\bar{\\eta}(\\tau)$ in the doublet of $SU(2)$. The field $g(\\tau)$ describes fluctuations in the angular momentum $J$ of the black hole in the 4D setup comes from the isometries of $S^2$. This theory has $\\cN=4$ Poincar\\'e supersymmetry but \\textbf{breaks} the emergent superconformal $PSU(1,1|2)$ space-time symmetry.\\footnote{Nonetheless, $PSU(1,1|2)$ is an important global symmetry for the action \\eqref{eq:intro-N=4-Schw-action}. We will clarify this in section \\ref{sec:space-time-and-global-symm}.} According to the unbroken $\\cN=4$ Poincar\\'e supersymmetry, supermultiplets organize into $\\mathbf{J}=(J)\\oplus2(J-\\frac{1}{2})\\oplus(J-1)$ for $E\\neq0$ and states $(J)$ for $E=0$. Solving this theory exactly gives the density of supermultiplet states labeled by their highest angular momentum $J$ as\n\\beq\n\\rho(J,E) = e^{S_0} \\delta_{J,0}\\delta(E)+ \n\\frac{e^{S_0}J}{4\\pi^2 \\Phi_r E^2}\\sinh \\left(2 \\pi \\sqrt{2\\Phi_r(E-E_0(J))} \\right)\\hspace{0.1cM} \\Theta\\Big(E-E_0(J)\\Big)\\,, \n\\eeq\nwhere we define $E_0(J)\\equiv J^2\/(2\\Phi_r)$. The details of this formula can be found in section \\ref{sec:N=4-super-Schw}. For example, states with zero angular momentum come from the contributions with $J=0$, $J=1\/2$ since $\\mathbf{1\/2}=(1\/2)\\oplus 2 (0)$ and $J=1$ since $\\mathbf{1}=(1)\\oplus 2(1\/2)\\oplus (0)$. This is the origin of the plot in the right panel of figure \\ref{fig:plot-of-rho}. In the continuous part states with angular momentum $J$ start at an energy $E_0(J)$. This is not necessarily surprising since the same feature appears in the non-supersymmetric case \\cite{Iliesiu:2020qvm}. From the gravity perspective this is the correction to the extremality bound of non-BPS extremal charged slightly-rotating black holes. The surprising feature in the supersymmetric theory is that there are no states with zero angular momentum in the range $0$ is a quadratic bilinear form invariant under adjoint transformations. Such a form can be obtained from the quadratic Casimir of $\\mathfrak{psu}(1,1|2)$.\\footnote{The quadratic Casimir of the superalgebra can be written as, \\be\nC_2 = L_0^2 - \\frac{1}2 \\{L_1, L_{-1}\\} - T_i T^i +\\frac{1}4 (i \\sigma^2)^\\b{}_\\a [G_p{}^\\a ,\\bar G^{\\,p}{}_\\b],.\\nn\n\\ee\nRewriting $C_2 = g_{AB} X^A X^B$ with $X = \\{L_0, L_\\pm,\\,T_i,\\,G_p{}^\\a,\\, \\bar G^{\\,p}{}_\\a\\}$, we can define the invariant quadratic form by using the metric, $\n \\eta_{AB}^{\\mathfrak{psu(1,1|2)}} = (-1)^{[X_A]} (g_{AB}^{-1})\n$, \nwhere $[X_A] = 0,1$ for bosonic, and respectively, fermionic generators. In such a case, we can define the supertrace as, $\n\\text{Str} B F = \\< B, \\, F\\> = \\eta_{AB} B^A F^B$.\n} We define the gauge field in terms of the supermultiplet of the frame $e^a$ and spin connection $\\omega$, also consisting of the SU(2) gauge field $B^i$ and the gravitinos $\\psi_{p}{}^{ \\a}$ as:\\footnote{We choose such conventions for the $\\mathfrak{sl}(2, \\mR)$ components of the gauge field in order to agree with \\cite{Saad:2019lba}. }\n\\be \n\\label{eq:gauge-field-ansatz}\nA(x) &= \\sqrt{\\frac{\\Lambda}{2}} \\left[e^1(x) L_0 + \\frac{e^2(x)}2 \\left(L_{1} - L_{-1}\\right) \\right]- \\frac{\\omega(x)}2 \\left(L_1+L_{-1}\\right) + B^i(x) T_i + \\nonumber \\\\ &+ \\left(\\frac{\\Lambda}{2}\\right)^{\\frac{1}{4}}\\left(\\bar{\\psi}^{p}{}_{ \\a}(x) G_{p}{}^{ \\a} + \\psi_{p}{}^{ \\a}(x) \\bar{G}^p{}_{ \\a}\\right)\\,.\n\\ee\nThe zero-form field $\\phi(x)$ is fixed in terms of the supermultiplet of the $SL(2, \\mR)$ Lagrange multipliers $\\phi^{1, 2}$ and $\\phi^0$, \n\\be\n\\label{eq:Lagr-multiplier}\n\\phi(x) &= 2\\phi^1(x) L_0 + {\\phi^2(x)} \\left(L_1 - L_{-1} \\right)- {\\phi^0(x)} \\left(L_1+L_{-1}\\right) + b^i (x) T_i + \\nn \\\\ &+ \\left(\\frac{\\Lambda}{2}\\right)^{-\\frac{1}{4}}\\left(\\bar{\\lambda}^{p}{}_{ \\a }(x) G_{p}{}^{ \\a}+ \\lambda_{p}{}^{ \\a }(x) \\bar G^{p}{}_{ \\a}\\right)\\,.\n\\ee\nHere, $\\lambda$ and $\\bar \\lambda$, and $\\psi$ and $\\bar \\psi$ should be understood as independent components of $A$ and are not related to the Hermitian conjugates of each other. In such a case, the action can be written as,\n\\be\n\\label{eq:JT-N=4-action}\nI_{JT}^{\\cN=4}& = {i} \\int \\bigg( \n\\underbrace{\\phi^0}_{\\sim\\text{ Dilaton}}\\bigg[\\underbrace{d\\omega + \\frac{\\Lambda}4 \\epsilon_{ab} e^a \\wedge e^b}_{\\frac{d^2 x}2 \\sqrt{g} (R+\\Lambda)} -\\underbrace{\\sqrt{2\\Lambda}\\bar{\\psi}^p{}_\\alpha \\wedge \\psi_p {}^\\alpha }_{\\substack{\\text{Gravitino $\\psi_p{}^\\a$ contribution}\\\\\\text{multiplying the dilaton}}}\\bigg] \\nn \\\\ &- \\sqrt{\\frac{\\Lambda}{2}}\\underbrace{\\phi^a}_{\\substack{\\text{Super-torsion}\\\\ \\text{Lagr.~multiplier}}}\\underbrace{\\left[de^a - \\epsilon_{ab}\\,\\omega\\wedge e^b +2\\left(\\bar \\gamma_a\\right)^{\\alpha}{}_{\\beta}\\bar{\\psi}^{p}{}_\\alpha\\wedge \\psi_p{}^{\\beta} \\right]}_{\\text{super-torsion component }\\tau^a} \\\\ \n&- \\tr_{SU(2)} \\underbrace{\\left[b \\left(dB + B \\wedge B \\right)\\right]}_{SU(2)\\text{ BF theory}} \\,\\,+ \\,\\, \\sqrt{\\frac{\\Lambda}{2}}\\underbrace{b^q{}_p \\bar{\\psi}^p{} \\wedge \\bar{\\gamma}_3\\psi_q{} }_{SU(2)\\text{ BF super-partner}}\\nn \n+ \\, \\underbrace{2\\lambda\\mathcal{D} \\bar{\\psi} +2\\mathcal{D}^*\\psi \\bar{\\lambda}}_{\\substack{\\text{Gravitino $\\psi^p{}_\\a$ and }\\\\\\text{dilatino $\\lambda_p{}^\\a$ contribution}}}\\bigg)\\,, \n\\ee\nwhere we have outlined the important terms in the action which will ease the comparison with the near-horizon action which we shall obtain in section \\ref{ssec:4dSugra}.\\footnote{Above, our normalization of the $SU(2)$ trace is given such that $\\tr_{SU(2)}(T^i T^j) = -\\frac{1}2 \\delta^{ij}$. The covariant derivative is given by $\\mathcal{D}=\\bar{\\gamma}_3d+ \\sqrt{\\frac{\\Lambda}{2}}\\bar{\\gamma}_ae^a+\\frac{1}{2}\\omega+\\frac{\\bar{\\gamma}_3}{2}B^i (\\sigma^i)$, and $\\cD^*$ is the conjugate of $\\cD$. We choose $\\bar{\\gamma}_1=\\sigma_1,\\,\\, \\bar{\\gamma}_2=-\\sigma_3$, and $\\bar \\gamma_3 = \\bar \\gamma_1 \\bar \\gamma_2 = i\\sigma_2$. }\n\n\nThe equations of motion for $\\phi^{1,2}$ act as Lagrange multipliers and imposes that the super-torsion vanishes. After integrating out $\\phi^{1,2}$, one can replace $e^1$, $e^2$ and $\\omega$ in terms of the metric $g_{\\mu \\nu}$ to obtain the supergravity action \\eqref{eq:JT-N=4-action} in the second order formalism. The JT gravity dilaton is given by $-i \\phi^0 \\equiv \\Phi$. When comparing the gravitational theory obtained from the dimensional reduction of the near-horizon region of near-BPS black holes to the $\\cN=4$ super-JT action we will use this latter form. For simplicity, in the remainder of this section we will fix the cosmological constant, $\\Lambda = 2$. Later, when discussing the effective action for the near-horizon region of the near-BP black holes we will revert to a general cosmological constant, determined by the radius of the black hole.\n\n\nWe can complete the dictionary between the BF theory \\eqref{eq:JT-N=4-action} and the second-order super-JT gravity by noting that infinitesimal $PSU(1,1|2)$ gauge transformations in the former, map to infinitesimal diffeomorphisms in the latter. In particular, the infinitesimal supersymmetric transformations on all the fields in \\eqref{eq:JT-N=4-action} can be obtained by considering the infintesimal gauge transformation whose gauge parameter takes the form $\\Lambda = \\epsilon^{p\\a} G_{p \\a} + \\bar \\epsilon^{p \\a} \\bar G_{p \\a}$. \n\n\nWith this mapping between the BF theory \\eqref{eq:JT-N=4-action} and $\\cN=4$ super-JT gravity in mind, we can now analyze the supersymmetric boundary conditions applied to the theory \\eqref{eq:JT-N=4-action} which will be necessary in section \\ref{sec:higherD} to understand the gluing of the near-horizon region of near-BPS black holes to asymptotic flatspace. As we will see, these boundary conditions will reduce the gravitational path integral to that of the $\\cN=4$ super-Schwarzian. \n\n\n\n\\subsection{The super-JT boundary conditions}\n\\label{sec:super-JT-bdy-cond}\n\nWe begin, just like in the non-supersymmetric case, by imposing that the boundary metric is fixed and proper boundary length is large, $L = \\beta\/\\varepsilon$, with $\\varepsilon$ small. Working in Fefferman-Graham gauge, where the metric can be written as\n\\be \n\\label{eq:FG-gauge-metric}\nds^2 = dr^2 + \\left(\\frac{1}4 e^{2r} - \\tilde \\cL(\\tau) + \\dots \\right) d\\tau^2 \\,,\n\\ee\nwe consider the boundary to be at fixed, but large, $r$. To satisfy the boundary conditions we identify $\\tau \\sim \\tau+\\beta$ and cut-off the geometry at $e^{-r} = \\varepsilon\/2$. We fix the leading component of the boundary metric and allow the sub-leading component, $\\tilde \\cL(\\tau)$, to vary. The $\\dots$ represent terms that are further sub-leading in $r$ which we do not need to fix.\n\nSimilarly, we require that the dilaton takes an asymptotically large and constant value at the boundary, $\\Phi|_{\\partial \\cM} \\equiv -i\\phi_0|_{\\partial \\cM} = \\Phi_r\/\\varepsilon$. Next, we discuss the boundary conditions for the super-partners of the frame and spin-connection. If working in the Fefferman-Graham gauge \\eqref{eq:FG-gauge-metric}, then, in order to preserve supersymmetry, we impose that the leading component of the gravitino is fixed and vanishes $\\psi^{p \\alpha} = O(e^{-r\/2})$ and $\\bar \\psi^{q \\alpha} = O(e^{-r\/2})$. Similarly, to again preserve supersymmetry, we impose that the leading contribution of the dilatino vanishes at asymptotically large values $\\lambda = O(e^{-r\/2})$. Finally, we describe the boundary conditions for the $SU(2)$ gauge field and its Lagrange multiplier. From the perspective of the higher dimensional black holes which we will study in section \\ref{sec:higherD}, the $SU(2)$ gauge field appears from the isometry of $S^2$ along which we perform the dimensional reduction. We would therefore, like to fix Dirichlet boundary conditions at the boundary of the asymptotically flat region. As we will describe in detail in section \\ref{sec:higherD}, imposing these boundary conditions in the asymptotically flat region translates to fixing a linear combination of the zero-form field $b$ and the $SU(2)$ gauge field $B$ at the boundary of the near-horizon region. \n\n\nWe would now like to translate the boundary conditions discussed above in the second-order formalism to boundary conditions in the BF theory \\eqref{eq:JT-N=4-action}. We will follow the steps outlined in \\cite{Grumiller:2017qao} (explained also recently in \\cite{Saad:2019lba}) in non-supersymmetric JT gravity and will obtain results similar to \\cite{Cardenas:2018krd}, which studied boundary conditions in JT supergravity with an $OSp(2, \\cN)$ isometry group. Fixing the Fefferman-Graham gauge for the metric \\eqref{eq:FG-gauge-metric} yields the value of the frame $e^{1, \\, 2}$ and spin connection $\\omega$ \\cite{Saad:2019lba}\n\\be \n\\label{eq:FG-1st-order-formalism}\ne^1 = dr \\,, \\qquad e^2= \\left(\\frac{1}2 e^r - \\tilde \\cL(\\tau) e^{-r}\\right)d\\tau \\,, \\qquad \\omega = -\\left(\\frac{1}2 e^r + \\tilde \\cL(\\tau) e^{-r}\\right)d\\tau \\,.\n\\ee \nFixing the decaying piece of the gravitino we can gauge fix all other components of the $PSU(1,1|2)$ gauge field along the boundary to be\n\\be\n\\label{eq:PSU(1,1|2)-gauge-field-form}\nA_\\tau(\\tau) &= L_1 \\frac{e^r}2 + L_{-1} \\tilde \\cL(\\tau) e^{-r} + B^i_\\tau(\\tau) T_i \\nn \\\\ &+ e^{-\\frac{r}2} \\frac{\\bar\\psi^p(\\tau)}2 G_{p, \\, -\\frac{1}2} + e^{-\\frac{r}2} \\frac{\\psi^p(\\tau)}2 \\bar G_{p,\\, -\\frac{1}2} + O(e^{-2r})\\,,\n\\ee\nwhere we have used the shorthand notation $\\psi^{p}(\\tau) \\equiv \\psi^{p,\\, \\frac{1}2} $ and $\\bar \\psi^{p}(\\tau) \\equiv \\bar \\psi^{p,\\, \\frac{1}2} $ on the boundary $\\partial \\cM$. \nWe can now impose the equations of motion of $A_\\tau$ in the proximity of the boundary $D_\\tau \\phi = 0$, or rather due to only fixing the leading orders of $A_\\tau$ and $\\phi$, $D_\\tau \\phi = O(e^{-r})$, where $D$ denotes the $PSU(1,1|2)$ covariant derivative. This implies that the zero-form field $\\phi$ should be constrained to take the form \n\\be\n\\label{eq:b.c.-for-dilaton}\n\\phi(\\tau) &= -{i\\Phi_r} L_1 e^r + {2 i\\Phi_r'} L_0 +(\\bar \\psi \\lambda + \\psi \\bar \\lambda - 2i \\tilde\\cL \\Phi_r - 2i\\Phi_r'')L_{-1} e^{-r} + b^i(x) T_i \\nn \\\\&+ \\bar \\lambda^p G_{p,\\frac{1}2} e^{r\/2} - \\bigg(2(\\bar \\lambda^p)' - i\\Phi_r \\bar \\lambda^p - B_\\tau^i (\\sigma^i{}^*)^p{}_q\\, \\bar \\lambda^q\\bigg) G_{p,-\\frac{1}2}e^{-r\/2} \\nn \\\\ &+ \\lambda^p \\bar G_{p,\\frac{1}2}e^{r\/2} - \\bigg(2 (\\lambda^p)' - i \\Phi_r \\lambda^p - B_\\tau^i (\\sigma^i\\,)^p{}_q\\, \\lambda^q\\bigg) \\bar G_{p,-\\frac{1}2} e^{-r\/2} + O(e^{-2r})\\,,\n\\ee\nwhere, again, we have used the short-hand notation $\\lambda^{p}(\\tau) = \\lambda^{p, \\frac{1}2}$ and $\\bar \\lambda^{p}(\\tau) =\\bar \\lambda^{p, \\frac{1}2}$ on the boundary $\\partial \\cM$ and where $\\Phi'_r \\equiv \\partial_\\tau \\Phi_r(\\tau)$ denotes the derivative with respect to the boundary time $\\tau$.\\footnote{We will motivate why the parameter $\\Phi_r$ in \\eqref{eq:b.c.-for-dilaton} can be identified with the renormalized dilaton shortly.}\n\nIf we want to impose the boundary conditions for the dilaton ($\\Phi_r = \\text{const}$), the dilatinos ($\\lambda^{p, \\,\\frac{1}2} = 0$ and $\\bar \\lambda^{p,\\,\\frac{1}2} =0$) and the zero-form field $b^i(x)$ we can then relate the gauge field in \\eqref{eq:PSU(1,1|2)-gauge-field-form} to the zero-form field $\\phi(\\tau)$ as $-2i \\Phi_r \\hspace{0.1cm}A_\\tau(\\tau) = \\phi(\\tau)$, where $\\Phi_r$ is the renormalized value of the dilaton. Thus, in the first-order formalism and in the gauge in which $A_\\tau$ takes the form \\eqref{eq:PSU(1,1|2)-gauge-field-form}, the boundary condition that we want to impose is $\\delta(2i\\Phi_r A_\\tau(\\tau) + \\phi(\\tau))|_{\\partial \\cM}=0$. The boundary term necessary for such a boundary condition to have a well defined variational principle is \\cite{Cardenas:2018krd}:\n\\be \n\\label{eq:bdy-term-JT-grav-first-order}\nI_{BF,\\,\\text{bdy.}} = \\frac{i}2 \\int_{\\partial \\cM} \\text{Str}\\, \\phi A = { \\Phi_r} \\int_{\\partial \\cM} d\\tau\\, \\text{Str}\\, A_\\tau^2 \\, . \n\\ee\nIntegrating out the field $\\Phi$ in the bulk we find that the bulk term yields no contribution. \nReplacing $A_\\tau$ in \\eqref{eq:bdy-term-JT-grav-first-order} we find that the action can then be rewritten as\n\\be \nI_{BF,\\,\\text{bdy.}} = -{\\Phi_r} \\int_{\\partial \\cM} d\\tau \\left[\\tilde \\cL(\\tau) + \\frac{1}2 \\left((B^1_\\tau)^2+(B^2_\\tau)^2+ (B^3_\\tau)^2\\right)\\right]\n\\ee\nThus, it is convenient to define \n\\be\n\\label{eq:def-cL(u)-and-bdy-Lagr}\n\\cL(\\tau) = \\tilde \\cL(\\tau) + \\frac{1}2 \\left((B^1_\\tau)^2+(B^2_\\tau)^2+ (B^3_\\tau)^2\\right)\\,, \\,\\, \\text{ from which } \\,\\, I_{BF,\\,\\text{bdy.}} = -{\\Phi_r} \\int_{\\partial \\cM} d\\tau \\, \\cL(\\tau)\\,.\n\\ee\nWe will not determine $\\cL(\\tau)$ explicitly. Rather we will see how $\\cL(\\tau)$ (and all other variables in \\eqref{eq:PSU(1,1|2)-gauge-field-form}) transform under the gauge transformations that preserve the asymptotic form of \\eqref{eq:PSU(1,1|2)-gauge-field-form}. In general in a BF theory with gauge group $G$, boundary gauge transformations are parametrized by functions $g$ in $\\text{loop}(G)\/G$. However, since we are preserving the asymptotic form \\eqref{eq:PSU(1,1|2)-gauge-field-form} which comes from working in the Fefferman-Graham gauge \\eqref{eq:FG-1st-order-formalism} the space of gauge transformations is instead parametrized by $\\text{Diff}(S^{1|4})\/PSU(1, 1|2)$. Therefore, the way in which $\\cL(\\tau)$ transforms under this special class of gauge transformations will yield how the boundary Lagrangian transforms under $\\text{Diff}(S^{1|4})\/PSU(1, 1|2)$. From here, we will show in section \\ref{sec:N=4-transf-law} that the boundary Lagrangian can be identified with the $\\cN=4$ super-Schwarzian derivative. Thus, to do this identification, we note that $\\cL(\\tau)$, $B_\\tau(\\tau)$, and $\\psi^p(\\tau)$ transform as \n\\bea \n\\label{eq:gauge-transf-preserving-asymp}\n\\delta_\\L \\cL &=&\\xi \\cL'+2 \\cL \\xi'+ \\xi'''- B^i_\\tau (t^i)'+\\frac{1}{2}\\left(3\\bar{\\psi} \\epsilon'+\\bar \\psi'\\epsilon -3\\bar{\\epsilon}' \\psi -\\bar{\\epsilon} \\psi'\\right), \\nn \\\\ \n\\delta_\\L \\psi^p &=& \\xi ( \\psi^p)'+\\frac{3}{2}\\psi^p \\xi'-\\epsilon^p \\cL -2(\\epsilon^p)''-\\frac{1}{2}t^i(\\sigma^i){}^p{}_q\\, \\psi^q + (B^i_\\tau)' (\\sigma^i){}^p_q \\epsilon^q +2 B^i_\\tau (\\sigma^i{})^p{}_q\\,(\\epsilon^q)',\\nn\\\\ \n\\delta_\\L B^i_\\tau&=& \\left( \\xi B^i_\\tau\\right)'-(t^i)'+ \\frac{1}2 \\bar \\psi \\sigma^i\\epsilon+ \\frac{1}2 \\bar{\\epsilon} \\sigma^i \\psi + i \\epsilon^{ijk} \\,t^j\\,B^k_\\tau\\,,\n\\eea\nunder the gauge transformation which preserves the form of $A_\\tau$ in \\eqref{eq:PSU(1,1|2)-gauge-field-form}:\n\\be\n\\Lambda(\\tau) &= \\frac{\\xi}2 L_1 - \\xi' L_{0} + \\left(\\frac{1}2 \\bar \\psi \\epsilon +\\frac{1}2 \\psi \\bar \\epsilon - \\frac{1}2 \\xi (B^a)^2 +\\cL \\xi + \\xi'' \\right) L_{-1} - (t^i -\\xi B^i_\\tau) T_i \\nn \\\\ &+ \\frac{1}2 \\bar \\epsilon^p G_{p,\\frac{1}2} - \\left((\\bar \\epsilon^p)' - \\frac{1}2 \\xi \\bar \\psi^p - \\frac{1}2 B_\\tau^i (\\sigma^i{}^*){}^p_q\\, \\bar \\epsilon^q\\right) G_{p,-\\frac{1}2} + \\frac{1}2 \\epsilon^p \\bar G_{p,\\frac{1}2} \\nn \\\\ &- \\left( \\epsilon^p - \\frac{1}2 \\xi \\psi^p - \\frac{1}2 B_\\tau^i (\\sigma^i)^p_q\\, \\epsilon^q\\right) \\bar G_{p,-\\frac{1}2}\n\\ee\nIt is not a coincidence that $\\Lambda(\\tau)$ takes the same form (up to the redefinition \\eqref{eq:def-cL(u)-and-bdy-Lagr}) as $\\phi(\\tau)$ in \\eqref{eq:b.c.-for-dilaton}. This follows from requiring that the leading result in both $D_\\tau \\Lambda$ (since we require that the gauge transformation $\\Lambda$ not change the asymptotic form of $A_\\tau$ \\eqref{eq:PSU(1,1|2)-gauge-field-form}) and $D_\\tau \\phi$ (since we impose the equation of motion for $A$ by also using \\eqref{eq:PSU(1,1|2)-gauge-field-form}) both vanish. With these transformations in mind, we now proceed by introducing the necessary technology to define the $\\cN=4$ Schwarzian derivative. Following that, we will finally show that this Schwarzian derivative can be identified with the boundary Lagrangian $\\cL(\\tau)$ from \\eqref{eq:def-cL(u)-and-bdy-Lagr}. \n\n\nFinally, we can extend this analysis to the case when the $SU(2)$ chemical potential, $\\a$, is turned on. In order to do this we can generalize the previous boundary condition from $ A_\\tau - \\frac{i \\phi}{2\\Phi_r} = 0$ to $ A_\\tau - \\frac{i\\phi}{2\\Phi_r} = \\frac{2\\pi i}{\\beta} \\alpha T^3 $, where $\\alpha\\sim \\alpha +1$. This does not modify the boundary conditions of gravity and the fermions but now the $SU(2)$ gauge field boundary condition is $B_\\tau - \\Phi^{-1}_r b = \\frac{2\\pi i}{\\beta} \\alpha T^3$, supporting the identification of $\\alpha$ with a chemical potential. In section \\ref{sec:subtleties} we explain how from the 4D near-extremal black hole perspective this boundary condition is equivalent to fixing the holonomy of the gauge field in the asymptotically flat region, which is related to fixing the boundary 4D angular velocity. \n\n\n\\subsection{The $\\cN=4$ supersymmetric boundary mode} \n\nSo far we have seen that $\\cN=4$ super-JT gravity can be reduced to a boundary theory. In this section, we will see this theory can be recasted as a $\\cN=4$ super-reparametrization mode with a super-Schwarzian action. We will first review in \\ref{sec:N=4-superDiffeos} the definition of super-diffeomorphisms. In \\ref{sec:super-Schw-action} we will define the super-Schwarzian derivative that will be the action and in \\ref{sec:N=4-transf-law} we will put everything together to write down the final boundary action. \n\n\\subsubsection{Super-diffeomorphisms}\n\\label{sec:N=4-superDiffeos}\n\nTo match with the $\\cN=4$ super-JT theory defined previously we will be interested in $SU(2)$ extended $\\cN=4$ supersymmetry.\\footnote{Other choices with also $\\cN=4$ are the $O(4)$ extended algebra studied for example in \\cite{Schoutens:1988ig}, we will not be interested in those for the purposes of this paper.} We will begin by giving a super-space description of $\\cN=4$ super-diffeomorphisms. \n\nConsider an $\\cN=4$ super-line parametrized by a bosonic variable $\\tau$ and fermionic variables $\\theta^p$ and $\\bar{\\theta}_q$, where $p,q=1,2$. The coordinates $\\theta^p$ and $\\bar{\\theta}_q$ transform respectively in the fundamental and antifundamental representations of a local $SU(2)$ symmetry. We will omit the indices and simply write $\\theta$ and $\\bar{\\theta}$ when it is clear by context. We define the four super-derivatives as \n\\beq\nD_p = \\frac{\\partial}{\\partial \\theta^p} + \\frac{1}{2} \\bar{\\theta}_p \\partial_\\tau ,~~~~~\\bar{D}^q = \\frac{\\partial}{\\partial \\bar{\\theta}_q} + \\frac{1}{2} \\theta^q \\partial_\\tau.\n\\eeq\nThe $SU(2)$ indices will be raised and lowered by the antisymmetric tensors $\\varepsilon_{pq}$ and $\\varepsilon^{pq}$ with $\\varepsilon_{12}=\\varepsilon^{21}=1$. We will denote by $\\sigma^i$ with $i=1,2,3$ the Pauli matrices and indices will be contracted from `left-bottom' to `right-up', e.g. $\\bar{\\theta} \\sigma^i \\theta = \\bar{\\theta}_p (\\sigma^i)^p_q \\theta^q$. We will sometimes group the coordinates of the $\\cN=4$ super-line as $Z=(\\tau, \\theta^p, \\bar{\\theta}_p)$. \n\nWe will study general reparametrizations of the super-line, which will become the degrees of freedom in the path integral that defines the Schwarzian theory. These have the following form \n\\beq\\label{reparametrizations}\n\\tau \\to \\tau'(\\tau, \\theta, \\bar{\\theta}) ,~~~~\\theta^p \\to \\theta'{}^p(\\tau, \\theta, \\bar{\\theta}), ~~~~\\bar{\\theta}_q \\to \\bar{\\theta}'_q(\\tau, \\theta, \\bar{\\theta}),\n\\eeq\nand satisfy a set of constrains given by \n\\bea\\label{repa_constraints}\nD_p \\bar{\\theta}'_q =0&,&~~~\\bar{D}^p \\theta'{}^b=0,\\\\\nD_p \\tau' - \\frac{1}{2} (D_p \\theta'{}^q)\\bar{\\theta}'_q=0&,&~~~~\\bar{D}^p \\tau' - \\frac{1}{2} (\\bar{D}^p \\bar{\\theta}'_q)\\theta'{}^q=0\n\\eea\nanalyzed in \\cite{Matsuda:1988qf} and \\cite{Matsuda:1989kp}. They guarantee that the superderivatives transform homogeneously and preserve the global $SU(2)$ symmetry. We will refer to the space of solutions of these constrains as $\\cN=4$ super-diffeomorphisms and denote it as ${\\rm Diff}(S^{1|4})$, indicating one bosonic and four fermionic directions. \n\nNext we will look at some examples. The simplest super-reparametrizations are purely bosonic. They are given in terms of an arbitrary function $f(\\tau)$ and an arbitrary $SU(2)$ matrix $g(\\tau)$. The solutions of the constraints have the form \n\\bea\\label{eq:repbos}\n\\tau &\\to& f(\\tau) + \\frac{1}{8} f''(\\tau) (\\bar{\\theta}\\theta)^2,\\\\\n\\theta^p &\\to& g\\Big( \\tau -\\frac{1}{2}\\bar{\\theta}\\theta\\Big)^p_q ~\\theta^q~ \\sqrt{f'\\Big(\\tau-\\frac{1}{2}\\bar{\\theta}\\theta\\Big)} , \\\\\n\\bar{\\theta}_p &\\to& \\bar{\\theta}_q~ \\bar{g}\\Big(\\tau + \\frac{1}{2}\\bar{\\theta}\\theta\\Big)^q_p ~\\sqrt{f'\\Big(\\tau+\\frac{1}{2}\\bar{\\theta}\\theta\\Big)}.\n\\eea\nAnother simple example are the `chiral' and `anti-chiral' fermionic transformations. The chiral ones are parametrized in terms of two fermionic functions $\\eta^p(\\tau)$ in the fundamental of $SU(2)$ and the reparametrization is given by\n\\bea\\label{eq:repfer}\n\\tau &\\to&\\tau + \\frac{1}{2} \\bar{\\theta} \\eta\\Big(\\tau+\\frac{1}{2}\\bar{\\theta}\\theta\\Big) + \\frac{1}{4} \\partial_\\tau (\\bar{\\theta} \\eta(\\tau))^2,\\\\\n\\theta^p &\\to& \\theta^p + \\eta^p\\Big(\\tau-\\frac{1}{2}\\bar{\\theta}\\theta\\Big) , \\\\\n\\bar{\\theta}_p &\\to& \\bar{\\theta}_p\\Big( 1 + \\bar{\\theta} \\eta'\\Big(\\tau-\\frac{1}{2}\\bar{\\theta}\\theta\\Big) \\Big).\n\\eea\nThe anti-chiral are parametrized by $\\bar{\\eta}_p(\\tau)$ in the antifundamental, and the reparametrization is given by\n\\bea\\label{eq:repfer2}\n\\tau &\\to&\\tau + \\frac{1}{2} \\theta \\bar{\\eta}\\Big(\\tau-\\frac{1}{2}\\bar{\\theta}\\theta\\Big) + \\frac{1}{4} \\partial_\\tau (\\theta \\bar{\\eta}(\\tau))^2,\\\\\n\\theta^p &\\to&\\theta^p\\Big( 1 +\\theta \\bar{\\eta}'\\Big(\\tau+\\frac{1}{2}\\bar{\\theta}\\theta\\Big) \\Big) , \\\\\n\\bar{\\theta}_p &\\to& \\bar{\\theta}_p +\\bar{\\eta}_p\\Big(\\tau+\\frac{1}{2}\\bar{\\theta}\\theta\\Big).\n\\eea\nThe most general element of ${\\rm Diff}(S^{1|4})$ is obtained by a general fermionic transformation followed by a bosonic one.\\footnote{ In writing the Schwarzian derivative, the order of the composition is important. In particular, to compute the $\\cN=4$ super-Schwarzian we compose the bosonic transformation with a fermionic one in this order. The order of compositions will however be unimportant when localizing the path integral.} We parametrize ${\\rm Diff}(S^{1|4})$ in terms of the degrees of freedom\n$\nf(\\tau),~~g(\\tau)\\in SU(2),~~\\eta^p(\\tau),~~\\bar{\\eta}_p(\\tau),\n$\nwhere $p=1,2$. It is hard to determine the finite form that has all parameters turned on since such answer contains a large number of terms. Therefore, we will not write it down explicitly although its straightforward to get from the results presented so far. Instead, it is useful to analyze the most general infinitesimal transformation. Up to $\\mathcal{O}(\\eta)$ is given by \n\\bea\n\\tau &\\to&f(\\tau-\\frac{1}{2}\\bar{\\eta}(\\tau-\\frac{1}{2}\\bar{\\theta}\\theta)\\theta+\\frac{1}{2}\\bar{\\theta}\\eta(\\tau+\\frac{1}{2}\\bar{\\theta}\\theta)) + \\frac{1}{8}f''(\\tau)\\left((\\bar{\\theta}+\\bar{\\eta})(\\theta+\\eta)\\right)^2\\\\\n\\theta^p&\\to& F^p{}_q\\left(\\tau-\\frac{1}{2}\\bar{\\theta}\\theta\\right)\\left(\\theta^q+\\eta^q\\left(\\tau-\\frac{1}{2}\\bar{\\theta}\\theta\\right)\\right)+\\partial_{\\tau}\\left(F^p{}_q\\left(\\tau\\right)\\theta^q \\theta \\bar{\\eta}\\left(\\tau\\right)\\right)\\\\ \n\\bar{\\theta}_p &\\to& \\left(\\bar{\\theta}_q+\\bar{\\eta}_q\\left(\\tau+\\frac{1}{2}\\bar{\\theta}\\theta\\right)\\right)F^q{}_p\\left(\\tau+\\frac{1}{2}\\bar{\\theta}\\theta\\right)+ \\partial_{\\tau}\\left(\\eta\\left(\\tau\\right)\\bar{\\theta}\\bar{\\theta}_qF^q{}_p\\left(\\tau\\right)\\right)\n\\eea\nwhere we use $F^p{}_q(\\tau)=\\sqrt{f'(\\tau)}g^p{}_q(\\tau) $.\n\nWe introduce now an important sub-group of the super-reparametrizations, the global superconformal group $PSU(1,1|2)$. It is generated by six bosonic variables (three from $SL(2)$ and three from $SU(2)$) and eight fermionic. The general case can be found in \\cite{Matsuda:1989kp}. Instead we will write down the bosonic generators \n\\bea\\label{infintesimalparameterb}\n\\tau &\\to& \\frac{a\\tau+b}{c\\tau+d} - \\frac{c}{4(c\\tau+d)^3}(\\bar{\\theta}\\theta)^2 ,\\\\\n\\theta^p &\\to&[e^{i \\vec{t} \\cdot \\vec{\\sigma}}]^p_q\\theta^q \\frac{1}{c(\\tau - \\frac{1}{2} \\bar{\\theta}\\theta)+d} ,\\\\\n\\bar{\\theta}_p &\\to& \\bar{\\theta}_q [e^{-i \\vec{t} \\cdot \\vec{\\sigma}}]^q_p \\frac{1}{c(\\tau + \\frac{1}{2} \\bar{\\theta}\\theta)+d}\\label{infintesimalparameterb-last} ,\n\\eea\nwith $a,b,c,d\\in \\mathbb{R}$ such that $a d- bc=1$ and $c>0$. This is precisely of the form \\eqref{eq:repbos} where $f(\\tau)$ is a global conformal transformation $PSL(2,\\mathbb{R})$ while $(t^1, t^2, t^3)$ parametrize a global $SU(2)$ transformation. The fermionic generators can be parametrized by two constant spinor doublets $\\eta$ and $\\tilde{\\eta}$ and act as \n\\bea\n\\label{infintesimalparameterf-first}\n\\tau &\\to& \\tau + \\frac{1}{2} (\\bar{\\theta}-\\bar{\\tilde{\\eta}}) \\eta - \\frac{1}{2} \\bar{\\eta} (\\theta-\\tilde{\\eta}) ,\\\\\n\\theta^p &\\to& \\theta^p+ \\eta^p - \\tilde{\\eta}^p,\\\\\n\\bar{\\theta}_p &\\to&\\bar{\\theta}_p + \\bar{\\eta}_p - \\bar{\\tilde{\\eta}}_p,\n\\label{infintesimalparameterf}\n\\eea\n As anticipated there are eight fermionic generators. As explained above, the most general case is a composition of bosonic and fermionic. \n\n\\subsubsection{Super-Schwarzian action}\n\\label{sec:super-Schw-action}\n\nThe Schwarzian derivative associated to reparametrizations of the $\\cN=4$ super-circle was defined by Matsuda and Uematsu in \\cite{Matsuda:1989kp} (see also \\cite{Matsuda:1988qf}). The Schwarzian derivative is given in terms of superspace variables as \n\\beq\\label{Sderivative}\nS^i (Z; Z') = - 2 D \\sigma^i \\bar{D} \\log \\left(\\frac{1}{2} (D_p \\theta'{}^q)(\\bar{D}^p \\bar{\\theta}'_q)\\right).\n\\eeq\nIt satisfies the following chain rule \n\\beq\n\\label{chainrule}\nS^i (Z; Z') = \\frac{1}{ 2} (\\sigma^i)^p_q(\\sigma^j)^{q'}_{p'} (D_p \\theta''{}^{p'})(\\bar{D}^q \\bar{\\theta}''_{q'}) S^j (Z'';Z')+S^i (Z;Z'').\n\\eeq\nAnother defining property is the fact that $S^i (Z;Z')=0$ whenever the super-reparametrization is an element of the global $PSU(1,1|2)$ as in \\eqref{infintesimalparameterb}. This will prove consequential in section \\ref{sec:space-time-and-global-symm} when studying the global symmetries of the $\\cN=4$ super-Schwarzian action which we shall define shortly. \n\nThe derivative $S^i$ is a superfield with several components. We can extract the bosonic piece we want to associate to the Schwarzian action. In the notation of \\cite{Matsuda:1989kp} it is \n\\beq\nS^i (Z; Z') \\supset - \\bar{\\theta} \\sigma^i \\theta \\hspace{0.1cm}S_b(\\tau, \\theta, \\bar{\\theta}; \\tau',\\theta',\\bar{\\theta}')\n\\eeq\nOne motivation of this choice is to look at purely bosonic transformations defined in equation \\eqref{eq:repbos}. For simplicity let's briefly consider $Z'=(\\tau',\\theta',\\bar{\\theta}')$ where $\\tau'$, $\\theta'$ and $\\bar \\theta'$ take the special form \\eqref{eq:repbos}, in terms of an arbitrary $f(\\tau)$ and set $g(\\tau)=1$. Then the definition above gives the super-Schwarzian \n\\beq\n\\label{eq:just-in-terms-of-Schw}\nS^i (Z,Z') = -{\\rm Sch}(f,\\tau) \\bar{\\theta} \\sigma^i \\theta.\n\\eeq\nAnother motivation is that when we interpret $S^i$ as a superconformal generator, that component generates bosonic translations along the circle and we want to identify this as the action of the Schwarzian theory. \n\nThe super-Schwarzian satisfies the constrains $D_p D_q S^i = \\bar{D}^p \\bar{D}^q S^i=0$. Therefore, as a superfield it can be expanded in the following components \n\\bea\\label{sschwarzian}\nS^i &=& 2S_{T}^i + \\bar{\\theta} \\sigma^i S_f + \\bar{S}_f \\sigma^i \\theta - \\bar{\\theta} \\sigma^i \\theta S_b + i \\epsilon^{ijk} \\bar{\\theta} \\sigma^j \\theta \\partial_\\tau S_{T}^k \\nonumber\\\\\n&&- \\frac{1}{2}(\\bar{\\theta}\\theta) \\bar{\\theta}\\sigma^i \\partial_\\tau S_f + \\frac{1}{2} (\\bar{\\theta} \\theta) \\partial_\\tau \\bar{S}_f \\sigma^i \\theta + \\frac{1}{4} (\\bar{\\theta} \\theta)^2 \\partial^2_\\tau S_T^i \n\\ea\nThen the super-Schwarzian action is \n\\beq\n\\label{Saction}\nI_{\\cN=4} = - \\Phi_r\\int d\\tau S_b[f(\\tau),g(\\tau),\\eta(\\tau)]\n\\eeq\nwhere $\\Phi_r$ can be viewed as a coupling constant whose role we shall soon discuss and the factor of $12$ above is chosen such that it simplifies the factor in \\eqref{eq:just-in-terms-of-Schw}. \n\nWe can rewrite the action in super-field notation by defining $S = \\bar{\\theta} \\sigma^i \\theta S^i,$ where we sum over $i=1,2,3$. Then, using the expansion of $S^i$ gives,\\footnote{This is a simpler expression of $S^i$ for which more components are present. We can construct $S$ from an $S^i$ as long as $S^i$ satisfies $D_p D_q S^i = \\bar{D}^p \\bar{D}^q S^i=0$. To derive this we use that \n\\bea\n&&(\\bar{\\theta} \\sigma^i \\theta)(\\bar{\\theta} \\sigma^i \\theta)=-3(\\bar{\\theta} \\theta)^2,~~~~~~\\varepsilon^{ijk}(\\bar{\\theta} \\sigma^i \\theta)(\\bar{\\theta} \\sigma^j \\theta)=0,\\nn\\\\\n&& (\\bar{\\theta} \\sigma^i \\theta) (\\bar{\\theta} \\sigma^i G) = -3 (\\bar{\\theta} \\theta) (\\bar{\\theta} G),~~~~(\\bar{\\theta} \\sigma^i \\theta) (\\bar{G} \\sigma^i \\theta) = 3 (\\bar{\\theta} \\theta) (\\bar{G} \\theta) \\nn\\,.\n\\eea\n}\n\\beq\nS =2 \\bar{\\theta} \\sigma^i S_T^i \\theta - 3 (\\bar{\\theta} \\theta) \\bar{\\theta} S_f + 3 (\\bar{\\theta} \\theta) \\bar{S}_f \\theta + 3 S_b (\\bar{\\theta}\\theta)^2\\,.\n\\eeq\nThen the action \\eqref{Saction} can be rewritten as $I_{\\cN=4}\\sim \\Phi_r \\int d\\tau d^4\\theta S$.\nNote that in terms of the $ S[f(\\tau),g(\\tau),\\eta(\\tau)]$ there is no obvious chain rule analogous to \\eqref{chainrule}. For this reason it will oftentimes be easier to work with $S^i$ instead of the super-field $S$. To make things concrete, it is informative to write the Schwarzian action when focusing on purely bosonic components, when setting $\\eta(\\tau) = 0$ in \\eqref{Saction}:\n\\be\n\\label{eq:bosonic-compoents-of-the-action}\nI_{\\cN=4,\\text{ bosonic}}[f(\\tau), g(\\tau), \\eta(\\tau) = 0] =- \\int_0^\\b d\\tau \\Phi_r\\left[\\text{Sch}( f, \\tau ) + \\Tr(g^{-1} \\partial_\\tau g)^2\\right] \\,.\n\\ee\nSince $\\eta(\\tau)=0$ is a solution to the equations of motion we will soon use the above action to extract the classical saddle point when quantizing the super-Schwarzian action at the level of the path integral.\n\n\n\\subsubsection{Transformation law and a match with the JT boundary term } \n\\label{sec:N=4-transf-law}\n\n\nIn this section, we shall derive explicitly the infinitesimal transformation rules of the $\\cN=4$ super-Schwarzian (\\ref{sschwarzian}). We will expand $f(\\tau)\\approx\\tau+\\xi(\\tau)$, $g \\approx 1 + i t^{i}(\\tau) \\sigma^i$, $\\eta \\approx \\epsilon(\\tau)$ and $\\bar{\\eta}\\approx\\bar{\\epsilon}(\\tau)$ and work to linear order in $\\xi$, $t^i$, $\\epsilon$ and $\\bar{\\epsilon}$. A convenient way to encode the infinitesimal reparametrizations of the super line (\\ref{reparametrizations}) that automatically satisfies the constraints (\\ref{repa_constraints}) is to use a super-field as in \\cite{Matsuda:1988qf,Matsuda:1989kp}: \n\\bea\\label{repara_superfield}\nE(Z)=\\xi(\\tau+\\frac{1}{2}\\bar{\\theta}\\theta)+\\xi(\\tau-\\frac{1}{2}\\bar{\\theta}\\theta)+\\bar{\\theta} \\epsilon(\\tau-\\frac{1}{2}\\bar{\\theta}\\theta)-\\bar{\\epsilon}(\\tau+\\frac{1}{2}\\bar{\\theta}\\theta)\\theta+\\frac{1}{2}\\bar{\\theta}\\sigma^i\\theta t^i(\\tau),\n\\eea\nUnder such a reparametrization (\\ref{repara_superfield}), the super-Schwarzian (\\ref{sschwarzian}) transforms in the same way as the super-energy momentum tensor, which is given in \\cite{Matsuda:1989kp}: \n\\bea\\label{SStransformation}\n\\delta_E S^i= \\partial_{\\tau}\\left(E(Z)S^i\\right)+D E(Z)\\bar{D} S^i+ \\bar{D}E(Z)D S^i - i \\epsilon^{ijk}\\left(D\\sigma^j\\bar{D}E\\right) S^k-2D\\sigma^i\\bar{D}\\partial_{\\tau}E(z),\n\\eea\n Now substitute (\\ref{sschwarzian}) and (\\ref{repara_superfield}) into (\\ref{SStransformation}), and collect in components: \n\\bea\n\\delta_E S^i= 2\\delta_E S_T^i + \\bar{\\theta} \\sigma^i \\delta_ES_f + \\delta_E\\bar{S}_f \\sigma^i \\theta - \\bar{\\theta} \\sigma^i \\theta \\delta_E S_b + i \\epsilon^{ijk} \\bar{\\theta} \\sigma^j \\theta \\partial_\\tau \\delta_E S_T^k+\\dots,\n\\eea\nwe obtain the infinitesimal transformation of $S_T^i, S_f, \\bar{S}_f, S_b$. Note that the terms in $\\dots$ are purely determined by lower components, and thus it is enough to focus on the terms up to $\\mathcal{O}(\\bar{\\theta}\\theta).$ As a result, the transformations of $S_T^i, S_f, \\bar{S}_f, S_b$ are given by:\n\\bea \n\\label{eq:Sb-transformation}\n\\delta_E S_b&=&\\xi S_b'+2S_b \\xi'+ \\xi'''- S_T^i (t^i)'+\\frac{1}{2}\\left(3\\bar{S}_f\\epsilon'+\\bar{S}_f'\\epsilon-3\\bar{\\epsilon}'S_f-\\bar{\\epsilon} S_f'\\right), \\nonumber\\\\ \n\\delta_E S_f^p&=& \\xi (S_f^{p})'+\\frac{3}{2}S_f^p \\xi'-\\epsilon^p S_b-2(\\epsilon^p)''-\\frac{1}{2}t^i (\\sigma^i){}^p_qS_f^q+(S_T^i)' (\\sigma^i{})^p_q\\epsilon^q+2 S_T^i (\\sigma^i{})^p_q(\\epsilon^q)',\\nonumber\\\\ \n\\delta_E S_T^i&=& \\left( \\xi S_T^i\\right)'-(t^i)'+\\frac{1}2 \\bar{S}_f\\sigma^i\\epsilon+ \\frac{1}2\\bar{\\epsilon}\\sigma^iS_f+i \\epsilon^{ijk}t^j S_T^k.\n\\eea\n\nWe note that they exactly agree with the infinitesimal transformation deduced from the boundary action of the BF theory \\eqref{eq:gauge-transf-preserving-asymp} with the field identification:\n\\be \n\\cL &\\leftrightarrow S_b\\,,\\nn \\\\ \n\\psi^p &\\leftrightarrow S_f^p\\,,\\nn \\\\ \nB^i_\\tau &\\leftrightarrow S_T^i\\,.\n\\ee\nSince the infinitesimal transformations and ${PSU}(1,1|2)$ invariance suffices to determine the global form of the action as the $\\cN=4$ super-Schwarzian, it then follows that the boundary action \\eqref{eq:def-cL(u)-and-bdy-Lagr} from BF theory agrees with our definition of the Schwarzian action \\eqref{Saction}. Therefore, the path integral in the $\\cN=4$ super-JT gravity with the boundary conditions discussed in section \\ref{sec:super-JT-bdy-cond} can be reduced to that for the $\\cN=4$ super-Schwarzian action defined by \\eqref{Saction}. \n\n\\subsection{Spacetime and global symmetries}\n\\label{sec:space-time-and-global-symm}\nBefore analyzing the quantization of the super-Schwarzian theory, it is useful to study the space-time and global symmetries present in this action. \n\nA useful way to discuss symmetries of a quantum field theory is to cast it in terms of \\emph{internal symmetries} and \\emph{spacetime symmetries}. The continuous internal symmetries of (\\ref{Saction}) form PSU$(1,1|2)$,\\footnote{It is sometimes confusing whether the group is SU$(1,1|2)$ or PSU$(1,1|2)$. We note that SU$(1,1|2)$ contains an extra $U(1)$ factor generated by identity, due to the cancellation of SL$(2)$ part and $SU(2)$ part of the super-trace in the algebra \\cite{BrittoPacumio:1999ax}. We do not have such an $U(1)$ factor in \\ref{infintesimalparameterb}, and, therefore, our symmetry group is PSU$(1,1|2).$ } generated by (\\ref{infintesimalparameterb})--(\\ref{infintesimalparameterf}), directly acting on $\\left(f(\\tau), g(\\tau), \\bar{\\eta}_p(\\tau), \\eta^p(\\tau)\\right).$ They are zero modes of the $\\cN=4$ super-Schwarzian derivative, and the action (\\ref{Saction}) is invariant due to the chain rule (\\ref{chainrule}). Specifically,\n\\be\n\\label{eq:invariance-under-PSU(1,1|2)}\nS^i(Z, h \\circ Z'(Z)) = S^i(Z, Z'(Z)) \\,,\n\\ee\nwhere $h$ is a composition of the bosonic and fermionic transformations in (\\ref{infintesimalparameterb})--(\\ref{infintesimalparameterf}) which we apply to the super-reparametrization $Z'(Z)$.\nThese transformations are the supersymmetric generalization of the $SL(2,\\,\\mR):f(\\tau) \\to \\frac{a f(\\tau)+b}{c f(\\tau) +d}$, and we will have to quotient out such transformations as we proceed to compute the partition function \\cite{Jensen:2016pah,Maldacena:2016upp,Engelsoy:2016xyb}, in order to obtain a well-defined partition function. Furthermore, aside from the $PSU(1, 1|2)$ that has an $SU(2)$ subgroup which acts on the left of the field $g(\\tau)$ there is an additional $SU(2)$ symmetry which acts on the right of the field $g(\\tau)$. This additional symmetry does not act on $f(\\tau)$ or on the fermionic fields $\\bar \\eta$ and $\\eta$. Thus, to summarize the continuous internal symmetry is, up to discrete factors, $PSU(1,1|2) \\times SU(2)$.\\footnote{There is also an outer $SU(2)$ inherited from the $PSU(1,1|2)$ algebra. It acts on $(\\eta^1\\left(\\tau\\right), -\\bar{\\eta}_2\\left(\\tau\\right))$ as a doublet. In the $4d$ setup we later consider, this is the $R$ symmetry of the $\\mathcal{N}=2$ supergravity that is broken by stringy effects. }\n\nThe $\\cN=4$ super-Schwarzian theory also admits \\emph{spacetime} symmetries. To see this consider the transformation \\eqref{eq:Sb-transformation}; for the transformation $\\xi(\\tau) = \\xi$ corresponding to $\\tau \\to \\tau + \\xi$ (time translations), $t^i(\\tau) =t^i$ corresponding to constant infinitesimal rotation of $\\theta^p$ ($R$-symmetry rotations), and $\\epsilon(\\tau) = \\epsilon$ corresponding to $\\theta \\to \\theta+\\epsilon$ (super-translations), the action $S_b$ is invariant up to a total derivative. Together, all such infinitesimal transformations generate the $\\cN = 4$ super-Poincar\\'e spacetime symmetry. They satisfy the algebra\n\\bea\n\\{\\bar{Q}_p, Q^q\\}=2\\delta^q_p H, ~~~\\text{with }~~\\bar{Q}_p=i\\frac{\\partial}{\\partial \\theta^p}+\\bar{\\theta}_p\\partial_{\\tau},\\,\\,\\, Q^p=-i\\frac{\\partial}{\\partial \\bar{\\theta}_p}-\\theta^p \\partial_{\\tau},\\,\\,\\,H=\\partial_\\tau\n\\eea\nwhile all other commutators vanish. \nIt is straightforward to identify these $\\cN=4$ supercharges from the Noether procedure using the transformation \\eqref{eq:Sb-transformation}: $S_b$ is the charge generating time translations (and is thus the Hamiltonian of the theory), $S_f$ is the generator of super-translations, and $S_T^i$ is the generator of R-symmetry rotations. Note that the Hamiltonian here is \\emph{not} one of the generators of PSU$(1,1|2),$ since it is in fact the super-Schwarzian itself, and thus proportional to the quadratic Casimir of the PSU$(1,1|2).$ This is quite analogous to the non-supersymmetric case \\cite{kitaevTalks, Mertens:2017mtv, Lin:2019qwu}. \n\nWe would like to stress that even though $PSU(1,1|2)$ plays a big role in constructing the Schwarzian action, the theory is not invariant under spacetime superconformal $PSU(1,1|2)$ symmetries. The spectrum is not organized according to $PSU(1,1|2)$ representations. Only the $\\cN=4$ super-Poincar\\'e sub-group is a spacetime symmetry. \n\nIt is also useful to briefly discuss some of the discrete internal and spacetime symmetries of the theory. The $\\cN=4$ super-Schwarzian has a time reversal symmetry $\\cT$ which acts on the fermionic fields as $\\cT \\eta(\\tau) = i \\eta(-\\tau)$ and $\\cT\\bar \\eta(\\tau) = i \\bar \\eta(-\\tau)$ and on the bosonic fields as $\\cT f(\\tau) = f(-\\tau)$ and $\\cT g(\\tau) = g(-\\tau)$; in such a case, $\\cT^2 = 1$ and the symmetry is $\\mathbb Z_2^\\cT$. There is also a $\\mathbb Z_2^F$ fermionic symmetry $(-1)^F$ which solely acts on the fermionic fields.\\footnote{The same time-reversal properties are also true in the $\\cN=1$ theory \\cite{Stanford:2019vob}. } Thus, the symmetry is $\\mZ_2^F \\times \\mZ_2^\\cT$. We can now address discrete factors for the internal symmetry group of the theory. In (\\ref{infintesimalparameterb})--(\\ref{infintesimalparameterf}) we note that the transformation given by the center of $SL(2, \\mR)$ with $a=d=-1$ and $b=c=0$ (acting as $\\eta \\to -\\eta$ and $\\bar \\eta\\to -\\bar \\eta$) is redundant with the composition of the two center transformations for the two $SU(2)$, the first of which acts as $g \\to -g$, $\\eta \\to -\\eta$, $\\bar \\eta\\to -\\bar \\eta$, and the second of which acts solely on $g \\to -g$. Furthermore, this transformation is again redundant with the $(-1)^F$ symmetry mentioned previously. Thus, the bosonic subgroup of the symmetry group for the theory is given by \n\\be\n\\frac{SL(2, \\mR) \\times SU(2) \\times SU(2) \\times \\mZ_2^F}{\\mZ_2} \\times \\mZ_2^\\cT\\,.\n\\ee \nConsequently, we note that even-dimensional representations of $SU(2)$ (half-integer spins) need to be fermionic and odd-dimensional representations of $SU(2)$ (integer spins) need to be bosonic. Since we will be able to decompose our partition function as a sum over $SU(2)$ characters, this fact will play an important role in easily obtaining the supersymmetric index from the theory by using the result for the partition function. \n\n\\section{Quantizing the $\\cN=4$ Schwarzian theory}\n\\label{sec:N=4-super-Schw}\n\nIn this section, we will study the $\\cN=4$ super-Schwarzian theory in more detail. In particular we will compute the exact partition function and density of states. We will do the calculation in two ways, first using the localization method of Stanford and Witten \\cite{Stanford:2017thb} and second using the 2D CFT approach of \\cite{Mertens:2017mtv}, and find agreement. Then we will analyze the spectrum that we derive and point out some salient features like the large zero temperature degeneracy and the presence of a gap.\n\n\\subsection{The action}\n\n\nThe $\\cN=4$ super-line can be parametrized in superspace by $(\\tau, \\theta^p, \\bar{\\theta}_p)$, $p=1,2$, where $\\theta$ and $\\bar{\\theta}$ are Grassman variables transforming as fundamental and anti-fundamental of an $SU(2)$ symmetry. $\\cN=4$ super-reparametrizations are parametrized by a bosonic field $f(\\tau)\\in {\\rm Diff}(S^1)$, a local transformation $g(\\tau) \\in SU(2)$ (or more precisely the loop group) and fermionic fields $\\eta^p(\\tau)$ and $\\bar{\\eta}_p(\\tau)$. In terms of a super-reparametrization these fields can be roughly written as \n\\bea\n\\tau &\\to& f(\\tau) + \\ldots,\\\\\n\\theta^p &\\to& g^p_{~q}(\\tau) \\theta^q \\sqrt{f'(\\tau)} + \\eta^p(\\tau) + \\ldots, \\\\\n\\bar{\\theta}_p &\\to& \\bar{\\theta}_q \\hspace{0.1cm}\\bar{g}^q_{~p}(\\tau) \\sqrt{f'(\\tau)} + \\bar{\\eta}_p(\\tau) + \\ldots.\n\\ea\nThe dots correspond to terms that are fixed by the super-reparametrization constrains and can be found in the previous section. We also defined a Schwarzian action $I_{\\cN=4} =-\\Phi_r \\int d\\tau S_b [ f, g, \\eta, \\bar{\\eta}]$ invariant under $PSU(1,1|2)$ transformations acting on the fields $(f,g,\\eta,\\bar{\\eta})$. The bosonic component of this action is \n\\beq\nI_{\\cN=4} =- \\Phi_r \\int_0^\\b d\\tau \\left[\\text{Sch}(f,\\tau) + \\Tr(g^{-1} \\partial_\\tau g)^2 + ({\\rm fermions})\\right]\n\\eeq\nwhich gives the usual Schwarzian action and a particle moving on $SU(2)$. The extra terms involve the fermions $\\eta$ and $\\bar{\\eta}$. \n\nIn this section, we will compute the Euclidean path integral giving the partition function,\\footnote{We leave the measure implicit in this formula. We take the measure to be the Pfaffian of the symplectic form over the integration space ${\\rm Diff}(S^{1|4})\/PSU(1,1|2)$, studied in \\cite{Aoyama:2018lfc}.}\n\\beq\nZ (\\beta,\\alpha) = \\int \\frac{\\mathcal{D} f \\mathcal{D} g \\mathcal{D} \\eta \\mathcal{D} \\bar{\\eta}}{PSU(1,1|2)} ~\\exp\\left( \\Phi_r \\int d\\tau S_b[f,g,\\eta,\\bar{\\eta}] \\right)\\,, \n\\eeq\nwhere $\\Phi_r$ is a dimensionful coupling constant of the theory. The inverse temperature $\\beta$ and chemical potentials $\\alpha$ appears in the path integral through the boundary conditions of the fields:\n\\beq\nf(\\tau+\\beta)=f(\\tau),~~~~g(\\tau+\\beta) = e^{2 \\pi i \\alpha \\sigma^3} g(\\tau),~~~ \\eta(\\tau+ \\beta) =- e^{2\\pi i \\alpha \\sigma^3} \\eta(\\tau),\n\\eeq\nand similarly for $\\bar{\\eta} $. In the rest of this section we will evaluate this path integral as a function of $\\beta$, $\\alpha$ and the coupling $\\Phi_r$, and rewrite it as a trace over a Hilbert space with a possibly continuous spectrum. \n \n \\subsection{The partition function}\n \n\\subsubsection{Method 1: Fermionic localization}\n\\label{sec:fermionic-localization}\n\nIn this section, we will solve the theory following \\cite{Stanford:2017thb}. The integration space is a coadjoint orbit and the super-Schwarzian action generates a $U(1)$ symmetry. Even though we will not work out the measure and symplectic form in detail, we will assume it is chosen such that we can apply the Duistermaat-Heckman theorem. Therefore, we will compute the classical saddles, the one-loop determinants, and put everything together into the final answer \\eqref{eq:localization-part-function}.\n\n\\paragraph{The $\\cN=4$ saddle-point:} As previously mentioned, the bosonic part of the $\\cN=4$ Schwarzian action is given by\n\\be\n\\label{eq:bosonic-compoents-of-the-action-2}\nI_{\\cN=4,\\text{ bosonic}} =- \\int_0^\\b d\\tau \\Phi_r\\left[\\text{Sch}(f,\\tau) + \\Tr(g^{-1} \\partial_\\tau g)^2\\right] \\,.\n\\ee\nThe equations of motion for $f(\\tau)$ and $g(\\tau)$ imply that:\n\\be\n\\partial_\\tau \\text{Sch}(f,\\tau) = 0\\,, \\qquad \\partial_\\tau \\Tr (g^{-1} \\partial_\\tau g)^2 = 0\n\\ee\nThe solution for the Schwarzian is well-known and is given by $f(\\tau) = \\tan(\\pi \\tau\/\\b)$. The solution for the $SU(2)$ adjoint field takes the form $g = \\exp(i t_i \\e^i \\tau)$, where $\\e^i$ is a constant that needs is set by the boundary conditions for the field $g(\\tau)$. To make the computation easier we note that all solutions can be transformed to the diagonal form ($g = \\exp(i \\sigma_3 \\e^3 \\tau)$) using an $SU(2)$ transformation. If require that the field $g$ be periodic, than we have that $\\e^{3} = 2\\pi n\/\\beta$, with $n \\in \\mZ$. More generally, the $SU(2)$ symmetry could have a fugacity which would imply that the field $g$ is no longer periodic; rather, it has $g(\\beta) = z\\, g(0)$ where $z \\in SU(2)$ is the fugacity. Once again, since the partition function only depends on the conjugacy class of $z$, we can perform an $SU(2)$ transformation to diagonalize $z = \\exp(2\\pi i \\a \\sigma_3 )$. The solution for $h$ is then given by $g = \\exp\\left(2\\pi i \\sigma_3 (n+\\a) \\frac{\\tau}\\beta\\right)$. \n\nIn such a case, the on-shell value of the action $I_{\\cN=4,\\text{bosonic}}$ is given by: \n\\be \n\\label{eq:on-shell-action-N=4}\nI_{\\cN=4,\\text{bosonic}}^{\\,\\text{on-shell}} = -\\frac{2\\pi^2 \\Phi_r} {\\b} \\left(1 -4 (n+\\a) ^2 \\right)\\,,\n\\ee\nNow that we have the on-shell action we can proceed by computing the one-loop determinant which is sufficient for fully computing the partition function. \n \n\n\\paragraph{The one-loop determinant:} To compute the one-loop determinant, we have to account for all quadratic fluctuations in the theory. The quadratic fluctuations of the Schwarzian field have been analyzed in great detail \\cite{Maldacena:2016upp, Stanford:2017thb}, and its contributions to the one-loop determinant is given, up to an overall proportionality constant, by\n\\be \n\\label{eq:Schwarzian-one-loop-determinant}\n\\det_{\\text{Schw., one-loop}} = \\left(\\frac{\\Phi_r}{\\beta}\\right)^\\frac{3}2\\,.\n\\ee\nThe quadratic fluctuation around the saddle-point of the $SU(2)$-group element can be parametrized as $g(\\tau) = \\exp\\left( \\sigma_3 \\left[2\\pi i(n+\\a) \\frac{\\tau}\\beta + \\epsilon^3(\\tau) \\right]\\right)\\exp\\left( \\epsilon^2(\\tau) \\sigma_2\\right) \\exp\\left( \\epsilon^1(\\tau) \\sigma_1\\right)$, and yields a contribution to the action \\cite{Picken:190160}\n\\be \nI_{SU(2), \\text{ quad}} &= \\frac{8\\pi^2 \\Phi_r} {\\b} (n+\\a) ^2\\nn \\\\ &-2 \\Phi_r \\int_0^\\b d\\tau \\left((\\epsilon^1(\\tau)')^2 + (\\epsilon^2(\\tau)')^2 - (\\epsilon^3(\\tau)')^2 + \\frac{8\\pi (n+\\a)}{\\b} \\epsilon^2(\\tau) \\epsilon^1(\\tau)' \\right)\\,,\n\\ee\nand the one-loop determinant obtained from integrating out these modes for each value of $n$ is given by \\cite{Picken:190160}:\n\\be\n\\label{eq:SO3-one-loop-determinant}\n \\det_{SU(2), \\text{ one-loop}} = \\frac{\\Phi_r^{3\/2} (n+\\a) }{ \\beta^{3\/2} \\sin(2\\pi \\a)} \\,.\n\\ee\nFinally, we discuss the quadratic contribution of the fermionic fields. By using the saddle-point solution for $f(\\tau)$ and $g(\\tau)$ and by quadratically expanding the super-Schwarzian action:\n\\be\n I_{\\text{ferm., quad.}} = \\Phi_r\\int_0^{\\beta} d\\tau\\, &\\bigg(\\eta^p \\left[\\frac{2\\pi^2}{\\beta^2}\\left(1+2(n+\\a)^2 \\right) \\partial_\\tau - \\partial_\\tau^3\\right]\\bar \\eta_p + \\nn \\\\&+ \\partial_\\tau \\eta^p \\left[\\frac{12\\pi^2(n+\\a)^2}{\\b^2} + \\frac{8i \\pi (n+\\a)\\partial_\\tau}{\\beta} - 3\\partial_\\tau^2 \\right] \\bar \\eta_p\\bigg) \n \\ee\nExpanding the fermionic fields in Fourier modes, \n\\bea \\eta^1(\\tau) &=&e^{i\\frac{2\\pi (n+\\a)\\tau}{\\b} }\\sum_{m_1 \\in \\dots, -\\frac{1}2, \\, \\frac{1}2, \\dots} \\sqrt{\\frac{\\b}{2\\pi}} \\,\\eta^1_{m_1} e^{-i\\frac{2\\pi m_1\\tau}{\\b} }\\\\\n\\eta^2(\\tau) &=&e^{-i\\frac{2\\pi (n+\\a)\\tau}{\\b} }\\sum_{m_2 \\in \\dots, -\\frac{1}2, \\, \\frac{1}2, \\dots} \\sqrt{\\frac{\\b}{2\\pi}}\\, \\eta^2_{m_2} e^{i\\frac{2\\pi m_2\\tau}{\\b}}\\,,\n\\ea\nwhere we impose anti-periodic boundary conditions for the fermionic fields when $\\alpha= 0$ and we impose boundary conditions consistent with the introduction of the fugacity $z$ when $\\alpha\\neq 0$. We can then rewrite the action as, \n\\be\n I_{\\text{ferm., quad.}} = \\frac{2\\pi^2 i \\Phi_r}{\\beta} \\left[\\sum_{p=1, 2}\\,\\sum_{m_p \\in \\dots, -\\frac{1}2, \\, \\frac{1}2, \\dots} (m_p - n - \t\\a) (4m_p^2-1) \\eta^p_{m_p} \\bar \\eta^p_{-m_p}\\right]\\,.\n\\ee\nWe are interested in computing the dependence of the one-loop determinant on $n$, $\\alpha$, $\\beta$ and $\\Phi_r$. The $\\beta$ and $\\Phi_r$ dependence is captured by the existence of the four-fermionic zero modes with $m_p = \\pm 1\/2$. As in \\cite{Stanford:2017thb}, to compute the rest of the one-loop determinant, we will regularize this result by dividing the result by the one-loop determinant with $n=0$ and $\\alpha = 0$. We thus find that the regularized one-loop determinant is given, again up to a proportionality constant, by \n\\be\n\\label{eq:ferm-one-loop}\n\\det_{\\text{ferm., one-loop}} = \\frac{\\beta^4}{\\Phi_r^4} \\prod_{p=1, 2}\\,\\,\\prod_{m_p \\in \\dots, -\\frac{5}2, -\\frac{3}2, \\frac{3}2, \\frac{5}2, \\dots} \\frac{m-n -\\a}{m} = \\frac{\\beta^4}{\\Phi_r^4} \\frac{\\cos(\\pi\\a)^2}{(1 - 4(n+\\a)^2)^2 }\\,. \n\\ee\n\n\n\\paragraph{Final answer:} Thus, accounting for the saddle-point value of the action \\eqref{eq:on-shell-action-N=4} together with the one-loop determinants \\eqref{eq:Schwarzian-one-loop-determinant}, \\eqref{eq:SO3-one-loop-determinant}, and \\eqref{eq:ferm-one-loop}, we find that the partition function of the $\\cN=4$ Schwarzian theory is given, up to an overall proportionality constant, by: \n\\be \n\\label{eq:localization-part-function}\nZ_{\\cN=4\\,\\,\\,\\text{Schw.}} &= \\sum_{n \\in \\mathbb Z} \\det_{\\text{Schw., one-loop}} \\,\\,\\det_{SU(2), \\text{ one-loop}}\\, \\det_{\\text{ferm., one-loop}} e^{-I_{\\cN=4,\\text{bosonic}}^{\\,\\text{on-shell}} } \\nn \\\\ &=\\sum_{n \\in \\mathbb Z} \\frac{ \\beta \\cot(\\pi \\a) (\\a+n)}{\\Phi_r (1- 4(n+\\a)^2)^2} e^{\\frac{2\\pi^2 \\Phi_r} {\\b} \\left(1 -4 (n+\\a) ^2 \\right)}\n\\ee \nWe will thus continue by matching this result using the completely distinct method of canonical quantization, after which we will come back to a detailed analysis of the spectrum associated to \\eqref{eq:localization-part-function} in section \\ref{sec:density-of-states}. \n\n\\subsubsection{Method 2: Canonical quantization} \\label{sec:meth2cq}\nIn this section, we will compute the partition function of the $\\cN=4$ super-Schwarzian theory using the canonical quantization approach of \\cite{Mertens:2017mtv} (for a very recent discussion explaining the connection to the localization approach see also \\cite{Alekseev:2020jja}). \n\nWe will illustrate briefly the idea first. The localization formula we used above can be applied to integrals that generally have the following form \n\\beq\nZ = \\int dqdp ~e^{-H(p,q)},\n\\eeq\nwhere the integral is over a symplectic space (a classical phase space) with coordinates $(q,p)$ and $H(q,p)$ generates via the Poisson brackets a $U(1)$ symmetry. In the case of the bosonic Schwarzian theory the integration manifold is ${\\rm Diff}(S^1)\/SL(2,\\mathbb{R})$ which a coadjoint orbit of the Virasoro group (and, therefore, symplectic), and $H(p,q)$ is the Schwarzian action. Instead of using localization, we can obtain this integral using the following identity \n\\beq\\label{eq:SKJDKD}\n\\lim_{\\hbar\\to 0} {\\rm Tr}\\left( e^{-H(p,q)}\\right) = \\int dqdp ~e^{-H(p,q)},\n\\eeq\nwhere the left-hand side is $\\hbar\\to 0$ limit of the trace evaluated over the Hilbert space obtained by quantizing the phase space. In the case of the Schwarzian, the quantization of ${\\rm Diff}(S^1)\/SL(2,\\mathbb{R})$ is the identity representation of the Virasoro algebra with central charge $c \\sim 1\/\\hbar$. The left hand side of \\eqref{eq:SKJDKD} can by very easily computed at finite $c$ as a Virasoro vacuum character by counting descendants, and a very simple calculation gives the Schwarzian path integral \\cite{Mertens:2017mtv}. In the bosonic case, the main advantage of this method is the possibility to compute correlation functions which are not available using localization. In this case, we will use it as a double check on our previous result.\n\n\nFor the case of the $\\cN=4$ super-Schwarzian the integration space is ${\\rm Diff}(S^{1|4})\/PSU(1,1|2)$ which is a coadjoint orbit of super-Virasoro and, therefore, also symplectic. We will assume that the quantization of this phase space, the Hilbert space in \\eqref{eq:SKJDKD}, is equivalent to the identity representation of the small $\\cN=4$ Virasoro algebra with central charge $c\\sim 1\/\\hbar$. The $\\cN=4$ super-Schwarzian partition function is then the semiclassical limit of the vacuum character. \n\nLets begin then by recalling the super-Virasoro algebra involved in this problem. The bosonic generators are $L_n$ and $T_n^i$ where $n$ is an integer and $i=1,2,3$ label the generators of a Kac-Moody $SU(2)$ at level $k$. Their algebra is \n\\bea\n[ L_m,L_n ]&=& (m-n)L_{m+n}+\\frac{k}{2} m(m^2-1)\\delta_{n+m,0} \\label{eq:smalln4gen1}\\\\\n\\text{[} T^i_m,T^j_n \\text{]} &=& i \\epsilon^{ijk}T^k_{m+n} + \\frac{k}{2} m \\delta_{m+n,0} \\delta_{i,j} \\label{eq:smalln4gen2}\\\\\n\\text{[} L_m , T_n^i\\text{]} & = & -n T_{m+n}^i.\\label{eq:smalln4gen3}\n\\ea\nThe central charge of the bosonic Virasoro sector is $c=6k$, which is fixed by a Jacobi identity. The fermionic generators are $G_r^p$ and $\\bar{G}_s^p$, $p=1,2$. They transform in the fundamental and antifundamental of the $SU(2)$. The Fourier mode parameter $r,s$ are integer in the Ramond sector or half-integer in the Neveu-Schwarz sector. The rest of the algebra, involving the fermionic generators, can be found for example in \\cite{Eguchi:1987sm}, and is given by\n\\bea\n&&\\{ G_r^p, \\bar{G}_s^q\\} = 2 \\delta^{pq} L_{r+s}-2(r-s)\\sigma^i_{pq} T^{i}_{r+s}+\\frac{k}{2}(4r^2-1)\\delta_{r+s,0},\\nn \\\\\n&& [T_m^i,G_r^p]=-\\frac{1}{2}\\sigma^i_{pq}G^q_{m+r},~~[T^i_m,\\bar{G}_r^p]=\\frac{1}{2}\\sigma^i_{pq}{}^\\star \\bar{G}_{m+r}^q,~~ \\{ G_r^p, G_s^q\\}=\\{\\bar{G}_r^p,\\bar{G}_s^q\\}=0\\nn \\\\\n&&[L_m,G_r^p]=\\left(\\frac{m}{2}-r\\right)G_{m+r}^p,~~[L_m,\\bar{G}_r^p]=\\left(\\frac{m}{2}-r\\right)\\bar{G}_{m+r}^p,\\label{eq:smalln4gen4}\n\\ea\nwhere $\\sigma^i_{pq}$ are the Pauli matrices. In that reference, Eguchi and Taormina also construct the unitary representations of the algebra. \n\nFor the application we have in mind in this paper, the Schwarzian path integral, we will only need the massless representations in the NS sector, due to the fact that we want the Schwarzian fermions to be antiperiodic as explained in \\cite{Mertens:2017mtv}.\\footnote{If we wanted the Schwarzian theory Witten index we would use the characters in the Ramond sector.} General representations are labeled by $h$, the eigenvalue of $L_0$, and $\\ell$, the spin of the $SU(2)$ representation, and the massless sector has $(h=\\ell, \\ell)$ with half-integer $\\ell=0, \\frac{1}{2}, \\ldots, \\frac{k}{2}$. For the Schwarzian path integral we will need the $\\ell=0$ representation. The characters are defined by\n\\beq\n\\chi_{\\ell} (k;q,z) \\equiv {\\rm Tr}_{NS}\\left[ (-1)^F q^{L_0-\\frac{c}{24}} z^{T^3_0}\\right],\n\\eeq\nover a representation $\\ell$ of the algebra. We need to insert $(-1)^F$ such that the fermions along the quantization direction are periodic and survive the semiclassical limit (see discussion in \\cite{Mertens:2017mtv}). \n\nThese characters were computed by Eguchi and Taormina \\cite{Eguchi:1987wf} by simply counting states. They are given by the following expression\\footnote{The origin of the first factor in the right hand side is explained in section 5 of \\cite{Kraus:2006nb}.}\n\\beq\n\\chi_{\\ell} (k;q,z=e^{2\\pi i y}) = e^{2\\pi i k \\frac{y^2}{\\tau}} q^{-\\frac{k}{4}} q^{\\ell+\\frac{1}{4}} \\frac{i \\theta_3(q,-z) \\theta_3(q,-z^{-1})}{\\eta(q)^3 \\theta_1(q,z^2)} \\left[ \\mu(z,q) - \\mu(z^{-1},q) \\right],\n\\eeq\nwhere $\\theta_3(q,z)$ is the Jacobi theta function and we defined the function \n\\beq\n\\mu(z,q) \\equiv \\sum_{n\\in\\mathbb{Z}} \\frac{q^{(k+1)n^2+(2\\ell+1)n}z^{2(k+1)n + 2 \\ell + 1}}{(1- z q^{n+\\frac{1}{2}})(1- z q^{n+\\frac{1}{2}})}.\n\\eeq\n\nNow we have all the ingredients to extract the Schwarzian partition function from the $\\hbar \\sim 1\/k \\to 0$ ($c\\to\\infty$) limit applied to the above expression for the identity $\\ell=0$ representation. As explained in \\cite{Mertens:2017mtv} we need to consider the following scaling \n\\beq\nz=e^{2 \\pi i \\alpha \\tau},~q=e^{2\\pi i \\tau}~~~~~{\\rm with}~~~~\\tau = \\frac{i}{k} \\frac{4\\pi \\Phi_r}{\\beta}.\n\\eeq\nWe then take $\\tau \\cdot k$ fixed in the limit and this constant is related to the ratio $\\Phi_r\/\\beta$ in the Schwarzian theory. This choice of $z$ and $q$ is written directly in terms of $\\alpha$ and $\\beta$ which will become the chemical potential and inverse temperature in the Schwarzian limit. \n\nWhen taking the Schwarzian limit we will only keep track of the dependence on $\\alpha$ and $\\beta$ since any prefactor can be absorbed in a redefinition of the zero-point entropy and energy. We will not go over all the details but some useful intermediate steps are \n\\beq\n\\mu(z,q)-\\mu(z^{-1},q) \\sim \\frac{8 e^{\\frac{2\\pi^2 \\Phi_r}{\\beta}4\\alpha^2}}{\\pi^2 |\\tau|^2} \\sum_{n\\in \\mathbb{Z}} \\frac{(\\alpha+n)}{(1-4(\\alpha+n)^2)^2} e^{-\\frac{2\\pi^2 \\Phi_r}{\\beta} 4(n+\\alpha)^2}.\n\\eeq\nUsing eq (3.15) of \\cite{Ahn:2003tt} gives the following limit\n\\beq\ni \\left( \\frac{\\theta_4(q,z)}{\\eta(q)^3}\\right)^2 \\frac{\\eta(q)^3}{\\theta_4(q,z^2q^{\\frac{1}{2}})} \\sim \\frac{\\tau}{\\tan \\pi \\alpha},\n\\eeq\nwhich is related in a simple way to the Jacobi theta functions appearing in the character. Including the rest of the terms the semiclassical $k\\to\\infty$ limit of the vacuum character is \n\\beq\n\\chi_{\\ell=0} (k\\to\\infty;q,z) \\sim \\sum_{n\\in \\mathbb{Z}} \\frac{\\beta \\cot(\\pi \\alpha)(\\alpha+n)}{\\Phi_r(1-4(n+\\alpha)^2)^2} e^{\\frac{2 \\pi^2 \\Phi_r}{\\beta}(1-4(n+\\alpha)^2)}.\n\\eeq\nThis precisely reproduces the partition function computed by localization, given in equation \\eqref{eq:localization-part-function} (An analogous match was checked in \\cite{Mertens:2017mtv} for the case of $\\cN=1$ and $\\cN=2$ super-Schwarzian).\n\nWe can mention some interesting features of this expression. First of all the factor of $\\cot \\pi \\alpha$ is important for the formula to make sense. When $\\alpha \\to 0$ or $1$ it is crucial to include this factor for the final answer to be finite. The same happens when $\\alpha \\to 1\/2$ since otherwise the sum would be divergent. \n\nFrom the 2D CFT perspective the identity representation is invariant under the generators of the global $PSU(1,1|2)$ algebra. In terms of the Virasoro algebra those generators are \n\\beq\n\\text{Bosonic:}~~~~L_{-1},L_0,L_1,~~~~T_0^1,T_0^2,T_0^3,~~~~\\text{Fermionic:}~~~~G_{\\pm \\frac{1}{2}}^a,~~~\\bar{G}^a_{\\pm \\frac{1}{2}},\n\\eeq\nwhich satisfy the same superalgebra as \\eqref{eq:psu(1,1|2)-superalgebra}. \nIt is important to take the fermionic generators in the NS sector. These produce the pre-factor of $\\beta^1$ in the character. In the localization calculation this factor basically counts the number of bosonic and fermionic zero modes $Z \\sim \\beta^{(\\#{\\rm fermion})\/2 - (\\#{\\rm bosons})\/2}$. In the case of the small $\\cN=4$ algebra there are $8$ fermionic zero modes and $6$ bosonic zero modes, giving a factor of $\\beta$. \n\n\\subsection{$\\cN=4$ supermultiplets}\n\nBefore extracting the spectrum from the exact partition function we first explain what properties we expect it to have. The super-Schwarzian theory we are studying captures the explicit breaking of the superconformal symmetry group $PSU(1,1|2)$. Still, as we have seen in section \\ref{sec:space-time-and-global-symm}, translations, super-translations and rigid $SU(2)$ rotations are symmetries. We can write the fermionic generators as $Q_p$ and $\\bar{Q}^p$ with $p=1,2$. Then a part of the algebra that we will use here is \n\\beq\n\\{ Q_p, \\bar{Q}^q\\} = 2 \\delta_a^b H,~~~\\{Q_p,Q_q\\}=\\{ \\bar{Q}_p, \\bar{Q}_q\\} =0\n\\eeq\nThese generators can be written in terms of the Schwarzian fields but we will not need it for the manipulations here. Imagine we first diagonalize $H$ and look at some states with energy $E$. Then as long as $E\\neq 0$ the operators $Q \\sim \\hat{a}$ act as a $SU(2)$ doublet of lowering fermionic operators and $\\bar{Q}\\sim \\hat{a}^\\dagger$ as a $SU(2)$ doublet of rising fermionic operators. To construct a representation we can begin with a state $|J\\rangle$ which transforms as a spin $J$ representation of $SU(2)$, constructed such that $Q_p|J\\rangle=0$. The supermultiplet will have states acting with a single charge $\\bar{Q}^q|J\\rangle$, which can be expanded into $(1\/2)\\otimes J = (J-1\/2) \\oplus (J+1\/2)$; and acting with two charges $\\bar{Q}_1 \\bar{Q}_2 |J\\rangle$ of spin $J$. Therefore, the supermultiplet with $E\\neq 0$, starting with $J\\neq 0$ is made of $(J-1\/2)\\oplus 2 (J) \\oplus (J+1\/2)$. When we construct a supermultiplet starting with a singlet $|0\\rangle$, the $\\bar{Q}^q|0\\rangle$ transforms as a doublet and $\\bar{Q}_1\\bar{Q}_2|0\\rangle$ as another singlet, giving $2(0)\\oplus(1\/2)$. Labeling the supermultiplet by the state with highest $SU(2)$ spin, the $E\\neq 0 $ part of the spectrum should organize as \n\\bea\n\\mathbf{J}&=&(J) \\oplus 2 (J-1\/2) \\oplus (J-1),~~~~J\\geq 1\\\\\n\\mathbf{1\/2}&=&(1\/2) \\oplus 2 (0).\n\\ea\nFinally we might also have states with $E=0$. Starting with a spin-$J$ representation $|J\\rangle$, having $H|J\\rangle =0$ implies that all supercharges annihilate the state and, therefore, that's the whole supermultiplet.\n\nTaking these considerations into account, we can expect the partition function of the $\\cN=4$ super-Schwarzian theory to be expanded in the following way\n\\bea\nZ(\\beta,\\alpha) &=& \\sum_J \\chi_J(\\alpha) \\rho_{\\rm ext}(J) + \\int dE ~e^{-\\beta E} \\left( \\chi_{1\/2}(\\alpha) +2\\chi_{0}(\\alpha)\\right) \\rho_{\\rm cont}(1\/2,E) \\nonumber\\\\\n&&+\\sum_{J\\geq 1} \\int dE ~e^{-\\beta E} \\left( \\chi_{J}(\\alpha) +2\\chi_{J-\\frac{1}{2}}(\\alpha)+ \\chi_{J-1}(\\alpha)\\right) \\rho_{\\rm cont}(J,E),\\label{sqwewq}\n\\ea\nwhere the sums are over half-integer $J$ and $\\chi_J(\\alpha)\\equiv \\sum_{m=-J}^J e^{4\\pi i \\alpha m} = \\frac{\\sin (2J+1)2\\pi \\alpha}{\\sin 2\\pi \\alpha}$ is the character of a spin-$J$ representation of $SU(2)$. In the first line, the first term corresponds to states with $E=0$ while the second term to the $E\\neq 0$ multiplet $\\mathbf{1\/2}$. The second line corresponds to the sum over all other $E\\neq 0$ supermultiplets. Therefore, $\\rho(J,E)$ is the density of supermultiplets with energy $E\\neq 0$ and highest spin $J$, while $\\rho_{\\rm ext}(J)$ is the density of $E=0$ states of spin $J$. \n\nWe will see in the next section that the spectrum of the $\\cN=4$ super-Schwarzian derived from the exact partition function we computed above has precisely this form (although with only singlet $J=0$ zero energy states). \n\n\n\n\n\\subsection{Exact density of states}\n\\label{sec:density-of-states}\nThe final answer for the exact $\\mathcal{N}=4$ super-Schwarzian theory partition function is given by the following function of inverse temperature $\\beta$ and $SU(2)$ chemical potential $\\alpha$ as\n\\beq\\label{exactPFN4d}\nZ(\\beta,\\alpha) =e^{S_0} \\sum_{n\\in \\mathbb{Z}} \\frac{\\beta}{\\Phi_r} \\frac{2 \\cot(\\pi \\alpha)(\\alpha+n)}{\\pi^3(1-4(n+\\alpha)^2)^2} e^{\\frac{2 \\pi^2 \\Phi_r}{\\beta}(1-4(n+\\alpha)^2)}\\,.\n\\eeq\nWe have fixed the overall normalization in a way that will be convenient later. We will write this answer as a trace over a Hilbert space (albeit with continuous spectrum) realizing it has precisely the form \\eqref{sqwewq}.\n\nTo understand the physics of this partition function we want to extract the density of states as a function of energy at fixed $SU(2)$ charge, which we will refer to as angular momentum (anticipating the application to near extremal black holes in 4D). To do that we begin by performing an inverse Laplace transform and define the fixed-chemical-potential density of states \n\\beq\nZ(\\beta,\\alpha) = \\int dE e^{-\\beta E} D(\\alpha, E).\n\\eeq\nApplying this to our result \\eqref{exactPFN4d} gives\n\\beq\nD(\\alpha,E) = D_{E=0}(\\alpha) \\delta(E) + D_{\\rm cont}(\\alpha,E),\n\\eeq\nwhere we separate the BPS and continuous part of the spectrum,\\footnote{The sum in $D_{E=0}(\\alpha)$ is at face value divergent. To regulate it we used the following prescription $\\lim_{N\\to\\infty} \\sum_{n=-N}^N\\frac{4(\\alpha+n)}{\\pi \\tan \\pi \\alpha (1-4(\\alpha+n)^2)} = 1$. We can verify that this is the correct prescription by checking that after integrating over energies, this gives back the original partition function.}\n\\bea\n\\label{toto}D_{E=0}(\\alpha) &=&e^{S_0} \\sum_{n\\in\\mathbb{Z}} \\frac{4(\\alpha+n)}{\\pi \\tan \\pi \\alpha (1-4(\\alpha+n)^2)} = e^{S_0}\\label{extD} \\\\\\label{contD}\nD_{\\rm cont}(\\alpha,E)&=&e^{S_0}\\sum_{n\\in \\mathbb{Z}} \\frac{4(\\alpha+n)}{\\pi \\tan \\pi \\alpha} \\frac{I_2\\left(2\\pi \\sqrt{2 \\Phi_r E(1-4(\\alpha+n)^2)}\\right)}{E(1-4(\\alpha+n)^2)}\n\\ea\nWe see the first line corresponding only to states with zero energy is independent of $\\alpha$. This means it only gets contributions from zero charge (angular momentum) states. We chose the normalization of the partition function such that this gives $\\exp \\left( S_0 \\right)$ and can be interpreted as the degeneracy of ground states.\n\nTo find the density of states we use the following identity to rewrite \\eqref{contD} as \n\\beq\\label{ble}\nD_{\\rm cont}(\\alpha,E) =e^{S_0} \\sum_{m=1}^\\infty \\frac{m \\sin 2\\pi m \\alpha}{ \\tan \\pi \\alpha} \\frac{\\sinh \\left(2 \\pi \\sqrt{2\\Phi_rE-\\frac{1}{4}m^2}\\right)}{2 \\pi^2 \\Phi_r E^2} \\Theta\\Big(E-\\frac{m^2}{8\\Phi_r}\\Big).\n\\eeq\nWe defined the Heaviside function $\\Theta(x)$ such that $\\Theta(x>0)=1$ and $\\Theta(x<0)=0$. The dependence with the chemical potential can be expanded in $SU(2)$ characters in the following simple way \n\\bea\\label{charactedID4}\n\\frac{2 \\sin 2\\pi m \\alpha}{ \\tan \\pi \\alpha} &=& \\chi_{J}(\\alpha) +2\\chi_{J-\\frac{1}{2}}(\\alpha)+ \\chi_{J-1}(\\alpha),~~~J\\equiv m\/2,~{\\rm with}~m>1\\\\\n\\frac{2 \\sin 2\\pi \\alpha}{ \\tan \\pi \\alpha} &=& \\chi_{1\/2}(\\alpha) +2\\chi_{0}(\\alpha),~~\\hspace{2.3cm}J\\equiv m\/2,~{\\rm with}~m=1\n\\eea\nwhere in the right hand side we defined the angular momentum $J$ in terms of the integer $m$. In principle we can use this formula to extract the density of states for each $SU(2)$ representation. Instead we will notice this is precisely the combination in equation \\eqref{sqwewq}\nwhere $J$ now labels the supermultiplet $\\mathbf{J}$. The second line with $m=1$ involves the special case $\\mathbf{1\/2}$. Comparing \\eqref{sqwewq} with \\eqref{ble} we can extract the density of supermultiplets $\\rho_{\\rm cont}(J,E)$ for $E\\neq0$ and using \\eqref{extD} we can write the density of $E=0$ states $\\rho_{\\rm ext}(J)$. The final answer is given by\n\\bea\n\\rho_{\\rm ext}(J) &=& e^{S_0} \\delta_{J,0}.\\label{sksks}\\\\\n\\rho_{\\rm cont}(J,E) &=& \n\\frac{e^{S_0}J}{4\\pi^2 \\Phi_r E^2}\\sinh \\left(2 \\pi \\sqrt{2\\Phi_r(E-E_0(J))} \\right)\\hspace{0.1cM} \\Theta\\Big(E-E_0(J)\\Big), \\hspace{0.1cm}\\text{for }J\\geq\\frac{1}{2},\\label{sksks2}\n\\ea\n where the gap for each supermultiplet labeled by $J$ is given by $E_0(J) \\equiv J^2\/(2\\Phi_r)$.\n\\begin{figure}\n \\centering\n \\begin{tikzpicture}[scale=0.65]\n \\pgftext{\\includegraphics[scale=0.5]{superdos.pdf}} at (0,0);\n \\draw (-5.7,-0.4) node {$e^{S_0}$};\n \\draw (-5.8,2.75) node {\\small $\\rho(E)$};\n \\draw (-2,-3) node {\\small $E_{\\rm gap}$};\n \\draw (-0,-3) node {\\small $E_0(1)$};\n \\draw (-0,-1.5) node {\\small $\\mathbf{1}$};\n \\draw (-2.3,-1.5) node {\\small $\\mathbf{1\/2}$};\n \\draw (-4.6,-1.5) node {\\small $\\mathbf{0}$};\n \\draw (5,-3) node {\\small $E$};\n \n \\end{tikzpicture}\n \\hspace{0.6cm}\n \\begin{tikzpicture}[scale=0.65]\n \\pgftext{\\includegraphics[scale=0.47]{dosj0.pdf}} at (0,0);\n \\draw (-5.4,-0.4) node {$e^{S_0}$};\n \\draw (-5.4,2.75) node {\\small $\\rho(E)$};\n \\draw (-2,-3) node {\\small $E_{\\rm gap}$};\n \\draw (0.2,-3) node {\\small $E_0(1)$};\n \\draw (5,-3) node {\\small $E$};\n \n \\end{tikzpicture}\n \\caption{\\textbf{Left:} Density of supermultiplets labeled by the highest spin $\\mathbf{J}$. We show $\\mathbf{0}$, which is simply a delta function at $E=0$; $\\mathbf{1\/2}$ which is continuous but starts at $E_{\\rm gap}\\equiv E_0(1\/2)$; and $\\mathbf{1}$ which is also continuous starting at $E_0(1)$. \\textbf{Right:} Degeneracy for all states with $J=0$. These come from $\\mathbf{0}$, the delta function at $E=0$, $\\mathbf{1\/2}$, starting at $E_{\\rm gap}$, and $\\mathbf{1}$, starting at $E_0(1)$. All other supermultiplets do not have a $J=0$ component.}\n \\label{fig:my_label}\n\\end{figure}\n\n\n\nUsing this result we can get a simple picture of the shape of the spectrum. First we have a number $e^{S_0}$ of states at exactly $E=0$ which are all in the supermultiplet $\\mathbf{0}$, an $SU(2)$ singlet. These are the extremal BPS states of the black hole as we will see in the next section. For small energies there are no states until we reach the gap in the spectrum given by the threshold energy for the supermultiplet $\\mathbf{\\frac{1}{2}}=(\\frac{1}{2})\\oplus 2(0)$, given by \n\\beq\nE_{\\rm gap} = \\frac{1}{8 \\Phi_r},\n\\eeq\nand for $E>E_{\\rm gap}$ we have a continuum of states. Something similar is true for higher multiplets $J > 1\/2$, but now the continuum starts at a supermultiplet-dependent gap\n\\beq\nE_{0}(J) = \\frac{1}{2\\Phi_r} J^2.\n\\eeq\nIt is perhaps not surprising that states with spin $J$ start at $E_{0}(J)$. The surprising feature is that there are no states with $J=0$ at energies $01\/2$ contribute in the following way \n\\beq\ne^{2\\pi i J} \\left[\\chi_J(\\alpha) - 2 \\chi_{J-1\/2}(\\alpha)+\\chi_{J-1}(\\alpha)\\right].\n\\eeq\nIt is easy to see that when $\\alpha=0$ this combination exactly vanishes since there is the same number of bosonic and fermionic states in the supermultiplet. The same is true for $\\mathbf{1\/2}$ which gives $\\chi_{1\/2}(\\alpha)-2\\chi_0(\\alpha)$ and also vanishes for $\\alpha=0$. Therefore, the Witten index of the $\\cN=4$ super-Schwarzian theory is given by $e^{S_0}$ and counts the number of ground states.\n\nAs a final comment, there are two different definitions of the $\\cN=2$ super-Schwarzian theory that differ on the presence of a 't Hooft anomaly, as we review in Appendix \\ref{app:N2}. Since the gauge group of the $\\cN=4$ super-Schwarzian is $SU(2)$ we think there cannot be such anomaly \\cite{Kapec:2019ecr} and the theory is unique, but this deserves further investigation.\n\n\n\\subsection{Comparison with a pure bosonic theory} \n\\label{sec:compbosth}\nTo finish this section we would like to compare this solution to a non-supersymmetric version of the theory, such as the one in \\cite{Iliesiu:2020qvm}. Imagine we have a bosonic Schwarzian theory coupled to an $SU(2)$ mode. The action is \n\\beq\\label{ejksjw}\nI =-\\Phi_r \\int d\\tau \\hspace{0.1cm} \\text{Sch}(f,\\tau) + K \\int d\\tau {\\rm Tr} \\left( g^{-1} \\partial_\\tau g\\right)^2 ,\n\\eeq\nwhere $K$ and $\\Phi_r$ are independent parameters. This theory can be solved exactly \\cite{Mertens:2019tcm}. The density of states as a function of energy and angular momentum $J$ is given by\n\\beq\n\\rho_{bos.}(J,E) = e^{S_0} \\sinh \\left( 2\\pi \\sqrt{2\\Phi_r\\left( E- E_{bos.}(J)\\right)}\\right) \\Theta (E-E_{bos.}(J)),~~~E_{bos.}(J)\\equiv\\frac{J(J+1)}{2K}.\n\\eeq\nThe bosonic sector of the supersymmetric theory is special in two ways. First of all it necessarily has $K=\\Phi_r$. Therefore, at least semiclassically one can compute the gap scale by measuring the following quantity \n\\beq\n\\left( \\frac{\\partial J}{\\partial \\Omega} \\right)_{T=0,\\Omega=0}= K = \\Phi_r,\n\\eeq\nwhere $\\Omega=i\\alpha\/\\beta$ is the potential conjugated to $J$ (from 4D perspective, angular velocity). The second, and more important, feature is the fact that for the bosonic theory \\eqref{ejksjw}, even if $K=\\Phi_r$, there are states with $J=0$ for any energy $E>0$, namely $\\rho(J=0,E>0)\\neq 0$ and $\\rho(J=0,E=0)=0$. The $\\cN=4$ supersymmetric Schwarzian theory is completely different. We find a delta function at $E=0$ describing $e^{S_0}$ states. Moreover, even for $J=0$, there are no states in the range $00$.\\footnote{For the opposite orientation, we can take $\\beta_L\\to0$ then we have large $P<0$.} Second, we take large $k\\gg 1$ so that the gravity description in the bulk is accurate. Finally, we take $\\beta_L \\sim k \\gg 1$, which implies that we are looking at very low temperatures or states with $E \\sim P$. Since the state without left-moving excitations preserves supersymmetry, this is also a near-BPS limit \\cite{Coussaert:1993jp}. \n\nWe will follow the calculation first in the fixed $\\beta_L,\\beta_R$ ensemble. As explained in \\cite{Ghosh:2019rcj} when taking this near-extremal limit we can inverse Fourier transform to obtain fixed $P$ ensemble by basically replacing $\\beta_R \\to 2\\pi \\sqrt{c\/(24 P)}$ and $\\beta_L \\to 2 \\beta$ at the end of the calculation. We also keep the left-moving $SU(2)$ chemical potential $\\alpha$ fixed in this limit, and consider zero right-moving charge. Either taking the limit of the character or doing the reduction, the near-extremal near-BPS limit of the partition function is \n\\beq\\label{eq:RReqnschwarzian}\nZ_{R-R}(\\beta,\\alpha) \\sim e^{2\\pi \\sqrt{k P}} \\sum_{n\\in \\mathbb{Z}} \\frac{\\beta}{k} \\frac{\\cot(\\pi \\alpha)(\\alpha+n)}{(1-4(n+\\alpha)^2)^2} e^{\\frac{ \\pi^2 k}{2\\beta}(1-4(n+\\alpha)^2)}\n\\eeq\nWe will not repeat the calculation here since it is completely analogous to section \\ref{sec:meth2cq}. The first term $e^{2\\pi \\sqrt{k P}}$ comes from the right-moving identity character which is basically evaluated in the usual Cardy limit, since $\\bar{q}' \\to 0$. The rest comes from the evaluation of the left-moving identity character, in the limit $q'\\to 1$. \n\nThe parameters describing the near-extremal near-BPS effective theory analyzed in section \\ref{sec:N=4-super-Schw} are given in terms of the level $k$ and the angular momentum $P$, by \n\\begin{equation}\n S_0 = 2 \\pi \\sqrt{k P} ,~~~~\\Phi_r = \\frac{k}{4}.\n\\end{equation}\nTaking the inverse Laplace transform of this, we obtain the same density of states as the $\\cN=4$ super-Schwarzian theory with these parameters. In particular, we find a large degeneracy of BPS states given by $e^{2\\pi \\sqrt{ k P}}$, we find a gap to the first excited black hole state $E_{gap}=1\/(2k)$, and this predicts the index matches with the black hole degeneracy. \n\nThis can be easily generalized to cases with non-zero $SU(2)_R$ charge $\\bar{T}_0^3=J_R$. In this cases states with $T_0^3=0$ are still BPS and the contribution from the right-moving sector replaces $S_0 \\to 2\\pi \\sqrt{k P - J_R^2}$. With this modification, the spectrum as a function of temperature and $SU(2)_L$ chemical potential is still given by \\eqref{eq:RReqnschwarzian}, controlled by the $\\mathcal{N}=4$ super-Schwarzian.\n\n\\paragraph{Alternative construction -- preserving $\\mathbf{(4,0)}$ SUSY:} In this case we obtain a similar conclusion for $\\beta_R \\to 0$ and large $\\beta_L$. The difference now is that we can take instead $\\beta_L \\to 0 $ and $\\beta_R$ large. This extremal limit breaks supersymmetry since the right-moving sector of the theory is purely bosonic. Therefore, we expect that in this case, the black hole spectrum has, to leading order, no extremal states and no gap, similar to \\cite{Ghosh:2019rcj} (or \\cite{Iliesiu:2020qvm}).\n\n\n\\subsection{Comparison to string theory constructions}\nIn this section, we will very briefly mention some string theory constructions using D-branes that can be dimensionally reduced to the $AdS_3$ supergravity theories studied above.\n\nFor example, take type IIB string theory compactified on $M^4$, being either $T^4$ or $K3$. We consider the D1-D5 system, which at low energies can be described by either a $(4,4)$ 2D superconformal field theory or supergravity on $AdS_3 \\times S^3$. Compactifying down to $AdS_3$ gives the $(4,4)$ supergravity theory we studied above. The bosonic part of the spectrum is a 3D metric on $AdS_3$, and a gauge symmetry coming from the $S^3$ factor in the metric $SO(4)\\sim SU(2)_L \\times SU(2)_R$ separated into left- and right-movers. For the reasons explained in the previous section, we expect the presence of other fields to leave the conclusions below unchanged. For this theory, we can derive the level of the $SU(2)$ current algebra by matching the chiral anomaly\n\\begin{equation}\n k = Q_1 Q_5 ~~{\\rm for}~T^4,~~~~k=Q_1Q_5+1~~{\\rm for}~K3.\n\\end{equation}\nFor concreteness, we look at the $T^4$ case below. \n\nSo far we have vacuum $AdS_3$. We can add some momentum $P$ along the D1-string direction, which is identified in the BTZ gravitational description with the angular momentum $P$ defined above \\cite{Cvetic:1998xh}. The BPS extremal states correspond to no left-moving excitations of the string. Looking at low temperatures we have a near-extremal near-BPS black hole string with $AdS_2 \\times S^1 \\times S^3$ horizon. The parameters of the effective low-energy $AdS_2$ theory from the microscopic model is\n\\begin{equation}\n S_0 = 2 \\pi \\sqrt{Q_1 Q_5 P},~~~~~\\Phi_r = \\frac{Q_1Q_5}{4}\n\\end{equation}\nUsing our solution we see we have $e^{S_0}$ states at extremality, consistent with \\cite{Strominger:1996sh}. Our analysis also explains why the index matches with the black hole degeneracy. This can be easily generalized to near BPS states with non-zero $SU(2)_R$ charges corresponding at zero temperature to BPS black holes with angular momentum in $S^3$ \\cite{Breckenridge:1996is}.\n\nFrom our gravitational analysis giving the low-temperature dependence of the partition function, we have also derived the gap to the first excited black hole, and it is $E_{gap}=1\/(8\\Phi_r)$. In terms of the microscopic model parameters, it is \n\\begin{equation}\n E_{gap} = \\frac{1}{2 Q_1 Q_5}.\n\\end{equation}\nThis answer matches with the string theory approach from \\cite{Maldacena:1996ds}. From this perspective, the extremal black hole states come from counting string configurations at the brane system with only left movers. The lowest energy excitation comes from the first excitation of long strings wound $Q_1Q_5$ times along the branes.\n\n\\paragraph{Alternative construction -- Black holes in type I string theory:} We can also analyze a similar model in type I string theory instead of type II. We consider a D1-D5 brane system but now the supergravity theory emerging in $AdS_3$ has $(4,0)$ supersymmetry \\cite{Johnson:1998vd, Oz:1999it}. When we have an extremal black hole made out of right-movers, we expect the spectrum near-extremality to be analogous to the $\\cN=4$ super-Schwarzian. On the other hand, when the extremal black hole is made out of left-movers, supersymmetry is broken, and the near-extremal spectrum will look like the non-supersymmetric cases studied in \\cite{Ghosh:2019rcj} or \\cite{Iliesiu:2020qvm}.\n\nFinally, there are other interesting compactifications which we do not analyze in this paper, but whose role we briefly mention in the discussion section.\n\n\\section{Discussion}\n\\label{sec:conclusion}\n\nIn this paper, we have defined and solved $\\cN=4$ super-JT gravity. We show it reduces to a $\\cN=4$ generalization of the Schwarzian theory, which can be exactly solved. We argue that this theory captures the temperature-dependence of the gravitational path integral evaluated around near-extremal black holes in higher dimensions. We showed that both $\\cN=2$ ungauged supergravity in 4D flat space and $(4,4)$ supergravity in $AdS_3$ reduce to $\\cN=4$ super-JT in the near-horizon region of near-extremal black hole backgrounds. We found a gravitational explanation of the large extremal black hole degeneracy and for the presence of a gap in the spectrum. Thus our work addresses the strong tension between the non-supersymmetric results of \\cite{Iliesiu:2020qvm} and past micro-state countings in string theory \\cite{Strominger:1996sh}.\n\nWe finish here with some open questions and future directions:\n\n\\subsection*{Generalization to other black holes in AdS}\n\nWhile in this paper, we have focused on near-BPS black holes in 4D $\\cN=2$ supergravity in flatspace or in $(4,4)$ supergravity in $AdS_3$, there are numerous other near-extremal black hole solutions which are of interest in the AdS\/CFT correspondence.\\footnote{Similar considerations are also useful in computing quantum corrections to the Hartle-Hawking wavefunction in dS \\cite{Maldacena:2019cbz}.}\n\nThe first near-BPS solutions which we have not fully analyzed are those on $AdS_3 \\times S^3 \\times S^3 \\times S^1$ \\cite{Elitzur:1998mm}. The special feature about this compactification is that the extremal solution exhibits a large $\\cN=4$ symmetry, with $SU(2)_k\\times SU(2)_{k'}\\times U(1)$ current algebra. It would be interesting to analyze the 2D theory emerging in the throat for near-extremal near-BPS black holes. In this case, the symmetry of the boundary mode is now $D(2,1,\\alpha)$. We do not yet know how to study this version of the $\\cN=4$ Schwarzian theory, and we hope to address such a construction in future work.\n\nWe would also like to briefly mention the existence of near-BPS solutions in higher dimensional AdS. Extremal black holes in such theories typically preserve a smaller amount of supersymmetry; for instance, in AdS$_4$, such black holes exhibit an $OSp(2|2)$ isometry in the near-horizon region. The effective theory capturing the breaking of $OSp(2|2)$ was found to be $\\cN=2$ super-JT gravity \\cite{Forste:2020xwx} and the boundary dynamics is analogously given by the $\\cN=2$ super-Schwarzian (whose properties we have reviewed in appendix \\ref{app:N2}). As we explain in appendix \\ref{app:N2}, the $\\cN=2$ super-Schwarzian has a gap depending on the value of $\\hat q$ (which gives the periodicity of the identification of the $U(1)$ field $\\sigma$) and on whether or not the theory exhibits an anomaly (related to how we weigh the different saddles in the path integral). Thus, to conclude whether near-BPS black holes in such a theory exhibit a mass gap, we need to perform a rigorous analysis to account for all possible massless Kaluza-Klein modes that can appear in the near-horizon region, determine the analog of $\\hat q$ in supergravity and understand the situations in which the action of the boundary mode can exhibit an anomaly. \n\n\nOne purpose for studying the partition function of near-BPS black holes from the bulk perspective is to understand the gap in scaling dimensions between BPS and the near-BPS states in the dual CFT. If we find that the effective theory which captures the near-horizon dynamics exhibits a gap (as it did for the black holes in flatspace studied in this paper), then this translates to a scaling dimension gap, $\\Delta_{\\text{gap}}\\sim 1\/N^2$. It would be interesting to understand whether this gap in scaling dimensions is consistent with predictions from the large charge bootstrap \\cite{Jafferis:2017zna} in SCFTs. Finally, in comparing the partition function on the CFT side to the black hole partition function within a fixed large charge sector, there may be a mismatch coming from configurations with multiple black holes. Thus, it would be interesting to understand such corrections coming from multi-centered black hole solutions \\cite{Denef:2007vg, Denef:2000nb, Bates:2003vx, Sen:2007pg}.\\footnote{We thank G.~Moore for pointing out past works on this issue.} \n\n\n\n\n\n\n\\subsection*{On a possible $\\cN=4$ SYK model}\n\nAnother interesting possibility is whether there is a UV completion of the $\\mathcal{N}=4$ Super-Schwarzian theory in some quantum mechanical models, along the lines of \\cite{Fu:2016vas} for $\\mathcal{N}=1,2.$ To be more specific, we would like a random quantum mechanical model, with a stable unitary nearly conformal fixed point at low energies, with unbroken $\\mathcal{N}=4$ supersymmetry. A model involving dynamical bosons typically causes instability and exhibits supersymmetry breaking in the infrared (shown either as an operator with complex scaling dimension in the spectrum as in \\cite{Giombi:2017dtl,Klebanov:2018fzb}, or the absence of supersymmetric Dyson-Schwinger solutions as in \\cite{Anninos:2016szt,Chang:2018sve} ), and is rather inconvenient to study at finite $N$. In fact, in such a theory with a supermultiplet with $(b, \\psi, \\dots),$ and in a supersymmetric nearly conformal fixed point, $\\Delta_{\\psi}=\\Delta_b+\\frac{1}{2}.$ The Dyson-Schwinger equation of the dominant interaction in the infrared would constrain the dimensions of various fields so that the sum of scaling dimensions of the fields in the interaction is one, i.e.~$ n \\Delta_b+ 2m \\Delta_{\\psi}+ \\dots=1$, with $n,m\\in \\mathbb{Z}_{\\geq 0}$. For the nearly conformal fixed point to be unitary, we require $\\Delta_{b},\\Delta_{\\psi}\\geq 0.$ Together with the supersymmetric constraint, we conclude the only non-trivial solutions possible is that \n\\begin{equation}\\label{bosondim}\n \\Delta_b=0, \\Delta_{\\psi}=\\frac{1}{2}\\,.\n\\end{equation}\nHowever, such a solution indicates that the two-point function of $b$ must be logarithmic and typically causes a divergence in the Dyson-Schwinger equations (or \\eqref{bosondim} ceases to be a solution as in \\cite{Popov:2019nja}). \n\nOn the other hand, we may consider a theory with a fermionic super-multiplet. This scenario brings about yet another complication. Unlike the case of $\\mathcal{N}=1,2$, there is no relevant deformation of the $\\mathcal{N}=4$ that exists in the UV free theory. To see this, we can work in the $\\mathcal{N}=4$ superspace,\\footnote{Here, we note that this argument does not rule out possible theories without any kind of superspace realization. In particular, in one dimension one can consider a first derivative action in bosons as in \\cite{Tikhanovskaya:2020elb}, and this modifies the supersymmetry constraint to $\\Delta_b=\\Delta_{\\psi}$. Thus it allows a greater number of relevant interactions. } and the allowed action is \n\\begin{equation} \n \\mathcal{L} \\sim \\int d^2\\theta W(\\Psi)+ h.c. + \\int d^4\\theta K(\\Psi, \\bar{\\Psi}),\n\\end{equation}\nwhere schematically \n\\begin{equation}\n \\Psi= \\psi+ \\theta b+ \\dots,\n\\end{equation}\n where $\\Psi$ can sit in any representation of $SU(2)$ with half-integer $J$, and $\\psi$ is the lowest component. As a result, any local interaction must have dimension at least one due to $\\int d^2\\theta \\dots $, which implies that it cannot be relevant. This contrasts with the constructions of $\\mathcal{N}=1,2$ SYK-like models, where $\\int d\\theta W(\\Psi)$ is allowed and can produce relevant interactions. We can still ask if it's possible to have a marginally relevant deformation. Even if this were the case, in the infrared, a similar argument to (\\ref{bosondim}) would suggest \n \\begin{equation}\n \\Delta_{\\psi}=0, \\Delta_b=\\frac{1}{2}, \\dots,\n \\end{equation}\nwhich coincides with the dimensions of the free theory. This analysis suggests that constructing an interacting IR fixed point is difficult when starting from a UV theory with the same amount of supersymmetry. Therefore, one might be tempted to consider a scenario in which the $\\cN=4$ supersymmetry solely emerges in the IR and is not present in the UV. We leave a more thorough investigation into these issues for future work. \n \n \n\\subsection*{Higher genus corrections to super-JT}\n\nMotivated by the existence of the gap in the leading density of states for the $\\cN=2$ and $\\cN=4$ super-Schwarzian, it would be interesting to understand whether the gap survives when accounting for corrections coming from higher genus geometries contributing to the 2D theory. Relatedly, due to the existence of the gap, it is interesting to note that the contribution from disk topologies to the spectral form factor $\\< Z(\\beta-i t) Z(\\beta + it)\\>$ dominates even at very late times. This result contrasts with non-supersymmetric or $\\cN=1$ JT gravity, where at late times, the cylindrical topology starts dominating, leading to a ``ramp'' in the spectral form factor, followed by a plateau at even later times. It would also be interesting to understand whether the genus expansion of the $\\cN=2$ and $\\cN=4$ super-JT gravity has an interpretation in terms of a matrix integral; this interpretation needs to go beyond the three Dyson\nensembles \\cite{Dyson:1962es} and the seven Altland-Zirnbauer ensembles \\cite{altland1997nonstandard}, whose gravitational interpretation was studied in \\cite{Stanford:2019vob}. \n\nThese non-perturbative corrections are relevant from the perspective of solving 2D $\\cN=4$ gravity exactly. It is not clear whether these corrections could be reliable from the higher dimensional picture, but it would be interesting if the presence of supersymmetry could help better understand issues of factorization in the D1\/D5 system, as one example. We leave this for future work.\n\n\n\n\n\n\\subsection*{Acknowledgements} \n\n\nWe thank R. Campos Delgado, A.~Castro, G.~Horowitz, I.~Klebanov, J.~Maldacena, S.~Pufu, D.~Stanford, H.~Verlinde and E.~Witten for valuable discussions and comments on the draft. MTH is supported in part by Department of Energy Grants DE-SC0007968, DE-SC0009988, and the Princeton Gravity Initiative. LVI was supported in part by the Simons Collaboration on the Conformal Bootstrap, a Simons Foundation Grant with No. 488653, and by the Simons Collaboration on Ultra-Quantum Matter, a Simons Foundation Grant with No. 651440. GJT is supported by a Fundamental Physics Fellowship. WZ was supported in part by the US NSF under Grants No. PHY-1620059 and PHY-1914860.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nIn order to tame the increasing data demands, small cell deployments are of key importance. To date, primary focus of small cell networks is to enhance the overall capacity by bringing users closer to serving base stations~\\cite{li2014throughput}. Small cells can be deployed as standalone self organizing networks, or operating in conjunction with the existing macro cellular network. Due to the ever-increasing data traffic demands, an ultra-dense deployment of small cells is not a cost-effective strategy due to CAPEX\/OPEX issues. At the same time, recent developments in unmanned aerial vehicles (UAVs), driven by Google and Facebook bring forward the idea of using UAVs for coverage extension and capacity enhancements. UAVs can be used as aerial access points acting as \\emph{pivot} between macro and small cell tiers. UAVs can autonomously provide a reliable multi-connectivity in areas prone to high demand or link failures. UAVs can be used as aerial access points, or relays between disconnected networks and enhanced connectivity~\\cite{guo2014performance}. A smart combination of all these networks can provide a vast range of applications in civilian networks~\\cite{merwaday2015uav}. One of the major issues faced by these networks is on-demand\/on-the-fly capacity provisioning. Capacity refers to data rate transmission towards ground users, whereas delays refer to latency of data transmission~\\cite{mozaffari2015unmanned}. Using drone small cells as aerial support to existing cellular network can handle these high traffic situations more cost-efficiently. While deploying a single UAV is relatively easy using a maximum coverage point over the demand area, deploying more UAVs operating in coordination is more challenging due to interference from other aerial nodes~\\cite{mozaffari2015drone}. Thus, an efficient approach is required that not only provides efficient topology for UAVs based on user demands, but also improved connectivity and enhanced coverage. Using multiple UAVs as relays between the existing macro cells and small cell networks is the primary focus of this letter, in which the goal is to increase capacity, and lower transmission delays.\n\nIn this letter, a cost function based multiple UAVs deployment model is presented. The proposed model uses user demand patterns to assign a cost and density function to each area and UAVs. These cost and density functions are then used to match each UAV to a particular demand zone via a reverse neural model based on user demand patterns~\\cite{chandrashekarappa2014forward}.\n\\begin{figure}[!hb]\n \\centering\n \n\\includegraphics[width=200px]{fig1}\\\\\n \\caption{Illustration of an UAV-overlaid network deployment, with UAVs acting as relays between MBS and UEs.}\\label{fig1}\n\\end{figure}\n\\section{System Model}\nThe proposed model aims at provisioning continuous data between macro and small cell user equipments (UEs). UAVs further enhance load balancing by forming multiple intermediate links between the macro cell and the small cell UEs. The proposed model focuses on UAV-to-UE links rather the MBS-to-UAV backhaul capacity links. A traditional small and macro cell network is shown in Fig.~\\ref{fig1}. This network provides a direct link between the UEs and the macro cell base station (MBS). However, with a continuous increase in number of users within the coverage of a particular small cell, it becomes almost impossible to sustain connectivity without any loss of data. This is a problematic issue for future generation 5G networks, whereby demand is set to be greater than the available capacity. This is the focus of this work harnessing the formation of UAVs for a reliable and load balanced network. The proposed approach leverages a cost based framework which uses neural demand patterns to identify areas with high demands. A predictive chart is formed at the MBS acting as a launch point for UAVs. This chart helps in finding appropriate positions and topology for UAVs. Network stability is attained by minimizing a cost function associated with both demand areas and deployed UAVs. A delay threshold is defined to analyze the network performance, defined as the network state with minimum delay in providing capacity coverage in high demand areas, minimum errors in mapping UAVs to particular demand area, and high reliability in terms of data rate delivery. The model consists of deploying $n$ UAVs each one capable of handling $S_{n}$ service requests in a zone governed by an MBS. If $S_{r}$ requests are made by active users in a small cell zone that the MBS is unable to handle, then the minimum number of UAVs required to handle this traffic is $\\frac{S_{r}}{S_{n}}$. Moreover, the optimal placement of multiple-UAVs in the required zone is a major issue. For this, the concept of \\emph{zone guider lines} is considered where \\emph{zone guider lines} divide a particular area into a set of small regular areas acting independently. For a low-complexity solution, the number of UAV is kept equal to the number of guider lines. The traditional hexagonal cell is divided into standard guider lines, then, the area with high user requests are marked. Next, the existing guider lines form the maximum and minimum limit for introducing new guider lines which will embark the area to be governed by a UAV. This procedure is presented in Fig~\\ref{fig3}.\n\\begin{figure}[!hb]\n \\centering\n \n\\includegraphics[width=200px,height=180px]{fig3}\\\\\n \\caption{Area distribution in the macro cell with an illustration of referral guider lines.}\\label{fig3}\n\\end{figure}\n\\subsection{Assumptions}\nIn order to validate the effectiveness of the proposed approach, some generic assumptions are as follows:\n\\begin{itemize}\n \\item UAVs operate on same frequency spectrum.\n \\item Each UAV is of the same make, whose configuration does not affect its positioning.\n\\end{itemize}\n\\section{Proposed Approach}\nUAVs are used as high altitude base stations to cover certain geographical areas. The proposed approach uses a cost based neural model to find the appropriate user demand zones where UAVs is placed. The cost function includes the cost of operation which has to be minimum, and the cost of handling UAVs. Thus, the proposed approach focuses on finding the appropriate cost function for demand areas and UAVs, allocating them to MBS, and defining a neural model to minimize the cost function. In the proposed model, UAVs operate at an altitude $h$ over an area $A$, with $x$ number of users. Users service request $S_{r}$ come with an arrival rate of $\\gamma$, and a mean packet size of 1\/$\\mu$. The load\/delay, denoted by $L$ (in seconds) for a user at location $y$, is computed as~\\cite{samarakoon2014opportunistic}:\n\\begin{equation}\\label{eq:d1}\nL (y) =\\frac{\\gamma}{W \\;\\log(1+SINR(y))\\times \\mu}.\n\\end{equation}\nThe channel modeling includes radio range and pathloss. Moreover, round robin approach is applied for scheduling. Further, the system model is defined with respect to UAVs rather than the MBS, assumed to operate on orthogonal band. The area load $L_{a}$ is given by~\\cite{samarakoon2014opportunistic}:\n\\begin{equation}\\label{eq:d2}\nL_{a}=\\int_{y \\in A} L(y) dy.\n\\end{equation}\nHere, $W$ is the system bandwidth, and assuming that UAVs operate on the same frequency spectrum, the signal-to-interference-plus-noise ratio $\\left(SINR\\right)$ from the $i^{th}$ UAV to a given UE at location $y$, considering UAV-to-UAV interference, is:\n\\begin{equation}\nSINR(y)= \\frac{\\frac{P \\; K }{R_{iy}^{\\alpha}}}{\\sum_{j=1, j\\neq i}^{n} \\frac{P \\;K}{ R_{jy}^{\\alpha}} + N_{0}},\n\\end{equation}\nwhere $P$ is the UAV transmission power, $K$ is a factor that accounts for the geometrical parameters such as transmitter and receiver antenna heights, $R_{iy}$ is the distance between the $i^{th}$ UAV and the UE at location $y$, $\\alpha$ is the path loss exponent, and $N_{0}$ is the noise power spectral density. The spectral efficiency $N_{S}$ for a user at location $y$ following round robin scheduling is given by:\n\\begin{equation}\nN_{S}=W.\\;\\frac{\\log_{2} \\left( 1+SINR(y) \\right)}{x}\n\\end{equation}\nThe cost function is a function of capacity, delay, availability of line of sight (LOS), and coverage. Further, let $D_{f}$ denote the density function that quantifies the population of active\/non-active users based on users' request patterns. $D_{f}$ accounts for the number of active users $x$, packet loss (call drops) $C_{d}$, service requests $S_{r}$, and the total amount of users a cell can handle is $T_{r}$. For the considered network, two variants of the density function, one for a given area $D_{f}^{A}$, and the other for UAVs $D_{f}^{U}$ are computed as\n\\begin{equation}\\label{eq:1}\nD_{f}^{A}=\\min\\left(\\frac{\\left(\\frac{x}{T_{r}}\\right)^{S_{r}}\\;e^{- \\left(\\frac{x}{T_{r}}\\right)}}{S_{r}!}\\right),\n\\end{equation}\nand\n\\begin{equation}\\label{eq:2}\nD_{f}^{U}=\\min\\left(\\frac{\\left(\\frac{L_{a}}{n}\\right)^{S_{n}}\\;e^{- \\left(\\frac{L_{a}}{n}\\right)}}{S_{n}!}\\right),\n\\end{equation}\nrespectively. $D_{f}^{A}$ accounts for user distribution over the area, in which a higher value requires more UAVs, and a minimum value shows the efficient connectivity with no further requirement of intermediate relays. $D_{f}^{U}$ accounts for pending user requests in area A with respect to the number of UAVs deployed. A higher value denotes the requirement of more UAVs, and a minimum denotes efficient service handling using current deployment. Here, $\\frac{x}{T_{r}}$ denotes the ratio of active users to the total users a cell can handle. For 100\\% accuracy in mapping, $\\frac{x}{T_{r}}=1$. (\\ref{eq:1})-(\\ref{eq:2}) account for the cost function provided constraint (\\ref{eq:3}) holds.\n\\begin{equation}\\label{eq:3}\n\\sqrt{\\frac{1}{S_{r}} \\sum_{i=1}^{S_{r}} \\left( T_{r}^{i}-C_{d}^{i}\\right)} \\leq \\frac{x}{T_{r}}.\n\\end{equation}\nFor an efficient operation, the deviation in (\\ref{eq:3}), the number of users with unhandled service requests, should be kept minimum. Furthermore, the per area and UAV cost function $C_{f}^{A}$ and $C_{f}^{U}$ is given as:\n\\begin{equation}\\label{eq:4}\nC_{f}^{A}=\\min\\left(D_{f}^{A}\\; L_{a}\\; \\left(\\eta_{1}S_{r} + \\eta_{2}T_{r} \\right)\\right),\n\\end{equation}\nand\n\\begin{equation}\\label{eq:5}\nC_{f}^{U}=\\min\\left(D_{f}^{U}\\; R^{\\alpha}_{iy}\\; \\left( \\eta_{1}S_{r}+\\eta_{2} x\\right)\\right), LOS=true,\n\\end{equation}\nrespectively. Here, $\\eta_{1}$ and $\\eta_{2}$ are network balancing constants such that $\\left(\\eta_{1}, \\eta_{2} \\right) \\in \\left(0,1\\right)$. In general, $\\eta_{1}$ is driven by the network bandwidth and link speed, whereas $\\eta_{2}$ is driven by the number of active connections. For ideal state, $\\eta_{1}$ and $\\eta_{2}$ equals 1. In general, $0.5 \\leq \\eta_{1} \\leq 1$ and $\\eta_{1} \\leq \\eta_{2} \\leq 1$ which denotes that the network transfer rate must be higher than half of the initial configured rate. Both cost functions $C_{f}^{A}$ and $C_{f}^{U}$ are governed by the constraints of $D_{f}^{A}$ and $D_{f}^{U}$. The complete availability of LOS is one of the key driving factor for continuous connectivity. The overall cost function $C_{f}^{O}$ is computed at the MBS to maintain the overall network connectivity such that\n\\begin{equation}\\label{eq:6}\nC_{f}^{O}=\\min\\left( \\frac{1}{n} \\sum_{i=1}^{n} \\left(C_{f}^{U}\\right)_{i} + \\sum_{j=1}^{A_{T}} \\left(\\frac{C_{f}^{A}}{U_{T}} \\right)_{j}\\right).\n\\end{equation}\nHere, $U_{T}$ is the number of UAVs allocated to a particular area, $A_{T}$ is the number of total demand areas. With more UAVs, more resources are available in terms of transmission power, yielding high throughput and reduced delay. Further, the delay ($L_{d}$) at each node is computed as:\n\\begin{equation}\nL_{d}=L_{transmission}+L_{propagation}+L_{queue}+L_{processing}.\n\\end{equation}\nHere, $L_{transmission}$ is the transmission delay defined as the load\/delay of a particular user and is equal to $L$ (\\ref{eq:d1}), $L_{propagation}$ is the ratio of the distance between nodes to the propagation speed, $L_{queue}$ is the waiting time of the packet, and $L_{processing}$ is the network operational time.\n\\section{Neural Demand Patterns and Network Capacity}\nThe goal is to optimize the density functions and minimize the cost functions defined in (\\ref{eq:1}), (\\ref{eq:2}), (\\ref{eq:4}), (\\ref{eq:5}), and (\\ref{eq:6}). By controlling $D_{f}^{A}$ and $D_{f}^{U}$, the user distribution with pending requests are controlled, which in turn, minimizes $C_{f}^{A}$, $C_{f}^{U}$, and $C_{f}^{O}$. This minimization provides guaranteed service to UEs. Thus, the aim of the neural model is to accurately map UAVs to demand areas so as to minimize these cost functions.\n\\begin{figure}[!ht]\n \\centering\n \n\\includegraphics[width=180px,height=170px]{fig4}\\\\\n \\caption{Reverse neural model modeling the user demand pattern: $C_{f}^{A}$ refers to area cost function, $C_{f}^{U}$ refers to UAV cost function, $C_{f}^{O}$ is the overall cost function.}\\label{fig4}\n\\end{figure}\nDemand patterns are used to minimize the demand and cost functions by efficiently deploying aerial nodes as intermediate nodes between high demand areas and the MBS. These demand patterns are driven by a reverse multi-hierarchical neural model which is a combination of input, hidden, and output layer. Although neural models are slow and complex in operation, the reverse neural model accounts for accurate mapping of UAVs to demand areas with lesser iterations. This provides a low-complexity approach for UAV-to-Area mapping. The output for the proposed model is computed in forward direction, i.e. from Layer 3 to Layer 1, but nodes are deployed in reverse direction i.e. from Layer 1 to Layer 3, as shown in Fig.~\\ref{fig4}. Input involves demand areas that require services of UAVs. MBS is the driving factor of this reverse neural model, and it act as an output layer. UAVs are the intermediates, and are not having permanent connections with the demand areas, thus, acts as a hidden layer. These neural patterns are then topologically rearranged to form a stable network with minimized cost function. Layer 3 defines the cost function for underlying demand areas, layer 2 is for aerial nodes, and output layer 1 is mapped to the MBS. At first, a mesh like interface is initialized for the neural model (Fig.~\\ref{fig4}), then, the whole model is rearranged to allocate links that will lower the cost functions of all the zones. This procedure is governed by series of steps given in Algorithm~\\ref{algo1}.\n\\begin{algorithm}[!ht]\n\\fontsize{7}{9}\\selectfont\n\\caption{UAV to Area Mapping}\n\\label{algo1}\n\\begin{algorithmic}[1]\n\\State \\textbf{Input}: $U \\longleftarrow UAVs$, $A \\longleftarrow Demand \\;areas$\n\\State Initialize Network\n\\State Divide A into subdivisions $D_{M}$\n\\While{(U are not mapped to subarea $D_{M}$ \\&\\& ($C_{f}^{O}$=minimum))}\n\\State Store $D_{M}$ in area[] following descending order for $C_{f}^{A}$\n\\State i=1\n\\While{($i \\leq count(area[])$)}\n\\State allocate U to $D_{M}$, such that min($C_{f}^{U}$) is mapped to max($C_{f}^{A}$)\n\\State Compute $C_{f}^{A}$, $C_{f}^{U}$, $C_{f}^{O}$\n\\If {((U is mapped to $area[i]$) \\&\\& ($C_{f}^{A}(i)$=minimum))}\n\\State continue\n\\Else\n\\State re-initialize $U$, $C_{f}^{A}$\n\\State reset\n\\EndIf\n\\State \\textbf{end if}\n\\State $i=i+1$\n\\EndWhile\n\\State \\textbf{end while}\n\\EndWhile\n\\State \\textbf{end while}\n\\end{algorithmic}\n\\end{algorithm}\nInitially, the demand area $A$ is subdivided into small segments $D_{M}$, and the cost function is computed for each of the subdivided area. Each of the cost function is ranked in descending order, in which the UAV with a minimum cost function is mapped to the area with maximum cost function. This helps in balancing the overall load of the network. After initial allocation, all the cost functions are recomputed, and UAVs are mapped to the next high demand area based on the cost function. This procedure continues till a minimum is attained. With an efficient deployment, the cost function is minimized by sub-dividing the density function based on the area so that active users becomes equal to the total registered users i.e. $\\frac{x}{T_{r}}=1$ such that equation (\\ref{eq:1}) reduces to\n\\begin{equation}\nD_{f,avg}^{A}=\\frac{1}{e\\times S_{r}!}\n\\end{equation}\nwhere e=2.71828 approx. For verification at any stage, the average density function of each area must be less than equal to $D_{f,avg}^{A}$.\n\\begin{table}[!ht]\n\\fontsize{7}{9}\\selectfont\n\\centering\n\\caption{Parameter Configurations}\\label{self_conf\n\\begin{tabular}{l l l}\n\\hline\\\\\n\\textbf{Parameter} & \\textbf{Value} & \\textbf{Description}\\\\\n\\hline\n\\hline\\\\\n$A$ & 10000x10000 sq. m. & Simulation Area\\\\\nMBS & 10& Number of Macro Cell Base Station\\\\\n$T_{r}$ & 1200 (per MBS) & Max Users in a Cell\\\\\n$n$& 6 (per MBS)& Number of UAVs\\\\\n$S_{n}$ & 200 & Service Requests handled by each UAV\\\\\n$N_{0}$ & -170 dBm\/Hz & Noise Power Spectral Density\\\\\n$\\frac{1}{\\mu}$ & 1024 B&Packet Size \\\\\n$h$ &200-500 Feet& UAV altitude\\\\\n$\\frac{\\gamma}{\\mu}$ & 256 kbps& Offered Traffic\\\\\n$\\alpha$& 4&Path loss Exponent\\\\\n$K$ & -11 dB & Transmission Constant\\\\\n$P$ & 35 dBm & UAV Transmission Power \\\\\n$S_{r}$& 30-50 per zone& Service Requests\\\\\n$W$ & 10 MHz& System Bandwdith\\\\\n$x$ & 400 & Active Users\\\\\n\\hline\n\\end{tabular}\n\\end{table\n\\begin{figure}[!ht]\n\\begin{minipage}[t]{0.20\\textwidth}\n\\centering\n\\includegraphics[width=120px,height=80px]{g1}\n\\caption{\\fontsize{6}{6}\\selectfont Networks Delays vs. Extra Users }\n\\label{g1}\n\\end{minipage}\n\\hspace{\\fill}\n\\begin{minipage}[t]{0.26\\textwidth}\n\\centering\n\\includegraphics[width=120px,height=80px]{g7}\n\\caption{\\fontsize{6}{6}\\selectfont Throughput Coverage vs. Path loss Exponent}\n\\label{g3}\n\\end{minipage}\n\\end{figure}\n\\begin{figure}[!ht]\n\\begin{minipage}[t]{0.20\\textwidth}\n\\centering\n\\includegraphics[width=120px,height=80px]{g8}\n\\caption{\\fontsize{6}{6}\\selectfont Throughput Coverage vs. Extra Users}\n\\label{g4}\n\\end{minipage}\n\\hspace{\\fill}\n\\begin{minipage}[t]{0.26\\textwidth}\n\\centering\n\\includegraphics[width=120px,height=80px]{g9}\n\\caption{\\fontsize{6}{6}\\selectfont 5th Percentile Spectral Efficiency vs. Extra Users }\n\\label{g5}\n\\end{minipage}\n\\end{figure}\n\\begin{figure}[!ht]\n\\begin{minipage}[t]{0.20\\textwidth}\n\\centering\n\\includegraphics[width=120px,height=80px]{g12}\n\\caption{\\fontsize{6}{6}\\selectfont 5th Percentile Spectral Efficiency vs. Path loss Exponent }\n\\label{g6}\n\\end{minipage}\n\\hspace{\\fill}\n\\begin{minipage}[t]{0.26\\textwidth}\n\\centering\n\\includegraphics[width=120px,height=80px]{g5}\n\\caption{\\fontsize{6}{6}\\selectfont Probability of Guaranteed SINR vs. Extra Users }\n\\label{g7}\n\\end{minipage}\n\\end{figure}\n\\section{Performance Evaluation}\nThe proposed model is analyzed using network simulations, to efficiently allocates areas with higher demand patterns to UAVs based on their cost function. All parameters and configurations are presented in Table~\\ref{self_conf}. Active users refer to the number of users in a cell requesting services. Thus, at any time instance, more than the maximum supported user requests can be present in the cell. Results were recorded for delays, network capacity, reliability, and value of cost function with and without use of UAVs. Delays were traced for complete data sharing with 1000 iterations of user requests over 1000 seconds. Availability of LOS is the primary condition for UAVs to form a link with the UE. Finding appropriate position in the user demand areas causes UAVs to adjust their altitude to guarantee LOS towards the UE. High altitude provides less interference and appropriate LOS, but also induces more delays. Thus, an optimum altitude with availability of LOS is required for better coverage. For analysis, the altitude was varied between 200 ft. and 500 ft. with a multi-antenna relay support for communication and backhaul link capacity of 1.2 Gbps. Delay threshold is fixed at 200 ms, which defines the upper limit above which the packet drop increases abruptly. Results show that the proposed approach leveraging UAVs yields 37.7\\% lesser delays in comparison with a network comprising of small cell and macro cell. The performance delay for various UAV altitudes is given in Fig.~\\ref{g1}. Throughput coverage defined as the percentage of users whose SINR is above the threshold (0.03 bps\/Hz) is shown in Fig.~\\ref{g3}. Therein, the use of UAVs increases the overall 5th percentile throughput coverage by 15.5\\%, as shown in Fig.~\\ref{g3} and Fig.~\\ref{g4}. Moreover the optimal placement of UAVs according to user demand leveraging the reverse neural deployment model enhances the 5th percentile spectral efficiency. The accurate mapping optimally places UAVs according to demand patterns, thus, improving the 5th percentile spectral efficiency by 38\\% approx., as shown in Fig.~\\ref{g5} and Fig.~\\ref{g6}. Finally, Fig.~\\ref{g7} plots the probability of guaranteed SINR for a particular user in a given macro cell. Clearly, it can be noticed that the use of UAVs provides much guaranteed SINR above the threshold defined by $\\eta_{1}$ in (\\ref{eq:5}). Here, the SINR threshold is kept at 0.55 defining the value below which the network is unable to provide efficient connectivity to users. Results show that the proposed user demand based network model is capable of providing better capacity and prolonged connectivity than the existing cellular network.\n\\section{Conclusion}\nIn this letter, user demand based network model is proposed using multiple UAVs. The proposed model uses density and cost functions to compute areas with higher demands, whereby UAVs are deployed based on these cost functions. Analysis proved that the proposed model is capable of providing better capacity, reliability, and prolonged connectivity in comparison to existing ground-based wireless networks.\n\\bibliographystyle{ieeetr}\n\\nocite{*}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\n\\section{Introduction}\nEvergrowing concerns about user-privacy, censorship and central authority in popular social media have motivated both the development of federated social networks such as Mastodon and Diaspora~\\cite{rochko2018mastodon,Bielenberg:2012:TGD}, as well as research in academia~\\cite{Anderson:2009:PSN, Shakimov:2009:PCA}.\nThese networks aim to promote user control by decentralizing authority and relying on open-source software and open standards.\nAt the time of this writing, Mastodon has over 1 million users and 3500 instances which demonstrates the increasing acceptance of distributed social networks.\nAs with traditional social media, one key success factor of such a network is an active and engaged community.\n\nAs a community grows, overwhelming amounts of content make it increasingly difficult for a user to find interesting topics and other users to interact with.\nFor that reason, popular platforms such as Twitter, LinkedIn and Facebook introduce recommender systems that set out to solve a particular recommendation task.\nOne prominent example is the ``Who to Follow'' service by Twitter~\\cite{Gupta:2013:WFS}.\nDue to their recent emergence, those recommender systems do not exist for federated social networks, yet.\nHowever, they are needed to make distributed social media attractive to large user groups as well as competitive to centralized networks.\nAt the same time, recommender systems will contribute to develop, grow and sustain an active community.\n\nTo make federated social networks more attractive and feature complete, we implement and evaluate a topology-based user recommender based on personalized PageRank~\\cite{Page:1999:PCR}, a commonly used algorithm for link-prediction in social networks.\nWe compare this method against collaborative filtering based on link intersections~\\cite{Hannon:2010:RTU} and a random link predictor baseline~\\cite{Liben-Nowell:2003:LPP}.\nThe experiments are carried out on Mastodon, a federated social network for which user relations do not require reciprocation, and the network forms a directed graph.\nWe expect that the method and results are transferable to any other federated social network with similar characteristics.\n\nWe evaluate the systems in an offline and online scenario. For the offline evaluation, we collect an unbiased sample of the Mastodon user graph.\nThis sample is created by performing a Metropolis-Hastings Random Walk (MHRW) adapted for directed graphs~\\cite{Wang:2011:UGS,Wang:2010:USD}.\nThe collected data contains about 25\\% of the entire userbase of Mastodon.\nWe then evaluate the recommender systems according to standard performance metrics used in ranked retrieval systems, and deploy the two best performing methods to an online setting.\nBoth algorithms generate a list of personalized recommendations for 19 Mastodon users participating in the online trial and performance is measured with the balanced interleaving approach~\\cite{Joachims:2003:ERP}.\n\nThis paper is structured as follows.~\\cref{sec:dataset} explains how data are collected for the offline experiments and discusses the recommendation algorithms and their evaluation.\nIn~\\cref{sec:results} we present and discuss experimental results.~\\cref{sec:conclusion} concludes this paper and provides directions for future work.\n\n\\section{Dataset and Methods}\n\\label{sec:dataset}\n\n\\subsection{Recommendation Algorithms}\n\\label{sec:recommendation-algorithms}\nThe user recommendation problem for social networks can be formalized as follows.\nGiven a graph $G = (V,E)$ where $V$ and $E$ are vertices and edges, we seek to predict an interaction between a user $u \\in V$ and $v \\in V$ denoted by edge $(u,v)$.\nIn networks such as Mastodon and Twitter, a user interaction does not require reciprocation.\nThus, the graph is directed.\nWe consider two broad approaches to generate recommendations: (1) collaborative filtering-based recommendation and (2) topology-based recommendation.\n\nWith respect to the collaborative filtering, we use an approach inspired by~\\cite{Hannon:2010:RTU}. Each user $u \\in V$ is represented by a profile and recommendations are generated based on the similarity of profiles.\nWe distinguish between the three best performing strategies in~\\cite{Hannon:2010:RTU}:\n\\begin{description}[leftmargin=!,labelwidth=\\widthof{$following(u)$}]\n\t\\item[$\\mathit{following}(u)$] The set of user ID's $u$ follows\n\t\\item[$\\mathit{followers}(u)$] The set of user ID's that follow $u$\n\t\\item[$\\mathit{combined}(u)$] The combined set of following and follower ID's\n\\end{description}\nWe consider these profiles as documents to be indexed in a general purpose search engine. In order to generate recommendations for a user, the corresponding profile is extracted first.\nAfterwards, the retrieval system is queried with the profile and it ranks the indexed documents by their relevance to the query.\nEach ID in the user profile is a token of the query. If a query consists of more than 10,000 tokens, we create a random subset of 10,000 tokens.\nUnlike~\\citet{Hannon:2010:RTU}, we use BM25 instead of TF-IDF to estimate the relevance score of each document and set parameters to common defaults ($k_1 = 1.2$, $b = 0.75$)~\\cite{Manning:2008:IIR:1394399}.\nThe final recommendation list contains the top-$k$ documents with highest retrieval score.\n\nThe collaborative filtering recommendations are compared to topology-based recommendations.\nSeveral methods have been proposed in literature which make use of link-based ranking algorithms such as HITS, PageRank and SALSA\\@.\nDue to the novelty of generating recommendations for federated social networks, we restrict our experiments to the personalized PageRank algorithm~\\cite{Page:1999:PCR} whose efficient computation is well-understood and which is used in the Twitter recommender system~\\citep{Gupta:2013:WFS}.\nWe apply the personalized PageRank for a seed node which is the user we want to generate recommendations for.\nAfter convergence, the list of user recommendations is constructed by taking the top-$k$ nodes with highest PageRank. Following~\\cite{Liben-Nowell:2003:LPP}, we set the damping factor $\\lambda = 0.85$.\n\n\\subsection{Data Collection}\nAcquiring the complete graph of a social network is always infeasible due to API limits and time constraints~\\cite{Wang:2011:UGS}.\nAn additional concern arises in a distributed social network.\nAs data is not stored at a central authority, there is no single API that provides access to all parts of the network.\nInstead, data is scattered around different sub-networks.\nBoth issues are addressed within this section.\n\nTo overcome the time constraint, we apply the Metropolis-Hastings Random Walk (MHRW) to acquire an unbiased sample that is still representative of the complete graph.\nMHRW is a Markov-Chain Monte Carlo algorithm that can be used to obtain node samples with a uniform probability distribution~\\cite{Wang:2011:UGS}.\nAs the MHRW is only applicable to undirected graphs, we apply a generalization that considers all directed edges as bidirectional edges~\\citep{Wang:2010:USD}.\nWe do not consider graph sampling methods such as Random Walk and Breadth-First Sampling as it has been shown that these methods yield samples biased towards high degree nodes~\\cite{Gjoka:2010:WFC}.\n\nDue to the fact that a distributed social network has no central API, one has to query the API of each individual sub-network referred to as \\textit{instance}.\nIn case of Mastodon, there are two public endpoints to acquire incoming and outgoing links: \\texttt{\/following} and \\texttt{\/followers}\\footnote{The following API URL pattern applies to any Mastodon instance:\\\\ \\texttt{https:\/\/\/users\/\/.json}}.\nWhenever the MHRW visits an unexplored node, followers and followings of that node are fetched and stored in a document-oriented database.\nThis database is also used as a cache: if the random walk transitions to a node which it has already visited, we use the cached result rather than querying the API again.\nDuring the data collection, we apply fair crawling policies. Only instances that allow crawling as defined by the \\texttt{robots.txt} are considered.\nFurthermore, concurrent requests are throttled such that no more than 10 requests per second are issued (a rate which we believe any web server can sustain).\n\n\\subsection{Dataset Statistics}\n\\label{sec:dataset-statistics}\n\\cref{tab:dataset-statistics} summarizes the properties of the collected graph. The initial graph ($t_1$) has been crawled from the 16\/05\/18 until 17\/05\/18.\nThe MHRW was executed for 5500 iterations.\nDuring the crawl, 138 instances were disregarded either because of their \\texttt{robots.txt} or because they were no longer available.\nIn order to acquire a newer version of that graph ($t_2$), we visited the same users five days later and recorded new relationships.\nThe number of visited users in $t_2$ is slightly lower than in $t_1$, as some profiles were deleted or their instances became unavailable.\nThe updated graph is used as the ground-truth when evaluating our recommender systems.\n\nIt can be observed that the Network Average Clustering Coefficient (NCC) and the fraction of nodes in the largest Strongly Connected Component (SCC) is almost equal for the two given graphs.\nFurthermore, the graph is mildly disassortative.\nIt is important to mention that although the total number of nodes found $|V|$ is high (253,000), accounting for about 25\\% of the total Mastodon users, the number of visited nodes $|V^*|$ is much smaller (about 3400).\nIncoming and outgoing edges are only known for visited nodes.\n\n\\begin{table}[t]\n\\centering\n\\caption{Statistics of crawled graphs. The initial crawl at $t_1$ and the newer crawl of the same users at $t_2$.}\n\\label{tab:dataset-statistics}\n\\begin{tabular}{@{}llllllll@{}}\n\\toprule\nGraph & $|V|$ & $|V^*|$ & $|E|$ & Assort. & Deg. & NCC & SCC \\\\ \\midrule\n$t_1$ & 253,822 & 3437 & 754,037 & -0.015 & 5.94 & 0.31 & 0.175 \\\\\n$t_2$ & 255,638 & 3383 & 754,667 & -0.016 & 5.9 & 0.31 & 0.173 \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\n\\subsection{Evaluation}\n\\label{sec:evaluation}\nThe algorithms presented in~\\cref{sec:recommendation-algorithms} are evaluated in two phases: an offline evaluation and an online evaluation.\nFor the offline evaluation we measure precision at rank $k$ (p@k), Mean Average Precision (MAP) and success at rank $k$ (s@k), which are popular metrics for the evaluation of ranked retrieval systems~\\cite{Manning:2008:IIR:1394399}.\nThe newer graph at time $t_2$ serves as the ground-truth, whereas the graph at time $t_1$ can be seen as the training graph.\nIn information retrieval terms, the generated list of recommendations are the retrieved documents and the list of users a target user follows at time $t_2$ are the relevant documents. Significance is tested using a two-tailed paired t-test. We denote improvements with $^{\\blacktriangle}$ ($p<0.01$), deteriorations with $^{\\blacktriangledown}$ ($p<0.01$), and no significance by $^{\\circ}$.\n\nDuring the offline evaluation, all systems generate a list of 100 recommendations based on the training graph at time $t_1$.\nThis list is then compared with the actual links added to the graph in between time $t_1$ and $t_2$ (see~\\cref{sec:dataset-statistics}).\nIn case of the collected dataset, 329 of 3437 visited users started to follow another individual, and thus added a link to the graph.\nOnly for this set of users, recommendations are generated and evaluated.\n\nThe online evaluation is performed as follows.\nA recommendation bot is created on the Mastodon instance associated with the institute of the authors\\footnote{See \\url{https:\/\/mastodon.utwente.nl\/@Followdon}}.\nAfterwards, we ask users to follow this bot if they wish to receive personalized recommendations. For each participant, we generate a static web page consisting of a list of $N$ recommendations with the option to start to follow a suggested user.\nA link to this web page is then send to the user and we track the user interactions. A recommendation is considered relevant if the participant starts to follow a suggested user.\nThe recommendations of two algorithms are presented using balanced-interleaving, which is a relatively inexpensive evaluation method for online experiments compared to conventional A\/B testing.\nWe refer the reader to~\\cite{Joachims:2003:ERP} for a thorough discussion of this evaluation method.\n\nOne complication arises in the online evaluation. As an up-to-date graph is unavailable at recommendation time, such a graph has to be created.\nFor this, we explore the vicinity of a recommendation target $u \\in V$ by applying an egocentric random walk for a fixed amount of iterations.\nThis strategy resembles the ``circle-of-trust'' used in the Twitter recommender system~\\cite{Gupta:2013:WFS}.\nThe random walk is performed as follows. At each iteration, the algorithm either transitions to a random neighbor of the current user with probability $\\gamma$, or jumps back to $u$ with probability $1 - \\gamma$. In our experiments, we execute the random walk for 200 iterations and set $\\gamma = 0.8$.\nHere, we do not claim that this is the most efficient way of generating recommendations in an online setup. It is merely a way to deal with incomplete data in federated social networks.\n\n\\section{Results}\n\\label{sec:results}\n\n\\subsection{Offline Evaluation}\nThe collaborative filtering approach shows a consistently higher performance than a topology-based system using PageRank (see~\\cref{tab:results-offline}).\nWith respect to the success at rank $k$ metric, profile-based approaches (R2--R4) have up to two times higher retrieval scores than PageRank (R5).\nThe individual profiling strategies perform all rather similarly, which aligns with the findings in~\\cite{Hannon:2010:RTU}.\nAlso, a baseline system (R1) which generates recommendations by selecting 100 random users from the network topology is outperformed by a large margin.\nIn \\cref{fig:precision-at-k}, it can be observed that shorter recommendation lists have a higher precision for all recommendation strategies.\nPrecision at rank $k$ remains stable starting from a list length of $k = 50$ items.\nThis suggests that shorter lists are to be preferred in an online scenario.\n\n\\begin{table}[t]\n\\centering\n\\caption{Experimental results of offline evaluation. Significance for model in line $i > 1$ is tested against line $i - 1$.}\n\\label{tab:results-offline}\n\\begin{tabular}{@{}clllll@{}}\n\\toprule\n\\textbf{ID} & \\textbf{System} & \\textbf{MAP} & \\textbf{s@1} & \\textbf{s@5} & \\textbf{s@10} \\\\ \\midrule\nR1 & Random & 0.001 & 0.000 & 0.000 & 0.055 \\\\\nR2 & Profile (following) & \\textbf{0.019}$^{\\blacktriangle}$ & \\textbf{0.033}$^{\\blacktriangle}$ & 0.085$^{\\blacktriangle}$ & 0.152$^{\\blacktriangle}$ \\\\\nR3 & Profile (followers) & \\textbf{0.019}$^{\\circ}$ & 0.030$^{\\circ}$ & 0.100$^{\\circ}$ & 0.167$^{\\circ}$ \\\\\nR4 & Profile (combined) & 0.018$^{\\circ}$ & \\textbf{0.033}$^{\\circ}$ & \\textbf{0.106}$^{\\circ}$ & \\textbf{0.173}$^{\\circ}$ \\\\\nR5 & Pers.\\ PageRank & 0.014$^{\\circ}$ & 0.018$^{\\circ}$ & 0.061$^{\\blacktriangledown}$ & 0.082$^{\\blacktriangledown}$ \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=.8\\columnwidth]{figures\/precisionatk-curve}\n\\caption{Precision for different recommendation list lengths ($k$) in offline evaluation.}\n\\label{fig:precision-at-k}\n\\end{figure}\n\nIt is important to mention that the list of possible suggestions from the profile-based recommender is smaller than the list from the PageRank recommender, which complicates the discussion.\nOnly visited nodes (see~\\cref{sec:dataset-statistics}) have been indexed in the document retrieval system.\nThis significantly reduces the pool size of possible users ($\\approx$3k).\nIn contrast, the PageRank recommender can suggest any user in the topology ($\\approx$255k).\nOne could overcome this issue as follows.\nEach user, regardless of whether or not it has been visited during the data collection, could be added to the search index.\nThen, incoming relationships can be inferred by inspecting the outgoing links of visited users.\nBy adding these relations as \\textit{followers} to the documents of unexplored nodes, the \\textit{following} strategy of the collaborative filtering can be applied.\nHowever, as no outgoing links are known for unexplored users, the \\textit{following} and \\textit{combined} strategies are not fully applicable.\nDue to time constraints, we did not further investigate this issue.\n\nFurthermore, it is worth to note that the chosen window of five days between $t_1$ and $t_2$ might not have been long enough to capture sufficient user activity.\nIn between the training snapshot at $t_1$ and the testing snapshot at $t_2$, six new connections were added to each user on average.\nThis gives rise to an interesting trade-off.\nFor a longer time span, one can capture larger amounts of activity within the network.\nIntuitively, more links will be added as users start to follow other users.\nHowever, the farther two snapshots are apart, the larger is the risk that the network deviates too much from the original structure.\nUsers might stop following other users or profiles could be deleted.\nMore severely, entire instances could become unavailable due to a temporary downtime, or they could even be discontinued.\nThis is a unique concern related to the distributed nature of federated social networks.\n\nFinally, we want to motivate which recommendation systems are evaluated in the online trial based on the results presented above.\nFrom~\\cref{tab:results-offline} it can be observed that the profile-based recommendation strategies perform rather similarly.\nHowever, the combined strategy (R4) performs best with respect to the success at rank 10 metric, which one seeks to maximize in an online system where 10 recommendations are presented to the user.\nTherefore, we pick R4 as the first recommendation system.\nAlthough the personalized PageRank recommender (R5) has a lower performance than the other profiling strategies, we expect that it produces valuable recommendations which are significantly different from the profile-based strategies.\nThis is due to the fact that it considers the network topology when generating recommendations.\nTherefore, we apply the balanced-interleaving evaluation to systems R4 and R5.\n\n\\subsection{Online Evaluation}\nThe online evaluation shows that neither the profile-based nor the topology-based system is superior (see~\\cref{tab:summary-online-evaluation}). Nineteen users participated in our online study. On average, they started to follow 1.8 users from our recommendations. For 5 users the profile-based approach performed best. For another 5 users, the topology-based approach performed best. For the remaining 9 users both system performed equally well, or no recommendation was followed.\nThe fact that valuable recommendations were generated that resulted in new followings shows that the two systems can be useful in practice.\nHowever, a larger group of participants is required to draw final conclusions on the recommender system performance.\n\n\\begin{table}[t]\n\\centering\n\\caption{Summary of online evaluation.}\n\\label{tab:summary-online-evaluation}\n\\begin{tabular}{@{}p{.7\\columnwidth}c@{}}\n\\toprule\n\\textbf{Characteristic} & \\textbf{Value} \\\\ \\midrule\nNumber of participants & 19 \\\\\nProfile-based recommender (R4) superior & 5\\\\\nPageRank recommender (R5) superior & 5\\\\\nDraw & 2 \\\\\nNo user interaction & 7 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{table}\n\n\\subsection{Practical Considerations}\nThe generation of online recommendations turned out to be costly because the complete network data is not available.\nIn contrast to centralized social media, federated social networks do not have a single authority which stores data about the entire network graph.\nThe proposed method of crawling the vicinity of a target user at recommendation time (see~\\cref{sec:evaluation}) comes with a high overhead in network traffic and is not suitable for real-time systems that have to support large amounts of users.\nIn addition to that, the method is sensitive to the size of the vicinity.\nWe expect that a larger number of iterations yields a better picture of a user's vicinity, which in turn increases the quality of recommendations. However, an exploration of different parameter settings has been out of scope of this study.\nThe data collection issue is even more severe in the offline evaluation which requires large and representative samples of the entire network.\n\nTo reduce the overhead associated with crawling in an online setting, one might attempt to gradually construct a cached representation of the entire network graph.\nWhenever a recommendation is generated for a user, the vicinity is added to that graph. On subsequent recommendations, one might reuse parts of this network to avoid additional crawling.\nThis approach has two important issues that have to be considered. First, one has to address the question when parts of the network are considered to be out of date (i.e., when the cache expires). Second, and more importantly, such an approach seems to be in conflict with the intentions behind decentralization.\nBy constructing a database that aims to capture the entire network graph, one starts to centralize the data of a federated social network.\\looseness=-1\n\n\\section{Conclusion}\\label{sec:conclusion}\nUser recommendation algorithms commonly applied to centralized social media can be applied to incomplete data from federated social networks with the goal of developing an engaged community.\nWe showed that collaborative filtering-based recommenders outperform a topology-based recommender on a large unbiased sample of the federated social network Mastodon. The two recommenders outperform a random recommender by a large margin.\nA subsequent live user experiment on Mastodon using balanced interleaving shows that the two recommender approaches perform on par.\nAcquiring a sufficiently large snapshot of the network topology for offline recommendation proofed to be difficult and costly. Keeping the snapshot up-to-date needs constant re-sampling. Online recommendation was done by sampling the graph neighborhood for the current user.\n\nThere are several directions for future work. First, studying the extent to which incomplete data impacts the recommender performance may derive methods that are tailored towards federated social networks which operate with limited amounts of data.\nSecond, user recommendation algorithms in popular social media increasingly utilize user context information such as location data and interests.\nIt remains unclear how such data can be effectively acquired and utilized in federated social networks while preserving privacy.\nThird, BM25 might not be the best ranking function for the presented recommender approach, and it should be compared to functions that also use popularity-based scoring.\nFinally, one may investigate how decentralized communication protocols such as ActivityPub can be extended to support community building algorithms while maintaining the notion of decentralized network data.\\looseness=-1\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{s1:introduction}\n\nThe Advanced LIGO (aLIGO) observatories completed their first observing run ``O1''\nearly-2016, operating at a factor of $3-4$ higher gravitational-wave\n(GW) strain sensitivity than their first-generation \ncounterparts~\\cite{Shoemaker2009}.\nDuring O1, they made the first direct observation of gravitational \nwaves~\\cite{LIGOVirgo2016a}. Emitted by a pair of coalescing black holes, these\nwaves heralded an era of observational GW astrophysics as they traveled \nthrough Earth.\nTowards the end of this decade, we expect aLIGO to reach its design sensitivity.\nIn addition to the US-based efforts, we also expect the French-Italian detector Advanced\nVirgo~\\cite{aVIRGO,aVirgo2}, Japanese detctor KAGRA~\\cite{kagra,Somiya:2011np},\nand LIGO-India~\\cite{2013IJMPD..2241010U} to begin observing at comparable\nsensitivities within a few years. With a global network of sensitive GW\n observatories, we can expect GW astronomy to face significant developments over the\ncoming years.\n\n\nCoalescing compact binaries of stellar-mass black holes (BH) and\/or\nneutron stars (NS) are the primary targets for the second\ngeneration GW detectors~\\cite{Timmes:1995kp,Fryer:1999mi,RevModPhys.74.1015,\n2010ApJ...714.1217B,2010ApJ...715L.138B,Dominik:2014yma,Belczynski:2006zi,\n2012ApJ...749...91F,\nWex:1998wt,1991ApJ...379L..17N,Mandel:2015spa,Abbott:2016nhf}.\nA binary system of black holes was recently observed by \naLIGO~\\cite{LIGOVirgo2016a}. Previously, stellar-mass black holes had only \nbeen observed by inference in mixed binaries with stellar companion (through\nelectromagnetic observations of the companion)~\\cite{Lewin2010,\nRemillard:2006fc,Fragos:2010tm}.\nNeutron stars, on the other hand, have had numerous sightings. Thousands of\nelectromagnetically emitting neutron stars, or pulsars, have been \ndocumented~\\cite{Manchester:2004bp},\nin varied situations: as radio pulsars~\\cite{Lattimer:2012nd,Manchester:2004bp},\nin binary systems with a stellar companion~\\cite{1971ApJ...169L..23M,\nBond:2002eh,Lattimer:2012nd,Manchester:2004bp},\nand in binary neutron stars (BNS)~\\cite{Hulse:1975uf,Taylor:1982wi,\nWeisberg:2010zz,Lattimer:2012nd,Manchester:2004bp}.\nMixed binaries of black holes and neutron stars, is an astrophysically\ninteresting class of systems~\\cite{Wex:1998wt,\n1991ApJ...379L..17N,Janka1999,Fryer:2015jpa}, that has not yet been detected.\nWe expect to observe $\\mathcal{O}(10)$ mixed binaries per year with\naLIGO~\\cite{Abadie:2010cf}.\n\n\n\n\n\nNSBH binaries are of interest for multiple reasons. For instance,\nthey have been long associated with (as possible progenitors of) short\nGamma-ray Bursts (SGRBs)~\\cite{eichler:89,1992ApJ...395L..83N,moch:93,Barthelmy:2005bx,\n2005Natur.437..845F,2005Natur.437..851G,Shibata:2005mz,Paschalidis2014,\nTanvir:2013}. Depending on their equation of state (EoS), NSs can get disrupted by\nthe tidal field of their companion BHs. Once disrupted, most of the NS\nmaterial falls into the hole over an $\\mathcal{O}(1$ms$)$ time-scale,\nwith the rest partly getting ejected as unbound material\nand partly forming an accretion disk around the BH.\nThis short lived ($0.1-1s$) disk-BH system is hypothesized to drive SGRBs\nthrough the production of relativistic jets~\\cite{Foucart:2015a,\nLovelace:2013vma,Deaton2013,Foucart2012,Shibata:2005mz,Paschalidis2014}.\nHowever, whether or not such a system forms depends also on the nature of\nthe BH. Massive BHs (with $m_\\mathrm{BH}\\gtrsim 12M_\\odot$), as well as BHs with\nlarge retrograde spins, tend to swallow the NS whole without forming a\ndisk~\\cite{Foucart:2013psa}.\nOn the other hand, {\\it low-mass} BHs with $m_\\mathrm{BH}\\in[3M_\\odot, 12M_\\odot]$\\footnote{\nThe upper limit on BH mass that allows for NS disruption may very well be\nhigher, depending strongly on the magnitude of BH spin~\\cite{Foucart:2014nda}.},\ncan disrupt their companion NSs much before merger, forming long-sustained disks\nthat are required to sustain SGRBs~\\cite{Shibata:2007zm,2010PhRvD..81f4026F,\nLovelace:2013vma,Foucart:2014nda,Kawaguchi:2015}.\nA {\\it coincident} detection of both GWs and gamma-rays from an NSBH merger,\nwill provide us with a unique opportunity to confirm this hypothesized link\nbetween NSBH mergers and GRBs~\\cite{Abbott:2016wya}.\n\n\n\n\n\nAnother question that compact object mergers can help answer is `what is the \nnature of matter at nuclear densities supported by NSs'?\nA large fraction of past work aimed at measuring NS matter effects from GW\nsignals has consisted of inquiries about BNSs~\\cite{Lee1999a,Lee1999b,Lee2000,\noechslin:07,Read:2008iy,Markakis:2010mp,Markakis:2011vd,stergioulas:11,\nEast:2011xa,Lackey2014,Wade:2014vqa,Bauswein:2014qla}. In this paper, we will\ninstead focus on NSBHs.\nDuring the course of early inspiral, the tidal field of the BH produces a\ndeformation in its companion NS. The quadrupolar moment of the star associated\nwith this deformation also depends on its material properties, through an EoS-dependent\ntidal deformability parameter $\\Lambda_\\mathrm{NS}$. This induced quadrupolar moment\nchanges over the orbital time-scale, resulting in the emission of GWs in {\\it\ncoherence} with the orbital waves.\nThese waves draw more energy from the orbit and increase the inspiral rate (as\ncompared to an equivalent BBH)~\\cite{Flanagan2008}.\nCloser to merger, the strong tidal field of the BH can disrupt the NS. The\nquadrupolar moment of the disrupted binary system falls monotonically over a\nmillisecond time-scale~\\cite{Kyutoku:2010zd,Lackey:2013axa,Lovelace:2013vma,\nFoucart:2015a,Pannarale:2015jia}, resulting in the damping of GW amplitude.\nThis penultimate stage also depends strongly on the internal structure and energy\ntransport mechanism of the NS, and carries the strongest tidal signature in the\nGW spectrum~\\cite{Foucart:2014nda,Deaton2013}.\n\n\n\n\nGravitational waves emitted by coalescing NSBH binaries carry subtle hints of\nthe NS EoS from inspiral through to merger. During early inspiral, the tidal\ndephasing is relatively weak and has a frequency dependence equivalent to a\n$5^{th}$ Post-Newtonian (PN) order effect~\\cite{Vines2011}. Closer to merger,\na disruptive fate of the NS\ncan result in a strong suppression of GW emission above a cut-off \nfrequency~\\cite{Pannarale:2015jia}. Some past studies of tidal measurements\nwith NSBH binaries have used PN inspiral-only waveforms~\\cite{Maselli:2013rza}.\nIn doing so, however, they ignore (i) the merger signal which could contain significant\ninformation for NSBHs, and (ii) the errors due to unknown vaccum terms in PN \nwaveforms, which could dominate over the tidal terms themselves~\\cite{Barkett2015,\nYagi:2014}.\nSome other studies that account for merger effects via the use of complete\nnumerical simulations~\\cite{Foucart:2013psa}, are limited in the binary\nparameter space they sample.\nOthers, that do the same through the use of phenomenological waveform\nmodels~\\cite{Lackey2011,Lackey:2013axa} use the Fisher matrix to estimate\n$\\Lambda_\\mathrm{NS}$ measurement errors. Fisher matrix estimates may become\nunreliable at realistic signal-to-noise ratios (SNR)~\\cite{Vallisneri:2007ev},\nsuch as those as we might expect in the upcoming observing runs of GW\ndetectors~\\cite{Abadie:2010cf}, and we improve such studies with a\nfully Bayesian treatment of the problem here.\n\n\n\n\n\nIn this paper we study the measurability of neutron star's tidal deformability\nfrom realistic binaries of {\\it low}-mass BHs and NSs by aLIGO. We also probe\nhow tidal effects affect the estimation of other binary parameters for the same\nclass of systems. This study improves upon previous work in the following ways.\nFirst, we include tidal effects during inspiral and merger in a consistent\nway, by using the waveform model of Lackey {\\it et al.}~\\cite{Lackey:2013axa}\n(abbreviated henceforth to ``LEA'').\nSecond, we include the effect of black hole spin on tidal GW signals, in\naddition to the effect of BH mass, tidal deformability of the NS, and the SNR.\nThird, we perform a complete Bayesian analysis, instead of using the Fisher matrix\napproximation.\nAnd fourth, we explore how our measurement errors decrease as we gain information\nfrom multiple (realistic) events.\n\n\nWe now outline the main questions and results discussed in this paper.\nFirst, we probe the effect of ignoring tidal effects in\nthe recovery of non-tidal binary parameters, such as \ncomponent masses and spins. This is the case for current and planned aLIGO\nefforts.\nTo do so, we first use the enhanced-LEA (or ``LEA+'', see Sec.~\\ref{s2:waveforms})\nmodel to generate a set of realistic signals;\nand then use non-tidal (BBH) waveform filters to estimate the underlying\nbinary masses and spins with a Markov-chain Monte Carlo. Here and throughout,\nwe use the zero-detuning high-power design sensitivity curve~\\cite{Shoemaker2009}\nto characterize the expected detector noise.\nWe find that, for individual events, ignoring tidal effects will affect mass\nand spin-estimation only marginally; only for very loud signals (SNRs $\\gtrsim 30$)\nwill the systematic biases be large enough to exceed the underlying\nstatistical uncertainty. Furthermore, detection searches can ignore tidal\neffects without loss of sensitivity.\n\n\n\nSecond, we study the ability of aLIGO to constrain neutron star tidal \ndeformability with a single observation of an NSBH merger. For this, we\nuse the same setup for signal waveforms as before, but replace the filter\ntemplate model with one that includes tidal effects from inspiral\nthrough to merger (i.e. LEA+)~\\cite{Lackey:2013axa}. For most binaries with\nBH masses outside of the mass-gap $(2-5M_\\odot)$~\\cite{Bailyn:1997xt,\nKalogera:1996ci,Kreidberg:2012,Littenberg:2015tpa} and\/or realistic signal-to-noise\nratios (SNR), we find it difficult to put better than a factor of $2$ bound\non $\\Lambda_\\mathrm{NS}$ with a single observation. As we can see from\nFig.~\\ref{fig:TT_LambdaCIWidths90_0_Lambda_SNR}, it is only at SNRs \n$\\rho\\gtrsim 20-30$ (under otherwise favorable circumstances, such as a stiff\nequation of state) that we are able\nto bring this down to a $\\pm 75\\%$ bound on $\\Lambda_\\mathrm{NS}$. For signals louder\nthan $\\rho =30$, we can constrain $\\Lambda_\\mathrm{NS}$ to a much more meaningful degree\n(within $\\pm 50\\%$ of its true value).\nWhile this is discouraging at first, we turn to ask: what if we combine\ninformation from a population of low-SNR observations?\n\n\n\n\nThe EoS of matter at nuclear densities is believed to be universal among all\nneutron stars. The Tolman-Oppenheimer-Volkoff equation~\\cite{Tolman:1939jz,\nOppenheimer:1939ne,1934PNAS...20..169T}\nwould then predict that NS properties satisfy a universal relationship\nbetween $\\Lambda_\\mathrm{NS}$ and $m_\\mathrm{NS}$. As the final part of\nthis paper, we combine information from multiple observations of realistic NSBH\nsystems and perform a fully-Bayesian analysis of how our estimation of\n$\\Lambda_\\mathrm{NS}$ changes as we accumulate detections. This is similar to an earlier\nstudy~\\cite{DelPozzo:13} aimed at binary neutron stars.\nWe restrict ourselves to a population of NSs with masses clustered very tightly\naround $1.35M_\\odot$ (with a negligible variance), and negligible spins. We\nsample different nuclear EoSs by sampling entire populations fixing different\nvalues for the NS tidal deformability.\nFor all populations, we take source locations to be uniformly distributed in\nspatial volume, and source orientations to be uniform on the $2-$sphere. To\nsummarize, we find the following:\n(a) Our median estimate for $\\Lambda_\\mathrm{NS}$ starts out prior dominated, but \nconverges to within $10\\%$ of the true value within $10-20$ detections.\n(b) Measurement uncertainties for $\\Lambda_\\mathrm{NS}$, on the other hand, depend on\n$\\Lambda_\\mathrm{NS}$ itself. We find that for hard equations of state (with \n$\\Lambda_\\mathrm{NS}\\geq 1000$), $10-20$ observations are sufficient to constrain\n$\\Lambda_\\mathrm{NS}$ within $\\pm 50\\%$. For softer equations of state, the same level\nof certainty would require substantially more ($25-40$) observations.\n(c) Further, if the astrophysical ``mass-gap''~\\cite{Bailyn:1997xt,\nKalogera:1996ci,Kreidberg:2012,Littenberg:2015tpa} is real, we find that $20-50\\%$\nadditional observations would be required to attain the same measurement\naccuracy as above. (d) Putting tighter constraints on the $\\Lambda_\\mathrm{NS}$ of a\npopulation would require $50+$ NSBH observations, in any scenario.\nAnd, (e) it is the loudest $5-10$ events that will furnish the bulk\nof tidal information, and not the combination of a large number of \nlow-SNR events.\nAll of the above is possible within a few years of design\naLIGO operation~\\cite{Abadie:2010cfa}.\n\n\n\n\nIn this paper, we restrict our parameter space to span mass-ratios\n$q:=m_\\mathrm{BH}\/m_\\mathrm{NS}\\in[2,5]$, dimensionless BH spin (aligned with orbit)\n$\\chi_\\mathrm{BH}\\in[-0.5, +0.75]$, and dimensionless NS tidal deformability \n$\\Lambda_\\mathrm{NS}:= G\\left(\\frac{c^2}{G m_\\mathrm{NS}}\\right)^5\\lambda \\in[500, 2000]$.\nThese ranges are governed by the calibration of the LEA+ model which we use as\nfilters. \nMost of the disruptive NSBH simulations that LEA+ has been calibrated to\ninvolve $1.35M_\\odot$ NSs, and it is unclear how reliable the model is \nfor different NS masses~\\cite{Lackey:2013axa,Pannarale:2015jka}. This\nmotivates us to conservatively fix NS masses to $1.35M_\\odot$ in our \nsimulated signals (not templates). But, since the domain of calibration of\nLEA+ excludes NS spin completely, we fix $\\chi_\\mathrm{NS}=0$ in both signals as well as\nfilter templates. We expect the effect of ignoring NS mass and spin variations\nin our NSBH populations to be less severe than for\nBNSs~\\cite{Agathos:2015a}, considering the higher mass-ratios of NSBHs.\nThe accuracy of our quantitative results depends on the reliability of LEA+,\nwhich is the only model of its kind in current literature. A more recent \nwork~\\cite{Pannarale:2015jka} improves upon the amplitude description of LEA+,\nbut needs to be augmented with a compatible phase model. Overall, we expect\nour broad conclusions here to hold despite modeling inaccuracies (with errors\nnot exceeding $\\mathcal{O}(10\\%)$~\\cite{Pannarale:2015jka}).\nFinally, our results apply to LIGO instruments at design sensitivity,\nwhich they are projected to attain by $2019$~\\cite{Shoemaker2009,\nAbbott:2016wya}.\n\n\nThe remainder of the paper is organized as follows. \nSec.~\\ref{s1:techniques} discusses data analysis techniques and resources \nused in this paper, such as the waveform model, and parameter estimation \nalgorithm.\nSec.~\\ref{s1:PEwithnoNS} discusses the consequences of ignoring tidal \neffects in parameter estimation waveform models.\nSec.~\\ref{s1:PEwithNS} discusses the measurability for the leading order\ntidal parameter $\\Lambda_\\mathrm{NS}$ at plausible SNR values.\nSec.~\\ref{s1:multiple_observations} discusses the improvement in our\nmeasurement of $\\Lambda_\\mathrm{NS}$ with successive (multiple) observations of\nNSBH mergers.\nFinally, in Sec.~\\ref{s1:discussion} we summarize our results and discuss\nfuture prospects with Advanced LIGO.\n\n\n\n\n\n\\section{Techniques}\\label{s1:techniques}\n\\begin{figure*}\n\\centering \n\\includegraphics[trim=20 18 18 18 0,clip=true,width=1.8\\columnwidth]{SingleSystemEta_q4_0_mc2_25_chi0_50}\\\\\n\\caption{{\\bf Illustrative posterior probability distributions for mass-ratio $\\eta$ at different SNR values:}\nWe show here probability distributions for mass ratio $\\eta$ as measured\nfor the same signal at different SNRs. The intrinsic parameters of the source\nare: $q = m_\\mathrm{BH}\/m_\\mathrm{NS} = 5.4M_\\odot\/1.35M_\\odot = 4$, $\\chi_\\mathrm{BH}=+0.5$, and $\\Lambda_\\mathrm{NS}=2000$;\nand the signal is injected at SNRs $\\rho=\\{20,30,50\\}$ (left to right). The templates\n{\\it ignore} tidal effects.\nIn each panel: the dashed red line marks the median value\n$\\eta^\\mathrm{Median}$, while the dashed green line show the true value\n$\\eta^\\mathrm{Injected}$. The darker shading shows\nthe recovered $90\\%$ credible interval for $\\eta$, $(\\Delta\\eta)^{90\\%}$.\nComparing systematic and statistical errors, we find that:\nat $\\rho=20$, $\\eta$ measurement is dominated by statistical\nerrors; at $\\rho=30$, the two become comparable; and \nfor louder signals ($\\rho\\simeq50$), the systematic errors dominate.\n}\n\\label{fig:SingleSystemEtaPDFvsSNR}\n\\end{figure*}\n\n\n\\subsection{Waveform Models}\\label{s2:waveforms}\n\n\n\nLackey {\\it et al.}~(LEA)~\\cite{Lackey:2013axa} developed a complete inspiral-merger\nwaveform model for disrupting NSBHs. Theirs is a frequency-domain\nphenomenological model that includes the effect of BH and NS masses and spins\n$\\{m_\\mathrm{BH}, \\chi_\\mathrm{BH}, m_\\mathrm{NS}\\}\\equiv\\vec{\\theta}$ and NS tidal deformability\n$\\Lambda_\\mathrm{NS}$. It was calibrated to a suite of $134$ numerical relativity (NR)\nsimulations of NSs inspiraling into spinning BHs, with\nNS masses ranging between $1.2M_\\odot\\leqm_\\mathrm{NS}\\leq 1.45M_\\odot$,\nmass-ratios $2\\leq q\\leq 5$, and BH spins $-0.5\\leq\\chi_\\mathrm{BH}\\leq+0.75$.\nThey also sample a total of $21$ two-parameter nuclear EoSs to cover the\nspectrum of NS deformability.\nThe GW strain $\\tilde{h}(f)$ per the LEA model can be written as\n\\begin{equation}\n \\tilde{h}_\\mathrm{NSBH}(f, \\vec{\\theta}, \\Lambda_\\mathrm{NS}) = \\tilde{h}_\\mathrm{BBH}(f, \\vec{\\theta})\\,A(f, \\vec{\\theta}, \\Lambda_\\mathrm{NS})\\,e^{\\mathrm{i} \\Delta\\Phi(f, \\vec{\\theta}, \\Lambda_\\mathrm{NS})},\n\\end{equation}\nwith NS spin $\\chi_\\mathrm{NS}=0$ identically. Here, $\\tilde{h}_\\mathrm{BBH}$ is\nan underlying BBH waveform model. In the original LEA model,\nthis was taken to be the SEOBNRv1\nmodel~\\cite{Taracchini:2012} of the Effective-one-body (EOB)\nfamily~\\cite{Buonanno99}. The factor $A(\\cdot)$ adjusts\nthe amplitude of the BBH model to match that of an NSBH merger of otherwise\nidentical parameters, with NS-matter effects parametrized by $\\Lambda_\\mathrm{NS}$.\nDuring early inspiral this term is set to\nunity, but is a sensitive function of $\\Lambda_\\mathrm{NS}$ close to merger. The term with\n$\\Delta\\Phi$ corrects the waveform phasing. During inspiral,\n$\\Delta\\Phi$ is set to the PN tidal phasing corrections,\nat the leading and next-to-leading orders~\\cite{Vines2011}; close to merger,\nadditional phenomenological terms are needed. Both $A$ and $\\Delta\\Phi$ are\ncalibrated to all $134$ available NR simulations.\n\n\nIn this paper we use LEA for our signal and template modeling, but switch the \nunderlying BBH model to SEOBNRv2 (and refer to it as enhanced-LEA or\n``LEA+'')~\\cite{Taracchini:2013rva}. We using the reduced-order\nfrequency-domain version of SEOBNRv2, which has the additional benefit of\nreducing computational cost~\\cite{Purrer:2015tud}. We expect this enhancement\nfrom LEA$\\rightarrow$LEA+ to make our conclusions more robust because: (a) the \nSEOBNRv2 model is more accurate~\\cite{Kumar:2015tha,Kumar:2016dhh}, and (b)\nthe differences between the two EOB models are caused by the\ninaccuracies of SEOBNRv1 during the {\\it inspiral} phase, many orbits before \nmerger~\\cite{Kumar:2015tha}.\nSince LEA only augments inspiral phasing with PN tidal terms, our\nchange in the underlying BBH model does not change LEA's construction, {\\it and}\nincreases the overall model accuracy during inspiral.\nFinally, we note that we approximate the full GW signal with its dominant\n$l=|m|=2$ modes, that are modeled by LEA+. For use in future LIGO science\nefforts, we have implemented the LEA+ model in the LIGO Algorithms\nLibrary~\\cite{LAL}.\n\n\\subsection{Bayesian methods}\\label{s2:bayesian}\n\n\n\nThe process of measuring systematic and statistical measurement errors\ninvolves simulating many artificial GW signals, and inferring source binary\nparameters from them using Bayesian statistics.\nWe start with generating a signal waveform, using the model LEA+, and injecting\nit in zero noise to obtain a stretch of data $d_n$. Source intrinsic parameters\n$\\vec{\\Theta}:=\\{m_\\mathrm{BH},m_\\mathrm{NS},\\chi_\\mathrm{BH},\\Lambda_\\mathrm{NS}\\}$ are reconstructed from this\ninjected signal. Extrinsic parameters $\\vec{\\theta}:=\\{t_c,\\phi_c\\}$ \nrepresenting the time of and phase at the arrival of signal are marginalized\nover numerically and analytically (respectively), while source location and\norientation parameters such as its luminosity distance, sky location, inclination\nand polarization angles are absorbed into a normalization, as describe later,\nand subsequently maximized over. This is justified because in this paper we\nconsider the {\\it single-detector case}. Using Bayes' theorem, the joint inferred\nprobability distribution of $\\vec{\\Theta}$ can be evaluated as\n\\begin{equation}\\label{eq:postprob}\n p(\\vec{\\Theta} | d_n, H) = \\dfrac{p(d_n|\\vec{\\Theta}, H)\\,p(\\vec{\\Theta} | H)}{p(d_n|H)}.\n\\end{equation}\nHere, $p(\\vec{\\Theta} | H)$ is the {\\it a priori} probability of binary parameters\n$\\vec{\\Theta}$\ntaking particular values, given $H$ - which denotes all our collective knowledge,\nexcept for expectations on binary parameters that enter\nour calculations explicitly. Throughout this paper, we impose priors that are\nuniform in individual component masses, BH spin, and the tidal deformability of\nthe NS. In addition, we restrict mass-ratios to $q\\geq 2$, as LEA+ is not\ncalibrated for $1\\leq q\\leq 2$. $p(d_n|\\vec{\\Theta}, H)$ is the {\\it likelihood}\nof obtaining the given stretch of data $d_n$ if we assume that a\nsignal parameterized by $\\vec{\\Theta}$ is buried in it, and is given by\n\\begin{equation}\\label{eq:likelihood}\n p(d_n| \\vec{\\Theta}, H) \\equiv \\mathcal{L}(\\vec{\\Theta}) = \\mathcal{N}\\, \\mathrm{exp}[-\\frac{1}{2} \\langle d_n - h | d_n - h\\rangle ],\n\\end{equation}\nwhere $h\\equiv h(\\vec{\\Theta})$ is a filter template with parameters \n$\\vec{\\Theta}$, $\\langle\\cdot|\\cdot\\rangle$ is a suitably defined\ndetector-noise weighted inner-product\\footnote{The inner product\n$\\langle\\cdot|\\cdot\\rangle$ is defined as\n\\begin{equation}\n\\langle a|b\\rangle \\equiv 4\\,\\mathrm{Re}\\left[\\int_0^\\infty \\dfrac{\\tilde{a}(f) \\tilde{b}(f)^*}{S_n(|f|)}\\,\\mathrm{d} f\\right],\n\\end{equation}\nwhere $\\tilde{a}(f)$ is the Fourier transform of the finite time series $a(t)$,\nand $S_n(|f|)$ is the one-sided amplitude spectrum of detector noise. In this\nwork, we use the zero-detuning high-power design sensitivity curve~\\cite{\nShoemaker2009} for Advanced LIGO, with $15$Hz as the lower frequency cutoff.},\nand $\\mathcal{N}$ is the normalization constant that absorbs source distance,\norientation and sky location parameters. As in Ref.~\\cite{Purrer:2015nkh} we\nuse a likelihood that is maximized over the template\nnorm, allowing us to ignore the extrinsic parameters that only enter in the\ntemplate norm through $\\mathcal{N}$. As a result, we only need to sample over\n$\\vec{\\Theta}$ (or $\\vec{\\Theta} - \\{\\Lambda_\\mathrm{NS}\\}$ in the case of non-tidal\ntemplates).\nThe denominator in Eq.~\\ref{eq:postprob} is the {\\it a priori} probability of finding\nthe particular signal in $d_n$ and we assume that each injected signal is as\nlikely as any other. From the joint probability distribution\n$p(\\vec{\\Theta} | d_n, H)$ so constructed, extracting the measured probability distribution\nfor a single parameter (say $\\alpha$) involves integrating\n\\begin{equation}\\label{eq:marginalize}\n p(\\alpha | d_n, H) = \\int\\mathrm{d} \\vec{\\Theta}_\\alpha\\, p(\\vec{\\Theta} | d_n, H),\n\\end{equation}\nwhere $\\vec{\\Theta}_\\alpha$ is the set of remaining parameters, i.e.\n$\\vec{\\Theta}_\\alpha:=\\vec{\\Theta} - \\{\\alpha\\}$.\n\nWe use the ensemble sampler Markov-chain Monte-Carlo algorithm implemented in\nthe {\\tt emcee} package~\\cite{emcee}, to sample the probability distribution \n$p(\\vec{\\Theta} | d_n, H)$. We run 100 independent chains, each of which is\nallowed to collect 100, 000 samples and combine samples from chains that have\na Gelman-Rubin statistic~\\cite{gelman1992} close to unity. This procedure yields\nabout 10,000 independent samples.\nOne simplification we make to mitigate computational cost is to set the\nfrequency sampling interval to $\\Delta f=0.4$~Hz, which we find to be\nsufficient for robust likelihoods calculations in zero noise~\\cite{Purrer:2015nkh}.\nWe integrate\nEq.~\\ref{eq:marginalize} to obtain marginalized probability distributions\nfor the NS tidal deformability parameter: $p(\\Lambda_\\mathrm{NS}|d_n,H)$. We will quote\nthe median value of this distribution as our {\\it measured} value for\n$\\Lambda_\\mathrm{NS}$, and the $90\\%$ credible intervals associated with the distribution\nas the statistical error-bars.\n\n\n\n\n\\section{How is PE affected if we ignore NS matter effects?}\\label{s1:PEwithnoNS}\n\\begin{figure*}\n\\centering \n\\includegraphics[trim=20 20 18 22 0,clip=true,width=1.95\\columnwidth]{TNMchirpBiasesOverCIWidths_CI90_0_Lambda_SNR30_70_linear}\n\\caption{{\\bf Ratio of systematic to statistical errors in measuring $\\mathcal{M}_c$, ignoring tidal effects:}\nWe show here the ratio of systematic and statistical\nmeasurement uncertainties for the binary chirp mass over the NSBH parameter \nspace. Each panel shows the same as a function of BH mass and spin. NS mass\nis fixed at $m_\\mathrm{NS}=1.35M_\\odot$, and its spin is set to zero. Down each column,\nwe can see the effect of the increasing tidal deformability of the NS at fixed\nSNR. Across each row, we can see the effect of increasing the signal strength\n(SNR), with the tidal deformability of the NS fixed. We show dashed contours\nfor $\\mathtt{R}_{\\mathcal{M}_c}=10\\%, 25\\%, 50\\%\\cdots$, with interleaving filled color\nlevels separated by $5\\%$.\nFor BBHs, the statistical errors dominate systematic ones for contemporary\nwaveform models~\\cite{Inprerp-LVC-WaveModels:2016,Kumar:2016dhh}. We find that\nits not much different for NSBH binaries, until we get to very high SNRs\n$\\rho\\gtrsim 70$.\n}\n\\label{fig:TN_chirpMassBias_vs_Lambda_SNR}\n\\end{figure*}\n\\begin{figure*}[!t]\n\\centering\n\\includegraphics[trim=20 20 18 21 0,clip=true,width=1.95\\columnwidth]{TNEtaBiasesOverCIWidths_CI90_0_Lambda_SNR20_50_linear}\n\\caption{{\\bf Ratio of systematic to statistical errors in measuring $\\eta$, ignoring tidal effects:}\nThis figure is similar to Fig.~\\ref{fig:TN_chirpMassBias_vs_Lambda_SNR}\nwith the difference that here we show the ratio of systematic and statistical\nerror sources for the symmetric mass-ratio $\\eta$ and not chirp mass. We find\nthat for fairly loud GW signals, at $\\rho\\simeq 50$, not including the\neffects of tidal deformation of the NS on GW emission can become the dominant\nsource of error for astrophysical searches with Advanced LIGO. However, for\nquieter signals with $\\rho\\leq 30$, it will have a negligible effect on the\nmeasurement of $\\eta$. We remind the reader that the SNRs here are always\nsingle detector values.\n}\n\\label{fig:TN_EtaBias_vs_Lambda_SNR}\n\\end{figure*}\n\\begin{figure*}\n\\centering\n\\includegraphics[trim=20 20 18 20 0,clip=true,width=1.95\\columnwidth]{TNChiBHBiasesOverCIWidths_CI90_0_Lambda_SNR_linear}\n\\caption{{\\bf Ratio of systematic to statistical errors in measuring $\\chi_\\mathrm{BH}$, ignoring tidal effects:}\nThis figure shows the ratio of the systematic and statistical\nmeasurement errors for BH spins $\\mathtt{R}_{\\chi_\\mathrm{BH}}$. Information is arranged identically\nto Fig.~\\ref{fig:TN_chirpMassBias_vs_Lambda_SNR}, \nand~\\ref{fig:TN_EtaBias_vs_Lambda_SNR}, with the level spacing of filled contours\nincreased to $15\\%$.\nSimilar to the case of mass parameters, we find that below $\\rho\\approx 30$,\nignoring tidal effects in templates introduces minor systematic effects,\nwhich remain sub-dominant to the statistical measurement uncertainties.\n}\n\\label{fig:TN_BHspinBias_vs_Lambda_SNR}\n\\end{figure*}\n\\begin{figure*}\n\\centering \n\\includegraphics[trim=10 20 8 18 0,clip=true,width=1.9\\columnwidth]{SingleSystemLambdaVary_q4_0_mc2_25_chi0_50_snr50}\n\\caption{{\\bf Illustrative posterior probability distributions for NS tidal\ndeformability $\\Lambda_\\mathrm{NS}$:}\nWe show here probability distributions recovered for the NS tidal\ndeformability parameter $\\Lambda_\\mathrm{NS}$ from three GW injections, with parameters:\n$q = m_\\mathrm{BH}\/m_\\mathrm{NS} = 5.4M_\\odot\/1.35M_\\odot = 4$, $\\chi_\\mathrm{BH}=+0.5$, and \n$\\Lambda_\\mathrm{NS}=\\{1000,1500,2000\\}$ from left to right. The injection SNR is fixed at\n$\\rho=50$. The templates {\\it include} tidal effects, with a prior $0\\leq\\Lambda_\\mathrm{NS}\\leq 4000$.\nIn each panel- the dashed red line marks the median value for\n$\\Lambda_\\mathrm{NS}$, and the dashed green line marks its {\\it true} value.\nThe darker shading shows the $90\\%$ credible interval, whose width\n$(\\Delta\\Lambda_\\mathrm{NS})^{90\\%}$ is a direct measure of our statistical uncertainty.\nBy comparing the measurement uncertainty for these three injections, we see\nthat $(\\Delta\\Lambda_\\mathrm{NS})^{90\\%}$ grows very slowly with $\\Lambda_\\mathrm{NS}$. Therefore,\nthe fractional measurement error - $(\\Delta\\Lambda_\\mathrm{NS})^{90\\%}\/\\Lambda_\\mathrm{NS}$ -\ndecreases monotonically as $\\Lambda_\\mathrm{NS}$ increases (with signal strength fixed).\n}\n\\label{fig:SingleSystemLambdaPDFvsSNR}\n\\end{figure*}\nPast (and future) efforts with Advanced LIGO have used (or plan to use) BBH\nwaveform templates to search for and characterize NSBH sources. In doing so,\nthey ignore the signature of NS tidal effects on the emitted GWs. In this\nsection we present a fully Bayesian analysis of the effect of this\nsimplification on the recovery of non-tidal parameters from NSBH signals.\n\n\nWe inject LEA+ NSBH signals into zero noise, and run an MCMC sampler on\nthem using equivalent BBH templates (same model, tidal terms $\\rightarrow 0$).\nWe fix $m_\\mathrm{NS}=1.35M_\\odot$ and $\\chi_\\mathrm{NS}=0$, and explore a range of NS\nequations of state via the single tidal deformability parameter\n$\\Lambda_\\mathrm{NS}\\in\\{500, 800, 1000, 1500, 2000\\}$. Our injections also span a\nrectangular grid in the BH parameter space, with vertices at\n$q\\in\\{2,3,4,5\\}$, i.e.\n$m_\\mathrm{BH}\\in\\{2.7M_\\odot, 4.05M_\\odot, 5.4M_\\odot, 6.75M_\\odot\\}$, and BH spins\n$\\chi_\\mathrm{BH}\\in\\{-0.5, 0, +0.5, +0.75\\}$. \nFinally, we sample all other source-related parameters, that determine the\nsignal strength but not character\\footnote{For aligned-spin signals and\naligned-spin templates both, we only consider the contribution of the dominant\n$l=|m|=2$ waveform multipoles. This approximation has the additional benefit\nof combining the dependence of the waveforms on inclination, polarization\nand sky location angles, as well as on distance, into the luminosity\nor {\\it effective} distance. This quantity only appears as an overall scaling\nfactor, and therefore only affects signal strength~\\cite{Sathyaprakash:2009xs}.\n}, by sampling the SNR $\\rho\\in\\{20,30,50,70\\}$. Our choice of injection\nparameters here\nis motivated by two factors: (i) previous studies of the signatures of NS tidal\neffects on gravitational waves~\\cite{FoucartEtAl:2011,Foucart:2013psa,\nFoucart:2014nda} (which suggest that necessary conditions for the observation\nof tidal effects with aLIGO include high SNRs and a low-mass spinning companion\nBH); and (ii) technical constraints of our chosen LEA+ model~\\cite{Lackey:2013axa}.\nAt design sensitivity, if we expect $0.2-300$ NSBH detections a \nyear~\\cite{Abadie:2010cfa}, we can expect to see $0.02-25$ \n{\\it disruptive}\\footnote{We assume here that BH mass values are {\\it uniformly}\nlikely from $2M_\\odot$ to $\\sim 35M_\\odot$~\\cite{LIGOVirgo2016a}, but NSs are\ndisrupted in NSBH mergers only if $q\\leq 6$ and $\\chi_\\mathrm{BH}\\geq 0$~\\cite{Foucart:2014nda,\nFoucart:2013psa}.\n} NSBH mergers a year, of which we will have $0.005-7$ observations with \n$\\rho\\geq 20$, and $0.002-3$ a year with $\\rho\\geq 30$.\nTherefore, our injection parameters span a physically interesting subset of NSBH\nbinaries, that is {\\it also} likely observable in the near future. \nFor our Bayesian priors, we choose uniform distributions for both component\nmasses and black hole spin: $m_\\mathrm{BH}\\in[1.2,25]M_\\odot$; $m_\\mathrm{NS}\\in[1.2,3]M_\\odot$;\nand $-0.75\\leq \\chi_\\mathrm{BH}\\leq +0.75$.\n\n\n\nThe effect of ignoring\ntidal corrections in templates will manifest as a systematic shift\nof recovered median parameter values away from what they would be if we had used\ntidal templates with identical priors. \nIn zero noise, we expect the probability distributions recovered using tidal\ntemplates to be multi-dimensional Gaussians with the maximum likelihood\nparameter values approaching their true values. If the priors are not\nrestrictive, we expect the recovered median to also converge to the true value.\nHowever,\nthe LEA model imposes significantly more restrictive priors (both mass-ratio\nand spin) than SEOBNRv2~\\cite{Taracchini:2013rva,Lackey:2013axa}, which shifts\nthe median value of parameters recovered using {\\it our} tidal templates away\nfrom their true value. If we use LEA+ priors for our non-tidal templates, it\nwould add a caveat to our original question 'can we estimate non-tidal NSBH\nparameters with equivalent BBH templates'. Instead, we approximate the median\ntidally recovered parameters by their true injected values, as one would expect\nto recover with an ideal model for tidally disruptive NSBH mergers. With this\ncaveat, we estimate {\\it systematic} measurement bias\/errors as the\ndifferences between median and {\\it injected} parameter values. \nAs an illustration, in Fig.~\\ref{fig:SingleSystemEtaPDFvsSNR} we show the\nrecovered probability distributions for binary mass ratio $\\eta$ for\nthree NSBH injections, with $\\rho=20$ (left), $30$ (middle), and $\\rho=50$\n(right), and other parameters held fixed ($m_\\mathrm{NS}=1.35M_\\odot$, $\\chi_\\mathrm{NS}=0$,\n$m_\\mathrm{BH}=5.4M_\\odot$, $\\chi_\\mathrm{BH}=+0.5$ and $\\Lambda_\\mathrm{NS}=2000$). In each panel, both\nthe {\\it true} and median values of $\\eta$ are marked, and we use\nthe shift between the red and green vertical lines as our estimate of systematic\nmeasurement errors. Darker shading in all panels marks $90\\%$ credible intervals,\nwhose width $(\\Delta\\eta)^{90.0\\%}$ we use as a direct measure of our\n{\\it statistical} measurement uncertainty\/error\\footnote{We generalize the\nnotation $(\\Delta X)^{90.0\\%}$ to mean the $90\\%$ credible interval width\nfor any measured source parameter $X$.}.\nFor the illustrated binary,\nwe see clearly that even when the signal is moderately loud, with $\\rho=20$,\nstatistical errors dominate over systematics for $\\eta$. As we turn up the SNR\nfurther, the two error sources become comparable at $\\rho\\sim 30$,\nand systematic errors dominate finally when $\\rho\\simeq 50$.\n\n\n\n\n\nCredible intervals $(\\Delta X)^{90\\%}$ showing the precision with which\n$X=\\{\\mathcal{M}_c,\\eta,\\chi_\\mathrm{BH}\\}$ can be measured, are presented in\nAppendix~\\ref{as1:nontidalerrors}.\nWe remind ourselves that this {\\it precision} is only meaningful so long as the\nmeasurement is {\\it accurate} to begin with. Therefore, we define $\\mathtt{R}_X$ as\nthe ratio between systematic and statistical errors associated with the\nmeasurement of parameter $X$,\n\\begin{equation}\\label{eq:arr}\n\\mathtt{R}_X = \\dfrac{(X^\\mathrm{Median} - X^\\mathrm{Injected})}{(\\Delta X)^{90\\%}},\n\\end{equation}\nin order to compare the relative magnitude of both. Only when\n$|\\mathtt{R}_X| \\ll 1$ can we ignore tidal effects in our templates\nwithout hampering the measurement of non-tidal parameters from NSBH signals.\nWhen $\\mathtt{R}_X$ approaches a few tens of percent of unity, we can begin to favor\ntidal templates for NSBH studies.\n\n\nWe start with calculating $\\mathtt{R}_{\\mathcal{M}_c}$ as a function of various source\nparameters and show it in Fig.~\\ref{fig:TN_chirpMassBias_vs_Lambda_SNR}.\n$\\mathcal{M}_c$ is the leading order\nmass combination that affects the GW strain emitted by compact binaries as they\nspiral in, and is therefore determined the most precisely. We notice\nimmediately that for $\\rho\\leq 30$ the systematics are well under control\nand we can obtain reliable chirp mass estimates for NSBH signals using BBH\ntemplates.\nFor louder and less likely SNRs ($\\rho\\simeq 50$), we find that\n$\\mathtt{R}_{\\mathcal{M}_c}$ can become comparable to unity, but only if the BH has\nprograde spin $\\chi_\\mathrm{BH}\\gtrsim 0.4$, {\\it and} the true NS tidal deformability\nis large enough, s.t. $\\Lambda_\\mathrm{NS} \\gtrsim 1000$.\nWe therefore conclude that only for very loud signals, with\n$\\rho\\gtrsim 50-70$, will the inclusion of tidal terms in template\nmodels improve $\\mathcal{M}_c$ estimation. For lower SNRs, inclusion of new\nphysical content in templates will instead get washed out by detector noise.\nIn addition, we also note that $\\mathtt{R}_{\\mathcal{M}_c}\\geq 0$ always,\ni.e. $\\mathcal{M}_c$ is always being over-estimated. This is to be expected since\nthe tidal deformation of the NS drains energy faster from the orbit during\ninspiral (as compared to the BBH case), and its disruption close to merger\nreduces GW signal power at high frequencies. Both of these effects make the\nresulting signal resemble a BBH signal of higher chirp (or total) mass, although\nwe expect the latter effect to be dominant~\\cite{Pannarale:2011pk}.\n\n\n\nNext, in Fig.~\\ref{fig:TN_EtaBias_vs_Lambda_SNR}, we show the ratio of\nmeasurement errors $\\mathtt{R}_\\eta$ for the symmetric mass-ratio.\nGoing through the figure from left to right, we find that for realistic\nSNRs ($\\rho\\leq 30$) the systematics remain below statistical errors for\n$\\eta$ measurement. The worst case is of the most deformable NSs\n($\\Lambda_\\mathrm{NS} = 2000$), but even for them systematics in $\\eta$ are $2\\times$\nsmaller than the statistical measurement errors.\nMoving to louder signals with $\\rho\\simeq 50$, we find that for binaries of\nfairly deformable NSs ($\\Lambda_\\mathrm{NS}\\gtrsim 1500$) and low-mass BHs\n($m_\\mathrm{BH}\\leq 5M_\\odot$) that have prograde spins ($\\chi_\\mathrm{BH}\\gtrsim +0.4$), our\nmeasurement of mass-ratio can be seriously compromised by ignoring tidal\nphysics in template models. \nThis pattern is continued at even higher SNRs, as we can see from\nFig.~\\ref{fig:TN_EtaBias_vs_Lambda_SNR}. We therefore conclude that, even if\nunder moderate restrictions on BH and NS parameters, $\\rho=30-50$ is loud enough\nto motivate the use of tidal templates in aLIGO data analyses.\nIn addition, we also notice that, unlike for $\\mathcal{M}_c$, the median value of\n$\\eta$ is always {\\it lower} than its true value, which is what we expect if we\nwant BBH templates to fit NSBHs that disrupt and merge at lower frequencies.\n\n\n\nMoving on from mass to spin parameters, we now consider the measurement of BH\nspin angular momentum $\\chi_\\mathrm{BH}$. The ratio of systematic and statistical errors\nfor $\\chi_\\mathrm{BH}$ are shown in\nFig.~\\ref{fig:TN_BHspinBias_vs_Lambda_SNR}. The presentation of information in this \nfigure is identical to that of Fig.~\\ref{fig:TN_chirpMassBias_vs_Lambda_SNR}\nand~\\ref{fig:TN_EtaBias_vs_Lambda_SNR}. A diverging colormap is used so that both \nextremes of the colorbar range point to large systematic biases, while its zero (or\nsmall) value lies in the middle.\nFor the lowest SNR considered ($\\rho=30$), $\\chi_\\mathrm{BH}$ bias is about $2\\times$\nsmaller than its statistical measurement uncertainty, and is therefore mostly\nnegligible. Both do become somewhat comparable, but only when we have the most\ndeformable NSs in orbit around low-mass BHs. \nAt higher SNRs $(\\rho\\simeq50-70)$, we find that the systematics in $\\chi_\\mathrm{BH}$\nmeasurement can dominate completely, especially for binaries containing\nmass-gap violating BHs and\/or deformable NSs with $\\Lambda_\\mathrm{NS}\\geq1000$.\nFrom Fig.~\\ref{fig:TN_BHspinBias_vs_Lambda_SNR} we additionally note that when\nthe source spin magnitudes approach the highest allowed, i.e. at both extremes\nof the $x$-axes, $\\chi_\\mathrm{BH}\\times\\mathtt{R}_{\\chi_\\mathrm{BH}}<0$. This is to be expected because\nthe median of the recovered posterior distributions for $\\chi_\\mathrm{BH}$ can only get\npushed inwards from the boundaries.\n\n\nSummarizing these results, we find that irrespective of system parameters,\nbelow a signal-to-noise ratio of $30$, our measurements of mass and spin\nparameters of astrophysical NSBH binaries will remain limited by the intrinsic\nuncertainty due to instrument noise, and do not depend on whether we include\ntidal effects in template models. However, when the signal-to-noise ratio\nexceeds $30$ the systematic bias in binary mass and spin measurements become\ncomparable to and can exceed the uncertainty due to noise. Of the different\nnon-tidal parameters considered, we find that the measurement of $\\eta$\ndegrades worst (in a relative-error sense) due to the use of BBH templates in\ndeciphering an NSBH signal. Of all the sub-categories, we find that tidal\ntemplates could especially help with the parameter estimation of astrophysical\n{\\it mass-gap violating} NSBH binaries,\n\n\n\n\n\n\n\n\\section{What do we gain by using templates that include NS matter effects?}\\label{s1:PEwithNS}\n\\begin{figure*}\n\\centering \n\\includegraphics[trim=35 21 15 21 0,clip=true,width=2.2\\columnwidth]{TTLambdaRawCIWidths90_0_Lambda_SNR}\n\\caption{{\\bf Statistical uncertainty in $\\Lambda_\\mathrm{NS}$ measurement:}\nHere we show the statistical uncertainty in the measurement of\n$\\Lambda_\\mathrm{NS}$. In each panel, the\nsame is shown as a function of the BH mass and spin, keeping $\\Lambda_\\mathrm{NS}$ and\ninjection's SNR $\\rho$ fixed (noted in the panel). Rows contain panels\nwith the same value of $\\Lambda_\\mathrm{NS}$, with $\\rho$ increasing from left to right.\nColumns contain panels with the same value of $\\rho$, with $\\Lambda_\\mathrm{NS}$ \nincreasing from top to bottom.\nContours at $(\\Delta\\Lambda_\\mathrm{NS})^{90\\%}=\\{50\\%, 75\\%, 100\\%, 150\\%, 200\\%\\}\\times\\Lambda_\\mathrm{NS}^\\mathrm{Injected}$ demarcate regions where we can constrain the\n$\\Lambda_\\mathrm{NS}$ parameter well (within a factor of two of the injected value).\nWe note that, as expected, the measurement accuracy for $\\Lambda_\\mathrm{NS}$ improves\nwith (i) increasing SNR, (ii) increasing $\\Lambda_\\mathrm{NS}$, (iii) increasing BH spin,\nand (iv) decreasing BH mass.\n}\n\\label{fig:TT_LambdaCIWidths90_0_Lambda_SNR}\n\\end{figure*}\n\\begin{figure}\n\\centering \n\\includegraphics[trim=10 10 0 10 0,clip=true,width=1.05\\columnwidth]{TTSNRThresholdFor100LambdaMeasurement_BHspin_BHmass_Lambda2000_0_CI90_0}\n\\caption{\nWe show here, as a function of BH mass and spin, the {\\it minimum} signal\nstrength (SNR) required to constrain $\\Lambda_\\mathrm{NS}$ within an interval of width\nequal to $100\\%$ of its true value, i.e. with $\\pm 50\\%$ error-bars. The NS mass\nis fixed at $1.35M_\\odot$, spin at zero, and $\\Lambda_\\mathrm{NS}=2000$.\nWe can see that, even in the most conducive circumstances with large aligned \n$\\chi_\\mathrm{BH}$ and a comparable mass BH, we can only constrain $\\Lambda_\\mathrm{NS}$ to better\nthan $\\pm 50\\%$ {\\it if} the SNR is $\\gtrsim 29$. In the era of design\nsensitivity LIGO instruments, we expect this to happen approximately once in a\nyear of observation~\\cite{Abadie:2010cfa}.\n}\n\\label{fig:TT_SNRThresholds_BHspin_BHmass_CI90_0}\n\\end{figure}\n\n\nIn the previous section, we showed that the effects of the tidal deformation of\nNSs by their companion BHs become discernible in\nthe GW spectrum under certain favorable conditions, including (a) BH mass is\nsufficiently small, (b) BH spin is positive aligned, i.e. $\\chi_\\mathrm{BH}\\gtrsim +0.4$,\n(c) the NS is not very compact, with $\\Lambda_\\mathrm{NS}\\gtrsim 1000$, and (d) the\nsource location and orientation are such that its GW SNR $\\gtrsim 30$.\nBoth condition (a) and (b) enhance the tidal distortion of the star and increase\nthe number of orbits the system goes through at small separation, where the\ndifferences between NSBH and BBH signals are maximal.\nConditions (a)-(c) also reduce the onset frequency of the disruption of the NS,\nallowing for it to happen earlier in the orbit. \nWe expect that these conditions are also the ones which should maximize the\nlikelihood of {\\it measuring} tidal effects in NSBH signals. Here,\nwe turn the question around to ask: under similarly favorable circumstances,\ncan we gain insights about the internal structure of neutron stars from GW\nobservations?\n\n\n\nIn this section, we calculate the accuracy with which we measure $\\Lambda_\\mathrm{NS}$ from\n{\\it single} GW observations. We sample the same set of disruptive NSBH mergers\nas in the previous section, i.e. those with $q=\\{2,3,4,5\\}$,\n$\\chi_\\mathrm{BH}=\\{-0.5,0,+0.5,+0.75\\}$, and $\\Lambda_\\mathrm{NS}=\\{500, 800, 1000, 1500, 2000\\}$;\nfixing the NS mass $m_\\mathrm{NS}=1.35 M_\\odot$ and $\\chi_\\mathrm{NS}=0$. For each unique\ncombination of these parameters, we inject LEA+ signals into zero noise and\nperform a fully Bayesian parameter estimation analysis of each with LEA+\ntemplates. Our priors on component masses and spins remain as in the\nprevious section, with mass-ratio additionally restricted to $2\\leq q\\leq 6$,\nand $\\Lambda_\\mathrm{NS}$ sampled uniformly from $[0,4000]$.\nAs an illustration of individual injections, we show the recovered probability\ndistribution for $\\Lambda_\\mathrm{NS}$ for three specific configurations in \nFig.~\\ref{fig:SingleSystemLambdaPDFvsSNR}. We fix\n$q = m_\\mathrm{BH} \/m_\\mathrm{NS} = 5.4M_\\odot\/1.35M_\\odot = 4$, with $\\chi_\\mathrm{BH}=+0.5$, and\nvary $\\Lambda_\\mathrm{NS}$ over $\\{1000, 1500, 2000\\}$ between the three panels.\nThe SNR is fixed at $\\rho=50$. The darker shaded regions mark the $90\\%$ credible\ninterval on $\\Lambda_\\mathrm{NS}$. We note that $\\Lambda_\\mathrm{NS}$ is estimated to within\n$\\pm 2000$ of its true value at this SNR. Another interesting thing to note \nis that while $(\\Delta\\Lambda_\\mathrm{NS})^{90\\%}$ slowly grows with $\\Lambda_\\mathrm{NS}$, the\nfractional uncertainty\n\\begin{equation}\n\\delta\\Lambda_\\mathrm{NS}^{90\\%}:= (\\Delta\\Lambda_\\mathrm{NS})^{90\\%}\/\\Lambda_\\mathrm{NS}\n\\end{equation}\ndecreases instead.\nFurther illustrations, showing the correlation between tidal and non-tidal\nparameters, are presented in Appendix~\\ref{as1:illustrations}.\nWe will continue here to focus on the measurement of $\\Lambda_\\mathrm{NS}$ itself.\n\n\n\n\nIn Fig.~\\ref{fig:TT_LambdaCIWidths90_0_Lambda_SNR} we show the main results of\nthis section. In each panel, as a function of black hole mass and spin, we show\nthe measured $90\\%$ credible interval widths $(\\Delta\\Lambda_\\mathrm{NS})^{90\\%}$. These\ncorrespond to the full width of the dark shaded regions in the illustrative\nFig.~\\ref{fig:SingleSystemLambdaPDFvsSNR}. The effect of increasing signal\nstrength can be seen as we go from left to right in each row. The effect of the\nNS tidal deformability parameter $\\Lambda_\\mathrm{NS}$ on its own measurability can be\nseen by comparing panels within each column, with the NS becoming more\ndeformable from top to bottom. \nA uniform pattern emerges in the left-most column, which corresponds to $\\rho=20$.\nWe find that at this signal strength, our measurement of $\\Lambda_\\mathrm{NS}$ is\ndominated by the width of our prior on it. The $90\\%$ credible intervals span\nthe entire allowed range for $\\Lambda_\\mathrm{NS}$, making a reasonable estimation of\n$\\Lambda_\\mathrm{NS}$ at $\\rho\\simeq20$ difficult.\nIncreasing the signal strength to $\\rho=30$ gives marginally better results,\nbringing down the statistical uncertainties to within $\\pm 75-100\\%$ of the\ntrue $\\Lambda_\\mathrm{NS}$ value~\\footnote{The symmetric error-bars of $\\pm\\mathrm{X}\\%$\ncorrespond to $\\delta\\lambdans^{90\\%} = 2\\mathrm{X}\\%$.}.\nIt is not until we reach an SNR as high as $\\rho\\gtrsim 50$, can we put\nmeaningful (i.e. $\\mathcal{O}(10\\%)$) constraints on $\\Lambda_\\mathrm{NS}$. For e.g.,\nwith a {\\it single} observation of a $q=4$ binary with $\\chi_\\mathrm{BH}\\geq 0.6$ and\n$\\rho = 50$~\\footnote{For an optimally oriented source with\n$q=4, m_\\mathrm{NS}=1.35M_\\odot, \\chi_\\mathrm{BH}=0.6$, an SNR of $\\rho = 50$ corresponds to\na luminosity distance of $\\approx 113$Mpc.}, we would be able to estimate \n$\\Lambda_\\mathrm{NS}$ to within $\\pm 40\\%$ of its true value (which is equivalent to\nmeasuring the ratio of NS radius to mass with an uncertainty of about\n$\\pm 10\\%$).\nThese results agree well with Sec.~\\ref{s1:PEwithnoNS}, and are consistent with\nFisher matrix estimates at high SNRs~\\cite{Lackey:2013axa}.\n\n\n\n\nAmongst other source parameters, BH mass and spin play a dominant role. A smaller\nBH with a larger spin always allows for a more precise measurement on $\\Lambda_\\mathrm{NS}$.\nWe can see this in the bottom right corner of each panel in\nFig.~\\ref{fig:TT_LambdaCIWidths90_0_Lambda_SNR}, which corresponds to low-mass BHs\nwith large spins, and is simultaneously the region of smallest measurement errors on $\\Lambda_\\mathrm{NS}$.\nThe actual deformability of the NS also plays an important role on its own\nmeasurability. For e.g., when $\\Lambda_\\mathrm{NS}\\leq 1000$, it is fairly difficult\nto meaningfully constrain $\\Lambda_\\mathrm{NS}$ without requiring the source to be\nclose ($\\approx 100$Mpc) with a GW SNR $\\rho\\gtrsim 50$. Quantifying this further,\nin Fig.~\\ref{fig:TT_SNRThresholds_BHspin_BHmass_CI90_0} we show the minimum\nsignal strength required to attain a certain level of credibility in our\n$\\Lambda_\\mathrm{NS}$ measurement, as a function of BH properties. The NS is allowed\nthe most favorable (hardest) EoS considered, with $\\Lambda_\\mathrm{NS}^\\mathrm{true}=2000$.\nWe first note that, even with the most favorable BH and NS properties, achieving\na $\\pm 50\\%$ measurement certainty on $\\Lambda_\\mathrm{NS}$ will require a GW SNR\n$\\rho\\gtrsim 30$. If we additionally restrict BH masses to lie outside of the so-called\nastrophysical mass-gap~\\cite{Bailyn:1997xt,Kalogera:1996ci,Kreidberg:2012,\nLittenberg:2015tpa}, we will simultaneously need to restrict BH spins\nto $\\chi_\\mathrm{BH}\\gtrsim +0.5$ to obtain the same measurement credibility at the same\nsource location.\n\n\nIt is interesting to note that the parameter ranges most favorable to\nthe measurability of $\\Lambda_\\mathrm{NS}$ are also those which produce\nrelatively more massive post-merger disks~\\cite{Foucart2012}. That is, \nthe subset of NSBHs that potentially produce SGRBs (using a sufficiently-large\ndisk mass as an indicator) would be the same subset most favorable\nfor measurement of tidal effects. Therefore the rate of SGRBs in the \nlocal universe (allowing for the fraction that are produced by NSBHs versus\nBNSs) would be an indicator of the rate of events most favorable for nuclear\nequation of state measurements.\n\n\nIn summary, with a single moderately loud ($\\rho\\lesssim 30$) GW signal from\na disruptive BHNS coalescence, we can constrain\nthe NS compactness parameter $\\Lambda_\\mathrm{NS}$ within $\\pm 100\\%$ of its true value.\nTo measure better with one observation, we will need a more fine-tuned source, with\n$\\rho\\geq 30$ and high BH spins, or $\\rho\\geq 50$.\nFinally, we note that these results are {\\it conservative}, and \nBHs with spins $\\chi_\\mathrm{BH} > 0.75$ will prove to be even more favorable laboratories\nfor $\\Lambda_\\mathrm{NS}$ measurement. However, we are presently unable to explore this case\nin quantitative detail due to waveform model restrictions~\\cite{Lackey:2013axa},\nwhich will also restrict our analyses of GW signals during the upcoming LIGO\nobserving runs.\n\n\n\n\\section{Combining observations: looking forward with Advanced LIGO}\\label{s1:multiple_observations}\n\\begin{figure}\n\\centering \n\\includegraphics[trim=18 18 18 10 0,clip=true,width=\\columnwidth]{pdfLambda_vs_N_L800.pdf}\\\\\n\\includegraphics[trim=18 18 18 10 0,clip=true,width=\\columnwidth]{FillBetweenErrorBarsLambda_vs_N_L800.pdf}\n\\caption{{\\bf Recovery of $\\Lambda_\\mathrm{NS}$ for an increasingly large population of BH-NS signals.}\n{\\it Top}: Posterior probability distributions for $\\Lambda_\\mathrm{NS}$ (colored curves), and\nassociated $90\\%$ credible intervals (grey vertical lines), shown for different number\nof accumulated observations N. Distributions are normalized to unit area. \n{\\it Bottom}: Measured median value of $\\Lambda_\\mathrm{NS}$ (as solid circles) and the\nassociated $90\\%$ credible intervals (as the vertical extent of filled region), shown as\na function of number of observations N. Solid horizontal line indicates the true value of\n$\\Lambda_\\mathrm{NS}=800$. Dashed and dotted horizontal lines (a pair for each line-style) demarcate\n$\\pm 25\\%$ and $\\pm 50\\%$ error bounds.\n}\n\\label{fig:TT_Lambda_vs_N_L800_CI90_0}\n\\end{figure}\n\\begin{figure}\n\\centering \n\\includegraphics[width=1.05\\columnwidth,trim=1cm 0 0 0]{FillBetweenRelErrorBarsLambda_vs_NShifted_AllLambda.pdf}\n\\caption{{\\bf Improvement in $\\Lambda_\\mathrm{NS}$ measurement accuracy for different NS EoS:}\nIn this figure, the filled regions show how our measurement of $\\Lambda_\\mathrm{NS}$\nimproves as the number of observed events ($N$, shown on $x$-axis) increases.\nEach color corresponds to an independent population with its true value of\n$\\Lambda_\\mathrm{NS}$ given in the legend. For each population, we show the median \n$\\Lambda_\\mathrm{NS}$ value (as filled circles), as well as the associated\n$90\\%$ credible intervals for the measurement (as the vertical extent of the\nfilled region about the median), as functions of $N$.\n}\n\\label{fig:TT_Lambda_vs_N_CI90_0}\n\\end{figure}\n\\begin{figure*}\n\\centering\n\\includegraphics[trim=1cm 0 0 0, width=1.025\\columnwidth]{LambdaMedian_vs_N_AllPopulation}\n\\includegraphics[trim=0 0 1cm 0, width=1.025\\columnwidth]{LambdaMedian_vs_N_AstroPopulation}\\\\\n\\includegraphics[trim=1cm 0 0 0, width=1.025\\columnwidth]{LambdaMedian90pc_vs_N_AllPopulation}\n\\includegraphics[trim=0 0 1cm 0, width=1.025\\columnwidth]{LambdaMedian90pc_vs_N_AstroPopulation}\\\\\n\\caption{\n{\\bf No Mass-Gap}, {\\it top left}: The top figure shows the median value of the recovered\nprobability distribution for $\\Lambda_\\mathrm{NS}$, as a function of the number of observed \nevents $N$. There are four ensembles of curves,\ncorresponding to $\\Lambda_\\mathrm{NS}=\\{500,1000,1500,2000\\}$, with a hundred\nindependent population draws within each ensemble. One curve in each ensemble\nis highlighted in color, representing the realizations already plotted in\nFig.~\\ref{fig:TT_Lambda_vs_N_CI90_0}.\nIn the same color we show $\\pm 10\\%$ error-bounds on $\\Lambda_\\mathrm{NS}$ with\nhorizontal dash-dotted lines.\n{\\bf No Mass-Gap}, {\\it bottom left}: Here we show the interval of $\\Lambda_\\mathrm{NS}$ values within\nwhich the median $\\Lambda_\\mathrm{NS}$ lies for $90\\%$ of the populations in\neach ensemble shown in the top left panel.\nWe observe that within $10-25$ observations, the median of the measured \ncumulative probability distribution for $\\Lambda_\\mathrm{NS}$ converges to within $10\\%$\nof its true value.\n{\\bf Mass-Gap}, {\\it right column}: These panels are identical to their counterparts on the left,\nwith the only difference that the BH masses in each population are restricted\nto lie {\\it outside} the astrophysical mass-gap (i.e. paradigm B). The\ndifference that\nwe observe under this paradigm is that we need more ($30+$) events to achieve \nthe same ($10\\%$) measurement accuracy for populations with $\\Lambda_\\mathrm{NS}<1000$.\nFor more deformable neutron stars, $10-25$ events would suffice.\n}\n\\label{fig:TT_LambdaMedian_vs_N_AllInOne}\n\\end{figure*} \n\\begin{figure*}\n\\centering \n\\includegraphics[width=1.025\\columnwidth,trim=1cm 0 0 0]{LambdaCIWidths90pc_vs_N_AllPopulation}\n\\includegraphics[width=1.025\\columnwidth,trim=0 0 1cm 0]{LambdaCIWidths90pc_vs_N_AstroPopulation}\n\\caption{{\\bf No Mass-Gap} ({\\it left}): This panel shows the width of $\\Lambda_\\mathrm{NS}$ interval within\nwhich the $90\\%$ credible intervals for $\\Lambda_\\mathrm{NS}$ lie, for $90\\%$ of \nthe populations in each ensemble, as a function of the number of observed events\n$N$. Details of how this is calculated are given in the text.\nThe populations are sampled under paradigm A, which allows BH masses to\nfall within the astrophysical mass-gap.\nEach panel corresponds to a unique value of populations' $\\Lambda_\\mathrm{NS}$,\ndecreasing from $2000\\rightarrow 500$ as we go from top to bottom.\nOne curve in each ensemble is highlighted in color (thin lines), representing the \nrealizations already plotted in Fig.~\\ref{fig:TT_Lambda_vs_N_CI90_0}.\n{\\bf Mass-Gap} ({\\it right}): This panel shows populations drawn under paradigm B, which\nrespects the mass-gap.\nWe find that with approximately $25$ or so events, we begin to put\nstatistically meaningful constraints on $\\Lambda_\\mathrm{NS}$, restricting it to within\n$\\pm 50\\%$ of the true value. We can expect to achieve this with a few years\nof design aLIGO operation~\\cite{Abadie:2010cfa}. Further tightening of \n$\\Lambda_\\mathrm{NS}$ credible intervals will require $40+$ events.\n}\n\\label{fig:TT_LambdaError_vs_N_L500_2000_CI90_0_AllInOne}\n\\end{figure*}\n\\begin{figure*}\n\\centering \n\\includegraphics[width=1.025\\columnwidth,trim=1cm 0 0 0]{LambdaMedianCIWidths90pc_vs_N_AllPopulation_SNRSorted}\n\\includegraphics[width=1.025\\columnwidth,trim=0 0 1cm 0]{LambdaCIWidths90pc_vs_N_AstroPopulation_SNRSorted}\n\\caption{This figure is similar to Fig.~\\ref{fig:TT_LambdaError_vs_N_L500_2000_CI90_0_AllInOne},\nwith the only difference being that events in each population\nhave been sorted according to their signal strength (SNR), instead of\ntheir simulated chronology. We note that information about\nthe tidal deformability of neutron stars comes primarily \nfrom the loudest $5-10$ events, whether we allow BH masses\nin the mass gap (left panel) or restrict them to\n$m_\\mathrm{BH}\\geq 5M_\\odot$ (right panel). Left inset zooms\nin on the main figure for the first $15$ events. Right inset\nshows the actual (ensemble mean) SNR value for each event. We find that\nevents with $\\rho\\gtrsim 20-30$ provide the bulk of tidal information\nin our analysis.\n}\n\\label{fig:TT_LambdaError_vs_N_L500_2000_CI90_0_AllInOne_SNRSorted}\n\\end{figure*}\n\n\nIn the previous section, we showed that single observations of NSBH\ncoalescences at moderate SNRs have little information about the internal\nstructure of neutron stars that will be accessible to Advanced LIGO at its\ndesign sensitivity. We expect all neutron stars to share the same equation of\nstate, and hence the same $\\Lambda_\\mathrm{NS}(m_\\mathrm{NS})$. In addition, we know that the mass\ndistribution of (most) NSs that have not been spun up to millisecond periods\n(which are the ones we focus on in this paper, by setting $\\chi_\\mathrm{NS}\\approx 0$) is\nnarrowly peaked around $\\sim 1.35M_\\odot$~\\cite{Kiziltan2013}. Therefore,\ninformation from multiple NSBH observations can be combined to improve our\nestimation of $\\Lambda_\\mathrm{NS}$. We explore the same in this section within a fully\nBayesian framework. We refer the reader to Ref.~\\cite{Mandel:2009pc,Lackey2014,\nWade:2014vqa} for similar analyses of BNS inspirals.\n\n\n\n\nAn intuitive understanding of the problem is gained by considering first\nmultiple {\\it identical} sources with realistic but different SNRs. Let us consider the case\nof a population of optimally oriented binaries~\\footnote{An optimally oriented\nbinary is one which is located directly overhead the detector, with the \norbital angular momentum parallel to the line joining the detector to the\nsource. Such a configuration maximizes the observed GW signal strength in \nthe detector.}, distributed uniformly in spatial volume out\nto a maximum {\\it effective} distance~\\footnote{{\\it effective} distance $D$ \nis a combination of distance to the source, its orientation, and its sky\nlocation angles; and has a one-to-one correspondence with SNR for non-precessing\nsources. This is so because for such sources, their location and orientation\nremain constant over the timescales within which they sweep through\naLIGO's sensitive frequency band.}.\n$D^\\mathrm{max}$. $D^\\mathrm{max}$ is set by the minimum SNR \nthreshold $\\rho_\\mathrm{min}$ at which a source is considered\ndetectable~\\footnote{which\nwe take as $\\rho_\\mathrm{min}=10$ throughout.}. Next, we divide this volume into $I$\nconcentric shells, with radii $D_i$. If we have a measurement error\n$\\sigma_0$ for $\\Lambda_\\mathrm{NS}$, associated with a source located at $D=D_0$,\nthe same error for the same source located within the $i-$th shell would\nbe $\\sigma_i=\\sigma_0 \\dfrac{D_i}{D_0}$. Ref.~\\cite{Markakis:2010mp}\ncalculated that the combined error $\\sigma$ from $N$ independent\nmeasurements of $\\Lambda_\\mathrm{NS}$ in such a setting to be\n\\begin{align}\\label{eq:1oversigma}\n\\frac{1}{\\sigma^2} =& \\sum_{i=1}^I \\frac{N_i}{\\sigma_i^2} = \\left(\\frac{D_0}{\\sigma_0}\\right)^2 \\sum_{i=1}^I\\frac{N_i}{D_i^2}\\\\ \\nonumber =& \\left(\\frac{D_0}{\\sigma_0}\\right)^2 \\int_0^{D^\\mathrm{max}} \\dfrac{4\\pi D^2 n}{D^2}\\mathrm{d} D = \\left(\\frac{D_0}{\\sigma_0}\\right)^2 \\dfrac{3N}{(D^\\mathrm{max})^2},\n\\end{align}\nwhere $N_i$ is the number of sources within the $i-$th shell (s.t.\n$N:=\\sum N_i$), and $n$ is the number density of sources in volume.\nThe root-mean-square (RMS) averaged measurement error from $N$ sources is \nthen~\\cite{Markakis:2010mp}\n\\begin{equation}\\label{eq:rmsSigmaIdenticalSources}\n \\sigma_{avg} := \\frac{1}{\\sqrt{1\/\\sigma^{2}}} = \\frac{\\sigma_0}{D_0} D^\\mathrm{max} \\frac{1}{\\sqrt{3 N}},\n\\end{equation}\ngiven a fiducial pair $(\\sigma_0, D_0)$. It is straightforward to deduce from\nEq.~\\ref{eq:rmsSigmaIdenticalSources} that measurement uncertainty scales as \n$1\/\\sqrt{N}$, and the uncertainty afforded by a single observation with a high\nSNR $\\rho_c$ can be attained with $N = \\rho_c^2\/300$ realistic observations\nthat have $\\rho\\geq\\rho_\\mathrm{min}$. E.g., to get to the\nlevel of certainty afforded by a single observation with $\\rho=70$, we would\nneed $49\/3\\approx 16-17$ realistic (low SNR) detections.\n\nWhile we discussed Eq.~\\ref{eq:rmsSigmaIdenticalSources} for a population\nof optimally oriented sources, it is valid for a more general population\ndistributed uniformly in effective volume~\\cite{Markakis:2010mp}\n($\\propto D^3$).\nHowever, it \nstill only applies to sources with identical masses and spins, and we \novercome this limitation by performing a fully Bayesian analysis next.\n\n\n\\textbf{Astrophysical source population: }\\label{s2:astro_multiple}\nImagine that we have $N$ stretches of data, $d_1, d_2, \\cdots, d_N$, each \ncontaining a single signal emitted by an NSBH binary. Each of these signals can\nbe characterized by the non-tidal source parameters\n$\\vec{\\theta} := \\{m_\\mathrm{BH}, m_\\mathrm{NS}, \\chi_\\mathrm{BH}, \\chi_\\mathrm{NS}, \\vec{\\alpha}\\}$,\nand $\\{\\Lambda_\\mathrm{NS}\\}$, where $\\vec{\\alpha}$ contains extrinsic parameters,\nsuch as source distance, inclination, and sky location angles.\nAs before, let $H$ denote all of our collective prior knowledge; for instance,\n$H$ includes our assumption that all NSs in a single population have the same\ndeformability parameter $\\Lambda_\\mathrm{NS}$, and that its cumulative measurement is\ntherefore possible.\nThe probability distribution for $\\Lambda_\\mathrm{NS}$, given $N$ unique and\nindependent events, is\n\\begin{eqnarray}\n p(\\Lambda_\\mathrm{NS} |&& \\hspace{-4mm}d_1, d_2, \\cdots, d_N, H)\\hspace{50mm}\\nonumber\\\\ &=& \\dfrac{p(d_1,d_2,\\cdots,d_N |\\Lambda_\\mathrm{NS} , H)\\,p(\\Lambda_\\mathrm{NS}|H)}{\\int p(\\Lambda_\\mathrm{NS} |H) p(d_1,d_2,\\cdots,d_N |\\Lambda_\\mathrm{NS} , H)\\mathrm{d}\\Lambda_\\mathrm{NS}},\\label{eq:p11}\\\\\n &=& \\dfrac{p(\\Lambda_\\mathrm{NS}|H) \\prod_{i} p(d_i|\\Lambda_\\mathrm{NS}, H)}{\\int p(\\Lambda_\\mathrm{NS} ) p(d_1,d_2,\\cdots,d_N |\\Lambda_\\mathrm{NS} , H)\\mathrm{d}\\Lambda_\\mathrm{NS}},\\label{eq:p12} \\\\\n &=& \\dfrac{p(\\Lambda_\\mathrm{NS}|H) \\prod_{i} \\left( p(\\Lambda_\\mathrm{NS} |d_i, H)\\dfrac{p(d_i)}{p(\\Lambda_\\mathrm{NS}|H)} \\right)}{\\int\\, p(\\Lambda_\\mathrm{NS}|H )\\, p(d_1,d_2,\\cdots,d_N |\\Lambda_\\mathrm{NS} , H)\\mathrm{d}\\Lambda_\\mathrm{NS}}\\label{eq:p13};\n\\end{eqnarray}\nwhere Eq.~\\ref{eq:p11} and Eq.~\\ref{eq:p13} are application of Bayes' theorem,\nwhile Eq.~\\ref{eq:p12} comes from the mutual independence of all events.\nAssuming in addition that all events are {\\it equally likely}: \n$p(d_i) = p(d_j) = p(d)$, we get\n\\begin{eqnarray}\n p(&&\\hspace{-4mm}\\Lambda_\\mathrm{NS} | d_1, d_2, \\cdots, d_N, H)\\hspace{50mm}\\nonumber\\\\\n &=& p(\\Lambda_\\mathrm{NS})^{1-N}\\times \\dfrac{p(d)^N}{\\int p(\\Lambda_\\mathrm{NS}) p(d_1, d_2, \\cdots, d_N |\\Lambda_\\mathrm{NS}, H)\\mathrm{d}\\Lambda_\\mathrm{NS}}\\nonumber\\\\ &&\\hspace{3mm}\\times\\prod_i p(\\Lambda_\\mathrm{NS} |d_i, H)\\label{eq:p22},\n\\end{eqnarray}\nwhere the prior probability $p(\\Lambda_\\mathrm{NS}|H)$ is written $p(\\Lambda_\\mathrm{NS})$ for\nbrevity. {\\it A priori}, we assume that no particular value of $\\Lambda_\\mathrm{NS}$ is\npreferred over another within the range $[0, 4000]$, i.e.\n\\begin{equation}\\label{eq:lprior}\n p(\\Lambda_\\mathrm{NS} | H) = \\dfrac{1}{4000}\\,\\mathrm{Rect}\\left(\\frac{\\Lambda_\\mathrm{NS}-2000}{4000}\\right).\n\\end{equation}\nWith a uniform prior, the first two factors in Eq.~\\ref{eq:p22} can be\nabsorbed into a normalization factor $\\mathcal{N}$, simplifying it to\n\\begin{equation}\\label{eq:lambdaMultiple}\n p(\\Lambda_\\mathrm{NS} | d_1, d_2, \\cdots, d_N; H) = \\mathcal{N}\\prod_{i=1}^N p(\\Lambda_\\mathrm{NS} | d_i, H).\n\\end{equation}\n\nIn the second set of terms in Eq.~\\ref{eq:lambdaMultiple} (of the form \n$p(\\Lambda_\\mathrm{NS} | d_i, H)$), each is the probability distribution for $\\Lambda_\\mathrm{NS}$\ninferred {\\it a posteriori} from the \\textit{i}-th observation by marginalizing\n\\begin{equation}\\label{eq:margpost}\n p(\\Lambda_\\mathrm{NS} | d_i, H) = \\int\\, p(\\vec{\\theta}, \\Lambda_\\mathrm{NS} | d_i, H)\\, \\mathrm{d} \\vec{\\theta},\n\\end{equation}\nwhere $p(\\vec{\\theta}, \\Lambda_\\mathrm{NS} | d_i, H)$ is the inferred joint probability \ndistribution of all source parameters $\\vec{\\theta}\\cup\\{\\Lambda_\\mathrm{NS}\\}$ for the \n$i$-th event, as given by Eq.~\\ref{eq:postprob}. We note that \nFig.~\\ref{fig:SingleSystemLambdaPDFvsSNR} illustrates $p(\\Lambda_\\mathrm{NS} | d_i, H)$\nfor three individual events. By substituting\nEq.~\\ref{eq:lprior}-\\ref{eq:margpost} into Eq.~\\ref{eq:lambdaMultiple}, we\ncalculate the probability distribution for $\\Lambda_\\mathrm{NS}$ as measured using $N$\nindependent events.\n\n\n\n\nOur goal is to understand the improvement in our measurement of $\\Lambda_\\mathrm{NS}$\nwith the number of recorded events. To do so, we simulate a population~\\footnote{%\nA population here is an ordered set of events, and an event itself is the \nset of parameters describing one astrophysical NSBH binary.}\nof $N$ events, and quantify what we learn from each successive observation \nusing Eq.~\\ref{eq:lambdaMultiple}. This allows us to quantify how\nrapidly our median estimate for $\\Lambda_\\mathrm{NS}$ converges to the true value,\nand how rapidly our credible intervals for the same shrink, with increasing\n$N$. Finally, we generate and analyze an ensemble of populations in order to\naverage over the stochastic process of population generation itself.\n\n\nIn order to generate each population, the first step is to fix\nthe NS properties: (i) NS mass $m_\\mathrm{NS}=1.35M_\\odot$, (ii) NS spin $\\chi_\\mathrm{NS}=0$\nand (iii) NS tidal deformability $\\Lambda_\\mathrm{NS}=$ fixed value chosen from\n$\\{500,800,1000,1500,2000\\}$. Next, we generate events, by sampling BH mass\n(uniformly) from $m_\\mathrm{BH}\\in[3M_\\odot,6.75M_\\odot]$, BH spin (uniformly)\nfrom $\\chi_\\mathrm{BH}\\in[0, 1]$, orbital inclination from $\\iota\\in[0,\\pi]$, and \nsource location uniform in spatial volume\\footnote{with a minimum SNR \n$\\rho_\\mathrm{min}=10$}.\nWe restrict ourselves to positive aligned BH spins, since binaries with\nanti-aligned spins have very little information to add at realistic SNRs,\nas demonstrated in Fig.~\\ref{fig:TT_LambdaCIWidths90_0_Lambda_SNR}. This is\nto be taken into account when the number of observations is related to detector\noperation time. \nWe repeat this process till we have an ordered set of $N$ events.\nSince we want to analyze not just a single realization of an astrophysical\npopulation, but an ensemble of them, we make an additional approximation to\nmitigate computational cost. Complete Bayesian parameter estimation is\nperformed for a set of simulated signals whose parameters are the vertices\nof a regular hyper-cubic grid (henceforth ``G'') in the space of\n$\\{q\\}\\times\\{\\chi_\\mathrm{BH}\\}\\times\\{\\rho\\}$, with each sampled at $q=\\{2,3,4,5\\}$,\n$\\chi_\\mathrm{BH}=\\{-0.5,0,0.5,0.75\\}$, and $\\rho=\\{10,20,30,50,70\\}$.\nAll events in each population draw are substituted by their respective nearest\nneighbours on the grid G.\nOur chosen signal parameter distribution is different from some other studies\nin literature, which often sample from more astrophysically motivated population\ndistribution functions~\\cite{Mandel:2009pc}. We chose one that is sufficiently\nagnostic in absence of actual known NSBHs, and pragmatic enough for generating\npopulation ensembles.\n\n\n\n\nIn Fig.~\\ref{fig:TT_Lambda_vs_N_L800_CI90_0} we show illustrative results\nfor a single population with neutron star deformability $\\Lambda_\\mathrm{NS}=800$.\nIn the top panel, each curve shows the \nprobability distributions for $\\Lambda_\\mathrm{NS}$ as inferred from $N$ events, with $N$\nranging from $1-80$. We also mark the $90\\%$ credible intervals associated\nwith each of the probability distribution curves. The first few observations\ndo not have enough information to bound $\\Lambda_\\mathrm{NS}$ much more than\nour prior from Eq.~\\ref{eq:lprior} does. \nIn the bottom panel, we present information derived from the top panel.\nThe line-circle curve shows the measured median value from $N$ observations.\nThe pair of dashed (dotted) horizontal lines mark\n$\\pm25\\%$ ($\\pm50\\%$) error bars. At each $N$, the range spanned by the \nfilled region is the $90\\%$ credible interval deduced from the same \nevents. This figure somewhat quantifies the qualitative deductions we made\nfrom the left panel. We find that the median does track the true value quickly,\nreaching within its $10\\%$ with $10-15$ observations. This is as one expects of \ninjections in zero noise where random fluctuations are unable to shift the\nmedian away from the true value, so long as the measurement is not restricted\nby the prior. With the same information, our credible intervals also shrink to $\\pm 25\\%$.\nIn Fig.~\\ref{fig:TT_Lambda_vs_N_CI90_0} we show further results from four\nindependent populations for $\\Lambda_\\mathrm{NS}=\\{500,1000,1500,2000\\}$. As in the \nright panel of Fig.~\\ref{fig:TT_Lambda_vs_N_L800_CI90_0}, the line-circle curves\ntrack the median $\\Lambda_\\mathrm{NS}$, while the filled regions show\nthe associated $90\\%$ credible intervals. From the figure, we observe\nthe following: (i) the shrinkage of credible interval widths with increasing\n$N$ happens in a similar manner for each $\\Lambda_\\mathrm{NS}$, \nand (ii) it takes\napproximately $20$ events to distinguish definitively (with $90\\%$ credibility)\nbetween deformable NSs with $\\Lambda_\\mathrm{NS}=2000$ and compact NSs with \n$\\Lambda_\\mathrm{NS}=500$, or equivalently to distinguish between hard, moderate and soft\nnuclear equations of state. This is comparable to what has been found for\nbinary neutron stars~\\cite{DelPozzo:13,Chatziioannou:2015uea,Agathos:2015a}.\n\n\n\nSo far we have discussed individual realizations of NSBH populations. The \nunderlying stochasticity of the population generation process makes it\ndifficult to draw generalized inferences (from a single realization of an\nNSBH population) about the measurability of $\\Lambda_\\mathrm{NS}$. In order to mitigate\nthis, we discuss ensembles of population draws next. In\nFig.~\\ref{fig:TT_LambdaMedian_vs_N_AllInOne} we show the median\n$\\Lambda_\\mathrm{NS}$ as a function of the number of observed\nevents, for four population ensembles, with a hundred population draws\nin each ensemble. Lets focus on the {\\it top left} panel first. In it, we\nshow the median $\\Lambda_\\mathrm{NS}$ for all populations in four ensembles,\nwith true $\\Lambda_\\mathrm{NS}=\\{2000,1500,1000,500\\}$ from top to bottom.\nPopulations highlighted in color are simply those that we discussed in\nFig.~\\ref{fig:TT_Lambda_vs_N_CI90_0}. Dash-dotted horizontal lines\ndemarcate $\\pm10\\%$ error intervals around the true $\\Lambda_\\mathrm{NS}$ values.\nThe panel just below it shows the range of $\\Lambda_\\mathrm{NS}$ that encloses the\nmedian $\\Lambda_\\mathrm{NS}$ for $90\\%$ of the populations in {\\it each}\nensemble. In other words, this panel shows the range of $\\Lambda_\\mathrm{NS}$ within which\nthe median $\\Lambda_\\mathrm{NS}$ value for $90\\%$ of NSBH populations is\nexpected to lie. From these panels, we observe that our median $\\Lambda_\\mathrm{NS}$\nvalues will be within $10\\%$ of the {\\it true} value after $\\sim 25$\ndetections of less deformable neutron stars ($\\Lambda_\\mathrm{NS}\\leq 1000$), or\nafter as few as $15$ detections of more deformable neutron stars\n($\\Lambda_\\mathrm{NS}\\geq 1500$). This is not surprising because we inject simulated\nsignals in {\\it zero} noise, which ensures that the median not be shifted away\nfrom the true value. That it takes $15+$ events for the median to approach\nthe true value is a manifestation of the fact that the measurement is limited\nby the prior on $\\Lambda_\\mathrm{NS}$ when we have fewer than $15$ events.\nThe results discussed in Fig.~\\ref{fig:TT_Lambda_vs_N_L800_CI90_0},\n\\ref{fig:TT_Lambda_vs_N_CI90_0} and the left two panels\nof Fig.~\\ref{fig:TT_LambdaMedian_vs_N_AllInOne} apply to the parameter\ndistribution spanned by the grid G. This distribution allows for $m_\\mathrm{BH}$\nas low as $2.7M_\\odot$ (i.e. $q=2$).\nGiven that disruptive signatures are strongest for small $m_\\mathrm{BH}$, we now\ninvestigate an alternate paradigm in which no black hole masses fall within\nthe mass gap $2-5M_\\odot$ suggested by astronomical\nobservations~\\cite{Bailyn:1997xt,Kalogera:1996ci,Kreidberg:2012,\nLittenberg:2015tpa}. We will henceforth denote our standard paradigm, which\ndoes not respect the mass-gap, as paradigm A; with paradigm B being\nthis alternate scenario.\nBoth right panels of the figure are identical to their corresponding left\npanels, but drawn under population paradigm B. Under this paradigm, we\nexpectedly find that information accumulation is much slower. It would\ntake $25-40$ detections with $\\rho\\geq10$ under this paradigm, for our median\n$\\Lambda_\\mathrm{NS}$ to converge within $10\\%$ of its true value.\n\n\nFinally, we investigate the statistical uncertainties associated with\n$\\Lambda_\\mathrm{NS}$ measurements. We use $90\\%$ credible intervals as our measure of\nthe same. First, we draw an ensemble of a hundred populations each for\n$\\Lambda_\\mathrm{NS}=\\{500,1000,1500,2000\\}$. For each population $i$ in each ensemble,\nwe construct\nits $90\\%$ credible interval $[{\\Lambda_\\mathrm{NS}^{90\\%}}_{i-},{\\Lambda_\\mathrm{NS}^{90\\%}}_{i+}]$.\nNext, we construct the interval $[X^-,Y^-]$ that contains ${\\Lambda_\\mathrm{NS}^{90\\%}}_{i-}$\nfor $90\\%$ of the populations in each ensemble; and similarly $[X^+,Y^+]$\nfor ${\\Lambda_\\mathrm{NS}^{90\\%}}_{i+}$. Finally, in the left panel of \nFig.~\\ref{fig:TT_LambdaError_vs_N_L500_2000_CI90_0_AllInOne}, we show the\nconservative width $|Y^+ - X^-|$ that contains the $90\\%$ credible\nintervals for $90\\%$ of all populations in each ensemble~\\footnote{Drawn\nunder paradigm A (mass-gap not respected).}.\nFrom top to bottom, the population $\\Lambda_\\mathrm{NS}$\ndecreases from $\\Lambda_\\mathrm{NS}=2000\\rightarrow 500$, corresponding to decreasingly\ndeformable NSs with softer equations of state. We observe the following:\n(i) for moderately-hard to hard equations of state with $\\Lambda_\\mathrm{NS}\\geq 1000$,\nwe can constrain $\\Lambda_\\mathrm{NS}$ within $\\pm 50\\%$ using only $10-20$ events, and\nwithin $\\pm 25\\%$ (marked by black circles) with $25-40$ events; (ii) for softer \nequations of state with\n$\\Lambda_\\mathrm{NS}<1000$, we will achieve the same accuracy with $20-30$ and $50+$ \nevents, respectively; and (iii) for the first $5$ or so observations, our\nmeasurement spans the entire prior allowed range:\n$\\Lambda_\\mathrm{NS}\\in[0,4000]$, as shown by the plateauing of the $90\\%$ \ncredible intervals towards the left edge to $90\\%$ of $4000$, i.e. $3600$.\nThe right panel in Fig.~\\ref{fig:TT_LambdaError_vs_N_L500_2000_CI90_0_AllInOne}\nis identical to the left one, with the difference that populations are drawn\nunder paradigm B, which does {\\it not} allow for BH masses to fall within\nthe mass-gap. We find that\nfor NSs with $\\Lambda_\\mathrm{NS}\\leq 1000$, it would take $25-40$ events to\nconstrain $\\Lambda_\\mathrm{NS}$ within $\\pm 50\\%$ and $50+$ events to constrain it\nwithin $\\pm 25\\%$. This is somewhat slower than paradigm A, as is to be\nexpected since here we preclude the lowest mass-ratios, which correspond to\nsignals with largest tidal signatures. For $\\Lambda_\\mathrm{NS}>1000$ we find that we\ncan constrain $\\Lambda_\\mathrm{NS}$ within $\\pm 50\\%$ with a similar number of events as\nfor paradigm A, but will need more ($30-40$, as compared to $25-40$) events\nto further constrain it to within $\\pm 25\\%$ of the true value.\nUnder either paradigms, we find that measuring $\\Lambda_\\mathrm{NS}$ better than\n$25\\%$ will require $\\mathcal{O}(10^2)$ observations of disruptive NSBH\nmergers.\nThese results demonstrate that our probed set of disruptive NSBH mergers is as\ninformative of NS tidal properties as are BNS populations, if we assume a uniform mass\ndistribution for NSs and zero NS spins, and possibly more if NS masses are\nnot distributed uniformly~\\cite{Agathos:2015a}.\nHowever, being a subset of all NSBH binaries, the accumulation of information\nfrom NSBH signals in general may be slower than from binary neutron stars,\ndepending on the distribution of BH masses in coalescing NSBH binaries.\n\n\n{\\it Do all events matter?} In Ref.~\\cite{Lackey2014} the authors demonstrate\nthat the overwhelming majority of information about the NS equation of state\ncomes from the loudest $\\sim 5$ events in the case of binary neutron star\ndetections, and not from the combined effect of a large number of low-SNR\nsystems.\nThe question naturally arises if the same is true for NSBH sources as well.\nTherefore, in Fig.~\\ref{fig:TT_LambdaError_vs_N_L500_2000_CI90_0_AllInOne_SNRSorted}\nwe re-evaluate the accumulation of information with each successive NSBH \ndetection, sorting the events in each population by their SNR instead of \nsimulated chronology. We find the same qualitative behavior as in the case of \nBNSs~\\cite{Lackey2014}. \nWhether or not there is an astrophysical mass gap, we find\nthat the bulk of tidal information will be furnished by the loudest $5-10$\nNSBH detections of aLIGO detectors. {\\it This result is especially encouraging\nto NR follow-up efforts for GW detections, as we now know that only a handful\nof loudest NSBH events (with SNRs $\\rho\\gtrsim 20-30$) are the ones that may merit\nfull numerical-GR + magnetohydrodynamical follow-up simulations.}\n\n\n\nTo summarize, in this section we study the improvement in our measurement of \nNS deformability parameter $\\Lambda_\\mathrm{NS}$ with an increasing number of events. We\ndo so by simulating plausible populations of disrupting NSBH binaries (with\n$\\rho\\geq 10$). We find that:\n(i) for more deformable neutron stars (harder equation of states), the median\nvalue of $\\Lambda_\\mathrm{NS}$ comes within $10\\%$ of the true value with as \nfew as $10$ events, while achieving the same accuracy for softer equations of \nstate will take $15-20$ source detections; (ii) the statistical uncertainty\nassociated with $\\Lambda_\\mathrm{NS}$ measurement shrinks to within $\\pm50\\%$ with\n$10-20$ events, and to within $\\pm 25\\%$ with $50+$ events, when source \n$\\Lambda_\\mathrm{NS}\\geq 1000$; (iii) for softer equations of state, the same could take\n$25-40$ and $50+$ events, respectively for the two uncertainty thresholds;\nand (iv) if BHs really do observe the astrophysical mass-gap, the information\naccumulation is somewhat slower than if they do not. We conclude that within\n$20-30$ observations, aLIGO would begin to place very interesting bounds on \nthe NS deformability, which would allow us to rule out or rank different\nequations of state for neutron star matter. Within this population, we also\nfind that it will be the loudest $5-10$ events that will furnish most of the\ntidal information. Our key findings are summarized in \nFig.~\\ref{fig:TT_LambdaMedian_vs_N_AllInOne} -\n\\ref{fig:TT_LambdaError_vs_N_L500_2000_CI90_0_AllInOne_SNRSorted}.\n\n\n\n\n\n\\section{Discussion}\\label{s1:discussion}\n\n\nThe pioneering observation of gravitational waves by Advanced LIGO\nharbingers the dawn of an era of gravitational-wave astronomy where observations\nwould finally drive scientific discovery~\\cite{Abbott:2016blz}. As confirmed by\nthe first observations~\\cite{Abbott:2016blz,Abbott:2016nmj,Abbott:2016nhf},\nstellar-mass compact binary mergers emit GWs right in the sensitive frequency\nband of the LIGO observatories, and are their primary targets.\nNeutron star black hole binaries form a physically distinct sub-class of\ncompact binaries. We expect to detect the first of them in the upcoming\nobserving runs~\\cite{Abbott:2016ymx}, and subsequently at a healthy rate of\n$0.2-300$ mergers a year when aLIGO detectors reach design\nsensitivity~\\cite{Abadie:2010cf}.\n\nNSBH binaries are interesting for various reasons. Unlike BBHs, the presence of\nmatter allows for richer phenomena to occur alongside the strong-field\ngravitational dynamics. The quadrupolar moment of the NS changes during the\ncourse of inspiral, which increases the inspiral rate of the binary and alters the\nform of the emitted gravitational waves. Close to merger, under restricted but\nplausible conditions, the neutron star is disrupted by the tidal field of its \ncompanion black hole and forms an accretion disk around it. This disruption\nreduces the quadrupolar moment of the system, and decreases the amplitude of\nthe emitted GWs from the time of disruption through to the end of ringdown.\nBoth of these phenomena are discernible in their gravitational-wave signatures\nalone. In addition, if the neutron star matter is magnetized, the magnetic\nwinding above the remnant black hole poles can build up magnetic fields\nsufficiently to power short gamma-ray bursts (SGRB)~\\cite{Foucart:2015a,\nLovelace:2013vma,Deaton2013,Foucart2012,Shibata:2005mz,Paschalidis2014}.\nTherefore a coincident observation of gravitational waves from an NSBH merger\nand a SGRB can potentially confirm the hypothesis that the former is a\nprogenitor of the latter~\\cite{eichler:89,1992ApJ...395L..83N,moch:93,\nBarthelmy:2005bx,2005Natur.437..845F,2005Natur.437..851G,Shibata:2005mz,\nTanvir:2013,Paschalidis2014}.\n\n\nIn this paper we study the observability of tidal signatures in the\ngravitational-wave spectrum of NSBH binaries. More specifically, we investigate\nthree questions. First, what is the effect of not including tidal effects in \ntemplates while characterizing NSBH signals? Second, if we do include tidal \neffects, how well can we measure the tidal deformability of the NS\n(parameterized by $\\Lambda_\\mathrm{NS}$) from individual NSBH signals? And third, as we\nobserve more and more signals, how does our knowledge of $\\Lambda_\\mathrm{NS}$ improve?\nIn the following, we summarize our main findings.\n\n\n\n\n\nFirst, we study the effects of not including tidal terms in our search\ntemplates while characterizing NSBH signals. We expect that the waveform\ntemplate that best fits the signal would compensate for the reduced number of\ndegrees of freedom in the template model by moving away from the true\nparameters of the binary. This should result in a {\\it systematic} bias in \nthe recovered values of non-tidal source parameters, such as its masses \nand spins. In order to quantify it, we inject tidal signals into zero noise,\nand perform a Bayesian parameter estimation analysis on them using templates\n{\\it without} tidal terms.\nWe use the LEA+ model (c.f. Sec.~\\ref{s2:waveforms}) to produce tidal waveforms\nthat incorporate the effect\nof NS distortion during inspiral, and of its disruption close to merger. Our\ninjected signals sample the region of NSBH parameter space where NS disruption\nprior to binary merger is likely {\\it and} can be modeled using LEA+. Their\nparameters are given by combinations of $q=m_\\mathrm{BH}\/m_\\mathrm{NS}=\\{2,3,4,5\\},\n\\chi_\\mathrm{BH}=\\{-0.5, 0, 0.5, 0.75\\}$ and $\\Lambda_\\mathrm{NS}=\\{500,800,1000,1500,2000\\}$.\nOther parameters, such as source location and orientation, that factor out of\n$h(t)$ as amplitude scaling are co-sampled by varying $\\rho=\\{20,30,50,70\\}$.\n\n\n\nAt low to moderate SNRs ($\\rho\\lesssim 30$), we find that using BBH templates\ndoes not significantly hamper our estimation of non-tidal parameters for NSBH\nsignals. In the worst case, when the BH mass is within the astrophysical \nmass-gap~\\cite{Bailyn:1997xt,Kalogera:1996ci,Kreidberg:2012,Littenberg:2015tpa}\nand its spin is positive aligned, the systematic biases in $\\eta$ and $\\chi_\\mathrm{BH}$\nmeasurements do become somewhat comparable to statistical errors (ratio\n$\\sim 0.5-0.8$) under very restrictive conditions~\\footnote{requiring a\ncompanion BH with mass $m_\\mathrm{BH}\\lesssim 4.5M_\\odot$ (i.e. in the astrophysical\nmass-gap), and the\nhardest NS EoS considered (with $\\Lambda_\\mathrm{NS}\\simeq 2000$).}, but never exceed them.\nAt high SNRs ($\\rho\\gtrsim 50$), systematic biases in $\\mathcal{M}_c$ become larger\nthan the statistical uncertainties. For $\\eta$ and $\\chi_\\mathrm{BH}$ the difference\nis more drastic with the systematics reaching up to $4\\times$ the statistical\nerrors. We therefore conclude that $\\rho\\simeq 30-50$ is loud enough to\nmotivate the use of tidal templates for even the estimation of non-tidal\nparameters from NSBH signals.\nWe also conclude that low-latency parameter estimation algorithms, designed to\nclassify GW signals into electromagnetically active (NSBH and NSNS) and\ninactive (BBH) sources, can use BBH templates to trigger GRB \nalerts~\\cite{2012A&A...541A.155A,Singer:2014qca,Singer:2015ema,Pankow:2015cra,\nAbbott:2016wya,Abbott:2016gcq} for NSBH signals with low to moderate SNRs\n($\\rho\\lesssim 30$).\nThis is so because the primary requirement of identifying NS-X binaries (X =\n\\{NS, BH\\}) can be achieved just as easily with BBH templates, on the basis of\nthe smaller component's mass\\footnote{The smaller component mass is unlikely\nto be significantly biased by missing tidal effects in filter templates below\n$\\rho\\simeq 30$, as we show above.}.\nWe also speculate that NSBH detection searches are unlikely to be\naffected by the choice of ignoring tidal effects in matched-filtering\ntemplates, if these effects are too subtle to manifest in parameter estimation\nbelow $\\rho\\simeq 30$.\n\n\n\n\n\nSecond, we turn the question around to ask: can we measure the tidal effects if\nour template models did account for them? Tidal effects in our waveform model\nare parameterized using a single deformability parameter \n$\\Lambda_\\mathrm{NS}\\propto (R\/M)_\\mathrm{NS}^5$. In order to quantify the \nmeasurability of $\\Lambda_\\mathrm{NS}$, we inject the same tidal signals as before, and\nthis time perform a Bayesian analysis on them using {\\it tidal} templates. \nThe results are detailed in Sec.~\\ref{s1:PEwithNS}.\nAt low SNRs ($\\rho\\simeq 20$), we find that the best we can do is to constrain\n$\\Lambda_\\mathrm{NS}$ within $\\pm 75\\%$ of its true value at $90\\%$ credible level. This\ntoo only if the BH is spinning sufficiently rapidly, with $\\chi_\\mathrm{BH}\\gtrsim +0.7$,\nand the NS has $\\Lambda_\\mathrm{NS}\\gtrsim 1000$. At moderate SNRs ($\\rho\\simeq 30$), we\ncan constrain $\\Lambda_\\mathrm{NS}$ a little better, i.e. within $\\pm 50\\%$ of its true\nvalue. This level of accuracy, however, again requires that BH spin\n$\\chi_\\mathrm{BH}\\gtrsim+0.7$ and $\\Lambda_\\mathrm{NS}\\gtrsim 1000$. Binaries with smaller BH spins\nand\/or softer NS EoSs will furnish worse than $\\pm 75\\%-\\pm 100\\%$ errors for\n$\\Lambda_\\mathrm{NS}$. This trend continues as we increase the SNR from $\\rho=30-50$. It\nis not before we reach an SNRs as high as $\\rho\\simeq 70$ that we can shrink\n$\\Lambda_\\mathrm{NS}$ errors substantially with a single observation (i.e. within\n$\\pm 25\\%$ of its true value).\nIn summary, we find that with a single but moderately loud NSBH signal,\nAdvanced LIGO can begin to put a factor of $1-2\\times$ constraints on NS tidal\ndeformability parameter. These constraints can subsequently be used to assess\nthe likelihood of various candidate equations of state for nuclear matter, and\npossibly to narrow the range they span.\n\n\n\n\n\nThird, knowing that single observations can furnish only so much information\nabout the NS equation of state, we move on to investigate how well we do with\nmultiple signals. In order to quantify how $\\Lambda_\\mathrm{NS}$ measurement improves\nwith the number of observed events $N$, we generate populations of NSBH signals\nand combine the information extracted from each event.\nThe population generation procedure is as follows. The neutron star mass is\nheld fixed at $1.35M_\\odot$, its spin at $\\chi_\\mathrm{NS}=0$, and its tidal\ndeformability is fixed to each of $\\Lambda_\\mathrm{NS}=\\{500,1000,1500,2000\\}$. Black hole\nmass is sampled uniformly from the range $[2,5]\\times 1.35=[2.7, 6.75]M_\\odot$,\nand spin from $\\chi_\\mathrm{BH}\\in[0,1]$. As before, our parameter choice here is given\nby the intersection set of the mass range that allows for neutron star disruption\nand the range supported by LEA+~\\cite{Foucart2012,Foucart:2013a,Lackey:2013axa}.\nIn order to keep the computational cost reasonable, we make an additional\napproximation. For every population generated, we replace the parameters of each\nevent by their nearest neighbor on the uniform grid G, which has vertices\nat: $q=\\{2,3,4,5\\}\\times\\chi_\\mathrm{BH}=\\{-0.5,0,0.5,0.75\\}\\times\\Lambda_\\mathrm{NS}=\\{500,800,\n1000,1500,2000\\}\\times\\rho=\\{10,20,30,50,70\\}$.\nThis way, we only have to run full Bayesian parameter estimation analysis on\nthis fixed set of signals. \nThere are two sources of error that enter the deductions we make from\na single population generated in the manner described above. First, since the\ninjection parameters are pushed to their nearest neighbor on a grid, we\nfind discrete jumps in $\\Lambda_\\mathrm{NS}$ errors as a function of $N$. And second, an\nindividual population is one particular realization of a stochastic process and\ncould have excursions that may never be found in another population. To\naccount for both of these limitations, we generate an ensemble of populations,\nand conservatively combine information from all of them\\footnote{See \nSec.~\\ref{s1:multiple_observations} for further details.}.\n\n\n\n\nWe probe two astrophysical paradigms, one that allows for BH masses to lie\nwithin the astrophysical mass-gap (paradigm A), and one that does not (paradigm\nB).\n{\\it For paradigm A}, we find the following: (i) for the softer equations of\nstate that result in less deformable neutron stars, $15-20$ detections bring\nthe measured probability distribution for $\\Lambda_\\mathrm{NS}$ entirely within the prior,\nwhich ensures that the median $\\Lambda_\\mathrm{NS}$ tracks the true value to within $10\\%$.\n(ii) For NSBH populations with more deformable NSs ($\\Lambda_\\mathrm{NS}> 1000$),\nthe same is achievable within as few as $10$ (or $15$ at most) realistic\nobservations. (iii) The statistical uncertainty associated with $\\Lambda_\\mathrm{NS}$\nmeasurement can be restricted to be within $\\pm50\\%$ using $10-20$ observations\nwhen $\\Lambda_\\mathrm{NS}> 1000$), and using $25-40$ observations for softer equations\nof state. All of the above is possible within a few years of design\naLIGO operation~\\cite{Abadie:2010cfa}, if astrophysical BHs are allowed\nmasses $< 5M_\\odot$ (i.e. in the mass-gap). However, further\nrestricting $\\Lambda_\\mathrm{NS}$ will require $50+$ NSBH observations.\n{\\it For paradigm B}, we find the information accumulation to be somewhat slower.\nWhile the quantitative inferences for populations with $\\Lambda_\\mathrm{NS}>1000$ are\nnot affected significantly, we find that $\\Lambda_\\mathrm{NS}< 1000$ populations require \n$10-20\\%$ more events to attain the same measurement accuracy as under\nparadigm A. In either case, the accumulation of information from the general\nNSBH population will likely be slower than from BNS inspirals~\\cite{Mandel:2009pc,\nLackey2014,Wade:2014vqa,Agathos:2015a}, depending on the mass distribution of \nstellar-mass black holes. Though, template models for the latter may be more\nuncertain due to missing point-particle PN terms at orders comparable to\ntidal terms~\\cite{Lackey2014}.\nWe conclude that within as few as $20-30$ observations of disruptive NSBH\nmergers, aLIGO will begin to place interesting bounds on NS deformability.\nThis, amongst other things, will allow us to rank different equations of \nstate for neutron star matter from most to least likely, within a few years'\ndetector operation.\nWe also find that, within this population, the loudest $5-10$ events (with SNRs\n$\\rho\\gtrsim 20-30$) will provide us with most of the tidal information,\nand will therefore merit full NR follow-up.\nOur methods and results are detailed in Sec.~\\ref{s1:multiple_observations}.\n\n\n\n\n\n\n\n\n\nFinally, we note that the underlying numerical simulations used to calibrate\nthe waveform model used here have not been verified against\nindependent codes so far.\nIt is therefore difficult to assess the combined modeling error of LEA+ and its\neffect\non our results. Our results here are, therefore, limited by the limitations of\nour waveform model, and presented with this caveat. However, we do expect the\ncombined effect of modeling errors to {\\it not} affect our {\\it qualitative}\nconclusions, especially since the underlying point-particle component of LEA+\nincludes all high-order terms, unlike past BNS studies~\\cite{Lackey2014,\nWade:2014vqa}\nIn future, we plan to further the results presented here by using more recent\ntidal models~\\cite{Pannarale:2015jka,Hinderer:2016eia}, that\nmay improve upon LEA+\\footnote{One of them~\\cite{Pannarale:2015jka} is only an\namplitude model though, which has to be augmented with a compatible phase model\nfirst.}.\n\n\n\n\n\\begin{acknowledgments}\n We thank Benjamin Lackey, Francesco Pannarale, Francois Foucart, and Duncan Brown\n for helpful discussions. We gratefully acknowledge support\n for this research at CITA from NSERC of Canada, the Ontario Early \n Researcher Awards Program, the Canada Research\n Chairs Program, and the Canadian Institute for Advanced Research.\n Calculations were performed at the Vulcan\n supercomputer at the Albert Einstein Institute;\n H.P. and P.K. thank the Albert-Einstein Institute,\n Potsdam, for hospitality during part of the time where this research\n was completed. M.P. thanks CITA for hospitality where part of the work\n was carried out.\n\\end{acknowledgments}\n\n\\begin{appendix}\n\n\\section{Statistical uncertainty in measuring non-tidal parameters}\\label{as1:nontidalerrors}\n\\begin{figure*}\n\\centering \n\\includegraphics[trim = {2cm 0 0 0},width=2.\\columnwidth]{TNMchirpCIWidths90_0_Lambda_SNR}\\\\\n\\includegraphics[trim = {2cm 0 0 0},width=2.\\columnwidth]{TNEtaCIWidths90_0_Lambda_SNR}\\\\\n\\includegraphics[trim = {2cm 0 0 0},width=2.\\columnwidth]{TNChiBHCIWidths90_0_Lambda_SNR}\n\\caption{{\\bf Statistical measurement uncertainty for NSBH parameters,\nignoring tidal effects:}\nWe show here the statistical uncertainty associated with our measurement of\nnon-tidal parameters $\\mathcal{M}_c, \\eta,$ and $\\chi_\\mathrm{BH}$ (at $90\\%$ credibility),\nover the signal parameter space. Individual panels show the same as a function\nof BH mass and spin. Across each row, we see the effect of increasing signal\nstrength (i.e. SNR) with the tidal deformability of the NS $\\Lambda_\\mathrm{NS}$ fixed.\nDown each column, we see the effect of increasing $\\Lambda_\\mathrm{NS}$, at fixed SNR.\nTidal effects are ignored in templates.\n}\n\\label{fig:CIWidths90_Lambda_SNR}\n\\end{figure*}\nIn Fig.~\\ref{fig:CIWidths90_Lambda_SNR}, we show how {\\it precisely} can we\nmeasure non-tidal NSBH parameters $X=\\{\\mathcal{M}_c,\\eta,\\chi_\\mathrm{BH}\\}$ using BBH templates.\nThe three panels correspond to $\\mathcal{M}_c$ (top), $\\eta$ (middle), and $\\chi_\\mathrm{BH}$\n(bottom), and show the width of these credible intervals $(\\Delta X)^{90\\%}$\nas a function of BH mass\/spin (within each sub-panel), and NS properties, i.e.\n$\\Lambda_\\mathrm{NS}$ (downwards in each column)~\\footnote{We restrict NS mass to\n$1.35M_\\odot$ and its spin to zero. Varying its tidal deformability $\\Lambda_\\mathrm{NS}$\ndoes not significantly change the measurement uncertainties for non-tidal\nbinary parameters, as is evident from comparing the two rows in each panel of\nFig.~\\ref{fig:CIWidths90_Lambda_SNR}.}.\nFrom the left-most column, we find that: (i) at $\\rho=20$ the chirp mass is\nmeasured remarkably well - to a precision of $0.16\\%$ of its true value, and\n(ii) so is $\\chi_\\mathrm{BH}$. (iii) The dimensionless mass-ratio $\\eta$ is determined\nmore loosely, with $25+\\%$ uncertainty. If the signal is even louder\n($\\rho\\geq 30$), all three measurements gain further precision, especially\n$\\eta$, for which the relative errors shrink down to single-digit percents.\nWe remind ourselves that these results do not tell the full story since the\nprecision of a measurement is only meaningful if the measurement is accurate \nto begin with. In our case there are tidal effects that have not been\nincorporated into our search (BBH) templates, which can lead to a systematic\nbias in parameter recovery. We refer the reader to Sec.~\\ref{s1:PEwithnoNS} for\na comparative study of both systematic and statistical errors.\n\n\n\n\n\\section{Illustrations of Bayesian posteriors}\\label{as1:illustrations}\n\\begin{figure*}\n\\centering\n\\includegraphics[width=1.05\\columnwidth,trim=2cm 0 0 0]{AllParamsMcEtPDF1D2D_q4_mc2_25_chi0_50_snr20\n\\includegraphics[width=1.05\\columnwidth,trim=0.7cm 0 1cm 0]{AllParamsMcEtPDF1D2D_q4_mc2_25_chi0_50_snr50\n\\caption{{\\bf Illustrative posterior probability distributions for NSBH parameters,\nfor signals at different SNRs:}\nWe illustrate here two sets of two-dimensional joint probability distributions,\ndiffering only in signal strength, with $\\rho=20$ in the left panel, and\n$\\rho=50$ in the right. The injected parameters are \n$q = m_\\mathrm{BH}\/m_\\mathrm{NS} = 5.4M_\\odot\/1.35M_\\odot = 4$, $\\chi_\\mathrm{BH}=+0.5$, and \n$\\Lambda_\\mathrm{NS}=2000$. Contours are shown for $\\{1-,2-,3-,\\cdots\\}\\sigma$ confidence levels.\nTemplates include tidal effects, as evident in the bottom rows\nof both panels which show the correlation of $\\Lambda_\\mathrm{NS}$ with non-tidal \nparameters. Contrasting the two panels illustrates the effect of increasing the\nSNR on various parameter measurements.\n}\n\\label{fig:SingleSystemLambda2DPDFs}\n\\end{figure*}\n\n\n\n\n\n\nIn Fig.~\\ref{fig:SingleSystemLambda2DPDFs} we show the correlation of\nmass, spin, and tidal parameter measurements. We keep the binary parameters\nas in Fig.~\\ref{fig:SingleSystemLambdaPDFvsSNR}, with $\\Lambda_\\mathrm{NS}=2000$, and\nset $\\rho=20$ (left panel) or $\\rho=50$ (right panel).\nWe find that the measurement of $\\Lambda_\\mathrm{NS}$ is weakly degenerate with\nother parameters, and at realistic SNRs it would improve by a few tens of \npercent if we knew non-tidal parameters to better accuracy. The predominant\nfactor that would enhance the measurement accuracy for $\\Lambda_\\mathrm{NS}$ is \nnevertheless the signal strength. Only when $\\rho\\gtrsim 50$ can we\nexpect $\\Lambda_\\mathrm{NS}$ measurement to be limited by its degeneracy with \nnon-tidal parameters (at a factor of few level), as also reported by previous\nstudies~\\cite{Lackey:2013axa}.\n\n\n\n\n\n\n\n\\section{Phenomenology of $\\Lambda_\\mathrm{NS}$ measurement errors}\n\\begin{figure}\n\\centering \n\\includegraphics[width=1.05\\columnwidth]{PowerLawCoefficient_LambdaErrorvsN_vs_N.pdf}\n\\caption{%\nAssuming a power-law dependence of the measurement error on the number of\nevents: $\\delta\\Lambda_\\mathrm{NS}\\propto 1\/N^\\alpha$, we show $\\alpha$ in this figure\nas a function of the number of observed events $N$. Shown are five families\nof $100$ population draws each, with each family corresponding to one of\n$\\Lambda_\\mathrm{NS}=\\{500,800,1000,1500,2000\\}$. Each grey curve corresponds to one\nof these $100\\times5 = 500$ populations. The thicker curves, one from each\nfamily, shows the population we discussed in\nFig.~\\ref{fig:TT_Lambda_vs_N_L800_CI90_0}-\\ref{fig:TT_Lambda_vs_N_CI90_0}.\nWe find that a power-law is a good approximation for the concerned dependence,\nand information accumulates {\\it faster} than $1\/\\sqrt{N}$. We estimate\n$\\alpha\\simeq 0.7^{+0.2}_{-0.2}$.\n}\n\\label{fig:TT_PowerLawLambdaErrorVsN}\n\\end{figure}\n\\begin{figure}\n\\centering \n\\includegraphics[width=\\columnwidth]{PowerLawCoefficient_LambdaErrorvsLambda_vs_N_AllPopulations.pdf}\n\\caption{%\nIn this figure, which is similar to Fig.~\\ref{fig:TT_PowerLawLambdaErrorVsN},\nwe quantify the dependence of $\\delta\\Lambda_\\mathrm{NS}$ on $\\Lambda_\\mathrm{NS}$ itself. Of \nthe five families of simulated NSBH populations, we construct $100$\nindependent sets taking one population from each family. With each of \nthese $100$ sets, and assuming a power-law dependence:\n$\\delta\\Lambda_\\mathrm{NS}\\propto\\Lambda_\\mathrm{NS}^\\beta$, we estimate $\\beta$ and show it in\nthis figure as a function of the number of observed events $N$. The thicker\ncurve corresponds to the populations discussed in\nFig.~\\ref{fig:TT_Lambda_vs_N_CI90_0}.\nWe find that $\\beta$ can be estimated to lie within $[1\/6,5\/6]$ with a\nlikely value close to $1\/2$. Since $0<\\beta<1$, the relative error\n$\\delta\\Lambda_\\mathrm{NS}\/\\Lambda_\\mathrm{NS}$ {\\it decreases} as the star gets more \ndeformable, while the absolute error $\\delta\\Lambda_\\mathrm{NS}$ {\\it increases}.\n}\n\\label{fig:TT_PowerLawLambdaErrorVsLambda}\n\\end{figure}\nHere, we quantitatively explore the dependence of our statistical\nuncertainties for $\\Lambda_\\mathrm{NS}$ on the number of events, as well as on the true\nNS deformability itself. First, we will focus on the dependence on $N$. We\nassume a power-law dependence of the form\n$\\delta\\Lambda_\\mathrm{NS}\\propto\\ 1\/N^\\alpha$. For each of the $100$ populations \nfor each of $\\Lambda_\\mathrm{NS}=500-2000$, we compute the exponent $\\alpha$ as a\nfunction of the number of observed events $N$, and show it in \nFig.~\\ref{fig:TT_PowerLawLambdaErrorVsN}. There are $100\\times5=500$ curves\non the figure, with one highlighted for each value of population's $\\Lambda_\\mathrm{NS}$.\nThese highlighted values are only special in the sense that they correspond to\npopulations discussed earlier in this section (c.f.\nFig.~\\ref{fig:TT_Lambda_vs_N_L800_CI90_0}-\\ref{fig:TT_Lambda_vs_N_CI90_0}).\nWe immediately observe two things, (i) there is a globally similar dependence\non $N$ for all populations, and (ii) information accumulates {\\it faster} than\n$1\/\\sqrt{N}$. In fact, we find that if\n$\\delta\\Lambda_\\mathrm{NS}\\propto\\frac{1}{N^\\alpha}$, $\\alpha$ lines in the range\n$0.7_{-0.2}^{+0.2}$.\nNext, we focus on the dependence of $\\delta\\Lambda_\\mathrm{NS}$ on $\\Lambda_\\mathrm{NS}$ of the\npopulation itself. As suggested by Fisher-matrix studies~\\cite{Lackey:2013axa},\nand as for $N$, we assume the form $\\delta\\Lambda_\\mathrm{NS}\\propto\\Lambda_\\mathrm{NS}^\\beta$.\nFrom each set of $100$ populations with a given $\\Lambda_\\mathrm{NS}$ value, we draw one\nat random, and form a set of $5$ similarly drawn populations, one for each of\n$\\Lambda_\\mathrm{NS}=\\{500,800,1000,1500,2000\\}$. With each set, we determine $\\beta$\nfor different number of observed events $N$. In all, we make $100$ independent\n$5-$population sets and show the value of $\\beta$ measured from each in \nFig.~\\ref{fig:TT_PowerLawLambdaErrorVsLambda}. We find that the assumed\nrelation $\\delta\\Lambda_\\mathrm{NS}\\propto\\Lambda_\\mathrm{NS}^\\beta$ gets fairly robust for \nlarger values of $N$, with $\\beta$ converging to $\\beta=0.5^{+0.33}_{-0.33}$.\nThe fact that $0<\\beta<1$ implies that the relative error\n$\\delta\\Lambda_\\mathrm{NS}\/\\Lambda_\\mathrm{NS}$ {\\it decreases} with increasing $\\Lambda_\\mathrm{NS}$, while\nthe absolute error {\\it increases}.\nFrom these results, we conclude that the measurement uncertainty for\n$\\Lambda_\\mathrm{NS}$ after $N$ observations is\n\\begin{equation}\n \\delta\\Lambda_\\mathrm{NS}\\propto \\dfrac{\\Lambda_\\mathrm{NS}^{0.5^{+0.33}_{-0.33}}}{N^{0.7_{-0.2}^{+0.2}}}.\n\\end{equation}\nWe also find that while these results are inferred from paradigm A populations,\nparadigm B gives very similar results.\n\n\n\n\n\\section{Choice of underlying BBH model in LEA}\n\\begin{figure}\n\\centering \n\\includegraphics[width=1.05\\columnwidth]{match-q-spin1-PhenomC-mf0_01.pdf}\n\\caption{%\nWe compare two alternatives of the tidal NSBH model from Ref.~\\cite{\nLackey:2013axa}, which differ in their underlying BBH prescriptions. One which\nwe use in this study uses SEOBNRv2, while the other uses IMRPhenomC as its\nbase. In this figure, we show the normalized overlap (match) between the two for\n$2,000,000$ points sampled uniformly in the NSBH parameter space. We find that\nfor $q\\gtrsim 4$ the discrepancies between IMRPhenomC and SEOBNRv2 as reported\nin~\\cite{Kumar:2015tha} dominate over tidal terms.\n\\label{fig:PhenomC_vs_SEOBNRv2_LEA}\n}\n\\end{figure}\nThe waveform model used in this paper is a variant of those calibrated\nin Ref~\\cite{Lackey:2013axa}. In that work, the authors also calibrate\na tidal prescription with the phenomenological model IMRPhenomC~\\cite{\nSantamaria:2010yb}\nas the base BBH model. Previous work~\\cite{Kumar:2015tha,Kumar:2016dhh} has \nshown that IMRPhenomC can exhibit pathological behavior for mass-ratios\n$q\\gtrsim 4$ and\/or non-zero black hole spins. We compute noise-weighted\ninner-products between the two variants for $2,000,000$ points sampled\nover the NSBH parameter space, and show the results in \nFig.~\\ref{fig:PhenomC_vs_SEOBNRv2_LEA}. We restrict the comparison to frequencies\nthat are affected by the tidal disruption of the NS, by integrating\nthe inner-products from $f = \\mathrm{max}(15, 0.01\/M)$~Hz\n(where $M$ is expressed in seconds ($1M_\\odot \\simeq 4.925\\mu$S, see\nEq.~(32-34) of~\\cite{Lackey:2013axa}).\nWe find that the differences between the two variants of LEA+ have mismatches\nof a few percent, while the tidal corrections contribute at a sub-percent\nlevel. We conclude that the differences of the underlying BBH model in LEA+\ndominate over its tidal calibration, and since SEOBNRv2 has been shown to\nbe more reliable than IMRPhenomC~\\cite{Kumar:2015tha,Kumar:2016dhh}, we \nrecommend the use of SEOBNRv2-based LEA+ in upcoming LIGO-Virgo analyses.\n\n\n\\end{appendix}\n\n\n\n\n\\section*{References}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbhzh b/data_all_eng_slimpj/shuffled/split2/finalzzbhzh new file mode 100644 index 0000000000000000000000000000000000000000..485f4ac6c6625bec48632b3a35f443ef2c3e9d22 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbhzh @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\\label{sec:intro}\n\n\\noindent\nElectronic excitations have been established as an alternative to nuclear recoils in direct detection of sub-GeV dark matter (DM). Nuclear recoil searches lose sensitivity at lower DM masses due to kinematic mismatch between the DM and heavier nuclei, whereas electronic transitions can potentially extract all of the DM kinetic energy during a DM-electron scattering event by excitation across an energy gap. Proposed targets, including noble gas atoms with ${\\cal O}(10\\, \\rm eV)$ ionization energies~\\cite{Essig:2011nj,Graham:2012su,Lee:2015qva,Essig:2017kqs,Catena:2019gfa,Agnes:2018oej,Aprile:2019xxb,Aprile:2020tmw},\nsemiconductors with ${\\cal O}(\\rm eV)$ electronic band gaps~\\cite{Essig:2011nj,Graham:2012su,Essig:2012yx,Lee:2015qva,Essig:2015cda,Derenzo:2016fse,Hochberg:2016sqx,Bloch:2016sjj,Kurinsky:2019pgb,Trickle:2019nya, Griffin:2019mvc,Griffin:2020lgd,Du:2020ldo}, and superconductors and Dirac materials with ${\\cal O}(\\rm meV)$ band gaps~\\cite{Hochberg:2015pha,Hochberg:2015fth,Hochberg:2016ajh,Hochberg:2017wce,Coskuner:2019odd,Geilhufe:2019ndy,inzani2021prediction}, extend the reach on DM mass well below the limit of nuclear recoil. Experimental searches using dielectric crystal targets are currently underway, specifically with Si (DAMIC~\\cite{deMelloNeto:2015mca,Aguilar-Arevalo:2019wdi,Settimo:2020cbq}, SENSEI~\\cite{Tiffenberg:2017aac,Crisler:2018gci,Abramoff:2019dfb,Barak:2020fql}, SuperCDMS~\\cite{Agnese:2014aze, Agnese:2015nto, Agnese:2016cpb, Agnese:2017jvy, Agnese:2018col, Agnese:2018gze, Amaral:2020ryn}) and Ge (EDELWEISS~\\cite{Armengaud:2018cuy, Armengaud:2019kfj, Arnaud:2020svb}, as well as SuperCDMS) which have been predicted to have excellent sensitivity down to $\\mathcal{O}(\\text{MeV})$ DM masses based on their $\\mathcal{O}(\\text{eV})$ band gaps.\n\nReliable theoretical predictions of target-specific transition rates are important not only for current experiments, but also for planning the next generation of detectors. Compared to the DM-induced electron ionization rate in noble gases like xenon~\\cite{Essig:2011nj,Graham:2012su,Lee:2015qva,Essig:2017kqs,Catena:2019gfa,Agnes:2018oej,Aprile:2019xxb,Aprile:2020tmw}, calculations for the DM-electron scattering rate in a crystal are more complicated. Ionization rates for noble gases can be calculated by considering each noble gas atom as an individual target, where the calculation simplifies to finding the ionization rate from an isolated atom, for which the wave functions and energy levels are well tabulated~\\cite{Bunge:1993jsz}. However, for crystal targets the atoms are not isolated and more involved techniques are required to obtain an accurate characterization of DM-electron interactions in a many-body system.\n\nThere have been a variety of approaches taken to compute the DM-electron scattering rate in crystals. One of the first attempts, Ref.~\\cite{Graham:2012su}, computed the rate with semi-analytic approximations for the initial and final state wave functions, and used the density of states to incorporate the electronic band structure. Later, Ref.~\\cite{Lee:2015qva} continued in this direction and used improved semi-analytic approximations for the initial state wave functions. Meanwhile, a fully numerical approach was advanced in Refs.~\\cite{Essig:2011nj, Essig:2015cda, Derenzo:2016fse} where density functional theory (DFT) was employed to calculate the valence and conduction electronic band structures and wave functions. The latter approach, as implemented in the \\texttt{QEdark} program and embedded in the \\textsc{Quantum ESPRESSO} package~\\cite{QE-2009,QE-2017,doi:10.1063\/5.0005082}, has become the standard for first-principles calculations of DM detection rates. Recently, in Refs.~\\cite{Trickle:2019nya, Griffin:2019mvc} we used a similar DFT approach as implemented in our own program for a study of DM-electron scattering in a variety of target materials. More recently there has been work utilizing the relation between the dielectric function and the spin-independent scattering rate~\\cite{Hochberg:2021pkt,Knapen:2021run,Knapen:2021bwg}, which also properly incorporates screening effects. \n\nThe goal of this work is to further extend the DM-electron scattering calculation in several key aspects, and present state-of-the-art predictions for Si and Ge detectors using a combination of DFT and semi-analytic methods. \nAs we will elaborate on shortly, the time- and resource-consuming nature of DFT calculations presents an intrinsic difficulty that has limited the scope of previous work in this direction to a restricted region of phase space; typically only bands within a few tens of eV above and below the band gap were included and electronic wave functions were cut off at a finite momentum.\nWe overcome this difficulty by implementing all-electron (AE) reconstruction (whose importance was previously emphasized in Ref.~\\cite{Liang:2018bdb}) to recover higher momentum components of DFT-computed wave functions, and by extending the calculation to bands farther away from the band gap using semi-analytic approximations along the lines of Refs.~\\cite{Graham:2012su,Lee:2015qva}. \nAs we will see, the new contributions computed here have a significant impact on detection prospects in cases where higher energy and\/or momentum regions of phase space dominate the rate, including scattering via a heavy mediator, and experiments with $\\mathcal{O}(10\\,\\text{eV})$ or higher energy thresholds. \nWe also stress that in contrast to the recent work emphasizing the relation between spin-independent DM-electron scattering rates and the dielectric function~\\cite{Hochberg:2021pkt,Knapen:2021run,Knapen:2021bwg}, \nour calculation can be straightforwardly extended to DM models beyond the standard spin-independent coupling. \nFurthermore, we do not make assumptions about isotropy for the majority of our calculation, and our framework is capable of treating anisotropic targets which exhibit smoking-gun daily modulation signatures~\\cite{Coskuner:2019odd,Trickle:2019nya,Geilhufe:2019ndy} (see also Refs.~\\cite{Griffin:2018bjn,Coskuner:2021qxo} for discussions of daily modulation for phonon excitations).\n\nOur calculation is implemented in an open-source program \\texttt{EXCEED-DM} (EXtended Calculation of Electronic Excitations for Direct detection of Dark Matter), to be released in an upcoming publication. \nCurrently a beta version of the program is available \\href{https:\/\/github.com\/tanner-trickle\/EXCEED-DM}{here}~\\cite{tanner_trickle_2021_4747696}. \nWe also make available our DFT-computed wave functions~\\cite{Trickle2021} and the output of \\texttt{EXCEED-DM}~\\cite{Trickle2021a} for Si and Ge.\n\n\\subsection{Overview of the Calculation and Key Results}\n\\label{subsec:key_results}\n\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.4\\textwidth]{figs\/Si_band_schematic.pdf}\n\t\\hspace{0.05\\textwidth}\n\t\\includegraphics[width=0.4\\textwidth]{figs\/Ge_band_schematic.pdf}\n \\caption{Schematic representation of electronic states in Si (left) and Ge (right), divided into core, valence (``val''), conduction (``cond'') and free. Shaded regions indicate the range of energies for each type of electronic states. In a scattering process, electrons transition from either core or valence states, below the Fermi surface at $E = 0$, to conduction or free states above the band gap $E_\\text{g}$. As outlined in Sec.~\\ref{subsec:key_results} and explained in detail in Sec.~\\ref{sec:elec_states}, we compute the valence and conduction states numerically using DFT (including all-electron reconstruction), model the core states semi-analytically with RHF wave functions, and treat the free states as plane waves.}\n\t\\label{fig:band_cartoon}\n\\end{figure}\n\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=\\textwidth]{figs\/key_results_summary.pdf}\n\t\\caption{\\label{fig:summary_plot}\n\t\tSelection of results from Sec.~\\ref{sec:projected_constraints}, for DM-electron scattering via a heavy mediator in a Ge target. \n \\textbf{Left:} Contribution from each of the four transition types, valence to conduction (v$\\to$c), valence to free (v$\\to$f), core to conduction (c$\\to$c), and core to free (c$\\to$f) to the scattering rate binned in energy deposition (with $\\Delta\\omega=1$\\,eV) for a 1\\,GeV DM at a given reference cross section $\\overline{\\sigma}_e = 10^{-40}\\; \\text{cm}^2$. \\textbf{Right:} 95$\\%$ C.L.\\ projected limit (3 events) on $\\overline{\\sigma}_e$ assuming 1\\,kg-year exposure, for energy thresholds corresponding to 1 and 5 electron-hole pairs. We compare our results with \\texttt{QEdark} calculations in Refs.~\\cite{Essig:2015cda,Derenzo:2016fse} and the semi-analytic model of Lee et al~\\cite{Lee:2015qva}; see text for details.}\n\\end{figure}\n\nBefore delving into the technical details, let us give a brief overview of the calculation and highlight some key results. \nWe divide the electronic states in a (pure) crystal into four categories: core, valence, conduction and free, as illustrated in Fig.~\\ref{fig:band_cartoon} for Si and Ge and discussed in more detail in Sec.~\\ref{sec:elec_states}. \nAt zero temperature, electrons occupy states up to the Fermi energy, defined as the maximum of the valence bands and denoted by $E=0$. \nThe band gap $E_\\text{g}$, {\\it i.e.}\\ the energy gap between the occupied valence bands and unoccupied conduction bands, is typically $\\mathcal{O}(\\text{eV})$ for semiconductors, {\\it e.g.}\\ 1.11\\,eV for Si and 0.67\\,eV for Ge; this sets a lower limit on the energy deposition needed for an electron transition to happen.\n\nThe electronic states near the band gap deviate significantly from atomic orbitals and need to be computed numerically. We apply DFT methods (including AE reconstruction) for this calculation, and refer to the DFT-computed states as valence and conduction. \nSpecifically, for both Si and Ge, we take the first four bands below the gap to be valence, which span an energy range of $-12\\,$eV to 0 and $-14\\,$eV to 0, respectively, and take bands above the gap up to $E_\\text{dft}=60\\,$eV to be conduction. \n\nWith more computing power we can in principle include more states, both below and above the band gap, in the DFT calculation. \nHowever, since the states far from the band gap can be modeled semi-analytically with reasonable accuracy, computing them with DFT is inefficient. \nBelow the valence bands, electrons are tightly bound to the atomic nuclei. We model them using semi-analytic atomic wave functions and refer to them as core states. \nThese include the 1s, 2s, 2p states in Si and 1s, 2s, 2p, 3s, 3p, 3d states in Ge (the 3d states in Ge are sometimes referred to as semi-core, and we will compare the DFT and semi-analytic treatment of them in Secs.~\\ref{subsec:atomic_wf} and \\ref{subsec:cc}). \nFinally, above $E_\\text{dft}=60\\,$eV, we model the states as free electrons as they are less perturbed by the crystal environment.\n\nWith the electronic states modeled this way, we compute the rate for valence to conduction (v$\\to$c), valence to free (v$\\to$f), core to conduction (c$\\to$c) and core to free (c$\\to$f) transitions induced by DM scattering, as discussed in detail in Sec.~\\ref{sec:general_calc}. \nThe total rate is the sum of all four contributions. \nWe then use our calculation to update the projected reach of direct detection experiments in Sec.~\\ref{sec:projected_constraints}, and compare our results with previous literature.\n\nFigure~\\ref{fig:summary_plot} gives a glimpse of some of our key results. \nHere we consider the case of DM scattering via a heavy mediator in a Ge target. \nThe impact of core (3d) to conduction contributions is clearly visible from both the differential rate (left panel, for $m_\\chi=1\\,$GeV) and the projected reach (right panel). \nThey dominate the total rate for $m_\\chi\\gtrsim 10\\,$MeV, and, as we can see from the right panel of Fig.~\\ref{fig:summary_plot}, lead to significantly more optimistic reach compared to previous DFT calculations implemented in \\texttt{QEdark}~\\cite{Essig:2015cda,Derenzo:2016fse}; this is especially true for higher detector thresholds (corresponding to higher $Q$ values). \nNote that while Refs.~\\cite{Essig:2015cda,Derenzo:2016fse} included the 3d states in their DFT calculation, their contributions were significantly underestimated due to the absence of AE reconstruction. \nThe importance of AE reconstruction is also seen from the valence to conduction differential rate in the left panel of Fig.~\\ref{fig:summary_plot}, where our calculation predicts a much higher rate at $\\omega \\gtrsim 15\\,$eV compared to the \\texttt{QEdark} calculation in Ref.~\\cite{Derenzo:2016fse}.\nMeanwhile, accounting for in-medium screening (see Sec.~\\ref{subsec:in-med}) we find, consistent with Ref.~\\cite{Knapen:2021run}, a lower rate at energy depositions just above the band gap, and as a result, weaker reach at low $m_\\chi$, compared to Refs.~\\cite{Essig:2015cda,Derenzo:2016fse}.\nOn the other hand, our modeling of the core (3d) states is similar to the semi-analytic approach of Ref.~\\cite{Lee:2015qva}, and indeed we find very similar reach at large $m_\\chi$; however, the approach of Ref.~\\cite{Lee:2015qva} overestimates the rate at smaller $m_\\chi$ due to reduced accuracy in the modeling of the valence and conduction states. \nWe reserve a more detailed comparison with the literature for Sec.~\\ref{subsec:compare_to_prev}.\n\n\\section{Electronic States}\n\\label{sec:elec_states}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/ae_no_ae_core_wfc.pdf}\n \\caption{Comparison of the Bloch wave function magnitudes, defined in Eq.~\\eqref{eq:av_bloch_state}, computed with DFT with (red, ``AE'') and without (blue, ``no AE'') AE reconstruction, and the semi-analytic core approximation of Eq.~\\eqref{eq:u_jnlmk} (green, ``core''). Shaded bands indicate the maximum and minimum values across all the bands belonging to the state type indicated in the upper right corner of each panel. AE reconstruction, discussed in Sec.~\\ref{subsubsec:ae}, recovers the large momentum behavior of the electronic wave functions. Core electronic states, such as those shown in the right panels and discussed in Sec.~\\ref{subsec:atomic_wf}, can be well modeled semi-analytically with atomic wave functions, as seen by the good agreement between the ``core'' and ``AE'' curves. When applicable, the semi-analytic parameterization is advantageous since the electronic wave functions are then known to arbitrarily large momentum.}\n \\label{fig:binned_core_wf}\n\\end{figure}\n\nTo compute the DM-electron scattering rate one must understand the electronic states of the target. In targets with a periodic potential, Bloch's theorem states that the energy eigenstates can be indexed by a momentum, $\\vect{k}$, which lies within the first Brillouin zone (1BZ). These Bloch states, $\\psi_{i,\\vect{k}}$, where $i$ represents additional quantum numbers, are eigenstates of the discrete translation operator such that $\\psi_{i,\\vect{k}}(\\vect{x} + \\vect{r}) = e^{i \\vect{k} \\cdot \\vect{r}} \\psi_{i,\\vect{k}}(\\vect{x})$, which means the electronic wave functions can be written as\n\\begin{align}\n \\label{eq:bloch_form}\n \\psi_{i,\\vect{k}}(\\vect{x}) = \\frac{1}{\\sqrt{V}}\\, e^{i \\vect{k} \\cdot \\vect{x}} \\,u_{i,\\vect{k}}(\\vect{x}) \\,,\n\\end{align}\nwhere $u_{i,\\vect{k}}(\\vect{x} + \\vect{r}) = u_{i,\\vect{k}}(\\vect{x})$ and $V$ is the target volume. For every $\\vect{k}$ there exists a tower of eigenstates (labeled by $i$) of the target Hamiltonian which constitutes the complete set of states in the target. Unfortunately this complete set is not known for a general material and therefore a combination of approximations must be used to calculate them. \nAs discussed in Sec.~\\ref{subsec:key_results} and illustrated in Fig.~\\ref{fig:band_cartoon}, we divide the states into core, valence, conduction and free, and use a combination of numerical calculations and semi-analytic modeling. \nIn this section, we expand on the treatment of each type of electronic states. \n\nWe first discuss the DFT calculation for valence and conduction states in Sec.~\\ref{subsec:dft}, and then move on to explain the semi-analytic treatment of core states in Sec.~\\ref{subsec:atomic_wf}. Our main results are contained in Fig.~\\ref{fig:binned_core_wf} where we compare the average magnitude of electronic wave functions,\nbinned in momentum (see Eq.~\\eqref{eq:av_bloch_state}), computed with and without AE reconstruction, discussed further in Sec.~\\ref{subsubsec:ae}, and, for the highest energy core states (2p in Si and 3d in Ge), those computed using the core approximation discussed in Sec.~\\ref{subsec:atomic_wf}. \nWe find that the AE reconstruction includes a significant contribution from wave functions at large momentum as expected, and that for the core states, the semi-analytic approach reproduces the large momentum components of these AE reconstructed DFT wave functions. \nLastly we will discuss the analytic treatment of the free states in Sec.~\\ref{subsec:pw_approx}.\n\n\\subsection{DFT Wave Functions and Band Structures}\n\\label{subsec:dft}\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.48\\textwidth]{figs\/bandstructure_Si.pdf}\n\\includegraphics[width=0.48\\textwidth]{figs\/bandstructure_Ge.pdf}\n\\caption{Calculated band structures of Si (left) using a PBE xc-functional within DFT and Ge (right) using a hybrid functional HSE06. The band gaps have been scissor corrected to their measured values near zero temperature, 1.11\\,eV and 0.67\\,eV for Si and Ge, respectively. The Fermi level is set to 0\\,eV in both panels.}\n\\label{fig:bandstructures}\n\\end{figure}\n\nIn principle, DFT provides an exact solution to the many-electron Schr\\\"odinger equation by the Hohenberg-Kohn theorems that treat all properties of a quantum many-body system as unique functionals of the ground state density. \nThey further show that the exact ground state density and energy can be found by minimizing the total energy of the system~\\cite{Hohenberg:1964zz, martin2020electronic}. This becomes tractable by the Kohn-Sham (KS) equations that reduce the many-body problem to non-interacting electrons moving in an effective potential, $V_{\\mathrm{eff}}$, \n\\begin{equation}\n\\left( \\frac{p^2}{2m_e} + V_{\\mathrm{eff}} - \\epsilon_i \\right) \\psi_i = 0 \\,,\\qquad\\quad\nV_{\\mathrm{eff}} = V_{\\mathrm{ext}} + V_{\\mathrm{H}} + V_{\\mathrm{xc}}\\,,\n\\label{eq:dft_hamiltonian_eq}\n\\end{equation}\nwhere $\\epsilon_i$ is the orbital energy of the KS orbital $\\psi_i$~\\cite{Kohn:1965zzb}. The external potential $V_{\\mathrm{ext}}$ and Hartree potential $V_{\\mathrm{H}}$, are known, while the exchange-correlation (xc) potential $V_{\\mathrm{xc}}$, which contains the many-body interactions, must be approximated. Herein lies the deviation from the exact solution, and although various formulations of xc-energy functionals have been successful, the choice of xc-functional will affect the predicted electronic states and hence calculated transition rates. For Si, we use PBE~\\cite{Perdew1996}, a type of generalized gradient approximation (GGA) xc-functional which is one of the most popular and low-cost choices. Local and semi-local based xc-functionals, such as PBE, suffer from a self-interaction error and band gap underestimation, which we modify with a ``scissor correction'' where the bands are shifted to match the experimentally determined values of band gap. For Ge, this underestimation results in zero band gap with PBE, therefore we instead use a hybrid functional, which mixes a parameterized amount of exact exchange into the xc-functional, correcting band gaps and band widths by error cancellation at the cost of increased computation time. We use the range-separated hybrid functional HSE06~\\cite{Heyd2003a,Heyd2006}, which applies a screened Coulomb potential to correct the long-range behavior of the xc-potential, giving high accuracy at a mid-level computational cost. Our computed band structures for Si and Ge are shown in Fig.~\\ref{fig:bandstructures}.\n\nThe periodic Bloch wave functions, $u_{i,\\vect{k}}(\\vect{x})$, Eq.~\\eqref{eq:bloch_form}, for band $i$ and Bloch wave vector $\\vect{k}$ are computed by finding the Fourier coefficients, $\\widetilde{u}_{i, \\vect{k}, \\vect{G}}$ (which satisfy the normalization condition, $\\sum_\\vect{G} | \\widetilde{u}_{i, \\vect{k}, \\vect{G}}|^2 = 1$):\n\\begin{align}\n u_{i, \\vect{k}}(\\vect{x}) = \\sum_\\vect{G} \\widetilde{u}_{i,\\vect{k},\\vect{G}} \\,e^{i \\vect{G} \\cdot \\vect{x}} \\, .\n \\label{eq:bloch_fourier}\n\\end{align}\nThe number of reciprocal lattice vectors $\\vect{G}$ kept in the sum is conventionally set by an energy cutoff, $E_\\text{cut}$, such that $|\\vect{k} + \\vect{G}|^2 < 2 m_e E_\\text{cut}$. These Bloch wave function coefficients $\\widetilde{u}_{i,\\vect{k},\\vect{G}}$ for both Si and Ge are computed with the projector augmented wave (PAW) method~\\cite{Blo,Kresse1999} within \\textsc{vasp}~\\cite{Kresse1993,Kresse1994,Kresse1996,Kresse1996a} up to $E_\\text{cut} = 1$ keV on a $10\\times10\\times10$ uniform $\\vect{k}$ mesh over the 1BZ. We then include AE reconstruction effects up to a higher energy cutoff, $E_\\text{AE} = 2$ keV, which recovers higher momentum components of the wave functions up to $|\\vect{k} + \\vect{G}|^2 < 2 m_e E_\\text{AE}$, as discussed in more detail in Sec.~\\ref{subsubsec:ae}. The Bloch wave function coefficients, $\\widetilde{u}_{i,\\vect{k},\\vect{G}}$, for Si and Ge used for this work can be found here~\\cite{Trickle2021}.\n\nA final consideration of using DFT wave functions is that DFT is fundamentally a ground state method, and the KS conduction band states are only approximations to excited states. Excited state methodologies are much more computationally expensive than ground state KS-DFT. Furthermore, since excited state quasiparticles, such as excitons, have been argued to have a negligible effect on the calculation of DM scattering rates~\\cite{Derenzo:2016fse}, they are neglected in our calculations. \n\n\\subsubsection{All-electron Reconstruction}\n\\label{subsubsec:ae}\n\nThere are many different approaches to find the eigenstates of Eq.~\\eqref{eq:dft_hamiltonian_eq}. The PAW method~\\cite{Blo} is one such standard approach. The main idea of the PAW method is to split up the calculation of the eigenstates: near the ionic centers the wave functions resemble the eigenstates of an isolated atom, while further away they can be computed numerically with a pseudopotential. This greatly simplifies the numeric calculation since the focus is then on large distance (small momentum), and the small distance (high momentum) pieces can be self-consistently reintroduced after the main part of the DFT calculation. The large distance components of the wave function are known as ``pseudo wave functions'' (PS wave functions) and the total wave functions are known as the ``all-electron wave functions'' (AE wave functions), indicating that all of the wave function components are included. We will now give a brief overview of how the AE wave functions can be reconstructed from the PS wave functions, computed with PAW-based DFT codes, and refer the reader to Refs.~\\cite{Blo,Kresse1999,rostgaard2009projector,pawpy} for more detailed information.\\footnote{It is possible to calculate all electronic eigenstates, including the core, self-consistently by other more complex methods such as the full-potential linearized augmented plane wave (FP-LAPW) method or the relaxed-core PAW (RC-PAW) method.} \n\nThe AE wave functions, $| \\Psi^\\text{AE} \\rangle$ are built from two components. Near the ionic core, or inside an ``augmentation sphere,'' $| \\Psi^\\text{AE} \\rangle$ is expanded in a set of basis functions, $| \\phi^\\text{AE}_j \\rangle$, which are simply taken to be the wave functions of an isolated atom,\n\\begin{align}\n | \\Psi^\\text{AE} \\rangle = \\sum_j c_j \\,| \\phi^\\text{AE}_j \\rangle \\, .\n\\end{align}\nOutside of the augmentation sphere, $| \\Psi^\\text{AE} \\rangle = | \\Psi^\\text{PS} \\rangle$. Near the ionic core the PS wave functions $| \\Psi^\\text{PS} \\rangle$ are expanded in a set of basis functions $| \\phi^\\text{PS}_j \\rangle$ that are computationally more convenient than the $| \\phi^\\text{AE}_j \\rangle$. Therefore,\n\\begin{align}\n | \\Psi^\\text{AE} \\rangle = | \\Psi^\\text{PS} \\rangle - \\sum_j c_j' \\,| \\phi^\\text{PS}_j \\rangle + \\sum_j c_j \\,|\\phi^\\text{AE}_j \\rangle \\, ,\n\\end{align}\nwhich simply replaces the components in $| \\Psi^\\text{PS} \\rangle$ within the augmentation sphere with the AE wave function. To find the $c$ coefficients we insert an identity, $\\mathds{1} = \\sum_j | \\phi^\\text{AE}_j \\rangle \\langle p^\\text{AE}_j | = \\sum_j | \\phi^\\text{PS}_j \\rangle \\langle p^\\text{PS}_j |$, where $|p^{\\text{AE}\/\\text{PS}}_j \\rangle$ are projector functions, defined to satisfy this identity within the augmentation sphere. Therefore, $c_j = \\langle p^\\text{AE}_j | \\Psi^\\text{AE} \\rangle$, $c_j' = \\langle p^\\text{PS}_j | \\Psi^\\text{PS} \\rangle$. The last ingredient to compute $| \\Psi^\\text{AE} \\rangle$ from $| \\Psi^\\text{PS} \\rangle$ is to require that $| \\phi^\\text{AE}_j \\rangle$ is related to $| \\phi^\\text{PS}_j \\rangle$ via a transformation, $| \\phi^\\text{AE} \\rangle = \\mathcal{T} | \\phi^\\text{PS} \\rangle$. This implies that all the PS states are related to AE states by this transformation $\\mathcal{T}$, such that $c_j = c_j'$ and the AE reconstruction can be written as\n\\begin{align}\n | \\Psi^\\text{AE} \\rangle = | \\Psi^\\text{PS} \\rangle + \\sum_j \\left( | \\phi^\\text{AE}_j \\rangle - | \\phi^\\text{PS}_j \\rangle \\right) \\langle p^\\text{PS}_j | \\Psi^\\text{PS} \\rangle \\, .\n\\end{align}\nIn practice, we implement the AE reconstruction with \\texttt{pawpyseed}~\\cite{pawpy}, and the plane wave expansion cutoff of $| \\Psi^\\text{AE} \\rangle$, $E_\\text{AE}$, can be increased from the initial $E_\\text{cut}$. We use $E_\\text{AE} = 2$ keV.\n\nTo visualize the effect of AE reconstruction, we plot in Fig.~\\ref{fig:binned_core_wf} the average magnitude of the Bloch wave functions, binned in $q$,\n\\begin{align}\n \\left\\langle \\left| \\widetilde{u}_i \\right|^2 \\right\\rangle (q; \\Delta q) \\equiv \\frac{1}{N_q} \\sum_{\\vect{k}}\\sum_{\\vect{G}} |\\widetilde{u}_{i,\\vect{k},\\vect{G}}|^2 \\,\\theta(q + \\Delta q - |\\vect{k} + \\vect{G}|) \\,\\theta(|\\vect{k} + \\vect{G}| - q) \\,,\n \\label{eq:av_bloch_state}\n\\end{align}\nwhere $\\widetilde{u}_{i,\\vect{k},\\vect{G}}$ are the Fourier components of the Bloch wave functions, defined in Eq.~\\eqref{eq:bloch_fourier}. Each bin in momentum space extends from $q$ to $q + \\Delta q$ with $\\Delta q=1\\,$keV, and $N_q$ is a normalization factor equal to the number of points in a bin, $N_q = \\sum_{\\vect{k}} \\sum_{\\vect{G}} \\theta(q + \\Delta q - |\\vect{k} + \\vect{G}|)\\,\\theta(|\\vect{k} + \\vect{G}| - q)$. \nWe see that AE reconstruction recovers the high momentum components, which as we will see can significantly affect the DM-induced transition rate for processes which favor large momentum transfers (such as processes mediated by heavy particles), or processes limited to larger $\\omega$ ({\\it e.g.}\\ higher experimental thresholds where large $q$ processes are the only kinematically allowed transitions).\nPrevious DFT calculations of DM-induced electron transition rates, with the exceptions of Ref.~\\cite{Liang:2018bdb, Trickle:2019nya, Griffin:2019mvc}, used only the pseudo wave functions, $| \\Psi^\\text{PS} \\rangle$ as opposed to the AE wave functions, $| \\Psi^\\text{AE} \\rangle $, and have therefore underestimated detection rates in several cases.\n\n\\subsection{Atomic Wave Functions}\n\\label{subsec:atomic_wf}\n\nIf one could reconstruct the AE wave functions arbitrarily deep into the band structure, and to arbitrarily high momentum, one could calculate an accurate representation of the complete set of electronic states with a DFT calculation. In practice, however, this is neither feasible nor necessary. States deep in the band structure are more isolated from the influence of the crystal environment, and so an isolated atomic approximation becomes valid. We refer to these inner, tightly bound electrons as core electrons. In Si, we will show that the 2p states and below can be treated as core, while in Ge, the 3d states and below can, as alluded to in Fig.~\\ref{fig:band_cartoon}. The purpose of this subsection is to expand on the atomic approximation for core electrons and discuss its accuracy. \n\nMore precisely, the initial states of a transition should be taken as a linear combination of isolated atomic wave functions that is in Bloch form (known as Wannier states):\n\\begin{align}\n \\label{eq:core_elec_wf}\n \\psi_{\\kappa nlm,\\vect{k}}(\\vect{x}) = \\frac{1}{\\sqrt{N}} \\sum_\\vect{r} e^{i \\vect{k} \\cdot (\\vect{r}+\\vect{x}_\\kappa)} \\,\\psi_{\\kappa nlm}^\\text{atom}(\\vect{x} - \\vect{r} - \\vect{x}_\\kappa) \\,,\n\\end{align}\nwhere $\\kappa$ labels the atom in the primitive cell, $n, l, m$ are the standard atomic quantum numbers, $\\vect{x}_\\kappa$ is the equilibrium position of the $\\kappa^\\text{th}$ atom, $\\sum_\\vect{r}$ sums over all primitive cells in the lattice, and $N$ is the total number of cells. \nIn contrast to the valence and conduction states discussed in the previous subsection, the core states are labeled by $(\\kappa nlm)$ rather than band index $i$. \nThe corresponding periodic (dimensionless) $u$ functions can be easily obtained via Eq.~\\eqref{eq:bloch_form}:\n\\begin{equation}\nu_{\\kappa nlm,\\vect{k}}(\\vect{x}) = \\sqrt{\\Omega}\\, \\sum_\\vect{r} e^{-i \\vect{k} \\cdot (\\vect{x}-\\vect{r}-\\vect{x}_\\kappa)} \\,\\psi_{\\kappa nlm}^\\text{atom}(\\vect{x} - \\vect{r} - \\vect{x}_\\kappa) \\,,\n\\label{eq:u_jnlmk}\n\\end{equation}\nwhere $\\Omega=V\/N$ is the primitive cell volume. \n\nIn general, the atomic wave functions $\\psi_{\\kappa nlm}^\\text{atom}$ are not known analytically, but are expanded in a basis of well-motivated analytic functions. The basis coefficients are then fit by solving the isolated atomic Hamiltonian, giving a semi-analytic expression for $\\psi_{\\kappa nlm}^\\text{atom}$. We use a basis of Slater type orbital (STO) wave functions whose radial component is\n\\begin{align}\n R_\\text{STO}(r; Z, n) & = a_0^{-3\/2} \\frac{\\left(2 Z \\right)^{n + \\frac{1}{2}}}{\\sqrt{(2n)!}} \\left( \\frac{r}{a_0} \\right)^{n - 1} e^{-Z r\/a_0} \\,, \n \\label{eq:sto_wf}\n\\end{align}\nwhere $a_0 =0.53$\\,\\AA\\;$= (3.7\\,\\text{keV})^{-1}$ is the Bohr radius, and $Z$ is an effective charge of the ionic potential. Including the angular part, the atomic wave functions are then\n\\begin{align}\n \\psi^{\\rm atom}_{\\kappa nlm}(\\vect{x}) = \\sum_j C_{jln,\\kappa} R_\\text{STO}(x; Z_{jl,\\kappa}, n_{jl,\\kappa}) Y_l^m(\\hat{\\vect{x}}) \\,,\n\\end{align}\nwhere $C_{jln,\\kappa}, Z_{jl,\\kappa}, n_{jl,\\kappa}$ are tabulated in Ref.~\\cite{Bunge:1993jsz}, and $Y_l^m(\\hat{\\vect{x}})$ are the spherical harmonics with the Condon-Shortley phase convention~\\cite{E.U.Condon1935}.\n\nTo assess the accuracy of the atomic wave function approximation, we temporarily push the DFT calculation beyond its default regime (valence and conduction), to the highest core states -- 2p states in Si and 3d states in Ge, where it is still computationally feasible -- and compare the numerical wave functions to the semi-analytic ones discussed above. \nThe results, in terms of the average magnitude of Bloch wave functions defined in Eq.~\\eqref{eq:av_bloch_state}, are shown in the right panels of Fig.~\\ref{fig:binned_core_wf}.\\footnote{The flatness of band structures offers a complementary check of the validity of the atomic approximation. We have verified that the DFT computed energy eigenvalues indeed have a small variance for the highest core states, as expected.}\nWe see that the atomic approximation accurately reproduces the numerical wave functions up to the momentum cutoff $\\sqrt{2m_e E_\\text{AE}}\\simeq 50\\,$keV for $E_\\text{AE}=2\\,$keV. \nThese plots also show the limitation of DFT calculations. \nWhile AE reconstruction recovers higher-momentum components of electronic wave functions, it is not feasible to expand the plane wave basis set to arbitrarily high cutoff. \nHowever, having verified the atomic approximation for the highest core states, we can use it for all core states with confidence, allowing us to more easily include the high momentum components beyond the DFT cutoff.\n\n\\subsection{Plane Wave Approximation}\n\\label{subsec:pw_approx}\n\nWith the inclusion of the semi-analytic core states, all of the states below the band gap have been modeled. States above the band gap can also be computed with DFT methods, as described in Sec.~\\ref{subsec:dft}. Similar to valence bands, there are practical limitations to how many conduction bands can be included. To remedy this in the simplest way possible, we model states far above the band gap as plane waves,\n\\begin{equation}\n \\label{eq:free_elec_wf}\n \\psi_{\\vect{G}, \\vect{k}}(\\vect{x}) = \\frac{1}{\\sqrt{V}}\\, e^{i \\left( \\vect{k} + \\vect{G} \\right) \\cdot \\vect{x}} \\,,\\qquad\\quad\n E_{\\vect{G}, \\vect{k}} = \\frac{| \\vect{k} + \\vect{G} |^2}{2 m_e}\\,,\n\\end{equation}\nwhere $\\vect{G}$ is a reciprocal lattice vector, and plays the role of a band index. (To understand this, simply note that every momentum can be decomposed into a $\\vect{k}$ vector inside the 1BZ and a reciprocal lattice vector. Integrating over the momentum of plane wave states amounts to a $\\vect{k}$ integral within the 1BZ and a $\\vect{G}$ sum.)\nFrom Eq.~\\eqref{eq:bloch_form} we see that the corresponding periodic $u$ functions are simply\n\\begin{equation}\nu_{\\vect{G},\\vect{k}}(\\vect{x}) = e^{i\\vect{G}\\cdot\\vect{x}} \\,.\n\\label{eq:u_free}\n\\end{equation}\nThe plane wave approximation is often used in atomic ionization rate calculations, with the inclusion of a \\textit{Fermi factor}, $F(\\nu)$,\n\\begin{align}\n \\label{eq:fermi_factor}\n F(\\nu) = \\frac{\\nu}{1 - e^{-\\nu}}\\,, \\qquad \\nu(Z_\\text{eff}, E) = 2 \\pi Z_\\text{eff} \\frac{\\alpha m_e}{\\sqrt{2 m_e E}} \\, , \n\\end{align}\nwhere $E$ is the final state electron energy, and $Z_\\text{eff}$ is an effective charge parameter, which enhances the transition rate at low $E$ to account for the long range behavior of the Coulomb potential. See Refs.~\\cite{Essig:2011nj,Graham:2012su, Lee:2015qva, Agnes:2018oej} for more details. In atomic ionization calculations one usually takes $Z_\\text{eff}$ to be related to the binding energy of the initial state, $E_B$,\n\\begin{align}\n \\label{eq:z_eff_binding_approx}\n Z_\\text{eff} = n \\sqrt{\\frac{E_B}{13.6 \\text{ eV}}} \\, ,\n\\end{align}\nwhere $n$ is the principal quantum number. Since the rate is proportional to the Fermi factor, $Z_\\text{eff} = 1$ is seen as the conservative choice. Later in Secs.~\\ref{subsec:vf} and \\ref{subsec:cf} we quantify how much of an effect this has on the transition rate. This uncertainty is only important for very high experimental thresholds, and generally we find that $Z_\\text{eff} = 1$ leads to a smoother match (within an $\\mathcal{O}(1)$ factor) to conduction band contributions from DFT calculations.\n\n\\section{Electronic Transition Rates}\n\\label{sec:general_calc}\n\nWe now present the DM-induced electron transition rate calculation. We begin with a general discussion and then in Secs.~\\ref{subsec:vc}-\\ref{subsec:cf} consider the four different transition types in turn: valence to conduction ($\\mathrm{v} \\rightarrow \\mathrm{c}$), valence to free ($\\mathrm{v} \\rightarrow \\mathrm{f}$), core to conduction ($\\mathrm{c} \\rightarrow \\mathrm{c}$) and core to free ($\\mathrm{c}\\rightarrow\\mathrm{f}$). Finally, in Sec.~\\ref{subsec:in-med} we discuss the treatment of in-medium screening.\n\nThe general derivation has been discussed previously (see {\\it e.g.}\\ Refs.~\\cite{Graham:2012su, Essig:2015cda,Catena:2019gfa, Trickle:2019nya, Liang:2018bdb}), and we repeat it here for completeness and clarity, as a variety of conventions have been used. Beginning with Fermi's Golden Rule, the transition rate between electronic states $\\ket{i, s}$ and $\\ket{f, s'}$ due to scattering with an incoming non-relativistic DM particle, $\\chi$, with mass $m_\\chi$, velocity $\\vect{v}$, and spin $\\sigma$ is given by\n\\begin{align}\n \\label{eq:fermi}\n \\Gamma_{i, s, \\sigma \\rightarrow f, s', \\sigma'}(\\vect{v}) = 2 \\pi V \\int \\frac{d^3 q}{(2 \\pi)^3}\\, \\big|\\bra{\\vect{p'}, \\sigma'; f, s'} \\,\\delta\\hat{H} \\,\\ket{\\vect{p}, \\sigma; i, s} \\big|^2 \\,\\delta(E_{f, s'} - E_{i, s} - \\omega_\\vect{q})\\,,\n\\end{align}\nwhere $\\ket{\\vect{p}, \\sigma; i, s} = \\ket{\\vect{p}, \\sigma} \\otimes \\ket{i, s}$, $\\vect{q}$ is the momentum deposited onto the target, $\\vect{p} = m_\\chi \\vect{v}$, $\\vect{p}' = \\vect{p} - \\vect{q}$, $\\delta\\hat{H}$ is the interaction Hamiltonian, $V$ is total volume of the target, and $\\omega_\\vect{q}$ is the energy deposition:\n\\begin{equation}\n \\omega_\\vect{q} = \\frac{1}{2} m_\\chi v^2 - \\frac{\\left( m_\\chi \\vect{v} - \\vect{q} \\right)^2}{2 m_\\chi} = \\vect{q} \\cdot \\vect{v} - \\frac{q^2}{2 m_\\chi} \\, .\n \\label{eq:w_kin}\n\\end{equation}\nWe assume that all quantum states are unit normalized. Modulo in-medium screening effects, discussed below in Sec.~\\ref{subsec:in-med}, we can write Eq.~\\eqref{eq:fermi} in terms of the standard QFT matrix element, defined with plane wave incoming and outgoing states, by inserting $\\mathbbm{1} = V \\sum_s \\int \\frac{d^3k}{(2\\pi)^3} \\ket{\\vect{k},s} \\bra{\\vect{k},s}$ and using \n\\begin{align}\n \\label{eq:qft_m}\n \\langle \\vect{p}', \\sigma'; \\vect{k}', s' | \\,\\delta \\hat H\\, | \\vect{p}, \\sigma; \\vect{k}, s \\rangle \\equiv \\frac{(2\\pi)^3}{V^2} \\frac{\\mathcal{M}_{\\sigma' s'\\sigma s}(\\vect{p}', \\vect{k}', \\vect{p}, \\vect{k})}{4 m_e m_\\chi} \\,\\delta^{(3)}\\left( \\vect{p}' + \\vect{k}' - \\vect{p} - \\vect{k}\\right) \\, .\n\\end{align}\nWe find\\\\[-4pt]\n\\begin{minipage}{\\textwidth}\n\\begin{align}\n \\label{eq:fermi_m}\n \\Gamma_{i, s, \\sigma \\rightarrow f, s', \\sigma'}(\\vect{v}) = &\\;\\frac{2 \\pi}{16 V m_e^2 m_\\chi^2} \\int \\frac{d^3 q}{(2 \\pi)^3} \\,\\delta(E_{f, s'} - E_{i, s} - \\omega_\\vect{q}) \\nonumber\\\\\n &\\times \\left| \\int \\frac{d^3k}{(2\\pi)^3} \\,\\mathcal{M}_{\\sigma' s'\\sigma s}(\\vect{p} -\\vect{q}, \\vect{k}+\\vect{q}, \\vect{p}, \\vect{k}) \\,\\widetilde{\\psi}_f^*(\\vect{k} + \\vect{q}) \\widetilde{\\psi}_i(\\vect{k}) \\right|^2 \\, ,\n\\end{align}\n\\end{minipage}\nwhere $\\widetilde{\\psi}_i(\\vect{k}) = \\sqrt{V} \\langle \\vect{k} | i \\rangle$. \n\nWe will limit our analysis to matrix elements which only depend on $\\vect{q}$, and assume that the electron energy levels are also spin independent, which allows the spin sums to be easily computed:\n\\begin{align}\n \\label{eq:fermi_spin_av}\n \\overline{\\Gamma}_{i \\rightarrow f} & \\equiv \\frac{1}{2} \\sum_{\\sigma, \\sigma'} \\sum_{s, s'} \\Gamma_{i, s, \\sigma \\rightarrow f, s', \\sigma'} \\nonumber\\\\\n & = \\frac{4 \\pi}{16 V m_e^2 m_\\chi^2}\\int \\frac{d^3 q}{(2 \\pi)^3} \\,\\overline{|\\mathcal{M}(\\vect{q})|^2}\\, |f_{i \\rightarrow f}|^2 \\,\\delta \\left( E_f - E_i - \\omega_\\vect{q} \\right) , \\\\\n f_{i \\rightarrow f} & \\equiv \\int \\frac{d^3k}{(2\\pi)^3} \\,\\widetilde{\\psi}_f^*(\\vect{k} + \\vect{q})\\, \\widetilde{\\psi}_i(\\vect{k}) = \\int d^3x \\, e^{i \\vect{q} \\cdot \\vect{x} }\\, \\psi_f^*( \\vect{x} )\\, \\psi_i(\\vect{x}) \\,,\n \\label{eq:wf_form_fac}\n\\end{align}\nwhere $\\overline{\\left| \\mathcal{M} \\right|^2}$ is the spin averaged matrix element squared and we have defined a crystal form factor $f_{i\\rightarrow f}$, written in terms of both momentum and position space representations of the wave functions. \n\nThe transition rate per target mass, $R_{i \\rightarrow f}$, is then given by\n\\begin{align}\n \\label{eq:rate}\n R_{i \\rightarrow f} = \\frac{1}{\\rho_T} \\frac{\\rho_\\chi}{m_\\chi} \\int d^3v f_\\chi(\\vect{v}) \\,\\overline{\\Gamma}_{i \\rightarrow f} \\,,\n\\end{align}\nwhere $\\rho_T$ is the target density, $\\rho_\\chi = 0.4 \\text{ GeV}\/\\text{cm}^{3}$ is the local DM density, and $f_\\chi$ is taken to be a boosted Maxwell-Boltzmann distribution. The total rate, $R$, is then simply the sum over all possible transitions from initial to final states. Since the only $\\vect{v}$ dependence in Eq.~\\eqref{eq:rate} comes from the energy conserving delta function, we perform the $\\vect{v}$ integral first and define $g(\\vect{q}, \\omega) = 2 \\pi \\int d^3v f_\\chi(v) \\delta(\\omega - \\omega_\\vect{q})$. This integral can be evaluated analytically (see {\\it e.g.}\\ Refs.~\\cite{Coskuner:2019odd,Trickle:2019nya,Coskuner:2021qxo}):\n\\begin{align}\n g(\\vect{q}, \\omega) &\n = \\frac{2\\pi^2 v_0^2}{N_0}\\frac{1}{q} \\left( e^{-v_-^2\/v_0^2} - e^{-v_\\text{esc}^2\/v_0^2} \\right) ,\\\\\n v_- & = \\text{min} \\left\\{ \\frac{1}{q} \\left| \\omega + \\frac{q^2}{2m_\\chi} + \\vect{q} \\cdot \\vect{v}_e \\right| , v_{\\text{esc}} \\right\\} ,\\label{eq:v_minus}\n\\end{align}\nwhere $\\omega = E_f - E_i$ is the deposited energy, and $N_0$ is a normalization factor such that $\\int d^3\\vect{v} f_\\chi(\\vect{v}) = 1$. \nWe take the DM velocity distribution parameters to be $v_0 = 230$ km$\/$s , $v_\\text{esc} = 600$ km$\/$s, and $v_e = 240$ km$\/$s. \nThe total rate then becomes\n\\begin{align}\n R = \\frac{2}{16 V m_e^2 m_\\chi^3} \\frac{\\rho_\\chi}{\\rho_T}\\sum_{i, f} \\int \\frac{d^3q}{(2\\pi)^3} \\overline{\\left| \\mathcal{M}(\\vect{q}) \\right|^2} \\,g(\\vect{q}, \\omega) \\left| f_{i \\rightarrow f}(\\vect{q}) \\right|^2 \\, .\n\\end{align}\nHere we focus on simple DM models, such as the kinetically mixed dark photon or leptophilic scalar mediator models. In these models $\\mathcal{M}(\\vect{q})$ can be factorized as $\\mathcal{M}(q) = \\mathcal{M}(q_0) \\mathcal{F}_\\text{med}(q_0\/q) \\left(f_e\/f_e^0 \\right)$, where $\\mathcal{F}_\\text{med}(q_0\/q) = 1$ for a heavy mediator and $\\mathcal{F}_\\text{med}(q_0\/q) = (q_0\/q)^2$ for a light mediator, and $f_e\/f_e^0$ is a screening factor discussed in more detail in Sec.~\\ref{subsec:in-med}. As in previous works, we choose the reference momentum transfer to be $q_0 = \\alpha m_e$. We can then finally write the rate in terms of a reference cross section,\n\\begin{align}\n \\overline{\\sigma}_e = \\frac{\\mu_{\\chi e}^2}{16\\pi m_\\chi^2 m_e^2} \\overline{|\\mathcal{M}(q_0)|^2} \\, ,\n\\end{align}\nand find\n\\begin{align}\n \\label{eq:rate_simple}\n R = \\frac{2 \\pi \\overline{\\sigma}_e}{V \\mu_{\\chi e}^2 m_\\chi} \\frac{\\rho_\\chi}{\\rho_T} \\sum_{i, f} \\int \\frac{d^3q}{(2\\pi)^3} \\left( \\frac{f_e}{f_e^0} \\right)^2 \\mathcal{F}_\\text{med}^2 \\,g(\\vect{q}, \\omega) \\left| f_{i \\rightarrow f}(\\vect{q}) \\right|^2 \\, .\n\\end{align}\nAnother useful quantity is the \\textit{binned} rate (the rate for energy deposition between $\\omega$ and $\\omega + \\Delta \\omega$), $\\Delta R_\\omega$, defined as\n\\begin{align}\n \\Delta R_\\omega = \\frac{2 \\pi \\overline{\\sigma}_e}{V \\mu_{\\chi e}^2 m_\\chi} \\frac{\\rho_\\chi}{\\rho_T}\\sum_{i, f}& \\, \\theta(\\omega + \\Delta \\omega - E_f + E_i) \n \\,\\theta(\\omega - E_f + E_i) \\nonumber \\\\\n& \\times \\int \\frac{d^3q}{(2\\pi)^3} \\left( \\frac{f_e}{f_e^0} \\right)^2\\,\\mathcal{F}_\\text{med}^2 \\,g(\\vect{q}, \\omega) \\left| f_{i \\rightarrow f}(\\vect{q}) \\right|^2 .\n & \n \\label{eq:binned_rate_E}\n\\end{align}\n\n\\subsection{Valence to Conduction}\n\\label{subsec:vc}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/vc_contribution_diff_rate.pdf}\n \\caption{DM-electron scattering rate from valence to conduction bands binned in energy deposition (with $\\Delta\\omega=1$\\,eV) for 1\\,GeV DM, light (top row) and heavy (bottom row) mediators, assuming $\\overline{\\sigma}_e = 10^{-40}\\; \\text{cm}^2$, computed with vs.\\ without AE reconstruction. Valence states included are the first four bands below the band gap, and conduction states included are all bands up to $E_\\text{dft}=60$\\,eV.}\n \\label{fig:vc_diff_rate}\n\\end{figure}\n\nWe begin with valence to conduction band transitions. The initial (final) states are indexed by band number, $i (f)$, and Bloch momentum, $\\vect{k}_i (\\vect{k}_f)$ inside the 1BZ. The wave functions in Eq.~\\eqref{eq:bloch_form} can be substituted into the crystal form factor in Eq.~\\eqref{eq:wf_form_fac},\n\\begin{align}\n f_{i, \\vect{k}_i \\rightarrow f, \\vect{k}_f} & = \\frac{1}{V} \\int d^3x \\,e^{i \\left( \\vect{k}_i - \\vect{k}_f + \\vect{q} \\right) \\cdot \\vect{x}}\\, u^*_{f, \\vect{k}_f}(\\vect{x}) \\,u_{i, \\vect{k}_i}(\\vect{x}) \\nonumber \\\\\n & = \\sum_\\vect{G} \\delta_{\\vect{q},\\, \\vect{k}_f - \\vect{k}_i + \\vect{G}} \\,\\frac{1}{\\Omega}\\, \\int_\\text{cell} d^3x \\,e^{i \\vect{G} \\cdot \\vect{x}} \\,u^*_{f, \\vect{k}_f}(\\vect{x}) \\,u_{i, \\vect{k}_i}(\\vect{x}) \\,,\n \\label{eq:f_val_to_cond}\n\\end{align}\nwhere the integral is over the primitive cell with volume $\\Omega$, and we have used the identity $\\sum_\\vect{r} e^{i \\vect{q} \\cdot \\vect{r}} = N \\sum_\\vect{G} \\delta_{\\vect{q}, \\vect{G}}$. The total rate in Eq.~\\eqref{eq:rate_simple} is then\n\\begin{align}\n R = \\frac{2 \\pi \\overline{\\sigma}_e}{\\mu_{\\chi e}^2 m_\\chi} \\frac{\\rho_\\chi}{\\rho_T} \\sum_{i = 1}^{N_\\text{v}} \\sum_{f = 1}^{N_\\text{c}} & \\int_\\text{1BZ} \\frac{d^3k_i}{(2\\pi)^3} \\frac{d^3k_f}{(2\\pi)^3} \\sum_\\vect{G} \\left( \\frac{f_e}{f_e^0} \\right)^2 \\mathcal{F}_\\text{med}^2 \\, g(\\vect{q}, \\omega) \\nonumber \\\\\n & \\times \\left| \\frac{1}{\\Omega} \\int_\\text{cell} d^3x \\, e^{i \\vect{G} \\cdot \\vect{x}} \\,u^*_{f, \\vect{k}_f}(\\vect{x}) \\,u_{i,\\vect{k}_i}(\\vect{x}) \\right|^2 ,\n \\label{eq:rate_val_to_cond}\n\\end{align}\nwhere $\\vect{q} = \\vect{k}_f - \\vect{k}_i + \\vect{G}$, $N_{\\text{v}(\\text{c})}$ is the number of valence (conduction) bands. This is identical to the rate formulae derived in~\\cite{Griffin:2019mvc, Trickle:2019nya, Essig:2015cda} but written in terms of the periodic Bloch functions, $u_{i,\\vect{k}}(\\vect{x})$, instead of their Fourier transformed components, $\\widetilde{u}_{i,\\vect{k},\\vect{G}}$, similar to Ref.~\\cite{Liang:2018bdb}. Numerically the position space form is superior since the integral over the primitive cell can be computed by Fast Fourier Transform. This reduces the computational complexity from $\\mathcal{O}(N_G^2)$ to $\\mathcal{O}(N_G \\log{N_G})$, where $N_G$ is the number of $\\vect{G}$ points, {\\it i.e.}\\ the number of Fourier components in the expansion of $\\widetilde{u}_{i,\\vect{k}}$ in Eq.~\\eqref{eq:bloch_fourier}.\n\nIn Fig.~\\ref{fig:vc_diff_rate} we show the scattering rate from valence to conduction transitions binned in energy deposition, defined in Eq.~\\eqref{eq:binned_rate_E}, for a 1\\,GeV DM. The main difference between the calculation performed here and in previous works is the effect of the AE reconstruction, as discussed in Sec.~\\ref{subsubsec:ae}. For the case of DM with a heavy mediator, the rate, even with experimental thresholds as low as $\\sim10$ eV, is significantly enhanced relative to previous work. The AE reconstruction plays less of a role in the light mediator case since the transition rate is dominated by small momentum transfers. However, at high thresholds, where only larger momentum components can contribute, the AE reconstruction can still significantly boost the scattering rate by fully including the contributions neglected in the pseudo wave functions. \n\nSince most earlier works computing DM-electron scattering include only valence to conduction transitions, it is useful to understand for which DM masses these are the only kinematically allowed transitions. If $\\omega < E_\\text{g} - E_\\text{max}^\\text{core}$, where $E_\\text{max}^\\text{core}$ is the maximum energy of the core states, then the core states cannot contribute; if $\\omega < E_\\text{dft}$ the free states are not available. Therefore if $\\omega < \\text{min}\\{ E_\\text{dft}, E_\\text{g} - E_\\text{max}^\\text{core} \\}$ only the valence to conduction transitions are allowed, which can be related to a DM mass via $\\omega_\\text{max}(m_\\chi) < \\text{min}\\{ E_\\text{dft}, E_\\text{g} - E_\\text{max}^\\text{core} \\}$, where\n\\begin{align}\n \\omega_\\text{max}(m_\\chi) = \\frac{1}{2} m_\\chi v_\\text{max}^2 = 3.9 \\text{ eV} \\left( \\frac{m_\\chi}{\\text{MeV}} \\right) \\left( \\frac{v_\\text{max}}{840 \\text{ km}\/\\text{s}} \\right)^2 \\, ,\n \\label{eq:w_max}\n\\end{align}\nwith $v_\\text{max} = v_e + v_\\text{esc}$, the maximum incoming DM velocity. For Si (Ge), $E_\\text{max}^\\text{core} = -116$\\,eV ($-28$\\,eV), this corresponds to \n\\begin{align}\n m_\\chi <\n \\begin{cases}\n 15.2 \\text{ MeV} & (\\text{Si}) \\,,\\\\\n 7.8 \\text{ MeV} & (\\text{Ge}) \\, .\n \\end{cases}\n\\end{align}\nRequiring that $\\omega_\\text{max} > E_\\text{g}$, where $E_\\text{g}$ is the band gap, sets a lower bound on the minimum detectable mass, $m_\\chi^\\text{min}$,\n\\begin{align}\n m_\\chi^\\text{min} = \\frac{2E_\\text{g}}{v_\\text{max}^2} = 0.25 \\text{ MeV} \\left( \\frac{E_\\text{g}}{\\text{eV}} \\right) \\left( \\frac{840 \\text{ km}\/\\text{s}}{v_\\text{max}} \\right)^2 \\, .\n\\end{align}\nFor Si (Ge), with a band gap of 1.11 (0.67) eV, $m_\\chi^\\text{min}$ is $0.28$ ($0.17$) MeV.\nLastly, we remark that for DM interactions characterized by higher-dimensional operators (not considered in this work), the scattering rate scales with higher powers of $q$ and therefore is even more sensitive to AE reconstruction (and also $\\mathrm{c} \\rightarrow \\mathrm{c}$ contributions discussed below in Sec.~\\ref{subsec:cc}), which must be included in the analysis.\n\n\\subsection{Valence to Free}\n\\label{subsec:vf}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/vc_vf_contribution_diff_rate.pdf}\n \\caption{DM-electron scattering rate from valence to conduction (v$\\to$c) bands and from valence bands to free states (v$\\to$f) binned in energy deposition (with $\\Delta\\omega=1$\\,eV) for 1\\,GeV DM, light (top row) and heavy (bottom row) mediators, assuming $\\overline{\\sigma}_e = 10^{-40}\\; \\text{cm}^2$. The upper edge of the shaded region corresponds to using $Z_\\text{eff}$ from Eq.~\\eqref{eq:z_eff_binding_approx}, while the bottom edge corresponds to $Z_\\text{eff} = 1$.}\n \\label{fig:vf_diff_rate}\n\\end{figure}\n\nFor valence to free transitions the initial states are identical to those from Sec.~\\ref{subsec:vc}, labeled by band number $i$ and Bloch momentum, $\\vect{k}_i$. The final state wave functions are simple plane waves given by Eq.~\\eqref{eq:free_elec_wf}, labeled by a momentum $\\vect{k}_f$ in the 1BZ with the bands labeled by $\\vect{G}$. \nWe can therefore directly substitute Eq.~\\eqref{eq:u_free} into Eq.~\\eqref{eq:f_val_to_cond} derived in the previous subsection, and obtain the crystal form factor:\n\\begin{align}\n f_{i, \\vect{k}_i \\rightarrow \\vect{G}_f,\\vect{k}_f } &=\n \\sum_\\vect{G} \\delta_{\\vect{q},\\, \\vect{k}_f - \\vect{k}_i + \\vect{G}} \\;\\frac{1}{\\Omega}\\, \\int_\\text{cell} d^3x \\,e^{i (\\vect{G}-\\vect{G}_f) \\cdot \\vect{x}} \\,u_{i, \\vect{k}_i}(\\vect{x}) \\nonumber\\\\\n &= \\sum_\\vect{G} \\delta_{\\vect{q},\\, \\vect{k}_f - \\vect{k}_i + \\vect{G}} \\,\\widetilde{u}_{i,\\vect{k}_i, \\vect{G}_f - \\vect{G}} \\,,\n\\end{align}\nwhere $\\widetilde{u}_{i,\\vect{k}_i,\\vect{G}}$ are the Fourier components of the Bloch wave functions defined in Eq.~\\eqref{eq:bloch_fourier}. \nIncorporating the Fermi factor correction discussed in Sec.~\\ref{subsec:pw_approx}, we find the rate in Eq.~\\eqref{eq:rate_simple} is given by\n\\begin{equation}\n R = \\frac{2 \\pi \\overline{\\sigma}_e}{\\mu_{\\chi e}^2 m_\\chi} \\frac{\\rho_\\chi}{\\rho_T} \\sum_{i = 1}^{N_\\text{v}} \\sum_{\\vect{G}_f} \\int_\\text{1BZ} \\frac{d^3k_i}{(2\\pi)^3} \\frac{d^3k_f}{(2\\pi)^3} F(\\nu_{i,\\vect{k}_i}) \\sum_\\vect{G} \\left( \\frac{f_e}{f_e^0} \\right)^2 \\mathcal{F}_\\text{med}^2 \\, g(\\vect{q}, \\omega) \\left| \\widetilde{u}_{i,\\vect{k}_i, \\vect{G}_f - \\vect{G}}\\right|^2 ,\n\\end{equation}\nwhere\n\\begin{equation}\n\\omega \\equiv \\frac{\\left| \\vect{k}_f + \\vect{G}_f \\right|^2}{2 m_e} - E_{i,\\vect{k}_i} \\,,\\qquad\n\\nu_{i,\\vect{k}_i} = \\nu(Z_\\text{eff}^{i, \\vect{k}_i},\\, \\omega + E_{i,\\vect{k}_i}) \\,.\n\\end{equation}\nWith a change of variables, $\\vect{G}' = \\vect{G}_f - \\vect{G}$ and defining $\\vect{k}' \\equiv \\vect{k}_f + \\vect{G}_f$ (and then dropping the prime for simplicity), the rate becomes\n\\begin{align}\n R & = \\frac{2 \\pi \\overline{\\sigma}_e}{\\mu_{\\chi e}^2 m_\\chi} \\frac{\\rho_\\chi}{\\rho_T} \\sum_{i = 1}^{N_\\text{v}} \\int_\\text{1BZ} \\frac{d^3k_i}{(2\\pi)^3} F(\\nu_{i,\\vect{k}_i}) \\sum_{\\vect{G}} \\left| \\widetilde{u}_{i,\\vect{k}_i,\\vect{G}}\\right|^2 \\int \\frac{d^3k}{(2\\pi)^3} \\left( \\frac{f_e}{f_e^0} \\right)^2 \\mathcal{F}_\\text{med}^2 \\, g(\\vect{q}, \\omega) \\, .\n\\end{align}\nwhere $\\vect{q} = \\vect{k} - \\vect{k}_i - \\vect{G}$.\n\nIn Fig.~\\ref{fig:vf_diff_rate} we compare the binned rate from the valence to conduction (v$\\to$c) calculation in the previous subsection to the valence to free (v$\\to$f) one performed here, again for a 1\\,GeV DM. We see that for large $\\omega$, where the v$\\to$c calculation is limited by the number of conduction bands included, the v$\\to$f calculation extrapolates the results to higher $\\omega$ as expected. There is some uncertainty due to the choice of the effective charge parameters, which is why the results are shown in bands. The lower edge corresponds to the conservative choice of $Z_\\text{eff}^{i, \\vect{k}_i} = 1$ for all $i, \\vect{k}_i$, and the upper edge corresponds to the value set by the binding energy, Eq.~\\eqref{eq:z_eff_binding_approx} with $E_B=-E_{i,\\vect{k}_i}$. We find that the conservative choice $Z_\\text{eff}^{i, \\vect{k}_i} = 1$ is a better match to the edge for the v$\\to$c calculation, and will use this in our final projections in Sec.~\\ref{sec:projected_constraints}. Note that as the threshold increases, the effect of v$\\to$f transitions becomes more important, and for a heavy mediator non-negligible constraints can be placed even with $\\mathcal{O}(100)$\\,eV energy thresholds.\n\n\\subsection{Core to Conduction}\n\\label{subsec:cc}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/cc_contribution_diff_rate.pdf}\n \\caption{DM-electron scattering rate from core states to conduction bands binned in energy deposition (with $\\Delta\\omega=5$\\,eV) for 1\\,GeV DM, light (top row) and heavy (bottom row) mediators, assuming $\\overline{\\sigma}_e = 10^{-40}\\; \\text{cm}^2$. The core states are labelled by the corresponding atomic orbitals, and the conduction states up to $E_\\text{dft}=60$\\,eV are included. For comparison we also show the v$\\to$c contribution (after AE reconstruction) from Fig.~\\ref{fig:vc_diff_rate} in gray.}\n \\label{fig:cc_diff_rate}\n\\end{figure}\n\nWe now turn to core to conduction transitions. The initial core states are indexed by $\\kappa$, the atom in the primitive cell, the usual atomic quantum numbers, $n, l, m$, and the Bloch momentum, $\\vect{k}_i$. The final states are the DFT computed conduction states. The crystal form factor is simply obtained from Eq.~\\eqref{eq:f_val_to_cond} by substituting $u_{i,\\vect{k}_i}\\to u_{\\kappa nlm,\\vect{k}_i}$:\n\\begin{equation}\nf_{\\kappa nlm, \\vect{k}_i \\rightarrow f, \\vect{k}_f} =\\sum_\\vect{G} \\delta_{\\vect{q},\\, \\vect{k}_f - \\vect{k}_i + \\vect{G}} \\,\\frac{1}{\\Omega}\\, \\int_\\text{cell} d^3x \\,e^{i \\vect{G} \\cdot \\vect{x}} \\,u^*_{f, \\vect{k}_f}(\\vect{x}) \\,u_{\\kappa nlm, \\vect{k}_i}(\\vect{x}) \\,,\n\\label{eq:f_c2c}\n\\end{equation}\nThe total scattering rate is then\n\\begin{align}\n R = \\frac{2\\pi\\overline{\\sigma}_e}{\\mu_{\\chi e}^2 m_\\chi} \\frac{\\rho_\\chi}{\\rho_T} \\sum_{\\kappa = 1}^{N_\\text{a}} \\sum_{n = 1}^{N_\\text{p}^\\kappa} & \\sum_{l = 0}^{n-1} \\sum_{m = -l}^{l} \\sum_{f = 1}^{N_\\text{c}} \\int_\\text{1BZ} \\frac{d^3k_i}{(2\\pi)^3} \\frac{d^3k_f}{(2\\pi)^3} \\sum_\\vect{G} \\left( \\frac{f_e}{f_e^0} \\right)^2 \\mathcal{F}_\\text{med}^2\\, g(\\vect{q}, \\omega) \\nonumber \\\\ \n & \\times \\left| \\frac{1}{\\Omega} \\int_\\text{cell} d^3x \\, e^{i \\vect{G} \\cdot \\vect{x}} u_{f\\vect{k}_f}^*(\\vect{x}) \\,u_{\\kappa nlm\\vect{k}_i}(\\vect{x}) \\right|^2 ,\n\\end{align}\nwhere $N_\\text{a}$ is the number of atoms in the primitive cell, $N_\\text{p}^\\kappa$ is the largest principal quantum number for atom $\\kappa$, and $\\omega = E_{f,\\vect{k}_f} - E_{\\kappa nl}$. \nThe core wave functions $u_{\\kappa nlm,\\vect{k}_i}(\\vect{x})$ are given by Eq.~\\eqref{eq:u_jnlmk}, and involves a sum over primitive cells. \nSince the integral in Eq.~\\eqref{eq:f_c2c} is just over one primitive cell, only the atoms in this and neighboring cells can have a significant contribution. \nIn other words, the sum over $\\vect{r}$ converges very quickly due to the localized nature of atomic wave functions. We therefore restrict $\\vect{r}$ to be summed over only the $3\\times 3\\times 3$ nearest cells.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/Ge_3d_focus.pdf}\n \\caption{Contribution to the DM-electron scattering rate binned in energy deposition (with $\\Delta\\omega=1$\\,eV) from 3d electrons to conduction bands in Ge, for 1\\,GeV DM, light (left) and heavy (right) mediators, assuming $\\overline{\\sigma}_e = 10^{-40}\\; \\text{cm}^2$. The three curves in each panel are computed using DFT with and without AE reconstruction, and using the semi-analytic core wave functions.}\n \\label{fig:Ge_3d_focus}\n\\end{figure}\n\nThe contribution of core to conduction (c$\\to$c) transitions to the binned rate, for $m_\\chi = 1$\\,GeV, can be seen in Fig.~\\ref{fig:cc_diff_rate}. In most cases the v$\\to$c transitions are dominant compared to the c$\\to$c, but there are two main scenarios where this is not true. First, when the experimental threshold is raised; this excludes the v$\\to$c transitions and causes the c$\\to$c contribution to be dominant. For example, consider a Si detector and a DM model with a heavy mediator (bottom left panel of Fig.~\\ref{fig:cc_diff_rate}). If the experimental threshold is $\\sim 50$\\,eV the c$\\to$c contribution from the 2p states in Si gives the dominant contribution. \nSecond, for a Ge target, and a DM model with a heavy mediator, the 3d states dominate the rate even at the lowest experimental threshold. To understand this in more detail we present Fig.~\\ref{fig:Ge_3d_focus} which compares the binned rate taking different modeling approaches for the 3d states in Ge. We see that the large momentum components of the wave function, recovered only after AE reconstruction in the DFT calculation, dominate the rate, which explains why previous works have underestimated the importance of 3d electrons. \nMeanwhile, we see explicitly at the scattering rate level that the semi-analytic approach accurately reproduces the DFT calculation at low $\\omega$, and extends the latter beyond its cutoff at high $\\omega$, consistent with the observation at the wave function level in Fig.~\\ref{fig:binned_core_wf}.\n\n\\subsection{Core to Free}\n\\label{subsec:cf}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/cc_cf_contribution_diff_rate.pdf}\n \\caption{DM-electron scattering rate from core states to conduction bands (c$\\to$c) and to free states (c$\\to$f) binned in energy deposition (with $\\Delta\\omega=10$\\,eV) for 1\\,GeV DM, light (top row) and heavy (bottom row) mediators, assuming $\\overline{\\sigma}_e = 10^{-40}\\; \\text{cm}^2$. As in the v$\\to$f calculation in Fig.~\\ref{fig:vf_diff_rate}, the upper edge of the shaded bands corresponds to $Z_\\text{eff}$ from Eq.~\\eqref{eq:z_eff_binding_approx}, and the lower edge corresponds to $Z_\\text{eff} = 1$.}\n \\label{fig:cf_diff_rate}\n\\end{figure}\n\nThe last transition type we consider involves a core electron initial state and a free electron final state.\nThe crystal form factor is most easily obtained by substituting Eqs.~\\eqref{eq:core_elec_wf} and \\eqref{eq:free_elec_wf} into its definition, Eq.~\\eqref{eq:wf_form_fac}:\n\\begin{align}\n f_{\\kappa nlm,\\vect{k}_i \\rightarrow \\vect{G}_f, \\vect{k}_f} & = \\frac{1}{\\sqrt{N V}} \\sum_\\vect{r} e^{i\\vect{k}_i \\cdot (\\vect{r}+\\vect{x}_\\kappa)} \\int d^3x\\, e^{i (\\vect{q} - \\vect{k}_f - \\vect{G}_f) \\cdot \\vect{x}} \\,\\psi_{\\kappa nlm}^\\text{atom}(\\vect{x} - \\vect{r} - \\vect{x}_\\kappa) \\nonumber \\\\\n & = \\frac{1}{\\sqrt{NV}} \\,e^{i(\\vect{k}_i+\\vect{q} - \\vect{k}_f - \\vect{G}_f) \\cdot \\vect{x}_\\kappa} \\sum_\\vect{r} e^{i (\\vect{q} - \\vect{k}_f + \\vect{k}_i) \\cdot \\vect{r}} \\int d^3x \\,e^{i (\\vect{q} - \\vect{k}_f - \\vect{G}_f) \\cdot \\vect{x}} \\,\\psi_{\\kappa nlm}^\\text{atom}(\\vect{x}) \\nonumber \\\\\n & = \\frac{1}{\\sqrt{\\Omega}} \\,e^{i(\\vect{k}_i+\\vect{q} - \\vect{k}_f - \\vect{G}_f) \\cdot \\vect{x}_\\kappa} \\sum_\\vect{G} \\delta_{\\vect{q} - \\vect{k}_f + \\vect{k}_i, \\vect{G}}\\, \\widetilde{\\psi}_{\\kappa nlm}^\\text{atom}(-\\vect{k}_i + \\vect{G} - \\vect{G}_f) \\,,\n\\end{align}\nwhere the Fourier transform of the RHF Slater type orbital (STO) core wave functions, given in Eq.~\\eqref{eq:sto_wf}, are known analytically \\cite{Belkic1989}:\n\\begin{align}\n \\widetilde{\\psi}_\\text{STO}(\\vect{q}; Z, n, l, m) & = \\int d^3x \\, e^{i \\vect{q} \\cdot \\vect{x}} R_\\text{STO}(x; Z, n) \\,Y_l^m(\\hat{\\vect{x}}) \\equiv \\chi_\\text{STO}(q; Z, n, l) \\,Y_l^m(\\hat{\\vect{q}}) \\,,\\\\\n \\chi_\\text{STO}(q; Z, n) & = 4 \\pi N (n - l)! (2 Z)^{n} \\left( \\frac{i a_0 q}{Z} \\right)^l \\sum_{s = 0}^{ \\lfloor (n - l)\/2 \\rfloor} \\frac{\\omega_s^{nl}}{\\left( (a_0 q)^2 + Z^2 \\right)^{n - s + 1}} \\,,\\\\\n \\omega_s^{nl} & = \\left(-4 Z^2\\right)^{-s} \\frac{(n - s)!}{s! (n - l - 2s)!} \\,.\n\\end{align}\nThe direct detection rate is then\n\\begin{align}\n R = &\\; \\frac{2\\pi\\overline{\\sigma}_e}{\\mu_{\\chi e}^2 m_\\chi} \\frac{\\rho_\\chi}{\\rho_T \\Omega} \\sum_{\\kappa = 1}^{N_\\text{a}} \\sum_{n = 1}^{N_\\text{p}^\\kappa} \\sum_{l = 0}^{n-1} \\sum_{m = -l}^{l} \\int_\\text{1BZ} \\frac{d^3k_i}{(2\\pi)^3} \\frac{d^3k_f}{(2\\pi)^3}\\nonumber \\\\ & \\times \\sum_{\\vect{G}_f} \\sum_\\vect{G} F (\\nu_{\\kappa nl}) \\left( \\frac{f_e}{f_e^0} \\right)^2 \\mathcal{F}_\\text{med}^2\\, g(\\vect{q}, \\omega) \\left| \\widetilde{\\psi}_{\\kappa nlm}^\\text{atom}\\left( -\\vect{k}_i + \\vect{G} - \\vect{G}_f \\right) \\right|^2 ,\n\\end{align}\nwhere $\\vect{q} = \\vect{k}_f - \\vect{k}_i + \\vect{G}$, and $\\nu_{\\kappa nl}=\\nu(Z_\\text{eff}^{\\kappa nl},\\, \\omega + E_{\\kappa nl})$. We can now shift the $\\vect{G}_f$ variable, $\\vect{G'} \\equiv \\vect{G}_f - \\vect{G}$ and define $\\vect{k} = \\vect{k}_i + \\vect{G}'$ and $\\vect{k}' = \\vect{k}_f + \\vect{G}$. Therefore, $\\vect{q} = \\vect{k}' - \\vect{k}$ and\n\\begin{align}\n R = \\frac{2\\pi\\overline{\\sigma}_e}{\\mu_{\\chi e}^2 m_\\chi} \\frac{\\rho_\\chi}{\\rho_T \\Omega} & \\sum_{\\kappa = 1}^{N_\\text{a}} \\sum_{n = 1}^{N_\\text{p}^\\kappa} \\sum_{l = 0}^{n - 1} \\sum_{m = -l}^{l} \\int \\frac{d^3k}{(2\\pi)^3} \\frac{d^3k'}{(2\\pi)^3} \\,F( \\nu_{\\kappa nl} ) \\left( \\frac{f_e}{f_e^0} \\right)^2 \\mathcal{F}_\\text{med}^2\\, g(\\vect{q}, \\omega) \\left| \\widetilde{\\psi}^\\text{atom}_{\\kappa nlm}\\left( \\vect{k} \\right) \\right|^2 ,\n\\end{align}\nwhich is the closest expression to the vacuum matrix element, with just the inclusion of the core wave functions acting as a form factor.\n\nIn Fig.~\\ref{fig:cf_diff_rate}, we compare the binned rate from the core to conduction (c$\\to$c) calculation to the core to free (c$\\to$f) calculation and see a reasonable extrapolation to higher $\\omega$. As with the transition region between v$\\to$c and v$\\to$f shown in Fig.~\\ref{fig:vf_diff_rate}, we find $Z_\\text{eff} = 1$ gives a better match between c$\\to$c and c$\\to$f. While the total number of electrons from these transitions is expected to be much less than lower energy transitions, this is the best available calculation for thresholds up to the kinematic limit of $\\omega_\\text{max}$. \n\n\\subsection{In-medium Screening}\n\\label{subsec:in-med}\n\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.45\\textwidth]{figs\/Si_dielectric.pdf}\n\t\\includegraphics[width=0.45\\textwidth]{figs\/Ge_dielectric.pdf}\n\t\\caption{Dielectric function $\\epsilon(q, \\omega)$, given by Eq.~\\eqref{eq:analytic_di} with the parameters in Table~\\ref{tab:in_med}, of Si (left) and Ge (right) used to incorporate screening effects. The solid line indicates the edge of the kinematically accessible region $\\omega \\lesssim q v$. The dashed line is the band gap of the target. While the static dielectric can be $\\mathcal{O}(10)$, in the kinematically allowed region $\\epsilon(q, \\omega)$ is an $\\mathcal{O}(1)$ number, leading to an $\\mathcal{O}(1)$ effect on the scattering rates when the latter are dominated by small $q, \\omega$ transitions.}\n\t\\label{fig:dielectrics}\n\\end{figure}\n\nDM-electron interactions mediated by a dark photon or scalar are screened due to the in-medium mixing between the mediator and the photon. \nThe relevance of screening has been recently emphasized in Ref.~\\cite{Knapen:2021run}. \nThe screening factor, $f_e\/f_e^0$, is related to the longitudinal dielectric, $f_e\/f_e^0 = (\\hat{\\vect{q}} \\cdot \\boldsymbol{\\epsilon} \\cdot \\hat{\\vect{q}})^{-1}$, where $\\boldsymbol{\\epsilon}$ is the dielectric tensor. \nIt can be computed from in-medium loop diagrams or extracted from optical data. \nHere we model the dielectric of Si and Ge following Ref.~\\cite{Cappellini1993}:\n\\begin{align}\n \\epsilon(q, \\omega) = 1 + \\left[ \\frac{1}{\\epsilon_0 - 1} + \\alpha \\left( \\frac{q}{q_\\text{TF}} \\right)^2 + \\frac{q^4}{4m_e^2 \\omega_p^2} - \\left( \\frac{\\omega}{\\omega_p} \\right)^2 \\right]^{-1} \\, ,\n \\label{eq:analytic_di}\n\\end{align}\nand $\\epsilon_{ij} = \\epsilon(q, \\omega) \\,\\delta_{ij}$. \nHere, $\\epsilon_0 \\equiv \\epsilon(0, 0)$ is the static dielectric, $\\alpha$ is a fitting parameter, $q_\\text{TF}$ is the Thomas-Fermi momentum, and $\\omega_p$ is the plasma frequency. The parameters used for Si and Ge are listed in Table~\\ref{tab:in_med}, and we plot the dielectric as a function of $q, \\omega$ in Fig.~\\ref{fig:dielectrics}. \n\n\\begin{table}[t]\n \\begin{center}\n \\begin{tabular}{c||c c c c}\n \\hline\n Target & $\\epsilon_0$ & $\\alpha$ & $\\omega_p$ $[\\text{eV}]$ & $q_\\text{TF}$ $[\\text{keV}]$ \\\\\n \\hline\n Si & 11.3 & 1.563 & 16.6 & 4.13 \\\\\n Ge & 14 & 1.563 & 15.2 & 3.99 \\\\\n \\hline\n \\end{tabular}\n \\caption{Parameters used in the model of dielectric function, Eq.~\\eqref{eq:analytic_di}, of Si and Ge from Ref.~\\cite{Cappellini1993}, which accounts for in-medium screening effects on the transition rate.}\n \\label{tab:in_med}\n \\end{center}\n\\end{table}\n\nNaively one might expect that the effect of the dielectric is to screen the rate by an $\\mathcal{O}(100)$ factor due to the fact that the static dielectric, $\\epsilon_0$, is $\\mathcal{O}(10)$. However, this is only the value of the dielectric function at $q = \\omega = 0$, while as $q \\rightarrow \\infty$ and $\\omega \\rightarrow \\infty$ the dielectric approaches unity. Therefore, the effect of the dielectric crucially depends on the region of the kinematic phase space being probed. For a given energy deposition, $\\omega$, the momentum transfer is limited to $q \\gtrsim \\omega \/ v$ where $v \\sim 10^{-3}$ is the DM velocity. Therefore, the absolute minimum momentum transfer is $q_\\text{min} \\sim E_\\text{g} \/ v \\sim \\mathcal{O}(\\text{keV})$, for $\\mathcal{O}(\\text{eV})$ band gap targets. This is parametrically the same size as the Thomas-Fermi momentum $q_\\text{TF}$, so the dielectric is expected to slightly deviate from one, which causes only an $\\mathcal{O}(1)$ shift to the scattering rate, as seen in Fig.~\\ref{fig:scr_effects}.\n\n\\begin{figure}[t]\n\\centering\n \\includegraphics[width=\\textwidth]{figs\/screening_comparison.pdf}\n \\caption{Effect of screening on the binned rate (top row, for 1\\,GeV DM) and total rate (bottom row, as a function of $m_\\chi$) from v$\\to$c transitions for DM models with a light (red) and heavy (blue) mediator. The unscreened rate $R^\\text{no scr}$ is obtained with $\\epsilon=1$, and the screened rate $R^\\text{scr}$ is obtained with the model of the dielectric function given in Eq.~\\eqref{eq:analytic_di}.}\n \\label{fig:scr_effects}\n\\end{figure}\n\n\\section{Projected Sensitivity}\n\\label{sec:projected_constraints}\n\nWe now compile the results from the previous sections to compute the projected sensitivity. We also compare the relative importance of each transition type, and discuss differences between our results and previous calculations in the literature. When there are large differences, it is typically because of the inclusion of AE reconstruction and core states in the calculation. Since AE reconstruction and core states contribute predominantly at higher momentum transfer and energy deposition, we will find the largest differences typically occur for a massive mediator and higher detector threshold, where the effects in some cases can be more than an order of magnitude (especially for Ge). For the case of a massless mediator and lower detection threshold, the differences with previous literature are much smaller and mostly due to the inclusion of in-medium effects.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/total_diff_rate_compare.pdf}\n \\caption{DM-electron scattering rate binned in energy deposition (with $\\Delta\\omega=1$\\,eV) for 1\\,GeV DM, light (top row) and heavy (bottom row) mediators, from all four transition types: valence to conduction (v$\\to$c), valence to free (v$\\to$f), core to conduction (c$\\to$c), and core to free (c$\\to$f). We assume $\\overline{\\sigma}_e = 10^{-40}\\; \\text{cm}^2$, and take $Z_\\text{eff} = 1$ for all effective charges in the Fermi factor. Note that the c$\\to$c and c$\\to$f transitions involve semi-analytic treatment of 2p (3d) states and below in Si (Ge), which has been validated with DFT calculations including AE reconstruction; see Fig.~\\ref{fig:binned_core_wf}. We also overlay the binned rate from Ref.~\\cite{Derenzo:2016fse} which computed the v$\\to$c contribution using \\texttt{QEdark} (treating 3d states in Ge as valence, without including AE reconstruction effects).}\n \\label{fig:compiled_diff_rate}\n\\end{figure}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/compiled_rate.pdf}\n \\caption{DM-electron scattering rate as a function of the DM mass, for light (top row) and heavy (bottom row) mediators, from all four transition types: valence to conduction (v$\\to$c), valence to free (v$\\to$f), core to conduction (c$\\to$c), and core to free (c$\\to$f). We assume $\\overline{\\sigma}_e = 10^{-40}\\; \\text{cm}^2$, take $Z_\\text{eff} = 1$ for all effective charges in the Fermi factor, and show results for several threshold $Q$ values which significantly impact the v$\\to$c contribution.}\n \\label{fig:compiled_rate}\n\\end{figure}\n\nIn Fig.~\\ref{fig:compiled_diff_rate} we show the contribution to the binned rate from each of the four transition types, for a 1\\,GeV DM. \nWe see that valence to conduction (v$\\to$c) has a higher peak than the other three transition types, except for the Ge, heavy mediator case, where core to conduction (c$\\to$c) has the highest peak. \nFor comparison, Refs.~\\cite{Essig:2015cda, Derenzo:2016fse} compute the valence to conduction rates with DFT, including also the 3d states in Ge, but without AE reconstruction. \nAs expected, we find a lower rate at the lowest energy depositions due to the inclusion of in-medium screening, and a much higher rate at high $\\omega$ due to AE reconstruction and inclusion of core states. \n\nThe impact of these observations on the reach depends on the energy threshold. \nAssuming charge readout ({\\it e.g.}\\ via a CCD), the relevant quantity is the number of electron-hole pairs, $Q$, produced in an event. \nFor an energy deposition $\\omega$, this is given by\n\\begin{align}\nQ = 1 + \\left\\lfloor \\frac{\\omega - E_\\text{g}}{\\varepsilon} \\right\\rfloor ,\n\\label{eq:Q_bin}\n\\end{align}\nwhere the values for $\\varepsilon$ are $3.6$ eV and $2.9$ eV for Si and Ge respectively. \nIn Fig.~\\ref{fig:compiled_rate}, we show the total rate as a function of the DM mass, for $Q\\ge1, 5, 10$.\nThe threshold only affects the v$\\to$ c rate, as the other three transition types involve energy depositions corresponding to $Q>10$, and are therefore always fully included. \nWe see that for $Q\\ge1$, the valence to conduction (v$\\to$c) contribution dominates the total rate with the exception of the Ge, heavy mediator scenario, where core to conduction (c$\\to$c) is dominant for $m_\\chi\\gtrsim 30\\,$MeV. \nHigher thresholds significantly cut out v$\\to$c contributions in all cases, and render c$\\to$c more important for Ge, even in the light mediator scenario. \nFor Si, on the other hand, the total rate is still dominated by v$\\to$c because the core states are much deeper and contribute a lower rate. \nWe also see that v$\\to$f and c$\\to$f contributions are subdominant in all cases.\n\nFinally, we present the projected reach on the DM-electron reference cross section $\\overline\\sigma_e$ in Figs.~\\ref{fig:reach_compare_Q1} and \\ref{fig:reach_compare_Q10}, for $Q\\ge1$ and $Q\\ge10$, respectively. \nOur new calculation yields several important differences compared to the previous literature, and we discuss them in detail in the following subsection.\n\n\\subsection{Comparison With Previous Results}\n\\label{subsec:compare_to_prev}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/reach_compare_Q1.pdf}\n \\caption{95\\% C.L.\\ exclusion reach (3 events) assuming 1 kg-year exposure, $Q \\ge 1$, for light (top row) and heavy (bottom row) mediators. The results shown are from this work, Griffin et al.~\\cite{Griffin:2019mvc}, Essig et al.~\\cite{Essig:2015cda}, Lee et al.~\\cite{Lee:2015qva}, and Knapen et al.~\\cite{Knapen:2021run} (with and without screening). See Sec.~\\ref{subsec:compare_to_prev} for detailed comparison.}\n \\label{fig:reach_compare_Q1}\n\\end{figure}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figs\/reach_compare_Q10.pdf}\n \\caption{95\\% C.L.\\ exclusion reach (3 events) assuming 1 kg-year exposure, $Q \\ge 10$, for light (top row) and heavy (bottom row) mediators. The results shown are from this work and Essig et al.~\\cite{Essig:2015cda}. See Sec.~\\ref{subsec:compare_to_prev} for detailed comparison.}\n \\label{fig:reach_compare_Q10}\n\\end{figure}\n\nWe begin by comparing to our previous work, Ref.~\\cite{Griffin:2019mvc}, shown in brown in Fig.~\\ref{fig:reach_compare_Q1}. We previously restricted our analysis to the light mediator scenario, and $Q \\ge 1$, which is relatively unaffected by AE reconstruction effects since the rate is peaked at small energy\/momentum transfers, as seen in Fig.~\\ref{fig:vc_diff_rate}. The main reason the reach here is weaker is the inclusion of in-medium screening discussed in Sec.~\\ref{subsec:in-med}. \n\nNext we compare to Ref.~\\cite{Essig:2015cda}, shown in red in Figs.~\\ref{fig:reach_compare_Q1} and \\ref{fig:reach_compare_Q10}. \nThose results were computed solely from valence to conduction (v$\\to$c) transitions. The largest discrepancy is in the high $m_\\chi$ regime scattering off a Ge target via a heavy mediator. This is due to high momentum contributions to the 3d wave functions in Ge. Ref.~\\cite{Essig:2015cda} computed the 3d states with DFT without AE reconstruction, which as we saw in Fig.~\\ref{fig:binned_core_wf} is crucial for recovering the dominant part of the 3d wave functions at high momentum. \nAs discussed in Sec.~\\ref{subsec:atomic_wf}, our modeling of 3d electrons in Ge as core states reproduces their DFT-computed wave functions up to the AE reconstruction cutoff, and provides a robust parameterization of higher momentum components.\nSince the valence states in Ge also contribute an appreciable amount, the $Q \\ge 1$ results in Fig.~\\ref{fig:reach_compare_Q1} only differ by about an order of magnitude. \nHowever, the difference is more stark when going to higher $Q$ thresholds in Fig.~\\ref{fig:reach_compare_Q10}, which essentially isolates the 3d electrons' contribution. \nIn the low mass regime the difference is less significant, and primarily due to the inclusion of screening effects. \nAnother difference that is important here is sampling of the 1BZ. Ref.~\\cite{Essig:2015cda} used a uniform $6\\times6\\times6$ mesh with extra 27 points chosen by hand close to the center of the 1BZ, whereas here (as well as in Ref.~\\cite{Griffin:2019mvc}) we use a uniform $10\\times10\\times10$ grid. While checking convergence we found our (unscreened) results using a $6\\times6\\times6$ uniform mesh were a closer match to Ref.~\\cite{Essig:2015cda}; generally, increasing the number of $\\vect{k}$ points reduces the rate toward convergence, {\\it i.e.}\\ $R_{10\\times10\\times10} < R_{9\\times9\\times9} < R_{8\\times8\\times8}$. This can be seen more directly in the difference between the brown and red lines in the light mediator scenario (as both are computed without screening), and it affects Ge more than Si, as is expected due to the smaller band gap and greater dispersions of nearby bands requiring denser $\\vect{k}$ point sampling for convergence.\n\nRef.~\\cite{Lee:2015qva} also computed DM-electron scattering rates in semiconductors, focusing on Ge. The approach taken in that paper was to semi-analytically model the Ge wave functions with the core wave functions (with the same set of RHF STO wave function coefficients tabulated in Ref.~\\cite{Bunge:1993jsz}) and treat the final states as free with a Fermi factor, analogous to the core to free calculation performed here. As we can see from Fig.~\\ref{fig:reach_compare_Q1}, while for most of the mass range and mediators the estimates are too optimistic due to incorrect modeling of the valence and conduction states, in the high mass region with a heavy mediator (bottom-right panel), where 3d states dominate, their estimates are in good agreement with ours presented here, as expected. \n\nFinally, we discuss the comparison with the most recent work, Ref.~\\cite{Knapen:2021run}, which was limited to valence to conduction transitions. To show the effect of screening, we show their projected reach with (purple) and without (green) screening in Fig.~\\ref{fig:reach_compare_Q1}. Again the largest discrepancy is in the heavy mediator scenario with a Ge target, primarily due to the neglect of the 3d states in Ref.~\\cite{Knapen:2021run}. When these are not important, {\\it i.e.}\\ the low mass regime or a light mediator, we generally find good agreement, with our reach being a bit stronger. Notably this does not seem due to a mis-model of the dielectric, since the effect of screening relative to our previous results, Ref.~\\cite{Griffin:2019mvc}, is consistent with their result. We also find that screening has a smaller effect at high masses in the heavy mediator scenario for Si. These small differences are harder to disentangle since they could be due to: 1) different xc-functionals used (PBE and HSE vs.\\ TB09); 2) local field effects which are only partially included here since we assume the screening factor is isotropic; 3) the plane wave expansion parameter, $E_\\text{cut}$, taken to be $500$\\,eV without AE reconstruction in Ref.~\\cite{Knapen:2021run}, vs.\\ 1\\,keV, AE corrected to 2 keV taken here; 4) DM velocity distribution parameters, studied in detail in Ref.~\\cite{Radick:2020qip}, for which Ref.~\\cite{Knapen:2021run} assumed $v_\\text{esc} = 500$ km$\/$s as opposed to $v_\\text{esc} = 600$ km$\/$s chosen here; and 5) Ref.~\\cite{Knapen:2021run} took a directionally averaged dielectric, whereas here we only assume isotropy in the screening factor but not the matrix element itself.\n\n\\section{Conclusions}\n\\label{sec:conclusions}\n\nDark matter-electron scattering in dielectric crystal targets, especially semiconductors like Si and Ge, are at the forefront of DM direct detection experiments. \nIt is therefore imperative to have accurate theoretical predictions for the excitation rates. In this work, we extended the scattering rate calculation in several key aspects. \nMuch of the focus of previous calculations has been on transitions from valence to conduction bands just across the band gap, which will be accessible to near-future experiments. \nWe performed state-of-the-art DFT calculations for these states, and highlighted the importance of all-electron reconstruction which has been neglected in most previous works. \nAlong with this, we extended the transition rate calculation by explicitly including the contributions from core electrons and additional states more than 60\\,eV above the band gap using analytic approximations. \n\nWe updated the projected reach with our new calculation and found important differences compared to previous results. \nIn particular, we found that in the heavy mediator scenario, 3d electrons in Ge give a dominant contribution to the detection rate for DM heavier than about 30\\,MeV. \nAlso, the rate can be significantly higher than predicted previously for higher experimental thresholds. \nThis is exciting because new DM parameter space will be within reach even before detectors reach the single electron ionization threshold. \n\nWe also release a beta version of \\texttt{EXCEED-DM} (available \\href{https:\/\/github.com\/tanner-trickle\/EXCEED-DM}{here}~\\cite{tanner_trickle_2021_4747696}) that implements our DM-electron scattering calculation for general crystal targets, and make the electronic wave function data for Si and Ge~\\cite{Trickle2021}, as well as the \\texttt{EXCEED-DM} output~\\cite{Trickle2021a}, publicly available so our present analysis can be reproduced.\nWe have previously used \\texttt{EXCEED-DM} for a target comparison study~\\cite{Griffin:2019mvc}, and to study the daily modulation signals that can arise in anisotropic materials~\\cite{Trickle:2019nya}. \nThe generality of \\texttt{EXCEED-DM} means that potential applications are vast. \nIt can be used to compute detection rates for other target materials (assuming DFT calculations of valence and conduction states are available), and can also be adapted to include additional DM interactions such as in an effective field theory framework similar to the study of atomic ionizations in Ref.~\\cite{Catena:2019gfa} (see Ref.~\\cite{Catena:2021qsr} for a recent effort in this direction).\nFor momentum-suppressed effective operators, a full calculation in our framework is even more important, as the effects of all-electron reconstruction and core states (overlooked in Ref.~\\cite{Catena:2021qsr}) are generally amplified. \nMoreover, the differential information that can be obtained from our program facilitates further studies including realistic backgrounds.\nDetails of \\texttt{EXCEED-DM} and additional example calculations will be presented in an upcoming publication.\n\n\\acknowledgments\nWe are grateful to Kyle Bystrom for assistance with \\texttt{pawpyseed}, and thank Alex Ganose, Thomas Harrelson, Andrea Mitridate, Michele Papucci and Tien-Tien Yu for helpful discussions. \nWe also thank Rouven Essig and Adrian Soto for early correspondence about \\texttt{QEdark}.\nThis material is based upon work supported by the U.S.\\ Department of Energy, Office of Science, Office of High Energy Physics, under Award No.~DE-SC0021431 (TT, ZZ, KZ), by a Simons Investigator Award (KZ) and the Quantum Information Science Enabled Discovery (QuantISED) for High Energy Physics (KA2401032). \nThis research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S.\\ Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No.~DE-AC02-05CH11231.\nSome of the computations presented here were conducted on the Caltech High Performance Cluster, partially supported by a grant from the Gordon and Betty Moore Foundation. \nWork at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S.\\ Department of Energy under Contract No.~DE-AC02-05CH11231.\n\n\\bibliographystyle{utphys}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction and summary}\n\\label{sec:intro}\n\nThis is an updated version of a Technical Report, \\cite{Hall-Wellner-79},\n that, although never published, has been referenced repeatedly in the literature: \n e.g., \\cite{MR1416657}, \\cite{MR931630}, \\cite{MR856407}, \\cite{MR1927772}, \n \\cite{MR1792793}, \\cite{MR2344641}.\n \n \n \n \n \nLet $X_1, \\ldots , X_n$ be a random sample from a continuous d.f. $F$ \non ${\\mathbb R}^+ = [0,\\infty)$ with finite mean $\\mu = E(X)$, \nvariance $\\sigma^2 \\le \\infty$, and density $f(x) > 0$. Let $\\overline{F} = 1-F$\ndenote the survival function, let ${\\mathbb F}_n$ and $\\overline{{\\mathbb F}}_n$ denote the \nempirical distribution function and empirical survival function respectively, \nand let \n\\begin{eqnarray*}\ne(x) \\equiv e_F (x) \\equiv E(X-x | X>x) = \\int_x^\\infty \\overline{F} dI \/ \\overline{F} (x), \n\\ \\ \\ \\ 0 \\le x < \\infty\n\\end{eqnarray*}\ndenote the {\\sl mean residual life function} or {\\sl life expectancy function} \nat age $x$.\nWe use a subscript $F$ or $\\overline{F}$ on $e$ interchangeably, and $I$\ndenotes the identity function and Lebesgue measure on ${\\mathbb R}^+$.\n\nA natural nonparametric or life table estimate of $e$ is the random function \n$\\hat{e}_n $ defined by \n\\begin{eqnarray*}\n\\hat{e}_n (x) = \\left \\{ \\int_x^\\infty \\overline{{\\mathbb F}}_n dI \/ \\overline{{\\mathbb F}}_n (x) \\right \\} \n1_{[0, X_{nn})} (x)\n\\end{eqnarray*}\nwhere $X_{nn} \\equiv \\max_{1 \\le i \\le n } X_i$; that is, the average, less $x$, of \nthe observations exceeding $x$. \n\\cite*{MR0471233}\nstudied $\\hat{e}_n$ on a fixed finite interval $0 \\le x \\le T < \\infty$.\nShe proved that $\\hat{e}_n$ is a strongly uniformly consistent estimator of $e$ \non $[0,T]$, and that, when properly centered and normalized, it \nconverges weakly to a certain limiting Gaussian process on $[0,T]$.\n\nWe first extend\nYang's (1978) \nresults to all of ${\\mathbb R}^+$ by introducing suitable \nmetrics. Her consistency result is extended in Theorem~\\ref{ConsistencyTh1} by \nusing the techniques of \n\\cite{MR0651528,MR0651392};\nthen her weak convergence \nresult is extended in Theorem~\\ref{ProcessConvergenceThm} using \n\\cite{MR0301846}\nand \\cite{MR0651392}. \n\nIt is intuitively clear that the variance of $\\hat{e}_n (x)$ is \napproximately $\\sigma^2 (x) \/ n(x)$ where \n\\begin{eqnarray*}\n\\sigma^2 (x) = Var[X-x| X> x]\n\\end{eqnarray*}\n is the residual variance and $n(x)$ is the number of observations exceeding $x$; \nthe formula would be justified if \nthese $n(x)$ observations were a random sample of fixed sized $n(x)$ from the \nconditional distribution $P( \\cdot | X>x)$. Noting that \n$\\overline{{\\mathbb F}}_n (x) = n(x)\/n \\rightarrow \\overline{F}(x)$ a.s., we would then have\n\\begin{eqnarray*}\nn Var[ \\hat{e}_n (x)] = n \\sigma^2 (x) \/ n(x) \\rightarrow \\sigma^2 (x) \/ \\overline{F} (x) .\n\\end{eqnarray*}\nProposition~\\ref{prop:VarianceCovarianceProp} and \nTheorem~\\ref{ProcessConvergenceThm} validate this (see (2.4) below): the variance of \nthe limiting distribution of $n^{1\/2} ( \\hat{e}_n (x) - e(x))$ is precisely \n$\\sigma^2 (x) \/ \\overline{F} (x)$. \n\nIn Section~\\ref{sec:AlternativeSufficientConditions} \nsimpler sufficient conditions for Theorems~\\ref{ConsistencyTh1} \nand \\ref{ProcessConvergenceThm} are given and the \ngrowth rate of the variance of the limiting process for large $x$ is considered;\nthese results are related to those of \n\\cite{MR0359049}. \nExponential, Weibull, and Pareto examples are considered in Section 4.\n\nIn Section~\\ref{sec:confidenceBands}, by transforming (and reversing) the time scale and rescaling\nthe state space, we convert the limit process to standard Brownian motion on \nthe unit interval (Theorem~\\ref{ReversedProcBMThm}); this enables construction of nonparametric simultaneous\nconfidence bands for the function $e_F$ (Corollary~\\ref{UniformConfBandsCor}).\nApplication to survival data of guinea pigs subject to infection with \ntubercle bacilli as given by \n\\cite{Bjerkedal-60} \nappears in Section~\\ref{sec:illustrationOfBands}.\n\nWe conclude this section with a brief review of other previous work.\nEstimation of the function $e$, and especially the discretized life-table version,\nhas been considered by Chiang; see pages 630-633 of \n\\cite{Chiang-60}\nand page 214 of \n\\cite{Chiang-68}. \n(Also see \\cite{Chiang-68},\npage 189, for some \nearly history of the subject.) The basis for {\\sl marginal} inference (i.e. at a specific\nage $x$) is that the estimate $\\hat{e}_n (x)$ is approximately normal with \nestimated standard error $S_k \/ \\sqrt{k}$, where $k = n \\overline{{\\mathbb F}}_n (x)$ \nis the observed number of observations beyond $x$ and $S_k$ is the \nsample standard deviation of those observations. \nA partial justification of this is in \n\\cite{Chiang-60},\npage 630, (and is made precise in \nProposition~\\ref{prop:PointwiseAsympNormality} below). \n\\cite{Chiang-68},\npage 214, gives the analogous marginal \nresult for grouped data in more detail, but again without proofs; \nnote the solumn $S_{\\hat{e}_i}$ in his Table 8, page 213, which is based on a \nmodification and correction of a variance formula due to \n\\cite{Wilson-38}. \nWe know of no earlier work on simultaneous inference (confidence\nbands) for mean residual life. \n \nA plot of (a continuous version of) the estimated mean residual life function of \n43 patients suffering from chronic gramulocytic leukemia is given \nby \n\\cite{MR0253494}. \n\\cite{Gross-Clark-75} \nbriefly mention the estimation of $e$ in a life - table setting, but \ndo not discuss the variability of the estimates (or estimates thereof).\nTests for exponentiality against decreasing mean residual life alternatives have\nbeen considered by \n\\cite{MR0395119}. \n\n\\section{Convergence on ${\\mathbb R}^+$; covariance function of the limiting process}\n\\label{sec:convergenceOnRplus}\nLet $\\{ a_n \\}_{n\\ge1}$ be a sequence of nonnegative numbers with $a_n \\rightarrow 0$\nas $n\\rightarrow \\infty$. For any such sequence and a d.f. $F$ as above, set \n$b_n = F^{-1} (1-a_n) \\rightarrow \\infty$ as $n\\rightarrow \\infty$. \nThen, for any function $f$ on ${\\mathbb R}^+$, define $f^*$ equal to $f$ for $x \\le b_n$ \nand $0$ for $x> b_n$: $f^* (x) = f(x) 1_{[0,b_n ]} (x)$. \nLet $\\| f \\|_a^b \\equiv \\sup_{a \\le x \\le b} | f(x)|$ and write $\\| f \\| $ \nif $a=0$ and $b=\\infty$. \n\nLet ${\\cal H}(\\downarrow)$ denote the set of all nonnegative, decreasing \nfunctions $h$ on $[0,1]$ for which $\\int_0^1 (1\/h) dI< \\infty$.\n\\medskip\n\n\\par\\noindent\n{\\bf Condition 1a.} There exists $h \\in {\\cal H} ( \\downarrow)$ such that\n\\begin{eqnarray*}\nM_1 \\equiv M_1 (h,F) \\equiv \\sup_x \\frac{\\int_x^\\infty h(F) dI\/ h(F(x))}{e(x)} < \\infty .\n\\end{eqnarray*}\nSince $0 < h(0) < \\infty$ and $e(0) = E(X) < \\infty$, \nCondition 1a implies that $\\int_0^\\infty h(F) dI < \\infty$.\nAlso note that $h(F)\/h(0)$ is a survival function on ${\\mathbb R}^+$\nand that the numerator in Condition 1a is simply $e_{h(F)\/h(0)}$; \nhence Condition 1a may be rephrased as: there exists $h \\in {\\cal H}(\\downarrow) $\nsuch that $M_1 \\equiv \\| e_{h(F)\/h(0)} \/ e_F \\| < \\infty$.\n\\medskip\n\n\\par\\noindent\n{\\bf Condition 1b.} \nThere exists $h \\in {\\cal H}(\\downarrow)$ for which \n$\\int_0^\\infty h(F) dI< \\infty$ and $\\| e h(F) \\| < \\infty$.\n\\medskip\n\n\\par\\noindent\nBounded $e_F$ and existence of a moment of order greater than $1$ \nis more than sufficient for Condition 1b (see Section 3).\n\\medskip\n\n\\begin{thm}\n\\label{ConsistencyTh1}\nLet $a_n = \\alpha n^{-1} \\log \\log n$ with $\\alpha>1$. \nIf Condition 1a holds for a particular $h \\in {\\cal H} (\\downarrow)$, then\n\\begin{eqnarray}\n&& \\rho_{h(F)e\/ \\overline{F}} ( \\hat{e}_n^* , e^* ) \\nonumber \\\\\n&& \\ \\ \\ \\equiv \\sup \\left \\{ \\frac{|\\hat{e}_n (x) - e(x) | \\overline{F} (x)}{h(F(x))e(x)} : \\ x \\le b_n \\right \\}\n\\rightarrow_{a.s.} 0 \\ \\ \\mbox{as} \\ \\ n \\rightarrow \\infty .\n\\label{WeightedConsistencyPart1}\n\\end{eqnarray}\nIf Condition 1b holds, then \n\\begin{eqnarray}\n&& \\rho_{1\/\\overline{F}} ( \\hat{e}_n^* , e^* ) \\nonumber \\\\\n&& \\ \\ \\ \\equiv \\sup \\{ |\\hat{e}_n (x) - e(x) | \\overline{F} (x) : \\ x \\le b_n \\}\n\\rightarrow_{a.s.} 0 \\ \\ \\mbox{as} \\ \\ n \\rightarrow \\infty .\n\\label{WeightedConsistencyPart2}\n\\end{eqnarray}\n\\end{thm}\n\nThe metric in (\\ref{WeightedConsistencyPart2}) turns out to be a natural one\n(see Section~\\ref{sec:confidenceBands}); that in (\\ref{WeightedConsistencyPart1}) is typically stronger.\n\n\\begin{proof}\nFirst note that for $x< X_{nn}$\n\\begin{eqnarray*}\n\\hat{e}_n (x) - e(x) \n= \\frac{\\overline{F}(x)}{\\overline{{\\mathbb F}}_n (x) }\n \\left \\{ \\frac{-\\int_x^\\infty ( {\\mathbb F}_n - F)dI}{\\overline{F} (x)} \n + \\frac{e(x)}{\\overline{F} (x)} ( {\\mathbb F}_n (x) - F(x) ) \\right \\} .\n\\end{eqnarray*}\nHence \n\\begin{eqnarray*}\n\\rho_{h(F)e\/ \\overline{F}} ( \\hat{e}_n^* , e^* ) \n& \\le & \\bigg \\| \\frac{\\overline{F}}{\\overline{{\\mathbb F}}_n } \\bigg \\|_0^{b_n} \n \\left \\{ \\sup_x \\frac{| \\int_x^\\infty ({\\mathbb F}_n - F) dI |}{h(F(x))e(x)} \n + \\sup_x \\frac{| {\\mathbb F}_n (x) - F(x) |}{h(F(x))} \\right \\} \\\\\n & \\le & \\bigg \\| \\frac{\\overline{F}}{\\overline{{\\mathbb F}}_n } \\bigg \\|_0^{b_n} \\cdot \n \\rho_{h(F)} ( {\\mathbb F}_n , F ) (M_1 + 1 ) \\\\\n & \\rightarrow_{a.s.} & 0\n \\end{eqnarray*}\nusing Condition 1a, Theorem 1 of \n\\cite{MR0651528} \nto show $\\rho_{h(F)} ( {\\mathbb F}_n , F ) \\rightarrow_{a.s.} 0$ a.s., \nand Theorem 2 of \n\\cite{MR0651392} \nto show that \n$\\limsup_n \\| \\overline{F} \/ \\overline{{\\mathbb F}}_n \\|_0^{b_n} < \\infty$ a.s..\n\nSimilarly, using Condition 1b,\n\\begin{eqnarray*}\n\\rho_{1\/ \\overline{F}} ( \\hat{e}_n^* , e^* ) \n& \\le & \\bigg \\| \\frac{\\overline{F}}{\\overline{{\\mathbb F}}_n } \\bigg \\|_0^{b_n} \n \\left \\{ \\sup_x | \\int_x^\\infty ({\\mathbb F}_n - F) dI | \n + \\sup_x e(x) | {\\mathbb F}_n (x) - F(x) | \\right \\} \\\\\n & \\le & \\bigg \\| \\frac{\\overline{F}}{\\overline{{\\mathbb F}}_n } \\bigg \\|_0^{b_n} \\cdot \n \\rho_{h(F)} ( {\\mathbb F}_n , F ) \\left ( \\int_0^\\infty h(F) dI + \\| e h(F) \\| \\right ) \\\\\n & \\rightarrow_{a.s.} & 0 .\n \\end{eqnarray*}\n\\end{proof} \n\nTo extend Yang's weak convergence results, we will use the \nspecial uniform empirical processes ${\\mathbb U}_n$ of the Appendix of \n\\cite{MR0301846} \nor \n\\cite{MR838963} \nwhich converge to a special Brownian bridge \nprocess ${\\mathbb U}$ in the strong sense that\n\\begin{eqnarray*}\n\\rho_q ({\\mathbb U}_n , {\\mathbb U}) \\rightarrow_p 0 \\ \\ \\mbox{as} \\ \\ n \\rightarrow \\infty \n\\end{eqnarray*}\nfor $q \\in {\\cal Q} (\\downarrow)$, the set of all continuous functions \non $[0,1]$ which are monotone decreasing on $[0,1]$ and \n$\\int_0^1 q^{-2} dI < \\infty$. Thus ${\\mathbb U}_n = n^{1\/2} (\\Gamma_n - I)$\non $[0,1]$ where $\\Gamma_n$ is the empirical d.f. of special uniform \n$(0,1)$ random variables $\\xi_1 , \\ldots , \\xi_n$.\n\nDefine the {\\sl mean residual life process} on ${\\mathbb R}^+$ by \n\\begin{eqnarray*}\nn^{1\/2} ( \\hat{e}_n (x) - e(x)) \n& = & \\frac{1}{\\overline{{\\mathbb F}}_n (x)} \\left \\{ - \\int_x^\\infty n^{1\/2} ( {\\mathbb F}_n - F) dI \n + e(x) n^{1\/2} ( {\\mathbb F}_n (x) - F(x)) \\right \\} \\\\\n& \\stackrel{d}{=} & \\frac{1}{\\overline{\\Gamma}_n (F(x))} \\left \\{ \n - \\int_x^\\infty {\\mathbb U}_n (F) dI + e(x) {\\mathbb U}_n (F(x)) \\right \\} \\\\\n& \\equiv & {\\mathbb Z}_n (x), \\ \\ \\ \\ 0\\le x < F^{-1} (\\xi_{nn} ) \n\\end{eqnarray*}\nwhere $\\xi_{nn} = \\max_{1 \\le i \\le n} \\xi_i$, and \n${\\mathbb Z}_n (x) \\equiv - n^{1\/2} e(x)$ for $x \\ge F^{-1} ( \\xi_{nn} )$. \nThus ${\\mathbb Z}_n$ has the same law as $n^{1\/2} (\\hat{e}_n - e)$ \nand is a function of the special process ${\\mathbb U}_n$. Define the corresponding \nlimiting process ${\\mathbb Z}$ by \n\\begin{eqnarray}\n{\\mathbb Z}(x) = \\frac{1}{\\overline{F} (x)} \\left \\{ - \\int_x^\\infty {\\mathbb U} (F) dI + e(x) {\\mathbb U}(F(x)) \\right \\}, \n\\ \\ \\ \\ 0 \\le x < \\infty .\n\\label{LimitProcess}\n\\end{eqnarray}\nIf $\\sigma^2 = Var(X) < \\infty$ (and hence under either Condition 2a or 2b below), \n${\\mathbb Z}$ is a mean zero Gaussian process on ${\\mathbb R}^+$ with covariance function \ndescribed as follows:\n\n\\begin{prop} \n\\label{prop:VarianceCovarianceProp}\nSuppose that $\\sigma^2 = Var(X) < \\infty$.\nFor $0 \\le x \\le y < \\infty$\n\\begin{eqnarray}\nCov [{\\mathbb Z}(x) , {\\mathbb Z}(y)] \n= \\frac{\\overline{F}(y)}{\\overline{F}(x)} Var [{\\mathbb Z}(y)] = \\frac{\\sigma^2 (y)}{\\overline{F}(y)} \n\\label{CovarianceFormulaOne}\n\\end{eqnarray}\nwhere\n\\begin{eqnarray*}\n\\sigma^2 (t) \\equiv Var[X-t|X>t] \n= \\frac{\\int_t^\\infty (x-t)^2 F(x)}{\\overline{F} (t)} - e^2 (t) \n\\end{eqnarray*}\nis the residual variance function; also \n\\begin{eqnarray}\nCov[{\\mathbb Z}(x) \\overline{F}(x) , {\\mathbb Z}(y) \\overline{F}(y)]\n= Var[{\\mathbb Z}(y) \\overline{F} (y)] = \\overline{F}(y) \\sigma^2 (y) .\n\\label{CovarianceFormulaTwo}\n\\end{eqnarray}\n\\end{prop}\n\n\\begin{proof} \nIt suffices to prove (\\ref{CovarianceFormulaTwo}). \nLet ${\\mathbb Z}' \\equiv {\\mathbb Z} \\overline{F}$; from (\\ref{LimitProcess}) we find\n\\begin{eqnarray*}\nCov[{\\mathbb Z}' (x) , {\\mathbb Z}' (y)] \n& = & e(x) e(y) F(x) \\overline{F} (y) - e(x) \\int_y^\\infty F(x) \\overline{F} (z) dz \\\\\n&& \\ \\ \\ - \\ e(y) \\int_x^\\infty (F(y \\wedge z) - F(y) F(z) ) dz \\\\\n&& \\ \\ \\ + \\ \\int_x^\\infty \\int_y^\\infty (F(z \\wedge w) - F(z) F(w) ) dz dw .\n\\end{eqnarray*}\nExpressing integrals over $(x,\\infty)$ as the sum of integrals over $(x,y)$\nand $(y,\\infty)$, and recalling the defining formula for $e(y)$, we find that the \nright side reduces to \n\\begin{eqnarray*}\n\\lefteqn{\n\\int_y^\\infty \\int_y^\\infty (F(z \\wedge z) - F(z) F(w) dz dw - e^2 (y) F(y) \\overline{F} (y) }\\\\\n&= & \\int_y^\\infty (t-y)^2 dF(t) - \\overline{F} (y) e^2 (y) \\\\\n& = & \\overline{F} (y) \\sigma^2 (y) \n\\end{eqnarray*}\nwhich, being free of $x$, is also $Var[{\\mathbb Z}' (y)]$. \n\\end{proof}\n\nAs in this proposition, the process ${\\mathbb Z}$ is often more easily studied \nthrough the process ${\\mathbb Z}' = {\\mathbb Z} \\overline{F}$; such a study continues\nin Section 5. \nStudy of the variance of ${\\mathbb Z}(x)$, namely $\\sigma^2 (x)\/ \\overline{F}(x)$,\nfor large $x$ appears in Section 3.\n\\medskip\n\n\\par\\noindent\n{\\bf Condition 2a}. $\\sigma^2 < \\infty$ and there exists \n$q \\in {\\cal Q}(\\downarrow)$ such that\n\\begin{eqnarray*}\nM_2 \\equiv M_2 (q,F) \\equiv \\sup_x \\frac{\\int_x^\\infty q(F) dI\/q(F(x))}{e(x)} < \\infty .\n\\end{eqnarray*}\nSince $0 < q(0) < \\infty$ and $e(0) = E(X) < \\infty$, Condition 2a implies\nthat $\\int_0^\\infty q(F) dI < \\infty$; Condition 2a may be rephrased as:\n$M_2 \\equiv \\| e_{q(F)\/q(0)}\/ e_F \\| < \\infty$ where \n$e_{q(F)\/q(0)}$ denotes the mean residual life function for the \nsurvival function $q(F)\/q(0)$. \n\\medskip\n\n\\par\\noindent\n{\\bf Condition 2b.} $\\sigma^2 < \\infty$ and there exists \n$q \\in {\\cal Q}(\\downarrow)$ such that $\\int_0^\\infty q(F)dI< \\infty$. \n\\medskip\n\nBounded $e_F$ and existence of a moment of order greater than $2$ \nis more than sufficient for 2b (see Section~\\ref{sec:AlternativeSufficientConditions}). \n\\medskip\n\n\\begin{thm} (Process convergence). \n\\label{ProcessConvergenceThm} \nLet $a_n \\rightarrow 0$, $na_n \\rightarrow \\infty$. \nIf Condition 2a holds for a particular $q \\in {\\cal Q}(\\downarrow)$, then\n\\begin{eqnarray}\n&& \\rho_{q(F) e \/ \\overline{F}} ( {\\mathbb Z}_n^* , {\\mathbb Z}^*) \\nonumber \\\\\n&& \\ \\ \\ \\ \\equiv \\sup \\left \\{ \\frac{| {\\mathbb Z}_n (x) - {\\mathbb Z}(x)| \\overline{F}(x)}{q(F(x))e(x)} : \\ \\ \nx \\le b_n \\right \\} \\rightarrow_p 0 \\ \\ \\ \\mbox{as} \\ \\ n \\rightarrow \\infty .\n\\label{ProcessConvergenceA}\n\\end{eqnarray}\nIf Condition 2b holds, then\n\\begin{eqnarray}\n\\label{ProcessConvergenceB}\n\\phantom{blablabla}\\rho_{1\/\\overline{F}} ( {\\mathbb Z}_n^*, {\\mathbb Z}^*) \n\\equiv \\sup \\{ | {\\mathbb Z}_n(x) - {\\mathbb Z}(x)| \\overline{F} (x): \\ x \\le b_n \\} \\rightarrow_p 0 \n\\ \\ \\ \\mbox{as} \\ \\ n \\rightarrow \\infty .\n\\end{eqnarray}\n\\end{thm}\n\n\\begin{proof}\nFirst write \n\\begin{eqnarray*}\n{\\mathbb Z}_n (x) - {\\mathbb Z}(x) \n= \\left \\{ \\frac{\\overline{F}(x)}{\\overline{\\Gamma}_n (F(x))} -1 \\right \\} {\\mathbb Z}_n^1 (x) \n + ({\\mathbb Z}_n^1 (x) - {\\mathbb Z}(x)) \n\\end{eqnarray*}\nwhere \n\\begin{eqnarray*}\n{\\mathbb Z}_n^1 (x) \\equiv \\frac{1}{\\overline{F}(x)} \\left \\{ - \n \\int_x^\\infty {\\mathbb U}_n (F) dI + e(x) {\\mathbb U}_n (F(x)) \\right \\}, \\ \\ \\ \\ 0 \\le x < \\infty .\n\\end{eqnarray*}\nThen note that, using Condition 2a, \n\\begin{eqnarray*}\n\\rho_{q(F)e\/\\overline{F}} ({\\mathbb Z}_n^1 , 0 ) \n& \\le & \\sup_x \\frac{| \\int_x^\\infty {\\mathbb U}_n (F) dI }{q(F(x))e(x)} + \\rho_q ({\\mathbb U}_n , 0 ) \\\\\n& \\le & \\rho_q ({\\mathbb U}_n , 0)\\{ M_2 +1) \\} = O_p (1);\n\\end{eqnarray*}\nthat $\\| \\overline{I}\/ \\overline{\\Gamma}_n -1 \\|_0^{1-a_n} \\rightarrow_p 0$\nby Theorem 0 of Wellner (1978) since $na_n \\rightarrow \\infty$; and,\nagain using Condition 2a, that \n\\begin{eqnarray*}\n\\rho_{q(F) e\/ \\overline{F}} ({\\mathbb Z}_n^1 , {\\mathbb Z}) \n& \\le & \\sup_x \\frac{| \\int_x^\\infty ( {\\mathbb U}_n (F) - {\\mathbb U}(F))dI|}{q(F(x))e(x)} \n + \\rho_q ({\\mathbb U}_n , {\\mathbb U})\\\\\n& \\le & \\rho_q ({\\mathbb U}_n , {\\mathbb U}) \\{ M_2 +1 \\} \\rightarrow_p 0 .\n\\end{eqnarray*}\nHence\n\\begin{eqnarray*}\n\\rho_{q(F)e\/ \\overline{F}} ({\\mathbb Z}_n^* , {\\mathbb Z}^* ) \n& \\le & \\bigg \\| \\frac{\\overline{I}}{\\overline{\\Gamma}_n} -1 \\bigg \\|_0^{1-a_n} \n \\rho_{q(F)e\/\\overline{F}} ({\\mathbb Z}_n^1, 0) + \\rho_{q(F)e\/\\overline{F}} ({\\mathbb Z}_n^1, {\\mathbb Z})\\\\\n& = & o_p (1) O_p (1) + o_p (1) = o_p (1) .\n\\end{eqnarray*}\nSimilarly, using Condition 2b\n\\begin{eqnarray*}\n\\rho_{1\/\\overline{F}} ({\\mathbb Z}_n^1, 0) \n& \\le & \\sup_x \\bigg | \\int_x^\\infty {\\mathbb U}_n (F) dI \\bigg | + \\sup_x e(x) | {\\mathbb U}_n (F(x)) | \\\\\n& \\le & \\rho_q ({\\mathbb U}_n , 0) \\left \\{ \\int_0^\\infty q(F) dI + \\| e q(F) \\| \\right \\} = O_p (1),\n\\end{eqnarray*}\n\\begin{eqnarray*}\n\\rho_{1\/\\overline{F}} ( {\\mathbb Z}_n^1, {\\mathbb Z}) \n& \\le & \\sup_x \\bigg | \\int_x^\\infty ({\\mathbb U}_n (F) - {\\mathbb U} (F) ) dI \\bigg | + \\sup_x e(x) | {\\mathbb U}_n (F(x)) - {\\mathbb U} (F(x)) |\\\\\n& \\le & \\rho_q ({\\mathbb U}_n , {\\mathbb U} ) \\left \\{ \\int_0^\\infty q(F) dI + \\| e q(F) \\| \\right \\} \\rightarrow_p 0,\n\\end{eqnarray*}\nand hence \n\\begin{eqnarray*}\n\\rho_{1\/\\overline{F}} ( {\\mathbb Z}_n^* , {\\mathbb Z}^*) \n& \\le & \\bigg \\| \\frac{\\overline{I}}{\\overline{\\Gamma}_n} -1 \\bigg \\|_0^{1-a_n} \n \\rho_{1\/\\overline{F}} ({\\mathbb Z}_n^1 , 0 ) + \\rho_{1\/\\overline{F}} ({\\mathbb Z}_n^1, {\\mathbb Z}) \\\\\n& = & o_p (1) O_p (1) + o_p (1) = o_p (1) .\n\\end{eqnarray*}\n\\end{proof}\n\n\\section{Alternative sufficient conditions; $Var[{\\mathbb Z}(x)]$ as $x \\rightarrow \\infty$.}\n\\label{sec:AlternativeSufficientConditions}\nOur goal here is to provide easily checked conditions which will imply the \nsomewhat cumbersome Conditions 2a and 2b; similar conditions also appear \nin the work of \n\\cite{MR0359049}, \nand we use their results to extend\ntheir formula for the residual coefficient of variation for large $x$ \n((\\ref{LimitingVarianceRelToMRLSquared}) below). \nThis provides a simple description of the behavior of \n$Var[{\\mathbb Z}(x)]$, the asymptotic variance of $n^{1\/2} (\\hat{e}_n (x) - e(x))$\nas $x\\rightarrow \\infty$.\n\\medskip\n\n\\par\\noindent\n{\\bf Condition 3.} $E(X^r) < \\infty$ for some $r>2$.\n\\smallskip\n\n\\par\\noindent\n{\\bf Condition 4a.} Condition 3 and $\\lim_{x \\rightarrow \\infty} \\frac{d}{dx} (1\/\\lambda (x))\n= c < \\infty$ where $\\lambda = f\/ \\overline{F}$, the hazard function.\n\\smallskip\n\n\\par\\noindent\n{\\bf Condition 4b.} Condition 3 and $\\limsup_{x \\rightarrow \\infty} \\{\n\\overline{F}(x)^{1+\\gamma} \/ f(x) \\} < \\infty$ for some \n$r^{-1} < \\gamma < 1\/2$. \n\\medskip\n\n\\begin{prop} \n\\label{RegVariationSuffCond}\nIf Condition 4a holds, then $0 \\le c \\le r^{-1}$, Condition 2a holds, \nand the squared residual coefficient of variation tends to $1\/(1-2c)$:\n\\begin{eqnarray}\n\\lim_{x \\rightarrow \\infty} \\frac{\\sigma^2 (x)}{e^2 (x)} = \\frac{1}{1-2c} .\n\\label{LimitingVarianceRelToMRLSquared}\n\\end{eqnarray}\nIf Condition 4b holds, then Condition 2b holds.\n\\end{prop}\n\n\\begin{cor}\nCondition 4a implies\n\\begin{eqnarray*}\nVar[ {\\mathbb Z}(x)] \\sim \\frac{e^2 (x)}{\\overline{F} (x)} (1- 2c)^{-1} \\ \\ \\ \n\\mbox{as} \\ \\ x \\rightarrow \\infty .\n\\end{eqnarray*}\n\\end{cor}\n\n\\begin{proof}\nAssume 4a. Choose $\\gamma$ between $r^{-1}$ and $1\/2$; define\na d.f. $G$ on ${\\mathbb R}^+$ by $\\overline{G} = \\overline{F}^{\\gamma}$ \nand note that $g\/\\overline{G} = \\gamma f \/ \\overline{F} = \\gamma \\lambda$.\nBy Condition 3 $x^r \\overline{F}(x) \\rightarrow 0$ as $x \\rightarrow \\infty$ \nand hence $x^{\\gamma r} \\overline{G}(x) \\rightarrow 0$ as $x \\rightarrow \\infty$. \nSince $\\gamma r > 1$, $G$ has a finite mean and therefore \n$e_G (x) = \\int_x^\\infty \\overline{G} dI \/ \\overline{G} (x)$ is well-defined.\n\nSet $\\eta = 1 \/ \\lambda = \\overline{F}\/f$, and note that \n$\\eta (x) \\overline{G} (x) \\rightarrow 0$ as $x \\rightarrow \\infty$. \n(If $\\limsup \\eta(x) < \\infty$, then it holds trivially; otherwise, $\\eta (x) \\rightarrow \\infty$\n(because of 4a) and $\\lim \\eta(x) \\overline{G} (x) = \\lim (\\eta(x)\/x) (x \\overline{G} (x)) \n= \\lim \\eta '' (x) x \\overline{G}(x) = 0$ by 4a and L'Hopital.\nThus by L'Hopital's rule\n\\begin{eqnarray*}\n0 & \\le & \\lim \\frac{\\eta(x)}{e_G (x)} \n = \\lim \\frac{\\eta(x) \\overline{G} (x)}{\\int_x^\\infty \\overline{G} dI} \\\\\n& = & \\lim \\frac{\\eta (x) g(x) - \\overline{G} (x) \\eta' (x)}{\\overline{G} (x)} \\\\\n& = & \\gamma - \\lim \\eta' (x) = \\gamma - c \\ \\ \\ \\mbox{by 4a} .\n\\end{eqnarray*}\nThus $c \\le \\gamma$ for any $\\gamma > r^{-1}$ and it follows that \n$c \\le r^{-1}$. It is elementary that $c \\ge 0$ since $\\eta = 1\/\\lambda$ \nis nonnegative.\n\nChoose $q(t) = (1-t)^{\\gamma}$. Then $\\gamma -c> 0$, \n$q \\in {\\cal Q}(\\downarrow)$, and to verify 2a \nit now suffices to show that $\\lim (\\eta(x) \/ e_F(x) ) = 1-c < \\infty$\nsince it then follows that \n\\begin{eqnarray*}\n\\lim \\frac{e_G (x)}{e_F (x)} = \\lim \\frac{\\eta (x) \/ e_F (x)}{\\eta (x) \/ e_G (x)} \n= \\frac{1-c}{\\gamma-c} < \\infty .\n\\end{eqnarray*}\nBy continuity and $e_G (0) < \\infty$, $0 < e_F (0) < \\infty$, \nthis implies Condition 2a. But $r>2$ implies that $x \\overline{F}(x) \\rightarrow 0$ \nas $x \\rightarrow \\infty$ so $\\eta (x) \\overline{F} (x) \\rightarrow 0$ and hence\n\\begin{eqnarray*}\n\\lim \\frac{\\eta(x)}{e_F (x)} \n= \\lim \\frac{\\eta \\overline{F} (x)}{\\int_x^\\infty \\overline{F} dI} \n= \\lim ( 1 - \\eta' (x)) = 1-c .\n\\end{eqnarray*}\nThat (\\ref{LimitingVarianceRelToMRLSquared}) holds will now follows from results of \n\\cite{MR0359049}, \nas follows:\nTheir Corollary to Theorem 7 implies that \n$P( \\lambda (t) (X-t) > x | X>t) \\rightarrow e^{-x}$ if $c=0$ \nand $\\rightarrow (1+cx)^{-1\/c}$ if $c>0$. \nThus, in the former case, $F$ is in the domain of attraction of the \nPareto residual life distribution and its related extreme value distribution.\nThen Theorem 8(a) implies convergence of the (conditional) mean \nand variance of $\\lambda (t) (X -t)$ to the mean and variance \nof the limiting Pareto distribution, namely $(1-c)^{-1}$ \nand $(1-c)^{-2} (1-2c)^{-1}$. \nBut the conditional mean of $\\lambda (t) (X_t)$ is simply \n$\\lambda (t) e(t)$, so that $\\lambda (t) \\sim (1-c)^{-1} \/ e(t)$ \nand (\\ref{LimitingVarianceRelToMRLSquared}) now follows.\n\nIf Condition 4b holds, let $q(F) = \\overline{F}^{\\gamma}$ again.\nThen $\\int_0^{\\infty} q(F) dI< \\infty$, \nand it remains to show that \n$\\limsup\\{ e(x) \\overline{F} (x)^{\\gamma} \\} < \\infty$.\nThis follows from 4b by L'Hopital. \n\\end{proof}\n\nSimilarly, sufficient conditions for Conditions 1a and 1b can be given:\nsimply replace ``2'' in Condition 3 and ``1\/2'' in Condition 4b with ``1'', \nand the same proof works. Whether (\\ref{LimitingVarianceRelToMRLSquared}) \nholds when $r$ in Condition 3 is exactly $2$ is not known.\n\n\\section{Examples.}\n\\label{sec:examples}\nThe typical situation, when $e(x)$ has a finite limit and Condition 3 holds, \nis as follows: $e\\sim \\overline{F}\/f \\sim f\/(-f')$ as $x \\rightarrow \\infty$ \n(by L'Hopital), and hence 4b, 2b, and 1b hold; also \n$\\eta' \\equiv (\\overline{F}\/ f)' = [ (F\/f) (-f\/f')] -1 \\rightarrow 0$\n(4a with $c=0$, and hence 2a and 1a hold), $\\sigma (x) \\sim e(x) $ \nfrom (\\ref{LimitingVarianceRelToMRLSquared}), and \n$Var[{\\mathbb Z}] \\sim e^2 \/ \\overline{F} \\sim (\\overline{F}\/f)^2 \/\\overline{F} \\sim 1\/(-f')$. \nWe treat three examples, not all `typical', in more detail.\n\n\\begin{exmpl} \n(Exponential). \nLet $\\overline{F} (x) = \\exp (-x\/\\theta)$, $x \\ge 0$, with \n$0 < \\theta < \\infty$. Then $e(x) = \\theta $ for all $x \\ge 0$. \nConditions 4a and 4b hold (for all $r$, $\\gamma \\ge 0$) with $c=0$, \nso Conditions 2a and 2b hold by Proposition~\\ref{RegVariationSuffCond}\nwith $q(t) = (1-t)^{1\/2-\\delta}$, $0 < \\delta < 1\/2$. \nConditions 1a and 1b hold with $h(t) = (1-t)^{1-\\delta}$, $0 < \\delta < 1$. \nHence Theorems~\\ref{ConsistencyTh1} and ~\\ref{ProcessConvergenceThm} hold where now \n\\begin{eqnarray*}\n{\\mathbb Z}(x) = \\frac{{\\mathbb U} (F(x))}{1-(x)} - \\frac{1}{1-F(x)} \\int_{F(x)}^1 \\frac{{\\mathbb U}}{1-I} dI \n\\stackrel{d}{=} \\theta {\\mathbb B} (e^{x\/\\theta} ) , \\ \\ \\ 0 \\le x < \\infty\n\\end{eqnarray*}\nand ${\\mathbb B}$ is standard Brownian motion on $[0,\\infty)$. \n(The process ${\\mathbb B}_1 (t) = {\\mathbb U} (1-t) - \\int_{1-t}^1 ( {\\mathbb U}\/ (1-I) ) dI$, \n$0 \\le t \\le 1$, is Brownian motion on $[0,1]$; and with \n${\\mathbb B}_2 (x) \\equiv x {\\mathbb B}_1 (1\/x)$ for $1 \\le x \\le \\infty$, \n${\\mathbb Z}(x) = \\theta {\\mathbb B}_2 (1\/\\overline{F} (x)) = \\theta {\\mathbb B}_2 (e^{x\/\\theta} )$.)\nThus, in agreement with (\\ref{CovarianceFormulaOne}), \n\\begin{eqnarray*}\nCov[ {\\mathbb Z}(x) , {\\mathbb Z}(y) ] = \\theta^2 e^{(x \\wedge y)\/\\theta}, \\ \\ \\ \n0 \\le x, y < \\infty .\n\\end{eqnarray*}\nAn immediate consequence is that \n$\\| {\\mathbb Z}_n^* \\overline{F} \\| \\rightarrow_d \\| \\overline{F} \\| \\stackrel{d}{=} \n\\theta \\sup_{0 \\le t \\le 1} | {\\mathbb B}_1 (t) |$; \ngeneralization of this to other $F$'s appears in Section 5. \n(Because of the ``memoryless'' property of exponential $F$, \nthe results for this example can undoubtedly be obtained by more\nelementary methods.)\n\\end{exmpl}\n \n\\begin{exmpl} (Weibull). \nLet $\\overline{F} (x) = \\exp (-x^{\\theta} )$, $x \\ge 0$, with $0 < \\theta < \\infty$.\nConditions 4a and 4b hold (for all $r$, $\\gamma >0$) with $c=0$, so \nConditions 1 and 2 hold with $h$ and $q$ as in Example 1 by Proposition~\\ref{RegVariationSuffCond}. \nThus Theorems~\\ref{ConsistencyTh1} and ~\\ref{ProcessConvergenceThm} hold. Also, $e(x) \\sim \\theta^{-1} x^{1-\\theta}$ as \n$x \\rightarrow \\infty$, and hence $Var[Z(x)] \\sim \\theta^{-2} x^{2(1-\\theta)} \\exp( x^{\\theta})$\nas $x \\rightarrow \\infty$.\n\\end{exmpl}\n\n\\begin{exmpl} (Pareto).\nLet $\\overline{F} (x) = (1+cx)^{-1\/c}$, $x \\ge 0$, with $0 < c < 1\/2$.\nThen $e(x) = (1-c)^{-1} (1+cx)$, and Conditions 4a and 4b hold for $r< c^{-1}$ \nand $\\gamma\\ge c$ (and $c$ of 4a is $c$). Thus Proposition~\\ref{RegVariationSuffCond} holds with $r>2$\nand $c>0$ and $Var[{\\mathbb Z}(x)] \\sim c^{2+(1\/c)} (1-c)^{-2} (1-2c)^{-1} x^{2 +(1\/c)} $\nas $x \\rightarrow \\infty$. \nConditions 1 and 2 hold with $h$ and $q$ as in Example 1, and Theorems~\\ref{ConsistencyTh1} \nand~\\ref{ProcessConvergenceThm} hold.\n\nIf instead $1\/2\\le c < 1$, then $E(X) < \\infty$ but $E(X^2 ) = \\infty$, \nand 4a and 4b hold with $1 < r < 1\/c \\le 2$ and $\\gamma\\ge c$. Hence Condition 1 and \nTheorem~\\ref{ConsistencyTh1} hold, but Condition 2 (and hence our proof of \nTheorem~\\ref{ProcessConvergenceThm}) fails. \nIf $c \\ge 1$, then $E(X) = \\infty$ and $e(x) = \\infty$ for all $x \\ge 0$.\n\\end{exmpl}\n\nNot surprisingly, the limiting process ${\\mathbb Z}$ has a variance which grows quite rapidly, \nexponentially in the exponential and Weibull cases, and as a power $(>4)$ of $x$ \nin the Pareto case.\n\n\\section{Confidence bands for $e$.} \n\\label{sec:confidenceBands}\nWe first consider the process ${\\mathbb Z}' \\equiv {\\mathbb Z} \\overline{F}$ on ${\\mathbb R}^+$ \nwhich appeared in (\\ref{CovarianceFormulaTwo}) of Proposition~\\ref{prop:VarianceCovarianceProp}.\nIts sample analog ${\\mathbb Z}_n' \\equiv {\\mathbb Z}_n \\overline{{\\mathbb F}}_n$ is easily seen to be a \ncumulative sum (times $n^{-1\/2}$) of the observations exceeding $x$, \neach centered at $x + e(x)$; as $x$ decreases the number of terms in the sum \nincreases. Moreover, the corresponding increments \napparently act asymptotically independently so that ${\\mathbb Z}_n'$, in reverse time,\nis behaving as a cumulative sum of zero-mean independent increments. \nAdjustment for the non-linear variance should lead to Brownian motion. \nLet us return to the limit version ${\\mathbb Z}'$.\n\nThe zero-mean Gaussian process ${\\mathbb Z}'$ has covariance function \n$Cov[{\\mathbb Z}' (x) , {\\mathbb Z}' (y) ] = Var[{\\mathbb Z}' (x \\vee y) ]$ (see (\\ref{CovarianceFormulaTwo})); \nhence, when viewed in reverse time, it has independent increments \n(and hence ${\\mathbb Z}'$ is a reverse martingale). \nSpecifically, with ${\\mathbb Z}'' (s) \\equiv {\\mathbb Z}' (-\\log s)$, \n${\\mathbb Z}'' $ is a zero-mean Gaussian process on $[0,1]$ with independent \nincrements and $Var[{\\mathbb Z}'' (s)] = Var[{\\mathbb Z}' (-\\log s)] \\equiv \\tau^2 (s)$. \nHence $\\tau^2$ is increasing in $s$, and, from (\\ref{CovarianceFormulaTwo}),\n\\begin{eqnarray}\n\\tau^2 (s) = \\overline{F} (-\\log s) \\sigma^2 (-\\log s) .\n\\label{FormulaForTau}\n\\end{eqnarray}\nNow $\\tau^2 (1) = \\sigma^2 (0) = \\sigma^2$, and \n$$\n\\tau^2 (0) = \\lim_{\\epsilon \\downarrow 0} \\overline{F} (-\\log \\epsilon) \\sigma^2 (-\\log \\epsilon)\n= \\lim_{x \\rightarrow \\infty} \\overline{F}(x) \\sigma^2 (x) = 0\n$$\nsince\n\\begin{eqnarray*}\n0 \\le \\overline{F} (x) \\sigma^2 (x) \\le \\overline{F}(x) E(X^2 | X> x) \n= \\int_x^\\infty y^2 dF(y) \\rightarrow 0.\n\\end{eqnarray*}\nSince $f(x)>0$ for all $x \\ge 0$, $\\tau^2 $ is strictly increasing. \n\nLet $g$ be the inverse of $\\tau^2$; then $\\tau^2 (g(t)) = t$, $g(0) = 0$,\nand $g(\\sigma^2 ) = 1$. Define ${\\mathbb W}$ on $[0,1]$ by \n\\begin{eqnarray}\n{\\mathbb W}(t) \\equiv \\sigma^{-1} {\\mathbb Z}'' ( g(\\sigma^2 t)) = \\sigma^{-1} {\\mathbb Z}' (-\\log g(\\sigma^2 t)) .\n\\label{DefnOfW}\n\\end{eqnarray}\n\n\\begin{thm} \n\\label{ReversedProcBMThm}\n${\\mathbb W}$ is standard Brownian motion on $[0,1]$.\n\\end{thm}\n\n\\begin{proof}\n${\\mathbb W}$ is Gaussian with independent increments and $Var[{\\mathbb W} (t)] =t$ \nby direct computation. \n\\end{proof}\n\n\\begin{cor}\n\\label{FBarWeightedProcessConvergence}\nIf (\\ref{ProcessConvergenceB}) holds, then\n\\begin{eqnarray*}\n\\rho ({\\mathbb Z}_n^{'*}, {\\mathbb Z}^{'*} ) \\equiv \\sup_{x \\le b_n } | {\\mathbb Z}_n (x) \\overline{{\\mathbb F}}_n (x) - {\\mathbb Z}(x) \\overline{F} (x)|\n= o_p (1) \n\\end{eqnarray*}\nand hence $\\| {\\mathbb Z}_n \\overline{{\\mathbb F}}_n \\|_0^{b_n} \\rightarrow_d \\| {\\mathbb Z} \\overline{F} \\| \n= \\sigma \\| {\\mathbb W} \\|_0^1 \\ \\ \\ \\mbox{as} \\ \\ n \\rightarrow \\infty $.\n\\end{cor}\n\n\\begin{proof}\nBy Theorem 0 of \n\\cite{MR0651392} \n$\\| \\overline{{\\mathbb F}}_n \/ \\overline{F} -1 \\|_0^{b_n} \\rightarrow_p 0$ \nas $n\\rightarrow \\infty$, and this together with (\\ref{ProcessConvergenceB}) implies the \nfirst part of the statement. The second part follows immediately from the \nfirst and (\\ref{DefnOfW}). \n\\end{proof}\n\nReplacement of $\\sigma^2$ by a consistent estimate $S_n^2$ (e.g. the sample variance based \non all observations), and of $b_n$ by $\\hat{b}_n = {\\mathbb F}_n^{-1} (1-a_n)$, the $(n-m)-$th order statistic \nwith $m = [ n a_n]$, leads to asymptotic confidence bands for $e = e_F$: \n\n\\begin{cor}\n\\label{UniformConfBandsCor}\nLet $0 < a < \\infty$, and set \n$\\hat{d}_n (x) \\equiv n^{-1\/2} a S_n \/ \\overline{{\\mathbb F}_n (x)}$. \n If (\\ref{ProcessConvergenceB}) holds, $S_n^2 \\rightarrow_p \\sigma^2$, and \n$na_n \/ \\log \\log n \\uparrow \\infty$, then, as $n \\rightarrow \\infty$\n\\begin{eqnarray}\n&& P \\left (\\hat{e}_n (x) - \\hat{d}_n (x) \\le e(x) \\le \\hat{e}_n (x) + \\hat{d}_n (x) \\ \\ \n\\mbox{for all \\ } 0 \\le x \\le \\hat{b}_n \\right ) \\nonumber \\\\\n&& \\ \\ \\rightarrow Q(a) \n\\label{ConfidenceProbConvergence}\n\\end{eqnarray}\nwhere \n\\begin{eqnarray*}\nQ(a) & \\equiv & P( \\| {\\mathbb W} \\|_0^1 < a) = \\sum_{k=-\\infty}^\\infty (-1)^k \\{ \\Phi ((2k+1)a) - \\Phi( (2k-1)a) \\} \\\\\n& = & 1 - 4 \\{ \\overline{\\Phi} (a) - \\overline{\\Phi} (3a) + \\overline{\\Phi} (5a) - \\cdots \\}\n\\end{eqnarray*}\nand $\\Phi$ denotes the standard normal d.f. \n\\end{cor}\n\n\\begin{proof}\nIt follows immediately from Corollary~\\ref{FBarWeightedProcessConvergence} \nand $S_n \\rightarrow_p \\sigma > 0$ that \n\\begin{eqnarray*}\n\\| {\\mathbb Z}_n \\overline{{\\mathbb F}}_n \\|_0^{b_n} \/ S_n \\rightarrow_d \\| {\\mathbb Z} \\overline{F} \\|\/ \\sigma = \\| {\\mathbb W} \\|_0^1 .\n\\end{eqnarray*}\nFinally $b_n $ may be replaced by $\\hat{b}_n$ without harm: letting $c_n = 2 \\log \\log \/(na_n) \\rightarrow 0$\nand using Theorem 4S of \n\\cite{MR0651392}, \nfor $\\tau>1$ and all $n \\ge N(\\omega , \\tau)$, \n$\\hat{b}_n \\equiv {\\mathbb F}_n^{-1} (1-a_n) \\stackrel{d}{=} F^{-1} (\\Gamma_n^{-1} (1-a_n)) \n\\le F^{-1} (\\{ 1 + \\tau c_n^{1\/2} \\} (1-a_n))$ w.p. 1. This proves the convergence claimed in the corollary; \nthe expression for $Q(a)$ is well-known (e.g. see \n\\cite{MR0233396}, \npage 79). \n\\end{proof}\n\nThe approximation $1- 4 \\overline{\\Phi} (a)$ for $Q(a)$ gives 3-place accuracy for $a>1.4$. \nA short table appears below:\n\n\n\\begin{table}[h]\n\\caption{$Q(a)$ for selected $a$}\n\\label{smallQTable}\n\\begin{tabular}{| c c | c c |}\n\\hline\n$a$ & $Q(a)$ & $a$ & $Q(a)$ \\\\\n\\hline\n2.807 & .99 & 1.534 & .75 \\\\\n2.241 & .95 & 1.149 & .50 \\\\\n1.960 & .90 & 0.871 & .25 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\nThus, choosing $a$ so that $Q(a) = \\beta$, (\\ref{ConfidenceProbConvergence}) provides a two-sided simultaneous confidence\nband for the function $e$ with confidence coefficient asymptotically $\\beta$. \nIn applications we suggest taking $a_n = n^{-1\/2}$ so that $\\hat{b}_n$ is the \n$(n-m)-$th order statistic with $m = [n^{1\/2}]$; we also want $m $ large enough for an adequate \ncentral limit effect, remembering that the conditional life distribution may be quite skewed. \n(In a similar fashion, one-sided asymptotic bands are possible, but they will be less trustworthy \nbecause of skewness.) \n\nInstead of simultaneous bands for all real $x$ one may seek (tighter) bands on $e(x)$ for one or two specific\n$x-$values. \nFor this we can apply Theorem~\\ref{ProcessConvergenceThm} and \nProposition~\\ref{prop:VarianceCovarianceProp} directly. \nWe first require a consistent estimator of the asymptotic variance of \n$n^{1\/2} ( \\hat{e}_n (x) - e(x))$, namely $\\sigma^2 (x) \/ \\overline{F} (x)$. \n\n\\begin{prop} \n\\label{prop:VarianceEstConsistent}\nLet $0 \\le x < \\infty$ be fixed and let $S_n^2 (x)$ be the sample variance of those observations \nexceeding $x$. If Condition 3 holds then \n$S_n^2 (x) \/ \\overline{{\\mathbb F}}_n (x) \\rightarrow_{a.s.} \\sigma^2 (x) \/ \\overline{F} (x) $. \n\\end{prop}\n\n\\begin{proof} \nSince $\\overline{{\\mathbb F}}_n (x) \\rightarrow_{a.s.} \\overline{F} (x) > 0$ and \n\\begin{eqnarray*}\nS_n^2 (x) = \\frac{2 \\int_x^\\infty (y-x) \\overline{{\\mathbb F}}_n (y) dy}{ \\overline{{\\mathbb F}}_n (x)} - \\hat{e}_n^2 (x), \n\\end{eqnarray*}\nit suffices to show that $\\int_x^\\infty y {\\mathbb F}_n (y) dy \\rightarrow_{a.s.} \\int_x^\\infty y \\overline{F} (y) dy$. \nLet $h(t) = (1-t)^{\\gamma+1\/2}$ and $q(t) = (1-t)^{\\gamma}$ with $r^{-1} < \\gamma < 1\/2$ \nso that $h \\in {\\cal H}(\\downarrow)$, $q \\in {\\cal Q}(\\downarrow)$, and \n$\\int_0^\\infty q(F) dI < \\infty$ by the proof of Proposition~\\ref{RegVariationSuffCond}. \nThen,\n\\begin{eqnarray*}\n\\bigg | \\int_x^\\infty y \\overline{{\\mathbb F}}_n (y) dy - \\int_x^\\infty y \\overline{F} (y) dy \\bigg | \n\\le \\rho_{h(F)} ( {\\mathbb F}_n , F) \\int_0^\\infty I h(F) dI \\rightarrow_{a.s.} 0 \n\\end{eqnarray*}\nby Theorem 1 of Wellner (1977) since\n\\begin{eqnarray*}\n\\int_0^\\infty I h(F) dI = \\int_0^\\infty (I^2 \\overline{F} )^{1\/2} q(F) dI < \\infty .\n\\end{eqnarray*}\n\\end{proof}\n\nBy Theorem~\\ref{ProcessConvergenceThm}, \nPropositions~\\ref{prop:VarianceCovarianceProp} and \n~\\ref{prop:VarianceEstConsistent}, and Slutsky's theorem we have:\n\n\\begin{prop} \n\\label{prop:PointwiseAsympNormality}\nUnder the conditions of Proposition~\\ref{prop:VarianceEstConsistent},\n\\begin{eqnarray*}\nd_n (x) \\equiv n^{1\/2} ( \\hat{e}_n (x) - e(x)) \\overline{{\\mathbb F}}_n^{1\/2} (x) \/ S_n (x) \n\\rightarrow_d N(0,1) \\ \\ \\ \\mbox{as} \\ \\ n \\rightarrow \\infty .\n\\end{eqnarray*}\n\\end{prop}\n\nThis makes feasible an asymptotic confidence interval for $e(x)$ (at this particular fixed $x$).\nSimilarly, for $x< y$, using the joint asymptotic normality of $(d_n (x) , d_n (y))$ \nwith asymptotic correlation $ \\{ \\overline{F} (y) \\sigma^2 (y) \/ \\overline{F} (x) \\sigma^2 (x) \\}^{1\/2}$\nestimated by \n$$\n\\{ \\overline{{\\mathbb F}}_n (y) S_n^2 (y) \/ \\overline{{\\mathbb F}}_n (x) S_n^2 (x) \\} ^{1\/2},\n$$ \nan asymptotic confidence ellipse for $(e(x), e(y))$ may be obtained. \n\n\\section{Illustration of the confidence bands.} \n\\label{sec:illustrationOfBands}\nWe illustrate with two data sets presented by \n\\cite{Bjerkedal-60} \nand briefly mention one appearing in Barlow and Campo (1975).\n\nBjerkedal gave various doses of tubercle bacilli to groups of $72$ guinea pigs and recorded their \nsurvival times. \nWe concentrate on Regimens 4.3 and 6.6 (and briefly mention 5.5, the only other complete data set \nin Bjerkedal's study M); see Figures 1 and 2 below. \n\nFirst consider the estimated mean residual life $\\hat{e}_n$, the center jagged line in each figure.\nFigure 1 has been terminated at day 200; the plot would continue approximately horizontally, \nbut application of asymptotic theory to this part of $\\hat{e}_n$, based only the last 23 survival\n times \n (the last at 555 days), seems unwise. \n Figure 2 has likewise been terminated at 200 days, omitting only nine survival times \n (the last at 376 days); the graph of $\\hat{e}_n$ would continue downward. \n The dashed diagonal line is $\\overline{X} -x$; if all survival times were equal, say $\\mu$, \n then the residual life function would be $(\\mu - x)^+$, a lower bound on $e(x)$ near the origin. \n More specifically, a Maclaurin expansion yields\n \\begin{eqnarray*}\n e(x) = \\mu + (\\mu f_0 -1) x + (1\/2)\\{ (2 \\mu f_0 -1) f_0 + f_0' \\} x^2 + o(x^2)\n \\end{eqnarray*}\n where $f_0 = f(0)$, $f_0' = f' (0)$, if $f'$ is continuous at $0$, or \n \\begin{eqnarray*}\n e(x) = \\mu - x + \\frac{\\mu d}{r!} x^r + o(x^r) \n \\end{eqnarray*}\n if $f^{(s)} (0) = 0$ for $s< (r-1)$ ($\\ge 0$) and $= d$ for $s = r-1$ \n (if $f^{(r-1)}$ is continuous at $0$). It thus seems likely from \n Figures 1 and 2 that in each of these cases either $f_0 = 0$ and $f_0' > 0$ \n or $f_0 $ is near $0$ (and $f_0'\\ge 0$). \n \n Also, for large $x$, $e(x) \\sim 1\/\\lambda (x)$, and Figure 1 suggests that the \n corresponding $\\lambda $ and $e$ have finite positive limits, whereas the $e$ \n of Figure 2 may eventually decrease ($\\lambda$ increase). \n We know of no parametric $F$ that would exhibit behavior quite like these. \n \n The upper and lower jagged lines in the figures provide $90\\%$ (asymptotic) \n confidence bands for the respective $e$'s, based on (\\ref{ConfidenceProbConvergence}). \n At least for Regimen 4.3, a constant $e$ (exponential survival) can be rejected.\n \n The vertical bars at $x=0$, $x=100$, and $x=200$ in Figure 1, and at \n $0$, $50$, and $100$ in Figure 2, are $90\\%$ (asymptotic) pointwise confidence \n intervals on $e$ at the corresponding $x-$values (based on \n Proposition~\\ref{prop:PointwiseAsympNormality}). \n Notice that these intervals are not much narrower than the simultaneous bands early \n in the survival data, but are substantially narrower later on. \n \n A similar graph for Regimen 5.5 (not presented) is somewhat similar to that \n in Figure 2, with the upward turn in $\\hat{e}_n$ occurring at 80 days \n instead of at $50$, and a possible downward turn at somewhere around 250 days\n (the final death occurring at 598 days).\n \n A similar graph was prepared for the failure data on 107 right rear tractor brakes presented \n by \n \\cite{MR0448771},\n page 462. \n It suggests a quadratic decreasing $e$ for the first 1500 to 2000 hours (with $f(0) $ at or near $0$\n but $f'(0)$ definitely positive), with $\\overline{X} = 2024$, and with \n a possibly constant or slightly increasing $e$ from 1500 or so to 6000 hours.\n The $e$ for a gamma distribution with $\\lambda =2$ and $\\alpha = .001$ ($e(x) = \\alpha^{-1} (\\alpha x +2 )\/ (\\alpha x +1)$\n with $\\alpha = .001$) fits reasonably well -- i.e. is within the confidence bands, even for $25\\%$ confidence. \n Note that this is in excellent agreement with Figures 2.1(b) and 3.1(d) of \n \\cite{MR0448771}. \n\n Bryson and Siddiqui's (1969) \n data set was too small ($n=43$) for these asymptotic methods, except \n possibly early in the data set.) \n \n \\begin{figure}\n\\centering\n\\includegraphics[width=1.00\\textwidth]{PLOTS\/GraphsMRL-1-ai.eps}\n\\caption{$90\\%$ confidence bands for mean residual life; Regimen 4.3}\n\\label{fig:Regimen4-3}\n\\end{figure}\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=1.00\\textwidth]{PLOTS\/GraphsMRL-2-ai.eps}\n\\caption{$90\\%$ confidence bands for mean residual life; Regimen 6.6}\n\\label{fig:Regimen6-6}\n\\end{figure}\n \n \\section{Further developments}\n \\label{sec:furtherDevelopments}\nThe original version of this paper, \\cite{Hall-Wellner-79}, ended with a one-sentence sketch of \ntwo remaining problems: \n``Confidence bands on the difference between two mean residual life functions, and for the case of censored data,\n will be presented in subsequent papers.'' Although we never did address these questions ourselves, others\n took up these further problems. \n \n Our aim in this final section is to briefly survey some of the developments since 1979 concerning \n mean residual life, including related studies of median residual life and other quantiles, as well as developments\n for censored data, alternative inference strategies, semiparametric models involving mean or median residual life,\n and generalizations to higher dimensions. \n For a review of further work up to 1988 see \\cite{Guess-Proschan:88}.\n\n \n\\subsection{Confidence bands and inference}\n\\cite{MR856407} gave a further detailed study of the asymptotic behavior \nof the mean residual life process as well as other related processes including the Lorenz curve.\n\\cite{MR931630} developed tests and confidence sets for comparing two mean residual life functions\nbased on independent samples from the respective populations. These authors also gave a brief \ntreatment based on comparison of {\\sl median residual life}, to be discussed in Subsection~\\ref{subsec:MedianRL}\nbelow.\n \\cite{MR1416657} introduced weighted metrics into the study of the asymptotic behavior of the\n mean residual life process, thereby avoiding the intervals $[0,x_n]$ changing with $n$ involved in our\n Theorems 2.1 and 2.2, and thereby provided confidence \n bands and intervals for $e_F$ in the right tail. \n \\cite{MR2256261} introduced empirical likelihood methods to the study of the mean residual life function.\n They obtained confidence intervals and confidence bands for compact sets $[0,\\tau]$ with \n $\\tau < \\tau_F \\equiv \\inf\\{ x : F(x) = 1\\}$.\n \n\\subsection{Censored data}\n \\label{subsec:CensoredMRL}\n \n \\cite{MR0458678} initiated the study of estimated mean residual life under random right censorship. \n She used an estimator $\\hat{F}_n$ which is asymptotically equivalent to the Kaplan - Meier estimator \n and considered, in particular,\n the case when $X$ is bounded and stochastically smaller than the censoring variable $C$.\n In this case she proved that $\\sqrt{n}(\\hat{e} (x) - e(x))$ converges weakly (as $n\\rightarrow \\infty$) \n to a Gaussian process with mean zero. \n \\cite{MR1416657} give a brief review of the challenges involved in this problem; see their page 1726.\n \\cite{MR2344641} extended their earlier study (\\cite{MR2256261}) of empirical likelihood methods to this case,\n at least for the problem of obtaining pointwise confidence intervals. The empirical likelihood methods\n seem to have superior coverage probability properties in comparison to the Wald type intervals which follow from \n our Proposition~\\ref{prop:PointwiseAsympNormality}. \n \\cite{MR1678973} introduced smooth estimates of mean residual life in the uncensored case. In\n \\cite{MR2459252} they introduce and study smooth estimators of $e_F$ based on corresponding smooth estimators \n of $\\overline{F} = 1-F$ introduced by \\cite{MR1661441}.\n\n\\subsection{Median and quantile residual life functions} \n \\label{subsec:MedianRL}\n \n Because mean residual life is frequently difficult, if not impossible, to estimate in the \n presence of right-censoring, it is natural to consider surrogates for it which do not depend\n on the entire right tail of $F$. Natural replacements include median residual life and \n corresponding {\\sl residual life quantiles}. \n The study of median residual life was apparently initiated in \\cite{MR628933}. \n Characterization issues and basic properties have been investigated by\n \\cite{MR732677}, \n \\cite{MR756012}, and \\cite{MR2131870}. \n \\cite{MR764974} proposed comparisons of two populations based on their corresponding \n median (and other quantile) residual life functions. \n As noted by Joe and Proschan, \n ``Some results differ notably from corresponding results for the mean residual life function''.\n \n \\cite{MR2422830} investigated estimation of median residual life with right-censored data for \n one-sample and two-sample problems. They provided an interesting illustration of\n their methods using a long-term follow-up \n study (the National Surgical Adjuvant Breast and Bowel Project, NSABP) involving breast cancer patients. \n \n \n\\subsection{Semiparametric models for mean and median residual life}\n\n \\cite{MR1064816}\n investigated a characterization \n related to a {\\sl proportional mean residual life} model: $e_G = \\psi e_F$ with $\\psi>0$.\n \\cite{MR1278221}\n studied several methods of estimation in a semiparametric \n regression version of the proportional mean residual life model, \n $e(x|z) = \\exp (\\theta^T z) e_0 (x) $ where $e(x|z)$ denotes the conditional mean\n residual life function given $Z = z$. \n \\cite{MR2135857}\n provide a nice review of various models and study estimation \n in the same semiparametric proportional mean residual life regression model\n considered by \\cite{MR1278221}, but in the presence of right censoring. Their\n proposed estimation method involves inverse probability of censoring weighted (IPCW) \n estimation methods (\\cite{MR0053460}; \n \\cite{Robins-Rotnitzky:92}). \n \\cite{MR2158607} use counting process methods to develop alternative estimators \n for the proportional mean residual life model in the presence of right censoring. \n The methods of estimation considered by \\cite{MR1278221}, \\cite{MR2135857}, and \\cite{MR2158607}\n are apparently inefficient.\n \\cite{MR2125050} \n consider information calculations and likelihood based estimation in a two-sample version \n of the proportional mean residual life model. Their calculations suggest that certain weighted ratio-type\n estimators may achieve asymptotic efficiency, but a definitive answer to the issue of efficient estimation\n apparently remains unresolved. \n \\cite{MR2278085} proposed an alternative additive semiparametric regression model involving mean \n residual life.\n \\cite{MR2753008} considered a large family of semiparametric regression models which includes \n both the additive model proposed by \\cite{MR2278085} and the proportional mean residual life \n model considered by earlier authors, but advocated replacing mean residual life by median residual life. \n \n \\cite{MR2155475} also developed a median residual life regression model with additive structure and took a \n semiparametric Bayesian approach to inference. \n \n\\subsection{Monotone and Ordered mean residual life functions}\n \n \\cite{MR1792793} consider estimation of $e_F$ subject to the shape restrictions that \n $e_F$ is increasing or decreasing. The main results concern ad-hoc estimators that are simple monotizations\n of the basic nonparametric empirical estimators $\\hat{e}_n$ studied here. These authors show that the \n nonparametric maximum likelihood estimator does not exist in the increasing MRL case and that although \n the nonparametric MLE exists in the decreasing MRL case, the estimator is difficult to compute. \n \\cite{MR1238352} and \\cite{MR1927772} study estimation of two mean residual life functions $e_F$ and $e_G$ in \n one- and two-sample settings \n subject to the restriction $e_F (x) \\le e_G(x)$ for all $x$. \\cite{MR1927772} also develop large sample confidence \n bands and intervals to accompany their estimators.\n \n \n\n\\subsection{Bivariate residual life} \n\n\\cite{MR663455} defined a multivariate mean residual life function and \nshowed that it uniquely determines the joint multivariate distribution, extending\nthe known univariate result of \\cite{MR0153061};\nsee \\cite{MR665274} for a review of univariate results of this type.\nSee \\cite{MR1412096, MR1649088} for further multivariate characterization results.\n\\cite{MR1941419} introduced a bivariate mean residual life function\nand propose natural estimators. \n\\medskip\n\n\n\\noindent {\\bf Remarks:}\nThis revision was accomplished jointly by the authors in 2011 and 2012. \nThe first author passed away in October 2012. Section 7 only covers further developments\nuntil 2012. A MathSciNet search for ``mean residual life'' over the period 2011 - 2017 yielded \n148 hits on 10 July 2017.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe framework within which astronomers -- a term that will be used\nrather loosely in the rest of the document to indicate individuals\nperforming research in Astronomy, Astrophysics and Space Physics\n(AA\\&SP) -- have been functioning in Greece is not too different from\nother European countries.\n\nAs in most other countries in Europe, the educational and research\nactivity in Astronomy, Astrophysics and Space Physics in Greece has\nbeen fostered within public Universities and Research Institutes. Even\nthough this may change in the near future currently no private\nacademic or research institutions in AA\\&SP are operating in\nGreece. Thus the individuals who are employed full time to teach or do\nresearch in AA\\&SP are typically civil servants in permanent, tenure\ntrack, or fixed-term research associate positions. Currently the\nmajority of them (see Sect. 3, Fig. 1) are in Universities and only a\nsmall fraction is associated with Research Institutes or\nObservatories.\n\nUp until the early 1980s the structure of the University system in\nGreece followed the German style. It was based on ``Chairs'' of\nProfessors in specific research fields (i.e. Astronomy, Classical\nMechanics, etc.). The few astronomy Professors, typically as numerous\nas the corresponding number of university departments pursuing\nresearch in the various areas of AA\\&SP, held their position until the\nage of retirement. They made all major administrative decisions\nrelated to both teaching priorities and research directions in their\ninstitutions. Several junior staff members did support them in these\nactivities but those members had only marginal control and rather\nlimited independence to pursue their own research directions.\n\nThe University system presently in place was put forward in 1983,\n(with a few minor modifications over the past $\\sim$25 years) and has a\nstructure similar to the current academic system of the United\nStates. There are two ranks of tenure track positions: Lecturer and\nAssistant Professor, and two of tenured positions: Associate Professor\nand Full Professor. A minimum of three years is required on each rank\nbefore applying for promotion the next. Tenure is obtained upon\nsuccessful evaluation after spending three years at the level of\nAssistant Professor. A university faculty can, in principle pursue\nhis\/her own research direction, teach courses, and supervise graduate\nstudents.\n\nHowever, when this change in the academic system took place in the\nearly 1980's, there was no provision on the age distribution of the\nfaculty to be hired. As a result a large number of individuals who\nwere already affiliated with the universities at the time in junior\nlevel -- the so-called ``Assistant'' appointments, automatically\nobtained tenure at the rank of Lecturer upon the completion of their\nPhD. Others, who already had a PhD, were considered for tenure at\nhigher ranks. The evaluation for this process though was often not\nvery strict and with criteria based mostly on social reasons or giving\na disproportional emphasis to the teaching responsibilities of the\nfaculty, rather than mostly based on their research background and\npotential or relevance of the field to the future direction of modern\nastrophysics. In addition, since most of the individuals who obtained\nthese positions were past graduate students of the same universities,\nthere was a disproportional hiring from ``within''. This phenomenon,\nknown as ``academic inbreeding'', was more prevalent in the older\ninstitutions in Athens and Thessaloniki, which had the largest number\nof staff at the time. Even today there are institutions in Greece\nwhere well over 70\\% of their permanent staff members are past alumni\nwho did their dissertation in the same institute and did not spend\nmore than a couple of years away from their alma matter before\nobtaining permanent positions. It is beyond the scope of the present\ndocument to discuss this phenomenon and the serious negative\nconsequences it has on both the quality of research performed and on\nthe opening of new research horizons in academic institutions. We\nshould note though that this phenomenon is not unique to Greece, as it\nalso appears for example in the French academic system and in\nKorea\\footnote{See: Science 18 December 1998: Vol. 282. no. 5397} ,\nbut it is practically absent in the United States. Greek universities\nin the periphery of the country did not suffer much from this problem\nfor two reasons. Either they had not produced their own PhDs due to\ntheir youth as institutions, or their faculty made a conscious\ndecision to have a broader perspective in their hiring process. For\nexample, in the Department of Physics of the University of Crete,\nwhere the author is currently employed, only 1 out of the 33 faculty\nmembers obtained his PhD from this institution. All these political\ndecisions had implications that continue to affect the evolution of\nGreek astronomy, and academic system in general, well into the 21st\ncentury.\n\nResearch Institutes in Greece have a similar structure to the\nUniversities, with also four ranks, which are loosely indicated as\nResearcher-D, -C, -B and -A. Each researcher also has to remain in a\ngiven rank for a minimum of three years and tenure is obtained upon\npromotion from Researcher-C to Researcher-B. Research Institutes can\nnot award academic degrees and as a result close collaboration with a\nUniversity is needed in order for a researcher to be able to\nco-supervise students.\n\nPublic funding for development of infrastructures, direct support of\nresearch in astronomy, or fellowships towards graduate studies in the\nfield, has been traditionally fairly limited. Such a low level support\nis not restricted to Greek astronomy but it is also the case in most\ndisciplines. In 2004 Greece spent only 0.58\\% of its Gross Domestic\nProduct (GDP) in R\\&D, which brings Greece as a nation in the last\nplace among the 15 EU countries in this category, a position it holds\nfor the past 5 years. At over the same period the European Union (EU)\naverage was 1.95\\%, more than 3 times higher\\footnote{Source EUROSTAT\nin http:\/\/europa.eu.int\/comm\/eurostat\/}. As a result the\npossibilities for Greek astronomers to join large international\ncollaborative projects, or just to obtain support to attend scientific\nmeetings outside Greece have been scarce. Even though the situation\nhas recently improved over the past decade, and the possibilities --\nmostly via the financial and organizational support of the European\nUnion -- are more numerous, the effects of this low level national\nfunding can be seen in most indices quantifying the overall astronomy\nscientific output from Greece. It is worth noting that Greece, which\njoined the European Union as the 10th member in 1981, is still not a\nmember state of the European Southern Observatory (ESO) and only\njoined the European Space Agency (ESA) in 2005.\n\n\\section{Governing bodies}\n\nThe policies that directly affect issues related to AA\\&SP in Greece\nare determined by the Ministry of Development, in particular the\nGeneral Secretariat for Research and Technology, and the Ministry of\nEducation. The first has administrative control over the research\ninstitutes and astronomy infrastructure and the latter controls the\nnational university system.\n\nGreek astronomers can express their opinion or shape policies on\nissues related AA\\&SP via the Greek National Committee for Astronomy\n(GNCA) or the Hellenic Astronomical Society (Hel.A.S.).\n\n\\subsection{The Greek National Committee for Astronomy (GNCA)}\n\nThe Greek National Committee for Astronomy (GNCA\\footnote{The web page\nof GNCA is: http:\/\/www.astro.noa.gr\/$\\sim$gnca}) was established, by Royal\nDecree, as the official advisory committee to the Greek Government for\nall matters relevant to Astronomical and Astrophysical research, in\n1957. It is the official body, responsible for the promotion and\ncoordination of Astronomy in Greece and for all matters related to\ninternational astronomical cooperation. The Minister of Development\nselects the members of GNCA and appoints them for a term of two\nyears. Its official seat is the National Observatory of Athens. Since\n1995, GNCA does not have its own budget, but obtains its funding from\nthe budget of the General Secretariat of Research and Technology\n(GSRT) of the Ministry of Development. The GNCA has the following\nprincipal objectives:\n\n\\begin{itemize}\n\n\\item To co-ordinate and promote the various astronomical activities\nin Greece, including research and education.\n\n\\item To act as the link between the Greek astronomical community and\nthe International Astronomical Union (IAU), officially representing\nGreece in the General Assembly of the IAU.\n\n\\item To facilitate the advancement of international collaboration\nbetween Greek and foreign astronomers and research groups.\n\n\\end{itemize}\n\nBesides the IAU, GNCA has taken responsibility for Greece's\nrepresentation to the Board of Directors of the journal \"Astronomy and\nAstrophysics\" (and its financial contributions), to the European Joint\nOrganization for Solar Observations (JOSO) and, recently, to the\nEuropean Union FP6, I3, Network OPTICON (see Sect. 8).\n\nThe board of GNCA consists of five ordinary and five substitute\nmembers. The current (2005-2007) ordinary members are\nProf. P. Laskarides (Chair - Univ. of Athens), Prof. T. Krimigis (Vice\nChair - Academy of Athens), Dr. I. Daglis (Nat. Obs. of Athens),\nProf. N. Kylafis (Univ. of Crete), and Prof. J.H. Seiradakis (Univ. of\nThessaloniki). The substitute members over the same period are:\nProf. S. Avgoloupis (Univ. of Thessaloniki), Dr. E. Dara (Academy of\nAthens), Prof. M. Kafatos (George Mason Univ., USA), Prof. A. Nindos\n(Univ. of Ioannina), and Prof. J. Papamastorakis (Univ. of Crete).\n\n\\begin{figure*} \n\\plotone{fig1.eps}\n\\figurenum{1} \n\n\\caption{Distribution of tenured and tenure track astronomers in the\nmajor research institutes in Greece. The total number of astronomers\nincluded in this study is 107.}\n\n\\end{figure*} \n\n\\subsection{The Hellenic Astronomical Society (Hel.A.S.)}\n\nThe Hellenic Astronomical Society (Hel.A.S.\\footnote{The web page of\nHel.A.S. is http:\/\/www.astro.auth.gr\/elaset}) exists for nearly 15\nyears and it is the major association of professional astronomers in\nGreece. Its overall structure and operation is similar to other\nnational societies such as the ``American Astronomical Society'' in\nthe US, or the ``Soci\\'{e}t\\'{e} Fran\\c{c}aise d' Astronomie et\nd'Astrophysique'' in France.\n\nHistorically, the first serious attempt to establish a Hellenic\nAstronomical Society was undertaken in 1982 during the XVIII General\nAssembly of the International Astronomical Union, which took place in\nPatras, Greece. There, during several meetings, a dozen astronomers\ngathered in order to put the foundations of the long sought\nSociety. The following years progress was slow even though material\nnecessary for setting up the framework for the Society was being\ncollected. It was much later, in November 1991, when\nProf. P. Laskarides (Univ. of Athens) issued the first announcement of\nthe 1st Hellenic Astronomical Conference, that the idea of the\nestablishment of an Astronomical Society was formally put forward\nagain. With the help of several colleagues Prof. J.H. Seiradakis\n(Univ. of Thessaloniki) drafted the first Constitution for the\nSociety. The final version was presented to the participants of the\n1st Hellenic Astronomical Conference, which was held in Athens in\nSeptember 1992.\n\nDuring the Athens Conference, several astronomers became founding\nmembers of the Hellenic Astronomical Society. A few more founding\nmembers signed the Constitution during the next weeks bringing the\ntotal number of founding members to sixty six (66). Following the\nappropriate legal procedures, the Hellenic Astronomical Society\n(Hel.A.S.) was recognized by the Court of Justice in Athens on May 25\n1993. The appointed Council of Hel.A.S. became aware of the verdict of\nthe Court of Justice in June 1993. The President of the Council,\nProf. B. Barbanis (Univ. of Thessaloniki), assisted by the members\ninitiated the procedure for the first elections of Hel.A.S. In the\nelections, which took place on June 2nd 1994, participated 83\\% of the\nfounding members.\n\nAccording to its Constitution the Governing Council of\nHel.A.S. consists of a President, six (6) members and three (3)\nauditors. The Council is elected for a two-year term and an individual\ncannot serve on it for more than two consecutive terms. The candidates\nfor the Council must be members of Hel.A.S. who live and work\npermanently in Greece during the term of their candidacy and at least\n42\\% of them should be affiliated with institutions outside the Athens\nmetro area. The current Council, whose mandate ends in June 2006,\nconsists of Prof. P. Laskarides (Univ. of Athens) as the president and\nProf. D. Hatzidimitriou (Univ. of Crete), Prof. K. Tsinganos (Univ. of\nAthens), Prof. V. Geroyannis (Univ. of Patras), Prof. K. Kokkotas\n(Univ. of Thessaloniki) and Prof. X. Moussas (Univ. of Athens) as\nmembers. The auditors for the same period are Prof. E. Danezis\n(Univ. of Athens), Prof. E. Mavromichalaki (Univ. of Athens) and\nDr. E. Xilouris (National Obs. of Athens)\n\nThe Hellenic Astronomical Society has been very active and currently\nhas 272 members, 27\\% of which live and work outside Greece. It has\nbeen recognized as an Affiliated Member of the European Astronomical\nSociety (EAS) and has established links with other international\nastronomical societies. It has been organizing a major science meeting\nevery two years and in the summer of 1997 organized the Joint European\nand National Astronomical Meeting (JENAM-97).\n\n\\begin{figure*} \n\\plotone{fig2.eps}\n\\figurenum{2} \n\\caption{A histogram of the age distribution of astronomers in Greece\nin 2006. The vertical line indicates the 67th year of age which is the\ncurrent compulsory retirement age for civil servants.}\n\n\\end{figure*} \n\n\\section{Academic Institutions}\n\n\\subsection{Human Resources in Astronomy, Astrophysics \\& Space Physics}\n\nAs one would expect, since more than half of the population in Greece\nis concentrated in the Athens and Thessaloniki metro areas, most of\nthe astronomers in Greece are also associated with institutes located\nin these two cities. This is depicted in Figure 1 where the fraction\nof tenured and tenure track astronomy faculty in the major AA\\&SP\ninstitutions in Greece is presented. \n\nAn additional issue, which affects the current state and has direct\nimplications to the future of Greek astronomy, is related to the age\ndistribution of Greek professional astronomers. In Figure 2, a\nhistogram of 135 astronomers working in Greece is presented, using the\ndatabase of the members of the Hellenic Astronomical Society, as well\nas ancillary information collected by the author. The study was\nlimited to individuals over the age of 30, since this is typically the\nage when one is competitive for tenure track or long term research\nassociate positions. Some individuals over the retirement age of 67,\nwho are on an emeritus-type position and\/or still active, were\nincluded in the analysis. The error on a single 5-year bin is of the\norder of 5\\% but it is very likely that the values of bins at ages\ngreater than 55 are somewhat underestimated. This is due to the fact\nthat there are a number of individuals who formally have a tenured\nastronomy positions but as they are no longer active they were not\nincluded in the database of Hel.A.S, on which analysis was based.\n\n\nInspection of Figure 2 clearly reveals that almost 30\\% of Greek\nastronomers are near or over the age of 60. This was a direct\nconsequence of the legislative changes that took place in Greece in\nthe early 1980s mentioned in Sect. 1. Furthermore, statistics over the\nlast 10 years indicate that on average there were less than 3 new\ntenure track astronomy position openings per year in the country,\nincluding both universities and research institutions. The fraction of\nastronomers near the age of retirement is even larger if we were to\nconsider only the two older universities of Athens and\nThessaloniki. This implies that within the next 5 to 10 years a large\nnumber of their current faculty members will retire and they will have\nto be replaced in a very short time scale. This will be an interesting\nchallenge for Greek astronomy. Will it be possible for these\ninstitutions to find enough, well qualified, candidates from the\navailable pool of post-docs and research associates for their needs?\nWill they be forced to lower their hiring standards in order to hire\nfaculty for their teaching needs, or they will be able to hire with a\nlower pace, being selective and identifying the key scientific\nresearch areas they should be investing in? In 2016 we will know the\nanswer to these questions!\n\nAnother topic worth touching upon is gender diversity in Greek\nastronomy. At the time of writing this report 13\\% of the permanent or\ntenure track astronomy positions in Greece were held by women. This\npercentage is less than in France\\footnote{The percentages for the\nother countries mentioned are based on the 2003 report by Dr. Florence\nDurret (Institute d'Astrophysique de Paris, France) available at:\nhttp:\/\/www2.iap.fr\/sf2a\/courrier.html}, which leads the way with\n$\\sim$26\\%, or in Italy, Russia and Spain, all above 15\\%, but higher\nthan the fraction of female astronomers in the United States which is\n$\\sim$10\\%. We should note though, that only recently one female\nastronomer in Greece reached for the first time the highest possible\nacademic rank (Full Professor or Researcher A), a statistic that will\nhopefully improve very soon.\n\n\n\\subsection{Research in Astronomy, Astrophysics \\& Space Physics}\n\nThe latest organized effort to map the research activity in AA\\&SP in\nthe various institutes in Greece took place in 1998. Dr. E. Kontizas,\nas the president of GNCA at the time, appointed an international\nsix-member committee, chaired by Prof. Y. Terzian (Cornell Univ.,\nUSA), to report on the status of astronomy in Greece and propose\nrecommendations for the future. The report\\footnote{The complete\n``Terzian Report'' is available at:\nhttp:\/\/www.astro.noa.gr\/gnca\/NEWS\/ca-report2000.htm} was presented\nduring the workshop ``Astronomy 2000+: Greek Prospects for the 21st\nCentury'' which took place at the National Observatory of Athens on\nNovember 1998. The description presented in the following paragraphs\ndraws from material included in this report with some modifications\nmostly related to changes in the human resources of the institutes\ninvolved.\n\nThere are eight institutions in Greece with Departments or Sections\ndevoted to teaching and research in Astronomy and Astrophysics. Three\nare located in Athens: the largest is the Section within the\nDepartment of Physics of the National Kapodistrian University,\nfollowed by the National Observatory of Athens, and an astronomy\nSection of the national Academy of Athens. In Thessaloniki there is a\nvery small group within the Faculty of Engineering (Polytechnic\nSchool) and a considerably larger one within the Department of Physics\nof the Aristotle University. In Crete there is a Section of\nAstrophysics and Space Physics in the Department of Physics in\nHeraklion, while the Universities of Patras and Ioannina each have\nsmall Astronomy groups within either their Physics or Engineering\nDepartments. Some research activity in very specific areas\n(i.e. cosmology or general relativity) also exists in a few\nDepartments of Mathematics but the numbers of permanent staff are very\nsmall and there is no critical mass to be considered groups.\n\nThe principal institutions devoted to research and technical\ndevelopment in space sciences is the Department of Electrical and\nComputer Engineering at the ``Democritus\" University of Thrace (in\nparticular the Laboratory of Space Electrodynamics in the Section of\nTelecommunications and Space Science) and the Institute for Space\nApplications and Remote Sensing of the National Observatory of\nAthens. Significant research in ground based ionospheric and\natmospheric work is also a component of the overall Astrophysics and\nSpace Science Section at the University of Crete. Activity relating to\nspace science also exists in the Section of Astronomy, Astrophysics,\nand Mechanics of the University of Athens, and at the Research Center\nfor Astronomy in the Academy of Athens.\n\nIn the following subsections we present a brief description of the\nvarious institutes in Greece hosting research groups with active\nresearch in AA\\&SP. More detailed annual activity reports from most\ninstitutions and groups are being collected by the Greek National\nCommittee for Astronomy and they are made available from its web site\nmentioned in section 2.1.\n\n\\subsubsection{University of Athens}\n\nThe ``National \\& Kapodistrian'' University of Athens was founded in\n1837, soon after the independence of Greece. It was the first\nUniversity in Greece as well as in the Balkan Peninsula and the whole\neastern Mediterranean region. The Department of Physics was created in\n1904 and its current Section of Astronomy, Astrophysics, and Mechanics\nwas formed in the mid 1980s by merging the previously independent\nChairs indicated in its name. The Section is the largest in Greece and\nconsists of 24 tenured or tenure tract faculty. Most of them have\nresearch interests in the area of Astronomy and Astrophysics, while\nMechanics is a rather small constituent. The Department of Physics\nstarted a graduate school in 1994, within which the Section has its\nown Masters and PhD programs with 12 graduate courses. Since January\n2000 the Section also operates a 40cm Cassegrain telescope within a 5m\nrotating dome located on the top of the Physics building. The\ntelescope was constructed by DFM engineering (USA) has an f\/3 focal\nratio and it is mainly used for educational activities.\n\nIn addition to the pursuit of astronomy, it should also be mentioned\nthat the faculty of the Physics Department have been involved over\nmany years in building a deep sea High Energy Neutrino telescope,\nknown as NESTOR. Recently this effort has been put under the auspices\nof the National Observatory of Athens as an independent institute for\nAstroparticle Physics (see Sect. 3.2.2).\n\n\\subsubsection{National Observatory of Athens}\n\nThe National Observatory of Athens (NOA\\footnote{More information on\nthe National Observatory of Athens the can be found at:\nhttp:\/\/www.noa.gr}) was founded in 1842 and is the oldest research\ninstitute in Greece. It currently consists of five institutes three of\nwhich, the Institute of Astronomy and Astrophysics, the Institute for\nSpace Applications and Remote Sensing and the Institute of\nAstroparticle Physics -- Nestor, conduct research in AA\\&SP. The\ncurrent director of NOA is Prof. C. Zerefos.\n\nThe Institute of Astronomy and Astrophysics has 11 permanent staff\nscientists as well as research associates and support personnel. Their\nresearch interests include a variety of topics in extragalactic\nastronomy, observational cosmology, interstellar matter, X-ray\nastronomy, and binary stars. The Institute supports the Astronomical\nObservatory in Kryoneri as well as the new Chelmos Observatory where\nthe new 2.3m ``Aristarchos'' telescope, the largest in Greece, is\nlocated (see Sect. 4.1, 4.3). The institute is also very active in\npublic outreach activities, among which are the operation of a Visitor\nCenter and an annual summer school, which introduces basic concepts of\nmodern astrophysics to high-school students since 1996. The current\ndirector of the Institute is Prof. C. Goudis.\n\nThe Institute for Space Applications and Remote Sensing has 11 tenure\nor tenure track research staff. The activities of the Institute\nencompass a wide area in Space Research and Applications. Its main\nobjective is to carry out R\\&D projects in these fields, which include\nRemote Sensing, Telecommunications, Space and Ionospheric\nPhysics. Additional activities include the systematic collection and\nprocessing of data derived from observations made either from the\nearth or space as well as the performance of autonomous studies in\nother specific subjects of space research and applications. The\nInstitute is equipped with satellite and ionospheric ground stations,\nvarious RF and electronic test and measurement equipment, as well as\nan advanced computing center connected to international networks. The\ncurrent director of the Institute is Dr. I. Daglis.\n\nThe Institute of Astroparticle Physics -- NESTOR (Neutrino Extended\nSubmarine Telescope with Oceanographic Research) became the fifth\ninstitute of the national Observatory of Athens in 2003. The institute\nis leading the development of a deep-sea high energy neutrino\ntelescope approximately 14km off the shore from the town of Pylos in\nPeloponnese, at water depth of 4000m. NESTOR will detect the Cherenkov\nradiation produced by muons traversing the water when their parent\nneutrinos emitted from astrophysical objects, such as X-ray binaries,\nblack holes, or Active Galactic Nuclei, interact with water. The\ncurrent director of the Institute is Prof. L. Resvanis.\n\n\n\\subsubsection{Academy of Athens}\n\nThe Academy of Athens was formally founded in 1926. It currently has\namong its members two Academicians (Prof. G. Contopoulos and\nProf. T. Krimigis) with a background and research interests in\nastronomy. One of the centers of the Academy, the Research Center for\nAstronomy and Applied Mathematics, consists of 11 permanent research\nstaff, and conducts research in solar and space physics, cosmology,\nparticle physics and dynamical astronomy.\n\n\\subsubsection{University of Thessaloniki}\n\nThe ``Aristotle'' University of Thessaloniki was the second university\nin Greece and it was founded in 1925. There are two units in the\nUniversity with activity in Astronomy. The smallest, in the\nPolytechnic School, consists of two faculty members and their research\nis concentrated mainly on flare stars. The largest is the Section of\nAstrophysics, Astronomy and Mechanics (AAM\\footnote{The online\ndescription of the AAM Section in Thessaloniki can be found at:\nhttp:\/\/www.astro.auth.gr}) of the Department of Physics, with 17\nfaculty members, several research associates, graduate students, and\nsupport personnel. The Section was formed in the mid-80s when the\nadministrative structure of the Laboratories of Astronomy (founded in\n1943) and Mechanics changed (see Sect. 1). The staff is active in many\nareas of theoretical and observational astrophysics, including an\nactive theoretical group on gravitation and general relativity, as\nwell as in education and public outreach. In addition to the\nStephanion Observatory (see Sect. 4.4) the Section operates a 20cm\nrefracting telescope (made by Secretan, Paris) in a rotating\n6m-diameter dome, which is located within the University campus, and\nit is used for educational purposes.\n\n\n\n\\subsubsection{University of Crete}\n\nThe University of Crete was founded in 1973 but accepted its first\nstudents in 1978. Its Department of Physics was founded in 1978 and is\nthe youngest of similar Departments in Greece. The Section of\nAstrophysics and Space Physics\\footnote{The web page of the Astronomy\nSection in Crete can be found at: http:\/\/www.physics.uoc.gr\/en\/} has 7\nfaculty members as well as several research staff and graduate\nstudents. Two (2) more tenured track astronomers, from the Foundation\nfor Research and Technology-Hellas and the Technical Education\nInstitute of Heraklion, are actively collaborating with the members of\nthe Section. Research at the Univ. of Crete covers a broad range in\ntheoretical and observational problems related to both galactic and\nextragalactic astrophysics. Significant efforts are being devoted to\nthe operations of an Ionospheric Physics laboratory. Observations for\nseveral astronomical projects are also taken at the Skinakas\nObservatory (see Sect. 4.2) and others are performed using\ninternational ground and space born telescopes. The Department has a\ngraduate program through which students can pursue their graduate\nstudies in astrophysics.\n\n\\subsubsection{University of Thrace}\n\nThe Laboratory of Space Electrodynamics (LSE) at Department of\nElectrical and Computer Engineering of the ``Democritus\" University of\nThrace is the largest space physics group in Greece with extensive\nexperience in hardware development. It consists of 6 faculty, several\nresearch associates, support personnel, and many graduate and\nundergraduate students. The scientists are co investigators or\nassociated scientists on several international spacecraft missions\n(e.g. Ulysses, Geotail, Cluster II, and others), successfully funded\nthrough European programs and bilateral collaborations with other\ncountries, including the U.S. The LSE has designed, developed and\nsuccessfully flown particle experiments on a number of Russian\nspacecraft, as well as component systems to instruments involving data\nprocessing units and ASICs (Application Specific Integrated\nCircuits). Such high technology hardware capability in space\ninstrumentation is rather unique within Greece. The LSE group has\nexpanded their activities to antennae and propagation, satellite\ncommunications, and other related areas.\n\n\\subsubsection{University of Patras}\n\nThe University of Patras has a Laboratory of Astronomy and a Section\nof Astronomy in the Division of Theoretical and Mathematical Physics\nin the Department of Physics. A total of nine tenured and tenure track\nfaculty teach courses and conduct research in a few astronomy areas\nand there is an active theoretical group on celestial mechanics.\n\n\\subsubsection{University of Ioannina}\n\nThis is the smallest group of Astronomy in a Department of Physics in\nGreece. It has three faculty members in the Section of Astrogeophysics,\nwithin the Department of Physics. The staff performs research mostly\nin solar physics and in multi-wavelength observations of flare stars.\n\n \n\\begin{figure*} \n\\plotone{fig3.eps}\n\\figurenum{3} \n\n\\caption{An optical satellite image of Greece, obtained in 2004, in\nwhich the locations of the major observatories hosting functioning\noptical telescopes with a primary mirror diameter larger than 0.5m are\nindicated (Image courtesy of MODIS Rapid Response Project at\nNASA\/GSFC)}\n\n\\end{figure*} \n \n\\section{National Facilities}\n\nThe limited funding of the Greek government towards basic and applied\nresearch has had, as a result, the small investment in major\ninfrastructures for astronomical facilities in Greece. This affected\nthe oldest observatories in Greece, such as Penteli, Kryoneri and\nStephanion Observatory, which have difficulties keeping up-to-date\nwith the modern developments in telescope design, aperture size of the\ntelescope primary mirrors, as well as the instrumentation\navailable. More recent facilities, such as Skinakas Observatory, which\ncurrently hosts the largest operational telescope in Greece which is\n1.29m in diameter, are more modern and do provide high quality\ninstruments to the observers. However, they also suffer from the\nlimited national financial support and they cannot function as\nfacilities that can provide access to all Greek astronomers who may\nwish to use them. A major effort in improving the current situation\nhas been the ongoing construction of the 2.3m ``Aristarchos\" telescope\nby the National Observatory of Athens. The telescope had its first\nlight in the end of 2005 and when it becomes fully operational, before\nthe end of 2007, will be the largest in Greece.\n\n\n\n\\subsection{Chelmos Observatory}\n\nThe site selected for the new 2.3m telescope is located in Northern\nPeloponnese, on top of Chelmos mountain, near the small town of\nKalavrita approximately 150km from Athens, with longitude:\n22$^{o}$13', latitude: 37$^{o}$58' N and an elevation of 2340m. The\ntotal cost for the project is expected to be about 5 million Euros and\nit was financed mainly by the European Union, as well as by the\nGeneral Secretariat for Research and Technology of the Ministry of\nDevelopment. The telescope named ``Aristarchos\" is a\nRitchey-Chr\u017dtien with a focal ratio f\/8 and a 10\u00d5 field of view as\nwell an RC-corrected field of view of 1degree. The telescope and dome\nare constructed by Carl Zeiss (Germany).\n \n\\begin{figure*}[h] \n\\plotone{fig4.eps}\n\\figurenum{4} \n\\caption{Left: A picture of the dome of the 2.3m Aristarchos\ntelescope at Chelmos. Right: A photograph of the telescope inside the\ndome (Images courtesy of National Observatory of Athens).}\n\\end{figure*} \n\n\nThe image scale on the focal plane is 1''=85$\\mu$m and a 1024x1024 CCD\ncamera was the first light instrument. A medium resolution (2.5\\AA -\n6\\AA) spectrometer covering the range between 4270\\AA and 7730\\AA as\nwell as a 4096x4096 optical CCD will be the first generation\ninstruments of the telescope. These will be followed by an echelle\nspectrometer covering the range between 3900\\AA and 7500\\AA with a\nresolution of 6 km s$^{-1}$, as well as other instruments. The\nsupervision of the telescope construction as well as its operation are\nmanaged by the Institute of Astronomy and Astrophysics of the National\nObservatory of Athens\\footnote{Details on the Chelmos Observatory is\navailable at: http:\/\/www.astro.noa.gr\/ASC\\_2.3m\/ngt\\_main.htm}.\n\n\\subsection{Skinakas Observatory}\n\nThe Skinakas Observatory\\footnote{More information on Skinakas\nObservatory can be obtained from: http:\/\/skinakas.physics.uoc.gr}\noperates as part of a scientific research collaboration between the\nUniversity of Crete, the Foundation for Research and Technology-Hellas\n(FORTH) and the Max-Planck-Institut f\\\"{u}r Extraterrestrische (MPE)\nPhysik of Germany.\n\nThe site of the Observatory (Longitude: 24$^{o}$53'57''E, Latitude:\n35$^{o}$12'43''N), chosen on scientific and functional grounds, is the\nSkinakas summit of Mount Ida (also known as Psiloritis), at an\naltitude of 1750m and a distance of 60km from Heraklion. The\nObservatory has two telescopes: a Modified Ritchey-Chr\u017dtien\ntelescope with a 1.29m aperture (focal ratio f\/7.6), which became\noperational in 1995, and a 30cm telescope (focal ratio f\/3.2). The\nbuilding for the small telescope was constructed in 1986, and\nobservations started in 1987. The site is one of the best in Greece\nwith weather conditions often permitting photometric sub-arcsecond\nseeing. It includes a modern guest house powered with solar arrays and\nan Internet connection.\n \n \n\\begin{figure*} \n\\plotone{fig5.eps}\n\\figurenum{5} \n\n\\caption{An areal view of the Skinakas Observatory summit with the\nlarger dome of the 1.29m telescope seen on the left, along with the\nsmaller domes the guest house facilities. The 1.29m telescope inside\nits dome is seen in the right (Images courtesy of the Physics Dept.,\nUniv. of Crete).}\n\n\\end{figure*} \n\n\nThe optical system of the 1.29m telescope were manufactured by Carl\nZeiss (Germany). The mechanical parts were built by DFM Engineering\n(USA). The instrumentation available includes a focal reducer, a\nnumber of optical CCD cameras, and a low resolution long slit\nspectrograph. A 1024x1024 near-IR camera and an echelle spectrograph\nwill soon be available on site, along with OPTIMA, a fast\nphoto-polarimeter with microsecond time resolution intended for\nobservations of compact objects. Various research projects, both\ngalactic and extragalactic, mostly led by members of the Department of\nPhysics of the University of Crete or MPE astronomers have been\nongoing since the facilities became operational. The close\ncollaboration with the MPE group and the FORTH engineering support has\nhelped the astrophysics group in Crete in keeping the telescope and\nthe instruments in the forefront of technology, always taking into\naccount the limitations in the budget.\n\n\\subsection{Kryoneri Observatory}\n\nThe Astronomical Station of Kryoneri\\footnote{Additional information\non Kryoneri Observatory are at:\nhttp:\/\/www.astro.noa.gr\/ASK\\_1.2m\/ask\\_main.htm} was established in\n1972. It is located in the Northern Peloponnese, on top of mountain\nKilini at an elevation of 930m, near the small village Kryoneri 110km\nfrom Athens (longitude: 22$^{o}$37'E, latitude: 37$^{o}$58'N). The\n1.2m Cassegrain Coude telescope of the Astronomical Station Kryoneri,\nmade by Grubb Parsons Co., Newcastle, was installed in 1975. Its\noptical system consists of a paraboloidal primary mirror of 1.23m in\ndiameter and f\/3 focal ratio, and a hyperboloidal secondary mirror (31\ncm). Both mirrors are made of Zerodur. The telescope focal ratio is\nf\/13, its field of view is about 40' and the image scale is\n12.5\"\/mm. As with Chelmos Observatory, Kryoneri is operated by the\nInstitute of Astronomy and Astrophysics of the National Observatory of\nAthens.\n\n\n\\subsection{Stephanion Observatory}\n\nThe first observations at the Stephanion Observatory, in eastern\nPeloponnese, were undertaken in March 1967 with a guest 38cm reflector\nand a UBV photometer that belonged to the Bergedorf Observatory of the\nUniversity of Hamburg, Germany. Since then a large number of\ninstruments have been hosted at the 800-m altitude observatory, which\nis located at longitude: 22$^{o}$49'45''E, latitude: 37$^{o}$45'9''N,\nincluding French telescopes, for monitoring satellites, and a 40cm\nreflector from the Utrecht Observatory, Netherlands. In June 1971, the\n30-inch (76cm) Cassegrain reflector of the University of Thessaloniki\nwas installed at the Observatory. Until 1975, when the 1.23m\nCassegrain Coude reflector at Kryoneri became operational, this was\nthe largest telescope in Greece.\n\nThe 30-inch reflector is mounted asymmetrically and its focal ratio is\nf\/3 for the primary hyperbolic mirror and f\/13.5 for the Cassegrain\nfocus. It was constructed by Astro Mechanics, USA, a firm that has\nlong ago discontinued making astronomical instruments. The majority of\nobservations are carried out with a Johnson dual channel photoelectric\nphotometer with an offset guider unit mounted in the Cassegrain\nfocus. It includes an RCA 1P21 and an RCA 7102 photo-multipliers, both\nof which are refrigerated by dry ice. Key photometric observations of\nvariable stars (flare stars, Cepheid variables, RS CVns, etc) have\nbeen undertaken in co-operation with large ground or space\ninstruments. The international demand for co-operative and\nsimultaneous observations at the Stephanion Observatory stems from the\nstrict differential method used for obtaining absolute, above\natmosphere, stellar magnitudes in the international UBV system. The\nerror in the calibrated magnitudes obtained is usually better than\n0.02 magnitudes.\n\n\\subsection{Penteli Observatory}\n\nThe Astronomical Station on Penteli Mountain, just 15km from downtown\nAthens, was established in 1937 when it became apparent that it was\nnecessary to move the telescopes from the grounds of the old National\nObservatory in the center of Athens. In 1955 the National Observatory\nof Athens accepted the donation offered by the University of\nCambridge, for a 62.5cm telescope designed by R. S. Newall, and\nconstructed by the firm Thomas Cooke \\& Sons in 1868. Its big tube\n(about 9m in legth), the German-type equatorial mount and its weight\nof about nine tons, required careful dismounting, transportation and\ninstallation in a new dome that was built in Penteli. This telescope,\nno longer used for research, is still available on site today.\n\n\\subsection{Eudoxos Educational Observatory}\n\nThe ``Eudoxos\" observatory is a web-accessible complex of optical and\nradio telescopes, founded in 1999, whose facilities are located 16km\nfrom Argostoli, in the Ionian island of Kefallinia at a plateau 600m\nbelow the peak of mount Ainos (1628m). It operates a 0.6m Cassegrain\nrobotic telescope named after Dr. Andreas Michalitsianos, a Greek\nastrophysicist who was born in the island and had a successful career\nin NASA (USA) until his early passing away. The observatory was formed\nby a consortium of Greek institutes involving the National Research\nCenter of Physical Sciences ``Democritus\", the Hellenic Naval Academy,\nthe Ministry of Education and the Prefecture of Kefallinia and\nIthaki. It is being operated by the same consortium with the addition\nof the University of Athens and has already received substantial\nsupport from the Hellenic Air Force and the Ministry of Education. The\n0.6m telescope consists of a fully autonomous computerized optical\ntube assembly, automated enclosure, GPS smart antenna for time\nsynchronization, a full set of meteorological sensors, a large format\nimaging CCD camera and UBVRI wheeled photometric filters, as well as a\nfleet of peripheral instruments currently under construction or\ntesting. All equipment is completely controlled by two supervisory\ncomputers, which communicate via the Internet to the participating\nsecondary schools and institutions.\n\n\\section{High School and Undergraduate Studies in Astronomy}\n\nThe Greek secondary education system does provide substantial training\nin physics and mathematics to the students who wish to follow\nuniversity studies in sciences. Even though there is no compulsory\nastronomy course in high school (only an elective introductory\nastronomy course is available for high-school juniors) basic astronomy\nideas related to the solar system, stars, galaxies, and the formation\nof the universe are presented in other courses. Since 1996 the\n``Society for Space and Astronomy\" of Volos (see Sect. 7) has been\norganizing a very successful national astronomy competition in which\nstudents from all over Greece attending the last three years of high\nschool (``Lyceum'' in greek) can participate. The top students are\nawarded various prizes while the first two are invited to attend an\nall-expenses-paid summer space-camp in the United States organized by\nNASA. This effort, mainly supported by private funds and volunteer\nwork, has helped substantially in popularizing astronomy among high\nschool students.\n\nAt the university level there is no Bachelors (BSc) degree in\nAstronomy or Space Science in Greece. Most individuals, who are now\nprofessionals in the field of AA\\&SP and did their undergraduate\nstudies in Greece, obtained their BSc degree in Physics following a\nfour-year program. Some, mostly theorists, have obtained their\nundergraduate degrees in Mathematics or Engineering. Even in the\nvarious Departments of Physics in Greece though, the curriculum of the\nastronomy courses varies depending on the number and research\nbackground of the astronomy faculty. Most Physics majors in Greece\nhave to follow at least one compulsory junior course in Astrophysics\nwhile some complementary topics on dynamical astronomy are typically\ncovered on compulsory sophomore and junior level courses in classical\nmechanics and modern physics. Most Departments of Physics offer the\npossibility of an astronomy specialization (or minor), even though\nthis is not formally awarded as a degree. Within this framework,\nstudents, who are interested in astronomy, have the opportunity to\nattend typically five to ten junior and senior level courses in\nastrophysics, space physics and celestial mechanics, thus obtaining a\nfairly solid background if they wish to continue for graduate studies.\n\nThe level of this University astronomy training is usually very good\nin the theoretical and encyclopedic part and the top students are\ncompetitive with international standards. What the students lack\nsometimes is the hands-on practical knowledge, which can only be\nobtained with access to engineering facilities or observatories. The\norganization of summer schools, such as the one taking place at the\nUniversity of Crete for the past 17 years, addressed to undergraduates\nat junior and senior level, can often fill this gap. There are also\nrecent efforts at various institutions, such as the Univ. of Athens,\nto enhance the observational astrophysics courses with a more\norganized usage of small telescopes and new instruments.\n\n\\section{Graduate Studies in Astronomy, Astrophysics \\& Space Physics}\n\nGraduate studies in Astronomy Astrophysics \\& Space Physics leading to\na Masters or a PhD degree can now be completed in most Greek\nUniversities. The first well-organized physics graduate program in\nGreece with coursework, qualifying exams, and at least partial\nfinancial support for students was developed in the University of\nCrete in 1984. This was soon to be followed by the University of\nAthens and other institutions.\n\nHowever, the system suffers from difficulties, which again stem from\nthe limited national funding. Less than a handful of state fellowships\nfor graduate studies in AA\\&SP are available each year. Providing\nfinancial support for graduate studies via European Union or national\nresearch proposals in astronomy is very challenging both due to\nlimited funds available in this field as well as due to various\nbureaucratic and organizational difficulties. As a result graduate\nstudents in Greece have to either work, or rely on other means to\nsupport themselves during their studies. This sometimes affects their\nability to invest the amount of time necessary for research in order\nto complete a very high quality PhD project.\n\nThese reasons have been pushing many of the Greek students to go\nabroad for their graduate studies. The most popular destinations are\nthe United States, the United Kingdom, Germany, France and The\nNetherlands. The improved facilities and competitive research\nenvironment in those countries do provide high quality training to the\nstudents but often decrease the likelihood of their return to work in\nGreece. Recently though, the opportunities made available by the\nEuropean Union, mostly via the Human Capital as well as Training and\nMobility programs, have ameliorated the situation. These new\npossibilities have provided the means to establish close links between\nGreek and other European institutions, which improves substantially\nthe training of local students thus bringing direct scientific return\nto the home institution.\n\n\\section{Amateur Astronomy in Greece}\n\nAmateur astronomy has been flourishing in Greece over the past\ndecade. The availability of high quality and low cost small telescopes\nand the use of Internet to organize and advertise the activities of\ngroups has greatly helped the development in the field. Many amateur\norganizations exist all over Greece. In particular one should mention\nthe ``Hellenic Astronomical Union\" which is the society of amateur\nastronomers in Athens, the ``Group of friends of Astronomy\" in\nThessaloniki, the ``Corfu Astronomical Society\", and the very active\n``Society for Space and Astronomy\" in the city of Volos. Since 1999\nthe Greek amateur astronomers have been organizing a national meeting\nevery two years where they present their results and discuss issues of\ncommon interest.\n\n\\section{Greece and International Astronomy Organizations}\n\nGreece joined the International Astronomical Union as a funding member\nin 1920. It also contributes to the support of the international\nrefereed journal of Astronomy \\& Astrophysics, which allows Greek\nastronomers to publish their scientific results without page charges.\nSince 2004 Greece also participates in OPTICON, a 19.2 million Euro\n5-year European FP6 Infrastructure Network, which provides access to a\nnumber of medium size telescope facilities around the world.\n\nIn early 2005 Greece joined the European Space Agency (ESA)\ncontributing to the annual budget of ESA with $\\sim$9 million\nEuros. This opens new opportunities for Astrophysics and Space Physics\nboth in terms of technology development as well as in science. Over\nthe past year significant organizational efforts have been taking\nplace in order to stimulate the Greek AA\\&SP community so that it will\nbe able to capitalize on this investment and join the rest of the\nwestern European countries in the forefront of space technology.\n\n\\section{Remarks}\n\nI believe that it is appropriate to end this article on the status of\nGreek astronomy with an optimistic note on the many improvements we\nhave all experienced over the past decade. As it can be seen from the\nmaterial presented in the previous sections, the environment, both\nresearch and academic, for the current and next generation of Greek\nastronomers is considerably better than what our predecessors had\nexperienced and worked through.\n\nI must also touch upon, a subject, which was mentioned earlier but\nonly briefly. Unfortunately Greece is still not a member of the\nEuropean Southern Observatory, the major astronomical organization in\nEurope. As a result it has no access to the current European\ninfrastructures of the Very Large Telescope (VLT), nor to the\ndevelopment of the Atacama Large Millimeter Array (ALMA) nor to the\ndesign of the future ESO projects, such as the 100m OverWhelmingly\nLarge telescope (OWL). I should stress that the report\\footnote{The\ncomplete ``Terzian Report'' is available at\nhttp:\/\/www.astro.noa.gr\/gnca\/NEWS\/ca-report2000.htm} of the\ninternational expert committee chaired by Prof. Terzian (Cornell\nUniv.) on the status of Greek Astronomy presented in 1998 during the\nworkshop ``Astronomy 2000+: Greek Prospects for the 21st Century''\nnoted that joining ESO should be the first astronomy priority for the\nnation. Current rough estimates indicate that the cost for Greece to\njoin ESO would be a one-time $\\sim$10 million Euros entrance fee,\nsimilar to our annual contribution to ESA, and an annual membership\nfee of only $\\sim$1 million Euros. Thus, if following the\nrecommendation of the international expert committee, ESO were to be\nour lofty astronomy goal for the present century, one can only hope\nthat the whole Greek community will embrace it and with a joined\neffort will convince the ``powers that be\" to turn the wheels and make\nit a reality before the end of the current decade.\n\n\\acknowledgments\n\nThis document used material from the online archives of the Hellenic\nAstronomical Society, the Greek National Committee for Astronomy, as\nwell as from the annual reports of the various institutes that were\navailable online. I would like to thank K. Kokkotas (Univ. of\nThessaloniki), N. Kylafis (Univ. of Crete), J.H. Seiradakis (Univ. of\nThessaloniki), K. Tsinganos (Univ. of Athens), and E. Xilouris\n(National Observatory of Athens) for making suggestions that improved\nthis article.\n\n\n\\end{document} \n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{#1}}\n\\renewcommand{\\vec}[1]{{\\bm #1}}\n\n\\usepackage{amsmath}\n\\usepackage{txfonts,wasysym,ulem,url}\n\\usepackage[usenames]{color}\n\n\\begin{document}\n\n\n\\title{\nChiral condensate with topological degeneracy in graphene \nand its manifestation in edge states\n}\n\n\\author{Yuji Hamamoto}\n \\affiliation{%\nInstitute of Physics, University of Tsukuba, Tsukuba 305-8571, Japan%\n}%\n\n\\author{Hideo Aoki}\n \\affiliation{%\nDepartment of Physics, University of Tokyo, Hongo, Tokyo 113-0033, Japan%\n}%\n\n\\author{Yasuhiro Hatsugai}%\n\\email{hatsugai@sakura.cc.tsukuba.ac.jp}\n \\affiliation{%\nInstitute of Physics, University of Tsukuba, Tsukuba 305-8571, Japan%\n}%\n \\affiliation{%\nTsukuba Research Center for Interdisciplinary Material Science, University of Tsukuba, Tsukuba 305-8571, Japan%\n}%\n\n\\date{\\today\n \n\n\\begin{abstract}\nRole of chiral symmetry in many-body states\nof\ngraphene in strong magnetic fields is theoretically studied with the honeycomb lattice model. \nFor a spin-split Landau level \nwhere the leading electron-electron interaction is \nthe nearest-neighbor repulsion,\na chiral condensate \nis shown to be, within the subspace of $n=0$ Landau level, an \nexact many-body ground state with \na finite gap, for which calculation of Chern numbers reveals that\nthe ground state is a Hall insulator with a {\\it topological degeneracy} \nof two. \nThe topological nature of the ground state is shown to manifest itself as \na Kekul\\'ean bond order along armchair edges,\nwhile the pattern melts in the bulk due to\nquantum fluctuations. \nThe whole story can be regarded as a realization of the \nbulk-edge correspondence peculiar to the chiral symmetry.\n\\end{abstract}\n\n\\pacs{73.22.Pr, 71.10.Fd, 73.43.-f\n \n \n\\maketitle\n\n\\mysection{Introduction}%\nWhile the physics of graphene started from the one-body \nelectronic structure as a Dirac fermion, \na possible relevance of electron correlation in graphene has \nbeen intensively studied after a gap opening in the $n=0$ \nLandau level (LL)\nwas experimentally observed in strong magnetic fields.~\\cite{PhysRevLett.96.136806,PhysRevLett.99.106802}\nSince it is difficult to explain the gap within a simple one-body problem,\nconsiderable theoretical efforts have ensued to clarify many-body effects \nin graphene quantum Hall (QH)\nsystems.~\\cite{PhysRevLett.96.256602,\nPhysRevB.74.075422,\nPhysRevB.74.161407,\nPhysRevB.74.195429,\nPhysRevB.75.165411,\n2007SSCom.143..504A,\nPhysRevLett.99.196802}\nHowever, little attention has been paid on how \nmany-body effects should reflect the {\\it chiral symmetry} \nin the graphene QH regime, which is after all \na fundamental symmetry inherent in graphene's honeycomb lattice.\nOn the one-body level, the effects of chiral symmetry in graphene is well \nunderstood: \nTo start with, the symmetry guarantees the \nemergence of doubled Dirac cones in the Brillouin zone, \nwhich can be interpreted as a two-dimensional analogue\nof the Nielsen-Ninomiya theorem well-known \nin the four-dimensional lattice gauge theory. \nWe can even examine the wave functions \nin terms of Aharonov-Casher argument, which states that\nchiral symmetry {\\it topologically protects}\nthe degeneracy of the $n=0$ LL against random gauge fields.~\\cite{PhysRevA.19.2461}\nA similar situation occurs for ripples in a graphene sheet, \nwhich can be modelled by random hopping amplitudes. \nKawarabayashi {\\it et al.} have shown that the $n=0$ LL exhibits\nan anomalously sharp (delta-function-like) density of \nstates (DOS) as soon as the spatial wavelength of the ripple \nexceed a few lattice constants.~\\cite{PhysRevLett.103.156804}\n\nIn the presence of electron-electron interactions, \non the other hand, \nthe role of chiral symmetry has primarily been investigated\nin zero magnetic fields \nin the context of spontaneous symmetry breaking.~\\cite{PhysRevB.74.195429,\nPhysRevLett.102.026802,\nPhysRevB.79.165425,\nPhysRevB.82.121403}\nWhile these studies mainly employ a Dirac field model \nin a continuum space to discuss many-body gap formation, \nsuch an effective treatment may well overlook \nthe essence of graphene's chiral symmetry,\nwhich is intimately related to the underlying honeycomb lattice.\n\nWith this background, we shed light in the present work on \nhow the chiral symmetry influences the many-body problem\nin graphene QH effect, by\nfully taking account of the lattice structure. \nWe first examine \nthe many-body problem with exact diagonalization\nin a subspace projected onto the $n=0$ LL.\nWorking on the subspace enables us to classify \nmany-body states according to a notion of the {\\it total chirality} of \nthe filled zero modes. In terms of this, \nfor a ``bipartite'' electron-electron interaction such as\nthe nearest neighbor repulsion,\nwhich is the dominant interaction for a spin-split LL, \nwe show that the many-body ground state is {\\it exactly} identified to be \na chiral condensate with a topological degeneracy of two. \nWe confirm numerically that there exists a finite energy \ngap to the first-excited state, \nwhich makes the Chern number of the ground state well-defined.\nThe total Chern number contributed by the filled zero modes\nalong with the negative energy states (``Dirac sea\") \nturns out to be zero, \nwhich implies the system is a Hall insulator with vanishing Hall conductance.\n\n\nDespite the cancellation of the Chern number in the bulk, however, \nwe move on to show that the topological nature of the chiral condensate \nis in fact made manifest as an emergence of a Kekul\\'ean bond order in the edge state \nalong armchair edges of the honeycomb lattice \n(in sharp contrast to zigzag edge states in one-body problem). \nOn the mean-field (MF) level \nthe Kekul\\'e pattern is shown to appear in the bulk \nwith a Kekul\\'ean degeneracy of three, \nso that the present result amounts that the mean-field \norder, while dissolved in the topologically-degenerate \nchiral condensate in the bulk, resurfaces along an armchair edge. \nThis can be interpreted as an example of bulk-edge correspondence,~\\cite{PhysRevLett.71.3697}\nwhich states that a topologically nontrivial bulk state\nshould always accompany a characteristic edge state.\nIn the case of graphene, the bulk-edge correspondence \nbecomes peculiar in that the chiral condensate \nmanifests itself as a freezing of a Kekul\\'ean bond order along a \nspecific (armchair) edges.\n\n\\mysection{Chiral symmetry}%\nTo model interacting electrons on a honeycomb lattice,\nwe assume that spin degeneracy is lifted by a Zeeman splitting, \nso that the leading Coulomb interaction reduces to the \nnearest-neighbor repulsion.\nThe\nHamiltonian then reads $\\mathcal{H}=\\mathcal{H}_{\\rm kin}+\\mathcal{H}_{\\rm int}$, where \nthe kinetic term\n$\\mathcal{H}_{\\rm kin}=-t\\sum_{\\langle ij\\rangle}\n(e^{i\\theta_{ij}}c^\\dagger_{i}c_{j}+{\\rm H.c.})\n\\equiv c^{\\dagger}H_{\\rm kin}c$\ndescribes electron hopping between adjacent sites\n$\\langle ij\\rangle$ with strength $t>0$.\nThe magnetic field is included as the Peierls phase\n$\\theta_{ij}$ such that magnetic flux per elementary hexagon equals\n$\\sum_{\\hexagon}\\theta_{ij}=2\\pi\\phi$ in units of a magnetic flux quantum $h\/e$.\nFor a honeycomb lattice with $N_{\\bullet(\\circ)}$\nsites in sublattice $\\bullet(\\circ)$,\n$c^{\\dagger}=(c^{\\dagger}_{\\bullet},c^{\\dagger}_{\\circ})$\nwith $c^{\\dagger}_{\\bullet(\\circ)}$ a row of creation operators\nfor sublattice $\\bullet(\\circ)$\nand $H_{\\rm kin}$ is a square matrix of dimension $N_{\\bullet}+N_{\\circ}.$\nIf we introduce $\\Gamma={\\rm diag}(I_{\\bullet},-I_{\\circ})$\nwith an identity matrix $I_{\\bullet(\\circ)}$ of dimension $N_{\\bullet(\\circ)}$,\nthe kinetic term satisfies an anticommutation relation\n$\\{H_{\\rm kin},\\Gamma\\}=0$, \nwhich defines the chiral symmetry.\nThe symmetry implies that, \nif $\\psi_{k}$ is the $k$-th eigenvector with energy $\\varepsilon_{k}$,\na chiral partner\n$\\Gamma\\psi_{k}$ exists with an energy $-\\varepsilon_{k}$.\nThis makes the $n=0$ LL special\nin that we can take\n$\\psi_{k}$ as an eigenstate of $\\Gamma$ \nas $\\Gamma\\psi_{k\\pm}=\\pm\\psi_{k\\pm}$.\nThe interaction between spin-polarized electrons \nis expressed in a particle-hole symmetric form as\n$\\mathcal{H}_{\\rm int}=\\sum_{i\\ne j}V_{ij}\n\\left(n_i-\\frac{1}{2}\\right)\\left(n_j-\\frac{1}{2}\\right)\n=\\frac{1}{2}\\sum_{i\\ne j}V_{ij}\n(c^\\dagger_ic^{\\dagger}_{j}c_jc_i\n+c_ic_{j}c^{\\dagger}_jc^{\\dagger}_i)+{\\rm const.}$,\nwhere $V_{ij}$ is the strength of electron-electron interaction,\n$n_i\\equiv c^\\dagger_ic_i$ the number operator at site $i$.\n\n\\mysection{Chiral condensate}%\nWe start with an investigation of \nthe many-body problem\nat half filling.\nSince it is difficult to treat all the many-body states exactly,\nwe shrink the Hilbert space\nby projecting onto the $n=0$ LL.\nSuch a treatment is valid as long as $|V_{ij}|$ is perturbatively small\ncompared with the Landau gaps around the $n=0$ LL.~\\cite{detail}\nIn the $n=0$ LL,\nwe take a zero mode multiplet $\\psi=(\\psi_{+},\\psi_{-})$\nwhere we have decomposed it into \neigenstates of the chiral operator, \n$\\psi_{\\pm}=(\\psi_{1\\pm},\\cdots,\\psi_{M_{\\pm}\\pm})$ with degeneracy $M_{\\pm}$.\nNote that the zero modes are localized on each of the sublattices\nas $\\psi_{+}=\\frac{1}{\\sqrt{2}}\\left(\n\\scriptsize{\n\\begin{array}{c}\n\\psi_{\\bullet}\\\\\n0\n\\end{array}\n}\n\\right)$ and $\\psi_{+}=\\frac{1}{\\sqrt{2}}\\left(\n\\scriptsize{\n\\begin{array}{c}\n0\\\\\n\\psi_{\\circ}\n\\end{array}\n}\n\\right)$.\nIf we introduce the negative-energy multiplet $\\varphi=(\\varphi_{1},\\varphi_{2},\\cdots)$\nsuch that $H_{\\rm kin}\\varphi_{k}=\\varepsilon_{k}\\varphi_{k}$\nwith $\\varepsilon_{k}<0$,\n$(\\psi_{+},\\psi_{-},\\varphi, \\Gamma\\varphi)$ form a complete set,\nso that the fermion operator is expanded as\n$c=\\psi d+\\varphi d_{<}+\\Gamma\\varphi d_{>}$,\nwhere\n$d=\\left(\n\\scriptsize{\n\\begin{array}{c}\nd_{+}\\\\\nd_{-}\n\\end{array}\n}\n\\right)$\nand $d_{\\lessgtr}$ are columns of fermion operators\nfor the zero modes and the $\\varepsilon\\lessgtr0$ states, respectively.\nIn the projected subspace,\nthe total Hamiltonian is written, up to a constant, as\n$\\tilde{\\mathcal{H}}=\\frac{1}{2}\\sum_{i\\ne j}V_{ij}\n(\\tilde{c}^{\\dagger}_{i}\\tilde{c}^{\\dagger}_{j}\\tilde{c}_{j}\\tilde{c}_{j}\n+\\tilde{c}_{i}\\tilde{c}_{j}\\tilde{c}^{\\dagger}_{j}\\tilde{c}^{\\dagger}_{i})$,\nwhere the projected fermion operator, $\\tilde{c}_{i}\\equiv\\psi d$,\nno longer satisfies the canonical anticommutation relations.\nStill, the generator of the total chirality of the filled zero modes\ncan be defined as $\\mathcal{G}=\\tilde{c}^{\\dagger}\\Gamma\\tilde{c}\n=d^{\\dagger}_{+}d_{+}-d^{\\dagger}_{-}d_{-}$.\nDue to the invariance of $\\tilde{\\mathcal{H}}$ for the chiral transformation\n$\\tilde{\\mathcal{H}}\\mapsto\ne^{i\\theta\\mathcal{G}}\\tilde{\\mathcal{H}}e^{-i\\theta\\mathcal{G}}\n=\\tilde{\\mathcal{H}}$,\nthe total chirality is conserved, $[\\tilde{\\mathcal{H}},\\mathcal{G}]=0$.\nThen the many-body states are classified according to the {\\it total chirality} \n$\\chi_{\\rm tot}$.\nFor repulsive interactions $V_{ij}>0$,\n$\\tilde{\\mathcal{H}}$ is semi-positive definite.\nFurthermore, when the interactions are bipartite (i.e., only \nact between different sublattices, $V_{i\\in\\bullet,j\\in\\bullet}=V_{i\\in\\circ,j\\in\\circ}=0$) as the case with the nearest-neighbor interaction, \nchiral condensates $|G_{\\pm}\\rangle=d^{\\dagger}_{1\\pm}\\cdots d^{\\dagger}_{M_\\pm\\pm}\n|D_{<}\\rangle$ (with $|D_<\\rangle\\equiv\\prod_{m}(c^{\\dagger}\\varphi)_{m}|0\\rangle$ denoting the Dirac sea) \nconstitute a {\\it ground state doublet} $\\Psi=(|G_{+}\\rangle,|G_{-}\\rangle)$,\nsince $c_{j}c_{i}|G_{\\pm}\\rangle=c^{\\dagger}_{j}c^{\\dagger}_{i}|G_{\\pm}\\rangle=0$\nfor $i\\in\\bullet$ and $j\\in\\circ$.~\\cite{detail,hamamoto}\nWe call this a topological degeneracy of two.\nUnlike a simple charge density wave (CDW),\none may mix $|G_{+}\\rangle$ and $|G_{-}\\rangle$\nthrough a unitary transformation $\\Psi\\mapsto\\Psi^{\\omega}\\omega$\nwith $\\omega\\in{\\rm U}(2)$ even in a finite system.\nOne can numerically confirm\nthat the chiral condensate remains the ground state\nunless the non-bipartite potential is large,~\\cite{PhysRevLett.99.196802}\neven though the ground-state energy is nonzero.\nIn the rest of the paper,\nwe focus on the leading interaction, i.e., the nearest-neighbor repulsion\nfor simplicity.\n\n\\begin{figure}[t]\n\\begin{center}\n \\includegraphics[scale=1.15]{gap_Ne30.eps}\n\\caption{\\label{fig:phi-dependence}\n(color online)\nMagnetic-field dependence of the many-body gap $\\Delta$ in the $n=0$ LL.\nThe result is displayed for 30 electrons \nwith the consecutive values of the linear dimension of \nthe system, $L$, connected by a line with the results \nfor $L\\equiv 3$ (mod 3) forming a lower envelope \n($\\Delta\\propto\\phi^2$). \nThe inset shows the gap at $L=3l$ against $1\/L$ \nfor the magnetic flux $\\phi=1\/12$ (circle), $\\phi=1\/27$ (triangle), \nand $\\phi=1\/48$ (square).\n}\n\\end{center}\n\\end{figure}\n\n\\mysection{Many-body gap}%\nWe next calculate excitation energies numerically\nwith the exact diagonalization method.\nIn the projected subspace,\nthe strength of the nearest-neighbor repulsion $V>0$ is the only \nenergy scale, which acts as the unit of energy.\nFull energy spectra of $\\tilde{\\mathcal{H}}$ for finite systems\nsuggest that the first excited state appears in the sector of\n$\\chi_{\\rm tot}=\\pm(M_\\pm-2)$,\nwhich is created from a chiral condensate $|G_{\\pm}\\rangle$\nby single chirality-flippings \nanalogous to the projected single-mode approximation.~\\cite{PhysRevLett.54.581,PhysRevB.33.2481,PhysRevLett.73.3568}\nNoticing this,\nwe further shrink the Hilbert space\nby focusing on the sector of $\\chi_{\\rm tot}=\\pm(M_\\pm-2)$.\nThis enables us to obtain the energy of the first excited state,\nor the energy gap $\\Delta$,\nwith calculation cost of the order of $\\mathcal{O}(M_\\pm^2)$.\nWe consider a system on a torus composed of $2L^2$ lattice sites\nwith a linear dimension $L$.\nFor investigating a weak-field regime, we adopt the string gauge,~\\cite{PhysRevLett.83.2246}\nwhere a magnetic flux is given by $\\phi=m\/L^2$ with an integer $m$ $(>0)$\nand $M_\\pm=m$ zero modes are obtained for each chirality.\n\nIn Fig.~\\ref{fig:phi-dependence},\nwe plot $\\Delta$ for $30$ electrons as a function of $\\phi$ with $L$ changed consecutively. We immediately \nnotice that \nthe result exhibits a marked periodicity of three, where \nthe values for $L\\equiv 3$ (mod 3) form a clear lower \nenvelope with a scaling $\\Delta\\propto\\phi^2$,\nwhile those for $L\\ne3l$ deviates from this. \nThe latter behavior is considered to be a finite-size effect, \nsince the deviation diminishes with the sample size. \nTo confirm the scaling, the inset plots $\\Delta\/\\phi^2$ at $L=3l$ \nagainst $1\/L$ for various values of $\\phi$,\nwhich indicates the scaling law $\\Delta\\propto\\phi^2$ is \nvery accurately obeyed.\n\n\\mysection{Hall conductance}%\nLet us now consider Hall conductance of the chiral condensate.\nBy the Niu-Thouless-Wu formula,~\\cite{PhysRevB.31.3372}\nthe Hall conductance is written with the Chern number~\\cite{PhysRevLett.49.405} as\n\\[\n\\sigma_{xy}=\\frac{e^{2}}{h}\\frac{1}{N_{D}}C,\\qquad\nC=\\frac{1}{2\\pii}\\int{\\rm Tr}_{N_{D}}dA,\\qquad\nA=\\Psi^{\\dagger}d\\Psi\n\\]\nwhere $N_{D}$ is the degeneracy\nand $A$ is the non-Abelian Berry connection \nthat describes multiplets.~\\cite{2004JPSJ...73.2604H}\nIn terms of the basis that diagonalizes $\\mathcal{G}$, we have \n$C=C_{+}+C_{-}$ with\n$C_{\\pm}=\\frac{1}{2\\pii}\\int\\langle dG_{\\pm}|dG_{\\pm}\\rangle$.\nEach term is further decomposed as $C_{\\pm}=C_{\\psi_{\\pm}}+C_{D_{<}}$\nwith $C_{\\psi_{\\pm}}=\\frac{1}{2\\pii}\\int{\\rm Tr}_{M_{\\pm}}\nd\\psi^{\\dagger}_{\\pm}d\\psi_{\\pm}$\nand $C_{D_{<}}=\\frac{1}{2\\pii}\\int{\\rm Tr}d\\varphi^{\\dagger}d\\varphi$.\nBy the charge conjugation,\nwe have $C_{\\psi_{+}}+C_{D_{<}}=-(C_{\\psi_{-}}+C_{D_{>}})$\nwith $C_{D_{>}}\n=\\frac{1}{2\\pii}\\int{\\rm Tr}(\\Gamma d\\varphi)^{\\dagger}\\Gamma d\\varphi=C_{D_{<}}$.\nThus the total Chern number of the ground-state doublet\nvanishes as $C=C_{\\psi_{+}}+C_{\\psi_{-}}+2C_{D_{<}}=0$,\nwhich may be called a topological cancellation.\nThis implies that the chiral condensate is a Hall insulator\nwith a nontrivial topological degeneracy $N_{D}=2$.\n\n\n\\begin{figure}[t]\n\\includegraphics[scale=1.15]{dos-obemf3_mf-bulk15x15p15v0.25e8i174s1323339888.eps}\n\\hfill\n\\includegraphics[scale=1.15]{wc-hbdmf3_mf-bulk15x15p15v0.25e8i174s1323339888.eps}\n\\caption{\\label{fig:bond-order}\n(color online) Mean-field results for the energy spectrum (a),\nits blowup around the $n=0$ LL (b),\nand the bond strength $|\\Delta_{ij}|$ plotted in a real-space (c).\nThe parameters are $L=15,\\phi=1\/15$, and $V\/t=0.25$.\nA dashed hexagon in (c) is an enlarged unit cell with arrows primitive vectors.}\n\\end{figure}\n\n\\mysection{Bond order}%\nAs have been confirmed in various systems,\nwhile topological phases are featureless in a bulk,\nthey show characteristic boundary states.~\\cite{PhysRevLett.71.3697}\nSo a natural question \nwe can pose here is: do the edge states in the present system \nexhibit special features despite the bulk Chern number \nbeing zero? \nBefore presenting the result, however, let us first \nhave a look at the mean-field state in the present \nsystem in the bulk, which will turn out to be instructive. \nOne virtue of a mean-field picture~\\cite{hatsugai08up} is that \nwe can introduce a bond order, \n$\\Delta_{ij}\\equiv V\\langle c^\\dagger_ic_j\\rangle$, \nfor adjacent sites $\\langle ij\\rangle$.~\\cite{PhysRevB.37.3774,\nPhysRevB.39.11413,\nPhysRevB.45.4027,\nPhysRevB.61.16377,\nPhysRevLett.98.146801}\nThe dominant part of the MF Hamiltonian is given by\n$\\mathcal{H}_{\\rm MF}=-\\sum_{\\langle ij\\rangle}[(te^{i\\theta_{ij}}\n+\\Delta_{ij}^\\ast)c^\\dagger_ic_j+{\\rm H.c.}]$,\nwhere $\\Delta_{ij}$ is determined self-consistently by diagonalizing\n$\\mathcal{H}_{\\rm MF}$.\nA spontaneous symmetry breaking is induced by the many-body effect for weak magnetic fields, \nwhere the density of states has a sharp peak at \nthe Fermi energy.\nIn Fig.~\\ref{fig:bond-order},\nwe show a typical MF result for the ordered phase.\nThe energy spectrum is plotted in panel (a),\nwhere the qualitative structure of the LLs is preserved.\nThis comes from the fact that the convergent order parameters turn out \nto retain the initial Peierls phase\nas $\\Delta_{ij}=|\\Delta_{ij}|e^{-i\\theta_{ij}}$.\nThe influence of the electron-electron interaction appears most prominently in the $n=0$ LL,\nwhere a finite gap of the order of $\\phi$ opens as shown in the blowup Fig.~\\ref{fig:bond-order}(b).\nTo see how the symmetry is broken in the mean field, \nwe show in panel (c) a real-space image of the \nbond order $|\\Delta_{ij}|$, \nwhich is seen to exhibit a Kekul\\'e pattern.~\\cite{2008PhyE...40.1530H,hatsugai08up}\nThis makes the unit cell enlarged, which causes $K$ and $K'$ points \nto be coupled, and this in turn opens a finite gap. In this sense \nwe can regard this a Peierls transition in the honeycomb lattice.\n\nOn the other hand,\nthe chiral condensate with its topological degeneracy of two\nexhibits in the bulk no bond order as we have seen in Fig.~\\ref{fig:bond-order}.\nDue to the quantum fluctuation, the bond order of the mean field\nis destroyed and the quantum liquid ground state is realized.\n\n\n\n\n\\mysection{Edge and defect states}%\nWe are now in a position to ask the question: what kind of \nedge states does the chiral condensate accommodate?\nBased on the bulk-edge correspondence,\nwe may expect a non-trivial behavior of the many-body states near edges.\nA prime example is the \nfractional\nQH states in a 2DEG, where \na CDW-like behavior emerges along edges\nof a\nribbon,~\\cite{PhysRevB.50.17199}\nwhile in the bulk it melts into the Laughlin liquid with no long-range order,\nwhich has a $q$-fold degeneracy of the fractional QH states at filling $\\nu=1\/q$.\nNote that a honeycomb lattice with edges\nhas QH edge states whose mode lies in a LL gap. \nTo perform the projection onto the $n=0$ LL we set an energy cutoff,\nthe choice of which is shown to have little influence on the edge states shown below.\n\n\\begin{figure}[t]\n\\includegraphics[scale=1.15]{wc-hbd_arm12x192p1q192n12z24.eps}\n\\hfill\n\\includegraphics[scale=1.15]{wc-hbd_zig24x192p1q192n12z58.eps}\n\\caption{\\label{fig:bond-chiral}\n(color online)\nBond strength near an armchair (left) or zigzag (right) edge of the doubly-degenerate chiral condensate.\nMagnetic flux is $\\phi=1\/192$ for which the magnetic length\nis $l_{B}\\simeq 8.9a$.\nThe bond order decays in a bulk away from the edges.\n}\n\\end{figure}\n\nIn Fig.~\\ref{fig:bond-chiral} we show\n$|\\Delta_{ij}|$\nfor the chiral condensate\nplotted in a real space near armchair and zigzag edges.\nIn panel (a),\nwe can see that a Kekul\\'e-type bond order \nreminiscent of the mean-field result in Fig.~\\ref{fig:bond-order}\nemerges along the armchair edge.\nThis is the\nkey result in the present work.\nThe enhancement in bonds rapidly decays away from the edge\nin a few lattice constants,\nand $|\\Delta_{ij}|$ slightly oscillates\nwith a length scale of the order of the magnetic length\n$l_{B}\\sim a\/\\sqrt{\\phi}$\nwith $a$ being the interatomic spacing.\nThis may naively seem to be analogous to the\nfractional QH edge states in a 2DEG, \nbut here the honeycomb lattice structure is essential \nin the ground state. Indeed,\nthe ring pattern is locked along the armchair edge \nin a Kekul\\'e pattern, \nwhile this is not the case with zigzag edges [see panel (b)].\nIn the latter case, \nthe ring pattern is blurred by the translational symmetry along a zigzag edge, \nand a very weak stripe pattern parallel to the edge appears.\nThese patterns related with the three-fold degeneracy of the \nKekul\\'e pattern are washed out in the bulk chiral condensate. \nAll these are a specific property of a honeycomb lattice model.\n\nWe can further endorse that the lattice structure is at the core \nby looking at the states around lattice defects.\nWhen a single atom is removed from the bulk honeycomb lattice,\none-body localized zero modes appear that are protected\nby the chiral symmetry.~\\cite{PhysRevLett.89.077002,2009SSCom.149.1061H}\nIn the presence of the electron-electron interactions, however,\nlocal chiral symmetry breaking occurs spontaneously to lower the energy\nby inducing effective hopping in the same sublattice.~\\cite{2004PhyE...22..679R,\n2009SSCom.149.1061H}\nThen what if two point defects come close to each other?\nSuch a divacancy\nconsists of two adjacent missing atoms, \nand is recently observed experimentally\nin ion-irradiated carbon samples.~\\cite{PhysRevB.85.121402}\nWe expect that the chiral symmetry may be partially recovered\nwith a reconfiguring of the two symmetry-breaking bonds.\nWe plot in Fig.~\\ref{fig:bond-defect} the \nbond order for the chiral condensate near the divacancy.\nWe do confirm that\nenhancement of the bond order near the divacancy\nwhich can be considered due to the revival of the chiral symmetry.\n\n\\begin{figure}[t]\n\\includegraphics[scale=1.15]{wc-hbd_z2def60x60p3q3600n3z6.eps}\n\\caption{\\label{fig:bond-defect}\n(color online)\nBond strength of the chiral condensate\nnear a divacancy composed of two adjacent sites missing.\nMagnetic flux is $\\phi=1\/1200$\nfor which the magnetic length is $l_B\\simeq 22.3a$. \nThe bond order reflects twofold axial symmetry of the divacancy.\n}\n\\end{figure}\n\nWe have thus shown that the bond order emerges along edges \nand around vacancies, \ndespite the topological cancellation $C=0$ \nthat might first seem to wipe out any signature of the chiral condensate.\nIndeed, the charge density $\\langle n_i\\rangle$ itself is uniform\nfor the\nchiral condensate even along edges,\nwhich is due to the invariance of the chiral condensate\nfor the charge conjugation.\nThus it is the bond order $|\\Delta_{ij}|$ that we have to \nlook at as a probe for the chiral condensate. \nThus the bond order provides a new probe for the many-body effect\nin half-filled graphene applied a magnetic field.\nThe bond order near\nedges\nshould be observable experimentally with some imaging techniques such as \nGreen's function scanning tunneling microscopes.~\\cite{PhysRevLett.74.306,PhysRevB.51.5502}\nSince the amplitude of\n$|\\Delta_{ij}|\/V=|\\langle c^\\dagger_ic_j\\rangle|$\nis of the order of the magnetic flux $\\phi$,\nthe magnetic field should have significant magnitudes.\n\n\n{\\it Summary.}---%\nThe many-body ground state\nat half filling\nin the honeycomb lattice is identified as a doubly-degenerate chiral condensate for a spin-split Landau level. \nThe many-body effect opens a finite energy gap,\nwhich makes the chiral condensate a generic topological insulator.\nHowever, the system has a peculiar manifestation of \nthe bulk-edge correspondence in topological systems \nas an emergence of a bond order with a Kekul\\'e pattern \nalong armchair edges in an exact ground state, \nwhile the pattern is dissolved in the bulk. \n\n{\\it Acknowledgement.}---%\nThe computation in this work has been done\nwith the facilities of the Supercomputer Center,\nInstitute for Solid State Physics, University of Tokyo.\nThis work was supported in part by Grants-in-Aid\nfor Scientific Research No. 23340112 and No. 23654128 from the JSPS.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Appendices}\n\\subsection{Full List of Incompatibility Rules}\\label{appd:corr1a}\nThe 12 incompatibility rules of entity type-constrained biRoR introduced in Section~\\ref{sec:corr1a} are\n\\begin{enumerate}\n\\item Part-Whole (arg0) and Per-Soc (arg0)\n\\item Part-Whole (arg0) and Gen-Aff (arg0)\n\\item Part-Whole (arg1) and Per-Soc (arg0)\n\\item Part-Whole (arg1) and Gen-Aff (arg0)\n\\item Per-Soc (arg0) and Org-Aff (arg1)\n\\item Per-Soc (arg0) and Art (arg1)\n\\item Org-Aff (arg0) and Art (arg1)\n\\item Org-Aff (arg1) and Art (arg1)\n\\item Org-Aff (arg1) and Gen-Aff (arg0)\n\\item Art (arg0) and Art (arg1)\n\\item Art (arg1) and Gen-Aff (arg0)\n\\item Art (arg1) and Gen-Aff (arg1)\n\\end{enumerate}\n\n\n\n\\end{document}\n\n\\subsection{The Tradeoff between precision and recall}\n\\begin{table*}[!ht]\n \n \\centering\n \\resizebox{\\textwidth}{!}{\n \\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc}\n \\toprule\n & \\multicolumn{3}{c|}{\\textbf{BC\\_dev}} & \\multicolumn{3}{c|}{\\textbf{BC\\_test}} & \\multicolumn{3}{c|}{\\textbf{CTS}} & \\multicolumn{3}{c|}{\\textbf{WL}} & \\multicolumn{3}{c}{\\textbf{Overall}} \\\\\n & \\textbf{P} & \\textbf{R} & \\textbf{Micro} & \\textbf{P} & \\textbf{R} & \\textbf{Micro} & \\textbf{P} & \\textbf{R} & \\textbf{Micro} & \\textbf{P} & \\textbf{R} & \\textbf{Micro} & \\textbf{P} & \\textbf{R} & \\textbf{Micro} \\\\\n \\hline\n \n \\textbf{$\\text{\\modelname{}}$} & 76.76 & 64.25 & 69.8& 71.64 & \\textbf{62.08} & 67.17& 63.52 & \\textbf{60.25} & 59.5& 60.89 & \\textbf{54.77} & 58.4 & 65.35\n& \\textbf{59.03}\n& 61.69\\\\\n \\textbf{$\\text{\\modelname{}}_\\text{local}$} & 77.01 & 61.69 & 68.44& \\textbf{77.52} & 61.94 & 68.89& \\textbf{71.95} & 57.93 & 59.87& 65.51 & 54.61 & 60.6 & \\textbf{71.66}\n& 58.16\n& 63.12\\\\\n \\textbf{$\\text{\\modelname{}}_\\text{non-local}$} & 79.57 & \\textbf{65.15} & \\textbf{70.97}& 72.64 & 61.78 & 67.88& 69.14 & 52.84 & 58.55& 63.17 & 52.96 & 58.52 & 68.32\n& 55.86\n& 61.65\\\\\n \\textbf{$\\text{\\modelname{}}_\\text{glocal}$} & \\textbf{80.30} & 62.98 & 70.36& 77.03 & 61.88 & \\textbf{69.82}& 70.20 & 59.64 & \\textbf{61.17}& \\textbf{65.94} & 54.48 & \\textbf{61.7} & 71.06\n\n&58.67& \\textbf{64.23}\\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{Precision and Recall on ACE05} \\label{tab:addlabel}\n\\end{table*}\n\n\n\n\\begin{table}[htbp]\n \\centering\n \\begin{tabular}{lccc}\n \\toprule\n \\textbf{Model} & \\textbf{P} & \\textbf{R} & \\textbf{Micro} \\\\\n \\midrule\n \\multicolumn{4}{c}{\\textbf{\\emph{Non-ensemble models}}} \\\\\n \\textbf{$\\text{\\modelname{}}$} & 32.05 & \\textbf{49.37} & 37.9 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{local}$} & 39.31 & 45.3 & 39.24 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{non-local}$} & 39.08 & 47.68 & 39.63 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{glocal}$} & \\textbf{44.24} & 46.86 & \\textbf{39.74} \\\\\n \\midrule\n \\multicolumn{4}{c}{\\textbf{\\emph{Ensemble models}}} \\\\\n \\textbf{E-$\\text{\\modelname{}}$} & 40.77 & \\textbf{54.03} & 42.39 \\\\\n \\textbf{E-$\\text{\\modelname{}}_\\text{local}$} & 51.03 & 51.98 & 43.12 \\\\\n \\textbf{E-$\\text{\\modelname{}}_\\text{non-local}$} & 52.31 & 51.39 & \\textbf{43.98} \\\\\n \\textbf{E-$\\text{\\modelname{}}_\\text{glocal}$} & \\textbf{56.48} & 47.44 & 43.35 \\\\\n \\bottomrule\n \\end{tabular\n \\caption{Results on SemEval 2018 Task 7.2}\n\n \\label{tab:res_semeval}%\n\\end{table}%\n\n\\subsection{Which model captures which feature well?}\n\n2. example\n\\subsection{Ablation Study on ACE05}\n\n\n\\subsection{Two-Stage Training}\nWe study the performance of each stage in the two-stage learning on ACE05 validation set in Table~\\ref{tab:ablation}. The first stage is binary classification to distinguish whether two entities have ``no-relation'' or a valid relation. In this stage, the glocal model which combines both the local learner and non-local supervisor performs the best, scoring +2.55 F1 scores higher than the base model.\n\nWe observe that in both datasets, there are a dominant number of negative labels. The experiments show that the difficulty is more in identifying whether there exists a relation, than in classifying the specific relation. So it is also worth exploring a two-stage learning strategy for local and non-local correlations. Following \\cite{DBLP:journals\/corr\/abs-1909-11898}, in the first stage, we train an extraction model on identifying binary relations (where the ``no-relation'' label is treated as negative, and all other relations are positive). In the second stage, a classification model, which is trained to classify the different positive relations, will be applied on the samples which the extraction model in Stage 1 labels as positive. We denote the combination of a local extracting model and local classification model as \\textbf{$\\text{\\modelname{}}_{\\text{ex:local}, \\text{cl:local}}$}, and the combination of a glocal extracting model and local classification model as \\textbf{$\\text{\\modelname{}}_{\\text{ex:glocal}, \\text{cl:local}}$}.\n\n\n \n\n\n\n\n\n\n\\section{Introduction}\\label{sec:intro}\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width= \\columnwidth]{img\/fig_intro.pdf}\n \\caption{An example of the RE task.}\n \\label{fig:intro-ex}\n\\end{figure}\n\nRelation Extraction (RE) is the task to identify the relation of given entities, based on the text that they appear in. As a fundamental task of knowledge extraction from text, RE has become an active area of research in the past several decades~\\cite{DBLP:conf\/tipster\/MillerCFRSSW98,DBLP:journals\/jmlr\/ZelenkoAR03,DBLP:conf\/naacl\/BunescuM05,DBLP:conf\/acl\/ZhouSZZ05,DBLP:conf\/semeval\/HendrickxKKNSPP10}. \n\nIn natural language, most text includes multiple entities. For example, the sentence in Figure~\\ref{fig:intro-ex} has seven entities. We find that 99.76\\% data instances of the widely used ACE 05 dataset \\cite{walker2006ace} have more than two entities, and there are 9.21 relations in each text on average. However, most previous research\nhas been confined to the simplified setting of only classifying the relation between every \\textit{two} entities at a time \\cite{DBLP:conf\/coling\/ZengLLZZ14,DBLP:conf\/semeval\/LuanOH18,DBLP:conf\/acl\/LiJ14,DBLP:conf\/emnlp\/GormleyYD15,DBLP:conf\/acl\/MiwaB16}. For the sentence in Figure~\\ref{fig:intro-ex} with 7 entities, most previous approaches would perform 49 independent relation classification tasks (if including self-reflexive relations). It is not feasible to reduce this number of classification tasks because existing methods require explicit annotation of the entities in the input. For example, to predict the relation between the entity pair (obstetricians, California), the input needs to be transformed into ``...$\\langle e1\\rangle$ obstetricians $\\langle \\backslash e1\\rangle$ in $\\langle e2\\rangle$ California $\\langle \\backslash e2\\rangle$ will pay \\$60,000 in Los Angeles ...''.\n\n\nThe problems exposed by such a previous paradigm is that it is not only inefficient, but also overlooks the interdependency among the multiple relations in one context. For example, in the 49 relations in Figure~\\ref{fig:intro-ex}, if we already know the relationship (Miami, is part of, south Florida), where ``is part of'' is a relation defined on two objects, then it is very unlikely for \\textit{Miami} to be in any other person-social relationship such as ``is the father of...''. \nRemember that, on average, there are 9.21 relations in each text in the ACE 05 dataset, for example, and each relation can provide information to other relations in the same text. We denote the frequently-appearing interdependency of the many relations in the same text as the ``relation of relations'' (RoR) phenomenon.\n\nTo capture RoR, we propose a new paradigm of RE by treating the predictions of all relations in the same text as a whole. Note that our work is distinct from \\cite{DBLP:conf\/acl\/WangTYCWXGP19}, which still treats the relation of each entity pair as independent classification tasks, but saves the computation power at the cost of accuracy by encoding all entities in one pass.\nInstead, our newly proposed paradigm is not about tradeoffs between computation cost and accuracy, but to increase the performance by capturing RoR.\n\nIn this paper, we first highlight the importance of RoR by identifying several types and their frequent occurrences in RE datasets in Section~\\ref{sec:ror_stats}. We then propose a data-driven approach {without} hand-crafted rules, using Graph Neural Networks (GNNs) to model each relation as a node to learn the pair-wise dependency of every two relations, and then a matrix transformer to learn the correlations involving multiple relations or numerical correlations of the count of relations. We evaluate the model on two benchmark datasets, ACE 2005 (ACE05)~\\cite{walker2006ace} and SemEval 2018 Task 7.2 (SemEval2018)~\\cite{DBLP:conf\/semeval\/GaborBSQZC18}. Our system outperforms the previous state-of-the-art (SOTA) models by +1.12\\% on ACE05 and +2.55\\% on SemEval2018. The contributions of our paper are as follows:\n\\begin{itemize}\n \\item We propose a new paradigm of RE, according to the frequent RoR phenomenon. It provides a new perspective for future research in RE.\n \\item We develop a model to learn the interdependency of all relations in the same text, based on a GNN and matrix transformer.\n \\item We validate the effectiveness of our model, which outperforms SOTA models by a clear margin on two benchmark datasets.\n \\item We open-source our model and evaluation codes.\n\\end{itemize}\n\n\\section{New formulation of RE}\nMost previous work formulates RE as multiple independent classification problems limited to two entities and the text: \n\\begin{quote}\n Given two entity mentions $\\bm{e}_1$ and $\\bm{e}_2$, a text sequence $\\bm{t}=\\{w_1, w_2, \\ldots, w_N\\}$ involving $\\bm{e}_1$ and $\\bm{e}_2$, and a finite set relation types $\\mathcal{R}$, the task is to predict the relation type between the two entities.\n\\end{quote}\nUnder this setting, RE can be solved by the well-researched sentence classification task.\n\nHowever, based on the motivations in Section~\\ref{sec:intro}, we propose a new paradigm of RE:\n\\begin{quote}\nGiven~a~text~sequence~$\\bm{t}=\\{w_1, w_2, \\ldots, w_N\\}$ and all the entities $\\bm{e}_1, \\ldots, \\bm{e}_M$ mentioned in $\\bm{t}$, the model needs to predict the relationship $r_{ij}$ between each two entities $(\\bm{e}_i, \\bm{e}_j)$, where $i, j \\in \\{1,\\ldots, M\\}$.\n\\end{quote}\nWe use a matrix $\\bm{R} = (r_{ij}) \\in \\mathbb{R}^{M \\times M}$ to represent all the relations of interest, as shown in Figure~\\ref{fig:problem_form}.\n\\begin{figure}[h]\n \\centering\n \\includegraphics[width= \\columnwidth]{img\/fig_prob.pdf}\n \\caption{New formulation of RE that considers all $M$ entities in the same text.}\n \\label{fig:problem_form}\n\\end{figure}\n\\begin{table*}[!t]\n \\centering\n \\begin{tabular}{lll}\n \\toprule\n \\textbf{Relation} & \\textbf{Arg0} & \\textbf{Arg1} \\\\\n \\midrule\n Person-Social (Per-Soc)\n (e.g., family) & PER & PER \\\\\n Part-Whole (e.g., city-and-country) & FAC, LOC, GPE, ORG & FAC, LOC, GPE, ORG \\\\\n Physical (Phys) (e.g., near) & PER, FAC, LOC, GPE & PER, FAC, LOC, GPE \\\\\n Organization-Affiliation (Org-Aff) (e.g., employment) & PER, ORG, GPE & ORG, GPE \\\\\n Agent-Artifact (Art) (e.g., owner-and-object) & PER, ORG, GPE & FAC \\\\\n General-Affiliation (Gen-Aff) (e.g., citizen) & PER & PER, LOC, GPE, ORG \n \\\\\n \\bottomrule\n \\end{tabular}%\n \\caption{Valid entity types for each relation. The entity type abbreviations refer to Person (PER), Facility (FAC), Location (LOC), Geo-PoliticalEntity (GPE), and Organization (ORG).}\n \\label{tab:ror_type1a}\n\\end{table*}\n\n\n\\section{Statistical analysis of RoR}\\label{sec:ror_stats}\nWe conduct our case study using the benchmark dataset ACE05 \\cite{walker2006ace}. We will introduce two forms of RoR: (1) biRoR, which is only between two relations, and (2) multiRoR, which involves three or more relations. \n\nNote that the purpose of the following analyses is to demonstrate the importance of RoR. Our model introduced in the subsequent section will \\textit{not} hand-craft such detailed rules but will learn RoR in a data-driven way.\n\n\\subsection{Data overview}\n\nACE05 \\cite{walker2006ace} is the most widely used dataset for RE. Its text is extracted from a variety of sources, including news programs, newspapers, newswire reports, and audio transcripts. The 6 relation types are shown in Table~\\ref{tab:ror_type1a}. There are 7 entity types valid for the relations: Facility (FAC), Geo-PoliticalEntity (GPE), Location (LOC), Organization (ORG), Person (PER), Vehicle (VEH), Weapon (WEA).\n\n\\subsection{BiRoR: Interdependency of two relations}\\label{sec:ror_local}\n\\subsubsection{Entity type-constrained biRoR }\\label{sec:corr1a}\nWe first introduce the simplest form of RoR, based on the entity types. Specifically, given the triple ($\\bm{e}_1$, $\\mathrm{RelType}_a$, $\\bm{e}_2$) of two entities and their relation, we can infer whether $\\bm{e}_1$ is unlikely to co-occur with a different relation type $\\mathrm{RelType}_b$. \n\nTo elucidate such constraint, we will make an example using the seven entity types in ACE05. As detailed in Table~\\ref{tab:ror_type1a}, only certain types are allowed to be the arguments of the relations. Therefore, we can deduct 12 rules of \\textit{incompatibility}. For example, \nthe same entity cannot be both the arg0 of Per-Soc and the arg0 of Part-Whole, because an entity must be a person (PER) in order to satisfy the Per-Soc relationship, but Part-Whole \\textit{cannot} involve PER. The full list of incompatibility rules are listed in Appendix~\\ref{appd:corr1a}.\n\n\n\\subsubsection{Semantic-constrained biRoR}\\label{sec:corr1b}\nAnother type of biRoR is constrained by the semantics of the relation. The intuition is that what a relation means can imply whether it can be shared or must be disjoint with another relation. For example, the Art relationship can describe a person (arg0) owning a facility (arg1), where the arg1 \\textit{must} be a facility. If such relation already exists, the same facility cannot be involved in the Part-Whole relation with a city, because cities (e.g., Boston) cannot be a part of a facility, semantically. This kind of incompatibility is not a hard constraint by the entity type, but it is implied by the semantics of relations.\n\nSemantics can also imply whether a relation should be symmetric. For example, Per-Soc is always symmetric because family and friends are commutative relations, whereas Org-Aff is always an asymmetric relation \\cite{ACE05eval}. Hence, if a relation $r_{ij}=$ Per-Soc, then $r_{ji}=$ Per-Soc. And if $r_{ij}=$ Org-Aff, then $r_{ji}\\neq$ Org-Aff.\n\n\n\n\n\n\\subsubsection{Empirical biRoR}\\label{sec:corr1c}\nTo form a more direct understanding of biRoR, we calculate the correlation of every two relations in Figure~\\ref{fig:corr2}. We can see that the incompatibility rules in Section~\\ref{sec:corr1a} is proven by the red negative color, the symmetric property of Per-Soc and Phys in Section~\\ref{sec:corr1b} is proven by the darker blue. \n\\begin{figure}[ht]\n \\centering\n \\includegraphics[width= \\columnwidth]{img\/fig_corr2.pdf}\n \\caption{Correlation of every two relations. Each cell $(\\mathrm{RelType}_i, \\mathrm{RelType}_j)$ represents the conditional probability that an entity will have $\\mathrm{RelType}_j$ given the existing $\\mathrm{RelType}_i$. For better visual effect, the zero probability is converted to $-1$ (dark red), signaling that these two relations have 0\\% co-occurrence.}\n \\label{fig:corr2}\n\\end{figure}\nThere are also other correlations such as frequent \nco-occurrences of Part-Whole and Phys relations.\n\n\n\n\\subsection{MultiRoR: Correlation of 3+ relations}\\label{sec:ror_nonlocal}\n\n\\subsubsection{Entity type-constrained multiRoR}\nOther than biRoR, which only involves two relations, there are more complicated rules acting on multiple relations, namely multiRoR. The entity type-constrained multiRoR extends Section~\\ref{sec:corr1a} from the incompatibility between two relations to among 3+ relations. For example, if we already know two relations of an entity, which is both the arg1 of the Org-Aff relation and the Phys relation, then this entity cannot be the arg1 of any Art relations (because this entity must be GPE). As the number of relations centered on one entity increases in Figure~\\ref{fig:corr3_1}, the percentage of invalid combinations of multiple relations among all possible combinations will soon be over 50\\%, and even reached 83\\% when there are 7 relations.\n\\begin{figure}[ht!]\n \\centering\n \\resizebox{0.85 \\columnwidth}{!}{%\n \\begin{tikzpicture}\n \\pgfplotsset{\n scale only axis,\n \n legend style={at={(0,0)},anchor=south west},\n }\n \n \\pgfplotsset{\npercentage plot\/.style={\nnodes near coords align=vertical,\n yticklabel=\\pgfmathprintnumber{\\tick}\\,$\\%$,\n ymin=0,\n ymax=100,\n enlarge y limits={upper,value=0}\n}\n}\n\\begin{axis}[\n legend style={cells={align=left},nodes={scale=0.7, transform shape},\n legend cell align={left}},\n title={Percentage of Invalid Combinations},\n xlabel={Number of Relations Centered on One Entity},\n ylabel={},\n xmin=0.5, xmax=7.5,\n ymin=0, ymax=100,\n xtick={1,2,3,4,5,6,7},\n ytick={0,20,40,60,80,100},\n legend pos=north west,\n ymajorgrids=true,\n grid style=dashed,\n width=7cm,\n height=5cm,\n every axis plot\/.append style={thick},\n percentage plot\n]\n\n\\addplot[\n color=blue,\n mark=square,\n ]\n coordinates {\n (1,0)(2,22)(3,41)(4,55)(5,65)(6,73)(7,83)\n };\n \\legend{}\n \n\\node [above,font=\\small] at (axis cs: 1, 3) {\\pgfmathprintnumber{0}\\%};\n\\node [above,font=\\small] at (axis cs: 1.9, 24) {\\pgfmathprintnumber{22}\\%};\n\\node [above,font=\\small] at (axis cs: 2.9, 41.5) {\\pgfmathprintnumber{41}\\%};\n\\node [above,font=\\small] at (axis cs: 4, 55.5) {\\pgfmathprintnumber{55}\\%};\n\\node [above,font=\\small] at (axis cs: 5, 65.5) {\\pgfmathprintnumber{65}\\%};\n\\node [above,font=\\small] at (axis cs: 6, 73.5) {\\pgfmathprintnumber{73}\\%};\n\\node [above,font=\\small] at (axis cs: 7, 83.5) {\\pgfmathprintnumber{83}\\%};\n\n\\end{axis}\n\\end{tikzpicture}\n}\n\\vspace{0px}\n\\caption{Percentage of invalid combinations over all possible set of relations of an entity, as the total number of non-empty relations centered on the entity increases.}\n\\label{fig:corr3_1}\n\\end{figure}\n\n\n\\subsubsection{Numerically correlated multiRoR}\n\n\nWe can also discover numerical correlations of multiple relations. From all relation matrices $\\bm{R}$ in the dataset, we find that the number of occurrences of a specific type of relations can correlate with the number of another relation type. Note that it is counted as multiRoR, because the correlation is defined not between two single relations, but the total count of two relation types (each of which can include multiple occurrences). From the correlation plot in Figure~\\ref{fig:corr3_3}, Per-Soc and Gen-Aff show a strong positive linear dependency, whereas Art and Org-Aff are negatively related by numbers.\n\n\\begin{figure}[ht]\n \\centering\n \\includegraphics[width= \\columnwidth]{img\/fig_corr3_3.png}\n \\caption{Numerical correlation of every two relation types. Each cell $(\\mathrm{RelType}_i, \\mathrm{RelType}_j)$ represents the linear correlation between the number of occurrences of $\\mathrm{RelType}_i$ and that of $\\mathrm{RelType}_j$.\n }\n \\label{fig:corr3_3}\n\\end{figure}\n\n\\section{Method}\nBased on the rich RoR phenomenon analyzed in Section~\\ref{sec:ror_stats}, we aim to design a model that can mine these properties from data. A naive solution is to hand-craft many rules to impose every type of RoR, but it is not scalable when there are datasets of different features, or when there are some RoR that are difficult to be manually identified. Hence, we aim to design a model that has the capacity to learn RoR with no hand-crafting. \n\n\nOur overall training strategy is in Figure~\\ref{fig:archi}. In the following, we will elaborate on three key components: (1) initial embeddings of entities and relations, (2) the GNN-based biRoR learner, and (3) the matrix transformer that learns multiRoR.\n\n \n \n\n\\subsection{Initialization of entities and relations} \\label{sec:method_init}\nAs a preparation step, we first obtain the embedding of each entity. We pass the text through a pretrained BERT model \\cite{DBLP:conf\/naacl\/DevlinCLT19}, and obtain each entity representation by average pooling over its tokens' hidden states in the last layer of BERT. Note that our framework can easily adapt to other ways to retrieve pretrained embeddings, such as \\cite{DBLP:conf\/nips\/YangDYCSL19,DBLP:journals\/corr\/abs-1907-11692}. \n\nThen, we obtain the initial embedding of each relation by applying an feedforward layer on the concatenated embeddings of the two involved entities.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width= \\columnwidth]{img\/fig_archi.pdf}\n \\caption{The model architecture of RoR.}\n \\label{fig:archi}\n\\end{figure}\n\n\\subsection{BiRoR Learner}\\label{sec:method_local}\nWe use a GNN to learn the interdependency between every two relations, namely biRoR.\nFor each text sequence $\\bm{t}$ with $M$ entities, we formulate a\ngraph $\\mathcal{G} = (\\mathcal{V},\\mathcal{E})$, where all entities and relations form the nodes of the graph. Accordingly, we link every relation node to its two entity nodes.\n\nWe use a GNN to learn node-to-node interactions, and especially among the relation nodes. The first layer of GNN is initialized with the node embeddings (including all entity embeddings and relation embeddings) obtained from Section~\\ref{sec:method_init}. In each layer $\\ell$, it aggregates all hidden states passed from the neighbors to update its representation in the next layer $\\ell +1$. More specifically, we have\n\\begin{align}\n h^{\\ell+1}_u = \\mathrm{FFN}\\left(W^O\\sum_{v \\in {\\mathcal{N}} (u)}\\alpha_{u,v}h^{\\ell}_v\\right)\n ,\n\\end{align}\nwhere $h_i^{\\ell}$ is the hidden state of the $i$-th vertex in the $\\ell$-th layer, $\\mathrm{FFN}(\\cdot)$ is a feed-forward network, $W^O$ is the weight matrix, ${\\mathcal{N}} (u)$ is the set of neighbor nodes to the vertex $u$, and $\\alpha_{u,v}$ is the attention weight that $u$ has to $v$. This attention weight $\\alpha_{u,v}$ is obtained by\n\\begin{align*}\n \\alpha_{u,v} = \\frac{ \\exp \\left[W^{Q}h^{\\ell}_v\\left(W^{K}h^{\\ell}_u\\right)^T\\right]}{ \\sum_{v' \\in {\\mathcal{N}} (u)}\\exp \\left[W^{Q}h^{\\ell}_{v'}\\left(W^{K}h^{\\ell}_u\\right)^T\\right]}\n ,\n\\end{align*}\nwhere $W^{K}$ and $W^{Q}$ are the key and query weight matrices when calculating the attention.\n\n\n\n\\subsection{MultiRoR Learner} \\label{sec:method_global}\nThe GNN introduced in Section~\\ref{sec:method_local} has a strong ability to model node-to-node interaction, which corresponds to the biRoR, but it is not as strong when seizing the more complicated multiRoR as analyzed in Section~\\ref{sec:ror_nonlocal}. For example, if an entity has both the relation Org-Aff and Phys, then it cannot have the relation Art. But the GNN structure does not necessarily capture such complicated multiRoR that involve nested conditions. \n\nTherefore, we need another module to directly model the relation matrix $\\bm{R}\\in \\mathbb{R}^{M\\times M}$, which takes into consideration the dynamics among all relations as a whole, in order to capture multiRoR.\n\nTo this end, we propose a simple but effective module, a relation matrix transformer. As each relation $r_{ij}$ in the relation matrix $\\bm{R}$ needs to attend to all other relations, we build our relation matrix transformer by customizing the Transformer encoder architecture, which allows extensive mutual attention among all elements \\cite{DBLP:conf\/nips\/VaswaniSPUJGKP17}. Specifically, we customize the position encoding in the Transformer into two parts: row encoding and column encoding, each of which is a learnable mapping from the position index to a $d$-dimensional vector space. \n\nOur relation matrix transformer adds the position encoding, namely the sum of the row and column embedding, to the initial representations of relations obtained by procedures in Section~\\ref{sec:method_init}. The input is thus a tensor $\\bm{T} \\in \\mathbb{R}^{M \\times M \\times d}$, where $d$ is the dimension of features in the embedding.\nThe matrix transformer then learns the dynamics among all relations as a whole, and outputs new features of all relations by a transformed matrix $\\bm{T_{\\mathrm{Transformed}}} \\in \\mathbb{R}^{M \\times M \\times d'}$ which captures the multiRoR. \n\nFinally, we add together the relation embeddings learned by the GNN and the matrix transformer, and feed them into the final classification layer to obtain the type of each relation.\n\n\n\n\\section{Experiments}\n\\subsection{Datasets}\nWe use two benchmark RE datasets to evaluate the performance of our model.\\footnote{Since our focus is RE, we do not use a newly published RE dataset, DocRED \\cite{yao2019docred}. It contains entity linking annotations which can be a multi-tasking objective, and turns the task into a different setting.}\n\n\\paragraph{ACE05} \nThe ACE 2005 Multilingual Training Corpus (ACE05)~\\cite{walker2006ace} is a standard RE dataset. It has six relation categories, detailed in Section~\\ref{sec:ror_stats}. We process and split the dataset following the practice in \\cite{DBLP:conf\/emnlp\/GormleyYD15,DBLP:conf\/acl\/PlankM13}.\\footnote{\\url{https:\/\/github.com\/mgormley\/ace-data-prep}} There are six subdomains in the dataset: Broadcast Conversations (BC), Broadcast News (BN), Conversational Telephone Speech (CTS), Newswire (NW), Usenet Newsgroups (UN), and Weblogs (WL). The statistics of the resulted dataset is in Table~\\ref{tab:ace05_stats}.\nThe macro F1-score is the primary evaluation metric of the data, as used by most works. As ``no-relation'' is regarded as the negative label, all non-negative relations on wrong entities as treated as false positives. \n\n\\begin{table}[!h]\n \\centering\n \\begin{tabular}{clc}\n \\toprule\n & \\textbf{Domain} & \\textbf{\\# Relations} \\\\\n \\midrule\n \\multirow{1}{*}{\\textbf{Train}} &\n NW+BN & 34,669 \\\\\n \\midrule\n \\multirow{1}{*}{\\textbf{Valid}} & BC\\_dev & 5,717 \\\\\n \\midrule\n \\multirow{3}{*}{\\textbf{Test}} & BC\\_test & 6,692 \\\\\n & CTS & 11,683 \\\\\n & WL & 11,022 \\\\\n \\bottomrule\n \\end{tabular}%\n \\caption{Statistics of ACE05 dataset. \n }\\label{tab:ace05_stats}\n\\end{table}\n\\paragraph{SemEval2018} \nWe use SemEval 2018 Task 7.2 \\cite{DBLP:conf\/semeval\/GaborBSQZC18} as the second RE dataset to evaluate our model.\\footnote{We choose Task 7.2 instead of the Task 7.1 used by \\citet{DBLP:conf\/acl\/WangTYCWXGP19} because Task 7.1 only tests the \\textit{classification} of positive relations, but Task 7.2 is the standard relation \\textit{extraction} which tests on both positive and negative relations.}\nThe corpus is collected from abstracts and introductions of\nscientific papers, and there are six types of semantic relations in total.\nNote that there are three subtasks of it: Subtask 1.1 and Subtask 1.2 are relation \\emph{classification} on clean and noisy data, respectively; Subtask 2 is the standard relation \\textit{extraction}, where the same training set as Subtask 1 is used, but the evaluation is to identify all relations including ``no-relation.''\nFollowing the main systems~\\cite{DBLP:conf\/semeval\/RotsztejnHZ18,DBLP:conf\/semeval\/NooralahzadehOL18}, we combine the training data of both Subtask 1.1 and 1.2 as the training data.\nThe dataset consists of 136,965 train, and 27,316 test examples. We count the relation of every entity pair as a sample, and if no specific relation is annotated for some entity pairs, we use ``no-relation'' as the label. The standard evaluation metric is also macro F1, which is used for the official ranking of submissions.\n\n\\subsection{Baselines for ACE05}\nFor ACE05, we compare our model with the following systems.\n\n\\paragraph{$\\text{BERT}_\\text{EntEmb}$}\n\nWe use the baseline from \\cite{DBLP:conf\/acl\/WangTYCWXGP19} (called ``$\\text{BERT}_\\text{sp}$ with entity-indicator on input-layer'' in the original paper), which is essentially a BERT with entity embedding on the input layer.\n\n\\paragraph{$\\text{OnePass}_\\text{MRE}$ and $\\text{OnePass}_\\text{SRE}$} \nWe also compare with the state-of-the-art systems on ACE05 -- \\text{OnePass} by \\citet{DBLP:conf\/acl\/WangTYCWXGP19}. OnePass has two variations: single relation extraction ($\\text{OnePass}_\\text{SRE}$) that treats \nevery single relation extraction as an independent classification task including separate encoding and classification, and multiple relation extraction ($\\text{OnePass}_\\text{MRE}$) that encodes all entities in one pass, and then do the pair-wises classification per relation. \n\n\n\\paragraph{Other models} \nWe also compare our model with other previous RE models such as \\cite{DBLP:conf\/emnlp\/GormleyYD15,DBLP:conf\/naacl\/NguyenG15,DBLP:conf\/ijcnlp\/FuNMG17,DBLP:conf\/emnlp\/ShiFHZJLH18}.\n\n\n\\subsection{Baselines for SemEval 2018 Task 7.2}\nFor SemEval2018, we compare our results with the top 3 systems on the leaderboard. For a fair comparison, we compare our models against the non-ensemble and ensemble models separately. \n\n\\paragraph{Ensemble models} The top 1 system \\cite{DBLP:conf\/semeval\/RotsztejnHZ18} is an ensemble of CNNs and RNNs. Its training uses an ensemble size of 20, data augmentation, multi-task learning, and many other meticulous designs.\nNotably, when implementing our models, we only use a small ensemble size of 5, with no further tricks.\n\n\n\\begin{table*}[!ht]\n \\centering\n \\begin{tabular}{l|c|cccc}\n \\toprule\n \\textbf{} & \\multicolumn{1}{c|}{\\textbf{Dev}} & \\multicolumn{4}{c}{\\textbf{Test}} \\\\\n \\textbf{} & BC\\_dev & BC\\_test & CTS & WL & \\textbf{Overall} \\\\\n \\hline\n \\textbf{HybridFCM \\cite{DBLP:conf\/emnlp\/GormleyYD15}} & -- & 63.48 & 56.12 & 55.17 & 58.26\n \\\\\n \\textbf{DAN \\cite{DBLP:conf\/ijcnlp\/FuNMG17}} & -- & 65.16 & 55.55 & 57.19 & 59.30\n \\\\\n \\textbf{GSN \\cite{DBLP:conf\/emnlp\/ShiFHZJLH18}} & -- & 66.38 & 57.92 & 56.84 & 60.38\n \\\\\n \\textbf{$\\text{BERT}_\\text{EntEmb}$ \\cite{DBLP:conf\/acl\/WangTYCWXGP19}} & 65.32 & 66.86 & 57.65 & 53.56 & 59.36 \\\\\n \\textbf{$\\text{OnePass}_\\text{MRE}$ \\cite{DBLP:conf\/acl\/WangTYCWXGP19}} & 67.46 & 69.25 & 61.7 & 58.48 & 63.14 \\\\\n \\textbf{$\\text{OnePass}_\\text{SRE}$ \\cite{DBLP:conf\/acl\/WangTYCWXGP19}} & 68.90 & \\textbf{69.76} & 63.71 & 57.20 & 63.14 \\\\\n \\hline\n \\textbf{$\\text{\\modelname{}}_\\text{base}$} & 69.95 & 66.51 & 61.84 & 57.67 & 62.01 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{bi-only}$} & 68.50 & \\textbf{68.86} & 64.18 & 59.57 & 64.20 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{multi-only}$} & \\textbf{71.64} & 66.77 & 59.90 & 57.62 & 61.43 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{full}$} & 70.59 & 68.63 & \\textbf{64.49} & \\textbf{59.67} & \\textbf{64.26} \\\\\n \n \n \n \n \n \n \n \n \\bottomrule\n \\end{tabular}%\n \\caption{Macro F1 on ACE05. Following \\cite{DBLP:conf\/acl\/WangTYCWXGP19}, we reported the performances on the development set, and all subsets of the test set. ``Overall'' is the average of all three subsets in the test set. }\\label{tab:ace05}\n\\end{table*}\n\n\\paragraph{Non-ensemble models} The 2nd model \\cite{DBLP:conf\/semeval\/LuanOH18} uses LSTMs to encode both word sequences and dependency\ntree structures, and perform relation extraction between concepts on top of them. And the 3rd model \\cite{DBLP:conf\/semeval\/NooralahzadehOL18} uses a CNN model over the shortest dependency path between two entities.\n\n\\subsection{Our Models}\nWe investigate the following four settings of our model.\n\n\\paragraph{$\\text{\\modelname{}}_\\text{base}$} For the base model in our architecture, we use a customized version of the $\\text{BERT}_\\text{EntEmb}$ baseline. Instead of the extra embedding for entities in $\\text{BERT}_\\text{EntEmb}$, we use the sentence index to indicate entities, so that the pretrained properties of BERT can be maintained as much as possible. We use this model as the base model that we build our biRoR and multiRoR learning modules on.\n\n\\paragraph{$\\modelname{}_\\text{bi-only}$ and $\\modelname{}_\\text{multi-only}$}\nBased on $\\text{\\modelname{}}_\\text{base}$, we implement {$\\modelname{}_\\text{bi-only}$}, which only the biRoR module based on GNN, and {$\\modelname{}_\\text{multi-only}$}, which uses only the multiRoR module based on a relation matrix transformer. \n\n\\paragraph{$\\modelname{}_\\text{full}$} Finally, we implement the full model $\\modelname{}_\\text{full}$ which adopts both the GNN and the relation matrix transformer to learn all types of RoR.\n\n\n\\subsection{Implementation details}\nWe follow \\cite{DBLP:conf\/acl\/WangTYCWXGP19}'s practice to use BERT-base (uncased) as the pretrained model. \nThe GNN and matrix transformer has 4 layers, 8-head attention, and the hidden size of attention layers is 512. The feedforward layers of GNN have 1024 hidden units, and that of matrix transformer has 4096 hidden units. The batch size is 8. The learning rate is set to 5e-5 on ACE05 and 1e-4 on SemEval2018, and the warmup is 0.1. We use the Adam optimizer and the cosine scheduler of the python package Transformers.\\footnote{\\url{https:\/\/github.com\/huggingface\/transformers}} We train the model for 30 epochs on both datasets. \nWe run all experiments with one Tesla V100 card on a Ubuntu system.\n\n\n\n\n\\section{Results and analysis}\n\\subsection{Main results}\n\\paragraph{ACE05}\nWe first analyze the experiments on the larger dataset, ACE05. From the main results in Table~\\ref{tab:ace05}, we can see that in the last column (overall performance), our full model $\\text{\\modelname{}}_\\text{full}$ achieves the strongest performance, outperforming all previous models. Specifically, our model improves over the strongest setting of OnePass by 1.12\\%, which is a large margin on the ACE05 dataset. Our full model Moreover, on the subsets of the test set, such as CTS and WL, which is different from the BC domain seen in the development set, our model also demonstrate consistent improvement over the previous best model.\n\\begin{table}[ht]\n \\centering\n \\begin{tabular}{lc}\n \\toprule\n \\textbf{Model} & \\textbf{Macro} \\\\\n \\midrule\n \\multicolumn{2}{c}{\\textbf{\\emph{Non-ensemble models}}} \\\\\n \\cite{DBLP:conf\/semeval\/LuanOH18} & 39.10 \\\\\n \\cite{DBLP:conf\/semeval\/NooralahzadehOL18} & 33.60 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{base}$} & 38.83 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{bi-only}$} & 41.99 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{multi-only}$} & 42.87 \\\\\n \\textbf{$\\text{\\modelname{}}_\\text{full}$} & \\textbf{45.46} \\\\\n \\midrule\n \\multicolumn{2}{c}{\\textbf{\\emph{Ensemble models}}} \\\\\n \\cite{DBLP:conf\/semeval\/RotsztejnHZ18} & 49.30 \\\\\n \\textbf{E-$\\text{\\modelname{}}_\\text{base}$} & 46.47 \\\\\n \\textbf{E-$\\text{\\modelname{}}_\\text{bi-only}$} & 51.50 \\\\\n \\textbf{E-$\\text{\\modelname{}}_\\text{multi-only}$} & \\textbf{51.85} \\\\\n \\textbf{E-$\\text{\\modelname{}}_\\text{full}$} & 51.56 \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{Macro F1 scores on SemEval 2018 Task 7.2.}\n\n \\label{tab:res_semeval}%\n\\end{table}%\n\n\\paragraph{SemEval2018}\n\nIn Table~\\ref{tab:res_semeval}, we compare our system with the top 3 systems \\cite{DBLP:conf\/semeval\/RotsztejnHZ18,DBLP:conf\/semeval\/LuanOH18,DBLP:conf\/semeval\/NooralahzadehOL18} on the SemEval 2018 Task 7.2 Leaderboard. For the non-ensemble setting, our model is +6.26\\% higher in performance than the most competitive models on the leaderboard. For the more difficult ensemble model leaderboard, we use a simpler setting (i.e., smaller ensemble size, and no data augmentation), but surpasses the top model by +2.55\\%. The improvement shows that our model with no complicated designs can demonstrate strong performance. \n\n\\paragraph{Summary of main experiments}\nExperiments on both datasets validate the effectiveness of our new perspective into RE. Because we model all the relation extraction tasks of the same text together, our method absorbs more knowledge from the same data than other models. Therefore, without data augmentation for SemEval2018, and without domain adaptation on ACE05 \\cite{DBLP:conf\/ijcnlp\/FuNMG17,DBLP:conf\/emnlp\/ShiFHZJLH18}, our model still surpasses all systems that utilizes extra data.\n\n\n\\subsection{Does the model learn multi-relation instances well?}\n\nWe want to further analyze our model's ability to learn complicated multiRoR relations, especially for some entities with a high number of non-empty relations. For the model outputs on the ACE05 test set, we look into the subset of relations centered on entities with $\\geq2$ relations. On this subset, the macro F1 of the previous model OnePass is 61.19\\%, which drops 2 percent under its overall reported performance. However, our method achieves 63.55\\%, keeping almost the same performance as the reported 64.26\\% on the whole test set. On the more challenging subset involving entities with $\\geq3$ relations, our model performs 56.63\\%, almost 4 percent over OnePass's F1 of 52.82\\%. This shows that the advantage of our model is stronger than others as the relation prediction gets more challenging.\n\n\n\n\\subsection{Does the model learn the conditional probability of relations well?}\n\nWe also compare our model's ability to learn biRoR versus OnePass. Remember the ground-truth conditional probability of every two relations mentioned before in Figure~\\ref{fig:corr2} of Section~\\ref{sec:corr1c}. For each row of the heatmap, it is normalized to probability 1, so each value in the row forms the probability distribution of any other relation conditioned on the observation of an existing relation. On the overall test set of ACE05, we analyze the output of our model and OnePass to obtain the corresponding heatmap, with each row representing a probability distribution. We calculate the average Jenson-Shannon (JS) distance of all the distributions. We find that OnePass is 0.0805 away from the gold distribution, and we are 0.0326 away from the gold distribution, $60\\%$ closer than OnePass, which is the strongest previous model.\n\n\n\n\n\\section{Related Work}\nRelation Extraction is one of the most important tasks in NLP. Conventional classification\napproaches have made use of contextual, lexical\nand syntactic features combined with richer linguistic and background knowledge such as WordNet and FrameNet \\cite{DBLP:conf\/semeval\/HendrickxKKNSPP10,DBLP:conf\/semeval\/RinkH10}, as well as kernel-based methods \\cite{DBLP:journals\/jmlr\/ZelenkoAR03,DBLP:conf\/naacl\/BunescuM05,DBLP:conf\/acl\/ZhouSZZ05}.\n\nThe recent advancement of deep neural networks result in a revolution in the methodology of RE. Many CNN-based \\cite{DBLP:conf\/coling\/ZengLLZZ14,DBLP:conf\/acl\/SantosXZ15,DBLP:conf\/naacl\/NguyenG15}, and RNN-based \\cite{DBLP:conf\/emnlp\/SocherHMN12,DBLP:journals\/corr\/ZhangW15a,DBLP:conf\/acl\/MiwaB16,DBLP:conf\/acl\/ZhouSTQLHX16} models achieve high performance in many datasets. The popularity of the field also gives birth to many shared tasks \\cite{DBLP:conf\/semeval\/HendrickxKKNSPP10} which turned into the cradle of many competitive, well-designed systems \\cite{DBLP:conf\/semeval\/LuanOH18,DBLP:conf\/semeval\/RotsztejnHZ18,DBLP:conf\/semeval\/JinDSMC18}.\nRecently, as the model innovation in single relation extraction gradually slow down, most work shifted to the direction of distant supervision \\cite{DBLP:conf\/acl\/MintzBSJ09,DBLP:conf\/emnlp\/ZengLC015,DBLP:conf\/acl\/LinSLLS16}.\n\nThese data-augmentation methods through distant supervision are orthogonal to the innovation in supervised models, and the focus of this paper is to innovate the supervised models to learn the same data with more thorough exploitation. To the best of our knowledge, we are the first paper in RE aiming to learn the correlation of relations in the given data.\n\nIn terms of our matrix formulation of the RoR, the most similar work is the table-filling approach of joint entity and relation extraction \\cite{miwa2014modeling,gupta2016table}. However, our basic unit is an entity as opposed to every word in the text. Moreover, our proposed GNN and Matrix Transformer are different from \\citeauthor{miwa2014modeling}'s \\citeyearpar{miwa2014modeling} history-based structured learning with complex features and heuristics, as well as \\citeauthor{gupta2016table}'s \\citeyearpar{gupta2016table} RNN sequence decoding model.\n\n\n\\section{Ethical Considerations}\nAs with any RE algorithms, there is the danger of using the model to analyze the massive amount of text online, and retrieve the relationship among users and mine their critical information. We are aware of this danger, and sought to minimize the risk. For this reason, we only work on anonymized data, and the set of relations only involves common knowledge graph relations such as part-whole. The algorithm provided in this paper should \\textit{not} be used to analyze any user-sensitive data, but as a tool to facilitate public knowledge graph construction.\n\n\\section{Conclusion}\nIn this paper, we proposed a new paradigm of RE, which is capable of modeling the interdependency among multiple relations in the same text. Our model uses a GNN and a matrix transformer to capture the RoR in data. Experiments validated that our model has a substantial improvement on the two benchmark datasets where most models compete for improvement on the decimal point. We also conducted analyses to reflect on our model's performance on learning multi-relation data and the proximity of distributions of correlation of the gold and predicted relations. \n\n\n\n\n\n\n\n\n\\section*{Acknowledgments}\nWe appreciate Prof Rada Mihalcea and Di Jin for their constructive suggestions on the storyline of this paper. We also thank Qipeng Guo for advice on the design of GNNs. Zhijing Jin appreciates Yixuan Zhang and Zhutian Yang for their support.","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbnag b/data_all_eng_slimpj/shuffled/split2/finalzzbnag new file mode 100644 index 0000000000000000000000000000000000000000..c9089c55fe4018260e42ab43f401b8dda2e8df57 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbnag @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nIn order to clearly state the question addressed here, it\nis important to recall some\npoints of the mathematical foundation of observables in quantum mechanics.\nThere will be two main\ncontributions, one related to some weak solutions of the Schr\\\"odinger\nequation and other to dimensional\ninterpretations.\n\nThe problem of finding the\ncorrect self-adjoint extension describing the quantum (Schr\\\"odinger)\noperator corresponding to a physical\nmodel can be subtle and difficult. Usually the physicist has a clear\nexpression for the operator, an\nunbounded one acting in a Hilbert space~$\\mathcal H$, but it is not\nobvious which\ndomain should be taken (some general references for what follows are\n\\cite{BEH,RS,Th}).\n\nLet $\\langle\\psi,\\phi\\rangle$ denote the inner product in~$\\mathcal\nH$; if $T$\nis a linear operator acting on its dense domain ${\\rm dom{\\,}} T\\subset \\mathcal\nH$, then to represent a physical\nobservable it is necessary that $T$ is hermitian, i.e.,\n\\[\n\\langle T\\psi,\\phi\\rangle = \\langle \\psi,T\\phi\\rangle, \\quad \\forall\n\\psi,\\phi\\in {\\rm dom{\\,}} T.\n\\]\nHowever this condition is not enough to guarantee that $T$ has real\nspectrum and the time evolution it generates is\nunitary; the right condition is self-adjointness. The domain of its\nadjoin $T^*$ is\n\\[\n{\\rm dom{\\,}} T^* = \\{\\xi\\in\\mathcal H: \\exists \\eta\\in{\\mathcal H} \\;{\\rm\nwith}\\; \\langle \\eta,\\phi\\rangle =\n\\langle \\xi,T\\phi\\rangle, \\; \\forall\\phi\\in{\\rm dom{\\,}} T \\},\n\\]\nand for $\\xi\\in{\\rm dom{\\,}} T^*,$ one has $T^*\\xi=\\eta.$ It follows that $T^*$\nis well defined if ${\\rm dom{\\,}} T$ is dense\nin $\\mathcal H$, and $T$ is hermitian if, and only if, $T^*$ is an\nextension of $T$. The operator $T$ is\nself-adjoint if $T=T^*$. Notice also that (often) for bounded operators the\ndistinction between hermitian and\nself-adjoint operators does not exist.\n\n\nAs already mentioned, usually $T$ is hermitian with dense domain, and\none asks if it is also self-adjoint or\nhas any self-adjoint extension; such extensions are the candidates for\nthe operator describing the related physical\nobservable. A nice situation that often occurs, in particular for the\nHamiltonian of the Hydrogen atom (and\nother atomic systems as well), is that $T$ is essentially self-adjoint,\ni.e., it has just one self-adjoint\nextension and the physical operator is well determined. However, there are\nsituations where there are\ninfinitely many self-adjoint extensions and each\none should correspond to a different physical circumstance; the choice\nis a physical one, not on mathematical bases. Even worse, some hermitian\noperators have no self-adjoint\nextensions!\n\nThe standard example of such framework is the momentum operator\n$P=-i\\frac{d}{dx}$ for a particle in a box\n$[0,1]$. In this case $\\mathcal H = L^2[0,1]$, it is natural to take\n${\\rm dom{\\,}} P$ as smooth functions\n$\\psi\\in\\mathcal H$ such that $\\psi(0)=0=\\psi(1)$ (so that the particle\nremains confined\nto the box); the self-adjoint extensions of this hermitian operator are\n$P_\\alpha$, where $\\alpha$ is a complex number with $|\\alpha|=1$, and\nall elements of ${\\rm dom{\\,}} P_\\alpha$ satisfy $\\psi(1)=\\alpha\\psi(0)$.\n\nIt is worth remarking that if $T$ is hermitian and ${\\rm dom{\\,}} T=\\mathcal H$,\nthen $T$ is bounded,\nso that in general such domain questions\nare not avoidable. These interesting problems are well explored in the\nliterature, and as additional\nreferences see\n\\cite{AGHKH} and for applications to the one-dimensional hydrogen\natom see~\\cite{FLM,Gesz}.\n\nNevertheless, there are some delicate issues in the mathematical\nfoundations of quantum mechanics that seem\nnot yet exploited from the physical point of view. The main goal of\nthis work is to discuss one of\nsuch issues and relate it to a physical situation.\n\n\nRecall that a self-adjoint Hamiltonian operator\n$H$ generates a time evolution $\\psi(t)=U(t,0)\\psi=e^{-itH}\\psi$,\nwhich is a solution of the Schr\\\"odinger equation\n\\[\ni\\frac{d}{dt}\\psi(t) = H\\psi(t),\\quad \\psi=\\psi(0)\\in{\\rm dom{\\,}} {H}.\n\\]\nSince $U(t,0)$ is a family of unitary operators, for any time $t$ its\ndomain is\nthe\nwhole Hilbert space $\\mathcal H$, so\nthat it is meaningful to consider $U(t,0)\\varphi$ for $\\varphi\\in\\mathcal\nH$ but with $\\varphi\\notin {\\rm dom{\\,}} H$,\ni.e., the time evolution is not\nrestricted to the domain of $H$. Sometimes $U(t,0)\\varphi$, for $\\varphi\\notin {\\rm dom{\\,}} H$, is called a\nweak solution of the Schr\\\"odinger\nequation.\n\nAn unusual situation will be presented. A self-adjoint operator $H$ with\ndense\n${\\rm dom{\\,}} H\\subset\\mathcal H=L^2(\\ensuremath{{\\mathrm{I\\!R}}}^3)$ will be considered,\n vectors\n$\\Xi\\in\\mathcal H$ not belonging to its domain will be given, although they are\npseudo-eigenvectors of $H$, that is,\n\\begin{equation}\\label{pseudoeigenvalue1}\nH\\Xi = \\lambda_\\Xi \\Xi,\n\\end{equation}\nfor $\\lambda_\\Xi\\in \\ensuremath{{\\mathrm{I\\!R}}}$. Some numerical calculations will indicate\nthat $\\Xi$ has a nonzero component in\nthe continuous subspace of $H$, so that the na\\\"{\\i}ve time evolution\nbuilt from (\\ref{pseudoeigenvalue1}) gives\nan incorrect answer. It will be argued that such solutions are related to\nthe same model in smaller\ndimensions. Furthermore, the physical system in question is one of the\nmost celebrated models in\nquantum mechanics, the three dimensional (3D) hydrogen atom.\n\n\n\\section{Pseudo-Eigenvectors as Weak Solutions}\n\nThe hermitian Hamiltonian of the 3D hydrogen atom is\n\\[\nH_0=-\\frac{\\hbar^2}{2\\mu}\\Delta - \\frac{e^2}{r},\\quad {\\rm dom{\\,}}\nH_0=C^\\infty_0(\\ensuremath{{\\mathrm{I\\!R}}}^3)\\subset L^2(\\ensuremath{{\\mathrm{I\\!R}}}^3),\n\\]\nwhere $\\mu$ is the electron mass, $e$ its electric charge and $C^\\infty_0(\\ensuremath{{\\mathrm{I\\!R}}}^3)$ denotes the set of\nsmooth functions with compact\nsupport. This operator is essentially\nself-adjoint and its unique self-adjoint extension\n$H_H$, {\\it the} 3D hydrogen atom\nHamiltonian, reads\n\\begin{equation}\\label{HH}\nH_H=-\\frac{\\hbar^2}{2\\mu}\\Delta - \\frac{e^2}{r},\\quad {\\rm dom{\\,}}\nH_H=H^2(\\ensuremath{{\\mathrm{I\\!R}}}^3),\n\\end{equation}\nwith $H^2(\\ensuremath{{\\mathrm{I\\!R}}}^3)$ denoting an appropriate Sobolev space; in particular,\n$H^2(\\ensuremath{{\\mathrm{I\\!R}}}^3)$ is a subspace of $L^2(\\ensuremath{{\\mathrm{I\\!R}}}^3)$,\nit is also the natural domain of the free particle Hamiltonian and all\nits\nelements are continuous functions~\\cite{RS}.\n\nThe usual spectral analysis of $H_H$ can be performed and its well-known\neigenvalues\n\\[\n-\\frac{\\mu e^4}{2 \\hbar^2 n^2},\\quad n\\ge1,\n\\]\ncan be found. Recall that the closed subspace $\\mathcal H_p$ generated by its\neigenvectors is named the point\nsubspace of\n$H_H$ and its orthogonal complement $\\mathcal H_{ac}$ is a nontrivial subspace (i.e., it has nonzero elements) and\nnamed the absolutely continuous (or scattering) subspace of\n$H_H$. Physically, the members of $\\mathcal H_p$ are the bound states while\nthe\nelements of $\\mathcal H_{ac}$ describe the\nionizing atomic states (this interpretation follows, for instance, by the\nRAGE Theorem\n\\cite{CFKS,deOdoC}).\n\nThe eigenvalue equation for the 3D hydrogen atom Hamiltonian is separable\nin spherical\ncoordinates $r\\ge0,\\, 0\\le \\theta\\le\\pi,\\,0\\le \\phi\\le2\\pi$, and by taking the standard representation\n\\begin{equation}\\label{RTP}\n\\Psi(r,\\theta,\\phi)=R(r)\\Theta(\\theta)\\Phi(\\phi),\n\\end{equation}\n the equation for\n$\\Theta(\\theta)$ is given by~\\cite{pauling}\n\\begin{equation}\\label{ThetaEq}\n\\frac{1}{\\sin\\theta}\\frac{d}{d\\theta}\\left(\\sin\\theta\n\\frac{d\\Theta}{d\\theta}\\right) -\\left(\\frac{m^2}{\\sin^2\\theta}-\\ell(\\ell+1)\n\\right)\\Theta=0,\n\\end{equation}\nwith $m$ and $\\ell\\ge0$ being integer constants. For each $\\ell$ value one\nhas\n$-\\ell\\le m\\le \\ell$.\n\nConsider first the particular\ncase $\\ell=0$; it follows that $m=0$ and (\\ref{ThetaEq}) reduces to\n\\begin{equation}\\label{danadaEq}\n\\frac{1}{\\sin\\theta}\\frac{d}{d\\theta}\\left(\\sin\\theta\n\\frac{d\\Theta}{d\\theta}\\right) =0.\n\\end{equation}\nThe usual normalized solution of this equation is\n$\\Theta_{0,0}(\\theta)=\\Theta_{l=0,m=0}({\\theta})=1\/\\sqrt2$. However, there\nis also\nthe additional solution (that will play a major role here)\n\\begin{equation}\\label{qsi}\n\\xi_{0,0}(\\theta) = \\frac{\\sqrt6}{\\pi}\\ln\\left[ \\tan\\left(\n\\frac{\\theta}{2}\\right) \\right].\n\\end{equation}\nThis is just one instance of\nadditional solutions $\\xi_{\\ell,m}$ of\n(\\ref{ThetaEq}) for $\\ell,m$ as above; such\nsolutions are Legendre function of the second kind~\\cite{arfken,math}.\nThe $\\xi_{\\ell,m}$ solutions have been discarded in the mathematical\nliterature\nsince\nthey are not continuous at\n$\\theta=0$ and $\\theta=\\pi$, and so via (\\ref{RTP}) they do not generate\nelements in the domain of\n$H_H$; and discarded in the physical literature~\\cite{pauling} by\narguing they are not bounded functions.\n\nBy taking the usual radial $R_{n,\\ell}(r)$ and azimuthal $\\Phi_m(\\phi)=\\frac\n1{\\sqrt{2\\pi}}e^{im\\phi}$ solutions for the 3D hydrogen\natom, set ($\\ell0.8$, as indicated in figure~\\ref{FigPN} for $n=1$ (in figure~\\ref{FigPN} the\nvalues of $P(N)$ are exact, since symbolic calculus was used); the parameter $P(N)^2\\equiv\\sum_{n'=1}^N\n\\sum_{l=0}^{n'-1} |C_{n',l}^{(1)}|^2$ is an approximation for\n$1-\\|\\chi^c_{1,0}\\|^2$. Similar results were found for other values\nof~$\\ell$.\n\n\nTherefore one concludes that $\\Xi_{n,\\ell,0}$ have both nonzero point and\ncontinuous components, so that their time\nevolutions actually are not described by (\\ref{naivesolution}), but give\n nonzero\nprobabilities $\\|\\chi^c_{n,\\ell}\\|^2$ of ionization (and then far from being\nbound states).\n\n\\begin{figure}\n\\begin{center}\n\\vskip 10pt\n\\includegraphics[width=6cm, height=6cm]{Fig1proj.eps}\n\\caption{$P$ as function of $N$.}\n\\label{FigPN}\n\\end{center}\n\\end{figure}\n\n\n\\section{Lower Dimensional Hydrogen Atom}\nThe fact that all $\\Xi_{n,\\ell,0}$ belong the Hilbert space raises the possibility of finding physical\nmeanings for them; this section aims at discussing possible physical contents of these pseudo-eigenvectors.\nThe first crucial remark is that formally $\\Xi_{n,\\ell,0}$ has null azimuthal angular\nmomentum ($m=0$). The solution $\\Xi_{n,0,0}$ has also null total angular\nmomentum (both\n$\\ell=0=m$), but there is a lack of rotational symmetry (see~(\\ref{qsi}));\nthis particular solution gives\na clue on the physical interpretation. In fact, in comparision with ordinary eigenfunctions\n$\\Xi_{n,\\ell,0}(r,\\theta)$ \nare elongated over the $z$-axis with a logarithmic divergence at\n$\\theta=0$ and $\\pi$.\nFigure~\\ref{Figzerozero} shows the absolutely values of\n$\\xi_{0,0}(\\theta)$ and $\\Theta_{0,0}(\\theta)$ as a\nfunction of~$\\theta$, and Figure~\\ref{function3D} a boundary surface of the 3D wavefunction $\\Xi_{1,0,0}$,\nwhich is to be compared with $\\Psi_{0,0,0}$ that has complete radial symmetry (its\nboundary surfaces are spheres centrered at the origin). Hence, there is a strong indication that\n$\\Xi_{n,0,0}$ are reminiscent of classical\ntrajectories performing one-dimensional (1D) like motion, in agreement with\nits null angular momentum and lack\n of rotational symmetry. So, it is natural to relate such\nwavefunctions to the 1D hydrogen atom, an interesting and\ncontroversial subject, popularized by the\nwork of Loudon \\cite{Lou} published in 1959.\n\n\\begin{figure}\n\\begin{center}\n\\vskip 10pt\n\\includegraphics[width=6cm, height=6cm]{FigQ00.eps}\n\\caption{$|\\xi_{0,0}(\\theta)|$ and $|\\Theta_{0,0}(\\theta)|$ (dashed) as\nfunction of~$\\theta$.}\n\\label{Figzerozero}\n\\end{center}\n\\end{figure}\n\n\nLoudon stated that the 1D hydrogen atom was twofold degenerate, having\neven and odd eigenfunctions for each\neigenvalue, except for the (even) ground state having infinite binding\nenergy. Typically 1D systems have no\ndegenerate eigenvalues, and Loudon justified the double degeneracy as a\nconsequence of the singular atomic\npotential. Andrews \\cite{And1} questioned the existence of a ground\nstate with infinite binding energy. Ten\nyears later Haines and Roberts \\cite{HR} revised Loudon's work and\nobtained that the even wave functions,\nwith continuous eigenvalues, were complementary to odd functions, but\nsuch results were criticized by\nAndrews \\cite{And2}, who did not accepted the continuous eigenvalues.\nGomes and Zimerman \\cite{GZ} argued\nthat the even states with finite energy should be excluded. Spector and\nLee \\cite{SL} presented a\nrelativistic treatment that removed the problem of infinite binding\nenergy of the ground state. Several other\nworks \\cite{DPST,BKB,NVS,LC,OL,FLM,XDD,LL} (see also references therein)\nhave discussed this (apparent)\nsimple problem.\n\nThe 1D hydrogen atom has been used as a simplification of the 3D model\nin several theoretical and\nnumerical studies \\cite{JSS,DKS,LCO3}. It is then interesting that Cole\nand Cohen\n\\cite{CC} and Wong et al.\\ \\cite{W} have\nreported some experimental evidence for the 1D hydrogen atom. The\n``quasi-1D'' solutions $\\Xi_{n,0,0}$ are\nnatural candidates to describe such experimental observations and may\nbe relevant for an appropriate\njustification for the use of 1D simplifications. Lastly, the\n1D eigenvalues coincide with the\neigenvalues of the 3D hydrogen model.\n\n\\begin{figure}\n\\begin{center}\n\\includegraphics[width=12cm, height=8cm]{Fig03D.eps}\n\\caption{A boundary surface of $|\\Xi_{n=1,\\ell=0,0}(r,\\theta,\\phi)|^2$. }\n\\label{function3D}\n\\end{center}\n\\end{figure}\n\nNow the solutions $\\Xi_{n,\\ell\\ne0,0}$ have nonzero total angular\nmomentum while zero angular\nmomentum in the $z$-direction, and the logarithm divergence for\n$\\theta=0$ and~$\\pi$ is also present for all\n$\\xi_{l,0}$, indicating that the $z$-axis plays a special role in the\nclassical trajectories analogy. So it\nis possible to interpret that $\\Xi_{n,\\ell\\ne0,0}$ is related to\ntwo-dimensional motions taking place in planes\ncontaining the $z$-axis, i.e., to the 2D hydrogen atom.\nFigure~\\ref{FigDoisDois} illustrates such\ninterpretation for\n$\\ell=2,m=0$. The\n2D hydrogen atom has also been considered in the literature (see\n\\cite{YGCWC,delCV,RR,VP,PP}\nand references therein), but its history is not as controversial as for\nthe 1D case.\n\n\nFinally, a word about $\\Xi_{n,\\ell,m\\ne0}$; since they do not belong to\nthe Hilbert space, based on the\nabove discussion and proceeding heuristically, it is tempting to interpret such solutions as the\n``contribution'' due to the classical\ntrajectories which come into collision with the nucleus, and the\nmathematical apparatus prudently avoids them\nexplicitly (maybe a mathematical consequence of the uncertainty principle).\n\n\n\\begin{figure}\n\\begin{center}\n\\vskip 10pt\n\\includegraphics[width=6cm, height=6cm]{FigQ22.eps}\n\\caption{$|\\xi_{2,0}(\\theta)|$ and $|\\Theta_{2,0}(\\theta)|$ (dashed) as\nfunction of $\\theta$.}\n\\label{FigDoisDois}\n\\end{center}\n\\end{figure}\n\n\n\\section{Conclusions}\nOne is naturally inclined to presume that higher dimensional quantum\nmodels carry somehow lower\ndimensional dynamics, and the study of such simpler models could mimics\nimportant aspects of\nthe original one. Of course, in general the difficulties of performing\nsuch dimensional reductions are enormous, and usually carried out by\n``brute force.''\n\nThe case of the 3D hydrogen atom discussed in this\nwork has revealed a particular and\ninteresting framework: there are experimental evidence for the 1D\nhydrogen atom; the 3D hermitian model has just one self-adjoint extension,\nand its\n 3D eigenvalue equation\npresents formal solutions $\\Xi_{n,\\ell,0}$ that do not belong to the\ndomain of the corresponding Hamiltonian\noperator; in spite of being formal eigenvectors, these solutions live in\nthe underlying Hilbert space\nand present a component in the continuous subspace\nof the Hamiltonian so that, for an electron in such state, ionization can\ntake place;\n these solutions have formally\nzero azimuthal angular momentum, with integrable probability\ndensities, and are concentrated around the $z$-axis, indicating their 1D\nand 2D\ncharacter for $\\Xi_{n,0,0}$ and $\\Xi_{n,\\ell\\ne0,0}$, respectively.\nSumming up, such solutions are\nreminiscent of 1D and 2D classical trajectories and give a connection\nbetween the hydrogen atom in different\ndimensions.\n\n\nHow general is this framework? This is a fascinating open question, whose\nanswer could\neventually improve the interpretations.\n\nIn addition, notice that there is an attractive relation between the\ndimensional interpretations advocated in\nthis work and the mathematical formalism, which exhausts the possibilities for (pseudo-)eigenvectors. For\ngenuine 3D motion it presents eigenfunctions in the Hamiltonian\noperator domain; for 1D and 2D reminiscent trajectories it presents\neigenfunctions in the Hilbert space but not\nin the domain of the operator; and for those colliding trajectories (axial\ndivergence) the\nformal eigenfunctions do not belong\nto the Hilbert space.\n\n\n\n\n\n\n\n\n\n\n\n\\subsubsection*{Acknowledgments} {\\small AL-C thanks FAPESP. CRdeO thanks\nthe partial\nsupport by CNPq}.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec intro}\n\\thispagestyle{empty}\n\nThe local and global deformation theories of irreducible holomorphic symplectic manifolds enjoy many beautiful properties. For example, unobstructedness of deformations \\cite{Bo78,Ti,To89} and the local Torelli theorem \\cite{B} are at the origin of many results on symplectic manifolds. Among the highlights of the global theory are Huybrechts' surjectivity of the period map \\cite{Huy99} and Verbitsky's global Torelli theorem \\cite{V13} (see for example Markman's survey article \\cite{markmantor} and Huybrechts' Bourbaki talk \\cite{huybourbaki}). \nVerbitsky's result has since paved the way for many important developments, with applications to a wide variety of questons such as: birational boundedness \\cite{Ch16}, lattice polarized mirror symmetry \\cite{Ca16}, algebraic cycles \\cite{CP}, hyperbolicity questions \\cite{KLV} and many more. Recent progress in MMP and interest in singular symplectic varieties \\cite{GKP} has made it apparent that a global deformation theory of singular symplectic varieties would be equally valuable.\n\nIn the present article we initiate a systematic study of the moduli theory of singular symplectic varieties. \nWe prove general results concerning their deformation theory and building on this we develop a global moduli theory for locally trivial families of singular irreducible symplectic varieties. \nFor the purpose of this article it is convenient to define an \\emph{(irreducible) symplectic variety} $X$ to be a compact normal K\\\"ahler space admitting a resolution $\\pi:Y\\to X$ by an (irreducible) holomorphic symplectic manifold $Y$, see section \\ref{sec symplektisch}. In this situation we refer to $\\pi$ as an \\emph{(irreducible) symplectic resolution}. In particular, if the varieties are projective, then our (irreducible) symplectic varieties are symplectic varieties in the sense of Beauville \\cite{Be} but our notion is of course much more restrictive.\n\nBy work of Namikawa \\cite{Na01,Na06}, a projective variety $X$ admitting a resolution $\\pi:Y\\to X$ by a symplectic manifold $Y$ also has unobstructed deformations, and the natural map ${\\operatorname{Def}}(Y)\\to{\\operatorname{Def}}(X)$ is finite. However, $\\pi$ deforms generically to an isomorphism, and as a consequence the only natural period map is that of the resolution $Y$.\nFrom a Hodge-theoretic perspective it is therefore more natural to consider the \\emph{locally trivial} deformations of $X$ as in this case the pure weight two Hodge structure on $H^2(X,{\\mathbb Z})$ varies in a local system, and the resulting theory is very closely analogous to the smooth situation. \nOur first result is the following\n\n\\begin{theorem}[see Proposition \\ref{prop defo}]\\label{theorem def smooth} Let $\\pi:Y\\to X$ be an irreducible symplectic resolution and $N=N_1(Y\/X)\\subset H_2(Y,{\\mathbb Z})$ the group of 1-cycles contracted by $\\pi$. The base space ${\\operatorname{Def}}^{\\rm{lt}}(X)$ of the universal locally trivial deformation is smooth, and there is a diagram\n\\[\n\\xymatrix{\n{\\mathscr Y} \\ar[d]\\ar[r]& {\\mathscr X} \\ar[d]\\\\\n{\\operatorname{Def}}(Y,N) \\ar[r]^{\\cong} & {\\operatorname{Def}}^{\\rm{lt}}(X) \\\\\n}\n\\]\nwhere ${\\mathscr X}\\to {\\operatorname{Def}}^{\\rm{lt}}(X)$ is the universal locally trivial deformation of $X$, ${\\mathscr Y}\\to{\\operatorname{Def}}(Y,N)\\subset{\\operatorname{Def}}(Y)$ is the restriction of the universal deformation of $Y$ to the closed subspace along which $N$ remains algebraic, and ${\\mathscr Y}\\to{\\mathscr X}$ specializes to $\\pi$.\n\\end{theorem}\n The case of a divisorial contraction $\\pi:Y\\to X$ of a projective symplectic variety was treated by Pacienza and the second author in \\cite{LP}, where it was shown that locally trivial deformations of $X$ correspond to deformations of $Y$ such that all irreducible components of the exceptional divisor ${\\operatorname{Exc}}(\\pi)$ of $\\pi$ deform along. The space ${\\operatorname{Def}}^{\\rm{lt}}(X)$ of locally trivial deformations of $X$ is smooth of dimension $h^{1,1}(X)=h^{1,1}(Y)-m$ where $m$ is the number of irreducible components of ${\\operatorname{Exc}}(\\pi)$. This description is equivalent to that of the theorem, since the Beauville--Bogomolov--Fujiki form $q$ on $H^2(Y,{\\mathbb Z})$ yields an isomorphism $\\tilde q: H^2(Y,{\\mathbb Q})\\xrightarrow{\\cong} H_2(Y,{\\mathbb Q})$ identifying the subspace of $H^2(Y,{\\mathbb Q})$ spanned by ${\\operatorname{Exc}}(\\pi)$ with $N_{\\mathbb Q}$. In the case where $\\pi$ is a small contraction, $X$ is not ${\\mathbb Q}$-factorial, but we can still recast the description in the theorem in terms of line bundles: under $\\tilde q$, the Hodge classes in the orthogonal $N^\\perp\\subset H^2(Y,{\\mathbb Q})$ are the ${\\mathbb Q}$-line bundles on $Y$ that vanish on $N$ and can therefore be pushed forward to ${\\mathbb Q}$-line bundles on $X$.\n\nAn important theorem of Huybrechts \\cite[Theorem 2.5]{Huy} shows that birational\\footnote{We will use the term birational instead of bimeromorphic also for K\\\"ahler varieties.} irreducible holomorphic symplectic manifolds are deformation equivalent. Of course, birational irreducible symplectic varieties are not necessarily locally trivially deformation equivalent, and the correct analog of Huybrechts' theorem is the following: \n\\begin{theorem}[See Theorem \\ref{theorem huybrechts}]\\label{intro theorem huybrechts}\nLet $\\pi:{Y}\\to X$ and $\\pi':Y'\\to X'$ be projective irreducible symplectic resolutions of projective irreducible symplectic varieties $X,X'$, and\nlet $\\phi:X\\dashrightarrow X'$ be a birational map. Then the following are equivalent:\n\\begin{enumerate}\n \\item There is an isomorphism $\\varphi:{\\operatorname{Def}}^{\\rm{lt}}(X)\\to {\\operatorname{Def}}^{\\rm{lt}}(X')$ such that for each $t\\in {\\operatorname{Def}}^{\\rm{lt}}(X)$ we have a birational map $\\phi_t: {\\mathscr X}_t \\dashrightarrow {\\mathscr X}'_{\\varphi(t)}$ which is an isomorphism in codimension one. For general $t \\in {\\operatorname{Def}}^{\\rm{lt}}(X)$, the map $\\phi_{t}$ is an isomorphism. In particular, $X$ and $X'$ are locally trivial deformations of one another.\n\\item The map $\\tilde\\phi^*: H^2({Y}',{\\mathbb C}) \\to H^2({Y},{\\mathbb C})$ induced by the birational map $\\tilde\\phi:{Y} \\dashrightarrow {Y}'$ is an isomorphism that sends $H^2(X',{\\mathbb C})$ isomorphically to $H^2(X,{\\mathbb C})$. \n\\end{enumerate}\n\\end{theorem} \nFor a criterion that is more intrinsic to $X$, let ${\\operatorname{Cl}}(X)$ denote the group of Weil divisors modulo linear equivalence.\n\\begin{corollary}\\label{intro cor div}\nSuppose $\\phi:X\\dashrightarrow X'$ is a birational map of irreducible projective symplectic varieties which is an isomorphism in codimension one. If $\\phi_*: {\\operatorname{Cl}}(X)_{\\mathbb Q} \\to {\\operatorname{Cl}}(X')_{\\mathbb Q}$ and its inverse send ${\\mathbb Q}$-line bundles to ${\\mathbb Q}$-line bundles, then $X$ and $X'$ are locally trivially deformation equivalent. \n\\end{corollary}\n\nFixing a lattice $\\Lambda$, there is a natural locally trivial $\\Lambda$-marked moduli space $\\mathfrak{M}^{\\rm{lt}}_\\Lambda$ obtained by gluing the universal locally trivial deformation spaces together, and we also have an associated notion of parallel transport operator. Further, there is a period map $P:\\mathfrak{M}^{\\rm{lt}}_\\Lambda\\to\\Omega_\\Lambda$ to the associated period domain $\\Omega_\\Lambda$ that is a local isomorphism. The two equivalent conditions in Theorem \\ref{intro theorem huybrechts} also imply -- as for smooth varieties -- that $X$ and $X'$ are inseparable in $\\mathfrak{M}^{\\rm{lt}}_\\Lambda$, see Theorem \\ref{theorem huybrechts strong}. \nAs a converse, we have a version of the global Torelli theorem, see Theorem \\ref{theorem global torelli}. The following is an analog of Markman's Hodge theoretic version \\cite[Theorem 1.3]{markmantor} of the global Torelli theorem.\n\n\\begin{theorem}[Global Torelli Theorem]\\label{intro global}\nLet $X$, $X'$ be irreducible symplectic varieties and let $f:H^2(X,{\\mathbb Z}) \\to H^2(X',{\\mathbb Z})$ be a locally trivial parallel transport operator which is a Hodge isometry. Then there is a birational map $\\phi:X \\dashrightarrow X'$ which induces\\footnote{By this we mean that the induced birational map on irreducible symplectic resolutions induces an isomorphism on cohomology which identifies $H^2(X,{\\mathbb Z})$ with $H^2(X',{\\mathbb Z})$.} a Hodge isometry $\\phi_*:H^2(X,{\\mathbb Z})\\to H^2(X',{\\mathbb Z})$.\n\\end{theorem}\n\nAs in the smooth case, this has an equivalent formulation saying that every connected component of the marked moduli space maps injectively to the period domain, points in one fiber are birational, and this birational map induces an isomorphism on the second cohomology, see Corollary \\ref{corollary global torelli}. Moreover, we have analogs of Huybrechts' surjectivity of the period map \\cite{Huy99} as well as of a well-known theorem of Sullivan \\cite{Sullivan} in the smooth case, see Corollary \\ref{corollary monodromy group} and Theorem \\ref{theorem almost surjectivity}. Put together, we have\n\n\\begin{theorem}\\label{intro theorem torelli}Let $X$ be an irreducible symplectic variety with $b_2(X)>3$ admitting a symplectic resolution $\\pi:Y\\to X$ with $b_2(Y) > 5$. Let $\\mathfrak{N}^{\\rm{lt}}_\\Lambda\\subset \\mathfrak{M}^{\\rm{lt}}_\\Lambda$ be a connected component containing $X$ (after choosing a marking). Then\n\\begin{enumerate}\n\\item The period map $P:\\mathfrak{N}^{\\rm{lt}}_\\Lambda\\to\\Omega_\\Lambda$ is surjective, and the points in any fiber are pairwise nonseparated. Moreover, the nonseparated points precisely correspond to birational symplectic varieties such that the birational map induces a Hodge isometry on the second cohomology lattices.\n\\item The locally trivial weight two monodromy group $\\operatorname{Mon}^2(X)^{\\rm{lt}}$ is a finite index subgroup of $\\O(H^2(X,{\\mathbb Z}))$, equal to the restriction of the subgroup of $\\operatorname{Mon}^2(Y)$ stabilizing $\\pi^*H^2(X,{\\mathbb Z})$. \n\\end{enumerate}\n\\end{theorem}\n\nThe analogous results in the smooth case heavily rely on the existence of a hyperk\\\"ahler metric, the theory of twistor lines, and deformations of complex structures. This presents a major difficulty for singular varieties as the aforementioned techniques are not available or much less understood as in the smooth case. \nWe therefore deduce the largeness of the image of the period map by density results built on Ratner's theorem (as first explored in this context by Verbitsky \\cite{V15}), and this is responsible for the numerical conditions on $b_2(X)$. To the best of our knowledge this is the first general result in this direction which makes a statement about \\emph{large} deformations of singular symplectic varieties. Note that $X$ with $b_2(X)=3$ are locally trivially rigid in the sense that their only locally trivial deformation is what should be the (unique) twistor deformation, and unlike in the smooth case we can give examples (see Remark \\ref{remark rigid}). On the other hand, all known smooth deformation types have $b_2(Y)>5$. \n\n \nThe locally trivial monodromy group interacts in an interesting way with the monodromy group of the resolution, permuting the different possible resolutions. We deduce from the second part of Theorem \\ref{intro theorem torelli} the following classification of the locally trivial deformation types of contractions in terms of the monodromy group of the resolution (see Corollary \\ref{cor mon det}):\n \\begin{corollary}\\label{intro theorem mono class} With the above restrictions, the locally trivial deformation type of $X$ is uniquely determined by the $\\operatorname{Mon}^2(Y)$ orbit of $N_1(Y\/X)$ in $H_2(Y,{\\mathbb Z})$. \n\\end{corollary}\n\nMuch more can be said when $X$ admits a resolution deformation equivalent to a Hilbert scheme of points on a $K3$ surface. The possible projective contractions $M\\to X$ of a moduli space of sheaves $M$ on a $K3$ surface is completely described by wall-crossing in the space of Bridgeland stability conditions by work of Bayer--Macr\\`i \\cite{BM}; \nTheorem \\ref{theorem def smooth} combined with density results then implies that no new singularities occur in the general $X$ (see Proposition \\ref{proposition def to bridge}):\n\n\\begin{theorem}Let $\\pi:Y\\to X$ be an irreducible symplectic resolution where $Y$ is a $K3^{[n]}$-type manifold. Provided $b_2(X)>3$, $X$ is locally trivially deformation equivalent to a wall-crossing contraction of a moduli space of Bridgeland stable objects on a K3 surface. \n\\end{theorem}\n\nExtremal contractions are particularly amenable to the lattice theory involved, and we for example have the following generalization of a beautiful result of Arbarello and Sacc\\`a \\cite{AS}:\n\n\\begin{theorem}\\label{intro theorem nakajima} Let $\\pi:Y\\to X$ be an irreducible symplectic resolution where $Y$ is a $K3^{[n]}$-type manifold, and assume ${\\rm rk}\\,N_1(Y\/X)=1$. Then for any closed point $x\\in X$ the analytic germ $(X,x)$ is isomorphic to that of a Nakajima quiver variety.\n\\end{theorem}\n\nAs another nice application, we are able to rule out the existence of certain contractions on $K3^{[n]}$-type varieties:\n\n\\begin{theorem} Let $\\pi:Y\\to X$ be a divisorial contraction of a $K3^{[n]}$-type manifold $Y$, and assume ${\\rm rk}\\,N_1(Y\/X)=1$. Then for a generic closed point $x\\in X^{\\operatorname{sing}}$ the analytic germ $(X,x)$ is isomorphic to $(S,0)\\times ({\\mathbb C}^{2n-2},0)$ where $(S,0)$ is an $A_1$-surface singularity.\n\\end{theorem}\n\nThe surprising fact is that relative Picard rank one contractions whose generic singularity is transversally an $A_2$-surface singularity \\emph{do not occur} on irreducible symplectic manifolds while on other arbitrary symplectic manifolds they do, see Corollary \\ref{corollary a2} and the discussion thereafter.\n\nWe expect most of our general results to apply to symplectic varieties not necessarily admitting a symplectic resolution by using a ${\\mathbb Q}$-factorial terminalization in place of the resolution but we do not pursue that level of generality here. We do note however that this would fit very nicely with another result of Namikawa \\cite{Na06} that every flat deformation of a ${\\mathbb Q}$-factorial terminal projective symplectic variety is locally trivial. As for the applications, we only restrict to $K3^{[n]}$-type varieties for simplicity, and our theory should yield similar results for the Kummer and O'Grady types. These directions are the topic of a forthcoming article.\n\n\\subsection*{Outline}\nIn sections \\ref{sec hodge} and \\ref{sec symplektisch} we explain some basic facts about irreducible symplectic varieties and their Hodge structures. We recall that the Hodge structure on the second cohomology of an irreducible symplectic variety is always pure and prove the degeneration of Hodge-de Rham spectral sequence for singular symplectic varieties in a suitable range. We also examine the Mumford--Tate group of a general irreducible symplectic manifold. \n\nThe locally trivial deformation theory of irreducible symplectic varieties is studied in section \\ref{sec defo}. This is one of the two technical centerpieces of the present paper. All our moduli theory builds on this in an essential way. The main results are the proof of smoothness of the Kuranishi space, the comparison of deformations of a singular symplectic variety and its resolution, the local Torelli theorem, and the analog of Huybrechts' theorem. We also include some remarks and applications to the question of existence of algebraically coisotropic subvarieties. In section \\ref{sec monodromy} we introduce and investigate marked moduli spaces of locally trivial families of irreducible symplectic varieties. We study the relation to moduli spaces of the resolution through compatibly marked moduli spaces of irreducible symplectic resolutions as well as various associated monodromy groups. The analysis of how these spaces and their monodromy groups are connected is the second main ingredient of this work. In this section we make use of Verbitsky's ergodicity of complex structures and Amerik-Verbitsky's concept of MBM classes.\n\nIn section \\ref{sec k3type} we give applications to $K3^{[n]}$-type varieties. We essentially make use of the description of the birational geometry of Bridgeland moduli spaces of stable objects by Bayer and Macr\\`i. In principle, our methods allow to extend any result on singularities that can be proven for contractions of Bridgeland moduli spaces to arbitrary $K3^{[n]}$-type varieties. The above-mentioned generalization of Arbarello--Sacc\\`a's result is proven here.\n\n\\subsection*{Acknowledgments.} We would like to thank J\\'anos Koll\\'ar for helpful discussions on birational contractions. We benefited from discussions, remarks, emails of Chiara Camere, Daniel Greb, Klaus Hulek, Manfred Lehn, Giovanni Mongardi, Gianluca Pacienza, Jacob Tsimerman, Thomas Peternell, Stefan Kebekus, and Stefano Urbinati.\n\nChristian Lehn was supported by the DFG through the research grants Le 3093\/2-1 and Le 3093\/3-1.\n\n\n\n\n\\section{Hodge structure for rational singularities}\\label{sec hodge}\n\nWe establish some basic facts about the Hodge structure on low degree cohomology groups of varieties with rational singularities. The following lemma is well-known, we include it for the reader's convenience.\n\n\\begin{lemma}\\label{lemma peternell}\nLet $\\pi:{Y}\\to X$ be a resolution of singularities of a compact normal variety $X$ with rational singularities. Then the sequence\n\\begin{equation}\\label{eq leray}\n 0 \\to H^2(X,{\\mathbb Z}) \\to[\\pi^*] H^2({Y},{\\mathbb Z}) \\to[\\varphi] H^0(R^2\\pi_*{\\mathbb Z})\n\\end{equation}\nis exact. In particular, if ${Y}$ is of class ${\\mathscr C}$, then $H^2(X,{\\mathbb Z})$ carries a pure Hodge structure. Moreover, the restriction of $\\pi^*$ to the transcendental lattice is an isomorphism over ${\\mathbb Q}$ and $\\pi^*\\operatorname{NS}(X)_{\\mathbb Q}$ is the subspace of $H^{1,1}({Y},{\\mathbb Q})$ of all ${\\mathbb Q}$-line bundles on ${Y}$ that vanish on the $\\pi$-exceptional curves.\n\\end{lemma}\n\\begin{proof}\nThe exponential sequence and rationality of singularities imply that $R^1\\pi_*{\\mathbb Z}=0$. Therefore, the sequence \\eqref{eq leray}, which comes from the Leray spectral sequence, is exact and by strictness of morphisms of Hodge structures, $H^2(X,{\\mathbb Z})$ carries a pure Hodge structure. \n\nThe last two statements follow from \\cite[(12.1.3) Theorem]{KM}.\n\\end{proof}\nThe proof shows that \\eqref{eq leray} is even defined and exact over ${\\mathbb Z}$ but we will not need this in the sequel.\n\nWe will investigate in how far the de Rham complex on a resolution or the one on the smooth part of a singular variety can be used to compute the singular cohomology and the Hodge decomposition on $X$. \n\\begin{lemma}\\label{lemma hodge}\nLet $X$ be a normal compact K\\\"ahler variety. Then the following hold:\n\\begin{enumerate}\n\t\\item\\label{lemma hodge item one} Let $\\pi:Y \\to X$ be a resolution of singularities by a compact K\\\"ahler manifold $Y$. If $X$ has rational singularities, then for all $k\\leq 2$ the canonical map $H^k(X,{\\mathbb C})\\to {\\mathbb H}^k(X,\\pi_*\\Omega_Y^\\bullet)$ is an isomorphism and the Hodge-de Rham spectral sequence\n\\begin{equation}\\label{eq spec seq}\n E^{p,q}_1=H^q(\\pi_*\\Omega_Y^p) \\Rightarrow {\\mathbb H}^{p+q}(X,\\pi_*\\Omega_Y^\\bullet)\n\\end{equation}\ndegenerates on $E_1$ in the region where $p+q \\leq 2$.\n\\item\\label{lemma hodge item two} Let us denote by $j:U=X^{\\operatorname{reg}}\\to X$ the inclusion of the regular part. If $X$ is projective with log terminal singularities, then for all $k\\leq 2$ the canonical map $H^k(X,{\\mathbb C})\\to {\\mathbb H}^k(X,j_*\\Omega_U^\\bullet)$ is an isomorphism and the Hodge-de Rham spectral sequence\n\\begin{equation}\\label{eq spec seq projective}\n E^{p,q}_1=H^q(j_*\\Omega_U^p) \\Rightarrow {\\mathbb H}^{p+q}(X,j_*\\Omega_U^\\bullet)\n\\end{equation}\ndegenerates on $E_1$ in the region where $p+q \\leq 2$.\n\\end{enumerate}\n\\end{lemma}\n\\begin{proof}\nWe may assume that $\\pi:{Y} \\to X$ is a log resolution of singularities which is an isomorphism over $U$, i.e., $\\pi^{-1}(X^{\\operatorname{sing}})$ is a divisor with simple normal crossings. \nLog terminal singularities are rational by \\cite[Theorem 5.22]{KM}. As a consequence of \\cite[Theorem 1.4]{GKKP} we have $j_*\\Omega_U^\\bullet=\\pi_*\\Omega_{Y}^\\bullet$ so that \\eqref{lemma hodge item two} is a consequence of \\eqref{lemma hodge item one}, and it suffices to prove the first statement.\nWe obtain a commuting diagram\n\\begin{equation}\\label{eq hodge diag}\n\\xymatrix{\nH^k(X,{\\mathbb C}) \\ar[r]\\ar[d]^{\\pi^*} & {\\mathbb H}^k(\\pi_*\\Omega_Y^\\bullet)\\ar[d]^{\\psi}\\\\\nH^k({Y},{\\mathbb C}) \\ar[r] & {\\mathbb H}^k(\\Omega_{Y}^\\bullet)\\\\\n}\n\\end{equation}\nwhere the right vertical morphism comes from the canonical morphism of complexes $\\pi_*\\Omega_{Y}^\\bullet \\to R\\pi_*\\Omega_{Y}^\\bullet$. The lower horizontal map is an isomorphism by Grothendieck's theorem and $\\pi^*$ is injective by Lemma \\ref{lemma peternell}. \n We will show that for $k\\leq 2$ the map $\\psi$ is injective and its codimension is the same as for $\\pi^*$. We may and will assume that $X_0={Y}$ is the beginning of a semi-simplicial resolution $\\begin{xy}\\xymatrix@C=1em{\n\\ldots \\ar[r]\\ar@<0.5ex>[r]\\ar@<-0.5ex>[r] & X_{1} \\ar@<0.5ex>[r]\\ar@<-0.5ex>[r] & X_0 \\ar[r]^\\varepsilon& X, \\\\}\\end{xy}$ which serves to calculate the Hodge structure on $H^k(X,{\\mathbb C})$, see \\cite[Chapter 5]{PS}. With this assumption, all morphisms in \\eqref{eq hodge diag} become filtered morphisms where on the left we speak about the Hodge filtrations and on the right about the filtrations on the cohomology of a complex $K^\\bullet$ given by \n$F^p{\\mathbb H}^k(K^\\bullet):=\\operatorname{im}({\\mathbb H}^k(K^{\\geq p})\\to {\\mathbb H}^k(K^\\bullet))$. All morphisms but $H^k(X,{\\mathbb C}) \\to {\\mathbb H}^k(j_*\\Omega_U^\\bullet)$ are clearly filtered and this last one is filtered because it comes from a morphism of filtered complexes\n\\[\n {\\mathbb C}_X \\to[\\textrm{qis}] {\\mathfrak s}\\left(R\\varepsilon_*\\Omega_{X_0}^\\bullet \\to R\\varepsilon_*\\Omega_{X_1}^\\bullet \\to \\ldots\\right) \\ot j_*\\Omega_U^\\bullet\n\\]\nwhere ${\\mathfrak s}(\\cdot)$ stands for the associated single complex.\n\nThe injectivity part of the claim will follow from the statement about degeneration of the spectral sequence.\nDegeneration on $E_1$ follows as the $E_1$-level of the spectral sequence \\eqref{eq spec seq} embeds into the $E_1$-level of the spectral sequence of the complex $\\Omega_{Y}^\\bullet$, which degenerates on $E_1$ by Hodge theory. \n\nWe have $H^k(X,{\\mathcal O}_X){\\ \\cong\\ } H^k({Y},{\\mathcal O}_{Y})$ by rationality of singularities for all $k \\in {\\mathbb N}_0$.\nThe inclusion $H^1(X,\\pi_*\\Omega_{Y}) \\subset H^1({Y},\\Omega_{Y})$ is deduced from the Leray spectral sequence. Thus, the spectral sequence for $\\pi_*\\Omega_Y^\\bullet$ degenerates in the realm $p+q \\leq 2$ and we obtain commutative diagrams\n\\begin{equation}\\label{eq hpq diag}\n\\xymatrix{\nH^{p,q}(X) \\ar@{^(->}[r]\\ar@{^(->}[d]^{\\pi^*} & H^q(\\pi_*\\Omega_Y^p)\\ar@{^(->}[d]^{\\psi^{p,q}}\\\\\nH^{p,q}({Y}) \\ar[r]^{\\ \\cong\\ } & H^q(\\Omega_{Y}^p)\\\\\n}\n\\end{equation}\nfor all such $p$ and $q$.\n\nIt remains to see that $H^1(X,\\pi_*\\Omega_{Y}) \\subset H^1({Y},\\Omega_{Y})$ has codimension equal to $m:=\\dim N_1(Y\/X)=\\dim H^{1,1}({Y})-\\dim H^{1,1}(X)$, where $N_1(Y\/X)$ is the kernel of the surjection $N_1(Y) \\to N_1(X)$, see \\cite[(12.1.5)]{KM} and note that log terminal singularities are rational.\nBut $\\operatorname{coker}\\psi^{1,1}$ is the image of $H^1(\\Omega_{Y}) \\to H^0(R^1\\pi_*\\Omega_{Y})$. So let $C_1, \\ldots, C_m$ be curves in ${Y}$ contracted to a point under $\\pi$ such that their classes form a basis of $N_1({Y}\/X)$ and let $L_1, \\ldots, L_m$ be line bundles on ${Y}$ such that their Chern classes $\\xi_i:={\\rm c}_1(L_i)\\in H^1(\\Omega_{Y})$ define linearly independent functionals on $N_1({Y}\/X)$. Choose irreducible components $F_1, \\ldots, F_m$ of fibers of $\\pi$ such that $C_i \\subset Y_i$ for all $i$. Take resolutions of singularities $\\nu_i:\\tilde F_i \\to F_i$ and curves $\\tilde C_i \\subset \\tilde F_i$ such that ${\\nu_i}_*\\tilde C_i = C_i$ for all $i$. If we denote $F:=\\coprod{\\tilde F_i}$ and by $\\nu : F\\to {Y}$ the composition of the resolutions with the inclusion, then by the projection formula $\\nu^*\\xi_i.\\tilde C_j= \\xi_i.C_j$ so that the $\\nu^*\\xi_i$ are still linearly independent. This implies that the $\\xi_i$ are mapped to an $m$-dimensional subspace of $H^1(F,\\Omega_F)$ under the composition $H^1(\\Omega_{Y}) \\to H^0(R^1\\pi_*\\Omega_{Y})\\to H^1(F,\\Omega_F)$. In particular, ${\\rm rk} \\left(H^1(\\Omega_{Y}) \\to H^0(R^1\\pi_*\\Omega_{Y})\\right) \\geq m$, which completes the proof of the lemma. \n\\end{proof}\nWe immediately deduce\n\\begin{corollary}\\label{corollary hodge}\nLet $X$ be a normal compact K\\\"ahler variety, let $\\pi:Y \\to X$ be a resolution of singularities by a compact K\\\"ahler manifold $Y$, and denote by $j:U=X^{\\operatorname{reg}}\\to X$ the inclusion of the regular part. \n\\begin{enumerate}\n\t\\item If $X$ has rational singularities and $k,p+q \\leq 2$ then \\begin{enumerate}\n \\item $H^{p,q}(X){\\ \\cong\\ } H^q(X,\\pi_*\\Omega_Y^p)$, \n \\item $H^k(X,{\\mathbb C}){\\ \\cong\\ } {\\mathbb H}^k(X,\\pi_*\\Omega_Y^\\bullet)$, and \n \\item $F^pH^k(X,{\\mathbb C}){\\ \\cong\\ } {\\mathbb H}^k(X,\\pi_*\\Omega_Y^{\\geq k})$.\n \\end{enumerate}\n\\item If $X$ is projective with log terminal singularities and $k,p+q \\leq 2$ then\n \\begin{enumerate}\n \\item $H^{p,q}(X){\\ \\cong\\ } H^q(X,j_*\\Omega_U^p)$, \n \\item $H^k(X,{\\mathbb C}){\\ \\cong\\ } {\\mathbb H}^k(X,j_*\\Omega_U^\\bullet)$, and \n \\item $F^pH^k(X,{\\mathbb C}){\\ \\cong\\ } {\\mathbb H}^k(X,j_*\\Omega_U^{\\geq k})$.\n \\end{enumerate}\n\\end{enumerate} \\qed\n\\end{corollary}\nNow we turn to the relative situation. The following result follows from the absolute case by homological algebra.\n\\begin{lemma}\\label{lemma hodge relativ}\nLet $X_0$ be a normal projective variety with log terminal singularities, let $f: X \\to S$ be a flat deformation of $X_0$ over a local Artinian base scheme $S$ of finite type over ${\\mathbb C}$, let $U_0 \\subset X_0$ be the regular locus and let $U\\to S$ be the induced deformation of $U_0$. Suppose that $j_*\\Omega^\\bullet_{U\/S}$ is flat over $S$ where $j:U{\\, \\hookrightarrow\\,} X$ is the inclusion. Then, for all $k\\leq 2$ the canonical map $H^k(X,f^{-1}{\\mathcal O}_S)\\to {\\mathbb H}^k(X,j_*\\Omega_{U\/S}^\\bullet)$ is an isomorphism and the Hodge-de Rham spectral sequence\n\\begin{equation}\\label{eq spec seq relativ}\n E^{p,q}_1=H^q(j_*\\Omega_{U\/S}^p) \\Rightarrow {\\mathbb H}^{p+q}(X,j_*\\Omega_{U\/S}^\\bullet)\n\\end{equation}\ndegenerates on $E_1$ in the region where $p+q \\leq 2$. Moreover, the ${\\mathcal O}_S$-modules $H^q(X,j_*\\Omega_{U\/S}^p)$ are free for $p+q \\leq 2$ and compatible with arbitrary base change.\n\nThe same holds true if $X \\to S$ is a locally trivial deformation of a normal K\\\"ahler variety $X_0$ with rational singularities such that for some resolution $\\pi:Y_0\\to X_0$ we have $\\pi_*\\Omega_{Y_0} = j_*\\Omega_{U_0}$.\n\\end{lemma}\n\\begin{proof}\nThe proof is the same in the projective and in the K\\\"ahler case.\nPut $R:=\\Gamma(S,{\\mathcal O}_S)$. The differentials on all $E_1^{p,q}$ with $p+q=n$ will be zero if and only if $\\sum_{p+q=n}\\lg_R E_1^{p,q}=\\lg_R {\\mathbb H}^n(X,j_*\\Omega_{U\/S}^\\bullet)$ where $\\lg_R$ denotes the length as an $R$-module. Note that both sides are finite.\n\nFlatness of $j_*\\Omega^\\bullet_{U\/S}$ entails that there is a bounded below complex $L^\\bullet$ of free $R$-modules such that there is an isomorphism $H^q(X,j_*\\Omega_{U\/S}^p \\otimes f^*M) {\\ \\cong\\ } H^q(L\\otimes_R M)$ which is functorial in the $R$-module $M$, see \\cite[Th\\'eor\\`eme (6.10.5)]{EGAIII2} in the projective case and \\cite[Ch~3, Th\\'eor\\`eme 4.1]{BS} in the K\\\"ahler case.\n By \\cite[(3.5.1)]{Deligne}, this implies that $\\lg_R H^q(X,j_*\\Omega_{U\/S}^p) \\leq \\lg R \\cdot \\lg_R H^q(X_0,j_*\\Omega_{U_0}^p)$ and equality holds if and only if $H^q(X,j_*\\Omega_{U\/S}^p)$ is $R$-free.\n \nFor $n\\leq 2$ we have \n\\begin{align*}\n\\lg_R {\\mathbb H}^n(X,j_*\\Omega_{U\/S}^\\bullet) &\\leq \\sum_{p+q=n}\\lg_R H^q(X,j_*\\Omega_{U\/S}^p)\\\\\n&\\leq \\lg R \\cdot \\sum_{p+q=n}\\dim_{\\mathbb C} H^q(X_0,j_*\\Omega_{U_0}^p) \\\\\n&= \\lg R \\cdot H^n(X_0,{\\mathbb C}) \n\\end{align*}\nwhere the first inequality is the existence of the spectral sequence, the second one was explained just before and the equality is the degeneracy of the spectral sequence for $X_0$, see Lemma \\ref{lemma hodge}.\nMoreover, ${\\mathbb H}^n(X,j_*\\Omega_{U\/S}^\\bullet) = H^n(X,f^{-1}{\\mathcal O}_S) = H^n(X,\\ul{R}_X)$ for $n \\leq 2$ where $\\ul{R}_X$ denotes the constant sheaf $R$ as can be seen by induction over the length of $R$ and the corresponding statement for $X_0$ from Lemma \\ref{lemma hodge}.\nThe base change property follows from the local freeness by \\cite[(7.8.5)]{EGAIII2} in the projective case and by \\cite[Ch~3, Corollaire 3.10]{BS} in the K\\\"ahler case.\n\\end{proof}\n\\section{Symplectic varieties}\\label{sec symplektisch}\n\nRecall that an \\emph{irreducible symplectic manifold} is a simply connected compact K\\\"ahler manifold ${Y}$ such that $H^0({Y},\\Omega_{Y}^2)={\\mathbb C} \\sigma$ for a holomorphic symplectic $2$-form $\\sigma$. There is a nondegenerate quadratic form $q_{Y}:H^2({Y},{\\mathbb Z})\\to {\\mathbb Z}$, the \\emph{Beauville--Bogomolov--Fujiki form}, whose associated bilinear form gives an injection $\\tilde q:H^2({Y},{\\mathbb Z}){\\, \\hookrightarrow\\,} H_2({Y},{\\mathbb Z})$ which becomes an isomorphism over ${\\mathbb Q}$.\nThroughout this article, by \\emph{symplectic variety} we mean a normal compact K\\\"ahler\\footnote{K\\\"ahler spaces were first introduced by Grauert \\cite[\\S 3.3]{Gra}; their study has been continued by Moishezon \\cite{Moi}.} space $X$ such that its smooth part admits a holomorphic symplectic $2$-form that extends holomorphically to one (and hence to any) resolution of singularities \\emph{and} such that $X$ admits a resolution by a symplectic manifold. \nNote that the existence of a symplectic resolution is not required in \\cite{Be}. We say that $X$ is an irreducible symplectic variety if the resolution is irreducible symplectic. An \\emph{irreducible symplectic resolution} is a resolution of a symplectic variety in our sense by an irreducible symplectic manifold.\n\nSuppose that $\\pi:{Y} \\to X$ is a proper birational morphism from an irreducible symplectic manifold ${Y}$ to a normal K\\\"ahler variety $X$ which is then automatically a symplectic variety. From \\cite[(12.1.3) Theorem]{KM} it follows that we have a short exact sequence \n\\[\n0\\to H_2(Y\/X,{\\mathbb Q}) \\to H_2(Y,{\\mathbb Q}) \\to H_2(X,{\\mathbb Q})\\to 0. \n\\]\n\nMoreover, by loc. cit. $H_2(Y\/X,{\\mathbb Q})$ is generated by algebraic cycles and therefore it coincides with $N_1(Y\/X)_{\\mathbb Q}$. Here, $N_1(Y\/X) \\subset N_1(Y)$ is defined as the kernel of the push forward map $\\pi_*:N_1(Y) \\to N_1(X)$.\n\n\\begin{lemma}\\label{lemma symplektisch}\nLet $\\pi:{Y} \\to X$ be an irreducible symplectic resolution. Then $\\pi^*:H^2(X,{\\mathbb C}) \\to H^2({Y},{\\mathbb C})$ is injective and the restriction of $q$ to $H^2(X,{\\mathbb C})$ is nondegenerate. \nThe $q$-orthogonal complement to $H^2(X,{\\mathbb C})$ in $H^2({Y},{\\mathbb C})$ is $N:=\\tilde q^{-1}(N_1({Y}\/X))$. In particular, the inclusion $H^2(X,{\\mathbb C})\\subset H^2({Y},{\\mathbb C})$ is an equality on the transcendental part $H^2(X,{\\mathbb C})_{\\rm tr}=H^2({Y},{\\mathbb C})_{\\rm tr}$. Moreover, $N$ is negative definite with respect to the Beauville--Bogomolov--Fujiki form.\n\\end{lemma}\n\\begin{proof}\nInjectivity follows from Lemma \\ref{lemma peternell}. To see that $N$ is $q$-orthogonal to $H^2(X,{\\mathbb C})$ we argue as follows: as the bilinear form $q$ is a morphism of Hodge structures and $N \\subset H^{1,1}({Y})$ is orthogonal to the $(2,0)$-part of $H^2({Y},{\\mathbb C})$ and hence to $H^2({Y},{\\mathbb C})_{\\rm tr}$ (which is by the way isomorphic to $H^2(X,{\\mathbb C})_{\\rm tr}$ by Lemma \\ref{lemma hodge}). So it suffices to prove that $N$ is $q$-orthogonal to $N^1(X) \\subset H^2(X,{\\mathbb Q}) \\subset H^2({Y},{\\mathbb Q})$. For this we only have to unravel the definition of $N$: let $C$ be a curve in ${Y}$ that is contracted to a point under $\\pi$. Then the class $D_C=q(C)\\in \\operatorname{Pic}(Y)_{\\mathbb Q}$ is the unique class such that $C.D'=q(D_C,D')$ for all $D'\\in \\operatorname{Pic}(Y)_{\\mathbb Q}$. In particular, $q(D_C,D')=0$ for all ${\\mathbb Q}$-line bundles $D'$ on $X$. So $H^2(X,{\\mathbb C}) \\subset N^\\perp$ and to conclude it is sufficient to exhibit a positive class in $H^2(X,{\\mathbb R})$. As $X$ is supposed to be K\\\"ahler, a K\\\"ahler class $h \\in H^{1,1}(X,{\\mathbb R})$ will do.\nThen $q(h)>0$ as $h$ is big and nef on $X$, so the orthogonal to $h$ in $H^{1,1}({Y})$ is negative definite and thus we see that $H^2({Y},{\\mathbb C})=H^2(X,{\\mathbb C}) \\oplus N$.\n\\end{proof}\n\\begin{proposition}\\label{proposition gkkp for symplectic}\nLet $\\pi:Y\\to X$ be an irreducible symplectic resolution and let $j:U=X^{\\operatorname{reg}}\\to X$ be the inclusion. Then, we have \n\\begin{enumerate}\n\t\\item $j_*\\Omega_U=\\pi_*\\Omega_Y$.\n\t\\item $T_X = j_*\\Omega_U$.\n\\item $H^p(X,j_*\\Omega_U^q)=H^p(X,\\pi_*\\Omega_Y^q)$ for all $p+q \\leq 2$. \n\\end{enumerate}\n\\end{proposition}\n\\begin{proof}\nLet $X_0=X^{\\operatorname{sing}}$ be the singular locus of $X$ with the reduced structure and let us consider $U_0=X {\\ \\setminus \\ } X_0^{\\operatorname{sing}}$. As a consequence of Kaledin's results \\cite{Kal}, see Remark \\ref{remark kaledin} and Corollary \\ref{corollary from kaledin} below, every point of $U_0$ is either smooth or a transversal ADE surface singularity. \n Let us denote $V = \\pi^{-1}(U)$, $V_0=\\pi^{-1}(U_0)$ and let us write for simplicity $j$ for all inclusions of $U,U_0,V,V_0$ to $X,Y$ and $j_0$ for the inclusions of $U,V$ to $U_0,V_0$. By semi-smallness \\cite[Lemma 2.11]{Kal}, we have $\\operatorname{codim}_Y (Y{\\ \\setminus \\ } V_0) \\geq 2$. Then the first two claims follow from\n\\[\nT_X=j_*{j_0}_*T_U= j_*{j_0}_*\\Omega_U=j_*\\pi_* \\Omega_{V_0}=\\pi_* j_*\\Omega_{V_0}=\\pi_* \\Omega_{Y},\n\\]\nwhere the first equality comes from the reflexivity of $T_X$, the second from the symplectic form on the regular part, the third is because the pushforward of the sheaf of holomorphic $p$-forms along the minimal resolution of ADE surface singularities is reflexive, the fourth is just functoriality of the push-forward, and the last equality holds for codimension reasons.\n\nLet us prove the third statement. As $X$ has rational singularities, we have that $R\\pi_* {\\mathcal O}_Y={\\mathcal O}_X=j_*{\\mathcal O}_U$ so that $H^k(j_*{\\mathcal O}_U)=H^k({\\mathcal O}_Y)$ for all $k$. The first statement implies that $H^k(j_*\\Omega_U)=H^k(\\pi_*\\Omega_Y)$ for all $k$ and the remaining equality $H^0(\\pi_*\\Omega^2_Y)=H^0(j_*\\Omega^2_U)$ \nfollows from $Y$ being an irreducible symplectic resolution. \n\\end{proof}\n\n\\begin{remark}\\label{remark kaledin}\nKaledin's article \\cite{Kal} is formulated for complex algebraic varieties, but we use his results in Proposition \\ref{proposition gkkp for symplectic} for arbitrary irreducible symplectic varieties.\nLet us comment on why they carry over to the compact K\\\"ahler setting as well. The crucial ingredient in Kaledin's proofs is the use of functorial mixed Hodge structures on cohomology groups of complex algebraic varieties and there is no such structure on the cohomology of arbitrary complex varieties. However, we have mixed a Hodge structure on the cohomology of compact K\\\"ahler varieties which is functorial for proper morphisms. It would even be sufficient to consider compact complex varieties of Fujiki class ${\\mathscr C}$, i.e., dominated by a K\\\"ahler manifold.\n\nWith this in mind, Kaledin's proofs work almost literally for compact K\\\"ahler varieties. More precisely, one first shows using mixed Hodge structures that Kaledin's proofs yield analogs of \\cite[Lemma 2.7]{Kal} and \\cite[Lemma 2.9]{Kal} in the compact K\\\"ahler setting. These are the key technical ingredients to prove the stratification and formal product decomposition \\cite[Theorem 2.3]{Kal} as well as \\cite[Theorem 2.5]{Kal} which relates the symplectic and Poisson structure. \nOther than mixed Hodge theory, Kaledin mainly uses Poisson structures, commutative algebra, or direct geometric arguments which all make sense also in our setting.\nFinally, also semi-smallness \\cite[Lemma 2.11]{Kal} is a consequence of geometric properties of the symplectic form and Lemma 2.9 of op. cit.\n\\end{remark}\nWe wish to be a bit more precise regarding the product decomposition on the level of germs, which has not been addressed by Kaledin. It follows easily using a well-known result of Artin.\n\\begin{corollary}\\label{corollary from kaledin}\nLet $X$ be a symplectic variety and let $X_1=X^{\\operatorname{sing}}$ be its singular locus with the reduced structure. Then $\\operatorname{codim}_X X_1^{\\operatorname{sing}} \\geq 4$ and for every point $p \\in U=X {\\ \\setminus \\ } X_1^{\\operatorname{sing}}$ we have an isomorphism of germs $(X,p) {\\ \\cong\\ } ({\\mathbb C}^{\\dim X -2},0) \\times (S,0)$ where $(S,0)$ is an ADE surface singularity.\n\\end{corollary}\n\\begin{proof}\nWith Remark \\ref{remark kaledin} in mind, we infer the claim on the codimension from \\cite[Theorem 2.3]{Kal}. The same theorem also gives the sought-for isomorphism on the level of formal complex spaces. Every local formal isomorphism between complex spaces is the formal completion of an analytic isomorphism by \\cite[Corollary 1.6]{Ar68}.\n\\end{proof}\n\n\nLater on we will need that the very general Hodge structure of a symplectic variety has no automorphisms different from $\\pm {\\rm id}$. As we were not able to find a reference, we include a short proof. In fact, we include a proof -- which is basically taken from \\cite{vGV} -- of a more general result about Mumford--Tate groups, which might be of independent interest, so let us recall this notion. Let $H$ be a finitely generated torsion free ${\\mathbb Z}$-module endowed with a pure Hodge structure of weight $k$ and consider the action $\\rho:\\SS^1\\to \\operatorname{End}(H_{\\mathbb C})$ of the unit circle $\\SS^1 \\subset {\\mathbb C}^\\times$ on $H_{\\mathbb C}=H\\otimes {\\mathbb C}$ defined by $\\rho(z).h:=z^{p-q}h$ for $h\\in H^{p,q}$. One easily checks that this action is defined over ${\\mathbb R}$.\n\n\n\\begin{definition}\nThe Mumford--Tate group of a pure Hodge structure $H$ is the smallest algebraic subgroup ${\\operatorname{MT}}(H) \\subset \\operatorname{GL}_H({\\mathbb R})$ which is defined over ${\\mathbb Q}$ and which contains the image of $\\rho$.\n\\end{definition}\n\nA \\emph{Hodge structure of hyperk\\\"ahler type} is a weight two Hodge structure $H$ with $h^{2,0}=h^{0,2}=1$ together with a nondegenerate bilinear form $q$ on $H$ of signature $(3,{\\rm rk} H -3)$ which is a morphism of Hodge structures and for which $(H^{1,1})^{\\perp_q} = H^{2,0}\\oplus H^{0,2}$ and the restriction of $q$ to $H^{2,0}\\oplus H^{0,2}$ is positive definite. Hodge structures of hyperk\\\"ahler type on $H$ are parametrized by the period domain $$\\Omega_H:=\\{\\omega \\in \\P(H_{\\mathbb C})\\mid (\\omega,\\omega)=0, (\\omega,\\bar\\omega)>0\\}.$$\n\nA Hodge structure of hyperk\\\"ahler type is called \\emph{Mumford--Tate general} if it is not contained in any Hodge locus $\\operatorname{Hdg}_\\alpha \\subset \\Omega_H$ of Hodge classes $\\alpha \\in H^s \\otimes (H^*)^r$ for some $r,s \\in {\\mathbb N}_0$ such that $\\operatorname{Hdg}_\\alpha \\subsetneq \\Omega_H$ is a proper subset.\n\n\\begin{proposition}\\label{proposition van geemen voisin}\nLet $(H,q)$ be a Mumford--Tate general Hodge structure of hyperk\\\"ahler type. Then ${\\operatorname{MT}}(H)=\\operatorname{SO}_H({\\mathbb R})$.\n\\end{proposition}\n\\begin{proof}The proof is basically identical to \\cite[Lemma 9]{vGV} so we only sketch it very briefly. It follows from the definition that ${\\operatorname{MT}}(H) \\subset \\operatorname{SO}_H({\\mathbb R})$. The main ingredient to show equality is that for every Hodge structure $H_\\omega$ corresponding to some $\\omega \\in \\Omega_H$ there is an inclusion of Mumford--Tate groups ${\\operatorname{MT}}(H_\\omega) \\subset {\\operatorname{MT}}(H)$. This is because the Mumford--Tate group may be characterized as the stabilizer group of all Hodge classes $\\alpha \\in H^s \\otimes (H^*)^r$ for all $r,s \\in {\\mathbb N}_0$. Then one proceeds inductively: for $\\alpha \\in H$ and $\\omega \\in \\alpha^\\perp \\subset \\Omega_H$ which is Mumford--Tate general with respect to the Hodge structure $\\alpha^\\perp$ we have that ${\\operatorname{MT}}(H_\\omega) = \\operatorname{SO}_{\\alpha^\\perp}({\\mathbb R})$. The result follows as $\\operatorname{SO}_H({\\mathbb R})$ is the smallest algebraic subgroup containing all $\\operatorname{SO}_{\\alpha^\\perp}({\\mathbb R})$.\n\\end{proof}\n\\begin{corollary}\\label{corollary mumford tate}\nA Mumford--Tate general Hodge structure of hyperk\\\"ahler type only has $\\pm {\\rm id}$ as automorphisms.\n\\end{corollary}\n\\begin{proof}\nThis is a consequence of Proposition \\ref{proposition van geemen voisin} together with the fact that $\\operatorname{End}_{{\\mathbb Q}-\\textrm{HS}}(H_{\\mathbb Q})=\\operatorname{End}(H_{\\mathbb Q})^{\\operatorname{SO}(H_{\\mathbb Q})}= {\\mathbb Q} {\\rm id}_H$.\n\\end{proof}\n\\section{Deformations}\\label{sec defo}\nAs usual in deformation theory, when we speak about the versal deformation ${\\mathscr Z} \\to {\\operatorname{Def}}(Z)$ of a complex space $Z$, then the complex space ${\\operatorname{Def}}(Z)$ has a distinguished point $0\\in{\\operatorname{Def}}(Z)$ such that the fiber of ${\\mathscr Z}\\to {\\operatorname{Def}}(Z)$ over $0$ is $Z$ and we should actually speak about the morphism of space germs $({\\mathscr Z},Z) \\to ({\\operatorname{Def}}(Z),0)$. All deformation theoretic statements have to be interpreted as statements about germs.\n\nLet $X$ be a normal compact complex variety with rational singularities and let $\\pi:Y \\to X$ be a resolution of singularities.\nRecall that by \\cite[Proposition 11.4]{KM} there is a morphism $p:{\\operatorname{Def}}(Y)\\to {\\operatorname{Def}}(X)$ between the Kuranishi spaces of $Y$ and $X$ and also between the versal families ${\\mathscr Y} \\to {\\operatorname{Def}}({Y})$ and ${\\mathscr X} \\to {\\operatorname{Def}}(X)$ fitting in a diagram\n\\begin{equation}\n\\label{eq defo diag}\n\\xymatrix{\n{\\mathscr Y} \\ar[d]\\ar[r]^{P}& {\\mathscr X} \\ar[d]\\\\\n{\\operatorname{Def}}({Y}) \\ar[r]^{p} & {\\operatorname{Def}}(X) \\\\\n}\n\\end{equation}\nRecall from \\cite{FK} that there exists a closed complex subspace ${\\operatorname{Def}}^{\\rm{lt}}(X)\\subset {\\operatorname{Def}}(X)$ parametrizing locally trivial deformations of $X$. More precisely, the restriction of the versal family to this subspace, which by abuse of notation we denote also by ${\\mathscr X} \\to {\\operatorname{Def}}^{\\rm{lt}}(X)$, is a locally trivial deformation of $X$ and is versal for locally trivial deformations of $X$.\n\n\nLet $\\pi:Y \\to X$ be an irreducible symplectic resolution of a compact singular symplectic variety $X$ of dimension $2n$. As $H^0(T_Y)=0$, every versal deformation of $Y$ is universal. We also have $H^0(T_X)=H^0(\\pi_*\\Omega_Y)=0$ by Proposition \\ref{proposition gkkp for symplectic} so that every versal deformation of $X$ is universal. \nLet us fix universal deformations of $X$ and $Y$ and a diagram as \\eqref{eq defo diag}. It is well-known that ${\\mathscr Y} \\to {\\operatorname{Def}}(Y)$ is a family of irreducible symplectic manifolds, at least in the sense of germs, i.e., possibly after shrinking the representative of ${\\operatorname{Def}}(Y)$. If $X$ is projective, then also ${\\mathscr X}\\to{\\operatorname{Def}}(X)$ is a family of irreducible symplectic varieties by \\cite[Theorem 2.2]{Na01}. \nThat this statement also holds without the projectivity assumption is highly probable, but it does not seem to be written somewhere in the literature. We will however not need this and even prove it for locally trivial deformations.\nOur first goal is to describe $p^{-1}({\\operatorname{Def}}^{\\rm{lt}}(X)) \\subset {\\operatorname{Def}}({Y})$. \n\nRecall from the introduction and Lemma \\ref{lemma symplektisch} that \nwe have an orthogonal decomposition\n\\begin{equation}\\label{eq n}\nH^2({Y},{\\mathbb Q})=H^2(X,{\\mathbb Q})\\oplus N \n\\end{equation}\nwhere $N$ corresponds under $\\tilde q$ to the curves contracted by $\\pi$ and put $m:=\\dim N$.\n\\begin{theorem}\\label{theorem deflt is smooth}\nLet $X$ be an irreducible symplectic variety. Then the space ${\\operatorname{Def}}^{\\rm{lt}}(X)$ of locally trivial deformations of $X$ is smooth of dimension $h^{1,1}(X) = h^{1,1}({Y}) - m$. \n\\end{theorem}\n\\begin{proof}\nSmoothness is shown using the $T^1$-lifting principle of Kawamata-Ran \\cite{Ran,Ka1,Ka2}, see also \\cite[\\S 14]{GHJ} or \\cite[VI.3.6]{mydiss} for more detailed introductions. The tangent space to ${\\operatorname{Def}}^{\\rm{lt}}(X)$ at the origin is $H^1(T_X)$ which thanks to the symplectic form can be identified with $H^1(j_*\\Omega_U)$ where $j:U=X^{\\operatorname{reg}} \\to X$ is the inclusion. For the $T^1$-lifting property one has to show that for every infinitesimal locally trivial deformation ${\\mathcal X} \\to S$ of $X$ over an Artinian base scheme $S$ the space $H^1(T_{{\\mathcal X}\/S})$ is locally ${\\mathcal O}_S$-free and compatible with arbitrary base change. We denote again by $j:{\\mathcal U} \\to {\\mathcal X}$ the inclusion of the smooth locus of ${\\mathcal X} \\to S$. Take an extension $\\sigma \\in H^0({\\mathcal U},j_* \\Omega_{{\\mathcal X}\/S}^2)$ of the symplectic form on $U\\subset X$. It remains nondegenerate and hence yields an isomorphism $T_{{\\mathcal X}\/S}\\to j_*\\Omega_{{\\mathcal X}\/S}$, consequently also $H^1(T_{{\\mathcal X}\/S}){\\ \\cong\\ } H^1(\\Omega_{{\\mathcal X}\/S})$ which is free by Proposition \\ref{proposition gkkp for symplectic} and Lemma \\ref{lemma hodge relativ}. Thus, it satisfies the $T^1$-lifting property and thus the space ${\\operatorname{Def}}^{\\rm{lt}}(X)$ is smooth.\nIt follows from Corollary \\ref{corollary hodge} that $\\dim H^1(T_X) = h^{1,1}(X)$ which shows the dimension statement and completes the proof.\n\\end{proof}\n\\begin{corollary}\\label{corollary deformation remains isv}\nLet $\\pi:Y \\to X$ be an irreducible symplectic resolution and consider the induced morphism $p:{\\operatorname{Def}}(Y)\\to {\\operatorname{Def}}(X)$. Then ${\\operatorname{Def}}^{\\rm{lt}}(X)$ is contained in the image of $p$. Moreover, a small locally trivial deformation of an irreducible symplectic variety is again an irreducible symplectic variety.\n\\end{corollary}\n\\begin{proof}\nFor the first claim, it suffices to note that the tangent space $H^1(X,T_X)$ to ${\\operatorname{Def}}^{\\rm{lt}}(X)$ by Proposition \\ref{proposition gkkp for symplectic} is a subspace to the tangent space $H^1(Y,T_Y)$ to ${\\operatorname{Def}}(Y)$ and as both spaces are smooth, every locally trivial deformation of $X$ induces a deformation of $Y$. A small deformation of a K\\\"ahler space with rational singularities remains K\\\"ahler by \\cite[Proposition 5]{Na01a} and again by Proposition \\ref{proposition gkkp for symplectic} the symplectic form extends sideways.\n\\end{proof}\n\\begin{corollary}\\label{corollary projective}\nEvery irreducible symplectic resolution $\\pi:Y \\to X$ has a small locally trivial deformation to an irreducible symplectic resolution $\\pi:Y' \\to X'$ where $Y'$ and $X'$ are projective.\n\\end{corollary}\n\\begin{proof}\nThe subspace $N$ from \\eqref{eq n} is negative definite and of Hodge type (1,1) by Lemma \\ref{lemma symplektisch}. As $Y$ is an irreducible symplectic manifold, the signature of $q_Y$ on $H^{1,1}(Y,{\\mathbb R})$ is $(1,b_2(Y)-3)$ so that by Theorem \\ref{theorem deflt is smooth} the contraction $\\pi:Y\\to X$ deforms in a positive dimensional locally trivial family. By \\cite[Proposition 26.6]{GHJ} arbitrarily close to $\\pi:Y\\to X$ there is a small deformation $Y'\\to X'$ of $\\pi$ such that $Y'$ is projective. As $X'$ has rational singularities, see e.g. \\cite[Theorem 3.3.3]{Kirschner}, and is K\\\"ahler, it is projective by Namikawa's result \\cite[Corollary 1.7]{Na02}.\n\\end{proof}\n\nFor $N\\subset H^2(Y,{\\mathbb Q})$ as in \\eqref{eq n} let us denote by ${\\operatorname{Def}}({Y},N)\\subset {\\operatorname{Def}}({Y})$ the subspace of those deformations of ${Y}$ where all line bundles on $Y$ with first Chern class in $N$ deform along. This is also the subspace where $N$ remains of type $(1,1)$. It is a smooth submanifold of ${\\operatorname{Def}}({Y})$ of codimension $m=\\dim N$ by \\cite[1.14]{Huy99}.\n\\begin{proposition}\\label{prop defo}\nLet $\\pi: {Y}\\to X$ be an irreducible symplectic resolution. Let ${\\mathscr Y} \\to {\\operatorname{Def}}({Y},N)$ and ${\\mathscr X} \\to{\\operatorname{Def}}^{\\rm{lt}}(X)$ be the (restrictions of the) universal deformations. Then there is a diagram \n\\begin{equation}\\label{eq diag lt}\n\\xymatrix{\n{\\mathscr Y} \\ar[d]\\ar[r]^P& {\\mathscr X} \\ar[d]\\\\\n{\\operatorname{Def}}({Y},N) \\ar[r]^{p} & {\\operatorname{Def}}^{\\rm{lt}}(X) \\\\\n}\n\\end{equation}\nwhere $p$ is the restriction of the natural morphism $p:{\\operatorname{Def}}({Y})\\to{\\operatorname{Def}}(X)$. Moreover, $p$ is an isomorphism.\n\\end{proposition}\n\\begin{proof}\nWe have to show that $p^{-1}({\\operatorname{Def}}^{\\rm{lt}}(X)) = {\\operatorname{Def}}({Y},N)$. We know by Corollary \\ref{corollary deformation remains isv} that for each $t\\in {\\operatorname{Def}}(Y)$ mapping to ${\\operatorname{Def}}^{\\rm{lt}}(X)$ the morphism $P_t:{\\mathscr Y}_t \\to {\\mathscr X}_{p(t)}$ is an irreducible symplectic resolution. By Lemma \\ref{lemma hodge relativ} and Proposition \\ref{proposition gkkp for symplectic} the second cohomology of locally trivial deformations of $X$ form a vector bundle on ${\\operatorname{Def}}^{\\rm{lt}}(X)$, in particular, $h^{1,1}({\\mathscr X}_{p(t)})=h^{1,1}(X)$. Thus, by the decomposition $H^2({Y},{\\mathbb C})=N\\oplus H^2(X,{\\mathbb C})$ from Lemma \\ref{lemma symplektisch} we see that the space $N_1({\\mathscr Y}_t\/{\\mathscr X}_{p(t)})$ of curves contracted by $P_t$ has dimension $m$ for all $t\\in p^{-1}({\\operatorname{Def}}^{\\rm{lt}}(X))$. As $N$ is the orthogonal complement of $H^2(X,{\\mathbb C})$, it also varies in a local system. This shows the sought-for equality.\n\nOne shows as in \\cite{LP} that $p$ is an isomorphism, we only sketch this: It suffices to show that the differential $T_{p,0}:T_{{\\operatorname{Def}}({Y},N),0}\\to T_{{\\operatorname{Def}}^{\\rm{lt}}(X),0}=H^1(T_X)$ is an isomorphism. \nWe know from \\cite[(1.8) and (1.14)]{Huy99} that $T_{{\\operatorname{Def}}({Y},N),0} \\subset H^1(T_{Y})$ can be identified with the orthogonal complement to $N \\subset H^{1,1}({Y})$ under the isomorphism $H^1(T_{Y}) {\\ \\cong\\ } H^{1,1}({Y})$ induced by the symplectic form. In other words, $T_{{\\operatorname{Def}}({Y},N),0} {\\ \\cong\\ } H^{1,1}(X) \\subset H^{1,1}({Y})$. That this is mapped to $H^1(T_X){\\ \\cong\\ } H^1(j_*\\Omega_U)$ under the restriction of $T_{p,0}:H^1(T_{Y})\\to \\operatorname{Ext}^1(\\Omega_X,{\\mathcal O}_X)$ is easily verified.\n\\end{proof}\nThe independence of the Beauville--Bogomolov--Fujiki pairing on the cohomology of a singular symplectic variety has been proven by Namikawa, see \\cite[Theorem 8]{Na01a}. In combination with the preceding result and the version for smooth varieties we immediately deduce the following variant of the local Torelli theorem for locally trivial deformations.\n\\begin{corollary}[Local Torelli Theorem]\\label{corollary local torelli}\nLet $X$ be an irreducible symplectic variety, let $q_X$ be its Beauville--Bogomolov--Fujiki form, and let \n\\begin{equation}\\label{eq local period domain}\n\\Omega(X):=\\{[\\sigma]\\in \\P(H^2(X,{\\mathbb C}))\\mid q_X(\\sigma)=0, q_X(\\sigma,\\bar\\sigma)>0\\} \\subset \\P(H^2(X,{\\mathbb C})) \n\\end{equation}\nbe the period domain for $X$. If $f:{\\mathscr X} \\to {\\operatorname{Def}}^{\\rm{lt}}(X)$ denotes the universal locally trivial deformation of $X$ and $X_t:=f^{-1}(t)$, then the period map \n\\[\n\\wp:{\\operatorname{Def}}^{\\rm{lt}}(X) \\to \\Omega(X), \\quad t \\mapsto H^{2,0}(X_t)\n\\]\nis a local isomorphism.\\qed\n\\end{corollary}\nIt should be mentioned that Namikawa has proven local Torelli theorem for certain singular projective symplectic varieties in loc. cit. and this has been generalized by Kirschner \\cite[Theorem 3.4.12]{Kirschner} to a larger class of varieties. In particular, Kirschner has proven a local Torelli theorem in the context of symplectic compact K\\\"ahler spaces. Let us emphasize, however, that neither Namikawa's version nor Kirschner's is what we need as they do not make any statement about local triviality. Also observe that our version of local Torelli---unlike Namikawa's or Kirschner's---does not make any assumption on the codimension of the singular locus of the variety $X$.\n\\begin{remark}\\label{remark identify base}\nWe will from now on identify the spaces ${\\operatorname{Def}}({Y},N)$ and ${\\operatorname{Def}}^{\\rm{lt}}(X)$ via the morphism $p$ if we are given a birational contraction. \\end{remark}\n\nThe following result is needed in the proof of this section's main theorem. For a variety $X$ we denote by ${\\operatorname{Cl}}(X)$ the group of Weil divisors modulo linear equivalence on it and we write ${\\operatorname{Cl}}(X)_{\\mathbb Q}:={\\operatorname{Cl}}(X) \\otimes_{\\mathbb Z} {\\mathbb Q}$.\n\n\\begin{proposition}\\label{proposition qcartier}\nConsider a diagram $X \\ot[\\pi] Y \\to[\\pi'] X'$ of normal projective varieties where $Y$ is smooth, $\\pi, \\pi'$ are birational and $\\pi^* H^2(X,{\\mathbb Z})\\subset (\\pi')^*H^2(X',{\\mathbb Z})\\oplus M$ as subsets of $H^2(Y,{\\mathbb Z})$ where $M \\subset H^2(Y,{\\mathbb Z})$ is the subspace generated by $\\pi'$-exceptional divisors.\nSuppose that the birational map $\\phi:= \\pi' \\circ \\pi^{-1}: X \\dashrightarrow X'$ is an isomorphism in codimension one. \nThen the map $\\phi_*: {\\operatorname{Cl}}(X)_{\\mathbb Q} \\to {\\operatorname{Cl}}(X')_{\\mathbb Q}$ sends ${\\mathbb Q}$-Cartier divisors to ${\\mathbb Q}$-Cartier divisors. \n\\end{proposition}\n\\begin{proof}\n Take a Cartier divisor $D$ on $X$ and write $\\pi^* D=\\tilde D + E$ where $\\tilde D$ is the strict transform and $E$ is $\\pi$-exceptional. By assumption, we have $\\pi^* D = (\\pi')^*D' + F$ for some Cartier divisor $D'$ on $X'$ and a $\\pi'$-exceptional divisor $F$ on $Z$. Then $\\pi'_*\\pi^* D = D'$ is Cartier. Now it suffices to note that $E$ is also $\\pi'$-exceptional so that $D'=\\pi'_* \\tilde D = \\phi_* D$ and the claim follows.\n\\end{proof}\n\nNote that is not necessary that $\\phi$ be an isomorphism in codimension one. It would be sufficient that every $\\pi$-exceptional divisor is also $\\pi'$-exceptional.\n\n\\begin{theorem}\\label{theorem huybrechts}\nLet $X$ and $X'$ be projective symplectic varieties, suppose that $X$ has a resolution of singularities $\\pi:{Y}\\to X$ by an irreducible symplectic manifold ${Y}$, and\nlet $\\phi:X\\dashrightarrow X'$ be a birational map which is an isomorphism in codimension one. Then, there exists a symplectic resolution $\\pi':{Y}' \\to X'$ by an irreducible symplectic manifold ${Y}'$ and \nthe following are equivalent.\n\\begin{enumerate}\n \\item \\label{aeq eins} There is an isomorphism $\\varphi:{\\operatorname{Def}}^{\\rm{lt}}(X)\\to {\\operatorname{Def}}^{\\rm{lt}}(X')$ such that for every $t\\in {\\operatorname{Def}}^{\\rm{lt}}(X)$ we have a birational map $\\phi_t: {\\mathscr X}_t \\dashrightarrow {\\mathscr X}'_{\\varphi(t)}$ which is an isomorphism in codimension one. For general $t \\in {\\operatorname{Def}}^{\\rm{lt}}(X)$, the map $\\phi_{t}$ is an isomorphism. In particular, $X$ and $X'$ are locally trivial deformations of one another.\n\\item \\label{aeq drei} The map $\\tilde\\phi^*: H^2({Y}',{\\mathbb C}) \\to H^2({Y},{\\mathbb C})$ induced by the birational map $\\tilde\\phi:{Y} \\dashrightarrow {Y}'$ is an isomorphism that sends $H^2(X',{\\mathbb C})$ to $H^2(X,{\\mathbb C})$. \n\\end{enumerate}\nIf these conditions are satisfied and if we denote $\\wp:{\\operatorname{Def}}^{\\rm{lt}}(X)\\to \\Omega(X)$, $\\wp':{\\operatorname{Def}}^{\\rm{lt}}(X')\\to \\Omega(X')$ the associated period maps, then the isomorphism $\\varphi$ from \\eqref{aeq eins} coincides with $(\\wp')^{-1}\\circ \\wp$ where we identify $\\Omega(X)$ and $\\Omega(X')$ through the isomorphism induced by $\\tilde\\phi^*$.\n\\end{theorem}\n\\begin{proof}\nThe statement on the existence of a symplectic resolution is well known, we include the references for convenience. Let $\\pi':{Y}'\\to X'$ be a ${\\mathbb Q}$-factorial terminalization, which exists by \\cite[Corollary 1.4.3]{BCHM}.\nIt follows from \\cite[Theorem 1]{Kaw08} and \\cite[Example (flops) p.98]{Na06} that ${Y}'$ is an irreducible symplectic manifold as well.\n\nThe birational map $\\tilde \\phi:{Y} \\dashrightarrow {Y}'$ between irreducible symplectic manifolds induces an isomorphism between $H^2({Y},{\\mathbb Z})$ and $H^2({Y}',{\\mathbb Z})$ compatible with the Beauville--Bogomolov--Fujiki forms and the local Torelli theorem gives an isomorphism ${\\operatorname{Def}}({Y})\\to {\\operatorname{Def}}({Y}')$. \nLet us consider the orthogonal decompositions $H^2({Y},{\\mathbb Q})=H^2(X,{\\mathbb Q})\\oplus N$ and $H^2({Y}',{\\mathbb Q})=H^2(X',{\\mathbb Q})\\oplus N'$ as in \\eqref{eq n}.\nCondition \\eqref{aeq drei} is equivalent to saying that the isomorphism ${\\operatorname{Def}}({Y})\\to {\\operatorname{Def}}({Y}')$ restricts to an isomorphism ${\\operatorname{Def}}({Y},N) \\to {\\operatorname{Def}}({Y}',N')$ and therefore yields an isomorphism $\\varphi:{\\operatorname{Def}}^{\\rm{lt}}(X) \\to {\\operatorname{Def}}^{\\rm{lt}}(X')$ via Proposition~\\ref{prop defo}. \nIn order to deduce \\eqref{aeq eins} from \\eqref{aeq drei}, it remains to show the existence of a birational map $\\phi_t: {\\mathscr X}_t \\dashrightarrow {\\mathscr X}'_{\\varphi(t)}$ for $t\\in {\\operatorname{Def}}^{\\rm{lt}}(X)$ which is an isomorphism at the general point and an isomorphism in codimension one for every point. In this situation we will identify the spaces $$S:={\\operatorname{Def}}^{\\rm{lt}}(X){\\ \\cong\\ }{\\operatorname{Def}}^{\\rm{lt}}(X'){\\ \\cong\\ } {\\operatorname{Def}}({Y},N){\\ \\cong\\ } {\\operatorname{Def}}({Y}',N')$$ and consider the universal families\n\\[\n {\\mathscr Y} \\to {\\mathscr X} \\to S \\ot {{\\mathscr X}'} \\ot {{\\mathscr Y}'}\n\\]\nfrom Proposition \\ref{prop defo}. For a point $t\\in S$ the fibers ${\\mathscr Y}_t$ and ${\\mathscr Y}'_{t}$ are deformation equivalent (by Huybrechts' theorem, see \\cite[Theorem 2.5]{Huy}) and have the same periods, hence they are birational by Verbitsky's global Torelli theorem \\cite[Theorem 1.17]{V13}. \nWe obtain a birational map ${\\mathscr X}_{t}\\dashrightarrow {\\mathscr X}'_{t'}$ for all $t\\in S$. \nAs the deformations of $X$ and $X'$ are locally trivial, their exceptional divisors deform all over $S$ and are contracted under the morphisms ${\\mathscr Y}_t \\to {\\mathscr X}_{t}$ and ${\\mathscr Y}'_t \\to {\\mathscr X}'_{t}$ for all $t$.\nThis follows just as in the proof of \\cite[Proposition 2.3]{LP} if we use that the $H^2({\\mathscr X}'_{t},{\\mathbb Z})$ and $H^2({\\mathscr X}_{t},{\\mathbb Z})$ form local systems, more precisely, local subsystems of those formed by $H^2({\\mathscr Y}'_{t},{\\mathbb Z})$ and $H^2({\\mathscr Y}_{t},{\\mathbb Z})$. Hence, the $\\phi_t$ are isomorphisms in codimension one.\nIn order to show that $\\phi_t$ is an isomorphism for general $t$, it is sufficient to consider projective deformations of $X$. Projective deformations of $X$ are dense over every positive dimensional subvariety of $\\deflt$ as the same holds for ${Y}$, see \\cite[Proposition 26.6]{GHJ}. For a very general $t \\in S$ corresponding to projective ${\\mathscr X}_t$ and ${\\mathscr X}'_t$ both varieties have Picard number one. Moreover, by Proposition \\ref{proposition qcartier} the pullback of an ample line bundle on ${\\mathscr X}'_t$ under the rational map $\\phi_t$ is again an ample line bundle and thus $\\phi_t$ is an isomorphism.\n\nFor the converse, consider $\\phi: X \\dashrightarrow X'$. The induced birational map $\\tilde\\phi:Y \\dashrightarrow Y'$ induces a Hodge isometry $\\tilde\\phi^*: H^2({Y}',{\\mathbb C}) \\to H^2({Y},{\\mathbb C})$ which maps $H^2(X,{\\mathbb C})_{\\rm tr}$ to $H^2(X',{\\mathbb C})_{\\rm tr}$. By assumption $\\tilde\\phi^*$ is an isomorphism in codimension one, so it fulfills the hypotheses of Proposition \\ref{proposition qcartier}. Thus, $H^2(X,{\\mathbb C})$ is sent to $H^2(X',{\\mathbb C})$ and the claim follows.\n\nThe last statement is clear from the proof.\n\\end{proof}\n\nWe denote by $\\Delta=\\{z\\in {\\mathbb C}\\mid \\abs{z}\\leq 1\\}$ the complex unit disk and by $\\Delta^\\times:=\\Delta{\\ \\setminus \\{0\\} }$ the complement of the origin.\n\n\\begin{theorem}\\label{theorem huybrechts strong}\nLet $X$ and $X'$ be projective symplectic varieties, suppose that $X$ has resolution of singularities $\\pi:{Y}\\to X$ by an irreducible symplectic manifold ${Y}$, and\nlet $\\phi:X\\dashrightarrow X'$ be a birational map which is an isomorphism in codimension one such that $\\phi_*: {\\operatorname{Cl}}(X)_{\\mathbb Q} \\to {\\operatorname{Cl}}(X')_{\\mathbb Q}$ sends ${\\mathbb Q}$-line bundles to ${\\mathbb Q}$-line bundles. Then there are one parameter deformations $f:{\\mathscr X} \\to \\Delta$, $f':{\\mathscr X}' \\to \\Delta$ such that ${\\mathscr X}$ and ${\\mathscr X}'$ are birational over $\\Delta$ and such that ${\\mathscr X}^* = f^{-1}(\\Delta^\\times) {\\ \\cong\\ } (f')^{-1}(\\Delta^\\times) = ({\\mathscr X}')^* $. \n\\end{theorem}\n\\begin{proof}\n The argument of Huybrechts works in this context almost literally, see \\cite[Theorem 1.1]{LP} for the necessary changes.\n\\end{proof}\n\nFollowing Voisin \\cite[Definition 0.6]{Vo15}, we call a subvariety $P\\subset Y$ of an irreducible symplectic variety an \\emph{algebraically coisotropic subvariety} if it is coisotropic and admits a rational map $\\phi: P \\dashrightarrow B$ onto a variety of dimension $\\dim Y - 2\\cdot\\operatorname{codim} P$ such that the restriction of the symplectic form to $P$ satisfies $\\sigma\\vert_P=\\phi^*\\sigma_B$ for some 2-form $\\sigma_B$ on $B$. \n\n\\begin{proposition}\\label{proposition cmsb}\nEvery irreducible component $P$ of the exceptional locus of an irreducible symplectic resolution $Y\\to X$ is algebraically coisotropic and the coisotropic fibration of $P$ is given by $\\pi:P\\to B:=\\pi(P)$. In particular, it is holomorphic. Moreover, the general fiber of $\\pi\\vert_P$ is rationally connected.\n\\end{proposition}\n\\begin{proof}\nBy Corollary \\ref{corollary projective} we may assume that $X$ and $Y$ are projective. Let $F$ denote a resolution of singularities of the general fiber of $P\\to B$. By \\cite[Lemma 2.9]{Kal} it follows that the pullback of the symplectic form $\\sigma$ of $X$ to $F$ vanishes identically so that $F$ is isotropic. It remains to show that $\\dim B = 2(n - \\dim F)$ where $\\dim X=2n$.\n This is a consequence of \\cite[Theorem 1.2]{Wierzba}. \n\nTo prove rational connectedness, we use that every rational curve $C$ on a symplectic variety the morphism $\\nu:\\P^1 \\to C \\subset X$ obtained by normalization and inclusion deforms in a family of dimension at least $2n-2$, see \\cite[Example 5.2]{Ran2} or \\cite[Proposition 3.1]{CP}. Being an exceptional locus $P$ is known to be uniruled. Let $H$ be an ample divisor on $Y$ and take an irreducible rational curve $C$ on $P$ contracted by $\\pi$ such that the intersection product $H.C$ is minimal among all rational curves on $P$. \nIn the Chow scheme of $Y$ we look at an irreducible component ${\\textrm{Ch}}$ containing $[C]$ of the locus parametrizing rational curves. \nBy what we noted above $\\dim {\\textrm{Ch}}\\geq 2n-2$ and by minimality of $H.C$ all points in $U$ correspond to irreducible and reduced rational curves.\nLet $U \\subset {\\textrm{Ch}} \\times Y$ be the graph of the universal family of cycles and denote by $p:U \\to {\\textrm{Ch}}$ and $q:U \\to Y$ the projections. As $C$ is contracted by $\\pi$, the curves in ${\\textrm{Ch}}$ cannot move out of the exceptional locus (this would e.g. change the intersection with a pullback of an ample divisor from $X$) and as $U$ is irreducible, we have in fact $U \\subset {\\textrm{Ch}} \\times P$. Moreover, by the Rigidity Lemma, $U\\to {\\textrm{Ch}}$ is a family of curves in the fibers of $P \\to B$. Now a simple dimension count together with the fact that a positive dimensional family of rational curves with two basepoints has to have reducible or nonreduced members (Bend and Break, see e.g. \\cite[Theorem (5.4.2)]{Ko}) imply that through the general point of a general fiber $F$ of $P\\to B$ there is a family of rational curves without further basepoints of dimension $i-1$. Consequently, $F$ is rationally connected.\n\\end{proof}\n\n\n\\begin{remark}\nRational connectedness of $F$ would follow from \\cite[Theorem 9.1]{CMSB} the proof of which however seems to be incomplete. Instead, it might also be possible to use \\cite[Theorem 2.8 (2)]{CMSB} and the well-known fact that a variety is rationally connected if and only if it contains a very free rational curve, see e.g. \\cite[Ch IV, 3.7 Theorem]{Ko}.\n\\end{remark}\n\n\\begin{remark}\n Rational chain connectedness follows from the much stronger result \\cite[Corollary 1.5]{HM}. This notion coincides for smooth varieties with rational connectedness, however, this is not the case for singular varieties. The cone over an elliptic curve is the easiest example of a variety which is rationally chain connected but not rationally connected. \n\\end{remark}\n\nRecall from \\cite[Theorem 1.1]{LP2} that given an algebraically coisotropic subvariety with almost holomorphic coisotropic fibration $\\phi$ whose generic fiber $F$ is smooth, then $F$ deforms all over its Hodge locus $\\operatorname{Hdg}_F \\subset {\\operatorname{Def}}(Y)$. Moreover, if for $t \\in \\operatorname{Hdg}_F$ we denote by $Y_t$ the corresponding deformation of $Y$, then the deformations of $F$ inside $Y_t$ cover an algebraically coisotropic subvariety $P_t \\subset Y_t$ with $F_t$ as a generic fiber of the coisotropic fibration. It seems, however, unclear how to relate the cycle class of $P_t$ with that of $P$ let alone to show that $P_t$ is a flat deformation. \n\nIn the context of birational contractions of symplectic varieties (in dimension $\\geq 4$ at least) it is rather common that the generic fiber of the exceptional locus over its image is smooth. Thus, it is worthwhile to mention that the main result of \\cite{LP2} can be strengthened in this special situation.\nBut first we need some notation. \n\nLet $F \\subset Y$ be a closed subvariety in an irreducible symplectic manifold. If ${\\mathscr Y} \\to {\\operatorname{Def}}(Y)$ denotes the universal deformation we let ${\\mathscr H} \\to {\\operatorname{Def}}(Y)$ be the union of all those components of the relative Hilbert scheme (or Douady space) of ${\\mathscr Y}$ over ${\\operatorname{Def}}(Y)$ which contain $[F]$. We define the closed subspace ${\\operatorname{Def}}(Y,F) \\subset {\\operatorname{Def}}(Y)$ to be the scheme theoretic image of ${\\mathscr H} \\to {\\operatorname{Def}}(Y)$; this is the space of deformations of $Y$ that contain a deformation of $F$. \n\n\\begin{theorem}\\label{theorem pacienza}\nLet $\\pi:Y\\to X$ be an irreducible symplectic resolution, let $P\\subset Y$ be the exceptional locus of $\\pi$, put $B:=\\pi(P)$, and let ${\\mathscr Y} \\to {\\mathscr X}$ be the restriction of the universal deformation of $Y\\to X$ over ${\\operatorname{Def}}(Y,N)$. Suppose that $P$ is irreducible and that a general fiber $F$ of $\\pi:P\\to B$ is smooth. Then we have ${\\operatorname{Def}}(Y,N) \\subset {\\operatorname{Def}}(Y,F)$ and the Hodge locus $\\operatorname{Hdg}_P$ of $P$ contains ${\\operatorname{Def}}(Y,N)$.\n\\end{theorem}\n\\begin{proof} \nLet ${\\mathscr F} \\to {\\mathscr H} \\to {\\operatorname{Def}}(F,Y)$ be the universal deformation of $F$ over the closed subspace ${\\mathscr H}$ of the relative Hilbert scheme of ${\\mathscr Y} \\to {\\operatorname{Def}}(Y,N)$. \nBy Proposition \\ref{proposition cmsb} the variety $P$ is algebraically coisotropic and the fibers of $\\pi:P\\to B$ are rationally connected so that $H^1(F,{\\mathcal O}_F)=0$ and \\cite[Theorem 1.1]{LP2} can be applied. We deduce that ${\\operatorname{Def}}(F,Y)$ and ${\\mathscr H}\\to{\\operatorname{Def}}(F,Y)$ are smooth at $0$ respectively $[F]$. In particular, ${\\mathscr H}$ is irreducible. Moreover, by \\cite[Corollary 1.2 and 1.3]{LP2} the period map identifies ${\\operatorname{Def}}(F,X)$ with $Q \\cap \\P(K)$ where $Q \\subset \\P(H^2(X,{\\mathbb C}))$ is the period domain of the irreducible symplectic manifold $Y$ and $K=\\ker(H^2(Y,{\\mathbb C})\\to H^2(F,{\\mathbb C}))$. If $p\\in B$ denotes the point with $F=\\pi^{-1}(b)$, then by commutativity of $$\\xymatrix{F \\ar[r]\\ar[d]& Y \\ar[d]\\\\ \\{p\\} \\ar[r] & X\\\\}$$ it follows that we have $H^2(X,{\\mathbb C}) \\subset K$ and hence using $H^2(X,{\\mathbb C})^\\perp = N$ and the period map once more we obtain ${\\operatorname{Def}}(Y,N) \\subset {\\operatorname{Def}}(F,Y)$.\n\nIn order to show that $P$ remains a Hodge class all over ${\\operatorname{Def}}(Y,N)$ we will construct flat families ${\\mathscr P}_\\Delta \\to \\Delta$ over curves $\\Delta \\subset {\\operatorname{Def}}(Y,N)$ passing through the origin such that the cycle underlying the central fiber ${\\mathscr P}_{\\Delta,0}$ is a multiple of $P$. To this end we replace ${\\mathscr F} \\to {\\mathscr H}$ as well as ${\\mathscr Y} \\to {\\mathscr X}$ by their restrictions to a given smooth curve germ $\\Delta \\subset {\\operatorname{Def}}(Y,N)$ and obtain morphisms\n$ \\xymatrix{\n{\\mathscr H} & \\ar[l] {\\mathscr F} \\ar[r]& {\\mathscr Y} \\ar[r] &{\\mathscr X}\\\\\n }$\nover $\\Delta$. The map in the middle is induced by the projection to the second factor of ${\\mathscr F} \\subset {\\mathscr H} \\times {\\mathscr Y}$. As $\\Delta$ is smooth and smoothness is stable under base change we may still assume that ${\\mathscr H}$, ${\\mathscr H} \\to \\Delta$ and ${\\mathscr F} \\to {\\mathscr H}$ are smooth at $[F]$ respectively in a neighborhood of $F\\subset {\\mathscr F}$. In particular, there is still a unique irreducible component of ${\\mathscr H}$ passing through $[F]$ and by shrinking the representative of ${\\operatorname{Def}}(F,Y)$ and throwing away components of ${\\mathscr H}$ we may assume that ${\\mathscr H}$ is irreducible.\n\nOn the other hand, as ${\\mathscr X} \\to \\Delta$ is a locally trivial deformation, it induces a flat (even locally trivial) deformation of all components of its singular locus (with the reduced structure). Let ${\\mathscr B} \\to {\\operatorname{Def}}(Y,N)$ be the so induced deformation of $B$. Then ${\\mathscr B} \\subset {\\mathscr X}$ is an irreducible (and reduced) subspace.\nIf we knew that also ${\\mathscr Y} \\to {\\mathscr X}$ were a locally trivial deformation of $Y\\to X$, the claim would follow immediately. As we cannot prove this so far, cf. Question \\ref{question contraction deforms locally trivially}, we have to argue differently.\n\nWe take the unique closed irreducible and reduced subspace ${\\mathscr F}' \\subset {\\mathscr F}$ that coincides with ${\\mathscr F}$ in a neighborhood of $F$. As $\\rho:{\\mathscr F}' \\to {\\mathscr H}$ is proper it therefore is surjective as well. If we take the Stein factorization, ${\\mathscr F}'\\to \\tilde{\\mathscr H} \\to {\\mathscr H}$, then\nby the Rigidity Lemma (see e.g. \\cite[Lemma 1.15]{Debarre}) there is a commutative diagram\n\\[\\xymatrix{\n{\\mathscr F} \\ar[d]\\ar[r]& {\\mathscr Y}\\ar[d]\\\\\n\\tilde{\\mathscr H} \\ar[r]&{\\mathscr X}\\\\\n}\\] \nNote that $\\tilde{\\mathscr H} \\to {\\mathscr H}$ is finite, birational, and an isomorphism over $[F]\\in {\\mathscr H}$ and that $\\tilde{\\mathscr H}$ is irreducible. Moreover, the image of $\\tilde{\\mathscr H} \\to {\\mathscr X}$ coincides in a neighborhood of $[f]\\in \\tilde{\\mathscr H}$ with the closed subvariety ${\\mathscr B} \\subset{\\mathscr X}$ thanks to the smoothness of ${\\mathscr F} \\to {\\mathscr H}$ in a neighborhood of $F\\subset {\\mathscr F}$. Invoking the irreducibility of $\\tilde{\\mathscr H}$ and ${\\mathscr B}$ we conclude that we have $\\tilde{\\mathscr H}{{\\twoheadrightarrow}} {\\mathscr B} \\subset {\\mathscr X}$. We define ${\\mathscr P} = {\\mathscr P}_\\Delta \\subset {\\mathscr Y}$ to be the image of ${\\mathscr F}' \\to {\\mathscr Y}$. The variety ${\\mathscr F}'$ being irreducible the same holds true for ${\\mathscr P}$ and hence the induced map $\\rho:{\\mathscr P} \\to \\Delta$ is flat. It remains to show that $P\\subset {\\mathscr P}$ is the unique component of the central fiber ${\\mathscr P}_0$ of $\\rho$ of dimension $\\dim P$. This follows from the irreducibility of $P$ by invoking the Rigidity Lemma once more.\n\\end{proof}\n\nRecall from \\cite[Definition 1.5]{Vo15} that a cohomology class $p \\in H^{2i}(Y,{\\mathbb C})$ on a smooth symplectic variety is called \\emph{coisotropic} if it is a Hodge class and $[\\sigma]^{n-i+1}\\cup p = 0$ in $H^{2n+2}(Y,{\\mathbb C})$ where $\\sigma$ is the symplectic form on $Y$. We refer to Huybrechts' article \\cite{Hu14} for the notion of a constant cycle subvariety. This is roughly speaking a subvariety of a given variety all of whose points have the same cycle class in the ambient variety.\n\n\\begin{corollary}\nIn the situation of Theorem \\ref{theorem pacienza}, the class of $[P]$ remains an effective coisotropic Hodge class all over $S={\\operatorname{Def}}(Y,N)$. Moreover, there are varieties $P_t \\subset Y_t$ for each $t\\in S$ representing (a multiple of) $[P]$ which are algebraically coisotropic with rationally connected fibers. In particular, the fibers are constant cycle subvarieties of $Y_t$.\n\\end{corollary}\n\\begin{proof}\nIt follows from the preceding theorem that the class $[P_t]$ of the subvarieties $P_t\\subset Y_t$ for a deformation $Y_t$ of $Y$ with $t \\in {\\operatorname{Def}}(Y,N)$ is (a multiple of) $[P]$. The proof showed moreover that $P_t$ is covered by deformations of the general fiber $F$ of the coisotropic fibration of $P$. The claim follows as $F$ was rationally connected and rational connectedness is known to be invariant under deformations (for smooth varieties).\n\\end{proof}\n\n \n\n\\begin{question}\\label{question contraction deforms locally trivially} Let $X$ be a symplectic variety, $Y\\to X$ a symplectic resolution, $N=\\tilde q^{-1}(N_1(Y\/X))$ (cf. Lemma \\ref{lemma symplektisch}), and\n${\\mathscr Y}\\to{\\mathscr X}$ be the morphism between universal families over $S:={\\operatorname{Def}}(Y,N)={\\operatorname{Def}}^{\\mathrm{lt}}(X)$ from diagram \\eqref{eq diag lt}. In this case we ask:\n\n\\emph{Is ${\\mathscr Y} \\to {\\mathscr X}$ a locally trivial deformation of $Y \\to X$? }\n\n\\noindent Note that if this were the case, the whole diagram \n\\[\\xymatrix{\n{\\mathscr E} \\ar[d]\\ar[r]& {\\mathscr Y}\\ar[d]\\\\\n{\\mathscr S} \\ar[r]&{\\mathscr X}\\\\\n}\\] \nof complex spaces over $S$ where ${\\mathscr S} \\to S$ is the singular locus of ${\\mathscr X} \\to S$ and ${\\mathscr E} \\to S$ is the exceptional locus of ${\\mathscr Y} \\to {\\mathscr X}$ were a locally trivial (in particular flat) deformation over $S$ of its central fiber.\n\\end{question}\n\n\n\n\n\n\n\n\n\\section{Period maps and monodromy groups}\\label{sec monodromy}\n\n\\subsection{Marked moduli spaces} \n\nRecall that given a lattice $\\Lambda$ of signature $(3,{\\rm rk}\\,\\Lambda-3)$, we define an analytic coarse moduli space $\\mathfrak{M}_{\\Lambda}$ of $\\Lambda$-marked irreducible holomorphic symplectic manifolds $(Y,\\mu)$ where $\\mu:\\Lambda\\xrightarrow{\\cong}H^2(Y,{\\mathbb Z})$ is a marking by gluing together the Kuranishi families. Likewise, we define $\\mathfrak{M}^{{\\rm{lt}}}_\\Lambda$ to be the analytic space obtained by gluing together the locally trivially Kuranishi spaces of $\\Lambda$-marked symplectic varieties. Note that $\\mathfrak{M}_\\Lambda$ itself is a disjoint union of connected components of $\\mathfrak{M}^{\\rm{lt}}_\\Lambda$.\n\nWe define the period domain\n\\[\\Omega_\\Lambda:=\\{{\\mathbb C}\\cdot\\sigma\\mid (\\sigma,\\sigma)=0, (\\sigma,\\bar\\sigma)>0\\}\\subset \\P(\\Lambda\\otimes{\\mathbb C}).\\]\nBy the local Torelli theorem, see Corollary \\ref{corollary local torelli}, there is a period map $P:\\mathfrak{M}^{\\rm{lt}}_\\Lambda\\to \\Omega_\\Lambda$ that is a local isomorphism. \n\n\nLet $X$ be a $\\Lambda$-marked symplectic variety. By Namikawa's result \\cite[Theorem 2.2]{Na01}, the deformation type of a symplectic resolution is constant along each connected component of $\\mathfrak{M}^{\\rm{lt}}_\\Lambda$. Given a signature $(3,{\\rm rk}\\,\\Lambda'-3)$ lattice $\\Lambda'$ and a primitive embedding $\\iota:\\Lambda{\\, \\hookrightarrow\\,}\\Lambda'$, we define a compatibly marked symplectic resolution $\\pi:(Y,\\mu)\\to (X,\\nu)$ to be a symplectic resolution $\\pi$ and a commutative diagram \n\n\n\\[\\xymatrix{\n\\Lambda'\\ar[r]^{\\mu}_\\cong&H^2(Y,{\\mathbb Z})\\\\\n\\Lambda\\ar[u]^\\iota\\ar[r]_\\nu^\\cong&H^2(X,{\\mathbb Z})\\ar[u]_{\\pi^*}\n}\\]\n\nWe define $\\mathfrak{M}^\\mathrm{res}_{\\Lambda',\\Lambda}$ to be the analytic space obtained by gluing together the bases of locally trivial Kuranishi spaces ${\\mathscr X}\\to {\\operatorname{Def}}^{\\rm{lt}}(X)$ together with a choice of compatibly marked simultaneous resolution ${\\mathscr Y}\\to{\\mathscr X}$ modulo the following equivalence relation: we identify $\\pi:(Y,\\mu) \\to (X,\\nu)$ with $\\pi':(Y',\\mu') \\to (X',\\nu')$ provided there is an isomorphism \n\\[\\xymatrix{Y\\ar[r]^\\cong\\ar[d]_{\\pi}&Y'\\ar[d]^{\\pi'}\\\\\nX \\ar[r]_\\cong&X'}\\]\n compatible with the marking. There are obvious forgetful maps that fit into a diagram\n \\[\\xymatrix{\n&\\mathfrak{M}^{\\rm{lt}}_\\Lambda\\ar[r]^P&\\Omega_{\\Lambda}\\ar[dd]\\\\ \n\\mathfrak{M}^\\mathrm{res}_{\\Lambda',\\Lambda}\\ar[ur]\\ar[dr]&&\\\\\n&\\mathfrak{M}_{\\Lambda'}\\ar[r]_P&\\Omega_{\\Lambda'}\\\\\n }\\]\n where the vertical arrow is the embedding of $\\Omega_\\Lambda$ into $\\Omega_{\\Lambda'}$ as the Noether-Lefschetz locus $\\Omega_{\\Lambda'}\\cap\\P(\\Lambda\\otimes{\\mathbb C})$.\n \n \\subsection{Monodromy groups}\n \n Each of the above moduli spaces $\\mathfrak{M}$ has a corresponding notion of parallel transport operator, which we call locally trivial parallel transport operators for $\\mathfrak{M}^{\\rm{lt}}_\\Lambda$ and simultaneously resolved parallel transport operators for $\\mathfrak{M}^\\mathrm{res}_{\\Lambda',\\Lambda}$. A simultaneously resolved parallel transport operator from $\\pi:Y\\to X$ to $\\pi':Y'\\to X'$ yields a diagram\n \\[\\xymatrix{\n H^2(Y,{\\mathbb Z})\\ar[r]^f&H^2(Y',{\\mathbb Z})\\\\\n H^2(X,{\\mathbb Z})\\ar[u]^{\\pi^*}\\ar[r]_g&H^2(X',{\\mathbb Z})\\ar[u]_{\\pi'^*}\n }\\]\n and $f,g$ are evidently locally trivial parallel transport operators.\n We adopt the following notation for the associated monodromy groups: \n \\begin{enumerate}\n \\item \n $\\operatorname{Mon}(X)^{\\rm{lt}}\\subset \\operatorname{GL}(H^*(X,{\\mathbb Z}))$ will be the monodromy group associated to locally trivial families, and $\\operatorname{Mon}^2(X)^{\\rm{lt}}\\subset\\O(H^2(X,{\\mathbb Z}))$ its image in $\\operatorname{GL}(H^2(X,{\\mathbb Z}))$ under the restriction of the action to the weight 2 cohomology. Here, the orthogonal group is taken with respect to the restriction of the Beauville--Bogomolov--Fujiki form to $H^2(X,{\\mathbb C}) \\subset H^2(Y,{\\mathbb C})$ for any resolution of singularities. As mentioned before, by \\cite[Theorem 8]{Na01a} the restriction of the quadratic form does not depend on the choice of resolution.\n \\item \n Likewise for a symplectic resolution $\\pi:Y\\to X$ we define $\\operatorname{Mon}(\\pi)$ and $\\operatorname{Mon}^2(\\pi)$ to be the monodromy groups associated to deformations of $\\pi$ that are locally trivial on $X$. \n \\item The monodromy groups associated to smooth deformations of $Y$ for which a given primitive sublattice $L\\subset H^2(Y,{\\mathbb Z})$ remains algebraic will be denoted $\\operatorname{Mon}(Y)_L$ and $\\operatorname{Mon}^2(Y)_L$.\n \n \\item We define $\\operatorname{Iso}^2(\\pi)\\subset \\operatorname{Mon}^2(Y)$ to be the stabilizer of $\\pi^*H^2(X,{\\mathbb Z})$, and $\\operatorname{Iso}^2(X)$ to be the image of $\\operatorname{Iso}^2(\\pi)$ in $\\O(H^2(X,{\\mathbb Z}))$.\n \\end{enumerate}\n Obviously $\\operatorname{Mon}^2(Y)_L\\subset\\operatorname{Iso}^2(\\pi)$ for $L=(\\pi^*H^2(X,{\\mathbb Z}))^\\perp$, the orthogonal complement of which is identified over ${\\mathbb Q}$ with $N_1(Y\/X)$ under the Beauville--Bogomolov--Fujiki pairing. Finally, in the presence of a compatible marking $(\\mu,\\nu)$ with $\\mu(\\Pi)=L$, we replace $Y,X,\\pi,L$ by $\\Lambda',\\Lambda,\\iota,\\Pi$ in the notation to denote the groups obtained in $\\O(\\Lambda')$ and $\\O(\\Lambda)$ using the marking.\n \nThe following is a corollary of a well-known theorem of Sullivan \\cite{Sullivan}, see Corollary \\ref{corollary monodromy group} for its analog for singular varieties.\n\\begin{corollary}\\label{corollary iso finite index} Let $\\pi:Y \\to X$ be an irreducible symplectic resolution. Then $\\operatorname{Iso}^2(X)$ is a finite index subgroup of $\\O(H^2(X,{\\mathbb Z}))$.\n\\end{corollary}\n\\begin{proof} Let $\\tilde\\O(H^2(X,{\\mathbb Z}))\\subset \\O(H^2(X,{\\mathbb Z}))$ be the subgroup acting as $\\pm 1$ on the discriminant group. Then $\\tilde\\O(H^2(X,{\\mathbb Z}))$ embeds to $\\O(H^2(Y,{\\mathbb Z}))$ by acting as the identity on $N=(\\pi^*H^2(X,{\\mathbb Z}))^\\perp$, by standard lattice theory \\cite[Corollary 1.5.2]{Ni}. Thus, $\\operatorname{Iso}^2(\\pi)$ contains $\\tilde\\O(H^2(X,{\\mathbb Z}))\\cap \\operatorname{Mon}^2(Y)$. By \\cite{Sullivan} the group $\\operatorname{Mon}^2(Y)$ has finite index in $\\O(H^2(Y,{\\mathbb Z}))$, see also \\cite[Lemma 7.5]{markmantor}, so that the intersection $\\tilde\\O(H^2(X,{\\mathbb Z}))\\cap \\operatorname{Mon}^2(Y)$ has finite index in $\\tilde\\O(H^2(X,{\\mathbb Z}))$. The inclusion $\\tilde\\O(H^2(X,{\\mathbb Z}))\\cap \\operatorname{Mon}^2(Y) \\subset \\operatorname{Iso}^2(\\pi)$ entails that $\\operatorname{Iso}^2(X)$ has finite index in $\\O(H^2(X,{\\mathbb Z}))$.\n\\end{proof}\n\n \n Before stating the main proposition of this subsection, we make a brief detour to describe an assumption which allows for a complete description of $\\operatorname{Mon}^2(X)^{\\rm{lt}}$. Recall that for $Y$ an irreducible holomorphic symplectic manifold, the positive cone $C^{1,1}_Y\\subset H^{1,1}(Y,{\\mathbb R})$ is the connected component of $\\{\\alpha \\in H^{1,1}(Y,{\\mathbb R})\\mid q(\\alpha)>0\\}$ containing the K\\\"ahler cone. \nIt has a wall-and-chamber decomposition such that the complement of the walls are the images of the K\\\"ahler cones of birational models \n(see \\cite{markmantor} for details).\n\\begin{property} \\label{property} For an irreducible holomorphic symplectic manifold $Y$ and a primitive negative definite sublattice let $L\\subset H^2(Y,{\\mathbb Z})$, we say that property $\\operatorname{Fin}_L(Y)$ holds if for the very general small deformation $Y'$ of $Y$ with $\\operatorname{Pic}(Y')=L$ there are only finitely many K\\\"ahler chambers. For an irreducible symplectic variety $X$, we say property $\\operatorname{Fin}(X)$ holds if property $\\operatorname{Fin}_L(Y)$ holds, where $\\pi:Y\\to X$ is a symplectic resolution and $L=(\\pi^*H^2(X,{\\mathbb Z}))^\\perp$. \n\\end{property}\nProperty $\\operatorname{Fin}_L(Y)$ follows for all such lattices $L$ from a version of the Kawamata-Morrison conjecture for the K\\\"ahler cone, and is known to hold provided $b_2(Y)\\neq 5$ (see \\cite{AV}). This in particular includes all known deformation types. $\\operatorname{Fin}_L(Y)$ is also trivially true for any deformation class if ${\\rm rk}\\,L=1$, see Proposition \\ref{proposition rank one} below.\n\\begin{proposition}\\label{proposition parallel transport} Let $X_1,X_2$ be locally trivially deformation equivalent irreducible symplectic varieties. \n\\begin{enumerate}\n\\item\\label{pto item one} Let $g:H^2(X_1,{\\mathbb Z})\\to H^2(X_2,{\\mathbb Z})$ be a locally trivial parallel transport operator. For any choice of symplectic resolutions $\\pi_i:Y_i\\to X_i$, there exists a parallel transport operator $f:H^2(Y_1,{\\mathbb Z})\\to H^2(Y_2,{\\mathbb Z})$ such that $f\\vert_{H^2(X_1,{\\mathbb Z})}=g$.\n\\item\\label{pto item two} Assume $b_2(X_1)>3$. For any choice of symplectic resolutions $\\pi_i:Y_i\\to X_i$, and any parallel transport operator $f:H^2(Y_1,{\\mathbb Z})\\to H^2(Y_2,{\\mathbb Z})$ sending $\\pi^*H^2(X_1,{\\mathbb Z})$ to $\\pi^*H^2(X_2,{\\mathbb Z})$, the restriction \\linebreak $H^2(X_1,{\\mathbb Z})\\to~H^2(X_2,{\\mathbb Z})$ is a locally trivial parallel transport operator.\n\\item\\label{pto item three} Additionally assume property $\\operatorname{Fin}(X_1)$. Then the locally trivial parallel transport operators $H^2(X_1,{\\mathbb Z})\\to H^2(X_2,{\\mathbb Z})$ are precisely those arising from restricting simultaneously resolved parallel transport operators from $\\pi_1$ to $\\pi_2$ for some choice of symplectic resolutions $\\pi_i:Y_i\\to X_i$.\n\\item\\label{pto item four} The simultaneously resolved parallel transport operators from $\\pi_1$ to $\\pi_2$ are precisely the parallel transport operators $H^2(Y_1,{\\mathbb Z})\\to H^2(Y_2,{\\mathbb Z})$ arising from families ${\\mathscr Y}\\to B$ along which $(\\pi_i^*H^2(X_i,{\\mathbb Z}))^\\perp$ remains algebraic.\n\\end{enumerate}\n\\end{proposition}\n \n \\begin{proof}\nFor the proof of \\eqref{pto item one}, let $\\pi_i:Y_i \\to X_i$ be resolutions and let ${\\mathscr X}\\to B$ be a locally trivial deformation of $X_1$ and $X_2$ over a connected base $B$. We can always choose the deformation in such a way that the very general fiber has Picard number zero. Let us call $b_1,b_2 \\in B$ the points corresponding to $X_1, X_2$. Let us choose a very general path $\\gamma$ from $b_1$ to $b_2$ that realizes the parallel transport operator $p$ and cover it with finitely many disks $D_1,\\ldots, D_n$ such that over each disk we have a simultaneous resolution ${\\mathscr Y}^{(i)}\\to {\\mathscr X}\\vert_{D_i} \\to D_i$, where for disks containing $b_1$ or $b_2$ the corresponding ${\\mathscr Y}^{(i)}$ should be a deformation of $Y_1$ respectively $Y_2$. We may assume that $D_i \\cap D_{i+1}$ is nonempty for each $i$ and that our path is obtained by concatenation of paths $\\gamma_i$ from $c_{i-1}$ to $c_i$ inside $D_i$ for some points $c_i \\in D_i \\cap D_{i+1}$ where $c_0=b_1, c_n=b_2$. We may furthermore assume that ${\\mathscr X}_{c_i}$ has Picard number zero for $i\\neq 0,n$. For each such $i$ the irreducible symplectic manifolds ${\\mathscr Y}^{(i)}_{c_i}$ and ${\\mathscr Y}^{(i+1)}_{c_i}$ are birational and therefore there exists a parallel transport operator $\\delta_i:H^2({\\mathscr Y}^{(i)}_{c_i},{\\mathbb Z}) \\to H^2({\\mathscr Y}^{(i+1)}_{c_i},{\\mathbb Z})$ which is a Hodge isometry. Combining with Lemma \\ref{lemma symplektisch} we see that $\\delta_i$ maps $H^2({\\mathscr X}_{c_i},{\\mathbb Z})$ to itself. If $f_i$ denotes the parallel transport operator induced by $\\gamma_i$ then $f:=f_n \\circ \\delta_n \\circ f_{n-1} \\circ \\ldots \\circ \\delta_1 \\circ f_1:H^2(Y_1,{\\mathbb Z})\\to H^2 (Y_2,{\\mathbb Z})$ is the sought-for parallel transport operator.\n\n For part \\eqref{pto item two}, let $\\mathfrak{N}_{\\Lambda'}$ be a given connected component of $\\mathfrak{M}_{\\Lambda'}$, and denote by $\\mathfrak{N}_{\\Lambda}^{\\rm{lt}}\\subset \\mathfrak{M}_{\\Lambda}^{\\rm{lt}}$ the set of $(X,\\nu)$ admitting a compatibly marked symplectic resolution $\\pi:(Y,\n \\mu)\\to (X,\\nu)$ such that $(Y,\\mu)\\in \\mathfrak{N}_{\\Lambda'}$. The subset $\\mathfrak{N}_{\\Lambda}^{\\rm{lt}}\\subset \\mathfrak{M}_{\\Lambda}^{\\rm{lt}}$ is open and closed by Proposition \\ref{prop defo}, and therefore it is a union of path-components as $\\mathfrak{M}^{\\rm{lt}}_\\Lambda$ is locally path-connected. It suffices to show $\\mathfrak{N}^{\\rm{lt}}_\\Lambda$ is path-connected, since if $(X_1,\\nu_1)\\in \\mathfrak{N}^{\\rm{lt}}_\\Lambda$ and $\\pi_1:(Y_1,\\mu_1)\\to (X_1,\\nu_1)$ is a compatibly marked resolution, then for $f$ as in the claim we have $(X_2,f\\nu_1)\\in \\mathfrak{N}_{\\Lambda}^{\\rm{lt}}$. The path-connectedness will in turn follow from the following\n \n\\begin{lemma}\\label{lemma density}The image of the period map $P:\\mathfrak{N}_{\\Lambda}^{\\rm{lt}}\\to \\Omega_\\Lambda$ is an open $\\operatorname{Iso}^2(\\Lambda)$-invariant path-connected subset and the preimage over any Picard rank 0 point of $\\Omega_\\Lambda$ consists of a unique point.\n\\end{lemma}\n \\begin{proof}The fact that $\\mathfrak{N}^{\\rm{lt}}_\\Lambda$ is invariant under $\\operatorname{Iso}^2(\\Lambda)$ is just a restatement of the above observation, so the image $P(\\mathfrak{N}_\\Lambda^{\\rm{lt}})$ is clearly open and $\\operatorname{Iso}^2(\\Lambda)$-invariant. In fact, it is the complement of countably many real analytic sub-manifolds of codimension $\\geq 2$ by a result of Verbitsky \\cite[Theorem 1.16]{V15} (see Remark \\ref{remark verbitsky} below), and is therefore path-connected (see for example \\cite[Lemma 4.10]{V13}). Here we used that $\\operatorname{Iso}^2(\\Lambda)$ has finite index in $\\O(\\Lambda)$ by Corollary \\ref{corollary iso finite index}.\n\nIt remains to prove the final claim. Suppose there are two points $(X_1,\\nu_1),$ $(X_2,\\nu_2)\\in \\mathfrak{N}^{\\rm{lt}}_\\Lambda$ with the same period and with Picard rank zero each.\n As for smooth varieties, neither $X_1$ nor $X_2$ contain any curves for otherwise the nondegeneracy of the intersection pairing would contradict the nonexistence of divisors.\n Note that $\\nu_2\\circ\\nu_1^{-1}:H^2(X_1,{\\mathbb Z})\\to H^2(X_2,{\\mathbb Z})$ is a Hodge isometry. Let us choose compatibly marked resolutions $(Y_1,\\mu_1),(Y_2,\\mu_2)\\in\\mathfrak{N}_{\\Lambda'}$. It follows that $\\mu_2\\circ\\mu_1^{-1}:H^2(Y_1,{\\mathbb Z})\\to H^2(Y_2,{\\mathbb Z})$ is a Hodge isometry as well. Thus, $(Y_1,\\mu_1)$ and $(Y_2,\\mu_2)$ have the same period and are therefore birational, by the global Torelli theorem. It follows that $X_1$ and $X_2$ are birational, and because neither has any effective curves, we have $X_1\\cong X_2$. This can be seen just as in the algebraic context, apply e.g. \\cite[Lemma 1.15 (b)]{Debarre} to a resolution of indeterminacy.\n\nLet us assume first, that the Hodge structure on $H^2(X_1,{\\mathbb Z})$ is Mumford--Tate general. Then the isomorphism induces $\\pm \\nu_2\\circ\\nu_1^{-1}$ on cohomology by Corollary \\ref{corollary mumford tate}. \nLet us recall from \\cite[Definition 3.5]{Markman} that Markman defined an orientation class in $H^2(C_Y,{\\mathbb Z}){\\ \\cong\\ } {\\mathbb Z}$ that is constant on the component $\\mathfrak{N}_{\\Lambda'}$, see also \\cite[\\S 4]{markmantor}, and we have a diagram\n \\[\\xymatrix{\n H^2(C_{Y_2},{\\mathbb Z})\\ar[r]^{(\\mu_2\\circ\\mu_1^{-1})^*}& H^2(C_{Y_1},{\\mathbb Z})\\\\\n H^2(C_{X_2}.{\\mathbb Z})\\ar[u]^{\\pi_2^*}_\\cong\\ar[r]_{(\\nu_2\\circ\\nu_1^{-1})^*}& H^2(C_{X_1},{\\mathbb Z})\\ar[u]_{\\pi_1^*}^\\cong\n }\\] \n so in fact $(X_1,\\nu_1)\\cong (X_2,\\nu_2)$.\n\n\tNow let $(X_1,\\nu_1),(X_2,\\nu_2)\\in \\mathfrak{N}^{\\rm{lt}}_\\Lambda$ be arbitrary Picard rank zero points with the same period. Also in this case, we have seen that $X_1,X_2$ are birational and having Picard rank zero, they are even biholomorphic. \n\t\nWe take a very general disk $\\Delta \\subset \\Omega_\\Lambda$ centered at $P(X_1,\\nu_1)=P(X_2,\\nu_2)$ and lift them to disks $\\Delta_1$ and $\\Delta_2$ in $\\mathfrak{N}^{\\rm{lt}}_\\Lambda$ centered at $(X_1,\\nu_1)$ respectively $(X_2,\\nu_2)$. Up to shrinking the disks, we may assume that there are locally trivial families ${\\mathscr X}^{(1)} \\to \\Delta_1$, ${\\mathscr X}^{(2)} \\to \\Delta_2$ of irreducible symplectic varieties with central fibers $X_1$ respectively $X_2$ such that $P(\\Delta_1)=\\Delta=P(\\Delta_2)$ where we endow these families with the markings induced by $\\nu_1$ and $\\nu_2$. \nAs $\\Delta$ was chosen very general, the above argument applies to the very general fiber of ${\\mathscr X}^{(1)}\\to\\Delta_1$, ${\\mathscr X}^{(2)}\\to\\Delta_2$ -- namely the Mumford--Tate general ones -- so that for the corresponding $t \\in \\Delta$ there is an isomorphism $\\phi_t:{\\mathscr X}^{(1)}_t \\to {\\mathscr X}^{(2)}_t$ such that $\\phi_t$ induces $\\nu_{2,t}\\circ \\nu_{1,t}^{-1}$ on cohomology.\nFor each such $t$ we consider the graph $\\Gamma_t' \\subset {\\mathscr X}^{(1)}_t \\times {\\mathscr X}^{(2)}_t$ and by the usual Hilbert scheme argument we obtain a family $\\Gamma \\subset {\\mathscr X}^{(1)}\\times_\\Delta {\\mathscr X}^{(2)}$ whose fibers $\\Gamma_t$ coincide with the above $\\Gamma_t'$ for uncountably many $t$.\nClearly, the locus of $t\\in \\Delta$ where the fiber $\\Gamma_t$ of $\\Gamma$ is an isomorphism is Zariski open and by shrinking our disk we may assume that $\\Gamma_t$ is the graph of an isomorphism everywhere outside $t=0$. \nUp to shrinking the disks once more we may assume that there are irreducible symplectic resolutions ${\\mathscr Y}^{(1)} \\to {\\mathscr X}^{(1)}$ and ${\\mathscr Y}^{(2)} \\to {\\mathscr X}^{(2)}$. We take the strict transform of $\\Gamma$ and obtain a cycle $\\wt\\Gamma \\subset {\\mathscr Y}^{(1)}\\times_\\Delta{\\mathscr Y}^{(2)}$. For each $t\\in \\Delta$ the induced morphism $[\\wt\\Gamma_t]_* : H^2({\\mathscr Y}^{(1)}_t,{\\mathbb Z}) \\to H^2({\\mathscr Y}^{(2)}_t,{\\mathbb Z})$ maps $H^2({\\mathscr X}^{(1)}_t,{\\mathbb Z})$ to $H^2({\\mathscr X}^{(2)}_t,{\\mathbb Z})$ and for $t\\neq 0$ the restriction to $H^2({\\mathscr X}^{(1)}_t,{\\mathbb Z})$ coincides with $\\nu_{2,t}\\circ \\nu_{1,t}^{-1}$. We conclude that it does so for $t=0$, too.\n\nIt remains to show that the restriction of $[\\wt\\Gamma_0]_*$ to $H^2(X_1,{\\mathbb Z})$ is induced by an isomorphism $X_1\\to[{\\ \\cong\\ }] X_2$. We argue as in the proof of \\cite[Theorem 4.3]{Huy99}: Let $Z$ be an irreducible component of $\\wt\\Gamma_0$. The restriction of $[Z]_*$ to $H^2(X_1,{\\mathbb Z})$ is a morphism of Hodge structures and hence either zero or an isomorphism as the Hodge structure on $H^2(X_1,{\\mathbb Z})$ is simple. Suppose that for the given $Z$ it is an isomorphism. Then there is $0\\neq \\lambda \\in {\\mathbb C}$ such that $[Z]_* \\sigma_1=\\lambda\\sigma_2$ where $\\sigma_1,\\sigma_2$ denote the symplectic forms on $Y_1={\\mathscr Y}^{(1)}_0,Y_2={\\mathscr Y}^{(2)}_0$. Observe that $\\sigma_i \\in H^2(X_i,{\\mathbb C}) \\subset H^2(Y_i,{\\mathbb C})$ by Lemma \\ref{lemma symplektisch} for $i=1,2$.\nDenoting $p_1$ and $p_2$ the projections from $Y_1\\times Y_2$ to its factors we have\n\\[\n\\begin{aligned}\n0 &\\neq \\lambda \\int_{Y_2} (\\sigma_2 \\bar\\sigma_2)^n = \\int_{Y_2} ({p_2}_*([Z] \\cup p_1^*\\sigma_1)) ({\\sigma_2}^{n-1} \\bar{\\sigma}_2^n)\\\\\n&= \\int_{Y_1\\times Y_2} [Z]\\cup p_1^*\\sigma_1 (p_2^*{\\sigma_2})^{n-1} (p_2^*\\bar{\\sigma}_2)^n = \\int_{Z} (p_1^*\\sigma_1) (p_2^*{\\sigma_2})^{n-1} (p_2^*\\bar{\\sigma}_2)^n.\\\\\n\\end{aligned}\n\\]\nReplacing $Z$ by a resolution of singularities we may assume it to be smooth. The integral can only be nonzero if $p_2:Z \\to Y_2$ (and hence also the composition $Z\\to X_2$) is dominant for otherwise the $(0,2n)$-form $(p_2^*\\bar{\\sigma}_2)^n$ would vanish. As $(p_1)_*[\\wt\\Gamma_t]=[({\\mathscr Y}_1)_t]$ and $(p_2)_*[\\wt\\Gamma_t]=[({\\mathscr Y}_2)_t]$ for $t\\neq 0$, the same is true for $t=0$ and therefore $Z$ has to map birationally onto $X_2$. We infer that $H^0(\\Omega_{Z}^2)= {\\mathbb C} \\cdot \\sigma_Z$ and $\\sigma_Z=p_2^*\t\\sigma_2$ is generically nondegenerate so that $Z\\to X_1$ is also birational (otherwise $p_1^*\\sigma_1=0$). Put together, the restriction of $[Z']_*$ to $H^2(X_1,{\\mathbb C})$ vanishes for all other irreducible components $Z'\\neq Z$ of $\\wt\\Gamma_0$ and hence $\\nu_2\\circ\\nu_1^{-1}=[Z]_*$ on $H^2(X_1,{\\mathbb C})$.\n The image of $Z$ in $X_1\\times X_2$ is the graph of a birational map which has to be an isomorphism $\\phi:X_2 \\to X_1$ as $X_1,X_2$ have Picard rank zero. The isomorphism $[Z]_*:H^2(Y_1,{\\mathbb C})\\to H^2(Y_2,{\\mathbb C})$ coincides with $p_{2*}\\circ p_1^*$ and we have that $\\phi \\circ \\pi_2 \\circ p_2 = \\pi_1 \\circ p_1$ so that\n\\[\n\\nu_2\\circ\\nu_1^{-1} = [Z]_* \\circ \\pi_1^* = \\pi_2^* \\circ \\phi^*\n\\]\nin cohomology which concludes the proof.\n \\end{proof}\n \nThe proofs of parts \\eqref{pto item three} and \\eqref{pto item four} proceed as in \\cite[Corollary 5.11]{Ma13} (see also Section 7.1 of \\cite{markmantor}), so we only sketch the argument. It will be enough if we can lift, for part \\eqref{pto item three}, families ${\\mathscr X}\\to B$ realizing locally trivial parallel transport operators and, for part \\eqref{pto item four}, families ${\\mathscr Y}\\to B$ realizing parallel transport operators, to simultaneously resolved families ${\\mathscr Y}'\\to{\\mathscr X}'\\to B'$. It suffices to consider a disk $B$, and the claim follows after covering the family with liftable disks and gluing them along nodes if there is a dense set of points with finitely many lifts. In both cases we use points corresponding to compatibly marked resolutions $\\pi:(Y,\\mu)\\to(X,\\nu)$ for which $\\operatorname{Pic}(X)=0$. Indeed, for part \\eqref{pto item four}, given such a $(Y,\\mu)$, there is at most one compatibly marked contraction $(Y,\\mu)\\to(X,\\nu)$ by the lemma. For part \\eqref{pto item three}, such a $(X,\\nu)$ admits finitely many compatibly marked resolutions assuming $\\operatorname{Fin}(X)$. \n \\end{proof}\n\n \\begin{remark}\nWith almost the same reasoning one can show that every $(X',\\nu'),(X'',\\nu'')\\in \\mathfrak{N}^{\\rm{lt}}_\\Lambda$ with the same period and with Picard group generated by an ample divisor are isomorphic as marked irreducible symplectic varieties.\n \\end{remark}\n\n \\begin{remark}\\label{remark verbitsky}\nWe make a remark about Verbitsky's result \\cite[Theorem 1.16]{V15}. The period domain $\\Omega_\\Lambda$ can alternatively be thought of as the space of positively oriented positive-definite planes in $\\Lambda_{\\mathbb R}$. Indeed, given a Hodge structure on $\\Lambda$, $(\\Lambda_ {\\mathbb C})^{2,0}\\oplus(\\Lambda_{\\mathbb C})^{0,2}$ is defined over ${\\mathbb R}$ and corresponds to a positively oriented positive definite plane $P\\subset \\Lambda_{\\mathbb R}$. The orientation is given by $(\\Re\\sigma,\\Im\\sigma)$, for $\\sigma\\in (\\Lambda_{\\mathbb C})^{2,0}$. Fixing such a plane $P_0$ as a basepoint, we obtain an isomorphism\n \\begin{equation} \\label{eq period homogeneous}\n\\Omega_\\Lambda\\cong \\operatorname{SO}(\\Lambda_{\\mathbb R})\/\\operatorname{SO}(P_0)\\times\\operatorname{SO}(P_0^\\perp)\n\\end{equation}\n Given an arithmetic lattice $\\Gamma\\subset \\operatorname{SO}(\\Lambda_{\\mathbb R})$, by an application of Ratner's theorem the orbit closure of a point $\\Gamma g\\in\\Gamma\\backslash\\operatorname{SO}(\\Lambda)$ under $\\operatorname{SO}(P_0)\\times\\operatorname{SO}(P_0^\\perp)$ is one of three possibilities, depending on $r:={\\rm rk}\\,(gP_0\\cap \\Lambda)$:\n \\begin{enumerate}\n \\item if $r=2$, the orbit is closed;\n \\item if $r=1$, the orbit closure is $\\Gamma g \\operatorname{SO}(gP_0^\\perp).\\operatorname{SO}(gP_0)$;\n \\item if $r=0$, the orbit is dense.\n \\end{enumerate} \n Correspondingly, there are three possibilities for the orbit closure of $\\omega\\in\\Omega_\\Lambda$ under $\\Gamma$ depending on the \\emph{transcendental rational rank} $r$ of the positive plane $P_\\omega$: it is closed if $r=2$, dense if $r=0$, and if $r=1$ it is a union of $G_{\\lambda}$ for $\\lambda\\in\\Gamma.\\lambda_0$ in the orbit of some primitive $0\\neq \\lambda_0\\in\\Lambda$, where $G_\\lambda$ is the subset of positive planes $P$ containing some $\\lambda$. Note that $G_\\lambda$ is a real-analytic submanifold of real codimension ${\\rm rk}\\, \\Lambda-2$ in $\\Omega_\\Lambda$. The $r=1$ case was omitted in \\cite{V15}.\n \\end{remark}\nFor an irreducible symplectic resolution $\\pi:Y\\to X$ we denote by $N$ the negative definite lattice $(\\pi^*H^2(X,{\\mathbb Z}))^\\perp$, compare to \\eqref{eq n}. In terms of monodromy groups, Proposition \\ref{proposition parallel transport} means:\n \\begin{corollary}\\label{corollary monodromy} Let $\\pi:Y\\to X$ be an irreducible symplectic resolution. \n \\begin{enumerate}\n\\item We have $\\operatorname{Mon}^2(X)^{\\rm{lt}}\\subset\\operatorname{Iso}^2(X)$ and if $b_2(X)>3$, then $\\operatorname{Mon}^2(X)^{\\rm{lt}} = \\operatorname{Iso}^2(X)$;\n\\item If in addition property $\\operatorname{Fin}(X)$ holds, then the image of $\\operatorname{Mon}^2(\\pi)$ in $\\operatorname{Mon}^2(X)^{\\rm{lt}}$ has finite index;\n\\item $\\operatorname{Mon}^2(\\pi)=\\operatorname{Mon}^2(Y)_N$.\n\n\\end{enumerate}\n \\end{corollary}\n In particular, we deduce the analog of Sullivan's theorem \\cite{Sullivan} in the smooth case:\n\n \\begin{corollary}\\label{corollary monodromy group} Let $\\pi:Y \\to X$ be an irreducible symplectic resolution and suppose $b_2(X) >3$. Then the following hold:\n\\begin{enumerate}\n \\item\\label{corollary monodromy group item one} $\\operatorname{Mon}^2(X)^{\\rm{lt}}$ is a finite index subgroup of $\\O(H^2(X,{\\mathbb Z}))$;\n \\item\\label{corollary monodromy group item two} If property $\\operatorname{Fin}(X)$ holds, then the forgetful map $\\operatorname{Mon}^2(\\pi)\\to\\operatorname{Mon}^2(X)^{\\rm{lt}}$ has finite kernel and finite index image.\n\\end{enumerate}\n\\end{corollary}\n\\begin{proof} By Corollary \\ref{corollary monodromy} it is sufficient to show that $\\operatorname{Iso}^2(X)$ has finite index in $\\O(H^2(X,{\\mathbb Z}))$, which has been done in Corollary \\ref{corollary iso finite index}. \nThe second claim follows from Corollary \\ref{corollary monodromy} and the fact that the kernel of $\\operatorname{Mon}^2(\\pi)\\to\\operatorname{Mon}^2(X)^{\\rm{lt}}$ is a subset of the finite group $O(N)$.\n \\end{proof}\n \n In fact, $\\operatorname{Mon}^2(X)^{\\rm{lt}}$ is a subgroup of the group $\\O^+(H^2(X,{\\mathbb Z}))$ of orthogonal transformations preserving the orientation of $H^2(C_X,{\\mathbb Z})$. \n \\begin{corollary}\\label{cor mon det} Fix a deformation type of irreducible holomorphic symplectic manifolds. The locally trivial deformation type of any irreducible symplectic variety $X$ with $b_2(X)>3$ admitting a symplectic resolution $\\pi: Y\\to X$ with $Y$ of that deformation type is determined by the $\\operatorname{Mon}^2(Y)$ orbit of $N_1(Y\/X)$ in $H_2(Y,{\\mathbb Z})$. If property $\\operatorname{Fin}(X)$ holds, it is uniquely determined.\n\\end{corollary}\n\nWe define the relative Picard rank of $X$ to be ${\\rm rk}\\, N_1(Y\/X)$ for a symplectic resolution $Y\\to X$; it is independent of the choice of resolution. In the special case that $X$ has relative Picard rank one, Property $\\operatorname{Fin}(X)$ trivially holds, and we can compute the relevant monodromy groups more precisely.\n\n\\begin{proposition}\\label{proposition rank one} Let $\\pi:Y\\to X$ be an irreducible symplectic resolution of relative Picard rank one and assume $b_2(X)>3$. Let $\\operatorname{Iso}^+(\\pi)\\subset \\operatorname{Iso}^2(\\pi)$ be the subgroup acting trivially on $(\\pi^* H^2(X,{\\mathbb Z}))^\\perp$. Then we have $\\operatorname{Mon}^2(\\pi)=\\operatorname{Iso}^+(\\pi)$.\n\\end{proposition}\n\\begin{proof} See \\cite{Ma13}. In the notation of the proof of Proposition \\ref{proposition parallel transport}, consider the fiber product\n\\[\\xymatrix{\n\\mathfrak{N}_{\\Lambda',\\Lambda}^\\mathrm{res}\\ar[d]\\ar[r]&\\mathfrak{N}_{\\Lambda'}\\times_{\\Omega_{\\Lambda'}}\\Omega_\\Lambda\\ar[d]\\\\\n\\mathfrak{N}_\\Lambda^{\\rm{lt}}\\ar[r]&\\Omega_\\Lambda\n}\\]\nThus, $\\mathfrak{N}_{\\Lambda',\\Lambda}^\\mathrm{res} \\subset \\mathfrak{M}_{\\Lambda',\\Lambda}^\\mathrm{res}$ is the space of those tuples $(Y',\\mu',X',\\nu',\\pi')$ for which $(X',\\nu')\\in \\mathfrak{N}_{\\Lambda}^{\\rm{lt}}$ and $(Y',\\mu')\\in \\mathfrak{N}_{\\Lambda'}$.\nLet $\\lambda\\in \\Lambda^\\perp\\subset \\Lambda'$ be a generator. The main point is that the sign of $(\\alpha,\\mu'(\\lambda))$ at a point $\\pi':(Y',\\mu')\\to (X',\\nu') \\in \\mathfrak{N}_{\\Lambda',\\Lambda}^\\mathrm{res}$ is locally constant on $\\mathfrak{N}^\\mathrm{res}_{\\Lambda',\\Lambda}$, $\\alpha\\in H^2(Y',{\\mathbb R})$ is a K\\\"ahler form, since at every point either $\\mu'(\\lambda)$ or $-\\mu'(\\lambda)$ as a class in $H_2(Y',{\\mathbb Z})\\supset H^2(Y',{\\mathbb Z})$ is a positive rational multiple of the class of a rational curve on $Y'$. Consequently, the space $\\mathfrak{N}_{\\Lambda',\\Lambda}^\\mathrm{res}$ has precisely two path-connected components, and $\\operatorname{Mon}^2(\\pi)\\subset \\operatorname{Iso}^+(\\pi)$. On the other hand, certainly $\\operatorname{Iso}^+(\\pi)\\subset \\operatorname{Mon}^2(\\pi)$. \n\\end{proof}\n\t\n\\begin{remark}\\label{remark rigid}\nIt should be noted that the assumption $b_2(X)>3$ that we used several times throughout this section is nontrivial. Unlike in the smooth case, we know that there are examples of irreducible symplectic varieties $X$ with $b_2(X)=3$: it may well happen that a birational contraction $Y \\to X$ of an irreducible symplectic manifold $Y$ contracts a negative definite subspace of $H^2(Y,{\\mathbb R})$ of maximal dimension. In the case of $K3$ surfaces for example, one may take $X$ to be $S\/G$ where $S$ is a $K3$ surface and $G$ is a group of symplectic automorphisms with minimal invariant second cohomology lattice $H^2(S,{\\mathbb Z})^G$ (i.e. of rank $3$).\n Finite groups of symplectic automorphisms were classified by Nikulin \\cite{Nikulin} and Mukai \\cite{Mukai}, explicit examples of groups with the sought-for rank of $H^2(S,{\\mathbb Z})^G$ may be found in \\cite{Xi,Ha}. An example of a different kind may be found in \\cite{OZ}. The minimal resolution $Y \\to X$ then gives us an example of a contraction of relative Picard rank $19$. The induced contraction ${\\rm Hilb}^n(Y) \\to {\\rm Sym}^n(Y) \\to {\\rm Sym}^n(X)$ gives an example of a contraction of a $K3^{[n]}$-type variety of dimension $2n$ with relative Picard rank $20$.\n\\end{remark}\n\t\nWe now prove the analog of Markman's Hodge theoretic formulation of the global Torelli theorem, see \\cite[Theorem 1.3]{markmantor}.\n\\begin{theorem}[Global Torelli Theorem]\\label{theorem global torelli}\nLet $X$, $X'$ be irreducible symplectic varieties and let $g:H^2(X,{\\mathbb Z}) \\to H^2(X',{\\mathbb Z})$ be a locally trivial parallel transport operator which is a Hodge isometry. Then there is a birational map $\\phi:X \\dashrightarrow X'$ which induces an isomorphism $\\phi_*:H^2(X,{\\mathbb Z})\\to H^2(X',{\\mathbb Z})$.\n\\end{theorem}\n\\begin{proof}\nThe choice of a marking $\\nu$ on $X$ induces via the parallel transport operator a marking $\\nu'$ on $X'$ and so the period maps give isomorphisms $S:={\\operatorname{Def}}^{\\rm{lt}}(X) \\to[(P')^{-1}\\circ P] {\\operatorname{Def}}^{\\rm{lt}}(X')$ thanks to the local Torelli theorem \\ref{corollary local torelli}. \nLet $\\pi:Y\\to X$, $\\pi':Y'\\to X'$ be irreducible symplectic resolutions. We infer from the local Torelli theorem for smooth varieties and Proposition \\ref{prop defo} that the period map in the same manner gives an isomorphism $S{\\ \\cong\\ } {\\operatorname{Def}}(Y,N){\\ \\cong\\ } {\\operatorname{Def}}(Y',N')$ where $N=(\\pi^*H^2(X,{\\mathbb Z}))^\\perp,N'=({\\pi'}^*H^2(X',{\\mathbb Z}))^\\perp$. Proposition \\ref{proposition parallel transport} \\eqref{pto item one} guarantees that $Y$ and $Y'$ lie in the same component of the moduli space of marked irreducible symplectic manifolds so that by Verbitsky's global Torelli theorem \\cite[Theorem 1.17]{V13} for each $t\\in S$ there is a birational map $\\phi_t:Y_t\\dashrightarrow Y'_{\\psi(t)}$ where $Y_t$ and $Y'_{\\psi(t)}$ are the corresponding deformations of $Y$ and $Y'$. \nIt is easily seen that the induced isomorphism on cohomology maps $N$ to $N'$. The claim follows therefore from Theorem \\ref{theorem huybrechts}.\n\\end{proof}\n\nAs in the smooth case an equivalent formulation of the global Torelli theorem is the following analog of \\cite[Theorem 1.16]{V13}, see also \\cite[Theorem 2.2]{markmantor}. \n\n\\begin{corollary}\\label{corollary global torelli}\nLet ${\\mathfrak{N}}_\\Lambda^{\\rm{lt}} \\subset {\\mathfrak{M}}_\\Lambda^{\\rm{lt}}$ be a connected component of the marked moduli space of irreducible symplectic varieties. Then the period map $P: {\\mathfrak{N}}_\\Lambda^{\\rm{lt}}\\to \\Omega_\\Lambda$ is generically injective and if $(X,\\nu),(X',\\nu')$ are in the same fiber of $P$, then $X$ and $X'$ are birational, isomorphic in codimension one, and the birational map induces a Hodge isometry $H^2(X,{\\mathbb Z})\\to H^2(X',{\\mathbb Z})$ compatible with the markings.\\qed\n\\end{corollary}\n\n Recall that a period $\\omega \\in \\Omega_\\Lambda$ may also be described by a positively oriented, positive-definite two plane $P_\\omega \\subset \\Lambda_{\\mathbb R}$. We call ${\\rm rk}\\left(P_\\omega \\cap \\Lambda\\right)$ the transcendental rational rank of $\\omega$.\n\\begin{theorem}[Surjectivity of the Period map]\\label{theorem almost surjectivity}Let ${\\mathfrak{N}}_\\Lambda^{\\rm{lt}} \\subset {\\mathfrak{M}}_\\Lambda^{\\rm{lt}}$ be a non\\-empty connected component with ${\\rm rk}\\Lambda >3$ such that the deformation type of the symplectic resolution has $b_2> 5$. Then the period map $P: {\\mathfrak{N}}_\\Lambda^{\\rm{lt}}\\to \\Omega_\\Lambda$ is surjective.\n\\end{theorem}\n\nBefore the proof we record an observation that is interesting in its own right. Recall that Amerik--Verbitsky \\cite[Definition 1.13]{AV} have introduced the notion of a monodromy birationally minimal (MBM) class. For a smooth irreducible holomorphic symplectic manifold $Y$, a nonzero class $\\alpha\\in H^{1,1}(Y,{\\mathbb Q})$ with $q_Y(\\alpha)<0$ is MBM if up to the action of the monodromy group $\\alpha^\\perp \\subset H^{1,1}(Y,{\\mathbb R})$ is a face of the K\\\"ahler cone of a birational model of $Y$. Note that here by a face of a convex cone $C$ in a vector space $V$ we mean a linear subspace $U$ whose intersection with the boundary $\\partial C$ has nonempty interior.\n Via the isomorphism $\\tilde q_Y:H^2(Y,{\\mathbb Q})\\to H_2(Y,{\\mathbb Q})$ coming from $q_Y$, we will also refer to MBM homology classes. MBM classes are deformation invariant along deformations for which they remain of Hodge type $(1,1)$ \\cite[Theorem 1.17]{AV}. \n\\begin{lemma}\\label{MBM}Let $\\pi:Y\\to X$ be an irreducible symplectic resolution with $b_2(Y)> 5$. Then $N=\\wt q^{-1}(N_1(Y\/X)) \\subset H^2(Y,{\\mathbb Z})$ is rationally generated by MBM classes.\n\\end{lemma}\n\\begin{proof} By the deformation invariance and Corollary \\ref{corollary projective}, we may assume $X$ and $Y$ are projective. Then $N^\\perp$ in $H^{1,1}(Y,{\\mathbb Q})_{\\mathbb R}$ is a face of the nef cone, since $N_1(Y\/X)$ is extremal in the effective cone of curves. Let $C \\subset H^{1,1}(Y,{\\mathbb Q})_{\\mathbb R}$ be the positive cone and denote $W$ the union of hyperplanes $\\alpha^\\perp$ over all MBM classes $\\alpha$.\n By \\cite[Theorem 1.19]{AV}, the ample cone is a connected component of the complement of $W$ in $C$ and by \\cite[Corollary 1.6]{AV14} integral MBM classes have bounded square. In particular, the collection $\\{\\alpha^\\perp\\}$ is locally finite (in the positive cone). Since $N$ is negative definite by Lemma \\ref{lemma symplektisch}, we can take a small ball $B\\subset N^\\perp$ which is both contained in the boundary of the ample cone and in the positive cone, so $B$ must be open in an intersection of finitely many $\\alpha^\\perp$. It follows that $N_{\\mathbb Q}$ is generated by MBM classes. \n\\end{proof}\n\\begin{proof}[Proof of Theorem \\ref{theorem almost surjectivity}]\nFor a point $(X',\\nu')\\in\\mathfrak{N}^{\\rm{lt}}_\\Lambda$, the marking $\\nu'$ induces markings on the universal locally trivial deformation ${\\mathscr X}' \\to S={\\operatorname{Def}}^{\\rm{lt}}(X')$ of $X'$ so that we may interpret $S$ as a small open neighborhood of $(X',\\nu')$ in ${\\gothN_\\Lambda^\\lt}$. We infer from the local Torelli theorem \\ref{corollary local torelli} that $P(S) \\subset \\Omega_\\Lambda$ is open as well. Evidently, the period map $P:\\mathfrak{N}_\\Lambda^{\\rm{lt}}\\to \\Omega_\\Lambda$ is $\\operatorname{Mon}^2(\\Lambda)^{\\rm{lt}}$-equivariant, so the complement of the image is a closed $\\operatorname{Mon}^2(\\Lambda)^{\\rm{lt}}$-invariant subset. By Remark \\ref{remark verbitsky}, and the fact that any transcendental rational rank one orbit closure contains a transcendental rational rank two orbit, it suffices to show that every maximal Picard rank period is in the image.\n\nAssume that $\\omega \\in \\Omega_\\Lambda$ corresponds to a Hodge structure whose Picard rank equals ${\\rm rk} \\Lambda$.\nAs in Proposition \\ref{proposition parallel transport}, there is a unique component $\\mathfrak{N}_{\\Lambda'}\\subset\\mathfrak{M}_{\\Lambda'}$ of compatibly marked resolutions, and via the embedding $\\omega\\in \\Omega_\\Lambda\\subset \\Omega_{\\Lambda'}$ and Verbitsky's global Torelli theorem, there is a smooth marked irreducible holomorphic symplectic manifold $(Y,\\mu)$ with period $\\omega$ and $Y$ is certainly projective, by Huybrechts' criterion. Furthermore, since $\\mu^{-1}(\\Lambda_{\\mathbb R}) \\subset H^2(Y,{\\mathbb R})$ is a wall in the decomposition of the positive cone in $H^{1,1}(Y,{\\mathbb R})$ into K\\\"ahler chambers by Lemma \\ref{MBM}, we can choose $(Y,\\mu)$ in its birational equivalence class so that $\\mu^{-1}(\\Lambda_{\\mathbb R})$ is a face of the ample cone of $Y$. Choosing a line bundle $L$ with $\\mathrm{c}_1(L)$ in the intersection of the nef cone and the positive cone of $Y$, $L$ is big and nef, so by the basepoint-free theorem we obtain a birational contraction $\\pi:Y\\to X$ to a symplectic variety $X$, and $\\pi$ is naturally compatibly marked by $(\\mu,\\nu)$. By Corollary \\ref{cor mon det}, $X$ is a locally trivial deformation of $X'$. To finish, by Lemma \\ref{lemma density} the point $(X,\\nu)$ is in the same component $\\mathfrak{N}_\\Lambda^{\\rm{lt}}$, and the claim is proven.\n\n\\end{proof}\n\\begin{remark}The moduli space of quasi-polarized locally trivially deformations of a given projective symplectic variety is thus a locally symmetric variety of orthogonal type. The geometry of such varieties, and in particular their Kodaira dimensions, have been studied using modular forms by Gritsenko--Hulek--Sankaran in a series of papers, see for example \\cite{GHS,GHS2,GHS3}. A rather general recent result in this direction has been obtained by Ma \\cite{Ma17}.\n\\end{remark}\n\n\\begin{remark}We needed to use Verbitsky's global Torelli theorem on the resolution, and this method of proof most likely cannot be used to give a different proof in the smooth case. Note however the same argument does provide a simple proof of the surjectivity of the period map of K3 surfaces using three facts: (1) any maximal Picard rank period is realized by a Kummer surface; (2) all K3 surfaces are deformation equivalent; and (3) the monodromy group is arithmetic. \n\\end{remark}\n\\section{Applications to \\texorpdfstring{$K3^{[n]}$}{K3\\textasciicircum {[n]}}-type manifolds}\\label{sec k3type}\nRecall that a compact K\\\"ahler manifold $Y$ is said to be of $K3^{[n]}$-type if it is deformation equivalent to a Hilbert scheme of $n$ points on $K3\\;$ surface. The $K3^{[n]}$-type manifolds form one of the two known infinite families of irreducible holomorphic symplectic manifolds. We assume throughout that $n\\geq 2$ (i.e. that $\\dim Y\\geq 4$). \n\nBy work of Markman \\cite[Corollary 9.5]{markmantor} there is a canonical extension of weight 2 integral Hodge structures\n\\begin{equation}\n0\\to H^2(Y,{\\mathbb Z})\\to \\tilde \\Lambda (Y,{\\mathbb Z})\\to Q\\to 0\\label{extperiod}\n\\end{equation}\nwhere $Q\\cong {\\mathbb Z}(-1)$. The lattice underlying $\\tilde\\Lambda(Y,{\\mathbb Z})$ is the Mukai lattice $\\tilde\\Lambda_{K3}=E_8(-1)^2\\oplus U^4$. We denote the primitive generator of the orthogonal to $H^2(Y,{\\mathbb Z})$ in $\\tilde\\Lambda(Y,{\\mathbb Z})$ by $v=v(Y)$, which is determined up to sign and satisfies $v^2=2-2n$. Note that\n\\[H^2(Y,{\\mathbb Z})\\cong \\Lambda_{K3^{[n]}}:=E_8(-1)^2\\oplus U^3\\oplus (2-2n).\\]\n\nDenote by $\\operatorname{Mon}^2_{K3^{[n]}}\\subset O(\\Lambda_{K3^{[n]}})$ the image of the weight two monodromy representation, which has been computed by Markman \\cite{markmanmon} to be the subgroup $\\tilde O^+(\\Lambda_{K3^{[n]}})$ preserving the orientation class and acting as $\\pm 1$ on the discriminant group $D(\\Lambda_{K3^{[n]}}):=\\Lambda_{K3^{[n]}}^*\/\\Lambda_{K3^{[n]}}$. We have the following well-known consequence of this computation and Verbitsky's global Torelli theorem: the extension \\eqref{extperiod} determines the birational class of $Y$. More precisely, for two symplectic manifolds $Y,Y'$, there is a Hodge isometry $\\phi:H^2(Y,{\\mathbb Z})\\to H^2(Y',{\\mathbb Z})$ lifting to a Hodge isometry $\\tilde\\phi:\\tilde\\Lambda(Y,{\\mathbb Z})\\to\\tilde\\Lambda(Y',{\\mathbb Z})$ of the Markman Hodge structures if and only if $Y$ is birational to $Y'$. We therefore refer to \\eqref{extperiod} as the \\emph{extended period}.\n\n\n\n\\subsection{Bridgeland stability conditions}\\label{subsec bridgeland stability}\n\nRecall that for a $K3\\;$ surface $S$, the total cohomology $H^*(S,{\\mathbb Z})$ carries the so-called Mukai Hodge structure\n\\[\\tilde H(S,{\\mathbb Z}):= H^0(S,{\\mathbb Z})(-1)\\oplus H^2(S,{\\mathbb Z})\\oplus H^4(S,{\\mathbb Z})(1)\\]\nwhich comes equipped with the Mukai pairing defined by\n\\[(a_0+a_2+a_4,b_0+b_2+b_4):=(a_2,b_2)_S - (a_0,b_4) - (a_4,b_0)\n\\]\nfor $a_i, b_i \\in H^i(S,{\\mathbb Z})$. For a generic Bridgeland stability condition $\\sigma$ on $S$, the moduli space $Y=M_\\sigma(v)$ of Bridgeland $\\sigma$-stable objects on $S$ of Mukai vector $v\\in \\tilde H(S,{\\mathbb Z})^{(1,1)}$ is a $K3$-type manifold, and we canonically have $\\tilde\\Lambda(Y,{\\mathbb Z})=\\tilde H(S,{\\mathbb Z}) $ with $v(Y)=v$. The identification $v^\\perp\\xrightarrow{\\cong} H^2(Y,{\\mathbb Z})$ is achieved by the Fourier--Mukai transform.\n\nNote that by work of Bayer-Macr\\`i \\cite{BM}, every symplectic birational model of a Bridgeland moduli space is a Bridgeland moduli space. We will need below the following Hodge-theoretic characterization of Bridgeland moduli spaces which follows from \\cite[Lemma 2.5]{Hu15} and \\cite[Proposition~4]{Addington}. \n\\begin{proposition}\nA projective $K3^{[n]}$-type manifold $Y$ is isomorphic to a Bridgeland moduli space on a twisted projective $K3\\;$ surface if and only if one of the following equivalent conditions holds:\n\\begin{enumerate}\n\\item $\\tilde\\Lambda(Y,{\\mathbb Q})_{\\textrm{alg}}$ contains $U$ as a sublattice;\n\\item The rational transcendental lattice $H^2(Y,{\\mathbb Q})_{\\textrm{tr}}\\cong \\tilde\\Lambda(Y,{\\mathbb Q})_{\\textrm{tr}}$ is Hodge-isometric to the rational transcendental lattice of a projective $K3\\;$ surface.\n\\end{enumerate}\nFurthermore, the projective $K3\\;$ surface can be taken untwisted if and only if one of the following equivalent conditions holds: \n\\begin{enumerate}\n\\item[$(1')$] $\\tilde\\Lambda(Y,{\\mathbb Z})_{\\textrm{alg}}$ contains $U$ as a primitive sublattice;\n\\item[$(2')$] The transcendental lattice $H^2(Y,{\\mathbb Z})_{\\textrm{tr}}\\cong \\tilde\\Lambda(Y,{\\mathbb Z})_{\\textrm{tr}}$ is Hodge-isometric to the transcendental lattice of a projective $K3\\;$ surface.\n\\end{enumerate}\n\\end{proposition}\n\n\\subsection{$K3^{[n]}$-type contractions}\nWe now turn to the singular case.\n\\begin{definition}Let $X$ be a symplectic variety and $\\pi:Y\\to X$ a symplectic resolution. We say $\\pi$ is a $K3^{[n]}$-type contraction if $Y$ is a $K3^{[n]}$-type manifold. We will often abuse terminology and refer to $X$ itself as a $K3^{[n]}$-type contraction as well.\n\\end{definition}\nNote that if $X$ is a $K3^{[n]}$-type contraction, \\emph{every} symplectic resolution is a $K3^{[n]}$-type manifold by Huybrechts' theorem \\cite[Theorem 2.5]{Huy}.\n\\begin{example}Our main source of examples of $K3^{[n]}$-type contractions come from contractions of Bridgeland moduli spaces, which we call Bridgeland contractions. Bridgeland moduli spaces of untwisted $K3$ surfaces will be called untwisted Bridgeland contractions for emphasis. Their geometry is beautifully described via wall-crossing by Bayer--Macr\\`i theory \\cite{BM}. Given a projective $K3$ surface $S$, a primitive Mukai vector $v\\in \\tilde H(S,{\\mathbb Z})_{\\mathrm{alg}}$, and an open chamber $\\mathcal{C}\\subset{\\operatorname{Stab}}^\\dagger(S)$ associated to $v$, then any $\\sigma_0\\in\\partial \\mathcal{C}$ yields a semiample class $\\ell_{\\sigma_0}$ on $M_\\mathcal{C}(v)$, and the associated morphism $\\pi:M_{\\mathcal{C}}(v)\\to M$ is a $K3^{[n]}$-type contraction. The morphism $\\pi$ contracts a curve if and only if two generic stable objects in the corresponding family are $S$-equivalent with respect to $\\sigma_0$.\n\\end{example}\nMuch is known about the singularities of Bridgeland contractions, and Theorem \\ref{theorem huybrechts} roughly says that arbitrary $K3^{[n]}$-type contractions exhibit no new singularities:\n\\begin{proposition}\\label{proposition def to bridge}Let $X$ be a $K3^{[n]}$-type contraction with $b_2(X)>3$. Then $X$ is locally trivially deformation-equivalent to a Bridgeland contraction $M$. Furthermore, if $b_2(X)>4$, $M$ may be taken to be an untwisted Bridgeland contraction.\n\\end{proposition}\n\\begin{proof} Since every smooth symplectic birational model of a smooth Bridgeland moduli space is a Bridgeland moduli space, and every contraction is a Bridgeland contraction, we need only argue that the periods of such moduli spaces are dense in $\\Omega_{\\Lambda}$ for any primitive signature $(3,b)$ sublattice $\\Lambda\\subset \\Lambda_{K3^{[n]}}$ with $b>0$. We prove the second part first. If we choose an arbitrary primitive embedding $U\\subset\\tilde\\Lambda_{K3}$ containing $\\Lambda_{K3^{[n]}}^\\perp$, then $U\\cap\\Lambda$ is of rank at most one and negative definite. Any period $\\omega\\in\\Omega_\\Lambda$ orthogonal to $U\\cap \\Lambda$ is of the required form, and by Remark \\ref{remark verbitsky} the monodromy orbits of such periods are dense in $\\Omega_\\Lambda$ since ${\\rm rk}\\,\\Lambda\\cap U^\\perp\\geq 4$ provided $b_2(X)>4$. \n\nFor the first claim, let $\\Pi$ be the orthogonal to $\\Lambda$ in $\\tilde\\Lambda$; we may assume ${\\rm rk}\\,\\Lambda=5$ by the last paragraph. As ${\\rm rk}\\,\\Pi>4$, we can find $U\\subset \\Pi_{\\mathbb Q}$ since $\\Pi$ has (many) isotropic vectors by a classical theorem of Meyer. The periods $\\omega\\in\\Omega_\\Lambda$ orthogonal to $U\\cap \\Lambda$ will now suffice.\n\\end{proof}\nThe work of Bayer-Macr\\`i \\cite{BM} in principle provides a complete description of the singularities of Bridgeland contractions as they are all realized by wall-crossing. A Bridgeland stability condition $\\sigma_0$ on a $K3\\;$ surface $S$ comes with a central charge $Z_0:\\tilde H(S,{\\mathbb Z})\\to {\\mathbb C}$, and we denote by ${\\mathcal{H}}_{\\sigma_0}(v)\\subset \\tilde H(S,{\\mathbb Z})_{\\mathrm{alg}}$ the primitive sublattice of algebraic vectors $a$ such that $\\Im \\frac{Z(a)}{Z(v)}=0$, \\emph{i.e.}, those vectors for which $Z(a)$ and $Z(v)$ are ${\\mathbb R}$-linearly dependent. A nearby generic stability condition $\\sigma$ yields a contraction $\\pi:M_\\sigma(v)\\to M$ which identifies $\\sigma$-stable sheaves which are $S$-equivalent with respect to $\\sigma_0$. The lattice $ {\\mathcal{H}}_{\\sigma_0}(v)$ is of signature $(1,\\rho)$, and \n\\[\nN_{\\sigma_0}(v):= {\\mathcal{H}}_{\\sigma_0}(v)\\cap H^2(M_{\\sigma}(v),{\\mathbb Z})={\\mathcal{H}}_{\\sigma_0}(v)\\cap v^\\perp\n\\]\n is negative definite. Denoting by $R_{\\sigma_0}(v)\\subset H_2(M,{\\mathbb Z})$ the primitive sublattice corresponding to $N_{\\sigma_0}(v)$ under the isomorphism $H^2(M_\\sigma(v),{\\mathbb Q})\\cong H_2(M_\\sigma(v),{\\mathbb Q})$, we see that $N_{\\sigma_0}(v)_ {\\mathbb Q}$ is naturally identified with the orthogonal to $\\pi^*H^2(M,{\\mathbb Q})$ in $H^2(M_{\\sigma}(v),{\\mathbb Q})$.\n\nWalls corresponding to relative Picard rank one contractions are particularly easy to analyze, and for instance we have:\n\n\\begin{proposition}\\label{nakajima} Let $X$ be a relative Picard rank one $K3^{[n]}$-type contraction and $x\\in X$ a (closed) point. The analytic germ $(X,x)$ is isomorphic to that of a Nakajima quiver variety.\n\\end{proposition}\n\\begin{proof}By \\cite[Theorem 1.1]{AS}, the statement is known for Gieseker moduli spaces $X=M_{H_0}(v)$ where $v$ is a primitive Mukai vector of a pure 1-dimensional sheaf on a $K3$ surface $S$ with $v^2\\geq 2$ and $H_0$ is a nongeneric polarization. Given a primitive sublattice ${\\mathcal{H}}\\subset\\tilde\\Lambda_{K3}$ associated to a relative Picard rank one contraction, we therefore only need to show that every such lattice arises from this construction up to the monodromy action. By \\cite{BM}, every such lattice ${\\mathcal{H}}$ contains $v$ and a class $a\\in{\\mathcal{H}}$ with $0\\leq(v,a)\\leq (v,v)\/2$ and $(a,a)\\geq -2$. Let $S$ be a $K3$ surface such that\n\\begin{enumerate}\n\\item $\\operatorname{Pic}(S)\\cong {\\mathcal{H}}$. Let $D\\in\\operatorname{Pic}(S)$ correspond to $v$ and $A$ to $a$.\n\\item $D-\\epsilon A$ is ample.\n\\end{enumerate}\nSuch a surface $S$ exists since ${\\mathcal{H}}$ embeds primitively into $\\Lambda_{K3}$ \\cite{Ni}. Then $D,A$ and $D-A$ are effective by the conditions on $a$, so $|D|$ contains reducible curves. Choose an ample $H_0$ and $\\delta,\\alpha\\in{\\mathbb Z}$ nonzero such that\n\\[\\frac{\\delta}{H_0.D}=\\frac{\\alpha}{H_0.A}.\\]\nThen there are strictly $H_0$-semistable sheaves of Mukai vector $v_0=(0,D,\\delta)$. Set $M_\\pm=M_{H^\\pm}(v_0)$ for $H^\\pm=H_0\\pm \\epsilon D$, and $M_0=M_{H_0}(v_0)$. We conclude by noticing that the lattice ${\\mathcal{H}}$ in $\\tilde H(S,{\\mathbb Z})=\\tilde\\Lambda(M_+,{\\mathbb Z})$ associated to the wall crossing \n\\[\\xymatrix{\nM_+\\ar[dr]\\ar@{-->}[rr]&&M_-\\ar[ld]\\\\\n&M_0&\n}\\]\nis \n\\[\\left\\langle (0,D,\\delta), (0,A,\\alpha)\\right\\rangle\\cong \\operatorname{Pic}(S)\\cong {\\mathcal{H}}\\]\nwhere the isomorphism takes $v_0$ to $v$.\n\\end{proof}\n\nThe proof of Proposition \\ref{nakajima} gives explicit models for every relative Picard rank one $K3^{[n]}$-type contraction among compactified Jacobians of linear systems on $K3$ surfaces. Knutsen, Lelli-Chiesa, and Mongardi \\cite{KLCM} have also used compactified Jacobians to construct contractible ruled subvarieties of $K3^{[n]}$-type manifolds, and analyze the geometry more closely. Models for such contractions have further been treated by Hassett--Tschinkel \\cite{HT15}, where it is shown that every wall ${\\mathcal{H}}$ can be realized on the Hilbert scheme of points for a projective $K3$ surface of Picard rank one. Note that we have the following simple consequence of Corollary \\ref{cor mon det}: \n\n\\begin{corollary}\\label{cor monodromy class} The locally trivial deformation type of a $K3^{[n]}$-type contraction $X$ is determined by $(\\lambda,\\lambda)$ and $\\div(\\lambda)$, for a primitive generator $\\lambda\\in (\\pi^*H^2(X,{\\mathbb Z}))^\\perp$ and a symplectic resolution $\\pi:Y\\to X$.\n\\end{corollary}\n\\begin{proof}Using Corollary \\ref{cor mon det}, this is a purely lattice-theoretic statement about the monodromy group of the $K3^{[n]}$-type deformation class, see \\cite[Section 10]{Eichler}.\n\\end{proof}\n\nThe Bayer--Macr\\`i picture strongly suggests that the answer to the following question is affirmative:\n\\begin{question}Let $\\pi:Y\\to X$ be a relative Picard rank one $K3^{[n]}$-type contraction, and let $E\\subset Y$ be an irreducible component of the exceptional locus. Is the generic fiber of the map $E\\to X$ isomorphic to $\\P^{\\operatorname{codim} E}$?\n\n\\end{question}\nIndeed, for a Bridgeland moduli space $Y=M_{\\sigma_+}(v)$ and a contraction induced by a wall-crossing, the Harder--Narasimhan filtration of a generic point $[F]\\in E$ with respect to a generic nearby stability condition $\\sigma_-$ on the other side of the wall is often of the form\n\\begin{equation}0\\to A\\to F\\to B\\to 0\\label{HNfilt}\\end{equation}\nfor $A,B$ $\\sigma_0$-stable. All such extensions are $\\sigma_+$-stable, and this yields a $\\P^k=\\P\\operatorname{Ext}^1(B,A)$ fiber that is contracted. Moreover, setting $a=v(A)$ and $b=v(B)$,\n\\begin{align*}\\dim E&=k+\\dim M_{\\sigma_0}^{\\operatorname{1st}}(a)+\\dim M^{\\operatorname{1st}}_{\\sigma_0}(b)\\\\\n&=\\left((a,b)-1\\right)+\\left(a^2+2\\right)+\\left(b^2+2\\right)\\\\\n&=\\left(v^2+2\\right)-k\\end{align*}\nso $k=\\operatorname{codim} E$. Thus, in this case, we are done if the Harder--Narasimhan filtration of the general point of $E$ has the form \\eqref{HNfilt} for fixed\\footnote{Note the constancy of the Mukai vectors is automatic if a universal family exists over $E$, by the existence of Harder--Narasimhan filtrations in families.} $a$ and $b$. By Corollary \\ref{cor monodromy class} and \\cite{LP2}, it would be sufficient to consider one model in each monodromy orbit, and many special cases have been established previously, see \\cite{HT15,KLCM}. It is also not difficult to prove the following special case:\n\n\\begin{proposition}\\label{proposition generic fiber}Let $\\pi:Y\\to X$ be a relative Picard rank one $K3^{[n]}$-type contraction, and let $E\\subset Y$ be an irreducible divisorial component of the exceptional locus. Then the generic fiber of the map $E\\to X$ is $\\P^1$.\n\\end{proposition}\n\\begin{proof}\nBy Proposition \\ref{proposition def to bridge}, the contraction $\\pi$ is a locally trivial deformation of a Bridgeland contraction. Using \\cite[Theorem 1.1]{LP2} we may assume that $\\pi$ is itself a Bridgeland contraction. In our special case it also follows from uniqueness of relative minimal resolutions of ADE surface singularities.\nSo let $Y=M_{\\sigma_+}(v)$ be a Bridgeland moduli space on a $K3\\;$ surface $S$ and consider a wall-crossing contraction $\\pi:Y\\to X$ associated to the rank two lattice $v\\in{\\mathcal{H}}\\subset \\tilde H(S,{\\mathbb Z})_\\mathrm{alg}$. Let $\\sigma_0$ be a generic stability condition on the wall. Note that there is at most one divisorial component to the exceptional locus of a relative Picard rank one contraction. It follows from the classification in \\cite{BM} that there are two cases:\n\\begin{enumerate}\n\\item ${\\mathcal{H}}$ contains no isotropic vectors. For a spherical class $s\\in {\\mathcal{H}}$, the spherical reflection $\\rho_s:\\tilde H(S,{\\mathbb Z})\\to \\tilde H(S,{\\mathbb Z})$ gives a parallel transport operator from $v^\\perp$ to $v'^\\perp$ for $v'=\\rho_s(v)$. As in \\cite[Section 7]{BM}, if the wall is totally semistable for $v$, we can produce $v'\\in {\\mathcal{H}}$ by a sequence of spherical reflections through spherical classes $s_i\\in{\\mathcal{H}}$ for which the wall is not totally semistable, so without loss of generality we may assume we are in this case. By \\cite[Lemma 7.4]{BM}, the Harder--Narasimhan filtration of a generic point $[F]\\in U$ for some \\'etale open $U\\to E$ is given by \n\\[0\\to S\\to F\\to A\\to 0\\mbox{\\indent or\\indent}0\\to A\\to F\\to S\\to 0\\]\nfor $S$ and $A$ both $\\sigma_0$-stable and $S$ spherical. Without loss of generality, assume we are in the first case (\\emph{i.e.} that $\\phi_{+}(S)\\leq \\phi_+(A)$). There is a unique such $S$ (since necessarily $(v(S),v)=0$), and all such extensions are $\\sigma_+$-stable, so $\\P^1\\cong \\P\\operatorname{Ext}^1(A,S)$ gives the unique curve through the general point that is contracted by $\\pi$.\n\\item ${\\mathcal{H}}$ contains an isotropic class. The above proof still holds if there are no isotropic classes $w\\in{\\mathcal{H}}$ such that $(v,w)=1,2$ (see \\cite[Proposition 8.6]{BM}). As in \\cite[Section 8]{BM}, there is an isotropic class $w_0\\in{\\mathcal{H}}$ such that $M_{\\sigma_0}(w_0)=M^{\\operatorname{1st}}_{\\sigma_0}(w_0)$, and we may assume $w=w_0$. The case $(v,w_0)=1$ is the Hilbert--Chow contraction, and the remaining case is similarly treated by \\cite[Lemma 8.7]{BM}.\n\\end{enumerate}\n\\end{proof}\n\nIn \\cite{BB} the first author classified contractions at the other extreme, namely those for which the exceptional locus contains a Lagrangian $\\P^n$.\n\n\nAs an application, assume that ${Y}$ is a symplectic $2n$-fold which admits a divisorial contraction $\\pi:{Y}\\to X$ of relative Picard rank one such that $X$ has transversal $A_2$ singularities. We call this an $A_2$-contraction. Note that while $ADE$ singularities admit unique symplectic resolutions, in the relative Picard rank one setting the monodromy action on the set of components of the general fiber of the exceptional locus yields a group of automorphisms of the $ADE$ graph in question acting transitively on the nodes, so only $A_1$ and $A_2$ singularities remain as possibilities. We would like to know whether an $A_2$ contraction exists if ${Y}$ is an irreducible symplectic manifold.\n\n\\begin{corollary}\\label{corollary a2}\nLet ${Y}$ be an irreducible symplectic manifold deformation equivalent to a Hilbert scheme of $n$ points on a K3 surface. Then ${Y}$ does not admit any $A_2$-contraction of relative Picard rank one.\n\\end{corollary}\n\nNote that there are however examples of $A_2$-contractions of relative Picard rank one of smooth and projective symplectic varieties. See e.g. \\cite[\\S 1.4, Example 2]{Wierzba} for an explicit construction.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{Abstract}\n\nMobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. We show that clustering and principal component analysis allows for a significant dimension reduction with limited loss of information. The most important features are related to geographical location. In particular, we observe that most people spend most of their time at only a few locations. With the help of clustering methods, we then robustly identify home and office locations and compare the results with official census data. Finally, we analyze the geographic spread of users' frequent locations and show that commuting distances can be reasonably well explained by a gravity model.\n\n\\section{Introduction}\nInformation and communication technologies have always been important sources of data and inspiration in sociology, especially in recent decades. These technologies influence the behavior of people, which is a subject of study in itself (e.g.\\cite{BALL1968,DEBAILLENCOURT-ET-AL2007,LICOPPESMOREDA2005}), but they also provide massive amounts of data that can be used to analyze various aspects of human behavior. \n\nTelephone and mobile phone data have already been used to study social networks,\nsometimes in conjunction with features such as gender and age\n\\cite{SMOREDALICOPPE2000}. More recently, the mobile phone data available to\nresearchers have been enriched with geographical information. This allows to\nanalyze regularities, or even\nlaws\\cite{Brockmann2006,GONZALEZETAL-NATURE-2008,Candia2008}, governing the\nhighly predictable mobility~\\cite{Song2010} in everyday life. These insights can\nbe vital in emergency situations~\\cite{Bagrow2011a}, or in (preventing)\nspreading of diseases~\\cite{Bajardi2011,Balcan2009,Rocha2011} or mobile\nviruses~\\cite{Wang2009a}. Furthermore, users' mobility and their social network\nare intertwined: the one could be used to predict the other~\\cite{Wang2011,\nCrandall2010}, and the probability of two people calling over a distance follows\na gravity like model~\\cite{Lambiotte2008a,Krings2009,Calabrese2011,Levy2010}.\nResearch has also shown there are geographical clusters of highly connected\nantennas~\\cite{Expert2011} (e.g. resembling provinces) as well as clusters in\nthe social network consisting of groups of well connected\npeople\\cite{Palla2007,Blondel2008,Onnela2007}, although the connections between\nthe two are not yet fully understood~\\cite{Onnela2011}. Similar results have\nalso been obtained in a virtual mobility setting~\\cite{Szell2011}.\n\nIn this paper, we analyze anonymized communication data from a telecom operator in Portugal. The data cover a period of 15 months and the following information is available for each communication: the times of initiation and termination, the users involved, and the transmitting and receiving antennas (at the beginning of the communication). In addition, we also know the locations (longitude and latitude) of all antennas.\n\nWe first present a statistical analysis of the data. We define a set of 50 general features that we compute for each user, and using principal component analysis and clustering methods, we show that these features are highly redundant: they can all be recovered, with a loss of accuracy of less than $5\\%$, using a reduced set of only five meta-features. \n\nObserving that the most important features are geographical, we then pay specific attention to the most common locations of each user. By developing a procedure to extract these frequent positions, we observe that people spend most of their time in only a few locations. We then cluster the different calling patterns for each user and each location, and from this, we observe that only two types of locations are clearly identifiable, namely home and work. We compare our results to census data obtained from the Portuguese National Institute of Statistics.\n\nFinally, we analyze in more detail the behavior of users who have exactly one\nhome location and one office location. This allows us to predict the number of\ncommuters between different regions of the country using a gravity model. More\nprecisely, we observe that two different regimes exist, the first involving\ndistances smaller than \\unit{150}{\\kilo\\meter} (which is half the distance\nbetween the two largest cities) and the second involving larger distances. In\nthe latter case, only the number of offices in the destination region is\nstatistically significant.\n\nThe fundamental contribution of this paper is that we improve the understanding\nof frequent locations. Building on previous location inference\nwork\\cite{ZANG2010}, we construct a method for rigorously determining the type\nof locations. It had already been observed that people have only a few top\nlocations \\cite{GONZALEZETAL-NATURE-2008}, but it remained unclear what type of\nlocations they represent. Although it is often (tacitly) assumed they represent\nhome and office (\\cite{GONZALEZETAL-NATURE-2008, Song2010}), this had never been\nrigorously analyzed. We confirm this hypothesis, and also conclude that these\nare the only type of locations that are robustly detectable in the data.\n\n\\section{Data Mining and Feature Analysis} \\label{sectanalysis}\nIn this section, we analyze the calling and geographic behaviors of mobile phone users based on features that summarize these behaviors. These features allow us to investigate interdependencies between characteristics such as call durations, the distances of calls, the distances of movements and the frequency of calls. This can be achieved, for example, by analyzing {correlations} between these features. In this section we also use principal component analysis and cluster analysis to better understand these interdependencies.\n\n\\subsection{Preprocessing} \\label{preprocsec}\nBefore proceeding with the analysis, some preprocessing of the raw data was necessary. The most important preprocessing step was the application of a {moving weighted average} filter on the calling positions of the users.\n\nThis filtering was crucial because the position of the antenna does not always accurately reflect the actual position of the user. Moreover, due to noise (such as that introduced by reflection and scattering in urban environments), the closest antenna is not always the one serving the call. Without proper filtering, these inaccuracies tend to accumulate, particularly for measures such as the total distance traveled.\n\nThe filtering was computed as follows. The positions were smoothed independently for all users. Assume that a user made calls at times $t(1), \\dots, t(n)$ and the coordinates of the antennas that served the calls are $x(1), \\dots, x(n)$. The smoothed positions of the user, denoted by $y(1), \\dots, y(n)$, can be calculated as\n\\begin{equation}\ny(i)= \\!\\! \\sum\\limits_{j \\in B_{\\delta} (i) } \\!\\!\\! w(j)\\, x(j)\n\\end{equation}\nwith $B_{\\delta}(i) = \\left\\{ \\, j : \\left| t(j) - t(i) \\right| \\leq \\delta \\,\n\\right\\}$ where $B_{\\delta}(i)$ denotes the indices of those calls that were initiated or received within a maximum interval $\\delta$ from the current time of the filtering. We used $\\delta=30$min for the dataset. Positions that were further distant from the current time of the decision had proportionally smaller weights:\n\n\\begin{equation}\nw(j) = 1 - \\frac{\\left|t(j) - t(i)\\right|}{\\delta},\n\\end{equation}\nwhere $i$ denotes the current index of the call that should be smoothed.\n\nIn addition to filtering, we took into account those customers who had made and\/or received at least $10$ calls during the period analyzed ($15$ months). Moreover, for compression and cluster analysis, we {normalized} (scaled) and {centered} the data. Most of the analysis was performed on $100\\,000$ randomly (uniformly) selected users. We performed Student's {t-tests} to examine the statistical significance of the results.\n\n\\subsection{Features}\nWe defined 50 features to summarize users' behavior. Each feature represents one particular aspect of users' behavior as a single number, such as the number of incoming or outgoing calls, the number of people who called or were being called by the user, the position (coordinates) of the user (mean and deviation), the coordinates of the two most frequently used antennas, the durations of incoming or outgoing calls (mean and deviation), the distances of the incoming or outgoing calls (mean and deviation), the directions of the incoming or outgoing calls (mean and deviation) and various movement measures.\n\n\\begin{figure}\n \\begin{center}\n \\includegraphics{1-features}\n\\end{center}\n\\caption{(a) The distribution of the average locations of the users. Brighter colors indicate areas in which higher numbers of users have their average locations. (b) Three methods of measuring customer movements: (1) gyration, (2) diameter of the convex hull and (3) total line segment length.}\n\\label{fig2}\n\\end{figure}\n\nIndividual features themselves can contain considerable information. For example, by analyzing the average locations of the users, we obtained information on the distribution of users across the country, and large cities can be recognized as bright spots in the left panel of Fig.~\\ref{fig2}.\n\nIn addition to a measure referred to as gyration~\\cite{GONZALEZETAL-NATURE-2008}, we propose two additional measures of customer movements: the {diameter of the convex hull} and the {total line segment length}. All three of these measures rely on the positions of the user during calls to give some indication of how much or how far he has traveled. We take into account both incoming (received) and outgoing (initiated) calls. The sequence of the positions (calls) is not important for the first two measures, but it is significant for determining line segment length. This is illustrated in the right panel of Fig.~\\ref{fig2}.\n\nGyration~\\cite{GONZALEZETAL-NATURE-2008} measures the deviation (mean square-error) of each of the user's positions from his average location. The diameter of the convex hull measures the maximum distance between any two positions of the user during a given period. The {total line segment length} sums all of the distances between each pair of consecutive positions of the user. Note that the filtering procedure explained earlier can have a large impact on this final measure.\n\n\\subsection{Correlation Analysis}\n\nAfter computing the values of each feature for each user, we analyzed the {interdependencies} between these features using a {correlation analysis}. As mentioned earlier, we considered $100\\,000$ randomly selected users. We used t-statistics to confirm that our results are also valid for the complete dataset. In some cases, the correlations are better analyzed on a {logarithmic} scale, and we have therefore also analyzed the logarithmic correlations.\n\n\\begin{table}\n\\caption{Selected Results of Correlation Analysis}\n\\begin{center}\n\\begin{tabular}{|l l| c c |} \\hline\n{\\em Feature A} & {\\em Feature B} & {\\em Cor.} & {\\em LogCor.}\\\\\n\\hline\n\\hline\nNo Calls & No Callers & .91 & .90\\\\\nDiam Conv Hull & No Antennas & .55 & .20\\\\\nAvg Duration & Avg Distance & .31 & .64\\\\\nNo Antennas & No Calls & .60 & .68\\\\\nDiam Conv Hull & Avg Duration & .05 & .18\\\\\nLine Segm Len & No Antennas & .45 & .75\\\\\nGyration & Std Dev Dist & .60 & .40\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\label{Corr-table}\n\\end{table}\n\nTable \\ref{Corr-table} shows some of the correlations for the $100\\,000$\nrandomly selected users. The data in this table shows that movement-related features are correlated with some but not all of the other features. Some pairs of features there, such as the number of calls and the number of callers, are highly correlated as expected. Other pairs of features, such as the diameter of the convex hull and the average duration of a call, exhibit weaker correlations. Note that the correlation measures only the {linear} dependencies between two features, and more detailed relationships might be uncovered using more complex methods. We do not pursue this further here, but instead turn to an analysis of the redundancy of the data.\n\n\\subsection{Principal Component Analysis}\n\n\\begin{figure}\n\\begin{center}\n \\includegraphics{2-clustering}\n\\end{center}\n\\caption{Illustration of the basic concept behind (a) principal component analysis and (b) cluster analysis.}\n\\label{fig4}\n\\end{figure}\n\nWe now analyze the interdependencies between features using another approach. To\nwhat extent are the analyzed features redundant? In other words, how much of the\ninformation represented by one feature can be expressed by (a combination of)\nother features? To address this question, we used {principal component analysis}\n(PCA), which is widely used in various disciplines. The basic goal of PCA is to\nreduce the dimensions of the data. It can be proven that PCA provides an optimal\nlinear transformation for mean-square-based dimensionality reduction\n\\cite{Jolliffe}.\n\nThe core idea of PCA is as follow. Let $x_1, \\dots, x_n \\in \\mathbb{R}^{d}$ be\n(independent) realizations of a random vector $X$, where we assume that\n$\\mathbb{E}[X]=0$ (which can be guaranteed, for example, by substracting the\nsample mean from the measurements). In our case, the vectors $(x_i)_{i=1}^n$\nrepresent users, while each entry corresponds to a feature. \n\nWe aim at finding {\\em orthonormal} vectors $w_1, \\dots, w_d \\in\n\\mathbb{R}^{d}$, called the principal components, with the property that\nfor all $k \\in \\{1,\\dots, d\\}$, the linearly transformed vector $Y_k =\nW_k^\\mathrm{T} X$, where $W_k$ is $[w_1, \\dots, w_k]$, explains the maximum\npossible variance of $X$. In other words, if we transform our dataset $D = [x_1,\n\\dots, x_n]$ by $S_k = W_k^\\mathrm{T} D$, then we can reconstruct the matrix $D \\in\n\\mathbb{R}^{d \\times n}$ from the matrix $S_k \\in \\mathbb{R}^{k \\times n}$ (using\n$W_k$) with the smallest possible mean square error. Note that sometimes the\nrows of $S_k$, i.e., $s_i = w_i^\\mathrm{T} D$ are called the principal\ncomponents and the $w_i$'s are referred to as loadings or coefficients.\n\n\\newcommand{\\argmax}{\\mathop{\\vphantom{\\max}\\mathchoice\n {\\hbox{arg\\,max}}\n {\\hbox{arg\\,max}}{\\mathrm{A}}{\\mathrm{A}}}\\displaylimits}\n\nA recursive formulation of PCA can be given as follows. Let\n\\begin{align}\nw_1 &= \\argmax_{\\|w\\|=1} \\frac{1}{n}\\sum_{i=1}^n (w^\\mathrm{T} x_i)^2 \\nonumber \\\\\n & \\approx \\argmax_{\\|w\\|=1} \\mathbb{E} \\left[(w^\\mathrm{T} X)^2\\right] =\n \\argmax_{\\|w\\|=1} \\mbox{Var}\\left[w^\\mathrm{T} X \\right].\n\\end{align}\nThe vector $w_1$ points toward the direction in which the sample variance of the data is maximized. This is of course an approximation of what we would get using the full (unknown) distribution of $X$. Having defined the first \\(k-1\\) vectors, the \\(k\\)-th is determined as\n\\begin{align}\n w_k &= \\argmax_{\\|w\\|=1} \\frac{1}{n}\\sum_{i=1}^n\n \\Bigg(w^\\mathrm{T}\\Big(x_i-\\sum_{j=1}^{k-1}{w_j w_j^\\mathrm{T}\n x_i}\\Big)\\Bigg)^{\\!\\!2} \\nonumber \\\\\n &\\approx\n\\argmax_{\\|w\\|=1,\\, w \\perp (w_i)_{i=1}^{k-1}}\\! \\mbox{Var} \\left[ w^\\mathrm{T}X \\right],\n\\end{align}\nwhich is thus chosen to achieve the highest variance possible while being\northogonal ($\\perp$) to the previous choices. The vectors $(w_i)_{i=1}^d$ can\nbe efficiently computed from the (estimate of the) covariance matrix \\(\\Sigma=\n\\mathbb{E}\\left[X X^\\mathrm{T}\\right]\\), since vector $w_i$, $i \\in \\{1,\\dots,\nd\\}$, is an eigenvector of the sample covariance matrix corresponding to its\n$i$-th largest eigenvalue. The basic concept behind PCA is illustrated in\nFig.~\\ref{fig4}.\n\n\\begin{table*}\n\\caption{Compression of Features by Principal Component Analysis}\n\\begin{center}\n\\begin{tabular}{|c | c || c | c |} \\hline\n{\\em Variance Kept} & {\\em Mean Square Err.} & {\\em Dimen.\\ Required} & {\\em Compress.\\ Rate}\\\\\n\\hline\n\\hline\n99\\% & 1\\% & 24 & 52\\%\\\\\n\\hline\n98\\% & 2\\% & 13 & 74\\%\\\\\n\\hline\n95\\% & 5\\% & 5 & 90\\%\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\label{PCA-table}\n\\end{table*}\n\nOur PCA analysis revealed high redundancy among the features analyzed. Table \\ref{PCA-table} shows the results of this analysis. It can be seen that if we allow a $1\\,\\%$ (mean square) error in the variance, the number of features can be reduced by more than $50\\,\\%$ (from $50$ to $24$). If the allowed error is raised to $5\\,\\%$, we can further reduce the number of features to $5$, which represents a compression rate of $90\\,\\%$. In other words, we can build five components using a linear combination of the original features, and using only the values of these five components, we can determine the values of any of the $50$ original features with a $5\\,\\%$ mean-square error. This implies that the features have many interdependencies and are highly redundant.\n\nIn order to identify which features are most relevant, we determined their\nimportance as follows. PCA produces a set of orthogonal vectors,\n$(w_i)_{i=1}^d$, which point toward the directions of maximum variance. As noted\nearlier they are eigenvectors corresponding to eigenvalues of the sample covariance\nmatrix. Furthermore, the $i$-th eigenvalue, $\\lambda_i$, equals to the (sample)\nvariance of $s_i = w_i^\\mathrm{T} D$. Then, each original feature can be\nidentified by an element of the canonical basis. For example, feature 1 can be\nidentified by $e_1 = \\left<1, 0, \\dots, 0 \\right>^\\mathrm{T}$. The {importance}\nof feature $i$ can then be defined as the max-norm of the projected vector $e_i$\non the basis defined by $(w_i\/{\\lambda_i})_{i=1}^{d}$. The basis was thus\nscaled to produce larger coordinates in directions of higher variances. Note\nthat we have also scaled the scores such that the most important feature has a\nrelative importance of $1$.\n\nFig.~\\ref{fig3} presents a list of the features in order of importance. The most\nimportant features, such as the average position of the user and the coordinates\nof the two most often used antennas, are geographic features. This indicates\nthat the locations of the users and their calls are among the most important\ncharacteristics.\n\n\\begin{figure*}\n\\begin{center}\n \\includegraphics{3-features-pca}\n\\end{center}\n\\caption{The relative importance of each feature according to PCA, defined as the max-norm of the projection of the feature basis on the principal components.}\n\\label{fig3}\n\\end{figure*}\n\n\\subsection{Cluster Analysis}\nAfter analyzing the data using PCA, we performed {cluster analysis}, to identify typical user classes based on calling behaviors. We used the {subtractive} clustering method~\\cite{Chiu} illustrated in Fig.~\\ref{fig4}, which is a variant of the classical mountain method. An advantage of the subtractive clustering method is that it can identify the number of clusters required.\n\nThe application of subtractive clustering to the {normalized} data for\n$100\\,000$ uniformly selected customers resulted in $5$ clusters. Each of these\nclusters is identified by its {central} element (a vector of feature values) and\nits range of influence. As with the PCA, we wished to identify the main\nconstituent features of these clusters. We therefore performed a similar\nanalysis as for the PCA, using the vectors of the cluster centers as the basis\nfor the dominant feature subspace. The results of this ordering, presented in\nFig.~\\ref{fig5}, indicate that location- and movement-related features are important characteristics, similar to PCA.\n\n\\begin{figure*}\n\\begin{center}\n \\includegraphics{4-features-clustering}\n\\end{center}\n\\caption{The relative importance of each feature according to cluster analysis.}\n\\label{fig5}\n\\end{figure*}\n\nNote that both PCA and cluster analysis reveal clear differences in importance\nbetween the $x$ and $y$ coordinates. This can be expected for an elongated\ncountry such as Portugal and is probably aggravated by the fact that most people\nlive along the coast.\n\nAlthough the features concerning the $y$ coordinates have a similar importance\nin both the PCA and cluster analysis, there are some clear differences as well.\nIn general, an explanation of this could be that PCA focuses on global\ncharacteristics, it tries to build components (by linear combination of feature\nvectors) which can explain the dataset with minimal mean square error.\nClustering, however, concentrates on local similarities, and tries to find\nclusters in which the feature vectors are ``close'' to each other.\nNevertheless, various geographical features have key importance according to\nboth orderings. The most notable difference concerns the diameter of the convex\nhull, which has a very high importance in clustering, while it has a relatively\nlow importance in PCA. From the PCA analysis this implies that the variance in\nthe diameter of the convex hull is not important for explaining a large part of\nthe data. From the cluster analysis, the differences in the diameter of the\nconvex hull are important, even though the variance might not contribute that\nmuch. This suggests an interesting effect of the diameter of the convex hull.\nBesides the obvious importance of the $y$ position when clustering people, the\ndiameter of the convex hull separates people that share similar $y$ positions.\nIn conclusion, the features that are important for clustering people are: (1)\nfirst antenna; (2) second antenna; (3) diameter of convex hull; and (4) average\nposition.\n\n\\section{Frequent Locations} \\label{sectarnaud}\n\nIn the previous section, we analyzed several features and concluded that the\nmost important ones are related to geography. Additionally, we observed that\nmost people spend most of their time in only a few locations. In this section,\nwe focus on characterizing these frequent locations for each user by analyzing\nweekly calling patterns. Once these frequent locations are characterized, we\nanalyze them in greater depth. A related, although different, concept of\nhabitats~\\cite{Bagrow2012} was recently introduced, where habitats are clusters\nof the associated Markov mobility network. However, a single habitat might\ncontain several frequent locations.\n\nAs explained in Section \\ref{preprocsec}, the data are noisy, and often, any one of multiple antennas can be used to make a call from a given position. Because this can be true for frequent locations such as home and the office, we first develop a method for estimating which antennas are relevant for characterizing such locations.\n\nAfter extracting the frequent locations for each user, we estimate these positions more precisely using a maximum likelihood approach. We then present various statistics using these estimated positions. In particular, we estimate the amount of time people spend at work and home, characterize different combinations of frequent locations (multiple `homes' or `offices'), estimate the geographical density of homes and offices, compare our estimates to independent statistics and, finally, analyze distances between home and office (commuting distances).\n\n\\subsection{Detection of Frequent Locations}\n\nDetecting the most common locations of a user is only possible if enough calls involving that user are recorded\\footnote{In this section, we include both calls and text messages because we want to maximize the information on antenna usage; we refer to both as ``calls''.}. For users who make only a few calls, no locations can be called ``frequent'' with any certainty. We therefore selected only users who make at least one call a day on average and who make consecutive calls within $24$ hours 80\\% of the time. The latter constraint requires a certain regularity of users, and excludes users with highly bursty behavior\\cite{GONZALEZETAL-NATURE-2008}. From this selection, we selected a random sample of $100\\,000$ users.\n\nFor detecting frequent locations, it is appropriate to begin by identifying the most frequently used antenna (MFA). However, as stated earlier, the same antenna is not always used for calls made from a given position (due to load balancing or the effects of noise on the signal). Hence, other antennas located near the MFA may also be used to serve the frequent location. We must therefore consider sets of antennas that are relatively close together.\n\nWe first performed a Voronoi tessellation, which partitions the space into cells based on the distance between each point and the closest antenna. Each Voronoi cell includes the set of points that are closer to the antenna located in that cell than to any other antenna. Based on the Voronoi tessellation, a graph can be created in which nodes are neighbors if their associated Voronoi cells are adjacent. Each node corresponds to an antenna, and its neighbors are called the Delaunay neighbors.\n\nWe next grouped antennas around the MFA based on Delaunay neighborship. More precisely, we defined the Delaunay radius of each antenna to be the largest distance between an antenna and any of its Delaunay neighbors (this is later used in the estimation of the position; see Section~\\ref{sec:Neighborhood} for more information). We then merged all antennas around the MFA that are within twice this radius\\footnote{We observe that taking twice the Delaunay radius yields an error of less than $0.1$\\% for estimating positions. See Section~\\ref{sec:Neighborhood} for more information.} and assigned them one ``location'', the position of which will be defined later.\n\nAfter identifying the first MFA and merging the surrounding antennas, we moved on to the remaining antennas, selecting the most frequently used of those and repeating the procedure described above. We continued iterating until we identified a set of antennas that represented less than 5\\% of a user's calls. We repeated this for each user in our selection and thus obtained a number of frequent locations for all users.\n\n\\begin{figure}\n \\begin{center}\n \\includegraphics{5-mfp_freq}\n \\end{center}\n \\caption{Histogram of the number of frequent locations per user}\n \\label{fig:histFL}\n\\end{figure}\n\nThe results of this procedure are summarized in Fig.~\\ref{fig:histFL} and indicate that the average number of frequent locations per user is approximately $2.14$ and that $95\\%$ of the users have fewer than $4$ frequent locations. This implies that the 3 or 4 most common locations are sufficient to predict the position of user, most of the time~\\cite{Song2010}. A substantial number of users have only one single frequent location, which is usually an office or a home location (as we will see later on). This could reflect the possession of separate business and private phones, one of which is (almost) exclusively used at work and the other only at home.\n\n\n\\subsection{Clustering of Weekly Calling Patterns}\n\nThe data show two clearly identifiable periodic dynamics in mobile phone use: a daily cycle and a weekly cycle, as illustrated in Fig.~\\ref{fig:avg_pattern}. The daily cycle largely follows the human circadian rhythm, with a clear drop in activity during the night, a gradual increase in the morning and a decrease in the evening, with a small dip around lunch time. The weekly dynamic is related to the workweek, with different behavior on weekends as compared to work days.\n\nWe collected all of the calls made using antennas associated with each frequent location. Because we have the time stamps (beginning and end) of each call, we know the times at which each frequent location is used. The description of this usage at the weekly scale seems to be especially suitable for further analysis. We therefore divided the week into 168 hours and aggregated the usage pattern of the whole period. This resulted in a $168-$dimensional vector per frequent location with the calling frequency for one hour in each entry.\n\nBased on the aggregated call vectors for all frequent locations, we performed k-means clustering. We ran this clustering for $k=\\{2,\\ldots,10\\}$ to investigate what patterns of usage could be distinguished. We found that using $k=3$ yielded clear results, as displayed in Fig.~\\ref{fig:home_office}. The first cluster clearly represents a pattern related to work. During the weekdays, an increase in the usage of these antennas occurs during the morning, followed by a small dip around noon, and a decrease in usage from around 6 p.m. on. During the weekend, these antennas are used far less. This pattern is in excellent agreement with independent statistics from the Portuguese National Institute of Statistics (INE) in terms of time spent at work, as shown in Fig.~\\ref{fig:home_office}. The second cluster reflects a pattern of usage that appears to be more closely associated with a home position. The usage of these antennas is lower during the day, and the maximum usage occurs during the evening. These antennas are also used more during the weekend than are the antennas in the first cluster. Finally, the third cluster appears simply to contain locations that do not follow the dynamics of the previous two clusters. This cluster follows the more general dynamic displayed in Fig.~\\ref{fig:avg_pattern}.\n\n\\begin{figure}\n \\begin{center}\n \\includegraphics{6-call_dynamics}\n \\end{center}\n \\caption{Weekly dynamics on average (a) and for the three clusters (b) detected using k-means clustering: home, office and the remainder, shown along with independent time usage statistics from the Portuguese National Institute of Statistics (INE)~\\cite{INE1999}. Using more clusters yields similar results. The dotted lines indicate noon of each day.}\n \\label{fig:home_office}\n \\label{fig:avg_pattern}\n\\end{figure}\n\n\nWe observed that when more than three clusters are considered, they tend to yield results very similar to those shown here. We expected that we would be able to identify additional patterns of usage, such as those of calls made by students with a different rhythm from working people or calls made from weekend houses that show no activity during the week, but we did not observe these patterns. Such patterns certainly do exist, but they appear to be marginal when compared to the established home and office routine. Hence, there appears to be no identifiable patterns of usage other than the home and office patterns described above. However, using only two clusters obfuscates this result, and the separation between home and office positions is less clear in this case.\n\n\\begin{table}\n\\begin{center}\n\\rowcolors{2}{gray!25}{white}\n\\begin{tabular}{cccc}\n \\rowcolor{white}\n$\\#$ Home & $\\#$ Office & $\\#$ Unidentified & \\%\\\\\n\\hline \\hline\n & 1& & 16.8\\\\ \n1& & & 16.5\\\\\n1& & 1& 9.1\\\\\n1& 1& & 6.6\\\\\n & 1& 1& 6.0\\\\\n1& & 2& 5.0\\\\\n & & 1& 3.5\\\\\n1& 1& 1& 3.5\\\\\n & & 2& 3.4\\\\\n & 1& 2& 2.7\n\\end{tabular}\n\\caption{The $10$ most frequent combinations of frequent locations. Each\n combination is composed of the number of homes, offices and unidentified\n locations a user has. Each row indicates such a combination. The empty entries\n indicate no such type of location is present in a combination. The last column\n contains the percentage of how often such a combination occurs.}\n\\label{locPattern}\n\\end{center}\n\\end{table}\n\nThe top 10 most frequent combinations of frequent locations are displayed in Table~\\ref{locPattern}. Approximately 32\\% of the users have either a single home location or a single office location alone, whereas only 3.5\\% have only a single unidentified location. For users with two frequent locations, the most common combination is one home location and one unidentified location. Only 6.6\\% of all users have the combination of one home location, one office location and no unidentified locations. Approximately 85\\% of the users have at most one home and\/or one office location, and approximately 12\\% of the users have exactly one home and one office location (and possibly multiple unidentified locations).\n\nOf all frequent locations, approximately $60\\%$ ca be classified as ``home'' or\n``office'' (as in the first two columns of Table~\\ref{locPattern}). We observed\nthat users tend to have no more than two identifiable positions, as depicted in\nFig.~\\ref{fig:histFLID}. The majority of users have only one identifiable location, which is by definition either home or office. For users with two identifiable locations, over 50\\% have both a home and an office, and the rest has either two homes or two offices.\n\n\\begin{figure}\n \\begin{center}\n \\includegraphics{7-barIdentifiable}\n \\end{center}\n \\caption{Histogram of the number of frequent locations per user, with visual separation between ``home'' locations, ``office'' location and mixed locations (i.e., users with one of each)}\n \\label{fig:histFLID}\n\\end{figure}\n\n\\subsection{Estimating the Position of Frequent Locations}\n\n \\subsubsection{Basic model}\n\n We propose a model to estimate the position of the home, the office and other frequent locations. We consider a simplified version of the model proposed in \\cite{ZANG2010}, which was also used in~\\cite{SocialEvent2011}. The underlying idea is that users connect to the antenna that has the highest signal strength, which is not necessarily the closest antenna. \n\n We begin by estimating the total signal strength of an antenna $i$ at a\n certain position $x$. We assume, similarly to~\\cite{ZANG2010}, that the total\n signal strength consists of three components: the power of the antennas,\n the loss of signal strength over distance and some stochastic fading of the signal due to scattering and reflection in the environment. Specifically, we use the following parameters.\n \\begin{itemize}\n \\item The position of antenna $i$ is denoted by $X_i$.\n \\item The power is denoted by $p_i$, and we assume this to be constant and equal for all antennas because we have no information regarding the power of the antennas. Therefore, $p_i=p$ for all $i$.\n \\item The loss of signal at position $x$ for antenna $i$ is modeled as\n \\begin{equation}\n L_i(x) = \\frac{1}{\\| x - X_i \\|^\\beta},\n \\end{equation}\n where $\\beta$ is a parameter indicating how quickly the signal decays.\n \\item The so-called Rayleigh fading of the signal from antenna $i$ can be modeled by a unit mean exponential random variable $R_i$~\\cite{Tse:2005p10323}, for which the cumulative distribution function (cdf) is\n \\begin{equation}\n \\Pr(R_i \\leq r) = F(r) = 1 - e^{-r}.\n \\end{equation}\n Furthermore, we assume all $R_i$ to be independent.\n \\end{itemize}\n The total signal strength $S_i(x)$ of antenna $i$ at location $x$ is then modeled as \n \\begin{equation}\n\t\tS_i(x) = p L_i(x) R_i,\n\t\\label{eq:signalstrength}\n\t\\end{equation}\n\tand we model the probability that a user at position $x$ connects to antenna $i$, $\\Pr(a=i | x)$, as the probability that the signal strength of antenna $i$ is larger than that of any other antenna:\n\t\\begin{align}\n\t\t\\Pr(a=i | x) &= \\Pr(S_i(x) > S_j(x), ~~ \\forall j) \\nonumber \\\\\n &= \\prod_j{\\Pr(S_i(x) > S_j(x))}.\n\t\t\\label{equ:probabilityAiX}\n\t\\end{align}\n This probability density is displayed in Fig.~\\ref{fig:antenna_dens}.\n \n\\begin{figure}\n \\begin{center}\n \\includegraphics{8-dens_plot}\n \\end{center}\n \\caption{Probability density $\\Pr(a=i | x)$ (represented by topographic curves) for a particular antenna $i$ (central black `X'), with neighboring antennas (red `X's) and the local Voronoi tessellation (dark lines) also shown. The probability density can be seen as a smoothed Voronoi tessellation in which there is a small probability of connection to antenna $i$ when the user is in another Voronoi cell.}\n \\label{fig:antenna_dens}\n\\end{figure}\n\n\t\n\tThis probability can be seen as a smoothed Voronoi tessellation, in which a user will always connect to the closest antenna, by taking the limit of $\\beta \\to \\infty$. In that case, we are essentially considering the situation in which the path loss is dominant over the Rayleigh fading. Hence, little noise is involved, and whenever a closer antenna exists, it will be used.\n\n \\subsubsection{Antenna neighborhoods}\\label{sec:Neighborhood}\n \n As mentioned in the previous section, the probability that a user will connect to a specific antenna depends on the position of other nearby antennas. The relevant set of antennas $\\mathcal{X}$ can be rather large, which can slow down the computation of the probabilities. Using a local approximation might accelerate this process without affecting the results.\n \n The idea of using a local approximation is tied to the decreased probability that a call will be linked to an antenna that is far away. Only some of the antennas around a given position are in fact relevant. It therefore seems natural to construct local neighborhoods of antennas so as to make the method more efficient without introducing any significant error.\n \n We define the neighborhood $\\mathcal{X}_i$ and the domain $\\mathcal{D}_i$ of antenna $i$ to consist of the smallest circle enclosing at least all of the Delaunay neighbors (and possibly more). As mentioned previously, the Delaunay neighbors are those antennas located in adjacent Voronoi cells.\n \\begin{itemize}\n \\item For each antenna, we select all Delaunay neighbors and then select the\n maximum distance between the focal antenna and any of these neighbors:\n \\begin{equation}\n \\rho_i = \\max \\{ d(X_i, X_j) | j \\text{~Delaunay neigh. of~} i\\},\n \\end{equation}\n where $d(X_i,X_j)$ is the distance between antenna $i$ and $j$.\n \\item We then define the domain \n \\begin{equation}\n \\mathcal{D}_i = \\{x | \\|x - X_i\\| \\leq \\delta \\rho_i\\}\n \\label{eq:domain}\n \\end{equation}\n as the region within radius $\\delta \\rho_i$, where $\\delta $ is a scaling factor. We observe that choosing $\\delta =2$ leads to an error of less than $0.1\\%$ in the computation of $\\Pr(a=i | x)$ compared\\footnote{average error based on $1000$ random points} to using the entire set $\\mathcal{X}$.\n \\item Finally, the set of Delaunay neighbors\\footnote{To deal with antennas near the border of the country (for which the Delaunay neighbors can be far away), we take this border into account, and create a slightly different neighbor set.} is taken as all antennas within this region:\n \\begin{equation} \\mathcal{X}_i = \\{j | X_j \\in \\mathcal{D}_i \\text{~for~} j \\in \\mathcal{X} \\}.\\end{equation}\n Note that this set contains at least all of the Delaunay neighbors and may also contain other antennas. \n \\end{itemize}\n \n Finally, using equation (\\ref{equ:probabilityAiX}), we approximate the probability as \n \\begin{equation}\n \\Pr(a=i | x) \\approx \\prod_{j \\in\\mathcal{X}_i}{Pr(S_i(x) > S_j(x))},\n \\label{eq:probabilityAisubX}\n \\end{equation}\n leading to a large reduction in the computational time required\n\t\n \\subsubsection{Maximum Likelihood Estimation}\n \n We use the model explained above to more accurately estimate the position of each frequent location. For each such location, we know the number of calls $k_i$ made using antenna $i$. The probability that $k_i$ calls were made using antenna $i$ given position $x$ is then $\\Pr(a=i | x)^{k_i}$. Hence, the log likelihood of observing call frequencies $k$ for the antennas in $\\mathcal{X}_f$, where $f$ is the MFA of a frequent location, for a certain position $x$ is\n \\begin{equation}\n \\log \\mathcal{L}(x|k) = \\sum_{i \\in \\mathcal{X}_f} k_i \\log \\Pr(a=i | x).\n \\label{equ:log_likelihood}\n \\end{equation}\n The maximum likelihood estimate (MLE) $\\hat{x}$ of the position of a frequent location is then given by\n \\begin{equation}\n \\hat{x} = \\arg \\max_{x} \\log \\mathcal{L}(x|k).\n \\label{equ:mle}\n \\end{equation}\n\nTo find the MLE, we employ a derivative-free optimization scheme because the\ngradient of the likelihood function is costly to evaluate. In particular, we use\nthe Nelder-Mead algorithm\\cite{NelderMead}, initialized with the weighted\naverage position of the antennas associated with the frequent location. The\ndistance between the average position of the antennas and the MLE is\n\\unit{1.7}{\\kilo\\meter} on average and reaches a maximum of approximately\n\\unit{35}{\\kilo\\meter}. This shows that although using the average position provides a reasonable approximation, it is not always accurate.\n\n\\subsection{Results}\nWe now analyze the results of the position estimation. First, we present our results concerning the geographical distribution of frequent locations around the country and compare these results to independent statistics. We then analyze commuting distances, i.e., the distances of travel between home and office, and develop a model of the number of commuters between each pair of counties\\footnote{We used the NUTS-3 data defined by Eurostat, which, in the case of Portugal, consists of groups of municipalities; we refer to these as ``counties'' for simplicity.}.\n\n\\subsubsection{Population density estimation}\n\n\\begin{figure*}\n \\begin{center}\n \\includegraphics{9-maps}\n \\end{center}\n \\caption{(a) Population sizes per county throughout the country (based on statistics from INE), (b) estimated number of homes per county, and (c) the distribution of all frequent locations. Lighter colors indicate higher values.}\n \\label{fig:distr_FL}\n \\label{fig:map_pop_dens}\n\\end{figure*}\n\nThe position estimates of all frequent locations can be used to analyze the population distribution throughout the country. Using the county level data, we counted the number of home locations for each county. We then compared these results to population density data obtained from the \\emph{Instituto Nacional de Estatistica\\footnote{http:\/\/www.ine.pt}} (INE). As shown in Fig.~\\ref{fig:map_pop_dens}, there is a strong correspondence between the INE population data for each county and our estimate. The correlation between the two is 0.92. This indicates that we can accurately estimate population size based on the mobile phone data. A more accurate density plot of the frequent locations is shown in Fig.~\\ref{fig:distr_FL}, which illustrate that these locations are concentrated in the cities. A comparison of Fig.~\\ref{fig:distr_FL} to the distribution of the average positions of users over the entire period (Fig.~\\ref{fig2}), shows that the distribution of frequent locations is more pronounced. Average positions are likely to be distorted by commutes and to interpolate between home and office.\n\n\\subsubsection{Commuting distances}\n\nThe home and office positions determined above can be used to estimate commuting distances. For individuals who have more than one home or one office, multiple commuting distances could be calculated, but it would be unclear which distance is the ``correct'' one. Therefore, for this analysis, we considered only the $12\\%$ of users who have exactly one home and one office (and possibly some unidentified frequent locations). This means that each user considered has exactly one commuting distance. These commutes are plotted in Fig.~\\ref{fig:commute_map}, with smaller distances indicated in brighter colors. Two things stand out on this map. First, the two largest cities in Portugal, Porto and Lisbon, are clearly discernible. Second, most of the cities appear to predominantly attract people living in the immediate surroundings.\n\n\\begin{figure}\n \\begin{center}\n \\includegraphics{10-commuting_map}\n \\end{center}\n \\caption{Commute map for our sample of users. Brighter colors indicate smaller commuting distances. Most of the commutes cover only small distances, although some commutes span half the country. The number of commutes decays approximately log-normally with distance.}\n \\label{fig:commute_map}\n\\end{figure}\n\n\nThe distribution of commuting distances depicted in Fig.~\\ref{fig:commute_dist}\nappears to be affected by the location of Porto and Lisbon. Two different\nregimes can be discerned: one regime reflecting commuting distances of less than\n\\unit{150}{\\kilo\\meter} and the other reflecting larger distances. This\ncoincides with the distance between Lisbon and Porto, which is approximately\n\\unit{300}{\\kilo\\meter}. In fact, most of Portugal is within\n\\unit{150}{\\kilo\\meter} of one of these two cities. This suggests that most\npeople tend to work no further away than the closest largest city, i.e., it is\nunlikely that people living near Porto work in Lisbon. The set of commuting\ndistances that are less than \\unit{150}{\\kilo\\meter} can be reasonably well fitted using a log-normal distribution with parameters $\\mu=2.35$ and $\\sigma=0.94$, as displayed in Fig.~\\ref{fig:commute_dist}.\n\n\\begin{figure}\n \\begin{center}\n \\includegraphics{11-commuting_dist_distr}\n \\end{center}\n \\caption{The distribution of commuting distances revealed by the analysis of\n mobile phone data. These distances exhibit a log-normal distribution for $d\n < \\unit{150}{\\kilo\\meter}$ (blue line). The full distribution is shown, with\n the line at \\unit{150}{\\kilo\\meter} separating the two different regimes.\n These two distinct regimes probably arise because almost all of continental\n Portugal is within \\unit{150}{\\kilo\\meter} of either Porto or Lisbon.}\n \\label{fig:commute_dist}\n\\end{figure}\n\nA common model for analyzing commuting distance is the gravity model~\\cite{Balcan2009, GravityModel}, although recently another parameterless model has been suggested~\\cite{Simini2012}. This model formulates the number of trips $w_{ij}$ made between two locations $i$ and $j$ as proportional to the population sizes at the origin $P_i$ and at the destination $P_j$, with some decay, depending on the distance $d_{ij}$ between $i$ and $j$. More precisely, the model is formulated as\n\\begin{equation}\n \\hat{w}_{ij} \\sim \\frac{P^\\alpha_iP^\\beta_j}{f(d_{ij})},\n\\end{equation}\nwhere $f(d_{ij})$ is usually taken as either a power law $d^\\gamma_{ij}$ or an exponential decay $e^{\\gamma d_{ij}}$, with parameters $\\alpha$, $\\beta$ and $\\gamma$ to be estimated from the data.\n\nHere, we formulate the gravity model in terms of the number of trips (commutes) made between county $i$ and county $j$. Instead of simply considering the population size as $P_i$ and $P_j$, we can take into account our previous calculations of the distributions of both home positions and office positions. The probability of a trip from $i$ to $j$ can then be formulated in terms of the number of home locations at the origin $H_i$ and the number of office locations at the destination $O_j$. \n\n\\begin{table*}\n \\begin{center}\n \\begin{tabular}{llll}\n Coefficient & Variable & $d_{ij} < \\unit{150}{\\kilo\\meter}$ & $d_{ij} \\geq \\unit{150}{\\kilo\\meter}$ \\\\\n \\hline \\hline\n $\\alpha$ & Number of homes at origin & $0.17^{**} \\pm 0.013$ & $0.018 \\pm 0.013$ \\\\\n $\\beta$ & Number of offices at destination & $0.21^{**} \\pm 0.013$ & $0.030^{*} \\pm 0.012$ \\\\\n $\\gamma$ & Distance & $0.37^{**} \\pm 0.018$ & $0.13 \\pm 0.11$ \\\\\n & $R^2$ & $0.52$ & $0.26$ \\\\\n & $R^2$ (exponential fit) & $0.46$ & $0.26$\n \\end{tabular}\n \\end{center}\n\n \\caption{Fitted parameters and $R^2$ of the gravity model with $f(d_{ij}) = d_ij^\\gamma$, with standard errors reported. We also report $R^2$ for the exponential fit $f(d_{ij}) = e^{\\gamma d_{ij}}$, which is slightly worse. ${}^{**}$ $p < 0.001$, ${}^*$ $p < 0.05$.}\n \\label{tab:gravity_fit}\n\\end{table*}\n\nAgain, we discern two regimes: a close-range regime with $d_{ij} < \\unit{150}{\\kilo\\meter}$ and a long-distance regime with $d_{ij} \\geq \\unit{150}{\\kilo\\meter}$. Fitting both the power law decay and the exponential decay, we find that the power law decay provides a slightly better fit. The results are displayed in Fig.~\\ref{fig:gravity_fit} and in Table~\\ref{tab:gravity_fit}. Interestingly, the decay distance parameter $\\gamma$ for large distances is not significant, suggesting that for distances $d_{ij} \\geq \\unit{150}{\\kilo\\meter}$, the number of trips no longer depends on the actual distance. In fact, the only coefficient that is significant for large distances is the coefficient of the number of offices at the destination. Thus, for larger distances, only the number of work opportunities at the destination appears to be important.\n\n\\begin{figure*}\n \\begin{center}\n \\subfloat[Power law decay]{\\includegraphics[width=0.5\\textwidth]{12-gravity_estimation_log_fit}}\n \\subfloat[Exponential decay]{\\includegraphics[width=0.5\\textwidth]{13-gravity_estimation_exp_fit}}\n \\end{center}\n \\caption{Plots of the prediction ratio $\\hat{w}_{ij}\/w_{ij}$ for commuting\n distance of $d < \\unit{150}{\\kilo\\meter}$ (left panels) and $d_{ij} \\geq\n \\unit{150}{\\kilo\\meter}$ (right panels) for (a) the power law decay $f(d_{ij})\n = d_{ij}^\\gamma$, and (b) the exponential decay $f(d_{ij}) = e^{\\gamma\n d_{ij}}$. Red squares indicate mean values, and blue circles indicate\n medians.}\n \\label{fig:gravity_fit}\n\\end{figure*}\n\nThe fit of the model is better when the numbers of home and office locations per county are used than when the population sizes are used. As shown in Table~\\ref{tab:gravity_fit}, the values of $R^2$ for the two regimes are $0.52$ and $0.26$, respectively, when the numbers of home and office locations are used, compared to $0.43$ and $0.24$, respectively, when population sizes are used. Hence, it is worth taking into account the numbers of offices and homes when modeling commuting distances instead of simply using population size as an approximation for both. \nIn the present case, the model slightly overestimates the number of shorter\ncommutes, indicating that there is room for improvement. This deviation might be\ndue to the aggregation of information at a small resolution. On the other hand,\nthis might also be due to a real effect: distances below some threshold have no\neffect. In this case, trips under about \\unit{2}{\\kilo\\meter} should be almost\nunaffected by distance. Higher resolution data is needed to investigate this in\nmore detail.\n\n\\section{Conclusion} \\label{sectpaul}\n\nIn this study, we analyzed the behavior of mobile phone customers based on their calling habits. We first sampled $100\\,000$ customers randomly and filtered their locations, as these are based on associated antenna locations, which are subject to disturbances. We then defined and computed $50$ features that describe the calling behaviors of the customers. We performed a correlation analysis on these features, which showed that movement- and location-related features are correlated with many other features. We then analyzed the data using principal component analysis (PCA). This showed that the original features are highly redundant and can be efficiently compressed if some reconstruction error (e.g., $5\\,\\%$) is allowed. We also performed a cluster analysis and that revealed a small number of typical user classes. We computed the relative importance of each feature in the PCA and the cluster analysis and found that location- and movement-related features are especially important in both cases. We therefore analyzed the users' most common locations.\n\nWe clustered these frequent locations based on weekly calling patterns and found that only home and office locations could be clearly identified. Other patterns of usage (such as use from weekend houses) are surely present in the data, but these are marginal when compared to the clear pattern of use from home and office locations. We characterized the number of frequent locations for each user and the most common combinations of frequent locations (e.g., multiple houses or offices). Finally, we estimated the positions of frequent locations based on a probabilistic inference framework. Using these positions, we derived a fairly accurate estimate of the distribution of the population, which showed a correlation of 0.92 with independent population statistics. These positions also allowed us to analyze commuting distances, and we found that the data are reasonably well explained by a gravity model. This model works better when the numbers of homes and offices are considered instead of population sizes. This indicates that when analyzing commuting distances, it is worth taking the distribution of home and office location into account.\n\nThe present study represents an exploratory analysis of the data. Further research into the frequent locations and associated user behavior should be undertaken. This data set contains both geographical data and social network data, and it would be interesting to further analyze the interaction between the two.\n\n\\section*{Acknowledgments}\nThe authors acknowledge support from the grant ``Actions de recherche concert\\'ees --- Large Graphs and Networks'' of the Communaut\\'e Fran\\c caise de Belgique and from the Belgian Network DYSCO (Dynamical Systems, Control, and Optimization), funded by the Interuniversity Attraction Poles Programme initiated by the Belgian State Science Policy Office. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\n\n\n\n\n\n\\bibliographystyle{model1-num-names}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe microscopic manifestation of temperature in lattice dynamics is generally understood through the \\textit{harmonic approximation}, where atomic motion is mapped onto a set of vibrational normal modes (i.e., phonons)~\\cite{Born1998}. \nModern studies into lattice dynamics of solids significantly expand on this view to account for anharmonic phenomena such as phase transitions~\\cite{Fultz2020,Bruce1981}, thermal expansion~\\cite{Barron1999} and thermal conductance~\\cite{Sun2010,Romero2015,Whalley2016}.\n\nIn contrast, studies that focus on the electronic properties of solids rarely go beyond a quasi-harmonic (QH) treatment, where the normal modes are re-normalized for each given temperature to account for thermal expansion~\\cite{Fultz2010}.\nThe effects of lattice dynamics are usually incorporated through the prism of electron-phonon interaction. \nThis can manifest in modified carrier lifetimes~\\cite{Eiguren2002,Eiguren2003}, corrections to the electronic band structure~\\cite{Gopalan1987}, mobility estimations~\\cite{Li2015,Fiorentini2016,Schweicher2019} and calculations for vibrational spectroscopy~\\cite{Strauch1995,Gillet2017,Cusco2007,Menedez1983}.\nFormally, almost any \\emph{ab initio} calculation of electron-phonon interactions relies on the harmonic approximation\\cite{Giustino2017}.\nA different manifestation of lattice dynamics in electronic properties is the temperature evolution of the material's dielectric response. \nThe need to extend anharmonic modeling to dielectric properties has been demonstrated perhaps most strikingly in halide perovskite semiconductors~\\cite{Zhu2016,Joshi2019,Martiradonna2018,Sender2016,Schilcher2021,Guo2019,Omer2017}.\nImportantly, by relying on molecular dynamics simulations, calculations of the dielectric response of a crystal are not limited to a perturbative treatment of anharmonicity.\n\nBecause inelastic light scattering originates in the vibrational modulation of a crystal's polarizability auto-correlation function~\\cite{Yu2010}, it is the ideal probe to study the effect of anharmonicity on dielectric properties.\nA main drawback of optical inelastic light scattering is that momentum conservation limits vibrational contributions to the $\\Gamma$, or zero crystal momentum point. \nThis limitation does not apply in 2\\textsuperscript{nd} order Raman scattering, which is driven by contributions from the entire Brillouin zone (BZ)~\\cite{Loudon1964}. \nSimply put, 2\\textsuperscript{nd} order Raman describes the inelastic scattering of a single photon with two phonons~\\cite{Cardona1982}. \nThe scattering intensity inside a given frequency interval $I\\left(\\Omega\\right)\\text{d}\\Omega$ emerges out of contributions from all possible phonon combinations with the appropriate energy and momentum conservation. \nIt is therefore sensitive to any anharmonic effect in either the phonon dispersion or the dielectric response.\n\nIn this study, we introduce an \\textit{ab initio} approach incorporating finite temperature effects into the dielectric response of a crystal. \nThe approach is based on the effective interatomic force constants used in the temperature dependent effective potential (TDEP) method~\\cite{Hellman2011,Hellman2013a,Hellman2013}. \nThe computational method is benchmarked with experimental measurements of dielectric response in terms of 2\\textsuperscript{nd} order Raman scattering.\nAs a model system we choose NaCl.\nIt is an ideal showcase since first order Raman is forbidden by symmetry, necessitating a computational methodology that goes beyond the lowest order.\nFurthermore, the central role of anharmonicity in NaBr was recently demonstrated~\\cite{Shen2020}, motivating further examination of the rock-salt structure.\nThe comparison between simulated and experimental results shows that the harmonic treatment is inadequate when considering dielectric response at finite temperatures.\nIn contrast, the method developed here not only reproduces experimental results more faithfully than conventional approaches, it also shows how higher order dielectric response is linked to other manifestations of anharmonicity.\nMore specifically, it accounts for phonon broadening, thermal transport, and demonstrates how a measurement of the 2\\textsuperscript{nd} order Raman spectrum can be used to conclude the dominant anharmonic expressions in a given material.\nImportantly, due to its \\emph{ab initio} nature, our method provides a full description of the linear dielectric response of the material and its dependence on atomic displacements. \n\n\n\n\n\n\n\n\n\n\n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=\\linewidth]{NaCl_measVSsim4.pdf}\n\\caption{Temperature dependence of the Raman spectrum for NaCl by measurement (black), by quasi-harmonic (red) and TDEP (blue) simulation. \\# symbols mark system artifacts.}\n\\label{fig:NaClTemp}\n\\end{figure}\n\n\n\nOur main results are presented in Fig.~\\ref{fig:NaClTemp}, where we compare the measured, temperature dependent, unpolarized \\footnote{measured\/calculated sums of the scattered intensities for a full rotation of incident polarization} Raman spectra of NaCl and corresponding simulations. \nThe technical details for the construction of an unpolarized spectrum, as well as the QH computation, are provided in Sec.~S\\rom{2},~S\\rom{7}, respectively, in the supplementary material (SM). \nOur experimental spectra are in good agreement with past measurements~\\cite{Welsh1949, Krauzman1969}. \nWe observe a continuous spectrum with some sharp non-Lorentzian features centered around 280 cm$^{-1}$.\n\nUnlike early analytical treatments\\cite{Birman1962,Birman1963,Lax1961}, our computation is able to predict the entire shape of the spectrum. \nIt is immediately evident that even at 300~K the QH calculation for the spectrum is imperfect.\nIt overestimates the high ($\\approx$300~cm$\\textsuperscript{-1}$) to low ($\\approx$100~cm$\\textsuperscript{-1}$) intensity ratio.\nIt also fails to capture the broad decaying intensity above 350~cm$\\textsuperscript{-1}$.\nThe discrepancies between experiment and QH calculation at 300~K are remarkable, as they demonstrate the significance of anharmonic effects in the dielectric response of an archetypal rock-salt crystal, even at room temperature. \nThese discrepancies get even larger at higher temperatures.\nAbove 500~K the main spectral feature around 300~cm$\\textsuperscript{-1}$ broadens and flattens but the QH calculation predicts an opposite trend. \nThese findings constitute direct evidence for the failure of a QH approach to explain the dielectric response of NaCl, due to finite temperature anharmonic effects.\n\nContrary to the QH approach, the TDEP-based computation shows much better agreement with experiment. \nAt 300~K the line-shape of the TDEP-based Raman spectrum (blue) follow more accurately the experimental spectrum (black). \nAgreement between experimental and TDEP spectra is even better above 500~K. \nThe main discrepancy remains the decaying intensity above 350~cm$\\textsuperscript{-1}$ which is not captured by either QH or TDEP. \nWe suspect this is the result of higher order ($>2$) scattering terms since, as discussed below, our calculation does not include them. \nHaving established that our computational method is both required and successful in predicting the Raman spectrum, we turn to discuss the TDEP-based calculation in more detail.\n\n\n\nWe start by briefly reiterating the main idea behind TDEP to describe the ionic motion. \nThe starting point is the lattice dynamical expansion for the crystal energy:\\cite{Born1998}\n\\begin{equation}\n\\begin{split}\n H = & H_0 + \n \\frac{1}{2}\n \\sum_{ij\\alpha\\beta} \\Phi_{ij}^{\\alpha\\beta} \n u_i^{\\alpha} u_j^{\\beta}\n +\n \\frac{1}{3!}\n \\sum_{ikj\\alpha\\beta\\gamma} \\Phi_{ijk}^{\\alpha\\beta\\gamma} \n u_i^{\\alpha} u_j^{\\beta} u_k^{\\gamma} +\n\\\\ & \n +\n \\frac{1}{4!}\n \\sum_{ikjl\\alpha\\beta\\gamma\\delta} \\Phi_{ijkl}^{\\alpha\\beta\\gamma\\delta} \n u_i^{\\alpha} u_j^{\\beta} u_k^{\\gamma} u_l^{\\delta}\n +\n \\ldots\n\\end{split}\n\\end{equation}\nwhere $u_i^\\alpha$ denote the displacement of atom $i$ in direction $\\alpha$. The interatomic force constants $\\Phi$ are determined by minimizing the difference between the model system and \\emph{ab initio} calculated forces $\\vec{f}$.\n\\begin{equation}\n \\vec{\\Phi} = \\arg\\min_{\\Phi} \\| \\vec{f}^{\\textrm{model}} - \\vec{f}^{\\textrm{ab initio}} \\|\n\\end{equation}\nThe \\emph{ab initio} calculations are performed in the canonical ensemble yielding an explicit temperature dependent model Hamiltonian -- either via molecular dynamics or a self-consistent stochastic sampling~\\cite{Shulumba2017}. \nIn this study we used the latter.\n\nSince the sampling is done at finite temperature, an explicit temperature dependence is built into the effective interaction parameters. The TDEP Hamiltonian is constructed to be the best possible fit at a certain temperature.\n\nTo asses the accuracy of our model Hamiltonian for the lattice dynamics we calculate the phonon spectral function and compare it with a neutron scattering experiment~\\cite{Raunio1969a}. \nThe calculated spectral functions agree well with experiments and previous non-harmonic calculations~\\cite{Ravichandran2018} (see Sec.~S\\rom{8} in the SM).\n\nThe need for a non-perturbative treatment when describing anharmonic lattice dynamics motivates an analogous treatment for the dielectric response. \nTo extend the formalism to dielectric properties we similarly expand the susceptibility $\\vec{P}$ and dipole moment $\\vec{M}$ in terms of atomic displacements $u$:\\cite{Born1998}\n\\begin{align}\n\\begin{split}\n \\label{eq:pexpansion}\n P^{\\mu\\nu} = & P_0^{\\mu\\nu} + \n \\sum_{i\\alpha}\n P^{\\mu\\nu\\alpha}_i u_i^{\\alpha} +\n \n \\frac{1}{2}\n \\sum_{ij\\alpha\\beta}\n P^{\\mu\\nu\\alpha\\beta}_{ij} u_i^{\\alpha}u_j^{\\beta} + \n \\ldots\n\\end{split}\n\\\\\n\\begin{split}\n \\label{eq:mexpansion}\n M^{\\mu} = & M_0^{\\mu} + \n \\sum_{i\\alpha}\n M^{\\mu\\alpha}_i u_i^{\\alpha} + \n \n \\frac{1}{2}\n \\sum_{ij\\alpha\\beta}\n M^{\\mu\\alpha\\beta}_{ij} u_i^{\\alpha}u_j^{\\beta} + \n \\ldots\n\\end{split}\n\\end{align}\nHere the indices $\\mu\\nu$ denote Cartesian components of electric field derivatives. \nIn principle this expansion is done for dynamic quantities -- including the electronic frequency dependence -- but in the present case for NaCl we are interested in a frequency range far from any resonances and can safely ignore the electronic frequency dependence.\n\nIn analogy with the process described for the force constants, the terms in the dipole moment and susceptibility expansions are determined by minimizing the difference between the model values and those obtained from simulation:\n\\begin{align}\n \\vec{M} & = \\arg\\min_{\\vec{M}} \\left\\| \n \\frac{\\partial \\vec{M}}{\\partial u}^{\\textrm{model}} - \n \\frac{\\partial \\vec{M}}{\\partial u}^{\\textrm{ab initio}}\n \\right\\|\n\\\\\n \\vec{P} & = \\arg\\min_{\\vec{P}} \\left\\| \n \\vec{P}^{\\textrm{model}} - \n \\vec{P}^{\\textrm{ab initio}}\n \\right\\|.\n\\end{align}\n\nThe end result is a set of interaction parameters that explicitly depend on temperature, incorporating all orders of non-harmonic effects in the linear dielectric response of a material. \nWe note that the inclusion of frequency dependence adds no conceptual difficulty, only the practical challenge of accurately determining the frequency dependent electronic dielectric response for a simulation with hundreds of atoms. \nThe algorithm for determining the interaction parameters is described in detail in Sec.~S\\rom{5} in the SM, and the specific computational details are given in Sec.~S\\rom{8}.\nHaving obtained the dielectric response we turn to compute the Raman scattering cross-section.\n\\begin{figure*}[t]\n\\centering\n\\includegraphics[width=\\linewidth]{NaCl_PO_maps_small.pdf}\n\\caption{NaCl Raman PO color map for measurement and TDEP simulation in 300~K. Frequency of spectral features in simulation slightly misses experimental values, so dashed color lines help guide comparison.\n}\n\\label{fig:NaClPO}\n\\end{figure*}\n\n\nTheoretical interpretations of 2\\textsuperscript{nd} order Raman spectra usually limit themselves to high symmetry areas of reciprocal space where most density of states singularities are expected~\\cite{Burstein1965,Agrawal1967,Karo1965,Weber1993,Wang1973,Smith2002}.\nThis approach adequately accounts for pronounced peaks, but severely limits the possibility of incorporating anharmonic effects.\nAlso, because of the symmetry breaking of moving away from the $\\Gamma$-point and generalization into multiple phonon states, selection rules in 2\\textsuperscript{nd} order Raman usually become too relaxed to place meaningful restrictions on the measured Raman tensor (see Sec.~S\\rom{4} in SM for more details).\nExisting \\textit{ab initio} approaches to 2\\textsuperscript{nd} order Raman all operated strictly within the harmonic approximation~\\cite{Gillet2017,Strauch1995}.\nA Shell-model based calculation by \\citet{Bruce1972} did incorporate phonon-phonon interactions, but his partial treatment lead him to conclude these are negligible in the Raman spectrum of NaCl.\n\nThe construction of the full 2\\textsuperscript{nd} order spectrum from first principles requires a few computational steps.\nThe starting point is to use the TDEP method to construct an anharmonic Hamiltonian that describes the dynamics of the ions.\nThis anharmonic Hamiltonian is then coupled to an expansion of the polarizability in terms of atomic displacements.\nThe coupled ionic-polarizability system can then be solved with many-body methods, providing the finite temperature spectrum.\nThe steps involved are discussed in detail below.\n\nFollowing \\citet{Cowley1963}, the Raman scattering cross-section is given by:\n\\begin{equation}\n \\sigma(\\Omega) \\propto \n \\sum_{\\mu\\nu\\xi\\rho}\n\tE^{\\textrm{out}}_{\\mu}\n\tE^{\\textrm{out}}_{\\xi}\n\tI_{\\mu\\nu,\\xi\\rho}(\\Omega)\n\tE^{\\textrm{in}}_{\\nu}\n\tE^{\\textrm{in}}_{\\rho} \n\\end{equation}\nwhere $\\vec{E}^{\\textrm{in}}$ and $\\vec{E}^{\\textrm{out}}$ are the electric field vectors of the incoming and outgoing light, with Greek letters standing for Cartesian components.\nThe tensor $I$ depends on the probing frequency $\\Omega$ and constitutes the material property governing Raman scattering. \nIt is given by\n\\begin{equation}\n\\label{eq:Cowley_Beast}\n I_{\\mu\\nu,\\xi\\rho}(\\Omega) = \\int \\text{d}t \\left\\langle P^{\\mu\\nu}(t)P^{\\xi\\rho}(0) \\right\\rangle e^{-i\\Omega t},\n\\end{equation}\nthe Fourier transformed thermal average of the polarizability-polarizability autocorrelation function.\nEq.~\\ref{eq:Cowley_Beast} says that the complete description for Raman scattering is given by a tensor detailing the vectorial relationship between incident and scattered fields.\nIt is therefore desirable to inspect polarization dependence when comparing theory and measurement.\n\nTo realize this, we performed a polarization-orientation (PO) Raman measurement of a NaCl single crystal. \nThe temperature dependent spectra presented in Fig.~\\ref{fig:NaClTemp} are actually calculated sums of the scattered intensities for a full rotation of incident polarization.\nWe probe separately the parallel and perpendicular scattered polarizations with respect to the linearly polarized incident beam orientation.\nDetails of the experimental setup are given in Sec.~S\\rom{1} in the SM.\n\nFigure~\\ref{fig:NaClPO} shows the full PO color map for measurement and simulation in 400~K (PO maps for all temperatures are given in Sec.~S\\rom{2} in the SM).\nOur theoretical calculation also fully accounts for the tensor nature of Raman scattering, making a detailed comparison possible.\nThe calculated model closely follows the PO dependence throughout the spectrum.\nDashed colored lines follow corresponding spectral features in simulation and experiment (some discrepancy in absolute frequency persists).\nOur calculation correctly predicts the periodicity with incident angle of each feature, as well as the relative phase between them, thereby faithfully reproducing the tensorial nature of the modulated dielectric response.\n\nThe full tensorial formalism for 2\\textsuperscript{nd} order Raman has already been rigorously worked out, but is rarely used and is repeated and extended here.\\cite{Cowley1964b,Wallis1971}\nFar from resonance, to lowest order, the tensor $I$ relating incident and outgoing light has four terms:\n\\begin{equation}\n I_{\\mu\\nu,\\xi\\rho}(\\Omega) = \n I^{(\\textrm{I})}_{\\mu\\nu,\\xi\\rho}+\n I^{(\\textrm{II})}_{\\mu\\nu,\\xi\\rho}+\n I^{(\\textrm{III})}_{\\mu\\nu,\\xi\\rho}+\n I^{(\\textrm{IV})}_{\\mu\\nu,\\xi\\rho}\n\\end{equation}\nwith the different terms given by\n\\begin{align}\n \\label{eq:ramanfirstorder}\n\tI^{(\\textrm{I})}_{\\mu\\nu,\\xi\\rho} = &\n\t(n(\\Omega)+1)\n\t\\sum_s P^{(\\textrm{I})}_{\\mu\\nu,\\xi\\rho}(s) \n\tJ_s(\\Omega) \n\\\\\n\\label{eq:ramansecondorder}\n\\begin{split}\n\tI^{(\\textrm{II})}_{\\mu\\nu,\\xi\\rho} = &\n\t2\n\t\\sum_{ \\vec{q} s_1 s_2 }\n\tP^{(\\textrm{II})}_{\\mu\\nu,\\xi\\rho}(\\vec{q},s_1,s_2)\n\t\t\\int \n\tJ_{s_1}(\\Omega')(n(\\Omega')+1)\n \\times \\\\\n\t&\n\tJ_{s_2}(\\Omega-\\Omega')(n(\\Omega-\\Omega')+1)\n\td\\Omega'\n\\end{split}\n\\\\\n\\begin{split}\n\tI^{(\\textrm{III})}_{\\mu\\nu,\\xi\\rho} = & \n\t-6(n(\\Omega)+1)\n\t\\sum_{\\vec{q}s_1 s_2 s_3}\n\tJ_{s_3}(\\Omega)\n\t\\times \\\\\n\t& P^{(\\textrm{III})}_{\\mu\\nu,\\xi\\rho}(\\vec{q},s_1,s_2,s_3)\n\t\\,\\Im\\left\\{ S(s_1,s_2,\\Omega) \\right\\}\n\t\\vspace{5pt}\n\\end{split}\n\\\\\n\\begin{split}\n\tI^{(\\textrm{IV})}_{\\mu\\nu,\\xi\\rho} = & \n\t3(n(\\Omega)+1)\n\t\\sum_{\\vec{q} s_1 s_2}\n\tP^{(\\textrm{IV})}_{\\mu\\nu,\\xi\\rho}(\\vec{q},s_1,s_2) \\times\n\t\\\\ & \n\t(2n_{s_2}+1)\n\tJ_{s_1}(\\Omega) \n\\end{split}\n\\end{align}\nwhere the matrix elements $\\vec{P}^{(\\textrm{I})}-\\vec{P}^{(\\textrm{IV})}$ are defined via the Fourier components of the terms in Eq.~\\ref{eq:pexpansion}, (see Sec.~S\\rom{7} in the SM for the explicit expressions and derivations of the above terms). \nThe phonon spectral function for mode $s$, $J_s = -\\Im\\left\\{ G_{s}(\\Omega) \\right\\}\/\\pi$ is obtained from\n\\begin{equation}\n G_{s}(Z) = \\frac{2\\omega_s}{\\omega_s^2 - 2\\omega_s\\Sigma_s(Z) - Z^2}\n\\end{equation} \nwhere\n\\begin{equation}\n\\begin{split}\n \\Sigma_{\\vec{q}s}(Z) = & -18 \\sum_{\\vec{q}_1\\vec{q}_2 s_1 s_2}\n \\left| \\Phi^{s s_1s_2}_{\\vec{q}\\vec{q}_1\\vec{q}_2} \\right|^2\n S(s_1,s_2,Z) +\n\\\\ & +\n 12 \\sum_{\\vec{q}_1 s_1}\n \\Phi^{s s s_1 s_1}_{\\vec{q}\\bar{\\vec{q}}\\vec{q}_1\\bar{\\vec{q}}_1}(2n_{\\vec{q}_1 s_1}+1)\n\\end{split} \n\\end{equation}\nand\n\\begin{equation}\n\\label{eq:Sfun}\n\\begin{split}\n &S(s_a,s_b,Z) = \n \\\\\n\t&(n_{a}+n_{b}+1)\n\t\\left[\n\t\\frac{1}{(\\omega_{a}+\\omega_{b}-Z)_p}-\n\t\\frac{1}{(\\omega_{a}+\\omega_{b}+Z)_p}\n\t\\right]\n\t\\\\\n\t&+\n\t(n_{a}-n_{b})\n\t\\left[\n\t\\frac{1}{(\\omega_{b}-\\omega_{a}+Z)_p}-\n\t\\frac{1}{(\\omega_{b}-\\omega_{a}-Z)_p}\n\t\\right]\n\\end{split}\t\n\\end{equation}\nThe first term, Eq.~\\eqref{eq:ramanfirstorder}, is the first order Raman scattering which comes down to the one-phonon spectral function weighted by the first order Raman matrix elements. \nThe spectral function contains any broadening and shifts due to anharmonicity, and also any deviation from a Lorentzian lineshape. \nThis term is what is commonly referred to as Raman scattering.\n\nThe second term, Eq.~\\eqref{eq:ramansecondorder}, is the second order Raman scattering. \nThe matrix elements contain the momentum conservation, and the convolution term contains the energy conservation. \nIf one were to set the matrix elements to unity it would yield a spectrum that follows the two-phonon density of states.\n\nThe third and fourth terms are more subtle, as they contain contributions from the whole BZ but are multiplied with the one-phonon lineshape. \nThey will thus decay rapidly away from the first order Raman peaks, with the net effect of slightly shifting and altering the shapes of the first order peaks. \nThese terms are one reason that neutron scattering and Raman scattering measurements might not coincide exactly. \n\nThe theoretical PO maps in Fig.~\\ref{fig:NaClPO} are calculated from Eq.~\\eqref{eq:ramansecondorder}. In NaCl first order Raman is forbidden by symmetry, which means $P^{(\\textrm{I})}$,$P^{(\\textrm{III})}$, and $P^{(\\textrm{IV})}$ are zero. We stress that the spectrum is calculated from the interacting phonons, i.e., a convolution of one-phonon spectral functions and includes explicit anharmonic effects.\n\n\nIt is worth dwelling on the temperature effects in 2\\textsuperscript{nd} order Raman, and their connection to anharmonic behavior.\nThe scattering is generated by phonons throughout the BZ. \nSince the TDEP method implicitly contains all orders of anharmonicity, this already imparts a non-trivial temperature dependence, but the effects of anharmonicity go beyond the temperature dependence of the bare phonon dispersion curve. \nInspecting Eq.~\\eqref{eq:ramansecondorder} one sees that it is a convolution of two one-phonon spectral functions. \nIf we replace the interacting spectral functions with the non-interacting ones we will recover the traditional result\\cite{Cowley1964b} (See SM, Eq.~S73).\nIn the present study we found it crucial to include the interacting spectral functions when calculating the 2\\textsuperscript{nd} order Raman spectra, without them agreement with experiment was markedly worse. \nIntuitively, if the one-phonon DOS is broadened, the two-phonon DOS must also broaden -- and since the two-phonon DOS is a convolution, non-Lorentzian lineshapes have a larger impact on two-phonon than on one-phonon spectra.\n\nBesides its own non-trivial temperature dependence, 2\\textsuperscript{nd} order Raman emerged as a uniquely sensitive probe for anharmonic lattice dynamics.\nThree-phonon scattering is ostensibly a straightforward perturbative anharmonic effect.\nOnly a small fraction of all possible three-phonon combinations satisfy energy and momentum conversion -- this is what we refer to as the scattering phase space. \nMathematically, this is defined via the imaginary part of Eq.~\\eqref{eq:Sfun}. \nThe temperature dependence of available phonon scattering phase space is, however, a non-perturbative anharmonic effect, and can lead to a host of surprising trends with temperature.\nThermal conductivity that does not follow $T^{-1}$\\cite{Romero2015}, phonon line widths that do not grow linearly with temperature\\cite{Delaire2011b}, or in our case, a Raman spectrum that does not follow the quasiharmonic approximation are all consequences of non-pertrubative anharmonicity.\n\nThe 2\\textsuperscript{nd} order Raman spectrum follows the same set of momentum and energy conservation rules as three-phonon scattering.\nIn fact, the spectra is directly proportional to the available scattering phase space. \nThis makes Raman scattering invaluable as a direct probe of anharmonicity: we can see how (a part) of the scattering phase space evolves with temperature, providing insight into the underlying mechanisms governing strongly anharmonic materials.\nIn this specific case we can verify prior theoretical studies. \\citet{Ravichandran2018} found it necessary to include temperature-dependent phonons to accurately describe the thermal transport in NaCl -- this can be verified experimentally by the change in the Raman spectra with temperature in Fig.~\\ref{fig:NaClTemp}. Moreover, four-phonon scattering was predicted to be important. The presence of higher order scattering is evident in the tail at large wavenumbers (second order Raman can only contribute up to wavenumbers equal to $2\\omega_{\\textrm{max}}$, third order Raman, which would involve four-phonon scattering, can contribute up to $3\\omega_{\\textrm{max}}$). \n\nThe methodology described in this letter is general with regard to the coupling between anharmonicity and dielectric response. \nIn Sec.~S\\rom{9} of the SM we present the infrared absorption spectra, as well as the temperature dependence of the dielectric tensor and Born charges. \nWe could not find any experimental results to verify these quantities with, so we leave them as predictions.\n\n\n\n\nIn conclusion, we have demonstrated the crucial role of anharmonic lattice dynamics effects in determining the phonon-modulated linear dielectric response of a crystal. \nThis was achieved by introducing an \\textit{ab initio} method to calculate the dielectric response of a crystal at finite temperatures and comparing it to the first continuous PO spectrum of an exclusively 2\\textsuperscript{nd} order Raman structure.\nOur generalized TDEP method incorporates all orders of non-harmonic effects through a sampling of temperature dependent lattice configurations, expressed as effective interatomic force constants.\nWe introduce analogous effective interaction tensors that govern the dielectric response, i.e., we expand the dipole moment and polarizability in terms of atomic displacements.\nUsing the new generalized TDEP method, other dielectric material properties such as ferroelectricity and capacitance may be calculated, along with their temperature dependence. \nBy isolating and evaluating the normal modes comprising the emergent temperature dependence of a material property, new design rules may be inferred for better functional materials operating at finite temperatures.\n\n\\section*{acknowledgments}\nThe authors would like to thank Dr. Lior Segev (WIS) for invaluable software development. O.Y. acknowledges funding from ISF(1861\/17), BSF (grant no. 2016650) and ERC (850041 -ANHARMONIC). O.H. acknowledges support from the Swedish Research Council (VR) program 2020-04630. Supercomputer resources were provided by the Swedish National Infrastructure for Computing (SNIC). \n\n\n\\section{Experimental setup} \\label{SM:Experiment}\nAll Raman measurements were performed on our home-built system. Figure \\ref{fig:SM_setup} shows a schematic of the system. \nThe beam path begins with a 488nm solid-state (Coherent Sapphire SF 488-100 CDRH) laser, which will excite the Raman scattering in the sample. \nThe laser is filtered for any amplified spontaneous emission by a volume holographic ASE (Ondax) filters. Next, the beam shape is optimized and culminated by a pinhole spatial filter.\nControl over incident and scattered polarization is realized by a set of two polarizers and two half-wave plates.\nFirst, linear polarization is insured by a (Thorlabds) calcite polarizer. A monochromatic half-wave plate (HW0 in Fig.~\\ref{fig:SM_setup}) is placed \\textit{before} the polarizer and rotated for optimal incident intensity, correcting for the original laser polarization.\nThe beam then passes through a (NoiseBlock 90\/10 Ondax) beam splitter (BS) with the experimental polarization orientation controlled by another monochromatic half-wave plate (HW1 in Fig.~\\ref{fig:SM_setup}). \nThe polarized beam then enters a microscope and focused on the sample by a 10X (Zeiss) objective.\nExcitation powers of 7-30 mW were used (depending on temperature), measured just before the microscope.\nThe sample (see \\ref{SM:Synthesis}) is placed inside a (TS1000 HiTemp) Linkam stage under Argon purge. \nPurging with a mono-atomic gas proved crucial, since under vacuum local laser heating would create defects, whisle nitrogen would dominate the Raman signal with its rotational excitations. \nA mono-atomic gas is also the best option for effective thermal coupling to the Linkam hot plate.\nInside the Linkam the magic happens, and laser light is scattered off the thermally excited crystal. \nThe back-scattered light is collected by the same objective and passes back through HW1 and through the BS, where 90\\% of the Rayleigh is eliminated and Raman signal transmitted.\nNote that the beam's polarization at this point is not known, since we can not assume the scattered light, especially the Raman signal, scattered back into the same incident polarization, as is described in \\ref{fig:SM_setup} (\\textbf{b}).\nIndeed, there is no reason to assume the beam is still linearly polarized. \nWe may, however, as always, describe the beam's polarization as the sum of two linear components, one parallel to the incident beam polarization, and the other perpendicular. \nAfter passing again through HW1, all light scattered in parallel to the incident beam polarization will now parallel the original polarization determined by the first polarizer. \nAll light scattered perpendicular to the incident beam, will now be also polarized perpendicular to the first polarizer. \nAfter the BS, the scattered beam (orange line in \\ref{fig:SM_setup}) passes through an achromatic half-wave plate (HW2 in \\ref{fig:SM_setup}) which either leaves or rotates the signal polarization by 90\\textsuperscript{0}. \nAnother identical polarizer (designated \"analyzer\" in Fig.~\\ref{fig:SM_setup}) filters the polarization of the signal. \nWe thus separate the two scattered polarization components.\nWith HW2 at 0\\textsuperscript{0}, only the parallel signal will make it past the analyzer, with HW2 at 45\\textsuperscript{0}, only the perpendicular signal is transmitted.\nThe system achieved an extinction ratio $(I_{\\perp}\/I_{\\parallel})$ between 1\/500 and 1\/100 depending on the orientation of HW1.\nAfter the analyzer, the remaining Rayleigh component of the signal is attenuated by two (Ondax SureBlock ultra-narrowband) notch filters and the signal is directed into a one meter (Horiba FHR 1000) spectrometer and detected by a (Horiba Synapse) CCD.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\textwidth]{setupANDfaceon.png}\n \\caption{({\\bfseries a}) Schematic of experimental setup. Laser light comes from top. Scattered signal goes right for detection. \n ({\\bfseries b}) Diagram for the different polarizations involved in the measurement. $\\overset{\\rightarrow}{\\vec{E}_i}$ is the incident linear polarization. $\\overset{\\rightarrow}{\\vec{E}_s}$ is the polarization for the scattered signal and need not be linearly polarized.\n Parallel measurement (HW2 at 0$\\textsuperscript{0}$) \n probes the scattered field's projection on $\\hat{\\vec{e}}_{\\parallel}$. \n The perpendicular measurement (HW2 at 45\\textsuperscript{0}) \n probes the scattered field's projection on \n \n \\label{fig:SM_setup}}\n\\end{figure}\n\n\\begin{figure*}[h]\n\\centering\n\\includegraphics[width=\\textwidth]{NaCl_400K_PO.pdf}\n\\caption{NaCl Raman PO color map for measurement and TDEP simulation in 400~K. Top Row is for experimental measurement, bottom row for calculation. Left column is for parallel configuration and right is for perpendicular.}\n\\label{fig:NaClPO400K}\n\\end{figure*}\n\n\\begin{figure*}[h]\n\\centering\n\\includegraphics[width=\\textwidth]{NaCl_500K_PO.pdf}\n\\caption{NaCl Raman PO color map for measurement and TDEP simulation in 500~K. Top Row is for experimental measurement, bottom row for calculation. Left column is for parallel configuration and right is for perpendicular.}\n\\label{fig:NaClPO500K}\n\\end{figure*}\n\n\\begin{figure*}[h]\n\\centering\n\\includegraphics[width=\\textwidth]{NaCl_600K_PO.pdf}\n\\caption{NaCl Raman PO color map for measurement and TDEP simulation in 600~K. Top Row is for experimental measurement, bottom row for calculation. Left column is for parallel configuration and right is for perpendicular.}\n\\label{fig:NaClPO600K}\n\\end{figure*}\n\n\\begin{figure*}[h]\n\\centering\n\\includegraphics[width=\\textwidth]{NaCl_700K_PO.pdf}\n\\caption{NaCl Raman PO color map for measurement and TDEP simulation in 700~K. Top Row is for experimental measurement, bottom row for calculation. Left column is for parallel configuration and right is for perpendicular.}\n\\label{fig:NaClPO700K}\n\\end{figure*}\n\n\\vspace{1.5cm}\n\\section{Measurement and analysis} \\label{SM:Measurement}\n\nHere We give the detailed description of all data acquisition processing and present all PO maps for all measured temperatures.\n\nTo accumulate a Raman crystallography data-set, we rotate the polarization orientation of the incident laser beam (polarization $\\overrightarrow{\\vec{E}}_i$\nin Fig.~\\ref{fig:SM_setup} (\\textbf{b}) in $10^0$ steps covering $\\theta=0-360^0$ in a plane parallel to the NaCl (001) crystal face. \nAt each $\\theta$ step, a Raman spectrum is collected for both HW2 configurations (see $\\hat{\\vec{e}}_{\\parallel}$ and $\\hat{\\vec{e}}_{\\perp}$ in Fig.~\\ref{fig:SM_setup}).\n\nThe signal of 2\\textsuperscript{nd} order Raman is very weak, so long exposure times and multiple accumulations are necessary. \nFor each measured spectrum, that is, for a given temperature, at a given incident polarization angle at given detection configuration, approximately 6 accumulations of 60 seconds were collected.\n\nSome residual baseline persisted in all measured spectra, resembling a Lorentzian tail towards lower frequencies. This was cancelled in all unpolarized figures via standard baseline reduction. \nAll PO maps are given in \\Cref{fig:NaClPO400K,fig:NaClPO500K,fig:NaClPO600K,fig:NaClPO700K}. These are presented with no augmentation other than a low-frequency cut-off, where the signal is governed by the notch filters and not the actual scattering. For each temperature, the corresponding TDEP calculation (see \\ref{SM:green}) is also presented.\n\n\\vspace{1.5cm}\n\\section{Single Crystal Synthesis} \\label{SM:Synthesis}\n\nThe NaCl single crystal used in Raman measurements was home grown.\nDouble-deionized water (DDW) was heated slowly to around $40-50^0C$.\nHigh purity $(\\geq99\\%)$ Sodium Chloride powder was added and stirred into the DDW until saturation.\nThe saturated solution was poured into another wide flask with a few fresh crystals for crystallization sites.\nThe flask was left covered with a filter paper (to avoid dust penetration and allow evaporation) in a chemical hood for several days until the crystals reached a size of $\\sim$1cm.\nFinally, the crystals were taken out of the dried flask and placed on a clean wipe to adsorb all residual water.\nA picture of the crystal used is given in figure \\ref{fig:Single_Crystal}.\n\n\\begin{figure*}[h]\n\\centering\n\\includegraphics[width=0.5\\textwidth]{Single_Crystal.jpg}\n\\caption{One of the NaCl single crystals synthesized for the experiment..}\n\\label{fig:Single_Crystal}\n\\end{figure*}\n\n\n\\vspace{1.5cm}\n\\section{Factor group analysis for 2\\textsuperscript{\\MakeLowercase{nd}} order Raman} \\label{SM:Group_theory}\n\nThe full factor group analysis for the rock-salt structure has already been worked out by Burnstein~\\cite{Burstein1965}, following methods established by Birman~\\cite{Birman1962} and Elliot~\\cite{Elliot1960}.\nThese ostensibly provide a complete tool-set to interpret 2\\textsuperscript{nd} order Raman spectra by assigning each spectral feature to a known combination of vibrational modes. \nAs mentioned in the main text, this treatment may prove useful for mode assignment of a known phononic dispersion relations to an observed spectrum. \nHowever, even in the high symmetry case of the cubic $O_h$ point group, selection rules for 2\\textsuperscript{nd} order Raman are too relaxed to offer complete predictive power, even with reliable dispersion relations at hand. \n\nTo demonstrate this, we perform the factor group analysis for for one high symmetry point in the Birllouin zone, specifically, for the $\\mathrm{X'_5 (TO)},\\:\\mathrm{X'_5 (TA)}$ phonon pair in the reciprocal space X-point in a rock-salt structure.\n\nThe basic selection rule governing Raman scattering is~\\cite{Yu2010}:\n\\begin{equation} \\label{eq:selection_rule}\n \\Gamma_f\\subseteq\\Gamma_{15}\\otimes\\Gamma_{15}\n\\end{equation}\nwith $\\Gamma_f$ the representation of the excited phonon state, and $\\Gamma_{15}$ the representation for the momentum operator in BSW notation (Raman scattering can be viewed as the product o two dipole transitions). The crux of the matter lies in the fact that in 2\\textsuperscript{nd} order Raman this final phonon state is generally composed of two off $\\Gamma$-point phonons, with its representation given by the Kronecker product of the two one phonon states. For example:\n\\begin{align}\n \\nonumber\n & \\textrm{Quantum state:}\\:\\:\n \\left|\\mathrm{X}_{2phonon}\\right\\rangle =\n \\left|\\mathrm{X'_5 (TO),\\:X'_5 (TA)}\\right\\rangle\\:\\to\\: \\\\ \\nonumber \n \\\\ \n & \\textrm{Representation:}\\:\\:\\:\n \\mathrm{X}_{2phonon} = \n \\mathrm{X'_5 (TO)\\otimes X'_5 (TA) = X_1 \\oplus X_2 \\oplus X_3 \\oplus X_4}\n\\end{align}\nwith all the irreducible representations in the direct sum being Raman active according to Eq.~\\eqref{eq:selection_rule}. This leads to the symmetry allowed Raman tensor form~\\cite{Bilbao2006}:\n\\begin{equation}\n R_{\\mathrm{X'}_5\\times\\mathrm{X'}_5} = R_{\\mathrm{X}_1}+R_{\\mathrm{X}_2}+R_{\\mathrm{X}_3}+R_{\\mathrm{X}_4}= \\\\\n \\begin{pmatrix}\n a & d & 0 \\\\\n d & b & 0 \\\\\n 0 & 0 & c \n \\end{pmatrix}\n\\end{equation}\nwhich is a very general Raman tensor indeed. Now, consider that this tensor is responsible for the contribution to the scattered intensity inside some frequency interval $I(\\Omega)\\mathrm{d}\\Omega$ from a single phonon combination.\nIn practice, equation $(11)$ allows a multitude of combinations to contribute to the same interval, such that accurately predicting the observed intensity, let alone the PO behavior of the signal, becomes intractable. \nThe situation becomes even worse for lower symmetry structures, where more phonon combinations will be Raman active and have more general Raman tensors.\n\n\n\n\\vspace{1.5cm}\n\\section{Numerical determination of interaction tensors} \\label{SM:Tensors}\n\nHere we briefly reiterate the TDEP procedure for consistency of notation and to clarify the analogy between how dipole and polarizibility interactions are determined and how interatomic force constants are determined. The starting point is a set of displacements $\\vec{u}$ and forces $\\vec{f}$ from a set of $N_c$ supercells sampled from a canonical ensemble at temperature $T$. Although omitted in the notation, all interaction tensors depend on temperature as well as the volume (or more generally strain). The interatomic force constants are determined as follows: consider a supercell with $N_a$ atoms and forces and displacements given by the $3N_{a} \\times 1$ vectors $\\vec{u}$ and $\\vec{f}$ (for clarity we will make note of the dimensions of the matrices in each step):\n\n\n\\begin{align}\n \\underbrace{\\vec{f}}_{3N_a \\times 1} \n & = - \n \\underbrace{\\vec{\\Phi}}_{3N_a \\times 3 N_a} \n \\underbrace{\\vec{u}}_{3N_a \\times 1} \n\\intertext{this can equivalently be written as}\n \\underbrace{\\vec{f}}_{3N_a \\times 1} \n & = - \n \\underbrace{(\\vec{I} \\otimes \\vec{u}^T)}_{3N_a \\times (3N_a)^2}\n \\underbrace{\\vec{\\Phi}_v}_{ (3N_a)^2 \\times 1}\n\\intertext{with the Kronecker product $\\otimes$. To express the forces from $N_c$ supercells the matrices are stacked on top of each other:}\n \\underbrace{\n \\begin{pmatrix}\n \\vec{f}_1 \\\\\n \\vdots \\\\\n \\vec{f}_{N_c}\n \\end{pmatrix}\n }_{3N_a N_c \\times 1} \n & = - \n \\underbrace{\n \\begin{pmatrix}\n \\vec{I} \\otimes \\vec{u}_1^T \\\\\n \\vdots \\\\\n \\vec{I} \\otimes \\vec{u}_{N_c}^T \\\\\n \\end{pmatrix}\n }_{3N_aN_c \\times (3N_a)^2}\n \\underbrace{\\vec{\\Phi}_v}_{ (3N_a)^2 \\times 1}\n %\n\\intertext{I can without any loss of generality express the second order force constants as a linear combination of $N_x$ irreducible components $\\vec{x}^{\\Phi\\textrm{II}}$, where $N_x \\le (3N_a)^2 $, via a matrix $\\vec{C}^{\\Phi\\textrm{II}}$:}\n \\underbrace{\n \\begin{pmatrix}\n \\vec{f}_1 \\\\\n \\vdots \\\\\n \\vec{f}_{N_c}\n \\end{pmatrix}\n }_{3N_a N_c \\times 1} \n & = - \n \\underbrace{\n \\underbrace{\n \\begin{pmatrix}\n \\vec{I} \\otimes \\vec{u}_1^T \\\\\n \\vdots \\\\\n \\vec{I} \\otimes \\vec{u}_{N_c}^T \\\\\n \\end{pmatrix}\n }_{3N_aN_c \\times (3N_a)^2}\n \\underbrace{\n \\vec{C}^{\\Phi\\textrm{II}}\n }_{(3N_a)^2 \\times N_x}\n }_{=\\vec{A}^{\\Phi\\textrm{II}}\\,, 3N_a N_c \\times N_x }\n \\underbrace{\\vec{x}^{\\Phi\\textrm{II}}}_{ N_x \\times 1}\n\\end{align}\nThe set of symmetry relations and invariances determines $\\vec{C}$ and $N_x$,\\cite{Leibfried1961,Maradudin1968,Born1998} and in general $N_x \\ll (3N_a)^2$ which considerably simplifies the problem of numerically determining $\\vec{x}$. In practice, only the matrix $\\vec{A}$ is determined and stored. The nominally very large matrices that go into the construction of $\\vec{A}$ are quite sparse and construction presents negligible computational cost. The second order force constants imply matrices that could possibly be treated naively, but the generalization to higher order quickly becomes intractable with dense matrix storage. The third order force constants, for example, comes down to\n\\begin{equation}\n \\underbrace{\n \\begin{pmatrix}\n \\vec{f}_1 \\\\\n \\vdots \\\\\n \\vec{f}_{N_c}\n \\end{pmatrix}\n }_{3N_a N_c \\times 1} \n = - \n \\underbrace{\n \\underbrace{\n \\begin{pmatrix}\n \\vec{I} \\otimes \\vec{u}_1^T \\otimes \\vec{u}_1^T\\\\\n \\vdots \\\\\n \\vec{I} \\otimes \\vec{u}_{N_c}^T \\otimes \\vec{u}_{N_c}^T \\\\\n \\end{pmatrix}\n }_{3N_aN_c \\times (3N_a)^3}\n \\underbrace{\n \\vec{C}^{\\Phi\\textrm{III}}\n }_{(3N_a)^3 \\times N_x}\n }_{=\\vec{A}^{\\Phi\\textrm{III}}\\,, 3N_a N_c \\times N_x }\n \\underbrace{\\vec{x}^{\\Phi\\textrm{III}}}_{ N_x \\times 1} \n\\end{equation}\nwhere the contracted matrix $\\vec{A}$ is many orders of magnitude smaller than the matrices it is built from. The interatomic force constants are determined in succession:\n\\begin{align}\n\\label{eq:f2}\n \\vec{A}^{\\Phi\\textrm{II}}\n \\vec{x}^{\\Phi\\textrm{II}} & = \\vec{f}\n\\\\\n\\label{eq:f3}\n \\vec{A}^{\\Phi\\textrm{III}}\n \\vec{x}^{\\Phi\\textrm{III}} & = \n \\vec{f}-\n \\vec{A}^{\\Phi\\textrm{II}}\n \\vec{x}^{\\Phi\\textrm{II}}\n\\\\ \n\\label{eq:f4}\n \\vec{A}^{\\Phi\\textrm{IV}}\n \\vec{x}^{\\Phi\\textrm{IV}} & = \n \\vec{f}\n -\n \\vec{A}^{\\Phi\\textrm{II}}\n \\vec{x}^{\\Phi\\textrm{II}}\n -\n \\vec{A}^{\\Phi\\textrm{III}}\n \\vec{x}^{\\Phi\\textrm{III}}\n\\end{align}\nWhere $\\vec{f}$ denotes the reference forces to be reproduced. Equations \\eqref{eq:f2}--\\eqref{eq:f4} are overdetermined and solved as a least squares problem. This ensures that the baseline harmonic part becomes as large as possible, and that the higher order terms become smaller and smaller.\n\nIn a similar manner we search for a way to determine $M$ and $P$ (the expansion of the dipole moment and polarizability with respect to position) to all orders. The input is again a supercell, where in addition to forces we also have determined $\\partial^2 U \/ \\partial E^2$ as well as $\\partial^2 U \/ \\partial E \\partial R$, that is the polarizability and Born charges. In a manner analogous to the force constants we determine the irreducible components of the interaction tensors and express the polarizability for a supercell as:\n\\begin{equation}\n\\begin{split}\n \\underbrace{ \\partial_E \\partial_E U (\\vec{u}) }_{9 \\times 1}\n &\n =\n P_0 + \n \\underbrace{ \\vec{I} \\otimes \\vec{u}^T }_{9 \\times 27 N_a }\n \\underbrace{ P }_{27 N_a \\times 1 }\n +\n \\underbrace{ \\vec{I} \\otimes \\vec{u}^T \\otimes \\vec{u}^T }_{9 \\times 9 (3N_a)^2 }\n \\underbrace{ P }_{9 (3N_a)^2 \\times 1 }\n +\n \\ldots\n\\\\ \n & = \n P_0 + \n \\vec{A}^{P\\textrm{I}}\n \\vec{x}^{P\\textrm{I}}\n +\n \\vec{A}^{P\\textrm{II}}\n \\vec{x}^{P\\textrm{II}}\n +\n \\ldots\n\\end{split} \n\\end{equation}\nThe terms in the polarizability expansion are determined slightly differently than the interatomic force constants. For a set of $N_c$ supercells I build the set of differences in polarizability between supercell $i$ and $j$:\n\\begin{align}\n \\Delta^{0} P_{ij} & = \n \\partial_E \\partial_E U(\\vec{u}_i) -\n \\partial_E \\partial_E U(\\vec{u}_j)\n\\intertext{and solve for $\\vec{x}^{P\\textrm{I}}$ from}\n \\left(\n \\vec{A}^{P\\textrm{I}}_{i} -\n \\vec{A}^{P\\textrm{I}}_{j}\n \\right)\\vec{x}^{P\\textrm{I}}\n & = \\Delta^{0} P_{ij}\n\\intertext{where the set of all pairs of supercell $ij$ are used. The fluctuations from the first order are removed to construct}\n \\Delta^{1} P_{ij} & = \n \\left(\\partial_E \\partial_E U(\\vec{u}_i) - \\vec{A}^{P\\textrm{I}}_{i}\\vec{x}^{P\\textrm{I}} \\right) -\n \\left(\n \\partial_E \\partial_E U(\\vec{u}_j) -\n \\vec{A}^{P\\textrm{I}}_{j}\\vec{x}^{P\\textrm{I}}\n \\right)\n\\\\\n \\left(\n \\vec{A}^{P\\textrm{II}}_{i} -\n \\vec{A}^{P\\textrm{II}}_{j}\n \\right)\\vec{x}^{P\\textrm{II}}\n & = \\Delta^{1} P_{ij}\n\\end{align}\nAnd so on for all orders considered. The baseline polarizability is then determined via\n\\begin{equation}\n P_0 + P^{\\textrm{th}} = \\left\\langle \n \\partial_E \\partial_E U -\n \\vec{A}^{P\\textrm{I}}\n \\vec{x}^{P\\textrm{I}} -\n \\vec{A}^{P\\textrm{II}}\n \\vec{x}^{P\\textrm{II}}\n - \\ldots\n \\right\\rangle\n\\end{equation}\nWhere $P^{\\textrm{th}}$ is the contribution to the susceptibility from the second order terms in the polarizability expansion:\\cite{Cowley1964b}\n\\begin{equation}\n P^{\\textrm{th}} = \\sum_{\\lambda} P_{\\lambda\\lambda}(2n_{\\lambda}+1)\n\\end{equation}\nwhere the matrix elements are defined below.\nFor a perfectly harmonic system (or any harmonic sampling of phase space) the average of the first order term disappears.\n\nThe interaction parameters for the dipole moment are determined from the set of Born charges calculated in the supercell. We deliberately do not work with polarization directly. The quantized nature of polarization makes finding the polarization on the same branch for a large number of supercells tedious if not outright problematic. The starting point is again the idea of expressing the Born charges for a supercell in terms of the irreducible components:\n\\begin{equation}\n\\begin{split}\n \\underbrace{ \\partial_E \\partial_R U }_{9N_a \\times 1}(\\vec{u})\n &\n =\n \\underbrace{ \\vec{M}^{\\textrm{I}} }_{9N_a \\times 1}\n +\n \\underbrace{ \\vec{I} \\otimes \\vec{u}^T }_{9N_a \\times (9N_a)(3 N_a) }\n \\underbrace{ M^{\\textrm{II}} }_{(9N_a) (3N_a) \\times 1 }\n +\n \\underbrace{ \\vec{I} \\otimes \\vec{u}^T \\otimes \\vec{u}^T }_{9N_a \\times (9N_a)(3 N_a)^2 }\n \\underbrace{ M^{\\textrm{III}} }_{(9N_a) (3N_a)^2 \\times 1 }\n +\n \\ldots\n \\\\\n & = \n \\vec{A}^{\\textrm{MI}}\\vec{x}^{\\textrm{MI}} + \n \\vec{A}^{\\textrm{MII}}\\vec{x}^{\\textrm{MII}} + \n \\vec{A}^{\\textrm{MIII}}\\vec{x}^{\\textrm{MIII}} + \n\\end{split} \n\\end{equation}\nWhere the terms are determined in succesion:\n\\begin{align*}\n \\vec{A}^{\\textrm{MI}}\\vec{x}^{\\textrm{MI}} & = \\partial_E \\partial_R U\n \\\\\n \\vec{A}^{\\textrm{MII}}\\vec{x}^{\\textrm{MII}} & = \\partial_E \\partial_R U -\\vec{A}^{\\textrm{MI}}\\vec{x}^{\\textrm{MI}}\n \\\\\n \\vec{A}^{\\textrm{MIII}}\\vec{x}^{\\textrm{MIII}} & = \\partial_E \\partial_R U -\\vec{A}^{\\textrm{MI}}\\vec{x}^{\\textrm{MI}}\n -\\vec{A}^{\\textrm{MII}}\\vec{x}^{\\textrm{MII}}\n\\end{align*}\n\n\n\\vspace{1.5cm}\n\\section{Long-ranged electrostatics in polar materials}\n\nLong-ranged interactions are not captured appropriately by the interatomic force constants and have to be treated with care. The formalism to deal with this in quasiharmonic calculations is well established, see e.g. \\citet{Gonze1997}. For finite temperature calculations of interactions beyond pair interaction the formalism requires a slight adjustment.\n\nWe will first discuss the problem in quite general terms. Our starting point is a general long-ranged (polar) pair interaction $f(r)$ that we range-separate in some way:\n\\begin{equation}\n f(r) = \n f^{\\textrm{sr}}(r)+f^{\\textrm{lr}}(r).\n\\end{equation}\nIf we set the range-separation such that the short-ranged $f^{\\textrm{sr}}$ part fits snugly in the simulation cell, that part of the interaction is taken care of by the usual force constants and requires no additional consideration. The long-ranged tail $f^{\\textrm{lr}}$ is what remains to be treated. This is done by calculating the Hessian of the long-ranged part:\n\\begin{equation}\n \\Phi^{\\textrm{lr}}_{ij\\alpha\\beta} = \\left. \\frac{\\partial^2 f^{\\textrm{lr}}(r)}{\\partial u_{i}^\\alpha \\partial u_{j}^\\beta} \\right|_{r=r_{ij}}\n\\end{equation}\nafter which the forces from the long-ranged interactions can be calculated\n\\begin{equation}\n f_{i\\alpha}^{\\textrm{lr}} = -\\sum_{j\\beta} \\Phi^{\\textrm{lr}}_{ij\\alpha\\beta} u_{j\\beta}\n\\end{equation}\nthese long-ranged forces are removed from the calculated forces, such that the procedure for determining the interatomic force constants becomes:\n\\begin{align}\n\\label{eq:ff2}\n \\vec{A}^{\\Phi\\textrm{II}}\n \\vec{x}^{\\Phi\\textrm{II}} & = \\vec{f} - \\vec{f}^{\\textrm{lr}}\n\\\\\n\\label{eq:ff3}\n \\vec{A}^{\\Phi\\textrm{III}}\n \\vec{x}^{\\Phi\\textrm{III}} & = \n \\vec{f}-\\vec{f}^{\\textrm{lr}}-\n \\vec{A}^{\\Phi\\textrm{II}}\n \\vec{x}^{\\Phi\\textrm{II}}\n\\\\ \n\\label{eq:ff4}\n \\vec{A}^{\\Phi\\textrm{IV}}\n \\vec{x}^{\\Phi\\textrm{IV}} & = \n \\vec{f}- \\vec{f}^{\\textrm{lr}}\n -\n \\vec{A}^{\\Phi\\textrm{II}}\n \\vec{x}^{\\Phi\\textrm{II}}\n -\n \\vec{A}^{\\Phi\\textrm{III}}\n \\vec{x}^{\\Phi\\textrm{III}}\n\\end{align}\nWhen determining the dynamical matrix, the long-ranged pair interactions are added back again:\n\\begin{equation}\n \\vec{\\Phi}(\\vec{q}) = \\vec{\\Phi}^{\\textrm{sr}}(\\vec{q})+\\vec{\\Phi}^{\\textrm{lr}}(\\vec{q})\n\\end{equation}\nThis procedure is beneficial in several ways. By subtracting forces (instead of dynamical matrices as in \\citet{Gonze1997}) numerical issues due to aliasing (finite size) errors are greatly reduced. More importantly, it also ensures there is no multiple counting: the long-ranged interactions are subtracted from interactions of all orders, ensuring the short-sightedness of higher order interactions. What we just described is the general procedure, for practical calculations one needs to assume an explicit form for the long-ranged interactions. We use the (now temperature-dependent) Born charges and dielectric constant, and the range-separatation is done using the standard Ewald technique. The resulting real-space force constants are then\\cite{Gonze1997}\n\\begin{align}\n\\Phi^{\\textrm{dd}}|^{\\alpha\\beta}_{ij} & = \\sum_{\\gamma\\delta} Z_{i}^{\\alpha\\gamma}Z_{j}^{\\beta\\delta} \\widetilde{\\Phi}^{\\gamma\\delta}_{ij} \\\\\n\\widetilde{\\Phi}^{\\alpha\\beta}_{ij} & =\n\\label{eq:SM_ddifc}\n\\frac{1}{4\\pi\\epsilon_0}\n\\frac{1}{\\sqrt{\\det \\epsilon}}\n\\left(\n\t\\frac\n\t{ \\epsilon^{-1}_{\\alpha\\beta} }\n\t{|\\Delta_{ij}|_{\\epsilon}^{3}}\n\t-3 \\frac\n\t{\\Delta_{ij}^{\\alpha}\\Delta_{ij}^{\\beta}}\n\t{|\\Delta_{ij}|_{\\epsilon}^{5}}\n\\right)\n\\end{align}\nHere $\\boldsymbol{\\epsilon}$ is the dielectric tensor and $\\Delta$ realspace distances using the dielectric tensor as a metric:\n\\begin{align}\n\\mathbf{r}_{ij} & = \\mathbf{R}_{j}+\\mathbf{\\tau}_j-\\mathbf{\\tau}_i \\\\\n\\mathbf{\\Delta}_{ij} & = \\boldsymbol{\\epsilon}^{-1}\\mathbf{r}_{ij} \\\\\n|\\Delta_{ij}|_{\\epsilon} & = \\sqrt{\\mathbf{\\Delta}_{ij} \\cdot \\mathbf{r}_{ij}}\n\\end{align}\nThis poses an issue when calculating the dynamical matrix: the interactions die off as $~1\/r^{3}$ which makes it necessary to extend the sum over lattice vectors to infinity. This is remedied with the usual Ewald technique:\n\\begin{equation}\n\\widetilde{\\mathbf{\\Phi}}_{ij} (\\mathbf{q})=\n\\widetilde{\\mathbf{\\Phi}}^\\textrm{r}+\\widetilde{\\mathbf{\\Phi}}^\\textrm{q}+\\widetilde{\\mathbf{\\Phi}}^\\textrm{c}\n\\end{equation}\nDividing the sum into a realspace part, a reciprocal part and a connecting part. The realspace part is given by\n\\begin{align}\n\\widetilde{\\mathbf{\\Phi}}^\\textrm{r}_{ij} & =\n-\\frac{\\Lambda^3}{4\\pi\\epsilon_0\\sqrt{\\det \\epsilon}}\n\\sum_{\\mathbf{R}}\n\\mathbf{H}(\\Lambda \\Delta_{ij},\\Lambda|\\Delta_{ij}|_{\\epsilon})\ne^{i \\mathbf{q} \\cdot \\mathbf{R}} \\\\\n\\frac{\\partial \\widetilde{\\mathbf{\\Phi}}^\\textrm{r}_{ij}}{\\partial q_\\alpha} & =\n-\\frac{\\Lambda^3}{4\\pi\\epsilon_0\\sqrt{\\det \\epsilon}}\n\\sum_{\\mathbf{R}}\niR_\\alpha\n\\mathbf{H}(\\Lambda \\Delta_{ij},\\Lambda|\\Delta_{ij}|_{\\epsilon})\ne^{i \\mathbf{q} \\cdot \\mathbf{R}}\n\\end{align}\nwhere\n\\begin{equation}\nH_{\\alpha\\beta}(\\mathbf{x},y) = \\frac{x_{\\alpha}x_{\\beta} }{y^2}\n\\left[\n\\frac{3\\,\\textrm{erfc}\\,y}{y^3}\n+\n\\frac{2 e^{-y^2}}{\\sqrt{\\pi}}\\left(\\frac{3}{y^2}+2 \\right)\n\\right]\n-\\epsilon^{-1}_{\\alpha\\beta}\n\\left[\n\\frac{\\textrm{erfc}\\,y}{y^3} + \\frac{2 e^{-y}}{\\sqrt{\\pi} y^2 }\n\\right]\\,.\n\\end{equation}\nIn reciprocal space we have\n\\begin{align}\n\\label{eq:SM_philongrange}\n\t\\widetilde{\\Phi}_\\textrm{q}|_{ij}\n\t&=\n\t\\sum_{\\mathbf{K}=\\mathbf{G}+\\mathbf{q}}\n\t\\chi_{ij}(\\mathbf{K},\\Lambda)\n\t\\left( \\mathbf{K}\\otimes\\mathbf{K} \\right) \\\\\n\t\\frac{\\partial}{\\partial q_x} \\widetilde{\\Phi}_\\textrm{q}|_{ij}\n\t%\n\t&=\n\t\\sum_{\\mathbf{K}=\\mathbf{G}+\\mathbf{q}}\n\t\\chi_{ij}(\\mathbf{K},\\Lambda)\n\t\\left( \\mathbf{K}\\otimes\\mathbf{K} \\right)\n\t%\n\t\\left(\n\ti\\tau^x_{ij} - \\left[\\sum_{\\alpha} K_\\alpha \\epsilon_{\\alpha x}\n\t\\right]\n\t\\left[ \\frac{1}{\\|\\mathbf{K}\\|_{\\epsilon}}+\\frac{1}{4\\Lambda^2} \\right]\n\t\\right) +\n\t%\n\t\\chi_{ij}(\\mathbf{K},\\Lambda)\n\t\\begin{pmatrix}\n\t2K_x & K_y & K_z \\\\\n\tK_y & 0 & 0 \\\\\n\tK_z & 0 & 0\n\t\\end{pmatrix} \\\\\n\t\\frac{\\partial}{\\partial q_y} \\widetilde{\\Phi}_\\textrm{q}|_{ij}\n\t%\n\t&=\n\t\\sum_{\\mathbf{K}=\\mathbf{G}+\\mathbf{q}}\n\t\\chi_{ij}(\\mathbf{K},\\Lambda)\n\t\\left( \\mathbf{K}\\otimes\\mathbf{K} \\right)\n\t%\n\t\\left(\n\ti\\tau^y_{ij} - \\left[\\sum_{\\alpha} K_\\alpha \\epsilon_{\\alpha y}\n\t\\right]\n\t\\left[ \\frac{1}{\\|\\mathbf{K}\\|_{\\epsilon}}+\\frac{1}{4\\Lambda^2} \\right]\n\t\\right) +\n\t%\n\t\\chi_{ij}(\\mathbf{K},\\Lambda)\n\t\\begin{pmatrix}\n\t0 & K_x & 0 \\\\\n\tK_x & 2K_y & K_z \\\\\n\t0 & K_z & 0\n\t\\end{pmatrix}\\\\\n\t\\frac{\\partial}{\\partial q_z} \\widetilde{\\Phi}_\\textrm{q}|_{ij}\n\t%\n\t&=\n\t\\sum_{\\mathbf{K}=\\mathbf{G}+\\mathbf{q}}\n\t\\chi_{ij}(\\mathbf{K},\\Lambda)\n\t\\left( \\mathbf{K}\\otimes\\mathbf{K} \\right)\n\t%\n\t\\left(\n\ti\\tau^z_{ij} - \\left[\\sum_{\\alpha} K_\\alpha \\epsilon_{\\alpha z}\n\t\\right]\n\t\\left[ \\frac{1}{\\|\\mathbf{K}\\|_{\\epsilon}}+\\frac{1}{4\\Lambda^2} \\right]\n\t\\right) +\n\t%\n\t\\chi_{ij}(\\mathbf{K},\\Lambda)\n\t\\begin{pmatrix}\n\t0 & 0 & K_x \\\\\n\t0 & 0 & K_y \\\\\n\tK_x & K_y & 2K_z\n\t\\end{pmatrix}\n\\end{align}\nwhere\n\\begin{align}\n\\chi_{ij}(\\mathbf{K},\\Lambda) & =\n\\frac{1}{\\Omega\\epsilon_0}\n\\frac{\n\\exp\\left(i \\mathbf{q} \\cdot \\mathbf{\\tau}_{ij} \\right)\n\\exp\\left( -\\frac{\\|\\mathbf{K}\\|_{\\epsilon}}{4\\Lambda^2} \\right)\n}\n{\\|\\mathbf{K}\\|_{\\epsilon}} \\\\\n\\|\\mathbf{K}\\|_{\\epsilon} & =\\sum_{\\alpha\\beta}\\epsilon_{\\alpha\\beta}K_\\alpha K_\\beta\n\\end{align}\nand finally a connecting part given by\n\\begin{equation}\n\\widetilde{\\mathbf{\\Phi}}_\\textrm{c}|_{ij}=\n\\delta_{ij}\n\\frac{\\Lambda^3}{3 \\epsilon_0 \\pi^{3\/2} \\sqrt{ \\det \\boldsymbol{\\epsilon} } }\n\\widetilde{\\boldsymbol{\\epsilon}}\n\\end{equation}\nThis expression constitutes the dipole-dipole interactions at all distances. As explained in the beginning of the section we only want to subtract the long-ranged components from the forces. This separation is realized by choosing the Ewald parameter $\\Lambda$ such that $\\widetilde{\\Phi}^r$ disappears at the supercell boundary, and keeping only the reciprocal space part of the Ewald sum. So, to summarize, the procedure is as follows\n\\begin{itemize}\n \\item Determine Ewald parameter $\\Lambda$ such that the short-ranged interactions die off at the supercell boundary\n \\item Calculate $\\vec{\\Phi}^{\\textrm{lr}}(\\vec{q}=0)$ for the simulation supercell via Eq.~\\eqref{eq:SM_philongrange}\n \\item Use $\\vec{\\Phi}^{\\textrm{lr}}(\\vec{q}=0)$ to calculate the forces $\\vec{f}^{\\textrm{lr}}$ originating from the long-ranged tails of the dipole-dipole interactions\n \\item Use equations \\eqref{eq:ff2}--\\eqref{eq:ff4} to determine interatomic force constants.\n \\item When determining the dynamical matrix at any $\\vec{q}$, add $\\vec{\\Phi}^{\\textrm{lr}}(\\vec{q})$ back again.\n\\end{itemize}\nFor reference we also specified the gradient of the dynamical matrix in case the group velocities are needed. \nTo conclude this section we note that there is no principal difference between this approach and what is regularly used, but in practice the presented approach is easier to implement, suffers from less numerical artifacts and makes avoiding double-counting easier. \nThe double-counting we refer to arises because if we only correct the dynamical matrices, some long-ranged polar interactions get picked up by the third and fourth order force constants. \nThe non-analytical behavior of Eq.~\\eqref{eq:SM_ddifc} is already included in the the long-ranged reciprocal space part and independent of Ewald coupling parameter.\n\n\\vspace{1.5cm}\n\\section{Determining Raman spectra}\n\\label{SM:green}\n\nIn this section we outline the procedure to determine Raman spectra of an anharmonic crystal. \nWe will begin by sketching the procedure to obtain the phonon-phonon correlation functions in the presence of three-phonon anharmonicity via the equation of motion approach, since intermediate results are needed to obtain the polarizability-polarizability correlation functions.\n\nIf we express the displacements in terms of creation and annihilation operators we can identify the reciprocal space coefficients needed for the perturbative expansion, repeated here for clarity and consistency of notation. \nStart with the standard creation and annihilation operators for a phonon with momentum $\\vec{q}$ and polarization $s$ (when there is no loss of clarity we use the compound index $\\lambda$ to denote $\\vec{q}s$):\n\\begin{align}\n\ta_{\\vec{q}s} = & \\frac{1}{\\sqrt{2N\\hbar}}\n\t\\sum_{i\\alpha} \\epsilon_{\\vec{q}s}^{i\\alpha}\n\t\\left( \\sqrt{m_i \\omega_{\\vec{q}s}} u_{i\\alpha}-i \\frac{p_{i\\alpha}}{ \\sqrt{ m_i \\omega_{\\vec{q}s}} } \\right) \n\te^{-i\\mathbf{q}\\cdot\\mathbf{r}_i} \n\\\\\n\ta_{\\vec{q}s}^\\dagger = & \\frac{1}{\\sqrt{2N\\hbar}}\n\t\\sum_{i\\alpha} \\epsilon_{\\vec{q}s}^{i\\alpha\\,\\dagger} \n\t\\left( \\sqrt{m_i \\omega_{\\vec{q}s}} u_{i\\alpha} + i \\frac{p_{i\\alpha}}{ \\sqrt{ m_i \\omega_{\\vec{q}s}} } \\right) \n\te^{i\\mathbf{q}\\cdot\\mathbf{r}_i}\n\\\\\n A_{\\vec{q}s} = a_{\\vec{q}s} + a_{\\bar{\\vec{q}}s}^\\dagger = &\n\\frac{2}{\\sqrt{2N\\hbar}}\n\t\\sum_{i\\alpha} \\epsilon_{\\vec{q}s}^{i\\alpha}\n\t\\sqrt{m_i \\omega_{\\vec{q}s}} u_{i\\alpha}\n\te^{-i\\mathbf{q}\\cdot\\mathbf{r}_i} \n\\\\\n B_{\\vec{q}s} = a_{\\vec{q}s} - a_{\\bar{\\vec{q}}s}^\\dagger = &\n-\\frac{2i}{\\sqrt{2N\\hbar}}\n\t\\sum_{i\\alpha} \\epsilon_{\\vec{q}s}^{i\\alpha}\n \\frac{p_{i\\alpha}}{ \\sqrt{ m_i \\omega_{\\vec{q}s}} }\t\n\te^{-i\\mathbf{q}\\cdot\\mathbf{r}_i} \n\\end{align}\nHere $\\epsilon$ are phonon eigenvectors ($\\vec{\\epsilon}_i \\cdot \\vec{\\epsilon}_j = \\delta_{ij}$), $m$ atomic masses, $\\omega$ frequencies. The vector $\\vec{r}$ is a lattice vector, and $u$ and $p$ are the position and momentum operators. We use the notation $\\bar{\\vec{q}}=-\\vec{q}$. \nI introduce scaled eigenvectors (to simplify notation) via\n\\begin{align}\n\t\\upsilon_{\\vec{q}s}^{i\\alpha} = & \\sqrt{ \\frac{\\hbar}{2 m_i \\omega_{\\vec{q}s} } } \\epsilon_{\\vec{q}s}^{i\\alpha}\n %\n\\intertext{In this notation the matrix elements pertaining to anharmonicity become}\n \\Phi_{\\lambda\\lambda'\\lambda''} & = \n \\frac{1}{3!}\n\t\\sum_{ijk\\alpha\\beta\\gamma}\n\t\\upsilon_{\\vec{q}s}^{i\\alpha}\n\t\\upsilon_{\\vec{q}'s'}^{j\\beta}\n\t\\upsilon_{\\vec{q}''s''}^{k\\gamma}\n\t\\Phi_{ijk}^{\\alpha\\beta\\gamma}\n\te^{-i \n\t(\\vec{q}\\cdot\\vec{r}_i+ \\vec{q}'\\cdot\\vec{r}_j+\\vec{q}''\\cdot\\vec{r}_k)}\n\\\\\n \\Phi_{\\lambda\\lambda'\\lambda''\\lambda'''} & = \n \\frac{1}{4!}\n\t\\sum_{ijkl\\alpha\\beta\\gamma\\delta}\n\t\\upsilon_{\\vec{q}s}^{i\\alpha}\n\t\\upsilon_{\\vec{q}'s'}^{j\\beta}\n\t\\upsilon_{\\vec{q}''s''}^{k\\gamma}\n\t\\upsilon_{\\vec{q}'''s'''}^{l\\delta}\t\n\t\\Phi_{ijkl}^{\\alpha\\beta\\gamma\\delta}\n\te^{-i\n\t(\\vec{q}\\cdot\\vec{r}_i +\n\t\\vec{q}'\\cdot\\vec{r}_j +\n\t\\vec{q}''\\cdot\\vec{r}_k +\n\t\\vec{q}'''\\cdot\\vec{r}_l)\n\t} \n\\intertext{so that the anharmonic part of the Hamiltonian becomes}\n\tH_A & = \\Phi_{\\lambda\\lambda'\\lambda''}A_{\\lambda}A_{\\lambda'}A_{\\lambda''}+\n\t\\Phi_{\\lambda\\lambda'\\lambda''\\lambda'''}A_{\\lambda}A_{\\lambda'}A_{\\lambda''}A_{\\lambda'''} + \\ldots\t\n\\end{align}\nIn an analogous way we define the coefficients of the expansion of the polarizability as\n\\begin{align}\n P^{\\mu\\nu}_{\\lambda} & =\n\t\\sum_{i\\alpha}\n\t\\upsilon_{\\vec{q}s}^{i\\alpha}\n\tP_{i}^{\\mu\\nu,\\alpha}\n\te^{-i\\vec{q}\\cdot\\vec{r}_i}\n\t\\Delta_{\\vec{q}} \n\t=\n\t\\sum_{i\\alpha}\n\t\\upsilon_{\\vec{\\Gamma}s}^{i\\alpha}\n\tP_{i}^{\\mu\\nu,\\alpha}\n\t=\n\tP^{\\mu\\nu}(s)\n\\\\\nP^{\\mu\\nu}_{\\lambda\\lambda'} & =\n \\frac{1}{2!}\n\t\\sum_{ij\\alpha\\beta}\n\t\\upsilon_{\\vec{q}s}^{i\\alpha}\n\t\\upsilon_{\\vec{q}'s'}^{j\\beta}\n\tP_{ij}^{\\mu\\nu,\\alpha\\beta}\n\te^{-i\\vec{q}\\cdot\\vec{r}_i}\n\te^{-i\\vec{q}'\\cdot\\vec{r}_j}\n\t\\Delta_{\\vec{q}\\vec{q}'} =\n\\\\\n\t& =\t\n\t\\frac{1}{2!}\n\t\\sum_{ij\\alpha\\beta}\n\t\\upsilon_{\\vec{q}s}^{i\\alpha}\n\t(\\upsilon_{\\vec{q}s'}^{j\\beta})^{\\dagger}\n\tP_{ij}^{\\mu\\nu,\\alpha\\beta}\n\te^{i\\vec{q}\\cdot(\\vec{r}_j-\\vec{r}_i)}\n\\\\\n\t& =\n\t\\frac{1}{2!}\n\t\\sum_{ij\\alpha\\beta}\n\t(\\upsilon_{\\vec{q}s}^{i\\alpha})^{\\dagger}\n\t\\upsilon_{\\vec{q}s'}^{j\\beta}\n\tP_{ij}^{\\mu\\nu,\\alpha\\beta}\n\te^{-i\\vec{q}\\cdot(\\vec{r}_j-\\vec{r}_i)} = \n\tP^{\\mu\\nu}(\\vec{q},s,s') = \n\tP^{\\mu\\nu}(\\bar{\\vec{q}},s,s')^\\dagger =\n\tP^{\\mu\\nu}(\\bar{\\vec{q}},s',s)\n\\\\\nP^{\\mu\\nu}_{\\lambda\\lambda'\\lambda''} & =\n \\frac{1}{3!}\n \\sum_{ijk\\alpha\\beta\\gamma}\n\t\\upsilon_{\\vec{q}s}^{i\\alpha}\n\t\\upsilon_{\\vec{q}'s'}^{j\\beta}\n\t\\upsilon_{\\vec{q}'s'}^{k\\gamma}\n\tP_{ijk}^{\\mu\\nu,\\alpha\\beta\\gamma}\n\te^{-i\n\t(\\vec{q}\\cdot\\vec{r}_i+\n\t\\vec{q}'\\cdot\\vec{r}_j+\n\t\\vec{q}''\\cdot\\vec{r}_k)\n\t}\n\\intertext{and the dipole moment matrix elements become}\n M^{\\mu}_{\\lambda} & =\n\t\\sum_{i\\alpha}\n\t\\upsilon_{\\vec{\\Gamma}s}^{i\\alpha}\n\tM_{i}^{\\mu,\\alpha}\n\t=\n\tM^{\\mu}(s)\n\\\\\nM^{\\mu\\nu}_{\\lambda\\lambda'} & =\n \\frac{1}{2!}\n\t\\sum_{ij\\alpha\\beta}\n\t(\\upsilon_{\\vec{q}s}^{i\\alpha})^{\\dagger}\n\t\\upsilon_{\\vec{q}s'}^{j\\beta}\n\tM_{ij}^{\\mu,\\alpha\\beta}\n\te^{-i\\vec{q}\\cdot(\\vec{r}_j-\\vec{r}_i)} = \n\tM^{\\mu}(\\vec{q},s,s') = \n\tM^{\\mu}(\\bar{\\vec{q}},s,s')^\\dagger =\n\tM^{\\mu}(\\bar{\\vec{q}},s',s)\n\\\\\nM^{\\mu}_{\\lambda\\lambda'\\lambda''} & =\n \\frac{1}{3!}\n \\sum_{ijk\\alpha\\beta\\gamma}\n\t\\upsilon_{\\vec{q}s}^{i\\alpha}\n\t\\upsilon_{\\vec{q}'s'}^{j\\beta}\n\t\\upsilon_{\\vec{q}'s'}^{k\\gamma}\n\tM_{ijk}^{\\mu,\\alpha\\beta\\gamma}\n\te^{-i\n\t(\\vec{q}\\cdot\\vec{r}_i+\n\t\\vec{q}'\\cdot\\vec{r}_j+\n\t\\vec{q}''\\cdot\\vec{r}_k)\n\t}\n\\intertext{Such that}\n\\label{eq:polarizabilityexpansion}\nP^{\\mu\\nu} & = P^{\\mu\\nu}_0 + P^{\\mu\\nu}_{\\lambda} A_{\\lambda}\n+ P^{\\mu\\nu}_{\\lambda\\lambda'} \nA_{\\lambda}A_{\\lambda'}\n+ P^{\\mu\\nu}_{\\lambda\\lambda'\\lambda''} \nA_{\\lambda}A_{\\lambda'}A_{\\lambda''} + \\ldots\n\\\\\nM^{\\mu} & = M^{\\mu}_0 + M^{\\mu}_{\\lambda} A_{\\lambda}\n+ M^{\\mu}_{\\lambda\\lambda'} \nA_{\\lambda}A_{\\lambda'}\n+ M^{\\mu}_{\\lambda\\lambda'\\lambda''} \nA_{\\lambda}A_{\\lambda'}A_{\\lambda''} + \\ldots\n\\end{align}\n\n\\subsection{Phonon Green's functions}\n\nWe briefly sketch the derivation of the phonon self-energy in the presence of third order anharmonicity since intermediate results will be used to express the Raman and infrared spectra. Starting from the retarded Green's function\n\\begin{equation}\n G^{X,Y} = -i\\theta(t)\\avg{[X(t),Y^\\dagger(0)]}\n\\end{equation}\nwe express the equations of motion of the phonon-phonon Green's function via\n\\begin{subequations}\n\\begin{align}\n\\label{eq:dr0}\n \\partial_t A(t) & = -i[H,A]\n\\\\\n\t\\frac{d}{d t} G^{AA}_{\\lambda\\lambda'} \n\t& = - i \\omega_{\\lambda} G^{BA}_{\\lambda\\lambda'}\n\\\\\n\t\\frac{d}{d t} G^{BA}_{\\lambda\\lambda'}\n\t& = i 2 \\delta_{ \\lambda\\lambda' }\\delta( t )\n\t- i \\omega_{\\lambda} G^{AA}_{\\lambda\\lambda'}\n\t- i 6 \\sum_{\\mu_1 \\mu_2} \\Phi_{\\lambda \\mu_1 \\mu_2} G^{AA,A}_{\\mu_1 \\mu_2,\\lambda'}\n\\\\\n\\label{eq:dr1}\n\ti \\frac{d}{d t} G^{AA,A}_{\\mu_1 \\mu_2,\\lambda'} & =\n\t\\omega_{\\mu_1} G^{BA,A}_{\\mu_1 \\mu_2,\\lambda'} +\n\t\\omega_{\\mu_2} G^{AB,A}_{\\mu_1 \\mu_2,\\lambda'}\n\\\\\n\\label{eq:dr2}\t\n\ti \\frac{d}{d t} G^{AB,A}_{\\mu_1 \\mu_2,\\lambda'} & =\n\t\\omega_{\\mu_1} G^{BB,A}_{\\mu_1 \\mu_2,\\lambda'} +\n\t\\omega_{\\mu_2} G^{AA,A}_{\\mu_1 \\mu_2,\\lambda'} +\n\t\\sum_{\\nu_1 \\nu_2}\n\t6 \\Phi_{\\mu_2 \\nu_1 \\nu_2} \n\tG^{AAA,A}_{\\mu_1 \\nu_1 \\nu_2,\\lambda'}\n\\\\\n\\label{eq:dr3}\t\n\ti \\frac{d}{d t} G^{BA,A}_{\\mu_1 \\mu_2,\\lambda'} & =\n\t\\omega_{\\mu_1} G^{AA,A}_{\\mu_1 \\mu_2,\\lambda'} +\n\t\\omega_{\\mu_2} G^{BB,A}_{\\mu_1 \\mu_2,\\lambda'} +\n\t\\sum_{\\nu_1 \\nu_2}\n\t6 \\Phi_{\\nu_1 \\nu_2 \\mu_1} \n\tG^{AAA,A}_{\\nu_1 \\nu_2 \\mu_2,\\lambda'}\n\\\\\n\\label{eq:dr4}\n\ti \\frac{d}{d t} G^{BB,A}_{\\mu_1 \\mu_2,\\lambda'} & =\n\t\\omega_{\\mu_1} G^{AB,A}_{\\mu_1 \\mu_2,\\lambda'} +\n\t\\omega_{\\mu_2} G^{BA,A}_{\\mu_1 \\mu_2,\\lambda'}\n\\intertext{\nthe four-point Green's functions are decoupled\\cite{Semwal1972,Zubarev1960} via \n$\\avg{abcd}\\approx \\avg{ab}\\avg{cd} + \\avg{ac} \\avg{bd} + \\avg{ad} \\avg{bc}$ to yield\n}\n\t\\sum_{\\nu_1 \\nu_2}\n\t6 \\Phi_{\\mu_2 \\nu_1 \\nu_2} \n\tG^{AAA,A}_{\\mu_1 \\nu_1 \\nu_2,\\lambda'}\n\t& =\n\t12 (2n_{\\mu_1}+1) \\sum_{\\nu_1}\\Phi_{\\mu_1 \\mu_2 \\nu_1 } G^{AA}_{\\nu_1 \\lambda'}\n\\\\\n\\label{eq:dr5}\n\t\\sum_{\\nu_1 \\nu_2}\n\t6 \\Phi_{\\nu_1 \\nu_2 \\mu_1} \n\tG^{AAA,A}_{\\nu_1 \\nu_2 \\mu_2,\\lambda'}\n\t& =\n\t12 (2n_{\\mu_2}+1) \\sum_{\\nu_1}\\Phi_{\\mu_1 \\mu_2 \\nu_1 } G^{AA}_{\\nu_1 \\lambda'}\n\\end{align}\n\\end{subequations}\nAfter Fourier transforming equations \\eqref{eq:dr0}--\\eqref{eq:dr5} to the frequency domain we get a solvable set of equations that recover the usual definition of three-phonon anharmonicity:\\cite{Leibfried1961,Cowley1963,wallace1998thermodynamics,Semwal1972}\n\\begin{equation}\n G_{\\lambda\\lambda'}(\\Omega)^{-1} = G^0_{\\lambda\\lambda'}(\\Omega)^{-1} + \\Sigma_{\\lambda\\lambda'}\n\\end{equation} \nwhere\n\\begin{equation}\n \\Sigma_{\\lambda\\lambda'}(Z) = -18 \\sum_{\\vec{q}_1\\vec{q}_2 s_1 s_2}\n \\Phi^{\\lambda s_1s_2}_{\\vec{q}\\bar{\\vec{q}}_1\\bar{\\vec{q}}_2}\n \\Phi^{\\lambda' s_1s_2}_{\\bar{\\vec{q}} \\vec{q}_1 \\vec{q}_2}\n S(s_1,s_2,Z)\n\\end{equation}\nand\n\\begin{equation}\n S(s_a,s_b,Z) = \n\t(n_{a}+n_{b}+1)\n\t\\left[\n\t\\frac{1}{(\\omega_{a}+\\omega_{b}-Z)_p}-\n\t\\frac{1}{(\\omega_{a}+\\omega_{b}+Z)_p}\n\t\\right]\n\t+\n\t(n_{a}-n_{b})\n\t\\left[\n\t\\frac{1}{(\\omega_{b}-\\omega_{a}+Z)_p}-\n\t\\frac{1}{(\\omega_{b}-\\omega_{a}-Z)_p}\n\t\\right]\n\\end{equation}\nNaturally if one considers the four-phonon interactions you recover the standard results, but the information we need right now is the intermediate result for the three-point Green's function expressed in terms of two-point Green's functions and three-phonon matrix elements:\n\\begin{equation}\n \\label{eq:threepointGF}\n G_{\\lambda\\lambda',\\lambda''} =\n 6 \\sum_{s} \\Phi_{\\bar{\\lambda}\\bar{\\lambda}'s} \n S(\\lambda,\\lambda')\n G_{ A_{s} A_{\\lambda''} }\n\\end{equation}\n\\subsection{Raman spectra}\nIf we start from the polarizability-polarizability correlation function and insert the expansion of the polarizability in terms of phonon coordinates, Eq.~\\eqref{eq:polarizabilityexpansion}, we get\n\\begin{equation}\n\\begin{split}\n \\avg{P(t)P(0)} = i\\overset{>}{G}(P,P) = &\n \\vec{P}_0 \\otimes \\vec{P}_0 + \n %\n \\vec{P}_{\\lambda} \\otimes \\vec{P}_{\\lambda'} i\\overset{>}{G}(A_{\\lambda},A_{\\lambda'})\n+ \\\\\n + &\n \\vec{P}_{\\lambda\\lambda'}\n \\otimes\n \\vec{P}_{\\lambda''\\lambda'''}\n i\\overset{>}{G}(A_{\\lambda}A_{\\lambda'},A_{\\lambda''}A_{\\lambda'''})\n+ \\\\ \n + &\n \\vec{P}_{\\lambda} \\otimes \\vec{P}_{\\lambda'\\lambda''}\n i\\overset{>}{G}(A_{\\lambda},A_{\\lambda'}A_{\\lambda''})\n+\n \\vec{P}_{\\lambda\\lambda'}\n \\otimes\n \\vec{P}_{\\lambda''}\n i\\overset{>}{G}(A_{\\lambda}A_{\\lambda'},A_{\\lambda''})\n+ \\\\\n + &\n\\vec{P}_{\\lambda} \\otimes \\vec{P}_{\\lambda'\\lambda''\\lambda'''}\n i\\overset{>}{G}(A_{\\lambda},A_{\\lambda'}A_{\\lambda''}A_{\\lambda'''})\n+\n \\vec{P}_{\\lambda\\lambda'\\lambda''}\n \\otimes\n \\vec{P}_{\\lambda'''}\n i\\overset{>}{G}(A_{\\lambda}A_{\\lambda'}A_{\\lambda''},A_{\\lambda'''})\n\\end{split}\n\\end{equation}\nwhere we have omitted terms of $A^4$ or higher. We will deal with the terms in order. The constant term does not contribute to the Raman spectrum. The first term that gives a contribution is\n\\begin{equation}\n\\label{eq:SMramanI}\n I^{\\textrm{I}} =\n \\vec{P}_{\\lambda} \\otimes \\vec{P}_{\\lambda'}\n \\int\n i \\overset{>}{G}(A_{\\lambda}(t),A_{\\lambda'}(0))\n e^{-i\\Omega t} dt\n =\n \\vec{P}_{\\lambda} \\otimes \\vec{P}_{\\lambda'}\n (n(\\Omega)+1)\n J_{\\lambda\\lambda'}(\\Omega)\n\\end{equation}\nwhere $J$ is the phonon spectral function\n\\begin{equation}\n\tJ(\\Omega) = \n\t- \\frac{1}{\\pi} \\Im\\left\\{ G(\\Omega) \\right\\} =\t\\frac{i}{(n(\\Omega,T)+1)}\\overset{>}{G}(\\Omega)\n\\end{equation}\nWe identify Eq.~\\eqref{eq:SMramanI} as the first order Raman spectra. \nThe next contribution comes from\n\\begin{equation} \\label{eq:2nd_ord_Raman}\n I^{\\textrm{II}} =\n \\vec{P}_{\\lambda\\lambda'} \\otimes \\vec{P}_{\\lambda''\\lambda'''}\n \\int\n \\overset{>}{G}(A_{\\lambda}(t)A_{\\lambda'}(t),A_{\\lambda''}(0)A_{\\lambda'''}(0))\n e^{-i\\Omega t} dt\n\\end{equation}\nThe first step is to decouple the four-point Green's function:\\cite{Zubarev1960}\n\\begin{equation}\n\\begin{split}\n \\overset{>}{G}(A_{\\lambda}(t)A_{\\lambda'}(t),A_{\\lambda''}(0)A_{\\lambda'''}(0))\n & \\approx \n \\overset{>}{G}(A_{\\lambda}(t),A_{\\lambda'''}(0))\n \\overset{>}{G}(A_{\\lambda'}(t),A_{\\lambda''}(0))\n +\n\\\\ & + \n \\overset{>}{G}(A_{\\lambda}(t),A_{\\lambda''}(0))\n \\overset{>}{G}(A_{\\lambda'}(t),A_{\\lambda'''}(0))\n +\n\\\\ & +\n \\underbrace{\\overset{>}{G}(A_{\\lambda}(t),A_{\\lambda'}(t))\n \\overset{>}{G}(A_{\\lambda''}(0),A_{\\lambda'''}(0))}_{= \\textrm{constant}}\n\\end{split} \n\\end{equation}\nand note that the Fourier transform of a product becomes a convolution:\n\\begin{equation}\n\\begin{split}\n I^{\\textrm{II}} & =\n \\vec{P}_{\\lambda\\lambda'} \\otimes \\vec{P}_{\\lambda''\\lambda'''}\n \\frac{1}{\\pi^2}\n \\bigg[ \n \\int\n (n(\\Omega')+1)\n J_{\\lambda\\lambda''}(\\Omega')\n (n(\\Omega-\\Omega')+1) J_{\\lambda'\\lambda'''}(\\Omega-\\Omega')\n d\\Omega'\n +\n\\\\\n & +\n \\int\n (n(\\Omega')+1)\n J_{\\lambda\\lambda'''}(\\Omega')\n (n(\\Omega-\\Omega')+1) J_{\\lambda'\\lambda''}(\\Omega-\\Omega')\n d\\Omega'\n \\bigg]\n =\n\\\\ \n& = 2\\Re\\left\\{ \n\\vec{P}_{\\lambda\\lambda'} \\otimes \\vec{P}_{\\lambda''\\lambda'''}\n \\right\\}\n \\int\n (n(\\Omega')+1)\n J_{\\lambda\\lambda''}(\\Omega')\n (n(\\Omega-\\Omega')+1) J_{\\lambda'\\lambda'''}(\\Omega-\\Omega')\n d\\Omega' \n\\end{split} \n\\end{equation}\nwhere we relabelled the summation indices in the second term. \nThis term is the second-order Raman spectra, slightly more general than it is usually presented. \nIf we assume a diagonal self-energy (and consequently a diagonal spectral function) and insert the non-interacting spectral function $J^0_{\\lambda} = \\delta(\\Omega-\\omega_{\\lambda}) - \\delta(\\Omega+\\omega_{\\lambda})$ the integral becomes\n\\begin{equation}\n\\begin{split}\n & \\int\n n(-\\Omega')\n \\left[\n \\delta(\\Omega'-\\omega_a)\n -\n \\delta(\\Omega'+\\omega_a)\n \\right]\n n(\\Omega'-\\Omega)\n \\left[\n \\delta(\\Omega-\\Omega'-\\omega_b)\n -\n \\delta(\\Omega-\\Omega'+\\omega_b)\n \\right]\n d\\Omega' =\n\\\\\n = &\n n(-\\omega_a)\n n(\\omega_a-\\Omega)\n \\left[\n \\delta(\\Omega-\\omega_a-\\omega_b)\n -\n \\delta(\\Omega-\\omega_a+\\omega_b)\n \\right] -\n\\\\ \n & \n n(\\omega_a)\n n(-\\omega_a-\\Omega)\n \\left[\n \\delta(\\Omega+\\omega_a-\\omega_b)\n -\n \\delta(\\Omega+\\omega_a+\\omega_b)\n \\right] =\n\\\\\n = &\n \\left[n(\\Omega)+1\\right]\n \\left[\n n(\\omega_a)+n(\\omega_b)+1\n \\right]\n \\left[\n \\delta(\\Omega+\\omega_a+\\omega_b)\n -\n \\delta(\\Omega-\\omega_a-\\omega_b)\n \\right]\n +\n\\\\\n & +\n \\left[n(\\Omega)+1\\right]\n \\left[\n n(\\omega_a)-n(\\omega_b)\n \\right]\n \\left[\n \\delta(\\Omega-\\omega_a+\\omega_b)\n -\n \\delta(\\Omega+\\omega_a-\\omega_b)\n \\right] =\n\\\\\n = & -\\frac{n(\\Omega)+1}{\\pi} \\Im\\left\\{ S(\\omega_a,\\omega_b,\\Omega) \\right\\}\n\\end{split}\n\\end{equation}\nwhere we made use of $n(a+b) = (1+n(a)+n(b))\/n(a)n(b)$ as well as $n(-a)=-n(a)-1$. \nWith this we recover the expression given by \\citet{Cowley1964b} as the limit of weak anharmonicity. \nIn this study, however, it was found necessary to keep the interacting spectral function when calculating the Raman spectra. \nIt is intuitive: the second order Raman is proportional to the two-phonon DOS, and if the phonons are broadened then the two-phonon DOS should also be broadened.\n\nNext we have a term given by\n\\begin{equation}\n I^{\\textrm{III}} =\n \\vec{P}_{\\lambda} \\otimes \\vec{P}_{\\lambda'\\lambda''}\n G^{>}(A_{\\lambda},A_{\\lambda'}A_{\\lambda''})\n+\n \\vec{P}_{\\lambda\\lambda'}\n \\otimes\n \\vec{P}_{\\lambda''}\n G^{>}(A_{\\lambda}A_{\\lambda'},A_{\\lambda''})\n\\end{equation}\nwhere we make use of the intermediate result for the three-point Green's functions in Eq.~\\eqref{eq:threepointGF} and arrive at\n\\begin{equation}\nI^{\\textrm{III}} =\n-6\n(n(\\Omega)+1)\n\\sum_{\\mu}\n\\left(\n\\vec{P}_{\\lambda''\\lambda'}\n\\otimes\n\\vec{P}_{\\lambda}\n\\Phi_{\\bar{\\lambda}''\\bar{\\lambda}'\\mu}\n+\n\\vec{P}_{\\lambda} \\otimes \\vec{P}_{\\lambda'\\lambda''}\n\\Phi_{\\bar{\\lambda}'\\bar{\\lambda}''\\mu}\n\\right)\n\\Im\\left\\{ \nS_{\\lambda''\\lambda'}\n\\right\\}\nJ_{\\mu \\lambda}(\\Omega)\n\\end{equation}\nwhere, if we assume a diagonal self-energy, we recover previous results.\\cite{Cowley1964b} \nIt is worth noting is that -- as far as we have discovered -- the last two terms are in practice several orders of magnitude smaller than the usual first and second order terms. \nIn term III we have a product between the two-phonon DOS and the one-phonon spectral function, so to have any sizeable contribution these need to peak simultaneously. \nThe final term\n\\begin{equation}\n I^{\\textrm{IV}} =\n\\vec{P}_{\\lambda} \\otimes \\vec{P}_{\\lambda'\\lambda''\\lambda'''}\n G(A_{\\lambda},A_{\\lambda'}A_{\\lambda''}A_{\\lambda'''})\n+\n \\vec{P}_{\\lambda\\lambda'\\lambda''}\n \\otimes\n \\vec{P}_{\\lambda'''}\n G(A_{\\lambda}A_{\\lambda'}A_{\\lambda''},A_{\\lambda'''}) \n\\end{equation}\nwe approach with the same decoupling procedure as before:\n\\begin{equation}\n\\begin{split}\n G^{>}(A_{\\lambda}(t),A_{\\lambda'}(0),A_{\\lambda''}(0)A_{\\lambda'''}(0))\n & \\approx \n G^{>}(A_{\\lambda}(t),A_{\\lambda'''}(0))\n G^{>}(A_{\\lambda'}(0),A_{\\lambda''}(0))\n +\n\\\\ & + \n G^{>}(A_{\\lambda}(t),A_{\\lambda''}(0))\n G^{>}(A_{\\lambda'}(0),A_{\\lambda'''}(0))\n +\n\\\\ & +\n G^{>}(A_{\\lambda}(t),A_{\\lambda'}(0))\n G^{>}(A_{\\lambda''}(0),A_{\\lambda'''}(0)) =\n\\\\\n & = 3 G^{>}(A_{\\lambda}(t),A_{\\lambda'''}(0))\\delta_{\\lambda'\\lambda''}(2n_{\\lambda'}+1)\n\\end{split} \n\\end{equation}\nwhere the factor 3 comes from relabelling indices. \nWe also used the equal-time thermal average of the phonon operators. This gives\n\\begin{equation}\n I^{\\textrm{IV}} =\n 3\n (n(\\Omega)+1)\n \\left(\n \\vec{P}_{\\lambda} \\otimes \\vec{P}_{\\lambda'\\lambda'\\lambda''}\n +\n \\vec{P}_{\\lambda''\\lambda'\\lambda'}\n \\otimes\n \\vec{P}_{\\lambda}\n \\right)\n (2n_{\\lambda'}+1)\n J_{\\lambda\\lambda''}\n\\end{equation}\nWhat we derived in the section above holds for any operator that is expressed as a constant multiplied with the phonon operator, i.e. a quantity that only depends on the position of the atoms (it's frequency dependence can be ignored, or at least the cross-terms between the frequency dependence and the position of atoms). The derivation of the infrared absorption spectrum is identical, one only needs to replace the matrix elements. If the real part of the susceptibility if of interest it is conveniently calculated via a Kramers-Kronig transformation of the imaginary part.\n\nIn the main text I have defined compound matrix element for the polarizability to simplify the notation, they are given via:\n\\begin{align}\n\tP^{(\\textrm{I})}_{\\mu\\nu,\\xi\\rho}(s_1) & = P^{\\mu\\nu}(s_1) P^{\\xi\\rho}(s_1)^\\dagger\t\n\\\\\n\tP^{(\\textrm{II})}_{\\mu\\nu,\\xi\\rho}(\\vec{q},s_1,s_2) & =\n\tP^{\\mu\\nu} (\\vec{q},s_1,s_2)\n\tP^{\\xi\\rho} (\\vec{q},s_1,s_2)^\\dagger\t\n\\\\\n\tP^{(\\textrm{III})}_{\\mu\\nu,\\xi\\rho}(\\vec{q},s_1,s_2,s_3) & =\n\t\\Phi_{\\vec{q} \\bar{\\vec{q}} \\vec{\\Gamma}}^{s_1 s_2 s_3}\n\tP^{\\mu\\nu}( \\vec{q},s_1,s_2 )\t\n\tP^{\\xi\\rho}(s_3)\n\t+\n\t\\Phi_{\\vec{q} \\bar{\\vec{q}} \\vec{\\Gamma}}^{s_1 s_2 s_3}\n\tP^{\\xi\\rho}( \\vec{q},s_1,s_2 )\t\n\tP^{\\mu\\nu}(s_3)\n\\\\\n\tP^{(\\textrm{IV})}_{\\mu\\nu,\\xi\\rho}(\\vec{q},s_1,s_2) & =\n\tP^{\\mu\\nu}(\\vec{\\Gamma},\\bar{\\vec{q}},\\vec{q},s_1,s_2,s_2)\n\tP^{\\xi\\rho}(s_1)\n\t+\n\tP^{\\xi\\rho}(\\vec{\\Gamma},\\bar{\\vec{q}},\\vec{q},s_1,s_2,s_2)\n\tP^{\\mu\\nu}(s_1)\n\\intertext{In exactly the same way I can define the following dipole matrix elements:}\n\t\\label{eq:ircomp1}\n\tM^{(I)}_{\\alpha\\beta}(s_1) & = M^{\\alpha}_{s_1} (M^{\\beta}_{s_1})^\\dagger\n\t%\n\t\\\\\n\t%\n\t\\label{eq:ircomp2}\n\tM^{(II)}_{\\alpha\\beta}(s_1,s_2,\\vec{q}) & =\n\tM^\\alpha (^{s_1 s_2}_{\\vec{q} })\n\tM^\\beta (^{s_1 s_2}_{\\vec{q} })^\\dagger\n\t%\n\t\\\\\n\t%\n\t\\label{eq:ircomp3}\n\tM^{(III)}_{\\alpha\\beta}(s_1,s_2,s_3,\\vec{q}) & =\t\n\t\\Phi_{\\vec{q} \\bar{\\vec{q}} \\vec{0}}^{s_1 s_2 s_3}\n\tM^\\beta(^{s_1 s_2}_{\\vec{q}} )\t\n\tM^\\alpha_{s_3}\n\t+\n\t\\Phi_{\\vec{q} \\bar{\\vec{q}} \\vec{0}}^{s_1 s_2 s_3}\n\tM^\\alpha(^{s_1 s_2}_{\\vec{q}} )\t\n\tM^\\beta_{s_3}\n\t%\n\t\\\\\n\t%\n\t\\label{eq:ircomp4}\n\tM^{(IV)}_{\\alpha\\beta}(s_1,s_2,\\vec{q}) & =\t\t\n\tM^\\alpha(^{s_1 s_2 s_2}_{ \\vec{0}\\bar{\\vec{q}}\\vec{q} })\n\tM^\\beta_{s_1}\n\t+\n\tM^\\beta(^{s_1 s_2 s_2}_{ \\vec{0}\\bar{\\vec{q}}\\vec{q} })\n\tM^\\alpha_{s_1}\n\\end{align}\nHere I have explicitly used momentum conservation in the matrix elements. There is only a single q-vector that remains, which means that the computational cost of determining the spectras (given interaction tensors) are modest, at most a single sum over the Brillouin zone.\n\n\\vspace{1.5cm}\n\\section{Details of calculations} \\label{SM:details}\n\n\\subsection{Self-consistency procedure}\n\nThe interatomic force constants are determined self-consistently as explained in \\citet{Shulumba2016b}, but it is worth explaining the practical procedure. \nThe basic idea is that a set of force constants gives us normal modes that can be thermally populated, generating thermally excited structures. \nThermally excited structures can, with the help of equation \\eqref{eq:ff2} yield a new set of force constants. \nThe procedure is terminated when the input and output force constants show no significant difference.\n\nThe self-consistency procedure is implemented via a geometric series:\n\\begin{itemize}\n \\item Create an initial guess for the second order force constants (see \\citet{Shulumba2016b}) and generate 1 thermally excited supercell\n \\item Generate new force constants and produce 2 thermally excited supercells.\n \\item Use both the one old and two new supercells to generate a new set of force constants, and 4 new supercells\n \\item Use 2+4 supercells to generate forceconstants and 8 supercells\n \\item Use 4+8 supercells to generate forceconstants and 16 supercells\n \\item Repeat until convergence\n\\end{itemize}\nThis procedure has the benefit of introducing some mixing (since you use supercells generated from different force constants) smoothing convergence. \nIn addition, the prescribed procedure leaves only 25\\% of calculations as waste. \nA straight implementation (generate N supercells each iteration, repeat until convergence) can waste a much larger fraction of calculations.\n\nFor this study all calculated spectra converged after 7 iterations, using 128+64 configurations. \nThe calculations were done on a grid of volumes and temperatures, and a few points were tested with one more iteration where no appreciable difference in calculated quantities could be seen.\n\n\\begin{figure*}[hbt]\n \\centering\n \\includegraphics[width=\\linewidth]{nacl_spectral_half.pdf}\n \\caption{Phonon spectral functions for NaCl at different temperatures calculated using the TDEP method. The white circles in panel (a) are experimental neutron measurements from \\citet{Raunio1969a}. We show an overall good agreement with experiment bar a slight uniform underestimation of frequencies.}\n \\label{fig:SMspectralfunction}\n\\end{figure*}\n\n\\subsection{Free energy minimization}\n\nTo determine the equilibrium volume at any given temperature we did calculations on a grid of volumes and temperatures. The grid is specified in Table \\ref{table:SM_simulationgrid}.\n\\begin{table*}\n\\begin{ruledtabular}\n\\begin{tabular}{r|llllll}\nTemperature (K) & \\multicolumn{6}{c}{Lattice constants (\\AA)} \\\\\n\\hline\n0 & 5.233848 & 5.324865 & 5.419088 & 5.510051 & 5.603468 & 5.717206 \\\\ \n150 & 5.255144 & 5.346531 & 5.441138 & 5.532472 & 5.626268 & 5.740469 \\\\ \n300 & 5.282018 & 5.373873 & 5.468963 & 5.560764 & 5.655040 & 5.769825 \\\\ \n500 & 5.326527 & 5.419155 & 5.515047 & 5.607621 & 5.702692 & 5.818444 \\\\ \n700 & 5.380951 & 5.474526 & 5.571398 & 5.664918 & 5.760960 & 5.877895 \\\\ \n1000 & 5.481180 & 5.576498 & 5.675174 & 5.770436 & 5.868267 & 5.987380 \\\\\n\\end{tabular}\n\\end{ruledtabular}\n\\caption{\\label{table:SM_simulationgrid}\nList of temperatures and lattice parameters used in the simulations.}\n\\end{table*}\nOnce simulations are converged the free energy was determined via\n\\begin{equation}\n F = U_0 + F_{\\textrm{ph}} + \\Delta F^{3\\textrm{ph}} + \\Delta F^{4\\textrm{ph}}\n\\end{equation}\nwhere $F_{\\textrm{ph}}$ is the usual phonon free energy and $U_0$ the renormalized baseline free energy given by\n\\begin{equation}\n U_0 = \\avg{U -\n \\frac{1}{2!}\\sum_{\\substack{ ij\\\\ \\alpha\\beta } }\\overset{\\textrm{lr}}{\\Phi}_{ij}^{\\alpha\\beta}\nu_i^\\alpha u_j^\\beta - \n \\frac{1}{2!}\\sum_{\\substack{ ij\\\\ \\alpha\\beta } }\\Phi_{ij}^{\\alpha\\beta}\nu_i^\\alpha u_j^\\beta - \n \\frac{1}{3!}\n\\sum_{\\substack{ijk\\\\ \\alpha\\beta\\gamma}}\\Phi_{ijk}^{\\alpha\\beta\\gamma}\nu_i^\\alpha u_j^\\beta u_k^\\gamma - \n\\frac{1}{4!}\n\t\\sum_{\\substack{\n\tijkl\\\\\n\t\\alpha\\beta\\gamma\\delta\n\t}}\n\\Phi_{ijkl}^{\\alpha\\beta\\gamma\\delta}\nu_i^\\alpha u_j^\\beta u_k^\\gamma u_l^\\delta \n }\n\\end{equation}\nHere it is important to note that we have to subtract the long-ranged polar interactions to avoid double-counting them. The explicit anharmonic contributions are given via\\cite{Leibfried1961,Cowley1963,wallace1998thermodynamics}\n\\begin{equation}\\label{eq:deltaF3}\n\t\\Delta F^{3\\textrm{ph}} =\n\t-6\n\t\\sum_{\\lambda\\lambda'\\lambda''}\n\t\\left|\n\t\t\\Phi_{\\lambda\\lambda'\\lambda''}\n\t\\right|^2 \n\t%\n\t\\left(\n\n\t\\frac{3n_{\\lambda} n_{\\lambda'} + 3n_{\\lambda} + 1}\n\t{(\\omega_{\\lambda}+\\omega_{\\lambda'}+\\omega_{\\lambda''})_p}\n\t+\n\t\\frac{ 6n_{\\lambda} n_{\\lambda''} - 3 n_{\\lambda} n_{\\lambda'} + 3n_{\\lambda''}}\n\t{(\\omega_{\\lambda}+\\omega_{\\lambda'}-\\omega_{\\lambda''})_p}\n\t\\right)\n\n\t%\n\t+9\\Phi_{\\lambda\\bar{\\lambda}\\lambda''}\\Phi_{\\lambda'\\bar{\\lambda}'\\bar{\\lambda}''}\n\t\\frac{4 n_{\\lambda}( n_{\\lambda'}+1)+1}\n\t{(\\omega_{\\lambda''})_p}\\,,\n\\end{equation}\nand\n\\begin{equation}\\label{eq:deltaF4}\n\t\\Delta F^{4\\textrm{ph}} =\n\t3\\sum_{\\lambda\\lambda'}\n\t\\Phi_{\\lambda\\bar{\\lambda}\\lambda'\\bar{\\lambda}'}(2n_{\\lambda}+1)(2n_{\\lambda'}+1)\n\\end{equation}\nThe free energy was fitted to a Birch-Murnaghan equation of state, and pressure was determined via $P = -dF\/dV$.\n\n\\subsection{Parameters in calculations}\n\nAll density functional theory calculations were done using the projector augmented wave (PAW)\\cite{Blochl1994a} method as implemented in VASP.\\cite{Kresse1996c,Kresse1999,Kresse1996,Kresse1993b} \nWe treated exchange-correlation within the AM05 approximation\\cite{Armiento2005,Mattsson2009}. \nWe used a 288 atom supercell, and the Brillouim zone integrations used a $2 \\times 2 \\times 2$ Monkhorst-Pack mesh,\\cite{Monkhorst1976a} and a Gaussian smearing of 0.2 eV was applied. \nPhonon self-energies and Raman spectra were integrated on a $51 \\times 51 \\times 51$ q-point mesh. \nThe plane-wave energy cutoff was set to 350eV.\n\nThe statistics needed to determine the polarizability and Born charges on a large set of supercells used a slightly smaller simulation cell to offset the increased computational cost. \nHere we used a 64 atom supercell with a tighter $6 \\times 6 \\times 6$ k-point mesh.\n\n\\vspace{1.5cm}\n\\section{Additional quantities you can get from the expansion parameters} \\label{SM:dielectric_things_not_Raman}\n\nAs noted in the main manuscript, what we have developed in this paper is a general procedure for the interplay between anharmonicity and dielectric properties. \nExperimentally we have the Raman spectra to verify our results with, which is the focus in the main text. \nTo illustrate the general nature of the method we present a few other quantities that are accessible (but without any accessible experimental confirmation we leave them as predictions).\n\nIn Fig.~\\ref{fig:SMZandeps} we show the temperature dependence of the Born charges and static dielectric tensor. \nThe temperature dependence is modest as one would expect in a wide-bandgap material. \nEven so, the quasiharmonic treatment of Born charges overestimates the temperature dependence by a factor 2 as compared with the full temperature dependence. \nIt is easy to imagine that in a material with a much smaller bandgap this discrepancy could be significantly larger.\nIn \\ref{fig:nacl_Z_and_eps} we show the IR spectra of NaCl for a few temperatures.\nThe procedure to calculate these is identical to the Raman spectra, the only difference being the matrix elements.\n\n\\begin{figure}[ht]\n \\centering\n \\includegraphics{nacl_Z_and_eps.pdf}\n \\caption{Temperature dependence of the Born effective charge and $\\epsilon_\\infty$ in NaCl treated three different ways. \n The most realistic, at constant pressure (i.e. volume changing with temperature) with full temperature dependence of all parameters is denoted NPT. The same treatment without thermal expansion is denoted NVT. \n For reference we also include the quasiharmonic results, denoted QH, where all interaction parameters are set to their values at 0K, and temperature is only included via thermal expansion.}\n \\label{fig:SMZandeps}\n\\end{figure}\n\n\\begin{figure}[ht]\n \\centering\n \\includegraphics{nacl_ir_spectra.pdf}\n \\caption{Temperature dependence of the IR absorption spectra in NaCl. The spectra are calculated via equations (10)--(13) with the Raman matrix elements replaced with the IR matrix elements, equations \\eqref{eq:ircomp1}--\\eqref{eq:ircomp4}}\n \\label{fig:nacl_Z_and_eps}\n\\end{figure}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\chapter*{Abstract}\n\n\\begin{tabularx}{\\textwidth}{lX}\t\n{\\bf Keywords:} & Black holes, Proca fields, Hawking radiation, TeV gravity, scalar fields, Maxwell fields, asymptotically anti-de Sitter spacetimes, quasinormal modes, superradiance.\\\\\n\\\\\n{\\bf Abstract:} & This thesis presents recent studies on test scalar and vector fields around black holes in the classical theory of General Relativity. It is separated in two parts according to the asymptotic properties of the spacetime under study. \\\\\n& In the first part, we investigate scalar and Proca fields on an asymptotically flat background. For the Proca field, we obtain a complete set of equations of motion in higher dimensional spherically symmetric backgrounds. These equations are solved numerically, both to compute Hawking radiation spectra and quasi-bound states. In the former case, for the first time, we carry out a precise study of the longitudinal degrees of freedom induced by the mass of the field. This can be used to improve the modeling of evaporation of black holes coupled to massive vector fields, and black hole event generators currently used at the Large Hadron Collider to probe TeV gravity models with extra dimensions. Regarding quasi-bound states, we find arbitrarily long lived modes for a charged Proca field in a Reissner-Nordstr\\\"om black hole. As a comparison, we also find such long lived modes for a charged scalar field. \\\\\n& The second part of this thesis presents research on superradiant instabilities of scalar and Maxwell fields on an asymptotically anti-de Sitter background. For the scalar case, we introduce a charge coupling between the field and the background, and show that superradiant instabilities do exist for all values of the total angular momentum, $\\ell$, in higher dimensions. This result corrects a statement in the literature that such instabilities only appear in even dimensions. For the Maxwell case, we first propose a general prescription to impose boundary conditions on the Kerr-anti-de Sitter spacetime, and obtain two Robin boundary conditions which give two different quasinormal modes even in a simpler Schwarzschild-anti-de Sitter black hole. Then these\n\\end{tabularx}\n\n\\begin{tabularx}{\\textwidth}{lX}\t\n{\\color{white}{Abstract:}} & two boundary conditions are implemented to study superradiant unstable modes and vector clouds. In particular, we find that the new branch of quasinormal modes may be unstable in a larger parameter space. Furthermore, the existence of vector clouds indicates that one may find a vector hairy black hole solution for the Einstein-Maxwell-anti-de Sitter system at the nonlinear level, which implies, in such system, that the Kerr-Newman-anti-de Sitter black hole is not a unique solution.\n\\end{tabularx}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\cleardoublepage\n\n\n\n\n\n\n\n\n\\begin{tabularx}{\\textwidth}{lX}\t\n{\\bf Palavras-chave:} & Buracos negros, Campos de Proca, Radia\\c{c}\\~ao de Hawking, gravidade \\`a escala do TeV, campos escalares, campos de Maxwell, asimt\\'oticamente anti-de Sitter, modos quasi-normais, super-radi\\^ancia.\\\\\n\\\\\n{\\bf Resumo:} & Nesta tese apresentamos estudos recentes sobre campos escalares e vetoriais de teste, em torno de buracos negros na teoria cl\\'assica da relatividade geral. A tese encontra-se dividida em duas partes, de acordo com as propriedades asimt\\'oticas do espa\\c{c}o-tempo em estudo. \\\\\n& Na primeira parte, investigamos os campos escalar e de Proca num espa\\c{c}o asimt\\'oticamente plano. Para o campo de Proca, obtemos um conjunto completo de equa\\c{c}\\~oes do movimento em espa\\c{c}os esfericamente sim\\'etricos em dimens\\~oes elevadas. Estas equa\\c{c}\\~oes s\\~ao resolvidas numericamente, tanto para o c\\'alculo de radia\\c{c}\\~ao de Hawking como para o c\\'alculo de estados quasi-ligados. No primeiro c\\'alculo, pela primeira vez, efetuamos um estudo preciso dos graus de liberdade longitudinais que s\\~ao induzidos pelo termo de massa do campo. Este estudo pode ser usado para melhorar o modelo da evapora\\c{c}\\~ao de buracos negros acoplados a campos vetoriais massivos e geradores de eventos de buraco negro usados presentemente no Grande Colisor de H\\'adrons para testar modelos de gravidade com dimens\\~oes extra \\`a escala do TeV. Relativamente aos estados quasi-ligados, encontramos estados com tempos de vida arbitrariamente longos para o campo de Proca carregado, no buraco negro de Reissner-Nordstr\\\"om. Como compara\\c{c}\\~ao, obtemos estados com tempos de vida arbitrariamente longos tamb\\'em para o campo escalar.\\\\\n& Na segunda parte da tese, apresentamos investiga\\c{c}\\~ao sobre instabilidades super-radiantes para os campos escalar e de Maxwell em espa\\c{c}o asimt\\'oticamente anti-de Sitter. No caso escalar introduzimos um acoplamento de carga entre o campo e o background e mostramos que instabilidades super-radiantes existem para todos os valores do momento angular total, $\\ell$, em dimens\\~oes mais elevadas. Este resultado\n\\end{tabularx}\n\n\\begin{tabularx}{\\textwidth}{lX}\t\n{\\color{white}{Abstract:}} & corrige a afirma\\c{c}\\~ao na literatura de que estas instabilidades aparecem apenas em dimens\\~oes \\'impares. Para o caso do campo de Maxwell, propomos primeiro uma prescri\\c{c}\\~ao para impor condi\\c{c}\\~oes fronteira no espa\\c{c}o tempo de Kerr-anti-de Sitter obtendo duas condi\\c{c}\\~oes fronteira do tipo de Robin que originam dois tipos diferentes de modos quasi-normais, mesmo no caso mais simples do buraco negro de Schwarzschild-anti-de Sitter. Estas duas condi\\c{c}\\~oes fronteira s\\~ao implementadas no estudo de modos super-radiantes inst\\'aveis e nuvens vetoriais. Em particular, encontramos um novo ramo de modos quasi-normais que podem conter instabilidades mais fortes. Mostramos ainda que a exist\\^encia de nuvens vetoriais indica a poss\\'ivel exist\\^encia de solu\\c{c}\\~oes de buraco negro com cabelo vetorial para o sistema Einstein-Maxwell-anti-de Sitter a n\\'ivel n\\~ao linear, o que implica, nesse sistema, que o buraco negro de Kerr-Newman-anti-de Sitter poder\\'a n\\~ao ser \\'unico.\n\\end{tabularx}\n\n\n\n\n\n\n\n\n\n\n\n\\chapter*{Acknowledgements}\n\nConfucius said, ``\\textit{lost time is never around the clock}''. Along my way to study physics during these years, I have received enormous encouragement and assistance from my family, my friends and my colleagues. I think it is the time to express my gratitude to all of them.\n\nFirst of all, I am very thankful to my supervisor, Prof. Carlos Herdeiro, for his support and inspiration, for teaching me how to work on physics and for giving me the freedom to explore my own interests.\n\nSpecial thanks are reserved for Marco Sampaio, for our enjoyable collaboration on various projects. As my co-supervisor, he has taught me a lot of physics and numerical techniques, while as a friend, his positiveness always reminds me to be optimistic when I face problems. I would also like to thank the other colleagues from the gravitation and high energy physics group at University of Aveiro, for all the great discussions about physics and others, they are Carolina Benone, Fl\\'avio Coelho, Pedro Cunha, Juan Carlos Degollado, Jai Grover, Antonio Morais, Jo\\~ao Rosa, Eugen Radu, and Helgi R\\'unarsson. In particular, I am grateful to Juan Carlos Degollado and Jo\\~ao Rosa for their interesting courses, and various discussions on the topic of superradiance.\n\nI would like to acknowledge the financial support from Funda\\c{c}\\~ao para a Ci\\^encia e a Tecnologia (FCT)--the International Doctorate Network in Particle Physics, Astrophysics and Cosmology (IDPASC) programme, with the grant SFRH\/BD\/51648\/2011 during the completion of this thesis.\n\nI owe my deepest gratitude to all my previous colleagues in China, especially Prof. Dezhi Huang, for encouraging me to work in a different field, and Prof. Jiliang Jing, for introducing me the black hole physics and supporting me to study aboard. I would also like to thank all my friends in China, who are too many to be listed individually, for their constant support.\n\nFinally, I would like to thank my parents, who teach me to be a virtuous man, and my sister, who is always on my side. Their endless love and support make me get better.\n\n\\chapter{Angular dependence of the news function}\n\\label{ch:analytics}\n\n\\epigraph{\\emph{Sym\\'etrie.} En ce qu'on voit d'une vue; fond\\'ee sur ce qu'il n'y a pas de raison de faire autrement.}{Blaise Pascal\\\\ \\emph{Pens\\'ees}}\n\nIn the previous chapter we showed that the radiation signal seen by an infinitely far away observer, which when squared and integrated gives the energy radiated, is encoded in a sole function of retarded time $\\tau$ and angular coordinate $\\theta$. Furthermore, this so called news function depends exclusively on the transverse metric components $h_{ij}$, whose formal solution was given in integral form in Chapter~\\ref{ch:dynamics}.\n\nIn this chapter we shall simplify that solution and study the angular dependence of the news function. We start in Section~\\ref{CL} by showing that the metric possesses a conformal symmetry at each order in perturbation theory which, at null infinity, implies a factorisation of the angular dependence of the news function at each order. By actually computing that angular factor, we shall write the inelasticity's angular distribution as a power series in $\\sin^2\\theta$. In particular, we show that a consistent truncation of this angular series at $O(n)$ requires knowledge of the metric perturbations up to $O(n+1)$. This clarifies the meaning of perturbation theory in this problem: it allows for an angular expansion of the news function off the collision axis. In Section~\\ref{asymp_waveforms} we simplify the integral solution and verify, explicitly, that it becomes effectively one-dimensional.\n\n\\section{A hidden symmetry}\n\\label{CL}\nD'Eath and Payne observed, in $D=4$, that if one starts with a shock wave and performs a boost followed by an appropriate scaling of all coordinates, the end result is just an overall conformal factor \\cite{D'Eath:1992hd}. This is easily understood in physical terms: since the only scale of the problem is the energy of the shock wave, the effect of the boost can be undone by rescaling the coordinates. In what follows we shall generalise this to $D\\geq4$.\n\nRecall the metric of one Aichelburg-Sexl shock wave in Rosen coordinates,\n\\begin{equation}\nds^2 = -d\\bar{u} d\\bar{v} + \\left(1+\\dfrac{\\kappa \\bar{u} \\theta(\\bar{u})}{2}\\Phi''\\right)^2d\\bar{\\rho}^{2} + \\bar{\\rho}^2\\left(1+ \\frac{\\kappa \\bar{u} \\, \\theta(\\bar{u})}{2 \\bar \\rho}\\Phi'\\right)^2 d\\bar{\\Omega}^2_{D-3} \\ .\n\\end{equation}\nNow let $L$ be the Lorentz transformation\n\\begin{equation}\n(\\bar{u},\\bar{v},\\bar{x}^i)\\xrightarrow{L}(e^{-\\beta}\\bar{u},e^\\beta\\bar{v},\\bar{x}^i)\\,,\n\\end{equation}\nand $C$ the conformal scaling\n\\begin{equation}\n(\\bar{u},\\bar{v},\\bar{x}^i)\\xrightarrow{C}e^{-\\frac{1}{D-3}\\beta}(\\bar{u},\\bar{v},\\bar{x}^i)\\,.\n\\end{equation}\nThen, under the combined action of $CL$,\n\\begin{eqnarray}\n(\\bar{u},\\bar{v},\\bar{x}^i)&\\xrightarrow{CL}&(e^{-\\frac{D-2}{D-3}\\beta}\\bar{u},e^{\\frac{D-4}{D-3}\\beta}\\bar{v},e^{-\\frac{1}{D-3}\\beta}\\bar{x}^i)\\,,\\\\\ng_{\\mu\\nu}(\\bar X)&\\xrightarrow{CL}&e^{\\frac{2}{D-3}\\beta}g_{\\mu\\nu}(\\bar X')\\; ,\n\\end{eqnarray}\nwhere $\\bar X$ and $\\bar X'$ denote the coordinates before and after the transformation respectively, i.e. $\\bar X\\xrightarrow{CL}\\bar X'$. The metric simply scales by an overall factor.\n\nSince this is a one parameter symmetry, one can find coordinates on $(D-1)$-dimensional sheets which are invariant under the transformation, and a normal coordinate parameterising inequivalent sheets. A suitable set of invariant coordinates on such sheets is $\\left\\{p,q,\\phi^a\\right\\}$, with\n\\begin{equation}\np\\equiv \\bar{v}\\bar{\\rho}^{D-4}\\,,\\qquad q\\equiv \\bar{u}\\bar{\\rho}^{-(D-2)}\\,, \n\\end{equation}\nand $\\phi^a$ the angles on the transverse plane. The normal coordinate along the orbits of the symmetry is simply $\\bar{\\rho}$ which transforms as\n\\begin{equation}\n\\bar{\\rho}\\rightarrow\\bar{\\rho}'=e^{-\\frac{1}{D-3}\\beta}\\bar{\\rho}\\,.\n\\end{equation} \n\nNow consider the superposition of two shocks. In the boosted frame with Rosen coordinates, the action of $CL$ is\n\\begin{equation}\ng_{\\mu\\nu}(\\nu,\\lambda;\\bar X)\\xrightarrow{CL}e^{\\frac{2}{D-3}\\beta}g_{\\mu\\nu}(\\nu,e^{-2\\beta}\\lambda;\\bar X')\\,.\n\\end{equation}\nThus the perturbative expansion remains the same except for $\\lambda\\rightarrow e^{-2\\beta}\\lambda$. In the centre-of-mass frame with Brinkmann coordinates, Eq.~\\eqref{metric_series} becomes\n\\begin{eqnarray}\\label{eq:CLonMetric}\ng_{\\mu\\nu}(X) \\xrightarrow{CL}g_{\\mu\\nu}(X')=e^{\\frac{2}{D-3}\\beta}\\left[\\eta_{ \\mu \\nu}+\\sum_{k=1}^\\infty e^{-2k\\beta} h_{\\mu\\nu}^{(k)}(X')\\right]\\,.\n\\end{eqnarray}\nQuite remarkably, in the future of the collision the metric possesses a conformal symmetry at each order in perturbation theory.\n\nNote that the metric functions $h_{\\mu\\nu}^{(k)}$ in Eq.~\\eqref{eq:CLonMetric} are the same before and after the transformation, the only change being the coordinates they are evaluated on. On the other hand, for a generic coordinate transformation, the metric transforms as a rank-2 tensor,\n\\begin{equation}\ng_{\\mu \\nu}(X)=\\frac{\\partial x^{\\mu'}}{\\partial x^\\mu}\\frac{\\partial x^{\\nu'}}{\\partial x^\\nu}g_{\\mu' \\nu'}(X')\\,.\n\\end{equation}\nTogether with Eq.~\\eqref{eq:CLonMetric} this implies that\n\\begin{equation}\\label{CL_transf}\nh_{\\mu\\nu}^{(k)}(X')=e^{(2k+N_u-N_v)\\beta}h_{\\mu\\nu}^{(k)}(X)\\,,\n\\end{equation}\nwhere $N_u$ and $N_v$ are the number of $u$-indices and $v$-indices. Thus a given metric function evaluated on the transformed coordinates is the same function evaluated on the initial coordinates multiplied by an appropriate factor.\n\nIn these coordinates, the invariants $p,q$ read\n\\begin{equation}\np=(v-\\Phi(\\rho))\\rho^{D-4}\\,,\\qquad q=u\\rho^{-(D-2)}\\,,\\label{def_pq}\n\\end{equation}\nwhile $\\rho$ transforms as\n\\begin{equation}\n\\rho\\rightarrow \\rho'=e^{-\\frac{1}{D-3}\\beta}\\rho\\,.\n\\end{equation}\n\n\\subsection{Reduction to two dimensions}\n\nSince $\\rho$ is the only coordinate that transforms under the action of $CL$, Eq.~\\eqref{CL_transf} implies a separation of variables in the form\n\\begin{equation}\\label{eq:separation_rho}\nh_{\\mu\\nu}^{(k)}(p,q,\\rho,\\phi^a)=\\rho^{-(D-3)(2k+N_u-N_v)}f^{(k)}_{\\mu\\nu}(p,q,\\phi^a)\\,.\n\\end{equation}\nSince the angles $\\phi^a$ are ignorable, the problem becomes two dimensional at each order in perturbation theory. Let us see how.\n\nIn Section~\\ref{reduction_3D} we defined scalar functions of $(u,v,\\rho)$ by factoring out the trivial dependence of each metric component on $\\phi^a$, Eqs.~\\eqref{gen_perts}, and similarly for the sources $T^{(k-1)}_{\\mu\\nu}$. Then Eq.~\\eqref{eq:separation_rho} implies that, for each of those functions generically denoted by $F(u,v,\\rho)$ as before,\n\\begin{equation}\nF^{(k)}(u,v,\\rho)= \\dfrac{f^{(k)}(p,q)}{\\rho^{(D-3)(2k+N_u-N_v)}}\\,.\\label{f_pq}\n\\end{equation}\nFor its respective source as defined in Eq.~\\eqref{def_S}, since the d'Alembertian operator scales as $\\rho^{-2}$ under $CL$,\n\\begin{equation}\nS^{(k)}(u,v,\\rho)=\\dfrac{s^{(k)}(p,q)}{\\rho^{(D-3)(2k+N_u-N_v)+2}}\\,.\\label{def_s}\n\\end{equation}\n\nThus our problem is effectively two-dimensional in $(p,q)$ coordinates: at each order, $f^{(k)}(p,q)$ is the solution of a differential equation (inherited from the wave equation) with a source $s^{(k)}(p,q)$ and subject to initial conditions on $q=0$. This is an enormous computational advantage. In Section~\\ref{app:Green2D} of Appendix~\\ref{app:Green} we obtain the differential equation obeyed by $f^{(k)}(p,q)$ and the reduced Green's function $G^k_m(p,q;p',q')$. However, there is another consequence of this symmetry which only becomes manifest when one goes to null infinity.\n\n\\subsection{The $CL$ symmetry at null infinity}\nIn terms of $(r,\\tau,\\theta)$ the new coordinates $p$ and $q$ read\n\\begin{eqnarray}\np&=&(\\tau+r(1+\\cos\\theta)-\\Phi(r\\sin\\theta))(r\\sin\\theta)^{D-4}\\,,\\\\\nq&=&(\\tau+r(1-\\cos\\theta))(r\\sin\\theta)^{-(D-2)}\\,.\n\\end{eqnarray}\nOne can see they are not well-defined in the limit $r\\rightarrow\\infty$ since\n\\begin{equation}\np\\rightarrow\\infty\\,,\\qquad q\\rightarrow0\\,.\n\\end{equation}\n\nHowever, it is reasonable to expect that the finite, non-trivial dependence of the news function in $(\\tau,\\theta)$ should be given in terms of combinations of $p,q$ that remain finite and non-trivial at null infinity. An appropriate choice is\n\\begin{equation}\n\\hat{p}\\equiv\n\\left\\{\n\\begin{array}{ll}\n \\frac{pq-1}{q}+2\\log q\\ , & D=4\\ \\vspace{2mm}\\\\\n\\displaystyle{ \\frac{pq-1}{q^{\\frac{1}{D-3}}}}\\ , & D>4\\ \\label{tau_transform}\\,\n\\end{array} \\right. \\ ,\n\\qquad \\hat{q}\\equiv q^{\\frac{1}{D-3}}\\,.\n\\end{equation}\n\nThis coordinate transformation, $(p,q)\\rightarrow(\\hat{p},\\hat{q})$, has a constant non-vanishing determinant,\n\\begin{equation}\n\\det \\left[\\frac{\\partial (p,q)}{\\partial(\\hat{p},\\hat{q})}\\right]=D-3\\,,\n\\end{equation}\nhence is well-defined everywhere.\n\nWhen $r\\rightarrow\\infty$,\n\\begin{eqnarray}\n\\hat{p}&\\rightarrow&2\\bar{\\tau}(\\tau,\\theta)+O\\left(r^{-1}\\right)\\,,\\\\\n\\hat{q}&\\rightarrow&(1-\\cos\\theta)^{\\frac{1}{D-3}}(\\sin\\theta)^{-\\frac{D-2}{D-3}}\\times r^{-1}+O\\left(r^{-2}\\right)\\,,\n\\end{eqnarray}\nwhere we have defined a new time coordinate\n\\begin{eqnarray}\n\\bar{\\tau}(\\tau,\\theta)=\\left\\{\n\\begin{array}{ll}\n \\tau\\times(1-\\cos\\theta)^{-1}+\\log\\left(\\frac{1-\\cos\\theta}{\\sin\\theta}\\right)\\ , & D=4\\ \\vspace{2mm}\\\\\n\\displaystyle{ \\tau\\times(1-\\cos\\theta)^{-\\frac{1}{D-3}}(\\sin\\theta)^{-\\frac{D-4}{D-3}}}\\ , & D>4\\ \\label{tau_transform}\\,\n\\end{array} \\right. \\ .\n\\end{eqnarray}\n\nRecall that we had already factored out the $\\rho$ dependence in Eq.~\\eqref{f_pq}, leaving only a function of $(p,q)$ to compute. But since $\\bar\\tau$ is the only surviving quantity in the asymptotic limit, we expect the news function to become effectively one-dimensional at null infinity: besides the trivial dependence on $\\theta$ coming from the known powers of $r$ and $\\rho$ (remember that $\\rho=r\\sin\\theta$), it must be a function of $\\bar\\tau(\\tau,\\theta)$ only.\n\nFrom Eqs.~\\eqref{news2} and \\eqref{f_pq},\n\\begin{equation}\n\\dot{\\mathcal{E}}^{(k)}(\\tau,\\theta)=(\\sin\\theta)^{\\frac{D-4}{2}-2k(D-3)}\\times\\lim_{r\\rightarrow\\infty}\\frac{d}{d\\tau}\\left[r^{\\frac{D-2}{2}-2k(D-3)}\\hat{f}^{(k)}(\\hat{p},\\hat{q})\\right]\\,,\n\\end{equation}\nwhere $\\hat{f}^{(k)}$ contains the relevant contribution from $E^{(k)}$ and $H^{(k)}$, as a function of $(\\hat{p},\\hat{q})$. \n\nIf the quantity inside brackets is to remain finite at null infinity, then necessarily\n\\begin{equation}\n\\hat{f}^{(k)}(\\hat{p},\\hat{q})\\simeq\\alpha^{(k)}(\\hat{p})\\hat{q}^{\\frac{D-2}{2}-2k(D-3)}+\\dots\\,,\n\\end{equation}\nwhere $\\dots$ denotes higher powers of $\\hat{q}$ and $\\alpha^{(k)}(\\hat{p})$ some unknown function of $\\hat{p}$. \n\nThus, after taking the limit $r\\rightarrow\\infty$,\n\\begin{equation}\n\\dot{\\mathcal{E}}^{(k)}(\\tau,\\theta)=\\frac{1}{1-\\cos\\theta}\\left(\\frac{1+\\cos\\theta}{1-\\cos\\theta}\\right)^{k-1+\\frac{1}{4}\\frac{D-4}{D-3}}\\frac{d\\bar\\tau}{d\\tau}(\\theta)\\times\\frac{d}{d\\bar\\tau}\\alpha^{(k)}(2\\bar\\tau)\\,.\n\\end{equation}\n\nUsing Eq.~\\eqref{tau_transform}, and evaluating this expression at $\\theta=\\tfrac{\\pi}{2}$ noting that $\\bar\\tau\\left(\\tau,\\tfrac{\\pi}{2}\\right)=\\tau$, we conclude that\n\\begin{equation}\n\\dot{\\mathcal{E}}^{(k)}(\\tau,\\theta)=\\left(\\frac{1}{1-\\cos\\theta}\\right)^2\\left(\\frac{1+\\cos\\theta}{1-\\cos\\theta}\\right)^{k-1-\\frac{1}{4}\\frac{D-4}{D-3}}\\dot{\\mathcal{E}}^{(k)}\\left(\\bar\\tau(\\tau,\\theta),\\frac{\\pi}{2}\\right)\\,,\\label{Eq:angular_factorization}\n\\end{equation}\nwhich proves our proposition.\n\n\\subsection{The meaning of perturbation theory}\n\\label{meaning}\n\nThe perturbative expansion of the metric, Eq.~\\eqref{metric_series}, implies an analogous series for the inelasticity's angular distribution $\\epsilon(\\theta)$ defined in Eq.~\\eqref{e1},\n\\begin{equation}\n\\epsilon(\\theta)=\\sum_{N=1}^\\infty\\epsilon^{(N)}(\\theta)\\,,\n\\end{equation}\nwhere\n\\begin{equation}\n\\epsilon^{(N)}(\\theta)=\\sum_{k=1}^{N}\\int d\\tau\\, \\dot{\\mathcal{E}}^{(k)}(\\tau,\\theta)\\dot{\\mathcal{E}}^{(N+1-k)}(\\tau,\\theta)\\,.\\label{N_N-k}\n\\end{equation}\n\nInserting the result of Eq.~\\eqref{Eq:angular_factorization} and changing the integration variable from $\\tau$ to $\\bar{\\tau}$, we conclude that\n\\begin{equation}\n\\epsilon^{(N)}(\\theta)=\\left(\\frac{1}{1-\\cos\\theta}\\right)^3 \\left(\\frac{1+\\cos\\theta}{1-\\cos\\theta}\\right)^{N-1}\\epsilon^{(N)}\\left(\\frac{\\pi}{2}\\right)\\, . \\label{seriesi2}\n\\end{equation}\nThus it suffices to compute the news function on the symmetry plane.\n\nThe whole series reads\n\\begin{eqnarray}\n\\epsilon(\\theta)&=&\\sum_{N=1}^\\infty \\epsilon^{(N)}(\\theta) =\\left(\\frac{1}{1-\\cos\\theta}\\right)^3\\sum_{N=1}^\\infty\\left(\\frac{1+\\cos\\theta}{1-\\cos\\theta}\\right)^{N-1}\\epsilon^{(N)}\\left(\\frac{\\pi}{2}\\right)\\,,\\\\\n&\\equiv&\\sum_{n=0}^\\infty\\alpha_n(\\theta) \\epsilon^{(n+1)}\\left(\\frac{\\pi}{2}\\right)\\,.\n\\label{series2}\n\\end{eqnarray}\n\nObserve that each individual $\\alpha_n(\\theta)$ is regular at $\\theta=\\pi$, where we saw that perturbation theory should be valid, but does not obey $\\alpha_n(\\theta)=\\alpha_n(\\pi-\\theta)$ since the transformation to Brinkmann coordinates broke the $\\mathbb{Z}_2$ symmetry. However, $\\epsilon(\\theta)$ should respect that symmetry. In particular, if it has a regular limit at the axis, it can be written as a power series in $\\sin^2\\theta$,\n\\begin{equation}\n\\epsilon(\\theta)=\\sum_{n=0}^\\infty \\epsilon_n(\\sin\\theta)^{2n}\\,.\\label{angular_series}\n\\end{equation}\n\nIndeed, writing $\\cos\\theta=-\\sqrt{1-\\sin^2\\theta}$, we see that near $\\theta=\\pi$,\n\\begin{equation}\n\\alpha_n(\\theta)\\sim (\\sin\\theta)^{2n}\\,.\n\\end{equation}\n\nFor example, the first three terms are given by\n\\begin{eqnarray}\n\\epsilon_0&=&\\frac{1}{8}\\epsilon^{(1)}\\left(\\frac{\\pi}{2}\\right)\\,,\\label{e0}\\\\\n\\epsilon_1&=&\\frac{1}{32}\\left[3\\epsilon^{(1)}\\left(\\frac{\\pi}{2}\\right)+\\epsilon^{(2)}\\left(\\frac{\\pi}{2}\\right)\\right]\\,,\\\\\n\\epsilon_2&=&\\frac{1}{128}\\left[9\\epsilon^{(1)}\\left(\\frac{\\pi}{2}\\right)+5\\epsilon^{(2)}\\left(\\frac{\\pi}{2}\\right)+\\epsilon^{(3)}\\left(\\frac{\\pi}{2}\\right)\\right] \\ .\n\\end{eqnarray}\n\nThus we conclude that a consistent truncation of the series in Eq.~\\eqref{angular_series}, i.e. the extraction of the coefficient $\\epsilon_n$, requires knowledge of $\\epsilon^{(n+1)}(\\tfrac{\\pi}{2})$ and hence, from Eq.~\\eqref{N_N-k}, of the metric up to $O(n+1)$. This clarifies the meaning of perturbation theory in this problem: it allows for the extraction of successive coefficients $\\epsilon_n$, thus amounting to an angular expansion off the collision axis.\n\nIt should be stressed that this result is a kinematical consequence of the $CL$ symmetry only: the dynamical (wave) equations have not yet been used (except for the background solution of course).\n\nFinally, the inelasticity is then given by\n\\begin{eqnarray}\n\\epsilon&=&\\sum_{n=0}^\\infty \\epsilon_n \\int_{-1}^1\\frac{d\\cos\\theta}{2}(\\sin\\theta)^{2n}\\,,\\\\\n&=&\\sum_{n=0}^\\infty \\frac{2^n n!}{(2n+1)!!}\\epsilon_n \\,,\\\\\n&=&\\epsilon_0+\\frac{2}{3}\\epsilon_1+\\ldots\\,.\\label{eq_second_order_e}\n\\end{eqnarray}\n\nIn the next section we shall obtain simplified expressions for the integrals giving the asymptotic metric functions that contribute to the news function. By working in Fourier space with respect to the retarded time $\\tau$, we shall also confirm, explicitly, that the coordinate transformation of Eq.~\\eqref{tau_transform} factorises the angular dependence out of the integrals.\n\n\n\\section{Asymptotic integral solution for the metric functions}\n\\label{asymp_waveforms}\nBack in Chapter~\\ref{ch:dynamics} we wrote the formal solution for each metric function $F(u,v,\\rho)$ in integral form, Eq.~\\eqref{sol_3D}. Since we are ultimately interested in the news function $\\dot{\\mathcal{E}}(\\tau,\\theta)$, we define the asymptotic waveform $\\dot{F}(\\tau,\\theta)$, obtained from $F(u,v,\\rho)$, according to Eq.~\\eqref{news2},\n\\begin{equation}\n\\dot{F}(\\tau,\\theta)\\equiv\\lim_{r\\rightarrow\\infty} r\\rho^{\\frac{D-4}{2}}\\frac{d}{d\\tau}F(u,v,\\rho)\\,.\n\\end{equation}\n\nActually, the Dirac delta in the Green's function is better dealt with in Fourier space, so we define\n\\begin{equation}\n\\hat{\\dot{F}}(\\omega,\\theta)\\equiv\\int d\\tau\\,\\dot{F}(\\tau,\\theta)e^{-i\\omega\\tau}\\,.\n\\end{equation}\nThis is equally useful to compute the inelasticity since, by the Parseval-Plancherel theorem,\n\\begin{equation}\n\\int d\\tau\\, \\dot{F}(\\tau,\\theta)^2=\\frac{1}{2\\pi}\\int d\\omega\\, |\\hat{\\dot{F}}(\\omega,\\theta)|^2\\,.\n\\end{equation}\n\nThe next steps are detailed in Appendix~\\ref{app:asympWF} due to their tedious and technical nature. \n\nIn short, one needs to take the asymptotic limit and integrate in $\\tau$ to obtain the Fourier transform. Dropping primes on integration variables for ease of notation, we get\n\\begin{equation}\n\\hat{\\dot{F}}(\\omega,\\theta)=-i^{\\frac{D-2}{2}+m}\\omega\\int_0^\\infty d\\rho\\,\\rho^{\\frac{D-2}{2}}J_{\\frac{D-4}{2}+m}(\\omega\\rho\\sin\\theta)\\hat{S}\\left(\\omega\\frac{1+\\cos\\theta}{2},\\omega\\frac{1-\\cos\\theta}{2};\\rho\\right)\\,,\\label{initial_volume}\n\\end{equation}\nwhere $J_\\nu(z)$ is the $\\nu$-th order Bessel function of the first kind and\n\\begin{equation}\n\\hat{S}(x,y;\\rho)=\\frac{1}{2}\\int du\\int dv\\, e^{-iux}e^{-ivy}\\, S(u,v,\\rho)\\,.\\label{S_hat}\n\\end{equation}\n\nNext, one finds it convenient to transform to a new frequency $\\Omega$,\n\\begin{equation}\n\\omega\\rightarrow\\Omega\\equiv\\omega^{D-3}\\left(\\frac{\\sin\\theta}{2}\\right)^{D-4}\\frac{1-\\cos\\theta}{2}\\,,\\label{omega_transformation}\n\\end{equation}\ntogether with a new waveform\n\\begin{equation}\n\\hat{\\mathcal{F}}(\\Omega,\\theta)\\equiv\\sqrt{\\omega'(\\Omega)}\\,\\hat{\\dot{F}}(\\omega(\\Omega),\\theta)\\,,\n\\end{equation}\nsuch that\n\\begin{equation}\n\\int d\\omega\\, |\\hat{\\dot{F}}(\\omega,\\theta)|^2=\\int d\\Omega\\, |\\hat{\\mathcal{F}}(\\Omega,\\theta)|^2\\,.\\label{oO_int}\n\\end{equation}\n\nThen, using the $CL$ symmetry, namely Eq.~\\eqref{def_s}, one shows that, at each order $k$ in perturbation theory,\n\\begin{equation}\n\\hat{\\mathcal{F}}^{(k)}(\\Omega,\\theta)=\\left(\\frac{1}{1-\\cos\\theta}\\right)^{\\frac{3}{2}}\\left(\\frac{1+\\cos\\theta}{1-\\cos\\theta}\\right)^{k-1}\\hat{\\mathcal{F}}^{(k)}\\left(\\Omega,\\frac{\\pi}{2}\\right)\\,, \\label{angular_factorization}\n\\end{equation}\nwhere the angular dependence is now completely factored out of the integral, and the (new) Fourier space waveform evaluated at $\\theta=\\tfrac{\\pi}{2}$, which we shall abbreviate to\n\\begin{equation}\n\\hat{\\mathcal{F}}(\\Omega)\\equiv\\hat{\\mathcal{F}}\\left(\\Omega,\\frac{\\pi}{2}\\right)\\,,\n\\end{equation}\nis given by\n\\begin{equation}\n\\hat{\\mathcal{F}}(\\Omega)\\equiv -\\sqrt{\\frac{8}{D-3}}i^{\\frac{D-2}{2}+m}\\,\\Omega^{2k-1}\\int_0^\\infty dR\\,R^{\\frac{D-2}{2}}J_{\\frac{D-4}{2}+m}(2R)\\hat{S}(\\Omega^{-1},\\Omega;R) \\; . \\label{Omega_form}\n\\end{equation}\n\nThis formulation in terms of the new frequency $\\Omega$ will prove to be useful later on in Chapter~\\ref{ch:surface} for the evaluation of surface terms. However, for the time being, we can invert the transformation in Eq.~\\eqref{omega_transformation} at $\\theta=\\tfrac{\\pi}{2}$, i.e.\n\\begin{equation}\n\\Omega\\rightarrow\\bar{\\omega}\\equiv2\\,\\Omega^{\\frac{1}{D-3}}\\,,\\qquad \\hat{\\mathcal{F}}(\\Omega)\\rightarrow\\sqrt{\\Omega'(\\bar{\\omega})}\\,\\hat{\\mathcal{F}}\\left(\\Omega(\\bar{\\omega})\\right)\\,.\n\\end{equation}\n\nThe net relationship between $\\omega$ and $\\bar{\\omega}$ is\n\\begin{equation}\n\\bar{\\omega}=\\omega\\times(1-\\cos\\theta)^{\\frac{1}{D-3}}(\\sin\\theta)^{\\frac{D-4}{D-3}}\\,,\n\\end{equation}\nwhich is equivalent, in real space, to a transformation of the time coordinate\n\\begin{equation}\n\\tau\\rightarrow\\bar{\\tau}(\\tau,\\theta)=\\tau\\times(1-\\cos\\theta)^{-\\frac{1}{D-3}}(\\sin\\theta)^{-\\frac{D-4}{D-3}}\\,.\n\\end{equation}\nApart from a $\\theta$-dependent shift in $D=4$ (indeed an example of a \\emph{supertranslation}~\\cite{Coelho:2012sy}) $\\bar{\\tau}$ is exactly the same as in \\eqref{tau_transform}.\n\nIn the next chapter we shall specialise this asymptotic solution to the surface case and compute all the terms that contribute to the inelasticity and depend linearly on the initial data. This will allows us to extract the isotropic coefficient $\\epsilon_0$.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\chapter{Angular momentum flux}\n\\label{app:angmomflux}\n\nThis appendix contains details on derivation of the angular momentum flux for the Maxwell field on the Kerr-AdS background. Our purpose is to show that the vanishing of the energy flux, a physical principle used in Chapter~\\ref{ch:KerrAdS} to impose boundary conditions for the Maxwell field on Kerr-AdS, leads to a vanishing angular momentum flux.\n\nFrom the definition of the energy-momentum tensor for the Maxwell field\n\\begin{equation}\nT_{\\mu \\nu}=F_{\\mu\\sigma}F^\\sigma_{\\;\\;\\;\\nu}+\\dfrac{1}{4}g_{\\mu\\nu}F^2\\;,\n\\end{equation}\nwe can calculate the angular momentum flux\n\\begin{equation}\n\\mathcal{J}=\\int_{S^2} \\sin\\theta d\\theta d\\varphi\\; r^2 \\left(T^r_{\\;\\;\\varphi,\\;\\uppercase\\expandafter{\\romannumeral1}}+T^r_{\\;\\;\\varphi,\\;\\uppercase\\expandafter{\\romannumeral2}}\\right)\\;,\\label{angmomf}\n\\end{equation}\nwith\n\\begin{align}\n&T^r_{\\;\\;\\varphi,\\;\\uppercase\\expandafter{\\romannumeral1}}=-a\\sin^2\\theta\\;T^r_{\\;\\;t,\\;\\uppercase\\expandafter{\\romannumeral1}}\\;,\\label{relation1}\\\\\n&T^r_{\\;\\;\\varphi,\\;\\uppercase\\expandafter{\\romannumeral2}}=-\\dfrac{i\\sin\\theta\\sqrt{\\Delta_\\theta}(r^2+a^2)}{2\\Xi\\rho^4}\\Phi_1(\\Phi_2^\\ast+\\Delta_r\\Phi_0^\\ast)+c.c\\;,\\label{relation2}\n\\end{align}\nwhere $T^r_{\\;\\;t,\\;\\uppercase\\expandafter{\\romannumeral1}}$ is given in Eq.~\\eqref{rtcom}, $c.c$ stands for the complex conjugate of the preceding terms, and\n\\begin{equation}\n\\Phi_0=\\phi_0\\;,\\;\\;\\;\\;\\;\\Phi_2=2\\bar{\\rho}^\\ast\\phi_2\\;,\\;\\;\\;\\;\\;\\bar{\\rho}=r+ia\\cos\\theta\\;.\n\\end{equation}\n\n\nFrom Eq.~\\eqref{relation1}, and considering the vanishing energy flux boundary conditions, i.e.\n\\begin{equation}\n\\int_{S^2} \\sin\\theta d\\theta d\\varphi\\; r^2 T^r_{\\;\\;t,\\;\\uppercase\\expandafter{\\romannumeral1}}\\rightarrow0\\;,\n\\end{equation}\nasymptotically, one concludes that there is no contributions for the angular momentum flux from the first term $T^r_{\\;\\;\\varphi,\\;\\uppercase\\expandafter{\\romannumeral1}}$.\n\n\nFor the second term, from Eq.~\\eqref{relation2}, we notice that $\\Phi_1$ is involved, which has been derived in Eq.~\\eqref{Phi1eq}. For convenience, we list this solution and related expressions below. The solution for $\\Phi_1$ is\n\\begin{equation}\n\\bar{\\rho}^\\ast\\Phi_1=g_{+1}\\bar{\\mathscr{L}}_1S_{+1}-ia f_{-1}\\mathscr{D}_0P_{-1}\\;,\n\\end{equation}\nwith\n\\begin{align}\n&g_{+1}=\\frac{1}{B}(r\\mathscr{D}_0P_{-1}-P_{-1})\\;,\\\\\n&f_{-1}=\\frac{1}{B}(\\cos\\theta\\bar{\\mathscr{L}}_1S_{+1}+\\sin\\theta\\sqrt{\\Delta_\\theta}S_{+1})\\;,\\\\\n&\\bar{\\mathscr{L}}_1S_{+1}=\\dfrac{(2a\\omega\\Xi\\cos\\theta-\\lambda)S_{+1}-BS_{-1}}{2\\mathcal{Q}\\sqrt{\\Delta_\\theta}}\\;,\n\\end{align}\nwhere\n\\begin{equation}\n\\mathscr{D}_0=\\dfrac{\\partial}{\\partial r}-\\dfrac{iK_r}{\\Delta_r}\\;,\\;\\;\\;\\mathcal{Q}=\\dfrac{\\Xi(a\\omega\\sin^2\\theta-m)}{\\sin\\theta\\Delta_\\theta}\\;,\\;\\;\\;P_{-1}=BR_{-1}\\;,\n\\end{equation}\nand the constant $B$ is given by Eq.~\\eqref{Bvalue}, $S_{+1}$ and $S_{-1}$ are spin weighted AdS spheroidal harmonics. From the properties of these spheroidal harmonic functions\n\\begin{equation}\nS_{+1}(\\pi-\\theta)=S_{-1}(\\theta)\\;,\\;\\;\\;\\;\\;\\;S_{-1}(\\pi-\\theta)=S_{+1}(\\theta)\\;,\n\\end{equation}\nwhich are guaranteed by the angular equations~\\eqref{Spluseq} and~\\eqref{Sminuseq}, then we have the following properties\n\\begin{align}\n\\int_0^\\pi d\\theta\\sin\\theta f_{odd}(\\theta)S_{+1}(\\theta)S^\\ast_{+1}(\\theta)=-\\int_0^\\pi d\\theta\\sin\\theta f_{odd}(\\theta)S_{-1}(\\theta)S^\\ast_{-1}(\\theta)\\;,\\nonumber\\\\\n\\int_0^\\pi d\\theta\\sin\\theta f_{even}(\\theta)S_{+1}(\\theta)S^\\ast_{+1}(\\theta)=\\int_0^\\pi d\\theta\\sin\\theta f_{even}(\\theta)S_{-1}(\\theta)S^\\ast_{-1}(\\theta)\\;,\\nonumber\\\\\n\\int_0^\\pi d\\theta\\sin\\theta f_{odd}(\\theta)S_{-1}(\\theta)S^\\ast_{+1}(\\theta)=-\\int_0^\\pi d\\theta\\sin\\theta f_{odd}(\\theta)S_{+1}(\\theta)S^\\ast_{-1}(\\theta)\\;,\\nonumber\\\\\n\\int_0^\\pi d\\theta\\sin\\theta f_{even}(\\theta)S_{-1}(\\theta)S^\\ast_{+1}(\\theta)=\\int_0^\\pi d\\theta\\sin\\theta f_{even}(\\theta)S_{+1}(\\theta)S^\\ast_{-1}(\\theta)\\;,\\label{intepro}\n\\end{align}\nwhere\n\\begin{equation}\nf_{odd}(\\pi-\\theta)=-f_{odd}(\\theta)\\;,\\;\\;\\;\\;\\;\\;f_{even}(\\pi-\\theta)=f_{even}(\\theta)\\;.\\nonumber\n\\end{equation}\nWith all of these expressions at hand, and making use of the integration properties of the spin weighted AdS spheroidal harmonics given in~\\eqref{intepro}, Eq.~\\eqref{relation2} becomes\n\\begin{eqnarray}\n&&T^r_{\\;\\;\\varphi,\\;\\uppercase\\expandafter{\\romannumeral2}}=-\\dfrac{i\\sin\\theta\\sqrt{\\Delta_\\theta}(r^2+a^2)}{2\\Xi\\rho^4}(\\mathcal{C}_1S_{+1}S^\\ast_{-1}+\\mathcal{C}_2S_{-1}S^\\ast_{-1})\n+c.c\\;,\\label{Trphi2rep}\n\\end{eqnarray}\nwhere terms that vanish under the angular integration, due to the properties listed above, have been discarded. The expressions for $\\mathcal{C}_1$ and $\\mathcal{C}_2$ are messy in general, but they can be simplified asymptotically. The asymptotic expression for $\\mathcal{C}_1$ goes as\n\\begin{equation}\n\\mathcal{C}_1\\sim c_0+\\mathcal{O}(1\/r)\\;,\\label{c1expasy1}\n\\end{equation}\nwhere $c_0$ is proportional to $T^r_{\\;\\;t,\\;\\uppercase\\expandafter{\\romannumeral1}}$ asymptotically, so that finally $\\mathcal{C}_1\\sim \\mathcal{O}(1\/r)$. Similar analysis can be done for $\\mathcal{C}_2$ as well. The asymptotic expression for $\\mathcal{C}_2$ is\n\\begin{equation}\n\\mathcal{C}_2\\sim \\hat{c}_0+\\mathcal{O}(1\/r)\\;,\\label{c1expasy2}\n\\end{equation}\nand, as in the former case, $\\hat{c}_0$ vanishes after the vanishing energy flux boundary conditions in Eq.~\\eqref{bc} are imposed. Then from Eq.~\\eqref{Trphi2rep}, together with Eqs~\\eqref{c1expasy1} and~\\eqref{c1expasy2}, we conclude that\n\\begin{eqnarray}\nr^2T^r_{\\;\\;\\varphi,\\;\\uppercase\\expandafter{\\romannumeral2}}\\sim \\mathcal{O}(1\/r)\\;,\n\\end{eqnarray}\nasymptotically, which leads to the vanishing of the angular momentum flux of Eq.~\\eqref{angmomf}.\n\n\n\n\n\n\n\\chapter{Functions and matrices for the charged Proca case}\n\\label{app:chargedP}\n\nThis appendix contains details on the functions and matrices used to rewrite the second order radial differential equations into a first order form, and recurrence relations used to initialize these radial differential equations close to the event horizon. This was used in Chapter~\\ref{ch:ChargedP}.\n\nThe functions that used in the text are (where $\\kappa_s^2=\\ell(\\ell+1)$):\n\\begin{align}\nA(r)&\\equiv \\sum^{2n+1}_{m=0}{a_m y^m}=r \\left[r^n-(1+Q^2)r+Q^2r^{n-2}\\right]^2 \\; ,\\nonumber\\\\\nB(r)&\\equiv \\sum^{2n}_{m=0}{b_m y^m}=2\\left[r^n-(1+Q^2)r+Q^2r^{n-2}\\right]^2\\; ,\\nonumber\\\\\nC(r)&\\equiv \\sum^{2n+1}_{m=0}{c_m y^m}= (\\omega r-qQ)^2r^{2n-1}\n-(\\kappa_s^2+\\mu_p^2r^2)r^{n-1}\\left[r^n-(1+Q^2)r+Q^2r^{n-2}\\right] \\; ,\\nonumber\\\\\nE(r)&\\equiv \\sum^{2n}_{m=0}{e_m y^m}=iqQr^{n-1}\\left[(1+Q^2)(n-1)r-2Q^2r^{n-2}\\right]\\nonumber\\\\& \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;+i\\omega r^n\\left[2r^{n}-(1+Q^2)(n+1)r+4Q^2r^{n-2}\\right] \\, ,\\nonumber\\\\\n\\tilde A(r)&\\equiv \\sum^{2n}_{m=0}{\\tilde a_m y^m}=\\left[r^n-(1+Q^2)r+Q^2r^{n-2}\\right]^2 \\; ,\\nonumber\\\\\n\\tilde B(r)&\\equiv 0 \\;,\\nonumber\n\\end{align}\n\\begin{align}\n\\tilde C(r)\\equiv \\sum^{2n}_{m=0}{\\tilde c_m y^m}=& (\\omega r-qQ)^2r^{2n-2}\n -\\left(\\kappa_s^2+\\mu_p^2r^2\\right)r^{n-2}\\left[r^n-(1+Q^2)r+Q^2r^{n-2}\\right] \\;,\\nonumber\\\\\n\\tilde E(r)\\equiv \\sum^{2n-2}_{m=0}{\\tilde e_m y^m}=&iqQ r^{n-1}\\left[(n-3)(1+Q^2)+2r^{n-1}\\right]\\nonumber\\\\&-i\\omega r^{n-1}\\left[(1+Q^2)(n-1)r-2Q^2r^{n-2}\\right]\\;.\\nonumber\n\\end{align}\nThe recurrence relations are\n\\begin{align}\n\\mu_0&=\\nu_0\\;,\\nonumber\\\\\n\\mu_1&=-\\frac{\\left[a_3\\rho(\\rho-1)+b_2\\rho +c_1+e_1\\right]\\nu_0+ e_0 \\nu_1}{a_2\\rho(\\rho+1)+c_0}\\;,\\nonumber\\\\\n\\mu_j&=\\frac{\\tilde a_2(\\rho+j)(\\rho+j-1)+\\tilde c_0}{D_j}f_j-\\frac{e_0}{D_j}\\tilde\nf_j\\;,\\nonumber\\\\\n\\nu_j&=\\frac{a_2(\\rho+j)(\\rho+j-1)+c_0}{D_j}\\tilde\nf_j-\\frac{\\tilde e_0}{D_j}f_j\\;,\n\\label{CPrecurone}\n\\end{align}\nwith\n\\begin{align}\nD_j&=\\left[a_2(\\rho+j)(\\rho+j-1)+c_0\\right]\\left[\\tilde a_2(\\rho+j)(\\rho+j-1)+\\tilde c_0\\right]-\\tilde e_0 e_0\\;,\\nonumber\\\\\nf_j&=-\\sum^j_{m=1}\\left[(a_{m+2}(\\rho+j-m)(\\rho+j-m-1)+b_{m+1}(\\rho+j-m)+c_m\\big)\\mu_{j-m}+e_m\\nu_{j-m}\\right]\\;,\\nonumber\\\\\n\\tilde f_j&=-\\sum^j_{m=1}\\left[\\big(\\tilde a_{m+2}(\\rho+j-m)(\\rho+j-m-1)+\\tilde c_m\\big)\\nu_{j-m}+\\tilde e_m\\mu_{j-m}\\right]\\;.\\nonumber\n\\end{align}\n\\begin{text}\nThe coefficients used in the asymptotic expansion in the text are\n\\begin{align}\nc^\\pm=&\\dfrac{i}{2\\omega}\\Big[-\\kappa_s^2+Q^2(\\mu_p^2-2\\omega^2)-\\dfrac{q^2Q^2\\mu_p^2}{k^2}\\mp i\\dfrac{qQ\\omega}{k}+(2\\omega^2-\\mu_p^2)(1+Q^2)\\delta_{3,n}\\nonumber\\\\&+\\Big(\\pm i\\dfrac{\\mu_p^2(1+Q^2)}{2k}-\\Big(\\dfrac{\\mu_p^2(1+Q^2)}{2k}\\Big)^2+(1+Q^2)^2(2\\omega^2-\\mu_p^2)\\nonumber\\\\&+\\dfrac{qQ\\omega(1+Q^2)(\\mu_p^2-2k^2)}{k^2}\\Big)\\delta_{2,n}\\Big] \\; .\\nonumber\\label{CPcpm}\n\\end{align}\nThe relation between the new first order radial functions $\\mathbf{\\Psi}$ used in~\\eqref{eq:ODEcoupled}, and the 4-vector $\\mathbf{V}^T=(\\psi,d_r\\psi,\\chi,d_r\\chi)$ for the original fields and derivatives is found from Eqs.~\\eqref{asymptoticpsi},~\\eqref{asymptoticpchi} and their derivatives. The corresponding $r$-dependent matrix transformation $\\mathbf{T}$ is defined\n\\begin{equation}\n\\mathbf{V}= \\mathbf{T} \\mathbf{\\Psi} \\; ,\n\\end{equation}\nand its form is\n\\begin{equation}\\label{eq:CPT}\n\\mathbf{T}=\\left(\\begin{array}{cccc}\\frac{e^{i\\Phi}}{r} & \\frac{e^{-i\\Phi}}{r} & e^{i\\Phi} & e^{-i\\Phi} \\vspace{2mm}\\\\ \\frac{ike^{i\\Phi}}{r} & -\\frac{ike^{-i\\Phi}}{r} & \\left[ik+\\frac{i\\varphi}{r}\\right]e^{i\\Phi} & -\\left[ik+\\frac{i\\varphi}{r}\\right]e^{-i\\Phi} \\vspace{2mm}\\\\ 0 & 0 & \\left(-\\frac{k}{\\omega}+\\frac{c^+}{r}\\right)e^{i\\Phi} & \\left(\\frac{k}{\\omega}+\\frac{c^-}{r}\\right)e^{-i\\Phi} \\vspace{2mm}\\\\ 0 & 0 & \\left[-\\frac{ik^2}{\\omega}+\\frac{ikc^+-\\frac{ik\\varphi}{\\omega}}{r}\\right]e^{i\\Phi}& -\\left[\\frac{ik^2}{\\omega}+\\frac{ikc^-+\\frac{ik\\varphi}{\\omega}}{r}\\right]e^{-i\\Phi} \\end{array}\\right) \\ ,\n\\end{equation}\nOn another hand, the original system~\\eqref{originalsys1} and\n~\\eqref{originalsys2}, can be written in a first order form\n\\begin{equation}\n\\dfrac{d\\mathbf{V}}{dr}=\\mathbf{X}\\mathbf{V} \\; ,\n\\end{equation}\nwhere the matrix $\\mathbf{X}$ is\n\\begin{equation}\\label{eq:CPX}\n\\mathbf{X}=\\left(\\begin{array}{cccc}0 & 1 & 0 & 0 \\vspace{2mm} \\\\ -\\dfrac{C}{A} & -\\dfrac{B}{A}& -\\dfrac{E}{A} & 0 \\vspace{2mm}\\\\0 & 0 & 0 & 1 \\vspace{2mm} \\\\ -\\dfrac{\\tilde E}{\\tilde A} & 0& -\\dfrac{\\tilde C}{\\tilde A}& 0 \\end{array}\\right) \\ ,\n\\end{equation}\nand the 2-vectors\n\\begin{equation}\\label{eq:CPyplus}\n\\mathbf{y}^{\\pm}=\\left(\\begin{array}{c}\\sqrt{\\dfrac{\\kappa_s^2 k}{\\omega^2}} a_0^{\\pm} \\\\ i\\sqrt{\\dfrac{k}{\\mu_p^2}}\\left[\\left(\\pm \\varphi+\\omega c^{\\pm}\\mp k(1+Q^2)\\delta_{n,2}\\pm\\dfrac{kqQ}{\\omega}\\right)a_0^{\\pm}\\pm ka_1^{\\pm}\\right] \\end{array}\\right) \\ ,\n\\end{equation}\n\\begin{equation}\\label{eq:hminus}\n\\mathbf{h}^-=\\left(\\begin{array}{c}\\sqrt{t} \\nu_0 \\\\ \\sqrt{\\dfrac{\\mu_p^2}{\\mu_p^4+q^2Q^2}}\\left(-i\\kappa_s^2\\nu_0+(\\omega-qQ)d \\right) \\end{array}\\right) \\; ,\n\\end{equation}\nwith\n\\begin{align}\nt&=\\dfrac{\\kappa_s^2}{\\mu_p^4+q^2Q^2}\\Big[(\\omega-qQ)\\left(qQa+b\\mu_p^2+2i\\rho \\mu_p^2\\alpha+\\dfrac{2qQ\\mu_p^2}{\\beta}\\right)-\\kappa_s^2\\mu_p^2\\Big]\\;,\\nonumber\\\\\nd&=\\left[\\left(1+\\rho-\\dfrac{iqQ}{\\mu_p^2}\\rho\\right)(a+ib)+\\left(1+\\dfrac{iqQ}{\\mu_p^2}\\right)\\left(\\dfrac{iqQ}{\\beta}-\\alpha\\rho\\right)\\right]\\nu_0+(1+2\\rho)\\left(\\dfrac{iqQ}{\\mu_p^2}-1\\right)\\nu_1 \\;,\\nonumber\\\\\na&=\\dfrac{qQ}{\\omega-qQ}\\;,\\;\\;\\;\\;\\;\\;\\;b=\\dfrac{qQ(2+n(n-3)(1+Q^2))}{\\beta^2}-\\dfrac{2\\omega\\alpha}{\\beta}+\\dfrac{\\kappa_s^2+\\mu_p^2}{\\omega-qQ}\\;,\\nonumber\\\\\n\\alpha&=\\dfrac{n(n-1)+(n-3)(n+2)Q^2}{2\\beta}\\;,\\;\\;\\;\\;\\;\\;\\;\\beta=(n-1)+(n-3)Q^2\\;.\\nonumber\n\\end{align}\n\\end{text}\n\n\n\n\n\n\\chapter{Functions and matrices for the neutral Proca case}\n\\label{app:neutralP}\n\nThis appendix contains details on the functions, recurrence relations and matrices, used in Chapter~\\ref{ch:NeutralP}.\n\nThe functions appeared in Eqs.~\\eqref{NPsysterm1} and~\\eqref{NPsysterm2} are $(\\kappa_s^2=\\ell(\\ell+n-1))$\n\\begin{align}\nM(r)&\\equiv \\sum^{2n-1}_{m=0}{\\alpha_m y^m}= r\\left(r^{n-1}-1\\right)^2 \\; ,\\nonumber\\\\\nN(r)&\\equiv \\sum^{2n-2}_{m=0}{\\beta_m y^m}=n\\left(r^{n-1}-1\\right)^2 \\; ,\\nonumber\\\\\nP(r)&\\equiv \\sum^{2n-1}_{m=0}{\\gamma_m y^m}=-(\\kappa_s^2+\\mu_p^2r^2)\\left(r^{2n-3}-r^{n-2}\\right) +\\omega^2r^{2n-1} \\; ,\\nonumber\\\\\nQ(r)&\\equiv \\sum^{2n-2}_{m=0}{\\sigma_m y^m}= i\\omega r^{n-1}\\left(2r^{n-1}-n-1\\right) \\; ,\\nonumber\\\\\n\\tilde M(r)&\\equiv \\sum^{2n}_{m=0}{\\tilde \\alpha_m y^m}=r^2\\left(r^{n-1}-1\\right)^2 \\; ,\\nonumber\\\\\n\\tilde N(r)&\\equiv \\sum^{2n-1}_{m=0}{\\tilde \\beta_m y^m}=(n-2)r\\left(r^{n-1}-1\\right)^2\\;,\\nonumber\\\\\n\\tilde P(r)&\\equiv \\sum^{2n}_{m=0}{\\tilde \\gamma_m y^m}=-\\left(\\kappa_s^2+\\mu_p^2r^2\\right)\\left(r^{2n-2}-r^{n-1}\\right)\n+\\omega^2r^{2n}-(n-2)\\left(r^{n-1}-1\\right)^2\\;,\\nonumber\\\\\n\\tilde Q(r)&\\equiv \\sum^{n}_{m=0}{\\tilde \\sigma_m y^m}=-i\\omega(n-1)r^n\\;.\\nonumber\n\\end{align}\nThe recurrence relations are\n\\begin{align}\n\\mu_0&=\\nu_0\\;,\\nonumber\\\\\n\\mu_1&=-\\frac{\\left(\\rho(\\rho-1)\\alpha_3+\\rho\\beta_2+\\gamma_1+\\sigma_1\\right)\\nu_0+\\sigma_0\\nu_1}{\\rho(\\rho+1)\\alpha_2+\\gamma_0}\\;,\\nonumber\\\\\n\\mu_j&=\\frac{\\omega^2+(n-1)^2(\\rho+j)(\\rho+j-1)}{D_j}f_j+\\frac{i\\omega(n-1)}{D_j}\\tilde\nf_j\\;,\\nonumber\\\\\n\\nu_j&=\\frac{\\omega^2+(n-1)^2(\\rho+j)(\\rho+j-1)}{D_j}\\tilde\nf_j+\\frac{i\\omega(n-1)}{D_j}f_j\\;,\n\\label{NPrecurone}\n\\end{align}\nwith\n\\begin{align}\nD_j&=(n-1)^2\\omega^2+\\left(\\omega^2+(n-1)^2(\\rho+j)(\\rho+j-1)\\right)^2\\;,\\nonumber\\\\\nf_j&=-\\sum^j_{m=1}{\\Big[\\big(\\alpha_{m+2}(\\rho+j-m)(\\rho+j-m-1)+\\beta_{m+1}(\\rho+j-m)+\\gamma_m\\big)\\mu_{j-m}+\\sigma_m\\nu_{j-m}\\Big]}\\;,\\nonumber\\\\\n\\tilde\nf_j&=-\\sum^j_{m=1}{\\left[\\big(\\tilde\\alpha_{m+2}(\\rho+j-m)(\\rho+j-m-1)+\\tilde\\beta_{m+1}(\\rho+j-m)+\\tilde\\gamma_m\\big)\\nu_{j-m}+\\tilde\\sigma_m\\mu_{j-m}\\right]}\\;.\\nonumber\n\\end{align}\nThe coefficients used in the asymptotic expansion in the text are\n\\begin{equation}\\label{eq:NPcpm}\nc^\\pm=\\dfrac{i}{2\\omega}\\left[-\\kappa_s^2+\\dfrac{n(2-n)}{4}+(2\\omega^2-\\mu_p^2)\\left(\\delta_{2,n}+\\delta_{3,n}\\right)+\\left(\\pm i\\dfrac{\\mu_p^2}{2k}-\\left(\\dfrac{\\mu_p^2}{2k}\\right)^2\\right)\\delta_{2,n}\\right] \\; .\n\\end{equation}\nThe matrices used in the text are as follows\n\\begin{equation}\\label{eq:NPT}\n\\mathbf{T}=\\frac{1}{r^{\\frac{n-2}{2}}}\\left(\\begin{array}{cccc}\\frac{e^{i\\Phi}}{r} & \\frac{e^{-i\\Phi}}{r} & e^{i\\Phi} & e^{-i\\Phi} \\vspace{2mm}\\\\ \\frac{ike^{i\\Phi}}{r} & -\\frac{ike^{-i\\Phi}}{r} & \\left[ik+\\frac{i\\varphi-\\frac{n-2}{2}}{r}\\right]e^{i\\Phi} & -\\left[ik+\\frac{i\\varphi+\\frac{n-2}{2}}{r}\\right]e^{-i\\Phi} \\vspace{2mm}\\\\ 0 & 0 & \\left(-\\frac{k}{\\omega}+\\frac{c^+}{r}\\right)e^{i\\Phi} & \\left(\\frac{k}{\\omega}+\\frac{c^-}{r}\\right)e^{-i\\Phi} \\vspace{2mm}\\\\ 0 & 0 & \\left[-\\frac{ik^2}{\\omega}+\\frac{ikc^+-\\frac{k}{\\omega}(i\\varphi-\\frac{n-2}{2})}{r}\\right]e^{i\\Phi}& -\\left[\\frac{ik^2}{\\omega}+\\frac{ikc^-+\\frac{k}{\\omega}(i\\varphi+\\frac{n-2}{2})}{r}\\right]e^{-i\\Phi} \\end{array}\\right) \\ ,\n\\end{equation}\n\\begin{equation}\\label{eq:X}\n\\mathbf{X}=\\left(\\begin{array}{cccc}0 & 1 & 0 & 0 \\vspace{2mm} \\\\ -\\dfrac{P}{M} & -\\dfrac{N}{M}& -\\dfrac{Q}{M} & 0 \\vspace{2mm}\\\\0 & 0 & 0 & 1 \\vspace{2mm} \\\\ -\\dfrac{\\tilde Q}{\\tilde M} & 0& -\\dfrac{\\tilde P}{\\tilde M}&-\\dfrac{\\tilde N}{\\tilde M} \\end{array}\\right) \\ ,\n\\end{equation}\nand the 2-vectors\n\\begin{equation}\\label{eq:NPyplus}\n\\mathbf{y}^{\\pm}=\\left(\\begin{array}{c}\\sqrt{\\dfrac{\\kappa_s^2 k}{\\omega}} a_0^{\\pm} \\\\ \\sqrt{\\dfrac{\\omega k}{\\mu_p^2}}\\left[(\\pm i\\varphi-\\frac{n-2}{2}+i\\omega c^{\\pm}\\mp ik\\delta_{n,2})a_0^{\\pm}\\pm ika_1^{\\pm}\\right] \\end{array}\\right) \\ ,\n\\end{equation}\n\\begin{equation}\\label{eq:NPhminus}\n\\mathbf{h}^-=\\left(\\begin{array}{c}\\sqrt{\\kappa_s^2} \\nu_0 \\\\ \\dfrac{i\\omega(\\mu_1-\\rho(\\frac{n}{2}\\nu_0+\\nu_1-\\mu_1))+\\kappa_s^2\\nu_0}{\\mu_p} \\end{array}\\right) \\ .\n\\end{equation} \n\\chapter{Proof of Theorem 1}\n\\label{app:ST1}\nIn this appendix, we prove Theorem~\\ref{thm1} given in Chapter~\\ref{ch:prelim}, by taking $\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}=BS_{-1}$, as an example. To do so, we start from Eq.~\\eqref{Spluseq}, by applying the operator $\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}$ on both sides,\n\\begin{align}\n-\\lambda\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}&=\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}(\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}-2a\\omega\\cos\\theta\\Xi)S_{+1}\\nonumber\\\\\n&=\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}S_{+1}-2a\\omega\\Xi\\bar{\\mathscr{L}_0}(\\cos\\theta\\bar{\\mathscr{L}_1}-\\sin\\theta\\sqrt{\\Delta_\\theta})S_{+1}\\nonumber\\\\\n&=\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}S_{+1}-2a\\omega\\Xi\\cos\\theta\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}+4a\\omega\\Xi\\sqrt{\\Delta_\\theta}\\sin\\theta \\bar{\\mathscr{L}_1}S_{+1}\\;,\\label{angularTeukol1}\n\\end{align}\nwhere relations\n\\begin{equation}\n\\bar{\\mathscr{L}_n}^\\dag\\cos\\theta=\\cos\\theta\\bar{\\mathscr{L}_n}^\\dag-\\sin\\theta\\sqrt{\\Delta_\\theta}\\;,\\;\\;\\;\\;\\;\\;\n\\sqrt{\\Delta_\\theta}\\sin\\theta\\bar{\\mathscr{L}}_{n+1}=\\bar{\\mathscr{L}_n}\\sqrt{\\Delta_\\theta}\\sin\\theta\\;,\n\\end{equation}\nhave been used. The first term in Eq.~\\eqref{angularTeukol1} can be further simplified\n\\begin{align}\n\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}S_{+1}&=\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}(\\bar{\\mathscr{L}_0}+2\\sqrt{\\Delta_\\theta}\\mathcal{Q})\\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&=\\bar{\\mathscr{L}_0}(\\bar{\\mathscr{L}_1}^\\dag-2\\sqrt{\\Delta_\\theta}\\mathcal{Q})\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}+2\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}\\sqrt{\\Delta_\\theta}\\mathcal{Q}\\bar{\\mathscr{L}_1}S_{+1}\\;,\n\\end{align}\nin which\n\\begin{align}\n\\bar{\\mathscr{L}_1}\\sqrt{\\Delta_\\theta}\\mathcal{Q}&=\\left(\\bar{\\mathscr{L}_0}+\\dfrac{1}{\\sin\\theta}\\dfrac{d}{d\\theta}(\\sqrt{\\Delta_\\theta}\\sin\\theta)\\right)\\sqrt{\\Delta_\\theta}\\mathcal{Q}\\nonumber\\\\\n&=\\sqrt{\\Delta_\\theta}\\mathcal{Q}\\bar{\\mathscr{L}_0}+\\sqrt{\\Delta_\\theta}\\dfrac{d}{d\\theta}(\\sqrt{\\Delta_\\theta}\\mathcal{Q})+\\dfrac{\\sqrt{\\Delta_\\theta}\\mathcal{Q}}{\\sin\\theta}\\dfrac{d}{d\\theta}(\\sqrt{\\Delta_\\theta}\\sin\\theta)\\nonumber\\\\\n&=\\sqrt{\\Delta_\\theta}\\mathcal{Q}\\bar{\\mathscr{L}_0}+\\dfrac{d}{d\\theta}(\\Xi H)+\\Xi H\\cot\\theta=\\sqrt{\\Delta_\\theta}\\mathcal{Q}\\bar{\\mathscr{L}_0}+2a\\omega\\Xi\\cos\\theta\\;,\n\\end{align}\nwhere $\\mathcal{Q}=\\tfrac{\\Xi H}{\\Delta_\\theta}$, as defined in Eq.~\\eqref{Qdef},\nand where the following relation is used\n\\begin{equation}\n\\dfrac{dH}{d\\theta}+H\\cot\\theta=2a\\omega\\cos\\theta\\;.\n\\end{equation}\nThen Eq.~\\eqref{angularTeukol1} becomes\n\\begin{align}\n-\\lambda\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}&=\\bar{\\mathscr{L}_0}(\\bar{\\mathscr{L}_1}^\\dag-2\\sqrt{\\Delta_\\theta}\\mathcal{Q})\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}+2\\bar{\\mathscr{L}_0}(\\sqrt{\\Delta_\\theta}\\mathcal{Q}\\bar{\\mathscr{L}_0}+2a\\omega\\Xi\\cos\\theta)\\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&\\;\\;\\;\\;-2a\\omega\\Xi\\cos\\theta\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}+4a\\omega\\Xi\\sqrt{\\Delta_\\theta}\\sin\\theta \\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&=\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}^\\dag\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}+4a\\omega\\Xi\\bar{\\mathscr{L}_0}\\cos\\theta\\bar{\\mathscr{L}_1}S_{+1}-2a\\omega\\Xi\\cos\\theta\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\&\\;\\;\\;\\;+4a\\omega\\Xi\\sqrt{\\Delta_\\theta}\\sin\\theta \\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&=\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}^\\dag\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}+4a\\omega\\Xi(\\cos\\theta\\bar{\\mathscr{L}_0}-\\sin\\theta\\sqrt{\\Delta_\\theta})\\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\&\\;\\;\\;\\;-2a\\omega\\Xi\\cos\\theta\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}\n+4a\\omega\\Xi\\sqrt{\\Delta_\\theta}\\sin\\theta \\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&=(\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}^\\dag+2a\\omega\\Xi\\cos\\theta)\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}\\;.\\label{angularTeukol2}\n\\end{align}\nComparing Eq.~\\eqref{angularTeukol2} with Eq.~\\eqref{Sminuseq} and remembering the definition in Eq.~\\eqref{newangoperator}, one concludes that $\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}$ is proportional to $S_{-1}$. We identify the proportionality constant as $B$, such that\n\\begin{equation}\n\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}=BS_{-1}\\;.\\label{eqB1}\n\\end{equation}\nFollowing the same procedures as above, one can also prove that\n\\begin{equation}\n\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}^\\dag S_{-1}=BS_{+1}\\;,\\label{eqB2}\n\\end{equation}\nwhere the same proportionality constant $B$ is used. This property is guaranteed by the normalization conditions given in Eq.~\\eqref{angnorm}\n\\\\\nTherefore, Theorem~\\ref{thm1} is proved. \n\\chapter{Marginal charged clouds around charged black holes}\n\\label{ch:ChargedClouds}\n\n\n\n\n\n\n\\section{Introduction}\nScattering processes for the Proca field were studied in Chapters~\\ref{ch:NeutralP} and~\\ref{ch:ChargedP}. An interesting generalization is to study this field in the context of quasinormal modes and quasi-bound states. In fact, such studies have been performed for a neutral Proca field on a Schwarzschild BH~\\cite{Rosa:2011my}, and on a slowly rotating Kerr BH~\\cite{Pani:2012vp}. In this chapter, we are going to study quasi-bound states for a charged Proca field (as well as a charged scalar field) on a Reissner-N\\\"ordstrom (RN) BH. This study is particularly interesting since we would like to know if the charge couplings between the background and the Proca field could balance the gravitational attraction, similarly to what occurs to scalar clouds.\n\nScalar clouds~\\cite{Hod:2012px,Hod:2013zza,Herdeiro:2014goa} are equilibrium configurations of a complex, massive scalar field in the background of a Kerr BH. They are stationary bound states, with a real frequency. These configurations are possible due to the existence of two qualitatively different types of quasi-bound states -- i.e. bound field configurations with a \\textit{complex} frequency:\n\\begin{itemize}\n\\item[i)] Time decaying quasi-bound states; this is the generic behavior expected for matter around a BH, due to the purely ingoing boundary condition at the horizon. Indeed, this is the only kind of quasi-bound state that can be found around Schwarzschild BHs\n\\item[ii)] Time growing quasi-bound states; this occurs for Kerr BHs in the superradiant regime,\n i.e. when the real part of the frequency, $\\omega$, of the quasi-bound state obeys $\\omega0$ in the square root and assuming, without loss of generality, $\\Re(\\omega)>0$):\n\\begin{itemize}\n\\item $\\Im(k)<0$: these are the quasinormal modes which describe time decaying oscillations. These modes are free to escape the BH potential and asymptotically grow exponentially~\\cite{Nollert:1999ji} (for large $r$).\n\\item $\\Im(k)>0$, these are quasi-bound states, i.e. they describe field configurations which are confined in the outside region of the BH and decay exponentially when $r\\rightarrow+\\infty$. They are possible if there is a confining potential well where the field can accumulate. The boundary condition for these states can thus be recast as\n\\begin{equation}\n\\lim_{r\\rightarrow+\\infty}\\psi_i=0\\;.\n\\end{equation}\n\n\\end{itemize}\nQuasi-bound states are very interesting in the presence of superradiance, since they provide the possibility of an instability as discussed in Chapter~\\ref{ch:intro}. If they exist within the superradiant regime, the field is able to extract energy from the BH which accumulates in the confining potential. This would be signaled by an exponential growth of the wave amplitude. The condition for the instability to appear is $\\omega_I\\equiv\\Im{(\\omega)}>0$, i.e. the time-dependence is\n\\begin{equation}\n\\psi_i\\sim e^{-i\\omega t}=e^{-i\\omega_Rt+\\omega_It} \\; .\n\\end{equation}\nIn the absence of instabilities (i.e. $\\omega_I<0$), the wave amplitude decays exponentially with a lifetime\n\\begin{equation}\n\\tau\\equiv |\\omega_I|^{-1}\\; .\n\\end{equation}\nIf $\\omega_I\\rightarrow 0$ is possible, then the state can be truly bound, or marginally bound if $\\omega_R\\rightarrow \\mu \\Leftrightarrow k_R\\rightarrow 0$. Observe that in this exact limit the wavelength diverges so the state becomes de-localized. Actually, $\\mu-\\omega_R$ is basically the binding energy of the state; since this goes to zero, in the limit, the state becomes marginally bound. Moreover, one can have arbitrarily long lived quasi-bound states near this limit which have a large amplitude in a compact domain (similarly to true bound states -- see the results in Section~\\ref{results}). If $\\tau$ is very large, then the state may be effectively considered bound (rather than quasi-bound) for many practical purposes.\n\n\\subsection{Effective potential for the transverse mode}\n\\label{sec:eff_potential}\nA natural strategy to investigate the possibility of quasi-bound states to appear is to recast the radial equations for the fields in a Schr\\\"odinger like form with an effective potential. In the case of the Proca field, this is not so straightforward for the coupled system. However, the transverse mode Eq.~\\eqref{TmodeEq}, can be easily recast in a Schr\\\"odinger like form ($dr_\\star \\equiv dr\/U $)\n\\begin{eqnarray}\n&&\\left[-\\dfrac{d^2}{dr_\\star^2}+V_{\\rm eff}\\right] \\Upsilon=0\\;,\\\\\n&&V_{\\rm eff}=\\left(\\tfrac{\\ell(\\ell+1)}{r^2}+\\mu^2\\right)U-\\left(\\omega -\\tfrac{qQ}{r}\\right)^2 \\; .\n\\end{eqnarray}\nTo classify the effective potential and investigate when a well forms, it is convenient to define a compactified coordinate $x\\equiv 1-1\/r\\in [0,1]$ such that\n\\begin{equation}\\label{eq:Veff}\nV_{\\rm eff}(x) = \\sum_{k=0}^4 b_k x^k \\ ,\n\\end{equation}\nwhere the coefficients $b_k$ are\n\\begin{align}\nb_0&=-(\\omega-qQ)^2 \\;,\\nonumber\\\\\nb_1&= (1-Q^2)(\\mu^2+\\ell(\\ell+1))-2qQ(\\omega-qQ)\\;,\\nonumber\\\\\nb_2&= -(2-3Q^2)\\ell(\\ell+1)+Q^2(\\mu^2-q^2)\\;,\\label{eq:bcoeffs}\\\\\nb_3&= \\ell(\\ell+1)(1-3Q^2)\\;,\\nonumber\\\\\nb_4&= \\ell(\\ell+1)Q^2\\; . \\nonumber\n\\end{align}\nThus we are dealing with a quartic polynomial. Taking into account its values at the end points a well can only form for two possible configurations with three roots (shown schematically in Fig.~\\ref{EffectiveVschematics}).\n\\begin{figure}\n\\begin{center}\n\\includegraphics[clip=true,trim = 40 450 50 50, width=0.44\\textwidth]{Veff\/Veff_schem.pdf}\n\\end{center}\n\\caption{\\label{EffectiveVschematics} Schematic representation of the effective potential (in the compactified coordinate $x$) for the two possible cases where a well may form.\n}\n\\end{figure}\nIn case~1 the derivative $V'_{\\rm eff}(x)$ must have three positive roots $x_1,x_2,x_3$. Then its form is $V'_{\\rm eff}(x)=A(x-x_1)(x-x_2)(x-x_3)$. But we also know that\n\\begin{eqnarray}\n V'_{\\rm eff}(x)&=&\\sum_{k=0}^3 (k+1) b_{k+1} x^k\\ .\n\\end{eqnarray}\nEquating the two forms we conclude that $x_1x_2x_3=-b_1\/(4b_4)$. We are interested in finding a well in the superradiant regime, $\\omega0 \\Leftrightarrow \\mu^2 >\\dfrac{2qQ\\omega}{1+Q^2} \\Leftrightarrow \\mu M >qQ\\dfrac{\\omega}{\\mu} \\Leftrightarrow \\dfrac{\\omega}{\\mu}<\\sqrt{\\dfrac{M}{r_H}}\\;,\\label{chargedclouds:case2relation}\n\\end{equation}\nwhere in the last step we have used the superradiance condition $\\omega r_H0$, the real part of the frequency, $\\omega_R$, moves to values closer to the field mass, $\\mu$, and the (negative) imaginary part $\\omega_I$ moves to values closer to zero.\n\nFor the coupled sector, the procedure is analogous if one recalls the scattering matrices defined in Chapter~\\ref{ch:NeutralP}. First one notes that the general solution of a system of $n$ coupled fields $\\psi_i$, with second order linear equations, can be represented by $2n$ integration constants. Those constants can be defined either at the event horizon or at infinity. The linearity of the system implies that a linear transformation relates the integration constants at the horizon, with those at infinity. We denote the ingoing and outgoing wave coefficients at the horizon ($+\/-$ respectively)\n \\[\\vec{\\mathbf{h}}=({\\mathbf h}^+,{\\mathbf h}^-)=(h^+_{i},h^-_{i}) \\ , \\] where $i=1,2$ for the current coupled system, and, similarly, the coefficients at infinity are defined\n \\[\\vec{\\mathbf{y}}=({\\mathbf y}^+,{\\mathbf y}^-)=(y^+_{i},y^-_{i}) \\ . \\]\nThe linear transformation is then represented as\n\\begin{equation}\n\\vec{\\mathbf{y}}=\\mathbf{S} \\vec{\\mathbf{h}} \\ \\ \\Leftrightarrow \\ \\ \\left(\\begin{array}{c} {\\mathbf y}^+ \\\\ {\\mathbf y}^- \\end{array} \\right)=\\left(\\begin{array}{c|c} {\\mathbf S}^{++} & {\\mathbf S}^{+-} \\\\ \\hline {\\mathbf S}^{-+} & {\\mathbf S}^{--} \\end{array} \\right)\\left(\\begin{array}{c} {\\mathbf h}^+ \\\\ {\\mathbf h}^- \\end{array} \\right) ,\n\\end{equation}\nwhere the scattering matrix $\\mathbf{S}$ depends on $\\omega$, $\\ell$, field couplings and the background. It encodes all the information on the scattering process and it can be constructed from specific combinations of modes with boundary conditions set at the horizon.\n\nSimilarly to the decoupled modes, we want to impose an ingoing boundary condition at the horizon i.e. ${\\mathbf h}^{+}=0$. Then the solution coefficients far away are\n\\begin{equation}\\label{scattering_ingoing}\n{\\mathbf y}^s={\\mathbf S}^{s-}{\\mathbf h}^{-} \\; .\n\\end{equation}\nWe then must impose that the coefficients of the solution which grows exponentially fast vanish (in analogy to Eq.~\\eqref{eq:illustratedecay}), i.e. we need that there is a particular initial condition $\\hat{\\mathbf h}$ at the horizon such that $\\mathbf{y}^{-}=0$, i.e.\n\\begin{equation}\n0={\\mathbf S}^{--}\\hat{\\mathbf h}^{-} \\; .\n\\end{equation}\nThus we need to choose the eigenvector with zero eigenvalue of ${\\mathbf S}^{--}$. The condition for this solution to be possible is then\n\\begin{equation}\n\\det{\\mathbf S}^{--}=0 \\; ,\n\\end{equation}\nwhich will occur at the quasi-bound state frequencies. Furthermore, one can check that (up to a normalization constant) the radial profile of the quasi-bound state solution is obtained using the following initial condition at the horizon\n\\begin{equation}\n\\left(\\begin{array}{c}\\hat{h}^{-}_1 \\\\ \\hat{h}^{-}_2\\end{array}\\right)\\propto\\left(\\begin{array}{c}-S^{--}_{12} \\\\ S^{--}_{11}\\end{array}\\right) \\; .\n\\end{equation}\nIn practice, there is numerical contamination of exponentially growing eigenmodes, which means we must again employ a minimization condition (similarly to the decoupled modes)\n\\begin{equation}\n \\min_{\\omega}\\left[|{\\mathbf S}^{--}|_{r_{far}}\\right] \\; .\n\\end{equation}\nIn Fig.~\\ref{SurfacesCoupled} we show an example of this quantity for $\\ell=1$ coupled modes with a non-zero background charge and a zoom around the corner where the higher frequency levels pile up (right). We discuss in more detail the effect of the field charge in Sections~\\ref{results} and~\\ref{Discussion} so here we only highlight the differences compared with the decoupled cases.\n\\begin{figure*}\n\\begin{center}\n\\hspace{-3mm}\\includegraphics[clip=true,trim = 90 400 50 30, width=0.505\\textwidth]{Figs\/ch6\/Surfaces\/SurfaceCoupledQ0.pdf}\\includegraphics[clip=true,trim = 90 30 50 380, width=0.505\\textwidth]{Figs\/ch6\/Surfaces\/SurfaceCoupledQ0zoom.pdf}\n\\end{center}\n\\caption{\\label{SurfacesCoupled} Magnitude of the determinant of ${\\mathbf S}^{--}$ for the coupled system at $r_{far}$ (i.e. in the asymptotic far region), as a function of the complex frequency $\\omega$ in the neutral limit for $\\ell=1$. The various levels are indexed by $(n,S)$ as discussed in the text. The right panel shows a zoom to better display the positions of a few higher levels.\n}\n\\end{figure*}\nOnce again the procedure is to find the local minimum for each valley found in the figure.\n\nSince the search for the quasi-bound state frequencies reduces to a (local) minimization problem, one needs in general a good starting guess which is close enough to the valley. Several analytic estimates have been developed in the literature for small parameters. The general conclusion is that $\\omega_R$ follows a hydrogen like spectrum. Of particular relevance to our problem is the charged scalar spectrum approximation found in~\\cite{Furuhashi:2004jk}:\n\\begin{equation}\\label{AnalyticFN}\n\\omega_R\\simeq \\mu\\left[1-\\frac{1}{2}\\frac{(\\mu M-qQ)^2}{N^2}\\right]\\;,\\;N=\\ell+n+1\\;,\n\\end{equation}\nwith $n\\footnote{In the remaining part of this chapter, $n$ refers to the overtone number.}\\in \\mathbb{N}_0$.\nThis approximation suggests that, as the charge is turned on to positive values, there is a critical value at\\footnote{In this limit $\\omega_R=\\mu$ and in fact $\\omega_I=0$ (see~\\cite{Furuhashi:2004jk}).} $\\mu M =qQ$, after which we expect an exit from the quasi-bound state regime (since the exponentially decaying tail disappears at this threshold).\n\nAlso in the low energy limit, but in the neutral case ($q=Q=0$), a low energy approximation was found for the Proca field in~\\cite{Rosa:2011my}. Their analytic approximations can in principle be generalized to our case by introducing a charge dependence. Even though in this study we have not developed such analytic matching calculation, we found an excellent agreement (for small parameters) with the following ansatz\n\\begin{equation}\\label{eq:ProcaOmegaR}\n\\omega_R\\simeq \\mu\\left[1-\\frac{1}{2}\\frac{(\\mu M-qQ)^2}{N^2}\\right]\\; \\;, \\; N=\\ell+S+n+1\n\\end{equation}\nwhich results from shifting $\\mu M\\rightarrow \\mu M-qQ$ in the formula of~\\cite{Rosa:2011my} as suggested by Eq.~\\eqref{AnalyticFN}. Here $S=0$ for the transverse modes and $S=1,-1$ for the coupled modes (for the exceptional mode $\\ell=0$ there is only $S=1$).\n\nIn Fig.~\\ref{SurfacesCoupled} we indicate the labels $(n,S)$ corresponding to this approximation, for the first few pairs of levels which arise in the coupled system. As expected, due to the degeneracy in $N$, i.e the same $N$ can be obtained by adding different combinations of $(n,S)$ for the same $\\ell$, the number of degrees of freedom is doubled. This can be observed in the existence of two distinct lines of valleys in the right panel. By continuity, this classification must hold also for larger parameters, so a natural strategy to obtain new frequencies is to perform a flow of the parameters in small steps from some reference quasi-bound state frequencies. This can be pictured as a flow of the valleys in the plots of Figs.~\\ref{SurfacesPsi0} and~\\ref{SurfacesCoupled}.\n\nThe two parameters we will want to vary continuously are the field mass $\\mu$ and charge $q$. Taking the mass $\\mu$ as an example, let us assume we have obtained a high precision quasi-bound state frequency $\\omega$ by minimization near a valley (with given mode labels $\\{\\ell,n,S\\}$ and fixed $\\mu,q,Q,M$). Then by continuity the frequency for this mode at a nearby mass $\\mu\\rightarrow \\mu+\\delta \\mu$ will shift by a small amount $\\omega\\rightarrow\\omega+\\delta \\omega$ (all other parameters are fixed). If the step is small enough, we can use the previous $\\omega$ as a first guess and refine it by minimization to obtain the new frequency. Thus, by using small steps, we can iterate this procedure to flow the frequencies as a function of a continuous parameter. The same procedure can be applied to flow the frequency with charge $q$.\n\nEven though analytic approximations give a very useful guide to the dependence of the frequencies, and help understanding the labels of the various levels, in practice (especially for larger parameters) we determined various reference initial estimates for the frequencies of each state graphically (using plots such as Fig.~\\ref{SurfacesPsi0}) and then refined them through minimization before using as seeds to the flows. In Table~\\ref{TabSeeds}, we provide a set of seed frequencies that were used as starting points to produce various of the plots in the results of Section~\\ref{results} for the Proca field. Some scalar frequencies that were used to obtain scalar profiles to compare with the Proca field in the region of parameters where long lived states were found (see Sections~\\ref{results} and~\\ref{Discussion}) are shown in Table~\\ref{TabSeedsScalar}.\n\\begin{table}\n\\begin{minipage}{\\textwidth}\n\\begin{center}\n$Qr_H=0$\\footnote{Note that the results shown in this table agree with the data used in~\\cite{Rosa:2011my}, to all of the significant figures quoted.}\\vspace{1mm}\\\\\n\\begin{tabular}{||c|c|c||}\n\\hline\n $N$ & $(\\ell,n,S)$ &$\\mu^{-1}(\\omega_R,\\omega_I)$ \\\\\n\\hline\n$1$ & $(1,0,-1)$ &$\\phantom{..}(0.95972157,-0.0037679893)\\phantom{.}$\\\\\n\\hline\n & $(1,1,-1)$ & $(0.99032863,-0.00062350043)$\\\\\n$2$ & $(0,0,1)$ & $(0.99031646,-0.00051223870)$\\\\\n & $(1,0,0)$ & $(0.99131023,-0.00001346974)$\\\\\n\\hline\n\\end{tabular}\\vspace{2mm}\\\\\n$Qr_H=0.3 \\; (Q\/M\\simeq 0.55\\;,\\;\\mu M \\simeq 0.27)$ \\vspace{1mm}\\\\\n\\begin{tabular}{||c|c|c||}\n\\hline\n $N$ & $(\\ell,n,S)$ &$\\mu^{-1}(\\omega_R,\\omega_I)$ \\\\\n\\hline\n$1$ & $(1,0,-1)$ & $(0.95270408,-0.0046419907)$\\\\\n\\hline\n & $(1,1,-1)$ & $(0.98838598,-0.00084694923)$\\\\\n $2$ & $(0,0,1)$ & $(0.98812624,-0.00095739793)$\\\\\n & $(1,0,0)$ & $(0.98953577,-0.00002375643)$\\\\\n\\hline\n\\end{tabular}\\vspace{2mm}\\\\\n$Qr_H=0.9\\;(Q\/M\\simeq 0.994\\;,\\;\\mu M \\simeq 0.45)$ \\vspace{1mm}\\\\\n\\begin{tabular}{||c|c|c||}\n\\hline\n $N$ & $(\\ell,n,S)$ &$\\mu^{-1}(\\omega_R,\\omega_I)$ \\\\\n\\hline\n$1$ & $(1,0,-1)$ & $\\phantom{..}(0.87274751,-0.031112471)\\phantom{...}$\\\\\n\\hline\n & $(1,1,-1)$ & $(0.96469871,-0.011098631)$\\\\\n$2$ & $(0,0,1)$ & $(0.96824273,-0.016591447)$\\\\\n & $(1,0,0)$ & $(0.96354486,-0.001705819)$\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\caption{\\label{TabSeeds} Some reference frequencies (found through minimization) used in the flow for the Proca field, with $\\mu r_H=0.5$ and $q=0$. We indicate the background charge both in inverse horizon radius units, and also in BH mass units for comparison.}\n\\begin{center}\n\\begin{tabular}{||c|c|c||}\n\\hline\n $N$ & $(\\ell,n)$ &$\\mu^{-1}(\\omega_R,\\omega_I)$ \\\\\n\\hline\n$1$ & $(0,0)$ & $\\phantom{..}(0.93713433,-0.083684629)\\phantom{...}$\\\\\n\\hline\n & $(0,1)$ & $(0.97694630,-0.016936300)$\\\\\n$2$ & $(1,0)$ & $(0.96780454,-0.000797486)$\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\caption{\\label{TabSeedsScalar} Some reference frequencies (found through minimization) used in the flow for the scalar field, with $\\mu r_H=0.5$ and $q=0$. We have focused on $Q\/M=0.994$ to compare with the Proca field.}\n\\end{minipage}\n\\end{table}\nAll frequencies in these tables were obtained by refining initial estimates through minimization. The initial estimates were obtained graphically by zooming surfaces such as Figs.~\\ref{SurfacesPsi0} and~\\ref{SurfacesCoupled} around each valley. Once an initial estimate is found which is inside the valley, the minimization routines refine the result to the required precision. These high precision frequencies can be used to flow either the mass $\\mu$ or the charge $q$ in small steps, to other values as shown in the plots of Section~\\ref{results}.\n\n\n\\section{Results}\n\\label{results}\nIn this section, we present a selection of numerical results, focusing mostly on the positive charge coupling case. First, we note that we did not find any quasi-bound states in the superradiant region $\\omega q Q >0$, that either decreasing the mass $\\mu$ or increasing the charge $q$ as to achieve the threshold condition $\\mu M=qQ$, one gets arbitrarily long lived states, since the imaginary part of the quasi-bound state frequency tends to zero. When taking this limit the frequency becomes equal to the field mass $\\omega\\rightarrow \\mu$, and so the states become marginally bound. Since they do not trivialize, we obtain configurations we have dubbed as marginal (charged) scalar and Proca clouds around RN BHs. But observe that these clouds are qualitatively different from those found in the Kerr case~\\cite{Hod:2012px,Hod:2013zza,Herdeiro:2014goa}, and recently extended to the Kerr-Newman case~\\cite{Hod:2014baa}, which are real bound states.\n\nBy analyzing the behavior of the field profiles when approaching the threshold condition, the region where the field has a large amplitude agrees well with the region where a potential well is present. The width of the well typically increases when approaching the marginally bound limit meaning that the field is large on a wider region away from the BH horizon. Since the quasi-bound state is localized away from the horizon, the gravitational and electromagnetic field interactions which are responsible for supporting the bound state should be dominated by their asymptotic Newtonian and Coulombian limit respectively. Thus the threshold condition $\\mu M=qQ$ should in fact correspond to a force balance condition between the Newtonian force and the electrostatic force.\n\nScalar clouds around Kerr BHs can be promoted to nonlinear hair. Indeed Kerr BHs with scalar hair exist that connect precisely to the Kerr solutions that allow the existence of the corresponding clouds~\\cite{Herdeiro:2014goa,Herdeiro:2014ima,Herdeiro:2014jaa}. One may therefore ask if the existence of these marginal (charged) scalar and Proca clouds hints at the existence of nonlinear solutions of RN BHs with scalar or Proca hair. One possibility is along the lines of the discussion in~\\cite{Degollado:2013eqa}, for the scalar case. It was observed therein that the marginal scalar clouds (albeit this terminology was not used therein) can be seen as the partial waves of a distribution of charged scalar particles in a no-force balance with the RN BH. This suggests the existence of multi-object nonlinear solutions -- of Majumdar-Papapetrou type~\\cite{Majumdar:1947eu,Papapetrou1945}, but not necessarily multiple BHs. One other possibility, however, is that since the clouds that are regular at the horizon are partial waves with $\\ell\\neq 0$, the corresponding nonlinear configurations will have non-zero angular momentum and thus will be a Kerr-Newman BH with charged scalar (or Proca) hair, a possibility already anticipated in~\\cite{Herdeiro:2014goa,Hod:2014baa}. Of course, the existence (or nonexistence) of any of these possible solutions, can only be decided by a fully nonlinear analysis of the Einstein-(charged)-Klein-Gordon or Einstein-(charged)-Proca systems.\n\n\n\n\n\n\\chapter{Hawking radiation for a Proca field: charged case}\n\\label{ch:ChargedP}\n\n\n\n\\section{Introduction}\nIn this chapter, we are going to study Hawking radiation for a charged Proca field, by solving the charged Proca equations derived in Chapter~\\ref{ch:GeneralInFlat} numerically. This is a generalization of the study presented in Chapter~\\ref{ch:NeutralP}, by adding charge both to the field and the BH, on the 3+1 dimensional SM brane. This charged brane background is motivated by TeV gravity scenarios, in which the SM particles are confined on a 4-dimensional brane, while gravity propagates in extra dimensions, see Section~\\ref{sc:BM} for more details. As shown in~\\cite{Sampaio:2009ra}, the Schwinger emission alone does not suffice to discharge the BH, which makes the study of Hawking radiation for charged BHs essential. The effects of charge on the Hawking evaporation process, for scalar and fermion fields, have been performed in~\\cite{Sampaio:2009ra,Sampaio:2009tp}. As a first motivation for this study, we are going to complete this picture by exploring the charged Proca fields.\n\n\n\n\n\n\n\n\n\n\n\n\n\nCompared to the study for neutral Proca fields in Chapter~\\ref{ch:NeutralP}, a new feature due to the charge of the background and of the field is the existence of superradiant modes. These modes are amplified through the extraction of Coulomb energy, as well as charge, from the charged BH. Furthermore, in a rotating background, the Proca field equation variables are not known to separate, which presents an extra difficulty added to the non decoupling of the modes, making it difficult to study exactly the superradiance phenomenon -- see~\\cite{paniPRL,paniPRD} for a recent study in the slow rotation approximation. Thus, as a second motivation for this study, the charged BH background with spherical symmetry, yields a setup where superradiance of a massive spin 1 field can be explored without any approximation, albeit numerically. Such analysis will be performed herein.\n\nThe structure of this chapter is organized as follows. In Section~\\ref{sec:nearhorizon} we introduce the background geometry and study the near horizon and asymptotic behaviors of the coupled charged Proca equations. In Section~\\ref{fluxes} we discuss how to construct the scattering matrix from the electric current. The numerical results for the transmission factor and the associated Hawking fluxes are presented in Section \\ref{chargedhresults} and we summarize our results in the last section. To keep the main part of this chapter compact and clear, some technical relations are left to Appendix~\\ref{app:chargedP}.\n\n\n\\section{Boundary conditions and first order system}\\label{sec:nearhorizon}\nBefore starting to deal with the Proca equations, we first present the following brane BH geometry\n\\begin{equation}\nds^2=-V(r)dt^2+\\dfrac{1}{V(r)}dr^2+r^2(d\\theta^2+\\sin^2\\theta d\\varphi^2) \\; ,\\label{chargebrane}\n\\end{equation}\nwith metric function\n\\begin{equation}\nV(r)=1-\\dfrac{M}{r^{n-1}}+\\dfrac{Q^2}{r^2}\\;,\\label{metricchbrane}\n\\end{equation}\nwhere $M$ and $Q$ are the parameters related with BH bulk mass and brane charge. For numerical convenience, we choose units such that the outer horizon radius is $r_H=1$, i.e. $M=1+Q^2$ at the outer horizon.\n\nIt is easy to map the line element in Eq.~\\eqref{chargebrane}, to the general background geometry with the Einstein space given in Eq.~\\eqref{KI:metric}. Then the charged Proca field equations we are interested herein, can be obtained by setting $n=2$ in Eqs.~\\eqref{originalsys1},~\\eqref{originalsys2},~\\eqref{Max} and~\\eqref{k0M}\\footnote{Note that the explicit $n$ appearing in these wave equations only depends on the dimension of the Einstein space. Therefore, we set $n=2$ for the background in Eq.~\\eqref{chargebrane}. But we keep $n$ in general in the metric function~\\eqref{metricchbrane} since gravity may propagate in the extra dimensions.}.\n\nThe procedure to rewrite the second order wave equation for the coupled system into a first order form, to impose an ingoing boundary condition at the horizon, and to extract the asymptotic expansion coefficients, is similar to what was done in Section~\\ref{sec:NPnearhorizon}. For clarity, we summarize the main steps in the reminder of this section.\n\nTo determine the transmission factors, we need to integrate the radial equations from the horizon to the far away region with ingoing boundary conditions. The standard procedure is to find a series expansion of the solution near the horizon, which can be used to initialize the solution (we do so at $r=1.001$). Focusing on the coupled system $\\left\\{\\psi,\\chi\\right\\}$, if we define $y=r-1$, Eqs.~\\eqref{originalsys1} and\n~\\eqref{originalsys2} become\n\\begin{eqnarray}\n\\left[A(r)\\dfrac{d^2}{dy^2}+B(r)\\dfrac{d}{dy}+C(r)\\right]\\psi+ E(r)\\chi&=&0\\label{systerm1}\\ ,\\\\\n\\left[\\tilde A(r)\\dfrac{d^2}{dy^2}+\\tilde\nB(r)\\dfrac{d}{dy}+\\tilde C(r)\\right]\\chi+\\tilde E(r)\\psi&=&0\\label{systerm2}\\ ,\n\\end{eqnarray}\nwhere the polynomials $A,B,C,E,\\tilde{A},\\tilde{B},\\tilde{C},\\tilde{E}$ are defined in Appendix~\\ref{app:chargedP}. Making use of Frobenius' method to expand $\\psi$ and $\\chi$, we insert the following expansions into Eqs.~\\eqref{systerm1} and~\\eqref{systerm2}\n\\begin{equation}\n\\psi=y^\\rho\\sum^{\\infty}_{j=0}{\\mu_jy^j}\\label{defpsi} \\; , \\;\\;\\; \\chi=y^\\rho\\sum^{\\infty}_{j=0}{\\nu_jy^j} \\; , \\;\\;\\; \\rho=\\dfrac{-i(\\omega-qQ)}{(n-1)+(n-3)Q^2}\\;,\n\\end{equation}\nwhere the sign of $\\rho$ was chosen to impose an ingoing boundary condition. We then obtain the recurrence relations~\\eqref{CPrecurone} for the coefficients $\\mu_j$ and $\\nu_j$ found in the Appendix. A general solution close to the horizon can be parameterized by two free coefficients $\\nu_0$ and $\\nu_1$.\n\nSimilarly, to understand the asymptotic behavior of the waves at infinity we now expand $\\psi$ and $\\chi$ as\n\\begin{equation}\n\\psi=e^{\\beta r}r^{p}\\sum_{j=0}\\dfrac{a_j}{r^j}\\label{psifar} \\; , \\qquad \\chi=e^{\\beta r}r^{p}\\sum_{j=0}\\dfrac{b_j}{r^j} \\ ,\n\\end{equation}\nwhich, after insertion into Eqs.~\\eqref{originalsys1} and~\\eqref{originalsys2}, yield\n\\begin{equation}\n\\beta = \\pm ik\\; , \\;\\;\\;\\;\\;\\;\\;k=\\sqrt{\\omega^2-\\mu_p^2}\\;,\\qquad p = \\pm i\\varphi \\; ,\n\\end{equation}\nwhere $\\varphi=\\delta_{n,2}(\\omega^2+k^2)(1+Q^2)\/(2k)-qQ\\omega\/k$. Thus one can show that asymptotically\n\\begin{equation}\n\\psi \\rightarrow \\left(a_0^++\\dfrac{a_1^+}{r}+\\ldots\\right)e^{i\\Phi}+\\left(a_0^-+\\dfrac{a_1^-}{r}+\\ldots\\right)e^{-i\\Phi} \\; , \\label{asymptoticpsi}\n\\end{equation}\n\\begin{eqnarray}\n\\chi \\rightarrow\n\\left[\\left(-\\frac{k}{\\omega}+\\dfrac{c^+}{r}\\right)a_0^++\\ldots\\right]e^{i\\Phi}\n+\\left[\\left(\\frac{k}{\\omega}+\\dfrac{c^-}{r}\\right)a_0^-+\\ldots\\right]e^{-i\\Phi} \\; ,\\label{asymptoticpchi}\n\\end{eqnarray}\nwhere $\\Phi\\equiv kr+\\varphi \\log r$ and $c^\\pm$ is defined in the Appendix, Eq.~\\eqref{CPcpm}.\nThus, as expected, each field is a combination of ingoing and outgoing waves at infinity.\nAsymptotically, the solution is parameterized by four independent coefficients $\\left\\{a_0^\\pm,a_1^\\pm\\right\\}$, two for each independent mode in the coupled system. In the same way as in Section~\\ref{sec:NPnearhorizon}, one can define a first order system of ODEs containing four radial functions $\\left\\{\\chi^\\pm,\\psi^\\pm\\right\\}$ which coincide with such coefficients at infinity, allowing for an easy extraction of the wave amplitudes. Our target system, which will be solved numerically in the remainder, is then\n\\begin{equation}\\label{eq:ODEcoupled}\n\\dfrac{d\\mathbf{\\Psi}}{dr}=\\mathbf{T}^{-1}\\left(\\mathbf{X}\\mathbf{T}-\\dfrac{d\\mathbf{T}}{dr}\\right) \\mathbf{\\Psi} \\ ,\n\\end{equation}\nwith $\\mathbf{\\Psi}^T=(\\psi_{+},\\psi_{-},\\chi_{+},\\chi_{-})$. The definition of the matrices $\\mathbf{X}$ and $\\mathbf{T}$, and how they relate with~\\eqref{systerm1} and~\\eqref{systerm2} can be found in Appendix~\\ref{app:chargedP}.\n\n\n\n\n\\section{Hawking fluxes}\n\\label{fluxes}\nWe shall now calculate the transmission factor for the coupled system as well as the Hawking fluxes generated from all the modes. In contrast to constructing a conserved flux from the energy-momentum tensor in Chapter~\\ref{ch:NeutralP} which was simple enough to extract the transmission factors, in the present case, we use the conserved electric current which is naturally defined for this charged field. One can show that such a current is given by\n\\begin{equation}\n\\mathcal{J}^{\\alpha}=W^{\\dagger\\alpha\\mu}W_{\\mu}+\\dfrac{1}{\\sqrt{-g}}\\partial_{\\beta}\\left(\\sqrt{-g}W^{\\dagger\\beta}W^{\\alpha}\\right)-c.c. \\;\\label{current}\n\\end{equation}\nThe radial flux at $r$ fixed is obtained by integrating the $\\alpha=r$ component on the sphere. We note that only the first term in Eq.~\\eqref{current} (denoted from now on $\\mathcal{J}^{\\alpha}_{\\uppercase\\expandafter{\\romannumeral1}}$) contributes, since the second term becomes a total derivative on the sphere.\n\nThe contribution for the flux of the coupled fields at infinity, is then found by simplifying the radial component of Eq.~\\eqref{current} using the equations of motion, and inserting the far away expansion at infinity\n\\begin{equation}\\label{eq:currentInf}\n\\mathcal{J}^{r, \\infty}_{\\uppercase\\expandafter{\\romannumeral1}-couple} =|y_0^-|^2-|y_0^+|^2+|y_1^-|^2-|y_1^+|^2\n\\equiv(\\mathbf{y}^-)^\\dagger \\mathbf{y}^--(\\mathbf{y}^+)^\\dagger \\mathbf{y}^+ \\; ,\n\\end{equation}\nwhere $y_i^s(s=\\pm; i=0,1)$ are linear combinations of the asymptotic coefficients $a_i^s$ given in the Appendix, Eq.~\\eqref{eq:CPyplus}.\nUsing the reflection matrix $(\\mathbf{R})$ defined in Chapter~\\ref{ch:NeutralP}, we obtain\n\\begin{equation}\\label{eq:TF1}\n\\mathcal{J}^{r, \\infty}_{\\uppercase\\expandafter{\\romannumeral1}-couple}=(\\mathbf{y}^-)^\\dagger\\left(\\mathbf{1}-\\mathbf{R}^\\dagger\\mathbf{R}\\right) \\mathbf{y}^-\\equiv (\\mathbf{y}^-)^\\dagger\\mathbf{T}\\, \\mathbf{y}^- \\; ,\n\\end{equation}\nwhere we have defined a (hermitian) transmission matrix $\\mathbf{T}$, which can be diagonalised to find the decoupled asymptotic fields.\n\nFollowing the same procedure, one can also calculate the electric current at the horizon\n\\begin{equation}\\label{eq:currentH}\n\\mathcal{J}^{r, H}_{\\uppercase\\expandafter{\\romannumeral1}-couple}=\\dfrac{1}{\\omega-qQ}\\left(\\mathbf{h^-}\\right)^\\dagger\\mathbf{h^-} \\; ,\n\\end{equation}\nwhere the $h^-_i$ coefficients are linear combinations of the two independent $\\nu_i$ coefficients \\mbox{($i=0,1$)}, given in the Appendix, Eq.~\\eqref{eq:hminus}. It shows an important point in Eq.~\\eqref{eq:currentH} that the current can be positive or negative. This is expected, because for a bosonic field, the electric coupling can trigger superradiance.\n\nFurthermore, from the conservation law of the electric current, one can find an alternative expression for the transmission matrix. Using the scattering matrix $(\\mathbf{S}^{--})$ defined in Chapter~\\ref{ch:NeutralP}, we find\n\\begin{equation}\\label{eq:TF2}\n\\mathbf{T}=\\dfrac{1}{\\omega-qQ}(\\mathbf{S}^{--}\\mathbf{S}^{\\dagger--})^{-1} \\; .\n\\end{equation}\n\nOnce we have obtained the transmission factors, the number and energy fluxes are given by\n\\begin{equation}\\label{eq:HawkFlux}\n\\dfrac{d\\left\\{N,E\\right\\}}{dt d\\omega}=\\dfrac{1}{2\\pi}\\sum_{\\ell,\\zeta} \\dfrac{(2\\ell+1)\\left\\{1,\\omega \\right\\}}{\\exp((\\omega-qQ)\/T_H)-1} \\mathbb{T}_{\\ell,\\zeta} \\;,\n\\end{equation}\nwhere $\\zeta$ labels the mode and $T_H$ is the Hawking temperature which, in our units, is\n\\begin{eqnarray}\nT_H=\\dfrac{(n-1)+(n-3)Q^2}{4\\pi}\\;.\\label{hawkingtemperature}\n\\end{eqnarray}\n\n\n\\section{Numerical Results}\n\\label{chargedhresults}\nWe now present a selection of numerical results for the transmission factor and the corresponding Hawking fluxes. In order to integrate the decoupled and coupled radial equations, we wrote independent codes in \\textsc{mathematica} and in \\textsc{c++}, finding agreement between the two codes. Using them we have generated a set of figures that we now describe.\n\nIn Fig.~\\ref{Superradiance01} transmission factors for different masses, spacetime dimensions and charges are shown to exhibit the superradiance phenomenon. As explained in Chapter~\\ref{ch:intro}, superradiant amplification of a bosonic field in a charged and\/or rotating BH occurs since there is Coulomb and\/or rotational energy that can be extracted without decreasing the BH area. The general condition of superradiance is $\\omega3$ as can be seen on the right panel, where the difference between the positive charge and negative charge flux spectrum is presented for various $n$. This inverted charge splitting effect was also observed for scalars and fermions~\\cite{Sampaio:2009tp}, and it results from the interplay between the thermal factor and the transmission factor appearing in the expression for the number fluxes. Whereas the thermal factor always favors same charge emission, the transmission factor favors opposite charge emission and these factors dominate different parts of the spectrum. We have considered the same parameters as in~\\cite{Sampaio:2009tp}, $Q=|q|=0.3$, wherein scalars and fermions have been studied, to allow for an easy comparison.\n\nIn Fig.~\\ref{FluxVarQ}, we present the number flux dependence on the background charge for different field charge, in the same row, and different spacetime dimensions, in the same column. Consider first the $q=0$ case (middle column); it shows that the fluxes are suppressed\/maintained\/enhanced with the increase of background charge for $n=2$\/$n=3$\/$n=4$. To understand this behavior observe, from the definition of Hawking temperature, Eq.~\\eqref{hawkingtemperature}, that the Hawking temperature is decreased\/maintained\/increased with increasing background charge for $n=2$\/$n=3$\/$n=4$, in these units. Since we are using horizon radius units, as we vary the charge parameter $Q$, we are actually varying the mass of the BH as well as the charge while keeping $r_H=1$. Nevertheless, it is easy to see that, up to a stretching of the horizontal axis, if we fix the BH mass and vary the dimensionful charge, these conclusions for the variation of the height of the curves do not change since the number flux is dimensionless~\\footnote{The integrated flux however will not be the same for all background charges as expected, scaling as $r_H^{-1}$ for fixed BH mass and varying charge.}. For higher temperature, one expects a larger flux of particles, which is indeed the behavior shown in the second column of Fig.~\\ref{FluxVarQ}. Turning on the field charge we observe a more involved behavior. For $n=3$, in which the Hawking temperature does not vary with $Q$, we see in the first\/third column and for sufficiently large energies a monotonic suppression\/enhancement of the Hawking flux when the BH has the opposite\/same charge as the field. This is in agreement with the discussion of the left panel of Figure~\\ref{asymmetryeffect}. For $n=2,4$, varying $Q$ one also varies the Hawking temperature and more complex patterns are observed. Another trend is that the number fluxes increase as the spacetime dimension increases, which may be understood from the existence of more modes that contribute to the transmission factor.\n\\begin{figure*}\n\\begin{center}\n\\begin{tabular}{ccc}\n\\hspace{-4.5mm}\\includegraphics[clip=true,width=0.32\\textwidth]{Figs\/ch5\/Fluxn2M04qm04VarQ.pdf} & \\hspace{-4.5mm}\n\\includegraphics[clip=true,width=0.32\\textwidth]{Figs\/ch5\/Fluxn2M04q0VarQ.pdf} & \\hspace{-5mm}\n\\includegraphics[clip=true,width=0.32\\textwidth]{Figs\/ch5\/Fluxn2M04q04VarQ.pdf}\n\\\\\n\\hspace{-2mm}\\includegraphics[clip=true,width=0.323\\textwidth]{Figs\/ch5\/Fluxn3M04qm04VarQ.pdf} &\n\\hspace{-1mm}\\includegraphics[clip=true,width=0.324\\textwidth]{Figs\/ch5\/Fluxn3M04q0VarQ.pdf} &\n\\hspace{-3mm}\\includegraphics[clip=true,width=0.318\\textwidth]{Figs\/ch5\/Fluxn3M04q04VarQ.pdf}\n\\\\\n\\includegraphics[clip=true,width=0.32\\textwidth]{Figs\/ch5\/Fluxn4M04qm04VarQ.pdf} &\n\\includegraphics[clip=false,width=0.32\\textwidth]{Figs\/ch5\/Fluxn4M04q0VarQ.pdf} &\n\\includegraphics[clip=true,width=0.32\\textwidth]{Figs\/ch5\/Fluxn4M04q04VarQ.pdf}\n\\end{tabular}\n\\end{center}\n\\caption{\\label{FluxVarQ} Number fluxes dependence on the background charge for different spacetime dimensions and field charge, with fixed field mass $\\mu_p=0.4$. We vary the field charge in the same row and vary the spacetime dimension in the same column.\n}\n\\end{figure*}\n\n\\subsection{Mass effect and bulk\/brane emission}\nIn Figure~\\ref{Mass_spin}, we perform a comparison of the effect of introducing a mass term for the various spins which are relevant for the Standard Model brane degrees of freedom. We have used the data of~\\cite{Sampaio:2009tp} for scalars and fermions. Note that for fermions we have multiplied the data by a factor of two to take into account the two helicities of the Dirac field, since here we are also considering all the three modes for the Proca field. The dashed curves are for increasingly larger field mass (we have used the cases $\\mu\\footnote{In this subsection, the parameter $\\mu$, if not specified, referes to the mass for different spin fields, include scalar, fermion and Proca.}=0$, $0.5$ and $1$, as can be seen from the threshold points where the curves start). For $D=n+2=4$ (left panel), we observe a striking similarity between the Proca ($s=1$) flux with the scalar ($s=0$) flux for $\\mu=0$ (except at the high energy tail), which is due to the dominance of the $\\ell=0$ mode. We can see this feature is always true at small energies for larger $n$ (middle and right panels); as we increase $n$, however, higher modes of the Proca field enhance the flux as compared to scalars. The extra modes of the Proca field contributing at higher energy, also explain the fact that the mass suppression is not as large as for Dirac fermions or scalars, as we see from the dashed curves corresponding to $\\mu=0.5$ for example. The high energy behavior contrasts with the low energy behavior, where scalar and Proca fields are dominated by the $s$-wave, whereas Dirac fermions are suppressed since they do not allow an $s$-wave. At high energy, we observe tails which are in the ratio $1:2:3$ following the number of degrees of freedom for the scalar, fermion and Proca fields respectively. This is in agreement with the fact that all transmission factors tend to one at high energy.\n\\begin{figure*}\n\\begin{center}\n\\begin{tabular}{ccc}\n\\includegraphics[clip=true,width=0.33\\textwidth]{Figs\/ch5\/Spincomparisonn2.pdf}\n\\includegraphics[clip=true,width=0.331\\textwidth]{Figs\/ch5\/Spincomparisonn3.pdf}\n\\includegraphics[clip=true,width=0.324\\textwidth]{Figs\/ch5\/Spincomparisonn4.pdf}\n\\end{tabular}\n\\end{center}\n\\caption{\\label{Mass_spin} Variation of the number fluxes for neutral particles with different spins (scalar, fermion and Proca) on the brane. For each type of particles we consider three different masses ($\\mu=0$, $0.5$ and $1$), which can be identified by the starting point of the curve. Note we have included the two helicities for the fermion field and all the three modes for the Proca field (we have used a small mass $\\mu_p=0.01$ for the latter, instead of $\\mu_p=0$).}\n\\end{figure*}\n\nIn the remainder, we combine our results for the Proca field on the brane with those in Chapter~\\ref{ch:NeutralP} for a Proca field in the bulk, to analyze the relative bulk-to-brane emissivity in the neutral case.\n\n\\begin{table}[h]\n\\begin{center}\n\\small\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n & $n=2$ & $n=3$ & $n=4$ & $n=5$ & $n=6$ & $n=7$\n\\\\\n\\hline\n$\\mu_p=0.1$ & 1 & 0.46 & 0.38 & 0.41 & 0.53 & 0.83\n\\\\\n\\hline\n$\\mu_p=0.3$ & 1 & 0.49 & 0.40 & 0.42 & 0.55 & 0.86\n\\\\\n\\hline\n$\\mu_p=0.6$ & 1 & 0.59 & 0.47 & 0.49 & 0.62 & 0.95\n\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\caption{Bulk-to-Brane relative energy emission rates for massive neutral vector fields for different mass $\\mu_p$ in terms of the spacetime dimension $D=2+n$.}\n\\label{bulkbranecomparison}\n\\end{table}\n\nThe total energy rate (or number rate) emitted in Hawking radiation for a given field, is obtained by integrating the fluxes of Eq.~\\eqref{eq:HawkFlux} (or their counterparts in the bulk) over $\\omega$. The bulk-to-brane energy emissivity ratio, for massive neutral Proca fields of different mass is shown in Table~\\ref{bulkbranecomparison} as a function of $n$. We have used the data in Chapter~\\ref{ch:NeutralP} to obtain the total emissivity for bulk fields, as well as the data presented here, for the brane emissivity. The entries of Table~\\ref{bulkbranecomparison}, show clearly that the emission of energy into brane-localized Proca particles is dominant, for $n$ larger than two, which is consistent with the argument that BHs radiate mainly on the brane~\\cite{EHMprl2000}. For fixed mass, as $n$ increases, the bulk-to-brane energy emission ratio initially decreases, reaching a minimum value for an intermediate $n$. However, if one increases $n$ further, the bulk-to-brane energy emission ratio increases again. Furthermore, we have also observed that the bulk-to-brane energy emission ratio increases with the field mass. A similar behavior was observed for scalar fields~\\cite{Harris:2003eg,Kanti:2010mk}. Finally, we found that the bulk-to-brane energy emission ratio for the Proca field is larger than that for the scalar field for all $n$, being more noticeable for large $n$, say $n=6$ and $n=7$ (note the different notation for $D=2+n$ in this chapter as compared with~\\cite{Harris:2003eg,Kanti:2010mk}).\n\n\n\\section{Summary}\n\\label{discussion}\nIn this chapter we have completed the analysis started in Chapter~\\ref{ch:NeutralP}, by computing the transmission factor for a charged Proca field propagating in the background of a charged BH on a brane. Furthermore, this work completes the study of the effect of mass and charge for particles evaporating on the brane, for all spins relevant for the SM in brane world scenarios~\\cite{Sampaio:2009ra,Sampaio:2009tp}.\n\nOne of the main, and novel features arising from considering a charged Proca field on a charged background is the existence of negative transmission factors, a signature of superradiant scattering, which we have presented and described. Our results are consistent with the condition of superradiance as allowed by the area theorem, and we found that generically increasing the field or background charge amplifies the effect (though some inversions are possible at small energies). As we explained in Chapter~\\ref{ch:intro}, it is worth commenting that although superradiant scattering is observed for both rotating and charged BHs, rotating BHs exhibit superradiant instabilities against massive scalar fields, and in particular against the Proca field~\\cite{paniPRL,paniPRD}, whereas charged (non-rotating) BHs do not exhibit analogous instabilities against charged, massive bosonic fields. This will be explored for the Proca field in Chapter~\\ref{ch:ChargedClouds}, by computing the frequencies of quasi-bound states, a study that can also teach us how long lived Proca hair around charged BHs can be.\n\nAn effect observed herein, is that similarly to scalars and fermions, there is an inverted charge splitting effect at small energies for more than one extra spacetime dimension, where particles with charge opposite to the BH are emitted dominantly. Nevertheless, for larger energies, the normal splitting order, favoring the emission of same charge particles as to discharge the BH is restored and overall (by integrating out the flux), this channel tends to discharge the BH. This effect has been suggested to be another signature that could be found in the energy spectrum of charged fermions \\cite{Sampaio:2009tp}. Since fermions in the final state are also produced indirectly from the decay of vector bosons such as $W^{\\pm}$ or the $Z$ particles one may ask whether the effect survives. Here we have verified that the effect is present for $W^{\\pm}$, so at least for the case when the final state decay products are a charged lepton $\\ell^\\pm$ and a neutrino, these contributions will certainly enhance the effect.\n\nIn the neutral case, we have performed two comparisons. First we have compared the effect of the mass and spin on the Hawking fluxes, for all spins in the SM. Our main findings are that the Proca field spectrum departs from being similar to a scalar field in four dimensions and becomes increasingly dominant for larger number of dimensions, peaking and extending towards larger energies. This also means that the mass suppression effect is smaller for the Proca field than it is for scalars and fermions. Second, we have compared the bulk-to-brane emission ratio for Proca fields, confirming brane dominance in general, and the suppression of brane dominance with increasing mass.\n\nThese results can be used to improve the modelling of BH evaporation in TeV gravity scenarios, in the BH event generators~\\cite{Frost:2009cf,Dai:2007ki} that are in use at the ATLAS and CMS experiments to put bounds on extra dimensions in this channel~\\cite{CMS:2012yf,ATLAS-CONF-2011-065,ATLAS-CONF-2011-068,Gingrich:2012vs,Aad:2015mzg}.\n\n\\chapter{Conclusions and outlook}\n\\label{ch:conclusion}\nIn this final chapter we draw our conclusions, and address some open questions\n\nThis thesis covers studies of Hawking radiation and superradiance, for scalar and vector fields in the probe limit, using perturbative methods.\n\nIn Chapter~\\ref{ch:GeneralInFlat}, we have studied the wave equations of a Proca field on spherically symmetric higher dimensional spacetimes. Such background was chosen to avoid the non-separation of variables for the Proca equations in a rotating spacetime; while still being an interesting setup in TeV gravity models. Using the Kodama-Ishibashi formalism, we achieved separation of variables, and obtained a set of coupled equations, as well as some decoupled ones.\n\nThese equations were used to study Hawking radiation, for a neutral Proca field on a $D$-dimensional Schwarzschild black hole in Chapter~\\ref{ch:NeutralP} and a charged Proca field on a brane charged black hole in Chapter~\\ref{ch:ChargedP}. We have designed a numerical strategy to solve the coupled equations and showed that the coupled systems may be treated with an {\\bf S}-matrix type formalism which allows decoupling in the asymptotic regions. This {\\bf S}-matrix was used to define a transmission matrix the eigenvalues of which give us the transmission factors. Then the Hawking fluxes were calculated using the standard formulas. For a neutral Proca field, we found distinctive features by introducing the mass term, such as the lifting of the degeneracy of the two transverse modes in four dimensions, the appearance of longitudinal modes and in particular the $s$-wave. When both background and Proca field charges were included, we observed the existence of superradiant modes, and a charge splitting effect for small energies and for two or more extra dimensions. We also compared the Proca bulk-to-brane ratio of energy emission, showing that most of the energy is emitted on the brane.\n\nIn Chapter~\\ref{ch:ChargedClouds}, we have studied quasi-bound states for both the charged massive scalar field and the charged Proca field in Reissner-Nordstr\\\"om black holes. We established that no such states exist in the superradiant regime for the Proca field, a similar behavior to that known for the scalar field. For both fields, however, decaying quasi-bound states with an arbitrary small imaginary part of the frequency exist and thus which are arbitrarily long lived. In the limit of vanishing imaginary part of the frequency, the fields did not trivialize and we dubbed the corresponding configurations as marginal scalar or Proca clouds, since they were only marginal bound.\n\nA problem which is still open is the study of the Proca field in the Kerr black hole without any approximations. Indeed, this problem has been partially addressed for slow rotation~\\cite{paniPRL,paniPRD}. This is still interesting because of the following open questions: (1) is it possible to construct a modified Newman-Penrose formalism to deal with linear perturbations for massive fields in general? (2) Is it possible to establish a numerical method to solve the type of two dimensional partial differential equation arising in this problem\n\nIn Chapter~\\ref{ch:scalarHD}, we have studied superradiant instabilities for a charged scalar field in a $D$-dimensional Reissner-Nordstr\\\"om-AdS black hole. By employing an analytic matching method and a numerical method, we proved that superradiant instabilities do exist for all $\\ell$ modes in higher dimensions. Inspired by the large $D$ general relativity~\\cite{Emparan:2013moa}, it would also be interesting to develop another analytic treatment to look for superradiant modes.\n\nIn Chapter~\\ref{ch:KerrAdS}, we have studied Maxwell perturbations on Kerr-AdS black holes. From the viewpoint that the AdS boundary may be regarded as a perfectly reflecting mirror, we proposed \\textit{vanishing energy flux} boundary conditions which are physically motivated. Imposing such conditions, we obtained a set of two Robin boundary conditions even for a Schwarzschild-AdS black hole, where only one of them has been reported in the literature. Applying these two boundary conditions to Kerr-AdS black holes, we have studied superradiant instabilities, and observed that the new branch of quasinormal modes may be unstable in a larger parameter space. Our results also showed that superradiant instabilities for the Maxwell field may exist for (moderately) larger black hole size, when comparing with the scalar case.\n\nTo study stationary vector clouds, we have solved the Teukolsky equations at the onset of the superradiant instability. We found that both boundary conditions can yield vector clouds, which are characterized by existence lines in the parameter space. These lines are bounded by pure AdS spaces and the extremal black holes, which differs from that observed for gravitational perturbations, for which only one of the sets of clouds are bounded by the extremal black holes~\\cite{Cardoso:2013pza}. The existence of clouds at the linear level indicates nonlinear hairy black hole solutions, so the open question is to find the nonlinear realization of these vector clouds. There is already a well-known exact black hole family within the Einstein-Maxwell-AdS system: the Kerr-Newman-AdS family. It will then be very interesting to study properties of the new type of ``hairy'' black hole solutions, and understand the interplay between the Kerr-Newman-AdS black holes and the new family of ``hairy'' black holes.\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\n\\chapter{Proca field equations\n\\label{ch:GeneralInFlat}\nIn the first part of this thesis, we are going to study Hawking radiation, for neutral and charged Proca fields; and quasi-bound states, for charged scalar and Proca fields. To explore these problems on a particular background, the first step is to obtain the wave equations for those fields. Two key properties that will allow the study of these wave equations are: separation of variables and decoupling of degrees of freedom. For scalar fields, which are governed by the Klein-Gordon equation, there is only one degree of freedom, and variables in general can be separated on Kerr-like spacetimes~\\cite{Carter:1968rr}. This is in part the reason why scalar fields have been explored so intensively in the literature\\footnote{It is now known that at least one fundamental scalar field exists in Nature with the discovery of the Higgs boson~\\cite{Chatrchyan:2012xdj,Aad:2012tfa}.}. The situation becomes complicated for massive bosonic fields, with spin.\n\nIn the standard model of particle physics (SM), massive spin-1 (Proca) fields describe the $Z$ and $W$ particles, where the former is neutral and the latter is charged. Perturbations of a neutral Proca field were first studied in~\\cite{Pawl:2004bx,Konoplya:2005hr} on spherically symmetric backgrounds. In these studies only the $\\ell=0$ mode was considered since the corresponding wave equation is directly decoupled and separated. Later on by using the Kodama-Ishibashi formalism, see Section~\\ref{sc:KI} for details, we were able to obtain a set of equations on $D$-dimensional spherically symmetric backgrounds, to cover all the possible modes for both neutral and charged Proca fields. This was done in the context of Hawking radiation and quasi-bound states~\\cite{Herdeiro:2011uu,Wang:2012tk,Sampaio:2014swa}. Similar equations for neutral Proca fields on Schwarzschild BHs were shortly after obtained using different perturbation variables, and these equations were applied to study quasinormal modes and quasi-bound states~\\cite{Rosa:2011my}. In the Kerr background, separation of variables for Proca fields can be only achieved in the slow rotation limit, and the corresponding coupled wave equations were obtained in~\\cite{paniPRL,paniPRD}, to study superradiant instabilities. A fully numerical study of the BH-Proca system was recently implemented~\\cite{Zilhao:2015tya}, in nonrotating spacetimes.\n\nIn this chapter, we present a complete set of Proca equations on spherically symmetric backgrounds, by using the Kodama-Ishibashi formalism introduced in Section~\\ref{sc:KI}.\n\n\nWe start by describing scalar and Proca fields, which may be complex and charged under a $U(1)$ electromagnetic field, with the Lagrangian\n\\begin{equation}\n\\mathcal{L}=-(\\mathcal{D}_\\mu\\Psi)^\\ast\\mathcal{D}^\\mu\\Psi-\\mu_s^2\\Psi^\\ast\\Psi -\\dfrac{1}{2}W^\\dagger_{\\mu\\nu}W^{\\mu\\nu}-\\mu_p^2W_\\mu^\\dagger W^\\mu-iqW_\\mu^\\dagger W_\\nu F^{\\mu\\nu} \\;,\\label{Lagrangian}\n\\end{equation}\nwhere $W_{\\mu\\nu}=\\mathcal{D}_\\mu W_\\nu-\\mathcal{D}_\\nu W_\\mu$, $\\mathcal{D}_\\mu\\equiv \\partial_\\mu-i q A_\\mu$ and the field charge is $q$\\footnote{In general, scalar and Proca fields may have different charge. Since they will be studied separately, for simplicity here we denote both charges by the same symbol $q$.}. Scalar and Proca fields are denoted by $\\Psi$ and $W_\\mu$, with mass $\\mu_s$ and $\\mu_p$, respectively. As one observes from the above Lagrangian, both scalar and Proca fields are coupled to the electromagnetic potential $A_\\mu$ through the gauge covariant derivative, while the latter one also couples to the electromagnetic field strength tensor $F_{\\mu\\nu}=\\partial_\\mu A_\\nu-\\partial_\\nu A_\\mu$, as determined by gauge invariance in the SM.\n\n\n\nThe equations of motion for Proca fields, when all the background fields are fixed, are\n\\begin{equation}\n\\dfrac{1}{\\sqrt{-g}}\\mathcal{D}_\\nu\\left(\\sqrt{-g}W^{\\mu\\nu}\\right)+\\mu_p^2W^\\mu+iqW_\\nu F^{\\mu\\nu}=0 \\;.\\label{Procaeq}\n\\end{equation}\nFor the gravitational background with an Einstein symmetric space shown in Eq.~\\eqref{KI:metric}, the Kodama-Ishibashi formalism~\\cite{Kodama:2000fa} can be applied.\nFor a Proca field $W_\\mu=\\{W_a,W_i\\}$, $W_a$ are $m$-scalars, with respect to the Einstein space $\\sigma_n$, so they must obey the scalar eigenvalue equations~\\eqref{intro:eigenscalar}. $W_i$ is a covector field, so it can be decomposed into a scalar $\\Phi$ obeying the scalar eigenvalue equation~\\eqref{intro:eigenscalar}, and a transverse covector $W^{T}_i$ obeying the vector eigenvalue equation~\\eqref{intro:eigenvector}.\n\n\nThis decomposition allows for an expansion of the various degrees of freedom $\\{W_a,\\Phi,W^T_i\\}$ in a basis of harmonics of the Einstein space.\nFurthermore, this decomposition allows for a decoupling of the field equations into an independent vector mode $W^T_i$ and $m+1$ coupled scalar fields for each set of quantum numbers labeling the basis of harmonic functions. Observe, however, that not all these modes correspond to physically independent degrees of freedom, as we will show.\n\\\\\nIn the following we shall consider, separately, two cases according to the scalar eigenvalue, i.e. $\\kappa_s$.\n\n\\section{Modes with $\\kappa_s\\neq 0$}\nExpanding the field equations~\\eqref{Procaeq}, with the decomposition~\\eqref{intro:decom} for $W_i$, and using conditions~\\eqref{intro:eigenscalar} for $\\{W_a,\\Phi\\}$ and~\\eqref{intro:eigenvector} for $W^T_i$, we obtain\n\\begin{align}\n&\\dfrac{\\kappa_s^2}{r^2}B_a+\\dfrac{h_{af}}{r^n}\\mathcal{D}_b\\left[h^{db}h^{cf}r^n\\left(\\mathcal{D}_cB_d-\\mathcal{D}_dB_c\\right)\\right]-\\mu_p^2B_a-\\mathcal{D}_a\\left[\\dfrac{1}{r^{n-2}}\\mathcal{D}_b\\left(r^{n-2}h^{bc}B_c\\right)\\right]+iqB_bF_a^{\\;\\;b}\\nonumber\\\\&=0 \\ , \\hspace{12mm}\\label{Ba}\\\\\n&\\dfrac{1}{r^{n-2}}\\mathcal{D}_a\\left(r^{n-2}h^{ab}B_b\\right)-\\mu_p^2\\Phi=0 \\ , \\label{Ge:PHIeq}\\\\\n&\\left[\\dfrac{1}{r^2}\\left(\\kappa_v^2+\\dfrac{\\hat{R}}{n}\\right)+\\mu_p^2+\\dfrac{1}{r^{n-2}}\\mathcal{D}_a\\left(r^{n-2}h^{ab}\\mathcal{D}_b\\right)\\right]\\hat\nW^{Tj}=0 \\label{WT}\\; ,\n\\end{align}\nwith definition\\footnote{Here we use the variables $B_a$, since $\\Phi$ can be determined by $B_a$ and becomes a non-dynamical degree of freedom, as shown in Eq.~\\eqref{Ge:PHIeq}.} $B_a\\equiv W_a-\\mathcal{D}_a\\Phi$, and $\\hat{W}^{Ti}=r(y)^2W^{Ti}$.\n\nWe consider the spherically symmetric case, by specifying the metric in Eq.~\\eqref{KI:metric} with $\\{y^a\\}=\\{t,r\\}$,\n$|h|=1$, $h_{ab}$ is diagonal,\n$h_{tt}=-1\/h_{rr}\\equiv -V$. Since $\\Phi$ is given by the second equation in terms of the other fields, it is a non-dynamical degree of freedom. In four dimensions, this agrees with the fact that a spin-1 massive field has three possible physical polarizations which in this case will be the two dynamical scalars and the transverse vector. In higher dimensions we will see that the transverse vector on the $n$-sphere contains more (degenerate) polarizations.\n\nWe can factor out the spherical harmonics through the decomposition\n\\begin{align}\nB_a&=\\beta_a^\\Lambda(y)\\mathcal{Y}_\\Lambda(x)\\ , \\nonumber \\\\\n\\hat{W}^{T i}&=q^\\Lambda(y)\\mathcal{Y}^i_{\\Lambda}(x) \\; ,\\label{Lexpansion}\n\\end{align}\nwhere $\\mathcal{Y}_\\Lambda$ is the scalar harmonic function with the definition~\\eqref{intro:ScalatrHarmonicFunction}, $\\mathcal{Y}^i_\\Lambda$ is the vector harmonic function with the definition~\\eqref{intro:HarmonicVector}, and $\\Lambda$ denotes the mode eigenvalues for the corresponding harmonic functions. Furthermore, making the ansatz\n\\[\n\\beta^{\\Lambda}_t=e^{-i\\omega t}\\psi(r) \\ , \\qquad \\beta^{\\Lambda}_r=e^{-i\\omega t}\\dfrac{\\chi(r)}{V} \\ , \\qquad q^{\\Lambda}=e^{-i\\omega t}\\Upsilon(r)\\ ,\n\\]\n and using Eqs.~\\eqref{Ba} and~\\eqref{WT}, we obtain\\footnote{Note that there is a symmetry for the coupled system, i.e. for real $\\omega$, if $(\\psi,\\chi)$ is a solution to the equations, $(-\\psi^\\ast,\\chi^\\ast)$ is also a solution. For complex $\\omega$, this statement still can be made as: if $(\\psi,\\chi)e^{-i\\omega t}$ is a solution to the equations, then $(-\\psi^\\ast,\\chi^\\ast)e^{-i\\omega^\\ast t}$ is also a solution.}\n\\begin{align}\n&\\left[V^2\\dfrac{d}{dr}\\left(\\dfrac{1}{r^{n-2}}\\dfrac{d}{dr}r^{n-2}\\right)+(\\omega+qA_t)^2-\\left(\\dfrac{\\kappa_s^2}{r^2}+\\mu_p^2\\right)V \\right]\\chi-i\\left((\\omega+qA_t) V^{\\prime}+2qA_t\\dfrac{V}{r}\\right)\\psi\\nonumber\\\\& =0\\ ,\\label{originalsys1}\\\\\n&\\left[\\dfrac{V^2}{r^n}\\dfrac{d}{dr}\\left(r^n\\dfrac{d}{dr}\\right)+(\\omega+qA_t)^2-\\left(\\dfrac{\\kappa_s^2}{r^2}+\\mu_p^2\\right)V\\right]\\psi+i\\left(\\dfrac{2\\omega V}{r}-(\\omega+qA_t)V^{\\prime}\\right)\\chi \\nonumber\\\\&=0 \\ ,\\label{originalsys2} \\\\\n&\\left[\\dfrac{V}{r^{n-2}}\\dfrac{d}{dr}\\left(r^{n-2}V\\dfrac{d}{dr}\\right)+(\\omega+qA_t)^2-\\left(\\dfrac{\\kappa_v^2+\\frac{\\hat{R}}{n}}{r^2}+\\mu_p^2\\right)V\\right] \\Upsilon=0 \\label{transverse}\n\\; ,\n\\end{align}\nwhere $A_t$ is the only nonvanishing component of electromagnetic potential due to the spherically symmetric background. Thus we obtain two second order coupled radial equations for $\\left\\{\\psi,\\chi\\right\\}$ and a decoupled equation for $\\Upsilon$. Note that $\\kappa_s^2=\\ell(\\ell+n-1)$ and $\\kappa_v^2=\\ell(\\ell+n-1)-1$ with $\\ell$ starting at zero and one respectively. The third combination is $\\kappa_v^2+\\frac{\\hat{R}}{n}=\\ell(\\ell+n-1)+n-2$.\n\n\nThe manipulations leading to the two coupled equations above are only valid for non-zero $\\mu_p$. In the exactly massless theory, a similar calculation leads to a single decoupled equation for one of the scalar modes which is\n\\begin{equation}\n\\left[V\\dfrac{d}{dr}\\left(\\dfrac{V}{r^{n-2}}\\dfrac{d}{dr}r^{n-2}\\right)-\\dfrac{2qd_rA_tV^2}{(\\omega+qA_t)r^{n-2}}\\dfrac{d}{dr}r^{n-2}\n+\\Big(\\omega+qA_t\\Big)^2-\\dfrac{\\kappa_s^2}{r^2}V\\right] \\chi=0 \\; , \\label{Max}\n\\end{equation}\nwhereas the other mode $\\psi=i V d_r(r^{n-2}\\chi)\/((\\omega+qA_t) r^{n-2})$ becomes non-dynamical\\footnote{Note that the coupled equations have only one dynamical degree of freedom in the massless limit. One can use either $\\psi$ or $\\chi$ to describe the dynamical mode, then the other one becomes non-dynamical and is determined in terms of the dynamical mode.}. Here $d_r\\equiv d\/dr$. The transverse mode -- described by equation (\\ref{transverse}) -- remains the same for any $\\mu_p$; in particular, for $\\mu_p=0$, and (only) $n=2$ it becomes equivalent to (\\ref{Max}). This will be manifest in the numerical results.\n\\section{Modes with $\\kappa_s= 0$}\nFor the exceptional modes with $\\kappa_s=0$, $\\Phi$ does not enter the wave equation so it is a free non-dynamical field. The corresponding equation for $W^{(0)}_a$ is (the superscript denotes it is the exceptional mode)\n\\begin{equation}\n\\dfrac{h_{af}}{r^n\\sqrt{|h|}}\\mathcal{D}_b\\left[h^{db}h^{cf}r^n\\sqrt{|h|}\\left(\\mathcal{D}_cW^{(0)}_d-\\mathcal{D}_dW^{(0)}_c\\right)\\right]+\\mu_p^2W^{(0)}_a+iqW_b^{(0)}F_a^{\\;\\;b}=0 \\; .\n\\end{equation}\nWhen $\\mu_p^2\\neq 0$ one uses an ansatz similar to the previous section to obtain a radial equation for a dynamical degree of freedom\n\\begin{align}\n&\\left[\\dfrac{\\mu_p^2}{r^n}\\dfrac{d}{dr}\\left(\\dfrac{r^nV}{(\\omega+qA_t)^2-\\mu_p^2V}\\dfrac{d}{dr}\\right)+\\dfrac{\\mu_p^2}{V}+\\dfrac{\\mu_p^2qd_rA_t}\n{((\\omega+qA_t)^2-\\mu_p^2V)^2}\\Big(2qVd_rA_t-(\\omega+qA_t)V'\\Big)\\right.\\nonumber\\\\\n&\\left.-\\dfrac{\\omega+qA_t}{r^n((\\omega+qA_t)^2-\\mu_p^2V)}\n\\dfrac{d}{dr}\\Big(qr^nd_rA_t\\Big)\\right]\\psi^{(0)}=0 \\; ,\\label{k0M}\n\\end{align}\nand a non-dynamical one,\n\\begin{equation}\n\\chi^{(0)}=\\dfrac{i V}{(\\omega+qA_t)^2-\\mu_p^2V}\\left((\\omega+qA_t)d_r\\psi^{(0)}-q\\psi^{(0)}d_rA_t\\right)\\;,\\nonumber\n\\end{equation}\nwhere $d_r\\equiv d\/dr$.\nOtherwise, for $\\mu_p^2=0$, we recover the well known result that all the exceptional modes are non-dynamical (see e.g. \\cite{Konoplya:2005hr}).\n\nNow that we have covered all possibilities, several comments are in order. First there is a discrete difference between the small mass limit and the exactly massless theory since we have different sets of equations for each case. This should not be surprising since there is an extra longitudinal mode for massive vector bosons. Second, the equations for the Maxwell theory case are all decoupled, in agreement with previous work \\cite{Page:1976df}.\n\nWith all of the Proca equations at hand, we are going to apply them to study Hawking radiation in Chapter~\\ref{ch:NeutralP} for a neutral Proca field, and in Chapter~\\ref{ch:ChargedP} for a charged Proca field. In Chapter~\\ref{ch:ChargedClouds}, we will then apply them to the computation of quasi--bound states in the Reissner-Nordstr\\\"om BH.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\chapter{Introduction}\n\\label{ch:intro}\n\n\n\n\n\\section{Background and motivation}\n\\label{sc:BM}\nGeneral Relativity (GR), as one of the pillars of modern physics, was established one hundred years ago by Albert Einstein in 1915. It unifies space and time, and in particular gravity is described by the curvature of spacetime. Mathematically, the theory is formulated by the elegant Einstein field equations~\\cite{Einstein:1916vd}, where the geometry is related to the distribution of matter and radiation. These equations were interpreted by John Wheeler in his famous statement that~\\textit{matter tells spacetime how to curve, and spacetime tells matter how to move}. GR has been tested with high accuracy in the regime of weak gravity~\\cite{Will:2014kxa}, while in the strong gravity regime the first direct observation on gravitational waves has been reported recently~\\cite{Abbott:2016blz}. From an analysis of the waves, black holes are identified as the source for such an event~\\cite{Abbott:2016blz}.\n\n\nThe concept of black hole (BH) can be dated back to the end of the eighteenth century. At that time, John Michell~\\cite{Michell:1784xqa} and\nPierre-Simon Laplace~\\cite{Israel:1987ae} put forward an idea that the largest bodies in the universe may be invisible since they are so massive\nthat even light could not escape, which were dubbed as \\textit{dark stars}. This idea was revived by Robert Oppenheimer and his collaborators, more than one century later, in their studies of gravitational collapse, where they concluded that neutron stars above the Tolman-Oppenheimer-Volkoff limit (approximately 1.5 to 3 solar masses) would collapse~\\cite{Oppenheimer:1939ne}. Such collapsed objects were called \\textit{frozen stars}. In 1967, the term \\textit{black hole} was introduced by John Wheeler~\\cite{Wheeler:1998vs}, and since then it was quickly accepted for general use.\n\nFrom a modern viewpoint, BHs are the simplest macroscopic objects in nature, in the sense that they can be uniquely characterized by their mass, spin and charge. This is the well known~\\textit{no hair conjecture}~\\cite{Misner:1974qy}. This conjecture has been circumvented in different contexts, for example in the Einstein-Yang-Mills theory~\\cite{Bizon:1990sr,Kuenzle:1990is,Volkov:1989fi,volkov1990black}, in the Horndeski theory~\\cite{Sotiriou:2013qea,Sotiriou:2014pfa,Babichev:2013cya} and recently in the Einstein-Klein-Gordon system where a Kerr BH with scalar hair was found~\\cite{Herdeiro:2014goa}. Among them, scalar hairy BH solutions are supported by the phenomenon of superradiance~\\cite{Brito:2015oca}, which provides a mechanism to generate hairy BH solutions in general~\\cite{Herdeiro:2014goa,Herdeiro:2014ima}. Another interesting phenomenon in BH physics is the Hawking radiation~\\cite{Hawking:1974rv}. Hawking radiation has been attracting a lot of attention, not only because it relates gravity to quantum theory which may provide a connection to a \\textit{quantum theory} of gravity, but also because it might be visible in high energy processes.\n\n\n\n\n\n\nIn this thesis, we are going to study BHs interacting with scalar and vector fields at the linear level, in the context of \\textit{Hawking radiation}\nand \\textit{superradiance}. The motivation for these studies is as follows.\n\n{\\bf TeV gravity scenarios}\\\\\nThe study of gravitational theories in higher dimensions has been discussed for a century, at least since the works by Nordstr\\\"om~\\cite{Nordstrom:1988fi}, Kaluza~\\cite{Kaluza:1921tu} and Klein~\\cite{Klein:1926tv}. During the last four decades, moreover, the naturalness of extra dimensions within supergravity and string theory made it a topic of intense research within high energy theoretical physics. At the end of the last century, this research led to models that, aiming at solving the hierarchy problem\\footnote{The hierarchy problem refers to the relative weakness of gravity, around sixteen orders of magnitude, by comparing to the other fundamental interactions.}, predicted that the extra dimensions could be very large (or even infinite) in size, as compared to the traditional Planck scale.\n\nWithin such scenario, the true fundamental Planck scale could be as low as the TeV scale~\\cite{Antoniadis:1990ew,Arkani-Hamed:1998rs,Antoniadis:1998ig,Arkani-Hamed:1998nn} so that the formation and evaporation of microscopic BHs could be visible in realistic man-made particle accelerators\\footnote{See, for example, the latest reports from CMS~\\cite{CMS:2015iwr} and ATLAS~\\cite{Aad:2015mzg} in the search for microscopic BHs.}, such as the Large Hadron Collider (LHC). This motivates our study on Hawking radiation. In particular, the second run of the LHC is ongoing, with a center of mass energy of 13 TeV, where such scenarios will be properly tested. Therefore, any improvements on the phenomenology of these models are quite timely.\n\n{\\bf Asymptotically anti-de Sitter spacetimes}\n\\\\\nAnti-de Sitter (AdS) spacetime is the unique maximally symmetric solution of the vacuum Einstein equations with a negative cosmological constant. Asymptotically AdS spacetimes, referring to spacetimes which share the conformal boundary with AdS but may be different in the bulk, have attracted a lot of attention in theoretical physics. One reason is the AdS\/CFT correspondence~\\cite{Maldacena:1997re} which conjectures a duality between gravity in the $d$-dimensional AdS bulk and a quantum conformal field theory living in the $(d-1)$-dimensional conformal boundary. Another reason is the timelike property of the AdS boundary, which leads to interesting novel features, as compared to asymptotically flat spacetimes, such as the weak turbulent instability~\\cite{Bizon:2011gg} and the superradiant instability for massless fields~\\cite{Cardoso:2013pza,Uchikata:2009zz,Wang:2015fgp}.\n\nWith these motivations in mind, in the following we will briefly describe the physical phenomena of Hawking radiation and superradiance.\n\n\n\n\n\n\n\n\\section{Hawking radiation}\n\\label{sc:HR}\nHawking radiation~\\cite{Hawking:1974rv} describes black body radiation that is predicted to be released by BHs, due to quantum effects\nclose to the event horizon. It is one of the most important features arising from quantum field theory in curved spacetime, discovered by Hawking \\mbox{in 1974}. This effect was derived in a semiclassical framework, in the sense that the background geometry is classical (governed by classical gravitational theories) while the propagating fields are quantized. Since Hawking radiation connects classical gravity with quantum theory, it has inspired many works to re-derive Hawking\nradiation through alternative methods, see for example~\\cite{Hawking:1974sw,Hartle:1976tp,Damour:1976jd,Parikh:1999mf,Robinson:2005pd}, with an expectation to get a deeper understanding of gravity itself.\n\nPhysically, Hawking radiation can be understood through an intuitive picture by considering virtual particles generated from the vacuum\\footnote{Note that this interpretation may lead to a flawed intuition on where does Hawking radiation originate~\\cite{Giddings:2015uzr}.}. As it is well known since Dirac, the quantum vacuum is not completely empty but it contains fluctuations which produce particle-antiparticle pairs. Close to the event horizon of a BH, strong gravity effects may separate particle-antiparticle pairs, and if the antiparticle is attracted into the interior of the hole then the particle can escape to infinity thus generating Hawking radiation.\n\nThe phenomenon of Hawking radiation found recently an interesting application in TeV gravity models. In such models, scattering processes with center of mass\nenergy well above \\mbox{the fundamental} Planck scale, should be dominated by classical gravitational interactions~\\cite{'tHooft:1987rb}. Then, for sufficiently small impact parameter, miniature BHs should form in particle collisions, and in particular, Hawking radiation would be the main observable signature~\\cite{Banks:1999gd,Dimopoulos:2001hw}. This motivation led to an intensive study of Hawking radiation from higher-dimensional BHs--\nsee~\\cite{Kanti:2014vsa} for a recent review and reference therein.\n\n\nIf a microscopic BH is produced, it is expected that the decay process can be modeled by the following four phases~\\cite{Giddings:2001bu}, namely\n\\begin{itemize}\n\\item \\textit{Balding phase}: all original ``hair'' inherited from incoming particles (except mass and angular momentum) is lost through gravitational and Hawking\nradiation and, at the end of this stage, the BH is axisymmetric and rotating.\n\\item \\textit{Spin-down phase}: then the BH emits Hawking radiation, losing mass and angular momentum evolving towards the end, into a spherically symmetric BH.\n\\item \\textit{Schwarzschild phase}: the spherically symmetric BH continues to radiate losing its mass, until it reaches the Planck scale.\n\\item \\textit{Planck phase}: the semiclassical approximation of Hawking radiation becomes invalid at this stage, and quantum gravity starts to play a significant role in the BH emission process.\n\\end{itemize}\nIt is believed that the spin-down and Schwarzschild phases will dominate the lifetime of the BH, therefore they are the most promising stages to generate\nobservational signatures of Hawking radiation. Indeed these phases have been modeled in BH event generators, such as \\textsc{charybdis2}\n~\\cite{Frost:2009cf,sampaio2010production} and~\\textsc{blackmax}~\\cite{Dai:2007ki} currently in use at the LHC. These event generators, however, can still be improved.\n\n\n\n\nOne of the Hawking radiation channels that has not been properly addressed in the literature is that of massive vector bosons, both electrically neutral and electrically charged, to describe the emission of $Z$ and $W^{\\pm}$ particles of the Standard Model. As our first goal in this thesis, we are going to study Hawing radiation\nfor both a neutral and a charged Proca field in higher dimensions, to bridge this gap.\n\n\n\n\\section{Superradiance}\n\\label{sc:SR}\nSuperradiance is a phenomenon which refers to a radiation enhancement process in several physical contexts. This term was coined by Dicke, to describe an effect in quantum optics that radiation of a group of emitters could be amplified in a coherent fashion~\\cite{Dicke:1954zz}. In 1971, Zel'dovich~\\cite{zeldovich1,zeldovich2} pointed out that scalar and electromagnetic radiation, impinging on a rotating cylinder with absorbing surfaces\ncan be amplified if the condition\n\\begin{equation}\n\\omegam\\Omega_H$, while $\\delta_1>0$ when $\\omega_{1,0}m\\Omega_H$, but $\\delta_2>0$ when $\\omega_{2,0}$. This is defined for a basis of decoupled modes. In our problem, we have a sub-set of modes, the transverse vector mode, and the $\\ell=0$ ($\\kappa_s=0$) mode, which are decoupled. But we also have a tower of modes which are coupled two by two for each $\\ell>0$, the two scalars $\\psi$ and $\\chi$. It is not obvious how to decouple them for all $r$ through an explicit transformation. Instead, let us try to understand how to extract the relevant information in the asymptotic regions.\n\n\nLet us denote the two coupled fields by a 2-vector $\\mathbf{U^T}=(\\psi,\\chi)$ and represent the coupled system of radial equations through a (linear) second order matrix differential operator $\\mathcal{D}^{(2)}$ acting on $\\mathbf{U}$, i.e. $\\mathcal{D}^{(2)}\\mathbf{U}=0$. The system is coupled because of the off diagonal elements of the $\\mathcal{D}^{(2)}$ operator. To decouple the system we would have to find a transformation of the fields $\\mathbf{U}=\\mathcal{A} \\mathbf{\\bar U}$, such that the new differential operator $\\bar{\\mathcal{D}}^{(2)}=\\mathcal{D}^{(2)}$\\textopenbullet$\\mathcal{A}$ is diagonal, i.e.\n\\begin{equation}\n\\bar{\\mathcal{D}}^{(2)}=\\left(\\begin{array}{cc}\n\\bar{\\mathcal{D}}^{(2)}_1 & 0 \\\\ 0 & \\bar{\\mathcal{D}}^{(2)}_2\n\\end{array}\\right) \\ .\n\\end{equation}\nEven without finding such a transformation explicitly, one can draw some conclusions by assuming its existence\\footnote{In fact, for example, if we consider $\\mathcal{A}$ to be a general $r$-dependent matrix, we can write down two conditions for the four arbitrary functions of such a matrix. Thus, in principle, there is enough freedom.}. In particular we may establish a map between our general solution of the coupled system and the actual decoupled solution, for each of the asymptotic regions (horizon and far field). To find such a map let us first summarize the information we have on the general solution of the coupled system.\n\nIn Section~\\ref{sec:NPnearhorizon} we have found that a general solution is parameterized by four independent coefficients in one of the asymptotic regions; either at the horizon or at infinity. Once we have chosen one set of coefficients, say at the horizon, due to the linearity of the equations, the four independent wave components at infinity are a linear combination of the four coefficients at the horizon. Let us formally denote the ingoing and outgoing wave coefficients at the horizon ($+\/-$ respectively) by\n \\[\\vec{\\mathbf{h}}=({\\mathbf h}^+,{\\mathbf h}^-)=(h^+_{i},h^-_{i}) \\ , \\] where $i=1,2$ since we have two fields.\nSimilarly, the coefficients at infinity are defined as the large $r$ limit of the $\\mathbf{\\Psi}$ field components (up to linear transformation which we will define next), i.e.\n \\[\\vec{\\mathbf{y}}=({\\mathbf y}^+,{\\mathbf y}^-)=(y^+_{i},y^-_{i}) \\ , \\]\nwith $i=1,2$ for $\\psi$ and $\\chi$ respectively. Due to linearity, we can define a scattering matrix\n\\begin{eqnarray}\n\\vec{\\mathbf{y}}=\\mathbf{S} \\vec{\\mathbf{h}} &&\\ \\ \\Leftrightarrow \\ \\ \\left(\\begin{array}{c} {\\mathbf y}^+ \\\\ {\\mathbf y}^- \\end{array} \\right)=\\left(\\begin{array}{c|c} {\\mathbf S}^{++} & {\\mathbf S}^{+-} \\\\ \\hline {\\mathbf S}^{-+} & {\\mathbf S}^{--} \\end{array} \\right)\\left(\\begin{array}{c} {\\mathbf h}^+ \\\\ {\\mathbf h}^- \\end{array} \\right) \\;,\\nonumber\\\\ &&\\ \\ \\Leftrightarrow \\ \\ \\left(\\begin{array}{c} {y}^+_i \\\\ { y}^-_i \\end{array} \\right)=\\sum_{j}\\left(\\begin{array}{c|c} S_{i j}^{++} & S_{i j}^{+-} \\\\ \\hline S_{i j}^{-+} & S_{i j}^{--} \\end{array} \\right)\\left(\\begin{array}{c} h_{j}^+ \\\\ h_{j}^- \\end{array} \\right) \\; ,\n\\end{eqnarray}\nwhich is a set of numbers (depending on energy, angular momentum, etc\\ldots) containing all the information on the scattering process. It can be fully determined by considering specific modes at the horizon and integrating them outwards.\nIn our problem, we have imposed an ingoing boundary condition at the horizon which is simply ${\\mathbf h}^{+}=0$. Then\n\\begin{equation}\\label{NPscattering_ingoing}\n{\\mathbf y}^s={\\mathbf S}^{s-}{\\mathbf h}^{-} \\; .\n\\end{equation}\nTaking the $s=-$ component, and denoting the inverse matrix of ${\\mathbf S}^{--}$ by $({\\mathbf S}^{--})^{-1}$, we invert~\\eqref{NPscattering_ingoing} to obtain the wave at the horizon given the ingoing wave at infinity\n\\begin{equation}\n{\\mathbf h}^{-} =\\left({\\mathbf S}^{--}\\right)^{-1}{\\mathbf y}^{-}\\; .\n\\end{equation}\nInserting this relation back in the $s=+$ component of~\\eqref{NPscattering_ingoing}, we obtain the outgoing wave in terms of the ingoing wave, at infinity\n\\begin{equation}\\label{Reflection}\n{\\mathbf y}^+={\\mathbf S}^{+-}({\\mathbf S}^{--})^{-1}{\\mathbf y}^-\\equiv{\\mathbf R} \\, {\\mathbf y}^-\\; ,\n\\end{equation}\nwhere in the last line we have defined the reflection matrix $\\mathbf{R}$. Before proceeding, we note that there is still some freedom in the definition of the asymptotic coefficients since any (non-singular) linear combination is equally good from the point of view of satisfying the boundary condition. This freedom can be written in terms of 3 matrices $\\mathbf{M}^s$, $\\mathbf{M}_H^-$ relating some new fields (hatted) to the old fields\n\\begin{equation}\n{\\mathbf y}^s=\\mathbf{M}^s\\hat{\\mathbf y}^s \\; ,\\; \\; \\; \\; \\; \\; \\; \\; {\\mathbf h}^{-}=\\mathbf{M}_H^-\\hat{\\mathbf h}^{-} \\; .\n\\end{equation}\nSince this represents the most general parametrization of the solution in the asymptotic regions, there must be a choice which decouples the fields in those regions. To find the correct transformation we need a physical prescription.\n\nTo obtain the transmission factor for the decoupled components, it is instructive to remind ourselves of the calculation of the transmission factor for a single decoupled field. It is defined as the fraction of the incident wave which is transmitted to the horizon. If we look at a wave with energy $\\omega$ (for an observer at infinity), with ingoing\/outgoing amplitudes $Y^{(\\infty)}_{\\mp}$, then~\\cite{Kanti:2004nr}\n\\begin{equation}\n\\mathbb{T}=\\dfrac{|Y^{(\\infty)}_{-}|^2-|Y^{(\\infty)}_{+}|^2}{|Y^{(\\infty)}_{-}|^2}=\\dfrac{\\omega\\left(|Y^{(\\infty)}_{-}|^2-|Y^{(\\infty)}_{+}|^2\\right)}{\\omega |Y^{(\\infty)}_{-}|^2}=\\dfrac{\\mathcal{F}^{in}_H}{\\mathcal{F}^{in}_\\infty} \\ ,\\label{deftrans}\n\\end{equation}\nwhere in the last step we note that $\\mathbb{T}$ can be re-expressed as a ratio between the total incident energy flux $\\mathcal{F}^{in}_H$ (which is the difference between the energy carried by the ingoing wave and the energy of the outgoing wave) and the incident energy flux associated with the ingoing wave at infinity ($\\mathcal{F}^{in}_\\infty$). The former is the flux of energy transmitted down to the horizon.\n\nWe now compute the energy fluxes through a sphere at radius $r$ using the energy-momentum tensor. This will allow us to identify the decoupled fields at infinity and at the horizon, and in particular, the ingoing and outgoing decoupled waves at infinity. Such a flux is shown to be conserved in our background, by using the conservation law for the energy-momentum tensor, combined with the fact that the spatial integral of $T_t^{\\phantom{t} t}$ for each energy eigen-mode is constant. It is defined, evaluated at $r$, as\n\\begin{equation}\\label{eq:FluxR}\n\\mathcal{F}|_r=-\\int_{S^n} d\\Sigma\\, T_t^{\\; r}\n\\end{equation}\nwhere $d\\Sigma$ is the volume element on a $t,r={\\rm constant}$ hyper-surface. The energy-momentum tensor for the complex neutral Proca field is\n\\begin{equation}\nT^{\\mu\\nu}=-\\dfrac{1}{2}\\left(W^{\\dagger \\mu \\alpha}W^{\\nu}_{\\; \\alpha}-\\mu_p^2W^{\\dagger \\mu}W^{\\nu}+c.c.\\right)-\\dfrac{g^{\\mu \\nu}}{2}\\mathcal{L} \\; ,\n\\end{equation}\nup to an irrelevant normalization. If we insert this in~\\eqref{eq:FluxR}, assume a field configuration with a well defined energy\/frequency $\\omega$, and make use of the equations of motion, then, for the non-trivial case of $\\mu_p^2\\neq 0 \\neq \\kappa_s^2$, we obtain\n\\begin{equation}\\label{eq:FluxR2}\n\\mathcal{F}|_r=\\sum_\\Lambda\\dfrac{i\\omega V\\Upsilon^{\\dagger}_\\Lambda}{2r^2}\\dfrac{d\\Upsilon_\\Lambda}{dr}+\\sum_\\Lambda\\left\\{\\dfrac{\\kappa_s^2}{2r^2}\\psi^\\dagger \\chi-\\dfrac{1}{2\\mu_p^2}\\left[\\dfrac{V}{r^n}\\dfrac{d(r^n\\xi^\\dagger)}{dr}-\\dfrac{\\kappa_s^2\\psi^\\dagger}{r^2}\\right]\\left[i\\omega\\xi+\\dfrac{\\kappa_s^2\\chi}{r^2}\\right]\\right\\}+c.c\n\\end{equation}\nwhere $\\Lambda$ denotes the mode eigenvalues for the corresponding harmonic functions, and for convenience we define $\\xi=d_r\\psi+i\\omega\\chi$. Modes with different angular momentum eigenvalues are clearly decoupled, as are the transverse vector mode contributions in the first sum. The terms in the second sum couple two fields for fixed $\\Lambda$. We can compute the flux at infinity and close to the horizon and express it in terms of the asymptotic coefficients in the corresponding region. Focusing on a specific mode and in the coupled part of the flux (second sum in~\\eqref{eq:FluxR2})\n\\begin{equation}\\label{eq:FluxInf}\n\\mathcal{F}^{\\mathrm{coupled}}_\\infty=|y_0^-|^2-|y_0^+|^2+|y_1^-|^2-|y_1^+|^2\\equiv(\\mathbf{y}^-)^\\dagger \\mathbf{y}^--(\\mathbf{y}^+)^\\dagger \\mathbf{y}^+ \\; ,\n\\end{equation}\nwhere $y_i^s$ are linear combinations of the asymptotic coefficients $a_i^s$ given in the Appendix, Eq.~\\eqref{eq:NPyplus}. This choice of $y_i^s$ is already in a form close to decoupled, since we have separated the modulus square of the incident contribution from the reflected contribution, without interference terms. This form is invariant under separate unitary transformation of $\\mathbf{y}^\\pm$. Using the reflection matrix we obtain\n\\begin{equation}\\label{eq:FluxInfT}\n\\mathcal{F}^{\\mathrm{coupled}}_\\infty=(\\mathbf{y}^-)^\\dagger\\left(\\mathbf{1}-\\mathbf{R}^\\dagger\\mathbf{R}\\right) \\mathbf{y}^-\\equiv (\\mathbf{y}^-)^\\dagger\\mathbf{T}\\, \\mathbf{y}^- \\; ,\n\\end{equation}\nwhere we have defined a (hermitian) transmission matrix $\\mathbf{T}$. Note that this matrix is composed of the transmission matrix given in Eq.~\\eqref{Reflection}, and it is a generalization of the transmission factor, defined in Eq.~\\eqref{deftrans}, to the coupled system. The transmission matrix can be diagonalized through a unitary transformation which is the remaining freedom we have for $\\mathbf{y}^-$. In fact we can do even better, and diagonalize the reflection matrix $\\mathbf{R}$ with a bi-unitary transformation using the arbitrary unitary $\\mathbf{M}^{\\pm}$ transformations. Then the fields are manifestly decoupled at infinity, both at the level of the reflection matrix and the transmission matrix. As a consequence, in the decoupled basis, an incident wave is reflected back in the same decoupled mode without interference with the other mode. Finally, the transmission factors are simply the eigenvalues of $\\mathbf{T}$, since they are each associated with a decoupled component.\n\nFurthermore, one can use the conservation law for the flux, to find an alternative expression for the transmission matrix, at the horizon (this will be useful to control numerical errors). The total flux at the horizon is\n\\begin{equation}\\label{eq:FluxH}\n\\mathcal{F}^{\\mathrm{coupled}}_H=\\left(\\mathbf{h^-}\\right)^\\dagger\\mathbf{h^-} \\; ,\n\\end{equation}\nwhere the $h^-_i$ coefficients are linear combinations of the two independent $\\nu_i$ coefficients ($i=0,1$), given in the Appendix, Eqs.~\\eqref{eq:NPhminus}. Eq.~\\eqref{eq:FluxH} establishes the important point that the flux is positive definite, so the transmission factors must be positive definite (as expected since there is no superradiance in Schwarzschild spacetime). Finally, using the relation between $\\mathbf{y}^-$ and $\\mathbf{h}^-$ through\\footnote{Note that the relation between $\\mathbf{h}^-$ and $(\\mathbf{M}^-)^{-1}\\mathbf{y}^-$ can be made diagonal using $\\mathbf{M}^{-}_H$, so the problem is also decoupled at the horizon.} $\\mathbf{S}^{--}$, we find\n\\begin{equation}\\label{eq:FluxH2}\n\\mathbf{T}=(\\mathbf{S}^{--}\\mathbf{S}^{\\dagger--})^{-1} \\; .\n\\end{equation}\nOnce we have obtained the transmission factors, the number and energy fluxes are given by the standard result\n\\begin{equation}\\label{eq:HawkFlux}\n\\dfrac{d\\left\\{N,E\\right\\}}{dt d\\omega}=\\dfrac{1}{2\\pi}\\sum_\\ell\\sum_\\zeta \\dfrac{\\left\\{1,\\omega \\right\\}}{\\exp(\\omega\/T_H)-1}d_\\zeta \\mathbb{T}_{\\zeta} \\ ,\n\\end{equation}\nwhere $\\zeta$ is a label running over the final set of decoupled scalar modes and the transverse mode, and $d_\\zeta$ are the degeneracies of the corresponding spherical harmonics. Labeling the scalar and vector harmonic degeneracies by $d_S$ and $d_V$ respectively we have \\cite{Ishibashi:2011ws}\n\\begin{eqnarray}\\label{eq:degen}\nd_S&=& \\dfrac{(n+2\\ell-1)(n+\\ell-2)!}{(n-1)!\\ell!}\\ , \\\\\nd_V&=& \\dfrac{(n+2\\ell-1)(n+\\ell-1)(n+\\ell-3)!}{(\\ell+1)(\\ell-1)!(n-2)!} \\; .\n\\end{eqnarray}\nThe Hawking temperature in horizon radius units is\n\\begin{equation}\nT_H=\\dfrac{n-1}{4\\pi} \\; .\n\\end{equation}\n\n\n\\section{Results}\n\\label{sec:resultsNP}\nIn this section we present a selection of numerical results to illustrate the behavior of the transmission factors and the corresponding Hawking fluxes. To integrate the coupled and decoupled radial equations, we first wrote test codes in \\textsc{mathematica} and then a code in the \\textsc{c++} language, using the numerical integration routines of the Gnu Standard Library (GSL). Besides using different programming frameworks we have also tested different integration strategies which all agreed within relative numerical errors smaller than 0.1~\\%. In fact, most of our numerical points have a precision which is one order of magnitude better. To check numerical errors we have integrated the radial equations up to a large radius of typically $r=10^4r_H$ and varied this up to a factor of 3 to check the precision. Furthermore we have used the two expressions for the transmission factor from Eqs.~\\eqref{eq:FluxInfT} and~\\eqref{eq:FluxH2} which agree within the quoted precision for almost all energies. The exception is for small energy, where the first definition converges poorly. This can be explained by a simple analysis of propagation of errors combined with the fact that the $\\mathbf{y}^{\\pm}$ coefficients grow very fast as we decrease energy, thus requiring a very large precision for some fine cancellations to occur. The second expression is thus more natural in that limit since it does not need such cancellations and does not require such large precision.\n\nWe have generated several samples of transmission factors, some of which are displayed in Fig.~\\ref{fig:Tfacs}. Hereafter, we shall denominate the partial waves associated to the different modes of the Proca field by $\\ell_1,\\ell_2,\\ell_T$ and $\\ell=0$, where $\\ell_1,\\ell_2$ correspond to the two coupled modes described by Eqs.~\\eqref{originalsys1} and~\\eqref{originalsys2}, $\\ell_T$ to the decoupled mode described by Eq.~\\eqref{transverse} and $\\ell=0$ to the $\\kappa_s=0$ mode, described by Eq.~\\eqref{k0M}. Moreover, partial waves associated to the Maxwell field shall be denoted by $\\ell_E$, and are described by Eq.~\\eqref{Max}.\n\\afterpage{\n\\begin{figure}[t]\n\\includegraphics[scale=0.68,clip=true,trim= 0 0 0 0]{Figs\/ch4\/M001n2Lvar.pdf}\n\\hspace{-1.5mm}\n\\includegraphics[scale=0.68,clip=true,trim= 0 0 0 0]{Figs\/ch4\/M001n3Lvar.pdf}\n\\hspace{-2.5mm}\n\\includegraphics[scale=0.68,clip=true,trim= 0 0 0 0]{Figs\/ch4\/M001n4Lvar.pdf} \\vspace{3mm}\\\\\n\\includegraphics[scale=0.68,clip=true,trim= 0 0 0 0]{Figs\/ch4\/M03n2Lvar.pdf} \\hspace{-2.5mm} \\includegraphics[scale=0.68,clip=true,trim= 0 0 0 0]{Figs\/ch4\/M03n3Lvar.pdf} \\hspace{-2.5mm} \\includegraphics[scale=0.68,clip=true,trim= 0 0 0 0]{Figs\/ch4\/M03n4Lvar.pdf} \\vspace{3mm} \\\\\n\\includegraphics[scale=0.68,clip=true,trim= 0 0 0 0]{Figs\/ch4\/M1n2Lvar.pdf} \\hspace{-2.5mm} \\includegraphics[scale=0.68,clip=true,trim= 0 0 0 0]{Figs\/ch4\/M1n3Lvar.pdf} \\hspace{-2.5mm} \\includegraphics[scale=0.68,clip=true,trim= 0 0 0 0]{Figs\/ch4\/M1n4Lvar.pdf}\n\\caption{\\label{fig:Tfacs} {\\em Transmission factors:} The three rows of panels, show the first few partial waves contributing to the Hawking spectrum. Each row corresponds to a fixed mass and each column to a fixed dimension. In particular, the first row shows the small mass limit of the Proca theory in order to compare it with Maxwell's theory.\n}\n\\end{figure}\n\\clearpage\n}\n\nIn the top row panels of Fig.~\\ref{fig:Tfacs}, we show the partial wave contributions for $n=2,3,4$ in the zero mass limit. Some general properties are as follows. The $\\mathbb{T}_\\ell$ curve becomes shifted towards higher frequencies both as $\\ell$ is increased, for $n$ fixed, and as $n$ is increased, for $\\ell$ fixed. The former can be understood from standard geometrical optics arguments. Moreover, for this choice, there is always a numerical coincidence between one of the partial waves ($\\ell_1$) obtained from the two coupled fields and the electromagnetic partial wave $\\ell_E$. The $\\ell=0$ and $\\ell_2$ modes are always absent in the Maxwell theory, so they can be associated with the longitudinal polarization of the massive vector field. Similarly, the $\\ell_T$ and $\\ell_1$ partial waves are associated with the transverse polarizations of the field. A qualitative dependence on dimension is that for $n=2$, $\\ell_T$ and $\\ell_1$ (or $\\ell_E$) modes are all equal. Curiously, this is in agreement with the fact that they describe the same number of transverse degrees of freedom as can be seen from the degeneracies~\\eqref{eq:degen} specialized for $n=2$. This degeneracy is lifted for $n>2$.\n\nFor non-zero mass (middle and bottom row panels of Fig.~\\ref{fig:Tfacs}), the degeneracy observed for $n=2$ in the massless limit is lifted. Also, we observe, for all $n$, that modes with higher $\\ell$ partial waves (especially $\\ell_1$ modes) become a more dominant contribution at lower energies, as compared to lower $\\ell$ partial waves of other modes. In particular for $\\mu_p=1$, the transmission factor for $\\ell_1$ becomes the largest for small energy. This effect of excitation of sub-dominant partial waves is well known to exist for example as we increase $n$ (and we can also observe such effect in our plots) as well as with the introduction of BH rotation \\cite{sampaio2010production}. If this effect persists cumulatively on a rotating background, then we may have enhanced angular correlations for massive Proca fields emitted from the BH, since higher $\\ell$ partial waves are less uniform.\n\nAnother outstanding point is that for large mass, when $n=2, 3$, it can be seen that the transmission factor starts from a constant non-zero value at the threshold $\\omega=\\mu_p$ ($k=0$), at least for small $\\ell$. We have checked that this does not happen for $n\\geq 4$ for masses as large as $\\mu_p=10\\sim 15$, where the curves always asymptote smoothly to zero at $k=0$. Note that the parameter in the radial equations is $\\mu_p^2$ so these are very large masses. A possible explanation for this phenomenon can be motivated from considerations about the range of the gravitational field in Rutherford scattering. In $n=2$, the total cross-section for Rutherford scattering diverges, so the Newtonian gravitational potential is long ranged. This means that the effective size of the gravitational potential is infinite. The same happens in $n=3$ but only at zero momentum $k=0$. This indicates that a possible reason is that an incident wave at infinity with a very small momentum will still be sufficiently attracted by the gravitational field so that a constant non-zero fraction is still absorbed by the potential. In particular we note that some of the radial equations are similar in form to those obeyed by massive scalar and massive fermion fields, so the same effect exists for such fields. To our knowledge, this feature has not been noted or discussed in the literature. The only exception is the paper by Nakamura and Sato~\\cite{Nakamura:1976nc} in four dimensions, where it is claimed that the reflection factor for a scalar field always goes to $1$ at $\\omega=\\mu_s$ (and thus the transmission factor goes to zero). Their result seems, however, inconsistent with Figs.~1,~2 and~3 of the paper by Page~\\cite{Page:1977um} (also in four dimensions), where the Hawking fluxes for massive fermions become constant at the $k=0$ threshold (in agreement with our result).\n\nOnce we obtain the transmission factors, the computation of the Hawking fluxes~\\eqref{eq:HawkFlux} follows straightforwardly by summing up partial waves with the appropriate degeneracy factors~\\eqref{eq:degen}. We have chosen to show the flux of number of particles. The flux of energy has similar features and is simply related by multiplying each point in the plots by $\\omega$.\n\nIn Fig.~\\ref{fig:NfluxM0} we compare the Hawking fluxes of the Maxwell theory with the small mass limit of the Proca theory. For the particular case of $n=2$ we have reproduced the results by Page~\\cite{Page:1976ki} for the electromagnetic field and found very good agreement. All panels show a red solid curve corresponding to the total Hawking flux summed up over partial waves. The partial waves included in the sum are also represented, scaled up by the appropriate degeneracy factor. As claimed in the discussion of the transmission factors, as we increase $n$, partial waves with larger $\\ell$ become more important for both Maxwell and Proca fields. One can clearly see that there is a large contribution to the total flux from the longitudinal degrees of freedom, since the vertical scales are larger for the Proca field. In particular the $\\ell=0$ mode enhances the spectrum greatly at small energies. Note that these extra contributions associated with the longitudinal degrees of freedom cannot in general (for arbitrary mass) be described by a scalar field, since there is always a contribution from the coupled modes $\\ell_1,\\ell_2$. That is, however, the approximation done so far in BH event generators, where the $W$ and $Z$ fields Hawking spectra in use are those of the electromagnetic field (for transverse polarizations) and a scalar field (for the longitudinal polarization). Thus, our methods can be readily applied to improve this phenomenological modeling.\n\\begin{figure}[t]\n\\includegraphics[scale=0.65,clip=true,trim= 0 0 0 0]{Figs\/ch4\/NfluxMaxwelln2.pdf}\\hspace{0mm} \\includegraphics[scale=0.65,clip=true,trim= 0 0 0 0]{Figs\/ch4\/NfluxMaxwelln3.pdf} \\hspace{-2.3mm} \\includegraphics[scale=0.64,clip=true,trim= 0 0 0 0]{Figs\/ch4\/NfluxMaxwelln4.pdf} \\vspace{0mm}\\\\\n\\includegraphics[scale=0.653,clip=true,trim= 0 0 0 0]{Figs\/ch4\/NfluxM001n2.pdf} \\hspace{-2.5mm} \\includegraphics[scale=0.653,clip=true,trim= 0 0 0 0]{Figs\/ch4\/NfluxM001n3.pdf} \\hspace{-2.5mm} \\includegraphics[scale=0.653,clip=true,trim= 0 0 0 0]{Figs\/ch4\/NfluxM001n4.pdf} \\vspace{0mm}\n\\caption{\\label{fig:NfluxM0} {\\em Number fluxes for $\\mu_p=0$ (top panels) and $\\mu_p\\rightarrow 0$ (bottom panels):} The red solid curve of the top panels shows the Hawking flux of particles summed over the dominant partial waves for the Maxwell theory. The different partial waves are multiplied by the corresponding degeneracies. In the bottom panels the small (but non zero) $\\mu_p$ limit of the Proca theory is shown for comparison. The $\\oplus$ symbol denotes the addition of modes which are numerically equal.}\n\\end{figure}\n\nIn Fig.~\\ref{fig:NfluxComparison} we show the variation of the total number flux with $n$ and $\\mu_p$. The left panel shows the expected variation with $\\mu_p$: that the flux not only gets cutoff at the energy threshold $\\omega=\\mu_p$, but it is also suppressed with $\\mu_p$ (the same holds for $n>2$). This is the same behavior as found in~\\cite{Sampaio:2009tp,Sampaio:2009ra}. As pointed out already, in event generators massive vector particles are modeled using the Hawking fluxes for the Maxwell field and a massless scalar, with a cutoff at the mass threshold. In \\cite{Sampaio:2009ra,Sampaio:2009tp} it was shown that simply imposing a sharp cut-off on the fluxes of massless scalars and fermions over-shoots the real amount of Hawking radiation emitted in the massive scalar and fermion channel. Qualitative inspection of our results suggests a similar effect for the $W$ and $Z$ channels in the evaporation. A quantitative comparison, however, requires a consideration of a Proca field confined to a thin brane, which will be studied in Chapter~\\ref{ch:ChargedP}. The middle and right panels show variation with $n$. In addition to the well known large scaling of the area under the curve and the shift of the spectrum to larger energies, we can also see that more partial waves start contributing to the shape of the curve which becomes more wavy. This is particularly true because the degeneracy factors for fixed $\\ell$ increase rapidly with $n$, which is a consequence of the larger number of polarizations available for a vector boson in higher dimensions. Finally, regarding $n=2,3$ we confirm the feature that the flux becomes a constant at $k=0$. This can be seen more clearly in the right panel in a logarithmic scale where the lines for $n\\geq 4$ curve down very sharply around that point, whereas for $n=2,3$ they tend to a constant.\n\\begin{figure}[t]\n\\includegraphics[scale=0.652,clip=true,trim= 0 0 0 0]{Figs\/ch4\/NfluxMVarn2.pdf} \\hspace{-2.5mm} \\includegraphics[scale=0.652,clip=true,trim= 0 0 0 0]{Figs\/ch4\/NfluxM1nVar.pdf} \\hspace{-2.5mm} \\includegraphics[scale=0.652,clip=true,trim= 0 0 0 0]{Figs\/ch4\/NfluxM1nVarLog.pdf} \\vspace{0mm}\n\\caption{\\label{fig:NfluxComparison} {\\em Number fluxes for various $\\mu_p$ and $n$:} (Left panel) Variation of the flux of particles for fixed $n=2$ and variable mass. (Middle and right panels) Variation of the flux with $n$ in a linear and logarithmic scale respectively. The logarithmic scale shows more clearly that the limiting flux at $k=0$ is finite for $n=2,3$.}\n\\end{figure}\n\n\n\\section{Summary}\n\\label{sec:sumProneu}\n\n\nIn this chapter, we have studied Hawking radiation for a neutral Proca field, by solving the coupled wave equations as well as decoupled equations numerically, on a $D$-dimensional Schwarzschild BH. Our results exhibit distinctive features as we introduce the mass term, such as the lifting of the degeneracy of the two transverse modes in four dimensions, the appearance of longitudinal mode contributions (absent for Maxwell's theory) and in particular the $s$-wave. As we have shown, there is a large contribution from the longitudinal modes, to the Hawking fluxes. \n\nOne feature that appears not to have been discussed in the literature is that in four and five spacetime dimensions, the transmission factor has a non-vanishing value in the limit of zero spatial momentum. We also find the expected suppression with mass of the Proca field, but perhaps the most relevant feature is to notice the increasing importance of the longitudinal modes and larger $\\ell$ partial waves.\n\nOur results could be applied to improve the model used in the \\textsc{charybdis2} Monte Carlo event generator \\cite{Frost:2009cf}. This simulates the production and decay of higher dimensional BHs in parton-parton collisions, a scenario which is being constrained at the second run of the LHC. It is therefore quite timely to improve the phenomenology of these models. Indeed our knowledge of Hawking evaporation process can still be improved greatly through the numerical study of various wave equations in BH backgrounds, which approximate the ones that could be produced at the LHC. This is illustrated by our results in this study, which alert for the importance of modelling the longitudinal modes correctly, instead of treating them as decoupled scalars as in current BH event generators.\n\n\n\n\n\\chapter{Preliminaries}\n\\label{ch:prelim}\n\n\nWe start in this chapter by introducing the mathematical tools to deal with perturbations of test fields around BHs, as the foundation to perform the study for Proca and Maxwell fields. Two types of perturbation methods, the Kodama-Ishibashi formalism and the Newman-Penrose formalism, will be illustrated, respectively, in the following.\n\nThroughout this thesis we will use the signature $(-,+,...,+)$ and natural units $G=c=\\hbar=1$, unless explicitly stated otherwise.\n\n\n\\section{Kodama-Ishibashi formalism}\n\\label{sc:KI}\nThe Kodama-Ishibashi (KI) formalism~\\cite{Kodama:2000fa,Kodama:2003jz,Ishibashi:2003ap,Kodama:2003kk} (for a review see~\\cite{Kodama:2007ph,Ishibashi:2011ws}), is the generalization of the Regge-Wheeler-Zerilli formalism~\\cite{Regge:1957td,Zerilli:1970se} to higher dimensions. This method is applicable to any higher dimensional spacetime with maximal symmetry whose manifold structure can be locally written as a warped product between a Lorentzian manifold and an Einstein space. The basic idea of this method is to classify the perturbations into different types (scalar, vector and tensor types), based on their tensorial behavior in the Einstein space.\n\nTo be specific, let us consider the following gravitational background with the manifold structure $\\mathcal{M}=\\mathcal{N}\\times\\mathcal{K}$ in the form~\\cite{Kodama:2000fa,Kodama:2007ph,Ishibashi:2011ws}\n\\begin{equation}\ng_{MN}dx^Mdx^N=h_{ab}(y)dy^a dy^b+r(y)^2d\\sigma_n^2 \\;,\\label{KI:metric}\n\\end{equation}\nwhere $x^M=(y^a,z^j)$. Note that the Lorentzian manifold is denoted by $\\mathcal{N}$ with metric $h_{ab}$, and the Einstein space is denoted by $\\mathcal{K}$ with constant curvature $K (K=0,\\pm1)$ and metric $\\sigma_{ij}$,\n\\begin{equation}\nd\\sigma_n^2=\\sigma_{ij}(z)dz^idz^j \\; .\n\\end{equation}\nThen the Riemann tensor and Ricci tensor on an Einstein space are given by\n\\begin{equation}\n\\hat{R}_{ijkl}=K (\\sigma_{ik}\\sigma_{jl}-\\sigma_{il}\\sigma_{jk})\\;,\\;\\;\\;\\;\\;\\;\\hat{R}_{ij}=(n-1)K \\sigma_{ij}\\;.\n\\end{equation}\nWe use indices $\\{a,b,c,\\ldots\\}$ for the first set of coordinates, $\\{y^a\\}$, spanning on the $m$-dimensional space with metric $h_{ab}(y)$; and indices $\\{i,j,k,\\ldots\\}$ for the second set of coordinates, $\\{z^i \\}$, spanning on the $n$-dimensional Einstein space. Then the spacetime dimension is $d=m+n$. We denote the covariant derivatives, the Christoffel connection coefficients and the Riemann tensors on the manifolds $\\{\\mathcal{M}$, $\\mathcal{N}$, $\\mathcal{K}\\}$, by $\\{\\nabla_M$, $D_a$, $\\hat{D}_i\\}$, $\\{\\Gamma^M_{NL}, \\bar{\\Gamma}^a_{bc}, \\hat{\\Gamma}^i_{jk}\\}$, and $\\{R_{MNLS}, \\bar{R}_{abcd}, \\hat{R}_{ijkl}\\}$, respectively. We also define the Laplace operator on the Einstein space as $\\hat{\\Delta}=\\hat{D}_i\\hat{D}^i$.\nThe metric form in Eq.~\\eqref{KI:metric} covers several interesting cases such as $2+n$-dimensional spherically symmetric BHs or a singly rotating BH in $4+n$-dimensions.\n\nThe expressions of $\\Gamma^M_{NL}$ and $R^M_{\\;\\;NLS}$ can be written in terms of the corresponding quantities on the manifold $\\mathcal{N}$ with metric $h_{ab}(y)$ and on the Einstein space with metric $\\sigma_{ij}$~\\cite{Kodama:2000fa}, i.e.\n\\begin{equation}\n\\Gamma^a_{bc}=\\bar{\\Gamma}^a_{bc}\\;,\\;\\;\\;\\Gamma^a_{ij}=-rD^ar\\sigma_{ij}\\;,\\;\\;\\;\\Gamma^i_{aj}=\\dfrac{D_ar}{r}\\delta^i_j\\;,\\;\\;\\;\\Gamma^i_{jk}=\\hat{\\Gamma}^i_{jk}\\;,\n\\end{equation}\nwhere the other components of $\\Gamma^M_{NL}$ vanish, and\n\\begin{align}\n&R^a_{\\;\\;bcd}=\\bar{R}^a_{\\;\\;bcd}\\;,\\;\\;\\;R^i_{\\;\\;ajb}=-\\dfrac{D_aD_br}{r}\\delta^i_j\\;,\\;\\;\\;R^a_{\\;\\;ibj}=-\\dfrac{D^aD_br}{r}g_{ij}\\;,\\nonumber\\\\\n&R^i_{\\;\\;jkl}=(K-D_arD^ar)(\\delta^i_k\\sigma_{jl}-\\delta^i_l\\sigma_{jk})\\;.\n\\end{align}\nThen the Ricci tensors and Einstein tensors can be derived directly~\\cite{Kodama:2000fa}\n\\begin{equation}\nR_{ab}=\\bar{R}_{ab}-\\dfrac{n}{r}D_aD_br\\;,\\;\\;\\;R_{ai}=0\\;,\\;\\;\\;R_{ij}=\\left(-\\dfrac{\\bar{\\Box} r}{r}+(n-1)\\dfrac{K-D_arD^ar}{r^2}\\right)g_{ij}\\;,\n\\end{equation}\nwhere we have defined $\\bar{\\Box}\\equiv D^aD_a$, and\n\\begin{align}\n&G_{ab}=\\bar{G}_{ab}-\\dfrac{n}{r}D_aD_br-\\left(\\dfrac{n(n-1)}{2r^2}(K-D_arD^ar)-\\dfrac{n}{r}\\bar{\\Box} r\\right)g_{ab}\\;,\\\\\n&G_{ij}=\\left(-\\dfrac{\\bar{R}}{2}-\\dfrac{(n-1)(n-2)}{2r^2}(K-D_arD^ar)+\\dfrac{n-1}{r}\\bar{\\Box} r\\right)g_{ij}\\;,\\\\\n&G_{ai}=0\\;,\n\\end{align}\nwith the definition $G_{MN}=R_{MN}-\\tfrac{1}{2}g_{MN}R$.\n\nTo write down the equations of motion, we shall decompose the perturbations in terms of their tensorial harmonics on $\\mathcal{K}$~\\cite{Kodama:2007ph,Ishibashi:2011ws}.\\\\\nFor a vector field $v_i$, it can be uniquely decomposed into a scalar field $v^{(s)}$ and a transverse vector field $v^{(t)}_i$ as\n\\begin{equation}\nv_i=\\hat{D}_iv^{(s)}+v^{(t)}_i\\;,\\;\\;\\;\\;\\;\\;\\hat{D}_iv^{(t)i}=0\\;,\\label{intro:decom}\n\\end{equation}\nwhere $v^{(s)}$ and $v^{(t)}_i$ satisfy the corresponding scalar and vector eigenvalue equations\n\\begin{align}\n&(\\hat{\\Delta}+\\kappa_s^2)v^{(s)}=0\\;,\\label{intro:eigenscalar}\\\\\n&(\\hat{\\Delta}+\\kappa_v^2)v^{(t)}_i=0\\;,\\label{intro:eigenvector}\n\\end{align}\non an Einstein space with spherical topology, with $\\kappa_s^2=\\ell(\\ell+n-1)$ and \\mbox{$\\kappa_v^2=\\ell(\\ell+n-1)-1$}. Note that the angular momentum quantum number, $\\ell$, starts from zero in the scalar eigenvalue $\\kappa_s$ and one in the vector eigenvalue $\\kappa_v$, respectively. Then taking a derivative $\\hat{D}_i$ on Eq.~\\eqref{intro:decom}, we have the relation\n\\begin{equation}\n\\hat{\\Delta}v^{(s)}=\\hat{D}_iv^i\\;,\n\\end{equation}\nwhich determines $v^{(s)}$ from Eq.~\\eqref{intro:eigenscalar}, and $v^{(t)}_i$ from Eq.~\\eqref{intro:decom} after $v^{(s)}$ is obtained.\n\nThe scalar and vector harmonic functions on $n$-spheres, used in Eqs.~\\eqref{intro:eigenscalar} and~\\eqref{intro:eigenvector}, are defined as follows~\\cite{Ishibashi:2011ws}. Let us denote the homogeneous cartesian coordinates on $n$-spheres by $\\Omega^A$($A=1,\\cdots,n+1$), and define the function $Y_{{\\bf a}}$ by\n\\begin{equation}\nY_{{\\bf a}}(\\Omega)=a_{A_1\\cdots A_\\ell}\\Omega^{A_1}\\cdots \\Omega^{A_\\ell}\\;,\n\\label{intro:ScalatrHarmonicFunction}\n\\end{equation}\nin terms of a constant tensor ${\\bf a}=(a_{A_1\\cdots A_\\ell})$ ($A_1,\\cdots,A_\\ell=1,\\cdots,n+1$). Then $Y_{{\\bf a}}$ is a scalar harmonic function with the eigenvalue $\\kappa_s^2$ if and only if $\\bf{a}$ satisfies the conditions\n\\begin{equation}\na_{A_1\\cdots A_\\ell}=a_{(A_1\\cdots A_\\ell)}\\;,\\;\\;\\;a_{A_1\\cdots A_{\\ell-2}}{}^B{}_B=0\\quad (\\ell\\ge2)\\;.\n\\end{equation}\nSimilarly, we can define the vector field $V_{{\\bf b}}^i$ by\n\\begin{equation}\nV_{{\\bf b}}^i=b_{A_1\\cdots A_\\ell ;B}\\Omega^{A_1}\\cdots\\Omega^{A_\\ell }\n \\hat{D}^i\\Omega^B \\;,\n\\label{intro:HarmonicVector}\n\\end{equation}\nin terms of a constant tensor ${\\bf b}=(a_{A_1\\cdots A_\\ell ;B})$($A_1,\\cdots,A_\\ell,B=1,\\cdots,n+1$). Then, $V_{{\\bf b}}^i$ is a vector harmonic function on $n$-spheres with eigenvalue $\\kappa_v^2$, if and only if the constant tensor ${\\bf b}$ satisfies the conditions\n\\begin{equation}\nb_{A_1\\cdots A_\\ell ;B}=b_{(A_1\\cdots A_\\ell );B}\\;,\\;\\;\\;b_{A_1\\cdots A_{\\ell -2}i}{}^i{}_{;B}=0\\;,\\;\\;\\;b_{(A_1\\cdots A_\\ell ;A_{\\ell +1})}=0\\;.\n\\end{equation}\nFor more details on these harmonic functions on $n$-spheres and the corresponding properties, we refer readers to~\\cite{Ishibashi:2011ws}.\n\n\n\n\n\n\n\n\n\n\\section{Newman-Penrose formalism}\n\\label{sc:NP}\nThe Newman-Penrose formalism~\\cite{Newman:1961qr}, as the name indicates, was developed by Newman and Penrose in 1962, as an alternative way to formulate field equations, such as the Einstein equations and the Maxwell equations. This formalism is extremely useful in various contexts in GR, for example to construct exact solutions of the Einstein equations~\\cite{Stephani:2003tm}, to study perturbations of massless test fields on various BH backgrounds~\\cite{Chandrasekhar:1985kt,Frolov:1998wf}, and to extract gravitational radiation in numerical relativity~\\cite{baumgarte2010numerical}. For the problems we are interested in this thesis, we focus on the application of this formalism in the context of perturbation theory.\n\nAs first exhibited in the celebrated work of Teukolsky~\\cite{Teukolsky:1972my}, linear perturbations of gravitational and electromagnetic fields on the Kerr background both separate and decouple, in terms of the Newman-Penrose variables. This was subsequently generalized to rotating BHs with a cosmological constant~\\cite{Khanal:1983vb,Wu:2003qc,Yoshida:2010zzb,Dias:2012pp}. In this section, we review the Newman-Penrose formalism with application to the Maxwell field on Kerr-AdS BHs, and further present some new ingredients which have not been derived in the literature, in the presence of a cosmological constant, with details, including\n\\begin{itemize}\n\\item the derivation of Teukolsky-Starbinski identities, which was given in~\\cite{Wu:2003qc} without proof,\n\\item the derivation for a complete set of solutions for the Maxwell field, in particular for $\\Phi_1$, which is relevant for proving Appendix~\\ref{app:angmomflux}.\n \n\\end{itemize}\n\n\n\n\n\n\n\n\n\n\n\n\\subsection{Basics}\nIn order to introduce the Newman-Penrose formalism, we first define a complex null tetrad $\\{l^\\mu,n^\\mu,m^\\mu,\\bar{m}^\\mu\\}$, where the normalization conditions\n\\begin{equation}\nl_\\mu n^\\mu=-1\\;,\\;\\;\\;\\;\\;\\;m_\\mu \\bar{m}^\\mu=1\\;,\\label{tetrad1}\n\\end{equation}\nare satisfied, and all the other scalar products vanish. Note that here $\\bar{m}^\\mu$ is the complex conjugate of $m^\\mu$.\nThe tetrad is related with the metric\\footnote{Note that this relation depends on the conventions.}\n\\begin{equation}\ng_{\\mu\\nu}=-l_\\mu n_\\nu-l_\\nu n_\\mu+m_\\mu\\bar{m}_\\nu+m_\\nu\\bar{m}_\\mu\\;.\\label{teme1}\n\\end{equation}\n\nThen spin coefficients are defined in terms of the tetrad\n\\begin{align}\n-\\kappa&=l_{\\mu;\\nu}m^\\mu l^\\nu\\;,\\;\\;\\;-\\rho=l_{\\mu;\\nu}m^\\mu \\bar{m}^\\nu\\;,\\;\\;\\;-\\sigma=l_{\\mu;\\nu}m^\\mu m^\\nu\\;,\\;\\;\\;-\\tau=l_{\\mu;\\nu}m^\\mu n^\\nu\\;,\\nonumber\\\\\n\\mu&=n_{\\mu;\\nu}\\bar{m}^\\mu m^\\nu\\;,\\;\\;\\;\\nu=n_{\\mu;\\nu}\\bar{m}^\\mu n^\\nu\\;,\\;\\;\\;\\;\\;\\;\\lambda=n_{\\mu;\\nu}\\bar{m}^\\mu \\bar{n}^\\nu\\;,\\;\\;\\;\\;\\;\\;\\pi=n_{\\mu;\\nu}\\bar{m}^\\mu l^\\mu\\;,\\nonumber\\\\\n-\\epsilon&=\\frac{1}{2}(l_{\\mu;\\nu}n^\\mu l^\\nu-m_{\\mu;\\nu}\\bar{m}^\\mu l^\\nu)\\;,\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;-\\beta=\\frac{1}{2}(l_{\\mu;\\nu}n^\\mu m^\\nu-m_{\\mu;\\nu}\\bar{m}^\\mu m^\\nu)\\;,\\nonumber\\\\\n-\\gamma&=\\frac{1}{2}(l_{\\mu;\\nu}n^\\mu n^\\nu-m_{\\mu;\\nu}\\bar{m}^\\mu n^\\nu)\\;,\\;\\;\\;\\;\\;\\;\\;\\;-\\alpha=\\frac{1}{2}(l_{\\mu;\\nu}n^\\mu \\bar{m}^\\nu-m_{\\mu;\\nu}\\bar{m}^\\mu \\bar{m}^\\nu)\\;.\\label{spincoeffs}\n\\end{align}\n\nNext we introduce the projection of the covariant derivatives in the null tetrad vectors, with the following notations\n\\begin{equation}\nD=l^\\mu\\partial_\\mu\\;,\\;\\;\\;\\Delta=n^\\mu\\partial_\\mu\\;,\\;\\;\\;\\delta=m^\\mu\\partial_\\mu\\;,\\;\\;\\;\\bar{\\delta}=\\bar{m}^\\mu\\partial_\\mu\\;.\\label{deriv}\n\\end{equation}\n\nWith the above spin coefficients in Eq.~\\eqref{spincoeffs} and the directional derivatives in Eq.~\\eqref{deriv} at hand, one may rewrite the Maxwell field equations.\nFor the reader who is interested in the equations of motion for other spin fields in this formalism, such as the gravitational field and the Dirac field, a detailed account can be found in~\\cite{Chandrasekhar:1985kt,Frolov:1998wf}.\n\nIn the Newman-Penrose formalism, the Maxwell tensor is decomposed into three complex scalars\n\\begin{equation}\n\\phi_0=F_{\\mu\\nu}l^{\\mu}m^{\\nu}\\;,\\;\\;\\;\\;\\;\\;\\phi_1=\\frac{1}{2}F_{\\mu\\nu}(l^{\\mu}n^{\\nu}+\\bar{m}^{\\mu}m^{\\nu})\\;,\\;\\;\\;\\;\\;\\;\\phi_2=F_{\\mu\\nu}\\bar{m}^{\\mu}n^{\\nu}\\;.\\label{Maxwellscalars}\n\\end{equation}\nThen the Maxwell equations become\n\\begin{align}\nD\\phi_1-\\bar{\\delta}\\phi_0&=(\\pi-2\\alpha)\\phi_0+2\\rho\\phi_1-\\kappa\\phi_2\\;,\\nonumber\\\\\nD\\phi_2-\\bar{\\delta}\\phi_1&=-\\lambda\\phi_0+2\\pi\\phi_1+(\\rho-2\\epsilon)\\phi_2\\;,\\nonumber\\\\\n\\delta\\phi_1-\\Delta\\phi_0&=(\\mu-2\\gamma)\\phi_0+2\\tau\\phi_1-\\sigma\\phi_2\\;,\\nonumber\\\\\n\\delta\\phi_2-\\Delta\\phi_1&=-\\nu\\phi_0+2\\mu\\phi_1+(\\tau-2\\beta)\\phi_2\\;,\\label{Maxwelleq1}\n\\end{align}\nwhere the differential operators appearing on the left hand side are defined in Eq.~\\eqref{deriv} and the spin coefficients on the right hand side are given by~Eq.\\eqref{spincoeffs}.\n\nTo obtain the explicit form of the Maxwell equations, we shall specify a background geometry. For that purpose and for later application in the problems we are interested in, we first review the Kerr-AdS spacetimes in the next subsection.\n\\subsection{Kerr-AdS black holes}\n\\label{subsec:KerrAdS}\nIn an asymptotically AdS background, the most general stationary and axisymmetric BH solution of the four dimensional Einstein-AdS system, is the Kerr-AdS BH. It was found by Carter~\\cite{Carter:1968ks} firstly, a few years after the finding of the Kerr solution.\n\nThe line element for a Kerr-AdS BH, in Boyer-Lindquist coordinates, can be written as\n\\begin{equation}\nds^2=-\\dfrac{\\Delta_r}{\\rho^2\\Xi^2}\\Big(dt-a\\sin^2\\theta d\\varphi\\Big)^2+\\rho^2\\left(\\dfrac{dr^2}{\\Delta_r}+\\dfrac{d\\theta^2}{\\Delta_\\theta}\\right)+\\dfrac{\\Delta_\\theta \\sin^2\\theta}{\\rho^2\\Xi^2}\\Big(a\\,dt-(r^2+a^2)d\\varphi\\Big)^2\\;,\\label{RKerrAdS}\n\\end{equation}\nwith metric functions\n\\begin{align}\n&\\rho^2\\equiv\\bar{\\rho}\\bar{\\rho}^\\ast=r^2+a^2\\cos^2\\theta\\;,\\;\\;\\Delta_r=\\Big(r^2+a^2\\Big)\\left(1+\\frac{r^2}{L^2}\\right)-2Mr\\;,\\;\\;\\nonumber\\\\\n&\\Delta_\\theta=1-\\dfrac{a^2\\cos^2\\theta}{L^2}\\;,\\;\\;\\Xi=1-\\dfrac{a^2}{L^2}\\;,\\label{RKerrAdSmetric}\n\\end{align}\nwhere $\\bar{\\rho}^\\ast$ is the complex conjugate of $\\bar{\\rho}$, and $\\bar{\\rho}=r+ia\\cos\\theta$.\nThe other parameters shown in Eq.~\\eqref{RKerrAdSmetric} include, $L$, which is the AdS radius; $M$ and $a$, which are the mass and spin parameters and relate to the BH energy and angular momentum.\n\nIn this frame, the angular velocity of the event horizon and the Hawking temperature are given by\n\\begin{align}\n&\\Omega_H=\\dfrac{a}{r_+^2+a^2}\\;,\\label{RHorizonV}\\\\\n&T_H=\\dfrac{1}{\\Xi}\\left[\\dfrac{r_+}{2\\pi}\\left(1+\\dfrac{r_+^2}{L^2}\\right)\\dfrac{1}{r_+^2+a^2}-\\dfrac{1}{4\\pi r_+}\\left(1-\\dfrac{r_+^2}{L^2}\\right)\\right]\\;,\\label{RTemp}\n\\end{align}\nwhere the event horizon $r_+$ is determined as the largest root of $\\Delta_r(r_+)=0$. For a given $r_+$, the mass parameter $M$ can be expressed as\n\\begin{equation}\nM=\\dfrac{(r_+^2+a^2)(L^2+r_+^2)}{2r_+L^2}\\;.\\nonumber\n\\end{equation}\nTo require the existence of BHs and to avoid singularities, one may impose the following constraints on the rotation parameter $a$\n\\begin{align}\n&\\dfrac{a}{L}\\leq \\dfrac{r_+}{L} \\sqrt{\\dfrac{3r_+^2+L^2}{L^2-r_+^2}}\\;,\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\text{for}\\;\\;\\;\\;\\; \\dfrac{r_+}{L}<\\dfrac{1}{\\sqrt{3}}\\;,\\nonumber\\\\\n&\\dfrac{a}{L}<1\\;,\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\text{for}\\;\\;\\;\\;\\; \\dfrac{r_+}{L}\\geq\\dfrac{1}{\\sqrt{3}}\\;,\\label{intro_secKerrAdS_rotation}\n\\end{align}\nwhere the equality sign in the first line corresponds to an extremal BH.\n\nThe Boyer-Lindquist coordinates are convenient to solve the perturbation equations for test fields. These coordinates, however, obscure the structure of the geometry at infinity. In fact, the metric in Eq.~\\eqref{RKerrAdS} describes the Kerr-AdS spacetime in a rotating frame, which can be seen by calculating the angular velocity at infinity~\\cite{Dias:2013sdc}\n\\begin{equation}\n\\Omega_\\infty=\\dfrac{a}{a^2-L^2}\\;,\n\\end{equation}\nwhich is apparently non-zero. In order to obtain a non-rotating frame, which is relevant to BH thermodynamics~\\cite{Gibbons:2004ai}, we can make the following coordinate transformation~\\cite{Henneaux:1985tv,Winstanley:2001nx}\n\\begin{equation}\n\\hat{t}=\\dfrac{t}{\\Xi}\\;,\\;\\;\\;\\hat{\\varphi}=\\varphi+\\dfrac{a}{L^2}\\dfrac{t}{\\Xi}\\;,\\;\\;\\;\\hat{r}^2=\\dfrac{r^2\\Delta_\\theta+a^2\\sin^2\\theta}{\\Xi}\\;,\\;\\;\\;\\hat{r}\\cos\\hat{\\theta}=r\\cos\\theta\\;,\\label{RNTrans}\n\\end{equation}\nso that in these new frame the Kerr-AdS geometry~\\eqref{RKerrAdS} is simply AdS space in the usual spherical coordinates\n\\begin{equation}\nds^2=-\\left(1+\\dfrac{\\hat{r}^2}{L^2}\\right)d\\hat{t}^2+\\left(1+\\dfrac{\\hat{r}^2}{L^2}\\right)^{-1}d\\hat{r}^2+\\hat{r}^2(d\\hat{\\theta}^2+\\sin^2\\hat\\theta d\\hat{\\varphi}^2)\\;,\n\\end{equation}\nwhen $r\\rightarrow\\infty (\\hat{r}\\rightarrow\\infty)$.\nThe angular velocity of the event horizon in these coordinates then becomes\n\\begin{equation}\n\\hat{\\Omega}_H=\\Omega_H\\Xi+\\dfrac{a}{L^2}\\;,\\label{NRHAV}\n\\end{equation}\nwhere $\\Omega_H$, defined in Eq.~\\eqref{RHorizonV}, is measured relative to a rotating observer at infinity.\nSince the metric in coordinates $(\\hat{t},\\hat{r},\\hat{\\theta},\\hat{\\varphi})$ is complicated, and also because we have the coordinate transformations in Eq.~\\eqref{RNTrans}, in practice we always work in Boyer-Lindquist coordinates. Our goal is to solve the Maxwell equations in the frequency domain, therefore it is useful to relate the frequencies in these two different coordinates. This is given by\n\\begin{equation}\ne^{-i\\omega t}e^{im\\varphi}=e^{-i\\hat{\\omega} \\hat{t}}e^{im\\hat{\\varphi}}\\;,\\;\\;\\;\\;\\;\\;\\Rightarrow\\;\\;\\;\\;\\;\\;\\hat{\\omega}=\\omega\\Xi+m\\dfrac{a}{L^2}\\;.\\label{nonrotatingfre\n\\end{equation}\n\n\\subsection{Maxwell equations on Kerr-AdS}\nBy specializing the general Maxwell equations~\\eqref{Maxwelleq1} to the Kerr-AdS background~\\eqref{RKerrAdS}, and using a generalization of the Kinnersley tetrad~\\cite{Khanal:1983vb}, i.e.\n\\begin{align}\nl^\\mu&=\\left(\\dfrac{(r^2+a^2)\\Xi}{\\Delta_r},1,0,\\dfrac{a\\Xi}{\\Delta_r}\\right)\\;,\\;\\;\\;\\;\\;\\;\nn^\\mu=\\dfrac{1}{2\\rho^2}\\Big((r^2+a^2)\\Xi,-\\Delta_r,0,a\\Xi\\Big)\\;,\\nonumber\\\\\nm^\\mu&=\\dfrac{1}{\\sqrt{2\\Delta_\\theta}\\bar{\\rho}}\\left(ia\\Xi\\sin\\theta,0,\\Delta_\\theta,\\dfrac{i\\Xi}{\\sin\\theta}\\right)\\;,\\;\\;\\;\\;\\;\\;\\bar{m}^\\mu=(m^\\mu)^\\ast\\;,\\label{pre:tetrad}\n\\end{align}\nwhere $\\bar{\\rho}=r+ia\\cos\\theta$, one obtains\n\\begin{align}\n\\left(\\mathscr{D}_0+\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)\\Phi_1&=\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_1-\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)\\Phi_0\\;,\\label{Maxwelleq2_1}\\\\\n\\left(\\mathscr{D}_0-\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)\\Phi_2&=\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_0+\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)\\Phi_1\\;,\\label{Maxwelleq2_2}\\\\\n-\\Delta_r\\left(\\mathscr{D}_1^\\dag-\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)\\Phi_0&=\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_0^\\dag+\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)\\Phi_1\\;,\\label{Maxwelleq2_3}\\\\\n-\\Delta_r\\left(\\mathscr{D}_0^\\dag+\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)\\Phi_1&=\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_1^\\dag-\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)\\Phi_2\\;,\\label{Maxwelleq2_4}\n\\end{align}\nwhere\n\\begin{align}\n\\mathscr{D}_n&=\\dfrac{\\partial}{\\partial r}-\\dfrac{i\\Xi K}{\\Delta_r}+\\dfrac{n}{\\Delta_r}\\dfrac{d\\Delta_r}{dr}\\;,\\nonumber\\\\\n\\mathscr{D}_n^\\dag&=\\dfrac{\\partial}{\\partial r}+\\dfrac{i\\Xi K}{\\Delta_r}+\\dfrac{n}{\\Delta_r}\\dfrac{d\\Delta_r}{dr}\\;,\\nonumber\\\\\n\\mathscr{L}_n&=\\dfrac{\\partial}{\\partial\\theta}-\\frac{\\Xi H}{\\Delta_\\theta}+\\dfrac{n}{\\sqrt{\\Delta_\\theta}\\sin\\theta}\\dfrac{d}{d\\theta}\\left(\\sqrt{\\Delta_\\theta}\\sin\\theta\\right)\\;,\\nonumber\\\\\n\\mathscr{L}_n^\\dag&=\\dfrac{\\partial}{\\partial\\theta}+\\frac{\\Xi H}{\\Delta_\\theta}+\\dfrac{n}{\\sqrt{\\Delta_\\theta}\\sin\\theta}\\dfrac{d}{d\\theta}\\left(\\sqrt{\\Delta_\\theta}\\sin\\theta\\right)\\;,\\label{Defoperator}\n\\end{align}\nwith\n\\begin{equation}\nK=\\omega(r^2+a^2)-am\\;,\\;\\;\\;H=a\\omega\\sin\\theta-\\dfrac{m}{\\sin\\theta}\\;,\\label{KHeq}\n\\end{equation}\nand where the following transformations have been made\n\\begin{equation}\n\\phi_0=\\Phi_0\\;,\\;\\;\\;\\phi_1=\\dfrac{1}{\\sqrt{2}\\bar{\\rho}^\\ast}\\Phi_1\\;,\\;\\;\\;\\phi_2=\\dfrac{1}{2(\\bar{\\rho}^\\ast)^2}\\Phi_2\\;.\\label{phirelations}\n\\end{equation}\nNote that the time and azimuthal dependence, $e^{-i\\omega t+im\\varphi}$, has been factored out. By acting with the operator $\\sqrt{\\Delta_\\theta}(\\mathscr{L}_0^\\dag+\\tfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast})$ on Eq.~\\eqref{Maxwelleq2_1}, and with the operator $(\\mathscr{D}_0+\\tfrac{1}{\\bar{\\rho}^\\ast})$ on Eq.~\\eqref{Maxwelleq2_3}, one obtains a decoupled equation for $\\Phi_0$ by eliminating $\\Phi_1$, i.e.\n\\begin{equation}\n[\\sqrt{\\Delta_\\theta}\\mathscr{L}_0^\\dag\\sqrt{\\Delta_\\theta}\\mathscr{L}_1+\\Delta_r\\mathscr{D}_1\\mathscr{D}_1^\\dag+2i\\omega\\Xi\\bar{\\rho}]\\Phi_0=0\\;,\\label{Phi0eq}\n\\end{equation}\nwith aid of the identities\n\\[\n\\begin{cases}\n\\left(\\mathscr{D}_0+\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)\\Delta_r\\left(\\mathscr{D}_1^\\dag-\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)=\\Delta_r\\mathscr{D}_1\\mathscr{D}_1^\\dag+\\dfrac{2i\\Xi K}{\\bar{\\rho}^\\ast}\\;,\\\\\n\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_0^\\dag+\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_1-\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)=\\sqrt{\\Delta_\\theta}\\mathscr{L}_0^\\dag\\sqrt{\\Delta_\\theta}\\mathscr{L}_1\n-\\dfrac{2ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\Xi H\\;.\n\\end{cases}\n\\]\n\nFollowing the same procedure, by acting with the operator $\\sqrt{\\Delta_\\theta}(\\mathscr{L}_0+\\tfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast})$ on Eq.~\\eqref{Maxwelleq2_4} and with the operator $\\Delta_r(\\mathscr{D}_0^\\dag+\\tfrac{1}{\\bar{\\rho}^\\ast})$ on Eq.~\\eqref{Maxwelleq2_2}, $\\Phi_1$ is again eliminated so that we obtain a decoupled equation for $\\Phi_2$\n\\begin{equation}\n[\\sqrt{\\Delta_\\theta}\\mathscr{L}_0\\sqrt{\\Delta_\\theta}\\mathscr{L}_1^\\dag+\\Delta_r\\mathscr{D}_0^\\dag\\mathscr{D}_0-2i\\omega\\Xi\\bar{\\rho}]\\Phi_2=0\\;,\\label{Phi2eq}\n\\end{equation}\nwith aid of the identities\n\\[\n\\begin{cases}\n\\Delta_r\\left(\\mathscr{D}_0^\\dag+\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)\\left(\\mathscr{D}_0-\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)=\\Delta_r\\mathscr{D}_0^\\dag\\mathscr{D}_0-\\dfrac{2i\\Xi K}{\\bar{\\rho}^\\ast}\\;,\\\\\n\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_0+\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_1^\\dag-\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)=\\sqrt{\\Delta_\\theta}\\mathscr{L}_0\\sqrt{\\Delta_\\theta}\\mathscr{L}_1^\\dag\n+\\dfrac{2ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\Xi H\\;.\n\\end{cases}\n\\]\nNow taking\n\\begin{equation}\n\\Phi_0=R_{+1}(r)S_{+1}(\\theta)\\;\\;\\;\\text{and}\\;\\;\\;\\Phi_2=R_{-1}(r)S_{-1}(\\theta)\\;,\\label{decompfields}\n\\end{equation}\nwe finally obtain the Maxwell equations with separated variables\n\\begin{align}\n&\\left(\\Delta_r\\mathscr{D}_1\\mathscr{D}_1^\\dag+2i\\omega\\Xi r\\right)R_{+1}=\\lambda R_{+1}\\;,\\label{Rpluseq}\\\\\n&\\left(\\sqrt{\\Delta_\\theta}\\mathscr{L}_0^\\dag\\sqrt{\\Delta_\\theta}\\mathscr{L}_1-2a\\omega\\Xi\\cos\\theta\\right)S_{+1}=-\\lambda S_{+1}\\;,\\label{Spluseq}\n\\end{align}\nand\n\\begin{align}\n&\\left(\\Delta_r\\mathscr{D}_0^\\dag\\mathscr{D}_0-2i\\omega\\Xi r\\right)R_{-1}=\\lambda R_{-1}\\;,\\label{Rminuseq}\\\\\n&\\left(\\sqrt{\\Delta_\\theta}\\mathscr{L}_0\\sqrt{\\Delta_\\theta}\\mathscr{L}_1^\\dag+2a\\omega\\Xi\\cos\\theta\\right)S_{-1}=-\\lambda S_{-1}\\;,\\label{Sminuseq}\n\\end{align}\nfrom Eqs.~\\eqref{Phi0eq} and~\\eqref{Phi2eq}. Note that $\\lambda$ refers to the separation constant from now on, and should not be confused with the spin coefficient.\nUsing the commutative property $\\Delta_r\\mathscr{D}_{n+1}=\\mathscr{D}_{n}\\Delta_r$, Eq.~\\eqref{Rpluseq} can be rewritten as\n\\begin{equation}\n\\left(\\Delta_r\\mathscr{D}_0\\mathscr{D}_0^\\dag+2i\\omega\\Xi r\\right)\\left(\\Delta_rR_{+1}\\right)=\\lambda \\left(\\Delta_rR_{+1}\\right)\\;,\\label{Ppluseq}\n\\end{equation}\nwhich shows that $\\Delta_rR_{+1}$ and $R_{-1}$ satisfy complex conjugate equations, by comparing with Eq.~\\eqref{Rminuseq}.\n\nThe decoupled equations~\\eqref{Maxwelleq2_1}-~\\eqref{Maxwelleq2_4} provide solutions for $\\Phi_0$, $\\Phi_1$ and $\\Phi_2$. In particular, $\\Phi_0$ and $\\Phi_2$ can be separated into radial parts which satisfy Eqs.~\\eqref{Rpluseq} and~\\eqref{Rminuseq}, and angular parts which satisfy Eqs.~\\eqref{Spluseq} and~\\eqref{Sminuseq}. The solution of $\\Phi_1$ is ignored for now and we will be back to this problem later.\nThere is an additional issue to discuss, which is related to the solutions of $\\Phi_0$ and $\\Phi_2$.\n\nAs we have already shown in Eqs~\\eqref{Rminuseq} and~\\eqref{Ppluseq}, $\\Phi_0$ and $\\Phi_2$ satisfy equations which are complex conjugate of each other, but the relative normalization between these two solutions still remains to be determined. The answer to this problem is given by the famous Starobinsky-Teukolsky identities. In the following we will address this issue by proving the Starobinsky-Teukolsky identities for the Maxwell field on the Kerr-AdS background.\n\n\\underline{\\bf Starobinsky-Teukolsky identities}\n\\begin{thm}\\label{thm1\n$\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}$ is a constant multiple of $S_{-1}$,\\;i.e. $\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}=BS_{-1}$, \\\\\n{\\color{white}{and}}$\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}^\\dag S_{-1}$ is a constant multiple of $S_{+1}$, i.e. $\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}^\\dag S_{-1}=BS_{+1}$.\n\\end{thm}\nNote that the new angular operators are defined as\n\\begin{equation}\n\\bar{\\mathscr{L}_n}=\\sqrt{\\Delta_\\theta}\\mathscr{L}_n\\;,\\;\\;\\;\\;\\;\\;\\bar{\\mathscr{L}_n}^\\dag=\\sqrt{\\Delta_\\theta}\\mathscr{L}_n^\\dag\\;,\\label{newangoperator}\n\\end{equation}\nso that this theorem can be written in the same form as its counterpart for the Kerr background~\\cite{Chandrasekhar:1985kt}. Also note that the two proportionality constants in this theorem have been set to the same (denoted by $B$), which is guaranteed if we normalize both $S_{+1}$ and $S_{-1}$ to unity, i.e.\n\\begin{equation}\n\\int_0^\\pi S_{+1}^2\\sin\\theta d\\theta=\\int_0^\\pi S_{-1}^2\\sin\\theta d\\theta=1\\;.\\label{angnorm}\n\\end{equation}\nTheorem~\\ref{thm1} is proved in Appendix~\\ref{app:ST1} with details, by taking $\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}=BS_{-1}$, as an example.\n\nTo evaluate $B$, we start by applying the operator $\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}^\\dag$ to the first expression in Theorem~\\ref{thm1} and with aid of the second expression in this theorem, then we have\n\\begin{align}\nB^2S_{+1}&=\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}^\\dag\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}=\\bar{\\mathscr{L}_0}^\\dag(\\bar{\\mathscr{L}_1}+2\\mathcal{Q}\\sqrt{\\Delta_\\theta})(\\bar{\\mathscr{L}_0}^\\dag-2\\mathcal{Q}\\sqrt{\\Delta_\\theta})\\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&=\\bar{\\mathscr{L}_0}^\\dag\\left(\\bar{\\mathscr{L}_1}\\bar{\\mathscr{L}_0}^\\dag-\\dfrac{2}{\\sin\\theta}\\dfrac{d}{d\\theta}\\left(\\sin\\theta H\\right)\\right)\\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&=\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}(2a\\omega\\Xi\\cos\\theta-\\lambda)S_{+1}-4a\\omega\\Xi\\bar{\\mathscr{L}_0}^\\dag\\cos\\theta\\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&=2a\\omega\\Xi\\bar{\\mathscr{L}_0}^\\dag(\\cos\\theta\\bar{\\mathscr{L}_1}-\\sin\\theta\\sqrt{\\Delta_\\theta})S_{+1}-\\lambda\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}S_{+1}-4a\\omega\\Xi\\bar{\\mathscr{L}_0}^\\dag\\cos\\theta\\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&=-2a\\omega\\Xi(\\cos\\theta\\bar{\\mathscr{L}_0}^\\dag-\\sin\\theta\\sqrt{\\Delta_\\theta})\\bar{\\mathscr{L}_1}S_{+1}-2a\\omega\\Xi\\sin\\theta\\sqrt{\\Delta_\\theta}\\bar{\\mathscr{L}_1}^\\dag S_{+1}-\\lambda\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}S_{+1}\\nonumber\\\\\n&=-(2a\\omega\\Xi\\cos\\theta+\\lambda)\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}S_{+1}+2a\\omega\\Xi\\sin\\theta\\sqrt{\\Delta_\\theta}(\\bar{\\mathscr{L}_1} -\\bar{\\mathscr{L}_1}^\\dag)S_{+1}\\nonumber\\\\\n&=-(2a\\omega\\Xi\\cos\\theta+\\lambda)(2a\\omega\\Xi\\cos\\theta-\\lambda)S_{+1}-4a\\omega\\sin\\theta\\Xi^2HS_{+1}\\nonumber\\\\\n&=\\left(\\lambda^2-4\\omega^2\\Xi^2(a^2-\\dfrac{am}{\\omega})\\right)S_{+1}\\;,\\label{derivaB}\n\\end{align}\nwhere the angular equation~\\eqref{Spluseq} was used in the above derivations, and $\\mathcal{Q}$ is defined as\n\\begin{equation}\n\\mathcal{Q}=\\dfrac{\\Xi H}{\\Delta_\\theta}\\;. \\label{Qdef\n\\end{equation}\nEq.~\\eqref{derivaB} finally gives the value of $B$, i.e.\n\\begin{equation}\nB^2=\\lambda^2-4\\omega\\Xi^2(\\omega a^2-ma)\\;.\\label{Bvalue}\n\\end{equation}\nThe sign of $B$ can be fixed by comparing with the spherical case $(a=0)$ when the angular functions reduce to the spin-weighted spherical harmonics~\\cite{goldberg1967spin}. This comparison requires us to choose the positive square root in Eq.~\\eqref{Bvalue}\n\\begin{thm}\\label{thm2}\n$\\Delta_r\\mathscr{D}_0\\mathscr{D}_0R_{-1}$ is a constant multiple of $\\Delta_rR_{+1}$,\\;i.e. $\\Delta_r\\mathscr{D}_0\\mathscr{D}_0R_{-1}=B\\Delta_rR_{+1}$, \\\\\n{\\color{white}{and}}$\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\Delta_r\\mathscr{D}_0^\\dag\\mathscr{D}_0^\\dag\\Delta_rR_{+1}$ is a constant multiple of $R_{-1}$, i.e. $\\Delta_r\\mathscr{D}_0^\\dag\\mathscr{D}_0^\\dag\\Delta_rR_{+1}=BR_{-1}$.\n\\end{thm}\nThe proportionality stated in this theorem can be proved as follows, by taking the first expression as an example. By applying the operator $\\mathscr{D}_0\\mathscr{D}_0$ to Eq.~\\eqref{Rminuseq}, we have\n\\begin{align}\n\\lambda\\mathscr{D}_0\\mathscr{D}_0R_{-1}&=\\mathscr{D}_0\\mathscr{D}_0(\\Delta_r\\mathscr{D}_0^\\dag\\mathscr{D}_0-2i\\omega\\Xi r)R_{-1}\\nonumber\\\\\n&=\\mathscr{D}_0\\mathscr{D}_0\\Delta_r\\mathscr{D}_0\\mathscr{D}_0R_{-1}+2i\\Xi\\mathscr{D}_0\\mathscr{D}_0K\\mathscr{D}_0R_{-1}-2i\\omega\\Xi\\mathscr{D}_0\\mathscr{D}_0(rR_{-1})\n\\nonumber\\\\\n&=\\mathscr{D}_0\\Delta_r\\mathscr{D}_1\\mathscr{D}_0\\mathscr{D}_0R_{-1}+2i\\Xi\\mathscr{D}_0(K\\mathscr{D}_0+2\\omega r)\\mathscr{D}_0R_{-1}\n-2i\\omega\\Xi\\mathscr{D}_0\\mathscr{D}_0(rR_{-1})\\nonumber\\\\\n&=\\mathscr{D}_0(\\Delta_r\\mathscr{D}_1+2i\\Xi K)\\mathscr{D}_0\\mathscr{D}_0R_{-1}+2i\\omega\\Xi(2\\mathscr{D}_0r\\mathscr{D}_0R_{-1}-r\\mathscr{D}_0\\mathscr{D}_0R_{-1}\n+2\\mathscr{D}_0R_{-1})\\nonumber\\\\\n&=\\mathscr{D}_0\\Delta_r\\mathscr{D}_1^\\dag\\mathscr{D}_0\\mathscr{D}_0R_{-1}+2i\\omega\\Xi r\\mathscr{D}_0\\mathscr{D}_0R_{-1}\\nonumber\\\\\n&=(\\Delta_r\\mathscr{D}_1\\mathscr{D}_1^\\dag+2i\\omega\\Xi r)(\\mathscr{D}_0\\mathscr{D}_0R_{-1})\\;.\n\\end{align}\nTherefore, $\\mathscr{D}_0\\mathscr{D}_0R_{-1}$ satisfies the same equation as $R_{+1}$, by comparing with Eq.~\\eqref{Rpluseq}. The second part in this theorem can be proved following the same logic. Notice that the constants of proportionality in this theorem have been fixed to $B$, the same constant used in Theorem~\\ref{thm1}. This fact, indeed, is a consequence of Theorem~\\ref{thm1}, and can be understood as follows. The Maxwell scalars $\\Phi_0$ and $\\Phi_2$ are governed by their equations \\eqref{Phi0eq} and \\eqref{Phi2eq}, but their relative normalization is still undetermined. To obtain this relative normalization constant, by applying the operator $\\sqrt{\\Delta_\\theta}(\\mathscr{L}_0+ia\\sin\\theta\/\\bar{\\rho}^\\ast)$ to Eq.~\\eqref{Maxwelleq2_1} and $(\\mathscr{D}_0+1\/\\bar{\\rho}^\\ast)$ to Eq.~\\eqref{Maxwelleq2_2}, and eliminating $\\Phi_1$, we obtain\n\\begin{equation}\n\\left(\\mathscr{D}_0+\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)\\left(\\mathscr{D}_0-\\dfrac{1}{\\bar{\\rho}^\\ast}\\right)\\Phi_2\n=\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_0+\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)\n\\sqrt{\\Delta_\\theta}\\left(\\mathscr{L}_1-\\dfrac{ia\\sin\\theta}{\\bar{\\rho}^\\ast}\\right)\\Phi_0\n\\end{equation}\nwhich can be further simplified\n\\begin{equation}\n\\mathscr{D}_0\\mathscr{D}_0\\Phi_2=\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}\\Phi_1\\;,\\label{complete1}\n\\end{equation}\nwith aid of the commutative property of the angular operator\n\\begin{equation}\n\\sqrt{\\Delta_\\theta}\\sin\\theta\\mathscr{L}_{n+1}=\\mathscr{L}_n\\sqrt{\\Delta_\\theta}\\sin\\theta\\;.\n\\end{equation}\nWith the field decompositions~\\eqref{decompfields} and the identity $\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}=BS_{-1}$ in Theorem~\\ref{thm1}, Eq.~\\eqref{complete1} becomes\n\\begin{equation}\n\\mathscr{D}_0\\mathscr{D}_0R_{-1}=BR_{+1}\\;.\n\\end{equation}\nThis gives the first identity in Theorem~\\ref{thm2}, by multiplying $\\Delta_r$ on both sides.\n\n\\underline{\\bf The solution for $\\Phi_1$}\n\nTo complete the solutions, now we step back to look for the solution for $\\Phi_1$.\nWe start from Eq.~\\eqref{Maxwelleq2_1}, by multiplying $\\bar{\\rho}^\\ast$ on both sides, then we have\n\\begin{equation}\n\\Big(\\bar{\\rho}^\\ast\\mathscr{D}_0+1\\Big)\\Phi_1=\\left(\\bar{\\rho}^\\ast\\bar{\\mathscr{L}_1}-ia\\sin\\theta\\sqrt{\\Delta_\\theta}\\right)\\Phi_0\\;,\n\\end{equation}\nwhich, from the definition of $\\mathscr{D}_0$, may be rewritten as\n\\begin{equation}\n\\mathscr{D}_0\\Big(\\bar{\\rho}^\\ast\\Phi_1\\Big)=\\left(\\bar{\\rho}^\\ast\\bar{\\mathscr{L}_1}-ia\\sin\\theta\\sqrt{\\Delta_\\theta}\\right)\\Phi_0\\;.\\label{Phi1sol1}\n\\end{equation}\nThen multiplying by $\\Delta_r$ and expanding $\\Phi_0$ as in Eq.~\\eqref{decompfields}, Eq.~\\eqref{Phi1sol1} becomes\n\\begin{align}\n\\Delta_r\\mathscr{D}_0\\Big(\\bar{\\rho}^\\ast\\Phi_1\\Big)&=\\left(\\bar{\\rho}^\\ast\\bar{\\mathscr{L}_1}-ia\\sin\\theta\\sqrt{\\Delta_\\theta}\\right)\\Big(\\Delta_r\\Phi_0\\Big)\\nonumber\\\\\n&=r\\Big(\\Delta_rR_{+1}\\Big)\\bar{\\mathscr{L}_1}S_{+1}-ia\\Big(\\Delta_rR_{+1}\\Big)\\left(\\cos\\theta\\bar{\\mathscr{L}_1}S_{+1}+\\sin\\theta\\sqrt{\\Delta_\\theta}S_{+1}\\right)\\nonumber\\\\\n&=\\Delta_r\\Big(\\mathscr{D}_0g_{+1}\\bar{\\mathscr{L}_1}S_{+1}-iaf_{-1}\\mathscr{D}_0\\mathscr{D}_0P_{-1}\\Big)\\;,\n\\end{align}\nwhich gives\n\\begin{equation}\n\\mathscr{D}_0\\Big(\\bar{\\rho}^\\ast\\Phi_1\\Big)=\\mathscr{D}_0\\Big(g_{+1}\\bar{\\mathscr{L}_1}S_{+1}-iaf_{-1}\\mathscr{D}_0P_{-1}\\Big)\\;,\\label{Phi11eq}\n\\end{equation}\nwhere we defined\n\\begin{align}\n&g_{+1}=\\dfrac{1}{B}\\Big(r\\mathscr{D}_0P_{-1}-P_{-1}\\Big)\\label{gpluseq}\\;,\\\\\n&f_{-1}=\\dfrac{1}{B}\\left(\\cos\\theta\\bar{\\mathscr{L}_1}S_{+1}+\\sin\\theta\\sqrt{\\Delta_\\theta}S_{+1}\\right)\\;,\\label{fminuseq}\n\\end{align}\nand\n\\begin{equation}\n\\Delta_r\\Phi_0\\equiv P_{+1}S_{+1}\\;,\\;\\;\\;\\Phi_2\\equiv P_{-1}S_{-1}\\;,\\;\\;\\;\\;\n\\end{equation}\nand where the Starobinski-Teukolsky identity in Theorem~\\ref{thm2}, i.e.\n\\begin{equation}\n\\Delta_r\\mathscr{D}_0\\mathscr{D}_0P_{-1}=BP_{+1}\\;,\n\\end{equation}\nhas been used.\\\\\nApplying a similar procedure to Eq.~\\eqref{Maxwelleq2_2}, we obtain\n\\begin{equation}\n\\bar{\\mathscr{L}_0}\\Big(\\bar{\\rho}^\\ast\\Phi_1\\Big)=\\bar{\\mathscr{L}_0}\\Big(g_{+1}\\bar{\\mathscr{L}_1}S_{+1}-iaf_{-1}\\mathscr{D}_0P_{-1}\\Big)\\;.\\label{Phi12eq}\n\\end{equation}\nBy comparing Eqs.~\\eqref{Phi11eq} and~\\eqref{Phi12eq}, and considering that $\\mathscr{D}_0$ is the differential operator only for the radial part while $\\bar{\\mathscr{L}_0}$ is the differential operator only for the angular part, we conclude that\\footnote{Strictly, we could add an extra function, say $F$, satisfying homogenous equations $\\mathscr{D}_0(F)=\\bar{\\mathscr{L}_0}(F)=0$, to the solution of $\\Phi_1$ in Eq.~\\eqref{Phi1eq}. The solution of $F$, however, is singular at $\\theta=0$ and $\\theta=\\pi\/2$, similar to the Kerr case~\\cite{Chandrasekhar:1985kt}. Therefore, we have not included $F$ in the solution for $\\Phi_1$.}\n\\begin{equation}\n\\bar{\\rho}^\\ast\\Phi_1=g_{+1}\\bar{\\mathscr{L}_1}S_{+1}-iaf_{-1}\\mathscr{D}_0P_{-1}\\;,\\label{Phi1eq}\n\\end{equation}\nwhich determines $\\Phi_1$ uniquely. This equation is relevant to derive the angular momentum flux for the Maxwell field on Kerr-AdS background, see Appendix~\\ref{app:angmomflux} for details.\n\nAlthough the scalar $\\Phi_1$ is obtained in Eq.~\\eqref{Phi1eq}, $\\bar{\\mathscr{L}_1}S_{+1}$ is still yet unknown. Indeed $\\bar{\\mathscr{L}_1}S_{+1}$ can be expressed in terms of $S_{+1}$ and $S_{-1}$, by\n\\begin{numcases}{}\n\\bar{\\mathscr{L}_1}S_{+1}=\\dfrac{(2a\\omega\\Xi\\cos\\theta-\\lambda)S_{+1}-BS_{-1}}{2\\sqrt{\\Delta_\\theta}\\mathcal{Q}}\\;,\\label{L1Sp1}\\\\\n\\bar{\\mathscr{L}_1}^\\dag S_{-1}=\\dfrac{(2a\\omega\\Xi\\cos\\theta+\\lambda)S_{-1}+BS_{+1}}{2\\sqrt{\\Delta_\\theta}\\mathcal{Q}}\\;,\\label{L1Sm1}\n\\end{numcases}\nwhere $B$ and $\\mathcal{Q}$ are given in Eqs.~\\eqref{Bvalue} and~\\eqref{Qdef}.\n\nTo prove Eq.~\\eqref{L1Sp1}, we start from Eq.~\\eqref{Spluseq},\n\\begin{align}\n\\left(\\bar{\\mathscr{L}_0}^\\dag\\bar{\\mathscr{L}_1}-2a\\omega\\Xi\\cos\\theta+\\lambda\\right)S_{+1}=0\\Rightarrow\n\\left(\\bar{\\mathscr{L}_0}+2\\mathcal{Q}\\sqrt{\\Delta_\\theta}\\right)\\bar{\\mathscr{L}_1}S_{+1}+\\left(\\lambda-2a\\omega\\Xi\\cos\\theta\\right)S_{+1}=0\\;,\n\\end{align}\nand considering $\\bar{\\mathscr{L}_0}\\bar{\\mathscr{L}_1}S_{+1}=BS_{-1}$ in Theorem~\\ref{thm2}, we obtain\n\\begin{equation}\nBS_{-1}+2\\mathcal{Q}\\sqrt{\\Delta_\\theta}\\bar{\\mathscr{L}_1}S_{+1}+\\left(\\lambda-2a\\omega\\Xi\\cos\\theta\\right)S_{+1}=0\\;,\n\\end{equation}\nwhich finally gives Eq.~\\eqref{L1Sp1}.\nFollowing a similar procedure, Eq.~\\eqref{L1Sm1} can be proved.\n\n\n\n\n\n\n\n\n\\chapter{List of publications}\nThis thesis is based on the following published papers by the author:\n\\begin{enumerate}\n\\item\n{\\textit{Hawking radiation for a Proca field in D-dimensions},\\\\\nCarlos~Herdeiro, Marco~O.~P.~Sampaio, Mengjie~Wang,\\\\\n{}\\href{http:\/\/journals.aps.org\/prd\/abstract\/10.1103\/PhysRevD.85.024005}{Phys.\\ Rev.\\ D {\\bf 85} (2012) 2, 024005}\\;$(${}\\href{http:\/\/arxiv.org\/abs\/1110.2485}{\\tt arXiv:1110.2485}$)$.\n}\n\n\\item\n{\\textit{Hawking radiation for a Proca field in D dimensions. II. charged field in a brane charged black hole},\\\\\nMengjie~Wang, Marco~O.~P.~Sampaio, Carlos~Herdeiro,\\\\\n{}\\href{http:\/\/journals.aps.org\/prd\/abstract\/10.1103\/PhysRevD.87.044011}{Phys.\\ Rev.\\ D {\\bf 87} (2013) 4, 044011}\\;$(${}\\href{http:\/\/arxiv.org\/abs\/1212.2197}{\\tt arXiv:1212.2197}$)$.\n}\n\n\\item\n{\\textit{Superradiant instabilities in a D-dimensional small Reissner-Nordstr\\\"om-anti-de Sitter black hole},\\\\\nMengjie~Wang, Carlos~Herdeiro,\\\\\n{}\\href{http:\/\/journals.aps.org\/prd\/abstract\/10.1103\/PhysRevD.89.084062}{Phys.\\ Rev.\\ D {\\bf 89} (2014) 8, 084062}\\;$(${}\\href{http:\/\/arxiv.org\/abs\/1403.5160}{\\tt arXiv:1403.5160}$)$.\n}\n\n\\item\n{\\textit{Marginal scalar and Proca clouds around Reissner-Nordstr\\\"om black holes},\\\\\nMarco~O.~P.~Sampaio, Carlos~Herdeiro, Mengjie~Wang,\\\\\n{}\\href{http:\/\/journals.aps.org\/prd\/abstract\/10.1103\/PhysRevD.90.064004}{Phys.\\ Rev.\\ D {\\bf 90} (2014) 6, 064004}\\;$(${}\\href{http:\/\/arxiv.org\/abs\/1406.3536}{\\tt arXiv:1406.3536}$)$.\n}\n\n\\item\n{\\textit{Maxwell perturbations on asymptotically anti-de Sitter spacetimes: generic boundary conditions and a new branch of quasinormal modes},\\\\\nMengjie~Wang, Carlos~Herdeiro, Marco O. P. Sampaio,\\\\\n{}\\href{http:\/\/journals.aps.org\/prd\/abstract\/10.1103\/PhysRevD.92.124006}{Phys.\\ Rev.\\ D {\\bf 92} (2015) 12, 124006}\\;$(${}\\href{http:\/\/arxiv.org\/abs\/1510.04713}{\\tt arXiv:1510.04713}$)$.\n}\n\n\\item\n{\\textit{Maxwell perturbations on Kerr-anti-de Sitter: quasinormal modes, superradiant instabilities and vector clouds},\\\\\nMengjie~Wang, Carlos~Herdeiro,\\\\\n{}\\href{https:\/\/journals.aps.org\/prd\/abstract\/10.1103\/PhysRevD.93.064066}\n{Phys.\\ Rev.\\ D {\\bf 93} (2016) 6, 064066}\\;$(${}\\href{http:\/\/arxiv.org\/abs\/1512.02262}{\\tt arXiv:1512.02262}$)$.\n}\n\\end{enumerate}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\chapter{Superradiant instabilities in higher dimensions}\n\\label{ch:scalarHD}\n\n\n\\section{Introduction}\n\\label{intro}\n\n\nSuperradiant instabilities are interesting phenomena with various applications from high energy physics to astrophysics, see a recent review~\\cite{Brito:2015oca} and references therein. Most of these studies focus on four dimensional spacetimes\\footnote{Exceptionally a few works were carried out in five dimensional spacetimes, see~\\cite{Aliev:2008yk,Dias:2011tj}, for example.}, but a generic analysis for superradiant instabilities in higher dimensions is still missing. In order to perform such a study, we choose asymptotically AdS BHs, one of the confining mechanisms discussed in Section~\\ref{sc:SR}, in which bound states of perturbation fields can be supported.\n\nThe goal of this chapter is to present a complete study of superradiant instabilities triggered by a charged scalar field interacting with a $D$-dimensional Reissner-Nordstr\\\"om-anti-de Sitter (RN-AdS) BH. As a spin off we shall clarify a claim, made in~\\cite{Aliev:2008yk}, that, in $D=5$, a subset of field modes -- those with odd angular momentum quantum number, $\\ell$ -- do not develop superradiant instabilities. We shall show otherwise: that in fact \\textit{all} $\\ell$ modes in \\textit{all} dimensions can develop superradiant instabilities.\n\nIn addition to the study of superradiant instabilities for a minimally coupled charged scalar field, other studies have been considered for charged AdS BHs. It was first observed in \\cite{Gubser:2000ec,Gubser:2000mm} that for BHs obtained within $\\mathcal{N}=8$ supergravity\\footnote{This is a theory containing four $U(1)$ gauge fields and three real scalar fields \\textit{non-minimally} coupled to the Maxwell fields.} in $D=4$, \\textit{large} RN-AdS BHs are dynamically unstable due to the existence of a tachyonic mode in the scalar field perturbations. On the other hand, in purely Einstein-Maxwell theory in $D$ dimensions, RN-AdS BHs have been argued to be stable against gravitational perturbations~\\cite{Konoplya:2008rq}. Thus the existence of scalar fields with some coupling to Maxwell fields is central to the instability of~\\cite{Gubser:2000ec,Gubser:2000mm}.\n\nA qualitatively different instability of RN-AdS BHs has been discussed in the context of holographic superconductors. It occurs in the presence of a massive scalar field that may or may not be charged and leads to the formation of scalar hair around the BH \\cite{Hartnoll:2008kx}. Unlike the superradiant instability, this other instability occurs even if the scalar field is not charged. In that case the, say, $D=4$ RN-AdS BH should be nearly extremal, which means its near horizon geometry has a two dimensional AdS factor; moreover, the scalar field should have a tachyonic mass above the Breitenlohner-Freedman bound \\cite{Breitenlohner:1982bm,Breitenlohner:1982jf} of the four dimensional AdS space, but below the Breitenlohner-Freedman bound of the two dimensional AdS factor in the near horizon geometry of the BH. This is why the scalar field provides an instability to the BH geometry, but not to four dimensional AdS space.\n\nIn order to investigate the superradiant instability of a charged, massive scalar field in a $D$-dimensional RN-AdS background we shall employ both an analytical and a numerical method. First, we solve the charged Klein-Gordon equation using a matching method, and obtain a solution near the BH region and another one far away from the BH. This is done for small BHs, i.e. BHs obeying $r_+\\ll L$, where $r_+$ and $L$ stand for the BH event horizon and the AdS radius, respectively. The analytical quasinormal frequency for small BHs is then obtained by matching the near and far solutions in an intermediate region. We find that the relation between $\\ell$ and $D$ plays a central role in determining the analytical quasinormal frequency formula. When $\\ell=(p+\\frac{1}{2})(D-3)$, where $p$ is a non-negative integer, the matching method fails. The reason is that the near region solution and the far region solution have different functional dependence in terms of the radial coordinate, which makes our matching impossible. Such difficulty in employing the matching method also occurs for extremal BHs as discussed in~\\cite{Rosa:2009ei}, where an alternative point matching method was used. For all other values of $\\ell$ and $D$, the matching method works and it may be used to show that a superradiance instability exists for all $\\ell$ modes, in a region of the parameters space.\n\nAfter the (approximate) analytical analysis, then we solve the charged Klein-Gordon equation numerically both to check the analytical results and to explore the special case where the analytical method fails. We find good agreement between the two methods in the regime where both are valid. For the special case $\\ell=(p+\\frac{1}{2})(D-3)$, the numerical results show that the superradiant instability does exist.\n\nThe structure of this chapter is organized as follows. In Section~\\ref{seceq} we introduce the background geometry and the scalar field equation. In Section~\\ref{secmatching} we solve the scalar field equation analytically using the matching method and obtain an analytical quasinormal frequency formula. We analyze this formula for different relations between $\\ell$ and $D$, and show the reason why the matching method fails for the special case $\\ell=(p+\\frac{1}{2})(D-3)$ in Section~\\ref{result analysis}. To confirm our analytical results and to be able to investigate if there is a superradiant instability for that special case, we resort to a numerical method to solve the Klein-Gordon equation in Section~\\ref{numerical}.\nConclusions are presented in the last section.\n\\section{Background and field equation}\n\\label{seceq}\nWe consider a $D$-dimensional RN-AdS BH with the line element\n\\begin{equation}\nds^2=-f(r)dt^2+\\dfrac{1}{f(r)}dr^2+r^2d\\Omega^2_n \\;,\\label{metric}\n\\end{equation}\nwhere $d\\Omega^2_n$ is the metric on the unit $n$-sphere. In the following it will be convenient to use $n=D-2$ rather than $D$ to parameterize the spacetime dimension. The metric function $f(r)$ takes the form\n\\begin{equation}\nf(r)=1-\\dfrac{M}{r^{n-1}}+\\dfrac{Q^2}{r^{2(n-1)}}+\\dfrac{r^2}{L^2} \\;,\\label{metricfunc}\n\\end{equation}\nwhere the parameters $M, Q$ and $L$ are related to the BH mass $\\mathcal{M}$, charge $\\mathbb{Q}$ and cosmological constant $\\Lambda$ through\n\\begin{equation}\nM=\\dfrac{16\\pi G\\mathcal{M}}{nS_n}\\;\\;,Q^2=\\dfrac{8\\pi G\\mathbb{Q}^2}{n(n-1)}\\;\\;,L^2=-\\dfrac{n(n+1)}{2\\Lambda}\\;,\\nonumber\n\\end{equation}\nand the area of a unit $n$-sphere is $S_n=\\frac{2\\pi^{\\frac{n+1}{2}}}{\\Gamma(\\frac{n+1}{2})}$.\nThe Hawking temperature is given by\n\\begin{equation}\nT=\\dfrac{1}{4\\pi}\\left[\\frac{(n-1)M}{r_+^n}-\\frac{2(n-1)Q^2}{r_+^{2n-1}}+\\frac{2r_+}{L^2}\\right]\\;,\\label{hawkingtemp}\n\\end{equation}\nwhere the event horizon $r_+$ is determined as the largest root of $f(r_+)=0$. For non-extremal BHs, we have $Q\\mu^2\\ge -(n+1)^2\/(4L^2)$. This is the well known Breitenlohner-Freedman bound already discussed in the introduction. In particular one may see that the bound is more negative for higher dimensional spaces. This is the reason why one may violate the bound for two dimensional AdS but obey it for four dimensional AdS.\n\nWhen the BH effects are taken into account, a correction to the frequency (which may be complex) will be generated\n\\begin{equation}\n\\omega=\\omega_N+i\\delta\\;,\n\\end{equation}\nwhere the real part of $\\delta$ is used to describe the damping of the quasinormal modes.\nThen, for small BHs, using the approximation $1\/\\Gamma(-N+\\epsilon)\\simeq (-1)^NN!\\epsilon$ for small $\\epsilon$, the first term inside the bracket of Eq.~\\eqref{farsolution@near} becomes\n\\begin{equation}\n{\\rm R}^{\\rm far}_{1\/r} = (-1)^{N+1} i\\delta N! \\dfrac{\\Gamma(\\ell+\\frac{n-1}{2})L^{n+\\ell}}{2\\Gamma(a)} \\;.\\nonumber\n\\end{equation}\nFinally, observe that there appears to be extra poles in ${\\rm R}_r^{\\rm far}$, Eq.~\\eqref{far_branches}, due to the Gamma function $\\Gamma(-\\ell-\\frac{n-1}{2})$ for odd $n$. In the ${\\rm R}_r^{\\rm far}$ expression, however, due to Eq.~\\eqref{AdSfrequency}, $\\Gamma(1-b)=\\Gamma(-\\ell-\\frac{n-1}{2}-N)$, which cancels the former poles.\n\n\n\\subsection{Overlap region}\nTo match the near region solution~(\\ref{NearsolFar}) and the far region solution~(\\ref{farsolution@near}) in the intermediate region, we impose the matching condition ${\\rm R}^{\\rm near}_r{\\rm R}^{\\rm far}_{1\/r}={\\rm R}^{\\rm far}_r{\\rm R}^{\\rm near}_{1\/r}$. Then $\\delta$ can be obtained perturbatively\n\\begin{align}\n\\delta=&(-1)^{N}2i\\dfrac{(r_+^{n-1}-r_-^{n-1})^{2\\alpha-1}}{N!L^{2\\ell+n}} \\dfrac{\\Gamma(1-2\\alpha)\\Gamma(\\alpha)}{\\Gamma(2\\alpha-1)\\Gamma(1-\\alpha)} \\dfrac{\\Gamma(a)}{\\Gamma(1-b)\\Gamma(c-b)} \\dfrac{\\Gamma(-\\ell-\\frac{n-1}{2})}{\\Gamma(\\ell+\\frac{n-1}{2})} \\nonumber\\\\ &\\times \\dfrac{\\Gamma(\\alpha-2i\\bar{\\omega})}{\\Gamma(1-\\alpha-2i\\bar{\\omega})}\\;.\\label{imaginarypart}\n\\end{align}\n\n\n\\section{Analytical result analysis}\n\\label{result analysis}\nTo analyze Eq.~\\eqref{imaginarypart}, we shall simplify the Gamma functions therein. Firstly, the following combination, which is independent of the relation between $\\ell$ and $n$, can be simplified as\n\\begin{align}\n\\dfrac{\\Gamma(a)}{\\Gamma(1-b)\\Gamma(c-b)}\\dfrac{\\Gamma\\left(-\\ell-\\frac{n-1}{2}\\right)}{\\Gamma\\left(\\ell+\\frac{n-1}{2}\\right)}\n=&\\dfrac{(-1)^N}{\\Gamma\\left(\\ell+\\frac{n-1}{2}\\right)} \\dfrac{\\Gamma\\left(N+\\frac{n+1}{2}+\\ell+\\sqrt{\\mu^2L^2+(\\frac{n+1}{2})^2}\\right)}{\\Gamma\\left(N+1+\\sqrt{\\mu^2L^2+(\\frac{n+1}{2})^2}\\right)}\\nonumber\\\\\n&\\times\\prod_{k=1}^{N}\\left(\\ell+\\frac{n-1}{2}+k\\right)\\;.\\nonumber\n\\end{align}\nThen one has to consider the following cases separately, because the simplification for the other Gamma functions in Eq.~\\eqref{imaginarypart} depends on the relation between $\\ell$ and $n$.\n\n\\subsection{$\\ell$ is an integer multiple of $(n-1)$}\nFor this case we can write $\\ell=p(n-1)$, where $p$ is a non-negative integer.\nThen, the corresponding Gamma functions in Eq.~\\eqref{imaginarypart} can be simplified to\n\\begin{equation}\n\\dfrac{\\Gamma(1-2\\alpha)\\Gamma(\\alpha)}{\\Gamma(2\\alpha-1)\\Gamma(1-\\alpha)}=\\dfrac{(-1)^{p+1}}{2}\\dfrac{(p!)^2}{(2p)!(2p+1)!}\\;,\\nonumber\n\\end{equation}\n\\begin{equation}\n\\dfrac{\\Gamma(\\alpha-2i\\bar{\\omega})}{\\Gamma(1-\\alpha-2i\\bar{\\omega})}=(-1)^{p+1} 2i\\bar{\\omega} \\prod_{k^{\\prime}=1}^p(k^{\\prime 2}+4\\bar{\\omega}^2)\\;.\\nonumber\n\\end{equation}\nTherefore, Eq.~\\eqref{imaginarypart} becomes\n\\begin{align}\n\\delta=&-2\\bar{\\omega}\\dfrac{(r_+^{n-1}-r_-^{n-1})^{1+\\frac{2\\ell}{n-1}}}{N! L^{2\\ell+n}\\Gamma\\left(\\ell+\\frac{n-1}{2}\\right)}\\dfrac{(p!)^2}{(2p)!(2p+1)!}\n\\dfrac{\\Gamma\\left(N+\\frac{n+1}{2}+\\ell+\\sqrt{\\mu^2L^2+(\\frac{n+1}{2})^2}\\right)}{\\Gamma\\left(N+1+\\sqrt{\\mu^2L^2+(\\frac{n+1}{2})^2}\\right)}\\nonumber\\\\ &\\times\\prod_{k=1}^N\\left(\\ell+\\frac{n-1}{2}+k\\right) \\prod_{k^{\\prime}=1}^p (k^{\\prime 2}+4\\bar{\\omega}^2)\\;.\\label{case1}\n\\end{align}\nThis equation shares a similar structure to the corresponding result in $D=4$. From the definition of $\\bar{\\omega}$ in Eq.~\\eqref{superradiance}, we find that in the superradiant regime, $\\bar{\\omega}<0$ which implies $\\delta>0$. In this superradiant regime the wave function of the scalar field will grow with time which means the BH is unstable. Moreover, from Eq.~\\eqref{superradiance}, one may get a condition for the onset of the superradiant instability, i.e.\n\\begin{equation}\n\\dfrac{Q}{Q_c} > \\sqrt{\\frac{2(n-1)}{n}} \\dfrac{\\omega_N}{q}\\;,\\label{onset1}\n\\end{equation}\nwhere $\\omega_N$ is given in Eq.~\\eqref{AdSfrequency}. For a massless field, Eq.~\\eqref{onset1} simplifies to\n\\begin{equation}\n\\dfrac{Q}{Q_c} > \\sqrt{\\frac{2(n-1)}{n}} \\dfrac{2N+n+1+\\ell}{q L}\\;.\\label{onset2}\n\\end{equation}\n\n\n\\subsection{$\\ell$ is not an integer multiple of $(n-1)$}\nFor this case, the corresponding Gamma function in Eq.~\\eqref{imaginarypart} can be simplified as\n\\begin{equation}\n\\dfrac{\\Gamma(1-2\\alpha)\\Gamma(\\alpha)}{\\Gamma(2\\alpha-1)\\Gamma(1-\\alpha)}=-\\dfrac{1}{2\\cos\\frac{\\pi \\ell}{n-1}}\\dfrac{\\Gamma^2(1+\\frac{\\ell}{n-1})}{\\Gamma(1+\\frac{2\\ell}{n-1})\\Gamma(2+\\frac{2\\ell}{n-1})}\\;.\\nonumber\n\\end{equation}\nIf $\\dfrac{\\ell}{n-1}\\neq p+\\frac{1}{2}$, then cos$\\frac{\\pi\\ell}{n-1}\\neq0$, and the parameter $\\delta$ becomes complex (not simply real as in the previous case).\nIn this case the real part of $\\delta$ reflects the instability, which is given by\n\\begin{align}\n\\mbox{Re} \\delta=&-2\\bar{\\omega}\\dfrac{(r_+^{n-1}-r_-^{n-1})^{1+\\frac{2\\ell}{n-1}}}{N! L^{2\\ell+n}} \\dfrac{\\Gamma^4(1+\\frac{\\ell}{n-1})}{\\Gamma(1+\\frac{2\\ell}{n-1})\\Gamma(2+\\frac{2\\ell}{n-1})\\Gamma(\\ell+\\frac{n-1}{2})}\\prod_{k=1}^N(\\ell+\\frac{n-1}{2}+k) \\nonumber\\\\\n&\\times\\dfrac{\\Gamma(N+\\frac{n+1}{2}+\\ell+\\sqrt{\\mu^2L^2+(\\frac{n+1}{2})^2})}{\\Gamma(N+1+\\sqrt{\\mu^2L^2+(\\frac{n+1}{2})^2})}\\;,\\label{case2}\n\\end{align}\nwhere we have expanded the terms $\\Gamma(x-2i\\bar{\\omega})$ around small $\\bar{\\omega}$ to clearly distinguish the superradiant regime. Thus, when $\\bar{\\omega}<0$, we obtain Re$\\delta$ $>0$ which implies that the BH is also unstable, and the corresponding onset of such instability is governed by Eq.~\\eqref{onset1} for a massive field and Eq.~\\eqref{onset2} for a massless field.\n\nIf $\\frac{\\ell}{n-1}=p+\\frac{1}{2}$, the matching method fails; a similar situation occurs for extremal Kerr BHs~\\cite{Rosa:2009ei}. In order to make this point clear, we can do the following analysis. First, from the definition of $\\alpha$ in Eq.~\\eqref{alpha} and the condition $\\frac{\\ell}{n-1}=p+\\frac{1}{2}$, one observes that the first expansion term inside the brackets of Eq.~\\eqref{NearsolFar} is divergent, which means that we cannot expand Eq.~\\eqref{NearSol} into Eq.~\\eqref{NearsolFar} anymore when $\\frac{\\ell}{n-1}=p+\\frac{1}{2}$. Alternatively, using a property of the hypergeometric function~\\cite{abramowitz+stegun}, we shall expand Eq.~\\eqref{NearSol} as\n\\begin{equation}\n{\\rm R} \\sim \\Gamma(1-2i\\bar{\\omega})\\left[-\\dfrac{(r_+^{n-1} - r_-^{n-1})^\\alpha \\zeta}{\\Gamma(1-\\alpha)\\Gamma(1-\\alpha-2i\\bar{\\omega})\\Gamma(2\\alpha)}\\dfrac{1}{r^{n-1+\\ell}}+\\dfrac{\\Gamma(2\\alpha-1)(r_+^{n-1}-r_-^{n-1})^{1-\\alpha}}{\\Gamma(\\alpha)\\Gamma(\\alpha-2i\\bar{\\omega})}r^{\\ell}\\right]\\;,\n\\label{NearsolFar2}\n\\end{equation}\nwith\n\\begin{equation}\n\\zeta=\\log\\left(\\dfrac{r_+^{n-1}-r_-^{n-1}}{r^{n-1}}\\right)+\\gamma+\\psi(\\alpha)-\\psi(2\\alpha)+\\psi(\\alpha-2i\\bar{\\omega})\\;,\\nonumber\n\\end{equation}\nwhere $\\gamma$ is the Euler constant and $\\psi(x)$ denotes the digamma function.\nBecause the $\\log r$ term is associated with distinct powers of $r$, it is impossible to match Eqs.~\\eqref{farsolution@near} and \\eqref{NearsolFar2}. For this case, we have to resort to a numerical solution, which is discussed in the next section.\n\n\n\\section{Numerical results}\n\\label{numerical}\nIn order to confirm the above analytical results and to calculate the quasinormal frequencies for the special cases $\\frac{\\ell}{n-1}=p+\\frac{1}{2}$ where the analytical method fails, we shall solve, in this section, Eq.~\\eqref{RadialEq2} numerically. We use a direct numerical integration method to obtain the quasinormal frequency of the BH. To do so, taking the boundary conditions near the horizon in Eq.~\\eqref{boundaryingoing} and at infinity in Eq.~\\eqref{boundarydecaying}, we expand the radial function near the horizon as\n\\begin{equation}\n{\\rm X} \\sim e^{-i(\\omega-\\omega_0)r_\\ast} \\sum_{j=0}^{\\infty}\\alpha_j(r-r_+)^j\\;,\\label{nearHExp}\n\\end{equation}\nand at infinity as\n\\begin{equation}\n{\\rm X} \\sim r^{-\\frac{1}{2}(1+\\sqrt{4\\mu^2L^2+(n+1)^2})} \\sum_{j=0}^{\\infty}\\frac{\\beta_j}{r^j}\\;.\\label{infExp}\n\\end{equation}\nThe series expansion coefficients can be derived directly after inserting these expansions into Eq.~(\\ref{RadialEq2}).\nWe use the series expansion near the horizon Eq.~\\eqref{nearHExp} to initialize the radial system Eq.~\\eqref{RadialEq2} from a point $r_s$ which is close to $r_+$ through the relation $r_s=(1+0.01)r_+$, and integrate the radial system outwards up to a radial value $r_m$. Similarly we can also use Eq.~\\eqref{infExp} as initial condition to integrate the radial system inward from $r_l=1000r_+$ down to $r_m$. Then we have two solutions at an intermediate radius $r_m$, and these two solutions are linearly dependent if their Wronskian vanishes at $r_m$. Using a secant method one can solve $W(\\omega,r_m)=0$ iteratively to look for the quasinormal frequency of the BH. We also varied $r_s$, $r_m$ and $r_l$ to check the numerical accuracy.\n\nWe list some numerical results in Tables~\\ref{3DRNAdS}-\\ref{compmass2}. Note that all physical quantities are normalized by the AdS radius $L$ and we set $L=1$. In the first three tables, we focus on the fundamental modes of massless fields because they are typically the most unstable modes. To check the mass effect on the validity of the analytical formulas, we also consider $\\mu=0.5$ and $\\mu=3.0$ in the last two tables. As a check of our numerical method, we have calculated the quasinormal frequencies for small Schwarzschild-AdS BHs and we obtained results which are in good agreement with those reported in~\\cite{Konoplya:2002zu}.\n\nIn order to address the special case for which the analytical method fails, i.e when $\\ell=(p+\\frac{1}{2})(n-1)$, we chose $n=3$ (five dimensional spacetime) and $\\ell=1$, corresponding to $p=0$ in our condition. The results are shown in Table~\\ref{3DRNAdS}, with $r_+=0.1$, field mass $\\mu=0$ and field charge $q=8$. It shows clearly that a superradiant instability appears when $Q\/Q_c$ satisfies the condition in Eq.~\\eqref{onset2}. Moreover, we also list numerical results for the $\\ell=0$ mode in Table~\\ref{3DRNAdS}. It shows that the frequencies of the odd modes ($\\ell=1$) and even modes ($\\ell=0$) have a similar behavior; in other words, there is nothing special for odd modes.\n\\begin{table}\n\\caption{\\label{3DRNAdS} Frequencies of the fundamental modes with different $\\ell$ for a BH with $r_+=0.1$, $q=8$, $\\mu=0$ in $D=5$.}\n\\begin{center}\n\\begin{tabular*}{\\textwidth}{@{\\extracolsep{\\fill}} l l l }\n\\hline\n\\hline\n$Q\/Q_c$ & $\\ell$=0 & $\\ell$=1 \\\\\n\\hline\n0.1 & 3.958 - 1.335$\\times 10^{-2}$ i & 4.978 - 2.689$\\times 10^{-4}$ i\\\\\n0.3 & 3.997 - 6.435$\\times 10^{-3}$ i & 4.998 - 1.367$\\times 10^{-4}$ i\\\\\n0.5 & 4.030 - 1.522$\\times 10^{-3}$ i & 5.014 - 5.053$\\times 10^{-5}$ i\\\\\n0.7 & 4.058 + 1.996$\\times 10^{-3}$ i & 5.028 - 2.596$\\times 10^{-6}$ i\\\\\n0.8 & 4.070 + 3.198$\\times 10^{-3}$ i & 5.034 + 7.524$\\times 10^{-6}$ i\\\\\n0.9 & 4.081 + 3.954$\\times 10^{-3}$ i & 5.040 + 9.597$\\times 10^{-6}$ i\\\\\n\\hline\n\\hline\n\\end{tabular*}\n\\end{center}\n\\end{table}\n\nTo confirm the validity of the analytical quasinormal frequency formulas in Eqs.~\\eqref{case1} and \\eqref{case2}, we also compare some analytical results with numerical data in Tables~\\ref{comp1}-\\ref{compmass2}. In Table~\\ref{comp1}, we present analytical results obtained from Eq.~\\eqref{case1} and numerical results with $r_+=0.01$, $q=6$ for the $\\ell=0$ massless fundamental mode in five dimensional spacetimes. They show good agreement; the difference is smaller than 1\\%. In Table~\\ref{comp2}, we present analytical results obtained from Eq.~\\eqref{case2} and numerical data for the $\\ell=1$ fundamental mode with $r_+=0.01$, $q=10$, $\\mu=0$ in $D=6$, and they show good agreement as well. From these two tables, we confirm the validity of the analytical matching method for \\mbox{$\\mu=0$}. Results for non-zero mass are reported in Tables~\\ref{compmass1} and~\\ref{compmass2}. Two conclusions may be drawn from these tables. First, as the mass increases the agreement between the analytical and numerical method becomes worse. This is expected in view of the discussion of the approximation employed in Section~\\ref{secmatching}. Second, as the mass increases, the mode with $Q\/Q_c=0.9$ becomes stable. This is in agreement with Eq.~\\eqref{onset1} since, for the parameters in Table~\\ref{compmass2}, superradiance is only expected for $Q\/Q_c \\gtrsim 1.1$.\n\\begin{table}[h]\n\\caption{\\label{comp1} Comparison of the frequencies for the $\\ell=0$ fundamental modes of a BH with $r_+=0.01$, $q=6$, $\\mu=0$ in $D=5$.}\n\\begin{tabular*}{\\textwidth}{@{\\extracolsep{\\fill}} l l l }\n\\hline\n\\hline\n$Q\/Q_c$ & Im$(\\omega)$ (numerical) & Im$(\\omega)$ (analytical) \\\\\n\\hline\n0.1 & -1.053$\\times 10^{-5}$ & -1.0441$\\times 10^{-5}$ \\\\\n0.3 & -7.369$\\times 10^{-6}$ & -7.3230$\\times 10^{-6}$ \\\\\n0.5 & -4.222$\\times 10^{-6}$ & -4.2050$\\times 10^{-6}$ \\\\\n0.7 & -1.088$\\times 10^{-6}$ & -1.0870$\\times 10^{-6}$ \\\\\n0.9 & 2.023$\\times 10^{-6}$ & 2.0310$\\times 10^{-6}$ \\\\\n\\hline\n\\hline\n\\end{tabular*}\n\\end{table}\n\\begin{table}[h]\n\\caption{\\label{comp2} Comparison of the frequencies for the $\\ell=1$ fundamental modes of a BH with $r_+=0.01$, $q=10$, $\\mu=0$ in $D=6$.}\n\\begin{tabular*}{\\textwidth}{@{\\extracolsep{\\fill}} l l l }\n\\hline\n\\hline\n$Q\/Q_c$ & Im$(\\omega)$ (numerical) & Im$(\\omega)$ (analytical) \\\\\n\\hline\n0.1 & -4.377$\\times 10^{-11}$ & -4.3678$\\times 10^{-11}$ \\\\\n0.3 & -2.830$\\times 10^{-11}$ & -2.8274$\\times 10^{-11}$ \\\\\n0.5 & -1.342$\\times 10^{-11}$ & -1.3418$\\times 10^{-11}$ \\\\\n0.7 & -1.538$\\times 10^{-12}$ & -1.5371$\\times 10^{-12}$ \\\\\n0.8 & 2.283$\\times 10^{-12}$ & 2.2846$\\times 10^{-12}$ \\\\\n0.9 & 3.778$\\times 10^{-12}$ & 3.7844$\\times 10^{-12}$ \\\\\n\\hline\n\\hline\n\\end{tabular*}\n\\end{table}\n\\begin{table}[h]\n\\caption{\\label{compmass1} Comparison of the frequencies for the $\\ell=0$ fundamental modes of a BH with $r_+=0.01$, $q=6$, $\\mu=0.5$ in $D=5$.}\n\\begin{tabular*}{\\textwidth}{@{\\extracolsep{\\fill}} l l l }\n\\hline\n\\hline\n$Q\/Q_c$ & Im$(\\omega)$ (numerical) & Im$(\\omega)$ (analytical) \\\\\n\\hline\n0.1 & -1.093$\\times 10^{-5}$ & -1.0844$\\times 10^{-5}$ \\\\\n0.3 & -7.711$\\times 10^{-6}$ & -7.6617$\\times 10^{-6}$ \\\\\n0.5 & -4.498$\\times 10^{-6}$ & -4.4797$\\times 10^{-6}$ \\\\\n0.7 & -1.300$\\times 10^{-6}$ & -1.2977$\\times 10^{-6}$ \\\\\n0.9 & 1.878$\\times 10^{-6}$ & 1.8842$\\times 10^{-6}$ \\\\\n\\hline\n\\hline\n\\end{tabular*}\n\\end{table}\n\\begin{table}\n\\caption{\\label{compmass2} Comparison of the frequencies for the $\\ell=0$ fundamental modes of a BH with $r_+=0.01$, $q=6$, $\\mu=3.0$ in $D=5$.}\n\\begin{tabular*}{\\textwidth}{@{\\extracolsep{\\fill}} l l l }\n\\hline\n\\hline\n$Q\/Q_c$ & Im$(\\omega)$ (numerical) & Im$(\\omega)$ (analytical) \\\\\n\\hline\n0.1 & -2.379$\\times 10^{-5}$ & -2.3423$\\times 10^{-5}$ \\\\\n0.3 & -1.886$\\times 10^{-5}$ & -1.8637$\\times 10^{-5}$ \\\\\n0.5 & -1.399$\\times 10^{-5}$ & -1.3850$\\times 10^{-5}$ \\\\\n0.7 & -9.155$\\times 10^{-6}$ & -9.0632$\\times 10^{-6}$ \\\\\n0.9 & -4.321$\\times 10^{-6}$ & -4.2765$\\times 10^{-6}$ \\\\\n\\hline\n\\hline\n\\end{tabular*}\n\\end{table}\n\n\n\n\\section{Summary}\n\\label{discussion}\nWe have studied the superradiant instability of small charged AdS BHs in $D$ dimensions, in the presence of a charged scalar field. Very recently our result has been generalized to the $D$-dimensional singly rotating Myers-Perry BHs, where the same conclusion was drawn, i.e. the superradiant instability exist for all $\\ell$ modes in \\textit{all} dimensions~\\cite{Aliev:2015wla,Delice:2015zga}.\n\nFirst, we solved the Klein-Gordon equation for a charged scalar field in charged AdS BHs with a standard matching method. We found that the relation between the angular momentum quantum number $\\ell$ and the spacetime dimension $D$ plays an important role in determining the analytical quasinormal frequency formula. When $\\ell=p(D-3)$, for a non-negative integer $p$, we found that the quasinormal frequencies of the small RN-AdS BHs have only an imaginary correction to the AdS normal frequencies. This is the case for all modes (i.e. all $\\ell$) in $D=4$, even $\\ell$ in $D=5$, $\\ell=0,3,6,9,\\dots$ in $D=6$, $\\ell=0,4,8,12,\\dots$ in $D=7$ and so on.\n\nA more subtle case occurs when $\\ell=(p+\\frac{1}{2})(D-3)$. For this case the matching method fails because a $\\log r$ term appears in the near region solution --~Eq.~\\eqref{NearsolFar2} -- which cannot be matched to Eq.~\\eqref{farsolution@near}. Failure to observe this limitation has led to a claim that odd $\\ell$ modes in $D=5$ did not exhibit superradiance~\\cite{Aliev:2008yk}. Here we have shown otherwise that the superradiant instability indeed exists using a numerical method which is mandatory for analyzing this case, in view of the invalidity of the matching method. A similar conclusion applies to all cases defined by $\\ell=(p+\\frac{1}{2})(D-3)$, i.e, odd $\\ell$ in $D=5$, $\\ell=2,6,10,14,\\dots$ in $D=7$ and so on. Observe that this case can only occur in odd dimensions.\n\nFinally, all other cases have a complex correction to the AdS normal frequencies, i.e. the real part of the frequency is also shifted.\n\nOur analytic results show good agreement with the numerical results in Section~\\ref{numerical}. In particular a central conclusion is that all $\\ell$ modes in all dimensions, for sufficiently large field charge $q$ display superradiance. Moreover, in $D=4$, the dependence of the instability on the various parameters seems to be in qualitative agreement with the study of cavity BHs in $D=4$~\\cite{Herdeiro:2013pia,Hod:2013fvl,Degollado:2013bha}, and it would be interesting to make a more detailed comparison between the two cases.\n\nLet us close this study with two questions. First, is there a simple pattern for the behaviour of the frequencies as $D\\rightarrow \\infty$? A preliminary analysis could not unveil a simple formula. Finding such behavior would be relevant in view of the recent interest on General Relativity in the large $D$ limit \\cite{Emparan:2013moa}. Second, can one follow this instability numerically into the non-linear regime? It has been recently shown that in four dimensions the end-point of the instability is a hairy charged AdS BH~\\cite{Bosch:2016vcp}. It would be interesting to generalize such study to higher dimensions, to display how the properties of hairy BHs depend on the spacetime dimension.\n\n\\part{Scalar and Proca fields on asymptotically flat spacetimes}\n\\label{main:p1}\n\\include{GeneralInFlat}\n\\include{NeutralP}\n\\include{ChargedP}\n\\include{ChargedClouds}\n\\part{Scalar and Maxwell fields on asymptotically AdS spacetimes}\n\\label{main:p2}\n\\include{scalarHD}\n\\include{KerrAdS}\n\\include{conclusion}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbqmq b/data_all_eng_slimpj/shuffled/split2/finalzzbqmq new file mode 100644 index 0000000000000000000000000000000000000000..91658a82b6fc87f1f33185c69682bbb47c2794cf --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbqmq @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\\label{intro}\nThe last years have seen a generalization of Riemannian geometry to sub-Rie\\-ma\\-nnian geometry \\cite{agrachev2012, gromov144, montgomery2006, strichartz1989, strichartz1986}.\nA sub-Riemannian manifold is a connected manifold $G$ with a distribution $D\\subset TG$ such that successive Lie brackets fields in $D$ generate all the tangent space $TG$. In addition, a positive definite scalar product $\\langle\\cdot,\\cdot\\rangle$ is defined at $D$ such that it is possible to calculate the length of admissible curves, i.e., the tangent curves on $D$. As in Riemannian geometry, the distance $\\rho$ between two points $p$ and $q$ in $G$ is defined as the infimum length of admissible curves that connect the points $p$ and $q$. By means of this distance, the sub-Riemannian manifold becomes a metric space \\cite{Bellaiche1996} and can be endowed with a Hausdorff measure. Also, there is an equivalent of the Riemannian volume, the so called Popp's volume. Introduced in \\cite{montgomery2006}, it is a smooth volume which is canonically associated with the sub-Riemannian structure and was used in \\cite{agrachev2012} to define the sub-Laplacian in sub-Riemannian geometry.\n\nOn sub-Riemannian manifolds which are nilpotent Lie groups (stratified Lie gro\\-u\\-ps) the spherical Hausdorff measure, the Popp's measure and the Haar measure are mutually constant multiples one of another \\cite{Agrachev2012a, ghezzi2014, Magnani2005, mitchell1985, barilari2013}. Then, it is natural to ask what means a submanifold of\na stratified Lie group to be of minimal measure.\n\nThe aim of this paper is, using the measure proposed by Magnani and Vittone for non-horizontal submanifolds in stratified Lie groups \\cite{Magnani2008}, to calculate the first variation of these submanifolds and determine the necessary conditions for a non-horizontal submanifold to be of minimal measure. We will also give new examples of non-horizontal minimal submanifolds in the Heisenberg group, in particular minimal surfaces in 5-dimensional Heisenberg group . In order to present the main results, we briefly introduce some concepts which will be dealt with in detail in the subsequent sections.\n\nLet $\\mathbb G$ be a stratified Lie group with a graded Lie algebra $\\mathfrak{g}=\\mathfrak{g}^1\\oplus\\cdots\\oplus\\mathfrak{g}^r$ with $r\\geq1$. If $\\langle\\cdot,\\cdot\\rangle$ is a scalar product on $\\mathfrak g^1$, we can extend it to $\\mathfrak g$ by induction (see Proposition \\ref{extend}).\nWe also consider the distribution $D\\subset T\\mathbb G$ generated by $\\mathfrak g^1$ and the scalar product in $D$ generated by $\\langle\\cdot,\\cdot\\rangle$. This way, $(\\mathbb G,D,\\langle\\cdot,\\cdot\\rangle)$ becomes a sub-Riemannian manifold, also called the Carnot group. Note that, traditionally the scalar product on $D$ is extended to $T\\mathbb G$ and the Riemannian connection on $T\\mathbb G$ is used to make calculations on $\\mathbb G$. This observation will be our starting point. We shall work with a covariant derivative $\\overline\\nabla$ defined by $\\overline\\nabla X=0$ for all $X\\in\\mathfrak g$. It has intrinsic torsion which is essentially the negative of the Lie bracket in $\\mathbb G$ and the zero curvature tensor. So, this covariant derivative permits us to establish an interesting parallel between the invariants of submanifolds in $\\mathbb R^n$ and the invariants of submanifolds in $\\mathbb G$.\n\nThe geometry of a submanifold $M$ of $\\mathbb G$ at each point depends on the relative position of\n$TM$ and $D$. The submanifolds with a ``high'' contact with $D$ at one point may have\nsingularities from the metric point of view, even though being a $C^\\infty$ submanifold. In this paper,\nwe avoid these situations by considering a non-horizontal submanifold $M$ transverse to D,\ni.e., $TM+D=T\\mathbb G$. For this submanifold the horizontal normal subspace $TM^\\perp$ play the same role as the normal space does to a submanifold in $\\mathbb R^n$. We define $TM^\\perp$ as the orthogonal subspace to $TM\\cap D$ in $D$, i.e., the horizontal distribution $D=(TM\\cap D)\\oplus TM^\\perp$. Hence, $T\\mathbb G=TM\\oplus TM^\\perp$ and we can use this decomposition\n(in general not orthogonal!) to project $\\overline\\nabla$ to a connection $\\nabla$ over $TM$.\n\nLet $e_1,\\ldots, e_n$ be a orthonormal basis of $\\mathfrak{g}$ with its dual $e^1,\\ldots,e^n$. Let $f_1,\\ldots,f_n$ be a adapted frame in the $T\\mathbb{G}$ such that $f_1,\\ldots,f_p$ are orthogonal to $TM\\cap D$ and $f_{p+1},\\ldots,f_{d_1}$ is a orthonormal basis of $TM\\cap D$ in $D$. We complete $f_{p+1},\\ldots,f_{d_1}$ to a basis $f_{p+1},\\ldots,f_n$ of $TM$ taking\n$\nf_j=e_j-\\sum_{\\alpha=1}^pA_j^\\alpha f_{\\alpha},\n$\nfor $j=d_1+1,\\ldots,n$. If we denote by $f^1,\\ldots, f^n$ its dual basis, then the sub-Riemannian volume form on $\\mathbb G$ is defined as $\\dif V=e^1\\wedge\\cdots\\wedge e^n=f^1\\wedge\\cdots\\wedge f^n$. When $M$ is a hypersurface, the $H$-perimeter measure is traditionally used \\cite{Montefalcone2007, Montefalcone2012, Danielli2007, Hladky2013}. For the non-horizontal submanifolds of codimension $p\\geq 1$, the spherical Hausdorff measure has the following representation proved in \\cite{Magnani2008}:\n\\begin{equation}\\label{M}\n\\int_M\\theta(\\tau^d_M(x))\\dif S^d_\\rho(x)=\\int_M|\\tau^d_{M}(x)|\\dif\\mbox{vol}_{h}(x) \\ ,\n\\end{equation}\nwhere $h$ is a fixed Riemannian metric, $d$ is the Hausdorff dimension of $M$, $\\theta(\\tau^d_M(x))$ is the metric factor (see Section \\ref{hspace}), $S^d_\\rho$ is the\t $d$-spherical Hausdorff measure and $\\dif\\mbox{vol}_{h}$ is the Riemannian volume form on $M$ induced by $(\\mathbb G,h)$.\n\nWe will denote by $\\mu$ the measure in non-horizontal submanifolds defined by $\\dif\\mu(x)=|\\tau^d_{M}(x)|\\dif\\mbox{vol}_{h}(x)$, which is a nat\\-u\\-ral can\\-di\\-date to de\\-fine the vol\\-ume for non-horiz\\-ontal submanifolds.\nIf the metric factor $\\theta(\\tau^d_M(x))$ is constant on $M$ (the case of the Heisenberg group $\\mathbb{H}^n$, see Section \\ref{hspace}) the measure $\\dif\\mu(x)$ is a multiple of $(Q-p)$-spherical Hausdorff measure on $M$. Writing the density $\\dif\\mu$ in the adapted frame $f^1,\\ldots,f^n$ we obtain a beautiful formula which we will use for the variational calculation, namely\n$\n\\dif \\mu=f^{p+1}\\wedge\\cdots\\wedge f^n\n$ (see Theorem \\ref{dS}).\n\nThe second result of this paper (Theorem \\ref{firstvar}) is a sufficient condition for minimality of non-horizontal submanifolds. We say that a non-horizontal submanifold is minimal if $H+\\sigma=0$ on $TM^\\perp$, where $H$ is the mean curvature (Definition \\ref{curvmedia}) and $\\sigma$ is the mean torsion (Definition \\ref{colchete}). In the case of hypersurfaces, the mean torsion is null and so the definition of minimality is the same as in \\cite{Montefalcone2007, Montefalcone2012, Danielli2007, Hladky2013, Hurtado2010, Ritore2008} and also of minimal submanifolds of Riemannian geometry.\n\nAs a direct application of Theorem \\ref{firstvar} we present the following example: if $M$ is minimal submanifold of $\\mathbb R^{2n}$, then $N=M\\times\\mathbb R$ is minimal submanifold of $\\mathbb{H}^n$.\nFurthermore, an interesting application of this theorem is discussed in section \\ref{H2}, where we find minimal non-horizontal surfaces in the $5$-dimensional Heisenberg group $\\mathbb H^2$. Observe that in the $3$-dimensional Heisenberg group $\\mathbb H^1$ the tangent horizontal curves to minimal surfaces are lines and hence this surfaces are ruled surfaces \\cite{Pansu1982}. Now, for $\\mathbb H^2$ the tangent horizontal curves to minimal surfaces can be more general. In section \\ref{H2}, we will present two cases: the curves are lines and we obtain ruled surfaces; the curves are circles and we obtain a family of circles, which we will call tubular surfaces.\n\nIn the last section, we prove for non-horizontal hypersurfaces the following: $-H_{f_1}=\\mbox{div}_{\\mathbb G}\\Big(\\frac{\\mbox{grad}_{D}\\phi}{|\\mbox{grad}_{D}\\phi|}\\Big)$, where $\\phi:\\mathbb G\\rightarrow\\mathbb R$ is smooth function, $\\mbox{div}_{\\mathbb G}$ is the divergence function on $\\mathbb G$ and $\\mbox{grad}_D$ is the horizontal gradient operator. With this formula we give a proof that the hyperboloid paraboloid is minimal.\n\n\\section{Stratified Lie groups}\\label{slg}\n\nA stratified Lie group $\\mathbb{G}$ is an $n$-dimensional connected, simply connected nilpotent Lie group whose Lie algebra $\\mathfrak{g}$ decomposes as $\\mathfrak{g}=\\mathfrak{g}^1\\oplus\\mathfrak{g}^2\\oplus\\cdots\\oplus\\mathfrak{g}^r$ and satisfies the condition $[\\mathfrak{g}^1,\\mathfrak{g}^j]=\\mathfrak{g}^{j+1}, j=1,\\ldots, r-1,\\quad [\\mathfrak{g}^j,\\mathfrak g^r]=0, j=1,\\ldots,r$. Write $d_i=\\dim\\mathfrak{g}^i$, choose a basis $e_1,\\ldots,e_n$ of $\\mathfrak{g}$ such that $e_{d_{j-1}+1},\\ldots,e_{d_j}$ is a basis of $\\mathfrak{g}^j$ and denote its dual basis by $e^1,\\ldots,e^n$. Then, we define the \\emph{degree} of $e_k$ as $\\deg k=j$ if $d_{j-1} 1$}\n\\label{tab:coeff}\n\\end{table*}\n\nFigure \\ref{fig:risk} shows the distribution of predicted risk values. The figure shows that even with all the features listed in Table \\ref{tab:variables}, there is still a considerable overlap of predicted probabilities between case and control patients. Better separation between these two classes can improve the risk prediction accuracy. The plot suggests incorporation of additional diagnoses or temporal aspects of existing diagnoses may be necessary to improve model performance.\n\n\\begin{figure}[htb]\n\\begin{center}\n\\includegraphics[width=0.49\\textwidth]{Risk.pdf}\n\\end{center}\n\\caption{Box plot of the predicted probabilities using a forward selection model on all the features.}\n\\label{fig:risk}\n\\end{figure}\n\nFigure \\ref{fig:perf} contains the performance plots for the forward selection models trained on feature set 1 and feature set 9. Figure \\ref{fig:roc} demonstrates the noticeable improvement using all the available features. Additionally, the model trained on feature set 1, demographics and family history features, barely outperforms random chance. The tradeoff between sensitivity, specificity, and positive predictive value can be seen in Figure \\ref{fig:ppv-sens}. Feature set 9 has a higher intersection between the sensitivity and specificity curves, which is summarized in Table \\ref{tab:intersect}. In addition, the full-featured model generally achieves a better positive predictive value for all threshold values. However, the positive predictive value and sensitivity curves cross at the value $\\sim 0.40$. At this point, we can accurately diagnose 40\\% of the case patients, but only 2 out of every 5 patients predicted to have a high risk of MS will be diagnosed with MS at the next office visit, a high number of false positives.\n\n\\begin{figure*}[htb]\n\\subfigure[ROC curves compared to random assignment]{\n\\includegraphics[width=0.48\\textwidth]{ROCR.pdf}\n\\label{fig:roc}\n}\n\\subfigure[Sensitivity, specificity, and positive predictive value as function of threshold]{\n\\includegraphics[width=0.48\\textwidth]{Threshold.pdf}\n\\label{fig:ppv-sens}\n}\n\\caption{Model performance plots for feature sets 1 and 9.}\n\\label{fig:perf}\n\\end{figure*}\n\n\\begin{table}[htb]\n\\begin{center}\n\\begin{tabular}{l c c c c}\n\\hline\nFeature Set & Cutoff & Sensitivity & Sensitivity & PPV \\\\\n\\hline\n1 & 0.212 & 0.528 & 0.528 & 0.218\\\\\n9 & 0.241 & 0.647 & 0.647 & 0.314 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\caption{The intersection of the sensitivity and specificity curve from Figure \\ref{fig:ppv-sens}.}\n\\label{tab:intersect}\n\\end{table}\n\\subsection{Discussion}\nThe results demonstrate reasonable predictive accuracy using all the available features. One potential hindrance lies in the current feature construction. As Figure \\ref{fig:encounter} shows, there are a limited number of encounters prior to $t_0$ for case patients. Thus, it is difficult to determine whether an unobserved diagnosis may be due to the lack of longitudinal data (the patient was diagnosed prior to the study period). Additionally, certain diagnoses, such as EBV, can only be verified through culture samples which are not performed for every patient.\n\nAnother limitation of our study is the reliance on ICD-9 and procedure codes. A patient may exhibit all the clinical symptoms for a specific disease but it is not present in the encounter data because the disorder has not been diagnosed. The ambiguity of ICD9-codes and diagnostic discrepancies between medical doctors can also impact our feature construction. Moreover, the blood test results' conversion to a categorical feature may be inaccurate as the testing protocol may have changed during the study window. Therefore, a patient's feature vector may not accurately reflect their medical history.\n\nOur study also suggests incorporating additional features. Given that some of the variables were unrecorded in the structured portion of the EMR, parsing through the clinical notes could result in information regarding lifestyle factors, diet, detailed family and medical history. In addition, temporal aspects of the medical diagnoses were not included in our feature set since the data was confined to medical encounters over a 6-year period. \n\n\\section{Conclusion}\nThis paper presented a risk prediction model from EMRs to help address the difficulty of early diagnosis in MS patients. A sparse set of features were selected to minimize model complexity while maintaining reasonable predictive performance. Our results show we are able to help identify patients at high-risk of developing MS, in spite of a limited sample of patient data. In addition, our models have the ability to generalize to other healthcare systems as we rely only on components commonly found in electronic patient data.\n\nThe work demonstrates the potential of leveraging EMRs to aid medical professionals with difficult tasks, especially with early disease diagnosis. Future work will focus on incorporating temporal components, such as time of diagnosis, into the model, decreasing the false positive rate, and integrating a larger control population.\n\n\\section{Acknowledgments}\nWe thank Afif Hentati and Demetrius \"Jim\" Maraganore for their guidance, advice and comments on this study. We acknowledge comments from and conversations with Kibaek Kim, Yubin Park, Xiang Zhong, and Sanjay Mehrotra. We are indebted to Justin Lakeman for extracting data from the NorthShore Enterprise Data Warehouse.\n\n\\bibliographystyle{IEEEtran}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction.}\n\nTraditional Density Functional Theory (DFT) \\cite{Dre90,Koh99,DFTLN} and its time-dependent generalization \\cite{Run84,Gro94}\nhave evolved into standard tools for the description of electronic properties in condensed-matter physics and quantum chemistry\nthrough the simple local density instead of the less tractable $N$-body wave function.\nStationary DFT is based on the Hohenberg-Kohn (HK) theorem \\cite{Hoh64}, which\nproves that, for any non-degenerate system of N Fermions\nor Bosons \\cite{Dre90} put into a local external potential, the $N$-body ground-state wave function can be written as a functional of the local ground-state density.\nA similar theorem exists for the time-dependent case \\cite{Run84,Gro94}, where a dependence on the initial state appears.\nThe Kohn-Sham (KS) scheme \\cite{Koh65} and its time-dependent generalization \\cite{Run84,Gro94} provide a straightforward method \nto compute self-consistently the density in a quantum framework,\ndefining the non-interacting system (i.e.\\ the local single-particle potential) which reproduces the exact density.\n\nTraditional DFT is particularly well suited to study the electronic properties in molecules \\cite{Kre01}.\nAs a molecule is a self-bound system, the corresponding Hamiltonian is translationally invariant \n(which ensures Galilean invariance of the wave function \\cite{foot1}),\nand one can apply the Jacobi coordinates method.\nThis permits to decouple the center-of-mass (c.m.) properties from the internal ones,\nand to treat correctly the redundant coordinate problem\n(i.e.\\ the fact that one coordinate is redundant for the description of the internal properties \\cite{Schm01a})\nand the c.m. correlations.\nBut as the nuclei are much heavier than the electrons,\nwe can apply the Jacobi coordinates method to the nuclei only,\nso that only the nuclei will carry the c.m. correlations,\nand use the clamped nuclei approximation.\nThen, one recovers the ``external'' potential of traditional DFT, of the form $\\sum_{i=1}^N v_{ext}(\\mathbf{r}_i)$,\nwhich accounts for the nuclear background as seen by the electrons in the frame attached to the c.m. of the nuclei.\nThus, traditional DFT is particularly adapted to the study of the electronic properties in molecules \\cite{Kre01}.\nIt is implicitely formulated in the nuclear c.m. frame \\cite{foot2} and the energy functional \ndoes not contain any c.m. correlations.\nOf course, contrary to the whole molecule, the pure electronic system is not a self-bound system:\nthe $v_{ext}$ potential breaks translational invariance and is compulsory in order to reach bound states in the stationary case.\n\nFor other self-bound systems, as isolated atomic nuclei or He droplets,\nthe situation is intrinsically different because the masses of all the particles (Fermions or Bosons) are of the same order of magnitude.\nAs a consequence, to decouple the c.m. properties from the internal ones,\none has to apply the Jacobi coordinates method\nto \\textit{all} the particules.\nThe redundant coordinate problem (thus the c.m. correlations) will now concern all the particles and should be treated properly.\nIf a DFT exists, the c.m. correlations \nshould be taken into account in the functional.\n\nMoreover, no \"external\" potential of the form $\\sum_{i=1}^N v_{ext}(\\mathbf{r}_i)$ can be justified in the corresponding self-bound Hamiltonians\n(we denote $\\mathbf{r}_i$ the $N$ \nparticules\ncoordinates related to any inertial frame as the laboratory).\nOne may be tempted to formulate a DFT using the traditional DFT conclusions in the limit $v_{ext} \\to 0$,\nbut this would lead to false and incoherent results because:\n\\begin{itemize}\n\\item in the stationary case, the Hohenberg-Kohn theorem is valid only for external potentials that lead to bound many-body states \\cite{Lie83},\nwhich is not the case anymore at the limit $v_{ext} \\to 0$ \nfor translational invariant particle-particle interactions \\cite{Mes09};\n\\item the form of $v_{ext}$ is not translationally invariant, but translational invariance is a key feature of self-bound systems \\cite{Schm01a,Pei62,Mes09};\n\\item traditional DFT concepts as formulated so far are not applicable in terms of a well-defined internal density $\\rho_{int}$, i.e.\\ the density relative \nto the system's c.m.\\ , which is of experimental interest \\cite{Kre01,Eng07,Mes09} (it is for example measured in nuclear scattering experiments).\n\\end{itemize}\n\nInstead of the traditional DFT potential $\\sum_{i=1}^N v_{ext}(\\mathbf{r}_i)$, one might be tempted to introduce an arbitrary translational invariant potential of the form $\\sum_{i=1}^N v_{int}(\\mathbf{r}_i - \\mathbf{R})$, where $\\vec{R}=\\frac{1}{N}\\sum_{j=1}^{N}\\vec{r}_j$ is the total c.m.\\ of the particles.\nThis potential is an \"internal\" potential, i.e. is seen in the c.m. frame,\nand in \\cite{Mes09} we underlined that it is the only form which satisfies all the key formal properties.\nHowever, $v_{int}$ should be zero in the purely isolated self-bound case.\nThis is why in \\cite{Mes09} we presented it as a mathematical \"auxiliary\"\nto reach our goal and showed that it can be dropped properly at the end, conserving all the conclusions.\nThrough it (and using the Jacobi coordinates), we proved, by a different way than those found in \\cite{Eng07,Bar07},\nthe stationary \"Internal DFT\" theorem:\nthe internal many-body state can be written as a functional of $\\rho_{int}$.\nThen we formulated rigorously the corresponding \"Internal\" KS scheme (in the c.m.\\ frame).\nThe main interest of this work is\nto give a first step towards a fundamental justification to the use of internal density functionals\nfor stationary mean-field like calculations of nuclei \\cite{Ben03} or He droplets \\cite{Bar06} with effective interactions,\nshowing that there exists an ultimate functional which permits to reproduce the exact internal density, which was not clear up to now.\n\nIt is to be noted that the stationary Internal DFT \/ KS formalism \ngives a more fundamental justification than the Hartree-Fock (HF) framework\nto the stationary nuclear mean-field like calculations. Indeed, HF does not contain quantum correlations,\nnor treats correctly the redundant coordinate problem, which introduces a spurious coupling between the\ninternal properties and the c.m. motion \\cite{RS80,Schm01a}.\nA way to overcome this problem in the stationary case is to perform projected HF\n(projection before variation on c.m.\\ momentum), which permits to restore \nGalilean invariance, but at the price of abandoning the independent-particle \ndescription \\cite{Schm01a,Pei62,Ben03}.\nWithin the Internal DFT \/ KS formalism,\nwe proved that the c.m.\\ correlations can be included in the energy functional \/ the KS potential \\cite{Mes09},\nso that there would be no need for a c.m.\\ projection if the ultimate functional was known.\n\nIt is a question of interest to generalize the stationary Internal DFT \/ KS formalism to the time-dependent case.\nIt would provide a first step towards a fundamental justification to the use of density functionals in nuclear\ntime-dependent calculations with an effective mean-field \\cite{tdhf_nucl,Neg82},\nand would prove that the c.m.\\ correlations can be included in the functional.\nThis last point is even more interesting that the spurious c.m.\\ motion problem remains in time-dependent HF \\cite{Irv80,Uma09},\nbut that then the projected HF method becomes unmanageable and is not used in practise \\cite{Uma09}.\n\nIn this paper, we propose to set up the time-dependent Internal DFT \/ KS formalism.\nThe paper is organized as follows:\nwe first apply the Jacobi coordinates method to the\ntime-dependent full many-body Hamiltonian to decouple the internal properties from the c.m. ones, and define some useful ``internal'' observables, including the internal density (section II);\nthen we show that the internal many-body wave function (and thus the ``internal'' mean values of all the observables) can be written as a functional of the internal density (section III);\nfinally, we develop the associated time-dependent Internal KS scheme as a practical scheme to compute the internal density (section IV).\n\n\n\n\n\n\n\\section{Time-dependent N body formulation.}\n\n\n\n\\subsection{General formulation.}\n\nIn the time-dependent domain, the introduction of an \\textit{explicitely} time-dependent internal potential of the form\n\\begin{eqnarray}\n\\label{eq:v}\n\\sum_{i=1}^N v_{int}(\\mathbf{r}_i - \\mathbf{R};t)\n\\end{eqnarray}\ntakes a true meaning.\nThis is because self-bound systems are plagued by a c.m. problem.\nFor instance, in the stationary case, the c.m. will be delocalized in the whole space for \\textit{isolated} self-bound systems \\cite{Eng07,Kre01,Mes09}.\nThis does not occur in experiments because experimentally observed self-bound systems are not \\textit{isolated} anymore\n(they interact with the piece of matter they are inserted in which localizes the c.m.).\nIn the time domain, the c.m. motion remains uncomparable to the experimental one (this will be discussed in more detail later),\nso that it would not make sense to introduce a time-dependent potential\nwhich would act on the c.m. motion.\nIt are the internal properties which are of true experimental interest (experimentalists always deduce those properties \\cite{foot4}).\nThis justifies\nthe introduction of an \\textit{explicitely} time-dependent potential \nof the form (\\ref{eq:v}),\nwhich would act on the internal properties only,\nand models the internal effect (only) of time-dependent potentials used in experiments.\nSuch a potential does not appear any more simply as a mathematical auxiliary (as for the stationary Internal DFT \/ KS) and should not necessarily be dropped at the end.\n\n\nWe thus start from a general translationally invariant $N$-body Hamiltonian\ncomposed of the usual kinetic energy term, a \ntranslationally invariant \ntwo-body potential $u$, which describes the particle-particle \ninteraction,\nand an arbitrary translationally invariant \"internal\" potential $v_{int}$ which contains an explicit time dependence\n\\begin{equation}\n\\label{eq:H}\nH\n= \\sum_{i=1}^{N} \\frac{\\vec{p}^2_i}{2m} \n + \\sum_{\\stackrel{i,j=1}{i > j}}^{N} u (\\vec{r}_i-\\vec{r}_j) \n + \\sum_{i=1}^{N} v_{\\text{int}} (\\vec{r}_i - \\vec{R} ; t)\n\\; .\n\\end{equation}\nFor the sake of simplicity we assume a 2-body interaction $u$ and $N$ identical Fermions or Bosons.\nThe generalization to 3-body etc interactions is straightforward;\nthe generalization to different types of particles is underway.\n\nWe rewrite the Hamiltonian (\\ref{eq:H}) using the ($N-1$) Jacobi coordinates $\\{\\xi_\\alpha;\\alpha=1,\\dots,N-1\\}$\nand the c.m.\\ coordinate $\\vec{R}$, defined as\n\\begin{eqnarray}\n&& \\mathbf{\\xi}_{1} = \\mathbf{r}_2-\\vec{r}_1, \n\\mathbf{\\xi}_2=\\mathbf{r}_3-\\frac{\\vec{r}_2+\\vec{r}_1}{2}, \\ldots,\n\\nonumber\\\\\n&& \\mathbf{\\xi}_{N-1} = \\frac{N}{N-1} \\, (\\vec{r}_N - \\vec{R}),\n\\nonumber\\\\\n&& \\vec{R}=\\frac{1}{N}\\sum_{j=1}^{N}\\vec{r}_j\n.\n\\label{eq:jacobi}\n\\end{eqnarray}\nThe $\\xi_\\alpha$ are relative to the c.m.\\ of the other \n$1, \\ldots, \\alpha-1$ particles and are independent from $\\vec{R}$. They are to be distinguished from the $N$ \n\"laboratory coordinates\" $\\vec{r}_i$, and the $N$ \"c.m. frame coordinates\" $(\\vec{r}_i-\\vec{R})$ relative to the total c.m. $\\vec{R}$.\nAs the $\\{\\vec{r}_i-\\vec{r}_{j\\ne i}\\}$ and the $\\{\\vec{r}_i - \\vec{R}\\}$ can be rewritten as functions of the $\\xi_\\alpha$\n(in Appendix \\ref{app:jacobi} is given the expression of the $\\{\\mathbf{r}_i-\\mathbf{R}\\}$ as a function of the $\\{\\xi_\\alpha\\}$ coordinates),\nthe interaction $u$ and the internal potential $v_{int}$\ncan be rewritten as functions of the $\\xi_\\alpha$. We denote $U$ and $V$\nthe interaction potential and the internal potential in the Jacobi coordinates representation:\n\\begin{eqnarray}\n\\sum_{\\stackrel{i,j=1}{i > j}}^{N} u (\\vec{r}_i-\\vec{r}_j)\n\\quad&\\rightarrow&\\quad\nU(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1}) \n\\nonumber\\\\\n\\sum_{i=1}^{N} v_{int} (\\vec{r}_i - \\vec{R} ; t)\n\\quad&\\rightarrow&\\quad\nV(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\n\\label{eq:V_int}\n.\n\\end{eqnarray}\nOf course we have $U[u]$ and $V[v_{int}]$.\nThe $V[v_{int}]$ potential is ($N-1$) body in the Jacobi coordinates representation and\n\\textit{cannot} be written in a simple form in this representation (see Appendix \\ref{app:jacobi}).\nMoreover, various $v_{int}$ can lead to the same $V$, which we will develop later.\n\nAfter having defined the conjugate momenta of $\\vec{R}$ and $\\xi_\\alpha$,\nwe can separate (\\ref{eq:H}) into $H = H_\\text{CM} + H_\\text{int}$, where ($M = Nm$ is the total mass)\n\\begin{equation}\nH_\\text{CM} = -\\frac{\\hbar^2 \\Delta_\\vec{R}}{2M} \n\\label{eq:H_cm}\n\\end{equation}\nis a one-body operator acting in $\\vec{R}$ space only,\nand ($\\tau_\\alpha$ is the conjugate momentum of $\\xi_\\alpha$ and $\\mu_\\alpha = m\\frac{\\alpha}{\\alpha+1}$ the corresponding reduced mass)\n\\begin{eqnarray}\nH_\\text{int}=\\sum_{\\alpha=1}^{N-1} \\frac{\\tau_\\alpha^2}{2\\mu_\\alpha} &+& U[u](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1}) \n\\nonumber\\\\\n&+& V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\n\\label{eq:H_int}\n\\end{eqnarray}\nis a $(N-1)$ body operator in the $\\{\\xi_\\alpha\\}$ space. It contains the \ninteraction and the internal potential.\n\nIn the time-dependent case, we can choose freely the initial state $\\psi(\\vec{r}_1, \\ldots , \\vec{r}_N ; t_0)$.\nWe start from an initial state which can be written \n\\begin{equation}\n\\label{eq:psi_init}\n\\psi(\\vec{r}_1, \\ldots , \\vec{r}_N ; t_0) \n= \\Gamma(\\vec{R} ; t_0) \\; \n \\psi_{int} ({\\boldmath{\\xi}}_1, \\ldots , {\\boldmath{\\xi}}_{N-1} ; t_0)\n\\end{equation}\nin the Jacobi coordinates representation.\nThis form does not mix the c.m. motion with the internal one\n(mixing them would not make sense because the c.m. motion does anyway not correspond to the experimental one)\nand corresponds to the form of the stationary state \\cite{Schm01a,Mes09}.\nAs $H_{CM}$ and $H_{int}$ act in two separate subspaces, the $\\mathbf{R}$ and $\\{\\xi_\\alpha\\}$ spaces\n(which implies $[H_{CM},H_{int}]=0$), it is easy to show that the state $|\\psi(t))$ can be built at all time $t\\geq t_0$ as a direct product of the form\n\\begin{equation}\n\\label{eq:psi}\n\\psi(\\vec{r}_1, \\ldots , \\vec{r}_N ; t) \n= \\Gamma(\\vec{R} ; t) \\; \n \\psi_{int} ({\\boldmath{\\xi}}_1, \\ldots , {\\boldmath{\\xi}}_{N-1} ; t) ,\n\\end{equation}\nwith\n\\begin{eqnarray}\n&& H_{CM}|\\Gamma(t)) = i\\hbar\\partial_t |\\Gamma(t))\n\\label{eq:schro_int}\\\\\n&& H_{int}|\\psi_{int}(t)) = i\\hbar\\partial_t |\\psi_{int}(t))\n\\label{eq:schro}\n\\; .\n\\end{eqnarray}\nHence, the $N$-body wave function $\\psi$ can be separated into a one-body wave function \n$\\Gamma$ that depends on the position $\\mathbf{R}$ of the c.m. only, \nand an \"internal\" ($N-1$) body wave function \n$\\psi_{int}$ that depends on the remaining ($N-1$) Jacobi \ncoordinates ${\\boldmath{\\xi}}_\\alpha$.\nOf course, $\\psi_{int}$ could also be written as a function of the $N$ laboratory coordinates $\\mathbf{r}_i$,\nbut one of them would be redundant.\n$\\Gamma$ is solution of the free Schr\\\"odinger equation and describes the motion of the \\textit{isolated} system as \na whole in any chosen inertial frame of reference (as the laboratory).\nIf one starts from a normalizable initial state $|\\Gamma(t_0))$, $|\\Gamma(t))$\nis condemned to spread more and more.\nIn the stationary limit, the only solutions of Eq. (\\ref{eq:schro_int}) are plane waves, which are infinitely spread (thus not normalizable).\nThis does not correspond to experimental situations, where the system is not isolated anymore: interactions with other systems\nof the experimental apparatus localize the c.m.\nBut the formal decoupling between the c.m.\\ motion and the internal properties obtained when using the Jacobi coordinates method\npermits to let the c.m.\\ motion to the choice of experimental conditions,\nthe internal properties being comparable to the experimental ones.\n\n\n\n\n\n\n\\subsection{Some useful definitions.}\n\\label{par:def}\n\nWe define some quantities and relations that will be useful for the next considerations.\nIn \\cite{Mes09,Gir08b,Kaz86} is defined the internal one-body density\n\\begin{eqnarray}\n\\lefteqn{\\rho_{int}(\\vec{r},t)\/N}\n\\label{eq:rho_int0}\n\\\\\n& = & \\int \\! d\\vec{r}_1 \\cdots d\\vec{r}_N \\;\n \\delta(\\mathbf{R})\n |\\psi_{int}(\\vec{r}_1, \\ldots, \\vec{r}_{N};t)|^2\n \\delta \\big( \\vec{r} - (\\vec{r}_i-\\mathbf{R}) \\big)\\, \n\\nonumber\\\\\n& = & \\Big(\\frac{N}{N-1}\\Big)^3 \n \\int \\! d\\vec{\\xi}_1 \\cdots d\\mathbf{\\xi}_{N-2} \\; \n \\big| \\psi_{int} \\big(\\mathbf{\\xi}_1, \\ldots, \\mathbf{\\xi}_{N-2},\n \\tfrac{N\\vec{r}}{N-1} ;t \\big) \\big|^2\n\\nonumber .\n\\end{eqnarray}\nIt is is normalized to $N$.\nThe laboratory density $\\rho(\\mathbf{r},t)$ is obtained by convolution of $\\rho_{int}$ with the c.m.\\ wave \nfunction (following \\cite{Gir08b,Kaz86}):\n$\n\\rho(\\mathbf{r},t) = \\int d\\mathbf{R} |\\Gamma(\\mathbf{R},t)|^2 \\rho_{int}(\\mathbf{r} - \\mathbf{R},t) .\n$\n\nWe also introduced in \\cite{Mes09} the local part of the two-body internal density matrix\n\\begin{eqnarray}\n\\label{eq:gamint0}\n\\lefteqn{\\gamma_{int}(\\vec{r},\\vec{r'};t)}\n \\\\\n& = & \\int \\! d\\vec{r}_1 \\cdots d\\vec{r}_N \\; \n \\delta(\\mathbf{R}) |\\psi_{int}(\\vec{r}_1, \\ldots, \\vec{r}_{N};t)|^2 \\,\n \\nonumber \\\\\n& & \\hspace{1.cm} \\times \n \\delta \\big( \\vec{r} - (\\vec{r}_i-\\mathbf{R}) \\big)\n \\delta \\big( \\vec{r'} - (\\vec{r}_{j\\ne i}-\\mathbf{R}) \\big)\n \\nonumber \\\\\n& = & \\frac{N(N-1)}{2} \\Big(\\frac{N-1}{N-2}\\Big)^3 \\Big(\\frac{N}{N-1}\\Big)^3\n \\int \\! d\\mathbf{\\xi}_1 \\cdots d\\mathbf{\\xi}_{N-3}\n \\nonumber\\\\\n& & \\hspace{1.cm} \\times \\Big| \\psi_{int} \\Big(\\mathbf{\\xi}_1, \\ldots, \\mathbf{\\xi}_{N-3}, \n \\tfrac{\\vec{r'}+(N-1)\\vec{r} }{N-2},\\tfrac{N\\vec{r'}}{N-1} ;t \\Big) \\Big|^2\n \\nonumber\n.\n\\end{eqnarray}\nIt has the required normalisation to $N(N-1)\/2$.\nFollowing similar steps than in \\cite{Gir08b,Kaz86},\nwe can show that the local part of the two-body laboratory density matrix \n$\\gamma(\\vec{r},\\vec{r'},t)$ is obtained by convolution of $\\gamma_{int}$ with the c.m.\\ wave \nfunction:\n$\n\\gamma(\\vec{r},\\vec{r'};t) = \\int d\\mathbf{R} |\\Gamma(\\mathbf{R},t)|^2 \\gamma_{int}(\\vec{r} - \\mathbf{R},\\vec{r'} - \\mathbf{R};t) .\n$\n\nThe definitions of $\\rho_{int}(\\vec{r},t)$ and $\\gamma_{int}(\\vec{r},\\vec{r'};t)$ show clearly that they are defined in the c.m. frame,\ni.e.\\ that the $\\vec{r}$, $\\vec{r'}$ coordinates are measured in the c.m. frame (see the $\\delta$ relations in (\\ref{eq:rho_int0}) and (\\ref{eq:gamint0})).\nCompared to the traditional definitions, a $\\delta(\\mathbf{R})$ appears in the definition of the internal densities calculated\nwith $\\psi_{int}$ in $\\{\\mathbf{r}_i\\}$ coordinates. As one of them is redundant, the $\\delta(\\mathbf{R})$ represents the dependence of the redundant coordinate on the others \\cite{foot3}.\n\nAnother quantity that will be very useful is the one-body internal probability current, defined in Appendix \\ref{app:int_current} ($c.c.$ denotes the complex conjugate)\n\\begin{eqnarray}\n\\lefteqn{\\mathbf{j}_{int}(\\mathbf{r},t)\/N}\n\\label{eq:j_int1}\\\\\n&=& \\frac{\\hbar}{2m i} \\Big(\\frac{N}{N-1}\\Big)^3 \\int d\\mathbf{\\xi}_1 ... d\\mathbf{\\xi}_{N-2} \\psi_{int}^*(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-2}, \\nu;t)\n\\nonumber\\\\\n&& \\hspace{1.5cm}\n\\times \\mathbf{\\nabla_{\\nu}} \\psi_{int}(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-2}, \\nu;t) \\Big|_{\\nu=\\frac{N}{N-1}\\mathbf{r}} + c.c.\n\\nonumber\n\\end{eqnarray}\nwhich satisfies the ``internal'' continuity equation\n\\begin{eqnarray}\n\\partial_t \\rho_{int}(\\mathbf{r},t) + \\mathbf{\\nabla} . \\mathbf{j}_{int}(\\mathbf{r},t) =0 .\n\\label{eq:cont_rel}\n\\end{eqnarray}\nUsing (\\ref{eq:j_int1}), (\\ref{eq:H_int}) and (\\ref{eq:schro}), we obtain the relation\n\\begin{widetext}\n\\begin{eqnarray}\n\\lefteqn{i \\frac{\\partial}{\\partial t} \\mathbf{j}_{int}(\\mathbf{r},t)}\n\\label{eq:partial_j_int}\n\\\\\n&=& \\frac{N}{2m i} \\Big(\\frac{N}{N-1}\\Big)^3 \\int d\\mathbf{\\xi}_1 ... d\\mathbf{\\xi}_{N-2} \\Big\\{ \n\\mathbf{\\nabla_{\\nu}} \\psi_{int}(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-2},\\nu;t) i\\hbar\\partial_t \\psi_{int}^*(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-2},\\nu;t) \\nonumber\\\\\n&& \\hspace{4.6cm} +\n\\mathbf{\\nabla_{\\nu}} \\Big( i\\hbar \\partial_t\\psi_{int}(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-2},\\nu;t) \\Big) \\psi_{int}^*(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-2},\\nu;t) + c.c.\n\\Big\\}\\Big|_{\\nu=\\frac{N}{N-1}\\mathbf{r}}\n\\nonumber\\\\\n&=& \\frac{N}{2m i} \\Big(\\frac{N}{N-1}\\Big)^3 \\int d\\mathbf{\\xi}_1 ... d\\mathbf{\\xi}_{N-2} \\Big\\{ \n\\mathbf{\\nabla_{\\nu}} \\psi_{int}(\\mathbf{\\xi}_1, ..., \\nu;t) \\frac{\\hbar^2\\Delta_{\\nu}}{2\\mu_{N-1}} \\psi_{int}^*(\\mathbf{\\xi}_1, ..., \\nu;t)\n- \\psi_{int}^*(\\mathbf{\\xi}_1, ..., \\nu;t) \\mathbf{\\nabla_{\\nu}} \\frac{\\hbar^2\\Delta_{\\nu}}{2\\mu_{N-1}} \\psi_{int}(\\mathbf{\\xi}_1, ..., \\nu;t)\n\\nonumber\\\\\n&& \\hspace{4.5cm} +\n\\psi^*_{int}(\\mathbf{\\xi}_1, ..., \\nu;t) \\mathbf{\\nabla_{\\nu}} \\Big( U[u](\\mathbf{\\xi}_1, ..., \\nu) + V[v_{int}](\\mathbf{\\xi}_1, ..., \\nu;t) \\Big) \\psi_{int}(\\mathbf{\\xi}_1, ... ,\\nu;t) \n+c.c.\n\\Big\\} \\Big|_{\\nu=\\frac{N}{N-1}\\mathbf{r}},\n\\nonumber\n\\end{eqnarray}\n\\end{widetext}\nwhich will be a key equation for the next considerations.\n\n\n\n\n\n\n\\section{Time-dependent Internal DFT theorem.}\n\n\n\n\\subsection{Preliminaries.}\n\\label{sub:int}\n\nTo prove the time-dependent Internal DFT theorem, we adapt the considerations of \\cite{Run84,Gro94}\nto the internal Schr\\\"odinger equation (\\ref{eq:schro}).\nThe main differences lie in the definition of the corresponding internal density (\\ref{eq:rho_int0}) and probability current (\\ref{eq:j_int1}),\nand in the fact that the potential $V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)$\ncannot be written as the sum of one-body potentials in the Jacobi coordinates representation\n(which introduces some subtleties due to the c.m. correlations and will bring us to use the integral mean value theorem to reach our goal).\n\nIn what follows, we consider a given type of Fermions or Bosons, i.e. a given particle-particle interaction $u$.\nSolving the ``internal'' Schr\\\"odinger equation (\\ref{eq:schro}) for a fixed initial state $|\\psi_{int}(t_0))$ and for various internal potentials $V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)$ defines two maps \\cite{Run84,Gro94}\n\\begin{eqnarray}\n&& F: V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\\rightarrow |\\psi_{int}(t))\n\\nonumber\\\\\n&& G: V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\\rightarrow \\rho_{int}(\\mathbf{r},t).\n\\label{eq:map}\n\\end{eqnarray}\nWe first notice that two potentials $v_{int}$ and $v'_{int}$ which lead to two potentials\n$V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)$ and $V[v'_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)$ that differ by a scalar function of time only $c(t)$, \nwill give two wave functions that differ by a phase $e^{-i\\alpha(t)\/\\hbar}$ only \\cite{Run84,Gro94}:\n\\begin{eqnarray}\n&&V[v'_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t) - V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t) = c(t)\n\\nonumber\\\\\n&&\\quad\\quad\\Rightarrow\\hspace{1mm}\n|\\psi'_{int}(t))=e^{-i\\alpha(t)\/\\hbar}|\\psi_{int}(t)) ,\n\\nonumber\\\\\n&& \\hspace{2.5cm} \\rm{with} \\hspace{2mm} \\Dot{\\alpha}(t)=c(t)\n\\label{eq:v'}\n.\n\\end{eqnarray}\nThen, $|\\psi_{int}(t))$ and $|\\psi'_{int}(t))$ will give the same density $\\rho_{int}(\\mathbf{r},t)=\\rho'_{int}(\\mathbf{r},t)$.\nThe consequence is that the map $G$ is not fully invertible.\n\nLet us discuss a bit about the condition (\\ref{eq:v'}).\nThe form (\\ref{eq:V_int}) for $V[v_{int}]$ implies $V[v'_{int}]-V[v_{int}]=V[v'_{int}-v_{int}]$.\nWe define\n\\begin{eqnarray}\n\\Delta v_{int}(\\mathbf{r};t)=v'_{int}(\\mathbf{r};t)-v_{int}(\\mathbf{r};t).\n\\label{eq:delta_v}\n\\end{eqnarray}\nIt is to be noted that the condition $\\Delta v_{int}(\\mathbf{r};t)\\ne c(t)\/N$\nis necessary but not sufficient to ensure the condition (\\ref{eq:v'}),\nwhich can be rewritten $V[\\Delta v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\\ne c(t)$.\nIndeed, it is possible to have $\\Delta v_{int}(\\mathbf{r};t)\\ne c(t)\/N$ and nevertheless\n$V[\\Delta v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t) = c(t)$,\nbecause compensations due to the c.m. correlations can happen.\n\nLet us reason on the two particules case, where only one Jacobi coordinate is sufficient to describe the internal properties.\nWe have (see Appendix \\ref{app:jacobi}):\n$V[\\Delta v_{int}](\\mathbf{\\xi}_1;t)=\\Delta v_{int} (-\\frac{1}{2}\\xi_1;t)+\\Delta v_{int} (\\frac{1}{2}\\xi_1;t) \\big( =\\sum_{i=1}^{2} v_{\\text{int}} (\\vec{r}_i - \\vec{R} ; t) \\big)$.\nWe see that if $\\Delta v_{int}(\\mathbf{r};t)$ is an odd function of $\\mathbf{r}$ at all $t$\n(up to an additional time-dependent function),\nwe have $V[\\Delta v_{int}](\\mathbf{\\xi}_1;t)=c(t)$\n$\\Rightarrow$ $\\rho_{int}=\\rho'_{int}$.\nThis is due to the c.m. correlations, that the non-trivial form of $V$ reflects.\nIf\n$\\Delta v_{int}$ tends to move the first particule in one direction,\nthe second particule will tend to move in the opposite direction because of the c.m. correlations.\nBut if this potential counter-acts perfectly the motion of the second particule (as does an odd potential in the c.m. frame), then the particules remain stuck and the density unchanged.\n\nThe same can occur for an arbitrary number of particules.\nFor instance, as $\\sum_{i=1}^N (\\mathbf{r}_i-\\mathbf{R})=0$,\nit is obvious with (\\ref{eq:V_int}) and (\\ref{eq:delta_v}) that every $\\Delta v_{int}(\\mathbf{r};t)=\\mathbf{b}(t).\\mathbf{r}+c(t)\/N$\nwill yield $V[\\Delta v_{int}] = c(t)$ (even if this form for $\\Delta v_{int}$ leads to internal potentials which are not null at infinity).\nAgain, this is because if a potential counter acts perfectly the motion due to the c.m. correlations, the particules remain stuck and the density unchanged.\nIn what follows, we consider only internal potentials $v_{int}$ and $v'_{int}$ that lead to $V[\\Delta v_{int}]\\ne c(t)$.\n\nWe come back to Eq. (\\ref{eq:v'}) and denote $|\\psi_{int}(t))=e^{-i\\alpha(t)\/\\hbar}|\\psi^0_{int}(t))$ where we define $\\psi^0_{int}$ as the wave function obtained for the choice $c(t)=0$,\ni.e. associated to a $V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)$ where no additive time-dependent function can be split.\nIf we prove that the map $G$ is invertible up to an additive time-dependent function $c(t)$, then $\\psi^0_{int}$ is fixed by $\\rho_{int}$\nthrough the relation $|\\psi^0_{int}(t))=F G^{-1} \\rho_{int}(\\mathbf{r},t)$,\nwhich implies that $|\\psi^0_{int}(t))$ can be written as a functional of the internal density $\\rho_{int}$ defined in (\\ref{eq:rho_int0}).\nConsequently, any expectation value of an operator $\\hat{O}$ which does not contain a time derivative can be written as a functional of the internal density (as the phase cancels out):\n$(\\psi_{int}(t)|\\hat{O}|\\psi_{int}(t))=(\\psi^0_{int}[\\rho_{int}](t)|\\hat{O}|\\psi^0_{int}[\\rho_{int}](t))$.\n\nWe thus have to show that \na propagation of (\\ref{eq:schro}) with two potentials $v_{int}$ and $v_{int}'$ that yield\n$V[\\Delta v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\\ne c(t)$\nwill produce two different internal densities $\\rho_{int}$ and $\\rho_{int}'$.\n\n\n\n\n\\subsection{The proof.}\n\nWe start from a \\textit{fixed initial state} $|\\psi_{int}(t_0))$ and\npropagate it with two with two potentials $v_{int}$ and $v_{int}'$ that give\n$V[\\Delta v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\\ne c(t)$.\nWe deduce from Eq. (\\ref{eq:partial_j_int})\n\\begin{widetext}\n\\begin{eqnarray}\ni \\frac{\\partial}{\\partial t} \\Big( \\mathbf{j}_{int}(\\mathbf{r},t) - \\mathbf{j}'_{int}(\\mathbf{r},t) \\Big) \\Big|_{t=t_0} =\n\\frac{N}{m i} \\Big(\\frac{N}{N-1}\\Big)^3\n\\int d\\mathbf{\\xi}_1 ... d\\mathbf{\\xi}_{N-2} |\\psi_{int}(\\mathbf{\\xi}_1, ..., \\nu;t_0)|^2\n\\mathbf{\\nabla_{\\nu}} V[\\Delta v_{int}](\\mathbf{\\xi}_1, ..., \\nu;t_0) \\Big|_{\\nu=\\frac{N}{N-1}\\mathbf{r}} .\n\\end{eqnarray}\nUsing the ``internal'' continuity relation (\\ref{eq:cont_rel}) we obtain\n\\begin{eqnarray}\n\\lefteqn{\\frac{\\partial^2}{\\partial t^2} \\Big( \\rho_{int}(\\mathbf{r},t) - \\rho'_{int}(\\mathbf{r},t) \\Big) \\Big|_{t=t_0} =}\n\\label{eq:partial_j_int0}\\\\\n&& \\frac{N}{m} \\Big(\\frac{N}{N-1}\\Big)^3 \\mathbf{\\nabla_{\\mathbf{r}}} .\\int d\\mathbf{\\xi}_1 ... d\\mathbf{\\xi}_{N-2}\n|\\psi_{int}(\\mathbf{\\xi}_1, ..., \\frac{N}{N-1}\\mathbf{r};t_0)|^2\n\\mathbf{\\nabla}_\\nu V[\\Delta v_{int}](\\mathbf{\\xi}_1, ..., \\nu;t_0) \\Big|_{\\nu=\\frac{N}{N-1}\\mathbf{r}}\n.\n\\nonumber\n\\end{eqnarray}\n\nWe now make the only hypothesis which is used in this derivation. Following \\cite{Run84,Gro94} we restrict the set of\npotentials $v_{int}$ to those that can be expanded into Taylor series with respect to the time at the initial time $t_0$\n(which is a reasonable hypothesis for physical potentials).\nAs we supposed that $V[\\Delta v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\\ne c(t)$, we have ($k$ is a positive integer)\n\\begin{eqnarray}\nV[\\Delta v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\\ne c(t)\n\\quad\\Rightarrow\\quad\n\\exists k :\nw_k(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t_0) \\ne constant ,\n\\label{eq:Vint}\n\\end{eqnarray}\nwhere\n\\begin{eqnarray}\nw_k(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t_0)\n= \\frac{\\partial^k}{\\partial t^k} V[\\Delta v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t) \\Big|_{t=t_0}\n\\label{eq:wk}\n.\n\\end{eqnarray}\nIt is to be noted that the condition $\\frac{\\partial^k}{\\partial t^k} \\Delta v_{int}(\\mathbf{r};t)\\Big|_{t=t_0} \\ne constant$\n$\\Rightarrow$ $\\mathbf{\\nabla_r} \\frac{\\partial^k}{\\partial t^k} \\Delta v_{int}(\\mathbf{r};t)\\Big|_{t=t_0}\\ne \\overrightarrow{0}$\nis necessary to ensure the condition (\\ref{eq:Vint}) (see (\\ref{eq:V_int}) and (\\ref{eq:wk})), but not sufficient.\n\nIn what follows, we consider $k$ as the \\textit{smallest} positive integer such that (\\ref{eq:Vint})\nis verified. Then, if we apply $k$ time derivatives to the Eq. (\\ref{eq:partial_j_int0}), we straightforwardly obtain\n\\begin{eqnarray}\n\\frac{\\partial^{k+2}}{\\partial t^{k+2}} \\Big( \\rho_{int}(\\mathbf{r},t) - \\rho_{int}'(\\mathbf{r},t) \\Big) \\Big|_{t=t_0} =\n\\frac{N}{m} \\Big(\\frac{N}{N-1}\\Big)^3 \\mathbf{\\nabla_{\\mathbf{r}}} . \\int d\\mathbf{\\xi}_1 ... d\\mathbf{\\xi}_{N-2} |\\psi_{int}(\\mathbf{\\xi}_1, ..., \\nu;t_0)|^2 \\mathbf{\\nabla_\\nu} w_k(\\mathbf{\\xi}_1, ..., \\nu;t_0) \\Big) \\Big|_{\\nu=\\frac{N}{N-1}\\mathbf{r}}\n.\n\\end{eqnarray}\nAs, for every physical potential, $\\mathbf{\\nabla}_{\\mathbf{\\xi}_{N-1}} w_k(\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t_0)$ is a real and continuous function in the whole position space, and as $|\\psi_{int}(\\mathbf{\\xi}_1, ...,\\mathbf{\\xi}_{N-1};t_0)|^2$ is a real and positive function in the whole position space, we can apply the integral mean value theorem generalized to many variables functions (demonstrated in Appendix \\ref{app:mean_val_th}) to the previous expression. We obtain\n\\begin{eqnarray}\n\\exists (\\beta_1,...,\\beta_{N-2}) \\hspace{1mm} :\n&& m \\frac{\\partial^{k+2}}{\\partial t^{k+2}} \\Big( \\rho_{int}(\\mathbf{r},t) - \\rho_{int}'(\\mathbf{r},t) \\Big) \\Big|_{t=t_0}\n\\nonumber\\\\\n&& \\hspace{5mm} = \\mathbf{\\nabla_{\\mathbf{r}}} . \\Big[\n\\mathbf{\\nabla}_\\frac{N\\mathbf{r}}{N-1} w_k(\\beta_1,...,\\beta_{N-2}, \\frac{N}{N-1}\\mathbf{r};t_0)\nN \\Big(\\frac{N}{N-1}\\Big)^3 \\int d\\mathbf{\\xi}_1 ... d\\mathbf{\\xi}_{N-2}|\\psi_{int}(\\mathbf{\\xi}_1, ..., \\frac{N}{N-1}\\mathbf{r};t_0)|^2 \\Big]\n\\nonumber\\\\\n&& \\hspace{5mm} = \\mathbf{\\nabla_{\\mathbf{r}}} . \\Big[\n\\mathbf{\\nabla}_\\frac{N\\mathbf{r}}{N-1} w_k(\\beta_1,...,\\beta_{N-2}, \\frac{N}{N-1}\\mathbf{r};t_0)\n\\rho_{int}(\\mathbf{r},t_0) \\Big]\n.\n\\label{eq:partial_j_int2}\n\\end{eqnarray}\nTo prove the one-to-one correspondence $V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)\\leftrightarrow \\rho_{int}(\\mathbf{r},t)$ it remains to show that (\\ref{eq:partial_j_int2})\ncannot vanish for $v_{int}$ and $v_{int}'$ that lead to the relation (\\ref{eq:Vint}).\nThen the internal densities $\\rho_{int}(\\mathbf{r},t)$ and $\\rho_{int}'(\\mathbf{r},t)$ would become different infinitesimally later than $t_0$.\nWe use the \\textit{reductio ad absurdum} method, in the spirit of Refs. \\cite{Run84,Gro94}. We suppose that (\\ref{eq:partial_j_int2}) vanishes, which implies:\n\\begin{eqnarray}\n0&=&\\frac{N-1}{N}\\int d\\mathbf{r} w_k(\\beta_1,...,\\beta_{N-2}, \\frac{N}{N-1}\\mathbf{r};t_0) \\mathbf{\\nabla_{\\mathbf{r}}} . \\Big[\n\\mathbf{\\nabla}_\\frac{N\\mathbf{r}}{N-1} w_k(\\beta_1,...,\\beta_{N-2}, \\frac{N}{N-1}\\mathbf{r};t_0)\n\\rho_{int}(\\mathbf{r},t_0) \\Big]\n\\nonumber\\\\\n&=& \n-\\int d\\mathbf{r} \\Big[ \\mathbf{\\nabla}_\\frac{N\\mathbf{r}}{N-1} w_k(\\beta_1,...,\\beta_{N-2}, \\frac{N}{N-1}\\mathbf{r};t_0) \\Big]^2\n\\rho_{int}(\\mathbf{r},t_0)\n.\n\\label{eq:absurdum2}\n\\end{eqnarray}\nAs $w_k$ is a many-body function, the Eq. (\\ref{eq:Vint}) does not imply that\n$\\forall (\\beta_1,...,\\beta_{N-2}):\\mathbf{\\nabla}_{\\xi_{N-1}} w_k(\\beta_1,...,\\beta_{N-2}, \\xi_{N-1};t_0)\\ne\\overrightarrow{0}$ in the general case.\nHowever, we check if this relation holds for the particular form (\\ref{eq:V_int}) we choose for $V$.\n\nInserting the results of Appendix \\ref{app:jacobi} in (\\ref{eq:V_int}) and (\\ref{eq:wk}), we obtain, if $N>2$ \n(the case $N=2$ will be discussed later on)\n\\begin{eqnarray}\n&& w_k(\\beta_1,...,\\beta_{N-2}, \\xi_{N-1};t_0)=\n\\label{eq:w_k}\\\\\n&&\\quad\n\\frac{\\partial^{k}}{\\partial t^{k}} \\Delta v_{int} \\big(\\frac{N-1}{N}\\xi_{N-1};t\\big) \\Big|_{t=t_0}\n+ \\sum_{i=1}^{N-2}\\frac{\\partial^{k}}{\\partial t^{k}} \\Delta v_{int} \\big(\\gamma_i-\\frac{1}{N}\\xi_{N-1};t\\big) \\Big|_{t=t_0}\n+\\frac{\\partial^{k}}{\\partial t^{k}} \\Delta v_{int} \\big(-\\sum_{i=1}^{N-2}\\gamma_i-\\frac{1}{N}\\xi_{N-1};t\\big) \\Big|_{t=t_0} ,\n\\nonumber\n\\end{eqnarray}\nwhere we defined\n\\begin{eqnarray}\n\\gamma_{N-2}=\\frac{N-2}{N-1}\\beta_{N-2}\n\\quad\\quad \\text{and} \\quad\\quad\n\\forall i \\in [1,N-3]: \\gamma_{i}=\\frac{i}{i+1}\\beta_{i} - \\sum_{\\alpha=i+1}^{N-2} \\frac{1}{\\alpha+1}\\beta_{\\alpha}.\n\\label{eq:gamma}\n\\end{eqnarray}\nThe form of the third term of the right hand side of Eq. (\\ref{eq:w_k}) comes from the fact that $\\sum_{i=1}^N(\\mathbf{r}_i-\\mathbf{R})=0$, which implies, using the Appendix \\ref{app:jacobi}, that $-\\sum_{\\alpha=1}^{N-2} \\frac{1}{\\alpha+1}\\beta_{\\alpha}=-\\sum_{i=1}^{N-2}\\gamma_i$. \nWe see from Eq. (\\ref{eq:gamma}) that the set $(\\gamma_1,...,\\gamma_{N-2})$ is perfectly defined by the set $(\\beta_1,...,\\beta_{N-2})$ and vice versa.\nWe now can calculate\n\\begin{eqnarray}\n&&\\mathbf{\\nabla}_{\\xi_{N-1}} w_k(\\beta_1,...,\\beta_{N-2}, \\xi_{N-1};t_0)=\n\\nonumber\\\\\n&&\\hspace{3cm}\n\\frac{N-1}{N} \\mathbf{D} \\big(\\frac{N-1}{N}\\xi_{N-1}\\big)-\\frac{1}{N}\\sum_{i=1}^{N-2} \\mathbf{D} \\big(\\gamma_i-\\frac{1}{N}\\xi_{N-1}\\big)\n-\\frac{1}{N} \\mathbf{D} \\big(-\\sum_{i=1}^{N-2}\\gamma_i-\\frac{1}{N}\\xi_{N-1}\\big)\n\\label{eq:w}\n,\n\\end{eqnarray}\nwhere we introduced\n\\begin{eqnarray}\n\\mathbf{D}(\\mathbf{r})=\\mathbf{\\nabla}_\\mathbf{r} \\frac{\\partial^{k}}{\\partial t^{k}} \\Delta v_{int} (\\mathbf{r};t) \\big|_{t=t_0}\n.\n\\label{eq:D}\n\\end{eqnarray}\nfor simplicity.\nWe now check if\n$\\exists (\\beta_1,...,\\beta_{N-2}):$ $\\mathbf{\\nabla}_{\\xi_{N-1}} w_k(\\beta_1,...,\\beta_{N-2}, \\xi_{N-1};t_0)=\\overrightarrow{0}$,\nwhich is equivalent, according to (\\ref{eq:gamma}) and (\\ref{eq:w}), to check if\n\\begin{eqnarray}\n\\exists (\\gamma_1,...,\\gamma_{N-2}):\\quad\n(N-1) \\mathbf{D} \\big((N-1)\\mathbf{r}\\big)=\\sum_{i=1}^{N-2} \\mathbf{D} \\big(\\gamma_i-\\mathbf{r}\\big)\n+\\mathbf{D} \\big(-\\sum_{i=1}^{N-2}\\gamma_i-\\mathbf{r}\\big)\n.\n\\end{eqnarray}\n\\end{widetext}\nSome mathematical considerations show that this equation cannot be fulfilled for all $\\mathbf{r}$ when $N>2$, whatever the set of $(\\gamma_1,...,\\gamma_{N-2})$,\ninstead if $\\mathbf{D} (\\mathbf{r})=\\overrightarrow{const}$.\nBut if $\\mathbf{D} (\\mathbf{r})=\\overrightarrow{const.}$, then $\\Delta v_{int} (\\mathbf{r};t)$ should be \nequal to $\\mathbf{b}(t).\\mathbf{r}+c(t)\/N$, according to (\\ref{eq:D}),\nwhich is forbidden by the condition (\\ref{eq:Vint}), cf. discussion of the \\S \\ref{sub:int}.\n\nIt remains to discuss the case $N=2$. It is easy to show that then, we have\n$\\mathbf{\\nabla}_{\\xi_{N-1}} w_k(\\xi_{N-1};t_0)=\n-\\frac{1}{2}\\mathbf{D}\\big(-\\frac{1}{2}\\xi_{N-1}\\big)+\\frac{1}{2}\\mathbf{D}\\big(\\frac{1}{2}\\xi_{N-1}\\big)$,\nwhich is null if $\\mathbf{D}(\\mathbf{r})$\nis any par function of $\\mathbf{r}$.\nBut if $\\mathbf{D} (\\mathbf{r})$ is par, then $\\frac{\\partial^{k}}{\\partial t^{k}}\\Delta v_{int} (\\mathbf{r};t)$ should be an odd function of $\\mathbf{r}$\n(up to an additional time-dependnt function), according to (\\ref{eq:D}),\nwhich is also forbidden by the condition (\\ref{eq:Vint}), cf. discussion of the \\S \\ref{sub:int}.\n\nThus, we can conclude that, in our case\n\\begin{eqnarray}\n\\forall (\\beta_1,...,\\beta_{N-2}):\n\\mathbf{\\nabla}_{\\xi_{N-1}} w_k(\\beta_1,...,\\beta_{N-2}, \\xi_{N-1};t_0)\\ne\\overrightarrow{0}\n.\n\\nonumber\n\\label{eq:absurdum1}\n\\end{eqnarray}\nWe immediately deduce the incompatibility of\nthis relation,\nwhich is a consequence of (\\ref{eq:Vint}) and of the particular form (\\ref{eq:V_int}) of $V$, with (\\ref{eq:absurdum2}). Thus, the hypothesis we made is absurd: Eq. (\\ref{eq:partial_j_int2}) cannot vanish if $V[\\Delta v_{int}]\\ne c(t)$, so that the internal densities $\\rho_{int}(\\mathbf{r},t)$ and $\\rho_{int}'(\\mathbf{r},t)$ become different infinitesimally later than $t_0$. As a consequence, the map $G$, defined in (\\ref{eq:map}), is invertible (up to an additive time-dependent function) and $|\\psi^0_{int}(t))$ can be written as a functional of the internal density (we use the notation (\\ref{eq:v'})).\nThus, any expectation value of an operator $\\hat{O}$ which does not contain a time derivative can be written as a functional of $\\rho_{int}$ as the phase cancels out.\nThis achieves to prove the time-dependent Internal DFT theorem\n(which is a variant of the Runge-Gross theorem \\cite{Run84,Gro94} for self-bound systems and internal densities).\n\nMind that all the previous reasonings hold only for a fixed initial state $\\psi_{int}(t_0)$ (and a given type of particle), so that $\\psi^0_{int}$ is not only a functional of $\\rho_{int}$, but also depends on $\\psi_{int}(t_0)$. This will be discussed further.\n\n\n\n\n\\subsection{Link with traditional (time-dependent) DFT.}\n\nWe stress here the link and differences between the traditional DFT and internal DFT potentials.\nWe recall that the form of the potential $v_{ext}$ of traditional DFT can be fundamentally justified starting from the\nlaboratory Hamiltonian of\nan isolated molecule where the nuclei are treated explicitely.\nAs a molecule is a self-bound system, one can apply the Jacobi coordinates method.\nWe denote the N electronic coordinates related to \nthe laboratory frame\nas $\\mathbf{r}_i$, \nthe nuclear c.m. coordinate as $\\mathbf{R}^{nucl}$ and\nthe N electronic coordinates related to the c.m. of the nuclei as $\\mathbf{r}'_i=\\mathbf{r}_i - \\mathbf{R}^{nucl}$.\nA key point concerning the molecules is that, as the nuclei are much heavier than the electrons,\nthe c.m. of the whole molecule coincides with $\\mathbf{R}^{nucl}$,\nand it is an excellent approximation to apply the Jacobi coordinates to the nuclear coordinates only.\nAs a result, the c.m. motion will be described by a $\\Gamma(\\mathbf{R}^{nucl})$ wave function.\nThe redundant coordinate problem (thus the c.m.\\ correlations) will concern the nuclei only, and will be ``external'' to the electronic problem:\nthe N electrons are still described by N coordinates.\nThen, if one decouples the electronic motion from the nuclear one doing the clamped nuclei approximation,\nthe interaction of the electrons with the nuclear background is described by a potential of the form $\\sum_{i=1}^N v_{ext}(\\mathbf{r}_i - \\mathbf{R}^{nucl})$,\nwhich becomes $\\sum_i v_{ext}(\\mathbf{r}'_i)$ when moving to the c.m. frame.\nWe then recover the form of the traditional DFT potential.\nThe potential $v_{ext}$, which is \\textit{internal} for the (self-bound) molecular problem, becomes \\textit{external} for the pure electronic problem.\nThose considerations also hold in the time domain, the difference being that the potential\n\\begin{eqnarray}\n\\sum_{i=1}^N v_{ext}(\\mathbf{r}_i - \\mathbf{R}^{nucl};t)\n\\label{eq:v_ext}\n\\end{eqnarray}\ncan then contain an explicit time dependence in addition to the part which\ndescribes the interaction of the electrons with the nuclear background.\nWe recover the traditional time-dependent DFT potential \\cite{Run84,Gro94,Gro90,Mar04} when moving in the c.m. frame.\n\n\nThose reasonings explicit the link between the traditional DFT potential expressed with the laboratory coordinates, Eq. (\\ref{eq:v_ext}),\nand the Internal DFT potential expressed with the laboratory coordinates, Eq. (\\ref{eq:v}).\nThey both act only on the internal properties, and not on the c.m. motion\n(because it is anyway not comparable to the experimental one).\nThe difference is that as, in the molecular case, some particules are much heavier than the other,\nit is a very good approximation to assimilate the c.m. of the whole molecule with $\\mathbf{R}^{nucl}$,\nwhich permits to neglect the c.m. correlations for the electronic system,\nand to justify the clamped nuclei approximation.\nThis simplifies greatly the electronic problem and the traditional DFT can be used to study it.\nWhen the particules constituting the self-bound system have nearly the same masses, as it is the case for the nuclei or the He droplets, \nthe total c.m. ($\\mathbf{R}$) should be calculated with \\textit{all} the particules,\nso that the c.m. correlations will concern all the particules, and no clamped approximation can be justified.\nThen, we should use the formalism proposed here.\n\n\n\n\n\n\n\\section{Time-dependent Internal Kohn-Sham scheme.}\n\nWe now provide a practical scheme to calculate the internal density $\\rho_{int}$, which consists in the generalization of the stationary Internal KS scheme of \\cite{Mes09} to the time-dependent case.\nFirst, we note that for any normalizable initial state $|\\psi_{int}(t_0))$, which are the only allowed, the ``internal'' Schr\\\"odinger equation (\\ref{eq:schro}) stems\nfrom a variational principle\non the ``internal'' quantum action \\cite{ker76,Run84,Vig08}\n\\begin{eqnarray}\nA_{int} = \\int_{t_0}^{t_1} dt (\\psi_{int}(t)|i\\hbar\\partial_t-H_{int}|\\psi_{int}(t)) .\n\\label{eq:action}\n\\end{eqnarray}\nAs the function $c(t)$ possibly contained in the potential $V_{int}$ is perfectly canceled by the time derivative of the corresponding phase $e^{-i\\alpha(t)\/\\hbar}$ of $\\psi_{int}$, see (\\ref{eq:v'}),\nwe have $A_{int}=\\int_{t_0}^{t_1} dt (\\psi_{int}^0[\\rho_{int}](t)|i\\hbar\\partial_t-H_{int}|\\psi_{int}^0[\\rho_{int}](t))$\nif $V_{int}$ is chosen so that no additive time-dependent function can be split.\nThus, the internal quantum action can be considered as a functional of $\\rho_{int}$.\nIts\n$\\int_{t_0}^{t_1} dt (\\psi_{int}^0(t)|i\\hbar\\partial_t-\\sum_{\\alpha=1}^{N-1} \\frac{\\tau_\\alpha^2}{2\\mu_\\alpha} - U[u]|\\psi_{int}^0(t))$\npart is a universal functional of $\\rho_{int}$ in the sense that, for a given type of particle (a given interaction $u$), the same dependence on $\\rho_{int}$ holds for every $V[v_{int}]$, thus $v_{int}$ (see (\\ref{eq:V_int})).\n\nUsing the Eq. (\\ref{eq:H_int}), we develop the ``internal'' quantum action as\n\\begin{eqnarray}\nA_{int}[\\rho_{int}]&=& \\int_{t_0}^{t_1} dt (\\psi_{int}^0(t)|i\\hbar\\partial_t-\\sum_{\\alpha=1}^{N-1} \\frac{\\tau_\\alpha^2}{2\\mu_\\alpha}|\\psi_{int}^0(t))\n\\label{eq:action2}\\\\\n&& - \\int_{t_0}^{t_1} dt (\\psi_{int}^0(t)|U[u](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1})|\\psi_{int}^0(t))\n\\nonumber\\\\\n&& - \\int_{t_0}^{t_1} dt (\\psi_{int}^0(t)|V[v_{int}](\\mathbf{\\xi}_1, ..., \\mathbf{\\xi}_{N-1};t)|\\psi_{int}^0(t))\n\\nonumber .\n\\end{eqnarray}\nTo rewrite its last two terms in a more convenient way, we establish a useful relation.\nFor any function $f(\\vec{r}_1,...,\\vec{r}_N;t)$ of the laboratory coordinates,\nexpressible with the Jacobi coordinates [we denote $F(\\xi_1,...,\\xi_{N-1};t)$], we have\n\\begin{eqnarray}\n\\label{eq:rel}\n\\lefteqn{(\\psi_{int}^0(t)| F(\\xi_1,...,\\xi_{N-1};t) |\\psi_{int}^0(t))}\n\\\\\n& = & \\int \\! d\\mathbf{\\xi}_1 \\cdots d\\mathbf{\\xi}_{N-1} F(\\xi_1,...,\\xi_{N-1};t) \n\\big| \\psi_{int}^0 (\\xi_1,...,\\xi_{N-1};t) \\big|^2\n\\nonumber\\\\\n& = & \\int \\! d\\mathbf{R} d\\mathbf{\\xi}_1 \\cdots d\\mathbf{\\xi}_{N-1} \\delta(\\mathbf{R}) F(\\xi_1,...,\\xi_{N-1};t)\n\\nonumber\\\\\n&& \\hspace{4.5cm} \\times \\big| \\psi_{int}^0 (\\xi_1,...,\\xi_{N-1};t) \\big|^2\n\\nonumber\\\\\n& = & \\int \\! d\\vec{r}_1 \\cdots d\\vec{r}_{N} \\delta(\\mathbf{R}) f(\\vec{r}_1,...,\\vec{r}_N;t) \\big| \\psi_{int}^0(\\vec{r}_1,...,\\vec{r}_N;t) \\big|^2\n\\nonumber\n\\, .\n\\end{eqnarray}\nWe see that the \"internal mean values\" calculated with $\\psi_{int}$\nexpressed as a function of the ($N-1$) coordinates $\\xi_\\alpha$, can also be calculated with $\\psi_{int}$\nexpressed as a function of the $N$ coordinates $\\mathbf{r}_i$.\nAs one of them is redundant, a $\\delta(\\mathbf{R})$ which\nrepresents the dependence of the redundant coordinate on the others appears \\cite{foot3}.\n\nThe relation (\\ref{eq:rel}) leads to\n\\begin{eqnarray}\n\\label{eq:Eext}\n\\lefteqn{\n(\\psi_{int}^0(t)|V[v_{int}](\\xi_1,...,\\xi_{N-1};t)|\\psi_{int}^0(t)) \n} \\nonumber\\\\\n& = & \\int \\! d\\vec{r}_1 \\cdots d\\vec{r}_N \\; \n \\delta(\\mathbf{R}) \\sum_{i=1}^N v_{int}(\\vec{r}_i - \\vec{R};t) |\\psi_{int}^0(\\vec{r}_1,...,\\vec{r}_N;t)|^2 \\, \n \\nonumber\\\\\n& = & \\sum_{i=1}^N \n \\int \\! d\\vec{r} \\; v_{int}(\\vec{r};t) \n \\int \\! d\\vec{r}_1 \\cdots d\\vec{r}_N \\; \\delta(\\mathbf{R})\n \\nonumber\\\\\n& & \\hspace{2cm} \\times |\\psi_{int}^0(\\vec{r}_1, \\ldots, \\vec{r}_{N};t)|^2 \\delta \\big( \\vec{r}-(\\vec{r}_i-\\vec{R}) \\big) \n \\nonumber\\\\\n& = & \\sum_{i=1}^N \n \\int \\! d\\vec{r} \\; v_{int}(\\vec{r};t) \\, \n \\, \\frac{\\rho_{int}(\\vec{r},t)}{N} \n \\nonumber\\\\\n& = & \\int \\! d\\vec{r} \\; v_{int}(\\vec{r};t) \\, \\rho_{int}(\\vec{r},t)\n,\n\\end{eqnarray}\nwhere we used (\\ref{eq:rho_int0}) to obtain the penultimate equality.\nWe see that the potential $\\sum_{i=1}^N v_{int}(\\vec{r}_i - \\vec{R};t)$ that is $N$ body \nwith respect to the laboratory coordinates (and $(N-1)$ body when \nexpressed with Jacobi coordinates), becomes one body (and local) when\nexpressed with the c.m. frame coordinates\n(mind that $\\rho_{int}$ is defined in the c.m. frame, i.e. that $\\mathbf{r}$ is measured in the c.m. frame, cf. \\S \\ref{par:def}).\n\nApplying (\\ref{eq:rel}) to the second term of the action integral (\\ref{eq:action2}) gives\n$(\\psi_{int}^0(t)| U[u](\\xi_1,...,\\xi_{N-1}) |\\psi_{int}^0(t))=\\frac{1}{2} \\int \\! d\\vec{r} \\, d\\vec{r'} \\gamma_{int}(\\vec{r},\\vec{r'};t) u(\\vec{r}-\\vec{r'})$,\nwhere $\\gamma_{int}$ is defined in (\\ref{eq:gamint0}).\n\nThe action integral (\\ref{eq:action2}) can thus be rewritten\n\\begin{eqnarray}\nA_{int}[\\rho_{int}] &=& \\int_{t_0}^{t_1} dt (\\psi_{int}^0(t)|i\\hbar\\partial_t-\\sum_{\\alpha=1}^{N-1} \\frac{\\tau_\\alpha^2}{2\\mu_\\alpha}|\\psi_{int}^0(t))\n\\nonumber\\\\\n&& - \\frac{1}{2} \\int_{t_0}^{t_1} dt \\int \\! d\\vec{r} \\, d\\vec{r'} \\gamma_{int}(\\vec{r},\\vec{r'};t) u(\\vec{r}-\\vec{r'})\n\\nonumber\\\\\n&& - \\int_{t_0}^{t_1} dt \\int \\! d\\vec{r} \\; v_{int}(\\vec{r};t) \\rho_{int}(\\vec{r},t)\n.\n\\label{eq:action3}\n\\end{eqnarray}\n\nUp to now we did not do any hypothesis.\nTo recover the \\old{associated} Internal time-dependent KS scheme, we assume, as to obtain the\ntraditional time-dependent KS scheme \\cite{Run84,Gro94}, that there exists, \\textit{in the c.m.\\ frame}, a $N$-body non-interacting system\n(i.e. a local single-particle potential $v_S$)\n\\begin{eqnarray}\n\\label{eq:td_KS}\n\\Big( -\\frac{\\hbar^2\\Delta}{2m} + v_S(\\mathbf{r},t) \\Big)\\varphi^i_{int}(\\mathbf{r},t) = i\\hbar\\partial_t \\varphi^i_{int}(\\mathbf{r},t)\n\\end{eqnarray}\nwhich reproduces \\textit{exactly} the density $\\rho_{int}$ of the interacting system\n(mind that $\\rho_{int}$ is defined in the c.m. frame)\n\\begin{eqnarray}\n\\rho_{int}(\\mathbf{r},t) =\\sum_{i=1}^N |\\varphi^i_{int}(\\mathbf{r},t)|^2 .\n\\label{eq:rho_int}\n\\end{eqnarray}\nEven if only ($N-1$) coordinates are sufficient to describe the internal properties,\nthey still describe a system of $N$ particles. Thus, we have to introduce $N$ orbitals in the KS scheme (as we did)\nif we want them to be interpreted (to first order only) as single-particle orbitals\nand obtain a scheme comparable (but not equivalent) to mean-field like calculations with effective interactions.\n\nIn (\\ref{eq:td_KS}) we implicitely supposed that the particles are Fermions (a KS scheme to describe Boson condensates can be set similarly equalling all the $\\varphi^i_{int}$).\nUniqueness of the potential $v_S(\\mathbf{r},t)$ for a given density $\\rho_{int}(\\mathbf{r},t)$\n(and initial $|\\varphi^i_{int}(t_0))$ which yield the correct initial density $\\rho_{int}(\\mathbf{r},t_0)$)\nis ensured by a direct application of the traditional time-dependent DFT formalism \\cite{Run84,Gro94}.\nOf course, the question of the validity of the KS hypothesis, known as the \\textit{non-interacting v-representability} problem,\nremains, as in traditional (time-dependent) DFT \\cite{Dre90,Gro94}.\n\nTo use similar kinds of notations than the traditional DFT ones, we add and substract to the internal action integral (\\ref{eq:action3}) the internal Hartree term\n\\\\\n$\nA_{H}[\\rho_{int}] = \\frac{1}{2} \\int_{t_0}^{t_1} dt \\int \\! d\\vec{r} \\, d\\vec{r'} \\, \n\\rho_{int}(\\vec{r},t) \\, \\rho_{int}(\\vec{r'},t) \\, u(\\vec{r}-\\vec{r'})\n$,\nthe non-interacting kinetic energy term\\\\\n$\\int_{t_0}^{t_1} dt \\sum_{i=1}^{N} (\\varphi^i_{int}(t)|\\frac{\\vec{p}^2}{2m}|\\varphi^i_{int}(t))$\nand the\n$\\int_{t_0}^{t_1} dt \\sum_{i=1}^{N} (\\varphi^i_{int}(t)|i\\hbar\\partial_t|\\varphi^i_{int}(t))$\nterm.\nThis permits to rewrite the ``internal'' action integral (\\ref{eq:action3}) as\n\\begin{eqnarray}\nA_{int}&=& \\int_{t_0}^{t_1} dt \\sum_{i=1}^{N} (\\varphi^i_{int}(t)|i\\hbar\\partial_t-\\frac{\\vec{p}^2}{2m}|\\varphi^i_{int}(t)) - A_{H}[\\rho_{int}]\n\\nonumber\\\\\n&& - A_{XC}[\\rho_{int}] - \\int_{t_0}^{t_1} dt \\int \\! d\\vec{r} \\; v_{int}(\\vec{r};t) \\, \\rho_{int}(\\vec{r},t) \n\\label{eq:action4}\n\\end{eqnarray}\nwhere the internal exchange-correlation part is defined as\n\\begin{widetext}\n\\begin{eqnarray}\nA_{XC}[\\rho_{int}]\n&=& \\frac{1}{2} \\int_{t_0}^{t_1} dt \\int \\! d\\vec{r} \\, d\\vec{r'} \\, \n \\Big( \\gamma_{int}(\\vec{r},\\vec{r'};t) - \\rho_{int}(\\vec{r},t) \\, \\rho_{int}(\\vec{r'},t) \\Big) \\, \n u(\\vec{r}-\\vec{r'})\n\\nonumber\\\\\n&& + \\int_{t_0}^{t_1} dt \\Big( \n (\\psi_{int}^0(t)|\\sum_{\\alpha=1}^{N-1} \\frac{\\tau_\\alpha^2}{2\\mu_\\alpha}|\\psi_{int}^0(t)) \n - \\sum_{i=1}^{N} (\\varphi^i_{int}(t)|\\frac{\\vec{p}^2}{2m}|\\varphi^i_{int}(t)) \\Big)\n\\nonumber\\\\\n&& - \\int_{t_0}^{t_1} dt \\Big( (\\psi_{int}^0(t)|i\\hbar\\partial_t|\\psi_{int}^0(t))\n - \\sum_{i=1}^{N} (\\varphi^i_{int}(t)|i\\hbar\\partial_t|\\varphi^i_{int}(t)) \\Big)\n.\n\\label{eq:Axc}\n\\end{eqnarray}\n\\end{widetext}\nWe see that it contains the exchange-correlation which comes from the interaction $u$ (first line of (\\ref{eq:Axc})), but also the correlations contained in the interacting\nkinetic energy (second line of (\\ref{eq:Axc})) and in the interacting ``$i\\hbar\\partial_t$'' term (third line of (\\ref{eq:Axc})).\nA key point is that, as the KS assumption implies $\\varphi^i_{int}[\\rho_{int}]$ \\cite{Run84,Gro94,Dre90},\n$A_{XC}[\\rho_{int}](t)$ can be written as a functional of $\\rho_{int}$\n(for given $|\\psi_{int}^0(t_0))$ and $\\{|\\varphi^i_{int}(t_0))\\}$ which yield the same initial density $\\rho_{int}(\\mathbf{r},t_0)$).\n\nIt remains to vary the ``internal'' quantum action (\\ref{eq:action4}) to obtain the equations of motion (which define $\\rho_{int}$).\nVignale, see Ref. \\cite{Vig08}, showed recently that the correct formulation of the variational principle is not to stationarize the quantum action, i.e. $\\delta A_{int}[\\rho_{int}]=0$ as done so far \\cite{ker76,Run84,Gro94}, but\n\\begin{eqnarray}\n\\delta A_{int}[\\rho_{int}]=&&\ni\\big(\\psi_{int}[\\rho_{int}](t_1)\\big|\\delta \\psi_{int}[\\rho_{int}](t_1)\\big)\n\\nonumber\\\\\n&& - i \\big(\\psi^S_{int}[\\rho_{int}](t_1)\\big|\\delta \\psi^S_{int}[\\rho_{int}](t_1)\\big)\n\\label{eq:action}\n\\end{eqnarray}\n(where $\\psi^S_{int}$ is the Slater determinant constructed from the $\\varphi^i_{int}$).\nThe two formulations lead to identical final results for theorems derived form symmetries of the action functional because\ncompensations occur \\cite{Vig08}, but Vignales's formulation permits to solve the causality paradox of the previous formulation.\n\nVarying (\\ref{eq:action}) with respect to the $\\varphi^{i*}_{int}(\\mathbf{r},t)$, with $t\\in[t_0,t_1]$,\nleads straightforwardly to the Internal time-dependent KS equations for the $\\varphi^i_{int}$\n\\begin{equation}\n\\label{eq:varphi_i}\n\\Big(\n- \\frac{\\hbar^2}{2m}\\Delta \n+ U_H[\\rho_{int}] \n+ U_{XC}[\\rho_{int}] \n+ v_{int}\n\\Big) \\varphi^i_{int} = i\\hbar\\partial_t \\varphi^i_{int}\n\\end{equation}\nwith the potentials\n\\begin{widetext}\n\\begin{eqnarray}\n&& U_{H}[\\rho_{int}](\\vec{r},t) \n= \\frac{\\delta A_{H}[\\rho_{int}]}{\\delta \\rho_{int}(\\vec{r},t)}\n\\nonumber\\\\\n&& U_{XC}[\\rho_{int}](\\vec{r},t) \n= \\frac{\\delta A_{XC}[\\rho_{int}]}{\\delta \\rho_{int}(\\vec{r},t)}\n-i\\big(\\psi_{int}[\\rho_{int}](t_1)\\big|\\frac{\\delta \\psi_{int}[\\rho_{int}](t_1)}{\\delta \\rho_{int}(\\vec{r},t)}\\big)\n+ i \\big(\\psi^S_{int}[\\rho_{int}](t_1)\\big|\\frac{\\delta \\psi^S_{int}[\\rho_{int}](t_1)}{\\delta \\rho_{int}(\\vec{r},t)}\\big)\n\\label{eq:Uxc}\n\\end{eqnarray}\n\\end{widetext}\nwhich are local as expected ($v_S=U_H[\\rho_{int}]+ U_{XC}[\\rho_{int}] + v_{int}$ with the notations of Eq. (\\ref{eq:td_KS})).\nNote that the variational formulation of Vignale \\cite{Vig08} leads to the addition of the last\ntwo terms in the definition of $U_{XC}[\\rho_{int}](\\vec{r},t)$, see Eq. (\\ref{eq:Uxc}),\ncompared to the traditional result obtained by stationarization of the action. It are those terms which permit to solve the causality paradox \\cite{Vig08}.\n\nEquations~(\\ref{eq:varphi_i}) have the same form as the traditional time-dependent KS \nequations formulated \\old{in the laboratory frame} for non-translationally \ninvariant Hamiltonians \\cite{Koh65,Run84,Gro94}\nand permit to define $\\rho_{int}$ through (\\ref{eq:rho_int}).\nHere, we have justified their form \\textit{in the c.m. frame} for \nself-bound systems described with translationally invariant Hamiltonians.\n\nBut there is a major difference with the traditional DFT formalism.\nFollowing similar steps as in Eq.~(\\ref{eq:rel}), one can show that the\ninteracting kinetic energy term and the interacting ``$i\\hbar\\partial_t$'' term can be rewritten \\cite{foot3}\n\\begin{widetext}\n\\begin{eqnarray}\n&& (\\psi_{int}^0(t)|\\sum_{\\alpha=1}^{N-1} \\frac{\\tau_\\alpha^2}{2\\mu_\\alpha}|\\psi_{int}^0(t))\n= \\int d\\vec{r}_1 \\cdots d\\vec{r}_N \\delta(\\mathbf{R}) \n\\psi_{int}^{0*}(\\vec{r}_1,...,\\vec{r}_N;t)\n\\sum_{i=1}^N \\frac{\\mathbf{p}_i^2}{2m}\\psi_{int}^0(\\vec{r}_1,...,\\vec{r}_N;t)\n\\nonumber\\\\\n&& (\\psi_{int}^0(t)|i\\hbar\\partial_t|\\psi_{int}^0(t)) =\n\\int d\\vec{r}_1 \\cdots d\\vec{r}_N \\delta(\\mathbf{R}) \\psi_{int}^{0*}(\\vec{r}_1,...,\\vec{r}_N;t)\ni\\hbar\\partial_t \\psi_{int}^0(\\vec{r}_1,...,\\vec{r}_N;t)\n,\n\\label{eq:cm_cor}\n\\end{eqnarray}\n\\end{widetext}\nwhich makes it clear that the differences with the non-interacting kinetic energy term\n$\\sum_{i=1}^{N} \\int d\\vec{r} \\varphi^{i*}_{int}(\\vec{r})\\frac{\\vec{p}^2}{2m}\\varphi^i_{int}(\\vec{r})$\nand the non-interacting ``$i\\hbar\\partial_t$ term'' $\\sum_{i=1}^{N} (\\varphi^i_{int}(t)|i\\hbar\\partial_t|\\varphi^i_{int}(t))$\n(found in the exchange-correlation functional (\\ref{eq:Axc}))\ncome, on the one hand, from the correlations neglected in the traditional independent-particle framework,\nbut also from the c.m. correlations described by the $\\delta(\\mathbf{R})$ term in (\\ref{eq:cm_cor}), which does not appear in traditional time-dependent DFT \\cite{Run84,Gro94}.\nThe inclusion of the c.m. correlations in the exchange-correlation functional (\\ref{eq:Axc}) and potential (\\ref{eq:Uxc})\nis the main difference with the traditional KS scheme, and is a key issue for self bound-systems as atomic nuclei.\n\nMind that all the previous considerations only hold for fixed initial states $|\\psi_{int}(t_0))$ and $\\{|\\varphi^i_{int}(t_0))\\}$ which should of course give the same initial density $\\rho_{int}(\\mathbf{r},t_0)$ (and also for a fixed type of particle).\nAs a consequence, $\\psi_{int}^0$ is not only a functional of $\\rho_{int}$, but also depends on the initial state $|\\psi_{int}(t_0))$, and $U_{XC}$, Eq. (\\ref{eq:Uxc}),\nalso depends on the initial orbitals $\\{|\\varphi^i_{int}(t_0))\\}$.\nAn important difference to the ground state Internal DFT formalism \/ KS scheme presented in \\cite{Mes09}\nis that $|\\psi_{int}(t_0))$ and the $\\{|\\varphi^i_{int}(t_0))\\}$\ncannot necessarily be written as functionals of $\\rho_{int}(\\mathbf{r},t_0)$.\nHowever, as underlined in \\cite{Run84,Gro94}, if one starts from initial states $|\\psi_{int}(t_0))$ and $\\{|\\varphi^i_{int}(t_0))\\}$\nthat are non-degenerate ground states, i.e. that can be written as functionals of $\\rho_{int}(\\mathbf{r},t_0)$ \\cite{Mes09},\n$\\psi_{int}$ and $U_{XC}$ become functionals of $\\rho_{int}(\\mathbf{r},t)$ alone.\nThen, in the limit of stationary ground states, the theory reduces to the stationary Internal DFT \/ KS.\n\nWe recall that, as in traditional DFT, the previously discussed functionals are defined only for internal densities $\\rho_{int}$ which correspond to some internal potential $v_{int}$, called \\textit{v-representable} internal densities \\cite{Run84,Gro94}.\nUp to now, we do not know exactly how large the set of v-representable densities is.\nThis has to be kept in mind when variations with arbitrary densities are done, as to obtain the time-dependent KS equations.\n\n\n\n\n\n\\section{Conclusion.}\n\nIn summary, we have shown that, for a fixed initial state, the internal wave function,\nwhich describes the internal properties of a time-dependent self-bound system, can be written (up to a trivial phase) as a functional of the internal density.\nThis implies that the \"internal\" expectation values of any observable (which does not contain a time derivative),\nthat are of experimental interest, can be regarded as functionals of the internal density.\nThen, we set up, in the c.m.\\ frame, a practical scheme which permits to calculate the internal density and whose form is similar to the traditional time-dependent KS equations,\nthe difference being that the exchange-correlation functional contains the c.m. correlations.\n\nThis work is a first step towards the justification to the use of density functionals\nfor time-dependent nuclear mean-field like calculations with effective interactions \\cite{tdhf_nucl,Neg82},\nproving that there exists an ultimate functional which permits to reproduce the exact internal density\n(up to the non-interacting v-representability question).\nIf this functional was known, there would be no need for a c.m.\\ correction.\n\nPractically speaking, the time-dependent Internal KS scheme can describe, for instance in the nuclear case,\nthe collision of two nuclei in the frame attached to the total c.m.\\ of the nuclei.\nThen, $v_{int}$ is zero but the dependency to the initial state allows to start from\na state which corresponds to two nuclei with different velocities, or ``boosts''\n(choosen such as the total kinetic momentum is zero because we are in the c.m. frame). According to the choice of the boosts, \nwe can describe a wide variety of physical phenomena, from nuclear fusion \\cite{tdhf_nucl} to\nCoulomb excitation \\cite{Ald56}. One of the nuclei can also simply consist in a particule as a proton,\nto describe the excitation of a nucleus by diffusion.\n\nA case where a non-zero $v_{int}$ would be interesting could be\nthe case of the laser irradiation ($v_{int}$ would then contain a laser potential switched on at $t>t_0$).\nThis is not of major interest in the nuclear case because, experimentally speaking,\nwe do not yet have lasers that are suited to the study of the laser irradiation of a nucleus.\nHowever, this could be interesting in view of a generalization of this work to the whole molecule\n(following from the generalization to different types of particules, which is underway).\n\nMany questions remain open.\nIn particular, the question of the form of the potential which describes the c.m.\\ correlations;\nin addition to its practical interest, this question would also give interesting arguments concerning the non-interacting v-representability question.\nGeneralization to different types of particles (Fermions or Bosons) appears desirable.\nFinally, the same reasoning should be applied\nto rotational invariance to formulate the theory in term of the so-called \"intrinsic\" one-body density \\cite{Gir08a}\n(which is not directly observable). This is more complicated because rotation\ndoes not decouple from internal motion, but it should be interesting concerning the symmetry breaking question.\n\n\n\n\n\n\n\\begin{acknowledgments}\n\nThe author is particularly grateful to M. Bender, E.K.U. Gross, and E. Suraud for enlightening discussions and\nreading of the manuscript,\nand thanks the referee for his pertinent remarks.\nThe author thanks the Centre d'Etudes Nucl\\'eaires de Bordeaux-Gradignan for warm hospitality,\nand the Institut Universitaire de France and the\nAgence Nationale de la Recherche (ANR-06-BLAN-0319-02) for financial support. \n\n\n\\end{acknowledgments}\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nHamel and Prahalad \\cite{2005Competing} point out that the rapid changes in technology and the non-stationary nature of market demand create dynamic behavioural characteristics of the environment, and that in today's society, where technological innovations are proliferating and transformations are taking place rapidly, the cumulative effects of technological innovation in firms are more likely to take on the characteristics of a complex, irregular and non-periodic non-linear dynamical system. This non-linear characteristic seems to be chaotic, but in fact it is in order. From the perspective of non-linear theory, the evolution of a system can be summarised as shown in Figure \\ref{fig:fig1} . McBride \\cite{2010Chaos} defines chaos as a qualitative study of the unstable, acyclic behaviour of a deterministic nonlinear dynamical system, and Sardar and Abrams \\cite{2004Introducing} argue that 'order and chaos coexist, order in chaos and chaos in order. The presence of chaos in a non-linear system should be categorised and discussed. There are situations where chaos should be avoided, times when it should be controlled, and times when it should be exploited. \n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[scale=0.7]{figure1.png}\n \\caption{System evolution illustration.}\n \\label{fig:fig1}\n\\end{figure}\n\nSo as of now, research on models of the cumulative effects of technological innovation in the textile industry is still very limited and far from systematic. The textile industry, as an indispensable part of the population, plays an important role in clothing, food, housing and transport, and has experienced a long period of development. The cumulative effect of technological innovation in the industry is also prominent, so the textile industry is chosen as the subject of this study. This paper discusses the following: 1. the application of a model of the cumulative effect of technological innovation in the textile industry from the perspective of chaos theory; and 2. the provision of countermeasures for technological innovation in the textile industry to appropriately control and manage chaos and increase the effective innovation rate of the industry.\n\n\n\\section{A chaotic model of the cumulative effect of technological innovation}\n\\subsection{Chaos theory}\nThe basic characteristics of chaotic motion are as follows: deterministic, non-linear, sensitive dependence on initial conditions and non-periodic. The connotation of chaos is reflected in the fact that it is a seemingly random dynamic behaviour generated by a non-linear system \\cite{R1976Simple} . A deterministic system is said to be chaotic if, in the absence of external stochastic influences, 1. the state of motion of the system is irregular and complex, similar to the Brownian motion of small molecules; 2. the system has a sensitive dependence on the initial conditions; and 3. some characteristic of the system (e.g. positive Lyapunov exponent, positive topological entropy, attractor of fractional dimension, etc.) has little to do with the choice of initial conditions.\nIn this paper, on top of the basic framework of previous studies, a chaotic economic model of the cumulative effect of technological innovation in the textile industry is developed using the worm mouth model in chaos theory as an example, with appropriately given parameters so that it can be used to describe the dynamic evolution of the textile industry.\n\n\\subsection{The textile industry is chaotic in nature}\nThe evolution of the textile industry is irregular and complex. The reason is that it is impossible to predict which textile company will launch a new garment that will be liked by the people, or to judge success or failure by its industry innovations. The system of cumulative effects of technological innovation in the textile industry is sensitive to the initial conditions - the cumulative effects of the initial innovation - and is dependent on them. Moreover, Mensh \\cite{1979Stalemate}, Houstein et al. \\cite{1982Long}, Kleinknecht \\cite{1987Are} and Silverberg et al. \\cite{1993Long} have successively conducted empirical studies on the distribution function of the temporal occurrence pattern of innovation using relevant statistical data, and the results show that the realisation of innovation is a similar Poisson distribution and has an exponential growth trend of stochastic process. Therefore, both the textile industry itself and its innovation accumulation are chaotic in nature.\n\n\\subsection{A chaotic model of the cumulative effect of innovation in the textile industry}\nLet $X_t$ be the marginal contribution of the accumulation of innovative technology in the textile industry system to the growth rate of the total economic volume of the textile industry system at a certain moment $t$, i.e. when the accumulation of innovation in the textile industry increases by $1\\%$, the total economic volume of the textile industry increases by $X_t\\%$, and call $X_t$ the accumulation effect of innovation. It is easy to see that $X_t$ is a function about time, that is, the value of $X_t$ is time-dependent. Similar to the variables used to describe the changing state of economic growth, such as capital output rate, labour productivity and capital-labour ratio, $X_t$ should also be a state variable to describe the evolution of the textile system.\nThe contribution of innovation to economic growth in the textile industry is much more than a simple linear accumulation of individual innovation contributors, and therefore $X_t$ is a more complex variable, as are variables such as the capital output rate, labour productivity and the capital-labour ratio. According to the definition of $X_t$, its variation mainly reflects fluctuations in the structure of productivity within the textile industry due to the continuous generation and accumulation of innovation. Silverberg \\cite{1993Long} developed a model of technological progress and its evolutionary chaos, which not only explains the existence of the impact of innovation, but also shows that innovation makes productivity move in an irregular cycle, i.e. chaos. This is the theoretical basis for this paper's $X_t$. \\\\\nIntroducing the model more commonly used in chaos economics for studying chaos in economic growth, as shown in equation \\ref{equ1}.\n\n\\begin{equation}\\label{equ1}\n X_{t+1} = X_{t}^{\\beta} \\frac{\\sigma A}{1+\\lambda} ( s-X_t )^{\\gamma}\n\\end{equation}\n\nwhere $X_t$ is the capital-labour ratio, $\\sigma (\\sigma >0)$ is the savings rate, $\\lambda (0<\\lambda <1)$ is the natural growth rate of labour, $A(A>0)$ is the technological progress factor, $\\beta (\\beta >0)$ is the elasticity of the capital-labour ratio, $\\gamma$ is a constant greater than zero, and $s$ is the maximum capital-labour rate. \\\\\nWhen $\\beta=\\gamma=s=1$, let $\\mu=\\sigma A\/(1+\\gamma) $, then \\ref{equ1} can be transformed into \\ref{equ2}.\n\n\\begin{equation}\\label{equ2}\n X_{t+1} = \\mu X_{t}(1-X_{t})\n\\end{equation}\n\nequation \\ref{equ2} is the general form of the insect-population model. The purpose of introducing the transformation of equation \\ref{equ1} into equation \\ref{equ2} is to construct a chaotic economic model of the cumulative effect of technological innovation by analogy with a chaotic initialization \\cite{2018Improving}. Drawing on the insect-population model, the following assumptions are introduced: 1) Technological innovation does not arise out of thin air, and existing innovations are usually the basis for subsequent innovations, similar to the relationship between parent and offspring insects in nature. 2) Technological innovation is measured by its level of innovation, and usually there is no mixture of parent and offspring, i.e., there is a certain amount of substitution of the firm's new product for the old one. 3) The technological innovation of an enterprise is limited by its own economic resources, just like the survival environment of a worm in nature. Therefore, this paper considers that technological innovation in textile enterprises meets the prerequisites of the insect-population model. According to the previous paper, the cumulative effect of technological innovation is a chaotic economic variable, which evolves in much the same way as the capital-labour ratio, with a non-linear evolution mechanism. Therefore, a chaotic economic model of the cumulative effect of technological innovation can be defined similarly - equation \\ref{equ3}.\n\n\\begin{equation}\\label{equ3}\n X_{t+1} = T \\epsilon X_{t}(1-X_{t})\n\\end{equation}\n\nwhere $X_t \\in (0,1)$, $\\epsilon \\in (0,10)$, $T\\epsilon \\in (0,4)$, $X_t$ represents the proportion of the cumulative effect of technological innovation at time $t$, as the state variable of the textile system; $\\epsilon$ denotes the government regulation parameter; $T$ is the specific coefficient of the textile firm (i.e. the combined coefficient of the growth rate of technological inputs $\\alpha$, the proportion of technological content of output $\\beta$ and the annual growth rate of labour force $n$ at a point in time, which The relationship between them is $T=\\frac{\\alpha + \\beta}{1+n}$, where $\\alpha, \\beta, n \\in (0,1)$, $T\\epsilon$ together constitute the innovation control parameters of the textile enterprises themselves.\n\n\\subsection{Chaos of the model}\nThe chaotic state of \\ref{equ3} can be determined using the Li-Yorke theorem. Constructing the function,\n\n\\begin{equation}\\label{equ4}\n f(x)=T\\epsilon x(1-x)\n\\end{equation}\n\nIt is not difficult to determine where $f(0)=0$ and $f(x)$ is a single-peaked function. \\\\\nFirst, determine the point $x^*$ at which $f(x)$ reaches its maximum value. $x^*$ represents the maximum technological innovation accumulation effect value of $f(x)$. $x^*$ can be obtained by solving the following first order partial derivative equation.\n\n\\begin{equation}\\label{equ5}\n \\frac{\\partial f(x^*)}{\\partial x^*}=0\n\\end{equation}\n\nThe mapping point $x^*$ corresponding to the value of the maximum reachable function is obtained,\n\n\\begin{equation}\\label{equ6}\n x^*=1\/2\n\\end{equation}\n\nThe maximum innovation accumulation effect value $X_{max}$ is,\n\n\\begin{equation}\\label{equ7}\n x_{max}=f(x^*)=\\frac{T\\epsilon}{2}\n\\end{equation}\n\nFrom the above definition, it follows that the value of the maximum innovation accumulation effect should satisfy,\n\n\\begin{equation}\\label{equ8}\n x_{max} \\le s\n\\end{equation}\n\nNext, determine the initial image point $x_i$ , and from equations \\ref{equ4} and \\ref{equ6}, we have,\n\n\\begin{equation}\\label{equ9}\n T\\epsilon x(1-x)=1\/2\n\\end{equation}\n\nThe left end of equation \\ref{equ9} is equation \\ref{equ4}, a single-peaked function, so the smaller root is taken to be $x_i$, \n\n\\begin{equation}\\label{equ10}\n x_i = \\frac{T\\epsilon - \\sqrt{(T\\epsilon)^2-T\\epsilon}}{2T\\epsilon}\n\\end{equation}\n\nFinally, determine its third-order mapping $f(x_{max})$,\n\n\\begin{equation}\\label{equ11}\n f(x_{max}) = \\frac{T^2\\epsilon ^2}{2}(1-T\\epsilon \/2)\n\\end{equation}\n\nprovided that the first-, second- and third-order mappings $x^*$,$x_{max}$, $f(x_{max})$ generated by the initial preimage $x_i$ are mapped from the interval $[0,s]$ to its own interval $[0,s]$ and satisfy the sufficient conditions for chaos in the Li-Yorke theorem, i.e.,\n\n\\begin{equation}\\label{equ12}\n 0 \\le f(x_{max}) \\le x_i \\le x^* \\le x_{max}\n\\end{equation}\n\nThen the textile industry system \\ref{equ3} appears chaotic, as shown in Figure \\ref{fig:fig2}, which is a chaotic phase diagram regarding the proportion of the cumulative effect of two adjacent generations of innovation in the textile industry innovation system. Where the curve is the image of equation (3) and the straight line is $X_{t+1} = X_t$, which represents the process of converting a preimage point to the next preimage point.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[scale=0.7]{figure2.png}\n \\caption{Chaotic phase diagrams in textile industry.}\n \\label{fig:fig2}\n\\end{figure}\n\n\n\\section{Chaotic nature of the model}\n\\subsection{Local structural stability and cyclic bifurcation propertiest}\nFor model \\ref{equ3}, specifically, when $T\\epsilon$ has a determined value, if the corresponding $x_t$ also has only one value (indicating that the firm's innovation reaches a uniquely determined proportion, i.e. the level of intensification). That is, the technological innovation effect of a textile firm reaches a uniquely determined level. Then the period of $x_t$ is said to be $1$; if at this point $x_t$ has two values corresponding to it, then the period of $x_t$ is said to be $2$; if at this point $x_t$ has n values corresponding to it, then the period of $x_t$ is said to be $n$. In particular, the structure of $x_t$ is unstable when there is a bifurcation and the steady state can only be maintained when the period of $x_t$ is determined. This also means that a stable accumulation of technological innovation in textile companies can only be guaranteed when $T\\epsilon$ takes on a value within a particular interval. In other words, a good accumulation of technological innovation in the textile industry can be maintained to a certain extent when the role of policy innovation and industry-specific coefficients are well controlled. A diagram of the iterative process for varying values of the control parameter $T\\epsilon$ from $0$ to large is shown in Figure \\ref{fig:fig3}.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[scale=0.7]{figure3.png}\n \\caption{Bifurcations.}\n \\label{fig:fig3}\n\\end{figure}\n\n\\subsection{Effect of parameter values on the initial innovation ratio in the textile industry}\nBased on its definition of firm intensification in a chaotic economic model, the effect of the $T\\epsilon$ parameter on the initial innovation ratio of textile firms is derived by analogy. \n\\begin{itemize}\n\t\\item When $03.5441$, the local structure of $x_t$ is stable and the period of $x_t$ is $8$. The rest of the process continues until $T\\epsilon=3.5699=T\\epsilon_{\\infty}$, when the period of $x_t$ is infinite, i.e. chaos emerges. In other words, there is no relationship between the firm's innovation accumulation and the control parameters at this point.\n\\end{itemize}\n\n\\subsection{Sexual analysis of different parameter values}\nFirstly, the values of $\\alpha$, $\\beta$, and $n$ are taken and defined as shown in Table \\ref{tab1}, and all data are obtained from the findings of the \"China Statistical Yearbook\".\n\n\\begin{table}[H]\n \\caption{\\label{tab1} Variable definition and value}\n \\centering\n \\resizebox{\\linewidth}{32pt}{\n \\begin{tabular}{ccc}\n \\hline\n Symbol & Description & Fetching method \\\\\n \\hline\n $\\alpha$ & Growth rate of investment in science and technology & Log difference of R\\&D inputs \\\\\n $\\beta$ & Proportion of technological content of output & Log difference in the number of active patents in the textile industry \\\\\n $n$ & Annual growth rate of the labour force & Log difference of the average number of workers employed \\\\\n $\\epsilon$ & Government regulation parameters & By taking the product of $T\\epsilon$ backwards or assuming that given \\\\\n \\hline\n \\end{tabular}}\n\\end{table}\n\nFrom an econometric perspective, log-differencing helps to enhance the significance of the main regression to some extent. As a corollary, the significance of log-differencing the variables in this paper is to smooth out the effects of unreasonable outliers and reduce the \"misleading\" effect of episodic events on the overall judgment. In addition, as the average number of workers before 14 years could not be found directly, the calculation of business income\/per capita business income was used, and the use of logarithmic difference to calculate the growth rate can also reduce the subtle influence of different quantiles on the conclusion. The log-difference results are shown in Table \\ref{tab2} \\footnote{All data are obtained from the China Statistical Yearbook, and are rounded to four decimal places after calculation by stata. The values of $\\alpha$ range from 1.46\\% to 47.44\\%, $\\beta$ from 9.58\\% to 51.69\\% and $n$ from -16.46\\% to 4.79\\%, so the values of $\\alpha+\\beta$ and $1+n$ are inferred from this.}.\n\n\\begin{table}[H]\n\t\\caption{\\label{tab2} Log-differential results}\n\t\\centering\n\t\t\\begin{tabular}{cccccc}\n\t\t\t\\hline\n\t\t\tYear & $\\alpha$ & $\\beta$ & $n$ & $\\alpha+\\beta$ & $1+n$ \\\\\n\t\t\t\\hline\n\t\t\t2009&\/&\/&\/&\/&\/\t\\\\\n\t\t\t2010&.2017&.5088&.0479&.7105&1.0479\\\\\n\t\t\t2011&.4744&.1671&-.0947&.6416&.9053\\\\\n\t\t\t2012&.0146&.1347&-.0566&.1494&.9434\\\\\n\t\t\t2013&.1382&.1418&-.0614&.2800&.9386\\\\\n\t\t\t2014&.1144&.5169&-.0653&.6313&.9347\\\\\n\t\t\t2015&.1558&.0958&-0.540&.2516&.9460\\\\\n\t\t\t2016&.0574&.2867&-.0627&.3441&.9373\\\\\n\t\t\t2017&.0584&.2688&-.1090&.3273&.8910\\\\\n\t\t\t2018&.0912&.2629&-.1646&.3541&.8354\\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\nTable \\ref{tab2} shows that the values of $\\alpha+\\beta$ range from 14.94\\% to 71.05\\%, and the values of $1+n$ range from 83.54\\% to 104.79\\%. Considering that the value of the parameter $T$ in equation \\ref{equ3} is proportional to $(\\alpha+\\beta)$ and inversely proportional to $(1+n)$, and that $T$ is related to the innovation accumulation $x_t$ of the textile enterprises in equation \\ref{equ3}, the sexual attitude of $x_t$ can be analysed through this. To facilitate the calculation, the three fixed cases of the lower, middle and upper limits of these elements are taken separately, and only $\\epsilon$ is adjusted to analyse the nature of $x_t$.\n\n\\begin{itemize}\n\t\\item Lower bound case. $\\alpha+\\beta=0.1494$ , $1+n=0.8354$ . When $\\epsilon=5.5917$, $T\\epsilon=1.0000$ . From equation \\ref{equ3} and Figure \\ref{fig:fig3}, $x_t=0$. when $\\epsilon=10$, $T\\epsilon=1.7884$ and $x_t=0.4408$ . This means that at $(\\alpha+\\beta=0.1494)$, the degree of innovation accumulation achieved by textile firms remains zero when the parameter of government regulation of textile firms is 5.5917. When adjusted to the maximum value $\\epsilon=10$, $T\\epsilon=1.7884$ (satisfying $10)$ is the savings rate, $\\lambda (0<\\lambda <1)$ is the natural growth rate of labour, $A(A>0)$ is the technological progress factor, $\\beta (\\beta >0)$ is the elasticity of the capital-labour ratio, $\\gamma$ is a constant greater than zero, and $s$ is the maximum capital-labour rate. \\\\\nWhen $\\beta=\\gamma=s=1$, let $\\mu=\\sigma A\/(1+\\gamma) $, then \\ref{equ1} can be transformed into \\ref{equ2}.\n\n\\begin{equation}\\label{equ2}\n X_{t+1} = \\mu X_{t}(1-X_{t})\n\\end{equation}\n\nequation \\ref{equ2} is the general form of the insect-population model. The purpose of introducing the transformation of equation \\ref{equ1} into equation \\ref{equ2} is to construct a chaotic economic model of the cumulative effect of technological innovation by analogy with a chaotic initialization \\cite{2018Improving}. Drawing on the insect-population model, the following assumptions are introduced: 1) Technological innovation does not arise out of thin air, and existing innovations are usually the basis for subsequent innovations, similar to the relationship between parent and offspring insects in nature. 2) Technological innovation is measured by its level of innovation, and usually there is no mixture of parent and offspring, i.e., there is a certain amount of substitution of the firm's new product for the old one. 3) The technological innovation of an enterprise is limited by its own economic resources, just like the survival environment of a worm in nature. Therefore, this paper considers that technological innovation in textile enterprises meets the prerequisites of the insect-population model. According to the previous paper, the cumulative effect of technological innovation is a chaotic economic variable, which evolves in much the same way as the capital-labour ratio, with a non-linear evolution mechanism. Therefore, a chaotic economic model of the cumulative effect of technological innovation can be defined similarly - equation \\ref{equ3}.\n\n\\begin{equation}\\label{equ3}\n X_{t+1} = T \\epsilon X_{t}(1-X_{t})\n\\end{equation}\n\nwhere $X_t \\in (0,1)$, $\\epsilon \\in (0,10)$, $T\\epsilon \\in (0,4)$, $X_t$ represents the proportion of the cumulative effect of technological innovation at time $t$, as the state variable of the textile system; $\\epsilon$ denotes the government regulation parameter; $T$ is the specific coefficient of the textile firm (i.e. the combined coefficient of the growth rate of technological inputs $\\alpha$, the proportion of technological content of output $\\beta$ and the annual growth rate of labour force $n$ at a point in time, which The relationship between them is $T=\\frac{\\alpha + \\beta}{1+n}$, where $\\alpha, \\beta, n \\in (0,1)$, $T\\epsilon$ together constitute the innovation control parameters of the textile enterprises themselves.\n\n\\subsection{Chaos of the model}\nThe chaotic state of \\ref{equ3} can be determined using the Li-Yorke theorem. Constructing the function,\n\n\\begin{equation}\\label{equ4}\n f(x)=T\\epsilon x(1-x)\n\\end{equation}\n\nIt is not difficult to determine where $f(0)=0$ and $f(x)$ is a single-peaked function. \\\\\nFirst, determine the point $x^*$ at which $f(x)$ reaches its maximum value. $x^*$ represents the maximum technological innovation accumulation effect value of $f(x)$. $x^*$ can be obtained by solving the following first order partial derivative equation.\n\n\\begin{equation}\\label{equ5}\n \\frac{\\partial f(x^*)}{\\partial x^*}=0\n\\end{equation}\n\nThe mapping point $x^*$ corresponding to the value of the maximum reachable function is obtained,\n\n\\begin{equation}\\label{equ6}\n x^*=1\/2\n\\end{equation}\n\nThe maximum innovation accumulation effect value $X_{max}$ is,\n\n\\begin{equation}\\label{equ7}\n x_{max}=f(x^*)=\\frac{T\\epsilon}{2}\n\\end{equation}\n\nFrom the above definition, it follows that the value of the maximum innovation accumulation effect should satisfy,\n\n\\begin{equation}\\label{equ8}\n x_{max} \\le s\n\\end{equation}\n\nNext, determine the initial image point $x_i$ , and from equations \\ref{equ4} and \\ref{equ6}, we have,\n\n\\begin{equation}\\label{equ9}\n T\\epsilon x(1-x)=1\/2\n\\end{equation}\n\nThe left end of equation \\ref{equ9} is equation \\ref{equ4}, a single-peaked function, so the smaller root is taken to be $x_i$, \n\n\\begin{equation}\\label{equ10}\n x_i = \\frac{T\\epsilon - \\sqrt{(T\\epsilon)^2-T\\epsilon}}{2T\\epsilon}\n\\end{equation}\n\nFinally, determine its third-order mapping $f(x_{max})$,\n\n\\begin{equation}\\label{equ11}\n f(x_{max}) = \\frac{T^2\\epsilon ^2}{2}(1-T\\epsilon \/2)\n\\end{equation}\n\nprovided that the first-, second- and third-order mappings $x^*$,$x_{max}$, $f(x_{max})$ generated by the initial preimage $x_i$ are mapped from the interval $[0,s]$ to its own interval $[0,s]$ and satisfy the sufficient conditions for chaos in the Li-Yorke theorem, i.e.,\n\n\\begin{equation}\\label{equ12}\n 0 \\le f(x_{max}) \\le x_i \\le x^* \\le x_{max}\n\\end{equation}\n\nThen the textile industry system \\ref{equ3} appears chaotic, as shown in Figure \\ref{fig:fig2}, which is a chaotic phase diagram regarding the proportion of the cumulative effect of two adjacent generations of innovation in the textile industry innovation system. Where the curve is the image of equation (3) and the straight line is $X_{t+1} = X_t$, which represents the process of converting a preimage point to the next preimage point.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[scale=0.7]{figure2.png}\n \\caption{Chaotic phase diagrams in textile industry.}\n \\label{fig:fig2}\n\\end{figure}\n\n\n\\section{Chaotic nature of the model}\n\\subsection{Local structural stability and cyclic bifurcation propertiest}\nFor model \\ref{equ3}, specifically, when $T\\epsilon$ has a determined value, if the corresponding $x_t$ also has only one value (indicating that the firm's innovation reaches a uniquely determined proportion, i.e. the level of intensification). That is, the technological innovation effect of a textile firm reaches a uniquely determined level. Then the period of $x_t$ is said to be $1$; if at this point $x_t$ has two values corresponding to it, then the period of $x_t$ is said to be $2$; if at this point $x_t$ has n values corresponding to it, then the period of $x_t$ is said to be $n$. In particular, the structure of $x_t$ is unstable when there is a bifurcation and the steady state can only be maintained when the period of $x_t$ is determined. This also means that a stable accumulation of technological innovation in textile companies can only be guaranteed when $T\\epsilon$ takes on a value within a particular interval. In other words, a good accumulation of technological innovation in the textile industry can be maintained to a certain extent when the role of policy innovation and industry-specific coefficients are well controlled. A diagram of the iterative process for varying values of the control parameter $T\\epsilon$ from $0$ to large is shown in Figure \\ref{fig:fig3}.\n\n\\begin{figure}[H]\n \\centering\n \\includegraphics[scale=0.7]{figure3.png}\n \\caption{Bifurcations.}\n \\label{fig:fig3}\n\\end{figure}\n\n\\subsection{Effect of parameter values on the initial innovation ratio in the textile industry}\nBased on its definition of firm intensification in a chaotic economic model, the effect of the $T\\epsilon$ parameter on the initial innovation ratio of textile firms is derived by analogy. \n\\begin{itemize}\n\t\\item When $03.5441$, the local structure of $x_t$ is stable and the period of $x_t$ is $8$. The rest of the process continues until $T\\epsilon=3.5699=T\\epsilon_{\\infty}$, when the period of $x_t$ is infinite, i.e. chaos emerges. In other words, there is no relationship between the firm's innovation accumulation and the control parameters at this point.\n\\end{itemize}\n\n\\subsection{Sexual analysis of different parameter values}\nFirstly, the values of $\\alpha$, $\\beta$, and $n$ are taken and defined as shown in Table \\ref{tab1}, and all data are obtained from the findings of the \"China Statistical Yearbook\".\n\n\\begin{table}[H]\n \\caption{\\label{tab1} Variable definition and value}\n \\centering\n \\resizebox{\\linewidth}{32pt}{\n \\begin{tabular}{ccc}\n \\hline\n Symbol & Description & Fetching method \\\\\n \\hline\n $\\alpha$ & Growth rate of investment in science and technology & Log difference of R\\&D inputs \\\\\n $\\beta$ & Proportion of technological content of output & Log difference in the number of active patents in the textile industry \\\\\n $n$ & Annual growth rate of the labour force & Log difference of the average number of workers employed \\\\\n $\\epsilon$ & Government regulation parameters & By taking the product of $T\\epsilon$ backwards or assuming that given \\\\\n \\hline\n \\end{tabular}}\n\\end{table}\n\nFrom an econometric perspective, log-differencing helps to enhance the significance of the main regression to some extent. As a corollary, the significance of log-differencing the variables in this paper is to smooth out the effects of unreasonable outliers and reduce the \"misleading\" effect of episodic events on the overall judgment. In addition, as the average number of workers before 14 years could not be found directly, the calculation of business income\/per capita business income was used, and the use of logarithmic difference to calculate the growth rate can also reduce the subtle influence of different quantiles on the conclusion. The log-difference results are shown in Table \\ref{tab2} \\footnote{All data are obtained from the China Statistical Yearbook, and are rounded to four decimal places after calculation by stata. The values of $\\alpha$ range from 1.46\\% to 47.44\\%, $\\beta$ from 9.58\\% to 51.69\\% and $n$ from -16.46\\% to 4.79\\%, so the values of $\\alpha+\\beta$ and $1+n$ are inferred from this.}.\n\n\\begin{table}[H]\n\t\\caption{\\label{tab2} Log-differential results}\n\t\\centering\n\t\t\\begin{tabular}{cccccc}\n\t\t\t\\hline\n\t\t\tYear & $\\alpha$ & $\\beta$ & $n$ & $\\alpha+\\beta$ & $1+n$ \\\\\n\t\t\t\\hline\n\t\t\t2009&\/&\/&\/&\/&\/\t\\\\\n\t\t\t2010&.2017&.5088&.0479&.7105&1.0479\\\\\n\t\t\t2011&.4744&.1671&-.0947&.6416&.9053\\\\\n\t\t\t2012&.0146&.1347&-.0566&.1494&.9434\\\\\n\t\t\t2013&.1382&.1418&-.0614&.2800&.9386\\\\\n\t\t\t2014&.1144&.5169&-.0653&.6313&.9347\\\\\n\t\t\t2015&.1558&.0958&-0.540&.2516&.9460\\\\\n\t\t\t2016&.0574&.2867&-.0627&.3441&.9373\\\\\n\t\t\t2017&.0584&.2688&-.1090&.3273&.8910\\\\\n\t\t\t2018&.0912&.2629&-.1646&.3541&.8354\\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\nTable \\ref{tab2} shows that the values of $\\alpha+\\beta$ range from 14.94\\% to 71.05\\%, and the values of $1+n$ range from 83.54\\% to 104.79\\%. Considering that the value of the parameter $T$ in equation \\ref{equ3} is proportional to $(\\alpha+\\beta)$ and inversely proportional to $(1+n)$, and that $T$ is related to the innovation accumulation $x_t$ of the textile enterprises in equation \\ref{equ3}, the sexual attitude of $x_t$ can be analysed through this. To facilitate the calculation, the three fixed cases of the lower, middle and upper limits of these elements are taken separately, and only $\\epsilon$ is adjusted to analyse the nature of $x_t$.\n\n\\begin{itemize}\n\t\\item Lower bound case. $\\alpha+\\beta=0.1494$ , $1+n=0.8354$ . When $\\epsilon=5.5917$, $T\\epsilon=1.0000$ . From equation \\ref{equ3} and Figure \\ref{fig:fig3}, $x_t=0$. when $\\epsilon=10$, $T\\epsilon=1.7884$ and $x_t=0.4408$ . This means that at $(\\alpha+\\beta=0.1494)$, the degree of innovation accumulation achieved by textile firms remains zero when the parameter of government regulation of textile firms is 5.5917. When adjusted to the maximum value $\\epsilon=10$, $T\\epsilon=1.7884$ (satisfying $1\\epsilon$, the exponential decay term of warp factor \\eqref{Warp_factor} is dominant in the whole bulk. It is clear that $V_I>0$ and $V_{II}<0$ in this case, namely, there is a positive tension brane located at the origin and a negative tension brane at $y_\\pi$. This is the model we will focus on in the rest of the paper. Especially, in the limit $\\epsilon e^{k y_\\pi}\\ll 1$, the warp factor \\eqref{Warp_factor} approaches the exponential decay form of RS model. Correspondingly, the fine-turning conditions reduce to the case of RS model as well,\n\\beq\nV_I\\approx-V_{II}\\approx6 k M^3, ~~~ \\Lambda=-6k^2.\n\\eeq\n\nBesides, there is another interesting case that both brane tensions are positive in case of $e^{-k|y_\\pi|}<\\epsilon<1$. It would generate a new brane configuration distinct from the RS1 model. Especially, the scenario that our universe is confined on the positive tension brane may potentially provide a possible model with better properties \\cite{Csaki1999,Shiromizu2000,Yang2012a}. This model will be studied in detail in our another work.\n\n\t\n\\section{Linear perturbations}\\label{Perturbation}\n\nIn order to investigate the stability of the model, we consider the full linear perturbations against the background. The perturbed metric is written as\n\\beq\nds^2=\\lt(g_{MN}+h_{MN}\\rt)dx^Mdx^N,\n\\label{Metric_NC}\n\\eeq\nwhere $h_{MN}$ represents the perturbations against the background metric $g_{MN}$ given in \\eqref{Brane_Metric}. Due to the 4D Lorentz symmetry of our background spacetime, it is convenient to decompose the perturbed metric into the scalar, transverse vector and transverse-traceless tensor modes, and to rewrite it by\n\\beqn\nh_{55}&=&-2\\xi,\\\\\nh_{\\mu 5}&=&-a \\lt(S_{\\mu}+\\pt_\\mu \\beta \\rt),\\\\\nh_{\\mu\\nu}&=&a^2\\lt[ D_{\\mu\\nu}+2\\eta_{\\mu\\nu}\\psi+\\fc{1}{2}\\lt(\\pt_{\\mu}F_{\\nu}+\\pt_{\\nu}F_{\\mu} \\rt)+2\\pt_\\mu\\pt_\\nu E \\rt],\n\\eeqn\nwhere the vector modes satisfy the transverse condition $\\pt^\\mu S_{\\mu}=\\pt^\\mu F_{\\mu}=0$, and the tensor mode satisfies the transverse-traceless (TT) condition $\\pt^\\mu D_{\\mu\\nu}=0$. \n\nCorrespondingly, the perturbed scalar fields are \n\\beq\n\\phi^a=x^a+\\pi^a,\n\\eeq \nwhere $\\pi^a=\\delta^a_\\mu\\pi^\\mu$ is the Goldstone excitation of the condensation. In order to maintain the scalar condensation \\eqref{Scalar_vacuum} under the general coordinate transformation $x^M \\to x^M+\\epsilon^M$, the Goldstone excitation has to transform opposite to the 4D coordinates simultaneously, i.e., $\n\\pi^\\mu\\to\\pi^\\mu-\\epsilon^\\mu$. This St\\\"uckelberg trick non-linearly restores the general covariance of the theory. Therefore, it behaves like a vector field and can be decomposed as $\\pi^\\mu=\\eta^{\\mu\\nu}\\lt(\\pt_\\nu \\varphi+A_\\nu \\rt)$, with $\\varphi$ a scalar field and $A_\\mu$ a transverse vector field.\n\nTherefore, there are 1 TT tensor, 3 transverse vector and 5 scalar modes in total. By decomposing $\\epsilon_\\mu =a^2(\\epsilon^{V}_{\\mu}+\\pt_\\mu \\epsilon^s)$ with $\\epsilon^{V}_{\\mu}$ a transverse vector and $\\epsilon^s$ a scalar, these 9 perturbed modes transform as follows under the general coordinate transformation,\n\\beqn\nD_{\\mu\\nu} &\\to& D_{\\mu\\nu}, \\quad S_{\\mu } \\to S_{\\mu } + a \\epsilon^{V}_{\\mu }{}' ,\\quad F_{\\mu } \\to F_{\\mu }-2 \\epsilon^{V}_{\\mu } ,\\nn\\\\\nA_{\\mu } &\\to& A_{\\mu }-{\\epsilon }^{V}_{\\mu }, \\quad \\psi \\to \\psi -H\\epsilon _5, \\quad E \\to E-\\epsilon^s, \\nn\\\\\n\\beta &\\to& \\beta+a {\\epsilon }^s{}'+\\frac{\\epsilon _5}{a}, \\quad \\xi \\to \\xi +\\epsilon '_5, \\quad \\varphi \\to \\varphi -{\\epsilon }^s.\n\\label{Gauge_transformation}\n\\eeqn\n\nNaively, we have 5 gauge freedoms to eliminate one vector and two scalar modes by fixing ${\\epsilon }^{V}_\\mu$, ${\\epsilon}^s$ and $\\epsilon _5$. A commonly used gauge choice is the so-called unitary gauge, in which the Goldstone excitations $\\pi^a$ of scalar fields are closed, i.e., $A_\\mu=\\varphi=0$. Moreover, we have another gauge freedom $\\epsilon _5$ to set $\\psi=0$. Then from the fact that $Z_\\mu-\\pi_\\mu$, with $Z_\\mu\\equiv a^2\\lt(F_\\mu\/2+\\pt_\\mu E \\rt)$, is a gauge invariant quantity, one observes that the 4 Goldstone excitations $\\pi^\\mu$ are ``eaten\" by the 5D graviton in the unitary gauge. Consequently, the 5D massless spin-2 graviton with 5 DOF gets weight and becomes massive, with 9 DOF on the spectrum. After gauge fixing, we have 1 TT tensor, 2 transverse vector and 3 scalar modes left. However, not all modes among them are physical, since some of them can be eliminated by some constraint equations, which can be easily extracted by utilizing the Arnowitt-Deser-Misner (ADM) formalism. \n\nThe RS metric \\eqref{Brane_Metric} in ADM formalism reads \n\\beq\nds^2=N^2dy^2+\\gamma _{\\mu \\nu } \\left(dx^{\\mu }+N^{\\mu }dy\\right) \\left(dx^{\\nu }+N^{\\nu }dy \\right),\n\\eeq\nwith $N=1$, $N^\\mu=0$ and $\\gamma_{\\mu\\nu}=a^2(y)\\eta_{\\mu\\nu}$. Correspondingly, the bulk action of \\eqref{Main_Action} is rewritten as \n\\beqn\nS&=&\\frac{M^3}{2}\\int d^5x\\sqrt{-\\gamma }\\Big[N \\lt(R^{(4)}-2 \\Lambda -m^2 \\gamma ^{\\mu \\nu } \\partial_\\mu\\phi ^a \\partial_\\nu\\phi ^a \\rt) -N^{-1}\\lt(E_{\\mu \\nu } E^{\\mu \\nu }-E^2\\rt)\\nn\\\\\n&& -m^2 N^{-1}\\left(\\phi^{a}{}'-N^{\\mu }\\partial_{\\mu }\\phi ^a\\right)\\left(\\phi^{a}{}'-N^{\\nu }\\partial_{\\nu }\\phi ^a\\right)\\Big], \\label{ADM_action}\n\\eeqn\nwhere $E_{\\mu \\nu }=\\frac{1}{2} \\left(\\gamma '_{\\mu \\nu }-\\nabla _{\\nu }N_{\\mu }-\\nabla _{\\mu }N_{\\nu }\\right)$. The constraint equations are yielded by varying with respect to the lapse function $N$ and shift vector $N_\\mu$, i.e.,\n\\beqn\nR^{(4)}-2 \\Lambda-m^2 \\gamma ^{\\mu \\nu } \\partial_\\mu\\phi^a \\partial_\\nu\\phi^a +N^{-2}\\lt(E_{\\mu \\nu } E^{\\mu \\nu }-E^2\\rt)&&\\nn\\\\\n+m^2N^{-2}\\left(\\phi^{a}{}'-N^{\\mu }\\partial _{\\mu }\\phi ^a\\right)\\left(\\phi^{a}{}'-N^{\\nu }\\partial_{\\nu }\\phi ^a\\right)&=&0, \\label{Constraint_Eq_I}\\\\\n\\nabla _{\\nu }\\lt[N^{-1}\\lt(E^{\\mu}_{\\nu }-\\delta^{\\mu}_{\\nu}E \\rt)\\rt]-m^2N^{-1}\\partial_{\\mu }\\phi ^a\\lt(\\phi^a{}'-N^{\\alpha } \\partial_\\alpha\\phi^a\\rt)&=&0. \\label{Constraint_Eq_II}\n\\eeqn\nBy perturbing the constraint equations, one has a constraint equation for vector modes and two constraint equations for scalar modes. Thus, one vector mode and two scalar modes can be worked out algebraically, leaving us one vector and one scalar modes eventually. \n\nAfter scalar-vector-tensor decomposition and expanding the action \\eqref{Main_Action} to the quadratic order of fluctuations, the TT tensor, transverse vector and scalar modes are decoupled with each other, so they can be treated separately.\n\n\\subsection{Tensor mode} \nBy including only the tensor perturbation in the perturbed metric and dropping the boundary terms, the quadratic action for tensor perturbation is read as\n\\beq\nS^{(2)}_T=\\frac{M^3}{8}\\int d^4xdya^2\\left[-{a^{2}} D_{ \\alpha \\beta }' D{}'^{\\alpha \\beta } -{\\partial_\\lambda D_{\\alpha \\beta }}\\partial ^{\\lambda }D ^{\\alpha \\beta }-2 m^2 D_{\\alpha \\beta } D^{\\alpha \\beta } \\right],\n\\eeq\nwhere the indices are raised and lowered by the 4D Minkowski metric $\\eta_{\\mu\\nu}$. Further, after a coordinate transformation $dy=adz$ that turns the background metric into a conformal form and a rescaling $\\tilde{D }={D }\/{2}$, a canonical form is obtained as\n\\beq\nS^{(2)}_T=\\frac{M^3}{2}\\int d^4y dz a^3 \\left[-\\dot{\\tilde{D }}_{\\alpha \\beta }\\dot{\\tilde{D }}^{\\alpha \\beta }-\\partial_\\lambda \\tilde D_{\\alpha \\beta } \\partial ^{\\lambda }\\tilde D ^{\\alpha \\beta }-2 m^2\\tilde D_{\\alpha \\beta} \\tilde D^{\\alpha \\beta } \\right],\n\\eeq\nwhere the dot denotes the derivative with respect to the extra dimension coordinate $z$. The tensor mode is free from the ghost instability due to the correct sign of kinetic term. Moreover, the action is expressed more concisely in the momentum space, where the d'Alembert operator $\\partial ^{\\alpha }\\partial_{\\alpha } $ is replaced by $ -k^2\\equiv -k^\\alpha k_\\alpha$, with $k^\\alpha$ the four-momentum of tensor mode. Then, it yields\n\\beq\nS^{(2)}_T=\\frac{M^3}{2}\\int d^4 k dz a^3 \\left[-\\dot{\\tilde{D }}_{\\alpha \\beta }\\dot{\\tilde{D }}^{\\alpha \\beta }-\\left({k^2}+{2 m^2}\\right)\\tilde D_{\\alpha \\beta }\\tilde D^{\\alpha \\beta } \\right].\n\\eeq\nBy variation with respect to $\\tilde{D }^{\\alpha \\beta }$, we have the equation of motion\n\\beq\n\\ddot{\\tilde{D} }_{\\alpha\\beta }+3H\\dot{\\tilde{D} }_{\\alpha\\beta }=(k^2+2m^2){\\tilde{D}_{\\alpha\\beta } }.\n\\label{eom_tensor}\n\\eeq\nAfter redefining $\\tilde{D}_{\\alpha\\beta } = a^{-\\fc{3}{2}}\\mathcal{D}_{\\alpha\\beta }$, a Schr\\\"odinger-like equation is obtained,\n\\beq\n-\\ddot{\\mathcal{D} }_{\\alpha\\beta } +\\left(\\frac{3}{2}\\dot H+\\frac{9}{4}H^2\\right) {\\mathcal{D} }_{\\alpha\\beta } =M_T^2{\\mathcal{D}}_{\\alpha\\beta } ,\n\\label{SE_Tensor}\n\\eeq\nwhere $M_T^2 \\equiv -k^2-2m^2$. The Hamiltonian can be further factorized as a supersymmetric quantum mechanics form, with $H_T=A_T^\\dag A_T=\\lt(\\pt_z+\\fc{3}{2}H\\rt)\\lt(-\\pt_z+\\fc{3}{2}H\\rt)$.\nThen with the boundary condition $\\pt_z \\tilde{D}_{ \\alpha \\beta } |_{z=0,z_b} =0$, the self-adjoint Hamiltonian gives non-negative eigenvalues \\cite{Yang2017}, i.e., $M_T^2\\geq 0$. Thus, with a positive 5D graviton mass $m$, the four-momentum of tensor mode $k^2=-M_T^2-2m^2<0$, i.e., the tensor excitations are all time-like particles. Thus, the model is also irrelevant to tachyonic instability. If $m$ is set to zero, then $M_T^2=-k^2\\geq 0$ gives us the well-known result that there is a massless graviton and a tower of massive gravitons in RS1 model.\n\n\\subsection{Vector modes} \n\nBy including only the vector perturbations in the perturbed metric and dropping the boundary terms, the quadratic action for vector perturbations is read\n\\beqn\nS_{V}^{(2)}&=&\\frac{M^3}{16}\\int d^4xdy a^2\\Big[-a^2\\pt_\\beta F_\\alpha{}'\\pt^\\beta F^\\alpha{}'-2m^2\\pt_\\beta F_\\alpha \\pt^\\beta F^\\alpha -8a^2m^2 A_\\alpha{}' A^\\alpha{}'-8m^2 \\pt_\\beta A_\\alpha \\pt^\\beta A^\\alpha \\nn\\\\\n&& -4\\partial_{\\beta }S_{\\alpha }\\partial^\\beta S^{\\alpha }-8m^2S_\\alpha S^\\alpha+8m^2 \\pt_\\beta F_\\alpha \\pt^\\beta A^\\alpha -4a\\pt_\\beta F_\\alpha{}'\\pt^\\beta S^\\alpha-16 a m^2 S_\\alpha A^\\alpha{}'\\Big].\n\\label{Action_SV}\n\\eeqn\nThe constraint equation can be obtained by counting the first order perturbations of \\eqref{Constraint_Eq_II}, or simplify by varying the above quadratic action with respect to $S^\\alpha$, i.e.,\n\\beq\n2\\partial _{\\beta }{\\partial^\\beta S_{\\alpha }}-4 m^2 S_{\\alpha }+a \\partial_{\\beta }{\\partial^\\beta F_{\\alpha }'}-4am^2A_\\alpha'=0.\n\\label{Vector_Constraint_Eq}\n\\eeq\nThen, $S^{\\alpha }$ can be worked out in momentum space, namely,\n\\beq\nS_{\\alpha }=-\\frac{a k^2 F_{\\alpha }' +4 a m^2 A_\\alpha '}{2 (k^2+2 m^2)}. \n\\label{Vector_Constraint}\n\\eeq\nWorking in the unitary gauge, $A_\\alpha$ is gauged away. Then, substituting the relation into the action \\eqref{Action_SV} yields\n\\beq\nS_V^{(2)}= M^3\\int d^4 k dy \\left[-\\frac{a^4 k^2 m^2 F_{\\alpha }{}' F^{\\alpha }{}'}{8 \\left(k^2+2 m^2\\right)}-\\frac{a^2 k^2 m^2}{8} F_{\\alpha } F^{\\alpha } \\right].\n\\eeq\nAfter a coordinate transformation into the coordinate $z$, a canonical normalized form can be obtained by redefining the vector perturbation as $\\tilde{F}_{\\alpha }=\\frac{k m F_{\\alpha } }{2 \\sqrt{k^2+2 m^2}}$, i.e.,\n\\beq\nS^{(2)}_V=\\frac{M^3}{2}\\int d^4 k dz a^3 \\left[-\\dot{\\tilde{F }}_{\\alpha} \\dot{\\tilde{F }}^{\\alpha}-\\left({k^2}+{2 m^2}\\right)\\tilde{F}_{\\alpha } \\tilde{F}^{\\alpha }\\right].\n\\eeq\nThe correct sign of the kinetic term ensures that the vector perturbation is free from the ghost instability. Then the equation of motion reads\n\\beq\n\\ddot{\\tilde{F} }_{\\alpha }+3H\\dot{\\tilde{F} }_{\\alpha }=(k^2+2m^2){\\tilde{F}_{\\alpha } }.\n\\eeq\nA Schr\\\"odinger-like equation is given by redefining $\\tilde{F}_{\\alpha }\\to a^{-\\frac{3}{2}} \\mathcal{F}_{\\alpha }$,\n\\beq\n-\\ddot{\\mathcal{F}}_\\alpha+\\left(\\frac{3}{2}\\dot H+\\frac{9}{4}H^2\\right) \\mathcal{F}_\\alpha=M_V^2\\mathcal{F}_\\alpha,\n\\label{SE_Vector}\n\\eeq\nwhere $M_V^2 \\equiv -k^2-2m^2$. The Hamiltonian can also be factorized as a supersymmetric quantum mechanics form, with $H_V=A_V^\\dag A_V=\\lt(\\pt_z+\\fc{3}{2}H\\rt)\\lt(-\\pt_z+\\fc{3}{2}H\\rt)$. With the boundary condition $\\pt_z \\tilde{F }_{\\alpha} |_{z=0,z_b} =0$, the eigenvalues are non-negative $M_V^2\\geq 0$. Thus, the vector excitations are also time-like particles and irrelevant to tachyonic instabilities for the cases of $M_V^2> 0$. \n\nHowever, for the case of $M_V^2=-k_0^2-2m^2=0$, the formula \\eqref{Vector_Constraint} is invalid, and the constraint equation \\eqref{Vector_Constraint_Eq} leads to \n\\beq\nF_\\alpha'=2A_\\alpha'.\n\\eeq\nThis implies that $F_\\alpha-2A_\\alpha \\equiv f_\\alpha(x)$ is a purely 4D field. Especially, the field $f_\\alpha(x)$ is a gauge invariant quantity under the general coordinate transformation by the observation from \\eqref{Gauge_transformation}. Then, the quadratic action \\eqref{Action_SV} reduces to\n\\beqn\nS^{(2)}_{V0} &=& -\\frac{M^3}{8}\\int dz a^3 m^2 k_0^2 \\left(F_{\\alpha }-2 A_{\\alpha }\\right) \\left(F^{\\alpha }-2 A^{\\alpha }\\right)=-\\frac{M^3}{8}\\int dz a^3 m^2 k_0^2 f_\\alpha f^\\alpha .\n\\eeqn\nThe equation of motion reads $k_0^2f_\\alpha=0$. Since $k_0^2=-2m^2$, it leads to $f_\\alpha=0$. This implies that the lightest vector mode does not exist in the mass spectrum. This is curial to recover the mass spectrum of RS1 model in the massless limit $m=0$, where no massless vector mode exists due to the lack of continuous isometries of the higher dimension in the presence of 3-branes \\cite{Randall1999}. Moreover, all the massive vector modes are gauge dependent in the massless limit $m=0$, therefore, they can be gauged away by gauge fixing.\n\n\\subsection{Scalar modes} \nBy including only the scalar perturbations in the perturbed metric and dropping the boundary terms, the quadratic action for scalar perturbations is read as\n\\beqn\nS^{(2)}_{S}&=&M^3\\int d^4xdy a^2\\Big(6 a^2 H^2 \\xi ^2+4 m^2 \\psi ^2-4 m^2 \\xi \\psi+6 a^2 \\psi '{}^2 +12 a^2 H \\xi \\psi ' +3\\pt_\\alpha \\psi \\pt^\\alpha \\psi\\nn\\\\\n&& -\\fc{m^2}{2} \\pt_\\alpha \\beta \\pt^\\alpha \\beta +3\\xi \\pt_\\alpha \\pt^\\alpha \\psi+3 aH\\xi \\pt_\\alpha \\pt^\\alpha \\beta + m^2 \\xi \\pt_\\alpha \\pt^\\alpha\\varphi-m^2 \\xi \\pt_\\alpha \\pt^\\alpha E -2m^2 \\psi \\pt_\\alpha \\pt^\\alpha \\varphi \\nn\\\\\n&& +2m^2\\psi \\pt_\\alpha \\pt^\\alpha \\varphi +3 a^2 H \\xi \\pt_\\alpha \\pt^\\alpha E' +3 a\\beta \\pt_\\alpha \\pt^\\alpha\\psi' + m^2 a \\beta \\pt_\\alpha \\pt^\\alpha \\varphi' -\\fc{m^2}{2} a^2 \\pt_\\alpha \\varphi' \\pt^\\alpha \\varphi'\\nn\\\\\n&&+3a^2 \\psi'\\pt_\\alpha \\pt^\\alpha E' -\\fc{m^2}{2} \\pt_\\alpha \\pt^\\alpha E \\pt_\\lambda \\pt^\\lambda E -\\fc{m^2 }{2} \\pt_\\alpha \\pt^\\alpha \\varphi \\pt_\\lambda \\pt^\\lambda \\varphi +m^2 \\pt_\\alpha \\pt^\\alpha \\varphi \\pt_\\lambda \\pt^\\lambda E \\Big).\n\\label{Scalar_full_action}\n\\eeqn\nFrom the first order perturbation of Eqs.~\\eqref{Constraint_Eq_I} and \\eqref{Constraint_Eq_II} or simply varying the above action respect to the modes $\\beta$ and $\\xi$ respectively, the constraint equations are obtained as \n\\beqn\n12 a^2 H^2 \\xi-4m^2\\psi+12 a^2 H \\psi'+3 \\pt_\\alpha \\pt^\\alpha \\psi +3aH\\partial _{\\alpha }\\partial ^{\\alpha }\\beta +m^2 \\partial _{\\alpha }\\partial ^{\\alpha }\\varphi\\nn\\\\\n-m^2\\partial _{\\alpha }\\partial ^{\\alpha }E +3a^2H\\partial _{\\alpha }\\partial ^{\\alpha }E'&=&0,\\\\\nm^2\\partial_\\alpha \\beta + 3aH\\partial _{\\alpha }\\xi + 3a \\pt_\\alpha \\psi' +m^2 a \\pt_\\alpha \\varphi' &=&0.\n\\eeqn\nAfter closing the scalar perturbations $\\varphi$ and $\\psi$ in the unitary gauge, we only have to take the remaining perturbations $\\xi$, $\\beta$ and $E$ into account in the quadratic action, i.e., \n\\beqn\nS^{(2)}_{S}&=&M^3\\int d^4xdy a^2\\Big(6 a^2 H^2 \\xi ^2 -\\frac{m^2}{2}\\partial _\\alpha \\beta \\partial ^{\\alpha }\\beta +3 a H \\xi\\partial _{\\alpha }\\partial ^{\\alpha }\\beta-m^2\\xi \\partial _{\\alpha }\\partial ^{\\alpha }E\\nn\\\\\n&&+3a^2H\\xi \\partial _{\\alpha }\\partial ^{\\alpha }E'-\\frac{m^2}{2}\\partial _{\\alpha }\\partial ^{\\alpha }E\\partial _{\\lambda }\\partial ^{\\lambda }E\\Big).\n\\label{Scalar_action}\n\\eeqn\nCorrespondingly, the constraint equations are rewritten as \n\\beqn\n12 a^2 H^2 \\xi+3aH\\partial _{\\alpha }\\partial ^{\\alpha }\\beta -m^2\\partial _{\\alpha }\\partial ^{\\alpha }E +3a^2H\\partial _{\\alpha }\\partial ^{\\alpha }E'&=&0,\\\\\nm^2\\partial_\\alpha \\beta+3aH\\partial _{\\alpha }\\xi &=&0.\n\\eeqn\nIn momentum space, $\\beta$ and $\\xi $ can be worked out from the constraint equations as \n\\beqn\n\\beta &=&-\\frac{3 aH}{m^2}\\xi ,\\\\\n\\xi &=&\\frac{k^2m^2(3 a^2 HE'- m^2 E)}{3 a^2 H^2 \\left(3 k^2+4 m^2\\right)}.\n\\eeqn\nThus, the perturbations $\\beta$ and $\\xi$ can be eliminated by substituting these relations into the action \\eqref{Scalar_action}, then a quadratic action for scalar perturbation $E$ is achieved,\n\\beq\nS^{(2)}_{S}\\supset M^3\\int dk^4 dy \\fc{3a^2k^4 m^2}{6 k^2+8 m^2} \\left[-a^2E' E' -\\lt(k^2+2 m^2\\rt)E^2\\right].\n\\eeq\nThe correct sign of the kinetic term ensures that the scalar perturbation is free from the ghost instability as well. Further, after redefining the scalar perturbation as $\\tilde E= \\sqrt{\\frac{3 k^4 m^2}{3 k^2+4 m^2}}E$, the above action can be rewritten as a canonical normalized form in the conformal coordinate $z$, i.e.,\n\\beq\nS^{(2)}_{S}\\supset\\frac{M^3}{2}\\int d^4 k dz a^3 \\left[-\\dot{\\tilde{E} }\\dot{\\tilde{E}} -\\left({k^2}+{2 m^2}\\right)\\tilde{E}^2\\right].\n\\eeq \nFurther, the equations of motion of scalar mode $\\tilde{E}$ is given by\n\\beq\n\\ddot{\\tilde{E} }+3H\\dot{\\tilde{E} }=(k^2+2m^2){\\tilde{E} }.\n\\eeq\nBy redefining $\\tilde{E}=a^{-\\fc{3}{2}}{\\varepsilon}$, it can be rewritten as a Schr\\\"odinger-like equation,\n\\beq\n-\\ddot{{\\varepsilon }}+\\left(\\frac{9 }{4}H^2+\\frac{3 }{2}\\dot H\\right){\\varepsilon } = M_S^2{\\varepsilon},\n\\label{SE_Scalar}\n\\eeq\nwhere $M_S^2\\equiv-k^2-2m^2$. The Hamiltonian can be factorized as a supersymmetric quantum mechanics form as well, $H_S=A_S^\\dag A_S=\\lt(\\pt_z+\\fc{3}{2}H\\rt)\\lt(-\\pt_z+\\fc{3}{2}H\\rt)$, so with the boundary condition $\\pt_z \\tilde E |_{z=0,z_b} =0$, the eigenvalues are non-negative $M_S^2\\geq 0$. Since the excitations of scalar mode are all time-like particles, it is also irrelevant to the tachyonic instability in scalar perturbations. \n\nIn RS1 model, the presence of IR brane abruptly ends AdS space, so it spontaneously breaks the conformal invariance of AdS bulk in the IR. The massless radion is just the Goldstone boson associated with the broken dilatation invariance \\cite{Arkani-Hamed2001a,Rattazzi2001}. For the current model, the scalar curvature reads $R=-20k^2+\\fc{4m^2e^{2k y}}{1+\\epsilon^2 e^{2ky}}$ in the bulk. Thus, the scalar condensation deforms the bulk geometry a little bit, which is not a pure AdS bulk anymore. Consequently, the radion acquires a tiny mass due to this explicit symmetry breaking. In the massless limit $m=0$, the quadratic action \\eqref{Scalar_full_action} will reduce to that of RS model, with only a massless radion in the mass spectrum \\cite{Goldberger1999a,Charmousis2000,Callin2005}. \n\n\n\n\\section{Mass Spectra and Gauge Hierarchy}\\label{Hierarchy}\n\nFor the lightest tensor and scalar mode corresponding to $M_{T,S}=0$, their wave functions can be easily solved from Eqs.~\\eqref{SE_Tensor} and \\eqref{SE_Scalar}. After returning to the coordinate space, and utilizing the KK decompositions, $\\mathcal{D}_{\\alpha\\beta }(x,z) =d_{\\alpha\\beta }(x)\\Psi(z)$ and $\\varepsilon(x,z) =e(x)\\Psi(z)$, Eqs.~\\eqref{SE_Tensor} and \\eqref{SE_Scalar} can be further reduced to the 4D Klein-Gordon equations, $\\Box^{(4)}d_{\\mu\\nu}=m_T^2 d_{\\mu\\nu}$ and $\\Box^{(4)}e(x)=m_S^2 e(x)$, with $m_{T,S}$ the effective mass of KK particles in 4D point of view, and the Schr\\\"odinger-like equation,\n\\beq\n-\\ddot{\\Psi} +\\left(\\frac{3}{2} \\dot{H}+\\frac{9}{4}H^2\\right)\\Psi =M_{T,S}^2\\Psi,\n\\eeq\nwhere $M_{T,S}^2=m_{T,S}^2-2m^2$. The wave function of ground states is easily achieved by setting $M_{T,S}=0$ or $m_{T,S}=\\sqrt{2}m$ in above Schr\\\"odinger-like equation, \n\\beq\n\\Psi^{(0)}(z)=N_0a(z)^{\\fc{3}{2}},\n\\eeq \nwith $N_0$ a normalization factor. The boundary conditions $\\pt_z \\tilde{D}_{ \\alpha \\beta } |_{z=0,z_b} =0$ and $\\pt_z \\tilde E |_{z=0,z_b} =0$ lead to $\\lt.\\lt( \\Psi-\\frac{3}{2}H \\rt)\\rt|_{z=0,z_b}=0$. Obviously, the wave function $\\Psi^{(0)}(z)$ satisfies the boundary condition. All KK particles are massive in this model, which is a significant difference from the RS1 model, where the lightest KK particles are massless spin-2 graviton and spin-0 radion. Especially, from the redefinitions $\\tilde{D}_{\\alpha\\beta } = a^{-\\fc{3}{2}}\\mathcal{D}_{\\alpha\\beta }$ and $\\tilde{E}=a^{-\\fc{3}{2}}{\\varepsilon}$, their canonical normalized field configurations are given by $\\tilde{D}^{(0)}_{\\alpha\\beta }=a^{-\\fc{3}{2}}\\mathcal{D}^{(0)}_{\\alpha\\beta }=d^{(0)}_{\\alpha\\beta }(x)$ and $\\tilde{E}^{(0)}=a^{-\\fc{3}{2}} {\\varepsilon}^{(0)} =e^{(0)}(x)$. Therefore, the lightest graviton and radion propagate only on the brane. \n\nHowever, the effective mass of 4D graviton is severely constrained by the gravitational experiments \\cite{Rham2017}, e.g., the bound of the graviton mass is $m_g\\leq 4.7\\times 10^{-23}$eV from the detection of gravitational waves \\cite{Abbott2019}, thus the parameter $m$ must be tinier than the experimental constraints, i.e., $m<3.3\\times 10^{-23}$eV. Since the lightweight radion has the same mass as the lightest graviton, exchanging such nearly massless scalar particle would cause a fifth force and violate experimental observations. Therefore, similar to the RS1 model, the radion must gain weight to meet the experimental expectations, which is realized through GW mechanism \\cite{Goldberger1999a} in the next section.\n\nWith the normalization condition $\\int^{z_b}_{-z_b}\\Psi_0^2dz=1$, the normalization factor is worked out as \n\\beqn\nN_0^{-2}=\\frac{1}{k}\\lt[1-e^{-2 k y_\\pi}+4\\epsilon^2 k y_\\pi -\\epsilon^4(1-e^{2 k y_\\pi})\\rt].\n\\label{Normalization_factor}\n\\eeqn\nBy shutting down the 5D graviton mass $m$, one recovers the result of RS1 model. However, if we remove the visible brane, i.e., $y_\\pi\\to\\infty$, the quasi-massless graviton is no longer normalizable. This means that the effective 4D gravity theory can not be recovered on the brane. So the RS2-like single brane model \\cite{Randall1999a} is not a physically available one in current massive gravity.\n\nThe braneworld scenario provides a natural way to solve the gauge hierarchy problem, which is a crucial motivation of the well-known ADD model \\cite{Arkani-Hamed1998} and RS1 model \\cite{Randall1999}. In our toy model, the low-energy effective theory is obtained by including the nearly-massless gravitons, i.e.,\n\\beq\nds^2=a^2(y)\\bar g_{\\mu\\nu}(x)dx^\\mu dx^\\nu+dy^2=a^2(y)\\lt[\\eta_{\\mu\\nu}+\\gamma_{\\mu\\nu}(x)\\rt]dx^\\mu dx^\\nu+dy^2,\n\\label{Metric_Lowenergy}\n\\eeq\nthen the 4D effective gravitational mass scale $M_{\\text{eff}}$ is read from the curvature term in the action \\eqref{Main_Action},\n\\beq\nM_{\\text{eff}}^2=M^3\\int^{y_\\pi}_{-y_\\pi} a^2 dy={N_0^{-2}}{M^3}. \\label{Mass_scale_relation}\n\\eeq\n\nOn the other hand, in order to produce a large hierarchy between the Planck scale and the electroweak scale, our Universe should be embedded on the IR brane located at $y_\\pi$. Then the Higgs field action on the brane is non-canonically normalized, \n\\beq\nS_\\text{H}\\supset\\int{ d^4x \\sqrt{| g_\\text{II}|}\\lt[-{g}_\\text{II}^{\\mu\\nu}D_{\\mu}H^{\\dag}D_{\\nu}H-\\lambda(H^{\\dag}H-v_0^2)^2 \\rt] },\n\\eeq\nwhere $g_{\\text{II}\\mu\\nu}=a(y_\\pi)\\tilde{g}_{\\mu\\nu}$ is the induced metric on the brane at $y_\\pi$, and $v_0$ the fundamental Higgs vacuum expectation value (VEV). Writting the warp factor explicitly, it is \n\\beq\nS_\\text{H}\\supset\\int{ d^4x \\sqrt{|\\tilde g|}\\lt[-a(y_\\pi)^{2}\\tilde{g}^{\\mu\\nu}D_{\\mu}H^{\\dag}D_{\\nu}H-a(y_\\pi)^4\\lambda(H^{\\dag}H-v_0^2)^2 \\rt] },\n\\eeq\n With a field renormalization, $H \\to \\tilde{H}\/a(y_\\pi)$, the effective action of Higgs on the brane is \n \\beq\n S_\\text{H}\\supset\\int{ d^4x \\sqrt{|\\tilde g|}\\lt[-\\tilde{g}^{\\mu\\nu}D_{\\mu}\\tilde{H}^{\\dag}D_{\\nu}H-\\lambda(\\tilde{H}^{\\dag}H-v_\\text{eff}^2)^2 \\rt] },\n \\eeq\n where the effective Higgs VEV, $v_\\text{eff}=a(y_\\pi) v_0$, sets the electroweak scale on the brane. \n \n Therefore, in order to solve the gauge hierarchy problem, the fundamental parameters $M$, $k$, $v_0$ are all set to be the order of Plank scale $M_\\text{Pl}$. Then, the warp factor has to provide enough redshift to recover a TeV scale of Higgs VEV on the brane, namely, $a(y_\\pi)\\sim10^{-16}$. In the condition $e^{-k |y_\\pi|}>\\epsilon$ and $m<3.3\\times 10^{-23}$eV, it leads to $e^{-ky_\\pi} \\approx a(y_\\pi) -\\fc{\\epsilon^2}{a(y_\\pi)} \\sim10^{-16}$. Thus, it requires $y_\\pi\\approx 37\/k$. \n\n\nSince the mass splitting scale of massive KK modes is inversely proportional to the conformal size of extra dimension $z_b$, i.e., $\\Delta m_T \\propto 1\/z_b$. From the coordinate transformation $dy=adz$, one has\n\\beq\nz_\\pi=\\frac{1}{k \\epsilon} \\left[\\text{arctan}\\left(\\epsilon e^{k y_\\pi}\\right)-\\text{arctan} \\left(\\epsilon \\right)\\right],\n\\eeq\nwhere the integral constant has been chosen so that $z(y=0)=0$. Since $e^{-ky_\\pi} \\sim10^{-16}$, $m<3.3\\times 10^{-23}$eV and $k\\sim M_\\text{Pl}$, one has $\\epsilon e^{k y_\\pi} \\ll 1$. Thus, the formula can be rewritten approximately as $z_\\pi \\approx \\fc{e^{ky_\\pi}}{k}\\lt[1+\\mathcal{O}(\\epsilon^2)\\rt]$. So the mass splitting scale is similar to the RS model, i.e., $\\Delta m_T \\sim k e^{-ky_\\pi} \\sim \\mathcal{O}(\\text{TeV})$.\n\n\t\n\\section{Radius stabilization}\\label{Radius_stabilization}\n\nIn RS1 model, the radius is not dynamically fixed, so there is a massless radion in the effective theory, which corresponds to the fluctuations of the radius of compact extra dimensions. However, the massless radion is phenomenologically unacceptable since it would contribute to Newton's law and cause a fifth force. It is well-known that the GW mechanism \\cite{Goldberger1999a} can be introduced to stabilize the size of the extra dimension and to increase the mass of radion. In our model, the light weight radion and the lightest graviton have equal mass, which is unacceptable in phenomenology as well. Therefore, we also utilize the GW mechanism here to increase the mass of radion. \n\nBy adding a bulk scalar field $\\Phi$ into the model, which has interaction with the two branes, the action is given by\n\\beqn\nS_\\Phi&=&\\fc{1}{2}\\int d^4x\\int^\\pi_{-\\pi}d\\theta\\sqrt{-g}\\lt(-g^{MN}\\pt_M\\Phi\\pt_N\\Phi-m_\\Phi^2\\Phi^2 \\rt)-\\int{}d^4x\\sqrt{-g_\\text{I}}\\lambda_\\text{I}(\\Phi^2-v_\\text{I}^2)^2\\nn\\\\\n&&-\\int{}\\sqrt{-g_{\\text{II}}}\\lambda_{\\text{II}}(\\Phi^2-v_{\\text{II}}^2)^2, \n\\eeqn\nwhere $g_{MN}$ is the background metric \\eqref{Brane_Metric} with the radius $r_c$ of compactified extra dimension given by $y=r_c \\theta$, $\\lambda_{\\text{I\/II}}$ is the coupling parameters, and $V_{\\text{I\/II}}=\\lambda_{\\text{I\/II}} v_{\\text{I\/II}}^4$.\n\nThe equation of motion of the scalar filed $\\Phi$ is achieved by varying with respect to $\\Phi$, \n\\beqn\n\\frac{1}{r_c}{\\partial_\\theta }\\left(a^4 {\\partial_\\theta \\Phi }\\right)-a^4 m_{\\Phi }^2\\Phi-{4 a^4 \\lambda_\\text{I} \\Phi \\left(\\Phi ^2-v_\\text{I}^2\\right)}\\delta (\\theta)-4 a^4\\lambda _\\text{II} \\left(\\Phi ^2-v_\\text{II}^2\\right) \\Phi \\delta (\\theta -\\pi ) =0.\n\\label{EoM_Phi}\n\\eeqn\nAway from the two branes at $\\theta=0,\\pi$, the general solution of this equation reads\n\\beq\n\\Phi(\\theta)=e^{2 \\sigma } \\left[A {}_2\\text{F}_1\\left(2,\\nu +2,\\nu +1, -\\epsilon ^2e^{2 \\sigma} \\right) e^{ \\nu \\sigma }+B {}_2\\text{F}_1\\left(2,2-\\nu ,1-\\nu ,-\\epsilon ^2e^{2 \\sigma } \\right) e^{-\\nu \\sigma }\\right],\n\\eeq\nwhere $\\sigma(\\theta)=k r_c \\theta$, $\\nu=\\sqrt{4+m_\\Phi^2\/k^2}$, ${}_2\\text{F}_1$ is the hypergeometric function, $A$ and $B$ are integration constants. Especially, under the condition $\\epsilon e^{\\sigma} \\ll 1$, up to the first order of correction, the solution can be approximately written as\n\\beq\n\\Phi(\\theta)\\simeq e^{2 \\sigma } \\left[A \\left(1-2 \\epsilon^2\\frac{\\nu +2}{\\nu +1}e^{2 \\sigma } \\right)e^{ \\nu \\sigma }+B \\left(1-2\\epsilon^2\\frac{\\nu -2}{\\nu -1} e^{2 \\sigma }\\right)e^{ -\\nu \\sigma }\\right].\n\\eeq\n If we close the mass of 5D graviton, i.e., $\\epsilon=0$, the solution reduces to the one in general relativity \\cite{Goldberger1999a}. The integration constants $A$ and $B$ can be fixed by the boundary conditions on the branes, which are obtained by inserting the approximate solution into the equation of motion \\eqref{EoM_Phi} and matching the delta functions,\n \\beqn\n k \\left[ (\\nu +2) \\left(1+2\\epsilon ^2\\frac{\\nu -2}{\\nu +1}\\right)A- (\\nu-2 ) \\left(1+2\\epsilon ^2\\frac{\\nu +2}{\\nu -1}\\right)B\\right]\\nn\\\\\n -2 \\lambda _\\text{I} \\left(1+4 \\epsilon ^2\\right) \\Phi(0)\\left( \\Phi(0)^2 -v_\\text{I}^2\\right)=0,~~~\\\\\n k e^{2 \\sigma(\\pi) } \\left[(\\nu +2) e^{\\nu \\sigma(\\pi)} \\left(1+2\\epsilon ^2\\frac{\\nu-2}{\\nu +1} e^{2 \\sigma(\\pi)}\\right)A -(\\nu-2) e^{ - \\nu \\sigma(\\pi) } \\left(1+2\\epsilon ^2\\frac{\\nu +2}{\\nu -1} e^{2 \\sigma(\\pi) }\\right) B \\right]\\nn\\\\\n +2 \\lambda _v \\left(1+4 \\epsilon ^2 e^{2 \\sigma(\\pi)}\\right)\\Phi(\\pi )\\left(\\Phi(\\pi)^2 -v_v^2\\right) =0.~~~\n \\eeqn \n Rather than solving the above equations, we employ the trick of Ref.~\\cite{Goldberger1999a} to simplify the calculation. By substituting the approximate solution back into the action and integrating over $\\theta$, it yields the effective potential of compactified radius $r_c$, \n \\beqn\n V_\\Phi(r_c)&\\approx& k A^2 (\\nu +2) e^{2 \\nu k r_c \\pi } \\left(1-\\frac{8 \\epsilon^2 e^{2 \\pi k r_c}}{\\nu +1}\\right)+\\lambda _\\text{I} \\left( \\Phi(0)^2 -v_\\text{I}^2\\right)^2\\nn\\\\\n&& +e^{-4 k r_c \\phi }\\lambda _\\text{II} \\left(1+4 \\epsilon ^2 e^{2 \\pi k r_c}\\right)\\left( \\Phi(\\pi)^2 -v_\\text{II}^2\\right)^2,\n\\label{Potential_Phi}\n \\eeqn\n where the limit of $e^{k r_c \\pi}\\gg 1$ has been used in the calculation. Assuming that the interaction parameters $\\lambda_\\text{I}$ and $\\lambda_\\text{II}$ are very large \\cite{Goldberger1999a}, this effective potential implies the solution $\\Phi(0)=v_\\text{I}$ and $\\Phi(\\pi)=v_\\text{II}$. Then, the integration constants $A$ and $B$ can be solved approximately as \n \\beqn\n A&\\approx & v_\\text{II} e^{-(\\nu +2) \\sigma(\\pi) } \\left[1-2\\epsilon^2\\frac{\\nu +2}{\\nu +1} e^{2 \\sigma(\\pi) } \\right]-v_\\text{I} e^{-2 \\nu \\sigma(\\pi) } \\left[1+4\\epsilon^2\\frac{ \\nu }{\\nu ^2-1} e^{2 \\sigma(\\pi) }\\right],\\\\\n B&\\approx & v_\\text{I} \\left[1+2\\epsilon^2\\frac{\\nu +2}{\\nu +1} \\left(e^{\\nu \\sigma(\\pi) }-e^{2 \\sigma(\\pi) }\\right)\\right]-v_\\text{II} e^{-(\\nu +2) \\sigma(\\pi) } \\left[1+2\\epsilon^2\\frac{\\nu +2}{\\nu +1} e^{\\nu \\sigma(\\pi) }\\right].\n \\eeqn\nFurther, assuming that the mass of the scalar filed is a small quantity, i.e., ${m_\\Phi}\/{k}\\ll 1$, so that $\\nu =\\sqrt{4+\\frac{m_\\Phi^2}{k^2}}\\simeq 2+\\delta$ with $\\delta=\\frac{m_\\Phi^2}{4 k^2}$ a tiny quantity. Then the effective potential can be rewritten as\n\\beqn\nV_\\Phi(r_c)&\\approx& (4+\\delta) k e^{-4 k r_c \\pi}\\left(1-\\frac{8 \\epsilon ^2e^{2 k r_c \\pi } }{\\delta +3}\\right) \\nn\\\\\n&&\\times \\left[\\left(1+\\frac{4 (\\delta +2) \\epsilon ^2e^{2 k r_c \\pi } }{\\delta ^2+4 \\delta+3 }\\right)e^{-\\delta k r_c \\pi} v_\\text{I} -\\left(1-\\frac{2 (\\delta +4) \\epsilon ^2e^{2k r_c \\pi }}{\\delta +3}\\right)v_\\text{II} \\right]^2.\n\\eeqn \nThus the effective potential has a minimum at \n\\beq\nr_c \\approx \\frac{1}{\\pi k\\delta }\\ln\\left(\\frac{v_h}{v_v}\\right)-\\frac{4\\epsilon ^2}{3\\pi k} \\left(\\frac{v_h}{v_v}\\right)^{\\fc{2}{\\delta }}\\left(1+\\frac{v_v}{v_h}\\right).\n\\eeq\nThe first leading term is just the result of GW mechanism in general relativity \\cite{Goldberger1999a}, and the second term proportional to $\\epsilon^2$ is a tiny correction stemming from the 5D graviton mass. The mechanism provides a dynamical way to stabilize the compactified radius of the extra dimension without introducing another large hierarchy. For instance, if $m_\\Phi\/k=0.1$ and ${v_h}\/{v_v}=1.34$, then one obtains $ky_\\pi=k r_c\\pi\\approx 37$ to generate a proper hierarchy. After the radius stabilization, the radion acquires a mass roughly $\\mathcal{O}(\\delta^2)$ TeV \\cite{Goldberger2000}, which is somewhat smaller than the TeV scale. \n\n \n\\section{Conclusions}\\label{Conclusions}\n\n\nIn this work, we generalized the RS1 model in a 5D extension of the Lorentz-violating massive gravity. It is found that the theory supports two distinct brane configurations. The configuration possessing both positive and negative tension branes is similar to the RS1 model, while the other possessing only two positive tension branes is distinct from RS1 model. The full linear perturbations against the background metric were also analyzed. It is found that the models are free from the ghost and tachyonic instabilities, and all KK particles are massive. \n\n\\begin{figure}[htb]\n\\begin{center}\n\\includegraphics[width=9cm]{Mass_spectra.eps}\n\\end{center}\n\\caption{The mass spectra of RS1 model and current model, where the blue lines refer to the tensor modes, red lines to vector modes, and green lines to scalar modes.}\n\\label{Mass_Spectra}\n\\end{figure}\n\nAs shown in Fig.~\\ref{Mass_Spectra}, the tensor and scalar modes have similar mass spectra which start from $\\sqrt{2}m$ with a mass splitting of TeV scale, nevertheless, the lightest vector mode does not exist in the mass spectrum. The ground states of tensor and scalar modes propagate only along the brane. The graviton mass $\\sqrt{2}m$ has to be tiny enough to fit the experimental constraints. However, the light weight radion would lead to a fifth force to violate experimental observations, so the GW mechanism was considered to stabilize the size of the extra dimension and to weight the mass of radion. After radius stabilization, the 4D effective theory on the brane includes a nearly massless graviton plus three towers of non-pathologic very massive spin-2, spin-1 and spin-0 particles. \n\nThe gauge hierarchy problem was also solved as an application of the model. Furthermore, due to the very distinct KK mass spectra between current model and RS1 model, the new KK towers of vector and scalar modes introduce some new reaction channels and suggest some new signals in colliders. This is potentially an interesting property of current model. However, it is beyond the scope of current work and left for our future consideration. \n\n\n\n\n\\section*{ACKNOWLEDGMENTS}\n\nWe would like to especially thank Prof.~Yu-Xiao Liu for a very helpful discussion of our paper. This work was supported by the National Natural Science Foundation of China under Grant Nos. 12005174 and 12165013. K. Yang acknowledges the support of Natural Science Foundation of Chongqing, China under Grant No. cstc2020jcyj-msxmX0370.\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbtvk b/data_all_eng_slimpj/shuffled/split2/finalzzbtvk new file mode 100644 index 0000000000000000000000000000000000000000..22459a513de09346bc1d3bc9635368c878fa75b3 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbtvk @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\\label{sec1}\n\n\\subsection{Background and motivation}\n\n\n\n\nThe study of the Boolean matrices \\cite{bapat, bapatbook, luce, rao} play an important role in linear algebra \\cite{belohlavek2015below, kim, rao72}, combinatorics \\cite{brual}, graph theory \\cite{berge} and network theory \\cite{ledley, li2015logical}. However, this becomes particularly challenging to store huge volumes of multidimensional data. This potential difficulty can be easily overcome, thanks to tensors, which are natural multidimensional generalizations of matrices \\cite{kolda, loan}. Here the notion of tensors is different in physics and engineering (such as stress tensors) \\cite{Nar93}, which are generally referred to as tensor fields in mathematics \\cite{de2008tensor}. However, it will be more appropriate if we study the Boolean tensors and the generalized inverses of Boolean tensors. Hence the generalized inverses of Boolean tensors will encounter in many branches of mathematics, including relations theory \\cite{plem}, logic, graph theory, lattice theory \\cite{birkhoff} and algebraic semigroup theory.\n\n\n\nRecently, there has been increasing interest in studying inverses \\cite{BraliNT13} and different generalized inverses of tensors based on the Einstein product \\cite{bm, wei18, stan, sun}, and opened new perspectives for solving multilinear systems \\cite{kolda, Mao19}. In \\cite{wei18, stan}, the authors have introduced some basic properties of the range and null space of multidimensional arrays. Further, in \\cite{stan}, it was discussed the adequate definition of the tensor rank, termed as reshaping rank. Corresponding representations of the weighted Moore-Penrose inverse introduced in \\cite{BehMM19, we17} and investigated a few characterizations in \\cite{PanMi19}. Though this work is focusing on the binary case; i.e., concentrating some interesting results based on the Boolean tensors and generalized inverses of Boolean tensors via the Einstein product. In many instances, the result in the general case does not immediately follow even though it is not difficult to conclude.\n\n\n\nOn the other hand, one of the most successful developments in the world of multilinear algebra is the concept of tensor decomposition \\cite{ Kolda01, kolda, LalmV00}. This concept gives a clear and convenient way to implement all basic operations efficiently. Recently this concept is extended in Boolean tensors \\cite{, erdos2013discovering, khamis2017Boolean, rukat2018tensormachine}. Further, the fast and scalable distributed algorithms for Boolean tensor decompositions were discussed in \\cite{miettinen2011Boolean}. In addition to that, a few applications of these decompositions are discussed in \\cite{erdos2013discovering, metzler2015clustering} for information extraction and clustering. At that same time, Brazell, et al. in \\cite{BraliNT13} discussed decomposition of tensors from the isomorphic group structure on the influence of the Einstein Product and demonstrated that they are special cases of the canonical polyadic decomposition \\cite{carroll1970analysis}. The vast work on decomposition on the tensors and its several applications in different areas of mathematics in the literature, and the recent works in \\cite{BraliNT13, sun}, motivate us to study the generalized inverses and space decomposition in the framework of Boolean tensors. \n This study leads to introduce the rank and the weight for the Boolean tensor with its application to generalized inverses.\n\n\n \n\n\n\\subsection{Organization of the paper}\n\n\nThe rest of the paper is organized as follows. In Section 2 we present some definitions, notations, and preliminary results, which are essential in proving the main results. The main results are discussed in Section 3. It has four subparts. In the first part, some identities are proved while the generalized inverses for Boolean tensor are discussed in the second part. The third part mainly focuses on weighted Moore-Penrose inverses. Space decomposition and its application to generalized inverses are discussed in the last part. Finally, the results along with a few questions are concluded in Section 4.\n \n \n\n\n\n\n\n\n\n\n\\section{Preliminaries}\nWe first introduce some basic definitions and notations which will be used throughout the article. \n\\subsection{Definitions and terminology}\nFor convenience, we first briefly explain some of the terminologies which will be used here onwards. The tensor notation and definitions are followed from the article \\cite{BraliNT13, sun}. We refer $\\mathbb{R}^{I_1\\times\\cdots\\times I_N}$ as the set of order $N$ real tensors. Indeed, a matrix is a second order tensor, and a vector is a first order tensor. Let $\\mathbb{R}^{I_1\\times\\cdots\\times I_N}$ be the set of order $N$ and dimension $I_1 \\times \\cdots \\times I_N$ tensors over the real\nfield $\\mathbb{R}$. $\\mc{A} \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N}$ is a tensor with $N$-th order tensor, and each entry of $\\mc{A}$ is denoted by $a_{i_1...i_N}$. Note that throughout the paper, tensors are represented in calligraphic letters like $\\mc{A}$, and the notation $(\\mc{A})_{i_1...i_N}= a_{i_1...i_N}$ represents the scalars. The Einstein product (\\cite{ein}) $ \\mc{A}{*_N}\\mc{B} \\in \\mathbb{R}^{I_1\\times\\cdots\\times\nI_N \\times J_1 \\times\\cdots\\times J_M }$ of tensors $\\mc{A} \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times K_1\n\\times\\cdots\\times K_N }$ and $\\mc{B} \\in\n\\mathbb{R}^{K_1\\times\\cdots\\times K_N \\times J_1 \\times\\cdots\\times\nJ_M }$ is defined\nby the operation ${*_N}$ via\n\\begin{equation}\\label{Eins}\n(\\mc{A}{*_N}\\mc{B})_{i_1...i_Nj_1...j_M}\n=\\displaystyle\\sum_{k_1...k_N}a_{{i_1...i_N}{k_1...k_N}}b_{{k_1...k_N}{j_1...j_M}}.\n\\end{equation}\nSpecifically, if $\\mc{B} \\in \\mathbb{R}^{K_1\\times\\cdots\\times K_N}$, then $\\mc{A}{*_N}\\mc{B} \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N}$ and \n\\begin{equation*}\\label{Einsb}\n(\\mc{A}{*_N}\\mc{B})_{i_1...i_N} = \\displaystyle\\sum_{k_1...k_N}\na_{{i_1...i_N}{k_1...k_N}}b_{{k_1...k_N}}.\n\\end{equation*}\nThis product is discussed in the area of continuum mechanics \\cite{ein} and the theory of relativity \\cite{lai}. Further, the addition of two tensors $\\mc{A}, ~\\mc{B}\\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times K_1 \\times\\cdots\\times K_N }$ is defined\nas \n\\begin{equation}\\label{Eins1}\n(\\mc{A} + \\mc{B})_{i_1...i_N k_1...k_N}\n=a_{{i_1...i_N}{k_1...k_N}} + b_{{i_1...i_N}{k_1...k_N}}.\n\\end{equation}\nFor a tensor $~\\mc{A}=(a_{{i_1}...{i_N}{j_1}...{j_M}})\n \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times J_1 \\times\\cdots\\times J_M},$ let $\\mc{B} =(b_{{i_1}...{i_M}{j_1}...{j_N}}) \\in \\mathbb{R}^{J_1\\times\\cdots\\times J_M \\times I_1 \\times\\cdots\\times I_N}$, be the {\\it transpose} of $\\mc{A}$, where $b_{i_1\\cdots i_Mj_1\\cdots j_N} = a_{j_1\\cdots j_M i_1\\cdots i_N}.$ The tensor $\\mc{B}$ is denoted by $\\mc{A}^T$. Also, we denote $\\mc{A}^T=\\left({a}_{{i_1}...{i_N}{j_1}...{j_M}}^t\\right).$ \n The trace of a tensor $\\mc{A}$ with entries $(\\mc{A})_{{i_1}...{i_N}{j_1}...{j_N}}$, denoted by $tr(\\mc{A})$,\n is defined as the sum of the diagonal entries, i.e., \n$tr(\\mc{A}) = \\displaystyle\\sum_{i_1 \\cdots i_N}a_{{i_1...i_N}{i_1...i_N}}.$ Further, a tensor $\\mc{O}$ denotes the {\\it zero tensor} if all the entries are zero. \n A tensor $\\mc{A}\\in\n\\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times I_1 \\times\\cdots\\times\nI_N}$ is {\\it symmetric} if $\\mc{A}=\\mc{A}^T,$ and {\\it orthogonal} if $\\mc{A}{*_M}\\mc{A}^T= \\mc{A}^T{*_N} \\mc{A}=\\mc{I}$. Further, a tensor\n$\\mc{A}\\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times I_1\n\\times\\cdots\\times I_N}$ is {\\it idempotent} if $\\mc{A}\n{*_N} \\mc{A}= \\mc{A}$. The definition of a diagonal tensor follows. \nFurther, a tensor with entries $(\\mc{D})_{{i_1}...{i_N}{j_1}...{j_N}}$ is\n called a {\\it diagonal\n tensor} if $d_{{i_1}...{i_N}{j_1}...{j_N}} = 0$ for $(i_1,\\cdots,i_N) \\neq (j_1,\\cdots,j_N).$\nA few more notations and definitions are discussed below for defining generalized inverses of Boolean tensors. We first recall the definition\nof an identity tensor below.\n\n\n\n\n\n\\begin{definition} (Definition 3.13, \\cite{BraliNT13}) \\\\\nA tensor \n with entries \n $ (\\mc{I})_{i_1 \\cdots i_Nj_1\\cdots j_N} = \\prod_{k=1}^{N} \\delta_{i_k j_k}$,\n where\n\\begin{numcases}\n{\\delta_{i_kj_k}=}\n 1, & $i_k = j_k$,\\nonumber\n \\\\\n 0, & $i_k \\neq j_k $.\\nonumber\n\\end{numcases}\n is called a {\\it unit tensor or identity tensor}.\n\\end{definition}\nThe permutation tensor is defined as follows.\n\n\\begin{definition}\\label{perm} \nLet $\\pi$ be a permutation map on $(i_1,i_2,\\cdots, i_N,j_1,j_2,\\cdots , j_N)$ defined by \n$$\\pi:=\\begin{pmatrix}\ni_1&i_2&\\cdots &i_N&j_1&j_2&\\cdots &j_N \\\\\n\\pi(i_1)&\\pi(i_2)&\\cdots &\\pi(i_N)&\\pi(j_1)&\\pi(j_2)&\\cdots&\\pi(j_N) \\\\\n\\end{pmatrix}.\n$$\nA tensor $\\mc{P}$\n with entries \n $ (\\mc{P})_{i_1 \\cdots i_Nj_1\\cdots j_N} = \\prod_{k=1}^{N} \\epsilon_{i_k} \\epsilon_{j_k}$,\n where\n\\begin{numcases}\n{\\epsilon_{i_k}\\epsilon_{j_k} = }\n 1, & $\\pi(i_k) = j_k$,\\nonumber\n \\\\\n 0, & otherwise.\\nonumber\n\\end{numcases}\n is called a {\\it permutation tensor}.\n\\end{definition}\n\n\nNow we recall the block tensor as follows. \n\\begin{definition}{\\cite{sun}}\nFor a tensor $\\mc{A} = (a_{i_1... i_Nj_1...j_M})\n \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times J_1 \\times\\cdots\\times\n J_M},\\\\\n \\mc{A}_{(i_1...i_N|:)}= (a_{i_1...i_N:...:})\\in \\mathbb{R}^{J_1\\times\\cdots\\times J_M}$ is a\n subblock of $\\mc{A}$. $Vec(\\mc{A})$ is obtained by lining up all the subtensors\n in a column, and $t$-th subblock of $Vec(\\mc{A})$ is $\\mc{A}_{(i_1...i_N|:)}$,\n where $$t=i_N + \\displaystyle\\sum_{K=1}^{N-1} \\left[ (i_K - 1) \\displaystyle\\prod_{L=K+1}^{N} I_L \\right].$$\n\\end{definition}\n\\vspace{-0.5cm}\nLet $\\mc{A} = (a_{i_1\\cdots i_N j_1 \\cdots j_M}) \\in\n\\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times J_1 \\times\\cdots\\times\nJ_M}$ and $\\mc{B} = (b_{i_1\\cdots i_N k_1 \\cdots k_M}) \\in\n\\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times K_1 \\times\\cdots\\times\nK_M}$. The {\\it row block tensor} consisting of $\\mc{A}$ and\n$\\mc{B}$ is denoted by\n$[\\mc{A} ~ \\mc{B}] \\in \\mathbb{R}^{\\alpha^N\\times\\beta_1\\times \\cdots \\times \\beta_M},$\nwhere $\\alpha^N = I_1\\times\\cdots\\times I_N, \\beta_i = J_i + K_i, i\n= 1, \\cdots, M$, and is defined by\n\\begin{equation*}\n[\\mc{A} ~ \\mc{B}]_{i_1 \\cdots i_N l_1 \\cdots l_M} =\n\\begin{cases}\na_{i_1 \\cdots i_N l_1 \\cdots l_M}, & i_1 \\cdots i_N \\in [I_1] \\times \\dots \\times [I_N], l_1 \\cdots l_M \\in [J_1] \\times \\cdots \\times [J_M];\n\\\\\nb_{i_1 \\cdots i_N l_1 \\cdots l_M}, & i_1 \\cdots i_N \\in [I_1] \\times \\dots \\times [I_N], l_1 \\cdots l_M \\in \\Gamma_1 \\times \\cdots \\times \\Gamma_M;\n\\\\\n0, & \\textnormal{otherwise}.\n\\end{cases}\n\\end{equation*}\nwhere $\\Gamma_i = \\{ J_i +1, \\cdots, J_i+K_i\\}, i=1,\\cdots, M.$\n\nLet $\\mc{C} = (c_{j_1 \\cdots j_M i_1 \\cdots i_N}) \\in\n\\mathbb{R}^{J_1\\times\\cdots\\times J_M \\times I_1 \\times\\cdots\\times\nI_N}$ and $\\mc{D} = (d_{k_1 \\cdots k_M i_1 \\cdots i_N}) \\in\n\\mathbb{R}^{K_1\\times\\cdots\\times K_M \\times I_1 \\times\\cdots\\times\nI_N}$. The {\\it column block tensor} consisting of $\\mc{C}$ and\n$\\mc{D}$ is\n\\begin{equation*}\\label{eq224}\n\\left[%\n\\begin{array}{c}\n \\mc{C} \\\\\n \\mc{D} \\\\\n\\end{array}%\n\\right]= [\\mc{C}^T ~ \\mc{D}^T]^T \\in \\mathbb{R}^{\\beta_1 \\times\n\\cdots \\times \\beta_M\\times\\alpha^N}.\n\\end{equation*}\nFor $\\mc{A}_1 \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times J_1\n\\times\\cdots\\times J_M}, \\mc{B}_1 \\in\n\\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times K_1 \\times\\cdots\\times\nK_M}, \\mc{A}_2 \\in \\mathbb{R}^{L_1\\times\\cdots\\times L_N \\times J_1\n\\times\\cdots\\times J_M}$ and $ \\mc{B}_2 \\in\n\\mathbb{R}^{L_1\\times\\cdots\\times L_N \\times K_1 \\times\\cdots\\times\nK_M}$, we denote $\\tau_1 = [\\mc{A}_1 ~ \\mc{B}_1]$ and $\\tau_2 =\n[\\mc{A}_2 ~ \\mc{B}_2]$ as the {\\it row block tensors}.\n The {\\it column block tensor} $ \\left[\n\\begin{array}{c}\n {\\tau}_1 \\\\\n {\\tau}_2 \\\\\n\\end{array}\n\\right]\n$ can be written as\n\\begin{equation*}\\label{eq225}\n\\left[\n\\begin{array}{c}\n \\mc{A}_1 ~~ \\mc{B}_1\\\\\n \\mc{A}_2 ~~ \\mc{B}_2\\\\\n\\end{array}\n\\right] \\in \\mathbb{R}^{\\rho_1\\times\\cdots\\times \\rho_N \\times \\beta_1 \\times\\cdots\\times \\beta_M},\n\\end{equation*}\nwhere $\\rho_i = I_i +L_i, i=1,\\cdots,N; \\beta_j = J_j + K_j$ and $j=1,\\cdots , M.$\n\n\n\n\n\n\n\n\n\n\\begin{definition} (Definition 2.1, \\cite{stan}) \\\\\nThe range space and null space of a tensor $\\mc{A}\\in \\mathbb{R}^{{I_1}\\times \\cdots\\times {I_M}\\times {J_1}\\times\\cdots \\times {J_N}}$ are defined as per the following: \n$$\n\\mathfrak{R}(\\mc{A}) = \\left\\{\\mc{A}{*_N}\\mc{X}:~\\mc{X}\\in\\mathbb{R}^{{J_1}\\times\\cdots\\times {J_N}}\\right\\}\\mbox{ and } \\mc{N}(\\mc{A})=\\left\\{\\mc{X}:~\\mc{A}{*_N}\\mc{X}=\\mc{O}\\in\\mathbb{R}^{{I_1}\\times \\cdots \\times {I_M}}\\right\\}.\n$$\n\\end{definition}\nThe relation of range space for tensors is discussed in \\cite{stan} as follows.\n\\begin{lemma}[Lemma 2.2. \\cite{stan}]\\label{range-stan}\nLet $\\mc{A}\\in \\mathbb{R}^{{I_1}\\times\\cdots\\times {I_M}\\times {J_1}\\times\\cdots\\times {J_N}}$, $\\mc{B}\\in \\mathbb{R}^{{I_1}\\times\\cdots\\times {I_M}\\times {K_1}\\times\\cdots\\times {K_L}}.$ Then $\\mathfrak{R}(\\mc{B})\\subseteq\\mathfrak{R}(\\mc{A})$ if and only if there exists $\\mc{U}\\in \\mathbb{R}^{{J_1}\\times\\cdots\\times {J_N}\\times {K_1}\\times\\cdots\\times {K_L}}$ such that \n$\\mc{B}=\\mc{A}{*_N}\\mc{U}.$\n\\end{lemma}\n\n\n\n\nThe next subsection is discussed the Boolean tensor and some useful definitions\n\\subsection{The Boolean tensor}\nThe binary Boolean algebra $\\mathfrak{B}$ consists of the set $\\{0,1\\}$ equipped with the operations of addition and multiplication defined as follows:\n\\begin{center}\n\\begin{tabular}{ c|c c } \n & 0 & 1 \\\\\n\\hline\n 0 & 0 & 1 \\\\ \n1 & 1 & 1 \\\\ \n\\end{tabular} \n\\hspace{2cm}\n\\begin{tabular}{ c|c c } \n. & 0 & 1 \\\\\n\\hline\n 0 & 0 & 0 \\\\ \n1 & 0 & 1 \\\\ \n\\end{tabular}\n\\end{center}\n\\begin{definition}\nLet $\\mc{A}=(a_{{i_1}...{i_M}{j_1}...{j_N}})\n \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_M \\times J_1 \\times\\cdots\\times J_N}.$ If $a_{{i_1}...{i_M}{j_1}...{j_N}}\\in\\{0,1\\},$ then the tensor $\\mc{A}$ is called Boolean tensor. \n \\end{definition}\n The addition and product of Boolean tensors are defined as in Eqs. (\\ref{Eins}) and (\\ref{Eins1}) but addition and product of two entries will follow addition and product rule of Boolean algebra.\nThe order relation for tensors is defined as follows.\n\\begin{definition}\nLet $\\mc{A}=(a_{{i_1}...{i_M}{j_1}...{j_N}})\n \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_M \\times J_1 \\times\\cdots\\times J_N} \\text{ and }~\\mc{B}\n =(b_{{i_1}...{i_M}{j_1}...{j_N}})~~ \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_M\n\\times J_1 \\times\\cdots\\times J_N}. $ Then $\\mc{A}\\leq \\mc{B}$ if and only if $a_{{i_1}...{i_M}{j_1}...{j_N}}\\leq b_{{i_1}...{i_M}{j_1}...{j_N}}$ for all $i_s$ and $j_t$ where $1\\leq s\\leq M$ and $1\\leq t\\leq N.$\n\\end{definition}\n\n\n\n We generalize the component-wise complement of the Boolean matrix \\cite{fitz} to Boolean tensors and defined below.\n\n\\begin{definition}\\label{CompDef} \nLet $\\mc{A}=(a_{{i_1}...{i_N}{j_1}...{j_M}})\n \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times J_1 \\times\\cdots\\times J_M}$ be a Boolean tensor. A tensor $\\mc{B}=(b_{{i_1}...{i_N}{j_1}...{j_M}})\n \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_N \\times J_1 \\times\\cdots\\times J_M}$ is called component-wise complement of $\\mc{A}$ if \n\\begin{equation*}\nb_{{i_1}...{i_N}{j_1}...{j_M}}=\\left\\{\\begin{array}{cc}\n 1, & \\mbox{ when } a_{i_1\\cdots i\n _Nj_1j_2\\cdots j\n _M}=0. \\\\\n 0, & \\mbox{ when } a_{i_1\\cdots i\n _Nj_1j_2\\cdots j\n _M}=1.\n \\end{array}\\right.\n \\end{equation*}\nThe tensor $\\mc{B}$ and its entries respectively, denoted by $\\mc{A}^C$ and $\\left(a_{i_1\\cdots i\n _Nj_1\\cdots j\n _M}^c\\right).$\n\\end{definition}\n\n\n\n\n \\section{Main Results} \nIn this section, we prove a few exciting results on tensors which are emphasized in the binary case. We divided this section into four folds. In the first part of this section, we discuss some identities on the Boolean tensors. Then, after having introduced some necessary ingredients, we study the generalized inverses of the Boolean tensor and some equivalence results to other generalized inverses in the second part. The existence and uniqueness of weighted Moore-Penrose inverses are discussed in the third part. The space decomposition and its connection to generalized inverses are presented in the final part.\n\n\\subsection{Some identities on Boolean tensors}\n \nBy the definition of Boolean tensor $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times I_1\\times \\cdots \\times I_M},$ we always get $\\mc{A}+\\mc{A}=\\mc{A}.$ The infinite series of the Boolean tensor, $\\displaystyle\\sum_{k=1}^\\infty\\mc{A}^k$, is convergent and reduces to a finite series, since there are only finite number of Boolean tensors of the same order. Now we denote $\\overline{\\mc{A}}$ for the infinite series of the Boolean tensors, i.e., $$\\overline{\\mc{A}}=\\displaystyle\\sum_{k=1}^ \\infty \\mc{A}^k.$$ \n\n\n\nSince $\\mc{A}\\leq\\mc{A}+\\mc{B}$ for any two Boolean tensor (suitable order for addition) $\\mc{A}$ and $\\mc{B}$, likewise $\\mc{A}=\\mc{A}+\\mc{A}\\geq \\mc{A}+\\mc{B}$ for any two Boolean tensor $\\mc{A}\\geq \\mc{B}$. This is stated in the next result.\n\n\\begin{theorem}\\label{thm3.11}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots \\times I_M\\times J_1 \\times\\cdots \\times J_N}$ and $\\mc{B}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1\\times\\cdots \\times J_N}.$ Then $\\mc{A}\\geq\\mc{B}$ if and only if $\\mc{A}+\\mc{B}=\\mc{A}$.\n\\end{theorem}\n\n\nIf we consider $\\mc{A}\\geq \\mc{I}$ in the above theorem, then it is easy to verify that \n$\\mc{I}+\\mc{A}+\\cdots+\\mc{A}^n=\\mc{A}^n$ and hence we can have the following result as a corollary. \n\\begin{corollary}\\label{cor3.4}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times\\cdots \\times I_N\\times I_1 \\times\\cdots \\times I_N}$ and $\\overline{\\mc{A}}=\\sum_{k=1}^\\infty \\mc{A}^k.$ If $\\mc{A}\\geq\\mc{I},$ then there exist $n,$ such that\n\\begin{enumerate}\n \\item[(a)] $\\overline{\\mc{A}}=\\mc{A}^{n};$\n \\item[(b)] $\\left(\\overline{\\mc{A}}\\right)^2=\\overline{\\mc{A}};$\n \\item[(c)] $\\overline{\\left(\\overline{\\mc{A}}\\right)}=\\overline{\\mc{A}}.$\n\\end{enumerate}\n \\end{corollary}\n\n\n \nUsing the above theorem, we now prove another result on the Boolean tensor. As follows,\n\n \n \n\\begin{theorem}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots \\times I_N\\times I_1 \\times \\cdots \\times I_N}$ and $\\mc{B}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_N\\times I_1\\times \\cdots \\times I_N},$ with $\\mc{A}\\geq \\mc{I}$ and $\\mc{B}\\geq \\mc{I}.$ Then $$\\overline{(\\mc{A}+\\mc{B})}=\\overline{(\\overline{\\mc{A}}{*_N}\\overline{\\mc{B}})}=\\overline{(\\overline{\\mc{B}}{*_N}\\overline{\\mc{A}})}.$$ \n\\end{theorem}\n\n\n\n\\begin{proof}\nSince $\\mc{A}\\geq \\mc{I}$ and $\\mc{B}\\geq \\mc{I}.$ So $\\overline{\\mc{A}}\\geq \\mc{I}$ and $\\overline{\\mc{B}}\\geq \\mc{I}.$ Also we have $\\overline{\\mc{A}}\\geq \\mc{A}$ and $\\overline{\\mc{B}}\\geq \\mc{B}.$ Combining these results, we get $\\overline{A}{*_N}\\overline{B}\\geq \\mc{A}$ and $\\overline{A}{*_N}\\overline{B}\\geq \\mc{B}.$ Thus $\\overline{A}{*_N}\\overline{B}\\geq \\mc{A}+\\mc{B}$ and hence\n\\begin{equation}\\label{eq3.61}\n \\overline{\\left(\\overline{\\mc{A}}{*_N}\\overline{\\mc{B}}\\right)}\\geq \\overline{\\mc{A}+\\mc{B}}. \n\\end{equation}\nNow $\\overline{\\mc{A}+\\mc{B}}\\geq \\overline{\\mc{A}}$ and $\\overline{\\mc{A}+\\mc{B}}\\geq \\overline{\\mc{B}}.$ By using Corollary \\ref{cor3.4} $(c)$, we get $\\overline{\\mc{A}}{*_N}\\overline{\\mc{B}}\\leq \\left(\\overline{\\mc{A}+\\mc{B}}\\right)^2=\\overline{\\mc{A}+\\mc{B}}.$ From Corollary \\ref{cor3.4} $(b)$, we have \n\\begin{equation}\\label{eq3.362}\n \\overline{\\left(\\overline{\\mc{A}}{*_N}\\overline{\\mc{B}}\\right)}\\leq \\overline{\\left(\\overline{\\mc{A}+\\mc{B}}\\right)}=\\overline{\\mc{A}+\\mc{B}}.\n\\end{equation}\nFrom Eqs.(\\ref{eq3.61}) and (\\ref{eq3.362}), the proof is complete. \n\\end{proof}\n\nIf $\\mathfrak{R}(\\mc{B}^T)=\\mathfrak{R}(\\mc{B}^T{*_M}\\mc{A}^T),$ then there exist a tensor $\\mc{U}$ such that $\\mc{B}=\\mc{U}{*_M}\\mc{A}{*_N}\\mc{B}$ and hence, we obtain $\\mc{B}{*_M}\\mc{C}=\\mc{U}{*_M}\\mc{A}{*_N}\\mc{B}{*_M}\\mc{C}=\\mc{U}{*_M}\\mc{A}{*_N}\\mc{B}{*_M}\\mc{D}=\\mc{B}{*_M}\\mc{D}.$ This leads the following result.\n\n\n\\begin{theorem}\\label{ltcan}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots \\times I_M \\times J_1\\times \\cdots \\times J_N}$, $\\mc{B}\\in\\mathbb{R}^{J_1\\times \\cdots \\times J_N\\times K_1\\times \\cdots \\times K_M} $, \\\\ $\\mc{C}\\in\\mathbb{R}^{K_1\\times \\cdots \\times K_M\\times J_1\\times \\cdots\\times J_N}$ and $\\mc{D}\\in\\mathbb{R}^{K_1\\times \\cdots \\times K_M \\times J_1\\times \\cdots \\times J_N}$ be Boolean tensors with\\\\ $\\mc{A}{*_N}\\mc{B}{*_M}\\mc{C}=\\mc{A}{*_N}\\mc{B}{*_M}\\mc{D}.$ If $\\mathfrak{R}(\\mc{B}^T)=\\mathfrak{R}(\\mc{B}^T{*_N}\\mc{A}^T),$ then $\\mc{B}{*_M}\\mc{C}=\\mc{B}{*_M}\\mc{D}.$\n\\end{theorem}\n\nSimilar way, we can prove the following corollary.\n\n\n\\begin{corollary}\\label{rtcan}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M \\times J_1 \\times \\cdots \\times J_N}$, $\\mc{B}\\in\\mathbb{R}^{J_1\\times \\cdots \\times J_N\\times K_1 \\times \\cdots \\times K_M} $, \\\\ $\\mc{C}\\in\\mathbb{R}^{K_1\\times \\cdots \\times K_M\\times I_1\\times \\cdots\\times I_M}$ and $\\mc{D}\\in\\mathbb{R}^{K_1 \\times \\cdots \\times K_M \\times I_1\\times I_2\\times\\cdots\\times I_M}$ be Boolean tensors with $\\mc{C}{*_M}\\mc{A}{*_N}\\mc{B}=\\mc{D}{*_M}\\mc{A}{*_N}\\mc{B}.$ If $\\mathfrak{R}(\\mc{A})=\\mathfrak{R}(\\mc{A}{*_N}\\mc{B}),$ then $\\mc{C}{*_M}\\mc{A}=\\mc{D}{*_M}\\mc{A}.$\n\\end{corollary}\n\n\nWe now discuss the important result on a transpose of an arbitrary order Boolean tensor, as follows.\n\\begin{lemma}\\label{lemma1}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots \\times I_M \\times J_1 \\times \\cdots \\times J_N}$ be any Boolean tensor. Then $\\mc{A}\\leq\\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{A}.$\n \\begin{proof}\n Let $\\mc{B} = \\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{A}.$ We need to show that\n \\begin{equation*}\n {a}_{i_1\\cdots i_M j_1\\cdots j_N}\\leq {b}_{i_1\\cdots i_M j_1\\cdots j_N}.\n \\end{equation*}\n This inequality is trivial if ${a}_{i_1\\cdots i_M j_1\\cdots j_N}= 0.$ Let us assume ${a}_{i_1\\cdots i_M j_1\\cdots j_N}=1.$ Now\n\\begin{equation*}\n{b}_{i_1\\cdots i_M j_1\\cdots j_N} =\\sum_{k_1\\cdots k_N}\\sum_{l_1\\cdots l_M}a_{{i_1\\cdots i_M}{k_1\\cdots k_N}}a_{{l_1\\cdots l_M}{k_1\\cdots k_N}}a_{{l_1\\cdots l_M}{j_1\\cdots j_N}}.\n\\end{equation*}\nFor $1\\leq s\\leq N,$ if $k_s=j_s$ and $l_s=i_s,$ then\n\\begin{equation*}\n {b}_{i_1\\cdots i_M j_1\\cdots j_N} \\geq ({a}_{i_1\\cdots i_M j_1\\cdots j_N})^3={a}_{i_1\\cdots i_M j_1\\cdots j_N}=1.\n\\end{equation*}\nHence the proof is complete.\n \\end{proof}\n\\end{lemma} \n\n\n\\begin{theorem}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots \\times I_N\\times J_1 \\times \\cdots \\times J_N}$ and $\\mc{B}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_N\\times J_1 \\times \\cdots \\times J_N}.$ Then the equation $\\mc{A}{*_N}\\mc{X}=\\mc{B}$ is solvable if and only if $\\mc{X}=\\mc{C},$ where \n$$ c_{i_1\\cdots i_N j_1\\cdots j_n}=\\left\\{\\begin{array}{cc}\n 1 & \\mbox{ if } a_{i_1\\cdots i\n _Ni_1\\cdots i\n _N}=0 \\mbox{ or } b_{i_1\\cdots i\n _Nj_1\\cdots j\n _N}=1 \\mbox{ for all } i_k,~1\\leq k\\leq N,\\\\\n 0 & otherwise.\n\\end{array}\\right.\n$$\n\\end{theorem}\n\n\n\\begin{proof}\nLet $\\mc{A}{*_N}\\mc{X}=\\mc{B}$ is solvable and $\\mc{A}{*_N}\\mc{X}=D.$ To claim $\\mc{D}=\\mc{B},$ it is enough to show $d_{i_1\\cdots i\n _Nj_1\\cdots j\n _N}=1$ if and only if $b_{i_1\\cdots i\n _Nj_1\\cdots j\n _N}=1.$ Let $d_{i_1\\cdots i\n _Nj_1\\cdots j\n _N}=1.$ This implies $a_{i_1\\cdots i\n _Np_1\\cdots p\n _N}=1$ and $c_{p_1\\cdots p\n _Nj_1\\cdots j\n _N}=1$ for some $p_k~~1\\leq k\\leq N.$ The condition $c_{p_1\\cdots p\n _Nj_1\\cdots j\n _N}=1$ yields either $a_{i_1\\cdots i\n _Np_1\\cdots p\n _N}=0 $ or $b_{p_1\\cdots p\n _Nj_1\\cdots j\n _N}=1$ for all $p_k~~1\\leq k\\leq N.$ Since $a_{i_1\\cdots i\n _Np_1\\cdots p\n _N}=1$ which makes $b_{p_1\\cdots p\n _Nj_1\\cdots j\n _N}=1$ for all $p_k,~~1\\leq k\\leq N.$ Therefore $b_{i_1\\cdots i\n _Nj_1\\cdots j\n _N}=1.$ Now if $b_{i_1\\cdots i\n _Nj_1\\cdots j\n _N}=1,$ then $a_{i_1\\cdots i\n _Nr_1\\cdots r\n _N}=1$ and $x_{r_1\\cdots r\n _Nj_1\\cdots j\n _N}=1$ for some $r_k,~~1\\leq k\\leq N.$ Suppose $c_{r_1\\cdots r\n _Nj_1\\cdots j\n _N}=0.$ Then $a_{q_1\\cdots q\n _Nr_1\\cdots r\n _N}=1$ and $b_{q_1\\cdots q\n _Nj_1\\cdots j\n _N}=0$ for some $q_k,~~1\\leq k\\leq N.$ Combining $a_{q_1\\cdots q\n _Nr_1\\cdots r\n _N}=1$ and $x_{r_1\\cdots r\n _Nj_1\\cdots j\n _N}=1$, we get $b_{q_1\\cdots q\n _Nj_1\\cdots j\n _N}=1.$ Which is the contradiction. So $c_{r_1\\cdots r\n _Nj_1\\cdots j\n _N}=1$ and hence $d_{i_1\\cdots i\n _Nj_1\\cdots j\n _N}=1.$ The converse part is trivial. \n\\end{proof}\n\n\nIn view of the Definition \\ref{CompDef} the following theorem is true for Boolean tensors.\n\n\\begin{preposition}\\label{equitc}\nLet $\\mc{A} \\in\\mathbb{R}^{I_1\\times \\cdots \\times I_M\\times J_1\\times \\cdots \\times J_N}$ be a Boolean tensor, then\n\\begin{enumerate}\n \\item[(a)] $(\\mc{A}^C)^C=\\mc{A};$\n \\item[(b)] $(\\mc{A}^C)^T=(\\mc{A}^T)^C = \\mc{A}^{CT}.$\n \\end{enumerate}\n\\end{preposition}\n \n \n \n \\begin{remark}\nIn general $ \\mc{B}^C *_N \\mc{A}^C \\neq (\\mc{A}*_N\\mc{B})^C \\neq \\mc{A}^C *_N \\mc{B}^C$ for any two tensor $\\mc{A},~\\mc{B} \\in\\mathbb{R}^{I_1\\times\\cdots \\times I_M\\times I_1\\times\\cdots \\times I_M}$\n \\end{remark}\n \n\n\\begin{example}\nConsider two Boolean tensor\n$~\\mc{A}=(a_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ and $~\\mc{B}=(b_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ such that\n\\begin{eqnarray*}\na_{ij11} =\n \\begin{pmatrix}\n 1 & 0 & 0 \\\\\n 0 & 0 & 1\n \\end{pmatrix},\na_{ij12} =a_{ij13} =a_{ij21}=a_{ij22}=a_{ij23}=\n \\begin{pmatrix}\n 0 & 0 & 0\\\\\n 0 & 0 & 1\n \\end{pmatrix}, \\mbox{ and }\n\\end{eqnarray*}\n\\begin{eqnarray*}\nb_{ij11} =b_{ij12}=b_{ij13}=b_{ij21}=b_{ij22}=b_{ij23}=\n \\begin{pmatrix}\n 1 & 0 & 0 \\\\\n 0 & 0 & 0\n \\end{pmatrix}.\n\\end{eqnarray*}\nIt is easy to verify $ \\mc{B}^C {*_2} \\mc{A}^C \\neq (\\mc{A}{*_2}\\mc{B})^C \\neq \\mc{A}^C {*_2} \\mc{B}^C$, where\n$~(\\mc{A}*_2\\mc{B})^C=\\mc{X}=(x_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ $~\\mc{A}^C*_2\\mc{B}^C=\\mc{Y} =(y_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ and \n$~\\mc{B}^C*_2\\mc{A}^C=\\mc{Z} =(z_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}},$ where\n\\begin{eqnarray*}\nx_{ij11} =x_{ij12} =x_{ij13} =x_{ij21} =x_{ij22} =x_{ij23} =\n \\begin{pmatrix}\n 0 & 1 & 1 \\\\\n 1 & 1 & 0\n \\end{pmatrix},\n\\end{eqnarray*}\n\\begin{eqnarray*}\ny_{ij11} =y_{ij12} =y_{ij13} =y_{ij21} =y_{ij22} =y_{ij23} =\n \\begin{pmatrix}\n 1 & 1 & 1 \\\\\n 1 & 1 & 0\n \\end{pmatrix}, \\mbox{ and }\n\\end{eqnarray*}\n\\begin{eqnarray*}\nz_{ij11} =z_{ij12} =z_{ij13} =z_{ij21} = z_{ij22} =z_{ij23} =\n \\begin{pmatrix}\n 0 & 1 & 1 \\\\\n 1 & 1 & 1\n \\end{pmatrix}.\n\\end{eqnarray*}\n\\end{example}\n\n\nThe next result is the one of the important tool to prove trace of a Boolean tensor.\n\n\\begin{theorem}\n Let $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_N\\times J_1 \\times \\cdots \\times J_N}.$ Then $\\mc{A}{*_N}\\mc{A}^C=\\mc{O}$ if and only either $\\mc{A}=\\mc{O}$ or $\\mc{A}=\\mc{O}^C.$\n\\end{theorem}\n \\begin{proof}\n Since the converse part is trivial, it is enough to show the sufficient part only. Let $\\mc{A}{*_N}\\mc{A}^C=\\mc{O}.$ Thus \n$\\displaystyle\\sum_{k_1\\cdots k_N}a_{i_1\\cdots i_Nk_1\\cdots k_N}a_{k_1\\cdots k_Nj_1\\cdots j_N}^c=0$. This implies, $ a_{i_1\\cdots i_Nk_1\\cdots k_N}a_{k_1\\cdots k_Nj_1\\cdots j_N}^c=0 $ for all $k_s,~1\\leq s\\leq N.$ Which again yields either $a_{i_1\\cdots i_Nk_1\\cdots k_N}=0 $ for all $i_s,~k_s$ or $a_{k_1\\cdots k_Nj_1\\cdots j_N}^c=0$ for all $j_s,~k_s,~1\\leq s\\leq N.$. Therefore either $\\mc{A}=\\mc{O}$ or $\\mc{A}^C=\\mc{O}.$ Hence completes the proof.\n \\end{proof}\n \nFurther, when $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_N\\times J_1 \\times \\cdots \\times J_N}$ is symmetric Boolean tensor, one can write \n \\begin{eqnarray*}\n tr(\\mc{A}{*_N}\\mc{A}^C)&=&\\sum_{i_1\\cdots i_N}\\sum_{k_1\\cdots k_N}a_{i_1\\cdots i_Nk_1\\cdots k_N}a_{k_1\\cdots k_Ni_1\\cdots i_N}^c\\\\\n &=&\\sum_{i_1\\cdots i_N}\\sum_{k_1\\cdot, k_N}a_{k_1\\cdots k_Ni_1\\cdots i_N}^ca_{i_1\\cdots i_Nk_1\\cdots k_N}\\\\\n &=&\\sum_{k_1\\cdots k_N}\\sum_{i_1\\cdots i_N}a_{k_1\\cdots k_Ni_1\\cdots i_N}^ca_{i_1\\cdots i_Nk_1\\cdots k_N}\\\\\n&=&tr(\\mc{A}^C{*_N}\\mc{A}).\n \\end{eqnarray*}\n\n Hence, the tensors in the trace of a product of symmetric tensor and its complement can be switched without changing the result. This is stated in the next result.\n \n \\begin{theorem}\\label{trresult}\n Let $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_N\\times J_1 \\times \\cdots \\times J_N}.$ If $\\mc{A}$ is symmetric, then \n $$\n tr(\\mc{A}{*_N}\\mc{A}^C)=tr(\\mc{A}^C{*_N}\\mc{A}).\n $$\n \\end{theorem}\n \n\n \n \\begin{remark}\\label{rmk3.10}\n In addition to the result of Theorem \\ref{trresult} one can write $tr(\\mc{A}{*_N}\\mc{A}^C)=tr(\\mc{A}^C{*_N}\\mc{A}) = 0$ . \nFurther, the symmetricity condition in Theorem \\ref{trresult} is only sufficient but not necessary.\n \\end{remark}\n One can verify the Remark \\ref{rmk3.10} by the following example.\n \n \\begin{example}\n Let a Boolean tensor\n$~\\mc{A}=(a_{ijkl}) \\in \\mathbb{R}^{{2\\times2}\\times{2 \\rtimes 2}}$ such that\n\\begin{eqnarray*}\na_{ij11} =\n \\begin{pmatrix}\n 1 & 0 \\\\\n 1 & 0\n \\end{pmatrix},\na_{ij12} =\n \\begin{pmatrix}\n 1 & 0 \\\\\n 1 & 0 \n \\end{pmatrix},\na_{ij21} =\n \\begin{pmatrix}\n 1 & 0 \\\\\n 1 & 0\n \\end{pmatrix},\na_{ij22} =\n \\begin{pmatrix}\n 1 & 0 \\\\\n 1 & 0\n \\end{pmatrix}.\n\\end{eqnarray*}\nIt is clear that $\\mc{A}$ is not symmetric but $tr(\\mc{A}*_2\\mc{A}^C) = tr(\\mc{A}^C*_2\\mc{A}) = 2$, where $~\\mc{A}*_2\\mc{A}^C=(x_{ijkl}) \\in \\mathbb{R}^{{2\\times2}\\times{2 \\rtimes 2}}$ and $~\\mc{A}^C*_2\\mc{A}=(y_{ijkl}) \\in \\mathbb{R}^{{2\\times2}\\times{2 \\rtimes 2}}$ with entries\n\\begin{eqnarray*}\nx_{ij11} =\n \\begin{pmatrix}\n 1 & 0 \\\\\n 1 & 0\n \\end{pmatrix},\nx_{ij12} =\n \\begin{pmatrix}\n 1 & 0 \\\\\n 1 & 0 \n \\end{pmatrix},\nx_{ij21} =\n \\begin{pmatrix}\n 1 & 0 \\\\\n 1 & 0\n \\end{pmatrix},\nx_{ij22} =\n \\begin{pmatrix}\n 1 & 0 \\\\\n 1 & 0\n \\end{pmatrix},\n\\end{eqnarray*}\n\\begin{eqnarray*}\ny_{ij11} =\n \\begin{pmatrix}\n 0 & 1 \\\\\n 0 & 1\n \\end{pmatrix},\ny_{ij12} =\n \\begin{pmatrix}\n 0 & 1 \\\\\n 0 & 1 \n \\end{pmatrix},\ny_{ij21} =\n \\begin{pmatrix}\n 0 & 1 \\\\\n 0 & 1\n \\end{pmatrix},\ny_{ij22} =\n \\begin{pmatrix}\n 0 & 1 \\\\\n 0 & 1\n \\end{pmatrix}.\n\\end{eqnarray*}\n \\end{example}\n \n\n \n \n Using the complement of a tensor, we now prove the following result.\n \n \\begin{lemma}\\label{complem}\n Let $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N},$ $\\mc{B}\\in\\mathbb{R}^{J_1 \\times \\cdots \\times J_N\\times K_1 \\times \\cdots \\times K_L}$ and $\\mc{C}\\in\\mathbb{R}^{K_1 \\times \\cdots \\times K_L\\times J_1 \\times \\cdots \\times J_N}$ be Boolean tensors. Then \n $$\\mc{A}{*_N}\\mc{B}{*_L}\\mc{C}\\leq \\mc{I}^C~~if ~and ~only ~if ~~\\mc{A}^C\\geq (\\mc{B}{*_L}\\mc{C})^T.$$\n \\end{lemma}\n \\begin{proof}\n $\\mc{A}{*_N}\\mc{B}{*_L}\\mc{C}\\leq \\mc{I}^C$ if and only $ \\sum_{j_1\\cdots j_N}\\sum_{k_1\\cdots k_L}a_{{i_1\\cdots i_M}{j_1\\cdots j_N}}b_{{j_1\\cdots j_N}{k_1\\cdots k_L}}c_{{k_1\\cdots k_L}{i_1\\cdots i_M}}=0$\nfor all $i_r,$ $1\\leq r\\leq M.$ This is equivalent to $a_{{i_1\\cdots i_M}{j_1\\cdots j_N}}b_{{j_1\\cdots j_N}{k_1\\cdots k_L}}c_{{k_1\\cdots k_L}{i_1\\cdots i_M}}=0$ \nfor all $i_r,$ $j_s$ and $k_t,$ where $1\\leq r\\leq M,$ $1\\leq s\\leq N,$ $1\\leq t\\leq L.$ This in turn is true if and only\n\\begin{eqnarray*}\n\\left(a_{{i_1\\cdots i_M}{j_1\\cdots j_N}}^c\\right)&\\geq& b_{{j_1\\cdots j_N}{k_1\\cdots k_L}}c_{{k_1\\cdots k_L}{i_1\\cdots i_M}} \\mbox{ for all $k_t$ }\\\\\n &=&\\left(c_{{i_1\\cdots i_M}{k_1\\cdots k_L}}^t\\right) \\left(b_{{k_1\\cdots k_L}{j_1\\cdots j_N}}^t\\right)~\\mbox{ for all $k_t$}\\\\\n &\\geq& \\sum_{k_1\\cdots k_L}\\left(c_{{i_1\\cdots i_M}{k_1\\cdots k_L}}^t\\right) \\left(b_{{k_1\\cdots k_L}{j_1\\cdots j_N}}^t\\right)\\\\\n &\\geq& \\left(\\mc{C}^T{*_N}\\mc{B}^T\\right)_{{i_1\\cdots i_M}{j_1\\cdots j_N}}=\\left(\\left(\\mc{B}{*_L}\\mc{C}\\right)^T\\right)_{{i_1\\cdots i_M}{j_1\\cdots j_N}}.\n \\end{eqnarray*}\n Thus the proof is complete.\n \\end{proof}\n Now we discuss the important result based on transpose and component-wise complement of an arbitrary order Boolean tensor, as follows.\n\\begin{theorem}\\label{lucethm}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}.$ Then $\\mc{X}{*_M}\\mc{A}\\leq \\mc{B}$ if and only if $\\mc{X}\\leq \\left(\\mc{B}^C{*_N}\\mc{A}^T\\right)^C$, and $\\mc{A}{*_N}\\mc{X}\\leq \\mc{B}$ if and only if $\\mc{X}\\leq \\left(\\mc{A}^T {*_M} \\mc{B}^C\\right)^C$.\n\\end{theorem}\n\\begin{proof}\nLet $\\mc{X}{*_M}\\mc{A}\\leq \\mc{B}.$ This yields \n$\\sum_{k_1\\cdots k_M}x_{i_1\\cdots i_M k_1\\cdots k_M}a_{k_1\\cdots k_M j_1\\cdots j_N }\\leq b_{i_1\\cdots i_M j_1\\cdots j_N }$ for all $i_r,~(1\\leq r\\leq M)$ and $j_s,~(1\\leq s\\leq N).$ This is equivalent to $x_{i_1\\cdots i_M k_1\\cdots k_M}a_{k_1\\cdots k_M j_1\\cdots j_N }\\leq b_{i_1\\cdots i_M j_1\\cdots j_N }$ for all $i_r$ and $j_s$ and $k_t~(1\\leq t\\leq M).$ This in turns is true {\\it if and only if} $x_{i_1\\cdots i_M k_1\\cdots k_M}a_{k_1\\cdots k_M j_1\\cdots j_N } b_{i_1\\cdots i_M j_1\\cdots j_N }^c=0,$ for all $j_s$ and $k_t.$ Which is equivalent to\\\\ $x_{i_1\\cdots i_M k_1\\cdots k_M}a_{k_1\\cdots k_M j_1\\cdots j_N } \\{b_{j_1\\cdots j_N i_1\\cdots i_M }^t\\}^c=0$ for all $j_s$ and $k_t.$ Summing over all $j_s$ and $k_t,$ we get, $\\sum_{k_1\\cdots k_M}\\sum_{j_1\\cdots j_N} x_{i_1\\cdots i_M k_1\\cdots k_M}a_{k_1\\cdots k_M j_1\\cdots j_N } \\{b_{j_1\\cdots j_N i_1\\cdots i_M }^t\\}^c=0.$ This is true if and only $\\mc{X}{*_M}\\mc{A}{*_N}\\left(\\mc{B}^T\\right)^C\\leq \\mc{I}^C.$ By Preposition \\ref{equitc} $(a)$, this is equivalent to $\\mc{X}{*_M}\\mc{A}{*_N}\\left(\\mc{B}^C\\right)^T\\leq \\mc{I}^C.$ By Lemma \\ref{complem}, this in turns true if and only $\\mc{X}^C\\geq \\left(\\mc{A}{*_N}(\\mc{B}^C)^T\\right)^T,$ that is, {\\it if and only if} $ \\mc{X}\\leq \\left(\\mc{B}^C{*_M}\\mc{A}^T\\right)^C.$\n\nThis completes first part of the theorem. Similar way, we can show the second part of the theorem.\n\\end{proof}\n\\begin{corollary}\nLet $\\mc{E}=\\mc{O}^C,$ where $\\mc{O}$ is the zero tensor. Then the following statements are equivalent:\n\\begin{enumerate}\n \\item[(a)] $\\mc{X}{*_M}\\mc{A}=\\mc{O};$\n \\item[(b)] $\\mc{X}\\leq \\left(\\left(\\mc{A}{*_N}\\mc{E}\\right)^T\\right)^C;$\n \\item[(c)] $\\mc{E}{*_N}\\mc{X}\\leq \\left(\\left(\\mc{A}{*_N}\\mc{E}\\right)^T\\right)^C.$\n\\end{enumerate}\n\\end{corollary}\nThe same result is also true for $\\mc{A}{*_N}\\mc{X}=\\mc{O}.$ Also the following corollary easily follow from Theorem \\ref{lucethm}.\n\n\\begin{corollary}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$ and $\\mc{X}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times I_1 \\times \\cdots \\times I_M}.$ Then $\\mc{X}{*_M}\\mc{A}=\\mc{B}$ has a solution if and only if $\\mc{B}\\leq \\left(\\mc{B}^C{*_N}\\mc{A}^T\\right)^C{*_M}\\mc{A}.$\n\\end{corollary}\n\n\n\n \n \n \n \n\\subsection{Generalized inverses of Boolean tensors}\nFor the generalization of the generalized inverses of Boolean matrix \\cite{rao}, we introduce the definition of $\\{i\\}$-inverses $(i = 1, 2, 3, 4)$ and the Moore-Penrose inverse of Boolean tensors via the Einstein product, as follows. \n\n \n \\begin{definition}\\label{defgi}\n For any Boolean tensor $\\mc{A} \\in \\mathbb{R}^{I_1\\times\\cdots\\times I_M \\times J_1 \\times\\cdots\\times J_N},$ consider the following equations in $\\mc{X} \\in\n\\mathbb{R}^{J_1\\times\\cdots\\times J_N \\times I_1 \\times\\cdots\\times\nI_M}:$\n\\vspace{-.4cm}\n\\begin{eqnarray*}\n&&(1)~\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A} = \\mc{A},\\\\\n&&(2)~\\mc{X}{*_M}\\mc{A}{*_N}\\mc{X} = \\mc{X},\\\\\n&&(3)~(\\mc{A}{*_N}\\mc{X})^T = \\mc{A}{*_N}\\mc{X},\\\\\n&&(4)~(\\mc{X}{*_M}\\mc{A})^T = \\mc{X}{*_M}\\mc{A}.\n\\end{eqnarray*}\n\\vspace{-.34cm}\nThen $\\mc{X}$ is called\n\\begin{enumerate}\n\\item[(a)] \na generalized inverse of $\\mc{A}$ if it satisfies $(1)$ and denoted by $\\mc{A}^{(1)}.$\n\\item[(b)] a reflexive generalized inverse of $\\mc{A}$ if it satisfies $(1)$ and $(2)$, which is denoted by $\\mc{A}^{(1,2)}.$\n\\item[(c)] a $\\{1,3\\}$ inverse of $\\mc{A}$ if it satisfies $(1)$ and $(3)$, which is denoted by $\\mc{A}^{(1,3)}.$\n\\item[(d)] a $\\{1,4\\}$ inverse of $\\mc{A}$ if it satisfies $(1)$ and $(4)$, which is denoted by $\\mc{A}^{(1,4)}.$\n\\item[(e)] the Moore-Penrose inverse of $\\mc{A}$ if it satisfies all four conditions $[(1)-(4)]$, which is denoted by $\\mc{A}^{\\dagger}.$\n\\end{enumerate}\n\\end{definition}\n\nThe following remark and corollary are follows from the Definition \\ref{defgi}.\n\n\\begin{remark}\\label{rm11}\n If $\\mc{X}$ is the generalized inverse of a Boolean tensor $\\mc{A} \\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$ then $\\mc{X}{*_M}\\mc{A}{*_N}\\mc{X}$ is the reflexive generalized inverse of $\\mc{A}.$\n\\end{remark}\n\n\\begin{corollary}\\label{corm1}\nIf $\\mc{X}$ is the generalized inverse of a Boolean tensor $\\mc{A} \\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$ Then \n\\begin{enumerate}\n \\item[(a)] $\\mc{X}^T$ is the generalized inverse of $\\mc{A}^T;$\n \\item[(b)] $(\\mc{X}_1+\\mc{X}_2)$ is the generalized inverse of of a Boolean tensor $\\mc{A}$ when $\\mc{X}_1$ and $\\mc{X}_2$ are two generalized inverse of $\\mc{A}.$\n\\end{enumerate}\n\\end{corollary}\n\n\nThus the existence of generalized inverse of a Boolean tensor guarantees the existence of a reflexive generalized inverse. In addition to that, the Remark \\ref{rm11} and Corollary \\ref{corm1} (b) ensures that the existence of one-generalized inverse implies the existence of finite number generalized inverses. In view of the fact, we define the maximum generalized inverse of a Boolean tensor, as follow:\n\n\\begin{definition}\n Let $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}.$ A tensor $\\mc{X}$ is called maximum generalized inverse of $\\mc{A}$ if $\\mc{G}\\leq \\mc{X}$ for every generalized inverse $\\mc{G}$ of $\\mc{A}.$\n\\end{definition}\n\nNote that, the generalized inverse of a Boolean tensor need not be unique which explained in the next example.\n\n\\begin{example}\\label{example18}\nConsider a Boolean tensor\n$~\\mc{A}=(a_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ with entries\n\\begin{eqnarray*}\na_{ij11} =a_{ij12} =a_{ij13} =a_{ij21} =a_{ij22} =a_{ij23} =\n \\begin{pmatrix}\n 1 & 0 & 0 \\\\\n 1 & 0 & 0\n \\end{pmatrix}.\n\\end{eqnarray*}\nThen it can be easily verified that both tensors\n$~\\mc{X}=(x_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ and $~\\mc{Y}=(y_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ with entries\n\\begin{eqnarray*}\nx_{ij11} =\n \\begin{pmatrix}\n 0 & 1 & 1 \\\\\n 1 & 1 & 1\n \\end{pmatrix},\nx_{ij12} =x_{ij13} =x_{ij21} = x_{ij22} =x_{ij23} =\n \\begin{pmatrix}\n 0 & 0 & 0\\\\\n 0 & 0 & 0\n \\end{pmatrix},\\mbox{ and }\n \\end{eqnarray*}\n\\begin{eqnarray*}\ny_{ij11} =\n \\begin{pmatrix}\n 1 & 0 & 0 \\\\\n 0 & 0 & 1\n \\end{pmatrix},\ny_{ij12} =y_{ij13} =y_{ij21} =y_{ij22} =y_{ij23} =\n \\begin{pmatrix}\n 0 & 0 & 0\\\\\n 0 & 0 & 0\n \\end{pmatrix},\n\\end{eqnarray*}\nare satisfies the required condition of the Definition \\ref{defgi}.\n\\end{example}\n \n Foa a Boolean tensor $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_N \\times J_1 \\times \\cdots \\times J_N},$ the number of generalized inverses are finite and the maximum number of generalized inverses is $2^{I_1 \\times \\cdots \\times I_N\\times I_1 \\times \\cdots \\times I_N}.$\n The next result assures the uniqueness and is true only for invertiable tensors.\n \\begin{lemma}\n Let $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_N\\times I_1 \\times \\cdots \\times I_N}$ be any Boolean tensor. If $\\mc{A}$ is invertiable then $\\mc{A}^{-1}$ is the only generalized inverse of $\\mc{A}.$\n \\end{lemma}\n Next, we discus the equivalence condition for consistent system and generalized inverse. \n \n \\begin{theorem}\\label{eqgen}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$ and $\\mc{X}\\in\\mathbb{R}^{J_1 \\times \\cdots \\times J_N\\times I_1 \\times \\cdots \\times I_M}.$ Then the followings are equivalent:\n\\begin{enumerate}\n \\item[(a)] $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}=\\mc{A}.$\n \\item[(b)] $\\mc{X}{*_M}\\mc{Y}$ is a solution of the tensor equation $\\mc{A}{*_N}\\mc{Z}=\\mc{Y}$ whenever $\\mc{Y}\\in\\mathfrak{R}(\\mc{A}).$\n \\item[(c)] $\\mc{A}{*_N}\\mc{X}$ is idempotent and $\\mathfrak{R}(\\mc{A})=\\mathfrak{R}(\\mc{A}{*_N}\\mc{X}).$\n \\item[(d)] $\\mc{X}{*_M}\\mc{A}$ is idempotent and $\\mathfrak{R}(\\mc{A^T})=\\mathfrak{R}(\\mc{A}^T{*_N}\\mc{X}^T).$\n\\end{enumerate}\n\\end{theorem}\n\n\n\n\n\n\\begin{proof}\nFirst we will claim $(a)$ {\\it if and only if} $(b).$ Let us assume $(a)$ holds and $\\mc{Y}\\in\\mathfrak{R}(\\mc{A}).$ Then there exists a Boolean tensor $\\mc{Z}\\in \\mathbb{R}^{J_1\\times J_2\\times\\cdots\\times J_N}$ such that $\\mc{A}{*_N}\\mc{Z}=\\mc{Y}.$ Now \n$$\\mc{A}{*_N}\\mc{X}{*_M}\\mc{Y}=\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}{*_N}\\mc{Z}=\\mc{A}{*_N}\\mc{Z}=\\mc{Y}.$$ Therefore, $\\mc{X}{*_M}\\mc{Y}$ is a solution of $\\mc{A}{*_N}\\mc{Z}=\\mc{Y}.$ Conversely assume $(b)$ is true. That is $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{Y}=\\mc{Y}$ for all $\\mc{Y}\\in\\mathfrak{R}(\\mc{A}).$ Since $\\mc{Y}\\in\\mathfrak{R}(\\mc{A})$ which implies there exists $\\mc{U}\\in \\mathbb{R}^{J_1\\times\\cdots \\times J_N}$ such that $\\mc{A}{*_N}\\mc{U}=\\mc{Y}.$ Thus $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}{*_N}\\mc{U}=\\mc{A}{*_N}\\mc{U}$ for all $\\mc{U}\\in \\mathbb{R}^{J_1\\times\\cdots\\times J_N}.$ Therefore $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}=\\mc{A}.$ Next we show the equivalence between $(a)$ and $(c).$ Clearly $(a)$ implies $\\mc{A}{*_N}\\mc{X}$ idempotent. Since $\\mc{A}=\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}$ and $\\mc{A}{*_N}\\mc{X}=\\mc{A}{*_N} {\\mc{X}{*_M}\\mc{A}{*_N}\\mc{X}},$ so by Lemma \\ref{range-stan} $\\mathfrak{R}(\\mc{A})=\\mathfrak{R}(\\mc{A}{*_N}\\mc{X}).$ Using the same idea, we can easily show the equivalence between $(a)$ and $(d).$ Hence completes the proof. \n\\end{proof}\n\n\n\n\nSince $\\mc{A}^T{*_M}\\mc{A}{*_N}\\mc{X}_1{*_N}\\mc{A}^T{*_M}\\mc{A}=\\mc{A}^T{*_M}\\mc{A}$, so by Theorem \\ref{ltcan}, $\\mc{A}{*_N}\\mc{X}_1{*_N}\\mc{A}^T{*_M}\\mc{A}=\\mc{A}.$ Which leads the following corollary. \n\n\\begin{corollary}\nLet $\\mathfrak{R}(\\mc{A}^T)=\\mathfrak{R}(\\mc{A}^T{*_M}\\mc{A}).$ If $\\mc{X}_1$ and $\\mc{X}_2$ are generalized inverses of $\\mc{A}^T{*_M}\\mc{A}$ and $\\mc{A}{*_N}\\mc{A}^T$ respectively, then $\\mc{X}_1{*_N}\\mc{A}^T$ and $\\mc{A}^T{*_M}\\mc{X}_2$ are generalized inverse of $\\mc{A}.$\n\\end{corollary}\n\n\n\nFurther, from the range conditions, if $\\mathfrak{R}(\\mc{A}^T)\\subseteq \\mathfrak{R}(\\mc{B}^T)$ and $\\mathfrak{R}(\\mc{C})\\subseteq \\mathfrak{R}(\\mc{B}).$ Then $\\mc{A}=\\mc{V}{*_M}\\mc{B}$ and $\\mc{C}=\\mc{B}{*_N}\\mc{U}$ for some tensors $\\mc{U}$ and $\\mc{V}.$ Now \n$\\mc{A}{*_N}\\mc{X}{*_M}\\mc{C}= \\mc{V}{*_M}\\mc{B}{*_N}\\mc{X}{*_M}\\mc{B}{*_N}\\mc{U}=\\mc{V}{*_M}\\mc{B}{*_N}\\mc{U}$ which does not rely on $\\mc{X}$. So it is invariant to the choice of $\\mc{X}.$ So, we conclude this observation in the following corollary.\n\n\n\n\n\n\n\n\n\\begin{corollary}\nLet $\\mc{A},$ $\\mc{B}$ and $\\mc{C}$ be suitable tensors such that $\\mathfrak{R}(\\mc{A}^T)\\subseteq \\mathfrak{R}(\\mc{B}^T)$ and $\\mathfrak{R}(\\mc{C})\\subseteq \\mathfrak{R}(\\mc{B}).$ If the generalized inverse of $\\mc{B}$ exists, then $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{C}$ is invariant to $\\mc{X},$ where $\\mc{X}$ is the generalized inverse of $\\mc{B}.$\n\\end{corollary}\n\n\n\n\nTo prove the next result, we define regular and singular of tensors, i.e., \nA tensor $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots \\times I_M\\times J_1\\times \\cdots J_N},$ is called regular if the tensor equation $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}=\\mc{A}$ has a solution, otherwise called \\textit{singular}.\n\n\\begin{theorem}\\label{thm3.43}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N},$ $\\mc{S}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times I_1 \\times \\cdots \\times I_M},$ and $\\mc{T}\\in\\mathbb{R}^{J_1 \\times \\cdots \\times J_M\\times J_1 \\times \\cdots \\times J_N}.$ If $\\mc{S}$ and $\\mc{T}$ are invertible, then the following are equivalent:\n\\begin{enumerate}\n \\item[(a)] $\\mc{A}$ is regular.\n \\item[(b)] $\\mc{S}{*_M}\\mc{A}{*_N}\\mc{T}$ is regular.\n \\item[(c)] $\\mc{A}^T$ is regular.\n \\item[(d)] $\\mc{T}{*_N}\\mc{A}^T{*_M}\\mc{S}$ is regular.\n\\end{enumerate}\n\\end{theorem}\n\n\n\nBased on the block tensor\\cite{sun} and their properties, we have the following lemma.\n\\begin{lemma}\\label{block}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}.$ Then $\\mc{A}$ is regular if and only if $\\begin{bmatrix}\n\\mc{A} &\\mc{O}\\\\\n\\mc{O} & \\mc{B}\n\\end{bmatrix}$ is regular for all regular tensors $\\mc{B}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}.$\n\\end{lemma}\n\n\\begin{proof}\nLet $\\mc{A}$ and $\\mc{B}$ be regular tensors. Then there exist tensors $\\mc{X}$ and $\\mc{Y}$ such that $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}=\\mc{A}$ and $\\mc{B}{*_N}\\mc{Y}{*_M}\\mc{B}=\\mc{B}.$ Let $\\mc{Z}=\\begin{bmatrix}\n\\mc{X} & \\mc{O}\\\\\n\\mc{O} & \\mc{Y}\\\\\n\\end{bmatrix}.$ Now \n\\begin{eqnarray*}\n\\begin{bmatrix}\n\\mc{A} & \\mc{O}\\\\\n\\mc{O} & \\mc{B}\\\\\n\\end{bmatrix}{*_N}\\mc{Z}{*_M}\\begin{bmatrix}\n\\mc{A} & \\mc{O}\\\\\n\\mc{O} & \\mc{B}\\\\\n\\end{bmatrix}&=&\\begin{bmatrix}\n\\mc{A} & \\mc{O}\\\\\n\\mc{O} & \\mc{B}\\\\\n\\end{bmatrix}{*_N}\\begin{bmatrix}\n\\mc{X} & \\mc{O}\\\\\n\\mc{O} & \\mc{Y}\\\\\n\\end{bmatrix}{*_M}\\begin{bmatrix}\n\\mc{A} & \\mc{O}\\\\\n\\mc{O} & \\mc{B}\\\\\n\\end{bmatrix}\\\\\n&=&\n\\begin{bmatrix}\n\\mc{A}{*_N}\\mc{X} & \\mc{O}\\\\\n\\mc{O} & \\mc{B}{*_N}\\mc{Y}\\\\\n\\end{bmatrix}{*_M}\\begin{bmatrix}\n\\mc{A} & \\mc{O}\\\\\n\\mc{O} & \\mc{B}\\\\\n\\end{bmatrix}\\\\\n&=&\\begin{bmatrix}\n\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A} & \\mc{O}\\\\\n\\mc{O} & \\mc{B}{*_N}\\mc{Y}{*_M}\\mc{B}\\\\\n\\end{bmatrix}=\\begin{bmatrix}\n\\mc{A} & \\mc{O}\\\\\n\\mc{O} & \\mc{B}\\\\\n\\end{bmatrix}.\n\\end{eqnarray*}\nThus $\\begin{bmatrix}\n\\mc{A} & \\mc{O}\\\\\n\\mc{O} & \\mc{B}\\\\\n\\end{bmatrix}$ is regular. The converse part can be proved in the similar way. \n\\end{proof}\n\n We now present another characterization of the generalized inverse of the Boolean tensor, as follows.\n\\begin{theorem}\\label{lemcomp}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}.$ Then \n$$\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}\\leq \\mc{A}~~ if ~and~ only~ if ~~\\mc{X}\\leq \\left(\\mc{A}{*_N}\\mc{A}^{CT}{*_M}\\mc{A}\\right)^{CT}.$$\n\\end{theorem}\n\\begin{proof}\nApplying Theorem \\ref{lucethm} repetitively, we get \n$\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}\\leq \\mc{A}$ if and only if $\\mc{X}{*_M}\\mc{A}\\leq \\left(\\mc{A}^T{*_M}\\mc{A}^C\\right)^C$, which equivalently if and only if \n\\begin{equation*}\n\\mc{X}\\leq \\left(\\left(\\left(\\mc{A}^T{*_M}\\mc{A}^C\\right)^C\\right)^C{*_N}\\mc{A}^T\\right)^C = \\left(\\mc{A}^T{*_M}\\mc{A}^C{*_N}\\mc{A}^T\\right)^C =\\left(\\mc{A}{*_N}\\mc{A}^{CT}{*_M}\\mc{A}\\right)^{CT}.\n\\end{equation*}\n\\end{proof}\nUsing the Theorem \\ref{lemcomp}, and the fact of transpose and component-wise complement of a Boolean tensor, we obtain an important result for finding the maximum generalized inverse of a Boolean tensor. \n\\begin{corollary}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$ be regular. Then the following are holds\n\\begin{enumerate}\n\\item[(a)] $\\mc{A}=\\mc{A}{*_N}\\left(\\mc{A}{*_N}\\mc{A}^{CT}{*_M}\\mc{A}\\right)^{CT}{*_M}\\mc{A};$\n \\item[(b)] $\\left(\\mc{A}{*_N}\\mc{A}^{CT}{*_M}\\mc{A}\\right)^{CT}$ is the maximum generalized inverse of $\\mc{A};$ \n \\item[(c)] $\\left(\\mc{A}{*_N}\\mc{A}^{CT}{*_M}\\mc{A}\\right)^{CT}{*_M}\\mc{A}{*_N}\\left(\\mc{A}{*_N}\\mc{A}^{CT}{*_M}\\mc{A}\\right)^{CT}$ is the maximum reflexive generalized inverse of $\\mc{A}.$\n\\end{enumerate} \n\\end{corollary}\n\n\n\n\nNext, we discuss some equivalence results between generalized and other inverses. \n\n\n\n\n\n\\begin{theorem}\\label{eqv14}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$ be any Boolean tensor, then the following statements are equivalent:\n\\begin{enumerate}\n\\item[(a)] $\\mc{A}^{(1,4)}$ exists.\n\\item[(b)] $\\mc{A}^{(1)}$ exists and $\\mathfrak{R}(\\mc{A}) = \\mathfrak{R}(\\mc{A}{*_N}\\mc{A}^T).$ \n\\item[(c)] $(\\mc{A}{*_N}\\mc{A}^T)^{(1)}$ exists and $\\mc{X}{*_M}\\mc{A}{*_N}\\mc{A}^T = \\mc{A}^T$ for some tensor $\\mc{X}.$\n\\end{enumerate}\n\n\n\\begin{proof}\nConsider $(a)$ is true and $\\mc{A}^{(1,4)}=\\mc{X}.$ Existence of $\\mc{A}^{(1)}$ is trivial and hence $\\mathfrak{R}(\\mc{A}) = \\mathfrak{R}(\\mc{A}{*_N}\\mc{A}^T).$ Now we claim $(b)\\Rightarrow (c).$ Let $\\mc{A}^{(1)}$ exists and $\\mathfrak{R}(\\mc{A}) = \\mathfrak{R}(\\mc{A}{*_N}\\mc{A}^T).$ Then there exist a Boolean tensor $\\mc{U}\\in\\mathbb{R}^{I_1\\times\\cdots \\times I_M\\times J_1\\times\\cdots\\times J_N}$ such that $\\mc{A} = \\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{U}.$ Which implies $\\mc{A}{*_N}\\mc{A}^{T}=\\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{U}{*_N}\\mc{U}^T{*_M}\\mc{A}{*_N}\\mc{A}^T.$ So generalized inverse of $\\mc{A}{*_N}\\mc{A}^T$ exists. If we take $\\mc{X}=\\mc{A}^T{*_N}(\\mc{A}{*_N}\\mc{A}^T)^{(1)},$ then \n\\begin{eqnarray*}\n\\mc{X}{*_M}\\mc{A}{*_N}\\mc{A}^T &=& {\\mc{A}^T}{*_M}(\\mc{A}{*_N}\\mc{A}^T )^{(1)}{*_M}\\mc{A}{*_N} \\mc{A}^T=\\mc{U}^T{*_M}\\mc{A}{*_N}\\mc{A}^T{*_M}(\\mc{A}{*_N}\\mc{A}^T )^{(1)}{*_M}\\mc{A}{*_N}\\mc{A}^T\\\\\n&=&\\mc{U}^T{*_M}\\mc{A}{*_N}\\mc{A}^T=\\mc{A}^T.\n\\end{eqnarray*}\nFinally, we claim $(c)\\Rightarrow (a).$ Let $\\mc{X}{*_M}\\mc{A}{*_N}\\mc{A}^T = \\mc{A}^T$. Taking transpose on both sides, we get $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A} = \\mc{A} $, As\n\\begin{eqnarray*}\n(\\mc{X}{*_M}\\mc{A})^T &= &\\mc{A}^T{*_M}\\mc{X}^T = \\mc{X}{*_M}\\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{X}^T\\\\\n&=& (\\mc{X}{*_M}\\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{X}^T)^T = (\\mc{A}^T{*_M}\\mc{X}^T)^T = \\mc{X}{*_M}\\mc{A}. \n\\end{eqnarray*}\nThus $\\mc{X}=\\mc{A}^{(1,4)}.$ Hence the proof is complete.\n\\end{proof}\n\\end{theorem}\n\n\n\n\n\nUsing the similar way, we can show the following theorem.\n\n\n\n\n\\begin{theorem}\\label{eqv13}\nLet $\\mc{A}$ be any Boolean tensor, then the following statements are equivalent:\n\\begin{enumerate}\n\\item[(a)] $\\mc{A}^{(1,3)}$ exists.\n\\item[(b)] $\\mc{A}^{(1)}$ exists and $\\mathfrak{R}(\\mc{A}^T) = \\mathfrak{R}(\\mc{A}^T{*_M}\\mc{A}).$ \n\\item[(c)] There exists a Boolean tensor $\\mc{X}$ such that $\\mc{A}^T=\\mc{A}^T{*_M}\\mc{A}{*_N}\\mc{X}.$ \n\\end{enumerate}\n\\end{theorem}\n\n\n\n\nWe now discuss the characterization of Moore-Penrose inverse of Boolean tensors. The similar proof of Theorem 3.2 in \\cite{sun}, we have the uniqueness of the Moore-Penrose inverse of a Boolean tensor in $\\mathbb{R}^{I_1\\times \\cdots \\times I_M\\times J_1\\times\\cdots\\times J_N}$, as follows.\n\n\n\n\n\\begin{lemma}\\label{mpiu}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$ be any Boolean tensor. If the Moore-Penrose inverse of $\\mc{A}$ exists then it is unique.\n\\end{lemma}\n In the next lemma, we discuss an estimate of Moore-Penrose inverse a tensor, as follows.\n \\begin{lemma}\\label{lemma2}\n Let $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$ be a Boolean tensor and suppose $\\mc{A}$ admits a Moore-Penrose inverse. Then $\\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{A}\\leq\\mc{A}$\n \\begin{proof}\n Let $\\mc{B}= \\mc{A}^T{*_M}\\mc{A}$. Since $\\mc{B}$ is a Boolean tensor of even order and there are finitely many Boolean tensors of same order, so there must exist positive integers $s,t\\in\\mathbb{N}$ such that $\\mc{B}^s$ = $\\mc{B}^{s+t}.$ Without loss of generality, we can assume that $s$ is the smallest positive integer for which $\\mc{B}^s= \\mc{B}^{s+t}$ for some $t\\in \\mathbb{N}.$ Now we will show $s =1.$ Suppose $s\\geq 2$. Let $\\mc{X}$ be the Moore-Penrose inverse of $\\mc{A}$. Since $\\mc{B}= \\mc{A}^T{*_M}\\mc{A}$ and $\\mc{B}^s= \\mc{B}^{s+t}$ which implies $\\mc{A}^T{{*_M}}\\mc{A}{{*_N}}\\mc{B}^{s-1} = \\mc{A}^T{{*_M}}\\mc{A}{{*_N}}\\mc{B}^{s+t-1}.$ Pre-multiplying both side $\\mc{X}^T$ yields $\\mc{A}{{*_N}}\\mc{B}^{s-1} = \\mc{A}{{*_N}}\\mc{B}^{s+t-1},$ which implies $\\mc{A}{*_N}\\mc{A}^T{{*_M}}\\mc{A}{{*_N}}\\mc{B}^{s-2} = \\mc{A}{*_N}\\mc{A}^T{{*_M}}\\mc{A}{{*_N}}\\mc{B}^{s+t-2}.$ Further, pre-multiplying both side $\\mc{X}$ yields $\\mc{A}^T{*_M}\\mc{X}^T{*_N}\\mc{A}^T{{*_M}}\\mc{A}{{*_N}}\\mc{B}^{s-2} =\\mc{A}^T{*_M}\\mc{X}^T{*_N}\\mc{A}^T{{*_M}}\\mc{A}{{*_N}}\\mc{B}^{s+t-2},$ which implies $\\mc{B}^{s-1}= \\mc{B}^{s+t-1}.$\n \nThus, the minimality of $s$ is false and hence $s=1.$ Therefore $\\mc{B}=\\mc{B}^{t+1}$ for some $t\\in\\mathbb{N}.$ Again we have $\\mc{B}=\\mc{B}^{t+1}$, which implies $\\mc{A}^T{{*_M}}\\mc{A} = \\mc{A}^T{{*_M}}\\mc{A}{{*_N}}\\mc{B}^{t}$. Pre-multiplying $\\mc{X}^T$ both sides yields\n$\\mc{A}{*_N}\\mc{X}{{*_M}}\\mc{A} = \\mc{A}{*_N}\\mc{X}{{*_M}}\\mc{A}{{*_N}}\\mc{B}^{t}.$\n Thus \n \\begin{equation}\\label{eqfl}\n \\mc{A} = \\mc{A}{{*_N}}\\mc{B}^{t}=\\mc{A}{*_N}(\\mc{A}^T{*_M}\\mc{A})^t.\n \\end{equation}\nApplying Lemma \\ref{lemma1} to $\\mc{A}{*_N}\\mc{A}^T{*_N}\\mc{A}$ repetitively and combining Eq. (\\ref{eqfl}), we obtain\n$$\\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{A}\\leq \\mc{A}{*_N}(\\mc{A}^T{*_M}\\mc{A})^2\\leq\\cdots\\leq \\mc{A}{*_N}(\\mc{A}^T{*_M}\\mc{A})^t=\\mc{A}.$$\n \\end{proof}\n\\end{lemma}\n\n\nUsing the Lemma \\ref{lemma1} and \\ref{lemma2} one can obtain an interesting result on invertibility of Boolean tensor as follows, \n\n\\begin{corollary}\nA Boolean tensor $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_N\\times I_1 \\times \\cdots \\times I_N}$ is invertible if and only if \n $$\\mc{A}{*_N}\\mc{A}^T=\\mc{A}^T{*_M}\\mc{A}=\\mc{I}.$$ \n\\end{corollary}\nFrom the Definition \\ref{perm}, we obtain\n\\begin{eqnarray*}\n(\\mc{P}{*_N}\\mc{P}^T)_{i_1 \\cdots i_Nj_1\\cdots j_N}&=&\\sum_{k_1\\cdots k_N}(\\mc{P})_{i_1 \\cdots i_Nk_1\\cdots k_N}(\\mc{P}^T)_{k_1 \\cdots k_Nj_1\\cdots j_N}\\\\\n&=&\\sum_{k_1\\cdots k_N}(\\mc{P})_{i_1 \\cdots i_Nk_1\\cdots k_N}(\\mc{P})_{j_1 \\cdots j_Nk_1\\cdots k_N}\\\\\n&=&(\\mc{P})_{i_1 \\cdots i_N\\pi(j_1)\\cdots \\pi(j_N)}(\\mc{P})_{j_1 \\cdots j_N\\pi(j_1)\\cdots \\pi(j_N)}\\\\\n&=& \\left\\{\\begin{array}{cc}\n 1 & \\mbox{ if } i_s=j_s \\mbox{ for all } 1\\leq s\\leq N. \\\\\n 0 & \\mbox{ otherwise.}\n\\end{array}\\right.\\\\\n&=&(\\mc{I})_{i_1 \\cdots i_Nj_1\\cdots j_N}.\n\\end{eqnarray*}\nSimilar way, we can also show $\\mc{P}^T{*_N}\\mc{P}=\\mc{I}.$ Therefore, every permutation tensors are orthogonal and invertible. Adopting this result, we now present a characterization of the permutation tensor, as follows.\n\\begin{preposition}\\label{permu}\nA Boolean tensor $\\mc{A}$ has an inverse {\\it if and only if} it is a permutation tensor.\n\\end{preposition}\nNext result contains five equivalent conditions involving the existence of Moore-Penrose inverse of a Boolean tensor.\n \\begin{theorem}\\label{thm1}\n Let $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M \\times J_1\\times \\cdots \\times J_N.}$ be any tensor. Then the following statements are equivalent:\n \\begin{enumerate}\n \\item[(i)] The Moore-Penrose inverse of $ \\mc{A}$ exists and unique.\n \\item[(ii)] $\\mc{A}{{*_N}}\\mc{A}^T{{*_N}}\\mc{A} \\leq \\mc{A}.$\n \\item[(iii)] $\\mc{A}{{*_N}}\\mc{A}^T{{*_N}}\\mc{A} = \\mc{A}.$\n\\item[(iv)] The Moore-Penrose inverse of $\\mc{A}$ exists and equals $\\mc{A}^T$.\n\\item[(v)] There exist a tensor $\\mc{G}$ such that \n $\\mc{G}{{*_N}}\\mc{A}{{*_N}}\\mc{A}^T=\\mc{A}^T$ and $\\mc{A}^T{{*_N}}\\mc{A}{{*_N}}\\mc{G}=\\mc{A}^T$.\n\\end{enumerate}\n \\end{theorem}\n \n \n \n \\begin{proof}\n If $(i)$ holds then by Lemma \\ref{lemma2} $(ii)$ holds. Also $(ii)\\Rightarrow (iii)$ by Lemma \\ref{lemma1}. The statements $(iii)\\Rightarrow (iv)$ and $(iv)\\Rightarrow (i)$ are trivial by definition. Now we will show equivalence between $(i)$ and $(v)$. Suppose $(i)$ holds. If we take $\\mc{G}=\\mc{A}^T$ then $(v)$ hold. Conversely assume $(v)$ is true. To prove Moore-Penrose inverse of $A$ exists, first we show the following results:\n \\begin{itemize}\n \\item $\\mc{A}{*_N}\\mc{G}{*_M}\\mc{A} = \\mc{A}$\\\\\n Since $\\mc{G}{*_M} \\mc{A}{*_N}\\mc{A}^T=\\mc{A}^T$ which implies $ \\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{G}^T = \\mc{A}. $ Pre multiplying $\\mc{G}$ and post multiplying $\\mc{A}^T$ both sides, we obtain $\\mc{G}{*_M}\\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{G}^T{*_N}\\mc{A}^T=\\mc{G}{*_M}\\mc{A}{*_N}\\mc{A}^T. $ \n Thus $\\mc{A}^T{*_M}\\mc{G}^T{*_N}\\mc{A}^T=\\mc{A}^T.$ \n Hence $\\mc{A}{*_N}\\mc{G}{*_M}\\mc{A} = \\mc{A}.$\n \\item $(\\mc{G}{*_M}\\mc{A})^T = \\mc{A}^T{*_M}\\mc{G}^T=\\mc{G}{{*_M}}\\mc{A}{{*_N}}\\mc{A}^T{*_M}\\mc{G}^T=\\mc{G}{*_M}\\mc{A}.$ Therefore $\\mc{G}{*_M}\\mc{A}$ is symmetric.\n \\item $(\\mc{A}{*_N}\\mc{G})^T = \\mc{G}^T{*_N}\\mc{A}^T=\\mc{G}^T{*_N}\\mc{A}^T{*_M}\\mc{A}{*_N}\\mc{G}=\\mc{A}{*_N}\\mc{G}.$ Thus $\\mc{A}{*_N}\\mc{G}$ is symmetric.\n \\end{itemize}\n Now we will show the tensor $\\mc{X}=\\mc{G}{*_M}\\mc{A}{*_N}\\mc{G}$ is the Moore-Penrose of $\\mc{A}.$ Since \n \\begin{enumerate}\n \\item [$\\bullet$] \n $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A} =\\mc{A}{*_N}\\mc{G}{*_M}\\mc{A}=\\mc{A}.$\n \\item [$\\bullet$] \n $\\mc{X}{*_M}\\mc{A}{*_N}\\mc{X} =\\mc{G}{*_M}\\mc{A}{*_N}\\mc{G}{*_M}\\mc{A}{*_N}\\mc{G} =\\mc{X}.$\n \\item [$\\bullet$] \n $(\\mc{A}{*_N}\\mc{X})^T=(\\mc{A}{*_N}\\mc{G}{*_M}\\mc{A}{*_N}\\mc{G})^T=(\\mc{A}{*_N}\\mc{G})^T{*_M}(\\mc{A}{*_N}\\mc{G})^T\n =\\mc{A}{*_N}\\mc{G}{*_M}\\mc{A}{*_N}\\mc{G}=\\mc{A}{*_N}\\mc{X}.$\n \\item [$\\bullet$] \n $(\\mc{X}{*_M}\\mc{A})^T=(\\mc{G}{*_M}\\mc{A}{*_N}\\mc{G}{*_M}\\mc{A})^T=(\\mc{G}{*_M}\\mc{A})^T{*_N}(\\mc{G}{*_M}\\mc{A})^T\n =\\mc{G}{*_M}\\mc{A}{*_N}\\mc{G}{*_M}\\mc{A}=\\mc{X}{*_M}\\mc{A}.$\n \\end{enumerate}\n\n\n Therefore, $\\mc{X}$ is the Moore-Penrose inverse of $\\mc{A}$ and By Lemma \\ref{mpiu} it is unique.\n \\end{proof}\n \n \n The reverse order law for the Moore-Penrose inverses of tensors yields a class of challenging problems that are fundamental research in the theory of generalized inverses. Research on reverse order law tensors has been very active recently \\cite{Mispa18, PanRad18} but as per the above theorem it is trivially true in case of Boolean tensors. \n\n \\begin{remark}\n If Moore-Penrose inverses of $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$, $\\mc{B}\\in\\mathbb{R}^{J_1 \\times \\cdots \\times J_N\\times K_1 \\times \\cdots \\times K_L},$ and $\\mc{A}{*_N}\\mc{B}$ exists, then the reverse-order law for the Moore-Penrose inverse is always exists, i.e., \n $$(\\mc{A}{*_N}\\mc{B})^\\dagger=\\mc{B}^\\dagger{*_N}\\mc{A}^\\dagger.$$\n \\end{remark}\n\n\n\n\n\\subsection{Weighted Moore-Penrose inverse}\n\nUtilizing the Einstein product, weighted Moore-Penrose inverse of even-order tensor and arbitrary-order tensor was introduced in \\cite{BehMM19, we17}, very recently. This work motivate us to study weighted Moore-Penrose inverse for Boolean tensors.\n\n\\begin{definition}\\label{wmpi}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$, $\\mc{M}\\in\\mathbb{R}^{I_1 \\times \\cdots \\times I_M\\times I_1 \\times \\cdots \\times I_M}$ and $\\mc{N}\\in\\mathbb{R}^{J_1 \\times \\cdots \\times J_N\\times J_1 \\times \\cdots \\times J_N}$ be three Boolean tensors. If a Boolean tensor $\\mc{Z}\\in\\mathbb{R}^{J_1 \\times \\cdots \\times J_N\\times I_1 \\times \\cdots \\times I_M}$ satisfying\n\\vspace{-.5cm}\n\\begin{eqnarray*}\n&(1)&\\mc{A}{*_N}\\mc{Z}{*_M}\\mc{A} = \\mc{A},\\\\\n&(2)&\\mc{Z}{*_M}\\mc{A}{*_N}\\mc{Z} = \\mc{Z},\\\\\n&(3)&(\\mc{M}{*_M}\\mc{A}{*_N}\\mc{Z})^T = \\mc{M}{*_M}\\mc{A}{*_N}\\mc{Z},\\\\\n&(4)&(\\mc{Z}{*_M}\\mc{A}{*_N}\\mc{N})^T = \\mc{Z}{*_M}\\mc{A}{*_N}\\mc{N},\n\\end{eqnarray*}\nis called weighted Moore-Penrose inverse of $\\mc{A}$ and it is denoted by $A^{\\dagger}_{\\mc{M},\\mc{N}}.$\n\\end{definition}\nNote that, the weighted Moore-Penrose inverse need not be unique in general. This can be verified by the following example.\n\\begin{example}\nLet the Boolean tensor\n$~\\mc{A}=(a_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ be defined as in Example \\ref{example18} with $\\mc{N}=\\mc{O} \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ and $\\mc{M}=(m_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ such that \n\\begin{eqnarray*}\nm_{ij11} =m_{ij21}=\n \\begin{pmatrix}\n 1 & 0 & 0 \\\\\n 0 & 0 & 0\n \\end{pmatrix},\nm_{ij12} = m_{ij22} =\n \\begin{pmatrix}\n 1 & 0 & 0\\\\\n 0 & 0 & 1\n \\end{pmatrix},\nm_{ij13} =m_{ij23}\n \\begin{pmatrix}\n 0 & 0 & 0\\\\\n 0 & 0 & 1\n\\end{pmatrix}.\n\\end{eqnarray*}\nThen it can be easily verified that both $~\\mc{X}=(x_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$, $~\\mc{Y}=(y_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ defined in Example \\ref{example18} satisfies all conditions of Definition \\ref{wmpi}. \\end{example}\nThe uniqueness and existence of weighted Moore-Penrose inverse and some equivalent properties will be discussed in the next part of this subsection.\n\n \\begin{theorem}\\label{uwmpi}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots\\times I_M\\times J_1 \\times \\cdots\\times J_N},~~\\mc{M}\\in\\mathbb{R}^{I_1 \\times \\cdots\\times I_M\\times I_1 \\times \\cdots\\times I_M},$\\\\ $\\mc{N}\\in\\mathbb{R}^{J_1 \\times \\cdots\\times J_N\\times J_1 \\times \\cdots\\times J_N}$ be three Boolean tensors with $\\mathfrak{R}(\\mc{A})=\\mathfrak{R}(\\mc{A}{*_N}\\mc{N})$ and $\\mathfrak{R}(\\mc{A}^T)=\\mathfrak{R}(\\mc{A}^T{*_M}\\mc{M}^T).$ If $\\mc{A}_{\\mc{M},\\mc{N}}^{\\dagger}$ exists, then\n\\begin{enumerate}\n\\item[(a)] $\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T = \\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T;$\n\\item[(b)] $\\mc{A}^T{*_M}\\mc{M}^T{*_M}\\mc{A} = \\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A}; $\n\\item[(c)] $ \\mc{A}_{\\mc{M},\\mc{N}}^{\\dagger} $ is unique.\n\\end{enumerate}\n\\end{theorem}\n \\begin{proof}\n Let $\\mc{X}$ be a weighted Moore-Penrose inverse of $\\mc{A}.$ Now \n \\begin{eqnarray*}\n \\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T &=&\\mc{A}{*_N} {\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{X}^T}{*_N}\\mc{A}^T\n =\\mc{A}{*_N} {(\\mc{X}{*_M}\\mc{A}{*_N}\\mc{N})^T}{*_N}\\mc{A}^T\\\\\n &=& {\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}}{*_N}\\mc{N}{*_N}\\mc{A}^T\n = \\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T.\n \\end{eqnarray*}\n This completes the proof of part $(a).$ Using the similar lines of part $(a)$ and relation $(3)$ of Definition \\ref{wmpi}, we can prove part $(b).$ Next we will claim the uniqueness of $A^\\dagger_{\\mc{M},\\mc{N}}.$ \\\\\n Suppose there exists two weighted Moore-Penrose inverses (say $\\mc{X}_1$ and $\\mc{X}_2$) for $\\mc{A}.$ Then \n \\begin{eqnarray*}\n \\mc{X}_1{*_M}\\mc{A}{*_N}\\mc{N} &=& \\mc{X}_1{*_N}\\mc{A}{*_N} {\\mc{X}_2{*_M}\\mc{A}{*_N}\\mc{N}}\n = \\mc{X}_1{*_M} {\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T}{*_M}\\mc{X}_2^T\\\\\n &= & {\\mc{X}_1{*_M}\\mc{A}{*_N}\\mc{N}}{*_N}\\mc{A}^T{*_M}\\mc{X}_2^T \n = \\mc{N}^T{*_N} {\\mc{A}^T{*_M}\\mc{X}_1^T{*_N}\\mc{A}^T}{*_M}\\mc{X}_2^T\\\\\n & =& \\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{X}_2^T\n = \\mc{X}_2{*_M}\\mc{A}{*_N}\\mc{N}.\n \\end{eqnarray*}\n Since $\\mathfrak{R}(\\mc{A})=\\mathfrak{R}(\\mc{A}{*_N}\\mc{N}).$ Which implies there exists $\\mc{U}$ such that $\\mc{A}{*_N}\\mc{N}{*_N}\\mc{U} = \\mc{A}.$ Thus $\\mc{X}_1{*_M}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{U} = \\mc{X}_2{*_M}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{U}.$ Hence $\\mc{X}_1{*_M}\\mc{A} =\\mc{X}_2{*_M}\\mc{A}$. Therefore\n \\begin{equation}\\label{eq4.51}\n \\mc{X}_1=\\mc{X}_1{*_M}\\mc{A}{*_N}\\mc{X}_1=\\mc{X}_2{*_M}\\mc{A}{*_N}\\mc{X}_1.\n \\end{equation}\n Now by using Eq. (\\ref{eq4.51}), we get\n \\begin{eqnarray*}\n \\mc{M}{*_M}\\mc{A}{*_N} {\\mc{X}_1} &=& {\\mc{M}{*_M}\\mc{A}{*_N}\\mc{X}_2}{*_M}\\mc{A}{*_N}\\mc{X}_1\n = \\mc{X}_2^T{*_N} {\\mc{A}^T{*_M}\\mc{M}^T{*_M}\\mc{A}}{*_N}\\mc{X}_1\\\\\n &=& \\mc{X}_2{*_N}\\mc{A}^T{*_M} {\\mc{M}{*_M}\\mc{A}{*_N}\\mc{X}_1}\n = \\mc{X}_2^T{*_N} {\\mc{A}^T{*_M}\\mc{X}_1^T{*_N}\\mc{A}^T}{*_M}\\mc{M}^T\\\\\n &=& \\mc{X}_2^T{*_N}\\mc{A}^T{*_N}\\mc{M}^T\n = \\mc{M}{*_M}\\mc{A}{*_N}\\mc{X}_2.\n \\end{eqnarray*}\n Again as $\\mathfrak{R}(\\mc{A}^T)=\\mathfrak{R}(\\mc{A}^T{*_M}\\mc{M}^T).$ This implies there exists $\\mc{V}^T$ such that $\\mc{A}^T{*_M}\\mc{M}^T{*_M}\\mc{V}^T = \\mc{A}^T.$ It leads $\\mc{V}{*_M}\\mc{M}{*_M}\\mc{A}=\\mc{A}.$ Thus $ \\mc{V}{*_M}\\mc{M}{*_M}\\mc{A}{*_N}\\mc{X}_1 = \\mc{V}{*_M}\\mc{M}{*_M}\\mc{A}{*_N}\\mc{X}_2.$ Hence $\\mc{A}{*_N}\\mc{X}_1 =\\mc{A}{*_N}\\mc{X}_2$. Therefore\n \\begin{equation}\\label{eq4.52}\n \\mc{X}_2=\\mc{X}_2{*_M}\\mc{A}{*_N}\\mc{X}_2=\\mc{X}_2{*_M}\\mc{A}{*_N}\\mc{X}_1.\n \\end{equation}\n Combining Eq. (\\ref{eq4.51}) and (\\ref{eq4.52}), we obtain $\\mc{X}_1=\\mc{X}_2$ and hence the proof is complete. \n \\end{proof}\n The existence of weighted Moore-Penrose inverse is not trivial like other generalized inverses. The next theorem discusses the existence of weighted Moore-Penrose inverse. \n \n \\begin{theorem}\\label{ewmpi}\n Let $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots\\times I_M\\times J_1\\times \\cdots\\times J_N},~~\\mc{M}\\in\\mathbb{R}^{I_1\\times \\cdots \\times I_M\\times I_1\\times \\cdots\\times I_M},$\\\\ $\\mc{N}\\in\\mathbb{R}^{J_1 \\times \\cdots\\times J_N\\times J_1 \\times \\cdots\\times J_N}$ be three Boolean tensors with $\\mathfrak{R}(\\mc{A})=\\mathfrak{R}(\\mc{A}{*_N}\\mc{N})$ and $\\mathfrak{R}(\\mc{A}^T)=\\mathfrak{R}(\\mc{A}^T{*_M}\\mc{M}^T).$ If\n $ \\mc{M}\\geq\\mc{I}$ and $\\mc{N}\\geq\\mc{I},$ then $\\mc{A}_{\\mc{M},\\mc{N}}^\\dagger$ exists if and only if any one of the following conditions holds:\n \\begin{enumerate}\n \\item[(a)] $\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A} = \\mc{A}.$\n \\item[(b)] $\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A} = \\mc{A}.$\n \\item[(c)] $\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T{*_M}\\mc{A} = \\mc{A}.$\n \\item[(d)] $\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T{*_M}\\mc{A} = \\mc{A}.$\n \\end{enumerate}\n In particular, $\\mc{A}_{\\mc{M},\\mc{N}}^\\dagger = \\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T.$\n \\end{theorem}\n \\begin{proof}\nAssume $\\mc{A}_{\\mc{M},\\mc{N}}^\\dagger$ exists and let $\\mc{X}=\\mc{A}_{\\mc{M},\\mc{N}}^\\dagger$. Let $\\mc{B}= \\mc{A}^T{*_M}\\mc{A}$. Since for every Boolean tensor, there are finitely many Boolean tensors of same order, so there must exist positive integers $s,t\\in\\mathbb{N}$ such that \n\\begin{equation}\\label{eq4.6}\n (\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^s = (\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s+t}.\n\\end{equation}\nWithout loss of generality, we can assume that $s$ is the smallest positive integer for which Eq. (\\ref{eq4.6}) holds. Now we will claim $s=1.$ Suppose on contradiction, assume $s>1.$ Now using Eq. (\\ref{eq4.6}), and properties of weighted Mooore-Penrose inverse, we get \n\\begin{eqnarray}\\label{eq4.7}\n\\nonumber\n {\\mc{X}{*_M} \\mc{A}{*_N}\\mc{N}}{*_N}\\mc{A}^T{*_M}\\mc{M}^T{*_M}&&\\hspace*{-0.7cm}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1} .\\\\\n\\nonumber\n&&\\hspace*{-3.5cm}= {\\mc{X}{*_M}\\mc{A}{*_N}\\mc{N}}{*_N}\\mc{A}^T{*_M}\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1+t}\\\\\n\\nonumber\\textnormal{This yield~~} \\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{X}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T&&\\hspace*{-0.7cm}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1} \\\\\n&&\\hspace*{-4.5cm}=\\mc{N}^T{*_N}\\mc{A}^T{*_N}\\mc{X}^T{*_M}\\mc{A}^T{*_M}\\mc{M}^T(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1+t}.\n\\end{eqnarray}\nSince $\\mathfrak{R}(\\mc{A})=\\mathfrak{R}(\\mc{A}{*_N}\\mc{N}),$ which implies there exists a tensor $\\mc{U}$ such that $\\mc{A}{*_N}\\mc{N}{*_N}\\mc{U}=\\mc{A}.$ Now premultiplying $\\mc{U}^T$ to Eq. (\\ref{eq4.7}) and using the properties $\\mc{U}^T{*_N}\\mc{N}^T{*_N}\\mc{A}^T=\\mc{A}^T$ and $\\mc{A}^T {*_M}\\mc{X}^T{*_N}\\mc{A}^T=\\mc{A}^T,$ we get\n\\begin{equation}\\label{eq4.71}\n \\mc{A}^T{*_M}\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1} = \\mc{A}^T{*_M}\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1+t}.\n\\end{equation}\n Again, premultiplying $\\mc{X}^T $ to Eq. (\\ref{eq4.71}) and using the symmetricity of $\\mc{M}{*_M}\\mc{A}{*_N}\\mc{X}$, we get\n$\\mc{M}{*_M} {\\mc{A}{*_N}\\mc{X}{*_M}(\\mc{A}}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1}= \\mc{M}{*_M} {\\mc{A}{*_N}\\mc{X}{*_M}(\\mc{A}}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1+t}.$ This gives \n\\begin{equation}\\label{eq4.8}\n \\mc{M}{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1} = \\mc{M}{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1+t}.\n\\end{equation} \n Since $\\mathfrak{R}(\\mc{A}^T)=\\mathfrak{R}(\\mc{A}^T{*_N}\\mc{M}^T),$ which implies there exists a tensor $\\mc{Z}$ such that $\\mc{Z}{*_M}\\mc{M}{*_M}\\mc{A}=\\mc{A}.$ Premultiplying $\\mc{Z}$ to Eq. (\\ref{eq4.8}) yields \n $$(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1} = (\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{s-1+t}.$$\n and contradicts the minimality of $s.$ Therefore \n \\begin{equation}\\label{eq4.9}\n \\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T = (\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{t+1},~\\mbox{for some }~t\\in\\mathbb{N}.\n \\end{equation}\n Premultiplying Eq. (\\ref{eq4.9}) by $\\mc{X},$ and using $(\\mc{X}{*_M}\\mc{A}{*_N}\\mc{N})^T=\\mc{X}{*_M}\\mc{A}{*_N}\\mc{N},$ we obtain \n$$\n\\mc{N}^T{*_N} {\\mc{A}^T{*_M}\\mc{X}^T{*_N}\\mc{A}^T}{*_M}\\mc{M}^T = \\mc{N}^T{*_M} {\\mc{A}^T{*_N}\\mc{X}^T{*_N}\\mc{A}^T}{*_M}\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^t.$$\nSince $ \\mc{A}^T{*_M}\\mc{X}^T{*_N}\\mc{A}^T=\\mc{A}^T,$ we get\n\\begin{equation}\\label{eqn121}\n \\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T = \\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^t. \n\\end{equation}\nPremultiplying Eq. (\\ref{eqn121}) by a tensor $\\mc{U}^T$ and using $\\mathfrak{R}(\\mc{A})=\\mathfrak{R}(\\mc{A}{*_N}\\mc{N}),$ we again obtain $ \\mc{A}^T{*_M}\\mc{M}^T = \\mc{A}^T{*_M}\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^t.$ Postmultiplying $\\mc{Z}^T$ and applying $\\mathfrak{R}(\\mc{A}^T)=\\mathfrak{R}(\\mc{A}^T{*_M}\\mc{M}^T),$ we have \n$$\n \\mc{A}^T = \\mc{A}^T{*_M}\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^{t-1}{*_M} \\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T.$$\n\nNow \n\\begin{eqnarray*}\n \\mc{A}^T &=& \\mc{A}^T{*_M} {\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T}{*_M}\\mc{M}^T)^{t-1}{*_M}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T\\\\\n &=&\\mc{A}^T{*_M}(\\mc{M}^T{*_M}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T){*_M} {\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T}{*_M}\\mc{M}^T)^{t-2}{*_N}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T\\\\\n &=&\\mc{A}^T{*_M}(\\mc{M}^T{*_M}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T)^2{*_M} {\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T}{*_M}\\mc{M}^T)^{t-3}{*_N}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T\\\\\n &=&\\cdots~~~~~~~~~~~~~~\\cdots~~~~~~~~~~~~~\\cdots\\\\\n &=&\\mc{A}^T{*_M}(\\mc{M}^T{*_M}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T)^{t-2}{*_M} {\\mc{M}^T{*_M}(\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T}{*_M} {\\mc{M}^T){*_N}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T}\\\\\n &=&\\mc{A}^T{*_M}(\\mc{M}^T{*_M}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T)^{t}=\\mc{A}^T{*_M}\\left[(\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M})^{t}\\right]^T.\n\\end{eqnarray*}\nThus \n\\begin{equation}\\label{eq4.10}\n \\mc{A}=(\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M})^{t}{*_M}\\mc{A}.\n\\end{equation}\nAs $\\mc{M}\\geq\\mc{I},\\mc{N}\\geq\\mc{I} $, so by Lemma \\ref{lemma1}\n\\begin{equation}\\label{eq4.11}\n \\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A}\\geq \\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{A}\\geq \\mc{A}.\n\\end{equation}\nPostmultiplying $\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A}$, we obtain \n \\begin{equation}\\label{eq4.12}\n \\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}\\leq(\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M})^2{*_M}\\mc{A}.\n \\end{equation}\n Combining Eqs.(\\ref{eq4.10}), (\\ref{eq4.11}) and (\\ref{eq4.12}), we have\n \n\n \n\\begin{eqnarray*}\n\\mc{A}&\\leq&\\mc{A}{*_N}\\mc{N}^T {*_N} \\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A}\n\\leq (\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T*_M\\mc{M})^2{*_M}\\mc{A} \\\\\n&\\leq & (\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M})^3{*_M}\\mc{A}\\leq \\cdots\\leq (\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M})^t{*_M}\\mc{A}=\\mc{A}.\n\\end{eqnarray*}\nTherefore \n\\begin{equation}\\label{eq4.13}\n \\mc{A} = \\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A},\n\\end{equation}\nand hence completes the proof of the condition $(b).$ By using Theorem \\ref{uwmpi}, the other conditions are holds since\n\\begin{eqnarray}\\nonumber\n \\mc{A} &=& {\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T}{*_M}\\mc{M}{*_M}\\mc{A} = \\mc{A}{*_N}\\mc{N}{*_N} {\\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A}}\\\\\\label{eq199} &=& {\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T}{*_M}\\mc{M}^T{*_M}\\mc{A} = \\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T{*_M}\\mc{A}.\n\\end{eqnarray}\nFurther, we will claim not only the four conditions holds but also $\\mc{A}^\\dagger_{\\mc{M},\\mc{N}}=\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T.$ Let $\\mc{X}=\\mc{A}_{\\mc{M},\\mc{N}} ^\\dagger.$ From Eq. (\\ref{eq199}), $\\mc{A}=\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}$ and\n\\begin{equation*}\n \\mc{X}{*_M}\\mc{A}{*_N}\\mc{X}=\\mc{N}^T{*_N} {\\mc{A}^T{*_M}\\mc{M}^T{*_M}\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T}{*_M}\\mc{M}^T=\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T=\\mc{X}.\n\\end{equation*}\nUsing Theorem \\ref{uwmpi}, we show \n\\begin{eqnarray*}\n\\mc{M}{*_M}\\mc{A}{*_N}\\mc{X} &=&\\mc{M}{*_M} {\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T}{*_M}\\mc{M}^T =\\mc{M}{*_M}\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}^T\\\\\n &=&(\\mc{M}{*_M}\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T)^T\n=(\\mc{M}{*_M}\\mc{A}{*_N}\\mc{X})^T.\n\\end{eqnarray*}\nTherefore, $\\mc{M}{*_M}\\mc{A}{*_N}\\mc{X}$ is symmetric. Similarly, we can show $\\mc{X}{*_M}\\mc{A}{*_N}\\mc{N}$ is symmetric. So $\\mc{A}_{\\mc{M},\\mc{N}} ^\\dagger=\\mc{N}^T{*_N}\\mc{A}^T{*_M}\\mc{M}^T $. Next we will show the converse part. Let $\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A} = \\mc{A}.$ Since $\\mc{M}\\geq \\mc{I}$ and $\\mc{N}\\geq\\mc{I}$, so by Lemma \\ref{lemma1}, \\begin{equation*}\n \\mc{A}\\leq\\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{A}\\leq\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{A}\\leq\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{M}{*_M}\\mc{A} = \\mc{A}\n\\end{equation*}\n and hence \n\\begin{equation}\\label{eq4.14}\n \\mc{A} =\\mc{A}{*_N}\\mc{A}^T{*_M}\\mc{A} = \\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{A}.\n\\end{equation}\nUsing the Eq. (\\ref{eq4.14}) and symmetricity of $ \\mc{A}{*_N}\\mc{A}^T$, we obtain\n\\begin{equation}\\label{eq4.15}\n \\mc{A}{*_N}\\mc{A}^T = \\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{A}{*_N}\\mc{A}^T\n=\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T\n=\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T .\n\\end{equation}\nSimilar argument yields,\n\\begin{eqnarray}\\label{eq4.16}\n\\nonumber\n \\mc{A}^T{*_M}\\mc{A}&=&\\mc{A}^T{*_M}\\mc{M}^T{*_M} {\\mc{A}{*_N}\\mc{N}^T{*_N}\\mc{A}^T}{*_M} \\mc{A}=\\mc{A}^T{*_M}\\mc{M}^T{*_M} {\\mc{A}{*_N}\\mc{N}{*_N}\\mc{A}^T{*_M}\\mc{A}}\\\\&=&\\mc{A}^T{*_M}\\mc{M}{*_N}\\mc{A} = \\mc{A}^T{*_M}\\mc{M}^T{*_M}\\mc{A}.\n\\end{eqnarray}\nUsing Eqs. (\\ref{eq4.14})-(\\ref{eq4.16}), it can be easily verified that $\\mc{X}=\\mc{N}^T{*_N}\\mc{A}^T {*_M}\\mc{M}^T$ is satisfies all four conditions of the weighted Moore-Penrose inverse. Similarly, one can start from other conditions to verify the same. Thus the proof is complete.\n \\end{proof}\n \\begin{remark}\n The equality condition in Theorem \\ref{ewmpi} $(a)$ can be replaced by ${\\bf{`\\geq'}}.$\n \\end{remark}\n\n\n\\subsection{Space Decomposition}\nUsing the theory of Einstein product, we introduce the definition of the space decomposition for Boolean tensors, which generalizes the matrix space decomposition \\cite{rao}. \n\n\n\\begin{definition}\\label{FRD}\n Let $\\mc{F}\\in\\mathbb{R}^{I_1 \\times \\cdots\\times I_M\\times K_1 \\times \\cdots\\times K_L}$ and $\\mc{R}\\in\\mathbb{R}^{K_1 \\times \\cdots\\times K_L\\times J_1 \\times \\cdots\\times J_N}$ be two tensors with\n \\vspace{-.5cm}\n\\begin{eqnarray*}\n &&(a)~\\mc{A} = \\mc{F}{*_L}\\mc{R};\\\\\n &&(b)~\\mathfrak{R}(\\mc{A}) = \\mathfrak{R}(\\mc{F});\\\\\n &&(c)~\\mathfrak{R}(\\mc{A}^T) = \\mathfrak{R}(\\mc{R}^T),\n\\end{eqnarray*}\nthen the tensor $\\mc{A}$ is called space decomposable and this decomposition is called a space decomposition of $\\mc{A}$.\n\\end{definition}\n\n\nIn connection with the fact of the above Definition \\ref{FRD} and Lemma \\ref{range-stan}, one can conclude the existence of a generalized inverse, as follows. \n\n\n\n\\begin{theorem}\\label{exisgen}\nLet $\\mc{A}=\\mc{F}{*_L}\\mc{R}$ be a space decomposition of $\\mc{A}\\in\\mathbb{R}^{I_1 \\times \\cdots\\times I_M\\times J_1 \\times \\cdots\\times J_N},$ where $\\mc{F}\\in\\mathbb{R}^{I_1 \\times \\cdots\\times I_M\\times K_1 \\times \\cdots\\times K_L}$ and $\\mc{R}\\in\\mathbb{R}^{K_1 \\times \\cdots\\times K_L\\times J_1 \\times \\cdots\\times J_N}$ . Then $\\mc{A}^{(1)}$ exists.\n\\end{theorem}\n\n\nWe now present one of our essential result which represents not only the existence of reflexive generalized inverse but also other inverses through this decomposition.\n\n\n\\begin{theorem}\\label{the336}\nLet $\\mc{X}$ be a generalized inverse of the Boolean tensor $\\mc{A}.$ If $\\mc{A}=\\mc{F}{*_L}\\mc{R}$ is a space decomposition of $\\mc{A},$ where $\\mc{F}\\in\\mathbb{R}^{I_1 \\times \\cdots\\times I_M\\times K_1 \\times \\cdots\\times K_L}$ and $\\mc{R}\\in\\mathbb{R}^{K_1 \\times \\cdots\\times K_L\\times J_1 \\times \\cdots\\times J_N}.$ Then the following are holds:\n\\begin{enumerate}\n\\label{eqvspace}\n \\item[(a)] $\\mc{F}^{(1)}$ and $\\mc{R}^{(1)}$ exists.\n \\item[(b)] $\\mc{F}^{(1)}{*_M}\\mc{F}=\\mc{R}{*_N}\\mc{R}^{(1)}.$\n \\item[(c)] $\\mc{F}^{(1)}{*_M}\\mc{A}=\\mc{R}$ and $\\mc{A}{*_N}\\mc{R}^{(1)}=\\mc{F}.$\n \\item[(d)] $\\mc{R}^{(1)}{*_M}\\mc{F}^{(1)}$ is a generalized inverse of $\\mc{A}.$\n \\item[(e)] $\\mc{R}{*_N}\\mc{X}$ is a reflexive inverse of $\\mc{F}$ and $\\mc{X}{*_M}\\mc{F}$ is a reflexive inverse of $\\mc{R}.$ \n\\end{enumerate}\n\\end{theorem}\n\\begin{proof}\nSince $\\mc{X}$ is the generalized inverse of $\\mc{A}.$ Then we have $\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}=\\mc{A},$ which implies\\\\ \n$\\mc{F}{*_L}\\mc{R}{*_N}\\mc{X}{*_M}\\mc{F}{*_L}\\mc{R}=\\mc{F}{*_L}\\mc{R}=\\mc{I}{*_M}\\mc{F}{*_L}\\mc{R}.$ Further, using Corollary \\ref{rtcan}, we get $\\mc{F}{*_L}\\mc{R}{*_N}\\mc{X}{*_M}\\mc{F}=\\mc{F}.$ Thus $\\mc{R}{*_N}\\mc{X}$ is a generalized inverse of $\\mc{F}.$ Similarly, one can determine $\\mc{X}{*_M}\\mc{F}$ is a generalized inverse of $\\mc{R}$. Hence $(a)$ is proved. Now using the result $(a),$ one can prove $(b)$ and $(c).$ To prove $(d)$ we use the fact $(a)$ and obtain. \n\\begin{equation*}\n \\mc{A}{*_N}\\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}{*_M}\\mc{A}= \\mc{A}{*_N}\\mc{X}{*_M}\\mc{F}{*_L}\\mc{R}{*_N}\\mc{X}{*_M}\\mc{A}= \\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}{*_N}\\mc{X}{*_M}\\mc{A}=\\mc{A}.\n\\end{equation*}\nHence $\\mc{R}^{(1)}{*_M}\\mc{F}^{(1)}$ is a generalized inverse of $\\mc{A}.$ \nIn a similar manner, one can prove $(e)$ using the fact $\\mc{R}{*_N}\\mc{X}{*_M}\\mc{F}{*_L}\\mc{R}{*_N}\\mc{X}\n=\\mc{R}{*_N}\\mc{X}$ and $\\mc{X}{*_M}\\mc{F}{*_L}\\mc{R}{*_N}\\mc{X}{*_M}\\mc{F}\n=\\mc{X}{*_M}\\mc{F}.$ This completes the proof.\n\\end{proof}\nIn view of the above theorem one can draw a conclusion, as follows. \n\\begin{remark}\\label{rmk3.43}\n Every generalized inverse of $\\mc{A}$ need not of the form $\\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}.$ \n \\end{remark}\n We verify the Remark \\ref{rmk3.43} with the following example.\n\\begin{example}\\label{example3.45}\nLet\n$~\\mc{A}=(a_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ be a Boolean tensor with\n\\begin{eqnarray*}\na_{ij11} =\n \\begin{pmatrix}\n 1 & 1 & 0 \\\\\n 1 & 0 & 0\n \\end{pmatrix},\na_{ij12}=a_{ij13} =a_{ij21}=a_{ij22}=a_{ij23}=\n \\begin{pmatrix}\n 0 & 0 & 0\\\\\n 0 & 0 & 0\n \\end{pmatrix}.\n\\end{eqnarray*}\nConsider $\\mc{A}^{(1)}=(x_{ijkl}) \\in \\mathbb{R}^{{2\\times3}\\times{2 \\rtimes 3}}$ is a generalized inverse of $\\mc{A}$ with \n\\begin{eqnarray*}\nx_{ij11} =\n \\begin{pmatrix}\n 1 & 0 & 0 \\\\\n 0 & 0 & 0\n \\end{pmatrix},\nx_{ij12} =\n \\begin{pmatrix}\n 0 & 1 & 0\\\\\n 0 & 0 & 0\n \\end{pmatrix},\nx_{ij13} =\n \\begin{pmatrix}\n 0 & 0 & 0\\\\\n 0 & 0 & 0\n\\end{pmatrix},\\\\\n %\n %\n %\nx_{ij21} =\n \\begin{pmatrix}\n 0 & 0 & 0\\\\\n 1 & 0 & 0\n \\end{pmatrix},\n x_{ij22} =\n \\begin{pmatrix}\n 0 & 0 & 0\\\\\n 0 & 0 & 0\n \\end{pmatrix},\nx_{ij23} =\n \\begin{pmatrix}\n 0 & 0 & 0\\\\\n 0 & 0 & 0\n \\end{pmatrix}.\n\\end{eqnarray*}\n\nIn light of the Theorem \\ref{the336} (e) one can conclude\n$$\\mc{R}^{(1)}*_2\\mc{F}^{(1)}=\\mc{A}^{(1)}*_2\\mc{F}*_2\\mc{R}*_2\\mc{A}^{(1)}=\\mc{A}^{(1)}*_2\\mc{A}*_2\\mc{A}^{(1)} \\neq \\mc{A}^{(1)}.$$\nTherefore, every generalized inverse of $\\mc{A}$ need not of the form $\\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}.$ \n\\end{example}\nAt this point one may be interested to know when does the generalized inverse of a Boolean tensor of the form $\\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}$ ? The answer to this question is explained in the following Remark. \n\\begin{remark}\\label{rmk3.46}\n If $\\mc{X}$ is a reflexive inverse of a Boolean tensor $\\mc{A}\\in \\mathbb{R}^{I_1 \\times \\cdots\\times I_M\\times J_1 \\times \\cdots\\times J_N}$ and $\\mc{A}=\\mc{F}{*_L}\\mc{R}$ has a space decomposition, where $\\mc{F}\\in\\mathbb{R}^{I_1 \\times \\cdots\\times I_M\\times K_1 \\times \\cdots\\times K_L}$ and $\\mc{R}\\in\\mathbb{R}^{K_1 \\times \\cdots\\times K_L\\times J_1 \\times \\cdots\\times J_N}.$ Then every generalized inverse is of the form $\\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}.$\n\\end{remark}\nNow considering the fact of Remark \\ref{rmk3.46} and the observation of the Example \\ref{example3.45}, one can get the desired result.\n\n\\begin{theorem}\\label{refeqv}\nLet $\\mc{A} \\in \\mathbb{R}^{I_1 \\times \\cdots\\times I_M\\times J_1 \\times \\cdots\\times J_N}$ be a Boolean tensor and \n$\\mc{A}=\\mc{F}{*_L}\\mc{R}$ be a space decomposition of $\\mc{A}, $ where $\\mc{F}\\in\\mathbb{R}^{I_1 \\times \\cdots\\times I_M\\times K_1 \\times \\cdots\\times K_L},$ $\\mc{R}\\in\\mathbb{R}^{K_1 \\times \\cdots\\times K_L\\times J_1 \\times \\cdots\\times J_N}.$ Assume that generalized inverse of either $\\mc{F}$ reflexive or $\\mc{R}$ reflexive. Then $\\mc{X}$ is a reflexive generalized inverse of $\\mc{A}$ if and only if $\\mc{X} = \\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}.$\n\\end{theorem}\n\\begin{proof}\nConsider the generalized inverse of $\\mc{F}$ is reflexive. Taking into account of Theorem \\ref{eqvspace} $(d)$, we obtain $\\mc{X} = \\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}$, which is a generalized inverse of $\\mc{A}$. Therefore, it is enough to show $\\mc{X}{*_M}\\mc{A}{*_N}\\mc{X}=\\mc{X}.$ Now using Theorem \\ref{eqvspace} $(c)$, we get\n\\begin{eqnarray*}\n\\mc{X}{*_M}\\mc{A}{*_N}\\mc{X} =\\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}{*_M} {\\mc{A}{*_N}\\mc{R}^{(1)}}{*_L}\\mc{F}^{(1)}\n=\\mc{R}^{(1)}{*_L} {\\mc{F}^{(1)}{*_M}\\mc{F}{*_L}\\mc{F}^{(1)}} = \\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}.\n\\end{eqnarray*}\nConversely, let $\\mc{X}$ be a reflexive inverse of $\\mc{A}.$ Then by Theorem \\ref{eqvspace} $(e)$,\n\\begin{eqnarray*}\n\\mc{X} = \\mc{X}{*_M}\\mc{A}{*_N}\\mc{X} = \\mc{X}{*_M}\\mc{F}{*_L}\\mc{R}{*_N}\\mc{X} = \\mc{R}^{(1,2)}{*_L}\\mc{F}^{(1,2)}=\\mc{R}^{(1)}{*_L}\\mc{F}^{(1)}.\n\\end{eqnarray*}\n\\vspace{-.3cm}\n\\end{proof}\n\n\n\n\n\\begin{remark}\\label{rk2.41}\nIf we drop the condition either $\\mc{F}$ or $\\mc{R}$ is reflexive generalized inverse of $\\mc{A}$ in Theorem \\ref{refeqv}, then the theorem will not true in general.\n \\end{remark}\n\n\n\nIn favour of the the Remark \\ref{rk2.41} we produce an example as follows. \n\n\n\n\n\\begin{example}\nLet $\\mc{A}$ be the Boolean tensor defined in Example \\ref{example3.45} and $\\mc{A}=\\mc{F}=\\mc{R}.$ Since $\\mc{A}{*_2}\\mc{I}{*_2}\\mc{A}=\\mc{I}$ and $\\mc{I}{*_2}\\mc{A}{*_2}\\mc{I}\\neq\\mc{I}$, it follows that $\\mc{I}$ is the generalized inverse for both $\\mc{F}$ and $\\mc{G}$ but not reflexive. In view of the Theorem \\ref{refeqv}, \none can conclude \n$\\mc{R}^{(1)}{*_2}\\mc{F}^{(1)}=\\mc{I}$\nis not a reflexive generalized inverse of $\\mc{A}.$\n\\end{example}\n\nIn \\cite{beasley} and \\cite{song}, the authors have defined the rank of a Boolean matrix through space decomposition. Next, we discuss the rank and weight of a Boolean tensors. \n\n\n\\begin{definition}\n Let $\\mc{A}\\in\\mathbb{R}^{I_1\\times\\cdots\\times I_M\\times J_1 \\times\\cdots\\times\n J_N}$ be a Boolean tensor. If there exist a least positive integer, $r=K_1 \\times\\cdots\\times K_L$ such that the Boolean tensors $\\mc{B}\\in\\mathbb{R}^{I_1 \\times\\cdots\\times I_M\\times K_1\\times\\cdots\\times K_L}$ and $\\mc{C}\\in\\mathbb{R}^{K_1 \\times\\cdots\\times K_L\\times J_1\\times\\cdots\\times J_N}$ satisfies $\\mc{A}=\\mc{B}{*_L}\\mc{C}$. Then $r$ is called the Boolean rank of $\\mc{A}$ and denoted by $r_b(\\mc{A}).$\n\\end{definition}\n\n\\begin{example}\\label{exrank}\nConsider a Boolean tensor\n$~\\mc{A}=(a_{ijkl}) \\in \\mathbb{R}^{{2\\times2}\\times{2 \\rtimes 2}}$ with entries\n\\begin{eqnarray*}\na_{ij11} =\n \\begin{pmatrix}\n 1 & 0 \\\\\n 0 & 0\n \\end{pmatrix},~\na_{ij12} =\n \\begin{pmatrix}\n 0 & 0\\\\\n 0 & 0\n \\end{pmatrix},~\na_{ij21} =\n \\begin{pmatrix}\n 0 & 0\\\\\n 1 & 0\n \\end{pmatrix},~\na_{ij22} =\n \\begin{pmatrix}\n 1 & 0\\\\\n 0 & 0\n \\end{pmatrix}.\n \\end{eqnarray*}\n There exist a least positive integer $r=2$ and two tensor\n $~\\mc{B}=(b_{ijk}) \\in \\mathbb{R}^{{2\\times2 \\times 2}}$ and $~\\mc{C}=(c_{ijk}) \\in \\mathbb{R}^{{2\\times2 \\times 2}}$ with entries\n \\begin{eqnarray*} \n b_{ij1} =\n \\begin{pmatrix}\n 1 & 0\\\\\n 0 & 0\n \\end{pmatrix},\n b_{ij2} =\n \\begin{pmatrix}\n 0 & 0\\\\\n 1 & 0\n \\end{pmatrix},\n c_{ij1} =\n \\begin{pmatrix}\n 1 & 0\\\\\n 0 & 1\n \\end{pmatrix},\n c_{ij2} =\n \\begin{pmatrix}\n 0 & 1\\\\\n 0 & 0\n \\end{pmatrix},\n \\end{eqnarray*}\n such that $\\mc{A}= \\mc{B}*_1\\mc{C}$. However, $r=1$ gives two matrices $B$ and $C$, which is impossible to get a tensor. Thus rank of the tensor is $2$.\n\\end{example}\n\nOn the other hand, the rank of the Boolean tensor is zero if it is zero tensor. Further, we have $\\mc{A}=\\mc{I}_m{*_M}\\mc{A}=\\mc{A}{*_N}\\mc{I}_n$, where $\\mc{A}\\in\\mathbb{R}^{I_1\\times\\cdots\\times I_M\\times J_1 \\times\\cdots\\times\n J_N} $. It is quite apparent that\n$$0\\leq r_b(\\mc{A})\\leq \\min\\{I_1 \\times\\cdots\\times I_M,~J_1\\times\\cdots\\times J_N\\}. $$\n\nTo prove the last result of this paper, we define weight of Boolean tensor as.\n\n\\begin{definition}\nThe weight of Boolean tensor is denoted by $w(\\mc{A})$ and defined as\n$$\nw(\\mc{A})=\\{\\mbox{ Total number of non zero elements of } \\mc{A}\\}.\n$$\n\\end{definition}\n\nThe existence of generalized inverse can be discussed through Boolean rank, as follows. \n\n\n\\begin{theorem}\\label{rankthm}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots \\times I_M\\times J_1 \\times \\cdots \\times J_N}$ be any tensor with $r_b(\\mc{A})\\leq 1.$ Then $\\mc{A}$ is regular.\n\\end{theorem}\n\n\\begin{proof}\nIt is trivial for $r_b(\\mc{A})=0,$ as a consequence of the fact $\\mc{O}$ tensor is always regular.\n Further, consider $r_b(\\mc{A})=1$ and define a tensor $\\mc{J},$ with no zero elements. Then there exist permutation tensors $P$ and $\\mc{Q}$ such that $\\mc{P}{*_M}\\mc{A}{*_N}\\mc{Q}=\\begin{bmatrix}\n\\mc{J} & \\mc{O}\\\\\n\\mc{O} & \\mc{O}\\\\\n\\end{bmatrix}.$ As $\\mc{J}$ is regular, it implies $\\mc{P}{*_M}\\mc{A}{*_N}\\mc{Q}$ is regular.\n In view of the Lemma \\ref{block} and Preposition \\ref{permu} one can conclude $\\mc{A}$ is regular \n\\end{proof}\nIt is clear, if the weight of a Boolean tensor is $1,$ then the rank is also $1$. In view of this we obtain the following result. \n\\begin{corollary}\\label{weightcoro}\nLet $\\mc{A}\\in\\mathbb{R}^{I_1\\times \\cdots I_M\\times J_1\\times\\cdots J_N}$ be any tensor with $w(\\mc{A})\\leq 1.$ Then $\\mc{A}$ is regular.\n\\end{corollary}\n\n\n\n\\section{Conclusion}\nIn this paper, we have introduced generalized inverses $(\\{i\\}$-inverses $(i = 1, 2, 3, 4))$ with the Moore-Penrose inverse and weighted Moore-Penrose inverse for Boolean tensors via the Einstein product, which is a generalization of the generalized inverses of Boolean matrices. In addition to this, we have discussed their existence and uniqueness. This paper also provides some characterization through complement and its application to generalized inverses. \nFurther, we explored the space decomposition for the Boolean tensors, at the same time, we have studied rank and the weight for the Boolean tensor. \nIn particular, we limited our study for Boolean tensors with $r_b(\\mc{A}) \\leq 1$ and $w(\\mc{A}) \\leq 1$. Herewith left as open problems for future studies.\\\\\n{\\bf Problem:} If the Boolean rank or weight of a tensor $\\mc{A}$ is greater than 1, then under which conditions the Boolean tensor $\\mc{A}$ is regular $?$\\\\\nAdditionally, it would be interesting to investigate more generalized inverses on the Boolean tensors; this work is currently underway.\n\n\n\n\n\n\n\n\\noindent {\\bf{Acknowledgments}}\\\\\nThis research work was supported by Science and Engineering Research Board (SERB), Department of Science and Technology, India, under the Grant No. EEQ\/2017\/000747.\n\n\\bibliographystyle{abbrv}\n\\bibliographystyle{vancouver}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:intro}\n\n\\paragraph{Background and Aim} Choreographies are high-level descriptions of communicating \nsystems, inspired by the ``Alice \nand Bob'' notation for security protocols, where the behaviours of participants is defined from a \nglobal viewpoint. Over the last two decades, they have become popular and have been applied in \ndifferent contexts, including: the specification of communication protocols \nin web service standards \\cite{wscdl}, business process notations \\cite{bpmn}, and \ntheoretical models of communications \\cite{HYC16}; the synthesis of correct-by-construction \nconcurrent software \\cite{CM13,chor:website,DGGLM17}; the runtime verification of (object-oriented) \nactor systems \\cite{NY17}; and the static verification of concurrent process models \n\\cite{LTY15,CLM17}. A notable application to software engineering is the Testable Architecture \nmethodology \\cite{savara:website}, a development lifecycle that keeps service implementations \naligned with the choreographies specified by designers.\n\nThe promise of choreographies is that they will improve correctness in concurrent programming. \nUnfortunately, this promise remains unfulfilled, because the choreography models explored so far have the unrealistic assumption that communications never fail. The only exception \nis the work in \\cite{APN17} (the state of the art in the topic \nof failures in choreographies so far), which equips choreographies with optional blocks that can be \ncancelled non-deterministically at runtime. This is an interesting direction, but it still has \nlimitations that impede its applicability (\\eg, communications are \nsynchronous\/instantaneous; we discuss more in related work) and, just as important, does not allow \nchoreographies to specify how the system should recover from a failure.\n\nThe aim of this work is to develop a choreography model that brings choreographies all the way to \nbeing applicable to settings with realistic communication failures.\nReaching this objective is challenging, because we need to provide enough \nexpressivity to program the local recovery strategies of participants (which may \nbe different) and at the same time retain the global viewpoint on communications offered by \nchoreographies.\nTo this end, we split choreographic communications into their declarations and \nimplementations as shown in the snippet below.\n\\begin{snippet}\n\\cnewframe{k}{\\pid s}{\\pid r}{}{\n\t\\\n\t\\code{sendExpBackoff}(k,\\pid s)\\keyword{;}\\,\n\t\\ \n\t\\code{recvTimeout}(k,\\pid r)\n}\n\\end{snippet}\nHere, we use a choreographic notation---$\\cframe{k}{\\pid s}{\\pid r}{}$---to declare a \ncommunication from a sender process $\\pid s$ to a receiver process $\\pid r$, and \nname it $k$. Then, we \\emph{implement} the communication $k$ by invoking the \nprocedures \\code{sendExpBackoff} and \\code{recvTimeout}, which respectively handle the send and \nreceive part of the communication. Both procedures handle communication failures and may perform \ndifferent retries at sending or receiving in case of failures, but with different policies: the \nfirst procedure uses exponential backoff between send attempts, while the second is based on a \nfixed timeout.\nThus, implementation needs not be symmetric between sender(s) and receiver(s).\n\n\\paragraph{Contribution}\nWe develop Robust Choreographies (RC\\xspace for short), a new choreographic programming model \nwith support for communication failures. In RC\\xspace, the programmer declares which communications \nshould take place using an Alice and Bob notation, and then defines how processes will enact these \ncommunications through asynchronous send and receive actions. Code regarding different \ncommunications can be interleaved freely, allowing for the modelling of dependencies between \nthe implementations of different communications.\n\nDifferently from previous work on choreographies, all send and receive actions might fail, \nmodelling that there may be connection problems and\/or timeouts on both ends.\nWe formalise this behaviour by giving an operational semantics for RC\\xspace.\nWhen a process tries to perform an action, it can later check whether this action succeeded (as in \ntypical mainstream APIs for networking), and it is possible to program recovery (\\eg, by retrying \nto perform the action, or by executing alternative code).\nRC\\xspace supports further features that are studied in the setting of choreographies with communication \nfailures for the first time, like name mobility, dynamic creation of processes (networks in RC\\xspace can \ngrow at runtime), and branching. This allows us, for example, to program processes that offload \ncommunication attempts to parallel computations.\n\nRC\\xspace is intended as an implementation model that sits on a lower level than previous work \non choreographies. The abstraction of state-of-the-art choreographic models can be recovered by \nimplementing their language constructs as procedures and thus offer them ``as a library''---or \nDomain-Specific Languages (DSLs), if we see them as macros. For example, for previous models that \ntake communication robustness for granted, we can write parametric procedures for robust send and \nreceive actions that attempt at performing the desired action until it succeeds (or follow \nbest-effort strategies).\nWe illustrate this idea by implementing in RC\\xspace different language constructs from previous work,\nincluding procedural choreographies \\cite{CM17:forte}, \none-to-any\/any-to-one interactions \\cite{LNN16,LH17}, and scatter\/gather \\cite{LMMNSVY15,CMP18}.\nWe also exemplify how some of these constructs can be extended to allow for failure and specify \ncompensations.\nA pleasant consequence of sharing RC\\xspace as underlying model for these constructs is that we can now \ncombine these features from different works. (For example, the calculus with map\/reduce\nin \\cite{LNN16} does not support parametric procedures and \\cite{CM17:forte} \\viceversa.)\n\nThe realistic failure model in RC\\xspace allows us to identify more programming mistakes and \nprogram properties than in previous work, in particular some related to robustness. In general, we \nare interested in studying which guarantees we can provide for each communication that has been \n(choreographically) declared in a program, based on an analysis of its following implementation.\nExamples of relevant questions are: Can we check whether a communication will be eventually \nimplemented, even if failures occur? And, will it have the right (type of) payload?\nWe develop a novel typing discipline that can answer questions of this kind, and apply it to the\nstatic verification of at-most-once and exactly-once delivery guarantees for user-defined code.\n\nWe end our development by showing that the foundations given by RC\\xspace are applicable in practice. \nSpecifically, we define a formal translation (a compiler, if you like) from choreographies in RC\\xspace to \na more standard process model, \\ie, an asynchronous variant of the $\\pi$-calculus equipped with \nstandard I\/O actions that might fail. These asynchronous fallible I\/O actions are the only way \nprocesses may interact: there is no shared memory or agreement primitive. \nWe prove that, if the original choreography is well-typed, the synthesised\ncode is operationally equivalent and enjoys deadlock-freedom.\nFor space reasons, the synthesis procedure and process model are given in \\cref{sec:synthesis}.\n\n\\section{Failure model}\n\\label{sec:failure-model}\n\nWe discuss the failure model that we adopt in this work.\n\nCommunications are asynchronous and may fail.\nA successful send action implies that the sent message is\nnow handed over to the communication stack of the sender, which will attempt at transmitting the \nmessage to the receiver.\nIf transmission succeeds, the message reaches the receiver and is stored by the communication stack \nof the receiver in a dedicated memory.\nA successful receive action means that a message has been consumed by the intended receiver, \n\\ie, the message has been successfully \\emph{delivered}---this \nrequires that transmission was successful.\nA receive action fails if it is executed when there is no message that it can consume.\nThis models that there may be connection problems on the end of the receiver or that a timeout \noccurred on the receive action. We assume that communication and node failures are transient, \nmeaning that failing to interact with a node does not impede eventually doing it in later retries. \nWe leave persistent failures to future work.\n\nThere are two settings that we consider, depending on the kind of system that the programmer is dealing with.\n\n\\begin{setting}[Reliable Transmission]\n\\label{fm:reliable}\nSuccessfully executing a send action means that the message has been reliably stored in the \ncommunication medium between sender and receiver. By reliably stored, we mean that the message is \nnot going to be lost until its transmission from the sender's to the receiver's communication stack \neventually takes place.\nThis is the case, for example, of local Inter-Process Communication (IPC) mechanisms, like unnamed \npipes in POSIX systems, shared memory, or file-based communications. It is also the case of \ndistributed systems using reliable message delivery protocols like TCP, under the assumption that \nthere are no connection resets (or similar issues)---in these cases, middleware can be employed to \nre-establish connections.\nCommunication failures happen in this setting, \\eg, because send actions may \nfail to hand over messages to the sender's communication stack, and receive actions may fail \nbecause of timing issues (trying to receive messages before they reach the receiver's communication \nstack, or trying to receive something that is never sent).\n\\end{setting}\n\n\\begin{setting}[Unreliable Transmission]\n\\label{fm:unreliable}\nThis setting is more low-level than the previous one. Here, we assume no reliable middleware for \nmessage transport.\nThis is the case, for example, of distributed systems that use protocols with unreliable \nmessage delivery. It is also the case for systems that use protocols which, in theory, guarantee \nmessage delivery (like TCP) but, in practice, messages acknowledged on the protocol level may fail \nin reaching the application of the receiver due to connection resets and no middleware to deal with \nthese issues is employed. We assume absence of corruption (\\eg, through checksums).\n\nSuccessfully executing a send action in this setting still means that the local communication \nstack of the sender has accepted the task of eventually sending the message, but there is no \nguarantee that the message is actually going to be successfully passed on to the receiver's stack:\ncommunication media can lose messages.\nTherefore, a sender cannot know if a successfully sent message is going to be transmitted\nto the receiver, unless the programmer explicitly implements an acknowledgement mechanism on the \napplication level and such acknowledgement is received by the sender.\n\\end{setting}\n\nNaturally, the first setting allows for stronger guarantees. We will show that our typing can be \nused to guarantee at-most once message delivery (reception by the receiver's application) in both \nsettings. For the first setting, we will also show that typing can be used to guarantee \nexactly-once message delivery. For the second setting this is unrealistic, and as typically done in \npractice we have to switch to a best-effort strategy. Therefore, we demonstrate that our typing can \nbe used to guarantee best-effort delivery, in the sense that every time an application is \nprogrammed to receive a message, it is guaranteed at least the chance to receive it correctly.\n\n\\section{Related Work}\n\\label{sec:related}\n\nThe work nearest to ours is \\cite{APN17}, where choreographies are used to specify communication \nprotocols that abstract from data---a variant of Multiparty Session Types \\cite{HYC16}.\nUnreliability is modelled by allowing parts of a choreography declared in special optional blocks \nto become no-op non-deterministically. A static analysis guarantees that the network cannot get \nstuck even if all optional blocks are not executed. We see our work as complementary to\n\\cite{APN17}: while our initial motivation is similar, the aim is different. Our focus is \nproviding guarantees on implementations, and consequently choreographies in RC\\xspace are concrete \nprograms, in contrast with protocol specifications. There are also several major technical \ndifferences that make our choreography language more expressive. We mention the most \nrelevant ones.\n\nCommunications are synchronous in \\cite{APN17}. This means that if \na participant succeeds in sending a message, it knows that the receiver has also succeeded. In RC\\xspace, \nwe are interested in systems with asynchronous message passing. This requires \ndefining and analysing send and receive actions separately, since succeeding in one does not \nnecessarily mean succeeding with the other.\nSeparating between send and receive actions is also essential to the programming of recovery \nstrategies in RC\\xspace, which may be asymmetric for sender and receiver. For example, a sender may \nhave different conditions to check (\\eg, a number of retries) than those at the intended receiver \nfor deciding whether an action should be retried, which cannot be captured in \\cite{APN17}. \nRecovery strategies cannot be specified at all in the choreographies of \\cite{APN17}, which is \nanother key distinction with our work.\nThe modelling of recovery strategies in RC\\xspace is also what allows us to develop our type system for \nthe static verification of at-most-once and exactly-once delivery, which is not studied in \n\\cite{APN17}.\nThe choreography model in \\cite{APN17} does not include features equivalent to our \nprimitives for process spawning, name mobility, or parametric procedures. Parametric procedures are \nparticularly important for RC\\xspace: including error handling code in choreographies makes them \nnecessarily more complicated, and having procedures to modularise programs is useful, as we \nillustrate with our examples. Procedures are also key to our implementation\nof language constructs from previous choreography models ``as libraries'' in RC\\xspace.\n\nFrom a broader perspective, we think that merging our research direction with that of \n\\cite{APN17} (choreographic programs with protocol specifications) would be a very interesting \nfuture work, because it may yield a static analysis for RC\\xspace to check whether a given recovery \nstrategy guarantees the eventual execution of a high-level protocol (the latter might even \nabstract from failures, leaving their handling to the implementation).\n\nIn \\cite{CGY16}, choreographies for protocol specifications are augmented with controlled \nexceptions. These are different from communication failures, because they are controlled by the \nprogrammer and their propagation is ensured through communications that are assumed never to fail \n(thus, they are also different from our compensations for communication failures, where we react to \nunexpected failures and do not make this assumption). This approach has been refined in \n\\cite{CVBZE16}, by allowing for more fine-grained propagation of errors (but errors are still \nuser-defined, so similar comments apply).\n\nIn \\cite{LNN16}, the authors present a choreography model that considers potential failures of \nnodes (processes in our terminology). This approach is far from ours and that in \\cite{APN17}, \nsince the idea is that a system has redundant copies of a node type, and a choreography can \nspecify how many nodes of a type are needed to continue operating. No recovery can be programmed, \nand there is no presentation of how the approach can be adopted in realistic process models \n(compilation). Communications among functioning nodes are assumed infallible.\n\nOur work is also related to the research line on Choreographic Programming \n\\cite{M13:phd,CM13,DGGLM17}, a paradigm where choreographies are used to define implementations of \ncommunicating systems. This is the first work that studies how communication failures can be dealt \nwith in this paradigm. Our primitives for name mobility and parametric procedures are inspired \nfrom \\cite{CM17:forte}, but our methods are different, since we brought them into an asynchronous \nsetting with potentially-failing communications. Also, the fact that interactions are \nimplemented through separate send and receive actions in RC\\xspace is new to choreographic programming. \nPrevious work \\cite{CM17:ice} explored this distinction to achieve asynchronous communications in \nchoreographies, but these cannot fail and the distinction is used only in the runtime semantics (the \nseparate terms cannot be used programmatically).\n\nPrevious work explored a notion of bisimulation for a process calculus with explicit locations and \nlinks, where both nodes and links may fail at runtime \\cite{FH08}. Differently from our \nsetting, communications are synchronous, messages cannot be lost, and failures are permanent.\nExploring a similar notion of behavioural equivalence for RC\\xspace and our target process calculus is \ndefinitely an interesting future work, because it may lead to a substitutability principle for \ngenerated processes wrt choreographies. For example, we could replace a block of process code \nprojected from a choreography with an equivalent one without having to re-run compilation.\nAnother interesting application could be extending RC\\xspace to allow for ``undefined'' processes, whose \nbehaviour is left to be defined in other choreographies, or even (legacy) process code. Previous \nwork showed how to design such extensions for choreographies based on multiparty session \ntypes, obtaining choreographies with ``partial'' terms that refer to externally-defined code \n\\cite{MY13}. A notion of bisimulation for such partial terms could lead to relaxing the conditions \nfor well-typedness given in \\cite{MY13}.\n\nOur scatter construct in \\cref{sec:examples} recalls the unreliable \nbroadcast studied in \\cite{KGG14} for the setting of process calculi. \nA key difference is that, in \\cite{KGG14}, recovery cannot re-attempt failed communications \n(a process exits all current sessions when a failure occurs). Moreover, \ncommunications are synchronous in \\cite{KGG14}.\n\nOur formalisation of messages in transit partly recalls that in \\cite{FMN07}, which presents an \nagreement protocol that works under the assumption of quasi-reliable communications and node \ncrashes. While we do not consider (permanent) node crashes in this work, the programming of recovery \nstrategies is similar in our setting. This is to be expected, since in a distributed setting a node \nmay suspect that another node crashed by being unable to communicate with it (over some time).\nA detailed study of consensus in RC (and extentions to node failures) is definitely interesting \nfuture work.\n\nPrevious works on choreographies include choice operators that behave non-de\\-term\\-in\\-ist\\-ic\\-ally, \\eg,\n$C + C'$, read ``run either $C$ or $C'$'' \n\\cite{QZCY07,LGMZ08,CHY12} (and their labelled variant, in \n\\cite{HYC16}).\nThese operators do not capture the communication failures that we are interested in, for two \nreasons. First, they are programmed explicitly and are thus predictable.\nSecond, their formalisations assume that the propagation of choice \ninformation among processes is reliable (for compilation).\nThus, similar comments to those for the comparison with \\cite{CGY16} apply.\nObserve also that the soundness of these models is ensured under the assumption that any two \nprocesses involved in some interactions together perform exactly the same number of (dual) \ncommunication actions. This is unrealistic, since sender and receiver can have different policies in \npractice, as we already discussed.\n\nSome previous choreography models, like \\cite{CHY12}, include explicit parallel \ncomposition, $C \\mid C'$. RC\\xspace captures process parallelism using out-of-order \nexecution---a practice shared by other choreography models, see also\nthe paper that introduced it \\cite{CM13}. In general, actions performed by distinct processes can \nbe performed in any order. However, $C \\mid C'$ in \\cite{CHY12} (and other works, like \n\\cite{LTY15}) allows processes to have internal threads that share (and compete for) resources, \npossibly leading to races to due internal sharing. This is not allowed for in RC\\xspace; if you \nlike, this follows Go's slogan ``do not communicate by sharing memory; instead, share memory by \ncommunicating'' \\cite{effective-go}.\n\n\\section{Choreography Model}\n\\label{sec:chor-model}\n\n\\paragraph{Syntax}\nAn RC\\xspace program is a pair $\\langle \\mathcal{D},C\\rangle$, where $C$ is a choreography and \n$\\mathcal{D}$ is a set of (global) procedure definitions following the syntax displayed below.\nWe assume the Barendregt convention and work up to $\\alpha$-equivalence, renaming bound names \n(frame identifiers, process references, and procedure parameters) as needed.\n\\begin{align*}\n\t\\mathcal{D} \\Coloneqq {} &\n\t\t\\procdef{X}{\\vec{P}}{C}, \\mathcal{D} \\mid \\emptyset\n\t\\\\\n\tP \\Coloneqq {} &\n\t\t\\cframe{k}{\\pid p}{\\pid q}{T} \\mid \\pid p\\colon T \\mid f\\colon T\n\t\t\\mid l\n\t\\\\\n\tC \\Coloneqq {} &\n\t\t\\cbindin{N}{C} \\mid \n\t\tI\\keyword{;}\\, C\\mid \\keyword{0} \n\t\\\\\n\tI \\Coloneqq {} &\n\t\\clocal{\\pid p}{f} \\mid\n\t\t\\csend{k}{S} \\mid\n\t\t\\crecv{k}{R} \\mid\n\t\tX(\\vec{A})\\mid\n\t\t\\cond{E}{C}{C'} \\mid \n\t\\\\ \\mid {} & \\keyword{0} \n\t\\\\\n\tS \\Coloneqq {} &\n\t\t\\pid p.f \\mid \\pid p.\\pid r \\mid \\pid p.l\n\t\\\\\n\tR \\Coloneqq {} &\n\t\t\\pid q.f \\mid \\pid q\n\t\\\\\n\tE \\Coloneqq {} &\n\t\t\\pid p.f \\mid \\pid p.\\csent{k} \\mid \\pid q.\\creceived{k} \\mid \\pid q.\\creceivedlbl{k}{l}\n\t\\\\\n\tA \\Coloneqq {} &\n\t\tk \\tteq k' \\mid \\pid p\\tteq \\pid p' \\mid f \\tteq f' \\mid l \\tteq l'\n\t\\\\\n\tN \\Coloneqq {} &\n\t\t\\cstart{\\pid p}{\\pid q}{f} \\mid \n\t\t\\cframe{k}{\\pid p}{\\pid q}{T}\n\\end{align*}\nProcess names ($\\pid p$,$\\pid q$,$\\pid r$,\\dots) identify processes that execute concurrently. Each process has exclusive access to a private memory cell for storing values of a fixed type $T$ from a fixed set $\\mathcal{V}$ of datatypes (\\eg $\\type{Nat}$, $\\type{Char}$, $\\type{Bool}$). Values are manipulated only via functions (terms $f$) specified in a \\emph{guest language} which is intentionally left as a parameter of the model. \nFollowing practices established in previous choreography models \\cite{M13:phd,CM13,DGGLM17,CM17:forte} we assume that evaluation of internal computations is local and terminates. \nThe only further assumptions about the guest language of internal computations \nare that it comes with a typing discipline and that it supports boolean values (or an equivalent mechanism). Typing judgements will have the form $\\vdash f\\colon T \\to S$ and the type of boolean values will be denoted as $\\type{Bool}$.\nBesides values used by internal computations, processes can communicate process names and label selections (terms $\\pid p.\\pid r$, $\\pid p.l$).\nThese payloads are inaccessible to the guest language and hence are assigned (disjoint) types $\\type{PID}$ and $\\type{LBL}$ not in $\\mathcal{V}$. For exposition convenience, we define $\\type{VAL}$ as the super type of all datatypes used by the guest language \\ie we define the subtyping relation $\\subtype$ as the smallest partial order such that $T \\subtype \\type{VAL}$ for any $T \\in \\mathcal{V}$. We assume a constructor $\\mid$ making (disjoint) union types based on $\\type{VAL}$, $\\type{PID}$, and $\\type{LBL}$ (\\eg $\\type{PID} \\mid \\type{LBL}$) and extend the relation $\\subtype$ accordingly ($\\type{PID} \\subtype \\type{PID} \\mid \\type{LBL}$, $\\type{LBL} \\subtype \\type{PID} \\mid \\type{LBL}$, \\etc). Note that we do not require the guest language to come with subtyping or unions.\nWe write $t \\in T$ to express that $t$ inhabits $T$.\n\nChoreography declarations ($N$) introduce new processes and frames in their continuation ($C$).\nTerm $\\cnewframe{k}{\\pid p}{\\pid q}{T}{C}$ declares a communication from $\\pid p$ to $\\pid q$ where $T$ is the payload type and $k$ is the frame identifier to be used by the implementation of the communication.\nTerm $\\cnewproc{\\pid p}{\\pid q}{f}{C}$ declares a new process $\\pid q$ where $\\pid p$ is the process that spawns $\\pid q$ and $f$ is a function used by $\\pid p$ to compute the initial value for the memory cell of $\\pid q$.\nChoreography statements ($I$) can be local computations, communication actions, conditionals, calls; all statements have continuations. Term \n$\\clocal{\\pid p}{f}$ represents an internal computation where process $\\pid p$ evaluates the \nfunction $f$ against its memory cell and updates its content. Send and receive actions in \nthe implementation of $k$ are described by terms of the form $\\csend{k}{S}$ and $\\crecv{k}{R}$ \nwhere subterms $S$ and $R$ depend on the payload type.\nIn value exchanges, terms $\\csend{k}{\\pid p.f}$ and $\\crecv{k}{\\pid q.f'}$ read ``$\\pid p$ applies \n$f$ to the content of its memory cell and attempts to send the result on frame $k$'' and ``$\\pid q$ \nattempts to receive a value on frame $k$ and, if successful, applies $f'$ to its memory cell and \nthe received value'', respectively. (We assume functions in sends and receives to accept \nrespectively exactly one argument and exactly two arguments, where the first argument is the \nprocess memory content.)\nIn label selections, terms $\\csend{k}{\\pid p.l}$ and $\\crecv{k}{\\pid q}$ read\n``$\\pid p$ attempts to send the selection of label $l$ on frame $k$'' and ``$\\pid p$ attempts \nto receive a selection on frame $k$''. Selections are meant to propagate information regarding \ninternal choices and as such have no side effects on process memory or network knowledge. (As we \nwill discuss in \\cref{sec:synthesis}, this mechanism is crucial for synthesising correct \nimplementations of conditionals.)\nIn process exchanges, terms $\\csend{k}{\\pid p.\\pid r}$ and $\\crecv{k}{\\pid q.\\pid r}$ read ``$\\pid \np$ attempts to send $\\pid r$ on frame $k$'' and ``$\\pid q$ attempts to receive $\\pid r$ on frame \n$k$'', respectively. The only side effect of process exchanges is on network knowledge of the \nreceiver which may learn a new process reference. This is necessary since networks may grow during \nthe execution of choreographic programs as new processes are spawn. \nIn a conditional term $\\cond{E}{C_1}{C_2}$, a process evaluates the guard $E$ and chooses between \nthe possible continuations $C_1$ and $C_2$ accordingly. We explain the meaning of each kind of \nguard $E$ in the following.\n\\begin{itemize}\n\t\\item $\\pid p.f$: $\\pid p$ chooses $C_1$ if applying $f$ to its memory content yields \n$\\literal{true}$, and $C_2$ otherwise; \n\t\n\t\\item $\\pid p.\\csent{k}$: $\\pid p$ chooses $C_1$ if its last send attempt \nfor $k$ was successful, and $C_2$ otherwise; \n\t\n\t\\item $\\pid q.\\creceived{k}$: $\\pid q$ chooses $C_1$ if its last receive \nattempt for $k$ was successful, and $C_2$ otherwise;\n\t\n\t\\item $\\pid q.\\creceivedlbl{k}{l}$: $\\pid q$ chooses $C_1$ if it successfully\nreceived the label $l$ on $k$, and $C_2$ otherwise.\n\\end{itemize}\nTerm $X(\\vec{A})$ is a call of procedure $X$ with the set of named arguments $\\vec{A}$; these can \nbe frame identifiers, process names, or (names of) functions in the guest language.\nTerm $\\keyword{0}$ is the \\emph{no-op} statement, also used to represent terminated choreographies.\n\nProcedures are defined by terms $\\procdef{X}{\\vec{P}}{C}$ where $X$ is the procedure name, \n$\\vec{P}$ is a set of parameter declarations, and the program term $C$ is the procedure body. A term \n$f\\colon T\\to S$ in $\\vec{P}$ binds a function (name) in $C$ and specifies its type. A term \n$\\cframe{k}{\\pid p}{\\pid q}{T}$ in $\\vec{P}$ binds the frame identifier $k$ in $C$ and specifies its \ntype, sender and receiver. A term $\\pid p\\colon T$ in $\\vec{P}$ binds the process name $\\pid p$ in \n$C$ and specifies the type of its memory cell. \nA set of procedure definitions $\\mathcal{D}$ is well-formed provided that its procedure definitions \nhave unique names, all free names in their bodies are captured by their parameters, and all calls \nare to procedures in $\\mathcal{D}$.\n\nIn the sequel, we may omit $\\keyword{0}$, empty $\\keyword{else}$ branches, and use basic logical connectors in guards as syntactic sugar. \nFor instance, we write $\\scond{\\neg\\pid p.\\csent{k}}{C}$ as sugar for\n$\\cond{\\pid p.\\csent{k}}{\\keyword{0}}{C}$.\nIn procedure calls, we may omit assignments if formal and actual parameters have the same name and,\n\\eg, simply write $X(k)$ instead of $X(k \\tteq k)$.\n\n\\paragraph{Semantics}\nDynamics of RC\\xspace is specified by the reduction semantics defined in \\cref{fig:chor-semantics}. The \nsemantics is parameterised over global procedures $\\mathcal{D}$, and its states (hereafter \n\\emph{runtime configurations}) are quadruples $\\langle C,\\sigma,\\phi,G\\rangle$. We describe\nthe components of runtime configurations in the following.\n\\begin{description}\n\t\\item[$C$]\n\tThe first component is the current term program.\n\t\n\t\\item[$\\sigma$]\n\tThe second component, called \\emph{memory configuration}, keeps track of the memory cell of each process in the system, which is\n\taccessible to the guest language for performing internal computation.\n\tFormally, it is a partial map from process names to values, \\ie $\\sigma(\\pid p) = v$ denotes \n\tthat the memory cell of process $\\pid p$ stores value $v$.\n\t\t\n\t\\item[$\\phi$]\n\tThe third component, called (concrete) \\emph{frame dictionary}, is a partial map from frame \n\tnames to representations of their states.\n\tMore specifically, $\\phi(k) = \\phiframe{\\pid s}{u}{\\pid r}{u'}$ denotes that ``frame $k$ has \n\tstate $\\phiframe{\\pid s}{u}{\\pid r}{u'}$.\n\tThe processes $\\pid s$ and $\\pid r$ are, respectively, the (intended) sender and receiver of \n\t$k$. The elements $u$ and $u'$ represent the states of the communication stacks of sender and \n\treceiver for the specific frame, respectively. Formally, $u$ and $u'$ can be a payload (a \n\tvalue, a label, or a process name), $\\bot$ (the payload did not enter the stack), or a \n\tpayload flagged as ``removed from the stack'' (denoted by the decoration $\\checkmark$, as \n\tin $\\removed{v}$). Removal from the sender stack happens when the stack attempts at \n\ttransmitting the payload to the intended receiver, and \n\tremoval from the receiver stack means a successful delivery to the receiver's application.\n\t\n\t\\item[$G$]\n\tThe fourth component, called \\emph{connection graph}, is a directed graph with process names as \n\tnodes. A process neighbourhood represents the processes that it knows (and thus can communicate\n\twith).\n\\end{description}\nTogether, the last two components form a \\emph{network configuration}.\n\n\\begin{figure*}[t]\n\\begin{infrules}\n\t\\infrule[\\rname[C]{NP}][rule:c-new-proc]{\n\t\tf(\\sigma(\\pid p)) \\downarrow v\n\t\t\\and\n\t\tG' = G\\cup \\{\\pid p \\leftrightarrow \\pid q\\} \\cup \\{\\pid q \\rightarrow \\pid r \\mid G_1 \\vdash \\pid p \\rightarrow \\pid r \\}\n\t}{\n\t\t\\langle\\cnewproc{\\pid p}{\\pid q}{f}{C},\\sigma,\\phi, G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\n\t\t\\langle C,\\sigma(\\pid q)[v],\\phi, G'\\rangle\n\t}\n\t\\infrule[\\rname[C]{NF}][rule:c-new-frame]{\n\t}{\n\t\t\\langle\\cnewframe{k}{\\pid p}{\\pid q}{T}{C},\\sigma,\\phi, G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\n\t\t\\langle C,\\sigma,\\phi(k)[\\phiframe{\\pid p}{\\bot}{\\pid q}{\\bot}], G\\rangle\n\t}\n\n\t\\infrule[\\rname[C]{Int}][rule:c-int-comp]{\n\t\tf(\\sigma(\\pid p)) \\downarrow v\n\t}{\n\t\t\\langle\\clocal{\\pid p}{f}\\keyword{;}\\, C ,\\sigma,\\phi\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C,\\sigma(\\pid p)[v],\\phi, G\\rangle \n\t}\n\t\\infrule[\\rname[C]{Snd}][rule:c-send]{\n\t\ts(\\sigma(\\pid p)) \\downarrow u \\and\n\t\t\\phi(k) = \\phiframe{\\pid p}{\\_}{\\pid q}{\\_} \\and\n\t\tG \\vdash \\pid p \\to \\pid q \\and\n\t}{\n\t\t\\langle\\csend{k}{\\pid p.s}\\keyword{;}\\, C ,\\sigma,\\phi\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C,\\sigma,\\phi(k)_2[u], G\\rangle \n\t}\n\t\\infrule[\\rname[C]{SndFail}][rule:c-send-fail]{}{\n\t\t\\langle\\csend{k}{S}\\keyword{;}\\, C ,\\sigma,\\phi, G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C,\\sigma,\\phi, G\\rangle \n\t}\n\\\\\n\t\\infrule[\\rname[C]{Loss}][rule:c-loss]{\n\t\t\\phi(k) = \\phiframe{\\pid s}{u}{\\pid q}{\\_}\n\t\t\\and\n\t\tG \\vdash \\pid p \\leftrightarrow \\pid q\n\t}{\n\t\t\\langle C,\\sigma,\\phi\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C,\\sigma,\\phi(k)_2[\\removed{u}], G\\rangle \n\t}\n\t\\infrule[\\rname[C]{Comm}][rule:c-comm]{\n\t\t\\phi(k) = \\phiframe{\\pid p}{u}{\\pid q}{\\_}\n\t\t\\and\n\t\tu \\in \\mathcal{U}\n\t\t\\and\n\t\tG \\vdash \\pid p \\leftrightarrow \\pid q\n\t}{\n\t\t\\langle C,\\sigma,\\phi\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C,\\sigma,\\phi(k)[\\phiframe{\\pid p}{\\removed{u}}{\\pid q}{u}], G\\rangle \n\t}\n\t\\infrule[\\rname[C]{RcvV}][rule:c-recv-val]{\n\t\t\\phi(k) = \\phiframe{\\pid p}{\\_}{\\pid q}{u}\n\t\t\\and\n\t\tu \\in \\{v,\\removed{v}\\}\n\t\t\\and\n\t\tf(\\sigma(\\pid q),v) \\downarrow w\n\t}{\n\t\t\\langle \\crecv{k}{\\pid q.f}\\keyword{;}\\, C ,\\sigma,\\phi,G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C,\\sigma(\\pid q)[w],\\phi(k)_4[\\removed{w}], G\\rangle \n\t}\n\t\\infrule[\\rname[C]{RcvP}][rule:c-recv-pid]{\n\t\t\\phi(k) = \\phiframe{\\pid p}{\\_}{\\pid q}{u}\n\t\t\\and\n\t\tu \\in \\{\\pid r,\\removed{\\pid r}\\}\n\t}{\n\t\t\\langle \\crecv{k}{\\pid q}\\keyword{;}\\, C ,\\sigma,\\phi,G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C,\\sigma,\\phi(k)_4[\\removed{\\pid r}], G \\cup \\{\\pid q \\to \\pid r\\}\\rangle \n\t}\n\t\\infrule[\\rname[C]{RcvL}][rule:c-recv-lbl]{\n\t\t\\phi(k) = \\phiframe{\\pid p}{\\_}{\\pid q}{u}\n\t\t\\and\n\t\tu \\in \\{l,\\removed{l}\\}\n\t}{\n\t\t\\langle \\crecv{k}{\\pid q}\\keyword{;}\\, C ,\\sigma,\\phi,G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C,\\sigma,\\phi(k)_4[\\removed{l}], G\\rangle \n\t}\n\t\\infrule[\\rname[C]{RcvFail}][rule:c-recv-fail]{\n \t\t\\phi(k)_4 = \\bot\n\t}{\n\t\t\\langle\\crecv{k}{R}\\keyword{;}\\, C ,\\sigma,\\phi, G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C,\\sigma,\\phi, G\\rangle \n\t}\n\t\\infrule[\\rname[C]{IfSnt}][rule:c-if-sent]{\n\t\t\\phi(k)_1 = \\pid p \\and\n\t\t\\text{if } \\phi(k)_2 \\neq \\bot\n\t\t\\text{ then } j = 1\n\t\t\\text{ else } j = 2\n\t}{\n\t\t\\langle \\cond{\\pid p.\\csent{k}}{C_1}{C_2}\\keyword{;}\\, C ,\\sigma,\\phi,G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C_j\\keyword{;}\\, C,\\sigma,\\phi,G\\rangle \n\t}\n\t\\infrule[\\rname[C]{IfRcv}][rule:c-if-recv]{\n\t\t\\phi(k)_3 = \\pid q \\and\n\t\t\\text{if } \\phi(k)_4 \\in \\mathcal{U}^\\checkmark\n\t\t\\text{ then } j = 1\n\t\t\\text{ else } j = 2\n\t}{\n\t\t\\langle \\cond{\\pid q.\\creceived{k}}{C_1}{C_2}\\keyword{;}\\, C ,\\sigma,\\phi,G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C_j\\keyword{;}\\, C,\\sigma,\\phi,G\\rangle \n\t}\n\t\\infrule[\\rname[C]{IfLbl}][rule:c-if-lbl]{\n\t\t\t\\phi(k)_3 = \\pid q \\and\n\t\t\\text{if } \\sigma(k)_4 = \\removed{l}\n\t\t\\text{ then } j = 1\n\t\t\\text{ else } j = 2\n\t}{\n\t\t\\langle \\cond{\\pid q.\\creceivedlbl{k}{l}}{C_1}{C_2}\\keyword{;}\\, C ,\\sigma,\\phi,G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C_j\\keyword{;}\\, C,\\sigma,\\phi,G\\rangle \n\t}\n\t\\infrule[\\rname[C]{IfExp}][rule:c-if-exp]{\n\t\t\\text{if } f(\\sigma(\\pid p)) \\downarrow \\literal{true}\n\t\t\\text{ then } j = 1\n\t\t\\text{ else } j = 2\n\t}{\n\t\t\\langle \\cond{\\pid p.f}{C_1}{C_2}\\keyword{;}\\, C ,\\sigma,\\phi,G\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C_j\\keyword{;}\\, C,\\sigma,\\phi,G\\rangle \n\t}\n\t\n\t\\infrule[\\rname[C]{Str}][rule:c-struct]{\n\t\tC_1 \\precongr_{\\mathcal{D}} C_1'\n\t\t\\and\n\t\t\\langle C_1' ,\\sigma_1,\\phi_1,G_1\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C_2',\\sigma_2,\\phi_2,G_2\\rangle\n\t\t\\and\n\t\tC_2' \\precongr_{\\mathcal{D}} C_2\n\t}{\n\t\t\\langle C_1 ,\\sigma_1,\\phi_1,G_1\\rangle \n\t\t\\reducesto_{\\mathcal{D}}\t\t\n\t\t\\langle C_2,\\sigma_2,\\phi_2,G_2\\rangle \n\t}\n\n\t\\infrule[\\rname[C]{Unfold}][rule:c-unfold]{\n\t\tX(\\vec{P}) = C_2 \\in \\mathcal{D}\n\t\t\\qquad\n\t\t\\vec{P} = \\dom(\\vec{A})\n\t}{\n\t\tX(\\vec{A})\\keyword{;}\\, C_1\n\t\t\\precongr_{\\mathcal{D}}\t\n\t\tC_2[\\vec{A}]\\keyword{;}\\, C_1\n\t}\n\t\\infrule[\\rname[C]{Nil}][rule:c-nil]{}{\n\t\t\\keyword{0}\\keyword{;}\\, C \\precongr_{\\mathcal{D}}\tC\n\t}\n\t\\infrule[\\rname[C]{Swap}][rule:c-swap]{\n\t\tC_1\n\t\t\\dotrel{\\congr}\n\t\tC_2\n\t}{\n\t\tC_1\n\t\t\\congr_{\\mathcal{D}}\t\n\t\tC_2\n\t}\n\t\\infrule[\\rname[C]{I-I}][rule:c-i-i]{\n\t\t\\mathrm{pn}(I_1) \\cap \\mathrm{pn}(I_2) = \\emptyset\n\t}{\n\t\tI_1\\keyword{;}\\, I_2\n\t\t\\dotrel{\\congr}\n\t\tI_2\\keyword{;}\\, I_1\n\t}\n\t\\infrule[\\rname[C]{I-N}][rule:c-i-n]{\n\t\t\\mathrm{pn}(I) \\cap \\mathrm{pn}(N) = \\emptyset\t\n\t}{\n\t\tI\\keyword{;}\\, \\cbindin{N}{C}\n\t\t\\dotrel{\\congr}\n\t\t\\cbindin{N}{I \\keyword{;}\\, C}\n\t}\n\t\\infrule[\\rname[C]{N-N}][rule:c-n-n]{\n\t\t\\mathrm{pn}(N_1) \\cap \\mathrm{pn}(N_2) = \\emptyset\t\n\t}{\n\t\t\\cbindin{N_1}{\\cbindin{N_2}{C}}\n\t\t\\dotrel{\\congr}\n\t\t\\cbindin{N_2}{\\cbindin{N_1}{C}}\n\t}\n\t\\infrule[\\rname[C]{I-If}][rule:c-i-if]{\n\t\t\\mathrm{pn}(I) \\cap \\mathrm{pn}(E) = \\emptyset\n\t}{\n\t\t\\cond{E}{I\\keyword{;}\\, C_1}{I\\keyword{;}\\, C_2}\n\t\t\\dotrel{\\congr}\n\t\tI\\keyword{;}\\, \\cond{E}{C_1}{C_2}\n\t}\n\t\\infrule[\\rname[C]{If-I}][rule:c-if-i]{}{\n\t\t\\cond{E}\n\t\t{C_1\\keyword{;}\\, I}\n\t\t{C_2\\keyword{;}\\, I}\n\t\t\\dotrel{\\congr}\n\t\t\\cond{E}{C_1}{C_2}\n\t\t\\keyword{;}\\, I\n\t}\n\t\\infrule[\\rname[C]{N-If}][rule:c-n-if]{\n\t\t\t\\mathrm{pn}(N) \\cap \\mathrm{pn}(E) = \\emptyset\t\t\n\t}{\n\t\t\\cond{E}{\\cbindin{N}{C_1}}{\\cbindin{N}{C_2}}\n\t\t\\dotrel{\\congr}\n\t\t\\cbindin{N}{\\cond{E}{C_1}{C_2}}\n\t}\n\t\\infrule[\\rname[C]{If-If}][rule:c-if-if]{\n\t\t\\mathrm{pn}(E_1) \\cap \\mathrm{pn}(E_2) = \\emptyset\n\t}{\n\t\t\\begingroup\\def1.1{1.1}\n\t\t\\begin{array}{r}\n\t\t\t\\cond{E_1}\n\t\t\t\t{\\cond{E_2}{C_{1}^{1}}{C_{1}^{2}}\\\\\\!}\n\t\t\t\t{\\cond{E_2}{C_{2}^{1}}{C_{2}^{2}}}\n\t\t\\end{array}\n\t\t\\endgroup\n\t\t\\dotrel{\\congr}\n\t\t\\begingroup\\def1.1{1.1}\n\t\t\\begin{array}{r}\n\t\t\t\\cond{E_2}\n\t\t\t\t{\\cond{E_1}{C_{1}^{1}}{C_{2}^{1}}\\\\\\!}\n\t\t\t\t{\\cond{E_1}{C_{1}^{2}}{C_{2}^{2}}}\n\t\t\\end{array}\n\t\t\\endgroup\n\t}\n\\end{infrules}\n\t\\caption{Choreographic model, operational semantics}\n\t\\label{fig:chor-semantics}\n\\end{figure*}\n\n\nWe define some convenient notation for the definition of our semantics.\nLet $\\mathcal U$ be the set of all payloads, \\ie, $\\mathcal{U} = \\type{VAL} \\uplus \\type{LBL} \n\\uplus \\type{PID}$. A frame state $\\phiframe{\\pid s}{u}{\\pid r}{u'}$ is thus an element of\n$\\type{PID} \\times \\mathcal{U}{}_\\bot^\\checkmark\\times\\type{PID}\\times\\mathcal{U}{}_\\bot^\\checkmark$,\nwhere: \n$\\mathcal{U}{}_\\bot^\\checkmark = \n\\mathcal{U} \\uplus \\{\\bot\\} \\uplus \\mathcal{U}^\\checkmark$, with $\\mathcal{U}^\\checkmark \n= \\{\\removed{u} \\mid u \\in \\mathcal{U}\\}$ the set of payloads flagged as ``removed'' from the stack.\nAn expression $\\phi(k)_i$ denotes the $i$-th component of the frame state $\\phi(k)$. A judgement $G \n\\vdash \\pid p \\to \\pid q$ states that $G$ has the edge $\\pid p \\to \\pid q$.\nUpdates to $\\sigma$ and $\\phi$ are written using a square bracket notation, \nspecifically: $\\sigma(\\pid p)[v]$ is the function defined as $\\sigma$\neverywhere except $\\pid p$, which is mapped to $v$;\n$\\phi(k)[\\phiframe{\\pid s}{u}{\\pid r}{u'}]$ is as $\\phi$, but $k$ is now mapped to\n$\\phiframe{\\pid s}{u}{\\pid r}{u'}$; $\\phi(k)_2[u]$ changes the second element (the sender's \nside) of the frame state for $k$ to $u$; and, likewise, $\\phi(k)_4[u]$ changes the fourth element \n(the receiver's side) of the frame state for $k$ to $u$.\n\n\n\\begin{figure*}[t]\n\\centering\n\\begin{tikzpicture}[auto,yscale=1.4,xscale=3.5,\n\t\tstate\/.style={\n\t\t\trectangle split, \n\t\t\trectangle split horizontal, \n\t\t\trectangle split parts=2,\n\t\t\tdraw,\n\t\t\trounded corners=5pt,\n\t\t\touter sep=2pt,\n\t\t\tinner sep=2pt,\n\t\t\tfont=\\small,\n\t text height=1.3ex,\n\t text depth=.1ex,\n\t text centered,\n\t minimum height=1.8em,\n\t\t},\n\t\ttransition\/.style={\n\t\t\t>=open triangle 60,->,\n\t\t\trounded corners=5pt,\n\t\t},\n\t\ttransition loss\/.style={\n\t\t\ttransition,\n\t\t\tdashed,\n\t\t\tblue\n\t\t},\n\t\ttransition label\/.style={\n\t\t\tpos=.45,\n\t\t\tfont=\\footnotesize,\n\t\t}\n\t]\n\n\t\\newcounter{stc}\n\t\\def\\state(#1) at (#2) <#3|#4>{\n\t\t\\stepcounter{stc}\n\t\t\\node[state,\n\t\t\tlabel={[label distance=-3pt]110:\\color{white!40!black}\\footnotesize(\\alph{stc})}\n\t\t] (#1) at (#2) {\t\t\t\t\n\t\t\t\\code{#3}\n\t\t\t\\nodepart{two}\n\t\t\t\\ensuremath{#4}\n\t\t};\n\t}\n\n\t\\state (nA) at (1,3) <1,2,3,4|>\n\t\\state (nB) at (1,2) < 2,3,4|\\bot,\\bot>\n\t\\state (nC) at (2,2) < 3,4|v,\\bot>\n\t\\state (nD) at (2,3) < 3,4|\\removed{v},v>\n\t\\state (nE) at (3,3) < 4|\\removed{v},\\removed{v}>\n\t\\state (nF) at (1,1) < 2,4|\\bot,\\bot>\n\t\\state (nG) at (0,2) < 3,4|\\bot,\\bot>\n\t\\state (nH) at (0,1) < 4|\\bot,\\bot>\n\t\\state (nI) at (2,1) < 4|v,\\bot>\n\t\\state (nJ) at (3,2) < 3,4|\\removed{v},\\bot>\n\t\\state (nK) at (3,1) < 4|\\removed{v},\\bot>\n\t\\state (nL) at (2,0) < 4|\\removed{v},v>\n\t\t\t\t\t\n\t\\draw[transition] (nA) -- \n\t\tnode[transition label] {\\ref{rule:c-new-frame}} \n\t\t(nB);\n\t\\draw[transition] (nB) --\n\t\tnode[transition label] {\\ref{rule:c-send}} \n\t\t(nC);\n\t\\draw[transition] (nC) --\n\t\tnode[transition label,swap] {\\ref{rule:c-comm}} \n\t\t(nD);\n\t\\draw[transition] (nD) --\n\t\tnode[transition label] {\\ref{rule:c-recv-val}} \n\t\t(nE);\n\t\\draw[transition] (nB) -- \n\t\tnode[transition label] {\\ref{rule:c-recv-fail}} \n\t\t(nF);\n\t\\draw[transition] (nB) -- \n\t\tnode[transition label,swap] {\\ref{rule:c-send-fail}} \n\t\t(nG);\n\t\\draw[transition] (nF) -- \n\t\tnode[transition label,swap] {\\ref{rule:c-send-fail}} \n\t\t(nH);\n\t\\draw[transition] (nF) --\n\t\tnode[transition label] {\\ref{rule:c-send}} \n\t\t(nI);\n\t\\draw[transition] (nG) -- \n\t\tnode[transition label] {\\ref{rule:c-recv-fail}} \n\t\t(nH);\n\t\\draw[transition] (nC) -- \n\t\tnode[transition label] {\\ref{rule:c-recv-fail}} \n\t\t(nI);\n\t\\draw[transition loss] (nC) -- \n\t\tnode[transition label] {\\ref{rule:c-loss}} \n\t\t(nJ);\n\t\\draw[transition] (nI) --\n\t\tnode[transition label] {\\ref{rule:c-comm}} \n\t\t(nL);\n\t\\draw[transition loss] (nI) -- \n\t\tnode[transition label] {\\ref{rule:c-loss}} \n\t\t(nK);\n\t\\draw[transition] (nJ) -- \n\t\tnode[transition label] {\\ref{rule:c-recv-fail}} \n\t\t(nK);\n\\end{tikzpicture}\n\\caption{An end-to-end communication and its execution.}\n\\label{fig:com-execution}\n\\end{figure*}\n\n\nFor compactness, the presentation relies on the structural precongruence $\\precongr_\\mathcal{D}$ via the standard mechanism of \\cref{rule:c-struct}; the relation is defined as the smallest relation on choreographies closed under rules in \\cref{fig:chor-semantics} (discussed below) and under syntactic constructors of the language. \nHerein, $C \\congr_\\mathcal{D} C'$ is a shorthand \nfor $C \\precongr_\\mathcal{D} C'$ and $C' \\precongr_\\mathcal{D} C$.\nUnnecessary schematic variables are omitted and replaced by the wildcard $\\_$.\n\n\\Cref{rule:c-new-proc} describes the creation of a new process which inherits the network knowledge \nof its parent---as common in standard process models like, \\eg, the $\\pi$-calculus \\cite{MPW92}.\nThe expression $f(\\sigma(\\pid p)) \\downarrow v$ in the rule premises states that the evaluation of \n$f$ against the content of the memory of $\\pid p$ yields value $v$. \nIn the reactum the memory of $\\pid p$ is initialised to $v$ and the connection graph is updated to \ninclude: the mutual connection between $\\pid p$ and $\\pid q$; and a connection from $\\pid q$ to \neach process in the neighbourhood of $\\pid p$.\n\\Cref{rule:c-new-frame} models the creation of a new frame. In the reactum the frame is given \nstatus $\\phiframe{\\pid p}{\\bot}{\\pid q}{\\bot}$, meaning that neither the sender's or the \nreceiver's stacks contain the frame payload.\n\\Cref{rule:c-send,rule:c-send-fail} describe the execution of a send attempt for frame $k$. In the \nfirst case the sender computes a payload for $k$ and its stack accepts it, whereas in the second \ncase it rejects it (the send action failed). In both cases no information about the attempt is \npropagated to the receiver side. For conciseness, we extend the notation of function evaluation \n($f(\\sigma(\\pid p)) \\downarrow v$) to process names and labels by regarding them as constants---we \nsignal this abuse of the notation by writing $s$ and $u$ in place of $f$ and $v$, respectively.\nObserve that \\cref{rule:c-send} does not check the frame's state, meaning that a sender may \nperform multiple send actions resulting in the transmission of different payloads for the \nsame frame.\n\\Cref{rule:c-comm,rule:c-loss} model frame transmission and its non-deterministic outcome.\nOnly in the first case the payload reaches the receiver (successful transmission), but the sender \nhas no knowledge of this outcome since the effect on its stack is the same \n(both reacti set $\\phi(k)_2$ to $\\removed{u}$).\n\\Cref{rule:c-recv-val,rule:c-recv-pid,rule:c-recv-lbl,rule:c-recv-fail} define the execution of a \nreceive attempt for frame $k$. The first three rules model the delivery of different types of \npayloads (values, processes, and labels) and the fourth models failure due to any payload for $k$ \nnot having reached the receiver end yet. \\Cref{rule:c-unfold} unfolds procedure calls by replacing \nall occurrences of formal arguments in its body ($C_2$) with actual ones as prescribed by the \nsubstitution $\\vec{A}$ (provided its domain of definition coincides with the set $\\vec{P}$) and all \nnames bound in the procedure body $C_2$ with fresh ones as per Barendregt's convention.\n\\Cref{rule:c-i-i,rule:c-i-n,rule:c-n-n,rule:c-i-if,rule:c-if-i,rule:c-n-if,rule:c-if-if} model the dynamic \nrescheduling of non-interfering operations, like communications involving different processes, \nwhere $\\mathrm{pn}(C)$ is the set of process names in $C$. We omit the symmetric rules for \nconditionals, for swapping statements in\/from the continuation of conditionals.\n\n\\paragraph{Failure Model}\nWe can now formalise our two settings described in \\cref{sec:failure-model}.\nSpecifically, in the remainder: whenever we discuss \\cref{fm:reliable} (Reliable Transmission), we \nrefer to RC\\xspace without \\cref{rule:c-loss} (without that rule, messages cannot be lost in \nthe transport phase); whenever we discuss \\cref{fm:unreliable} (Unreliable Transmission), we use \nfull RC\\xspace (\\cref{rule:c-loss} is included).\nWe illustrate the (formal) difference between the two settings with an end-to-end communication \nexample.\nConsider the program term below.\n\\begin{snippet}\n\t\\cnewframe{k}{\\pid s}{\\pid r}{T}{\\\\\n\t\\indent \\csend{k}{\\pid s.f}\\keyword{;}\\, \\\\\n\t\\indent \\crecv{k}{\\pid r.g}\\keyword{;}\\, \\\\\n\t\\indent \\keyword{0}}\n\\end{snippet}\nAssume w.l.o.g.~that $v$ is the payload computed using $f$, that $f$, $g$, $v$, and memory cells of \nsender and receiver have the correct types, and that $\\pid s$ and $\\pid r$ are \nconnected.\nThe transition system in \\cref{fig:com-execution} depicts all possible executions for this program.\nIn \\cref{fm:reliable}, the dashed edges are not included since they require \\cref{rule:c-loss}. In \n\\cref{fm:unreliable}, instead, all transitions are possible (even the dashed ones).\nStates represent runtime configurations and edges reductions.\nFor exposition convenience states are assigned letters from (a) to (l) and only a subset of the data forming a runtime configuration is included:\nprogram terms are represented by the list of lines from the program above (left half of each state);\nprocess memory cells are omitted;\nonly stacks of $\\pid s$ and $\\pid r$ for $k$ are included (right half of each state);\nconnection graphs are omitted.\nEdges are labelled with the names of the reduction rules used to derive them; rules about structural precongruence are omitted---the reduction from (b) to (f) is the only one that requires also \\cref{rule:c-struct}.\n\nThe program execution begins in state (a) and every run reaches a configuration with program term \n$\\keyword{0}$, \\ie, every execution eventually terminates---there are configurations like (i) where $\\keyword{0}$ \nmay still admit some reductions but these are derivable only using \\cref{rule:c-comm,rule:c-loss} and eventually reach a configuration that cannot be further reduced.\nWe describe the different situations after executing all terms.\n\\begin{description}\n\\item[(e)] This is the only configuration where $v$ is marked as delivered and there is exactly one path from (a) to (e) that is the only chain of events without failures.\n\n\\item[(h)] This configuration is reached only if both the send and receive fail. There are two paths to (h), one passing through (g) and one through (f).\nIn the former the failure at the receiver side is consequential to the failure at the sender side \nwhereas in the second the two failures are independent. In fact, (f) may also reduce to (i).\n\n\\item[(i)] This configuration is reached only if the send succeeds and the receive fails regardless \nof the relative ordering of such events. Although the program has been reduced to $\\keyword{0}$, (i) \nadmits further reductions modelling a transmission attempt by the sender stack.\n\n\\item[(l)] Considerations made for (i) apply (l) too since it is reachable only from (i) and via a reduction modelling actions performed by the communication stack.\n\\end{description}\nOnly if we assume \\cref{fm:unreliable} (\\ie we include \\cref{rule:c-loss}) then, (j) and (k) become \nreachable. In particular, (k) configuration is reached only if the send succeeds, the receive fails, \nand the frame is lost during transmission. Similarly to (h), the last two events are consequential \nonly along the path through (j). Indeed (i) admits a reduction to a configuration unreachable from \n(j).\nIf we exclude any form of failure instead (\\ie \\cref{fm:reliable} and the additional assumption \nsend and receive operations may never fail) then, only configurations on the path to (e) remain \nreachable from (a)---as expected.\n\n\n\\section{Application Examples}\n\\label{sec:examples}\n\nConsider the procedure in the snippet below.\n\\begin{snippet}\n\t\\procdef{\\code{sendWhile}}{\n\t\t\\cframe{k}{\\pid s}{\\pid r}{T_k},\n\t\t\\pid s\\colon T_{\\pid s},\n\t\tf\\colon T_{\\pid s} \\to T_k,\n\t\tc\\colon T_{\\pid s} \\to T_{\\pid s},\n\t\tg\\colon T_{\\pid s} \\to \\type{Bool}\n\t}{\\\\\\indent\n\t\t\\scond{\\clocal{\\pid s}{g} \\land \\neg \\pid s.\\csent{k}}{\n\t\t\t\\\\\\indent\\indent\n\t\t\t\\csend{k}{\\clocal{\\pid s}{f}}\\keyword{;}\\,\n\t\t\t\\\\\\indent\\indent\n\t\t\t\\clocal{\\pid s}{c}\\keyword{;}\\,\n\t\t\t\\\\\\indent\\indent\n\t\t\t\\code{sendWhile}(k,\\pid s,f,c,g)\n\t\t\t\\\\\\indent\\mspace{-10.0mu}\n\t\t}\n\t}\n\\end{snippet}\nAbove, process $\\pid s$ tries to send a payload computed by $f$ for frame $k$ \nuntil its stack accepts it or the guard $g$ is falsified; $c$ is \n(locally) computed between attempts.\nWe can use \\code{sendWhile} to implement different recovery strategies, as other procedures:\n\\begin{itemize}\n\t\\item\n\ta procedure \\code{send} that never gives up sending until successful is implemented as a call \n\tto \\code{sendWhile} with a guard $g$ that is always true;\n\n\t\\item\n\ta procedure \\code{sendN} that gives up after $n$ attempts is implemented as a call to \n\t\\code{sendWhile} where $g$ and $c$ are used to test and increment a counter respectively;\n\n\t\\item\n\ta procedure \\code{sendT} that gives up after a timeout $t$ is implemented as a call to \n\t\\code{sendWhile} where $g$ tests a timer;\n\n\t\\item\n\tvariations of the above that use exponential backoff or (\\eg procedure\n\t\\code{sendExpBackoff} from \\cref{sec:intro}) are implemented by passing a delay computation \n\t(``sleep'') as $c$.\n\\end{itemize}\nProcedure \\code{recvWhile}, the analogue of \\code{sendWhile}, is likewise implemented: one just has \nto replace every frame operation with its dual.\n\n\\paragraph{Procedural Choreographies}\nAssume \\cref{fm:reliable}, and consider the procedure below.\n\\begin{snippet}\n\t\\procdef{\\code{com}}{\n\t\t\\pid s\\colon T_{\\pid s},\n\t\t\\pid r\\colon T_{\\pid r},\n\t\tf\\colon T_{\\pid s} \\to T_k,\n\t\tg\\colon T_{\\pid r}\\times T_{k} \\to T_{\\pid r}\n\t}{\\\\\\indent\n\t\t\\cnewframe{k}{\\pid s}{\\pid r}{T}{\n\t\t\\\\\\indent\n\t\t\\code{send}(k,\\pid s,f)\\keyword{;}\\,\n\t\t\\\\\\indent\n\t\t\\code{recv}(k,\\pid r,g)\n\t\t}\n\t}\n\\end{snippet}\nProcedure \\code{com} implements exactly-once delivery since \\code{send} terminates only if the \npayload is accepted by the sender's stack, which in turn ensures transmission, and finally \n\\code{recv} terminates only after the payload is delivered.\n\nAs a consequence, we can recover the language of Procedural Choreographies (PC) \\cite{CM17:forte}, \nwhich abstracts from communication failures, ``as a library''. For more evocative notation we \ndefine ${\\pid s.f \\ttTo \\pid r.g}$, ${\\pid s \n\\ttTo \\pid r[l]}$, and ${\\pid s.\\pid p \\ttTo \\pid r}$ as syntactic sugar for\n$\\code{com}(\\pid s,\\pid r,f,g)$ and its equivalent versions for label and process name \ncommunications, respectively.\nThen, translating PC programs into RC\\xspace is a matter of rewriting a\nfew symbols, \\eg, $\\pid s.f \\ttto \\pid r.f'$ in PC becomes ${\\pid s.f \\ttTo \\pid r.g}$ in RC\\xspace.\n\n\\paragraph{A search engine}\nWe now present a more sophisticated scenario, where a search process $\\pid s$ queries\nproviders $\\pid p_1,\\dots,\\pid p_m$ making a limited number of attempts.\nProcedures allow us to hide the request-response implementation and write\n\\begin{snippet*}\n\t\\code{reqRes}(\\pid s,\\pid p_1,req,\\overline{req},\\overline{resp},resp_1)\\keyword{;}\\,\n\t\\\\\\dots\\keyword{;}\\,\\\\\n\t\\code{reqRes}(\\pid s,\\pid p_m,req,\\overline{req},\\overline{resp},resp_m)\\keyword{;}\\,\n\\end{snippet*}\\looseness=-1\nwhere handling of internal representations and computations is delegated to functions $req$, $\\overline{req}$, $\\overline{resp}$, $resp_i$ written in the guest language.\nSince all queries are independent we can offload them to worker processes spawned by $\\pid s$ as shown in procedure $\\code{reqRes}$ below.\n\\begin{snippet}[\n\t\t\\def\\alignedcomment#1{%\n\t\t\t\\hfill\\quad%\n\t\t\n\t\t\t\\commentline{#1}%\n\t\t\n\t\t}\n\t]\n\t\\procdef{\\code{reqRes}}{\n\t\t\\pid s\\colon T_\\pid{s},\n\t\t\\pid p\\colon T_\\pid{p},\n\t\t\\\\\\indent\\indent\n\t\treq\\colon T_\\pid{s} \\to \\type{Maybe(Str)},\n\t\t\\overline{req}\\colon \\type{Maybe(Str)} \\to T_\\pid{p},\n\t\t\\\\\\indent\\indent\n\t\t\\overline{resp}\\colon T_\\pid{p} \\to \\type{Str},\n\t\tresp\\colon \\type{Maybe(Str)} \\to T_{\\pid s}\n\t}{\n\t\\\\\\indent\n\t\\cnewproc{\\pid s}{\\pid w}{req}{}\n\t\\alignedcomment{start a worker initialised with $req$}\n\t\\\\\\indent\t\n\t\\code{comPID}(\\pid s,\\pid p,\\pid w)\\keyword{;}\\,\n\t\\alignedcomment{introduce worker and provider}\n\t\\\\\\indent\t\n\t\\cnewframe{k_1}{\\pid w}{\\pid p}{\\type{Str}}{}\n\t\\alignedcomment{declare a frame for the request}\n\t\\\\\\indent\n\t\\code{sendN}(k_1,\\pid w,req)\\keyword{;}\\,\n\t\\alignedcomment{sends the query; $n$ attempts}\n\t\\\\\\indent\n\t\\code{recvT}(k_1,\\pid p,\\overline{req})\\keyword{;}\\,\n\t\\alignedcomment{receive the query but set a timeout}\n\t\\\\\\indent\n\t\\cnewframe{k_2}{\\pid p}{\\pid w}{\\type{Maybe(Str)}}{}\n\t\\alignedcomment{a frame for the response}\n\t\\\\\\indent\t\n\t\\cond{\\pid p.\\creceived{k_2}}{\n\t\\\\\\indent\\indent\n\t\t\\code{comN}(k_2,\\pid p,\\pid w,\\code{some}(resp),id)\\keyword{;}\\,\n\t\t\\alignedcomment{send response}\n\t\\\\\\indent\\mspace{-10.0mu}\n\t}{\n\t\\\\\\indent\\indent\n\t\t\\code{comN}(k_2,\\pid p,\\pid w,\\code{none},id)\\keyword{;}\\,\n\t\t\\alignedcomment{send empty response}\n\t\\\\\\indent\\mspace{-10.0mu}\n\t}\n\t\\\\\\indent\n\t\\code{com}(\\pid w,\\pid s,id,\\overline{resp})\\keyword{;}\\,\n\t\\alignedcomment{rely response}\n\t}\n\\end{snippet}\n\n\\paragraph{Best-effort strategies}\nAssume \\cref{fm:unreliable}.\nIn this setting, procedure \\code{com} is not robust any more, for \npayloads may now be lost during transmission, leaving the receiver looping forever.\n\nThis is a common problem in practice, which is addressed by switching to ``best-effort'' strategies where delivery is possible (to varying degrees) but not certain.\nBelow is a procedure that implements a simple communication protocol with capped retries and acknowledgements to the sender. In this, the strategy implemented by \\code{comACK} can be regarded as a simplification of that of TCP; four-phase handshakes or other protocols are implementable in RC\\xspace as well.\n\\begin{snippet}\n\t\\procdef{\\code{comACK}}{\n\t\t\\cframe{k}{\\pid s}{\\pid r}{T_k},\n\t\t\\cframe{k_{ack}}{\\pid r}{\\pid s}{\\type{Unit}},\n\t\t\\pid s\\colon T_{\\pid s},\n\t\t\\pid r\\colon T_{\\pid r},\n\t\tf\\colon T_{\\pid s} \\to T_k,\n\t\tg\\colon T_{\\pid r}\\times T_{k} \\to T_{\\pid r}\n\t}{\n\t\t\\\\\\indent\n\t\t\\code{sendT}(k,\\pid s,f)\\keyword{;}\\,\n\t\t\\\\\\indent\n\t\t\\code{recvT}(k, \\pid r,g)\\keyword{;}\\, \n\t\t\\\\\\indent\n\t\t\\code{sendT}(k_{ack}, \\pid r, \\literal{unit})\\keyword{;}\\,\n\t\t\\\\\\indent\n\t\t\\code{sendUntilACK}(k,k_{ack},\\pid s,f)\\keyword{;}\\,\n\t}\n\t\\\\\n\t\\procdef{\\code{sendUntilACK}}{\n\t\t\\cframe{k}{\\pid s}{\\pid r}{T_k},\n\t\t\\cframe{k_{ack}}{\\pid r}{\\pid s}{\\type{Unit}},\n\t\t\\pid s\\colon T_{\\pid s},\\\\\\indent\\indent\n\t\tf\\colon T_{\\pid s} \\to T_k\n\t}{\n\t\t\\\\\\indent\n\t\t\\scond{\\pid s.\\code{n > 0} \\land \\neg \\pid s.\\creceived{k}}{\n\t\t\t\\\\\\indent\\indent\n\t\t\t\\pid s.\\code{n-{}-}\n\t\t\t\\\\\\indent\\indent\n\t\t\t\\code{send}(k,\\pid s,f)\\keyword{;}\\,\n\t\t\t\\\\\\indent\\indent\n\t\t\t\\code{recv}(k_{ack},\\pid s,\\code{noop})\\keyword{;}\\,\n\t\t\t\\\\\\indent\\indent\n\t\t\t\\code{sendUntilACK}(k,k_{ack},\\pid s, f)\\keyword{;}\\,\n\t\t\t\\\\\\indent\\mspace{-10.0mu}\n\t\t}\n\t}\n\\end{snippet}\n\n\\paragraph{Compensations}\nWith \\code{comACK} we can also use RC\\xspace to develop a new variant of PC that does not assume reliable transmission, \\ie, for \\cref{fm:unreliable}. \nIn this setting, a common patter to deal with failures of best-effort communications are \n\\emph{compensations}. Fault compensations can be defined in RC\\xspace (for both \n\\cref{fm:reliable,fm:unreliable}) using conditionals, \\code{comACK} (or variations thereof), and \nsome syntax sugar to improve readability.\nAn expression ${\\pid s.f \\ttTo^{BE} \\pid r.f'}\\{C_{\\pid s}\\}\\{C_{\\pid r}\\}$ is a communication as \nin $\\code{comACK}(\\pid s,\\pid r,f,g)$ where choreographies $C_{\\pid s}$ and $C_{\\pid r}$ are \nexecuted as compensations for faults detected by the sender $\\pid s$ (no ack) or the receiver $\\pid \nr$, respectively.\nAn example of communications with fault compensations is the communication construct defined in \n\\cite{APN17} where communication operations specify default values as compensations; this construct \nis recovered in RC\\xspace using local computations as, \\eg, in ${\\pid s.f \\ttTo^{BE} \\pid r.f'}\\{\\pid \ns.\\literal{foo}\\}\\{\\pid r.\\literal{42}\\}$.\n\n\\paragraph{Any\/Many communications}\nWe can also implement more complex communication primitives, like those in \\cite{LNN16,CMP18}.\nBelow are procedures that iteratively attempt\nat sending some frames until the sender \nstack accepts all or any of them, respectively, using a round-robin strategy.\n\\begin{snippet}\n\t\\procdef{\\code{sendAll}}{\n\t\t\\pid s\\colon T_{\\pid s},\n\t\t\\cframe{k_1}{\\pid s}{\\pid r_1}{T},\n\t\t\\dots,\n\t\t\\cframe{k_n}{\\pid s}{\\pid r_n}{T},\n\t\t\\\\\\indent\\indent\n\t\tf\\colon T_{\\pid s} \\to T\n\t}{\\\\\\indent\n\t\t\\csend{k_n}{\\pid s.f}\\keyword{;}\\, \n\t\t\\\\\\indent\n\t\t\\cond{\\pid s.\\csent{k_n}}{\n\t\t\\\\\\indent\\indent\n\t\t\t\t\\code{sendAll}(\n\t\t\t\t\t\\pid s,\n\t\t\t\t\tk_1,\n\t\t\t\t\t\\dots,\n\t\t\t\t\tk_{n-1},\n\t\t\t\t\tf)\n\t\t\\\\\\indent\\mspace{-10.0mu}\n\t\t}{\n\t\t\\\\\\indent\\indent\n\t\t\t\\code{sendAll}(\\pid s,\n\t\t\t\tk_1 \\tteq k_2,\n\t\t\t\t\\dots,\n\t\t\t\tk_{n-1} \\tteq k_n,\n\t\t\t\tk_n \\tteq k_1,\n\t\t\t\tf)\n\t\t\\\\\\indent\\mspace{-10.0mu}\n\t\t}\n\t}\n\\end{snippet}\n\\begin{snippet}\n\t\\procdef{\\code{sendAny}}{\n\t\t\\pid s\\colon T_{\\pid s},\n\t\t\\cframe{k_1}{\\pid s}{\\pid r_1}{T},\n\t\t\\dots,\n\t\t\\cframe{k_n}{\\pid s}{\\pid r_n}{T},\n\t\t\\\\\\indent\\indent\n\t\tf\\colon T_{\\pid s} \\to T\n\t}{\\\\\\indent\n\t\t\\csend{k_1}{\\pid s.f}\\keyword{;}\\, \n\t\t\\\\\\indent\n\t\t\\scond{\\pid s.\\neg\\csent{k_1}}{\n\t\t\\\\\\indent\\indent\n\t\t\t\\code{sendAny}(\\pid s,\n\t\t\t\tk_1 \\tteq k_2,\n\t\t\t\t\\dots,\n\t\t\t\tk_{n-1} \\tteq k_n,\n\t\t\t\tk_n \\tteq k_1,\n\t\t\t\tf)\n\t\t\\\\\\indent\\mspace{-10.0mu}\n\t\t}\n\t}\n\\end{snippet}\nWe omit the dual procedures for receiving all or some frames, which are similarly defined.\nCombining these it is possible to implement scatter\/gather communication primitives from \n\\cite{LNN16}. For instance, below is an implementation of scatter.\n\\begin{snippet}\n\t\\procdef{\\code{scatterAll}}{\n\t\t\\pid s\\colon T_{\\pid s},\n\t\t\\pid r_1\\colon T_{\\pid r},\n\t\t\\dots,\n\t\t\\pid r_n\\colon T_{\\pid r},\n\t\tf\\colon T_{\\pid s} \\to T,\n\t\tg\\colon T_{\\pid r}\\times T_{k} \\to T_{\\pid r}\n\t}{\\\\\\indent\n\t\t\\cbindin{\\cframe{k_1}{\\pid s}{\\pid r_1}{T}\n\t\t\\dots\n\t\t\\cframe{k_n}{\\pid s}{\\pid r_2}{T}}{\n\t\t\\\\\\indent\n\t\t\\code{sendAll}(k_1,\\dots,k_n,f)\\keyword{;}\\,\n\t\t\\\\\\indent\n\t\t\\code{recv}(k,\\pid r_1,g)\\keyword{;}\\,\n\t\t\\dots\n\t\t\\code{recv}(k,\\pid r_n,g)\n\t\t}\n\t}\n\\end{snippet}\n\n\\begin{remark}\nFor clarity, we remark that RC\\xspace would require a version of the above procedures (\\eg \n\\code{send<$T_k$,$T_{\\pid s}$,$T_{\\pid r}$>}) for each signature used by the program at hand, since \nwe do not support type variables. Extending RC\\xspace with \nparametric polymorphism for procedures or an erasure step seems straightforward. \n\\end{remark}\n\n\\section{Typing, Progress, and Robustness}\n\\label{sec:typing}\n\nIt is easy to write programs in RC\\xspace that get stuck or have inconsistent communication implementations: parties may not be connected, payload types may not be respected, communication attempts may be mismatched. \nTo address these issues we introduce a typing discipline for RC\\xspace that checks that:\n\\begin{enumerate}\n\\item types of processes, functions, and procedures are respected;\n\\item processes that need to communicate are properly connected;\n\\item the delivery of frames is guaranteed to be at-most-once and best-effort;\n\\item there are no unnecessary checks on network actions (to avoid dead branches).\n\\end{enumerate}\nAdditionally, in \\cref{fm:reliable} exactly-once delivery is also checkable.\n\nChoreography programs can be regarded as ``network transformers''. Under this perspective, typing judgements are naturally of form\n\\[\n\t\\Gamma \\vdash \\langle \\mathcal{D}, C\\rangle \\colon \\mathcal{N} \\to \\mathcal{N}'\n\\]\nand read ``under the environment $\\Gamma$, running $\\langle\\mathcal{D},C\\rangle$ on a network configuration of type $\\mathcal{N}$ yields one of type $\\mathcal{N}'$''. \n\nTyping environments specify labels, procedures, and process names that may be used as well as their type; they are collections of the following form\n\\[\\Gamma \\Coloneqq\n\t\t\\Gamma, \\pid p\\colon T \\mid \n\t\t\\Gamma, l \\mid\n\t\t\\Gamma, \\cframe{k}{\\pid p}{\\pid q}{T} \\mid \n\t\t\\Gamma, X(\\vec{P})\\colon \\mathcal{N} \\to \\mathcal{N}' \\mid\n\t\t\\varnothing\n\\]\nwhere labels are unique, processes are assigned unique types in $\\mathcal{V}$, and procedure may be assigned multiple types.\nA type of network configurations $\\mathcal{N}$ is a pair $\\hnet{F}{G}$ formed by an abstract frame dictionary $F$ and a connection graph $G$. Abstract frame dictionaries specify possible states of frames while abstracting payload of a value type and any information non accessible to a program: a frame sender and receiver may only test its status using conditionals: a sender may only know whether its component is $\\bot$ and the receiver only whether its component is in $\\removed{\\mathcal{U}}$. \nFormally, abstract frames are collections of the form\n\\[\n\tF \\Coloneqq F, \\hframe[k]{U}{U'} \\mid \\emptyset\n\\]\nwhere $U,U' \\subseteq \\mathcal{U}_\\hbot$ for $\\mathcal{U}_\\hbot \\triangleq \\{\\hbot,\\bullet\\} \\uplus \\type{PID} \\uplus \\type{LBL}$.\nThe sender and receiver components of a frame status are abstracted by the function $\\alpha^s\\colon \\mathcal{U}_\\bot^\\checkmark \\to \\mathcal{U}_\\hbot$ and $\\alpha^r\\colon \\mathcal{U}_\\bot^\\checkmark \\to \\mathcal{U}_\\hbot$, respectively.\nThe first is given by the assignments \n\\[\n\t\\bot \\mapsto \\hbot\n\t\\qquad\n\tv,\\removed{v} \\mapsto \\bullet\n\t\\qquad\n\tl,\\removed{l} \\mapsto l\n\t\\qquad\n\t\\pid p,\\removed{\\pid p} \\mapsto \\pid p\n\\]\nand the second by\n\\[\n\t\\bot,v,l,\\pid p \\mapsto \\hbot\n\t\\qquad\n\t\\removed{v} \\mapsto \\bullet\n\t\\qquad\n\t\\removed{l} \\mapsto l\n\t\\qquad\n\t\\removed{\\pid p} \\mapsto \\pid p\n\\]\nwhere $v \\in \\type{VAL}$, $\\pid p \\in \\type{PID}$, and $l \\in \\type{LBL}$.\nConsider for instance a value exchange $k$, $\\hframe[k]{\\{\\bullet\\}}{\\{\\hbot,\\bullet\\}}$ is inhabited by any frame status of $k$, $\\hframe[k]{\\{\\hbot,\\bullet\\}}{\\{\\hbot\\}}$ by any frame status where the payload is not delivered to the receiver, and $\\hframe[k]{\\{\\bullet\\}}{\\{\\hbot,\\bullet\\}}$ by any frame status where the sender stack accepted the payload.\nA type of network $\\hnet{F}{G}$ is well-formed under $\\Gamma$ (written $\\Gamma \\vdash \\hnet{F}{G}$) if the following conditions are met:\n\\begin{enumerate}[label={\\em(\\alph{*})}]\n\t\\item \n\tif $\\Gamma \\vdash \\cframe{k}{\\pid p}{\\pid q}{T}$, then\n\t$G \\vdash \\pid p \\leftrightarrow \\pid q$,\n\t$\\hframe[k]{U}{U'} \\in F$, and payloads in $U$ and $U'$ are of type $T$;\n\t\n\t\\item \n\tif $\\Gamma \\vdash \\pid p\\colon T$, then $\\pid p \\in G$;\n\t\n\t\\item\n\tif $\\hframe[k]{U}{U'} \\in F$, then $\\Gamma \\vdash \\cframe{k}{\\pid p}{\\pid q}{T}$ for some $\\pid p$, $\\pid q$, $T$;\n\t\n\t\\item\n\tif $\\hframe[k]{U}{U'} \\in F$ and $\\pid r \\in U \\cup U'$ then $\\pid r \\in G$;\n\t\n\t\\item\n\tif $\\pid p \\in G$, then $\\Gamma \\vdash \\pid p\\colon T$ for some $T$.\n\\end{enumerate}\nHereafter network types are assumed well-formed whenever appearing in a judgement together with an environment.\nJudgements of (concrete) network configurations have form\n\\[\n\t\\Gamma \\vdash {\\phi,G} \\colon \\hnet{F}{G'}\n\\]\nand hold whenever the following conditions are met:\n\\begin{enumerate}[label={\\em(\\alph{*})}]\n\t\\item \n\tif $\\Gamma \\vdash \\cframe{k}{\\pid p}{\\pid q}{T}$ and $\\hframe[k]{U}{U'} \\in F$, then\n\t$\\phi(k) = \\phiframe{\\pid p}{u}{\\pid q}{u'}$,\n\t$\\alpha^s(u) \\in U$, $\\alpha^r(u') \\in U'$, and payloads are of type $T$;\n\n\t\\item\n\t$G \\subseteq G'$.\n\\end{enumerate}\n\n\\begin{figure*}\n\\begin{infrules}\n\t\\infrule[\\rname[T]{Weaken}][rule:t-weaken]{\n\t\t\\Gamma_1 \\vdash C \\colon \\hnet{F_1}{G_1} \\to \\hnet{F_2}{G_2}\n\t}{\n\t\t\\Gamma_0,\\Gamma_1 \\vdash C \\colon \n\t\t\\hnet{F_0,F_1}{G_0\\cup G_1}\t\\to\t\\hnet{F_0,F_2}{G_0\\cup G_2}\t\t\n\t}\n\n\t\\infrule[\\rname[T]{Tell}][rule:t-tell]{\n\t\t\\Gamma \\vdash \\cframe{k}{\\pid p}{\\pid q}{\\type{PID}} \\and\n\t\t\\Gamma \\vdash \\pid r\\colon T \\and\n\t\t\\Gamma \\vdash C \\colon \\hnet{F_0,\\hframe[k]{U}{\\{\\pid r\\}}}{\\{\\pid q \\to \\pid r\\} \\uplus G_0} \\to \\hnet{F_1}{G_1}\t\t\n\t}{\n\t\t\\Gamma \\vdash C \\colon \n\t\t\\hnet{F_0,\\hframe[k]{U}{\\{\\pid r\\}}}{G_0} \\to\t\\hnet{F_1}{G_1}\t\t\n\t}\n\n\t\\infrule[\\rname[T]{Swap}][rule:t-swap]{\n\t\tC_0 \\dotrel{\\congr} C_1 \\and\n\t\t\\Gamma \\vdash C_1 \\colon \\hnet{F_0}{G_0} \\to \\hnet{F_1}{G_1}\n\t}{\n\t\t\\Gamma \\vdash C_0 \\colon \n\t\t\\hnet{F_0}{G_0} \\to \\hnet{F_1}{G_1}\t\t\n\t}\n\n\t\n\t\\infrule[\\rname[T]{Int}][rule:t-local]{\n\t\t\\vdash f\\colon T \\to T \n\t}{\n\t\t\\pid p\\colon T \\vdash \\pid p.f \\colon\n\t\t\\hnet{\\varnothing}{\\varnothing} \\to \\hnet{\\varnothing}{\\varnothing}\n\t}\n\t\n\t\\infrule[\\rname[T]{Nil}][rule:t-nil]{\n\t}{\n\t\t\\varnothing \\vdash \\keyword{0} \\colon \\hnet{\\varnothing}{\\varnothing}\\to\\hnet{\\varnothing}{\\varnothing}\n\t}\n\n\t\\infrule[\\rname[T]{;}][rule:t-conc]{\n\t\t\\Gamma \\vdash\tC_0 \\colon \\hnet{F_0}{G_0} \\to \\hnet{F_1}{G_1}\n\t\t\\and\n\t\t\\Gamma \\vdash\tC_1 \\colon \\hnet{F_1}{G_1} \\to \\hnet{F_2}{G_2}\n\t}{\n\t\t\\Gamma \\vdash C_0\\keyword{;}\\, C_1 \\colon \\hnet{F_0}{G_0}\t\\to \\hnet{F_2}{G_2}\n\t}\n\t\t\n\t\\infrule[\\rname[T]{NP}][rule:t-new-proc]{\n\t\tG_{\\pid q} = \\{\\pid p \\leftrightarrow \\pid q\\} \\cup \\{\\pid q \\rightarrow \\pid r \\mid G_1 \\vdash \\pid p \\rightarrow \\pid r \\}\n\t\t\\and\n\t\t\\vdash f \\colon T \\to T' \\and\n\t\t\\Gamma \\vdash \\pid p \\colon T \\and\n\t\t\\Gamma, \\pid q \\colon T' \\vdash C \\colon \n\t\t\\hnet{F_0}{G_0 \\cup G_{\\pid q} } \\to \n\t\t\\hnet{F_1}{G_1}\n\t}{\n\t\t\\Gamma \\vdash \\cbindin{\\cstart{\\pid p}{\\pid q}{f}}{C} \\colon\n\t\t\\hnet{F_0}{G_0} \\to \\hnet{F_1}{G_1\\setminus \\{\\pid q\\}}\n\t}\n\t\n\t\\infrule[\\rname[T]{NF}][rule:t-new-frame]{\n\t\tG_0 \\vdash \\pid p \\leftrightarrow \\pid q\n\t\t\\and\n\t\t\\Gamma, \\cframe{k}{\\pid p}{\\pid q}{T} \\vdash C \\colon \n\t\t\\hnet{F_0, \\hframe[k]{\\{\\hbot\\}}{\\{\\hbot\\}}}{G_0} \\to \n\t\t\\hnet{F_1, \\hframe[k]{\\_}{\\_}}{G_1}\n\t}{\n\t\t\\Gamma \\vdash \\cbindin{\\cframe{k}{\\pid p}{\\pid q}{T}}{C} \\colon\n\t\t\\hnet{F_0}{G_0} \\to\\hnet {F_1}{G_1}\n\t}\n\t\t\n\t\\infrule[\\rname[T]{Call}][rule:t-call]{\n\t\t\\Gamma \\vdash X(P_1,\\dots,P_n)\\colon \\hnet{F_0}{G_0} \\to \\hnet{F_1}{G_1}\n\t\t\\and\n\t\t\\Gamma \\vdash P_i[\\vec{A}] \\text{ for any } 1 \\leq i \\leq n \n\t}{\n\t\t\\Gamma \\vdash\n\t\tX(\\vec{A}) \\colon \n\t\t\\hnet{F_0[\\vec{A}]}{G_0[\\vec{A}]} \\to\n\t\t\\hnet{F_1[\\vec{A}]}{G_1[\\vec{A}]}\n\t}\n\n\t\\infrule[\\rname[T]{IfExp}][rule:t-if-exp]{\n\t\t\\Gamma \\vdash \\pid p\\colon T \\and\n\t\t\\vdash f\\colon T \\to \\type{Bool}\n\t\t\\and\n\t\t\\Gamma \\vdash C_1 \\colon\n\t\t\t\t\\hnet{F_0}{G_0} \\to \\hnet{F_1}{G_1}\n\t\t\\and\n\t\t\\Gamma \\vdash C_2 \\colon\n\t\t\t\t\\hnet{F_0}{G_0} \\to \\hnet{F_2}{G_2}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\cond{\\pid p.f}{C_1}{C_2} \\colon\n\t\t\\hnet{F_0}{G_0} \\to \\hnet{F_1 \\Ydown F_2}{G_1\\cap G_2}\n\t}\n\n\t\\infrule[\\rname[T]{IfSnd}][rule:t-if-send]{\n\t\t\\Gamma \\vdash \\cframe{k}{\\pid p}{\\pid q}{T} \n\t\t\\and\n\t\t\\Gamma \\vdash C_1 \\colon\n\t\t\t\t\\hnet{F_0,\\hframe[k]{\\{u\\}}{U}}{G_0} \\to \\hnet{F_1}{G_1}\n\t\t\\and\n\t\t\\Gamma \\vdash C_2 \\colon\n\t\t\t\t\\hnet{F_0,\\hframe[k]{\\{\\hbot\\}}{U}}{G_0} \\to \\hnet{F_2}{G_2}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\cond{\\pid p.\\csent{k}}{C_1}{C_2} \\colon\n\t\t\\hnet{F_0,\\hframe[k]{\\{\\hbot,u\\}}{U}}{G_0} \\to \\hnet{F_1 \\Ydown F_}{G_2\\cap G_2}\n\t}\n\n\t\\infrule[\\rname[T]{IfRcv}][rule:t-if-recv]{\n\t\t\\Gamma \\vdash \\cframe{k}{\\pid p}{\\pid q}{T} \n\t\t\\and\n\t\t\\Gamma \\vdash C_1 \\colon\n\t\t\t\t\\hnet{F_0,\\hframe[k]{U}{\\{u\\}}}{G_0} \\to \\hnet{F_1}{G_1}\n\t\t\\and\n\t\t\\Gamma \\vdash C_2 \\colon\n\t\t\t\t\\hnet{F_0,\\hframe[k]{U}{\\{\\hbot\\}}}{G_0} \\to \\hnet{F_2}{G_2}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\cond{\\pid q.\\creceived{k}}{C_1}{C_2} \\colon\n\t\t\\hnet{F_0,\\hframe[k]{U}{\\{\\hbot,u\\}}}{G_0} \\to \\hnet{F_1 \\Ydown F_2}{G_1\\cap G_2}\n\t}\n\n\t\\infrule[\\rname[T]{IfLBL}][rule:t-if-recv-lbl]{\n\t\t\\Gamma \\vdash \\cframe{k}{\\pid p}{\\pid q}{\\type{LBL}} \\and\n\t\t\\Gamma \\vdash l \n\t\t\\and\n\t\t\\Gamma \\vdash C_1 \\colon\n\t\t\t\t\\hnet{F_0,\\hframe[k]{U}{\\{l\\}}}{G_0} \\to \\hnet{F_1}{G_1}\n\t\t\\and\n\t\t\\Gamma \\vdash C_2 \\colon\n\t\t\t\t\\hnet{F_0,\\hframe[k]{U}{\\{\\hbot\\}}}{G_0} \\to \\hnet{F_1}{G_2}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\cond{\\pid q.\\creceivedlbl{k}{l}}{C_1}{C_2} \\colon\n\t\t\\hnet{F_0,\\hframe[k]{U}{\\{\\hbot,l\\}}}{G_0} \\to \\hnet{F_1 \\Ydown F_2}{G_1\\cap G_2}\n\t}\n\t\n\t\\infrule[\\rname[T]{RcvV}][rule:t-recv-val]{\n\t\t\\Gamma = \\cframe{k}{\\pid p}{\\pid q}{T}, \\pid q \\colon T'\n\t\t\\and\n\t\t\\vdash f\\colon T \\times T' \\to T'\n\t\t\\and\n\t\tU \\subseteq \\{\\hbot\\}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\crecv{k}{\\pid q.f} \\colon \n\t\t\\hnet{\\hframe[k]{U \\cup \\{\\bullet\\}}{\\{\\hbot\\}}}{\\varnothing} \\to \n\t\t\\hnet{\\hframe[k]{U \\cup \\{\\bullet\\}}{\\{\\hbot,\\bullet\\}}}{\\varnothing}\n\t}\n\n\t\\infrule[\\rname[T]{RcvP}][rule:t-recv-pid]{\n\t\t\\Gamma = \\cframe{k}{\\pid p}{\\pid q}{\\type{PID}},\\pid r \\colon T\n\t\t\\and\n\t\tU \\subseteq \\{\\hbot\\}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\crecv{k}{\\pid q} \\colon \n\t\t\\hnet{\\hframe[k]{U \\cup \\{\\pid r\\}}{\\{\\hbot\\}}}{\\varnothing} \\to \n\t\t\\hnet{\\hframe[k]{U \\cup \\{\\pid r\\}}{\\{\\hbot,\\pid r\\}}}{\\varnothing}\n\t}\n\n\t\\infrule[\\rname[T]{RcvL}][rule:t-recv-lbl]{\n\t\t\\Gamma = \\cframe{k}{\\pid p}{\\pid q}{\\type{LBL}}, l\n\t\t\\and\n\t\tU \\subseteq \\{\\hbot\\}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\crecv{k}{\\pid q.f} \\colon \n\t\t\\hnet{\\hframe[k]{U \\cup \\{l\\}}{\\{\\hbot\\}}}{\\varnothing} \\to \n\t\t\\hnet{\\hframe[k]{U \\cup \\{l\\}}{\\{\\hbot,l\\}}}{\\varnothing}\n\t}\n\\end{infrules}\n\t\\caption{Typing choreographies, shared rules}\n\t\\label{fig:chor-typing-shared}\n\\end{figure*}\n\n\\begin{figure*}\n\\begin{infrules}\t\n\t\\infrule[\\rname[R]{SndV}][rule:r-send-val]{\n\t\t\\Gamma = \\cframe{k}{\\pid p}{\\pid q}{T}, \\pid p \\colon T',\tf\\colon T' \\to T\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\csend{k}{\\pid p.f} \\colon \n\t\t\\hnet{\\hframe[k]{\\{\\hbot\\}}{\\{\\hbot\\}}}{\\varnothing} \\to \n\t\t\\hnet{\\hframe[k]{\\{\\hbot,\\bullet\\}}{\\{\\hbot\\}}}{\\varnothing}\n\t}\n\n\t\\infrule[\\rname[R]{SndP}][rule:r-send-pid]{\n\t\t\\Gamma = \\pid r \\colon T,\t\\cframe{k}{\\pid p}{\\pid q}{\\type{PID}}\n\t\t\\and\n\t\tG = \\{\\pid p \\rightarrow \\pid r\\}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\csend{k}{\\pid p.\\pid r} \\colon \n\t\t\\hnet{\\hframe[k]{\\{\\hbot\\}}{\\{\\hbot\\}}}{G} \\to \n\t\t\\hnet{\\hframe[k]{\\{\\hbot,\\pid r\\}}{\\{\\hbot\\}}}{G}\n\t}\n\t\n\t\\infrule[\\rname[R]{SndL}][rule:r-send-lbl]{\n\t\t\\Gamma = l, \\cframe{k}{\\pid p}{\\pid q}{\\type{LBL}}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\csend{k}{\\pid p.\\pid r} \\colon \n\t\t\\hnet{\\hframe[k]{\\{\\hbot\\}}{\\{\\hbot\\}}}{\\varnothing} \\to \n\t\t\\hnet{\\hframe[k]{\\{\\hbot,l\\}}{\\{\\hbot\\}}}{\\varnothing}\n\t}\n\\end{infrules}\n\t\\caption{Typing choreographies, rules for \\cref{fm:reliable}.}\n\t\\label{fig:chor-typing-reliable}\n\\end{figure*}\n\n\\begin{figure*}\n\\begin{infrules}\n\t\\infrule[\\rname[U]{SndV}][rule:u-send-val]{\n\t\tU \\subseteq \\{\\hbot,\\bullet\\} \\and\n\t\t\\Gamma = \\cframe{k}{\\pid p}{\\pid q}{T}, \\pid p \\colon T',\tf\\colon T' \\to T\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\csend{k}{\\pid p.f} \\colon \n\t\t\\hnet{\\hframe[k]{U}{\\{\\hbot\\}}}{\\varnothing} \\to \n\t\t\\hnet{\\hframe[k]{\\{\\hbot,\\bullet\\}}{\\{\\hbot\\}}}{\\varnothing}\n\t}\n\t\n\t\\infrule[\\rname[U]{SndP}][rule:u-send-pid]{\n\t\tU \\subseteq \\{\\hbot,\\pid r\\}\n\t\t\\and\n\t\t\\Gamma = \\pid r \\colon T,\t\\cframe{k}{\\pid p}{\\pid q}{\\type{PID}}\n\t\t\\and\n\t\tG = \\{\\pid p \\rightarrow \\pid r\\}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\csend{k}{\\pid p.\\pid r} \\colon \n\t\t\\hnet{\\hframe[k]{U}{\\{\\hbot\\}}}{G} \\to \n\t\t\\hnet{\\hframe[k]{\\{\\hbot,\\pid r\\}}{\\{\\hbot\\}}}{G}\n\t}\n\t\n\t\\infrule[\\rname[U]{SndL}][rule:u-send-lbl]{\n\t\tU \\subseteq \\{\\hbot,l\\}\n\t\t\\and\n\t\t\\Gamma = l, \\cframe{k}{\\pid p}{\\pid q}{\\type{LBL}}\n\t}{\n\t\t\\Gamma \\vdash \n\t\t\\csend{k}{\\pid p.\\pid r} \\colon \n\t\t\\hnet{\\hframe[k]{U}{\\{\\hbot\\}}}{\\varnothing} \\to \n\t\t\\hnet{\\hframe[k]{\\{\\hbot,l\\}}{\\{\\hbot\\}}}{\\varnothing}\n\t}\n\\end{infrules}\n\t\\caption{Typing choreographies, rules for \\cref{fm:unreliable}.}\n\t\\label{fig:chor-typing-unreliable}\n\\end{figure*}\n\nJudgements of choreography terms have form \n\\[\n\t\\Gamma \\vdash C \\colon \\hnet{F}{G} \\to \\hnet{F'}{G'}\n\\]\nand are derived using rules in \\cref{fig:chor-typing-shared} together with either rules in \\cref{fig:chor-typing-reliable} or \\cref{fig:chor-typing-unreliable} according to whether \\cref{fm:reliable} or \\cref{fm:reliable} is assumed.\n\n\\Cref{rule:t-recv-val,rule:t-recv-pid,rule:t-recv-lbl} specify that a receive operation takes any network where the sender will eventually produce a payload of the expected type, the receiver knows the sender, and the payload has not been consumed yet, and yields a network where the payload may be consumed.\nIn particular, choreographies where receives cannot be matched to sends (\\eg $\\cnewframe{k}{\\pid p}{\\pid q}{T}{\\crecv{k}{\\pid q.f}}$) or have consecutive receive operations (\\eg $\\crecv{k}{\\pid q.f}\\keyword{;}\\,\\crecv{k}{\\pid q.f}$) are rejected since delivery is either impossible or inconsistencies may arise (\\eg the second operation shadows a successful outcome of the first).\nGiven the same receive statement multiple typing can be derived for the same receive statement under the same environment, even once weakening is taken into account. Likewise for send operations (discussed below).\nFor instance, judgement \n$\\Gamma \\vdash \\crecv{k}{\\pid q.f} \\colon \\hnet{\\hframe[k]{\\{\\hbot,\\bullet\\}}{\\{\\hbot\\}}}{G} \\to \\hnet{\\hframe[k]{\\{\\hbot,\\bullet\\}}{\\{\\hbot,\\bullet\\}}}{G}$\nis derivable if and only if \n$\\Gamma \\vdash \\crecv{k}{\\pid q.f} \\colon \\hnet{\\hframe[k]{\\{\\bullet\\}}{\\{\\hbot\\}}}{G} \\to \\hnet{\\hframe[k]{\\{\\bullet\\}}{\\{\\hbot,\\bullet\\}}}{G}$ is derivable.\n\\Cref{rule:t-recv-pid} cannot update the connection graph since the outcome of a receive operation cannot be statically known; the update can only be done using \\cref{rule:t-tell} once the delivery is certain.\n\\Cref{rule:r-send-val,rule:r-send-pid,rule:r-send-lbl} and \n\\Cref{rule:u-send-val,rule:u-send-pid,rule:u-send-lbl} are intended for typing under the assumptions of \\cref{fm:reliable} and \\cref{fm:unreliable}, respectively.\nRules of the two groups are alike save for the requirements imposed on the sender stack: rules of both groups require that the frame is of the expected type, that the receiver is known, and that it may be so once the statement is executed, but only those from the former require that the stack has yet to accept a payload for the frame. \nThe more stringent set of rules forbids any send operation for a frame with a potentially accepted (hence transmitted) payload (\\eg $\\csend{k}{\\pid p.f}\\keyword{;}\\,\\csend{k}{\\pid p.f}$) since this is a programming error in \\cref{fm:reliable} but not in \\cref{fm:unreliable} where transmission is not guaranteed. In fact, when the stack does not guarantee transmission this has to be programmed at the application level \\eg by resending frames not acknowledged.\n\\Cref{rule:t-if-exp,rule:t-if-send,rule:t-if-recv,rule:t-if-recv-lbl} require branches to be live; graphs are intersected to remove connections created only in one branch; frame dictionaries are merged ($\\Ydown$) by pointwise union under the condition that whenever both branches specify a payload they agree on it \\ie whenever $U_1$ and $U_2$ are merged it must hold that the set $(U_1 \\cup U_2) \\cap (\\{\\bullet\\} \\uplus \\type{PID} \\uplus \\type{LBL})$ has at most one element.\n\\Cref{rule:t-call} requires that the substitution $\\vec{A}$ respects types of formal and actual parameters and that the call type is obtained applying $\\vec{A}$ to the selected procedure type---the discipline admits \\adhoc polymorphism.\n\\Cref{rule:t-swap} allow each step of a derivation to switch to any element in the (finite) equivalence class $[C]_{\\dotrel{\\congr}}$. As a direct consequence, $\\dotrel{\\congr}$ implies type equivalence \\ie types are unaffected by instruction scheduling---recursive calls are not unfold by $\\dotrel{\\congr}$.\nObserve that \\cref{rule:t-swap} is only required for typing choreographies that would be otherwise rejected like \\eg $\\crecv{k}{\\pid q.g}\\keyword{;}\\,\\csend{k}{\\pid p.f}$.\n\n\\begin{lemma}\nIf $\\Gamma \\vdash C\\colon \\mathcal{N} \\to \\mathcal{N'}$ has a derivation where \\ref{rule:t-swap} is used, then either:\n\\begin{itemize}\n\\item $\\Gamma \\vdash C\\colon \\mathcal{N} \\to \\mathcal{N'}$ has a derivation without \\cref{rule:t-swap} or \n\\item no judgement for $C$ has a derivation without \\cref{rule:t-swap}.\n\\end{itemize}\n\\end{lemma}\n\nTyping judgements for procedure definitions are derived using the rule\n\\begin{infrules}\n\t\\infrule[\\rname[T]{Proc}]{\n\t\t\\Gamma \\vdash X(\\vec{P}) \\colon \\hnet{F_1}{G_1} \\to \\hnet{F_2}{G_2}\n\t\t\\\\\n\t\t\\Gamma|_{\\mathcal{D}}, \\vec{P} \\vdash C \\colon \\hnet{F_1}{G_1} \\to \\hnet{F_2}{G_2}\n\t}{\n\t\t\\Gamma \\vdash \\procdef{X}{\\vec{P}}{C}\n\t}\n\\end{infrules}\nwhere $\\Gamma|_{\\mathcal{D}}$ is the restriction of $\\Gamma$ to procedures in $\\mathcal{D}$. This restriction guarantees every label, free process name, and free frame name in the procedure body $C$ is bound by a formal parameter.\nA procedure definition is is well-typed under $\\Gamma$ if $\\Gamma \\vdash \\procdef{X}{\\vec{P}}{C}$.\nA set of procedure definitions $\\mathcal{D}$ is well-typed under $\\Gamma$ (written $\\Gamma \\vdash \\mathcal{D}$) provided that all of its elements are well-typed.\n\nA memory configuration $\\sigma$ is well-typed under $\\Gamma$ (written $\\Gamma \\vdash \\sigma$) provided that $\\vdash \\sigma(\\pid p)\\colon T$ whenever $\\Gamma \\vdash \\pid p\\colon T$.\n\n\\begin{definition}[Well-typedness]\n\\label{def:well-typedness}\nFor $\\mathcal D$ a set of procedure definitions and $\\langle C, \\sigma, \\phi,G \\rangle$ a runtime \nconfiguration, $\\langle C, \\sigma, \\phi,G \\rangle$ is \\emph{well-typed} under $\\mathcal{D}$ if there exist $\\Gamma$, $F_1$, $G_1$, $F_2$, and $G_2$ such that \n$\\Gamma \\vdash \\mathcal D$,\n$\\Gamma \\vdash C\\colon \\hnet{F_1}{G_1} \\to \\hnet{F_2}{G_2}$,\n$\\Gamma \\vdash \\sigma$, and\n$\\Gamma \\vdash \\phi,G\\colon \\hnet{F_1}{G_1}$.\n\nA choreographic program $\\langle \\mathcal{D}, C\\rangle$ is \\emph{well-typed} if there exist $\\sigma$, \n$\\phi$ and $G$ s.t.~the runtime configuration $\\langle C,\\sigma,\\phi, G \\rangle$ is well-typed.\n\\end{definition}\n\nFor any configuration there is an environment and a finite set of pairs of network types that subsume any other typing judgement.\n\n\\begin{theorem}[Existence of minimal typing]\n\\label{thm:chor-minimal-type}\nLet $\\langle C, \\sigma, \\phi,G \\rangle$ be \\emph{well-typed} under $\\mathcal{D}$. There are $\\Gamma$, $(\\mathcal{N}_0 \\to \\mathcal{N}'_0),\\dots,(\\mathcal{N}_n \\to \\mathcal{N}'_n)$ with the property that\nwhenever $\\Gamma' \\vdash \\langle \\mathcal{D}, C, \\sigma, \\phi, G \\rangle \\colon {\\mathcal{N}} \\to {\\mathcal{N}'}$ there is $i \\leq n$ such that:\n\\[\n\t\\infer{\n\t\t\\Gamma' \\vdash \\langle \\mathcal{D}, C, \\sigma, \\phi, G \\rangle \\colon {\\mathcal{N}} \\to {\\mathcal{N}'}\n\t}{\n\t\t\\infer*{}{\n\t\t\\Gamma \\vdash \\langle \\mathcal{D}, C, \\sigma, \\phi, G \\rangle \\colon {\\mathcal{N}_i} \\to {\\mathcal{N}'_i}\n\t\t}\n\t}\n\t\\text{.}\n\\]\n\\end{theorem}\n\n\\begin{theorem}[Decidability of typing]\n\\label{thm:decidability}\nGiven a set of procedure definitions $\\mathcal D$ and a runtime configuration $\\langle C, \\sigma, \n\\phi,G \\rangle$, it is decidable whether $\\langle C, \\sigma, \\phi,G \\rangle$ is \\emph{well-typed} \nunder $\\mathcal{D}$.\nGiven a program $\\langle \\mathcal{D},C\\rangle$, it is decidable whether $\\langle \n\\mathcal{D},C\\rangle$ is well-typed.\n\\end{theorem}\n\n\\begin{proof}[Sketch]\nTyping for elements of the guest language is known by assumption.\nObserve that building derivations for typing judgements from \\cref{def:well-typedness} is completely mechanical:\nrule selection is deterministic save \\cref{rule:t-tell,rule:t-swap} which only introduce a finite \nnumber of cases and can be used a finite amount of times (\\ref{rule:t-tell} uses disjoint union and \n\\ref{rule:t-swap} cannot unfold calls). A heuristic is to delay uses of these rules until it is \nnecessary to infer new connections or consider scheduling alternatives in order to proceed. Hence, \nderivations can be built using straightforward non-deterministic case exploration. Furthermore, \nrules in \\cref{fig:chor-typing-shared,fig:chor-typing-reliable,fig:chor-typing-unreliable} can be \nused to construct network types.\nThe only nontrivial part is constructing typing environments but only a finite number of cases need \nto be checked since processes, frames, and labels that need to be part of the typing environment \nare inferred from free names in program terms and formal parameters.\nFinally, observe that the definition domain of memory configurations and concrete frame \ndictionaries is bounded by typing environments and network types, and that actual values in $\\sigma$ \nor $\\phi$ (the only possible source of infinity) are irrelevant provided that they are of the right \ntypes (which is checkable by the assumptions on the guest language).\n\\end{proof}\n\nTyping is preserved by all reductions.\n\\begin{theorem}[Type preservation]\n\t\\label{thm:type-preservation}\n\tIf $\\langle C,\\sigma,\\phi,G \\rangle$ is well-typed under $\\mathcal{D}$ and there is a reduction $\\langle C,\\sigma,\\phi,G\\rangle \\reducesto_{\\mathcal{D}} \\langle C',\\sigma',\\phi',G'\\rangle$, then the reductum $\\langle C',\\sigma',\\phi',G'\\rangle$ is well-typed under $\\mathcal{D}$.\n\\end{theorem}\n\nIn general, RC\\xspace programs may deadlock if \nthe types of functions, memory cells, or procedures are not respected.\nInstead, well-typed programs enjoy progress, \\ie, they either terminate or diverge.\n\\begin{theorem}[Progress]\n\t\\label{thm:progress}\n\tIf a runtime configuration $\\langle C,\\sigma,\\phi,G \\rangle$ is well-typed under $\\mathcal{D}$ then either\n\t\t $C \\preceq_{\\mathcal{D}} \\keyword{0}$ or\n\t\tthere are $C'$, $\\sigma'$, $\\phi'$, $G'$ such that $\\langle C,\\sigma,\\phi,G\\rangle \\reducesto_{\\mathcal{D}} \\langle C',\\sigma',\\phi',G'\\rangle$.\n\\end{theorem}\n\nFrames are never delivered (to the receiver application level) more than one time.\n\\begin{theorem}[At-most-once delivery]\n\\label{thm:chor-at-most-once}\nLet $\\langle C,\\sigma,\\phi,G \\rangle$ be well-typed under $\\mathcal{D}$.\nIf $C\\precongr_{\\mathcal{D}} \\crecv{k}{R}\\keyword{;}\\, C'$, then $\\phi(k)_4 \\notin \\removed{\\mathcal{U}}$.\n\\end{theorem}\n\nIn \\cref{fm:reliable}, typing identifies frames that are guaranteed to be delivered.\n\\begin{theorem}[At-least-once delivery]\n\\label{thm:chor-at-least-once}\nAssume \\cref{fm:reliable} and that\n$\\Gamma \\vdash \\langle \\mathcal{D}, C, \\sigma, \\phi, G \\rangle \\colon \\hnet{F_1}{G_1} \\to \\hnet{F_2}{G_2}$.\nFor $\\hframe[k]{U}{U'} \\in F_2$ such that $\\hbot \\notin U'$, \nif $\\langle C,\\sigma,\\phi,G \\rangle \\reducesto_{\\mathcal{D}}^\\ast \\langle C',\\sigma',\\phi',G' \\rangle$ and $k \\notin \\mathrm{fn}(C')$ then $\\phi'(k)_4 \\in \\removed{\\mathcal{U}}$.\n\\end{theorem}\n\nThere is always an execution where a given frame is delivered.\n\\begin{theorem}[Best-effort delivery]\n\\label{thm:chor-best-effort}\nAssume \\cref{fm:unreliable} and that $\\Gamma \\vdash \\langle \\mathcal{D}, C, \\sigma, \\phi, G \\rangle \\colon \\hnet{F_1}{G_1} \n\\to \\hnet{F_2}{G_2}$.\nFor any $\\hframe[k]{U}{U'} \\in F_2$ such that $\\hbot \\notin U$, \nif there is a sequence of reductions $\\langle C,\\sigma,\\phi,G \\rangle \\reducesto_{\\mathcal{D}}^\\ast \n\\langle \\crecv{k}{R}\\keyword{;}\\, C',\\sigma',\\phi',G' \\rangle$ such that $k \\notin \\mathrm{fn}(C')$, then \nthere exist $C''$, $\\sigma''$, $\\phi''$, $G''$ such that $\\langle \nC,\\sigma,\\phi,G \\rangle \\reducesto^\\ast \\langle \\crecv{k}{R}\\keyword{;}\\, C'',\\sigma'',\\phi'',G'' \\rangle$ and $\\phi''(k)_4 \\in \\mathcal{U}$.\n\\end{theorem}\n\nWe conclude this section pointing out a limitation of the type system and a possible future extension. Consider procedure \\code{sendAnyOfTwo} reported below.\n\\begin{snippet*}\n\t\\procdef{\\code{sendAnyOfwo}}{\n\t\t\\pid p \\colon T_{\\pid p},\n\t\t\\cframe{k_1}{\\pid p}{\\pid q_1}{T_k},\n\t\t\\cframe{k_2}{\\pid p}{\\pid q_2}{T_k},\n\t\t\t\t\\\\\\indent\\indent\n\t\tf \\colon T_{\\pid p} \\to T_K\n\t}{\\\\\\indent\n\t\t\\csend{k_1}{\\pid p.f}\\keyword{;}\\, \n\t\t\\\\\\indent\n\t\t\\scond{\\pid p.\\neg\\csent{k_1}}{\n\t\t\\\\\\indent\\indent\n\t\t\t\\code{sendAnyOfTwo}(\\pid p \\tteq \\pid p, k_1 \\tteq k_2,k_2 \\tteq k_1, f \\tteq f)\n\t\t\\\\\\indent\\mspace{-10.0mu}\n\t\t}\n\t}\n\\end{snippet*}\nThis procedure alternates attempts for $k_1$ and $k_2$ until exactly one is successful. However, \nthis property is not captured by its type:\n\\begin{multline*}\n\t\\code{sendAnyOfwo}(\n\t\t\\pid p \\colon T_{\\pid p},\n\t\t\\cframe{k_1}{\\pid p}{\\pid q_1}{T_k},\n\t\t\\cframe{k_2}{\\pid p}{\\pid q_2}{T_k}) \\colon\\\\\n\t\\hnet{\n\t\t\\hframe[k_1]{\\{\\hbot\\}}{\\{\\hbot\\}},\n\t\t\\hframe[k_2]{\\{\\hbot\\}}{\\{\\hbot\\}}\n\t}{G}\n\t\\to\\\\\n\t\\hnet{\n\t\t\\hframe[k_1]{\\{\\hbot,\\bullet\\}}{\\{\\hbot\\}},\n\t\t\\hframe[k_2]{\\{\\hbot,\\bullet\\}}{\\{\\hbot\\}}\n\t}{G}\n\\end{multline*}\nwhere $G = \\{\\pid p \\to \\pid q_1,\\pid p \\to \\pid q_2\\}$.\nIndeed, the type system is designed to verify single communications, not groups.\n\n\\section{Synthesis of implementations}\n\\label{sec:synthesis}\n\n\nIn this section we present an EndPoint Projection (EPP) procedure which compiles a choreography to a concurrent implementation represented in terms of a process calculus. This calculus assumes the same failure model assumed for the choreography model but foregoes global data like globally unique frame identifiers since these are unrealistic in distributed settings.\n\n\\subsection{Process model}\nThe target process model is an extension of Procedural Processes \\cite{CM17:forte} where send and receive operations may fail and exchanged messages are tagged with numeric identifiers. Differently from frame identifiers used at the choreography level, numeric ones are strictly local: \n\\begin{itemize}\n\\item each process maintains a counter for each known process (its neighbourhood in the choreography model);\n\\item frame declarations increment counters locally \\ie without synchronising with the other party (which may not even have a matching frame declaration);\n\\item frames are assigned the value held by the corresponding counter.\n\\end{itemize}\nNumeric frame identifiers may be regarded as sequence numbers. However, the model does not offer any mechanism for maintaining counters synchronised among connected processes nor can such mechanism be programmed since these counters are inaccessible. The only way to maintain synchrony is to write programs where frame declarations are carefully matched on each involved party.\n\n\\paragraph{Syntax} A network is a pair $\\langle \\mathcal{B}, N\\rangle$ where $\\mathcal{B}$ is a set of procedure definitions and $N$ is a parallel composition of processes and messages in transit. A process is written as $\\actor{\\pid p}{B}{\\sigma_\\pid p}{\\theta_\\pid p}$ where $\\pid p$ is its name, $\\sigma_{\\pid p}$ is its memory cell, and $\\theta_{\\pid p}$ is the memory reserved to the runtime for storing information about:\n\\begin{itemize}\n\t\\item open connections (known process, last frame index), \n\t\\item method requests (labels received), and\n\t\\item frame status (the last send\/receive operation succeeded).\n\\end{itemize}\nFormally, $\\theta$ is a function given as the combination of:\n\\begin{itemize}\n\\item \n$\\theta^{\\mathrm{fc}}\\colon \\type{PID} \\rightharpoonup \\type{FID}$, \n\\item\n$\\theta^{\\mathrm{lb}}\\colon \\type{PID} \\times \\type{FID} \\rightharpoonup \\type{LBL}$, and\n\\item\n$\\theta^{\\mathrm{fs}}\\colon \\type{PID} \\times \\type{FID} \\to \\type{Bool}$.\n\\end{itemize}\nWe will omit superscripts $\\mathrm{fc}$, $\\mathrm{lb}$, and $\\mathrm{fs}$ provided that the intended component is clear from the context.\nThe full syntax of the language for programming in this model is defined by the grammar below.\n\\begin{align*}\n\t\\mathcal{B} \\Coloneqq {} & \n\t\t\\procdef{X}{\\vec{P}}{B}, \\mathcal{B} \\mid \\varnothing\n\t\\\\\n\tN\t\\Coloneqq {} & \n\t\t\\actor{\\pid p}{B}{\\sigma_\\pid p}{\\theta_\\pid p} \\mid\n\t\tN \\mathrel{\\keyword{\\bfseries |}} N' \\mid \n\t\t\\keyword{0}\n\t\\\\\n\tB \\Coloneqq {} & \n\t\t\\astart{\\pid q}{f}{B'} \\keyword{;}\\, B \\mid\n\t\t\\acomdecl{\\pid p}{k} \\keyword{;}\\, B \\mid\n\t\t\\arecv{\\pid p}{k}{}\\keyword{;}\\, B\\mid\n\t\t\\\\ \\mid {} & \n\t\t\\arecv{\\pid p}{k}{R}\\keyword{;}\\, B\\mid\n\t\t\\asend{\\pid q}{k}{S}\\keyword{;}\\, B\\mid\n\t\t\\abranch{\\pid p}{k}{l_i\\keyword{:} B_i}[i \\in I] \\mid\n\t\tX(\\vec{P})\\keyword{;}\\, B \\mid\n\t\t\\keyword{0} \\mid\n\t\t\\\\ \\mid {} &\n\t\t\\cond{E}{B}{B'}\\keyword{;}\\, B''\n\t\\\\\n\tS \\Coloneqq {} & \n\t\tf \\mid \\pid r \\mid l\n\t\\\\\n\tR \\Coloneqq {} & \n\t\tf \\mid (\\pid r)\n\t\\\\\n\tE \\Coloneqq {} &\n\t\te \\mid \\adelivered{\\pid p}{k}\n\t\\\\\n\tP \\Coloneqq {} &\n\t\tk \\mid \\pid p\n\\end{align*}\nTerm $\\acomdecl{\\pid p}{k}$ describes a behaviour that creates a new frame for its continuation. Send actions for $k$ are described by terms $\\asend{\\pid q}{k}{f}$, $\\asend{\\pid q}{k}{\\pid r}$, $\\asend{\\pid q}{k}{l}$, and receive ones by $\\arecv{\\pid p}{k}{f}$, $\\arecv{\\pid p}{k}{(\\pid r)}$, $\\arecv{\\pid p}{k}{}$, for values, process names, or label exchanges, respectively. We remark that terms for receiving process names bind them in their continuation.\nTerm $\\abranch{\\pid p}{k}{l_i\\keyword{:} B_i}[i \\in I]$ describes a selection based on a label communicated as frame $k$, if any label $l_i$ has been successfully received then, the process proceeds with the corresponding behaviour $B_i$ otherwise it proceeds with the one labelled with $\\lbl{\\color{keyword} default}$. This label is reserved exclusively for this purpose and cannot be sent. If $I = \\emptyset$, then the term is simply discarded. Guards $\\adelivered{\\pid p}{k}$ state that the last communication action for frame $k$ with $\\pid p$ has been successfully completed. Remaining terms are standard.\n\n\nPrograms are written using frame names which are replaced by numeric identifiers assigned at runtime when frames are created.\nTerms where frame names ($k$) are replaced by numeric identifiers ($n$) are reserved to the runtime. Messages in transit from $\\pid p$ to $\\pid q$ are represented by ``bags'' \\ie terms (of sort $N$) like $\\abag{n}{\\pid p}{\\pid q}{M}$ where the subterm $M$ stands for the payload. \n\n\\begin{figure*}[t]\n\\begin{infrules}\n\t\\infrule[\\rname[P]{NP}][rule:p-new-proc]{\n\t\tf(\\sigma) \\downarrow v\n\t\t\\and\n\t\t\\theta_{\\pid p}' = \\theta_{\\pid p}[\\pid q \\mapsto 0]\n\t\t\\and\n\t\t\\theta_{\\pid q} = \\{\\pid p \\mapsto 0\\}\n\t}{\n\t\t\\actor{\\pid p}{\\astart{\\pid q}{f}{B'}\\keyword{;}\\, B}{\\sigma}{\\theta_{\\pid p}}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\actor{\\pid p}{B'}{\\sigma}{\\theta_{\\pid p}'}\n\t\t\\mathrel{\\keyword{\\bfseries |}}\n\t\t\\actor{\\pid q}{B}{v}{\\theta_{\\pid q}}\n\t}\n\t\\infrule[\\rname[P]{NF}][rule:p-new-frame]{\n\t\tn = \\mathrm{next}(\\theta(\\pid q)) \\and\n\t\t\\theta' = \\theta[\\pid q \\mapsto n, (\\pid q,n) \\mapsto \\bot]\n\t}{\n\t\t\\actor{\\pid p}{\\keyword{new}(\\pid q,k) \\keyword{;}\\, B[n\/k]}{\\sigma}{\\theta}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\actor{\\pid p}{B}{\\sigma}{\\theta'}\n\t}\n\t\\infrule[\\rname[P]{Int}][rule:p-comp]{\n\t\tf(\\sigma) \\downarrow v\n\t}{\n\t\t\\actor{\\pid p}{\\alocal{\\pid q}{f} \\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\actor{\\pid p}{B}{v}{\\theta}\n\t}\n\t\\infrule[\\rname[P]{Snd}][rule:p-send]{\n\t\ts(\\sigma) \\downarrow u \\and\n\t\t\\and\n\t\t\\theta' = \\theta[(\\pid q,n) \\mapsto \\top]\n\t}{\n\t\t\\actor{\\pid p}{\\asend{\\pid q}{n}{s} \\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\actor{\\pid p}{B}{\\sigma}{\\theta'}\n\t\t\\mathrel{\\keyword{\\bfseries |}}\n\t\t\\abag{n}{\\pid p}{\\pid q}{u}\n\t}\n\t\\infrule[\\rname[P]{SndFail}][rule:p-send-fail]{\n\t}{\n\t\t\\actor{\\pid p}{\\asend{\\pid q}{n}{s} \\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\actor{\\pid p}{B}{\\sigma}{\\theta}\n\t}\n\t\\infrule[\\rname[P]{Loss}][rule:p-loss]{\n\t}{\n\t\t\\abag{n}{\\pid p}{\\pid q}{u}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\keyword{0}\n\t}\n\t\\infrule[\\rname[P]{RcvV}][rule:p-recv-val]{\n\t\tf(\\sigma,v) \\downarrow w\n\t\t\\and\n\t\t\\theta' = \\theta[(\\pid p,n) \\mapsto \\top]\n\t}{\n\t\t\\actor{\\pid q}{\\arecv{\\pid p}{n}{f} \\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\mathrel{\\keyword{\\bfseries |}}\n\t\t\\abag{n}{\\pid p}{\\pid q}{v}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\actor{\\pid q}{B}{w}{\\theta'}\n\t}\n\t\\infrule[\\rname[P]{RcvP}][rule:p-recv-pid]{\n\t\t\\theta' = \\theta[\n\t\t\t(\\pid p,n) \\mapsto \\top,\n\t\t\t\\pid r \\mapsto 0\n\t\t]\n\t}{\n\t\t\\actor{\\pid q}{\\arecv{\\pid p}{n}{(\\pid s)} \\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\mathrel{\\keyword{\\bfseries |}}\n\t\t\\abag{n}{\\pid p}{\\pid q}{\\pid r}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\actor{\\pid q}{B[\\pid r \/ \\pid s]}{\\sigma}{\\theta'}\n\t}\n\t\\infrule[\\rname[P]{RcvL}][rule:p-recv.lbl]{\n\t\t\\theta' = \\theta[(\\pid p,n) \\mapsto \\top,(\\pid p,n) \\mapsto l]\n\t}{\n\t\t\\actor{\\pid q}{\\arecv{\\pid p}{n} \\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\mathrel{\\keyword{\\bfseries |}}\n\t\t\\abag{n}{\\pid p}{\\pid q}{l}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\actor{\\pid q}{B}{\\sigma}{\\theta'}\n\t}\n\t\\infrule[\\rname[P]{RcvFail}][rule:p-rcv-fail]{\n\t}{\n\t\t\\actor{\\pid q}{\\arecv{\\pid p}{n}{R} \\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\reducesto_{\\mathcal{B}}\n\t\t\\actor{\\pid q}{B}{\\sigma}{\\theta}\n\t}\n\t\\infrule[\\rname[P]{IfFrame}][rule:p-if-frame]{\n\t\t\\text{if } \\theta(\\pid q,n) = \\top\n\t\t\\text{ then } i = 1\n\t\t\\text{ else } i = 2\n\t}{\n\t\t\\actor{\\pid p}{\\cond{\\adelivered{\\pid q}{n}}{B_1}{B_2}\\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\reducesto_{\\mathcal{B}}\t\t\n\t\t\\actor{\\pid p}{B_i\\keyword{;}\\, B}{\\sigma}{\\theta}\n\t}\n\t\\infrule[\\rname[P]{IfExp}][rule:p-if-exp]{\n\t\t\\text{if } f(\\sigma) \\downarrow \\literal{true}\n\t\t\\text{ then } i = 1\n\t\t\\text{ else } i = 2\n\t}{\n\t\t\\actor{\\pid p}{\\cond{f}{B_1}{B_2}\\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\reducesto_{\\mathcal{B}}\t\t\n\t\t\\actor{\\pid p}{B_i\\keyword{;}\\, B}{\\sigma}{\\theta}\n\t}\n\t\\infrule[\\rname[P]{Branch}][rule:p-branch]{\n\t\t\\theta(\\pid p,n) = l_i\t\n\t\t\\and\n\t\t\\lbl{default} \\neq l_i\n\t}{\n\t\t\\actor{\\pid q}{\\abranch{\\pid p}{n}{l_i\\keyword{:} B_i}[i \\in I]\\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\reducesto_{\\mathcal{B}}\t\t\n\t\t\\actor{\\pid p}{B_i\\keyword{;}\\, B}{\\sigma}{\\theta}\n\t}\n\t\\infrule[\\rname[P]{BranchFail}][rule:p-branch-fail]{\n\t\t\\theta(\\pid p,n) = \\bot\n\t\t\\and\n\t\t\\lbl{default} = l_i\n\t}{\n\t\t\\actor{\\pid q}{\\abranch{\\pid p}{n}{l_i\\keyword{:} B_i}[i \\in I]\\keyword{;}\\, B}{\\sigma}{\\theta}\n\t\t\\reducesto_{\\mathcal{B}}\t\t\n\t\t\\actor{\\pid p}{B_i\\keyword{;}\\, B}{\\sigma}{\\theta}\n\t}\n\t\\infrule[\\rname[P]{Par}][rule:p-par]\n\t\t{N \\reducesto_{\\mathcal{B}} N'}\n\t\t{N \\mathrel{\\keyword{\\bfseries |}} M \\reducesto_{\\mathcal{B}} N' \\mathrel{\\keyword{\\bfseries |}} M}\t\t\n\t\\infrule[\\rname[P]{Str}][rule:p-str]\n\t\t{N \\precongr_{\\mathcal{B}} M \\qquad M \\reducesto_{\\mathcal{B}} M' \\qquad M' \\precongr_{\\mathcal{B}} N'}\n\t\t{N \\reducesto_{\\mathcal{B}} N'}\n\t\\infrule[\\rname[P]{NilBeh}][rule:p-nil-beh]{}{\n\t\t\\keyword{0}\\keyword{;}\\, B \\precongr_{\\mathcal{B}} B\n\t}\n\t\\infrule[\\rname[P]{NilProc}][rule:p-nil-proc]\n\t\t{}\n\t\t{\\actor{\\pid p}{\\keyword{0}}{\\sigma}{\\theta} \\precongr_{\\mathcal{B}} \\keyword{0}}\n\n\t\\infrule[\\rname[P]{NilRecv}][rule:p-nil-recv]\n\t\t{}\n\t\t{\\abag{n}{\\pid p}{\\pid q}{M} \\mathrel{\\keyword{\\bfseries |}} \\actor{\\pid q}{\\keyword{0}}{\\sigma}{\\theta} \\precongr_{\\mathcal{B}} \\actor{\\pid q}{\\keyword{0}}{\\sigma}{\\theta}}\n\t\\infrule[\\rname[P]{NilNet}][rule:p-nil-net]\n\t\t{}\n\t\t{\\keyword{0} \\mathrel{\\keyword{\\bfseries |}} N \\precongr_{\\mathcal{B}} N}\n\t\\infrule[\\rname[P]{Unfold}][rule:p-unfold]\n\t\t{\\procdef{X}{\\vec{P}}{B'} \\in D}\n\t\t{X(\\vec{A})\\keyword{;}\\, B \\precongr_{\\mathcal{B}} B'[\\vec{A}\/\\vec{P}]\\keyword{;}\\, B}\n\\end{infrules}\n\t\\caption{Process model, operational semantics}\n\t\\label{fig:proc-semantics}\n\\end{figure*}\n\n\\paragraph{Semantics} \nThe calculus semantics is given by the reduction relation on networks $\\reducesto_{\\mathcal{B}}$ in \\cref{fig:proc-semantics} and is parameterised in the set $\\mathcal{B}$ of procedure definitions. For compactness, the presentation relies on the structural precongruence $\\precongr_\\mathcal{B}$.\nAll rules follow the intuitive description of terms and their description will be omitted.\n\n\\begin{figure*}[t]\n\t\\begin{center}\n\t\\begin{autoflow}\t\n\t\t\\newcommand{\\afdbox}[1]{{$\\displaystyle#1$}\\endmath\\autoflow@AND\\math\\displaystyle}\n\n\t\t\\afdbox{\n\t\t\t\\epp{\\keyword{0}}[m][\\pid r] \n\t\t\t\\triangleq \\keyword{0}\n\t\t}\n\t\t\n\t\t\\afdbox{\n\t\t\t\\epp{\\keyword{0} \\keyword{;}\\, C}[m][\\pid r]\n\t\t\t\\triangleq \n\t\t\t\\keyword{0}\\keyword{;}\\,\\epp{C}[m][\\pid r]\n\t\t}\n\t\t\n\t\t\\afdbox{\n\t\t\t\\epp{\\cstart{\\pid p}{\\pid q}{f}{C}}[m][\\pid r] \n\t\t\t\\triangleq \n\t\t\t\\begin{cases}\n\t\t\t\t\\astart{\\pid q}{f}{\\epp{C}[m][\\pid q]}\\keyword{;}\\, \\epp{C}[m][\\pid r] & \\text{if } \\pid r = \\pid p\\\\\n\t\t\t\t\\epp{C}[m][\\pid r] & \\text{otherwise}\n\t\t\t\\end{cases}\n\t\t}\n\t\t\n\t\t\\afdbox{\n\t\t\t\\epp{X(\\vec{A})\\keyword{;}\\, C}[m][\\pid r] \n\t\t\t\\triangleq \n\t\t\t\\begin{cases}\n\t\t\t\tX_{\\pid p}(m(\\vec{A}\\setminus \\pid p\\tteq \\pid r))\\keyword{;}\\, \\epp{C}[m][\\pid r] & \\text{if } \n\t\t\t\t\\pid p\\tteq \\pid r \\in \\vec{A} \\text{ for some } \\pid p\\\\\n\t\t\t\t\\epp{C}[m][\\pid r] & \\text{otherwise}\n\t\t\t\\end{cases}\n\t\t}\n\t\t\n\t\t\\afdbox{\n\t\t\t\\epp{\\cnewframe{k}{\\pid p}{\\pid q}{T}{C}}[m][\\pid r] \n\t\t\t\\triangleq \n\t\t\t\\begin{cases}\n\t\t\t\t\\acomdecl{\\pid q}{k}\\keyword{;}\\, \\epp{C[k^{T,\\pid r,\\pid q}\/k]}[m[k \\mapsto k]][\\pid r] & \\text{if } \\pid r = \\pid p\\\\\n\t\t\t\t\\acomdecl{\\pid p}{k}\\keyword{;}\\, \\epp{C[k^{T,\\pid p,\\pid r}\/k]}[m[k \\mapsto k]][\\pid r] & \\text{if } \\pid r = \\pid q\\\\\n\t\t\t\t\\epp{C}[m][\\pid r] & \\text{otherwise}\n\t\t\t\\end{cases}\n\t\t}\n\t\t\n\t\t\\afdbox{\n\t\t\t\\epp{\\csend{k^{T,\\pid p,\\pid q}}{\\pid p.s}\\keyword{;}\\, C}[m][\\pid r] \n\t\t\t\\triangleq \n\t\t\t\\begin{cases}\n\t\t\t\t\\asend{\\pid q}{m(k)}{s}\\keyword{;}\\, \\epp{C}[m][\\pid r] & \\text{if } \\pid r = \\pid p\\\\\n\t\t\t\t\\epp{C}[m][\\pid r] & \\text{otherwise}\n\t\t\t\\end{cases}\n\t\t}\n\t\t\n\t\t\\afdbox{\n\t\t\t\\epp{\\crecv{k^{T,\\pid p,\\pid q}}{R}\\keyword{;}\\, C}[m][\\pid r] \n\t\t\t\\triangleq \n\t\t\t\\begin{cases}\n\t\t\t\t\\arecv{\\pid p}{m(k)}{f}\\keyword{;}\\, \\epp{C}[m][\\pid r] & \\text{if } R = \\pid r.f\\\\\n\t\t\t\t\\arecv{\\pid p}{m(k)}{(\\pid s)}\\keyword{;}\\, \\epp{C}[m][\\pid r] & \\text{if } R = \\pid r \\text{ and } T\\eqtype\\type{PID}\\\\\n\t\t\t\t\\arecv{\\pid p}{m(k)}\\keyword{;}\\, \\epp{C}[m][\\pid r] & \\text{if } R = \\pid r \\text{ and } T\\eqtype\\type{LBL}\\\\\n\t\t\t\t\\epp{C}[m][\\pid r] & \\text{otherwise}\n\t\t\t\\end{cases}\n\t\t}\n\t\t\t\t\n\t\t\\afdbox{\n\t\t\t\\epp{\\cond{E}{C_1}{C_2}\\keyword{;}\\, C}[m][\\pid r] \n\t\t\t\\triangleq \n\t\t\t\\begin{cases}\n\t\t\t\t\\cond{e}{\\epp{C_1}[m][\\pid r]}{\\epp{C_2}[m][\\pid r]}\\keyword{;}\\, \\epp{C}[m][\\pid r] & \n\t\t\t\t\t\\text{if } E = \\pid r.e\\\\\n\t\t\t\t\\cond{\\adelivered{\\pid q}{m(k)}}{\\epp{C_1}[m][\\pid r]}{\\epp{C_2}[m][\\pid r]}\\keyword{;}\\, \\epp{C}[m][\\pid r] & \n\t\t\t\t\t\\text{if } E = \\pid r.\\csent{k^{T,\\pid r,\\pid q}}\\\\\n\t\t\t\t\\cond{\\adelivered{\\pid p}{m(k)}}{\\epp{C_1}[m][\\pid r]}{\\epp{C_2}[m][\\pid r]}\\keyword{;}\\, \\epp{C}[m][\\pid r] & \n\t\t\t\t\t\\text{if } E = \\pid r.\\creceived{k^{T,\\pid p,\\pid r}}\\\\\n\t\t\t\t\\abranch{\\pid p}{m(k)}{l\\colon \\epp{C_1}[m][\\pid r],\\lbl{\\color{keyword} default}\\keyword{:} \\epp{C_2}[m][\\pid r]}\\keyword{;}\\, \\epp{C}[m][\\pid r] & \n\t\t\t\t\t\\text{if } E = \\pid r.\\creceivedlbl{k^{T,\\pid p,\\pid r}}{l}\\\\\n\t\t\t\t(\\epp{C_1}[m][\\pid r] \\merge \\epp{C_2}[m][\\pid r])\\keyword{;}\\, \\epp{C}[m][\\pid r] & \t\n\t\t\t\t\t\\text{otherwise}\n\t\t\t\\end{cases}\n\t\t}\n\n\t\\end{autoflow}\n\t\\end{center}\n\t\\caption{Behaviour projection.}\n\t\\label{fig:behaviour-projection}\n\\end{figure*}\n\n\\subsection{EndPoint Projection}\n\\label{sec:epp}\n\nRecall that at process level, active frames have numeric identifiers that are locally and independently generated by each process as soon as a frame is declared whereas at the choreography level frames have unique global identifiers. As a consequence, a coherent mapping from the former to the latter is needed in order to project choreographies with free frame names as in the case of running ones. By \\emph{frame mapping} we mean any mapping taking frame names to frame numbers or to themselves---there is no reason for assigning a numeric identifier or a different name to a bound frame name. \nWrite $\\phi|_{\\pid p}$ for the set\n$\n\t\\{k \\mid \\phi(k) = (\\pid q,\\pid r,t,u) \\text{ and } \\pid p \\in \\{\\pid q,\\pid r\\}\\}\n$\nof all frames in $\\phi$ to or from $\\pid p$.\nA frame mapping $m$ is said to be \\emph{compatible with $\\phi$} if for any $\\pid p$ that occurs in $\\phi$, $m$ assigns to frames in $\\phi|_{\\pid p}$ unique and sequential numbers \\ie:\n\\[\n\t\\{m(k) \\mid k \\in \\phi|_{\\pid p}\\} = \\{1,2,\\dots,\\left|\\,\\phi|_{\\pid p}\\,\\right|\\}\\text{.}\n\\]\nAny $\\phi$ admits a compatible mapping under the mild assumption that frame names can be totally ordered.\nConsider the choreography $C = \\csend{k}{\\pid p.f}\\keyword{;}\\, \\csend{k}{\\pid q.f'}$.\nIf $C$ is part of an execution (of a well-typed program) then, $k$ must occur in $\\phi$ and hence the projections of $\\pid p$ and $\\pid q$ must refer to this frame via a numeric identifier (\\cf \\cref{rule:p-new-frame}) and they must agree on it. However, since the process model does not offer any mechanism for processes to negotiate an agreement on their internal frame counters this property must be derived from the choreography level, hence the necessity of $m$. We remark that this situation is limited to free names only: programs in RC\\xspace are projected with $m = id$.\n\n\nGiven a choreography $C$ and a frame mapping $m$, the projected behaviour of process $\\pid p$ in $C$ is defined as $\\epp{C}[m][\\pid p]$ where $\\epp{-}[m][\\pid p]$ is the partial function defined by structural recursion in \\cref{fig:behaviour-projection}---for conciseness, each frame occurring in the choreography $C$ is pre-annotated with its senders and receivers.\nEach case in the definition follows the intuition of projecting, for each choreographic term, the local actions performed by the given process.\nFor instance, $\\csend{k}{\\pid p.f}$ is skipped during the projection of any process but $\\pid p$ for which case the send action $\\asend{\\pid p}{m(k)}{f}$ is produced. Cases for frame reception, procedure calls, frame and process creation, are similar. \nThe case for conditionals is more involved but follows a standard approach (see \\eg \\cite{BCDLDY08,LGMZ08,CHY12,CM16:facs,CM17:forte}). The (partial) merging operator $\\merge$ from \\cite{CHY12} is used to merge the behaviour of a process that does not know (yet) which branch has been chosen by the the process evaluating the guard. Intuitively, $B \\merge B'$ is isomorphic to $B$ and $B'$ up to branching, where branches of $B$ or $B'$ with distinct labels are also included. One proceeds homomorphically (\\eg $\\csend{k}{\\pid p.f}\\keyword{;}\\, B \\merge \\csend{k}{\\pid p.f}\\keyword{;}\\, B'$ is $\\csend{k}{\\pid p.f}\\keyword{;}\\, (B \\merge B')$) on all terms but branches which are handled defining the merge of\n$\n\t\\abranch{\\pid p}{k}{l_i \\keyword{:} B_i}[i \\in I]\\keyword{;}\\, B \n$ and $\n\t\\abranch{\\pid p}{k}{l_j \\keyword{:} B'_j}[j \\in J]\\keyword{;}\\, B'\n$\nas\n$\\abranch{\\pid p}{k}{l_h \\keyword{:} B''_h}[h \\in H]\\keyword{;}\\, (B \\merge B')$\nwhere $\\{l_h\\colon B''_h\\}_{h \\in H}$ is the union of\n$\\{l_i \\keyword{:} B_i\\}_{i \\in I\\setminus J}$, $\\{l_j \\keyword{:} B'_j\\}_{j \\in J\\setminus I}$, and $\\{l_g \\keyword{:} B_g \\merge B'_g\\}_{g \\in I\\cap J}$.\n\nProjection of procedure definitions follows the approach introduced by Procedural Choreographies \\cite{CM17:forte}. For $\\mathcal{D}$ a set of procedure definitions, its projection is defined as follows:\n\\[\n\t\\epp{\\mathcal{D}} \\triangleq \\bigcup_{\\procdef{X}{\\vec{P}}{C} \\in \\mathcal{D}}\n\t\t\t\\left\\{\n\t\t\t\t\\procdef{X_{\\pid p}}{\\vec{P}\\setminus \\pid p}{\\epp{C}[id][\\pid p]}\n\t\t\t\\,\\middle|\\,\n\t\t\t\t\\pid p: T \\in \\vec{P}\n\t\t\t\\right\\}\n\t\\text{.}\n\\]\nObserve that since a procedure $X$ may be called multiple times on any combination of its arguments (hence assigning to a process different r\\^oles at each call) it is necessary to project the behaviour of each possible process parameter in $\\vec{P}$ as the procedure $X_{\\pid r}$. Here typing is crucial otherwise processes may be called to play r\\^oles for which they lack the necessary connections.\n\nTo designate a network as the projection of a configuration $\\langle C,\\sigma,\\phi,G\\rangle$ it remains only to distribute the information contained in the global state $\\sigma$, $\\phi$, $G$.\nReserved memory for process $\\pid p$ ($\\theta_{\\pid p}$) in the process model is completely determined (up to frame numbering) by $\\phi$ and $G$ from the choreography level as these contain all data regarding processes known to $\\pid p$ and frames exchanged by $\\pid p$. \nSpecifically, $\\epp{\\phi,G}[m][\\pid p]$ is defined as the function $\\theta$ where\n\\begin{align*}\n\t\\theta^{\\mathrm{fc}}(\\pid q) \\triangleq{}&\n\t\\begin{cases}\n\t\t\\left|\\phi|_{\\pid p}\\right|\n\t\t & \\text{if } G \\vdash \\pid p \\to \\pid q \\\\\n\t\t\\bot & \\text{otherwise}\n\t\\end{cases}\n\t\\\\\n\t\\theta^{\\mathrm{ln}}(\\pid q,n) \\triangleq{}&\n\t\\begin{cases}\n\t\tl & \\text{if } \\exists k \\in m^{-1}(n) \\text{ s.t.~}\\phi(k) = \\phiframe{\\pid p}{\\_}{\\pid q}{\\removed{l}} \\\\\n\t\t\\bot & \\text{otherwise}\n\t\\end{cases}\n\t\\\\\n\t\\theta^{\\mathrm{fn}}(\\pid q,n) \\triangleq{}&\n\t\\begin{cases}\n\t\t\\top & \n\t\t\t\\mspace{-10mu}\\array{l}\n\t\t\t\\text{if } \\exists k \\in m^{-1}(n) \\text{ s.t. either } \n\t\t\t\\phi(k) = \\phiframe{\\pid p}{u}{\\pid q}{\\_} \\\\\n\t\t\t\\text{and } u \\neq \\bot \\text{ or } \n\t\t\t\\phi(k) = \\phiframe{\\pid q}{\\_}{\\pid p}{u'} \\text{ and } u' \\in \\removed{\\mathcal{U}}\n\t\t\t\\endarray\\mspace{-15mu}\\\\\n\t\t\\bot & \\text{otherwise}\n\t\\end{cases}\n\\end{align*}\nThe only information of $\\phi$ and $G$ that cannot be reconstructed from the distributed state of the processes in a network is that of frames in transit. To this end, a term $\\abag{m(k)}{\\pid p}{\\pid q}{u}$ is added to the network for each $\\phi(k) = \\phiframe{\\pid p}{\\removed{u}}{\\pid q}{\\bot}$.\nThe projection $\\epp{C,\\sigma,\\phi,G}[m]$ of $\\langle C,\\sigma,\\phi,G\\rangle$ \nis defined as the network:\n\\[\n\t\\prod_{\\phi(k) = \\phiframe{\\pid p}{\\removed{u}}{\\pid q}{\\bot}}\n\t\\abag{m(k)}{\\pid p}{\\pid q}{u}\n\t\\mathrel{\\keyword{\\bfseries |}}\n\t\\prod_{\\pid r \\in \\mathrm{pn}(C)} \\actor{\\pid p}{\\epp{C}[m][\\pid p]}{\\sigma(\\pid p)}{\\epp{\\phi,G}[m][\\pid p]}\n\\]\nwhere $m$ is any mapping compatible with $\\phi$.\nObserve that mappings are all equivalent up to $\\alpha$-conversion and that if $\\epp{C,\\sigma,\\phi,G}[m]$ is defined for some $\\sigma$, $\\phi$, and $G$ then $\\epp{C,\\sigma',\\phi',G'}[m']$ is defined for any $\\sigma'$, $\\phi'$, and $G'$. We say that $C$ is projectable whenever $\\epp{C,\\sigma,\\phi,G}[m]$ is defined for some $m$, $\\sigma$, $\\phi$, and $G$.\n\nThere is an operational correspondence between choreographies and their projections---up to the ``pruning'' relation $\\pruning$ (\\cite{CHY12,CM13}) that eliminates ``dead branches'' due to the merging operator $\\merge$ when they are no longer needed to follow the originating choreography. \n\n\\begin{theorem}[EPP]\n\t\\label{thm:epp}\n\tLet $\\langle \\mathcal{D},C\\rangle$ be a projectable program.\n\tFor any $\\langle C,\\sigma,\\phi,G \\rangle$ well-typed under $\\mathcal{D}$:\n\t\\begin{description}\n\t\t\\item[Compl.] \n\t\t\tIf $\\langle C,\\sigma,\\phi,G \\rangle \\reducesto_{\\mathcal{D}} \\langle C',\\sigma',\\phi',G' \\rangle$, \n\t\t\tthen there are mappings $m \\subseteq m'$ such that $|\\dom(m')\\setminus\\dom(m)| \\leq 1$ and\n\t\t\t$\\epp{C,\\sigma,\\phi,G}[m] \\reducesto_{\\epp{\\mathcal{D}}} N$\n\t\t\tfor some $N \\pruning \\epp{C',\\sigma',\\phi',G'}[m']$.\n\t\t\\item[Sound.]\n\t\t\tIf $\\epp{C,\\sigma,\\phi,G}[m] \\reducesto_{\\epp{\\mathcal{D}}} N$, \n\t\t\tthen there are $N'$,$C'$,$\\sigma'$,$\\phi'$,$G'$, and $m'$ s.t.~$N \\reducesto_{\\epp{\\mathcal{D}}}^\\ast N'$,\n\t\t\t$\\langle C,\\sigma,\\phi,G \\rangle \\reducesto_{\\mathcal{D}} \\langle C',\\sigma',\\phi',G' \\rangle$, \n\t\t\t$\\epp{C',\\sigma',\\phi',G'}[m'] \\pruning N'$,\n\t\t\t$|\\dom(m')\\setminus\\dom(m)| \\leq 1$, and\n\t\t\t$m \\subseteq m'$.\n\t\\end{description}\n\\end{theorem}\n\nIt follows from the operational correspondence in \\cref{thm:epp} that projected networks exhibit all relevant properties ensured by the typing disciplines introduced in \\cref{sec:typing}, namely: progress (\\cref{thm:progress}), at-most-once delivery (\\cref{thm:chor-at-least-once}), best-effort delivery (\\cref{thm:chor-best-effort}), and at-lest-once delivery (\\cref{thm:chor-at-least-once}).\n\n\\section{Conclusions}\n\\label{sec:concl}\n\nProgramming methodologies based on structured communications, like choreographies, have been \ninvestigated for a long time now \\cite{Aetal16,Hetal16}. This is the first paper that investigates \nhow this research line can be applied to the programming of robust distributed systems in the \nsetting of communication failures, bringing us one step nearer to improved reliability in concurrent \ncomputing in the future.\n\nWe believe that the results achieved in this paper unlock a very promising research direction. A \nnatural continuation is to consider different failure models that take into account node failures \nand explore adversaries models (\\eg to include message loss, duplication, or forging).\nNode failures and adversaries are crucial for reasoning about agreement problems in distributed systems. These problems are as challenging as common in real-world distributed programming. Results in this direction may advance the development of correct-by-construction agreement protocols and their implementations.\n\nAnother interesting direction is to explore quantitative properties of programs in RC\\xspace. To this end we plan to develop quantitative semantics for the RC\\xspace model. For instance, in a probabilistic settings, failures are characterised by probability distributions and properties like progress, at-most-once, and exactly-once delivery are formalised as almost-certain properties (their complement event has null measure). Then it is possible to reason about reliability assumptions on communication links \\eg to understand how a certain failure probability impacts our program. Another interesting property is the expected number of retransmissions. Estimates of this value allow to optimise failure-recovery strategies. Likewise, stochastic or timed semantics will enable models with explicit timeouts.\n\nThe typing disciplines introduced in this work ensure that well-typed distributed programs have at-most-once or exactly-once delivery guarantees. As pointed out at the end of \\cref{sec:typing}, these guarantees are limited to single communications but our approach can be reasonably extended to communication groups. This extension has immediate applications \\eg to the statical verification of replication protocols where an update is deemed successful only if it accepted by enough replicas.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\nIn this paper we study the following type of (markovian) backward\nstochastic differential equations with infinite horizon (that we\nshall call \\textit{ergodic} BSDEs or EBSDEs for short):\n\\begin{equation}\\label{EBSDE*}\nY^x_t=Y^x_T +\\int_t^T\\left[\\psi(X^x_\\sigma,Z^x_\\sigma)-\n\\lambda\\right]d\\sigma-\\int_T^T Z^x_\\sigma dW_\\sigma, \\quad 0\\le\nt\\le T <\\infty.\n\\end{equation}\nIn equation (\\ref{EBSDE*}) $X^x$ is the solution of a forward\nstochastic differential equation with\n values in a Banach space $E$\nstarting at $x$ and $(W_t)_{t\\geq 0}$ is a cylindrical Wiener\nprocess in a Hilbert space $\\Xi$.\n\n\nOur aim is to find a triple\n $(Y,Z,\\lambda)$, where $Y,Z$ are\n adapted processes taking values in\n $\\mathbb{R}$ and $\\Xi^*$ respectively and $\\lambda$\n is a real number. $\\psi:E\\times \\Xi^*\\to \\mathbb R$ is\n a given function.\nWe stress the fact that $\\lambda$ is part of the unknowns of\nequation (\\ref{EBSDE*}) and this is the reason why the above is a\nnew class of BSDEs.\n\n\n$ $\n\n\nIt is by now well known that BSDEs provide an efficient\nalternative tool to study optimal control problems, see, e.g.\n\\cite{peng93}, \\cite{ElKaMaz} or, in an infinite dimensional\nframework, \\cite{{FuTe1}}, \\cite{masiero}. But up to our best\nknowledge, there exists no work in which BSDE techniques are\napplied to\n optimal control problems with \\emph{ergodic} cost functionals that is\n functionals depending only on the asymptotic behavior of the state\n (see e.g. the cost defined in formula\n (\\ref{ergodic-cost*}) below).\n\n\n$ $\n\n\n\\noindent The purpose of the present paper is to show that\nbackward\n stochastic differential equations, in particular\n the class of EBSDEs mentioned above, are a very useful tool in the\n treatment of ergodic control problems as\n well, especially in an infinite dimensional framework.\n\n\n $ $\n\n\n \\noindent There is a fairly large amount of literature dealing by\n analytic techniques with optimal ergodic control problems\n for finite dimensional stochastic state\n equations.\n We just mention the basic papers by Bensoussan and Frehse\n \\cite{BeFr} and by Arisawa and Lions \\cite{ArLi} where the\nproblem is treated through the study of the corresponding\nHamilton-Jacobi-Bellman (HJB) equation (solutions are understood\n in a classical sense and in a viscosity sense,\nrespectively).\n\n\nConcerning the infinite dimensional case it is known that both\nclassical and viscosity notions of solutions are not so suitable\nconcepts. Maslowski and Goldys in \\cite{GoMa} employ a mild\nformulation of the Hamilton-Jacobi-Bellman equation in a\n Hilbertian framework (see \\cite{C} and references within for the\ncorresponding mild formulations in the standard cases). In\n\\cite{GoMa} the authors prove, by a fixed point argument that\nexploits the smoothing properties of the Ornstein-Uhlenbeck\nsemigroup corresponding to the state equation, existence and\nuniqueness of the solution of the stationary HJB equation for\ndiscounted infinite horizon costs. Then they pass to the limit, as\nthe discount goes to zero, to obtain a mild solution of the HJB\nequation for the ergodic problem (see also \\cite{duncans}). Such\ntechniques need to assume, beside natural condition on the\ndissipativity of the state equation, also non-degeneracy of the\nnoise and a limitation on the lipschitz constant (with respect to\nthe gradient variable) of the hamiltonian function. This last\ncondition carries a bound on the size of the control domain (see\n\\cite{FuTe-ell} for similar conditions in the infinite horizon\ncase).\n\n\n$ $\n\n\n\n\nThe introduction of EBSDEs allow us to treat Banach valued state\nequations with general monotone nonlinear term and possibly\ndegenerate noise. Non-degeneracy is replaced by a structure\ncondition as it usually happens in BSDEs approach, see, for\ninstance, \\cite{ElKaMaz}, \\cite{FuTe1}. Moreover the use of\n$L^{\\infty}$ estimates specific to infinite horizon backward\nstochastic differential equations (see \\cite{bh}, \\cite{royer},\n\\cite{HuTe}) allow us to eliminate conditions on the lipschitz\nconstant of the hamiltonian. On the other side we will only\nconsider bounded cost functionals.\n\n\n$ $\n\n\n To start being more precise we consider a forward equation\n$$dX_t^x=(AX_t^x+F(X_t^x))dt+G dW_t,\\qquad X_0=x$$\nwhere $X$ has values in a Banach space $E$, $F$ maps $E$ to $E $\nand $A$ generates a strongly continuous semigroup of contractions.\nAppropriate dissipativity assumptions on $A+F$ ensure the\nexponential decay of the difference between the trajectories\nstarting from different points $x,x'\\in E$.\n\n\nThen we introduce the class of strictly monotonic backward\nstochastic differential equations\n\\begin{equation}\\label{bsderoyer*}\n{Y}^{x,\\alpha}_t={Y}^{x,\\alpha}_T +\\int_t^T(\\psi(X^{x}_\\sigma,\nZ^{x,\\alpha}_\\sigma)-\\alpha Y^{x,\\alpha}_\\sigma)d\\sigma-\\int_t^T\nZ^{x,\\alpha}_\\sigma dW_\\sigma, \\quad 0\\le t\\le T <\\infty.\n\\end{equation}\nfor all $\\alpha>0$ (see \\cite{bh}, \\cite{royer} or \\cite{HuTe})\nwhere $\\psi: E\\times\\Xi^*\\rightarrow \\mathbb{R}$ is bounded in the\nfirst variable and Lipschitz in the second. By estimates based on\na Girsanov argument introduced in \\cite{bh} we obtain uniform\nestimates on $\\alpha{Y}^{x,\\alpha}$ and\n${Y}^{x,\\alpha}-{Y}^{x',\\alpha}$ that allow us to prove that,\nroughly speaking, $({Y}^{x,\\alpha}-{Y}^{0,\\alpha}_0,\n{Z}^{x,\\alpha}, \\alpha {Y}^{0,\\alpha}_0)$ converge to a solution\n$(Y^x,Z^x,\\lambda)$ of the EBSDE (\\ref{EBSDE*}), for all $x\\in E$.\nWe also show that $\\lambda$ is unique under very general\nconditions. On the contrary, in general we can not expect\nuniqueness of the solution to (\\ref{EBSDE*}), at least in the non\nmarkovian case. On the other side in the markovian case we show\nthat we can find a solution of (\\ref{EBSDE*}) with\n$Y^x_t=v(X^x_t)$ and $Z^x_t=\\zeta(X^x_t)$ where $v$ is Lipschitz\nand $v(0)=0$. Moreover $(v, \\zeta)$ are unique at least in a\nspecial case where $\\psi$ is the Hamiltonian of a control problem\nand the processes $X^x$ are recurrent (see Section \\ref{sec-uniq}\nwhere we adapt an argument from \\cite{GoMa}).\n\n\n$ $\n\n\nIf we further assume differentiability of $F$ and\n $\\psi$ (in the Gateaux sense) then $v$ is differentiable,\n moreover $\\zeta =\\nabla v G$\n and finally $(v,\\lambda)$ give a mild solution of the HJB equation\n \\begin{equation}\n\\mathcal{L}v(x)\n+\\psi\\left( x,\\nabla v(x) G\\right) = \\lambda, \\quad x\\in E, \\label{hjb*}%\n\\end{equation}\nwhere linear operator $\\mathcal{L}$ is formally defined by\n\\[\n\\mathcal{L}f\\left( x\\right) =\\frac{1}{2}Trace\\left(\nGG^{\\ast}\\nabla ^{2}f\\left( x\\right) \\right) +\\langle Ax,\\nabla\nf\\left( x\\right) \\rangle_{E,E^{\\ast}}+\\langle F\\left( x\\right)\n,\\nabla f\\left( x\\right) \\rangle_{E,E^{\\ast}}.\n\\]\nMoreover if the Kolmogorov semigroup satisfies the smoothing\nproperty in Definition \\ref{strongly-feller} and $F$ is genuinely\ndissipative (see Definition \\ref{gen-diss}) then $v$ is bounded.\n\n\n\n$ $\n\n\n\nThe above results are then applied to a control problem with cost\n\\begin{equation}\\label{ergodic-cost*}\nJ(x,u)=\\limsup_{T\\rightarrow\\infty}\\frac{1}{T}\\, \\mathbb E\\int_0^T\nL(X_s^x,u_s)ds,\n\\end{equation}\n where $u$ is an adapted process (an admissible control)\nwith values in a separable metric space $U$, and the state\nequation is a Banach valued evolution equation of the form\n$$dX_t^x=(AX_t^x+F(X_t^x))\\, dt+G(dW_t+R(u_t)\\,dt),$$\nwhere $R: U \\rightarrow \\Xi$ is bounded. It is clear that the\nabove functional depends only on the asymptotic behavior of the\ntrajectories of $X^x$. After appropriate formulation\n we prove that, setting $\\psi(x,z)= \\inf_{u\\in U} [L(x,u)+ zR(u)]$ in\n (\\ref{EBSDE*}), then $\\lambda$ is optimal, that is\n $$\\lambda=\\inf_{u}J(x,u)$$\n where the infimum is over all admissible controls.\n Moreover $Z$ allows to construct on optimal feedback in the\n sense that $$\\lambda=J(x,u) \\hbox{ if and only if } L(X_t^x,u_t)+Z_t\nR(u_t)=\\psi(X_t^x,Z_t).$$\n\n\n\nFinally, see Section \\ref{section-heat-eq}, we show that our\nassumptions allow us to treat ergodic optimal control problems for\na stochastic heat equation with polynomial nonlinearity and\nspace-time white noise. We notice that the Banach space setting is\nessential in order to treat nonlinear terms with superlinear\ngrowth in the state equation.\n\n\n\n\n$ $\n\n\n\n\n\n\n\nThe paper is organized as follows.\nAfter a section on notation, we introduce the forward SDE; in section 4 we\nstudy the ergodic BSDEs; in section 5 we show in\naddition the differentiability of the\nsolution assuming that the coefficient is Gateaux differentiable.\nIn section 6 we study the ergodic Hamilton-Jacobi-Bellman\nequation and we apply our result to optimal\nergodic control in section 7. Section 8 is devoted to\nshow the uniqueness of Markovian solution and the last section\ncontains application to the ergodic control of a nonlinear stochastic\nheat equation.\n\n\n\\section{Notation}\n Let $E,F$ be Banach spaces, $H$ a Hilbert space, all\nassumed to be defined over the real field and to be separable. The\nnorms and the scalar product will be denoted $|\\,\\cdot\\,|$,\n$\\langle\\,\\cdot\\,,\\,\\cdot\\,\\rangle$, with subscripts if needed.\nDuality between the dual space $E^*$ and\n $E$ is denoted $\\langle\\,\\cdot\\,,\\,\\cdot\\,\\rangle_{E^*,E}$.\n$L(E,F)$ is the space of linear bounded operators $E\\to F$, with\nthe operator norm.\nThe domain of a linear (unbounded) operator $A$ is denoted $D(A)$.\n\nGiven a bounded function\n$ \\phi: E\\rightarrow \\mathbb{R}$ we denote\n$\\Vert\\phi\\Vert_0=\\sup_{x\\in E}|\\phi(x)|$. If, in addition,\n$\\phi$ is also Lipschitz continuous then\n$\\Vert\\phi\\Vert_{\\hbox{lip}}=\\Vert\\phi\\Vert_0+\n\\sup_{x,x'\\in E,\\,x\\ne x'}|\\phi(x)-\\phi(x')||x-x'|^{-1}$.\n\n\nWe say that a function $F:E\\to F$ belongs to\nthe class ${\\cal G}^1(E,F)$ if it is continuous, has a Gateaux\ndifferential $\\nabla F(x)\\in L(E,F)$ at any point $x \\in E$, and\nfor every $k\\in E$ the mapping $x\\to \\nabla F(x) k$ is continuous\nfrom $E$ to $F$ (i.e. $x\\to \\nabla F(x) $ is continuous from $E$\nto $L(E,F)$ if the latter space is endowed the strong operator\ntopology). In connection with stochastic equations,\nthe space ${\\cal G}^1$ has been introduced in \\cite{FuTe1},\nto which we refer the reader for further properties.\n\n\n Given a probability space $\\left(\n\\Omega,\\mathcal{F},\\mathbb{P}\\right) $ with a filtration\n$({\\cal F}_t)_{t\\ge 0}$ we consider the following classes of\nstochastic processes with values in a real separable Banach space\n$K$.\n\n\\begin{enumerate}\n\\item\n$L^p_{\\mathcal{P}}(\\Omega,C([0,T],K))$, $p\\in [1,\\infty)$,\n$T>0$, is the space\nof predictable processes $Y$ with continuous paths\non $[0,T]$\nsuch that\n$$\n|Y|_{L^p_{\\mathcal{P}}(\\Omega,C([0,T],E))}^p\n= \\mathbb E\\, \\sup_{t\\in [0,T]}|Y_t|_K^p<\\infty.\n$$\n\\item\n$L^p_{\\mathcal{P}}(\\Omega,L^2([0,T];K))$, $p\\in [1,\\infty)$,\n$T>0$, is the space\nof predictable processes $Y$ on $[0,T]$ such that\n$$\n|Y|^p_{L^p_{\\mathcal{P}}(\\Omega,L^2([0,T];K))}=\n\\mathbb E\\,\\left( \\int_{0}^{T}|Y_t|_K^2\\,dt\\right)^{p\/2}<\\infty.\n$$\n\n\n\n\\item\n$L_{\\cal P, {\\rm loc}}^2(\\Omega;L^2(0,\\infty;K))$\nis the space\nof predictable processes $Y$ on $[0,\\infty)$ that belong\nto the space $L^2_{\\mathcal{P}}(\\Omega,L^2([0,T];K))$\nfor every $T>0$.\n\\end{enumerate}\n\n\n\n\\section{The forward equation}\nIn a complete probability space $\\left(\n\\Omega,\\mathcal{F},\\mathbb{P}\\right) ,$ we consider the following\nstochastic differential equation with values in a Banach\nspace $E$:\n\\begin{equation}\n\\left\\{\n\\begin{array}[c]{l} dX_t =AX_t d t+F(X_t) dt +GdW_t ,\\text{ \\ \\ \\\n} t\\geq 0, \\\\\nX_0 =x\\in \\, E.\n\\end{array}\n\\right. \\label{sde}\n\\end{equation}\nWe assume that $E$ is continuously and densely embedded in a\nHilbert space $H$, and that both spaces are real separable.\n\n\n We will work under the following\ngeneral assumptions:\n\n\n\\begin{hypothesis}\n\\label{general_hyp_forward}\n\\begin{enumerate}\n \\item The operator $A$ is the generator of a strongly\n continuous semigroup of contractions in\n$E$. We assume that the semigroup\n $\\{e^{tA},\\, t\\geq0\\}$\n of bounded linear operators on $E$ generated by $A$\n admits an extension to a strongly\ncontinuous semigroup of bounded linear operators on $H$ that we\ndenote by $\\{S(t),\\, t\\geq 0\\}$.\n\n\n\n\\item $W$ is a cylindrical Wiener process in another real separable\nHilbert space $\\Xi$. Moreover by ${\\cal F}_{t}$ we denote the\n$\\sigma$-algebra generated by $\\{W_s,\\; s\\in [0,t]\\}$ and\n by the sets of ${\\cal F}$ with $\\mathbb P$-measure zero.\n\n\n\n\\item $F:E\\to E$ is continuous and has polynomial growth (that is\nthere exist $c>0, k\\ge 0$ such that $|F(x)|\\leq c (1+|x|^k)$,\n$x\\in E$). Moreover there exists $\\eta>0$\n such that $A+F+\\eta I$ is dissipative.\n\n\n\\item $G$ is a bounded linear operator from $\\Xi$ to $H$. The\nbounded linear, positive and symmetric operators on $H$ defined\nby the formula\n\\[\nQ_{t}h=\\int_{0}^{t}S(s)GG^{\\ast}S^*(s)h\\,ds,\\qquad t\\geq 0,\\; h\\in\nH,\n\\]\nare assumed to be of trace class in $H$. Consequently we can\ndefine the stochastic convolution\n$$\nW^{A}_t =\\int_{0}^{t}S(t-s) GdW_s,\\quad t\\geq 0,\n$$\nas a family of $H$-valued stochastic integrals. We assume that the\nprocess $\\{W^{A}_t,\\, t \\geq 0\\}$ admits an $E$-continuous\nversion.\n\\end{enumerate}\n\\end{hypothesis}\n\n\n\n\nWe recall that, for every $x\\in E$, with $x\\neq 0$, the\nsubdifferential of the norm at $x$, $\\partial\\left( |x| \\right) $,\nis the set of functionals $x^{\\ast}\\in E^{\\ast}$ such that\n$\\left\\langle x^{\\ast },x\\right\\rangle _{E^{\\ast},E}=| x| $ and $|\nx^{\\ast}|_{E^{\\ast}}=1$. If $x=0$ then $\\partial\\left( | x|\\right)\n$ is the set of functionals $x^{\\ast}\\in E^{\\ast}$ such that\n$|x^{\\ast}|_{E^{\\ast}}\\leq 1$. The dissipativity assumption on\n$A+F$ can be explicitly stated as follows: for $x,x'\\in\nD(A)\\subset E$ there exists $x^{\\ast } \\in\\partial\\left( \\left|\nx-x' \\right| \\right) $ such that\n$$\\left\\langle\nx^{\\ast} ,A( x-x' ) +F\\left( x \\right) -F\\left( x' \\right)\n\\right\\rangle _{E^{\\ast},E}\n \\leq-\\eta\\left|\nx-x' \\right|.\n$$\n\n\nWe can state the following theorem, see e.g. \\cite{DP1}, theorem\n7.13 and \\cite{DP2}, theorem 5.5.13.\n\\begin{theorem}\n\\label{teo2 forward}Assume that Hypothesis\n\\ref{general_hyp_forward} holds true. Then for every $x\\in E$\nequation (\\ref{sde}) admits a unique mild solution, that is an\nadapted $E$-valued process with continuous paths satisfying\n$\\mathbb{P}$-a.s.\n\\[\nX_{t}=e^{ t A}x+\\int_{0}^{t}e^{ (t-s ) A}F\\left( X_{s}\\right)\nds+\\int_{0}^{t}e^{(t-s ) A}GdW_{s},\\text{ \\ \\ \\ }t\\geq 0 .\n\\]\n\\end{theorem}\n\n\nWe denote the solution by $X^x $, $x\\in E$.\n\n\n Now we want to investigate the dependence of\nthe solution on the initial datum.\n\n\n\\begin{proposition}\n\\label{prop lip X} Under Hypothesis \\ref{general_hyp_forward} it\nholds:\n\\[\n\\left| X_t^{x_1} -X_t^{x_2} \\right| \\leq e^{-\\eta t }\\left|\nx_{1}-x_{2}\\right| ,\\text{ }t\\ge 0, \\;\\; x_{1},x_{2}\\in E.\n\\]\n\n\n\\end{proposition}\n\n\n\\begin{proof}\nLet $X_{1}\\left( t\\right) =X^{x_1}_{t} $ and $X_{2}\\left(\nt\\right) =X^{x_2}_{t} $, $x_{1},x_{2}\\in E$. For $i=1,2$ we set\n$X_{i}^{n}\\left( t\\right) =J_n X_{i}\\left( t\\right) $, where\n$J_n\n =n\\left( nI-A\\right) ^{-1}$. Since $X_{i}^{n}\\left( t\\right)\n\\in {D}\\left( A\\right) $ for every $t\\geq 0 $, and\n\\[\nX_{i}^{n}\\left( t\\right) =e^{t A}J_n x_{i}+\\int\n_{0}^{t}e^{\\left( t-s\\right) A}J_n F\\left( X_{i}\\left( s\\right)\n\\right) ds+\\int_{0}^{t}e^{\\left( t-s\\right) A}J_n GdW_{s},\n\\]\nwe get\n\\[\n\\frac{d}{dt}\\left( X_{1}^{n}\\left( t\\right) -X_{2}^{n}\\left(\nt\\right) \\right) =A\\left( X_{1}^{n}\\left( t\\right) -X_{2}\n^{n}\\left( t\\right) \\right) +J_n \\left[ F\\left( X_{1}\\left(\nt\\right) \\right) -F\\left( X_{2}\\left( t\\right) \\right) \\right]\n.\n\\]\nSo, by proposition II.8.5 in \\cite{S}\\ also $\\left|\nX_{1}^{n}\\left( t\\right) -X_{2}^{n}\\left( t\\right) \\right| $\nadmits the left and right derivatives with respect to $t$ and\nthere exists $x_{n}^{\\ast }\\left( t\\right) \\in\\partial\\left(\n\\left| X_{1}^{n}\\left( t\\right) -X_{2}^{n}\\left( t\\right) \\right|\n\\right) $ such that the left derivative of $\\left|\nX_{1}^{n}\\left( t\\right) -X_{2}^{n}\\left( t\\right) \\right| $\nsatisfies the following\n\\[\n\\frac{d^{-}}{dt}\\left| X_{1}^{n}\\left( t\\right) -X_{2}^{n}\\left(\nt\\right) \\right| =\\left\\langle x_{n}^{\\ast}\\left( t\\right)\n,\\frac{d}{dt}\\left( X_{1}^{n}\\left( t\\right) -X_{2}^{n}\\left(\nt\\right) \\right) \\right\\rangle _{E^{\\ast},E}.\n\\]\nSo we have\n$$ \\begin{array}{ll}\\displaystyle \\frac{d^{-}}{dt}\\left| X_{1}^{n}\\left(\nt\\right) -X_{2}^{n}\\left( t\\right) \\right| & =\\left\\langle\nx_{n}^{\\ast}\\left( t\\right) ,A\\left( X_{1}^{n}\\left( t\\right)\n-X_{2}^{n}\\left( t\\right) \\right) +F\\left( X_{1} ^{n}\\left(\nt\\right) \\right) -F\\left( X_{2}^{n}\\left( t\\right)\n\\right) \\right\\rangle _{E^{\\ast},E}\\\\\n& \\quad +\\left\\langle x_{n}^{\\ast}\\left( t\\right) ,J_n F\\left(\nX_{1}\\left( t\\right) \\right) -F\\left( X_{1}\n^{n}\\left( t\\right) \\right) \\right\\rangle _{E^{\\ast},E}\\\\\n& \\quad -\\left\\langle x_{n}^{\\ast}\\left( t\\right) ,J_n F\\left(\nX_{2}\\left( t\\right) \\right) -F\\left( X_{2}\n^{n}\\left( t\\right) \\right) \\right\\rangle _{E^{\\ast},E}\\\\\n& \\leq-\\eta\\left|\n X_{1}^{n}\\left( t\\right) -X_{2}^{n}\\left(\nt\\right) \\right| +\\left| \\delta_{1}^{n}\\left( t\\right)\n-\\delta_{2}^{n}\\left( t\\right) \\right| ,\n\\end{array}$$\nwhere for $i=1,2$ we have set $\\delta_{i}^{n}\\left( t\\right) =J_n\nF\\left( X_{i}\\left( t\\right) \\right) -F\\left( X_{i}^{n}\\left(\nt\\right) \\right) $.\n\n\nMultiplying the above by $e^{\\eta t}$ we get\n$$\\frac{d^{-}}{dt}\\left( e^{\\eta t}\\left| X_{1}^{n}\\left( t\\right)\n-X_{2}^{n}\\left( t\\right) \\right|\\right)\\leq e^{\\eta t} \\left|\n\\delta_{1}^{n}\\left( t\\right) -\\delta_{2}^{n}\\left( t\\right)\n\\right|.$$ We note that $\\delta_{i} ^{n}\\left( t\\right) $ tends\nto $0$\\ uniformly in $t\\in \\left[0,T\\right] $ for arbitrary\n$T>0$. Indeed,\n\\[\n\\delta_{i}^{n}\\left( t\\right) =nR\\left( n,A\\right) \\left[\nF\\left( X_{i}\\left( t\\right) \\right) -F\\left( X_{i}^{n}\\left(\nt\\right) \\right) \\right] +\\left( nR\\left( n,A\\right) -I\\right)\nF\\left( X_{i}\\left( t\\right) \\right) ,\n\\]\nand the convergence to $0$ follows by a classical argument, see\ne.g. the proof of theorem 7.10 in \\cite{DP1}, since\n$X_{i}^{n}\\left( t\\right) $ tends to $X_{i}\\left( t\\right) $\nuniformly in $t\\in\\left[ 0,T\\right] $ and the maps $t\\mapsto\nX_{i}\\left( t\\right) $ and $t\\mapsto F\\left( X_{i}\\left(\nt\\right) \\right) $ are continuous with respect to $t$.\n\n\n Thus letting $n\\rightarrow\\infty$ we can conclude\n\\[\n\\left| X_{1}\\left( t\\right) -X_{2}\\left( t\\right) \\right| \\leq\ne^{-\\eta t } \\left| x_{1}-x_{2}\\right| .\n\\]\nand the claim is proved. \\end{proof}\n\n\n$ $\n\n\n\\noindent We will also need the following assumptions.\n\\begin{hypothesis}\n\\label{hyp_W_A F(W_A)} We have $\\sup_{t\\geq 0}\\,\n\\mathbb E\\,|W^A_t|^2<\\infty.$\n\\end{hypothesis}\n\\begin{hypothesis}\n\\label{hyp-convol-determ} $e^{tA}G\\,(\\Xi)\\subset E$ for all $t>0$\nand $\\displaystyle \\int_0^{+\\infty} |e^{tA} G|_{L(\\Xi,E)} dt <\n\\infty$.\n\\end{hypothesis}\n\n\nWe recall that for arbitrary gaussian random variabile $Y$ with\nvalues in the Banach space $E$, the inequality\n$$\n\\mathbb E \\,\\phi (|Y|-\\mathbb E\\,|Y|)\\le \\mathbb E \\,\\phi (2\\sqrt{\\mathbb E\\,|Y|^2}\\,\\gamma)\n$$\nholds for any convex nonnegative continuous function $\\phi$\non $E$ and\nfor $\\gamma$ a real standard gaussian random variable, see e.g.\n\\cite{kw-woy}, Example 3.1.2. Upon taking $\\phi(x)=|x|^p$, it\nfollows that for every $p\\ge 2$ there exists $c_p>0$ such that $\\mathbb E\n\\,|Y|^p\\le c_p(\\mathbb E \\,|Y|^2)^{p\/2}$. By the gaussian character of\n$W^A_t$ and the polynomial growth condition on $F$ stated in\nHypothesis \\ref{general_hyp_forward}, point 3, we see that\nHypothesis \\ref{hyp_W_A F(W_A)} entails that for every $p\\ge 2$\n\\begin{equation}\\label{stimegaussunif}\n\\sup_{t\\geq 0} \\mathbb E\\left[ |W^A_t|^p+ |F(W^A_t)|^p \\right] <\\infty.\n\\end{equation}\n\n\n\\begin{proposition}\\label{prop-X-L^p}\nUnder Hypothesis \\ref{general_hyp_forward} it holds, for arbitrary\n$T>0$ and arbitrary $p\\geq 1$\n\\begin{equation}\\label{prop-X-L^p-1}\n\\mathbb E\\sup_{t\\in [0,T]} |X_t^x|^p \\leq C_{p,T}(1+|x|^p),\\qquad x\\in\nE.\n\\end{equation}\n If, in addition, Hypothesis\n\\ref{hyp_W_A F(W_A)} holds then, for a suitable constant C\n\\begin{equation}\\label{prop-X-L^p-2}\\sup_{t\\geq 0} \\mathbb E |X_t^x| \\leq C(1+|x|)\n,\\qquad x\\in E.\\end{equation} Moreover if, in addition, Hypothesis\n\\ref{hyp-convol-determ} holds, $\\gamma$ is a bounded, adapted,\n$\\Xi$-valued process and $X^{x,\\gamma}$ is the mild solution of\nequation\n\\begin{equation}\n\\left\\{\n\\begin{array}{l}\ndX^{x,\\gamma}_t =AX^{x,\\gamma}_{t} dt+F(\nX^{x,\\gamma}_{t} ) dt+GdW_{t}+G\\gamma_{t}\\,dt ,\\quad t\\geq 0, \\\\\nX^{x,\\gamma}_{0} =x\\in E.\n\\end{array} \\right. \\label{sde-gamma}\n\\end{equation}\nthen it is still true that\n\\begin{equation}\\label{rel-estimate-Xgamma}\n \\sup_{t\\geq 0} \\mathbb E |X^{x,\\gamma}_t| \\leq C_{\\gamma}(1+|x|),\\qquad x\\in E,\n\\end{equation}\nfor a suitable constant $C_{\\gamma}$ depending only on\n a uniform bound for $\\gamma$.\n\\end{proposition}\n\\begin{proof} We let $Z_t=X^x_t-W^A_t$,\n$Z^n_t=J_n Z_t $, then\n$$\\frac{d}{dt } Z^n_t =\nAZ^n_t +J_nF(X^x_t) = AZ^n_t +\\left[F(Z^n_t+J_n W^A_t) - F(J_n\nW^A_t)\\right]+F( W^A_t)+\\delta^n_t\n$$ where $$\\delta^n_t= J_n F(X^x_t)-F(J_n X^x_t)\n+F(J_n W^A_t)-F( W^A_t).$$ Proceeding as in the proof of\nProposition \\ref{prop lip X} observing that, for all $t>0$,\n $\\displaystyle \\int_0^{t}|\\delta^n_s| ds \\rightarrow 0$ as $n\\rightarrow\\infty$, we get:\n$$|Z_t|\\leq e^{-\\eta t}|x|+\\int_0^{t} e^{-\\eta (t-s)}\n|F(W^A_s)|ds,\\;\\;\\; \\mathbb{P}-\\hbox{a.s.}$$ and\n(\\ref{prop-X-L^p-2}) follows from (\\ref{stimegaussunif}).\n\n\nIn the case in which $X^x$ is replaced by $X^{x, \\gamma}$ the\nproof is exactly the same just replacing $W^A_t$ by\n$W^{A,\\gamma}_t=W^A_t+\\int_0^t e^{(t-s)A}G\\gamma_s ds$.\n\n\n\nFinally to prove (\\ref{prop-X-L^p-1}) we notice that (see the\ndiscussion in \\cite{masiero}) the process $W^A$ is a Gaussian\nrandom variable with values in $C([0,T],E)$. Therefore by the\npolynomial growth of $F$ we get\n$$ \\mathbb E\\sup_{t\\in [0,T]} \\left[|W^A_t|^p + |F(W^A_t)|^p\\right]\\leq\nC_{p,T}(1+|x|^p),$$ and the claim follows as above.\n\\end{proof}\n\n\n$ $\n\n\n Finally the following result is proved exactly as\nTheorem 6.3.3. in \\cite{DP2}.\n\\begin{theorem}\\label{ergodicity}\nAssume that Hypotheses \\ref{general_hyp_forward} and\n \\ref{hyp_W_A F(W_A)} hold then equation (\\ref{sde}) has a unique\n invariant measure in $E$ that we will denote by $\\mu$. Moreover\n $\\mu$ is strongly\nmixing (that is, for all $x\\in E$, the law of $X_t^x$ converges\nweakly to $ \\mu$ as\n $t\\rightarrow \\infty$).\n Finally\nthere exists a constant $C>0$ such that for any bounded Lipschitz\nfunction $\\phi: E\\rightarrow \\mathbb{R}$,\n$$\\left|\\mathbb{E}\\phi(X^x_t)-\\int_E \\phi\\, d\\mu \\right|\\leq C(1+|x|)\ne^{-\\eta t \/2} \\Vert\\phi\\Vert_{\\hbox{\\em lip}}.$$\n\\end{theorem}\n\n\n\n\\section{Ergodic BSDEs (EBSDEs)}\n\n\nThis section is devoted to the following type of BSDEs with\ninfinite horizon\n\\begin{equation}\\label{EBSDE}\nY^x_t=Y^x_T +\\int_t^T\\left[\\psi(X^x_\\sigma,Z^x_\\sigma)-\n\\lambda\\right]d\\sigma-\\int_t^T Z^x_\\sigma\\, dW_\\sigma, \\quad 0\\le\nt\\le T <\\infty,\n\\end{equation}\nwhere $\\lambda$ is a real number and is part of the unknowns of\nthe problem; the equation is required to hold for every $t$ and\n$T$ as indicated. On the function $\\psi: E\\times \\Xi^*\n\\rightarrow {\\mathbb R}$ and assume the following:\n\n\n\\begin{hypothesis}\\label{hypothesisroyer} $ $ There exists\n$K_x, K_z>0$ such that\n$$ |\\psi(x,z)\n-\\psi(x',z')|\\le K_x|x-x'|+ K_z |z-z'|, \\qquad\n x,x'\\in E,\\;\nz,z'\\in\\Xi^*.\n$$\nMoreover $\\psi(\\,\\cdot\\,,0)$ is bounded. We denote $\\sup_{x\\in E\n}|\\psi(x,0)|$ by $M$.\n\\end{hypothesis}\nWe start by considering an infinite horizon equation with strictly\nmonotonic drift, namely, for $\\alpha>0$, the equation\n\\begin{equation}\\label{bsderoyer}\n{Y}^{x,\\alpha}_t={Y}^{x,\\alpha}_T +\\int_t^T(\\psi(X^{x}_\\sigma,\nZ^{x,\\alpha}_\\sigma)-\\alpha Y^{x,\\alpha}_\\sigma)d\\sigma-\\int_t^T\nZ^{x,\\alpha}_\\sigma dW_\\sigma, \\quad 0\\le t\\le T <\\infty.\n\\end{equation}\n\n\n\n\nThe existence and uniqueness of solution to (\\ref{bsderoyer})\nunder Hypothesis \\ref{hypothesisroyer} was first studied by Briand\nand Hu in \\cite{bh} and then generalized by Royer in \\cite{royer}.\n They have established the following result when $W$ is a finite dimensional\n Wiener process but the extension to the case in which $W$ is a\n Hilbert-valued Wiener process is immediate (see also \\cite{HuTe}).\n\n\n\\begin{lemma}\\label{lemmaroyer} Let us suppose that Hypotheses\n\\ref{general_hyp_forward} and\n\\ref{hypothesisroyer} hold.\n Then\n there exists a unique solution $(Y^{x,\\alpha},Z^{x,\\alpha})$\n to BSDE (\\ref{bsderoyer})\nsuch that $Y^{x,\\alpha}$ is a bounded continuous process, and\n$Z^{x,\\alpha}$ belongs to $L_{\\cal P, {\\rm\nloc}}^2(\\Omega;L^2(0,\\infty;\\Xi^*))$.\n\n\nMoreover $|Y^{x,\\alpha}_t|\\leq {M}\/{\\alpha}$, $\\mathbb{P}$-a.s.\nfor all $t\\geq 0$.\n\\end{lemma}\nWe define $$v^{\\alpha}(x)=Y^{\\alpha,x}_0.\n$$\nWe notice that by the above $|v^{\\alpha}(x)|\\leq {M}\/{\\alpha}$ for\nall $x\\in E$. Moreover by the uniqueness of the solution of\nequation (\\ref{bsderoyer}) it follows that\n$Y^{\\alpha,x}_t=v^{\\alpha}(X^x_t)$\n\n\n\nTo establish Lipschitz continuity of $ v^{\\alpha}$ (uniformly in\n$\\alpha$) we use a Girsanov argument due to P. Briand and Y. Hu,\nsee \\cite{bh}. Here and in the following we use an\ninfinite-dimensional version of the Girsanov formula that can be\nfound for instance in \\cite{DP1}.\n\\begin{lemma}\\label{lemma-lip-v} Under Hypotheses \\ref{general_hyp_forward}\nand \\ref{hypothesisroyer} the following\nholds for any $\\alpha>0$:\n$$|v^{\\alpha}(x) - v^{\\alpha}(x')| \\leq \\frac{K_x}{\\eta} |x-x'|,\n\\qquad x,x'\\in E. $$\n\\end{lemma}\n\\begin{proof} We briefly report the argument for the reader's convenience.\n\n\nWe set $\\tilde{Y}=Y^{\\alpha,x}-Y^{\\alpha,x'}$,\n$\\tilde{Z}=Z^{\\alpha,x}-Z^{\\alpha,x'},$\n$$\\beta_t=\\begin{cases}\n\\frac{\\displaystyle \\psi(X^{x'}_t,Z^{\\alpha,\nx'}_t)-\\psi(X^{x'}_t,Z^{\\alpha,x}_t)} {\\displaystyle\n|Z^{\\alpha,x}_t - Z^{\\alpha,x'}_t|_{\\Xi^*}^2}\\left( Z^{\\alpha,x}_t\n- Z^{\\alpha,x'}_t\\right)^*,& \\hbox{ if } Z^{\\alpha,x}_t \\neq Z^{\\alpha,x'}_t \\\\\n0, & \\hbox{ elsewhere, }\n \\end{cases}\n$$\n$$f_t=\\psi(X^{x}_t,\nZ^{x,\\alpha}_t)-\\psi(X^{x'}_t, Z^{x,\\alpha}_t). $$ By\nHypothesis \\ref{hypothesisroyer}, $\\beta$ is a bounded\n$\\Xi$-valued, adapted process thus there exists a probability\n$\\tilde{\\mathbb{P}}$ under which $\\tilde{W_{t}}=\\int_0^{t} \\beta_s\nds + W_{t}$ is a cylindrical $\\Xi$-valued Wiener process for\n${t}\\in [0,T]$. Then $(\\tilde{Y},\\tilde{Z})$ verify, for all\n$0\\le t\\le T <\\infty$,\n\\begin{equation}\\label{bsderoyer-girsanov}\n\\tilde{Y}_t=\\tilde{Y}_T -\\alpha \\int_t^T \\tilde{Y}_\\sigma d\\sigma\n+\\int_t^T f_{\\sigma}d\\sigma- \\int_t^T \\tilde{Z}_\\sigma\nd\\tilde{W}_\\sigma.\n\\end{equation}\nComputing $d (e^{-\\alpha t}\\tilde{Y}_t)$, integrating over\n$[0,T]$, estimating the absolute value and finally taking the\nconditional expectation\n $\\tilde{\\mathbb{E}}^{\\mathcal{F}_t}$ with respect to\n$\\tilde{\\mathbb{P}}$ and $\\mathcal{F}_t$ we get:\n$$ |\\tilde{Y}_t| \\leq e^{-\\alpha(T-t)} \\tilde{\\mathbb{E}}^{\\mathcal{F}_t}\n| \\tilde{Y}_T |+\n \\tilde{\\mathbb{E}}^{\\mathcal{F}_t}\n \\int_{t}^T e^{-\\alpha(s-t)} |f_s| ds $$\nNow we recall that $ \\tilde{Y}$ is bounded and that $|f_t|\\leq\nK_x |X^{x}_t-X^{x'}_t|\\leq K_x e^{-\\eta t}|x-x'|$ by Proposition\n\\ref{prop lip X}. Thus if $T\\rightarrow \\infty$ we get $\n|\\tilde{Y}_t| \\leq K_x (\\eta+\\alpha)^{-1}e^{\\alpha t} |x-x'| $\nand the claim follows setting $t=0$.\n\\end{proof}\n\n\n$ $\n\n\n\\noindent By the above Lemma if we set\n$$\\overline{v}^{\\alpha}(x)= {v}^{\\alpha}(x)- {v}^{\\alpha}(0),$$\nthen $ | \\overline{v}^{\\alpha}(x)|\\leq K_x \\eta^{-1}|x|$ for\nall $x\\in E$ and all $\\alpha>0$. Moreover by Lemma\n\\ref{lemmaroyer} $\\alpha |{v}^{\\alpha}(0)|\\leq M$.\n\n\n \\noindent Thus by a diagonal procedure we can construct a\n sequence $\\alpha_n\\searrow 0$ such that for all $x$ in a\ncountable dense subset $D\\subset E$\n \\begin{equation}\\label{def-of-lambda}\n {\\overline{v}}^{\\alpha_n}(x)\\rightarrow \\overline{v}(x),\\qquad\n\\alpha_n v^{\\alpha_n}(0)\\rightarrow \\overline{\\lambda},\n \\end{equation}\nfor a suitable function $ \\overline{v}: D \\rightarrow\n\\mathbb{R}$ and for a suitable real number $\\overline{\\lambda}$.\n\n\n Moreover, by Lemma \\ref{lemma-lip-v}, $ | \\overline{v}^{\\alpha}(x)-\n\\overline{v}^{\\alpha}(x')|\\leq K_x \\eta^{-1}|x-x'|$ for all\n$x,x'\\in E$ and all $\\alpha>0$. So $\\overline{v}$ can be extended\nto a Lipschitz function defined on the whole $E$ (with Lipschitz\nconstant $K_x \\eta^{-1} $) and\n\\begin{equation}\\label{def-of-v} {\\overline{v}}^{\\alpha_n}(x)\\rightarrow\n\\overline{v}(x),\\qquad x\\in E.\\end{equation}\n\n\n\\begin{theorem} \\label{main-EBSDE} Assume Hypotheses\n\\ref{general_hyp_forward} and\n\\ref{hypothesisroyer} hold. Moreover let $\\bar \\lambda$ be the\nreal number in (\\ref{def-of-lambda}) and define $\\bar Y^x_t= \\bar\nv(X^x_t)$ (where $\\overline{v}$ is the Lipschitz function with\n$\\overline{v}(0)=0$ defined in (\\ref{def-of-v})). Then there\nexists a process $\\overline{Z}^{x}\\in L_{\\cal P, {\\rm\nloc}}^2(\\Omega;L^2(0,\\infty;\\Xi^*))$\n such that $\\mathbb{P}$-a.s. the EBSDE\n (\\ref{EBSDE}) is satisfied by\n $(\\bar Y^x,\\bar Z^x, \\bar \\lambda)$ for all $0\\leq t\\leq T$.\n\n\nMoreover there exists a measurable function $\\overline{\\zeta}:\nE\\rightarrow \\Xi^*$ such that\n$\\overline{Z}^{x}_t=\\overline{\\zeta}(X^x_t)$.\n\\end{theorem}\n\n\n\\begin{proof} Let $\\overline{Y}^{x,\\alpha}_t={Y}^{x,\\alpha}_t-v^{\\alpha}(0)=\n\\overline{v}^{\\alpha}({X}^{x}_t)$. Clearly we have,\n$\\mathbb{P}$-a.s.,\n\\begin{equation}\\label{equation-proof-main-1}\n \\overline{Y}^{x,\\alpha}_t=\\overline{Y}^{x,\\alpha}_T +\\int_t^T(\\psi(X^{x}_\\sigma,\nZ^{x,\\alpha}_\\sigma)-\\alpha \\overline{Y}^{x,\\alpha}_\\sigma-\\alpha\n{v}^{\\alpha}(0))d\\sigma -\\int_t^T Z^{x,\\alpha}_\\sigma dW_\\sigma,\n\\quad 0\\le t\\le T <\\infty.\n\\end{equation}\nSince $|\\bar v^{\\alpha}(x)|\\leq K_x|x|\/\\eta $, inequality\n(\\ref{prop-X-L^p-1}) ensures that\n$\\mathbb{E}\\sup_{t\\in[0,T]}\\left[\\sup_{\\alpha>0}\n|\\overline{Y}^{x,\\alpha}_t|^2\\right]< +\\infty$ for any $T>0$.\nThus, if we define $\\overline{Y}^x=\\overline{v}(X^x)$, then by\ndominated convergence theorem\n$$\\mathbb{E} \\int_0^T |\\overline{Y}^{x,\\alpha_n}_t -\\overline{Y}^{x}_t|^2 dt\n \\rightarrow 0\\quad \\hbox{and}\\quad\n\\mathbb{E} |\\overline{Y}^{x,\\alpha_n}_T-\\overline{Y}^{x}_T|^2\n\\rightarrow 0\n$$\nas $n\\rightarrow \\infty$ (where $\\alpha_n \\searrow 0$ is a\nsequence for which (\\ref{def-of-lambda}) and (\\ref{def-of-v})\nhold).\n\n\n\nWe claim now that there exists $\\overline{Z}^{x}\\in L_{\\cal P,\n{\\rm loc}}^2(\\Omega;L^2(0,\\infty;\\Xi^*))$ such that\n $$\\mathbb{E} \\int_0^T |{Z}^{x,\\alpha_n}_t -\\overline{Z}^{x}_t|_{\\Xi^*}^2 dt\n \\rightarrow 0$$\nLet $\\tilde{Y}={\\bar Y}^{x,\\alpha_n}-{\\bar Y}^{x,\\alpha_m}$,\n$\\tilde{Z}={Z}^{x,\\alpha_n}-{Z}^{x,\\alpha_m}$. Applying It\\^o's\nrule to $\\tilde{Y}^2$ we get by standard computations\n$$\\tilde{Y}^2_0+\\mathbb{E}\\int_0^T |\\tilde{Z}_t|_{\\Xi^*}^2 dt\n=\\mathbb{E}{\\tilde Y}^2_T + 2\\mathbb{E}\\int_0^T \\tilde \\psi_t\n\\tilde Y_t dt -2 \\mathbb{E}\\int_0^T \\left[\\alpha_n\n{Y}^{x,\\alpha_n}_t - \\alpha_m {Y}^{x,\\alpha_m}_t\\right] \\tilde\nY_t\\,dt\n$$\nwhere $\\tilde\n\\psi_t=\\psi(X^x_t,Z^{x,\\alpha_n}_t)-\\psi(X^x_t,Z^{x,\\alpha_m}_t)$.\nWe notice that $|\\tilde\\psi_t| \\leq K_z|\\tilde Z _t|$ and\n$\\alpha_n |{Y}^{x,\\alpha_n}_t|\\leq M$. Thus\n$$\n\\mathbb{E}\\int_0^T |\\tilde{Z}_t|_{\\Xi^*}^2 dt \\leq c\\left[\n\\mathbb{E} (\\tilde Y^x_T)^2 +\\mathbb{E}\\int_0^T (\\tilde{Y}^x_t)^2\ndt +\\mathbb{E}\\int_0^T |\\tilde{Y}^x_t| dt \\right].$$ It follows\nthat the sequence $\\{{Z}^{x,\\alpha_m}\\}$ is Cauchy in\n$L^2(\\Omega;L^2(0,T;\\Xi^*))$ for all $T>0$ and our claim is\nproved.\n\n\nNow we can pass to the limit as $n\\rightarrow \\infty$ in equation\n(\\ref{equation-proof-main-1}) to obtain\n\\begin{equation}\\label{equation-proof-main-2}\n \\overline{Y}^{x}_t=\\overline{Y}^{x}_T +\\int_t^T(\\psi(X^{x}_\\sigma,\n\\overline{Z}^{x}_\\sigma)-\\overline{\\lambda })d\\sigma-\\int_t^T\n\\overline{Z}^{x}_\\sigma dW_\\sigma, \\quad 0\\le t\\le T <\\infty.\n\\end{equation}\nWe notice that the above equation also ensures continuity of the\ntrajectories of $\\overline{Y}$ It remains now to prove that we can\nfind a measurable function $\\bar \\zeta:E\\rightarrow \\Xi^*$ such\nthat\n $\\overline{Z}^{x}_t=\\bar \\zeta (X^x_t)$, $\\mathbb{P}$-a.s. for almost every $t\\geq 0$.\n\n\nBy a general argument, see for instance \\cite{Fu}, we know that\nfor all $\\alpha>0$ there exists $\\zeta^{\\alpha}:E\\rightarrow\n\\Xi^*$ such that\n ${Z}^{x,\\alpha}_t=\\zeta^{\\alpha} (X^x_t)$, $\\mathbb{P}$-a.s.\n for almost every $t\\geq 0$.\n\n\nTo construct $\\zeta$ we need some more regularity of the processes\n${Z}^{x,\\alpha}$ with respect to $x$.\n\n\nIf we compute $d ({Y}^{x,\\alpha}_t-{Y}^{x',\\alpha}_t)^2$ we get by\nthe Lipschitz character of $\\psi$:\n$$ \\begin{array} {l}\n\\displaystyle \\mathbb{E}\\int_0^T\n|Z^{x,\\alpha}_t-Z^{x',\\alpha}_t|_{\\Xi^*}^2 dt \\leq \\mathbb{E}\n(v^{\\alpha}(X^x_T)- v^{\\alpha}(X^{x'}_T))^2\n \\\\\n\\quad + \\displaystyle \\mathbb{E}\\int_0^T\n\\left(K_x|X^x_s-X^{x'}_s|\n+K_z|Z^{x,\\alpha}_s-Z^{x',\\alpha}_s|\\right)\n\\left|v^{\\alpha}(X^x_s)- v^{\\alpha}(X^{x'}_s)\\right| ds\n \\end{array}$$\nBy the Lipschitz continuity of $v^{\\alpha}$ (uniform in $\\alpha$)\nthat of $\\psi$ and Proposition \\ref{prop lip X} we immediately\nget:\n\\begin{equation}\\label{lip-of-Z}\n \\mathbb{E}\\int_0^T |Z^{x,\\alpha}_t-Z^{x',\\alpha}_t|_{\\Xi^*}^2 dt \\leq c |x-x'|^2.\n\\end{equation}\nfor a suitable constant $c$ (that may depend on $T$).\n\n\nNow we fix an arbitrary $T>0$ and, by a diagonal procedure (using\nseparability of $E$) we construct a subsequence\n$(\\alpha_n')\\subset (\\alpha_n)$ such that $\\alpha_n' \\searrow 0$\nand\n$$\\mathbb{E}\\int_0^T |Z^{x,\\alpha_n'}_t-Z^{x',\\alpha_m'}_t|_{\\Xi^*}^2 dt \\leq 2^{-n}\n$$ for all $m\\geq n$ and for all $x\\in E$.\nConsequently $Z^{x,\\alpha_n'}_t\\rightarrow \\overline{Z}^x_t$,\n$\\mathbb{P}$-a.s. for a.e. $t\\in [0,T]$. Then we set:\n$$\\bar \\zeta(x)=\\left\\{\\begin{array}{ll} \\lim_n \\zeta^{\\alpha_n'}(x),\n& \\hbox{ if the limit exists in }\\Xi^*,\\\\\n0, & \\hbox{ elsewhere.}\\end{array}\\right.$$ Since\n$Z^{x,\\alpha_n'}_t= \\zeta^{\\alpha_n'}(X^x_t)\\rightarrow\n\\overline{Z}^{x}_t$ $\\mathbb{P}$-a.s. for a.e. $t\\in [0,T]$ we\nimmediately get that, for all $x\\in E$, the process $X^x_t$\nbelongs $\\mathbb{P}$-a.s. for a.e. $t\\in [0,T]$ to the set where\n$\\lim_n \\zeta^{\\alpha_n'}(x)$ exists and consequently\n $\\overline{Z}^{x}_t=\\bar \\zeta(X^x_t)$.\n\\end{proof}\n\\begin{remark}\\begin{em} We notice that the solution we\nhave constructed above has the following ``linear growth''\nproperty with respect to $X$: there exists $c>0$ such that,\n$\\mathbb{P}$-a.s.,\n\\begin{equation}\\label{growt-of-Y}\n|\\overline{Y}^x_t|\\leq c |X^x_t| \\hbox{ for all $t\\geq 0$}.\n\\end{equation}\n\\end{em}\n\\end{remark}\nIf we require similar conditions then we immediately obtain\nuniqueness of $\\lambda$.\n\\begin{theorem}\\label{th-uniq-lambda} Assume that,\nin addition to Hypotheses \\ref{general_hyp_forward}, \\ref{hyp_W_A\nF(W_A)} and \\ref{hypothesisroyer}, Hypothesis\n\\ref{hyp-convol-determ} holds as well. Moreover suppose that, for\nsome $x\\in E$, the triple $(Y',Z',\\lambda')$ verifies\n$\\mathbb{P}$-a.s. equation\n (\\ref{EBSDE}) for all $0\\leq t\\leq T$,\nwhere\n $Y'$ is a progressively measurable continuous process, $Z'$ is a process\n in $L_{\\cal P, {\\rm loc}}^2(\\Omega;L^2(0,\\infty;\\Xi^*))$ and\n $\\lambda'\\in \\mathbb{R}$.\n Finally assume that there exists $c_x>0$ (that may depend\n on $x$) such that\n$\\mathbb{P}$-a.s.\n$$\n |Y'_t|\\leq c_x (|X^x_t|+1) , \\hbox{ for all $t\\geq 0$}.\n$$ Then $\\lambda'=\\bar \\lambda$.\n\\end{theorem}\n\\begin{proof}\nLet $\\tilde \\lambda=\\lambda'-\\lambda$, $\\tilde\nY=Y'-\\overline{Y}^x$, $\\tilde Z=Z'-\\overline{Z}^x$. By easy\ncomputations:\n$$\\tilde \\lambda=T^{-1}\\left[\\tilde Y_T-\\tilde Y_0\\right]+T^{-1}\\int_0^T \\tilde Z_t \\gamma_t dt\n-T^{-1}\\int_0^T \\tilde Z_t dW_t$$ where\n$$\\gamma_t:=\\begin{cases} \\frac{\\displaystyle \\psi(X^{x}_t,Z'_t)-\\psi(X^{x}_t,\\overline{Z}^{x}_t)}{\\displaystyle |Z'_t - \\overline{Z}_t|_{\\Xi^*}^2}\\left(Z'_t - \\overline{Z}_t \\right)^*,& \\hbox{ if } Z'_t \\neq \\overline{Z}_t, \\\\\n0, & \\hbox{ elsewhere },\n \\end{cases}\n$$\nis a bounded $\\Xi$-valued progressively measurable process. By the\nGirsanov Theorem there exists a probability measure\n$\\mathbb{P}_{\\gamma}$ under which $W^{\\gamma}_t=-\\int_0^t \\gamma_s\nds+W_t$, $t\\in [0,T]$, is a cylindrical Wiener process in $\\Xi$.\nThus computing expectation with respect to $\\mathbb{P}_{\\gamma}$\nwe get\n$$\\tilde \\lambda=T^{-1}\\mathbb{E}^{\\mathbb{P}_{\\gamma}}\n\\left[\\tilde Y_T-\\tilde Y_0\\right].$$ Consequently, taking into\naccount (\\ref{growt-of-Y}),\n \\begin{equation}\\label{eq-proof-uniq-lambda}\n|\\tilde \\lambda|\\leq c T^{-1}\\mathbb{E}^{\\mathbb{P}_{\\gamma}}\n(|X^x_T|+1)+ c T^{-1}(|x|+1)\n \\end{equation}\nWith respect to $W^{\\gamma}$, $X^x$ is the mild solution of\n$$\n\\left\\{\n\\begin{array}{l}\ndX^{x,\\gamma}_t =AX^{x,\\gamma}_{t} dt+F( X^{x,\\gamma}_{t} )\ndt+GdW^{\\gamma}_{t}+G\\gamma_{t}\\,dt ,\n\\quad t\\geq 0 \\\\\nX^{x,\\gamma}_{0} =x\\in E.\n\\end{array} \\right.\n$$\nand by (\\ref{rel-estimate-Xgamma}) we get\n$\\sup_{T>0}\\mathbb{E}^{\\mathbb{P}_{\\gamma}}|X^x_T|<\\infty$. So if\nwe let $T\\rightarrow\\infty$ in (\\ref{eq-proof-uniq-lambda}) we\nconclude that $\\tilde\\lambda=0$.\n\\end{proof}\n\n\n\\begin{remark} \\em\nThe solution to EBSDE (\\ref{EBSDE}) is, in general, not unique.\nIt is evident that the equation is invariant with respect to\naddition of a constant to $Y$ but we can also construct an\narbitrary number of solutions that do not differ only by a\nconstant (even if we require them to be bounded). On the contrary\nthe solutions we construct are not Markovian.\n\n\n\nIndeed, consider the equation:\n\\begin{equation}\\label{eq:nouniqueness}\n-dY_t=[\\psi(Z_t)-\\lambda]dt-Z_tdW_t.\n\\end{equation}\nwhere $W$ is a standard brownian motion and\n$\\psi:\\mathbb{R}\\rightarrow \\mathbb{R}$ is differentiable bounded\nand has bounded derivative.\n\n\nOne solution is $Y=0;Z=0;\\lambda=\\psi(0)$ (without loss of\ngenerality we can suppose that $\\psi(0)=0$).\n\n\nLet now $\\phi:\\mathbb{R}\\rightarrow \\mathbb{R}$ be an arbitrary\ndifferentiable function bounded and with bounded derivative. The\nfollowing BSDE on $[t,T]$ admits a solution:\n$$\\left\\{\\begin{array}{rcl}\n-dY_s^{x,t}&=&\\psi(Z_s^{x,t})ds-Z_s^{x,t}dW_s,\\\\\nY_T^{x,t}&=&\\phi(x+W_T-W_t).\n\\end{array}\\right.$$\nIf we define $u(t,x)=Y_t^{x,t}$ then both $u$ and $\\nabla u$ are\nbounded. Moreover if $\\tilde{Y}_t=Y_t^{0,0}=u(t,W_t),\\\n\\tilde{Z}_t=Z_t^{0,0}=\\nabla u(t,W_t)$ then\n$$\\left\\{\\begin{array}{rcl}\n-d\\tilde{Y}_t&=&\\psi(\\tilde{Z}_t)dt-\\tilde{Z}_tdW_t,\\quad\nt\\in [0,T],\\\\\n\\tilde{Y}_T&=&\\phi(W_T).\n\\end{array}\\right.$$\n Then it is enough to extend with\n$\\tilde{Y}_t=\\tilde{Y}_T,\\ \\tilde{Z}_t=0$ for $t>T$ to construct a\nbounded solution to (\\ref{eq:nouniqueness}).\n\\end{remark}\n\\begin{remark}\\em The existence result in Theorem \\ref{main-EBSDE}\ncan be easily extended to the case of $\\psi$ only satisfying\nthe conditions\n$$ |\\psi(x,z)\n-\\psi(x',z)|\\le K_x|x-x'|,\\quad |\\psi(x,0)|\\le M,\n\\quad |\\psi(x,z)|\n\\le K_z(1+|z|).\n$$\nIndeed we can construct a sequence $\\{\\psi_n : n\\in \\mathbb{N}\\}$\nof functions Lipschitz in $x$ and $z$ such that for all $x,x'\\in\nH$, $z \\in \\Xi^*$, $n\\in \\mathbb{N}$\n$$ |\\psi^n(x,z)\n-\\psi^n(x',z)|\\le K'_x|x-x'|;\\quad |\\psi^n(x,0)|\\leq M';\\quad\n\\lim_{n\\rightarrow \\infty}|\\psi^n(x,z) -\\psi(x,z)|=0.\n$$\nThis can be done by projecting $x$ to the subspaces generated by\na basis in $\\Xi^*$ and then regularizing by the standard\nmollification techniques, see \\cite{FuTeBE}.\nWe know that if $(\\bar Y^{x,n}, \\bar Z^{x,n},\\lambda_n)$ is the\nsolution of the EBSDE (\\ref{EBSDE}) with $\\psi$ replaced by\n$\\psi^n$ then $\\bar Y^{x,n}_t=\\bar v^n(X^x_t)$ with\n$$ |\\bar v^n(x)\n-\\bar v^n(x')|\\le \\dfrac{K'_x}{\\eta}|x-x'|;\\quad \\bar v^n(0)=0\n;\\quad |\\lambda_n|\\leq M'\n$$\nThus we can assume (considering, if needed, a subsequence) that\n$\\bar v^n(x) \\rightarrow \\bar v(x)$ and $\\lambda_n \\rightarrow\n\\lambda$.\nThe rest of the proof is identical to the one of Theorem\n\\ref{main-EBSDE}.\n\\end{remark}\n\n\n\\section{Differentiability}\n\n\n\nWe are now interested in the differentiability of the\nsolution to the EBSDE (\\ref{EBSDE}) with respect to $x$.\n\n\\begin{theorem}\\label{th-diff} Assume that Hypotheses\n\\ref{general_hyp_forward} and\n\\ref{hypothesisroyer} hold. Moreover assume that $F$ is of class\n${\\cal G}^1(E,E)$ with $\\nabla F$ bounded on bounded sets of $E$.\nFinally assume that $\\psi$ is of class ${\\cal G}^1(E\\times\n\\Xi^*,E)$. Then the function $\\overline{v}$ defined in\n(\\ref{def-of-v}) is of class ${\\cal G}^1(E,\\mathbb{R})$.\n\\end{theorem}\n\\begin{proof} In \\cite{masiero} it is proved that for arbitrary $T>0$ the map\n$x\\rightarrow X^x$ is of class $\\mathcal{G}^1$ from $E$ to\n$L^p_{\\mathcal{P}}(\\Omega,C([0,T],E))$. Moreover Proposition\n\\ref{prop lip X} ensures that for all $h\\in E$,\n\\begin{equation}\\label{proof-diff-estim-nabla-X}\n |\\nabla X^x_t h|\\leq e^{-\\eta t}|h|,\\quad \\hbox{$\\mathbb{P}$-a.s.,\n for all $t\\in [0,T]$}.\n\\end{equation}\nUnder the previous conditions one can proceed exactly as\nin Theorem 3.1 of \\cite{HuTe} to\nprove that for all $\\alpha >0$ the map $v^{\\alpha}$ is of class\n$\\mathcal{G}^1$.\n\n\n$ $\n\n\nThen we consider again\nequation (\\ref{bsderoyer}):\n$$\n{Y}^{x,\\alpha}_t ={Y}^{x,\\alpha}_T\n+\\int_t ^T(\\psi(X^{x}_\\sigma, Z^{x,\\alpha}_\\sigma)-\\alpha\nY^{x,\\alpha}_\\sigma)d\\sigma-\\int_t ^T Z^{x,\\alpha}_\\sigma\ndW_\\sigma, \\quad 0\\le t \\le T <\\infty,\n$$\nwe recall that ${Y}^{x,\\alpha}_T={v}^{\\alpha}(X^{x}_T)$,\n and apply again \\cite{masiero} (see Proposition 4.2 there) and \\cite{FuTe1}\n (see Proposition 5.2 there) to obtain that for all $\\alpha >0 $\n the map $x\\rightarrow Y^{x,\\alpha}$ is of class $\\mathcal{G}^1$\n from $E$ to $L^2_{\\mathcal{P}}(\\Omega,C([0,T],\\mathbb{R}))$ and the map\n$x\\rightarrow Z^{x,\\alpha}$ is of class $\\mathcal{G}^1$ from $E$\nto $L^2_{\\mathcal{P}}(\\Omega,L^2([0,T],\\Xi^*))$. Moreover for all\n$h\\in E$ it holds (for all $t>0$ since $T$ was arbitrary)\n$$\n-d\\nabla Y^{\\alpha,x}_th=[\\nabla_x\\psi(X^x_t,Z_t^{\\alpha,x})\n\\nabla X_t^xh+\\nabla_z\\psi(X^x_t,Z_t^{\\alpha,x})\\nabla\nZ_t^{\\alpha,x}h-\\alpha\\nabla Y^{\\alpha,x}_th]dt\n-\\nabla Z^{\\alpha,x}h dW_t.\n$$\nWe also know that $|Y^{\\alpha,x}_t|\\le {M}\/{\\alpha}$. Now we set\n$$U^{\\alpha,x}_t=e^{\\eta t}\\nabla Y^{\\alpha,x}_t h,\n\\quad V^{\\alpha,x}=e^{\\eta t}\\nabla Z^{\\alpha,x}_t h.$$ Then\n$(U^{\\alpha,x},V^{\\alpha,x})$ satisfies the following BSDE:\n\\begin{eqnarray*}\n-dU^{\\alpha,x}_t&=&[e^{\\eta t}\\nabla_x\\psi(X^x_t,Z_t^{\\alpha,x})\n\\nabla X_t^x-(\\alpha+\\eta)U^{\\alpha,x}_t +\\nabla_z\n\\psi(X^x_t,Z_t^{\\alpha,x}) V^{\\alpha,x}_t]dt-V^{\\alpha,x}_tdW_t.\n\\end{eqnarray*}\nBy (\\ref{proof-diff-estim-nabla-X}) and the usual Girsanov\nargument (recall the $\\nabla_x \\psi$ and $\\nabla_z \\psi$ are\nbounded),\n$$|U^{\\alpha,x}_t|\\le \\frac{c}{\\alpha+\\eta},\\;\n\\forall t\\geq 0,\\; \\hbox{$\\mathbb P-$a.s. $\\qquad$ i.e. } \\qquad\n|\\nabla Y_t^{x,\\alpha}|\\le e^{-\\eta t}\\frac{c}{\\alpha+\\eta}.$$\nMoreover, consider the limit equation, with unknown\n$(U^{x},V^{x})$,\n\\begin{equation}\\label{eq:limit}\n-dU^x_t=[e^{\\eta t}\\nabla_x\\psi(X^x_t,\\bar Z_t^{x}) \\nabla\nX_t^x-\\eta U^x_t+\\nabla_z\\psi(X^x_t,\\bar Z_t^{x}) V^x]dt-V^xdW_t,\n\\end{equation}\nwhich, since $|e^{\\eta t}\\nabla_x\\psi \\nabla_x X_t^x|$ is bounded,\nhas a unique solution such that $U^x$ is bounded and $V^x$\nbelongs to $L_{\\cal P, {\\rm loc}}^2(\\Omega;L^2(0,\\infty;\\Xi^*))$\n(see \\cite{bh} and \\cite{royer}).\n\n\nWe know that for a suitable sequence $\\alpha_n \\searrow 0$,\n$$\\bar v^{\\alpha}(x)= Y^{x,\\alpha_n}_0-Y^{0,\\alpha_n}_0\\rightarrow \\bar{Y}^x_0,$$\nand we claim now that\n$$ \\nabla \\bar v^{\\alpha_n}(x)=\\nabla Y_0^{x,\\alpha_n}=U_0^{x,\\alpha_n}\n\\rightarrow U_0^x.$$ To prove this we introduce the finite horizon\nequations: for $t\\in [0,N]$,\n$$\\begin{cases}\n& -dU_t^{x,\\alpha,N}=[e^{\\eta t}\\nabla_x\\psi(X^x_t,Z_t^{x,\\alpha})\n \\nabla X_t^x-(\\alpha+\\eta)U_t^{x,\\alpha,N}\n +\\nabla_z \\psi (X^x_t,Z_t^{x,\\alpha}) V_t^{x,\\alpha,N}]dt\\\\\n& \\qquad\\qquad\\qquad - V^{x,\\alpha,N}_tdW_t,\\\\\n& U_N^{x,\\alpha,N}=0.\n\\end{cases}$$\n$$\\begin{cases}& -dU_t^{x,N}=[e^{\\eta t}\\nabla_x\\psi(X^x_t,\\bar Z_t^{x})\n\\nabla X_t^x-(\\alpha+\\eta)U_t^{x,N}\n+\\nabla_z \\psi (X^x_t,\\bar Z^{x}_t) V_t^{x,N}]dt-V^{x,N}_tdW_t,\\\\\n& U_N^{x,N}=0.\n\\end{cases}$$\nSince $\\displaystyle \\mathbb E\\int_0^N |Z^{x,\\alpha_n}_s-\\bar Z^{x}_s|^2\nds\\rightarrow 0$ it is easy to verify that, for all fixed $N>0$,\n$U_0^{x,\\alpha_n,N}\\rightarrow U_0^{x,N}$.\n\n\nOn the other side a standard application of Girsanov Lemma gives\n see \\cite{HuTe},\n$$|U_0^{x,\\alpha_n,N}-U_0^{x,\\alpha_n}|\\le \\frac{c}{\\alpha_n+\\eta}e^{-\\eta N}, \\qquad |U_0^{x,N}-U_0^{x}|\\le \\frac{c}{\\eta}e^{-\\eta N}.$$\nfor a suitable constant $c$.\n\n\nThus a standard argument implies $U_0^{x,\\alpha_n}\\rightarrow\nU_0^{x}$. An identical argument also ensures continuity of\n$U_0^{x}$ with respect to $x$ (also taking into account\n\\ref{lip-of-Z}). The proof is therefore completed.\n\\end{proof}\n\n\n$ $\n\n\nAs usual in the theory of markovian BSDEs, the differentiability\nproperty allows to identify the process $\\bar Z^x$ as a function\nof the process $X^x$. To deal with our Banach space setting we\nneed to make the following extra assumption:\n\n\\begin{hypothesis}\\label{Hyp-masiero}\nThere exists a Banach space $\\Xi_0$, densely and continuously\nembedded in $\\Xi$, such that $G\\, (\\Xi_0) \\subset \\Xi$ and $G\n:\\Xi_0 \\rightarrow E$ is continuous.\n\\end{hypothesis}\n\nWe note that this condition is satisfied in most applications. In\nparticular it is trivially true in the special case $E=H$ just by\ntaking $\\Xi_0=\\Xi$, since $G$ is assumed to be a linear bounded\noperator from $\\Xi$ to $H$. The following is proved in\n\\cite[Theorem 3.17]{masiero}:\n\n\n\\begin{theorem}\n \\label{theorem-identif-Z}\nAssume that Hypotheses \\ref{general_hyp_forward},\n \\ref{hypothesisroyer} and \\ref{Hyp-masiero} hold.\nMoreover assume that $F$ is of class ${\\cal G}^1(E,E)$ with\n$\\nabla F$ bounded on bounded subsets of $E$ and $\\psi$ is of\nclass ${\\cal G}^1(E\\times \\Xi^*,E)$. Then $\\bar Z^x_t=\\nabla \\bar\nv(X^x_t)G$, $\\mathbb{P}$-a.s. for a.e. $t\\geq 0$.\n\\end{theorem}\n\\begin{remark} \\label{precision}\\begin{em}\nWe notice that $\\nabla \\bar v(x)G\\xi$ is only defined for $\\xi\\in\n\\Xi_0$ in general, and the conclusion of\nTheorem \\ref{theorem-identif-Z} should be stated more precisely\nas follows: for $\\xi\\in\n\\Xi_0$ the equality $Z^x_t\\xi=\\nabla \\bar v(X^x_t)G\\xi$\nholds $\\mathbb{P}$-a.s. for almost every $t\\geq 0$. However,\nsince $\\bar Z^x$\nis a process with values in $\\Xi^*$, and more specifically\na process in $\nL^2_{\\mathcal{P}}(\\Omega,L^2([0,T],\\Xi^*))$, it follows that\n$\\mathbb P$-a.s. and\nfor almost every\n$t$ the\noperator $\\xi \\rightarrow \\nabla \\bar v(X^x_t)G\\xi$ can be\nextended to a bounded linear operator defined on the whole $\\Xi$.\nEquivalently,\nfor almost every\n$t$ and for almost all $x\\in E$ (with respect to the law of $X_t$)\nthe linear\noperator $\\xi \\rightarrow \\nabla \\bar v(x)G\\xi$ can be\nextended to a bounded linear operator defined on the whole $\\Xi$\n(see also Remark 3.18 in \\cite{masiero}).\n\\end{em}\n\\end{remark}\n\\begin{remark}\\label{boundedpsibar}\n\\begin{em} The above representation together with the fact that\n$\\bar v$ is Lipschitz with Lipschitz constant $K_x\\eta^{-1}$\nimmediately implies that, if $F$ is of class ${\\cal G}^1(E,E)$ and\n$\\psi$ is of class ${\\cal G}^1(E\\times \\Xi^*,E)$, then $|\\bar\n{Z}^x_t|_{\\Xi_0^*}\\leq K_x\\eta^{-1} |G|_{L(\\Xi_0,E)}$ for all $x\\in\nE$, $\\mathbb{P}$-a.s. for almost every $t\\geq 0$. Consequently we\ncan construct $\\bar \\zeta$ in Theorem \\ref{main-EBSDE}\n in such a way that it is bounded in the\n$\\Xi_0^*$ norm by $K_x\\eta^{-1} |G|_{L(\\Xi_0,E)}$.\n\n\nOnce this is proved we can extend the result to the case in which\n$\\psi$ is no longer differentiable but only Lipschitz, namely\nwe can prove than even in this case the process $\\bar\n{Z}^x$ is bounded. Indeed if we\nconsider a sequence $\\{\\psi_n : n\\in \\mathbb{N}\\}$ of functions of\nclass ${\\cal G}^1(E\\times \\Xi^*,E)$ such that for all $x,x'\\in H$,\n$z,z'\\in \\Xi^*$, $n\\in \\mathbb{N}$,\n$$ |\\psi_n(x,z)\n-\\psi_n(x',z')|\\le K_x|x-x'|+ K_z |z-z'|;\\quad \\lim_{n\\rightarrow\n\\infty}|\\psi_n(x,z) -\\psi(x,z)|=0.\n$$\nWe know that if $(\\bar Y^{x,n}, \\bar Z^{x,n},\\lambda_n)$ is the\nsolution of the EBSDE (\\ref{EBSDE}) with $\\psi$ replaced by\n$\\psi_n$ then $|\\bar {Z}^{x,n}_t|_{\\Xi_0^*}\\leq K_x\\eta^{-1}\n|G|_{L(\\Xi_0,E)}$. Then as we did above we can show (showing that the\ncorresponding equations with monotonic generator converge\nuniformly in $\\alpha$) that $\\mathbb{E}\\int_0^T|\\bar {Z}^{x,n}_t\n-\\bar {Z}^{x}_t|_{\\Xi_0^*}^2dt\\rightarrow 0$ and the claim\nfollows.\n\n\nWe also notice that by the same argument we also have $ |\\bar\n\\zeta^{\\alpha}(x)|_{\\Xi_0^*}\\leq K_x\\eta^{-1} |G|_{L(\\Xi_0,E)}$,\n$\\forall \\alpha>0$.\n\\end{em}\n\\end{remark}\nNow we introduce the Kolmogorov semigroup corresponding to $X$:\n for measurable and bounded $\\phi:\nE\\rightarrow \\mathbb{R}$ we define\n\\begin{equation}\\label{def-of-p}\nP_t[\\phi](x)=\\mathbb{E}\\, \\phi(X^x_t)\\qquad t\\ge 0,\\, x\\in E.\n\\end{equation}\n\\begin{definition}\\label{strongly-feller}\nThe semigroup $(P_t)_{t\\geq 0}$ is called strongly Feller if for\nall $t>0$ there exists $k_t$ such that for all measurable and\nbounded $\\phi: E\\rightarrow \\mathbb{R}$,\n$$|\nP_t[\\phi](x)- P_t[\\phi](x')|\\leq k_t \\Vert\\phi\\Vert_0 |x-x'|,\n\\qquad x,x'\\in E,\n$$\nwhere $\\Vert\\phi\\Vert_0=\\sup_{x\\in E}|\\phi(x)|$.\n\\end{definition}\n\nThis terminology is somewhat different from the classical one\n(namely, that $P_t$ maps measurable bounded functions into\ncontinuous ones,\n for all\n$t>0$), but it will be convenient for us.\n\n\n\n\\begin{definition}\\label{gen-diss} We say that $F$\nis genuinely dissipative if there exist $\\epsilon>0$ and $c>0$\nsuch that, for all $x,x'\\in E$, there exists $z^*\\in \\partial\n|x-x'|$ such that $_{E^*,E}\\leq c\n|x-x'|^{1+\\epsilon}$.\n\\end{definition}\n\n\n\n\\begin{lemma}\\label{lemma-SF-dissip}\nAssume that Hypotheses \\ref{general_hyp_forward} and \\ref{hyp_W_A\nF(W_A)} hold.\nIf the Kolmogorov\nsemigroup $(P_t)$ is strongly Feller then for all bounded\nmeasurable $\\phi: E\\rightarrow\\mathbb{R}$,\n$$\\left|P_t[\\phi](x)-\\int_E \\phi(x)\\mu (dx)\\right|\n\\leq c e^{-\\eta (t\/4)}(1+|x|)\\Vert\\phi\\Vert_0.$$\nIf in addition $F$ is genuinely dissipative then\n$$\\left|P_t[\\phi](x)-\\int_E \\phi(x)\\mu (dx)\\right|\n\\leq c e^{-\\eta (t\/4)}\\Vert\\phi\\Vert_0.$$\n\\end{lemma}\n\\begin{proof} We fix $\\epsilon >0$. For $t>2$ we have,\nby Theorem \\ref{ergodicity},\n$$\n\\begin{array}{r}\\displaystyle\\left|P_t[\\phi](x)-\\int_E \\phi(x)\\mu (dx)\\right|=\n\\left|P_{t-1}[P_1[\\phi]](x)-\\int_E P_{1}[\\phi](x)\\mu (dx)\\right|\n\\leq C(1+|x|)\ne^{-\\eta t \/4} \\Vert P_{1}[\\phi]\\Vert_{\\hbox{lip}}\\\\\n\\displaystyle \\leq C(1+|x|) e^{-\\eta t \/4} k_{1}\\Vert\\phi\\Vert_0,\n\\end{array}$$\nand the first claim follows since $\\left|P_t[\\phi](x)-\\int_E\n\\phi(x)\\mu (dx)\\right|\\leq 2 \\Vert\\phi\\Vert_0$.\n\n\nIf now $F$ is genuinely dissipative then in \\cite{DP2}, Theorem\n6.4.1 it is shown that\n$$\\left|\\mathbb{E}\\phi(X^x_t)-\\int_E \\phi\\, d\\mu \\right|\\leq\nC e^{-\\eta t \/2} \\Vert\\phi\\Vert_{\\hbox{lip}}$$ and the second\nclaim follows by the same argument.\n\\end{proof}\n\nWe are now able to state and prove two corollaries\nof Theorems \\ref{th-diff} and \\ref{theorem-identif-Z}.\n\n\n\\begin{corollary}\\label{characterization of lambda}\nAssume that Hypotheses \\ref{general_hyp_forward}, \\ref{hyp_W_A\nF(W_A)}, \\ref{hypothesisroyer} and \\ref{Hyp-masiero} hold.\nMoreover assume that $F$ is of class $\\mathcal{G}^1$ with $\\nabla\nF$ bounded on bounded subsets of $E$, and that $\\psi$ is bounded\non each set $E\\times B$, where $B$ is any ball of $\\Xi_0^*$.\nFinally assume that the Kolmogorov semigroup $(P_t)$ is strongly\nFeller.\n\n\n\nThen the following holds:\n$$\\lambda=\\int_E \\psi(x,\\bar \\zeta(x))\\mu (dx),$$\nwhere $\\mu$ is the unique invariant measure of $X$.\n\\end{corollary}\n\\begin{proof} First notice that $\\overline{\\psi}:=\n\\psi(\\,\\cdot\\, , \\bar\\zeta(\\,\\cdot\\,))$ is bounded, by\nRemark \\ref{boundedpsibar}.\n Then\n$$T^{-1}\\mathbb{E}[\\bar Y ^x_0-\\bar Y ^x_T]=\nT^{-1}\\mathbb E \\int_0^T\\left (\\psi(X^x_t,\\bar \\zeta( X^x_t))- \\int_E\n\\bar \\phi\\, d\\mu \\right)dt+ \\left(\\int_E \\bar \\phi\\, d\\mu\n-\\lambda\\right).$$ We know that $T^{-1}\\mathbb{E}[\\bar Y ^x_0-\\bar\nY ^x_T]\\rightarrow 0$, by the argument\nin Theorem \\ref{th-uniq-lambda}.\nMoreover by the first conclusion of Lemma\n\\ref{lemma-SF-dissip}\n$$ T^{-1}\\mathbb E \\int_0^T\\left (\\psi(X^x_t,\\bar \\zeta( X^x_t))-\n\\int_E \\bar \\phi\\, d\\mu \\right)dt \\rightarrow 0,$$ and the claim\nfollows. \\end{proof}\n\n\\begin{corollary}\\label{boundedness of v}\nIn addition to the assumptions of Corollary \\ref{characterization\nof lambda} suppose that $F$ is genuinely dissipative. Then $\\bar\nv$ is bounded.\n\\end{corollary}\n\\begin{proof}\nLet $(Y^{x,\\alpha},Z^{x,\\alpha})$ be the solution of\n(\\ref{bsderoyer}). We know that $Y^{x,\\alpha}_t=v^{\\alpha}(X^x_t)$\nand $Z^{x,\\alpha}_t= \\zeta^{\\alpha}(X^x_t)$ with $v^{\\alpha}$\nLipschitz uniformly with respect to $\\alpha$ and $\\zeta^{\\alpha}$\nbounded in $\\Xi^*$ uniformly with respect to $\\alpha$. Let\n$\\psi^{\\alpha}=\\psi(\\,\\cdot\\,,\\bar \\zeta^{\\alpha}(\\,\\cdot\\,))$.\nUnder the present assumptions we conclude that also the maps\n$\\psi^{\\alpha}$ as well are bounded in $\\Xi^*$ uniformly with\nrespect to $\\alpha$.\n\n\nComputing $d (e^{-\\alpha t} \\bar Y^{x\\alpha}_t)$ we obtain,\n$$Y^{x,\\alpha}_0=\\mathbb{E} e^{-\\alpha T} Y^{x,\\alpha}_T+\n\\mathbb{E} \\int_0^T e^{-\\alpha t} \\psi^{\\alpha} (X^x_t)dt,$$ and\nfor $T\\rightarrow\\infty$,\n$$Y^{x,\\alpha}_0=\n\\mathbb{E} \\int_0^\\infty e^{-\\alpha t} \\psi^{\\alpha} (X^x_t)dt.$$\nSubtracting to both sides $\\alpha^{-1}\\int_E\n\\psi^{\\alpha}(x)\\mu(dx)$ we obtain\n$$\\left|Y^{x,\\alpha}_0-\\alpha^{-1}\\int_E \\psi^{\\alpha}(x)\\mu(dx)\\right|=\n\\left| \\int_0^\\infty e^{-\\alpha t} \\left[P_t[\\psi^{\\alpha}]\n(x)-\\int_E \\psi^{\\alpha}(x)\\mu(dx)\\right]dt\\right|\\leq 4c\n\\eta^{-1} \\Vert \\psi^\\alpha\\Vert_0 $$ where the last inequality\ncomes from the second conclusion of Lemma \\ref{lemma-SF-dissip}.\n\n\nThus $\\left|Y^{x,\\alpha}_0-Y^{0,\\alpha}_0\\right| \\leq 8 c\n\\eta^{-1} \\Vert \\psi^\\alpha\\Vert_0 $ and the claim follows since\nby construction $Y^{x,\\alpha}_0-Y^{0,\\alpha}_0 \\rightarrow \\bar v\n(x)$.\n\\end{proof}\n\\section{Ergodic Hamilton-Jacobi-Bellman equations}\nWe briefly show here that if $\\bar Y_0^x=\\bar v(x)$ is of class\n${\\cal G}^1$ then the couple $(v,\\lambda)$ is a mild solution of\nthe following ``ergodic'' Hamilton-Jacobi-Bellman equation:\n\\begin{equation}\n\\mathcal{L}v(x)\n+\\psi\\left( x,\\nabla v(x) G\\right) = \\lambda, \\quad x\\in E, \\label{hjb}%\n\\end{equation}\nWhere linear operator $\\mathcal{L}$ is formally defined by\n\\[\n\\mathcal{L}f\\left( x\\right) =\\frac{1}{2}Trace\\left(\nGG^{\\ast}\\nabla ^{2}f\\left( x\\right) \\right) +\\langle Ax,\\nabla\nf\\left( x\\right) \\rangle_{E,E^{\\ast}}+\\langle F\\left( x\\right)\n,\\nabla f\\left( x\\right) \\rangle_{E,E^{\\ast}},\n\\]\nWe notice that we can define the transition semigroup\n $(P_t)_{t\\geq 0}$ corresponding to $X$ by the formula (\\ref{def-of-p})\nfor all measurable functions $\\phi:E\\to\\mathbb{ R}$ having\npolynomial growth, and we notice that $\\mathcal{L}$ is the formal\ngenerator of $(P_t)_{t\\geq 0}$.\n\n\n Since we are dealing with an elliptic equation it is natural to consider\n$(v,\\lambda)$ as a mild solution of equation (\\ref{hjb}) if and\nonly if, for arbitrary $T>0$, $v(x)$ coincides with the mild\n solution $u(t,x)$ of the corresponding parabolic equation\n having $v$ as a terminal condition:\n\\begin{equation}\\left\\{\n\\begin{array}{l}\n \\dfrac{\\partial u(t,x)}{\\partial t}+\\mathcal{L}u\\left( t,x\\right)\n+\\psi\\left( x,\\nabla u\\left( t,x\\right) G\\right)\n -\\lambda=0, \\quad t\\in [0,T],\\; x\\in E, \\\\ \\\\\nu(T,x)=v(x), \\quad x\\in E.\n \\end{array}\\right. \\label{hjb-parab}\n\\end{equation}\nThus we are led to the following definition (see also\n\\cite{FuTe-ell}):\n\\begin{definition}\n\\label{defsolmildkolmo} A pair $(v,\\lambda)$ ($v: E\\rightarrow\n\\mathbb{R}$ and $\\lambda\\in \\mathbb{R}$) is a mild solution of the\nHamilton-Jacobi-Bellman equation (\\ref{hjb}) if the following are\nsatisfied:\n\n\n\\begin{enumerate}\n\\item $v\\in\\mathcal{G}^{1}\\left( E,\\mathbb R \\right) $;\n\n\n\\item there exists $C>0$ such that $\\left| \\nabla v\\left(\nx\\right)\nh\\right| \\leq C\\left| h\\right| _{E}\\left( 1+\\left| x\\right| _{E}%\n^{k}\\right) $ for every $x,h\\in E$ and some positive integer\n$k$;\n\n\n\\item for $0\\le t\\le T$ and $x\\in E$,\n\\begin{equation}\nv(x)=P_{T-t}\\left[ v\\right] \\left( x\\right)\n+\\int_{t}^{T}\\left(P_{s -t }\\left[ \\psi(\\cdot,\\nabla v\\left(\n\\cdot\\right) G)\\right] \\left( x\\right) -\\lambda \\right) \\,ds.\n\\label{mild sol hjb}\n\\end{equation}\n\n\n\\end{enumerate}\n\\end{definition}\n\n\n\n\nIn the right-hand side of (\\ref{mild sol hjb}) we notice\noccurrence of the term $\\nabla v\\left( \\cdot\\right) G$, which is\nnot well defined as a function $E\\to\\Xi^*$, since $G$ is not\nrequired to map $\\Xi$ into $E$.\nThe situation is similar to Remark \\ref{precision}.\nIn general,\n for $x \\in E$, $\\nabla \\bar\nv(x)G\\xi$ is only defined for $\\xi\\in \\Xi_0$.\nIn (\\ref{mild sol hjb}) it is implicitly required\nthat, $\\mathbb P$-a.s. and\nfor almost every\n$t$, the\noperator $\\xi \\rightarrow \\nabla \\bar v(X^x_t)G\\xi$ can be\nextended to a bounded linear operator defined on the whole $\\Xi$.\nNoting that\n$$\nP_{t }\\left[ \\psi(\\cdot,\\nabla v\\left( \\cdot\\right) G)\\right]\n\\left( x\\right) = \\mathbb E \\, \\psi(X^x_{t},\\nabla v\\left( X^x_{t}\\right)\nG)\n$$\nthe equation (\\ref{mild sol hjb}) is now meaningful.\n\nUsing the results for the parabolic case, see \\cite{masiero}, we\nget existence of the mild solution of equation (\\ref{hjb})\nwhenever we have proved that the function\n$\\bar v$ in Theorem \\ref{main-EBSDE} is differentiable.\n\n\n\\begin{theorem}\\label{th-EHJB}\nAssume that Hypotheses \\ref{general_hyp_forward},\n\\ref{hypothesisroyer} and \\ref{Hyp-masiero} hold.\nMoreover assume that $F$ is of class ${\\cal G}^1(E,E)$ with\n$\\nabla F$ bounded on bounded subsets of $E$ and $\\psi$ is of\nclass ${\\cal G}^1(E\\times \\Xi^*,E)$.\n\n\nThen $(\\bar v, \\bar\\lambda)$ is a mild solution of the\nHamilton-Jacobi-Bellman equation (\\ref{hjb}).\n\nConversely, if $(v,\\lambda)$ is a mild solution of\n (\\ref{hjb}) then, setting $ Y^x_t=\nv(X^x_t)$ and ${Z}^{x}_t=\n\\nabla v( X^x_t) G$,\nthe triple\n $( Y^x, Z^x, \\lambda)$ is a solution of\n the EBSDE\n (\\ref{EBSDE}).\n\n\\end{theorem}\n\n\n\n\\section{Optimal ergodic control}\n\\label{optcontr}\n\nAssume that Hypothesis \\ref{general_hyp_forward} holds and let\n$X^x$ denote the solution to equation (\\ref{sde}).\n Let $U$ be a separable\n metric space. We define a control $u$ as an\n$({\\cal F}_t)$-progressively measurable $U$-valued process. The cost\n corresponding to a given control\nis defined in the following way. We assume that the functions\n$R:U\\rightarrow \\Xi^*$ and $L:E\\times U \\rightarrow \\mathbb R$ are\nmeasurable and satisfy, for some constant $c>0$,\n\\begin{equation}\\label{condcosto}\n|R(u)|\\leq c,\\quad |L(x,u)|\\leq c, \\quad |L(x,u)-L(x',u)|\\leq\nc\\,|x-x'|,\\qquad u\\in U,\\,x,x'\\in E.\n\\end{equation}\nGiven an arbitrary control $u$ and $T>0$, we introduce the\nGirsanov density\n$$ \\rho_T^u=\\exp\\left(\\int_0^T R(u_s)dW_s\n-\\frac{1}{2}\\int_0^T |R(u_s)|_{\\Xi^*}^2 ds\\right)$$ and the\nprobability $\\mathbb P_T^u=\\rho_T^u\\mathbb P$ on ${\\cal F}_T$. The\nergodic cost corresponding to $u$ and the starting point $x\\in E$\nis\n\\begin{equation}\\label{def-ergodic-cost}\n J(x,u)=\\limsup_{T\\rightarrow\\infty}\\frac{1}{T} \\mathbb\nE^{u,T}\\int_0^T L(X_s^x,u_s)ds,\n\\end{equation}\nwhere $\\mathbb E^{u,T}$ denotes expectation with respect to\n$\\mathbb P_T^u$. We notice that $W_t^u=W_t-\\int_0^t R(u_s)ds$ is a\nWiener process on $[0,T]$ under $\\mathbb P^u$ and that\n$$dX_t^x=(AX_t^x+F(X_t^x))dt+G(dW_t^u+R(u_t)dt),\n\\quad t\\in [0,T]$$ and this justifies our formulation of the\ncontrol problem. Our purpose is to minimize the cost over all\ncontrols.\n\n\n To this purpose we first define the Hamiltonian in the\nusual way\n\\begin{equation}\\label{defhamiton}\n\\psi(x,z)=\\inf_{u\\in U}\\{L(x,u)+z R(u)\\},\\qquad x\\in E,\\,z\\in\n\\Xi^*,\n\\end{equation}\nand we remark that if, for all $ x,z$, the infimum is attained\nin (\\ref{defhamiton}) then there exists a measurable function\n$\\gamma:E\\times \\Xi^*\\rightarrow U$ such that\n$$\\psi(x,z)=l(x,\\gamma(x,z))+z R(\\gamma(x,z)).$$\nThis follows from an application of Theorem 4 of \\cite{McS-War}.\n\nWe notice that under the present assumptions $\\psi$ is a\nLipschitz function and $\\psi(\\cdot,0)$ is bounded (here the fact\nthat $R$ depends only on $u$ is used). So if we assume Hypotheses\n\\ref{general_hyp_forward} and \\ref{hyp_W_A F(W_A)} then in Theorem\n\\ref{main-EBSDE} we have constructed, for every $x\\in E$, a triple\n\\begin{equation}\\label{richiamoebsde}\n(\\bar Y^x,\\bar Z^x, \\bar \\lambda)= (\\bar v (X^x),\\bar \\zeta(X^x),\n\\bar \\lambda)\n\\end{equation} solution to\n the EBSDE\n (\\ref{EBSDE}).\n\n\n\n\n\n\n\n\\begin{theorem}\\label{Th-main-control}\nAssume that Hypotheses \\ref{general_hyp_forward}, \\ref{hyp_W_A\nF(W_A)} and \\ref{hyp-convol-determ} hold, and that\n(\\ref{condcosto}) holds as well.\n\n\nMoreover suppose that, for some $x\\in E$, a triple $(Y,Z,\\lambda)$\nverifies $\\mathbb{P}$-a.s. equation\n (\\ref{EBSDE}) for all $0\\leq t\\leq T$,\nwhere\n $Y$ is a progressively measurable continuous process, $Z$ is a process\n in $L_{\\cal P, {\\rm loc}}^2(\\Omega;L^2(0,\\infty;\\Xi^*))$ and\n $\\lambda\\in \\mathbb{R}$.\n Finally assume that there exists $c_x>0$ (that may depend\n on $x$) such that\n$\\mathbb{P}$-a.s.\n$$\n |Y_t|\\leq c_x (|X^x_t|+1) , \\hbox{ for all $t\\geq 0$}.\n$$\n\n\n\nThen the following holds:\n\\begin{enumerate}\n \\item[(i)] For arbitrary control\n $u$ we have $J(x,u)\\ge \\lambda=\\bar\\lambda,$\nand the equality holds if and only if $L(X_t^x,u_t)+Z_t\nR(u_t)=\\psi(X_t^x,Z_t)$, $\\mathbb P$-a.s. for almost every $t$.\n\n\n\\item[(ii)] If the infimum is attained in (\\ref{defhamiton}) then\nthe control $\\bar u_t=\\gamma(X_t^x,Z_t)$ verifies $J(x,\\bar u)=\n\\bar\\lambda.$\n\\end{enumerate}\n\n\nIn particular, for the solution (\\ref{richiamoebsde}) mentioned\nabove, we have:\n\\begin{enumerate}\n \\item[(iii)] For arbitrary control\n $u$ we have $J(x,u)=\\bar\\lambda$ if and only if\n$L(X_t^x,u_t)+\\bar\\zeta (X_t^x) R(u_t)=\\psi(X_t^x,\\bar \\zeta\n(X_t^x))$, $\\mathbb P$-a.s. for almost every $t$. \\item[(iv)] If the\ninfimum is attained in (\\ref{defhamiton}) then the control $\\bar\nu_t=\\gamma(X_t^x,\\bar\\zeta (X_t^x))$ verifies $J(x,\\bar u)=\n\\bar\\lambda.$\n\\end{enumerate}\n\n\n\\end{theorem}\n\n\n\\begin{remark}\\em\n\\begin{enumerate}\n \\item\nThe equality $\\lambda=\\bar\\lambda$ clearly follows from Theorem\n\\ref{th-uniq-lambda}. \\item Points $(iii)$ and $(iv)$ are\nimmediate consequences of $(i)$ and $(ii)$. \\item The conclusion\nof point $(iv)$ is that there exists an optimal control in\nfeedback form, with the optimal feedback given by the function\n$x\\mapsto \\gamma(x,\\bar\\zeta (x))$. \\item Under the conditions of\nTheorem \\ref{th-EHJB}, the pair $(\\bar v, \\bar \\lambda)$ occurring\nin (\\ref{richiamoebsde}) is a mild solution of the\nHamilton-Jacobi-Bellman equation (\\ref{hjb}). \\item It follows\nfrom the proof below that if $\\limsup$ is changed into $\\liminf$\nin the definition (\\ref{def-ergodic-cost}) of the cost, then the\nsame conclusions hold, with the obvious modifications, and the\noptimal value is given by $\\bar\\lambda$ in both cases.\n\\end{enumerate}\n\\end{remark}\n\n\n\\begin{proof}\n As $(Y,{Z}, \\bar\\lambda)$ is a solution of the\nergodic BSDE, we have\n\\begin{eqnarray*}\n-d{Y}_t&=&[\\psi(X_t^x,{Z}_t)-\\bar\\lambda]dt-{Z}_tdW_t\\\\\n&=&[\\psi(X_t^x,{Z}_t)- \\bar\\lambda]dt-{Z}_tdW_t^u-{Z}_t R(u_t)dt,\n\\end{eqnarray*}\nfrom which we deduce that\n\\begin{eqnarray*}\n\\bar\\lambda&=&\\frac{1}{T}\\mathbb E^{u,T}[Y_T-Y_0]\n+\\mathbb E^{u,T}\\frac{1}{T}\\int_0^T[\\psi(X_t^x,{Z}_t)-{Z}_t r(u_t)-L(X_t^x,{Z}_t)]dt\\\\\n& &+\\frac{1}{T}\\mathbb E^{u,T}\\int_0^T L(X_t^x,{Z}_t)dt.\n\\end{eqnarray*}\n\n\nThus\n$$\\frac{1}{T}\\mathbb E^{u,T}\\int_0^T L(X_t^x,{Z}_t)dt\\ge\n \\frac{1}{T}\\mathbb E^{u,T}[Y_0-Y_T]+\\bar\\lambda.$$\nBut by (\\ref{rel-estimate-Xgamma}) we have\n$$|\\mathbb E^{u,T} Y_T|\\le c\\mathbb E^{u,T}(|X_T^x|+1)\\le c(1+|x|).$$\nConsequently $T^{-1}\\mathbb E^{u,T}[Y_0-Y_T]\\rightarrow 0,$ and\n$$\\limsup_{T\\rightarrow\\infty } \\frac{1}{T}\\mathbb E^{u,T}\\int_0^T L(X_t^x,{Z}_t)dt\n\\ge \\bar\\lambda.$$\n\n\nSimilarly, if $L(X_t^x,u_t)+ Z_t R(u_t)=\\psi(X_t^x,Z_t)$,\n$$\\frac{1}{T}\\mathbb E^{u,T}\\int_0^T L(X_t^x,{Z}_t)dt=\n\\frac{1}{T}\\mathbb E^{u,T}[Y_0-Y_T]+\\bar\\lambda,$$ and the claim\nholds.\n\\end{proof}\n\n\n\n\n\\section{Uniqueness}\\label{sec-uniq}\nWe wish now to adapt the argument in \\cite{GoMa} in order to\nobtain uniqueness of markovian solutions to the EBSDE. This will\nbe done by a control thoretic interpretation the requires that the\nMarkov process related to the state equation with continuous\nfeedback enjoys recurrence properties. In this section we assume\n\\begin{equation}\\label{addizionali}\nE=H \\qquad\\hbox{ and }\\qquad F \\hbox{ is bounded.}\n\\end{equation}\n\n\\noindent We recall here a result due to \\cite{seid} on recurrence\nof solution to SDEs.\n\\begin{theorem}\\label{th-rec-seidler}\nConsider\n\\begin{equation}\\label{eq:u}\nd{X}_t=(A{X}_t+g({X}_t))dt+GdW_t.\n\\end{equation}\nwhere $g: H \\rightarrow H$ is bounded and weakly continuous (that\nif $x\\rightarrow\\<\\xi,g(x)\\>$ is continuous for all $\\xi\\in H$).\nLet\n$$Q_t=\\int_0^t e^{sA}GG^*e^{sA^*}ds.$$\nand assume the following\n\\begin{enumerate}\n \\item $\\sup_{t\\ge 0} \\hbox{Trace}\\,(Q_t)<\\infty$;\n\\item $Q_t$ is injective for $t>0$; \\item $ e^{t A}(H)\\subset\n(Q_t)^{1\/2}(H)$ for $t>0$; \\item $\\int_0^t\n|Q_s^{-1\/2}e^{sA}|ds<\\infty$ for $t>0$; \\item there exists\n$\\beta>0$ such that $\\int_0^t s^{-\\beta}\\,\n\\hbox{Trace}\\,(S(s)S(s)^*)\\, ds<\\infty$\n for $t>0$.\n\\end{enumerate}\nThen, for all $T>0$, equation (\\ref{eq:u}) admits a martingale\nsolution on $[0,T]$, unique in law. The associated transition\nprobabilities $P(t,x,T,\\cdot)$ on $H$ ($0\\le t\\le T, x\\in H$)\nidentify a recurrent Markov process on $[0,\\infty)$.\n\\end{theorem}\n\n\nConsider now the ergodic control problem with state equation:\n$$d{X}^{x,u}_t=(A{X}^{x,u}_t+F({X}^{x,u}_t)+GR(u_t))dt+GdW_t, \\ X_0^{x,u}=x,$$\nand cost\n$$\\limsup_{T\\to\\infty}\\frac{1}{T}\\,\n\\mathbb E\\int_0^T l(X_s,u_s)ds$$ where $R:U\\rightarrow\n\\Xi$ is continuous and bounded.\n\n\nWe restrict ourselves to the class of controls given by continuous\nfeedbacks, i.e. given arbitrary\n continuous $u: H\\rightarrow U$ (called feedback) we define the\n corresponding trajectory as the solution of\n$$d{X}^{x,u}_t=(A{X}^{x,u}_t+F({X}^{x,u}_t))dt+G(R(u(X_t^{x,u}))dt+dW_t),\n\\ X_0^{u,x}=x.$$\nWe notice that for all $T>0$ there exists a weak solution $X^{x,u}$ of\nthis equation, and it is unique in law.\n\n\n\n$ $\n\n\n We set as usual\n$$\\psi(x,z)=\\inf_{u\\in U}\\{L(x,u)+zR(u)\\},$$\nand assume that $\\psi$ is continuous and there exists a continuous\n$\\gamma:H\\times\\Xi\\rightarrow U$ such that\n$$\\psi(x,z)=L(x,\\gamma(x,z))+zR(\\gamma(x,z)).$$\n\n\n\\begin{theorem}\\label{th-uniqueness}\nSuppose (\\ref{addizionali})\nand suppose that the assumptions of Theorem\n\\ref{th-rec-seidler} hold.\nLet $(v,\\zeta,\\lambda)$ with $v:H\\rightarrow \\mathbb{R}$ continuous,\n$\\zeta:H\\rightarrow \\mathbb{R}$ continuous, and $\\lambda$ a real number\nsatisfy the following conditions:\n\n\n\\begin{enumerate}\n \\item $|v(x)|\\le c|x|$;\n\\item\nfor an arbitrary filtered probability space with\na Wiener process\n$(\\hat{\\Omega},\\hat{\\mathcal{F}},\n\\{\\hat{\\mathcal{F}}_t\\}_{t>0},\\hat{\\mathbb{P}},\\{\n\\hat{W}_t\\}_{t>0})$ and\nfor any solution of\n$$d\\hat{X}_t=(A\\hat{X}_t+F(\\hat{X}_t))dt+Gd\\hat{W}_t,\\qquad t\\in [0,T],$$\nsetting $Y_t=v(\\hat{X}_t),\\\nZ_t=\\zeta(\\hat{X}_t)$, we have\n$$-dY_t=[\\psi(\\hat{X}_t,Z_t)-\\lambda]dt-Z_tdW_t\\quad t\\in [0,T].$$\n\\end{enumerate}\nLet\n$$\\tau_r^T=\\inf \\{s\\in [0,T]:|X_s^{u,x}|< r\\},$$\nwith the convention $\\tau_r^T=T$ if the indicated set\nis empty,\nand\n$$J(x,u)=\\limsup_{r\\rightarrow 0}\\limsup_{T\\rightarrow \\infty}\n\\mathbb E\\int_0^{\\tau_r^T} [\\psi(X_s^{x,u},u(X_s^{x,u}))-\\lambda]ds.$$\nThen\n$$v(x)=\\inf_u J(x,u),$$\nwhere the infimum (that is a minimum) is taken over all continuous\nfeedbacks $u$.\n\\end{theorem}\n\\begin{proof} Let $u:H\\to U$ be continuous.\nWe notice that $X^{x,u}$ solves on $[0,T]$:\n$$dX_t^{x,u}=(AX_t^{x,u}+F(X_t^{x,u}))ds+Gd\\tilde{W}_t^u,\\ t\\in [0,T],$$\nwhere $\\tilde{W}_t=\\int_0^t R(u(X_r^{x,u})dr+W_t$ is a Wiener\nprocess on $[0,T]$ under a suitable probability $\\hat{\\mathbb{P}}^{u,T}$.\n\n\nTherefore $Y_t=v(X_t^{x,u})$, $Z_t=\\zeta(X_t^{x,u})$ satisfy:\n$$\n-dY_t=[\\psi(X_t^{x,u},u(X_t^{x,u}))-\\lambda]dt-Z_t\nR(u(X_t^{x,u}))]dt-Z_tdW_t.$$ Integrating in $[0,\\tau_r^T]$ we get\n$$v(x)=\\mathbb E(v(X_{\\tau_r^T}^{x,u}))+\\mathbb E\\int_0^{\\tau_r^T}\n[\\psi(X_s^{u,x},u(X_s^{x,u}))-\\lambda-Z_s R(X_s^{x,u})]ds.$$ Thus,\n\\begin{equation}\\label{eq:x}\nv(x)\\le\\mathbb E(v(X_{\\tau_r^T}^{x,u}))+\\mathbb E\\int_0^{\\tau_r^T}\n[L(X_s^{u,x},u(X_s^{x,u}))-\\lambda]ds.\n\\end{equation}\nNow\n\\begin{eqnarray*}\n|\\mathbb E(v(X_{\\tau_r^T}^{x,u}))|\\le c\\mathbb\nE|X^{x,u}_{\\tau_r^T}|&\\le& c r+ (\\mathbb\nE(|X_T^{x,u}|^2))^{1\/2}(\\mathbb P(\\tau_r^T=r))^{1\/2}\\\\ &\\le & c r+\nc (\\mathbb P(\\tau_r^T=r))^{1\/2}\n\\end{eqnarray*}\nNotice that $\\mathbb P(\\tau_r^T=r)=\\tilde{\\mathbb P}(\\inf_{t\\in\n[0,T]}|\\tilde{X}_t|\\geq r),$ where $\\tilde{X}$ is the Markov\nprocess on the whole $[0,+\\infty)$ corresponding to the equation\n(\\ref{eq:u}) with $g=F(\\cdot)+GR(u(\\cdot))$.\n\n\n$ $\n\n\n\\noindent Since $\\tilde{X}$ is recurrent, for all $ r>0$ it holds\n$\\tilde{\\mathbb P}(\\inf_{t\\in [0,T]}|\\tilde{X}_t|>r)\\rightarrow 0$\nas $T\\rightarrow \\infty.$ Thus\n$$\\limsup_{r\\rightarrow 0}\\limsup_{T\\rightarrow \\infty}|\n\\mathbb E(v(X_{\\tau_r^T}^{x,u}))|\\rightarrow 0.$$\nHence,\n$$v(x)\\le \\limsup_{r\\rightarrow 0}\\limsup_{T\\rightarrow \\infty}\n\\mathbb E\\int_0^{\\tau_r^T}\n[l(X_s^{x,u},u(X_s^{x,u}))-\\lambda]ds.$$ The proof is completed\nnoticing that if $u$ is chosen as ${u}(x)=\\gamma(x,\\zeta(x))$\nthen the above\ninequality becomes an equality.\n\\end{proof}\n\nThis result combines with Theorems \\ref{th-uniq-lambda}\nand \\ref{th-EHJB}\nto give the following\n\n\\begin{corollary}\\label{HJB-uniqueness}\nSuppose that all the assumptions of\nTheorems \\ref{th-uniq-lambda}, \\ref{th-EHJB} and \\ref{th-uniqueness} hold.\nThen $(\\bar v, \\bar\\lambda)$ is the unique mild solution of the\nHamilton-Jacobi-Bellman equation (\\ref{hjb}) satisfying\n$|\\bar v (x)|\\le c|x|$.\n\\end{corollary}\n\n\n\\section{Application to ergodic control of a semilinear heat equation}\n\\label{section-heat-eq}\n\nIn this section we show how our results can be applied to perform\nthe synthesis of the ergodic optimal control when the state\nequation is a semilinear heat equation with additive noise. More\nprecisely, we treat a stochastic heat equation in space dimension\none, with a dissipative nonlinear term and with control and noise\nacting on a subinterval. We consider homogeneous Dirichlet\nboundary conditions.\n\n\n\\noindent In $\\left( \\Omega,\\mathcal{F},\\mathbb{P}\\right) $ with\na filtration $\\left( \\mathcal{F}_{t}\\right) _{t\\geq0}$ satisfying\nthe usual conditions, we consider, for $t \\in\\left[ 0,T\\right] $\nand $\\xi\\in\\left[ 0,1\\right] $, the following equation\n\\begin{equation}\n\\left\\{\n\\begin{array}\n[c]{l} d_{t }X^{u}\\left( t ,\\xi\\right) =\\left[\n\\frac{\\partial^{2}}{\\partial \\xi^{2}}X^{u}\\left( t ,\\xi\\right)\n+f\\left( \\xi,X^{u}\\left( t ,\\xi\\right) \\right)\n+\\chi_{[a,b]}(\\xi) u\\left( t ,\\xi\\right) \\right] dt\n+\\chi_{[a,b]}(\\xi) \\dot{W}\\left(\nt ,\\xi\\right) dt ,\\\\\nX^{u}\\left( t ,0\\right) =X^{u}\\left( t ,1\\right) =0,\\\\\nX^{u}\\left( t,\\xi\\right) =x_{0}\\left( \\xi\\right) ,\n\\end{array}\n\\right. \\label{heat equation}\n\\end{equation}\nwhere $\\chi_{[a,b]}$ is the indicator function of $[a,b]$ with\n$0\\leq a\\leq b\\leq 1$; $\\dot{W}\\left( t ,\\xi\\right) $ is a\nspace-time white noise on $\\left[ 0,T\\right] \\times\\left[\n0,1\\right] $.\n\n\n\\noindent We introduce the cost functional\n\\begin{equation}\nJ\\left( x,u\\right) = \\limsup_{T\\rightarrow\\infty}\\dfrac{1}{T}\n\\mathbb{E}\\int_{0}^{T}\\int_{0}^{1}l\\left( \\xi ,X^{u}_s\\left(\n\\xi\\right) ,u_s(\\xi)\\right) \\mu\\left( d\\xi\\right) \\, ds,\n \\label{heat costo diri}\n\\end{equation}\nwhere $\\mu$ is a finite Borel measure on $\\left[ 0,1\\right] $.\nAn admissible control $u\\left( t ,\\xi\\right) $ is a predictable\nprocess such that for all $t \\geq 0$, and\n $\\mathbb{P}$-a.s.\n $u\\left( t ,\\cdot\\right)\n\\in U:=\\{v\\in C\\left( \\left[ 0,1\\right] \\right) :\\left\\vert\nv\\left( \\xi\\right) \\right\\vert \\leq\\delta\\}$. We denote by\n$\\mathcal{U}$ the set of such admissible controls. We wish to\nminimize the cost over $\\mathcal{U}$, adopting the formulation of\nSection \\ref{optcontr}, i.e. by a change of probability in the\nform of (\\ref{def-ergodic-cost}). The cost introduced in\n(\\ref{heat costo diri}) is well defined on the space of continuous\nfunctions on the interval $\\left[ 0,1\\right] $, but for an\narbitrary $\\mu$\\ it is not well defined on the Hilbert space of\nsquare integrable functions.\n\n\nWe suppose the following:\n\n\n\\begin{hypothesis}\n\\label{heatipotesi}\n\\begin{enumerate}\n\\item $f:\\left[ 0,1\\right] \\times\\mathbb{R} \\to\\mathbb{R}$ is\ncontinuous and for every\n $\\xi\\in\\left[0,1\\right] $, $ f(\\xi,\\,\\cdot\\,)$ is decreasing.\nMoreover there exist $C>0$ and $m>0$ such that for every\n$\\xi\\in\\left[0,1\\right] ,$ $x\\in\\mathbb{R}$,\n$$ |f\\left(\n\\xi,x\\right)|\\leq C(1+|x|)^m, \\qquad f\\left( 0,x\\right)= f\\left(\n1,x\\right)=0.\n$$\n\n\n\\item $l:\\left[ 0,1\\right] \\times\\mathbb{R} \\times\n[-\\delta,\\delta]\\rightarrow\\mathbb{R}$ is continuous and bounded,\nand $l(\\xi,\\cdot,u)$ is Lipschitz continuous uniformly with\nrespect to $\\xi \\in\\left[ 0,1\\right]$, $u\\in [-\\delta,\\delta]$.\n\n\n\n\n\\item $x_{0}\\in C\\left( \\left[ 0,1\\right] \\right) $,\n$x_{0}(0)=x_{0}(1)=0$.\n\\end{enumerate}\n\\end{hypothesis}\n\n\n\\noindent To rewrite the problem in an abstract way we set\n $H=\\Xi=L^{2}\\left( 0,1 \\right) $\n and $E=C_0\\left(\\left[ 0,1\\right] \\right)\n =\\{y\\in C\\left(\\left[ 0,1\\right] \\right)\\,:\\, y(0)=y(1)=0\\} $.\n We define an operator $A$ in $E$\\ by\n\\[\nD\\left( A\\right) =\\{y\\in C^{2}\\left( \\left[ 0,1\\right]\n\\right)\\,:\\, y,y''\\in C_{0}\\left( \\left[ 0,1\\right] \\right)\\}\n,\\text{ \\ \\ \\ \\ }\\left( Ay\\right) \\left( \\xi\\right)\n=\\frac{\\partial^{2}}{\\partial\\xi^{2}}y\\left( \\xi\\right) \\text{\nfor }y\\in D\\left( A\\right).\n\\]\nWe notice that $A$ is the generator of a $C_0$ semigroup in $E$,\nadmitting and extension to $H$, and $\\left| e^{tA}\\right|\n_{L\\left( E,E\\right) }\\leq e^{- t}$ see, for instance, Theorem\n11.3.1 in \\cite{DP2}. As a consequence, $A+ F+I$ is\n dissipative in $E$.\n\nWe set, for $x\\in E$, $\\xi \\in [0,1]$, $z\\in \\Xi$, $u\\in U$,\n\\begin{equation}\nF\\left( x\\right) \\left( \\xi\\right) =f\\left( \\xi,x\\left(\n\\xi\\right) \\right) ,\\ \\ \\left( Gz\\right) \\left( \\xi\\right)\n =\\chi_{[a,b]}\\left( \\xi\\right) z\\left(\n\\xi\\right) ,\\ \\ L\\left( x,u\\right)\n=\\displaystyle\\int_{0}^{1}l\\left( \\xi,x\\left( \\xi\\right) ,u\\left(\n\\xi\\right) \\right) \\mu\\left( d\\xi\\right) ,\n\\label{heatnotazioni}\n\\end{equation}\nand let $R$ denote the canonical imbedding of $C( \\left[\n0,1\\right])$ in\n $L^2( 0,1)$.\n\n\n\\noindent Finally $\\left\\{ W_{t },t \\geq0\\right\\} $ is a\ncylindrical Wiener process in $H$ with respect to the filtration\n$\\left( \\mathcal{F}_{t }\\right) _{t \\geq0}$\n\n\n$ $\n\n\n\\noindent It is easy to verify that Hypotheses\n\\ref{general_hyp_forward} and \\ref{hyp_W_A F(W_A)} are satisfied\n(for the proof of point $4$ in Hypothesis\n\\ref{general_hyp_forward} and of Hypothesis \\ref{hyp_W_A F(W_A)}\nsee again \\cite{DP2} Theorem 11.3.1.).\n\n\n\\noindent Moreover, see for instance \\cite{C}, for some $C>0$,\n\\[\n\\left| e^{tA}\\right| _{L\\left( H,E\\right) }\\leq Ct^{-1\/4}, \\qquad\n t\\in(0,1] ,\n\\]\nthus Hypothesis \\ref{hyp-convol-determ} holds.\n\n\n\\noindent Also Hypothesis \\ref{Hyp-masiero} is satisfied by taking\n$\\Xi _{0}=\\left\\lbrace f\\in C_0\\left( \\left[ 0,1\\right]\n\\right):f(a)=f(b)=0 \\right\\rbrace $.\n\n\n$ $ \\noindent Clearly the controlled heat equation (\\ref{heat\nequation}) can now be written in abstract way in the Banach space\n$E$ as\n\\begin{equation}\n\\left\\{\n\\begin{array}\n[c]{l}\ndX_{t }^{x_0,u}=\\left[ AX_{t }^{x_0,u}+F\\left( X_{t\n}^{x_0,u}\\right) \\right] dt +GRu_{t }dt +GdW_{t }\\text{\\ \\ \\ }t\n\\in\\left[\nt,T\\right] \\\\\nX^{x_0,u}_0=x_{0},\n\\end{array}\n\\right. \\label{heat eq abstract}\n\\end{equation}\nand the results of the previous sections can be applied to the\nergodic cost (\\ref{heat costo diri}) (reformulated by a change of\nprobability in the form of (\\ref{def-ergodic-cost})).\n\n\n\\noindent In particular if we define,\nfor all $x\\in C_0([0,1])$, $z\\in L^2(0,1)$, $u\\in U$\n(identifying $L^2(0,1)$ with its dual)\n$$\\psi(x,z)=\\inf _{u\\in U}\\left\\{\\int_0^1 l (\\xi,x(\\xi),u(\\xi))\n \\mu (d\\xi)+ \\int_a^b z(\\xi) u(\\xi) d\\xi\\right\\}$$\nthen there exist $\\overline v: E \\rightarrow \\mathbb{R}$\nLipschitz continuous and with $\\overline v(0)=0$, $\\overline \\zeta : E\n\\rightarrow \\Xi^*$ measurable and $\\overline \\lambda \\in\n\\mathbb{R}$ such that if $X^{x_0}=X^{x_0,0}$ is the solution of\nequation (\\ref{heat eq abstract}) then $(\\overline v(X^{x_0}),\n\\overline \\zeta(X^{x_0}),\\overline \\lambda)$ is a solution of the\nEBSDE (\\ref{EBSDE}) and the characterization of the optimal\nergodic control stated in Theorem \\ref{Th-main-control} holds (and\n $\\overline \\lambda$ is unique in the sense of Theorem\n\\ref{th-uniq-lambda}).\n\n $ $\n\n\n\\noindent Moreover if $ f$ is of class $C^1(\\mathbb{R})$\n (consequently $F$ will be of class ${\\cal G}^1(E,E)$) and $\\psi$\n is of class ${\\cal G}^1(E\\times \\Xi^*,E)$ then by Theorem \\ref{th-diff}\n $ \\overline v$ is of class ${\\cal G}^1(E,E)$ and, by Theorem \\ref{th-EHJB},\nit is a mild solution of the ergodic HJB equation (\\ref{hjb}) and it holds\n $\\overline \\zeta=\\nabla \\overline v G$.\n\n\n$ $\n\n\n\\noindent Let us then consider the particular case in which $[a,b]\n=[0,1]$, $f(x,\\xi)=f(x)$ is of class $C^1$ with derivative having polynomial\ngrowth, and satisfies $f(0)=0$,\n$[f(x+h)-f(x)]h\\leq - c |h|^{2+\\epsilon}$\nfor suitable $c,\\epsilon\n>0$ and all $x,h\\in \\mathbb{R}$ (for instance, $f(x)=-x^3$).\nIn that case the Kolmogorov semigroup corresponding to the process\n$X^{x_0}$ is strongly Feller, see\n \\cite{C} and \\cite{masiero2}, and it is easy to verify that\n$F$ is genuinely dissipative (see Definition \\ref{gen-diss}).\nMoreover we can choose $\\Xi_0=C_0([0,1])$ and it turns out that\n$\\psi$\n is bounded\non each set $E\\times B$, where $B$ is any ball of $\\Xi_0^*$. Thus\nthe claims of Corollaries \\ref{characterization of lambda} and\n\\ref{boundedness of v} hold true, and in particular $\\overline v$\nis bounded.\n\n\n$ $\n\n\n\n\\noindent Finally if we assume that $\\mu$ is Lebesgue measure and\n$f$ is bounded and Lipschitz we can choose\n$E=\\Xi=\\Xi_0=H=L^2(0,1)$. Then the assumptions of Theorem\n\\ref{th-rec-seidler} are satisfied and we can apply Theorem\n\\ref{th-uniqueness} to characterize the function $\\overline v$. In\nparticular if $f$ is of class $C^1(\\mathbb{R})$ and $\\psi$ is of\nclass ${\\cal G}^1(H\\times \\Xi^*,H)$ then $\\overline v$ is the\nunique mild solution of the ergodic HJB equation (\\ref{hjb}).\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nAutonomous trucks are expected to fundamentally transform the freight transportation industry, and the enabling technology is progressing rapidly.\nMorgan Stanley estimates the potential savings from automation at \\$168 billion annually for the US alone \\cite{Greene2013-AutonomousFreightVehicles}.\nAdditionally, autonomous transportation may improve on-road safety, and reduce emissions and traffic congestion \\cite{ShortMurray2016-IdentifyingAutonomousVehicle,SlowikSharpe2018-AutomationLongHaul}.\n\nSAE International defines different levels of driving automation, ranging from L0 to L5, corresponding to no-driving automation to full-driving automation \\cite{SAEInternational2018-TaxonomyDefinitionsTerms}.\nThe current focus is on L4 technology (high automation), which aims at delivering automated trucks that can drive without any need for human intervention in specific domains, e.g., on highways.\nThe automotive industry is actively involved in making L4 vehicles a reality.\nDaimler Trucks, one of the leading heavy-duty truck manufacturers in North America, is working with both Torc Robotics and Waymo, and will be testing the latest generation of L4 trucks in the Southwest in early 2021 \\cite{Engadget2020-WaymoDaimlerTeam}.\nIn 2020, truck and engine maker Navistar announced a strategic partnership with technology company TuSimple to develop L4 trucks, to go into production by 2024 \\cite{TransportTopics2020-NavistarTusimplePartner}.\nOther companies developing self-driving vehicles include Argo AI, Aurora, Cruise, Embark, Ford, Kodiak, Lyft, Motional, Nuro, and Volvo Cars \\cite{FleetOwner-TusimpleAutonomousTruck}.\n\nA study by Viscelli \\cite{Viscelli-Driverless?AutonomousTrucks} describes different scenarios for the adoption of autonomous trucks by the industry.\nThe most likely scenario, according to some of the major players, is the \\emph{transfer hub business model} \\cite{Viscelli-Driverless?AutonomousTrucks,RolandBerger2018-ShiftingGearAutomation,ShahandashtEtAl2019-AutonomousVehiclesFreight}.\nAn Autonomous Transfer Hub Network (ATHN) makes use of autonomous truck ports, or \\emph{transfer hubs}, to hand off trailers between human-driven trucks and driverless autonomous trucks.\nAutonomous trucks then carry out the transportation between the hubs, while conventional trucks serve the first and last miles.\nFigure~\\ref{fig:autonomous_example} presents an example of an autonomous network with transfer hubs.\nOrders are split into a first-mile leg, an autonomous leg, and a last-mile leg, each of which served by a different vehicle.\nA human-driven truck picks up the cargo at the customer location, and drops it off at the nearest transfer hub.\nA driverless autonomous truck moves the trailer to the transfer hub closest to the destination, and another human-driven truck performs the last leg.\n\n\\begin{figure}[!t]\n\t\\centering\n\t\\includegraphics[scale=0.6]{images\/autonomous_example_ryder.pdf}\n\t\\caption{An Example of an Autonomous Transfer Hub Network.}\n\t\\label{fig:autonomous_example}\n\\end{figure}\n\nATHNs apply automation where it counts: monotonous highway driving is automated, while more complex local driving and customer contact is left to humans.\nEspecially for long-haul transportation, the benefit of automation is expected to be high.\nGlobal consultancy firm Roland Berger \\cite{RolandBerger2018-ShiftingGearAutomation} estimates that operational cost savings may be between 22\\% and 40\\% in the transfer hub model, based on the cost difference between driverless trucks and conventional trucks.\nThese estimates are based on single trips and it is not clear that they can be realized in practice: in particular, they do not take into account the empty miles traveled by autonomous trucks to pick up their next orders.\n\nThis paper proposes a Constraint Programming (CP) model to schedule the ATHN operations for a given set of orders.\nThe resulting schedule details the autonomous operations and the first\/last-mile operations at each of the hubs, and specifies the movements of every load, vehicle, and driver.\n\\emph{The CP model is then used to provide, for the first time, a detailed quantitative study of the benefits of ATHNs by considering a real case study where actual operations are modeled and optimized with high fidelity.}\nIt examines whether the savings predicted by \\cite{RolandBerger2018-ShiftingGearAutomation} materialize when the network effects, e.g., empty miles for relocation, are taken into account.\nIt is found that it is computationally feasible to solve this large-scale optimization problem with more than 100,000 variables and 100,000 constraints, and that the benefits of ATHNs may indeed be realized in practice.\n\nThe remainder of this paper is organized as follows.\nSection~\\ref{sec:problem} defines the problem of scheduling freight operations on an ATHN, and Section~\\ref{sec:formulation} formulates a CP model to solve this problem.\nThe case study is presented in Section~\\ref{sec:casestudy}.\nThe final section of the paper summarizes the findings and provides the conclusions.\n\n\n\\section{Problem Statement}\n\\label{sec:problem}\n\nThis section defines the problem of scheduling freight operations on an ATHN to perform a given set orders with a given set of vehicles, with the objective to minimize the cost of driving empty.\nThe problem is defined on a directed graph $G=(V,A)$, with vertices $V$ and arcs $A$.\nThe vertices represent locations, and are partitioned into hub locations $V_H$ and customer locations $V_C$.\nArcs between the transfer hubs correspond to autonomous transportation, and the other arcs represent human-driven legs.\nEvery arc $(i,j) \\in A$ is associated with a non-negative travel time $\\tau_{ij}$ and a cost $c_{ij}$.\nFor convenience, define $\\tau_{ii} = 0$ and $c_{ii} = 0$ for all $i \\in V$.\n\nThe set of customer orders is given by $R$.\nOrder $r\\in R$ is supposed to be picked up at time $p(r)$ at the origin $o(r) \\in V_C$, and to be transported through the ATHN to the destination $d(r) \\in V_C$.\nBased on order $r\\in R$, three tasks are defined: the first-mile task $t_r^f$, the autonomous task $t_r^a$, and the last-mile task $t_r^l$.\nThe first-mile task consists of loading the trailer at the customer location, moving the freight to the closest transfer hub, and unloading the trailer.\nSimilarly, the autonomous task and the last-mile task consist of loading, driving (between the hubs and to the destination, respectively), and unloading.\n\nLet $T$ be the set of all tasks generated by the orders.\nEvery task $t\\in T$ corresponds to a single leg, and is defined by an origin $o(t) \\in V$, a destination $d(t) \\in V$, and a pickup time $p(t)$.\nThe duration of a task equals $\\tau_{o(t),d(t)} + 2S$, where $S \\ge 0$ is the fixed time for loading or unloading a trailer.\nThe pickup time $p(t_r^f)$ of the first-mile task is equal to the order pickup time $p(r)$, while subsequent pickup times are based on the time the freight is supposed to be available.\nThat is, $p(t_r^a) = p(t_r^f) + \\tau_{o(t_r^f),d(t_r^f)} + 2S$, and $p(t_r^l) = p(t_r^a) + \\tau_{o(t_r^a),d(t_r^a)} + 2S$.\n\nTo create a feasible schedule, every task must be given a starting time, and be assigned to one of the available trucks.\nIt is assumed that an appointment flexibility of $\\Delta \\ge 0$ minutes is permitted, which means that task $t\\in T$ may start anywhere in the interval $[p(t)-\\Delta, p(t)+\\Delta]$.\nThe set of trucks $K$ is partitioned into autonomous trucks $K_A$, and regular trucks $K_h$ at every hub $h \\in V_H$.\nTasks can only be assigned to the corresponding set of trucks, and tasks performed by the same vehicle must not overlap in time.\nIf $t\\in T$ and $t'\\in T$ are subsequent tasks for a single truck, and $d(t) \\neq o(t')$, then an empty relocation is necessary, which takes $\\tau_{d(t) o(t')}$ time units and has cost $c_{d(t) o(t')}$.\nThe objective is to assign the tasks such that the total relocation cost is minimized.\n\nNote that the problem described above can be decomposed and solved independently for the autonomous network and for the operations at each of the hubs.\nThis is possible because different trucks are used for each part of the ATHN, and because each task is given an independent pickup time, based on the expected time the freight is available.\nOne potential problem is that using the appointment flexibility in one part of the network may lead to an infeasibility in another part of the network, but the case study shows that this is not an issue in practice: The first and last-mile schedules are not very constrained, and shifting the schedule to accommodate flexibility in the autonomous network is straightforward.\nAlternatively, one may first optimize the autonomous network, and update the first and last-mile pickup times accordingly.\n\n\n\\section{Mathematical Formulation}\n\\label{sec:formulation}\n\nThis section presents a CP model to schedule orders on the ATHN.\nWithout loss of generality, the set of tasks and the set of trucks represent a single part of the network that can be optimized independently.\nThat is, either the autonomous operations, or the first\/last-mile operations at one of the hubs are considered.\n\n\\newsavebox{\\modelbox}\n\\begin{lrbox}{\\modelbox}\n\\begin{varwidth}{1.15\\textwidth}\n\\begin{lstlisting}\nrange Trucks = ...;\nrange Tasks = ...;\nrange Sites = ...;\nrange Horizon = ...;\nrange Types = Sites union { shipType }; \nint or[Tasks] = ...; \nint de[Tasks] = ...; \nint pickupTime[Tasks] = ...;\nint loadTime = ...;\nint flexibility = ...;\nint travelTime[Types,Types] = ...;\nint travelCost[Types,Types] = ...; \n\ndvar interval task[t in Tasks] in Horizon\n size travelTime[or[t],de[t]] + 2*loadTime;\ndvar interval ttask[k in Trucks,t in Tasks] optional in Horizon\n size travelTime[or[t],de[t]] + 2*loadTime;\ndvar interval load[Trucks,Tasks] optional in Horizon size loadTime;\ndvar interval ship[k in Trucks,t in Tasks] optional in Horizon\n size travelTime[ort],de[t]];\ndvar interval unload[Trucks,Tasks] optional in Horizon size loadTime;\ndvar sequence truckSeq[k in Trucks]\n in append(all(t in Tasks)load[k,t],all(t in Tasks)ship[k,t],all(t in Tasks)unload[k,t])\n types append(all(t in Tasks)or[t],all(t in Tasks)shipType,all(t in Tasks)de[t]);\ndvar int emptyMilesCost[Trucks,Tasks];\ndvar int truckEmptyMilesCost[Trucks];\n\nminimize sum(k in Trucks) truckEmptyMilesCost[k];\n\nconstraints {\n\n forall(t in Tasks) \n startOf(task[t]) >= pickupTime[t] - flexibility;\n startOf(task[t]) <= pickupTime[t] + flexibility;\n\t\n forall(k in Trucks,t in Tasks)\n span(ttask[k,t],[load[k,t],ship[k,t],unload[k,t]]);\n startOf(ship[k,t]) == endOf(load[k,t])\n startOf(unload[k,t]) == endOf(ship[k,t])\t \n\t\n forall(k in Trucks)\n alternative(task[t],all(k in Trucks) ttask[k,t])\t\n\t\n forall(k in Trucks,t in Tasks)\n emptyMilesCost[k,t] = travelCost[destination[t],typeOfNext(truckSeq[k],ttask[k,t],destination[t],destination[t])];\n\t\n forall(k in Trucks)\n truckEmptyMilesCost[k] = sum(t in Tasks) emptyMilesCost[k,t];\n\t\n forall(k in Trucks)\n noOverlap(truckSeq,travelTime);\n\n}\n\\end{lstlisting}\n\\end{varwidth}\n\\end{lrbox}\n\n\\begin{figure}[!t]\n\\makebox[\\textwidth][c]{%\n\\fbox{\\begin{minipage}{1.13\\textwidth}\n\t\\usebox{\\modelbox}\n\\end{minipage}}\n}\n\\caption{Formulation for Scheduling Freight Operations on an ATHN.}\n\\label{fig:formulation}\n\\end{figure}\n\nThe model is depicted in Figure \\ref{fig:formulation} using OPL\nsyntax \\cite{VanHentenryck1999-OplOptimizationProgramming}. The data of the model is given in lines 1--12. It consists of\na number of ranges (line 1--5), information about the tasks (lines\n6--8) that include their origins, destinations, and pickup times, the\ntime to load\/unload a trailer (line 9), the flexibility around the\npickup times (line 10), and the matrices of travel times and travel\ncosts. These matrices are defined between the sites but also\ninclude a dummy location {\\tt shipType} for reasons that will become\nclear shortly.\n\nThe main decision variables are the interval variables {\\tt task[t]}\nthat specify the start and end times of task {\\tt t} when processed\nby the autonomous network, and the optional interval variables {\\tt\n\tttask[k,t]} that are present if task {\\tt t} is transported by\ntruck {\\tt k}. These optional variables consist of three subtasks that\nare captured by the interval variables {\\tt load[k,t]} for loading,\n{\\tt ship[k,t]} for transportation, and {\\tt unload[k,t]} for\nunloading. The other key decision variables are the sequence variables\n{\\tt truckSeq[k]} associated with every truck: these variables\nrepresent the sequence of tasks performed by every truck. They\ncontain the loading, shipping, and unloading interval variables\nassociated with the trucks, and their types. The type of a loading\ninterval variable is the origin of the task, the type of an unloading\ninterval variable is the destination of the task, and the type of the\nshipping interval variable is the specific type {\\tt shipType} that is\nused to represent the fact that there is no transition cost and\ntransition time between the load and shipping subtasks, and the\nshipping and destination subtasks. The model also contains two\nauxiliary decision variables to capture the empty mile cost between a\ntask and its successor, and the empty mile cost of the truck\nsequence.\n\nThe objective function (line 28) minimizes the total costs of empty\nmiles. The constraints in lines 32--34 specify the potential start\ntimes of the tasks, and are defined in terms of the pickup times and\nthe flexibility parameter. The {\\sc span} constraints (line 37) link\nthe task variables and their subtasks, while the constraints in lines\n38--39 link the subtasks together. The {\\sc alternative} constraints\non line 42 specify that each task is processed by a single truck. The\nempty mile costs between a task and its subsequent task (if it\nexists) is computed by the constraints in line 45: they use the {\\sc\n\ttypeOfNext} expression on the sequence variables. The total empty\nmile cost for a truck is computed in line 48. The {\\sc noOverlap}\nconstraints in line 51 impose the disjunctive constraints between the\ntasks and the transition times. \n\n\n\\section{Case Study}\n\\label{sec:casestudy}\n\nTo quantify the impact of autonomous trucking on a real transportation network, a case study is presented for the dedicated transportation business of Ryder System, Inc., commonly referred to as \\emph{Ryder}.\nRyder is one of the largest transportation and logistics companies in North America, and provides fleet management, supply chain, and dedicated transportation services to over 50,000 customers.\nIts dedicated business, \\emph{Ryder Dedicated Transportation Solutions}, offers supply-chain solutions in which Ryder provides both drivers and trucks, and handles all other aspects of managing the fleet.\nRyder's order data is used to design an ATHN, and to create a detailed plan for how it would operate.\nThis allows for a realistic evaluation of the benefits of autonomous trucking.\n\n\n\\subsection{Data Description}\n\\label{sec:inputdata}\n\nRyder prepared a representative dataset for its dedicated transportation business in the Southeast of the US, reducing the scope to orders that were strong candidates for automation.\nThe dataset consists of trips that start in the first week of October 2019, and stay completely within the following states: AL, FL, GA, MS, NC, SC, and TN.\nIt contains 11,264 rows, which corresponds to 2,090 orders, formatted as in Table~\\ref{tab:order_data}.\nEvery order has a unique \\emph{OrderNumber}, and every row corresponds to a stop for a particular order.\nStops have a unique identifier \\emph{StopNumber}, and the \\emph{Stop} column indicates the sequence within the order.\nThe columns \\emph{StopArrivalDate} and \\emph{StopDepartureDate} indicate the scheduled arrival and departure times, and \\emph{City} and \\emph{ZipCode} identify the location of the stop.\nThe \\emph{Status} column gives a code for the status of the vehicle on arrival, and the \\emph{Event} column indicates what happens at the stop.\n\n\\begin{adjustbox}{center,float={table}[!t]}\n\t\\centering\n\t\\scriptsize\n\t\\begin{threeparttable}\n\t\t\\caption{An Example of the Order Data.}\n\t\t\\label{tab:order_data}%\n\t\t\\begin{tabular}{rrrrrrrrrr}\n\t\t\t\\multicolumn{1}{l}{StopNum} & \\multicolumn{1}{l}{OrderNum} & \\multicolumn{1}{l}{StopArrivalDate} & \\multicolumn{1}{l}{StopDepartureDate} & \\multicolumn{1}{l}{Stop} & \\multicolumn{1}{l}{City} & ZipCode & \\multicolumn{1}{l}{Status} & Event & \\\\\n\t\t\t\\toprule\n\t\t\t68315760 & 7366366 & 2-10-2019 09:01 & 2-10-2019 09:02 & 1 & Atlanta & 30303 & LD & HPL \\\\\n\t\t\t68315761 & 7366366 & 2-10-2019 16:29 & 2-10-2019 18:33 & 2 & Tennessee & 37774 & LD & LUL \\\\\n\t\t\t68315762 & 7366366 & 3-10-2019 11:00 & 3-10-2019 11:30 & 3 & Atlanta & 30303 & MT & DMT \\\\\n\t\t\n\t\t\n\t\t\t\\dots & \\dots & \\dots & \\dots & \\dots & \\dots & \\dots & \\dots & \\dots\\\\\n\t\t\t46798427 & 5207334 & 7-10-2019 02:35 & 7-10-2019 02:50 & 1 & Alpharetta & 30009 & NaN & LLD \\\\\n\t\t\t46798428 & 5207334 & 7-10-2019 08:10 & 7-10-2019 08:49 & 2 & Macon & 31201 & LD & LUL \\\\\n\t\t\t46798429 & 5207334 & 7-10-2019 15:16 & 7-10-2019 15:45 & 3 & Alpharetta & 30009 & LD & LUL \\\\\n\t\t\\end{tabular}%\n\t\\end{threeparttable}\n\\end{adjustbox}%\n\nThe example data in Table~\\ref{tab:order_data} displays two orders.\nThe first order is a trip from Atlanta to Tennessee and back.\nBased on the status code, the truck arrived in Tennessee loaded (LD) and returned to Atlanta empty (MT).\nThe event codes show that a preloaded trailer was hooked in Atlanta (HPL), followed by a live unload (LUL) in Tennessee, after which the truck dropped the empty trailer (DMT) in Atlanta.\nThe exact codes are not important for the purpose of this paper.\nWhat is important, is the ability to derive the parts of the trip when the truck is moving freight, and the parts when the truck is driving empty.\nIf a vehicle returns to the starting location after only making deliveries, it is assumed that the return trip is empty, and the data is corrected if needed.\n\nRoad system data was obtained from OpenStreetMap \\cite{OpenStreetMap2020}, and route distance and mileage were calculated with the GraphHopper library.\nThe provided orders are long-haul trips, with an average trip length of 431 miles.\nMost of this distance is driven on highways: 65\\% of the distance is driven on interstates and US highways, and this number goes up further to 87\\% if state highways are included.\nSignificant highway usage is typical for long-haul transportation, and indicates that a significant part of each trip can potentially be automated.\n\n\n\\subsection{ATHN Design}\n\nThe design of ATHNs needs to decide the locations of the transfer hubs, which are the gateways to the autonomous parts of the network.\nA natural choice is to locate the hubs in areas where many trucks currently enter or exit the highway system.\nHistorical order data is used to identify these common highway access points.\nFor a given order, the truck is routed through the existing road network, and the highway segments, and their access points, can easily be identified.\nThe transfer hubs are then placed in areas with many access points.\nThe case study considers two different sets of hubs: a \\emph{small network} with 17 transfer hubs in the areas where Ryder trucks most frequently access the highway system, and a \\emph{large network} that includes 13 additional hubs in locations with fewer highway access points.\nThe small and the large network are visualized in Figure~\\ref{fig:designsmall} and Figure~\\ref{fig:designlarge}, respectively.\nThe exact hub locations are masked, but the figures are accurate within a 50 mile range.\nThe large network extends further northeast into North-Carolina, and further south into Florida.\nIt also makes the network more dense in the center of the region.\n\n\\begin{figure}[!t]\n\t\\centering\n\t\\subfloat[Small Network (17 hubs).\\label{fig:designsmall}]{%\n\t\t\\centering\n\t\t\\includegraphics[width=0.45\\textwidth, trim=28cm 3cm 29cm 15cm, clip]{images\/obfuscated_design_small.png}\n\t}\n\t\\hfill\n\t\\subfloat[Large Network (30 hubs).\\label{fig:designlarge}]{%\n\t\t\\centering\n\t\t\\includegraphics[width=0.45\\textwidth, trim=28cm 3cm 29cm 15cm, clip]{images\/obfuscated_design_large.png}\n\t}\n\t\\caption{ATHN Network Designs for the Southeast.}\n\t\\label{fig:designs}\n\\end{figure}\n\n\n\\subsection{Order Selection}\n\nThe case study focuses on scheduling the 494 most \\emph{challenging orders}.\nThese orders consist of a single delivery, followed by an empty return trip.\nBecause they require empty travel for 50\\% of the trip, these challenging orders are an excellent target for cost savings.\nThey also make up 24\\% of the dataset, and account for 53\\% of the empty mileage.\n\nFor a given ATHN and given arc costs $c_{ij}$ for all $(i,j) \\in A$, it is first determined which of these orders may benefit from the autonomous network, and which are better served by human-driven trucks.\nFor example, a direct trip with a human-driven trucks may be preferred for trips that are short, or for trips between locations that are far from any hub.\nTwo options are compared for every order: The first option is to serve the order with a conventional truck, which necessitates an empty return leg.\nThe second option uses the autonomous network, which amounts to driving to the nearest hub with a human-driven truck (first mile), shipping the load over the autonomous network to the hub closest to the destination, and then serving the last mile with another human-driven truck.\nNo empty returns are added in this case, as every truck is immediately available for the next task.\nThe two options are compared in terms of arc costs, and the cheapest one is selected.\nDifferent costs are used for autonomous and non-autonomous arcs, as will be explained in the next section.\nThe CP model only considers the orders that may benefit from the ATHN, while the other orders are scheduled separately.\n\n\n\\subsection{Parameters and Settings}\n\nThe following scenario is defined as the \\emph{base case} for the upcoming experiments.\nThe base case uses the small network, presented by Figure~\\ref{fig:designsmall}.\nFor conventional trucks, the cost $c_{ij}$ for driving arc $(i,j)\\in A$ is equal to the road distance.\nFor autonomous trucks, this cost is reduced by the percentage $\\alpha$.\nThe value of $\\alpha$ is a parameter, and the case study uses values ranging from $\\alpha=25\\%$ to $\\alpha=40\\%$, with $\\alpha = 25\\%$ for the base case.\nThis results in a conservative estimate of the benefits of autonomous trucking, which is predicted to be 29\\% to 45\\% cheaper per mile \\cite{EngholmEtAl2020-CostAnalysisDriverless}.\n\nThe time for loading or unloading is estimated at $S = 30$ minutes, and the appointment time flexibility is set to one hour ($\\Delta=60$).\nThe number of autonomous trucks is set to $\\lvert K_A \\rvert=50$.\nThe CP model is used to schedule the orders for each independent part of the network: the autonomous operations, and the first\/last-mile operations at each of the hubs.\nEach model is solved with the CPLEX CP Optimizer version 12.8 \\cite{LaborieEtAl2018-IbmIlogCp}.\n\n\n\\subsection{Base Case Results}\n\nOut of the 494 challenging orders, 437 (88\\%) are found to potentially benefit from the autonomous network, and the CP model is used to schedule these orders.\nScheduling the autonomous part of the ATHN is the most challenging: the model has close to 110,000 decision variables and more than 110,000 constraints.\nThe model is given an hour of CPU time and returns the best found solution, which is visualized in Figure~\\ref{fig:basecase_schedule}.\nThis figure shows both the autonomous tasks and the and the relocation tasks for the first week of October.\n\n\\begin{figure}[p]\n\t\\centering\n\t\\includegraphics[trim=20 50 10 40,clip,width=\\linewidth]{images\/small_25_gantt.pdf}\n \\caption{Autonomous Truck Schedule for the Base Case.}\n\t\\label{fig:basecase_schedule}\n\\end{figure}\n\n\\begin{figure}[p]\n\t\\centering\n\t\\includegraphics[width=0.7\\linewidth]{images\/obfuscated_vehicle_20.png}\n\t\\caption{Single Autonomous Truck Route in the Base Case (blue is loaded, red is empty).}\n\t\\label{fig:singleroute}\n\\end{figure}\n\nFigure~\\ref{fig:basecase_schedule} shows that the transportation tasks are close together, and only a relatively small amount of relocation is necessary.\nIt is interesting to see that only a small number of autonomous trucks is driving during the weekend.\nThis is because the appointment times are still based on the current agreements with the customers, and having drivers work during the weekends is typically avoided.\nFor autonomous trucks, this would not be a problem, which again underlines that making full use of autonomous transportation requires adapting the business model and current practices.\nIn Section~\\ref{sec:flexibility}, the importance of time flexibility is considered in more detail.\n\nThe routes that are driven by the autonomous trucks consist of serving autonomous legs of different orders, with relocations in between.\nFigure~\\ref{fig:singleroute} shows a representative single truck route from the schedule.\nBlue arrows indicate that freight is being moved, and red arrows indicate that the truck is driving empty.\nThe truck starts at the west coast of Florida, where it picks up a load that has been delivered to the hub by a first\/last-mile truck driver.\nThe freight is then transported to the east coast, where it is unloaded so that a regular truck with driver can complete the last-mile.\nThe autonomous truck immediately starts serving the autonomous leg of the next order, returning to the Tampa area.\nAfter that, legs are served that are going north.\nThe first time a relocation is needed, is when the truck makes a delivery near Valdosta, close to the Georgia and Florida border.\nNo freight is immediately available, and the vehicle drives empty to the next location to pick up a load there.\nThe routes are clearly complex, which emphasizes the power of optimization: It is unlikely that this solution can be found by manual planners, but it is possible with optimization techniques.\n\nCompared to the autonomous trucks, the total distance driven by regular trucks is relatively short.\nThe optimization model was used to schedule the first and last-mile operations at selected hubs, and it was found that the amount of work is often insufficient to keep drivers occupied throughout the week.\nTo prevent driver idle time, it may be beneficial to outsource these legs, or to consolidate them with other operations in the area.\nIn terms of mileage, the percentage of empty miles at the hubs is typically under 25\\%, which is used as an estimate for the first\/last-mile efficiency in the remainder.\n\nTable~\\ref{tab:basecase_costs} quantifies the impact of autonomous trucking on the operating costs for the 437 selected orders.\nThe \\emph{Mileage} column indicates the total miles driven for both the current network and for the ATHN.\nThe numbers are separated based on whether the distance was driven while loaded or empty, and percentages are shown in the \\emph{\\% of total} column.\nThe \\emph{Cost without autonomous trucks} converts the miles into dollars, using \\$2 per mile as an approximation for the cost of human-driven trucks.\nRecall that driving autonomously is assumed to be $\\alpha=25\\%$ cheaper, which is reflected by the \\emph{Cost adjustment} column.\nThe \\emph{Cost} column presents the cost when autonomous trucks are available, and is obtained by multiplying the cost without autonomous trucks by the cost adjustment factor.\n\n\\begin{adjustbox}{center,float={table}[!t]}\n\t\\centering\n\t\\footnotesize\n\t\\begin{threeparttable}\n\t\t\\caption{Cost Table for the Base Case (437 orders).}\n\t\t\\label{tab:basecase_costs}%\n\t\t\\begin{tabular}{ccrrrrrrr}\n\t\t\t\\toprule\n\t\t\t&\t\t &\t\t &\t\t &\t\t\t & Cost without & & \\\\\n\t\t\t& & & \\quad Mileage & \\quad \\% of total & \\quad auton. trucks & \\quad Cost adj. & \\quad\\quad\\quad Cost \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{3}[0]{*}{Current network} & & \\multicolumn{1}{l}{Loaded} & 96,669 & 50\\% & \\$ 193,338 & 1.00 & \\$ 193,338 \\\\\n\t\t\t& & \\multicolumn{1}{l}{Empty} & 96,698 & 50\\% & \\$ 193,396 & 1.00 & \\$ 193,396 \\\\\n\t\t\t& & \\multicolumn{1}{l}{Total} & 193,367 & 100\\% & \\$ 386,734 & 1.00 & \\$ 386,734 \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{9}[0]{*}{\\parbox{2.5 cm}{Autonomous transfer\\\\hub network}} & \\multirow{3}[0]{*}{Autonomous} & \\multicolumn{1}{l}{Loaded} & 91,618 & 67\\% & \\$ 183,235 & 0.75 & \\$ 137,426 \\\\\n\t\t\t& & \\multicolumn{1}{l}{Empty} & 44,217 & 33\\% & \\$ 88,433 & 0.75 & \\$ 66,325 \\\\\n\t\t\t& & \\multicolumn{1}{l}{Total} & 135,834 & 100\\% & \\$ 271,668 & 0.75 & \\$ 203,751 \\\\\n\t\t\t\\cmidrule{2-8}\n\t\t\t& \\multirow{3}[0]{*}{First\/last mile} & \\multicolumn{1}{l}{Loaded} & 29,286 & 75\\% & \\$ 58,573 & 1.00 & \\$ 58,573 \\\\\n\t\t\t& & \\multicolumn{1}{l}{Empty\\tnote{*}} & 9,762 & 25\\% & \\$ 19,524 & 1.00 & \\$ 19,524 \\\\\n\t\t\t& & \\multicolumn{1}{l}{Total} & 39,049 & 100\\% & \\$ 78,097 & 1.00 & \\$ 78,097 \\\\\n\t\t\t\\cmidrule{2-8}\n\t\t\t& Total & & 174,883 & & \\$ 349,766 & & \\$ 281,848 \\\\\n\t\t\t\\cmidrule{2-8}\n\t\t\t& Savings & & 18,484 & & \\$ 36,969 & & \\$ 104,886 \\\\\n\t\t\t& Savings (\\%) & & 10\\% & & 10\\% & & 27\\% \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}%\n\t\t\\begin{tablenotes}\n\t\t\t\\item[*] estimated\n\t\t\\end{tablenotes}\n\t\\end{threeparttable}\n\\end{adjustbox}%\n\nCompared to the current network, the ATHN allows for significant savings for the selected orders: Table~\\ref{tab:basecase_costs} shows that the total cost goes down by 27\\%.\nAt a cost of \\$2 per mile, this corresponds to \\$104,886 per week, or \\$5.5M per year.\nThe \\emph{Mileage} column shows that almost 80\\% of the mileage in the ATHN can be automated, which partly explains the large savings.\nWhat is very interesting to observe is that the total mileage for the ATHN is actually \\emph{less} than the total mileage for the direct trips in the current network.\nIn the transfer hub network, there is no need to return back empty after a delivery, and there is no need to limit working hours or to return to a domicile at the end of the day.\nAs a result, only 33\\% of the automated distance is driven empty, compared to 50\\% for the current system.\nThis means that even if autonomous trucks would be as expensive as trucks with drivers, costs would still go down by 10\\% due to the additional flexibility that automation brings.\n\n\n\\subsection{Impact of the Size of the Network}\n\nA larger autonomous network results in shorter first\/last-mile trips, and may have a larger area of coverage.\nTo evaluate the impact of the size of the network, the calculations for the base case are repeated using the large network (Figure~\\ref{fig:designlarge}) with 30 hubs, instead of the small network (Figure~\\ref{fig:designsmall}) with 17 hubs.\nFor the large network, 468 of the 494 orders (95\\%) may benefit from the autonomous network, compared to only 88\\% for the base case.\nThis immediately implies that there is more potential for savings.\nIt also means a higher utilization of the autonomous trucks, as more legs are served by the same 50 vehicles.\n\nTable~\\ref{tab:large_25_costs} shows that the relative cost savings for the large network (29\\%) are similar to those for the small network (27\\%).\nThis means that the average benefit of automation is similar for both designs, \\emph{for the orders that are automated}.\nHowever, the large network allows more trips to benefit from automation, which is why the cost savings of \\$ 116,582 are 11\\% higher than the savings for the small network (\\$ 104,886).\nThe average benefit of automation is similar for the two designs due to two effects that cancel out.\nFirst, the same autonomous trucks have to serve more orders on the large network.\nThis increases the utilization of the vehicles, but also increases the percentage of empty miles from 33\\% to 35\\%.\nThe reason for this increase is that there is less time available to wait around at a hub for the next order, as the trucks are needed to perform other orders in the meantime.\nOn the other hand, the first and last-mile trips are shorter due to the additional hubs, which saves costs.\n\n\\begin{adjustbox}{center,float={table}[!t]}\n\t\\centering\n\t\\footnotesize\n\t\\begin{threeparttable}\n\t\t\\caption{Cost Table for the Large Network (468 orders).}\n\t\t\\label{tab:large_25_costs}%\n\t\t\\begin{tabular}{ccrrrrrr}\n\t \\toprule\n\t\t&\t\t &\t\t &\t\t &\t\t\t & Cost without & & \\\\\n\t\t& & & \\quad Mileage & \\quad \\% of total & \\quad auton. trucks & \\quad Cost adj. & \\quad\\quad\\quad Cost \\\\\n\t\t\\midrule\n\t\t\\multirow{3}[0]{*}{Current network} & & \\multicolumn{1}{l}{Loaded} & 101,213 & 50\\% & \\$ 202,425 & 1.00 & \\$ 202,425 \\\\\n\t\t& & \\multicolumn{1}{l}{Empty} & 96,698 & 50\\% & \\$ 193,396 & 1.00 & \\$ 193,396 \\\\\n\t\t& & \\multicolumn{1}{l}{Total} & 202,476 & 100\\% & \\$ 404,953 & 1.00 & \\$ 404,953 \\\\\n\t\t\\midrule\n\t\t\\multirow{8}[0]{*}{\\parbox{2.5 cm}{Autonomous transfer\\\\hub network}} & \\multirow{3}[0]{*}{Autonomous} & \\multicolumn{1}{l}{Loaded} & 97,326 & 65\\% & \\$ 194,653 & 0.75 & \\$ 145,990 \\\\\n\t\t& & \\multicolumn{1}{l}{Empty} & 53,247 & 35\\% & \\$ 106,493 & 0.75 & \\$ 79,870 \\\\\n\t\t& & \\multicolumn{1}{l}{Total} & 150,573 & 100\\% & \\$ 301,146 & 0.75 & \\$ 225,860 \\\\\n\t\t\\cmidrule{2-8}\n\t\t& \\multirow{3}[0]{*}{First\/last mile} & \\multicolumn{1}{l}{Loaded} & 23,442 & 75\\% & \\$ 46,883 & 1.00 & \\$ 46,883 \\\\\n\t\t& & \\multicolumn{1}{l}{Empty \\tnote{*}} & 7,814 & 25\\% & \\$ 15,628 & 1.00 & \\$ 15,628 \\\\\n\t\t& & \\multicolumn{1}{l}{Total} & 31,256 & 100\\% & \\$ 62,511 & 1.00 & \\$ 62,511 \\\\\n\t\t\\cmidrule{2-8}\n\t\t& Total & & 181,829 & & \\$ 363,657 & & \\$ 288,371 \\\\\n\t\t\\cmidrule{2-8}\n\t\t& Savings & & 20,648 & & \\$ 41,296 & & \\$ 116,582 \\\\\n\t\t& Savings (\\%) & & 10\\% & & 10\\% & & 29\\% \\\\\n\t\t\\bottomrule\n\t\t\\end{tabular}%\n\t\t\\begin{tablenotes}\n\t\t\t\\item[*] estimated\n\t\t\\end{tablenotes}\n\t\\end{threeparttable}\n\\end{adjustbox}%\n\n\n\\subsection{Impact of the Cost of Autonomous Trucking}\n\nFor the base case, it was assumed that autonomous trucks are $\\alpha= 25\\%$ cheaper per mile than trucks with a driver.\nHowever, this number is yet far from certain, and higher cost reductions have also been reported in the literature.\nTo investigate the impact of the cost of autonomous trucking, Table~\\ref{tab:overview_costs} presents results for $\\alpha$ ranging from $25\\%$ to $40\\%$, for both the small and the large network.\nThe column \\emph{Autom.\n\torders} gives the number of orders that may benefit from automation, and are considered in the ATHN.\nThe relative cost savings (\\emph{Rel.\n\tsavings}) state the cost reduction compared to serving these orders with conventional trucks.\nThe \\emph{Cost savings} column gives the absolute cost savings in dollars.\nThe final column compares the absolute savings to the savings obtained for the baseline (small network, $\\alpha=25\\%$).\n\nTable~\\ref{tab:overview_costs} shows that, as autonomous trucking gets cheaper, and as more hubs are added to the network, the savings compared to the current system go up.\nAdditionally, more orders start using the ATHN, which increases the absolute savings further.\nEven though the autonomous trucks only perform the transportation between the hubs, the relative cost savings for the complete system often exceed the mileage cost reduction for autonomous trucks ($\\alpha$).\nThis again shows that the benefit of autonomous trucks is not only the lower cost per mile, but also the additional flexibility.\nCompared to the base case, cheaper autonomous transportation results in significantly larger savings.\nSimilar as in the previous section, increasing the size of the network does not strongly impact the average cost benefit per order, but does increase the total amount of orders that can be automated, which leads to more profits.\nIn the best case (large network, $\\alpha=40\\%$), the total benefit of the ATHN is \\$ 161,762 per week for the challenging orders, which corresponds to \\$8.4M savings per year.\n\n\\begin{table}[!t]\n\t\\centering\n\t\\footnotesize\n\t\\caption{Overview of Cost Savings under Different Assumptions.}\n\t\\label{tab:overview_costs}%\n\t\\begin{tabular}{crrrrr}\n\t\t\\toprule\n\t\t& & & & & Additional savings \\\\\n\t\t\\multicolumn{1}{l}{Network} & $\\alpha$ & \\quad Autom. orders & \\quad Rel. savings & \\quad Cost savings & \\quad comp. to base case \\\\\n\t\t\\midrule\n\t\t\\multirow{4}[0]{*}{Small} & 25\\% & 437 & 27\\% & \\$ 104,886 & +0\\% \\\\\n\t\t& 30\\% & 439 & 32\\% & \\$ 122,396 & +17\\% \\\\\n\t\t& 35\\% & 443 & 35\\% & \\$ 135,572 & +29\\% \\\\\n\t\t& 40\\% & 443 & 38\\% & \\$ 148,984 & +42\\% \\\\\n\t\t\\midrule\n\t\t\\multirow{4}[0]{*}{Large} & 25\\% & 468 & 29\\% & \\$ 116,582 & +11\\% \\\\\n\t\t& 30\\% & 469 & 33\\% & \\$ 131,798 & +26\\% \\\\\n\t\t& 35\\% & 472 & 37\\% & \\$ 149,586 & +43\\% \\\\\n\t\t& 40\\% & 472 & 40\\% & \\$ 161,762 & +54\\% \\\\\n\t\t\\bottomrule\n\t\\end{tabular}%\n\\end{table}%\n\n\\subsection{Impact of Appointment Flexibility}\n\\label{sec:flexibility}\n\nFor the base case, the appointment flexibility was assumed to be $\\Delta = 60$ minutes.\nDeviating from a previously agreed appointment must be negotiated with the customer, but if there are significant benefits in terms of efficiency, this may be worth the effort.\nTo determine the impact of appointment flexibility, Table~\\ref{tab:time_flexibility} presents results for the base case (small network, $\\Delta=60$), in which the value of $\\Delta$ is varied.\nThe model is given four hours of CPU time for each setting.\nThe columns are similar to the previous table, and show the number of automated orders, the relative and absolute savings, and the additional savings compared to the base case.\nNote that the appointment flexibility does not affect the amount of orders that may benefit from automation, which is 437 for all four experiments.\n\nTable~\\ref{tab:time_flexibility} reveals that, if the appointment flexibility is already limited to one hour, limiting it further to 30 minutes to increase the service level is relatively inexpensive: the cost savings would only go down by 0.8\\%.\nIncreasing the flexibility by 30 minutes, on the other hand, goes a long way.\nUsing $\\Delta=90$ instead of $\\Delta=60$ results in 5\\% additional savings.\nThis indicates that the impact of appointment flexibility can be substantial.\nAlso note that the additional benefit is almost half that of the additional benefit for extending the network (+11\\%).\n\n\\begin{table}[!t]\n\t\\centering\n\t\\footnotesize\n\t\\caption{Overview of Cost Savings under Different Values of $\\Delta$.}\n\t\\label{tab:time_flexibility}%\n\t\\begin{tabular}{crrrrr}\n\t\t\\toprule\n\t\t& & & & & Additional savings \\\\\n\t\t\\multicolumn{1}{l}{Network} & $\\Delta$ & Autom. Orders & Rel. savings & Cost savings & comp. to base case \\\\\n\t\t\\midrule\n\t\t\\multirow{4}[0]{*}{Small} & 30 & 437 & 29\\% & \\$ 110,887 & -0.8\\% \\\\\n\t\t& 60 & 437 & 29\\% & \\$ 111,802 & 0.0\\% \\\\\n\t\t& 90 & 437 & 30\\% & \\$ 117,354 & 5.0\\% \\\\\n\t\t& 120 & 437 & 30\\% & \\$ 116,865 & 4.5\\% \\\\\n\t\t\\bottomrule\n\t\\end{tabular}%\n\\end{table}%\n\nIt is surprising to see that increasing $\\Delta$ from 90 to 120 actually leads to a schedule that is less efficient, while more flexibility is available.\nThis is due to the CP model not finding the optimal solution.\nFinding the best schedule is a very challenging task, and increasing the flexibility increases the search space, which makes this task even more challenging.\nFurther investigation is needed to determine the actual additional savings that can be realized for $\\Delta=120$.\nThe results do suggest that no schedule could easily be found that was significantly better than the schedule for $\\Delta=90$, which hints that the advantage of additional flexibility is leveling off after $\\Delta=90$.\n\n\n\\section{Conclusion}\n\\label{sec:conclusion}\n\nAutonomous freight transportation is expected to completely transform the industry, and the technology is advancing rapidly, with different players developing and testing high automation L4 trucks.\nA crucial factor for the adoption of autonomous trucks is its return on investment, which is still uncertain.\nThis study contributes to the discussion by quantifying the benefits of the ATHN model, which is one of the most likely future scenarios.\nThe benefits are estimated based on a real transportation network, taking into account the detailed operations of the autonomous network.\n\nA CP model was presented to schedule the orders on an ATHN, and the model was used to conduct a case study on a real transportation network.\nIt was found that solving this large-scale optimization problem with CP is computationally feasible.\nFurthermore, ATHN may lead to substantial cost savings.\nFor some of the most challenging orders in the Southeast (orders that make a single delivery and return empty), operational cost may be reduced by 27\\% to 40\\%, which could save an estimated \\$5.5M to \\$8.4M per year on these orders.\nThis shows that the cost savings estimated by \\cite{RolandBerger2018-ShiftingGearAutomation} (22\\%-40\\%) may indeed be realized.\nThe savings are mainly attributed to a reduction in labor cost, but the increased flexibility of autonomous trucks also plays a significant role: Even if autonomous trucks would have the same cost per mile as human-driven trucks, cost savings would still be possible.\n\nIt was also explored how different assumptions impact the ATHN.\nIncreasing the size of the autonomous network mainly increases the number of orders that can benefit from automation, while the average benefit per automated order remains similar.\nThe impact of the cost per mile for autonomous trucking was also studied.\nAs autonomous trucks become cheaper, it is cost-efficient to automate more orders, and existing trips become cheaper as well.\nDue to the additional flexibility, it was found that the system benefit of automation often exceeds the benefit of the lower cost per mile.\nFinally, it was analyzed how appointment flexibility impacts the efficiency, and it was found that allowing deviations to be even 30 minutes larger can go a long way.\n\nThis paper quantified the impact of autonomous trucking on a real transportation network, and substantial benefits were found in terms of labor costs and flexibility.\nThese results strengthen the business case for autonomous trucking, and major opportunities may arise in the coming years.\nTo seize these opportunities, transport operators will have to update their business models, and use optimization technology to operate the more complex systems.\nDeveloping more detailed models and solution methods to support this transition is an interesting direction for future research.\n\n\n\\subsection*{Acknowledgements}\n\nThis research was funded through a gift from Ryder. Special thanks to the Ryder team for their invaluable support, expertise, and insights.\n\n\\clearpage\n\n\\bibliographystyle{splncs04}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec:1}\n\nElectromagnetic waves propagating in disordered media are progressively scrambled by refractive index fluctuations and,\nthanks to interference, result into mesoscopic phenomena, such as speckle correlations and weak\nlocalization~\\cite{Akkermans2011, Sheng2010}. Polarization is an essential characteristic of electromagnetic waves that,\nconsidering the ubiquity of scattering processes in science, prompted the development of research in statistical\noptics~\\cite{Goodman2015, Brosseau1998} and impacted many applications, from optical imaging in biological\ntissues~\\cite{Tuchin2006} to material spectroscopy (e.g.\\@\\xspace, rough surfaces)~\\cite{Maradudin2007}, and radiation transport\nin turbulent atmospheres~\\cite{Andrews2005, Shirai2003}. Although the topic has experienced numerous developments and\noutcomes in the past decades, recent studies have revealed that much remains to be explored and understood on the\nrelation between the microscopic structure of scattering media and the polarization properties of the scattered field.\nIn particular, it was found that important information about the morphology of a disordered medium is contained in\nthe three-dimensional (3D) polarized speckles produced in the near-field above its surface~\\cite{Apostol2003,\nCarminati2010, Parigi2016} and in the spontaneous emission properties of a light source in the bulk~\\cite{Caze2010,Sapienza2011a}.\nSimilarly, the light scattered by random ensembles of large spheres was shown to exhibit unusual polarization features\ndue to the interplay between the various multipolar scatterer resonances~\\cite{Schmidt2015}.\n\nThe fact that light transport is affected by the microscopic structural properties of disordered media is well known.\nStructural correlations, coming from the finite scatterer size or from the specific morphology of porous\nmaterials~\\cite{Torquato2005, RojasOchoa2004, Garcia2007}, typically translate into an anisotropic phase function,\n$p(\\cos \\theta)$, which describes the angular response of a single scattering event with the scattering angle $\\theta$. The\naverage cosine of the phase function, known as the anisotropic scattering factor, $g=\\left\\langle \\cos \\theta \\right\\rangle$ (with\n$-1 \\leq g \\leq 1$), then leads to the standard definition of the transport mean free path (the average distance after\nwhich the direction of light propagation is completely randomized) as $\\ell^*=\\ell\/(1-g)$, where $\\ell$ is the\nscattering mean free path (the average distance between two scattering events). Single scattering anisotropy naturally\naffects how the polarization diffuses in disordered media, one of the most notable findings being that circularly\npolarized light propagates on longer distances compared to linearly polarized light in disordered media exhibiting\nforward single scattering ($g>0$) ---the so-called ``circular polarization memory effect''~\\cite{MacKintosh1989a,\nXu2005a, Gorodnichev2007}.\n\nRecent observations in mesoscopic optics also motivate deeper investigations on polarized light transport in correlated\ndisordered media. Indeed, numerical simulations revealed that uncorrelated ensembles of point scatterers cannot exhibit\n3D Anderson localization due to the vector nature of light~\\cite{Skipetrov2014, Bellando2014}. By contrast, it was found that the\ninterplay between short-range structural correlations and scatterer resonances could yield the opening of a 3D photonic\ngap in disordered systems~\\cite{Edagawa2008, Liew2011} and promote localization phenomena at its\nedges~\\cite{Imagawa2010}. To date, the respective role of polarization and structural correlations on mesoscopic optical\nphenomena remains largely to be clarified.\n\nTheoretically describing the propagation of polarized light in disordered media exhibiting structural correlations is a\ndifficult task. A first approach consists in using the vector radiative transfer equation~\\cite{Chandrasekhar1960,\nPapanicolaou1975, Mishchenko2006}, in which electromagnetic waves are described via the Stokes parameters and the\nscattering and absorption processes are related via energy conservation arguments. The various incident polarizations\n(linear, circular) and the single scattering anisotropy are explicitly implemented, thereby allowing for the\ninvestigation of a wide range of problems~\\cite{Amic1997, Gorodnichev2014}. A second approach relies on a transfer\nmatrix formalism based on a scattering sequence picture, where each scattering event (possibly anisotropic) yields a partial\nredistribution of the light polarization along various directions~\\cite{Akkermans1988, Xu2005, Rojas-Ochoa2004a}. The\napproach is phenomenological, yet very intuitive, making it possible to gain important physical insight into mesoscopic\nphenomena such as coherent backscattering~\\cite{Akkermans1988}.\n\nThe most \\textit{ab-initio} approach to wave propagation and mesoscopic phenomena in disordered systems is the so-called\nmultiple scattering theory, which directly stems from Maxwell's equations and relies on perturbative expansions on the\nscattering potential~\\cite{Sheng2010, Akkermans2011}. The formalism is often used to investigate mesoscopic\nphenomena, such as short and long-range (field and intensity) correlations or coherent backscattering, in a large\nvariety of complex (linear or nonlinear) media, including disordered dielectrics and atomic clouds. Unfortunately, it\nalso rapidly gains in complexity when the vector nature of light is considered. In fact, multiple scattering theory for\npolarized light has so far been restricted to uncorrelated disordered media only~\\cite{Stephen1986, MacKintosh1988,\nOzrin1992, VanTiggelen1996, VanTiggelen1999, Muller2002, Vynck2014}.\n\nIn this article, we present a model based on multiple scattering theory that describes how the diffusion of polarized\nlight is affected by short-range structural correlations, thereby generalizing previous models limited to uncorrelated\ndisorder. We do not aim at developing a complete theory for polarization-related mesoscopic phenomena in correlated\ndisordered media but at showing that, by a series of well-controlled approximations, important steps towards this\nobjective can be made. Starting from the (exact) Dyson and the Bethe-Salpeter equations for the average field and the\nfield correlation function, we derive a radiative transfer equation for the polarization-resolved specific\nintensity in the limit of short-range structural correlations and weak scattering. To analyze the impact of short-range\nstructural correlations on the diffusion of polarization, we then apply a $P_1$ approximation and decompose the\npolarization-resolved energy density into ``polarization eigenmodes'', as was done previously for uncorrelated\ndisordered media~\\cite{Ozrin1992, Muller2002, Vynck2014}. An interesting outcome of this decomposition is the\nobservation that each polarization eigenmode is affected independently and differently by short-range structural\ncorrelations. More precisely, each mode is characterized by a specific transport mean free path, and thus a specific\nattenuation length (describing the depolarization process) for its intensity. The transport mean free path of each\neigenmode depends non-trivially on the anisotropy factor $g$, and differently from the $(1-g)^{-1}$ rescaling\nwell known for the diffusion of scalar waves.\n\nThe paper is organized as follows. The radiative transfer equation for polarized light is derived\n\\textit{ab-initio} in Sect.~\\ref{sec:2}. The diffusion limit and the eigenmode decomposition are applied in\nSect.~\\ref{sec:3}. In Sect.~\\ref{sec:4}, we discuss the model and the results deduced from it, paying special attention to \nthe consistency of the approximations that have been made. Our conclusions are given in Sect.~\\ref{sec:5}. \nTechnical details about the average Green's function, the range of validity of the short-range structural correlation approximation, \nand the particular case of uncorrelated disorder, are presented in Appendices~\\ref{sec:A1}--\\ref{sec:A3}, respectively.\n\n\\section{Radiative transfer for polarized light}\\label{sec:2}\n\n\\subsection{Spatial field correlation}\n\nWe consider a disordered medium described by a real dielectric function of the form\n$\\epsilon(\\bm{r})=1+\\delta\\epsilon(\\bm{r})$, where $\\delta\\epsilon(\\bm{r})$ is the fluctuating part with the\nstatistical properties\n\\begin{equation}\\label{eq:disorder}\n \\left\\langle \\delta\\epsilon(\\bm{r}) \\right\\rangle = 0,\n \\qquad \\left\\langle \\delta\\epsilon(\\bm{r}) \\delta\\epsilon(\\bm{r}') \\right\\rangle = u f(\\bm{r}-\\bm{r}')\n\\end{equation}\nwhere $\\left\\langle\\ldots\\right\\rangle$ indicates ensemble averaging. The function\n$f(\\bm{r}-\\bm{r}')$ describes the structural correlation of the medium and $u$ is an amplitude whose expression will be\nderived below. We assume that the medium is statistically isotropic and invariant by translation. Considering a\nmonochromatic wave with free-space wavevector $k_0=\\omega\/c=2\\pi\/\\lambda$, $\\omega$ being the frequency,\n$\\lambda$ the wavelength and $c$ the speed of light in vacuum, the electric field $\\textbf{E}$ satisfies the vector \npropagation equation\n\\begin{equation}\n \\nabla \\times \\nabla \\times \\bm{E}(\\bm{r})-k_0^2 \\epsilon(\\bm{r}) \\bm{E}(\\bm{r})=i \\mu_0 \\omega \\bm{j}(\\bm{r}),\n\\end{equation}\nwhere the current density $\\bm{j}(\\bm{r})$ describes a source distribution in the disordered medium. \nIntroducting the dyadic Green's function $G_{ik}$, the $i$th component of the electric field reads\n\\begin{equation}\\label{eq:Efield-green}\nE_i(\\bm{r})=i \\mu_0 \\omega \\int G_{ik}(\\bm{r},\\bm{r}') j_k(\\bm{r}') d\\bm{r}',\n\\end{equation} \nwhere implicit summation of repeated indices is assumed.\nThe spatial correlation function of the electric field $\\left\\langle E_i(\\bm{r}) E_j^\\star(\\bm{r}') \\right\\rangle$ obeys the Bethe-Salpeter equation\n\\begin{multline}\\label{eq:BS_field}\n \\left\\langle E_i(\\bm{r}) E_j^\\star(\\bm{r}') \\right\\rangle = \\left\\langle E_i(\\bm{r}) \\right\\rangle \\left\\langle E_j^\\star (\\bm{r}') \\right\\rangle\n\\\\\n + k_0^4 \\int \\left\\langle G_{im}(\\bm{r}-\\bm{r}_1) \\right\\rangle \\left\\langle G_{jn}^\\star(\\bm{r}'-\\bm{r}_1') \\right\\rangle\n\\\\\n \\times \\Gamma_{mnrs} (\\bm{r}_1,\\bm{r}_1',\\bm{r}_2,\\bm{r}_2') \\left\\langle E_r (\\bm{r}_2) E_s^\\star (\\bm{r}_2') \\right\\rangle d\\bm{r}_1 d\\bm{r}_1' d\\bm{r}_2 d\\bm{r}_2'\n\\end{multline}\nthat can be derived from diagrammatic calculations~\\cite{Akkermans2011,Sheng2010}. In this expression\nthe superscript $\\star$ denotes complex conjugation, and\n$\\Gamma_{mnrs}$ is the four-point irreducible vertex that describes all possible scattering sequences\nbetween four points. In Eq.~(\\ref{eq:BS_field}), the first term in the right-hand side corresponds to the ballistic \nintensity, that is attenuated due to scattering at the scale of the scattering mean free path $\\ell$, and\nthe second term describes the multiple-scattering process. Note that at this level, Eq.~(\\ref{eq:BS_field}) \nis an exact closed-form equation.\n\nIt is also interesting to remark that the field correlation function $\\left\\langle E_i(\\bm{r}) E_j^\\star(\\bm{r}') \\right\\rangle$ is\none of the key quantities in statistical optics (where it is usually denoted by cross-spectral density matrix), since it\nencompasses the polarization and coherence properties of fluctuating fields in the frequency domain~\\cite{Goodman2015, Brosseau1998}. \nThe study of light fluctuations in 3D multiple scattering media has stimulated a revisiting of the concepts of degree of polarization \nand coherence~\\cite{Setala2002, Dennis2007, Refregier2014, Gil2014,Dogariu2015}, initially defined for 2D paraxial fields.\n\nTo proceed further, we assume weak disorder, such that the scattering mean free path $\\ell$\n is much larger than the wavelength ($k_0\\ell \\gg 1$). In this regime, only the two diagrams\nfor which the field and its complex conjugate follow the same trajectories (the so-called ladder and most-crossed\ndiagrams) contribute to the average intensity. The ladder diagrams are the root of radiative transport theory, that describes\nthe transport of intensity as an incoherent process. The most-crossed diagrams are responsible for weak localization and\ncoherent backscattering. In the ladder approximation and assuming independent scattering, the four-point irreducible\nvertex reduces to\n\\begin{multline}\n \\Gamma_{mnrs}(\\bm{r}_1,\\bm{r}_1',\\bm{r}_2,\\bm{r}_2')\n\\\\\n \\begin{split}\n & = \\left\\langle \\delta\\epsilon(\\bm{r}_1) \\delta\\epsilon(\\bm{r}_1') \\right\\rangle \\delta(\\bm{r}_1-\\bm{r}_2) \\delta(\\bm{r}_1'-\\bm{r}_2') \\delta_{mr} \\delta_{ns}\n \\\\\n & = u f(\\bm{r}_1-\\bm{r}_1') \\delta(\\bm{r}_1-\\bm{r}_2) \\delta(\\bm{r}_1'-\\bm{r}_2') \\delta_{mr} \\delta_{ns},\n \\end{split}\n\\end{multline}\nyielding\n\\begin{multline}\\label{eq:BS_field2}\n \\left\\langle E_i(\\bm{r}) E_j^\\star(\\bm{r}') \\right\\rangle = \\left\\langle E_i(\\bm{r}) \\right\\rangle \\left\\langle E_j^\\star (\\bm{r}') \\right\\rangle\n\\\\\n + uk_0^4 \\int \\left\\langle G_{im}(\\bm{r}-\\bm{r}_1) \\right\\rangle \\left\\langle G_{jn}^\\star(\\bm{r}'-\\bm{r}_1') \\right\\rangle\n\\\\\n \\times f(\\bm{r}_1-\\bm{r}_1') \\left\\langle E_m (\\bm{r}_1) E_n^\\star (\\bm{r}_1') \\right\\rangle d\\bm{r}_1 d\\bm{r}_1'.\n\\end{multline}\nWe consider the source to be a point electric dipole located at $\\bm{r}_0$, such that\n\\begin{equation}\n j_k(\\bm{r})=-i\\omega p_k \\delta(\\bm{r}-\\bm{r}_0),\n\\end{equation}\nwhere $p_k$ is the dipole moment along direction $k$. \nEquation~(\\ref{eq:Efield-green}) simplifies into $E_i(\\bm{r})= \\mu_0 \\omega^2 \\, G_{ik}(\\bm{r}-\\bm{r}_0) p_k$ and the\nBethe-Salpeter equation (\\ref{eq:BS_field2}) can be rewritten in terms of the dyadic Green's function in the form\n\\begin{multline}\\label{eq:BS_green}\n \\left\\langle G_{ik}(\\bm{r}-\\bm{r}_0) G_{jl}^\\star(\\bm{r}'-\\bm{r}_0) \\right\\rangle = \\left\\langle G_{ik}(\\bm{r}-\\bm{r}_0) \\right\\rangle \\left\\langle G_{jl}^\\star (\\bm{r}'-\\bm{r}_0) \\right\\rangle\n\\\\\n + uk_0^4 \\int \\left\\langle G_{im}(\\bm{r}-\\bm{r}_1) \\right\\rangle \\left\\langle G_{jn}^\\star(\\bm{r}'-\\bm{r}_1') \\right\\rangle f(\\bm{r}_1-\\bm{r}_1')\n\\\\\n \\times \\left\\langle G_{mk} (\\bm{r}_1-\\bm{r_0}) G_{nl}^\\star (\\bm{r}_1'-\\bm{r}_0) \\right\\rangle d\\bm{r}_1 d\\bm{r}_1'.\n\\end{multline}\nUsing the change of variables $\\bm{r}-\\bm{r}_0=\\bm{R}+\\bm{X}\/2$ and $\\bm{r}'-\\bm{r}_0=\\bm{R}-\\bm{X}\/2$, and \ntransforming Eq.~(\\ref{eq:BS_green}) into reciprocal space, with $\\bm{K}$ and $\\bm{q}$ the reciprocal variables of\n$\\bm{R}$ and $\\bm{X}$ respectively, we finally obtain\n\\begin{widetext}\n \\begin{multline}\\label{eq:BS_green_fourier}\n \\left\\langle G_{ik}\\left(\\bm{q}+\\frac{\\bm{K}}{2}\\right) G_{jl}^\\star\\left(\\bm{q}-\\frac{\\bm{K}}{2}\\right) \\right\\rangle\n = \\left\\langle G_{ik}\\left(\\bm{q}+\\frac{\\bm{K}}{2}\\right) \\right\\rangle \\left\\langle G_{jl}^\\star\\left(\\bm{q}-\\frac{\\bm{K}}{2}\\right) \\right\\rangle\n + uk_0^4 \\left\\langle G_{im}\\left(\\bm{q}+\\frac{\\bm{K}}{2}\\right) \\right\\rangle \\left\\langle G_{jn}^\\star\\left(\\bm{q}-\\frac{\\bm{K}}{2}\\right) \\right\\rangle\n \\\\\n \\times \\int f(\\bm{q}-\\bm{q}') \\left\\langle G_{mk} \\left(\\bm{q}'+\\frac{\\bm{K}}{2}\\right) G_{nl}^\\star\n \\left(\\bm{q}'-\\frac{\\bm{K}}{2}\\right) \\right\\rangle \\frac{d\\bm{q}'}{8\\pi^3}.\n \\end{multline}\n\\end{widetext}\nA direct resolution of Eq.~(\\ref{eq:BS_green_fourier}) is possible for $f(\\bm{q}-\\bm{q}')=1$,\nand this approach was used in Ref.~\\cite{Vynck2014} to study the coherence and polarization properties of light in\nan uncorrelated disordered medium. In the case of a medium with structural correlations, a direct resolution is out of reach\nand we need to follow a different strategy.\n\n\\subsection{From field correlation to radiative transfer}\n\nIn this section we derive a radiative transfer equation for polarized light.\nWe proceed by evaluating the average Green's tensor $\\left\\langle \\bm{G} \\right\\rangle$, that obeys the Dyson\nequation~\\cite{Akkermans2011}. In its most general form, it reads~\\cite{Tai1993}\n\\begin{equation}\\label{eq:averageG}\n \\left\\langle \\bm{G} (\\bm{q}) \\right\\rangle = \\left[ k_0^2 \\bm{I} - q^2 \\bm{P}(\\hat{\\bm{q}}) - \\bm{\\Sigma}(\\bm{q}) \\right]^{-1},\n\\end{equation}\nwith $\\bm{I}$ the unit tensor, $\\bm{P}(\\hat{\\bm{q}})=\\bm{I}-\\hat{\\bm{q}} \\otimes \\hat{\\bm{q}}$ the transverse\nprojection operator, $\\hat{\\bm{q}}=\\bm{q}\/q$ and $q=|\\bm{q}|$. $\\bm{\\Sigma}(\\bm{q})$ is the self-energy, which contains\nthe sum over all multiple scattering events that cannot be factorized in the averaging process. As shown in\nAppendix~\\ref{sec:A1}, for arbitrary structural correlations, $\\bm{\\Sigma}(\\bm{q})$ is non-scalar. \nThe problem can be simplified by assuming short-range structural correlations,\nin which case $\\bm{\\Sigma}(\\bm{q})=\\Sigma(\\bm{q})\\bm{I}$. The average Green's tensor can then be written as\n\\begin{equation}\\label{eq:Green-mulet}\n \\left\\langle \\bm{G} (\\bm{q}) \\right\\rangle = \\left\\langle G(\\bm{q}) \\right\\rangle \\left( \\bm{I} - \\frac{\\bm{q} \\otimes \\bm{q}}{k_0^2-\\Sigma(\\bm{q})} \\right),\n\\end{equation}\nwith $\\left\\langle G(\\bm{q}) \\right\\rangle =[k_0^2-q^2-\\Sigma(\\bm{q})]^{-1}$ the scalar Green's function. In a dilute medium,\nthe scattering events are assumed to take place on large distances compared to the wavelength (near-field interactions between \nscatterers can be neglected). In this case, the average Green's tensor $\\left\\langle \\bm{G} (\\bm{q}) \\right\\rangle$ can be reduced to its transverse\ncomponent~\\cite{Arnoldus2003}, yielding\n\\begin{equation}\n \\left\\langle \\bm{G} (\\bm{q}) \\right\\rangle \\simeq \\left\\langle G(\\bm{q}) \\right\\rangle \\bm{P}(\\hat{\\bm{q}}).\n\\end{equation}\nAfter some simple algebra, the first term in the right-hand side in Eq.~(\\ref{eq:BS_green_fourier}) can be written as\n\\begin{multline}\n \\left\\langle G_{ik}\\left(\\bm{q}+\\frac{\\bm{K}}{2}\\right) \\right\\rangle \\left\\langle G_{jl}^\\star\\left(\\bm{q}-\\frac{\\bm{K}}{2}\\right) \\right\\rangle\n\\\\\n = M_{ik} M'_{jl}\\frac{\\left\\langle G(\\bm{q}+\\bm{K}\/2) \\right\\rangle - \\left\\langle G^\\star(\\bm{q}-\\bm{K}\/2) \\right\\rangle}\n {2 \\bm{q} \\cdot \\bm{K} + \\Sigma(\\bm{q}+\\bm{K}\/2) - \\Sigma^\\star(\\bm{q}-\\bm{K}\/2)},\n\\end{multline}\nwhere we have defined the polarization factors $M_{ik}=\\delta_{ik} - (q_i + K_i\/2) (q_k + K_k\/2)\/|\\bm{q}+\\bm{K}\/2|^2$ and\n$M'_{jl}=\\delta_{jl} - (q_j - K_j\/2) (q_l - K_l\/2)\/|\\bm{q}-\\bm{K}\/2|^2$. In a dilute medium, we can assume that $|\\bm{K}| \\ll\n|\\bm{q}|$. This means that there are two different space scales in the correlation function of Green's tensor: A short scale associated to $\\bm{q}$ and corresponding to the dependence on direction of the specific intensity that we will introduce in Eq.~(\\ref{eq:specific_intensity}), and a large scale associated to $\\bm{K}$ and corresponding to the dependence of the specific intensity on position. This leads to\n\\begin{multline}\\label{eq:Green1}\n \\left\\langle G_{ik}\\left(\\bm{q}+\\frac{\\bm{K}}{2}\\right) \\right\\rangle \\left\\langle G_{jl}^\\star\\left(\\bm{q}-\\frac{\\bm{K}}{2}\\right) \\right\\rangle\n\\\\\n = (\\delta_{ik} - \\hat{q}_i \\hat{q}_k) (\\delta_{jl} - \\hat{q}_j \\hat{q}_l)\n \\frac{\\left\\langle G(\\bm{q}) \\right\\rangle - \\left\\langle G^\\star(\\bm{q}) \\right\\rangle}{2 \\bm{q} \\cdot \\bm{K} + 2i \\operatorname{Im}[\\Sigma(\\bm{q})]}.\n\\end{multline}\nThe self-energy $\\Sigma(\\bm{q})$ renormalizes the propagation constant in the medium by defining a complex effective\npermittivity $\\epsilon_\\text{eff}=1-\\Sigma(\\bm{q})\/k_0^2$. The real part of $\\Sigma$ yields a change in the phase velocity,\nand the imaginary an attenuation of the field amplitude due to scattering. Hence, we can write\n\\begin{equation}\\label{eq:avGreen}\n \\left\\langle G(\\bm{q}) \\right\\rangle = \\frac{1}{k_0^2 \\operatorname{Re}[\\epsilon_\\text{eff}] - q^2 + i k_0^2 \\operatorname{Im}[\\epsilon_\\text{eff}]}.\n\\end{equation}\nSince $\\operatorname{Im}[\\epsilon_\\text{eff}] \\ll \\operatorname{Re}[\\epsilon_\\text{eff}]$ in a dilute medium, we can rewrite\nEq.~(\\ref{eq:avGreen}) using the identity\n\\begin{equation}\n \\lim_{\\varepsilon \\rightarrow 0} \\frac{1}{x-x_0-i \\varepsilon} = \\operatorname{PV}\\left[ \\frac{1}{x-x_0} \\right] - i \\pi \\delta (x-x_0),\n\\end{equation}\nwhere $\\operatorname{PV}$ stands for principal value.\nDefining $q_e=k_0\\sqrt{\\operatorname{Re}[\\epsilon_\\text{eff}]}$ as an effective wavevector, Eq.~(\\ref{eq:Green1}) becomes\n\\begin{multline}\\label{eq:Green2}\n \\left\\langle G_{ik}\\left(\\bm{q}+\\frac{\\bm{K}}{2}\\right) \\right\\rangle \\left\\langle G_{jl}^\\star\\left(\\bm{q}-\\frac{\\bm{K}}{2}\\right) \\right\\rangle\n\\\\\n = (\\delta_{ik} - \\hat{q}_i \\hat{q}_k) (\\delta_{jl} - \\hat{q}_j \\hat{q}_l) \\frac{\\pi \\delta(q_e^2-q^2)}{ i \\bm{q} \\cdot \\bm{K} - \\operatorname{Im}[\\Sigma(\\bm{q})]}.\n\\end{multline}\nIn order to derive a radiative transfer equation, we then introduce the quantity $L_{ijkl}$ by the relation\n\\begin{multline}\\label{eq:specific_intensity}\n \\left\\langle G_{ik}\\left(\\bm{q}+\\frac{\\bm{K}}{2}\\right) G_{jl}^\\star\\left(\\bm{q}-\\frac{\\bm{K}}{2}\\right) \\right\\rangle\n\\\\\n = \\frac{4\\pi^2}{q_e} \\delta(q_e^2 - q^2) L_{ijkl}(\\bm{K},q_e \\hat{\\bm{q}}).\n\\end{multline}\nHere, we assume that the correlation function of Green's tensor propagates on shell, {\\it i.e.} with a wavevector $q=q_e$. The impact of the on-shell approximation, which is the key step to solve the Bethe-Salpeter equation in the presence of structural correlations, will be discussed in Sec.~\\ref{sec:4}. From Eqs.~(\\ref{eq:Green2}) and (\\ref{eq:specific_intensity}), we can rewrite the Bethe-Salpeter equation (\\ref{eq:BS_green_fourier}) in the form\n\\begin{multline}\n \\frac{4\\pi^2}{q_e} \\delta(q_e^2 - q^2) L_{ijkl}(\\bm{K},q_e \\hat{\\bm{q}})\n\\\\\n = \\frac{\\pi \\delta(q_e^2 - q^2)}{ i \\bm{q} \\cdot \\bm{K} - \\operatorname{Im}[\\Sigma(\\bm{q})]}\n \\left[\\vphantom{\\int} (\\delta_{ik} - \\hat{q}_i \\hat{q}_k) (\\delta_{jl} - \\hat{q}_j \\hat{q}_l)\\right.\n\\\\\n + u k_0^4 (\\delta_{im} - \\hat{q}_i \\hat{q}_m) (\\delta_{jn} - \\hat{q}_j \\hat{q}_n)\n\\\\\n \\left. \\times \\frac{4\\pi^2}{q_e} \\int f(\\bm{q}-\\bm{q}') \\delta(q_e^2 - q'^2) L_{mnkl}(\\bm{K},q_e \\hat{\\bm{q}}')\n \\frac{d\\bm{q}'}{8\\pi^3} \\right].\n\\end{multline}\nIntegrating both sides of the equation over $q$, performing the integral on the right-hand side over $q'$, and using the relation $\\int_0^\\infty f(\\bm{r}) \\delta(r^2-r_0^2) r^2 dr=r_0f(\\bm{r}=r_0 \\hat{\\bm{r}})\/2$, we obtain\n\\begin{multline}\\label{eq:preRTE}\n L_{ijkl}(\\bm{K},q_e \\hat{\\bm{q}})\n\\\\\n =\\frac{q_e}{4\\pi}\\frac{1}{ i q_e \\hat{\\bm{q}} \\cdot \\bm{K} - \\operatorname{Im}[\\Sigma(q_e \\hat{\\bm{q}})]}\n \\left[\\vphantom{\\int} (\\delta_{ik} - \\hat{q}_i \\hat{q}_k) (\\delta_{jl} - \\hat{q}_j \\hat{q}_l) \\right.\n\\\\\n + \\frac{u k_0^4}{4\\pi} (\\delta_{im} - \\hat{q}_i \\hat{q}_m) (\\delta_{jn} - \\hat{q}_j \\hat{q}_n)\n\\\\\n \\left.\\times \\int f(q_e (\\hat{\\bm{q}}-\\hat{\\bm{q}}')) L_{mnkl}(\\bm{K}, q_e \\hat{\\bm{q}}') d\\hat{\\bm{q}}' \\right].\n\\end{multline}\nThe quantity $L_{ijkl}(\\bm{K},q_e \\hat{\\bm{q}})$ is proportional to the specific intensity introduced in radiative transfer theory~\\cite{Chandrasekhar1960}, and has the meaning of a local and directional radiative flux. Actually, Eq.~(\\ref{eq:preRTE}) can be cast in the form of a radiative transfer equation, as we will now show.\n\nSince the disordered medium is statistically isotropic and translational-invariant, the correlation function $f$ only depends on $|\\hat{\\bm{q}}-\\hat{\\bm{q}}'|$, or equivalently on $\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}'$. It is directly related to the classical phase function $p(\\hat{\\bm{q}}\\cdot\\hat{\\bm{q}}')$ of radiative transfer theory as\n\\begin{equation}\\label{eq:correlation-phase}\n f(q_e |\\hat{\\bm{q}}-\\hat{\\bm{q}}'|) = A \\, p(\\hat{\\bm{q}}\\cdot\\hat{\\bm{q}}'),\n\\end{equation}\nwhere $A$ is a constant whose value is determined by energy conservation, and $\\int p(\\hat{\\bm{q}}\\cdot\\hat{\\bm{q}}') d\\hat{\\bm{q}} = 4\\pi$.\nTo order $(k_0\\ell)^{-1}$ and for short-range structural correlations, one has $\\operatorname{Im}[\\Sigma(q_e \\hat{\\bm{q}})] =\n-q_e\/\\ell$ and $u=6\\pi\/k_0^4\\ell$ (these results are derived in Appendix~\\ref{sec:A1}). This allows us to rewrite Eq.~(\\ref{eq:preRTE})\nin its final form \n\\begin{widetext}\n \\begin{equation}\\label{eq:RTE}\n \\left[ i \\hat{\\bm{q}} \\cdot \\bm{K} + \\frac{1}{\\ell} \\right] L_{ijkl}(\\bm{K},\\hat{\\bm{q}})\n = \\frac{1}{4\\pi} (\\delta_{ik} - \\hat{q}_i \\hat{q}_k) (\\delta_{jl} - \\hat{q}_j \\hat{q}_l)\n + \\frac{3 A}{8 \\pi \\ell} (\\delta_{im} - \\hat{q}_i \\hat{q}_m) (\\delta_{jn} - \\hat{q}_j \\hat{q}_n)\n \\int p(\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}') L_{mnkl}(\\bm{K}, \\hat{\\bm{q}}') d\\hat{\\bm{q}}'\n \\end{equation}\n\\end{widetext}\nwhere an implicit summation over $m$ and $n$ is assumed.\nThis expression takes the form of a radiative transfer equation (RTE) for the polarization-resolved specific intensity.\nIt differs from the standard vector radiative transfer equation~\\cite{Chandrasekhar1960} in the sense that it is not written\nin terms of Stokes vector, but using a fourth-order tensor representing the specific intensity for polarized light, and relating two\ninput and two output polarization components. Nevertheless, the various terms in Eq.~(\\ref{eq:RTE}) have a very clear\nphysical meaning. The first and second terms on the left-hand-side respectively describe the total variation of specific\nintensity along direction $\\hat{\\bm{q}}$ and the extinction of the ballistic light due to scattering (i.e.\\@\\xspace, Beer-Lambert's\nlaw). The first and second terms on the right-hand-side describe the increase of specific intensity along direction\n$\\hat{\\bm{q}}$ due to the presence of a source, and to the light originally propagating along direction $\\hat{\\bm{q}}'$\nand being scattered along $\\hat{\\bm{q}}$, respectively.\n\nConservation of energy requires the scattering losses to be compensated by the gain due to scattering after\nintegration over all angles. The energy conservation relation has to be written on the intensity, i.e.\\@\\xspace by setting $i=j$ and\nsumming over polarization components in Eq.~(\\ref{eq:RTE}), in the form\n\\begin{multline}\n \\frac{1}{\\ell}\\sum_i \\int L_{iikl}(\\bm{K},\\hat{\\bm{q}}) d\\hat{\\bm{q}} = \\frac{3 A}{8\\pi\\ell}\n\\\\\n \\times \\sum_{i,m} \\int (\\delta_{im} - \\hat{q}_i \\hat{q}_m)^2 p(\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}') L_{mmkl}(\\bm{K}, \\hat{\\bm{q}}') d\\hat{\\bm{q}}' d\\hat{\\bm{q}}.\n\\end{multline}\nThis leads to the following relation on the coefficient $A$\n\\begin{equation}\\label{eq:energy_conservation_constant}\n \\frac{3}{8\\pi} \\sum_m \\int (\\delta_{im} - \\hat{q}_i \\hat{q}_m)^2 p(\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}') d\\hat{\\bm{q}} = \\frac{1_i}{A},\n\\end{equation}\nwhere $1_i$ is the unit vector. At this stage, we have obtained a transport equation for polarized light\n[Eq.~(\\ref{eq:RTE})] that takes the form of a RTE. This equation stems directly from the Dyson and Bethe-Salpeter equations, \nfulfills energy conservation, and is valid for dilute media and short-range correlated disorder.\n\n\\section{Diffusion of polarization}\\label{sec:3}\n\n\\subsection{$P_1$ approximation}\n\nIn short-range correlated media, the phase function $p(\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}')$ is expected to be\nquasi-isotropic. It can therefore be expanded into a Legendre series, which, to order $\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}'$,\nreads\n\\begin{equation}\\label{eq:phasefunction}\n p(\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}') = 1 + 3g (\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}'),\n\\end{equation}\nwhere $g$ is the anisotropic scattering factor, defined as\n\\begin{equation}\n g =\\frac{1}{4\\pi} \\int p({\\bm{q}} \\cdot \\hat{\\bm{q}}') \\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}' d\\hat{\\bm{q}},\n\\end{equation}\nand satisfying\n\\begin{equation}\n g \\hat{\\bm{q}}' =\\frac{1}{4\\pi} \\int p({\\bm{q}} \\cdot \\hat{\\bm{q}}') \\hat{\\bm{q}} d\\hat{\\bm{q}}.\n \\end{equation}\nInserting Eq.~(\\ref{eq:phasefunction}) into Eq.~(\\ref{eq:RTE}), the RTE can be rewritten as\n\\begin{multline}\\label{eq:newRTE}\n \\left[ i \\hat{\\bm{q}} \\cdot \\bm{K} + \\frac{1}{\\ell} \\right] L_{ijkl}(\\bm{K},\\hat{\\bm{q}})\n = \\frac{1}{4\\pi} (\\delta_{ik} - \\hat{q}_i \\hat{q}_k) (\\delta_{jl} - \\hat{q}_j \\hat{q}_l)\n\\\\\n + \\frac{3 A}{2 \\ell} (\\delta_{im} - \\hat{q}_i \\hat{q}_m) (\\delta_{jn} - \\hat{q}_j \\hat{q}_n)\n \\\\\n \\times \\left[\n L^{(0)}_{mnkl}(\\bm{K})+ \\frac{3g}{4 \\pi } \\bm{j}_{mnkl}(\\bm{K}) \\cdot \\hat{\\bm{q}}\\right],\n\\end{multline}\nwhere $L^{(0)}_{ijkl}$ and $\\bm{j}_{ijkl}$ are the (polarization-resolved)\nirradiance and radiative flux vector, respectively, defined as\n\\begin{align}\n L^{(0)}_{ijkl}(\\bm{K}) & = \\frac{1}{4\\pi} \\int L_{ijkl}(\\bm{K},\\hat{\\bm{q}}) d\\hat{\\bm{q}},\n\\\\\n \\bm{j}_{ijkl}(\\bm{K}) & = \\int \\hat{\\bm{q}} L_{ijkl}(\\bm{K},\\hat{\\bm{q}}) d\\hat{\\bm{q}}.\n\\end{align}\n\nTo gain insight into the effect of short-range correlations on the propagation of polarized light, it is convenient to\ninvestigate the diffusion limit, which is reached after propagation on distances much larger than the \nscattering mean free path $\\ell$. In this limit, the specific intensity becomes quasi-isotropic. \nExpanding $L_{ijkl}$ into Legendre polynomials $P_n$ to first order in $\\hat{\\bm{q}}$, we have \n\\begin{equation}\\label{eq:P1_approx}\n L_{ijkl}(\\bm{K},\\hat{\\bm{q}}) = L^{(0)}_{ijkl}(\\bm{K}) + \\frac{3}{4\\pi} \\bm{j}_{ijkl}(\\bm{K}) \\cdot \\hat{\\bm{q}}\n\\end{equation}\nwhich is the so-called $P_1$ approximation.\nInserting Eq.~(\\ref{eq:P1_approx}) into Eq.~(\\ref{eq:newRTE}) and calculating the zeroth and first moments of the\nresulting equation (which amounts to performing the integrations $\\int - d\\hat{\\bm{q}}$ and $\\int - \\hat{\\bm{q}} d\\hat{\\bm{q}}$,\nrespectively), we eventually arrive to a pair of equations relating $L^{(0)}_{ijkl}$ and $\\bm{j}_{ijkl}$:\n\\begin{widetext}\n \\begin{align}\n i \\bm{K} \\cdot \\bm{j}_{ijkl}(\\bm{K}) + \\frac{4\\pi}{\\ell} L^{(0)}_{ijkl}(\\bm{K})\n & = \\frac{2}{3} S_{ijkl} + \\frac{4\\pi}{\\ell} A S_{ijmn} L^{(0)}_{mnkl}(\\bm{K}), \\label{eq:zeroth_moment}\n \\\\\n -\\frac{4\\pi}{3} K^2 \\ell L^{(0)}_{ijkl}(\\bm{K}) + i \\bm{K} \\cdot \\bm{j}_{ijkl}(\\bm{K})\n & = i g \\frac{9A}{8\\pi} \\int (\\delta_{im} - \\hat{q}_i \\hat{q}_m) (\\delta_{jn} - \\hat{q}_j \\hat{q}_n)\n \\left( \\bm{j}_{mnkl}(\\bm{K}) \\cdot \\hat{\\bm{q}} \\right) \\left( \\bm{K} \\cdot \\hat{\\bm{q}} \\right)\n d\\hat{\\bm{q}}.\\label{eq:first_moment}\n \\end{align}\n\\end{widetext}\nHere, we have defined\n\\begin{equation}\n S_{ijkl}=\\frac{3}{8\\pi} \\int (\\delta_{ik} - \\hat{q}_i \\hat{q}_k) (\\delta_{jl} - \\hat{q}_j \\hat{q}_l) d\\hat{\\bm{q}},\n\\end{equation}\nand used the relations $\\int (\\delta_{im} - \\hat{q}_i \\hat{q}_m) (\\delta_{jn} - \\hat{q}_j \\hat{q}_n) \\hat{\\bm{q}}\nd\\hat{\\bm{q}}=0$, $\\int \\hat{q}_i \\hat{q}_j d\\hat{\\bm{q}}= 4\\pi \/ 3\\delta_{ij}$ and $\\int \\hat{q}_i \\hat{q}_j\n\\hat{q}_k d\\hat{\\bm{q}}= 0$. The additional complexity of the polarization mixing due to structural correlations can be\napprehended from Eq.~(\\ref{eq:first_moment}), where the relation between $L^{(0)}_{ijkl}$ and $\\bm{j}_{ijkl}$ in terms\nof input and output polarization components becomes particularly intricate as soon as $g \\neq 0$. Much deeper insight\ninto the diffusion of polarized light can be gained via an eigenmode decomposition, as shown below.\n\n\\subsection{Polarization eigenmodes}\n\nAnalytical expressions for all terms in the $L_{ijkl}^{(0)}(\\bm{K})$ and $\\bm{j}_{ijkl}(\\bm{K})$ tensors can be\nobtained by solving Eqs.~(\\ref{eq:zeroth_moment}) and (\\ref{eq:first_moment}), which we have done imposing $\\bm{K}$ to\nbe along one of the main spatial directions, without loss of generality, and using the software Mathematica~\\cite{Mathematica}. The\nobtained expressions at this stage are long and complicated, containing in particular high-order terms in powers of $K$\nand $g$ (that are not physical and will be neglected below). We now introduce a polarization-resolved energy density\n$U_{ijkl}=6\\pi \/ cL_{ijkl}^{(0)}$ and decompose it in terms of ``polarization eigenmodes''\nas in Refs.~\\cite{Ozrin1992, Muller2002, Vynck2014}:\n\\begin{equation}\\label{eq:eigenmode_decomposition}\n U_{ijkl}(\\bm{K}) = \\sum_p U^{(p)}(\\bm{K}) \\left|ij\\right\\rangle_p \\left\\langle kl \\right|_p.\n\\end{equation}\nThe eigenvalues $U^{(p)}$ provide the characteristic length and time scales of the diffusion of each eigenmode and the projectors\n$\\left|ij\\right\\rangle_p \\left\\langle kl \\right|_p$, which will be denoted by ``polarization eigenchannels'', relate input polarization pairs\n$(k,l)$ to output polarization pairs $(i,j)$. The $U_{ijkl}$ is represented as a $9 \\times 9$ matrix (9 pairs of\npolarization components in input and output) and is diagonalized using Mathematica, leading again to full analytical expressions.\n\nAt this stage, the obtained expressions still depend on the coefficient $A$, originally defined in\nEq.~(\\ref{eq:correlation-phase}) and used to ensure energy conservation in the RTE, Eq.~(\\ref{eq:RTE}). To predict how\n$A$ depends on structural correlations, we rely on the particular case of the Henyey-Greenstein (HG) phase function~\\cite{Henyey1941}\n\\begin{equation}\\label{eq:HG-phasefunction}\n p_\\text{HG}(\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}') = \\frac{1-g}{\\left[1+g^2-2g (\\hat{\\bm{q}} \\cdot \\hat{\\bm{q}}')\\right]^{3\/2}}.\n\\end{equation}\nThe HG phase function is very convenient since it provides a closed-form expression with $g$ as a single parameter, and\napproximates the phase functions of a wide range of disordered media (e.g.\\@\\xspace, interstellar dust clouds, biological\ntissues). The energy conservation equation, Eq.~(\\ref{eq:energy_conservation_constant}), can be solved analytically in\nthis case, yielding the surprisingly simple relation\n\\begin{equation}\\label{eq:A_HG}\n A_\\text{HG} = \\frac{1}{1+g^2\/2}.\n\\end{equation}\nNote that the modification in energy conservation due to structural correlations appears at order $g^2$.\n\nWe can finally insert Eq.~(\\ref{eq:A_HG}) into the eigenvalues and eigenvectors found from\nEq.~(\\ref{eq:eigenmode_decomposition}) and develop analytical expressions valid to orders $K^2$ (diffusion\napproximation) and $g$ (weakly correlated disorder). The eigenvectors take the expressions already obtained for\nuncorrelated disorder~\\cite{Ozrin1992, Muller2002, Vynck2014}\n\\begin{align}\\label{eq:eigenvectors}\n \\left|kl\\right\\rangle_{1} & = \\frac{1}{\\sqrt{3}} \\delta_{kl}, \\nonumber \\\\\n \\left|kl\\right\\rangle_{2,3,4} & = \\frac{1}{\\sqrt{2}} (\\delta_{ka} \\delta_{lb} - \\delta_{kb} \\delta_{la}), \\nonumber \\\\\n \\left|kl\\right\\rangle_{5} & = \\frac{1}{\\sqrt{2}} (\\delta_{ka} \\delta_{la} - \\delta_{kb} \\delta_{lb}), \\nonumber \\\\\n \\left|kl\\right\\rangle_{6,7,8} & = \\frac{1}{\\sqrt{2}} (\\delta_{ka} \\delta_{lb} + \\delta_{kb} \\delta_{la}), \\nonumber \\\\\n \\left|kl\\right\\rangle_{9} & = \\frac{1}{\\sqrt{6}} (\\delta_{ka} \\delta_{la} + \\delta_{kb} \\delta_{lb}- 2 \\delta_{kc} \\delta_{lc}).\n\\end{align}\nThe first eigenchannel is the scalar mode, relating uniformly pairs of identical polarization components ($xx$, $yy$ and\n$zz$), which describe the classical intensity, between themselves. The other eigenchannels either redistribute\nnonuniformly the energy between pairs of identical polarization ($p=5$ and $9$), thereby participating as well in the\npropagation of the classical intensity, or are concerned with pairs of orthogonal polarizations ($xy$, $xz$, etc), which\ncan participate, for instance, in magneto-optical media in which light polarization can rotate~\\cite{MacKintosh1988,\nVanTiggelen1996, VanTiggelen1999}.\n\nThe eigenvalues take the form of the solution of the diffusion equation in reciprocal space\n\\begin{equation}\\label{eq:diffusionsolution}\n U^{(p)}(\\bm{K}) = \\frac{1}{\\mathcal{D}^{(p)} K^2 + \\mu_a^{(p)} c},\n\\end{equation}\nwhere $\\mathcal{D}^{(p)}$ and $\\mu_a^{(p)}$ are the diffusion constant and attenuation coefficient of the $p$th\npolarization mode. The eigenmode energy densities in real space therefore read\n\\begin{equation}\n U^{(p)}(\\bm{R}) = \\frac{1}{4\\pi \\mathcal{D}^{(p)} R} \\exp\\left[- \\frac{R}{\\ell_\\text{eff}^{(p)}} \\right],\n\\end{equation}\nwith $R=|\\bm{R}|$ and $\\ell_\\text{eff}^{(p)}=\\sqrt{\\mathcal{D}^{(p)}\/\\mu_a^{(p)} c}$, which is an effective attenuation\nlength, describing the depolarization process.\n\nTable~\\ref{tab:diffcorr} summarizes the diffusion constants, attenuation coefficients and effective attenuation lengths\nof the different polarization eigenchannels. As in the case of uncorrelated\ndisorder previously studied in Ref.~\\cite{Vynck2014}, all modes exhibit different diffusion constants, thereby spreading at different speeds, and\nonly the scalar mode persists at large distances ($\\ell_\\text{eff}^{(1)}=\\infty$), all other modes being attenuated on a\nlength scale on the order of a mean free path.\n\n\\begin{table*}\n \\caption{Summary of the diffusion constants $\\mathcal{D}^{(p)}$, attenuation coefficients $\\mu_a^{(p)}$ and effective\n attenuation lengths $\\ell_\\text{eff}^{(p)}$ characterizing the diffusion properties of the energy density through the\n individual polarization eigenchannels and the depolarization process. Note that all quantities are given to order\n $g$. Quite remarkably, structural correlations, via the scattering asymmetry factor $g$, are found to affect\n differently and independently each mode.}\\label{tab:diffcorr}\n \\begin{ruledtabular}\n \\def1.5{1.5}\n \\begin{tabular}{c|c|c|c|c|c|c}\n $p$ & 1 & 2 & 3,4 & 5,6 & 7,8 & 9 \\\\ \\hline\n $\\mathcal{D}^{(p)}$ & $ \\left(1-g\\right)^{-1} \\frac{c\\ell}{3}$ & $\\left(\\frac{1}{2}-\\frac{9}{20}g\\right)^{-1} \\frac{c\\ell}{3}$ & $\\left(\\frac{1}{2}-\\frac{3}{20}g\\right)^{-1} \\frac{c\\ell}{3}$ & $\\left(\\frac{7}{10}-\\frac{69}{100}g\\right)^{-1} \\frac{c\\ell}{3}$ & $\\left(\\frac{7}{10}-\\frac{39}{100}g\\right)^{-1} \\frac{c\\ell}{3}$ & $\\left(\\frac{7}{10}-\\frac{29}{100}g\\right)^{-1} \\frac{c\\ell}{3}$ \\\\ \\hline\n \n $\\mu_a^{(p)}$ & $0$ & $\\frac{1}{\\ell}$ & $\\frac{1}{\\ell}$ & $\\frac{3}{7\\ell}$ & $\\frac{3}{7\\ell}$ & $\\frac{3}{7\\ell}$ \\\\ \\hline\n $\\ell_\\text{eff}^{(p)}$ & $\\infty$ & $\\left( 1-\\frac{9}{20}g \\right)^{-1} \\sqrt{\\frac{2}{3}}\\ell$ & $\\left( 1-\\frac{3}{20}g \\right)^{-1} \\sqrt{\\frac{2}{3}}\\ell$ & $\\left( 1-\\frac{69}{140}g \\right)^{-1} \\frac{\\sqrt{10}}{3}\\ell$ & $\\left( 1-\\frac{39}{140}g \\right)^{-1} \\frac{\\sqrt{10}}{3}\\ell$ & $\\left( 1-\\frac{29}{140}g \\right)^{-1} \\frac{\\sqrt{10}}{3}\\ell$\n \n \\end{tabular}\n \\end{ruledtabular}\n\\end{table*}\n\nMore interestingly, our study brings new information on the influence of short-range structural correlations on transport and depolarization.\nLet us first remark that we properly recover the diffusion constant of the scalar mode, $\\mathcal{D}=c\\ell^*\/3$ with $\\ell^*=\\ell\/(1-g)$ the\ntransport mean free path, which is a good indication of the validity of the model. The second and\nmore interesting finding in this study is the fact that the propagation characteristics of each polarization mode is\naffected independently and differently by short-range structural correlations. One may have anticipated that the\ndiffusion constant of each polarization mode would be simply rescaled by the $(1-g)^{-1}$ factor relating scattering and\ntransport mean free paths. Instead, we show that a transport mean free path can be defined for each polarization mode,\n$\\ell^{*(p)}=3\\mathcal{D}^{(p)}\/c$ and its dependence on the anisotropy factor $g$ can change significantly, as shown in Fig.~\\ref{fig1}(a). This, in\nturn, implies that the spatial attenuation of each polarization mode (due to depolarization) is affected differently by\nstructural correlations, as shown in Fig.~\\ref{fig1}(b).\n\n\\begin{figure}\n \\centering\n\t\\includegraphics[width=0.5\\textwidth]{fig1}\n \\caption{(Color online only) Evolution of (a) the\n transport coefficient, $1\/\\ell^{*(p)}$, and (b) the attenuation coefficient, $1\/\\ell_\\text{eff}^{(p)}$, of\n polarization eigenmodes with short-range structural correlations. The coefficients are given in units of $1\/\\ell$ and\n shown on a restricted range of $g$ since the model is expected to remain valid to first order near $g=0$. The scalar\n mode ($p=1$, cyan solid curve) has a transport coefficient scaling as $(1-g)$ and an attenuation coefficient equal to\n zero (not shown). The polarization modes $p=2$--$4$ (gray dashed curves) and $p=5$--$9$ (orange dot-dashed curves)\n exhibit different slopes, indicating that both their transport properties are affected differently by short-range\n structural correlations.}\n \\label{fig1}\n\\end{figure}\n\n\\section{Discussion}\\label{sec:4}\n\nPrevious studies based on the multiple scattering theory for the propagation of polarized light relied on the\n\\textit{direct} resolution of the Bethe-Salpeter equation, Eq.~(\\ref{eq:BS_green_fourier}), using an expansion of the average Green's\ntensors and its correlation function to order $K^2$ (diffusion approximation). This strategy is however\npossible only for uncorrelated disorder, for which $f(\\bm{q}-\\bm{q}')=1$. Here, we proposed an alternative strategy based on\nthe derivation of a transport equation taking the form of an RTE, which allowed us to reach the same final goal (eigenmode decomposition) including\nshort-range structural correlations. This strategy, however, involves an additionnal approximation that has some implications. To\nclarify this point, let us consider our predictions in the limit of an uncorrelated disorder. Setting $g=0$ in the\npredictions of Table~\\ref{tab:diffcorr} yields the values reported in Table~\\ref{tab:diffuncorr}. An alternative\nstraightforward derivation from Eqs.~(\\ref{eq:zeroth_moment}) and (\\ref{eq:first_moment}), which yields the same\nresults, is proposed in Appendix~\\ref{sec:A3}. Compared to previous results (see, e.g.\\@\\xspace, Ref.~\\onlinecite{Vynck2014}), we\nobserve that the eigenvectors, or polarization eigenchannels, remain unchanged, but the eigenvalues are now 1, 3 and\n5-fold degenerate, yielding the same attenuation coefficients $\\mu_a^{(p)}$ but different diffusion constants\n$\\mathcal{D}^{(p)}$. This apparent discrepancy can be explained by the on-shell approximation, which ``smoothes out''\nthe polarization dependence in the correlation function of Green's tensor. Nevertheless, it is important to note that the\n\\textit{average} diffusion constants for the various degenerate modes are strictly identical:\n\\begin{equation}\n\\frac{1}{3} \\left(\\frac{6}{5}c\\ell + 2\\frac{2}{5}c\\ell \\right) = 2 \\frac{c\\ell}{3},\n\\end{equation}\nand\n\\begin{equation}\n\\frac{1}{5} \\left(2\\frac{230}{343}c\\ell + 2\\frac{130}{343}c\\ell + \\frac{290}{1029}c\\ell \\right) = \\frac{10}{7}\\frac{c\\ell}{3}\n\\end{equation}\nThis brings us to the conclusion that the model is consistent with the approximations that have been made.\n\n\\begin{table}[H]\n \\caption{Summary of the diffusion constants $\\mathcal{D}^{(p)}$, attenuation coefficients $\\mu_a^{(p)}$ and effective\n attenuation lengths $\\ell_\\text{eff}^{(p)}$ characterizing the diffusion properties of the energy density through the\n individual polarization eigenchannels for an uncorrelated disorder ($g=0$).}\\label{tab:diffuncorr}\n \\begin{ruledtabular}\n \\def1.5{1.5}\n \\begin{tabular}{l|l|l|l}\n $p$ & 1 & 2-4 & 5-9 \\\\ \\hline\n $\\mathcal{D}^{(p)}$ & $\\frac{c\\ell}{3}$ & $2 \\frac{c\\ell}{3}$ & $\\frac{10}{7}\\frac{c\\ell}{3}$ \\\\ \\hline\n $\\mu_a^{(p)}$ & $0$ & $\\frac{1}{\\ell}$ & $\\frac{3}{7\\ell}$ \\\\ \\hline\n $\\ell_\\text{eff}^{(p)}$ & $\\infty$ & $\\sqrt{\\frac{2}{3}}\\ell$ & $\\frac{\\sqrt{10}}{3}\\ell$\n \\end{tabular}\n \\end{ruledtabular}\n\\end{table}\n\nA second point deserving a comment is the fact that the attenuation length $1\/\\mu_a^{(p)}$ of the polarization eigenmodes\ndoes not depend on $g$ to first order, the effect of short-range structural correlations on the spatial\ndecay of polarization away from the source being implemented via the definition of mode-specific transport mean free\npaths. This picture contrasts with previous studies based on the phenomenological transfer matrix approach~\\cite{Xu2005,\nRojas-Ochoa2004a}, which relate the depolarization length $\\ell_p$ for linearly polarized light to the \\textit{scalar}\ntransport mean free path via a linear relation with $g$. In this sense, our model provides a different perspective on\nthis basic problem of light transport in disordered media. Intuitively, this picture also appears more physically sound,\nsince it is known that the relation between depolarization and transport mean free path varies with the incident\npolarization (linear, circular) or in presence of magneto-optical effects~\\cite{MacKintosh1988, VanTiggelen1996}.\n\nRelated to this point, it is also important to discuss the validity of the diffusion limit to retrieve depolarization\ncoefficients. Reaching the regime of diffusive transport typically requires light to experience several multiple\nscattering events. However, as pointed out previously (see, e.g.\\@\\xspace, Ref.~\\onlinecite{Gorodnichev2014}), this limit can hardly be\nachieved for the polarization modes, for which the depolarization occurs on the scale of a mean free path. It is then\nlegitimate to question the accuracy of the expressions reported in Table~\\ref{tab:diffcorr}. Nevertheless,\nwe do not expect this question to impact our claim that different polarization modes are individually and differently\naffected by short-range structural correlations. Actually, the established RTE for the polarization-resolved specific\nintensity, Eq.~(\\ref{eq:RTE}), like the standard vector radiative transfer equation, does not assume diffusive\ntransport. On this aspect, our study constitutes a very good starting point to investigate the validity of the diffusion\napproximation, which may be done either numerically by solving the RTE by Monte-Carlo methods, or analytically by adding\nhigher-order Legendre polynomials $P_n$ in the following steps.\n\nFinally, let us remark that the results of our model, in which disorder is described by a continuous and randomly fluctuating function of position [Eq.~(\\ref{eq:disorder})], should apply not only to heterogeneous materials with complex textit{connected} morphologies (e.g., porous media) but also to random ensembles of finite-size scatterers. Indeed, the Fourier transform of the structural correlation $f(\\bm{r}-\\bm{r}')$ directly leads to the definition of the phase function $p(\\hat{\\bm{q}}\\cdot\\hat{\\bm{q}}')$ [Eq.~(\\ref{eq:correlation-phase})], which is the same function to which one arrives when investigating light scattering by finite-size scatterers (it is, in this case, defined from the differential scattering cross-section). For the sake of broadness of applications and convenience, the final results here have been given for the HG phase function [Eq.~(\\ref{eq:HG-phasefunction})] but other phase functions (e.g., Mie for spherical scatterers) may be used to describe specific disordered media. Note that for ensembles of finite-size scatterers, the short-range correlation approximation restricts the validity range of the model to small scatterers.\n\n\\section{Conclusion}\\label{sec:5}\n\nTo conclude, we have proposed a model based on multiple scattering theory to describe the propagation of polarized light\nin disordered media exhibiting short-range structural correlations. Our results assume weak disorder ($k_0\\ell \\gg 1$),\nshort-range structural correlations (first order in $g$), and are obtained in the ladder approximation. Starting from the exact\nDyson and Bethe-Salpeter equations for the average field and the field correlation, we have derived a RTE for the\npolarization-resolved specific intensity [Eq.~(\\ref{eq:RTE})] and applied the $P_1$ approximation to investigate the\npropagation of polarized light in the diffusion limit. Interestingly, we have found that the polarization modes, described\nso far for uncorrelated disorder only, are independently and differently affected by short-range structural\ncorrelations. In practice, each mode is described by its own transport mean free path, which does not trivially depend\non $g$ (see Table~\\ref{tab:diffcorr}). \n\nIn essence, our study partly unveils the intricate relation between the complex morphology of disordered media and the\npolarization properties of the scattered intensity. The road towards a possible description of polarization-related\nmesoscopic phenomena in correlated disorder is long, yet we hope that the present work, which highlights several\ntheoretical challenges when dealing with polarized light and structural correlations, will motivate future\ninvestigations. The model may be generalized, for instance, by including the most-crossed diagrams in the derivation to\nenable the study of phenomena such as weak localization, or frequency dependence to investigate ---via a generalized\nRTE--- the temporal response to incident light pulses. Another line of research could be to study the impact of\nshort-range structural correlations on spatial coherence properties, which appears extremely relevant to the optical\ncharacterization of complex nanostructured media~\\cite{Dogariu2015}.\n\n\\section*{Acknowledgements}\n\nThe authors acknowledge John Schotland for stimulating discussions. This work is supported by LABEX WIFI (Laboratory of\nExcellence within the French Program ``Investments for the Future'') under references ANR-10-LABX-24 and\nANR-10-IDEX-0001-02 PSL$^*$, by INSIS-CNRS via the LILAS project and the CNRS ``Mission for Interdisciplinarity''\nvia the NanoCG project.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzekhb b/data_all_eng_slimpj/shuffled/split2/finalzzekhb new file mode 100644 index 0000000000000000000000000000000000000000..ec41e6f8c30262b9a484872f4085b9e36961c1a3 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzekhb @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nThe work of Seiberg and Witten \\cite{sewi,sewi2} on the exact solution \nto the low-energy effective action of $N=2$ supersymmetric Yang-Mills theory \nhas afforded not only a renewed insight into the charge confinement \n\\cite{sewi2} and the chiral symmetry breaking \\cite{sewi2}, \nbut also a marvelous insight into topological \ninvariants \\cite{wi} and conformal field theories in four \ndimensions \\cite{scft}. The key ingredients in obtaining these exact results\nare duality and the appearance of massless monopoles\/dyons in the strong\ncoupling regions of the theory. \nIn the weak coupling region, the exact solution enables us to\ndetermine full non-peturbative instanton corrections to the \neffective coupling constant, whose evaluation is otherwise quite cumbersome\nin the standard framework of quantum field theory.\n\nThe low-energy effective action is described in terms of a prepotential\nwhich is a single holomorphic function of superfields of $N=2$ \n$U(1)$ vector multiplets. \nThe exact solution for the prepotential may be \ncharacterized by the period integrals of \nthe special type of one-form on a hyper-elliptic curve.\nThe curves associated with a variety of $N=2$ \nsupersymmetric Yang-Mills theories and QCD \nhave been studied extensively \\cite{KlLeThYa}$-$\\cite{itsa}.\nThe moduli space of the curve contains singularities, at which \nsome solitons become massless. \nIn order to investigate the strong coupling physics, one needs to \nevaluate the period integrals near the singular locus.\nAn efficient approach to study this problem is to use the Picard-Fuchs \nequation, the differential \nequation which the periods obey \\cite{Cer,KlLeTh,Ma,ItYa}.\n\nIn the present article, we shall investigate the quantum moduli space\nof $N=2$ supersymmetric Yang-Mills theories with massless hypermultiplets\nand gauge groups $SU(2)$ and $SU(3)$.\nWe shall derive the Picard-Fuchs equation for the scalar part of $N=2$ \n$U(1)$ vector multiplets and their duals.\nBy solving the non-linear differential equation obeyed by the prepotential\nfor $G=SU(2)$, we will explicitly evaluate\nthe non-perturvative contributions in the prepotential \nin both weak and strong coupling regions.\n\n\n\\section{$N=2$ Supersymmetric Yang-Mills Theory with Massless Hypermultiplets}\nWe begin with reviewing some basic properties of the low-energy \neffective action of the $N=2$ supersymmetric $G=SU(N_{c})$ QCD\n\\cite{sewi2,arplsh}. \nIn $N=1$ superfield formulation, \nthe theory contains chiral \nmultiplets $\\Phi^{a}$ and chiral field strength $W^{a}$ \n($a=1,\\ldots, {\\rm dim}G$) both in the adjoint representation of $G$, \nand chiral superfields $Q^{i}$ in the $N_{c}$ and \n$\\tilde{Q}^{i}$ ($i=1,\\cdots, N_{f}$)\nin the $\\bar{N}_{c}$ representation of the gauge group.\nThe superpotential is given by \n${\\cal W}=\\tilde{Q}T^{a}\\Phi^{a}Q+M^{i}_{j}\\tilde{Q}_{i} Q^{j}$\nwhere $T^{a}$ is the generator of $G$ and $M^{i}_{j}$ is the mass matrix.\nAlong the flat direction the scalar fields $\\phi$ of $\\Phi$ get\nvacuum expectation values, which break the gauge group to the Cartan subgroup \n$U(1)^{r}$ where $r=N_{c}-1$ is the rank of $G=SU(N_{c})$. \nWhen the squark fields do not have vacuum expectation values, the low-energy\neffective theory is in \nthe Coulomb branch and contains $r$ $U(1)$ vector multiplets \n$(A^{i}, W_{\\alpha}^{i})$ $(i=1,\\cdots,r)$, where $A^{i}$ are $N=1$\nchiral superfields and $W_{\\alpha}^{i}$ are $N=1$ vector superfields. \nThe quantum moduli space may be characterized by \nthe low-energy effective Lagrangian ${\\cal L}$ \nwith the prepotential ${\\cal F}(A)$\n\\begin{equation}\n{\\cal L}={1\\over 4\\pi} {\\rm Im }\n\\left(\\int d^2\\theta d^2\\bar{\\theta}A_{D i}\\bar{A}^{i}+{1\\over2}\n\\int d^2\\theta \\tau^{i j}W_{\\alpha}^{i} W^{j \\alpha}\\right), \n\\end{equation}\nwhere $A_{D i}={\\partial {\\cal F}\\over \\partial A^{i}}$ is a field dual to $A^{i}$ \nand \n$\\tau^{i j}={\\partial^{2}{\\cal F}\\over \\partial A^{i} \\partial A^{j}}$ the effective\ncoupling constants.\nWe denote the scalar component of $A^{i}$, $A_{D i}$ by $a^{i}$, $a_{D i}$.\nThe pairs $(a_{D i},a_{i})$ are the $Sp(2r,{\\bf Z})$ section over the \nspace of gauge invariant parameters $s_{i}$ $(i=2,\\cdots, N_{c})$\ndefined by ${\\rm det}(x-\\phi)=x^{N_{c}}-\\sum_{i=2}^{N_{c}} s_{i} x^{N_{c}-i}$.\nThe quantum moduli space of the Coulomb branch is parametrized by the \ngauge invariants $s_{i}$ and the eigenvalues $m_{1},\\ldots, m_{N_{f}}$ \nof the mass matrix.\n\nThe sections $(a_{D i},a_{i})$ are obtained as\nthe period integrals of a meromorphic differential\n$\\lambda$ over the hyper-elliptic curve ${\\cal C}$ \\cite{haoz}:\n\\begin{eqnarray}\ny^{2}&=&C(x)^2-G(x), \\nonumber \\\\\nC(x)&=& x^{N_{c}}-\\sum_{i=2}^{N_{c}} s_{i} x^{N_{c}-i}\n +{\\Lambda^{2N_{c}-N_{f}}\\over 4} \\sum_{i=0}^{N_{f}-N_{c}}\n t_{i}(m) x^{N_{f}-N_{c}-i},\n\\nonumber \\\\\nG(x)&=& \\Lambda^{2N_{c}-N_{f}}\\prod_{i=1}^{N_{f}}(x+m_{i}),\n\\end{eqnarray}\nwhere $\\Lambda$ stands for the QCD scale parameter and $t_{i}(m)$ is defined by\n$\\prod_{i=1}^{N_{f}}(x+m_{i})=\\sum_{i=0}^{N_{f}} t_{i}(m) x^{N_{f}-i}$ with\n$t_{0}(m)=1$.\nThe terms proportional to $\\Lambda^{2N_{c}-N_{f}}$ in $C(x)$ are absent \nin the case of $N_{f}p_c$ (actually, it is limited only by the system size). At criticality,\n$\\langle N (t) \\rangle$ is found to be constant in the large time limit.\n\nIn the usual scaling picture of absorbing phase transitions, the critical\nexponent $\\beta$ is related to the probability that a given site belongs to an\ninfinite cluster generated from a fully occupied lattice at $t=-\\infty$. This\nquantity tends to zero as the control parameter approaches the critical value\nfrom above. Similarly, the exponent $\\beta'$ is related to the probability\nthat a localized seed generates an infinite cluster extending to\n$t=+\\infty$. Therefore, in the supercritical phase $(\\Delta >0)$, the averaged\nactivity of the site at the origin for $t\\to\\infty$ \\textit{measured in seed\n simulations} averaging over all runs, scales as $\\rho^s \\sim\n\\Delta^{\\beta+\\beta'}$, where the superscript `s' stands for `stationary'. At\ncriticality, this function is expected to decay as $\\rho(t) \\sim\nt^{-(\\beta+\\beta')\/\\nu_\\parallel}$, where $\\nu_\\parallel$ is the correlation\ntime exponent. Moreover, in the DP class a special {\\it time reversal\n symmetry} implies that $\\beta=\\beta'$ \\cite{Hinrichsen00}.\n\nAs shown in~\\cite{DeloubriereWijland02}, time reversal symmetry also holds in\nthe present type of models. This implies that, in supercritical seed\nsimulations, the density of active sites at the boundary is expected to\nsaturate as:\n\\begin{equation}\n \\rho_0^s\\sim \\Delta^{2\\beta}\\,,\n\\end{equation}\nwhile, at criticality:\n\\begin{equation}\n \\rho_0(t) \\sim t^{-2\\beta\/\\nu_\\parallel},\n\\end{equation}\nimplying that $\\alpha$ in Eq.~(\\ref{rhodecay}) is\n\\begin{equation}\n\\alpha=2\\beta\/\\nu_\\parallel\\,.\n\\label{alpha}\n\\end{equation}\nAssuming that $\\alpha=1\/2$, then $\\beta\/\\nu_\\parallel = 1\/4$.\n\\begin{figure}\n\\includegraphics[width=87mm]{.\/fig3.eps}\n\\vspace{2mm}\n\\caption{(Color online) Left panel: Density of particles at the leftmost site,\n at the time when it reaches its minimum value, as a function of the distance\n from criticality $\\Delta$. This gives the exponent $\\beta= 0.68(5)$. Right\n panel: The same quantity, at criticality and in the stationary state, as a\n function of the external field $h$, giving $\\delta_h^{-1}= 0.29(5)$, compatible\n with the conjectured value $1\/3$.\n\\label{fig:off}}\n\\end{figure}\n\n\\subsection{Stationary properties}\n\\label{stationary}\nIn numerical simulations in the active phase, it takes a very long time,\nspecially for small values of $\\Delta$, to reach the steady state. Moreover,\nwe observed the unusual fact that, for $\\Delta>0$, the density $\\rho_0$ goes\nthrough a minimum before reaching the stationary state (see\nFig.~\\ref{fig:decay} and also \\cite{FES}, where similar non-monotonous curves\nwere reported). However, it turns out that the value $\\rho_0^m$ at the minimum\nand the saturation value $\\rho_0^s$ differ by a constant factor, entailing\nthat both quantities scale in the same way, i.e.:\n\\begin{equation}\n\\rho_0^m\\sim \\Delta^{2\\beta} \\,.\n\\end{equation}\nNote that this can be true only if the density $\\rho_0(t)$ in seed simulations\nobeys the scaling relation:\n\\begin{equation}\n\\rho_0(t) = \\Delta^{2\\beta}\\, R(t\\Delta^{\\nu_\\parallel})\n\\end{equation}\ni.e. if it is possible to collapse the data by plotting\n$\\rho_0\\Delta^{-2\\beta}$ versus $t\\Delta^{4\\beta}$. Indeed, this will be shown\nto be the case in Sec.~\\ref{non-Markovian} for a $0$-dimensional non-Markovian\nprocess argued to be in the same universality class.\n\nRelying on this observation, one can determine the value of the exponent\n$\\beta$ by measuring the density $\\rho_0^m$ at the minimum, which is reached\nmuch earlier than the stationary state. In Fig.~\\ref{fig:off} we plot $\\rho_m$\nas a function of $\\Delta$, inferring $\\beta= 0.68(5)$.\n\n\\subsection{External field}\n\\label{external}\nIn ordinary directed percolation, an external field, conjugate to the order\nparameter, can be implemented by creating active sites at some constant rate\n$h$, thereby destroying the absorbing nature of the empty configuration. At\ncriticality, the external field is known to drive a $d$+1-dimensional DP\nprocess towards a stationary state with $\\rho^s \\sim h^{1\/\\delta_h}$ where\n$\\delta_h^{-1}=\\beta\/(\\nu_\\parallel+d\\nu_\\perp-\\beta')$, and $\\nu_\\perp$ is the\ncorrelation length critical exponent.\n\nIn the present model, the external field, conjugate to the order parameter\n$\\rho_0$, corresponds to spontaneous creation of activity at the leftmost site\nat rate $h$. The above hyperscaling relation for $\\delta_{h}$ is thus expected\nto be fulfilled by taking $d=0$:\n\\begin{equation}\n \\rho_0^s\\sim h^{1\/\\delta_h}.\n\\end{equation}\nwith\n\\begin{equation}\n \\delta_h^{-1}= \\beta\/(\\nu_\\parallel-\\beta').\n\\end{equation}\nFrom this expression, exploiting the fact that $\\beta=\\beta'$ and using\nEq.(\\ref{alpha}) as well as the conjectured rational value $\\alpha= 1\/2$, a\nprediction $\\delta_h^{-1}=1\/3$ is obtained. Our numerical estimate, $\\delta_h^{-1}=\n0.29(5)$ (see Fig.~\\ref{fig:off}) is compatible with this result.\n\n\\subsection{Survival probability}\n\\label{survival}\n\n\n\\begin{figure}\n\\includegraphics[width=87mm]{.\/fig4.eps}\n\\vspace{-2mm}\n\\caption{(Color online) Survival probability $P_s(t)$ as function of time\n below, above, and at criticality. At the critical point, it decays with the\n exponent $\\delta=0.15(2)$, different from $\\beta\/\\nu_\\parallel$, and in\n agreement with the conjectured value $1\/6$.\n\\label{fig:survival}}\n\\end{figure}\nThe survival probability $P_s(t)$ is defined as the fraction of runs that,\nstarting with a single seed at the boundary, survive \\textit{at least} until\ntime $t$. At criticality, this quantity is expected to decay algebraically:\n\\begin{equation}\nP_s(t)\\sim t^{-\\delta},\n\\end{equation}\nwith the so-called survival exponent $\\delta$, while in the super-critical\nregime it saturates in the long time limit. Since $P_s(\\infty)$ coincides with\nthe probability for a seed to generate an infinite cluster, the saturation\nvalue of the survival probability as a function of the distance from\ncriticality gives the exponent $\\beta'$. As in DP, one expects $P_s(t)$ to\ndecay in time with an exponent $\\delta=\\beta'\/\\nu_\\parallel =1\/4$. However, as\nshown in Fig.~\\ref{fig:survival}, one finds a much smaller exponent $\\delta=\n0.15(2)$. Therefore, the usual relation $\\delta= \\beta'\/\\nu_\\parallel$ does\nnot hold. We also observed that it is not possible to collapse different\ncurves of $P_s(t)$ for different values of $\\Delta$, i.e. the survival\nprobability seems to exhibit an anomalous type of scaling behavior. We expect\nthat off-critical simulations of the survival probability give the exponent\n$\\beta'$ but the simulation times needed to reach steady state are\nprohibitively long.\n\nAn explanation for the value $\\delta= 0.15(2)$, differing from\n$\\beta\/\\nu_\\parallel$, is given in the following subsection.\n\n\n\n\\subsection{Time reversal symmetry}\n\n\\label{time_reversal}\n\nIn ordinary bond DP, the statistical weight of a configuration of percolating\npaths does not depend on the direction of time. More specifically, the\nprobability to find an open path from at least one site at time $t=0$ to a\nparticular site at time $t$ coincides with the probability to find an open\npath from a particular site at time $t=0$ to at least one site at time\n$t$. This implies that, in bond DP, {\\it i)} the density $\\rho(t)$ in simulations with\nfully occupied initial state and {\\it ii)} the survival probability $P_s(t)$ in seed\nsimulations coincide; hence $\\beta=\\beta'$. In other realizations of DP\n(e.g. site DP), this time reversal symmetry is not exact but only\nasymptotically realized.\n\nApplying the same arguments to the present model, the survival probability\n$P_s(t)$ in seed simulations should scale in the same way as the density of\nactive sites at the boundary $\\rho_0(t)$ in a process starting with a\n\\textit{fully occupied lattice} in the bulk. A numerical test, which\napproximates such a situation, confirms this conjecture, i.e. one has\n$\\rho_0(t)\\sim t^{-\\delta}$ with $\\delta \\approx 0.15$ for a fully occupied\ninitial state.\n\nFollowing the arguments of~\\cite{BaratoHinrichsen09} in a related model, this\nobservation can be used to provide an heuristic explanation for the fact that\n$\\delta\\neq\\beta\/\\nu_\\parallel$.\n\nIt is known that, if the boundary acts as a sink or perfect trap (e.g. if\n$p=0$), then, in a process starting with a fully occupied lattice, one\nobserves a growing depletion zone around the boundary whose linear size\n$l'(t)$ increases as $l'\\sim t^{\\alpha_{l}}$, with $\\alpha_{l}=1\/2$ (see\n\\cite{rabbits} and the next subsection). Thus, the density of active sites\ndecays as $t^{-1\/2}$. Hence, the influx of particles from the bulk to the\nleftmost site may be considered as an effective time-dependent external field\n$h(t)\\sim t^{-1\/2}$. Making the assumption that this field varies so slowly\nthat the response of the process (i.e. the actual average activity at the\nboundary) behaves adiabatically, as if the field was constant, then in a\ncritical process starting from an initially fully occupied state:\n\\begin{equation}\n \\rho_0(t) \\sim t^{-\\frac{1}{2\\delta_h}} \\sim t^{-1\/6}.\n\\end{equation}\nOwing to the time reversal property, this quantity should decay as the\nsurvival probability. This chain of heuristic arguments leads to the\nconjecture that the survival exponent is given by $ \\delta = 1\/6$, in\nagreement with the numerical estimate $\\delta=0.15(2)$.\n\nThis unusual value of the exponent $\\delta$ is clearly related to the fact\nthat the present problem is inhomogeneous. The argumentation presented above\ndoes not work for the CP, for example, since there is no special site and,\ntherefore, a fully occupied lattice cannot be interpreted as a time dependent\nfield acting on a special site.\n\n\n\\subsection{Density profile}\n\\label{density}\n\\begin{figure}\n\\includegraphics[width=87mm]{.\/fig5.eps}\n\\vspace{-2mm}\n\\caption{(Color online) Left: Data collapse of the rescaled profiles of the\n particle density at criticality for $t_0=64,128,\\ldots, 8192$ (blue) compared\n to a Gaussian distribution (red). Inset: The same data collapse in a\n double-logarithmic representation. Right: Density of particles (blue) and\n pairs (green) at $t_0=10^6$, showing the presence of correlations which\n decay in space as $x^{-1\/2}$, indicating that $\\beta\/\\nu_\\perp=1\/2$.\n \\label{fig:profile}}\n\\end{figure}\nNow, we consider the density profile $\\rho(x,t)$ in the bulk, where $x \\in\n\\mathbb{N}$ is the spatial coordinate (distance to the boundary), computed at\nthe critical point. In the left panel of Fig.~\\ref{fig:profile}, we compare\nthe data collapse of the curves $\\rho(x,t)t^{1\/2}$ as a function of\n$x\/t^{1\/2}$ with a Gaussian and observe an excellent agreement, indicating\nrandom-walk like behavior with a dynamical exponent $z=2$. However, in\ncontrast to a simple random walk, particles are mutually correlated. This is\nillustrated in the right panel of Fig.~\\ref{fig:profile}, where the connected\ncorrelation function between two nearest neighbors:\n\\begin{equation}\n \\rho^{\\rm pair}(x,t)= \\langle\\rho(x+1,t)\\rho(x,t)\\rangle- \n \\langle\\rho(x+1,t)\\rangle\\langle\\rho(x,t)\\rangle\n\\end{equation}\nin a system at the critical point is plotted against time. One observes an\nalgebraic decay, $x^{-1\/2}$, with distance. According to the standard scaling\ntheory this implies that $\\beta\/\\nu_\\perp=1\/2$ , confirming that\n$z=\\nu_\\parallel\/\\nu_\\perp=2$. Moreover, these results are in full agreement\nwith field theoretical calculations presented in\nRef.~\\cite{DeloubriereWijland02} (see section \\ref{vanWijland}), which predict\n$z=2$ and $\\alpha=1\/2$.\n\n\\section{Mean field approximation}\n\\label{mean_field}\nHere, we study mean field approximations at different levels. Let us denote by\n$\\eta_i$ the probability to find a particle at site~$i$; the temporal\nevolution within a simple (one-site) mean field approximation is given by:\n\\begin{eqnarray}\n\\frac{d\\eta_0}{dt}&=& -(1-p)\\eta_0+ \\frac{1}{2}\\eta_1(1-\\eta_0),\n\\label{eqmf1}\\\\\n\\frac{d\\eta_1}{dt}&=& p\\eta_0(1-\\eta_1)+\n\\frac{1}{2}\\left(\\eta_{2}+\\eta_{0}\\eta_1-2\\eta_{1}\\right),\n\\label{eqmf2}\\\\\n\\frac{d\\eta_i}{dt}&=& \\frac{1}{2}\\left(\\eta_{i+1}+\n\\eta_{i-1}-2\\eta_{i}\\right),~~ \\mbox{ for } i=2,3,\\hdots\\,\n\\label{eqmf3}\n\\end{eqnarray}\nNote that the equations for the boundary site and its neighbor,\nEq.~(\\ref{eqmf1}) and Eq.~(\\ref{eqmf2}), include quadratic terms due to the\nexclusion constraint, while the equation for sites at the bulk,\nEq.~(\\ref{eqmf3}), describes in this approximation a symmetric random walk,\n{\\it i.e.} it is a diffusion equation. The critical point within simple mean\nfield theory (where the equation for $\\eta_1$ also becomes a diffusion\nequation) is $p_c= 1\/2$.\n\nConsidering a localized initial condition at the boundary,\n$\\eta_i=\\delta_{i,0}$, after a transient time the densities at sites $0$ and\n$1$ should, approximately, coincide. Therefore, from Eq.~(\\ref{eqmf1}) with\n$\\eta_0 \\approx \\eta_1$, it follows that, at criticality, $\\eta_0\\sim\nt^{-1\/2}$.\n\nIn the stationary regime, Eq.~(\\ref{eqmf1}) leads to $\\eta_0\\sim (p-1\/2)$ for\n$p\\ge 1\/2$. From these results we have:\n\\begin{equation}\n\\alpha^{MF}=1\/2\\,,\\qquad \\beta^{MF}=1\\,.\n\\end{equation}\nTo obtain the survival exponent, $\\delta$, we follow the arguments of the\npreceding section and study the decay of activity from a fully occupied\nlattice, $\\eta_i=1$ for all $i$. Integrating Eqs.~(\\ref{eqmf1}),\nEq.~(\\ref{eqmf2}) and Eq.~(\\ref{eqmf3}) numerically with this initial\ncondition, we obtain an exponent in agreement with\n\\begin{equation}\n\\delta^{MF}= 1\/4\\,.\n\\end{equation}\nA more accurate approximation can be obtained by keeping the correlation\nbetween the first two sites, which is expected to be more relevant than the\ncorrelation between other neighboring sites. Such a pair-approximation was\nused recently in a model where a boundary site also plays a special\nrole~\\cite{Sugden07}. In this approximation, the master equation reads:\n\\begin{eqnarray}\n\\frac{d\\sigma_{00}}{dt}&=& (1-p)\\sigma_{10}+\n\\frac{1}{2}[\\sigma_{01}(1-\\eta_2)- \\sigma_{00}\\eta_2],\n\\\\ \\frac{d\\sigma_{01}}{dt}&=& (1-p)\\sigma_{11}+\n\\frac{1}{2}[\\sigma_{00}\\eta_2-\\sigma_{01}(2-\\eta_2)],\\nonumber\n\\\\ \\frac{d\\sigma_{10}}{dt}&=& -\\sigma_{10}+\n\\frac{1}{2}[\\sigma_{01}-\\sigma_{10}\\eta_2+\\sigma_{11}(1-\\eta_2)],\\nonumber\n\\\\ \\frac{d\\sigma_{11}}{dt}&=& p\\sigma_{10}- (1-p)\\sigma_{11}+\n\\frac{1}{2}[\\sigma_{10}\\eta_2-\\sigma_{11}(1-\\eta_2)],\\nonumber\n\\\\ \\frac{d\\eta_2}{dt}&=& \\frac{1}{2}\\left(\\eta_{3}+ \\sigma_{11}+ \\sigma_{01}\n-2\\eta_{2}\\right),\\nonumber \\\\ \\frac{d\\eta_i}{dt}&=&\n\\frac{1}{2}\\left(\\eta_{i+1}+ \\eta_{i-1}-2\\eta_{i}\\right) \\mbox{ for }\ni=3,4,\\hdots\\,\\nonumber\n\\end{eqnarray}\nwhere $\\sigma_{s_0s_1}$ is the probability that the occupation numbers of the\nfirst two sites are $s_0$ and $s_1$. Numerical integration of these equations\nleads to an improved critical point estimation, $p_c\\approx 0.634$, but to the\nsame mean-field exponents as above.\n\\section{Related models and field theoretical approaches}\n\\label{related_models}\n\n\\subsection{Bosonic variant}\n\\label{bosonic}\nThe model defined above is fermionic in the sense that each site can be\noccupied by, at most, one particle. We now consider a bosonic variant without\nsuch a constraint. This means that diffusion is independent of the\nconfiguration of particles and that particles can be created at the boundary\nsite without restriction. More specifically, the update rules are:\n\\begin{enumerate}\n\\item[(a)] A particle is chosen randomly.\n\\item[(b)] If the particle is located at the leftmost site it can:\ncreate another particle at the leftmost site ($s_0=s_0+1$) at rate $\\lambda$,\ndie ($s_0=s_0-1$) at rate $\\sigma$, or\ndiffuse to the next neighbor at rate $D$.\n \\item[(c)] If the particle is located in the bulk, it diffuses to the\n right or to the left at equal rates $D$.\n\\end{enumerate}\nThe corresponding master equation is:\n\\begin{eqnarray}\n\\frac{dP(\\{n\\},t)}{dt} &=& \\lambda\\bigl[(n_0-1)P(n_0-1,...,t)-\n n_0P(\\{n\\},t)\\bigr]\\nonumber \\\\\n& +&\\sigma\\bigl[(n_0+1)P(n_0+1,...,t)- n_0P(\\{n\\},t)\\bigr]\\nonumber \\\\\n&+& D \\Bigl[ \\sum_{\\langle ij\\rangle}P(...,n_i-1,n_j+1,...,t) \\\\\n&+&P(...,n_i+1,n_j-1,...,t)-2P(\\{n\\},t) \\Bigr] \\nonumber\n\\label{eqboson1}\n\\end{eqnarray} \nwhere $P(\\{n\\},t)$ is the probability to find a given configuration $\\{n\\}=\nn_0, n_1, n_2\\ldots$ and the sum runs over all nearest neighbors, $j$, of site\n$i$ (recall that site $0$ has only one neighbor). Defining the state vector:\n\\begin{equation}\n|\\psi(t)\\rangle= \\sum_{\\{n\\}}P(\\{n\\},t)|\\{n\\}\\rangle,\n\\end{equation}\nwhere $|\\{n\\}\\rangle=\\otimes_i|n_i\\rangle$ denotes the usual configuration\nbasis, the master equation can be expressed in the form\n\\begin{equation}\n\\frac{d}{dt} |\\psi(t)\\rangle= -\\hat{H}|\\psi(t)\\rangle\\,,\n\\end{equation}\nwhere $\\hat{H}$ is the time evolution operator. Using bosonic creation and\nannihilation operators, defined by $\\hat{a}_i|n_i\\rangle= n_i|n_i-1\\rangle$\nand $\\hat{a}_i^\\dagger|n_i\\rangle= |n_i+1\\rangle$, the master equation\nEq.~(\\ref{eqboson1}) can be shown to correspond to the time evolution\noperator:\n\\begin{eqnarray}\n\\hat{H}&=& D\\sum_{\\langle\n ij\\rangle}(\\hat{a}^\\dagger_i-\\hat{a}^\\dagger_j)(\\hat{a}_i-\\hat{a}_j)\n\\label{eqbosonH}\n\\\\ &&+\\sigma(\\hat{a}_0^\\dagger-1)\\hat{a}_0 +\\lambda\n\\hat{a}_0^\\dagger(1-\\hat{a}_0^\\dagger)\\hat{a}_0.\\nonumber\n\\end{eqnarray}\nIn this formalism, the expectation value of an operator $\\hat{B}$ is given by\n$\\langle\\hat{B}\\rangle= \\langle 1|\\hat{B} |\\psi(t)\\rangle$ where $\\langle 1|=\n\\sum_{\\{n\\}}\\langle \\{n\\}|$. As is the case for the bosonic contact process\n\\cite{Baumann05}, the equations for the time evolution of the density of\nparticles close. From the Heisenberg equation of motion, $\\frac{d\\hat{B}}{dt}=\n[\\hat{H},\\hat{B}]$ and Eq.~(\\ref{eqbosonH}), one obtains:\n\\begin{eqnarray} \n\\frac{d\\rho_0}{dt} &=& D(\\rho_1-\\rho_0)+ \\Delta\\rho_0 \\label{eqboson1b}\n\\\\ \\frac{d\\rho_i}{dt} &=& D(\\rho_{i+1}+\\rho_{i-1}-2\\rho_i)\\qquad\ni=1,2,3\\ldots\\nonumber\n\\end{eqnarray}\nwhere $\\rho_i(t)= \\langle a^\\dagger_i(t)a_i(t)\\rangle= \\langle a_i(t)\\rangle$\nand $\\Delta= \\lambda-\\sigma$. Alternatively, one could have written a\nLangevin equation equivalent to Eq.(\\ref{eqbosonH}), and from it, averaging\nover the resulting noise, one readily arrives at the same set of equations\nEq.(\\ref{eqboson1b}).\n\nFrom these equations, we can see that the critical point is $\\Delta= 0$, where\nEq.(\\ref{eqboson1b}) is a diffusion equation. In the continuum limit,\nEq.~(\\ref{eqboson1b}) reads:\n\\begin{equation}\n\\frac{\\partial\\rho(x,t)}{\\partial t}= \\frac{\\partial^2\\rho(x,t)} {\\partial\n x^2}+ \\Delta\\delta(x)\\rho(x,t)\\,\n\\label{eqboson2}\n\\end{equation}\nwhere $x$ is the spatial coordinate and, without loss of generality, we have\nset $D=1$. We note that in order to take the continuum limit in equation (\\ref{eqboson1b}), a site $-1$, with $\\rho_{-1}= \\rho_{0}$, has to be introduced, so that appropriate boundary conditions are satisfied. The solution of this inhomogeneous diffusion equation is:\n\\begin{equation}\n\\rho(x,t)= \\int_0^\\infty \\delta(\\zeta)G(x,\\zeta,t)d\\zeta+\\nonumber\n\\end{equation}\n\\begin{equation}\n\\int_0^t\\int_0^\\infty\\Delta\\delta(\\zeta)\\rho(\\zeta,\\tau)G(x,\\zeta,t-\\tau),\nd\\zeta d\\tau \\,\n\\label{eqboson3}\n\\end{equation}\nwhere $G(x,\\zeta,t)= (e^{-(x+\\zeta)^2\/(4t)}+e^{-(x-\\zeta)^2\/(4t)})\/(\\sqrt{\\pi t})$ is the Green\nfunction and the first term in the right hand side comes from the initial\ncondition $\\rho(x,0)= \\delta(x)$. From Eq.~(\\ref{eqboson3}) we have\n\\begin{equation}\n\\rho_0(t)= \\frac{2}{\\sqrt{\\pi t}}+2\\Delta\\frac{d^{-1\/2}}{dt^{-1\/2}}\\rho_0(t) \\,\n\\label{eqboson4}\n\\end{equation}\nwhere $\\rho_0(t)= \\rho(0,t)$, and the operator $\\frac{d^{-1\/2}}{dt^{-1\/2}}$,\ndefined by\n\\begin{equation}\n\\frac{d^{-1\/2}}{dt^{-1\/2}}f(t)= \\int_0^t\\frac{f(\\tau)}{\\sqrt{\\pi (t-\\tau)}}d\\tau,\n\\end{equation}\nis a half integral operator \\cite{Oldham}. Equation~(\\ref{eqboson4}) involves\n(owing to the delta function in the interaction term in Eq.~(\\ref{eqboson2}))\nonly the density at the leftmost site. This justifies the mapping of this\nmodel onto an effective one-site non-Markovian process (see next\nsection). Using some rules for half integration \\cite{Oldham} to solve\nEq.~(\\ref{eqboson4}), we find:\n\\begin{equation} \n\\rho_0(t)= \\frac{2}{\\sqrt{\\pi t}}+ 4\\Delta\\exp(4\\Delta^2t)\\mbox{erf}(-2\\Delta\\sqrt{t}),\n\\label{eqboson5}\n\\end{equation}\nwhere $\\mbox{erf(x)}$ is the error function. This implies that, above the\ncritical point, $\\rho_0$ grows exponentially in the long time limit, and does\nnot reach a stationary value, {\\it i.e.} there is a first order transition\nand, hence, $\\beta= 0$ in this bosonic model. From equation\nEq.~(\\ref{eqboson5}), we deduce $\\beta'=1$ and $\\nu_\\parallel=2$. We have not\nbeen able to calculate the survival-probability exponent exactly, but\nnumerical simulations suggest $\\delta= 1\/4$, in agreement with the mean field\nexponent.\n\n\\subsection{Partially bosonic variant}\n\\label{partially_bosonic}\n\nLet us now introduce a {\\it partially bosonic} variant of the previous model\nby retaining the exclusion constraint only at the boundary, but not in the\nbulk. The rules, in this case, are:\n\\begin{enumerate}\n\\item[(a)] A particle is randomly chosen.\n\\item[(b)] If it is at the leftmost site, it can generate a particle at site\n $1$ (provided that $s_1=0$) with probability $p$ or die ($s_0:=0$) with\n probability $1-p$.\n\\item[(c)] Particles in the bulk diffuse to the right or to the left with the\n same probability, $1\/2$.\n\\end{enumerate}\nNumerical simulations show that this variant exhibits the same critical\nbehavior as the original model, even if the critical point is shifted to $p_c=\n0.6973(1)$. This shows that the fermionic constraint is relevant only at the\nboundary, where it induces a saturation of the particle density and leads the\ntransition to become continuous.\n\n\\subsection{Models with pair annihilation at the boundary}\n\\label{vanWijland}\nIn the models discussed so far, particles at the boundary either create an\noffspring or die spontaneously at some rate. Instead, a very similar model was\nintroduced in Ref.~\\cite{DeloubriereWijland02}, for which particles at the\nboundary annihilate only in \\textit{pairs}. In its fermionic variant,\nparticles at sites $0$ and $1$ annihilate with each other (provided that both\nsites are occupied) at some rate, while isolated particles at the boundary\ncannot disappear:\n\\begin{eqnarray*}\n\\mbox{present models:} & A\\to2A \\,,\\quad A\\to\\emptyset\\,, \\\\ \\mbox{models of\n Ref.~\\cite{DeloubriereWijland02}:} & A\\to2A \\,,\\quad 2A\\to\\emptyset\\,.\n\\end{eqnarray*}\nAnalogously, one can define a bosonic version, in which two particles at the\nboundary can annihilate. In the following discussion we consider these two\nvariants in $d$ spatial dimensions where, as is the case $d=1$, only a single\nsite has ``special\" dynamics.\n \nA detailed field theoretical analysis of these pair-annihilating models was\npresented in \\cite{DeloubriereWijland02}. In the bosonic case, proceeding as\nabove (see Eq.(\\ref{eqbosonH})) one obtains the following time evolution\noperator:\n\\begin{eqnarray}\n\\hat{H}&=& D\\sum_{\\langle ij\\rangle}(\\hat{a}^\\dagger_i-\\hat{a}^\\dagger_j)\n(\\hat{a}_i-\\hat{a}_j) \\nonumber\n\\\\ &&+\\sigma[(\\hat{a}_0^\\dagger)^2-1]\\hat{a}_0^2 +\\lambda\n\\hat{a}_0^\\dagger(1-\\hat{a}_0^\\dagger)\\hat{a}_0.\n\\label{H2}\n\\end{eqnarray} \nwhich, after eliminating higher order terms and taking the continuum limit, is\nequivalent to a Langevin equation identical to the one for DP except for the\nfact that\nall terms, except for the Laplacian, are multiplied by a $\\delta$ function at\nthe boundary; {\\it i.e.} the non-diffusive part of the dynamics operates only\nat the boundary. An $\\epsilon$-expansion analysis of Eq.(\\ref{H2}) (see\n\\cite{DeloubriereWijland02}) leads to $\\alpha=1\/2$ and $z=2$ as exact results\nin all orders of perturbation theory, and to $\\beta=1-3(4-3d)\/8$, up to first\norder in $\\epsilon =4\/3-d$ around the critical dimension $d_c=4\/3$. Also, it\nwas shown that the time reversal symmetry is preserved.\n\nWe have verified all these predictions in computer simulations of the bosonic\nannihilation model. For instance, from the time decay of $\\rho_0(t)$, as shown\nin Fig.~\\ref{FSS}, we determine $\\delta = 0.21(3)$, while from a finite size\nscaling analysis of the saturation values of the order parameter at\ncriticality we measure $\\beta\/\\nu_{\\perp}=0.51(2)$ (see Fig.~\\ref{FSS}), in\nreasonable agreement with the expected results, $\\delta= 1\/6$ and\n$\\beta\/\\nu_{\\perp}=1\/2$, respectively. Moreover, from spreading simulations\n(not shown) we estimate $\\alpha \\approx 1\/2 $ and $z \\approx 2$. All the\nexponents are in agreement with the ones presented in the previous section for\nsingle particle annihilation models.\n\nActually, a simple argument explains why the model of section\n\\ref{simulations} and the pair-annihilation model share the same\ncritical behavior. This is plausible because the chain reaction $A \\to\n2A \\to \\emptyset$ in the model with pair annihilation generates\neffectively the reaction $A\\to\\emptyset$ of the model considered with\nCP-like dynamics.\n\nHence, the field theoretical predictions discussed above\n\\cite{DeloubriereWijland02,BaratoHinrichsen08} apply also to the CP-like\nmodel. In $d=1$, the one-loop prediction $\\beta= 5\/8= 0.625$\n\\cite{DeloubriereWijland02}, is not far from the exponent measured in section\n\\ref{simulations}, $\\beta=0.68(5)$.\n\nOn the other hand, the fermionic version of the pair-annihilating model has\nbeen conjectured to yield in a different universality class, and a prediction\nfor its critical exponents is made in \\cite{DeloubriereWijland02} (for\ninstance, $\\beta=1$). Our numerical simulations disprove such a claim; all\nthe measured critical exponents for the fermionic variant of the\npair-annihilation model are numerically indistinguishable from their bosonic\ncounterparts (see Fig.~\\ref{FSS}).\n\\begin{figure}[t]\n\\begin{center}\n \\includegraphics[height=5cm]{fig6.eps}\n\\end{center}\n\\caption{\\footnotesize{(Color online) Temporal behavior of $\\rho_{0}$ for the bosonic\n pair-annihilating model, starting from a homogeneous initial condition for\n different system sizes (from $L=64$ to $L=2048$). The exponent\n $\\beta\/\\nu_{\\perp}$ can be measured from the scaling of the different\n saturation values as a function of system size (see inset; yellow\n line). Also, in the inset (dashed green line), we show the scaling of saturation\n values for the fermionic version of the same model, showing the same type\n of scaling. }}\n\\label{FSS}\n\\end{figure}\n\nIn summary, all the defined models, either with single particle annihilation\nor with pair-annihilation, fermionic or bosonic, exhibit a boundary induced\nphase transitions and, except for one of them, they all are continuous and\nshare the same critical behavior. The exception to this rule is the CP-like\nmodel without a fermionic constraint at the boundary, which lacks of a\nsaturation mechanism in the active phase, leading to unbounded growth of \nparticle density at the leftmost site above the critical point and to a discontinuous transition.\n\\section{Relation to a $(0+1)$-dimensional non-Markovian process}\n\\label{non-Markovian}\n\nIn Ref.~\\cite{DeloubriereWijland02}, by integrating out the fields related to\ndiffusion in the bulk from the corresponding action, it was shown that the\nclass of boundary-induced phase transitions into an absorbing state considered\nhere can be related to a non-Markovian single site process. The properties of\nsuch a spreading process on a time line has been studied in further detail in\nRef.~\\cite{BaratoHinrichsen09}.\n\nOn an heuristic basis, the relation can be explained as follows: consider the\nCP-like model only from the perspective of the leftmost site. A particle at\nthe origin may die or create a new particle that will go for a random walk\ncoming back to the origin after a time $\\tau$. What happens during this random\nwalk is irrelevant from the perspective of the leftmost site; the only\nrelevant aspect is the time needed for a created particle to come back to the\nboundary. Once it returns it may die or create new offsprings which, on their\nturn, will undergo random walks in the bulk.\n\nOur simulations above show that the fermionic constraint is irrelevant in the\nbulk. Therefore, we can consider without lost of generality the bulk-bosonic\nversion in which there is no effective interaction among diffusing\nparticles. In this case, the probability distribution of the returning time to\nthe origin has the well-known asymptotic form~\\cite{Redner01}:\n\\begin{equation}\n\\label{WaitingTimeDistribution}\nP(\\tau)\\sim \\tau^{-3\/2}\\,.\n\\end{equation}\nTaking all these elements into account we define the following\nnon-Markovian model on a single site~\\cite{BaratoHinrichsen08}:\n\\begin{enumerate}\n\\item[(a)] Set initially $s(t):= \\delta_{t,0}$ for all times, $t$.\n\\item[(b)] Select the lowest $t$ for which $s(t)=1$.\n\\item[(c)] With probability $\\mu$, generate a waiting time $\\tau$ according to\n the distribution~Eq.~(\\ref{WaitingTimeDistribution}), truncate it to an\n integer, and set $s(t+\\tau):=1$; otherwise (with probability $1-\\mu$) set\n $s(t):=0$.\n\\item[(d)] Go back to (b).\n\\end{enumerate}\nThe process runs until the system enters the absorbing state ($s(t')=0$ for\nall $t'>t$) or a predetermined maximum time is exceeded.\n\\begin{figure}\n\\includegraphics[width=87mm]{.\/fig7.eps}\n\\vspace{-2mm}\n\\caption{(Color online) Off-critical data collapse with the one-site model:\n $\\langle s(t)\\rangle\\Delta^{-2\\beta}$ as a function of $t\\Delta^{4\\beta}$\n for different values of $\\Delta$, with $\\beta= 0.71(2)$.\n \\label{fig:datacollapse}}\n\\end{figure}\nThe density of particles at the leftmost site of the original model is related\nto $\\langle s(t)\\rangle$ in the single-site model, the survival probability at\ntime $t$ is given by the fraction of runs surviving at least up to $t$, and\nthe initial condition $s(t):= \\delta_{t,0}$ corresponds to start with a single\nparticle at the boundary in the full model. Critical exponents can be defined\nas in the original model. However, the simulation results for the single-site\nnon-Markovian model are more reliable because it is possible to perform much\nlonger runs and, in the case of off-critical simulations, one can work with\nsmaller values of $\\Delta$. With time-dependent simulations at the critical\npoint $\\mu_c=0.574262(2)$, we obtained $\\alpha=0.500(5)$ and\n$\\delta=0.165(3)$, in good agreement with the conjectured values $\\alpha= 1\/2$\nand $\\delta= 1\/6$. As an example, we show the results of supercritical\nsimulations in Fig.~\\ref{fig:datacollapse}, where we obtained a convincing\ndata collapse by plotting $\\langle s(t)\\rangle\\Delta^{-2\\beta}$ as a function\nof $t\\Delta^{4\\beta}$ for different values of $\\Delta$ with $\\beta=\n0.71(2)$. The latter estimate is in agreement with $\\beta= 0.68(5)$, coming\nfrom the original model.\n\nAs shown in previous studies (see e.g.~\\cite{Hinrichsen07} and references\ntherein), a non-Markovian time evolution with algebraically distributed\nwaiting times $P(\\tau) \\sim \\tau^{-1-\\kappa}$ is generated by so-called\nfractional derivatives $\\partial_t^\\kappa$ which are defined by:\n\\begin{equation}\n\\label{eq:IntegralTime}\n\\partial_t^\\kappa \\, \\rho(t) \\;=\\; \\frac{1}{\\mathcal{N}_\\parallel(\\kappa)}\n\\int_0^{\\infty} {\\rm d}t' \\, {t'}^{-1-\\kappa} [\\rho(t)-\\rho(t-t')]\\,,\n\\end{equation}\nwhere $\\kappa\\in[0,1]$ and $\\mathcal{N}_\\parallel(\\kappa)=-\\Gamma(-\\kappa)$ is\na normalization constant. Hence, we expect this model to be described by a\nDP-like $0$-dimensional Langevin equation with a half-time derivative, instead\nof the usual one, to account for the non-Markovian character of the model:\n\\begin{equation}\n\\label{Langevin}\n\\partial_t^{1\/2} \\rho(t) = a \\rho(t) - \\rho(t)^2 + \\xi(t)\\,\n\\end{equation}\nwhere $a$ is proportional to the distance from criticality and $\\xi$ is a\nmultiplicative noise with correlations $\\langle \\xi(t)\n\\xi(t')\\rangle=\\rho(t)\\delta(t-t')$. This equation can be obtained from the\neffective action that arises when the fields related to diffusion in the bulk\nare integrated out, and the relation of the order of the fractional derivative\nin a generalized one-site model with the dimension in the full model is\n$\\kappa= (2-d)\/2$ \\cite{DeloubriereWijland02}. An analysis of this one-site\nmodel with general $\\kappa$ and a comparison with the results coming from\nfield theory is presented in \\cite{BaratoHinrichsen09}.\n\\section{Conclusion}\n\\label{conclusions}\n\nWe have studied boundary-induced phase transitions into an absorbing\nstate in one-dimensional systems with creation\/annihilation dynamics\nat the boundary and simple diffusive dynamics in the bulk. The\nnon-trivial dynamics at the boundary induces a phase transition in the\nbulk. We have analyzed such a transition for different though similar\nmodels, including different ingredients: either single-particle\nannihilation or pairwise annihilation, fermionic constraint or lack of\nit, etc.\n\nA particular bosonic version can be exactly solved; owing to the lack of any\nsaturation mechanism, the density of particles grows unboundedly in the active\nphase, leading to a discontinuous transition with trivial critical exponents.\n\nThe rest of the analyzed models exhibit a continuous transition and define a\nunique universality class. At the bulk, the dynamics is governed by\nrandom-walks, entailing the exponent values $z=2$ and $\\alpha=1\/2$. On the\nother hand, some critical exponents take non-trivial values:\n{\\it i)} the survival probability from a localized seed at\nthe boundary exponent, which from an heuristic argument supported by\nsimulations results, turns out to be $\\delta=1\/6$, as well as {\\it ii)} the\norder parameter exponent, $\\beta= 0.71(2)$. The remaining exponents can be\nobtained from these ones using scaling relations.\n\nFinally, it has been shown that the class of boundary induced phase\ntransitions studied here can be related to a single-site non-Markovian\nprocess. This process is particularly suitable for numerical\nsimulations and it is also of conceptual interest in the sense that it\nshows that nonequilibrium phase transitions can occur even in $0+1$\ndimensions by choosing an adequate non-Markovian dynamics. It is also\nconvenient for the comparison of the results obtained form the\n$\\epsilon$-expansion and simulations\n\\cite{BaratoHinrichsen09,DeloubriereWijland02}.\n\nThe models studied here possibly constitute the simplest universality class of\nnonequilibrium phase transition into an absorbing state, in the sense that the transition occurs \nbecause of the special dynamics of just one site and, in contrast to DP, some critical exponents\ncan be obtained exactly from the field theory.\n\n\\begin{acknowledgments}\n We thank X. Durang and M. Henkel for helpful discussions. \n Financial support by the Deutsche Forschungsgemeinschaft (HI\n 744\/3-1), by the Spanish MEyC-FEDER, project FIS2005-00791, and from\n Junta de Andaluc{\\'\\i}a as group FQM-165 is gratefully acknowledged.\n\\end{acknowledgments}\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nAt the eve of data taking at the LHC, the electroweak standard\nmodel~(SM) with a fundamental scalar Higgs doublet remains an\nextremely successful effective description of all data collected in\nparticle physics experiments at colliders. Nevertheless, the\nmicroscopic dynamics of the electroweak symmetry breaking~(EWSB)\nsector has not yet been tested directly. Therefore, detailed studies\nof the SM and realistic alternative scenarios for EWSB are an\nessential part of the LHC experimental program.\n\nIn the past decade, additional dimensions of space-time at the\nTeV-scale have become an important paradigm for electroweak~(EW) model\nbuilding. Planck-scale extra dimensions have long been a solid\nprediction of superstring theory, but they are outside of the\nexperimental range of collider experiments. In contrast, TeV-scale\nextra dimensions will be tested at the LHC.\n\nModels with just one additional space dimension that have the geometry of\na five dimensional Anti-de Sitter space~($\\text{AdS}_5$)~\\cite{Randall:1999ee},\nplay a special role, because the conjectured AdS\/CFT\ncorrespondence~\\cite{Maldacena:1997re} reveals such models as dual to conformal\nfield theories~(CFT) on the four dimensional boundary branes. In\nparticular, a weakly interacting~$\\text{AdS}_5$ model turns out to be\ndual to a strongly interacting Technicolor~(TC) like model for EWSB.\nIf the conjectured AdS\/CFT correspondence is exact, the extra\ndimension can be viewed as a technically convenient description\nof a strongly interacting dynamics.\n\nModel building for EWSB with extra dimensions does not require them to\nbe continuous, instead they can be\ndeconstructed~\\cite{ArkaniHamed:2001ca} as a discrete lattice with a\nfinite number of sites. In this approach, the extra dimensions play a\nmetaphorical role as a organization principle for gauge theories with\nlarge non-simple gauge groups and complicated matter representations,\nsimilar to moose models. It turns out that a minimal version of\nwarped higgsless models can be (de)constructed on just three lattice\nsites in the extra dimension and is known as the Three Site Higgsless\nModel~(3SHLM)~\\cite{SekharChivukula:2006cg}.\n\nIn order to be compatible with the EW precision tests~(EWPT), any\nadditional heavy gauge bosons should couple weakly to the SM fermions.\nThe 3SHLM ensures this by ``ideal fermion\ndelocalization''~\\cite{SekharChivukula:2006cg,SekharChivukula:2005xm}\nand the predominant production mechanism at the LHC will be in vector\nboson fusion~\\cite{He:2007ge}. However, it has been pointed out\nrecently~\\cite{Abe:2008hb}, that the EWPT actually require a small,\nbut nonvanishing coupling of the heavy gauge bosons to SM fermions.\nThis allows their production in the $s$-channel at the LHC. In fact,\na measurement of the relative strengths of the production mechanisms\nfor heavy vector bosons will be required to constrain higgsless models\nof EWSB.\n\nIn this paper, we complement the existing phenomenological\nstudies~\\cite{He:2007ge} of the 3SHLM by allowing for non ideal\ndelocalization and the production of~$W'$ and $Z'$~bosons in the\n$s$-channel at LHC. We perform parton level Monte Carlo studies to\nidentify the regions of parameter space where the coupling of the\n$W'$~boson to SM fermions can be measured at the LHC. We show how the\ncontributions from the nearly degenerate ~$W'$ and $Z'$~bosons can be\nseparated for this purpose.\n\nThis paper is organized as follows: in section~\\ref{sec:3shl} we\nreview the features of the 3SHLM that are relevant for our\ninvestigation. In section~\\ref{sec-cpl} we discuss the relevant\ncouplings, masses and widths that are used in our Monte Carlo\neventgenerator described in section~\\ref{sec:whizard}.\nIn sections~\\ref{sec-hzprod} and~\\ref{sec-hwprod} we discuss our\nresults for the production of heavy~$Z'$ and $W'$~bosons, respectively.\nLimitations arising from finite jet mass resolutions are described\nin~\\ref{sec-jetres}. We conclude in section~\\ref{sec:concl}.\n\n\\section{The Three Site Higgsless Model}\n\\label{sec:3shl}\n\nThe Three Site Higgsless Model~\\cite{SekharChivukula:2006cg} can be\nviewed as a warped 5D model of EWSB,\ndimensionally deconstructed~\\cite{ArkaniHamed:2001ca}\nto three lattice sites. The structure and field content of the model\nis shown in moose notation in figure~\\ref{fig-moose}.\n\\begin{figure}\n\\centerline{\\includegraphics{moose}}\n\\caption{The field content and structure of the 3SHLM in moose notation. The dashed lines\nconnecting fermions represent Yukawa couplings, the dotted blob illustrates the nontrivial\n$\\mathbf{U}(1)_2$ charge carried by all fermions.}\n\\label{fig-moose}\n\\end{figure}\nThe gauge group consists of two $\\mathbf{SU}(2)$ group factors located at the\nlattice sites~$0$ and~$1$ with gauge fields $A^\\mu_{0\/1}$ and gauge couplings $g_{0\/1}$ and a\n$\\mathbf{U}(1)$ gauge group located at the third lattice site with the gauge field\n$A^\\mu_2$ and gauge coupling $g_2$. Note that the continuous 5D\nanalogue of this is a bulk $\\mathbf{SU}(2)$\nbroken to $\\mathbf{U}(1)$ on one brane by boundary conditions. The lattice sites are\nlinked by $\\mathbf{SU}(2)$ valued Wilson line fields $\\Sigma_{0\/1}$\nthat transform bi-unitarily under\ngauge transformations as\n\\begin{equation*}\n \\Sigma_0 \\longrightarrow U_0\\Sigma_0 U_1^\\dagger \\quad,\\quad\n \\Sigma_1 \\longrightarrow U_1\\Sigma_0 e^{-i\\theta\\frac{\\sigma_3}{2}}\\,.\n\\end{equation*}\nIf the potential for the Wilson line fields is arranged such that these acquire a vacuum\nexpectation value\n\\begin{equation*}\n \\left<\\Sigma_{0\/1}\\right> = \\sqrt{2}v\\,,\n\\end{equation*}\nthe symmetry group is broken spontaneously\nto the electromagnetic~$\\mathbf{U}(1)_\\text{em}$. The kinetic terms for~$\\Sigma_{0\/1}$\ncontain covariant derivatives which produce mass terms for the gauge bosons;\nafter diagonalization we find a massless photon, two massive charged\ngauge bosons~$W$ and~$W^\\prime$ and two neutral massive gauge bosons~$Z$ and~$Z^\\prime$. \n\nChoosing~$g_1\\gg g_0,g_2$, the mass gap between the massive\ngauge bosons becomes large and the lighter ones can be identified\nwith the SM~$W$ and $Z$~bosons. These are mostly localized at the\nbrane sites, while the heavy modes\nare strongly localized at the bulk site. This symmetry breaking setup is similar to the\nBESS model~\\cite{Casalbuoni:1985kq}. After fixing the electric charge and\nthe~$W$ and $Z$~masses from the observed values,\nthe only remaining free parameter in the gauge sector is the $W^\\prime$ mass~$m_{W'}$.\n\nFermions are incorporated into the model by putting left-handed $\\mathbf{SU}(2)$\ndoublets $\\Psi_{0\/1,L}$ on the sites~$0$ and~$1$, a right-handed doublet $\\Psi_{1,R}$ on\nsite~$1$ and singlets $\\Psi^{u\/d}_{2,R}$ on site~$2$ for every SM\nfermion (cf.~figure~\\ref{fig-moose}). The $\\mathbf{U}(1)_2$~charges\nof the $\\Psi^{u\/d}_{2,R}$ fermions are taken from the SM hypercharge\nassignments for the corresponding righthanded singlets, whereas the\n$\\mathbf{U}(1)_2$~charges of all other left- and righthanded fermions\nare taken from the SM hypercharge assignments for the corresponding\n\\emph{left}handed doublets.\n\nIn addition to the kinetic terms,\nYukawa couplings are added to the fermion Lagrangian\n\\begin{multline}\n\\label{equ-lgr-fermion}\n\\mathcal{L}_\\text{Yukawa} = \\\\\n\\lambda\\sum_i\n\\left[\\epsilon_L\\overline{\\Psi}_{0,L}^i\\Sigma_0\\Psi_{1,R}^i +\n\\sqrt{2}v\\overline{\\Psi}_{1,L}^i\\Psi_{1,R}^i +\n\\overline{\\Psi}_{1,L}^i\\Sigma_1\n \\begin{pmatrix}\n \\epsilon_{u,R}^i & 0 \\\\\n 0 & \\epsilon_{d,R}^i\n \\end{pmatrix}\n\\Psi_{2,R}^i\\right]\n\\end{multline}\nwith the index~$i$ running over all SM fermions.\nThe parameter $\\epsilon_L$ is chosen universally for all fermions and\nsuch that the tree-level corrections to the EWPT\nvanish. This will be referred to as ``ideal fermion\ndelocalization''~\\cite{SekharChivukula:2006cg,SekharChivukula:2005xm}.\nThe parameter $\\lambda$\nis also chosen universally for all fermions; only the~$\\epsilon_{u\/d,R}$ have a nontrivial\nflavor structure and are used to implement the mass splitting of\nquarks and leptons, as well as CKM flavor mixing. The\nvacuum expectation value $v$ breaks the symmetry and the mass\neigenstates are the SM\nfermions (localized mostly at the branes) and heavy partner fermions (localized\nmostly in the bulk).\n\nThe only remaining free parameter in the fermion sector after fixing the SM fermion\nmasses and $\\epsilon_L$ is the heavy fermion mass scale $m_\\text{bulk}=\\sqrt{2}\\lambda v$.\nTherefore the model is fixed uniquely by setting the SM parameters,\n$m_{W'}$ and $m_\\text{bulk}$ and \nby the requirement of ideal delocalization.\nLoop corrections to the EWPT\nand other phenomenological bounds limit the minimal values for these two parameters,\nrequiring $m_{W'}>\\unit[380]{GeV}$ and\n$m_\\text{bulk}>\\unit[2]{TeV}$~\\cite{SekharChivukula:2006cg}.\n\nThe spectrum of the model consists of the SM gauge bosons and\nfermions, the $W^\\prime$ and $Z^\\prime$ and a heavy partner fermion\nfor each SM fermion. The masses of the two new heavy gauge bosons are\nquasi-degenerate ($|m_{W'}-m_{Z'}|=\\mathcal{O}(\\unit[1]{GeV})$), and the\nmasses of the partner fermions are of the order $m_\\text{bulk}$ with\nthe~$t^\\prime$ being slightly heavier than the rest.\n\n\\section{Couplings, widths and branching ratios}\n\\label{sec-cpl}\n\nIdeal fermion delocalization implies that the couplings of the light SM fermions\nto the $W^\\prime$ vanish and that those to the $Z^\\prime$ are small\n($\\mathcal{O}(10^{-2}))$. The both heavy gauge bosons also\ncouple to the SM $Z$ and $W$~bosons with couplings of order $\\mathcal{O}(10^{-2})$.\n\n\\begin{figure}\n\\centerline{\\includegraphics[angle=270,width=8cm]{widths}}\n\\caption{\\label{plot-widths}%\nThe relative width~$\\Gamma_V\/m_V$ of the\n$W^\\prime$ and $Z^\\prime$ bosons as a function of $m_{W'}$ with ideal\ndelocalization and $m_\\text{bulk}=\\unit[5]{TeV}$.}\n\\end{figure}\nTherefore, the only decay channel for the $W^\\prime$ in the ideally delocalized scenario is\nthe decay into a $W$ and a $Z$. The $Z^\\prime$ can in principle also decay into SM\nfermions; however, the decay of the longitudinal mode enhances the $WZ$ decay\nchannel by a factor of\n\\begin{equation*}\n \\frac{m_{Z^\\prime}^4}{16m_W^2m_Z^2}\n\\end{equation*}\nover the decay into a fermion pair causing the latter decay to be highly\nsuppressed by a factor of the order of $\\mathcal{O}(10^{-2})$ (cf.~\\cite{He:2007ge}).\nLooking at figure~\\ref{plot-widths} we find that the resonances are rather narrow\n($\\Gamma_V\/m_V\\approx1-3\\%$) improving the prospects for observing these particles at the LHC.\n\nThe new heavy fermions decay into their light partner and a gauge boson, the resulting\nwidths being of the order $\\Gamma_f\/m_f\\approx0.1$, which, combined with their large mass\n($>\\unit[2]{TeV}$), will make the direct detection as a resonance at a collider rather\nchallenging.\n\nFor a massless SM fermion, the Yukawa coupling between the sites~$1$ and~$2$ vanishes.\nFrom~$\\mathcal{L}_{\\text{Yukawa}}$ we find that the wave function is\ncompletely fixed by the delocalization parameter $\\epsilon_L$. Therefore, the influence of\n$m_\\text{bulk}$ on the wave functions of the light SM fermions and their couplings is very\nsmall and the dependence of the cross section on $m_\\text{bulk}$ is\nalmost negligible at LHC energies.\n\nAlthough ideal delocalization guarantees compatibility with the\nconstraints from EWPT at tree level~\\cite{SekharChivukula:2005xm},\na recent 1-loop analysis~\\cite{Abe:2008hb} has shown that a deviation from\nideal delocalization is necessary to comply with the EWPT constraints at loop\nlevel. According to the authors of~\\cite{Abe:2008hb}, this deviation corresponds to an\non-shell coupling between the $W^\\prime$ and the light fermions as large as $1-2\\%$ of\nthe isospin gauge coupling $g_W\\approx g_0$.\n\n\\begin{figure}\n\\centerline{\\includegraphics{operator1}}\n\\caption{The tree-level diagram generating the operator~$O_1$~(cf.~(\\ref{equ-op1})) after\nintegrating out the bulk fermions.}\n\\label{fig-op1}\n\\end{figure}\nThe coupling $g_{W'ff}$ to which the bounds derived in~\\cite{Abe:2008hb} apply is defined\nin the effective theory obtained by integrating out the bulk fermions and is renormalized\nat the $W^\\prime$ mass shell. There are two operators contributing to this coupling in the\none loop analysis in addition to the coupling of the left-handed\nfermions to the component of\nthe $W^\\prime$ sitting at site $0$. The first one\n\\begin{equation} \n\\label{equ-op1}\n O_1 = \\overline{\\Psi}_{0,L}\\Sigma_0\\fmslash{A_1}\\Sigma_0^\\dagger\\Psi_{0,L}\n\\end{equation}\nencodes a coupling between the component of the $W^\\prime$ sitting at site $1$ and the\nleft-handed SM fermion and is generated by integrating out the bulk fermion\nfrom the diagram in figure~\\ref{fig-op1} (see\nalso~\\cite{SekharChivukula:2006cg}). The second operator\n\\begin{equation}\n\\label{equ-op2}\n O_2 = \n \\overline{\\Psi}_{0,L}\\left(D_\\nu\\left(\\Sigma_0 F_1^{\\mu\\nu}\\Sigma_0^\\dagger\\right)\n \\right)\\gamma_\\mu\\Psi_{0,L}\n\\end{equation}\narises from loop corrections. Although this operator also contains a contribution to the\ncoupling between the left-handed fermion and the gauge bosons at site $1$, it has a\nnontrivial momentum structure. However, using a non-linear field redefinition in the\nspirit of on-shell effective field theory~\\cite{Georgi:1991ch}, the\ncorresponding part of~$O_2$ \ncan be converted to the same form as~$O_1$ at the price of introducing additional\nhigher dimensional operators coupling at least two gauge bosons to two fermions whose\ncontributions are suppressed by another power of the gauge couplings. This allows the\noperator~$O_2$ to be included into $g_{W^\\prime ff}$ where it contributes to\nthe bounds derived by the authors of~\\cite{Abe:2008hb}.\n\nTherefore, the contributions of both these operators can be accounted for by adjusting the\ndelocalization parameter $\\epsilon_L$ in the tree level\nLagrangian $\\mathcal{L}_{\\text{Yukawa}}$\nto generate the coupling $g_{W^\\prime ff}$. The model parameters then should be understood\nto be renormalized at the $W^\\prime$~mass.\n\n\\begin{figure}\n\\centerline{\\includegraphics[angle=270,width=8cm]{gWff_500}}\n\\caption{$g_{W'ff}$, $g_{Z'uu,L}$ and $g_{Z'dd,L}$ normalized to the site $0$ gauge\ncoupling as a function of the delocalization parameter $\\epsilon_L$.\nThe gray rectangle marks the range for $g_{W^\\prime ff}$ allowed by the EWPT as\nderived by the authors of~\\cite{Abe:2008hb}.}\n\\label{fig-gwff-500}\n\\end{figure}\nIn the case of light SM fermions and their partners, only the wave functions of the\nleft-handed fermions depend on the delocalization parameter $\\epsilon_L$. Therefore, the\nright-handed couplings between the new gauge bosons and the light SM fermions are not\naffected by the departure from ideal delocalization. Denoting the wave functions\nby~$\\phi_{f,L,i}$ and~$\\phi_{Z',i}$ and using the normalization of the fermion\nwave functions, the left-handed coupling of a fermion\nto the~$Z^\\prime$ can be written as\n\\begin{equation}\n \\sum_{i=0}^{1}\\phi_{f,L,i}^2\\left(\\pm\\frac{1}{2} g_i\\phi_{Z',i}\n + Y g_2\\phi_{Z',2}\\right)\n = \\pm\\frac{1}{2}\\sum_{i=0}^1 g_i\\phi_{Z',i}\\phi_{f,L,i}^2 + Yg_2\\phi_{Z',2}\\,,\n\\label{equ-cpl-zff}\\end{equation}\nwith the sign depending on the isospin of the fermion and $Y$ denoting the hypercharge.\nAs tuning away from ideal delocalization shifts the light mode of the\nfermion towards the heavy $Z^\\prime$ sitting at site $1$, the isospin dependent part\nin~(\\ref{equ-cpl-zff}) grows, while the correction to~$g_{Z'uu}$ differs only in sign\nfrom the correction to~$g_{Z'dd}$.\n\nFigure~\\ref{fig-gwff-500} shows the dependence of\n$g_{W'ff}$, $g_{Z'dd}$ and $g_{Z'uu}$ on the delocalization parameter and clearly\ndemonstrates this behavior. Considering that we have both $u\\bar u$\nand $d\\bar d$ initial states at\nthe LHC, that both couplings start with positive values of the same order of magnitude at\nthe point of ideal delocalization and that we also have right-handed couplings of the same\norder of magnitude which don't depend on $\\epsilon_L$ at all, we don't\nexpect a large impact from changing $\\epsilon_L$ on $Z^\\prime$~production in the $s$-channel.\nOn the other hand, the effect on the $s$-channel $W^\\prime$~production\nshould be sizable, because $\\epsilon_L$ interpolates between this channel\nbeing forbidden and being about the same order of magnitude as\n$Z^\\prime$ production.\n\\begin{figure}\n\\centerline{\\begin{tabular}{cc}\n\\includegraphics[angle=270,width=6.5cm]{gWff_380} &\n\\includegraphics[angle=270,width=6.5cm]{gWff_600}\n\\end{tabular}}\n\\caption{\\label{fig-gwff-380-600}%\nThe same plots as figure~\\ref{fig-gwff-500}, but for the other parts of parameter\nspace probed by our Monte Carlo simulations.}\n\\end{figure}\n\nFigure~\\ref{fig-gwff-380-600} shows the same plot as\nfigure~\\ref{fig-gwff-500} for the other\nregions of parameter space probed in our Monte Carlo simulations. In all three plots,\nchanging $m_\\text{bulk}$ doesn't generate a visible change of the actual couplings, but\ndoes move the gray band of acceptable $g_{W'ff}$ values.\n\n\\section{Implementation}\n\\label{sec:whizard}\n\nWe have coded a FORTRAN 90 module which diagonalizes the lagrangian of the model\nand calculates all masses and couplings. Furthermore, the module calculates the tree\nlevel widths of all new particles. Non ideal delocalization is implemented by tuning the\nparameter $\\epsilon_L$ away from the value required for vanishing $g_{W'ff}$.\nFor the automatized generation of tree level matrix\nelements, we encoded the model in unitarity gauge into the optimizing\nmatrix element generator O'Mega~\\cite{Moretti:2001zz,Kilian:2007gr}\nwhich is part of the Monte Carlo eventgenerator generator WHIZARD~\\cite{Kilian:2007gr}.\nThe results presented below are based on Monte\nCarlo simulations using WHIZARD~\\cite{Kilian:2007gr}.\n\nWe checked the couplings calculated by our FORTRAN code against all the couplings for\nwhich analytic expressions are given in~\\cite{SekharChivukula:2006cg}. To check the\nvalidity of our implementation of the model, we compared the cross sections for a number of\n$2\\rightarrow 2$ processes to the SM, taking $m_{W^\\prime}$,\n$m_\\text{bulk}$ and $m_\\text{Higgs}$ to be huge. The widths calculated by the FORTRAN\nmodule using analytic formulae were checked against numeric results obtained from\namplitudes generated by O'Mega.\n\nWe also checked gauge invariance by numerically checking the Ward Identities in the model \nobtained by taking the limit\n\\begin{equation*}\n \\sqrt{2}v=\\left<\\Sigma_{0\/1}\\right>\\rightarrow 0\\,,\n\\end{equation*}\nwhere the exact $\\mathbf{SU}(2)_0\\times\\mathbf{SU}(2)_1\\times\\mathbf{U}(1)_2$\ngauge symmetry is restored.\n\nIn addition, we compared several $2\\rightarrow 2$ cross sections\nto the CalcHep~\\cite{Pukhov:2004ca} implementation of the model used by the authors\nof~\\cite{He:2007ge}. After plugging in the correct $W^\\prime$ and $Z^\\prime$ widths,\nthe results turn out to be in perfect agreement.\n\n\\section{$Z^\\prime$ production in the $s$-channel}\n\\label{sec-hzprod}\n\nIn the ideally delocalized scenario, only the $Z^\\prime$ has nonvanishing tree level\ncouplings to the SM fermions, while the $W^\\prime$ is perfectly\nfermiophobic. As explained above, the $Z^\\prime$ decays with a branching ratio of over\n$95\\%$ into a $W^+W^-$ pair, rendering the resulting four fermion final state highly\nfavored over the two lepton one. This is in sharp contrast to many new heavy neutral\ngauge bosons predicted by other extensions of the SM (Little Higgs, GUTs\netc.) which usually have larger fermion couplings but small or vanishing couplings to the\nSM gauge bosons, because they typically originate from different gauge group factors and have little\nor no mixing with the SM gauge bosons~\\cite{Rizzo:2006nw,Langacker:2008yv}.\n\nThe most interesting final states for $Z^\\prime$ production are thus $jjjj$, $l\\nu jj$ and\n$l\\nu l\\nu$. The four jet final state however is highly contaminated from\nbackgrounds containing gluon jets, and the two neutrino final state suffers from\nthe momentum information missing for the two neutrinos, leaving $l\\nu jj$ as\nthe most promising candidate assuming one can cope with the missing neutrino momentum.\n\\begin{figure}\n\\centerline{\\includegraphics{hzprod}}\n\\caption{Representative of the class of diagrams contributing to the $Z^\\prime$\nproduction signal in $pp\\rightarrow l\\nu jj$.}\n\\label{fig-diag-hzprod}\n\\end{figure}\nFigure~\\ref{fig-diag-hzprod} shows a representative of the class of diagrams contributing to\nthe signal in this process.\nIn addition to the signal, there are also reducible backgrounds from events with\nneutral jet pairs and an irreducible background from diagrams not of the type\nfigure~\\ref{fig-diag-hzprod} contributing to the same final state. In\nthis and the next section, we assume that a veto on forward tagging\njets is effective in suppressing the background from vector boson fusion.\n\nFor the construction of an observable that can deal with the missing longitudinal neutrino\nmomentum, consider the decay of an on-shell $W$ into a lepton with momentum $p_l=q$ and a\nneutrino with momentum $p_\\nu=p$. The mass shell conditions of neutrino and $W$ boson then give\ntwo equations involving the neutrino energy $p_0$ and longitudinal\nmomentum~$p_L$\n\\begin{subequations}\n\\begin{align}\n\\label{equ-nurec1}\n p_0^2 - p_L^2 - \\left|\\vec p_\\perp\\right|^2 &= 0 \\\\\n\\label{equ-nurec2}\n p_0 q_0 - p_L q_L - \\vec p_\\perp \\vec q_\\perp &= \\frac{m_W^2}{2}\n\\end{align}\n\\end{subequations}\n(assuming the lepton to be massless), with~$\\vec p_\\perp$ and~$\\vec\nq_\\perp$ the projections of the momenta onto the transverse plane.\n(\\ref{equ-nurec1}) describes a hyperbola in the $p_L-p_0$\nplane and~(\\ref{equ-nurec2}) describes a straight line with the modulus of the slope\nsmaller than $1$. These curves are parametrized by $\\vec p_\\perp$, $q$ and $m_W$ and one\nof their (two in general) intersections gives the neutrino energy and longitudinal\nmomentum as a function of these quantities. This geometrical situation is depicted in\nfigure~\\ref{fig-nurec}.\n\\begin{figure}\n\\centerline{\\includegraphics[angle=270,width=8cm]{mnurec}}\n\\caption{The two curves generated by the mass shell conditions for $W$ and neutrino in the\ncase of a $W$ decaying to $l\\nu_l$.}\n\\label{fig-nurec}\n\\end{figure}\n\nThis construction allows us to reconstruct the full neutrino momentum from the lepton\nmomentum and the missing $p_T$ for the events coming from the decay of a quasi-on-shell\n$W$. However, owing to the modulus of the slope of the straight line being smaller than\none, we always have two solutions, none of which is preferred on kinematical\ngrounds. We have elected to deal with this by counting \\emph{both} solutions in the histograms,\neffectively doubling the amount of background events while preserving the size of the signal.\nThe two points of intersection can be obtained analytically by\n\\begin{equation}\np_0 = \\frac{q_0^2\\left(m_W^2 + 2\\vec{p}_\\perp\\vec{q}_\\perp\\right) \\pm q_L A}\n\t{2q_0\\left(q_0^2 - q_L^2\\right)} \\quad,\\quad\np_L = \\frac{q_L\\left(m_W^2 + 2\\vec{p}_\\perp\\vec{q}_\\perp\\right) \\pm A}\n\t{2\\left(q_0^2 - q_L^2\\right)}\\,,\n\\label{equ-nurec3}\n\\end{equation}\nwith the abbreviation\n\\[\nA = q_0\\sqrt{\\left(m_W^2 + 2\\vec{p}_\\perp\\vec{q}_\\perp\\right)^2 +\n\t4\\vec{p}_\\perp^2\\left(q_L^2 - q_0^2\\right)} \\,.\n\\]\n\nTo investigate the possibility of discovering the $Z^\\prime$ in\n$pp\\rightarrow jjl+p_{T,\\text{miss}}$ at the LHC we have performed full parton-level Monte\nCarlo simulations for an integrated luminosity of $\\int\\mathcal{L}=\\unit[100]{fb^{-1}}$,\nthe lepton being either an electron or a\nmuon and each jet being either a quark (excluding the top) or a gluon. To suppress the\nbackgrounds, we have applied $p_T$-cuts to all visible particles and to\n$p_{T,\\text{miss}}$\n\\[ p_T \\ge \\unit[50]{GeV}\\,. \\]\nIn addition, we have required the polar and intermediary angles of all visible particles\nto lie within\n\\[ -0.95 \\le \\cos\\theta \\le 0.95 \\]\nand also applied a small-$x$ cut to the ingoing partons\n\\[ x \\ge 1.4\\cdot 10^{-3} \\]\nto avoid infrared singularities in the amplitude. For identifying the intermediary $W$ we\napplied a cut to the invariant mass of the two jets\\footnote{See\n section \\ref{sec-jetres} for a discussion of the effects of finite jet \n resolution on the identification of the $W$.}\n\\[ \\unit[75]{GeV} \\le m_{jj} \\le \\unit[85]{GeV}\\,. \\]\nWe used~(\\ref{equ-nurec3}) to reconstruct the neutrino momentum, counting both solutions\ninto the histograms and discarding those with negative neutrino energy.\n\n\\begin{figure}\n\\centerline{\\begin{tabular}{cc}\n\\includegraphics[angle=270,width=6.5cm]{hist_hz_nurec} &\n\\includegraphics[angle=270,width=6.5cm]{hist_hz_deloc}\n\\end{tabular}}\n\\caption{\\emph{Left:} Invariant mass distribution in $pp\\rightarrow l\\nu_ljj$ obtained\nfrom the reconstructed neutrino momenta vs. the distribution obtained from $p_\\nu$\ntaken from Monte Carlo data. \\emph{Right:} The effect of tuning $\\epsilon_L$ away from\nideal delocalization (cf.~figure~\\ref{fig-gwff-500}).}\n\\label{hist-hzprod-nurec}\n\\end{figure}\nThe plot on the left of figure~\\ref{hist-hzprod-nurec} compares the\ninvariant mass distribution obtained\nfrom the reconstructed neutrino momenta to that obtained from\nthe unobservable neutrino momenta taken from Monte Carlo\ndata for $m_{W^\\prime}=\\unit[500]{GeV}$ and $m_\\text{bulk}=\\unit[3.5]{TeV}$.\nIn both cases the peak from the $Z^\\prime$ is clearly visible. As expected, counting\nboth solutions obtained from the reconstruction doubles the amount of background events\nwhile the number of events contained in the peak stays roughly the same. However, the peak is\nbroadened by the reconstruction, which can been seen when comparing to a\nSM simulation (dotted line). The broadening at the center of the peak is mainly\ncaused by the mismatch between reconstructed and true neutrino momentum of the signal events\ndue to the $W$ not being exactly on-shell; the sidebands of the peak are caused by the\nsecond solutions for $p_\\nu$ of events at the center of the peak.\n\nThe plot on the right of figure~\\ref{hist-hzprod-nurec} shows the\neffect of changing the delocalization parameter\n$\\epsilon_L$ in the range allowed by the EWPT at one loop\n(cf.~section \\ref{sec-cpl}),\nagain for $m_{W^\\prime}=\\unit[500]{GeV}$ and $m_\\text{bulk}=\\unit[3.5]{TeV}$. As argued\nbefore, the impact on the invariant mass distribution is not strong, the peak\nstaying clearly visible over the whole range of allowed values of $\\epsilon_L$.\n\n\\begin{figure}\n\\centerline{\\includegraphics[angle=270,width=8cm]{hist_hz_fullrange}}\n\\caption{Invariant mass distribution in $pp\\rightarrow l\\nu_ljj$ for different values of\n$m_{W^\\prime}$ and $m_\\text{bulk}$.}\n\\label{hist-hzprod-fullrange}\n\\end{figure}\nFigure~\\ref{hist-hzprod-fullrange} shows the invariant mass distributions obtained for\n\\[ m_{W^\\prime}\\in\\left\\{\\unit[380]{GeV},\\unit[500]{GeV},\\unit[600]{GeV}\\right\\}\\,,\\]\nwhich covers the whole range of values allowed by the EWPT\nat one loop\\footnote\n{We also changed $m_\\text{bulk}$ as shown in figure~\\ref{hist-hzprod-fullrange} to\ncomply with the EWPT; however, as explained in section \\ref{sec-cpl}, this has no\nnoticeable effect on the cross section.}~\\cite{SekharChivukula:2006cg,Abe:2008hb}. As\nthe masses of the $Z^\\prime$ and $W^\\prime$ are quasi-degenerate,\nthe $Z^\\prime$ peak moves with\nchanging $m_{W^\\prime}$. The histogram shows that the peak stays clearly observable,\nalthough it decreases in size as $m_{W^\\prime}$ becomes larger owing\nto the smaller parton\ndistribution functions for the sea quarks at larger values of~$x$.\n\nTo get a quantitative handle on the significance of the signal and to estimate the minimal\nluminosity necessary for discovering the $Z^\\prime$, we define the raw signal $N$ to be the\nnumber of events in the $\\pm\\unit[20]{GeV}$ region around the peak. To estimate the\nbackground we have generated SM events for an integrated luminosity of\n$\\int\\mathcal{L}=\\unit[400]{fb^{-1}}$, analyzed this data the in the same way\nas the Monte Carlo data for the three site model and then downscaled the resulting\ndistributions by a factor of $4$ to reduce the error coming\nfrom fluctuations in the background. We denote the number of background events in the\n$\\pm\\unit[20]{GeV}$ region around the peak obtained this way by $N_b$.\n\nWe define the signal $N_s$ as\n\\begin{equation}\n N_s = N - N_b\\,.\n\\end{equation}\nThe number of background events in the original Monte Carlo data $N^\\prime_b$\nis roughly doubled by our momentum reconstruction\n\\begin{equation*}\n N_b = 2N^\\prime_b\n\\end{equation*}\nand the standard deviation of $\\sigma_{N_b}$ of $N_b$ must scale\naccordingly, resulting in\n\\begin{equation*}\n \\sigma_{N_b} = 2\\sigma_{N^\\prime_b} = 2\\sqrt{N^\\prime_b} = \\sqrt{2N_b}\\,.\n\\end{equation*}\nWe then define the significance in the usual way:\n\\begin{equation}\ns = \\frac{N_s}{\\sigma_{N_b}} = \\frac{N - N_b}{\\sqrt{2N_b}}\\,.\n\\label{equ-sgn-rec}\\end{equation}\n\n\\begin{figure}\n\\centerline{\\includegraphics[angle=270,width=8cm]{significance_hz}}\n\\caption{The significance as defined in the text as a function of the integrated\nluminosity. The dotted lines mark the $3\\sigma$ resp. $5\\sigma$ discovery thresholds.}\n\\label{fig-sig-hz}\n\\end{figure}\nThe significance of the signal in the ideally delocalized scenario thus calculated is\nshown in figure~\\ref{fig-sig-hz} together with the $5\\sigma$ and $3\\sigma$ discovery\nthresholds. The $5\\sigma$ thresholds are approx.~$\\unit[1]{fb^{-1}}$,\n$\\unit[2]{fb^{-1}}$, $\\unit[5]{fb^{-1}}$ for\n$m_{W^\\prime}=\\unit[380]{GeV}$, $\\unit[500]{GeV}$, $\\unit[600]{GeV}$, respectively.\nConsidering the fact that tuning\n$\\epsilon_L$ into the region allowed by the EWPT does not\nsignificantly change the signal,\nthe three-site $Z^\\prime$ may be discovered as early as in the first\n$\\unit[1-2]{fb^{-1}}$ and even in the worst case can be expected to\nmanifest itself in the first\n$\\unit[10-20]{fb^{-1}}$ of data.\n\n\\section{$W^\\prime$ production in the $s$-channel without ideal delocalization}\n\\label{sec-hwprod}\n\n\\begin{figure}\n\\centerline{\\begin{tabular}{cc}\n\\includegraphics{hwjj} &\n\\includegraphics{hwll}\n\\end{tabular}}\n\\caption{\\emph{Left: }Representative of the class of diagrams contributing to the\n$W^\\prime$ production signal in $pp\\rightarrow l\\nu_ljj$. \\emph{Right: }One of the signal\ndiagrams in the $lljj$ decay channel of the $W^\\prime$.}\n\\label{fig-diag-hwprod}\n\\end{figure}\nAs discussed in section \\ref{sec-cpl}, the deviation from ideal delocalization required by\nthe EWPT at one loop leads to non-vanishing couplings of the\n$W^\\prime$ to the SM fermions of the same order of magnitude as the $Z^\\prime ff$\ncouplings. This allows for the possibility of producing the $W^\\prime$ in the $s$-channel\nat the LHC.\n\nThere are two possible decay channels for the $W^\\prime$ that are promising candidates for\ndiscovering this resonance. The first possibility is the decay $W^\\prime\\rightarrow\nWZ\\rightarrow l\\nu_ljj$ (cf.~the left plot in figure~\\ref{fig-diag-hwprod}),\nwhich is the final state already discussed in the last section\nand which can be treated the same way (replacing the cut on the $W$ mass with a cut on the\n$Z$ mass). The second possibility is the decay of the $ZW$ pair into two leptons and two\njets (cf.~the right plot in figure~\\ref{fig-diag-hwprod}). The absence\nof missing $p_T$ is a clear advantage\nof this decay mode allowing for background suppression by cutting on the invariant mass of\nthe lepton pair; unfortunately, the branching ratio is smaller than that for the $l\\nu_ljj$\nmode.\n\nTo probe the $l\\nu_ljj$ final state we have used the same Monte Carlo data and cuts as in\nsection \\ref{sec-hzprod} replacing the cut on the invariant mass\\footnote{See\n section~\\ref{sec-jetres} for a discussion of the effects of finite jet \n resolution on the separation of $W$ and $Z$.}\nof the jet pair with\n\\[ \\unit[86]{GeV} \\le m_{jj} \\le \\unit[96]{GeV}\\,. \\]\nFor probing the $lljj$ final state we again performed Monte Carlo simulations\nfor an integrated luminosity of $\\int\\mathcal{L}=\\unit[100]{fb^{-1}}$. We applied the\nsame $p_T$, $x$ and angular cuts as in the last section together with the\nidentification cuts\n\\begin{equation}\n\\unit[75]{GeV} \\le m_{jj} \\le \\unit[85]{GeV} \\quad,\\quad\n\\unit[86]{GeV} \\le m_{ll} \\le \\unit[96]{GeV}\n\\label{equ-cut-wzident}\\end{equation}\non the invariant mass of the jet pair and on that of the dilepton system.\n\n\\begin{figure}\n\\centerline{\\begin{tabular}{cc}\n\\includegraphics[angle=270,width=6.5cm]{hist_hwjj_fullrange} &\n\\includegraphics[angle=270,width=6.5cm]{hist_hwll_fullrange}\n\\end{tabular}}\n\\caption{\\emph{Left: }Invariant mass distribution for $W^\\prime$ production in\n$pp\\rightarrow l\\nu_ljj$ for different $W^\\prime$ masses and large $g_{W'ff}$.\n\\emph{Right: }The same distribution for the $lljj$ final state.}\n\\label{hist-hw-fullrange}\n\\end{figure}\nFigure~\\ref{hist-hw-fullrange} shows the invariant mass distributions obtained for both final\nstates for $m_{W^\\prime}=\\unit[380]{GeV}$, $\\unit[500]{GeV}$, $\\unit[600]{GeV}$ and\n$\\epsilon_L$ chosen from the allowed range such as to give large\nvalues\\footnote{Even larger values of $g_{W'ff}$ are allowed by\n increasing $m_\\text{bulk}$, but we are more interested in the lowest\n possible value for which the $W^\\prime$ might still be detected in\n this channel at the LHC.}\nof~$g_{W'ff}$ (cf.~figures~\\ref{fig-gwff-500} and~\\ref{fig-gwff-380-600}). For both final\nstates, the resonance peaks can be clearly seen for all three values of $m_{W^\\prime}$.\nThe total number of events for $lljj$ is much smaller compared to $l\\nu_l jj$\nowing to the smaller branching\nratio, but the cuts on both $m_Z$ and $m_W$ and the absence of the double counting\nintroduced by the neutrino reconstruction significantly improve the\nsignal to background ratio.\n\n\\begin{figure}\n\\centerline{\\begin{tabular}{cc}\n\\includegraphics[angle=270,width=6.5cm]{hist_hwjj_38_deloc} &\n\\includegraphics[angle=270,width=6.5cm]{hist_hwll_38_deloc} \\\\\n\\includegraphics[angle=270,width=6.5cm]{hist_hwjj_50_deloc} &\n\\includegraphics[angle=270,width=6.5cm]{hist_hwll_50_deloc} \\\\\n\\includegraphics[angle=270,width=6.5cm]{hist_hwjj_60_deloc} &\n\\includegraphics[angle=270,width=6.5cm]{hist_hwll_60_deloc}\n\\end{tabular}}\n\\caption{\\emph{Left column: }The $W^\\prime$ resonance peak in the invariant mass\ndistribution for $pp\\rightarrow l\\nu_ljj$ for different values of the delocalization\nparameter. \\emph{Right column: }The same distributions in the case of the $lljj$ final\nstate.}\n\\label{hist-hw-deloc}\n\\end{figure}\nThe dependence of the resonance peak on the delocalization parameter $\\epsilon_L$ is shown\nin figure~\\ref{hist-hw-deloc}. The left column shows the $\\pm\\unit[50]{GeV}$ region around\nthe peak for the $l\\nu_ljj$ final state for different values of $\\epsilon_L$ \nand for the case of ideal delocalization. For\n$m_{W^\\prime}=\\unit[500]{GeV}$ and $m_{W^\\prime}=\\unit[600]{GeV}$ the peak vanishes in the\ncase of ideal delocalization which demonstrates that the cut~(\\ref{equ-cut-wzident}) is\nsufficient to discriminate between jets coming from the decay of $W$ and those coming from\na $Z$. In the case of\n$m_{W^\\prime}=\\unit[380]{GeV}$, a small peak remains even in the case of ideal\ndelocalization which stems from jets coming from\n$pp\\rightarrow Z^\\prime\\rightarrow l\\nu_ljj$ misidentified as a $Z$ (we will discuss the\npossibility of unfolding these two contribution in the next section).\n\nThe histograms show\nthat tuning $\\epsilon_L$ towards the point of ideal delocalization quickly decreases the\nsize of the peak making it invisible for the lowest chosen values of~$\\epsilon_L$.\nThe right column shows the same region around the peak for the final state\n$lljj$ and the same values of $\\epsilon_L$. As should be expected, the same decrease of\nthe peak size is visible.\n\nTo obtain a numerical estimate for the integrated luminosity required\nfor a $s=5\\sigma$ or~$3\\sigma$\ndiscovery of the $W^\\prime$ at some given value of the delocalization\nparameter $\\epsilon_L$ we exploit the fact that the significance of\nthe signal scales as~$g_{W'ff}^2$\nwith the coupling of $W^\\prime$ to left-handed SM fermions. This allows\nus to estimate the integrated luminosity required for\nobtaining a signal with significance $s_0$ in terms of the significance of the signal\nfor other values of coupling and integrated luminosity.\n\nFor the actual determination of $s$ from Monte Carlo data we define the signal as in section\n\\ref{sec-hzprod}. In case of the $l\\nu_ljj$ final state we calculate~$s$\nvia~(\\ref{equ-sgn-rec}), while for the case of $lljj$ it can be calculated simply as\n\\begin{equation}\n s = \\frac{N_s}{\\sqrt{N_b}}\\,,\n\\end{equation}\nbecause we don't have the additional doubling of the\nbackground events by the neutrino momentum reconstruction in this case\\footnote\n{Because of the lower number of events in the final state $lljj$, the background for this\ncase was calculated for an integrated luminosity of $\\int\\mathcal{L}=\\unit[1000]{fb^{-1}}$\nand scaled down.}.\n\n\\begin{table}\n\\centerline{\n\\begin{tabular}{|c|c|c||c|}\n\\hline\\multicolumn{4}{|c|}{$W^\\prime\\rightarrow l\\nu_ljj$} \\\\\\hline\\hline\n$m_{W^\\prime}\\:\\left[\\unit{GeV}\\right]$ & $m_\\text{bulk}\\:\\left[\\unit{TeV}\\right]$\n& $\\epsilon_L$ & $s$ \\\\\n\\hline 380 & 3.5 & 0.338 & 5.6 \\\\\n\\hline 500 & 3.5 & 0.254 & 8.6 \\\\\n\\hline 600 & 4.3 & 0.211 & 6.4 \\\\ \\hline\n\\end{tabular}\\hspace{1cm}\n\\begin{tabular}{|c|c|c||c|}\n\\hline\\multicolumn{4}{|c|}{$W^\\prime\\rightarrow lljj$} \\\\\\hline\\hline\n$m_{W^\\prime}\\:\\left[\\unit{GeV}\\right]$ & $m_\\text{bulk}\\:\\left[\\unit{TeV}\\right]$\n& $\\epsilon_L$ & $s$ \\\\\n\\hline 380 & 3.5 & 0.338 & 5.1 \\\\\n\\hline 500 & 3.5 & 0.254 & 8.5 \\\\\n\\hline 600 & 4.3 & 0.211 & 8.3 \\\\ \\hline\n\\end{tabular}}\n\\caption{The significance of the signal calculated at different points in parameter space\nfor both final states.}\n\\label{tab-sgn-hw}\n\\end{table}\nThe significances calculated this way at different points in parameter\nspace are shown in\ntable~\\ref{tab-sgn-hw}. For $m_{W^\\prime}=\\unit[380]{GeV}$ and\n$m_{W^\\prime}=\\unit[500]{GeV}$, both final states seem to do equally well at revealing the\nfermionic couplings of the $W^\\prime$; however, for $m_{W^\\prime}=\\unit[600]{GeV}$ the\ndilepton final state appears to give a slightly better signal owing to the\nbetter ratio of signal to background.\n\\begin{figure}\n\\centerline{\\begin{tabular}{cc}\n\\multicolumn{2}{c}{\\includegraphics[angle=270,width=6.5cm]{sign_hw_380}} \\\\\n\\includegraphics[angle=270,width=6.5cm]{sign_hw_500} &\n\\includegraphics[angle=270,width=6.5cm]{sign_hw_600}\n\\end{tabular}}\n\\caption{The integrated luminosity required for a $5\\sigma$ resp. $3\\sigma$ discovery of the\n$W^\\prime$ in the $s$-channel.}\n\\label{fig-lumi-hw}\n\\end{figure}\nThe integrated luminosity necessary for a $5\\sigma$ resp. $3\\sigma$ discovery of the\n$W^\\prime$ in the $s$-channel is shown in figure~\\ref{fig-lumi-hw} together with the range of the\ndelocalization parameter $\\epsilon_L$ allowed for the different choices of $m_{W^\\prime}$\nand $m_\\text{bulk}$. Taking the integrated luminosity collected over the full LHC running\ntime to be around $\\unit[400]{fb^{-1}}$ and considering the fact that the band of allowed\n$\\epsilon_L$ (and $g_{W^\\prime ff}$) can be moved further towards smaller values by\nlowering $m_\\text{bulk}$, it is evident from figure~\\ref{fig-lumi-hw} that there is a part of\nthe allowed parameter space in which the $W^\\prime$ would appear perfectly fermiophobic\nat the LHC. However, there also is a big region of parameter space in which the coupling of\nthe $W^\\prime$ to the SM fermions eventually should be discovered, although\nthis still would take several years of running time as the lowest integrated luminosity\nrequired for $3\\sigma$ is around $\\unit[10]{fb^{-1}}$ even at the point in parameter space\nmost easily accessible.\n\n\\section{Finite jet resolution and $W$\/$Z$ identification}\n\\label{sec-jetres}\n\nSince flavor tagging is impossible for light quark flavors, we have to\nrely on invariant mass cuts for the jet pairs to be able to separate\nthe case of the two jets in $l\\nu_ljj$ coming from the decay of a~$W$\nin $Z^\\prime$~production from that of the jets being produced by a\ndecaying $Z$ in $W^\\prime$~production.\nHowever, it may very well be impossible to obtain a resolution of order\n$\\pm\\unit[5]{GeV}$ in the jet invariant mass from experimental data.\nIn this section, we discuss the effect of a gaussian smearing of\nthe invariant mass of the jets on our analysis.\n\nIn the ideal case of exact $m_{jj}$ measurement, events coming from the decay of a\nintermediary $W$\/$Z$ are distributed according to a Breit-Wigner distribution\n\\[ p_b(x,m,\\Gamma)\\:dx =\n\\frac{n_b(m,\\Gamma)^{-1}}{\\left(x^2-m^2\\right)^2+\\Gamma^2 m^2}\\:dx\\,, \\]\nwith the normalization factor\n\\[ n_b(m,\\Gamma) = \\frac{\\pi}{4m^3}\\left(1+\\frac{\\Gamma^2}{m^2}\\right)^{-\\frac{3}{4}}\n\\sin^{-1}\\left(\\frac{1}{2}\\atan\\frac{\\Gamma}{m}\\right)\\,. \\]\nEmulating the measurement error in the jet mass by convoluting $p_\\text{bw}$ with a\ngaussian of standard deviation $\\sigma$\n\\[ \np_\\text{g}(x,\\sigma)\\:dx = \\frac{1}{\\sqrt{2\\pi}\\sigma}e^{-\\frac{x^2}{2\\sigma^2}}\\:dx\n\\]\nwe obtain the smeared distribution\n\\[ p_\\text{sm}(x,m,\\Gamma,\\sigma)\\: dx = \\int_0^\\infty dy\\:p_b(y,m,\\Gamma)\np_\\text{g}(x-y,\\sigma)\\,.\n\\]\n\\begin{figure}\n\\centerline{\\includegraphics[angle=270,width=8cm]{smearedpeaks}}\n\\caption{The effect of a gaussian smearing on the Breit-Wigner shape of the $W$ and $Z$\nresonances for various widths $\\sigma$ of the gaussian.}\n\\label{fig-smearedpeaks}\n\\end{figure}\n\nFigure~\\ref{fig-smearedpeaks} shows the effect of this smearing on the Breit-Wigner peaks of\nthe $Z$ and the $W$. Turning on the smearing and increasing $\\sigma$ causes the sharp\nBreit-Wigner peaks to decay rapidly, and for $\\sigma=\\unit[10]{GeV}$, only two very broad\nbumps are left. The result is that, if a cross section has one contribution which stems\nfrom the decays of a virtual $Z$ and one coming from a virtual $W$, any attempt to isolate\nthe $Z$ contribution by cutting on the resonance will inevitably also select events coming\nfrom the $W$ decay contaminating the sample (and vice versa). Therefore, our analysis of\nthe $l\\nu_ljj$ final state will show a $W^\\prime$ peak even in the case of ideal\ndelocalization which is caused by jet pairs from a decaying $W$ misidentified as a $Z$.\n\nIf we try to isolate the $W$ peak with a cut on the invariant mass $m_{jj}$\n\\[ L_W \\le m_{jj} \\le U_W \\]\nand the $Z$ peak with a cut\n\\[ L_Z \\le m_{jj} \\le U_Z\\,, \\]\nthen the resulting event counts $\\widetilde{N}_W, \\widetilde{N}_Z$ can be\ncalculated from the true event counts $N_W, N_Z$ coming from a decaying $W$ or $Z$\nvia a matrix $T$ as\n\\[ \\begin{pmatrix} \\widetilde{N}_W \\\\ \\widetilde{N}_Z \\end{pmatrix} =\n\\begin{pmatrix} T_{WW} & T_{WZ} \\\\ T_{ZW} & T_{ZZ} \\end{pmatrix}\n\\begin{pmatrix} N_W \\\\ N_Z \\end{pmatrix} \\]\nwith entries\n\\[ T_{ij} = \\int_{L_i}^{U_i}dm\\:p_\\text{sm}(m,m_j,\\Gamma_j,\\sigma)\\,. \\]\nInverting $T$ we can calculate the event counts $N_W$ and $N_Z$\n\\begin{equation} \\begin{pmatrix} N_W \\\\ N_Z \\end{pmatrix} = T^{-1}\n\\begin{pmatrix} \\widetilde{N}_W \\\\ \\widetilde{N}_Z \\end{pmatrix}\\,.\n\\label{equ-trans-mat}\\end{equation}\nThe entries of $T$ give the probability of misidentifying an event and can be readily\ncalculated numerically; for example, choosing cuts\n\\[ L_W=\\unit[60]{GeV} \\quad,\\quad U_W=\\unit[85]{GeV} \\quad,\\quad\nL_Z=\\unit[86]{GeV} \\quad,\\quad U_Z=\\unit[111]{GeV} \\]\nyields\n\\[ T \\approx \\begin{pmatrix} 0.64 & 0.27 \\\\ 0.29 & 0.62 \\end{pmatrix}\n\\quad,\\quad\nT^{-1} \\approx \\begin{pmatrix} 1.9 & -0.85 \\\\ -0.89 & 2.0 \\end{pmatrix}\\,. \\]\nThis way, we can in principle use $T$ to disentangle the\ncontributions from $W$ and $Z$ resonances\nto the signal in the presence of a measurement error which causes the Breit-Wigner\npeaks to lose their shape. However, to apply this to actual data, it is vital\nto separate the signal from both the reducible and the irreducible\nbackgrounds, because they don't follow a Breit-Wigner distribution.\n\nIn order to estimate the significance of a signal obtained this way, we calculate the\nstandard deviation $\\sigma_{N_i}$ of $N_i$ according to\n\\[ \\sigma_{N_i} = \\sqrt{\\sum_{j\\in W,Z}\\left(T^{-1}_{ij}\\right)^2\\sigma_{\\widetilde{N}_j}^2}\\,. \\]\nIn our analysis, we obtain the signal events inside the smeared Breit-Wigner peaks\n$\\widetilde{N}_i$ by subtracting the background $N_{b,i}$ from the total number of events\n$N_{t,i}$. The error on $N_{t,i}$ is\n\\[ \\sigma_{N_{t,i}} = \\sqrt{N_i + 2N_{b,i}} = \\sqrt{N_{t,i} + N_{b,i}}\\,, \\]\nbecause of the neutrino momentum reconstruction doubling the amount of background events\n(cf.~section~\\ref{sec-hzprod}), and we finally arrive at\n\\begin{equation}\n\\sigma_{N_i} = \\sqrt{\\sum_{j\\in W,Z}\\left(T^{-1}_{ij}\\right)^2\\left(N_{t,j} + N_{b,j}\\right)}\n\\label{equ-sigma-after-transfer}\\end{equation}\nFor a simulation of the effect of the measurement error our analysis we have\nrandomly distributed the invariant mass of the jet pairs within a gaussian with width\n$\\sigma=\\unit[10]{GeV}$ centered around the correct value calculated from Monte Carlo data. We\nthen did the same analysis as in sections \\ref{sec-hzprod} and \\ref{sec-hwprod}\nwith $m_{W^\\prime}=\\unit[500]{GeV}$ and $m_\\text{bulk}=\\unit[3.5]{TeV}$ both for\n$\\epsilon_L=0.254$ and for the ideally delocalized scenario. The only difference to the\nprevious analysis are the cuts on $m_{jj}$ which we enlarged to\n\\[ \\unit[60]{GeV}\\le m_{jj}\\le\\unit[85]{GeV} \\quad\\text{resp.}\\quad\n\\unit[86]{GeV}\\le m_{jj} \\le\\unit[111]{GeV}\\,. \\]\n\\begin{figure}\n\\centerline{\\begin{tabular}{cc}\n\\includegraphics[angle=270,width=6.5cm]{hist_w_smear_ideloc} &\n\\includegraphics[angle=270,width=6.5cm]{hist_w_smear_254}\n\\end{tabular}}\n\\caption{\\emph{Left: }Signal in the $W^\\prime$ detection channel for the case of ideal\ndelocalization smeared with a gaussian error. \\emph{Right: }The same for the case\nof nonzero $g_{W'ff}$}\n\\label{hist-wsmear}\n\\end{figure}\nFigure~\\ref{hist-wsmear} shows the resulting effect on the $W^\\prime$ peak for the cases\nof ideal delocalization (left) and for $\\epsilon_L=0.254$ (right). In both cases a peak is\nclearly visible, which in the ideally delocalized scenario is only composed of events\nwith jets coming from a decaying $W$ misidentified as a $Z$.\n\nThe number of signal events\n$\\widetilde{N}_{W\/Z}$ after smearing, the significance $s_{W\/Z}$ of these\ncalculated via~(\\ref{equ-sgn-rec}), $N_{W\/Z}$ obtained from applying the transfer\nmatrix $T^{-1}$ (\\ref{equ-trans-mat}) and the resulting significance\n$N_i\/\\sigma_{N_i}$ obtained from~(\\ref{equ-sigma-after-transfer}) are shown in\ntable \\ref{tab-sgn-wzsep}. All peaks are significant with $s>5\\sigma$; however, after\napplying the transfer matrix, the $W^\\prime$ peak vanishes within one standard deviation\nfor ideal delocalization,\nwhile in the case of $\\epsilon_L=0.254$ a residue as big as $2\\sigma$ remains. The\n$Z^\\prime$ peak remains significant after applying the transfer matrix, however, the\nsignificance is reduced because the transfer matrix enlarges the error.\n\\begin{table}\n\\centerline{\n\\begin{tabular}{|c||c|c|c|c|}\n\\hline\\multicolumn{5}{|c|}{ideal delocalization}\\\\\\hline\\hline\n & $\\widetilde{N}_i$ & $s_i$ & $N_i$ & $\\frac{N_i}{\\sigma_{N_i}}$ \\\\\\hline\\hline\n$i=W$ & $3193$ & $17$ & $5126$ & $13$ \\\\\\hline\n$i=Z$ & $1371$ & $7.5$ & $-96.10$ & $0.24$ \\\\\\hline\n\\end{tabular}\n\\hspace{1cm}\n\\begin{tabular}{|c||c|c|c|c|}\n\\hline\\multicolumn{5}{|c|}{$\\epsilon_L=0.254$}\\\\\\hline\\hline\n & $\\widetilde{N}_i$ & $s_i$ & $N_i$ & $\\frac{N_i}{\\sigma_{N_i}}$ \\\\\\hline\\hline\n$i=W$ & $3767$ & $21$ & $5628$ & $14$ \\\\\\hline\n$i=Z$ & $2083$ & $11$ & $811.6$ & $2.0$ \\\\\\hline\n\\end{tabular}\n}\n\\caption{Comparison of the signals $\\widetilde{N}_{W\/Z}$ obtained with an gaussian\nsmearing of the invariant mass of the jets with $\\sigma=\\unit[10]{GeV}$ to the ``true''\nsignals $N_{W\/Z}$ calculated from the measured ones via the transfer matrix $T^{-1}$.}\n\\label{tab-sgn-wzsep}\n\\end{table}\n\nWhat are the consequences for the detection of $Z^\\prime$ and $W^\\prime$ in the $l\\nu_ljj$\nfinal state? The detection of the $Z^\\prime$ is not affected by inaccuracies in the\njet mass resolution as the peak is always present with little variations of its size over\nthe whole parameter space, and we can always compensate for the smearing of the jet\nmass by enlarging the cut window on $m_{jj}$. However, the separation of a possible $W^\\prime$\ncontribution to the peak (which depends heavily on the point in parameter space) by\ncutting on $m_{jj}$ alone is spoiled by the error in $m_{jj}$; we have to apply additional\ntricks like the transfer matrix~(\\ref{equ-trans-mat}) to disentangle the two contributions.\nWhile this seems to work in principle, the significance of the $W^\\prime$ signal is\nreduced by this analysis, rendering this final state much less suitable for detecting a\ncoupling between $W^\\prime$ and SM fermions than the decay into $lljj$ which\nis not contaminated by a contribution of the $Z^\\prime$.\n\n\\section{Conclusions}\n\\label{sec:concl}\n\nWe have studied the production of the heavy~$W'$ and $Z'$ bosons of\nthe three site higgsless model in the $s$-channel at the LHC. Unlike\nvector boson fusion, this production mode allows to directly measure\nthe couplings of the new bosons to standard model fermions. These\ncouplings are constrained by electroweak precision tests and their\nmeasurement is therefore crucial for consistency checks of models of\nelectroweak symmetry breaking with extended gauge sectors.\n\nWe have found a method that will allow the separation of~$W'$ from\n$Z'$~processes at the parton level. Our results show that the\nobservation of $s$-channel production of $Z'$ bosons will not require\na lot of integrated luminosity for all of the allowed parameter space.\nIn contrast, $W'$ production in the $s$-channel is much more sensitive\nto the model parameters and there are regions of parameter space where\nan observation will be very challenging, if not impossible. A more\ndetailed experimental analysis should investigate the effects of\nhadronization and detector response on our results.\n\n\n\n\\section*{Acknowledgments}\nThis research is supported by Deutsche Forschungsgemeinschaft through\nthe Research Training Group 1147 \\textit{Theoretical Astrophysics and\nParticle Physics}, by Bundesministerium f\\\"ur Bildung und Forschung\nGermany, grant 05HT6WWA and by the Helmholtz Alliance \\textit{Physics\nat the Terascale}.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\\indent Throughout this paper, the term ``polyhedron'' refers only to convex polyhedron in Euclidean 3-space $\\mathbb{E}^3$ (convex 3-polytope). For any polyhedron $P$, we denote the set of all vertices, edges and faces of $P$ by $F_0(P)$, $F_1(P)$ and $F_2(P)$ respectively. These sets together with the empty set $\\emptyset$ and $P$ itself, form a lattice $F(P)$ under inclusion, with $P$ as maximum and $\\emptyset$ as minimum. The lattice $F(P)$ of $P$ is determined by the {\\it incidence matrix} $M_P$ of $P$, where we label the vertices by $v_1,\\ldots, v_r$ and the polygonal faces by $A_1,\\ldots,A_s$, say, and define $M_P$ to be the $r \\times s$ matrix whose $(i,j)th$ element $m_{i,j}$ is 1 if $v_i \\in A_j$ and 0 otherwise. The number $\\mu=\\mu(M_P)=\\mu(P)$, of nonzero elements of $M_P$ is called the {\\it multiplicity} of $P$. Since each edge determines four incidences of the adjacent faces and vertices and each incidence corresponds to two edges, we have $\\mu=\\mu(P)=2e$, where $e=e(P)$ is the number of edges of $P$.\n\n\\indent Two polyhedra $P$ and $Q$ are face equivalent (also called combinatorially equivalent), written $P \\approx Q$, if there is a lattice isomorphism from $F(P)$ to $F(Q)$. The set $[P]$ of all $Q \\approx P$ is the \\emph{face type} or, as often called, the \\emph{isomorphism type} of $P$. Since $P$ is the convex hull of the set $F_0(P)$ of its vertices and since $P$ determines $\\F$, we can identify $P$ with the point $(v_1,\\ldots,v_r)$ in $\\mathbb{E}^{3r}=(\\mathbb{E}^3)^r$, where $\\F = \\{ v_1,\\ldots, v_r \\} $. Thus we may identify a neighborhood of $P$ in $[P]$ with a neighborhood of $(v_1,\\ldots, v_r)$ in $\\mathbb{E}^{3r}$, since any $P'\\approx P$ sufficiently close to $P$ may have its vertices labeled $v'_1,\\ldots v'_r$ in a unique way so that $v'_i$ is close to $v_i$, $i=1,\\ldots,r$.\n\nA graph $\\mathscr{G}=(V,E)$ with vertex set $V$ and edge set $E$ is said to be $3$-connected if any two vertices are connected by three internally disjoint paths (each pair of paths have only the two vertices in common); $\\mathscr{G}$ is planar if it can be embedded in the plane such that no two edges intersect internally.\n\nNow, for any polyhedron $P$ each edge has exactly two vertices. Therefore we can define the $1$-skeleton, or edge graph, $\\mathscr{G}_P$ of any polyhedron $P$ as follows. The vertices of $\\mathscr{G}_P$, are vertices of $P$, and two vertices are connected by an edge in graph $\\mathscr{G}_P$ if they are vertices of the same edge of $P$. This planar graph which is called ``\\emph{Schlegel diagram}'' of $P$, can be obtained by projecting $P$ on one of its faces (outside face).\n\nDue to Steinitz's famous Theorem called ``Fundamental theorem of convex types'' a graph $\\mathscr{G}_P=(V,E)$ is the edge graph of a polyhedron $P$ if and only if it is planar and $3$-connected. Beyond this remarkable result, it is interesting that, Steinitz discovered some important facts about the topological structure of $[P]$ which is called the realization space of $P$ (see Section 2 for the relevant definitions).\n\nIndeed, a careful inspection of the proof of Steinitz's Theorem shows that $[P]$ is smooth manifold with dimension $\\dim[P]=e-1$, up to similarities, where $e=e(P)$ is the number of edges of $P$ .\n\nPerhaps the naturality of the above result of Steinitz was the main reason for other mathematicians to believe that the realization space of polytopes, in dimensions higher than three, might also be a smooth manifolds. Theorem, on page 18 of \\cite{Rob}, asserts that $[P]$ is manifold in general case, where $P$ is an $n$-dimensional convex polytope. But a striking result of Mnev \\cite{RG,Mnev} shows that, from the topological point of view, the realization space of a convex polytope may be arbitrarily complicated. The argument given in \\cite{Rob} does not take full account of coplanarity conditions on the intersections of the faces, but works for $n=3$ (see Section 3).\n\nThe structure of this note is as follows. In Section 2 it is introduced the explicit definition of the {\\it realization space} of a polyhedron in terms of a relevant metric. Then, we give a proof that the realization space is in fact a smooth manifold with a specific dimension. The proof, which is independent of implicit proof of Steinitz himself, is illustrated with an example adopted from (\\cite{Rob}, p. 21).\n\n\nIn Section 3, for any polyhedron $P$ we define $\\langle P \\rangle$ the {\\it symmetry type} of $P$, together with giving some basic facts about the actions of transformation groups on manifolds. Then, we recall the {\\it stratification} of $\\mathbb{E}^3$ and the decomposition of the manifold of the space of all polyhedra into {\\it strata} of symmetry types under the action of the point groups.\n\nIn Section 4, we first consider finite point groups generated by reflections. Then, with the help of the familiar notation of {\\it fundamental region} of a reflection group, we prove our main theorem which relates the dimension of the symmetry type of polyhedron $P$ to the number of its edge orbits, under the action of $G(P)$, the symmetry group of $P$ on its face lattice $F(P)$. This is the proof of Deicke's conjecture in \\cite{Rob} as well, for polyhedra having reflection symmetry groups. For polyhedra with rotation groups we refer to \\cite{Ros1}, and for the basic properties of polyhedra with symmetry we refer to Robertson's book \\cite{Rob}.\n\n\n\n\\section{The realization space of a polyhedron}\n\n\\indent Our purpose in this Section is to define realization space and determine its topological structure. But first we present some basic definitions.\n\nLet $\\left( \\mathbb{E}^n , \\, d \\right)$ be the $n$-dimensional Euclidean space with induced norm\n$$\nd(x,y) \\, = \\, \\|x-y \\|, \\, x,y\\in \\mathbb{E}^n \\, .\n$$\n\nDenote by $\\mathbb{K}^n$ hyperspace of all nonempty compact convex subsets (convex bodies) of $\\mathbb{E}^n$. Then, $\\mathbb{K}^n$ is endowed with the following familiar metric, called \\emph{Hausdorff metric}.\n\n\\begin{defi}\nFor $P\\in \\mathbb{K}^n$ and $\\varepsilon >0$, let\n$$\n\\mathcal{U}_{\\varepsilon}(P) \\, = \\, \\left\\{ x\\in \\mathbb{E}^n \\, \\mid \\, d(x,P) < \\varepsilon \\right\\} \\, ,\n$$\n\n\\noindent where $d(x,P) \\, = \\, \\stackrel[p\\in P]{}{\\inf} \\left\\{ d(x,p)=\\|x-p \\| \\right\\}$ .\\\\\n\nNow, for $P, \\, P' \\in \\mathbb{K}^n$, let $\\rho(P,P') \\, = \\, \\inf \\left\\{ \\varepsilon \\, \\mid \\, P' \\subseteq \\, \\, \\mathcal{U}_{\\varepsilon}(P) \\right\\}$. Then,\n$$\\,\n\\,\n\\begin{array}{rcl}\n d_{\\mathcal{H}}(P,P') & = & \\max \\left\\{ \\rho(P,P') \\, , \\, \\rho(P',P) \\right\\} \\\\[4pt]\n & = & \\inf \\left\\{ \\varepsilon >0 \\, \\mid \\, P \\subseteq \\, \\, \\mathcal{U}_{\\varepsilon}(P') \\text{ and } P' \\subseteq \\, \\, \\mathcal{U}_{\\varepsilon}(P) \\right\\} \\, ,\n\\end{array}\n$$\n\n\\noindent is a metric on $\\mathbb{K}^n$ which is called Hausdorff metric (distance).\n\\end{defi}\n\n\nWe denote by $\\mathscr{P}$ the set of all polyhedra in $\\mathbb{E}^3$ as a topological subspace of the metric space $\\left( \\mathbb{K}^3, \\, d_{\\mathcal{H}} \\right)$.\n\n\n\\begin{defi}\nLet $P$ be a polyhedron (convex 3-polytope). Denote by $[P]$ the face type of $P$, the set of all polyhedra $Q$ face equivalent to $P$.\n$$\n[P] \\, = \\, \\left\\{ Q \\, \\mid \\, Q {\\text{ is polyhedron and }} Q\\approx P \\right\\} \\, .\n$$\n\n\\noindent Then, $[P]$ together with its natural subspace topology induced by Hausdorff metric $d_{\\mathcal{H}}$ is called the realization space of $P$.\n\\end{defi}\n\nAlternatively, any polyhedron $P$ determines, and it is determined by, the set $F_0(P) = \\{ v_1,\\ldots,v_r \\}$ of its vertices. Since we can identify $P$ with the point $(v_1,\\ldots,v_r)\\in \\left( \\mathbb{E}^3 \\right)^r$, $[P]$ can be interpreted as a topological subspace of $\\mathbb{E}^{3r}$ with its topology induced by the vertices.\n\nGiven $\\varepsilon >0$, sufficiently small, there is an open neighborhood $V_{\\varepsilon}(P)$ of $P$ in $[P]$ such that, for all $P' \\in V_{\\varepsilon}(P)$ we have $F_0(P') = \\{ v_1^{'},\\ldots,v_r^{'} \\}$, where $\\| v_i - v_i^{'} \\| < \\varepsilon$, $i=1, \\ldots , r$.\n\nTherefore, $[P]$ can be topologized locally by $V_{\\varepsilon}(P)$ neighborhood of $P$. Thus $V_{\\varepsilon}(P)$, in fact, is the open space of the small perturbations of the vertices of $P$.\n\n\\vspace*{0,2cm}\nNow, let $\\Pi_j$ be the plane containing $A_j$. Then, we may suppose without loss of generality that for all $j=1,\\ldots,s$, the origin $O$ does not lie in $\\Pi_j$. Otherwise translate $P$ to ensure this condition. Then, for all $j$ there is a unique $a_j \\in \\mathbb{E}^3$ such that $\\Pi_j$ is given by the equation $\\langle x,a_j\\rangle=1, x\\in \\mathbb{E}^3$. Thus $\\Pi_j$ is given by $a_j$ and $P$ itself by the point\n$$(v_1,\\ldots,v_r,a_1,\\ldots,a_s)\\in \\mathbb{E}^{3r}\\times \\mathbb{E}^{3s}=\\mathbb{E}^{3(r+s)} \\, ,$$\n\n\\noindent where $\\langle v_i,a_j\\rangle=1$ for all $i,j$ such that $m_{i,j}=1$. Now let $P'$ be the polyhedron with $P\\approx P'$, having vertices $v'_1,\\ldots,v'_r$ and faces given by $a'_1,\\ldots,a'_s$ where the plane $\\Pi'_j$ of $A'_j$ has equation $\\langle x,a'_j\\rangle= 1$ and again $\\langle v'_i,a'_j \\rangle=1$ if $m_{ij}^{'}=1$ (with obvious notation).\nSuppose that $P'$ is close to $P$, so that\n$$v'_i=v_i+\\xi_i,\\,\\,\\,\\,a'_j=a_j+\\eta_j \\, ,$$\n\n\\noindent for some $\\xi_j,\\eta_j \\in \\mathbb{E}^3$ with $||\\xi_i||$ and $||\\eta_j||$ small ($i=1,\\ldots,r;j=1,\\ldots,s$). Then,\n\\begin{equation}\\label{eq1}\n\\bf{\\Phi_{[i,j]}(\\xi,\\eta):=\\langle v_i,\\eta_j\\rangle+\\langle \\xi_i,a_j\\rangle+\\langle\\xi_i,\\eta_j\\rangle=0},\n\\end{equation}\n\n\\noindent if $m_{ij}=1$ (and hence $m_{ij}^{'}=1$).\n\n\\vspace*{0,2cm}\nConversely, for any sufficiently small $\\xi \\in \\mathbb{E}^{3r}$ and $\\eta \\in \\mathbb{E}^{3s}$, the point\n$$(\\nu_1+\\xi_1,\\ldots,\\nu_r+\\xi_r,a_1+\\eta_1,\\ldots,a_s+\\eta_s)$$\n\n\\noindent represents a unique polyhedron $P'\\approx P$, provided equations (\\ref{eq1}) hold. Let us now consider the polynomial map $\\Phi: \\mathbb{E}^{3r}\\times \\mathbb{E}^{3s}\\rightarrow \\mathbb{E}^\\mu$ given by $\\Phi(\\xi,\\eta)=(y_1,\\ldots,y_\\mu)$ where the pairs $(i,j)$ with $m_{ij}=1$ are arranged in lexicographical order, and if $(i,j)$ is the $[i,j]th$ such pair then, $y_{[i,j]}=\\Phi_{[i,j]}(\\xi,\\eta)$ is defined as in (\\ref{eq1}). We may for convenience suppress component suffices of $\\xi_i$ and $\\eta_j$, and write\n$$\\frac{\\partial y_{[i,j]}}{\\partial \\xi_i}=a_j+\\eta_j,\\,\\,\\,\\,\\frac{\\partial y_{[i,j]}}{\\partial \\eta_j}=\\nu_i+\\xi_i \\, .$$\n\nThus the Jacobian matrix $J_{\\Phi}(0,0)$, has order $\\mu\\times(3(r+s))$ with $a_j$ in the $[i,j]$th row and the $i$th triplet of columns, and $\\nu_i$ in the $[i,j]$th row and the $(r+j)$th triplet of columns. All the other elements of $J_{\\Phi}(0,0)$ are $0$.\n\nA simple example may helps to clarify these remarks. Let $P$ be the polyhedron shown, with its accompanying Schlegel diagram in the following figure.\n{ \\begin{figure}[!htb] \\centering\n\\hspace*{.75cm} \\includegraphics[scale=.2]{FIG_1.eps}\n\\vspace*{-0,4cm}\\caption{{\\small{Schlegel diagram of $P$ with ``outside face'' $A_1$}}}\n{\\label{im1}}\n\\end{figure}}\n\n\\newpage\nIn this example, $r=7,s=8$ and $\\mu=26$. Notice that $3(r+s)=45>26=\\mu$. In fact, for any polyhedron, $3(r+s)=3(e+2)=3e+6=\\mu+e+2>\\mu$, by Euler's Theorem. Thus $\\mu$ is the largest value of the rank of $J_{\\Phi}(0,0)$. In the example, $J_{\\Phi}(0,0)$ has 26 rows and 45 columns, shown below in the truncated $26\\times 15$ form.\n\n\\begin{center} {\\scriptsize{\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n\n & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\\\\n \\hline\n 15 & $a_5$ & & & & & & & & & & $\\nu_1$ & & & & \\\\\n \\hline\n 16 & $a_6$ & & & & & & & & & & & $\\nu_1$ & & & \\\\\n \\hline\n 17 & $a_7$ & & & & & & & & & & & & & $\\nu_1$ & \\\\\n \\hline\n 18 & $a_8$ & & & & & & & & & & & & & &$\\nu_1$ \\\\\n \\hline\n 22 & & $a_2$ & & & & & & & $\\nu_2$ & & & & & & \\\\\n \\hline\n 23 & & $a_3$ & & & & & & & & $\\nu_2$ & & & & & \\\\\n \\hline\n 25 & & $a_5$ & & & & & & & & & & $\\nu_2$ & & & \\\\\n \\hline\n 26 & & $a_6$ & & & & & & & & & & & $\\nu_2$ & & \\\\\n \\hline\n 32 & & & $a_2$ & & & & & & $\\nu_3$ & & & & & & \\\\\n \\hline\n 34 & & & $a_4$ & & & & & & & & $\\nu_3$ & & & & \\\\\n \\hline\n 35 & & & $a_5$ & & & & & & & & & $\\nu_3$ & & & \\\\\n \\hline\n 38 & & & $a_8$ & & & & & & & & & & & & $\\nu_3$\\\\\n \\hline\n 41 & & & & $a_1$ & & & & $\\nu_4$ & & & & & & & \\\\\n \\hline\n 44 & & & & $a_4$ & & & & & & & $\\nu_4$ & & & & \\\\\n \\hline\n 47 & & & & $a_7$ & & & & & & & & & & $\\nu_4$ & \\\\\n \\hline\n 48 & & & & $a_8$ & & & & & & & & & & &$\\nu_4$\\\\\n \\hline\n 51 & & & & & $a_1$ & & & $\\nu_5$ & & & & & & & \\\\\n \\hline\n 53 & & & & & $a_3$ & & & & & $\\nu_5$ & & & & & \\\\\n \\hline\n 56 & & & & & $a_6$ & & & & & & & & $\\nu_5$ & & \\\\\n \\hline\n 57 & & & & & $a_7$ & & & & & & & & & $\\nu_5$ & \\\\\n \\hline\n 61 & & & & & & $a_1$ & & $\\nu_6$ & & & & & & &\\\\\n \\hline\n 62 & & & & & & $a_2$ & & & $\\nu_6$ & & & & & &\\\\\n \\hline\n 63 & & & & & &$ a_3$ & & & & $\\nu_6$ & & & & &\\\\\n \\hline\n 71 & & & & & & & $a_1$ & $\\nu_7$ & & & & & & & \\\\\n \\hline\n 72 & & & & & & & $a_2$ & & $\\nu_7 $ & & & & & & \\\\\n \\hline\n 74 & & & & & & & $a_4$ & & & & $\\nu_7$ & & & &\\\\\n \\hline\n\\end{tabular}}} \\end{center}\n\n\\begin{center}\n[truncated $J_{\\Phi}$]\n\\end{center}\n\n\nRecall that a similarity is a map\n\\vspace*{-.3cm}\n$$ s:\\mathbb{E}^3 \\rightarrow \\mathbb{E}^3$$\n\n\\vspace*{-.3cm}\n\\noindent such that for some real $r>0$, and all $x,y\\in \\mathbb{E}^3$\n\\vspace*{-.3cm}\n$$\nrd(x,y)=d(s(x), s(y)) \\, .\n$$\n\n\\vspace*{-.3cm}\nThe set of all similarities is a group which is denoted by $Sim(3)$, and called similarity group of $\\mathbb{E}^3$.\n\nThe map $\\varphi: Sim(3)\\rightarrow \\mathbb{R}_\\ast$ defined by $\\varphi(f)=r$, where $r$ is as above and $\\mathbb{R}_\\ast$ is multiplicative group of positive real numbers, is a group homomorphism.\nIt is well known that the kernel of this homomorphism is the isometry group or the Euclidian group $\\mathbb{E}(3)$. We may identify $Sim(3)$ as $\\mathbb{E}(3) \\times \\mathbb{R}_\\ast$. Since the manifold $\\mathbb{E}(3)$ has dimension $6$, the dimension of $Sim(3)$ is equal to $7$.\n\n\\vspace*{.2cm}\nNow we are ready to prove our main theorem in this Section:\n\\begin{teo} Let $P$ be a convex 3-polyhedron. Then, $[P]\/Sim(3)$, the realization space of $P$ modulo similarities, is a manifold of dimension $\\dim \\left( [P]\/Sim(3) \\right) \\, = \\, e-1$, where $e=e(P)$ the number of edges of $P$.\n\\end{teo}\n\\vspace*{-.1cm} {\\bf Proof:} To show, equivalently, that for any $P$, the face type $[P]$ is a manifold of dimension $e+6$, it is enough to show that $J_{\\Phi}(0,0)$ has rank $\\mu$. The result follows by the implicit function Theorem, since $e-1=3(r+s)-\\mu-7$.\n\nWe want to show that the rows of $J_{\\Phi}(0,0)$ are linearly independent. Suppose then, that for some real number $\\alpha_{[i,j]}$, we have\n\\vspace*{-.1cm}\n\\begin{equation}\\label{eq2}\n\\sum_j \\alpha_{[i,j]}a_j=\\sum_i \\alpha_{[i,j]}\\nu_i=0 \\, ,\n\\end{equation}\n\n\\vspace*{-.3cm}\n\\noindent for each $i=1,\\ldots,r$ and each $j=1,\\ldots,s$. We know that for any $i$ and any three values of $j_1,j_2,j_3$ of $j$ with $m_{ij}=1$, $a_{j_1},a_{j_2},a_{j_3}$ are linearly independent. Hence for each $i$, the equations $\\sum \\alpha_{[i,j]}a_j=0$ have solution space of dimension $s_i-3$ where $s_i$ is the number of the values of $j$ with $m_{i,j}=1$, and we can express any three of the numbers $\\alpha_{[i,j]}$ as linear functions of the remaining $s_i-3$. Hence the $\\mu$ numbers $\\alpha_{[i,j]}$ are expressible as linear functions of $\\sum_{i=1}^r(s_i-3)=\\mu-3r$ independent variables. In particular, for each $i$ for which $s_i=3$, all the numbers $\\alpha_{[i,j]}$ are 0.\n\nNow consider the equations $\\sum \\alpha_{[i,j]}\\nu_i=0$. Again, for each $i$, any three of the vertices $v_i$ are linearly independent. So again we can express any three of the numbers $\\alpha_{[i,j]}$ as linear combinations of the remaining $r_j-3$. So the numbers $\\alpha_{[i,j]}$ are expressible as linear functions of $\\sum_{i=1}^s(v_j-3)=\\mu-3s$ independent variables.\n\nBut the labels $\\alpha_{[i,j]}$ for each $j$ are already expressed as linear functions of $\\mu-3r$ independent variables, as described above. Hence the numbers $\\alpha_{[i,j]}$ are over determined by the two sets of equations in (\\ref{eq2}), since the solution space of the equations in (\\ref{eq2}) has dimension $\\mu-3(r+s)$. But $\\mu-3(r+s)<0$ by Euler formula. It follows that $\\alpha_{[i,j]}=0$ for all $i,j$ where $m_{i,j}=1$.\\hfill$\\square$\n\n\n\\vspace*{.5cm}\nReferring to the above example, we may express each of, say $\\alpha_{[1,6]}$, $\\alpha_{[1,7]}$ and $\\alpha_{[1,8]}$ as linear function of $\\alpha_{[15]}$ that is as a multiple of $\\alpha_{[15]}$. Thus $\\alpha_{[16]}=\\lambda_{16}\\alpha_{[15]}$, $\\alpha_{[17]}=\\lambda_{17}\\alpha_{[15]}$, $\\alpha_{[18]}=\\lambda_{18}\\alpha_{[15]}$. Likewise, taking the first number $\\alpha_{[i,j]}$ in each block parameter where possible we have $\\alpha_{[23]}=\\lambda_{23}\\alpha_{[22]}$, $\\alpha_{[25]}=\\lambda_{25}\\alpha_{[22]}$ and $\\alpha_{[26]}=\\lambda_{26}\\alpha_{[22]}$, and so for each of the values $i=1,2,3,4,5$. For $i=6$ and for $i=7$, $s_i=3$ and immediately we find that $\\alpha_{[6i]}=\\alpha_{[7j]}=0$ for each $j$ with $m_{6j}=1$ or $m_{7j}=1$.\n\nNow repeat this procedure with numbers $\\alpha_{[i,j]}$ with $j$ fixed. Thus $\\alpha_{[51]}=\\mu_{51}\\alpha_{[41]}$, $\\alpha_{[61]}=\\mu_{61}\\alpha_{[41]}$, and $\\alpha_{[71]}=\\mu_{71}\\alpha_{[41]}$, and likewise, for $j=2$. For $j=3,4,5,6,7$ and $8$, only three values of $i$ corresponds to each values of $j$, because the associated faces $A_j$ are triangles. So all $\\alpha_{[ij]}=0$ for all $i,j$ with $m_{i,j}=1$, as in general case.\n\n\n\\begin{obs}\n{\\label{obs1}}\n{\\rm It is worth mentioning that Richter-Gebert in (\\cite{RG}, Section 13.3) by fixing a suitable affine basis, proves that the realization space of a polyhedron $P$ (denoted there by $\\mathcal{R}(P)$), that is the space of coordinatization for the combinatorial type of $P$ with $e(P)=e$ edges is a smooth open manifold of dimension $e-6$, module the natural action of $12$-dimensional affine transformation group.\n\nThe realization space $\\mathcal{R}(P)$ is understood as a subspace of $\\mathbb{E}^{3n}$ by identifying the $3n$ coordinates of the $n$ vertices of $P$ with points in $\\mathbb{E}^{3n}$. It is described by the set of all solutions of a collection of polynomial equations and inequalities with integer coefficients. Such sets are called a simple semi-algebraic variety.\n\nFurthermore, by fixing an affine basis in the definition of the realization space, one makes sure that the ``reflection'' (mirror images in Steinitz's proof) do not create a second component of the realization space. Therefore, $\\mathcal{R}(P)$ is indeed (path connected) \\emph{contractible} and has \\emph{Steinitz's isotopy property} (\\cite{Stei}, Section 69), i. e., any two realizations $P_1$ and $P_2$ of $P$ can be continuously deformed into each other while maintaining the same structure throughout.\n\nHowever, our approach to study the face type manifold $[P]$ of a polyhedron $P$, is different. Although the realization spaces such as $[P]$ are usually (for example as above) defined modulo affine or Euclidian groups, in this work we consider $[P]\/Sim(3)$, $[P]$ modulo similarity group. The topology of the realization space $[P]$ of polyhedron $P$ is induced by Hausdorff metric, and since the action $Sim(3)$ on $[P]$ is not (fixed-point) free (see Section 3 for definition), the manifold $[P]\/Sim(3)$ is not contractible, and clearly does not satisfy Steinitz's isotopy property.}\n\\end{obs}\n\n\\begin{obs}\n{\\rm Let $P$ be a polyhedron with $f_0(P)=r$ vertices, $f_1(P)=e$ edges and $f_2(P)=s$ faces. Consider the face type $[P]$ of $P$. In (\\cite{Rob}, p. 75) the dimension of this manifold is given by\n$$\\dim [P]=3(r+s)-\\mu(P) \\, .$$\n\nAn intuitive derivation of the above formula may be given as follows. If the vertices and faces of $P$ were allowed to move independently in $\\mathbb{E}^3$ then, they would have $3(r+s)$ \\emph{degrees of freedom} (see page 11 for definition). But they are not independent. In fact for each incidence relation $(v,F)\\in F_0(P)\\times F_2(P)$ with $v \\in F$, the whole space loses one degree of freedom. Hence we have\n$$\\dim[P] \\, = \\, 3(r+s)-\\mu(P)\\,.$$\n\n\nNote that, since the similarity Lie group $Sim(3)$ has dimension 7, by Euler's formula\n\\vspace*{-.1cm}\n$$n-e+f=2 \\: , $$\n\n\\vspace*{-.1cm}\n\\noindent we have\n\\vspace*{-.1cm}\n$$\n\\begin{array}{rcl}\n \\dim \\left( [P]\/Sim(3) \\right) & = & 3(r+s)-\\mu(P)-7 \\\\\n \\, & = & 3(r+s)-2e-7 \\, = \\, e-1\\, (\\text{\\small{\\emph{module similarities}}}) \\, .\n\\end{array}\n$$}\n\\end{obs}\n\n\n\\section{Symmetry type of a polyhedron and stratifications}\n\n{\\bf Transformation groups}\n\n\\vspace*{0,2cm}\n\\indent We start this Section by introducing some basic definitions and standard results from the theory of Lie groups acting on smooth manifolds and refer the reader to \\cite{Dei} and \\cite{Kaw} for more details.\n\n\\vspace*{0,2cm}\nLet $G$ be a Lie group, with identity element $e$, and $M$ a smooth manifold. A smooth action of $G$ on $M$ is a $C^{\\infty}$ mapping:\n\\vspace*{-0,2cm}\n$$\\phi: G\\times M \\rightarrow M \\: , \\:\\: \\phi(g,x)=\\phi_g(x)=g(x) \\, ,$$\nsuch that\n$$\n\\begin{array}{rcll}\n e(x) & = & x, & x\\in M \\, ,\\\\\n (g_1g_2)(x) & = & g_1(g_2(x)), & g_1, g_2 \\in G \\, , \\: x\\in M \\, .\n\\end{array}\n$$\n\n\nIn this case we say that $M$ is a $G$-manifold. For any $x\\in M$ the subgroup\n\\vspace*{-.2cm}\n$$G_x=\\{g \\in G \\mid g(x)=x\\} \\, ,$$\nof $G$ is called the \\emph{stabilizer} or \\emph{isotropy} subgroup at $x$.\n\nThe set $ \\, G(x)=\\{ g(x) \\mid g\\in G \\} \\,$ is called the \\emph{$G$-orbit} of $x$. The \\emph{orbit space} of the action of $G$ on $M$ is the space $M\/G$, the space of all $G$-orbits endowed with the quotient topology given by canonical projection\n\\vspace*{-.2cm}\n$$\n\\begin{array}{ccccl}\n \\pi & : & M & \\rightarrow & M\/G\\\\\n & & x & \\mapsto & G(x)\n\\end{array} \\, ,\n$$\n\\noindent and the differentiable structure of $M\/G$ is induced by the same structure of $M$.\n\n\nThe action is called \\emph{free} if, for each $x\\in M$, $ \\: G_x=\\{ e \\} \\, $ .\n\nIf a Lie group $G$ acts on a smooth manifold $M$ via $\\phi$, we call $(M,G):=(M,G,\\phi)$ a transformation group, and $M$ is said to be a $G$-manifold.\n\nNow, for each polyhedron $P$, a symmetry of $P$ is a rigid transformation (or self isometry) $f:\\mathbb{E}^3\\rightarrow \\mathbb{E}^3$ such that $f(P)=P$. Any such symmetry maps vertices to vertices, edges to edges and faces to faces and preserves inclusions (incidences). Hence any symmetry induces an automorphism on $F(P)$. The set $G(P)$ of all symmetries of $P$ is a finite subgroup of the Euclidean group $\\mathbb{E}(3)$ acting on $F(P)$ as a group of automorphisms. We may assume that the centroid is $O$, so $G(P)$ is a finite subgroup of the orthogonal group $\\mathcal{O}(3)$. If a finite subgroup $G$ of $\\mathcal{O}(3)$ is the symmetry group of a convex polyhedron $P$, we also call $P$ a $G$-polyhedron.\n\n\n\n\\begin{defi}\nTwo polyhedra $P$ and $Q$ are \\emph{symmetry equivalent}, and write $P\\cong Q$, if there is an isomorphism\n$$\\lambda: F(P)\\rightarrow F(Q)$$\n\n\\noindent of the face lattices and some isometry $f:\\mathbb{E}^3 \\rightarrow \\mathbb{E}^3$ such that for all\n$g \\in G(P)$ and all $x \\in F(P)$,\n$$\\lambda(gx)=(f g f^{-1})(\\lambda(x)) \\, .$$\n\\end{defi}\n\nIf we further assume that $P$ and $Q$ are both $G$-polyhedra, that is having the same (rather than conjugate) subgroups then, with the above condition, we say that $P$ and $Q$ are $G$-equivalent. Hence, in this case\n$$\\lambda(gx)=g\\lambda(x) \\, .$$\n\n\\begin{defi}\nLet $P$ be a $G$-polyhedron. The symmetry type $\\langle P \\rangle$ of $P$ is defined by\n$$\\langle P \\rangle = \\{ Q \\, | \\, Q \\text{ is $G$-polyhedron and $Q$ is $G$-equivalent to $P$}, \\,\\, Q\\cong P \\} \\, . $$\n\\end{defi}\n\nNow consider $\\mathscr{P}$ the space of all convex polyhedra in $\\mathbb{E}^3$. Since the subdivision of $\\mathscr{P}$ into face types and symmetry types both respect the Euclidian similarities, it is convenient to look at the action of the similarity group $Sim(3)$ on the space $\\mathscr{P}$ of all polyhedra in $\\mathbb{E}^3$. This action partitions the quotient space\n$$ \\, \\mathscr{S}:=\\mathscr{P}\/Sim(3) \\, ,$$\n\n\\vspace*{-.2cm}\n\\noindent of similarity classes or ``shapes'' of polyhedra, into orbit types, where each orbit type consists of all those orbits on which the isotropy subgroups at any polyhedron in the orbit are conjugate.\n\nThus the symmetry types partitions each $[P]$ into mutually disjoint subsets refining the partitions of $\\mathscr{S}$ into face types.\n\nBut the isotropy subgroup at $P$ is just the symmetry group $G(P)$ itself. It follows that the symmetry types are composed of components of the orbit types. The principal orbit type corresponds to the trivial isotropy subgroup, that is to say to the symmetry type of any polyhedron $Q$ in $[P]$ with trivial symmetry group $G(Q)=\\{ e \\}$. This type is open in $[P]\/Sim(3)$ of dimension $e(P)-1$. All other symmetry types have lower dimensions.\n\nAs an example, let $P$ be a polyhedron combinatorially equivalent to cube. Then, $[P]\/Sim(3)$ has dimension 11. The principal orbit type corresponds to the realization space of polyhedron $Q\\approx P$ with trivial group, is an open and dense submanifold of $[P]\/Sim(3)$ of dimension $e(Q)-1=11$.\n\nNow, since each orbit type is a submanifold of $[P]$, by the Slice Theorem of transformation groups (see \\cite{Kaw}, Th. 4.11, and \\cite{Rob}, p.42), we have the following well known theorem.\n\n\n\n\\begin{teo} {\\rm (in \\cite{Rob}, p. 42 and \\cite{Ros2})} Let $P$ be a polyhedron in $\\mathbb{E}^3$. Then, $\\langle P \\rangle$, the symmetry type of $P$, is a smooth manifold.\\end{teo}\n\nIn order to study the dimension of the symmetry type of $P$, $\\dim \\langle P \\rangle$, we simply study those polyhedra $Q$ that lie in some neighborhood of $P$ in $\\mathscr{S}$ and are symmetry equivalent to $P$. We can simplify our discussion by restricting our attention to those $Q$ whose symmetry group is not merely conjugate to $G(P)$ but $G(P)$ itself. Along this restriction we also factor out components that came from similarities. In this way, we may determine the value of $\\dim \\langle P \\rangle$. We first look at a simple example again.\n\nLet $P$ be the right pyramid over a square (Figure \\ref{image1}) with symmetry group $G(P)$ which is dihedral reflection group.\n\n\nSuppose we fix the group $G = G(P)$. Then, vertex $v_1$ can be chosen only on the axis of $G$. Therefore it has one degree of freedom (see page 11). Likewise $v_2$ must lie on reflection plane, hence has only two degrees of freedom. Having chosen $v_2$, the vertices $v_3$,$v_4$ and $v_5$ which are on the same orbit of $v_2$ , have no degree of freedom at all, since they are determined by our choice of G and $v_2$ . Hence the vertices have a total of $1 + 2 = 3$ degrees of freedom. Similarly for the faces, each triangular face or equivalently the plane that contains it has only two degrees of freedom in the space of affine plane in $\\mathbb{E}^3$, since each plane is invariant under a reflection element of $G$. But the square face has only one degree freedom, because it is orthogonal to the axis of rotation of $G$. Therefore the faces have just $2 + 1 = 3$ degrees of freedom. Of course the faces and vertices can not be chosen independently of one another. The incidence of $v_1$ with respect to any of the four triangular faces adjacent to it, determines the incidence of that vertex to the other three faces under the action of $G$. Hence $v_1$ has only one ``independent'' incidence. The vertices $v_2$, $v_3$, $v_4$ and $v_5$ are in the same $G$-orbit and each one is incident with two triangles and one square faces. Take one of them say $v_2$. There is a reflection which fixes $v_2$ and sends adjacent triangular faces each one to the other. Thus the number of independent multiplicity (to be defined later) of $P$ is $1 + 2 = 3$. Each such incidence relation in the form of the condition that a vertex lies in a particular face, reduces the dimension of the symmetry type by one. Now, if we take into account the fact that the center of $P$ can be chosen only on the fixed point set of $G$, which is one dimensional and considering also the dilation of $P$ which in each case reduces $\\dim\\langle P\\rangle $ by one, we get\n$$\\dim\\langle P\\rangle \\, = \\, (1 + 2) + (1 + 2) - (1 + 2) - 2 \\, = \\, 1 \\, .$$\n\n\\vspace*{.5cm}\n{ \\begin{figure}[!htb] \\centering\n\\vspace*{-.5cm} \\hspace*{.75cm} \\includegraphics[scale=.25]{figura1.eps}\n\\vspace*{-0,4cm}\\caption{$\\,$}\n\\label{image1}\n\\end{figure}}\n\n\\vspace*{.5cm}\nIn fact, in Figure \\ref{image1} or in any right pyramid with a regular polygon as base, if we denote the height and radius of the base of P by $h$ and $r$, respectively and consider the ratio $\\zeta=\\frac{h}{r}$ the two such pyramids are similar if they have the same ratio $\\zeta$. Therefore we can parameterize the symmetry type of $P$ by $\\zeta$ with $0<\\zeta$. Hence $\\langle P \\rangle$ has the structure of the open interval and $\\dim \\langle P\\rangle = 1$. Indeed the action of $G(P)$ on $F(P)$ has $\\epsilon=2$ edge orbits. Hence\n$\\dim \\langle P\\rangle =\\epsilon -1 = 1$. Note that in the right pyramid with regular base the ratio $\\zeta$ is similarity invariant and the symmetry type is a connected 1-manifold with boundary 0-dimensional symmetry types one for regular base and the other a segment (1-polytope). The following figure illustrates this idea.\n\n\\vspace*{.5cm}\n\\begin{center}\n\\begin{tikzpicture}[scale=0.5]\n\\filldraw [black] (0,0) circle (2pt)\n (15,0) circle (2pt);\n\\draw (15,0) -- (0,0) node[above] {\\small{$h\\rightarrow 0$}};\n\\draw (15,0) -- (0,0) node[below] {\\footnotesize{regular base}};\n\\draw (0,0) -- (15,0) node[above] {\\small{$r\\rightarrow 0$}};\n\\draw (0,0) -- (15,0) node[below] {\\footnotesize{1-polytope}};\n\\end{tikzpicture}\n\\end{center}\n\n\\begin{center}\n\\vspace*{-1.1cm} {\\footnotesize{right pyramid}}\n\\end{center}\n\n\n\\begin{center}\n{ \\begin{figure}[!htb] \\centering\n\\vspace*{-.55cm} \\includegraphics[scale=.075]{extra.eps}\n\\caption{$\\,$}\n\\label{extra}\n\\end{figure}}\n\\end{center}\n\n\\vspace*{-1cm}\nThe idea of this example can be applied in general to find the dimension of the symmetry type of any polyhedron $P$.\n\nLet us denote by $F_0(P), F_1(P)$ and $F_2(P)$ the set of all vertices, edges and faces of $P$, respectively, with symmetry group $G=G(P)$.\n\n\\begin{defi}\\label{TwoP}\nTwo ordered pairs $(v, F)$ and $(v', F')$ in $F_0(P)\\times F_2(P)$ are called $G$-independent incidences or simply independent incidences, if and only if, there exists no $g \\in G$ such that $g(v)=v'$ and $g(F)=F'$. By $\\mu_*(P)$ we mean the number of $G$-independent incidences $(v, F)$ where $v \\in F_0(P)$, $F \\in F_2(P)$ and $v \\in F$. Therefore $G(P)$ acts on the set of all such incident pairs $(v, F)$ with $\\mu_*(P)$ orbits of independent incidences or ``incident orbits''.\n\\end{defi}\n\nFor example let $P$ be a rhombic dodecahedron (Figure \\ref{f2}) then, $\\mu(P) = 2e = 48$ but $\\mu_*(P)=2$.\n\\vspace*{-.25cm}\n{ \\begin{figure}[!htb] \\centering\n\\hspace*{.5cm} \\includegraphics[scale=0.15]{figura2.eps}\n\\vspace*{-0,4cm}\\caption{$\\,$}\n\\label{f2}\n\\end{figure}}\n\n\\vspace*{.5cm}\n{\\bf Stratifications}\n\n\n\nFor a transformation group $(M,G)$ the structure of the orbit space $M\/G$ usually is complicated, for example it is not necessarily a manifold.\n\nHowever, when the Lie group $G$ is compact and the manifold $M$ is without boundary it can be shown that they are stratified into smooth manifolds.\n\nWe now describe briefly a stratification of $\\mathbb{E}^3$ associated with a finite subgroup $G$ of the orthogonal group $O(3)$ that will help us to understand the relationship between the action of $G$ and the number $\\dim \\langle P\\rangle $.\n\n\\begin{defi} ({\\rm Stratification}) Let $X$ be a topological subspace of some Euclidian space $\\mathbb{E}^3$.\n\nA partition $\\sum=\\{M_i \\, | \\, i=1,\\ldots,k\\}$ of (pairwise disjoint) subsets of $X$ is called a stratification of $X$ if $\\sum$ satisfies the followings:\n\n\\begin{enumerate}\n\\item Each $M_i,\\,i=1,\\ldots,k$ is a connected smooth submanifold of $\\mathbb{E}^3$, called a \\newline $\\sum$-stratum.\n\n\\item For each $i$, the closure $\\overline{M_i}$ is the union of $M_i$ and the $M_j$'s with lower dimensions than the dimension of $M_i$, that is, the relative closure $X \\cap \\overline{M_i}$ is the union of elements of $\\sum$, one being $M_i$ itself and the others being of dimension less than the $\\dim M_i$.\n\n\\end{enumerate}\n\nThis condition is called {\\it frontier condition}. The dimension $\\dim(X)$ is\n\\vspace*{-0.4cm}\n$$\\max \\{\\dim(M_i) \\mid i=1,\\ldots,k\\} \\ .$$\n\\end{defi}\n\nThe stratification mainly is done by the help of the theorem so called Slice Theorem which is fundamental in studding the structure of the transformation groups (see \\cite{Kaw}, Th. 4.11).\n\n\\vspace*{.25cm}\nLet $X=\\mathbb{E}^3$ and $G$ a compact subgroup of $O(3)$, acting via $\\phi$ on $\\mathbb{E}^3$ as above.\nFor each $x \\in \\mathbb{E}^3$ let $\\: \\: G_x = \\{g \\in G \\mid g(x)=x\\} \\: \\:$ be the isotropy subgroup of $G$ at $x$ and $F_x=Fix \\left( G_x \\right)$ be the set of all fixed points of $G_x$. Thus\n\\vspace*{-0,2cm}\n$$F_x=\\{y \\in \\mathbb{E}^3 \\mid \\text{ for all } g \\in G_x, \\, g(y)=y\\} \\, .$$\n\n\nSince $x \\in F_x$ and for all $g \\in G$, $g(0)=0$ and for $y,z$ in $F_x$ and $\\lambda,\\mu\\in \\mathbb{R}$ we have $g(\\lambda y+\\mu z)=\\lambda y+\\mu z$, $F_x$ is a linear subspace of $\\mathbb{E}^3$. Define an equivalence relation $\\sim_{G}$ on $\\mathbb{E}^3$ as follows. Put $x\\sim_{G} y$ if $F_x=F_y$.\n\nNow let $x \\in \\mathbb{E}^3$ and $y \\in F_x$. If $F_x=F_y$ then, $y \\in [x]$, the equivalence class of $x$ in $\\sim_{G}$. However $y \\in F_x$ implies that $F_y \\subseteq F_x$, since $G_x \\subseteq G_y$. Thus $ F_y$ is a linear subspace of $F_x$. For $z\\in \\mathbb{E}^3-F_x$, we cannot have $z \\sim_{G} x$. Therefore $[x]=\\{y\\in F_x: F_y=F_x\\}$. So $[x]$ is complement in $F_x$ of finitely many subspaces of $F_x$. Hence the equivalence classes $[x]$, $x \\in \\mathbb{E}^3$ stratify $\\mathbb{E}^3$ with finitely many such strata (orbit types) (see \\cite{Dei}, Th. 5.11). The dimension of $F_x$ denoted by $\\delta(x)$ is called the \\emph{degree of freedom} of $x$.\n\nFor example, let $G$ be a group generated by rotation matrix\n$$A=\\left(\n \\begin{array}{ccc}\n \\cos(\\theta) & -\\sin(\\theta) & 0 \\\\\n \\sin(\\theta)& \\cos(\\theta) & 0 \\\\\n 0 & 0 & 1 \\\\\n \\end{array}\n \\right),\\,\\,\\,\\,\\,\\,\\,\\theta=\\frac{2\\pi}{n},\n$$\n\n\\noindent about $z$-axis through $\\theta$.\n\nThen, there are just four strata under this group action on $\\mathbb{E}^3$, namely the $0$-stratum $\\{0\\}$, the open rays $x=y=0, z>0$ and $x=y=0, z<0$, and the complement of the $z$-axis.\n\nNow let $P$ be a polyhedron with $G=G(P)$ some finite subgroup of $\\mathcal{O}(3)$ and $Q\\in \\langle P \\rangle$ as above.\nUnder the restriction imposed on $Q$ within the symmetry type of $P$, each vertex of $Q$ may be moved along a line, or within a plane, or in any direction in $\\mathbb{E}^3$ near (without changing the symmetry type) its initial position in $P$ itself, having one, two or three degrees of freedom. Likewise, each face $F$ of $Q$ may have one, two or three degrees of freedom close to the corresponding face of $P$, according as F intersects a 1-stratum in an interior point of $F$ (necessarily at right angles), or intersects a 2-stratum in the interior of $F$ (again at right angles), or neither of these.\n\nConsidering the action of $G=G(P)$ on $F(P)$, let\n$$\n\\overline{v} \\, = \\, G(v) \\, = \\, \\left\\{ g(v) \\, \\mid \\, g\\in G \\right\\} \\: , \\: \\: \\overline{F} \\, = \\, G(F) \\, = \\, \\left\\{ g(F) \\, \\mid \\, g\\in G \\right\\} \\, , \\, \\,\\, \\text{ and }\n$$\n\\vspace*{-.6cm}\n$$\nF_0(P)\/ G \\, = \\, \\left\\{ \\overline{v} \\, \\mid \\, v\\in F_0(P) \\right\\} \\: , \\: \\: F_2(P)\/ G \\, = \\, \\left\\{ \\overline{F} \\, \\mid \\, F\\in F_2(P) \\right\\}\n$$\n\n\\noindent be the collection of orbits under the action of $G$ on face lattice $F(P)$.\n\nDefine $\\mathcal{M}_P$ to be the set of all pairs $(v,F)\\in F_0(P) \\times F_2(P)$ for which $v\\in F$ and\n\\vspace*{-.2cm}\n$$\n\\mathcal{M}_P\/ G \\, = \\, \\left\\{ \\left( g(v), g(F) \\right) \\, \\mid \\, g\\in G \\text{ and } (v,F)\\in \\mathcal{M}_P \\right\\} \\, .\n$$\n\nClearly $\\mu (P)$ and $\\mu _{*}(P)$ are the cardinalities of $\\mathcal{M}_P$ and $\\mathcal{M}_P\/ G$, respectively.\n\n\\vspace*{.2cm}\nWe observe that if $v,u\\in F_0(P)$ and $\\overline{v}=\\overline{u}$ then,\n\\vspace*{-.2cm}\n$$\n\\dim(v) \\, = \\, \\dim(u) \\, .\n$$\n\n\\vspace*{-.2cm}\nTherefore we can define the fixed dimensions $\\delta (\\xi)$, $\\xi \\in F_0(P)\/ G$, $\\delta (\\xi)=\\dim(v)$, the degree of freedom of $v$ for an arbitrary $\\overline{v}\\in \\xi$.\nThe same holds for $\\delta (\\zeta)$, $\\zeta \\in F_2(P)\/ G$.\n\nOur aim is to count the number of the vertices and the faces with dimensions $k=1,2,3$ and then, by subtracting the independent incidences $\\mu _{*}(P)$, express the $\\dim \\langle P \\rangle$ in terms of edge orbits alone.\n\n\n\n\n\n\n\\section{Fundamental regions and main theorem}\n\nIn this Section we consider finite subgroups of isometries which are generated by reflections namely $[q], [2, q], [3, 3], [3, 4]$ and $[3,5]$ (Table 2).\n\n\\vspace*{.2cm}\nThe finite subgroups of $\\mathbb{E}(3)$ which are generated by reflections in the plane, are given in the following table. They are called reflection groups, for obvious reason.\n\\begin{center}\n\\vspace*{-.2cm} {\\small{\\begin{tabular}{|c|l|c|}\n \\hline\n \n Symbol & description & order \\\\\n \\hline\n & $q=1$: One plane of reflection; $q\\geq 2$: $q$ equally inclined planes & \\\\\n $[q],q\\geq 1$ & of reflection passing through a $q$-fold axis of rotation, dihedral \\hspace{11cm}& $2q$ \\\\\n & reflection group. & \\\\\n \\hline\n & $q$ equally inclined planes of reflection passing through a $q$-fold & \\\\\n $[2,q]$ & axis of rotation and reflection in a equatorial plane. & 4q \\\\\n & $q$ 2-fold axes of rotation. The group of $q$-prism.& \\\\\n\\hline\n $[3,3]$ & Four 3-fold and three 2-fold axes. Six planes of reflection. & \\\\\n & Symmetry group of the regular tetrahedron. & 24 \\\\\n\\hline\n & Three 4-fold and four 3-fold and six 2-fold axes of rotation. & \\\\\n $[3,4]$ & Nine planes of reflection. Symmetry group of the cube. & 48 \\\\\n\\hline\n & Six 5-fold, ten 3-fold and fifteen 2-fold axes of rotation. & \\\\\n $[3,5]$ & Fifteen planes of reflection. Symmetry group of the icosahedron. & 120 \\\\\n \\hline\n\\end{tabular}}}\n\\end{center}\n\n\\begin{center} Table 2: Reflection groups \\end{center}\n\nIt is well known that the \\emph{fundamental region} $\\Delta$ for the action of $[3, 3], [3, 4], [3, 5]$ and $[2, q]$ on the sphere $S^2$ are spherical triangles [1]. For $[q]$ the dihedral reflection group generated by two reflections, the fundamental region is a ``lune'' of angle $\\frac{\\pi}{q}$.\nWe may use the fundamental region of a reflection group to construct a stratification of $\\mathbb{E}^3$. For instance consider the tetrahedron $OABC$ (or its spherical projection) as a fundamental region of [3, 4] in Figure \\ref{f3} (\\cite{Rob}, p. 81).\nWe take the origin $O$ as a $0{\\text{-stratum}}$. By removing the origin from the rays $OA$, $OB$ and $OC$ we get three $1{\\text{-strata}}$, the interiors of the region $AOB$, $AOC$ and $BOC$ are $0{\\text{-strata}}$.\n\nFinally, the interior points of $\\mathbb{E}^3$ bounded by sectors $AOB$, $AOC$ and $BOC$ is $3{\\text{-strata}}$. By transferring these strata under the action of [3, 4] we obtain the required stratification of $\\mathbb{E}^3$.\n\nFor other reflection groups a stratification of $\\mathbb{E}^3$ is constructed in analogues fashion.\nReturning to our main problem, we now consider the following notion. Suppose that a finite reflection group $G$ in $\\mathcal{O}(3)$ has its fundamental region a spherical triangle $\\Delta$ and, let $P$ be a polyhedron with $G(P)=G$.\n{ \\begin{figure}[!htb] \\centering\n\\hspace*{.5cm} \\includegraphics[scale=.2]{figura3.eps}\n\\vspace*{-0,4cm}\\caption{$\\,$}\n\\label{f3}\n\\end{figure}}\n\nWe denote by $\\Delta_p$ that portion of the surface of $P$ (namely those vertices, edges and subpolygonal faces) which lie within $\\Delta$, and call $\\Delta_p$ a \\emph{basic region} of $P$ (Figure \\ref{f4}).\n{ \\begin{figure}[!htb] \\centering\n\\hspace*{.75cm} \\includegraphics[scale=.225]{figura4.eps}\n\\vspace*{-0,4cm}\\caption{$\\,$}\n\\label{f4}\n\\end{figure}}\n\n\\vspace*{-0.1cm}\nHence $\\Delta_p$ is a simple closed planar polygonal region. If $n_{\\Delta_p}, e_{\\Delta_p}$ and $f_{\\Delta_p}$ denote the total number of distinct vertices, edges and subpolygonal faces of $\\Delta_p$ respectively then, from Euler's formula by stereographic projection we get\n\\vspace*{-0,2cm}\n$$n_{\\Delta_p} - e_{\\Delta_p} + f_{\\Delta_p}=1 \\, .$$\n\nAs illustrated example, let $P$ be truncated cuboctahedron (Figure \\ref{f4}) with symmetry group $G(P)=[3,4]$ of cube, with order 48. Thus the basic region $\\Delta_p$ has $n_{\\Delta_p}=7$, $e_{\\Delta_p}=9$, $f_{\\Delta_p}=3$, and $n_{\\Delta_p} - e_{\\Delta_p} + f_{\\Delta_p}=7-9+3=1$.\n\n\\vspace*{0.1cm}\nNow having our necessary tools, we are in the position to state and prove our main theorem in this Section.\n\n\\begin{teo}Let $G$ be a finite reflection group in $\\mathbb{E}(3)$ and $P$ a polyhedron with $G(P)=G$. Then, $\\dim \\langle P\\rangle= \\epsilon-1$ , where $\\epsilon$ is the number of edge orbits of the action of $G$ on the set of edges of $P$.\n\\end{teo}\n\nFirst we prove the following lemma.\n\n\\begin{lema} Assuming the hypothesis of the theorem, let $\\Delta_p$ be a basic region for $P$ such that the corners of fundamental region of $\\Delta$ of $G$ are vertices of $P$ (see Figure \\ref{f5}). Then, the number of incident pairs of vertices and faces of $\\Delta_p$, the multiplicity $\\mu(\\Delta_p)$ of $\\Delta_p$ is given by $\\mu(\\Delta_p)=2e-\\beta$ where $e$ is the total number of edges of $\\Delta_p$ and $\\beta$ the number of vertices on the boundary of the fundamental region.\n\\end{lema}\n{\\bf Proof:} By adjoining an extra face, say $K$, to $\\Delta_p$, namely the complement of $\\Delta_p$ itself with respect to the sphere we get a map $M_P$ on sphere. But the number of edges (and vertices) of $M_P$ is equal to the number of edges (and vertices) of $\\Delta_p$. Hence $\\mu(\\Delta_p)=2e$. Since there are $\\beta$ vertices on boundary of $\\Delta$, with respect to that extra face $K$, we have $\\mu(\\Delta_p)=\\mu(P)-\\beta=2e - \\beta$.\n\\vspace*{-0.3cm}\n{ \\begin{figure}[!htb] \\centering\n\\hspace*{.75cm} \\includegraphics[scale=.15]{figura5.eps}\n\\vspace*{-0,1cm}\\caption{$\\,$}\n\\label{f5}\n\\end{figure}}\n\n\\vspace*{-0,5cm}\n$\\:$ \\hfill$\\square$\n\n\\vspace*{-0,1cm}\n \\begin{obs}\n {\\rm Let $P$ be a polyhedron and $v$ a vertex of $P$. According to stratification of $\\mathbb{E}^3$ with reflection group $G(P)$ of $P$, $\\delta(v)$, the degree of freedom of $v$, is one, two or three if $v$ is on a corner or side or within the interior of $\\Delta_p$ respectively. Similarly if a face $F$ of $P$ has as its interior point a corner of $\\Delta$ then, $\\delta(F)=1$. If $F$ is orthogonal to a side of $\\Delta_p$ or lies inside $\\Delta_p$ then, $\\delta(F)=2 $ or 3, respectively.}\n \\end{obs}\n\n \\noindent {\\bf Proof of theorem:} Let $\\eta(1),\\eta(2)$ and $\\eta(3)$ be the number of vertices of $\\Delta_p$ and $\\phi(1),\\phi(2)$ and $\\phi(3)$ the number of faces of $\\Delta_p$ with one, two and three degrees of freedom respectively.\n\n First we assume that $P$ has no face $F$ with $\\delta(F)$ equal to one or two. Then, $\\mu_*(P)=\\mu(\\Delta_p)=2e-\\beta$. After factoring out the effect of dilation we get\n $$\n \\begin{array}{rcl}\n \\dim\\langle P \\rangle & = & 1\\eta(1)+2\\eta(2)+3\\eta(3)+3\\phi(3)-\\mu(\\Delta_p)-1 \\\\[8pt]\n & = & 3(n_{\\Delta_p}+\\phi_{\\Delta_p})-2e_{\\Delta_p}-4 \\, ,\n \\end{array}$$\n\\noindent since $\\eta(1)=3$.\n\nBut $n_{\\Delta_p}+\\phi_{\\Delta_p}=e_{\\Delta_p}+1$. Hence,\n$$\\dim\\langle P \\rangle=3(e_{\\Delta_p}+1)-2e_{\\Delta_p}-4= e_{\\Delta_p}-1 \\, .$$\n\\noindent Now clearly the number of edges of $\\Delta_p$ is exactly the number of edge orbits of $P$. Therefore the theorem follows in this case.\n\nNext suppose $P$ has one face $F$ with $\\delta(F)=1$. This means that there is a face $F$ such that one corner say $v$ of $\\Delta_p$ is an interior point of $F$.\n\nLet $P'$ be a polyhedron which we get, by changing ``fake'' edges in $\\Delta_p$ into real ones (see Figure \\ref{f6}). This is done as follows.\n\nWe remove the constraint that the plane of $F \\cap \\Delta_p$ is perpendicular to the ray $ov$. The edges of $F \\cap \\Delta_p$ that lie in the boundary of $\\Delta_p$ are also edges of $P'$ where $P'$ has a basic region $\\Delta_{p'}$ , say, with the same combinatorial structure as $\\Delta_p$, and with vertices arbitrarily close to those of $\\Delta_p$.\nSince we have substituted a face with one degree of freedom by a face with three degrees of freedom and since the new vertex and its incidence cancel each other and hence do not effect our calculation for $ \\dim\\langle P \\rangle $, we have $ \\dim\\langle P' \\rangle = \\dim\\langle P \\rangle + 2$. But $\\dim\\langle P' \\rangle = \\epsilon'-1$ and $\\epsilon'=\\epsilon+2$ with obvious notations. Hence\n$$\\dim\\langle P \\rangle = \\dim\\langle P' \\rangle -2= \\epsilon'-1-2=\\epsilon-1 \\, .$$\n\nThe process will continue if $P$ has two or three (on other corners of $\\Delta$) faces of degree one.\n{ \\begin{figure}[!htb] \\centering\n\\vspace*{-.5cm} \\hspace*{.25cm} \\includegraphics[scale=.175]{figura6.eps}\n\\vspace*{-0,4cm}\\caption{\\small{\\emph{Broken lines represent ``fake'' edges and ``o'' a fake vertex.}}}\n\\label{f6}\n\\end{figure}}\n\nHere we remark that, in the process of changing ``fake'' edges in $\\Delta_p$ into real ones, since the transforms of $\\Delta_{p'}$ under corresponding group action is a polyhedral graph (planar and 3-connected), by the Theorem of Steinitz \\cite{Stei}, there exists a polyhedron P' which geometrically realizes $\\Delta_{P'}$.\n\n\\vspace*{.25cm}\nFinally, suppose $P$ has a face $F$ with $\\delta(F)=2$ (Figure \\ref{f7}). We construct $P'$ by adjoining the fake edge to $\\Delta_p$. Then, $\\dim\\langle P' \\rangle $ differs by one from\n$\\dim\\langle P \\rangle$ for replacement of a face with two degrees of freedom, by a face of three degrees of freedom. Hence\n$\\dim\\langle P' \\rangle = \\dim\\langle P \\rangle +1$. Because of $\\dim\\langle P' \\rangle =\\epsilon'-1$ and $\\epsilon=\\epsilon'-1$, we have\n\\vspace*{-0,3cm}\n$$\\dim\\langle P \\rangle +1=\\epsilon'-1=\\epsilon\\,\\,\\,\\,\\text{ and }\\,\\,\\,\\, \\dim\\langle P \\rangle =\\epsilon-1 \\, .$$\n\n{ \\begin{figure}[!htb] \\centering\n\\hspace*{.25cm} \\includegraphics[scale=.175]{figura7.eps}\n\\vspace*{-0,4cm}\\caption{$\\,$}\n\\label{f7}\n\\end{figure}}\n\n\\newpage\nNow this inductive process can be continued if $P$ has any number of faces with two degrees of freedom. In each step of construction, $\\dim\\langle P \\rangle$ and $\\epsilon$ each increase by one, while the operation leaves every other quantity in our calculation fixed. For the case of the group $[q]$ where $\\Delta$ is a ``lune'', the proof proceeds in similar way and is omitted here to avoid repetition. The proof of the theorem now is complete. \\hfill$\\square$\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nIn Sun-as-a-star helioseismology, it is common practice to fix the amplitude ratios between the $m$-components of the $l = 2$ and 3 multiplets (the so-called $m$-amplitude ratio) during the peak-fitting procedure when estimating the p-mode characteristics (e.g., Salabert et al. [1]), while the amplitudes of the $l=1$, 2, and 3 modes relative to the $l=0$ modes are left free (the so-called mode visibility). However, in asteroseismology, the mode visibilities are fixed to theoretical values due to lower signal-to-noise ratio (SNR) and shorter time series, and the $m$-amplitude ratios are expressed as a function of the inclination of the rotation axis only (Appourchaux et al. [2]; Garc\\'\\i a et al. [3]). In both cases, they are supposed to not depend on the star magnetic activity. However, in the near future, this situation could change when stellar activity cycles will be measured in asteroseismic targets (e.g., Garc\\'\\i a et al. [4]). After several years of observations collected by the Kepler mission, the SNR will be high enough to measure these parameters in a wide range of solar-like stars in the HR diagram at different evolution stages (Bedding et al. [5]; Chaplin et al. [6]). Moreover, simultaneous observations from SONG (Doppler velocity) and Kepler (intensity) will be extremely useful to better understand stellar atmospheres. \nAlthough, variations with the height in the solar atmosphere at which the measurements are obtained have been observed in the intensity VIRGO\/SPM data at the beginning of the SoHO mission (Fr{\\\"o}hlich et al. [7]), it has never been verified if these values change in the radial velocity GOLF measurements between the blue- and red-wing observing periods. \n\n\\section{Observations and analysis}\nWe used observations collected by the space-based instruments Global Oscillations at Low Frequency (GOLF) and Variability of Solar Irradiance and Gravity Oscillations (VIRGO) onboard the {\\it Solar and Heliospheric Observatory} (SoHO) spacecraft. GOLF (Gabriel et al. [8]) measures the Doppler velocity at different heights in the solar atmosphere depending on the wing -- Blue or Red -- of the sodium doublet -- D1 and D2 -- in which the observations were performed (Garc\\'\\i a et al. [9]). VIRGO (Fr{\\\"o}hlich et al. [10]) is composed of three Sun photometers (SPM) at 402~nm (blue), 500~nm (green) and 862~nm (red). A total of 5021 days of GOLF and VIRGO observations starting on 1996 April 11 and ending on 2010 January 8 were analyzed, with respective duty cycles of 95.4\\% and 94.7\\%. The power spectra of the time series were fitted to extract the mode parameters (Salabert et al. [11]) using a standard likelihood maximization function (power spectrum with a $\\chi^2$ with 2 d.o.f. statistics). Each mode component was parameterized using an asymmetric Lorentzian profile. Since SoHO observes the Sun equatorwards, only the $l+|m|$ even components are visible in Sun-as-a-star observations of GOLF and VIRGO. In order to obtain observational estimates of the $m$-amplitude ratios, the $m = \\pm2$ and $m = 0$ components of the $l = 2$ multiplet, and the $m = \\pm3$ and $m = \\pm1$ components of the $l = 3$ multiplet were fitted using independent amplitudes, assuming that components with opposite azimuthal order $m$ have the same amplitudes ($H_{l,n,-m}=H_{l,n,+m}$). Note that the blue and red periods of GOLF were also analyzed separately, as well as the mean power spectrum of the three VIRGO SPMs.\n\n\n\n\\section{Mode visibilities and $m$-amplitude ratios}\nThe amplitude of a given multiplet $(l,n)$ is defined as the sum of the amplitudes of its $m$-components, as $H_{l,n} = \\sum_{m=-l}^{m=+l} H_{l,n,m}$. \nThen, the visibilities of the $l=1$, 2, and 3 modes relative to the $l=0$ mode are respectively defined as the ratios $H_{l=1,n} \/ H_{l=0,n}$, $H_{l=2,n-1} \/ H_{l=0,n}$, and $H_{l=3,n-1}$. The left panel of Fig.~\\ref{fig:visi} shows these visibilities for both radial velocity (GOLF) and intensity (VIRGO) measurements as a function of frequency. These raw mode visibilities present a variation with frequency -- especially for the $l=1$ mode -- that is due to the large variation of the mode amplitudes with frequency, even over half a large frequency separation.\nThus, the visibilities are biased and in order to correct them we interpolated (using a spline interpolation) the amplitudes of the $l=1$, 2, and 3 modes to the frequencies of the $l=0$ mode (right panel of Fig.~\\ref{fig:visi}). \nFigure~\\ref{fig:meanvisi} shows the mode visibilities averaged over frequency for both GOLF and VIRGO observations as a function of $l$ (see Table~\\ref{tab:visigolf}). The amplitude ratios between the $m$-components of the $l = 2$ and $l = 3$ multiplets, defined as $H_{l=2,m=0}\/H_{l=2,m=\\pm2}$ and $H_{l=3,m=\\pm1}\/H_{l=3,m=\\pm3}$ respectively, are represented on Fig.~\\ref{fig:mratio} in the case of the radial velocity GOLF measurements and are also reported in Table \\ref{tab:mratiogolf}.\n\n\n\n\\begin{figure*} \n\\begin{center} \n\\includegraphics[scale=0.31]{GOLF_visi_nocorrection_forpaper.eps} \\includegraphics[scale=0.31]{GOLF_visi_withcorrection_forpaper.eps}\n\\end{center} \n\\caption{\\label{fig:visi} Raw (left) and corrected (right) mode visibilities of $l = 1$ ($\\opencircle$), $l=2$ ($\\opensquare$), and $l=3$ ($\\opendiamond$) relative to $l = 0$ as a function of frequency in GOLF ($\\full$) and VIRGO ($\\dashed$) observations.} \n\\end{figure*} \n\n\n\\begin{figure}\n\\includegraphics[width=2.5in]{visib_vs_degree_forpaper.eps}\\hspace{0.5pc}%\n\\begin{minipage}[b]{22pc}\\caption{\\label{fig:meanvisi} Mode visibilities as a function of angular degree $l$ in GOLF ($\\full$, $\\fullcircle$) and VIRGO ($\\dashed$, $\\opensquare$) measurements. For comparison, the mode visibilities of the CoRoT target HD49385 measured by Deheuvels et al. [12] are also represented ($\\dotted$, $\\opentriangledown$).}\n\\end{minipage}\n\\end{figure}\n\n\n\\begin{figure}\n\\includegraphics[scale=0.25]{GOLF_mratio_forpaper.eps}\\hspace{0.5pc}%\n\\begin{minipage}[b]{20.5pc}\\caption{\\label{fig:mratio} $m$-amplitude ratios of the $l = 2$ (top) and $l = 3$ (bottom) modes as a function of frequency in the GOLF measurements.}\n\\end{minipage}\n\\end{figure}\n\n\n\\begin{table}\n\\caption{\\label{tab:visigolf} Mode visibilities in radial velocity GOLF and intensity VIRGO measurements.}\n\\begin{center}\n\\begin{tabular}{lllll}\n\\br\nMode visibility & GOLF & GOLF & GOLF& \\\\\nRadial velocity & & Blue wing & Red wing&\\\\\n\\mr\n$H_{l=1}\/H_{l=0}$ & 1.69$\\pm$0.04 & 1.60$\\pm$0.05 & 1.85$\\pm$0.06&\\\\\n$H_{l=2}\/H_{l=0}$ & 0.81$\\pm$0.03 & 0.74$\\pm$0.04 & 0.98$\\pm$0.05&\\\\\n$H_{l=3}\/H_{l=0}$ & 0.17$\\pm$0.01 & 0.14$\\pm$0.02 & 0.28$\\pm$0.03&\\\\\n\\br\nMode visibility & VIRGO & VIRGO & VIRGO & VIRGO \\\\\nIntensity & & Blue & Green & Red \\\\\n\\mr\n$H_{l=1}\/H_{l=0}$ & 1.53$\\pm$0.05 & 1.55$\\pm$0.05 & 1.52$\\pm$0.05 & 1.39$\\pm$0.05\\\\\n$H_{l=2}\/H_{l=0}$ & 0.59$\\pm$0.03 & 0.63$\\pm$0.03 & 0.57$\\pm$0.03 & 0.42$\\pm$0.03\\\\\n$H_{l=3}\/H_{l=0}$ & 0.09$\\pm$0.02 & 0.10$\\pm$0.02 & 0.09$\\pm$0.02 & 0.05$\\pm$0.02\\\\\n\\br\n\\end{tabular}\n\\end{center}\n\\end{table}\n\n\n\\begin{table}\n\\caption{\\label{tab:mratiogolf} $m$-amplitude ratios in radial velocity GOLF and intensity VIRGO measurements.}\n\\begin{center}\n\\begin{tabular}{lll}\n\\br\n$m$-amplitude ratio & GOLF & VIRGO\\\\\n\\mr\n$H_{l=2,m=0}\/H_{l=2,m=\\pm2}$ & 0.63$\\pm$0.03 & 0.75$\\pm$0.06\\\\\n$H_{l=3,m=\\pm1}\/H_{l=3,m=\\pm3}$ & 0.40$\\pm$0.02 & 0.63$\\pm$0.06\\\\\n\\br\n\\end{tabular}\n\\end{center}\n\\end{table}\n\n\n\n\n\\section{Models}\nWhen the contribution of a solar-disk element to the total flux depends only on its distance to the limb, the mode visibility and the $m$-amplitude ratio are decoupled (e.g. Gizon \\& Solanki [13]; Ballot et al. [14]). For VIRGO observations, this is verified since the contribution depends mainly on the limb-darkening. However, for GOLF, this is no more the case and we have performed complete computation taking into account the instrumental response, which differ for the blue and red wings. Results of these computations are listed in Table~\\ref{tab:visimodel} . The limb-darkening law of Neckel \\& Labs [15] has been used. Indeed, visibility values for GOLF vary with frequency by a few percents due to the horizontal motions of modes that increase at low frequency.\nIn general, these predictions agree with the observations. There is nevertheless some shortcomings: (i) even if the trend is correct, the difference between blue and red wings for GOLF is larger than expected; (ii) the visibility of the $l = 3$ modes in VIRGO are sensitively higher than expected. That could be explained by stronger effects of limb-darkening.\n\n\n\\begin{table}\n\\caption{\\label{tab:visimodel} Modeled visibilities and $m$-amplitude ratios in intensity VIRGO and radial velocity GOLF measurements.}\n\\begin{center}\n\\begin{tabular}{llll}\n\\br\nMode visibility \\& & VIRGO & GOLF & GOLF\\\\\n $m$-amplitude ratio & & Blue wing & Red wing\\\\\n\\mr\n$H_{l=1}\/H_{l=0}$ & 1.51 & 1.84 & 1.86\\\\\n$H_{l=2}\/H_{l=0}$ & 0.53 & 1.09 & 1.14\\\\\n$H_{l=3}\/H_{l=0}$ & 0.025 & 0.27 & 0.31\\\\\n\\mr\n$H_{l=2,m=0}\/H_{l=2,m=\\pm2}$ & 0.67 & 0.59 & 0.58\\\\\n$H_{l=3,m=\\pm1}\/H_{l=3,m=\\pm3}$ & 0.60 & 0.43 & 0.40\\\\\n\\br\n\\end{tabular}\n\\end{center}\n\\end{table}\n\n\n\\ack\nThe authors want to thank Catherine Renaud and Antonio Jim\\'enez for the calibration and preparation of the GOLF and VIRGO datasets. The GOLF and VIRGO instruments onboard SoHO are a cooperative effort of many individuals, to whom we are indebted. SoHO is a project of international collaboration between ESA and NASA. DS acknowledges the support of the grant PNAyA2007-62650 from the Spanish National Research Plan. This work has been partially supported by the CNES\/GOLF grant at the SAp\/CEA-Saclay.\n\n\\section*{References}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzfywc b/data_all_eng_slimpj/shuffled/split2/finalzzfywc new file mode 100644 index 0000000000000000000000000000000000000000..7053a26454eb1d06e73ad9eecfd6436d6b213af2 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzfywc @@ -0,0 +1,5 @@ +{"text":"\\section{Supplementary information}\n\\subsection{Extraction of the exciton population from measured data}\n\\label{population}\n\nThe exciton population created by a pump pulse tuned into the phonon sideband\ncan be extracted from our two-pulse spectra (see Fig.~2) in three different ways\nby analyzing the peak heights of the three transitions: \n$|0\\rangle \\rightarrow |X\\rangle$, $|0\\rangle \\rightarrow |\\bar X\\rangle$\nand $|X\\rangle \\rightarrow |2X\\rangle$. The relation\nbetween the photocurrent ($PC$) signal and the exciton population is as follows. The $PC$ measured in our experiment is determined by the number of\nelectron-hole pairs in the sample, including pairs in the quantum dot (QD) and pairs\nexcited in the surrounding material by the pump and probe pulses, according to:\n\\begin{equation}\nPC=\\alpha(C^{'}_X+C^{'}_{\\bar{X}})+2\\beta C^{'}_{2X}+\\gamma N^{'}_\\text{s},\n\\label{Eq:PC}\n\\end{equation}\nwhere $C^{'}_X$ and $C^{'}_{\\bar{X}}$ are the populations of the two exciton states \nwith opposite spins that are created in the QD \nafter circularly polarized pump and probe pulses, while $C^{'}_{2X}$ is the biexciton population. \n$N^{'}_\\text{s}$ is the number of electron-hole pairs created \nin the surrounding material which scales linearly with the laser intensity.\n$\\alpha$, $\\beta$ and $\\gamma$ are the detection efficiencies of our \nphotocurrent measurement for each type of charge complexes. \nSpecifically, for our pump-probe measurement, $PC$ can be further subdivided into two parts: \na reference level $PC_\\text{R}$ and the change of the photocurrent signal originating \nfrom the QD induced by the probe pulse $\\Delta PC$ according to:\n\n\\begin{subequations}\n\\begin{align}\nPC&=PC_\\text{R} + \\Delta PC,\\\\\nPC_\\text{R} &= (\\alpha(C_X+C_{\\bar{X}})+ 2\\beta C_{2X})e^{-\\tau_\\text{delay}\/T_1}\n+ \\gamma N^{'}_\\text{s},\\\\\n\\Delta PC &= \\alpha (\\Delta C_X +\\Delta C_{\\bar{X}})+2\\beta \\Delta C_{2X},\n\\end{align}\n\\label{Eq:C}\n\\end{subequations}\nwhere $C_{X,\\bar{X},2X}$ are the exciton and biexciton\npopulations created immediately after the pump pulse\nand $\\Delta C_{X,\\bar{X},2X}$ is the change of the exciton population induced\nby the probe pulse in the QD. Since the electron can\ntunnel out from the QD, the exciton and biexciton populations decay exponentially with\ntime. Thus, $C_{X,\\bar{X},2X}e^{(-\\tau_\\text{delay}\/T_1)}=C^{'}_{X,\\bar{X},2X}$ represent the pump-created exciton or biexciton\npopulations that have remained in the QD until the arrival of the probe pulse. \nHere, $\\tau_\\text{delay}$ is the delay time between the pump and probe\npulses and $T_1$ is the electron tunnelling time determined using inversion recovery measurement \\cite{Kolodka2007}. In the above discussion, we have neglected \nthe loss of the exciton population due to the radiative decay, since the radiative decay time\n($\\sim 600$~ps) is much longer than our typical delay times $\\tau_\\text{delay}$ (10 - 30ps). \n$PC_\\text{R}$ is determined by measuring the photocurrent signal from \nan off-resonant measurement where the pump pulse is tuned into the phonon \nsideband and the probe frequency is far from any of the resonances of the dot or the surrounding material\nsuch that $\\Delta PC$ becomes negligible and the photocurrent signal coincides with $PC_\\text{R}$\nin this case. \n$\\Delta PC$ is the differential photocurrent signal that is discussed in the paper, \nwhich is determined from our experiment by subtracting the $PC_\\text{R}$ obtained from the\noff-resonant measurement from the total photocurrent signal $PC$. \nThe detection efficiency $\\alpha$ can be extracted from the single $\\pi$ pulse experiment. \nDenoting by $\\Delta PC(\\pi)$ the maximum of the differential photocurrent signal reached \nwith a single $\\pi$ pulse we find:\n\\begin{equation}\n\\Delta PC(\\pi)=\\alpha C_X(\\pi)=\\alpha.\n\\label{Eq:alpha}\n\\end{equation}\nHere, we have used that according to our path-integral simulations,\nthe phonon-induced deviation of $C_X(\\pi)$ from the ideal value of 1 \nis negligible at low temperatures.\n\n\nNow let us derive the relation between the differential PC signal and the exciton population for \nthe case when pump and probe pulse are co-circularly polarized and the probe pulse\nis resonant to the $|0\\rangle \\rightarrow |X\\rangle$ transition.\nIn this case, firstly, the $\\sigma^{+}$ polarized pump pulse creates a certain exciton population $C_X$\nand consequently the ground-state occupation after the pump pulse is given by $C_{0}=1-C_{X}$.\nThen, in the time interval until the probe pulse arrives, the exciton population\nis reduced to $C_Xe^{-\\tau_\\text{delay}\/T_1}$ due to the tunneling. The ground-state occupation, on the other hand,\nis not affected by the tunneling and thus stays at the value of $C_{0}=1-C_{X}$ until the arrival of the probe.\nFinally, the $\\pi$-pulse $\\sigma^{+}$ polarized probe exchanges the populations of the\nstates $|0\\rangle$ and $|X\\rangle$ resulting in an exciton population after the probe of $C^{'}_{X}=1-C_{X}$.\nTherefore, the change of the $X$-exciton occupation induced by the probe pulse with the energy of $\\hbar \\omega_X$ \nis $\\Delta C_X = 1-(1+ e^{-\\tau_\\text{delay}\/T_1})\\,C_{X}$. \nUsing this result \ntogether with the fact, that for co-polarized $\\sigma^{+}$-pulses the populations of the $\\bar X$-exciton \nand the biexciton never build up, we find from Eq.~2(c):\n\\begin{equation}\n\\Delta PC_{0-X}=\\alpha[1-(1+ e^{-\\tau_\\text{delay}\/T_1})\\,C_{X}]\n\\label{Eq:Hph}\n\\end{equation}\nand thus with the help of Eq.~(\\ref{Eq:alpha}) we eventually end up with:\n\\begin{equation}\nC_X=\\dfrac{1}{1+e^{-\\tau_\\text{delay}\/T_1}}\\left(1-\\dfrac{\\Delta PC_{0-X}}{\\Delta PC(\\pi)}\\right).\n\\end{equation}\n\nThe situation is different when cross-circularly polarized pulses are used. Let\nus first discuss the case where the $\\bar X$ polarized probe pulse is resonant\nto the $|0\\rangle \\rightarrow |\\bar X\\rangle$ transition. After the action of\nthe circularly $\\sigma^{+}$ polarized pump pulse, the QD again has a certain probability $C_X$\nto be in the $|X\\rangle$ state and the probability to find the dot in the ground-state is $C_{0}=1-C_{X}$. The $\\sigma^{-}$ polarized probe pulse induces transitions\nfrom the ground-state to the $\\bar X$ exciton. Since the probe pulse has a pulse\narea of $\\pi$ and the occupation of $|\\bar X\\rangle$ is zero before the arrival\nof the probe, the probe pulse fully converts the occupation that was left in \nthe ground-state after the pump pulse \ninto an occupation of the $\\bar X$ exciton, i.e., $C^{'}_{\\bar X}=C_{0}=1-C_{X}$.\nAgain, the ground-state occupation is not affected by the electron tunnelling\nand therefore no correction involving the tunnelling time $T_{1}$ should be applied.\nSince the probe pulse is off-resonant to the $X-2X$ transition we can neglect the probe induced change\nof the $|X\\rangle$ and $|2X\\rangle$ occupations.\nRecalling that $|\\bar X\\rangle$ is unoccupied before the probe, we find for\nthe resulting differential photocurrent signal $\\Delta PC_{0-\\bar{X}}$ with the \nprobe pulse being in resonance to the exciton transition:\n\\begin{equation}\n\\Delta PC_{0-\\bar{X}}=\\alpha (1-C_X),\n\\label{Eq:Hph}\n\\end{equation}\nwhich yields:\n\\begin{equation}\nC_X=1-\\Delta PC_{0-\\bar{X}}\/ \\Delta PC(\\pi).\n\\label{Eq:Hph}\n\\end{equation}\n\nBesides from the data measured at the $|0\\rangle \\rightarrow |X\\rangle$ \nand $|0\\rangle \\rightarrow |\\bar X\\rangle$ transitions, the exciton population\ncreated by the pump can also be extracted from the exciton to biexciton\ntransition. An $\\sigma^{+}$ polarized pump pulse tuned into the high-energy phonon\nsideband of the neutral exciton transition again creates a certain exciton population\n$C_X$ which evolves into $C_Xe^{-\\tau_\\text{delay}\/T_1}$ until the arrival of the probe.\nBiexcitons are not created, i.e., we have $C_{2X}=0$. A cross-polarized\n$\\pi$-power probe pulse resonant to the \n$|X\\rangle \\rightarrow |2X\\rangle$ transition\nconverts the $|X\\rangle$ population completely into\nan $|2X\\rangle$ population, which gives $C^{'}_X=0$ and\n$C^{'}_{2X}=C_Xe^{-\\tau_\\text{delay}\/T_1}$. Since the probe is now off-resonant to the\n$|0\\rangle \\rightarrow |\\bar X\\rangle$ transition, the occupation of the\n$|\\bar X\\rangle$ exciton induced by the probe is negligible \nand the ground-state occupation is not affected. \nThus, the $\\Delta PC$ signal resulting from a probe pulse \nin resonance to the $|X\\rangle \\rightarrow |2X\\rangle$\ntransition is given by:\n\\begin{equation}\n\\Delta PC_{X-2X}=2\\beta \\Delta C_{2X}+\\alpha \\Delta C_X=(2\\beta - \\alpha)C_Xe^{-\\tau_\\text{delay}\/T_1}.\n\\label{Eq:PCbiexciton}\n\\end{equation}\n$\\beta$ can be determined from a separate experiment in whihc the pump is a $\\pi$ pulse resonant with $X$ and the probe is a $\\pi$ pulse resonant with $2X$. According to Eqs.~\\eqref{Eq:alpha} and \\eqref{Eq:PCbiexciton}, we have:\n\\begin{equation}\n\\beta=0.5(e^{\\tau_\\text{delay}\/T_1}\\Delta PC_{X-2X}(\\pi)+\\Delta PC(\\pi)).\n\\label{Eq:beta}\n\\end{equation}\n\nInserting Eqs.~\\eqref{Eq:alpha} and \\eqref{Eq:beta} into Eq.~\\eqref{Eq:PCbiexciton} \nwe can extract the exciton population after the pump from:\n\\begin{equation}\nC_X= \\dfrac{\\Delta PC_{X-2X}}{\\Delta PC_{X-2X}(\\pi)}.\n\\end{equation}\n\n\\subsection{Model}\n\\label{Model}\n\nFor our calculations we used the same model for an optically driven strongly \nconfined quantum dot as in Ref.~\\cite{Glassl2013},\nwhich is based on the Hamiltonian\n\\begin{align}\n \\label{eq:Hamiltonian}\n H = H_{\\rm{QD-light}} + H_{\\rm{QD-phonon}},\n\\end{align}\nwhere \n\\begin{align}\n H_{\\rm{QD-light}} = \\hbar\\omega^{0}_{X}| X\\rangle\\langle X|\n+\\frac{\\hbar\\Omega(t)}{2} \\left[ | 0\\rangle\\langle X| + |X\\rangle \\langle 0| \\right],\n\\end{align}\nand\n\\begin{align}\n H_{\\rm{QD-phonon}} \\!=\\! \\sum_{\\bf q} \\hbar\\omega_{\\bf q}\\,b^\\dag_{\\bf q} b_{\\bf q} \n\\!+\\! \\sum_{\\bf q} \\hbar \\big( \\gamma_{\\bf q} b_{\\bf q} \\!+\\! \\gamma^{\\ast}_{\\bf q} b^\\dag_{\\bf q}\n \\big) |X \\rangle\\langle X|.\n\\label{dot-ph}\n\\end{align} \nThe ground-state $|0\\rangle$ is chosen as the zero of the energy and\nthe phonon-free energy of the transition to the single exciton state $|X\\rangle$ is denoted\nby $\\hbar\\omega^{0}_{X}$. The Rabi frequency $\\Omega(t)$ is proportional to the\nelectric field envelope of a circularly polarized Gaussian laser pulse with\nfrequency $\\omega_{L}$, which is detuned from the ground-state to exciton\ntransition by $\\Delta = \\omega_{L}-\\omega_{X}$, where $\\omega_{X}$ is the\nfrequency of the single exciton resonance which deviates from $\\omega^{0}_{X}$\nby the polaron shift that results from the dot-phonon coupling\nin Eq.~(\\ref{dot-ph}). The coupling to the laser field\nis treated in the common rotating wave and dipole approximations. The operator\n$b^\\dag_{\\bf q}$ creates a longitudinal acoustic (LA) bulk phonon with wave\nvector $\\bf{q}$ and energy $\\hbar \\omega_{\\bf{q}}$. We assume a linear\ndispersion relation $\\omega_{\\bf{q}} = c_{s} |\\bf{q}|$, where $c_{s}$ denotes\nthe speed of sound. The phonons are coupled via the deformation potential only\nto the exciton state. This coupling is expressed by the exciton-phonon coupling\n$\\gamma_{\\bf{q}}=\\frac{|\\bf{q}|}{\\sqrt{2V\\rho \\hbar \\omega_{\\bf{q}}}}\n\\left(D_{\\rm{e}} \\Psi^{\\rm{e}}({\\bf q}) - D_{\\rm{h}} \\Psi^{\\rm{h}}({\\bf\nq})\\right)$, where $\\rho$ denotes the mass density of the crystal, $V$ the mode\nvolume, $D_{\\rm{e\/h}}$ the deformation potential constants, and\n$\\Psi^{\\rm{e\/h}}(\\bf{q})$ the formfactors of electron and hole, respectively. As\nexplained in the main article, we calculate the formfactors from the\nground-state wavefunctions of a spherical symmetric, parabolic confinement\npotential. It should be noted that, in the pure dephasing model for the\ndot-phonon coupling, no transitions between the bare electronic states can be\ninduced by the continuum of LA phonons, which can change the electronic\noccupations only in the presence of the laser field. We assume the system to be\ninitially in a product state of a thermal phonon-distribution at the temperature\nof the cryostat and a pure ground-state of the electronic subsystem. We use the\nmaterial parameters given in Ref.~\\cite{Krummheuer2002} for GaAs, which are: $\\rho =\n5370 \\; \\rm{kg}\/\\rm{m}^3$, $c_{s} = 5110 \\; \\rm{m}\/\\rm{s}$, $D_{\\rm{e}} = 7.0 \\;\n\\rm{eV}$, and $D_{\\rm{h}} = -3.5 \\; \\rm{eV}$.\n\nTo obtain the time evolution of the electronic density matrix elements predicted \nby this model, we make use of a numerically exact real-time path-integral\napproach, described in detail in Ref.~\\cite{Vagov2011}. This gives us the\nopportunity to calculate the dynamics of the quantum dot with a high and\ncontrollable numerical precision and without further approximations to the\ngiven Hamiltonian. This includes taking into account all multi-phonon processes\nand non-Markovian effects.\n\n\\end{document}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Conclusion and Future Work}\n\\label{sec:conclusion}\n\nWe present Flexible BFT, a protocol that supports diverse clients with different assumptions to use the same ledger. Flexible BFT allows the clients to tolerate combined (Byzantine plus alive-but-corrupt\\xspace) faults exceeding 1\/2 and 1\/3 for synchrony and partial synchrony respectively. At a technical level, under synchrony, we show a synchronous protocol where the replicas execute a network speed protocol and only the commit rule uses the synchrony assumption. For partial synchrony, we introduce the notion of Flexible Byzantine Quorums by deconstructing existing BFT protocols to understand the role played by the different quorums. We combine the two to form Flexible BFT which obtains the best of both worlds.\n\nOur liveness proof in Section~\\ref{sec:proof}\nemploys a strong assumption that all clients have correct commit rules.\nThis is because our alive-but-corrupt\\xspace fault model did not specify \nwhat these replicas would do if they can violate safety for some clients.\nIn particular, they may stop helping liveness. \nHowever, we believe this will not be a concern \nonce we move to a more realistic rational model.\nIn that case, the best strategy for alive-but-corrupt\\xspace replicas\nis to attack the safety of clients with unsafe commit rules \nwhile preserving liveness for clients with correct commit rules. \nSuch an analysis in the rational fault model\nremains interesting future work.\nOur protocol also assumes that all replicas have clocks that advance at the same rate. It is interesting to explore whether our protocol can be modified to work with clock drifts.\n\n\n\\section{Discussion}\n\\label{sec:discussion}\n\nAs we have seen, three parameters \n${q_{r}}$, ${q_c}$, and $\\Delta$ determine the protocol. \n${q_{r}}$ is the only parameter for the replicas\nand is picked by the service administrator.\nThe choice of ${q_{r}}$\ndetermines a set of client assumptions that can be supported. \n${q_c}$ and $\\Delta$ are chosen by clients to commit blocks.\nIn this section, we first discuss the client assumptions supported by a\ngiven ${q_{r}}$ and then discuss the trade-offs \nbetween different choices of ${q_{r}}$.\n\n\\subsection{Client Assumptions Supported by ${q_{r}}$}\n\\label{sec:client-beliefs-given}\n\n\\begin{figure}[tbp]\n \\centering\n \\includegraphics[width=0.45\\textwidth]{single-curve.pdf}\n \\caption{\\textbf{Clients supported for ${q_{r}} = 2\/3$.}}\n \\label{fig:clients-supported}\n\\end{figure}\n\nFigure~\\ref{fig:clients-supported} represents\nthe clients supported at ${q_{r}} = 2\/3$. \nThe x-axis represents Byzantine faults\nand the y-axis represents total faults (Byzantine plus a-b-c\\xspace). \nEach point on this graph \nrepresents a client fault assumption as a pair: (Byzantine faults, total faults).\nThe shaded gray area indicates an ``invalid area'' since we\ncannot have fewer total faults than Byzantine faults.\nA missing dimension\nin this figure is the choice of $\\Delta$. \nThus, the synchrony guarantee shown in this figure is for clients\nthat choose a correct synchrony bound. \n\nClients with partial-synchrony assumptions\ncan get fault tolerance on (or below) the starred orange line. \nThe right most point on the line is $(1\/3, 1\/3)$, i.e., we\ntolerate less than a third of Byzantine replicas and no additional\na-b-c\\xspace replicas. \nThis is the setting of existing partially synchronous consensus\n protocols~\\cite{DLS88,castro1999practical,yin2018hotstuff}. \nFlexible BFT\\xspace generalizes these protocols by giving clients the option of\nmoving up-left along the line, \ni.e., tolerating fewer Byzantine and more total faults.\nBy choosing ${q_c}>{q_{r}}$, a client tolerates \n$< {q_c}+{q_{r}}-1$ total faults for safety \nand $\\leq 1-{q_c}$ Byzantine faults for liveness. \nIn other words, as a client moves left, \nfor every additional vote it requires,\nit tolerates one fewer Byzantine fault and gains overall\none higher total number of faults (i.e., two more a-b-c\\xspace faults).\nThe left most point on this line $(0, 2\/3)$ tolerating no \nByzantine replicas and the highest fraction of a-b-c\\xspace replicas. \n\nMoreover, for clients who believe in synchrony,\nif their $\\Delta$ assumption is correct,\nthey enjoy 1\/3 Byzantine tolerance and 2\/3 total tolerance\nrepresented by the green diamond.\nThis is because synchronous commit rules are not parameterized by\nthe number of votes received. \n\n\\paragraph{How do clients pick their commit rules?}\nIn Figure~\\ref{fig:clients-supported}, the shaded starred orange\nportion of the plot represent fault tolerance provided \nby the partially synchronous commit rule (CR1). \nSpecifically, setting ${q_c}$ to the total fault \nfraction yields the necessary commit rule. On the other hand, if\na client's required fault tolerance lies in the circled green portion\nof the plot, then the synchronous commit rule (CR2) with an\nappropriate $\\Delta$ picked by the client yields the necessary\ncommit rule. Finally, if a client's target fault tolerance corresponds to the\nwhite region of the plot, then \nit is not achievable with this ${q_{r}}$.\n\n\\paragraph{Clients with incorrect assumptions and recovery.}\nIf a client has incorrect assumption with respect to the fault\nthreshold or synchrony parameter $\\Delta$, then it can lose\nsafety or liveness. If a client believing in synchrony\npicks too small a $\\Delta$ and commits a value $b$, \nit is possible that a conflicting\nvalue $b'$ may also be certified. Replicas may\nchoose to extend the branch containing $b'$, effectively\nreverting $b$ and causing a safety violation. Whenever a client\ndetects such a safety violation, it may need to revert\nsome of its commits and increase $\\Delta$ to recover.\n\nFor a client with partial-synchrony assumption, if it loses safety, \nit can update its fault model to move left along\nthe orange starred line, i.e., tolerate higher total faults but fewer\nByzantine. On the other hand, if it observes no progress as its\nthreshold ${q_c}$ is not met, then it moves towards the\nright. However, if the true fault model is in the circled green\nregion in Figure~\\ref{fig:clients-supported}, then the client\ncannot find a partially synchronous commit rule that is both safe and live\nand eventually has to switch to using a synchronous commit rule.\n\nRecall that the goal of a-b-c\\xspace replicas is to attack \nsafety. Thus, clients with\nincorrect assumptions may be exploited by\na-b-c\\xspace replicas for their own gain (e.g., by double-spending). \nWhen a client updates to a correct assumption \nand recovers from unsafe commits, their subsequent commits\nwould be safe and final. This is remotely analogous to Bitcoin \n-- if a client commits to a transaction when it is a few blocks deep and a\npowerful adversary succeeds in creating an alternative longer fork, the\ncommit is reverted. \n\n\\subsection{Comparing Different ${q_{r}}$ Choices}\n\\label{sec:comp-diff-qmins}\n\\begin{figure}[tbp]\n \\centering\n \\includegraphics[width=0.45\\textwidth]{pbft.pdf}\n \\caption{Clients supported by Flexible BFT\\xspace at different\n ${q_{r}}$'s. The legend represents the different ${q_{r}}$ values.}\n \\label{fig:varying-qmin}\n\\end{figure}\nWe now look at the service administrator's choice at picking\n${q_{r}}$. \nIn general, the service administrator's goal is to tolerate a large number of Byzantine\nand a-b-c\\xspace faults, i.e., move towards top and\/or right of the figure.\nFigure~\\ref{fig:varying-qmin} shows the trade-offs\nin terms of clients supported by different ${q_{r}}$ values in Flexible BFT\\xspace.\n\nFirst, it can be observed that for clients with partial-synchrony assumptions, \n${q_{r}} \\geq 2\/3$ dominates ${q_{r}} < 2\/3$. \nObserve that the fraction of Byzantine\nreplicas $(B)$ are bounded by $B < {q_c}+{q_{r}}-1$ and $B \\leq\n1-{q_c}$, so $B \\leq {q_{r}}\/2$. \nThus, as ${q_{r}}$ decreases, Byzantine fault tolerance decreases.\nMoreover, since the total fault tolerance is \n${q_c} + {q_{r}} - 1$, a lower ${q_{r}}$ also tolerates a\nsmaller fraction of total faults for a fixed ${q_c}$.\n\nFor ${q_{r}} \\geq 2\/3$ or for clients believing in synchrony, \nno value of ${q_{r}}$ is Pareto optimal. \nFor clients with partial-synchrony assumptions, as ${q_{r}}$ increases, \nthe total fault tolerance for safety increases.\nBut since ${q_c} \\geq {q_{r}}$, we have $B \\leq 1 - {q_{r}}$, and\nhence the Byzantine tolerance for liveness decreases.\nFor clients believing in synchrony, the total fault tolerance\nfor safety is $< {q_{r}}$ and the Byzantine fault tolerance for\nliveness is $\\geq 1-{q_{r}}$. \nIn both cases, the choice of ${q_{r}}$ represents a safety-liveness\ntrade-off.\n\n\n\\subsection{Separating Alive-but-corrupt\\xspace Resilience from Diversity}\n\nSo far, we presented the Flexible BFT\\xspace techniques and protocols \nto simultaneously support diverse client support and stronger a-b-c\\xspace fault tolerance.\nIndeed, we believe both properties are desirable and they strengthen each other.\nBut we remark that these two properties can be provided separately.\n\nIt is relatively straightforward to provide stronger fault tolerance in the a-b-c\\xspace model in a classic uniform setting. \nFor example, under partial-synchrony, one can simply use a larger quorum in PBFT (without the ${q_{r}}$\/$q$ replica\/client quorum separation).\nBut we note that a higher total (a-b-c\\xspace plus Byzantine) tolerance comes at the price of a lower Byzantine tolerance.\nIn a uniform setting, this means \\emph{all} clients have to sacrifice some Byzantine tolerance.\nIn the diverse setting, Flexible BFT\\xspace gives clients the freedom to choose the fault assumption they believe in,\nand a client can choose the classic Byzantine fault model.\n\nOn the flip side, if one hopes to support diverse clients in the classic Byzantine fault (no a-b-c\\xspace faults),\nthe ``dimension of diversity'' reduces.\nOne example is the network speed replica protocol in Section~\\ref{sec:overv-synchr-flex},\nwhich supports clients that believe in different synchrony bounds.\nThat protocol can be further extended to support clients with a (uniform) partial-synchrony assumption.\nClients with partial-synchrony assumption are uniform since\nwe have not identified any type of ``diversity'' outside a-b-c\\xspace faults for them. \n\n\n\n\\section{Introduction}\n\\label{sec:introduction}\n\nByzantine fault tolerant (BFT) protocols are used to build replicated services~\\cite{PSL80,LSP82,schneider1990implementing}.\nRecently, they have received revived interest\nas the algorithmic foundation of what is known as decentralized ledgers, or blockchains. \n\nIn the classic approach to BFT protocol designs,\na protocol designer or a service administrator first picks a set of assumptions \n(e.g., the fraction of Byzantine faults and certain timing assumptions)\nand then devises a protocol (or chooses an existing one) tailored for that particular setting.\nThe assumptions made by the protocol designer are imposed upon all parties involved --- \nevery replica maintaining the service as well as every client (also known as the\n\"learner\" role) using the service.\nSuch a protocol collapses if deployed under settings that differ from the one it is designed for. \nIn particular, optimal-resilience partially synchronous solutions~\\cite{DLS88,castro1999practical} \nbreak (lose safety and liveness) if the fraction of Byzantine faults exceeds\n$1\/3$. \nSimilarly, optimal-resilience synchronous solutions~\\cite{abraham2018synchronous,hanke2018dfinity}\ndo not obtain safety or liveness if the fraction of Byzantine faults exceeds\n$1\/2$ or if the synchrony bound is violated.\n\nIn this work, we introduce a new approach for BFT protocol design called \\emph{Flexible BFT\\xspace}. \nOur approach offers advantages in the two aspects above.\nFirst, the Flexible BFT\\xspace approach enables protocols \nthat tolerate more than $1\/3$ (resp.\\ $1\/2$) corruption faults in the\npartial-synchrony (resp.\\ synchrony) model\n--- provided that the number of Byzantine faults do not exceed the respective resilience bounds. \nSecond, the Flexible BFT\\xspace approach allows a certain degree of separation between \nthe fault model and the protocol design.\nAs a result, Flexible BFT\\xspace allows diverse clients with different \nfault assumptions and timing assumptions (synchrony or not)\nto participate in the same protocol.\nWe elaborate on these two aspects below.\n\n\\paragraph{Stronger resilience.}\nWe introduce a mixed fault model with a new type of fault called\n\\emph{alive-but-corrupt\\xspace} (a-b-c\\xspace for short) faults. \nAlive-but-corrupt\\xspace replicas actively try to disrupt the system from maintaining a safe consensus decision \nand they might arbitrarily deviate from the protocol for this purpose. \nHowever, if they cannot break safety,\nthey will not try to prevent the system from reaching a (safe) decision.\nThe rationale for this new type of fault is that \nviolating safety may provide the attacker gains (e.g., a double spend attack)\nbut preventing liveness usually does not.\nIn fact, a-b-c\\xspace replicas may gain rewards from keeping the replicated service\nlive, e.g., by collecting service fees.\nWe show a family of protocols \nthat tolerate a combination of Byzantine and a-b-c\\xspace faults \nthat exceeds $1\/3$ in the partially synchronous model\nand exceeds $1\/2$ in the synchronous model.\nOur results do not violate existing resilience bounds \nbecause the fraction of Byzantine faults is always smaller than the respective bounds.\n\n\n\\paragraph{Diversity.}\nThe Flexible BFT\\xspace approach further provides certain separation between the fault model\nand the protocol. The design approach builds a protocol whose transcript can be\ninterpreted by external clients with diverse beliefs, who draw different consensus\ncommit decisions based on their beliefs.\nFlexible BFT\\xspace guarantees safety and liveness so far as the\nclients' beliefs are correct; thus two clients with correct\nassumptions agree with each other.\nClients specify (i) the fault threshold they need to tolerate, \nand (ii) the message delay bound, if any, they believe in. \nFor example, one instance of Flexible BFT\\xspace can support a client that requires tolerance against $1\/5$ Byzantine faults plus $3\/10$ a-b-c\\xspace faults, \nwhile simultaneously supporting another client who requires tolerance against $1\/10$ Byzantine faults plus $1\/2$ a-b-c\\xspace faults,\nand a third client who believes in synchrony and requires \n$3\/10$ Byzantine plus $2\/5$ a-b-c\\xspace tolerance.\n\nThis novel separation of fault model from protocol design\ncan be useful in practice in several ways.\nFirst, different clients may naturally hold different assumptions about the system.\nSome clients may be more cautious and require a higher resilience than others;\nsome clients may believe in synchrony while others do not.\nMoreover, even the same client may assume a larger fraction of faults \nwhen dealing with a \\$1M transaction compared to a \\$5 one. \nThe rationale is that more replicas may be willing to collude\nto double spend a high-value transaction.\nIn this case, the client can wait for more votes before committing the \\$1M transaction.\nLast but not least, a client may update its assumptions based on certain events it observes.\nFor example, if a client receives votes for conflicting values,\nwhich may indicate an attempt at attacking safety,\nit can start requiring more votes than usual;\nif a client who believes in synchrony notices abnormally long message delays,\nwhich may indicate an attack on network infrastructure,\nit can update its synchrony bound to be more conservative or switch to a partial-synchrony assumption.\n\n\nThe notion of ``commit'' needs to be clarified in our new model.\nClients in Flexible BFT\\xspace have different assumptions and hence different commit rules.\nIt is then possible and common that a value is committed by one client but not another.\nFlexible BFT\\xspace guarantees that any two clients whose assumptions are correct \n(but possibly different) commit to the same value.\nIf a client's assumption is incorrect, however,\nit may commit inconsistent values which may later be reverted.\nWhile this new notion of commit may sound radical at first, \nit is the implicit behavior of existing BFT protocols.\nIf the assumption made by the service administrator is violated in a classic BFT protocol\n(e.g., there are more Byzantine faults than provisioned),\nclients may commit to different values and they have no recourse.\nIn this sense, Flexible BFT\\xspace is a robust generalization of classic BFT protocols. \nIn Flexible BFT\\xspace, if a client performs conflicting commits,\nit should update its assumption to be more cautious\nand re-interpret what values are committed under its new assumption.\nIn fact, this ``recovery'' behavior is somewhat akin to Bitcoin.\nA client in Bitcoin decides how many confirmations are needed \n(i.e., how ``deeply buried'') to commit a block.\nIf the client commits but subsequently an alternative longer fork appears, its commit is reverted.\nGoing forward, the client may increase the number of confirmations it requires.\n\n\n\\paragraph{Key techniques.}\nFlexible BFT\\xspace centers around two new techniques.\nThe first one is a novel synchronous BFT protocol with replicas\n executing at \\emph{network speed}; \nthat is, the protocol run by the replicas does not assume synchrony.\nThis allows clients in the same protocol \nto assume different message delay bounds and commit at their own\npace. \nThe protocol thus \n separates timing assumptions of replicas from timing assumptions of clients.\nNote that this is only possible via Flexible BFT\\xspace's separation of\nprotocol from the fault model:\nthe action of committing is only carried out by clients, not by replicas.\nThe other technique involves a breakdown of the different roles that \nquorums play in different steps of partially synchronous BFT protocols.\nOnce again, made possible by the separation in Flexible BFT\\xspace,\nwe will use one quorum size for replicas to run a protocol,\nand let clients choose their own quorum sizes for committing in the protocol.\n\n\\paragraph{Contributions.}\nTo summarize, our work has the following contributions.\n\n\\begin{enumerate}[topsep=8pt,itemsep=8pt]\n\t\t\t\t\n\\item \n\\textbf{Alive-but-corrupt faults.} We introduce a new type of fault, called\nalive-but-corrupt\\xspace fault, which attack safety but not liveness.\n\n\\item \\textbf{Synchronous BFT with network speed replicas.} \n\tWe present a synchronous protocol in which \n\tonly the commit step requires synchrony.\n\tSince replicas no longer perform commits in our approach,\n\tthe protocol simultaneously supports clients assuming different synchrony bounds.\n\t\t\n\\item \\textbf{Flexible Byzantine Quorums.} \n\tWe deconstruct existing BFT protocols to understand the role played by different quorums \n\tand introduce the notion of Flexible Byzantine Quorums.\n\tA protocol based on Flexible Byzantine Quorums simultaneously supports clients assuming different fault models.\n\n\\item \\textbf{One BFT Consensus Solution for the Populace.} \n\tPutting the above together, we present a new approach for BFT design, Flexible BFT\\xspace.\n\tOur approach has stronger resilience and diversity: Flexible BFT\\xspace tolerates a fraction of combined (Byzantine plus a-b-c\\xspace) faults \n\tbeyond existing resilience bounds. And clients with diverse fault\nand timing beliefs are supported in the same protocol. \n\n\\end{enumerate}\n\n\\paragraph{Organization.}\nThe rest of the paper is organized as follows.\nSection~\\ref{sec:model} defines the Flexible BFT\\xspace model where replicas and clients are separated.\nWe will describe in more detail our key techniques \nfor synchrony and partial-synchrony in\nSections~\\ref{sec:overv-synchr-flex}~and~\\ref{sec:overview-async}, respectively. \nSection~\\ref{sec:protocol} puts these techniques together and presents the final protocol.\nSection~\\ref{sec:discussion} discusses the result obtained by the\nFlexible BFT design and \nSection~\\ref{sec:related-work} describes related work.\n\n\n\n\\section*{Acknowledgement}\nWe thank Ittai Abraham and Ben Maurer for many useful discussions\non Flexible BFT. We thank Marcos Aguilera for many insightful\ncomments on an earlier draft of this work\n\\bibliographystyle{plain}\n\n\\section{Modeling Flexible BFT\\xspace}\n\\label{sec:model}\n\nThe goal of Flexible BFT\\xspace is to build a replicated service that takes requests from clients\nand provides clients an interface of a single non-faulty server,\ni.e., it provides clients with the same totally ordered sequence of values. \nInternally, the replicated service uses multiple servers,\nalso called replicas, to tolerate some number of faulty servers. \nThe total number of replicas is denoted by $n$. In this paper, whenever we\nspeak about a set of replicas or messages, we denote the set size as its fraction over $n$.\nFor example, we refer to a set of $m$ replicas as ``$q$ replicas'' where $q=m\/n$.\n\nBorrowing notation from Lamport~\\cite{lamport2006fast}, \nsuch a replicated service has three logical actors: \n\\emph{proposers} capable of sending new values,\n\\emph{acceptors} who add these values to a totally ordered sequence (called a blockchain), \nand \\emph{learners} who decide on a sequence of values based on the transcript of the protocol and execute them on a state machine. \nExisting replication protocols provide the following two properties:\n\n\\begin{description}[topsep=8pt,itemsep=4pt]\n\\item[-] \\textbf{Safety.} Any two learners learn the same sequence of values.\n\\item[-] \\textbf{Liveness.} A value proposed by a proposer will\n eventually be executed by every learner.\n\\end{description}\n\nIn existing replication protocols, \nthe learners are assumed to be \\emph{uniform}, \ni.e., they interpret a transcript using the same rules \nand hence decide on the same sequence of values.\nIn Flexible BFT\\xspace, we consider diverse learners with different assumptions. \nBased on their own assumptions, \nthey may interpret the transcript of the protocol differently. \nWe show that so far as the assumptions \nof two different learners are both correct, \nthey will eventually learn the same sequence of values.\nA replication protocol in the Flexible BFT\\xspace approach\nsatisfies the following properties:\n\n\\begin{description}[topsep=8pt,itemsep=4pt]\n\\item[-] \\textbf{Safety for diverse learners.} Any two\n learners with correct but potentially different assumptions\n learn the same sequence of values.\n\\item[-] \\textbf{Liveness for diverse learners.} A value\n proposed by a proposer will eventually be executed by every learner\n with a correct assumption.\n\\end{description}\n\nIn a replicated service, clients act as proposers and learners, \nwhereas the replicas (replicated servers) are acceptors.\nThus, safety and liveness guarantees are defined with respect to clients.\n\n\\paragraph{Fault model.} \nWe assume two types of faults within the replicas: Byzantine and \\emph{alive-but-corrupt\\xspace} (a-b-c\\xspace for short). \nByzantine replicas behave arbitrarily. \nOn the other hand, the goal of a-b-c\\xspace replicas\nis to attack safety but to preserve liveness.\nThese replicas will take any actions that help them break safety of the protocol.\nHowever, if they cannot succeed in breaking safety, they will help provide liveness.\nConsequently, in this new fault model, \nthe safety proof should treat a-b-c\\xspace replicas similarly to Byzantine.\nThen, \\emph{once safety is proved},\nthe liveness proof can treat a-b-c\\xspace replicas similarly to honest.\nWe assume that the adversary is static, i.e., the adversary determines which\nreplicas are Byzantine and a-b-c\\xspace before the start of the protocol. \n\n\\paragraph{Other assumptions.} \nWe assume hash functions, digital signatures and a public-key infrastructure (PKI).\nWe use $\\sig{x}_R$ to denote a message $x$ signed by a replica $R$. \nWe assume pair-wise communication channels between replicas. \nWe assume that all replicas have clocks that advance at the same\nrate.\n\n\n\\section{Flexible Byzantine Quorums for Partial Synchrony - Overview}\n\\label{sec:overview-async}\n\nIn this section, we explain the high-level insights of Flexible\nByzantine Quorums in Flexible BFT\\xspace. \nAgain, for ease of exposition, we\nfocus on a single-shot consensus and do not consider termination. \nWe start by reviewing the Byzantine Quorum\nSystems~\\cite{Malkhi:1997:BQS:258533.258650} that underlie\nexisting partially synchronous protocols \nthat tolerate 1\/3 Byzantine faults (Section~\\ref{sec:byz-quorums}). \nWe will illustrate that multiple uses of 2\/3-quorums \nactually serve different purposes in these protocols.\nWe then generalize these protocols to use \n\\emph{Flexible Byzantine Quorums}~(Section~\\ref{sec:flexible-byz-quorum}),\nthe key idea that enables more than 1\/3 fault tolerance \nand allows diverse clients with varying assumptions to co-exist. \n\n\\subsection{Background: Quorums in PBFT}\n\\label{sec:byz-quorums}\n\nExisting protocols for solving consensus in the partially synchronous setting \nwith optimal $1\/3$-resilience\nrevolve around voting by \\emph{Byzantine quorums} of replicas. \nTwo properties of Byzantine quorums are utilized for achieving safety and liveness.\nFirst, any two quorums intersect at one honest replica -- quorum intersection.\nSecond, there exists a quorum that contains no Byzantine faulty replicas -- quorum availability.\nConcretely, when less than $1\/3$ the replicas are Byzantine,\nquorums are set to size ${q_{r}}=2\/3$.\n(To be precise, ${q_{r}}$ is slightly larger than 2\/3, \ni.e., $2f+1$ out of $3f+1$ where $f$ is the number of faults,\nbut we will use ${q_{r}}=2\/3$ for ease of exposition.)\nThis guarantees an intersection of size at least $2{q_{r}}-1=1\/3$, \nhence at least one honest replica in the intersection.\nAs for availability, there exist ${q_{r}}=2\/3$ honest replicas to form\na quorum.\n\nTo dissect the use of quorums in BFT protocols, consider their use in\nPBFT~\\cite{castro1999practical} for providing safety and liveness. \nPBFT operates in a view-by-view manner.\nEach view has a unique leader and consists of the following steps: \n\n\\begin{itemize}\n\n\\item[-] \\textbf{Propose.} \n A leader $L$ proposes a value $b$.\n\n\\item[-] \\textbf{Vote 1.} \n On receiving the first value $b$ for a view $v$, a replica votes for $b$ \n if it is \\emph{safe}, as determined by a locking mechanism described below.\n A set of ${q_{r}}$ votes form a certificate $\\mathcal{C}^{{q_{r}}}(b)$.\n\n\\item[-] \\textbf{Vote 2.} \n On collecting $\\mathcal{C}^{{q_{r}}}(b)$,\n a replica ``locks'' on $b$ and votes for $\\mathcal{C}^{{q_{r}}}(b)$.\n\n\\item[-] \\textbf{Commit.} \n On collecting ${q_{r}}$ votes for $\\mathcal{C}^{{q_{r}}}(b)$, a client learns that proposal\n$b$ becomes a committed decision. \n\n\\end{itemize}\nIf a replica locks on a value $b$ in a view, then it votes only for $b$ in subsequent views \nunless it ``unlocks'' from $b$.\nA replica ``unlocks'' from $b$ if it learns that ${q_{r}}$ replicas are \\emph{not}\nlocked on $b$ in that view or higher \n(they may be locked on other values or they may not be locked at all).\n\nThe properties of Byzantine quorums are harnessed in PBFT for safety and liveness as follows:\n\n\\begin{description}[topsep=8pt,itemsep=4pt]\n\n\\item[Quorum intersection within a view.]\nSafety within a view is ensured by the first round of votes.\nA replica votes only once per view.\nFor two distinct values to both obtain certificates, \none honest replica needs to vote for both, which cannot happen.\n\n\\item[Quorum intersection across views.]\nSafety across views is ensured by the locking mechanism.\nIf $b$ becomes a committed decision in a view, \nthen a quorum of replicas lock on $b$ in that view.\nFor an honest replica among them to unlock from $b$,\na quorum of replicas need to claim they are not locked on $b$.\nAt least one replica in the intersection is honest \nand would need to falsely claim it is not locked, which cannot happen.\n\n\\item[Quorum availability within a view.]\nLiveness within each view is guaranteed by having an honest quorum respond to a\nnon-faulty leader.\n\\end{description}\n\n\n\\subsection{Flexible Byzantine Quorums}\n\\label{sec:flexible-byz-quorum}\n\nOur Flexible BFT\\xspace approach separates the quorums used in \nBFT protocols for the replicas (acceptors) from the quorums used for learning when a decision becomes\ncommitted. \nMore specifically,\nwe denote the quorum used for forming certificates (locking) by ${q_\\text{lck}}$\nand the quorum used for unlocking by ${q_\\text{ulck}}$. \nWe denote the quorum employed by clients for learning certificate uniqueness by\n${q_\\text{unq}}$, and the quorum used for learning commit safety by ${q_\\text{cmt}}$. \nIn other words, clients mandate ${q_\\text{unq}}$ first-round votes and ${q_\\text{cmt}}$ second-round votes in order\nto commit a decision. \nBelow, we outline a modified PBFT-like protocol that uses these different quorum sizes instead of a single quorum size $q$.\nWe then introduce a new definition, Flexible Byzantine Quorums, that\ncapture the requirements needed for these quorums to provide\nsafety and liveness.\n\n\\begin{figure}[h]\n\\centering\n\\begin{boxedminipage}{\\columnwidth}\n\\begin{itemize}[itemsep=4pt,leftmargin=*]\n\n\\item[-] \\textbf{Propose.} \n A leader $L$ proposes a value $b$.\n\n\\item[-] \\textbf{Vote 1.} \n On receiving the first value $b$ for a view $v$, a replica votes for $b$ \n if it is \\emph{safe}, as determined by a locking mechanism described below.\n A set of ${q_\\text{lck}}$ votes forms a certificate $\\mathcal{C}^{{q_\\text{lck}}}(b)$.\n\n\\item[-] \\textbf{Vote 2.} \n On collecting $\\mathcal{C}^{{q_\\text{lck}}}(b)$,\n a replica ``locks'' on $b$ and votes for $\\mathcal{C}^{{q_\\text{lck}}}(b)$.\n\n\\item[-] \\textbf{Commit.} \n On collecting ${q_\\text{unq}}$ votes for $b$ and ${q_\\text{cmt}}$ votes for $\\mathcal{C}^{{q_\\text{lck}}}(b)$, \n a client learns that proposal $b$ becomes a committed decision. \n\n\\end{itemize}\nIf a replica locks on a value $b$ in a view, then it votes only for $b$ in subsequent views \nunless it ``unlocks'' from $b$ by learning that ${q_\\text{ulck}}$\n replicas are not locked on $b$.\n\\end{boxedminipage}\n\\end{figure}\n\n\\begin{description}[topsep=8pt,itemsep=4pt]\n\\item[Flexible quorum intersection (a) within a view.]\nContrary to PBFT, in Flexible BFT\\xspace, a pair of ${q_\\text{lck}}$ certificates need not necessarily\nintersect in an honest replica. \nIndeed, locking on a value does not preclude conflicting locks.\nIt only mandates that every ${q_\\text{lck}}$ quorum\nintersects with every ${q_\\text{unq}}$ quorum at at least one honest replica. \nFor safety, it is essential\nthat the fraction of faulty replicas is less than ${q_\\text{lck}}+{q_\\text{unq}}-1$.\n\n\\item[Flexible quorum intersection (b) across views.]\nIf a client commits a value $b$ in a view, \n${q_\\text{cmt}}$ replicas lock on $b$ in that view.\nFor an honest replica among them to unlock from $b$,\n${q_\\text{ulck}}$ replicas need to claim they are not locked on $b$.\nThis property mandates that every ${q_\\text{ulck}}$ quorum intersects\nwith every ${q_\\text{cmt}}$ quorum at at least one honest replica.\nThus, for safety, it is essential that the fraction of faulty\nreplicas is less than ${q_\\text{ulck}} + {q_\\text{cmt}} - 1$. \n\n\\item[Flexible quorum availability within a view.]\nFor liveness, Byzantine replicas cannot exceed \n{$1-\\max({q_\\text{unq}}, {q_\\text{cmt}}, {q_\\text{lck}}, {q_\\text{ulck}})$} \nso that the aforementioned quorums can be formed at different stages of the protocol. \n\\end{description}\n\nGiven the above analysis, Flexible BFT\\xspace ensures safety \nif the fraction of faulty replicas is less than\n$\\min({q_\\text{unq}} + {q_\\text{lck}} - 1, {q_\\text{cmt}} + {q_\\text{ulck}} - 1)$,\nand provides liveness if the fraction of Byzantine replicas \nis at most $1-\\max({q_\\text{unq}}, {q_\\text{cmt}}, {q_\\text{lck}}, {q_\\text{ulck}})$.\nIt is optimal to use \\emph{balanced quorum sizes}\nwhere ${q_\\text{lck}} = {q_\\text{ulck}}$ and ${q_\\text{unq}} = {q_\\text{cmt}}$.\nTo see this, first note that we should make sure \n${q_\\text{unq}} + {q_\\text{lck}} = {q_\\text{cmt}} + {q_\\text{ulck}}$;\notherwise, suppose the right-hand side is smaller, \nthen setting $({q_\\text{cmt}},{q_\\text{ulck}})$ to equal $({q_\\text{unq}},{q_\\text{lck}})$\nimproves safety tolerance without affecting liveness tolerance.\nNext, observe that if we have ${q_\\text{unq}} + {q_\\text{lck}} = {q_\\text{cmt}} + {q_\\text{ulck}}$\nbut ${q_\\text{lck}} > {q_\\text{ulck}}$ (and hence ${q_\\text{unq}} < {q_\\text{cmt}}$),\nthen once again setting $({q_\\text{cmt}},{q_\\text{ulck}})$ to equal $({q_\\text{unq}},{q_\\text{lck}})$\nimproves safety tolerance without affecting liveness tolerance.\n\nThus, in this paper, we set ${q_\\text{lck}} = {q_{r}}$ and ${q_\\text{unq}} =\n{q_\\text{cmt}} = {q_c}$. Since replicas use ${q_{r}}$ votes to lock,\nthese votes can always be used by the clients to commit\n${q_\\text{cmt}}$ quorums. \nThus, ${q_c} \\geq {q_{r}}$.\nThe Flexible Byzantine Quorum requirements collapse\ninto the following two conditions.\n\\begin{description}\n\\item[Flexible quorum intersection.]\nThe fraction of faulty replicas is $< {q_c} + {q_{r}} - 1$.\n\n\\item[Flexible quorum availability.]\nThe fraction of Byzantine replicas is $\\leq 1-{q_c}$.\n\\end{description}\n\n\\paragraph{Tolerating a-b-c\\xspace faults.}\nIf all faults in the system are Byzantine faults, \nthen the best parameter choice is ${q_c}={q_{r}} \\geq 2\/3$\nfor $<1\/3$ fault tolerance,\nand Flexible Byzantine Quorums degenerate to basic Byzantine quorums.\nHowever, in our model, a-b-c\\xspace replicas are only interested in attacking safety but not liveness.\nThis allows us to tolerate \n${q_c} + {q_{r}} - 1$ total faults (Byzantine plus a-b-c\\xspace), which can be more than $1\/3$. \nFor example, if we set ${q_{r}} = 0.7$ and ${q_c} = 0.8$, \nthen such a protocol can tolerate $0.2$ Byzantine faults plus\n$0.3$ a-b-c\\xspace faults.\nWe discuss the choice for ${q_{r}}$ and ${q_c}$ and their rationale in Section~\\ref{sec:discussion}.\n\n\\paragraph{Separating client commit rules from the replica protocol.}\n\\label{sec:client-coexist-partial}\nA key property of the Flexible Byzantine Quorum approach is that\nit decouples the BFT protocol from \nclient commit rules. \nThe decoupling allows \nclients assuming different fault models\nto utilize the same protocol.\nIn the above protocol, the propose and two voting steps \nare executed by the replicas and they are only parameterized by ${q_{r}}$.\nThe commit step can be carried by different clients using different commit thresholds ${q_c}$. \nThus, a fixed ${q_{r}}$ determines a possible set of clients with\nvarying commit rules (in terms of Byzantine and a-b-c\\xspace adversaries).\nRecall that a Byzantine adversary can behave arbitrarily and thus may not\nprovide liveness whereas an a-b-c\\xspace adversary only\nintends to attack safety but not liveness.\nThus, a client who believes that a large fraction of\nthe adversary may attempt to break safety, not progress, can choose a larger ${q_c}$.\nBy doing so, it seeks stronger safety against dishonest replicas, while trading \n liveness.\nConversely, a client that assumes that a large fraction of the adversary\nattacks liveness must choose a smaller ${q_c}$. \n\n\n\n\\subsection{Safety and Liveness}\n\\label{sec:proof}\n\nWe introduce the notion of \\emph{direct} and \\emph{indirect} commit to aid the proofs.\nWe say a block is committed \\emph{directly} under \\textbf{CR1} if\nthe block and its immediate successor both get ${q_c}$ votes in the same view.\nWe say a block is committed \\emph{directly} under \\textbf{CR2} if some honest replica\nreports an undisturbed-$2\\Delta$ period after its successor block\nwas obtained.\nWe say a block is committed \\emph{indirectly} if neither condition applies to it but\nit is committed as a result of a block extending it being committed directly.\nWe remark that the direct commit notion, especially for \\textbf{CR2}, is merely a proof technique.\nA client cannot tell whether a replica is honest,\nand thus has no way of knowing whether a block is directly committed under \\textbf{CR2}. \n\n\\begin{lemma}\nIf a client directly commits a block $B_l$ in view $v$ using a correct commit rule,\nthen a certified block that ranks no lower than $\\CommitCert_{v}(B_{l})$ must equal or extend $B_l$. \n\\label{lemma:unique-cert}\n\\end{lemma}\n\n\\begin{proof}\nTo elaborate on the lemma, \na certified block $\\CommitCert_{v'}(B'_{l'})$ ranks no lower than $\\CommitCert_{v}(B_{l})$\nif either (i) $v'=v$ and $l' \\geq l$, or (ii) $v'>v$.\nWe need to show that if $B_l$ is directly committed, \nthen any certified block that ranks no lower either equals or extends $B_l$.\nWe consider the two commit rules separately. \nFor both commit rules, we will use induction on $v'$ to prove the lemma.\n\n\\bigskip\nFor $\\textbf{CR1}$ with parameter ${q_c}$ to be correct, \nflexible quorum intersection needs to hold, i.e.,\nthe fraction of faulty replicas must be less than ${q_c}+{q_{r}}-1$.\n$B_l$ being directly committed under $\\textbf{CR1}$ with parameter ${q_c}$ implies that \nthere are ${q_c}$ votes in view $v$ for $B_l$ and $B_{l+1}$ where $B_{l+1}$ extends $B_l$. \n\nFor the base case, a block $B'_{l'}$ with $l'\\geq l$ that does not extend $B_l$ cannot get certified in view $v$,\nbecause that would require ${q_c}+{q_{r}}-1$ replicas to vote for two equivocating blocks in view $v$.\n\nNext, we show the inductive step.\nNote that ${q_c}$ replicas voted for $B_{l+1}$ in view $v$,\nwhich contains $\\CommitCert_{v}(B_{l})$.\nThus, they lock $B_l$ or a block extending $B_l$ by the end of view $v$.\nDue to the inductive hypothesis,\nany certified block that ranks equally or higher from view $v$ up to view $v'$ \neither equals or extends $B_l$. \nThus, by the end of view $v'$,\nthose ${q_c}$ replicas still lock $B_l$ or a block extending $B_l$.\nSince the total fraction of faults is less than ${q_c}+{q_{r}}-1$,\nthe status $\\mathcal{S}$ shown by the leader of view $v'+1$ \nmust include a certificate for $B_l$ or a block extending it;\nmoreover, any certificate that ranks equal to or higher than $\\CommitCert_{v}(B_{l})$\nis for a block that equals or extends $B_l$. \nThus, only a block that equals or extends $B_l$ can gather votes from those ${q_c}$ replicas in view $v'+1$\nand only a block that equals or extends $B_l$ can get certified in view $v'+1$.\n\n\\bigskip\nFor $\\textbf{CR2}$ with synchrony bound $\\Delta$ to be correct, \n$\\Delta$ must be an upper bound on worst case message delay \nand the fraction of faulty replicas is less than ${q_{r}}$. \n$B_l$ being directly committed under $\\textbf{CR2}$ with $\\Delta$-synchrony implies that\nat least one honest replica voted for $B_{l+1}$ extending $B_l$ in view $v$,\nand did not hear an equivocating block or view change within $2\\Delta$ time after that.\nCall this replica $h$.\nSuppose $h$ voted for $B_{l+1}$ extending $B_l$ in view $v$ at time $t$,\nand did not hear an equivocating block or view change by time $t+2\\Delta$. \n\nWe first show the base case: a block $B'_{l'}$ with $l'\\geq l$ certified in view $v$ must equal or extend $B_l$.\nObserve that if $B'_{l'}$ with $l'\\geq l$ does not equal or extend $B_l$,\nthen it equivocates $B_l$.\nNo honest replica voted for $B'_{l'}$ before time $t+\\Delta$,\nbecause otherwise $h$ would have received the vote for $B'_{l'}$ by time $t+2\\Delta$,\nNo honest replica would vote for $B'_{l'}$ after time $t+\\Delta$ either,\nbecause by then they would have received (from $h$) and voted for $B_l$.\nThus, $B'_{l'}$ cannot get certified in view $v$.\n\nWe then show the inductive step.\nBecause $h$ did not hear view change by time $t+2\\Delta$,\nall honest replicas are still in view $v$ by time $t+\\Delta$,\nwhich means they all receive $B_{l+1}$ from $h$ by the end of view $v$.\nThus, they lock $B_l$ or a block extending $B_l$ by the end of view $v$.\nDue to the inductive hypothesis,\nany certified block that ranks equally or higher from view $v$ up to view $v'$ \neither equals or extends $B_l$. \nThus, by the end of view $v'$,\nall honest replicas still lock $B_l$ or a block extending $B_l$.\nSince the total fraction of faults is less than ${q_{r}}$,\nthe status $\\mathcal{S}$ shown by the leader of view $v'+1$ \nmust include a certificate for $B_l$ or a block extending it;\nmoreover, any certificate that ranks equal to or higher than $\\CommitCert_{v}(B_{l})$\nis for a block that equals or extends $B_l$. \nThus, only a block that equals or extends $B_l$ can gather honest votes in view $v'+1$\nand only a block that equals or extends $B_l$ can get certified in view $v'+1$.\n\\end{proof}\n\n\n\\begin{theorem}[Safety]\nTwo clients with correct commit rules commit the same block $B_k$ for each height $k$.\n\\label{thm:safety}\n\\end{theorem}\n\n\\begin{proof}\nSuppose for contradiction that two distinct blocks \n$B_k$ and $B'_k$ are committed at height $k$.\nSuppose $B_k$ is committed as a result of $B_{l}$ being directly committed in view $v$\nand $B'_k$ is committed as a result of $B'_{l'}$ being directly committed in view $v'$.\nThis implies $B_l$ is or extends $B_k$;\nsimilarly, $B'_{l'}$ is or extends $B'_k$.\nWithout loss of generality, assume $v \\leq v'$.\nIf $v=v'$, further assume $l \\leq l'$ without loss of generality.\nBy Lemma~\\ref{lemma:unique-cert},\nthe certified block $\\CommitCert_{v'}(B'_{l'})$ must equal or extend $B_l$.\nThus, $B'_k=B_k$.\n\\end{proof}\n\n\n\n\\begin{theorem}[Liveness]\nIf all clients have correct commit rules, \nthey all keep committing new blocks. \n\\label{thm:liveness}\n\\end{theorem}\n\n\\begin{proof}\nBy the definition of a-b-c\\xspace faults,\nif they cannot violate safety, they will preserve liveness.\nTheorem~\\ref{thm:safety} shows that if all clients have correct commit rules,\nthen safety is guaranteed \\emph{even if a-b-c\\xspace replicas behave arbitrarily}.\nThus, once we proved safety, we can treat a-b-c\\xspace replicas \nas honest when proving liveness. \n\nObserve that a correct commit rule tolerates at most $1-{q_{r}}$ Byzantine faults.\nIf a Byzantine leader prevents liveness, \nthere will be ${q_{r}}$ blame messages against it,\nand a view change will ensue to replace the leader. \nEventually, a non-Byzantine (honest or a-b-c\\xspace) replica becomes the leader \nand drives consensus in new heights.\nIf replicas use increasing timeouts,\neventually, all non-Byzantine replicas stay in the same view for sufficiently long.\nWhen both conditions occur, \nif a client's commit rule is correct (either \\textbf{CR1} and \\textbf{CR2}),\ndue to quorum availability,\nit will receive enough votes in the same view to commit. \n\\end{proof}\n\n\n\\section{Flexible BFT Protocol}\n\\label{sec:protocol}\n\nIn this section, we combine the ideas presented in\nSections~\\ref{sec:overv-synchr-flex} and \\ref{sec:overview-async}\nto obtain a final protocol that supports both types of clients.\nA client can either assume partial synchrony, with freedom to choose ${q_c}$ as described\nin the previous section, or assume synchrony with its own choice of\n$\\Delta$, as described in Section~\\ref{sec:overv-synchr-flex}. \nReplicas execute a protocol at the network speed\nwith a parameter ${q_{r}}$.\nWe first give the protocol executed by the replicas \nand then discuss how clients commit depending on their assumptions. \nMoreover, inspired by Casper~\\cite{DBLP:journals\/corr\/abs-1710-09437} and\nHotStuff~\\cite{yin2018hotstuff}, we show a protocol where the\nrounds of voting can be pipelined. \n\n\n\\subsection{Notation}\nBefore describing the protocol, we will first define some\ndata structures and terminologies that will aid presentation.\n\n\\paragraph{Block format.} \nThe pipelined protocol forms a chain of values.\nWe use the term \\emph{block} to refer to each value in the chain.\nWe refer to a block's position in the chain as its \\emph{height}. \nA block $B_k$ at height $k$ has the following format\n$$B_k := (b_k, h_{k-1})$$ \nwhere $b_k$ denotes a proposed value at height $k$ \nand $h_{k-1} := H(B_{k-1})$ is a hash digest of the predecessor block.\nThe first block $B_1=(b_1, \\bot)$ has no predecessor.\nEvery subsequent block $B_k$ must specify a predecessor block $B_{k-1}$ by including a hash of it.\nWe say a block is \\emph{valid} if \n(i) its predecessor is valid or $\\bot$, and \n(ii) its proposed value meets application-level validity conditions and is consistent with its chain of ancestors (e.g., does not double spend a transaction in one of its ancestor blocks). \n\n\\paragraph{Block extension and equivocation.}\nWe say $B_l$ \\emph{extends} $B_k$, \nif $B_k$ is an ancestor of $B_l$ ($l>k$). \nWe say two blocks $B_l$ and $B'_{l'}$ \\emph{equivocate} one another \nif they are not equal and do not extend one another.\n\n\n\\paragraph{Certificates and certified blocks.} \nIn the protocol, replicas vote for blocks by signing them.\nWe use $\\CommitCert_{v}{(B_{k})}$ to denote a set of signatures \non $h_{k} = H(B_{k})$ by ${q_{r}}$ replicas in view $v$. \n${q_{r}}$ is a parameter fixed for the protocol instance. \nWe call $\\CommitCert_{v}{(B_{k})}$ a certificate for $B_{k}$ from view $v$.\nCertified blocks are ranked \nfirst by the views in which they are certified and then by their heights.\nIn other words, a block $B_k$ certified in view $v$ \nis ranked \\emph{higher} than a block $B_{k'}$ certified in view $v'$\nif either (i) $v > v'$ or (ii) $v = v'$ and $k>k'$. \n\n\\paragraph{Locked blocks.} \nAt any time, a replica locks the highest certified block to its knowledge.\nDuring the protocol execution, each replica keeps track of all signatures for all blocks\nand keeps updating its locked block.\nLooking ahead, the notion of locked block will be\nused to guard the safety of a client commit.\n\n\\subsection{Replica Protocol}\n\nThe replica protocol progresses in a view-by-view fashion. Each view has a\ndesignated leader who is responsible for driving consensus on a\nsequence of blocks. Leaders can be chosen\nstatically, e.g., round robin, \nor randomly using more sophisticated techniques~\\cite{CKS05,Algorand}. \nIn our description, we assume a round robin selection of leaders,\ni.e., ($v$ {\\sf mod} $n$) is the leader of view $v$. \n\nAt a high level, the protocol does the following: \nThe leader proposes a block to all replicas. \nThe replicas vote on it if safe to do so. \nThe block becomes certified once ${q_{r}}$ replicas vote on it. \nThe leader will then propose another block extending the previous one, \nchaining blocks one after another at increasing heights.\nUnlike regular consensus protocols\nwhere replicas determine when a block is committed, in\nFlexible BFT\\xspace, replicas only certify blocks while committing is\noffloaded to the clients.\nIf at any time replicas detect malicious leader behavior or lack of\nprogress in a view, they blame the leader \nand engage in a view change protocol \nto replace the leader and move to the next view. \nThe new leader collects a status from different replicas and continues to propose\nblocks based on this status.\nWe explain the steady state and view change protocols in more detail below.\n\n\\begin{figure*}[tb]\n\\centering\n\\begin{boxedminipage}{\\textwidth}\n\nLet $v$ be the current view number and \nreplica $L$ be the leader in this view. \nPerform the following steps in an iteration.\n\n\\begin{enumerate}[topsep=8pt,itemsep=8pt,leftmargin=*]\n\\setlength\\itemsep{0.5em}\n\\item \\textbf{Propose. } \\label{step:propose} \n\\Comment{Executed by the leader of view $v$}\n\nThe leader $L$ broadcasts $\\sig{\\mathsf{propose}, B_k, v, \\CommitCert_{v'}(B_{k-1}), \\mathcal{S}}_L$.\nHere, $B_k := (b_k, h_{k-1})$ is the newly proposed block\nand it should extend the highest certified block known to $L$.\nIn the steady state, an honest leader $L$ would extend the\nprevious block it proposed,\nin which case $v'=v$ and $\\mathcal{S} = \\bot$. \nImmediately after a view change, \n$L$ determines the highest certified block from\nthe status $\\mathcal{S}$ received during the view change. \n\n\\item \\label{step:vote} \\textbf{Vote.} \n\\Comment{Executed by all replicas}\n\nWhen a replica $R$ receives a valid proposal \n$\\sig{\\mathsf{propose}, B_k, v, \\CommitCert_{v'}(B_{k-1}), \\mathcal{S}}_L$\nfrom the leader $L$, $R$ broadcasts the proposal and a vote $\\sig{\\mathsf{vote}, B_k, v}_R$ \nif (i) the proposal is the first one in view $v$,\nand it extends the highest certified block in $\\mathcal{S}$, \nor (ii) the proposal extends the last proposed block in the view. \n\n\nIn addition, replica $R$ records the following based on the messages it receives.\n\\begin{itemize}[topsep=8pt,itemsep=4pt]\n\\item[-] $R$ keeps track of the number of\n votes received for this block in this view as $q_{B_{k},v}$. \n\\item[-] If block $B_{k-1}$ has been proposed in view\n $v$, $R$ marks $B_{k-1}$ as a locked block and\n records the locked time as ${\\mathsf{t\\text{-}{lock}}}_{k-1,v}$.\n\\item[-] If a block equivocating $B_{k-1}$\n is proposed by $L$ in view $v$ (possibly received through a vote),\n $R$ records the time ${\\mathsf{t}\\text{-}\\mathsf{equiv}}_{k-1,v}$ at\n which the equivocating block is received. \n\\end{itemize}\n\nThe replica then enters the next iteration.\nIf the replica observes no progress or equivocating blocks in the same view $v$, \nit stops voting in view $v$ \nand sends $\\sig{\\mathsf{view\\text{-}change},v}_r$ message to all replicas. \n\\end{enumerate}\n\n\\end{boxedminipage}\n\\caption{Flexible BFT steady state protocol.}\n\\label{fig:steady}\n\\end{figure*}\n\n\\paragraph{Steady state protocol.}\nThe steady state protocol is described in Figure~\\ref{fig:steady}.\nIn the steady state, there is a unique leader who, in an iteration,\nproposes a block, waits for votes from ${q_{r}}$\nreplicas and moves to the next iteration.\nIn the steady state, an honest leader always extends the previous block it proposed.\nImmediately after a view change, \nsince the previous leaders could have been\nByzantine and may have proposed equivocating blocks, \nthe new leader needs to determine a safe block to propose. \nIt does so by collecting a status of\nlocked blocks from ${q_{r}}$ replicas denoted by $\\mathcal{S}$\n(described in the view change protocol).\n\nFor a replica $R$ in the steady state, on receiving a proposal for block\n$B_{k}$, a replica votes for it if \nit extends the previous proposed block in the view or \nif it extends the highest certified block in $\\mathcal{S}$.\nReplica $R$ can potentially receive blocks out of\norder and thus receive $B_{k}$ before its ancestor blocks. \nIn this case, replica $R$ waits until it receives the ancestor blocks, \nverifies the validity of those blocks and $B_k$ before voting for $B_k$.\nIn addition, replica $R$ records the following to aid a client commit:\n\\begin{itemize}\n\\item[-] \\textbf{Number of votes.} It records the number of votes received\n for $B_{k}$ in view $v$ as $q_{B_{k},\n v}$. Observe that votes are broadcast by \n all replicas and the number of votes for a block can be greater\n than ${q_{r}}$. $q_{B_{k}, v}$ will be updated each time the\n replica hears about a new vote in view $v$.\n\\item[-] \\textbf{Lock time.} If $B_{k-1}$ was proposed in the\n same view $v$, it locks $B_{k-1}$ and records the locked time as\n ${\\mathsf{t\\text{-}{lock}}}_{k-1, v}$.\n\\item[-] \\textbf{Equivocation time.} If the replica ever observes an\n equivocating block at height $k$ in view $v$ through a\n proposal or vote, \n it stores the time of equivocation as ${\\mathsf{t}\\text{-}\\mathsf{equiv}}_{k,v}$.\n\\end{itemize}\nLooking ahead, the locked time ${\\mathsf{t\\text{-}{lock}}}_{k-1,v}$ and\nequivocation time ${\\mathsf{t}\\text{-}\\mathsf{equiv}}_{k-1,v}$ will\nbe used by clients with synchrony assumptions to commit,\nand the number of votes $q_{B_{k}, v}$\nwill be used by clients with partial-synchrony assumptions to commit.\n\n\\paragraph{Leader monitoring.}\nIf a replica detects a lack of progress in view $v$ or\nobserves malicious leader behavior\nsuch as more than one height-$k$ blocks in the same view,\nit blames the leader of view $v$\nby broadcasting a $\\sig{\\mathsf{view\\text{-}change}, v}$ message. It quits\nview $v$ and stops voting and broadcasting blocks in\nview $v$. \nTo determine lack of progress, the replicas may simply guess a time bound for\nmessage arrival or use increasing timeouts for each view~\\cite{castro1999practical}.\n\n\\paragraph{View change.}\nThe view change protocol is described in Figure~\\ref{fig:vc}.\nIf a replica gathers ${q_{r}}$ $\\sig{\\mathsf{view\\text{-}change}, v}$\nmessages from distinct replicas,\nit forwards them to all other replicas and enters a new view\n$v + 1$ (Step~\\ref{step:new_view}). It records the time\nat which it received the blame certificate as ${\\mathsf{t\\text{-}{viewchange}}}_{v}$.\nUpon entering a new view,\na replica reports to the leader of the new view $L'$ its locked block \nand transitions to the steady state (Step~\\ref{step:status}). \n${q_{r}}$ status messages form the status $\\mathcal{S}$.\nThe first block $L'$ proposes in the new view \nshould extend the highest certified block among these ${q_{r}}$ status messages.\n\n\\begin{figure*}[htbp]\n\\centering\n\\begin{boxedminipage}{\\textwidth}\n\nLet $L$ and $L'$ be the leaders of views $v$ and $v+1$, respectively.\n\\begin{enumerate}[topsep=8pt,itemsep=8pt,leftmargin=*,label=(\\roman*)]\n\\setlength\\itemsep{0.5em}\n\\item \\label{step:new_view} \\textbf{New-view.}\nUpon gathering ${q_{r}}$ $\\sig{\\mathsf{view\\text{-}change}, v}$ messages,\nbroadcast them and enter view $v+1$. Record the time as ${\\mathsf{t\\text{-}{viewchange}}}_{v}$.\n\n\\item \\label{step:status} \\textbf{Status.}\nSuppose $B_j$ is the block locked by the replica. Send a\nstatus of its locked block to the leader $L'$ using\n$\\sig{\\mathsf{status}, v, B_j, \\CommitCert_{v'}(B_j)}$ and transition\nto the steady state. Here, $v'$ is the view in which $B_j$\nwas certified.\n\n\\end{enumerate}\n\n\\end{boxedminipage}\n\\caption{Flexible BFT view change protocol.}\n\\label{fig:vc}\n\\end{figure*}\n\n\n\n\\subsection{Client Commit Rules}\n\\label{sec:commit-rules}\n\n\n\\begin{figure*}[htbp]\n \\centering\n \\begin{boxedminipage}{\\textwidth}\n \\begin{enumerate}[itemsep=8pt,leftmargin=0.2cm,label=]\n \n \\item \\textbf{(CR1) Partially-synchronous commit.} A\n block $B_k$ is committed under the partially synchronous rule with parameter ${q_c}$ \n iff there exist $l \\geq k$ and $v$ such that\n \\begin{enumerate}[topsep=8pt,itemsep=4pt]\n\t\t\t\t\\item $\\CommitCert_{v}(B_l)$ and $\\CommitCert_{v}(B_{l+1})$ \n\t\t\t\t\t\texist where $B_{l+1}$ extends $B_l$ and $B_k$ (if $l = k$, $B_l = B_k$).\n \\item $q_{B_{l}, v} \\geq {q_c}$ and $q_{B_{l+1}, v} \\geq {q_c}$.\n \\end{enumerate}\n \n\t\t\\item \\textbf{(CR2) Synchronous commit.}\n A block $B_k$ is committed assuming $\\Delta-$synchrony \n\t\tiff the following holds for ${q_{r}}$ replicas.\n\t\tThere exist $l \\geq k$ and $v$ (possibly\n different across replicas) such that,\n \\begin{enumerate}[topsep=8pt,itemsep=4pt]\n \\item $\\CommitCert_{v}(B_l)$ exists where $B_l$ extends $B_k$ (if $l = k$, $B_l = B_k$).\n \\item An undisturbed-$2\\Delta$ period is observed after\n $B_{l+1}$ is obtained, i.e., no equivocating block\n or view change of view $v$ were\n observed before $2\\Delta$ time after $B_{l+1}$\n was obtained, i.e.,\n $$\\min({\\mathsf{current}\\text{-}\\mathsf{time}}, {\\mathsf{t}\\text{-}\\mathsf{equiv}}_{l,v},\n {\\mathsf{t\\text{-}{viewchange}}}_v) - {\\mathsf{t\\text{-}{lock}}}_{l,v}\n \\geq 2\\Delta$$\n \\end{enumerate}\n \\end{enumerate}\n \\end{boxedminipage}\n \\caption{Flexible BFT commit rules}\n \\label{fig:commit-rules}\n\\end{figure*}\n\nAs mentioned in the introduction, Flexible BFT\\xspace supports \nclients with different assumptions. \nClients in Flexible BFT\\xspace learn the\nstate of the protocol from the replicas and based on their\nown assumptions determine whether a block has been committed.\nBroadly, we supports two types of clients:\nthose who believe in synchrony and those who believe in partial synchrony.\n\n\\subsubsection{Clients with Partial-Synchrony Assumptions (CR1)}\nA client with partial-synchrony assumptions deduces whether a block has been committed\nby based on the number of votes received by a block. \nA block $B_l$ (together with its ancestors) is committed with parameter ${q_c}$\niff $B_l$ and its immediate\nsuccessor both receive $\\geq {q_c}$ votes in the same view. \n\n\\paragraph{Safety of CR1.}\nA CR1 commit based on ${q_c}$ votes is safe \nagainst $<{q_c}+{q_{r}}-1$ faulty replicas (Byzantine plus a-b-c\\xspace).\nObserve that if $B_l$ gets ${q_c}$ votes in view $v$, \ndue to flexible quorum intersection, \na conflicting block cannot be certified in view $v$, \nunless $\\geq {q_c}+{q_{r}}-1$ replicas are faulty. \nMoreover, $B_{l+1}$ extending $B_l$ \nhas also received ${q_c}$ votes in view $v$. \nThus, ${q_c}$ replicas lock block $B_l$ in view $v$.\nIn subsequent views, honest replicas that have locked \n$B_l$ will only vote for a block \nthat equals or extends $B_l$ unless they unlock. \nHowever, due to flexible quorum intersection, \nthey will not unlock \nunless $\\geq {q_c}+{q_{r}}-1$ replicas are faulty. \nProof of Lemma~\\ref{lemma:unique-cert} formalizes this argument. \n\n\\subsubsection{Client with Synchrony Assumptions (CR2)}\nIntuitively, a CR2 commit involves ${q_{r}}$ replicas collectively stating that \nno ``bad event'' happens within ``sufficient time'' in a view.\nHere, a bad event refers to either leader equivocation or view change\n(the latter indicates sufficient replicas believe leader is\nfaulty) and the ``sufficient time'' is $2\\Delta$; \nwhere $\\Delta$ is a synchrony bound chosen by the client. \nMore formally, a replica states that a synchronous commit for block $B_k$\nfor a given parameter $\\Delta$ (set by a client) is satisfied iff the following holds.\nThere exists $B_{l+1}$ that extends $B_l$ and $B_k$,\nand the replica observes an undisturbed-$2\\Delta$ period after\nobtaining $B_{l+1}$ during which (i) no equivocating block\nis observed, and (ii) no blame certificate\/view\nchange certificate for view $v$ was obtained, i.e., \n$$\\min({\\mathsf{current}\\text{-}\\mathsf{time}}, {\\mathsf{t}\\text{-}\\mathsf{equiv}}_{l,v},\n {\\mathsf{t\\text{-}{viewchange}}}_v) - {\\mathsf{t\\text{-}{lock}}}_{l,v}\n \\geq 2\\Delta$$\nwhere ${\\mathsf{t}\\text{-}\\mathsf{equiv}}_{l,v}$ denotes the time \nequivocation for $B_l$ in view $v$ was\nobserved ($\\infty$ if no equivocation), ${\\mathsf{t\\text{-}{viewchange}}}_v$\ndenotes the time at which view change happened from view $v$\nto $v + 1$ ($\\infty$ if no view change has happened\nyet), and ${\\mathsf{t\\text{-}{lock}}}_{l,v}$ denotes the time at which \n$B_l$ was locked (or $B_{l+1}$ was proposed) in\nview $v$.\nNote that the client does not require the ${q_{r}}$ fraction of replicas \nto report the same height $l$ or view $v$.\n\n\\paragraph{Safety of CR2.}\nA client believing in synchrony assumes that all messages between replicas\narrive within $\\Delta$ time after they were sent. \nIf the client's chosen $\\Delta$ is a correct upper bound on message delay, \nthen a CR2 commit is safe against ${q_{r}}$ faulty replicas (Byzantine plus a-b-c\\xspace),\nas we explain below.\nIf less than ${q_{r}}$ replicas are faulty, at least one honest replica reported \nan \\emph{undisturbed-$2\\Delta$} period.\nLet us call this honest replica $h$ and \nanalyze the situation from $h$'s perspective\nto explain why an undisturbed $2\\Delta$ period ensures safety.\nObserve that replicas in Flexible BFT\\xspace forward the proposal when voting. \nIf $\\Delta$-synchrony holds, every other honest replica \nlearns about the proposal $B_l$ at most $\\Delta$ time after $h$ learns about it.\nIf any honest replica voted for a conflicting block \nor quit view $v$, $h$ would have known within $2\\Delta$ time. \n\n\\input{proof}\n\n\\subsection{Efficiency}\n\n\\paragraph{Latency.}\nClients with a synchrony assumption incur a latency of\n$2\\Delta$ plus a few network speed rounds. In terms of the\nmaximum network delay $\\Delta$, this matches the \nstate-of-the-art synchronous\nprotocols~\\cite{abraham2019sync}. The distinction though is that\n$\\Delta$ now depends on the client assumption and \nhence different clients may commit with different latencies\nClients with partial-synchrony assumptions incur a latency of two\nrounds of voting; this matches PBFT~\\cite{castro1999practical}.\n\n\\paragraph{Communication.}\nEvery vote and new-view messages are broadcast to all replicas,\nincurring $O(n^2)$ communication messages. This is the same\ncomplexity of PBFT~\\cite{castro1999practical} and Sync\nHotStuff~\\cite{abraham2019sync}.\n\n\n\\section{Related Work}\n\\label{sec:related-work}\n\n\\begin{figure}[htbp]\n \\centering\n \\begin{subfigure}{0.45\\textwidth}\n \\includegraphics[width=1\\linewidth]{compare.pdf}\n \\end{subfigure}\n \n \\begin{subfigure}{0.45\\textwidth}\n \n \n \\begin{tabular}{c l}\n $+$ & Partially Synchronous protocols~\\cite{castro1999practical,Yin03,Martin06,Zyzzyva07,yin2018hotstuff,buchman2016tendermint}\\\\\n $\\times$ & Synchronous Protocols~\\cite{pass2018thunderella,hanke2018dfinity,abraham2019sync,abraham2018synchronous}\\\\\n $\\blacktriangle$ & Thunderella, Sync HotStuff ($\\vartriangle$: optimistic)~\\cite{pass2018thunderella,abraham2019sync}\\\\\n $\\blacklozenge$ & Zyzzyva, SBFT ($\\lozenge$: optimistic)~\\cite{Zyzzyva07}\n \\end{tabular}\n \n \n \\end{subfigure}\n \\caption{\\textbf{Comparing Flexible BFT\\xspace to existing consensus\n protocols.} The legend represent different ${q_{r}}$ values.}\n \\label{fig:compare}\n\\end{figure}\n\nMost BFT protocols are designed with a uniform assumption about the\nsystem. The literature on BFT consensus is vast and is largely beyond scope for\nreview here; we refer the reader to the standard\ntextbooks in distributed computing~\\cite{lynch1996distributed,attiya2004distributed}.\n\n\n\\paragraph{Resilience.} Figure~\\ref{fig:compare} compares resilience in Flexible BFT\\xspace\nwith some existing consensus protocols. The x axis represents a Byzantine\nresilience threshold, the y axis the total resilience against\ncorruption under the a-b-c\\xspace fault mode. The three\ndifferent colors (red, green, blue) represent three possible instantiations of\nFlexible BFT\\xspace at different ${q_{r}}$'s. \n\nEach point in the figure represents an abstract ``client'' belief. For the partial\nsynchrony model, client beliefs form lines, and for synchronous settings,\nclients beliefs are individual circles.\nThe locus of points on a given color represents all client\nassumptions supported for a corresponding ${q_{r}}$, representing the diversity of\nclients supported. \nThe figure depicts state-of-art resilience combinations by existing consensus solutions via \nuncolored shapes, $+, \\times, \\vartriangle, \\blacktriangle, \\lozenge, \\blacklozenge$.\nPartially synchronous protocols~\\cite{castro1999practical, yin2018hotstuff,buchman2016tendermint} that tolerate one-third Byzantine faults can all be represented by the `+' symbol at $(1\/3, 1\/3)$. \nSimilarly, synchronous\nprotocols~\\cite{hanke2018dfinity,abraham2018dfinity,abraham2018synchronous}\nthat tolerate one-half Byzantine faults are represented by the\n`$\\times$' symbol at $(1\/2, 1\/2)$.\nIt is worth noting that some of these works \nemploy two commit rules that differ in number of votes \nor synchrony~\\cite{Martin06,Zyzzyva07,pass2018thunderella,abraham2019sync}.\nFor instance, Thunderella and Sync HotStuff optimistically commit \nin an asynchronous fashion based on quorums of size $\\geq 3\/4$, \nas represented by a hollow triangle at $(1\/4, 1\/2)$.\nSimilarly, FaB~\\cite{Martin06}, Zyzzyva~\\cite{Zyzzyva07} and SBFT~\\cite{gueta2018sbft}\noptimistically commit when they receive all votes but wait for two rounds of votes otherwise.\nThese are represented by two points in the figure. \nDespite the two commit rules, \nthese protocols do not have client diversity, \nall parties involved (replicas and clients) make the same assumptions\nand reach the same commit decisions.\n\n\\paragraph{Diverse client beliefs.}\nA simple notion of client diversity exists in Bitcoin's probabilistic commit rule.\nOne client may consider a transaction committed after six confirmations while another may require only one confirmation.\nGenerally, the notion of client diversity has been discussed informally at public blockchain forums. \n\nAnother example of diversity is considered in the XFT protocol~\\cite{XFT}.\nThe protocol supports two types of clients: clients that assume crash faults under partial synchrony,\nor clients that assume Byzantine faults but believe in synchrony.\nYet another notion of diversity is considered \nby the federated Byzantine consensus model and the Stellar\nprotocol~\\cite{mazieres2015stellar}.\nThe Stellar protocol allows nodes to pick their own quorums.\nOur Flexible BFT\\xspace approach instead considers diverse clients \nin terms of a-b-c\\xspace adversaries and synchrony.\nThe model and techniques in~\\cite{mazieres2015stellar} and our paper\nare largely orthogonal and complementary.\n\n\\paragraph{Flexible Paxos.} Flexible Paxos by Howard et\nal.~\\cite{DBLP:conf\/opodis\/HowardMS16} observes that \nPaxos may use non-intersecting quorums within a view \nbut an intersection is required across views.\nOur Flexible Quorum Intersection (b) can be viewed as\nits counterpart in the Byzantine and a-b-c\\xspace setting.\nIn addition, Flexible BFT\\xspace applies the flexible quorum idea\nto support diverse clients with different fault model and timing assumptions.\n\n\\paragraph{Mixed fault model.}\nFault models that mix Byzantine and crash faults have been considered in various\nworks, e.g., FaB~\\cite{Martin06} and SBFT~\\cite{abraham2019sync}. The a-b-c\\xspace\nfaults are in a sense the opposite of crash faults, mixing Byzantine with\n``anti-crashes''. \nOur a-b-c\\xspace adversary bears similarity to a rational adversary in the BAR\nmodel~\\cite{aiyer2005bar}, with several important differences.\nBAR assumes no collusion exists among rational replicas themselves and between\nrational and Byzantine replicas, whereas a-b-c\\xspace replicas have no such\nconstraint.\nBAR solutions are designed to expose cheating behavior and thus deter rational\nreplicas from cheating. The Flexible BFT\\xspace approach does not rely on deterrence for good\nbehavior, and breaks beyond the $1\/3$ ($1\/2$) corruption tolerance threshold in\nasynchronous (synchronous) systems. \nLast, BAR solutions address only the partial synchrony settings. \nAt the same time, BAR provides a game theoretic proof of rationality. \nMore generally, game theoretical modeling and analysis with collusion have been performed to other problems \nsuch as secret sharing and multiparty computation~\\cite{abraham2006distributed,lysyanskaya2006rationality,gordon2006rational,kol2008cryptography}. \nAnalyzing incentives for the a-b-c\\xspace model remains an open challenge.\n\n\n\n\\section{Synchronous BFT with Network Speed Replicas - Overview}\n\\label{sec:overv-synchr-flex}\n\\begin{figure*}[ht]\n\\begin{boxedminipage}{\\textwidth}\n\\paragraph{Protocol executed by the replicas.}\n\\begin{enumerate}\n\\setlength\\itemsep{0.5em}\n\n\\item \\textbf{Propose. } \\label{step:sync-propose} \n The leader $L$ of view $v$ \n proposes a value $b$.\n\\item \\textbf{Vote.} \\label{step:sync-vote}\n On receiving the first value $b$ in a view $v$, a replica broadcasts $b$ and votes for $b$ if it is \\emph{safe} to do so, as determined by a locking mechanism described later. \nThe replica records the following.\n\\begin{itemize}\n\\item[-] If the replica collects ${q_{r}}$ votes on $b$, \n\t\tdenoted as $\\mathcal{C}^{q_{r}}_v(b)$ and called a certificate of $b$ from view $v$, \n\t\tthen it ``locks'' on $b$ and records the lock time as ${\\mathsf{t\\text{-}{lock}}}_v$.\n\\item[-] If the replica observes an equivocating value signed by $L$ at any time after entering view $v$, it records the time of equivocation as ${\\mathsf{t}\\text{-}\\mathsf{equiv}}_v$. It blames the leader by broadcasting $\\sig{\\mathsf{view\\text{-}change}, v}$ and the equivocating values.\n\\item[-] If the replica does not receive a proposal for sufficient time in view $v$, it times out and broadcasts $\\sig{\\mathsf{view\\text{-}change}, v}$. \n\\item[-] If the replica collects a set of ${q_{r}}$ $\\sig{\\mathsf{view\\text{-}change}, v}$ messages, \nit records the time as ${\\mathsf{t\\text{-}{viewchange}}}_v$, broadcasts them and enters view $v+1$. \n\\end{itemize}\n\\end{enumerate}\n\nIf a replica locks on a value $b$ in a view, then it votes only for $b$ in subsequent views \nunless it ``unlocks'' from $b$ by learning that ${q_{r}}$ replicas\nare not locked on $b$ in that view or higher views (they may be locked on other values or they may not be locked at all).\n\n\\paragraph{Commit rules for clients.}\nA value $b$ is said to be committed by a client assuming $\\Delta$-synchrony \niff ${q_{r}}$ replicas\neach report that there exists a view $v$ such that, \n\\begin{enumerate}\n\\item $b$ is certified, i.e., $\\mathcal{C}^{q_{r}}_v(b)$ exists.\n\\item the replica observed an undisturbed-$2\\Delta$ period after certification, i.e., \n\t\tno equivocating value or view change was observed at a time before $2\\Delta$ after it was certified, \n\t\tor more formally, $\\min({\\mathsf{current}\\text{-}\\mathsf{time}}, {\\mathsf{t}\\text{-}\\mathsf{equiv}}_v, {\\mathsf{t\\text{-}{viewchange}}}_v) - {\\mathsf{t\\text{-}{lock}}}_v \\geq 2\\Delta$\n\\end{enumerate}\n\\end{boxedminipage}\n\\caption{Synchronous BFT with network speed replicas.}\n\\label{fig:sync-bft}\n\\end{figure*}\n\nEarly synchronous protocols~\\cite{DS83,katz2009expected,micali2017optimal} have relied on synchrony in two ways.\nFirst, the replicas assume a maximum network delay $\\Delta$ for communication between them. Second, they require a lock step execution, i.e., all replicas are in the same round at the same time.\nHanke et al. showed a synchronous protocol without lock step execution~\\cite{hanke2018dfinity}. \nTheir protocol still contains a synchronous step in which all replicas perform a blocking wait of $2\\Delta$ time before proceeding to subsequent steps. \nSync HotStuff~\\cite{abraham2019sync} improves on it further to remove replicas' blocking waits during good periods (when the leader is honest),\nbut blocking waits are still required by replicas during bad situations (view changes).\n\nIn this section, we show a synchronous protocol where the\nreplicas do not ever have blocking waits and execute at the network speed.\nIn other words, replicas run a partially synchronous protocol and do not rely on synchrony at any point. \nClients, on the other hand, rely on synchrony bounds to commit.\nThis separation is what allows our protocol to support clients with different assumptions on the value of $\\Delta$. \nTo the best of our knowledge, this is the first synchronous protocol to achieve such a separation.\nIn addition, the protocol tolerates a combined Byzantine plus a-b-c\\xspace fault ratio greater than a half (Byzantine fault tolerance is still less than half). \n\nFor simplicity, in this overview, we show a protocol for single shot consensus.\nIn our final protocol in Section~\\ref{sec:protocol}, we will consider a pipelined version of the protocol for consensus on a sequence of values. \nWe do not consider termination for the single-shot consensus protocol in this overview \nbecause our final replication protocol is supposed to run forever. \n\nThe protocol is shown in Figure~\\ref{fig:sync-bft}. It runs in a sequence of views. Each view has a designated leader who may be selected in a round robin order. The leader drives consensus in that view.\nIn each view, the protocol runs in two steps -- propose and vote. In the propose step, the leader proposes a value $b$. In the vote step, replicas vote for the value if it is \\emph{safe} to do so.\nThe vote also acts as a \\emph{re-proposal} of the value.\nIf a replica observes a set of ${q_{r}}$ votes on $b$, called a\ncertificate $\\CommitCert(b)$, it ``locks'' on $b$. \nFor now, we assume ${q_{r}} = 1\/2$.\n(To be precise, ${q_{r}}$ is slight larger than 1\/2, e.g., $f+1$ out of $2f+1$.)\nWe will revisit the choice\nof ${q_{r}}$ in Section~\\ref{sec:discussion}.\nIn subsequent views, a replica will not vote for a value other than $b$ unless it learns that ${q_{r}}$ replicas are not locked on $b$. In addition, the replicas switch views (i.e., change leader) if they either observe an equivocation or if they do not receive a proposal from the leader within some timeout. \nA client commits $b$ if ${q_{r}}$ replicas state that there exists a view in which $b$ is certified \nand no equivocating value or view change was observed at a time before $2\\Delta$ after it was certified. \nHere, $\\Delta$ is the maximum network delay the client believes in. \n\nThe protocol ensures safety if there are fewer than ${q_{r}}$ faulty replicas.\nThe key argument for safety is the following: \nIf an honest replica $h$ satisfies the commit condition for some value $b$ in a view, then \n(a) no other value can be certified and \n(b) all honest replicas are locked on $b$ at the end of that view.\nTo elaborate, satisfying the commit condition implies that some honest replica $h$ has observed an undisturbed-$2\\Delta$ period after it locked on $b$, i.e., it did not observe an equivocation or a view change. \nSuppose the condition is satisfied at time $t$. \nThis implies that other replicas did not observe an equivocation or a view change before $t-\\Delta$. \nThe two properties above hold if the quorum honesty conditions described below hold.\nFor liveness, if Byzantine leaders equivocate or do not propose a safe value, they will be blamed by both honest and a-b-c\\xspace replicas and a view change will ensue. \nEventually there will be an honest or a-b-c\\xspace leader to drive consensus if quorum availability holds.\n\n\\begin{description}[topsep=8pt,itemsep=4pt]\n\\item[Quorum honesty (a) within a view.] \nSince the undisturbed period starts after $b$ is certified, \n$h$ must have voted (and re-proposed) $b$ at a time earlier than $t-2\\Delta$. \nEvery honest replica must have received $b$ before $t - \\Delta$.\nSince they had not voted for an equivocating value by then, they must have voted for $b$.\nSince the number of faults is less than ${q_{r}}$,\nevery certificate needs to contain an honest replica's vote.\nThus, no certificate for any other value can be formed in this view. \n\t\t\n\\item[Quorum honesty (b) across views.] \n$h$ sends $\\mathcal{C}^{q_{r}}_v(b)$ at time $t-2\\Delta$. \nAll honest receive $\\mathcal{C}^{q_{r}}_v(b)$ by time $t-\\Delta$ and become locked on $b$. \nFor an honest replica to unlock from $b$ in subsequent views, \n${q_{r}}$ replicas need to claim that they are not locked on $b$. \nAt least one of them is honest \nand would need to falsely claim it is not locked, which cannot happen.\n\n\\item[Quorum availability.]\tByzantine replicas do not exceed $1-{q_{r}}$ so that ${q_{r}}$ replicas respond to the leader. \n\\end{description}\n\n\n\\paragraph{Tolerating a-b-c\\xspace faults.}\nIf we have only honest and Byzantine replicas (and no a-b-c\\xspace replicas), quorum honesty requires the fraction of Byzantine replicas $B < {q_{r}}$. Quorum availability requires $B \\leq 1-{q_{r}}$. If we optimize for maximizing $B$, we obtain ${q_{r}} \\geq 1\/2$.\nNow, suppose $P$ represents the fraction of a-b-c\\xspace replicas. Quorum honesty requires $B + P < {q_{r}}$, and quorum availability requires $B \\leq 1-{q_{r}}$. Thus, the protocol supports varying values of $B$ and $P$ at different values of ${q_{r}} > 1\/2$ such that safety and liveness are both preserved.\n\n\\paragraph{Separating client synchrony assumption from the replica protocol.}\nThe most interesting aspect of this protocol is the separation of the client commit rule from\nthe protocol design. In particular, although this is a synchronous protocol, the replica protocol does not rely on any synchrony bound. This allows clients to choose their own message delay bounds. \nAny client that uses a correct message delay bound enjoys safety. \n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nConvolutional neural network (CNN) has gained success in many fields and become the main method in pattern recognition and computer vision since AlexNet won the 2012 ImageNet competition \\cite{krizhevsky2012imagenet,deng2009imagenet}. Nowadays, the technology has also been used in real-world applications, such as applications in smartphones, intelligent recommendations on products or music, target recognition and face detection \\cite{lecun2015deep}.\n\nHowever, designing a high performance architecture is still a challenging problem. This usually needs expertise and takes long time of research by trail and error \\cite{hanxiao}. Because of numerous configurations with respect to the number of layers and details in each layer, it is impossible to explore the whole architecture space. Hence, how to automatically generate a competitive architecture becomes an important problem \\cite{bergstra2012random,miller1989designing,negrinho2017deeparchitect}.\n\nReinforcement Learning and Evolutionary Algorithm are two main architecture learning methods used for CNN structure learning \\cite{Baker,zhao,Zoph,Barret,zhao,Esteban}. Reinforcement learning uses models' performances on validation dataset as rewards and uses an agent to maximize the cumulative rewards when interacting with an environment. Evolutionary algorithm simulates evolution processes to iteratively improve models' performances.\n\nHowever, performances of the generated networks are limited by architecture search space which is determined by the algorithm's encoding system. If the encoding system can not represent one architecture, that architecture can never be learned. For example, \\cite{Lingxi} used fixed-number stages to represent network structures. Different stages are separated by pooling layer. Each stage is a directed acyclic graph (DAG). In a DAG, nodes represent layers and edges represent operations such as 1$\\times$1 convolution and 3$\\times$3 convolution. Therefore, the architecture space is directly limited by the fixed-number stages and pooling layers. Because of this, it is impossible to generate many network architectures. For example, it can't generate one network where there is a skip connection between one layer in first stage and another layer in last stage. Some other encoding systems just represent plain architectures which are composed of stacking layers. But plain networks usually suffer from learning problems such as vanishing gradient problem and exploding gradient problem. In this paper, we propose a new encoding system which have little limitations on search space. For example, the order of layers are randomly set. In our encoding system, we use short DNA strands which are composed of a series of DNA molecules (A,G,C,T) to represent layers and long DNA strands composed of short strands to represent architectures.\n\nIt is well known that network depth is of crucial importance because it can raise the ``levels'' of features. However, simply stacking layers causes the degradation problem. Skip-connections can solve the degradation problem when deepening networks \\cite{srivastava2015highway,srivastava2015training,he2016deep}. ResNet \\cite{he2016deep} uses skip-connections via residual blocks. Skip-connections containing in ResNet provide a shorter path between bottom layers and target output layer compared with normal plain architectures. Parameters of bottom layers are thus easier to be trained. DenseNet \\cite{huang2017densely} also adopts skip-connections by reusing feature maps. Because skip-connections existing in ResNet and DenseNet ease learning problem and improve model's capacity, we explore neural network architectures with skip-connections.\n\nDNA Computing is a method of computation using molecular biology techniques \\cite{braich2000solution,adleman1994molecular,boneh1996computational,kari2000using}. DNA molecules (A, G, C, T) can be used to encode information just like binary strings. DNA strands are composed of DNA molecules and can be regarded as a piece of information. DNA computing uses DNA strands to carry information. It generates large numbers of short DNA strands and then put them into DNA soup. In DNA soup, short DNA strands connect to each other to form longer DNA strands based on base pairing principle if provided suitable reaction environment. After a period of time, the soup contains a sets of candidate DNA strands that represent desired results. Then we can pick DNA strands representing desired results from the soup. For example, ``travelling salesman problem\" can be solved by DNA computing \\cite{adleman1994molecular}. We can generate different DNA strands and use them to represent a city that have to be visited. Each strand has a linkage with other strands. Within seconds, the strands form bigger ones that represent different travel routes. Then the DNA strands representing longer routes can be eliminated through chemical reaction. The remains are the solutions. In DNA soup, all connecting processes happen at the same time so that DNA computing can reduce reaction time.\n\nIn this paper, we use DNA computing algorithm to generate neural network architectures. In DNA computing algorithm, short DNA strand denoted as Layer Strand encodes one layer architecture and a piece of skip-connection information which determines whether the layer has a skip-connection with one of its previous layers. Long strands denoted as Architecture Strands are composed of short Layer Strands via base pairing. Because each layer in network have at most one skip connection with one its previous layers, DNA computing algorithm aims to explore networks with skip-connections. We have little limitations on search space. We don't limit the number of pooling layers and the depth of the architecture. The skip-connections are also randomly set for that any two layers can have a skip-connection. During DNA computing algorithm, we use Layer Strands (representing layers) as our reaction units and learn Architecture Strands (representing architectures) via base paring. After getting models (Architecture Strands) via DNA computing algorithm, we train those models on training data set and select one model according to their performance on the validation data set. We achieve 0.27\\% test error on MINST data set and 4.9\\% test error on CIFAR-10 data set.\n\n\\section{Related Work}\nIn this section, we introduce convolutional neural networks firstly. Then we introduce reinforcement learning and evolutionary algorithms for structure learning of deep networks.\n\\subsection{Convolutional Neural Networks}\nConvolutional neural networks (CNN) \\cite{Alex,Simonyan} have achieved great success in various computer tasks \\cite{Backpropagation}. Convolution neural networks are usually composed of convolution layers, pooling layers and fully connected layers. By stacking convolution layers, pooling layers and fully connected layers, we can get plain architectures.\n\nSpecialists have tried a lot to improve neural network's capacity and find that increased depth can help a lot. For example, deep models, from depth of sixteen \\cite{simonyan2014very} to thirty \\cite{ioffe2015batch}, perform well on ImageNet dataset. However, vanishing gradients and exploding gradients prevent models from being deeper \\cite{he2016deep}. By normalized initialization \\cite{lecun1998efficient,glorot2010understanding,saxe2013exact,he2015delving} and intermediate normalization layers, the problem has been solved a lot and networks can extend to tens of layers. But degradation problem happens when the models become deeper and deeper. Degradation problems mean that models' performance degrades with increased depth.\n\nResNet \\cite{he2016deep} and DenseNet \\cite{huang2017densely} can solve degradation problems well via skip-connections. Both have gained good results in ImageNet and CIFAR-10. They can also be generalized to many other data sets. Skip-connections make bottom layers have shorter pathes to output layer which makes learning easily and enriches the features. ResNet uses residual blocks to form whole architecture. In each residual block, input layer is added to output layer which is called a skip-connection. So bottom layers in ResNet have a very short path to output layer. The gradients can thus easily and effectively flow to the bottom layers via skip-connections. DenseNet reuses feature maps and increases width of each layer with little increased parameters. The input layer becomes one part of the output layer which can also be called a skip-connection. \\\\\nAs the neural networks containing skip-connections perform well in many tasks, we explore neural network architectures with skip-connections. In our encoding system, we don't limit search space. we don't limit the number of pooling layer compared with \\cite{Lingxi}. Locations of convolution layers and pooling layers are all randomly set and skip-connections are also randomly set for that one layer can have a skip-connection with any one of its previous layers.\n\\subsection {Reinforcement Learning and Evolutionary Algorithm}\nEven though Resnet and DenseNet perform well in many data sets, network architectures still need to be carefully designed for specific data set. Therefore, how to design a convolutional neural network is a very worthwhile issue. The traditional neural network is designed based on a large number of experimental experience. Recently, more and more researchers focus their research on automatically generating networks and networks have been automatically generated through Reinforcement Learning \\cite{Baker} and genetic algorithms \\cite{zhao}.\n\nReinforcement Learning usually uses a meta-controller to determine neural network architectures. It uses architectures' performance on validation data set as reward to update the meta-controller. Thus, a neural network architecture can be treated as a training sample. Because deep learning is a data driving technology, Reinforcement Learning needs a lot of samples to learn a high performance meta-controller. However, training a model spends huge computation resources. Evolutionary Algorithm simulates the process of evolution. It iterates improve its performance by operators such as mutation, crossover and selection. Evolutionary algorithm selects models according to their performances on validation data set. Thus, Evolutionary Algorithm also spends huge computation resources and time.\n\nAs the Reinforcement Learning and Evolutionary Algorithm are all data hungry methods and need huge computation sources, we aim to address that we can get a good model via DNA computing algorithm from high quality search space and only train a few of models.\n\\begin{figure*}[ht]\n\\centering\n\\includegraphics[width=3in,height=3in]{DNA_strands.jpg} \n\\caption{ Layer Strand 9 in one Architecture Strand whose maximum depth is 24 (not including fully connected layers). \\textcircled{1}: Left: Head fragment AAACG (9) of Layer Strand 9; Right: Tail of Layer Strand 8. The tail pairs with the head to form a longer strand. \\textcircled{2}: Layer type fragment AAGTC (30). Because the result of 30 mod 24 is 6 and 6 is more than 4, the layer belongs to a convolution layer. \\textcircled{3}: kernel size fragement AAAGC (6). 6 mod 4 is 2. Because the layer is a convolution layer, thus the number 2 represents 5*5 kernel size. \\textcircled{4}: Channel number fragment AAGAC (18). The result of 18 mod 6 is 0, so the channel number is 32. \\textcircled{5}: Skip-connection fragment AAAGC (6). 6 mod 9 is 6. So there is a skip-connection between layer 6 and layer 9. \\textcircled{6}: Left: The head fragment of layer 10. Right: The tail fragment of layer 9.}\n\\label{f1}\n\\end{figure*}\n\\section{Our Approach}\nIn this section, we introduce how DNA strands encode neural network architectures. And then, we introduce how to generate architectures using DNA Computing Algorithm. After introduction of model generation, we introduce how to train the learned models.\n\\subsection{Coding System}\\label{codesys}\nDNA computing algorithm uses DNA strands to encode neural network architectures and use DNA computing algorithm to generate the strands that represent architectures. In our DNA computing algorithm, we use short DNA strands denoted as Layer Strands to represent layer architectures (convolution layers and pooling layers) and long strands denoted as Architecture Strands to represent overall neural network architectures. In DNA computing algorithm, Layer Strands can form Architecture Strands via base paring between the exposed heads and tails of different Layer Strands, which is like the process that stacking layers to form architectures. The skip-connection information is encoded in each Layer Strand. And one layer can have at most one skip-connection with one of its previous layer. Those architectures with skip-connections form architecture space learned by DNA computing algorithm. We must emphasize we don't limit the search space for that we only set the maximum depth of the model. The number of pooling layers, location of convolution layers and skip-connection in each layer are all random. The detail of encoding method are described below.\n\nBecause the similarity between pooling layer and convolution layer, pooling layer and convolution layer are represented by Layer Strands that have same constructions. Each Layer Strand is composed of 6 DNA-fragments representing specific parameters of pooling layer or convolution layer, such as kernel size or channel number. Those 6 DNA-fragments include head fragment, layer type fragment, kernel size fragment, channel number fragment, skip-connection fragment and tail fragment. The head fragment represents layer number. For example, some Layer Strands represent first layer while some Layer strands represent second layer and so on. If one Layer Strand represent ith layer, it is called Layer Strand i-1. The tail fragment are specially designed to pair the head fragment of its previous Layer strand so that two Layer Strands can form a longer strand by base paring method. The layer type fragment determines which kinds of layer this strand represents, convolution layer or pooling layer. The channel number fragment determines the number of channels. The number of channels is chosen from \\{32, 64, 96, 128, 160, 192\\}. The channel number fragment of pooling layer is useless for that the channel number of pooling layer is same as its previous layer. The kernel size fragment determines the kernel size. For convolution layer, the kernel size is chosen from \\{1$\\times$1, {3$\\times$3}, {5$\\times$5}, {7$\\times7$}\\}. For pooling layer, the kernel size is chosen from \\{2$\\times$2, 3$\\times$3\\}. As for skip-connection fragment, it determines whether this layer has a skip-connection with one of its previous layer. Each layer has at most one skip-connection. Six DNA fragments are arranged according to head, layer type, kernel size, channel number, skip-connection, and tail order. In reality, DNA is a double-stranded structure with exposed head and tail. So in addition to the head and tail fragments, the other four fragments belong to double-stranded structures. The head and tail fragments are exposed with single layer structure. Thus different Layer Strands can be connected between their exposed head and tail fragments' base paring. Six fragments compose a Layer strand which is a basic unit in DNA computing.\n\nJust as Figure \\ref{f1} shows, each fragment of Layer Strand is composed of five pairs of molecules or five single molecules (head and tail). For double-stranded structure fragments, only the strand along the same side as the tail and head is useful during decoding. Therefore, the length of a Layer Strand is 30. We use molecules A, G, C, T to represent 0, 1, 2, 3 respectively. According to the quaternary decoding method, five molecules represent integers from 0 to 1023. Thus one fragment can encode 1024 (4$^5$) kinds of information. There are redundant integers in each strands and we use different groups of integers to represent different parameter values in the four double-structured fragments so that all the permutation of five molecules can be utilized. The detail will be described below.\n\nHyper-parameter N specifies the maximum number of layers in each neural network. Each fragment can be translated into a real number and the specific parameter are determined by the real number. The head fragment can be translated into integer n and the integer n represents layer n. In generation, only Layer Strands represent layer 0 (first layer) to layer N-1 can be generated. Thus, only head fragments represent layer 0 to layer N-1 can be generated. The Layer Strand representing layer i can be denoted as Layer Strand i. In this way, we can define maximum depth of neural network. Because the tail fragment representing layer L needs to pair the head fragment of layer L+1 to form a longer strand, the tail fragment of layer L is thus determined by the tail fragment of layer L+1. For example, the head fragment representing layer one is AAAAG (can be translated into number 1), then the tail fragment of the layer 0 must be TTTTC (A pairs with T and G pairs with C). Thus, the two Layer Strands can be connected by base paring between exposed AAAAG and TTTTC single-layer fragments to form a long strand. So, only N kinds of tail fragments corresponding to head tails can be generated. As for the other four kinds of fragment, they are not limited in generation. All kinds of permutation of five molecules can be generated.\n\nDuring decoding, one fragment is translated into real number n and the concrete meanings of fragments are determined by integer groups which the integers belong to. Layer type fragment can be translated into n$_t$. If the result of n$_t$ mod N is less than 4, the layer belong to pooling layer. Otherwise, it is a convolution layer. That means we may get about 4 pooling layers in the N layers. The kernel size fragment can be translated into number n$_k$. If the layer is a convolution layer, the result of n$_k$ mod 4 determines the kernel size and 0, 1, 2, 3 represent 1$\\times$1, {3$\\times$3}, {5$\\times$5}, {7$\\times7$} kernel sizes respectively. If the layer is a pooling layer, the result of n$_k$ mod 2 determines the kernel size and 0, 1 represent 2$\\times$2, 3$\\times$3 kernel sizes respectively. The channel number fragment can be translated into real number n$_c$. The results of n$_c$ mod 6 determine the channel numbers and 0, 1, 2, 3, 4, 5 represent 32, 64, 96, 128, 160, 192 respectively. The skip-connection fragment can be translated into real number n$_s$. If the layer number is L and the result of n$_c$ mod L is l, there is a skip-connection between the layer L and layer l. l should be less than L-1. Otherwise, the skip-connection fragments are useless.\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=3in,height=5.8in]{1234.jpg} \n\\caption{One of our models on CIFAR-10 that gains high accuracy. }\n\\label{f3}\n\\end{figure}\n\\subsection{Generation via DNA Computing Algorithm}\nLayer Strands are our basic reaction units of DNA computing algorithm. Hyper-parameter P defines the number of architectures need to be generated. In generation, we generate P Layer Strands i (0$\\leq$i$\\le$N-1). We can get P$\\times$N Layer Strands. In each Layer Strand, only head fragments and tail fragments are generated specially while other fragments are randomly generated. \n\nWe then put all the Layer Strands into DNA soup. In DNA soup, Layer Strand i and Layer Strand i+1 are connected via base paring between their exposed head and tail. The head fragment of Layer Strand i pairs with the tail of Layer Strand i+1 to form a double-structure fragment. Thus, the two strands form a longer strand. All the connection processes can happen at the same time.\n\nThe process finish within seconds. Then we can select DNA strands from DNA soup and eliminate the strands whose start part is not Layer Strand 0. After we get Architecture Strands, we translate them into real architectures. In the translating, we add a fully connected layer at the end of each architecture. We train them on training data set and select one model based on their performances on validation data set. We simulate DNA computing algorithm via computer. Algorithm \\ref{alg:1} illustrates the process of DNA computing algorithm.\n\\begin{algorithm}[ht]\n\t\\renewcommand{\\algorithmicrequire}{\\textbf{Input:}}\n\t\\renewcommand{\\algorithmicensure}{\\textbf{Output:}}\n\t\\caption{DNA Computing Algorithm}\n\t\\label{alg:1}\n\t\\begin{algorithmic}[1]\n\t\t\\REQUIRE {Dataset, maximum number of layers in one architecture (N), number of Layer Strands representing one layer (P);\\\\}\n\t\t\\ENSURE The network structure with the highest accuracy on test data set.\n \\FOR { i=1 to N }\n \\FOR { j=1 to P }\n\t\t \\STATE {Generate one Layer Strand i randomly. }\n \\ENDFOR\n \\ENDFOR\n\t\t\\STATE{Put all the Layer Strands into DNA soup and provide proper reaction environment.}\\\\\n \\STATE{Select Architecture Strands from DNA soup. Count the number of Architecture Strands and get number Num. }\n\t\t\\FOR{ i=1 to Number}\n\t\t\\STATE {{S}=Generate real CNN network.}\n \\ENDFOR\n \\STATE {{G} = Randomly select 100 models.}\n\t\t\\FOR { i=1 to 100 }\n\t\t \\STATE { Training network i in {G} on training data set and record its network structure and final accuracy on validation data set}\n \\ENDFOR\n\t\t\\STATE {Select the model R from G that has highest validation accuracy.}\n \\STATE {Train model R on the whole training data set and validation data set and get its accuracy A on test data set.}\n\t\t\\STATE \\textbf{return} R, A\n\t\\end{algorithmic}\n\\end{algorithm}\n\n\\subsection{Model Training}\nJust as algorithm \\ref{alg:1}, we train all the models on the training data set and get their accuracies on validation data set after getting model via DNA Computing Algorithm. We select the best model according to their performances on validation data set. After getting the best model, we merge the training data set and validation data set and train the model again. we then use the model's performance on test data set as our algorithm output. The training details are described below.\n\nTo compress the search space, we used a pre-activated convolution unit (PCC). That's to say, we use batch normalization(BN) \\cite{ioffe2015batch} and ReLU activation \\cite{Alex} before convolution operation. The stride of convolution layer is set as 1 while the stride of pooling layer is set as 2. As for skip-connections, if the layer i and layer j (i$\\leq$j) has a skip-connection. We then add layer i and layer j as output of layer j. If the two layers' channels don't map, we use 1$\\times$1 convolution with stride 1 to change channels. If the feature map size don't map, we use 1$\\times$1 convolution with stride 2 to down sample.\n\nAs for optimizer, we use momentum optimizer with momentum set to 0.9. The initial learning rate is 0.1 and the weight decay is 0.0001. The total training epochs is 60. In the tenth epoch, the learning rates is set to 0.01. In the thirtieth epoch, the learning rates is set to 0.001. \n\nThe models are trained for at most 60 epochs on training sets (CIFAR-10). As for MNIST, 10 epochs is enough for the models to be converged. Carefully designing total epochs reduces huge time. At training time, we find that with a fixed learning rate, the algorithm is converged around some epochs and had a little progress on the further epochs. If we do not decrease the learning rate, the accuracy increase little. So it's necessary to immediately decrease the learning rate after the accuracy increase slowly. If the learning rate is set properly, it reduce huge time. During the training process, we used L2 regularization.\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=3in]{6.png} \n\\caption{The learning curve of MNIST and CIFAR10 is very similar and proves the early stop strategy can also be used on MNIST.}\n\\label{f6}\n\\end{figure}\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=3in]{5.png} \n\\caption{ We randomly chose eight of all our generated models without early stop strategy. As the figure shows, the competitive models perform well all the time, but the poor models are contrary. That demonstrates we can eliminate the poor model according their learning curve. }\n\\label{f5}\n\\end{figure}\n\\subsection{Early Stop Strategy}\nIn order to further reduce running time, we used early stop strategy. Just as shown in Figure \\ref{f5} and Figure \\ref{f6}, the epochs can be reduced by carefully design. We have two main discoveries. Firstly, all the models have similar learning curves. That indicates us we can eliminate models without finishing all the training epochs. Secondly, most of models in our search space perform similarly showing that we can get a high performance model with fewer times of training. We can stop training if the model can not perform well until the specified epoch. The early stop strategy eliminates poor performance models with high probability for that good models usually perform well on an early stage. This strategy reduces the huge time and has little impact on accuracy. We set three model performance thresholds on CIFAR-10 data set that are trained for at most 60 epochs. (1) 10th epoch, 80\\% test accuracy. (2) 20th epoch, 85\\% accuracy. (3) 45th epoch, 90\\% accuracy. If the model does not reach the specified accuracy after the there epochs respectively, the models will be eliminated.\n\n\n\n\\section{Results}\nIn this section, we introduce our results on CIFAR-10 and MNIST data sets. We simulate DNA computing algorithm by computer.\n\\begin{table}[h]\n \\centering\n \\setlength{\\tabcolsep}{0.8mm}\n \\scalebox{0.9}[0.9]{\n \\begin{tabular} {|l|l|l|l|}\n \\hline\n Model&MNIST\\\\\n \\hline\n Lecun\\emph{et.al}\\cite{lecun1998gradient}&0.7\\\\\n \\hline\n Lauer\\emph{et.al}\\cite{lauer2007trainable}&0.54\\\\\n \\hline\n Jarrett\\emph{et.al}\\cite{jarrett2009best}&0.53\\\\\n \\hline\n Ranzato\\emph{et.al}\\cite{poultney2007efficient}&0.39\\\\\n \\hline\n Cirecsan\\emph{et.al}\\cite{cirecsan2012multi}&0.23\\\\\n\n \\hline\n {Our Method}&{0.27}\\\\\n \\hline\n \\end{tabular}\n }\n \\caption{Comparison of the recognition error rate (\\%) on MNIST.}\n\\end{table}\n\n\\subsection{Results on the MNIST Data Set}\nThe MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. We test our algorithm on MNIST data set in order to reduce time. We divide the training data set into two parts. 55,000 images are used as training set while 5,000 images are used as validation data set. After we get the best model according their performances on validation data set, the whole 60,000 images are used for training the model. We then test the model on test data set and use the accuracy as our method's final output.\n\nWe use DNA Computing algorithm to generate model architectures. Many of our models have gained test accuracies higher than 99.60\\% and the highest test accuracy is 99.73\\%. We show one hundred models' learning in Figure \\ref{f7}. Only few of them perform poorly indicating that our neural network architectures with random skip-connection composes a high performance search space.\n\\begin{table}[h]\n \\centering\n \\setlength{\\tabcolsep}{1.0mm}\n\n \\scalebox{0.9}[0.9]{\n \\begin{tabular}{p{1.2cm}|p{6cm}|p{1.4cm}}\n \\hline\n &Model&CIFAR-10\\\\\n \\hline\n &Maxout \\cite{goodfellow2013maxout}&9.38\\\\\n human&ResNet(depth=110) \\cite{he2016deep}&6.61\\\\\n designed&ResNet(pre-activation)\\cite{he2016identity}&4.62\\\\\n &DenseNet(L=40,k=12)\\cite{huang2017densely}&5.24\\\\\n\n \\hline\n &GeNet\\#2(G-50) \\cite{Lingxi}&7.10\\\\\n auto&Large-Scale Evolution\\cite{Esteban}&5.40\\\\\n designed&NAS (depth=39)\\cite{Lee}&6.05\\\\\n &MetaQNN\\cite{Baker}&6.92\\\\\n &EAS\\cite{cai2018efficient}&4.23\\\\\n \\hline\n &{Our Method(depth=49)}& {4.9}\\\\\n \\hline\n \\end{tabular}\n }\n \\caption{Comparison of the recognition error rate (\\%) on CIFAR-10.}\n\n\n\\end{table}\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=3in]{7.png} \n\\caption{One hundred models test accuracy on MNIST. Most of models have gained similar performances.}\n\\label{f7}\n\\end{figure}\n\n\\subsection{Results on the CIFAR-10 Data Set}\nThe CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50,000 training images and 10,000 test images. The training data set are divided into two parts, new training data set(55,000 images) and validation data set(5,000 images).\n\n\\begin{table}[h]\n \\centering\n \\setlength{\\tabcolsep}{0.8mm}\n \\scalebox{0.9}[0.9]{\n \\begin{tabular} {p{5.5cm}|p{2cm}}\n \\hline\n Model&CIFAR-10\\\\\n \\hline\n DNA computing algorithm (depth=13)&7.34\\\\\n \\hline\n DNA computing algorithm (depth=17)&6.7\\\\\n \\hline\n DNA computing algorithm (depth=21)&6.1\\\\\n \\hline\n DNA computing algorithm (depth=25)&5.65\\\\\n \\hline\n DNA computing algorithm (depth=49)&4.9\\\\\n \\hline\n \\end{tabular}\n }\n \\caption{Comparison of our models' recognition error rate (\\%) with different depth of models on CIFAR-10 data set.}\n\\end{table}\nOur best model gain 95.10\\% test accuracy with 49 layers architectures composed of convolution layers, pooling layers and a fully connected layer. Note that we use data augmentation (flip, crop, and data normalization). With few fully trained models, we still gain high test accuracy. It proves that learning models from carefully designed search space via DNA computing algorithm can gain high accuracy. It is possible for non-experts without expertise to get a high accuracy models in specific task. One of our high performance models are showed in Figure \\ref{f3}.\n\n\\section{Conclusion}\nWe propose DNA computing algorithm to learn neural networks from a well defined architecture search space. Our search space is defined by architectures with skip-connections. During training models, we use early stop strategy which saves time and computation sources. We find most models perform similarly in our search space and have similar learning curves. We prove that learning neural networks via DNA computing algorithm is feasible and gain high accuracy. And we find that local minimal is not of importance during training models and using early stop strategy can eliminate models just after several epochs of training. We conduct the algorithm in two data sets (CIFAR-10 and MNIST) and we get competitive results in comparison with evolutionary algorithm and Reinforcement Learning but training fewer models.\nWe simulate DNA computing algorithm by computer. In future work, We consider doing biochemical experiments to verify the feasibility of the method. \n\\bibliographystyle{aaai}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\nThis document serves as an example submission. It illustrates the format\nwe expect authors to follow when submitting a paper to ECCV. \nAt the same time, it gives details on various aspects of paper submission,\nincluding preservation of anonymity and how to deal with dual submissions,\nso we advise authors to read this document carefully.\n\n\\section{Initial Submission}\n\n\\subsection{Language}\n\nAll manuscripts must be in English.\n\n\\subsection{Paper length}\nPapers submitted for review should be complete. \nThe length should match that intended for final publication. \nPapers accepted for the conference will be allocated 14 pages (plus additional pages for references) in the proceedings. \nNote that the allocated 14 pages do not include the references. The reason for this policy\nis that we do not want authors to omit references for sake of space limitations.\n\nPapers with more than 14 pages (excluding references) will be rejected without review.\nThis includes papers where the margins and\nformatting are deemed to have been significantly altered from those\nlaid down by this style guide. Do not use the TIMES, or any other font than the default. The reason such papers will not be reviewed is that there is no provision for supervised revisions of manuscripts. The reviewing process cannot determine the suitability of the paper for presentation in 14 pages if it is reviewed in 16.\n\n\\subsection{Paper ID}\n\nIt is imperative that the paper ID is mentioned on each page of the manuscript.\nThe paper ID is a number automatically assigned to your submission when \nregistering your paper submission on the submission site.\n\n\nAll lines should be numbered in the initial submission, as in this example document. This makes reviewing more efficient, because reviewers can refer to a line on a page. Line numbering is removed in the camera-ready.\n\n\n\\subsection{Mathematics}\n\nPlease number all of your sections and displayed equations. Again,\nthis makes reviewing more efficient, because reviewers can refer to a\nline on a page. Also, it is important for readers to be able to refer\nto any particular equation. Just because you didn't refer to it in\nthe text doesn't mean some future reader might not need to refer to\nit. It is cumbersome to have to use circumlocutions like ``the\nequation second from the top of page 3 column 1''. (Note that the\nline numbering will not be present in the final copy, so is not an\nalternative to equation numbers). Some authors might benefit from\nreading Mermin's description of how to write mathematics:\n\\url{www.pamitc.org\/documents\/mermin.pdf}.\n\\section{Policies}\nTo avoid confusion, in case of discrepancies between policies mentioned here and those in the ECCV 2022 webpage, the web page is the one that is updated regularly and its policies shall overrule those appearing here. \n\n\\subsection{Review Process}\nBy submitting a paper to ECCV, the authors agree to the review process and understand that papers are processed by the Toronto system to match each manuscript to the best possible chairs and reviewers.\n\\subsection{Confidentiality}\nThe review process of ECCV is confidential. Reviewers are volunteers not part of the ECCV organisation and their efforts are greatly appreciated. The standard practice of keeping all information confidential during the review is part of the standard communication to all reviewers. Misuse of confidential information is a severe professional failure and appropriate measures will be taken when brought to the attention of ECCV organizers. It should be noted, however, that the organisation of ECCV is not and cannot be held responsible for the consequences when reviewers break confidentiality.\n\nAccepted papers will be published by Springer (with appropriate copyrights) electronically up to three weeks prior to the main conference. Please make sure to discuss this issue with your legal advisors as it pertains to public disclosure of the contents of the papers submitted.\n\\subsection{Dual and Double Submissions}\nBy submitting a manuscript to ECCV 2022, authors acknowledge that it has not been previously published or accepted for publication in substantially similar form in any peer-reviewed venue including journal, conference, or workshop. Furthermore, no paper substantially similar in content has been or will be submitted to a journal, another conference or workshop during the review period (March 07, 2022 \u2013 July 3, 2022). The authors also attest that they did not submit substantially similar submissions to ECCV 2022. Violation of any of these conditions will lead to rejection and the violation will be reported to the other venue or journal, which will typically lead to rejection there as well. \n\nThe goals of the dual submission policy are (i) to have exciting new work be published for the first time at ECCV 2022, and (ii) to avoid duplicating the efforts of the reviewers.\nTherefore, all papers under review are checked for dual submissions and this is not allowed, independent of the page size of submissions. \n\nFor already published papers, our policy is based upon the following particular definition of ``publication''. A publication, for the purposes of the dual submission policy, is defined to be a written work longer than four pages that was submitted for review by peers for either acceptance or rejection, and, after review, was accepted. In particular, this definition of publication does not depend upon whether such an accepted written work appears in a formal proceedings or whether the organizers declare that such work ``counts as a publication''. \n\nAn arXiv.org paper does not count as a publication because it was not peer-reviewed for acceptance. The same is true for university technical reports. However, this definition of publication does include peer-reviewed workshop papers, even if they do not appear in a proceedings, if their length is more than 4 pages including citations. Given this definition, any submission to ECCV 2022 should not have substantial overlap with prior publications or other concurrent submissions. As a rule of thumb, the ECCV 2022 submission should contain no more than 20 percent of material from previous publications. \n\n\\subsection{Requirements for publication}\nPublication of the paper in the ECCV 2022 proceedings of Springer requires that at least one of the authors registers for the conference and present the paper there. It also requires that a camera-ready version that satisfies all formatting requirements is submitted before the camera-ready deadline. \n\\subsection{Double blind review}\n\\label{sec:blind}\nECCV reviewing is double blind, in that authors do not know the names of the area chair\/reviewers of their papers, and the area chairs\/reviewers cannot, beyond reasonable doubt, infer the names of the authors from the submission and the additional material. Avoid providing links to websites that identify the authors. Violation of any of these guidelines may lead to rejection without review. If you need to cite a different paper of yours that is being submitted concurrently to ECCV, the authors should (1) cite these papers, (2) argue in the body of your paper why your ECCV paper is non trivially different from these concurrent submissions, and (3) include anonymized versions of those papers in the supplemental material.\n\nMany authors misunderstand the concept of anonymizing for blind\nreview. Blind review does not mean that one must remove\ncitations to one's own work. In fact it is often impossible to\nreview a paper unless the previous citations are known and\navailable.\n\nBlind review means that you do not use the words ``my'' or ``our''\nwhen citing previous work. That is all. (But see below for\ntechnical reports).\n\nSaying ``this builds on the work of Lucy Smith [1]'' does not say\nthat you are Lucy Smith, it says that you are building on her\nwork. If you are Smith and Jones, do not say ``as we show in\n[7]'', say ``as Smith and Jones show in [7]'' and at the end of the\npaper, include reference 7 as you would any other cited work.\n\nAn example of a bad paper:\n\\begin{quote}\n\\begin{center}\n An analysis of the frobnicatable foo filter.\n\\end{center}\n\n In this paper we present a performance analysis of our\n previous paper [1], and show it to be inferior to all\n previously known methods. Why the previous paper was\n accepted without this analysis is beyond me.\n\n [1] Removed for blind review\n\\end{quote}\n\n\nAn example of an excellent paper:\n\n\\begin{quote}\n\\begin{center}\n An analysis of the frobnicatable foo filter.\n\\end{center}\n\n In this paper we present a performance analysis of the\n paper of Smith [1], and show it to be inferior to\n all previously known methods. Why the previous paper\n was accepted without this analysis is beyond me.\n\n [1] Smith, L. and Jones, C. ``The frobnicatable foo\n filter, a fundamental contribution to human knowledge''.\n Nature 381(12), 1-213.\n\\end{quote}\n\nIf you are making a submission to another conference at the same\ntime, which covers similar or overlapping material, you may need\nto refer to that submission in order to explain the differences,\njust as you would if you had previously published related work. In\nsuch cases, include the anonymized parallel\nsubmission~\\cite{Authors14} as additional material and cite it as\n\\begin{quote}\n1. Authors. ``The frobnicatable foo filter'', BMVC 2014 Submission\nID 324, Supplied as additional material {\\tt bmvc14.pdf}.\n\\end{quote}\n\nFinally, you may feel you need to tell the reader that more\ndetails can be found elsewhere, and refer them to a technical\nreport. For conference submissions, the paper must stand on its\nown, and not {\\em require} the reviewer to go to a techreport for\nfurther details. Thus, you may say in the body of the paper\n``further details may be found in~\\cite{Authors14b}''. Then\nsubmit the techreport as additional material. Again, you may not\nassume the reviewers will read this material.\n\nSometimes your paper is about a problem which you tested using a tool which\nis widely known to be restricted to a single institution. For example,\nlet's say it's 1969, you have solved a key problem on the Apollo lander,\nand you believe that the ECCV audience would like to hear about your\nsolution. The work is a development of your celebrated 1968 paper entitled\n``Zero-g frobnication: How being the only people in the world with access to\nthe Apollo lander source code makes us a wow at parties'', by Zeus.\n\nYou can handle this paper like any other. Don't write ``We show how to\nimprove our previous work [Anonymous, 1968]. This time we tested the\nalgorithm on a lunar lander [name of lander removed for blind review]''.\nThat would be silly, and would immediately identify the authors. Instead\nwrite the following:\n\\begin{quotation}\n\\noindent\n We describe a system for zero-g frobnication. This\n system is new because it handles the following cases:\n A, B. Previous systems [Zeus et al. 1968] didn't\n handle case B properly. Ours handles it by including\n a foo term in the bar integral.\n\n ...\n\n The proposed system was integrated with the Apollo\n lunar lander, and went all the way to the moon, don't\n you know. It displayed the following behaviours\n which show how well we solved cases A and B: ...\n\\end{quotation}\nAs you can see, the above text follows standard scientific convention,\nreads better than the first version, and does not explicitly name you as\nthe authors. A reviewer might think it likely that the new paper was\nwritten by Zeus, but cannot make any decision based on that guess.\nHe or she would have to be sure that no other authors could have been\ncontracted to solve problem B. \\\\\n\nFor sake of anonymity, it's recommended to omit acknowledgements\nin your review copy. They can be added later when you prepare the final copy.\n\n\\section{Manuscript Preparation}\n\nThis is an edited version of Springer LNCS instructions adapted\nfor ECCV 2022 first paper submission.\nYou are strongly encouraged to use \\LaTeX2$_\\varepsilon$ for the\npreparation of your\ncamera-ready manuscript together with the corresponding Springer\nclass file \\verb+llncs.cls+.\n\nWe would like to stress that the class\/style files and the template\nshould not be manipulated and that the guidelines regarding font sizes\nand format should be adhered to. This is to ensure that the end product\nis as homogeneous as possible.\n\n\\subsection{Printing Area}\nThe printing area is $122 \\; \\mbox{mm} \\times 193 \\;\n\\mbox{mm}$.\nThe text should be justified to occupy the full line width,\nso that the right margin is not ragged, with words hyphenated as\nappropriate. Please fill pages so that the length of the text\nis no less than 180~mm.\n\n\\subsection{Layout, Typeface, Font Sizes, and Numbering}\nUse 10-point type for the name(s) of the author(s) and 9-point type for\nthe address(es) and the abstract. For the main text, please use 10-point\ntype and single-line spacing.\nWe recommend using Computer Modern Roman (CM) fonts, which is the default font in this template.\nItalic type may be used to emphasize words in running text. Bold\ntype and underlining should be avoided.\nWith these sizes, the interline distance should be set so that some 45\nlines occur on a full-text page.\n\n\\subsubsection{Headings.}\n\nHeadings should be capitalized\n(i.e., nouns, verbs, and all other words\nexcept articles, prepositions, and conjunctions should be set with an\ninitial capital) and should,\nwith the exception of the title, be aligned to the left.\nWords joined by a hyphen are subject to a special rule. If the first\nword can stand alone, the second word should be capitalized.\nThe font sizes\nare given in Table~\\ref{table:headings}.\n\\setlength{\\tabcolsep}{4pt}\n\\begin{table}\n\\begin{center}\n\\caption{Font sizes of headings. Table captions should always be\npositioned {\\it above} the tables. The final sentence of a table\ncaption should end without a full stop}\n\\label{table:headings}\n\\begin{tabular}{lll}\n\\hline\\noalign{\\smallskip}\nHeading level & Example & Font size and style\\\\\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\nTitle (centered) & {\\Large \\bf Lecture Notes \\dots} & 14 point, bold\\\\\n1st-level heading & {\\large \\bf 1 Introduction} & 12 point, bold\\\\\n2nd-level heading & {\\bf 2.1 Printing Area} & 10 point, bold\\\\\n3rd-level heading & {\\bf Headings.} Text follows \\dots & 10 point, bold\n\\\\\n4th-level heading & {\\it Remark.} Text follows \\dots & 10 point,\nitalic\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\end{table}\n\\setlength{\\tabcolsep}{1.4pt}\n\nHere are some examples of headings: ``Criteria to Disprove Context-Freeness of\nCollage Languages'', ``On Correcting the Intrusion of Tracing\nNon-deterministic Programs by Software'', ``A User-Friendly and\nExtendable Data Distribution System'', ``Multi-flip Networks:\nParallelizing GenSAT'', ``Self-determinations of Man''.\n\n\\subsubsection{Lemmas, Propositions, and Theorems.}\n\nThe numbers accorded to lemmas, propositions, and theorems etc. should\nappear in consecutive order, starting with the number 1, and not, for\nexample, with the number 11.\n\n\\subsection{Figures and Photographs}\n\\label{sect:figures}\n\nPlease produce your figures electronically and integrate\nthem into your text file. For \\LaTeX\\ users we recommend using package\n\\verb+graphicx+ or the style files \\verb+psfig+ or \\verb+epsf+.\n\nCheck that in line drawings, lines are not\ninterrupted and have constant width. Grids and details within the\nfigures must be clearly readable and may not be written one on top of\nthe other. Line drawings should have a resolution of at least 800 dpi\n(preferably 1200 dpi).\nFor digital halftones 300 dpi is usually sufficient.\nThe lettering in figures should have a height of 2~mm (10-point type).\nFigures should be scaled up or down accordingly.\nPlease do not use any absolute coordinates in figures.\n\nFigures should be numbered and should have a caption which should\nalways be positioned {\\it under} the figures, in contrast to the caption\nbelonging to a table, which should always appear {\\it above} the table.\nPlease center the captions between the margins and set them in\n9-point type\n(Fig.~\\ref{fig:example} shows an example).\nThe distance between text and figure should be about 8~mm, the\ndistance between figure and caption about 5~mm.\n\\begin{figure}\n\\centering\n\\includegraphics[height=6.5cm]{eijkel2}\n\\caption{One kernel at $x_s$ ({\\it dotted kernel}) or two kernels at\n$x_i$ and $x_j$ ({\\it left and right}) lead to the same summed estimate\nat $x_s$. This shows a figure consisting of different types of\nlines. Elements of the figure described in the caption should be set in\nitalics,\nin parentheses, as shown in this sample caption. The last\nsentence of a figure caption should generally end without a full stop}\n\\label{fig:example}\n\\end{figure}\n\nIf possible (e.g. if you use \\LaTeX) please define figures as floating\nobjects. \\LaTeX\\ users, please avoid using the location\nparameter ``h'' for ``here''. If you have to insert a pagebreak before a\nfigure, please ensure that the previous page is completely filled.\n\n\n\\subsection{Formulas}\n\nDisplayed equations or formulas are centered and set on a separate\nline (with an extra line or halfline space above and below). Displayed\nexpressions should be numbered for reference. The numbers should be\nconsecutive within the contribution,\nwith numbers enclosed in parentheses and set on the right margin.\nFor example,\n\\begin{align}\n \\psi (u) & = \\int_{0}^{T} \\left[\\frac{1}{2}\n \\left(\\Lambda_{0}^{-1} u,u\\right) + N^{\\ast} (-u)\\right] dt \\; \\\\\n& = 0 ?\n\\end{align}\n\nPlease punctuate a displayed equation in the same way as ordinary\ntext but with a small space before the end punctuation.\n\n\\subsection{Footnotes}\n\nThe superscript numeral used to refer to a footnote appears in the text\neither directly after the word to be discussed or, in relation to a\nphrase or a sentence, following the punctuation sign (comma,\nsemicolon, or full stop). Footnotes should appear at the bottom of\nthe\nnormal text area, with a line of about 2~cm in \\TeX\\ and about 5~cm in\nWord set\nimmediately above them.\\footnote{The footnote numeral is set flush left\nand the text follows with the usual word spacing. Second and subsequent\nlines are indented. Footnotes should end with a full stop.}\n\n\n\\subsection{Program Code}\n\nProgram listings or program commands in the text are normally set in\ntypewriter font, e.g., CMTT10 or Courier.\n\n\\noindent\n{\\it Example of a Computer Program}\n\\begin{verbatim}\nprogram Inflation (Output)\n {Assuming annual inflation rates of \n years};\n const\n MaxYears = 10;\n var\n Year: 0..MaxYears;\n Factor1, Factor2, Factor3: Real;\n begin\n Year := 0;\n Factor1 := 1.0; Factor2 := 1.0; Factor3 := 1.0;\n WriteLn('Year \n repeat\n Year := Year + 1;\n Factor1 := Factor1 * 1.07;\n Factor2 := Factor2 * 1.08;\n Factor3 := Factor3 * 1.10;\n WriteLn(Year:5,Factor1:7:3,Factor2:7:3,Factor3:7:3)\n until Year = MaxYears\nend.\n\\end{verbatim}\n\\noindent\n{\\small (Example from Jensen K., Wirth N. (1991) Pascal user manual and\nreport. Springer, New York)}\n\n\n\n\\subsection{Citations}\n\nThe list of references is headed ``References\" and is not assigned a\nnumber\nin the decimal system of headings. The list should be set in small print\nand placed at the end of your contribution, in front of the appendix,\nif one exists.\nPlease do not insert a pagebreak before the list of references if the\npage is not completely filled.\nAn example is given at the\nend of this information sheet. For citations in the text please use\nsquare brackets and consecutive numbers: \\cite{Alpher02},\n\\cite{Alpher03}, \\cite{Alpher04} \\dots\n\n\\section{Submitting a Camera-Ready for an Accepted Paper}\n\\subsection{Converting Initial Submission to Camera-Ready}\nTo convert a submission file into a camera-ready for an accepted paper:\n\\begin{enumerate}\n \\item First comment out \\begin{verbatim}\n \\usepackage{ruler}\n \\end{verbatim} and the line that follows it.\n \\item The anonymous title part should be removed or commented out, and a proper author block should be inserted, for which a skeleton is provided in a commented-out version. These are marked in the source file as \\begin{verbatim}\n \n \\end{verbatim} and \\begin{verbatim}\n \n \\end{verbatim}\n \\item Please write out author names in full in the paper, i.e. full given and family names. If any authors have names that can be parsed into FirstName LastName in multiple ways, please include the correct parsing in a comment to the editors, below the \\begin{verbatim}\\author{}\\end{verbatim} field.\n \\item Make sure you have inserted the proper Acknowledgments.\n \\end{enumerate} \n \n\\subsection{Preparing the Submission Package}\nWe need all the source files (LaTeX files, style files, special fonts, figures, bib-files) that are required to compile papers, as well as the camera ready PDF. For each paper, one ZIP-file called XXXX.ZIP (where XXXX is the zero-padded, four-digit paper ID) has to be prepared and submitted via the ECCV 2022 Submission Website, using the password you received with your initial registration on that site. The size of the ZIP-file may not exceed the limit of 60 MByte. The ZIP-file has to contain the following:\n \\begin{enumerate}\n \\item All source files, e.g. LaTeX2e files for the text, PS\/EPS or PDF\/JPG files for all figures.\n \\item PDF file named ``XXXX.pdf\" that has been produced by the submitted source, where XXXX is the four-digit paper ID (zero-padded if necessary). For example, if your paper ID is 24, the filename must be 0024.pdf. This PDF will be used as a reference and has to exactly match the output of the compilation.\n \\item PDF file named ``XXXX-copyright.PDF\": a scanned version of the signed copyright form (see ECCV 2022 Website, Camera Ready Guidelines for the correct form to use). \n \\item If you wish to provide supplementary material, the file name must be in the form XXXX-supp.pdf or XXXX-supp.zip, where XXXX is the zero-padded, four-digit paper ID as used in the previous step. Upload your supplemental file on the ``File Upload\" page as a single PDF or ZIP file of 100 MB in size or less. Only PDF and ZIP files are allowed for supplementary material. You can put anything in this file \u2013 movies, code, additional results, accompanying technical reports\u2013anything that may make your paper more useful to readers. If your supplementary material includes video or image data, you are advised to use common codecs and file formats. This will make the material viewable by the largest number of readers (a desirable outcome). ECCV encourages authors to submit videos using an MP4 codec such as DivX contained in an AVI. Also, please submit a README text file with each video specifying the exact codec used and a URL where the codec can be downloaded. Authors should refer to the contents of the supplementary material appropriately in the paper.\n \\end{enumerate}\n\nCheck that the upload of your file (or files) was successful either by matching the file length to that on your computer, or by using the download options that will appear after you have uploaded. Please ensure that you upload the correct camera-ready PDF\u2013renamed to XXXX.pdf as described in the previous step as your camera-ready submission. Every year there is at least one author who accidentally submits the wrong PDF as their camera-ready submission.\n\nFurther considerations for preparing the camera-ready package:\n \\begin{enumerate}\n \\item Make sure to include any further style files and fonts you may have used.\n \\item References are to be supplied as BBL files to avoid omission of data while conversion from BIB to BBL.\n \\item Please do not send any older versions of papers. There should be one set of source files and one XXXX.pdf file per paper. Our typesetters require the author-created pdfs in order to check the proper representation of symbols, figures, etc.\n \\item Please remove unnecessary files (such as eijkel2.pdf and eijkel2.eps) from the source folder. \n \\item You may use sub-directories.\n \\item Make sure to use relative paths for referencing files.\n \\item Make sure the source you submit compiles.\n\\end{enumerate}\n\nSpringer is the first publisher to implement the ORCID identifier for proceedings, ultimately providing authors with a digital identifier that distinguishes them from every other researcher. ORCID (Open Researcher and Contributor ID) hosts a registry of unique researcher identifiers and a transparent method of linking research activities to these identifiers. This is achieved through embedding ORCID identifiers in key workflows, such as research profile maintenance, manuscript submissions, grant applications and patent applications.\n\\subsection{Most Frequently Encountered Issues}\nPlease kindly use the checklist below to deal with some of the most frequently encountered issues in ECCV submissions.\n\n{\\bf FILES:}\n\\begin{itemize}\n \\item My submission package contains ONE compiled pdf file for the camera-ready version to go on Springerlink.\n\\item I have ensured that the submission package has all the additional files necessary for compiling the pdf on a standard LaTeX distribution.\n\\item I have used the correct copyright form (with editor names pre-printed), and a signed pdf is included in the zip file with the correct file name.\n\\end{itemize}\n\n{\\bf CONTENT:}\n\\begin{itemize}\n\\item I have removed all \\verb| \\vspace| and \\verb|\\hspace| commands from my paper.\n\\item I have not used \\verb|\\thanks| or \\verb|\\footnote| commands and symbols for corresponding authors in the title (which is processed with scripts) and (optionally) used an Acknowledgement section for all the acknowledgments, at the end of the paper.\n\\item I have not used \\verb|\\cite| command in the abstract.\n\\item I have read the Springer author guidelines, and complied with them, including the point on providing full information on editors and publishers for each reference in the paper (Author Guidelines \u2013 Section 2.8).\n\\item I have entered a correct \\verb|\\titlerunning{}| command and selected a meaningful short name for the paper.\n\\item I have entered \\verb|\\index{Lastname,Firstname}| commands for names that are longer than two words.\n\\item I have used the same name spelling in all my papers accepted to ECCV and ECCV Workshops.\n\\item I have inserted the ORCID identifiers of the authors in the paper header (see http:\/\/bit.ly\/2H5xBpN for more information).\n\\item I have not decreased the font size of any part of the paper (except tables) to fit into 14 pages, I understand Springer editors will remove such commands.\n\\end{itemize}\n{\\bf SUBMISSION:}\n\\begin{itemize}\n\\item All author names, titles, and contact author information are correctly entered in the submission site.\n\\item The corresponding author e-mail is given.\n\\item At least one author has registered by the camera ready deadline.\n\\end{itemize}\n\n\n\\section{Conclusions}\n\nThe paper ends with a conclusion. \n\n\n\\clearpage\\mbox{}Page \\thepage\\ of the manuscript.\n\\clearpage\\mbox{}Page \\thepage\\ of the manuscript.\n\nThis is the last page of the manuscript.\n\\par\\vfill\\par\nNow we have reached the maximum size of the ECCV 2022 submission (excluding references).\nReferences should start immediately after the main text, but can continue on p.15 if needed.\n\n\\clearpage\n\\bibliographystyle{splncs04}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Conclusion}\nIn this paper, we addressed the problem of long-horizon exploration and planning by introducing a novel Long-HOT benchmark. Further, we proposed a modular hierarchical transport policy (HTP) that builds a topological graph of the scene to perform exploration with the help of weighted frontiers and simplify navigation in long-horizons through a combination of motion planning and RL policy robust to imperfect hand-offs.\nOur sub-task policies are connected in novel ways with different levels of hierarchical control requiring different state representations to perform object transport. We show how our approach leads to large improvements in performance on the transport task, it's ability to generalize to harder long-horizon task settings while only training on simpler versions, and also achieve state-of-the-art numbers on MultiON. \n\n\n\\section{Experiments}\n\\input{figures\/results}\n\nWe evaluate all approaches on two tasks set in photo-realistic Matterport3D scenes (MP3d)\\cite{Matterport3D} in Habitat~\\cite{habitat_iccv2019}: Long-HOT transport, and Multi-ON~\\cite{wani2020multion} object navigation. \n\n\n\\vspace{0.4cm}\n\\noindent \\textbf{Long-HOT:}\nWe split MP3D\\cite{Matterport3D} scenes into disjoint train, validation, and test scenes~\\cite{anderson2018evaluation} each with 61\/14\/15 scenes respectively. We generate 10,000 training task configurations among the training scenes, and 3000 each of validation and test configurations. \nEach task configuration consists of a specific configuration of objects, container, goal location, and agent starting location and pose. \nFirst, we sample a goal $(x,y)$ location in the map, then sample the four object locations. These object locations (1) lie in a specified range of distances from the goal (``goal-range''), (2) at a specified minimum distance (``obj-dist-min'') away from other objects, and at (3) within a specified maximum distance (``obj-dist-max'') from at least one other object. Next, container and agent starting locations are also sampled to lie within the same goal-range as objects. All distances are geodesic. These settings permit modulating complexity: for example, large goal distances lead to harder tasks that are more exploration-intensive and need a longer task horizon. \n\n\nTable \\ref{tab:dataset} shows settings for different task levels used in our experiments. We train all methods on \\emph{default}-level tasks on 61 scenes.\nAfter training, we first evaluate them on 15 disjoint test scenes in the \\textit{default} setting and call this as ``Standard Long-HOT Task''.\nWe then perform a more focused ``Large Long-HOT Task'' evaluation on \\textit{large} scenes that have at least one dimension $>40m$ and sample \\emph{default}, \\emph{hard} and \\emph{harder} level tasks from Table \\ref{tab:dataset}.\n\n\n\\input{tables\/dataset_table}\n\n\n\n\n\n\n\\vspace{0.4cm}\n\\noindent \\textbf{MultiOn Dataset:}\nMultiOn\\cite{wani2020multion} is a sequential multi-object navigation task where the agent is required to visit objects in a predefined sequence.\nWe adapt our proposed transport policy to test it on the challenging MultiOn (3-On) task using the object goal vector as input to the high-level controller (more details in supplementary). \n\n\\input{tables\/results}\n\n\\vspace{0.4cm}\n\\noindent \\textbf{Baselines:} We compare our proposed transport policy with several baseline methods and ablations. \n\\begin{itemize}[leftmargin=*]\n\\item \\textbf{NoMap:} This baseline policy, trained using PPO\\cite{ppo}, maps RGBD image $I$, hand state $O_h$ and goal state $O_g$ directly to low-level robot actions.\n\\item \\textbf{OracleMap:} This method improves NoMap by assuming additional access to a ground truth \n2D occupancy map of the $10m\\times 10m$ area centered on the agent in the overhead view. Similar to \\cite{wani2020multion}, we evaluate two versions of OracleMap: with occupancy alone (``Occ''), and with extra annotated true locations of the task-relevant objects and container (``Occ+Obj''). \n\\item \\textbf{OracleMap-Waypoints:} This baseline represents the a popular hierarchical approach in embodied navigation~\\cite{krantz2021waypoint,xia2021relmogen}: setting navigation waypoints for a motion planner, such as A\\text{*}~\\cite{astar}. It trains an RL policy to select discretized $(x, y)$ waypoints on the map. We provide access to OracleMap (Occ+Obj) for this baseline. See supplementary for details.\n\n\\item \\textbf{MultiOn Baselines:} For Long-HOT object transport, we adapt the ProjNeuralMap baseline from \\cite{wani2020multion} that projects perspective features in top view to our task. To this, we add additional hand-state and goal state embeddings instead of object goal embeddings as in MultiOn.\nFor MultiOn, we compare against the authors' baselines~\\cite{wani2020multion}, as well as the best-performing methods from the public leaderboard.\n\n\\item \\textbf{Exploration Ablations:} We study three variants of our method with different exploration strategies: NearestFrontier, CNN, GCN. HTP-NearestFrontier uses vanilla frontier exploration\\cite{frontier} and picks the closest frontier from the agent location as the next exploration subgoal. HTP-CNN and HTP-GCN use our proposed CNN and GCN-based exploration scores for weighted frontier exploration, explained in Sec~\\ref{sec:score_pred}.\n\n\n\\end{itemize}\n\n\n\n\\subsection{Metrics}\nWe use standard evaluation metrics following previous works \\cite{Weihs_2021_CVPR,wani2020multion,gupta2019cognitive,jain2020cordial,chen2020soundspaces,anderson2018evaluation,habitat_iccv2019} and adapt a few other metrics relevant to our task setting. \n\\vspace{-0.3cm}\n\\paragraph{\\bf \\%Success:} It measures the percentage of successful episodes across the test set. An episode is successful if the agent moves all $K$ objects to the goal location. \n\n\\vspace{-0.3cm}\n\\paragraph{\\bf \\%Progress:} It measures the percentage of target objects successfully transported to the goal location.\n\n\\vspace{-0.3cm}\n\\paragraph{\\bf SPL \\& PPL:} SPL is Success weighted-by Path Length, and PPL is Progress weighted by Path Length. Since there multiple ways in which one can complete this task we substitute optimal path length in SPL and PPL calculations with a reference path length $G_{ref}$ (details in supplemetary). Any execution with path length $G_{pl} \\leq G_{ref}$ weights the success and progress values by $1.0$. Hence $\\texttt{SPL} = 1_{\\texttt{success}}\\times \\min(G_{pl}\/G_{ref},1.0)$ and $\\texttt{PPL} = \\texttt{Progress}\\times \\min(G_{pl}\/G_{ref},1.0)$. \n\n\\vspace{-0.3cm}\n\\paragraph{\\bf Episode Energy:} We adapt a similar metric from \\cite{Weihs_2021_CVPR} to our task setting. \nIt measures the amount of remaining energy to complete the episode and gives partial credit if the agent successfully moves the object closer to goal. It is defined as $E = \\sum_{k=1}^K d_{g2t^k}\/ \\sum_{k=1}^K D_{g2t^k}$ where numerator and denominator represent sum of geodesic distance of target objects to goal location at the ending and starting of the episode respectively. \n\n\\vspace{-0.3cm}\n\\paragraph{\\bf \\% Picked:} This metric measures the percentage of target objects that are successfully picked\n\\input{tables\/largeLHOT_multion}\n\n\\subsection{Results} \\label{ssec:quant_compare}\n\n\n\n\\paragraph{Standard Long-HOT Task:} \n\nTable \\ref{tab:standard} shows the results of evaluations on Standard Long-HOT task for 1000 test episodes generated using the \\emph{default} task level.\nAll variants of HTP clearly outperform NoMap and ProjNeuralMap on all six metrics. \nFig. \\ref{fig:example_results} visualizes an episode of HTP-CNN. We show video results of our work in supplementary.\n\n\n\n\\vspace{0.2cm}\n\\noindent\\emph{Are hierarchies good?} HTP-NearestFrontier already outperforms the non-hierarchical flat baselines by a large margin, showing the importance of our modular hierarchical approach involving separate policies for different task phases, coupled with a topological map. Interestingly, not all hierarchies are good: in particular, OracleMap-Waypoints, which sets waypoint subgoals for a motion planner, performs clearly worse than flat OracleMap (``Occ+Obj''). Note that OracleMap methods have access to ground truth map information and are not directly comparable with HTP, but can be meaningfully compared among themselves. \n\n\\vspace{0.2cm}\n\\noindent\\emph{Does weighted frontier exploration work?} Among HTP variants, both HTP-GCN and HTP-CNN, which use predicted scores for weighted frontier exploration, clearly outperform HTP-NearestFrontier. Between them, GCN and CNN are roughly equivalent in this setting.\n\n\\vspace{0.2cm}\n\\noindent\\emph{How important is good agent-centered occupancy and object location information?} On this test set, access to the ground truth occupancy and object maps centered around the agent significantly improves performance, with OracleMap (Occ+Obj) performing the best out of all methods.\n\n\n\n\n\n\n\\vspace{-0.2cm}\n\\paragraph{Large Long-HOT Task:} We now evaluate these same trained policies on more challenging settings in large scenes, with more difficult transport task levels. This evaluates generalization and highlights the benefits of effective hierarchy and modularity. Note that scenes used for testing in Large Long-HOT have some overlap with training scenes but not with the same episodes. This is due to limited large scenes available in the disjoint test set. But our observation of better generalization stands since all methods have the same advantage, but others suffer severe drops compared to ours.\n\nTable \\ref{tab:deep_transport} (left) shows the results. Flat end-to-end approaches like NoMap and OracleMap deteriorate catastrophically on \\textit{hard} and \\textit{harder} level tasks. \nNoMap's \\%Success drops from 39\\% on \\emph{default} to\na mere 5.2\\% and 2.8\\% on \\emph{hard} and \\emph{harder} levels. \nHTP methods degrade more gracefully, achieving up to 33\\% and 22\\% on \\textit{hard} and \\textit{harder}. In fact, as task difficulty increases, HTP significantly closes the performance gap to OracleMap-Waypoints despite the oracle method's access to ground truth map information. We believe this is because our modular approach with weighted frontier exploration leads to better generalization compared to waypoint setting as in OracleMap-Waypoints. Further, OracleMap-Waypoints performs better than OracleMap (Occ+Obj) confirming the benefits of subgoals in long-horizon settings. \nTo further study the dependence of HTP's model components on the task performance and its generalization to increasing number of object goals we conduct several ablations, results for which are available in the supplementary. \n\n\n\nOverall, this large effect of small increments in the spatial task scale at \\textit{hard} and \\textit{harder} levels (Tab~\\ref{tab:dataset}) shows how Long-HOT stress-tests planning, exploration and reasoning over long spatial and temporal horizons. This is different from prior efforts~\\cite{wani2020multion} that extend a task by adding new objects, but pre-specify a sequence of single-object sub-tasks.\nOur HTP approach, which leverages hierarchical policies and topological maps, is a first step towards addressing these unique challenges induced by Long-HOT. \nNote that, difficulty of Long-HOT is expected to be a lot higher with the end criterion of MultiON, analysis for which will be in the supplementary.\n\n\n\n\n\n\n\n\n\n\n\\vspace{-0.2cm}\n\\paragraph{Results on MultiOn:} Finally, we also evaluate our proposed HTP framework on the MultiOn\\cite{wani2020multion} challenge. Table \\ref{tab:deep_transport} (right) shows that our method significantly outperforms other baselines from \\cite{wani2020multion}. Moreover, its performance is nearly on par or better with the CVPR 2021 Embodied AI workshop challenge winner \\cite{marza2021teaching}. Note that the techniques proposed in \\cite{marza2021teaching} are complementary to ours. \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\\section{Introduction}\n\\input{figures\/teaser}\n\nA robot tasked with finding an object in a large environment or executing a complex maneuver must reason over a long horizon. Existing end-to-end reinforcement learning (RL) approaches often suffer in long-horizon embodied navigation tasks due to a combination of challenges: (a) inability to provide exploration guarantees when the point or object of interest is not visible, (b) difficulty in backtracking previously seen locations and (c) difficulty in planning over long horizons. In this work, we address these issues by proposing a novel long-horizon embodied transport task, as well as modular hierarchical methods for embodied transport and navigation.\n\nOur proposed long-horizon object transport task, Long-HOT, is designed to study modular approaches in the Habitat environment \\cite{habitat_iccv2019}. It requires an embodied agent to pick up objects placed at unknown locations in a large environment and drop them at a known goal location, while satisfying load constraints, which may be relaxed by picking up a special container object (Fig.~\\ref{fig:teaser}). While tasks like MultiON for sequential navigation also benefit from long-range planning \\cite{wani2020multion}, the proposed transport task requires more complex decision-making, such as order of pick-up and exploration-exploitation trade-off with respect to searching for the container. We abstract away the physical reasoning for pickup and drop actions, since unlike TDW-Transport \\cite{threedworld_transport}, our focus is on deeper exploration and long-horizon planning to find and transport objects in large scenes.\n\nWe argue that modularity is a crucial choice for tackling the above challenge, whereby navigation and interaction policies can be decoupled through temporal and state abstractions that significantly reduce training cost and enhance semantic interpretability compared to end-to-end approaches. This is distinct from existing hierarchical methods for subgoal generation \\cite{krantz2021waypoint,xia2021relmogen} in long horizon tasks, where the expressivity of subgoals is largely limited to goal reaching for embodied navigation and which still face scalability challenges when the task requires long trajectory demonstrations. \n\nOur modular approach for long-horizon embodied tasks constitutes a topological graph based exploration framework and atomic policies to execute individual sub-tasks (Fig.~\\ref{fig:teaser}). The higher level planner is a finite state machine that decides on the next sub-routine to execute from one of \\texttt{\\small \\{Explore, Pickup, Drop\\}} actions. The topological map representation consists of nodes connected in the form of a graph that serves to infuse geometric and odometry information to aid deeper exploration and facilitate backtracking. Unlike methods that utilize 360-degree panoramic images as input \\cite{chaplot2020neural,VGM,krantz2021waypoint}, we divide every node to aggregate representations from several directions in its vicinity. The representation within a specific node and direction consists of latent features $\\mathcal{F}_A$ from a pre-trained encoder, an exploration score $\\mathcal{F}_E$ that captures the likelihood of the agent finding an object if it explores a frontier in that direction and object closeness score $\\mathcal{F}_O$ that indicates distance to the object within the agent's field of view. We also propose a novel weighted improvement of frontier exploration \\cite{frontier} using the predicted exploration scores.\n\nUnlike methods \\cite{xia2021relmogen,krantz2021waypoint,VGM,habitat2o} that completely rely on either motion planning algorithms \\cite{planningalgo,roboticsbook} or use pure RL for low-level actions \\cite{VGM,wani2020multion}, our approach uses the best of both worlds with motion planning for point goals within explored regions and RL policies to travel the last-mile towards semantic targets at unknown locations. Indeed, on both Long-HOT and MultiON , we show that our proposed modular hierarchical approach performs significantly better, especially on longer horizons, compared to agents operating without map or other hierarchical approaches that sample navigation subgoals for task completion. Moreover, it realizes a key benefit of modularity, namely, adaptability to harder settings when trained on a simpler version of the task.\n\nIn summary, our contributions are the following:\n\\begin{tight_itemize}\n \\item A novel object transport task, Long-HOT, for evaluating embodied methods in complex long-horizon settings.\n \\item A modular transport framework that builds a topological graph and explores an unknown environment using weighted frontiers.\n \\item Strong empirical performance on object transport and navigation tasks, with adaptability to longer horizons . \n\\end{tight_itemize}\n\n\n\\if 0,\n\nMany real world tasks require agent to be robust towards varying task complexities. Imagine a robot trying to find a car key within a room and within the whole building.\nExisting end-to-end RL approaches fail in adapting to such long-horizon settings when the complexity of the problem scales. These methods face three main challenges in embodied navigation, a) their inability to provide exploration guarantees when the point or object of interest is not visible, b) difficulty in backtracking previously seen locations and c) difficulty in planning over long horizons. \nThus far, hierarchical approaches \\cite{gupta2019relay,Barto03recentadvances} have been better at handling such temporally extended problems. In this work, we aim to address these issues by proposing modular hierarchical methods for embodied transport and navigation.\n\\JD{Feels a little disjointed. What constitutes task complexity? Are we studying specifically navigation settings?}\n\nAs our first contribution, to study the benefits of modular approaches in we build a long horizon object transport task called Long-HOT in Habitat\\cite{habitat_iccv2019} environment. Our task requires agents to transport objects to a goal locations with access to a container where these objects are scattered randomly at different locations across the floor on Matterport\\cite{Matterport3D} scenes. While there exists previous approaches\\cite{threedworld_transport} with similar task definition requiring additional physics based reasoning for object pickup\nwe abstract such physics in our setup and focus on defining these tasks in large environments where agent requires deeper exploration and long-horizon planning to find and transport objects more efficiently Fig. \\ref{fig:teaser}.\n\nFurther, we study hierarchical control in the context of object transport and navigation tasks. Existing hierarchical approaches have looked into the problem of subgoal generation \\cite{krantz2021waypoint,xia2021relmogen} for solving complex long horizon tasks but the expressivity of these subgoals have mostly been limited to goal reaching for embodied navigation and still suffer from the problem of scalability when the task requires long trajectory demonstrations. Modular approaches are arguably more a natural choice to this problem where navigation and interaction policies can be decoupled through means of temporal and state abstractions that significantly reduce the training cost and provide means for more semantic interpretability compared to end-to-end approaches.\n\nIn this work we present a novel modular approach for long-horizon transport problems by building a topological graph based exploration framework and atomic policies to execute individual sub-tasks. \nOur higher level planner is a finite state machine that decides on next sub-routine to execute from one of \\texttt{\\small \\{Explore, Pickup, Drop\\}} actions.\nWe build a topological map representation which consists of nodes that are connected in the form of a graph. Unlike methods \\cite{chaplot2020neural,VGM,krantz2021waypoint} that utilize 360-degree panoramic images as input we divide every node to aggregate representations from $\\theta = 12$ directions \nwithin its vicinity. A representation within a specific node and direction consists of latent features from a pre-trained encoder ($\\mathcal{F}_A$),\nan exploration ($\\mathcal{F}_E$) score that captures how likely will the agent find an object if it explores a frontier in that direction and object closeness ($\\mathcal{F}_O$) scores that indicates distances to object within the agent's field of view. Further, we propose a novel weighted improvement of the frontier exploration\\cite{frontier} \nusing the predicted exploration scores ($\\mathcal{F}_E$). Unlike methods\\cite{xia2021relmogen,krantz2021waypoint,VGM,habitat2o} that completely rely on either motion planning algorithms \\cite{planningalgo,roboticsbook} or use pure RL for low-level actions \\cite{VGM,wani2020multion} we propose to use the best of both worlds where we use motion planning algorithms for point goals within explored regions and make use of RL policies to travel the last-mile towards semantic targets at unknown locations. \nWe show that our proposed modular hierarchical approach performs significantly better especially in long-horizon compared to agents operating without map or other hierarchical approaches that samples navigation subgoals for task completion. \n\nIn summary, our contributions are the following:\n\\begin{itemize}\\itemsep-2pt\n \\item A novel object transport task called Long-HOT, focused on evaluating the performance of methods in complex long-horizon settings.\n \\item A hierarchical transport framework that builds a topological graph and explores the environment using weighted frontiers.\n \\item Strong empirical performance on the proposed object transport and navigation tasks \\cite{wani2020multion}. Further, we show better adaptability of our method to much harder settings when trained on a simple version of the same task. \n \n\\end{itemize}\n\n\\fi\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\\section{Habitat Transport Policy}\n\n\n\n\n\n\\section{Hierarchical Transport Policy (HTP)}\nWe now describe a modular policy (Fig. \\ref{fig:framework}) that builds a topological map of the environment and operates at different levels of temporal abstraction. Our framework consists of three modules: {\\it a high-level controller}, {\\it an exploration module} and {\\it a pick-drop module}. The {\\it high-level controller} decides on the next high-level action to execute from one of $\\mathcal{A}_H =$\\texttt{\\small \\{Explore, Pickup[Object], Drop\\}} actions. The appropriate sub-task module then takes over to perform the given high-level action. At any point, if the high-level controller decides to execute a different sub-task, the current execution is interrupted and control is passed to the module executing the next one. The modules in our framework are made of several functions which we describe briefly in Sec. \\ref{ssec:components} and then provide details on how those components are connected to our overall framework.\n\\input{figures\/hierarchy}\n\n\\subsection{HTP Model Components}\\label{ssec:components}\nAn overview of our model components along with their connections in the HTP framework is shown in Fig. \\ref{fig:pipeline}. Our sub-task modules consist of the following components: a) a topological graph builder, b) exploration score predictor c) object-closeness predictor, d) object navigation policy and e) a low-level controller. We now describe each of them in detail. \n\\input{figures\/pipeline}\n\n\\subsubsection{Topological Graph Builder}\\label{ssec:graphupdate}\nThis function is responsible for creating a topological map of the environment as a graph ${G} = ({V}, {E})$, where $V \\in \\mathbb{R}^{N_t\\times f_d}$ and $E$ represent spatial nodes and connecting edges respectively. Here, $N_t$ represents number of nodes at timestep $t$ and $f_d$ represents the length of node features. Node features for a node $V_i \\in {V}$ consist of concatenated ``node-direction'' features $V_{i, \\theta}$ corresponding to $D=12$ directions $\\theta \\in \\{1,...,D\\}$ spanning 360 degrees, centered on the node.\nThese node-direction features $V_{i, \\theta}$ in turn are computed by encoding perspective RGB images through an encoder $\\mathcal{F}_A$, pretrained in an autoencoder (details in supplementary).\n\n\n\n\n\n\nAt every timestep $t$, the graph builder updates the map as follows. \nIt takes the pose information ($P_{\\phi xy}$)\nand encoded egocentric image features from $\\mathcal{F}_A$ as input.\nThe agent's location $P_{xy}$ is mapped to the nearest existing graph node $V_i$, and its heading angle $P_{\\phi}$ is mapped onto the nearest node-direction $\\theta$. Then that corresponding node-direction representation \n$V_{i,\\theta}$ is updated to the image feature vector.\nWe add a new node $V_{i+1}$ if the agent is not within a distance threshold $l_{th} = 2m$ of existing nodes, and store the corresponding node coordinate $P_{xy}$. \nWhen the agent transitions between two nodes in the graph, the graph is updated to add an edge between them.\nSimilar to \\cite{VGM}, we also keep track of the last visited time-step for every node to provide additional context for downstream processing.\n\n\n\n\n\n\\subsubsection{Exploration and Object Closenes Scores}\\label{sec:score_pred}\\label{ssec:scorepred}\nAt every node-direction, indexed by $(i, \\theta)$, aside from the feature vector above, we store two score predictions: (a) an {\\it exploration score} for frontiers\\cite{frontier} that predicts the likelihood of finding an object by exploring that frontier and (b) an {\\it object closeness score} that indicates distance to various objects in the current field of view.\n\n\\vspace{-0.2cm}\n\\paragraph{Exploration Score Predictor ($\\mathcal{F}_E$):}\\label{para:exp_score_pred} \nWe explore two variants of this function: (1) a Q learning based graph convolutional network (GCN)\\cite{kipf2017semi} that reasons for frontiers over the entire topological graph and (2) a convolutional network (CNN) predicts frontier scores on a per-frame basis.\n\n\\vspace{0.2cm}\n\\noindent{\\it GCN Exploration Score:} This function operates on (1) \nthe current topological map\n$G = (V,E)$ with associated features as computed above, and (2) a binary mask $M^{N_t \\times \\theta_d}$ indicating the availability of a frontier at every node-direction, computed using the method described in Sec. \\ref{ssec:expmodule}.\nProvided these inputs, we train a graph convolutional network (GCN)~\\cite{kipf2017semi} to produce reinforcement learning Q-values for each node-direction, representing future rewards for finding objects after visiting each frontier associated with that node-direction.\nThe object-finding reward function $r_t^e$ at every timestep $t$ is:\n\\begin{equation}\n r_t^e = \\mathbb{I}_{success} \\cdot r^e_{success} + r^e_{slack} + \\sum_o \\mathbb{I}^o_{found} \\cdot r^e_{found},\n\\end{equation}\nwhere $\\mathbb{I}^o_{found}$ is the indicator if object $o$ was found at timestep $t$, $r^e_{found}$ is the reward for finding a new object, $r^e_{slack}$ is the time penalty for every step that encourages finding the objects faster, $\\mathbb{I}_{success}$ is an indicator if \\textit{all} objects were found and $r^e_{success}$ is the associated success bonus. We consider the object to be found if the object is in the agent's field of view with distance less than maximum pre-defined distance. \nOur GCN architecture involves three layers of graph convolution layers and a fully connected layer. \n\n\n\n\\vspace{0.2cm}\n\\noindent{\\it CNN Exploration Score:} This variant of the exploration score is computed directly from the agent's current RGBD view.\nGiven this view, a CNN predicts three exploration scores:\neach score represents the chances of finding an object if the agent explores the farthest frontier available within a corresponding range of angles centered on the current agent heading: $-45^\\circ$ to $-15^\\circ$, $-15^\\circ$ to $+15^\\circ$, and $+15^\\circ$ to $+45^\\circ$ respectively for the three scores. This CNN is trained with labels set to $\\max(\\max_o(d_{a,o} - d_{f,o})\/5, 0)$ where $d_{a,o}, d_{f,o}$ represent geodesic distance to object $o$ from the agent and frontier respectively. If a frontier is not available then the score is set to 0. These three scores are then stored respectively to three consecutive node-directions $\\theta-1, \\theta, \\theta+1$, centered on the current direction $\\theta$, and at the current node $i$. \n\n\n\n\n\n\n\\vspace{-0.2cm}\n\\paragraph{Object Closeness Predictor ($\\mathcal{F}_O$):} This CNN maps the current RGBD observation $I$ to a ``closeness score'' for every object. It is trained with supervised learning to predict target closeness labels for each object, which are set to $\\max(1-d\/5, 0)$ where $d$ is the true distance to the object in m. So, objects farther than $5m$ away (or invisible) have labels $0$, and very close objects have labels $\\approx 1$. Each node-direction has an associated closeness score for each object.\n\n\\subsubsection{Object Navigation Policy}\\label{ssec:objectnav}\nNext, we discuss our object navigation policy. Given the RGBD observation $I$ and a one-hot encoding $k_o$ of a target object, the policy must select navigation actions from \\texttt{\\small \\{FORWARD, TURN-LEFT, TURN-RIGHT\\}} that take it closer towards the target. This policy is trained with the following reward $r^n_t$ \\cite{wani2020multion} at each timestep $t$:\n\\begin{equation}\nr^n_t = \\mathbb{I}_{[reached-obj]} \\cdot r^n_{obj} + r^n_{slack} + r^n_{d2o} + r^n_{collision},\n \n \n \n\\end{equation}\nwhere $r^n_{obj}$ is the success reward if it reaches closer than a threshold distance $d_{th}$ with the target object, $r_{slack}$ is a constant time penalty for every step, $r^n_{d2o} = (d_{t-1} - d_t)$ is the decrease in geodesic distance with the target object and $r^n_{collision}$ is the penalty for collision with the environment. \n\nWe train this policy using the proximal policy optimization (PPO) \\cite{ppo} reinforcement learning algorithm, for approximately 40M iterations using 24 simulator instances. We use mini-batch size of 4 and perform 2 epochs in each PPO update. We use other hyper-parameters similar to \\cite{wani2020multion}.\n\n\\subsubsection{Low-Level Navigation Controller}\\label{ssec:lc}\nOur final module is a low-level controller that takes a goal location (from within the explored regions)\nto be reached as input. \nIt then plans a path towards the specified goal location using the classical A*\\cite{astar} planning algorithm using a pre-built occupancy map.\n\n\\subsection{HTP Control Flow}\\label{ssec:method} \nWe are now ready to describe how HTP manages the flow of control between these components to perform long-horizon transport tasks.\nNote that while we describe the HTP algorithm for object transport, we show in Sec.~\\ref{ssec:quant_compare} that HTP also works for other embodied navigation tasks.\n\n\n\n\\subsubsection{High-Level Controller}\\label{ssec:highlevel}\nThe high-level controller ($\\pi^H$) is a finite state machine. Based on object closeness scores $\\mathcal{F}_O$ (Sec. \\ref{sec:score_pred}), hand state $O_h$, and goal state $O_g$,\nit selects one subtask from among $\\mathcal{A}_H =$\\texttt{\\small \\{Explore, Pickup[Object], Drop\\}}. \nAt timestep $t$, if the next high level action predicted by the controller is different from the current sub-task that is being executed, the controller interrupts the execution, and agent performs the updated high-level action. For example, during exploration if the agent finds an object with closeness score higher than a some threshold it then switches control from exploration to picking the object if the hand is not full or if it holds a container. \n\n\n\n\\subsubsection{Weighted Frontier Exploration}\\label{ssec:expmodule}\n\nIf $\\pi^H$ selects the $<$\\texttt{\\small Explore}$>$ sub-task, the exploration module is executed. For exploration, we introduce a weighted frontier technique based on the predicted exploration score function $\\mathcal{F}_E$ (Sec \\ref{sec:score_pred}). For every timestep $t$, we calculate the set of frontiers ${\\bf S}$ over the explored and unexplored regions using occupancy information \\cite{frontier}.\nWhen a new frontier is identified, we assign a parent node-direction $Y_r = (i,\\theta^n)_r$ for the $r^{th}$ frontier, where $(i,\\theta)$ is the current localized node-direction and $\\theta^n$ is calculated based on the angle made by the frontier with the agent. Here the agent's field of view is $90^\\circ$, so $\\theta^n$ for the newly found frontiers can assume one of $\\{\\theta-1,\\theta,\\theta+1\\}$ directions.\nFor all existing frontiers from timestep $t-1$, we copy the same parent node-direction from the previous timestep. Finally, we calculate a representative frontier $ S^{(i,\\theta)}$ for node-direction $(i,\\theta)$, as: $S^{(i,\\theta)} = \\{s_k: \\argmin_k \\|s_k - X_c\\|\\ \\forall\\ Y_k = (i,\\theta)\\}$ where $s_k \\in {\\bf S}$ and $X_c$ is the center of frontiers associated with $Y_k=(i,\\theta)$. \n\n\nAt each timestep during its execution, the exploration module selects a node-direction $(i,\\theta)$ from the topological graph $G$ that has the highest exploration score $\\mathcal{F}_E$. Its corresponding frontier $S^{(i,\\theta)}$ is then set as the goal location for the agent's low-level controller, which begins to move towards this goal. \nThe highest-score goal frontier is recomputed at every timestep, and may switch as new views are observed during exploration. \n\n\n\\subsubsection{Pick-Drop Module}\\label{ssec:pickdrop}\nThis module performs the pick or drop actions in the object transport task when the controller $\\pi^H$ selects an action $a_H \\in$\\ \\{\\texttt{\\small Pickup[Object], DropAtGoal}\\}. \nWhen called, this module first selects a node $(i,\\theta)$ from graph $G$ with the highest object closeness score $\\mathcal{F}_O$ for the target object.\nIf the agent is not already in the selected $i^{th}$ node, then its location $P_{xy}(i)$ is set as the goal for the low-level controller. Once the agent is localized to the $i^{th}$ node, it orients in the direction of $\\theta$. At this point, control is passed to the Object Navigation policy, targeting the object selected by $\\pi_H$. \nThe module then selects the pickup or drop action whenever the object closeness score $\\mathcal{F}_O$ for the target object, based on the current view, exceeds a threshold. \nThe sub-task is successful when the hand state or goal state is changed accordingly and the controller $\\pi^H$ predicts the next high level action to execute. We execute this module till it performs the pick\/drop or for a maximum of $T_p$ steps after which the control is given back to the high-level controller $\\pi^H$.\n\n\n\n\n\n\\section{Related Work}\n\\paragraph{\\bf Embodied intelligence.} \n\nThe community has developed several simulation environments \\cite{habitat_iccv2019,ai2thor,shen2021igibson,threedworld_transport,chen2020soundspaces,xiazamirhe2018gibsonenv,RoboTHOR} and associated tasks to study embodied agents in tasks like object goal navigation \\cite{batra2020objectnav,chaplot2020object,wortsman2019learning,yang2018visual,wani2020multion}, point goal navigation \\cite{anderson2018evaluation,habitat_iccv2019,wijmans2020ddppo,ramakrishnan2020occupancy}, rearrangement \\cite{threedworld_transport,shridhar2020alfred,Weihs_2021_CVPR,habitat2o}, instruction following \\cite{shridhar2020alfred,anderson2018visionandlanguage} and several others in this regard.\nWhile there are handful of previous works designed for navigation \\cite{batra2020objectnav,chaplot2020object,wani2020multion} or rearrangement \\cite{threedworld_transport,Weihs_2021_CVPR}, they do not extensively stress tests methods with increasing task complexities. We find the typically used flat policy architectures \\cite{wani2020multion} in embodied AI tasks fail completely when executing over longer horizons. Hence, we propose a new benchmark called Long-HOT that has potential to serve as a testbed for, and accelerate the development of novel architectures for planning, exploration, and reasoning over long spatial and temporal horizons. \n\n\nOur task builds on previous transport tasks defined in embodied intelligence \\cite{Weihs_2021_CVPR,habitat2o,threedworld_transport,wani2020multion} but differs in ways that it requires deeper exploration and long horizon planning. While previous work like \\cite{Weihs_2021_CVPR} focus on identifying state changes using visual inputs to perform rearrangement or \\cite{habitat2o} use geometrically specified goals in single apartment environments these works operate in minimal exploration scenarios where the focus is shifted more towards perception or interaction with objects. \nOur task is closest to \\cite{threedworld_transport}, while \\cite{threedworld_transport} focuses on performing transport including physics based simulations, we abstract our interactions and focus more on complex long-horizon planning.\nOur work extends \\cite{wani2020multion} but rather than focusing on navigation in a predefined sequential fashion, our task requires more complex decision making to determine the order of picking and decide whether to perform a greedy transport if it sees the goal or to explore more in hopes of finding the container for efficient transport.\n\n\n\n\n \n\n\n\\paragraph{\\bf Modular-Hierarchical Frameworks.} \nSolutions to long-horizon tasks typically involved hierarchical\\cite{NIPS1997_hierarchy,subgoal_discovery,Sutton:1999,bacon2016optioncritic} policies in reinforcement learning. They provide better exploration behavior through long-term commitment towards a particular subtask. \\cite{xia2021relmogen,krantz2021waypoint} present one such approach where they sample navigation subgoals to be executed by the low-level controller. While these methods can temporally abstract navigation to an extent we find their performance to drop significantly in longer horizon settings. In HTP we show that modularity enables generalization while only training on the simpler versions of the task. \n\n\nCloser to our work are modular approaches\\cite{NMCdas2019,chaplot2020neural,krantz2021waypoint,xia2021relmogen} that provide an intuitive way to divide complex long horizon tasks as a combination of individual sub-tasks that can be solved using existing approaches. Das et al. \\cite{NMCdas2019} present a modular approach to solve embodied question answering \\cite{das2017embodied} through a combination of several navigation policies each for finding an object, to find a room or to exit one. \nThis can blow-up with number of sub-routines required to navigate across a building or inability of the agent to find a room of particular type in large environments. Rather than navigating to individual rooms our method proposes a weighted frontier technique that provides exploration guarantees.\nChaplot et al. \\cite{chaplot2020neural} propose a method for image goal navigation by generating a topological map of the scene using 360-degree panoramic images. Our approach operates on perspective images and divides a node representation into segments across different directions.\n\nOur work also closely relates to task and motion planning (TAMP) literature\\cite{garrett2020integratedtamp,ffrob} where the closest work in this domain is \\cite{yamada2020motion} which proposes a MP augmented RL approach in manipulation settings where they realize large displacements of a manipulator through a MP. \nWhile \\cite{yamada2020motion} tackles a simple manipulation domain for 2D block pushing where target objects are fully observable, we tackle a more complex navigation setting and propose to use a combination of MP and object navigation policies where a motion planner first moves to a region with high likelihood of object presence and gives control to the navigation policy that takes it closer to the goal object.\n\n\n\n\n\n\n\\section{Habitat Transport Task}\nWe propose a novel transport task for embodied agents that simulates object search, interaction, and transport in large indoor environments. A robot assistant might be expected to perform such tasks in a warehouse or a hospital.\nIn each episode, the agent must transport $K$ target objects to a specified object goal location in a large partially observed Habitat~\\cite{habitat_iccv2019} 3D indoor environment with many rooms and obstacles.\nThe environment also contains a special ``container'' object (in yellow) that can be used to transport other objects, another special goal object (in green) whose position is the goal location. In our setting, all objects are cylinders of various colors, placed into the environment.\nUnlike previously studied tasks such as~\\cite{habitat2o}, the agent needs to explore the environment to find all objects, and does not have access to their geometric coordinates. \nAt each step, the agent can turn by angle $\\alpha=30^{\\circ}$ to the left or right, move forward by $x=0.25m$, or execute object ``Pickup'' or ``Drop'' actions. \n\n\nThe agent has access to standard egocentric perspective RGB and depth views. Fig.~\\ref{fig:pipeline} (left) shows an example of the agent's view of a scene with a prominent red object. Aside from this, the agent has access to odometry ($P_{\\phi xy}$),\nas well as the hand state $O_h$ and goal state $O_g$, which indicate if a target\/container object is either held by the agent or already at the goal respectively. \nFollowing \\cite{wani2020multion,Weihs_2021_CVPR}, if the agent is within $R = 1.5m$ of any pick-able object and a Pickup action is called, the closest object is removed from the scene and the hand state $O_h$ is updated to include it. For the Drop action, any objects in the agent's hand are dropped near the agent's location. If the goal is within distance $R$, the goal state is updated to include the object.\nThe agent can hold limited items in its hands at once, and is therefore constrained to carry at most two objects at a time unless it picks up the container, in which case any number of objects may be carried. Picking up the container requires the agent's hands to be empty. Each episode runs for a maximum of $T = 2500$ timesteps.\n\n\nThis transport task naturally entails additional complexity compared to previously proposed navigation settings, and has properties that are not emphasized in previous benchmark tasks for embodied agents~\\cite{wani2020multion,habitat2o,habitat_iccv2019,xiazamirhe2018gibsonenv}. It includes multiple task phases (searching for, navigating to, and interacting with objects), reasoning about the environment at various scales (such as coarse room connectivity charts over the explored map for planning long trajectories, and fine-grained local occupancy maps for object interaction), accounting for carrying capacity constraints. It also involves dynamically selecting among various sub-task sequences under uncertainty: for example, having found an object far from the goal, should an agent immediately return to drop it off at the goal, or should it look for another object before returning for efficiency?\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Further Analysis for Long-HOT and HTP}\n\n\\subsection{Ablation Study}\nHere, we study the dependence of various components in HTP to the overall task performance and also their generalizability to increasing number of target objects for the transport task. For creating a test episode, we sample additional targets with object colors that were used during the training. \nNote that we only test our methods with increasing number of object goals by training them on a \\emph{default} task level with 4 objects. The ablations were conducted using the GCN variant in HTP on 250 test episodes with 15 scenes.\n\n\\vspace{-0.3cm}\n\\paragraph{\\bf HTP w\/o graph:}\nWe perform an ablation of HTP without a graph memory for closeness scores. When the high-level action is one of \\texttt{\\small\\{Pickup, Drop\\}} action, the Pick-Drop module provides direct control to object-nav policy bypassing the low-level controller. Here, instead of reaching the node with highest closeness score we directly use object-nav policy to move closer towards target objects. \n\n\\input{figures\/ablation}\n\n\\vspace{-0.3cm}\n\\paragraph{\\bf HTP - random closeness scores:} To study how the values of closeness scores affect the task performance, we replace closeness values to be random numbers. Intuitively, this should affect agent the most as random closeness values will take agent to a very different node location compared to the one closest to the target object. Further, it also makes agent execute pick-drop operations at unintended locations thereby affecting the task performance. \n\n\\vspace{-0.3cm}\n\\paragraph{\\bf HTP - random exploration scores:} In this ablation, we replace the frontier exploration scores to random values which affects the frontier selection and the exploration strategy. \n\n\\vspace{-0.3cm}\n\\paragraph{\\bf HTP - random object navigation (50\\%):} Here we replace the actions from the object navigation policy with random actions 50\\% of the time. \n\n\nFig. \\ref{fig:ablation} reports the success rate and SPL of various HTP variants for increasing number of target objects. As shown, the performance of HTP is considerably higher than the all other ablations indicating the purpose of each of these model components. \nHTP w\/o graph provides significant drop in performance compared to HTP due to the long-horizon exploration and navigation required by object-nav for finding target objects. With increasing number of target objects the performance of HTP w\/o graph deteriorates even further relatively indicating the importance of topological graph. HTP with random closeness score affect agents the most due to its influence on pick drop operations as incorrect values take agent to a completely different node compared to the closest one and the performance even reduces close to 0\\% with increase in number of target objects. \n\nHTP with random exploration score provides low success rates as incorrect frontier values makes agent switch between frontiers that are farther away making exploration less efficient as the agent travels within the explored regions for significant portion of its time while switching. \nHTP is not affected much even when perturbed with random actions for object navigation 50\\% of the time, indicating the robustness of HTP. This could be due to HTP simplifying the navigation process through a combination of motion planning and RL where, we use motion planning for navigation within explored regions and RL policies for navigating towards semantic targets at unknown locations. \n\nWhile the performance of all the HTP variants decrease with increase in number of target objects, HTP still provides good enough performance for 8 object transport while only training on transport task involving 4 objects. This also shows HTP's ability to generalize towards increasing number of target objects.\n\n\n\n\n\\input{tables\/standard_wtermination}\n\\subsection{Episode termination for wrong pickup}\nTable \\ref{tab:standard_wterminate} compares the performance of HTP and baseline methods with early termination criteria for a wrong pickup action. An episode is terminated if there are no objects within agent's vicinity when a pickup action is called. The numbers indicate Long-HOT's difficulty to be a lot higher with an end criterion similar to MultiOn\\cite{wani2020multion}. We relax those hard constraints as training becomes more difficult with sparse rewards for long-horizon tasks and rather focus on task completion for our agents. While the performance of RL methods drop significantly in Table \\ref{tab:standard_wterminate}, the proposed HTP is nearly unaffected. It can be attributed to the modular hierarchical design of HTP where pick-drop actions are executed only when the object closeness score is higher than a threshold.\n\n\n\n\n\\subsection{Long-HOT episode statistics}\nFig. \\ref{fig:dataset_statistics} shows a histogram of geodesic distances with fraction of total episodes in the corresponding histogram bin for various datasets used in Long-HOT experiments. Note the range of reference path length increases with different task configurations in Large Long-HOT indicating a increasing task complexity.\n\\input{figures\/data_statistics}\n\n\\vspace{-0.4cm}\n\\subsection{Discussion and limitations}\nThe accompanying video shows some failure cases of HTP which includes situations where an agent is unable to move around an obstacle or has small frontier regions which are ignored by the exploration module and situations where the closeness scores for some object visible only from some particular node direction is overwritten by values obtained when the perspective image does not contain the object viewed from a different location localized to the same node direction.\n\nThe work assumes noiseless odometry and depth for task completion, but earlier works like \\cite{chaplot2020object} have shown that semantic mapping and navigation work well in the real world even with noisy pose and depth. Future works can relax these assumptions to build methods that work more robustly with different forms of noisy inputs.\n\n\n\\section{Todo}\n \n\n\\section{Further Details on Implementation, Training and Metrics}\n\nIn this section, we provide additional implementation details of baseline architectures and the proposed HTP.\n\n\\subsection{No Map baseline}\n\nWe adapt an architecture similar to \\cite{wani2020multion} for the No Map policy. \n\n\\vspace{-0.4cm}\n\\paragraph{Inputs and Outputs:} No Map takes an RGBD image of size $256\\times256$ along with the hand state $O_h$ (size $5\\times1$), goal state $O_g$ (size $4\\times1$) and previous action as inputs to the policy. It then predicts one of \\texttt{\\{\\small FORWARD, TURN LEFT, TURN RIGHT, PICKUP, DROP\\}} actions at every timestep.\n\n\\vspace{-0.4cm}\n\\paragraph{Architecture:} The RGBD image is passed through a sequence of three convolutional layers + ReLU\\cite{relu} and a linear layer + ReLU that transforms the input into a feature vector of length $512$. The convolutional layers consist of kernels with size $\\{8,4,3\\}$, strides $\\{4,2,1\\}$ and output channels $\\{32,64,32\\}$ respectively. The hand state $O_h$ and goal state $O_g$ are passed through dense layers to get respective feature vectors (dim $32$). The previous action is embedded through an embedding layer of length $32$. Finally, image features, hand-goal features, and previous action embedding are concatenated and passed through a recurrent unit to output features that are used to predict actions and the approximate value function.\n\n\n\\vspace{-0.4cm}\n\\paragraph{Rewards:} The following rewards $r_t$ is provided at every timestep $t$ to train the No Map agent:\n\\begin{equation}\n \\begin{split}\n r_t = & \\mathbb{I}_{success} \\cdot r_{success} + \\mathbb{I}_{pick} \\cdot r_{pick} + r_{d2o} +r_{d2g} \\\\ & + \\sum_o \\mathbb{I}^o_{goal} \\cdot r_{goal} + r_{collision} + r_{fpd} + r_{slack}\n \\end{split}\n\\end{equation}\nwhere, $\\mathbb{I}_{success}, \\mathbb{I}_{pick}, \\mathbb{I}^o_{goal}$ are functions that indicate successful completion of the episode, any target object picked for the first time and target objects that are transported to the goal respectively. $r_{success}, r_{pick}, r_{goal}$ are the rewards associated with $\\mathbb{I}_{success}, \\mathbb{I}_{pick}, \\mathbb{I}^o_{goal}$. $r_{d2o} = (d^o_{t-1} - d^o_t)$ is the decrease in geodesic distance from the agent's position to the closest object. $r_{d2g} = \\max_o (d^o_{t-1} - d^o_t)$ is the maximum decrease in geodesic distance of target objects with the goal. $r_{collision}$ is the collision penalty for agents and $r_{slack}$ is the slack reward for every timestep the agent delays in completing the episode.\n\n\n\\vspace{-0.4cm}\n\\paragraph{Training:} The policy is trained using proximal policy optimization (PPO) \\cite{ppo} technique, for approximately 40M iterations using 24 simulator instances. The hyper-parameters used are similar to \\cite{wani2020multion}.\n\n\\subsection{OracleMap Baselines}\nWe first describe the OracleMap (Occ) and OracleMap (Occ+Obj) baselines and then provide details on OracleMap-Waypoints policy.\n\n\\subsubsection{OracleMap (Occ \/ Occ+Obj)}\nThe policy architecture for OracleMap (Occ \/ Occ + Obj) is similar to No Map agent with an additional map input that covers an area of $10m\\times10m$. First top view map embeddings (dim. 16) are generated and then passed through a map encoder. The encoder consists of convolutions with kernels $\\{4,3,2\\}$, stride $\\{3,1,1\\}$ and output channels $\\{32,64,32\\}$. The map encoder produces a feature vector of length $256$ and is concatenated as one of the inputs to the recurrent unit. The output action space and rewards used to train OracleMap (Occ \/ Occ + Obj) is similar to the No Map baseline. \n\n\n\n\n\n\\subsubsection{OracleMap-Waypoints}\nThe inputs to the baseline are OracleMap (Occ+Obj), hand state $O_h$ and goal state $O_g$. It then predicts waypoints to be reached as $(x,y)$ locations on the map. The prediction is discretized into $M=100$ bins within a $5m\\times5m$ range centered on the agent. The agent then selects a bin as one of its action and uses A*\\cite{astar} to reach its location. The predicted subgoal is also associated with one of \\texttt{\\small \\{Pickup, Drop\\}} high-level actions. The action space in Waypoints policy contains $M\\times2$ in total. Once the agent reaches the predicted subgoal, the corresponding high level action \\texttt{\\small \\{Pickup, Drop\\}} is executed. The subgoals are updated for every $t_k$ steps irrespective of agent reaching previously assigned subgoal. Agent's prediction range is kept higher than maximum traversable distance in $t_k$ steps for agents to provide subgoals that avoid taking pickup or drop actions within $t_k$ timesteps. The policy architecture and rewards used are similar to OracleMap (Occ+Obj) baselines.\n\n\n\n\n\n\\subsection{Hierarchical Transport Policy}\n\n\\paragraph{High-level Controller:} \nThe controller takes hand state $O_h$, goal state $O_g$ and closeness scores $\\mathcal{F}_O$ of objects with respect to the nodes as inputs. The high-level action is assigned based on the following conditions, if the agent already holds maximum number of objects in its hand and goal object was discovered (closeness score of goal $> F_O^{th}$ in one of the nodes) a \\texttt{\\{Drop\\}} action is executed else the agent executes \\texttt{\\{Explore\\}} to find the goal object. If the agent has capacity to carry more objects, and some closeness scores for objects are greater than $F_O^{th}$ in the nodes, a \\texttt{\\{Pickup[Object]\\}} action is executed for objects that are not either held by the agent or transported already. The agent executes \\texttt{\\{Explore\\}} if none of the objects satisfy the closeness score criteria. \\texttt{\\{Pickup[Container]\\}} action is only executed when the container is discovered and the agent does not hold any object in its hand.\n\n\n\n\n\n\n\\paragraph{Pre-trained Encoder $\\mathcal{F}_A$ :}\nLatent features from a pre-trained auto-encoder is used as node features in HTP. It consists of a ResNet-18\\cite{resnet18} style encoder-decoder architecture with latent features of $32$ dimensions. The auto-encoder is trained with a weighted MSE loss that weighs pixels of target objects with a weight $\\lambda=2.0$.\n\n\n\\subsection{Reference Trajectory Calculation}\nA reference demonstration in the transport task first picks up the container and then picks up consecutive closest objects from its previous location to finally drop them at goal. The sum of geodesic distances in executing this reference trajectory in an episode from agent's starting location is used as the reference path length $G_{ref}$.\n\n\n\n\n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nHelioseismology -- the study of solar oscillations -- is a powerful probe of the\nstructure and dynamics of the Sun which has provided great improvements in our\nunderstanding of stellar evolution and structure \\citep[][ and references\ntherein]{TurckChieze1993, JCD2002}. Those successes push the community to apply\nseismic techniques to other stars, opening the doors to asteroseismology, the\nstudy of stellar oscillations. These oscillations have already been observed\nfrom ground and space. The ground-based observations are limited by the\nday-night cycle, which introduces aliases in the observations, but allow to use\nDoppler velocity measurements. They have provided data with {sufficient\nquality} to detect solar-like oscillations \\citep[see][ and references\ntherein]{BouchyCarrier2003, BeddingKjeldsen2003}. To reduce the aliases,\nmulti-site campaigns have been carried out but they are too short to have a good\nfrequency resolution. Space photometry missions and ground-based velocity\nnetworks must be used to provide observations of stellar oscillations without\nthese limitations. With the current MOST\\footnote{Microvariability and\nOscillations of STars \\citep{Matthews1998}} and WIRE\\footnote{Wide-field Infra\nRed Explorer \\citep{Buzasi2000}} satellites and the future\nCOROT\\footnote{Convection Rotation and planetary Transits \\citep{Baglin2001}}\nmission asteroseismology is blooming. However, we still have to deal -- in the\ncase of solar-like oscillations -- with very small signal-to-noise ratio\n(hereafter $S\/N$) observations as a consequence of the weakness of the\nluminosity variations. Moreover, stars cannot be spatially resolved yet. Only\nglobal oscillation modes can be observed. In addition, we cannot have access to\nthe rotation rates and the rotation-axis inclination separately. Without knowing\nthese two key stellar properties, the tagging of the modes in terms of their\nproperties ($\\ell, m$) and successive $n$ may be extremely difficult. In fact,\nthe main problem to face will not be to fit the peaks (``peak-bagging'') but to\nprovide a good description of the model to be fitted after having put the\ncorrect labels on the modes (``peak tagging''). To do this, it has been proposed\nto use the echelle diagram where the modes follow ridges depending on the\nstellar properties. To improve the S\/N ratio \\citet{Bedding2004} proposed to\nfilter this diagram by a vertical {smoothing}. However the\n{smoothing} works well only when the ridges are quasi-vertical which\nmeans a very good \\textit{a priori} knowledge of the large difference and is\nrestricted to the asymptotic part of the spectrum. We propose here to follow a\nsimilar approach but using new mathematical denoising techniques better suited\nto the study of curved ridges.\n\nAt the end of the last decade, the application of mathematical transforms based\non wavelets to analyze astronomical images has been widely developed. The first\nwavelet algorithms were well adapted to treat images with isotropic elements.\nHowever, this description presented a limitation in the context of astrophysics,\nwhere objects such as filaments or spiral structures exhibit a highly\nanisotropic character (in shape and scale). New transforms, the ridgelet\n\\citep{Candes1998} and curvelet transforms \\citep{CandesDonoho1999, Starck2002},\nwere then developed to deal efficiently with such objects. Astrophysical\napplications (image denoising) of this technique have been presented in\n\\citet{Starck2003, Starck2004} to analyze images of gravitational arcs, the\nSaturn rings or the CMB (Cosmic Microwave Background) map.\n\nIn this paper we suggest to use the curvelet transform to analyze asteroseismic\nobservations (more precisely the stellar echelle diagrams), in order to improve\nthe ``peak tagging'' of the oscillation modes and even the resultant ``peak\nbagging''. To illustrate the application of this denoising technique in the\nasteroseismic case, we have performed Monte Carlo simulations of ideal\nasteroseismic data contaminated by different levels of stochastic noise. We\nstart in Sect.~2 by a quick reminder of the properties of stellar oscillation\nmodes in the solar-like case and the construction of the echelle diagram. In\nSect.~3 we introduce multiscale transforms, in particular the ridgelet and the\ncurvelet transforms. In Sect.~4, the simulated data of a star with an\noscillation spectrum similar to the Sun but with different rotation axis\ninclinations and rotation rates, are presented. In Sect.~5 we discuss the\nresults obtained in the simulations.\n\n\\section{Properties of solar-like oscillations}\n\n\\begin{figure}\n\t\\includegraphics[scale=0.35,angle=0]{Lambert2006_fig1a.eps}\n\t\\includegraphics[scale=0.35,angle=90]{Lambert2006_fig1b.eps}\n\t\\caption{Portion of the theoretical spectrum (top) and echelle diagram (bottom)\nfor a sun spinning ten times faster than the Sun and seen under an angle of\n50$\\degr$. This is the ideal power spectrum used in the simulations described in\nSect.~5.}\n\t\\label{theorique}\n\\end{figure}\n\nOnly low-degree stellar oscillation modes can be detected and observed with the\npresent generation of instruments. The asymptotic theory of oscillation modes\n($n\\gg \\ell$) is then adequate and can be used to study them. First order\n\\citep{Tassoul1980} and second order developments \\citep{Vorontsov1991,\nLopes1994, Roxburgh2000a, Roxburgh2000b} have been made to describe solar and\nstellar oscillations. In the case of solar-like stars, where p-modes\npredominate, the frequencies can be developed as:\n\\begin{equation}\\label{secondordre}\n\t\\nu_{n,\\ell} \\approx \\Delta\\nu_0 \\big( n+\\frac{\\ell}{2}+\\frac{1}{4}\n+\\alphaup(\\nu) \\big) + \\frac{\\Delta\\nu_0}{4\\pi^2\\nu_{n,\\ell}}\\big((\\ell +\n1\/2)^2A + \\psi\\big) \n\\end{equation}\nin this expression $\\ell$ and $n$ are respectively the degree and the radial\norder of the modes and \n\\begin{eqnarray*}\n\\tau_c\t\t\t&=&\t\\int_{r_{in}}^{r_{out}}\\frac{dr}{c_s} \\\\\n\\Delta\\nu_0\t&=&\t\\frac{1}{2\\tau_c} \\\\\nA\t\t\t\t&=&\t\\frac{1}{4\\pi^2\\nu_{n,\\ell}}\\big(\\frac{c_s(R_\\star)}{R_\\star} -\n\\int_{r_{in}}^{r_{out}}\\frac{dc_s}{dr}\\frac{dr}{r}\\big) \n\\end{eqnarray*}\n$c_s$ is the internal stellar sound speed, $\\alphaup$ is a phase-shift term and\n$\\psi$ is a function which allows to take into account the gravitational\npotential in the central region \\citep{Lopes1994}. From the asymptotic approach,\nwe can extract general properties of modes and better understand the physics\nhidden in the frequencies behavior. The large frequency spacing, defined as\n$\\Delta\\nu_{n,\\ell}=\\nu_{n+1,\\ell} - \\nu_{n,\\ell}$, tends asymptotically to\n$\\Delta\\nu_0$, related to the mass and radius of the star; the small frequency\nspacing, $\\delta_{\\ell,\\ell+2}\\nu=\\nu_{n,\\ell}-\\nu_{n-1,\\ell+2}$, can be\napproximated to first order by\n$(4\\ell+6)\\Delta\\nu_0\/(4\\pi^2\\nu_{n,\\ell})\\int_0^{R_\\star}\\frac{dc_s}{dr}\\frac{dr}{r}$.\nThis variable is related to the derivative of the sound speed and enhances the\neffect coming from the central regions, providing constraints on the age of the\nstar. Finally the second difference is defined as\n$\\delta_2\\nu=\\nu_{n+1,\\ell}-2\\nu_{n,\\ell}+\\nu_{n-1,\\ell}$. Its variations\nprovide information about the extent of the convective zone \\citep{Monteiro2000,\nBallot2004} or the helium abundance in the stellar envelope \\citep{Basu2004}.\n\nUnder the rotation effects the azimuthal order $m$ ($-\\ell \\leqslant m \\leqslant\n\\ell$) is needed to characterize the oscillation spectrum. If the angular\nvelocity $\\Omega$ is uniform \\citep{Ledoux1951}, the mode frequencies are\nasymptotically approximated by:\n\\begin{equation}\\label{unifangvel}\n\t\\nu_{n,\\ell,m}\\approx\\nu_{n,\\ell}+m\\Omega\/2\\pi = \\nu_{n,\\ell}+m\\delta\\nu\n\\end{equation}\nwhere $\\delta\\nu$ is the rotational splitting. Equation~\\ref{unifangvel} shows\nthat modes are ($2\\ell+1$)-times degenerated among the azimuthal order: a single\npeak in the spectrum becomes a multiplet. Its corresponding structure depends on\nthe rotation rate, the inclination axis of the star and its stochastic\nexcitation. The solar-like mode lifetimes (a few days) are expected to be much\nshorter than the length of the future space observations (a few months). In\nconsequence, the relative amplitude ratios inside a multiplet will only depend,\nin average, on the inclination angle and the spacing between these different\nm-components \\citep{GizonSolanki2003}. Thus if the different m-components of a\nmultiplet can be identified and tagged with the correct $(\\ell,m)$, they can\nprovide a good estimation of both the rotation-axis inclination $i$ and the\nrotational splitting $\\delta\\nu$, allowing a better mode parameter extraction\nthrough the fitting of the spectra. The effect of the stochastic excitation on\nan isolated mode could be minimized by computing the average of these parameters\non several modes \\citep[see for example the\nn-collapsogramme;][]{BallotYale2004}.\n \nEquation~\\ref{secondordre} shows that the even ($\\ell=0,2$) and odd ($\\ell=1,3$)\nmodes have respectively almost the same frequency, only separated by the small\nspacing $\\delta_{\\ell,\\ell+2}\\nu$. In addition, they are separated regularly in\nfrequency by the large spacing $\\Delta \\nu_{n,\\ell}$. This property allows us to\nbuild the so-called echelle diagram \\citep{Grec1983}, which is currently used to\nidentify modes for solar-like oscillations. It is a 2D representation of the\npower spectrum where this one is folded onto itself in units of the large\nspacing. In such representation the modes appear as almost locally vertical\nridges (see Fig.~\\ref{theorique}). The echelle diagram is a powerful tool for\nthe ``peak tagging'' since assigning the correct $(\\ell,m)$ values to the peaks\nis easier when the multiplet structure is well identified in this diagram. The\nsuccessive $n$ values are obtained from each individual horizontal line. \n\n \\section{Multiscale Transforms}\n \\subsection{The Wavelet Transform}\n\nThe wavelet transform provides a framework for decomposing images into their\nelementary constituents across scales by reducing the number of significant\ncoefficients necessary to represent an image. The continuous wavelet transform\nof a 2D signal is defined as:\n\n\\begin{eqnarray}\n W(a,b_i, b_j) = \\frac{1}{\\sqrt{a}}\\int\\!\\!\\!\\int\nf(x,y)\\psi^*\\left(\\frac{x-b_i}{a},\\frac{y-b_j}{a}\\right)dxdy\n\\end{eqnarray}\nwhere $W(a,b)$ are the wavelet coefficients of the function $f(x)$, $\\psi(x)^*$\nis the conjugate of the analyzing wavelet, $a>0$ is the scale parameter and $b$\nis the position parameter. The continuous wavelet transform is the sum over all\nthe positions of the signal $f(x,y)$ multiplied by the scaled and shifted\nversions of the wavelet $\\psi((x-b_i) \/ a,(y-b_j) \/ a)$ (cf.\nFig.~\\ref{examples}, top panels). This process produces wavelet coefficients\nthat are a function of scale and position.\n\nHowever, the classical wavelet transform only address a portion of the whole\nrange of interesting phenomena: isotropic features at all scales and locations.\nOne of the drawbacks of the two-dimensional wavelet transform is that it does\nnot achieve an efficient analysis of images which present high anisotropy. For\ninstance, the wavelet transform does not efficiently approximate 2D edges, since\na large number of large wavelet coefficients, scale after scale, are required,\nmaking difficult its analysis. In order to solve this problem two new\nmathematical transforms, namely the ridgelet transform and the curvelet\ntransform, were introduced.\n\n\\begin{figure}\n \\centering\n \\includegraphics[scale=0.245]{Lambert2006_fig2a.ps}\n \\includegraphics[scale=0.245]{Lambert2006_fig2b.ps}\n \\includegraphics[scale=0.245]{Lambert2006_fig2c.ps}\n \\includegraphics[scale=0.245]{Lambert2006_fig2d.ps}\n \\caption{Examples of 2D wavelets (top panels) and ridgelets (bottom panels).\nThe top right wavelet has a greater scale parameter than this on the left. The\nbottom right ridgelet has different orientation and width than the left one.}\n \\label{examples}\n\\end{figure}\n\n \\subsection{The Ridgelet transform}\n\nThe ridgelet transform was developed to process images including ridges elements\n\\citep{Candes1998}. It provides a representation of perfectly straight edges.\nGiven a function $f(x_1,x_2)$, the representation of this latter is the\nsuperposition of elements of the form\n$a^{-1\/2}\\psi((x_1\\cos\\theta+x_2\\sin\\theta-b)\/a)$, where $\\psi$ is a wavelet,\n$a>0$ a scale parameter, $b$ a location parameter and $\\theta$ an orientation\nparameter. The ridgelet is constant along lines\n$x_1\\cos\\theta+x_2\\sin\\theta=\\mathrm{const}$, and transverse to these ridges it\nis a wavelet. Thus, contrary to a unique wavelet transform, the ridgelet has two\nsupplementary characteristics: a length, equal to this of the image and an\norientation, allowing the analysis of an image in every direction and so\nexhibiting the edge structure. Fig.~\\ref{examples} (bottom panels) shows two\nexamples of ridgelets. The problem is that in the nature edges are typically\ncurved rather than straight so ridgelets alone cannot yield an efficient\nrepresentation.\n\n\t\\subsection{The Curvelet transform}\n\t\t\\subsubsection{Description}\n\n\\begin{figure}\n \\centering\n \\includegraphics[scale=0.32]{Lambert2006_fig3.eps} \n \\caption{Sketch illustrating the curvelet transform applied to an image. The\nimage is decomposed into subbands followed by a spatial partitioning of each\nsubband. The ridgelet transform is applied to each block. The finest details\ncorrespond to the highest frequencies.}\n \\label{curveletgraphe}\n\\end{figure}\n\nRidgelets can be adapted to represent objects with curved edges using an\nappropriate multiscale localization: at a sufficiently fine scale a curved edges\ncan be considered as almost straight. \\citet{CandesDonoho1999} developed the\ncurvelet transform using ridgelets in this localized manner.\nFig.~\\ref{curveletgraphe} shows the different steps of the curvelet analysis of\nan image:\n\n\\begin{enumerate}\n\t\\item Image decomposition into subbands: as a set of wavelets bands through a\n2D isotropic wavelet transform. Each band corresponds to a different scale.\n\t\\item Smooth partitioning: each subband is partitioned into squares -- blocks\n--, whose size is appropriate to each scale. The finest is the scale, the\nsmaller are the blocks.\n\t\\item Ridgelet analysis: it's applied to each square.\n\\end{enumerate}\n\nThe implementation of the curvelet transform offers an exact reconstruction and\na low computational complexity. Like ridgelets, curvelets occur at all scales,\nlocations and orientations. Moreover contrary to ridgelets, which have a given\nlength (the image size) and a variable width, the curvelets have also a variable\nlength (the block size) and consequently a variable anisotropy. The finest the\nscale is, the more sensitive to the curvature the analysis is. As a consequence,\ncurved singularities can be well approximated with very few coefficients.\n\n\t\t\\subsubsection{Denoising images: filtering curvelet coefficients}\n\nTo remove noise a simple thresholding of the curvelet coefficients has been\napplied to select only significant coefficients. One possible thresholding of a\nnoisy image consists in setting to $0$ all non-significant curvelet coefficients\n$\\tilde c_{i,j,l}$, $i$, $j$ and $l$ respectively the indexes of the line, row\nand scale: it is the so-called hard-thresholding:\n\\begin{eqnarray}\n\t\\tilde c_{i,j,l} = \\left\\{ \\begin{array}{ll} \t\\mbox{1} & \\mbox{if } \tc_{i,j,l}\n\\mbox{ is significant} \\\\ \n\t\t\t\t\t\t\t\t\t\t\t\\mbox{0} & \\mbox{if } \tc_{i,j,l} \\mbox{ is not significant}\n\\end{array} \\right.\n\\end{eqnarray}\nCommonly, $c_{i,j,l}$ is significant if the probability that the curvelet\ncoefficient is due to noise is small, i.e., if the curvelet coefficient is\ngreater than a given threshold. A basic problem remains: the choice of the\nthreshold. Usually, this threshold is taken equal to $k\\sigma_j$, where\n$\\sigma_j$ is the noise standard deviation at the scale $j$ and $k$ is a\nconstant taken equal to 5 in our filterings.\n\nSimple thresholding of the curvelet coefficients is very competitive\n\\citep{Starck2002} with ``state of the art'' techniques based on wavelets,\nincluding thresholding of decimated or undecimated wavelet transforms.\n\n\\section{Simulation of data}\n\n\\begin{figure*}\n \\centering\n \\includegraphics[scale=0.65]{Lambert2006_fig4_lowres.eps}\n \\caption{Effect of the curvelet denoising on the mode visibility for\n$S\/N=5$. Each picture shows 120 realizations out of the 500 done in our Monte\nCarlo simulation. Each horizontal line corresponds to a single realization. The\ntop panel is the raw spectra and the bottom is the curvelet filtered one. \n\\label{montecarlo}}\n\\end{figure*}\n\nTo characterize the curvelet denoising technique applied to the asteroseismic\ndata, we have simulated typical solar-like observations varying different\nparameters: S\/N ratios, observational lengths, rotation-axis inclinations,\nrotation rates... With this approach we know the input parameters in advance and\nwe can evaluate the quality of the results given by the curvelet analysis and\nits limits.\n\nIn the simulations shown in this paper, we use the oscillation spectrum of a\nstar similar to the Sun but seen under different conditions. Different rotation-axis inclinations ($i=50\\degr$ and $90\\degr$) and rotation\nrates ($\\Omega= \\Omega_{\\sun}$, $5\\Omega_{\\sun}$, and $10\\Omega_{\\sun}$) have\nbeen considered. An ideal power spectrum were constructed first. Only the modes\n$\\ell\\le3$, $n=12$--$25$ were simulated. The mode parameters -- frequencies\n($\\nu$), amplitudes ($A$) and widths ($\\Gamma$) -- were obtained from the\nanalysis of GOLF (Global Oscillations at Low Frequency) data \\citep{Garcia2004}.\nThe amplitudes were corrected to take into account the difference between\nintensity and velocity observations. Modes were simulated with symmetrical\nLorentzian profiles as the asymmetry is expected to be at the level of the\nnoise. Following the method described in \\citet{FierryFraillon1998}, a\nmultiplicative noise, a $\\chi^2$ with 2 d.o.f. statistics, has been introduced\nto reproduce the stochastic excitation of such modes \\citep[see\nalso][]{Anderson1990}. The $S\/N$ ratio of the ``resultant'' raw power spectrum\nwas defined as the maximum of the bell-shaped p-mode power (i.e. the highest\nsimulated p mode) divided by the noise dispersion. The simulated background is\nflat assuming that it has been previously fitted and removed as it is usually\ndone for the Sun \\citep{Harvey1985}.\n\nSeveral Monte Carlo simulations have been performed for each ideal spectrum.\nRealistic $S\/N$, with values ranging from 5 to 15, have been used to cover a\nwide range of situations (compatible with what it is expected, \\citep[see\n][]{Baglin2001}). In each realization of the Monte Carlo simulation the same\nlevel of noise has been randomly added to the corresponding ideal spectra.\nTherefore all the realizations, in a given Monte Carlo simulation, have the same\n$S\/N$ ratio. The simulated spectra have been computed for two resolutions,\n$\\approx0.38$ and $\\approx0.077~\\mu$Hz, corresponding respectively to 30-day and\n150-day observations. The first are representative of MOST observations and the\nshort CoRoT runs while the latter are of the same length than the long CoRoT\nruns.\n\nSimulations of other stars, like some potential main CoRoT targets, with\ndifferent masses, ages and, in consequence, internal structures have been made.\nThe results have already been presented and discussed during the CoROT workshops\n\\#8 and \\#9 obtaining the same qualitative results. For the sake of clarity,\nthey are not shown here.\n\n\\section{Discussion}\n\\begin{figure*}\n \t\\includegraphics[scale=0.35,angle=90.]{Lambert2006_fig5a.eps}\n \t\\includegraphics[scale=0.35,angle=90.]{Lambert2006_fig5b.eps}\n \t\\includegraphics[scale=0.35,angle=90.]{Lambert2006_fig5c.eps}\n \t\\includegraphics[scale=0.35,angle=90.]{Lambert2006_fig5d.eps}\n \t\\includegraphics[scale=0.35,angle=90.]{Lambert2006_fig5e.eps}\n\t\\includegraphics[scale=0.35,angle=90.]{Lambert2006_fig5f.eps}\n\t\\caption{Raw (left) and filtered (right) power spectra (top and middle panels)\nand echelle diagrams (bottom panels) for a $S\/N=5$ realization. The short dashed\nlines in the power spectra represent the position of the theoretical\nfrequencies. From left to right, the three first equidistant lines indicate the\ncomponents $m=-1,0,1$ of $\\ell=1$ modes, the two next indicate the strongest\ncomponents of $\\ell=2$ ($m=-1$ and $1$), and the the last indicates $\\ell=0$. In\nthis case only two components of the $\\ell=1$ and the $\\ell=0$ mode are slightly\nvisible in the raw diagram. On the curvelet filtered one, the three $\\ell=1$\ncomponents appear as well as the $\\ell=0$ and the components $m=\\pm 1$ of the\n$\\ell=2$ modes.\\label{diagechellesnr5}}\n\\end{figure*}\n\nOnce the spectra have been computed, the echelle diagrams can be built with a\nfixed folding frequency. This one corresponds to the mean large frequency\nspacing $\\Delta\\nu_0$, identified either by computing the FFT, the\nautocorrelation of the spectra or any other technique \\citep[see for\nexample][]{Regulo2002}. The denoising based on the curvelet transform is then\napplied to this echelle diagrams. It is important to note that artifacts may\nappear in the filtered spectra at frequencies $\\nu^*$=$\\nu_0$+$k\\Delta\\nu_0$,\nwith $k$ an integer, when random small structures appear in the echelle\ndiagrams. However, their appearance and position strongly depend on the folding\nfrequency and are very sensitive to its value. Therefore they can be easily\nidentified. The artifacts can be reduced (in contrast to the regions containing\nsignal) by building echelle diagrams with slightly different folding frequencies\nand averaging the resultant filtered spectra.\n\nIn order to present the results of data analysis using the curvelet denoising\nmethod, we have selected the case of a sun-like star seen with an inclination\nangle $i=50\\degr$ and with a rotation $\\Omega=10 \\Omega_{\\sun}$. A portion of\nthe ideal spectra constructed for this star can be seen in Fig.~\\ref{theorique}\n(top panel). Monte Carlo simulations were then performed, giving rise to\ndifferent sets (each one with 500 realizations) of raw spectra with different\n$S\/N$ ratios. The echelle diagrams were constructed using a folding frequency of\n135.18$~\\mu$Hz, obtained by computing the FFT of the raw spectrum.\n\n \\subsection{Peak tagging}\n\nIn those cases, with a high $S\/N$ (typically 15), the mode structure is clearly\nvisible in each raw spectrum and also on the echelle diagram. The different\nridges can be easily identified and tagged. Although the filtering gives\nenhanced denoised diagrams and unfolded spectra, it does not contribute\nsignificantly to the mode identification. \n\nIn the lower $S\/N$ cases, however, the situation is different.\nFigure~\\ref{montecarlo} shows some of the results of the Monte Carlo simulation\nfor $S\/N$=5. \nThe upper panel corresponds to 120 realizations among the 500 computed for the\nraw spectra in the frequency range 2450--2920$~\\mu$Hz. Each horizontal line\ncorresponds to a single realization. Some patterns can hardly be seen. The lower\npanel represents the same spectra after applying the curvelet filtering. A\nseries of vertical ridges clearly appears. From the left to the right on the\npanels, they can be identified as the ($\\ell=2$; $m=\\pm 1$), the $\\ell=0$\n(blended with the $\\ell=2$; $m=+2$ ) and the ($\\ell=1$; $m=-1,0,+1$). The\nimprovement of the contrast is important in all the realizations and allows to\ndistinguish the different components of a mode, making easier the identification\nand the tagging. \n\nThe identification is harder when looking at each individual spectrum and\nrequires the use of the echelle diagram. Fig.~\\ref{diagechellesnr5} shows an\nexample of raw (left) and filtered (right) 150-day observation power spectra\n(top and middle panels) and the corresponding echelle diagrams (bottom panels)\nfor a $S\/N=5$ realization. Input frequencies are indicated by the short dashed\nlines above the spectra. The mode peaks can hardly be distinguished in the raw\nspectrum and can easily be confused with noise. For the range 2780-2920$~\\mu$Hz,\nonly a strong peak at 2900$~\\mu$Hz can be considered not to be noise. In the\nregion 3060--3180$~\\mu$Hz the peaks are visible and we can attempt to identify\nthe $\\ell$=1 and $\\ell$=0 modes but it is still unclear. On the contrary, on the\ncorresponding parts of the filtered spectrum, the structures of the $\\ell$=1\nmode with three components, the $\\ell$=0 mode and even the strongest components\nof the $\\ell$=2 mode are visible. \nThe raw echelle diagram gives no extra information because of the very weak\nridges and low contrast with the background. The weakest components can hardly\nbe detected and no tagging can be done. The curvelet filtering provides a\ncontrast enhancement of the ridges on the echelle diagram. Thus three almost\nequidistant strong ridges appear on the left of the diagram and one strong ridge\nwith two weaker ones on the right. The corresponding patterns can be seen on the\nfiltered spectrum corresponding well to the theoretical frequencies. Since the\nmodes $\\ell=3$ are not visible, and according to the amplitude of the strongest\npeak on the left, we can suggest that the three strongest peaks correspond to a\n$\\ell=1$ multiplet and the other ones to the $\\ell=2$ and $\\ell=0$ modes. \n\nConsequently, when the tagging is done it is also easier to have a first\nestimation of both the mean rotational splitting and the rotation-axis\ninclination, since the visibility of the multiplet is increased. From the\nspacing between the components of the mode $\\ell=1$, a first estimation of the\nmean rotational splitting of the star can be done, as well as an estimation of\nthe inclination angle, according to their relative amplitude ratios. We have\nselected the extraction of one parameter: the mean rotational splitting of the\n$\\ell$=1 mode at low frequency (2540--2550$~\\mu$Hz), to quantify the improvement\nof the curvelet filtering. This region is particularly interesting because the\nline width is still small and the modes, when they are visible, can be easily\nidentified. Thus, in a sample of 100 realizations of the Monte Carlo simulation,\nwe have obtained in 90 of them a better estimation of this parameter in the\nfiltered spectra. In fact, in the raw spectra it was very exceptional to obtain\na good result. With the filtered spectra a mean rotational splitting of $\\langle\n\\delta\\nu \\rangle=4.05\\pm0.30~\\mu$Hz was found, which is very close to the\nactual splitting included in the ideal spectra $\\langle \\delta\\nu\n\\rangle=4.0~\\mu$Hz. In addition, specific methods can be applied to improve the\nextraction of these parameters by using different strategies of spectra fitting\nas the ones developed by \\citet{GizonSolanki2003} or \\citet{Ballot2006}. \nIn the case of the 30-day observations, the curvelet filtered echelle diagram is\nstill very noisy and it does not help in recognizing the ridges. However the\ncorresponding denoised power spectrum is much better despite the lower\nresolution (5 times less than in the long runs), even for small $S\/N$ ratios\n($\\sim5$). The modes $\\ell=0,2$ and $\\ell=1$ can be distinguished, at the\nmaximum power, while it is not obvious to do so in the raw spectra. Therefore,\nwe consider that a \n30-day run is the minimum length needed to have reliable results with the\ncurvelet denoising technique.\n\n\\citet{Garcia2005} analyzed the first available MOST public Procyon A data\n(32-day observation) using the curvelet technique. Previous analysis by\n\\citet{Matthews2004} did not reveal the presence of any p-mode structure in this\nstar. Therefore, due to its tiny S\/N ratio the results of the curvelet denoising\nshould be taken with care. Nevertheless, an excess of power seems to appear in\nthe region where it is expected and taking the 15 most prominent peaks in this\nregion, many are in agreement, inside the error bars, with previous tagged modes\nusing ground-based velocity observations.\n\n\\subsection{Extraction of p-mode parameters}\n\nOnce the mode identification and tagging are done, the extraction of the mode\nparameters can be performed. To illustrate how this extraction can be improved\nby using the denoised spectrum we have extracted the central frequency of the\nmodes in both the raw and the filtered spectra. To determine this parameter,\nmodes have been fitted by Lorentzian profiles using a maximum-likelihood\nestimator in the classical way: adjacent pairs of even ($\\ell=0$ and $\\ell=2$)\nmodes are fitted together, while $\\ell=1$ is fitted alone, due to the small\namplitudes of $\\ell=3$ modes. For each multiplet, the fitted parameters are the\ncentral frequency $\\tilde\\nu_{n,\\ell}$, the amplitude $\\tilde A_{n,\\ell}$, the\nlinewidth $\\tilde\\Gamma_{n,\\ell}$ and the background $b$. The amplitude ratios\ninside the multiplets and the rotational splittings have been fixed thanks to\nthe preliminary estimation done in the previous section (cf. 5.1). The fitting\nprocedure provides for each adjusted parameter $\\tilde{X}$ an associated error\n$\\sigma(\\tilde{X})$ {computed by Hessian-matrix inversion}.\n \nThe raw spectra follow a $\\chi^2$ with 2 d.o.f. statistics, whereas the filtered\nspectra have a $\\chi^2$ with a higher d.o.f. statistics (close to a Gaussian\ndistribution depending on the number of filtered coefficients). {According to \\citet{Appourchaux2003}, it is possible to fit spectra following a\n$\\chi^2$ with more than\n2 d.o.f. statistics with a classical procedure developed for a $\\chi^2$ with 2\nd.o.f. statistics: parameters\nare correctly fitted, but computed errors have to be adapted \\textit{a\nposteriori}. However in our case, \ndue to filtering, points of filtred spectra are correlated (we have estimated\nthat one point is correlated with $\\sim$10 neighbouring points). This\ncorrelation should have to be considered, but we have neglected its \neffect on the fitting procedure in the present study. This assumption is\nvalidated by the Monte Carlo simulations.\nSuch a global filtering induces also correlations between the different lines\nof the echelle diagram. Thus the errors on parameters of different modes\n(typically $(n,\\ell)$ and $(n+1,\\ell)$)\ncan be correlated. These correlations will have to be taken into account\nespecially during the comparison of frequencies extracted by this way to stellar\nmodels.}\n\nFrom the 500 realizations of the Monte Carlo simulation, we derived for each\nmode and for both the raw and the filtered spectra the mean value of the\nextracted frequencies $\\langle \\tilde\\nu_{n,\\ell} \\rangle$, their mean computed\nerrors $\\langle\\sigma(\\tilde\\nu_{n,\\ell})\\rangle$ and the dispersion of\nfrequency distribution $\\sigma^{*}(\\tilde\\nu_{n,\\ell})$ (the real error). We\nhave verified that $\\sigma^{*}(\\tilde\\nu_{n,\\ell}) \\approx\n\\langle\\sigma(\\tilde\\nu_{n,\\ell})\\rangle$ for fits performed on the raw spectra\nand {we have $\\sigma^{*}(\\tilde\\nu_{n,\\ell}) <\n\\langle\\sigma(\\tilde\\nu_{n,\\ell})\\rangle$ for fits performed on the filtered\nones. As expected, the error bars on the fitted frequencies, computed by Hessian-matrix inversion, are overestimated.\n}\n\n\nFigure~\\ref{ecart} shows the difference between the mean fitted frequencies\n$\\langle \\tilde\\nu_{n,\\ell} \\rangle$ and the theoretical frequencies $\\nu_{in}$\nof the simulated star discussed in the previous section ($S\/N=5$). The error\nbars correspond to the dispersion $\\sigma^{*}(\\tilde\\nu_{n,\\ell})$. For each\n$\\ell$, the error bars of the filtered spectra are smaller than those of the raw\nspectra. In addition, the range where modes can be detected, tagged and fitted\nis extended. While the difference $\\langle \\tilde\\nu_{n,\\ell} \\rangle -\n\\nu_{in}$ is only flat in the central region of the raw power spectrum (e.g. for\n$\\ell=0$, in the range $n=18$--$22$), it extends at higher and lower frequencies\n(e.g. for $\\ell=0$, the range is extended to $n=16$--$23$) in the filtered one. \n\n\\begin{figure*}\n \\centering\n \\includegraphics[scale=0.5,angle=90]{Lambert2006_fig6.eps}\n \\caption{Differences between the mean fitted frequencies $\\langle \\tilde\n\\nu_{n,\\ell} \\rangle$ and the input frequencies $\\nu_{in}$, for $\\ell=0,1,2$,\nfor the raw (dashed line with triangles) and filtered (full line with diamonds)\nspectra ($S\/N=5$, 150-day observation). The error bars correspond to the\ndispersion $\\sigma^*(\\tilde\\nu_{n,\\ell})$ of the frequency distribution. For\nclarity the values for the raw case are shifted by $20~\\mu$Hz towards the\nright.\\label{ecart}}\n\\end{figure*}\n\n\\section{Conclusions}\n\nThe application of a noise reduction technique based on the curvelet transform\nto echelle diagrams improves the identification -- ``peak tagging'' -- of\nstellar acoustic modes. In observations with a $S\/N$ ratio as small as 5 we are\nstill able to recover the mode pattern and extract reliable asteroseismic\ninformation in both small and long runs (30-day and 150-day observations\nrespectively). Below this S\/N and with shorter observations, the method\nefficiency is reduced drastically. The rotational splittings and the\nrotation-axis inclination can be better estimated using the filtered spectrum.\nIn particular, Monte Carlo simulations showed that a better extraction of the\nmean rotational splitting from modes at low frequency can be done in 90 out of\n100 realizations using the filtered spectra. The uncertainty on the extracted\nrotational splitting of a typical sun-like star seen with an inclination angle\n$i=50\\degr$ and with a rotation $\\Omega=10 \\Omega_{\\sun}$ is very small,\n$\\sim$0.30 $\\mu$Hz. These parameters can then be used to have a set of guesses\nor \\textit{a priori} values to perform individual fits of the spectra. We have\nalso shown that the range of the frequency extraction can be extended at higher\nand lower frequencies using the filtered spectra. Finally, simulations of the\nshort run observations have demonstrated that this method can also be applied to\nlower resolution spectra with good results.\n\n\\begin{acknowledgements}\nP. Lambert thanks Dr. D. Neuman for useful discussions. \n\\end{acknowledgements}\n\\bibliographystyle{aa}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzgfks b/data_all_eng_slimpj/shuffled/split2/finalzzgfks new file mode 100644 index 0000000000000000000000000000000000000000..afbb7ea3773007cb3e927c367b4df438f4371c83 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzgfks @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nThe binding energy of the nucleus, or its mass, contains\ninformation about interactions between its constituent protons and\nneutrons. Precision mass data as well as separation energies\nextracted from them can unveil much about the underlying nuclear\nstructure~\\cite{Blaum,Blaum2}. For instance, magic numbers can easily be seen from\nseparation energies which reveal a huge drop (depending on the\nsize of the closed shell), or a jump or a gap (depending on the way the\nseparation energy is constructed) after a magic number~\\cite{Novikov}. \nIn addition, separation energies can reflect\ncollective effects as we will discuss below. Such data are\ntherefore valuable in understanding the underlying shell structure\nin nuclei and the evolution of collective effects, and are\ntherefore essential to provide the basis for the development and\ntesting of a comprehensive theory of nuclei. In turn, a reliable\nnuclear theory is of utmost importance for calculating the\nproperties of unknown nuclei, which are needed, for example, in\nthe modelling of the rapid neutron capture process (r-process) of\nnucleosynthesis in stars~\\cite{Bertulani}. \nTherefore, new data on neutron-rich heavy\nnuclei are essential.\n\nHowever, such data are scarce mainly due to the complexity of\nproducing these exotic nuclei. This becomes evident if one\nglances at the chart of nuclides (see e.g. Ref.~\\cite{nchart}) where\nfor elements $Z\\sim70-80$ the number of observed neutron-rich\nnuclides is very limited. Very recently, several tens of new\nisotopes were discovered in projectile fragmentation of uranium\nbeams, though no spectroscopic information could yet be obtained for\nthese nuclei~\\cite{kurcewicz}. Their production rates are tiny, \nwhich requires very efficient measurement\ntechniques. One such technique for mass measurements is\nstorage-ring mass spectrometry~\\cite{FGM}.\n\nIn this paper we report on direct mass measurements of\nneutron-rich nuclei in the element range from lutetium to osmium.\nMasses for nine nuclei were measured for the first time, and for\nthree nuclei the mass uncertainty was improved. \nIt is known that nuclear collective effects can be seen in the behavior of nucleon separation energies~\\cite{Cakirli2}.\nHere, we investigate the relation between rather subtle effects in two-neutron separation energies, $S_{2n}$,\nand changes in both collectivity and neutron number.\nObserved irregularities in the smooth two-neutron separation\nenergies for Hf and W isotopes are linked to changes in\ncollective observables. The importance of the number of valence\nnucleons is discussed in the context of collective\ncontributions to binding energies calculated with the IBA\nmodel~\\cite{IBA}.\n\n\\section{Experiment}\n\\begin{figure*}[t!]\n\\centering\n\\includegraphics[width=\\textwidth]{fig1m.pdf}\n\\caption{Example of a Schottky frequency spectrum with the\ncorresponding isotope identification. This spectrum is\ncombined from eight independent injections acquired for the electron\ncooler voltage $U_c=209$~kV. Nuclides with known and previously\nunknown masses are indicated by different fonts (see legend).}\n\\label{id}\n\\end{figure*}\n\\begin{figure}[b]\n\\centering\n\\includegraphics[width=\\linewidth]{fig2_u}\n\\caption{A zoom of the Schottky frequency spectrum illustrated in\nFig.~\\ref{id} on a quadruplet of $A=190$ isobars. The peaks\ncorresponding to nuclides in isomeric states are labeled with\n$m$.} \\label{spzoom}\n\\end{figure}\n\\begin{figure*}[t]\n\\centering\n\\includegraphics[width=\\linewidth]{fig3}\n\\caption{(Color online) A part of the chart of nuclides indicating the\nnuclides measured in this work as well as the nuclides in the\nground and isomeric states identified in the other part of this\nexperiment devoted to the search for new K-isomers in this\nregion~\\cite{Matt,Matt2}.} \\label{nchart}\n\\end{figure*}\nThe experiment was conducted at GSI Helmholtzzentrum f\\\"ur\nSchwerionenforschung in Darmstadt. Heavy neutron-rich nuclei of\ninterest were produced in projectile fragmentation of $^{197}$Au\nprimary beams. The experiment described here was part of a\nlarger experimental campaign, some results of which are described\nin Refs.~\\cite{Matt,Matt2,Matt25,Matt3}. The $^{197}$Au beams were accelerated\nto the energy of 11.4~MeV\/u in the linear accelerator UNILAC and\nthen injected into the heavy ion synchrotron SIS-18~\\cite{sis},\nwhere they were further accelerated to an energy of 469.35~MeV\/u.\nThe $^{197}$Au$^{65+}$ beams were fast extracted (within about\n1~$\\mu$s) and focused on a production target located at the\nentrance of the fragment separator FRS~\\cite{frs,Geissel1992NIM}.\nAs target we used 1036~mg\/cm$^2$ thick $^9$Be with a 221~mg\/cm$^2$\nNb backing for more efficient electron stripping. The reaction\nproducts emerged from the target as highly-charged ions having\nmostly 0, 1, or 2 bound electrons. The nuclides of interest were\ntransported through the FRS, being operated as a pure magnetic\nrigidity ($B\\rho$) analyzer~\\cite{Geissel1992NIM}, and injected\ninto the cooler-storage ring ESR~\\cite{esr}. The transmission\nthrough the FRS and the injection into the ESR were optimized with\nthe primary beam, and the magnetic setting of FRS-ESR was fixed at\n$B\\rho=7.9$~Tm throughout the entire experiment. All ion species\nwithin the acceptance of the FRS-ESR of about $\\pm0.2$\\% were\ninjected and stored. Only $25$\\% of the ESR acceptance is filled\nat the injection. We note, that in contrast to the settings\ndescribed in Refs.~\\cite{Matt,Matt2}, in this experiment no\nenergy-loss degraders were employed in the FRS.\n\nThe relationship between relative revolution frequencies ($f$),\nrelative mass-over-charge ratios ($m\/q$) and velocities ($v$) of\nthe particles stored in a ring is given\nby~\\cite{FGM,Ra-PRL,Ra-NPA,LiBo}:\n\\begin{equation}\n\\label{sms1}\n\\frac{\\Delta f}{f}=-\\alpha_p\\frac{\\Delta \\frac{m}{q}}{\\frac{m}{q}}+(1-\\alpha_p\\gamma^2)\\frac{\\Delta v}{v},\n\\end{equation}\nwhere $\\gamma$ is the relativistic Lorentz factor, $\\alpha_p$\nis the momentum compaction factor, which characterizes\nthe relative variation of the orbit length of stored particles per\nrelative variation of their magnetic rigidity (for more details\nsee Refs.~\\cite{FGM,Ra-PRL,Ra-NPA,LiBo}). For the ESR, $\\alpha_p$\nis nearly constant for the entire revolution frequency acceptance\nand is $\\alpha_p\\approx0.179$. From Eq.~\\eqref{sms1} it becomes\nobvious that the revolution frequency is a measure of the\nmass-over-charge ratios of the stored ions provided that the\nsecond term on the right hand side, containing the velocity spread\n($\\Delta v\/v$), can be eliminated. The latter is achieved by\napplying electron cooling~\\cite{elcool}. For this purpose the\nstored ions are merged over a length of about 2.5~m with a\ncontinuous beam of electrons in the electron cooler device. The\nmean circumference of the ESR is 108.4~m and at our energies the\nions circulate with a frequency of about 2~MHz passing the\nelectron cooler at each revolution. The energy of the electrons is\nvery accurately defined by the applied acceleration potential.\nWithin a few seconds the mean velocity of the ions becomes equal\nto the mean velocity of the electrons. The velocity spread of the\nstored ions, which is $\\Delta v\/v\\approx4\\cdot10^{-3}$ at the injection,\nis thereby reduced to $\\Delta v\/v\\approx10^{-7}$~\\cite{elcool}.\n\nThe Schottky mass spectrometry (SMS) technique has been applied to\nthe electron cooled ions~\\cite{Ra-NPA,FGM}. In this technique,\nevery stored highly-charged ion at each revolution in the ESR\ninduces mirror charges on a couple of parallel electrostatic\ncopper plates, the Schottky pick-up installed inside the ring aperture.\nThe noise from the pick-up, which is dominated by the\nthermal noise, is amplified by a broad-band\nlow-noise amplifier~\\cite{Schaaf}. In the present experiment, we analyzed the\nnoise power at about 60~MHz, corresponding to the 30$^{th}$\nharmonic of the revolution frequency of the stored ions. The\npick-up signal was down-mixed using a $\\sim$60~MHz reference\nfrequency from an external frequency generator. The acceptance of\nthe ESR at the 30$^{th}$ harmonic corresponds to about\n320~kHz~\\cite{Li-2004,Li-NPA}. Therefore, to cover the entire ESR\nacceptance we digitized the signal with a sampling frequency of\n640~kHz using a commercial 16-bit ADC~\\cite{Kaza}. A Fourier\ntransform of the digitized data yielded the noise-power density\nspectrum, or the Schottky frequency spectrum~\\cite{FGM,Li-NPA}.\n\nNew ions were injected every few minutes. At the injection into\nthe ESR, the previously stored ions were removed. Several Schottky\nfrequency spectra were created for each injection. The parameters\nof the Fourier transform algorithm were optimized offline. A\nfrequency resolution of 4.77~Hz\/channel was chosen, which\ncorresponds to the time resolution of 0.21~s per spectrum.\nFurthermore, every 50 consecutive Schottky spectra were averaged\nto enhance the signal-to-noise ratio. Thus, Schottky spectra\nintegrated over 10~s were produced. The latter means that several\nindependent subsequent frequency spectra were obtained for each\ninjection of the ions into the ESR.\n\nThe electron cooling forces the ions to the same mean velocity\nthus filling the entire acceptance of the ESR of $\\Delta\nB\\rho\/B\\rho\\sim\\pm1.5\\%$ (see Ref.~\\cite{Ra-NPA}). Since $B\\rho=m\nv \\gamma \/ q$, by changing the velocity of the electrons in\ndifferent injections, ions with different $m\/q$ can be studied. In\nthe present experiment we varied the electron cooler voltage in\nthe range from 204~kV to 218~kV. On average eight injections were\nrecorded for each cooler setting. In order to facilitate the\nassignment of the revolution frequencies with the corresponding\nisotope identification, all spectra within each cooler setting\nwere combined together. In this case the maximum number of ion\nspecies present in each setting can be used for the\nidentification. The latter is done based on Eq.~\\eqref{sms1}.\nAs a starting point for the identification we used\nthe frequency of the stored $^{197}$Au$^{76+}$ primary ions. An\nexample of the combined Schottky frequency spectrum for an electron\ncooler voltage of $U_c=209$~kV is illustrated in Fig.~\\ref{id}.\nFig.~\\ref{spzoom} shows a zoom on a quadruplet of lines of\n$A=190$ isobars present in ground and\/or isomeric states. The\nlatter are indicated with a label $m$. The peak finding and the\nisotope identification were done automatically with a dedicated\nROOT-based~\\cite{ROOT} software~\\cite{Dasha}. The nuclides\nobserved in this experiment are illustrated on the chart of nuclides\nin Fig.~\\ref{nchart} together with the nuclides in the ground\nand isomeric states identified in the other part of this\nexperiment (see Refs.~\\cite{Matt,Matt2,Matt25,Matt3}).\n\n\\section{Data Analysis and Results}\n\\begin{table}[t!]\n\\caption{Nuclides with accurately known masses used as references\nto calibrate Schottky frequency spectra. Listed are the proton\n($Z$) and mass ($A$) numbers, the number of experimental settings \n($N_{set}$) in which this reference mass was observed, literature\nmass excess values from the Atomic-Mass Evaluation ~\\cite{AME} \n($ME_{AME}$) as well as the re-determined mass excess values\n($ME$) (see text) with the corresponding $\\sigma_{stat}$ uncertainty \nand its difference to the literature value\n($\\delta=ME-ME_{AME}$). \nNote that the systematic uncertainty of $\\sigma_{syst}=38$~keV (see text) is \nnot added here.\n\\label{references}}\n\\begin{center}\n\\begin{tabular}{cccccc}\n\\hline\n\\hline\nZ & A & $N_{set}$ & $ME_{\\rm AME}$ & $ME$& $\\delta$ \\\\\n & & & (keV) & (keV) & (keV) \\\\\n\\hline\n72 & 181 & 1 & -47412(2) & -47412(40) & 0(40) \\\\\n\\hline\n73 & 181 & 1 & -48442(2) & -48383(40)& 59(40) \\\\\n73 & 182 & 3 & -46433(2) & -46466(29)& -32(29) \\\\\n73 & 183 & 3 & -45296(2) & -45276(16)& 20(16) \\\\\n73 & 185 & 6 & -41396(14)& -41350(14)& 46(20) \\\\\n\\hline\n74 & 184 & 5 & -45707(1) & -45663(17)& 44(17) \\\\\n74 & 186 & 7 & -42510(2) & -42493(12)& 17(13) \\\\\n74 & 187 & 8 & -39905(2) & -39863(8)& 41(8) \\\\\n\\hline\n75 & 189 & 9 & -37978(8) & -38063(10)& -85(13) \\\\\n75 & 191 & 9 & -34349(10)& -34364(3)& -15(11) \\\\\n\\hline\n76 & 188 & 7 & -41136(1) & -41115(12)& 21(12) \\\\\n76 & 190 & 7 & -38706(2) & -38637(15)& 69(15) \\\\\n76 & 192 & 7 & -35881(3) & -35833(8)& 48(8) \\\\\n76 & 193 & 7 & -33393(3) & -33329(8)& 63(8) \\\\\n\\hline\n77 & 191 & 1 & -36706(2) & -36650(71)& 56(71) \\\\\n\\hline\n78 & 194 & 4 & -34763(1) & -34779(24)& -16(24) \\\\\n78 & 196 & 9 & -32647(1) & -32655(4)& -7(4) \\\\\n\\hline\n79 & 196 & 5 & -31140(3) & -31126(4)& 14(5) \\\\\n\\hline\n\\hline\n\\end{tabular}\\end{center}\n\\end{table}\n\nIn order to determine the unknown mass-over-charge ratios, the\nSchottky frequency spectra have to be\ncalibrated~\\cite{Ra-NPA,Li-NPA}. For this purpose we selected the\nnuclides which were identified in our spectra and for which masses are\nknown experimentally according to the Atomic-Mass Evaluation 2003\n(AME)~\\cite{AME}. We note that an update of the AME was made\navailable in 2011~\\cite{AME11}, which however contains no new\ninformation in the mass region studied here. \nThe data of the present work were already included in the latest AME published very recently~\\cite{AME12}.\nFurthermore we\nrequired that the reference masses were obtained by more than one\nindependent measurement technique and that there must exist no\nother ionic species with a close mass-to-charge ratio which could simultaneously be stored in the ESR.\nAlso the peaks corresponding to long-lived isomeric states (we observed\n$^{182}$Hf$^{\\rm m1}$, $^{186}$W$^{\\rm m2}$, $^{190}$Os$^{\\rm m1}$ and $^{190}$Ir$^{\\rm m2}$ isomers)\nwere not used for calibration. The list of reference masses is\ngiven in Table~\\ref{references}.\n\n\\begin{figure}[b]\n\\centering\n\\includegraphics[width=\\linewidth]{fig4} \n\\caption{Top: Mass-over-charge ratio $m\/q$ as a function of revolution\nfrequency. The solid line illustrates a straight line fit through\nthe calibration $m\/q$-values. Bottom: The same as top but with\nthe subtracted linear fit.} \\label{mqf}\n\\end{figure}\n\nThe momentum compaction factor $\\alpha_p$, although nearly\nconstant, is a complicated function of the revolution frequency\n$f$. If $\\alpha_p$ were exactly constant, then the $m\/q$ would linearly\ndepend on $f$. Fig.~\\ref{mqf} (top) shows an example of a linear\nfit through the calibration $m\/q$-values for one of the measured spectra. The\nresiduals of the fit (bottom panel of the same figure) clearly\nshow that the calibration function is more complicated than a\nlow-order polynomial function. Polynomials of up to 4th order (5\nfree coefficients) were employed, but different compared to the analyses\nperformed in Refs.~\\cite{Ra-NPA, Li-NPA,Chen2012}, the quality of\nthe fits was found unacceptable. \nA possible reason for the latter is the small number of reference masses in individual 10-s Schottky spectra.\nFurthermore, due to time variations of the storage ring and electron cooler parameters, \nsuch as, e.g., their magnetic fields, it is not possible to establish a universal calibration curve. \nTherefore, we employed an analysis procedure \nin which we used linear splines to approximate the calibration curve in each individual spectrum. \n\nChanges of the electron cooler voltage were done in steps of 0.5~kV\nso that for adjacent cooler settings the measured frequency spectra have a significant overlap.\nFurthermore, the same nuclide can be present in different charge states, which allows for a\nredundant analysis. The nuclides whose masses have been measured\nfor the first time or which mass accuracy was improved in this work are listed in Table~\\ref{newm}.\n\n\\begin{table}[t!]\n\\caption{Nuclides whose masses were determined for\nthe first time (in boldface) or whose mass uncertainty was improved in this work.\nListed are the proton ($Z$) and mass ($A$) numbers, the number of\nexperimental settings ($N_{set}$) in which this nuclide was\nobserved, and the obtained mass excess value ($ME$) with the\ncorresponding $1\\sigma$ total ($\\sqrt{\\sigma^2_{stat}+\\sigma^2_{syst}}$) uncertainty ($\\sigma(ME)$).\\label{newm}}\n\\begin{center}\n\\begin{tabular}{rrrrr}\n\\hline \\hline\nZ & A & $N_{set}$ &$ME$& $\\sigma(ME)$ \\\\\n & & & (keV) & (keV) \\\\\n\\hline\n{\\bf 71}& {\\bf 181}& {\\bf 1} & {\\bf -44797}& {\\bf 126}\\\\\n{\\bf 71}& {\\bf 183}& {\\bf 1} & {\\bf -39716}& {\\bf 80}\\\\\n\\hline\n{\\bf 72}& {\\bf 185}& {\\bf 1} & {\\bf -38320}& {\\bf 64}\\\\\n{\\bf 72}& {\\bf 186}& {\\bf 1} & {\\bf -36424}&{\\bf 51}\\\\\n\\hline\n{\\bf 73}& {\\bf 187}& {\\bf 2} & {\\bf -36896}&{\\bf 56}\\\\\n{\\bf 73}&{\\bf 188}& {\\bf 2} & {\\bf -33612}&{\\bf 55}\\\\\n\\hline\n74& 189& 5 & -35618& 40\\\\\n74& 190& 7 & -34388& 41\\\\\n{\\bf 74}&{\\bf 191}&{\\bf 1} & {\\bf -31176}&{\\bf 42}\\\\\n\\hline\n{\\bf 75}&{\\bf 192}&{\\bf 1} & {\\bf -31589}&{\\bf 71}\\\\\n{\\bf 75}&{\\bf 193}&{\\bf 7} & {\\bf -30232}&{\\bf 39}\\\\\n\\hline\n76& 195& 1 & -29512& 56\\\\\n\\hline\n\\hline\n\\end{tabular}\\end{center}\n\\end{table}\n\nSince the calibration curve is not known exactly and is approximated with linear splines, and\nsince the number of calibration points in each spectrum is small,\nthere is inevitably a systematic error introduced by the analysis method.\nWays to estimate systematic uncertainty have been described in our previous works~\\cite{Ra-NPA,Li-NPA,Chen2012}.\nFor this purpose in the present work, we re-determined the mass of each reference nuclide. \nThis was done consecutively by setting each of the references as ``no''-reference and\nobtaining its mass from the remaining 17 references. \nThe re-determined mass excess values are listed in Table~\\ref{references} along with their literature values~\\cite{AME}. \nThe systematic error $\\sigma_{syst}$ has been obtained from solving the following equation:\n\\begin{equation}\n\\sum_{i=1}^{N_{ref}}\\frac{(ME^{(i)}_{AME}-ME^{(i)})^2}{\\sigma_{AME(i)}^2+\\sigma_{(i)}^2+\\sigma_{syst}^2}=N_{ref},\n\\end{equation}\nwhere $N_{ref}=18$ is the number of reference nuclides, $ME^{(i)}$ ($\\sigma_{(i)}$) \nand $ME_{AME}^{(i)}$ ($\\sigma_{AME(i)}$) are the re-calculated and literature mass excess\nvalues (statistical uncertainties) of the $i$-th reference nuclide, respectively.\nThe systematic uncertainty of the present analysis amounts to $\\sigma_{syst}=38 $~keV. \nThe final uncertainties listed in Table~\\ref{newm} were obtained from a quadratic sum of the systematic and statistical uncertainties.\n\nWe note, that in contrast to Ref.~\\cite{Chen2012} we do not observe any significant systematic dependence \nof the re-calculated mass values versus their proton number and correspondingly do not reduce the systematic errors.\nA dedicated study should be performed to investigate the origin of this inconsistency.\n\n\\section{Discussion}\n\n\nThe new masses allow us to obtain interesting information on\nnuclear structure. Fig.~\\ref{s2n_e2} (left, middle) shows\ntwo-neutron separation energies ($S_{2n}$) as a function of\nneutron number for $Z=66-78$ in the $A\\sim180$ region.\nFig.~\\ref{s2n_e2} (left) is for even proton numbers while\nFig.~\\ref{s2n_e2} (middle) is for odd proton numbers. The new\n$S_{2n}$ values, for $^{181,183}$Lu, $^{185,186}$Hf,\n$^{187,188}$Ta, $^{191}$W, $^{192,193}$Re, calculated from the\nmasses measured in this study, are marked in red color. For known\nmasses whose values were improved in this experiment,\n$^{189,190}$W and $^{195}$Os, the literature $S_{2n}$ values are\nillustrated with black color.\n\nBy inspecting Fig.~\\ref{s2n_e2} (left), one can notice, that in\nthe $S_{2n}$ values of the even-$Z$ nuclei a flattening in Yb, Hf\nand W is seen at almost the last neutron numbers experimentally\nknown. The flattening in $S_{2n}$(W), using the improved W masses,\nis confirmed and $S_{2n}$ at $N=117$ continues with the same\nbehavior. In contrast to Hf and W, the new $S_{2n}$(Os) point,\nwhich is a bit lower at $N=119$ than in previous measurements,\nshows no flattening.\n\n\\begin{figure*}\n\\includegraphics[height=5.4cm]{fig5a}\\hspace{8mm}\n\\includegraphics[height=5.3cm]{fig5b}\n\\includegraphics[height=5.3cm]{fig5c}\n\\caption{(Color online) Data for the $A\\sim180$ region,\ntwo-neutron separation energies \\cite{AME} as a function of\nneutron number from $N=100$ to $N=124$ for even-$Z$ (left) from Dy\nto Pt and odd-$Z$ (middle) from Ho to Re. The new $S_{2n}$ values\nobtained from this work are shown in red color while the\nliterature $S_{2n}$ values are shown in black color. Right :\nEnergies of the first excited 2$^+$ states \\cite{NDC} for the same\neven-even nuclei as in the left panel.\\label{s2n_e2}}\n\\end{figure*}\n\nFig.~\\ref{s2n_e2} (middle) with the new measured masses does not\nshow similar effects in $S_{2n}$ as in Fig.~\\ref{s2n_e2} (left).\nHowever, there is a small change in slope (a more rapid fall-off)\nat $N=110$ compared to the lower-$N$ trend in $S_{2n}$($_{71}$Lu),\n$S_{2n}$($_{73}$Ta) and $S_{2n}$($_{75}$Re) (we also see this drop\nfor $_{72}$Hf and $_{74}$W in Fig.~\\ref{s2n_e2} (left)), and maybe\nat $N=115$ in $S_{2n}$($_{73}$Ta) as well. It is highly desirable\nto have more odd-$Z$ mass measurements in this region for a\ncomparison with even-$Z$ where more data are available. Thus, we\nconcentrate below on discussing even-$Z$ nuclei.\n\n\nFig.~\\ref{s2n_e2} (right) shows the energy of the first excited\n2$^+$ states against neutron number for $Z=70-78$ in the\n$A\\sim180$ region. This important, simple, observable has a high\nenergy (can be a few MeV) at magic numbers where nuclei are\nspherical and very low energies (less than 100 keV for well\ndeformed heavy nuclei) near mid-shell. The $E$(2$_1^+$) values\nusually decrease smoothly between the beginning and middle of a\nshell except when there is a sudden change in structure. Since\n$N\\sim104$ is mid-shell for the nuclei illustrated in\nFig.~\\ref{s2n_e2}, $E$(2$_1^+$) has a minimum at or close to\n$N=104$. After the mid-shell, the energy increases towards the\n$N=126$ magic number.\n\nLet us now focus on the W-Pt nuclei in Fig.~\\ref{s2n_e2} and\ncompare the behavior of $E$(2$_1^+$) and $S_{2n}$. In particular,\nwe look at $S_{2n}$ in isotopes where $E$(2$_1^+$) changes\nrapidly, indicating a sudden change in structure. In W,\n$E$(2$_1^+$) increases from $N=114$ to 116 by a considerably\nlarger amount compared to the other W isotopes (see\nRef.~\\cite{podolyak2000}). This jump signals a structural change\nfrom approximately constant deformation to decreasing deformation\nat $N=116$ (after $N=114$). Note that this neutron number is\nexactly where $S_{2n}$ exhibits flattening.\n\nSimilar to W, Os at $N=120$ (after $N=118$) has a jump in\n$E$(2$_1^+$). However, $S_{2n}$($^{196}$Os) does not reveal an\nobvious change at the same neutron number. At first glance, this\nseems inconsistent with the interpretation explained for W above\nbut, in fact, there might be an explanation for this different\nbehavior which would provide additional insight into the relation\nof binding to collectivity.\n\nReference~\\cite{Cakirli2} showed the structural sensitivity of\ncalculated collective contributions to binding. In addition\nRef.~\\cite{Cakirli2} stressed that collective binding is very\nsensitive to the number of valence nucleons.\nCalculated collective contributions to binding using the IBA-1\nmodel for boson numbers $N_{B}=5$ (left) and $N_{B}=16$ (right)\nare illustrated in the symmetry triangle \\cite{Cakirli2} in\nFig.~\\ref{triangle}. The three corners of the triangle describe\nthree dynamical symmetries, U(5) (vibrator), SU(3) (rotor) and\nO(6) ($\\gamma$-soft) (for more details, see Ref.~\\cite{Iachello}).\nThe color code in Fig.~\\ref{triangle} changes from yellow to red\nwhen the collective effects increase. Needless to say, nuclei have\nmore valence particles (so boson numbers) around the SU(3) corner\nthan the U(5) (and also O(6)) corner. One sees that the collective\nbinding energy (B.E.) rapidly increases for nuclei with axial\ndeformation, that is, near SU(3). Note that the triangles are\npresented for fixed boson numbers. In both, the color scale is\nkept the same to point out that the collective B.E.s are larger in\nN$_B$=16 than 5. As shown in Fig.~4 of Ref.~\\cite{Cakirli2}, the\ncollective binding energies vary approximately as the square of\nthe number of valence nucleons in the context of IBA calculations.\nTherefore, for a lower number of valence nucleons,\nFig.~\\ref{triangle} shows similar trends for both $N_{B}=5$ and\n$N_{B}=16$, but the overall binding is considerably less (compare\n(left) and (right) of Fig.~\\ref{triangle}). We now suggest that\nthe behavior of $E(2_1^+)$ and $S_{2n}$ in Fig.~\\ref{s2n_e2} can\nbe understood in terms of this dual dependence of binding on\ncollectivity and valence nucleon number.\n\nIf $^{190}$W and $^{196}$Os are mapped in the symmetry triangle,\n$^{190}$W will likely be closer to the SU(3) corner than\n$^{196}$Os \\cite{Cakirli-pri}. One of the ways to understand this\nis from the $P$-factor \\cite{RCasten2}, defined as $P= N_p\\cdot N_n \/ (N_p +\nN_n)$, where $N_p$ denotes the number of valence protons (proton\nholes) and $N_n$ the number of valence neutrons (neutron holes).\nThus $P$ is a quantity that can provide a guide to structure. For\nexample, the onset of deformation in heavy nuclei corresponds to\nthe $P$-factor around 4 and 5. Generally, if $P$ is larger than 3,\ncollective effects increase. That is, nuclei become deformed and\napproach closer to the SU(3) corner.\n\nFig.~\\ref{Pfactor} shows color-coded values for the $P$-factor\nfor the $Z=50-82$, $N=82-126$ region, and indicates the\n$P$-factors for the nuclei relevant to this discussion. $^{190}$W\nhas 8 valence protons and 10 valence neutrons while $^{196}$Os has\n$N_p=6$ and $N_n=6$. Correspondingly, these nuclei have\n$P$-factors of 4.4 and 3, respectively. The greater collectivity\nof $^{190}$W compared to $^{196}$Os suggested by their $P$-factors\nis reflected in its lower 2$_1^+$ energies as seen in\nFig.~\\ref{s2n_e2} (right). Thus, for two reasons -- both greater\ncollectivity and more valence nucleons -- the collective binding\nshould be much greater in $^{190}$W than in $^{196}$Os and changes\nin binding energies ($S_{2n-coll}$ values) should be on a larger\nscale. We suggest that this accounts for the fact that we see a\nflattening in $^{190}$W clearly but not in $^{196}$Os in\nFig.~\\ref{s2n_e2} (left). Obviously, it is very important to have\nnew data, both masses and spectroscopic information, on even more\nneutron rich W isotopes although such experiments are difficult.\nEven the mass of $^{192}$W alone would be telling since the trend\nin $E$(2$_1^+$) is quite clear already.\n\n\n\\begin{figure}\n\\includegraphics[width=\\linewidth]{fig6}\n\\caption{(Color online) The symmetry triangle of the IBA showing\nthe three dynamical symmetries at the vertices. The colors\nindicate calculated collective contributions in MeV to binding\nenergies for $N_B = 5$ (left) and $N_B=16$ (right). A similar\ntriangle for $N_B=16$ was presented in\nRef.~\\cite{Cakirli2}.\\label{triangle}}\n\\end{figure}\n\n\n\\begin{figure}\n\\includegraphics[width=\\linewidth]{fig7}\n\\caption{(Color online) $P$-factor values illustrated with a color\ncode for even-even nuclei in the $Z=50-82$ and $N=82-126$ shells.\nBlack points marked are for the key nuclei discussed, namely,\n$^{190}$W, $^{196}$Os, $^{188}$Pt, $^{198}$Pt, and\n$^{152}$Sm.\\label{Pfactor}}\n\\end{figure}\n\n\n\\begin{figure}\n\\includegraphics[width=\\linewidth]{fig8}\n\\caption{Calculated $E$(2$_1^+$) (top), $\\delta E$(2$_1^+$) (middle)\nand $\\delta S_{2n-coll}$ (bottom) values from IBA calculations as\na function of boson number $N_B$. The points for\n$\\delta E$(2$_1^+)$ and $\\delta S_{2n-coll}$ correspond to a set\nof schematic IBA calculations in which $\\kappa$, and $\\chi$ are\nconstant (at 0.02 and -1.32, respectively) while $\\epsilon$, and\n$N_B$ vary in a smooth way to simulate a spherical-to-deformed\ntransition region. The following equations are used for\n$\\delta E$(2$_1^+$) and $\\delta S_{2n-coll}$:\n$\\delta E(2_1^+)(Z,N)=[E(2_1^+)(Z,N) - E(2_1^+)(Z,N+2)] \/\nE(2_1^+)(Z,N)$ and\n$\\delta S_{2n-coll}=-[S_{2n-coll}(Z,N) -\nS_{2n-coll}(Z,N+2)$], respectively.\\label{dS2n-E2}}\n\\end{figure}\n\nExisting data on Pt nicely illustrate and support these ideas.\nFig.~\\ref{s2n_e2} (right) shows two jumps in $E$(2$_1^+$) for\nPt, around $N\\sim110$ and $N\\sim118-120$. Looking at $S_{2n}$ for\nPt, there is a kink near $N\\sim110$ but a smooth behavior near\n$N\\sim118$. For $^{188}$Pt, $N_p$ is 4 and $N_n$ is 16 so the\n$P$-factor is 3.2. This isotope, with 20 valence nucleons, is\nrelatively collective and once again one sees an anomaly in\n$S_{2n}$ as well. In contrast, for $^{198}$Pt$_{120}$ with only 10\nvalence nucleons, and a $P$-factor of only 2.4, the lower\ncollectivity (seen in the much higher 2$^+$ energy) and the lower\nnumber of valence nucleons are such that $S_{2n}$ shows no\nanomaly, but rather a nearly straight behavior.\n\nThis qualitative interpretation is supported by collective model\ncalculations. A thorough and detailed study of this or any\ntransition region requires a very careful and systematic\nassessment of all the data on energies, transition rates, and\nbinding energies, the choice for the specific terms to include in\nthe Hamiltonian and the optimum approach to fitting the data. We\nare undertaking such a study and will present the results in a\nfuture publication \\cite{Cakirli3}. Nevertheless, it is useful to\npresent an example of the model results here to validate the ideas\npresented above. To this end, we have carried out a schematic set\nof IBA calculations using the Hamiltonian \\cite{7,8}\n\n\\begin{equation}\n\\label{eqH}\nH = \\epsilon \\hat{n}_d - \\kappa{Q} \\cdot {Q}\n\\end{equation}\n\n\\noindent where $Q$ is a quadrupolar operator\n\n\\noindent\n\n\\begin{equation}\n\\label{eqQ} \n{Q}= (s^{\\dagger}\\tilde{d} +\nd^{\\dagger}s) + \\chi(d^{\\dagger}\\tilde{d})^{(2)}.\n\\end{equation}\n\n\\noindent The first term in Eq.~\\eqref{eqH} drives nuclei spherical while\nthe $Q\\cdot Q$ term induces collectivity and deformation. Therefore a\nspherical-deformed transition region involves a systematic change\nin the ratio of $\\epsilon$ to $\\kappa$. No generality is lost by\nkeeping $\\kappa$ constant (at 0.02 MeV). We follow a trajectory\nalong the bottom axis of the triangle corresponding to $\\chi\n=-1.3228$. Fig.~\\ref{dS2n-E2} illustrates the results, for $N_B\n= 6-16$ showing $E$(2$_1^+$) and the differentials of $E$(2$_1^+$) (for\n$N_B = 6-15$) and for the collective contributions to $S_{2n}$,\n$S_{2n-coll}$, (for $N_B = 6-14$). There is a clear change in\nstructure at $N_B\\sim$10 which is seen in a change in trend of\n$E$(2$_1^+$). Between $N_B = 10$ and 11, R$_{4\/2}$ changes from 2.60\nto 3.13. This corresponds to a maximum in the normalized\ndifferential of $E$(2$_1^+$). Confirming our association of\nstructural changes with kinks in $S_{2n}$, the differential of the\ncollective part of $S_{2n}$ also shows an extremum at exactly the\nsame point. These ideas will be expanded in our future publication\n\\cite{Cakirli3}.\n\nBesides the experimental examples of a correlation of $E$(2$_1^+$) energies \nand $S_{2n}$ values discussed in the context of Fig.~\\ref{s2n_e2}, \nour interpretation can\neasily be illustrated with the Sm isotopes around $N=90$. As is well\nknown, there is a sudden onset of deformation for the rare earth\nnuclei from $N=88$ to 90. This effect is clear from various\nobservables. One example is seen in Fig.~\\ref{Sm-S2n-E2} which\nshows the experimental $E$(2$_1^+$) energies (top) and $S_{2n}$\n(bottom) as a function of neutron number. Note that we plot these\nagainst decreasing neutron number so that the deformed nuclei are\non the left and spherical ones on the right to make the comparison\nwith Fig.~\\ref{s2n_e2} easier. Note also that the overall trend in\n$S_{2n}$ is opposite from that in Fig.~\\ref{s2n_e2} since $S_{2n}$\nvalues decrease with increasing neutron number (going to the left in Fig.~\\ref{Sm-S2n-E2} (bottom) which simply \nreflects the filling of the shell model orbits. The noticeable\ndeviation occurs near $N\\sim90$ where there is\na distinct flattening. To correlate the trends in these two\nobservables in the $N=90$ region, one can use the same\ninterpretation as above for W at $N=116$, namely, if there is a\nvisible change at neutron number $N$ in $E$(2$_1^+$) and there are\nmany valence nucleons, we expect to see a change in the behavior\nof $S_{2n}$. In Fig.~\\ref{Sm-S2n-E2} (top), the $E$(2$_1^+$) change\noccurs at $N\\sim90$. The isotope $^{62}$Sm at $N=90$ has $N_p=12$ and\n$N_n=8$ so it has 10 bosons and its $P$-factor is 4.8 (see\nFig.~\\ref{Pfactor}). One therefore expects to see a change in\n$S_{2n}$. Fig.~\\ref{Sm-S2n-E2} (bottom) confirms this\nexpectation. The clear structural change at $N=90$ shown in\n$E$(2$_1^+$) is correlated with a larger binding compared to the\ngeneral trend in $S_{2n}$ as a function of $N$.\n\nSimilar correlations can be seen in some other nuclei as well.\nFurther details will be discussed in Ref.~\\cite{Cakirli3}.\nHowever, here, it is worth mentioning two more examples marked in\nFig.~\\ref{s2n_e2}. The case of Yb-isotopes is interesting. Yb at\n$N=107$ starts to change slope in $S_{2n}$ and a flattening occurs\nat $N=108$. The $P$-factor is $\\sim7$. With the interpretation\nabove, one would expect to see a change in $E$(2$_1^+$) after\n$N=106$, at $N=108$. However, there is no sudden change in\n$E$(2$_1^+$) in $^{178}$Yb. To understand Yb around $N\\sim108$\nbetter, it might therefore be useful to have additional $S_{2n}$\nvalues (mass measurements) and also more spectroscopic results for\nthe neutron-rich Yb isotopes.\n\n\nHf at $N=114$ has a $P$-factor 5.4 and one sees a flattening in\n$S_{2n}$. The corresponding 2$^+$ energies, however, are not\nknown. Thus, similarly as in Yb, we need more spectroscopic\nresults for Hf.\n\nTo summarize, we observed a correlation between the behavior of\n$S_{2n}$ obtained from our measured masses with the spectroscopic\ndata for $E(2_1^+)$, which could be related to nuclear\ncollectivity and valence nucleon number.\n\n\\begin{figure}\n\\includegraphics[width=\\linewidth]{fig9}\n\\caption{Experimental $E$(2$_1^+$) (top) and\n$S_{2n}$ (bottom) values against neutron number for $_{62}$Sm\n\\cite{AME11, NDC}. \\label{Sm-S2n-E2}}\n\\end{figure}\n\n\\section{Conclusion}\n\nDirect mass measurements of neutron-rich $^{197}$Au projectile\nfragments at the cooler-storage ring ESR yielded new mass data.\nMasses of nine nuclides were obtained for the first time and for\nthree nuclei the mass uncertainty was improved.\n\nWith the new masses, two-neutron separation energies, $S_{2n}$,\nare investigated. We showed that changes in structure, as\nindicated by changes in the collective observable $E$(2$^+_1$),\nare reflected in $S_{2n}$ values in nuclei such as $^{190}$W and\n$^{188}$Pt, which have large $P$-factors, are collective, and\nhave large valence nucleon numbers. For nuclei with similar\nchanges in $E$(2$_1^+$), such as $^{196}$Os, and $^{198}$Pt, which\nhave lower collectivity and $P$-factors, and fewer valence\nnucleons, the sensitivity of collective binding to structure is\ngreatly reduced and smooth trends in $S_{2n}$ are observed. In Hf,\nthere are new $S_{2n}$ values at $N=113$, 114 where we see a\nflattening but there is no spectroscopic data at $N=114$. To\nconfirm the ideas discussed in this paper and also in\nRef.~\\cite{Cakirli3}, it would be useful to measure the\n$E$(2$_1^+$) for Hf at $N=114$. Similarly, mass and spectroscopic\nmeasurements are suggested for nuclei such as Yb with $N\\sim108$.\nTo conclude, these new data illustrate subtle changes in structure\nand the correlation with $E$(2$_1^+$) reveals a valuable way to\ncorrelate changes in structure in terms of both masses and\nspectroscopic observables. Of course, to quantitatively test these\nideas requires a systematic collective model study of the\nmass-structure relationship in this region. Such a project has\nbeen initiated~\\cite{Cakirli3} and we illustrated some of the\nresults here.\n\n\n\n\n\\section{Acknowledgments}\n\n\nThe authors would like to thank the GSI accelerator team for the excellent technical support. \nThis work was supported by the BMBF Grant in the framework of the Internationale Zusammenarbeit in Bildung und Forschung Projekt-FKZ 01DO12012,\nby the Alliance Program of the Helmholtz Association (HA216\/EMMI), by the Max-Planck Society and the US DOE under Grant No. DE-FG02-91ER-40609.\nD.S. is supported by the International Max Planck Research School for Precision Tests of Fundamental Symmetries at MPIK. \nR.B.C. thanks the Humboldt Foundation for support.\nK.B. and Y.A.L. thank ESF for support within the EuroGENESIS program.\nK.B. acknowledge support by the Nuclear Astrophysics Virtual Institute (NAVI) of the Helmholtz Association. \nZ.P. would like to acknowledge the financial support by Narodowe Centrum Nauki (Poland) grant No. 2011\/01\/B\/ST2\/05131. \nM.S.S. acknowledges the support by the Helmholtz International Centre for FAIR within the framework of the LOEWE program launched by the State of Hesse. \nB.S. is partially supported by NCET, NSFC (Grants No. 10975008, 11105010 and 11035007).\nP.M.W. acknowledges the support by the UK STFC and AWE plc.\nT.Y. is grateful for a grant-in-aid for a scientific research No. A19204023 by the Japanese Ministry of Education, Science and Culture. \n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{\\label{sec:intro}Introduction}\n\nDespite the fantastic successes of the Standard Model (SM)\nof electroweak interactions,\nits scalar sector remains largely untested~\\cite{hhg}.\nAn alternative to the single Higgs doublet of the SM\nis provided by the two-Higgs-doublet model (THDM),\nwhich can be supplemented by symmetry requirements\non the Higgs fields $\\Phi_1$ and $\\Phi_2$.\nSymmetries leaving the kinetic terms unchanged\\footnote{It has\nbeen argued by Ginsburg~\\cite{Gin} and by\nIvanov~\\cite{Ivanov1,Ivanov2} that one should also consider\nthe effect of non-unitary global symmetry transformations\nof the two Higgs fields, as the most general renormalizable\nHiggs Lagrangian allows for kinetic mixing of the two Higgs fields.\nIn this work, we study the possible\nglobal symmetries of the effective low-energy Higgs theory that arise\n\\textit{after} diagonalization of the Higgs kinetic energy terms.\nThe non-unitary transformations that diagonalize the Higgs kinetic\nmixing terms also transform the parameters of the Higgs potential,\nand thus can determine the structure of the remnant Higgs flavor symmetries\nof effective low-energy Higgs scalar potential. It is the latter \nthat constitutes the main focus of this work.}\nmay be of two types. On the one hand,\none may relate $\\Phi_a$ with some unitary transformation\nof $\\Phi_b$.\nThese are known as Higgs Family symmetries, or HF symmetries.\nOn the other hand,\none may relate $\\Phi_a$ with some unitary transformation\nof $\\Phi_b^\\ast$.\nThese are known as generalized CP symmetries,\nor GCP symmetries.\nIn this article we consider all such symmetries that are\npossible in the THDM,\naccording to their impact on the Higgs potential.\nWe identify three classes of GCP symmetries.\n\nThe study is complicated by the fact that one may perform a basis\ntransformation on the Higgs fields, thus hiding what might otherwise\nbe an easily identifiable symmetry. The need to seek basis invariant\nobservables in models with many Higgs was pointed out by Lavoura and\nSilva \\cite{LS}, and by Botella and Silva \\cite{BS}, stressing\napplications to CP violation. Refs.~\\cite{BS,BLS} indicate how to\nconstruct basis invariant quantities in a systematic fashion for any\nmodel, including multi-Higgs-doublet models. Work on basis\ninvariance in the THDM was much expanded upon\nby Davidson and Haber \\cite{DavHab},\nby Gunion and Haber \\cite{GunHab,Gun},\nby Haber and O'Neil \\cite{HabONe},\nand by other authors \\cite{others}.\nThe previous approaches highlight the role\nplayed by the Higgs fields. An alternative approach, spearheaded by\nNishi \\cite{Nishi1,Nishi2}, by Ivanov \\cite{Ivanov1,Ivanov2} and by\nManiatis {\\em et al}~\\cite{mani}, highlights the role played by\nfield bilinears, which is very useful for studies of the vacuum\nstructure of the model \\cite{Barroso,earlier_bilinears}. In this\npaper, we describe all classes of HF and GCP symmetries in both\nlanguages.\nOne problem with two classes of GCP identified here is that\nthey lead to an exceptional region of parameter space\n(ERPS) previously identified as problematic\nby Gunion and Haber \\cite{GunHab} and\nby Davidson and Haber \\cite{DavHab}.\nIndeed, no basis invariant quantity exists in the literature that\ndistinguishes between the $Z_2$ and $U(1)$ HF symmetries in\nthe ERPS.\n\nIf evidence for THDM physics is revealed in future experiments, then\nit will be critical to employ analysis techniques that are free\nfrom model-dependent assumptions. It is for this reason that\na basis-independent formalism for the THDM is so powerful.\nNevertheless, current experimental data already impose significant\nconstraints on the most general THDM. In particular, we know that\ncustodial symmetry breaking effects, flavor changing neutral\ncurrent (FCNC) constraints, and (to a lesser extent) CP-violating phenomena \nimpose some significant restrictions on the structure of the THDM\n(including the Higgs-fermion interactions). For example,\nthe observed suppression of FCNCs implies that either the two heaviest\nneutral Higgs bosons of the THDM have masses above 1 TeV,\nor certain Higgs-fermion Yukawa couplings must be absent~\\cite{Pas}.\nThe latter can be achieved by imposing certain discrete symmetries on\nthe THDM. Likewise, in the most general THDM, mass splittings between\ncharged and neutral Higgs bosons can yield custodial-symmetry breaking\neffects at one-loop that could be large enough to be in \nconflict with the precision electroweak data~\\cite{precision}. \nOnce again, symmetries\ncan be imposed on the THDM to alleviate any potential disagreement\nwith data. The implications of such symmetries for THDM phenomenology\nhas recently been explored by Gerard and collaborators~\\cite{Gerard}\nand by Haber and O'Neil~\\cite{custodial}.\n\nThus, if THDM physics is discovered, it will be important to \ndevelop experimental methods that can reveal the presence or absence\nof underlying symmetries of the most general THDM. This requires two\nessential pieces of input. First, one must identify all possible\nHiggs symmetries of interest. Second, one must relate these\nsymmetries to basis-independent observables that can be probed by\nexperiment. In this paper, we primarily address the first step,\nalthough we also provide basis-independent characterizations of these\nsymmetries. Our analysis focuses the symmetries of the THDM scalar\npotential. In principle, one can extend our study of these symmetries to the\nHiggs-fermion Yukawa interactions, although this lies beyond the scope\nof the present work.\n\n\nThis paper is organized as follows.\nIn section~\\ref{sec:notation} we\nintroduce our notation and define an invariant that does\ndistinguish the $Z_2$ and $U(1)$ HF symmetries in\nthe ERPS.\nIn section~\\ref{sec:vacuum} we explain the role\nplayed by the vacuum expectation values in\npreserving or breaking the $U(1)$ symmetry,\nand we comment briefly on renormalization.\nIn section~\\ref{sec:GCP} we introduce the GCP\ntransformations and explain why they are organized\ninto three classes.\nWe summarize our results and set them in the\ncontext of the existing literature in section~\\ref{sec:summary},\nand\nin section~\\ref{sec:allisCP} we prove a surprising result:\nmultiple applications of\nthe standard CP symmetry can be used to\nbuild all the models we identify,\nincluding those based on HF symmetries.\nWe draw our conclusions in\nsection~\\ref{sec:conclusions}.\n\n\n\\section{\\label{sec:notation}The scalar sector of the THDM}\n\n\\subsection{Three common notations for the scalar potential}\n\nLet us consider a $SU(2) \\otimes U(1)$ gauge theory with\ntwo Higgs-doublets $\\Phi_a$,\nwith the same hypercharge $1\/2$,\nand with vacuum expectation values (vevs)\n\\begin{equation}\n\\langle \\Phi_a \\rangle\n=\n\\left(\n\\begin{array}{c}\n0\\\\\nv_a\/\\sqrt{2}\n\\end{array}\n\\right).\n\\label{vev}\n\\end{equation}\nThe index $a$ runs from $1$ to $2$,\nand we use the standard definition for the electric\ncharge,\nwhereby the upper components of the $SU(2)$ doublets are\ncharged and the lower components neutral.\n\nThe scalar potential may be written as\n\\begin{eqnarray}\nV_H\n&=&\nm_{11}^2 \\Phi_1^\\dagger \\Phi_1 + m_{22}^2 \\Phi_2^\\dagger \\Phi_2\n- \\left[ m_{12}^2 \\Phi_1^\\dagger \\Phi_2 + \\textrm{H.c.} \\right]\n\\nonumber\\\\[6pt]\n&&\n+ \\tfrac{1}{2} \\lambda_1 (\\Phi_1^\\dagger\\Phi_1)^2\n+ \\tfrac{1}{2} \\lambda_2 (\\Phi_2^\\dagger\\Phi_2)^2\n+ \\lambda_3 (\\Phi_1^\\dagger\\Phi_1) (\\Phi_2^\\dagger\\Phi_2)\n+ \\lambda_4 (\\Phi_1^\\dagger\\Phi_2) (\\Phi_2^\\dagger\\Phi_1)\n\\nonumber\\\\[6pt]\n&&\n+ \\left[\n\\tfrac{1}{2} \\lambda_5 (\\Phi_1^\\dagger\\Phi_2)^2\n+ \\lambda_6 (\\Phi_1^\\dagger\\Phi_1) (\\Phi_1^\\dagger\\Phi_2)\n+ \\lambda_7 (\\Phi_2^\\dagger\\Phi_2) (\\Phi_1^\\dagger\\Phi_2)\n+ \\textrm{H.c.}\n\\right],\n\\label{VH1}\n\\end{eqnarray}\nwhere $m_{11}^2$, $m_{22}^2$, and $\\lambda_1,\\cdots,\\lambda_4$\nare real parameters.\nIn general,\n$m_{12}^2$, $\\lambda_5$, $\\lambda_6$ and $\\lambda_7$\nare complex. ``H.c.''~stands for Hermitian conjugation.\n\nAn alternative notation,\nuseful for the construction of invariants\nand championed by Botella and Silva \\cite{BS} is\n\\begin{eqnarray}\nV_H\n&=&\nY_{ab} (\\Phi_a^\\dagger \\Phi_b) +\n\\tfrac{1}{2}\nZ_{ab,cd} (\\Phi_a^\\dagger \\Phi_b) (\\Phi_c^\\dagger \\Phi_d),\n\\label{VH2}\n\\end{eqnarray}\nwhere Hermiticity implies\n\\begin{eqnarray}\nY_{ab} &=& Y_{ba}^\\ast,\n\\nonumber\\\\\nZ_{ab,cd} \\equiv Z_{cd,ab} &=& Z_{ba,dc}^\\ast.\n\\label{hermiticity_coefficients}\n\\end{eqnarray}\nThe extremum conditions are\n\\begin{equation}\n\\left[ Y_{ab}\n+ Z_{ab,cd}\\, v_d^\\ast v_c \\right]\\ v_b = 0\n\\hspace{3cm}(\\textrm{for\\ } a = 1,2).\n\\label{stationarity_conditions}\n\\end{equation}\nMultiplying by $v_a^\\ast$ leads to\n\\begin{equation}\nY_{ab} (v_a^\\ast v_b) = - Z_{ab,cd}\\,\n(v_a^\\ast v_b)\\, (v_d^\\ast v_c).\n\\label{aux_1}\n\\end{equation}\n\nOne should be very careful when comparing Eqs.~(\\ref{VH1})\nand (\\ref{VH2}) among different authors,\nsince the same symbol may be used for quantities\nwhich differ by signs, factors of two, or complex conjugation.\nHere we follow the definitions of Davidson and Haber\n\\cite{DavHab}.\nWith these definitions:\n\\begin{eqnarray}\nY_{11}=m_{11}^2, &&\nY_{12}=-m_{12}^2,\n\\nonumber \\\\\nY_{21}=-(m_{12}^2)^\\ast && Y_{22}=m_{22}^2,\n\\label{ynum}\n\\end{eqnarray}\nand\n\\begin{eqnarray}\nZ_{11,11}=\\lambda_1, && Z_{22,22}=\\lambda_2,\n\\nonumber\\\\\nZ_{11,22}=Z_{22,11}=\\lambda_3, && Z_{12,21}=Z_{21,12}=\\lambda_4,\n\\nonumber \\\\\nZ_{12,12}=\\lambda_5, && Z_{21,21}=\\lambda_5^\\ast,\n\\nonumber\\\\\nZ_{11,12}=Z_{12,11}=\\lambda_6, && Z_{11,21}=Z_{21,11}=\\lambda_6^\\ast,\n\\nonumber \\\\\nZ_{22,12}=Z_{12,22}=\\lambda_7, && Z_{22,21}=Z_{21,22}=\\lambda_7^\\ast.\n\\label{znum}\n\\end{eqnarray}\n\nThe previous two notations look at the Higgs fields $\\Phi_a$ individually.\nA third notation is used by Nishi \\cite{Nishi1,Nishi2} and\nIvanov \\cite{Ivanov1,Ivanov2},\nwho emphasize\nthe presence of field bilinears $(\\Phi_a^\\dagger \\Phi_b)$\n\\cite{earlier_bilinears}.\nFollowing Nishi \\cite{Nishi1} we write:\n\\begin{equation}\nV_H = M_\\mu r_\\mu + \\Lambda_{\\mu \\nu} r_\\mu r_\\nu,\n\\label{VH3}\n\\end{equation}\nwhere $\\mu = 0,1,2,3$ and\n\\begin{eqnarray}\nr_0 &=&\n\\frac{1}{2}\n\\left[(\\Phi_1^\\dagger \\Phi_1) + (\\Phi_2^\\dagger \\Phi_2) \\right],\n\\nonumber\\\\\nr_1 &=&\n\\frac{1}{2}\n\\left[(\\Phi_1^\\dagger \\Phi_2) + (\\Phi_2^\\dagger \\Phi_1) \\right]\n= \\textrm{Re}\\, (\\Phi_1^\\dagger \\Phi_2),\n\\nonumber\\\\\nr_2 &=&\n- \\frac{i}{2}\n\\left[(\\Phi_1^\\dagger \\Phi_2) - (\\Phi_2^\\dagger \\Phi_1) \\right]\n= \\textrm{Im}\\, (\\Phi_1^\\dagger \\Phi_2),\n\\nonumber\\\\\nr_3 &=&\n\\frac{1}{2}\n\\left[(\\Phi_1^\\dagger \\Phi_1) - (\\Phi_2^\\dagger \\Phi_2) \\right].\n\\label{r_Ivanov}\n\\end{eqnarray}\nIn Eq.~(\\ref{VH3}),\nsummation of repeated indices is\nadopted with Euclidean metric.\nThis differs from Ivanov's notation \\cite{Ivanov1,Ivanov2},\nwho pointed out that $r_\\mu$ parametrizes the gauge orbits\nof the Higgs fields,\nin a space equipped with a Minkowski metric.\n\nIn terms of the parameters of Eq.~(\\ref{VH1}),\nthe $4$-vector $M_\\mu$ and $4 \\times 4$ matrix $\\Lambda_{\\mu\\nu}$ are written\nrespectively as:\n\\begin{equation}\nM_\\mu =\n\\left(\n\\begin{array}{cccc}\nm_{11}^2 + m_{22}^2,\n&\n-2\\, \\textrm{Re}\\, m_{12}^2,\n&\n2\\, \\textrm{Im}\\, m_{12}^2,\n&\nm_{11}^2 - m_{22}^2\n\\end{array}\n\\right),\n\\label{M_mu}\n\\end{equation}\nand\n\\begin{equation}\n\\Lambda_{\\mu \\nu} =\n\\left(\n\\begin{array}{cccc}\n(\\lambda_1+\\lambda_2)\/2 + \\lambda_3\\\n&\\,\\,\\,\n\\textrm{Re}\\, (\\lambda_6 + \\lambda_7)\n&\\,\\,\\,\n- \\textrm{Im}\\, (\\lambda_6 + \\lambda_7)\n&\\,\\,\\,\n(\\lambda_1 - \\lambda_2)\/2\n\\\\\n \\phantom{-}\\textrm{Re}\\, (\\lambda_6 + \\lambda_7)\\\n&\\,\\,\\,\n\\lambda_4 + \\textrm{Re}\\, \\lambda_5\n&\\,\\,\\,\n- \\textrm{Im}\\, \\lambda_5\n&\\,\\,\\,\n \\phantom{-}\\textrm{Re}\\, (\\lambda_6 - \\lambda_7)\\\n\\\\\n- \\textrm{Im}\\, (\\lambda_6 + \\lambda_7)\\\n&\\,\\,\\,\n- \\textrm{Im}\\, \\lambda_5\n&\\,\\,\\,\n\\lambda_4 - \\textrm{Re}\\, \\lambda_5\n&\\,\\,\\,\n- \\textrm{Im}\\, (\\lambda_6 - \\lambda_7)\\\n\\\\\n(\\lambda_1 - \\lambda_2)\/2\n&\\,\\,\\,\n\\textrm{Re}\\, (\\lambda_6 - \\lambda_7)\n&\\,\\,\\,\n- \\textrm{Im}\\, (\\lambda_6 - \\lambda_7)\n&\\,\\,\\,\n\\ (\\lambda_1+\\lambda_2)\/2 - \\lambda_3\n\\end{array}\n\\right).\n\\label{Lambda_munu}\n\\end{equation}\nEq.~(\\ref{VH3}) is related to Eq.~(\\ref{VH2}) through\n\\begin{eqnarray}\nM^\\mu &=&\n\\sigma^\\mu_{ab}\\, Y_{ba},\n\\label{M_vs_Y}\n\\\\\n\\Lambda^{\\mu \\nu} &=&\n\\tfrac{1}{2} Z_{ab,cd}\\, \\sigma^\\mu_{ba} \\sigma^\\nu_{dc},\n\\label{Lambda_vs_Z}\n\\end{eqnarray}\nwhere the matrices $\\sigma^i$ are the three Pauli matrices,\nand $\\sigma^0$ is the $2 \\times 2$ identity matrix.\n\n\n\n\\subsection{Basis transformations}\n\nWe may rewrite the potential in terms of new fields $\\Phi^\\prime_a$,\nobtained from the original ones by a simple \n(global) basis transformation\n\\begin{equation}\n\\Phi_a \\rightarrow \\Phi_a^\\prime = U_{ab} \\Phi_b,\n\\label{basis-transf}\n\\end{equation}\nwhere $U\\in U(2)$ is a $2 \\times 2$ unitary matrix.\nUnder this unitary basis transformation,\nthe gauge-kinetic terms are unchanged,\nbut the coefficients $Y_{ab}$ and $Z_{ab,cd}$ are transformed as\n\\begin{eqnarray}\nY_{ab} & \\rightarrow &\nY^\\prime_{ab} =\nU_{a \\alpha}\\, Y_{\\alpha \\beta}\\, U_{b \\beta}^\\ast ,\n\\label{Y-transf}\n\\\\\nZ_{ab,cd} & \\rightarrow &\nZ^\\prime_{ab,cd} =\nU_{a\\alpha}\\, U_{c \\gamma}\\,\nZ_{\\alpha \\beta,\\gamma \\delta}\\, U_{b \\beta}^\\ast \\, U_{d \\delta}^\\ast ,\n\\label{Z-transf}\n\\end{eqnarray}\nand the vevs are transformed as\n\\begin{equation}\nv_a \\rightarrow v_a^\\prime = U_{a b} v_b.\n\\label{vev-transf}\n\\end{equation}\nThus,\nthe basis transformations $U$ may be utilized in order to absorb\nsome of the degrees of freedom of $Y$ and\/or $Z$,\nwhich implies that not all parameters of Eq.~(\\ref{VH2})\nhave physical significance.\n\n\n\n\\subsection{\\label{subsec:HFsymmetry}Higgs Family symmetries}\n\nLet us assume that the scalar potential in\nEq.~(\\ref{VH2}) has some explicit internal symmetry.\nThat is,\nwe assume that the coefficients of $V_H$ stay\n\\textit{exactly the same} under a transformation\n\\begin{equation}\n\\Phi_a \\rightarrow \\Phi_a^S = S_{ab} \\Phi_b.\n\\label{S-transf-symmetry}\n\\end{equation}\n$S$ is a unitary matrix,\nso that the gauge-kinetic couplings\nare also left invariant by this Higgs Family symmetry\n(HF symmetry).\nAs a result of this symmetry,\n\\begin{eqnarray}\nY_{a b} & = &\nY^S_{a b} =\nS_{a \\alpha}\\, Y_{\\alpha \\beta}\\, S_{b \\beta}^\\ast ,\n\\label{Y-S}\n\\\\\nZ_{ab,cd} & = &\nZ^S_{ab,cd} =\nS_{a \\alpha}\\, S_{c \\gamma}\\,\nZ_{\\alpha \\beta, \\gamma \\delta}\\, S_{b \\beta}^\\ast \\, S_{d \\delta}^\\ast .\n\\label{Z-S}\n\\end{eqnarray}\nNotice that this is \\textit{not} the situation considered\nin Eqs.~(\\ref{basis-transf})--(\\ref{Z-transf}).\nThere,\nthe coefficients of the Lagrangian\n\\textit{do change}\n(although the quantities that are physically\nmeasurable are invariant with respect to any change of basis).\nIn contrast, Eqs.~(\\ref{S-transf-symmetry})--(\\ref{Z-S})\nimply the existence of a HF symmetry $S$ of the scalar potential\nthat leaves the coefficients of $V_H$ unchanged.\n\nThe Higgs Family symmetry group must be a subgroup of full $U(2)$\ntransformation group of $2\\times 2$ unitary \nmatrices employed in Eq.~(\\ref{basis-transf}). Given the most\ngeneral THDM scalar potential, there is always a $U(1)$ subgroup\nof $U(2)$ under which the scalar potential is invariant.\nThis is the global hypercharge $U(1)_Y$ symmetry group:\n\\begin{equation}\nU(1)_Y:\n\\hspace{4ex}\n\\Phi_1 \\rightarrow e^{i \\theta} \\Phi_1,\n\\hspace{4ex}\n\\Phi_2 \\rightarrow e^{i \\theta} \\Phi_2,\n\\label{U1Y}\n\\end{equation}\nwhere $\\theta$ is an arbitrary angle (mod $2\\pi$). The invariance\nunder the global $U(1)_Y$ is trivially guaranteed by the \ninvariance under the $SU(2)\\otimes U(1)$ electroweak gauge symmetry.\n\\textit{Since the global hypercharge $U(1)_Y$ is always present, we shall \nhenceforth define the \nHF symmetries as those Higgs Family symmetries that are \northogonal to $U(1)_Y$.}\n\nWe now turn to the interplay between\nHF symmetries and basis transformations.\nLet us imagine that,\nwhen written in the basis of fields $\\Phi_a$,\n$V_H$ has a symmetry $S$.\nWe then perform a basis transformation from\nthe basis $\\Phi_a$ to the basis $\\Phi^\\prime_a$,\nas given by Eq.~(\\ref{basis-transf}).\nClearly,\nwhen written in the new basis,\n$V_H$ does \\textit{not} remain invariant under $S$.\nRather, it will be invariant under\n\\begin{equation}\nS^\\prime = U S U^\\dagger .\n\\label{S-prime}\n\\end{equation}\nAs we change basis,\nthe form of the potential changes\nin a way that may obscure the presence\nof a HF symmetry. In particular, two HF symmetries\nthat naively look distinct\nwill actually yield precisely the same physical predictions\nif a unitary matrix $U$ exists such that Eq.~\\eqref{S-prime} is satisfied.\n\nHF symmetries in the two-Higgs-doublet model (THDM) have a long\nhistory.\nIn papers by Glashow and Weinberg and by Paschos~\\cite{Pas}, the discrete\n$Z_2$ symmetry was introduced,\n\\begin{equation}\nZ_2:\n\\hspace{4ex}\n\\Phi_1 \\rightarrow \\Phi_1,\n\\hspace{4ex}\n\\Phi_2 \\rightarrow - \\Phi_2,\n\\label{Z2}\n\\end{equation}\nin order to preclude flavour-changing neutral currents \\cite{Pas}.\nThis is just the interchange\n\\begin{equation}\n\\Pi_2:\n\\hspace{4ex}\n\\Phi_1 \\leftrightarrow \\Phi_2,\n\\label{Pi2}\n\\end{equation}\nseen in a different basis,\nas shown by applying Eq.~(\\ref{S-prime}) in the form\n\\begin{equation}\n\\left(\n\\begin{array}{cc}\n0 & 1 \\\\\n1 & 0 \\\\\n\\end{array}\n\\right)\n=\n\\frac{1}{\\sqrt{2}}\n\\left(\n\\begin{array}{cc}\n1 & 1 \\\\\n1 & -1 \\\\\n\\end{array}\n\\right)\n\\\n\\left(\n\\begin{array}{cc}\n1 & 0 \\\\\n0 & -1 \\\\\n\\end{array}\n\\right)\n\\\n\\frac{1}{\\sqrt{2}}\n\\left(\n\\begin{array}{cc}\n1 & 1 \\\\\n1 & -1 \\\\\n\\end{array}\n\\right).\n\\label{Z2ToPi2}\n\\end{equation}\nPeccei and Quinn~\\cite{PQ} introduced the continuous $U(1)$ symmetry\n\\begin{equation}\nU(1):\n\\hspace{4ex}\n\\Phi_1 \\rightarrow e^{-i \\theta} \\Phi_1,\n\\hspace{4ex}\n\\Phi_2 \\rightarrow e^{i \\theta} \\Phi_2,\n\\label{U1}\n\\end{equation}\ntrue for any value of $\\theta$,\nin connection with the strong CP problem.\nOf course,\na potential invariant under $U(1)$ is also invariant\nunder $Z_2$.\n\nFinally, we examine the largest possible Higgs Family symmetry group\nof the THDM, namely $U(2)$. In this case, a basis transformation\nwould have no effect on the Higgs potential parameters. Since\n$\\delta_{ab}$ is the only $U(2)$-invariant tensor, it follows that\n\\begin{eqnarray}\nY_{ab}&=& c_1 \\delta_{ab}\\,,\\label{u2y}\\\\\nZ_{ab,cd} &=& c_2 \\delta_{ab} \\delta_{cd} + c_3 \\delta_{ad} \\delta_{bc}\\,,\n\\label{u2z}\n\\end{eqnarray}\nwhere $c_1$, $c_2$ and $c_3$ are arbitrary real \nnumbers.~\\footnote{Note that there is no $\\delta_{ac} \\delta_{bd}$ term\ncontributing to $Z_{ab,cd}$, as such a term is not invariant under\nthe transformation of Eq.~(\\ref{Z-transf}).}\nOne can easily check from Eqs.~(\\ref{Y-transf}) and (\\ref{Z-transf})\nthat the unitarity of $U$ implies that $Y'=Y$ and $Z'=Z$ \nfor any choice of basis, as required\nby the $U(2)$-invariance of the scalar potential.\nEqs.~(\\ref{u2y}) and (\\ref{u2z}) impose the following constraints on\nthe parameters of the THDM scalar potential (independently of the\nchoice of basis):\n\\begin{eqnarray}\nm_{22}^2 = m_{11}^2,\n& \\hspace{4ex} &\nm_{12}^2=0,\n\\nonumber\\\\\n\\lambda_1 = \\lambda_2=\\lambda_3+\\lambda_4\\,,\n& \\hspace{4ex} &\n\\lambda_5 = \\lambda_6 = \\lambda_7= 0\\,.\n\\label{u2pot}\n\\end{eqnarray}\nAs there are no non-zero potentially complex scalar potential parameters,\nthe $U(2)$-invariant THDM is clearly CP-invariant.\n\nAs previously noted, the\n$U(2)$ symmetry contains the global hypercharge\n$U(1)_Y$ as a subgroup. Thus, in order to identify\nthe corresponding HF symmetry that is orthogonal to $U(1)_Y$,\nwe first observe that\n\\begin{equation}\nU(2)\\mathchoice{\\cong}{\\cong}{\\isoS}{\\cong} SU(2)\\otimes U(1)_Y\/Z_2 \\mathchoice{\\cong}{\\cong}{\\isoS}{\\cong} SO(3)\\otimes U(1)_Y\\,.\n\\end{equation}\nTo prove the above isomorphism, simply note that any $U(2)$ matrix can\nbe written as $U=e^{i\\theta}\\hat{U}$, where $\\hat U\\in SU(2)$.\nTo cover the full $U(1)_Y$ group, we must take $0\\leq\\theta<2\\pi$.\nBut since both $\\hat U$ and $-\\hat U$ are elements of $SU(2)$\nwhereas $+1$ and $-1=e^{i\\pi}$ are elements of $U(1)_Y$, we must\nidentify $\\hat U$ and $-\\hat U$ as the same group element in order not to\ndouble cover the full $U(2)$ group. The identification of $\\hat U$\nwith $-\\hat U$ in $SU(2)$ is isomorphic to $SO(3)$, using the well known\nisomorphism $SO(3)\\mathchoice{\\cong}{\\cong}{\\isoS}{\\cong} SU(2)\/Z_2$. Consequently, we have identified\nSO(3) as the HF symmetry that constrains the scalar potential\nparameters as indicated in Eq.(\\ref{u2pot}).\n\n\n\nThe impact of these symmetries on the potential parameters\nin Eq.~(\\ref{VH1}) is shown in section~\\ref{sec:summary}.\nAs mentioned above,\nif one makes a basis change, the potential parameters\nchange and so does the explicit form of the symmetry and\nof its implications.\nFor example,\nEq.~(\\ref{Z2ToPi2}) shows that the symmetries $Z_2$ and $\\Pi_2$\nare related by a basis change.\nHowever,\nthey have a different impact on the parameters in their\nrespective basis.\nThis can be seen explicitly in Table~\\ref{master1}\nof section~\\ref{sec:summary}. One can also easily prove that\nthe existence of either the $Z_2$, $\\Pi_2$ or Peccei-Quinn $U(1)$\nsymmetry is sufficient to guarantee the existence of a basis choice in\nwhich all scalar potential parameters are real. That is, \nthe corresponding scalar Higgs sectors are explicitly CP-conserving.\n\nBasis invariant signs of HF symmetries were discussed\nextensively in Ref.~\\cite{DavHab}.\nRecently,\nFerreira and Silva~\\cite{FS2} extended these methods to include\nHiggs models with more than two Higgs doublets.\n\nConsider first the THDM scalar potentials that are invariant under \nthe so-called \\textit{simple} HF symmetries of Ref.~\\cite{FS2}.\nWe define a simple HF symmetry to be a symmetry\ngroup $G$ with the following property: the requirement that the\nTHDM scalar potential is invariant under a particular element \n$g\\in G$ (where $g\\neq e$ and $e$ is the identity element)\nis sufficient to guarantee invariance under the entire\ngroup $G$. The discrete cyclic group \n$Z_n=\\{e\\,,\\,g\\,,\\,g^2\\,,\\,\\ldots\\,,\\,g^{n-1}\\}$,\nwhere $g^n = e$,\nis an example of a possible simple HF symmetry group. \nIf we restrict the TDHM scalar potential to include terms of\ndimension-four or less (e.g., the tree-level scalar potential of the THDM),\nthen one can show that the Peccei-Quinn $U(1)$ symmetry is also\na simple HF symmetry. For example, consider the matrix\n\\begin{equation}\nS=\\left( \\begin{array}{cc}\ne^{- 2 i \\pi\/3} & 0\\\\\n0 & e^{2 i \\pi\/3}\n\\end{array} \\right)\\,.\n\\end{equation}\nNote that $S$ is an element of the cyclic sub-group\n$Z_3=\\{S\\,,\\,S^2\\,,\\,S^3=1\\}$ of the\nPeccei-Quinn $U(1)$ group.\nAs shown in Ref.~\\cite{FS2}, the \ninvariance of the tree-level THDM scalar potential\nunder $\\Phi_a\\to S_{ab}\\Phi_b$ automatically implies the\ninvariance of the scalar potential under the full Peccei-Quinn $U(1)$\ngroup. In contrast, the maximal HF symmetry, $SO(3)$, introduced\nabove is not a simple HF symmetry, as there is no single element of\n$S\\in SO(3)$ such that invariance under $\\Phi_a\\to S_{ab}\\Phi_b$ \nguarantees invariance of the tree-level THDM \nscalar potential under the\nfull SO(3) group of transformations.\n\n\nTypically, the simple HF\nsymmetries take on a simple form for a particular choice of basis\nfor the Higgs fields.\nWe summarize here a few of the results of Ref.~\\cite{FS2}:\n\\begin{enumerate}\n\\item In the THDM, there are only two\n\\textit{independent} classes of \\textit{simple} symmetries:\na discrete $Z_2$ flavor symmetry, and a continuous Peccei-Quinn $U(1)$\nflavor symmetry.\n\\item Other discrete flavor symmetry groups $G$ that are subgroups of $U(1)$\nare not considered independent. That is, if $S\\in G$ (where\n$S\\neq e$), then invariance under the\nthe discrete symmetry $\\Phi\\to S\\Phi$\nmakes the scalar potential automatically invariant under the full\nPeccei-Quinn $U(1)$ group;\n\\item In most regions of parameter space,\none can build quantities invariant under basis transformations\nthat detect these symmetries;\n\\item There exists a so-called exceptional region of parameter space (ERPS)\ncharacterized by\n\\begin{eqnarray}\nm_{22}^2 = m_{11}^2,\n& \\hspace{4ex} &\nm_{12}^2=0,\n\\nonumber\\\\\n\\lambda_2 = \\lambda_1,\n& \\hspace{4ex} &\n\\lambda_7 = - \\lambda_6.\n\\label{ERPS}\n\\end{eqnarray}\nAs shown by Davidson and Haber \\cite{DavHab},\na theory obeying these constraints does have a $Z_2$ symmetry,\nbut it may or not have a $U(1)$ symmetry.\nWithin the ERPS, the invariants in the literature cannot be\nused to distinguish the two cases.\n\\end{enumerate}\n\nThe last statement above is a result of the following considerations.\nIn order to distinguish between $Z_2$ and $U(1)$,\nDavidson and Haber \\cite{DavHab} construct two\ninvariant quantities given by Eqs.~(46) and (50) of Ref.~\\cite{DavHab}.\nOutside the ERPS,\nthese quantities are zero if and only if $U(1)$ holds.\nUnfortunately,\nin the ERPS these quantities vanish automatically\nindependently of whether or not $U(1)$ holds.\nSimilarly,\nFerreira and Silva \\cite{FS2} have\nconstructed invariants detecting HF symmetries.\nBut their use requires the existence of a matrix, obtained\nby combining $Y_{ab}$ and $Z_{ab,cd}$,\nthat has two distinct eigenvalues.\nThis does not occur when the ERPS is due to a symmetry.\nFinally, in the ERPS,\nIvanov \\cite{Ivanov1} states that the symmetry might be\n``$(Z_2)^2$ or $O(2)$''\n[our $Z_2$ \\textit{or} our $U(1)$]\nand does not provide a way to distinguish the\ntwo possible flavor symmetries \\cite{oversight}.\n\nGunion and Haber \\cite{GunHab} have shown that\nthe ERPS conditions of Eq.~(\\ref{ERPS})\nare basis independent;\nif they hold in one basis, then they hold in any basis.\nMoreover, for a model in the ERPS,\na basis may be chosen such that all parameters are \nreal.\\footnote{Given a scalar potential whose parameters satisfy\nthe ERPS conditions\nwith ${\\rm Im}(\\lambda_5^* \\lambda_6^2)\\neq 0$, the unitary matrix\nrequired to transform into a basis in which all the scalar potential\nparameters are real can be determined only by numerical means.}\nHaving achieved such a basis,\nDavidson and Haber \\cite{DavHab} demonstrate that\none may make one additional basis transformation\nsuch that\n\\begin{eqnarray}\nm_{22}^2 = m_{11}^2,\n& \\hspace{4ex} &\nm_{12}^2=0,\n\\nonumber\\\\\n\\lambda_2 = \\lambda_1,\n& \\hspace{4ex} &\n\\lambda_7 = \\lambda_6 = 0,\n\\hspace{4ex} \\textrm{Im}\\, \\lambda_5 = 0.\n\\label{ERPS2}\n\\end{eqnarray}\nThese conditions express the ERPS for a specific basis choice.\n\nOne might think that this is such a special region of parameter\nspace that it lacks any relevance.\nHowever,\nthe fact that the conditions in Eq.~(\\ref{ERPS}) hold in\n\\textit{any} basis is a good indication that a\nsymmetry may lie behind this condition.\nIndeed,\nas pointed out by Davidson and Haber \\cite{DavHab},\ncombining the two symmetries $Z_2$ and $\\Pi_2$\n\\textit{in the same basis} one is lead immediately to\nthe ERPS in the basis of Eq.~(\\ref{ERPS2}).\nUp to now,\nwe considered the impact of imposing\non the Higgs potential only one symmetry.\nThis was dubbed a simple symmetry.\nNow we are considering the possibility that the\npotential must remain invariant under one symmetry\nand \\textit{also} under a second symmetry;\nthis implies further constraints on the parameters\nof the Higgs potential.\nWe refer to this possibility as a multiple symmetry.\nAs seen from Table~\\ref{master1} of section~\\ref{sec:summary},\nimposing $Z_2$ and $\\Pi_2$ in the same basis leads\nto the conditions in Eq.~(\\ref{ERPS2}).\nIncidentally,\nthis example shows that a model which lies in the\nERPS,\nis automatically invariant under $Z_2$.\n\nIn section~{\\ref{sec:GCP}} we will show that\nall classes of non-trivial CP transformations lead\ndirectly to the ERPS,\nreinforcing the importance of this particular region of\nparameter space.\n\n\n\\subsection{Requirements for $U(1)$ invariance}\n\nIn the basis in which the $U(1)$ symmetry takes the form\nof Eq.~(\\ref{U1}),\nthe coefficients of the potential must obey\n\\begin{equation}\nm^{\\prime\\,2}_{12}= 0,\n\\hspace{4ex}\n\\lambda^\\prime_5 = \\lambda^\\prime_6 = \\lambda^\\prime_7 = 0.\n\\label{U1_conditions}\n\\end{equation}\nImagine that we have a potential of Eq.~(\\ref{VH1})\nin the ERPS:\n$m_{11}^2 = m_{22}^2$,\n$m_{12}^2=0$,\n$\\lambda_2=\\lambda_1$,\nand $\\lambda_7 = - \\lambda_6$.\nWe now wish to know whether a transformation $U$ may be chosen\nsuch that the potential coefficients in the new basis\nsatisfy the $U(1)$ conditions in Eq.~(\\ref{U1_conditions}).\nUsing the transformation rules in Eqs.~(A13)-(A23) of Davidson and\nHaber \\cite{DavHab},\nwe find that such a choice of $U$ is possible if and only if the\ncoefficients in the original basis satisfy\n\\begin{equation}\n2 \\lambda_6^3 - \\lambda_5 \\lambda_6(\\lambda_1 - \\lambda_3 - \\lambda_4)\n- \\lambda_5^2 \\lambda_6^\\ast = 0,\n\\label{can_change_to_usual_U1}\n\\end{equation}\nsubject to the condition that $\\lambda_5^\\ast \\lambda_6^2$ is real.\n\n\n\\subsection{\\label{subsec:D}The D invariant}\n\nHaving established the importance of the ERPS\n(as it can arise from a symmetry),\nwe will now build a basis invariant quantity that\ncan be used to detect the presence of a U(1)\nsymmetry in this special case.\n\nThe quadratic terms of the Higgs potential are always\ninsensitive to the difference between $Z_2$ and $U(1)$.\nMoreover,\nthe matrix $Y$ is proportional to the unit matrix in the ERPS.\nOne must thus look at the quartic terms.\nWe were inspired by the expression of $\\Lambda_{\\mu \\nu}$\nin Eq.~(\\ref{Lambda_munu}),\nwhich appears in the works of Nishi \\cite{Nishi1,Nishi2} and\nIvanov \\cite{Ivanov1,Ivanov2}.\nIn the ERPS of Eq.~(\\ref{ERPS}),\n$\\Lambda_{\\mu \\nu}$ breaks into a $1 \\times 1$ block\n($\\Lambda_{00}$),\nand a $3 \\times 3$ block\n($\\tilde{\\Lambda} = \\left\\{\\Lambda_{ij}\\right\\}$; $i,j=1,2,3$).\nA basis transformation $U$ belonging to $SU(2)$ on the $\\Phi_a$ fields\ncorresponds to an orthogonal $SO(3)$ transformation\nin the $r_i$ bilinears,\ngiven by\n\\begin{equation}\nO_{ij} = \\hbox{$\\frac{1}{2}$}\\,\\textrm{Tr} \n\\left[ U^\\dagger \\sigma_i U \\sigma_j \\right].\n\\label{O}\n\\end{equation}\nAny matrix $O$ of $SO(3)$ can be obtained by considering an\nappropriate matrix $U$ of $SU(2)$\n(unfortunately this property does not generalize for\nmodels with more than two Higgs doublets).\nA suitable choice of $O$ can be made that diagonalizes\nthe $3 \\times 3$ matrix $\\tilde{\\Lambda}$,\nthus explaining Eq.~(\\ref{ERPS2}).\nIn this basis,\nthe difference between the usual choices for $U(1)$ and $Z_2$ corresponds\nto the possibility that $\\textrm{Re} \\lambda_5$ might\nvanish or not, respectively.\n\nWe will now show that,\nonce in the ERPS,\nthe condition for the existence of $U(1)$ is that\n$\\tilde{\\Lambda}$ has two eigenvalues which are equal.\nThe eigenvalues of a $3 \\times 3$ matrix are the\nsolutions to the secular equation\n\\begin{equation}\nx^3 + a_2 x^2 + a_1 x + a_0 = 0,\n\\label{secular}\n\\end{equation}\nwhere\n\\begin{eqnarray}\na_0 &=&\n\\det \\tilde{\\Lambda}\n= - \\tfrac{1}{3} \\textrm{Tr\\,} (\\tilde{\\Lambda}^3)\n- \\tfrac{1}{6} ( \\textrm{Tr\\,} \\tilde{\\Lambda} )^3\n+ \\tfrac{1}{2} ( \\textrm{Tr\\,} \\tilde{\\Lambda} )\n\\textrm{Tr\\,} (\\tilde{\\Lambda}^2)\n\\nonumber\\\\\n&=&\n- \\tfrac{1}{3} Z_{ab,cd}\n\\left( Z_{dc,gh} Z_{hg,ba} - \\tfrac{3}{2} Z^{(2)}_{dc} Z^{(2)}_{ba} \\right)\n+ \\tfrac{1}{2} Z_{ab,cd} Z_{dc,ba}\n\\textrm{Tr\\,} \\left( Z^{(1)} - \\tfrac{1}{2} Z^{(2)} \\right)\n\\nonumber\\\\\n& &\n-\\tfrac{1}{6} \\left( \\textrm{Tr\\,} Z^{(1)}\\right)^3\n+\\tfrac{1}{4} \\left( \\textrm{Tr\\,} Z^{(1)}\\right)^2 \\textrm{Tr\\,} Z^{(2)}\n- \\tfrac{1}{2} \\textrm{Tr\\,} Z^{(1)} \\left( \\textrm{Tr\\,} Z^{(2)}\\right)^2,\n\\\\\na_1 &=&\n\\tfrac{1}{2} ( \\textrm{Tr\\,} \\tilde{\\Lambda} )^2\n- \\tfrac{1}{2} \\textrm{Tr\\,} (\\tilde{\\Lambda}^2)\n\\nonumber\\\\\n&=&\n\\tfrac{1}{2}\n\\left[\n\\left( \\textrm{Tr\\,} Z^{(1)}\\right)^2\n- \\textrm{Tr\\,} Z^{(1)} \\textrm{Tr\\,} Z^{(2)}\n+ \\left( \\textrm{Tr\\,} Z^{(2)}\\right)^2\n- Z_{ab,cd} Z_{dc,ba}\n\\right],\n\\\\\na_2 &=&\n- \\textrm{Tr\\,} \\tilde{\\Lambda}\n\\nonumber\\\\\n&=&\n\\tfrac{1}{2} \\textrm{Tr\\,} Z^{(2)} - \\textrm{Tr\\,} Z^{(1)},\n\\end{eqnarray}\nand\n\\begin{eqnarray}\nZ_{ab}^{(1)}\n&\\equiv&\nZ_{a \\alpha,\\alpha b} =\n\\left( \\begin{array}{cc}\n \\lambda_1 + \\lambda_4 & \\quad \\lambda_6 + \\lambda_7 \\\\\n \\lambda_6^\\ast + \\lambda_7^\\ast & \\quad \\lambda_2 + \\lambda_4\n\\end{array} \\right),\n\\\\\nZ_{ab}^{(2)}\n&\\equiv&\nZ_{\\alpha \\alpha,a b} =\n\\left( \\begin{array}{cc}\n \\lambda_1 + \\lambda_3 & \\quad \\lambda_6 + \\lambda_7 \\\\\n \\lambda_6^\\ast + \\lambda_7^\\ast & \\quad \\lambda_2 + \\lambda_3\n\\end{array} \\right).\n\\end{eqnarray}\nThe cubic equation, Eq.~(\\ref{secular}), has at least two\ndegenerate solutions if \\cite{AbrSte}\n\\begin{equation}\nD \\equiv\n\\left[ \\tfrac{1}{3} a_1 - \\tfrac{1}{9} a_2^2 \\right]^3\n+ \\left[ \\tfrac{1}{6} (a_1 a_2 - 3 a_0) - \\tfrac{1}{27} a_2^3 \\right]^2\n\\end{equation}\nvanishes.\n\nThe expression of $D$ in terms of the parameters in Eq.~(\\ref{VH1})\nis rather complicated,\neven in the ERPS.\nBut one can show by direct computation that if the $U(1)$-symmetry\ncondition of Eq.~(\\ref{can_change_to_usual_U1}) holds\n(subject to $\\lambda_5^\\ast \\lambda_6^2$ being real),\nthen $D=0$.\nWe can simplify the expression for $D$ by changing to a\nbasis where all parameters are real \\cite{GunHab},\nwhere we get\n\\begin{equation}\nD =\n- \\tfrac{1}{27}\n\\left[ \\lambda_5 (\\lambda_1 - \\lambda_3 - \\lambda_4 + \\lambda_ 5)\n - 2 \\lambda_6^2 \\right]^2\n\\left[ (\\lambda_1 - \\lambda_3 - \\lambda_4 - \\lambda_ 5)^2\n + 16 \\lambda_6^2 \\right].\n \\label{eq:D}\n\\end{equation}\nIf $\\lambda_6 \\neq 0$, then $D=0$ means\n\\begin{equation}\n2 \\lambda_6^2 = \\lambda_5 (\\lambda_1 - \\lambda_3 - \\lambda_4 + \\lambda_ 5).\n\\label{L6NEQ0}\n\\end{equation}\nIf $\\lambda_6 = 0$,\nthen $D=0$ corresponds to one of three possible conditions:\n\\begin{equation}\n\\lambda_5 = 0,\n\\hspace{4ex}\n\\lambda_5 = \\pm (\\lambda_1 - \\lambda_3 - \\lambda_4).\n\\label{L6EQ0}\n\\end{equation}\nNotice that Eqs.~(\\ref{L6NEQ0}) and (\\ref{L6EQ0}) are\nequivalent to Eq.~(\\ref{can_change_to_usual_U1})\nin any basis where the coefficients are real.\n\nAlthough $D$ can be defined outside the ERPS,\nthe condition $D=0$ only guarantees that the model is invariant under\n$U(1)$ inside the ERPS of Eq.~(\\ref{ERPS}).\nOutside this region one can detect the presence of a $U(1)$ symmetry\nwith the invariants proposed by Davidson and Haber \\cite{DavHab}.\nThis closes the last breach in the literature concerning basis-invariant\nsignals of discrete symmetries in the THDM.\nThus, in the ERPS $D=0$ is a necessary and sufficient condition for\nthe presence of a $U(1)$ symmetry.\n\n\n\\section{\\label{sec:vacuum}Vacuum structure and renormalization}\n\nThe presence of a $U(1)$ symmetry in the Higgs potential\nmay (or not) imply the existence of a massless scalar, the axion,\ndepending on whether (or not) the $U(1)$ is broken by the vevs.\nIn the previous section we related the basis-invariant condition\n$D=0$ in the ERPS with the presence of a $U(1)$ symmetry.\nIn this section we will show that,\nwhenever the basis-invariant condition $D=0$ is\nsatisfied in the ERPS,\nthere is always a stationary point for which a massless scalar,\nother than the usual Goldstone bosons, exists.\n\nWe start by writing the extremum conditions for the THDM in the\nERPS.\nFor simplicity, we will be working in a basis where all the\nparameters are real~\\cite{GunHab}.\nFrom Eqs.~\\eqref{stationarity_conditions} and~\\eqref{znum},\nwe obtain\n\\begin{align}\n0 = & \\;\\;\nY_{11}\\,v_1\\,+\\,\\frac{1}{2}\\,\\left[\\lambda_1\\,v_1^3\\,+\n\\,\\lambda_{345}\\,v_1\\,v_2^2\\,+\\,\n\\lambda_6\\,(3\\,v_1^2\\,v_2\\,-\\,v_2^3)\\right],\n\\nonumber\\\\\n0 = & \\;\\;\nY_{11}\\,v_2\\,+\\,\\frac{1}{2}\\,\\left[\\lambda_1\\,v_2^3\\,+\n\\,\\lambda_{345}\\,v_2\\,v_1^2\\,+\\,\n\\lambda_6\\,(v_1^3\\,-\\,3\\,v_2^2\\,v_1)\\right],\n\\label{eq:stat}\n\\end{align}\nwhere we have defined\n$\\lambda_{345}\\equiv\\lambda_3 + \\lambda_4 + \\lambda_5$.\nWe now compute the mass matrices.\nAs we will be considering only vacua with real vevs,\nthere will be no mixing between the real and imaginary\nparts of the doublets.\nAs such, we can define the mass matrix of the CP-even scalars as given by\n\\begin{equation}\n\\left[M^2_h\\right]_{ij}\\;=\\;\\frac{1}{2}\\,\\frac{\\partial^2\nV}{\\partial \\mbox{Re}(\\Phi_i^0)\\,\n\\partial \\mbox{Re}(\\Phi_j^0)}\n\\end{equation}\nwhere $\\Phi_i^0$ is the neutral (lower) component of the $\\Phi_i$\ndoublet. Thus, we obtain, for the entries of this matrix, the\nfollowing expressions:\n\\begin{align}\n\\left[M^2_h\\right]_{11}\\;=& \\;\nY_{11}\\,+\\,\\frac{1}{2}\\,\\left(3\\,\\lambda_1\\,v_1^2\\,+\n\\,\\lambda_{345}\\,v_2^2\n\\,\n+\\,6\\,\\lambda_6\\,v_1\\,v_2\\right)\\nonumber \\vspace{0.2cm} \\\\\n\\left[M^2_h\\right]_{22}\\;=&\\;\nY_{11}\\,+\\,\\frac{1}{2}\\,\\left(3\\,\\lambda_1\\,v_2^2\\,+\n\\,\\lambda_{345}\\,v_1^2\n\\,\n+\\,6\\,\\lambda_6\\,v_1\\,v_2\\right)\\nonumber \\vspace{0.2cm} \\\\\n\\left[M^2_h\\right]_{12}\\;=& \\;\\lambda_{345}\\,v_1\\,v_2\\,\n\\,+\\,\\frac{3}{2}\\,\\lambda_6\\,(v_1^2\\,-\\,v_2^2)\\;\\;\\; .\n\\label{eq:mh}\n\\end{align}\nLikewise, the pseudoscalar mass matrix is defined as\n\\begin{equation}\n\\left[M^2_A\\right]_{ij}\\;=\\;\\frac{1}{2}\\,\\frac{\\partial^2\nV}{\\partial \\mbox{Im}(\\Phi_i^0)\n\\partial \\mbox{Im}(\\Phi_j^0)}\n\\end{equation}\nwhose entries are given by\n\\begin{align}\n\\left[M^2_A\\right]_{11}\\;=&\\;\nY_{11}\\,+\\,\\frac{1}{2}\\,\\left[\\lambda_1\\,v_1^2\\,+\\,\n\\left(\\lambda_3\\,+\\,\\lambda_4\\,-\\,\\lambda_5\\right)\\,v_2^2\n\\,+\\,2\\,\\lambda_6\\,v_1\\,v_2 \\right]\n\\nonumber \\vspace{0.2cm} \\\\\n\\left[M^2_A\\right]_{22}\\;=&\\;\nY_{11}\\,+\\,\\frac{1}{2}\\,\\left[\\lambda_1\\,v_2^2\\,+\\,\n\\left(\\lambda_3\\,+\\,\\lambda_4\\,-\\,\\lambda_5\\right)\\,v_1^2\n\\,-\\,2\\,\\lambda_6\\,v_1\\,v_2 \\right]\n\\nonumber \\vspace{0.2cm} \\\\\n\\left[M^2_A\\right]_{12}\\;=& \\;\\lambda_5\\,v_1\\,v_2 \\,+\\,\n\\frac{1}{2}\\,\\lambda_6\\,(v_1^2\\,-\\,v_2^2)\\;\\;\\; .\n\\label{eq:mA}\n\\end{align}\nThe expressions ~\\eqref{eq:mh} and~\\eqref{eq:mA} are valid for all\nthe particular cases we will now consider.\n\n\n\\subsection{Case $\\lambda_6\\,=\\,0$, $\\{v_1\\,,\\,v_2\\}\\,\\neq\\,0$}\n\nLet us first study the case $\\lambda_6\\,=\\,0$, wherein we may solve\nthe extremum conditions in an analytical manner. It is trivial\nto see that Eqs.~\\eqref{eq:stat} have three types of solutions: both\nvevs different from zero, one vev equal to zero (say, $v_2$) and\nboth vevs zero (trivial non-interesting solution). For a solution\nwith $\\{v_1\\,,\\,v_2\\}\\,\\neq\\,0$, a necessary condition must be\nobeyed so that there is a solution to Eqs.~\\eqref{eq:stat}:\n\\begin{equation}\n\\lambda_1^2\\,-\\,\\lambda_{345}^2 \\;\\neq\\;0\\;\\;\\; . \\label{eq:det}\n\\end{equation}\nIf we use the extremum conditions to evaluate\n$\\left[M^2_h\\right]$, we obtain\n\\begin{equation}\n\\left[M^2_h\\right]\\;=\\;\\begin{pmatrix} \\lambda_1\\,v_1^2 & \\quad\n\\lambda_{345}\\,v_1\\,v_2 \\\\ \\lambda_{345}\\,v_1\\,v_2 & \\quad\n\\lambda_1\\,v_2^2\n\\end{pmatrix}\n\\end{equation}\nwhich only has a zero eigenvalue if Eq.~\\eqref{eq:det} is broken.\nThus, there is no axion in this matrix in this case. As for\n$\\left[M^2_A\\right]$, we get\n\\begin{equation}\n\\left[M^2_A\\right]\\;=\\;-\\,\\lambda_5\\,\\begin{pmatrix}\nv_1^2 &\\quad v_1\\,v_2 \\\\\nv_1\\,v_2 &\\quad v_2^2 \\end{pmatrix}\n\\end{equation}\nwhich clearly has a zero eigenvalue corresponding to the $Z$\nGoldstone boson. Further, this matrix will have an axion if\n$\\lambda_5\\,=\\,0$, which is the first condition of\nEq.~\\eqref{L6EQ0}.\n\n\\subsection{Case $\\lambda_6\\,=\\,0$, $\\{v_1\\,\\neq\\,0,\\,v_2\\,=\\,0\\}\\,$}\n\nReturning to Eq.~\\eqref{eq:stat}, this case gives us\n\\begin{equation}\nY_{11}\\,=\\,-\\,\\frac{1}{2}\\,\\lambda_1\\,v_1^2\\;\\;\\; ,\n\\end{equation}\nwhich implies $Y_{11}\\,<\\,0$. With this condition, the mass matrices\nbecome considerably simpler:\n\\begin{equation}\n\\left[M^2_h\\right]\\;=\\;\\begin{pmatrix} \\lambda_1\\,v_1^2 & \\quad 0 \\\\\n 0 & \\quad \\frac{1}{2}\\,(\\lambda_{345}\\,-\\,\\lambda_1)\\,v_1^2\n\\end{pmatrix}\n\\label{eq:mhll}\n\\end{equation}\nand\n\\begin{equation}\n\\left[M^2_A\\right]\\;=\\;\\frac{1}{2}\\,\\begin{pmatrix} 0 & \\quad 0 \\\\\n 0 & \\quad (\\lambda_3\\,+\\,\\lambda_4\\,-\\lambda_5\\,-\\,\\lambda_1)\\,v_1^2\n\\end{pmatrix} \\;\\;\\; .\n\\label{eq:mAll}\n\\end{equation}\nSo, we can have an axion in the matrix~\\eqref{eq:mhll} if\n\\begin{equation}\n\\lambda_{345}\\,-\\,\\lambda_1\\,=\\,0\\;\\;\\Leftrightarrow\\;\\;\\lambda_5\\,=\\,\n\\lambda_1\\,-\\,\\lambda_3\\,-\\,\\lambda_4 \\label{eq:c1}\n\\end{equation}\nor an axion in matrix~\\eqref{eq:mAll} if\n\\begin{equation}\n\\lambda_5\\,=\\, -\\lambda_1\\,+\\,\\lambda_3\\,+\\,\\lambda_4 \\;\\;\\; .\n\\label{eq:c2}\n\\end{equation}\nThat is, we have an axion if the second or third conditions of\nEq.~\\eqref{L6EQ0} are satisfied. The other possible case,\n$\\{v_1\\,=\\,0,\\,v_2\\,\\neq\\,0\\}\\,$, produces exactly the same\nconclusions.\n\n\\subsection{Case $\\lambda_6\\,\\neq\\,0$}\n\nThis is the hardest case to treat, since we cannot obtain analytical\nexpressions for the vevs. Nevertheless a full analytical treatment is\nstill possible. First, notice that with $\\lambda_6\\,\\neq\\,0$\nEqs.~\\eqref{eq:stat} imply that both vevs have to be non-zero. At\nthe stationary point of Eqs.~\\eqref{eq:stat}, the pseudoscalar mass\nmatrix has a Goldstone boson and an eigenvalue given by\n\\begin{equation}\n-\\lambda_5\\,(v_1^2\\,+\\,v_2^2)\\,-\n\\,\\lambda_6\\,\\frac{v_1^4\\,-v_2^4}{2\\,v_1\\,v_2}\n\\;\\;\\; .\n\\end{equation}\nSo, an axion exists if we have\n\\begin{equation}\n\\frac{v_1^2\\,-\\,v_2^2}{v_1\\,v_2}\\;=\\;-\n\\,\\frac{2\\,\\lambda_5}{\\lambda_6}\\;\\;\\;.\n\\label{eq:ves}\n\\end{equation}\nOn the other hand, after some algebraic manipulation, it is simple\nto obtain from~\\eqref{eq:stat} the following condition:\n\\begin{equation}\n\\lambda_1\\,-\\,\\lambda_{345}\\;=\\;\n\\lambda_6\\,\\left(\\frac{v_1^2\\,-\\,v_2^2}{v_1\\,v_2}\\,-\n\\,\\frac{4\\,v_1\\,v_2}{v_1^2\\,-\\,v_2^2}\\right)\n\\label{eq:les}\n\\end{equation}\nSubstituting Eq.~\\eqref{eq:ves} into~\\eqref{eq:les}, we obtain\n\\begin{equation}\n\\lambda_1\\,-\\,\\lambda_{345}\\,=\\,\n\\lambda_6\\,\\left(-\\,\\frac{2\\,\\lambda_5}{\\lambda_6}\\,+\n\\,\\frac{2\\,\\lambda_6}{\\lambda_5}\\right)\n\\;\\Longleftrightarrow\\; 2 \\lambda_6^2 \\,=\n\\, \\lambda_5 (\\lambda_1 -\n\\lambda_3 - \\lambda_4 + \\lambda_ 5).\n\\end{equation}\n\nThus, we have shown that all of the conditions stemming from the\nbasis-invariant condition $D=0$\nguarantee the existence of some stationary point for\nwhich the scalar potential yields an axion.\nNotice that, however,\nthis stationary point need not coincide with the global minimum\nof the potential.\n\n\n\\subsection{Renormalization group invariance}\n\nWe now briefly examine the renormalization group (RG) behavior of our\nbasis-invariant condition $D=0$. It would be meaningless to say that\n$D=0$ implies a $U(1)$ symmetry if that condition were only valid at\na given renormalization scale. That is, it could well be that a numerical\naccident forces $D=0$ at only a given scale. To avoid such a\nconclusion, we must verify if\n$D=0$ is a RG-invariant condition (in addition to being\nbasis-invariant). For a given renormalization scale $\\mu$, the\n$\\beta$-function of a given parameter $x$ is defined as\n$\\beta_x\\,=\\,\\mu\\,\\partial x\/\\partial \\mu$. For simplicity, let us\nrewrite $D$ in Eq.~\\eqref{eq:D} as\n\\begin{equation}\nD\\;=\\;-\\,\\frac{1}{27}\\,D_1^2\\,D_2\\;\\;\\; ,\n\\end{equation}\nwith\n\\begin{align}\nD_1 &=\\; \\lambda_5 (\\lambda_1 - \\lambda_3 - \\lambda_4 + \\lambda_ 5)\n - 2 \\lambda_6^2 \\nonumber \\\\\nD_2 &=\\; (\\lambda_1 - \\lambda_3 - \\lambda_4 - \\lambda_ 5)^2\n + 16 \\lambda_6^2 \\;\\;\\;.\n\\end{align}\nIf we apply the operator $\\mu\\,\\partial \/\\partial \\mu$ to $D$, we\nobtain\n\\begin{equation}\n\\beta_D\\,=\\,-\\,\\frac{1}{27}\\,\n\\left(2\\,D_1\\,D_2\\,\\beta_{D_1}\\,+\\,D_1^2\\,\\beta_{D_2}\\right)\n\\;\\;\\;.\n\\end{equation}\n\nIf $D_1=0$ (which corresponds to three of the conditions presented\nin Eqs.~\\eqref{L6NEQ0} and~\\eqref{L6EQ0}) then we immediately have\n$\\beta_D\\,=\\,0$. That is, if $D=0$ at a given scale, it is zero at\nall scales.\n\nIf $D_2=0$ and $D_1\\neq 0$ we will only have $\\beta_D\\,=\\,0$ if\n$\\beta_{D_2}\\,=\\,0$, or equivalently,\n\\begin{equation}\n2\\,(\\lambda_1 - \\lambda_3 - \\lambda_4 - \\lambda_ 5)\\,\n(\\beta_{\\lambda_1} - \\beta_{\\lambda_3} - \\beta_{\\lambda_4} -\n\\beta_{\\lambda_ 5}) \\,+\\,32\\,\\beta_{\\lambda_6}\\,\\lambda_6\\;=\\;0\n\\;\\;\\; .\n\\end{equation}\nGiven that $D_2=0$ implies that $\\lambda_6\\,=\\,0$ and\n$\\lambda_5\\,=\\,\\lambda_1 - \\lambda_3 - \\lambda_4$, we once\nagain obtain $\\beta_D\\,=\\,0$.\n\nThus, the condition $D=0$ is RG-invariant. A direct verification of\nthe RG invariance of Eqs.~\\eqref{L6NEQ0} and~\\eqref{L6EQ0}, and of\nthe conditions that define the ERPS itself, would require the\nexplicit form of the $\\beta$ functions of the THDM involving the\n$\\lambda_6$ coupling. That verification will be made\nelsewhere~\\cite{drtj}.\n\n\\section{\\label{sec:GCP}Generalized CP symmetries}\n\nIt is common to consider the standard CP transformation\nof the scalar fields as\n\\begin{equation}\n\\Phi_a (t, \\vec{x}) \\rightarrow\n\\Phi^{\\textrm{CP}}_a (t, \\vec{x}) = \\Phi_a^\\ast (t, - \\vec{x}),\n\\label{StandardCP}\n\\end{equation}\nwhere the reference to the time ($t$) and space ($\\vec{x}$)\ncoordinates will henceforth be suppressed.\nHowever,\nin the presence of several scalars with the same quantum numbers,\nbasis transformations can be included in the definition of the\nCP transformation.\nThis yields generalized CP transformations (GCP),\n\\begin{eqnarray}\n\\Phi^{\\textrm{GCP}}_a\n&=& X_{a \\alpha} \\Phi_\\alpha^\\ast\n\\equiv X_{a \\alpha} (\\Phi_\\alpha^\\dagger)^\\top,\n\\nonumber\\\\\n\\Phi^{\\dagger \\textrm{GCP}}_a\n&=& X_{a \\alpha}^\\ast \\Phi_\\alpha^\\top\n\\equiv X_{a \\alpha}^\\ast (\\Phi_\\alpha^\\dagger)^\\ast,\n\\label{GCP}\n\\end{eqnarray}\nwhere $X$ is an arbitrary unitary\nmatrix~\\cite{GCP1,GCP2}.\\footnote{Equivalently, one can \nconsider a generalized time-reversal\ntransformation proposed in Ref.~\\cite{Branco:1983tn}\nand considered further in Appendix A of Ref.~\\cite{GunHab}.}\n\n\nNote that the transformation\n$\\Phi_a\\to\\Phi^{\\rm GCP}_a$, where $\\Phi^{\\rm GCP}_a$ is given\nby Eq.~\\eqref{GCP},\nleaves the kinetic terms invariant.\nThe GCP transformation of a field bilinear yields\n\\begin{equation}\n\\Phi^{\\dagger \\textrm{GCP}}_a\n\\Phi^{\\textrm{GCP}}_b\n=\nX_{a \\alpha}^\\ast X_{b \\beta}\n(\\Phi_\\alpha \\Phi_\\beta^\\dagger)^\\top.\n\\end{equation}\nUnder this GCP transformation,\nthe quadratic terms of the potential may be written as\n\\begin{eqnarray}\nY_{ab} \\Phi^{\\dagger \\textrm{GCP}}_a\n\\Phi^{\\textrm{GCP}}_b\n&=&\nY_{ab} X_{a \\alpha}^\\ast X_{b \\beta}\n\\Phi_\\beta^\\dagger \\Phi_\\alpha\n\\nonumber\\\\\n&=&\nX_{b \\beta} Y_{ba}^\\ast X_{a \\alpha}^\\ast\n\\Phi_\\beta^\\dagger \\Phi_\\alpha\n\\nonumber\\\\\n&=&\nX_{\\alpha a} Y_{\\alpha \\beta}^\\ast X_{\\beta b}^\\ast\n\\Phi_a^\\dagger \\Phi_b\n=\n( X^\\dagger\\, Y\\, X )^\\ast_{ab}\n\\Phi_a^\\dagger \\Phi_b.\n\\end{eqnarray}\nWe have used the Hermiticity condition\n$Y_{ab}=Y_{ba}^\\ast$ in going to the second line;\nand changed the dummy indices $a \\leftrightarrow \\beta$\nand $b \\leftrightarrow \\alpha$ in going to the third line.\nA similar argument can be made for the quartic terms.\nWe conclude that the potential is invariant\nunder the GCP transformation\nof Eq.~\\eqref{GCP} if and only if the coefficients obey\n\\begin{eqnarray}\nY_{ab}^\\ast\n&=&\nX_{\\alpha a}^\\ast Y_{\\alpha \\beta} X_{\\beta b}\n= ( X^\\dagger\\, Y\\, X )_{ab},\n\\nonumber\\\\\nZ_{ab,cd}^\\ast\n&=&\nX_{\\alpha a}^\\ast X_{\\gamma c}^\\ast\nZ_{\\alpha \\beta, \\gamma \\delta} X_{\\beta b} X_{\\delta d}.\n\\label{YZ-CPtransf}\n\\end{eqnarray}\n\nIntroducing\n\\begin{eqnarray}\n\\Delta Y_{ab}\n&=&\nY_{ab} -\nX_{\\alpha a} Y_{\\alpha \\beta}^\\ast X_{\\beta b}^\\ast\n= \\left[Y - ( X^\\dagger\\, Y\\, X )^\\ast \\right]_{ab},\n\\nonumber\\\\\n\\Delta Z_{ab,cd}\n&=&\nZ_{ab,cd} -\nX_{\\alpha a} X_{\\gamma c}\nZ_{\\alpha \\beta, \\gamma \\delta}^\\ast X_{\\beta b}^\\ast X_{\\delta d}^\\ast.\n\\label{DY-DZ}\n\\end{eqnarray}\nwe may write the conditions for invariance under GCP as\n\\begin{eqnarray}\n\\Delta Y_{ab}\n&=&\n0,\n\\label{DY-GCP}\n\\\\\n\\Delta Z_{ab,cd}\n&=&\n0.\n\\label{DZ-GCP}\n\\end{eqnarray}\nGiven Eqs.~\\eqref{hermiticity_coefficients},\nit is easy to show that\n\\begin{eqnarray}\n\\Delta Y_{ab} &=& \\Delta Y_{ba}^\\ast,\n\\nonumber\\\\\n\\Delta Z_{ab,cd} \\equiv \\Delta Z_{cd,ab} &=& \\Delta Z_{ba,dc}^\\ast.\n\\label{DY-DZ-hermiticity}\n\\end{eqnarray}\nThus, we need only consider the real coefficients\n$\\Delta Y_{11}$, $\\Delta Y_{22}$,\n$\\Delta Z_{11,11}$, $\\Delta Z_{22,22}$,\n$\\Delta Z_{11,22}$, $\\Delta Z_{12,21}$,\nand the complex coefficients\n$\\Delta Y_{12}$, $\\Delta Z_{11,12}$,\n$\\Delta Z_{22,12}$, and $\\Delta Z_{12,12}$.\n\n\n\n\n\n\\subsection{GCP and basis transformations}\n\nWe now turn to the interplay between GCP transformations and basis\ntransformations.\nConsider the potential of Eq.~\\eqref{VH2} and call it\n$V(\\Phi)$.\nNow consider the potential obtained from $V(\\Phi)$\nby the basis transformation\n$\\Phi_a \\rightarrow \\Phi^\\prime_a = U_{ab} \\Phi_b$:\n\\begin{equation}\nV (\\Phi^\\prime) =\nY_{ab}^\\prime (\\Phi_a^{\\prime \\dagger} \\Phi^\\prime_b) +\n\\tfrac{1}{2}\nZ^\\prime_{ab,cd} (\\Phi_a^{\\prime \\dagger} \\Phi^\\prime_b)\n(\\Phi_c^{\\prime \\dagger} \\Phi^\\prime_d),\n\\end{equation}\nwhere the coefficients in the new basis are given by\nEqs.~(\\ref{Y-transf}) and (\\ref{Z-transf}).\nWe will now prove the following theorem: If $V(\\Phi)$ is invariant under\nthe GCP transformation of Eq.~(\\ref{GCP}) with the matrix $X$,\nthen $V (\\Phi^\\prime)$ is invariant under a new GCP transformation\nwith matrix\n\\begin{equation}\nX^\\prime = U X U^\\top.\n\\label{X-prime}\n\\end{equation}\nBy hypothesis $V(\\Phi)$ is invariant under\nthe GCP transformation of Eq.~(\\ref{GCP}) with the matrix $X$.\nEq.~(\\ref{YZ-CPtransf}) guarantees that $Y^\\ast = X^\\dagger Y X$.\nNow,\nEq.~(\\ref{Y-transf}) relates the coefficients in the two\nbasis through $Y = U^\\dagger Y^\\prime U$.\nSubstituting gives\n\\begin{equation}\nU^\\top Y^{\\prime \\ast} U^\\ast\n= X^\\dagger (U^\\dagger Y^\\prime U) X,\n\\end{equation}\nor\n\\begin{equation}\nY^{\\prime \\ast}\n= (U^\\ast X^\\dagger U^\\dagger) Y^\\prime (U X U^\\top)\n= X^{\\prime \\dagger} Y^\\prime X^\\prime,\n\\end{equation}\nas required.\nA similar argument holds for the quartic terms and the proof is complete.\n\nThe fact that the transpose $U^\\top$ appears in Eq.~(\\ref{X-prime})\nrather than $U^\\dagger$ is crucial.\nIn Eq.~(\\ref{S-prime}),\napplicable to HF symmetries,\n$U^\\dagger$ appears.\nConsequently,\na basis may be chosen where the HF symmetry is represented by\na diagonal matrix $S$.\nThe presence of $U^\\top$ in Eq.~(\\ref{X-prime}) implies\nthat, \ncontrary to popular belief,\n\\textit{it is not possible to reduce all GCP transformations\nto the standard CP transformation} of Eq.~(\\ref{StandardCP})\nby a basis transformation.\nWhat is possible,\nas we shall see below,\nis to reduce an invariance of the THDM potential under any GCP\ntransformation,\nto an invariance under the standard CP transformation\nplus some extra constraints.\n\nTo be more specific, the following result is easily established. If\nthe unitary matrix $X$ is symmetric, then it follows\nthat\\footnote{Here, we make use of a theorem in linear algebra that\nstates that for any unitary symmetric matrix $X$, a unitary matrix\n$V$ exists such that $X=VV^\\top$. A proof of this result can be\nfound, e.g., in Appendix B of Ref.~\\cite{GunHab}.}\na unitary matrix\n$U$ exists such that $X'=UXU^\\top=1$, in which case\n$Y^{\\prime\\,*}=Y^\\prime$. In this case, a basis exists in which the\nGCP is a standard CP transformation.\nIn contrast, if the unitary matrix $X$ is not symmetric, then no\nbasis exists in which $Y$ and $Z$ are real for generic values of the\nscalar potential parameters.\nNevertheless, as we shall demonstrate below, by \\textit{imposing}\nthe GCP symmetry on the scalar potential, the parameters of the\nscalar potential are constrained in such a way that for an\nappropriately chosen basis change, $Y^{\\prime\\,*}\n=X^{\\prime\\,\\dagger}Y'X'=Y'$ (with a similar result for $Z'$).\n\n\nGCP transformations were studied in \nRefs.~\\cite{GCP1,GCP2}. In\nparticular, Ecker, Grimus, and Neufeld \\cite{GCP2} proved that for\nevery matrix $X$ there exists a unitary matrix $U$ such that\n$X^\\prime$ can be reduced to the form\n\\begin{equation}\nX^\\prime = UXU^\\top=\n\\left(\n\\begin{array}{cc}\n \\phantom{-}\\cos{\\theta} & \\quad \\sin{\\theta}\\\\\n - \\sin{\\theta} & \\quad \\cos{\\theta}\n\\end{array}\n\\right),\n\\label{GCP-reduced}\n\\end{equation}\nwhere $0 \\leq \\theta \\leq \\pi\/2$. Notice the restricted range for\n$\\theta$. The value of $\\theta$ can be determined in either of two\nways: (i) the eigenvalues of $(X+X^\\top)^\\dagger(X+X^\\top)\/2$ are\n$\\cos{\\theta}$, each of which is twice degenerate; or (ii) $X\nX^\\ast$ has the eigenvalues $e^{\\pm 2 i \\theta}$.\n\n\n\\subsection{The three classes of GCP symmetries}\n\nHaving reached the special form of $X^\\prime$ in\nEq.~(\\ref{GCP-reduced}),\nwe will now follow the strategy adopted by Ferreira and\nSilva \\cite{FS2} in connection with HF symmetries.\nWe substitute Eq.~(\\ref{GCP-reduced}) for $X$ in\nEq.~(\\ref{YZ-CPtransf}),\nin order to identify the constraints imposed by this\nreduced form of the GCP transformations on\nthe quadratic and quartic couplings.\nFor each value of $\\theta$,\ncertain constraints will be forced upon the couplings.\nIf two different values of $\\theta$ enforce the same constraints,\nwe will say that they are in the same class\n(since no experimental distinction between the two will then be\npossible).\nWe will start by considering the special cases of $\\theta=0$\nand $\\theta=\\pi\/2$,\nand then turn our attention to $0 < \\theta < \\pi\/2$.\n\n\n\\subsubsection{CP1: $\\theta=0$}\n\nWhen $\\theta=0$,\n$X^\\prime$ is the unit matrix,\nand we obtain the standard CP transformation,\n\\begin{eqnarray}\n\\Phi_1 &\\rightarrow& \\Phi_1^\\ast,\n\\nonumber\\\\\n\\Phi_2 &\\rightarrow& \\Phi_2^\\ast, \\label{eq:cp1}\n\\end{eqnarray}\nunder which Eqs.~(\\ref{YZ-CPtransf}) take the very simple\nform\n\\begin{eqnarray}\nY_{ab}^\\ast\n&=&\n Y_{ab} ,\n\\nonumber\\\\\nZ_{ab,cd}^\\ast\n&=&\nZ_{ab,cd}.\n\\end{eqnarray}\nWe denote this CP transformation by CP1.\nIt forces all couplings to be real.\nSince most couplings are real by the Hermiticity of\nthe Higgs potential,\nthe only relevant constraints are\n$\\textrm{Im}\\, m_{12}^2 = \\textrm{Im}\\, \\lambda_5 =\n\\textrm{Im}\\, \\lambda_6 = \\textrm{Im}\\, \\lambda_7 = 0$.\n\n\n\\subsubsection{CP2: $\\theta=\\pi\/2$}\n\nWhen $\\theta=\\pi\/2$,\n\\begin{equation}\nX^\\prime =\n\\left(\n\\begin{array}{cc}\n \\phantom{-} 0 & \\quad 1\\\\\n - 1 & \\quad 0\n\\end{array}\n\\right), \\label{eq:cp2}\n\\end{equation}\nand we obtain the CP transformation,\n\\begin{eqnarray}\n\\Phi_1 &\\rightarrow& \\Phi_2^\\ast,\n\\nonumber\\\\\n\\Phi_2 &\\rightarrow& - \\Phi_1^\\ast,\n\\end{eqnarray}\nwhich we denote by CP2.\nThis was considered by Davidson and Haber \\cite{DavHab}\nin their Eq.~(37),\nwho noted that if this symmetry holds in one basis,\nit holds in \\textit{all} basis choices.\nUnder this transformation,\nEq.~(\\ref{DY-GCP}) forces the matrix of quadratic\ncouplings to obey\n\\begin{equation}\n0 =\n\\Delta Y =\n\\left(\n\\begin{array}{cc}\n m_{11}^2 - m_{22}^2 & \\quad -2 m_{12}^2\\\\\n - 2 m_{12}^{2 \\ast} & \\quad m_{22}^2 - m_{11}^2,\n\\end{array}\n\\right)\n\\end{equation}\nleading to $m_{22}^2 = m_{11}^2$ and $m_{12}^2=0$.\nSimilarly,\nwe may construct a matrix of matrices containing\nall coefficients $\\Delta Z_{ab,cd}$.\nThe uppermost-leftmost matrix corresponds to $\\Delta Z_{11,cd}$.\nThe next matrix along the same line corresponds\nto $\\Delta Z_{12,cd}$, and so on.\nTo enforce invariance under CP2,\nwe equate it to zero,\n\\begin{equation}\n0 =\n\\left(\n\\begin{array}{cc}\n \\left(\n \\begin{array}{cc}\n \\lambda_1 - \\lambda_2 & \\quad\n \\lambda_6 + \\lambda_7 \\\\\n \\lambda_6^\\ast + \\lambda_7^\\ast & \\quad\n 0\n \\end{array}\n \\right)\n &\n \\left(\n \\begin{array}{cc}\n \\lambda_6 + \\lambda_7 & \\quad\n 0 \\\\\n 0 & \\quad\n \\lambda_6 + \\lambda_7\n \\end{array}\n \\right)\n \\\\*[7mm]\n \\left(\n \\begin{array}{cc}\n \\lambda_6^\\ast + \\lambda_7^\\ast & \\quad\n 0 \\\\\n 0 & \\quad\n \\lambda_6^\\ast + \\lambda_7^\\ast\n \\end{array}\n \\right)\n &\n \\left(\n \\begin{array}{cc}\n 0 & \\quad\n \\lambda_6 + \\lambda_7 \\\\\n \\lambda_6^\\ast + \\lambda_7^\\ast & \\quad\n \\lambda_2 - \\lambda_1\n \\end{array}\n \\right)\n\\end{array}\n\\right).\n\\end{equation}\nWe learn that invariance under CP2 forces\n$m_{22}^2 = m_{11}^2$ and $m_{12}^2=0$,\n$\\lambda_2=\\lambda_1$, and $\\lambda_7 = - \\lambda_6$,\nleading precisely to the ERPS of Eq.~(\\ref{ERPS}).\nRecall that Gunion and Haber \\cite{GunHab} found that,\nunder these conditions we can always find a basis where\nall parameters are real.\nAs a result,\nif the potential is invariant under CP2,\nthere is a basis where CP2 still holds and in which\nthe potential is also invariant under CP1.\n\n\n\n\\subsubsection{CP3: $0 < \\theta < \\pi\/2$}\n\nFinally we turn to the cases where $0 < \\theta < \\pi\/2$.\nImposing Eq.~(\\ref{DY-GCP}) yields\n\\begin{eqnarray}\n0 = \\Delta Y_{11} &=&\n\\left[ (m_{11}^2 - m_{22}^2)\\ s - 2\\ \\textrm{Re}\\, m_{12}^2\\ c \\right] s,\n\\nonumber\\\\\n0 = \\Delta Y_{22} &=&\n- \\Delta Y_{11},\n\\nonumber\\\\\n0 = \\Delta Y_{12} &=&\n\\textrm{Re}\\, m_{12}^2\\ ( c_2 - 1) - 2 i\\ \\textrm{Im}\\, m_{12}^2\n + \\tfrac{1}{2} (m_{22}^2 - m_{11}^2)\\ s_2,\n\\end{eqnarray}\nwhere we have used $c=\\cos{\\theta}$, $s=\\sin{\\theta}$,\n$c_2=\\cos{2 \\theta}$, and $s_2=\\sin{2 \\theta}$.\nSince $\\theta \\neq 0, \\pi\/2$,\nthe conditions $m_{22}^2 = m_{11}^2$ and $m_{12}^2=0$ are imposed,\nas in CP2.\nSimilarly, Eq.~(\\ref{DZ-GCP}) yields\n\\begin{eqnarray}\n0 = \\Delta Z_{11,11} &=&\n\\lambda_1 (1-c^4) - \\lambda_2 s^4\n- \\tfrac{1}{2} \\lambda_{345} s_2^2\n+ 4\\ \\textrm{Re}\\, \\lambda_6 c^3 s + 4\\ \\textrm{Re}\\, \\lambda_7 c s^3,\n\\nonumber\\\\\n0 = \\Delta Z_{22,22} &=&\n\\lambda_2 (1-c^4) - \\lambda_1 s^4\n- \\tfrac{1}{2} \\lambda_{345} s_2^2\n- 4\\ \\textrm{Re}\\, \\lambda_7 c^3 s - 4\\ \\textrm{Re}\\, \\lambda_6 c s^3,\n\\nonumber\\\\\n0 = \\Delta Z_{11,22}\n&=&\n- \\tfrac{1}{4} s_2\n\\left[\n4 \\textrm{Re}\\, (\\lambda_6 - \\lambda_7) c_2\n+ (\\lambda_1 + \\lambda_2 - 2 \\lambda_{345})s_ 2\n\\right],\n\\nonumber\\\\\n0 = \\Delta Z_{12,21} &=&\n\\Delta Z_{11,22}\n\\nonumber\\\\\n0 = \\textrm{Re}\\, \\Delta Z_{11,12} &=&\n\\tfrac{1}{4} s \\left[\n(-3 \\lambda_1 + \\lambda_2 + 2 \\lambda_{345}) c\n- (\\lambda_1 + \\lambda_2 - 2 \\lambda_{345}) c_3\n\\right.\n\\nonumber\\\\\n& & \\hspace{7mm}\n\\left.\n+ 4 \\textrm{Re}\\, \\lambda_6 (2 s + s_3)\n- 4 \\textrm{Re}\\, \\lambda_7 s_3\n\\right],\n\\nonumber\\\\\n0 = \\textrm{Re}\\, \\Delta Z_{22,12} &=&\n\\tfrac{1}{4} s \\left[\n(- \\lambda_1 + 3 \\lambda_2 - 2 \\lambda_{345}) c\n+ (\\lambda_1 + \\lambda_2 - 2 \\lambda_{345}) c_3\n\\right.\n\\nonumber\\\\\n& & \\hspace{7mm}\n\\left.\n- 4 \\textrm{Re}\\, \\lambda_6 s_3\n+ 4 \\textrm{Re}\\, \\lambda_7 (2 s + s_3)\n\\right],\n\\nonumber\\\\\n0 = \\textrm{Re}\\, \\Delta Z_{12,12} &=&\n\\Delta Z_{11,22}\n\\label{Real-5}\n\\\\\n0 = \\textrm{Im}\\, \\Delta Z_{11,12} &=&\n\\tfrac{1}{2}\n\\left[\n\\textrm{Im}\\, \\lambda_6 (3+c_2)\n+ \\textrm{Im}\\, \\lambda_7 (1-c_2)\n- \\textrm{Im}\\, \\lambda_5 s_2\n\\right],\n\\nonumber\\\\\n0 = \\textrm{Im}\\, \\Delta Z_{22,12} &=&\n\\tfrac{1}{2}\n\\left[\n\\textrm{Im}\\, \\lambda_6 (1-c_2)\n+ \\textrm{Im}\\, \\lambda_7 (3+c_2)\n+ \\textrm{Im}\\, \\lambda_5 s_2\n\\right],\n\\nonumber\\\\\n0 = \\textrm{Im}\\, \\Delta Z_{12,12} &=&\n2 c\n\\left[\n\\textrm{Im}\\, \\lambda_5 c + \\textrm{Im}\\,(\\lambda_6-\\lambda_7)s\n\\right],\n\\label{Im-3}\n\\end{eqnarray}\nwhere $\\lambda_{345}=\\lambda_3 + \\lambda_4 + \\textrm{Re}\\, \\lambda_5$,\n$c_3 = \\cos{3 \\theta}$, and $s_3 = \\sin{3 \\theta}$.\n\nThe last three equations may be written as\n\\begin{equation}\n0=\n\\left[\n\\begin{array}{ccc}\n-s_2 &\\quad (3+c_2) & \\quad (1-c_2)\\\\\n\\phantom{-}s_2 &\\quad (1-c_2) &\\quad (3+c_2)\\\\\n(1+c_2) &\\quad s_2 &\\quad -s_2\n\\end{array}\n\\right]\n\\left[\n\\begin{array}{c}\n\\textrm{Im}\\, \\lambda_5 \\\\\n\\textrm{Im}\\, \\lambda_6 \\\\\n\\textrm{Im}\\, \\lambda_7\n\\end{array}\n\\right].\n\\end{equation}\nThe determinant of this homogeneous system of three equations\nin three unknowns is $32 c^2$,\nwhich can never be zero since we are assuming that $\\theta \\neq \\pi\/2$.\nAs a result,\n$\\lambda_5$, $\\lambda_6$, and $\\lambda_7$ are real,\nwhatever the value of $0 < \\theta < \\pi\/2$ chosen for the GCP\ntransformation.\nSince $m_{12}^2=0$,\nall potentially complex parameters must be real.\nWe conclude that a potential invariant\nunder any GCP with $0 < \\theta < \\pi\/2$\nis automatically invariant under CP1.\nCombining this with what we learned from CP2,\nwe conclude the following:\nif a potential is invariant under some GCP transformation,\nthen a basis may be found in which it is also invariant\nunder the standard CP transformation,\nwith some added constraints on the parameters.\n\nThe other set of five independent homogeneous equations in\nfive unknowns has a determinant equal to zero,\nmeaning that not all parameters must vanish.\nWe find that\n\\begin{eqnarray}\n0=\n\\Delta Z_{11,11} - \\Delta Z_{22,22}\n&=&\n2s \\left[\ns\\ (\\lambda_1-\\lambda_2) + c\\ 2 \\textrm{Re}\\, (\\lambda_6 + \\lambda_7)\n\\right],\n\\nonumber\\\\\n0=\n\\textrm{Re}\\, \\Delta Z_{11,12}\n- \\textrm{Re}\\, \\Delta Z_{22,12}\n&=&\ns \\left[\n-c\\ (\\lambda_1-\\lambda_2) + s\\ 2 \\textrm{Re}\\, (\\lambda_6 + \\lambda_7)\n\\right].\n\\end{eqnarray}\nSince $s \\neq 0$,\nwe obtain the homogeneous system\n\\begin{equation}\n0=\n\\left[\n\\begin{array}{cc}\n\\phantom{-}s & \\quad c\\\\\n-c & \\quad s\n\\end{array}\n\\right]\n\\left[\n\\begin{array}{c}\n\\lambda_1 - \\lambda_2 \\\\\n2 \\textrm{Re}\\, (\\lambda_6 + \\lambda_7)\n\\end{array}\n\\right],\n\\end{equation}\nwhose determinant is unity.\nWe conclude that $\\lambda_2 = \\lambda_1$ and $\\lambda_7 = - \\lambda_6$.\nThus,\nGCP invariance with any value of $0 < \\theta \\leq \\pi\/2$ leads\nto the ERPS of Eq.~(\\ref{ERPS}).\nSubstituting back we obtain\n$\\Delta Z_{11,11} = \\Delta Z_{22,22} = - \\Delta Z_{11,22}$\nand\n$\\textrm{Re}\\, \\Delta Z_{11,12} = - \\textrm{Re}\\, \\Delta Z_{22,12}$,\nleaving only two independent equations:\n\\begin{eqnarray}\n0=\n\\Delta Z_{11,11}\n&=&\n\\tfrac{1}{2} s_2 \\left[\n(\\lambda_1 - \\lambda_{345}) s_2 + 4 \\lambda_6 c_2 \\right],\n\\nonumber\\\\\n0=\n\\textrm{Re}\\, \\Delta Z_{22,12}\n&=&\n\\tfrac{1}{2} s_2 \\left[\n(\\lambda_1 - \\lambda_{345}) c_2 - 4 \\lambda_6 s_2 \\right],\n\\end{eqnarray}\nwhere we have used $c + c_3 = 2 c c_2$ and $s + s_3 = 2 c s_2$.\nSince $s_2 \\neq 0$,\nthe determinant of the system does not vanish,\nforcing $\\lambda_1=\\lambda_{345}$ and $\\lambda_6=0$.\n\nNotice that our results do not depend on which exact\nvalue of $ 0 < \\theta < \\pi\/2$ in Eq.~(\\ref{GCP-reduced}) we have chosen.\nIf we require invariance of the potential under GCP with some\nparticular value of $ 0 < \\theta < \\pi\/2$,\nthen the potential is immediately invariant under GCP\nwith any other value of $ 0 < \\theta < \\pi\/2$.\nWe name this class of CP invariances, CP3.\nCombining everything,\nwe conclude that invariance under CP3 implies\n\\begin{eqnarray}\nm_{11}^2 = m_{22}^2,\n& \\hspace{4ex} &\nm_{12}^2=0,\n\\nonumber\\\\\n\\lambda_2 = \\lambda_1,\n& \\hspace{4ex} &\n\\lambda_7 = \\lambda_6 = 0,\n\\nonumber\\\\\n\\textrm{Im}\\, \\lambda_5 = 0,\n& \\hspace{4ex} &\n\\textrm{Re}\\, \\lambda_5 = \\lambda_1 - \\lambda_3 -\\lambda_4.\n\\label{Region-CP3}\n\\end{eqnarray}\nThe results of this section are all summarized in Table~\\ref{master1}\nof section~\\ref{sec:summary}.\n\n\\subsection{The square of the GCP transformation}\n\nIf we apply a GCP transformation twice to the scalar fields, we will\nhave, from Eq.~\\eqref{GCP}, that\n\\begin{equation}\n\\left(\\Phi^{\\textrm{GCP}}_a\\right)^{\\textrm{GCP}}\n\\;=\\;\nX_{a \\alpha} \\left(\\Phi^{\\textrm{GCP}}_\\alpha\\right)^\\ast\n\\;=\\;\nX_{a \\alpha}\\, X_{\\alpha b}^\\ast\\ \\Phi_b \\;\\;\\; ,\n\\end{equation}\nso that the square of a GCP transformation is given by\n\\begin{equation}\n(GCP)^2 \\;=\\; XX^\\ast \\;\\;\\; .\n\\label{eq:cpq}\n\\end{equation}\nIn particular, for a generic unitary matrix $X$, $(GCP)^2$ is a Higgs Family \nsymmetry transformation.\n\nUsually, only GCP transformations with $(GCP)^2 = \\boldsymbol{1}$\n(where $\\boldsymbol{1}$ is the unit matrix)\nare considered in the literature.\nFor such a situation,\n$X=X^\\dagger=X^*$,\nand one can always find\na basis in which $X=\\boldsymbol{1}$.\nIn this case,\na GCP transformation is equivalent to a standard CP\ntransformation in the latter basis choice.\nFor example,\nthe restriction that $(GCP)^2 = \\boldsymbol{1}$\n(or equivalently, requiring the squared of the corresponding generalized \ntime-reversal transformation to equal the unit matrix)\nwas imposed in \nRef.~\\cite{GunHab} and more recently in Ref.~\\cite{mani}. \nHowever, as we have illustrated in this section, the invariance\nunder a GCP transformation, in which $(GCP)^2 \\neq \\boldsymbol{1}$ \n(corresponding to a unitary matrix $X$ that is not symmetric)\nis a \\textit{stronger} restriction on the parameters of the \nscalar potential than the invariance under a standard CP transformation. \n\nAs we see from the results in the previous sections,\n$X$ is {\\em not} symmetric for the symmetries CP2 and CP3.\nIn fact, this feature provides a strong distinction among the\nthree GCP symmetries previously introduced. \nLet us briefly examine $(GCP)^2$ for the three possible\ncases $CP1$, $CP2$ and $CP3$.\n\n\\subsubsection{$(CP1)^2$}\n\nComparing Eqs.~\\eqref{GCP} and~\\eqref{eq:cp1}, we come to the\nimmediate conclusion that $X_{CP1}\\,=\\,\\boldsymbol{1}$, so that\nEq.~\\eqref{eq:cpq} yields\n\\begin{equation}\n(CP1)^2\\;=\\;\\boldsymbol{1}\\,.\n\\end{equation}\nThis implies that a CP1-invariant scalar potential \nis invariant under the symmetry group\n$Z_2=\\{\\boldsymbol{1}\\,,\\,CP1\\}$.\n\n\n\\subsubsection{$(CP2)^2$}\n\nThe matrix $X_{\\textrm{CP2}}$ is shown in Eq.~\\eqref{eq:cp2} so that, by\nEq.~\\eqref{eq:cpq}, we obtain\n\\begin{equation}\n(CP2)^2\\;=\\;-\\,\\boldsymbol{1}\\,.\n\\end{equation}\nAlthough this result significantly distinguished CP2 from CP1, \nthe authors of Ref.~\\cite{mani} noted (in considering their\n$CP_g^{(i)}$ symmetries) that the transformation law for $\\Phi_a$\nunder (CP2)$^2$ can be reduced to the identity by a global\nhypercharge transformation. That is,\nif we start with the symmetry group $Z_4=\\{\\boldsymbol{1}\\,,\\,CP2\\,,\\,\n-\\boldsymbol{1}\\,,\\,-CP2\\}$, we can impose an equivalence relation\nby identifying two elements of $Z_4$ related by multiplication \nby $-\\boldsymbol{1}$. If we denote $(Z_2)_Y=\\{\\boldsymbol{1}\\,,\n-\\boldsymbol{1}\\}$ as the two-element\ndiscrete subgroup of the global hypercharge\n$U(1)_Y$, then the discrete symmetry group that is orthogonal to $U(1)_Y$\nis given by $Z_4\/(Z_2)_Y\\mathchoice{\\cong}{\\cong}{\\isoS}{\\cong} Z_2$. Hence,\nthe CP2-invariant scalar potential exhibits\na $Z_2$ symmetry orthogonal to the Higgs flavor symmetries\nof the potential. \n\n\n\\subsubsection{$(CP3)^2$}\n\nThe matrix $X_{\\textrm{CP3}}$ is given in Eq.~\\eqref{GCP-reduced}, with\n$0<\\theta<\\pi\/2$, so that, by Eq.~\\eqref{eq:cpq}, we obtain\n\\begin{equation}\n(CP3)^2\\;=\\;\\left(\n\\begin{array}{cc}\n \\phantom{-} \\cos{2\\theta} & \\quad \\sin{2\\theta}\\\\\n - \\sin{2\\theta} & \\quad \\cos{2\\theta}\n\\end{array}\n\\right)\\,,\n\\end{equation}\nwhich once again is {\\em not} the unit matrix.\nHowever, the transformation law for $\\Phi_a$ under (CP3)$^2$ \n\\textit{cannot} be reduced to the identity by a global\nhypercharge transformation. \nThis is the reason why Ref.~\\cite{mani} did not consider CP3.\nHowever, $(CP3)^2$ is a non-trivial HF symmetry of the CP3-invariant\nscalar potential.\\footnote{In Section~\\ref{sec:summary}B, we shall\nidentify $(CP3)^2$ with the Peccei Quinn U(1) symmetry defined as\nin Eq.~(\\ref{U1}) and then transformed to a new basis\naccording to the unitary matrix defined in Eq.~(\\ref{UPQ}).}\nThus, one can always reduce the square of \nCP3 to the identity by applying a suitable HF symmetry transformation.\nIn particular, a CP3-invariant scalar potential also exhibits a $Z_2$ symmetry\nthat is orthogonal to the Higgs flavor symmetries of the potential.\n\n\nIn this paper, we prove that there are three and only three\nclasses of GCP transformations.\nOf course, within each class,\none may change the explicit form of the\nscalar potential by a suitable basis transformation;\nbut that will not alter its physical consequences.\nSimilarly,\none can set some parameters to zero in some ad-hoc fashion,\nnot rooted in a symmetry requirement. \nBut, as we have shown, the constraints imposed on the scalar potential\nby a single GCP symmetry can be grouped into three classes:\nCP1, CP2, and CP3.\n\n\n\\section{\\label{sec:summary}Classification of the HF and\nGCP transformation classes in the THDM}\n\n\\subsection{Constraints on scalar potential parameters}\n\nSuppose that one is allowed one single symmetry requirement\nfor the potential in the THDM.\nOne can choose an invariance under one particular Higgs Family\nsymmetry.\nWe know that there are only two independent classes\nof such simple symmetries: $Z_2$ and Peccei-Quinn $U(1)$.\nOne can also choose an invariance under a particular\nGCP symmetry. \nWe have proved that there are three classes of\nGCP symmetries, named CP1, CP2, and CP3.\nIf any of the above symmetries is imposed on the THDM scalar\npotential (in a specified basis), then the coefficients\nof the scalar potential are constrained, as summarized in\nTable~\\ref{master1}. For completeness, we also exhibit\nthe constraints imposed by $SO(3)$,\nthe largest possible continuous HF symmetry that is orthogonal to\nthe global hypercharge $U(1)_Y$ transformation.\n\\begin{table}[ht!]\n\\caption{Impact of the symmetries on the coefficients\nof the Higgs potential in a specified basis.}\n\\begin{ruledtabular}\n\\begin{tabular}{ccccccccccc}\nsymmetry & $m_{11}^2$ & $m_{22}^2$ & $m_{12}^2$ &\n$\\lambda_1$ & $\\lambda_2$ & $\\lambda_3$ & $\\lambda_4$ &\n$\\lambda_5$ & $\\lambda_6$ & $\\lambda_7$ \\\\\n\\hline\n$Z_2$ & & & 0 &\n & & & &\n & 0 & 0 \\\\\n$U(1)$ & & & 0 &\n & & & &\n0 & 0 & 0 \\\\\n$SO(3)$ & & $ m_{11}^2$ & 0 &\n & $\\lambda_1$ & & $\\lambda_1 - \\lambda_3$ &\n0 & 0 & 0 \\\\\n\\hline\n$\\Pi_2$ & & $ m_{11}^2$ & real &\n & $ \\lambda_1$ & & &\nreal & & $\\lambda_6^\\ast$\n\\\\\n\\hline\nCP1 & & & real &\n & & & &\nreal & real & real \\\\\nCP2 & & $m_{11}^2$ & 0 &\n & $\\lambda_1$ & & &\n & & $- \\lambda_6$ \\\\\nCP3 & & $m_{11}^2$ & 0 &\n & $\\lambda_1$ & & &\n$\\lambda_1 - \\lambda_3 - \\lambda_4$ (real) & 0 & 0 \\\\\n\\end{tabular}\n\\end{ruledtabular}\n\\label{master1}\n\\end{table}\n\nEmpty entries in Table~\\ref{master1} correspond to a lack of constraints on the\ncorresponding parameters.\nTable~\\ref{master1}has been constructed for those basis choices in which\n$Z_2$ and $U(1)$ have the specific forms in Eqs.~(\\ref{Z2}) and\n(\\ref{U1}), respectively.\nIf, for example,\nthe basis is changed and $Z_2$ acquires the form $\\Pi_2$ in\nEqs.~(\\ref{Pi2}),\nthen the constraints on the coefficients are altered,\nas shown explicitly on the\nfourth line of Table~\\ref{master1}.\nHowever,\nthis does not correspond to a new model.\nAll physical predictions are the same since\nthe specific forms of $Z_2$ and $\\Pi_2$ differ only by\nthe basis change in Eq.~(\\ref{Z2ToPi2}).\nThe constraints for CP1, CP2, and CP3 shown in Table I\napply to the basis in which the GCP transformation\nof Eq.~(\\ref{GCP}) is used where $X$ has been transformed into $X^\\prime$\ngiven by Eq.~(\\ref{GCP-reduced}),\nwith $\\theta=0$, $\\theta = \\pi\/2$,\nand $0 < \\theta < \\pi\/2$, respectively.\n\n\n\n\n\\subsection{Multiple symmetries and GCP}\n\nWe now wish to consider the possibility of simultaneously imposing\nmore than one symmetry requirement on the Higgs potential.\nFor example, one can require that $Z_2$ and $\\Pi_2$ be enforced\n\\textit{within the same basis}. In what follows, we shall indicate\nthat the two symmetries are enforced simultaneously by writing\n$Z_2\\oplus\\Pi_2$.\nCombining the constraints from the appropriate\nrows of Table~\\ref{master1},\nwe conclude that,\nunder these two simultaneous requirements\n\\begin{eqnarray}\nm_{22}^2 = m_{11}^2,\n& \\hspace{4ex} &\nm_{12}^2=0,\n\\nonumber\\\\\n\\lambda_2 = \\lambda_1,\n& \\hspace{4ex} &\n\\lambda_7 = \\lambda_6 = 0,\n\\hspace{4ex} \\textrm{Im}\\, \\lambda_5 = 0.\n\\label{Z2+Pi2}\n\\end{eqnarray}\nThis coincides exactly with the conditions of the ERPS\nin a very special basis,\nas shown in Eq.~(\\ref{ERPS2}).\nSince CP2 leads to the ERPS of Eq.~(\\ref{ERPS}),\nwe conclude that\n\\begin{equation}\nZ_2 \\oplus \\Pi_2 \\equiv \\textrm{CP2 in some specific basis}.\n\\label{equiv-CP2}\n\\end{equation}\nThis was noted previously by Davidson and Haber \\cite{DavHab}.\nNow that we know what all classes of HF and CP symmetries can\nlook like,\nwe can ask whether all GCP symmetries can be written as\nthe result of some multiple HF symmetry.\n\nThis is clearly not possible for CP1 because of parameter counting.\nTable~\\ref{master1} shows that CP1 reduces the scalar potential to ten\nreal parameters.\nWe can still perform an orthogonal basis change while keeping\nall parameters real.\nThis freedom can be used to remove one further parameter;\nfor example, setting $m_{12}^2=0$ by diagonalizing the $Y$\nmatrix.\nNo further simplification is allowed.\nAs a result, CP1 leaves nine independent parameters.\nThe smallest HF symmetry is $Z_2$.\nTable~\\ref{master1} shows that $Z_2$ reduces the potential to six\nreal and one complex parameter.\nThe resulting eight parameters could never account for the\nnine needed to fully describe the most general model\nwith the standard CP invariance CP1.\\footnote{In\nIvanov's language, this is clear since CP1 corresponds to\na $Z_2$ transformation of the vector $\\vec{r}$,\nwhich is the simplest transformation on $\\vec{r}$ one\ncould possibly make.\nSee section~\\ref{more_multiple}.}\n\n\nBut one can utilize two HF symmetries in order\nto obtain the same constraints obtained by invariance under CP3.\nLet us impose \\textit{both} $U(1)$ and $\\Pi_2$\n\\textit{in the same basis}.\nFrom Table~\\ref{master1},\nwe conclude that,\nunder these two simultaneous requirements\n\\begin{eqnarray}\nm_{22}^2 = m_{11}^2,\n& \\hspace{4ex} &\nm_{12}^2=0,\n\\nonumber\\\\\n\\lambda_2 = \\lambda_1,\n& \\hspace{4ex} &\n\\lambda_7 = \\lambda_6 = 0,\n\\hspace{4ex} \\lambda_5 = 0.\n\\label{U1+Pi2}\n\\end{eqnarray}\nThis does not coincide with the\nconditions for invariance under CP3 shown in Eq.~(\\ref{Region-CP3}).\nHowever,\none can use the transformation rules in Eqs.~(A13)-(A23)\nof Davidson and Haber \\cite{DavHab},\nin order to show that a basis transformation, \n\\begin{equation} \\label{UPQ}\nU=\\frac{1}{\\sqrt{2}}\\left(\\begin{array}{cc} \\phantom{-}1 & \\quad -i \\\\\n-i & \\quad\\phantom{-}1\\end{array}\\right)\\,, \n\\end{equation}\nmay be chosen which takes us from Eqs.~(\\ref{Region-CP3}),\nwhere $\\textrm{Re}\\, \\lambda_5 = \\lambda_1 -\\lambda_3 -\\lambda_4$,\nto Eqs.~(\\ref{U1+Pi2}),\nwhere $\\lambda_5=0$ (while maintaining the other relations among\nthe scalar potential parameters).\nWe conclude that\n\\begin{equation}\nU(1) \\oplus \\Pi_2 \\equiv \\textrm{CP3 in some specific basis}.\n\\label{equiv-CP3}\n\\end{equation}\nNote that in the basis in which the CP3 relations of Eq.~(\\ref{Region-CP3})\nare satisfied with $\\lambda_5\\neq 0$, the discrete HF symmetry\n$\\Pi_2$ is still respected.\nHowever, using Eq.~(\\ref{UPQ}), it follows that the U(1)-Peccei Quinn\nsymmetry corresponds to the invariance of the scalar potential under\n$\\Phi_a\\to \\mathcal{O}_{ab}\\Phi_b$, where $\\mathcal{O}$ is an arbitrary\n$SO(2)$ matrix.\n\nThe above results suggest that it should be possible to distinguish CP1,\nCP2, and CP3 in a basis invariant fashion.\nBotella and Silva \\cite{BS} have built three so-called $J$-invariants\nthat detect any signal of CP violation (either explicit or\nspontaneous) after the minimization\nof the scalar potential. However, in this paper we are concerned\nabout the symmetries of the scalar potential independently of the\nchoice of vacuum. Thus, we shall consider\nthe four so-called $I$-invariants built by\nGunion and Haber \\cite{GunHab} in order to detect any\nsignal of \\textit{explicit} CP violation present (before the vacuum state\nis determined).\nIf any of these invariants is nonzero, then CP is explicitly violated,\nand neither CP1, nor CP2, nor CP3 hold.\nConversely,\nif all $I$-invariants are zero, then CP is explicitly conserved, but we cannot\ntell a priori which GCP applies.\nEqs.~(\\ref{equiv-CP2}) and (\\ref{equiv-CP3}) provide the crucial hint.\nIf we have CP conservation, $Z_2\\oplus\\Pi_2$ holds,\nand $U(1)$ does not,\nthen we have CP2.\nAlternatively,\nif we have CP conservation, and $U(1)\\oplus\\Pi_2$ also holds,\nthen we have CP3.\nWe recall that both CP2 and CP3 lead to the ERPS,\nand that the general conditions for the ERPS in Eq.~(\\ref{ERPS})\nare basis independent.\nThis allows us to distinguish CP2 and CP3 from CP1.\nBut, prior to the present work,\nno basis-independent quantity had been identified in the literature\nthat could distinguish $Z_2$ and $U(1)$ in the ERPS.\nThe basis-independent quantity $D$ introduced\nin subsection~\\ref{subsec:D} is precisely the invariant required for\nthis task. That is,\nin the ERPS $D\\neq0$ implies CP2, whereas $D=0$ implies CP3.\n\nOne further consequence of the results of Table~\\ref{master1}\ncan be seen by simultaneously imposing the U(1) Peccei-Quinn symmetry\nand the CP3 symmetry \\textit{in the same basis}. The resulting\nconstraints on the scalar potential parameters are precisely those of\nthe SO(3) HF symmetry. Thus, we conclude that\n\\begin{equation}\nU(1) \\oplus \\textrm{CP3} \\equiv SO(3).\n\\label{equiv-O3}\n\\end{equation}\nIn particular, $SO(3)$ is not a simple HF symmetry, as the invariance\nof the scalar potential under a single element of SO(3) is not\nsufficient to guarantee invariance under the full SO(3) group of\ntransformations. \n\n\n\n\\subsection{Maximal symmetry group of the scalar potential\northogonal to $U(1)_Y$}\n\nThe standard CP symmetry, CP1,\nis a discrete $Z_2$ symmetry that transforms the scalar\nfields into their complex conjugates, and hence \nis not a subgroup of the $U(2)$ transformation group\nof Eq.~\\ref{basis-transf}. We have previously noted that\nTHDM scalar potentials that exhibit \\textit{any} non-trivial\nHF symmetry $G$ is automatically CP-conserving. Thus, the actual\nsymmetry group of the scalar potential is in fact \nthe semidirect product\\footnote{In general, the non-trivial element of\n$Z_2$ will not commute with all elements of $G$, in which case the\nrelevant mathematical structure is that of a semidirect product. In\ncases where the non-trivial element of $Z_2$ commutes with all\nelements of $G$, we denote the corresponding direct product as\n$G\\otimes Z_2$.}\nof $G$ and $Z_2$, which we write as $G\\rtimes Z_2$.\nNoting that $U(1)\\rtimes Z_2\\mathchoice{\\cong}{\\cong}{\\isoS}{\\cong} SO(2)\\rtimes Z_2\\mathchoice{\\cong}{\\cong}{\\isoS}{\\cong} O(2)$, and\n$SO(3)\\otimes Z_2\\mathchoice{\\cong}{\\cong}{\\isoS}{\\cong} O(3)$, we conclude that the maximal \nsymmetry groups of the scalar potential orthogonal to $U(1)_Y$\nfor the possible choices of HF symmetries are given in \nTable~\\ref{maximal}.\\footnote{For ease of notation, we denote\n$Z_2\\otimes Z_2$ by $(Z_2)^2$ and $Z_2\\otimes Z_2\\otimes Z_2$\nby $(Z_2)^3$.}\n\n\n\n\\begin{table}[ht]\n\\caption{Maximal symmetry groups [orthogonal to global $U(1)_Y$\nhypercharge] of the scalar sector of the THDM.}\n\\begin{ruledtabular}\n\\begin{tabular}{ccc}\ndesignation & HF symmetry group & maximal symmetry group\\\\\n\\hline\n$Z_2$ & $Z_2$ & $(Z_2)^2$ \\\\\nPeccei-Quinn & $U(1)$ & $O(2)$ \\\\\n$SO(3)$ & $SO(3)$ & $O(3)$ \\\\\nCP1 & --- & $Z_2$ \\\\\nCP2 & $(Z_2)^2$ & $(Z_2)^3$ \\\\\nCP3 & $O(2)$ & $O(2)\\otimes Z_2$\n\\\\\n\\end{tabular}\n\\end{ruledtabular}\n\\label{maximal}\n\\end{table}\n\nFinally, we reconsider CP2 and CP3. Eq.~(\\ref{equiv-CP2})\nimplies that the CP2 symmetry is equivalent to a $(Z_2)^2$ HF\nsymmetry. To prove this statement, we note that in the\ntwo-dimensional flavor space of Higgs fields, the $Z_2$ and $\\Pi_2$\ndiscrete symmetries defined by Eqs.~(\\ref{Z2}) and (\\ref{Pi2})\nare given by:\n\\begin{equation}\nZ_2=\\{S_0\\,,\\,S_1\\}\\,,\\qquad\\qquad \\Pi_2=\\{S_0\\,,\\,S_2\\}\\,,\n\\end{equation}\nwhere $S_0\\equiv \\boldsymbol{1}$ is the $2\\times 2$ identity matrix and\n\\begin{equation}\nS_1=\\left(\n\\begin{array}{cc}\n1 & \\quad\\phantom{-} 0 \\\\\n0 & \\quad -1 \\\\\n\\end{array}\n\\right)\\,,\\qquad\\qquad\nS_2=\\left(\n\\begin{array}{cc}\n0 & \\quad 1 \\\\\n1 & \\quad 0 \\\\\n\\end{array}\n\\right)\\,.\n\\end{equation}\nIf we impose the $Z_2$ and $\\Pi_2$ symmetry in the same basis, then the\nscalar potential is invariant under the dihedral group of eight elements,\n\\begin{equation}\nD_4=\\{S_0\\,,\\,S_1\\,,\\,S_2\\,,\\,S_3\\,,\\,-S_0\\,,\\,-S_1\\,,\\,-S_2\\,,\\,-S_3\\}\\,,\n\\end{equation}\nwhere $S_3=S_1 S_2=-S_2 S_1$. \nAs before, we identify $(Z_2)_Y\\equiv\\{S_0\\,,\\,-S_0\\}$ as the\ntwo-element discrete subgroup of the global hypercharge $U(1)_Y$.\nHowever, we have defined the HF symmetries to be orthogonal to $U(1)_Y$.\nThus, to determine the HF symmetry group of CP2, we identify as\nequivalent those elements of $D_4$ that are related by multiplication\nby $-S_0$. Group theoretically, we identify the HF symmetry group\nof CP2 as\n\\begin{equation}\nD_4\/(Z_2)_Y\\mathchoice{\\cong}{\\cong}{\\isoS}{\\cong} Z_2\\otimes Z_2\\,.\n\\end{equation}\n\nThe HF symmetry group of CP2 is not the maximally allowed symmetry\ngroup. In particular, the constraints of CP2 on the scalar potential\nimply the existence of a basis in which all scalar potential\nparameters are real. Thus, the scalar potential is explicitly\nCP-conserving. The $Z_2$ symmetry associated with this CP\ntransformation is orthogonal to the HF symmetry as previously noted. \n(This is easily checked explicitly by employing a four-dimensional real\nrepresentation of the two complex scalar fields.) Thus,\nthe maximal symmetry group of the CP2-symmetric scalar potential\nis $(Z_2)^3$. Similarly, Eq.~(\\ref{equiv-CP3})\nimplies that the CP3 symmetry is equivalent to a $U(1)\\rtimes Z_2$ HF\nsymmetry. This is isomorphic to an $O(2)$ HF symmetry, which is\na subgroup of the maximally allowed $SO(3)$ HF symmetry group. \nHowever, the constraints of CP3 on the scalar potential\nimply the existence of a basis in which all scalar potential\nparameters are real. Thus, the scalar potential is explicitly\nCP-conserving. Once again, the $Z_2$ symmetry associated with this CP\ntransformation is orthogonal to the HF symmetry noted above. Thus,\nthe maximal symmetry group of the CP3-symmetric scalar potential\nis $O(2)\\otimes Z_2$. \n\nThe above results are also summarized in \nTable~\\ref{maximal}. In all cases, the maximal symmetry group is\na direct product of the HF symmetry group and the $Z_2$ corresponding\nto the standard CP-transformation, whose square is the identity operator.\n\nOne may now ask whether Table~\\ref{maximal} exhausts all\npossible independent symmetry constraints that one\nmay place on the Higgs potential.\nPerhaps one can choose other combinations,\nor maybe one can combine three, four, or more\nsymmetries.\nWe know of no way to answer this problem\nbased only on the transformations of the scalar\nfields $\\Phi_a$.\nFortunately,\nIvanov has solved this problem~\\cite{Ivanov1} by looking\nat the transformation properties of field bilinears,\nthus obtaining for the first time the list of symmetries given in the last\ncolumn of Table~\\ref{maximal}.\n\n\n\\subsection{\\label{more_multiple}More on multiple symmetries}\n\n\nWe start by looking at the implications of the symmetries we have\nstudied so far on the vector $\\vec{r} = \\{ r_1, r_2, r_3\\}$,\nwhose components were introduced in Eq.~(\\ref{r_Ivanov}).\nNotice that a unitary transformation $U$ on\nthe fields $\\Phi_a$ induces an orthogonal\ntransformation $O$ on the vector of bilinears\n$\\vec{r}$,\ngiven by Eq.~(\\ref{O}).\nFor every pair of unitary transformations $\\pm U$\nof $SU(2)$,\none can find some corresponding transformation $O$\nof $SO(3)$,\nin a two-to-one correspondence.\nWe then see what these symmetries imply\nfor the coefficients of Eq.~(\\ref{VH3})\n(recall the $\\Lambda_{\\mu \\nu}$ is a symmetric matrix).\nBelow, we list the transformation of $\\vec{r}$ under which the\nscalar potential is invariant, followed by the \ncorresponding constraints on the\nquadratic and quartic scalar potential parameters, $M_\\mu$ and\n$\\Lambda_{\\mu\\nu}$. \n\nUsing the results of Table I, we find that $Z_2$ implies\n\\begin{equation}\n\\vec{r} \\rightarrow\n\\left[\n\\begin{array}{c}\n-r_1\\\\\n-r_2\\\\\n\\phantom{-}r_3\n\\end{array}\n\\right],\n\\hspace{10mm}\n\\left[\n\\begin{array}{c}\nM_0\\\\\n0\\\\\n0\\\\\nM_3\n\\end{array}\n\\right],\n\\hspace{4ex}\n\\left[\n\\begin{array}{cccc}\n\\Lambda_{00} &\\,\\,\\, 0 &\\,\\,\\, 0 &\\,\\,\\, \\Lambda_{03}\\\\\n 0 &\\,\\,\\, \\Lambda_{11} &\\,\\,\\, \\Lambda_{12} &\\,\\,\\, 0\\\\\n 0 & \\,\\,\\,\\Lambda_{12} &\\,\\,\\, \\Lambda_{22} &\\,\\,\\, 0\\\\\n\\Lambda_{03} &\\,\\,\\, 0 &\\,\\,\\, 0 &\\,\\,\\, \\Lambda_{33}\n\\end{array}\n\\right],\n\\label{Iv-Z2}\n\\end{equation}\n$U(1)$ implies\n\\begin{equation}\n\\vec{r} \\rightarrow\n\\left[\n\\begin{array}{ccc}\nc_2 & -s_2 & \\phantom{-}0\\\\\ns_2 &\\phantom{-}c_2 & \\phantom{-}0\\\\\n0 & \\phantom{-}0 & \\phantom{-}1\n\\end{array}\n\\right]\\ \\vec{r},\n\\hspace{10mm}\n\\left[\n\\begin{array}{c}\nM_0\\\\\n0\\\\\n0\\\\\nM_3\n\\end{array}\n\\right],\n\\hspace{4ex}\n\\left[\n\\begin{array}{cccc}\n\\Lambda_{00} &\\,\\,\\, 0 & \\,\\,\\,0 & \\,\\,\\,\\Lambda_{03}\\\\\n 0 &\\,\\,\\, \\Lambda_{11} &\\,\\,\\, 0 &\\,\\,\\, 0\\\\\n 0 & \\,\\,\\,0 & \\,\\,\\,\\Lambda_{11} & \\,\\,\\,0\\\\\n\\Lambda_{03} & \\,\\,\\,0 & \\,\\,\\,0 &\\,\\,\\, \\Lambda_{33}\n\\end{array}\n\\right],\n\\label{Iv-U1}\n\\end{equation}\nand SO(3) implies \n\\begin{equation}\n\\vec{r} \\rightarrow\n\\mathcal{O} \\vec{r},\n\\hspace{10mm}\n\\left[\n\\begin{array}{c}\nM_0\\\\\n0\\\\\n0\\\\\n0\n\\end{array}\n\\right],\n\\hspace{4ex}\n\\left[\n\\begin{array}{cccc}\n\\Lambda_{00} &\\,\\,\\, 0 & \\,\\,\\,0 & \\,\\,\\,0\\\\\n 0 &\\,\\,\\, \\Lambda_{11} &\\,\\,\\, 0 &\\,\\,\\, 0\\\\\n 0 & \\,\\,\\,0 & \\,\\,\\,\\Lambda_{11} & \\,\\,\\,0\\\\\n0 & \\,\\,\\,0 & \\,\\,\\,0 &\\,\\,\\, \\Lambda_{11}\n\\end{array}\n\\right],\n\\label{Iv-SO3}\n\\end{equation}\nwhere $\\mathcal{O}$ is an arbitrary $3\\times 3$ orthogonal \nmatrix of unit determinant.\nIn the language of bilinears, a basis invariant condition for the presence of\n$SO(3)$ is that the three eigenvalues of $\\tilde{\\Lambda}$ are equal.\n(Recall that $\\tilde{\\Lambda} = \\left\\{\\Lambda_{ij}\\right\\}$; $i,j=1,2,3$).\n\n\nAs for the GCP symmetries,\nCP1 implies\n\\begin{equation}\n\\vec{r} \\rightarrow\n\\left[\n\\begin{array}{c}\n\\phantom{-}r_1\\\\\n-r_2\\\\\n\\phantom{-}r_3\n\\end{array}\n\\right],\n\\hspace{10mm}\n\\left[\n\\begin{array}{c}\nM_0\\\\\nM_1\\\\\n0\\\\\nM_3\n\\end{array}\n\\right],\n\\hspace{4ex}\n\\left[\n\\begin{array}{cccc}\n\\Lambda_{00} & \\,\\,\\,\\Lambda_{01} & \\,\\,\\, 0 & \\,\\,\\, \\Lambda_{03}\\\\\n\\Lambda_{01} & \\,\\,\\, \\Lambda_{11} & \\,\\,\\, 0 & \\,\\,\\, \\Lambda_{13}\\\\\n 0 & \\,\\,\\, 0 & \\,\\,\\, \\Lambda_{22} & \\,\\,\\,0\\\\\n\\Lambda_{03} & \\,\\,\\,\\Lambda_{13} & \\,\\,\\,0 & \\,\\,\\, \\Lambda_{33}\n\\end{array}\n\\right],\n\\label{Iv-CP1}\n\\end{equation}\nCP2 implies\n\\begin{equation}\n\\vec{r} \\rightarrow\n\\left[\n\\begin{array}{c}\n-r_1\\\\\n-r_2\\\\\n-r_3\n\\end{array}\n\\right],\n\\hspace{10mm}\n\\left[\n\\begin{array}{c}\nM_0\\\\\n0\\\\\n0\\\\\n0\n\\end{array}\n\\right],\n\\hspace{4ex}\n\\left[\n\\begin{array}{cccc}\n\\Lambda_{00} &\\,\\,\\, 0 &\\,\\,\\, 0 &\\,\\,\\, 0\\\\\n0 &\\,\\,\\, \\Lambda_{11}\\,\\,\\, &\\,\\,\\, \\Lambda_{12} & \\,\\,\\,\\Lambda_{13}\\\\\n0 &\\,\\,\\, \\Lambda_{12} &\\,\\,\\, \\Lambda_{22} &\\,\\,\\, \\Lambda_{23}\\\\\n0 &\\,\\,\\, \\Lambda_{13} & \\,\\,\\,\\Lambda_{23} & \\,\\,\\,\\Lambda_{33}\n\\end{array}\n\\right],\n\\label{Iv-CP2}\n\\end{equation}\nand CP3 implies\n\\begin{equation}\n\\vec{r} \\rightarrow\n\\left[\n\\begin{array}{ccc}\n\\phantom{-}c_2 & \\phantom{-}0 & \\phantom{-}s_2\\\\\n\\phantom{-}0 & -1 & \\phantom{-}0\\\\\n-s_2 & \\phantom{-}0 & \\phantom{-}c_2\n\\end{array}\n\\right]\\ \\vec{r},\n\\hspace{10mm}\n\\left[\n\\begin{array}{c}\nM_0\\\\\n0\\\\\n0\\\\\n0\n\\end{array}\n\\right],\n\\hspace{4ex}\n\\left[\n\\begin{array}{cccc}\n\\Lambda_{00} &\\,\\,\\, 0 & \\,\\,\\,0 & 0\\,\\,\\,\\\\\n0 &\\,\\,\\, \\Lambda_{11} &\\,\\,\\, 0 &\\,\\,\\, 0\\\\\n0 &\\,\\,\\, 0 &\\,\\,\\, \\Lambda_{22} &\\,\\,\\, 0\\\\\n0 &\\,\\,\\, 0 &\\,\\,\\, 0 & \\,\\,\\,\\Lambda_{11}\n\\end{array}\n\\right].\n\\label{Iv-CP3}\n\\end{equation}\nNotice that in CP3 two of the eigenvalues of $\\Lambda$ are equal,\nin accordance with our observation that $D$ can be used\nto distinguish between CP2 and CP3.\n\nBecause each unitary transformation on the fields $\\Phi_a$\ninduces an $SO(3)$ transformation on the vector\nof bilinears $\\vec{r}$,\nand because the standard CP transformation\ncorresponds to an inversion of $r_2$\n(a $Z_2$ transformation on the vector $\\vec{r}$),\nIvanov \\cite{Ivanov1}\nconsiders all possible proper and improper transformations\nof $O(3)$ acting on $\\vec{r}$. \nHe identifies the following six classes of transformations:\n(i) $Z_2$; (ii) $(Z_2)^2$; (iii) $(Z_2)^3$;\n(iv) $O(2)$; (v) $O(2) \\otimes Z_2$; and (vi) $O(3)$.\nNote that these symmetries are all orthogonal to the global $U(1)_Y$\nhypercharge symmetry, as the bilinears $r_0$ and $\\vec{r}$\nare all singlets under a $U(1)_Y$ transformation.\nThe six classes above identified by Ivanov\ncorrespond precisely to the six possible maximal\nsymmetry groups identified in Table~\\ref{maximal}.\nNo other independent symmetry transformations are possible.\n\n\nOur work permits one to identify the abstract transformation\nof field bilinears utilized by Ivanov in terms of\ntransformations on the scalar fields themselves,\nas needed for model building.\nCombining our work with Ivanov's,\nwe conclude that there is only one new type\nof symmetry requirement which one can place on\nthe Higgs potential via multiple symmetries.\nCombining this with our earlier results,\nwe conclude that all possible symmetries on the scalar\nsector of the THDM can be reduced to multiple HF symmetries,\nwith the exception of the standard CP transformation (CP1).\n\n\n\n\n\\section{\\label{sec:allisCP}Building all symmetries with the standard CP}\n\nWe have seen that there are only six independent\nsymmetry requirements, listed in Table~\\ref{maximal},\nthat one can impose on the Higgs potential.\nWe have shown that all possible symmetries of the scalar\nsector of the THDM can be reduced to multiple HF symmetries,\nwith the exception of the standard CP transformation (CP1).\nNow we wish to show a dramatic result:\n\\textit{all possible symmetries on the scalar\nsector of the THDM can be reduced to multiple applications of\nthe standard CP symmetry.}\n\nUsing Eq.~(\\ref{X-prime}),\nwe see that the basis transformation of Eq.~(\\ref{basis-transf}),\nchanges the standard CP symmetry of Eq.~(\\ref{StandardCP})\ninto the GCP symmetry of Eq.~(\\ref{GCP}),\nwith\n\\begin{equation}\nX=U U^\\top.\n\\end{equation}\nIn particular,\nan orthogonal basis transformation does not affect the\nform of the standard CP transformation.\nSince we wish to generate $X \\neq 1$,\nwe will need complex matrices $U$.\n\nNow we wish to consider the following situation.\nWe have a basis (call it the original basis) and\nimpose the standard CP symmetry CP1 on that original basis.\nNext we consider the same model in a different basis\n(call it $M$) and impose the standard CP symmetry on that basis $M$.\nIn general, this procedure of imposing\nthe standard CP symmetry in the original basis \\textit{and also}\nin the rotated basis $M$ leads to two independent impositions.\nThe first imposition makes all parameters real in\nthe original basis.\nOne way to combine the second imposition with the first\nis to consider the basis transformation $U_M$ taking us\nfrom basis $M$ into the original basis.\nAs we have seen,\nthe standard CP symmetry in basis $M$ turns,\nwhen written in the original basis,\ninto a symmetry under\n\\begin{eqnarray}\n\\Phi^{\\textrm{CP}}_a\n&=& (X_M)_{a \\alpha} \\Phi_\\alpha^\\ast,\n\\nonumber\\\\\n\\Phi^{\\dagger \\textrm{CP}}_a\n&=&\n(X_M)^\\ast_{a \\alpha} (\\Phi_\\alpha^\\dagger)^\\ast,\n\\label{CGP-M}\n\\end{eqnarray}\nwith $X_M=U_M U_M^\\top$.\nNext we consider several such possibilities.\n\nWe start with\n\\begin{equation}\nU_{A} =\n\\left(\n\\begin{array}{cc}\n\\phantom{-}c_{\\pi\/4} & \\quad -i s_{\\pi\/4} \\\\\n- i s_{\\pi\/4} & \\quad \\phantom{-}c_{\\pi\/4}\n\\end{array}\n\\right),\n\\hspace{3ex}\nX_{A} =\n\\left(\n\\begin{array}{cc}\n\\phantom{-}0 & \\quad -i \\\\\n- i & \\quad \\phantom{-}0\n\\end{array}\n\\right).\n\\end{equation}\nHere and henceforth $c$ ($s$) with a subindex indicates the\ncosine (sine) of the angle given in the subindex.\nWe denote by CP1$_A$ the imposition of the CP symmetry\nin Eq.~(\\ref{CGP-M}) with $X_M=X_A$\n(which coincides with the imposition of the standard CP\nsymmetry in the basis $M=A$).\n\nNext we consider\n\\begin{equation}\nU_{B} =\n\\left(\n\\begin{array}{cc}\ne^{-i \\pi\/4} & \\quad 0 \\\\\n0 & \\quad e^{i \\pi\/4}\n\\end{array}\n\\right),\n\\hspace{3ex}\nX_{B} =\n\\left(\n\\begin{array}{cc}\n-i & \\quad 0 \\\\\n\\phantom{-}0 & \\quad i\n\\end{array}\n\\right).\n\\end{equation}\nWe denote by CP1$_B$ the imposition of the CP symmetry\nin Eq.~(\\ref{CGP-M}) with $X_M=X_B$\n(which coincides with the imposition of the standard CP\nsymmetry in the basis $M=B$).\n\nA third possible choice is\n\\begin{equation}\nU_{C} =\n\\left(\n\\begin{array}{cc}\ne^{i \\delta\/2} & \\quad 0 \\\\\n0 & \\quad e^{-i \\delta\/2}\n\\end{array}\n\\right),\n\\hspace{3ex}\nX_{C} =\n\\left(\n\\begin{array}{cc}\ne^{i \\delta} & \\quad 0 \\\\\n0 & \\quad e^{-i \\delta}\n\\end{array}\n\\right),\n\\end{equation}\nwhere $\\delta \\neq n \\pi\/2$ with $n$ integer.\nWe denote by CP1$_C$ the imposition of the CP symmetry\nin Eq.~(\\ref{CGP-M}) with $X_M=X_C$\n(which coincides with the imposition of the standard CP\nsymmetry in the basis $M=C$).\n\nFinally, we consider\n\\begin{equation}\nU_{D} =\n\\left(\n\\begin{array}{cc}\n\\phantom{i}c_{\\delta\/2} & \\quad i s_{\\delta\/2} \\\\\ni s_{\\delta\/2} & \\phantom{i}\\quad c_{\\delta\/2}\n\\end{array}\n\\right),\n\\hspace{3ex}\nX_{D} =\n\\left(\n\\begin{array}{cc}\n\\phantom{i}c_\\delta & \\quad i s_\\delta \\\\\ni s_\\delta & \\quad \\phantom{i}c_\\delta\n\\end{array}\n\\right),\n\\end{equation}\nwhere $\\delta \\neq n \\pi\/2$ with $n$ integer.\nWe denote by CP1$_D$ the imposition of the CP symmetry\nin Eq.~(\\ref{CGP-M}) with $X_M=X_D$\n(which coincides with the imposition of the standard CP\nsymmetry in the basis $M=D$).\n\nThe impact of the first three symmetries on the coefficients of the\nHiggs potential are summarized in\nTable~\\ref{master3}.\n\\begin{table}[ht!]\n\\caption{Impact of the CP1$_M$ symmetries on the coefficients\nof the Higgs potential.\nThe notation ``imag'' means that the\ncorresponding entry is purely imaginary.\nCP1 in the original basis has been included for reference.}\n\\begin{ruledtabular}\n\\begin{tabular}{ccccccccccc}\nsymmetry & $m_{11}^2$ & $m_{22}^2$ & $m_{12}^2$ &\n$\\lambda_1$ & $\\lambda_2$ & $\\lambda_3$ & $\\lambda_4$ &\n$\\lambda_5$ & $\\lambda_6$ & $\\lambda_7$ \\\\\n\\hline\nCP1 & & & real &\n & & & &\nreal & real & real\\\\\n\\hline\nCP1$_A$ & & $m_{11}^2$ & &\n & $\\lambda_1$ & & &\n & & $\\lambda_6$ \\\\\nCP1$_B$ & & & imag &\n & & & &\nreal & imag & imag \\\\\nCP1$_C$ & & & $|m_{12}^2| e^{i \\delta}$ &\n & & & & $|\\lambda_5| e^{2 i \\delta}$ &\n$|\\lambda_6| e^{i \\delta}$ & $|\\lambda_7| e^{i \\delta}$\n\\\\\n\\end{tabular}\n\\end{ruledtabular}\n\\label{master3}\n\\end{table}\n\n\\noindent\nImposing CP1$_D$ on the Higgs potential leads to the more complicated\nset of equations:\n\\begin{eqnarray}\n2 \\textrm{Im} \\left( m_{12}^2 \\right)\\, c_\\delta\n+ (m_{22}^2 - m_{11}^2)\\, s_\\delta\n&=& 0,\n\\nonumber\\\\\n2 \\textrm{Im} \\left( \\lambda_6 - \\lambda_ 7 \\right)\\, c_{2 \\delta}\n+ \\lambda_{12345}\\, s_{2 \\delta}\n&=& 0,\n\\nonumber\\\\\n2 \\textrm{Im} \\left( \\lambda_6 + \\lambda_ 7 \\right)\\, c_\\delta\n+ \\left( \\lambda_1 - \\lambda_2 \\right)\\, s_\\delta\n&=& 0,\n\\nonumber\\\\\n\\textrm{Im} \\lambda_5\\, c_\\delta\n+ \\textrm{Re} \\left( \\lambda_6 - \\lambda_ 7 \\right)\\, s_\\delta\n&=& 0,\n\\end{eqnarray}\nwhere\n\\begin{equation}\n\\lambda_{12345} = \\tfrac{1}{2} \\left( \\lambda_1 + \\lambda_ 2 \\right)\n- \\lambda_3 - \\lambda_4 + \\textrm{Re} \\lambda_5.\n\\end{equation}\n\nCombining these results with those in Table~\\ref{master1},\nwe have shown that\n\\begin{eqnarray}\n\\textrm{CP1} \\oplus \\textrm{CP1}_B\n&=& Z_2\\ \\ \\textrm{in some specific basis},\n\\nonumber\\\\\n\\textrm{CP1} \\oplus \\textrm{CP1}_C\n&=& U(1),\n\\nonumber\\\\\n\\textrm{CP1} \\oplus \\textrm{CP1}_A \\oplus \\textrm{CP1}_B\n&=& \\textrm{CP2}\\ \\ \\textrm{in some specific basis},\n\\nonumber\\\\\n\\textrm{CP1} \\oplus \\textrm{CP1}_A \\oplus \\textrm{CP1}_C\n&=& \\textrm{CP3}\\ \\ \\textrm{in some specific basis},\n\\nonumber\\\\\n\\textrm{CP1} \\oplus \\textrm{CP1}_C \\oplus \\textrm{CP1}_D\n&=& SO(3).\n\\label{incredible}\n\\end{eqnarray}\nLet us comment on the ``specific basis choices'' needed.\nImposing $\\textrm{CP1} \\oplus \\textrm{CP1}_B$ leads to\n$ m_{12}^2=\\lambda_6=\\lambda_7=0$ and $\\textrm{Im} \\lambda_5=0$,\nwhile imposing $Z_2$ leads to $ m_{12}^2=\\lambda_6=\\lambda_7=0$\nwith no restriction on $\\lambda_5$.\nHowever, when $Z_2$ holds one may rephase $\\Phi_2$\nby the exponential of $-i \\arg(\\lambda_5)\/2$,\nthus making $\\lambda_5$ real.\nIn this basis,\nthe restrictions of $Z_2$ coincide with the restrictions\nof $\\textrm{CP1} \\oplus \\textrm{CP1}_B$.\nSimilarly,\nimposing $\\textrm{CP1} \\oplus \\textrm{CP1}_A \\oplus \\textrm{CP1}_C$\nleads to $m_{12}^2=\\lambda_5=\\lambda_6=\\lambda_7=0$,\n$m_{22}^2=m_{11}^2$ and $\\lambda_2=\\lambda_1$.\nWe see from Table~\\ref{master1} that CP3 has these features,\nexcept that $\\lambda_5$ need not vanish; it is real and\n$\\textrm{Re} \\lambda_5 = \\lambda_1-\\lambda_3-\\lambda_4$.\nStarting from the CP3 conditions and\nusing the transformation rules in Eqs.~(A13)-(A23) of Davidson and\nHaber \\cite{DavHab},\nwe find that a basis choice is possible such that\n$\\textrm{Re} \\lambda_5=0$.\\footnote{Notice that, in the new basis,\n$\\lambda_1$ differs in general from $\\lambda_3 + \\lambda_4$;\notherwise the larger $SO(3)$ Higgs Family symmetry would hold.}\nPerhaps it is easier to prove the equality\n\\begin{equation}\n\\textrm{CP1} \\oplus \\textrm{CP1}_B \\oplus \\textrm{CP1}_D\n= \\textrm{CP3}\\ \\ \\textrm{in some specific basis}.\n\\end{equation}\nIn this case,\nthe only difference between the impositions from the\ntwo sides of the equality come from the sign of $\\textrm{Re} \\lambda_5$,\nwhich is trivial to flip through the basis change\n$\\Phi_2 \\rightarrow - \\Phi_2$.\nFinally,\nimposing $\\textrm{CP1} \\oplus \\textrm{CP1}_A \\oplus \\textrm{CP1}_B$\nwe obtain $m_{12}^2=\\textrm{Im} \\lambda_5=\\lambda_6=\\lambda_7=0$,\n$m_{22}^2=m_{11}^2$ and $\\lambda_2=\\lambda_1$.\nThis does not coincide with the conditions of CP2 which\nlead to the ERPS of Eq.~(\\ref{ERPS}).\nFortunately,\nand as we mentioned before,\nDavidson and Haber \\cite{DavHab} proved that\none may make a further basis transformation\nsuch that Eq.~(\\ref{ERPS2}) holds,\nthus coinciding with the conditions imposed by\n$\\textrm{CP1} \\oplus \\textrm{CP1}_A \\oplus \\textrm{CP1}_B$.\n\nNotice that our description of CP2 in terms of several\nCP1 symmetries is in agreement with the results found by the\nauthors of Ref.~\\cite{mani}.\nThese authors also showed a very interesting\nresult, concerning spontaneous symmetry breaking in 2HDM models\npossessing a CP2 symmetry.\nNamely, they prove (their Theorem 4)\nthat electroweak symmetry breaking will {\\em necessarily}\nspontaneously break CP2.\nHowever, they also show that the vacuum\nwill respect at least one of the CP1 symmetries which compose\nCP2.\nWhich is to say, in a model which has a CP2 symmetry,\nspontaneous symmetry breaking necessarily respect the CP1\nsymmetry.\n\nIn summary,\nwe have proved that all possible symmetries on the scalar\nsector of the THDM,\nincluding Higgs Family symmetries,\ncan be reduced to multiple applications of\nthe standard CP symmetry.\n\n\n\\section{\\label{sec:conclusions}Conclusions}\n\nWe have studied the application of generalized CP symmetries\nto the THDM,\nand found that there are only two independent classes\n(CP2 and CP3),\nin addition to the standard CP symmetry (CP1).\nThese two classes lead to an exceptional region of parameter,\nwhich exhibits either a $Z_2$ discrete symmetry or\na larger $U(1)$ Peccei-Quinn symmetry.\nWe have succeeded in\nidentifying a basis-independent invariant quantity that can\ndistinguish between the $Z_2$ and $U(1)$ symmetries.\nIn particular, such an invariant is required\nin order to distinguish between CP2 and CP3,\nand completes the description of all symmetries in the THDM\nin terms of basis-invariant quantities.\nMoreover, CP2 and CP3 can be obtained by combining\ntwo Higgs Family symmetries and that this is not possible\nfor CP1. \n\nWe have shown that all symmetries of the THDM previously identified\nby Ivanov \\cite{Ivanov1} can be achieved through simple symmetries.\nwith the exception of $SO(3)$.\nHowever, the $SO(3)$ Higgs Family symmetry\ncan be achieved by imposing a $U(1)$ Peccei-Quinn \nsymmetry and the CP3-symmetry in the same basis.\nFinally, we have demonstrated that\nall possible symmetries of the scalar\nsector of the THDM can be reduced to multiple applications of\nthe standard CP symmetry.\nOur complete description of the symmetries on the scalar fields\ncan be combined with symmetries in the quark and lepton sectors,\nto aid in model building.\n\n\n\\begin{acknowledgments}\nWe would like to thank Igor Ivanov and Celso Nishi for their\nhelpful comments on the first version of this manuscript.\nThe work of P.M.F. is supported in part by the Portuguese\n\\textit{Funda\\c{c}\\~{a}o para a Ci\\^{e}ncia e a Tecnologia} (FCT)\nunder contract PTDC\/FIS\/70156\/2006. The work of H.E.H. is\nsupported in part by the U.S. Department of Energy, under grant\nnumber DE-FG02-04ER41268. The work of J.P.S. is supported in\npart by FCT under contract CFTP-Plurianual (U777).\n\nH.E.H. is most grateful for the kind hospitality and support of the\nCentro de F\\'{\\i}sica Te\\'orica e Computacional at Universidade de\nLisboa\n(sponsored by the Portuguese FCT and\nFunda\\c{c}\\~{a}o Luso-Americana para o Desenvolvimento)\nand the Centro de F\\'{\\i}sica Te\\'orica de Part\\'{\\i}culas at\nInstituto Superior T\\'ecnico during his visit to Lisbon. This work\nwas initiated during a conference in honor of Prof. Augusto Barroso,\nto whom we dedicate this article.\n\\end{acknowledgments}\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction} \\label{sec:intro}\nYoung high-mass stars (M $ \\geq \\,$ 8 M$_\\odot$) probed in cm-wavelength interferometric studies typically appear as fairly bright (flux densities of $\\sim$ few mJy to Jy) regions of ionized gas that are classified according to their size and emission measure, e.g., compact, ultracompact (UC), and hypercompact (HC) H{\\small II} regions \\citep[e.g.,][]{2005IAUS..227..111K}. It is generally thought that once nuclear burning has begun the star produces enough UV radiation to photoionize the surrounding gas. However, theories of the earliest stages remain poorly constrained by observations mainly due to the characteristics of the regions where they are born, which are highly dust-obscured, distant ($\\gtrsim$ 1 kpc) regions that undergo rapid evolution, and they reach the zero-age main sequence (ZAMS) while still heavily accreting. In fact, an evolutionary sequence for high-mass stars has not yet been established \\citep[e.g.,][]{2013A&A...550A..21S, 2014prpl.conf..149T}, although significant progress has been achieved on both observational and theoretical fronts \\citep[e.g.,][]{2018ARA&A..56...41M}. The identification and study of\nobjects in the early stages of their evolution will help us to\n discriminate among proposed mechanisms for their formation; the two main\nscenarios being core accretion (i.e., scaled-up version of low-mass\nstar formation) and competitive accretion (i.e., in which stars in a cluster attract each other while they accrete from a shared reservoir of gas; see \\citealp{2014prpl.conf..149T}). The low-mass star formation process is modeled by\naccretion via a circumstellar disk and a collimated jet\/outflow that removes angular momentum and allows accretion to proceed \\citep[e.g.,][]{1988ApJ...328L..19S}. The jet\/outflow system is powered magnetohydrodynamically by rotating magnetic fields coupled to either the disk (disk winds: e.g., \\citealp{2000prpl.conf..759K}) and\/or the protostar (X-winds: e.g., \\citealp{1987ARA&A..25...23S}). Additionally, protostellar collisions have been proposed as an alternative mechanism for the formation of high-mass stars \\citep{1998MNRAS.298...93B, 2005AJ....129.2281B}. \\\\\n\nMassive molecular outflows are a common phenomenon in high-mass star forming regions (e.g., \\citealp{1996ApJ...457..267S, 2002A&A...383..892B}); hence accretion disks and ionized jets similar to those found towards low-mass protostars are also expected. \n In addition, several surveys toward high-mass star forming regions in the NIR spectral lines of H$_2$ have detected a large number of molecular jets \\citep[e.g.,][]{2017ApJ...844...38W, 2015MNRAS.450.4364N}.\nHowever, the current sample of known high-mass protostars associated with disks \\citep[see review by][]{2016A&ARv..24....6B} and collimated jets \\citep[e.g.,][]{1995ApJ...449..184M, 1998ApJ...502..337M, 2006ApJ...638..878C, 2008AJ....135.2370R} is inadequate to draw conclusions about the entire population.\nThe detection of sources at the onset of high-mass star formation and the measurement of their physical properties is essential to test theoretical models of high-mass star formation \\citep[e.g.,][]{2014prpl.conf..149T}. Furthermore, the most sensitive instruments are necessary to place significant constraints on the occurrence rate and parameters of these detections.\\\\\n\nIn \\citet[][hereafter Paper I]{2016ApJS..227...25R} we described our high sensitivity ($\\sim$3 -- 10 $\\mu$Jy beam$^{-1}$) continuum survey, which aimed to identify candidates in early evolutionary phases of high-mass star formation and to study their centimeter continuum emission. We observed 58 high-mass star forming region candidates using the Karl G. Jansky Very Large Array (VLA)\\footnote{The National Radio Astronomy \nObservatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.} at 1.3 and 6 cm wavelengths at an angular resolution ${\\scriptstyle <}\\,$0\\rlap.$^{\\prime \\prime}$6. The 58 targets were grouped into three categories based on their mid and far-IR luminosity as well as the temperature of the cores: 25 hot molecular cores (HMCs), 15 cold molecular cores with mid-IR point source association (CMC--IRs), and 18 cold molecular cores (CMCs) devoid of IR point source associations. The cores in our sample cover a wide range of parameters such as bolometric luminosity and distance. They have similar masses and densities, however, the latter two types of cores---mainly found within infrared dark clouds (IRDCs)---have lower temperatures (T$\\sim$ 10--20 K) than HMCs (T${\\scriptstyle >}\\,$50 K; depending on the probe and scale). In \\citetalias{2016ApJS..227...25R} we reported detection rates of 1\/18 (6$\\%$) CMCs, 8\/15 (53$\\%$) CMC-IRs and 25\/25 (100$\\%$) HMCs.\nIn several cases, we detected multiple sources within a region, which resulted in a total detection of 70 radio sources associated with 1.2 mm dust clumps.\nThe 100$\\%$ detection rate of centimeter emission in the HMCs is a higher fraction than previously reported. This suggests that radio continuum may be present, albeit weak, in {\\it all} HMCs although in many cases it is only detectable with the superior sensitivity now available with the upgraded VLA. Our results show further evidence for an evolutionary sequence in the formation of high-mass stars, from very early stage cold cores (i.e., CMCs) to relatively more evolved ones (i.e., HMCs).\\\\\n \n\n\nA number of physical processes can cause centimeter continuum emission associated with high-mass star forming regions (see \\citealt{2012ApJ...755..152R} and \\citealt{2013ApJ...766..114S} summaries of thermal and non-thermal emission detected at centimeter wavelengths from YSOs). Recently, \\citet*[][hereafter TTZ16]{2016ApJ...818...52T} developed a model to predict the radio emission from high-mass stars forming via core accretion. The \\citetalias{2016ApJ...818...52T} model predicts that during the first stages of ionization the H{\\small II} region is initially confined to the vertical (or outflow) axis and produces free-free emission with similar features and parameters as observed towards ionized jets. Ionized jets are detected as weak and compact centimeter continuum sources. At subarcsecond resolutions, they usually show a string-like morphology, often aligned with a large-scale molecular outflow of size up to a few parsecs. Ionized jets trace outflows on smaller scales, providing the location of the driving protostar, that otherwise are deeply embedded in the natal clump and generally remain undetected at other wavelengths due to the high extinction in the region \\citep{1998AJ....116.2953A}. However, less extincted sources may\nhave molecular jet counterparts visible in H$_2$ line emission from shocked gas.\nThese sources are also called `thermal radio jets' due to their characteristic rising spectrum which is consistent with free-free radiation from ionized gas. \nThe ionization mechanism of these jets has been proposed to be \\emph{shock-induced ionization} when the wind from the central protostar ionizes itself through shocks due to variations in velocity of the flow or variations of the mass loss rate (\\citealt*{1987RMxAA..14..595C}; \\citealt{1989ApL&C..27..299C}). Unlike the simple model of a uniform electron density H{\\small II} region, ionized jets and winds have a radial density gradient and thus are partially optically thick. \\citet{1986ApJ...304..713R} discussed the behavior of collimated jets and the dependency of their physical parameters (such as temperature, velocity, density and ionization fraction) on morphology, independently of the mechanism of ionization, and showed that the spectral index of a partially ionized jet ranges between $-0.1 \\leq \\alpha \\leq 1.1$.\\\\\n\nThe detection of ionized jets toward high-mass stars at their early stages, as predicted by \\citetalias{2016ApJ...818...52T}, can help to distinguish between accretion scenarios (highly organized outflows are expected from core accretion but not from competitive accretion scenarios; \\citealt{2016ApJ...821L...3T}), and ultimately will give us insight about accretion disks around high-mass stars. Several systematic studies searching for ionized jets have been reported in the literature. \\citet{2012ApJ...753...51G}, from a sample of 33 IR luminous objects, detected 2 ionized jets using the Australia Telescope Compact Array (ATCA) with a 4$\\sigma$ detection limit and an image rms ($\\sigma$) of $\\sim$0.1-0.2 mJy beam$^{-1}$ at 4.8 and 8.6 GHz. \\citet{2016A&A...585A..71M} observed 11 high-mass YSOs using the Jansky VLA and detected 5 collimated ionized jets and 6 ionized wind candidates with a 3$\\sigma$ detection limit and an rms $\\sim$ 11 $\\mu$Jy beam$^{-1}$ at $\\sim$ 6.2 GHz. \\citet{2016MNRAS.460.1039P} observed 49 high-mass YSOs using the ATCA and detected 16 ionized jets and 12 jet candidates with a 3$\\sigma$ detection limit and an rms $\\sim$ 17 $\\mu$Jy beam$^{-1}$ at $\\sim$ 5.5 GHz. Additionally, the Protostellar Outflow at the EarliesT Stage (POETS) survey is undertaking a search of radio-jets using the VLA with an angular resolution of $\\sim$0\\rlap.$^{\\prime \\prime}$1 and an image rms of $\\sim$10 $\\mu$Jy beam$^{-1}$ \\citep{2018A&A...619A.107S, 2019A&A...623L...3S}. In our radio continuum Jansky VLA survey we observed 58 high-mass star forming regions and detected 70 radio sources with a \n5$\\sigma$ detection limit and an rms $\\sim$ 5 $\\mu$Jy beam$^{-1}$ at $\\sim$ 6 GHz \\citep{2016ApJS..227...25R}. \\citealt*{2018A&ARv..26....3A} is a recent comprehensive review of ionized jets in star forming regions. \\\\\n\nThe main goal of this paper is to investigate the nature of the 70 detected radio sources \nreported in \\citetalias{2016ApJS..227...25R}. The observations along with the complete list of targets, coordinates, radio detections and derived observational parameters are presented in \\citetalias{2016ApJS..227...25R}. In Section \\ref{sec:models} we examine several scenarios to explain the origin of the ionized gas emission and we study the physical properties of the detected sources. Section \\ref{discussion_paperII} contains a discussion of the viability of the different scenarios. In Section \\ref{conclusions_paperII}\nwe summarize our findings. Additionally, Appendix \\ref{app:lum} shows the bolometric luminosity estimates for these high-mass star forming regions using {\\it Herschel}\/Hi--GAL data and Appendix \\ref{mom_rate_appe} shows a study of the momentum rate of ionized jets.\n\n\n\n\n\n\n\n\n\n\n\\section[Models for the Radio Emission]{ Models Considered for the Radio Emission} \\label{sec:models}\n\n\\subsection{Low-mass Young Stellar Objects}\\label{yso}\n\nThe main goal of our high sensitivity continuum survey presented in \\citetalias{2016ApJS..227...25R} \nwas to detect radio emission from high-mass protostars. However, there exists a variety of sources that could also appear as radio detections in our images.\nIn \\citetalias{2016ApJS..227...25R} we considered contamination by extragalactic radio\nsources, and found that only a small number of extragalactic sources are expected to be observed within\nthe typical dust clump size of $\\sim$30$^{\\prime \\prime}$ (8 and 2 sources in the 6 and $1.3\\,$cm bands, respectively for the entire sample). \nA more likely source of contamination would be the presence of low-mass YSOs which are expected\nto be present in regions of high-mass star formation \\citep[e.g.,][]{2013A&A...554A..48R}. We are thus interested in identifying possible low-mass class 0 -- class III YSOs that could have \nbeen detected in our survey toward high-mass star forming regions. \n\nA large sample of low-mass YSOs has been observed with the VLA at 4.5 and 7.5 GHz as part of the Gould Belt survey (i.e., Ophiuchus at a distance of 120 pc: \n\\citealp{2013ApJ...775...63D}; Orion at 414 pc: \\citealp{2014ApJ...790...49K}; Serpens at 415 pc: \\citealp{2015ApJ...805....9O}; Taurus-Auriga at 140 pc: \\citealp{2015ApJ...801...91D}, and\nPerseus at 235 pc: \\citealp{2016ApJ...818..116P}). \nThe brightest low-mass YSO in the entire Gould Belt survey (excluding the Orion region) was found in the Ophiuchus region (source J162749.85--242540.5,\na class III YSO, i.e., weak-lined T-Tauri star, with S$_{7.5\\,GHz} = 8.51\\,$mJy, \\citealt{2013ApJ...775...63D}). To determine whether such an object would have been detected in our survey,\nwe scaled its flux density to the assumed distance\\footnote{Distances were taken from the literature, and are listed in Table \\ref{SED_Parameters}. \nMost distances are kinematic; only a few regions have trigonometric parallax measurements.}\nof each of our targets, and compared its scaled flux density to our adopted detection limit of $\\geq$ 5 times the image rms\nat 7.4 GHz for each of our regions. We found that such a YSO would not be detected in any of our targets located at distances beyond $2\\,$kpc.\nSince the majority\nof our targets exceed this distance (see Figure \\ref{T_Tauri_hist}), we conclude that for most of our observed regions the detected radio sources are not low-mass YSOs.\\\\\n\n\nThere are 10 regions in our survey that are located at distances $\\leq$ 2 kpc. However, given the 7.4 GHz image rms for these regions, only in 7 of them would we have detected the brightest low-mass YSO of Ophiuchus.\nThese regions are five HMCs: 18517$+$0437, 20126$+$4104, 20293$+$3952, 20343$+$4129, G34.43$+$00.24mm1, and two CMC--IRs: LDN1657A$-$3 and\nUYSO1. In these 7 regions we detected a total of 13 radio sources within the FWHM of the mm clumps: 10 towards HMCs and 3 towards CMC--IRs. That some of these \nsources are possibly low-mass YSOs can be seen in the case of IRAS 20126$+$4104: Besides the well-studied high-mass protostars associated\nwith radio sources 20126$+$4104 A and 20126$+$4104 B, the radio source G$78.121+3.632$ in this region (see \\citetalias{2016ApJS..227...25R} , Table 4) corresponds to the source I20var, which was\n discussed by \\citet{2007A&A...465..197H}. This is a highly \nvariable radio source and has observational properties consistent with a flaring T-Tauri star. In the same region, we have also detected a new object of similar characteristics. Radio source 20126$+$4104 C \nwas detected for the first time in our survey although several high sensitivity observations of this region have been made in the past \\citep[][]{2007A&A...465..197H}.\n\nHence, 20126$+$4104 C is clearly \nvariable in the radio regime, and is a candidate for a low-mass pre-main sequence star.\nAdditionally, the radio source LDN1657A$-$3 A, which has a negative spectral index ($\\alpha=-1.2$), is also a candidate for a variable radio source, where the emission is probably\ncaused by non-thermal processes on the surface of a T-Tauri star. While the observational properties of these sources are consistent with low mass YSOs, we note that alternative explanations are possible \\citep[e.g.,][]{2018A&A...612A.103C}.\n\nIn summary, while some degree of contamination by low-mass YSOs probably exists in our survey for the nearest sources,\nfor the majority of our targets the detected radio sources are \nvery likely not contaminated by emission from low-mass YSOs. \n\n\n\\begin{figure}[!h]%\n \\centering\n\n \\includegraphics[width=0.7\\linewidth, clip=True]{Regions_T_Tauri}%\n \\caption[The distance distribution of our targets is shown as a black line]{\\small{The distance distribution of our targets is shown as a black line. The color histogram shows the number of targets where we expect the detection of T-Tauri \n stars which are as bright as J162749.85--242540.5 in Ophiuchus. This is the case for 5 HMCs and 2 CMC-IRs.}\n}%\n \\label{T_Tauri_hist}%\n\\end{figure}\n\n\\subsection{H{\\small II} Regions}\nIn \\citetalias{2016ApJS..227...25R} we reported the detection of 70 radio continuum sources associated with three different types of mm clumps and we calculated their 5--25 GHz spectral index ($\\alpha$) using power-law fits of the form $S_{\\nu}$ $\\propto$ $\\nu^{\\alpha}$. The spectral index values and the fits to the data for all the radio detections are reported in \\citetalias{2016ApJS..227...25R} in Table 4 (electronic version) and in Figure 4, respectively. The range of spectral indices found was $-$1.2 to 1.8 (see Figure 5 in \\citetalias{2016ApJS..227...25R}). Based on their radio spectra, we classify these sources as flat spectral index ($-$0.25$<\\alpha<$0.2), positive spectral index ($\\alpha \\geq$ 0.2), and negative spectral index ($\\alpha <$-0.25). Thus, we have 10 sources with flat, 44 sources with positive, and 9 sources with negative spectral index. For the remaining 7, there is not a clear estimate of the spectral index.\n\nThe radio sources have a variety of morphologies. Excluding the sources without spectral index information, there are 6 extended sources, 8 sources with elongated structures,\nand the majority of sources (49) are compact with respect to our synthesized beam. In this section we consider whether a family of H{\\small II} region models could explain sources with flat and positive spectral index.\n\n\n\\subsubsection{Extended Sources}\\label{ext_sources}\n\nAmong the sources detected in our survey associated with $1.2\\,$mm dust emission, there are six sources that are clearly extended at cm wavelengths with\nrespect to the $\\sim$0\\rlap.$^{\\prime \\prime}$4 resolution of the maps, hence they are candidates for H{\\small II} regions,\ni.e. photoionized gas.\n These sources are relatively bright (S$_{25.5\\,GHz} \\approx$ 1\\,mJy), and are found mostly toward HMCs.\nMoreover, they generally show a flat spectral index, indicative of optically thin free-free emission. \nFor five of these sources, we calculate the physical properties from the $25.5\\,$GHz continuum flux using the formulae from \\citet{1994ApJS...91..659K},\nwhich assume spherical symmetry, and optically thin emission from a uniform density plasma with T$_{e}= 10^{4}$\\,K. The results are listed in Table \\ref{HII_Parameters}, where column \n1 is the region name, column 2 is the \nspecific radio source, and columns 3 and 4 are the frequency ($\\nu$) and radio flux (S$_{\\nu}$), respectively. Column 5 is the observed linear size (diameter)\nof the radio source ($\\Delta s$) at 3$\\sigma$ rms level in the image, column 6 is the emission measure (EM), column 7 is the electron density (n$_e$),\ncolumn 8 is the \nexcitation parameter (U) and column 9 is the logarithm of the Lyman continuum flux (N$^{\\prime}_{Ly}$) required for ionization. We use log\\,N$^{\\prime}_{Ly}$ to \nestimate \nthe spectral type of the ionizing star (listed in column 10) using the tabulation in \\citet{1973AJ.....78..929P}, further assuming that a single ZAMS star is photoionizing the nebula and \nproducing the \nLyman continuum flux. The distances used for these calculations are listed in Table \\ref{SED_Parameters}, and the near kinematic distance is adopted when the region has a distance ambiguity.\n\n\n\n\n\\begin{deluxetable}{l c c c c c c c c c}\n\\tabletypesize{\\scriptsize}\n\\tablecaption{Extended Sources: Parameters from Radio Continuum \\label{HII_Parameters}}\n\\tablewidth{0pt}\n\\tablehead{\n\\colhead{Region} & \n\\colhead{Radio } &\n\\colhead{$\\nu$} &\n\\colhead{S$_{\\nu}$ } & \n\\colhead{$\\Delta s$} & \n\\colhead{EM\/$10^{5}$ } & \n\\colhead{n$_{e}\/10^{3}$ } & \n\\colhead{U } & \n\\colhead{log\\,N$^{\\prime}_{Ly}$ } &\n\\colhead{Spectral } \n \\\\[2pt]\n\\colhead{} & \n\\colhead{Source} & \n\\colhead{(GHz)} & \n\\colhead{($\\mu$Jy)} & \n\\colhead{(pc)} & \n\\colhead{(pc\\,cm$^{-6}$)} & \n\\colhead{(cm$^{-3}$)} & \n\\colhead{(pc\\,cm$^{-2}$)} & \n\\colhead{(s$^{-2}$)} &\n\\colhead{Type\\tablenotemark{a}} \\\\[-20pt]\\\\}\n\\startdata\n\\input{latex_classic_HII_table_mod.txt}\n\\enddata\n\\tablenotetext{\\text{a}}{Using the tabulation in \\citet{1973AJ.....78..929P}.}\n\\tablenotetext{\\text{b}}{Includes radio source 20293$+$3952 B (see Figure 2 in \\citetalias{2016ApJS..227...25R}).}\n\\end{deluxetable}\n\nThe measured sizes of the five sources listed in Table \\ref{HII_Parameters} are all below $0.1\\,$pc, which according to \\citet{2005IAUS..227..111K} would suggest a classification as ultra (UC)- or hypercompact (HC)\nH{\\small II} regions. However, the calculated emission measures and electron densities are an order of magnitude or more smaller than typical values for such H{\\small II} regions. The typical values of the emission measure and electron density for UCH{\\small II} regions are EM$\\gtrsim 10^{7}$ pc cm$^{-6}$ and n$_{e}\\gtrsim 10^{4}$ cm$^{-3}$ and for HCH{\\small II} regions are EM$\\gtrsim 10^{10}$ pc cm$^{-6}$ and n$_{e}\\gtrsim 10^{6}$ cm$^{-3}$ \\citep{2005IAUS..227..111K}.\nWe note that four of these resolved extended radio sources (18470--0044 A, 18521$+$0134 B, 19035$+$0641 B and 20293$+$3952 C) are offset $\\sim$ 2\/3 of the radius from the center of the mm clumps,\nand are thus located at their outskirts. A plausible explanation for the lower electron densities and emission measures is that early B-type stars have\nformed near the edge of the dust clumps where the density of the surrounding medium is much lower than in the center.\n\nWe will comment on two additional resolved and extended radio sources detected in this survey. The first one is 18470--0044 A, which was previously observed by \\citet{2011ApJ...739L..17H} at 25.5 GHz using the VLA in the C-configuration. This radio source has an offset of 7\\rlap.$^{\\prime \\prime}$2 with respect to the peak of the mm clump associated with IRAS 18470--0044 reported by \\citet{2002ApJ...566..945B}. Our image towards this region is affected by sidelobes due to the large flux and extended emission of a \nnearby radio source, and we were not able to accurately measure the radio flux at 1.3 cm. This source is among the detections without a clear estimate of its spectral index. However, based on the flux \nreported by \\citet{2011ApJ...739L..17H}, and our measured flux at 6 cm (see \\citetalias{2016ApJS..227...25R}), this radio source has a flat spectrum, and it is likely an H{\\small II} region ionized by a \nB2 ZAMS star. \n\nThe second bright, and resolved, radio source is G34.43$+$00.24mm2 A, which is the only extended source detected towards an IRDC clump in our survey. Interestingly, this radio source \nhas a spectral index of $\\alpha= -0.5$ (see \\citetalias{2016ApJS..227...25R}) and is associated with a 24 $\\mu$m point source, and at least two molecular outflows \\citep{2007ApJ...669..464S}. This radio \nsource was originally detected at 6 cm by \\citet[labeled by them as Mol 74]{1998A&A...336..339M} and \\citet{2004ApJ...602..850S} using the VLA (FWHM $\\sim$6\\rlap.$^{\\prime \\prime}$$\\times\n$3\\rlap.$^{\\prime \\prime}$). The flux densities at 6 cm reported in those studies are consistent with our data. This radio source has an offset of 7\\rlap.$^{\\prime \\prime}$8 with respect to the \ncenter of the $1.3\\,$mm clump G34.43$+$00.24mm2 detected by \\citet{2006ApJ...641..389R}.\\\\\n\nWe found that at least 36$\\%$ of the HMCs have an extended radio source within the 1\\rlap.$^{\\prime}$8 FWHM primary beam at 25.5 GHz. However, most of them are located slightly outside the FWHM of the mm clump, and are not discussed in this study. These extended radio sources are: 18089$-$1732 G12.890$+$0.495, 18182$-$1433 G16.584$-$0.053, 18470$-$0044 G32.113$+$0.097, 18521$+$0134 G34.749$+$0.021, 19012$+$0536 \nG39.389$-$0.143 and 19266$+$1745 G53.037$+$0.115. All these sources and their radio continuum parameters are reported in \\citetalias{2016ApJS..227...25R}. \n\n\n\\subsubsection{Compact Sources}\\label{compact_sources_sect}\n\nIn \\citetalias{2016ApJS..227...25R} we characterized the change in flux density with frequency using a power law. \nWe reported the detection of 36 compact radio sources (51$\\%$ of total detections) with a rising spectrum ($\\alpha >$0.2), of which \n7 were not detected at 6 cm, thus a lower limit for their spectral index was estimated. We also detected 5 compact radio sources with a flat cm spectrum.\nIn this section we investigate whether a uniform density UC\/HC H{\\small II} region can explain the observed fluxes and spectral indices for compact sources with rising spectra.\n\nWhile optically thin free-free emission results in a flat spectral index ($\\alpha = -0.1$), a rising spectrum implies appreciable optical depth in the emitting gas.\nTo fit our spectra, we thus will require a turnover, (i.e. $\\tau_{\\nu} \\sim 1$) near the intermediate frequency of our observing bands, around $\\nu_{t}=$14.7 GHz,\nwhich in turn requires an emission measure near 9$\\times$10$^{8}$ pc\\,cm$^{-6}$. For fitting the data we use a uniform density, spherical H{\\small II} region model\nwith electron temperature of $T_{e}=$ 10$^{4}$\\,K\nas shown in equation (11) from \\citet{1975A&A....39..217O}. We find that \nfor all compact, rising spectrum sources detected in our survey, within the given uncertainties, a uniform density H{\\small II} region spectrum can be reasonably fit to the radio continuum data.\nExamples of the fits are shown by the continuous blue line in Figure \\ref{HII_fit_examples}. \n The fits for all the 36 compact radio sources with rising spectral index is shown in Appendix \\ref{all_HII_fit} Figure \\ref{HII_fit_app}.\n\n\n\n\\begin{figure}[!h]%\n \\centering\n \\hspace*{-0.5cm} \n \\includegraphics[width=0.35\\linewidth, clip]{g23_01_alpha_HII_A}%\n \\includegraphics[width=0.35\\linewidth, clip]{18440_alpha_HII_A}%\n \\includegraphics[width=0.35\\linewidth, clip]{20343_alpha_HII_B}%\n \\caption[Spectra of the compact radio sources G23.01--0.41 A (left), 18440--0148 A (center) and 20343$+$4129 B (right)]{\\small{Spectra of the compact radio sources G23.01--0.41 A (left), 18440--0148 A (center) and 20343$+$4129 B (right). Error bars are an assumed uncertainty of 10$\\%$ from the flux densities added in quadrature with an assumed 10$\\%$ error in calibration. The continuous blue line is the H{\\small II} region fit using a spherical, constant density model. The red arrow indicates the frequency where $\\tau_{\\nu}=$1 in each model fit. The dashed line is the best fit to the data from a power-law of the form $S_{\\nu}$ $\\propto$ $\\nu^{\\alpha}$.}}\\label{HII_fit_examples}%\n\\end{figure}\n\n\nOur fitting results show that the generally quite weak emission from these very compact, rising spectrum sources implies a very small size\nfor the emitting regions. The sizes are much smaller than our angular resolution, and are on the order of the initial Str\\\"{o}mgren sphere radius.\nThe initial Str\\\"{o}mgren sphere radius ($R_{s}$) depends on the Lyman continuum (N$_{Ly}$) flux and the ambient molecular density (n$_{H_{2}}$) as\nstated in equation 1 from \\citet{1996ApJ...473L.131X}:\n\n\\begin{equation}\nR_{s}= 4104.7 \\left( \\frac{N_{Ly}}{10^{49}s^{-1}}\\right)^{1\/3}\\left(\\frac{n_{H_{2}}}{10^{5}cm^{-3}}\\right)^{-2\/3} \\text{[au]} .\n\\end{equation}\n\nWe show the relation R$_{s}$ versus N$_{Ly}$, represented by the solid lines, for n$_{H_{2}}=$ 10$^{5}$, 10$^{6}$, 10$^{7}$ and 10$^{8}$ cm$^{-3}$ in Figure \n\\ref{Stromgren_sphere}. To place our sources in this diagram, we estimated the Lyman continuum flux (N$_{Ly}$) from our $25.5\\,$GHz flux density, with\nthe formulae of \\citet{1994ApJS...91..659K}, and use the radii ($\\Delta\\,s\/2$) derived from the spectral fitting. These data are listed in Table~\\ref{tab:fig3}.\nOur sources, represented as solid purple dots\nin Figure \\ref{Stromgren_sphere}, cluster around the expected Str\\\"{o}mgren radius for an initial density of $10^{6}$\\,cm$^{-3}$. \n\n\\begin{figure}[!h]%\n \\vspace{1.2cm}\n \\centering\n \\includegraphics[width=0.5\\linewidth, clip]{R_strongrem}%\n \\caption{\\small{Initial Str\\\"{o}mgren sphere radius as a function of the Lyman continuum for compact sources with rising spectra. The solid lines represent the ambient molecular density for n$_{H_{2}}=$ 10$^{5}$ -- 10$^{8}$ cm$^{-3}$. The solid purple dots represent the radius of the\n H{\\small II} regions ($\\Delta\\,s\/2$) as implied by the spherical, constant density H{\\small II} region model fits. The dashed green line represents the lower limit of $R_{Turb}$ (for $\\xi =$1) and the dashed blue line presents $R_{Th}$ if the initial ambient molecular density for both cases is n$_{H_{2}}=$ 10$^{7}$\\,cm$^{-3}$. Shaded areas represent the path from their initial radius up to their final Str\\\"{o}mgren sphere radius if the sources were born at a density of n$_{H_{2}}=$ 10$^{7}$\\,cm$^{-3}$ (green and blue shaded areas for turbulence and thermal pressure confinement, respectively).\nThe error bars in the bottom right corner correspond to a 20$\\%$ calibration uncertainty. \n}}%\n \\label{Stromgren_sphere}%\n\\end{figure}\n\n\n\nWhen nuclear burning begins and a high-mass star produces enough UV photons to photo-ionize the surrounding material, the initial Str\\\"{o}mgren sphere radius is reached (to within a few percent) in a recombination timescale, $t_{r}= $(n$_{H_{2}}$$\\beta_{2}$)$^{-1}$[s], where $\\beta_{2}$= 2.6$\\times$10$^{-13}$\\,cm$^{3}$s$^{-1}$ is the recombination coefficient \\citep{1980pim..book.....D}. For any reasonable initial density this time scale is extremely short ($< 1\\,$yr), and the initial\nStr\\\"{o}mgren sphere radius is reached almost instantaneously. The highly over-pressured ionized region will then begin to expand, and hence the initial\nStr\\\"{o}mgren sphere is a very short-lived configuration, and therefore it is unlikely that the large number of sources detected represent this evolutionary stage.\n\n\n\n\nAfter formation of the initial Str\\\"{o}mgren sphere around a star, the UC H{\\small II} region is highly overpressured, and as a result, it expands approximately at the sound speed until approaching pressure equilibrium with the ambient medium. \n\\citet{1995RMxAA..31...39D} and \\citet{1996ApJ...473L.131X} have studied the confinement of UC H{\\small II} regions in a molecular core by thermal, and thermal plus turbulent pressure, respectively. \nAssuming pressure equilibrium between ionized and surrounding molecular gas, \\citet{1996ApJ...473L.131X} gives the final radius of the ionized region (R) as :\n\n\n\\begin{equation}\nR = R_{s} \\left( \\frac{2k\\xi T_{H^{+}}}{m_{H_{2}}\\sigma_{v}^{2} + kT_{k}}\\right)^{2\/3} ,\n\\end{equation}\n\n\\noindent\nwhere $T_{H^{+}}$ is the temperature of the ionized region, $\\xi$ is a turbulence factor ($>$ 1) that takes into account the pressure due to stellar winds and turbulence in the ionized gas, $\\sigma_{v}$ is the velocity dispersion produced by turbulence and $T_{k}$ is the kinetic temperature of the surrounding molecular gas. \nUsing typical values for the physical conditions in regions where high-mass stars form, we can test whether the sources discussed in this section could be ionized regions in pressure equilibrium with the surrounding molecular gas.\nWhile molecular line observations with single dish instruments indicate average densities of n$_{H_{2}}=$ 10$^{5}$\\,cm$^{-3}$ over the $1\\,$pc clump sizes \\citep[e.g.,][]{2000ApJ...536..393H}, interferometric measurements of high-mass star forming cores\nhave revealed central densities of n$_{H_{2}}= 10^7 - 10^{10} $\\,cm$^{-3}$ on scales $< 0.1\\,$pc \\citep[e.g.,][]{1990ApJ...362..191G, 2015A&A...573A.108G}. Following \\citet{1996ApJ...473L.131X}, we adopt values of $T_{H^{+}}=$ 10$^{4}$\\,K, $\\sigma_{v}=$ 2 km\\,s\n$^{-1}$ (FWHM$\\,\\sim$\\,5 km\\,s$^{-1}$) and $T_{k}=$ 100 K. Assuming $\\xi = 1$, and evaluating the above equation with these numbers we get $R_{turb} = 11.2 R_{s} $ for the case of thermal plus turbulent pressure, and\n$R_{th} = 54.3 R_{s}$ for thermal pressure only. These relations are shown in Figure \\ref{Stromgren_sphere} for n$_{H_{2}}= 10^7$\\,cm$^{-3}$ as green and blue dashed lines, respectively. \nConsidering the location of our data points in Figure \\ref{Stromgren_sphere}, we can exclude the extremely high densities of $10^{10} $\\,cm$^{-3}$ as found by \\citet{2015A&A...573A.108G}, which would predict\nmuch smaller source sizes. On the other hand, our data points are located within the shaded areas that represent the path from their initial Str\\\"omgren radius up to their final radius in pressure equilibrium,\nif the sources were born at a density of n$_{H_{2}}=$ 10$^{7}$\\,cm$^{-3}$ (green and blue shaded areas for turbulence and thermal pressure confinement, respectively).\n\n\nAn estimate of the expansion time $\\tau_{expansion}$ for an ionized region can be obtained assuming that it expands at its sound speed ($C_{s} \\sim$ 10 km\\,s$^{-1}$). To expand to $\\sim$\\,200 au,\nthen $\\tau_{expansion} \\sim R\/C_{s} \\sim $ 100 yr. Thus, an initial Str\\\"omgren sphere will expand fairly quickly and, as suggested by \\citet{1995RMxAA..31...39D} and \\citet{1996ApJ...473L.131X}, \nthe ionized regions can remain compact as long as the molecular core provides the outside pressure. Observations of UCH{\\small II} regions and HMCs suggest that this time is on order $10^5\\,$yr\n\\citep{1989ApJ...340..265W,2001ApJ...550L..81W}, and it hence appears that our sources could be ionized regions around newly formed stars in pressure equilibrium in their\nmolecular cores. While our calculations have not been fitted to a particular source, the observed scatter in Figure \\ref{Stromgren_sphere} can be accounted for with a varying amount of turbulence in the molecular gas,\ni.e. if the molecular line FWHM varies between $\\sim$ 7 -- 20 km\\,s$^{-1}$ for the case of an ambient molecular density of n$_{H_{2}}=$ 10$^{7}$\\,cm$^{-3}$.\n\n\nIt is interesting to note that we found that the radius of the extended sources discussed above are within the pressure equilibrium zone for an initial density of n$_{H_{2}}=$ 10$^{5}$\\,cm$^{-3}$. \nThese sources are located on the outskirts of the mm core, and one might ask whether they have migrated out of the molecular core center, or if they were born in their current location. Assuming stellar velocities between 2 and 12 km\\,s$^{-1}$ \n\\citep{2007ApJ...660.1296F} in order for them to travel to the half power point of the cores (FWHM median angular size for HMC $=$18\\rlap.$^{\\prime \\prime}$ at a distance of 4 kpc), times between\naround 10$^{5}$ yr and 10$^{4}$ yr, respectively, are needed. While migration toward lower density regions is thus possible, we note that we do not find any strong evidence for cometary regions which would be predicted due to\nbow shocks between molecular and ionized gas \\citep{1990ApJ...353..570V}.\n\n\\subsubsection{Lyman Continuum}\n\nAn additional point to consider to understand the nature of our detections is the Lyman continuum photon rate as a function of the bolometric luminosity. We analyze this relation for all the sources \nwith a flat or a rising spectrum (including extended, elongated structure, as well as compact morphology) as shown in Figure \\ref{Lyman_cont_plot}. The Lyman continuum photon rate is estimated from the radio \ncontinuum flux at 25.5 GHz and the bolometric luminosities for our regions are estimated from {\\it Herschel}\/Hi--GAL fluxes, and from ancillary data (see \\S \\ref{app:lum}). We list these data in Table~\\ref{tab:fig4}.\nFor data taken from the literature, care was taken that the Lyman continuum flux and bolometric luminosities refer to the same distance. For sources with distance ambiguity, we use the near kinematic distance.\nIn Figure \\ref{Lyman_cont_plot}, compact and elongated sources are represented by filled circles if the bolometric luminosities are estimated in this work (see \\S \\ref{app:lum}), or open circles if the luminosity\nis taken from the literature. The extended sources from \\S \\ref{ext_sources} are represented by the $\\color{blue} \\times $ symbol. \nThe continuous black line is the expected Lyman continuum photon rate from a single zero-age main-sequence (ZAMS) star at a given luminosity, and the shaded area\nbounded by the solid black line shows the expected Lyman continuum from a stellar cluster of the same N$_{Ly}$. For more details on these curves see \\citet{2013A&A...550A..21S}.\nThus, H{\\small II} regions ionized by stellar UV photons from a single early-type star are expected to lie on the black line. If, on the other hand, the Lyman continuum comes from a cluster of stars (a likely scenario for high-mass \nstars) rather than from a single ZAMS star, the expected N$_{Ly}$ is lower, and should be located within the shaded area \\citep{2015A&A...579A..71C}.\n\n\n\n As seen in Figure \\ref{Lyman_cont_plot}, only a small fraction of our sources fall in the shaded area of the plot indicating direct stellar photoionization. Most of the HMC sources (red open\/filled circles) lie\n below the curve of the expected Lyman continuum flux, and hence are underluminous at radio wavelengths.\n On the other hand, the majority of the sources detected towards CMCs and CMC--IRs are located in the so-called ``forbidden area'' above the Lyman continuum line, showing an excess of Lyman continuum compared \n to the expected value based \non their luminosities. This is true even if the sources are corrected by the distance, i.e., when there is ambiguity in the kinematic distance of the source or the value of the distance is incorrect. For reference, the arrow in the plot indicates the amount \nthat a point will move if the distance increases by a factor of 2. If the distance changes by any other factor the point will move parallel to the arrow. Additionally, there is a possibility that some bolometric luminosities are underestimated (see \\S \\ref{app:lum}), however if this is the case we believe that the luminosities will shift to the right by less than 0.5 dex.\n\n\\begin{figure}[!h]\n \n \\centering\n \\includegraphics[width=0.5\\linewidth, clip]{Lyman_plot_v3}\n \\caption{\\small{Lyman continuum measured at 25.5 GHz as a \n function of the bolometric luminosity for all detected sources with flat or rising spectra in our sample. The bolometric luminosity is mainly estimated from {\\it Herschel}\/Hi--GAL data (except for the open circles for\n whose the bolometric luminosity is from the literature). The circles represent the compact sources with flat or rising spectra, while the blue $\\color{blue} \\times $ symbol represents the flat spectrum extended \n sources from \\S \\ref{ext_sources}. UC H{\\small II} regions from \\citet{1994ApJS...91..659K} are represented by the gray $\\color{gray} \\times $ symbol. The continuous black line is the expected Lyman continuum photon rate of a \n single ZAMS star at a given luminosity, and the shaded area gives these quantities for the case of a cluster \\citep{2013A&A...550A..21S}. The arrow indicates how much a point would move if the distance were increased by \n a factor of 2. \nThe error bars in the bottom right corner correspond to a 20$\\%$ calibration uncertainty. \n}}\n \\label{Lyman_cont_plot}\n\n\\end{figure} \n \n\n\n\n\\citet{2013A&A...550A..21S} have reported Lyman continuum excess for several sources in an 18 and 22.8 GHz survey of high-mass star forming regions with the Australian Telescope Compact Array (ATCA).\nInterestingly, $\\sim$70$\\%$ of their H{\\small II} regions with Lyman excess are associated with molecular clumps belonging to two types of sources that are in the earliest evolutionary stages of high-mass stars based on their classification (equivalent to our CMCs and CMC-IRs clumps). \nAdditionally, \\citet{2015A&A...579A..71C} found Lyman continuum excess for about 1\/3 of their sample of 200 compact and UC H{\\small II} regions selected from the CORNISH survey \\citep{2013ApJS..205....1P}. Their sources with Lyman continuum excess are also in an earlier evolutionary phase within their sample. Both studies argued that the Lyman excess is not easily justified, leaving room for two possible scenarios, invoking additional\nsources of UV photons from an ionized jet, or from an accretion shock in the protostar\/disk system. \\citet{2016A&A...588L...5C} suggested that the Lyman excess is produced by accretion shocks, based on outflow (SiO) and infall (HCO$^{+}$) tracer observations towards the 200 H{\\small II} regions studied in \\citet{2015A&A...579A..71C}. \n\nIt is important to mention that due to our selection criteria (see \\citetalias{2016ApJS..227...25R}) the sources studied by \\citet{2013A&A...550A..21S} and \\citet{2015A&A...579A..71C} are much brighter at radio wavelengths than the ones from our work, with radio luminosities at 5 GHz of $\\sim$ 10$^{2}$--10$^{6}$ mJy kpc$^{2}$ vs 10$^{-2}$--10 mJy kpc$^{2}$ in our sample. In Figure \\ref{Lyman_cont_plot} we also show several UC H{\\small II} regions from \\citet{1994ApJS...91..659K}, denoted by the $\\color{gray} \\times $ symbol. These sources seem to be produced by higher free-free emission compared with our sample, suggesting that our sources represent a different population of radio sources. \\citet{1999RMxAA..35...97C} based on selection criteria similar to ours, detected sources with low radio luminosities like the ones in this work. Furthermore, these low radio luminosities are typical of thermal jets, with UV photons that are produced by shocks from collimated winds from the protostar with the surrounding material \\citep[e.g.,][]{1996ASPC...93....3A}. Thus, while the above analysis of the cm SEDs suggests a model of pressure confined H{\\small II} regions for our compact sources, the Lyman continuum photon rate as a function of the bolometric luminosity\nshown in Figure \\ref{Lyman_cont_plot} does not lend strong support to this model. A further possible explanation for the compact sources with rising spectra, as well as for several elongated sources\ndetected in our survey is that they arise from thermal jets. We explore this scenario in the following section. \n\n\\subsection{Ionized Jets}\\label{ionized_jet_sect}\n\nBased on the low radio luminosities (S$_{5\\,GHz}\\,$d$^{2}$ $\\sim$ 10$^{-2}$ -- 10 mJy kpc$^{2}$) of our detected sources, we need to consider the possibility that the source of ionization is not a ZAMS star, but rather \nthat their nature is that of a thermal, ionized jet produced by shock ionization as described in \\S \\ref{sec:intro}.\nSupport for this hypothesis comes from a subset of resolved sources from our survey. We have characterized 12 jet candidates based on their elongated, or string-like morphology in conjunction with an\nassociation with a molecular outflow. These sources are listed in Table \\ref{jet_cand_list}, where column 1 is the name of the region, column 2 are the radio sources that are thought to be part of the ionized jet, and \ncolumn 3 lists the approximate direction of the ionized jet. Column 4 shows the approximate direction of the molecular outflows associated with the centimeter continuum emission as found in the literature. Column 5 \nindicates if the centimeter continuum emission is a new detection or if it has been detected in previous studies. Column 6 lists the references for the molecular outflow detections and previous centimeter continuum \ndetections if any. Examples for these sources are shown in Figures \\ref{fig:spitzer_examples}a, \\ref{fig:spitzer_examples}b, and \\ref{fig:UKIDSS_examples}a, \\ref{fig:UKIDSS_examples}b. To our knowledge, 6 of these ionized jet candidates are new detections. In the cases of previous detections of centimeter continuum emission towards the listed regions, our high-sensitivity observations described in Paper I\ngenerally show the elongation or string-like morphology of a jet for the first time \\citep[e.g.,][]{2017ApJ...843...99H}. Furthermore, this subset of resolved jet candidates have the expected spectral index ( $0.2 \\leq \\alpha \\leq 1.2$) for ionized jets, several of them are associated with 6.7 GHz CH$_{3}$OH masers and H$_{2}$O \nmasers, and they have excess emission at 4.5 $\\mu$m, which may trace shocked gas via H$_{2}$ emission in outflows or scattered continuum from an outflow cavity \\citep[e.g.,][]{2011ApJ...729..124C, \n2013ApJS..208...23L}. In some cases, like towards the ionized jet in 18182$-$1433, some of the radio sources have negative spectral indices, consistent with non-thermal lobes, since it is thought that when very strong shock waves from a fast jet move through a magnetized medium, some of the electrons are accelerated to relativistic velocities producing synchrotron emission \\citep{2003ApJ...587..739G, 2010Sci...330.1209C}. \\citet{2016MNRAS.460.1039P} and \\citep{2018A&A...619A.107S, 2019A&A...623L...3S} also reported the detection of ionized jets with non-thermal lobes (see also review by \\citealt{2018A&ARv..26....3A}). \n\n\n\n\n\\begin{deluxetable}{l c c c c c c}\n\\tabletypesize{\\scriptsize}\n \\renewcommand*{\\arraystretch}{1.5}\n\\tablecaption{Ionized Jets \\label{jet_cand_list}}\n\\tablewidth{0pt}\n\\tablehead{\n\\colhead{Region} & \n\\colhead{Radio Source} &\n\\colhead{Jet Direction} &\n\\colhead{Outflow Direction} &\n\\colhead{ H$_{2}-$Jet Direction} &\n\\colhead{New Detection} &\n \\colhead{Reference} \\\\[-20pt]\\\\}\n\\startdata\n\\setcounter{iso}{0}\t\nG11.11$-$0.12P1 & A, C, D & NE$-$SW & E$-$W, NE$-$SW\\tablenotemark{a} & E$-$W & y & \\rxn \\label{rxn:Wang2014} \\rxn \\label{rxn:Rosero2014} \\rxn \\label{rxn:Lee2013} \\\\\n18089$-$1732 & A & N$-$S & N$-$S & no\/very weak\\tablenotemark{b} & n & \\rxn \\label{rxn:Beuther2004} \\rxn \\label{rxn:Beuther2010} \\rxn \\label{rxn:Zapata2006} \\\\\n18151$-$1208 & B & NE$-$SW & NW$-$SE\\tablenotemark{c} & NW-SE & n & \\rxn \\label{rxn:Fallscheer2011} \\rxn \\label{rxn:Hofner2011} \\rxn \\label{rxn:Davis2004} \\rxn \\label{rxn:Varricat2010} \\\\\n18182$-$1433 & A$-$C\\tablenotemark{d} & E$-$W & NE$-$SW, NW$-$SE & E$-$W & n & \\rxn \\label{rxn:Beuther2006} \\rxn \\label{rxn:Moscadelli2013} \\rxn \\label{rxn:Lee2012} \\\\\nIRDC18223$-$3 & A$-$B\\tablenotemark{e} & NE$-$SW & NW$-$SE\\tablenotemark{f} & SE-NW & y & \\rxn \\label{rxn:Fallscheer2009} \\rxn \\label{rxn:Beuther2005} \\\\ \n G23.01$-$0.41 & A & NE$-$SW & NE$-$SW & non-detection & n & \\rxn \\label{rxn:Sanna2016} \\rxn \\label{rxn:Araya2008} \\rxn \\label{rxn:Sanna2018} \\osref{rxn:Lee2013}\\\\\n18440$-$0148 & A & NW-SE & \\nodata\\tablenotemark{g} & non-detection & y & \\rxn \\label{rxn:Navarete2015} \\\\\n18566$+$0408 & A$-$D\\tablenotemark{h} & E$-$W & NW$-$SE & non-detection & n & \\rxn \\label{rxn:Zhang2007}\\rxn \\label{rxn:Araya2007} \\rxn \\label{rxn:Hofner2017} \\osref{rxn:Lee2013} \\\\\n19035$+$0641 & A & NE$-$SW & NW$-$SE & no\/very weak\\tablenotemark{b} & y & \\rxn \\label{rxn:Lopez_Sep2010} \\\\\n19411$+$2306 & A & NE$-$SW & NE$-$SW & detection\\tablenotemark{b} & y & \\rxn \\label{rxn:Beuther2002bb} \\\\\n20126$+$4104 & A$-$B & NW$-$SE & NW$-$SE, S$-$N & NW$-$SE & n & \\rxn \\label{rxn:Su2007} \\rxn \\label{rxn:Shepherd2000} \\rxn \\label{rxn:Hofner2007} \\rxn \\label{rxn:Cesaroni1999} \\rxn \\label{rxn:Cesaroni2013} \\\\\n20216$+$4107 & A & NE$-$SW & NE$-$SW & NE$-$SW & y & \\osref{rxn:Lopez_Sep2010} \\osref{rxn:Navarete2015} \\\\\n\\enddata\n\\tablenotetext{\\text{a}}{ALMA unpublished data (Rosero et al. in prep).}\n\\tablenotetext{\\text{b}}{ T. Stanke and H. Beuther (private communication).}\n\\tablenotetext{\\text{c}}{A blue-shifted component of a molecular outflow going in the direction of 18151$-$1208 B is seen in Figure 4 of \\citet{2011ApJ...729...66F} but it is not discussed by the authors.}\n\\tablenotetext{\\text{d}}{Radio source B has a negative spectral index and radio source A has an upper limit value in its spectral index. Their fluxes are not included in \n Figures \\ref{fig:rad_bol_lum} and \\ref{fig:Tanaka_tracks} (right panel). \n}\n\\tablenotetext{\\text{e}}{Radio source B has an upper limit value for the flux at 4.9 GHz and its value is not included in \n Figures \\ref{fig:rad_bol_lum} and \\ref{fig:Tanaka_tracks} (right panel). \n}\n\\tablenotetext{\\text{f}}{A blue-shifted component of a molecular outflow going in the direction of IRDC18223$-$3 is seen in Figure 5 of \\citet{2011ApJ...729...66F} but it is not discussed by the authors.}\n\n\\tablenotetext{\\text{g}}{\\citet{2002ApJ...566..931S} report the presence of CO (2--1) wings towards this region, but contour maps of the molecular outflow are not available.}\n\\tablenotetext{\\text{h}}{Radio sources C and D have upper limit spectral indices that are consistent with being negative and their fluxes were not included in \n Figures \\ref{fig:rad_bol_lum} and \\ref{fig:Tanaka_tracks} (right panel). \n}\n\\tablecomments{Generally there are multiple molecular outflows in each of these high-mass star forming region. We reference the ones that are located closest to the centimeter continuum emission. The `y' and `n' indicates if the centimeter radio continuum detection is new or if it has been detected in a previous study, respectively.\\\\\n \\osref{rxn:Wang2014} \\citet{2014MNRAS.439.3275W}; \\osref{rxn:Rosero2014} \\citet{2014ApJ...796..130R}; \\osref{rxn:Lee2013} \\citet{2013ApJS..208...23L}; \\osref{rxn:Beuther2004} \\citet{2004ApJ...616L..23B}; \\osref{rxn:Beuther2010} \\citet{2010ApJ...724L.113B}; \\osref{rxn:Zapata2006} \\citet{2006AJ....131..939Z}; \\osref{rxn:Fallscheer2011} \\citet{2011ApJ...729...66F}; \\osref{rxn:Hofner2011} \\citet{2011ApJ...739L..17H}; \\osref{rxn:Davis2004} \\citet{2004A\\string&A...425..981D}; \\osref{rxn:Varricat2010} \\citet{2010MNRAS.404..661V}; \\osref{rxn:Beuther2006} \\citet{2006A\\string&A...454..221B}; \\osref{rxn:Moscadelli2013} \\citet{2013A\\string&A...558A.145M}; \\osref{rxn:Lee2012} \\citet{2012ApJS..200....2L}; \\osref{rxn:Fallscheer2009} \\citet{2009A\\string&A...504..127F}; \\osref{rxn:Beuther2005} \\citet{2005ApJ...634L.185B}; \\osref{rxn:Sanna2016} \\citet{2016A\\string&A...596L...2S}; \\osref{rxn:Araya2008} \\citet{2008ApJS..178..330A}; ; \\osref{rxn:Sanna2018} \\citet{2019A\\string&A...623A..77S}; \\osref{rxn:Navarete2015} \\citet{2015MNRAS.450.4364N}; \\osref{rxn:Zhang2007} \\citet{2007A\\string&A...470..269Z}; \\osref{rxn:Araya2007} \\citet{2007ApJ...669.1050A}; \\osref{rxn:Hofner2017} \\citet{2017ApJ...843...99H}; \\osref{rxn:Lopez_Sep2010} \\citet{2010A\\string&A...517A..66L}; \\osref{rxn:Beuther2002bb} \\citet{2002A\\string&A...383..892B}; \\osref{rxn:Su2007} \\citet{2007ApJ...671..571S}; \\osref{rxn:Shepherd2000} \\citet{2000ApJ...535..833S}; \\osref{rxn:Hofner2007} \\citet{2007A\\string&A...465..197H}; \\osref{rxn:Cesaroni1999} \\citet{1999A\\string&A...345..949C}; \\osref{rxn:Cesaroni2013} \\citet{2013A\\string&A...549A.146C}\n }\n\\end{deluxetable}\n\n\n\n\n\n\\begin{figure}[htbp]\n\\centering\n\\begin{tabular}{c}\n\n\\includegraphics[width=0.7\\textwidth,clip=true, trim = 10 240 10 80, clip, angle = 0]{18089} \\\\\n\\includegraphics[width=0.68\\textwidth,clip=true, trim = 10 240 10 60, clip, angle = 0]{19411} \n\\end{tabular}\n\\caption{\\small{\\emph{Spitzer} IRAC GLIMPSE three-color (3.6$\\mu m$-blue, 4.5$\\mu m$-green and 8.0$\\mu m$-red )\nimages of two ionized jet candidates, overlayed with VLA 6 cm continuum emission contours. \nNote that both regions show 4.5 $\\mu$m excess emission. In the right panel we show\nan enlarged version of the radio continuum from \\citetalias{2016ApJS..227...25R}.\n{\\bf Top: 18089-1732 A:} The arrows represent the direction of a the north-south bipolar SiO outflow detected by \\citet{2004ApJ...616L..23B, 2010ApJ...724L.113B}. The black circle and the square are the 6.7 GHz CH$_{3}$OH and H$_{2}$O masers reported in \\citep{2002A&A...390..289B}, respectively.\nVLA 6 cm contour levels are ($-$2.0, 3.0, 10.0, 25.0, 40.0) $\\times $6 $\\mu$Jy beam$^{-1}$, and 1.3 cm contour levels ($-$1.5, 3.0, 5.0, 7.5, 15.0, 25.0, 45.0, 95.0) $\\times$ 10 $\\mu$Jy beam$^{-1}$.\n{\\bf Bottom: 19411+2306 A:} The arrows represent the direction of the detected CO outflow by \\citet{2002A&A...383..892B}. \\citet{2002ApJ...566..931S} reported that 6.7 GHz CH$_{3}$OH and H$_{2}$O\n masers were not detected for this source in their survey. VLA 6 cm contour levels are ($-$2.0, 2.0, 3.0, 6.0, 10.0, 13.0, 15.0) $\\times$ 5.5 $\\mu$Jy beam$^{-1}$, and 1.3 cm contour levels ($-$2.0, 2.0, 3.0, 6.0, 8.0, 10.0, 12.0) $\\times$ 8 $\\mu$Jy beam$^{-1}$. }}\n \\label{fig:spitzer_examples}\n\\end{figure}\n\n\nAs listed in Table~\\ref{jet_cand_list}, at least 5 of the ionized jet candidates are aligned in the same direction as a large scale molecular outflow (see Figures \\ref{fig:spitzer_examples} for examples). The other \nionized jet candidates appear to be associated with molecular outflows where the directions are approximately perpendicular. In Figure \\ref{fig:UKIDSS_examples} we present the examples of 18151$-$1208 B and 19035+0641 \nA where we show VLA~6$\\,$cm continuum emission contours overlayed on a UKIDSS\\footnote{United Kingdom Infrared Telescope (UKIRT) Infrared Deep Sky Survey (UKIDSS) Galactic Plane Survey \\citep{2007MNRAS.379.1599L}.} {\\emph K}-band (2.2 $\\mu m$) image. It is interesting to note that the putative ionized jets and the \nUKIDSS {\\emph K}-band emission in both cases are elongated in the same direction. This together with the fact that the ionized jets are located nearly at the peak of the UKIDSS {\\emph K}-band emission could indicate \nthat the latter is tracing scattered light from the central protostar that is escaping from an outflow cavity \\citep{2013ApJS..208...23L}. The observed misalignment between cm continuum emission and the dominating molecular flow in the region could be explained by the existence of two flows, where the molecular outflow associated\nwith the jet is weaker, and hence undetected. This could in fact be the case for 18151$-\n$1208 B, where a blue-shifted component of a CO molecular outflow observed with the Submillimeter Array appears to be aligned in the direction of the ionized jet\n(see Figure 4 of \\citealt{2011ApJ...729...66F}), although this outflow component is not discussed by the authors. Another possible explanation for the misalignment in the directions of the ionized jet and the molecular outflow is that they are subjected to precession, where \nthe flow axis changes from the small to the large scale as suggested by e.g., \\citet{2000ApJ...535..833S} and \\citet{2005A&A...434.1039C} for the case of 20126$+$4104, \\citet{2013A&A...558A.145M} to explain the \ncase of 18182$-$1433 and \\citet{2007ApJ...669.1050A} for 18566$+$0408. \n\nFor a further test of their jet nature, we have also attempted to estimate the deconvolved sizes of the central jet components using the CASA task {\\tt imfit}. This was possible for 3 sources within the subsample of jet candidates listed in\nTable~\\ref{jet_cand_list}. Figure \\ref{fig:size_freq} shows the deconvolved major axis as a function of frequency for 18151$-$1208 B and 18440$-$0148 A (the case of 18566$+$0408 B is reported in \\citealt{2017ApJ...843...99H}). Within the uncertainties these radio sources follow the relation $\\theta_{maj} \\propto \\nu^{\\gamma}$, where a major axis index of $\\gamma = -0.7$ is expected for a biconical ionized wind or jet \\citep{1986ApJ...304..713R}. Therefore, at least in these 3 cases, we have further evidence for the jet nature of these specific radio sources. \n\nIn summary, for the subsample of elongated continuum sources listed in Table~\\ref{jet_cand_list} it is very likely that the nature of these sources are ionized jets at the base of a molecular outflow.\n\n\n\n\n\n\n\n\n\n\\begin{figure}[htbp]\n\\centering\n\\begin{tabular}{c}\n\\includegraphics[width=0.7\\textwidth,clip=true, trim = 10 200 10 80, clip, angle = 0]{18151} \\\\\n\\includegraphics[width=0.68\\textwidth, clip=true, trim = 10 150 10 80, clip, angle = 0]{19035} \n\\end{tabular}\n\\caption{\\small{UKIDSS {\\emph K}-band images of two ionized jet candidates, overlayed with VLA 6 cm continuum emission contours. In the right panel we show\nan enlarged version of the radio continuum from \\citetalias{2016ApJS..227...25R}. \n{\\bf Top: 18151$-$1208 B:} The arrows represent the direction of the two nearly perpendicular CO outflows detected by \\citet{2011ApJ...729...66F}. A blue-shifted component of a molecular outflow going in the direction\nof 18151$-$1208 B is seen in Figure 4 of \\citet{2011ApJ...729...66F} but it is not discussed by the authors. The black circle is the 6.7 GHz CH$_{3}$OH maser from \\citet{2002A&A...390..289B}. The x symbol represents the position of an \nadditional radio source detected at 1.3 cm reported in \\citet{2016ApJS..227...25R}. VLA 6 cm contour levels are ($-$2.0, 3.0, 9.0, 15.0) $\\times$ 6 $\\mu$Jy beam$^{-1}$, and 1.3 cm contour levels ($-$2.0, 3.0, 6.0, 8.5, 20.0, 40.0, 60.0) $\\times$ 8 $\\mu$Jy beam$^{-1}$.\n{\\bf Bottom: 19035+0641 A:} The arrows represent the direction of the detected CO and HCO$^{+}$ outflows \\citep{2002A&A...383..892B, 2010A&A...517A..66L}. The black circle and the square are the 6.7 GHz CH$_{3}$OH and \nH$_{2}$O masers from \\citet{2002A&A...390..289B}, respectively. VLA 6 cm contour levels are ($-$2.5, 3.0, 10.0, 25.0, 120.0, 280.0, 380.0) $\\times$ 4 $\\mu$Jy beam$^{-1}$, and 1.3 cm contour levels ($-$2.0, 5.0, 10.0, 20.0, 30.0, 50.0, 90.0, 170.0) $\\times$ 8 $\\mu$Jy beam$^{-1}$.}}\n \\label{fig:UKIDSS_examples}\n\\end{figure}\n\n\n\n\n\n\\begin{figure}[!h]\n\\centering\n\\begin{tabular}{cc}\n\n\\includegraphics[width=0.34\\textwidth, clip, angle = 0]{18151alpha_size_freq} &\n\\includegraphics[width=0.34\\textwidth, clip, angle = 0]{18440alpha_size_freq} \\\\\n \\vspace{-1.cm} \n\\end{tabular}\n\\caption{\\small{Deconvolved major angular axis as a function of frequency for the ionized jet candidates 18151$-$1208 B, 18440$-$0148 A and 18566$+$0408 B. The arrows represent the size limit value from the synthesized beam of the map at the given frequency. The dashed line is the power law fit of the form $\\theta_{maj} \\propto \\nu^{\\gamma}$.}}\n \\label{fig:size_freq}\n\\end{figure}\n\n\n\\defcitealias{2011MNRAS.415..893A}{AMI Consortium: Scaife et al. (2011}\n\\defcitealias{2012MNRAS.420.1019A}{2012)}\n\nAs mentioned above, most of our detected radio sources with a rising spectrum are compact, i.e., spatially unresolved, or marginally resolved. Several of these sources are associated with molecular \noutflows, and 6.7 GHz CH$_{3}$OH and 22 GHz H$_{2}$O masers as found in the literature. More precisely, only 6 of the 25 regions where we detected a radio source with a rising spectrum, are not \nassociated with molecular outflows, or adequate data that would trace such outflows do not seem to exist.\nFurthermore, after taking into account the shape of the synthesized beam of our VLA observations some of \nthese radio sources appear slightly elongated in a certain direction. Examples are 18264$-$1152 F and G53.25$+$00.04mm2 A (see \\citetalias{2016ApJS..227...25R}, Figure~2). Therefore, we now investigate the \npossibility that the compact radio continuum sources with a rising spectrum represent ionized jets. \n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{ccc}\n\\hspace*{\\fill}%\n\\includegraphics[width=0.5\\textwidth, clip=true, angle = 0]{Rad_LumVSBol_Lum_paper}\n\\end{tabular}\n\\caption{\\small{Radio luminosity at 4.9 GHz as a function of the bolometric luminosity. The red stars and octagons are our ionized jet and jet candidates listed in Table \\ref{jet_cand_list} and \\ref{candidates} towards HMCs and CMC-IRs, respectively. The bolometric luminosity for the red symbols is mainly estimated from our {\\it Herschel}\/Hi--GAL data (except for the open symbols whose bolometric luminosity information is from the literature). The green circles represent ionized jets associated with low-mass protostars ($1\\,L_{\\odot} \\leq L_{bol} \\leq 1000\\,L_{\\odot}$) from \\citet{2018A&ARv..26....3A} and the yellow circles are the very low luminosity objects (VeLLOs) and low-mass protostars from \\citetalias{2011MNRAS.415..893A, 2012MNRAS.420.1019A}.\nThe purple triangles represent ionized jets from high-mass stars as found in the literature,\nfrom \\citet{2008AJ....135.2370R} and \\citet{2016A&A...585A..71M}. The $\\times$ symbols are UC and HC H{\\small II} regions from \\citet{1994ApJS...91..659K}. \nThe dashed line relation shows the positive correlation found by \\citet{2015aska.confE.121A} derived for jets from low-mass stars. The red dotted line is our best fit to the data including ionized jets from low, intermediate and high-mass YSOs, but excluding the sources from \\citetalias{2011MNRAS.415..893A, 2012MNRAS.420.1019A}.\nThe error bars in the bottom right corner correspond to a 20$\\%$ calibration uncertainty.\n}}\n \\label{fig:rad_bol_lum}\n\\end{figure}\n\n\nA statistical way of investigating the nature of our compact sources is to study the energy contained in the ionized gas. In Figure \\ref{fig:rad_bol_lum} we show the \nradio luminosity S$_\\nu\\,$d$^2$ of all the components of the ionized jet (or the jet candidate) as a function of the bolometric luminosity of the region. As in Figure \\ref{Lyman_cont_plot} above, the black line is the radio luminosity expected from the Lyman continuum\nflux at a given bolometric luminosity if it arises from photoionization of a single ZAMS star. In addition to the compact, rising spectra sources from our survey we also show in Figure \\ref{fig:rad_bol_lum} as green circles\nthe radio luminosity from low-mass stars ($1\\,L_{\\odot} \\leq L_{bol} \\leq 1000\\,L_{\\odot}$) associated with ionized jets from \\citet{2018A&ARv..26....3A} and as yellow circles the radio luminosity of very low luminosity objects (VeLLOs) and low-mass protostars detected at 1.8 cm, and reported in \\citetalias{2011MNRAS.415..893A, 2012MNRAS.420.1019A}. In order to compare the sources from \\citetalias{2011MNRAS.415..893A, 2012MNRAS.420.1019A} and \\citet{2018A&ARv..26....3A} with our $4.9\\,$GHz data, we scaled their\nfluxes using a factor of 0.48 and 0.76, respectively, assuming that those sources have a spectral index $\\alpha=0.6$, which is the canonical value of ionized jets. \n The scaling factors are calculated using \n$\\frac{S_{\\lambda_{1}}}{S_{\\lambda_{2}}}= \\left(\\frac{\\lambda_{2}}{\\lambda_{1}} \\right)^{\\alpha}$. \n\nIt is well known that for low mass YSOs the radio luminosities are correlated with the bolometric luminosity, and we show the correlation\n$\\frac{S_{\\nu}d^{2}}{\\text{mJy kpc}^{2}}= 8.7 \\times 10^{-3} \\left( \\frac{L_{\\text{bol}}}{L_{\\odot}} \\right)^{0.54}$ first found by \\citet{1995RMxAC...1...67A} and recently updated by \\citet{2018A&ARv..26....3A}. The black dashed line is the best fit to the green circles in Figure \\ref{fig:rad_bol_lum}, which are the low-mass ionized jets presented by \\citet{2018A&ARv..26....3A}.\nIt is clear from Figure \\ref{fig:rad_bol_lum} that the sources from \\citetalias{2011MNRAS.415..893A, 2012MNRAS.420.1019A} also follow this relation, although their data were observed at low resolution ($\\sim$ 30\\rlap.$^{\\prime \\prime}$) and there is not enough information that proves that they correspond to ionized jets. \n\\citet{1995RMxAC...1...67A} used this observed correlation to explain the apparent excess ionization levels from low mass YSOs by shock induced ionization from jets,\nas modeled by \\citet*{1987RMxAA..14..595C} and \\citet{1989ApL&C..27..299C}. \nA handful of detections of ionized jets towards high-mass stars in recent years suggested that this correlation appears to also hold for stars with luminosities up to $\\sim$ 10$^{5}$ L$_{\\odot}$ (see \n\\citealt{2016MNRAS.460.1039P}). We have added these objects from \\citet{2008AJ....135.2370R} and \\citet{2016A&A...585A..71M} as purple triangles in Figure \\ref{fig:rad_bol_lum} and the data has been properly scaled to our frequency of 4.9~GHz assuming $\\alpha=0.6$. The data from our survey (\\citetalias{2016ApJS..227...25R}) in conjunction with improved estimates of the \nluminosities based on Herschel data (see \\S \\ref{app:lum}) allow us to further populate this plot and test if a correlation exists. In Figure \\ref{fig:rad_bol_lum} the red stars and octagons are our radio sources with rising spectrum detected\ntowards HMCs and CMC-IRs, respectively, and we see that most sources are located very close to the relation found by \\citet{1995RMxAC...1...67A} up to luminosities of $\\sim$ 10$^{5}$ L$_{\\odot}$. In fact, a fit of the data \nincluding low, intermediate and high-mass YSOs is shown as a red dotted line and the result is similar to what was found by \\citet{1995RMxAC...1...67A}. We excluded the sources from \\citetalias{2011MNRAS.415..893A, 2012MNRAS.420.1019A} from our fit since it is unclear if those source are indeed ionized jets.\n We take this result as a strong indication that the weak, and compact radio sources which we found in our survey are caused by the same mechanism which causes the radio emission the low mass YSOs, namely it is caused by ionized jets. We also note that of the 6 compact radio sources where currently no observational association with molecular flows is known,\n 5 match our fit (red dotted line in Fig 8) of the $S_\\nu\\,d^2$ vs $L_{bol}$ relationship.\n \n \n\\defcitealias{2011MNRAS.415..893A}{AMI Consortium: Scaife et al. (2011}\n\\defcitealias{2012MNRAS.420.1019A}{2012)} \n \nFurthermore, in Figure \\ref{fig:Curiel_plot} we show the momentum rate ($\\dot{P}$) of the molecular outflow as a function of the radio luminosity (S$_\\nu\\,$d$^2$) of the ionized jet estimated from our flux values at 4.9 GHz (symbols and colors are the same as in Figure \\ref{fig:rad_bol_lum}). The momentum rate of the molecular outflows comes from information from the literature for our ionized jets (and jet candidates), if available, and the values are in most cases from single dish observations. For consistency, we have scaled the physical values, so that they are based on the same distance. However,\nmany uncertainties remain due to the inhomogeneity of the data set. In particular, the values for the momentum rate come from observations taken\nby different authors, using different spectral lines, as well as different telescopes. Hence, the large scatter in Figure \\ref{fig:Curiel_plot} is not unexpected,\nand the creation of a homogenous data set for the $\\dot{P}$ versus $S_\\nu\\,d^2$ relation will be an important future task.\n\nIn spite of the large scatter, the correlation seen in Figure \\ref{fig:Curiel_plot} indicates that the more radio luminous the protostar are the more powerful they are in pushing outflowing material. This correlation, which has been studied by several authors (e.g., \\citealt{1995RMxAC...1...67A}, \\citealt{2008AJ....135.2370R}, \\citetalias{2011MNRAS.415..893A, 2012MNRAS.420.1019A}, \\citealt{2018A&ARv..26....3A}), follows the shocked-induced ionization model introduced by \\citet{1987RMxAA..14..595C, 1989ApL&C..27..299C}, suggesting that the ionization of thermal jets is due to shocks. The shocked-induced ionization model implies $\\left(\\frac{S_{\\nu}d^{2}}{mJy\\,kpc^{2}}\\right) = 10^{3.5} \\eta \\left(\\frac{\\dot{P}}{M_{\\odot}\\,yr^{-1}\\,km\\,s^{-1}}\\right)$ at $\\nu=5$ GHz where $\\eta$ is the shock efficiency fraction or the fraction of material that gets ionized by the shocks, which for low-mass protostars has been observationally found to be around 10$\\%$ (or $\\eta=0.1$). \\citet{2018A&ARv..26....3A} suggested that the ionization fraction of jets in general is low ($\\sim 1 - 10\\%$). With the current data, and due to the large scatter seen in the correlation of $\\dot{P}$ vs S$_\\nu\\,$d$^2$, we cannot yet properly quantify how the efficiency fraction changes with the luminosity of the protostar (e.g., if the ionization in thermal jets associated with high-mass protostars is higher than for low-mass protostars). Therefore, a uniform survey to measure the momentum rate of the molecular outflows associated with ionized jets (ideally with comparable resolutions) will be fundamental to further constrain this model. \\citet{2018A&ARv..26....3A} discussed both correlations shown in Figure~\\ref{fig:rad_bol_lum} and Figure~\\ref{fig:Curiel_plot} in great detail and they interpreted them as an indication that the mechanism of ionization, accretion and ejection of outflows associated with protostars do not depend on their luminosities.\n\n\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.5\\textwidth, clip=true, angle = 0]{Momentum_Rate_Survey}\n\\caption{\\small{Momentum rate of the molecular outflow as a function of the radio luminosity at 4.9 GHz. The red stars and octagons are our ionized jets and jet candidates listed in Table \\ref{jet_cand_list} and \\ref{candidates} towards HMCs and CMC-IRs, respectively, symbols for which there is information of the momentum rate. The momentum rate values of the molecular outflow for all the sources including our data are collected from the literature. The green circles represent ionized jets associated with low-mass protostars ($1\\,L_{\\odot} \\leq L_{bol} \\leq 1000\\,L_{\\odot}$) from \\citet{2018A&ARv..26....3A} and the yellow circles are the very low luminosity objects (VeLLOs) and low-mass protostars from \\citetalias{2011MNRAS.415..893A, 2012MNRAS.420.1019A}.\nThe purple triangles represent ionized jets from high-mass stars as found in the literature,\nfrom \\citet{2008AJ....135.2370R} and \\citet{2016A&A...585A..71M}. The $\\times$ symbols are UC and HC H{\\small II} regions from \\citet{1994ApJS...91..659K}. \nThe dashed line relation shows the positive correlation found by \\citet{1995RMxAC...1...67A} derived for jets from low-mass stars. The red dotted line is our best fit to the data including ionized jets from low, intermediate and high-mass YSOs, but excluding the sources from \\citetalias{2011MNRAS.415..893A, 2012MNRAS.420.1019A}. The gray shaded area corresponds to the momentum rate as predicted by the shock ionization model from \\citet*{1987RMxAA..14..595C} for values of the shock efficiency fraction of $\\eta = 0.1$ and $\\eta = 1.0$. \n The error bar in the middle right corresponds to a 20$\\%$ calibration uncertainty. The error in $\\dot{P}$ is not represented in the figure because it \nvaries widely, and depends strongly on how different authors have gathered the data.\n}}\n \\label{fig:Curiel_plot}\n\\end{figure}\n\n\n\n\n\n\n\n\\section{Discussion}\\label{discussion_paperII}\nResults reported in \\citetalias{2016ApJS..227...25R} of detection rates of CMC (6$\\%$), CMC-IR (53$\\%$) and HMCs (100$\\%$) provide further evidence for an evolutionary sequence in the formation of high-mass stars, from a very early stage type of cores (i.e., CMCs) to relatively more evolved ones (i.e., HMCs). The fraction of centimeter wavelength sources detected towards HMCs is higher than previously expected towards this type of cores and suggests that radio continuum may be detectable at weak levels in all HMCs. The lack of radio detections for some objects in the sample (including most CMCs) provides interesting constraints and are ideal follow up candidates for studies of the earliest stages of high-mass stars.\nIt is important to note that it is likely that the ionized material from jets or HC H{\\small II} regions associated with these type of cores remains undetected at our sensitivity, thus in order to rule out these regions as pre-stellar cores deeper observations are required or alternative tracers for ongoing star formation in these cores need to be identified.\n\nHere we consider some constraints on the nature of the centimeter continuum emission detected in these cores and clumps towards high-mass star forming regions. As described in \\S \\ref{yso}, most of our radio detections arise from high-mass YSOs and at least for 7 regions some of the radio detections could potentially arise from (solar-like mass) T-Tauri stars. Also, we detected at least 10 radio sources associated with the mm cores\/clumps with a flat spectral index, most of them resolved sources, which are most likely UC H{\\small II} regions. Understanding the nature of the rising spectral index sources has proven to be more challenging. These compact radio sources appear to be well fitted (within the uncertainties) when using either a homogeneous H{\\small II} region or a power-law fit (as shown in Figure \\ref{HII_fit_examples}). \nTherefore, we will discuss two plausible scenarios, that the radio sources are either UC\/HC H{\\small II} regions or that the emission arises from shock ionized jets. \n\n\n\\subsection{H{\\small II} regions}\n\nFor the first scenario, when fitting the sources in terms of a homogeneous H{\\small II} region, the solutions required a significantly smaller size (several of them an order of magnitude smaller) for the H{\\small II} region than the upper limit given by the FWHM synthesized beam. However, since these calculations assumed a pure hydrogen nebula, we must consider whether internal dust absorption can make the regions as small as the H{\\small II} region model is predicting. \\citet{1989ApJS...69..831W} suggested that, even if the dust absorbs 90$\\%$ of the UV photons, the radius of the H{\\small II} region is reduced by only a factor of 0.46. Thus, dust absorption alone appears insufficient to explain the small region sizes predicted by the H{\\small II} region model used to fit our data. As shown in Figure \\ref{Stromgren_sphere}, these sources could be explained as turbulence-pressured confined H{\\small II} regions if they are born in a clump with density of n$_{H_{2}}=$ 10$^{7}$\\,cm$^{-3}$ and assuming velocity dispersions of $\\sigma \\sim$ 3--8 km~s$^{-1}$ (FWHM$\\sim$7--20 km~s$^{-1}$). However, it is not clear if high velocity dispersions are common towards the dense clumps harboring high-mass stars, since $\\sigma \\sim$ 2 km s$^{-1}$ seems to be more typical. Measuring the line width of an optically thin tracer on $\\sim$100 au scale would provide a decisive constraint on the velocity dispersion. We also found that the sources could be consistent with having been born in a denser environment of $n_{H_{2}}\\approx$ 10$^{8}$\\,cm$^{-3}$. Arguably, \\citetalias{2016ApJ...818...52T} predicts that such a density for the ionized region is already too high for a protostar of 8 M$_{\\odot}$ to 24 M$_{\\odot}$. Recently, \\citet{2016A&A...585A..71M} detected compact radio sources towards high-mass YSOs with similar physical characteristics to the ones found in this survey, where the Lyman continuum derived from the bolometric luminosities always exceeds the one obtained from the radio luminosities (as seen in Figure \\ref{fig:rad_bol_lum}). From their analysis they conclude that those sources cannot be HC or UC H{\\small II} regions, unless the ionized gas has a density gradient (e.g., model IV of \\citealt{1975A&A....39..217O}). \n\n\nAdditionally, for the extended UC H{\\small II} regions detected at the outskirts of the mm cores there are two scenarios: either they were born in a low density clump of $n_{H_{2}}\\approx$ 10$^{5}$\\,cm$^{-3}$ or they were born at a higher density and have migrated out of the center potential. The latter scenario requires large stellar dispersion velocities ($\\gtrsim$10 km~s$^{-1}$), which are not typical unless the source is a runaway OB star that has been dynamically ejected. Observed stellar dispersion velocities, for instance for Orion's brightest population is only $\\lesssim$3 km~s$^{-1}$ \\citep[e.g.,][]{2005AJ....129..363S,2009ApJ...697.1103T}, which makes the former scenario more plausible. However, \\citet{2007ApJ...660.1296F} predicted that stellar velocities up to $\\lesssim$13 km~s$^{-1}$ are likely for core densities of 10$^{7}$\\,cm$^{-3}$, and that these high stellar velocities carry the star to lower density regions of the core\/clump, where the H{\\small II} region is free to expand. We are leaning to favor the scenario where the sources have migrated since it allows \nto explain the occurrence of both compact and extended emission in the same protocluster (e.g., \\citealt{2007prpl.conf..181H}). \n\\newpage \n\\subsection{Radio Jets}\n \nNow we discuss the second scenario where the radio emission of the radio compact sources with rising spectrum is due to shock ionization. \nThe observable properties of several of our radio detections indicate that they likely have a jet nature: one can argue that the low centimeter emission from the majority of the sources detected in this survey, their free-free\nspectral index being in the range $0.2 \\leq \\alpha \\leq 1.8$ and their association with molecular outflows indicate that even those sources without an elongated radio morphology are also ionized jets or stellar winds that are conical, accelerating and\/or recombining. From the analysis in \\S\\ref{compact_sources_sect} and \\S \\ref{ionized_jet_sect} we inferred that from the 44 sources with rising spectral index, approximately 12 of them are ionized jets (see Table \\ref{jet_cand_list}) and 13 are jet\/wind candidates (see Table \\ref{candidates}). \nIn fact, half of the jet candidates in Table \\ref{candidates} have a spectral index \n$\\alpha \\approx 0.6$ and all but two of them (UYSO1 A and 18521$+$0134 A) have a spectral index $\\alpha \\leq 1.0$, which is consistent with the expected value of a spherical, isothermal and constant velocity ionized wind \\citep[e.g.,][]{1975A&A....39....1P}. As stated before, the deviation from the value $\\alpha = 0.6$ could be due to acceleration or recombination within the flow. \n\n\n\n\\begin{deluxetable}{l c c c c}\n\\tabletypesize{\\scriptsize}\n \\renewcommand*{\\arraystretch}{1.5}\n\\tablecaption{Ionized Jet\/Wind Candidates \\label{candidates}}\n\\tablewidth{0pt}\n\\tablehead{\n\\colhead{Region} & \n\\colhead{Radio Source} &\n\\colhead{Outflow Direction} &\n\\colhead{ H$_{2}-$Jet Direction} &\n \\colhead{Reference} \\\\}\n\\startdata\n\\setcounter{iso}{0}\t\nUYSO1 & A & NW$-$SE & \\nodata & \\rxn \\label{rxn:Forbrich04} \\\\\n18264$-$1152 & F & NW$-$SE & E$-$W & \\rxn \\label{rxn:Sanchez-Monge2013} \\rxn \\label{rxn:Navarete2015} \\\\\n18345$-$0641 & A & NW$-$SE & very weak &\\rxn \\label{rxn:Beuther2002} \\rxn \\label{rxn:Varricat2013} \\rxn \\label{rxn:Varricat2010} \\\\\n18470$-$0044 & B & E$-$W & no\/very weak\\tablenotemark{a} & \\osref{rxn:Beuther2002} \\\\\n18517$+$0437 & A & N$-$S & very weak & \\rxn \\label{rxn:Lopez_Sep10} \\osref{rxn:Varricat2010} \\\\\n18521$+$0134 & A & \\nodata\\tablenotemark{b} & non-detection & \\rxn \\label{rxn:Cooper13} \\\\\n G35.39$-$00.33mm2 & A & \\nodata & \\nodata & \\nodata \\\\\n18553$+$0414 & A & \\nodata\\tablenotemark{c} & non-detection & \\osref{rxn:Navarete2015} \\\\\n19012$+$0536 & A & NE$-$SW & non-detection & \\osref{rxn:Beuther2002} \\osref{rxn:Navarete2015} \\\\\nG53.25$+$00.04mm2 & A & \\nodata & \\nodata & \\nodata \\\\\n 19413$+$2332 & A & \\nodata\\tablenotemark{d} & \\nodata & \\osref{rxn:Beuther2002} \\\\\n20293$+$3952 & E\\tablenotemark{e} & NE$-$SW & detection & \\rxn \\label{rxn:Beuther04} \\rxn \\label{rxn:Palau07_a} \\osref{rxn:Varricat2010} \\\\ \n 20343$+$4129 & B & E$-$W & non-detection & \\rxn \\label{rxn:Palau07} \\osref{rxn:Cooper13}\\\\ \n\\enddata\n\\tablenotetext{\\text{a}}{ T. Stanke and H. Beuther (private communication).}\n\\tablenotetext{\\text{b}}{\\citet{2002ApJ...566..931S} reports non-detection of CO (2--1) wings towards this region, although an outflow could be present at an inclination angle of $< 10^{\\circ}$ to the plane of the sky.}\n\\tablenotetext{\\text{c}}{\\citet{2002ApJ...566..931S} reports the presence of CO (2--1) wings towards this region, but contour maps of the molecular outflow are not available.}\n\\tablenotetext{\\text{d}}{CO outflow is detected in the region, but the data does not show a clear bipolar structure.}\n\n\\tablenotetext{\\text{e}}{Radio source E has an upper limit value for the flux at 4.9 GHz and its value is not included in Figures \\ref{fig:rad_bol_lum} and \\ref{fig:Tanaka_tracks} (right panel).}\n\\tablecomments{The dots indicates that there is not enough information available about observations of the molecular outflow in the literature.\\\\\n\\osref{rxn:Forbrich04} \\citet{2004ApJ...602..843F}; \\osref{rxn:Sanchez-Monge2013} \\citet{2013A\\string&A...557A..94S}; \\osref{rxn:Navarete2015} \\citet{2015MNRAS.450.4364N}; \\osref{rxn:Beuther2002} \\citet{2002A\\string&A...383..892B}; \\osref{rxn:Varricat2013} \\citet{2013A\\string&A...554A...9V}; \\osref{rxn:Varricat2010} \\citet{2010MNRAS.404..661V}; \\osref{rxn:Lopez_Sep10} \\citet{2010A\\string&A...517A..66L}; \\osref{rxn:Cooper13} \\citet{2013MNRAS.430.1125C}, \\osref{rxn:Beuther04} \\citet{2004ApJ...608..330B}; \\osref{rxn:Palau07_a} \\citet{2007A\\string&A...465..219P}; \\osref{rxn:Palau07} \\citet{2007A\\string&A...474..911P}.}\n\n\\end{deluxetable}\n\n\n\\subsection{H{\\small II} regions vs Radio Jets}\n\nIn Figure \\ref{fig:rad_bol_lum} we compared the radio luminosity with the bolometric luminosity using the radio flux at 4.9 GHz. When fitting the ionized jets (and jet candidates) from low, intermediate and high-mass protostars using a power-law (represented by the dotted red line) we find an index of 0.63$\\pm$0.04 with a correlation coefficient of $r=$0.89 which yields the relation $S_{\\nu}d^{2}$ [mJy kpc$^{-2}$]=$ 6.5 \\times 10^{-3}$ (L$_{bol}$\/L$_{\\odot}$)$^{0.63}$. This result is comparable with the index found by \\citet{2016MNRAS.460.1039P} of 0.64$\\pm$0.04 for jets spanning luminosities from $\\sim 10^{-1}$ to $10^{5}$ L$_{\\odot}$, although their fit has a lower correlation coefficient ($r=$0.73). Their estimates for bolometric luminosities, which include {\\it Herschel}\/Hi--GAL data for most of their sources, are similar to ours. Therefore, the scatter in their data may come from the radio fluxes. \\citet{2016MNRAS.460.1039P} have stated that some of their jets have high flux densities probably because those objects represent a transition between a jet and H{\\small II} region stages. \n Further, it is important to note that a similar relation between the bolometric luminosity and the luminosity of shocked H$_2$ emission from molecular jets\nhas been reported by \\citet{2015A&A...573A..82C} for sources with a wide range of bolometric luminosities. These studies together with our\nrefined relation point to a common flow mechanism from YSOs of any luminosity.\n\nUntil very recently, the stellar evolutionary models that have been used to analyze this type of sources correspond to more evolved objects (i.e., ZAMS star). However, the recently introduced \\citetalias{2016ApJ...818...52T} model predicts the ionizing luminosity of a protostar which will allow us to compare our data with a more appropriate part of the evolutionary track. These evolutionary stellar models mainly depend on the accretion history, this is the mass of the core (M$_{c}$) and the mass surface density of the ambient clump ($\\Sigma_{cl}$). Figure \\ref{fig:Tanaka_tracks} shows the same relations as those in Figs. \\ref{Lyman_cont_plot} and \\ref{fig:rad_bol_lum}, but now we also consider an evolutionary track for a YSO which is represented by the cyan continuous line for an initial core mass of M$_{c}=$ 60 M$_{\\odot}$ and a mass surface density of ambient clump of $\\Sigma_{cl}=$~1~g~cm$^{-2}$ (\\citetalias{2016ApJ...818...52T} fiducial case). This cyan track shows the evolutionary sequence of the ionizing photon luminosity as a function of the protostellar luminosity. Its shape shows each of the physical stages in the evolution of the protostar: accretion stage, swelling stage (as seen with the decrease in the ionizing luminosity as the temperature decreases), contraction stage (increase of the ionizing luminosity as the temperature also increases) and nuclear burning stage when the protostar reaches the ZAMS (represented by the black continuous line; for more discussion on this evolutionary track see \\citealt{2014ApJ...788..166Z}; \\citetalias{2016ApJ...818...52T}). The left panel of Figure \\ref{fig:Tanaka_tracks} shows that\n for the majority of the radio sources detected towards CMCs and CMC--IRs, the Lyman continuum excess (for the fiducial case L$_{bol} \\sim$10$^{2}$--10$^{3}$) is still evident and it is not likely due to photoionization. Additionally, the evolutionary track for a YSO shows how the ionizing luminosity decreases as the protostar swells while accreting its mass and before it enters the Kelvin-Helmholz contraction (for the fiducial case L$_{bol} \\sim$10$^{3}$--10$^{4}$). This further indicates that the measured radio flux for most of our radio sources detected towards HMCs are also very unlikely to be photoionized by the central object.\n \nModel calculations presented by \\citet{2002ApJ...568..754K,2003ApJ...599.1196K,2007ApJ...666..976K} predict that high accretion rates on the order of $10^{-4} - 10^{-3}$ M$_{\\odot}$yr$^{-1}$ can choke off the H{\\small II} region to very small sizes producing very low radio continuum; see also Section 5 of \\citet{1995RMxAC...1..137W}. This might be a possible scenario for some of our more compact sources, but additional evidence is necessary such as high-resolution mm observations of infall tracers to determine mass infall rates for these sources. Based on the analysis and discussion presented above, we are inclined to favor the scenario that most of our compact sources (see Table \\ref{candidates}) with rising spectrum are ionized jets. However, the confirmation of these radio sources as shocked ionized gas requires further observational and theoretical work. Additional observations and tests are necessary in order to have conclusive information of the nature of these detections. Higher resolution data ($\\lesssim$ 0\\rlap.$^{\\prime \\prime}$1) of the radio continuum is required to resolve the ionized jets and estimate their degree of collimation. Additionally, high resolution millimeter data will help us to disentangle multiple outflows, possibly being driven by protostellar clusters as expected toward high-mass star forming regions and to study the kinematics of the outflow material. Masers arise from the hot core regions and their association with ionized material is very important. They indicate the evolutionary stage \\citep[e.g.,][]{2018A&A...619A.107S} of the exciting object and allow detailed studies of the kinematics at a smaller scale, very close to the powering high-mass YSO and the disk\/jet interface. For resolved sources, long-term monitoring of the ionized jet is necessary in order to estimate proper motions,\nvelocities of the radio jets and evolution in the morphology of the jet. \n\n\n\n\\begin{figure}[htbp]\n\\centering\n\\begin{tabular}{cc}\n\\vspace{-0.7cm}\n\\includegraphics[width=0.46\\textwidth, clip=true, angle = 0]{Lyman_plot_Tanaka} &\\hspace{-1.6em} \n\\includegraphics[width=0.46\\textwidth, clip=true, angle = 0]{Tanaka_M60} \n\\end{tabular}\n\n\\caption{\\small{Lyman continuum (left) and radio luminosity (right) as a function of the bolometric luminosity. Symbols and colors are the same as used in Figures \\ref{Lyman_cont_plot} and \\ref{fig:rad_bol_lum}, except that now we also show the estimated Lyman continuum from the \\citetalias{2016ApJ...818...52T} model for an optically thin H{\\small II} region based on the ionization of a protostar (cyan continuous line). The stellar model evolution starts with a core mass of M$_{c}=$ 60 M$_{\\odot}$ and a mass surface density of ambient clump of $\\Sigma_{cl}=$~1~g~cm$^{-2}$. The black continuous line is the Lyman continuum from a ZAMS star.\n The error bars in the bottom right corner correspond to a 20$\\%$ calibration uncertainty. \n}}\n \\label{fig:Tanaka_tracks}\n\\end{figure}\n\n\n\n\n\n\n\n\n\\section{Summary and Conclusions}\\label{conclusions_paperII}\nIn this work we investigate the nature of the 70 radio sources\nreported in \\citetalias{2016ApJS..227...25R}. These radio sources were observed using the VLA at 6 and 1.3 cm towards a sample of high-mass star forming region candidates having either no previous radio continuum detection or a relatively weak detection at the 1 mJy level. We have explored several scenarios such as pressure confined H{\\small II} regions and ionized jets to explain the origin of the ionized gas emission and we have studied the physical properties of the detected sources. Based on our results we favor the scenario that $\\sim 30 - 50 \\%$ of our radio detections are ionized jets and\/or jet knots. These sources, listed in Tables \\ref{jet_cand_list} and \\ref{candidates}, have observational properties that are not expected towards regular H{\\small II} regions such as the correlation of their radio luminosity and bolometric luminosity and the correlation of the momentum rate of the molecular outflow with the radio luminosity of the ionized jet. Such correlations have been found observationally towards ionized jets associated with high-mass protostars of different luminosities and are also predicted in recent theoretical models such as the \\citetalias{2016ApJ...818...52T} model. However, for the most compact radio continuum detections we cannot rule out the scenario that they correspond to pressure confined H{\\small II} regions. Our main results from this survey are summarized below: \n\n\\begin{itemize}\n\\item We detected centimeter wavelength sources in 100$\\%$ of our HMCs, which is a higher fraction than previously expected and suggests that radio continuum may be detectable at weak levels in all HMCs. The lack of radio detections for some objects in the sample (including most CMCs) contributes evidence that these clumps are in an earlier evolutionary stage than HMCs, providing interesting constraints and ideal follow up candidates for studies of the earliest stages of high-mass stars.\n\n\\item At least 10$\\%$ of our detected radio sources are consistent with non-thermal emission and likely due to either active magnetospheres in T-Tauri stars (possibly for the few regions located at a distance $<$ 2 kpc) or synchrotron emission from fast shocks in disks or jets.\n\n\\item For the most compact radio detections, the sources are consistent with being small pressure confined H{\\small II} regions. Also, we cannot completely exclude the possibility that these sources are gravitationally trapped H{\\small II} regions. \n\n\\item The majority of our detected radio continuum sources ($\\sim$80$\\%$) have spectral indices ($-$0.1$<\\alpha<$2) that are consistent with thermal (free-free) emission from ionized gas. \n\n\\item Most of the radio sources with a rising spectrum detected towards clumps at an earlier evolutionary stage (i.e., CMCs and CMC--IRs) show Lyman continuum excess, consistent with previous results. This can be explained either by UV photons from shocks producing an ionized jet or shocks in an accretion flow onto the disk.\n\n\\item For most of the radio sources with a rising spectrum detected towards HMCs, the estimated Lyman continuum is lower than expected if the radio flux comes from a single ZAMS star. This could indicate that the origin of the measured radio flux is not from HC\/UC H{\\small II} regions but shock ionized jets.\n\n\\item We detected at least 12 ionized jets (6 of them are new detections to the\nknowledge of the authors) based on their spectral index, morphology and molecular outflow associations. For several of the previously detected jets, we detected additional knots or lobes that are part of the collimated structure. Additionally, we detected at least 13 jet\/wind candidates. \n\n\\item We found that ionized jets from low and high-mass stars are very well correlated. This is consistent with previous studies and is further evidence of a common origin for jets of any luminosity.\n\n\n\n\\end{itemize}\n\n\n\n\n\\acknowledgments\n We thank the anonymous referee, whose comments improved this manuscript.\nSupport for this work was provided by the NSF through the Grote Reber Fellowship Program administered by Associated Universities, Inc.\/National Radio Astronomy Observatory. P. H. acknowledges support from NSF grant AST--1814011. C. C-G. acknowledges support from UNAM DGAPA--PAPIIT grant number IA102816, IN10818. E. D. A. is partially supported by NSF grant AST--1814063. We thank K. E. I. Tanaka for providing the stellar model evolutionary tracks predicted by the \\citetalias{2016ApJ...818...52T} model. We thank K. E. I. Tanaka, K. Johnston and J. Marvil for useful discussions. Herschel is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA. This work is based in part on observations made with the Spitzer Space telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. P.H. acknowledges support from NSF grant AST$-$0908901 for this project. Some of the data reported here were obtained as part of the UKIRT Service Program. The United Kingdom Infrared Telescope is operated by the Joint Astronomy Centre on behalf of the UK Particle Physics and Astronomy Research Council.\nThis research made use of APLpy, an open-source plotting package for Python hosted at http:\/\/aplpy.github.com.\n\n\\vspace{5mm}\n\n\\software{CASA \\citep{2007ASPC..376..127M}, APLpy \\citep{2012ascl.soft08017R}.}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction }\nThe Batalin--Vilkovisky formalism (BV--formalism) is the most\ngeneral method of quantization\nof gauge field theories~\\cite{bat1},~\\cite{bat2}.\nIn recent years the interest\nin studying its geometrical nature has increased.\nIt was stimulated by Witten's paper \\cite{witten},\nwhere the necessity of such an investigation is pointed out and\nparticularly for the\nformulation of the background independent open-string\nfield theory on the base of the BV-formalism.\nThe realization of this program began in \\cite{witten1}, \\cite{witten2}.\n\nIt is known that the BV-formalism uses unusual structures -- odd\nPoisson brackets (antibrackets) and the operator $\\Delta$.\nAs one of the main obstacles to the construction of\na background independent open\nstring field theory, indicated by Witten \\cite{witten},\n was the nonexistence of an invariant\ndefinition of the operator $\\Delta$, and of a\nnaturally defined integral measure.\nHowever, such a definition of the operator $\\Delta$\nwas shown by one of us (O. K.) in\n\\cite{khud} before the cited Witten's paper appeared (see also \\cite{km}).\nIts realization on K\\\"ahlerian supermanifolds \\cite{ners} and its\nsimplest properties\n\\cite{knjinr} were considered.\n\nIn the present paper we study the operator $\\Delta$ in more detail.\nWe propose an invariant definition of this operator and show that\nthe condition of its nilpotency defines (in some sense) the choice of the\nintegration measure.\n\nIn Section 2 we propose an invariant definition of the operator $\\Delta$\n on supermanifolds, given by the odd symplectic structure\nand by the volume form as the divergence of the Hamiltonian vector field.\n We show that all the relations between the antibrackets and the operator\n$\\Delta$, which are satisfied for canonical ones in the BV-formalism, are\nsatisfied for\nany generalized operator $\\Delta$ and the corresponding odd brackets.\nHowever\nthe nilpotency condition for such an operator holds only for\na certain class of the integral density.\n\nIn Section 3 we consider the realization of the operator $\\Delta $ on\nsupermanifolds, given by the odd (and even) K\\\"ahlerian structure,\nand show that it is\nnilpotent, if the integral density is the\ncharacteristic class of the basic K\\\"ahlerian manifold (the function from\nChern classes). Then it corresponds to the\ndivergence operator $\\delta =* d *$\nof the basic manifold.\n\nIn Section 3 we discuss the geometrical nature of\nthe Bat\\-al\\-in-Vil\\-kov\\-isky formalism.\\\\\n\nWhen this paper was in preparation, we received two very important papers of\nA.S. Schwarz \\cite {schwarz}, \\cite{schwarz2} where the geometry of\nthe BV-formalism is analyzed in detail and in particular the same definition\nof the operator $\\Delta$, as in \\cite{khud}, \\cite{ners}, \\cite{knjinr} and\nin the present paper is given.\n \\setcounter{equation}0\n\\section{Odd Poisson Brackets and Operator $\\Delta$}\nThe odd Poisson bracket (odd bracket, antibracket, Buttin bracket)\n of the functions $f$ and $g$ on the supermanifold ${\\cal M}$ is\ndefined by the following conditions \\cite{ber}, \\cite{leit} :\n\\begin{equation}\n\\begin{array}{l}\n\\{ f, ga +hb \\}_{1} = \\{ f, g\\}_{1}a + \\{ f, h \\}_{1}b,\n \\quad {\\rm where}\\quad a,b = const \\\\[3mm]\np(\\{ f, g \\}_{1} )= p(f)+ p(g) + 1\n \\quad {\\rm (grading \\ condition)} \\label{eq:bgrad} \\\\[3mm]\n\\{ f, g \\}_1 = -(-1)^{(p(f)+1)(p(g)+1)}\\{ g, f \\}_1\n \\quad {\\rm ( \"antisymmetricity\"\\ condition )} \\label{eq:anti} \\\\[3mm]\n\\{ f, gh \\}_1 =\\{ f, g \\}_{1}h +(-1)^{(p(f)+1)p(g)} g\\{ f, h \\}_1\n \\quad {\\rm ( Leibnitz \\ rule )} \\label{eq:bLieb} \\\\[3mm]\n ( -1)^{(p(f)+1)(p(h)+1)}\\{ f,\\{ g, h \\}_{1}\\}_1\n +{\\rm {cycl. perm.}}( f,g,h) = 0\n \\quad{\\rm {(Jacobi\\ id.)}}\n\\end{array}\n\\label{eq:bjac}\n\\end{equation}\n Locally, the odd bracket can be written as:\n\\begin{equation}\n\\{ f, g \\}_1 = \\frac{\\partial ^R f}{\\partial x^A} \\Omega^{AB}\n \\frac{\\partial ^L g}{\\partial x^B}\n\\label{eq:bloc}\n\\end{equation}\nwhere $\\Omega^{AB}$ satisfies to conditions\n\\begin{eqnarray}\n& & p(\\Omega^{AB} )= p(A)+ p(B) + 1\n\\quad {\\rm (grading\\ condition)} \\nonumber \\\\[3mm]\n& & \\Omega^{AB} = -(-1)^{(p(A)+1)(p(B)+1)} \\Omega^{BA}\n\\quad {\\rm ( \"antisymmetricity\" \\ condition )} \\nonumber \\\\[3mm]\n& & ( -1)^{(p(A)+1)(p(C)+1)}\\frac{\\partial^{R}\n \\Omega^{AB}}{\\partial x^D }\\Omega^{DC}\n +{\\rm {cycl.\\ perm.}}(A,B,C ) = 0\n \\quad{\\rm {(Jacobi\\ id.)}}\n\\nonumber\n\\end{eqnarray}\nwhere\n$x^A$ are the local coordinates of ${\\cal M}$, $p_A \\equiv p( x^A ) $.\n$ \\frac{\\partial ^R }{\\partial x^A}$ and\n$\\frac{\\partial ^L}{\\partial x^A} $ denote\ncorresponding right and left derivatives.\nThey are connected with each other by:\n$$\\frac{\\partial ^R f}{\\partial x^A}=\n (-1)^{(p(f)+1)p_A}\\frac{\\partial ^l f}{\\partial x^A}. $$\nIf ${\\cal M}$ has an equal number of\neven and odd coordinates, the odd bracket\ncan be nondegenerate. Then one can associate with it the\nodd symplectic structure\n\\begin{equation}\n\\Omega =dx^A \\Omega^{AB}dx^B \\label{eq:symp}\n\\end{equation}\n where $\\Omega_{AB}\\Omega^{BC} =\\delta_{A}^{C}$.\nThis form is closed because of the Jacobi identities (\\ref{eq:bjac}).\nLocally, one can reduce (\\ref{eq:symp}) to the canonical form \\cite{leit}:\n\\begin{equation}\n\\Omega^{\\rm can} =\\sum_{i=1}^{N} dx^i \\wedge d \\theta_i\n \\label{eq:sympcan}\n\\end{equation}\nwhere $( x^i,\\theta _i) $ are some local coordinates (Darboux coordinates)\n($p( x^i )= 0, p(\\theta_i)= 1 $).\nThe corresponding odd bracket takes the form\n\\begin{equation}\n\t\t \\{f,g\\}_1 =\n\t\t\t\\sum_{i=1}^{N}\\left(\n\t\t\\frac{\\partial^{R} f}{\\partial x^i}\n\t\t\\frac{\\partial^{L} g}{\\partial\\theta_i}\n\t\t\t +\n \\frac{\\partial^{R} f}{\\partial \\theta_i}\n\t\t\\frac{\\partial^{L} g}{\\partial x^i}\n\t\t \\right) \\label{eq:bcan}\n\\end{equation}\n Transformations preserving odd symplectic structures (or odd brackets)\n have (locally) the hamiltonian form:\n \\begin{equation}\n{\\cal L}_{{\\bf V}}\\Omega = O\n\\quad {\\rm iff}\\quad {\\bf V}=\\{.,H\\}_1 \\equiv {\\bf D}_{H} \\label{ham}\n\\end{equation}\n where $ H$ is an arbitrary function (Hamiltonian) on ${\\cal M}$,\n${\\cal L}_{{\\bf V}}$ denotes the Lie derivative\nalong the vector field {\\bf V}.\n\nIt is well known that any supermanifold can be associated with some vector\n bundle \\cite{ber}. The odd symplectic structure\ncan be globally defined on the supermanifolds\n which are associated with the cotangent bundles of manifolds.\n\n Let $T^* M$ be the cotangent bundle of the manifold $M$.\n $x^{i}$ are local coordinates on $M$\n and $( x^{i}, v_{i} )$ are the corresponding\n local coordinates on $T^* M$.\n From map to map\n \\begin{equation}\nx^{i} \\rightarrow {\\tilde x^{i}} = {\\tilde x^{i}} ( x),\\quad\n v_{i} \\rightarrow {\\tilde v_{i}} = \\sum_{i=1}^N\n \\frac{\\partial x^{j}}{\\partial {\\tilde x^{i}}} v_{j}.\n \\label{eq:trans}\n\\end{equation}\n Considering for every map the superalgebra generated by\n $(x^{i}, \\theta _{i})$, where the $x^{i}$ are even and\nthe $\\theta_{i}$ are odd coordinates, transforming from map\n to map like $(x^{i}, v_{i})$ in ref{eq:trans})\n ($v\\leftrightarrow \\theta$), we come to the\n supermanifold ${\\cal M}$ which is associated with $T^*M$ in the\n coordinates $(x^{i}, \\theta_{i})$.\n\n Obviously, on this supermanifold, in the coordinates $(x^i, \\theta_{i})$,\none can globally define the\n canonical odd symplectic structure (\\ref{eq:sympcan}) \\cite{leit}.\n\n\nFor the coordinates $(x^i, \\theta_{i})$ on ${\\cal M}$,\none can admit a more general class of transformations:\n $$\nx^i\\rightarrow {\\tilde x}^i(x,\\theta) \\quad\n\t\\theta_{i}\\rightarrow {\\tilde \\theta}_{i}(x,\\theta ) $$\n which do not correspond to (\\ref{eq:trans}).\n In particular,\nif $\\theta_i \\to {\\tilde \\theta}^i = \\omega^{ij}\\theta _j $,\n where $\\omega _{ij}$ is the matrix of some nondegenerate Poisson bracket\n on $M$, then the supermanifold\n ${\\cal M}$ in the coordinates $(x^i,{\\tilde \\theta}^i )$ is\n associated with the tangent bundle $TM$ of $M$,\ne.g. ${\\tilde \\theta^i}$ transform\n under (\\ref{eq:trans}) as $dx^i$.\n\n On the supermanifolds which can be associated in some\n coordinates with the tangent or cotangent bundle the\n superstructures are evidently reduced to standard geometrical objects.\n\n Concerning the integration, the\nproperties of the odd brackets strongly\ndiffer from the properties of odd brackets \\cite {khud}, \\cite {km},\nsuch as:\\\\\n\n -- the odd bracket hasn't an invariant volume form and invariant\n integral densities;\\\\\n\n -- it has semidensities, which depend on higher order derivatives.\\\\\n\nThe first of this properties plays an essential role in\nthe Batalin--Vilkovisky quantization\nformalism.\nUsing this property, we can construct on ${\\cal M}$\nan invariant generalization of the\nimportant object of the BV-formalism -- the operator $\\Delta$.\n\nLet the supermanifold ${\\cal M}$ be\n provided with the odd symplectic structure (\\ref{eq:symp}) and\n the volume form\n\\begin{equation}\ndv=\\rho(x, \\theta)d^{N}x d^{N}\\theta. \\label{eq:vol}\n\\end{equation}\nHere $\\rho (x,\\theta)$\nis some integral density. Under coordinate transformation\n${\\tilde x}^{A} = {\\tilde x}^{A}(x)$,\nit transforms as:\n\\begin{equation}\n {\\tilde \\rho}({\\tilde x}) = \\rho (x ({\\tilde x})) {\\rm Ber}\n \\frac{\\partial^{R} x^A}{\\partial {\\tilde x}^B} \\label{eq:denstrans}\n\\end{equation}\nOn this supermanifold one can invariantly define a second order odd\ndifferential operator, which we call the \"generalized operator $ \\Delta $\",\nand which is invariant\n under the transformations preserving the symplectic structure\n and the volume form \\cite {khud}. Its action on a function $f(x,\\theta)$\n is the divergence of the Hamiltonian\n vector field ${\\bf D}_{f}$\n with the volume form $dv$:\n\\begin{equation}\n\\Delta _{\\rho} f =\\frac{1}{2}div_{\\rho} {\\bf D}_{f}\n\\equiv \\frac{1}{2}\\frac{{\\cal L}_{{\\bf D}_f} dv}{dv}, \\label{eq:delta}\n\\end{equation}\nwhere ${\\cal L}_{{\\bf D}_f}$ is the Lie derivative\nalong ${{\\bf D}_f}$ \\cite {ber}, \\cite {voronov}.\nIn coordinate form:\n\\begin{equation}\n \\Delta f=\\frac{1}{2\\rho}\n \\frac{\\partial^R}{\\partial x^A}\n\\left(\\rho\\{x^A,f\\}_1\\right) \\label{eq:deltaloc}\n\\end{equation}\n It has no analog within even symplectic structures.\nThe oddness of the Poisson bracket (\\ref{eq:bloc}) forces\n the nontrivial grading of $\\Delta $,\nand the \"antisymmetricity\" condition (\\ref{eq:anti}) forces\nits dependence on second derivatives.\n\nIf the Poisson bracket in (\\ref{eq:delta}) is canonical,\nand $\\rho =constant$,\nthe generalized operator $\\Delta$ takes the canonical form\n\\begin{equation}\n \\Delta^{\\rm can} = \\frac{\\partial^R}{\\partial x^i}\n\\frac{\\partial ^L}{\\partial \\theta_i} \\label{eq:deltacan}\n\\end{equation}\nused in the BV-formalism.\n\n From the Leibnitz rule (\\ref{eq:bLieb}) and the\ndefinition (\\ref{eq:delta}) follows\n\\begin{equation}\n(-1)^{p(g)}\\{f,g \\}_1 = \\Delta(fg) - f\\Delta g\n -(-1)^{p(g)}(\\Delta f)g \\label{eq:Liebdelta}\n\\end{equation}\n\n From the Jacobi identity (\\ref{eq:bjac}) and the\ndefinition (\\ref{eq:delta}) follows\n\\begin{equation}\n\\Delta \\{f,g \\}_1 = \\{f,\\Delta g \\}_1\n+(-1)^{p(g)+1}\\{\\Delta f ,g \\}_1 \\label{eq:jacdelta}\n\\end{equation}\nThe density transformation rule (\\ref{eq:denstrans}) implies for\nthe generalized operator $\\Delta$\nthe following transformation rule\nunder canonical transformations:\n\\begin{equation}\n \\Delta'f = \\Delta f +\\frac{1}{2}\n\\{\\log {\\cal J} ,f \\}_1 , \\label{eq:transdelta}\n\\end{equation}\nwhere ${\\cal J}$ is the Jacobian of the canonical transformation of\nthe odd bracket, $\\Delta'$ is the\ngeneralized operator $\\Delta$ in the new coordinates.\nFor example, let us demonstrate the derivation of (\\ref{eq:jacdelta}):\n\\begin{eqnarray}\n&&\\Delta \\{f,g \\}_{1}dv = {\\cal L}_{{\\bf D}_{\\{f,g \\}_1}}dv =\\nonumber\\\\[3mm]\n&&=\\left ( {\\cal L}_{{\\bf D}_f}{\\cal L}_{{\\bf D}_g} -\n(-1)^{(p(f)+1)(p(g)+1)}{\\cal L}_{{\\bf D}_g}{\\cal L}_{{\\bf D}_f} \\right )dv\n= \\nonumber\\\\[3mm]\n&&={\\cal L}_{{\\bf D}_f} {\\Delta g}dv\n-(-1)^{(p(f)+1)(p(g)+1)}{\\cal L}_{{\\bf D}_g} {\\Delta f}dv = \\nonumber\\\\[3mm]\n&& = \\left ( \\{f,\\Delta g \\}_{1}\n+(-1)^{p(g)+1}\\{\\Delta f ,g \\}_1 \\right ) dv \\nonumber\n\\end{eqnarray}\nLet us write the following useful expressions, too:\n\\begin{equation}\n \\Delta f(g)=f'(g) \\Delta g + \\frac{1}{2}f''(g)\n\\{g,g \\}_1, \\label{eq:difdelta}\n\\end{equation}\n where $f(g)$ is an even complete function, and $g$ is\nan even function on ${\\cal M}$.\n\nThe properties (\\ref{eq:Liebdelta}) -\n(\\ref{eq:difdelta}) are satisfied for any\n$\\rho$ and in the same manner as\nthe relations between canonical Poisson brackets\n(\\ref{eq:bcan}) and (\\ref{eq:deltacan}) in the\nBV-formalism \\cite{bat2}, \\cite{witten}.\nThis can br derived in the same way as (\\ref{eq:jacdelta}).\n\nHowever (\\ref{eq:deltacan}) satisfies the\n nilpotency condition\n\\begin{equation}\n\t \\Delta^2=0 \\label{eq:nilp}\n\\end{equation}\nwhich is very important in the BV-formalism.\n\nThe latter condition is violated for arbitrary $\\rho (x,\\theta)$.\n Indeed, if we have two densities $\\rho$ and ${\\tilde \\rho}$,\nand ${\\tilde \\rho} = \\lambda\\rho,\\ p(\\lambda )=0$,\nthen the corresponding operators $\\Delta$ are related by\n\\begin{equation}\n \\Delta_{\\tilde \\rho} f =\\Delta_{\\rho} f +\n\\frac{1}{2} \\{\\log \\lambda, f\\}_{1} \\label{eq:deltacon}\n\\end{equation}\nIt is easy to see that\n \\begin{equation}\n \\Delta^{2}_{\\tilde \\rho} f =\\Delta^{2}_{\\rho} f\n+ \\{ \\Gamma_{\\lambda}, f \\}_{1} , \\label{eq:deltasqcon}\n\\end{equation}\nwhere\n\\begin{equation}\n \\Gamma_{\\lambda}\n=\\lambda^{-\\frac{1}{2}}\\Delta_{\\rho} \\lambda^{\\frac{1}{2}},\n\\quad p(\\Gamma_{\\lambda}) = 1\n\\end{equation}\nIf for some $\\Delta_{\\rho}$ the nilpotency condition\n(\\ref{eq:nilp}) is satisfied,\nthen it is also satisfied for $\\Delta_{\\tilde \\rho}$ (\\ref{eq:deltacon})\nif $ \\Gamma_{\\lambda} =$odd constant$=0$.\n\nFor example, if the symplectic structure is canonical,\n(\\ref{eq:nilp}) holds if\n$\\rho (x,\\theta)$ satisfies the equation\n\\begin{equation}\n\\Delta^{{\\rm can}} \\sqrt \\rho =0.\\label{eq:sqrt}\n\\end{equation}\nBut this is the master equation of the\nBV-formalism for the action $ S= -i\\frac{1}{2}\\log\\rho $.\n\n The geometrical meaning of the\nnilpotency condition (and correspondingly of the master equation)\nwill be illustrated on a simple example\nin the next Section.\n\\setcounter{equation}0\n\\section{Example: The operator $\\Delta$ on K\\\"ahlerian Supermanifolds}\n\nAs we saw in the previous section, in contrary to the case of an even\nsymplectic structure, on supermanifolds with an odd symplectic\nstricture there arises\na nontrivial differential geometry.\n\n It is sufficient to show\nthe correspondence between the generalized operator\n$ \\Delta$ and geometrical objects on basic manifolds in the case of a\nK\\\"ahlerian basic manifold,\nbecause\non K\\\"ahlerian manifolds the symplectic structure corresponds to a\nRiemannian one,\nand a Riemmannian structure has a rich differential geometry.\nMoreover, in this case there also exists\n on ${\\cal M}$ an even K\\\"ahlerian\nstructure, and, using it, we can construct a natural\nintegral density \\cite {ners}, \\cite{knjinr}.\n\nLet ${\\cal M}$ be a\ncomplex supermanifold, and $z^A$ local complex coordinates\non ${\\cal M}$.\nA symplectic structure $\\Omega^{\\kappa}$ -- here and further\n$\\kappa =0(1)$ if\nthe symplectic structure is even (odd) -- on ${\\cal M}$ is\ncalled K\\\"ahlerian,\nif in local coordinates $z^{A}$ it takes the following form:\n\\begin{equation}\n\\Omega^{\\kappa}=i(-1)^{p(A)(p(B)+\\kappa+1)}g^\\kappa_{A {\\bar B}}\n dz^A \\wedge d{\\bar z}^B,\n\\end{equation}\n where\n$$ g^\\kappa_{A {\\bar B}} =\n (-1)^{(p(A)+\\kappa+1)(p(B)+\\kappa+1)+\\kappa +1}\n \\overline {g^\\kappa _{B {\\bar A}}},\n \\quad p(g^\\kappa _{A\\bar B})=p_A +p_B+\\kappa$$\n\nThen there exists a local real even (odd) function\n$K^\\kappa(z,{\\bar z})$\n (K\\\"ahlerian potential), such that\n\\begin{equation}\n\t g^\\kappa_{A {\\bar B}} =\n \\frac{\\partial ^L}{\\partial z^A}\n\t\t\\frac{\\partial ^R}{\\partial {\\bar z}^B}\n\t\t\t K^\\kappa (z,{\\bar z})\n\\end{equation}\n To $\\Omega^{\\kappa}$ there corresponds\n the Poisson bracket\n \\begin{equation}\n\t\t \\{ f,g\\}_\\kappa\n =\n\t\t i\\left(\n\t \\frac{\\partial ^R f}{\\partial \\bar z^A}\n\t\t\t g^{{\\bar A}B}_\\kappa\n\t \\frac{\\partial ^L g}{\\partial z^B}\n\t\t -\n (-1)^{(p(A)+\\kappa)(p(B)+\\kappa)}\n \t\t \\frac{\\partial ^R f}{\\partial z^A}\n\t\t g^{{\\bar A}B}_\\kappa\n\t\t \\frac{\\partial ^L g }{\\partial \\bar z^B}\n\t\t \\right),\n\\end{equation}\n where\n$$g^{{\\bar A}B}_\\kappa g_{B{\\bar C}}^\\kappa=\n \\delta^{\\bar A}_{\\bar C} \\;\\;,\\;\\;\\;\\; \\overline{g^{{\\bar A}B}_\\kappa}\n = (-1)^{(p(A)+\\kappa)(p(B)+\\kappa)}g^{{\\bar B}A}_\\kappa.$$\n Its satisfies the conditions of reality and \"antisymmetricity\"\n \\begin{equation}\n \\overline{\\{ f, g\\}_\\kappa }\n =\\{\\bar f,\\bar g \\}_\\kappa,\\;\\;\\;\\{ f, g \\}_\\kappa = -\n(-1)^{(p(f)+\\kappa)(p(g)+\\kappa)}\\{ g, f \\}_\\kappa,\n\\end{equation}\nand the Jacobi identities :\n\\begin{equation}\n( -1)^{(p(f)+\\kappa)(p(h)+\\kappa)}\n\\{ f,\\{ g, h \\}_{\\kappa}\\}_\\kappa +{\\rm {cycl. perm.}}(f,g,h) = 0\n\\end{equation}\nLet ${\\cal M}$ be associated with the tangent bundle $TM$ of the\nK\\\"ahlerian manifold $M$,\nand $z^A = (w^{a}, \\theta^a )$ local coordinates on it,\n$\\theta^a$ transforming from map to map\nlike $dw^a$.\nLet\n\\begin{equation}\ng_{a{\\bar b}}(w,{\\bar w})\n =\\frac{\\partial^{2} K(w,{\\bar w})}\n{{\\partial \\omega^{a}}{\\partial {\\bar\\omega^b}}}\n\\end{equation}\nbe a K\\\"ahlerian metric on $M$, with $K$ its K\\\"ahlerian\npotential \\cite{kobnom}.\nThen the local functions\n\\begin{eqnarray}\nK_0(w,{\\bar w},\\sigma,{\\bar \\sigma})&=& K(w,{\\bar w})+\nig_{a{\\bar b}}(w,{\\bar w})\\sigma^a{\\bar \\sigma}^b,\n\\quad p( K_0 )=0 \\label{eq:evenpot}\\\\[3mm]\n K_{1}(w,{\\bar w},\\sigma,{\\bar \\sigma})&=&\n \\epsilon\\frac{\\partial K(w,{\\bar w})}{\\partial w^a}\\sigma^a+\n {\\bar \\epsilon}\\frac{\\partial K(w,{\\bar w})}\n {\\partial {\\bar w^a}}{\\bar \\sigma^a} \\quad p( K_1 )=1\n \\label{eq:oddpot}\n \\end{eqnarray}\n(where $\\epsilon$ is\nan arbitrary complex constant) correctly define an even and an\nodd symplectic structures on ${\\cal M}$\n(this is not the most general form of K\\\"ahlerian potentials\non such supermanifolds \\cite {ners})\n\nThe odd K\\\"ahlerian potential (\\ref{eq:oddpot})\ndefines on ${\\cal M}$ the following odd bracket:\n\\begin{equation}\n \\{ f,g\\}_1 = \\frac{i}{\\epsilon}\\left (\n\t \\frac{\\partial ^R f}{\\partial \\theta^a} \\nabla^{a}g -\n \\nabla^{a}f \\frac{\\partial^L g }{\\partial\n\\theta^a } \\right ) +{\\rm c.c} \\label {eq:oddbk}\n\\end{equation}\nwhere\n\\begin{equation}\n{\\overline {\\nabla^a}}=g^{{\\bar a}b}\\nabla_b \\quad \\nabla_a=\n\\frac{\\partial}{\\partial w^a} -\n \\Gamma^c_{ab}\\theta^b\\frac{\\partial^L}{\\partial\\theta^c},\n\\end{equation}\nand $\\Gamma^c_{ab}=g^{\\bar d c}g_{a \\bar d,b} $ are\nthe Christoffel symbols of the K\\\"ahlerian metric on $M$.\n\nIt is easy to see, that in the coordinates\n$(w^a, \\theta_a =i g_{a\\bar b}{\\bar \\sigma}^b )$,\nin which ${\\cal M}$ is associated with $ T^* M$, the\nodd Poisson bracket takes the canonical form.\n\nThe generalized operator $\\Delta$ corresponding to (\\ref{eq:oddbk}) takes the\nform\n \\begin{equation}\n \\Delta f = \\left(\\frac{1}{\\epsilon}\\nabla^a\n\\frac{\\partial^L}{\\partial\\theta^a} +\n\t\t\\frac{1}{\\bar \\epsilon}\n{\\overline {\\nabla^a}}\\frac{\\partial^L}{\\partial{\\bar \\theta}^a}\\right)f +\n \\frac{1}{2}\\{\\log \\rho,f \\}_{1}\n\\end{equation}\nIf $\\nabla_{a} \\rho =0$ (or, in fact, if $\\rho$ is a characteristic class\nof $M$) then\n \\begin{equation}\n\t \\Delta f = \\frac{1}{\\sqrt \\rho}\n \\left(\\frac{1}{\\epsilon}\\nabla^a \\frac{\\partial^L}{\\partial\\theta^a} +\n \\frac{1}{\\bar \\epsilon} {\\overline {\\nabla^a}}\\frac{\\partial^L}\n {\\partial{\\bar \\theta}^a}\\right)(\\sqrt\\rho f), \\label{eq:deltan}\n\\end{equation}\nis obviously nilpotent.\n\nThe invariant density which corresponds to (\\ref{eq:evenpot}) is\n \\begin{equation}\n\t \\rho= det(\\delta^a_b+i\n\t R^a_{bc{\\bar d}}\\theta^c \\bar\\theta^d)\n\\end{equation}\nwhere $ R^a_{bc\\bar d}=(\\Gamma^a_{bc})_{,\\bar d}$ is\nthe curvature tensor on $M$.\nIt is associated with the\ngenerating functions of the Chern classes of the underlying\n K\\\"ahlerian manifold \\cite{kobnom}.\n\nObviously (\\ref {eq:deltan}) corresponds to the operator\nof covariant divergence $\\delta = \\ast d \\ast$ on $M$ with some effective\nweight.\n\n \\setcounter{equation}0\n\\section { Discussion}\n\nAs we have seen in the previous Sections, the operator $\\Delta$ has\na simple geometrical\nnature on the supermanifolds associated with the\ncotangent bundles of manifolds.\nObviously, the same construction holds, if we replace the basic\nmanifold $M$ in the previous Sections by some\nsupermanifold ${\\cal M}_{0}$. Then if $x^i$ are local coordinates on\n${\\cal M}_{0}$ ($p( x^i)\\neq 0$), then, on the supermanifold ${\\cal M}$ which\nis associated with the cotangent bundle $T^*{\\cal M}_{0}$,\none can naturally define\nthe odd Poisson bracket (\\ref{eq:bcan})\n where $\\theta_i$ corresponds to coordinates of the bundle, $p(\\theta_i) =\n p( x^i ) +1$.\nThese coordinates are the analogs of the antifields of the BV-formalism.\n\nBut what is the reason for the introduction of antifields\n(and, correspondingly,\nfor the structure of supermanifolds with an odd symplectic structure)\n in the BV-formalism ?\n\nIn our opinion, this is connected to the\npeculiarity of the integration on supermanifolds.\nIndeed, if we have some differential form $\\omega ( x^i, dx^i )$\non the supermanifold ${\\cal M}_{0}$, its integral over ${\\cal M}_{0}$\ndefined in the following way \\cite{bernleit}, \\cite{voronov}:\n\\begin{equation}\n\\int_{{\\cal M}_{0}}\\omega \\equiv \\int_{\\hat{\\cal M}_{0}}\n\\omega (x^i, \\theta^i ) D(x,\\theta)[dxd\\theta] \\label{eq:blint}\n\\end{equation}\nwhere $ p(\\theta^{i})=p(x^{i}) + 1$, and $\\hat{\\cal M}_{0}$\ndenotes the supermanifold\nassociated with the tangent bundle of (supermanifold) ${\\cal M}_{0}$\n(i.e. $\\theta^{i}$ transforms\nlike $dx^{i}$), then $D(x,\\theta)$ is\nthe natural density on $\\hat{\\cal M}$ \\cite{bernleit2}.\n\nTransiting from the description on $\\hat{\\cal M}_{0}$ to that on\n${\\cal M} ={\\cal T}_{*}{\\cal M}_{0}$ --\nthe supermanifold associated with the cotangent bundle of\nthe supermanifold ${\\cal M}_{0}$ -- we saw that\nthe integral (4.2) takes the form of the partition function of the\nBV-formalism.\n\nCorrespondingly, the master-equation of the BV-formalism\ncorresponds to the closeness of the\ninitial differential form. This is clearly seen in the case where\nthe basic manifold,\nis K\\\"ahlerian (in the general case this proposition was\nstrongly proved in \\cite{schwarz}.\nThen the gauge invariance of the partition function in the BV-formalism\nfollows from Stokes' theorem \\cite{bernleit2}, \\cite{voronov}.\n\n\\section {Acknowledgments}\nWe are very indebted to I. A. Batalin for valuable discussions.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\\label{intro}\n\nRecent discovery of charge density waves (CDW) in hole-doped cuprates has raised a new wave of interest to the physics of these materials. The CDWs have been detected in underdoped samples of YBCO \\cit\n{y123REXS-3,y123REXS-4,y123REXS-5,y123REXS-6,y123XRD-3,y123XRD-4\n, Bi-2201 \\cite{bi2201STM-1,bi2201STM-2}, Bi-2212 \\cit\n{Bi2212REXS-2,Bi2212STM-2,Bi2212STM-3}\nand Hg-1201 \\cite{HgREXS,HgXRD} by direct methods such as resonant X-ray\nscattering, hard X-ray diffraction and scanning tunneling microscopy(STM).\nA wide variety of techniques, that can be sensitive to a CDW indirectly, also confirm its presence, among them transport measurements \\cite{y123Transp,HgTransp}, nuclear magnetic resonance \\cite{y123NMR-2}, ultrasound propagation \\cite{y123US} and pump-probe experiments \\cite{y123Refl}.\n\n\nSeveral common properties of this CDW state in hole-doped cuprates have been identified. The transition temperature $T_{CDW}$ is higher than $T_c$ but lower or equal to the pseudogap temperature $T^*$. The temperature and magnetic filed dependence of the CDW amplitude({\\it e.g.} \\cite{y123XRD-3}) is consistent with the CDW state competing with the superconductivity.\n\nThe CDW wave vectors seen in the experiments \\cite{y123REXS-5,y123XRD-3,HgXRD,Bi2212STM-0} are directed along the $Cu-O-Cu$ bonds of the $CuO_2$ plane (axes of the Brillouin zone, axial CDW). The CDW period is approximately equal along both the axes and increases with doping \\cite{y123XRD-3,bi2201STM-1,Bi2212REXS-2}.\n\n\nRecent studies have also revealed important information about the distribution of the modulated charge inside the unit cell, {\\it i.e.} the CDW form factor. It has been found for Bi-2212 \\cite{Bi2212STM-2} and YBCO \\cite{y123REXS-5} that\nthe charge is modulated approximately in antiphase at two oxygen sites of the unit cell with the charge at $Cu$ site being constant. In other words, the CDW form factor is characterized by a dominant d- component. The properties mentioned up to now are quite different from the stripe state of the La-based compounds \\cite{StripeRev,y123REXS-6}.\n\n\nConsiderable attention has been also drawn to the nanoscale structure of the CDW. Quantum resistance oscillation experiments \\cite{y123QO-1,y123QO-2,HgQO} have been interpreted \\cite{seb1} as being due to a checkerboard modulation, where CDWs with two orientations uniformly coexist throughout the sample. Results of studies \\cite{Bi2212STM-0,y123REXS-4} suggest, however, show that the charge ordered state consists of domains where CDW is unidirectional.\n\n\nThere have been a number of attempts to obtain the CDW state with the properties discussed above from microscopic calculations. In a model of fermions interacting with antiferromagnetic critical spin fluctuations \\cite{SFREV} (spin fermion (SF) model) a charge order appears in perturbation theory as a subleading instability \\cite{MetSach} hindered by the curvature of the Fermi surface. This order is a checkerboard CDW with d-form factor and wave vectors directed along the diagonals of the BZ \\cite{Efetov2013,Sach2013}. The nearest-neighbor Coulomb interaction can, in principle, make this state leading as has been shown in Ref. \\cite{SachSau}. Moreover, thermal fluctuations between this charge order and SC have been shown to be able to destroy both the orders while pertaining a single-particle gap \\cite{Efetov2013}, which can explain the pseudogap phase. Qualitative aspects of CDW-SC competition are also well-captured in the SF model: moderate magnetic fields suppressing the superconductivity have been shown to favor CDW \\cite{MEPE} resembling the experiment \\cite{y123US}. The vortex cores in the SC state have been shown to contain CDW \\cite{EMPE} which is seen in STM \\cite{hoffman,hamidian}. The diagonal direction of modulation wavevectors contrasting the experiments, however, has proved to be quite robust.\n\n\nSome proposals have been put forward to overcome this contradiction. A CDW with the correct wavevector direction has been\nobtained in Refs. \\cite{cascade,WangChub2014}, however the form factor has been found to lack the dominant d-symmetry with a large s- component. In Ref. \\cite{pepin} a mixture of the states proposed in Ref. \\cite{Efetov2013} and Ref. \\cite{WangChub2014} has been considered, which should contain either the diagonal modulation or an axial CDW with a non d-form factor. CDW considerations using other models \\cite{Punk2015,DavisDHLee2013,Kampf2013,yamakawa2015,Kampf2014,ChowdSach2014,ThomSach}also do not seem to explain the robustness of the axial d-form factor CDW in the cuprates.\n\nIn this contribution we review the treatment of the SF model allowing the neighboring hot spots to overlap, such that eight hot spots merge into two hot regions entirely covering the antinodal portions of the Fermi\nsurface. This corresponds to sufficiently small values $|\\varepsilon\n(\\pi ,0)-E_{F}|\\lesssim \\Gamma $, where $E_{F}$ is the Fermi energy, $\\varepsilon \\left( \\pi ,0\\right) $ is the energy in the middle of the Brillouin zone edge, and $\\Gamma $ is a characteristic energy of the\nfermion-fermion interaction due to the antiferromagnetic fluctuations.\nConsideration of this limit is motivated by ARPES data \\cite{Bi2201ARPES-1,Bi2201ARPES-2,Bi2212ARPES-1,Bi2212ARPES-2} showing that the energy separation between the hot spots and $(\\pi ,0);(0,\\pi )$ is actually\nquite small. In addition to the electron-electron interaction via\nparamagnons, we consider also the effects of low-energy (low-momentum) part of\nthe Coulomb interaction, which should not contradict the philosophy of the\nlow energy SF model. A detailed derivation and discussion of the results can\nbe found in our paper \\cite{preprint}.\n\n\\section{Model and main equations}\n\\label{sec1}\n\nWe consider a single band of fermions interacting through critical antiferromagnetic (AF) fluctuations (paramagnons) represented by a spinful bosonic field as well as the Coulomb force. As the AF fluctuations peak at momentum transfer $(\\pi,\\pi)$ we restrict our model to two regions of the Fermi surface connected with this vector represented in Fig. \\ref{fig1}. Inside these regions we do not specify individual hot spots, {\\it i.e.} points on the FS connected by $(\\pi,\\pi)$ as we assume the interaction to be important in all the whole region. This assumption is supported by ARPES experiments \\cite{Bi2201ARPES-1,Bi2201ARPES-2,Bi2212ARPES-1,Bi2212ARPES-2} showing that $|\\varepsilon (\\pi ,0)-E_{F}|$ is actually smaller than the pseudogap energy, which can be taken as the interaction scale.\n\\begin{figure}[tbp]\n\\includegraphics[width=0.5\\linewidth]{1.eps}\n\\centering\n\\caption{A typical cuprate Fermi Surface with two regions connected\nby the antiferromagnetic wavevector $(\\pi,\\pi)$ }\n\\label{fig1}\n\\end{figure}\n\nThe fermion-paramagnon part of the Lagrangian takes the form:\n\\begin{equation}\n\\begin{split}\nL_{\\mathrm{SF}} =\\sum_{\\mathbf{p,}\\nu =1,2} \\chi\n_{\\mathbf{p}}^{\\nu \\dagger } \\left[\\partial _{\\tau }+\\varepsilon _{\\nu}\\left(\\mathbf{p}\\right) -\\mu_0 \\right] \\chi _{\\mathbf{p}}^{\\nu }+\n\\\\\n+\\sum_{q}\\vec{\\varphi}_{-\\mathbf{q}}(-v_{s}^{-2}\\partial _{\\tau }^{2}+\n\\mathbf{q}^{2}+\\xi^{-2})\\vec{\\varphi}_{\\mathbf{q}} \\\\ +\\lambda\n^{2}\\sum_{\\mathbf{p,q}}\\left[ \\chi _{\\mathbf{p+q}}^{1\\dagger }\n\\vec{\\varphi}_{\\mathbf{q}}\\vec{\\sigma}\\chi _{\\mathbf{p}}^{2}+\\chi _{\\mathbf{\np+q}}^{2\\dagger }\\vec{\\varphi}_{\\mathbf{q}}\\vec{\\sigma}\\chi\n_{\\mathbf{p}}^{1} \\right].\n\\end{split}\n\\label{sf_h}\n\\end{equation}\nwhere $\\varepsilon _{\\nu}(\\mathbf{p})$ is the electron dispersion in region $\\nu=1,\\;2$ (including the chemical potential), $v_s$ is the velocity of spin waves and $\\xi$ is the magnetic correlation length. We shall not write explicitly the terms corresponding the Coulomb interaction as we will take their effect into account qualitatively.\n\nAssuming that the regions 1 and 2 occupy a small portion of the BZ we expand $\\varepsilon _{1(2)}(\\mathbf{p})$ around $[\\pi,0]([0,\\pi])$ resulting in $\\varepsilon _{p}^{1}=\\alpha p_{x}^{2}-\\beta p_{y}^{2}-\\mu_0,\\;\\varepsilon_{p}^{2}=\\alpha p_{y}^{2}-\\beta p_{x}^{2}-\\mu_0$, where $\\mu _{0}$ is the chemical potential counted from $\\varepsilon (\\pi ,0)=\\varepsilon (0,\\pi )$. Moreover, we will average the curvature term (the one with $\\beta$) inside each region leading to the final form:\n\\begin{equation}\n\\varepsilon _{p}^{1}=\\alpha p_{x}^{2}-\\mu ,\\quad \\varepsilon _{p}^{2}=\\alpha\np_{y}^{2}-\\mu , \\label{mod_disp}\n\\end{equation}\nwhere $\\mu =\\mu _{0}+\\langle \\beta p_{\\parallel }^{2}\\rangle $. To study particle-hole instabilities we define the order parameter:\n\\begin{equation}\nW_{\\mathbf{Q}}(\\tau-\\tau',\\mathbf{k})=\\langle \\chi_{\\mathbf{k}-\\mathbf{Q}\/2,\\sigma}^{\\dagger}(\\tau')\n\\chi_{\\mathbf{k}+\\mathbf{Q}\/2,\\sigma }(\\tau)\\rangle \\label{mod_op}\n\\end{equation}\nAs has been shown in \\cite{preprint} this order parameter is related to density modulations at the three atoms of the unit cell in the following way:\n\\begin{equation}\n\\begin{split}\n\\delta n_{Cu}(\\mathbf{r}) =2e^{i\\mathbf{Q}\\mathbf{r}}\\sum_{\\mathbf{k}}W_{\\mathbf{Q}}(0,\\mathbf{k})+c.c, \\\\\n\\delta n_{O_{x}}(\\mathbf{r}) =\\frac{p}{4}e^{i\\mathbf{Q}\\mathbf{r}}\\sum_{\\mathbf{k}}\\cos(k_{x}a_{0})W_{\\mathbf{Q}}(0,\\mathbf{k})+c.c., \\\\\n\\delta n_{O_{y}}(\\mathbf{r}) =\\frac{p}{4}e^{i\\mathbf{Q}\\mathbf{r}}\\sum_\n\\mathbf{k}}\\cos (k_{y}a_{0})W_{\\mathbf{Q}}(0,\\mathbf{k})+c.c.\n\\end{split}\n\\label{mod_dens}\n\\end{equation}\nAs both the regions we consider yield approximately $\\cos (k_{x}a_{0})+\\cos (k_{y}a_{0})\\approx 0$ we have $\\delta n_{O_{x}}(\\mathbf{r}) + \\delta n_{O_{y}}(\\mathbf{r})\\approx 0$ in or model, {i.e.} charge is modulated in antiphase at the two oxygen sites of the unit cell.\n\n\nNow we can discuss the qualitative effects of the Coulomb interaction in the $CuO_2$ plane. The strong on-site repulsion prohibits any real charge modulations on the $Cu$ sites leading to the constraint: $\\delta n_{Cu}=0$ for the order parameter. Together with $\\delta n_{O_{x}}(\\mathbf{r}) + \\delta n_{O_{y}}(\\mathbf{r})\\approx 0$ discussed above this leads to the conclusion that the charge modulations obtained in our model will have the d-form factor in accord with the experiments \\cite{Bi2212STM-2,y123REXS-5}.\n\nThe nearest-neighbor Coulomb interaction has been shown in \\cite{SachSau} to suppress superconductivity and support charge ordering, explaining $T_{CDW}>T_c$. This allows one to consider the particle-hole channel of the model separately from the particle-particle one.\n\n\n\\section{Pomeranchuk instability and intra-cell charge modulation.}\nOur main finding is that for sufficiently small $\\mu$ the leading particle-hole instability is the one with ${\\bf Q}=0$. The ordered state is then characterized not by a CDW, but rather a deformation of the FS (this type of transition is known as Pomeranchuk instability \\cite{pomeranchuk}, \\cite{yamase2005}). Moreover, it follows from (\\ref{mod_dens}) that such a deformation leads to a redistribution of charge between the oxygen sites of the unit cell (see Fig.\\ref{figpom}).\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.5\\linewidth]{6.eps}\n\\caption{Pictorial representation of two possible shapes of the Fermi\nsurface below the Pomeranchuk transition and the corresponding\nintra-unit-cell charge redistributions. Grey arrows mark the emergent nesting vectors for regions $1$ and $2$.}\n\\label{figpom}\n\\end{figure}\n\nOne can obtain this result analytically for a simplified BCS-like model where the paramagnon propagator is replaced by a constant. For that case a mean-field analysis yields that if $\\mu\/T_{Pom}\\leq1.1$ then it is the leading instability. $T_{Pom}$ is given in this case by $\\frac{1}{2\\alpha }\\left( \\frac{\\lambda _{0}\\Lambda }{4\\pi ^{2}}\\right) ^{2}$ where $\\lambda_0$ is the dimensionless coupling constant and $\\Lambda$ is the size of a single region in the momentum space. This expression contrasts the usual exponential dependence obtained in BCS-like theories. A detailed account on this simplified case is presented in \\cite{preprint}.\n\n\nNow let us turn to the model presented here. As a starting approximation we will use the self-consistent equations represented by diagrams in Fig. \\ref{figFeyn}\n\\begin{figure}[h]\n\\includegraphics[trim = 100 150 0 0,width=0.8\\linewidth]{7.eps}\n\\caption{Feynman diagrams for fermionic and bosonic propagators illustrating\nthe approximations used.}\n\\label{figFeyn}\n\\end{figure}\nThe integral over momentum in the fermionic self-energy can be greatly simplified provided $\\mu v_{s}^{2}\/\\alpha \\ll (v_{s}\/\\xi )^{2}$, i.e. that the correlation length is not too large. Then the self-energy and polarization operator do not depend on the momentum. To analyze the FS deformation we distinguish the 'even' $(\\Sigma_1+\\Sigma_2)\/2\\equiv i\\varepsilon _{n}-if(\\varepsilon _{n})$ and 'odd' $(\\Sigma_1-\\Sigma_2)\/2\\equiv P$ contributions to self-energy, with the latter being zero in the normal state. After the momentum integration one can introduce an energy scale $\\Gamma =\\left( \\frac{\\lambda ^{2}v_{s}}{\\sqrt{\\alpha }\\hbar ^{2}}\\right)^{2\/3}$ and write the self-consistency equations in the dimensionless form (see Eq. \\ref{sf_Pom}), where $\\bar{a}$ denotes $\\overline{(v_s\/\\xi)}$.\n\\begin{figure*}\n\\begin{centering}\n\\begin{equation}\n\\begin{gathered}\n\\bar{f}(\\bar{\\varepsilon}_{n})-\\bar{\\varepsilon}_{n}=\n0.75\\bar{T}\\sum_{\\bar{\\varepsilon}_{n}^{\\prime }}\n\\frac{1}{\\sqrt{\\bar{\\Omega}(\\bar{\\varepsilon}_{n}-\\bar{\\varepsilon}_{n}^{\\prime})+\\bar{a}}}\n\\frac{\\mathrm{sgn}(\\mathrm{Re}[f(\\bar{\\varepsilon}_{n}^{\\prime })])}{2}\n\\left[\n\\frac{1}{\\sqrt{i\\bar{f}(\\bar{\\varepsilon}_{n}^{\\prime })+\\bar{\\mu}+\\bar{P}(\\bar{\\varepsilon}_{n}^{\\prime })}}\n\\frac{1}{\\sqrt{i\\bar{f}(\\bar{\\varepsilon}_{n}^{\\prime })+\\bar{\\mu}-\\bar{P}(\\bar\n\\varepsilon}_{n}^{\\prime })}}\n\\right]\n\\\\\n\\bar{P}(\\bar{\\varepsilon}_{n})=i\\cdot 0.75\\bar{T}\\sum_{\\bar{\\varepsilon\n_{n}^{\\prime }}\\frac{1}{\\sqrt{\\bar{\\Omega}(\\bar{\\varepsilon}_{n}-\\bar\n\\varepsilon}_{n}^{\\prime })+\\bar{a}}}\\frac{\\mathrm{sgn}(\\mathrm{Re}[f(\\bar\n\\varepsilon}_{n}^{\\prime })])}{2}\\left[ \\frac{1}{\\sqrt{i\\bar{f}(\\bar\n\\varepsilon}_{n}^{\\prime })+\\bar{\\mu}-\\bar{P}(\\bar{\\varepsilon}_{n}^{\\prime })}}\n\\frac{1}{\\sqrt{i\\bar{f}(\\bar{\\varepsilon}_{n}^{\\prime })+\\bar{\\mu}+\\bar{P}(\\bar\n\\varepsilon}_{n}^{\\prime })}}\\right]\n\\\\\n\\bar{\\Omega}(\\bar{\\omega}_{n})-\\bar{\\omega}_{n}^{2}=-\\frac{\\bar{T}}{2}\\sqrt{\\frac{v_{s}^{2}\/\\alpha }{\\Gamma }}\n\\sum_{\\bar{\\varepsilon}_{n}}\n\\left[\n\\frac\n{\\mathrm{sgn}(\\mathrm{Re}[f(\\varepsilon_{n})])}\n{\\sqrt{i\\bar{f}(\\bar{\\varepsilon}_n)+\\bar{\\mu}+\\bar{P}(\\bar{\\varepsilon}_n)}}\n\\frac\n{\\mathrm{sgn}(\\mathrm{Re}[f(\\varepsilon _{n}+\\omega _{n})])}\n{\\sqrt{i\\bar{f}(\\bar{\\varepsilon}_{n}+\\omega _{n})+\\bar{\\mu}-\\bar{P}(\\bar{\\varepsilon}_{n}+\\omega _{n})}}\n+\n\\right.\n\\\\\n\\left.\n\\frac\n{\\mathrm{sgn}(\\mathrm{Re}[f(\\varepsilon_{n})])}\n{\\sqrt{i\\bar{f}(\\bar{\\varepsilon}_{n})+\\bar{\\mu}-\\bar{P}(\\bar{\\varepsilon}_n)}}\n\\frac\n{\\mathrm{sgn}(\\mathrm{Re}[f(\\varepsilon _{n}+\\omega _{n})])}\n{\\sqrt{i\\bar{f}(\\bar{\\varepsilon}_{n}+\\omega _{n})+\\bar{\\mu}+\\bar{P}(\\bar{\\varepsilon}_{n}+\\omega _{n})}}\n\\right]\n.\n\\end{gathered}\n\\label{sf_Pom}\n\\end{equation}\n\\end{centering}\n\\end{figure*}\nNote that the polarization operator $\\bar{\\Omega}(\\bar{\\omega}_{n})-\\bar{\\omega}_{n}^{2}$ contains a factor $\\sqrt{v_{s}^{2}\/\\alpha\\Gamma}$ absent in the fermionic self-energy part. This factor will also arise if one calculates the vertex correction, as there one has to integrate a product of fermionic Green's functions like in the polarization operator. This allows us to use $\\sqrt{v_{s}^{2}\/\\alpha\\Gamma}$ as a small parameter to justify the Eliashberg-like approximation given by Fig. \\ref{figFeyn}. We shall not neglect, however, the polarization operator, as it behaves linearly at low frequencies and might outpower the initial quadratic dispersion.\n\n\n\nThe equations (\\ref{sf_Pom}) have been numerically solved by an iteration scheme, yielding the transition temperature $T_{Pom}$ where the 'odd' self-energy $P$ becomes non-zero. To show that this transition can be indeed leading we have also computed the transition temperature for a CDW with wavevector along the BZ diagonal. This instability has been found to be universally leading in previous studies. The transition temperature can be found from the linearized equation for the CDW order parameter $W_{diag}(\\varepsilon_{n})$:\n\\begin{equation}\n\\begin{gathered}\n\\bar{W}_{diag}(\\bar{\\varepsilon}_{n})\n=\\frac{0.75\\bar{T}}{2}\\sum_{\\varepsilon _{n}^{\\prime\n}}\\frac{\\bar{W}_{diag}(\\bar{\\varepsilon}_{n}^{\\prime\n})}{\\sqrt{\\bar{\\Omega}(\\bar{\\varepsilon}_{n}-\\bar{\\varepsilon _{n}^{\\prime\n}})+\\bar{a}}}\n\\\\\n\\times \\frac{\\mathrm{sgn}\\left(\n\\mathrm{Re}[f(\\bar{\\varepsilon}_{n}^{\\prime })]\\right)\n}{\\bar{f}(\\bar{\\varepsilon}_{n}^{\\prime\n})\\sqrt{i\\bar{f}(\\bar{\\varepsilon}_{n}^{\\prime })+\\bar{\\mu}}}.\n\\end{gathered}\n\\label{sf_tdiag}\n\\end{equation}\nThe results of the numerical solutions are presented in Fig.\\ref{figpomdiag}.\n\n\\begin{figure}[tbp]\n\\includegraphics[width=\\linewidth]{9.eps}\n\\caption{$\\bar{T}_{Pom}(\\bar{\\protect\\mu})$ (dashed line) and $\\bar{T}_{diag}(\\bar{\\protec\n\\mu})$ (dotted line) for $\\overline{(v_s\/\\xi)}=0.1$, $\\sqrt{\\frac{v_{s}^{2}\/\\alpha }\n\\Gamma }}=0.5(a),\\;0.1(b)$.}\n\\label{figpomdiag}\n\\end{figure}\nOne can clearly see that for $\\bar{\\mu}$ less than a certain value Pomeranchuk instability is the leading one. Note that the ratio $\\mu\/T_{Pom}$ can be as high as $12$ for $\\sqrt{v_{s}^{2}\/\\alpha\\Gamma}=0.5$ and $9$ for $\\sqrt{v_{s}^{2}\/\\alpha\\Gamma}=0.1$.\n\nAs the Fermi Surface seen in ARPES experiments is universally found to be $C_4$-symmetric and in the light of the domained CDW structure \\cite{Bi2212STM-3,y123REXS-4}, we assume that Pomeranchuk order should also be organized in domains with different sign of the order parameter. This constitutes a way of 'masking' a $C_4$ breaking alternative to the one proposed in \\cite{yamase2009}.\n\\section{Incommensurate charge modulation.}\nThe deformed Fermi surface of Fig.\\ref{figpom} can be unstable to CDW formation at lower temperatures. The direction and the magnitude of the wavevector are directly related to the sign and the magnitude of $P$. We assume that the CDW wavevector should yield nesting in the region where the FS 'expands' due to the FS deformation. Then one has:\n\\begin{equation}\nQ^{SF}(T)=2\\sqrt{(\\mu +0.5\\left\\vert P(-\\pi T)+P(\\pi T)\\right\\vert )\/\\alpha }. \\label{sf_q}\n\\end{equation}\nIn our model the FS in the second region 'closes' moving out of the considered region for $P>\\mu$. However, as is seen from Fig.\\ref{figpom}, in reality such a deformation can lead to emergent nesting in this region with the same vector direction as in the first one. The best-case scenario is that the nesting vectors in both regions coincide also in magnitude, {\\it i.e.} $Q_1=Q_2$ (see Fig.\\ref{figpom}). We shall assume that this is indeed the case, thus providing an upper limit on the $T_{CDW}$. In this case the equation for the CDW transition is:\n\\begin{eqnarray}\n&&\\bar{W}(\\bar{\\varepsilon}_{n})=0.75\\;i\\frac{\\bar{T}_{CDW}}{2\n\\sum_{\\varepsilon _{n}^{\\prime }}\\frac{\\bar{W}(\\bar{\\varepsilon}_{n}^{\\prime\n})}{\\sqrt{\\bar{\\Omega}(\\bar{\\varepsilon}_{n}-\\bar{\\varepsilon _{n}^{\\prime }\n)+\\bar{a}}} \\notag \\\\\n&&\\times \\frac{\\mathrm{sgn}(\\mathrm{Re}[f(\\bar{\\varepsilon}_{n}^{\\prime })]\n}{(\\left[ i\\bar{f}(\\bar{\\varepsilon}_{n}^{\\prime })+\\bar{P}(\\bar{\\varepsilon\n_{n}^{\\prime })-P(0)\\right] g\\left( \\bar{\\varepsilon}^{\\prime }\\right) }.\n\\label{c11}\n\\end{eqnarray\nThe results of numerical calculations are presented in Fig.\\ref{figCDW}. It turns out that the CDW transition can closely follow the onset of the FS deformation.\n\\begin{figure}[tbp]\n\\includegraphics[width=\\linewidth]{12.eps}\n\\caption{$T_{Pom}(\\bar{\\protect\\mu})$ (dashed line) and $\\bar{T}_{CDW}(\\bar\n\\protect\\mu})$ (dotted line) determined from Eq. (\\protect\\ref{c11}) for $\\overline{(v_s\/\\xi)}=0.1$, $\\protect\\sqrt{\\frac{v_{s}^{2}\/\\protect\\alpha }{\\Gamma }\n=0.5(a),\\;0.1(b)$.}\n\\label{figCDW}\n\\end{figure}\n\n\\section{Comparison with experiments and conclusions.}\n\n\\label{sec4}\n\nMotivated by the existing ARPES data \\cit\n{Bi2201ARPES-1,Bi2201ARPES-2,Bi2212ARPES-1} we have considered the SF model\nwith overlapping hotspots and demonstrated that the d-wave Fermi\nsurface distortion can be the leading instability. The\ntransition is further followed at a lower temperature by a transition into\na state with a d-form factor CDW directed along one of the BZ axes. The corresponding transition temperatures $T_{Pom}$ and $T_{CDW}$ can be not far away from each other.\n\nThe results obtained allow us to draw the following qualitative picture of\nthe charge order formation:\n\n$\\bullet $ At $T_{Pom}\\geq T^{\\ast }$ $C_{4}$ symmetry is broken by a Pomeranchuk\ntransition. The Fermi surface is deformed(see Fig. \\ref{figpom}) and doped holes\nare redistributed between the oxygen orbitals of the unit cell. The sample consists\nof domains with different signs of the order parameter corresponding to two alternatives presented in Fig.\\ref{figpom}.\n\n$\\bullet $ At $T_{CDW} 0$ is an appropriate regularization parameter and $\\|\\cdot\\|_\\mathcal{D}$ is the atomic norm with respect to the set $\\mathcal{D}$ (see e.g.~\\cite{chandrasekaran2012convex}), i.e.\n\\begin{equation}\\label{eq:D_norm}\n\t\\|x\\|_\\mathcal{D} := \\inf \\left\\{ \\sum_{g \\in \\mathcal{D}} |c_g| : x = \\sum_{g \\in \\mathcal{D}} c_g \\, g \\right\\}.\n\\end{equation}\nWhile such an approach is quite popular and has its uses, it might not always result in the most appropriate solution since it essentially changes the target function in order to promote sparsity of the solution.\n\nAnother way of obtaining a sparse minimizer (without changing the optimization problem) is to procedurally construct a sequence of minimizers with an increasing support, or, more generally, to design an algorithm that after $m$ iterations provides a point $x_m$ such that $E(x_m)$ is close to the $\\inf_{x \\in S} E(x)$ and that $x_m$ is $m$-sparse with respect to $\\mathcal{D}$, i.e.\n\\[\n\tx_m = \\sum_{j=1}^m c_j \\, g_j\n\t\\ \\ \\text{with}\\ \\ \n\tg_1, \\ldots, g_m \\in \\mathcal{D}\n\t\\ \\ \\text{and}\\ \\ \n\tc_1, \\ldots, c_m \\in \\mathbb{R}.\n\\]\nA wide class of algorithms that fit such requirements is the greedy algorithms in approximation theory, see e.g. \\cite{D}, \\cite{VTbook}.\nA typical problem of greedy approximation is the following.\nLet $X$ be a Banach space with the norm $\\|\\cdot\\|$ and let $\\mathcal{D}$ be a dictionary, i.e. a dense set of semi-normalized elements of $X$.\nThe goal of a greedy algorithm is to obtain a sparse (with respect to the dictionary $\\mathcal{D}$) approximation of a given element $f \\in X$.\nGreedy algorithms are iterative by design and generally after $m$ iterations a greedy algorithm constructs an $m$-term linear combination with respect to $\\mathcal{D}$ that approximates the element $f$.\n\nIt is easy to reframe a greedy approximation problem as a convex optimization problem.\nIndeed, for a given dictionary $\\mathcal{D}$ consider the set of all $m$-term linear combinations with respect to $\\mathcal{D}$ ($m$-sparse with respect to $\\mathcal{D}$ elements):\n\\[\n\t\\Sigma_m(\\mathcal{D}) := \\left\\{ x \\in X: x = \\sum_{j=1}^m c_j \\, g_j, \\ \\ g_1, \\ldots, g_m \\in \\mathcal{D} \\right\\}.\n\\]\nGreedy algorithms in approximation theory are designed to provide a simple way to build good approximants of $f$ from $\\Sigma_m(\\mathcal{D})$, hence the problem of greedy approximation is the following:\n\\begin{equation}\\label{eq:opt_ga}\n\t\\text{find}\\ \\ x_m = \\mathop{\\operatorname{argmin}}_{x \\in \\Sigma_m} \\|f - x\\|.\n\\end{equation}\nClearly, problem~\\eqref{eq:opt_ga} is a constrained optimization problem of the real-valued convex function $E(x) := \\|f - x\\|$ over the manifold $\\Sigma_m(\\mathcal{D}) \\subset X$.\n\nAt first glance the settings of approximation and optimization problems appear to be very different since in approximation theory our task is to find a sparse approximation of a given element $f \\in X$, while in optimization theory we want to find an approximate sparse minimizer of a given target function $E : X \\to \\mathbb{R}$ (for instance, energy function or loss function).\nHowever it is now well understood that similar techniques can be used for solving both problems.\nNamely, it was shown in~\\cite{VT140} and in follow up papers (see, for instance, \\cite{DT}, \\cite{GP}, \\cite{NP}, \\cite{VT141}, and~\\cite{VT148}) how methods developed in nonlinear approximation theory (greedy approximation techniques in particular) can be adjusted to find an approximate sparse (with respect to a given dictionary $\\mathcal{D}$) solution to the optimization problem~\\eqref{eq:opt}.\nMoreover, there is an increasing interest in building such sparse approximate solutions using different greedy-type algorithms, for example, \\cite{BD1}, \\cite{BD2}, \\cite{chandrasekaran2012convex}, \\cite{Cl}, \\cite{Ja2}, \\cite{JS}, \\cite{SSZ}, \\cite{TRD}, and~\\cite{Z}.\n\nWith an established framework it is straightforward to adjust a greedy strategy to a context of convex optimization; however, each of these modified techniques requires an individual analysis to guarantee a desirable performance.\nOn the other hand, it is known that the behavior of a greedy method is largely determined by the underlying geometry of the problem setting.\nIn particular, in~\\cite{VT165} we present a unified way of analyzing different greedy-type algorithms in Banach spaces.\nSpecifically, we define the class of Weak Biorthogonal Greedy Algorithms ($\\mathcal{WBGA}$) and prove convergence and rate of convergence results for algorithms from this class.\nSuch an approach allows for a simultaneous analysis of a wide range of seemingly different greedy algorithms based on the smoothness characteristic of the problem.\n\nIn this paper we adopt the approach of unified analysis for the setting of convex minimization.\nIn Section~\\ref{sec:wbga} we adjust the class $\\mathcal{WBGA}$ of algorithms designed for greedy approximation in Banach spaces and derive the class of Weak Biorthogonal Greedy Algorithms for convex optimization ($\\mathcal{WBGA}$(co)), which consists of greedy algorithms designed for convex optimization.\nWe prove convergence and rate of convergence results for algorithms from the class $\\mathcal{WBGA}$(co) in Theorems~\\ref{thm:wbga_conv} and~\\ref{thm:wbga_rate} respectively.\nThus, results in Section~\\ref{sec:wbga} address two important characteristics of an algorithm~--- convergence and rate of convergence.\n\nThe rate of convergence is an essential characteristic of an algorithm, though in certain practical applications resistance to various perturbations might be of equal importance.\nA systematic study of the stability of greedy algorithms in Banach spaces was started in~\\cite{T7} and further advanced in~\\cite{De}, where necessary and sufficient conditions for the convergence of a certain algorithm were obtained.\nA transition to the optimization setting was performed in~\\cite{DT} and~\\cite{VT148}, where stability results for greedy-type algorithms for convex optimization were obtained.\nIn Section~\\ref{sec:awbga} we discuss the stability of the algorithms from the $\\mathcal{WBGA}$(co) by analyzing convergence properties of the algorithms from $\\mathcal{WBGA}$(co) under the assumption of imprecise calculations in the steps of the algorithms.\nWe call such algorithms {\\it approximate greedy algorithms} or {\\it algorithms with errors}.\nWe prove convergence and rate of convergence results for the Weak Biorthogonal Greedy Algorithms with errors, which describes the stability of the algorithms from the class $\\mathcal{WBGA}$(co)~--- an important characteristic that is crucial for practical implementation.\n\nSince theoretical analysis cannot always predict the practical behavior of an algorithm, it is of interest to observe its actual implementation for particular problems.\nIn Section~\\ref{sec:numerics} we demonstrate the performance of some algorithms from the class $\\mathcal{WBGA}$(co) by employing them to solve various minimization problems.\nAdditionally, we compare these algorithms with a conventional method of obtaining sparse minimizers~--- optimization with $\\ell_1$-regularization~\\eqref{eq:opt_reg}.\nLastly, in Sections~\\ref{sec:proofs_wbga} and~\\ref{sec:proofs_awbga} we prove the results stated in Sections~\\ref{sec:wbga} and~\\ref{sec:awbga} respectively.\n\n\n\n\\section{Weak Biorthogonal Greedy Algorithms for Convex Optimization}\\label{sec:wbga}\nIn this section we introduce and discuss the class of Weak Biorthogonal Greedy Algorithms for convex optimization, denoted as $\\mathcal{WBGA}$(co).\nWe begin by recalling the relevant terminology.\n\n\\subsection{Preliminaries}\nLet $X$ be a real Banach space with the norm $\\|\\cdot\\|$.\nWe say that a set of elements $\\mathcal{D}$ from $X$ is a dictionary if each $g \\in \\mathcal{D}$ has the norm bounded by one and $\\mathcal{D}$ is dense in $X$, that is\n\\[\n\t\\|g\\| \\le 1\n\t\\ \\ \\text{for any}\\ \\ \n\tg \\in \\mathcal{D},\n\t\\ \\ \\text{and}\\ \\\n\t\\overline{\\operatorname{span}\\mathcal{D}} = X.\n\\]\nFor notational convenience in this paper we consider {\\it symmetric dictionaries}, i.e. such that\n\\[\n\tg\\in \\mathcal{D} \\ \\ \\text{implies} \\ \\ -g \\in \\mathcal{D}.\n\\]\nWe denote the closure (in $X$) of the convex hull of $\\mathcal{D}$ by $\\mathcal{A}_1(\\mathcal{D})$:\n\\begin{equation}\\label{eq:A_1(D)}\n\t\\mathcal{A}_1(\\mathcal{D}) := \\overline{\\mathrm{conv} \\mathcal{D}},\n\\end{equation}\nwhich is the standard notation in relevant greedy approximation literature. \n\nThe modulus of smoothness $\\rho(E,S,u)$ of a function $E : X \\to \\mathbb{R}$ on a set $S \\subset X$ is defined as\n\\begin{equation}\\label{eq:mod_smt}\n\t\\rho(E,S,u) := \\frac{1}{2} \\sup_{x\\in S, \\|y\\|=1} \\Big| E(x + uy) + E(x - uy) - 2E(x) \\Big|.\n\\end{equation}\nWe note that, in comparison to the modulus of smoothness of a norm (see, for instance,~\\cite[Part~3]{beauzamy2011introduction}), the modulus of smoothness of a function additionally depends on the chosen set $S \\subset X$.\nThat is because a norm is a positive homogeneous function, thus its smoothness on the whole space is determined by its smoothness on the unit sphere, which is not the case for a general function on a Banach space.\n\nThe function $E$ is uniformly smooth on $S \\subset X$ if $\\rho(E,S,u) = o(u)$ as $u \\to 0$.\nWe say that the modulus of smoothness $\\rho(E,S,u)$ is of power type $1 \\leq q \\leq 2$ if $\\rho(E,S,u) \\leq \\gamma u^q$ for some $\\gamma > 0$.\nNote that the class of functions with the modulus of smoothness of a nontrivial power type is completely different from the class of uniformly smooth Banach spaces with the norms of a nontrivial power type since any uniformly smooth norm is not uniformly smooth as a function on any set containing $0$.\nHowever, it is shown in~\\cite{borwein2009uniformly} that if a norm $\\|\\cdot\\|$ has the modulus of smoothness of power type $q \\in [1,2]$, then the function $E(\\cdot) := \\|\\cdot\\|^q$ has the modulus of smoothness $\\rho(E,S,u)$ of power type $q$ for any set $S \\subset X$.\nIn particular, it implies (see e.g.~\\cite[Lemma B.1]{donahue1997rates}) that for any $1 \\le p < \\infty$ the function $E : L_p \\to \\mathbb{R}$ defined as\n\\[\n\tE_p(x) = \\|x\\|_{L_p}^p\n\\]\nhas the modulus of smoothness that satisfies\n\\[\n\\rho_p(E,X,u) \\le\n\t\\left\\{\\begin{array}{ll}\n\t\t\\frac{1}{p} u^p & 1 \\le p \\le 2,\n\t\t\\\\\n\t\t\\frac{p-1}{2} u^2 & 2 \\le p < \\infty,\n\t\\end{array}\\right.\n\\]\ni.e. $\\rho_p(E,X,u)$ is of power type $\\min\\{p,2\\}$.\n\\\\\nA typical smoothness assumption in convex optimization is of the form\n\\[\n\t|E(x + uy) - E(x) - \\| \\le Cu^2\n\\]\nwith some constant $C > 0$ and any $u \\in \\mathbb{R}$, $x \\in X$, $\\|y\\| = 1$.\nIn terms of the modulus of smoothness~\\eqref{eq:mod_smt} such an assumption corresponds to the case $\\rho(E,X,u) \\le Cu^2 \/ 2$, i.e. that the modulus of smoothness of $E$ is of power type $2$.\n\nThroughout the paper we assume that the target function $E$ is Fr{\\'e}chet-differentiable, i.e. that at any $x \\in X$ there is a bounded linear functional $E'(x) : X \\to \\mathbb{R}$ such that\n\\[\n\t\\sup_{\\|y\\|=1} \\Big( \\lim_{u \\to 0} \\frac{E(x + uy) - E(x)}{u} - \\< E'(x), y \\> \\Big) = 0.\n\\]\nThen the convexity of $E$ implies that for any $x,y \\in D$\n\\begin{equation}\\label{eq:E'_conv1}\n\tE(y) \\ge E(x) + \\,\n\\end{equation}\nor, equivalently,\n\\begin{equation}\\label{eq:E'_conv2}\n\tE(x) - E(y) \\le \\ = \\<-E'(x),y-x\\>.\n\\end{equation}\n\n\\begin{Remark}\nThe condition of Fr{\\'e}chet-differentiability is not necessary and can be relaxed by considering support functionals in place of the derivative of $E$, as is done in~\\cite[Chapter~5]{dereventsov2017convergence}.\nAlthough the existence of support functionals is guaranteed by the convexity of the target function, we additionally impose the assumption of differentiability for the convenience of presentation.\n\\end{Remark}\n\n\n\\subsection{Weak Biorthogonal Greedy Algorithms}\nTypically in greedy approximation one has to perform a greedy selection from a given dictionary $\\mathcal{D}$, which might not always be possible.\nIn order to guarantee the feasibility of algorithms, it is conventional to perform a {\\it weak} greedy step where the greedy search is relaxed.\nSuch relaxations are represented by a given sequence $\\tau := \\{t_m\\}_{m=1}^\\infty$, referred to as a {\\it weakness sequence}.\n\nFor a convex Fr{\\'e}chet-differentiable target function $E : X \\to \\mathbb{R}$ we define the following class of greedy algorithms.\n\\\\[.5em]\\noindent\n{\\bf Weak Biorthogonal Greedy Algorithms ($\\boldsymbol{\\mathcal{WBGA}}$(co)).\\\\}\nWe say that an algorithm belongs to the class $\\mathcal{WBGA}$(co) with a weakness sequence $\\tau = \\{t_m\\}_{m=1}^\\infty$, $t_m\\in[0,1]$, if sequences of approximators $\\{G_m\\}_{m=0}^\\infty$ and selected elements $\\{\\varphi_m\\}_{m=1}^\\infty$ of the dictionary $\\mathcal{D}$ satisfy the following conditions at every iteration $m \\ge 1$:\n\\begin{enumerate}[label=\\bf(\\arabic*), leftmargin=.5in]\n\t\\item\\label{wbga_gs}\n\t\tGreedy selection: ${\\displaystyle \\<-E'(G_{m-1}), \\varphi_m\\> \\ge t_m \\sup_{\\varphi\\in\\mathcal{D}} \\< -E'(G_{m-1}), \\varphi \\>}$;\n\t\\item\\label{wbga_er}\n\t\tError reduction: ${\\displaystyle E(G_m) \\le \\inf_{\\lambda\\ge0} E(G_{m-1} + \\lambda\\varphi_m)}$;\n\t\\item\\label{wbga_bo}\n\t\tBiorthogonality: ${\\displaystyle \\ = 0}$.\n\\end{enumerate}\n\n\\smallskip\\noindent\nWe assume that for a given target function $E : X \\to \\mathbb{R}$ the set\n\\[\n\tD = D(E) := \\big\\{ x \\in X : E(x) \\le E(0) \\big\\} \\subset X\n\\]\nis bounded.\nCoupled with the assumption that $E$ is convex and Fr{\\'e}chet-differentiable, boundedness of $D$ guarantees that\n\\[\n\t\\inf_{x \\in X} E(x) = \\inf_{x \\in D} E(x) > -\\infty\n\t\\ \\ \\text{and}\\ \\\n\t\\mathop{\\operatorname{argmin}}_{x \\in X} E(x) = \\mathop{\\operatorname{argmin}}_{x \\in D} E(x) \\in D,\n\\]\ni.e. there is a nontrivial and attainable minimum of $E$.\nThen by condition~\\ref{wbga_er} the sequence of $m$-sparse approximants $\\{G_m\\}_{m=0}^\\infty$ constructed by an algorithm from the $\\mathcal{WBGA}$(co) satisfies the relation\n\\[\n\tE(0) = E(G_0) \\ge E(G_1) \\ge E(G_2) \\ge \\dots,\n\\]\nwhich guarantees that $G_m \\in D$ for all $m \\ge 0$.\n\n\\begin{Remark}\nIn the case $E(x) := \\|f - x\\|^q$ with any $f\\in X$ and $q \\ge 1$, the class $\\mathcal{WBGA}$(co) coincides with the class $\\mathcal{WBGA}$ from the approximation theory, which is introduced and analyzed in~\\cite{VT165}.\n\\end{Remark}\n\n\n\\subsection{Examples of algorithms from the $\\mathcal{WBGA}$(co)}\\label{sec:wbga_ga}\nIn this section we briefly overview a few particular algorithms from the class $\\mathcal{WBGA}$(co) that will be utilized in the numerical experiments presented in Section~\\ref{sec:numerics}.\nBy $\\tau := \\{t_m\\}_{m=1}^\\infty$ we denote a weakness sequence, i.e. a given sequence of non-negative numbers $t_m \\le 1$, $m = 1,2,3,\\dots$.\n\nWe first define the Weak Chebyshev Greedy Algorithm for convex optimization that is introduced and studied in~\\cite{VT140}.\n\\\\[.5em]\\noindent\n{\\bf Weak Chebyshev Greedy Algorithm (WCGA(co)).\\\\}\nSet $G^c_0 = 0$ and for each $m \\ge 1$ perform the following steps:\n\\begin{enumerate}\n\t\\item Take any $\\varphi^{c}_m \\in \\mathcal{D}$ satisfying\n\t\t\\[\n\t\t\t\\< -E'(G^c_{m-1}), \\varphi^c_m \\> \\ge t_m \\sup_{\\varphi\\in\\mathcal{D}} \\< -E'(G^c_{m-1}), \\varphi \\>;\n\t\t\\]\n\t\\item Denote $\\Phi_m^c = \\operatorname{span} \\{\\varphi^c_k\\}_{k=1}^m$ and find $G_m^c \\in \\Phi_m^c$ such that\n\t\t\\[\n\t\t\tE(G_m^c) = \\inf_{G \\in \\Phi_m^c} E(G).\n\t\t\\]\n\\end{enumerate}\n\n\\smallskip\\noindent\nAnother algorithm, which utilizes a simpler approach to updating the approximant is the Weak Greedy Algorithm with Free Relaxation for convex optimization (see~\\cite{VT140}).\n\\\\[.5em]\\noindent\n{\\bf Weak Greedy Algorithm with Free Relaxation (WGAFR(co)).\\\\}\nSet $G^f_0 = 0$ and for each $m \\ge 1$ perform the following steps:\n\\begin{enumerate}[label=\\bf(\\arabic*), leftmargin=.5in]\n\t\\item Take any $\\varphi^f_m \\in \\mathcal{D}$ satisfying\n\t\t\\[\n\t\t\t\\< -E'(G^f_{m-1}), \\varphi^f_m \\> \\ge t_m \\sup_{\\varphi\\in\\mathcal{D}} \\< -E'(G^f_{m-1}), \\varphi \\>;\n\t\t\\]\n\t\\item Find $\\omega_m \\in \\mathbb{R}$ and $ \\lambda_m \\in \\mathbb{R}$ such that\n\t\t\\[\n\t\t\tE\\big( (1-\\omega_m) G^f_{m-1} + \\lambda_m \\varphi^f_m \\big)\n\t\t\t= \\inf_{ \\lambda, \\omega \\in \\mathbb{R}} E\\big( (1 - \\omega) G^f_{m-1} + \\lambda \\varphi^f_m \\big)\n\t\t\\]\n\t\tand define $G^f_m = (1 - \\omega_m) G^f_{m-1} + \\lambda_m \\varphi^f_m$.\n\\end{enumerate}\n\n\\smallskip\\noindent\nThe next algorithm~--- the Rescaled Weak Relaxed Greedy Algorithm for convex optimization~--- is an adaptation of its counterpart from the approximation theory (see~\\cite{VT165}) that can be viewed as a generalization of the Rescaled Pure Greedy Algorithm, introduced in~\\cite{Pet} and adapted for convex optimization in~\\cite{GP}.\n\\\\[.5em]\\noindent\n{\\bf Rescaled Weak Relaxed Greedy Algorithm (RWRGA(co)).\\\\}\nSet $G^r_0 = 0$ and for each $m \\ge 1$ perform the following steps:\n\\begin{enumerate}\n\t\\item Take any $\\varphi^r_m \\in \\mathcal{D}$ satisfying\n\t\t\\[\n\t\t\t\\< -E'(G^r_{m-1}), \\varphi^r_m \\> \\ge t_m \\sup_{\\varphi\\in\\mathcal{D}} \\< -E'(G^r_{m-1}), \\varphi \\>;\n\t\t\\]\n\t\\item Find $\\lambda_m \\ge 0$ such that\n\t\t\\[\n\t\t\tE(G^r_{m-1} + \\lambda_m \\varphi^r_m) = \\inf_{\\lambda \\ge 0} E(G^r_{m-1} + \\lambda \\varphi^r_m);\n\t\t\\]\n\t\\item Find $\\mu_m \\in \\mathbb{R}$ such that\n\t\t\\[\n\t\t\tE\\big( \\mu_m (G^r_{m-1} + \\lambda_m \\varphi^r_m) \\big)\n\t\t\t= \\inf_{\\mu \\in \\mathbb{R}} E\\big( \\mu (G^r_{m-1} + \\lambda_m \\varphi^r_m) \\big)\n\t\t\\]\n\t\tand define $G^r_m = \\mu_m (G^r_{m-1} + \\lambda_m \\varphi^r_m)$.\n\\end{enumerate}\n\n\\begin{Proposition}\\label{prp:ga_wbga}\nThe WCGA(co), the WGAFR(co), and the RWRGA(co) belong to the class $\\mathcal{WBGA}$(co).\n\\end{Proposition}\n\n\n\\subsection{Convergence results for the $\\mathcal{WBGA}(co)$}\nIn this section we state the results related to convergence and the rate of convergence for algorithms from the class $\\mathcal{WBGA}$(co).\n\nOur setting of an infinite dimensional Banach space makes the formulation of convergence results nontrivial, and thus we require a special sequence which is defined for a given modulus of smoothness $\\rho(u) := \\rho(E,D,u)$ and a given weakness sequence $\\tau = \\{t_m\\}_{m=1}^\\infty$.\n\nLet $E : X \\to \\mathbb{R}$ be a convex uniformly smooth function, then $\\rho(u) := \\rho(E,D,u) : \\mathbb{R} \\to \\mathbb{R_+}$ is an even convex function.\nAssume that $\\rho(u)$ has the property $\\rho(1\/\\theta_0) \\ge 1$ for some $\\theta_0 \\in (0,1]$ and\n\\[\n\t\\lim_{u\\to 0} \\rho(u)\/u = 0.\n\\]\nNote that assumptions on uniform smoothness of $E$ and boundedness of domain $D \\subset X$ guarantee the above properties.\nThen for a given $0 < \\theta \\le \\theta_0$ define $\\xi_m := \\xi_m(\\rho,\\tau,\\theta)$ as the solution of the equation\n\\begin{equation}\\label{eq:theta}\n\t\\rho(u) = \\theta t_m u.\n\\end{equation}\nNote that conditions on $\\rho(u)$ imply that the function\n\\[\n\ts(u) := \\left\\{\\begin{array}{ll}\n\t\t\\rho(u)\/u, & u \\neq 0\n\t\t\\\\\n\t\t0, & u = 0\n\t\\end{array}\\right.\n\\]\nis continuous and increasing on $[0,\\infty)$ with $s(1\/\\theta_0) \\ge \\theta_0$.\nThus equation~\\eqref{eq:theta} has the unique solution $\\xi_m = s^{-1}(\\theta t_m)$ such that $0 < \\xi_m \\le 1\/\\theta_0$.\n\n\\noindent\nWe now formulate our main convergence result for the $\\mathcal{WBGA}$(co).\n\\begin{Theorem}\\label{thm:wbga_conv}\nLet $E$ be a uniformly smooth on $D \\subset X$ convex function with the modulus of smoothness $\\rho(E,D,u)$.\nAssume that a sequence $\\tau := \\{t_m\\}_{m=1}^\\infty$ satisfies the condition that for any $\\theta \\in(0,\\theta_0]$ we have\n\\[\n\t\\sum_{m=1}^\\infty t_m \\xi_m(\\rho,\\tau,\\theta) = \\infty.\n\\]\nThen for any algorithm from the class $\\mathcal{WBGA}$(co) we have\n\\[\n\t\\lim_{m\\to\\infty} E(G_m) = \\inf_{x\\in D} E(x).\n\\]\n\\end{Theorem}\n\n\\noindent\nHere are two simple corollaries of Theorem~\\ref{thm:wbga_conv}.\n\\begin{Corollary}\nLet $E$ be a uniformly smooth on $D \\subset X$ convex function.\nThen any algorithm from the class $\\mathcal{WBGA}$(co) with a constant weakness sequence $\\tau = t \\in (0,1]$ converges, i.e.\n\\[\n\t\\lim_{m\\to\\infty} E(G_m) = \\inf_{x\\in D} E(x).\n\\]\n\\end{Corollary}\n\n\\begin{Corollary}\nLet $E$ be a convex function with the modulus of smoothness of power type $1 < q \\le 2$, that is, $\\rho(E,D,u) \\le \\gamma u^q$.\nLet a sequence $\\tau := \\{t_m\\}_{m=1}^\\infty$, $t_m \\in (0,1]$ for $m = 1,2,3,\\ldots$ be such that \n\\[\n\t\\sum_{m=1}^\\infty t_m^p = \\infty, \\ \\ p = \\frac{q}{q-1}.\n\\]\nThen any algorithm from the class $\\mathcal{WBGA}$(co) with the weakness sequence $\\tau$ converges, i.e.\n\\[\n\t\\lim_{m \\to \\infty} E(G_m) = \\inf_{x\\in D} E(x).\n\\]\n\\end{Corollary}\n\n\\noindent\nWe now proceed to the rate of convergence estimates, which are of interest in both finite dimensional and infinite dimensional settings.\nA typical assumption in this regard is formulated in terms of the convex hull $\\mathcal{A}_1(\\mathcal{D})$ of the dictionary $\\mathcal{D}$, defined by~\\eqref{eq:A_1(D)}.\n\\begin{Theorem}\\label{thm:wbga_rate}\nLet $E$ be a convex function with the modulus of smoothness of power type $1 < q \\le 2$, that is, $\\rho(E,D,u) \\le \\gamma u^q$.\nTake an element $f^\\epsilon \\in D$ and a number $\\epsilon \\ge 0$ such that\n\\[\n\tE(f^\\epsilon) \\le \\inf_{x\\in D} E(x) + \\epsilon, \\ \\ f^\\epsilon\/A(\\epsilon) \\in \\mathcal{A}_1(\\mathcal{D})\n\\]\nwith some number $A(\\epsilon) \\ge 1$.\nThen for any algorithm from the class $\\mathcal{WBGA}$(co) we have\n\\[\n\tE(G_m) - \\inf_{x\\in D} E(x) \\le \\max\\left\\{ 2\\epsilon, C(q,\\gamma) A(\\epsilon)^q \\left(C(E,q,\\gamma) + \\sum_{k=1}^m t_k^p\\right)^{1-q} \\right\\},\n\\]\nwhere $p = q\/(q-1)$.\n\\end{Theorem}\n\n\\begin{Corollary}\nLet $E$ be a convex function with the modulus of smoothness of power type $1 < q \\le 2$, that is, $\\rho(E,D,u) \\le \\gamma u^q$.\nIf $\\operatorname{argmin}_{x \\in D} E(x) \\in \\mathcal{A}_1(\\mathcal{D})$ then for any algorithm from the class $\\mathcal{WBGA}$(co) we have\n\\[\n\tE(G_m) - \\inf_{x \\in D} E(x) \\le C(q,\\gamma) \\left(C(E,q,\\gamma) + \\sum_{k=1}^m t_k^p\\right)^{1-q},\n\\]\nwhere $p = q\/(q-1)$.\n\\end{Corollary}\n\n\\begin{Remark}\nWhile the results stated in this section are known for the WCGA(co) and the WGAFR(co) (see~\\cite{VT140}), they are novel for the RWRGA(co).\n\\end{Remark}\n\n\n\n\\section{Weak Biorthogonal Greedy Algorithms with errors for Convex Optimization}\\label{sec:awbga}\nIn this section we address the question of the stability of algorithms from the class $\\mathcal{WBGA}$(co) by introducing the wider class $\\mathcal{WBGA}(\\Delta,\\text{co})$, which allows for imprecise calculations in the realization of algorithms.\nSuch an approach is of a practical interest since computational inaccuracies often occur naturally in applications.\nTo account for imprecise computations we introduce a sequence $\\Delta := \\{\\delta_m, \\epsilon_m\\}_{m=1}^\\infty$, where $\\delta_m \\in [0,1]$ and $\\epsilon_m \\ge 0$ for $m = 1,2,3,\\dots$, that represents the allowed inaccuracies in the steps of the algorithms.\nIn accordance with the conventional notation (see e.g.~\\cite{gribonval2001approximate}, \\cite{galatenko2003convergence}), we refer to a given sequence $\\Delta := \\{\\delta_m, \\epsilon_m\\}_{m=1}^\\infty$ as an {\\it error sequence}.\n\nFor a convex Fr{\\'e}chet-differentiable target function $E : X \\to \\mathbb{R}$ we define the following class of greedy algorithms with errors.\n\\\\[.5em]\\noindent\n{\\bf Weak Biorthogonal Greedy Algorithms with errors ($\\boldsymbol{\\mathcal{WBGA}(\\Delta,\\text{co})}$).\\\\} \nWe say that an algorithm belongs to the class $\\mathcal{WBGA}(\\Delta,\\text{co})$ with a weakness sequence $\\tau = \\{t_m\\}_{m=1}^\\infty$, $t_m\\in[0,1]$ and an error sequence $\\Delta = \\{\\delta_m,\\epsilon_m\\}_{m=1}^\\infty$, $\\delta_m\\in[0,1], \\epsilon_m\\ge0$, if sequences of approximators $\\{G_m\\}_{m=0}^\\infty$ and selected elements $\\{\\varphi_m\\}_{m=1}^\\infty$ of the dictionary $\\mathcal{D}$ satisfy the following conditions at every iteration $m \\ge 1$:\n\\begin{enumerate}[label=\\bf(\\arabic*), leftmargin=.5in]\n\t\\item\\label{awbga_gs}\n\t\tGreedy selection: ${\\displaystyle \\<-E'(G_{m-1}), \\varphi_m\\> \\ge t_m \\sup_{\\varphi\\in\\mathcal{D}} \\<-E'(G_{m-1}), \\varphi\\>}$;\n\t\\item\\label{awbga_er}\n\t\tError reduction: ${\\displaystyle E(G_m) \\le \\inf_{\\lambda\\ge0} E(G_{m-1} + \\lambda\\varphi_m) + \\delta_m}$;\n\t\\item\\label{awbga_bo}\n\t\tBiorthogonality: ${\\displaystyle |\\| \\le \\epsilon_m}$;${\\displaystyle\\phantom{\\inf_\\lambda}}$\n\t\\item\\label{awbga_bd}\n\t\tBoundedness: ${\\displaystyle E(G_{m}) \\le E(0) + C_0}$.\n\\end{enumerate}\n\n\\smallskip\\noindent\nNote that in addition to conditions~\\ref{wbga_gs}--\\ref{wbga_bo} from the definition of the class $\\mathcal{WBGA}$(co), for the $\\mathcal{WBGA}(\\Delta,\\text{co})$ we require the boundedness condition~\\ref{awbga_bd} to account for the magnitude of allowed errors $\\Delta$.\nIn particular, if the error sequence $\\Delta$ is summable, i.e. $\\sum_{m=1}^\\infty \\delta_m < \\infty$, then condition~\\ref{awbga_bd} follows directly from~\\ref{awbga_er} with $C_0 = \\sum_{m=1}^\\infty \\delta_m$.\n\\\\\nMoreover, we assume that the set\n\\[\n\tD \\subset D_1 := \\{x \\in X : E(x) \\le E(0) + C_0\\} \\subset X,\n\\]\nwhere $C_0$ is the constant from condition~\\ref{awbga_bd}, is bounded.\nThen condition~\\ref{awbga_bd} guarantees that $G_m \\in D_1$ for all $m \\ge 0$ for any algorithm from the $\\mathcal{WBGA}(\\Delta,\\text{co})$.\n\n\\begin{Remark}\nIn the error reduction condition~\\ref{awbga_er} from the definition of the class $\\mathcal{WBGA}(\\Delta,co$) the infimum is taken over all $\\lambda \\ge 0$.\nIn order to simplify this problem, one can consider a wider than the $\\mathcal{WBGA}(\\Delta,co)$ class~--- the class $\\mathcal{WBGA}(\\Delta,[0,1],co)$ of algorithms satisfying conditions~\\ref{awbga_gs}, \\ref{awbga_bo}, \\ref{awbga_bd}, and the following condition instead of~\\ref{awbga_er}:\n\\[\n\t\\text{{\\bf (2')} {\\rm Restricted error reduction: }}\n\tE(G_m) \\le \\inf_{\\lambda\\in[0,1]} E(G_{m-1} + \\lambda\\varphi_m) + \\delta_m.\n\\]\nThen finding such $\\lambda\\in[0,1]$ is a line search problem, which is known to be a simple one-dimensional convex optimization problem (see e.g.~\\cite{boyd2004convex}, \\cite{N}).\n\\end{Remark}\n\n\n\\subsection{Examples of algorithms from the $\\mathcal{WBGA}(\\Delta,co)$}\\label{sec:awbga_ga}\nIn this section we briefly overview particular algorithms from the class $\\mathcal{WBGA}(\\Delta,\\text{co})$ that correspond to the approximate versions of the algorithms considered in Section~\\ref{sec:wbga_ga}.\nDenote by $\\tau := \\{t_m\\}_{m=1}^\\infty$ and $\\Delta := \\{\\delta_m,\\epsilon_m\\}_{m=1}^\\infty$ a weakness sequence and an error sequence respectively, i.e. given sequences of numbers $t_m \\in [0,1]$, $\\delta_m \\in [0,1]$, and $\\epsilon_m \\ge 0$ for $m = 1,2,3,\\ldots$.\n\nWe begin with the Weak Chebyshev Greedy Algorithm with errors for convex optimization. \n\\\\[.5em]\\noindent\n{\\bf Weak Chebyshev Greedy Algorithm with errors (WCGA($\\Delta,\\text{co}$)).\\\\}\nSet $G^c_0 = 0$ and for each $m \\ge 1$ perform the following steps:\n\\begin{enumerate}\n\t\\item Take any $\\varphi^{c}_m \\in \\mathcal{D}$ satisfying\n\t\t\\[\n\t\t\t\\< -E'(G^c_{m-1}), \\varphi^c_m \\> \\ge t_m \\sup_{\\varphi\\in\\mathcal{D}} \\< -E'(G^c_{m-1}), \\varphi \\>;\n\t\t\\]\n\t\\item Denote $\\Phi_m^c = \\operatorname{span} \\{\\varphi^c_k\\}_{k=1}^m$ and find $G_m^c \\in \\Phi_m^c$ such that\n\t\t\\[\n\t\t\tE(G_m^c) \\le \\inf_{G \\in \\Phi_m^c} E(G) + \\delta_m.\n\t\t\\]\n\\end{enumerate}\n\n\\smallskip\\noindent\nNext, we state the Weak Greedy Algorithm with Free Relaxation and errors for convex optimization, introduced and studied in~\\cite{DT}.\n\\\\[.5em]\\noindent\n{\\bf Weak Greedy Algorithm with Free Relaxation and errors (WGAFR($\\Delta,\\text{co}$)).\\\\}\nSet $G^f_0 = 0$ and for each $m \\ge 1$ perform the following steps:\n\\begin{enumerate}\n\t\\item Take any $\\varphi^f_m \\in \\mathcal{D}$ satisfying\n\t\t\\[\n\t\t\t\\< -E'(G^f_{m-1}), \\varphi^f_m \\> \\ge t_m \\sup_{\\varphi\\in\\mathcal{D}} \\< -E'(G^f_{m-1}), \\varphi \\>;\n\t\t\\]\n\t\\item Find $\\omega_m \\in \\mathbb{R}$ and $ \\lambda_m \\in \\mathbb{R}$ such that\n\t\t\\[\n\t\t\tE\\big( (1-\\omega_m) G^f_{m-1} + \\lambda_m \\varphi^f_m \\big)\n\t\t\t\\le \\inf_{\\lambda, \\omega \\in \\mathbb{R}} E\\big( (1 - \\omega) G^f_{m-1} + \\lambda \\varphi^f_m \\big) + \\delta_m\n\t\t\\]\n\t\tand define $G^f_m = (1 - \\omega_m) G^f_{m-1} + \\lambda_m \\varphi^f_m$.\n\\end{enumerate}\n\n\\smallskip\\noindent\nLastly, we introduce a new algorithm~--- the Rescaled Weak Relaxed Greedy Algorithm with errors for convex optimization.\n\\\\[.5em]\\noindent\n{\\bf Rescaled Weak Relaxed Greedy Algorithm with errors (RWRGA($\\Delta,\\text{co}$)).\\\\}\nSet $G^r_0 = 0$ and for each $m \\ge 1$ perform the following steps:\n\\begin{enumerate}\n\t\\item Take any $\\varphi^r_m \\in \\mathcal{D}$ satisfying\n\t\t\\[\n\t\t\t\\< -E'(G^r_{m-1}), \\varphi^r_m \\> \\ge t_m \\sup_{\\varphi\\in\\mathcal{D}} \\< -E'(G^r_{m-1}), \\varphi \\>;\n\t\t\\]\n\t\\item Find $\\lambda_m \\ge 0$ such that\n\t\t\\[\n\t\t\tE(G^r_{m-1} + \\lambda_m \\varphi^r_m) \\le \\inf_{\\lambda \\ge 0} E(G^r_{m-1} + \\lambda \\varphi^r_m) + \\delta_m\/2;\n\t\t\\]\n\t\\item Find $\\mu_m \\in \\mathbb{R}$ such that\n\t\t\\[\n\t\t\tE\\big( \\mu_m (G^r_{m-1} + \\lambda_m \\varphi^r_m) \\big)\n\t\t\t\\le \\inf_{\\mu \\in \\mathbb{R}} E\\big( \\mu (G^r_{m-1} + \\lambda_m \\varphi^r_m) \\big) + \\delta_m\/2\n\t\t\\]\n\t\tand define $G^r_m = \\mu_m (G^r_{m-1} + \\lambda_m \\varphi^r_m)$.\n\\end{enumerate}\n\n\\begin{Proposition}\\label{prp:ga_awbga}\nThe WCGA($\\Delta$,co), the WGAFR($\\Delta$,co), and the RWRGA($\\Delta$,co) belong to the class $\\mathcal{WBGA}(\\Delta,co)$ with\n\\[\n\t\\epsilon_m = \\inf_{u > 0} \\frac{\\delta_m + 2\\rho(E,D_1,u \\|G_m\\|)}{u}.\n\\]\n\\end{Proposition}\n\n\n\\subsection{Convergence results for the $\\mathcal{WBGA}(\\Delta,co)$}\nIn this section we discuss the convergence and rate of convergence results for algorithms from the class $\\mathcal{WBGA}(\\Delta,\\text{co})$.\n\n\\noindent\nFirst, we state the convergence result.\n\\begin{Theorem}\\label{thm:awbga_conv}\nLet $E$ be a uniformly smooth on $D_1 \\subset X$ convex function.\nAssume that an error sequence $\\Delta := \\{\\delta_m,\\epsilon_m\\}_{m=1}^\\infty$ is such that $\\delta_m \\to 0$ and $\\epsilon_m \\to 0$ as $m \\to \\infty$.\nThen any algorithm from the class $\\mathcal{WBGA}(\\Delta,\\text{co})$ with a constant weakness sequence $\\tau = t \\in (0,1]$ converges, i.e.\n\\[\n\t\\lim_{m\\to\\infty} E(G_m) = \\inf_{x\\in D_1} E(x).\n\\]\n\\end{Theorem}\n\n\\noindent\nSecond, we provide the rate of convergence estimate.\n\\begin{Theorem}\\label{thm:awbga_rate}\nLet $E$ be a convex function with the modulus of smoothness of power type $1 < q \\le 2$, that is, $\\rho(E,D_{1},u) \\le \\gamma u^q$.\nTake an element $f^\\epsilon \\in D_1$ and a number $\\epsilon \\ge 0$ such that\n\\[\n\tE(f^\\epsilon) \\le \\inf_{x \\in D_1} E(x) + \\epsilon, \\ \\ \n\tf^\\epsilon\/A \\in \\mathcal{A}_1(\\mathcal{D}),\n\\]\nwith some number $A := A(\\epsilon) \\ge 1$.\nThen for any algorithm from the class $\\mathcal{WBGA}(\\Delta,\\text{co})$ with a constant weakness sequence $\\tau = t \\in (0,1]$ and an error sequence $\\Delta = \\{\\delta_m,\\epsilon_m\\}_{m=1}^\\infty$ with $\\delta_m + \\epsilon_m \\le cm^{-q}$, $m = 1,2,3,\\dots$ we have\n\\[\n\tE(G_m) - \\inf_{x\\in D_1} E(x) \\le \\epsilon + C(E,q,\\gamma,t,c) A(\\epsilon)^q \\, m^{1-q}.\n\\]\n\\end{Theorem}\n\n\\begin{Corollary}\nUnder the conditions of Theorem~\\ref{thm:awbga_rate}, specifying \n\\[\n\tA(\\epsilon) := \\inf \\Big\\{ A > 0 : \\exists f \\in D_1 : f\/A \\in \\mathcal{A}_1(\\mathcal{D}),\\ \\ E(f) \\le \\inf_{x\\in D_1}E(x) + \\epsilon \\Big\\}\n\\]\nand denoting\n\\[\n\t\\eta_m := \\inf \\big\\{ \\epsilon > 0: A(\\epsilon)^q \\, m^{1-q} \\le \\epsilon \\big\\},\n\\]\nwe obtain for any algorithm from the class $\\mathcal{WBGA}(\\Delta,co)$\n\\[\n\tE(G_m) - \\inf_{x\\in D_1} E(x) \\le C(E,q,\\gamma,t) \\, \\eta_m.\n\\]\n\\end{Corollary}\n\n\\begin{Remark}\nIt follows from the proofs of Theorems~\\ref{thm:awbga_conv} and~\\ref{thm:awbga_rate}, given in Section~\\ref{sec:proofs_awbga}, that the results stated in this section also hold for the class $\\mathcal{WBGA}(\\Delta,[0,1],\\text{co})$.\n\\end{Remark}\n\n\n\n\\section{Numerical experiments}\\label{sec:numerics}\nIn this section we demonstrate the performance of the algorithms from the class $\\mathcal{WBGA}$(co) that are discussed in Section~\\ref{sec:wbga_ga}: the Weak Chebyshev Greedy Algorithm (WCGA(co)), the Weak Greedy Algorithm with Free Relaxation (WGAFR(co)), and the Rescaled Weak Relaxed Greedy Algorithm (RWRGA(co)).\n\nFor each of the numerical experiments presented below we consider the Banach space $X = \\ell_1^{(\\mathrm{dim})}$ of dimensionality $\\mathrm{dim}$, a target function $E : X \\to \\mathbb{R}$, and a dictionary $\\mathcal{D} \\in X$.\nWe then employ the aforementioned algorithms to solve the optimization problem~\\eqref{eq:opt}, i.e. to find a sparse (with respect to the dictionary $\\mathcal{D}$) minimizer\n\\[\n\tx^* = \\mathop{\\operatorname{argmin}}_{x \\in X} E(x).\n\\]\nSince greedy algorithms are iterative by design, in Examples~1--2 we obtain and present the trade-off between the sparsity of the solution $x^*$ and the value of $E(x^*)$.\nIn Examples~3--4, we additionally compare the greedy algorithms for convex optimization with a conventional method of finding sparse solutions~--- the optimization with $\\ell_1$-regularization, see~\\eqref{eq:opt_reg}.\nSpecifically, we solve the problem\n\\[\n\t\\text{find}\\ \\ x^* = \\mathop{\\operatorname{argmin}}_{x \\in X} \\Big( E(x) + \\lambda \\|x\\|_\\mathcal{D} \\Big),\n\\]\nwhere $\\|\\cdot\\|_\\mathcal{D}$ is the atomic norm with respect to the dictionary $\\mathcal{D}$, defined by~\\eqref{eq:D_norm}.\nTo obtain minimizers of different sparsities, the values of the regularization parameter $\\lambda$ are taken from the sequence $\\{0.1 \\times (0.9)^k\\}_{k=0}^{49}$, i.e. $50$ regularized optimization problems are solved in every setting.\n\nTo avoid an unintentional bias in the selection of dictionary $\\mathcal{D}$ and target function $E$, we generate those randomly, based on certain parameters that are described in the setting of each example.\nIn order to provide a reliable demonstration that is independent of a particular random generation, we compute $100$ simulations for each presented example and provide the distribution of the optimization results (shown in Figures~1--4).\nIn the presented pictures the solid lines represent the mean minimization values for each algorithm and the filled areas represent the minimization distribution across all $100$ simulations.\nFinally, to make the results consistent across simulations, we rescale the optimization results to be in the interval $[0,1]$, i.e. instead of reporting the value of $E(x^*)$ we report\n\\[\n\t\\frac{E(x^*) - \\inf_{x \\in X} E(x)}{E(0) - \\inf_{x \\in X} E(x)} \\in [0,1].\n\\]\n\nNumerical experiments presented in this section are performed in Python~3.6 with the use of NumPy and SciPy libraries.\nThe source code is available at~\\url{https:\/\/github.com\/sukiboo\/wbga_co_2020}.\n\n\n\\subsection{Example 1}\n\\begin{figure}[t]\n\t\\includegraphics[width=\\linewidth]{.\/images\/ex1.pdf}\n\t\\caption{Distribution of optimization results for Example~1.}\n\\end{figure}\nIn this example we consider the space $X = \\ell_1^{(500)}$, and construct a dictionary $\\mathcal{D}$ of size $1000$ as linear combinations of the canonical basis $\\{e_i\\}_{i=1}^{500}$ of $X$ with uniformly distributed coefficients, i.e.\n\\[\n\t\\mathcal{D} = \\{\\varphi_j\\}_{j=1}^{1000},\n\t\\ \\ \\text{where}\\ \\ \n\t\\varphi_j = \\sum_{i=1}^{500} c^i_j \\, e_i\n\t\\ \\ \\text{with}\\ \\ \n\tc^i_j \\sim \\mathcal{U}(0,1).\n\\]\nThe target function $E : X \\to \\mathbb{R}$ is chosen as\n\\[\n\tE(x) = \\|x - f\\|_p^p,\n\\]\nwhere $p = 1.2$ and $f \\in X$ is randomly generated as a linear combination of $60$ randomly selected elements of $\\mathcal{D}$ with normally distributed coefficients, i.e.\n\\[\n\tf = \\sum_{k=1}^{60} a_k \\, \\varphi_{\\sigma(k)},\n\t\\ \\ \\text{where}\\ \\ \n\ta_k \\sim \\mathcal{N}(0,1)\n\t\\ \\ \\text{and}\\ \\\n\t\\sigma\\ \\ \\text{is a permutation of}\\ \\ \\{1,\\ldots,1000\\}.\n\\]\nPerformance of the greedy algorithms in this setting is presented in Figure~1.\nThe average number of iterations required to obtain a minimizer of sparsity $50$ is $185$ for the RWRGA(co), $93$ for the WGAFR(co), and $50$ for the WCGA(co).\n\n\n\\subsection{Example 2}\n\\begin{figure}[t]\n\t\\includegraphics[width=\\linewidth]{.\/images\/ex2.pdf}\n\t\\caption{Distribution of optimization results for Example~2.}\n\\end{figure}\nIn this example we once again consider the space $X = \\ell_1^{(500)}$ and a dictionary $\\mathcal{D}$ of size $1000$, constructed as linear combinations of the canonical basis $\\{e_i\\}_{i=1}^{500}$ of $X$ with uniformly distributed coefficients, i.e.\n\\[\n\t\\mathcal{D} = \\{\\varphi_j\\}_{j=1}^{1000},\n\t\\ \\ \\text{where}\\ \\ \n\t\\varphi_j = \\sum_{i=1}^{500} c^i_j \\, e_i\n\t\\ \\ \\text{with}\\ \\ \n\tc^i_j \\sim \\mathcal{U}(0,1).\n\\]\nThe target function $E : X \\to \\mathbb{R}$ is chosen as\n\\[\n\tE(x) = \\|x - f\\|_p^p \\, \\|g\\|_q^q + \\|x - g\\|_q^q \\, \\|f\\|_p^p,\n\\]\nwhere $p = 3$, $q = 1.2$, and the elements $f,g \\in X$ are each randomly generated as linear combinations of $30$ randomly selected elements of $\\mathcal{D}$ with normally distributed coefficients, i.e.\n\\begin{gather*}\n\tf = \\sum_{k=1}^{30} a^1_k \\, \\varphi_{\\sigma_1(k)}\n\t\\ \\ \\text{and}\\ \\ \n\tg = \\sum_{k=1}^{30} a^2_k \\, \\varphi_{\\sigma_2(k)},\n\t\\\\\n\t\\text{where}\\ \\ \n\ta^1_k, a^2_k \\sim \\mathcal{N}(0,1)\n\t\\ \\ \\text{and}\\ \\\n\t\\sigma_1, \\sigma_2\\ \\ \\text{are permutations of}\\ \\ \\{1,\\ldots,1000\\}.\n\\end{gather*}\nPerformance of the greedy algorithms in this setting is presented in Figure~2.\nThe average number of iterations required to obtain a minimizer of sparsity $50$ is $125$ for the RWRGA(co), $78$ for the WGAFR(co), and $50$ for the WCGA(co).\n\n\n\\subsection{Example 3}\n\\begin{figure}[t]\n\t\\includegraphics[width=\\linewidth]{.\/images\/ex3.pdf}\n\t\\caption{Distribution of optimization results for Example~3.}\n\\end{figure}\nIn this example we additionally compare the greedy algorithms with conventional optimization with $\\ell_1$-regularization, see~\\eqref{eq:opt_reg}.\nSince obtaining the minimization-sparsity trade-off with $\\ell_1$-regularization is more expensive computationally than it is for the greedy algorithms, we restrict ourselves to work in a space of smaller dimensionality.\nNamely, we consider the space $X = \\ell_1^{(100)}$, and construct a dictionary $\\mathcal{D}$ of size $200$ as linear combinations of the canonical basis $\\{e_i\\}_{i=1}^{100}$ of $X$ with uniformly distributed coefficients, i.e.\n\\[\n\t\\mathcal{D} = \\{\\varphi_j\\}_{j=1}^{200},\n\t\\ \\ \\text{where}\\ \\ \n\t\\varphi_j = \\sum_{i=1}^{100} c^i_j \\, e_i\n\t\\ \\ \\text{with}\\ \\ \n\tc^i_j \\sim \\mathcal{U}(0,1).\n\\]\nThe target function $E : X \\to \\mathbb{R}$ is chosen as\n\\[\n\tE(x) = \\|x - f\\|_p^p \\, \\|g\\|_q^q + \\|x - g\\|_q^q \\, \\|f\\|_p^p,\n\\]\nwhere $p = 4$, $q = 1.5$, and the elements $f,g \\in X$ are each randomly generated as linear combinations of $30$ randomly selected elements of $\\mathcal{D}$ with normally distributed coefficients, i.e.\n\\begin{gather*}\n\tf = \\sum_{k=1}^{30} a^1_k \\, \\varphi_{\\sigma_1(k)}\n\t\\ \\ \\text{and}\\ \\ \n\tg = \\sum_{k=1}^{30} a^2_k \\, \\varphi_{\\sigma_2(k)},\n\t\\\\\n\t\\text{where}\\ \\ \n\ta^1_k, a^2_k \\sim \\mathcal{N}(0,1)\n\t\\ \\ \\text{and}\\ \\\n\t\\sigma_1, \\sigma_2\\ \\ \\text{are permutations of}\\ \\ \\{1,\\ldots,200\\}.\n\\end{gather*}\nPerformance of the greedy algorithms and optimization with $\\ell_1$-regularization in this setting is presented in Figure~3.\nThe average number of iterations required to obtain a minimizer of sparsity $50$ is $105$ for the RWRGA(co), $74$ for the WGAFR(co), and $50$ for the WCGA(co).\n\n\\subsection{Example 4}\n\\begin{figure}[t]\n\t\\includegraphics[width=\\linewidth]{.\/images\/ex4.pdf}\n\t\\caption{Distribution of optimization results for Example~4.}\n\\end{figure}\nIn this example we compare the greedy algorithms with conventional optimization with $\\ell_1$-regularization in a classical setting of canonical basis instead of a randomly-generated dictionary.\nNamely, we consider the space $X = \\ell_1^{(200)}$, and set a dictionary $\\mathcal{D}$ to be the canonical basis $\\{e_i\\}_{i=1}^{200}$ of $X$, i.e.\n\\[\n\t\\mathcal{D} = \\{e_j\\}_{j=1}^{200},\n\t\\ \\ \\text{where}\\ \\ \n\te_j = (\\underbrace{0,\\ldots,0}_{j-1},1,\\underbrace{0,\\ldots,0}_{200-j}).\n\\]\nThe target function $E : X \\to \\mathbb{R}$ is chosen as\n\\[\n\tE(x) = \\|x - f\\|_p^p \\, \\|g\\|_q^q + \\|x - g\\|_q^q \\, \\|f\\|_p^p,\n\\]\nwhere $p = 7$, $q = 3$, and $f,g$ are randomly generated as elements of $X$ with normally distributed coefficients, i.e.\n\\[\n\tf = \\sum_{k=1}^{200} a^1_k \\, e_k\n\t\\ \\ \\text{and}\\ \\ \n\tg = \\sum_{k=1}^{200} a^2_k \\, e_k,\n\t\\ \\text{where}\\ \\ \n\ta^1_k, a^2_k \\sim \\mathcal{N}(0,1).\n\\]\nPerformance of the greedy algorithms and optimization with $\\ell_1$-regularization in this setting is presented in Figure~4.\nNote that in this case all greedy algorithms~--- the RWRGA(co), the WGAFR(co), and the WCGA(co)~--- coincide due to the fact that elements of the dictionary $\\mathcal{D}$ are mutually disjoint.\nHence the number of iterations required to obtain a minimizer of sparsity $100$ is exactly $100$ for all three greedy algorithms.\n\n\n\n\\section{Proofs for Section~\\ref{sec:wbga}}\\label{sec:proofs_wbga}\nIn this section we provide the proofs of the results from Section~\\ref{sec:wbga}.\nWe begin with a known lemma.\n\\begin{Lemma}[{\\cite[Lemma~6.1]{VT140}}]\\label{lem:E'_L=0}\nLet $E$ be a uniformly smooth Fr{\\'e}chet-differentiable convex function on a Banach space $X$ and $L$ be a finite-dimensional subspace of $X$.\nLet $x_L$ denote the point from $L$ at which $E$ attains the minimum, i.e.\n\\[\n\tx_L = \\mathop{\\operatorname{argmin}}_{x \\in L} E(x) \\in L.\n\\]\nThen for any $\\phi \\in L$ we have\n\\[\n\t\\ = 0.\n\\]\n\\end{Lemma}\n\n\\noindent\nWe now prove that the algorithms stated in Section~\\ref{sec:wbga_ga} belong to the class $\\mathcal{WBGA}$(co).\n\\begin{proof}[Proof of Proposition~\\ref{prp:ga_wbga}]\nIt is easy to see that conditions~\\ref{wbga_gs} and~\\ref{wbga_er} from the definition of the class $\\mathcal{WBGA}$(co) are satisfied for all three algorithms.\nCondition~\\ref{wbga_bo} for any $m \\ge 1$ follows directly from Lemma~\\ref{lem:E'_L=0} with $x_L = \\phi = G_m$ and\n\\[\n\tL = \\Phi^c_m = \\operatorname{span}\\{\\varphi_1^c, \\ldots, \\varphi_m^c\\}\n\\]\nfor the WCGA(co), and\n\\[\n\tL = \\operatorname{span}\\{G_{m-1}^f, \\varphi_m^f\\}\n\t\\ \\ \\text{or}\\ \\ \n\tL = \\operatorname{span}\\{G_{m-1}^r, \\varphi_m^r\\}\n\\]\nfor the WGAFR(co) \/ RWRGA(co) respectively.\n\\end{proof}\n\n\n\\noindent\nWe proceed by listing the lemmas that will be utilized later in the proofs of the main results.\nThe following simple lemma is well-known (see, for instance, \\cite{VT140}).\nFor the reader's convenience we present its proof here.\n\\begin{Lemma}[{\\cite[Lemma~6.3]{VT140}}]\\label{lem:E'_rho}\nLet $E$ be a Fr{\\'e}chet-differentiable convex function.\nThen the following inequality holds for any $x \\in S \\subset X$, $y \\in X$, and $u \\in \\mathbb{R}$\n\\[\n\t0 \\le E(x + uy) - E(x) - u\\ \\le 2\\rho(E,S,u\\|y\\|).\n\\]\n\\end{Lemma}\n\\begin{proof}\nThe left inequality follows directly from~\\eqref{eq:E'_conv1}.\nNext, from the definition of modulus of smoothness~\\eqref{eq:mod_smt} it follows that\n\\[\n\tE(x + uy) + E(x - uy) \\le 2\\big( E(x) + \\rho(E,S,u\\|y\\|) \\big).\n\\]\nFrom inequality \\eqref{eq:E'_conv1} we get\n\\[\n\tE(x - uy) \\ge E(x) - u\\. \n\\]\nCombining the above two estimates, we obtain\n\\[\n\tE(x + uy) \\le E(x) + u\\ + 2\\rho(E,S,u\\|y\\|),\n\\]\nwhich proves the second inequality. \n\\end{proof}\n\n\n\\begin{Lemma}[{\\cite[Lemma~6.10]{VTbook}}]\\label{lem:F_A1(D)}\nFor any bounded linear functional $F$ and any dictionary $\\mathcal{D}$, we have\n\\[\n\t\\sup_{g\\in \\mathcal{D}} \\ = \\sup_{f\\in\\mathcal{A}_1(\\mathcal{D})} \\.\n\\]\n\\end{Lemma}\n\n\n\\noindent\nThe following lemma is similar to the result from~\\cite{T13}.\nFor the reader's convenience we present a brief proof of this lemma here.\n\\begin{Lemma}\\label{lem:y_k}\nSuppose that a sequence $y_1 \\ge y_2 \\ge y_3 \\ge \\ldots > 0$ satisfies inequalities\n\\[\n\ty_k \\le y_{k-1} (1 - w_k y_{k-1}), \\ \\ w_k \\ge 0\n\\]\nfor any $k > n$.\nThen for any $m > n$ we have\n\\[\n\t\\frac{1}{y_m} \\ge \\frac{1}{y_n} + \\sum_{k=n+1}^m w_k.\n\\]\n\\end{Lemma}\n\\begin{proof}\nThe proof follows directly from the chain of inequalities\n\\[\n\t\\frac{1}{y_k} \\ge \\frac{1}{y_{k-1}} (1 - w_k y_{k-1})^{-1}\n\t\\ge \\frac{1}{y_{k-1}} (1 + w_k y_{k-1})\n\t= \\frac{1}{y_{k-1}} + w_k.\n\\]\n\\end{proof}\n\n\n\\noindent\nThe following lemma is our key tool for establishing convergence and rate of convergence of algorithms from the class $\\mathcal{WBGA}$(co).\n\\begin{Lemma}[{{\\bf Error Reduction Lemma}}]\\label{lem:erl}\nLet $E$ be a uniformly smooth on $D \\subset X$ convex function with the modulus of smoothness $\\rho(E,D,u)$.\nTake a number $\\epsilon\\ge 0$ and an element $f^\\epsilon \\in D$ such that\n\\[\n\tE(f^\\epsilon) \\le \\inf_{x \\in X} E(x) + \\epsilon, \\ \\ \n\tf^\\epsilon \/ A \\in \\mathcal{A}_1(\\mathcal{D}),\n\\]\nwith some number $A := A(\\epsilon) \\ge 1$.\nThen for any algorithm from the class $\\mathcal{WBGA}$(co) we have for any $m \\ge 1$\n\\begin{multline*}\n\tE(G_m) - E(f^\\epsilon) \\le E(G_{m-1}) - E(f^\\epsilon)\n\t\\\\\n\t+ \\inf_{\\lambda\\ge0} \\Big(-\\lambda t_m A^{-1} (E(G_{m-1})-E(f^\\epsilon)) + 2\\rho(E,D,\\lambda) \\Big).\n\\end{multline*}\n\\end{Lemma}\n\\begin{proof}\nThe main idea of the proof is the same as in the proof of the corresponding one-step improvement inequality for the WCGA (see, for instance, \\cite[Lemma~6.11]{VTbook}).\nIt follows from~\\ref{wbga_er} of the definition of the class $\\mathcal{WBGA}$(co) that\n\\[\n\tE(0) \\ge E(G_1) \\ge E(G_2) \\ldots.\n\\]\nThus if $E(G_{m-1}) - E(f^\\epsilon) \\le 0$ then the claim of Lemma~\\ref{lem:erl} is trivial.\nAssuming $E(G_{m-1}) - E(f^\\epsilon) > 0$, Lemma~\\ref{lem:E'_rho} provides for any $\\lambda \\ge 0$\n\\[\n\tE(G_{m-1} + \\lambda \\varphi_m) \\le E(G_{m-1}) - \\lambda \\<-E'(G_{m-1}),\\varphi_m\\> + 2 \\rho(E,D,\\lambda)\n\\]\nand by~\\ref{wbga_gs} from the definition of the class $\\mathcal{WBGA}$(co) and Lemma~\\ref{lem:F_A1(D)} we get\n\\begin{align*}\n\t\\<-E'(G_{m-1}),\\varphi_m\\> \n\t&\\ge t_m \\sup_{g\\in \\mathcal{D}} \\<-E'(G_{m-1}),g\\> \n\t\\\\\n\t&= t_m\\sup_{\\phi \\in \\mathcal{A}_1(\\mathcal{D})} \\<-E'(G_{m-1}),\\phi\\>\n\t\\ge t_m A^{-1} \\<-E'(G_{m-1}),f^\\epsilon\\>.\n\\end{align*}\nBy~\\ref{wbga_bo} from the definition of the class $\\mathcal{WBGA}$(co) and by convexity~\\eqref{eq:E'_conv2} we obtain\n\\[\n\t\\<-E'(G_{m-1}),f^\\epsilon\\> = \\<-E'(G_{m-1}),f^\\epsilon-G_{m-1}\\> \\ge E(G_{m-1})-E(f^\\epsilon).\n\\]\nThus, by~\\ref{wbga_er} from the definition of the $\\mathcal{WBGA}$(co) we deduce\n\\begin{align*}\n\tE(G_m) &\\le \\inf_{\\lambda\\ge0} E(G_{m-1} + \\lambda\\varphi_m)\n\t\\\\\n\t&\\le E(G_{m-1}) + \\inf_{\\lambda\\ge0} \\Big( -\\lambda t_m A^{-1} (E(G_{m-1}) - E(f^\\epsilon)) + 2\\rho(E,D,\\lambda) \\Big),\n\\end{align*}\nwhich proves the lemma.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:wbga_conv}]\nThe error reduction property~\\ref{wbga_er} of the class $\\mathcal{WBGA}$(co) implies that the sequence of minimizers $\\{G_m\\}_{m=0}^\\infty$ is in $D$ and the sequence $\\{E(G_m)\\}_{m=0}^\\infty$ is non-increasing.\nTherefore, we have\n\\[\n\t\\lim_{m\\to \\infty} E(G_m) = a \\ge \\inf_{x\\in D}E(x).\n\\]\nDenote\n\\[\n\tb := \\inf_{x\\in D} E(x)\n\t\\ \\ \\text{and}\\ \\ \n\t\\alpha := a - b.\n\\]\nWe prove that $\\alpha = 0$ by contradiction.\nIndeed, assume that $\\alpha > 0$.\nThen for any $m \\ge 0$ we have\n\\[\n\tE(G_m) - b \\ge \\alpha.\n\\]\nWe set $\\epsilon = \\alpha\/2$ and find $f^\\epsilon \\in D$ such that\n\\[\n\tE(f^\\epsilon) \\le b + \\epsilon \\ \\ \\text{and}\\ \\ f^\\epsilon\/A \\in \\mathcal{A}_1(\\mathcal{D})\n\\]\nwith some $A := A(\\epsilon) \\ge 1$.\nThen by Lemma~\\ref{lem:erl} we get\n\\[\n\tE(G_m) - E(f^\\epsilon) \\le E(G_{m-1}) - E(f^\\epsilon) + \\inf_{\\lambda\\ge0} (-\\lambda t_m A^{-1}\\alpha\/2 + 2\\rho(E,D,\\lambda)).\n\\]\nSpecify $\\theta := \\min\\left\\{ \\theta_0,\\frac{\\alpha}{8A} \\right\\}$ and take $\\lambda = \\xi_m(\\rho,\\tau,\\theta)$ given by~\\eqref{eq:theta}.\nThen we obtain\n\\[\n\tE(G_m) \\le E(G_{m-1}) - 2\\theta t_m\\xi_m.\n\\]\nThe assumption\n\\[\n\t\\sum_{m=1}^\\infty t_m\\xi_m =\\infty\n\\]\nimplies a contradiction, which proves the theorem.\n\\end{proof}\n\n \n\\begin{proof}[Proof of Theorem~\\ref{thm:wbga_rate}]\nDenote\n\\[\n\ta_n := E(G_n) - E(f^\\epsilon),\n\\]\nthen the sequence $\\{a_n\\}_{n=0}^\\infty$ is non-increasing.\nIf for some $n \\le m$ we have $a_n \\le 0$ then $E(G_m) - E(f^\\epsilon) \\le 0$, which implies\n\\[\n\tE(G_m) - \\inf_{x \\in D} E(x) \\le \\epsilon,\n\\]\nand hence the statement of the theorem holds.\nThus we assume that $a_n > 0$ for $n \\le m$.\nBy Lemma~\\ref{lem:erl} we have\n\\begin{equation}\\label{B3}\n\ta_m \\le a_{m-1} + \\inf_{\\lambda\\ge0} \\left(-\\frac{\\lambda t_m a_{m-1}}{B} + 2\\gamma \\lambda^q\\right).\n\\end{equation}\nChoose $\\lambda$ from the equation\n\\[\n\t\\frac{\\lambda t_m a_{m-1}}{A} = 4\\gamma \\lambda^q,\n\\]\nwhich implies that\n\\[\n\t\\lambda = \\left(\\frac{ t_m a_{m-1}}{4\\gamma A}\\right)^{\\frac{1}{q-1}} .\n\\]\nLet\n\\[\n\tA_q := 2(4\\gamma)^{\\frac{1}{q-1}}.\n\\]\nUsing the notation $p := q\/(q-1)$ we get from~\\eqref{B3}\n\\[\n\ta_m \\le a_{m-1}\\left(1-\\frac{\\lambda t_m}{2A} \\right)\n\t= a_{m-1}\\left(1 - \\frac{t_m^p}{A_q A^{p}} a_{m-1}^{\\frac{1}{q-1}}\\right).\n\\]\nRaising both sides of this inequality to the power $1\/(q-1)$ and taking into account the inequality $x^r\\le x$ for $r\\ge 1$, $0\\le x\\le 1$, we obtain\n\\[\n\ta_m^{\\frac{1}{q-1}} \\le a_{m-1}^{\\frac{1}{q-1}} \\left(1 - \\frac{t^p_m}{A_q A^{p}} a_{m-1}^{\\frac{1}{q-1}}\\right).\n\\]\nThen Lemma~\\ref{lem:y_k} with $y_k := a_k^{\\frac{1}{q-1}}$, $n=0$, $w_k=t^p_m\/(A_qA^{p})$, which provides\n\\[\n\ta_m^{\\frac{1}{q-1}} \\le C(q,\\gamma) A^{p}\\left(C(E,q,\\gamma) + \\sum_{k=1}^m t_k^p\\right)^{-1},\n\\]\nthat implies\n\\[\n\ta_m \\le C(q,\\gamma) A^q\\left(C(E,q,\\gamma) + \\sum_{k=1}^m t_k^p\\right)^{1-q},\n\\]\nwhich proves the theorem.\n\\end{proof}\n\n\n\n\\section{Proofs for Section~\\ref{sec:awbga}}\\label{sec:proofs_awbga}\nIn this section we state the proofs for the results from Section~\\ref{sec:awbga}.\nWe begin with the proof that the algorithms stated in Section~\\ref{sec:awbga_ga} belong to the class $\\mathcal{WBGA}(\\Delta,\\text{co})$.\n\\begin{proof}[Proof of Proposition~\\ref{prp:ga_awbga}]\nIt is easy to see that conditions~\\ref{awbga_gs} and~\\ref{awbga_er} from the definition of the class $\\mathcal{WBGA}(\\Delta,\\text{co})$ are satisfied for all three algorithms.\nCondition~\\ref{awbga_bd} holds with $C_0 = 1$ since for all three algorithms we have for any $m \\ge 1$\n\\[\n\tE(G_m) \\le E(0) + \\delta_m \\le E(0) + 1.\n\\]\nTo guarantee condition~\\ref{awbga_bo}, first note that for any $m \\ge 1$ and any $u > 0$ the definition of modulus of smoothness~\\eqref{eq:mod_smt} provides\n\\[\n\tE((1+u) G_m) + E((1-u) G_m) \\le 2E(G_m) + 2\\rho(E,D_1,u\\|G_m\\|).\n\\]\nAssume that $\\ \\ge 0$ (the case $\\ < 0$ is handled similarly).\nThen from convexity~\\eqref{eq:E'_conv1} we get\n\\[\n\tE((1+u) G_m) \\ge E(G_m) + u \\\n\\]\nand from the definitions of the corresponding algorithms we obtain\n\\[\n\tE((1-u) G_m) \\ge E(G_m) - \\delta_m.\n\\]\nCombining the above estimates we deduce\n\\[\n\t\\ \\le \\frac{\\delta_m + 2\\rho(E,D_1, u \\|G_m\\|)}{u}.\n\\]\nTaking infimum over $u > 0$ completes the proof.\n\\end{proof}\n\n\n\\noindent\nNext, we state necessary technical lemmas that will be utilized in the proof of main results.\n\\begin{Lemma}[{\\cite[Lemma~3.2]{VT148}}]\\label{lem:a_delta_0}\nLet $\\rho(u)$ be a non-negative convex on $[0,1]$ function with the property $\\rho(u)\/u\\to0$ as $u\\to 0$.\nAssume that a nonnegative sequence $\\{\\alpha_k\\}_{k=1}^\\infty$ is such that $\\alpha_k\\to0$ as $k\\to\\infty$.\nSuppose that a nonnegative sequence $\\{a_k\\}_{k=0}^\\infty$ satisfies the inequalities\n\\[\n\ta_m \\le a_{m-1} + \\inf_{0\\le\\lambda\\le1}(-\\lambda va_{m-1} + B\\rho(\\lambda)) + \\alpha_m, \\ \\ m = 1,2,3,\\dots\n\\]\nwith positive numbers $v$ and $B$.\nThen\n\\[\n\t\\lim_{m\\to\\infty} a_m = 0.\n\\]\n\\end{Lemma}\n\n\n\\begin{Lemma}[{\\cite[Lemma~3.3]{VT148}}]\\label{lem:a_delta_q}\nSuppose a nonnegative sequence $a_0,a_1,\\dots$ satisfies the inequalities for $m = 1,2,3,\\dots$\n\\[\n\ta_m\\le a_{m-1} + \\inf_{0\\le \\lambda\\le 1}(-\\lambda va_{m-1}+B\\lambda^q) + \\alpha_m, \\ \\ \\alpha_m \\le cm^{-q},\n\\]\nwhere $q\\in (1,2]$, $v\\in(0,1]$, and $B > 0$.\nThen\n\\[\n\ta_m \\le C(q,v,B,a_0,c) \\, m^{1-q} \\le C'(q,B,a_0,c) \\, v^{-q} \\, m^{1-q}.\n\\]\n\\end{Lemma}\n\n\n\\noindent\nLastly, we establish a generalized version of Lemma~\\ref{lem:erl}.\n\\begin{Lemma}[{{\\bf General Error Reduction Lemma}}]\\label{lem:gerl}\nLet $E$ be a uniformly smooth on $S \\subset X$ convex function with the modulus of smoothness $\\rho(E,S,u)$.\nTake a number $\\epsilon \\ge 0$ and an element $f^\\epsilon \\in S$ such that\n\\[\n\tE(f^\\epsilon) \\le \\inf_{x\\in X} E(x) + \\epsilon, \\ \\ \n\tf^\\epsilon\/B \\in \\mathcal{A}_1(\\mathcal{D}),\n\\]\nwith some number $B \\ge 1$.\nSuppose that $G \\in S$ and $\\varphi \\in \\mathcal{D}$ satisfy the following conditions\n\\begin{gather}\n\t\\label{C1}\n\t\\<-E'(G),\\varphi\\> \\ge \\theta \\sup_{g\\in \\mathcal{D}} \\<-E'(G),g\\>, \\ \\ \\theta \\in (0,1];\n\t\\\\\n\t\\label{C2}\n\t|\\| \\le \\delta, \\ \\ \\delta \\in [0,1].\n\\end{gather}\nThen we have\n\\begin{align*}\n\t\\inf_{0\\le\\lambda\\le1} E(G+\\lambda\\varphi)\n\t&\\le E(G)\n\t\\\\\n\t&+ \\inf_{0\\le\\lambda\\le1} (-\\lambda \\theta B^{-1} (E(G) - E(f^\\epsilon)) + 2\\rho(E,S,\\lambda)) + \\delta.\n\\end{align*}\n\\end{Lemma}\n\\begin{proof}\nIf $E(G_{m-1}) - E(f^\\epsilon) \\le 0$ then the claim of Lemma~\\ref{lem:gerl} is trivial.\nAssuming $E(G_{m-1}) - E(f^\\epsilon) > 0$, Lemma~\\ref{lem:E'_rho} provides for any $\\lambda \\ge 0$\n\\[\n\tE(G + \\lambda \\varphi) \\le E(G) - \\lambda \\<-E'(G),\\varphi\\> + 2 \\rho(E,S,\\lambda).\n\\]\nBy~\\eqref{C1} and Lemma~\\ref{lem:F_A1(D)} we get\n\\begin{align*}\n\t\\<-E'(G),\\varphi\\>\n\t&\\ge \\theta \\sup_{g \\in \\mathcal{D}} \\<-E'(G),g\\>\n\t\\\\\n\t&= \\theta\\sup_{\\phi \\in \\mathcal{A}_1(\\mathcal{D})} \\<-E'(G),\\phi\\> \\ge \\theta B^{-1} \\<-E'(G),f^\\epsilon\\>.\n\\end{align*}\nBy~\\eqref{C2} and by convexity~\\eqref{eq:E'_conv2} we obtain\n\\[\n\t\\<-E'(G),f^\\epsilon\\> = \\<-E'(G),f^\\epsilon-G\\> + \\<-E'(G),G\\> \\ge E(G)-E(f^\\epsilon)-\\delta.\n\\]\nThus\n\\[\n\tE(G + \\lambda\\varphi) \\le E(G) - \\lambda \\theta B^{-1} (E(G) - E(f^\\epsilon)) + 2\\rho(E,S,\\lambda)) + \\delta,\n\\]\nwhich proves the lemma.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:awbga_conv}]\nAssumption~\\ref{awbga_bd} from the definition of the class $\\mathcal{WBGA}(\\Delta,\\text{co})$ implies that for any $m \\ge 0$\n\\[\n\tE(G_m) \\le E(0) + C_0\n\t\\ \\ \\text{and}\\ \\\n\tG_m \\in D_1.\n\\]\nThen from Lemma~\\ref{lem:gerl} with $S = D_1$, $G = G_{m-1}$, $\\varphi = \\varphi_m$, $\\delta = \\epsilon_m$, $\\theta = t$, $B = A(\\epsilon)$ and property~\\ref{awbga_er} from the definition of the class $\\mathcal{WBGA}(\\Delta,\\text{co})$ we obtain\n\\begin{align}\\nonumber\n\tE(G_m)\n\t&\\le \\inf_{0\\le\\lambda\\le1} E(G_{m-1} + \\lambda\\varphi_m) + \\delta_m\n\t\\\\\\nonumber\n\t&\\le E(G_{m-1}) + \\inf_{0\\le\\lambda\\le 1} \\big(-\\lambda t A^{-1} (E(G_{m-1}) - E(f^\\epsilon))\n\t\\\\\\label{eq:E(G_m)}\n\t&\\phantom{E(G_{m-1}) + \\inf_{0\\le\\lambda\\le 1} \\big(-\\lambda}\n\t+ 2\\rho(E,D_1,\\lambda) \\big) + \\delta_m + \\epsilon_m.\n\\end{align}\nDenote\n\\[\n\ta_n := \\max\\big\\{ E(G_n) - E(f^\\epsilon), 0 \\big\\}.\n\\]\nNote that under our assumptions $t \\in (0,1]$ and $A := A(\\epsilon) \\ge 1$ we always have\n\\[\n\ta_{m-1} + \\inf_{0\\le\\lambda\\le 1}(-\\lambda t A^{-1} a_{m-1} + 2\\rho(E,D_1,\\lambda)) \\ge 0.\n\\]\nTherefore estimate~\\eqref{eq:E(G_m)} implies\n\\begin{equation}\\label{eq:a_m}\n\ta_m \\le a_{m-1} + \\inf_{0\\le\\lambda\\le 1} (-\\lambda t A^{-1} a_{m-1} + 2\\rho(E,D_1,\\lambda)) + \\delta_m + \\epsilon_m.\n\\end{equation}\nWe apply Lemma~\\ref{lem:a_delta_0} with $v = tA^{-1}$, $B = 2$, $\\rho(u) = \\rho(E,D_1,\\lambda)$, and $\\alpha_m = \\delta_m + \\epsilon_m$ to obtain\n\\[\n\t\\lim_{m\\to\\infty} a_m = 0,\n\\]\nwhich implies\n\\[\n\t\\limsup_{m\\to\\infty} E(G_m) \\le \\epsilon + \\inf_{x\\in D_1} E(x)\n\\]\nand, due to the arbitrary nature of choice of $\\epsilon > 0$,\n\\[\n\t\\lim_{m\\to\\infty} E(G_m) = \\inf_{x\\in D_1} E(x),\n\\]\nwhich completes the proof.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:awbga_rate}]\nFrom estimate~\\eqref{eq:a_m} we get\n\\begin{align*}\n\ta_m\n\t&\\le a_{m-1} + \\inf_{0\\le\\lambda\\le 1} (-\\lambda t A^{-1} a_{m-1} + 2\\rho(E,D_1,\\lambda)) + \\delta_m + \\epsilon_m\n\t\\\\\n\t&\\le a_{m-1} + \\inf_{0\\le\\lambda\\le 1}(-\\lambda t A^{-1} a_{m-1} + 2\\gamma\\lambda^q) + \\delta_m + \\epsilon_m.\n\\end{align*}\nApplying Lemma~\\ref{lem:a_delta_q} with $v = t A^{-1}$, $B = 2\\gamma$, and $\\alpha_m = \\delta_m + \\epsilon_m$ completes the proof.\n\\end{proof}\n\n\n\n\n\n\\section*{Acknowledgments}\nThe first author acknowledges support given by the Oak Ridge National Laboratory, which is operated by UT-Battelle, LLC., for the U.S. Department of Energy under Contract DE-AC05-00OR22725.\n\nThe work was supported by the Russian Federation Government Grant N{\\textsuperscript{\\underline{o}}}14.W03.31.0031. The paper contains results obtained in frames of the program \"Center for the storage and analysis of big data\", supported by the Ministry of Science and High Education of Russian Federation (contract 11.12.2018 N{\\textsuperscript{\\underline{o}}}13\/1251\/2018 between the Lomonosov Moscow State University and the Fond of support of the National technological initiative projects).\n\n\\section*{References}\n\\bibliographystyle{amsplain}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThis article is a complementary part to the work done in \\cite{CMFixed} with \nits own independent interest. We discuss geometric conditions under which \nthere are no invariant Beltrami differentials supported on the dissipative set \nof a rational map $R$. \n\nIn this paper we will always assume that the conservative set of the action of \n$R$ belongs to the Julia set.\n\nNow, let us introduce the geometric objects to be treated in this \npaper. \n\nWe denote by $P(R)$ the closure of the postcritical set of $R$ and consider \nthe surface $S_R:=\\bar{\\C}\\setminus P(R)$. The surface $S_R$ is not always \nconnected, however, on each connected component of $S_R$ we fix a \nPoincar\\'e hyperbolic metric and denote by $\\lambda$ the family of all these \nmetrics.\n\nLet $Q(S_R)$ be the subspace of $ L_1(S_R)$ of holomorphic \nintegrable functions on $S_R.$\n\nA rational map $R$ defines a complex Push-Forward map on $L_1(\\C)$, with \nrespect to the Lebesgue measure $m$, which is a contracting endomorphism and \nis called the complex Ruelle-Perron-Frobenius, for shortness \nRuelle operator. The Ruelle operator has the following formula:\n\n\\[R^*(\\phi)(z)=\\sum_{y \\in R^{-1}(z)} \\frac{\\phi(y)}{R'(y)^2}\nR(\\zeta)\\]\n\\[=\\sum_i \\phi(\\zeta_i)(z)\\zeta'_i(z)\\] where $\\zeta_i$ is any local complete \nsystem of branches of $R^{-1}.$ The space $Q(S_R)$ is invariant under the action \nof the Ruelle operator. \nThe Beltrami operator $Bel:L_\\infty(\\C)\\rightarrow L_\\infty(\\C)$ given by \n\\[Bel(\\mu)=\\mu(R)\\frac{\\overline{R'}}{R'}\\] is dual to the Ruelle operator \nacting on $L_1(\\C)$. \n\nThe fixed point space $Fix(B)$ of the Beltrami operator is called the \n\\textit{space of invariant Beltrami differentials}. An element $\\alpha \\in \nL_\\infty(\\C)$ is called non trivial if and only if the functional given by \n\\[v_\\alpha(\\phi) = \\int \\phi \\alpha\\] is non zero on $Q(S_R).$ The norm of \n$v_\\alpha$ in $Q^*(S_R)$, for a non trivial element $\\alpha$, is called the \n\\textit{Teichm\\\"uller norm} of $\\alpha$ and it is denoted by $\\| \\alpha \n\\|_{T}.$ \n\nA non trivial element $\\alpha$ is called \\textit{extremal} if and only if the \n$\\|\\alpha\\|_\\infty=\\|\\alpha\\|_T.$ \n\nA sequence of unit vectors $\\{\\phi_i\\}$ is called a \n\\textit{Hamilton-Krushkal} sequence, for short HK-sequence, for an extremal \nelement $\\alpha$ if and only if \\[ \\lim_{i\\rightarrow \n\\infty}|v_\\alpha(\\phi_i)|=\\|\\alpha\\|_\\infty.\\]\n\nA HK sequence $\\{\\phi_i\\}$ is called \\textit{degenerated} if converge to $0$ \nuniformly on compact sets.\n \nLet $T:\\mathcal{B}\\rightarrow \\mathcal{B}$ be a linear contraction of a Banach \nspace $\\mathcal{B}$. An element \n$b\\in \\mathcal{B}$ is called \\textit{mean ergodic} with respect to $T$ if and \nonly if the sequence of Ces\\`aro averages with respect to $T$, given by\n$C_n(b)=\\frac{1}{n}\\sum_{i=0}^{n-1} T^i(b)$, forms a weakly precompact family. \nIndeed (see Krengel \\cite{Krengel}), when $\\mathcal{B}$ is weakly complete then, for \na mean ergodic element $b$, the sequence $C_n(b)$ converges in norm to its limit, \nthis limit always is a fixed element of $T$. If every element $b\\in \\mathcal{B}$ \nis mean ergodic with respect to $T$ then the operator $T$ is called \nmean-ergodic.\n\nBy the Bers Representation Theorem, the space $Q^*(S_R)$ is linearly \nquasi-isome\\-trically isomorphic to the \\textit{Bergman} space $B(S_R)$ which \nis \nthe space of holomorphic functions $\\phi$ on $S_R$ with the \nnorm $\\|\\lambda^{-2}\\phi\\|_{L_\\infty(S_R)}.$\n\nIn the case where $S_R$ has finitely many components, a classical theorem, \nsee for example \\cite{Matsuzaki} and references within, states that \n$Q(S_R)\\subset B(S_R)$ if and only if the infimum of the length of simple \nclosed geodesics is bounded away from $0$.\n\n\\section{Main Theorem}\nLet $X$ be an $R$ invariant measurable set, then the set $W:=\\bigcup \nR^{-n}(X)$ is completely invariant. In the following theorem we will only \nconsider Ces\\`aro averages with respect to the Ruelle operator $R^*$ in \n$L_1(W).$\n\n\n\\begin{theorem}\\label{MainTechnicalThm}\nLet $X$ be an $R$ invariant measurable subset such that the restriction map \n$r(\\phi)= \\phi|_X$ from $Q(S_R)$ to $L_1(X) $ is weakly precompact. Then \nevery $\\phi \\in Q(S_R)$ is mean ergodic with respect to $R^*$ in $L_1(W)$. \n\\end{theorem}\n\n\\begin{proof}\nIf $X$ is $R$ invariant then the Ruelle operator $R^*$ defines an endomorphism \nof $L_1(X)$. Given $\\phi \\in Q(S_R)$, the family of Ces\\`aro averages \n$C_n(\\phi)$ restricted on $X$ forms a weakly precompact subset of $L_1(X).$ \nWe claim that $C_n(\\phi)$ converges in norm on $L_1(X).$ Indeed, first \nwe show that every weak accumulation point of $C_n(\\phi)$ is a fixed point for \nthe Ruelle operator. Let $f$ be the weak limit of $C_{n_i}(\\phi)$ for some \nsubsequence $\\{n_i\\},$ then $R^*(f)$ is the weak limit of $R^*(C_{n_i}(\\phi))$.\nBy the Fatou Lemma \n$$\\int_X |f-R^*(f)| \\leq \\liminf \\int_X |C_{n_i}(\\phi)-R^*(C_{n_i}(\\phi))|$$\n$$\\leq \\liminf \\| C_{n_i}(\\phi)-R^*(C_{n_i}(\\phi))\\|_{L_1(S_R)}$$ \n$$\\leq \\limsup \\|C_{n_i}(I-R^*)(\\phi)\\|_{L_1(S_R)}.$$\n\nBut \n$$ \\|C_{n_i}(I-R^*)(\\phi)\\|_{L_1(S_R)}\\leq \\frac{2}{n_i}\\|\\phi\\|_{L_1(S_R)}.$$\nThen $f$ is a non zero fixed point of Ruelle operator. As in \\cite{MakRuelle} \nwe have that $|f|$ defines a finite absolutely continuous invariant measure. \nHence, the support of $f$ is a non trivial subset of the conservative set of \n$R.$ By Lyubich's Ergodicity theorem (see \\cite{Mc1} and \\cite{LyuTypical}) and \nthe fact \nthat $X$ does not intersect the postcritical set we have $X=W=S_R$. But, \nMcMullen's Theorem (Theorem 3.9 of \\cite{Mc1}) implies that in this case $R$ is \na, so called, \\textit{flexible Latt\\`es} map. Furthermore, \nthe space $Q(S_R)$ is finitely dimensional and hence $R^*$ is a compact \nendomorphism of $Q(S_R)$, it follows that $R^*$ is mean ergodic on $Q(S_R)$. \n\nTherefore, if $R$ is not a flexible Latt\\`es map then any weak limit of \n$C_n(\\phi)$ is $0$. Since the weak closure of convex bounded sets is equal to \nthe closure in norm of convex bounded sets, we conclude our claim.\n\nNow let $W_n=R^{-n}(X)$, one can inductively prove that \n$\\phi|_{W_n}$ is mean ergodic on $L_1(W_n)$. Indeed, let $\\psi_n=\\phi|_{W_n}$, \nsince $R^*:L_1(W_n)\\rightarrow L_1(W_{n-1}) \\subset L(W_n)$ \nand $R^*(\\psi_n)=R^*(\\phi)|_{W_{n-1}}$, then by arguments above we are done. \n\nNow consider $\\phi|_{W}-\\phi|_{W_n}$, the $L_1$ norm of this difference \nconverges to $0$ in $L_1(W)$, since the Ces\\`aro averages does not expand the \n$L_1$ norm we have $$\\|C_k(\\phi|_{W}-\\phi|_{W_n})\\| \\leq \n\\|\\phi|_{W}-\\phi|_{W_n}\\|.$$\n\nHence $C_k(\\phi|_{W})$ converges to $0$ and $\\phi$ is mean ergodic on $L_1(W)$. \n\n\\end{proof}\n\nNow we state our Main Theorem.\n\n\\begin{theorem}\\label{MainTheorem}\nLet $R$ be a rational map and let $X\\subset S_R$ be an invariant \nmeasurable set of positive Lebesgue measure.\nAssume that the restriction map $r(\\phi)=\\phi|_X$ from $Q(S_R)$ into \n$L_1(X)$ is weakly precompact. If $\\mu$ is a non trivial invariant Beltrami \ndifferential, then $m(supp (\\mu)\\cap X)>0$ if and only if $R$ is a flexible \nLatt\\`es map.\n\\end{theorem}\n\n\\begin{proof}\nAssume that $R$ is a flexible Latt\\`es map. Then $R$ is ergodic on the Riemann \nsphere and therefore the support of any invariant Beltrami differential $\\mu$ \nis the whole Riemann sphere. Hence, if $X$ is invariant of positive Lebesgue \nmeasure then $m(supp(\\mu)\\cap X)=m(X)>0.$ \n\nAgain let $W=\\bigcup R^{-n}(X)$. Now let $\\mu$ be a non \ntrivial invariant Beltrami differential supported on $W$. If $R$ is not \nLatt\\`es, then for any $\\phi \\in Q(S_R)$ we have $$\\int_{S_R} \\phi \\mu \n=\\int_{S_R} \\mu C_k(\\phi)=\\int_{W} \\mu C_k(\\phi).$$ \n\nBy Theorem \\ref{MainTechnicalThm}, the right hand side converges to $0$ as $k$ \nconverges to $\\infty$. Hence $\\int \\phi \\mu=0$ for every quadratic \ndifferential $\\phi$ and the functional $\\phi\\mapsto \\int \\phi \\mu$ is $0$ on \n$Q(S_R).$ Which contradicts the assumption that $\\mu$ is non trivial. \n\n\\end{proof}\n\n\nIn the proofs of the previous theorems, the only ingredient was the\nprecompactness of the Ces\\`aro averages $C_n(\\phi)$. Hence, \nit is enough to assume the weak precompactness only of \nCes\\`aro averages on elements of $Q(S_R)$. \nBy results of the second author in \\cite{MakRuelle}, see also a related work \non \\cite{CMFixed}, it is enough to consider the Ces\\`aro averages of \nrational functions in $Q(S_R)$ having poles only on the set of critical values. \n\n\\section{Compactness}\nWe want to discuss conditions under which the restriction map $\\phi\\mapsto \n\\phi|_A$ is weakly precompact. Unfortunately, so far we have not found \nconditions where the restriction is weakly precompact but not compact.\nLet us start with the following observations and definitions. \n\n\\begin{definition}\nA rational map $R$ satisfies the $B$-condition if and only if for any $\\phi\\in \nQ(S_R)$ we have $$\\|\\lambda^{-2}(z) \n\\phi(z)\\|_{L_\\infty(S_R)}\\leq C \\|\\phi(z)\\|_{L_1(S_R)},$$ where $C$ \nis a constant independent of $\\phi.$ \n\\end{definition}\nIn other words, if $R$ satisfies the $B$-condition, then\n$Q(S_R)\\subset B(S_R)$ and the inclusion\nmap $Q(S_R)\\rightarrow B(S_R)$ is continuous. As it was noted on the \nintroduction, this happens when $S_R$ has finitely many components and \nthe infimum of the length of the simple closed geodesics is bounded away from \n$0.$\n\n\\begin{proposition}\\label{prop.Bcond.comp}\nIf $R$ satisfies the $B$-condition and $\\lambda(X)<\\infty$ then the \nrestriction map is compact.\n\\end{proposition}\n\n\\begin{proof}\nIf $R$ satisfies the $B$-condition then $$\\lambda^{-2}|\\phi(z)|\\leq sup_{z\\in \nS_R} |\\lambda^{-2}(z) \\phi(z) | \\leq C \\|\\phi \\|_1,$$ hence $|\\phi(z)|\\leq \nC\\| \n\\phi\\|_1 \\lambda^{2}(z),$ by Lebesgue Theorem \nthe restriction map is compact.\n\\end{proof}\n\nUsing Theorem \\ref{MainTheorem} and Proposition \\ref{prop.Bcond.comp} \nwe have the following.\n\\begin{corollary}\\label{cor.area}\nIf $R$ satisfies the $B$-condition and $X$ is an invariant set of \npositive Lebesgue measure with $Area_\\lambda(X)<\\infty$. If $\\mu$ is a non \nzero invariant Beltrami differential, then $m(supp(\\mu)\\cap X)>0$ if and only \nif $R$ is a flexible Latt\\`es map. \n\\end{corollary}\nIn general, the finiteness of the hyperbolic area of $X$ does not \nimply the finiteness of hyperbolic area of $W$. Generically, it \ncould be that the hyperbolic area of $W$ is infinite regardless of the area of \n$X$. \nOn the other hand, by Corollary \\ref{cor.area}, if $R$ satisfies the \n$B$-condition and the hyperbolic area \n$Area_\\lambda(J(R))$ is bounded then $R$ satisfies Sullivan's conjecture. \nHowever, in this situation, we believe that the following stronger statement \nholds true:\n\nThe $Area_\\lambda(J(R))<\\infty$ if \nand only if either $m(J(R))=0$ or $R$ is postcritically finite. \n\nIn fact, we do not know if the $B$-condition is sufficient on this statement.\n\nNow we consider the more general condition when the restriction map $r_X$ is \ncompact. This condition, in some sense, reflects the geometry of the \npostcritical set.\n \nOn the product $S_R\\times S_R\\subset \\C^2$ there exist a unique \nfunction $K(z,\\zeta)$ which is characterized by the following conditions.\n\n\\begin{enumerate}\n \\item $K(\\zeta,z)=-\\overline{K(z,\\zeta)}$\n \\item For any $\\zeta_0\\in S_R$, the function $\\phi_{\\zeta_0}(z)=K(z,\\zeta_0)$\nbelongs to the intersection $Q(S_R)\\cap B(S_R).$ \n \\item If $z_0,\\zeta_0$ belong to different components of $S_R$, then \n$K(z_0,\\zeta_0)=0.$\n\\item The operator $P(f)(z)=\\int \\lambda^{-2}(\\zeta) K(z,\\zeta)f(\\zeta) d\\zeta \nd\\bar{\\zeta}$ from $L_1(S_R)$ to $Q(S_R)$ is a continuous surjective \nprojection.\n\\end{enumerate}\n\nIn fact, the function $K(z,\\zeta)$ is defined on any planar hyperbolic Riemann \nsurface $S$. In particular, when the surface $S$ is the unit disk $\\mathbb{D}$ \nthe function $K(z,\\zeta)$ has the formula $$K(z,\\zeta)=\\frac{3}{2}\\pi i \nK_\\mathbb{D}(z,\\zeta)^2$$ where \n$K_\\mathbb{D}(z,\\zeta)=[\\pi(1-z\\bar{\\zeta})^2]^{-1}$ is the classical Bergman \nKernel function on the unit disk. For further details on these facts see for \nexample Chapter 3, \\S 7 of the book of I. Kra \n\\cite{KraBook} .\n\n\nNow we consider the following function $$\\omega(\\zeta,z)=\\lambda^{-2}(\\zeta) \nK(z,\\zeta)$$ and $$w(z)=\\omega(z,z).$$ \n\n\nThe following proposition is a consequence of H\\\"older inequality and appear as \nLemma 2 on Ohtake's paper \\cite{Ohtakedeform}.\n\n\\begin{proposition}\\label{prop.bound.comp}\n If $X$ has positive measure and $$\\int_X |w|<\\infty$$ then the restriction \n$r_X:\\phi\\mapsto \\phi|_X$ from $Q(S_R)$ to $L_1(X)$ is compact.\n\\end{proposition}\n\\begin{proof}\n\n\nWe follow arguments of Lemma 2 in \\cite{Ohtakedeform}. If $D$ is a \ncomponent of $S_R$, then by H\\\"older's \ninequality as in Lemma 2 of \\cite{Ohtakedeform}, we have that $$|(\\phi|_D)(z)| \n\\leq \nC|(w|_D)(z)|\\int_D |\\phi| $$ where the constant $C$ does not depend on $D$. \nSince $S_R$ is a countable union of components, then \n\n$$|\\phi(z)|\\leq C |w| \\|\\phi(z)\\|.$$ As $w$ is integrable on $X$ then by \napplying once again the Lebesgue Theorem we complete the proof.\n\\end{proof}\n\nAs a consequence we have:\n\\begin{corollary}\\label{cor.finite}\n If $\\int_{J(R)} |w|<\\infty$ then $R$ satisfies Sullivan's conjecture.\n\\end{corollary}\n\n\\begin{proof}\n Follows from Theorem \\ref{MainTheorem} and Proposition \\ref{prop.bound.comp}.\n\\end{proof}\n\n\nRemarks: \n\\begin{enumerate}\n\\item If $R$ satisfies the $B$ condition then by Classical results, see the \ncomments before Proposition 1 in \\cite{Ohtake}, we have that $w(z)\\leq C \n\\lambda^2(z)$ where \n$C$ does not depend on $z.$ Partially, if $X$ has bounded hyperbolic area then \n$w(z)$ is integrable on $X$, hence the conditions of Proposition \n\\ref{prop.bound.comp} implies Proposition \\ref{prop.Bcond.comp}.\nAs it is mentioned in \\cite{Ohtake}, the conditions in Proposition \n\\ref{prop.Bcond.comp} are strictly weaker than conditions of Proposition \n\\ref{prop.bound.comp}.\n\\item Moreover, by other result of Ohtake (Proposition 3 in \n\\cite{Ohtakedeform}) we note \nthat in general, the boundedness of the hyperbolic area is not a quasiconformal \ninvariant.\n\n\n\\end{enumerate}\n\n\nIn other words, Proposition \\ref{prop.Bcond.comp} and Proposition \n\\ref{prop.bound.comp} states that if $X$ is completely invariant positive \nmeasure set and satisfying an integrability condition then $X$ can not support \nextremal \ndifferentials with Hamilton-Krushkal degenerated sequences.\n\nHence, Corollary \\ref{cor.area} and Corollary \\ref{cor.finite}, in the case \nwhen $X$ is a completely invariant, derive from results in \n\\cite{CMFixed}. Together, the corollaries mean that \nif a map $R$ has an invariant line field which does not allow a \nHamilton-Krushkal degenerated sequences on $Q(S_R)$, then $R$ is a Latt\\`es map \nif and only if the postcritical set has Lebesgue measure zero.\n\nLet $Y_n$ be an exhaustion of $S_R$ by compact subsets such that the Lebesgue \nmeasure of \n$Y_{n+1}\\setminus Y_n$ converge to zero. Let $P_n$ be the sequence of \nrestrictions \n$P_n:L_1(S_R)\\rightarrow L_1(S_R)$ given by $P_n(f)=\\chi_{n} P(f)$ where \n$\\chi_{n}$ is the characteristic function on $Y_n$. \nImmediately from the definition we have the following facts:\n\n\\begin{enumerate}\n \\item For each $n$, the map $P_n$ is a compact operator.\n \\item The limit \\[\\lim_{n\\rightarrow \\infty} \n\\|P_n(f)-P(f)\\|_{L_1(S_R)}\\rightarrow 0\\] for all $f$ on $L_1(S_R)$.\n\\end{enumerate}\n\nWe have the following Theorem:\n\n\\begin{theorem}\\label{th.exhaustion}\n Let $\\mu\\neq 0$ be an extremal invariant Beltrami differential, then the \nfollowing conditions are equivalent:\n\n\\begin{itemize}\n \\item The map $R$ is a flexible Latt\\`es map. \n \\item There exist an exhaustion of compact sets $Y_n$ as defined above \nsuch that the following inequality is true: \\[\\inf_{n} \\|P_n -P\\|_{L_1(supp \n(\\mu))}<1.\\]\n\\end{itemize}\n \n\\end{theorem}\n\n\\begin{proof}\nAssume that $R$ is a flexible Latt\\`es map, then $Q(S_R)$ is finitely \ndimensional then the operators $P_n$ converge to $P$ by norm. Hence,\nthe infimum $\\inf_{n} \\|P_n -P\\|_{L_1(supp(\\mu))}=0.$ \n\nNow, let us assume that $\\inf_{n} \\|P_n -P\\|_{L_1(supp (\\mu))}<1$. We show \nthat this condition implies that $\\mu$ does not accept degenerated \nHamilton-Krushkal sequences. Indeed, assume that $\\{\\phi_n\\}$ is a degenerated \nHamilton-Krushkal sequence for $\\mu$. By assumption, there exist $n_0$ such \nthat \n\\[\\sup_{f\\in L_1(supp(\\mu)),\\|f\\|=1} \\int |P_{n_0}(f)-P(f)|=r<1.\\] Since \n$\\phi_n$ is degenerated and by the compactness of $P_{n_0}$ we have that \n\\[\\lim_{j\\rightarrow \\infty} \\|P_{n_0}(\\phi_j)\\|_{L_1(S_R)}\\rightarrow 0.\\] \nHence\n\\[\\|\\mu\\|_\\infty=\\lim_{j} \\bigg |\\int \\mu \\phi_j\\bigg |=\\lim_j \\bigg \n|\\int_{supp(\\mu) }\\mu \n\\phi_j\\bigg |\\]\n\\[=\\lim_{j} \\bigg |\\int_{supp(\\mu)} \\mu(P_{n_0}(\\phi_j)-P(\\phi_j))\\bigg |\\]\n\\[\\leq \\|\\mu\\|_\\infty \\sup_{f\\in L_1(supp(\\mu)),\\|f\\|=1} \\int \n|P_{n_0}(f)-P(f)|=r \\|\\mu\\|_\\infty < \\|\\mu\\|_\\infty.\\] \nWhich is a contradiction. \n\nApplying the Corollary 1.5 in \\cite{EarleLi}, the extremal differential $\\mu$ \ndoes \nnot accept Hamilton{-}Krushkal degenerated sequences if \nand only if there exist $\\phi$ in $Q(S_R)$ and a suitable constant $K$ such \nthat \n\\[v_\\mu(\\gamma)=K \\int \\frac{|\\phi|}{\\phi} \\gamma.\\]\n\n\n\nHence, for any $\\gamma$ in $Q(S_R)$ we have\n\n$$\\int \\frac{|\\phi|}{\\phi}R^*(\\gamma)=\\int\\frac{|\\phi|}{\\phi} \\gamma$$ and\n$$1=\\int\\frac{|\\phi|}{\\phi}R^*(\\phi).$$ This implies that \n$$\\frac{|R^*(\\phi)|}{R^*(\\phi)}=\\frac{|\\phi|}{\\phi}$$ but since $\\phi$ is \nholomorphic then $\\phi$ is a non zero fixed point on $Q(S_R)$. \nUsing arguments of the proof of Theorem \\ref{MainTechnicalThm} we are \ndone. \n\\end{proof}\n\nThe following Proposition is an illustration of when the conditions of Theorem \n\\ref{th.exhaustion} are fulfilled. \n\n\\begin{proposition} If $R$ is a rational map satisfying the $B$ condition.\nIf $A$ is a measurable subset of $S_R$ so that \n\\[\\int_A \\int_{S_R} |K(z,\\zeta)| dz\\wedge d\\bar{z} \\wedge d\\zeta \\wedge \nd\\bar{\\zeta}<\\infty\\]\nthen for any exhaustion of $S_R$ by compact sets $Y_n$ and operators \n$P_n$ defined as above we have $\\lim \\|P_n-P\\|_{L_1(A)}=0.$ \n\\end{proposition}\n\n\\begin{proof} Let $Y_n$ be an exhaustion of compact sets as above. Since \n$K(z,\\zeta)$ is absolutely integrable on $A\\times S_R$ then \n$$|\\chi_{n} K(z,\\zeta)|\\leq |K(z,\\zeta)|$$ and\n$\\chi_{n}K(z,\\zeta)\\rightarrow K(z,\\zeta)$ pointwise on $A\\times S_R.$ By the \nLebesgue theorem $$\\inf \\int_{A}\\int_{S_R} |K(z,\\zeta)-\\chi_{n} \nK(z,\\zeta)|=0.$$\nFor all $\\phi \\in Q(S_R)$, we have $$\\|P_n(\\phi)-P(\\phi)\\|_{L_1(A)}$$ \n\n\\[\\leq \\int_A |P_n(\\phi)-P(\\phi)|\\leq \\int_{A} \\int_{S_R} \n|\\lambda^{-2}(\\zeta)\\phi(\\zeta)(K(z,\\zeta)-\\chi_n K(z,\\zeta))|d\\zeta dz\\]\n\n\n\\[\\leq \\|\\lambda^{-2} \\phi\\|_\\infty \\int_{A} \\int_{S_R} |K(z,\\zeta)-\\chi_n \nK(z,\\zeta)| d\\zeta dz \n\\]\nwhich by the $B$-condition we have that the latter is \n\\[\\leq C \\|\\phi\\|_{L_1(S_R)}\\int_{A} \\int_{S_R} |K(z,\\zeta)-\\chi_n \nK(z,\\zeta)| d\\zeta dz .\\] For some constant $C$ which does not depend on \n$\\phi$.\n\nNow let $f\\in L_1(A)$, since $P$ is a projection then $f=\\phi+\\omega$ where \n$\\phi\\in \nQ(S_R),$ $P(\\omega)=P_n(\\omega)=0$ and $$\\|\\phi\\|_{Q(S_R)}\\leq \\|P\\| \n\\|f\\|_{L_1(A)}.$$\n Hence $\\lim \\|P_n-P\\|_{L_1(A)}= 0.$ \n\n\n\\end{proof}\nFinally we characterize a Latt\\`es map in terms of the geometry of $Q^*(S_R).$ \nWe start with the following definitions.\n\\begin{definition} \n\\begin{enumerate} \n \\item A set $L$ in $Q^*(S_R)$ is called a geodesic ray if $L$ \nis an isometric image of the non negative real numbers $\\mathbb{R}_+$. \n \\item Let $L_1$ and $L_2$ be geodesic rays with parameterizations \n$\\psi_1:\\mathbb{R}_+\\longrightarrow L_1$ and \n$\\psi_2:\\mathbb{R}_+\\longrightarrow L_2$ respectively. The pair of rays $L_1$ \nand $L_2$ are called equivalent if \n$$\\limsup_{t\\rightarrow \\infty} \\| \\psi_1(t)-\\psi_2(t) \\|_T\\leq d <\\infty$$ for \nsome $d.$\n\\item An element $v$ in $Q^*(S_R)$ is called asymptotically finite if the \nnumber of equivalence classes of geodesic rays in $Q^*(S_R)$ containing $0$ and \n$v$ is finite.\n\\end{enumerate}\n \n\n \n\\end{definition}\n\n\nNow we characterize rational maps which have asymptotically finite non trivial \ninvariant Beltrami differentials.\n\n\\begin{theorem}\\label{thm. uniq.equiv}\nAssume that $S_R$ is connected and let $\\mu$ be non trivial an invariant \nBeltrami differential for $R$ supported on $S_R$. Then the functional \n$v_\\mu(\\phi)=\\int \\phi\\mu$ is asymptotically finite if and only if $R$ is \nLatt\\`es.\n\\end{theorem}\n \n\n\\begin{proof}\n If $R$ is a Latt\\`es map then $Q^*(S_R)$ is finitely dimensional and then \nthere is only a unique geodesic ray passing through any \npair of points in $Q^*(S_R)$ see \\cite{EarleLi} and \\cite{GardLakic}. \n\nReciprocally, suppose that the functional $v_\\mu$ is asymptotically \nfinite. Let us first assume \nthat $\\|v_\\mu \\|_{Q^*(S_R)}=\\|\\mu\\|_{L_\\infty}.$ Then by Corollary 6.4 in \n\\cite{EarleLi}, if $\\mu$ accept degenerated \nHamilton-Krushkal sequences there exist $\\C$-linear isometry \n$I:\\ell_\\infty\\rightarrow Q^*(S_R)$ such that if $m$ is the constant \nsequence \nwith value $\\|\\mu\\|_\\infty$ then $I(m)=v_\\mu.$\n \n\n\nNow let $\\{e_i\\}$ be the canonical basis of $\\ell_\\infty$. Then as in \n\\cite{EarleLi}, we define geodesic rays in $\\ell_\\infty$ as follows:\n\nFor any $r\\geq \\|\\mu\\|_\\infty$ and \n\n$$\\psi_{r,i}(t)=\\bigg \\{ \\begin{array}{l} \nt\\cdot m \\textnormal{ for } t\\leq r. \\\\\nr\\cdot m + (t-r)\\|\\mu\\|_\\infty e_i \\textnormal{ for } t>r.\n\\end{array}$$\n\nBut for all $i_0, r_1,r_2$, $$\\limsup_{t\\rightarrow \\infty}\n\\|\\psi_{i_0,r_1}(t)-\\psi_{i_0,r_2}(t)\\|_{\\ell_\\infty}\\leq \n|r_1-r_2|\\|\\mu\\|_\\infty.$$\n\n\nAlso for all $i\\neq j$ and all $r$ we have \n\\[\\limsup_{t\\rightarrow \\infty} \\|\\psi_{j,r}(t)-\\psi_{i,r}(t)\\|_\\infty\\]\n\\[=\\limsup_{t\\rightarrow \\infty}\\|\\mu\\|_\\infty t \\|e_i-e_j\\|=\\infty.\\]\n\n\nBut the existence of the isometry $I$ gives a contradiction. \nHence $\\mu$ does not accept Hamilton-Krushkal degenerated sequences.\n\nNow using similar arguments as in the proof of Theorem \\ref{th.exhaustion}, we \ncomplete the proof in the case where $\\mu$ is extremal.\n\n\nFinally we show that if $\\mu$ is a non trivial invariant Beltrami differential, \nthen there exist an extremal invariant differential $\\nu$ such that \n$v_\\nu(\\gamma)=v_\\mu(\\gamma)$ for all $\\gamma$ in $Q(S_R).$ \n\n\nIndeed, if $\\mu$ is not extremal then by the Banach Extension Theorem and \nRiesz Representation Theorem there exist is another Beltrami differential \n$\\alpha$ which is extremal satisfying \n$\\|\\alpha\\|_\\infty=\\|\\mu\\|_T<\\|\\mu\\|_\\infty$ and \nsuch that defines the same functional as $\\mu$ in $Q(S_R)$. Let $\\beta$ be a \n$*$-weak limit of the Ces\\`aro averages $C_n(\\alpha)=\\frac{1}{n} \n\\sum_{i=0}^{n-1} \n\\alpha(R^i)\\frac{\\overline{(R^i)'}}{(R^i)'}$, then \n$\\beta(R)\\frac{\\overline{R'}}{R'}=\\beta$ and $\\|\\beta\\|_\\infty\\leq \n\\|\\alpha\\|_\\infty.$ Then we claim that $v_\\beta=v_\\mu$.\nLet $\\{C_{n_i}(\\alpha)\\}$ be a sequence of averages $*$-weakly converging to \n$\\beta$. For any $\\gamma \\in Q(S_R)$ we have $$\\int \\gamma \\beta=\\lim \\int \nC_{n_i}(\\alpha)\\gamma$$ by duality the previous limit is \nequal to $$\\lim \\int \\alpha \\frac{1}{n_i}\\sum_{k=0}^{n_i-1} R^{*k}(\\gamma)=\\lim \n\\int \\mu \\frac{1}{n_i}\\sum_{k=0}^{n_i-1} R^{*k}(\\gamma)$$ but $\\mu$ is an \ninvariant differential and again using duality the previous limit becomes \n$$\\lim \\int \\mu \\gamma=\\int \\mu \\gamma.$$\n\nHence for any $\\gamma$ in $Q(S_R)$ we have $$\\int \\beta \\gamma= \\lim \n\\int C_{n_i}(\\alpha)\\gamma=\\int \\mu \\gamma.$$\nSince $\\alpha$ is extremal we have \n$\\| \n\\beta \\|_\\infty=\\| \\alpha \\|_\\infty=\\|\\mu\\|_T$. Thus \n$\\beta$ is the desired extremal invariant differential. \n\n\\end{proof}\n\n\nTo conclude, let us note that the arguments of the theorems in this paper \nwork for entire and meromorphic functions in the class\nof Eremenko-Lyubich. This is the class of all entire or meromorphic functions \nwith finitely many critical and singular values. It is not completely clear \nwhether this arguments can be carried on entire or meromorphic functions whose \nasymptotic value set contains a compact set of positive Lebesgue measure. \n\n\n\n \\bibliographystyle{amsplain} \n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:introduction}\n\n\n\\IEEEPARstart{D}{iabetic} \nretinopathy (DR) has become a worldwide major medical concern for the large population of diabetic patients and has been the leading cause of blindness in the working-age population today\\cite{thomas2019idf,ciulla2003diabetic,raman2016diabetic}. DR lesions often present as microaneurysms (MAs), hemorrhages (HEs), soft exudates (SEs), and hard exudates (EXs) which can be observed in colorful fundus images and are the basis of diagnosis for ophthalmologists. \n\nHowever, until now there has been no valid treatment to cure this disease completely. The most recognized treatment is the early diagnosis and intervention to controll the progression of the disease and to avoid eventual loss of vision\\cite{wong2018guidelines}. Thus, many national health institutions are promoting DR screening, which has been proven effective in reducing the rate of blindness caused by DR\\cite{ciulla2003diabetic,ting2016diabetic}. However, screening is a heavy burden for primary care systems during the promotion, since the ophthalmologists are in very short supply and have already engaged in post-DR treatment. For this reason, automatic segmentation technology for DR lesions has become a trend towards assisting ophthalmologists in diagnosis.\n\n\n\n\\begin{figure}[!t]\n\\centerline{\\includegraphics[width=\\columnwidth]{fig1.png}}\n\\caption{Illustration of fundus image with characteristics of DR lesion. \nSE: soft exudate; HE: hemorrhage; EX: hard exudate; MA: microaneurysm.\n(a) Original image with different clusters of lesions denoted by red and blue bounding boxes;\n(b) magnified lesion regions where green, purple, red and blue area represent SE, HE, EX and MA, respectively;\n(c) location statistics of certain lesions on IDRiD dataset in pixels. Specifically, from left to right are the distances from SE center to nearest HE center, from EX cluster center to nearest MA center, from SE center to the nearest vascular tree midline, and from MA center to the nearest vascular tree midline.}\n\\label{fig1}\n\\end{figure}\n\n\nRecent research efforts have been directed towards automatic DR segmentation based on deep learning (DL). \nGuo et al. \\cite{guo2019seg} adopted a pretrained Vgg16\\cite{simonyan2014very} as backbone with multi-scale feature fusion block for DR lesion segmentation. Specifically, they extracted side features from each convolution layer in Vgg16 and fused them in a weighted way. Further, a multi-channel bin loss was proposed to alleviate class-imbalance and loss-imbalance problems.\nZhou et al. \\cite{zhou2019collaborative} designed a collaborative learning network to jointly improve the performance of DR grading and DR lesion segmentation with attention mechanism. The attention mechanism allowed features with image-level annotations to be refined by class-specific information, and generated pixel-level pseudo-masks for the training of segmentation model.\nHowever, previously published studies paid too much attention to the designs of networks and many of the researches up to now have only achieved the segmentation of just one or two lesions \\cite{tavakoli2020automated,mamilla2017extraction,wu2017automatic,khojasteh2019novel,mo2018exudate}. It should be noted that as a complex medical lesion segmentation task, the pathological connections have not received enough attention.\n\nAfter comprehensive investigation in possible causes of DR lesions, we found two interesting presentation phenomena shown in Fig.\\ref{fig1}: 1) lesions are usually closed to specific veins and arteries. For example, SEs are generally distributed at the margins of the main trunk of the upper and lower arteries, and MAs are generally distributed at the margins of the capillaries; 2) most lesions have certain spatial interactions with each other. Specifically, SEs commonly appear at the edge of HEs, while EXs are usually arranged in a circular pattern around one or several MAs, which is consistent with the occurrence of pathology.\n\nMotivated by the above observations, we propose a relation transformer block (RTB), comprised of a cross-attention and a self-attention head, to explore dependencies among lesions and other fundus tissues. For specific, \nthe cross-attention head is designed to capture implicit relations between lesions and vessels. We design a dual-branch network to employ both lesion and vascular information, and cross-attention head is integrated between the two branches to make effective use of vascular information in lesion segmentation branch. \nAs we know, the fundus tissues, \\textit{e.g.,} vessels, optic disc, nerves and other lesions, are complex and easily confused with DR lesions of interest, but considering that vascular information describes certain distribution patterns of the above tissues, the cross-attention head is able to locate more lesions through the layout provided and eliminate the false positives far from certain vessels.\nThe self-attention head is employed to investigate the relationships of multi-lesion themselves. Since some of the lesions look similar, \\textit{e.g.,} HE and MA (both red-lesion), SE and EX (both exduate-lesion), misclassifications between lesions occur frequently. Through impactful information emphasized by self-attention head, the distinction and connection of lesions play roles in reducing confusion in segmentation.\nNote that before DL was utilized in medical imaging, vessels were easily mistaken for red lesions, and vessel detection was regarded as a routine step in the segmentation with detected vessels being removed straight away. However, as no fundus dataset is annotated with both vessels and DR lesions, the DL-based lesion segmentation task no longer extracts vessel features separately. To our best knowledge, this is the first trial that utilizes vascular information for deep based fundus lesion segmentation.\n\n\n\nIn addition, some special lesion patterns, such as MA with small size and SE with blurred border, are hard to be situated accurately due to the lack of fine-grained details in high-level features.\nTo alleviate this, we propose a Global Transformer Block (GTB) inspired by GCNet\\cite{GCNet} to further extract detail information, which can preserve the detailed lesion information and suppress the less useful information of channels in each position. In our network, GTB is also adopted to generate dual branches. Specifically, after the backbone, the shared fundus features are obtain as input of the two GTBs, and then GTBs generate more specific features of vessels and lesions respectively, which would be further investigated at the pathological connection level by RTB.\n\nWe have evaluated our network on two publicly available datasets - IDRiD and DDR. Experimental results show that our network outperforms the state-of-the-art DR lesion segmentation reports and achieves the best performance in EX, MA and SE. Furthermore, we also implement ablative experiments on IDRiD dataset and validate the effectiveness of RTB and GTB in improving DR lesion segmentation outcomes.\n\nIn summary, our contributions are as follows:\n\\begin{itemize}\n \\item We propose a dual-branch architecture to obtain vascular information, which contributes to locate the position of DR lesions. For effective use of vascular information in multi-lesion segmentation, we design a relation transformer block (RTB) based on transformer mechanism. To our best knowledge, this is the first work to employ multi-heads transformer structure in lesion segmentation in fundus medical images.\n \\item We present global transformer block (GTB) and relation transformer block (RTB) to detect the special medical patterns with small size or blurred border. The design explores the internal relationship between DR lesions which improves the performance in capture the details of interest.\n \\item Experiments on the IDRiD dataset show that our method achieves a front row finish on DR multi-lesion segmentation. Specifically, our method achieves the best performance in exduates segmentation and ranks second in HE lesion segmentation.\n Experiments on the DDR dataset show that our method outperforms other methods on EX, MA and SE segmentation task and ranks second on HE segmentation task.\n\\end{itemize}\n\n\n\\section{Related Work} \n\\subsection{Pathological Analysis of the DR Lesions}\nDR lesions segmentation is a complex topic due to large intra-class variance.\nFurthermore, DR lesions vary with different stages of disease as well, which also brings challenges to segmentation. \nHowever, instead of discovering lesions directly, we notice that there are pathological associations between these lesions, which can be depicted in the spatial distribution. \n\nWe first investigate the possible pathological causes of DR lesions. Briefly, MA is the earliest lesion of DR observed as spherical lateralized swelling which is produced by vascular atresia; EX looks like yellowish-white well-defined waxy patch, generally thought to be lipid produced by the rupture of the retinal nerve tissues, incidentally, which is also resulted from the vascular atresia. Additionally, when the rupture of vessels happens after the vascular atresia, bloods leak out from the vessels, which leads to the lipoproteins in vessels leaking into the retina as well. The leaking bloods form the HE patterns and the leaking lipoproteins form the SE patterns with poorly defined borders. In summary, as shown in Fig.\\ref{fig1}(b), most of the EXs are observed arranged in a circular pattern around one or several MAs (the bottom line) and most of the SEs appear at the edge of HEs (the upper line).\n\nIn addition to the intra-class dependencies among lesions, the inter-class relations between DR lesions and vessels also make great sense. As mentioned above, vascular abnormalities are the direct or indirect causes of DR lesions, specifically, we found the fact that SEs are often distributed near the trunk of upper and lower arteries, and MAs are generally distributed among the capillaries. Furthermore, intricate fundus tissues often confuse the identification of lesions, but we notice that there are certain pattern rules of fundus, especially in the distribution of various veins and arteries, which would provide valuable prior information. \n\nTo confirm the above pathological analysis, we count the distances in pixel between different fundus tissues on the IDRiD dataset. Fig.\\ref{fig1} (c) illustrates the distance between cluster center of EX and the nearest neighbor MA, SE and the nearest neighbor HE, MA and the closet capillaries, SE and closest upper and lower arteries, respectively. The statistical results verify that there is an exploitable pattern in the distribution of DR lesions. \n\n\\subsection{Deep Neural Networks in DR Lesion Segmentations}\n\nDR lesion segmentation task based on traditional image processing techniques\\cite{walter2007automatic,Automatic2005,alipour2012analysis} is facing two main challenges: the great morphological differences of the same lesions in different disease stages and the confusions of DR lesions and similar structures in the fundus. These two problems have not been effectively solved until \ndeep neural networks (DNNs) exploded in the field of computer vision (CV) \\cite{krizhevsky2012imagenet} and have also been widely applied to DR lesion segmentations\\cite{CABNet,CANet,CENet}.\n\nHowever, DNNs also raise new difficulties. For instance, the detailed information is easily lost by deep networks but most of the DR lesions are very small and even just one or two pixels. Besides, considering the balance of different characteristics of red and exudate lesions, the accuracy of multi-task model is limited. \n\nTo deal with the above issues, researchers have proposed many improvements, which can be summarised as two directions:\n\nFirstly, some researchers focus on the designs of attention models fusing low-level and high-level features together to avoid details lost in the deep network.\nZhang et al. \\cite{zhang2019detection} fused multiple features with distinct target features in each layer based on attention mechanism and achieved preliminary MA detection.\nWang et al. \\cite{wang2017zoom} designed a dual-branch attention network, with one producing a 5-graded score map and the other producing an attention gate map combined to the score map to highlight suspicious regions.\nZhou et al. \\cite{zhou2019collaborative} applied low-level and high-level guidances to different lesion features and obtained the refined multi-lesion attention maps, which were further employed as pseudo-masks to train the segmentation model.\n\nSecondly, the task of segmenting DR lesions is divided into segmenting red lesions and exudate lesions separately, which evades the balanced cost of inter-class disparities and enables fully learning of the same type of lesions.\nMo et al. \\cite{mo2018exudate} designed a fully convolutional residual network incorporating multi-level hierarchical information to segment the exudates without taking the red lesions into account.\nXie et al. \\cite{xie2020sesv} built a general framework to predict the errors generated by existing models and then correct them, which performed well in MA segmentation.\n\nHowever, the first direction pays much attention to the network designs, with seldom considering the pathological connections of DR lesions, while the second direction leads to time consuming and large memory requirements. In order to take advantages of the pathological connections and improve the efficiency of the multi-task model, we propose a RTB consisting of self-attention and cross-attention head to segment DR lesions simultaneously.\n\n\\subsection{Transformer in Medical Images}\n\nTransformer network has been one of the fundamental architecture for natural language processing (NLP) since 2017 due to the efficient and effective self-attention mechanism\\cite{vaswani2017attention}. It improves the performance on many NLP tasks, such as text classification, general language understanding and question answering. Compared with recurrent networks, transformer network achieves parallel computation and reduces the computational complexity. \nIn a basic transformer attention block, Query ($Q$), Key ($K$), Value ($V$) are the three typical inputs to a attention operation. At first, $Q$ and $K$ are computed in the form of pairwise function to obtain the corresponding attention of all points on $K$ for each point on $Q$. The pairwise function can optionally be Gaussian, Embedding Guassian, Dot-Product, Concatenation and \\textit{etc.}. Then, the product is multiplied by $V$ and passes through a column-wise softmax operator to ensure every column sum to 1. Every position on the output contains the recoded global information by attention mechanism. In self-attention operator, $Q = K = V$, so that the output has the same shape with input.\n\nInspired by the success in the domain of NLP, a standard transformer was applying to CV in 2020\\cite{dosovitskiy2020image} with the fewest modifications, called as Vision Transformer (ViT). The input to a ViT is a sequence of cropping images which are linearly encoded by aliquoting the original image. The image patches are treated the same way as tokens (words) in an NLP application.\n\n\nRecently, the transformer architecture has also been applied to the field of medical image processing. \n Liu et al. \\cite{GPT} proposed a global pixel transformer (GPT) to predict several target fluorescent labels in microscopy images. The GPT is similar to a three-headed transformer with different sizes of query inputs, which allows it to adequately capture features at different scales.\nGuo et al. \\cite{guo2021transformer} applied the ViT to anisotropic 3D medical image segmentation, with the self-attention model arranged at the bottom of the Unet architecture.\nSong et al.\\cite{DRT} built a Deep Relation Transformer (DRT) to combine OCT and VF information for glaucoma diagnosis. They modified the standard transformer to an interactive transformer that utilizes a relationship map of VF features interacting with OCT features. \n\n\\section{Methodology}\nIn this section, we first give a brief overview of our proposed network, and then elaborate on the key network components, \\textit{i.e.,} global transformer block (GTB) and relation transformer block (RTB). Finally, designed loss function is further provided. \n\n\n\\subsection{Overview}\nGiven an input fundus image, the proposed network is designed to output one vascular mask and four lesion masks in parallel.\nFig.\\ref{fig2} depicts its overall architecture, which is comprised of four key components: backbone, global transformer block (GTB), relation transformer block (RTB), and segmentation head.\nA dual-branch architecture is employed upon the backbone to explore vascular and pathological features separately, where the transformers based on GTB and RTB\nare incorporated to reason about interactions among both features.\n\n\n\nTo be specific, the fundus image first passes through a backbone to obtain an abstracted feature map $\\mathbf{F}$, with a spatial resolution of $W\\times H$ and $C$ number of channels.\nThen, two parallel branches comprised of global transformer block (GTB) are incorporated to exploit long-range dependencies among pixels in $\\mathbf{F}$, resulting in specific vessel features $\\mathbf{F}_v$ and primary lesion features $\\mathbf{F}_l$ fueled with global contextual information, respectively.\nUpon the branch providing lesion features, we further integrate a relation transformer block (RTB) to model spatial relations between vessels and lesions due to their inherent pathological connections using a self-attention and a cross-attention head:\nthe self-attention head inputs only the lesion features $\\mathbf{F}_l$, and exploits long-range contextual information to generate self-attentive features $\\mathbf{F}_{s}$ through a self-attention mechanism; the cross-attention head inputs both the lesion and vessel features $\\mathbf{F}_{l}$, $\\mathbf{F}_v$,\nand incorporates beneficial fine-grained vessel structural information into $\\mathbf{F}_v$, producing cross-attentive features $\\mathbf{F}_{c}$. \nThe resulting $\\mathbf{F}_{s}$ and $\\mathbf{F}_{c}$ are concatenated together to form the output of the RTB. \nFinally, two sibling heads, each of which contains a Norm layer and a $1\\times 1$ convolution, are used to predict vascular and pathology masks based on the vessel features and concatenated lesion features, respectively.\n\nGTB contains one head while RTB contains two heads. Although the basic heads of GTB and RTB are based on the transformer structure that generates query, key and value for relation reasoning,\nthey are structurally different in our work. \nThe query of head in GTB is similar to a channel-wise weights and the one in RTB has the same size in spatial dimension with the input.\nIn a training process, GTB is employed to generate specific multi-lesion and vessel features independently which maintain more details of interest, and RTB further exploits the inherent pathogenic relationships between multi-lesion and vessels, which eliminate noise and imply the location information. \n\n\\begin{figure*}[htbp]\n\\centerline{\\includegraphics[width=\\textwidth]{fig2.png}}\n\\caption{Pipeline of the proposed method. The input image passes through a backbone to obtain the shared feature $\\mathbf{F}$. Then the shared feature $\\mathbf{F}$ takes two branches to achieve vessels and multi-lesion segmentation respectively. Two Global Transformer Blocks (GTB) are applied to both branches to generate specific features, and a Relation Transformer Block (RTB) is incorporated after GTB to explore the inherent pathological connections among multi-lesion and between multi-lesion and vessels.}\n\\label{fig2}\n\\end{figure*}\n\n\\begin{figure}[htbp]\n\\centerline{\\includegraphics[width=\\columnwidth]{fig3.png}}\n\\caption{The overall structure of Global Transformer Block (GTB).}\n\\label{fig3}\n\\end{figure}\n\n\\begin{figure}[htbp]\n\\centerline{\\includegraphics[width=\\columnwidth]{fig4.png}}\n\\caption{The details of Relation Transformer Block (RTB).}\n\\label{fig4}\n\\end{figure}\n\n\n\\subsection{Global Transformer Block}\nThe Global Transformer Block (GTB) contains two parallel branches of the same architecture to extract features for lesions and vessels separately. Such a dual-branch design owns to the fact that lesions and vessels generally have dramatically different visual patterns. To be concrete, lesions are discrete patterns, with nearly random spatial distribution. While vessels are topological connected structures, and the layout of vascular trunks, containing central retinal artery, ciliary artery and etc, generally follows some common rules. \nIt is hence necessary to use specialized branches to learn specific characteristics of different objects of interest.\n\n \nFig.\\ref{fig3} presents the detailed structure of each GTB branch.\nIt takes an input $\\mathbf{F}\\in \\mathbb{R}^{\\: C\\times W\\times H}$ generated from the backbone, and outputs attentively-refined feature maps $\\mathbf{F}_{i}\\in \\mathbb{R}^{\\: C\\times W\\times H},i \\in\\left\\{l,v\\right\\}$ of lesions and vessels respectively.\nSpecifically, GTB follows the typical framework of transformer networks. \nThree generators, denoted as $\\mathcal{Q}$, $\\mathcal{K}$ and $\\mathcal{V}$ are first employed to transform the input $\\mathbf{F}$ into query, key and value, respectively. In GTB, the generator $\\mathcal{Q}$ is implemented with a $3 \\times 3$ convolution followed by a global average pooling, and outputs a query vector $\\mathcal{Q}(\\mathbf{F}) \\in \\mathbb{R}^{\\: C^{'}\\times 1}$ with the channel number designed as $C^{\\prime}=C\/8$; the generator $\\mathcal{K}$ and $\\mathcal{V}$ have the same architecture as $\\mathcal{Q}$ expect for replacing the global averaging pooling with a reshape operation, leading to the key and value $\\mathcal{K}(\\mathbf{F}),\\mathcal{V}(\\mathbf{F})\\in \\mathbb{R}^{\\:C^{'}\\times HW}$.\n\nWe define the pairwise function of query and key as a matrix multiplication:\n\\begin{equation}\n \\mathcal{F}(\\mathbf{F})=\\mathcal{K}(\\mathbf{F})^T\\mathcal{Q}(\\mathbf{F}) ,\n\\end{equation}\nwhere the superscript $T$ denotes a transpose operator for matrix. \nNote that the query of GTB acts as a channel-wise query instead of the position query as NLNet\\cite{wang2018NLnet}. To be specific, the query vector is considered as a feature selector for channels of key matrix. \nSubsequently, the product $\\mathcal{F}(\\mathbf{F})\\in\\mathbb{R}^{\\:HW\\times 1}$ also acts as a feature selector for spatial positions of value matrix. \nIn summary, the GTB can be roughly described as a attention mechanism which fuses channel-wise first and then spatial-wise weighted features together with input information.\n\nNext, we consider the global transform operation defined as:\n\\begin{equation}\n \\mathcal{G}(\\mathbf{F}) = \\mathcal{V}(\\mathbf{F})softmax(\\mathcal{F}(\\mathbf{F}))\\in \\mathbb{R}^{\\:C^{'}\\times 1},\n\\end{equation}\nwhere $softmax$ is a softmax function to normalize the $\\mathcal{F}(\\mathbf{F})$.\n\nThen, We take the obtained attentive features $\\mathcal{G}(\\mathbf{F})$ with a linear embedding as a residual term to the input $\\mathbf{F}$, and get the final output through a residual connection:\n\\begin{equation}\n \\mathbf{F}_{i} = W\\mathcal{G}(\\mathbf{F})+\\mathbf{F},\\ i\\in\\left \\{ l,v\\right \\},\n\\end{equation}\nwhere the $+$ operation denotes broadcasting element-wise sum operation; the $W$ is a linear embedding, implemented as $1 \\times 1$ convolution to convert the channel number of intermediate feature map from $C^{'}$ back to $C$. As a result, the output features are received with the same format as the input, but have been enriched with specialized vessel and lesion features, respectively.\n\n\n\n\n\n\nThe GTB structure is inspired by the GCNet\\cite{GCNet}. They both follow the idea of transformer mechanism but generate the per-channel weights. The weight vectors in GCNet and GTB both obtained by a matrix multiplication, but different from GCNet, GTB attains the two multipliers with three generator to further highlight the useful channels in each position, realizing both channel-wise and spatial-wise attention. Due to the fundus lesion features, especially the small discrete ones, are easily confused with artifacts or idiosyncratic tissues, \nuseful information tends to exist in only a few pixels of certain channels. It is an improved method aimed at the feasibility of small discrete pattern segmentation in fundus images.\n\n\\subsection{Relation Transformer Block}\nRelation Transformer Block (RTB) consists of a self-attention and a cross-attention head, used to capture intra-class dependencies among lesions and inter-class relations between lesions and vessels, respectively, as shown in Fig.\\ref{fig4}. In each head, three trainable linear embeddings, implemented with a $3 \\times 3$ convolution followed by a reshape operation, are employed as the query, key and value generator $\\mathcal{G}_i, \\mathcal{K}_i, \\mathcal{V}_i,i \\in\\left\\{s,c\\right\\}$, respectively.\nThe pairwise computations of query and key in self-attention head and cross-attention head are described as:\n\\begin{equation}\n \\begin{split}\n \\mathcal{F}_{s}(\\mathbf{F}_l)&=\\mathcal{K}_{s}(\\mathbf{F}_l)^T\\mathcal{Q}_{s}(\\mathbf{F}_l) \\\\\n \\mathcal{F}_{c}(\\mathbf{F}_l,\\mathbf{F}_v)&=\\mathcal{K}_{c}(\\mathbf{F}_v)^T\\mathcal{Q}_{c}(\\mathbf{F}_l),\n \\end{split}\n\\end{equation}\nwhere the subscripts $s$ and $c$ denote the self-attention and the cross-attention head, respectively.\nIt is important to emphasize that different from the self-attention head that derives the query, key all from input lesion features $\\mathbf{F}_l$, the cross-attention head generates the key from the vessel features $\\mathbf{F}_v$ instead to integrate vascular information. \n\n\n\nNext, the individual attentive features of the two heads are computed respectively as:\n\\begin{equation}\n\\begin{split}\n \\mathcal{G}_{s}(\\mathbf{F}_{l})&=\\mathcal{V}_{s}(\\mathbf{F}_{l})softmax(\\mathcal{F}_{s}(\\mathbf{F}_{l}))\\\\\n \\mathcal{G}_{c}(\\mathbf{F}_{l},\\mathbf{F}_v)&=\\mathcal{V}_{c}(\\mathbf{F}_{v})softmax(\\mathcal{F}_{c}(\\mathbf{F}_{l},\\mathbf{F}_v)).\n\\end{split}\n\\end{equation}\n\nWe adopt residual learning to each head as well and get the outputs:\n\n\n\\begin{equation}\n\\begin{split}\n \\mathbf{F}_{i} = W_{i}\\mathcal{G}_{i}(\\mathbf{F}_{l},\\mathbf{F}_v)\\oplus\\mathbf{F}_l\\\\i\\in \\left\\{s, c \\right\\},\n\\end{split}\n\\end{equation}\nwhere the $W_i$ is a linear embedding implemented as $1 \\times 1$ convolution, and the $\\oplus$ operation is performed by a residual connection of element-wise addition. \n\nAs such, the self-attention head computes the response in a position as a weighted sum of the features in all positions, and thus well captures long-range dependencies. \nGiven the fact that DR lesions are usually dispersed over a broad range, the self-attention can exchange message among multiple lesions, regardless of their positional distance, and thus allows the modeling of intra-class pairwise relations of lesions. The head is supposed to distinguish the mixtures of more than two lesions and further refine the edges of large patterns in lesion segmentation.\n\n\nThe cross-attention head queries global vascular structures from the vessel features, thus incorporating interactions between lesions and vessels.\nConsidering that lesions and vessels have strong inherent pathogenic connections, the cross attention help to better locate MA and SE, and meanwhile eliminate false positives of EX caused by vessel reflection and MA caused by capillary confusion.\n\nWe concatenate the resulting features $\\mathbf{F}_{s}$ from the self-attention head and that $\\mathbf{F}_{c}$ from the cross-attention head, leading to the final RTB output:\n\n\\begin{equation}\n \\mathbf{F}_{out} = [\\mathbf{F}_s;\\mathbf{F}_c],\n\\end{equation}\nwhere the $[\\cdot\\ ;\\ \\cdot]$ denotes the concatenation at channel dimension.\n\n\n\n\\subsection{Loss Function}\nWe employ two loss functions, \\textit{i.e.}, $\\mathcal{L}_{lesion}$ and $\\mathcal{L}_{vessel}$ for the multi-lesion and vessel segmentation branches respectively, and the total loss of our network is defined as:\n\\begin{equation}\n \\mathcal{L} =\\mathcal{L}_{lesion}+\\lambda \\mathcal{L}_{vessel},\n\\end{equation}\nwhere the $\\mathcal{L}_{lesion}$ denotes the 5-class weighted cross-entropy loss for multi-lesion segmentation and the $\\mathcal{L}_{vessel}$ is a binary weighted cross-entropy loss to learn vascular features; the $\\lambda$ is set as the weight in the loss function. When $\\lambda = 0.0$, the network is optimized by the multi-lesion features only, and as $\\lambda$ grows, vascular information plays an increasing role in optimization.\n\n\n\\section{Experiments And Results}\n\\subsection{Datasets}\n\\textbf{IDRiD Dataset} is available for the segmentation and grading of retinal image challenge 2018\\cite{porwal2018indian, porwal2020idrid}. The segmentation part of the dataset contains 81 $4288 \\times 2848$ sized fundus images, accompanied by four pixel-level annotations, \\textit{i.e.,} EX, HE, MA and SE if the image has this type of lesion. In total, there are 81 EX annotations, 81 MA annotations, 80 HE annotations, and 40 SE annotations. The partition of training set and testing set is provided on IDRiD already, with 54 images for training and the rest 27 images for testing. \n\n\\textbf{DDR Dataset} is provided by Ocular Disease Intelligent Recognition (ODIR-2019) for lesion segmentation and lesion detection\\cite{LI2019}. This dataset consists 13,673 fundus images from 147 hospitals, covering 23 provinces in China. For segmentation task, 757 fundus images are provided with pixel-level annotation for EX, HE, MA and SE if the image has this type of lesion. In total, there are 486 EX annotations, 570 MA annotations, 601 HE annotations, and 239 SE annotations. The partition of training set, validation set and testing set is provided on DDR already, with 383 images for training, 149 images for validation and the rest 225 images for testing.\n\n\\begin{table*}\n \\centering\n \\caption{Performance Comparison with the state-of-the-art works reported on the IDRiD dataset, where the \\textbf{separate} and \\textbf{same} indicates the way in which the method segments the lesions separately by different models or at the same time by one model}\n \\label{tab1}\n \\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n \\multirowcell{2}{Method} & \\multirowcell{2}{sepa-\\\\rate} & \\multirowcell{2}{same} & \\multicolumn{2}{c|}{Hard Exudates} &\\multicolumn{2}{c|}{Haemorrhages} \n & \\multicolumn{2}{c|}{Microaneurysms} \n & \\multicolumn{2}{c|}{Soft Exudates} \\\\ \\cline{4-11}\n & & & AUC\\_PR & AUC\\_ROC & AUC\\_PR & AUC\\_ROC & AUC\\_PR & AUC\\_ROC & AUC\\_PR & AUC\\_ROC \\\\ \\hline\n VRT(1st) & \\checkmark & & 0.7127 & - & 0.6804 & - & 0.4951 & - & 0.6995 & - \\\\ \n PATech(2nd) & \\checkmark & & 0.8850 & - & 0.6490 & - & 0.4740 & - & - & - \\\\ \n iFLYTEK-MIG(3rd) & \\checkmark & & 0.8741 & - & 0.5588 & - & 0.5017 & - & 0.6588 & - \\\\ \n \n DRUNet\\cite{kou2019microaneurysms} & \\checkmark & & - & - & - & - & - & 0.9820 & - & - \\\\ \n SESV\\cite{xie2020sesv} & \\checkmark & & - & - & - & - & \\textbf{0.5099} & - & - & - \\\\\n L-Seg\\cite{guo2019seg} & & \\checkmark & 0.7945 & - & 0.6374 & - & 0.4627 & - & 0.7113 & - \\\\\n SSCL\\cite{zhou2019collaborative} & & \\checkmark & 0.8872 & 0.9935 & \\textbf{0.6936} & \\textbf{0.9779} & 0.4960 & 0.9828 & 0.7407 & 0.9936 \\\\ \n RTN(Ours) & & \\checkmark & \\textbf{0.9024} & \\textbf{0.9980} & 0.6880 & 0.9731 & 0.4897 & \\textbf{0.9952} & \\textbf{0.7502} & \\textbf{0.9938} \\\\ \\hline\n \\end{tabular}\n\\end{table*}\n\n\\begin{table*}\n\\caption{Performance Comparison with state-of-the-art segmentation methods on the DDR dataset, where * denotes the results are reproduced by ourselves}\n\\label{tab2}\n \\centering\n \\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n \\multirowcell{2}{Method} & \\multicolumn{2}{c|}{Hard Exudates} &\\multicolumn{2}{c|}{Haemorrhages} \n & \\multicolumn{2}{c|}{Microaneurysms} \n & \\multicolumn{2}{c|}{Soft Exudates} \\\\ \\cline{2-9}\n & AUC\\_PR & AUC\\_ROC & AUC\\_PR & AUC\\_ROC & AUC\\_PR & AUC\\_ROC & AUC\\_PR & AUC\\_ROC \\\\ \\hline\n HED\\cite{xie2015holistically} & 0.4252 & 0.9612 & 0.2014 & 0.8878 & 0.0652 &0.9299 & 0.1301 & 0.8215 \\\\ \n DeepLab v3+\\cite{chen2018deeplabv3} & 0.5405 & 0.9641 & 0.3789 & 0.9308 & 0.0316 & 0.9245 & 0.2185 & 0.8642 \\\\\n UNet\\cite{guan2019fully,Yakubovskiy2019} & 0.5505 & 0.9741 & \\textbf{0.3899} & \\textbf{0.9387} & 0.0334 & 0.9366 & 0.2455 & 0.8778 \\\\\n L-seg*\\cite{guo2019seg} & 0.5645 & 0.9726 & 0.3588 & 0.9298 & 0.1174 & 0.9423 & 0.2654 & 0.8795\\\\ \n RTN(Ours) & \\textbf{0.5671} & \\textbf{0.9751} & 0.3656 & 0.9321 & \\textbf{0.1176} & \\textbf{0.9452} & \\textbf{0.2943} & \\textbf{0.8845} \\\\ \\hline\n \\end{tabular}\n\\end{table*}\n\n\\subsection{Implementation Details}\n\n\\subsubsection{Data Preparation}\nTo prepare more trainable data, some operations are performed on the original images. First, the images are input into the a segmentation model pretrained on DRIVE\\cite{staal2004ridgeb91} and STARE\\cite{hoover2000locatingb92} dataset with vessel annotations and the pseudo vascular masks are obtained. Next, \nlimited by the memory received, the large images are random resized and cropped into small pieces of $512 \\times 512$ size, additionally we apply random horizontal flips, vertical flips, and random rotation as forms of data augmentation to reduce overfitting. Then, in order to enhance image contrast while preserving local details, we process Contrast Limited Adaptive Histogram Equalization (CLAHE) on all input images with ClipLimit=2 and GridSize=8 by setting. The CLAHE is proven to be effective due to the anomalousness distinguished from the background in diabetic fundus, and the quantitative results are presented in Table \\ref{tab5}.\n\n\\subsubsection{Model Settings}\nThe typical UNet architecture\\cite{Yakubovskiy2019} is \na popular method for medical images segmentation, constructed by an encoder and a decoder with skip connections in channel-wise concatenation manner. In this paper, we apply DenseNet-161\\cite{huang2017densely} pretrained on ImageNet dataset as the backbone of UNet encoder\\cite{guan2019fully} to achieve better performance. The channel number $C$ of output of UNet is set to 32.\n\n\\subsubsection{Experiment settings}\nOur framework is implemented using pytorch backend and performed on NVIDIA GeForce RTX 3090 GPU with 24GB of memory. During the training, the batch-size is set to 16. The initial learning rate is set to 0.001 and is decay in a step-wise manner to 0.1 times of the previous every 120 epochs. All models are trained for 250 epochs with the SGD optimizer with momentum 0.9 and weight decay 0.0005.\n\nThe loss function settings are as follows: a) the return loss ratio $\\lambda$ is set to 0.1; b) the weights of $\\mathcal{L}_{lesion}$ are set as 0.001, 0.1, 0.1, 1.0, 0.1 for background, EX, HE, MA and SE respectively; c) The coefficients of background and vessels in $\\mathcal{L}_{vessel}$ are set as 0.01 and 1.0.\n\n\\subsection{Evaluation Metrics}\nTo evaluate the performance of the proposed method, we employ the area-under-the-curve (AUC) of both the precision and recall (PR) curve and receiving operating characteristic (ROC) curve \\cite{scikit-learn}, which are also recognized as metrics of fundus image segmentation in previous competitions and researches. The former is more concerned with the accuracy of the true data in prediction, while the latter reflects the performance of the data predicted positively. There is more emphasis on recall metric in medical images, which indicates the performance of true samples being successfully predicted, \\textit{i.e.,} the AUC\\_PR drawn by recall metric shows more practical value, and the AUC\\_ROC also characterizes the effectiveness of the model.\n\n\\subsection{Comparisons on Other State-of-the-art Methods}\nWe compare our method with previous works reported on the IDRiD dataset (Table \\ref{tab1}). Our method ranks first in AUC\\_ROC of EX, MA and SE and AUC\\_PR of EX and SE, ranks second in AUC\\_ROC and AUC\\_PR of HE. \nNote that the first five methods of Table \\ref{tab1} all employed individual models segmenting the four lesions separately: according to the conference reports of the top 3 IDRiD competition teams, four models were developed for segmentation of four lesions respectively; DRUNet\\cite{kou2019microaneurysms} and SESV\\cite{xie2015holistically} proposed a specific network to segment MA only. Although SESV achieved the best AUC\\_PR of MA, such individual designs require modifying a large number of hyper-parameters in training stage and lead to time consuming in inference.\nThe rest methods of Table \\ref{tab1}: L-Seg\\cite{zhou2019collaborative}, SSCL\\cite{zhou2019collaborative} and our network, all propose one model to segment the four lesions at the same time. SSCL performs better than ours in AUC\\_PR of MA segmentation but worse in AUC\\_ROC, that might result from the fact that although RTB reduces false positives of MA away from the vessels, it also introduces the false negatives of MA.\n\nWe also verify the effectiveness of the proposed method on DDR dataset. Compared with the IDRiD dataset, the DDR dataset is an updated dataset with few results reported on it, so we apply some state-of-the-art segmentation methods on the DDR dataset to make comparisons. As shown in Table \\ref{tab2}, in the comparison with other state-of-the-arts, our method achieves the best performance in EX, MA and SE and ranks second in HE. It is obvious that our method is not doing the best job in HE segmentation both in IDRiD and DDR dataset, which may be explained by the fact that the large HE patterns are formed by blood irregularly haloing on the retina, and the specific bleeding points have been blurred. In this case, RTB, an approach that is more concerned with theoretical correlations, does not contribute useful information in the segmentation of large HEs. As mentioned in the dataset section, considering there are many low quality images with uneven illumination, underexposure, overexposure, image blurring, retinal artifacts and other disturbing lesion tissues in DDR dataset, DDR dataset is more challenging than IDRiD dataset, which results in the performance of the former lagging behind that of the latter. \n\n\n\n\n\\begin{table}[]\n \\centering\n \\caption{Performance Comparison of the different backbones with our network}\n \\scalebox{0.9}{\n \\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n \\multirowcell{2}{Framework} &\n \\multirowcell{2}{Encoder}&\n \\multicolumn{4}{c|}{AUC\\_PR}\\\\\\cline{3-6}\n & & EX & HE & MA & SE \\\\\n \\hline\n \\multirowcell{5}{UNet\\cite{guan2019fully}} & ResNet-34\\cite{he2016deep} & 0.8778 & 0.6764 & 0.4659 & 0.7407 \\\\\n & ResNet-50\\cite{he2016deep} & 0.8858 & 0.6874 & 0.4692 & 0.7475 \\\\\n & Xception\\cite{chollet2017xception} & 0.8924 & 0.6806 & 0.4851 & 0.7424 \\\\\n & Vgg19-bn\\cite{simonyan2014very} & 0.8821 & 0.6832 & 0.4789 & 0.7423 \\\\\n & DenseNet-161\\cite{huang2017densely} & \\textbf{0.9024} & \\textbf{0.6880} & \\textbf{0.4897} & \\textbf{0.7502} \\\\\n \\hline\n \\end{tabular}}\n \\label{tab4}\n\\end{table}\n\n\\subsection{Ablation Studies on IDRiD Dataset}\nWe conduct ablation studies to better understand the impact of each component of our network. First, results of several encoding architectures are available to select the proper one as backbone. Then, considering the topology of vessels, regularization terms of loss function are discussed. Next, we analyze the effect of GTB based on the baseline, which is defined as the complete workflow without GTB and RTB. In order to identify whether vascular information contributes to lesion segmentation in advanced, we apply concatenation operation to the two outputs of GTBs directly. Finally, the roles of both self-attention head and cross-attention head in RTB are discussed thoroughly.\nSince AUC\\_PR is considered as the most important clinical evaluation metric for lesion segmentation in fundus images, we simplify the metrics to AUC\\_PR only for ablative comparisons. The loss and PR curves obtained from different experiments are set out in Fig.\\ref{fig5} and detailed AUC\\_PR values can be compared in Table \\ref{tab3}.\n\n\n\\subsubsection{Analysis on the Backbone}\nTo compare the effectiveness of backbone models, we perform experiments to select proper encoding architecture. \nResNet-34\\cite{he2016deep}, ResNet-50\\cite{he2016deep}, Xception\\cite{chollet2017xception}, Vgg19-bn\\cite{simonyan2014very} and DenseNet-161\\cite{huang2017densely} integrated with UNet are implemented with the segmentation heads of our network. As can be seen from the Table \\ref{tab4}, the DenseNet-161 integrated with UNet achieves the best performance in all lesions and is utilized as the following backbone.\n\n\\begin{figure*}[htbp]\n\\centerline{\\includegraphics[width=\\textwidth]{fig5.png}}\n\\caption{Loss and PR curves for segmentation over four DR lesions. Ablation studies are compared to explore the effectiveness of the baseline itself and stacked by GTB, self-attention head (sah), cross-attention head (rah) and RTB one by one.}\n\\label{fig5}\n\\end{figure*}\n\n\n\\begin{figure}[htbp]\n\\centerline{\\includegraphics[width=\\columnwidth]{fig6.png}}\n\\caption{Visualization of (a) Original images with annotations; (b) spatial attention features with Convolutional Block Attention Module (CBAM) and (c) query attention features $\\mathcal{F}$ with Global Transformer Block (GTB) for three images from IDRiD dataset. The GTB picks more discrete and small lesions up and fine-grains the patterns of interest.}\n\\label{fig6}\n\\end{figure}\n\n\n\n\\subsubsection{Analysis on the Regularization Term}\nConsidering the topology of vessels, an extension of loss function regularization terms has been conducted. In the result of vessel segmentation, there are two common cases, the neglected ends and the truncated trunks. \nBased on the above cases, we propose two regularization terms $R_{thin}$ and $R_{cl}$. The former inspired by \\cite{yang2021hybrid} applies focal-loss function specifically on peripheral vessels, and the latter utilizes the center-line idea of \\cite{shit2021cldice} to ensure the connectivity. Table \\ref{tab_loss} indicates that the $R_{thin}$ improves the performance of MA segmentation and the $R_{cl}$ achieves the best grades in AUC\\_PR of HE and MA. However, the alterations are unremarkable, probably due to the fact that the groundtruths of vessels are pseudo-masks generated by semi-supervision, rather than manually annotated masks. The upper bound on the performance of the vessel segmentation restricts the improvement of the regularization terms on the final results.\n\n\\begin{table}[]\n \\centering\n \\caption{Performance Comparison of Different Regularization Terms on IDRiD Dataset}\n \\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n \\multirowcell{2}{$R_{thin}$} &\\multirowcell{2}{$R_{cl}$} & \\multicolumn{4}{c|}{AUC\\_PR}\\\\\\cline{3-6}\n & & EX & HE & MA & SE \\\\\n \\hline\n&&\\textbf{0.9024} & 0.6880 & 0.4897 & \\textbf{0.7502}\\\\\n\\checkmark&&0.8975&0.6845&0.4899&0.7432\\\\\n&\\checkmark&0.8912&\\textbf{0.6891}&\\textbf{0.4901}&0.7435\\\\\n\\checkmark&\\checkmark&0.8923&0.6808&0.4895&0.7426\\\\\n \\hline\n \\end{tabular}\n \\label{tab_loss}\n\\end{table}\n\n\n\\begin{figure*}[htbp]\n\\centerline{\\includegraphics[width=\\textwidth]{fig7.png}}\n\\caption{Visualization of the query position (red points) of different lesions and their two query-specific attention maps with Relation Transformer Block (RTB). The red borders denote the self-attention maps, and the blue denote the cross-attention maps. The attention of different query positions in EX, HE, MA and SE varies.}\n\\label{fig7}\n\\end{figure*}\n\n\\begin{table*}\n \\centering\n \\caption{Performance Comparison of different attention blocks on the IDRiD dataset}\n \\label{tab5}\n \\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n lesion & \\multicolumn{2}{|c|}{Hard Exudates} &\\multicolumn{2}{|c|}{Haemorrhages} \n & \\multicolumn{2}{|c|}{Microaneurysms} \n & \\multicolumn{2}{|c|}{Soft Exudates} \\\\ \\hline\n method & AUC\\_PR & AUC\\_ROC & AUC\\_PR & AUC\\_ROC & AUC\\_PR & AUC\\_ROC & AUC\\_PR & AUC\\_ROC \\\\ \\hline\n baseline(without CLAHE) & 0.8025 & 0.9912 & 0.6031 & 0.9498 & 0.3912 & 0.9803 & 0.5478 & 0.9120\\\\ \\hline\n baseline & 0.8593 & 0.9919 & 0.6284 & 0.9553 & \\textbf{0.4279} & 0.9830 & 0.5766 & 0.9171 \\\\ \n baseline+SENet\\cite{hu2018squeeze} & 0.8653 & 0.9931 & 0.6408 & 0.9497 & 0.3861 & 0.9869 & 0.5683 & 0.9299 \\\\ \n baseline+CBAM\\cite{woo2018cbam} & 0.8606 & 0.9918 & 0.6470 & 0.9480 & 0.3979 & 0.9871 & 0.5525 & 0.9373 \\\\ \n baseline+GC\\cite{GCNet} & 0.8633 & 0.9929 & 0.6406 & 0.9488 & 0.4031 & 0.9809 & 0.5540 & 0.9251 \\\\ \n baseline+GTB(Ours) & \\textbf{0.8659} & \\textbf{0.9933} & \\textbf{0.6570} & \\textbf{0.9534} & 0.4071 & \\textbf{0.9879} & \\textbf{0.5968} & \\textbf{0.9458} \\\\ \\hline\n \\end{tabular}\n\\end{table*}\n\n\n\\begin{table*}[]\n \\centering\n \\caption{Performance Comparison of different components of our network on the IDRiD dataset, where \\textbf{cat} denotes a simple concatenate of multi-lesion and vessel features in channel-wise, \\textbf{cah} and \\textbf{sah} is abbreviations for cross-attention head and self-attention head respectively}\n \\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n \\multirowcell{2}{Framework} & \\multirowcell{2}{GTB} &\\multirowcell{2}{cat} & \\multicolumn{2}{c|}{RTB} & \\multicolumn{4}{c|}{AUC\\_PR}\\\\\\cline{4-9}\n & & & cah & sah & EX & HE & MA & SE \\\\\n \\hline\n \\multirowcell{6}{baseline} & & & & & 0.8593 & 0.6284 & 0.4071 & 0.5766 \\\\\n & \\checkmark & & & & 0.8659 & 0.6570 & 0.4279 & 0.5968 \\\\\n & \\checkmark & \\checkmark & & & 0.8672 & 0.6756 & 0.4294 & 0.6663 \\\\\n & \\checkmark & & \\checkmark & & 0.8682 & 0.6818 & 0.4847 & 0.7463 \\\\\n & \\checkmark & & & \\checkmark & 0.8862 & 0.6846 & 0.4663 & 0.7422 \\\\\n & \\checkmark & & \\checkmark & \\checkmark & \\textbf{0.9024} & \\textbf{0.6880} \n & \\textbf{0.4897} & \\textbf{0.7502} \\\\\n \\hline\n \\end{tabular}\n \\label{tab3}\n\\end{table*}\n\n\n\n\\subsubsection{Analyze the Effect of GTB}\n\nTable \\ref{tab3} shows that GTB improves the performance on the basis of baseline, which indicates the weights assigned to channels in each position by GTB benefit the segmentation of all four lesions. To highlight the performance of GTB further, we compare it with other popular attention blocks under the same model parameters. \nTable \\ref{tab5} illustrates that the performance of GTB on IDRiD dataset is better than others. Different from the pooling operation as other attention blocks do, channel-wise weights of GTB are specific in each pixel, so that different channels are enlightened in different pixels.\n\nAs shown in Fig.\\ref{fig6}, the snapshots of attentive features after softmax function are taken in color. In the comparison of the spatial attention features of CBAM\\cite{woo2018cbam} and the attention features $\\mathcal{F}$ of GTB, many discrete and small patterns overlooked by the former are noticed by the latter.\n\n\\subsubsection{Analyze the Effect of RTB}\nTable \\ref{tab3} lists the ablation results. We first reaffirm the idea that vascular information contributes to multi-lesion segmentation by concatenating the vascular and multi-lesion features in channel-wise. In the comparison with and without concatenations, the greatest performance gains are obtained for HE and SE, which is consistent with the previous analysis that the HE and SE have strong relations with vessels. In order to make full use of vascular information, RTB is applied instead of simple concatenation.\nCompared with the simple concatenation, cross-attention head incorporating multi-lesion and vascular features in a transformer way enhances the scores of all four lesions further. Likewise, with the integration of self-attention head, the scores get a huge improvement as well. \n\nAs visualized in Fig.\\ref{fig7}, query-specific attention maps focus on specialized tissues. Take the query pixel on MA as an example, the self-attention tends to smaller patterns, and the cross-attention specializes the vascular tributaries, which are supposed to assist in reducing false negatives mistaken for tributaries and eliminating false alarms far from tributaries.\nBoth self-attention head and cross-attention head play a role in improving the network performance, evincing the fact that exploring the internal relationships of multi-lesion and vessels makes sense.\n\nFinally both GTB and RTB are incorporated and the network achieves the highest results. As shown in the bottom half of the Fig.\\ref{fig5}, the curve corresponding to the complete network wraps almost entirely around the others. \n\n\n\n\\begin{figure*}[htbp]\n\\centerline{\\includegraphics[width=\\textwidth]{fig8.png}}\n\\caption{Visualization of segmentation results for multi-lesion segmentation on IDRiD dataset. The different columns represent the original images, the segmentation results generated by baseline, baseline+GTB, baseline+GTB+RTB and groundtruths respectively. The yellow boxes denote the improvements over baseline brought about by GTB, in the form of pickups of missing detections, while the green boxes denote the improvements over baseline+GTB brought about by RTB, mainly in the form of fewer false alarms.\n}\n\\label{fig8}\n\\end{figure*}\n\n\\subsection{Generalization Studies on DDR and IDRiD Dataset}\nFor medical images, it is challenging but meaningful to realize the generalization over different domains under different imaging conditions. In purpose to validate the generalization capability, models are trained with the images from the train set of DDR dataset and tested on test set of IDRiD dataset which is captured from another source. Table \\ref{tab6} compares the results obtained from the preliminary analysis of generalization. From the chart, it can be seen that our method achieves the best performance by narrowing down the gap between images under different conditions.\n\n\n\n\n\n\n\n\\subsection{Qualitative Results}\nTo better illustrate the effect of GTB and RTB, we visualize the results of certain images. \nFig.\\ref{fig8} compares the segmentation results with corresponding original images and groundtruths. We take the segmentation maps of baseline, baseline with GTB and baseline with GTB and RTB to present the improvements of different components of our network.\nThe yellow boxes present the improvements of GTB, where the missing detections are discovered. The green boxes are steps up from the yellow boxes by RTB, which picks up missing detections further and reduces false alarms. Additionally, the edges of the large lesion patterns are more precisely fine-tuned by RTB, especially for the SE with blurred edges.\n\n\n\\begin{table}[]\n \\centering\n \\caption{Performance Comparison of different methods on the generalization from DDR dataset to IDRiD dataset}\n \\begin{tabular}{|c|c|c|c|c|}\n \\hline\n \\multirowcell{2}{Framework} & \\multicolumn{4}{c|}{AUC\\_PR}\\\\\\cline{2-5}\n & EX & HE & MA & SE \\\\\n \\hline\n HED\\cite{xie2015holistically} & 0.5420 & 0.2104 & 0.1245 & 0.1278 \\\\\n DeepLab v3+\\cite{chen2018deeplabv3} & 0.6480 & 0.4472 & 0.1823 & 0.2926 \\\\\n UNet\\cite{guan2019fully,Yakubovskiy2019} & 0.6472 & 0.4452 & 0.1965 & 0.2845 \\\\\n Lseg\\cite{guo2019seg} & 0.6501 & 0.4405 & 0.1986 & 0.3059 \\\\\n RTN(Ours) & \\textbf{0.6799} & \\textbf{0.4504} & \\textbf{0.2114} & \\textbf{0.3401} \\\\\n \\hline\n \\end{tabular}\n \\label{tab6}\n\\end{table}\n\n\n\n\\section{Conclusion And Discussion}\nIn this paper, we present a novel network that employs a dual-branch architecture with GTB and RTB to segment the four DR lesions simultaneously. \nOutstanding experiment results of our network can be attributed to GTB and RTB, which investigate the intra-class dependencies among multi-lesion and inter-class relations of multi-lesion and vessels.\n\n\nHowever, limited to the considerable cost of expertise pixel-level annotations, the vessel pseudo masks provided by semi-supervised learning are inevitably coarse-grained and lead to the inadequacy of our network. Therefore,\nin our future work, we will further modify the vascular semi-supervised learning strategy and keep improving the transformer structures to achieve better performance in DR multi-lesion segmentation with less memory requirement. \n\n\n\n\n\n\\input{ref.bbl}\n\\bibliographystyle{IEEEtran}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{Introduction and statement of results}\n\nThis paper gives a systematic treatment of two topologies for spaces of smooth functions from a finite-dimensional manifold to a (possibly infinite-dimensional) manifold modeled on a locally convex space.\n\nIn particular, we establish the continuity of certain mappings between spaces of smooth mappings, e.g.\\ the continuity of the joint composition map.\nAs a first application we prove that the bisection group of an arbitrary Lie groupoid (with finite-dimensional base) is a topological group.\nFor the most part, these results are generalizations of well known constructions to spaces of smooth functions with infinite-dimensional range.\nWe refer to \\cite{illman,michor,hirsch} for topologies on spaces of smooth functions between finite-dimensional manifolds.\n\\medskip\n\nTo understand these results of the present article, recall first the situation for spaces of smooth functions between finite-dimensional manifolds. \nFor $0 \\leq r \\leq \\infty$, let $C^r (M,N)$ denote the set of $r$-times continuously differentiable functions between manifolds $M$ and $N$\nIn the case where $r$ is finite, the standard choice for a topology on $C^r (M,N)$ is the well known Whitney $C^r$-topology (cf.\\ \\cite{illman,hirsch}). \nFor $r=\\infty$ and $M$ non-compact there are several choices for a suitable topology. \nOne can for example choose the topology generated by the union of all Whitney $C^r$-topologies. \nWe call this topology the strong $C^\\infty$-topology and write $C^\\infty_S (M,N)$ for the smooth functions with this topology.\\footnote{The strong topology is in the literature often also called the ``Whitney $C^\\infty$-topology''. Following Illman in \\cite{illman}, we will not use this naming convention as it can be argued that the strong $C^\\infty$-topology is not a genuine $C^\\infty$-topology. See ibid. for more information.}\nNote that each basic neighborhood of the strong $C^\\infty$-topology allows one to control derivatives of functions only up to a fixed upper bound.\nHowever, in applications one wants to control the derivatives of up to arbitrary high order (this is made precise in Section \\ref{sect: vsTop}).\nTo achieve this one has to refine the strong topology, obtaining the \\emph{very strong} topology\\footnote{In \\cite{michor} this topology is called the $\\mathcal{D}$-topology.} in the process (cf.\\ \\cite{illman} for an exposition).\nWe denote by $C_{\\textup{vS}}^\\infty (M,N)$ the smooth functions with the very strong topology and note that this topology is fine enough for many questions arising from differential topology.\n\nUnfortunately, as is argued in \\cite{michor} this topology is still not fine enough, if one wants to obtain manifold structures on $C^\\infty (M,N)$ (and subsequently on the group of diffeomorphisms $\\Diff (M)$).\nHence Michor constructed a further refinement of the very strong topology, called the $\\mathcal{FD}$-topology. \nIn the present paper, we call this topology the \\emph{fine very strong} topology and denote the space of smooth functions with this topology by $C_{\\textup{fS}}^\\infty (M,N)$.\n\nNote that the topologies discussed so far coincide if the source manifold is compact. \nIn fact in this case, all of these topologies coincide with the compact open $C^\\infty$-topology (see e.g.\\ \\cite[Definition 5.1]{neeb}). \nThe compact open $C^\\infty$-topology for infinite-dimensional target manifolds is already well understood and has been used in many investigations, for example in infinite-dimensional Lie theory, e.g.\\ \\cite{glock1}.\nHence our investigation will only turn up new results for non-compact source manifolds and infinite-dimensional target manifolds.\n\\smallskip\n\nWe will now go into some more detail and explain the main results of the present paper.\nOur aim is now to generalize the construction of the very strong and fine very strong topology to the set of smooth functions $C^\\infty (M,X)$, where $X$ is a locally convex manifold.\nHere smooth maps are understood in the sense of Bastiani's calculus \\cite{bastiani} (often also called Keller's $C^r$-theory~\\cite{keller}).\nWe refer to \\cite{milnor1983,glock1,neeb} for streamlined expositions, but have included a brief recollection in Appendix \\ref{calculus}.\n\nWorking in this framework we construct the very strong and the fine very strong topology for $C^\\infty (M,X)$, where $M$ is finite-dimensional and $X$ is a locally convex manifold.\nOur exposition mostly follows Illman's article \\cite{illman} and we adapt his arguments to our setting. \nIn particular, we describe the topology in terms of local charts as in \\cite{illman} (cf.\\ also \\cite{hirsch}).\nFor finite-dimensional manifolds one can alternatively introduce the topology using jet bundles and it is well known that both approaches yield the same topology.\nThis fact seems to be a folklore theorem, but we were not able to locate a proof in the literature. \nAs this fact is needed later on, a proof is given in Appendix \\ref{folklore}. \nThe advantage of the approach using local charts can be summarized as follows: Arguments and proofs often split into two distinct steps.\nFirst one establishes a property of the function space topology only for the (easier) special case of vector space valued smooth mappings. \nThen a localization argument involving manifold charts allows one to establish the result for smooth maps between manifolds.\n\nTo our knowledge the topologies discussed in the present paper have so far only been studied for finite-dimensional manifolds. A topology somewhat similar to the very strong topology but for infinite-dimensional manifolds can be found in \\cite[Section 41]{KM97}. Albeit the similar look, be aware that the jet bundles used in the construction are only manifolds in the inequivalent convenient setting of calculus. In particular, the topology in loc.cit.\\ does not coincide with the one constructed here if $M$ is non-compact (cf.\\ \\cite[42.2 Remarks]{KM97}). We refer to Remark \\ref{rem: topgeneral} for related topologies on function spaces between Banach manifolds.\nFor finite-dimensional manifolds, our construction recovers exactly the ones in the literature. \nWe exploit this and recall that the set $\\Prop (N,N) \\subseteq C^\\infty (M,N)$ of all proper maps is open in the very strong and the fine very strong topology.\nThen one can establish continuity of certain composition mappings, in particular our results subsume the following theorem.\\smallskip\n\n\\textbf{Theorem A} \\emph{Let $M$, $N$ be finite-dimensional manifolds, $X$ and $Y$ be (possibly infinite-dimensional) manifolds. In the following, endow all function spaces either with the very strong or the fine very strong topology. Then \n the joint composition \n \\begin{displaymath}\n \\Gamma \\colon \\Prop (M,N) \\times C^\\infty (N,X) \\rightarrow C^\\infty (M,X) ,\\quad (f,g) \\mapsto g\\circ f\n \\end{displaymath}\n is continuous.}\n \n \\emph{Further, for any smooth map $h \\colon X \\rightarrow Y$, the pushforward $$h_* \\colon C^\\infty (M,X) \\rightarrow C^\\infty (M,Y) , \\quad f \\mapsto h\\circ f$$ \n is continuous.}\n\nHaving this theorem at our disposal, we construct an interesting class of topological groups: \nSuppose $\\mathcal{G} = (G \\ensuremath{\\nobreak\\rightrightarrows\\nobreak} M)$ is a Lie groupoid. This means that $G,M$ are smooth manifolds, equipped with submersions $\\alpha,\\beta \\colon G\\rightarrow M$ and an associative and smooth multiplication $G\\times _{\\alpha,\\beta}G \\rightarrow G$ that\n admits a smooth identity map $1 \\colon M\\rightarrow G$ and a smooth inversion $\\iota\\colon G\\rightarrow G$. \n Then the bisections $\\Bis(\\mathcal{G})$ of $\\mathcal{G}$ are the sections\n $\\sigma\\colon M\\rightarrow G$ of $\\alpha$ such that $\\beta \\circ \\sigma$ is a\n diffeomorphism of $M$. This becomes a group with respect to \n \\begin{equation*}\n (\\sigma \\star \\tau ) (x) := \\sigma ((\\beta \\circ \\tau)(x))\\tau(x)\\text{ for } x \\in M.\n \\end{equation*}\nMany interesting groups from differential geometry such as diffeomorphism groups, automorphism groups and gauge transformations of principle bundles can be realised as bisection groups of suitable Lie groupoids. \nBy construction $\\Bis (\\mathcal{G}) \\subseteq C^\\infty (M,G)$ and with respect to the topologies on the space of smooth functions we obtain the following.\n\n\\textbf{Theorem B} \n\\emph{Let $\\mathcal{G} = (G\\ensuremath{\\nobreak\\rightrightarrows\\nobreak} M)$ be a Lie groupoid with finite-dimensional base $M$. Then $(\\Bis (\\mathcal{G}),\\star)$ is a topological group with respect to the subspace topology induced by either the very strong or the fine very strong topology on $C^\\infty (M,G)$.}\n\nThis result is a first step needed to turn the bisection group into an infinite-dimensional Lie group. \nIn fact, it turns out that one can establish this result quite easily (see below) once Theorem B is available.\nThe key step to establish the applications mentioned below, is to work out the continuity of certain composition mappings (which has been done in Theorem A).\nThen Proposition C and Theorem D below can be established using standard techniques from the literature.\nIn the present paper we will be only concerned with properties of the topology on function spaces. \nHence the next results are stated without a proof. We provide only some references to the literature and hope to provide details in future work.\n\n\n\\textbf{Proposition C} \\emph{\nLet $M$ be a finite-dimensional manifold and $X$ be a possibly infinite-dimensional manifold which admits a local addition.\\footnote{This is for example satisfied if $X$ is a Lie group, see also \\cite[Section 42.4]{KM97} for a definition of local additions and more examples.} \nThen $C_{\\textup{fS}}^\\infty (M,X)$ can be turned into a manifold modeled on spaces of compactly supported sections of certain bundles.}\n\nIt turns out that once the space of smooth functions is endowed with the correct topology it is not hard to prove Proposition C.\nMore details and references to literature containing the necessary auxiliary facts can be found at the end of Section \\ref{sect: vsTop}.\nProposition C generalizes \\cite[Theorem 10.4]{michor} in so far as it admits arbitrary infinite-dimensional manifolds as target manifolds (whereas loc.cit.\\ was confined to finite-dimensional targets). \nWe remark that in \\cite[42.4 Theorem]{KM97} the smooth functions $C^\\infty (M,X)$ for $M$ and $X$ as in Proposition C have been endowed with a manifold structure in the inequivalent convenient setting of calculus.\nHowever, following \\cite[42.2 Remarks]{KM97} the topology on $C^\\infty (M,X)$ used in the construction does not coincide with the fine very strong topology if $M$ is non-compact. \nHence both constructions are inequivalent even if both $M$ and $X$ are finite-dimensional (and \\( M \\) is non-compact).\n\nThe manifold structure provided by Proposition C allows one to establish the Lie group structure for a general class of bisection groups. Adapting arguments from \\cite{michor} and \\cite{Schmeding2015} one can prove that \n\n\\textbf{Theorem D} \\emph{\nThe group of bisections of a Lie groupoid $\\mathcal{G} = (G\\ensuremath{\\nobreak\\rightrightarrows\\nobreak} M)$ with $M$ finite-dimensional and $G$ a Banach manifold\\footnote{Assuming certain mild conditions on $G$ (i.e.\\ an adapted local addition, cf. \\cite{Schmeding2015}), it is not necessary to assume that $G$ is a Banach manifold.} is an infinite-dimensional Lie group. }\n\nThis generalizes the construction from \\cite{Schmeding2015}, where the group of bisections of a Lie groupoid with \\emph{compact} base was turned into an infinite-dimensional Lie group. \nThus one obtains a conceptual approach to the Lie group structures of many groups which are of interest in differential geometry (e.g.\\ automorphism groups and gauge transformation groups of principle bundles over a \\emph{non compact} base).\nMoreover, Theorem D is a crucial ingredient if one wants to extend the strong connection between Lie groupoids and infinite-dimensional Lie groups which was developed in \\cite{SchmedingWockel15}.\n\n\\newpage\n\\section{The very strong topology}\\label{sect: vsTop}\nIn this section, we introduce the \\emph{very strong topology} on the space $ C^\\infty (M,X) $ of smooth maps from a finite-dimensional smooth manifold $ M $ to a possibly infinite-dimensional smooth manifold $ X $. \nThe very strong topology allows us to control derivatives of smooth maps up to arbitrarily high order on certain families of compact sets.\nThis is a straightforward generalization of the very strong topology on the space of smooth maps between finite-dimensional manifolds as described in \\cite{illman}.\n\n\\textbf{Notation and conventions.} We write $ \\mathbb{N} := \\lbrace 1,2,\\dots \\rbrace $ and $ \\mathbb{N}_0 := \\lbrace 0,1,\\dots \\rbrace $, and will only work with vector spaces over the field of real numbers $ \\RR $. Finite-dimensional manifolds are always assumed to be $\\sigma$-compact, i.e. a countable union of compact subspaces (which for finite-dimensional manifolds is equivalent to being second countable). We always endow \\( \\RR^n \\) with the supremum norm $ \\Vert \\cdot \\Vert_\\infty $ unless otherwise stated. We define $ B_\\epsilon^n (x) := \\lbrace y \\in \\mathbb{R}^n : \\Vert y - x \\Vert_\\infty < \\epsilon \\rbrace $. Notation and conventions regarding locally convex vector spaces, smooth maps, and infinite-dimensional manifolds is covered in Appendix \\ref{calculus}. Typically, $ M $ and $ N $ will be finite-dimensional smooth manifolds, $ X $ a smooth manifold modeled on a locally convex vector space, and $ E $ a locally convex vector space.\n\n\\begin{definition}\n\t\\label{norm}\n\tLet $E$ be a locally convex vector space, $p$ a continuous seminorm on $E$, $f \\colon \\mathbb{R}^m \\to E $ smooth, $A \\subseteq \\mathbb{R}^m $ compact, $r \\in \\mathbb{N}_0 $, and $e_1, \\dots, e_m $ the standard basis vectors in $\\mathbb{R}^m$. Then define\n\t\\begin{displaymath}\n\t\\Vert f \\Vert (r,A,p) = \\sup \\lbrace p(\\dd^{(k)} f(a;\\alpha)) : a \\in A, \\alpha \\in \\lbrace e_1, \\dots , e_m \\rbrace^k, 0 \\leq k \\leq r \\rbrace .\n\t\\end{displaymath}\n\\end{definition}\n\\begin{remark}\n\t\\label{normremark}\n\tThe symbol $ \\dd^{(k)} f $ is defined in Definition \\ref{Crmap}. Elsewhere in the literature, $ \\dd^{(k)} f (x;y) = \\dd^{(k)} f (x;y_1,\\dots,y_k) $ is often denoted\n\t\\begin{align*}\n\t\\frac{\\partial^k}{\\partial y_k \\cdots \\partial y_1}f(x) && \\mbox{or} && \\frac{\\partial}{\\partial y} f(x),\n\t\\end{align*}\n\twhere $ y = (y_1,\\dots,y_k) $.\n\t\n\tIn the definition above we require $ \\alpha \\in \\lbrace e_1,\\dots,e_m \\rbrace^k $. But for any $ \\alpha \\in B_1^n (0) $ and $ a \\in A $ and $ k \\leq r $ we have $ p (\\dd^{(k)} f (a;\\alpha)) \\leq K \\Vert f \\Vert (r,A,p) $ for some constant $ K $ depending only on $ r $ and $ m $, by \\eqref{schwarzrule} in Proposition \\ref{chainruleprop}.\n\t\n\tIf $ E = \\RR^n $, any norm generates the topology on $ E $ and norms are in particular seminorms. By Proposition \\ref{generatingfamilies}, the very strong topology is not affected if we always assume that the seminorm $p$ on $ \\RR^n $ is the supremum norm $ \\Vert\\cdot \\Vert_\\infty $. In this case we simply write $ \\Vert f \\Vert (r,A) $ for $ \\Vert f \\Vert (r,A,\\Vert\\cdot \\Vert_\\infty ) $.\n\\end{remark}\n\n\\begin{lemma}[Triangle inequality]\n\t\\label{triangleinequality}\n\tLet $ E,p,A,r $ be as in Definition \\ref{norm}. Then the map\n\t\\begin{displaymath}\n\t\t\\Vert\\cdot \\Vert (r,A,p) : C^\\infty (\\mathbb{R}^m,E) \\to \\mathbb{R}\n\t\\end{displaymath}\n\tsatisfies the triangle inequality. In fact it is a seminorm on $ C^\\infty (\\mathbb{R}^m,E) $.\n\\end{lemma}\n\\begin{proof}\n\t\tUse linearity of $ d(-)(a,\\alpha) $ for fixed $ (a, \\alpha) $, and the fact that $ p $ satisfies the triangle inequality.\n\\end{proof}\n\n\\begin{definition}[Elementary neighborhood]\n\t\\label{elementarynbh}\n\tLet $E$, $p$, and $r$ be as in Definition \\ref{norm}, $M$ an $m$-dimensional smooth manifold, $ X $ a smooth manifold modeled on $ E $. Consider \\( f \\colon M \\to X \\) smooth, $(U,\\phi)$ a chart on $M$, $ (V,\\psi) $ a chart on $ X $, $ A \\subseteq U $ compact such that $ f(A) \\subseteq V $, and $ \\epsilon > 0 $. Define\n\t\\begin{align*}\n\t\\mathcal{N}^r (f; A,(U,\\phi),(V,\\psi),p,\\epsilon ) = \\lbrace h &\\in C^\\infty (M,E) : \\mbox{$ h(A) \\subseteq V $ and } \\\\\n\t&\\Vert \\psi \\circ h \\circ \\phi^{-1} - \\psi \\circ f \\circ \\phi^{-1} \\Vert (r, \\phi (A), p) < \\epsilon \\rbrace .\n\t\\end{align*}\n\tWe call this set an \\emph{elementary ~$C^r$-neighborhood of }~$ f $ in ~$ C^\\infty (M,E) $.\n\\end{definition}\n\n\\textbf{Conventions for elementary neighborhoods}\nIf $ X = \\RR^n $, we will assume that $ p $ is the supremum norm and omit the $ p $ when writing down the elementary neighborhoods.\n\nWhen there is a canonical choice of charts for our manifolds, e.g.\\ if $ X = E $ is a locally convex vector space, we omit the obvious charts when writing down elementary $ C^r $-neighborhoods.\nThus for $ f \\colon M \\to E $ we write e.g.\\ $ \\mathcal{N}^r (f;A,(U,\\phi),p,\\epsilon) := \\mathcal{N}^r (f;A,(U,\\phi),(X,\\id ),p,\\epsilon ) .$ \n\n\n\\begin{remark}\n\t\\begin{enumerate}\n\t \\item The conditions $ f(A) \\subseteq V $ and $ h(A) \\subseteq V $ ensure that the map $ \\psi \\circ h \\circ \\phi^{-1} - \\psi \\circ f \\circ \\phi^{-1} $ makes sense. \n\tFurther, the conditions enable us to control the open sets into which a (given) compact set is mapped, i.e.\\ the kind of control provided by the well known compact open topology (cf.\\ \\cite[Definition I.5.1]{neeb}).\n Indeed, by restricting to elementary $C^0$-neighborhoods, one would recover a subbase of the compact open topology on $C^\\infty (M,X)$.\n\t \\item We define elementary neighborhoods only for finite-dimensional source manifolds as the seminorms in Definition \\ref{norm} make only sense for these manifolds. \n\t Compare Remark \\ref{rem: topgeneral} for more information on alternative approaches to the topology which avoid this problem. \n\t\\end{enumerate}\n\\end{remark}\n\nWe now define what will become the basis sets in the very strong topology on $ C^\\infty (M,X) $. \n\\begin{definition}[Basic neighborhood]\n\t\\label{basicnbh}\n\tLet $ f \\colon M \\to X $ be a smooth map from a finite-dimensional smooth manifold $M$ to a smooth manifold $ X $ modeled on a locally convex vector space $E$. A \\emph{basic neighborhood of $f$ in ~$ C^\\infty (M,X) $} is a set of the form\n\t\\begin{displaymath}\n\t\\bigcap_{i \\in \\Lambda } \\mathcal{N}^{r_i} (f; A_i,(U_i, \\phi_i),(V_i,\\psi_i),p_i, \\epsilon_i),\n\t\\end{displaymath}\n\twhere $ \\Lambda $ is a possibly infinite indexing set, for all \\( i \\) the other parameters are as in Definition \\ref{elementarynbh}, and $ \\lbrace A_i \\rbrace_{i \\in \\Lambda} $ is locally finite. We call $ \\lbrace A_i \\rbrace_{i \\in \\Lambda} $ the \\emph{underlying compact family} of the neighborhood.\n\\end{definition}\nWithout loss of generalization, \\( \\Lambda = \\mathbb{N} \\), since every locally finite family over a \\( \\sigma \\)-compact space is countable.\n\nAs Proposition \\ref{basicnbhsisbasis} show, the basic neighborhoods in ~$ C^\\infty (M,X) $ form a basis for a topology on ~$ C^\\infty (M,X) $. In order to prove the proposition we need the following lemma.\n\\begin{lemma}\n\t\\label{trianglelemma}\n\tLet $ f : M \\to X $ be smooth, and ~$ g \\in \\mathcal{N} := \\mathcal{N}^r (f; A, (U,\\phi),(V,\\psi),p,\\epsilon) $. \n\tThen there exists $ \\epsilon' > 0 $ such that ~$ \\mathcal{N}' := \\mathcal{N}^r (g; A, (U, \\phi ),(V,\\psi), p, \\epsilon') \\subseteq \\mathcal{N} $.\n\\end{lemma}\n\\begin{proof}\n\tFor $ h,\\tilde{h} \\in C^\\infty (M,X) $ with $ h(A),\\tilde{h}(A) \\subseteq V $, let\n\t\\begin{displaymath}\n\t\td(h,\\tilde{h}) = \\Vert \\psi \\circ \\tilde{h} \\circ \\phi^{-1} - \\psi \\circ h \\circ \\phi^{-1} \\Vert (r,\\phi(A),p).\n\t\\end{displaymath}\n\tNote that $ d $ satisfies the triangle inequality by Lemma \\ref{triangleinequality}, and that $ h \\in \\mathcal{N} $ is equivalent to $ d(f,h)<\\epsilon $.\n\t\n\tSet $ \\epsilon' = \\epsilon - d(f,g) $, and let $ \\mathcal{N}' $ be as in the statement of the lemma. If $ h \\in \\mathcal{N}' $, then \n\t\\begin{displaymath}\n\td(f,h) \\leq d(f,g) + d(g,h) < d(f,g) + (\\epsilon - d(f,g)) = \\epsilon.\n\t\\end{displaymath}\n\tHence $ h \\in \\mathcal{N} $, and $ \\mathcal{N}' \\subseteq \\mathcal{N} $.\n\\end{proof}\n\\begin{proposition}\n\t\\label{basicnbhsisbasis}\n\tLet $ \\mathcal{U} $ and $ \\mathcal{U}' $ be basic neighborhoods of $ f $ and $ f' $ in $ C^\\infty (M,X) $, respectively. If $ g \\in \\mathcal{U} \\cap \\mathcal{U}' $, then there exists a basic neighborhood $ \\mathcal{V} $ of $ g $ such that $ \\mathcal{V} \\subseteq \\mathcal{U} \\cap \\mathcal{U}' $.\n\t\n\tHence the basic neighborhoods form a basis for a topology on $ C^\\infty (M,E) $, called \\emph{the very strong topology on $ C^\\infty (M,E) $}.\n\\end{proposition}\n\\begin{proof}\n\tWe may write\n\t\\begin{align*}\n\t\\mathcal{U} = \\bigcap_{i \\in \\Lambda} \\mathcal{N}_i && \\mbox{and} && \\mathcal{U}' = \\bigcap_{j \\in \\Lambda'} \\mathcal{N}'_j \n\t\\end{align*}\n\tfor some sets $ \\Lambda $ and $ \\Lambda' $, where $ \\mathcal{N}_i $ and $ \\mathcal{N}'_i $ are elementary neighborhoods of $ f $ and $ f' $, respectively.\n\tFor all $ i \\in \\Lambda $ and $ j \\in \\Lambda $ choose as in Lemma \\ref{trianglelemma} elementary neighborhoods $ \\mathcal{M}_i $ and $ \\mathcal{M}'_j $ of $ g $ such that $ \\mathcal{M}_i \\subset \\mathcal{N}_i $ and $ \\mathcal{M}'_j \\subset \\mathcal{N}'_j $. Then \n\t\\begin{displaymath}\n\t\\mathcal{V} := \\left( \\bigcap_{i \\in \\Lambda} \\mathcal{M}_i \\right) \\cap \\left( \\bigcap_{j \\in \\Lambda'} \\mathcal{M}'_i \\right) \\subseteq \\mathcal{U} \\cap \\mathcal{U}'. \n\t\\end{displaymath} \n\tIt remains to check that $ \\mathcal{V} $ is in fact a basic neighborhood of $ g $. The set $ \\mathcal{V} $ is a basic neighborhood of $ g $ provided that the underlying compact family of $ \\mathcal{V} $ is locally finite. This is indeed the case since the underlying compact families of $ \\mathcal{U} $ and $ \\mathcal{U}' $ are locally finite and finite unions of locally finite families are locally finite.\n\\end{proof}\nThe preceding proposition justifies the following definition.\n\\begin{definition}[Very strong topology]\n\tThe \\emph{very strong topology on $ C^\\infty (M,X) $} is the topology on $ C^\\infty (M,X) $ with basis the basic neighborhoods in $ C^\\infty (M,X) $.\\\\\n\tThe set $ C^\\infty (M,X) $ equipped with the very strong topology will be denoted by $ C_{\\textup{vS}}^\\infty (M,X) $.\n\\end{definition}\n\n\\begin{remark}\n We will work later on with $C_{\\textup{vS}}^\\infty (M,E)$, where $E$ is a locally convex space. To this end, we considered $E$ as a manifold with the canonical atlas given by the identity.\n This may seem artificial at first glance as one in principle needs to take all ``manifold charts'' of $E$ into account.\n Note however that by Lemma \\ref{lem: atlaschoice} the very strong topology on $C^\\infty (M,E)$ is generated by all basic neighborhoods of the form \n \\begin{equation}\\label{loc: charts}\n \\bigcap_{i \\in \\Lambda } \\mathcal{N}^{r_i} (f; A_i,(U_i, \\phi_i),(E,\\id_E),p_i, \\epsilon_i),\n \\end{equation}\n i.e.\\ it suffices to consider elementary neighborhoods with respect to the identity chart.\n Hence the topology on $C^\\infty (M,E)$ is quite natural.\n \n Similarly for $C^\\infty (\\mathbb{R}^n,E)$ the charts $(U_i,\\phi_i)$ in \\eqref{loc: charts} can be replaced by $(\\mathbb{R}^n,\\id_{\\mathbb{R}^n})$ by Lemma \\ref{lem: atlaschoice2}. \n In the following, we will always assume that our elementary and basic neighborhoods are constructed with respect to the identity if one (or both) of the manifolds are a locally convex space. \n\\end{remark}\n\n\n\\begin{remark}\n\tThere are other well-known topologies on $ C^\\infty (M,X) $. The \\emph{strong} topology (or \\emph{Whitney} $C^\\infty$-topology) and the \\emph{compact open} $C^\\infty$-topology (or \\emph{weak} topology) have as bases neighborhoods of the form described in Definition \\ref{basicnbh}, with some additional restrictions. For the strong topology the collection $\\lbrace r_i \\rbrace_{i \\in \\Lambda} $ of indices giving differentiation order is bounded, and for the compact open $C^\\infty$-topology we require that the indexing set $ \\Lambda $ is finite. \n\t\n\tThe very strong topology is finer than the strong topology which is finer than the compact open $C^\\infty$-topology, and in the case that $ M $ is compact all of these topologies coincide (since every locally finite family meets a compact set only finitely many times). We refer the reader to section 2.1 in \\cite{hirsch} for information about the strong and compact open $C^\\infty$ topologies in the case that $ X $ is finite-dimensional. A comparison of the strong topology and the very strong topology can be found in the introduction of \\cite{illman}.\n\t\n\tSince the very strong topology is finer than the strong topology, subsets of $ C^\\infty (M,X) $ that are open in the strong topology are also open in the very strong topology. \\cite[Section 2.1]{hirsch} has several results stating that certain subsets of $ C^\\infty (M,N) $ are open in the strong topology, consequently also in the very strong topology. In particular, the set $ \\Prop (M,N) $ of proper smooth maps is open in $ C_{\\textup{vS}}^\\infty (M,N) $. We write $ \\Prop_{\\textup{vS}} (M,N) $ for the subspace $ \\Prop (M,N) $ of $ C_{\\textup{vS}}^\\infty (M,N) $ equipped with the subspace topology.\n\\end{remark}\n\n\\begin{remark}\\label{rem: topgeneral}\nOne can also define the very strong topology on the space $ C^\\infty (X,Y) $ where $ X $ and $ Y $ are Banach manifolds (i.e. modeled on Banach spaces). \nTo this end one needs to redefine the seminorms generating the topology, which in the vector space case will take the following form:\n\nIf \\( X,Y \\) are Banach spaces, \\( f \\colon X \\to Y \\) smooth, \\( r \\in \\mathbb{N}_0 \\), and \\( A \\subseteq X \\) compact define\n\\begin{equation}\\label{semi:Frechet}\n\t\\Vert f \\Vert (r,A) = \\sup \\left\\lbrace \\Vert D^k f (x) \\Vert_Y : \\mbox{ \\( 0 \\leq k \\leq r \\) and \\( x \\in A \\)} \\right\\rbrace,\n\\end{equation}\nwhere \\( D^k f \\) denotes the \\( k \\)-th \\emph{\\Frechet derivative} of \\( f \\).\n\nHere we use that every smooth Bastiani map is also smooth in the sense of \\Frechet differentiability by \\cite[Lemma 2.10]{milnor1983}. It is easy to see that all statements made on elementary neighborhoods in the present section remain valid. Hence we obtain a very strong topology on smooth functions between Banach manifolds. \n\n\tNote that one can prove as in Appendix \\ref{folklore} that the ``very strong topology'' constructed with respect to the seminorms \\eqref{semi:Frechet} induces again the (original) very strong topology on $ C^\\infty (X,Y) $ if $ X $ is finite-dimensional. \n\tUnfortunately, for an infinite-dimensional Banach manifold $ X $ this topology does not allow us to control the behavior of functions ``at infinity'' (or anywhere for that matter since compact subsets of infinite-dimensional Banach spaces have empty interior).\nTo see this recall that manifolds modeled on infinite-dimensional Banach space don't have a locally finite compact exhaustion by the Baire category theorem. \n\n\tRecall however, that one can define a Whitney $ C^\\infty $-topology for $X,Y$ Banach manifolds via jet bundles (see e.g.\\ \\cite{michor,KM97} or Appendix \\ref{folklore} for a short exposition). As shown in \\cite[Chapter 9]{marg}, this topology then allows one to control the behavior of a function on all of $ X $. \n\tThe key difference is that the Whitney topology defined in this way controls the behavior of jets on locally finite families of \\textbf{closed} sets. \n\tObviously, one can not hope to describe it via the seminorms as the existence of the suprema in the seminorms is tied to the compactness of the sets. Even worse, for an infinite-dimensional manifold $ X $ and $Y=E$ a locally convex space, the largest topological vector space contained in $C^\\infty(X,E)$ with respect to this topology is trivial (cf. \\cite[437]{KM97}). For these reasons we work exclusively with the very strong topology for finite-dimensional source manifolds.\n\\end{remark}\n\n\\textbf{Additional facts about the very strong topology.} Sometimes it is convenient to assume that the continuous seminorms $ p $ used in constructing very strong neighborhoods are of a certain form, as we have already remarked. There is no loss of generality in making such assumptions if the family of seminorms that we restrict to is ``big enough''.\n\n\\begin{proposition}\n\t\\label{generatingfamilies}\n\tLet $ M $ be a finite-dimensional smooth manifold and $ X $ a smooth manifold modeled on a locally convex vector space $ E $. Suppose $ \\mathcal{P} $ is a generating family of seminorms for $ E $ (see Definition \\ref{seminormfamilydef}). \n\t\n\tIf we replace every instance of ``$p$ is a continuous seminorm on $ E $'' in the definitions and results earlier in this section with ``$ p \\in \\mathcal{P} $'', then the resulting very strong topology on $ C^\\infty (M,X) $ is unaffected.\n\\end{proposition}\n\\begin{proof}\n\tLet $ \\mathcal{T} $ be the very strong topology on $ C^\\infty (M,X) $ constructed with respect to all continuous seminorms on $ E $, and let $ \\mathcal{T}' $ be the very strong topology on $ C^\\infty (M,X) $ obtained by restricting to seminorms in $ \\mathcal{P} $. Then $ \\mathcal{T}' $ is obviously coarser than $ \\mathcal{T} $ since every $ p \\in \\mathcal{P} $ is continuous, so it suffices to show that $ \\mathcal{T} $ is coarser than $ \\mathcal{T}' $. This will be the case if for every basic $ \\mathcal{T} $-very strong neighborhood $ \\mathcal{U} = \\bigcap_{i\\in\\Lambda} \\mathcal{N}_i $ of $ f \\in C^\\infty (M,X) $, where each $ \\mathcal{N}_i $ is an elementary $ \\mathcal{T} $-very strong neighborhood $$ \\mathcal{N}_i = \\mathcal{N}^{r_i} (f;A_i,(U_i,\\phi_i),(V_i,\\psi_i),p_i,\\epsilon_i ), $$ there exists a basic $ \\mathcal{T}'$-very strong neighborhood $ \\mathcal{U}' $ of $ f $ such that $ \\mathcal{U}' \\subseteq \\mathcal{U} $.\n\t\n\tFix $ i \\in \\Lambda $. By \\eqref{generating family criterion} in Proposition \\ref{locally convex prop} there exist $ n_i \\in \\mathbb{N} $ and $ p_{i,1},\\dots,p_{i,n_i} \\in \\mathcal{P} $ and $ c_i > 0 $ such that $ p_i \\leq c_i \\sup_{1\\leq j \\leq n_i} p_{i,j} $. And then\n\t\\begin{align*} \n\t\\mathcal{V}_i := \\bigcap_{j=1}^{n_i} \\mathcal{N}^{r_i} \\left( f;A_i,(U_i,\\phi_i),(V_i,\\psi_i),p_{i,j}, \\frac{ \\epsilon_i }{2c_i} \\right) \\subseteq \\mathcal{N}_i.\n\t\\end{align*}\n\tIndeed, if $ g \\in \\mathcal{V}_i $, then for $ a \\in A_i $, $ \\alpha \\in \\lbrace e_1,\\dots,e_m \\rbrace^k $, $ 0 \\leq k \\leq r_i $, and $ 0 \\leq j \\leq n_i $, we have $$ c_i p_{i,j} ( \\dd (\\psi_i \\circ g \\circ \\phi_i^{-1} - \\psi_i \\circ f \\circ \\phi_i^{-1})^{(k)}(a,\\alpha) ) < \\frac{ \\epsilon_i}{2}, $$ which together with $ p_i \\leq c_i \\sup p_{i,j} $ clearly implies that $ g \\in \\mathcal{N}_i $.\n\t\n\tNow set $ \\mathcal{U}' := \\bigcap_{i \\in \\Lambda} \\mathcal{V}_i $. This is a basic $ \\mathcal{T}' $-very strong neighborhood of $ f $ such that $ \\mathcal{U}' \\subseteq \\mathcal{U} $.\n\\end{proof}\n\nThe following lemma is useful when constructing certain basic neighborhoods. The proof given here is fairly detailed, but throughout the remainder of this text the details of similar arguments will be omitted.\n\\begin{lemma}\n\t\\label{hackinglemma}\n\tLet \\( M \\) be a finite-dimensional smooth manifold, \\( X \\) a locally convex manifold, and \\( f \\colon M \\to X \\) a smooth map. Suppose \\( \\lbrace K_n \\rbrace_{n \\in \\mathbb{N}} \\) is a locally finite family of compact subsets of \\( M \\). Then there exist families of charts \\( \\lbrace (V_i,\\psi_i) \\rbrace_{i\\in\\mathbb{N}} \\) for \\( X \\) and \\( \\lbrace (U_i,\\phi_i) \\rbrace_{i\\in\\mathbb{N}} \\) for \\( M \\), and a locally finite family \\( \\lbrace A_i \\rbrace_{i\\in\\mathbb{N}} \\) of compact subsets of \\( M \\) such that \n\t\\begin{enumerate}\n\t\t\\item \\( \\bigcup_{i\\in\\mathbb{N}} A_i = \\bigcup_{n \\in \\mathbb{N}} K_n \\),\n\t\t\\item \\( A_i \\subseteq U_i \\) for all \\( i \\in \\mathbb{N} \\),\n\t\t\\item \\( f(U_i) \\subseteq V_i \\) for all \\( i \\in \\mathbb{N} \\).\n\t\\end{enumerate}\n\\end{lemma}\n\\begin{proof}\n\tFix \\( n \\in \\mathbb{N} \\). For every \\( x \\in K_n \\) choose a chart \\( (V_{n,x},\\psi_{n,x}) \\) around \\( f(x) \\) and a chart \\( (U_{n,x},\\phi_{n,x}) \\) around \\( x \\). By shrinking \\( U_{n,x} \\) we may assume that \\( f(U_{n,x}) \\subseteq V_{n,x} \\). Since \\( M \\) is locally compact there exists a compact neighborhood \\( A_{n,x}' \\) around \\( x \\) such that \\( A_{n,x}' \\subseteq U_{n,x} \\). Now set \\( A_{n,x} = K_n \\cap A_{n,x}' \\). By compactness of \\( K_n \\) there exist finitely many \\( x_{n,1},\\dots,x_{n,k_n} \\in K_n \\) such that \\( \\lbrace A_{n,x_{n,j}} \\rbrace_{i=1}^{k_n} \\) covers \\( K_n \\). \n\t\n\tThe families \\( \\lbrace (V_{n,x_{n,i}},\\psi_{n,x_{n,i}}) \\rbrace_{n,i} \\), \\( \\lbrace (U_{n,x_{n,i}},\\phi_{n,x_{n,i}}) \\rbrace_{n,i} \\) and \\( \\lbrace A_{n,x_{n,i}} \\rbrace_{n,i} \\) have the desired properties. By relabeling the indices we can take the indexing set to be \\( \\mathbb{N} \\).\n\\end{proof}\n\n\\begin{lemma}\n\t\\label{biglemma}\n\tLet $ M $ be a finite-dimensional smooth manifold, $ X $ a smooth manifold modeled on a locally convex vector space $ E $, and let \\( U \\subseteq M \\) and \\( V \\subseteq X \\) be open subsets. Consider the subspace \\( C^\\infty_{\\text{vS,sub}} (U,V) := \\left\\lbrace f \\in C^\\infty (M,X) : f(U) \\subseteq V \\right\\rbrace \\subseteq C_{\\textup{vS}}^\\infty (M,X) \\).\n\t\\begin{enumerate}\n\t\t\\item \\( C^\\infty_{\\text{vS,sub}} (U,V) \\) is an open subset of \\( C_{\\textup{vS}}^\\infty (M,X) \\).\n\t\t\\item The restriction \\( \\res_{\\text{vS}} \\colon C^\\infty_{ \\text{vS,sub}} (U,V) \\to C_{\\textup{vS}}^\\infty (U,V) \\) is continuous.\n\t\t\\item If $f \\in C^\\infty (U,V)$ and $\\mathcal{N}^r (f; A,(U_\\phi,\\phi),(V_\\psi,\\psi),p,\\epsilon )$ is an elementary neighborhood of $f$ such that $\\psi (V_\\psi)$ is a convex set, then there exists $g \\in C^\\infty (M,X)$ with\n\t\t\t \\begin{equation}\\label{eq: elres}\n\t\t \\res_{\\text{vS}}^{-1} (\\mathcal{N}^r (f; A,(U_\\phi,\\phi),(V_\\psi,\\psi),p,\\epsilon )) = \\mathcal{N}^r (g; A,(U_\\phi,\\phi),(V_\\psi,\\psi),p,\\epsilon )\n\t\t \\end{equation}\n\t\\end{enumerate}\n\\end{lemma}\n\\begin{proof}\n\t\\begin{enumerate}\n\t\t\\item Suppose \\( f \\in C^\\infty(U,V) \\).\n\t\t\tSince $ U $ is an open subset of $ M $, the subspace \\( U \\) is metrizable and locally compact, hence $ \\sigma $-compact.\n\t\t\tCombining Lemma \\ref{dugundjifact} and Lemma \\ref{hackinglemma}, we find a locally finite exhaustion \\( \\lbrace A_n \\rbrace_{n \\in \\mathbb{N}} \\) of \\( U \\) by compact sets, charts \\( \\lbrace (U_n,\\phi_n) \\rbrace_{n \\in \\mathbb{N}} \\) for \\( M \\), and charts \\( \\lbrace (V_n,\\psi_n) \\rbrace_{n \\in \\mathbb{N}} \\) for \\( X \\) such that \\( A_n \\subseteq U_n \\) and \\( f(A_n) \\subseteq V_n \\) for all \\( n \\in \\mathbb{N} \\).\n\t\t\tSince \\( f(A_n) \\subseteq V \\), shrink the \\( V_n \\) if necessary such that \\( V_n \\subseteq V \\) for all \\( n \\in \\mathbb{N} \\) (while still \\( f( A_n ) \\subseteq V_n \\)).\n\t\t\tTake any continuous seminorm $ p $ on $ E $ and define \n\t\t\t\\[ \\mathcal{U} = \\bigcap_{n \\in \\mathbb{N}} \\mathcal{N}^0 \\left( f; A_n,(U_n,\\phi_n),(V_n,\\psi_n),p,1 \\right). \\] \n\t\t\tIf $ g \\in \\mathcal{U} $, then $ g(A_n) \\subseteq V_n \\subseteq V $ for all $ n \\in \\mathbb{N} $, from which it follows that $ g(U) = g \\left( \\bigcup A_n \\right) \\subseteq V $. So $ \\mathcal{U} $ is a neighborhood of $ f $ in $ C_{\\textup{vS}}^\\infty (M,X) $ such that $ \\mathcal{U} \\subseteq C^\\infty (U,V) $.\n\t\t\\item Take an arbitrary basic neighborhood\n\t\t\t\\[\n\t\t\t\t\\mathcal{U} = \\bigcap_{i\\in\\Lambda} \\mathcal{N}^{r_i} \\left( f;A_i,(U_i,\\phi_i),(V_i,\\psi_i),p_i,\\epsilon_i \\right)\n\t\t\t\\]\n\t\t\tin \\( C_{\\textup{vS}}^\\infty (U,V) \\). We will show that given \\( g \\in \\res_{\\text{vS}}^{-1} (\\mathcal{U}) \\), there exists a basic neighborhood \\( \\mathcal{V} \\) of \\( g \\) in \\( C_{\\textup{vS}}^\\infty (M,X) \\) such that \\( \\mathcal{V} \\subseteq \\res_{\\text{vS}}^{-1} (\\mathcal{U}) \\), i.e. \\( \\res_{\\text{vS}}^{-1} (\\mathcal{U}) \\) is open. By Lemma \\ref{basicnbhsisbasis} there are elementary neighborhoods of $\\res_{\\text{vS}} (g)$ such that \n\t\t\t\\[\n\t\t\t\t\\mathcal{V} = \\bigcap_{i\\in\\Lambda} \\mathcal{N}^{r_i} \\left( \\res_{\\text{vS}} (g) ;A_i,(U_i,\\phi_i),(V_i,\\psi_i),p_i,\\delta_i \\right).\n\t\t\t\\]\n\t\t\tis contained in $\\mathcal{U}$. \n\t\t\tNow clearly $\\bigcap_{i\\in\\Lambda} \\mathcal{N}^{r_i} \\left( g ;A_i,(U_i,\\phi_i),(V_i,\\psi_i),p_i,\\delta_i \\right)$ is contained in $\\res_{\\text{vS}}^{-1} (\\mathcal{V}) \\subseteq \\res_{\\text{vS}}^{-1} (\\mathcal{U})$.\n \t\t\\item Since $M$ is finite-dimensional, whence paracompact, we can choose a neighborhood $W$ of $A$ and a smooth cutoff function $\\rho \\colon M \\rightarrow \\RR$ with $\\rho|_{W} \\equiv 1$ and $\\rho_{M \\setminus f^{-1} (V_\\psi) \\cap U_\\phi}\\equiv 0$.\n \t\tComposing with a suitable translation, we may assume without loss of generality that $\\psi(V_\\psi)$ is a convex $0$-neighborhood.\n \t\tSuppressing the translation we can thus define $$g \\colon M \\rightarrow X , \\quad x \\mapsto \\begin{cases}\n \t\t \\psi^{-1} ( \\rho(x) \\cdot \\psi \\circ f (x)) & \\text{if } x \\in U_\\phi, \\\\\n \t\t \\psi^{-1} (0) & \\text{else}.\n \t\t \\end{cases}\n\t\t $$\n\t\tNow as $g|_W = f|_W$ the identity \\eqref{eq: elres} is satisfied. \\qedhere\n\t\\end{enumerate}\n\\end{proof}\n\n\n\\section{Composition of maps in the very strong topology}\\label{sect: compo}\nThroughout this section, $ M $ and $ N $ are finite-dimensional smooth manifolds and $ X $ denotes a smooth manifold modeled on a locally convex vector space $ E $. \n\nIt is a desirable property of the very strong topology on \\( C^\\infty (M,X) \\) that composition\n\\begin{align*}\n\t\\Gamma : C_{\\textup{vS}}^\\infty (M,N) \\times C_{\\textup{vS}}^\\infty (N,X) &\\to C_{\\textup{vS}}^\\infty (M,X) \\\\\n\t(f,h) &\\mapsto h \\circ f\n\\end{align*}\nis continuous. But this is not the case in general, a counterexample can be found in Example \\ref{counterexample}. However, the restriction of the composition map\n\\begin{displaymath}\n\\Gamma \\colon \\Prop_{\\textup{vS}} (M,N) \\times C_{\\textup{vS}}^\\infty (N,X) \\to C_{\\textup{vS}}^\\infty (M,X)\n\\end{displaymath}\n\\textit{is} continuous, where $ \\Prop_{\\textup{vS}} (M,N) $ denotes the subspace of $ C_{\\textup{vS}}^\\infty (M,N) $ consisting of all the proper maps. This is precisely what Theorem \\ref{compiscont} says, and proving it is the main goal of this section.\n\nAs we will see, the crucial property of proper maps needed is the fact that if $ \\lbrace A_i \\rbrace $ is a locally finite family of subsets of $ M $ and $ f : M \\to N $ is proper, then $ \\lbrace f(A_i) \\rbrace $ is locally finite. This will enable us to choose for a basic neighborhood $ \\mathcal{V} $ of some composition $ h \\circ f $ in $ C_{\\textup{vS}}^\\infty (M,X) $ basic neighborhoods $ \\mathcal{U} $ and $ \\mathcal{U}' $ of $ f $ and $ h $, respectively, such that $ \\Gamma (\\mathcal{U} \\times \\mathcal{U}' ) \\subseteq \\mathcal{V} $. The challenge is to choose $ \\mathcal{U}' $ in such a way that the underlying compact family of the neighborhood is locally finite.\n\nWe now give the promised counterexample to the statement that composition of maps in the very strong topology is continuous in general. This example is inspired by the proof of \\cite[Proposition 2.2(b)]{counterglock}.\n\\begin{example}\n\t\\label{counterexample}\n\tThe composition map\n\t\\begin{align*}\n\t\t\\Gamma \\colon C_{\\textup{vS}}^\\infty (\\mathbb{R},\\mathbb{R}) \\times C_{\\textup{vS}}^\\infty (\\mathbb{R},\\mathbb{R}) \\to C_{\\textup{vS}}^\\infty (\\mathbb{R},\\mathbb{R}), \\quad (f,h) \\mapsto h \\circ f\n\t\\end{align*}\n\tis not continuous.\n\\end{example}\n\\begin{proof}\n\tNote that for every basic neighborhood $ \\mathcal{U} $ of $ f \\in C_{\\textup{vS}}^\\infty (\\mathbb{R},\\mathbb{R}) $ there exists a basic neighborhood $ \\mathcal{U}' $ of $ f $ with underlying compact family $ \\lbrace [2n-1,2n+1] \\rbrace_{n\\in\\mathbb{Z}} $ such that $ f \\in \\mathcal{U}' \\subseteq \\mathcal{U} $, since each compact interval $ [2n-1,2n+1] $ intersects only finitely many sets belonging to the locally finite underlying compact family of $ \\mathcal{U} $.\n\t\n\tTo show discontinuity of $ \\Gamma $ it suffices to show discontinuity at $ (0,0) $. Let $ \\mathcal{V} $ be the basic neighborhood of 0 given by $$ \\mathcal{V} := \\bigcap_{n\\in\\mathbb{N}} \\mathcal{N}^{n} (0;[2n-1,2n+1],1) . $$ We will show that for any pair of basic neighborhoods\n\t\\begin{align*}\n\t\\mathcal{U} = \\bigcap_{n\\in\\mathbb{Z}} \\mathcal{N}^{r_n} (0;[2n-1,2n+1],\\epsilon_n), \\\\\n\t\\mathcal{U}' = \\bigcap_{n\\in\\mathbb{Z}} \\mathcal{N}^{r_n'} (0;[2n-1,2n+1],\\epsilon_n'),\n\t\\end{align*}\n\tthere exists a pair of functions $ (f,h) \\in \\mathcal{U}' \\times \\mathcal{U} $ such that $ h \\circ f \\notin \\mathcal{V} $. \n\t\n\tConstruct $ h \\in C^\\infty (\\mathbb{R},\\mathbb{R}) $ such that in a neighborhood of $ 0 $, $ h $ is given by the equation $ h(x) = x^{r_0 + 1} $, and such that $ \\operatorname{supp} h \\subseteq ]-1,1[ $. For some sufficiently small $ k > 0 $ we will have $ kh \\in \\mathcal{U} $. For every $ m \\in \\mathbb{N} $ define $$ h_m (x) := \\frac{k}{m^{r_0}} h(mx) $$ and note that $ h_m \\in \\mathcal{U} $, since $$ | h_m^{(j)} (x) | = \\frac{k m^j}{m^{r_0}} | h^{(j)} (mx) | \\leq k | h^{(j)}(mx) | < \\epsilon_0 $$ for $ j \\leq r_0 $, where we use the notation \\( g^{(j)} (y) = \\dd^{(j)} g(y;1,\\dots,1) \\) for smooth maps \\( g \\colon \\RR \\to \\RR \\).\n\t\n\tLet $ 2n \\geq r_0 + 1 $ and construct $ \\tilde{f} \\in C^\\infty (\\mathbb{R},\\mathbb{R}) $ such that $ \\tilde{f}(x) = x - 2n $ in a neighborhood of $ 2n $ and $ \\operatorname{supp} \\tilde{f} \\subseteq ]2n-1,2n+1[ $. Then for some sufficiently small $ s > 0 $ we have $ f := s \\tilde{f} \\in \\mathcal{U}' $.\n\t\n\tSo far we have a sequence $ \\lbrace h_m \\rbrace_{m\\in\\mathbb{N}} \\subset \\mathcal{U} $ and $ f \\in \\mathcal{U}' $. By construction, $ h_m \\circ f (x) = kms^{r_0+1} (x-2n)^{r_0+1} $ in a neighborhood of $ 2n $. Hence $$ |(h_m \\circ f)^{(r_0+1)}(2n)| = kms^{r_0+1} (r_0+1)! \\geq 1 $$ for large enough $ m $, in which case $ h_m \\circ f \\notin \\mathcal{V} $.\n\\end{proof}\nHaving given the example above, we return our focus to the main task of the section, which is proving Theorem \\ref{compiscont}. Leading up to the theorem is a sequence of lemmata. \n\nAlthough we are actually interested in mapping spaces between manifolds, we first give a lemma that only applies to vector spaces. In a sense, this lemma resolves the main difficulty, and generalizing to manifolds is only a matter of dealing with charts.\n\\begin{lemma}\n\t\\label{realdomains}\n\tConsider the composition map\n\t\\begin{displaymath}\n\t\t\\Gamma : C_{\\textup{vS}}^\\infty (\\mathbb{R}^m,\\mathbb{R}^n) \\times C_{\\textup{vS}}^\\infty (\\mathbb{R}^n,E) \\to C_{\\textup{vS}}^\\infty (\\mathbb{R}^m,E),\\quad (f,h) \\mapsto h \\circ f.\n\t\\end{displaymath}\n\tLet $ (f,h) \\in C_{\\textup{vS}}^\\infty (\\mathbb{R}^m,\\mathbb{R}^n) \\times C_{\\textup{vS}}^\\infty (\\mathbb{R}^n,E) $, and consider an arbitrary elementary neighborhood\n\n\t$\t\\mathcal{N} = \\mathcal{N}^r (h\\circ f;A,p,\\epsilon) \\subseteq C_{\\textup{vS}}^\\infty (\\mathbb{R}^m,E)$\n\n\tof $ h \\circ f $. For all compact neighborhoods \\( A' \\) of \\( f(A) \\) there exist \\( \\delta, \\delta' > 0 \\) such that the elementary neighborhoods\n\t\\begin{align*}\n\t\t\\mathcal{M} = \\mathcal{N}^r (f;A,\\delta) && \\mbox{and} && \\mathcal{M}' = \\mathcal{N}^r (h;A',p,\\delta')\n\t\\end{align*}\n\tsatisfy $ \\Gamma (\\mathcal{M} \\times \\mathcal{M}') \\subseteq \\mathcal{N} $.\n\\end{lemma}\n\\begin{proof}\n\tLet $ A' $ be any compact neighborhood of $ f(A) $. We proceed in several steps.\n\t\n\t\\textbf{Step 1.} Our first goal is to define $ \\mathcal{M} $. We want a $ \\delta > 0 $ such that for all $ \\hat{f} \\in C^\\infty (\\mathbb{R}^m, \\mathbb{R}^n) $, the inequality $ \\Vert \\hat{f} - f \\Vert (r,A) < \\delta $ implies\n\t\\begin{align*}\n\t\t\\Vert h \\circ (\\hat{f} - f) \\Vert (r,A,p) < \\frac{\\epsilon}{2} && \\mbox{and} && \\hat{f} (A) \\subseteq A'.\n\t\\end{align*}\n\tBy Lemma \\ref{greenlemma} it is possible to choose $ \\delta $ such that the first property holds. We may choose $ \\delta $ such that the second property also holds, because $ \\hat{f}(A) = (\\hat{f}-f)(A) + f(A) \\subseteq B_\\delta^n (0) + f(A) $. Pick such a $ \\delta $ and define\n\t\\begin{displaymath}\n\t\t\\mathcal{M} := \\mathcal{N}^r (f;A,\\delta).\n\t\\end{displaymath}\n\t\n\tObserve that by the triangle inequality (Lemma \\ref{triangleinequality}) there exists an $ R > 0 $ such that every $ \\hat{f} \\in \\mathcal{M} $ satisfies $ \\Vert \\hat{f} \\Vert (r,A) \\leq R $.\n\t\n\t\\textbf{Step 2.} Our second goal is to define $ \\mathcal{M}' $. We want a $ \\delta' > 0 $ such that for all $ \\hat{h} \\in C^\\infty (\\mathbb{R}^n,E) $ and all $ \\hat{f} \\in \\mathcal{M} $,\n\t\\begin{align*}\n\t\t\\Vert \\hat{h} - h \\Vert (r,A',p) < \\delta' && \\implies && \\Vert (\\hat{h} - h) \\circ \\hat{f} \\Vert (r,A,p) < \\frac{\\epsilon}{2}.\n\t\\end{align*}\n\tA $ \\delta' $ having this property exists by Lemma \\ref{greenlemma} and the observation at the end of step 1. Now define\n\t\\begin{displaymath}\n\t\t\\mathcal{M}' := \\mathcal{N}^r (h;A',p,\\delta').\n\t\\end{displaymath}\n\t\n\t\\textbf{Step 3.} Now we must show that $ \\mathcal{M} $ and $ \\mathcal{M}' $ have the desired property. Let $ \\hat{f} \\in \\mathcal{M} $ and $ \\hat{h} \\in \\mathcal{M}' $. By the triangle inequality,\n\t\\begin{displaymath}\n\t\t\\Vert \\hat{h} \\circ \\hat{f} - h \\circ f \\Vert (r,A,p) \\leq \\Vert (\\hat{h} - h) \\circ \\hat{f} \\Vert (r,A,p) + \\Vert h \\circ (\\hat{f} - f) \\Vert (r,A,p) < \\epsilon.\n\t\\end{displaymath} \n\tSo $ \\hat{h}\\circ \\hat{f} \\in \\mathcal{N} $. Thus $ \\Gamma (\\mathcal{M} \\times \\mathcal{M}') \\subseteq \\mathcal{N} $.\n\\end{proof}\n\nA version of the preceding lemma still holds if we replace $ \\mathbb{R}^m $ and $ \\mathbb{R}^n $ with finite-dimensional smooth manifolds and $ E $ with an infinite-dimensional smooth manifold. This is our next result.\n\n\\begin{lemma}\n\t\\label{illmannslemma}\n\tGiven $ f \\in C^\\infty (M,N) $ and $ h \\in C^\\infty (N,X) $ and an arbitrary elementary neighborhood\n\t\\begin{displaymath}\n\t\t\\mathcal{N} := \\mathcal{N}^r (h\\circ f;A,(U,\\phi),(W,\\eta),p,\\epsilon) \\subseteq C_{\\textup{vS}}^\\infty (M,X)\n\t\\end{displaymath}\n\tof $ h\\circ f $ there exist finitely many\n\t\\begin{align*}\n\t\t\\mathcal{M}_j := \\mathcal{N}^r (f;A_j,(U,\\phi),(V_j,\\psi_j),\\delta_j) && \\mbox{and} && \\mathcal{M}_j' := \\mathcal{N}^r (h;A_j',(V_j,\\psi),(W,\\eta),p,\\delta_j')\n\t\\end{align*}\n\tsuch that\n\t\\begin{enumerate} \n\t\t\\item $\\displaystyle\\Gamma \\left( \\bigcap_j \\left( \\mathcal{M}_j \\times \\mathcal{M}_j' \\right) \\right) \\subseteq \\mathcal{N} $,\n\t\t\\item $\\displaystyle \\bigcup_j A_j = A $,\n\t\t\\item $ f(A_j) \\subseteq \\interior A_j' \\subseteq A_j' \\subseteq V_j $ for all $ j $.\n\t\\end{enumerate}\n\t\n\tMoreover, given any neighborhood $ Q $ of $ f(A) $ we may choose the $ V_j $ such that all $ V_j \\subseteq Q $.\n\\end{lemma}\n\\begin{proof}\n\tSince $ f(A) $ is compact we may choose finitely many sets\n\t\\begin{align*}\n\t\tD_j \\subseteq \\interior A_j' \\subseteq A_j' \\subseteq V_j \\subseteq N && \\mbox{such that} && f(A) \\subseteq \\bigcup_j D_j,\n\t\\end{align*}\n\twhere $ D_j $ and $ A_j' $ are compact, and $ V_j $ is a chart domain for a chart $ (V_j, \\psi_j) $ on $ N $. Shrinking the $ V_j $ we may assume that every $ V_j \\subseteq Q $. Set\n\t\\begin{displaymath} \n\t\tA_j := A \\cap f^{-1} (D_j),\n\t\\end{displaymath} \n\tto obtain compact sets that satisfy\n\t\\begin{align*}\n\t\t\\bigcup_j A_j = A && \\mbox{and} && f(A_j) \\subseteq D_j \\mbox{, for all $ j $.}\n\t\\end{align*}\n\t\n\tLet\n\t\\( \\mathcal{N}_j := \\bigcap_j \\mathcal{N}^r (h\\circ f ; A_j, (U,\\phi),(W,\\eta),p,\\epsilon) \\),\n\tand note that $ \\mathcal{N} = \\bigcap_j \\mathcal{N}_j $. For each $ j $ apply Lemma \\ref{realdomains} to the maps $ \\psi_j \\circ f \\circ \\phi^{-1} $ and $ \\eta \\circ h \\circ \\psi_j^{-1} $ and the elementary neighborhood\n\t\\begin{displaymath}\n\t\t\\tilde{\\mathcal{N}}_j := \\mathcal{N}^r (\\eta \\circ h \\circ f \\circ \\phi^{-1};\\phi(A_j),p,\\epsilon)\n\t\\end{displaymath}\n\tto obtain elementary neighborhoods\n\t\\begin{align*}\n\t\t\\tilde{\\mathcal{M}}_j = \\mathcal{N}^r (\\psi_j \\circ f \\circ \\phi^{-1};\\phi(A_j),\\delta_j) && \\mbox{and} && \\tilde{\\mathcal{M}}_j' = \\mathcal{N}^r (\\eta \\circ h \\circ \\psi_j^{-1};\\psi_j (A_j'),p,\\delta_j')\n\t\\end{align*}\n\tsuch that $ \\Gamma (\\tilde{\\mathcal{M}}_j \\times \\tilde{\\mathcal{M}}_j') \\subseteq \\tilde{\\mathcal{N}}_j $. \n\t\n\tThe elementary neighborhoods $ \\tilde{\\mathcal{M}}_j $ and $ \\tilde{\\mathcal{M}}_j' $ of $ \\psi_j \\circ f \\circ \\phi^{-1} $ and $ \\eta \\circ h \\circ \\psi_j^{-1} $, respectively, induce elementary neighborhoods\n\t\\begin{align*}\n\t\t\\mathcal{M}_j = \\mathcal{N}^r (f;A_j,(U,\\phi),(V_j,\\psi_j),\\delta_j) && \\mbox{and} && \\mathcal{M}_j' = \\mathcal{N}^r (h;A_j',(V_j,\\psi_j),(W,\\eta),p,\\delta_j')\n\t\\end{align*}\n\tof $ f $ and $ h $, respectively. These neighborhoods correspond to each other in the sense that $ \\hat{f} \\in \\mathcal{M}_j $ if and only if $ \\psi_j \\circ \\hat{f} \\circ \\phi^{-1} \\in \\tilde{\\mathcal{M}}_j $, and $ \\hat{h} \\in \\mathcal{M}_j' $ if and only if $ \\eta \\circ \\hat{h} \\circ \\psi_j^{-1} \\in \\tilde{\\mathcal{M}}_j' $. Similarly for $ \\tilde{\\mathcal{N}}_j $ and $ \\mathcal{N}_j $. Since $ \\Gamma (\\tilde{\\mathcal{M}}_j \\times \\tilde{\\mathcal{M}}_j') \\subseteq \\tilde{\\mathcal{N}}_j $, one has by the correspondence described here that $ \\Gamma (\\mathcal{M}_j \\times \\mathcal{M}_j') \\subseteq \\mathcal{N}_j $. \n\t\n\tNow just observe that\n\t\\begin{displaymath}\n\t\t\\Gamma \\left( \\bigcap_j \\left( \\mathcal{M}_j \\times \\mathcal{M}_j' \\right) \\right) \\subseteq \\bigcap_j \\Gamma \\left( \\mathcal{M}_j \\times \\mathcal{M}_j' \\right) \\subseteq \\bigcap_j \\mathcal{N}_j = \\mathcal{N}.\n\t\\end{displaymath}\n\\end{proof}\n\\begin{lemma}\n\t\\label{compislemma}\n\tConsider smooth maps $ f \\in C_{\\textup{vS}}^\\infty (M,N) $ and $ h \\in C_{\\textup{vS}}^\\infty (N,X) $ and a basic neighborhood $ \\mathcal{U} = \\bigcap_{i \\in \\Lambda} \\mathcal{N}_i $, where each\n\t\\begin{displaymath} \n\t\\mathcal{N}_i = \\mathcal{N}^{r_i} (h\\circ f;A_i,(U_i,\\phi_i),(W_i,\\eta_i),p_i,\\epsilon_i)\n\t\\end{displaymath}\n\tis an elementary neighborhood of $ h\\circ f $. \n\t\n\tIf $ \\lbrace f(A_i) \\rbrace_{i \\in \\Lambda} $ is locally finite, then there exist basic neighborhoods $ \\mathcal{V} $ and $ \\mathcal{V}' $ of $ f $ and $ h $, respectively, such that $ \\Gamma (\\mathcal{V} \\times \\mathcal{V}') \\subseteq \\mathcal{U} $.\n\\end{lemma}\n\\begin{proof}\n\tSince $ \\lbrace f(A_i) \\rbrace_{i\\in\\Lambda} $ is locally finite, there exist compact neighborhoods $ Q_i $ of $ f(A_i) $ such that $ \\lbrace Q_i \\rbrace_{i \\in \\Lambda} $ is locally finite, by \\cite[30.C.10]{cech}. Here we use our assumption that finite-dimensional manifolds are $ \\sigma $-compact.\n\t\n\tFor each $ i \\in \\Lambda $, Lemma \\ref{illmannslemma} implies that there exist\n\t\\begin{align*}\n\t\t\\mathcal{W}_i :=& \\bigcap_{j=1}^{n_i} \\mathcal{N}^{r_i} (f;A_{i,j},(U_i,\\phi_i),(V_{i,j},\\psi_{i,j}),\\delta_{i,j}), \\\\\n\t\t\\mathcal{W}_i' :=& \\bigcap_{j=1}^{n_i} \\mathcal{N}^{r_i} (h;A_{i,j}',(V_{i,j},\\psi_{i,j}),(W_i,\\eta_i),p_i,\\delta_{i,j}')\n\t\\end{align*}\n\tsuch that\n\t\\begin{enumerate}\n\t\t\\item $\\displaystyle \\Gamma (\\mathcal{W}_i \\times \\mathcal{W}_i') \\subseteq \\mathcal{N}_i $,\n\t\t\\item $\\displaystyle \\bigcup_{j=1}^{n_i} A_{i,j} = A_i $,\n\t\t\\item $ A_{i,j}' \\subseteq V_{i,j} \\subseteq Q_i $ for all \\( j \\).\n\t\\end{enumerate}\n\tThen $ \\lbrace A_{i,j} \\rbrace_{i,j} $ is locally finite by (2) and since $ \\lbrace A_i \\rbrace_i $ is locally finite, and $ \\lbrace A_{i,j}' \\rbrace_{i,j} $ is locally finite by (3) and since $ \\lbrace Q_i \\rbrace_i $ is locally finite.\tHence\n\t\\begin{align*}\n\t\t\\mathcal{V} := \\bigcap_{i\\in\\Lambda} \\mathcal{W}_i && \\mbox{and} && \\mathcal{V}' := \\bigcap_{i\\in\\Lambda} \\mathcal{W}_i'\n\t\\end{align*}\n\tare basic neighborhoods of $ f $ and $ h $, respectively, such that\n\t\\begin{displaymath}\n\t\t\\Gamma \\left( \\mathcal{V} \\times \\mathcal{V}' \\right) = \\Gamma \\left( \\bigcap_{i\\in\\Lambda} \\left( \\mathcal{W}_i \\times \\mathcal{W}_i' \\right) \\right) \\subseteq \\bigcap_{i\\in\\Lambda} \\Gamma (\\mathcal{W}_i \\times \\mathcal{W}_i') \\subseteq \\bigcap_{i\\in\\Lambda} \\mathcal{N}_i = \\mathcal{U}.\n\t\\end{displaymath}\n\\end{proof}\n\\begin{theorem}\n\t\\label{compiscont}\n\tLet $M$ and $N$ be finite-dimensional smooth manifolds and let $ X $ be a smooth manifold modeled on a locally convex vector space $E$. Then the composition map\n\t\\begin{displaymath}\n\t\\Gamma \\colon \\Prop_{\\textup{vS}} (M,N) \\times C_{\\textup{vS}}^\\infty (N,X) \\to C_{\\textup{vS}}^\\infty (M,X)\n\t\\end{displaymath}\n\tsending $ (f,h) $ to $ h \\circ f $ is continuous.\n\\end{theorem}\n\\begin{proof}\n\tIt suffices to show that given maps $ f \\in \\Prop_{\\textup{vS}} (M,N) $ and $ h \\in C_{\\textup{vS}}^\\infty (N,E) $ and a basic neighborhood\n\t\\begin{displaymath}\n\t\t\\mathcal{U} = \\bigcap_{i\\in\\Lambda} \\mathcal{N}^{r_i} (h\\circ f;A_i,(U_i,\\phi_i),(V_i,\\psi_i),p_i,\\epsilon_i)\n\t\\end{displaymath}\n\tof $ h\\circ f $ in $ C_{\\textup{vS}}^\\infty (M,X) $, there exist basic neighborhoods $ \\mathcal{V} $ and $ \\mathcal{V}' $ of $ f $ and $ h $, respectively, such that $ \\Gamma (\\mathcal{V} \\times \\mathcal{V}') \\subseteq \\mathcal{U} $.\n\t\n\tSo suppose that we are given $ f , h $ and $ \\mathcal{U} $ as above. Then $ \\lbrace f(A_i) \\rbrace_{i \\in \\Lambda} $ is locally finite since $ f $ is proper, by \\cite[Lemma 3.10.11]{engelking}. Thus we may apply Lemma \\ref{compislemma} to obtain the desired neighborhoods $ \\mathcal{V} $ and $ \\mathcal{V}' $.\n\\end{proof}\nUnfortunately, precomposition is not continuous in general as an examination of Example \\ref{counterexample} reveals. However, precomposition by a proper map is continuous. \n\\begin{proposition}\n\t\\label{precomposition}\n\tLet $ f \\in \\Prop_{\\textup{vS}} (M,N)$. Then the following map is continuous \n\t\\begin{align*}\n\t\tf^* \\colon C_{\\textup{vS}}^\\infty (N,X) &\\to C_{\\textup{vS}}^\\infty (M,X), \\quad\t\th \\mapsto h \\circ f \n\t\\end{align*}\n\\end{proposition}\n\\begin{proof}\n\tThe map $ \\iota_f \\colon C_{\\textup{vS}}^\\infty (N,X) \\to \\Prop (M,N) \\times C_{\\textup{vS}}^\\infty (N,X) $ given by $ \\iota_f (h) = (f,h) $ is continuous. Hence $ f^* $, which is the composition \n\t\\begin{displaymath}\n\t\tC_{\\textup{vS}}^\\infty (N,X) \\xrightarrow{\\iota_f} \\Prop_{\\textup{vS}} (M,N) \\times C_{\\textup{vS}}^\\infty (N,X) \\xrightarrow{\\Gamma} C_{\\textup{vS}}^\\infty (M,X),\n\t\\end{displaymath}\n\tis also continuous. \n\\end{proof}\n\nWe will now prove that postcomposition is always continuous. \nThis result is needed even for postcomposition by a map $f\\colon X \\rightarrow Y$ between infinite-dimensional manifolds.\nThus the next proposition can not readily be deduced from Theorem \\ref{compiscont} (or the other results in this section)\\footnote{For the finite-dimensional case, a proof along these lines can be found in \\cite{illman}.}. \nInstead we have to take a detour using results on the (coarser) compact open $C^\\infty$-topology (cf.\\ to the proof of Lemma \\ref{lem: atlaschoice}). \n\n\\begin{proposition}\\label{postcomposition}\n Let $f \\colon X \\rightarrow Y$ be smooth, where $Y$ is a (possibly infinite-dimensional) manifold. \n Then for any finite-dimensional manifold $M$ the following map is continuous \n \\begin{displaymath}\n f_* \\colon C_{\\textup{vS}}^\\infty (M,X) \\rightarrow C_{\\textup{vS}}^\\infty (M,Y) , \\quad h \\mapsto f\\circ h\n \\end{displaymath}\n\\end{proposition}\n\n\\begin{proof}\n To see that $f_*$ is continuous, we proceed in several steps.\n \n \\textbf{Step 1} \\emph{Special elementary neighborhoods.} \n Consider first an arbitrary elementary neighborhood $\\mathcal{N} = \\mathcal{N}^{r} (f\\circ h; A,(U, \\phi),(V,\\psi),q, \\epsilon)$ in $C_{\\textup{vS}}^\\infty (M,Y)$.\n Since $h(A)$ is compact, there are finitely many manifold charts $(W_i,\\kappa_i)$ of $X$ with $h(A) \\subseteq \\bigcup_{i} W_i$.\n Now the open sets $h^{-1} (W_i)$ cover $A$ and thus there are finitely many compact sets $K_j$ such that $A=\\bigcup_{j} K_j$ and $h(K_j) \\subseteq W_{i_j}$.\n Thus we replace $A$ by the finitely many compact sets. Note that this will ensure that the families of compact sets considered later remain locally finite.\n To shorten the notation, assume without loss of generality that there is a manifold chart $(W,\\kappa)$ of $X$ such that $h(A) \\subseteq W$ and $f(W) \\subseteq V$.\n In particular, we can thus consider the mapping $f_{\\kappa}^\\psi := \\psi \\circ f \\circ \\kappa^{-1} \\colon \\kappa (W) \\rightarrow \\psi (V)$\n \n \\textbf{Step 2} \\emph{The preimage of a special elementary neighborhood of $f\\circ h$ is a neighborhood of $h$.} We work locally in charts.\n Let $Y$ be modeled on the locally convex space $F$ and $X$ be modeled on the locally convex space $E$.\n Recall that the compact open $C^\\infty$-topology (see \\cite[Definition I.5.1]{neeb}) controls the derivatives of functions on compact sets.\n Moreover, the elementary neighborhoods of the very strong topology form a subbase of the compact open $C^\\infty$-topology.\n We denote by $C^\\infty (M,F)_{\\text{co}}$ the vector space of smooth functions with the compact open $C^\\infty$-topology. \n \n Choose a compact neighborhood $C \\subseteq U$ of $A$ such that $h(C) \\subseteq W$ (this entails $f\\circ h(C)\\subseteq V$). \n We endow the subset $\\lfloor C,\\kappa(W)\\rfloor := \\{g\\in C^\\infty (M,E) \\mid g(C) \\subseteq \\kappa(W)\\}$ with the subspace topology induced by the compact open $C^\\infty$-topology.\n As $\\kappa (W) \\subseteq E$ is open, we note that $\\lfloor C,\\kappa(W)\\rfloor$ is open in $C^\\infty (M,E)_{\\text{co}}$.\n Now \\cite[Proposition 4.23 (a)]{glockomega} shows that \n \\begin{displaymath}\n (f_{\\kappa}^\\psi)_* \\colon \\lfloor C,\\kappa(W) \\rfloor \\rightarrow C^\\infty (\\interior C,F)_{\\text{co}},\\quad h \\mapsto (\\psi\\circ f\\circ \\kappa^{-1}) \\circ h\n \\end{displaymath}\n is continuous. \n Moreover, $\\mathcal{N}_{loc} := \\mathcal{N}^{r} (\\psi \\circ f\\circ h|_{\\interior C}; A,(U\\cap \\interior C, \\phi),(F,\\id_F),q, \\epsilon)$ is open in $C^\\infty (\\interior C,F)_{\\text{co}}$.\n Further, $f_{\\kappa}^\\psi \\circ \\kappa \\circ h|_{\\interior C} = \\psi \\circ f \\circ h|_{\\interior C}$. \n Observe that thus $\\kappa \\circ h \\in ((f_{\\kappa}^\\psi)_*)^{-1} (\\mathcal{N}_{loc})$. \n As the elementary neighborhoods form a subbase of the compact open $C^\\infty$-topology, Lemma \\ref{lem: atlaschoice} together with continuity of $(f_{\\kappa}^\\psi)_*$ yields\n \\begin{equation}\\label{eq: locpush}\n \\lfloor C, \\kappa (W)\\rfloor \\cap \\bigcap_{k=1}^N \\mathcal{N}^{r} (\\kappa \\circ h; A_k,(U_k, \\phi_k),(E,\\id_E),p_k, \\epsilon_k) \\subseteq ((f_{\\kappa}^\\psi)_*)^{-1} (\\mathcal{N}_{loc}).\n \\end{equation}\n Recall from the proof of \\cite[Proposition 4.23 (a)]{glockomega} that the compact sets $A_k$ are contained by construction in $\\interior C$. \n Thus one easily deduces from \\eqref{eq: locpush} that \n \\begin{displaymath}\n \\mathcal{N}^{0} (h; C,(U, \\phi),(W,\\kappa),p_1, 1) \\cap \\bigcap_{k=1}^N \\mathcal{N}^{r} (h; A_k,(U_k, \\phi_k),(W,\\kappa),p_k, \\epsilon_k) \\subseteq (f_*)^{-1} (\\mathcal{N})\n \\end{displaymath}\n Summing up, we see that $(f_*)^{-1} (\\mathcal{N})$ is a neighborhood of $h$. \n\n\n Further, this \\emph{finite} family of neighborhoods controls the behavior of mappings only on a pre chosen compact set $C$ (which depends of course on $h$). \n \n \\textbf{Step 3} \\emph{Preimages of basic neighborhoods are open} \n Let $\\mathcal{M} = \\bigcap_{i \\in \\mathbb{N}} \\mathcal{N}_i$ be a basic neighborhood of $f\\circ h \\in C^\\infty (M,Y)$ with $\\{A_k\\}_{k \\in \\mathbb{N}}$ its the underlying compact family.\n We will prove that for arbitrary $g \\in (f_*)^{-1} (\\mathcal{M})$ the preimage is a neighborhood of $g$.\n Choose with Proposition \\ref{basicnbhsisbasis} a basic neighborhood of $f\\circ g$ which is contained in $\\mathcal{M}$. \n Replacing $\\mathcal{M}$ with this basic neighborhood, it suffices thus to consider the case $g=h$. \n Splitting each $A_k$ as in Step 1 we may assume without loss of generality that each $\\mathcal{N}_i$ is of the form considered in Step 2.\n Use \\cite[30.C.10]{cech} to construct for every $A_k$ a compact neighborhood $C_k$ such that $\\{C_k\\}_{k \\in \\mathbb{N}}$ is locally finite.\n Now we proceed for every elementary neighborhood $\\mathcal{N}_k$ as above (replace $C$ in Step 2 by $C_k$ and shrink $C_k$ if necessary!). \n Since the family $\\{C_k\\}_{k \\in \\mathbb{N}}$ is locally finite, we thus end up with a basic neighborhood $\\mathcal{M}_h$ around $h$ which mapped by $f_*$ to $\\mathcal{M}$.\n We conclude that $(f_*)^{-1} (M)$ is a neighborhood of $h$, whence of every of its elements.\n Hence preimages of basic neighborhoods under $f_*$ are open in $C_{\\textup{vS}}^\\infty (M,X)$, whence $f_*$ is continuous.\n\\end{proof}\n\n\n\nAs an application, we can now identify (as topological spaces) spaces of maps into a product with products of spaces of mappings to the factors.\n\n\\begin{theorem}\n\t\\label{product theorem}\n\tLet $ M $ be a finite-dimensional manifold, and let $ X_1 $ and $ X_2 $ be smooth manifolds modeled on locally convex vector spaces $ E_1 $ and $E_2$, respectively. Then \n\t\\begin{align*}\n\t\t\\iota : C_{\\textup{vS}}^\\infty (M, X_1 \\times X_2) \\to C_{\\textup{vS}}^\\infty (M, X_1) \\times C_{\\textup{vS}}^\\infty (M, X_2), \\quad f \\mapsto (\\pr_1 \\circ f, \\pr_2 \\circ f)\n\t\\end{align*}\n\tis a homeomorphism, where for \\( i \\in \\lbrace 1,2 \\rbrace \\) \\( \\pr_i \\colon X_1 \\times X_2 \\to X_i \\) is the canonical projection.\n\\end{theorem}\n\\begin{proof}\n\tClearly $ \\iota $ is a bijection, and it is continuous by Proposition \\ref{postcomposition}. We will prove that $ \\iota^{-1} $ is continuous, i.e. that $ \\iota $ is open.\n\tBy \\eqref{locally convex product} in Proposition \\ref{locally convex prop}, the set \n\t\t\\begin{displaymath}\n\t\t\t\\mathcal{P} := \\lbrace p \\circ \\pr_i : p \\mbox{ is a continuous seminorm on } E_i \\rbrace\n\t\t\\end{displaymath} \n\tis a generating family of seminorms on $ E_1 \\times E_2 $.\n\tConsider a basic neighborhood $ \\mathcal{U} = \\bigcap_{i\\in\\Lambda} \\mathcal{N}_i $ of $ f \\in C_{\\textup{vS}}^\\infty (M,X_1 \\times X_2) $, where each\n\t$\n\t\t\\mathcal{N}_i = \\mathcal{N}^{r_i} (f;A_i,(U_i,\\phi_i),(V_i,\\psi_i),p_i,\\epsilon_i).\n\t$\n\tBy Proposition \\ref{generatingfamilies} we may assume that each $ p_i \\in \\mathcal{P} $. Take an arbitrary $ i \\in \\Lambda $. If $ p_i = p\\circ \\pr_1 $ for some continuous seminorm $ p $ on $ E_1 $, let\n\t\\begin{align*}\n\t\t\\mathcal{M}_i = \\mathcal{N}^{r_i} (\\pr_1 \\circ f; A_i,(U_i,\\phi_i),(V_i,\\psi_i),p,\\epsilon_i) && \\mbox{and} && \\mathcal{M}_i' = C_{\\textup{vS}}^\\infty (M,X_2).\n\t\\end{align*}\n\tIf $ p_i = q\\circ \\pr_2 $ for a continuous seminorm $ q $ on $ E_2 $, reverse the roles of $ \\mathcal{M}_i $ and $ \\mathcal{M}_i' $.\n\t\n\tNow suppose without loss of generalization that $ p_i = p \\circ \\pr_1 $ for some continuous seminorm $ p $ on $ E_1 $. For $ g \\colon \\mathbb{R}^m \\supseteq \\phi_i (A_i) \\to \\psi_i (V_i) \\subseteq E_1 \\times E_2 $, one has\n\t\\begin{displaymath}\n\t\\dd^{(k)} g = \\dd^{(k)} (\\pr_1 \\circ g , \\pr_2 \\circ g) = ( \\dd^{(k)} \\pr_1 \\circ g, \\dd^{(k)} \\pr_2 \\circ g ),\n\t\\end{displaymath}\n\tso the condition\n\t\\( p_i \\left( \\dd^{(k)}g (a;\\alpha) \\right) < \\epsilon_i \\)\n\tis equivalent to\n\t\\( p \\left( \\dd^{(k)} (\\pr_1 \\circ g) (a;\\alpha) \\right) < \\epsilon_i \\).\n\tHence $ \\mathcal{M}_i \\times \\mathcal{M}_i' = \\iota (\\mathcal{N}_i) $. Since $ \\iota $ is bijective one has\n\t\\begin{displaymath}\n\t\t\\iota (\\mathcal{U}) = \\bigcap_{i\\in\\Lambda} \\iota (\\mathcal{N}_i) = \\bigcap_{i\\in\\Lambda} \\mathcal{M}_i \\times \\bigcap_{i\\in\\Lambda} \\mathcal{M}_i'.\n\t\\end{displaymath}\n\tSo $ \\iota $ is open, and a homeomorphism.\n\\end{proof}\n\n\\begin{corollary}\n\t\\label{product corollary}\n\tIf $ Q $ is a compact smooth manifold, then following map is continuous\n\t\\begin{align*}\n\t\t\\chi \\colon C_{\\textup{vS}}^\\infty (M,X) \\to C_{\\textup{vS}}^\\infty (Q \\times M, Q \\times X), \\quad f \\mapsto \\id \\times f.\n\t\\end{align*}\n\\end{corollary}\n\\begin{proof}\n\tBy Theorem \\ref{product theorem} it suffices to show that the maps\n\t\\begin{align*}\n\t\t\\chi_1 \\colon C_{\\textup{vS}}^\\infty (M,X) &\\to C_{\\textup{vS}}^\\infty (Q\\times M, Q) && &\\mbox{and} && \\chi_2 \\colon C_{\\textup{vS}}^\\infty(M,X) &\\to C_{\\textup{vS}}^\\infty (Q \\times M, X) \\\\\n\t\tf &\\mapsto \\pr_1 \\circ (\\id \\times f) && & && f &\\mapsto \\pr_2 \\circ (\\id \\times f)\n\t\\end{align*}\n\tare continuous. For $ (q,m) \\in Q \\times M $, one has\n\t\\begin{align*}\n\t\t\\chi_1(f)(q,m) &= \\pr_1 \\circ (\\id \\times f) (q,m) = \\pr_1 (q,f(m)) = q = \\pr_1 (q,m), \\\\ \n\t\t\\chi_2(f)(q,m) &= \\pr_2 \\circ (\\id \\times f) (q,m) = \\pr_2 (q,f(m)) = f(m) \\\\ &= f \\circ \\pr_2 (q,m) = \\pr_2^* (f) (q,m).\n\t\\end{align*}\n\tThe map \\( \\chi_1 \\) is constant in $ f $, hence continuous, and the map \\( \\chi_2 = \\pr_2^* \\). Since $ Q $ is compact, $ \\pr_2 $ is proper, so Proposition \\ref{precomposition} implies that $ \\chi_2 = \\pr_2^* $ is also continuous.\n\\end{proof}\n\n\\section{The fine very strong topology}\n\nIn the end, we would like a structure on \\( C^\\infty (M,X) \\) as a locally convex manifold, where \\( M \\) is a finite-dimensional smooth manifolds and \\( X \\) is a manifold modeled on a locally convex vector space \\( E \\), but for this purpose the very strong topology is not fine enough. A first step in the direction of making \\( C^\\infty (M,X) \\) into a locally convex manifold would be having a similar structure on \\( C^\\infty (M,E) \\). One might hope that \\( C_{\\textup{vS}}^\\infty (M,E) \\) itself with the vector space structure induced by pointwise operations would be a locally convex vector space. But as Corollary \\ref{supportcorollary} points out, this is not the case when $ E $ is a (non-trivial) locally convex vector space and $ M $ is a non-compact manifold. However, we will see in the next section that the subspace of \\( C_{\\textup{vS}}^\\infty (M,E) \\) consisting of maps with compact support, denoted \\( C_{\\text{vS,c}}^\\infty (M,E) \\), is a locally convex vector space. Following \\cite{michor}, we refine the topology on \\( C_{\\textup{vS}}^\\infty (M,E) \\) to obtain a structure on \\( C^\\infty (M,E) \\) as a smooth manifold modeled on \\( C_{\\text{vS,c}}^\\infty (M,E) \\). The resulting topology on \\( C^\\infty (M,E) \\), or more generally \\( C^\\infty (M,X) \\), is called the \\emph{fine very strong} topology on $ C^\\infty (M,X) $. The space \\( C^\\infty (M,X) \\) equipped with the fine very strong topology is denoted $ C_{\\textup{fS}}^\\infty (M,X) $. \n\nFortunately, the results of the previous sections are easily extended to hold in the fine very strong topology. This is done in Proposition \\ref{fsmoothprop}.\n\nIt is a folklore fact (Proposition \\ref{prop: topologiescoincide}) that in the finite-dimensional case, the very strong topology is equivalent to the $ \\mathcal{D} $-topology as described in \\cite[36]{michor}.\\footnote{The $\\mathcal{D}$-topology was defined using jet bundles (also reviewed in Appendix \\ref{folklore}). Our treatment of the topology has the advantage that only elementary arguments are needed. Further, only our approach generalizes to arbitrary locally convex target manifolds.} Consequently, the fine very strong topology is equivalent to the $ \\mathcal{FD} $-topology defined in \\cite[40]{michor}.\n\n\\begin{proposition}\n\t\\label{supportprop}\n\tLet $ M $ be a finite-dimensional smooth manifold and $ E $ be a locally convex vector space. Consider a sequence $ \\lbrace f_n \\rbrace_{n\\in \\mathbb{N}} \\subseteq C_{\\textup{vS}}^\\infty (M,E) $ which converges in the very strong topology towards \\( f \\in C^\\infty (M,E) \\). \n\tThen there exist a compact $ K \\subseteq M $ and an $ N \\in \\mathbb{N} $ such that for all $ n \\geq N $ we have $$ \\osupp{f}{f_n} := \\lbrace y \\in M : f_n (y) \\neq f(y) \\rbrace \\subseteq K. $$\n\\end{proposition}\n\\begin{proof}\n\tFor $ f \\in C_{\\textup{vS}}^\\infty (M,X) $, we will show that $ f $ cannot be a limit of $ \\lbrace f_n \\rbrace $ if for all compact $ K \\subseteq M $ and all $ N \\in \\mathbb{N} $ there exists $ n \\geq N $ such that $ \\osupp{f}{f_n} \\nsubseteq K $.\n\n\tLet \\( \\lbrace A_n \\rbrace_{n \\in \\mathbb{N}} \\) be a locally finite exhaustion of \\( M \\) by compact sets (exists by Lemma \\ref{dugundjifact} since \\( M \\) is \\( \\sigma \\)-compact), and for \\( n \\in \\mathbb{N} \\) set \\( K_n = \\bigcup_{i=1}^n A_i \\).\n\n\tConstruct a basic neighborhood of \\( f \\) recursively, using the following procedure. Let \\( n_0 = 1 \\), \\( m_0 = 1 \\). For \\( i \\in \\mathbb{N} \\), choose \\( n_i > n_{i-1} \\) such that \\( \\osupp{f}{f_{n_i}} \\nsubseteq K_{m_{i-1}} \\). By construction there exists \\( m_i > m_{i-1} \\) such that \\( \\osupp{f}{f_{n_i}} \\cap \\left( M \\setminus K_{m_{i-1}} \\right) \\cap A_{m_i} \\neq \\emptyset \\). Take any \\( x \\) in this nonempty set. Since \\( f (x) \\neq f_{n_i} (x) \\), there exists a continuous seminorm \\( p_i \\) on \\( E \\) such that \\( 2 \\epsilon_i := p_i (f_{n_i} (x) - f(x)) > 0 \\), and then\n\t\\[\n\t\tf_{n_i} \\notin \\mathcal{N}_i := \\mathcal{N}^0 \\left( f; A_{m_i}, p_i, \\epsilon_i \\right).\n\t\\]\n\tNow \\( \\mathcal{U} := \\bigcap_{i \\in \\mathbb{N}} \\mathcal{N}_i \\) is a basic neighborhood of \\( f \\) such that for all \\( N \\in \\mathbb{N} \\) there exists \\( n \\geq N \\) such that \\( f_n \\notin \\mathcal{U} \\). So the sequence \\( \\lbrace f_n \\rbrace_{n \\in \\mathbb{N}} \\) does not converge to \\( f \\).\n\\end{proof}\n\\begin{remark}\n\tOne can easily prove the proposition above for \\( E \\) a locally convex manifold rather than a locally convex vector space, by ``hacking'' the compact sets \\( A_i \\) in the proof into smaller compact sets that are contained in charts.\n\\end{remark}\n\\begin{corollary}\n\t\\label{supportcorollary}\n\tLet $ M $ be a finite-dimensional non-compact manifold and $ E \\neq \\lbrace 0 \\rbrace $ a locally convex vector space. Then $ C_{\\textup{vS}}^\\infty (M,E) $ with the vector space structure induced by pointwise operations is not a topological vector space.\n\\end{corollary}\n\\begin{proof}\n\tLet $ f \\in C_{\\textup{vS}}^\\infty (M,E) $ be a non-zero constant map. Then Proposition \\ref{supportprop} shows that $ \\lim_{\\lambda \\to 0} (\\lambda f) \\neq 0 = (\\lim_{\\lambda \\to 0} \\lambda) f $, hence scalar multiplication is not continuous.\n\\end{proof}\n\\begin{remark}\n\t\\label{supportcorollaryremark}\n\tAlthough $ C_{\\textup{vS}}^\\infty (M,E) $ is not a topological vector space, it is a topological group under pointwise addition by Lemma \\ref{continuous addition}. And $ C_{\\textup{vS}}^\\infty (M,\\mathbb{R}) $ is a topological ring under the pointwise operations induced by addition and multiplication in $ \\mathbb{R} $.\n\\end{remark}\n\\begin{definition}[The fine very strong topology]\n\tDefine an equivalence relation $ \\sim $ on $ C^\\infty (M,X) $ by declaring that $ f \\sim g $ whenever $$ \\csupp{f}{g} := \\overline{\\lbrace y \\in M : f(y) \\neq g(y) \\rbrace} $$ is compact. Now refine the very strong topology on $ C^\\infty (M,X) $ by demanding that the equivalence classes are open in $ C^\\infty (M,X) $. In other words, equip $ C^\\infty (M,X) $ with the topology generated by the very strong topology and the equivalence classes. This is the \\emph{fine very strong topology} on $ C^\\infty (M,X) $. We write $ C_{\\textup{fS}}^\\infty (M,X) $ for $ C^\\infty (M,X) $ equipped with the fine very strong topology.\n\\end{definition}\n\\begin{remark}\n\t\\label{fvs remark}\n\tHere is another way to look at the fine very strong topology. Start with $ C_{\\textup{vS}}^\\infty (M,X) $ and equip the equivalence classes $ [f] $ with the subspace topology. Then $$ C_{\\textup{fS}}^\\infty (M,X) = \\bigsqcup_{[f] \\in C^\\infty (M,X)\/\\sim} [f] $$ as topological spaces.\n\tTaking the family of all sets of the form $ \\mathcal{U} \\cap [f] $, where $ \\mathcal{U} $ runs through the basic neighborhoods in $ C^\\infty (M,X) $ and $ [f] $ runs through the equivalence classes, yields a basis for the fine very strong topology on $ C^\\infty (M,X) $.\n\\end{remark}\n\\begin{remark}\n\tIf $ f \\in C^\\infty (M,X) $ is a proper map and $ f \\sim \\hat{f} $, then $ \\hat{f} $ is also proper. Indeed, if $ K \\subseteq X $ is compact, then $ \\hat{f}^{-1} (K) \\subseteq f^{-1}(K) \\cup \\csupp{f}{\\hat{f}} $. Since closed subspaces of compact spaces are compact, $ \\hat{f}^{-1} (K) $ is compact.\n\\end{remark}\nWe would obviously like the results of the previous sections to remain true in the fine very strong topology. Fortunately, it is easy to extend the results to this case using the following lemma.\n\\begin{lemma}\n\t\\label{corollarylemma}\n\tLet $ T $ be a topological space, and $ \\zeta \\colon T \\to C^\\infty (M,X) $ a function. If $ \\zeta $ is continuous as a map to $ C_{\\textup{vS}}^\\infty (M,X) $ and $ \\zeta^{-1} ([f]) \\subseteq T $ is open for all equivalence classes $ [f] \\subseteq C^\\infty (M,X) $, then $ \\zeta $ is continuous as a map to $ C_{\\textup{fS}}^\\infty (M,X) $.\n\\end{lemma}\n\\begin{proof}\n\tThe map $ \\zeta $ is continuous if preimages of basis elements are open. Basis elements for $ C_{\\textup{fS}}^\\infty (M,X) $ are of the form $ \\mathcal{U} \\cap [f] $ for some basic neighborhood $ \\mathcal{U} $ and some equivalence class $ [f] $, and $ \\zeta^{-1}(\\mathcal{U}\\cap [f]) = \\zeta^{-1}(\\mathcal{U}) \\cap \\zeta^{-1} ([f]) $.\n\\end{proof}\n\\begin{proposition}\n\t\\label{fsmoothprop}\n\tTheorem \\ref{compiscont}, Proposition \\ref{precomposition}, Proposition \\ref{postcomposition}, Theorem \\ref{product theorem}, and Corollary \\ref{product corollary} still hold if we in every case replace the very strong topology with the fine very strong topology.\n\t\n\tIn the cases that we consider $ \\Prop_{\\textup{vS}} (M,N) $, replace this with $ \\Prop_{\\mbox{fS}} (M,N) $, by which is meant the subset $ \\Prop (M,N) \\subseteq C_{\\textup{fS}}^\\infty (M,N) $ equipped with the subspace topology.\n\\end{proposition}\n\\begin{proof}\n\tThe proof is case by case. In all cases except for the generalization of Theorem \\ref{product theorem} and its corollary, it suffices by \\ref{corollarylemma} to check that preimages of equivalence classes are open. Unless otherwise stated, letters (such as $ f $ or $ N $) are always assumed to have the same role here as in the statement of the corresponding result.\n\n\t\\textit{Theorem \\ref{compiscont} (the full composition map is continuous).} Suppose that $ f \\sim \\hat{f} $ and $ h \\sim \\hat{h} $. We have $ \\csupp{h \\circ f}{ \\hat{h} \\circ \\hat{f} } \\subseteq \\csupp{f}{\\hat{f}} \\cup f^{-1}\\left( \\csupp{h}{\\hat{h}} \\right) $. The right hand side is compact since $ f $ is proper, so $ \\csupp{h \\circ f}{ \\hat{h} \\circ \\hat{f} } $ is a closed subset of a compact space, hence compact. By definition this means that $ h \\circ f \\sim \\hat{h} \\circ \\hat{f} $. \n\t\t\n\tConsider an equivalence class $ [g] \\subseteq C_{\\textup{fS}}^\\infty (M,X) $. By what we just observed, if $ h \\circ f \\sim g $ and $ \\hat{f} \\sim f $ and $ \\hat{h} \\sim h $, then $ \\hat{h} \\circ \\hat{f} \\sim g $. Hence $$ \\Gamma^{-1} ([g]) = \\bigcup_{h \\circ f \\sim g} [f] \\times [h], $$ which is open.\n\t\n\t\\textit{Proposition \\ref{precomposition} (precomposition is continuous).} If $ h \\sim \\hat{h} $, then $ h \\circ f \\sim \\hat{h} \\circ f $ by the same argument as before. So for any equivalence class $ [g] \\subseteq C^\\infty (M,X) $, we have $$ (f^*)^{-1} ([g]) = \\bigcup_{h \\circ f \\sim g} [h]. $$\n\t\n\t\\textit{Proposition \\ref{postcomposition} (postcomposition is continuous).} If $ h, \\hat{h} \\in C^\\infty (M,X) $, then it is easy to see that $ \\csupp{f \\circ h}{f \\circ \\hat{h}} \\subseteq \\csupp{h}{\\hat{h}} $. So if $ h \\sim \\hat{h} $, then $ f \\circ h \\sim f \\circ \\hat{h} $, since closed subsets of compact spaces are compact. It follows that for any equivalence class $ [g] \\subseteq C^\\infty (M,X) $, we have $ (f_*)^{-1}([g]) = \\bigcup_{f \\circ h \\sim g} [h]. $ \n\t\n\t\\textit{Theorem \\ref{product theorem} (the product theorem).} For the same reasons as in the proof of the very strong version of the theorem, $ \\iota $ is clearly a bijective continuous map. So by Lemma \\ref{corollarylemma} it suffices to show that images of equivalence classes are open. \n\t\n\tObserve that for $ f, \\hat{f} \\in C^\\infty (M, X_1 \\times X_2) $, we have $ \\csupp{f}{\\hat{f}} = \\csupp{\\pr_1 \\circ f}{\\pr_1 \\circ \\hat{f}} \\cup \\csupp{\\pr_2 \\circ f}{\\pr_2 \\circ \\hat{f}} $. Hence $ f \\sim \\hat{f} $ if and only if $ \\pr_1 \\circ f \\sim \\pr_1 \\circ \\hat{f} $ and $ \\pr_2 \\circ f \\sim \\pr_2 \\circ \\hat{f} $. Another way of stating this fact is $ \\iota ([f]) = [\\pr_1 \\circ f] \\times [\\pr_2 \\circ f] $ for all $ f \\in C^\\infty (M,X_1 \\times X_2) $.\n\t\n\t\\textit{Corollary \\ref{product corollary}.} Same proof as in the very strong case.\n\\end{proof}\n\n\n\\section{The manifold structure on smooth vector valued functions}\\label{sect: smmfd}\nThroughout this section, $ M $ is a finite-dimensional manifold, $ E $ is a locally convex vector space, and \\( X \\) is a locally convex manifold.\n\nRecall from Corollary \\ref{supportcorollary} that \\( C_{\\textup{vS}}^\\infty (M,E) \\) with pointwise operations is not a locally convex vector space, in fact it is not even a topological vector space. Neither is \\( C_{\\textup{fS}}^\\infty (M,E) \\). However, we will in this section make \\( C_{\\textup{fS}}^\\infty (M,E) \\) into a locally convex manifold. This is a first step towards making \\( C_{\\textup{fS}}^\\infty (M,X) \\) into a locally convex manifold (but we will not do this). The modeling space for \\( C_{\\textup{fS}}^\\infty (M,E) \\) as a locally convex manifold is \\( C_{\\text{vS,c}}^\\infty (M,E) \\), defined below.\n\n\\begin{definition}\n\tWe define $ C_{\\text{vS,c}}^\\infty (M,E) $ to be the subspace of $ C_{\\textup{vS}}^\\infty (M,E) $ consisting of the functions with compact support, i.e. \n\t$$ C_{\\text{vS,c}}^\\infty (M,E) = \\lbrace f \\in C_{\\textup{vS}}^\\infty (M,E) : \\mbox{$\\csupp{f}{0}$ is compact} \\rbrace $$ equipped with the subspace topology from $ C_{\\textup{vS}}^\\infty (M,E) $.\n\tNote that $ C_{\\text{vS,c}}^\\infty (M,E) = [0] $ in $ C_{\\textup{fS}}^\\infty (M,E) $.\n\\end{definition}\n\nAs a first step towards proving that \\( C_{\\text{vS,c}}^\\infty (M,E) \\) with pointwise operations is a locally convex vector space, we show that \\( C^\\infty (M,E) \\) with pointwise addition is a topological group in the very strong and fine very strong topologies.\n\n\\begin{lemma}\n\t\\label{continuous addition}\n\tAddition\n\t\\begin{align*}\n\t\t\\Sigma \\colon C^\\infty (M,E) \\times C^\\infty (M,E) &\\to C^\\infty (M,E), \\quad (f,g) &\\mapsto f+h = \\left[ m \\mapsto f(m) + h(m) \\right]\n\t\\end{align*}\n\tis continuous when $ C^\\infty (M,E) $ is equipped with the very strong topology or fine very strong topology.\n\\end{lemma}\n\\begin{proof}\n\tWe prove the assertion only for the very strong topology as the proof carries over verbatim to the fine very strong topology.\n\tBy Theorem \\ref{product theorem} there is a canonical homeomorphism $ \\iota \\colon C_{\\textup{vS}}^\\infty (M,E) \\times C_{\\textup{vS}}^\\infty (M,E) \\cong C_{\\textup{vS}}^\\infty (M,E \\times E ) $. Since addition $ S \\colon E \\times E \\to E $ in $ E $ is smooth, induced postcomposition $ S_* \\colon C_{\\textup{vS}}^\\infty (M,E\\times E) \\to C_{\\textup{vS}}^\\infty (M,E) $ is continuous.\n\tHence $ \\Sigma = S_* \\circ \\iota $ is continuous.\n\\end{proof}\n\nOnce we have established the following proposition, it will be easy to make \\( C_{\\textup{fS}}^\\infty (M,E) \\) into a locally convex manifold modeled on \\( C_{\\text{vS,c}}^\\infty (M,E) \\). The hard work lies here.\n\\begin{proposition}\n\t\\label{locally convex vs prop}\n\tThe topological space $ C_{\\text{vS,c}}^\\infty (M,E) $ with vector space structure induced by pointwise operations in $ E $ is a locally convex vector space.\n\\end{proposition}\n\\begin{proof}\n\tIn Lemma \\ref{continuous addition} we showed that addition is continuous, and the topological space $ C_{\\text{vS,c}}^\\infty (M,E) $ is Hausdorff since the compact open \\( C^\\infty \\)-topology on \\( C^\\infty(M,E) \\) is Hausdorff and the very strong topology is finer than the compact open \\( C^\\infty \\)-topology. It is therefore only necessary to check that scalar multiplication is continuous in order to conclude that $ C_{\\text{vS,c}}^\\infty (M,E) $ is a topological vector space. Finally, we must verify that this topological vector space is locally convex.\n\t\n\t\\textit{Scalar multiplication $\\mu \\colon \\mathbb{R} \\times C_{\\text{vS,c}}^\\infty (M,E) \\to C_{\\text{vS,c}}^\\infty (M,E),\\ \n\t\t(\\lambda, f) \\mapsto \\lambda f $ is continuous.} \n\tLet $ (\\lambda,f) \\in \\mathbb{R} \\times C_{\\text{vS,c}}^\\infty (M,E) $, and consider a basic neighborhood $ \\mathcal{V} = \\bigcap_{i \\in \\Lambda} \\mathcal{N}_i $ of $ \\lambda f $, where each $\\mathcal{N}_i = \\mathcal{N}^{r_i} (\\lambda f; A_i, (U_i,\\phi_i),p_i,\\epsilon_i) $ is an elementary neighborhood of $ \\lambda f $. We will show that there exists open sets $ I \\subseteq \\RR $ and $ \\mathcal{U} \\subseteq C_{\\textup{vS}}^\\infty (M,E) $ such that $ \\mu (I \\times \\mathcal{U}) \\subseteq \\mathcal{V} $. \n\t\n\tSince $ \\csupp{f}{0} $ is compact, only finitely many $ A_i $ intersect $ \\csupp{f}{0} $, say only for $ i = i_1, \\dots , i_n $. Define $ \\epsilon := \\min (\\epsilon_{i_1},\\dots,\\epsilon_{i_n}) $. \n\t\n\tSet $m_1 := \\max\\left\\{\\sup_{1 \\leq j \\leq n} \\Vert f \\circ \\phi_{i_j}^{-1} \\Vert (r_{i_j}, \\phi_{i_j}(A_{i_j}),p_{i_j}), 1\\right\\}$ and \n\t\\[ I := B_{\\frac{\\epsilon}{2 m_1 }}^1 (\\lambda) = \\left] \\lambda - \\frac{\\epsilon}{2 m_1}, \\lambda + \\frac{\\epsilon}{2 m_1} \\right[ . \\] \n\t\n\tDefine $ m_2 := \\sup \\lbrace | t | : t \\in I \\rbrace $, and set $ \\mathcal{U} := \\bigcap_{i \\in \\Lambda} \\mathcal{N}^{r_i} \\left( f;A_i,(U_i,\\phi_i),p_i,\\frac{\\epsilon_i}{2 m_2} \\right). $\n\tSuppose $ (\\lambda',f') \\in I \\times \\mathcal{U} $. \n\tFor all $ i \\in \\Lambda $, $ x \\in A_i $, $ 1 \\leq k \\leq r_i $, and $ \\alpha \\in \\lbrace e_1, \\dots , e_{\\dim M} \\rbrace^k $, we have\n\t\\begin{align*}\n\t\t& p_i \\left( \\dd^{(k)} (\\lambda' f' \\circ \\phi_i^{-1} - \\lambda f \\circ \\phi_i^{-1})(\\phi_i(x);\\alpha) \\right) \\\\\n\t\t\\leq& | \\lambda' | p_i \\left( \\dd^{(k)} (f' \\circ \\phi_i^{-1} - f \\circ \\phi_i^{-1})(\\phi_i(x);\\alpha) \\right) + | \\lambda' - \\lambda | p_i \\left( \\dd^{(k)} (f \\circ \\phi_i^{-1})(\\phi_i(x);\\alpha) \\right) \\\\\n\t\t<& \\frac{\\epsilon_i}{2} + \\frac{\\epsilon}{2 m_1} p_i \\left( \\dd^{(k)} (f \\circ \\phi_i^{-1})(\\phi_i(x);\\alpha) \\right) =: C.\n\t\\end{align*}\n\tIf $ i \\notin \\lbrace i_1,\\dots,i_n \\rbrace $, then $ p_i \\left( \\dd^{(k)} (f \\circ \\phi_i^{-1})(\\phi_i(x);\\alpha) \\right) = 0 $, in which case $ C \\leq \\epsilon_i $. And if $ i \\in \\lbrace i_1, \\dots,i_n \\rbrace $, then $ \\epsilon \\leq \\epsilon_i $ and $ p_i \\left( \\dd^{(k)} (f \\circ \\phi_i^{-1})(\\phi_i(x);\\alpha) \\right) \\leq m_1 $, in which case we still have $ C \\leq \\epsilon_i $. Hence $ \\lambda' f' \\in \\mathcal{V} $, and $ \\mu (I \\times \\mathcal{U}) \\subseteq \\mathcal{V} $. Consequently, $\\mu$ is continuous.\n\t\n\t\\textit{The space is locally convex.} We have now established that $ C_{\\mbox{vS,c}}^\\infty (M,E) $ is a topological vector space. It remains to see that this topological vector space is locally convex. For $ r \\in \\mathbb{N}_0 $, $ (U,\\phi) $ a chart on $ M $, $ A \\subseteq U $ compact, and $ p $ a continuous seminorm on $ E $, define\n\t\\begin{align*}\n\t\t\\Vert\\cdot \\Vert(r,A,(U,\\phi),p) \\colon C_{\\mbox{vS,c}}^\\infty (M,E) \\to [0,\\infty), \\quad f \\mapsto \\Vert f \\circ \\phi^{-1} \\Vert (r,\\phi_i (A_i),p) \n\t\\end{align*}\n\tThis is a seminorm on $ C_{\\mbox{vS,c}}^\\infty (M,E) $. Consider a family $ \\lbrace \\Vert\\cdot \\Vert (r_i,A_i,(U_i,\\phi_i),p_i) \\rbrace_{i\\in \\Lambda} $ of such seminorms, where $ \\lbrace A_i \\rbrace_{i \\in \\Lambda} $ is locally finite. For some family $ \\lbrace \\epsilon_i \\rbrace_{i \\in \\Lambda} $ define $ q \\colon C_{\\mbox{vS,c}}^\\infty (M,E) \\to [0,\\infty) $ by \\[ q(f) = \\sup_{i\\in\\Lambda} \\epsilon_i \\Vert f \\Vert (r_i,A_i,(U_i,\\phi_i),p_i). \\] \n\tEvery $ f \\in C_{\\mbox{vS,c}}^\\infty (M,E) $ has compact support, so $ \\operatorname{supp}(f,0) $ intersects only finitely many of the $ A_i $, from which it follows that $ \\Vert f \\Vert (r_i,A_i,(U_i,\\phi_i),p_i) \\neq 0 $ for only finitely many $ i \\in \\Lambda $. Hence $ q(f) < \\infty $, so $ q $ is well-defined. Clearly $ q $ is a seminorm. Also $ q $ is continuous as for all $ \\lambda > 0 $, the preimage $ q^{-1} [0,\\lambda) $ is a basic neighborhood of $ 0 $, e.g.\\ \n\t\\[ q^{-1}[0,1) = \\left( \\bigcap_{i \\in \\Lambda } \\mathcal{N}^{r_i} (0;A_i,(U_i,\\phi_i),p_i,\\epsilon_i) \\right) \\cap C_{\\text{vS,c}}^\\infty (M,E). \\] So every basic neighborhood of 0 arises as a preimage of a continuous seminorm. Consequently, $ C_{\\text{vS,c}}^\\infty (M,E) $ is locally convex (see \\cite[\\S 18]{koethe}).\n\\end{proof}\n\nWe will now provide an alternative description of the topology on $ C_{\\text{vS,c}}^\\infty (M,E) $ as an inductive limit of certain locally convex spaces.\nThis characterization also implies that $ C_{\\text{vS,c}}^\\infty (M,E) $ is a locally convex space (thus providing an elegant proof of Proposition \\ref{locally convex vs prop}).\nNote however: Though the proof of Proposition \\ref{locally convex vs prop} is a bit cumbersome, it is also completely elementary and does not use auxiliary results on inductive limits.\n\n\n\\begin{definition}\nLet $K \\subseteq M$ be a compact subset and $E$ be a locally convex space.\nThen we define \n \\begin{displaymath}\n C^\\infty_K (M,E) := \\{f\\in C^\\infty (M,E) \\mid \\operatorname{supp} (f,0) \\subseteq K\\} \n \\end{displaymath}\n and topologize this space with the compact open $C^\\infty$-topology, i.e.\\ the topology generated by the subbase $\\mathcal{N} \\cap C^\\infty_K (M,E)$ where $\\mathcal{N}$ runs through all elementary neighborhoods of $C^\\infty_{\\text{vS}} (M,E)$.\n Recall from \\cite[Proposition 4.19]{glockomega} that $C^\\infty_K (M,E)$ is a locally convex vector space.\n\\end{definition}\n\n\\begin{remark}\n Since all functions $C^\\infty_K (M,E)$ have compact support contained in $K$ one can prove that the compact open $C^\\infty$-topology coincides with the subspace topologies induced by $C_{\\textup{vS}}^\\infty (M,E)$ and $C_{\\textup{fS}}^\\infty (M,E)$.\n However, we will not need this. \n\\end{remark}\n\nDenote by $\\mathcal{K} (M)$ the set of compact subsets of $M$. \nObserve that as sets $C^\\infty_{\\text{vS,c}} (M,E) = \\bigcup_{K \\in \\mathcal{K} (M)} C^\\infty_K (M,E)$.\nWe claim that the topology on the compactly supported functions is determined by the smaller locally convex spaces:\nTo see this, recall that with respect to inclusion, $\\mathcal{K} (M)$ is a directed set. \nFurther, for $K,L \\in \\mathcal{K} (M)$ with $K \\subseteq L$ the canonical inclusion $\\iota_K^L\\colon C^\\infty_K (M,E) \\rightarrow C^\\infty_L (M,E)$ is continuous linear by definition of the topology.\nHence we can form the locally convex inductive limit $\\displaystyle \\lim_{\\rightarrow} C^\\infty_K (M,E)$ (cf.\\ \\cite[\\S 19 3.]{koethe}) of the family $\\{C^\\infty_K (M,E)\\}_{\\mathcal{K} (M)}$ (with respect to the canonical inclusions).\n\n\\begin{lemma}\\label{lem: indlim}\n Let $E$ be a locally convex space, then as locally convex spaces\n \\begin{displaymath}\n C^\\infty_{\\text{vS,c}} (M,E) = \\lim_{\\rightarrow} C^\\infty_K (M,E).\n \\end{displaymath}\n\\end{lemma}\n\n\\begin{proof}\n Since as sets $C^\\infty_{\\text{vS,c}} (M,E) = \\displaystyle\\lim_{\\rightarrow} C^\\infty_K (M,E)$, we only have to prove that the topologies coincide.\n However, since $M$ is $\\sigma$-compact, \\cite[Proposition 8.13 (d)]{glockomega} implies that a basis for the inductive limit topology on $C^\\infty_{\\text{vS,c}} (M,E) = \\displaystyle\\lim_{\\rightarrow} C^\\infty_K (M,E)$ is given by the basic neighborhoods of the very strong topology.\n\\end{proof}\n\n\n\\begin{proposition}\n\tFor each class $ [f] $ in $ C_{\\textup{fS}}^\\infty (M,E) $ define $ \\phi_{[f]} \\colon [f] \\to C_{\\text{vS,c}}^\\infty (M,E) $ by $ \\phi_{[f]}(g) = g-f $. \n\tThen $ \\mathcal{A} = \\lbrace (\\phi_{[f]},[f]) \\rbrace_{f \\in C^\\infty (M,E)} $ is a smooth atlas for $ C_{\\textup{fS}}^\\infty (M,E) $. Hence $ C_{\\textup{fS}}^\\infty(M,E) $ is a smooth manifold modeled on $ C_{\\text{vS,c}}^\\infty (M,E) $.\n\\end{proposition}\n\\begin{proof}\n\tWe will first show that every chart $ \\phi_{[f]} $ is a homeomorphism. First of all, note that $ \\phi_{[f]} $ is well-defined since $ g - f $ is smooth and compactly supported for $ g \\in [f] $. It is bijective with inverse $ \\phi_{[f]}^{-1} (h) = h + f $. Both $ \\phi_{[f]} $ and $ \\phi_{[f]}^{-1} $ are continuous by Lemma \\ref{continuous addition}.\n\t\n\tThe chart domains of $ \\mathcal{A} $ cover $ C_{\\textup{fS}}^\\infty $, whence we have to check that chart transformations are smooth. Let $ (\\phi_{[f]},[f]) $ and $(\\phi_{[g]},[g]) $ be charts with $ [f] \\cap [g] \\neq \\emptyset $.\n\tThen $ [f] = [g] $ and $ \\phi_{[g]} \\circ \\phi_{[f]}^{-1}(h) = h + f - g $, whence it is smooth in $ h $ as addition in $C_{\\text{vS,c}}^\\infty (M,E)$ is so.\n\\end{proof}\nStructurally, the manifold $ C_{\\textup{fS}}^\\infty (M,E) $ is just a collection of (affine) copies of $ C_{\\text{vS,c}}^\\infty (M,E)$. For this reason, it is also called in \\cite{michor} a \\emph{local topological affine space}.\n\nTo construct a manifold structure on $ C_{\\textup{fS}}^\\infty (M,X) $ for an arbitrary locally convex manifold $X$ one needs a so called \\emph{local addition} on $X$ (cf.\\ \\cite{michor,KM97}).\nA local addition replaces the vector space addition. \nIt allows to ``smoothly choose'' charts on $X$ (see \\cite{stacey} for more information). \nThe details are similar to \\cite[Section 10]{michor} but require certain analytical tools (e.g.\\ a suitable version of the $\\Omega$-Lemma, \\cite[Appendix F]{glockomega})\\footnote{To apply the $\\Omega$-Lemma as stated in \\cite{glockomega}, one needs a topology on spaces of compactly supported sections in vector bundles. In ibid.\\ the compact open $C^\\infty$-topology is used, however by arguments similar to Lemma \\ref{lem: indlim} one proves that this topology coincides with the very strong topology.}.\n\n\\section{Application to bisection groups}\nIn this section we use our results on the very strong and the fine very strong topology to turn certain groups into topological groups.\nThe groups envisaged here are the bisection groups associated to certain Lie groupoids.\nA reference on (finite-dimensional) Lie groupoids is \\cite{mackenzie}, see \\cite{Schmeding2015,SchmedingWockel15} for infinite-dimensional Lie groupoids.\n\\begin{definition}[Lie groupoid]\n\tLet $ M $ be a finite-dimensional smooth manifold and $ G $ a smooth manifold modeled on a locally convex vector space. Then a groupoid $ \\mathcal{G} = (G \\rightrightarrows M) $ with source projection $ \\alpha \\colon G \\to M $ and target projection $ \\beta \\colon G \\to M $ is a \\emph{(locally convex) Lie groupoid} if $ \\alpha $ and $ \\beta $ are smooth submersions (i.e. locally projections), partial multiplication $ m \\colon G \\times_{\\alpha,\\beta} G \\to G $ is smooth, object inclusion $ 1 \\colon M \\to G $ is smooth, and inversion $ i \\colon G \\to G $ is smooth.\n\\end{definition}\n\\begin{definition}[Bisection group]\n\tThe \\emph{group of bisections} $ \\Bis (\\mathcal{G}) $ of a Lie groupoid $ \\mathcal{G} = (G \\rightrightarrows M) $ is the set of sections $ \\sigma \\colon M \\to G $ of $ \\alpha $ such that $ \\beta \\circ \\sigma $ is a diffeomorphism of $ M $. The group operation $ \\star $ is given by $$ (\\sigma \\star \\tau)(x) := \\sigma ((\\beta \\circ \\tau)(x)) \\tau (x). $$ With this operation, the object inclusion $ 1 \\colon M \\to G $ becomes the neutral element and the inverse of a section $ \\sigma $ is $ \\sigma^{-1}(x) = i(\\sigma((\\beta \\circ \\sigma)^{-1}(x))). $\n\\end{definition}\n\\begin{example}\n\t\\begin{enumerate}\n\t\t\\item For a finite-dimensional manifold $ M $, the \\emph{unit Lie groupoid} is the groupoid $ (M \\rightrightarrows M) $ with both source and target projection $ \\id_M $. The bisection group of this groupoid is trivial.\n\t\t\\item Let $ M $ be a finite-dimensional smooth manifold. Then $ \\mathcal{P}(M) := (M \\times M \\rightrightarrows M) $ with source projection $ \\alpha = \\pr_2 $ and target projection $ \\beta = \\pr_1 $ is a Lie groupoid. Multiplication in the groupoid is given by $ (x,y)(y,z) = (x,z) $. Postcomposition $ \\beta_* $ induces an isomorphism $ \\Bis (\\mathcal{P}(M)) \\cong \\Diff(M) $ of groups, where \\( \\Diff (M) \\) is the group of smooth diffeomorphisms of \\( M \\).\n\t\t\\item Suppose $ G $ is a locally convex Lie group, and $ * $ is the one-point space. Then $ (G \\rightrightarrows *) $ is a Lie groupoid with bisection group $ G $.\n\t\\end{enumerate}\n\\end{example}\n\nTo prepare the construction of a topological group structure on bisection groups, recall the following facts on diffeomorphism groups of finite-dimensional manifolds.\n\\begin{remark}\\label{rem: topgroup}\n Let $M$ be a finite-dimensional manifold and $\\Diff (M)$ be the group of smooth diffeomorphisms of $M$.\n As $\\Diff (M) \\subseteq C^\\infty (M,M)$, we can endow $\\Diff (M)$ either with the subspace topology induced by the very strong topology (write $\\Diff_{\\text{vS}} (M)$) or with respect to the fine very strong topology (we write $\\Diff_{\\text{fS}} (M)$).\n Now as a consequence of \\cite[Corollary 7.7]{michor} and Proposition \\ref{prop: topologiescoincide}, both $\\Diff_{\\text{vS}} (M)$ and $\\Diff_{\\text{fS}} (M)$ are topological groups.\n Note that $\\Diff_{\\text{fS}} (M)$ is even a locally convex Lie group by \\cite[Theorem 11.11]{michor}.\n In particular, we remark that the (subspace topology induced by the) fine very strong topology is the Lie group topology of $\\Diff (M)$.\n\\end{remark}\n\n\\begin{proposition}\\label{prop: topgp}\n\tIf $ \\Bis (\\mathcal{G}) $ is equipped with the subspace topology with respect to $ C_{\\textup{vS}}^\\infty (M,G) $ or $ C_{\\textup{fS}}^\\infty (M,G) $, then $ \\Bis (\\mathcal{G}) $ becomes a topological group.\n\\end{proposition}\n\\begin{proof}\n\tWe will prove that $ \\Bis (\\mathcal{G}) $ becomes a topological group when equipped with the subspace topology with respect to $ C_{\\textup{vS}}^\\infty (M,G) $. The case where we consider the subspace topology with respect to $ C_{\\textup{fS}}^\\infty (M,G) $ can be proven identically, since we only use results that hold in both topologies.\n\tLet $ \\Omega \\colon \\Bis (\\mathcal{G}) \\times \\Bis (\\mathcal{G}) \\to \\Bis (\\mathcal{G}) $ be the multiplication map defined by $ \\Omega (\\sigma,\\tau) = \\sigma \\star \\tau $, and let $ \\iota $ be the inclusion $ \\Bis (\\mathcal{G}) \\to C_{\\textup{vS}}^\\infty (M,G) $. Observe that we can write $$ \\Omega(\\sigma,\\tau)(x) = \\sigma((\\beta \\circ \\tau)(x))\\tau(x) = m(\\Gamma(\\beta \\circ \\tau, \\sigma)(x),\\tau(x)). $$ So $ \\iota \\circ \\Omega $ can be written as a composition of continuous maps; the diagram\n\t\\begin{align*}\n\t\t\\xymatrix{\n\t\t\t\\Bis (\\mathcal{G}) \\times \\Bis (\\mathcal{G}) \\ar[d]^{(\\beta_* \\circ \\pr_2, \\iota \\times \\iota )} \\ar@{.>}[rr]^{\\iota \\circ \\Omega} &&C_{\\textup{vS}}^\\infty (M,G)\t\\\\\n\t\t\t\\Prop_{\\textup{vS}} (M,M) \\times C_{\\textup{vS}}^\\infty(M,G) \\times C_{\\textup{vS}}^\\infty (M,G) \\ar[d]^{\\Gamma \\times \\id} && \\\\\n\t\t\tC_{\\textup{vS}}^\\infty (M,G) \\times C_{\\textup{vS}}^\\infty (M,G) \\ar[rr]^-{\\cong} & & C_{\\textup{vS}}^\\infty (M,G \\times G) \\ar[uu]^{m_*}\n\t\t\t}\n\t\\end{align*}\n\tcommutes. Here we have used that $ \\beta_* (\\Bis (\\mathcal{G})) \\subseteq \\Diff (M) \\subseteq \\Prop (M,M) $ by definition of bisections. All of the maps represented by normal arrows in the diagram are continuous by results in the previous sections. Since $ \\iota \\circ \\Omega $ is continuous, so is $ \\Omega $.\n\t\n\tLet $ \\Phi \\colon \\Bis (\\mathcal{G}) \\to \\Bis (\\mathcal{G}) $ be the inversion map. \n\tInversion $ \\Inv \\colon \\Diff_{\\text{vS}} (M) \\to \\Diff_{\\text{vS}}(M) $ is continuous by \\cite[Theorem 7.6]{michor} and Proposition \\ref{prop: topologiescoincide}. The diagram\n\t\\begin{align*}\n\t\t\\xymatrix{\n\t\t\t\\Bis(\\mathcal{G}) \\ar[d]^{\\beta_*} \\ar@{.>}[rr]^{\\iota \\circ \\Phi} && C_{\\textup{vS}}^\\infty(M,G) \\\\\n\t\t\t\\Diff_{\\text{vS}}(M) \\ar[r]^{\\Inv} & \\Diff_{\\text{vS}}(M) \\ar[r]^{\\sigma_*} & C_{\\textup{vS}}^\\infty (M,G) \\ar[u]^{i_*}\n\t\t\t}\n\t\\end{align*}\n\tcommutes as $ \\Phi(\\sigma)(x) = i(\\sigma((\\beta \\circ \\sigma)^{-1}(x))) = (i_*(\\sigma_*(\\Inv(\\beta_*(\\sigma))))(x)$. \n\tAll maps represented by normal arrows are continuous. Thus $ \\iota \\circ \\Phi $ and also $ \\Phi $ are continuous.\n\\end{proof}\n\nAs noted in Remark \\ref{rem: topgroup}, $\\Diff (M)$ is a topological group with respect to the subspace topologies induced by the (fine) very strong topology on $C^\\infty (M,M)$.\nThus we obtain the following morphisms of topological groups.\n\\begin{corollary}\n\tThe target projection \\( \\beta \\colon G \\to M \\) of a locally convex Lie groupoid \\( \\mathcal{G} = (G \\rightrightarrows M) \\) induces a map \\( \\beta_* \\colon \\Bis (\\mathcal{G}) \\to \\Diff(M) \\) given by postcomposition. This is a homomorphism of topological groups with respect to the very strong and fine very strong topologies on both groups.\n\\end{corollary}\n\\begin{proof}\n\tSince $\\beta_* \\colon C_{\\textup{vS}}^\\infty (M,G) \\rightarrow C_{\\textup{vS}}^\\infty (M,M)$ is continuous, so is the (co)restriction of \\( \\beta_* \\) to $\\Bis (\\mathcal{G})$ and \\( \\Diff (M) \\). The same argument holds in the fine very strong topology.\n\tThe map \\( \\beta_* \\) is also a group homomorphism, since \n\t\\[ \\left( \\beta_* ( \\sigma \\star \\tau ) \\right) (x) = \\beta \\left( \\sigma ((\\beta \\circ \\tau)(x)) \\tau (x) \\right) = \\beta ( \\sigma ( \\beta \\circ \\tau ) (x)) = \\left( \\beta_*(\\sigma) \\circ \\beta_* (\\tau)\\right) (x).\\qedhere \\]\n\\end{proof}\n\nThe results of this section enable the construction of a Lie group structure on $\\Bis (\\mathcal{G})$. \nIt is worth noting that the key step in constructing the Lie group structure is sorting out the topology of the function spaces.\nUsing the manifold structure on $C_{\\textup{fS}}^\\infty (M,G)$ (see comments in Section \\ref{sect: smmfd}) one establishes the smoothness of joint composition and postcomposition with respect to these structures.\nSince Theorem A and the $\\Omega$-Lemma \\cite[Appendix F]{glockomega} are at our disposal, one can copy exactly the arguments from the finite-dimensional case outlined in \\cite[\\S 10 and \\S 11]{michor}.\nAfter that one can proceed as in \\cite{Schmeding2015} and establish smoothness of the group operations following the proof of Proposition \\ref{prop: topgp}.\nAgain, results of this type are beyond the scope of the present paper. \n\n\\newpage\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nHigh-fidelity numerical simulations play a critical role in modern-day engineering and scientific investigations. The computational cost of high-fidelity or full-order models (FOMs) is, however, often prohibitively expensive. This limitation has led to the emergence of reduced-order modeling techniques. Reduced-order models (ROMs) are formulated to \\textit{approximate} solutions to a FOM on a low-dimensional manifold. Common reduced-order modeling techniques include balanced truncation~\\cite{balanced_truncation_moore,balanced_truncation_roberts}, Krylov subspace techniques~\\cite{krylov_rom}, reduced-basis methods~\\cite{Hesthaven2016}, and the proper orthogonal decomposition approach~\\cite{chatterjee_pod_intro}.\nReduced-order models based on such techniques have been implemented in a wide variety of disciplines and have been effective in reducing the computational cost associated with high-fidelity numerical simulations~\\cite{kerschen_mech_pod,padhi_neural_net_pod,cao_meteorology_pod}.\n\nProjection-based reduced-order models constructed from proper orthogonal decomposition (POD) have proved to be an effective tool for model order reduction of complex systems. In the POD-ROM approach, snapshots from a high-fidelity simulation (or experiment) are used to construct an orthonormal basis spanning the solution space. A small, truncated set of these basis vectors forms the \\emph{trial} basis.\nThe POD-ROM then seeks a solution within the range of the trial basis via projection. Galerkin projection, in which the FOM equations are projected onto the same trial subspace, is the simplest type of projection. The Galerkin ROM (G ROM) has been used successfully in a variety of problems. When applied to general non-self-adjoint and non-linear problems, however, theoretical analysis and numerical experiments have shown that Galerkin ROM lacks \\textit{a priori} guarantees of stability, accuracy, and convergence~\\cite{rowley_pod_energyproj}. This last issue is particularly challenging as it demonstrates that enriching a ROM basis does not necessarily improve the solution~\\cite{huang_combustion_roms}. The development of stable and accurate reduced-order modeling techniques for complex non-linear systems is the motivation for the current work.\n\\begin{comment}\nResearch examining the stability and accuracy of ROMs is typically approached from either a stabilization viewpoint or from a closure modeling viewpoint. \n\\end{comment}\n\nA significant body of research aimed at producing accurate and stable ROMs for complex non-linear problems exists in the literature. These efforts include, but are not limited to, ``energy-based\" inner products~\\cite{rowley_pod_energyproj,Kalashnikova_sand2014}, symmetry transformations~\\cite{sirovich_symmetry_trans}, basis adaptation~\\cite{carlberg_hadaptation,adeim_peherstorfer}, $L^1$-norm minimization~\\cite{l1}, projection subspace rotations~\\cite{basis_rotation}, and least-squares residual minimization approaches~\\cite{bui_resmin_steady,bui_unsteady,rovas_thesis,carlberg_thesis,bui_thesis,carlberg_lspg,carlberg_lspg_v_galerkin,carlberg_gnat}. The Least-Squares Petrov--Galerkin (LSPG)~\\cite{carlberg_lspg} method comprises a particularly popular least-squares residual minimization approach and has been proven to be an effective tool for non-linear model reduction. Defined at the fully-discrete level (i.e., after spatial and temporal discretization), LSPG relies on least-squares minimization of the FOM residual at each time-step. While the method lacks \\textit{a priori} stability guarantees for general non-linear systems, it has been shown to be effective for complex problems of interest~\\cite{carlberg_gnat, carlberg_lspg_v_galerkin, huang_scitech19}. Additionally, as it is formulated as a minimization problem, physical constraints such as conservation can be naturally incorporated into the ROM formulation~\\cite{carlberg_conservative_rom}. At the fully-discrete level, LSPG is sensitive to both the time integration scheme as well as the time-step. For example, in Ref.~\\cite{carlberg_lspg_v_galerkin} it was shown that LSPG produces optimal results at an intermediate time-step. Another example of this sensitivity is that, when applied to explicit time integration schemes, the LSPG approach reverts to a Galerkin approach. This limits the scope of LSPG to implicit time integration schemes, which can in turn increase the cost of the ROM~\\cite{carlberg_lspg_v_galerkin}\\footnote{It is possible to use LSPG with an explicit time integrator by formulating the ROM for an implicit time integration scheme, and then time integrating the resulting system with an explicit integrator.}. This is particularly relevant in the case where the optimal time-step of LSPG is small, thus requiring many time-steps of an implicit solver. Despite these challenges, the LSPG approach is arguably the most robust technique that is used for ROMs of non-linear dynamical systems.\n\n\\begin{comment}\nOne method of producing stable and accurate ROMs is through stabilization, or the addition of dissipative effects to otherwise-unstable dynamics. Research on ROM stabilization seeks to address the issue that, in general, Galerkin ROMs lack any \\textit{a priori} guarantees of stability~\\cite{Kalashnikova_sand2014}. A variety of model reduction techniques have been developed to address this issue, including ``energy-based\" inner products~\\cite{Kalashnikova_sand2014,rowley_pod_energyproj}, symmetry transformations~\\cite{sirovich_symmetry_trans}, projection subspace rotations~\\cite{basis_rotation}, and the Least-Squares Petrov--Galerkin (LSPG) approach~\\cite{carlberg_lspg}. Typically defined at the fully-discrete level (i.e., after spatial and temporal discretization), the LSPG approach relies on least-squares minimization of the FOM residual at each time-step. While the method lacks \\textit{a priori} stability guarantees for general non-linear systems, it has been shown to be effective for complex problems of interest~\\cite{carlberg_gnat, carlberg_lspg_v_galerkin, huang_scitech19}. Additionally, as it is formulated as a minimization problem, physical constraints such as conservation can be naturally incorporated into the ROM formulation~\\cite{carlberg_conservative_rom}. At the fully-discrete level, LSPG is sensitive to both the time integration scheme as well as the time-step. For example, in Ref.~\\cite{carlberg_lspg_v_galerkin} it was shown that LSPG produces optimal results at an intermediate time-step. Another example of this sensitivity is that, when applied to explicit time integration schemes, the LSPG approach reverts to a Galerkin approach. This limits the scope of LSPG to implicit time integration schemes, which can in turn increase the cost of the ROM~\\cite{carlberg_lspg_v_galerkin}\\footnote{It is possible to use LSPG with an explicit time integrator by formulating the ROM for an implicit time integration scheme, and then time integrating the resulting system with an explicit integrator.}. This is particularly relevant in the case where the optimal time-step of LSPG is small, thus requiring many time-steps of an implicit solver. Despite these drawbacks, the LSPG approach is arguably the most robust technique that is used for ROMs of non-linear dynamical systems.\n\\end{comment}\n\nA second school of thought addresses stability and accuracy of ROMs from a closure modeling viewpoint. This follows from the idea that instabilities and inaccuracies in ROMs can, for the most part, be attributed to the truncated modes. While these truncated modes may not contain a significant portion of the system energy, they can play a significant role in the dynamics of the ROM~\\cite{Wang_ROM_thesis}. This is analogous to the closure problem encountered in large eddy simulation. Research has examined the construction of mixing length~\\cite{aubry_mixlength_pod}, Smagorinsky-type~\\cite{Wang_ROM_thesis,Ullmann_smag,wang_smag,smag_ROM}, and variational multiscale (VMS) closures~\\cite{Wang_ROM_thesis,san_iliescu_geostrophic,Bergmann_pod_vms,Stabile2019} for POD-ROMs. The VMS approach is of particular relevance to this work. Originally developed in the context of finite element methods, VMS is a formalism to derive stabilization\/closure schemes for numerical simulations of multiscale problems. The VMS procedure is centered around a sum decomposition of the solution $u$ in terms of resolved\/coarse-scales $\\tilde{u}$ and unresolved\/fine-scales ${u}^{\\prime}$. The impact of the fine-scales on the evolution of the coarse-scales is then accounted for by devising an approximation to the fine-scales. This approximation is often referred to as a ``subgrid-scale'' or ``closure'' model. \n\nResearch has examined the application of both phenomenological and residual-based subgrid-scale models to POD-ROMs. In Refs.~\\cite{san_iliescu_geostrophic,iliescu_pod_eddyviscosity,iliescu_vms_pod_ns}, Iliescu and co-workers examine the construction of eddy-viscosity-based ROM closures via the VMS method. These eddy-viscosity methods are directly analogous to the eddy-viscocity philosophy used in turbulence modeling. While they do not guarantee stability \\textit{a priori}, these ROMs have been shown to enhance accuracy on a variety of problems in fluid dynamics. However, as eddy-viscosity methods are based on phenomenological assumptions specific to three-dimensional turbulent flows, their scope may be limited to specific types of problems. Residual-based methods, which can also be derived from VMS, constitute a more general modeling strategy. The subgrid-scale model emerging from a residual-based method typically appears as a term that is proportional to the residual of the full-order model; if the governing equations are exactly satisfied by the ROM, then the model is inactive. While residual-based methods in ROMs are not as well-developed as they are in finite element methods, they have been explored in several contexts. In Ref.~\\cite{Bergmann_pod_vms}, ROMs of the Navier-Stokes equations are stabilized using residual-based methods. This stabilization is performed by solving a ROM stabilized with a method such as streamline upwind Petrov--Galerkin (SUPG) and augmenting the POD basis with additional modes computed from the residual of the Navier-Stokes equations. In Ref.~\\cite{iliescu_ciazzo_residual_rom}, residual-based stabilization is developed for velocity-pressure ROMs of the incompressible Navier-Stokes equations. Both eddy-viscosity and residual-based methods have been shown to improve ROM stability and performance. The majority of existing work on residual-based stabilization (and eddy-viscosity methods) is focused on ROMs formulated from continuous projection (i.e., projecting a continuous PDE using a continuous basis). In this instance, the ROM residual is defined at the continuous level and is directly linked to the governing partial differential equation. In many applications (arguably the majority~\\cite{Kalashnikova_sand2014}), however, the ROM is constructed through discrete projection (i.e., projecting the spatially discretized PDE using a discrete basis). In this instance, the ROM residual is defined at the semi-discrete level and is tied to the \\textit{spatially discretized} governing equations. Residual-based methods for ROMs developed through discrete projections have, to the best of the authors' knowledge, not been investigated.\n \nAnother approach that displays similarities to the variational multiscale method is the Mori-Zwanzig (MZ) formalism. Originally developed by Mori~\\cite{MoriTransport} and Zwanzig~\\cite{ZwanzigLangevin} and reformulated by Chorin and co-workers~\\cite{ChorinOptimalPrediction,ChorinOptimalPredictionMemory,Chorin_book,ProblemReduction}, the MZ formalism is a type of model order reduction framework. The framework consists of decomposing the state variables in a dynamical system into a resolved (coarse-scale) set and an unresolved (fine-scale) set. An exact reduced-order model for the resolved scales is then derived in which the impact of the unresolved scales on the resolved scales appears as a memory term. This memory term depends on the temporal history of the resolved variables. In practice, the evaluation of this memory term is not tractable. It does, however, serve as a starting point to develop closure models. As MZ is formulated systematically in a dynamical system setting, it promises to be an effective technique for developing stable and accurate ROMs of non-linear dynamical systems. A range of research examining the MZ formalism as a multiscale modeling tool exists in the community. Most notably, Stinis and co-workers~\\cite{stinisEuler,stinisHighOrderEuler,Stinis-rMZ,stinis_finitememory,PriceMZ,PriceMZ2} have developed several models for approximating the memory, including finite memory and renormalized models, and examined their application to the semi-discrete systems emerging from Fourier-Galerkin and Polynomial Chaos Expansions of Burgers' equation and the Euler equations. Application of MZ-based techniques to the classic POD-ROM approach has not been undertaken.\n\nThis manuscript leverages work that the authors have performed on the use of the MZ formalism to develop closure models of partial differential equations~\\cite{parishAIAA2016,parishMZ1,parish_dtau,GouasmiMZ1,parishVMS}. In addition to focusing on the development and analysis of MZ models, the authors have examined the formulation of the MZ formalism within the context of the VMS method~\\cite{parishVMS}. By expressing MZ models within a VMS framework, similarities were discovered between MZ and VMS models. In particular, it was discovered that several existing MZ models are residual-based methods. \n\n The contributions of this work include:\n\\begin{enumerate}\n\\item The development of a novel projection-based reduced-order modeling technique, termed the Adjoint Petrov--Galerkin (APG) method. The method leads to a ROM equation that is driven by the residual of the discretized governing equations. The approach is equivalent to a Petrov--Galerkin ROM and displays similarities to the LSPG approach. The method can be evolved in time with explicit integrators (in contrast to LSPG). This potentially lowers the cost of the ROM.\n\n\\item Theoretical error analysis examining conditions under which the \\textit{a priori} error bounds in APG may be smaller than in the Galerkin method.\n\n\\item Computational cost analysis (in FLOPS) of the proposed APG method as compared to the Galerkin and LSPG methods. This analysis shows that the APG ROM is twice as expensive as the G ROM for a given time step, for both explicit and implicit time integrators. In the implicit case, the ability of the APG ROM to make use of Jacobian-Free Newton-Krylov methods suggests that it may be more efficient than the LSPG ROM.\n\n\\item Numerical evidence on ROMs of compressible flow problems demonstrating that the proposed method is more accurate and stable than the G ROM on problems of interest. Improvements over the LSPG ROM are observed in most cases. An analysis of the computational cost shows that the APG method can lead to lower errors than the LSPG and G ROMs for the same computational cost.\n\n\\item Theoretical results and numerical evidence that provides a relationship between the time-scale in the APG ROM and the spectral radius of the right-hand side Jacobian. Numerical evidence suggests that this relationship also applies to the selection of the optimal time-step in LSPG.\n\n\\end{enumerate}\n\n\nThe structure of this paper is as follows: Section~\\ref{sec:FOM} outlines the full-order model of interest and its formulation in generalized coordinates. Section~\\ref{sec:ROM} outlines the reduced-order modeling approach applied at the semi-discrete level. Galerkin, Petrov--Galerkin, and VMS ROMs will be discussed. Section~\\ref{sec:MZ} details the Mori-Zwanzig formalism and the construction of the Adjoint Petrov--Galerkin ROM. Section~\\ref{sec:analysis} provides theoretical error analysis. Section~\\ref{sec:cost} discusses the implementation and computational cost of the Adjoint Petrov--Galerkin method. Numerical results and comparisons with Galerkin and LSPG ROMs are presented in Section~\\ref{sec:numerical}. Conclusions are provided in Section~\\ref{sec:conclude}.\n\nMathematical notation in this manuscript is as follows: matrices are written as bold uppercase letters (e.g. ${\\mathbf{V}}$), vectors as lowercase bold letters (e.g. ${\\mathbf{u}}$), and scalars as italicized lowercase letters (e.g. $a_i$). Calligraphic script may denote vector spaces or special operators (e.g. $\\mathcal{V}$, $\\mathcal{L}$). Bold letters followed by parentheses indicate a matrix or vector function (e.g. $\\mathbf{R}(\\cdot)$, ${\\mathbf{u}} (\\cdot)$), and those followed by brackets indicate a linearization about the bracketed argument (e.g. $\\mathbf{J}[\\cdot]$).\n\n\\section{Full-Order Model and Generalized Coordinates}\\label{sec:FOM}\nConsider a full-order model that is described by the dynamical system,\n\\begin{equation}\\label{eq:FOM}\n\\frac{d }{dt}{\\mathbf{u}}(t) = \\mathbf{R}({\\mathbf{u}}(t)), \\qquad {\\mathbf{u}}(0) = {\\mathbf{u}}_0, \\qquad t \\in [0,T], \n\\end{equation}\nwhere $T \\in \\mathbb{R}^+$ denotes the final time, ${\\mathbf{u}} : [0,T] \\rightarrow \\RR{N}$ denotes the state, and ${\\mathbf{u}}_0 \\in \\mathbb{R}^N$ the initial conditions. The function $\\mathbf{R}: \\mathbb{R}^N \\rightarrow \\mathbb{R}^N$ with $\\mathbf{y} \\mapsto \\mathbf{R}(\\mathbf{y})$ is a (possibly non-linear) function and will be referred to as the ``right-hand side\" operator. Equation~\\ref{eq:FOM} arises in many disciplines, including the numerical discretization of partial differential equations. In this context, $\\mathbf{R}(\\cdot)$ may represent a spatial discretization scheme with source terms and applicable boundary conditions. \n\nIn many practical applications, the computational cost associated with solving Eq.~\\ref{eq:FOM} is prohibitively expensive due to the high dimension of the state. The goal of a ROM is to transform the $N$-dimensional dynamical system presented in Eq.~\\ref{eq:FOM} into a $K$ dimensional dynamical system, with $K \\ll N$. To achieve this goal, we pursue the following agenda:\n\\begin{enumerate}\n\\item Develop a weak form of the FOM in generalized coordinates.\n\\item Decompose the generalized coordinates into a $K$-dimensional resolved coarse-scale set and an $N-K$ dimensional unresolved fine-scale set.\n\\item Develop a $K$-dimensional ROM for the coarse-scales by making approximations to the fine-scale coordinates.\n\\end{enumerate}\nThe remainder of this section will address task 1 in the above agenda.\n\n\nTo develop the weak form of Eq.~\\ref{eq:FOM}, we start by defining a trial basis matrix comprising $N$ orthonormal basis vectors,\n\\begin{equation*}\n\\mathbf{V} \\equiv \\begin{bmatrix}\n\\mathbf{v}_1 & \\mathbf{v}_2 & \\cdots & \\mathbf{v}_N\n\\end{bmatrix},\n\\end{equation*}\nwhere $\\mathbf{v}_i \\in \\mathbb{R}^N$, $\\mathbf{v}_i^T \\mathbf{v}_j = \\delta_{ij}$.\nThe basis vectors may be generated, for example, by the POD approach. \nWe next define the \\textit{trial space} as the range of the trial basis matrix,\n$${\\MC{V}} \\trieq \\text{Range}({\\mathbf{V}}).$$\nAs $\\mathbf{V}$ is a full-rank $N \\times N$ matrix, $\\mathcal{V} \\equiv \\RR{N}$ and the state variable can be exactly described by a linear combination of these basis vectors,\n\\begin{equation}\\label{eq:genCord}\n{\\mathbf{u}}(t) = \\sum_{i=1}^N \\mathbf{v}_i a_i(t). \n\\end{equation}\nFollowing~\\cite{carlberg_lspg}, we collect the basis coefficients $a_i(t)$ into $\\mathbf{a} : [0,T] \\rightarrow \\RR{N}$ and refer to $\\mathbf{a}$ as the \\emph{generalized coordinates}. We similarly define the test basis matrix, ${\\mathbf{W}}$, whose columns comprise linearly independent basis vectors that span the \\textit{test space}, $\\mathcal{W}$,\n\\begin{equation*}\n{\\mathbf{W}} \\equiv \\begin{bmatrix}\n\\mathbf{w}_1 & \\mathbf{w}_2 & \\cdots& \\mathbf{w}_N\n\\end{bmatrix}, \\qquad \n\\mathcal{W} \\trieq \\text{Range}({\\mathbf{W}}) ,\n\\end{equation*}\nwith $\\mathbf{w}_i \\in \\mathbb{R}^N$. \n\nEquation~\\ref{eq:FOM} can be expressed in terms of the generalized coordinates by inserting Eq.~\\ref{eq:genCord} into Eq.~\\ref{eq:FOM}, \n\\begin{equation}\\label{eq:FOM2}\n{\\mathbf{V}} \\frac{d }{dt}\\mathbf{a}(t)= \\mathbf{R}({\\mathbf{V}} \\mathbf{a}(t)).\n\\end{equation}\nThe weak form of Eq.~\\ref{eq:FOM2} is obtained by taking the $L^2$ inner product with ${\\mathbf{W}}$,\\footnote{The authors recognize that many types of inner products are possible in formulating a ROM. To avoid unnecessary abstraction, we focus here on the simplest case.}\n\\begin{equation}\\label{eq:FOM3}\n{\\mathbf{W}}^T {\\mathbf{V}} \\frac{d }{dt}\\mathbf{a}(t) = {\\mathbf{W}}^T \\mathbf{R}({\\mathbf{V}} \\mathbf{a}(t)).\n\\end{equation}\nManipulation of Eq.~\\ref{eq:FOM3} yields the following dynamical system,\n\\begin{equation}\\label{eq:FOM_generalized}\n\\frac{d }{dt}\\mathbf{a}(t) = [{\\mathbf{W}}^T {\\mathbf{V}} ]^{-1} {\\mathbf{W}}^T \\mathbf{R}({\\mathbf{V}} \\mathbf{a}(t)), \\qquad \\mathbf{a}(t=0) = \\mathbf{a}_0, \\qquad t \\in [0,T],\n\\end{equation}\nwhere $\\mathbf{a}_0 \\in \\RR{N}$ with $\\mathbf{a}_0 = [{\\mathbf{W}}^T {\\mathbf{V}} ]^{-1} {\\mathbf{W}}^T {\\mathbf{u}}_0.$ \nNote that Eq.~\\ref{eq:FOM_generalized} is an $N$-dimensional ODE system and is simply Eq.~\\ref{eq:FOM} expressed in a different coordinate system. It is further worth noting that, since ${\\mathbf{W}}$ and ${\\mathbf{V}}$ are invertible (both are square matrices with linearly independent columns), one has $[{\\mathbf{W}}^T {\\mathbf{V}} ]^{-1} {\\mathbf{W}}^T = {\\mathbf{V}}^{-1}.$ This will not be the case for ROMs.\n\n\\section{Reduced-Order Models}\\label{sec:ROM}\n\\subsection{Multiscale Formulation}\nThis subsection addresses task 2 in the aforementioned agenda. Reduced-order models seek a low-dimensional representation of the original high-fidelity model. To achieve this, we examine a multiscale formulation of Eq.~\\ref{eq:FOM_generalized}. Consider sum decompositions of the trial and test space,\n\\begin{equation}\n{\\MC{V}} = \\tilde{\\MC{V}} \\oplus {\\MC{V}^{\\prime}}, \\qquad \\mathcal{W} = \\mathcal{\\tilde{W}} \\oplus \\mathcal{W}'.\n\\end{equation}\nThe space $\\tilde{\\MC{V}}$ is referred to as the coarse-scale trial space, while ${\\MC{V}^{\\prime}}$ is referred to as the fine-scale trial space. We refer to $\\tilde{\\MC{W}}$ and ${\\MC{W}^{\\prime}}$ in a similar fashion.\nFor simplicity, define $\\tilde{\\MC{V}}$ to be the column space of the first $K$ basis vectors in ${\\mathbf{V}}$ and ${\\MC{V}^{\\prime}}$ to be the column space of the last $N-K$ basis vectors in ${\\mathbf{V}}$. This approach is appropriate when the basis vectors are ordered in a hierarchical manner, as is the case with POD. Note the following properties of the decomposition:\n\\begin{enumerate}\n\\item The coarse-scale space is a subspace of $\\mathcal{V}$, i.e., $\\tilde{\\MC{V}} \\subset {\\MC{V}}$.\n\\item The fine-scale space is a subspace of $\\mathcal{V}$, i.e., ${\\MC{V}^{\\prime}} \\subset {\\MC{V}}$.\n\\item The fine and coarse-scale subspaces do not overlap, i.e., $\\tilde{\\MC{V}} \\cap {\\MC{V}^{\\prime}} = \\{ 0 \\}.$\n\\item The fine-scale and coarse-scale subspaces are orthogonal, i.e., $\\tilde{\\MC{V}} \\perp {\\MC{V}^{\\prime}}.$ This is due to the fact that the basis vectors that comprise $\\mathbf{V}$ are orthonormal.\n\\end{enumerate}\nFor notational purposes, we make the following definitions for the trial and test spaces:\n\\begin{equation*}\n{\\mathbf{V}} \\equiv \\begin{bmatrix} \\tilde{\\mathbf{V}} & ; & {\\mathbf{V}^{\\prime}} \\end{bmatrix}, \\quad {\\mathbf{W}} \\equiv \\begin{bmatrix} \\tilde{\\mathbf{W}} & ; & {\\mathbf{W}^{\\prime}} \\end{bmatrix}, \n\\end{equation*}\nwhere $[ \\cdot \\hspace{0.05 in}; \\hspace{0.05 in} \\cdot ]$ denotes the concatenation of two matrices and,\n\\begin{alignat*}{2}\n&\\tilde{\\mathbf{V}} \\equiv \\begin{bmatrix}\n\\mathbf{v}_1 & \\mathbf{v}_2 & \\cdots & \\mathbf{v}_K\n\\end{bmatrix}, && \\quad \\tilde{\\MC{V}} \\trieq \\text{Range}(\\tilde{\\mathbf{V}}) , \\\\\n&{\\mathbf{V}^{\\prime}} \\equiv \\begin{bmatrix}\n\\mathbf{v}_{K+1} & \\mathbf{v}_{K+2} & \\cdots & \\mathbf{v}_{N}\n\\end{bmatrix}, && \\quad {\\MC{V}^{\\prime}} \\trieq \\text{Range}({\\mathbf{V}^{\\prime}}). \\\\\n&\\tilde{\\mathbf{W}} \\equiv \\begin{bmatrix}\n\\mathbf{w}_1 & \\mathbf{w}_2 & \\cdots & \\mathbf{w}_K\n\\end{bmatrix}, && \\quad \\tilde{\\MC{W}} \\trieq \\text{Range}(\\tilde{\\mathbf{W}}) , \\\\\n&{\\mathbf{W}^{\\prime}} \\equiv \\begin{bmatrix}\n\\mathbf{w}_{K+1} & \\mathbf{w}_{K+2} & \\cdots & \\mathbf{w}_{N}\n\\end{bmatrix}, && \\quad {\\MC{W}^{\\prime}} \\trieq \\text{Range}({\\mathbf{W}^{\\prime}}) .\n\\end{alignat*}\nThe coarse and fine-scale states are defined as,\n\\begin{equation*}\n\\tilde{\\mathbf{u}}(t) \\trieq \\sum_{i=1}^K \\mathbf{v}_i a_i(t) \\equiv \\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}(t), \\qquad {\\mathbf{u}^{\\prime}}(t) \\trieq \\sum_{i = K+1}^N \\mathbf{v}_i a_i(t) \\equiv {\\mathbf{V}^{\\prime}} {\\mathbf{a}^{\\prime}}(t),\n\\end{equation*}\nwith $\\tilde{\\mathbf{u}} : [0,T] \\rightarrow \\tilde{\\mathcal{V}}$, ${\\mathbf{u}^{\\prime}} : [0,T] \\rightarrow \\mathcal{V}', \\tilde{\\mathbf{a}}: [0,T] \\rightarrow \\RR{K},$ and ${\\mathbf{a}^{\\prime}} : [0,T] \\rightarrow \\RR{N-K}.$\n\\begin{comment}\nWe make similar definitions for the test space,\n\\begin{equation*}\n{\\mathbf{W}} \\overset{\\Delta}{=} \\begin{bmatrix} \\tilde{\\mathbf{W}} & ; & {\\mathbf{W}^{\\prime}} \\end{bmatrix}, \n\\end{equation*}\nwhere,\n\\begin{alignat*}{2}\n&\\tilde{\\mathbf{W}} \\overset{\\Delta}{=} \\begin{bmatrix}\n\\mathbf{w}_1, \\mathbf{w}_2, \\hdots, \\mathbf{w}_K\n\\end{bmatrix}, && \\quad \\text{Range}(\\tilde{\\mathbf{W}}) \\overset{\\Delta}{=} \\tilde{\\MC{W}}, \\\\\n&{\\mathbf{W}^{\\prime}} \\overset{\\Delta}{=} \\begin{bmatrix}\n\\mathbf{w}_{K+1}, \\mathbf{w}_{K+2}, \\hdots, \\mathbf{w}_{N}\n\\end{bmatrix}, && \\quad \\text{Range}({\\mathbf{W}^{\\prime}})\\overset{\\Delta}{=} {\\MC{W}^{\\prime}}.\n\\end{alignat*}\n\\end{comment}\nThese decompositions allow Eq.~\\ref{eq:FOM3} to be expressed as two linearly independent systems,\n\\begin{equation}\\label{eq:FOM_VMS_coarse}\n\\tilde{\\mathbf{W}}^T \\tilde{\\mathbf{V}} \\frac{d }{dt}\\tilde{\\mathbf{a}}(t) + \\tilde{\\mathbf{W}}^T {\\mathbf{V}^{\\prime}} \\frac{d }{dt}{\\mathbf{a}^{\\prime}}(t) =\\tilde{\\mathbf{W}}^T \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}(t) + {\\mathbf{V}^{\\prime}} {\\mathbf{a}^{\\prime}}(t)),\n\\end{equation}\n\\begin{equation}\\label{eq:FOM_VMS_fine}\n{\\mathbf{W}^{\\prime}}^T \\tilde{\\mathbf{V}} \\frac{d }{dt} \\tilde{\\mathbf{a}}(t) + {\\mathbf{W}^{\\prime}}^T {\\mathbf{V}^{\\prime}} \\frac{d }{dt}{\\mathbf{a}^{\\prime}}(t) ={\\mathbf{W}^{\\prime}}^T \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}(t) + {\\mathbf{V}^{\\prime}} {\\mathbf{a}^{\\prime}}(t)).\n\\end{equation}\nEquation~\\ref{eq:FOM_VMS_coarse} is referred to as the coarse-scale equation, while Eq.~\\ref{eq:FOM_VMS_fine} is referred to as the fine-scale equation. It is important to emphasize that the system formed by Eqs.~\\ref{eq:FOM_VMS_coarse} and~\\ref{eq:FOM_VMS_fine} is still an exact representation of the original FOM.\n\nThe objective of ROMs is to solve the coarse-scale equation. The challenge encountered in this objective is that the evolution of the coarse-scales depends on the fine-scales. This is a type of ``closure problem\" and must be addressed to develop a closed ROM.\n\n\\subsection{Reduced-Order Models}\nAs noted above, the objective of a ROM is to solve the (unclosed) coarse-scale equation. We now develop ROMs of Eq.~\\ref{eq:FOM} by leveraging the multiscale decomposition presented above. This section addresses task 3 in the mathematical agenda.\n\n The most straightforward technique to develop a ROM is to make the approximation,\n\\begin{equation*}\\label{eq:ufine_ansatz}\n{\\mathbf{u}^{\\prime}} \\approx \\mathbf{0}.\n\\end{equation*}\nThis allows for the coarse-scale equation to be expressed as,\n\\begin{equation}\\label{eq:FOM_VMS_coarse_2}\n\\tilde{\\mathbf{W}}^T \\tilde{\\mathbf{V}} \\frac{d }{dt}\\tilde{\\mathbf{a}}(t) =\\tilde{\\mathbf{W}}^T \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}(t) ).\n\\end{equation}\nEquation~\\ref{eq:FOM_VMS_coarse_2} forms a $K$-dimensional reduced-order system (with $K \\ll N$) and provides the starting point for formulating several standard ROM techniques. The Galerkin and Least-Squares Petrov--Galerkin ROMs are outlined in the subsequent subsections.\n\n\\subsubsection{The Galerkin Reduced-Order Model}\nGalerkin projection is a common choice for producing a reduced set of ODEs. In Galerkin projection, the test basis is taken to be equivalent to the trial basis, i.e. $\\tilde{\\mathbf{W}} = \\tilde{\\mathbf{V}}$. The Galerkin ROM is then,\n\\begin{equation}\\label{eq:GROM}\n\\tilde{\\mathbf{V}}^T \\frac{d }{dt}\\tilde{\\mathbf{u}}(t) = \\tilde{\\mathbf{V}}^T \\mathbf{R}(\\tilde{\\mathbf{u}}(t)), \\qquad \\tilde{\\mathbf{u}}(0) = \\tilde{\\mathbf{u}}_0, \\qquad t \\in [0,T].\n\\end{equation}\nGalerkin projection can be shown to be optimal in the sense that it minimizes the $L^2$-norm of the FOM ODE residual over $\\text{Range}(\\tilde{\\mathbf{V}})$~\\cite{carlberg_lspg}. As the columns of $\\tilde{\\mathbf{V}}$ no longer spans ${\\MC{V}}$, it is possible that the initial state of the full system, ${\\mathbf{u}}_0$, may differ from the initial state of the reduced system, $\\tilde{\\mathbf{u}}_0$. For simplicity, however, it is assumed here that the initial conditions lie fully in the coarse-scale trial space, i.e. ${\\mathbf{u}}_0 \\in \\tilde{\\MC{V}}$ such that,\n\\begin{equation}\\label{eq:ROM_IC}\n\\tilde{\\mathbf{u}}_0 = {\\mathbf{u}}_0.\n\\end{equation}\n Note that this issue can be formally addressed by using an affine trial space to ensure that $\\tilde{\\mathbf{u}}_0 = {\\mathbf{u}}_0$.\n\nEquation~\\ref{eq:GROM} can be equivalently written for the generalized coarse-scale coordinates, $\\tilde{\\mathbf{a}}$,\n\\begin{equation}\\label{eq:GROM_modal}\n\\frac{d }{dt}\\tilde{\\mathbf{a}}(t) = \\tilde{\\mathbf{V}}^T \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}(t)), \\qquad \\tilde{\\mathbf{a}}(0) = \\tilde{\\mathbf{a}}_0, \\qquad t \\in [0,T],\n\\end{equation}\nwhere $\\tilde{\\mathbf{a}}_0 \\in \\RR{K}$ with $\\tilde{\\mathbf{a}}_0 =\\tilde{\\mathbf{V}}^T {\\mathbf{u}}_0.$\nEquation~\\ref{eq:GROM_modal} is a $K$-dimensional ODE system (with $K\\ll N$) and is hence of lower dimension than the FOM. Note that, similar to Eq.~\\ref{eq:FOM_generalized}, the projection via $\\tilde{\\mathbf{V}}^T$ would normally be $\\big[ \\tilde{\\mathbf{V}}^T \\tilde{\\mathbf{V}} \\big]^{-1} \\tilde{\\mathbf{V}}$. When $\\tilde{\\mathbf{V}}$ is constructed via POD, its columns are orthonormal and $\\tilde{\\mathbf{V}}^T \\tilde{\\mathbf{V}} = \\mathbf{I}$; the projector has been simplified to reflect this. Non-orthonormal basis vectors will require the full computation of $\\big[ \\tilde{\\mathbf{V}}^T \\tilde{\\mathbf{V}} \\big]^{-1} \\tilde{\\mathbf{V}}$.\n\nIt is important to note that, in order to develop a computationally efficient ROM, some ``hyper-reduction'' method must be devised to reduce the cost associated with evaluating the matrix-vector product, $\\tilde{\\mathbf{V}}^T \\mathbf{R}(\\tilde{\\mathbf{u}}(t))$. Gappy POD~\\cite{everson_sirovich_gappy} and the (discrete) empirical interpolation method~\\cite{eim,deim} are two such techniques. More details on hyper-reduction are provided in Appendix~\\ref{appendix:hyper}\n\nWhen applied to unsteady non-linear problems, the Galerkin ROM is often inaccurate and, at times, unstable. Examples of this are seen in Ref.~\\cite{carlberg_lspg_v_galerkin}. These issues motivate the development of more sophisticated reduced-order modeling techniques. \n\n\\subsubsection{Petrov--Galerkin and Least-Squares Petrov--Galerkin Reduced-Order Models}\nIn the Petrov--Galerkin approach, the test space is different from the trial space. Petrov--Galerkin approaches have a rich history in the finite element community~\\cite{brooks_supg,hughes_petrovgalerkin} and can enhance the stability and robustness of a numerical method. In the context of reduced-order modeling for dynamical systems, the Least-Squares Petrov--Galerkin method (LSPG)~\\cite{carlberg_lspg} is a popular approach. The LSPG approach is a ROM technique that seeks to minimize the fully discrete residual (i.e., the residual after spatial and temporal discretization) at each time-step. The LSPG method can be shown to be optimal in the sense that it minimizes the $L^2$-norm of the \\textit{fully discrete} residual at each time-step over $\\text{Range}(\\tilde{\\MC{V}})$. To illustrate the LSPG method, consider the algebraic system of equations for the FOM obtained after an implicit Euler temporal discretization,\n\\begin{equation}\\label{eq:coarse_implicit_euler_0}\n\\frac{{\\mathbf{u}}^{n} - {\\mathbf{u}}^{n-1} }{\\Delta t} - \\mathbf{R}({\\mathbf{u}}^{n}) = \\mathbf{0},\n\\end{equation}\nwhere ${\\mathbf{u}}^n \\in \\RR{N}$ denotes the solution at the $n^{th}$ time-step.\nThe FOM will exactly satisfy Eq.~\\ref{eq:coarse_implicit_euler_0}. The ROM, however, will not. The LSPG method minimizes the residual of Eq.~\\ref{eq:coarse_implicit_euler_0} over each time-step. For notational purposes, we define the residual vector for the implicit Euler method,\n\\begin{equation*}\n\\mathbf{r}_{\\text{IE}}: (\\mathbf{y};{\\mathbf{u}}^{n-1}) \\mapsto \\frac{\\mathbf{y} - {\\mathbf{u}}^{n-1} }{\\Delta t} - \\mathbf{R}(\\mathbf{y}).\n\\end{equation*}\nThe LSPG method is defined as follows,\n\\begin{equation*}\n{\\mathbf{u}}^n = \\underset{\\mathbf{y} \\in \\text{Range}(\\tilde{\\mathbf{V}}) }{\\text{arg min}}|| \\mathbf{A}(\\mathbf{y}) \\mathbf{r}_{\\text{IE}}(\\mathbf{y};{\\mathbf{u}}^{n-1}) ||_2^2,\n\\end{equation*}\nwhere $\\mathbf{A}(\\cdot) \\in \\mathbb{R}^{z \\times n}$ with $z \\le N$ is a weighting matrix. The standard LSPG method takes $\\mathbf{A} = \\mathbf{I}$. For the implicit Euler time integration scheme (as well as various other implicit schemes) the LSPG approach can be shown to have an equivalent continuous representation using a Petrov--Galerkin projection~\\cite{carlberg_lspg_v_galerkin}. For example, the LSPG method for any backward differentiation formula (BDF) time integration scheme can be written as a Petrov--Galerkin ROM with the test basis,\n\\begin{equation*}\n\\mathbf{\\tilde{\\mathbf{W}}} =\\big( \\mathbf{I} - \\alpha \\Delta t \\mathbf{J}[\\tilde{\\mathbf{u}}(t)] \\big) \\tilde{\\mathbf{V}},\n\\end{equation*}\nwhere $\\mathbf{J}[\\tilde{\\mathbf{u}}(t)] = \\frac{\\partial \\mathbf{R}}{\\partial \\mathbf{y}}(\\tilde{\\mathbf{u}}(t))$ is the Jacobian of the right-hand side function evaluated about the coarse-scale state and $\\alpha$ is a constant, specific to a given scheme (e.g. $\\alpha = 1$ for implicit Euler, $\\alpha = \\frac{2}{3}$ for BDF2, $\\alpha = \\frac{6}{11}$ for BDF3, etc.).\nWith this test basis, the LSPG ROM can be written as,\n\\begin{equation}\\label{eq:LSPGROM}\n \\tilde{\\mathbf{V}}^T \\bigg( \\frac{d }{dt}\\tilde{\\mathbf{u}}(t) - \\mathbf{R}(\\tilde{\\mathbf{u}}(t)) \\bigg) = \\tilde{\\mathbf{V}}^T \\mathbf{J}^T [\\tilde{\\mathbf{u}}(t)] \\alpha \\Delta t\\bigg( \\frac{d }{dt}\\tilde{\\mathbf{u}}(t) - \\mathbf{R}(\\tilde{\\mathbf{u}}(t)) \\bigg) , \\qquad \\tilde{\\mathbf{u}}(0) = \\tilde{\\mathbf{u}}_0, \\qquad t \\in [0,T].\n\\end{equation}\nIn writing Eq.~\\ref{eq:LSPGROM}, we have coupled all of the terms from the standard Galerkin ROM on the left-hand side, and have similarly coupled the terms introduced by the Petrov--Galerkin projection on the right-hand side. One immediately observes that the LSPG approach is a residual-based method, meaning that the stabilization added by LSPG is proportional to the residual. The LSPG method is similar to the Galerkin\/Least-Squares (GLS) approach commonly employed in the finite element community~\\cite{hughes_GLS,hughes0}. This can be made apparent by writing Eq.~\\ref{eq:LSPGROM} as,\n\\begin{equation*}\n \\bigg( \\mathbf{v}_i , \\frac{d }{dt}\\tilde{\\mathbf{u}}(t) - \\mathbf{R}(\\tilde{\\mathbf{u}}(t)) \\bigg)= \\bigg(\\mathbf{J} [\\tilde{\\mathbf{u}}(t)] \\mathbf{v}_i , \\tau \\big[ \\frac{d }{dt}\\tilde{\\mathbf{u}}(t) - \\mathbf{R}(\\tilde{\\mathbf{u}}(t)) \\big] \\bigg) , \\qquad i = 1,2,\\hdots,K, \n\\end{equation*}\nwhere $(\\mathbf{a},\\mathbf{b}) = \\mathbf{a}^T \\mathbf{b}$ and $\\tau = \\alpha \\Delta t$ is the stabilization parameter. Compare the above to, say, Eq. 70 and 71 in Ref.~\\cite{hughes0}. A rich body of literature exists on residual-based methods, and viewing the LSPG approach in this light helps establish connections with other methods.\nWe highlight several important aspects of LSPG. Remarks 1 through 3 are derived by Carlberg et al. in Ref.~\\cite{carlberg_lspg_v_galerkin}:\n\\begin{enumerate}\n\\item The LSPG approach is inherently tied to the temporal discretization. For different time integration schemes, the ``stabilization\" added by the LSPG method will vary. For optimal accuracy, the LSPG method requires an intermediary time-step size.\n\\item In the limit of $\\Delta t \\rightarrow 0$, the LSPG approach recovers a Galerkin approach.\n\\item For explicit time integration schemes, the LSPG and Galerkin approach are equivalent.\n\\item For backwards differentiation schemes, the LSPG approach is a type of GLS stabilization for non-linear problems. \n\\item While commonalities exist between LSPG and multiscale approaches, the authors believe that the LSPG method should \\textit{not} be viewed as a subgrid-scale model. The reason for this is that it is unclear how Eq.~\\ref{eq:LSPGROM} can be derived from Eq.~\\ref{eq:FOM_VMS_coarse}. This is similar to the fact that, in Ref.~\\cite{hughes0}, \\textit{adjoint} stabilization is viewed as a subgrid-scale model while GLS stabilization is not. The challenge in deriving Eq.~\\ref{eq:LSPGROM} from Eq.~\\ref{eq:FOM_VMS_coarse} lies primarily in the fact that the Jacobian in Eq.~\\ref{eq:LSPGROM} contains a transpose operator. We thus view LSPG as mathematical stabilization rather than a subgrid-scale model.\n\\end{enumerate}\nWhile the LSPG approach has enjoyed much success for constructing ROMs of non-linear problems, remarks 1, 2, 3, and 5 suggest that improvements over the LSPG method are possible. Remark 1 suggests improvements in computational speed and accuracy are possible by removing sensitivity to the time-step size. Remark 3 suggests that improvements in computational speed and flexibility are possible by formulating a method that can be used with explicit time-stepping schemes. Lastly, remark 5 suggests that improvements in accuracy are possible by formulating a method that accounts for subgrid effects.\n\n\n\\subsection{Mori-Zwanzig Reduced-Order Models}\\label{sec:MZ}\nThe optimal prediction framework formulated by Chorin et al.~\\cite{ChorinOptimalPrediction,ChorinOptimalPredictionMemory,Chorin_book}, which is a (significant) reformulation of the Mori-Zwanzig (MZ) formalism of statistical mechanics, is a model order reduction tool that can be used to develop representations of the impact of the fine-scales on the coarse-scale dynamics. In this section, the optimal prediction framework is used to derive a compact approximation to the impact of the fine-scale POD modes on the evolution of the coarse-scale POD modes. For completeness, the optimal prediction framework is first derived in the context of the Galerkin POD ROM. It is emphasized that the content presented in Sections~\\ref{sec:liouville} and \\ref{sec:projOpsLangevin} is simply a formulation of Chorin's framework, with a specific projection operator, in the context of the Galerkin POD ROM. \n\n\nWe pursue the MZ approach on a Galerkin formulation of Eq.~\\ref{eq:FOM_generalized}. Before describing the formalism, it is beneficial to re-write the original FOM in terms of the generalized coordinates with the solution being defined implicitly as a function of the initial conditions,\n\\begin{equation}\\label{eq:FOM_generalized_b}\n\\frac{d }{dt} \\mathbf{a} (\\mathbf{a}_0,t) = {\\mathbf{V}}^T \\mathbf{R}({\\mathbf{V}} \\mathbf{a} (\\mathbf{a}_0,t)), \\qquad \\mathbf{a}(0) = \\mathbf{a}_0, \\qquad t \\in [0,T],\n\\end{equation}\nwith $\\mathbf{a}: \\mathbb{R}^N \\times [0,T] \\rightarrow \\mathbb{R}^N$, $\\mathbf{a} \\in \\RR{N} \\otimes \\RRC{N} \\otimes \\MC{T}$ the time-dependent generalized coordinates, $\\RRC{N}$ the space of (sufficiently smooth) functions acting on $\\RR{N}$, $\\MC{T}$ the space of (sufficiently smooth) functions acting on $[0,T]$, and $\\mathbf{a}_0 \\in \\mathbb{R}^N$ the initial conditions. Here, $\\mathbf{a}(\\mathbf{a}_0,t)$ is viewed as a function that maps from the coordinates $\\mathbf{a}_0$ (i.e., the initial conditions) and time to a vector in $\\mathbb{R}^N$. It is assumed that the right-hand side operator $\\mathbf{R}$ is continuously differentiable on $\\mathbb{R}^N$.\n\\subsubsection{The Liouville Equation}\\label{sec:liouville}\nThe starting point of the MZ approach is to transform the non-linear FOM (Eq.~\\ref{eq:FOM_generalized_b}) into a linear partial differential equation. \nEquation~\\ref{eq:FOM_generalized_b} can be written equivalently as the following partial differential equation in $\\mathbb{R}^N \\times [0,T]$~\\cite{ChorinOptimalPredictionMemory},\n\\begin{equation}\\label{eq:Liouville}\n\\frac{\\partial }{\\partial t}v(\\mathbf{a}_0,t) = \\mathcal{L} v(\\mathbf{a}_0,t); \\qquad\nv(\\mathbf{a_0},0) = g(\\mathbf{a}_0),\n\\end{equation}\nwhere $v: \\mathbb{R}^N \\times [0,T] \\rightarrow \\mathbb{R}^{N_v}$ with $v \\in \\RR{N_v} \\otimes \\RRC{N} \\otimes \\MC{T}$ is a set of $N_v$ observables and $g: \\mathbb{R}^N \\rightarrow \\mathbb{R}^{N_v}$ is a state-to-observable map. The operator $\\mathcal{L}$ is the Liouville operator, also known as the Lie derivative, and is defined by,\n\\begin{align*}\n\\mathcal{L} &: \\mathbf{q} \\mapsto \\bigg[ \\frac{\\partial }{\\partial \\mathbf{a_0}} \\mathbf{q} \\bigg] {\\mathbf{V}}^T \\mathbf{R}( {\\mathbf{V}} \\mathbf{a_0} ),\\\\\n &: \\RR{q} \\otimes \\RRC{N} \\rightarrow \\RR{q} \\otimes \\RRC{N},\n\\end{align*}\nfor arbitrary q.\nEquation~\\ref{eq:Liouville} is referred to as the Liouville equation and is an exact statement of the original dynamics. The Liouville equation describes the solution to Eq.~\\ref{eq:FOM_generalized_b} for \\textit{all} possible initial conditions. The advantage of reformulating the system in this way is that the Liouville equation is linear, allowing for the use of superposition and aiding in the removal of the fine-scales.\n\nThe solution to Eq.~\\ref{eq:Liouville} can be written as,\n\\begin{equation*}\nv(\\mathbf{a_0},t) = e^{t \\mathcal{L}} g(\\mathbf{a_0}) .\n\\end{equation*}\nThe operator $e^{t \\mathcal{L}}$, which has been referred to as a ``propagator\", evolves the solution along its trajectory in phase-space~\\cite{ZwanzigBook}. The operator $e^{t \\mathcal{L}}$ has several interesting properties. Most notably, the operator can be ``pulled\" inside of a non-linear functional~\\cite{ZwanzigBook},\n\\begin{equation*}\ne^{t \\mathcal{L}} g(\\mathbf{a}_0) = g( e^{t \\mathcal{L}} \\mathbf{a}_0).\n\\end{equation*}\nThis is similar to the composition property inherent to Koopman operators~\\cite{Koopman}. With this property, the solution to Eq.~\\ref{eq:Liouville} may be written as,\n\\begin{equation*}\nv(\\mathbf{a_0},t) = g( e^{t \\mathcal{L}} \\mathbf{a}_0).\n\\end{equation*\nThe implications of $e^{t \\mathcal{L}}$ are significant. It demonstrates that, given trajectories $\\mathbf{a}(\\mathbf{a_0},t)$, the solution $v$ is known for any observable $g$. \n\nNoting that $\\mathcal{L}$ and $e^{t \\mathcal{L}}$ commute, Eq.~\\ref{eq:Liouville} may be written as,\n\\begin{equation*}\n\\frac{\\partial }{\\partial t} v(\\mathbf{a_0},t) = e^{t \\mathcal{L}} \\mathcal{L} v(\\mathbf{a_0},0).\n\\end{equation*}\nA set of partial differential equations for the resolved generalized coordinates can be obtained by taking $g(\\mathbf{a_0}) = \\tilde{\\mathbf{a}}_0$,\n\\begin{equation}\\label{eq:Liouville_sg_res}\n\\frac{\\partial }{\\partial t} e^{t \\mathcal{L}} \\tilde{\\mathbf{a}}_0 = e^{t \\mathcal{L}} \\mathcal{L} \\tilde{\\mathbf{a}}_0.\n\\end{equation}\nThe remainder of the derivation is performed for $g(\\mathbf{a_0}) = \\tilde{\\mathbf{a}}_0$, thus $N_v = K$. \n\\subsubsection{Projection Operators and the Generalized Langevin Equation}\\label{sec:projOpsLangevin}\nThe objective now is to remove the dependence of Eq.~\\ref{eq:Liouville_sg_res} on the fine-scale variables.\nSimilar to the VMS decomposition, $\\RRC{N}$ can be decomposed into resolved and unresolved subspaces,\n\\begin{equation*}\n\\RRC{N} = \\tilde{\\MC{H}} \\oplus \\MC{H}',\n\\end{equation*} \nwith $\\tilde{\\MC{H}}$ being the space of all functions of the resolved coordinates, $\\tilde{\\mathbf{a}}_0$, and $\\MC{H}'$ the complementary space.\nThe associated projection operators are defined as $\\mathcal{P}: \\RRC{N} \\rightarrow \\tilde{\\MC{H}}$ and $\\mathcal{Q} = I - \\mathcal{P}$. Various types of projections are possible, and here we consider,\n\\begin{equation*}\n\\mathcal{P}f( \\mathbf{a}_0 ) = \\int_{\\RR{N}} f( \\mathbf{a}_0 ) \\delta({\\mathbf{a}^{\\prime}}_0) d {\\mathbf{a}^{\\prime}}_0,\n\\end{equation*}\nwhich leads to\n\\begin{equation*}\n\\mathcal{P}f(\\mathbf{a}_0 )= f([\\tilde{\\mathbf{a}}_0;\\mathbf{0}]).\n\\end{equation*}\n\nThe projection operators can be used to split the Liouville equation,\n\\begin{equation}\\label{eq:Liouville_sg_split}\n\\frac{\\partial }{\\partial t} e^{t \\mathcal{L}} \\tilde{\\mathbf{a}}_0 = e^{t \\mathcal{L}} \\mathcal{PL} \\tilde{\\mathbf{a}}_0 + e^{t \\mathcal{L}}\\mathcal{QL}\\tilde{\\mathbf{a}}_0.\n\\end{equation}\nThe objective now is to remove the dependence of the right-hand side of Eq.~\\ref{eq:Liouville_sg_split} on the fine-scales, ${\\mathbf{a}_0^{\\prime}}$ (i.e. $\\mathcal{QL}\\tilde{\\mathbf{a}}_0$). This may be achieved by Duhamel's principle,\n\\begin{equation}\\label{eq:duhamel}\ne^{t \\mathcal{L}} = e^{t \\mathcal{Q} \\mathcal{L}} + \\int_0^t e^{(t - s)\\mathcal{L}} \\mathcal{P}\\mathcal{L} e^{s \\mathcal{Q} \\mathcal{L}} ds.\n\\end{equation}\nInserting Eq.~\\ref{eq:duhamel} into Eq.~\\ref{eq:Liouville_sg_split}, the generalized Langevin equation is obtained,\n\\begin{equation}\\label{eq:MZ_Identity}\n\\frac{\\partial }{\\partial t} e^{t \\mathcal{L}} \\tilde{\\mathbf{a}}_0 = \\underbrace{e^{t\\mathcal{L}}\\mathcal{PL} \\tilde{\\mathbf{a}}_0}_{\\text{Markovian}} + \\underbrace{e^{t\\mathcal{QL}}\\mathcal{QL} \\tilde{\\mathbf{a}}_0}_{\\text{Noise}} + \n \\underbrace{ \\int_0^t e^{{(t - s)}\\mathcal{L}} \\mathcal{P}\\mathcal{L} e^{s \\mathcal{Q} \\mathcal{L}} \\mathcal{QL}\\tilde{\\mathbf{a}}_0 ds}_{\\text{Memory}}.\n\\end{equation}\nBy the definition of the initial conditions (Eq.~\\ref{eq:ROM_IC}), the noise-term is zero and we obtain,\n\\begin{equation}\\label{eq:MZ_Identity3}\n\\frac{\\partial}{\\partial t} e^{t \\mathcal{L}} \\tilde{\\mathbf{a}}_0 = e^{t\\mathcal{L}}\\mathcal{PL} \\tilde{\\mathbf{a}}_0+ \\int_0^t e^{{(t - s)}\\mathcal{L}} \\mathcal{P}\\mathcal{L} e^{s \\mathcal{Q} \\mathcal{L}} \\mathcal{QL} \\tilde{\\mathbf{a}}_0 ds.\n\\end{equation}\nThe system described in Eq.~\\ref{eq:MZ_Identity} is precise and not an approximation to the original ODE system. For notational purposes, define,\n\\begin{equation}\\label{eq:kerndef}\n\\mathbf{K}(\\tilde{\\mathbf{a}}_0,t) \\equiv \\mathcal{PL}e^{t\\mathcal{QL}}\\mathcal{QL}\\tilde{\\mathbf{a}}_0.\n\\end{equation}\nThe term $\\mathbf{K}: \\mathbb{R}^K \\times [0,T] \\rightarrow \\mathbb{R}^K$ with $\\mathbf{K} \\in \\RR{K} \\otimes \\tilde{\\MC{H}} \\otimes \\MC{T}$ is referred to as the memory kernel. \n\nUsing the identity $e^{t \\mathcal{L}} \\mathcal{PL}\\tilde{\\mathbf{a}}_0 = \\tilde{\\mathbf{V}}^T \\mathbf{R}(\\tilde{\\mathbf{u}}(t))$ and Definition~\\eqref{eq:kerndef}, Equation~\\ref{eq:MZ_Identity3} can be written in a more transparent form,\n\\begin{equation}\\label{eq:MZ_Identity_VMS}\n\\tilde{\\mathbf{V}}^T \\bigg( \\frac{\\partial }{ \\partial t}\\tilde{\\mathbf{u}}(t) - \\mathbf{R}(\\tilde{\\mathbf{u}}(t)) \\bigg) = \\int_0^t \\mathbf{K}(\\tilde{\\mathbf{a}}(t-s),s) ds,\n\\end{equation}\nNote that the time derivative is represented as a partial derivative due to the Liouville operators embedded in the memory.\n\nThe derivation up to this point has cast the original full-order model in generalized coordinates (Eq.~\\ref{eq:FOM_generalized_b}) as a linear PDE. Through the use of projection operators and Duhamel's principle, an \\textit{exact} equation (Eq.~\\ref{eq:MZ_Identity_VMS}) for the coarse-scale dynamics \\textit{only in terms of the coarse-scale variables} was then derived. The effect of the fine-scales on the coarse-scales appeared as a memory integral. This memory integral may be thought of as the closure term that is required to exactly account for the unresolved dynamics.\n \n\\subsubsection{The $\\tau$-model and the Adjoint Petrov--Galerkin Method}\\label{sec:tau-model}\nThe direct evaluation of the memory term in Eq.~\\ref{eq:MZ_Identity_VMS} is, in general, computationally intractable. To gain a reduction in computational cost, an approximation to the memory must be devised. A variety of such approximations exist, and here we outline the $\\tau$-model~\\cite{parish_dtau,BarberThesis}. The $\\tau$-model can be interpreted as the result of assuming that the memory is driven to zero in finite time and approximating the integral with a quadrature rule. This can be written as a two-step approximation,\n\\begin{equation*}\n\\int^t_0 \\mathbf{K}(\\tilde{\\mathbf{a}}(t-s),s) ds \\approx \\int^t_{t-\\tau} \\mathbf{K}(\\tilde{\\mathbf{a}}(t-s),s) ds \\approx \\tau \\mathbf{K}(\\tilde{\\mathbf{a}}(t),0).\n\\end{equation*}\nHere, $\\tau \\in \\RR{}$ is a stabilization parameter that is sometimes referred to as the ``memory length.\" It is typically static and user-defined, though methods of dynamically calculating it have been developed in \\cite{parish_dtau}. The \\textit{a priori} selection of $\\tau$ and sensitivity of the model output to this selection are discussed later in this manuscript. \n\nThe term $\\mathbf{K}(\\tilde{\\mathbf{a}}(t),0)$ can be shown to be~\\cite{parishVMS},\n\\begin{equation*}\n\\mathbf{K}(\\tilde{\\mathbf{a}}(t),0) = \\tilde{\\mathbf{V}}^T \\mathbf{J}[\\tilde{\\mathbf{u}}(t)] {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}(t)),\n\\end{equation*}\nwhere ${\\Pi^{\\prime}}$ is the ``orthogonal projection operator,\" defined as ${\\Pi^{\\prime}} \\equiv \\big(\\mathbf{I}-\\tilde{\\mathbf{V}} \\tilde{\\mathbf{V}}^T\\big)$. We define the corresponding coarse-scale projection operator as $\\tilde{\\Pi} \\equiv \\tilde{\\mathbf{V}} \\tilde{\\mathbf{V}}^T$. The coarse-scale equation with the $\\tau$-model reads,\n\\begin{equation}\\label{eq:MZ_coarse_tau_NL}\n\\tilde{\\mathbf{V}}^T \\bigg( \\frac{d }{dt}\\tilde{\\mathbf{u}}(t) - \\mathbf{R}(\\tilde{\\mathbf{u}}(t)) \\bigg) = \\tau \\tilde{\\mathbf{V}}^T \\mathbf{J}[\\tilde{\\mathbf{u}}(t)] {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}(t)).\n\\end{equation}\nEquation~\\ref{eq:MZ_coarse_tau_NL} provides a closed equation for the evolution of the coarse-scales. The left-hand side of Eq.~\\ref{eq:MZ_coarse_tau_NL} is the standard Galerkin ROM, and the right-hand side can be viewed as a subgrid-scale model. \n\nWhen compared to existing methods, the inclusion of the $\\tau$-model leads to a method that is analogous to a non-linear formulation of the \\textit{adjoint} stabilization technique developed in the finite element community. The ``adjoint\" terminology arises from writing Eq.~\\ref{eq:MZ_coarse_tau_NL} in a Petrov--Galerkin form,\n\\begin{equation}\\label{eq:adjoint_Galerkin}\n \\bigg[ \\bigg( \\mathbf{I} + \\tau {\\Pi^{\\prime}}^T \\mathbf{J}^T[\\tilde{\\mathbf{u}}(t)]\\bigg) \\tilde{\\mathbf{V}} \\bigg]^T \\bigg( \\frac{d }{dt}\\tilde{\\mathbf{u}}(t) - \\mathbf{R}(\\tilde{\\mathbf{u}}(t)) \\bigg) = \\mathbf{0}.\n\\end{equation}\nIt is seen that Eq.~\\ref{eq:adjoint_Galerkin} involves taking the inner product of the coarse-scale ODE with a test-basis that contains the adjoint of the coarse-scale Jacobian. Unlike GLS stabilization, adjoint stabilization can be derived from the multiscale equations~\\cite{hughes0}. Due to the similarity of the proposed method with adjoint stabilization techniques, as well as the LSPG terminology, the complete ROM formulation will be referred to as the Adjoint Petrov--Galerkin (APG) method. \n\n\\subsubsection{Comparison of APG and LSPG}\nThe APG method displays similarities to LSPG. From Eq.~\\ref{eq:adjoint_Galerkin}, it is seen that the test basis for the APG ROM is given by,\n\\begin{equation}\\label{eq:MZ_testbasis}\n\\tilde{\\mathbf{W}}_{A} = \\bigg( \\mathbf{I} + \\tau {\\Pi^{\\prime}}^T \\mathbf{J}^T[\\tilde{\\mathbf{u}}]\\bigg) \\tilde{\\mathbf{V}} .\n\\end{equation}\nRecall the LSPG test basis for backward differentiation schemes,\n\\begin{equation}\\label{eq:LSPG_testbasis}\n\\tilde{\\mathbf{W}}_{LSPG} = \\big( \\mathbf{I} - \\alpha \\Delta t \\mathbf{J}[\\tilde{\\mathbf{u}}] \\big) \\tilde{\\mathbf{V}}.\n\\end{equation}\nComparing Eq.~\\ref{eq:LSPG_testbasis} to Eq.~\\ref{eq:MZ_testbasis}, we can draw several interesting comparisons between the LSPG and APG method. Both contain a time-scale: $\\tau$ for APG and $\\alpha \\Delta t$ for LSPG. Both include Jacobians of the non-linear function $\\mathbf{R}(\\tilde{\\mathbf{u}})$. The two methods differ in the presence of the orthogonal projection operator in APG, a transpose on the Jacobian, and a sign discrepancy on the Jacobian. These last two differences are consistent with the discrepancies between GLS and adjoint stabilization methods used in the finite element community. See, for instance, Eqs. 71 and 73 in Ref~\\cite{hughes0}.\n\n\n\n\n\\section{Analysis}\\label{sec:analysis}\nThis section presents theoretical analyses of the Adjoint Petrov--Galerkin method. Specifically, error and eigenvalue analyses are undertaken for linear time-invariant (LTI) systems.\nSection~\\ref{sec:error_bound} derives \\textit{a priori} error bounds for the Galerkin and Adjoint Petrov--Galerkin ROMs. Conditions under which the APG ROM may be more accurate than the Galerkin ROM are discussed. Section~\\ref{sec:selecttau} outlines the selection of the parameter $\\tau$ that appears in APG. \n\\subsection{A Priori Error Bounds}\\label{sec:error_bound}\nWe now derive \\textit{a priori} error bounds for the Galerkin and Adjoint Petrov--Galerkin method for LTI systems. Define ${\\mathbf{u}}_F$ to be the solution to the FOM, $\\tilde{\\mathbf{u}}_G$ to be the solution to the Galerkin ROM, and $\\tilde{\\mathbf{u}}_A$ the solution to the Adjoint Petrov--Galerkin ROM. The full-order solution, Galerkin ROM, and Adjoint Petrov--Galerkin ROMs obey the following dynamical systems,\n\\begin{equation}\\label{eq:fom_error}\n\\frac{d }{dt}{\\mathbf{u}}_F(t) = \\mathbf{R}({\\mathbf{u}}_F(t)), \\qquad {\\mathbf{u}}_F(0) = {\\mathbf{u}}_0,\n\\end{equation}\n\\begin{equation}\\label{eq:grom_error}\n\\frac{d }{dt}\\tilde{\\mathbf{u}}_G(t) = \\mathbb{P}_G \\mathbf{R}(\\tilde{\\mathbf{u}}_G(t)), \\qquad {\\mathbf{u}}_G(0) = {\\mathbf{u}}_0,\n\\end{equation}\n\\begin{equation}\\label{eq:ag_error}\n\\frac{d }{dt}\\tilde{\\mathbf{u}}_A(t) = \\mathbb{P}_{A} \\mathbf{R}(\\tilde{\\mathbf{u}}_A(t)), \\qquad {\\mathbf{u}}_A(0) = {\\mathbf{u}}_0,\n\\end{equation}\nwhere the Galerkin and Adjoint Petrov--Galerkin projections are, respectively,\n\\begin{equation*}\n\\mathbb{P}_G = \\tilde{\\Pi}, \\qquad \\mathbb{P}_A = \\tilde{\\Pi} \\big[ \\mathbf{I} + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}_A] {\\Pi^{\\prime}} \\big]. \n\\end{equation*}\nThe residual of the full-order model is defined as,\n\\begin{equation*}\n\\mathbf{r}_F : {\\mathbf{u}} \\mapsto \\frac{d {\\mathbf{u}}}{dt} - \\mathbf{R}({\\mathbf{u}}).\n\\end{equation*}\nWe define the error in the Galerkin and Adjoint Petrov--Galerkin method as,\n\\begin{equation*}\n\\mathbf{e}_G \\overset{\\Delta}{=} {\\mathbf{u}}_F - \\tilde{\\mathbf{u}}_G, \\qquad \\mathbf{e}_A \\trieq {\\mathbf{u}}_F - \\tilde{\\mathbf{u}}_A. \n\\end{equation*}\nSimilarly, the coarse-scale error is defined as,\n\\begin{equation*}\n\\mathbf{\\tilde{e}}_G \\overset{\\Delta}{=} \\tilde{\\Pi} {\\mathbf{u}}_F - \\tilde{\\mathbf{u}}_G, \\qquad \\tilde{\\mathbf{e}}_A \\trieq \\tilde{\\Pi} {\\mathbf{u}}_F - \\tilde{\\mathbf{u}}_A. \n\\end{equation*}\nIn what follows, we assume Lipschitz continuity of the right-hand side function: there exists a constant $\\kappa > 0$ such that $\\forall \\mathbf{x},\\mathbf{y} \\in \\mathbb{R}^N$,\n\\begin{equation*}\n\\norm{ \\mathbf{R}(\\mathbf{x}) - \\mathbf{R}(\\mathbf{y})} \\le \\kappa \\norm{ \\mathbf{x} - \\mathbf{y}}. \n\\end{equation*}\nTo simplify the analysis, the Adjoint Petrov--Galerkin projection is approximated to be stationary in time. Note that the Galerkin projection is stationary in time. For clarity, we suppress the temporal argument on the states when possible in the proofs.\n\\begin{theorem}\n\\textit{A priori} error bounds for the Galerkin and Adjoint Petrov--Galerkin ROMs are, respectively,\n\\begin{equation}\\label{eq:g_nlbound} \\norm{\\mathbf{e}_G(t)} \\le \\int_0^t e^{ \\norm{\\mathbf{P}_G} \\kappa s }\\norm{\\big[\\mathbf{I} - \\mathbb{P}_G \\big] \\mathbf{R}( {\\mathbf{u}}_F(t-s) ) } ds .\\end{equation}\n\\begin{equation}\\label{eq:ag_nlbound} \\norm{\\mathbf{e}_A(t)} \\le \\int_0^t e^{\\norm{\\mathbf{P}_A} \\kappa s }\\norm{\\big[\\mathbf{I} - \\mathbb{P}_A \\big] \\mathbf{R}({\\mathbf{u}}_F(t-s))} ds .\\end{equation}\n\\end{theorem}\n\n\\begin{proof}\nWe prove only Eq.~\\ref{eq:ag_nlbound} as Eq.~\\ref{eq:g_nlbound} is obtained through the same arguments. Following~\\cite{carlberg_lspg_v_galerkin}, start by subtracting Eq.~\\ref{eq:ag_error} from Eq.~\\ref{eq:fom_error}, and adding and subtracting $\\mathbb{P}_A\\mathbf{R} ( {\\mathbf{u}}_F$),\n\\begin{equation*}\\label{eq:ea_lti_1}\n\\frac{d \\mathbf{e}_A}{dt} = \\mathbf{R} ({\\mathbf{u}}_F) + \\mathbb{P}_A \\mathbf{R} ({\\mathbf{u}}_F) - \\mathbb{P}_A \\mathbf{R} ({\\mathbf{u}}_F) - \\mathbb{P}_A \\mathbf{R}(\\tilde{\\mathbf{u}}_G), \\qquad \\mathbf{e}_A(0) = \\mathbf{0}.\n\\end{equation*}\nTaking the $L^2$-norm,\n\\begin{equation*}\\label{eq:ea_lti_2}\n\\norm{ \\frac{d \\mathbf{e}_A}{dt} } = \\norm{ \\mathbf{R} ({\\mathbf{u}}_F) + \\mathbb{P}_A \\mathbf{R} ({\\mathbf{u}}_F) - \\mathbb{P}_A \\mathbf{R} ({\\mathbf{u}}_F) - \\mathbb{P}_A \\mathbf{R}(\\tilde{\\mathbf{u}}_G)}.\n\\end{equation*}\nApplying the triangle inequality,\n\\begin{equation*}\\label{eq:ea_lti_3}\n\\norm{ \\frac{d \\mathbf{e}_A}{dt} }\\le \\norm{\\big[\\mathbf{I} - \\mathbb{P}_A \\big] \\mathbf{R}( {\\mathbf{u}}_F) } + \\norm{ \\mathbb{P}_A \\big( \\mathbf{R} {\\mathbf{u}}_F) - \\mathbf{R}(\\tilde{\\mathbf{u}}_G) \\big) } .\n\\end{equation*}\nInvoking the assumption of Lipschitz continuity,\n\\begin{equation*}\\label{eq:ea1}\n\\norm{ \\frac{d \\mathbf{e}_A}{dt} }\\le \\norm{\\big[\\mathbf{I} - \\mathbb{P}_A \\big] \\mathbf{R}( {\\mathbf{u}}_F)} +\\norm{ \\mathbb{P}_A} \\kappa \\norm{\\mathbf{e}_A}.\n\\end{equation*}\nNoting that $\\frac{d \\norm{ \\mathbf{e}_A } }{dt} \\le \\norm{ \\frac{d \\mathbf{e}_A}{dt}}$ \nwe have\n\\footnote{ \n$$\\frac{d \\norm{ \\mathbf{e}_A } }{dt} = \\frac{1}{\\norm{\\mathbf{e}_A}} \\mathbf{e}_A^T \\frac{d \\mathbf{e}_A}{dt} \\le \\norm{ \\frac{1}{\\norm{\\mathbf{e}_A}} \\mathbf{e}_A} \\norm{ \\frac{d \\mathbf{e}_A}{dt}} \\le \\norm{ \\frac{d \\mathbf{e}_A}{dt}}\n$$},\n\\begin{equation}\\label{eq:ea2}\n\\frac{d \\norm{\\mathbf{e}_A }}{dt} \\le \\norm{\\big[\\mathbf{I} - \\mathbb{P}_A \\big] \\mathbf{R}( {\\mathbf{u}}_F)} +\\norm{ \\mathbb{P}_A} \\kappa \\norm{\\mathbf{e}_A}.\n\\end{equation}\n\\begin{comment}\nNext, we show $\\int_0^t \\norm{ \\frac{d \\mathbf{e}_A}{ds} }ds$ is bounded from below by $\\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds }$. Expanding $\\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds }^2$,\n\\begin{equation*}\n\\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds }^2 = \\bigg[ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds \\bigg]^T\\bigg[ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds \\bigg] .\n\\end{equation*}\nPulling the first integral on the right-hand side into the second integral as it is independent of $s$,\n\\begin{equation*}\n\\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds }^2 = \\int_0^t \\bigg[ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds \\bigg]^T \\bigg[\\frac{d \\mathbf{e}_A}{ds} \\bigg] ds \n\\end{equation*}\nUsing $\\mathbf{a}^T \\mathbf{b} \\le \\norm{\\mathbf{a}}\\norm{\\mathbf{b}}$,\n\\begin{equation*}\n\\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds }^2\\le \\int_0^t \\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds } \\norm{ \\frac{d \\mathbf{e}_A}{ds} } ds .\n\\end{equation*}\nPulling the first term on the right-hand side outside of the integral,\n\\begin{equation*}\n\\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds }^2 \\le \\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds } \\int_0^t \\norm{ \\frac{d \\mathbf{e}_A}{ds} } ds \n\\end{equation*}\nDividing through by $\\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds }$ gives,\n\\begin{equation*}\n\\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds } \\le \\int_0^t \\norm{ \\frac{d \\mathbf{e}_A}{ds} } ds.\n\\end{equation*}\nNext, it is noted that,\n\\begin{align*}\n\\norm{ \\int_0^t \\frac{d \\mathbf{e}_A}{ds} ds } &= \\norm{\\mathbf{e}_A(t) - \\mathbf{e}_A(0)} \\\\\n&= \\norm{\\mathbf{e}_A(t)} , \\\\\n&= \\int_0^t \\frac{d \\norm{\\mathbf{e}_A}} {ds} ds .\n\\end{align*}\nTherefore,\n\\begin{equation}\\label{eq:norm_inequality}\n\\int_0^t \\frac{d \\norm{\\mathbf{e}_A}} {ds} ds \\le \\int_0^t \\norm{ \\frac{d \\mathbf{e}_A}{ds} } ds.\n\\end{equation}\n\\end{comment}\nAn upper bound on the error for the Adjoint Petrov--Galerkin method is then obtained by solving Eq.~\\ref{eq:ea2} for $\\norm{\\mathbf{e}_A}$, which yields,\n\\begin{equation*}\n\\norm{ \\mathbf{e}_A(t) } \\le \\int_0^t e^{\\norm{ \\mathbb{P}_A } \\kappa s }\\norm{\\big[\\mathbf{I} - \\mathbb{P}_A \\big] \\mathbf{R} ({\\mathbf{u}}_F(t-s))} ds .\n\\end{equation*}\n\n\n \\end{proof}\nNote that, for the Adjoint Petrov--Galerkin method, the error bound provided in Eq.~\\ref{eq:ag_nlbound} is not truly an \\textit{a priori} bound as $\\mathbb{P}_A$ is a function of $\\tilde{\\mathbf{u}}_A$.\nEquation~\\ref{eq:g_nlbound} (and~\\ref{eq:ag_nlbound}) indicates an exponentially growing error and contains two distinct terms. The term $e^{\\norm{\\mathbb{P}_A} \\kappa s }$ indicates the exponential growth of the error in time. The second term of interest is $\\norm{\\big[\\mathbf{I} - \\mathbb{P}_A \\big] \\mathbf{R} ({\\mathbf{u}}_F(t))}$. This term corresponds to the error introduced at time $t$ due to projection. It is important to note that the first term controls how the error will grow in time, while the second term controls how much error is added at a given time. \n\nUnfortunately, for general non-linear systems, \\textit{a priori} error analysis provides minimal insight beyond what was just mentioned. To obtain a more intuitive understanding of the APG method, error analysis in the case that $\\mathbf{R}(\\cdot)$ is a linear time-invariant operator is now considered. \n\n\\input{proofs}\n\n\\begin{comment}\n\\begin{corollary}\\label{cor:1}\nIf $\\norm{ \\mathbb{P}_A} \\le \\norm{\\mathbb{P}_G }$, then the upper bound on the error will accumulate at a slower rate in the Adjoint Petrov--Galerkin method than in the Galerkin method.\n\\end{corollary}\n\\begin{proof}\nIf $\\norm{ \\mathbb{P}_A} \\le \\norm{\\mathbb{P}_G }$ then $e^{\\norm{\\mathbb{P}_A} s} < e^{ \\norm{\\mathbb{P}_G} s}$ $\\forall s > 0$, and the desired result is obtained.\n\\end{proof}\nCorollary~\\ref{cor:1} shows that it is possible for the APG ROM to be more accurate than the G ROM. Theorem 2 demonstrates one possible situation in which this will occur.\n\n\\begin{theorem}\\label{theorem:symmetric}\nIf $ \\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}} $ is a diagonalizable symmetric matrix with negative eigenvalues, then \n$$ \\norm{ \\mathbb{P}_A} \\le \\norm{\\mathbb{P}_G }; \\qquad \\forall \\tau \\in \\bigg[0, \\frac{2}{ \\rho( \\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}} )} \\bigg],$$\nand errors in the Adjoint Petrov--Galerkin method grow slower than in the Galerkin method. \\footnote{Note that the function $\\rho(\\cdot)$ indicates the spectral radius, not to be confused with the physical density $\\rho$ which appears later in this manuscript.}\n\\end{theorem}\n\n\\begin{proof}\nStart by defining an upper bound on $\\mathbb{P}_A$. It is helpful to note that by the orthonormality of $\\tilde{\\mathbf{V}}$,\n\\begin{equation}\\label{eq:picoarse_norm}\n\\norm{\\mathbb{P}_G} = \\norm{\\tilde{\\Pi}} = 1.\n\\end{equation}\nBy the sub-multiplicative property of the $L^2$-norm,\n$$\\norm{ \\mathbb{P}_A } \\le \\norm{\\tilde{\\Pi}} \\norm{\\big[ \\mathbf{I} + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}} \\big]}.$$\nBy Eq.~\\ref{eq:picoarse_norm},\n$$\\norm{ \\mathbb{P}_A }\\le \\norm{\\big[ \\mathbf{I} + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}} \\big]}.$$\nInvoking the assumption that $ \\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}} $ is diagonalizable, the eigendecomposition can be written as,\n$$ \\mathbf{I} + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}} = \\mathbf{S} (\\tau \\Lambda + \\mathbf{I}) \\mathbf{S}^{-1},$$\nwhere $\\Lambda$ is a diagonal matrix containing the $N-K$ non-zero eigenvalues of $\\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}}$. \nInvoking the assumption that $ \\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}} $ is symmetric, one has $\\norm{\\mathbf{S}}=\\norm{\\mathbf{S}^{-1}} = 1$, obtaining the upper bound,\n$$\\norm{ \\mathbb{P}_A }\\le \\norm{ (\\tau \\Lambda + \\mathbf{I}) }.$$\nThe norm of a diagonal matrix is the maximum absolute value of its diagonal elements.\nKnowing that the eigenvalues of $\\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}}$ are all negative, this can be written as,\n$$\\norm{ \\mathbb{P}_A }\\le |\\tau \\cdot \\underset{i}{min} (\\lambda_i) + 1|.$$\nNoting that $\\norm{ \\mathbb{P}_G } = 1$, we can enforce that $\\norm{ \\mathbb{P}_A } \\le \\norm{ \\mathbb{P}_G}$ via the inequality,\n$$ |\\tau \\cdot \\underset{i}{min} (\\lambda_i) + 1| \\leq 1 $$\nRecognizing that $\\underset{i}{min} (\\lambda_i) = - \\rho( \\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}} )$, we can solve for bounds on $\\tau$ to arrive at,\n$$\\norm{ \\mathbb{P}_A } \\le \\norm{ \\mathbb{P}_G}; \\qquad \\forall \\tau \\in \\bigg[0, \\frac{2}{ \\rho( \\mathbf{J}[\\tilde{\\mathbf{u}}_A]{\\Pi^{\\prime}} ) } \\bigg] .$$\n\\end{proof}\n\n\n\n\nTheorem~\\ref{theorem:symmetric} is interesting. First, it provides theoretical results showing the conditions under which the Adjoint Petrov--Galerkin method will have a lower rate of error growth than the Galerkin method. Further, it provides valuable insight into the selection of the stabilization parameter $\\tau$. Specifically, it is an upper bound on $\\tau$ and shows that this bound varies inversely with the spectral radius of the Jacobian.\n\\end{comment}\n\n\\subsection{Selection of Memory Length $\\tau$}\\label{sec:selecttau}\nThe APG method requires the specification of the parameter $\\tau$. Theorem~\\ref{theorem:errorbound_symmetric} showed that, for a self-adjoint linear system, bounds on the value of $\\tau$ are related to the eigenvalues of the Jacobian of the full-dimensional right-hand side operator. While such bounds provide intuition into the behavior of $\\tau$, they are not particularly useful in the selection of an optimal value of $\\tau$ as they 1.) are conservative due to repeated use of inequalities and 2.) require the eigenvalues of the full right-hand side operator, which one does not have access to in a ROM. Further, the bounds were derived for a self-adjoint linear system, and the extension to non-linear systems is unclear. \n\nIn practice, it is desirable to obtain an expression for $\\tau$ using only the coarse-scale Jacobian, $\\tilde{\\mathbf{V}}^T \\mathbf{J}[\\tilde{\\mathbf{u}}]\\tilde{\\mathbf{V}}$.\nIn Ref.~\\cite{parishMZ1}, numerical evidence showed a strong correlation between the optimal value of $\\tau$ and this coarse-scale Jacobian. Based on this numerical evidence and the analysis in the previous section, the following heuristic for selecting $\\tau$ is used:\n\\begin{equation}\\label{eq:taueq}\n\\tau = \\frac{C}{\\rho (\\tilde{\\mathbf{V}}^T \\mathbf{J}[\\tilde{\\mathbf{u}}] \\tilde{\\mathbf{V}})},\n\\end{equation}\nwhere $C$ is a model parameter and $\\rho(\\cdot)$ indicates the spectral radius. In Ref.~\\cite{parishMZ1}, $C$ was reported to be $0.2.$ In the numerical experiments presented later in this manuscript, the sensitivity of APG to the value of $\\tau$ and the validity of Eq.~\\ref{eq:taueq} are examined. \n\nSimilar to the selection of $\\tau$ in the APG method, the LSPG method requires the selection of an appropriate time-step~\\cite{carlberg_lspg_v_galerkin}. In practice, this fact can be problematic as finding an optimal time-step for LSPG which minimizes error may result in a small time-step and, hence, an expensive simulation. The selection of the parameter $\\tau$, on the other hand, does not impact the computational cost of the APG ROM.\n\n\\section{Implementation and Computational Cost of the Adjoint Petrov--Galerkin Method}\\label{sec:cost}\n\nThis section details the implementation of the Adjoint Petrov--Galerkin ROM for simple time integration schemes. Algorithms for explicit and implicit time integration schemes are provided, and the approximate cost of each method in floating-point operations (FLOPs) is analyzed. Here, a FLOP refers to any floating-point addition or multiplication; no distinction is made between the computational cost of either operation. The notation used here is as follows: $N$ is the full-order number of degrees of freedom, $K$ is the number of modes retained in the POD basis, and $\\omega N$ is the number of FLOPs required for one evaluation of the right-hand side, $\\mathbf{R}(\\tilde{\\mathbf{u}}(t))$. For sufficiently complex problems, $\\omega$ is usually on the order of $\\mathcal{O}(10) < \\omega < \\mathcal{O}(1000)$. The analysis presented in this section does not consider hyper-reduction. The analysis can be approximately extended to hyper-reduction by replacing the full-order degrees of freedom with the dimension of the hyper-reduced right-hand side.\\footnote{An accurate cost-analysis for hyper-reduced ROMs should consider over sampling of the right-hand side and the FOM stencil.}\n\n\\subsection{Explicit Time Integration Schemes}\nThis section explores the cost of the APG method within the scope of explicit time integration schemes. For simplicity, the analysis is carried out only for the explicit Euler scheme. The computational cost of more sophisticated time integration methods, such as Runge-Kutta and multistep schemes, is generally a proportional scaling of the cost of the explicit Euler scheme. Algorithm~\\ref{alg:alg_apg_exp} provides the step-by-step procedure for performing an explicit Euler update to the Adjoint Petrov--Galerkin ROM. Table~\\ref{tab:alg_apg_exp} provides the approximate floating-point operations for the steps reported in Algorithm~\\ref{alg:alg_apg_exp}. The algorithm for an explicit update to the Galerkin ROM, along with the associated FLOP counts, is provided in Algorithm~\\ref{alg:alg_g_exp} and Table~\\ref{tab:alg_g_exp} in Appendix~\\ref{appendix:algorithms}. As noted previously, LSPG reverts to the Galerkin method for explicit schemes, and so is not detailed in this section. Table~\\ref{tab:alg_apg_exp} shows that, in the case that $K \\ll N$ (standard for a ROM) and $\\omega \\gg 1$ (sufficiently complex right-hand side), the Adjoint Petrov--Galerkin ROM is approximately twice as expensive as the Galerkin ROM.\n\n\n\n\n\n\\begin{algorithm}\n\\caption{Algorithm for an explicit Euler update for the APG ROM}\n\\label{alg:alg_apg_exp}\nInput: $\\tilde{\\mathbf{a}}^n$\\;\n\\newline\nOutput: $\\tilde{\\mathbf{a}}^{n+1}$\\;\n\\newline\nSteps:\n\\begin{enumerate}\n\\item Compute the state from the generalized coordinates, $\\tilde{\\mathbf{u}}^n =\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^{n+1}$\n\\item Compute the right-hand side from the state, $\\mathbf{R}(\\tilde{\\mathbf{u}}^n)$\n\\item Compute the projection of the right-hand side, $\\tilde{\\Pi} \\mathbf{R}(\\tilde{\\mathbf{u}}^n) = \\tilde{\\mathbf{V}} \\tilde{\\mathbf{V}}^T \\mathbf{R}(\\tilde{\\mathbf{u}}^n)$\n\\item Compute the orthogonal projection of the right-hand side, ${\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}^n) = \\mathbf{R}(\\tilde{\\mathbf{u}}^n) - \\tilde{\\Pi} \\mathbf{R}(\\tilde{\\mathbf{u}}^n)$\n\\item Compute the action of the Jacobian on ${\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}^n)$ using either of the two following strategies:\n \\begin{enumerate}\n \\item Finite difference approximation:\n \\begin{equation*}\n \\mathbf{J}[\\tilde{\\mathbf{u}}^n] {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}^n) \\approx \\frac{1}{\\epsilon} \\Big[ \\mathbf{R}\\big(\\tilde{\\mathbf{u}}^n + \\epsilon {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}^n) \\big) - \\mathbf{R}(\\tilde{\\mathbf{u}}^n ) \\Big], \n \\end{equation*}\n where $\\epsilon$ is a small constant value, usually $\\sim \\mathcal{O}(10^{-5})$.\n \\item Exact linearization:\n \\begin{equation*}\n \\mathbf{J}[\\tilde{\\mathbf{u}}^n] {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}^n) = \\mathbf{R'}[\\tilde{\\mathbf{u}}^n]({\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}^n)),\n \\end{equation*}\n where $\\mathbf{R}'[\\tilde{\\mathbf{u}}^n]$ is right-hand side operator linearized about $\\tilde{\\mathbf{u}}^n$.\n \\end{enumerate}\n\\item Compute the full right-hand side: $\\mathbf{R}(\\tilde{\\mathbf{u}}^n) + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}^n] {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}^n)$\n\n\\item Project: $\\tilde{\\mathbf{V}}^T \\bigg[ \\mathbf{R}(\\tilde{\\mathbf{u}}^n) + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}^n] {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}^n) \\bigg]$\n\\item Update the state $\\tilde{\\mathbf{a}}^{n+1} = \\tilde{\\mathbf{a}}^n + \\Delta t \\tilde{\\mathbf{V}}^T \\bigg[ \\mathbf{R}(\\tilde{\\mathbf{u}}^n) + \\tau\\mathbf{J}[\\tilde{\\mathbf{u}}^n] {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}^n) \\bigg]$\n\\end{enumerate}\n\\end{algorithm}\n\n\\begin{table}\n\\begin{tabular}{p{7cm} p{8cm}}\n\\hline\nStep in Algorithm~\\ref{alg:alg_apg_exp}& Approximate FLOPs \\\\\n\\hline\n1 & $2 N K - N$ \\\\\n2 & $\\omega N$ \\\\\n3 & $4 N K - N - K$ \\\\\n4 & $N $ \\\\\n5 & $(\\omega + 4)N $ \\\\\n6 & $2N $ \\\\\n7 & $2NK - K $ \\\\\n8 & $2K $ \\\\\n\\hline\nTotal & $8 N K + (2\\omega + 5) N$ \\\\\nTotal for Galerkin Method & $4 N K + (\\omega-1) N + K$ \\\\\n\\hline\n\\end{tabular}\n\\caption{Approximate floating-point operations for an explicit Euler update to the Adjoint Petrov--Galerkin method reported in Algorithm~\\ref{alg:alg_apg_exp}. The total FLOP count for the Galerkin ROM with an explicit Euler update is additionally reported for comparison. A full description of the Galerkin update is provided in Appendix~\\ref{appendix:algorithms}.}\n\\label{tab:alg_apg_exp}\n\\end{table}\n\n\n\n\\subsection{Implicit Time Integration Schemes}\nThis section evaluates the computational cost of the Galerkin, Adjoint Petrov--Galerkin, and Least-Squares Petrov--Galerkin methods for implicit time integration schemes. For non-linear systems, implicit time integration schemes require the solution of a non-linear algebraic system at each time-step. Newton's method, along with a preferred linear solver, is typically employed to solve the system. For simplicity, the analysis provided in this section is carried out for the implicit Euler time integration scheme along with Newton's method to solve the non-linear system. Before proceeding, the full-order residual, Galerkin residual, and APG residual at time-step $(n + 1)$ are denoted as,\n\\begin{align*}\n &\\mathbf{r}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^{n+1}) = \\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^{n+1} - \\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^n - \\Delta t \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^{n+1}),\\\\\n&\\mathbf{r}_G(\\tilde{\\mathbf{a}}^{n+1}) = \\tilde{\\mathbf{a}}^{n+1} - \\tilde{\\mathbf{a}}^n - \\Delta t \\tilde{\\mathbf{V}}^T \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^{n+1}),\\\\\n&\\mathbf{r}_{A}(\\tilde{\\mathbf{a}}^{n+1}) = \\tilde{\\mathbf{a}}^{n+1} - \\tilde{\\mathbf{a}}^n - \\Delta t \\tilde{\\mathbf{V}}^T\\bigg[\\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^{n+1}) + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}]{\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^{n+1} ) \\bigg].\n\\end{align*}\nAs the future state, $\\tilde{\\mathbf{a}}^{n+1}$, is unknown, we denote an intermediate state, $\\tilde{\\mathbf{a}}_k$, that is updated after every Newton iteration until some convergence criterion is met.\nNewton's method is defined by the iteration,\n\\begin{equation}\\label{eq:newton_linear}\n \\frac{\\partial \\mathbf{r}(\\tilde{\\mathbf{a}}_k)}{\\partial \\tilde{\\mathbf{a}}_k} \\big[ \\tilde{\\mathbf{a}}_{k+1} - \\tilde{\\mathbf{a}}_k\\big] = - \\mathbf{r}(\\tilde{\\mathbf{a}}_k).\n\\end{equation}\nNewton's method solves Eq.~\\ref{eq:newton_linear} for the change in the state, $\\tilde{\\mathbf{a}}_{k+1} - \\tilde{\\mathbf{a}}_k$, for $k = 1,2,\\hdots$, until the residual converges to a sufficiently-small number. For a ROM, both the assembly and solution of this linear system is the dominant cost of an implicit method.\n\nTwo methods are considered for the solution to the non-linear algebraic system arising from implicit time discretizations of the G and APG ROMs: Newton's method with direct Gaussian elimination and Jacobian-Free Newton-Krylov GMRES. The Gauss-Newton method with Gaussian elimination is considered for the solution to the least-squares problem arising in LSPG.\n\nAlgorithm~\\ref{alg:alg_apg_imp} provides the step-by-step procedures for performing an implicit Euler update to the Adjoint Petrov--Galerkin ROM with the use of Newton's method and Gaussian elimination. Table~\\ref{tab:alg_apg_imp} provides the approximate floating-point operations for the steps reported in these algorithms. Analogous results for the Galerkin and LSPG ROMs are reported in Algorithms~\\ref{alg:alg_g_imp} and~\\ref{alg:alg_LSPG}, and Tables~\\ref{tab:alg_g_imp} and~\\ref{tab:alg_LSPG} in Appendix~\\ref{appendix:algorithms}.\nIn the limit that $K \\ll N$ and $\\omega \\gg 1$, the total FLOP counts reported show that APG is twice as expensive as both the LSPG and Galerkin ROMs. It is observed that the dominant cost for all three methods lies in the computation of the low-dimensional residual Jacobian. Computation of the low-dimensional Jacobian requires $K$ evaluations of the unsteady residual. Depending on values of $\\omega$, $N$, and $K$, this step can consist of over $50\\%$ $(K \\ll N)$ of the CPU time.\\footnote{It is noted that the low-dimensional Jacobian can be computed in parallel.}\n\nTo avoid the cost of computing the low-dimensional Jacobian required in the linear solve at each Netwon step, the Galerkin and APG ROMs can make use of Jacobian-Free Netwon-Krylov (JFNK) methods to solve the linear system, opposed to direct methods such as Gaussian elimination. JFNK methods are iterative methods that allow one to circumvent the expense associated with computing the full low-dimensional Jacobian. Instead, JFNK methods only compute the \\textit{action} of the Jacobian on a vector at each iteration of the linear solve. This can drastically decrease the cost of the implicit solve. JFNK utilizing the Generalized Minimal Residual (GMRES) method~\\cite{gmres}, for example, is guaranteed to converge to the solution $\\tilde{\\mathbf{a}}_k$ in at most $K$ iterations. It takes $K$ residual evaluations just to form the Jacobian required for direct methods.\n\nThe LSPG method is formulated as a non-linear least-squares problem. The use of Jacobian-free methods to solve non-linear least-squares problems is significantly more challenging. The principle issue encountered in attempting to use Jacobian-free methods for such applications it that one requires the action of the \\textit{transpose} of the residual Jacobian on a vector. This quantity cannot be computed via a standard finite difference approximation or linearization. It is only recently that true Jacobian-free methods have been utilized for solving non-linear least-squares problems. In Ref~\\cite{nlls_JacobianFree}, for example, automatic differentiation is utilized to compute the action of the transposed Jacobian on a vector. Due to the challenges associated with Jacobian-free methods for non-linear least-squares problems, this method is not considered here as a solution technique for LSPG.\n\nAlgorithm~\\ref{alg:alg_apg_jfnk} and Table~\\ref{tab:alg_apg_jfnk} report the algorithm and FLOPs required for an implicit Euler update to APG using JFNK GMRES. The term $\\eta \\le K$ is the number of iterations needed for convergence of the GMRES solver at each Newton iteration. For a concise presentation, the same update for the Galerkin ROM is not presented. Figure~\\ref{fig:implicitcost} shows the ratio of the cost of the various implicit ROMs as compared to the Galerkin ROM solved with Gaussian elimination. The standard LSPG method is seen to be approximately the same cost of Galerkin, while APG is seen to be approximately $2$x the cost of Galerkin. The success of the JFNK methods depends on the number of GMRES iterations required for convergence. If $\\eta = K$, which is the maximum number of iterations required for GMRES, the cost of JFNK methods is seen to be the same as their direct-solve counterparts. For cases where JFNK converges at a rate of $\\eta < K,$ the iterative methods out-perform their direct-solve counterparts.\n\nThe analysis presented here shows that, for a given basis dimension, the Adjoint Petrov--Galerkin ROM is approximately twice the cost of the Galerkin ROM for both implicit and explicit solvers. In the implicit case, the APG ROM utilizing a direct linear solver is approximately 2x the cost of LSPG. It was highlighted, however, that APG can be solved via JFNK methods. For cases where one either doesn't have access to the full Jacobian, or the full Jacobian can't be stored, JFNK methods can significantly decrease the ROM cost. The use of JFNK methods within the LSPG approach is more challenging due to the presence of the transpose of the residual Jacobian. Lastly it is noted that, although hyper-reduction can decrease the cost of a residual evaluation, it does not entirely alleviate the cost of forming the Jacobian.\n\n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.65\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{implicit_cost.pdf}\n\\end{subfigure}\n\\end{center}\n\\caption{Estimates of the G, APG, and LSPG reduced-order models for an implicit Euler update. This plot is generated for values of $N=1000$ and $\\omega = 50$, and $\\eta = \\{K,K\/2,K\/5\\},$ where $\\eta$ is the total number of iterations required for the GMRES solver at each Newton step.}\n\\label{fig:implicitcost}\n\\end{figure}\n\n\n\n\n\n\\begin{comment} \n\nLSPG, on the other hand, cannot leverage these methods as the full residual Jacobian must be formed in computing the test basis. Unless hyper-reduction methods are implemented, this fact severely limits the practical application LSPG to problems with many degrees of freedom.\n\nThis linear system solve is generally the dominant cost of the implicit method, and many methods are available for computing the solution are available. In calculating the computational cost for this step, we use Gaussian elimination, which is the simplest but most expensive method (and is also potentially unstable).\n\nAlgorithms~\\ref{alg:alg_g_imp},~\\ref{alg:alg_apg_imp}, and~\\ref{alg:alg_LSPG} provide step-by-step procedures for performing an implicit Euler update to the Galerkin, Adjoint Petrov--Galerkin, and Least-Squares Petrov--Galerkin ROMs, respectively. Tables~\\ref{tab:alg_g_imp},~\\ref{tab:alg_apg_imp}, and~\\ref{tab:alg_LSPG} provide the approximate floating-point operations for the steps reported these algorithms. \n\nThe analysis reveals a couple key points in comparing the three ROM methods. Again, we see that in the case of $N \\gg K$ and $\\omega \\gg 1$, the cost of the implicit APG ROM is roughly double the cost of implicit Galerkin ROM. Additionally, we see that the cost of LSPG suffers from the fact that the original problem is posed as a minimization of the full-order residual. This results in the evaluation of an $N\\times K$ residual Jacobian. In the case of $N \\gg K$, as is generally the case for most ROMs, this cost far outpaces the extra cost incurred by computing the modified right-hand side of the APG method. In general, note that the computational cost of all methods is dominated by computing the residual Jacobian. In fact, for $N \\gg K$, it is likely that computing this Jacobian is even more expensive than computing $\\Delta \\tilde{\\mathbf{a}}_k$ via Gaussian elimination. This leads to another critical point, as we scrutinize what linear system is solved to compute the Newton update $\\Delta \\tilde{\\mathbf{a}}$ in each method. For a residual Jacobian $\\mathbf{J}_k$, the linear system for the Galerkin and APG methods takes the form,\n\\begin{equation}\n \\mathbf{J}_k \\Delta \\tilde{\\mathbf{a}} = - \\mathbf{r}(\\tilde{\\mathbf{a}}_k),\n\\end{equation}\nwhile the LSPG linear system takes the form,\n\\begin{equation}\n \\big[ \\tilde{\\mathbf{V}}^T \\mathbf{J}_k^T \\mathbf{J}_k \\tilde{\\mathbf{V}}] \\Delta \\tilde{\\mathbf{a}}_k = - \\tilde{\\mathbf{V}}^T \\mathbf{J}_k^T \\mathbf{r}(\\tilde{\\mathbf{u}}_k)\n\\end{equation}\nThat is, the APG\/Galerkin methods result in a strict Newton's method form. Instead of first computing the residual Jacobian and then solving a generic linear system, indirect Jacobian-free Newton-Krylov methods may be used to solve the system without the explicit computation of the residual Jacobian. The popular Generalized Minimal Residual (GMRES) method, for example, is guaranteed to converge to the solution $\\tilde{\\mathbf{a}}_k$ in $K$ iterations, although the residual Jacobian is never actually generated. LSPG, on the other hand, cannot leverage these methods as the full residual Jacobian must be formed in computing the test basis. Unless hyper-reduction methods are implemented, this fact severely limits the practical application LSPG to problems with many degrees of freedom.\n\\end{comment} \n\n\n\n\n\n\n\\begin{algorithm}\n\\caption{Algorithm for an implicit Euler update for the APG ROM using Newton's Method with Gaussian Elimination}\n\\label{alg:alg_apg_imp}\nInput: $\\tilde{\\mathbf{a}}^n$, residual tolerance $\\xi$ \\;\n\\newline\nOutput: $\\tilde{\\mathbf{a}}^{n+1}$\\;\n\\newline\nSteps:\n\\begin{enumerate}\n\\item Set initial guess, $\\tilde{\\mathbf{a}}_k$\n\\item Loop while $\\mathbf{r}^k > \\xi$\n\\begin{enumerate}\n \\item Compute the state from the generalized coordinates, $\\tilde{\\mathbf{u}}_k = \\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_k$\n \\item Compute the right-hand side from the full state, $\\mathbf{R}(\\tilde{\\mathbf{u}}_k)$\n \\item Compute the projection of the right-hand side, $\\tilde{\\Pi} \\mathbf{R}(\\tilde{\\mathbf{u}}^n) = \\tilde{\\mathbf{V}} \\tilde{\\mathbf{V}}^T \\mathbf{R}(\\tilde{\\mathbf{u}}^n)$\n \\item Compute the orthogonal projection of the right-hand side, ${\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}_k) = \\mathbf{R}(\\tilde{\\mathbf{u}}_k) - \\tilde{\\Pi} \\mathbf{R}(\\tilde{\\mathbf{u}}_k)$\n \\item Compute the action of the right-hand side Jacobian on ${\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}_k)$, as in Alg.~\\ref{alg:alg_apg_exp}.\n \\item Compute the modified right-hand side, $ \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_k) + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}]{\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_k )$\n \\item Project the modified right-hand side, $\\tilde{\\mathbf{V}}^T\\Big[\\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_k) + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}]{\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_k ) \\Big]$\n \\item Compute the APG residual, $\\mathbf{r}_A(\\tilde{\\mathbf{a}}_k) = \\tilde{\\mathbf{a}}_k - \\tilde{\\mathbf{a}}^n - \\Delta t \\tilde{\\mathbf{V}}^T\\Big[\\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_k) + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}]{\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_k ) \\Big]$\n \\item Compute the residual Jacobian, $\\frac{\\partial \\mathbf{r}(\\tilde{\\mathbf{a}}_k)}{\\partial \\tilde{\\mathbf{a}}_k}$\n \\item Solve the linear system via Gaussian Elimination: $\\frac{\\partial \\mathbf{r}(\\tilde{\\mathbf{a}}_k)}{\\partial \\tilde{\\mathbf{a}}_k} \\Delta \\tilde{\\mathbf{a}} = - \\mathbf{r}(\\tilde{\\mathbf{a}}_k)$\n \\item Update the state: $\\tilde{\\mathbf{a}}_{k+1} = \\tilde{\\mathbf{a}}_k + \\Delta \\tilde{\\mathbf{a}}$\n \\item $k = k + 1$\n\\end{enumerate}\n\\item Set final state, $\\tilde{\\mathbf{a}}^{n+1} = \\tilde{\\mathbf{a}}_k$\n\\end{enumerate}\n\\end{algorithm}\n\n\n\\begin{table}[]\n\\centering\n\\begin{tabular}{p{7cm} p{8cm}}\n\\hline\nStep in Algorithm~\\ref{alg:alg_apg_imp}& Approximate FLOPs \\\\\n\\hline\n2a & $2 N K - N $ \\\\\n2b & $ \\omega N $ \\\\\n2c & $4 N K - N - K$ \\\\\n2d & $ N $ \\\\\n2e & $ (\\omega + 4) N $ \\\\\n2f & $ 2N $ \\\\\n2g & $ 2NK - K $ \\\\\n2h & $ 3K $ \\\\\n2i & $ (2\\omega + 5) NK + K^2 + 8NK^2 $ \\\\\n2j & $ K^3 $ \\\\\n2k & $ K $ \\\\\n\\hline\nTotal & $ (2\\omega + 5)N + 2K + (2\\omega + 13) NK + K^2 + 8NK^2 + K^3 $ \\\\\nGalerkin ROM FLOP count & $ (\\omega - 1)N + 3K + (\\omega + 3)NK + 2K^2 + 4NK^2 + K^3 $ \\\\\nLSPG ROM FLOP count& $ (\\omega + 2)N + (\\omega + 6) NK - K^2 + 4NK^2 + K^3 $ \\\\\n\n\\end{tabular}\n\\caption{Approximate floating-point operations for one Newton iteration for the implicit Euler update to the Adjoint Petrov--Galerkin method reported in Algorithm~\\ref{alg:alg_apg_imp}. FLOP counts for the Galerkin ROM and LSPG ROM with an implicit Euler update are additionally reported for comparison. A full description of the Galerkin and LSPG ROM updates are provided in Appendix~\\ref{appendix:algorithms}}\n\\label{tab:alg_apg_imp}\n\\end{table}\n\n\n\n\n\n\n\n\n\\begin{algorithm}\n\\caption{Algorithm for an implicit Euler update for the APG ROM using JFNK GMRES}\n\\label{alg:alg_apg_jfnk}\nInput: $\\tilde{\\mathbf{a}}^n$, residual tolerance $\\xi$ \\;\n\\newline\nOutput: $\\tilde{\\mathbf{a}}^{n+1}$\\;\n\\newline\nSteps:\n\\begin{enumerate}\n\\item Set initial guess, $\\tilde{\\mathbf{a}}_k$\n\\item Loop while $\\mathbf{r}^k > \\xi$\n\\begin{enumerate}\n\n \\refstepcounter{enumii}\\item[$(a\\text{--}h)$] Compute steps 2a through 2h in Algorithm~\\ref{alg:alg_apg_imp}\n \\setcounter{enumii}{8}\n \\item Solve the linear system, $\\frac{\\partial \\mathbf{r}(\\tilde{\\mathbf{a}}_k)}{\\partial \\tilde{\\mathbf{a}}_k} \\Delta \\tilde{\\mathbf{a}}_k = \\mathbf{r}_A(\\tilde{\\mathbf{a}}_k)$ using Jacobian-Free GMRES\n \\item Update the state: $\\tilde{\\mathbf{a}}_{k+1} = \\tilde{\\mathbf{a}}_k + \\Delta \\tilde{\\mathbf{a}}$\n \\item $k = k + 1$\n\\end{enumerate}\n\\item Set final state, $\\tilde{\\mathbf{a}}^{n+1} = \\tilde{\\mathbf{a}}_k$\n\\end{enumerate}\n\\end{algorithm}\n\n\n\\begin{table}[]\n\\centering\n\\begin{tabular}{p{6cm} p{9cm}}\n\\hline\nStep in Algorithm~\\ref{alg:alg_apg_jfnk}& Approximate FLOPs \\\\\n\\hline\n2a & $2 N K - N $ \\\\\n2b & $ \\omega N $ \\\\\n2c & $4 N K - N - K$ \\\\\n2d & $ N $ \\\\\n2e & $ (\\omega + 4) N $ \\\\\n2f & $ 2N $ \\\\\n2g & $ 2NK - K $ \\\\\n2h & $ 3K $ \\\\\n2i & $ (2\\omega + 5) N \\eta + K \\eta + 8N K \\eta + \\eta^2 K $\\\\\n2k & $ K $ \\\\\n\\hline\nTotal & $ \\big((2\\eta + 2) \\omega + 5\\eta + 5) \\big)N + (\\eta^2 + \\eta + 2)K + (8\\eta + 8) NK $ \\\\\n\\end{tabular}\n\\caption{Approximate floating-point operations for one Newton iteration for the implicit Euler update to the Adjoint Petrov--Galerkin method using Jacobian-Free GMRES reported in Algorithm~\\ref{alg:alg_apg_jfnk}.}\n\\label{tab:alg_apg_jfnk}\n\\end{table}\n\n\n\n\\begin{comment}\nThe Adjoint Petrov--Galerkin method is straightforward to implement in a Jacobian-free fashion and requires minimal modifications to a standard Galerkin ROM. The Jacobian-free implementation of the additional terms in the Adjoint Petrov--Galerkin method is as follows:\n\\begin{enumerate}\n \\item Compute the coarse-scale right-hand side, $\\mathbf{R}(\\tilde{\\mathbf{u}})$.\n \\item Compute the orthogonal projection of the right-hand side, ${\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}) = \\mathbf{R}(\\tilde{\\mathbf{u}}) - \\tilde{\\Pi} \\mathbf{R}(\\tilde{\\mathbf{u}})$.\n \\item Compute the action of the Jacobian on ${\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}})$. This can be done without explicitly forming the Jacobian using either of the two following strategies:\n \\begin{enumerate}\n \\item Finite difference approximation:\n \\begin{equation*}\n \\mathbf{J}[\\tilde{\\mathbf{u}}] {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}) = \\frac{1}{\\epsilon} \\Big[ \\mathbf{R}\\big(\\tilde{\\mathbf{u}} + \\epsilon {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}) \\big) - \\mathbf{R}(\\tilde{\\mathbf{u}} ) \\Big] + \\mathcal{O}(\\epsilon^2),\n \\end{equation*}\n where $\\epsilon$ is a small constant value, usually $\\sim \\mathcal{O}(10^{-5})$.\n \\item Exact linearization:\n \\begin{equation*}\n \\mathbf{J}[\\tilde{\\mathbf{u}}] {\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}}) = \\mathbf{R'}[\\tilde{\\mathbf{u}}]({\\Pi^{\\prime}} \\mathbf{R}(\\tilde{\\mathbf{u}})),\n \\end{equation*}\n where $\\mathbf{R}'[\\tilde{\\mathbf{u}}]$ is right-hand side operator linearized about $\\tilde{\\mathbf{u}}$.\n \\end{enumerate}\n \\item Multiply by $\\tau \\tilde{\\mathbf{V}}^T$ to compute the subgrid-scale term.\n\\end{enumerate}\nThe Adjoint Petrov--Galerkin method is observed to require an extra right-hand side (or linearized right-hand side) evaluation. Assuming the right-hand side evaluation is the dominant cost of the ROM, the Adjoint Petrov--Galerkin method is roughly twice as expensive as the standard Galerkin ROM.\n\\end{comment}\n\n\n\\section{Numerical Examples}\\label{sec:numerical}\n\nApplications of the APG method are presented for ROMs of compressible flows: the 1D Sod shock tube problem and 2D viscous flow over a cylinder. In both problems, the test bases are chosen via POD. The shock tube problem highlights the improved stability and accuracy of the APG method over the standard Galerkin ROM, as well as improved performance over the LSPG method. The impact of the choice of $\\tau$ (APG) and $\\Delta t$ (LSPG) time-scales are also explored. The cylinder flow experiment examines a more complex problem and assesses the predictive capability of APG in comparison with Galerkin and LSPG ROMs. The effect of the choice of $\\tau$ on simulation accuracy is further explored. \n\n\\subsection{Example 1: Sod Shock Tube with reflection}\nThe first case considered is the Sod shock tube, described in more detail in \\cite{sod}. The experiment simulates the instantaneous bursting of a diaphragm separating a closed chamber of high-density, high-pressure gas from a closed chamber of low-density, low pressure gas. This generates a strong shock, a contact discontinuity, and an expansion wave, which reflect off the shock tube walls at either end and interact with each other in complex ways. The system is described by the one-dimensional compressible Euler equations with the initial conditions,\n\\begin{comment}\n\\begin{equation}\\label{eq:euler_1D}\n \\frac{\\partial {\\mathbf{u}}}{\\partial t} + \\frac{\\partial \\mathbf{f}}{\\partial x} = 0, \\quad\n {\\mathbf{u}} = \n \\begin{Bmatrix} \\rho \\\\ \\rho u \\\\ \\rho E \\end{Bmatrix}, \\quad \n \\mathbf{f} = \\begin{Bmatrix} \\rho u \\\\ \\rho u^2 + p \\\\ u(\\rho E + p) \\end{Bmatrix}.\n\\end{equation}\nThe problem setup is given by the initial conditions,\n\\end{comment} \n\n\\begin{equation*}\n\\rho = \n\\begin{cases} \n 1 & x\\leq 0.5 \\\\\n 0.125 & x > 0.5 \n \\end{cases},\n\\qquad\np = \n\\begin{cases} \n 1 & x\\leq 0.5 \\\\\n 0.1 & x > 0.5 \n \\end{cases},\n\\qquad\nu = \n\\begin{cases} \n 0 & x\\leq 0.5 \\\\\n 0 & x > 0.5 \n \\end{cases},\n\\end{equation*} \nwith $x \\in [0,1]$. Impermeable wall boundary conditions are enforced at x = 0 and x = 1.\n\n\\subsubsection{Full-Order Model}\nThe 1D compressible Euler equations are solved using a finite volume method and explicit time integration. The domain is partitioned into 1,000 cells of uniform width. The finite volume method uses the first-order Roe flux~\\cite{roescheme} at the cell interfaces. A strong stability-preserving RK3 scheme~\\cite{SSP_RK3} is used for time integration. The solution is evolved for $t \\in [0.0,1.0]$ with a time-step of $\\Delta t = 0.0005$, ensuring CFL$\\leq 0.75$ for the duration of the simulation. The solution is saved every other time-step, resulting in 1,000 solutions snapshots for each conserved variable.\n\n\\subsubsection{Solution of the Reduced-Order Model}\nUsing the FOM data snapshots, trial bases for the ROMs are constructed via the proper orthogonal decomposition (POD) approach. A separate basis is constructed for each conserved variable. The complete basis construction procedure is detailed in Appendix~\\ref{appendix:basisconstruction}. Once a coarse-scale trial basis $\\tilde{\\mathbf{V}}$ is built, a variety of ROMs are evaluated according to the following formulations:\n\n\\begin{enumerate}\n\\item Galerkin ROM:\n\\begin{equation*}\\label{eq:galerkin_ROM}\n\\tilde{\\mathbf{V}}^T \\bigg( \\frac{d \\tilde{\\mathbf{u}}}{dt} - \\mathbf{R}(\\tilde{\\mathbf{u}}) \\bigg) = 0, \\qquad t \\in [0,1].\n\\end{equation*}\n\n\\item Adjoint Petrov--Galerkin ROM:\n\\begin{equation*}\\label{eq:MZPG_ROM}\n\\tilde{\\mathbf{V}}^T\\bigg(\\mathbf{I} + \\tau \\mathbf{J}[\\tilde{\\mathbf{u}}] {\\Pi^{\\prime}} \\bigg) \\bigg( \\frac{d \\tilde{\\mathbf{u}}}{dt} - \\mathbf{R}(\\tilde{\\mathbf{u}}) \\bigg) = 0 , \\qquad t \\in [0,1].\n\\end{equation*}\n\\textit{Remark: The Adjoint Petrov--Galerkin ROM requires specification of $\\tau$.}\n\n\\item Least-Squares Petrov--Galerkin ROM (Implicit Euler Time Integration):\n\\begin{equation*}\n{\\mathbf{u}}^n = \\underset{\\mathbf{y} \\in \\text{Range}(\\tilde{\\mathbf{V}}) }{\\text{arg min}}\\norm{ \\frac{\\mathbf{y} - \\tilde{\\mathbf{u}}^{n-1}}{\\Delta t} - \\mathbf{R}(\\mathbf{y}) }^2, \\qquad \\text{for } n = 1,2,\\hdots , \\text{ceil}\\big(\\frac{1}{\\Delta t}\\big).\n\\end{equation*}\n\\textit{Remark: The LSPG approach is strictly coupled to the time integration scheme and time-step.}\n\n\n\\end{enumerate}\n\n\\subsubsection{Numerical Results}\nThe first case considered uses $50$ basis vectors each for the conserved variables $\\rho, \\rho u,$ and $\\rho E$. The total dimension of the reduced model is thus $K = 150$. Roughly 99.9--99.99\\% of the POD energy is captured by this 150-mode basis. In fact, 99\\% of the energy is contained in the first 5-12 modes of each conserved variable.\n\nThe Adjoint Petrov--Galerkin ROM requires specification of the memory length $\\tau$. Similarly, LSPG requires the selection of an appropriate time-step. The sensitivity of both methods to this selection will be discussed later in this section.\nThe simulation parameters are provided in Table~\\ref{tab:sod_tab1}.\n\nDensity profiles at $t = 0.25$ and $t = 1.0$ for explicit Galerkin and APG ROMs, along with an implicit LSPG ROM, are displayed in Fig.~\\ref{fig:sod_density}. All three ROMs are capable of reproducing the shock tube density profile in Fig.~\\ref{fig:sod_density_0p25}; a normal shock propagates to the right and is followed closely behind by a contact discontinuity, while an expansion wave propagates to the left. All three methods exhibit oscillations at $x = 0.5$, the location of the imaginary burst diaphragm, and near the shock at $x = 0.95$. At $t = 1.0$, when the shock has reflected from the right wall and interacted with the contact discontinuity, much stronger oscillations are present, particularly near the reflected shock at $x = 0.45$. These oscillations are reminiscent of Gibbs phenomenon, and are an indicator of the inability to accurately reconstruct sharp gradients. The Galerkin ROM exhibits the largest oscillations of the ROMs considered, while LSPG exhibits the smallest.\n\n\\begin{table}\n\\centering\n\\begin{tabular}{ l l l l l}\\hline\n ROM Type & Time Scheme & $\\Delta t$ & $\\tau$ & $\\int ||e||_2 dt$\\\\ \\hline\n Galerkin & SSP-RK3 & 0.0005 & N\/A & 1.5752 \\\\\n Galerkin & Imp. Euler & 0.0005 & N\/A & 1.0344 \\\\\n APG & SSP-RK3 & 0.0005 & 0.00043 & 1.0637 \\\\\n APG & Imp. Euler & 0.0005 & 0.00043 & 0.8057 \\\\\n APG & Imp. Euler & 0.001 & 0.00043 & 0.7983 \\\\\n LSPG & Imp. Euler & 0.0005 & N\/A & 1.1668 \\\\\n LSPG & Imp. Euler & 0.001 & N\/A & 1.4917 \\\\ \\hline\n\\end{tabular}\n\\caption{Computational details for Sod shock tube ROM cases, $K = 150$}\n\\label{tab:sod_tab1}\n\\end{table}\n\n\\begin{comment}\n\\begin{figure}\n \\centering\n \\begin{minipage}{0.49\\linewidth}\n \\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/PODspectrumZoomed.png}\n \\caption{Sod shock tube POD energy spectrum}\n \\label{fig:pod_spectrum}\n \\end{minipage}\\hfill\n \\begin{minipage}{0.49\\linewidth}\n \\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/err_consVars_k50_apgExplicit.png}\n \\caption{Conserved variable error profiles, APG w\/ SSP-RK3 time integration, $K = 150$, $\\Delta t = 0.0005$}\n \\label{fig:sod_error_consVars}\n \\end{minipage}\\hfill\n\\end{figure}\n\\end{comment}\n\n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/rho_k50_dt0p0005_t0p25__explicit.png}\n\\caption{$t=0.25$.}\n\\label{fig:sod_density_0p25}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/rho_k50_dt0p0005_t1p00_explicit.png}\n\\caption{$t=1$.}\n\\label{fig:sod_density_1p0}\n\\end{subfigure}\n\\end{center}\n\\caption{Density profiles for the Sod shock tube with $K = 150$, $\\Delta t = 0.0005$.}\n\\label{fig:sod_density}\n\\end{figure}\n\n\nFigure~\\ref{fig:sod_error} shows the evolution of the error for all of the ROMs listed in Table~\\ref{tab:sod_tab1}. The $L^2$-norm of the error is computed as,\n\\begin{equation*}\n||e||_2 = \\sqrt{ \\sum_{i=1}^{1000} \\Big[(\\tilde{\\rho}_{i,ROM} - \\tilde{\\rho}_{i,FOM} )^2 + (\\widetilde{\\rho u}_{i,ROM} - \\widetilde{\\rho u}_{i,FOM} )^2 + (\\widetilde{\\rho E}_{i,ROM} - \\widetilde{\\rho E}_{i,FOM})^2 \\Big] }.\n\\end{equation*}\n Here, the subscript $i$ denotes each finite volume cell. The FOM values used for error calculations are projections of the FOM data onto $\\tilde{\\MC{V}}$, e.g. $\\tilde{\\rho}_{FOM} = \\tilde{\\Pi} \\rho_{FOM}$. This error measure provides a fair upper bound on the accuracy of the ROMs, as the quality of the ROM is generally dictated by the richness of the trial basis and the projection of the FOM data is the maximum accuracy that can be reasonably hoped for.\n\nIn Figure~\\ref{fig:sod_error}, it is seen that the APG ROM exhibits improved accuracy over the Galerkin ROM. The LSPG ROM for $\\Delta t = 0.0005$ performs slightly better than the explicit Galerkin ROM, and worse than the implicit Galerkin ROM. Increasing the time-step to $\\Delta t = 0.001$ results in a significant increase in error for the LSPG ROM. This is due to the fact that the performance of LSPG is influenced by the time-step. For a trial basis containing much of the residual POD energy, LSPG will generally require a very small time-step to improve accuracy; this sensitivity will be explored later. Lastly, it is observed that the APG ROM is \\textit{not} significantly affected by the time-step. The APG ROM with $\\Delta t = 0.001$ shows moderately increased error prior to $t = 0.3$ and similar error afterwards when compared against the $\\Delta t = 0.0005$ APG ROM case. \n\n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/err_k50_explicit.png}\n\\caption{Explicit Galerkin\/APG, implicit LSPG, $\\Delta t = 0.0005$}\n\\label{fig:sod_err_explicit}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/err_k50_implicit.png}\n\\caption{All implicit, various $\\Delta t$}\n\\label{fig:sod_err_implicit}\n\\end{subfigure}\n\\end{center}\n\\caption{$L^2$-norm error profiles for the Sod shock tube with $150$ basis vectors.}\n\\label{fig:sod_error}\n\\end{figure}\n\nFigure~\\ref{fig:sod_mode_study} studies the effect of the number of modes retained in the trial basis on the stability and accuracy, over the range $K = 60,75,90,\\ldots,180$. Missing data points indicate an unstable solution. Values of $\\tau$ for the APG ROMs are again selected by user choice. The most striking feature of these plots is the fact that even though the explicit Galerkin ROM is unstable for $K \\leq 135$ and the implicit Galerkin ROM is unstable for $K \\leq 75$, the APG and LSPG ROMs are stable for all cases. Furthermore, the APG and LSPG ROMs are capable of achieving stability with a time-step twice as large as that of the Galerkin ROM. The cost of the APG and LSPG ROMs are effectively halved, but they are still able to stabilize the simulation. Interestingly, the Galerkin and APG ROMs both exhibit abrupt peaks in error at $K = 120$, while the LSPG ROMs do not. The exact cause of this is unknown, but displays that a monotonic decrease in error with enrichment of the trial space is not guaranteed. \n\nSeveral interesting comparisons between APG and LSPG arise from Figure~\\ref{fig:sod_mode_study}. First, with the exception of the $K = 120$ case, Fig.~\\ref{fig:sod_err_explicit} shows that the APG ROM with explicit time integration exhibits accuracy comparable to that of the LSPG ROM with implicit time integration. As can be seen in comparing Tables~\\ref{tab:alg_apg_exp} and~\\ref{tab:alg_LSPG}, the cost of APG with explicit time integration is significantly lower than the cost of LSPG. This is an attractive feature of APG, as it is able to use inexpensive explicit time integration while LSPG is restricted to implicit methods. Additionally, we draw attention to the poor performance of LSPG at high $K$ for a moderate time-step in Fig.~\\ref{fig:sod_modeSens_implicit}. Increasing the time-step to $\\Delta t = 0.001$ to decrease simulation cost only exacerbates this issue; as the trial space is enriched, LSPG requires a smaller time-step to yield accurate results. If we wish to improve the LSPG solution for $K = 150$, we must decrease the time-step below that of the FOM. The accuracy of the APG ROM does not change when the time-step is doubled from $\\Delta t = 0.0005$ to $\\Delta t = 0.001$. This halves the cost of the APG ROM with no significant drawbacks. \n\n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/modeSensExplicit.png}\n\\caption{Explicit Galerkin\/APG, implicit LSPG, fixed $\\Delta t$}\n\\label{fig:sod_modeSens_explicit}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/modeSensImplicit.png}\n\\caption{All implicit, various $\\Delta t$}\n\\label{fig:sod_modeSens_implicit}\n\\end{subfigure}\n\\end{center}\n\\caption{Integrated error mode sensitivity study for the Sod shock tube.}\n\\label{fig:sod_mode_study}\n\\end{figure}\n\n\\subsubsection{Optimal Memory Length Investigations}\nAs mentioned previously, the success of LSPG is tied to the physical time-step and the time integration scheme, while the parameter $\\tau$ in the APG method may be chosen independently from these factors. In minimizing ROM error, finding an optimal value of $\\tau$ for the APG ROM may permit the choice of a much larger time-step than the optimal LSPG time-step. Further, the APG method may be applied with explicit time integration schemes, which are generally much less expensive than the implicit methods which LSPG is restricted to. To demonstrate this, the APG ROM and LSPG ROM with $K=150$ are simulated for a variety of time scales ($\\tau$ for APG and $\\Delta t$ for LSPG). \n\nFigure~\\ref{fig:sod_memlength_sensitivity} shows the integrated error of the ROMs versus the relevant time scale. For this case, the optimal value of $\\Delta t$ for LSPG is less than $0.0001$ and is not shown. The optimal value of $\\tau$ is not greatly affected by the choice of time integration scheme (implicit or explicit) or time-step. Furthermore, because $\\tau$ can be chosen independently from $\\Delta t$ for APG, the APG ROM can produce low error at a much larger time-step ($\\Delta t = 0.001$) than the optimal time-step for the LSPG ROM. This highlights the fact that the ``optimal\" LSPG ROM may be computationally expensive due to a small time-step, whereas the ``optimal\" APG ROM requires only the specification of $\\tau$ and can use, potentially, much larger time-steps than LSPG. It has to be mentioned, however, that the choice of $\\tau$ has an impact on performance --- selections of $\\tau$ larger than those plotted in Fig.~\\ref{fig:sod_memlength_sensitivity} caused the ROM to lose stability. \n\nAlso discussed previously, computing an optimal value of $\\tau$ \\textit{a priori} may be linked to the spectral radius of the coarse-scale Jacobian, (i.e. $\\rho \\big( \\tilde{\\mathbf{V}}^T \\mathbf{J}[\\tilde{\\mathbf{u}}_0]\\tilde{\\mathbf{V}} \\big)$, not to be confused with the physical density $\\rho$). We consider the APG ROM for basis sizes of $K=30,60,90,150,180,240$. For each case, an ``optimal\" $\\tau$ is found by minimizing the misfit between the ROM solution and the projected FOM solution. The misfit is defined as follows,\n\\begin{equation}\\label{eq:misfit}\n\\mathcal{J}(\\tau) = \\sum_{i=1}^{200} ||e(\\tau,t = i10\\Delta t)||_2.\n\\end{equation}\nEquation~\\ref{eq:misfit} corresponds to summing the $L^2$-norm of the error at every $10^{th}$ time-step. Figure~\\ref{fig:sod_tau_specRad} shows the resulting optimal $\\tau$ for each case plotted against the inverse of the spectral radius of the coarse-scale Jacobian evaluated at $t=0$. \nThe strong linear correlation suggests that a near-optimal value of $\\tau$ may be chosen by evaluating the spectral radius, $\\rho \\big( \\tilde{\\mathbf{V}}^T \\mathbf{J}[\\tilde{\\mathbf{u}}_0]\\tilde{\\mathbf{V}} \\big)$ and using the above linear relationship to $\\tau$. \n\nTwo points are emphasized here:\n\\begin{enumerate}\n\\item The spectral radius plays an important role in both implicit and explicit time integrators and is often the determining factor in the choice of the time-step. Theoretical analysis on the stability of explicit methods (and convergence of implicit methods) shows a similar dependence to the spectral radius. Choosing the memory length to be $\\tau = \\Delta t$ is one simple heuristic that may be used.\n\\item\nWhile a linear relationship between $\\tau$ and the spectral radius of the coarse-scale Jacobian has been observed in every problem the authors have examined, the slope of the fit is somewhat problem dependent. For the purpose of reduced-order modeling, however, this is only a minor inconvenience as an appropriate \nvalue of $\\tau$ can be selected by assessing the performance of the ROM on the training set, i.e., on the simulation used to construct the POD basis. \n\\item Finally, more complex methods may be used to compute $\\tau$. A method to dynamically compute $\\tau$ based on Germano's identity, for instance, was proposed in~\\cite{parish_dtau} in the context of the simulation of turbulent flows with Fourier-Galerkin methods. Extension of this technique to projection-based ROMs and the development of additional techniques to select $\\tau$ will be the subject of future work.\n\\end{enumerate}\n\n\\begin{figure}\n \\centering\n \\begin{minipage}{0.49\\linewidth}\n \\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/dt_tau_sensitivity_semilog.png}\n \\caption{Error as a function of time scale, $K = 150$.}\n \\label{fig:sod_memlength_sensitivity}\n \\end{minipage}\\hfill\n \\begin{minipage}{0.49\\linewidth}\n \\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{sodFigs\/tauSpecRadCorr_revis.png}\n \\caption{Optimal $\\tau$ as a function of the spectral radius evaluated at $t=0$.}\n \\label{fig:sod_tau_specRad}\n \\end{minipage}\\hfill\n\\end{figure}\n\n\\input{cyl_new}\n\n\n\n\n\\section{Conclusion}\\label{sec:conclude}\nThis work introduced the Adjoint Petrov--Galerkin method for non-linear model reduction. Derived from the variational multiscale method and Mori-Zwanzig formalism, the Adjoint Petrov--Galerkin method is a Petrov--Galerkin projection technique with a non-linear time-varying test basis. The method is designed to be applied at the semi-discrete level, i.e., after spatial discretization of a partial differential equation, and is compatible with both implicit and explicit time integration schemes. The method displays commonalities with the adjoint-stabilization method used in the finite element community as well as the Least-Squares Petrov--Galerkin approach used in non-linear model-order reduction. Theoretical error analysis was presented that showed conditions under which the Adjoint Petrov--Galerkin ROM may have lower \\textit{a priori} error bounds than the Galerkin ROM. The theoretical cost of the Adjoint Petrov--Galerkin method was considered for both explicit and implicit schemes, where it was shown to be approximately twice that of the Galerkin method. In the case of implicit time integration schemes, the Adjoint Petrov--Galerkin ROM was shown to be capable of being more efficient than Least-Squares Petrov--Galerkin when the non-linear system is solved via Jacobian-Free Newton-Krylov methods. \n\nNumerical experiments with the Adjoint Petrov--Galerkin, Galerkin, and Least-Squares Petrov--Galerkin method were presented for the Sod shock tube problem and viscous compressible flow over a cylinder parameterized by the Reynolds number. In all examples, the Adjoint Petrov--Galerkin method provided more accurate predictions than the Galerkin method for a fixed basis dimension. Improvements over the Least-Squares Petrov--Galerkin method were observed in most cases. In particular, the Adjoint Petrov--Galerkin method was shown to provide relatively accurate predictions for the cylinder flow at Reynolds numbers outside of the training set used to construct the POD basis. The Galerkin method, with both an equivalent and an enriched trial space, failed to produce accurate results in these cases. Additionally, numerical evidence showed a correlation between the spectral radius of the reduced Jacobian and the optimal value of the stabilization parameter appearing in the Adjoint Petrov--Galerkin method.\n\nWhen augmented with hyper-reduction, the Adjoint Petrov--Galerkin ROM was shown to be capable of producing accurate predictions within the POD training set with computational speedups up to 5000 times compared to the full-order models. This speed-up is a result of hyper-reduction of the right-hand side, as well as the ability to use explicit time integration schemes at large time-steps. A study of the Pareto front for simulation error versus relative wall time showed that, for the compressible cylinder problem, the Adjoint Petrov--Galerkin ROM is competitive with the Galerkin ROM, and more efficient than the LSPG ROM for the problems considered. \n\n\n\n\n\n\\section{Acknowledgements}\\label{sec:acknowledge}\nThe authors acknowledge support from the US Air Force Office of Scientific Research through the Center of Excellence Grant FA9550-17-1-0195 (Tech. Monitors: Mitat Birkan \\& Fariba Fahroo) and the project LES Modeling of Non-local effects using Statistical Coarse-graining (Tech. Monitors: Jean-Luc Cambier \\& Fariba Fahroo). E. Parish acknowledges an appointment to the Sandia National Laboratories John von Neumann fellowship. This paper describes objective technical results and analysis. Any subjective\nviews or opinions that might be expressed in the paper do not necessarily\nrepresent the views of the U.S. Department of Energy or the United States\nGovernment Sandia National Laboratories is a multimission laboratory managed\nand operated by National Technology and Engineering Solutions of Sandia, LLC.,\na wholly owned subsidiary of Honeywell International, Inc., for the U.S.\nDepartment of Energy's National Nuclear Security Administration under contract\nDE-NA-0003525.\n\n\\begin{appendices}\n\n\\section{Hyper-reduction for the Adjoint Petrov--Galerkin Reduced-Order Model}\\label{appendix:hyper}\n\nIn the numerical solution of non-linear dynamical systems, the evaluation of the non-linear right-hand side term usually accounts for a large portion (if not the majority) of the computational cost. Equation~\\ref{eq:GROM_modal} shows that standard projection-based ROMs are incapable of reducing this cost, as the evaluation of $\\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}})$ still scales with the number of degrees of freedom $N$. If this issue is not addressed, and there is no reduction in temporal dimensionality, the ROM will typically be \\emph{more} expensive than the FOM due to additional matrix-vector products from projection onto the reduced-order space. Techniques for overcoming this bottleneck are typically referred to as hyper-reduction methods. The thesis of hyper-reduction methods is that, instead of computing the entire right-hand side vector, only a few entries are calculated. The missing entries can either be ignored (as done in collocation methods) or reconstructed (as done in the discrete interpolation and gappy POD methods). This section outlines the Gappy POD method, selection of the sampling points through QR factorization, and the algorithm for the hyper-reduced Adjoint Petrov--Galerkin ROM.\n\n\\subsection{Gappy POD}\nThe gappy POD method~\\cite{everson_sirovich_gappy} seeks to find an approximation for $\\mathbf{R}(\\cdot)$ that evaluates the right-hand side term at a reduced number of spatial points $r \\ll N$. This is achieved through the construction of a trial space for the right-hand side and least-squares reconstruction of a sampled signal. The offline steps required in the Gappy POD method are given in Algorithm~\\ref{alg:gappy_hyper_offline}.\n\n\\begin{algorithm}\n\\caption{Algorithm for the offline steps required for hyper-reduction via gappy POD.}\n\\label{alg:gappy_hyper_offline}\nOffline Steps:\n\\begin{enumerate}\n \\item Compute the full-order solution, storing $n_t$ time snapshots in the following matrices,\n \\begin{alignat*}{2}\n \n &\\text{Right-hand side snapshots:} \\; &&\\mathbf{F} = [\\mathbf{R}({\\mathbf{u}}_1) \\quad \\mathbf{R}({\\mathbf{u}}_2) \\quad ... \\quad \\mathbf{R}({\\mathbf{u}}_{n_t})] \\in \\mathbb{R}^{N \\times n_t}\n \\end{alignat*} \n \n \\item Compute the right-hand side POD basis $\\mathbf{U} \\in \\mathbb{R}^{N \\times r}$, from $\\mathbf{F}$.\n \\item Compute the sampling point matrix $\\mathbf{P} = [\\mathbf{e}_{p_1} \\quad \\mathbf{e}_{p2} \\quad ... \\mathbf{e}_{p_r}] \\in \\mathbb{R}^{N_{p} \\times N}$, where $\\mathbf{e}_i$ is the $i$th cannonical unit vector and $N_p$ is the number of sampling points.\n \\item Compute the stencil matrix $\\mathbf{P}_s = [\\mathbf{e}_{s_1} \\quad \\mathbf{e}_{s2} \\quad ... \\mathbf{e}_{s_r}] \\in \\mathbb{R}^{N_{s} \\times N}$, where $\\mathbf{e}_i$ is the $i$th cannonical unit vector and $N_s$ is the number of stencil points required to reconstruct the residual at the sample points. Note $N_s \\ge N_p$.\n\n \\item Compute the least-squares reconstruction matrix: $\\big[ \\mathbf{P}^T \\mathbf{U} \\big]^{+}$, where the superscript $+$ denotes the pseudo-inverse. \n Note that this matrix corresponds to the solution of the least-squares problem for a gappy signal $\\mathbf{f} \\in \\mathbb{R}^N$:\n$$ \\mathbf{a}_{\\mathbf{f}} = \\underset{\\mathbf{b} \\in \\mathbb{R}^r}{\\text{argmin}} || \\mathbf{P}^T \\mathbf{U} \\mathbf{b} - \\mathbf{P}^T \\mathbf{f}||,$$\nwhich has the solution,\n$$\\mathbf{a}_{\\mathbf{f}} = \\bigg[ \\mathbf{P}^T \\mathbf{U} \\bigg]^{+} \\mathbf{f}.$$\n\\end{enumerate}\n\\end{algorithm}\n\n\nNote that, because the gappy POD approximation of the right-hand side only samples the right-hand side term at $N_p$ spatial points, the cost of the ROM no longer scales with the full-order degrees of freedom $N$, but instead with the number of POD basis modes $K$ and the number of sample points $N_p$. Thus, the cost of evaluating the full right-hand side may be drastically reduced at the price of storing another snapshot matrix $\\mathbf{F}$ and computing another POD basis $\\mathbf{U}$ in the offline stage of computation. Furthermore, the product $\\tilde{\\mathbf{V}}^T \\mathbf{U} \\big[ \\mathbf{P}^T \\mathbf{U} \\big]^{+}$ may be precomputed during the offline stage if both $\\mathbf{P}$ and $\\mathbf{U}$ remain static throughout the simulation. This results in an relatively small $K\\times N_p$ matrix. As such, an increase in offline computational cost may produce a significant decrease in online computational cost.\n\n\\subsection{Selection of Sampling Points}\nStep 3 in Algorithm~\\ref{alg:gappy_hyper_offline} requires the construction of the sampling point matrix, for which several methods exist. In the discrete interpolation method proposed by Chaturantabut and Sorensen~\\cite{deim}, the sample points are selected inductively from the basis $\\mathbf{U}$, based on an error measure between the basis vectors and approximations of the basis vectors via interpolation. The method proposed by Drmac and Gugercin~\\cite{qdeim_drmac} leverages the rank-revealing QR factorization to compute $\\mathbf{P}$. Dynamic updates of the $\\mathbf{U}$ and $\\mathbf{P}$ via periodic sampling of the full-order right-hand side term is even possible via the methods developed by Peherstorfer and Willcox~\\cite{adeim_peherstorfer}. \n\nThe 2D compressible cylinder simulations presented in this manuscript uses a modified version of the rank-revealing QR factorization proposed in~\\cite{qdeim_drmac} to obtain the sampling points. The modifications are added to enhance the stability and accuracy of the hyper-reduced ROM within the discontinuous Galerkin method. Algorithm~\\ref{alg:qdeim} outlines the steps used in this manuscript to compute the sampling points.\n\n\\begin{algorithm}\n\\caption{Algorithm for QR-factorization-based selection of sampling matrix.}\n\\label{alg:qdeim}\nInput: Right-hand side POD basis $\\mathbf{U} \\in \\mathbb{R}^{N \\times r}$ \\;\n\\newline\nOutput: Sampling matrix $\\mathbf{P}$ \\;\n\\newline\nSteps:\n\\begin{enumerate}\n \n \\item Transpose the right-hand side POD basis, $\\mathbf{U}' = \\mathbf{U}^T$\n \\item Compute the rank-revealing QR factorization of $\\mathbf{U}'$, generating a permutation matrix $\\Gamma \\in \\mathbb{R}^{N \\times N}$, unitary $\\mathbf{Q} \\in \\mathbb{R}^{r \\times r}$, and orthonormal $\\mathbf{R} \\in \\mathbb{R}^{r \\times N}$ such that\n \\begin{equation*}\n \\mathbf{U}' \\Gamma = \\mathbf{QR}\n \\end{equation*}\n Details on computing rank-revealing QR decompositions can be found in~\\cite{qr_decomp_gu}, though many math libraries include optimized routines for this operation.\n \\item From the permutation matrix $\\Gamma$, select the first $r$ columns to form the interpolation point matrix $\\mathbf{P}$. \n \\item When applied to systems of equations, sampling approaches that select only specific indices of the residual can be inaccurate due to the fact that, at a given cell, the residual of all unknowns at that cell may not be calculated. This issue is further exacerbated in the discontinuous Galerkin method, where each cell has a number of quadrature points. As such, we perform the additional step: \n \\begin{enumerate}\n \\item Augment the sampling point matrix, $\\mathbf{P}$, with additional columns such that all unknowns are computed at the mesh cells selected by Step 3. In the present context, these additional unknowns correspond to each conserved variable and quadrature point in the selected cells. This step leads to $N_p > r$.\n \\end{enumerate}\n\\end{enumerate}\n\n\n\\end{algorithm}\n\n\n\\subsection{Hyper-Reduction of the Adjoint Petrov--Galerkin ROM}\nLastly, the online steps required for an explicit Euler update to the Adjoint Petrov--Galerkin ROM with Gappy POD hyper-reduction is provided in Algorithm~\\ref{alg:alg_ag_hyper}. It is worth noting that Step 3 in Algorithm~\\ref{alg:alg_ag_hyper} requires one to reconstruct the right-hand side at the stencil points. Hyper-reduction via a standard collocation method, which provides no means to reconstruct the right-hand side, is thus not compatable with the Adjoint Petrov--Galerkin ROM. \n\\begin{algorithm}\n\\caption{Algorithm for an explicit Euler update for the Adjoint Petrov--Galerkin ROM with gappy POD hyper-reduction.}\n\\label{alg:alg_ag_hyper}\nInput: $\\tilde{\\mathbf{a}}^n$ \\;\n\\newline\nOutput: $\\tilde{\\mathbf{a}}^{n+1}$\\;\n\\newline\nOnline steps at time-step $n$:\n\\begin{enumerate}\n\\item Compute the state at the stencil points: $\\tilde{\\mathbf{u}}_s^n = \\mathbf{P}_s^T \\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^n,$ with $\\tilde{\\mathbf{u}}_s^n \\in \\mathbb{R}^{N_s}$ \n\\item Compute the generalized coordinates to the right-hand side evaluation via,\n\\begin{equation*}\\label{eq:RHSapprox}\n \\mathbf{a}_{\\mathbf{R}}^n = \\big[ \\mathbf{P}^T \\mathbf{U} \\big]^{+} \\mathbf{P}^T \\mathbf{R}(\\tilde{\\mathbf{u}}_s^n),\n\\end{equation*}\nwith $\\mathbf{a}_{\\mathbf{R}}^n \\in \\mathbb{R}^r$.\nNote that the product $\\mathbf{P}^T \\mathbf{R}(\\tilde{\\mathbf{u}}_s^n)$ requires computing $\\mathbf{R}(\\tilde{\\mathbf{u}}_s^n)$ \\emph{only at the sample points}, as given by the unit vectors stored in $\\mathbf{P}$.\n\\item Reconstruct the right-hand side at the stencil points: $\\overline{\\mathbf{R}_s(\\tilde{\\mathbf{u}}_s^n)} = \\mathbf{P}_s^T \\mathbf{U} \\mathbf{a}_{\\mathbf{R}}^n$\n\\item Compute the orthogonal projection of the approximated right-hand side at the stencil points:\n$${\\Pi^{\\prime}} \\overline{\\mathbf{R}_s(\\tilde{\\mathbf{u}}^n)} = \\overline{\\mathbf{R}_s(\\tilde{\\mathbf{u}}^n)} - \\tilde{\\mathbf{V}} \\tilde{\\mathbf{V}}^T \\overline{ \\mathbf{R}_s(\\tilde{\\mathbf{u}}^n)}$$\n\\item Compute the generalized coordintes for the action of the Jacobian on ${\\Pi^{\\prime}} \\overline{\\mathbf{R}_s(\\tilde{\\mathbf{u}}^n)}$ at the sample points using either finite difference or exact linearization. For finite difference:\n \\begin{equation*}\n \\mathbf{a}_{\\mathbf{J}}^n \\approx \\frac{1}{\\epsilon} \\big[ \\mathbf{P}^T \\mathbf{U} \\big]^{+} \\mathbf{P}^T \\Big[ \\mathbf{R}_s\\big(\\tilde{\\mathbf{u}}^n_s + \\epsilon {\\Pi^{\\prime}} \\overline{\\mathbf{R}_s}(\\tilde{\\mathbf{u}}^n_s) \\big) - {\\mathbf{R}_s(\\tilde{\\mathbf{u}}^n_s )} \\Big], \n \\end{equation*}\n with $\\mathbf{a}_{\\mathbf{J}}^n \\in \\mathbb{R}^r$. Note $\\epsilon$ is a small constant value, usually $\\sim \\mathcal{O}(10^{-5})$.\n\\item Compute the combined sampled right-hand side: $\\tilde{\\mathbf{V}}^T \\mathbf{U} \\bigg[ \\mathbf{a}_{\\mathbf{R}}^n + \\tau \\mathbf{a}_{\\mathbf{J}}^n \\bigg]$\n\\item Update the state: $\\tilde{\\mathbf{a}}^{n+1} = \\tilde{\\mathbf{a}}^n + \\Delta t \\tilde{\\mathbf{V}}^T \\mathbf{U} \\bigg[ \\mathbf{a}_{\\mathbf{R}}^n + \\tau \\mathbf{a}_{\\mathbf{J}}^n \\bigg] $\n\\end{enumerate}\n\n\\end{algorithm}\n\n\n\n\\section{POD Basis Construction}\\label{appendix:basisconstruction}\n\nFor the 1D Euler case detailed in this manuscript, the procedure for constructing separate POD bases for each conserved variables is as follows:\n\n\\begin{enumerate}\n\\item Run the full-order model for $t \\in (0,1)$ at a time-step of $\\Delta t = 0.0005$. The state vector is saved at every other time step to create 1000 state snapshots.\n\\item Collect the snapshots for each state into three state snapshot matrices:\n$$\n\\mathbf{S}_{\\rho} = \\begin{bmatrix}\n\\mathbf{\\rho}_1 & \\mathbf{\\rho}_2 & \\hdots & \\rho_{1000}\n\\end{bmatrix},\n\\mathbf{S}_{\\rho u} = \\begin{bmatrix}\n\\mathbf{\\rho u}_1 & \\mathbf{\\rho u}_2 & \\hdots & \\mathbf{\\rho u}_{1000}\n\\end{bmatrix},\n\\mathbf{S}_{\\rho E} = \\begin{bmatrix}\n\\mathbf{ \\rho E}_1 & \\mathbf{ \\rho E}_2 & \\hdots & \\mathbf{\\rho E}_{1000}\n\\end{bmatrix},\n$$\nwhere $\\mathbf{\\rho}_i, \\mathbf{\\rho u}_i, \\mathbf{\\rho E}_i \\in \\mathbb{R}^{1000}$.\n\\item Compute the singular-value decomposition (SVD) of each snapshot matrix, e.g. for $\\mathbf{S}_{\\rho}$,\n$$\\mathbf{S}_{\\rho} \\mathrel{\\overset{\\makebox[0pt]{\\mbox{\\normalfont\\tiny\\sffamily SVD}}}{=}} \\mathbf{V}_{\\rho} {\\Sigma}_{\\rho} \\mathbf{U}^T_{\\rho}.$$\nThe columns of $\\mathbf{V}_{\\rho}$ and $\\mathbf{U}_{\\rho}$ are the left and right singular vectors of $\\mathbf{S}_{\\rho}$, respectively. ${\\Sigma}_{\\rho}$ is a diagonal matrix of the singular values of $\\mathbf{S}_{\\rho}$. The columns of $\\mathbf{V}_{\\rho}$ form a basis for the solution space of $\\rho_i$. \n\n\\textit{Remarks}\n\\begin{enumerate}\n\\item In this example, a separate basis is computed for each conserved quantity. It is also possible to construct a global basis by stacking $\\mathbf{S}_{\\rho}$, $\\mathbf{S}_{\\rho \\mathbf{u}}$, and $\\mathbf{S}_{\\rho \\mathbf{E}}$ into one snapshot matrix and computing one ``global\" SVD.\n\\end{enumerate}\n\\item Decompose each basis into bases for the resolved and unresolved scales by selecting the first $K$ columns and last $1000-K$ columns, respectively, e.g.\n$$\\mathbf{V}_\\rho = \\begin{bmatrix} \\tilde{\\mathbf{V}}_{\\rho} & ; & {\\mathbf{V}^{\\prime}}_{\\rho} \\end{bmatrix},$$\nwhere $\\tilde{\\mathbf{V}}_{\\rho} \\in \\mathbb{R}^{1000 \\times K}$ and ${\\mathbf{V}^{\\prime}} \\in \\mathbb{R}^{1000 \\times 1000 - K}.$ \\\\\n\\textit{Remarks}\n\\begin{enumerate}\n\\item Each basis vector is orthogonal to the others, hence the coarse and fine-scales are orthogonal.\n\\item In this example, we have selected 1000 snapshots such that the column space of ${\\mathbf{V}}$ spans ${\\MC{V}}$. In general, this is not the case. As the APG method requires no processing of the fine-scale basis functions, however, this is not an issue.\n\\end{enumerate}\n\\item Construct a global coarse-scale basis,\n$$\\tilde{\\mathbf{V}} = \\begin{bmatrix}\n\\tilde{\\mathbf{V}}_{\\rho} & \\mathbf{0} & \\mathbf{0} \\\\\n\\mathbf{0} & \\tilde{\\mathbf{V}}_{\\rho u} & \\mathbf{0} \\\\\n\\mathbf{0} & \\mathbf{0} & \\tilde{\\mathbf{V}}_{\\mathbf{\\rho E}} \\\\\n\\end{bmatrix}.$$\n\\end{enumerate}\n\n\n\\section{Algorithms for the Galerkin and LSPG ROMs}\\label{appendix:algorithms}\nSection~\\ref{sec:cost} presented an analysis on the computational cost of the Adjoint Petrov--Galerkin ROM. This appendix presents similar algorithms and FLOP counts for the Galerkin and LSPG ROMs. The following algorithms and FLOP counts are reported:\n\\begin{enumerate}\n \\item An explicit Euler update to the Galerkin ROM (Algorithm~\\ref{alg:alg_g_exp}, Table~\\ref{tab:alg_g_exp}).\n \\item An implicit Euler update to the Galerkin ROM using Newton's method with Gaussian elimination (Algorithm~\\ref{alg:alg_g_imp}, Table~\\ref{tab:alg_g_imp}).\n \\item An implicit Euler update to the Least-Squares Petrov--Galerkin ROM using the Gauss-Newton method with Gaussian elimination (Algorithm~\\ref{alg:alg_LSPG}, Table~\\ref{tab:alg_LSPG}).\n\\end{enumerate}\n\n\\begin{algorithm}[h!]\n\\caption{Algorithm for an explicit Euler update for the Galerkin ROM.}\n\\label{alg:alg_g_exp}\nInput: $\\tilde{\\mathbf{a}}^n$ \\;\n\\newline\nOutput: $\\tilde{\\mathbf{a}}^{n+1}$\\;\n\\newline\nSteps:\n\\begin{enumerate}\n\\item Compute the state from the generalized coordinates, $\\tilde{\\mathbf{u}}^n =\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}^{n}$\n\\item Compute the right-hand side from the state, $\\mathbf{R}(\\tilde{\\mathbf{u}}^n)$\n\\item Project the right-hand side, $\\tilde{\\mathbf{V}}^T\\mathbf{R}(\\tilde{\\mathbf{u}}^n)$\n\\item Update the state $\\tilde{\\mathbf{a}}^{n+1} = \\tilde{\\mathbf{a}}^n + \\Delta t \\tilde{\\mathbf{V}}^T\\mathbf{R}(\\tilde{\\mathbf{u}}^n)$\n\\end{enumerate}\n\\end{algorithm}\n\n\\begin{table}[h!]\n\\begin{tabular}{p{7cm} p{8cm}}\n\\hline\nStep in Algorithm~\\ref{alg:alg_g_exp} & Approximate FLOPs \\\\\n\\hline\n1 & $2NK - N$ \\\\\n2 & $\\omega N$ \\\\\n3 & $2NK - K$ \\\\\n4 & $2K $ \\\\\n\\hline\nTotal & $4 N K + (\\omega-1) N + K$ \\\\\n\\hline\n\\end{tabular}\n\\caption{Approximate floating-point operations for an explicit Euler update to the Galerkin method reported in Algorithm~\\ref{alg:alg_g_exp}.}\n\\label{tab:alg_g_exp}\n\\end{table}\n\n\n\\begin{algorithm}[h!]\n\\caption{Algorithm for an implicit Euler update for the Galerkin ROM using Newton's Method with Gaussian Elimination}\n\\label{alg:alg_g_imp}\nInput: $\\tilde{\\mathbf{a}}^n$, residual tolerance $\\xi$ \\;\n\\newline\nOutput: $\\tilde{\\mathbf{a}}^{n+1}$\\;\n\\newline\nSteps:\n\\begin{enumerate}\n\\item Set initial guess, $\\tilde{\\mathbf{a}}_k$\n\\item Loop while $\\mathbf{r}^k > \\xi$\n\\begin{enumerate}\n \\item Compute the state from the generalized coordinates, $\\tilde{\\mathbf{u}}_k = \\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_k$\n \\item Compute the right-hand side from the full state, $\\mathbf{R}(\\tilde{\\mathbf{u}}_k)$\n \\item Project the right-hand side, $\\tilde{\\mathbf{V}}^T \\mathbf{R}(\\tilde{\\mathbf{u}}_k)$\n \\item Compute the Galerkin residual, $\\mathbf{r}_G(\\tilde{\\mathbf{a}}_k) = \\tilde{\\mathbf{a}}_k - \\tilde{\\mathbf{a}}^n - \\Delta t \\tilde{\\mathbf{V}}^T \\mathbf{R}(\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_k)$\n \\item Compute the residual Jacobian, $\\frac{\\partial \\mathbf{r}(\\tilde{\\mathbf{a}}_k)}{\\partial \\tilde{\\mathbf{a}}_k}$\n \\item Solve the linear system via Gaussian Elimination: $\\frac{\\partial \\mathbf{r}(\\tilde{\\mathbf{a}}_k)}{\\partial \\tilde{\\mathbf{a}}_k} \\Delta \\tilde{\\mathbf{a}} = - \\mathbf{r}(\\tilde{\\mathbf{a}}_k)$\n \\item Update the state: $\\tilde{\\mathbf{a}}_{k+1} = \\tilde{\\mathbf{a}}_k + \\Delta \\tilde{\\mathbf{a}}$\n \\item $k = k + 1$\n\\end{enumerate}\n\\item Set final state, $\\tilde{\\mathbf{a}}^{n+1} = \\tilde{\\mathbf{a}}_k$\n\\end{enumerate}\n\\end{algorithm}\n\n\n\n\\begin{table}[h!]\n\\centering\n\\begin{tabular}{p{7cm} p{8cm}}\n\\hline\nStep in Algorithm~\\ref{alg:alg_apg_imp}& Approximate FLOPs \\\\\n\\hline\n2a & $2 N K - N $ \\\\\n2b & $ \\omega N $ \\\\\n2c & $2 N K - K$ \\\\\n2d & $ 3K $ \\\\\n2e & $ 4 N K^2 + (\\omega - 1) N K + 2K^2 $ \\\\\n2f & $ K^3 $ \\\\\n2g & $ K $ \\\\\n\\hline\nTotal & $ (\\omega - 1)N + 3K + (\\omega + 3)NK + 2K^2 + 4NK^2 + K^3 $\n\\end{tabular}\n\\caption{Approximate floating-point operations for one Newton iteration for the implicit Euler update to the Galerkin method reported in Algorithm~\\ref{alg:alg_g_imp}.}\n\\label{tab:alg_g_imp}\n\\end{table}\n\n\\clearpage\n\n\\begin{algorithm}[H]\n\\caption{Algorithm for an implicit Euler update for the LSPG ROM using a Gauss-Newton method with Gaussian Elimination}\n\\label{alg:alg_LSPG}\nInput: $\\tilde{\\mathbf{a}}^n$, residual tolerance $\\xi$ \\;\n\\newline\nOutput: $\\tilde{\\mathbf{a}}^{n+1}$\\;\n\\newline\nSteps:\n\\begin{enumerate}\n\\item Set initial guess, $\\tilde{\\mathbf{a}}_k$\n\\item Loop while $\\mathbf{r}_k > \\xi$\n\\begin{enumerate}\n \\item Compute the state from the generalized coordinates, $\\tilde{\\mathbf{u}}_k =\\tilde{\\mathbf{V}} \\tilde{\\mathbf{a}}_{k}$\n \\item Compute the right-hand side from the full state, $\\mathbf{R}(\\tilde{\\mathbf{u}}_k)$\n \\item Compute the residual, $\\mathbf{r} (\\tilde{\\mathbf{u}}_k) = \\tilde{\\mathbf{u}}_k - \\tilde{\\mathbf{u}}^n - \\Delta t \\mathbf{R}(\\tilde{\\mathbf{u}}_k)$\n \\item Compute the test basis, $\\mathbf{W}_k = \\frac{\\partial \\mathbf{r}(\\tilde{\\mathbf{u}}_k)}{\\partial \\tilde{\\mathbf{u}}_k} \\tilde{\\mathbf{V}} = \\frac{\\partial \\mathbf{r}(\\tilde{\\mathbf{u}}_k)}{\\partial \\tilde{\\mathbf{a}}_k} $\n \\item Compute the product, $\\tilde{\\mathbf{W}}_k^T \\tilde{\\mathbf{W}}_k$\n \\item Project the residual onto the test space, $\\tilde{\\mathbf{W}}^T \\mathbf{r}(\\tilde{\\mathbf{a}}_k)$\n \\item Solve $\\tilde{\\mathbf{W}}^T \\tilde{\\mathbf{W}} \\Delta \\tilde{\\mathbf{a}} = - \\tilde{\\mathbf{W}}^T \\mathbf{r}(\\tilde{\\mathbf{a}}_k) $ for $\\Delta \\tilde{\\mathbf{a}}$ via Gaussian elimination\n \\item Update solution, $\\tilde{\\mathbf{a}}_{k+1} = \\tilde{\\mathbf{a}}_k + \\Delta \\tilde{\\mathbf{a}}$\n \\item k = k + 1\n\\end{enumerate}\n\\item Set final state, $\\tilde{\\mathbf{a}}^{n+1} = \\tilde{\\mathbf{a}}_k$ \n\\end{enumerate}\n\\end{algorithm}\n\n\n\\begin{table}[H]\n\\begin{tabular}{p{7cm} p{8cm}}\n\\hline\nStep in Algorithm~\\ref{alg:alg_LSPG}& Approximate FLOPs \\\\\n\\hline\n2a & $ 2NK - N $ \\\\\n2b & $ \\omega N $ \\\\\n2c & $ 3N $ \\\\\n2d & $ (\\omega + 2)NK + 2NK^2 $ \\\\\n2e & $ 2NK^2 - K^2 $ \\\\\n2f & $ 2NK - K $ \\\\\n2g & $ K^3 $ \\\\\n2h & $ K $ \\\\\n\\hline\nNewton Iteration Total & $ (\\omega + 2)N + (\\omega + 6) NK - K^2 + 4NK^2 + K^3 $ \\\\\n\n\\end{tabular}\n\\caption{Approximate floating-point operations for one Newton iteration for the implicit Euler update to the LSPG method reported in Algorithm~\\ref{alg:alg_LSPG}.}\n\\label{tab:alg_LSPG}\n\\end{table}\n\n\n\n\\end{appendices}\n\n\\clearpage\n\n\\bibliographystyle{aiaa}\n\n\n\n\\subsection{Example 2: Flow Over Cylinder}\\label{sec:cylinder}\nThe second case considered is viscous compressible flow over a circular cylinder. The flow is described by the two-dimensional compressible Navier-Stokes equations. A Newtonian fluid and a calorically perfect gas are assumed.\n\\begin{comment} \nThe flow is described by the two-dimensional compressible Navier-Stokes equations,\n\\begin{equation}\\label{eq:compressible_ns}\n\\frac{\\partial \\mathbf{u}}{\\partial t} + \\nabla \\cdot \\big( \\mathbf{F}(\\mathbf{u} ) - \\mathbf{F}_v (\\mathbf{u},\\nabla \\mathbf{u} ) \\big) =0,\n\\end{equation}\nwhere $\\mathbf{F}$ and $ \\mathbf{F}_v$ are the inviscid and viscous fluxes, respectively. For a two-dimensional flow the state vector and inviscid fluxes are,\n$$\n\\mathbf{u} = \\begin{Bmatrix}\n\\rho \\\\ \\rho u_1 \\\\ \\rho u_2 \\\\ \\rho E \\end{Bmatrix}, \\qquad \\mathbf{F}_{1} = \\begin{Bmatrix} \\rho u_1 \\\\ \\rho u_1^2 + p \\\\ \\rho u_1 u_2 \\\\ u_1(E + p) \\end{Bmatrix}, \n\\qquad \\mathbf{F}_{2} = \\begin{Bmatrix} \\rho u_2 \\\\ \\rho u_1 u_2 \\\\ \\rho u_2^2 + p \\\\ u_2(E + p) \\end{Bmatrix}.\n$$\nThe viscous fluxes are given by,\n$$\n\\qquad \\mathbf{F}_{v_1} = \\begin{Bmatrix} 0 \\\\ \\tau_{11} \\\\ \\tau_{12} \\\\ u_j \\tau_{j1} + c_p \\frac{\\mu}{\\text{Pr}} \\frac{\\partial T}{\\partial x_1} \\end{Bmatrix}, \n\\qquad \\mathbf{F}_{v_2} = \\begin{Bmatrix} 0 \\\\ \\tau_{21} \\\\ \\tau_{22} \\\\ u_j \\tau_{j2} + c_p \\frac{\\mu}{\\text{Pr}} \\frac{\\partial T}{\\partial x_2} \\end{Bmatrix}.\n$$\nWe assume a Newtonian fluid, which leads to a viscous stress tensor of the form,\n\\begin{equation*}\n\\tau_{ij} = 2\\mu S_{ij},\n\\end{equation*}\nwhere,\n\\begin{equation*}\n S_{ij} = \\frac{1}{2} \\big( \\frac{\\partial u_i}{\\partial x_j} + \\frac{\\partial u_j}{\\partial x_i} \\big) - \\frac{1}{3} \\frac{\\partial u_k}{\\partial x_i} \\delta_{ij}.\n\\end{equation*}\nThe Navier-Stokes equations are closed with a constitutive relationship for a calorically perfect gas,\n$$p = (\\gamma - 1)( \\rho E - \\frac{1}{2} \\rho u_1^2 - \\frac{1}{2} \\rho u_2^2 \\big),$$\nwhere $\\gamma = 1.4$ is the heat-capacity ratio. \n\\end{comment}\n\\subsubsection{Full-Order Model}\\label{sec:cylinder_fom}\nThe compressible Navier-Stokes equations are solved using a discontinuous Galerkin (DG) method and explicit time integration. Spatial discretization with the discontinuous Galerkin method leads to a semi-discrete system of the form,\n\\begin{equation*}\n \\frac{d {\\mathbf{u}}}{dt} = \\mathbf{M}^{-1}\\mathbf{f}({\\mathbf{u}}), \\qquad {\\mathbf{u}}(t=0) = {\\mathbf{u}}_0,\n\\end{equation*}\nwhere $\\mathbf{M}\\in \\mathbb{R}^{N \\times N}$ is a block diagonal mass matrix and $\\mathbf{f}({\\mathbf{u}}) \\in \\mathbb{R}^N$ is a vector containing surface and volume integrals. Thus, using the notation defined in Eq.~\\ref{eq:FOM}, the right-hand side operator for the DG discretization is defined as,\n$$\\mathbf{R}({\\mathbf{u}}) = \\mathbf{M}^{-1}\\mathbf{f}(\\tilde{\\mathbf{u}}).$$\n\nFor the flow over cylinder problem considered in this section, a single block domain is constructed in polar coordinates by uniformly discretizing in $\\theta$ and by discretizing in the radial direction by,\n$$r_{i+1} = r_i + r_i (R_g - 1),$$\nwhere $R_g$ is a stretching factor and is defined by,\n$$R_g = r_{max}^{1\/N_r}.$$\n\nThe DG method utilizes the Roe flux at the cell interfaces and uses the first form of Bassi and Rebay~\\cite{BR1} for the viscous fluxes. Temporal integration is again performed using a strong stability preserving RK3 method. Far-field boundary conditions and linear elements are used. Details of the FOM are presented in Table~\\ref{tab:cylinder}. \n\n\n\n\n\\begin{table}\n\\centering\n\\begin{tabular}{|c | c | c | c | c | c | c | c | c | c |}\\hline\n $r_{max}$ & $N_r$ & $N_{\\theta}$ & $p_r$ & $p_{\\theta}$ & $\\Delta t$ & Mach & $a_{\\infty}$ & $p_{\\infty}$ & $T_{\\infty}$ \\\\ \\hline\n 60 & 80 & 80 & 3 & 3 & $5e-3$ & 0.2 & $1.0$ & $1.0$ & $\\gamma^{-1}$ \\\\\\hline\n\\end{tabular}\n\\caption{Details used for flow over cylinder problem. In the above table, $N_r$ and $N_{\\theta}$ are the number of cells in the radial and $\\theta$ direction, respectively. Similarly, $p_r$ and $p_{\\theta}$ are the polynomial orders in the radial and $\\theta$ direction. Lastly, $a_{\\infty}$, $p_{\\infty}$, and $T_{\\infty}$ are the free-stream speed of sound, pressure, and temperature.}\n\\label{tab:cylinder}\n\\end{table}\n\n\n\\subsubsection{Solution of the Full-Order Model and Construction of the ROM Trial Space}\\label{sec:cyl_romsteps}\nFlow over a cylinder at Re=$100,200,$ and $300$, where Re=$ \\rho_{\\infty} U_{\\infty} D \/ \\mu$ is the Reynolds number, are considered. These Reynolds numbers give rise to the well studied von K\\'arm\\'an vortex street. Figure~\\ref{fig:vonkarman} shows the FOM solution at Re=100 for several time instances to illustrate the vortex street.\n\nThe FOM is used to construct the trial spaces used in the ROM simulations. The process used to construct these trial spaces is as follows:\n\\begin{enumerate}\n \\item Initialize FOM simulations at Reynold's numbers of Re=$100,200,$ and $300.$ The Reynold's number is controlled by raising or lowering the viscosity.\n \\item Time-integrate the FOM at each Reynolds number until a statistically steady-state is reached.\n \\item Once the flow has statistically converged to a steady state, reset the time coordinate to be $t=0$, and solve the FOM for $t \\in [0,100]$.\n \\item Take snapshots of the FOM solution obtained from Step 3 at every $t=0.5$ time units over a time-window of $t \\in [0,100]$ time units, for a total of $200$ snapshots at each Reynolds number. This time window corresponds to roughly two cycles of the vortex street, with $100$ snapshots per cycle.\n \\item Assemble the snapshots from each case into one global snapshot matrix of dimension $N \\times 600$. This snapshot matrix is used to construct the trial subspace through POD. Note that only one set of basis functions for all conserved variables is constructed. \n \\item Construct trial spaces of dimension $N\\times 11$, $N\\times 43$, and $N \\times 87$. These subspace dimensions correspond to an energy criterion of $99, 99.9,$ and $99.99\\%$. The different trial spaces are summarized in Table~\\ref{tab:rom_basis_1}.\n\\end{enumerate}\n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.05\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/colorbar.png}\n\\label{fig:vonkarman0}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.3\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/cyl0000.png}\n\\caption{$x$-velocity at $t=0.0$}\n\\label{fig:vonkarman1}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.3\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/cyl0005.png}\n\\caption{$x$-velocity at $t=25.0$}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.3\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/cyl0010.png}\n\\caption{$x$-velocity at $t=50.0$}\n\\label{fig:vonkarman3}\n\\end{subfigure}\n\\end{center}\n\\caption{Evolution of the von K\\'arm\\'an vortex street at Re=100.}\n\\label{fig:vonkarman}\n\\end{figure}\n\n\\subsubsection{Solution of the Reduced-Order Models}\nThe G ROM, APG ROM, and LSPG ROMs are considered. Details on their implementation are as follows:\n\\begin{enumerate}\n\\item Galerkin ROM: The Galerkin ROM is evolved in time using both explicit and implicit time integrators. In the explicit case, a strong stability RK3 method is used. In the implicit case, Crank-Nicolson time integration is used. The non-linear algebraic system is solved using SciPy's Jacobian-Free Netwon-Krylov solver. LGMRES is employed as the linear solver. The convergence tolerance for the max-norm of the residual is set at the default ftol=6e-6. \n\n\\item Adjoint Petrov-Galerkin ROM: The APG ROM is evolved in time using the same time integrators as the Galerkin ROM. The extra term appearing in the APG model is computed via finite difference\\footnote{It is noted that, when used in conjunction with a JFNK solver that utilizes finite difference to approximate the action of the Jacobian on a vector, approximating the extra RHS term in APG via finite difference leads to computing the finite difference approximation of a finite difference approximation. While not observed in the examples presented here, this can have a detrimental effect on accuracy and\/or convergence.} with a step size of $\\epsilon = $1e-5. Unless noted otherwise, the memory length $\\tau$ is selected to be $\\tau = \\frac{0.2}{\\rho(\\tilde{\\mathbf{V}}^T \\mathbf{J}[\\tilde{\\mathbf{u}}(0)] \\tilde{\\mathbf{V}} ) } .$ The impact of $\\tau$ on the numerical results is considered in the subsequent sections. \n\n\\item LSPG ROM: The LSPG ROM is formulated from an implicit Crank-Nicolson temporal discretization. The resulting non-linear least-squares problem is solved using SciPy's least-squares solver with the `dogbox' method~\\cite{scipy_leastsquares_dogbox}. The tolerance on the change to the cost function is set at ftol=1e-8. The tolerance on the change to the generalized coordinates is set at xtol=1e-8. The SciPy least-squares solver is comparable in speed to our own least-squares solver that utilizes the Gauss-Newton method with a thin QR factorization to solve the least-squares problem. The SciPy solver, however, was observed to be more robust in driving down the residual than the basic Gauss-Newton method with QR factorization, presumably due to SciPy's inclusion of trust-regions, and hence results are reported with the SciPy solver.\n\\end{enumerate}\nAll ROMs are initialized with the solution of the Re=$100$ FOM at time $t=0$, the $x$-velocity of which is shown in Figure~\\ref{fig:vonkarman1}.\n\n\n\n\\begin{table}[]\n\\begin{center}\n\\begin{tabular}{c c c c}\n\\hline\nBasis \\# & Trial Basis Dimension ($K$) & Energy Criteria & $\\tau$ (Adjoint Petrov-Galerkin) \\\\\n\\hline\n1 & $ 11$ & $99\\%$ & $1.0$ \\\\\n2 & $ 42$ & $99.9\\%$ & $0.3$ \\\\\n3 & $ 86$ & $99.99\\%$ & $0.1$ \\\\\n\\hline\n\\end{tabular}\n\\caption{Summary of the various basis dimensions used for Example 2}\n\\label{tab:rom_basis_1}\n\\end{center}\n\\end{table}\n\n\n\n\\subsubsection{Reconstruction of Re=100 Case}\\label{sec:re100_rec}\nReduced-order models of the Re=100 case are first considered. This case was explicitly used in the construction of the POD basis and tests the ability of the ROM to reconstruct previously ``seen\" dynamics. Unless otherwise noted, the default time-step for all ROMs is taken to be $\\Delta t = 0.5$. The values of $\\tau$ used in the APG ROMs are selected from the spectral radius heuristic and are given in Table~\\ref{tab:rom_basis_1}. Figures~\\ref{subfig:re100a} and~\\ref{subfig:re100b} show the lift coefficient as well as the mean squared error (MSE) of the full-field ROM solutions for the G ROM, APG ROM, and LSPG ROMs for Basis \\#2, while Figure~\\ref{subfig:re100c} shows the integrated MSE for $t \\in [0,200]$ for Basis \\#1, 2, and 3. Figure~\\ref{subfig:re100d} shows the integrated error as a function of relative CPU time for the various ROMs. The relative CPU time is defined with respect to the FOM, which is integrated with an explicit time-step 100 times lower than the ROMs. The lift coefficients predicted by all three ROMs are seen to overlay the FOM results. The mean squared error shows that, for a given trial basis dimension, the APG ROM is more accurate than both the Galerkin and LSPG ROMs. This is the case for both explicit and implicit time integrators. As shown in Figure~\\ref{subfig:re100c}, the APG ROM converges at a similar rate to the G ROM as the dimension of the trial space grows. For Basis \\#2 and \\#3, the implicit time-marching schemes are slightly less accurate than the explicit time-marching schemes. Finally, Figure~\\ref{subfig:re100d} shows that, for a given CPU time, the G ROM with explicit time-marching produces the least error. The APG ROM with explicit time-marching is the second-best performing method. In the implicit case, both the G and APG ROMs lead to lower error at a given CPU time than LSPG. This decrease in cost is due to the fact that the G and APG ROMs utilize Jacobian-Free Netwon-Krylov solvers. As discussed in Section~\\ref{sec:cost}, it is much more challenging for LSPG to utilize Jacobian-Free methods. Due to the increased cost associated with implicit solvers, only explicit time integration is used for the G ROM and APG ROM beyond this point. \n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.48\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/cl_999case.pdf}\n\\caption{Lift Coefficient as a function of time for Basis \\# 2.}\n\\label{subfig:re100a}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.48\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/mse_vs_t_999case.pdf}\n\\caption{Normalized error as a function of time for Basis \\# 2.}\n\\label{subfig:re100b}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.48\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/converge.pdf}\n\\caption{Integrated normalized error vs trial subspace dimension.}\n\\label{subfig:re100c}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.48\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/rom_pareto.pdf}\n\\caption{Integrated normalized error vs relative CPU time.}\n\\label{subfig:re100d}\n\\end{subfigure}\n\\end{center}\n\\caption{ROM results for flow over cylinder at Re=100. The lift coefficient is defined as $C_L = \\frac{2L}{\\rho U_{\\infty}^2 D}$, with $L$ being the integrated force on the cylinder perpendicular to the free-stream velocity vector.}\n\\label{fig:re100}\n\\end{figure}\n\nNext, we investigate the sensitivity of the different ROMs to the time-step size. Reduced-order models of the Re=$100$ case using Basis \\#2 are solved using time-steps of $\\Delta t = \\big[0.1,0.2,0.5,1]$. Note that the largest time-step considered is $200$ times larger than the FOM time-step, thus reducing the temporal dimensionality of the problem by 200 times. The mean-squared error of each ROM solution is shown in Figure~\\ref{fig:re100_dtvary}. The G and APG ROMs are stable for all time-steps considered. Further, it is seen that varying the time-step has a minimal effect on the accuracy of the G and APG ROMs. In contrast, the accuracy of LSPG deteriorates if the time-step grows too large. This is due to the fact that, as shown in Ref.~\\cite{carlberg_lspg_v_galerkin}, the stabilization added by LSPG depends on the time-step size. Optimal accuracy of the LSPG method requires an intermediate time-step. The ability of the APG and G ROMs to take large time-steps without a significant degradation in accuracy allows for significant computational savings. This advantage is further amplified when large time-steps can be taken with an explicit solver, as is the case here. This will be discussed in more detail in Section~\\ref{sec:cylhyper}.\n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/mse_vs_t_dtvary}\n\\caption{Normalized error as a function of time.}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/converge_dtvary.pdf}\n\\caption{Integrated normalized error as a function of the time-step.}\n\\end{subfigure}\n\\end{center}\n\\caption{Results for time-step study of flow over cylinder at Re$=100$.}\n\\label{fig:re100_dtvary}\n\\end{figure}\n\nLastly, we numerically investigate the sensitivity of APG to the parameter $\\tau$ by running simulations for $\\tau=[0.001,0.01,0.1,0.3,0.5,1.]$. All simulations are run at $\\Delta t = 0.5$. The results of the simulations are shown in Figure~\\ref{fig:re100_tau}. It is seen that, for all values of $\\tau$, the APG ROM produces a better solution than the G ROM. The lowest error is observed for an intermediate value of $\\tau$, in which case the APG ROM leads to over a $50\\%$ reduction in error from the G ROM. As $\\tau$ approaches zero, the APG ROM solution approaches the Galerkin ROM solution. Convergence plots for LSPG as a function of $\\Delta t$ are additionally shown in Figure~\\ref{subfig:re100b_tau}. It is seen that the optimal time-step in LSPG is similar to the optimal value of $\\tau$ in APG.\n\n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.45\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/mse_vs_t_tauvary.pdf}\n\\caption{Normalized error as a function of time}\n\\label{subfig:re100a_tau}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.45\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/converge_tauvary.pdf}\n\\caption{Integrated normalized error as a function of $\\tau$. Results for how the time-step $\\Delta t$ impacts LSPG are included for reference.}\n\\label{subfig:re100b_tau}\n\\end{subfigure}\n\\end{center}\n\\caption{Summary of numerical results investigating the impact of the parameter $\\tau$ on the performance of the Adjoint Petrov-Galerkin method.}\n\\label{fig:re100_tau}\n\\end{figure}\n\n\n\\begin{comment}\n\n\\subsubsection{Prediction at Re$=150$}\nSimulations at a Reynolds number of Re$=150$ are now considered. The Re$=150$ case was not considered in the construction of the POD basis, and thus this case tests the predictive ability of the ROM. The case is initialized using the flow field from the Re$=100$ simulation. The Reynolds number is modified by lowering the viscosity. Figure~\\ref{fig:re150_unsteady} shows the temporal evolution of the lift coefficient as predicted by the FOM, G ROM, APG ROM, and LSPG ROM. The G ROM and LSPG ROMs are seen to behave similarly for this case, both under-predicting the growth in amplitude of the lift coefficient and shedding frequency. The solution generated by the APG ROM provides a better prediction for both the lift coefficient, as well as the full-field mean-squared error. By $t=200$, the instantaneous MSE of the APG ROM is approximately an order of magnitude better than both the G ROM and LSPG ROM. It is interesting to again observe that, at early time, the APG ROM is less accurate than both the G ROM and LSPG ROM. This further supports the theoretical analysis in Section~\\ref{sec:analysis}. \n \\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/re150_cl999case.pdf}\n\\caption{Lift coefficient prediction}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/re150_mse_vs_t_999case.pdf}\n\\caption{Extended time integration of ROM cases}\n\\label{fig:re150_longtime}\n\\end{subfigure}\n\\end{center}\n\\caption{Temporal evolution of cylinder case for Re$=150$.}\n\\label{fig:re150_unsteady}\n\\end{figure}\n\n\\end{comment}\n\n\\subsubsection{Parametric Study of Reynolds Number Dependence}\nNext, the ability of the different ROMs to interpolate between between different Reynolds numbers is studied. Simulations at Reynolds numbers of Re=100,150,200,250, and 300 with Basis $\\#2$ and $\\#3$ are performed. All cases are initialized from the Re=100 simulation. Note that the trial spaces in the ROMs were constructed from statistically steady-state FOM simulations of Re=100,200,300. The Reynolds number is modified by changing the viscosity.\n\nFigure~\\ref{fig:ROM_summary} summarizes the amplitude of the lift coefficient signal as well as the shedding frequency for the various methods. The values reported in Figure~\\ref{fig:ROM_summary} are computed from the last 150s of the simulations\\footnote{Not all G ROMs reached a statistically steady state over the time window considered}. The Galerkin ROM is seen to do poorly in predicting the lift coefficient amplitude for both Basis $\\#2$ and Basis $\\#3$. Unlike in the Re=100 case, enhancing the basis dimension does not improve the performance of the ROMs. Both the LSPG and APG ROMs are seen to offer much improved predictions over the Galerkin and ROM. This result is promising, as the ultimate goal of reduced-order modeling is to provide predictions in new regimes.\n\nThe results presented in this example highlight the shortcomings of the Galerkin ROM. To obtain results that are even qualitatively correct for the Re=\\{150,200,250,300\\} cases, either APG or LSPG must be used. As reported in Figure~\\ref{subfig:re100d}, explicit APG is over an order of magnitude faster than LSPG, and implicit APG with a JFNK solver is anywhere from 2x to 5x faster than LSPG. Therefore, APG is the best-performing method for this example. \n\n\n\n \\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/re_vs_amp.pdf}\n\\caption{Prediction for lift coefficient amplitudes}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/re_vs_st.pdf}\n\\caption{Prediction for shedding frequency}\n\\end{subfigure}\n\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/cl_re150.pdf}\n\\caption{Re=$150$}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/cl_re200.pdf}\n\\caption{Re=$200$}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/cl_re250.pdf}\n\\caption{Re=$250$}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/cl_re300.pdf}\n\\caption{Re=$300$}\n\\end{subfigure}\n\n\n\\end{center}\n\\caption{Summary of ROM simulations}\n\\label{fig:ROM_summary}\n\\end{figure}\n\n\n\n\\subsubsection{Flow Over Cylinder at Re=$100$ with Hyper-Reduction}\\label{sec:cylhyper}\nThe last example considered is again flow over a cylinder, but this time the reduced-order models are augmented with hyper-reduction. The purpose of this example is to examine the performance of the different reduced-order models when fully equipped with state-of-the-art reduction techniques. For hyper-reduction, an additional snapshot matrix of the right-hand side is generated. This additional snapshot matrix is generated by following steps one through five provided in Section~\\ref{sec:cyl_romsteps}. Hyper-reduction for the G ROM and the APG ROM is achieved through the Gappy POD method~\\cite{everson_sirovich_gappy}. Hyper-reduction for LSPG is achieved through collocation using the same sampling points.\\footnote{It is noted that collocated LSPG out-performed the GNAT method for this example, and thus GNAT is not considered.} When augmented with hyper-reduction, the trial basis dimension ($K$), right-hand side basis dimension ($r$), and number of sample points $(N_s)$ can impact the performance of the ROMs. Table~\\ref{tab:rom_basis_details} summarizes the various permutations of $K$, $r$, and $N_s$ considered in this example. The sample points are selected through a QR factorization of the right-hand side snapshot matrix~\\cite{qdeim_drmac}. These sample points are then augmented such that they contain every conserved variable and quadrature point at the selected cells. The sample mesh corresponding to Basis numbers 4,5, and 6 in Table~\\ref{tab:rom_basis_details} is shown in Figure~\\ref{fig:re100_sample}. Details on hyper-reduction and its implementation in our discontinuous Galerkin code are provided in Appendix~\\ref{appendix:hyper}. \n\n\\begin{table}[]\n\\begin{tabular}{c c c c c}\n\\hline\nBasis \\# & Trial Basis Dimension ($K$) & RHS Basis Dimension ($r$) & Sample Points ($N_S$) & Maximum Stable $\\Delta t$\\\\\n\\hline\n1 & $ 11$ & $103$ & $4230$ & 4.0\\\\\n2 & $ 42$ & $103$ & $4230$ & 2.0\\\\\n3 & $ 86$ & $103$ & $4230$ & 1.0\\\\\n4 & $ 11$ & $268$ & $8460$ & 4.0\\\\\n5 & $ 42$ & $268$ & $8460$ & 2.0\\\\\n6 & $ 86$ & $268$ & $8460$ & 1.0\\\\\n\\hline\n\\end{tabular}\n\\caption{Summary of the various reduced-order models evaluated on the flow over cylinder problem. The maximum stable $\\Delta t$ for each basis is reported for the SSP-RK3 explicit time-marching scheme and was empirically determined.}\n\\label{tab:rom_basis_details}\n\\end{table}\n\n\n\nFlow at Re=$100$ is considered. All simulations are performed at the maximum stable time-step for a given basis dimension, as summarized in Table~\\ref{tab:rom_basis_details}. Figure~\\ref{fig:re100_qdeim_pareto} shows the integrated error as a function of relative wall time for the various ROM techniques and basis numbers. All methods show significant computational speedup, with the G and APG ROMs producing wall-times up to 5000 times faster than the FOM while retaining a reasonable MSE. It is noted that the majority of this speed up is attributed to the increase in time-step size.\nFor a given level of accuracy, LSPG is significantly more expensive than the G and APG ROMs. The reason for this expense is three-fold. First, LSPG is inherently implicit. For a given time-step size, an implicit step is more expensive than an explicit step. Second, LSPG requires an intermediate time-step for optimal accuracy. In this example, this intermediate time-step is small enough that the computational gains that could be obtained with an implicit method are negated. The third reason is that, for each Gauss-Newton iteration, LSPG requires the computation of the action of the Jacobian on the trial space basis, $\\tilde{\\mathbf{V}}$. This expense can become significant for large basis dimensions as it requires the computation of a dense Jacobian. It is noted that it may be possible to achieve computational speed-ups in our implementation of LSPG through the development of a least-squares solver more tailored to LSPG, sparse Jacobian updates, or Jacobian approximation techniques.\n\n\n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.48\\textwidth}\n\\includegraphics[trim={4cm 0cm 4cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/samplemesh1.png}\n\\caption{Full sample mesh}\n\\label{subfig:re100_sampling_full}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.48\\textwidth}\n\\includegraphics[trim={0cm 0cm 4cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/samplemesh_wake2.png}\n\\caption{Close up of wake}\n\\label{subfig:re100_sampling_zoom}\n\\end{subfigure}\n\\end{center}\n\\caption{Mesh used for hyper-reduction. Cells colored in red are the sampled cells. Note that all conserved variables and quadrature points are computed within a cell.}\n\\label{fig:re100_sample}\n\\end{figure}\n\n\n\\begin{figure}\n\\begin{center}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/qdeim_pareto.pdf}\n\\caption{Wall time vs normalized error for hyper-reduced ROMs.}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/qdeim_mse_999case.pdf}\n\\caption{Normalized error as a function of time for Basis \\#5}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/qdeim_cl_999case.pdf}\n\\caption{Lift coefficient as a function of time for Basis \\# 5.}\n\\end{subfigure}\n\\begin{subfigure}[t]{0.49\\textwidth}\n\\includegraphics[trim={0cm 0cm 0cm 0cm},clip,width=1.\\linewidth]{figs_cylnew\/qdeim_cl_9999case.pdf}\n\\caption{Lift coefficient as a function of time for Basis \\# 6.}\n\\end{subfigure}\n\n\\end{center}\n\\caption{Results for hyper-reduced ROMs at Re$=100$.}\n\\label{fig:re100_qdeim_pareto}\n\\end{figure}\n\n\n\n\\subsubsection{Discussion}\\label{sec:theorem_discussion}\nTheorems~\\ref{theorem:apg_error} and~\\ref{theorem:errorbound_symmetric} contain three interesting results that are worth discussing. First, as discussed in Corollary~\\ref{corollary:resid_bound}, Theorem~\\ref{theorem:apg_error} shows that, in the limit $\\tau \\rightarrow 0^+$, the upper-bound provided in Theorem~\\ref{theorem:residualbounds} on the error introduced at time $t$ in the APG ROM due to the fine-scales is less than that introduced in the Galerkin ROM (per unit $\\tau$). This is an appealing result as APG is derived as a subgrid-scale model. Two remarks are worth making regarding this result. First, while the result was demonstrated in the limit $\\tau \\rightarrow 0^+$, it will hold so long as the norm of the truncation error in the quadrature approximation is less than the norm of the integral it is approximating; i.e. the approximation is doing a better job than neglecting the integral entirely. Second, the result derived in Theorem~\\ref{theorem:apg_error} does not \\textit{directly} translate to showing that,\n\\begin{equation}\\label{eq:resid_discussion}\n\\norm{\\tilde{\\mathbf{V}}^T \\mathbb{P}_A \\mathbf{r}_F(\\tilde{\\mathbf{u}}_F(t)) } \\le \\norm{\\tilde{\\mathbf{V}}^T \\mathbb{P}_G \\mathbf{r}_F(\\tilde{\\mathbf{u}}_F(t)) }.\n\\end{equation}\nThis is a consequence of Theorem~\\ref{theorem:residualbounds}, in which the integral that defines the fine-scale solution was split into two intervals. The APG ROM attempts to approximate the first integral, while the Galerkin ROM ignores both terms. Theorem~\\ref{theorem:apg_error} showed that, in the limit $\\tau \\rightarrow 0^+$, the APG approximation to the first integral is better than in the case of Galerkin (i.e., the APG approximation is better than no approximation). The only time that the result provided in Theorem~\\ref{theorem:apg_error} will \\textit{not} translate to APG providing a better approximation to the \\textit{entire} integral (and thus proving Eq.~\\ref{eq:resid_discussion}), is when integration over the second interval ``cancels out\" the integration over the first interval. To make this idea concrete, consider the integral,\n$$\\int_0^{2 \\pi} \\sin(x) dx = \\int_0^{\\pi} \\sin(x) dx + \\int_{\\pi}^{2 \\pi} \\sin(x) dx .$$\nClearly, $\\int_0^{2 \\pi} \\sin(x) dx = 0$. If the entire integral was approximated using just the interval $0$ to $\\pi$ (which is analogous to APG), then one would end up with the approximation $\\int_0^{2 \\pi} \\sin(x) dx \\approx \\int_0^{\\pi} \\sin(x) dx = 2$. Alternatively, if one were to ignore the integral entirely (which is analogous to Galerkin) and make the approximation $\\int_0^{2 \\pi} \\sin(x) dx \\approx 0$ (which in this example is exact), a better approximation would be obtained.\n\n The next interesting result is presented in Theorem~\\ref{theorem:errorbound_symmetric}, where it is shown that for a self-adjoint system with negative eigenvalues, the eigenvalues associated with the APG ROM error equation are \\textit{greater} than the Galerkin ROM. This implies that APG is \\textit{less} dissipative than Galerkin, and means that errors may be slower to decay in time. Thus, while Theorem~\\ref{theorem:apg_error} shows that the \\textit{a priori} contributions to the error due to the closure problem may be smaller in the APG ROM than in the Galerkin ROM, the errors that \\textit{are} incurred may be slower to decay. Finally, Corollary~\\ref{corollary:errorbound_symmetric} shows that, for self-adjoint systems with negative eigenvalues, the bounds on the parameter $\\tau$ such that all eigenvalues associated with the evolution of the error in APG remain negative depends on the spectral content of the Jacobian of $\\mathbf{A}$. This observation has been made heuristically in Ref~\\cite{parishMZ1}. Although the upper bound on $\\tau$ in Eq.~\\ref{eq:tau_bound} is very conservative due to the repeated use of inequalities, it provides insight into the selection and behaviour of $\\tau$.\n\n\n\n\n\\section{Significance}\n\n\nThis work develops a new reduced-order modeling technique for discretely projected ROMs. We show that the method outperforms the standard Galerkin-ROM and, in some cases, the popular least-squares Petrov-Galerkin approach. This work is novel to both the reduced-order modeling community and the Mori-Zwanzig community. For the reduced-order modeling community, the novel aspects and contributions of this work are:\n\\begin{enumerate}\n\\item A new reduced-order model derived from the Mori-Zwanzig formalism and variational multiscale method that is developed specifically for discretely projected ROMs. The method leads to a coarse-scale ROM equation that is driven by the coarse-scale residual. The method can be evolved in time with explicit integrators (in contrast to the popular least-squares Petrov Galerkin approach), potentially lowering the cost of the ROM. \n\n\\item An analysis of the present approach as a Petrov-Galerkin Method. This setting exposes similarities between the present approach and the least-squares Petrov-Galerkin (LSPG) approach. We find that the proposed method displays similarities to the well known adjoint stabilization technique, while LSPG displays similarities with the Galerkin least squares technique. \n\n\\item A comprehensive analysis of the algorithmic implementation of the proposed method as well as the estimation of the computational cost (in FLOPS) for the proposed method, Galerkin, and least-squares Petrov-Galerkin approaches. Implicit and explicit time marching schemes are considered.\n\n\\item Detailed $\\textit{a priori}$ error analysis, where we show circumstances in which the presented technique is expected to be more accurate than the Galerkin method.\n\n\\item Numerical evidence on ROMs of compressible flow problems demonstrating that the proposed method is more accurate and stable than a Galerkin-ROM. Improvements over the LSPG ROM are observed in most cases. \n\n\\item A thorough examination of errors vs CPU time for the presented method, Galerkin method, and LSPG method. We show that the presented method leads to lower errors for a given CPU time than the LSPG method. Improvements over Galerkin are observed in some cases. These results are presented for cases both with and without hyper-reduction.\n\n\\end{enumerate}\nThis work is also novel to the Mori-Zwanzig research community. The contributions include:\n\\begin{enumerate}\n\\item This is the first application of an analytical MZ method for a POD ROM. All previous work with analytic MZ models have focused on analytical basis.\n\\item By formulating MZ in the context of the variational multiscale method, we express the proposed model in a general \"residual-based\" context that allows us to apply it to complex systems. The applications presented in this work (POD-ROMs of the Sod shocktube and flow over a cylinder) mark a significant step up in complexity as compared to existing numerical problems that have been explored in the MZ community. In the authors' viewpoint, this is one of the first MZ methods that can, out of the box, be directly applied to complex systems of engineering interest.\n\\item We provide numerical evidence of a relationship between the time-scale in the proposed model and the spectral radius of the right-hand side Jacobian. This relationship applies to the selection of the optimal time-step in LSPG as well.\n\\end{enumerate}\n\n\\end{document}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzgxro b/data_all_eng_slimpj/shuffled/split2/finalzzgxro new file mode 100644 index 0000000000000000000000000000000000000000..c76e877ffe480cee7f9e67ec483f6bf33eb82937 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzgxro @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\\label{intr}\n\nThe Kontsevich integral was invented by M.~Kontsevich \\cite{Kon}\nas a tool to prove the fundamental theorem of the theory of\nfinite type (Vassiliev) invariants (see \\cite{BN1,BNe}).\nIt provides an invariant exactly as strong as the totality\nof all Vassiliev knot invariants.\n\nThe Kontsevich integral is defined for oriented tangles (either framed or\nunframed) in $\\R^3$, therefore it is also defined in the particular cases\nof knots, links and braids.\n\n$$\n \\includegraphics[width=12cm]{tang_br_link_knot.eps}\n$$\n\nAs a starter, we give two examples where simple versions of the Kontsevich\nintegral have a straightforward geometrical meaning. In these examples, as\nwell as in the general construction of the Kontsevich integral, we represent\n3-space $\\R^3$ as the product of a real line $\\R$ with coordinate $t$ and a\ncomplex plane $\\C$ with complex coordinate $z$.\n\\smallskip\n\n\\noindent\n\\begin{minipage}{4.5in}\n\\textbf{Example 1.} The number of twists in a braid with two strings $z_1(t)$ \nand $z_2(t)$ placed in the slice $0\\le t\\le 1$ is equal to\n$$\\frac{1}{2\\pi i}\\int_0^1\\frac{dz_1-dz_2}{z_1-z_2}.$$\n\\end{minipage}\n\\quad\n\\raisebox{-12mm}{\\includegraphics[height=25mm]{2braid.eps}}\n\n\\medskip\n\n\\noindent\n\\begin{minipage}{4.5in}\n\\textbf{Example 2.} The linking number of two spatial curves\n$K$ and $K'$ can be computed as\n$$ \n lk(K,K')=\\frac{1}{2\\pi i}\n \\int_{m 0 $ between $k_y \\in (-\\pi\/2-k_0,-\\pi\/2+k_0)$, and again there is a Fermi arc connecting these points on the $k_y$ axis for the same surface. This situation is illustrated in Fig. \\ref{fig:bulk}.\nNote that for $H'$ (i.e., after the unitary transformation), states on the Fermi arcs are eigenvectors of $\\sigma_y$ rather than $\\sigma_x$.\n\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.45\\textwidth]{E_n0.pdf}\\\\\n\t\\caption{(Top) Minimum solution of $\\chi'$ for values of $M^{\\prime}_{\\eta}$. When $M'_{\\eta} \\rightarrow -\\infty$, $\\chi' \\rightarrow 0$, when $M'_{\\eta} =0 $, $\\chi' = (\\pi\/2)^2$ and for large $M'_{\\eta}$, $\\chi \\approx M^{\\prime 2}_{\\eta} + \\pi^2$. (Bottom) The lowest energy solution for $q_x=0$ for various values of $L_z$ from 5 to 30 are shown for half of the Brillouin zone, containing two Weyl nodes. The energy values decreases exponentially with increasing $L_z$ depicting surface states for $k_y$ between the Weyl nodes.}\\label{fig:solchi}\n\\end{figure}\n\n\nNear the Weyl nodes, if we can write the low-energy Hamiltonian in the form of $H=k_{\\mu}A_{\\mu\\nu}\\sigma_{\\nu}$, then the chirality of the node is given by sgn(Det[$\\mathbf{A}$]). Writing, $\\textbf{\\textit{k}} = (q_x,\\eta\\frac{\\pi}{2}+\\xi k_0 +q_y,q_z) $ and expanding to first order in $q_i$, we arrive at the low-energy Hamiltonian\n\\begin{align}\nH^{{\\rm low}}_{\\eta\\xi} &\\approx \\lambda(\\sigma_y q_x - \\sigma_x q_z) + \\eta\\xi \\alpha \\sigma_z q_y,\n\\end{align}\nwith $\\alpha =\\sqrt{1-(m\/\\lambda)^2}$. The chiralities of the four nodes may\nthen be written as ${\\rm sgn}({\\rm Det}[\\mathbf{A}_{\\eta,\\sigma}])= -\\eta\\xi$.\n\n\\subsection{Infinite mass boundary condition}\nTo make progress analytically, we need to construct appropriate boundary conditions of the Dirac Hamiltonian Eq.~(\\ref{eq:blokH}) for a slab geometry, such that the properties of the Fermi arc can be recovered. In general boundary conditions for Dirac equation can be cumbersome [REF], but our goal is to recover the properties of the surface modes (i.e, Fermi arc states). We construct boundary conditions by taking the Hamiltonian of the vacuum\n(outside the slab, which extends from $z=0$ to $z=L_z$), similar to Eq.~(\\ref{eq:newH}), except for the mass term, whose form is taken as $M^{\\rm vac}_{\\eta} = \\eta m_0$, with $m_0 \\rightarrow \\infty$. This construction is required to ensure that for momentum between the Weyl nodes the effective mass term ($M_{\\eta}(k_y)$) for the Weyl semimetal and the vacuum ($M_{\\eta}^{\\rm vac}$) are oppositely signed. \n\nThe eigenfunctions for the Hamiltonian $H_{\\rm vac} = \\lambda(q_x\\sigma_y - q_z\\sigma_x) + M_{\\eta}^{{\\rm vac}}\\sigma_z$ are\n\\begin{align}\n\\psi_{\\rm vac} \\propto \\left(\\begin{array}{c}\n\\lambda(q_z+i q_x) \\\\\nM_{\\eta}^{{\\rm vac}}- E\n\\end{array} \\right)e^{i(q_z z + q_x x)},\n\\end{align}\nwith eigenvalue $E =\\pm \\sqrt{m_0^2 +\\lambda^2(q_z^2 + q_x^2)}$. For $m_0 \\gg E$, the eigenfunctions are normalizeable if\n\\begin{align}\n& q_z = i\\kappa, \\quad {\\rm for} ~~ z\\ge L_z, \\nonumber\\\\\n&q_z = -i\\kappa, \\quad {\\rm for} ~~ z\\le 0,\\nonumber\n\\end{align}\nwith $\\kappa = \\sqrt{m_0^2 +q_x^2 -E^2}$. Thus, in the limit $m_0 \\rightarrow \\infty$, we have $\\kappa \\rightarrow m_0$. For $z>L_z$,\n\\begin{align}\\label{eq:vacone}\n\\psi_{>} \\propto \\left(\\begin{array}{c}\nim_0+iq_x \\\\\n\\eta m_0 -E\n\\end{array} \\right)e^{-m_0z} \\approx \\left(\\begin{array}{c}\ni\\\\\n\\eta\n\\end{array} \\right)e^{-m_0z}.\n\\end{align}\nFor $z<0$,\n\\begin{align}\\label{eq:vactwo}\n\\psi_{<} \\propto \\left(\\begin{array}{c}\n-im_0+iq_x \\\\\n\\eta m_0 -E\n\\end{array} \\right)e^{m_0 z} \\approx \\left(\\begin{array}{c}\ni\\\\\n-\\eta\n\\end{array} \\right)e^{m_0z}.\n\\end{align}\nAt $z=0, L_z$, these spinors become the Fermi arc wavefunctions, and are recognizable as eigenvectors of $\\sigma_y$.\n\n\n\\begin{figure*}[ht]\n\t\\centering\n\t\\includegraphics[width=0.85\\textwidth]{Top.pdf}\n\t\\caption{ The RKKY coupling between two spins (connected to the same orbital) put on the same surface (along $x$-direction \\textit{i.e.} $\\mathbf{R}=(R,0,0)$) of the WSM slab with (a) the analytical wave-functions and keeping only the $n=0$ bands, (b), (c) numerically evaluated Green's function in the real-space. With increasing thickness, the all components except $J_{xx}$ becomes essentially thickness independent after certain thickness, as shown in (d). Inset of (d) shows the RKKY coupling vs slab thickness calculated using analytical wave-functions and $n=0$ bands. Results shown are for $\\mu=0$ (i.e., Fermi wavevector $k_F=0$)\nand $m=0.5\\lambda$. $R\/a=40$ in panel (d).\n}\\label{fig:top}\n\\end{figure*}\n\n\n\nMatching the wavefunction $\\psi(z)$ within the slab to these boundary forms yields the conditions\n\\begin{align}\\label{bc}\n\\psi(z=0) \\propto \\psi_{{\\rm}<}(z=0)~~{\\rm and}~~\\psi(z=L) \\propto \\psi_{{\\rm}>}(z=L_z),\n\\end{align}\nwhere\n\\begin{align}\\label{eq:states}\n\\psi(z) =& a\\left(\\begin{array}{c}\n\\lambda(q_z+iq_x) \\\\\nM_{\\eta}(k_y) - E\n\\end{array} \\right)e^{iq_z z}\\nonumber\\\\\n& + b\\left(\\begin{array}{c}\n\\lambda(-q_z+iq_x) \\\\\nM_{\\eta}(k_y) - E\n\\end{array} \\right)e^{-iq_z z},\n\\end{align}\nwith $q_z =(1\/\\lambda)\\sqrt{E^2 - M_{\\eta}^2 -\\lambda^2q_x^2}$. Non-trivial solutions of Eq.~\\ref{bc} exists if\n\n\\begin{widetext}\n\\begin{align}\n{\\rm Det}\\left(\\begin{array}{cccc}\ni & \\lambda(q_z+iq_x) & \\lambda(-q_z+iq_x)& 0\\\\\n-\\eta & M_{\\eta}(k_y) - E & M_{\\eta}(k_y) - E & 0\\\\\n0 & \\lambda(q_z+iq_x)e^{iq_z L_z} & \\lambda(-q_z+iq_x)e^{-iq_z L_z}& i\\\\\n0 & (M_{\\eta}(k_y) - E)e^{iq_zL_z} & (M_{\\eta}(k_y) - E)e^{-iq_z L_z} & \\eta\n\\end{array} \\right) = 0.\n\\end{align}\n\\end{widetext}\nSimplifying this condition, we obtain a transcendental equation,\n\\begin{align}\n&\\frac{{\\rm tanh}\\left(L_z\\sqrt{(M_{\\eta}\/\\lambda)^2 - \\chi}\\right)}{L_z\\sqrt{(M_{\\eta}\/\\lambda)^2 -\\chi}} = -\\frac{\\lambda}{L_z\\eta M_{\\eta}},\n\\label{eq:chi}\n\\end{align}\nwhere $\\chi = (E\/\\lambda)^2 - q_x^2$. For all real solutions $\\chi$ of this equation, the energy has values $E = \\pm\\lambda\\sqrt{\\chi+q_x^2}$. No solutions of Eq. \\ref{eq:chi} exist with $\\chi<0$.\n\n\\begin{figure*}[ht]\n\t\\centering\n\t\\includegraphics[width=0.99\\textwidth]{Two.pdf}\n\t\\caption{The RKKY coupling between two spins (connected to same orbital) on opposite surfaces of the WSM slab, with the positions of the two spins at $(x,y,z) = (0,0,0)$ and $ (R,0,L_z=N_za)$. (a), (c) and (e) are results for the analytical wavefunctions keeping only the $n=0$ bands. (b), (d) and (f) show numerically evaluated results from the Green's function approach. With increasing thickness, all components decrease rapidly (shown in more detail in Fig.~\\ref{fig:withN} and tabulated in Table II). These results are for $\\mu=0$\n($k_F=0$). For all panels, $m=0.5~\\lambda$. \n}\\label{fig:two}\n\\end{figure*}\n\n\nFor bound-state solutions, i.e, when $q_z$ is imaginary, $\\chi < (M_{\\eta}\/\\lambda)^2$. The left hand side of Eq. \\ref{eq:chi} is a positive function with values between 0 and 1. Thus, such bound-state solutions are only possible when $\\eta M_{\\eta}<0$ as well as when $|L_zM_{\\eta}\/\\lambda|>1$, i.e., when $|M_{\\eta}|>\\lambda\/L_z$.\nDefining $\\chi' = L_z^2\\chi$ and $M'_{\\eta} = \\eta L_zM_{\\eta}\/\\lambda$, we rewrite\nEq. \\ref{eq:chi} as\n\\begin{align}\\label{eq:chim}\n&\\frac{{\\rm tanh}(\\sqrt{M_{\\eta}^{\\prime 2} - \\chi'})}{\\sqrt{M^{\\prime 2}_{\\eta} -\\chi'}} = -\\frac{1}{M^{\\prime }_{\\eta}}.\n\\end{align}\nThe various solutions of $\\chi'$ from Eq. \\ref{eq:chim} can be labeled by an index $n=0,1,..$ (with increasing values of $n$ corresponding to larger values of $\\chi'$) and the corresponding energy solutions $E_{n,\\pm}(q_x,q_y) = \\pm \\lambda \\sqrt{\\chi_n + q_x^2}$ gives rise to particle-hole symmetric bands. The minimum solution of $\\chi'$ is shown in Fig.~\\ref{fig:solchi}. The bands with $n=0$ contains all the Fermi arc states (when $k_y$ is between the Weyl nodes, in the Fermi arc interval) and low-energy bulk states (when $k_y$ is outside the interval).\n\nThe coefficient $(a\/b)$ for the states, Eq.~(\\ref{eq:states}), can be found from the boundary conditions at $z=0$ to be\n\\begin{align}\n\\frac{a}{b} = -\\frac{M_{\\eta} -E + \\eta \\lambda(q_x +i q_z)}{M_{\\eta} -E + \\eta \\lambda(q_x - i q_z)}.\n\\end{align}\nWe can then write down the wavefunctions. Defining\n$K = M_{\\eta}(k_y) -E, f=\\lambda(q_x-iq_z), g=\\lambda(q_x+iq_z)$, one finds\n\\begin{widetext}\n\\begin{align}\\label{eq:wf1}\n|\\psi \\rangle &= \\frac{1}{\\sqrt{N}} \\left\\{ (K+\\eta g)\\left(\\begin{array}{c}\ni f \\\\\nK\n\\end{array} \\right)e^{iq_z z} +(K+\\eta f)\\left(\\begin{array}{c}\n-i g \\\\\n-K\n\\end{array} \\right)e^{-iq_z z} \\right\\}.\n\\end{align}\n\\end{widetext}\nFor real $q_z = \\sqrt{\\chi - M_{\\eta}(k_y)^2}$ (when $\\chi>m^2$, $f=g^*$) the normalization factor has the form\n\\begin{align}\nN =& 2|K+\\eta f|^2(K^2+|f|^2)L\\nonumber \\\\\n&+{\\rm Im}\\left[(K+\\eta f)^2(K^2+g^2)\\left(\\frac{e^{-2iLq_z}-1}{q_z}\\right) \\right].\n\\end{align}\nFor purely imaginary $q_z=i\\kappa$ (when $\\chi r_2 \\geq r_3$ and in Sec.~\\ref{s:gaussian} we take $r_i=\\rho$ ($i=1,\\dots,d$).\n\nSolving Eq.~\\eqref{PDE} requires the knowledge of the effective nucleation and growth rates, $J(t)$ and $\\mathbf{v}(\\mathbf{r},t)$, respectively. It is important to realize that though the microscopic rates $I_0$ and $v_0$ are constant, the {\\it effective} rates are time-dependent. Assuming that no nuclei can form in the volume of an existing grain, the probability of formation of a new nucleus must decay in time since the volume available for its formation decreases in time. Kolmogorov, Mehl, Johnson and Avrami (KMJA) have derived an exact expression for the effective nucleation rate $J(t)$ in the case of RNG processes in $d$ dimensions\\cite{kolmogorov37,mehljohnson,avrami39,*avrami40,*avrami41}\n\\begin{eqnarray}\\label{Jt}\nJ(t) = I_0 e^{-\\left(\\frac{t-t_0}{t_{cI}}\\right)^{d+1}}\\Theta\\left(\\frac{t-t_o}{t_{cI}}\\right) \\equiv I_0 f(t).\n\\end{eqnarray}\nThe expression for $f(t)$ is well-established as discussed in numerous publications (see references in Ref.~\\onlinecite{teranPRB10}) and applies to RNG processes. $t_0$ is the incubation time, $t_{cI} = \\left[(d+1)\/I_0v_0\\omega_d\\right]^{1\/d+1}$ is the critical time for nucleation and $\\Theta$ is the Heaviside function. $\\omega_d$ is the constant appearing in the volume $\\Omega_d = \\omega_d r_1\\cdots r_d$ of the hypersphere.\\cite{teranPRB10} For example for $d=1,2,3$ we have $\\omega_1 = 2$, $\\omega_2 = \\pi$, $\\omega_3 = 4\\pi\/3$, respectively.\n\nThe effective growth rate similarly decreases in time since impingement stops the growth of a grain in the direction perpendicular to the contact line between grains. Once completely surrounded by other grains a given grain cannot grow anymore (see Fig.~\\ref{fig:tessellation}). The time-dependence of the effective growth rate is postulated to have a form similar to the one for $J(t)$. The discussion of Refs.~\\onlinecite{bergmannJCG08,teranPRB10,billMRS09} lead to consider an exponential time decay of the effective growth rate\n\\begin{eqnarray}\\label{vt}\nv(t) = v_0 e^{-\\left(\\frac{t-t_0}{t_{cv}}\\right)} \\Theta\\left(\\frac{t-t_0}{t_{cv}}\\right) \\equiv v_0 g(t)\\,.\n\\end{eqnarray}\nBoth the microscopic growth rate $v_0$ and the critical time $t_{cv}$ can be determined experimentally.\\cite{bergmannJCG08}\n\nFinally, to solve Eq.~\\eqref{PDE} we need an expression for the source term of nuclei, $D(\\mathbf{r})$. We consider two cases. In one case, we assume that only nuclei of a specific critical volume $\\Omega_c$ can form. In this case, $D(\\mathbf{r}) = \\delta\\left(\\Omega - \\Omega_c\\right)$ is given by a Dirac distribution. This assumption allows for an analytic treatment of the equation\\cite{bergmannJCG08,teranPRB10} and will be considered in the next section, Sec.~\\ref{s:anisotropic}, dealing with anisotropic growth rates. In Sec.~\\ref{s:gaussian} we generalize the theory by relaxing this condition. We consider the more physical standpoint according to which a cluster is thermodynamically stable when within a range of volumes around a mean value. We consider the case of a Gaussian distribution\n\\begin{eqnarray}\\label{DrgaussianOm}\nD(\\mathbf{r}) = \\frac{1}{\\epsilon\\,\\sqrt{2\\,\\pi}}\\,e^{-\\frac{1}{2\\epsilon^2}(\\Omega - \\Omega_c)^2},\n\\end{eqnarray}\nwhere $\\epsilon$ is a small real number and $\\Omega_c$ is now the mean value of a nucleus' volume. The study of the nucleation barrier that has to be overcome by a cluster of atoms or molecules and stabilizes the nucleus has been studied in detail in Ref.~\\onlinecite{shiJMR91,*shiJMR91b,*shiMCP94}. Their study gives an expression for $\\epsilon$ that can be used to estimate its value for specific systems.\n\n\n\\section{Anisotropic growth rate}\\label{s:anisotropic}\n\n\\begin{figure*} \n\\begin{center}\n\\includegraphics[scale=0.5]{Lokovic_Fig2a.eps}\n\\includegraphics[scale=0.5]{Lokovic_Fig2b.eps}\n\\end{center}\n\\caption[Anisotropic grain and space of grain sizes]{(a) Ellipsoidal grains resulting from an anisotropic growth rate (see text). (b) Space of grain sizes spanned by the semi-axes of the ellipsoid. The non-equilibrium GSD $N(\\mathbf{r},t)$ is calculated in this space. The growth rate $\\mathbf{v}$ points along a radial line in that space since we assume that the anisotropic growth changes the volume of the grain but keeps its shape invariant (see text).}\n\\label{fig:ellipsoid}\n\\end{figure*}\n\nIn the theory developed in Refs.~\\onlinecite{bergmannJCG08,teranPRB10,billMRS09} we assumed that the growth of grains is isotropic, leading to spherical grains when they do not impinge on each other. This assumption is not always valid as even isolated grains often appear non-spherical in shape. The shape is determined by either extrinsic or intrinsic factors. A patterned substrate containing steps may lead to filamentary grains and is an example of an extrinsic factor. On the other hand, when the interaction between atoms or molecules is directional, the grain may also become non-spherical. For example, in Silicon the growth rate is different along different principal axes of the crystal.\\cite{kakinumaJVSTA95,*hartman73} Another example is the case of planar molecules that often experience a strong binding when stacked, but only weakly interact with each other through the edges of the molecules.\\cite{*gentryPRB09} Although thermodynamic fluctuations will favor the stacking, the binding through the edges may not be completely neglected, leading to an ellipsoidal shape of grains (see Fig.~\\ref{fig:ellipsoid}).\\\\\nIn compounds made of atoms rather than molecules ({\\it e.g.} polycrystalline Si) the formation of periodic bond chains and the mobility of atoms determine the crystal axis along which the growth preferably occurs.\\cite{hartman73} Hence, interactions between the entities constitute an intrinsic factor leading to anisotropic growth of grains. It is important to note that while extrinsic factors generally impose the growth of grains along specific directions of space, the intrinsic factors allow for a random orientation of the ellipsoids in the volume of the sample. Furthermore, since microscopic interactions between molecules are responsible for the shape of the grain, the growth of a grain will mainly consist of an increase in volume and keep the shape invariant. That is, the ratio of semi-axes of the ellipse (for $d=2$) or the ellipsoid ($d=3$) is constant: $r_j\/r_1 = r_{cj}\/r_{c1}$ for $j=2,3$, $r_1$ ($r_{c1}$) being the major semi-axis of the grain (nucleus). In spherical coordinates it means that $\\varphi$ and $\\theta$ are constant in $\\mathbf{r}$-space (see Fig.~\\ref{fig:ellipsoid}). The calculations performed below rely on these physical assumptions. When considering anisotropic growth we will assume it is due to intrinsic factors. We also assume the absence of nucleation centers in the amorphous sample, and thus a homogeneous and isotropic nucleation rate.\n\nThe effective growth rate can be written in the form\n\\begin{eqnarray}\\label{vrt}\n\\mathbf{v}(\\mathbf{r}, t)&= v_1(t)\\hat{r}_1 + v_2(t)\\hat{r}_2 + v_3(t)\\hat{r}_3 = v(t)\\,\\hat{r},\n\\end{eqnarray}\nwhere the last equality is obtained by introducing spherical coordinates in $\\mathbf{r}$-space and using the fact mentioned above that the shape of the ellipsoid does not change as the grain grows. $v(t)$ is given by Eq.~\\eqref{vt}. We emphasize that the vector is written in the space of semi-axes of the ellipsoid $(r_1,r_2,r_3)$ and not in real space $(x,y,z)$. This expression for $\\mathbf{v}(\\mathbf{r},t)$ is valid in absence of diffusion of atoms during the crystallization process as is for example the case in solid phase crystallization.\\cite{bergmannJCG08} We also write the nucleation source term in polar (spherical) coordinates\n\\begin{eqnarray}\\label{Drdelta}\nD(\\mathbf{r}) = \\delta(\\Omega_d - \\Omega_{c,d}) = \\frac{1}{A_{c,d}}\\delta(r-r_c)\n\\end{eqnarray}\nwhere $A_{c,d}$ is the area of the nucleus in $d$ dimensions\n\\begin{subequations}\\label{Acd}\n\\begin{eqnarray}\nA_{c,2} &=& 2\\,\\pi\\,r_c\\,\\cos\\varphi\\,\\sin\\varphi,\\\\\nA_{c,3} &=& 4\\,\\pi\\,r_c^2\\,\\sin^2\\theta \\cos\\theta \\sin\\varphi \\cos\\varphi.\n\\end{eqnarray}\n\\end{subequations}\n\nWith Eqs.~(\\ref{Jt},\\ref{vrt}-\\ref{Acd}) the partial differential equation, Eq.~\\eqref{PDE}, can be solved analytically for the anisotropic case. The calculation is more involved than for the isotropic growth rate but follows the procedure presented in Ref.~\\onlinecite{teranPRB10}. Hence, we do not repeat the derivation here. The general result for the non-equilibrium GSD with anisotropic growth rate is\n\\begin{widetext}\n\\begin{eqnarray}\\label{solanistropic}\nN(\\gamma,\\tau) &=& \\left(\\frac{I_o}{v_0}\\right) \\frac{1}{A_{\\infty,d}}\\,\\gamma^{d-1}\\,\\frac{f[\\sigma(\\gamma,\\tau)]}{g[\\sigma(\\gamma,\\tau)]\n\\left\\{\\Theta\\left(\\frac{\\gamma-\\gamma_c}{1-\\gamma_c}\\right) - \\Theta\\left[\\frac{\\gamma-\\gamma_{max}(\\tau)}{1-\\gamma_c}\\right]\\right\\},\n\\end{eqnarray}\n\\end{widetext}\nwhere $f$ and $g$ are given by Eqs.~(\\ref{Jt},\\ref{vt}) with time replaced by $\\sigma(\\gamma,\\tau)$ below. This expression is written in terms of the dimensionless quantities\n\\begin{eqnarray} \\label{dim}\n\\gamma=\\frac{r}{r_\\infty},\\quad \\tau=\\frac{t}{\\sqrt{t_{cv}t_{cI}}},\n\\end{eqnarray}\nwhere $r_\\infty$ is the magnitude of $\\mathbf{r}$ for the largest grain found at full crystallization, $\\gamma_c = r_c\/r_\\infty$, and $\\sigma(t)$ is given by\\cite{teranPRB10}\n\\begin{eqnarray}\\label{sigmart}\n\\sigma(\\gamma,\\tau) = \\tau_0 + t_r\\,\\ln\\left( \\frac{\\gamma-\\gamma_c}{\\mathcal{V}_0} + e^{-(\\tau-\\tau_0)\/t_r} \\right)^{-1},\n\\end{eqnarray}\nwith $t_r^2 = t_{cv}\/t_{cI}$ and ${\\mathcal V}_0 = t_{cv}v_0\/r_\\infty$.\n\nThe central result, Eq.~\\eqref{solanistropic}, is a generalization of Eq.~(21) in Ref.~\\onlinecite{teranPRB10} to the case of anisotropic growth rates. It can be implemented in a variety of ways to describe experimental data or extract specific parameters of the model by fitting to experimental findings.\\cite{billMRS09} The result has several interesting features we now describe.\n\nThe term in curly parenthesis is easily understood. It states that the non-equilibrium GSD $N(\\gamma,\\tau)$ is only non-zero in the interval $\\gamma_c\\leq \\gamma \\leq \\gamma_{\\rm max}$, that is, for grain sizes between those of a nucleus ($\\gamma_c$) and of the largest grain found at time $t$ in the sample [$\\lim_{\\tau\\to\\infty} \\gamma_{\\rm max}(\\tau) = 1$ since $r_{\\rm max}(t\\to\\infty) = r_\\infty$)].\nFurther, the non-equilibrium GSD is proportional to the ratio of microscopic nucleation and growth rates $I_0\/v_0$. It is also proportional to the rate $f(\\sigma)\/g(\\sigma)$. Note, however, that since $\\sigma(\\gamma,\\tau)$ is a non-linear function of time and grain size, the ratio is {\\it not} simply the ratio of Eqs.~\\eqref{Jt} and \\eqref{vt}. Finally, the non-equilibrium GSD is inversely proportional to the surface area $A_{\\infty,d}$ of the largest grain found at full crystallization. The expression for $A_{\\infty,d}$ is the same as in Eq.~\\eqref{Acd} with $r_c$ replaced by $r_\\infty$.\n\nEq.~\\eqref{solanistropic} is a remarkable result. It states that the only difference between isotropic and anisotropic growth rates is the presence of $A_{\\infty,d}$ in the prefactor. Thus, the results derived in Refs.~\\onlinecite{bergmannJCG08,teranPRB10} are robust against the generalization to anisotropic growth rates!\n\n\\begin{figure}[htb] \n\\begin{center}\n\\includegraphics[scale=0.55]{Lokovic_Fig3.eps}\n\\end{center}\n\\caption[Normalized GSD, anisotropic growth, $\\tau$ and $d=3$]{Normalized grain size distribution in the early stage ($\t\\tau = 1$), intermediate stage ($\\tau=2.4$) and late stage ($\\tau=5$) of crystallization for $d=3$. In the early stage only the \tnucleation peaks is observed. At intermediate stage the nucleation peak appears concomitantly with the broader growth \tpeak. At late stages of crystallization, nucleation has disappeared and only the growth peak remains. The curve at full \tcrystallization $\\tau \\geq 5$ is lognormal like.\\cite{teranPRB10} Parameters are $\\gamma_c = 0.0001, \\tau_0 = 0, t_r = \t0.75, \\mathcal{V}_0 = 1$ (see Refs.~\\onlinecite{bergmannJCG08,billMRS09}).}\n\\label{fig:GSDtau}\n\\end{figure}\n\nFig.~\\ref{fig:GSDtau} displays the normalized non-equilibrium GSD $\\bar{N}(\\gamma,\\tau) = N(\\gamma,\\tau)\/N(\\tau)$, where\n\\begin{eqnarray}\\label{Nt}\nN(\\tau) = \\int_{\\gamma_c}^{\\gamma_{\\rm max}(\\tau)} N(\\gamma,\\tau')\\,d\\tau',\n\\end{eqnarray}\nfor early, intermediate and late stages of crystallization\\cite{teranPRB10} in three dimensions ($d=3$).\nIn the early stage, only a nucleation peak appears as the initial process is dominated by the formation of nuclei. The intermediate stage is characterized by the simultaneous appearance of a nucleation peak (near $\\gamma_c$) and a growth peak. That stage occurs over a very short period; in the present calculation, full crystallization is reached for $\\tau \\gtrsim 5$ and the time interval of the intermediate stage is $2\\lesssim \\tau \\lesssim 3$. The late stage of crystallization is obtained for $\\tau \\gtrsim 3$ where nucleation is almost completely suppressed and grains continue growing in areas of untransformed material until complete tessellation of the sample is achieved. Thus, in the late stage of crystallization the shape of the distribution remains unchanged but shifts towards larger grain sizes. As discussed in Refs.~\\onlinecite{bergmannJCG08,teranPRB10} the GSD for $\\tau \\geq 5$ in Fig.~\\ref{fig:GSDtau} displays a lognormal like distribution.\nThe results of the GSD for $d=2$, that is for a thin film where the average grain size at full crystallization is larger than the thickness of the film, are qualitatively similar to the $d=3$ case depicted in Fig.~\\ref{fig:GSDtau} and is therefore not shown here (see Ref.~\\onlinecite{teranPRB10}). \n\nThe remarkable conclusion of this section is that the non-equilibrium GSD for isotropic and anisotropic growth rates are qualitatively similar as they only differ by the constant prefactor $A_{\\infty,d}^{-1}$.\n\n\n\\section{Gaussian nucleation rate}\\label{s:gaussian}\n\nThe theory developed in Refs.~\\onlinecite{bergmannJCG08,teranPRB10,billMRS09} and used in the previous section considered the simplified expression, Eq.~\\eqref{Drdelta}, for the source term of nuclei $D(\\mathbf{r})$. We assumed that only nuclei of a well-defined specific volume $\\Omega_c$ are stable in the system. Such assumption is physically not entirely realistic as grains with a few more or a few less molecules are likely to be thermodynamically stable as well. In the present section we analyze how the non-equilibrium GSD is affected if one relaxes the condition imposed by Eq.~\\eqref{Drdelta}. Contrary to the previous section but in accordance with Refs.~\\onlinecite{bergmannJCG08,teranPRB10,billMRS09} we assume isotropic growth of grains, leading to spherically shaped unimpinged grains.\n\nTo generalize the theory we assume that the Dirac distribution appearing in Eq.~\\eqref{Drdelta} is replaced by the Gaussian distribution, Eq.~\\eqref{DrgaussianOm}. In dimensionless quantities and using spherical coordinates $D(\\mathbf{r})$ reads\n\\begin{eqnarray}\nD(\\gamma) = \\frac{1}{r_\\infty\\, \\epsilon'\\,\\sqrt{2\\,\\pi}}\\,e^{-\\frac{1}{2\\,\\epsilon'^2}(\\gamma - \\gamma_c)^2}\\,\n\\end{eqnarray}\nwhere $\\epsilon' \\equiv \\epsilon\/r_\\infty$. This term has to be inserted into the right hand side of Eq.~\\eqref{PDE}. Contrary to all previous calculations the present form of the differential equation allows solving for the non-equilibrium GSD analytically except for one integral. Defining\n\\begin{eqnarray}\\label{Ntilde}\n\\tilde{N}(\\gamma,\\tau) = \\gamma^{d-1}\\,A_{\\infty, d}\\,r_\\infty^2\\,N(\\gamma,\\tau),\n\\end{eqnarray}\nthe normalized GSD for a Gaussian nucleation rate becomes\n\\begin{widetext}\n\\begin{eqnarray}\\label{solgaussian}\n\\tilde{N}(\\gamma,\\tau) &=& \\frac{2\\,\\epsilon'\\,t_r^2}{\\gamma_c\\sqrt{2\\,\\pi}}e^{-\\frac{\\gamma_c^2}{2\\,\\epsilon'^2}}\\,\\frac{I(\\sigma)}{v(\\sigma)}\\,\\delta_{d,2\n\\mp \\frac{t_r}{2\\,\\gamma_c^{d-1}}\\int_{\\tau_0}^\\tau \\frac{I(\\tau')}{\\epsilon' \\sqrt{2\\,\\pi}}\\left[\\gamma - u(\\tau',\\tau)\\right]^{d-1\n\\exp\\left\\{-\\frac{\\left[\\gamma \\mp \\gamma_c - u(\\tau',\\tau)\\right]^2}{2\\,\\epsilon'^2}\\right\\}d\\tau',\n\\end{eqnarray}\n\\end{widetext}\nwhere the minus (plus) sign is for $d=2$ ($d=3$), Eq.~\\eqref{sigmart} defines $\\sigma$, and\n\\begin{eqnarray}\nu(\\tau',\\tau) = \\mathcal{V}_0 \\left[\\exp\\left(-\\frac{\\tau-\\tau_0}{t_r}\\right) - \\exp\\left(-\\frac{\\tau'-\\tau_0}{t_r}\\right)\\right].\n\\end{eqnarray}\nBecause of the Kronecker symbol $\\delta_{d,2}$ the first term is absent for $d=3$. That term vanishes in the limit $\\epsilon\\to 0$, in which case the Gaussian in the second term reduces to the Dirac distribution and the integral can be solved, leading to the result previously established in Refs.~\\onlinecite{bergmannJCG08,teranPRB10}.\n\nSince for $\\epsilon\\neq 0$ the above integral cannot be solved analytically we performed numerical calculations to study the time dependent form of the GSD. To comply with typical experimental results (see, {\\it e.g.}, Ref.~\\onlinecite{shiJMR91,*shiJMR91b,*shiMCP94}) we assumed that the width of the Gaussian is of the order of a tenth of the average nuclei radius, $\\epsilon\\sim 0.1 r_c$. The results for the normalized distribution differ from those of the previous section represented in Fig.~\\ref{fig:GSDtau} only for early stages of crystallization, and only marginally. The nucleation peak is slightly wider in the early and intermediate stages. Hence, Eq.~\\eqref{solgaussian} should only be used to discuss experimental studies of early stages of nucleation. For all other cases, a nucleation source term with a Dirac distribution is sufficient.\n\n\\section{Conclusion}\\label{s:conclusion}\nWe studied two major generalizations of the theory established in Refs.~\\onlinecite{bergmannJCG08,teranPRB10} to determine the non-equilibrium grain size distribution during the crystallization of a solid. In the first, we assumed that the growth of grains can be anisotropic, as observed in some experiments. We showed that the final non-equilibrium grain size distribution remains essentially unaffected by this generalization [except for a constant prefactor in $N(\\mathbf{r},t)$]. In the second extension of the theory we considered the case where nuclei with variable size within a physically realistic range can be thermodynamically stable and develop into grains. Using a Gaussian distribution to model the source of nuclei, the partial differential equation could not be completely solved analytically. The numerical results showed that only for early stages of crystallization, when nucleation dominates the process, did the generalization affect the grain size distribution quantitatively. The main conclusion of this work is that the theory established in Refs.~\\onlinecite{bergmannJCG08,teranPRB10} is very robust against these important generalizations. This is particularly surprising for the generalization to anisotropic grain growth. This is explained in part by the fact that we assume the shape of unimpinged grains to remain unaltered during the growth. This assumption of our model is physically justified by the fact that the formation of a nucleus and its growth into a grain are determined by the microscopic interactions between atoms and molecules, the intrinsic factors described in the introduction.\n\nIn both generalizations we found the previously obtained behavior of the non-equilibrium grain size distribution, which goes through three stages during the crystallization: a nucleation dominated early stage, an intermediate stage where nucleation and growth have similar strength and a late stage of crystallization where growth dominates.\\cite{teranPRB10} We also note that in all cases studied the non-equilibrium grain size distribution is found to depend on the ratio of the effective nucleation and growth rates, $N(r,t) \\propto I[\\sigma(r,t)]\/v[\\sigma(r,t)]$ with, however, a non-trivial function $\\sigma(r,t)$ of grain size and time. The assumptions leading to that result are the random nucleation and growth of grains at fixed thermodynamic conditions and in the absence of coalescence or secondary grain formation. When using our expression to describe the crystallization of specific materials it is important to carefully consider the latter assumption since coalescence may affect the grain size distribution.\n In conclusion, Eq.~\\eqref{solanistropic} provides an excellent description of the non-equilibrium grain size distribution in systems where random nucleation and growth occurs and can be used to analyze experimental data.\n\n\\begin{acknowledgements}\nWe gratefully acknowledge the support of the Research Corporation, the Army Research Laboratory and the CNSM block grant at CSU Long Beach.\n\\end{acknowledgements}\n\n\\newpage\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\section{Combining Gaussian variables}\n\\label{sec:combining_gauss}\n\\input{gauss_appendix.tex}\n\n\\section{Variational Bayes}\n\\label{sec:variational_bayes}\nVariational Bayes is an approximation technique commonly used when evaluation of the posterior distribution or evaluation of expectations with respect to the posterior distribution is computationally intractable. This can be because of high dimensionality of the posterior distribution or the analytic intractability of its expression.\n\nTo resolve the computational expense the posterior distribution $p(z|x)$ \nis approximated with an analytically tractable distribution $q(z|x)$. \nIn this setting the log-likelihood of the data is given by,\n\\begin{equation}\n \\log p(x) = \\text{KL}(q||p) + \\mathcal{L}(q)\n\\end{equation}\nwhere the first term is the KL-divergence,\n\\begin{equation*}\n \\text{KL}(q||p) = -\\int q(z) \\log \\frac{p(x|z)}{q(z)} dz\n\\end{equation*}\nand the second term is the lower bound,\n \\begin{equation*}\n \\mathcal{L}(q) = \\int q(z) \\log \\frac{p(x,z)}{q(z)} dz\n \\end{equation*}\n\n\\section{Derivations for transition dynamics (Sec \\ref{sec:op_estim})}\n\\input{derivation_op_estim}\n\n\\section{Derivations for PPCA (Sec \\ref{sec:im_op_estim})}\n\\input{derivation_img_op_estim}\n\n\\section{Derivations for PNPCA (Sec \\ref{sec:nonlin_im_op_estim})}\n\\input{derivation_nonlinear_img_op_estim}\n\n\\section{Introduction}\\label{sec:introduction}}\n\n\n\n\n\n\\IEEEraisesectionheading{\\section{Introduction}\n\\label{sec:learing_lie_generators_intro}}\n\\input{intro.tex}\n\n\\section{Estimating transition dynamics}\n\\label{sec:op_estim}\n\\label{sec:learning_lie_generators_operator_estim}\n\\input{operator_estim.tex}\n\n\\section{Joint estimation of PPCA image representations and transition dynamics}\n\\label{sec:im_op_estim}\n\\label{sec:learning_lie_generators_image_op_estim}\n\\input{image_op_estim.tex}\n\n\\section{Joint estimation of PNPCA image representations and transition dynamics}\n\\label{sec:nonlin_im_op_estim}\n\\label{sec:learning_lie_generators_nonlin_image_op_estim}\n\\input{nonlin_image_op.tex}\n\n\\section{Related Work}\n\\label{sec:learning_lie_generators_related_work}\n\\input{rw.tex}\n\n\\section{Conclusion}\n\\input{conclusion.tex}\n\\appendices\n\\label{sec:appendix}\n\\input{appendix.tex}\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\\ifCLASSOPTIONcompsoc\n \n \\section*{Acknowledgments}\n\\else\n \n \\section*{Acknowledgment}\n\\fi\nThe authors would like to thank Andrew Jaegle and Camillo J. Taylor for their insights and invaluable conversation. Support was provided by the following grants: NSF IIS 1703319, NSF\nTRIPODS 1934960, NSF CPS 2038873, ARL RCTA W911NF-10-2-0016, ARL DCIST\nCRA W911NF-17-2-0181, and ONR N00014-17-1-2093. \n\n\\ifCLASSOPTIONcaptionsoff\n \\newpage\n\\fi\n\n\n\n\n\n\n\n\\bibliographystyle{unsrt}\n\n\n\n\\subsection{Posterior distribution of the image representation}\n\\label{sec:posterior_image}\nThe posterior distribution of the latent image representation given in equation (\\ref{eq:im_op_im_rep_posterior}) is derived using the equations in Section \\ref{sec:combining_gauss}. The conditional distribution of the image given\nthe latent image representation is a linear Gaussian distribution and the prior distribution on the latent\nimage representation is Gaussian. Using the equations in Section \\ref{sec:combining_gauss}, the posterior distribution is given by\n\\begin{equation*}\n p(z^i|x^i, \\theta_R) = \\mathcal{N}(z^i|u, \\sigma^{-2}\\Sigma)\n\\end{equation*}\nwhere $\\theta_R = (W, \\mu, \\sigma)$ are the representation embedding parameters and\n\\begin{equation*}\n \\Sigma = (I + W^TW)^{-1}, \\quad u = \\Sigma^{-1} W^T x^i_\\mu\n\\end{equation*}\nwhere $x^i_\\mu=x^i - \\mu$ is $x_i$ mean-centered.\n\n\\subsection{Complete-data likelihood}\nThe complete-data likelihood is the product of the data and latent variable distributions,\n\\begin{align*}\n \\prod_i& p(\\lambda^i, z^{i+1}, x^{i+1}, z^i| x^i, \\theta_T, \\Lambda, \\theta_R) = \\\\&\\prod_i p(x^{i+1} | z^{i+1}, \\theta_R)\\, p(z^{i+1} | z^i, \\lambda^i , \\theta_T, \\Lambda)\\, p(\\lambda^i| \\Lambda) \\,p(z^i | x^i, \\theta_R).\n\\end{align*}\nwhere $\\theta_T=(G,\\Omega)$ are the transformation parameters.\nUsing the result from equation (\\ref{eq:deriv_op_estim_complete_data})\nthe complete-data likelihood can be expressed\n\\begin{align*}\n \\prod_i p(&\\lambda^i, z^{i+1}, x^{i+1}, z^i | x^i, \\theta_T, \\Lambda, \\theta_R) \n =\\\\& \\prod_i p(x^{i+1} | z^{i+1}, \\theta_R) \\,p(\\lambda^i, z^{i+1} | z^i, \\theta_T, \\Lambda)\\, p(z^i | x^i, \\theta_R)\n\\end{align*}\nThe distribution\n\\begin{align*}\n p(\\lambda^i, z^{i+1} | z^i, \\theta_T, \\Lambda) &= p(z^{i+1} | z^i, \\lambda^i, \\theta_T, \\Lambda)p(\\lambda^i| \\Lambda) \\\\&= \\mathcal{N}(\\lambda^i, z^{i+1} | m, R^{-1})\n\\end{align*}\nwith\n\\begin{equation*}\n R^{-1} =\n \\begin{pmatrix}\n \\Lambda & \\Lambda A^T\\\\\n A\\Lambda & \\Omega + A\\Lambda A^T\n \\end{pmatrix}, \\quad\n m = \\begin{pmatrix}\n 0\\\\\n z^i\n \\end{pmatrix}\n\\end{equation*}\nand\n\\begin{equation*}\nA_{\\cdot,j} = G^j z^i,\n\\end{equation*}\nis given in equation (\\ref{eq:deriv_op_estim_complete_data}).\n\n\\subsection{Posterior distribution of the latent variables}\n\\label{sec:im_op_estim_latent}\nThe latent posterior distribution of the combination coefficients $\\lambda^i$ and the latent image distributions $z^i$ and $z^{i+1}$ given in equation \n(\\ref{eq:latent_posterior})\nis derived using the equations in Section \\ref{sec:combining_gauss}.\n\nThe conditional distribution of the transformed image $x^{i+1}$ given the latent representation of the transformed image $z^{i+1}$ is a linear Gaussian distribution and the conditional distribution of the latent representation of the transformed image $z^{i+1}$ given the latent representation of the initial image $z^i$ is Gaussian. Using the equations in Section \\ref{sec:combining_gauss}, these distributions are combined to give the posterior distribution of the latent representation of the transformed image $z^{i+1}$ as\n\\begin{align*}\n p(& z^{i+1} | x^{i+1}, z^i, \\lambda^i, \\theta_T, \\theta_R) =\\\\ &\\frac{ p(x^{i+1} | z^{i+1}, \\theta_R)p(z^{i+1} | z^i, \\lambda^i, \\theta_T) }{p(x^{i+1} | \\theta_R)} = \\mathcal{N}(z^{i+1}| \\gamma, \\Gamma)\n\\end{align*}\nwhere\n\\begin{align*}\n \\gamma = \\Gamma \\{ \\sigma^{-2}W^T x^{i+1}_\\mu + \\Omega^{-1}(z^i + A\\lambda^i)\\}, \\\\\n \\Gamma = (\\Omega^{-1} + \\sigma^{-2} W^T W)^{-1}, \\quad A_{\\cdot,m} = G^j z^i.\n\\end{align*}\nThe latent posterior distribution can then be expressed,\n\\begin{align*}\n p(&\\lambda^i, z^{i+1}, z^i | x^{i+1}, x^i, \\theta_T, \\Lambda, \\theta_R) = \\\\& p( z^{i+1} | x^{i+1}, z^i, \\lambda^i, \\theta_T, \\theta_R )\\, p(\\lambda^i| \\Lambda)\\, p(z^i | x^i, \\theta_R).\n\\end{align*}\nThis latent posterior distribution is non-Gaussian, however, \nit is still in the exponential family\nand can be expressed in the canonical form,\n\\begin{equation*}\np(x|\\eta) = h(x)\\exp\\{\\eta(\\theta)^T T(x)-A(\\theta)\\},\n\\end{equation*}\nwhere,\n\\begin{align*}\n h(x) =& 3\\log \\frac{1}{(2\\pi)^{d\/2}}\\\\\n A(\\theta) =& \\Gamma\\sigma^{-2}W^T x^{i+1}_\\mu(x^{i+1}_\\mu)^T W\\sigma^{-2} \\\\&+ \\sigma^2\\Sigma^{-2}\\Sigma^{-1}W^T x^{i+1}_\\mu (x^{i+1}_\\mu)^T W\\Sigma^{-1}\\\\& + \\log\\frac{1}{|\\Gamma|^{1\/2}} + \\log\\frac{1}{|\\Lambda|^{1\/2}} + \\log\\frac{1}{|\\sigma^{-2}\\Sigma|^{1\/2}}\\\\\n \\eta(\\theta)= & (\\Gamma^{-1}, -2\\sigma^{-2}W^T x^{i+1}_\\mu, -2\\Omega^{-1}, -2\\Omega^{-1}G^j,\\\\& \\Omega^{-1}\\Gamma\\Omega^{-1} + \\sigma^2\\Sigma^{-1}, (G^k)^T\\Omega^{-1}\\Gamma\\Omega^{-1}G^j,\\\\& 2\\Omega^{-1}\\Gamma\\sigma^{-2}W^T x^{i+1}_\\mu-2\\sigma^2\\Sigma^{-1}\\Sigma^{-1}W^T x^i_\\mu,\\\\& 2(G^j)^T\\Omega^{-1}\\Gamma\\sigma^{-2}W^T x^{i+1}_\\mu, 2(G^j)^T\\Omega^{-1}\\Gamma\\Omega^{-1}, \\Lambda)\\\\\n T(x) = & (z^{i+1}(z^{i+1})^T, z^{i+1}, z^i(z^{i+1})^T, \\lambda^i_j z^i(z^{i+1})^T, z^i(z^i)^T,\\\\& \\lambda^i_j\\lambda^i_k z^i(z^i)^T, z^i, \\lambda^i_j(z^i)^T, \\lambda^i_j z^i(z^i)^T, \\lambda^i(\\lambda^i)^T)\n\\end{align*}\n\nThe sufficient statistics, $T(x)$, are computed by taking partial derivatives of $A(\\theta)$ with respect to $\\eta(\\theta)$.\n\n\\subsection{Update equations for the model parameters}\nThe update equation for each distribution parameter is found by computing the partial derivative of the complete-data log-likelihood function with respect to the parameter and solving for the parameter value at the critical point.\nThe complete-data log-likelihood is given by\n\\begin{align*}\n \\log p(&\\lambda^i, z^{i+1}, x^{i+1}, z^i | \\theta_T, \\Lambda, \\theta_R) =\\\\& \\log \\mathcal{N}(\\lambda^i, z^{i+1} | m, R^{-1}) + \\log \\mathcal{N}(z^{i+1}|u^{i+1}, \\Sigma) \\\\&+ \\log \\mathcal{N}(z^i|u^i, \\Sigma)\n\\end{align*}\nSetting the partial derivative of the complete-data log-likelihood function with respect to parameter $G$ equal to zero gives, \n\\begin{equation}\n \\label{eq:deriv_im_op_estim_G}\nG = \\left(\\sum_i \\Delta z^i(z^i\\otimes\\lambda^i)^T\\right)\\left(\\sum_i z^i(z^i)^T\\otimes \\lambda^i(\\lambda^i)^T \\right)^{-1}\n\\end{equation}\nwhere $\\Delta z^i = z^{i+1} - z^i$ is the difference between sequential image representations.\n\nSetting the partial derivative of the complete-data log-likelihood function with respect to parameter $\\Omega$ equal to zero gives, \n\\begin{align}\n\\label{eq:deriv_im_op_estim_Omega}\n \\Omega = \\frac{1}{M}\\Big( \\sum_i& \\Delta z^i(\\Delta z^i)^T - 2A\\lambda^i(\\Delta z^i)^T + A\\lambda^i(\\lambda^i)^TA^T\\Big).\n\\end{align}\nUpdate equations \\ref{eq:deriv_im_op_estim_G} and \\ref{eq:deriv_im_op_estim_Omega} are derived similarly to the update equations in Section \\ref{sec:op_estim}.\nSetting the partial derivative of the complete-data log-likelihood function with respect to parameter $W$ equal to zero gives, \n\\begin{align*}\n 0 = \\sum_i& \\frac{\\partial}{\\partial W}\\log \\mathcal{N}(x^{i+1}|W z^{i+1} + \\mu, \\sigma^{2}I) \\\\&+ \\frac{\\partial}{\\partial W}\\log \\mathcal{N}(x^i|W z^i + \\mu, \\sigma^2I).\n\\end{align*}\nReordering terms gives\n\\begin{align*}\n W&\\left(\\sum_i z^{i+1}(z^{i+1})^T + z^i(z^i)^T\\right) =\\\\ &\\left(\\sum_i x^{i+1}_\\mu (z^{i+1})^T + x^i_\\mu (z^i)^T\\right),\n\\end{align*}\nand solving for $W$ gives\n\\begin{align*}\n W = &\\left(\\sum_i x^{i+1}_\\mu (z^{i+1})^T + x^i_\\mu (z^i)^T\\right) \\\\&\\left(\\sum_i z^{i+1}(z^{i+1})^T + z^i(z^i)^T\\right)^{-1}.\n\\end{align*}\nSetting the partial derivative of the complete-data log-likelihood function with respect to parameter $\\sigma$ equal to zero gives, \n\\begin{align*}\n 0 =& \\sum_i \\frac{\\partial}{\\partial \\sigma^{-2}}\\log \\mathcal{N}(x^{i+1}|W z^{i+1} + \\mu, \\sigma^{2}I) \\\\&\\qquad+ \\frac{\\partial}{\\partial \\sigma^{-2}}\\log \\mathcal{N}(x^i|W z^i + \\mu, \\sigma^2I)\\\\\n =& \\sum_i \\frac{\\partial}{\\partial \\sigma^{-2}} \\log|\\sigma^{-2}I| + \\frac{\\partial}{\\partial \\sigma^{-2}}\\sigma^{-2}I(x^i_\\mu - W z^i)(x^i_\\mu - W z^i)^T \\\\ &\\qquad+ \\frac{\\partial}{\\partial \\sigma^{-2}}\\sigma^{-2}I (x^{i+1}_\\mu - W z^{i+1})(x^{i+1}_\\mu - W z^{i+1})^T.\n\\end{align*}\nReordering terms gives,\n\\begin{align*}\n N\\sigma^2I = \\sum_i&(x^i_\\mu - W z^i)(x^i_\\mu - W z^i u)^T \\\\&+ (x^{i+1}_\\mu - W z^{i+1})(x^{i+1}_\\mu - W z^{i+1})^T\n\\end{align*}\nand application of the trace operator gives the update,\n\\begin{align*}\n \\sigma^2 =& \\frac{1}{ND} \\text{tr}\\Big(\\sum_i x^i_\\mu (x^i_\\mu)^T + x^{i+1}_\\mu (x^{i+1}_\\mu)^T \\\\&\\qquad\\qquad\\qquad+ W(z^i(z^i)^T + z^{i+1}(z^{i+1})^T)W^T\\Big) \\\\&-\\frac{2}{ND} \\text{tr}\\left(\\sum_i W(z^i(x^i_\\mu)^T + z^{i+1}(x^{+1}i_\\mu)^T) - \\mu\\mu^T\\right)\n\\end{align*}\n\n\\subsection{Posterior distribution of the image representation}\nComputation of the posterior distribution of the latent image representation given in Section \\ref{sec:nonlin_im_op_estim} is analytically intractable. A common approach in this setting is to approximate the distribution by an analytically tractable variational distribution. In Section \\ref{sec:nonlin_im_op_estim} the posterior distribution of the latent image representation is approximated by the variational posterior\n\\begin{equation}\n \\label{eq:deriv_nonlin_var_posterior}\n q(z^k|x^k, w_\\phi) = \\mathcal{N}(z^k | \\phi(x^k)_\\mu, \\phi(x^k)_\\sigma)\n\\end{equation}\nwhere the mean and standard deviation of the distribution are functions of the encoding network parameters and the image $x^k$.\n\\subsection{Complete-data likelihood}\nThe complete-data likelihood is the product of the data and latent variable distributions,\n\\begin{align*}\n \\prod_i &p(\\lambda^i, z^{i+1}, x^{i+1}, z^i | x^i, \\theta_T, \\Lambda, w_\\psi, w_\\phi) = \\\\&\\prod_i p(x^{i+1} | z^{i+1}, w_\\psi)\\, p(z^{i+1} | z^i, \\lambda^i ,\\theta_T)\\, p(\\lambda^i| \\Lambda)\\, p(z^i | x^i, w_\\psi\n\\end{align*}\nSince the posterior distribution is intractable, the\nvariational posterior in equation (\\ref{eq:deriv_nonlin_var_posterior}) is used instead. To ensure the variational\nposterior is a good approximation of the true posterior distribution\nrequires that the KL-divergence between the variational posterior and the true posterior (see Section \\ref{sec:variational_bayes}) is added to the complete-data log-likelihood function. The resulting form of the complete-data log-likelihood is\n\\begin{align*}\n \\sum_i &\\log p(\\lambda^i, z^{i+1}, x^{i+1}, z^i | x^i, \\theta_T, \\Lambda, w_\\psi, w_\\phi) = \\\\\n &\\sum_i \\log p(x^{i+1} | z^{i+1}, w_\\psi) + \\log p(z^{i+1} | z^i, \\lambda^i , \\theta_T) \\\\ &\\qquad + \\log p(\\lambda^i| \\Lambda) + \\log p(z^i | x^i, w_\\psi)\\\\\n \\geq & \\sum_i \\log p(x^{i+1} | z^{i+1}, w_\\psi) + \\log p(z^{i+1} | z^i, \\lambda^i , \\theta_T) \\\\ &\\qquad +\\log p(\\lambda^i| \\Lambda) + \\mathcal{L}(q)\\\\\n = & \\sum_i \\log p(x^{i+1} | z^{i+1}, w_\\psi) + \\log p(z^{i+1} | z^i, \\lambda^i , \\theta_T) \\\\ &\\qquad +\\log p(\\lambda^i| \\Lambda) - \\text{KL}(q(z|x, w_\\phi)\\,||\\,p(z | w_\\psi)) \\\\&\\qquad + \\mathbb{E}_{q(z|x, w_\\phi)}[p(x|z, w_\\psi)],\n\\end{align*}\nwhere, for convenience, $p(z | w_\\psi)$ is chosen to be the standard-Gaussian.\n\n\\subsection{Posterior distribution of the latent variables}\n\\label{sec:nonlin_op_estim_latent}\nThe latent posterior distribution of the combination coefficients $\\lambda^i$ and the latent image distributions $z^i$ and $z^{i+1}$ given in equation \n(\\ref{eq:nonlin_op_estim_latent_posterior}) is given by\n\\begin{align*}\n p(&\\lambda^i, z^{i+1}, z^i | x^i, x^{i+1}, \\theta_T, \\Lambda, w_\\psi, w_\\phi) = \\\\& \\frac{ p(x^{i+1} | z^{i+1}, w_\\psi)}{p(x^{i+1} | w_\\psi)} p(z^{i+1} | z^i, \\lambda^i, \\theta_T) p(\\lambda^i| \\Lambda) p(z^i | x^i, w_\\phi)=\\\\\n & \\frac{p(z^{i+1} | x^{i+1},w_\\psi) }{p(z^{i+1} | w_\\psi)} p(z^{i+1} | z^i, \\lambda^i, \\theta_T)p(\\lambda^i| \\Lambda) p(z^i | x^i, w_\\psi)\n\\end{align*}\nwhere the substitution\n\\begin{equation*}\n p(x^{i+1} | z^{i+1}, w_\\psi) = \\frac{p(z^{i+1} | x^{i+1}, w_\\psi) p(x^{i+1} | w_\\psi)}{p(z^{i+1} | w_\\psi)}\n\\end{equation*}\nis due to Bayes rule.\nSince the posterior distribution of the latent image representation is analytically intractable, the posterior distribution of the latent variables is approximated by\n\\begin{align*}\n p(&\\lambda^i, z^{i+1}, z^i | x^i, x^{i+1}, \\theta_T, \\Lambda, w_\\psi, w_\\psi) \\approx \\\\& \\frac{q(z^{i+1} | x^{i+1}, w_\\phi)}{p(z^{i+1} | w_\\psi)} p(z^{i+1} | z^i, \\lambda^i, \\theta_T) p(\\lambda^i| \\Lambda) q(z^i | x^i, w_\\phi).\n\\end{align*}\n This latent posterior distribution is non-Gaussian, however, \nit is still in the exponential family\nand can be expressed in the canonical form,\n\\begin{equation*}\np(x|\\eta) = h(x)\\exp\\{\\eta(\\theta)^T T(x)-A(\\theta)\\},\n\\end{equation*}\nwhere,\n\\begin{align*}\n h(x) =& 3\\log \\frac{1}{(2\\pi)^{d\/2}}\\\\\n A(\\theta) =& \\text{tr}(\\phi_\\sigma(x^{i+1})^{-1}\\phi_\\mu(x^{i+1})\\phi_\\mu(x^{i+1})^T \\\\&+ \\phi_\\sigma(x^i)^{-1}\\phi_\\mu(x^i)\\phi_\\mu(x^i)^T) \\\\&+ \\log\\frac{1}{|\\phi_\\sigma(x^{i+1})|^{1\/2}}+ \\log\\frac{1}{|\\phi_\\sigma(x^i)|^{1\/2}} \\\\&+ \\log\\frac{1}{|\\Lambda|^{1\/2}} - \\log\\frac{1}{|I|^{1\/2}}\\\\\n \\eta(\\theta) =& (\\phi_\\sigma(x^{i+1})^{-1} - I + \\Omega,\\, -2\\phi_\\mu(x^{i+1})\\phi_\\sigma(x^{i+1})^{-1},\\,\\\\& -2\\Omega^{-1},\\, -2\\Omega^{-1}G^j,\\, \\phi_\\sigma(x^i)^{-1} + \\Omega^{-1},\\, \\\\&-2\\Omega^{-1}G^j,\\, (G^j)^T\\Omega^{-1}G^k,\\, \\Lambda,\\, -2\\phi_\\mu(x^i)\\phi_\\sigma(x^i)^{-1})\\\\\n T(x) =& (z^{i+1}(z^{i+1})^T,\\, z^{i+1},\\, z^i(z^{i+1})^T,\\, \\lambda^i_j z^i(z^{i+1})^T,\\, z^i(z^i)^T,\\,\\\\& \\lambda^i_j z^i(z^i)^T,\\, \\lambda^i_j\\lambda^i_k z^i(z^i)^T,\\, z^i,\\,\n \\lambda^i(\\lambda^i)^T,\\, z^i)\n\\end{align*}\nThe sufficient statistics, $T(x)$, are computed by taking partial derivatives of $A(\\theta)$ with respect to $\\eta(\\theta)$.\n\n\\subsection{Update equations for the model parameters}\nThe update equation for each distribution parameter is found by computing the partial derivative of the complete-data log-likelihood function with respect to the parameter and solving for the parameter value at the critical point.\nThe complete-data log-likelihood is given by\n\\begin{align*}\n \\sum_i &\\log p(\\lambda^i, z^{i+1}, x^{i+1}, z^i | x^i, \\theta_T, \\Lambda, w_\\psi, w_\\phi) \\approx \\\\\n &\\sum_i \\log p(x^{i+1} | z^{i+1}, w_\\psi) + \\log p(z^{i+1} | z^i, \\lambda^i , \\theta_T) \\\\ &\\qquad + \\log p(\\lambda^i| \\Lambda) + \\log q(z^i | x^i, w_\\phi) + \\mathcal{L}(q).\n\\end{align*}\nSetting the partial derivative of the complete-data log-likelihood function with respect to parameters $G$ and $\\Omega$ equal to zero give the same results as in \nSection \\ref{sec:complete_data_op} where\n\\begin{equation*}\nG = \\left(\\sum_i \\Delta z^i(z^i\\otimes \\lambda^i)^T\\right)\\left(\\sum_i z^i(z^i)^T \\otimes \\lambda^i(\\lambda^i)^T \\right)^{-1}\n\\end{equation*}\nand\n\\begin{align*}\n \\Omega = \\frac{1}{M}\\Big( \\sum_i& \\Delta z^i(\\Delta z^i)^T - 2A\\lambda^i(\\Delta z^i)^T + A\\lambda^i(\\lambda^i)^TA^T\\Big).\n\\end{align*}\nThe parameters $w_\\phi$ and $w_\\psi$ are updated by backpropagation using the reparameterization trick (\\cite{kingma2013auto}).\n\n\\subsection{Complete-data likelihood}\n\\label{sec:complete_data_op}\nThe complete-data likelihood is the product of the data and latent variable distributions,\n\\begin{align*}\n \\prod_i p(\\lambda^i, z^{i+1} | z^i, \\theta_T, \\Lambda) &= \\prod_i p(z^{i+1} | z^i, \\lambda^i, \\theta_T)\\, p(\\lambda^i | \\Lambda)\n \\\\ &= \\prod_i\\mathcal{N}(z^{i+1}| z^i + A\\lambda^i, \\Omega)\\, \\mathcal{N}(\\lambda^i| 0, \\Lambda)\n\\end{align*}\nwhere $\\theta_T=(G, \\Omega)$ are the transformation parameters.\nThe form given in\nequation (\\ref{eq:op_estim_complete-data_td}) is derived using the equations in Section \n\\ref{sec:combining_gauss}. \nThe transformed image distribution is a linear Gaussian distribution and the prior distribution on the combination coefficients is Gaussian. Using the equations in Section \\ref{sec:combining_gauss}, the complete-data likelihood can be expressed\n\\begin{equation}\n \\label{eq:deriv_op_estim_complete_data}\n p(\\lambda^i, z^{i+1} | z^i, \\theta_T, \\Lambda) = \\mathcal{N}(\\lambda^i, z^{i+1} | m, R^{-1})\n\\end{equation}\nwhere the parameters $R^{-1}$ and $m$ are given by\n\\begin{equation*}\n R^{-1} =\n \\begin{pmatrix}\n \\Lambda & \\Lambda A^T\\\\\n A\\Lambda & \\Omega + A\\Lambda A^T\n \\end{pmatrix}, \\quad\n m = \\begin{pmatrix}\n 0\\\\\n z^i\n \\end{pmatrix}\n\\end{equation*}\nand\n\\begin{equation*}\n A_{\\cdot,j} = G^j z^i.\n\\end{equation*}\n\n\\subsection{Posterior distribution of the latent variables}\n\\label{sec:latent_posterior_op}\nThe latent posterior distribution of the combination coefficients $\\lambda^i$ given in equation (\\ref{eq:op_estim_latent_posterior_td}) is derived using the equations in Section \\ref{sec:combining_gauss}. \nThe transformed image distribution is a linear Gaussian distribution and\nthe prior distribution on the combination coefficients is Gaussian.\nUsing the equations in Section \\ref{sec:combining_gauss}, the latent posterior distribution on the combination coefficients is given by\n\\begin{equation*}\n p(\\lambda^i | z^{i+1}, z^i, \\theta_T,\\Lambda) = \\mathcal{N}(\\lambda | q, K )\n\\end{equation*}\nwhere $\\theta_T=(G, \\Omega)$ are the transformation parameters, and where\n\\begin{equation*}\n K = (\\Lambda^{-1} + A^T\\Omega^{-1} A)^{-1}, \\quad q = K A^T\\Omega^{-1}\\Delta z^i\n\\end{equation*}\nwhere $\\Delta z_i=z^{i+1} - z^i$ is the difference between sequential image representations, and\n\\begin{equation*}\n A_{\\cdot,j} = G^j z^i.\n\\end{equation*}\n\n\\subsection{Update equations for the model parameters}\n\\label{sec:optim_estim_m_step}\nThe update equation for each distribution parameter is found by computing the partial derivative of the complete-data log-likelihood function with respect to the parameter and solving for the parameter value at the critical point. The complete-data log-likelihood is given by\n\\begin{equation*}\n\\sum_i \\log p(\\lambda^i, z^{i+1} | z^i, \\theta_T, \\Lambda) = \\sum_i \\log \\mathcal{N}(\\lambda^i, z^{i+1} | m, R^{-1})\n\\end{equation*}\nwith\n\\begin{equation*}\n R^{-1} =\n \\begin{pmatrix}\n \\Lambda & \\Lambda A^T\\\\\n A\\Lambda & \\Omega + A\\Lambda A^T\n \\end{pmatrix}, \\quad\n m = \\begin{pmatrix}\n 0\\\\\n z^i\n \\end{pmatrix}\n\\end{equation*}\nand\n\\begin{equation}\n \\label{eq:deriv_op_estim_A_def}\n A_{\\cdot,j} = G^j z^i.\n\\end{equation}\nSetting the partial derivative of the complete-data log-likelihood function with respect to parameter $G$ equal to zero gives, \n\\begin{equation*}\n 0 =\\sum_i \\frac{\\partial}{\\partial R} \\log \\mathcal{N}(\\lambda^i, z^{i+1} | m, R^{-1})\\frac{\\partial R}{\\partial A}\\frac{\\partial A}{\\partial G}\n\\end{equation*}\nyielding\n\\begin{align}\n \\label{eq:partialA}\n 0 &= \\sum_i \\frac{\\partial}{\\partial R} \\log \\mathcal{N}(\\lambda^i, z^{i+1} | m, R^{-1}) \\\\&\\propto -\\sum_i\\frac{\\partial}{\\partial R}\\log|R| + \\frac{\\partial}{\\partial R}\\text{tr}(\\sum_i R y y^T) \\\\&= -\\sum_i R^{-1} + \\sum_i y y^T\n\\end{align}\nwith\n\\begin{equation*}\n y = \\begin{pmatrix}\n \\lambda^i\\\\\n \\Delta z^i\n \\end{pmatrix}\n\\end{equation*}\nwhere $\\Delta z^i=z^{i+1}-z^i$ is the difference between sequential image representations.\nThe right most terms give a system of equations,\n\\begin{align}\n \\label{eq:system}\n &\\begin{pmatrix}\n \\Lambda & \\Lambda A^T\\\\\n A\\Lambda & \\Omega + A\\Lambda A^T\n \\end{pmatrix}=\n \\begin{pmatrix}\n \\lambda^i(\\lambda^i)^T & \\lambda^i(\\Delta z^i)^T\\\\\n \\Delta z^i(\\lambda^i)^T & \\Delta z^i(\\Delta z^i)^T\n \\end{pmatrix}\n\\end{align}\nand by substitution,\n\\begin{equation*}\n \\sum_i A \\lambda^i(\\lambda^i)^T = \\sum_i \\Delta z^i(\\lambda^i)^T.\n\\end{equation*}\nSubstituting equation (\\ref{eq:deriv_op_estim_A_def}) gives,\n\\begin{equation*}\n \n \\sum_i G (z^i \\otimes \\lambda^i) (\\lambda^i)^T = \\sum_i \\Delta z^i(\\lambda^i)^T.\n\\end{equation*}\nBy a property of the Kronecker product, multiplying both sides by $(z^i \\otimes \\lambda^i)$ gives, \n\\begin{equation*}\nG = \\left(\\sum_i \\Delta z^i(z^i\\otimes \\lambda^i)^T\\right)\\left(\\sum_i z^i(z^i)^T\\otimes \\lambda^i (\\lambda^i)^T\\right)^{-1}\n\\end{equation*}\n\nThe update equation for $\\Omega$ is derived similarly.\nTaking the relevant terms in the system of equations in (\\ref{eq:system}) gives,\n\\begin{align*}\n \\sum_i A\\lambda^i(\\Delta z^i)^T =& \\sum_i A\\lambda^i(\\lambda^i)^TA^T\\\\\n M \\Omega + \\sum_i A\\lambda^i(\\Delta z^i)^T =& \\sum_i \\Delta z^i(\\Delta z^i)^T\n\\end{align*}\ncombining the above gives,\n\\begin{align*}\n \\Omega = \\frac{1}{M}\\Big( \\sum_i& \\Delta z^i(\\Delta z^i)^T - 2A\\lambda^i(\\Delta z^i)^T + A\\lambda^i(\\lambda^i)^TA^T\\Big).\n\\end{align*}\n\n\n\\subsection{Linear Gaussian variable}\nIn this section a form for the marginal, conditional and joint distributions\nare given for the case where the conditional distribution $p(y|x)$\nis a linear Gaussian model, that is, when $p(y|x)$\nhas a mean that is a linear function of $x$ and a covariance that\nis independent of $x$. The material in this section is adapted\nprimarily from \\cite{bishop2006pattern}, details of the derivations are given in Section 2.3 of the same text.\n\nBeginning with the linear Gaussian $p(y|x)$ and $p(x)$,\n\\begin{align*}\n p(x) &= \\mathcal{N}(x|\\mu, \\Lambda^{-1})\\\\\n p(y|x) &= \\mathcal{N}(y| A x + b, L^{-1}),\n\\end{align*}\nthe marginal distribution $p(y)$ and conditional distribution $p(x|y)$\nare given by\n\\begin{align*}\n p(y) &= \\mathcal{N}(y| A\\mu + b, L^{-1} + A\\Lambda^{-1}A^T)\\\\\n p(x|y) &= \\mathcal{N}(x|\\Sigma\\{A^TL(y-b)+\\Lambda\\mu\\}, \\Sigma)\n\\end{align*}\nwhere\n\\begin{align*}\n \\Sigma =& (\\Lambda + A^TLA)^{-1}.\n\\end{align*}\nThe joint distribution $p(x,y)$ is given by\n\\begin{align*}\n p(x,y) &=\\mathcal{N}(x, y | m, R^{-1})\n\\end{align*}\nwhere\n\\begin{align*}\n R^{-1} =\n \\begin{pmatrix}\n \\Lambda^{-1} & \\Lambda^{-1}A^T\\\\\n A\\Lambda^{-1} & L^{-1} + A\\Lambda^{-1}A^T\n \\end{pmatrix}, \\quad\n m = \\begin{pmatrix}\n \\mu\\\\\n A\\mu + b\n \\end{pmatrix}.\n\\end{align*}\n\n\\subsubsection{Gaussian joint distribution}\nIn this section the conditional and marginal distributions of two sets of variables are given for the case when their joint distribution is Gaussian. The material\nin this section is adapted primarily from \\cite{bishop2006pattern}, details of the derivations are given in\nSection 2.3 of that text.\n\nFor the variable $x\\sim\\mathcal{N}(\\mu, \\Lambda^{-1})$ partitioned into two disjoint subsets $x_a$ and\n$x_b$ so that\n\\begin{equation*}\n x = \\begin{pmatrix}\n x_a\\\\\n x_b\n \\end{pmatrix}.\n\\end{equation*}\nThe corresponding partitions of the mean and precision matrix are given by\n\\begin{equation*}\n \\mu =\n \\begin{pmatrix}\n \\mu_a\\\\\n \\mu_b\n \\end{pmatrix}, \\quad\n \\Lambda =\n \\begin{pmatrix}\n \\Lambda_{aa} & \\Lambda_{ab}\\\\\n \\Lambda_{ab} & \\Lambda_{bb}\n \\end{pmatrix}\n\\end{equation*}\nand the conditional distribution $p(x_a|x_b)$ is Gaussian with\nsufficient statistics\n\\begin{equation*}\n \\mu_{a|b} = \\mu_a - \\Lambda_{aa}^{-1}\\Lambda_{ab}(x_b - \\mu_b), \\quad\n \\Sigma_{a|b}=\\Lambda_{aa}^{-1}.\n\\end{equation*}\n\n\n\n\n\n\n\n\\subsubsection{Estimating Lie generators}\n\\cite{freeman1991design, perona1995deformable, teo1998design}, and \\cite{bansal2014steerability}\npropose techniques for estimating a steering basis on linear Lie groups. \nSteering can be described as transforming a function defined on a group \nby the group action using a linear combination of basis functions and combination coefficients soley dependent on the steering direction.\n The steering basis is determined from a known Lie transformation group and not from\n a sequence of images.\n\n\\subsubsection{Probabilistic modeling with Lie dynamics}\n\\cite{miao2007learning} estimate Lie transition generators and combination coefficients from an image sequence in an EM framework.\nThe image representations from which\nthe dynamics are estimated are assumed to be given and sequential images are assumed to be close in the transformation space.\n\n\\cite{sohl-dickstein2010unsupervised} use a similar framework to estimate Lie transition generators and their combination coefficients but do not require sequential images to be close in the transformation space. The resulting nonconvexity in inference is addressed using a coarse-to-fine estimation of the transformation generators. The generators themselves are constrained to be diagonalizable and consequently do not capture transformations such as constrast, scaling and translation without periodic boundary. By relaxing the Lie group assumption, however, \\cite{sohl-dickstein2010unsupervised} demonstrate how their technique can be used to capture a fuller set of transformations.\n\n\\cite{cohen2014learning} introduce an approach\nfor probabilistic estimation of special orthogonal transition dynamics\nfrom an image sequence. The authors model the transformation coefficients\nusing the von-Mises distribution and show that the posterior distribution of the transformation coefficients is also von-Mises.\n\n\\cite{falorsi2018explorations} introduce an approach for probabilistic estimation of low-dimensional image representations that are compatible with the action of special orthogonal transformations in 3D. Images are mapped to the Fourier domain where they are transformed by a group action. The representation of the group action is given. \\cite{falorsi2019reparameterizing} extends \\cite{falorsi2018explorations} to accomodate other Lie transformations groups but also requires the group representation to be provided.\n\n\\subsubsection{Probabilistic modeling of transition dynamics}\nThere are many other approaches for estimating\ndisentangled image representations from video sequences\nusing deep neural networks. These approaches typically do not\ndirectly model the trasition dynamics in matrix form\nand when they do, the dynamics are not constrained to have the Lie group structure.\n\nFor example,\n\\cite{watter2015embed} learn to estimate state dependent\nlocally linear discrete-time transition dynamics. \nA learned transformation of\nan image gives the (low-dimensional) state vector, and\na learned embedding of the state vector gives the\nlocally linear transition dynamics. During training,\nthe authors must regularize their loss function \nto ensure estimates in the state space correspond\nto embeddings of the observation space.\n\nAnother example comes from\n\\cite{whitney2019disentangling} where latent representations\nof varying factors are learned from a video sequence by defining a set of\nfactors which which evolve in time\nand from which reconstruction of an image is possible.\nThe time evolution of each factor is\ndetermined by an MLP and is conditionally\ndependent on its representation at the prevous time step.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Appendix}\n\\label{sec:app}\n\n\\subsection{Propositional Logic}\nWe briefly present propositional logic, which underpins the dataset generation. Let $S$ be a (finite) set $S$ of propositional variables. A \\emph{literal} is defined as $v$, or $\\bar{v}$ (resp., $\\neg v$), where $v \\in S$. A disjunction of literals is a \\emph{clause}. The \\emph{width} of a clause is defined as the number of literals it contains. \nA formula $\\phi$ is in \\emph{conjunctive normal form (\\CNF)} if it is a conjunction of clauses. A \\CNF has width $k$ if it contains clauses of width at most $k$, and is referred to as a $k-$\\CNF. To illustrate, the formula ${\\phi=(x_1 \\lor \\neg x_3) \\land (x_4 \\lor x_1)}$ is a \\CNF with clauses of width 2.\n\n\nAn assignment $\\nu: S \\mapsto \\{0,1\\}$ maps variables to False (0), or True (1), and \\emph{satisfies} $\\phi$, which we denote by $\\nu\\models \\phi$, in the usual sense, where $\\models$ is propositional entailment. \nGiven a propositional formula $\\phi$, the \\emph{satisfiability problem}, commonly known as \\SAT, consists of determining whether $\\phi$ admits a satisfying assignment, and is NP-complete \\cite{cook1971complexity}. \n\n\\subsection{Proof of \\Cref{theo:uni}}\n\n\nWe first prove a Boolean version of the theorem.\n\n\\begin{lemma}\\label{lem:boolean}\n Let $n\\ge 1$, and let $f:\\CG_n\\to\\{0,1\\}$ be an\n invariant\n Boolean\n function. Then, for all $\\epsilon,\\delta>0$ there is a MPNN with RNI\n that $(\\epsilon,\\delta)$-approximates $f$.\n\\end{lemma}\n\nTo prove this lemma, we use a logical characterization of the\nexpressiveness of MPNNs, which we always assume to admit global\nreadouts. Let $\\LC$ be the extension of first-order predicate logic using\ncounting quantifiers of the form $\\exists^{\\ge k}x$ for $k\\ge 0$,\nwhere $\\exists^{\\ge k}x\\phi(x)$ means that there are at least $k$\nelements $x$ satisfying $\\phi$.\n\nFor example, consider the formula\n\\begin{equation}\n \\label{eq:a1}\n \\phi(x):=\\neg\\exists^{\\ge 3}y\\big(\n E(x,y)\\wedge \\exists^{\\ge 5} z E(y,z)\\big).\n\\end{equation}\nThis is a formula in the language of graphs; $E(x,y)$ means that there\nis an edge between the nodes interpreting $x$ and $y$.\nFor a graph $G$ and a vertex $v\\in V(G)$, we have\n$G\\models\\phi(v)$ (``$G$ satisfies $\\phi$ if the variable $x$ is\ninterpreted by the vertex $v$\") if and only if $v$ has at most $2$\nneighbors in $G$ that have degree at least $5$.\n\nWe will not only\nconsider formulas in the language of graphs, but also formulas in\nthe \\emph{language of colored graphs}, where in addition to the binary\nedge relation we also have unary relations, that is, sets of\nnodes, which we may view as colors of the nodes. For example, the\nformula\n\\[\n \\psi(x):=\\exists^{\\ge 4}y\\big(E(x,y)\\wedge \\textit{RED}(y)\\big)\n\\]\nsays that node $x$ has at least $4$ red neighbors (more precisely,\nneighbors in the unary relation $\\textit{RED}$). Formally, we assume\nwe have fixed infinite list $R_1,R_2,\\ldots$ of color symbols that we\nmay use in our formulas. Then a \\emph{colored graph} is a graph together\nwith a mapping that assigns a finite set $\\rho(v)$ of colors $R_i$ to\neach vertex (so we allow one vertex to have more than one, but only\nfinitely many, colors).\n\nA \\emph{sentence} (of the logic $\\LC$ or any other logic) is a formula\nwithout free variable. Thus a sentence expresses a property of a\ngraph, which we can also view as a Boolean function. For a sentence\n$\\phi$ we denote this function by $\\llbracket\\phi\\rrbracket$. If\n$\\phi$ is a sentence in the language of (colored) graphs, then for\nevery (colored) graph\n$G$ we have $\\llbracket\\phi\\rrbracket(G)=1$ if $G\\models\\phi$ and\n$\\llbracket\\phi\\rrbracket(G)=0$ otherwise.\n\nIt is easy to see that $\\LC$ is only a syntactic extension of first\norder logic $\\FO$---for every $\\LC$-formula there is a logically\nequivalent $\\FO$-formula. To see this, note that we can simulate \n$\\exists^{\\ge k}x$ by $k$ ordinary existential quantifiers:\n$\\exists^{\\ge k}x$ is equivalent to $\\exists x_1\\ldots\\exists\nx_k\\Big(\\bigwedge_{1\\le i0$ there is an\n MPNN that $\\epsilon$-approximates $\\llbracket\\phi\\rrbracket$.\n\\end{lemma}\n\nSince here we are talking about deterministic MPNNs, there is no\nrandomness involved, and we just say \\emph{``$\\epsilon$-approximates''}\ninstead of ``$(\\epsilon,1)$-approximates''.\n\nLemma~\\ref{lem:barcelo} not only holds for sentences in the language\nof graphs, but also for sentences in the language of colored\ngraphs. Let us briefly discuss the way MPNNs access such colors. We\nencode the colors using one-hot vectors that are part of the\ninitial states of the nodes. For example, if we have a formula that\nuses color symbols among $R_1,\\ldots,R_k$, then we reserve $k$\nplaces in the initial state $\\vec x_v=(x_{v1},\\ldots,x_{v\\ell})$ of each\nvertex $v$ (say, for convenience, $x_{v1},\\ldots,x_{vk}$) and we\ninitialize $\\vec x_v$ by letting $x_{vi}=1$ if $v$ is in $R_i$ and\n$x_{vi}=0$ otherwise.\n\nLet us call a colored graph $G$ \\emph{individualized} if for any two\ndistinct vertices $v,w\\in V(G)$ the sets $\\rho(v),\\rho(w)$ of colors\nthey have are distinct.\nLet us say that a sentence $\\chi$ \\emph{identifies} a (colored) graph $G$ if for\nall (colored) graphs $H$ we have $H\\models\\chi$ if and only if $H$ is\nisomorphic to $G$.\n\n\\begin{lemma}\n For every individualized colored graph $G$ there is a\n $\\LC^2$-sentence $\\chi_G$ that identifies $G$. \n\\end{lemma}\n\n\\begin{proof}\n Let $G$ be an individualized graph. For every vertex $v\\in V(G)$,\n let\n \\[\n \\alpha_v(x):=\\bigwedge_{R\\in\\rho(v)}R(x)\\wedge\\bigwedge_{R\\in\\{R_1,\\ldots,R_k\\}\\setminus\\rho(x)}\\neg\n R(x).\n \\]\n Then $v$ is the unique vertex of $G$ such that\n $G\\models\\alpha_v(v)$. For every pair $v,w\\in V(G)$ of\n vertices, we let\n \\[\n \\beta_{vw}(x,y):=\n \\begin{cases}\n \\alpha_v(x)\\wedge\\alpha_w(y)\\wedge E(x,y)&\\text{if }(v,w)\\in\n E(G),\\\\\n \\alpha_v(x)\\wedge\\alpha_w(y)\\wedge \\neg E(x,y)&\\text{if }(v,w)\\not\\in\n E(G).\n \\end{cases}\n \\]\n We let\n \\begin{align*}\n \\chi_G:=&\\bigwedge_{v\\in V(G)}\\big(\\exists\n x\\alpha_v(x)\\wedge\\neg\\exists^{\\ge\n 2}x\\alpha_v(x)\\big)~\\wedge \\\\\n &\\bigwedge_{v,w\\in V(G)}\\exists x\\exists y\\beta_{vw}(x,y).\n \\end{align*}\n It is easy to see that $\\chi_G$ identifies $G$.\n\\end{proof}\n\nFor $n,k\\in\\Nat$, we let $\\CG_{n,k}$ be the class of\nall individualized colored graphs that only use colors among $R_1,\\ldots,R_k$.\n\n\\begin{lemma}\n Let $h:\\CG_{n,k}\\to\\{0,1\\}$ be an invariant Boolean function. Then\n there exists a $\\LC^2$-sentence $\\psi_h$ such that for all\n $G\\in\\CG_{n,k}$ it holds that $\\llbracket\\psi_h\\rrbracket(G)=h(G)$.\n\\end{lemma}\n\n\\begin{proof}\n Let $\\mathcal H\\subseteq\\CG_{n,k}$ be the subset consisting of all graphs\n $H$ with $h(H)=1$. We let\n \\[\n \\psi_h:=\\bigvee_{H\\in\\mathcal H}\\chi_H.\n \\]\n We eliminate duplicates in the disjunction. Since up to isomorphism,\n the class $\\CG_{n,k}$ is finite, this makes the disjunction finite and\n hence $\\psi_h$ well-defined.\n\\end{proof}\n\nThe \\emph{restriction} of a colored graph $G$ is the underlying plain\ngraph, that is, the graph $G^\\vee$ obtained from the colored graph $G$ by\nforgetting all the colors. Conversely, a colored graph $G^\\wedge$ is an\n\\emph{expansion} of a plain graph $G$ if $G=(G^\\wedge)^\\vee$. %\n\n\\begin{corollary}\\label{cor:inv}\n Let $f:\\CG_{n}\\to\\{0,1\\}$ be an invariant Boolean function. Then\n there exists a $\\LC^2$-sentence $\\phi^\\wedge_f$ (in the language\n of colored graphs) such that for all $G\\in\\CG_{n,k}$ it holds that\n $\\llbracket\\psi^\\wedge_f\\rrbracket(G)=f(G^\\vee)$.\n\\end{corollary}\n\nTowards proving Lemma~\\ref{lem:boolean}, we fix an $n\\ge 1$ and a\n$\\epsilon,\\delta>0$. We let \n\\[\n c:=\\left\\lceil\\frac{2}{\\delta}\\right\\rceil\n \\quad\n \\text{and}\n \\quad\n k:=c^2\\cdot n^3\n\\]\nThe technical\ndetails of the proof of Lemma~\\ref{lem:boolean} and\nTheorem~\\ref{theo:uni} depend on the exact choice of the random\ninitialization and the activation functions used in the neural\nnetworks, but the idea is always the same. For simplicity, we assume\nthat we initialize the states $\\vec x_v=(x_{v1},\\ldots,x_{v\\ell})$ of all\nvertices to $(r_v,0,\\ldots,0)$, where $r_v$ for $v\\in V(G)$ are chosen\nindependently uniformly at random from $[0,1]$. As our activation\nfunction $\\sigma$, we choose the linearized sigmoid function defined\nby $\\sigma(x)=0$ for $x<0$, $\\sigma(x)=x$ for $0\\le x<1$, and\n$\\sigma(x)=1$ for $x\\ge 1$.\n\n\\begin{lemma}\\label{lem:prob}\n Let $r_1,\\ldots,r_n$ be chosen\n independently uniformly at random from the interval $[0,1]$.\n For $1\\le i\\le n$ and $1\\le j\\le c\\cdot n^2$, let\n \\[\n s_{ij}:=k\\cdot r_i-(j-1)\\cdot\\frac{k}{c\\cdot n^2}.\n \\]\n Then with probability greater than $1-\\delta$, the following\n conditions are satisfied.\n \\begin{enumerate}\n \\item[(i)] For all $i\\in\\{1,\\ldots,n\\},j\\in\\{1,\\ldots,c\\cdot n^2\\}$ it\n holds that $\\sigma(s_{ij})\\in\\{0,1\\}$.\n \\item[(ii)] For all distinct $i,i'\\in\\{1,\\ldots,n\\}$ there exists a\n $j\\in\\{1,\\ldots,c\\cdot n^2\\}$ such that\n $\\sigma(s_{ij})\\neq\\sigma(s_{i'j})$.\n \\end{enumerate}\n\\end{lemma}\n\n\\begin{proof}\n For every $i$, let $p_i:=\\lfloor r_i\\cdot k\\rfloor$. Since $k\\cdot\n r_i$ is uniformly random from the interval $[0,k]$, the integer\n $p_i$ is uniformly random from $\\{0,\\ldots,k-1\\}$. Observe that\n $0<\\sigma(s_{ij})<1$ only if $p_i- (j-1)\\cdot\\frac{k}{c\\cdot n^2}=0$ (here\n we use the fact that $k$ is divisible by $c\\cdot n^2$). The probability that\n this happens is $\\frac{1}{k}$. Thus, by the Union Bound,\n \\begin{equation}\n \\label{eq:a3}\n \\Pr\\big(\\exists i,j:\\;0<\\sigma(s_{ij})<1\\big)\\le\\frac{c\\cdot n^3}{k}.\n \\end{equation}\n Now let $i,i'$ be distinct and suppose that\n $\\sigma(s_{ij})=\\sigma(s_{i'j})$ for all $j$. Then for all $j$ we\n have $s_{ij}\\le 0\\iff s_{i'j}\\le 0$ and therefore $\\lfloor\n s_{ij}\\rfloor\\le 0\\iff \\lfloor\n s_{i'j}\\rfloor\\le 0$. This implies that\n \\begin{align}\n \\begin{split}\n &\\forall j\\in\\{1,\\ldots,c\\cdot n^2\\}:\\\\\n &\\quad p_i\\le\n (j-1)\\cdot\\frac{k}{c\\cdot n^2}\\iff p_{i'}\\le\n (j-1)\\cdot\\frac{k}{c\\cdot n^2}. \\label{eq:a4}\n \\end{split}\n \\end{align}\n Let $j^*\\in\\{1,\\ldots, c\\cdot n^2\\}$ such that\n \\[p_i\\in\\Big\\{(j^*-1)\\cdot\\frac{k}{c\\cdot\n n^2},\\ldots,j^*\\cdot\\frac{k}{c\\cdot n^2}-1\\Big\\}.\\] Then\n by (\\ref{eq:a4}) we have: \\[p_i'\\in\n \\Big\\{(j^*-1)\\cdot\\frac{k}{c\\cdot n^2},\\ldots,j^*\\cdot\\frac{k}{c\\cdot n^2}-1\\Big\\}.\\] As\n $p_{i'}$ is independent of $p_i$ and hence of $j^*$, the probability\n that this happens is at most $\\frac{1}{k}\\cdot\n \\frac{k}{c\\cdot n^2}=\\frac{1}{c\\cdot n^2}$.\n This proves that for all distinct $i,i'$ the probability that \n $\\sigma(s_{ij})=\\sigma(s_{i'j})$ is at most $\\frac{1}{c\\cdot\n n^2}$. Hence, again by the Union Bound,\n \\begin{equation}\n \\label{eq:a5}\n \\Pr\\big(\\exists i\\neq i'\\forall\n j:\\;\\sigma(s_{ij})=\\sigma(s_{i'j})\\big)\\le \\frac{1}{c}.\n \\end{equation}\n (\\ref{eq:a3}) and (\\ref{eq:a5}) imply that the probability that either (i) or (ii) is\n violated is at most\n \\[\n \\frac{c\\cdot n^3}{k}+\\frac{1}{c}\\le \\frac{2}{c}\\le\\delta.\n \\qedhere\n \\]\n\\end{proof}\n\n\n\n\n\\begin{proof}[Proof of Lemma~\\ref{lem:boolean}]\n For given function $f:\\CG_n\\to\\{0,1\\}$, we choose the sentence\n $\\psi^\\wedge_f$ according to Corollary~\\ref{cor:inv}. Applying\n Lemma~\\ref{lem:barcelo} to this sentence and $\\epsilon$, we obtain\n an MPNN $\\CN_f$ that on a colored graph $G\\in \\CG_{n,k}$ computes\n an $\\epsilon$-approximation of $f(G^\\vee)$.\n \n Without loss of generality, we assume that the vertex set of the\n input graph to our MPNN is $\\{1,\\ldots,n\\}$. We choose $\\ell$ (the\n dimension of the state vectors) in such a way that\n $\\ell\\ge c\\cdot n^2$ and $\\ell$ is at least as large as the\n dimension of the state vectors of $\\CN_f$. Recall that\n the state vectors are initialized as $\\vec\n x^{(0)}_i=(r_i,0,\\ldots,0)$ for values $r_i$ chosen independently\n uniformly at random from the interval $[0,1]$.\n\n In the first step, our MPNN computes the purely local\n transformation (no messages need to be passed) that maps\n $\\vec x^{(0)}_i$ to\n $\\vec x^{(1)}_i=(x^{(1)}_{i1},\\ldots,x^{(1)}_{i\\ell})$ with\n \\[\n x^{(1)}_{ij}=\n \\begin{cases}\n \\sigma\\Big(k\\cdot r_i-(j-1)\\cdot\\frac{k}{c\\cdot n^2}\\Big)&\\text{for }1\\le j\\le\n c\\cdot n^2,\\\\\n 0&\\text{for }c\\cdot n^2+1\\le j\\le\\ell.\n \\end{cases}\n \\]\n Since we treat $k,c,n$ as constants, the mapping \\[r_i\\mapsto k\\cdot\n r_i-(j-1)\\cdot\\frac{k}{c\\cdot n^2}\\] is just a linear mapping\n applied to $r_i=x^{(0)}_{i1}$.\n\n By Lemma~\\ref{lem:prob}, with probability at least $1-\\delta$, the\n vectors $\\vec x_i^{(1)}$ are mutually distinct $\\{0,1\\}$-vectors,\n which we view as encoding a coloring of the input graph with colors\n from $R_1,\\ldots,R_k$. Let $G^\\wedge$ be the resulting colored\n graph. Since the vectors $\\vec x^{(0)}_i$ are\n mutually distinct, $G^\\wedge$ is individualized and thus in the\n class $\\CG_{n,k}$. We now apply the MPNN $\\CN_f$, and it computes a\n value $\\epsilon$-close to \\[\\llbracket\\psi_f^\\wedge\\rrbracket(G^\\wedge)=f((G^\\wedge)^\\vee)=f(G).\\]\n \\end{proof}\n\n \\begin{proof}[Proof of Theorem~\\ref{theo:uni}]\n Let $f:\\CG_n\\to \\Real$ be invariant. Since $\\CG_n$ is finite, the range\n $Y:=f(\\CG_n)$ is finite. To be precise, we have\n $N:=|Y|\\le|\\CG_n|=2^{\\binom{n}{2}}$. \n\n Say, $Y=\\{y_1,\\ldots,y_N\\}$. For\n $i=1,\\ldots,N$, let $g_i:\\CG_n\\to\\{0,1\\}$ be the Boolean function\n defined by\n \\[\n g_i(G)=\n \\begin{cases}\n 1&\\text{if }f(G)=y_i,\\\\\n 0&\\text{otherwise}.\n \\end{cases}\n \\]\n Note that $g_i$ is invariant. Let $\\epsilon,\\delta>0$ and $\\epsilon':=\\frac{\\epsilon}{\\max Y}$\n and $\\delta':=\\frac{\\delta}{N}$. By Lemma~\\ref{lem:boolean}, for\n every $i\\in\\{1,\\ldots,N\\}$ there is an MPNN with RNI $\\CN_i$ that\n $(\\epsilon',\\delta)$-approximates $g_i$. Putting all the $\\CN_i$\n together, we obtain an invariant MPNN $\\CN$ that computes a\n function $g:\\CG_n\\to\\{0,1\\}^N$. We only need to apply the linear\n transformation\n \\[\n \\vec x\\mapsto\\sum_{i=1}^N x_i\\cdot y_i\n \\]\n to the output of $\\CN$ to obtain an approximation of $f$.\n \\end{proof}\n\n\\begin{remark}\n Obviously, our construction yields MPNNs with a prohibitively large state\n space. In particular, this is true for the brute force step from\n Boolean to general functions. We doubt that there are much more\n efficient approximators, after all we make no assumption whatsoever\n on the function $f$.\n\n The approximation of Boolean functions\n is more interesting. It may still happen that\n the GNNs get exponentially large in $n$; this seems\n unavoidable. However, the nice thing here is that our construction\n is very adaptive and tightly linked to the descriptive complexity of\n the function we want to approximate. This deserves a more thorough\n investigation, which we leave for future work.\n\n As opposed to other universality results for GNNs, our construction\n needs no higher-order tensors defined on tuples of nodes, with\n practically infeasible space requirements on all but very small\n graphs. Instead, the complexity of our construction goes entirely\n into the dimension of the state space. The advantage of this is that\n we can treat this dimension as a hyperparameter that we can easily\n adapt and that gives us more fine-grained control over the space\n requirements. Our experiments show that usually in practice a small\n dimension already yields very powerful networks.\n\\end{remark}\n\n\\begin{remark}\n In our experiments, we found that partial RNI, which assigns random values to a fraction of all node embedding vectors, often yields very good results, sometimes better than a full RNI. There is a theoretical plausibility\n to this. For most graphs, we do not\n lose much by only initializing a small fraction of vertex embeddings, because in a few message-passing rounds GNNs can propagate the randomness and individualize the full input\n graph with our construction. On the other hand, we reduce the amount of noise our models\n have to handle when we only randomize partially.\n\\end{remark}\n\n\n\n\\subsection{Details of Dataset Construction}\n\\label{app:corePair}\nThere is an interesting universality result for functions defined on planar graphs. It is known that 3-WL can distinguish between planar graphs \\cite{KieferPS19}. Since 4-GCNs can simulate 3-WL, this implies that functions over planar graphs can be approximated by 4-GCNs. This result can be extended to much wider graph classes, including all graph classes excluding a fixed graph as a minor \\cite{GroheWL}. \n\nInspired by this, we generate planar instances, and ensure that they can be distinguished by 2-WL, by carefully constraining these instances further. Hence, any GNN with 2-WL expressive power can approximate solutions to these planar instances. This, however, does not imply that these GNNs will solve \\EXP in practice, but only that an appropriate approximation function exists and can theoretically be learned.\n\n\n\n\\subsubsection{Construction of \\EXP}\n\n\n\\EXP consists of two main components, (i) a pair of \\emph{cores}, which are non-isomorphic, planar, 1-WL indistinguishable, 2-WL distinguishable, and decide the satisfiability of every instance, and (ii) an additional randomly generated and satisfiable \\emph{planar component}, identically added to the core pair, to add variability to \\EXP and make learning more challenging. We first present both components, and then provide further details about graph encoding and planar embeddings. \n\n\\paragraph{Core pair.}\n\n\\begin{figure*}[t] %\n\t\\centering\n\t\\begin{subfigure}[t]{\\linewidth} \n\t\\centering\n\t\t\\begin{tikzpicture}[node distance = 1cm,line width=0.8pt,shorten >=2pt, shorten <=2pt,-, scale=0.3]\n\t\t\\tikzstyle{var} = [text width=1.2em, text centered, text=black, circle, inner sep=2pt, draw=black, fill=white, thick]\n\t\t\\tikzstyle{disj} = [text width=1.2em ,fill=gray!120,text centered, text=white, circle, inner sep=2pt, draw=black, thick]\n\t\t\\node[var] (x0) {$x_0$};\n\t\t\\node[var, right=0.5cm of x0] (x0n) {$\\bar{x_0}$};\n\t\t\\node[var, right = 0.5cm of x0n] (x1) {$x_1$};\n\t\t\\node[var, right=0.5cm of x1] (x1n) {$\\bar{x_1}$};\n\t\t\\node[var, right = 0.5cm of x1n] (x2) {$x_2$};\n\t\t\\node[var, right=0.5cm of x2] (x2n) {$\\bar{x_2}$};\n\t\t\\node[var, right = 0.5cm of x2n] (x3) {$x_3$};\n\t\t\\node[var, right= 0.5cm of x3] (x3n) {$\\bar{x_3}$};\n\t\t\\node[disj, above= 0.5cm of x0n] (d0) {$d_0$};\n\t\t\\node[disj, above= 0.5cm of x1n] (d1) {$d_1$};\n\t\t\\node[disj, above= 0.5cm of x2n] (d2) {$d_2$};\n\t\t\\node[disj, above right = 1.5cm and 0.2 of x1] (d6) {$d_6$};\n\t\t\\node[disj, above right = 2cm and 0.2cm of x0] (d4) {$d_4$};\n\t\t\\node[disj, above = 2.5cm of x0] (d3) {$d_3$};\n\t\t\\node[disj, below = 0.5cm of x1n] (d7) {$d_7$};\n\t\t\\node[disj, below = 1cm of x0n] (d5) {$d_5$};\n\t\t\n\t\t\\draw[color=black,dashed] (x0.east) -- (x0n.west);\n\t\t\\draw[color=black,dashed] (x1.east) -- (x1n.west);\n\t\t\\draw[color=black,dashed] (x2.east) -- (x2n.west);\n\t\t\\draw[color=black,dashed] (x3.east) -- (x3n.west);\n\t\t\\draw[color=black] (x0n.north) |- (d0.south);\n\t\t\\draw[color=black] (x1.north) |- (d0.east);\n\t\t\\draw[color=black] (x1n.north) -- (d1.south);\n\t\t\\draw[color=black] (x2.north) |- (d1.east);\n\t\t\\draw[color=black] (x2n.north) -- (d2.south);\n\t\t\\draw[color=black] (x3.north) |- (d2.east);\n\t\t\\draw[color=black] (x0.north) -- (d3.south);\n\t\t\\draw[color=black] (x3n.north) |- (d3.east);\n\t\t\n\t\t\\draw[color=black] (x0.60) |- (d4.west);\n\t\t\\draw[color=black] (x3.60) |- (d4.east);\n\t\t\\draw[color=black] (x0n.south) -- (d5.north);\n\t\t\\draw[color=black] (x3n.south) |- (d5.east);\n\t\t\n\t\t\\draw[color=black] (x1.60) |- (d6.west);\n\t\t\\draw[color=black] (x2.60) |- (d6.east);\n\t\t\\draw[color=black] (x1n.south) -- (d7.north);\n\t\t\\draw[color=black] (x2n.south) |- (d7.east);\n\t\t\\end{tikzpicture}\n\t\t\\caption{The encoding of the formula $\\phi_1$.}\\label{fig:phi1}\t\t\n\t\\end{subfigure}\n\t\\\\ \\vspace{0.25cm}\n\t\\begin{subfigure}[t]{\\linewidth} \n\t\\centering\n\t\t\\begin{tikzpicture}[node distance = 1cm,line width=0.8pt,shorten >=2pt, shorten <=2pt,-, scale=0.3]\n\t\t\\tikzstyle{var} = [text width=1.2em, text centered, text=black, circle, inner sep=2pt, draw=black, fill=white, thick]\n\t\t\\tikzstyle{disj} = [text width=1.2em ,fill=gray!120,text centered, text=white, circle, inner sep=2pt, draw=black, thick]\n\t\t\\node[var] (x0) {$x_0$};\n\t\t\\node[var, right=0.5cm of x0] (x0n) {$\\bar{x_0}$};\n\t\t\\node[var, right =0.5cm of x0n] (x1) {$x_1$};\n\t\t\\node[var, right=0.5cm of x1] (x1n) {$\\bar{x_1}$};\n\t\t\\node[var, right =0.5cm of x1n] (x2n) {$\\bar{x_2}$};\n\t\t\\node[var, right=0.5cm of x2] (x2) {$x_2$};\n\t\t\\node[var, right = 0.5cm of x2] (x3n) {$\\bar{x_3}$};\n\t\t\\node[var, right= 0.5cm of x3] (x3) {$x_3$} ;\n\t\t\\node[disj, below = 0.5cm of x0n] (d0) {$d_0$};\n\t\t\\node[disj, below = 1cm of x0] (d1) {$d_1$};\n\t\t\\node[disj, below = 0.5cm of x2] (d3) {$d_3$};\n\t\t\\node[disj, below = 1cm of x2n] (d2) {$d_2$};\n\t\t\\node[disj, above = 0.5cm of x1n] (d7) {$d_7$};\n\t\t\\node[disj, above = 1cm of x1] (d6) {$d_6$};\n\t\t\\node[disj, above= 1.5cm of x0n] (d5) {$d_5$};\n\t\t\\node[disj, above= 2cm of x0] (d4) {$d_4$};\n\t\t\n\t\t\\draw[color=black,dashed] (x0.east) -- (x0n.west);\n\t\t\\draw[color=black,dashed] (x1.east) -- (x1n.west);\n\t\t\\draw[color=black,dashed] (x2.west) -- (x2n.east);\n\t\t\\draw[color=black,dashed] (x3.west) -- (x3n.east);\n\t\t\\draw[color=black] (x0.north) -- (d4.south);\n\t\t\\draw[color=black] (x0.south) -- (d1.north);\n\t\t\\draw[color=black] (x0n.north) -- (d5.south);\t\t\n\t \\draw[color=black] (x0n.south) -- (d0.north);\n\t \\draw[color=black] (x1.north) -- (d6.south);\t\t\n\t\t\\draw[color=black] (x1.south) |- (d0.east);\n \\draw[color=black] (x1n.north) -- (d7.south);\t\t\n\t\t\\draw[color=black] (x1n.south) |- (d1.east);\t\n\t \\draw[color=black] (x2.north) |- (d6.east);\t\t\n\t\t\\draw[color=black] (x2.south) -- (d3.north);\n \\draw[color=black] (x2n.north) |- (d7.east);\t\t\n\t\t\\draw[color=black] (x2n.south) -- (d2.north);\n\t \\draw[color=black] (x3.north) |- (d4.east);\t\t\n\t\t\\draw[color=black] (x3.south) |- (d2.east);\n \\draw[color=black] (x3n.north) |- (d5.east);\t\t\n\t\t\\draw[color=black] (x3n.south) |- (d3.east);\t\t\n\t\t\\end{tikzpicture}\n\t\t\\caption{The encoding of the formula $\\phi_2$.}\\label{fig:phi2}\t\t\n\t\\end{subfigure}\n\t\\caption{Illustration of planar embeddings for the formulas $\\phi_1$ and $\\phi_2$ for $n=2$.}\n\\label{fig:planEmb}\n\\end{figure*}\nIn \\EXP, a core pair consists of two \\CNF formulas $\\phi_1, \\phi_2$, both defined using $2n$ variables, $n \\in \\Nbb^{+}$, such that $\\phi_1$ is unsatisfiable and $\\phi_2$ is satisfiable, and such that their graph encodings are 1-WL indistinguishable and planar. $\\phi_1$ and $\\phi_2$ are constructed using two structures which we refer to as \\emph{variable chains} and \\emph{variable bridges} respectively. \n\nA \\emph{variable chain} $\\phi_{chain}$ is defined over a set of $n \\geq 2$ Boolean variables%\n, and imposes that all variables be equally set. The variable chain can be defined in increasing or decreasing order over these variables. More specifically, given variables $x_i, ..., x_j$, \n\\begin{align}\n\\text{Chain}_\\text{Inc}(i, j) &= \\bigwedge_{k=i}^{j-1} (\\bar{x_k} \\vee x_{i+(k+1)\\%(j-i+1)}),~\\text{and}\\\\\n\\text{Chain}_\\text{Dec}(i, j) &= \\bigwedge_{k=i}^{j-1} ({x_k} \\vee \\bar{x}_{i+(k+1)\\%(j-i+1)}).\n\\end{align}\nAdditionally, a \\emph{variable bridge} is defined over an even number of variables $x_0,..., x_{2n-1}$, as \n\\begin{align}\n{\\phi_{bridge} = \\bigwedge_{i=0}^{n-1} \\big((x_i \\vee x_{2n-1-i}) \\land (\\bar{x_i} \\vee \\bar{x}_{2n-1-i})\\big)}.\n\\end{align}\nA variable bridge makes the variables it connects forcibly have opposite values, e.g., $x_0 = \\bar{x_1}$ for $n=1$. We denote a variable bridge over $x_0,..., x_{2n-1}$ as $\\text{Bridge}(2n)$. \n\nTo get $\\phi_1$ and $\\phi_2$, we define $\\phi_1$ as a variable chain and bridge on all variables, yielding contrasting and unsatisfiable constraints. To define $\\phi_2$, we ``cut'' the chain in half, such that the first $n$ variables can differ from the latter $n$, satisfying the bridge. The second half of the ``cut'' chain is then flipped to a decrementing order, which preserves the satisfiability of $\\phi_2$, but maintains the planarity of the resulting graph. More specifically, this yields:\n\\begin{align}\n\\phi_1 &= \\text{Chain}_\\text{Inc}(0, 2n) \\land \\text{Bridge}(2n)\\text{, and} \\\\\n\\phi_2 &= \\text{Chain}_\\text{Inc}(0, n) \\land \\text{Chain}_\\text{Dec}(n, 2n) \\land \\text{Bridge}(2n).\n\\end{align}\n\\paragraph{Planar component.}\nFollowing the generation of $\\phi_1$ and $\\phi_2$, a disjoint satisfiable planar graph component $\\phi_\\text{planar}$ is added. $\\phi_\\text{planar}$ shares no variables or disjunctions with the cores, so is primarily introduced to create noise and make learning more challenging. $\\phi_\\text{planar}$ is generated starting from random 2-connected (i.e., at least 2 edges must be removed to disconnect a component within the graph) bipartite planar graphs from the Plantri tool \\cite{brinkmann2007fast}, such that (i)\nthe larger set of nodes in the graph is the variable set\\footnote{Ties are broken arbitrarily if the two sets are equally sized.}, (ii)\nhighly-connected disjunctions are split in a planarity-preserving\nfashion to maintain disjunction widths not exceeding 5, (iii) literal signs for variables are uniformly randomly assigned, and (iv) redundant disjunctions,\nif any, are removed. If this $\\phi_\\text{planar}$ is satisfiable, then it is accepted and used. Otherwise, the formula is discarded and a new $\\phi_\\text{planar}$ is analogously generated until a satisfiable formula is produced.\n\nSince the core pair and $\\phi_{\\text{planar}}$ are disjoint, it clearly follows that the graph encodings of $\\phi_\\text{planar} \\land \\phi_1$ and $\\phi_\\text{planar} \\land \\phi_2$ are planar and 1-WL indistinguishable. Furthermore, $\\phi_\\text{planar} \\land \\phi_1$ is satisfiable, and $\\phi_\\text{planar} \\land \\phi_2$ is not. Hence, the introduction of $\\phi_\\text{planar}$ maintains all the desirable core properties, all while making any generated \\EXP dataset more challenging. \n\nThe structural properties of the cores, combined with the combinatorial difficulty of \\SAT, make \\EXP a challenging dataset. For example, even minor formula changes, such as flipping a literal, can lead to a change in the \\SAT outcome, which enables the creation of near-identical, yet semantically different instances. Moreover, \\SAT is NP-complete \\cite{cook1971complexity}, and remains so on planar instances \\cite{hunt1998complexity}. Hence, \\EXP is cast to be challenging, both from an expressiveness and computational perspective. \n\n\\paragraph{Remark 3.} Intuitively, $\\phi_1$ and $\\phi_2$, generated as described, can be distinguished by 2-WL, as 2-WL can detect the break in cycles resulting from the ``cut''. In other words, 2-WL can identify that the chain has been broken in between these two formulas, and thus will return distinct colorings. Hence, $\\phi_1$ and $\\phi_2$ can be distinguished by 3-GCNs.\n\\paragraph{Graph encoding.}\nWe use the following graph encoding, denoted by $Enc$: (i) Every variable is encoded by two nodes, representing its positive and negative literals, and connected by an edge, (ii) Every disjunction is represented by a node, and an edge connects a literal node to a disjunction node if the literal appears in the disjunction, and (iii) Variable and disjunction nodes are encoded with their respective types. We opt for this encoding, as it is commonly used in the literature \\cite{Selsam-ICLR2019}, and, for the sake of our empirical evaluation, yields planar encodings for \\EXP graph pairs. \n\n\\paragraph{Planar embeddings for core pair.}\nWe show planar embeddings for $Enc(\\phi_1)$ and $Enc(\\phi_2)$ for $n=2$ in \\Cref{fig:planEmb}, and these embeddings can naturally be extended to any $n$. %\n $Enc(\\phi_1)$ and $Enc(\\phi_2)$ can also be shown to be 1-WL indistinguishable. This can be observed intuitively, as node neighborhoods in both graphs are identical and very regular: all variable nodes are connected to exactly one other variable node and two disjunction nodes, and all disjunction nodes are connected to exactly two variables. %\n\n\n\\subsubsection{Construction of \\CEXP}\n\\label{app:CExpConstruction}\nGiven an \\EXP dataset with $N$ pairs of graphs, we create \\CEXP by selecting $N \/ 2$ graph pairs and modifying them to yield \\Corrupt. The unmodified graph pairs are therefore exactly identical in type to \\EXP instances, and we refer to these instances within \\CEXP as \\EXPTwo. Then, for every graph pair, we discard the satisfiable graph and construct a new graph from a copy of the unsatisfiable graph as follows:\n\\begin{enumerate}\n \\item Randomly introduce new literals to the existing disjunctions of the copy of the unsatisfiable graph, such that no redundancies are created (e.g., adding $x$ to a disjunction when $x$ or $\\bar{x}$ is already present), until 3 literals are added \\emph{and} the formula becomes satisfiable. Literal addition is done by creating new edges in the graph between disjunction and literal nodes. To do this, disjunctions with less than 5 literals are uniformly randomly selected, and the literal to add is uniformly randomly sampled from the set of all non-redundant literals for the disjunction.\n \\item Once a satisfiable formula is reached, iterate sequentially over all added edges, and eliminate any edge whose removal does not restore unsatisfiability. This ensures that a minimal number of new edges, relative to the original unsatisfiable graph, are added. \n\\end{enumerate}\n\nObserve that these modifications have several interesting effects on the dataset. First, they preserve the existing UNSAT core nodes and edges, while flipping the satisfiability of their overall formulas, which makes the learning task go beyond structure identification. Second, they introduce significant new variability to the dataset, in that the planar component and cores can share edges. Finally, they make the graph pairs 1-WL distinguishable, which gives standard GNNs a chance to perform well on \\Corrupt. \n\n\\subsubsection{Dataset generation for experiments}\nTo create the \\EXP dataset, we randomly generate 600 core pairs, where $n$ is uniformly randomly set between 2 and 4 inclusive. Then, we generate the additional planar component using Plantri, such that 500 $\\phi_\\text{planar}$ formulas are generated from 12-node planar bipartite planar graphs, and the remaining 100 from planar bipartite graphs with 15 nodes. \n\nThis generation process implies that every formula has a number of variables ranging between 10 (4 core variables when $n=2$ plus a minimum 6 variables from the larger bipartite set during $\\phi_\\text{planar}$ generation from 12-node graphs) and 22 variables (8 core variables for $n=4$ plus a maximally-sized variable subset of 14 nodes for $\\phi_\\text{planar}$ generation from 15-node graphs). \n\nFurthermore, the number of disjunctions also ranges from 10 (8 core disjunctions for $n=2$ plus the minimum 2 disjunctions for the case where $\\phi_\\text{planar}$, generated from 12-node graphs, has 10 variables and 2 disjunctions) to 30 disjunctions (16 core disjunctions for $n=4$ plus at most 14 disjunctions for the case where $\\phi_\\text{planar}$, generated from 15-node graphs, initially has 8 variables and 7 disjunctions, which can at most lead to 14 final disjunctions following step (ii)).\n\n\n\n\\subsection{Standard Deviation of GCN-50\\%RNI on \\EXP over training}\n\n\\begin{figure}\n\\centering\n\\begin{tikzpicture}[scale=0.7]\n\n\\definecolor{color0}{rgb}{0.12156862745098,0.466666666666667,0.705882352941177}\n\\definecolor{color4}{rgb}{1,0.498039215686275,0.0549019607843137}\n\\definecolor{color2}{rgb}{0.172549019607843,0.627450980392157,0.172549019607843}\n\\definecolor{color3}{rgb}{0.83921568627451,0.152941176470588,0.156862745098039}\n\\definecolor{color1}{rgb}{0.4,0.4,0.4}\n\n\\begin{axis}[\nlabel style={font=\\large},\ntick label style={font=\\normalsize},\ntick align=outside,\ntick pos=left,\nx grid style={white!69.01960784313725!black},\nxlabel={Training Epoch},\nxmajorgrids,\nxmin=0, xmax=500, \ny grid style={white!69.01960784313725!black},\nymajorgrids,\nymin=0, ymax=0.10\n]\n\n\\addplot [line width=0.4mm, 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0.002499999999999991\\\\\n340 0.0033333333333333214\\\\\n345 0.002499999999999991\\\\\n350 0.005335936864527385\\\\\n355 0.0\\\\\n360 0.010034662148993576\\\\\n365 0.005335936864527385\\\\\n370 0.005527707983925673\\\\\n375 0.0055901699437494795\\\\\n380 0.0\\\\\n385 0.010034662148993576\\\\\n390 0.005527707983925671\\\\\n395 0.002499999999999991\\\\\n400 0.002499999999999991\\\\\n405 0.005000000000000016\\\\\n410 0.002499999999999991\\\\\n415 0.002499999999999991\\\\\n420 0.002499999999999991\\\\\n425 0.005000000000000016\\\\\n430 0.002499999999999991\\\\\n435 0.002499999999999991\\\\\n440 0.0\\\\\n445 0.0\\\\\n450 0.005335936864527384\\\\\n455 0.0\\\\\n460 0.0033333333333333214\\\\\n465 0.005335936864527384\\\\\n470 0.007637626158259738\\\\\n475 0.005000000000000016\\\\\n480 0.0\\\\\n485 0.0033333333333333214\\\\\n490 0.0\\\\\n495 0.007637626158259738\\\\\n500 0.002499999999999991\\\\\n};\n\n\\end{axis}\n\\end{tikzpicture}\n\\caption{Standard deviation of test accuracy over all 10 validation splits for GCN-50\\%RNI on \\EXP.}\\label{fig:stdev}\n\\end{figure}\n\\begin{figure*}[t!]\n\t\\centering\n\t\\begin{subfigure}{.48\\textwidth}\n\t\t\\centering\n\t\t\\input{fig\/fig_core}\n\t\t\\caption{Learning curves on \\EXP.}\n\t\t\\label{app:fig:modelConvergence}\n\t\\end{subfigure}%\n\t\\begin{subfigure}{.48\\textwidth}\n\t\t\\centering\n\t\t\\input{fig\/fig_coreSparse}\n\t\t\\caption{Learning curves on \\SparseEXP.}\n\t\t\\label{app:fig:modelConvergenceSparse}\n\t\\end{subfigure}%\n\t\\caption{Model convergence results for Experiment 1 on the datasets \\EXP and \\SparseEXP.}\n\t\\label{app:fig:resEXP}\n\\end{figure*}\n In this subsection, we investigate the variability of \\GCNRNI~learning across validation folds, and do so with a representative model and dataset, namely the semi-randomized GCN-50\\%RNI model and the standard \\EXP dataset. The standard deviation of the test accuracy of GCN-50\\%RNI over \\EXP, across all 10 cross-validation folds relative to the number of epochs, is shown in \\Cref{fig:stdev}. From this figure, we see that standard deviation spikes sharply at the start of training, and only begins dropping after 100 epochs. This suggests that the learning behavior of GCN-50\\% RNI is quite variable, sometimes requiring few epochs to converge, and in other cases requiring a very high number of epochs. Furthermore, standard deviation converges to almost zero following 200 epochs, corresponding to the phase where all validation folds have achieved near-perfect test performance. From these findings, we further confirm that RNI introduces volatility to GCN training, this time manifesting in variable convergence times across validation folds, but that this volatility does not ultimately hinder convergence and performance, as all folds eventually reach satisfactory performance within a reasonable amount of epochs, and subsequently stabilize. \n\n\\subsection{Additional Experiments}\nIn addition to the experiments in the main body of the paper, we additionally evaluate RNI on sparser analog datasets to \\EXP and \\CEXP, namely \\SparseEXP and \\SparseCEXP. These datasets only contain 25\\% of the number of instances of their original counterparts, and are used to study the behavior and impact of RNI when data is sparse.\n\n\\subsubsection{Experiment 1: \\SparseEXP}\nIn this experiment, we generate \\SparseEXP analogously to \\EXP, except that this dataset only consists of 150 graph pairs, i.e., 300 graphs in total. We then train \\ThreeGNNFull for 200 epochs, and all other systems for 1000 epochs on \\SparseEXP, as opposed to 100 and 500 respectively for \\EXP, to give all evaluated models a better opportunity to compensate for the smaller dataset size. We show the learning curves for all models on \\SparseEXP, and reproduce the original figure for \\EXP, in \\Cref{app:fig:resEXP} for easier comparison.\n\nFirst, we observe that all models converge slower on \\SparseEXP compared to \\EXP. This is not surprising, as a lower data availability makes learning a well-performing function slower and more challenging. More specifically, sparsity implies that (i) fewer weight updates are made per epoch, and (ii) these updates are of lower quality, as they are computed from a less complete dataset. Nonetheless, the same convergence patterns for \\GCNRNI~models and \\ThreeGNNFull are also visible in this setting, further highighting the increased convergence time required by \\GCNRNI~models. \n\nWe also observe that all \\GCNRNI~models, though also eventually converging, do so in a more volatile fashion. Indeed, \\GCNRNI~models suffer from the sparseness of the dataset, as this makes them more sensitive to RNI. As a result, these models require more training to effectively learn robustness against RNI values, and learn this from a smaller sample set, increasing their variability further. Moreover, the nature of \\SparseEXP makes learning more difficult, as it fully relies on RNI for MPNNs to have a chance of achieving above-random performance, and thus encourages MPNNs to fit specific RNI values. Hence, RNI introduces significant volatility and variability to training, particularly with sparser data, and requires substantial training and epochs for \\GCNRNI~models to effectively develop a robustness to RNI instantiations. \n\n\\subsubsection{Experiment 2: \\SparseCEXP}\n\\begin{figure*}[t!]\n\t\\centering\n\t\\begin{subfigure}{.48\\textwidth}\n\t\t\\centering\n\t\t\\input{fig\/fig_coreplus}\n\t\t\\caption{Learning curves on \\CEXP.}\n\t\t\\label{app:fig:modelConvergencePlus}\n\t\\end{subfigure}%\n\t\\begin{subfigure}{.48\\textwidth}\n\t\t\\centering\n\t\t\\input{fig\/fig_LRNEXP}\n\t\t\\caption{Learning curves on \\EXPTwo (\/E) and \\Corrupt (\/C).}\n\t\t\\label{app:fig:modelConvergenceSplit}\n\t\\end{subfigure}%\n\t\\break\n\t\\begin{subfigure}{.48\\textwidth}\n\t\t\\centering\n\t\t\\input{fig\/fig_coreplusSparse}\n\t\t\\caption{Learning curves on \\SparseCEXP.}\n\t\t\\label{app:fig:modelConvergencePlusSparse}\n\t\\end{subfigure}%\n\t\\begin{subfigure}{.48\\textwidth}\n\t\t\\centering\n\t\t\\input{fig\/fig_LRNEXPSparse}\n\t\t\\caption{Learning curves on\\EXPTwo (\/E) and \\Corrupt (\/C).}\n\t\t\\label{app:fig:modelConvergenceSplitSparse}\n\t\\end{subfigure}%\n\t\\caption{Model convergence results for Experiment 2 on \\CEXP and \\SparseCEXP.}\n\t\t\\label{app:fig:res}\n\\end{figure*}\n\nAnalogously to Experiment 1, we generate a \\SparseCEXP dataset similarly to \\CEXP, but only generate 150 graph pairs. Then, we select 75 graph pairs and modify them, as described in \\Cref{app:CExpConstruction}. We report the learning curves for all models on \\SparseCEXP, as well as the original curves for \\CEXP from the main body, in \\Cref{app:fig:res}.\n\nAs in the previous subsection, similar behavior is observed on \\SparseCEXP compared with \\CEXP, only differing by slower convergence in the former case. However, we note that the ``struggle'' phase described in the main paper, which only occurs during the first 100 epochs over \\CEXP, lasts for around 500 epochs on \\SparseEXP. Intuitively, this ``struggle'' phenomenon is due to conflicting learning requirements, stemming from \\Corrupt and \\EXPTwo, which effectively require models to ``isolate'' deterministic dimensions for \\Corrupt, and other randomized dimensions for \\EXPTwo. This in itself is already challenging on \\CEXP, but is made even more difficult on \\SparseCEXP due to its sparsity. Indeed, sparsity makes that further samples are needed in expectation to find a reasonable solution, leading to a lengthy ``struggle'' phase, in which both \\Corrupt and \\EXPTwo data points conflict with one another during optimization. \n\n\n\\subsection{Hyper-parameter details}\nAll GCN models with (partially or completely) deterministic initial node embeddings map a 2-dimensional one-hot encoding of node type (literal or disjunction) to a $k$-dimensional embedding space, where $k$ corresponds to the dimensionality of the deterministic embeddings. Furthermore, the final prediction for every graph is computed by aggregating all node embeddings using the $\\max$ function, and then passing the result through a multi-layer perceptron (MLP) of 3 layers with dimensionality $x$, 32 and 2 respectively, where $x$ is the embedding dimensionality used in the given model. The activation function for the first two MLP layers is the ELU function \\cite{ClevertUH15}, and the softmax function is used to make a final prediction at the final MLP layer.\n\nAll neural networks in this work are optimized using the Adam optimizer \\cite{Kingma-ICLR2014}. All training is conducted with a fixed learning rate $\\lambda$, for fairer comparison between all models. Initially, decaying learning rates were used, but these were discarded, as they yielded sub-optimal convergence for all \\GCNRNI~models. Finally, all experiments were run on a V100 GPU. Detailed hyper-parameters, namely learning rate $\\lambda$ and RNI distribution $p$, per model on every evaluation dataset are shown in \\Cref{tab:hyperParams}.\n\n\\begin{table}[t!]\n\t\\centering\n\t\\caption{Hyper-parameter configurations for all experiments.} \n\t\\begin{tabular}{lcccc}\n\t\t\\toprule \n\t\tDataset & \\multicolumn{2}{c}{\\EXP} & \\multicolumn{2}{c}{\\CEXP}\\\\\n\t\t & $\\lambda$ & $p$ & $\\lambda$ & $p$\\\\\n\t\t \\cmidrule(r){2-3}\n\t\t \\cmidrule(r){4-5}\n\t\t GCN & $1\\times10^{-4}$ & N\/A & $1\\times10^{-4}$ & N\/A \\\\\n\t\t GCN-12.5\\%RNI & $2\\times10^{-4}$ & N & $2\\times10^{-4}$ & N \\\\\n\t\t GCN-50\\%RNI & $2\\times10^{-4}$ & N & $2\\times10^{-4}$ & N \\\\\n\t\t GCN-87.5\\%RNI & $2\\times10^{-4}$ & N & $5\\times10^{-4}$ & N \\\\\n\t\t \\GCNRNI & $5\\times10^{-4}$ & N & $5\\times10^{-4}$ & N \\\\\n\t\t \\ThreeGNNFull & $5\\times10^{-4}$ & N\/A & $2\\times10^{-4}$ & N\/A \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\t\\label{tab:hyperParams}\n\\end{table}\n\n\\subsubsection{Results for \\GCNRNI~with hyperbolic tangent activation}\n\n\\begin{table}[t!]\n\\centering\n\\caption{Performance of \\GCNRNI on \\EXP dataset with tanh.}\n\\begin{tabular}{lHc} \n\\toprule\nModel & Training Accuracy (\\%) & Testing Accuracy (\\%)\\\\\n\\midrule\n \\GCNRNI(U) & 93.0 $\\pm$ 6.32 & 92.7 $\\pm$ 5.61 \\\\ \n\\textbf{\\GCNRNI(N)} & \\textbf{95.7 $\\pm$ 2.64} & \\textbf{96.0 $\\pm$ 2.11} \\\\ \n\\GCNRNI(XU) & 65.1 $\\pm$ 20.6 & 64.6 $\\pm$ 19.9 \\\\ \n\\GCNRNI(XN) & 63.2 $\\pm$ 21.2 & 63.0 $\\pm$ 20.9 \\\\ \n\\bottomrule\n\\end{tabular}\n\\label{tab:exp1Tanh}\n\\end{table}\n\nIn addition to experimenting with the RNI probability distribution, we also experimented with different activation functions for the GCN message passing iterations. Results are shown in \\Cref{tab:exp1Tanh}. Performance with $tanh$ is significantly more variable across distributions than ELU, which shows that RNI is highly sensitive to choices of hyper-parameters.\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\\section{Introduction}\nGraph neural networks (GNNs) \\cite{Scarselli09,Gori2005} are neural architectures designed for learning functions over graph domains, and naturally encode desirable properties such as permutation invariance (resp., equivariance) relative to graph nodes, and node-level computation based on message passing. These properties provide GNNs with a strong inductive bias, enabling them to effectively learn and combine both local and global graph features \\cite{BattagliaGraphNetworks}. GNNs have been applied to a multitude of tasks, ranging from protein classification \\cite{GilmerSRVD17} and synthesis \\cite{YouLYPL18}, protein-protein interaction \\cite{FoutBSB17}, and social network analysis \\cite{HamiltonYL17}, to recommender systems \\cite{YingHCEHL18} and combinatorial optimization \\cite{BengioTourDHorizon}. %\n\nWhile being widely applied, popular GNN architectures, such as message passing neural networks (MPNNs), are limited in their expressive power. Specifically, MPNNs are at most as powerful as the Weisfeiler-Leman (1-WL) graph isomorphism heuristic \\cite{MorrisAAAI19,Keyulu18}, and thus cannot discern between several families of non-isomorphic graphs, e.g., sets of regular graphs \\cite{CaiFI92}. \nTo address this limitation, alternative GNN architectures with provably higher expressive power, such as $k$-GNNs~\\cite{MorrisAAAI19} and invariant (resp., equivariant) graph networks~\\cite{MaronInvEqui19}, have been proposed. These models, which we refer to as \\emph{higher-order GNNs}, are inspired by the generalization of 1-WL to $k-$tuples of nodes, known as $k$-WL \\cite{CaiFI92}. \nWhile these models are very expressive, they are computationally very demanding. As a result, MPNNs, despite their limited expressiveness, remain the standard for graph representation learning. \n\nIn a rather recent development, MPNNs have achieved empirical improvements using \\emph{random node initialization} (RNI), in which initial node embeddings are randomly set. Indeed, RNI enables MPNNs to detect \\emph{fixed} substructures, so extends their power beyond 1-WL, and also allows for a better approximation of a class of combinatorial problems \\cite{SatoRandom2020}. While very important, these findings do not explain the overall theoretical impact of RNI on GNN learning and generalization for \\emph{arbitrary} functions. \n\nIn this paper, we thoroughly study the impact of RNI on MPNNs. Our main result states that MPNNs enhanced with RNI are \\emph{universal}, and thus can approximate every function defined on graphs of any fixed order. This follows from a logical characterization of the expressiveness of MPNNs \\cite{BarceloKM0RS20} combined with an argument on order-invariant definability. \nImportantly, MPNNs enhanced with RNI preserve the \\emph{permutation-invariance} of MPNNs in expectation, and possess a strong inductive bias. \nOur result strongly contrasts with 1-WL limitations of deterministic MPNNs, and provides a foundation for developing expressive and memory-efficient MPNNs with strong inductive bias.\n\nTo verify our theoretical findings, we carry out a careful empirical study. We design \\EXP, a synthetic dataset requiring 2-WL expressive power for models to achieve above-random performance, and run MPNNs with RNI on it, to observe \\emph{how well} and \\emph{how easily} this model can learn and generalize. Then, we propose \\CEXP, a modification of \\EXP with partially 1-WL distinguishable data, and evaluate the same questions in this more variable setting. Overall, the contributions of this paper are as follows:\n\\begin{enumerate}[-,leftmargin=*]\n\n\t\\item We prove that MPNNs with RNI are universal, while being permutation-invariant in expectation. This is a significant improvement over the 1-WL limit of standard MPNNs and, to our knowledge, a first universality result for memory-efficient GNNs.\n\t\n\t\\item We introduce two carefully designed datasets, \\EXP and \\CEXP, based on graph pairs only distinguishable by 2-WL or higher, to rigorously evaluate the impact of RNI.\n\t\n\t\\item We analyze the effects of RNI on MPNNs on these datasets, and observe that (i)~MPNNs with RNI closely match the performance of higher-order GNNs, (ii)~the improved performance of MPNNs with RNI comes at the cost of slower convergence, and (iii)~partially randomizing initial node features improves model convergence and accuracy.\n\t\\item We additionally perform the same experiments with analog sparser datasets, with longer training, and observe a similar behavior, but more volatility.\n\\end{enumerate}\nThe proof of the main theorem, as well as further details on datasets and experiments, can be found in the appendix of this paper.\n\n\\section{Graph Neural Networks}\n\n\n\nGraph neural networks (GNNs) \\cite{Gori2005,Scarselli09} are neural models for learning functions over graph-structured data.\nIn a GNN, graph nodes are assigned vector representations, which are updated iteratively through series of \\emph{invariant} or \\emph{equivariant} computational layers. \nFormally, a function $f$ is \\emph{invariant} over graphs if, for isomorphic graphs $G,H\\,{\\in}\\,\\CG$ it holds that $f(G)\\,{=}\\,f(H)$. Furthermore, a function $f$ mapping a graph $G$ with vertices $V(G)$ to vectors ${\\boldsymbol x\\in\\Rbb^{|V(G)|}}$ is \\emph{equivariant} if, for every permutation $\\pi$ of $V(G)$, it holds that ${f(G^\\pi)=f(G)^\\pi}$.\n\n\n\\subsection{Message Passing Neural Networks} \nIn MPNNs \\cite{GilmerSRVD17}, node representations aggregate \\emph{messages} from their neighboring nodes, and use this information to iteratively update their representations. Formally, given a node $x$, its vector representation $v_{x,t}$ at time $t$, and its\nneighborhood $N(x)$, an update can be written as:\n\\[\nv_{x,t+1} = combine \\Big(v_{x,t},aggregate\\big(\\{v_{y,t}|~y \\in N(x)\\}\\big)\\Big),\n\\]\nwhere \\emph{combine} and \\emph{aggregate} are functions, and \\emph{aggregate} is typically permutation-invariant. \nOnce message passing is complete, the final node representations are then used to compute target outputs. Prominent MPNNs include graph convolutional networks~(GCNs)~\\cite{Kipf16} and graph attention networks~(GATs)~\\cite{VelickovicCCRLB18}.\n\nIt is known that standard MPNNs have the same power as the 1-dimensional Weisfeiler-Leman algorithm (1-WL) \\cite{Keyulu18,MorrisAAAI19}. This entails that graphs (or nodes) cannot be distinguished by MPNNs if $1$-WL does not distinguish them. For instance, 1-WL cannot distinguish between the graphs $G$ and $H$, shown in Figure \\ref{fig:indistinguishable}, despite them being clearly non-isomorphic. Therefore, MPNNs cannot learn functions with different outputs for $G$ and $H$. \n\n\\begin{figure}[t!]\n\t\\centering\n\t\\begin{tikzpicture}[\n\tvertex\/.style = {draw,fill=black!70,circle,inner sep=0pt, minimum height= 2mm},\n\t]\n\t\\begin{scope}\n\t\\node[vertex] (v0) at (90.00:1cm) {};\n\t\\node[vertex] (v1) at (141.43:1cm) {};\n\t\\node[vertex] (v2) at (192.86:1cm) {};\n\t\\node[vertex] (v3) at (244.29:1cm) {};\n\t\\node[vertex] (v4) at (295.71:1cm) {};\n\t\\node[vertex] (v5) at (347.14:1cm) {};\n\t\\node[vertex] (v6) at (398.57:1cm) {};\n\t\\draw[thick] (v0) edge (v1) edge (v6) (v1) edge (v6) (v2) edge\n\t(v3) edge (v5) (v4) edge (v5) edge (v3);\n\t\\node at (-1,1) {$G$};\n\t\\end{scope}\n\t\n\t\\begin{scope}[xshift=3cm]\n\t\\node[vertex] (v0) at (90.00:1cm) {};\n\t\\node[vertex] (v1) at (141.43:1cm) {};\n\t\\node[vertex] (v2) at (192.86:1cm) {};\n\t\\node[vertex] (v3) at (244.29:1cm) {};\n\t\\node[vertex] (v4) at (295.71:1cm) {};\n\t\\node[vertex] (v5) at (347.14:1cm) {};\n\t\\node[vertex] (v6) at (398.57:1cm) {};\n\t\\draw[thick] (v0) edge (v1) edge (v6) (v2) edge (v1) edge (v3)\n\t(v4) edge (v3) edge (v5) (v5) edge (v6);\n\t\\node at (-1,1) {$H$};\n\t\\end{scope}\n\t\\end{tikzpicture}\n\t\\caption{$G$ and $H$ are indistinguishable by $1$-WL} %\n\t\\label{fig:indistinguishable}\n\\end{figure}\nAnother somewhat trivial limitation in the expressiveness of MPNNs is that information is only propagated along edges, and hence can never be shared between distinct connected components of a graph \\cite{BarceloKM0RS20,Keyulu18}. \nAn easy way to overcome this limitation is by adding \\emph{global readouts}, that is, permutation-invariant functions that aggregate the current states of all nodes. Throughout the paper, we therefore focus on MPNNs with global readouts, referred to as \\emph{ACR-GNNs} \\cite{BarceloKM0RS20}. \n\n\\subsection{Higher-order Graph Neural Networks}\nWe now present the main classes of higher-order GNNs.\n\n\\paragraph{Higher-order MPNNs.} The $k-$WL hierarchy has been directly emulated in GNNs, such that these models learn embeddings for \\emph{tuples} of nodes, and perform message passing between them, as opposed to individual nodes. This higher-order message passing approach resulted in models such as $k$-GNNs \\cite{MorrisAAAI19}, which have $(k-1)$-WL expressive power.\\footnote{In the literature, different versions of the Weisfeiler-Leman algorithm have inconsistent dimension counts, but are equally expressive. For example, $(k+1)$-WL and $(k+1)$-GNNs in \\cite{MorrisAAAI19} are equivalent to $k$-WL of \\cite{CaiFI92,GroheWL}. We follow the latter, as it is the standard in the literature on graph isomorphism testing.} These models need $O(|V|^k)$ memory to run, leading to \\emph{excessive memory requirements}. \n\n\\paragraph{Invariant (resp., equivariant) graph networks.} Another class of higher-order GNNs is invariant (resp., equivariant) graph networks \\cite{MaronInvEqui19}, which represent graphs as a tensor, and implicitly pass information between nodes through invariant (resp., equivariant) computational blocks. %\nFollowing intermediate blocks, \\emph{higher-order} tensors are typically returned, and the order of these tensors correlates directly with the expressive power of the overall model. Indeed, invariant networks \\cite{MaronFSL19}, and later equivariant networks \\cite{KerivenP19}, are shown to be universal, but with tensor orders of $O(|V|^2)$, where $|V|$ denotes the number of graph nodes. Furthermore, invariant (resp., equivariant) networks with intermediate tensor order $k$ are shown to be equivalent in power to $(k-1)$-WL \\cite{MaronBSL19}, which is strictly more expressive as $k$ increases \\cite{CaiFI92}. Therefore, universal higher-order models require \\emph{intractably-sized intermediate tensors} in practice. \n\n\\paragraph{Provably powerful graph networks.} A special class of invariant GNNs is provably powerful graph networks (PPGNs)\\cite{MaronBSL19}. PPGNs are based on ``blocks'' of multilayer perceptrons (MLPs) and matrix multiplication, which theoretically have 2-WL expressive power, and only require memory $O(|V|^2)$ (compared to $O(|V|^3)$ for 3-GNNs). However, PPGNs theoretically require \\emph{exponentially many samples} in the number of graph nodes to learn necessary functions for 2-WL expressiveness \\cite{PunyLRGA}.\n\n\\section{MPNNs with Random Node Initialization}\n\\label{sec:RNIUniversal}\nWe present the main result of the paper, showing that RNI makes MPNNs universal, in a natural sense. \nOur work is a first positive result for the universality of MPNNs. This result is not based on a new model, but rather on random initialization of node features, which is widely used in practice, and in this respect, it also serves as a theoretical justification for models that are empirically successful.\n\n\\subsection{Universality and Invariance}\nIt may appear somewhat surprising, and even counter-intuitive, that randomly initializing node features on its own would deliver such a gain in expressiveness.\nIn fact, on the surface, random initialization no longer preserves the invariance of MPNNs, since the result of the computation of an MPNN with RNI not only depends on the structure (i.e., the isomorphism type) of the input graph, but also on the random initialization.\nThe broader picture is, however, rather subtle, as we can view such a model as computing a random variable (or as generating an output distribution), and this random variable would still be invariant. \nThis means that the outcome of the computation of an MPNN with RNI does still \\emph{not} depend on the specific representation of the input graph, which fundamentally maintains invariance. Indeed, the mean of random features, in expectation, will inform GNN predictions, and is identical across all nodes, as randomization is i.i.d. However, the variability between different samples and the variability of a random sample enable graph discrimination and improve expressiveness. Hence, in expectation, all samples fluctuate around a unique value, preserving invariance, whereas sample variance improves expressiveness. \n\n\n\nFormally, let $\\mathcal G_n$ be the class of all $n$-vertex graphs, i.e., graphs that consist of at most $n$ vertices, and let ${f:\\Gmc_n\\to\\Rbb}$. \nWe say that a randomized function $\\Xmc$ that associates with every graph $G\\in \\Gmc_n$ a random variable $\\Xmc (G)$ is an \\emph{$(\\epsilon,\\delta)$-approximation} of $f$ if for all $G\\in\\Gmc_n$ it holds that ${\\Pr\\big(|f(G)-\\Xmc(G)|\\le\\epsilon\\big)\\ge 1-\\delta}$. \nNote that an MPNN $\\Nmc$ with RNI computes such functions $\\Xmc$. If $\\Xmc$ is computed by $\\Nmc$, we say that \\emph{$\\Nmc$ $(\\epsilon,\\delta)$-approximates $f$}.\n\\begin{theorem}[Universal approximation]\n\\label{theo:uni}\n\tLet $n\\ge 1$, and let $f:\\mathcal G_n\\to\\mathbb R$ be invariant. Then, for all\n\t$\\epsilon,\\delta>0$, there is an MPNN with RNI that $(\\epsilon,\\delta)$-approximates $f$.\n\\end{theorem}\nFor ease of presentation, we state the theorem only for real-valued functions, but note that it can be extended to equivariant functions. The result can also be extended to weighted graphs, but then the function $f$ needs to be continuous.\n\n\\subsection{Result Overview}\nTo prove \\Cref{theo:uni}, we first show that MPNNs with RNI can capture arbitrary Boolean functions, by building on the result of \\cite{BarceloKM0RS20}, which states that any logical sentence in $\\LC^2$ can be captured by an MPNN (or, by an ACR-GNN in their terminology). The logic $\\LC$ is the extension of first-order predicate logic using counting quantifiers of the form $\\exists^{\\ge k}x$ for $k\\ge 0$, where $\\exists^{\\ge k}x\\phi(x)$ means that there are at least $k$ elements $x$ satisfying $\\phi$, and $\\LC^2$ is the two-variable fragment of $\\LC$.\n\nWe establish that any graph with identifying node features, which we call \\emph{individualized graphs}, can be represented by a sentence in $\\LC^2$. Then, we extend this result to sets of individualized graphs, and thus to Boolean functions mapping these sets to True, by showing that these functions are represented by a $\\LC^2$ sentence, namely, the disjunction of all constituent graph sentences. \nFollowing this, we provide a construction with node embeddings based on RNI, and show that RNI individualizes input graphs w.h.p. \nThus, RNI makes that MPNNs learn a Boolean function over individualized graphs w.h.p. Since all such functions can be captured by a sentence in $\\LC^2$, and an MPNN can capture any Boolean function, MPNNs with RNI can capture arbitrary Boolean functions. \nFinally, the result is extended to real-valued functions via a natural mapping, yielding universality.\n\n\n\n\nThe concrete implications of \\Cref{theo:uni} can be summarized as follows. First, MPNNs with RNI can distinguish individual graphs with an embedding dimensionality polynomial in the inverse of desired confidence $\\delta$ (namely, $O(n^2 \\delta^{-1})$, where $n$ is the number of graph nodes). \nSecond, universality also holds with partial RNI, and even with only one randomized dimension. \nThird, the theorem is adaptive and tightly linked to the descriptive complexity of the approximated function. That is, for a more restricted class of functions, there may be more efficient constructions than the disjunction of individualized graph sentences, and our proof does not rely on a particular construction. \nFinally, our construction provides a \\emph{logical characterization}for MPNNs with RNI, and substantiates how randomization improves expressiveness. This construction therefore also enables a more logically grounded theoretical study of randomized MPNN models, based on particular architectural or parametric choices.\n\nSimilarly to other universality results, Theorem~\\ref{theo:uni} can potentially result in very large constructions. This is a simple consequence of the generality of such results: \\Cref{theo:uni} applies to families of functions, describing problems of \\emph{arbitrary} computational complexity, including problems that are computationally hard, even to approximate.\nThus, it is more relevant to empirically verify the formal statement, and test the capacity of MPNNs with RNI relative to higher-order GNNs. \nHigher-order GNNs typically suffer from prohibitive space requirements, but this not the case for MPNNs with RNI, and this already makes them more practically viable. \nIn fact, our experiments demonstrate that MPNNs with RNI indeed combine expressiveness with efficiency in practice.\n\n\n\\section{Datasets for Expressiveness Evaluation} \n\\label{sec:dataset} \n\nGNNs are typically evaluated on real-world datasets \\cite{KKMMN2016}, which are not tailored for evaluating expressive power, as they do not contain instances indistinguishable by $1$-WL. In fact, higher-order models only marginally outperform MPNNs on these datasets \\cite{BenchmarkingGNNs}, which further highlights their unsuitability.\nThus, we developed the synthetic datasets \\EXP and \\CEXP. \\EXP explicitly evaluates GNN expressiveness, and consists of graph instances $\\{G_1, \\ldots ,G_n$, $H_1, \\ldots ,H_n\\}$, where each instance encodes a propositional formula. The classification task is to determine whether the formula is satisfiable (\\SAT). %\nEach pair $(G_i$, $H_i)$ respects the following properties: \n(i)~$G_i$ and $H_i$ are non-isomorphic, \n(ii)~$G_i$ and $H_i$ have different \\SAT outcomes, that is, $G_i$ encodes a satisfiable formula, while $H_i$ encodes an unsatisfiable formula, \n(iii)~$G_i$ and $H_i$ are 1-WL indistinguishable, so are \\emph{guaranteed} to be classified in the same way by standard MPNNs, and \n(iv)~$G_i$ and $H_i$ are 2-WL distinguishable, so \\emph{can} be classified differently by higher-order GNNs. \n\nFundamentally, every $(G_i, H_i)$ is carefully constructed on top of a basic building block, the \\emph{core pair}. \nIn this pair, both cores are based on propositional clauses, such that one core is satisfiable and the other is not, both \\emph{exclusively} determine the satisfiability of $G_i$ (resp., $H_i$), and have graph encodings enabling all aforementioned properties. \nCore pairs and their resulting graph instances in \\EXP are \\emph{planar} and are also carefully constrained to ensure that they are 2-WL distinguishable. Thus, core pairs are key substructures within \\EXP, and distinguishing these cores is essential for a good performance. \n\n\nBuilding on \\EXP, \\CEXP includes instances with varying expressiveness requirements. Specifically, \\CEXP is a standard \\EXP dataset where 50\\% of all satisfiable graph pairs are made 1-WL distinguishable from their unsatisfiable counterparts, only differing from these by a small number of added edges.\nHence, \\CEXP consists of 50\\% ``corrupted'' data, distinguishable by MPNNs and labelled \\Corrupt, and 50\\% unmodified data, generated analogously to \\EXP, and requiring expressive power beyond 1-WL, referred to as \\EXPTwo. \nThus, \\CEXP contains the same core structures as \\EXP, but these lead to different \\SAT values in \\EXPTwo and \\Corrupt, which makes the learning task more challenging than learning \\EXPTwo or \\Corrupt in isolation.\n\n\\section{Experimental Evaluation} \n\nIn this section, we first evaluate the effect of RNI on MPNN expressiveness based on \\EXP, and compare against established higher-order GNNs. \nWe then extend our analysis to \\CEXP. \nOur experiments use the following models: \n\\paragraph{1-WL GCN (\\OneGCN).} A GCN with 8 distinct message passing iterations, ELU non-linearities \\cite{ClevertUH15}, 64-dimensional embeddings, and deterministic learnable initial node embeddings indicating node type. \n\tThis model is guaranteed to achieve 50\\% accuracy on \\EXP. \n\t\n\\paragraph{GCN - Random node initialization (\\GCNRNI).} A 1-GCN enhanced with RNI. We evaluate this\n\tmodel with four initialization distributions, namely, \n\tthe standard normal distribution $\\mathcal N(0,1)$ (N),\n\tthe uniform distribution over $[-1,1]$ (U), Xavier normal (XN), and the Xavier uniform distribution (XU) \\cite{GlorotB10}. %\n\tWe denote the respective models \\GCNRNI($D$), where $D \\in \\{\\text{N,\\,U,\\,XN,\\,XU}\\}$. \n\t\n\\paragraph{GCN - Partial RNI (GCN-$x$\\%RNI).}\n\tA \\GCNRNI~model, where $\\floor{\\frac{64x}{100}}$ dimensions are initially randomized, and all remaining dimensions are set deterministically from one-hot representation of the two input node types (literal and disjunction). We set $x$ to the extreme values 0 and 100\\%, 50\\%, as well as near-edge cases of 87.5\\% and 12.5\\%, respectively.\n\t\n\\paragraph{PPGN.} A higher-order GNN with 2-WL expressive power \\cite{MaronBSL19}. We set up PPGN using its original implementation, and use its default configuration of eight 400-dimensional computational blocks.\n\t\n\\paragraph{\\OneTwoThreeLocal.} A higher-order GNN~\\cite{MorrisAAAI19} emulating $2$-WL on 3-node tuples. \\OneTwoThreeLocal operates at increasingly coarse granularity, starting with single nodes and rising to 3-tuples. This model uses a \\emph{connected} relaxation of 2-WL, which slightly reduces space requirements, but comes at the cost of some theoretical guarantees. We set up \\OneTwoThreeLocal with 64-dimensional embeddings, 3 message passing iterations at level 1, 2 at level 2, and 8 at level 3.\n\t\n\\paragraph{\\ThreeGNNFull.} A GCN analog of the \n\t\\emph{full} 2-WL procedure over 3-node tuples, thus preserving all theoretical guarantees.\n\n\n\\subsection{How Does RNI Improve Expressiveness?}\n\nIn this experiment, we evaluate GCNs using different RNI settings on \\EXP, and compare with standard GNNs and higher-order models.\nSpecifically, we generate an \\EXP dataset consisting of 600 graph pairs. Then, we evaluate all models on \\EXP using 10-fold cross-validation. We train \\ThreeGNNFull for 100 epochs per fold, and all other systems for 500 epochs, and report \\emph{mean test accuracy} across all folds.\n\n\\begin{table}[t!]\n\t\\centering\n\t\\begin{tabular}{HlHc} \n\t\t\\toprule\n\t\t\\multicolumn{2}{c}{Model} & Training Accuracy (\\%) & Test Accuracy (\\%)\\\\\n\t\t\\midrule\n\t\t\\multirow{4}{*}{\\textit{\\GCNRNI}} & \\GCNRNI(U) & 97.9 $\\pm$ 0.64 & 97.3 $\\pm$ 2.55 \\\\ \n\t\t& \\textbf{\\GCNRNI(N)} & \\textbf{98.0 $\\pm$ 0.53} & \\textbf{98.0 $\\pm$ 1.85} \\\\ \n\t\t& \\GCNRNI(XU) & 97.0 $\\pm$ 0.83 & 97.0 $\\pm$ 1.43 \\\\\n\t\t& \\GCNRNI(XN) & 97.0 $\\pm$ 1.20 & 96.6 $\\pm$ 2.20 \\\\ \n\t\t\\cmidrule{1-4}\n\t\t\\multirow{3}{*}{\\textit{HO-GNNs}} & PPGN & 50.0 & 50.0\\\\ \n\t\t& \\OneTwoThreeLocal & 50.0 & 50.0\\\\ \n\t\t& \\textbf{\\ThreeGNNFull} & \\textbf{99.9 $\\pm$ 0.002} & \\textbf{99.7 $\\pm$ 0.004} \\\\ \n\t\t\\bottomrule\n\t\\end{tabular}\n\t\t\\caption{Accuracy results on \\EXP.}\n\t\\label{tab:exp1Results}\n\\end{table}%\n\n\n\\begin{figure*}[t!]\n\t\\centering\n\t\\begin{subfigure}{.32\\textwidth}\n\t\t\\centering\n\t\t\\input{fig\/fig_core}\n\t\t\\caption{\\EXP.}\n\t\t\\label{fig:modelConvergence}\n\t\\end{subfigure}%\n\t\\begin{subfigure}{.32\\textwidth}\n\t\t\\centering\n\t\t\\input{fig\/fig_coreplus}\n\t\t\\caption{\\CEXP.}\n\t\t\\label{fig:modelConvergencePlus}\n\t\\end{subfigure}%\n\t\\begin{subfigure}{.32\\textwidth}\n\t\t\\centering\n\t\t\\input{fig\/fig_LRNEXP}\n\t\t\\caption{\\EXPTwo (\/E) and \\Corrupt (\/C).}\n\t\t\\label{fig:modelConvergenceSplit}\n\t\\end{subfigure}%\n\t\\label{fig:res}\n\t\\caption{Learning curves across all experiments for all models.}\n\\end{figure*}\nFull test accuracy results for all models are reported in Table \\ref{tab:exp1Results}, and model convergence for \\ThreeGNNFull and all \\GCNRNI~models are shown in \\Cref{fig:modelConvergence}.\nIn line with \\Cref{theo:uni}, \\GCNRNI~achieves a near-perfect performance on \\EXP, substantially surpassing 50\\%. Indeed, \\GCNRNI~models achieve above 95\\% accuracy with all four RNI distributions. %\nThis finding further supports observations made with rGNNs \\cite{SatoRandom2020}, and shows that RNI is also beneficial in settings beyond structure detection. \nEmpirically, we observed that \\GCNRNI~is highly sensitive to changes in learning rate, activation function, and\/or randomization distribution, and required delicate tuning to achieve its best performance.\n\n\nSurprisingly, PPGN does not achieve a performance above 50\\%, despite\nbeing theoretically 2-WL expressive. \nEssentially, PPGN learns an approximation of 2-WL, based on power-sum multi-symmetric polynomials (PMP), but fails to distinguish \\EXP graph pairs, despite extensive training. This suggests that PPGN struggles to learn the required PMPs, and we could not improve these results, both for training and testing, with hyperparameter tuning. \nFurthermore, PPGN requires exponentially many data\nsamples in the size of the input graph \\cite{PunyLRGA} for learning. Hence, PPGN is likely struggling to discern between \\EXP graph pairs due to the smaller sample size and variability of the dataset. \n\\OneTwoThreeLocal also only achieves 50\\% accuracy, which can be attributed to theoretical model limitations. Indeed, this algorithm only considers 3-tuples of nodes that form a connected subgraph, thus discarding disconnected 3-tuples, where the difference between \\EXP cores lies. This further highlights the difficulty of \\EXP, as even relaxing 2-WL reduces the model to random performance.\nNote that \\ThreeGNNFull achieves near-perfect performance, as it explicitly has the necessary theoretical power, irrespective of learning constraints, and must only learn appropriate injective aggregation functions for neighbor aggregation \\cite{Keyulu18}.\n\nIn terms of convergence, we observe that \\ThreeGNNFull converges\nsignificantly faster than \\GCNRNI~models, for all randomization\npercentages. Indeed, \\ThreeGNNFull only requires about 10 epochs to\nachieve optimal performance, whereas \\GCNRNI~models all require over 100 epochs. \nIntuitively, this slower convergence of \\GCNRNI~can be attributed to a harder learning task compared to \\ThreeGNNFull: Whereas\n\\ThreeGNNFull learns from deterministic embeddings, and can naturally discern between dataset cores, \\GCNRNI~relies on RNI to discern between \\EXP data points, via an artificial node ordering. This implies that \\GCNRNI~must leverage RNI to detect structure, then subsequently learn robustness against RNI variability, which makes its learning task especially challenging.\n\n\n\nOur findings suggest that RNI practically improves MPNN expressiveness, and makes them competitive with higher-order models, despite being less demanding computationally. Indeed, for a 50-node graph, GCN-RNI only requires 3200 parameters (using 64-dimensional embeddings), whereas \\ThreeGNNFull requires 1,254,400 parameters. Nonetheless, GCN-RNI performs comparably to \\ThreeGNNFull, and, unlike the latter, can easily scale to larger instances. This increase in expressive power, however, comes at the cost of slower convergence. Even so, RNI proves to be a promising direction for building scalable yet powerful MPNNs. \n\\subsection{How Does RNI Behave on Variable Data?} \n\nIn the earlier experiment, RNI practically improves the expressive power of GCNs over \\EXP. However, \\EXP solely evaluates expressiveness, and this leaves multiple questions open: How does RNI impact learning when data contains instances with varying expressiveness requirements, and how does RNI affect generalization on more variable datasets? We experiment with \\CEXP to explicitly address these questions. \n\n\nWe generated \\CEXP by generating another 600 graph pairs, then selecting 300 of these and modifying their satisfiable graph, yielding \\Corrupt.\n\\CEXP is well-suited for holistically evaluating the efficacy of RNI, as it evaluates the contribution of RNI on \\EXPTwo conjointly with a second learning task on \\Corrupt involving very similar core structures, and assesses the effect of different randomization degrees on overall and subset-specific model performance. \n\nIn this experiment, we train \\GCNRNI~(with varying randomization degrees) and \\ThreeGNNFull on \\CEXP, and compare their accuracy. For \\GCNRNI, we observe the effect of RNI on learning \\EXPTwo and \\Corrupt, and the interplay between these tasks. %\nIn all experiments, we use the normal distribution for RNI, given its strong performance in the earlier experiment.\n\n\n\nThe learning curves of all \\GCNRNI~ and \\ThreeGNNFull on \\CEXP are shown in \\Cref{fig:modelConvergencePlus}, and the same curves for the \\EXPTwo and \\Corrupt subsets are shown in \\Cref{fig:modelConvergenceSplit}. As on \\EXP, \\ThreeGNNFull converges very quickly, exceeding 90\\% test accuracy within 25 epochs on \\CEXP. By contrast, \\GCNRNI, for all randomization levels, converges much slower, around after 200 epochs, despite the small size of input graphs ($\\sim$70 nodes at most). Furthermore, fully randomized \\GCNRNI~ performs worse than partly randomized \\GCNRNI, particularly on \\CEXP, due to its weak performance on \\Corrupt.\n\nFirst, we observe that partial randomization significantly improves performance. This can clearly be seen on \\CEXP, where GCN-12.5\\%RNI and GCN-87.5\\%RNI achieve the best performance, by far outperforming GCN-RNI, which struggles on \\Corrupt.\nThis can be attributed to having a better inductive bias than a fully randomized model. Indeed, GCN-12.5\\%RNI has mostly deterministic node\nembeddings, which simplifies learning over \\Corrupt. This also applies to GCN-87.5\\%RNI, where the number of deterministic dimensions, though small, remains sufficient. Both models also benefit from randomization for \\EXPTwo, similarly to a fully randomized GCN.\nGCN-12.5\\%RNI and GCN-87.5\\%RNI effectively achieve the best of both worlds on \\CEXP, leveraging inductive bias from deterministic node embeddings, while harnessing the power of RNI to perform strongly on \\EXPTwo. This is best shown in \\Cref{fig:modelConvergenceSplit}, where standard GCN fails to learn \\EXPTwo, fully randomized GCN-RNI struggles to learn \\Corrupt, and the semi-randomized GCN-50\\%RNI achieves perfect performance on both subsets. We also note that partial RNI, when applied to several real datasets, where 1-WL power is sufficient, did not harm performance \\cite{SatoRandom2020}, and thus at least preserves the original learning ability of MPNNs in such settings. Overall, these are surprising findings, which suggest that MPNNs can viably improve across all possible data with partial and even small amounts of randomization. \n\nSecond, we observe that the fully randomized \\GCNRNI~ performs substantially worse than its partially randomized counterparts. Whereas fully randomized \\GCNRNI~ only performs marginally worse on \\EXP (cf. \\Cref{fig:modelConvergence}) than partially randomized models, this gap is very large on \\CEXP, primarily due to \\Corrupt. \nThis observation concurs with the earlier idea of inductive bias: Fully randomized \\GCNRNI~ loses all node type information, which is key for \\Corrupt, and therefore struggles. Indeed, the model fails to achieve even 60\\% accuracy on \\Corrupt, where other models are near perfect, and also relatively struggles on \\EXPTwo, only reaching 91\\% accuracy and converging slower. \n\nThird, all \\GCNRNI~models, at all randomization levels, converge significantly slower than \\ThreeGNNFull on both \\CEXP and \\EXP. However, an interesting phenomenon can be seen on \\CEXP: All \\GCNRNI ~models fluctuate around 55\\% accuracy within the first 100 epochs, suggesting a struggle jointly fitting both \\Corrupt and \\EXPTwo, before they ultimately improve. This, however, is not observed with \\ThreeGNNFull. Unlike on \\EXP, randomness is not necessarily beneficial on \\CEXP, as it can hurt performance on \\Corrupt.\nHence, RNI-enhanced models must additionally learn to isolate deterministic dimensions for \\Corrupt, and randomized dimensions for \\EXPTwo. \nThese findings consolidate the earlier observations made on \\EXP, and highlight that the variability and slower learning for RNI also hinges on the complexity of the input dataset. \n\nFinally, we observe that both fully randomized \\GCNRNI, and, surprisingly, \\OneGCN, struggle to learn \\Corrupt relative to partially randomized \\GCNRNI. We also observe that \\OneGCN does not ``struggle'', and begins improving consistently from the start of training. \nThese observations can be attributed to key conceptual , but very distinct hindrances impeding both models. For \\OneGCN, the model is jointly trying to learn both \\EXPTwo and \\Corrupt, when it provably cannot fit the former. This joint optimization severely hinders \\Corrupt learning, as data pairs from both subsets are highly similar, and share identically generated UNSAT graphs. \nHence, \\OneGCN, in attempting to fit SAT graphs from both subsets, knowing it cannot distinguish \\EXPTwo pairs, struggles to learn the simpler difference in \\Corrupt pairs. For \\GCNRNI, the model discards key type information, so must only rely on structural differences to learn \\Corrupt, which impedes its convergence. All in all, this further consolidates the promise of partial RNI as a means to combine the strengths of both deterministic and random features.\n\n\n\n\\section{Related Work}\n\\label{sec:rw}\n\nMPNNs have been enhanced with RNI \\cite{SatoRandom2020}, such that the model trains and runs with partially randomized initial node features. These models, denoted rGNNs, are shown to near-optimally approximate solutions to specific combinatorial optimization problems, and can distinguish between 1-WL indistinguishable graph pairs based on fixed local substructures. Nonetheless, the precise impact of RNI on GNNs for learning arbitrary functions over graphs remained open. Indeed, rGNNs are only shown to admit parameters that can detect a \\emph{unique, fixed} substructure, and thus tasks requiring \\emph{simultaneous} detection of multiple combinations of structures, as well as problems having no locality or structural biases, are not captured by the existing theory. \n\nOur work improves on Theorem 1 of \\cite{SatoRandom2020}, and shows \\emph{universality} of MPNNs with RNI. Thus, it shows that arbitrary real-valued functions over graphs can be learned by MPNNs with RNI. Our result is distinctively based on a logical characterization of MPNNs, which allows us to link the size of the MPNN with the descriptive complexity of the target function to be learned.\nEmpirically, we highlight that the power of RNI in a significantly more challenging setting, using a target function (\\SAT) which does not rely on local structures, is \\emph{hard} to approximate. %\n\nSimilarly to RNI, random pre-set color features have been used to disambiguate between nodes \\cite{DasoulasSSV20}. This approach, known as CLIP, introduces randomness to node representations, but explicitly makes graphs distinguishable by construction. By contrast, we study random features produced by RNI, which (i)~are not designed a priori to distinguish nodes, (ii)~do not explicitly introduce a fixed underlying structure, and (iii)~yield potentially infinitely many representations for a single graph. In this more general setting, we nonetheless show that RNI adds expressive power to distinguish nodes with high probability, leads to a universality result, and performs strongly in challenging problem settings. \n\n\\section{Summary and Outlook}\nWe studied the expressive power of MPNNs with RNI, and showed that these models are universal and preserve MPNN invariance in expectation. \nWe also empirically evaluated these models on carefully designed datasets, and observed that RNI improves their learning ability, but slows their convergence. \nOur work delivers a theoretical result, supported by practical insights, to quantify the effect of RNI on GNNs. An interesting topic for future work is to study whether polynomial functions can be captured via efficient constructions; see, e.g., \\cite{GroheLogicGNN} for related open problems.\n\n\\section*{Acknowledgments}\nThis work was supported by the Alan Turing Institute under the UK EPSRC grant EP\/N510129\/1, by the AXA Research Fund, and by the EPSRC grants EP\/R013667\/1 and EP\/M025268\/1. Ralph Abboud is funded by the Oxford-DeepMind Graduate Scholarship and the Alun Hughes Graduate Scholarship. Experiments were conducted on the Advanced Research Computing (ARC) cluster administered by the University of Oxford.\n\n\n\\bibliographystyle{named}\n{\\small\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzmstn b/data_all_eng_slimpj/shuffled/split2/finalzzmstn new file mode 100644 index 0000000000000000000000000000000000000000..aa41a83330ac9644c6aaad846fd37b147dce1ed9 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzmstn @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nFor a graph $G=(V,E)$ with vertex set $V$ and edge set $E$, a \\textit{binary edge-labeling} is a surjection $f : E \\to \\{ 0,1 \\}$. Let $i \\in \\{ 0,1 \\}$. An edge labeled $i$ is called an \\textit{$i$-edge} and let $e(i)$ denote the total number of $i$-edges in $G$ with respect to a binary edge-labeling $f$. In the case where $|e(1)-e(0)| \\leq 1$, a binary edge-labeling is called \\textit{edge-friendly}. Call the number of $i$-edges incident with a vertex $v$ the \\textit{$i$-degree} of $v$, denoted $\\deg_i(v)$, so that the degree of $v$ is $\\deg(v) = \\deg_1(v) + \\deg_0(v)$. An edge-friendly labeling of $G$ will induce a (possibly partial) \\textit{vertex-labeling} where a vertex $v$ is labeled $1$ when $\\deg_1(v) > \\deg_0(v)$, is labeled $0$ when $\\deg_0(v) > \\deg_1(v)$, and is unlabeled when $\\deg_1(v) = \\deg_0(v)$. Call a vertex labeled $i$ an \\textit{$i$-vertex} and let $v(i)$ denote the total number of $i$-vertices in $G$ with respect to an edge-friendly labeling $f$. The \\textit{edge-balanced index set} of $G$ is defined as\n\\begin{align*}\nEBI(G) = \\big\\{ |v(1)-v(0)|: \\text{over all edge-friendly labelings of $G$} \\big\\}.\n\\end{align*} \nMore information about graph labelings can be found in Gallian's dynamic survey~\\cite{GallianYYYY}.\n\nThe idea of a balanced labeling was introduced in 1992 by Lee, Liu, and Tan~\\cite{LLT1992}. In 1995, Kong and Lee provided results concerning edge-balanced graphs~\\cite{KL1995}. In~\\cite{KWL2009}, Kong, Wang, and Lee introduced the problem of finding the $EBI$ of complete bipartite graphs by solving the cases where the smaller part has cardinality 1, 2, 3, 4, or 5, and the special case where both parts have the same cardinality, but left all other cases open. In~\\cite{KMPR2014}, Krop, Minion, Patel, and Raridan concluded the edge-balanced index set problem for complete bipartite graphs with both parts of odd cardinality. \n\nA natural next step in the problem is to find the $EBI$ of the complete bipartite graphs where at least one part has even cardinality. In this paper, we conclude the problem for complete bipartite graphs where the larger part is of odd cardinality and the smaller is of even cardinality.\n\nFor positive integers $a,b$, where $a \\leq b$, let $[a]$ denote the set of integers $\\{ 1, \\dots, a \\}$ and let $[a,b]$ denote the set of integers $\\geq a$ but $\\leq b$; in the case where $a=1$, $[a]=\\{1\\}$, and in the case where $a=b$, $[a,a]=\\{a\\}$. \n\nThroughout the rest of this paper, let $K_{m,n}$ be a complete bipartite graph with part $A$ of cardinality $m$ and part $B$ of cardinality $n$, where $m$ is odd, $n$ is even, and $m>n \\geq 2$. Let $q$ be the quotient when $m$ is divided by $\\frac{n}{2}+1$ and let $r$ be the remainder. If $r=0$, then we further partition $A$ into sets $A_i$ and denote the vertices of $A_i$ by $v_j^i$, where $i \\in [q]$ and $j \\in \\left[ \\frac{n}{2}+1 \\right]$. If $r \\geq 1$, then we partition $A$ as in the $r=0$ case with the addition of another partition $A_*$ having vertices denoted $v_i^*$, where $i \\in [r]$. Denote the vertices of $B$ by $u_i$, where $i \\in [n]$. \n\nIn the case where $n=2$, we have $q = \\frac{m-1}{2} \\geq 1$ and $r=1$. If $n \\geq 4$ and $q=1$, then $m = \\frac{n}{2}+1+r$, where $r \\geq 2$; for, if $r=0,1$, then $m \\leq \\frac{n}{2}+2 \\leq n$, a contradiction. In fact, if $n \\geq 4$, $q=1$, and $r \\geq 2$ is even, say $r=2j$ for some $j \\geq 1$, then $\\frac{n}{2}$ is even since $m$ is odd. That is, $n = 4k$ for some $k \\geq 1$. We have $m>n$ if and only if $j \\geq k$ and $r \\leq \\frac{n}{2}$ if and only if $j \\leq k$; hence, $j=k$ and $ r = \\frac{n}{2}$. Similarly, if $n \\geq 4$, $q=1$, and $r \\geq 3$ is odd, we have $r = \\frac{n}{2}$. Thus, for $n \\geq 4$, if $q=1$, then $m=n+1$ and $r = \\frac{n}{2}$. \n\nAny edge-friendly labeling of $K_{m,n}$ has $e(1) = e(0) = \\frac{mn}{2}$. Every vertex in $A$ has even degree so each vertex in this part is a $1$-vertex, a $0$-vertex, or is unlabeled, and every vertex in $B$ has odd degree so each is labeled either $1$ or $0$. Let $v_A(i)$ and $v_B(i)$ represent the number of $i$-vertices in $A$ and $B$, respectively, so that $v(i) = v_A(i)+v_B(i)$, where $i=0,1$. Without loss of generality, we assume that $v_A(1) \\geq v_A(0)$ and $v_B(1) \\geq v_B(0)$, which implies that $v(1) \\geq v(0)$; with this assumption, every element in $EBI(K_{m,n})$ can be computed as $v(1)-v(0)$. Note that not every vertex in $B$ can be a $1$-vertex. If every vertex in $B$ were a $1$-vertex, then the number of $1$-edges incident with these vertices would be at least $n\\left(\\frac{m+1}{2}\\right) > \\frac{mn}{2}$, a contradiction. However, it is possible to have only one $0$-vertex in $B$ since $(n-1)\\left(\\frac{m+1}{2}\\right) < \\frac{mn}{2}$. We have similar results for the vertices in $A$.\n\n\\section{Two Particular Edge-Friendly Labelings}\n\\label{sec:2-e-f}\n\nIn this section, we describe two edge-friendly labelings $f,f'$ of $K_{m,n}$. The labeling $f$ will show that $0 \\in EBI(K_{m,n})$ and $f'$ will show that $n-2 \\in EBI(K_{m,n})$. When $n=2$, the labelings $f$ and $f'$ give the same index $0$, so we do not construct $f'$ when $n=2$. For larger values of $n$, we obtain two distinct edge-friendly labelings that give different indices. \n\n\\subsection{Initializing the Labelings $f,f'$}\n\\label{subsec:initialize-f-f'}\n\nSet $f(v_1^i u_j)=f'(v_1^i u_j) = 1$, where $i \\in [q]$ and $j \\in \\left[ \\frac{n}{2} \\right]$, and label the remaining edges incident with each vertex $v_1^i$ by $0$. Then $v_1^i$ is unlabeled. If $r \\geq 1$, set $f(v_1^* u_j)=f'(v_1^* u_j) = 1$, where $j \\in \\left[ \\frac{n}{2} \\right]$, and label the remaining edges incident with vertex $v_1^*$ by $0$, so that $v_1^*$ is unlabeled.\n\n\\subsection{The Labeling $f$}\n\\label{subsec:f}\n\nAfter initializing $f$ as described above, we continue to label the edges of $K_{m,n}$ as follows to create an edge-friendly labeling $f$: For $i \\in [q]$, $j \\in \\left[ 2, \\frac{n}{2}+1 \\right]$, and $k \\in \\left[ \\frac{n}{2} \\right]$, set $f(v_j^i u_k) = 0$ and label the remaining edges incident with each vertex $v_j^i$ by $1$, so that $v_j^i$ is unlabeled. If $r \\geq 2$, for $j \\in [2, r]$ and $k \\in \\left[ \\frac{n}{2} \\right]$, set $f(v_j^* u_k) = 0$ and label the remaining edges incident with vertex $v_j^*$ by $1$, so that $v_j^*$ is unlabeled. Note that under $f$, all edges in the graph have been labeled either $0$ or $1$ and $f$ is edge-friendly by construction. \n\nAll vertices in $A$ are unlabeled. If $n=2$, then $\\deg_1(u_1) = \\deg_0(u_2) = q+1$ and $\\deg_0(u_1) = \\deg_1(u_2) = q$, so vertex $u_1$ is a $1$-vertex and $u_2$ is a $0$-vertex. For $n \\geq 4$ and $i \\in \\left[ \\frac{n}{2} \\right]$, we have $\\deg_0(u_i) = \\deg_1(u_{i+\\frac{n}{2}}) > \\deg_1(u_i) = \\deg_0(u_{i+\\frac{n}{2}})$, so vertex $u_i$ is a $0$-vertex and $u_{i+\\frac{n}{2}}$ is a $1$-vertex. Thus, $v(1)=v(0)=\\frac{n}{2}$, which gives $0 \\in EBI(K_{m,n})$. \n\n\\subsection{The Labeling $f'$}\n\\label{subsec:f'}\n\nBy previous remarks, we know that it is possible to have an edge-friendly labeling of $K_{m,n}$ where one vertex of $B$, say $u_n$, is a $0$-vertex and the remaining vertices $u_1, \\dots, u_{n-1}$ are $1$-vertices. \n\nAfter initializing $f'$ as described above, we continue to label the edges of $K_{m,n}$, where $n \\geq 4$, as follows to create an edge-friendly labeling $f'$. We begin by labeling the edges (which are not already labeled) incident with vertex $u_n$ by $0$ so that $\\deg_0(u_n)=m$ and $u_n$ is a $0$-vertex. The remaining edge labels will be determined based on the parity of $i \\in [q]$. For $i \\in [q]$ with $i$ odd, $j \\in \\left[2,\\frac{n}{2}+1\\right]$, $k \\in \\left[\\frac{n}{2}-1\\right]$, and $\\ell=j+k-2$, set $f'(v_j^i u_\\ell)=0$ and label the remaining unlabeled edges incident with each vertex $v_j^i$ by $1$, so that $v_j^i$ is unlabeled. For $i \\in [q]$ with $i$ even, $j \\in \\left[2,\\frac{n}{2}\\right]$, $k \\in \\left[\\frac{n}{2}\\right]$, and $\\ell = j+k-1$, set $f'(v_j^i u_\\ell)=1$ and label the remaining unlabeled edges incident with each vertex $v_j^i$ by $0$, so that $v_j^i$ is unlabeled. For $i \\in [q]$ with $i$ even, $j = \\frac{n}{2}+1$, $k \\in \\left[\\frac{n}{2}-1\\right]$, and $\\ell=j+k-1$, set $f'(v_j^i u_\\ell)=1$ and $f'(v_j^i u_1)=1$, and label the remaining unlabeled edges incident with each vertex $v_j^i$ by $0$, so that $v_j^i$ is unlabeled. If $r \\geq 2$, then similar to the even $i$ case, we set $f'(v_j^* u_\\ell)=1$, where $j \\in \\left[2,r\\right]$, $k \\in \\left[\\frac{n}{2}\\right]$, and $\\ell = j+k-1$, and label the remaining unlabeled edges incident with each vertex $v_j^*$ by $0$, so that $v_j^*$ is unlabeled. Under $f'$, all edges in the graph have been labeled either $0$ or $1$, and since $e(0)=e(1)$, the constructed labeling $f'$ is edge-friendly. \n\nAll vertices in $A$ are unlabeled, vertex $u_n$ is a $0$-vertex, and $\\deg_1(u_i) > \\deg_0(u_i)$ for $i \\in [n-1]$, so vertex $u_i$ is a $1$-vertex. Thus, $v(1)=n-1$ and $v(0)=1$, which gives $n-2 \\in EBI(K_{m,n})$. \n\n\\section{Main Result}\n\\label{sec:main-result}\n\nWe are now ready to prove the following:\n\\begin{thm}\n\\label{thm:max-EBI-odd-even}\nLet $K_{m,n}$ be a complete bipartite graph with parts of cardinality $m$ and $n$, where $m$ is odd, $n$ is even, and $m>n \\geq 2$. Then $EBI(K_{m,2}) = \\{0\\}$. For $n\\geq 4$, let $q$ be the quotient when $m$ is divided by $\\frac{n}{2}+1$ and let $r$ be the remainder. Then\n\\begin{align}\nEBI(K_{m,n}) =\n\\begin{cases}\n\\left\\{ 0,1, \\dots, m+n-2q-2 \\right\\}, &\\text{if~$r = 0$}, \\\\\n\\left\\{ 0,1, \\dots, m+n-2q-3 \\right\\}, &\\text{if~$r = 1$}, \\\\\n\\left\\{ 0,1, \\dots, m+n-2q-4 \\right\\}, &\\text{if~$r \\geq 2$}.\n\\end{cases}\n\\end{align}\n\\end{thm}\n\n\\begin{proof}\nLet $n=2$. Then the labeling $f$ given in Section~\\ref{subsec:f} shows that $0 \\in EBI(K_{m,2})$. To see that $0$ is the only index in the edge-balanced index set of $K_{m,2}$, consider switches on pairs of edges incident with a vertex $u \\in B$, say $e=uv$ and $e'=uv'$, where $f(e)=1$ and $f(e')=0$. Such switches will not alter the edge-friendliness of the labeling, nor alter the label on vertex $u$, but each switch will change the unlabeled vertex $v$ to a $0$-vertex and the unlabeled vertex $v'$ to a $1$-vertex. No matter how many switches are performed (up to labeling all but one vertex in part $A$), we will always have $v_A(1)=v_A(0)$ and $v_B(1)=v_B(0)=1$. It is impossible to label all the vertices in part $A$, so $EBI(K_{m,2}) = \\{ 0 \\}$.\n\n\nFor the remainder of the proof, let $n \\geq 4$, let $q$ be the quotient when $m$ is divided by $\\frac{n}{2}+1$, and let $r$ be the remainder.\n\n\nConsider the labeling $f'$ given in Section~\\ref{subsec:f'}, which provides an edge-friendly labeling of $K_{m,n}$ and shows that $n-2 \\in EBI(K_{m,n})$. We perform edge label switches on pairs of $0$-edges and $1$-edges incident with the same vertex in part $B$, noting that such a switch will not alter the edge-friendliness of the labeling. For $i \\in [q]$, switch the label on edge $v_1^i u_1$ with the label on edge $u_1 v_2^i$. These edge label switches will not change the label on vertex $u_1$, but will cause $v_1^i$ to change from an unlabeled vertex to a $0$-vertex and will cause $v_2^i$ to change from unlabeled vertex to a $1$-vertex. After performing these edge label switches, we note that the number of $1$-vertices increased by $q$ and the number of $0$-vertices increased by $q$, so we still have $n-2 \\in EBI(K_{m,n})$. Continuing our $\\{0,1\\}$-edge-pair switches, for $i \\in [q]$ and $j \\in \\left[2,\\frac{n}{2}\\right]$, switch the label on edge $v_1^i u_j$ with the label on edge $u_j v_{j+1}^i$. Each such switch increases the edge-balanced index by one. Moreover, after all of these $\\{0,1\\}$-edge-pair switches, we have that $\\deg_0(v_1^i)=n$, implying that each $v_1^i$ is a $0$-vertex with no incident $1$-edges, and that $\\deg_1(v_j^i) = \\frac{n}{2}+1$, where $j \\in \\left[2,\\frac{n}{2}+1\\right]$, implying that each $v_j^i$ is a $1$-vertex (but just barely). Thus, all vertices in $A_i$, where $i \\in [q]$, are labeled either $0$ or $1$, and we have attained each index from $n-2$ to $n-2+q\\left(\\frac{n}{2}-1\\right)=m+n-2q-2-r$ in the edge-balanced index set. If $r=0$, then we are done as we cannot increase the edge-balanced index further. That is, if $r=0$, then the maximal index in the edge-balanced index set is $m+n-2q-2$. Similarly, if $r=1$, then we do not have any extra $1$-edges incident with vertices $v_j^i$, where $i \\in [q]$ and $j \\in \\left[2,\\frac{n}{2}+1\\right]$, that could be used to change vertex $v_1^*$ into a $1$-vertex, so we cannot increase the edge-balanced index further. That is, for $r=1$, the maximal index is $m+n-2q-3$. For values of $r \\geq 2$, we may perform additional $\\{0,1\\}$-edge-pair switches to force all vertices in part $A$ to be labeled, increasing the number of $0$-vertices in $A$ by one and the number of $1$-vertices by $r-1$. In particular, for $j \\in [r-1]$, switch the label on edge $v_1^* u_j$ with the label on edge $u_j v_{j+1}^*$. Then $v_1^*$ is a $0$-vertex and $v_{j+1}^*$ is a $1$-vertex. In this case, we have that $v_A(0)=q+1$ and $v_A(1)=m-q-1$, which means that the maximal index in the edge-balanced index set is $v(1)-v(0)=v_A(1)+v_B(1)-v_A(0)-v_B(0)=m-q-1+n-1-(q+1)-1=m+n-2q-4$. \n\n\nNow, consider the labeling $f$ given in Section~\\ref{subsec:f}. Performing the same $\\{0,1\\}$-edge-pair switches described above, we find that we are able to achieve subsets of the edge-balanced index set based on the value of $r$. If $r=0$, then we achieve the indices $\\{ 0,1, \\dots, m-2q \\}$. If $r=1$, then we achieve the indices $\\{ 0,1, \\dots, m-2q-1 \\}$. If $r \\geq 2$, then we achieve the indices $\\{ 0,1, \\dots, m-2q-2 \\}$. \n\n\nFor the last part of the proof, note that if $r=0$, then we have that $\\{ 0,1, \\dots, m-2q \\} \\cup \\{ n-2, \\dots, m+n-2q-2 \\} = \\{ 0,1, \\dots, m+n-2q-2 \\}$, since $q = \\frac{2m}{n+2}$ and $m > n+1$ implies $m-2q = \\frac{m(n-2)}{n+2}> n-3 + \\frac{4}{n+2} > n-3$. That is, if $r=0$, then $EBI(K_{m,n}) = \\{ 0,1, \\dots, m+n-2q-2 \\}$. Now, if $r=1$, then $q = \\frac{2m-2}{n+2}$ and $m \\geq n+3$ implies $m-2q-1 = \\frac{(m-1)(n-2)}{n+2} \\geq n-2$, and $EBI(K_{m,n}) = \\{ 0,1, \\dots, m+n-2q-3 \\}$. Finally, for values of $r \\geq 2$, we consider three cases: (i)~$m=n+1$, (ii)~$m=n+3$, and (iii)~$m \\geq n+5$. For case~(i), if $m=n+1$, then $q=1$ and $m-2q-2=n-3$. For case~(ii), if $m=n+3$, then $q=2$ and $m-2q-2=n-3$. For case~(iii), if $m \\geq n+5$, then $m-2q-2 = \\frac{(m-2)(n-2)}{n+2} + \\frac{4r-8}{n+2} \\geq \\frac{(n+3)(n-2)}{n+2} > n-2$. Thus, if $r \\geq 2$, then $m-2q-2 \\geq n-3$ and $EBI(K_{m,n}) = \\{ 0,1, \\dots, m+n-2q-4 \\}$.\n\\end{proof}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:intro}\nEnd of 1998 the concept of ``Grid computing\" was introduced in the monograph ``The Grid: Blueprint for a New\nComputing Infrastructure\" by I. Foster and C. Kesselman \\cite{Foster:1998:GBN}. Two years earlier, in 1997, the\ndevelopment of the UNICORE - Uniform Interface to Computing Resources - system was initiated to enable German\nsupercomputer centers to provide their users with a {\\em seamless, secure, and intuitive access} to their\nheterogeneous computing resources. Like in the case of the Globus Toolkit\\textsuperscript{\\textregistered}\n\\cite{Foster:1997:GMI} UNICORE was started before ``Grid Computing\" became the accepted new paradigm for\ndistributed computing.\n\nThe UNICORE vision was proposed to the German Ministry for Education and Research (BMBF) and received funding. A\nfirst prototype was developed in the UNICORE\\footnote{funded in part by BMBF grant 01 IR 703, duration: August\n1997 - December 1999} project \\cite{Erwin:2000:UNI}. The foundations for the current production version were laid\nin the follow-up project UNICORE Plus\\footnote{funded in part by BMBF grant 01 IR 001 A-D, duration: January 2000\n- December 2002} \\cite{Erwin:2003:UNI}, which was successfully completed in 2002. Since then UNICORE was used in\noperation at German supercomputing centers and became a solid basis for numerous European projects. In this paper\nwe will describe the evolution of UNICORE from a prototype software developed in research projects to a Grid\nmiddleware used today in the daily operation of production Grids.\n\nAlthough already set out in the initial UNICORE project proposal in 1997, the goals and objectives of the UNICORE\ntechnology are still valid:\n\\bi\n\\item Foremost, the aim of UNICORE is to hide the rough edges resulting from different hardware architectures, vendor\nspecific operating systems, incompatible batch systems, different application environments, historically grown\ncomputer center practices, naming conventions, file system structures, and security policies -- just to name the\nmost obvious.\n\\item Equally, security is a constituent part of UNICORE's design relying on X.509 certificates for the\nauthentication of users, servers, and software, and the encryption of the communication over the internet.\n\\item Finally, UNICORE is usable by scientists and engineers without having to study vendor or site-specific\ndocumentation. A Graphical User Interface (GUI) is available to assist the user in creating and managing jobs.\n\\ei\n\nAdditionally, several basic conditions are met by UNICORE: the Grid middleware supports operating systems and\nbatch systems of all vendors present at the partner sites. In 1997 these were for instance large Cray T3E systems,\nNEC and Hitachi vector machines, IBM SP2s, and smaller Linux clusters. Nowadays the spectrum is even broader, of\ncourse with modern hardware, such as IBM p690 systems. The deployed software has to be non-intrusive, so that it\ndoes not require changes in the computing centers hard- and\/or software infrastructure. Maintaining site autonomy\nis still a major issue in Grid computing, when aspects of acceptability and usability in particular from the\nsystem administrator's point of view are addressed. In addition to UNICORE's own security model, site-specific\nsecurity requirements (\\eg firewalls) are supported.\n\nNear the end of the initial funding period of the UNICORE Plus project, a working prototype was available, which\nshowed that the initial concept works. By combining innovative ideas and proven components over the years, this\nfirst prototype evolved to a {\\em vertically integrated} Grid middleware solution.\n\nThe remainder of this paper is structured as follows. In section 2 the architecture of UNICORE and its core\nfeatures are described. European funded projects, which use UNICORE as a basis for their work are described in\nSection 3, and in Section 4 the usage of UNICORE in production is described. Section 5 gives an outlook on the\nfuture development of UNICORE. The paper closes with conclusions and acknowledgements.\n\n\n\n\\section{The Architecture of UNICORE}\n\\label{sec:arch}\n\\figref{fig:UNICORE-arch} shows the layered Grid architecture of UNICORE consisting of user, server and target\nsystem tier \\cite{Romber:2002:UGI}. The implementation of all components shown is realized in Java. UNICORE meets\nthe Open Grid Services Architecture (OGSA) \\cite{Foster:2003:TPG} concept following the paradigm of 'Everything\nbeing a Service'. Indeed, an analysis has shown that the basic ideas behind UNICORE already realizes this paradigm\n\\cite{Snelling:2002:UGI,Snelling:2003:UOG}.\n\n\\subsection{User Tier}\n\\label{sec:arch-userT}\nThe UNICORE Client provides a graphical user interface to exploit the entire set of services offered by the\nunderlying servers. The client communicates with the server tier by sending and receiving Abstract Job Objects\n(AJO) and file data via the UNICORE Protocol Layer (UPL) which is placed on top of the SSL protocol. The AJO is\nthe realization of UNICORE's job model and central to UNICORE's philosophy of abstraction and seamlessness. It\ncontains platform and site independent descriptions of computational and data related tasks, resource information\nand workflow specifications along with user and security information. AJOs are sent to the UNICORE Gateway in form\nof serialized and signed Java objects, followed by an optional stream of bytes if file data is to be transferred.\n\\begin{figure}[htb]\n\\centering\n\\includegraphics[width=\\textwidth]{UNICORE-architecture.eps}\n\\caption{The UNICORE architecture.} \\label{fig:UNICORE-arch}\n\\end{figure}\n\nThe UNICORE client assists the user in creating complex, interdependent jobs that can be executed on any UNICORE\nsite (Usite) without requiring any modifications. A UNICORE job, more precisely a job group, may recursively\ncontain other job groups and\/or tasks and may also contain dependencies between job groups to generate job\nworkflows. Besides the description of a job as a set of one or more directed a-cyclic graphs, conditional and\nrepetitive execution of job groups or tasks are also included. For the monitoring of jobs, their status is\navailable at each level of recursion down to the individual task. Detailed log information is available to analyze\npotential error conditions. At the end of the execution of the job it is possible to retrieve the {\\sf stdout} and\n{\\sf stderr} output of the job. Data management functions like import, export, and transfer are available through\nthe GUI as explicit tasks. This allows the user to specify data transfer from one target system to another (\\eg\nfor workflows), from or to the local workstation before or after the execution of a job, or to store data\npermanently in archives.\n\nThe previously described features already provide an effective tool to use resources of different computing\ncenters both for capacity or capability computing, but many scientists and engineers use application packages. For\napplications without a graphical user interface, a tool kit simplifies the development of a custom built UNICORE\nplug-in. Over the years many plug-ins were developed, so that plug-ins already exist for many standard scientific\napplications, as \\eg for CPMD (Car-Parrinello Molecular Dynamics) \\cite{Huber:2001:SCP}, Fluent or MSC Nastran.\n\n\\subsection{Server Tier}\n\\label{sec:arch-serverT}\nThe server tier contains the Gateway and the Network Job Supervisor (NJS). The Gateway controls the access to a\nUsite and acts as the secure entry point accepting and authenticating UPL requests. A Usite identifies the\nparticipating organization (\\eg a supercomputing center) to the Grid with a symbolic name that resolves into the\nURL of the Gateway. An organization may be part of multiple Grids offering the same or different resources to\ndifferent communities. The Gateway forwards incoming requests to the underlying Network Job Supervisor (NJS) of a\nvirtual site (Vsite) for further processing. The NJS represents resources with a uniform user mapping scheme and\nno boundaries like firewalls between them.\n\nA Vsite identifies a particular set of resources at a Usite and is controlled by a NJS. A Vsite may consist of a\nsingle supercomputer, \\eg a IBM p690 System with LoadLeveler, or a Linux cluster with PBS as resource management\nsystem. The flexibility of this concept supports different system architectures and gives the organization full\ncontrol over its resources. Note that, there can be more than one Vsite inside each USite as depicted in\n\\figref{fig:UNICORE-arch}.\n\nThe NJS is responsible for the virtualization of the underlying resources by mapping the abstract job on a\nspecific target system. This process is called ``incarnation\" and makes use of the Incarnation Database (IDB).\nSystem-specific data are stored in the IDB describing the software and hardware infrastructure of the system.\nAmong others, the available resources like software, incarnation of abstract commands (standard UNIX command like\nrm, cp, ...) and site-specific administrative information are stored. In addition to the incarnation the NJS\nprocesses workflow descriptions included in an AJO, performs pre- and post-staging of files and authorizes the\nuser via the UNICORE User Database (UUDB). Typically the Gateway and NJS are running on dedicated secure systems\nbehind a firewall, although the Gateway could be placed outside a firewall or in a demilitarized zone.\n\n\\subsection{Target System Tier}\n\\label{sec:arch-targetsysT}\nThe Target System Interface (TSI) implements the interface to the underlying supercomputer with its resource\nmanagement system. It is a stateless daemon running on the target system and interfacing with the local resource\nmanager realized either by a batch system like PBS \\cite{openPBS:url} or CCS \\cite{Hovestadt:2003:SHR}, a batch\nsystem emulation on top of \\eg Linux, or a Grid resource manager like Globus' GRAM\n\\cite{GRAM:url,Menday:2003:GEU}.\n\n\\subsection{Single Sign-On}\n\\label{sec:arch-signon}\nThe UNICORE security model relies on the usage of permanent X.509 certificates issued by a trusted Certification\nAuthority (CA) and SSL based communication across `insecure' networks. Certificates are used to provide a single\nsign-on in the client. The client unlocks the user's keystore when it is first started, so that no further\npassword requests are handed to the user. All authentication and authorization is done on the basis of the user\ncertificate. At each UNICORE site user certificates are mapped to local accounts (standard UNIX uid\/gid), which\nmay be different at each site, due to existing naming conventions. The sites retain full control over the\nacceptance of users based on the identity of the individual -- the distinguished name -- or other information that\nmight be contained in the certificate. UNICORE can handle multiple user certificates, \\ie it permits a client to\nbe part of multiple, disjoint Grids. It is also possible to specify project accounts in the client allowing users\nto select different accounts for different projects on one execution system or to assume different roles with\ndifferent privileges.\n\nThe private key in the certificate is used to sign each job and all included sub-jobs during the transit from the\nclient to sites and between sites. This protects against tampering while the job is transmitted over insecure\ninternet connections and it allows to verify the identity of the owner at the receiving end, without having to\ntrust the intermediate sites which forwarded the job.\n\n\n\n\\section{UNICORE Based Projects}\n\\label{sec:projects}\nDuring the evolutionary development of the UNICORE technology, many European and international projects have\ndecided to base their Grid software implementations on UNICORE or to extend the growing set of core UNICORE\nfunctions with new features specific to their project focus. The goals and objectives of projects using UNICORE\nare not limited to the computer science community alone. Several other scientific domains such as bio-molecular\nengineering or computational chemistry are using the UNICORE technology as the basis of their work. In the\nfollowing we present short overviews of goals and objectives of UNICORE-based projects and describe additional\nfunctions and services contributed to the UNICORE development.\n\n\\subsection{EUROGRID -- Application Testbed for European Grid Computing}\n\\label{sec:projects-eurogrid}\nIn the EUROGRID\\footnote{funded in part by EC grant IST-1999-20247, duration: November 2000 - January 2004}\nproject \\cite{eurogrid:url} a Grid network of leading European High Performance Supercomputing centers was\nestablished. Based on the UNICORE technology application-specific Grids were integrated, operated and\ndemonstrated: \\bi\n\\item Bio-Grid for biomolecular science\n\\item Meteo-Grid for localized weather prediction\n\\item CAE-Grid for coupling applications\n\\item HPC-Grid for general HPC end-users\n\\ei\n\nAs part of the project, the UNICORE software was extended by an efficient data transfer mechanism, resource\nbrokerage mechanisms, tools and services for Application Service Providers (ASP), application coupling methods,\nand an interactive access feature \\cite{Mallmann:2001:EAT}. Efficient data transfer is a important issue, as Grids\ntypically rely on public parts of Internet connections. The available limited bandwidth has to be used efficiently\nto reduce the transfer time and the integrity of the transferred data has to be maintained, even if the transfer\nis interrupted. Depending on the application domain, additional security and confidentiality concerns need to be\nconsidered. This UNICORE high performance data transfer also uses X.509 certificates for authentication and\nencryption. To achieve not only a fast and secure transfer of data, but also high-performance capabilities,\nnetwork Quality of Service (QoS) aspects, overlapping of streamed data transfers, and packet assembling and\ncompression techniques are included.\n\nIn order to optimize the selection of resources -- either done by the users manually or by a metascheduler\nautomatically -- resource brokerage mechanisms and detailed resource description abilities are important. Within\nthe EUROGRID project, mechanisms were added to UNICORE, which allow users to specify their jobs in an abstract way\nimproving the overall resource selection and accounting. In particular for the benefit of the industrial user\naspects of security, convenience, and cost efficiency were addressed. To this end, the already existing security\nconcepts of UNICORE were thoroughly evaluated and assessed as being adequate, hence no additional development had\nto be done. The task of the developed resource broker is to match the abstract specification of the users jobs and\ntheir requirements with the available resources in the Grid. The resource broker reports the best match back to\nthe user including an estimate of the costs, which than allows the user to assign the appropriate resources to the\njob. For the suppliers of Grid resources (\\eg supercomputing centers) the resource broker allows to specify\ninformation about computational resources, architectures, processing power, storage and archiving facilities,\npost-processing facilities like visualization equipment, available software packages, and security guarantees. All\nthis data is enhanced by billing information.\n\nSupercomputing centers converge from pure providers of raw supercomputing power to Application Service Providers\n(ASP) running relevant scientific applications. For accounting and billing purposes the ASP needs to know the\nexact resources consumed by each customer in each run. For measuring the usage of supercomputers standard\nmechanisms provided by the resource management and operating system can be used, but measuring the usage of\nlicenses requires a sophisticated approach. For some applications, \\eg from the Computer Aided Engineering (CAE)\ndomain, this includes a connection to the applications licence manager. Establishing a link to the above mentioned\nresource broker is required to influence their decisions.\n\nFor solving complex problems applications from different domains, \\eg fluid-structure or\nelectromagnetism-structure, need to be coupled. This is established by using the EUROGRID resource broker\nfunctionality and combining it with the available Metacomputing functionality developed in the UNICORE Plus\nproject (\\cf \\secref{sec:intro}), which allows different schedulers of compute and application resources to\ncooperate. Finally, an interactive access to control and steer running application is needed for many scientific\napplications. The interactive use includes an interactive shell to actually login to computing resources using the\nUNICORE technology and security infrastructure.\n\nEUROGRID used the UNICORE technology to provide the above described services and functionalities by developing new\ncomponents. After the project ended, the developed components were revised and useful additions to the core\nUNICORE functions are now part of the available UNICORE software.\n\n\n\\subsection{GRIP -- Grid Interoperability Project}\n\\label{sec:projects-grip}\nGrid computing empowers users and organizations to work effectively in an information-rich environment. Different\ncommunities and application domains have developed distinct Grid implementations some based on published open\nstandards or on domain and community specific features. GRIP\\footnote{funded in part by EC grant IST-2001-32257,\nduration: January 2002 - February 2004} \\cite{GRIP:url} had the objective to demonstrate that the different\napproaches of two distinct grids can successfully complement each other and that different implementations can\ninteroperate. Two prominent Grid systems were selected for this purpose: UNICORE and Globus\\texttrademark\n\\cite{globus:url}, a toolkit developed in the United States. In contrast to UNICORE, Globus provides a set of APIs\nand services which requires more in-depth knowledge from the user. Globus is widely used in numerous international\nprojects and many centers have Globus installed as Grid middleware.\n\nThe objectives of GRIP were: \\bi\n\\item Develop software to enable the interoperation of independently developed Grid solutions\n\\item Build and demonstrate prototype inter-Grid applications\n\\item Contribute to and influence international Grid standards\n\\ei\n\nDuring the runtime of the GRIP project the Open Grid Service Architecture was proposed by the Global Grid Forum\n(GGF) \\cite{GGF:url}. The arrival of OGSA also was an opportunity to influence the standards directly which were\nto be created and to start developments that allow UNICORE to interoperate not only with Globus but with services\non the Grid in general, once the definition of the services and their interfaces became mature. OGSA did not\nchange the overall objectives of GRIP, however, it influenced directly some of the technical results.\n\nA basic requirement of GRIP was that the Grid interoperability layer should not change the well-known UNICORE user\nenvironment. As developers from both communities cooperated in the GRIP project, this goal was reached with only\nlittle changes of the UNICORE server components and no changes of the Globus Toolkit. This was achieved by the\ndevelopment of the so called Globus Target System Interface (Globus TSI), which provides UNICORE-access to\ncomputational resources managed by Globus. The Globus TSI was integrated into a heterogeneous UNICORE and Globus\ntestbed.\n\nTo achieve the main objective of GRIP, the interoperability between UNICORE and Globus and initial OGSA services,\nthe following elements had to be implemented: \\bi\n\\item The interoperability layer between UNICORE and Globus Version 2\n\\item The interoperability layer between UNICORE and Globus Version 3\n\\item The Access from UNICORE to simple Web services as a first step towards full integration of Web services\n\\item The Interoperability of the certificate infrastructures of UNICORE and Globus\n\\item A resource broker capable of brokering between UNICORE and Globus resources\n\\item The Ontology of the resource description on an abstract level\n\\ei\n\nIn GRIP, two important application areas were selected to prove that the interoperability layers work as\nspecified: \\bi\n\\item Bio-molecular applications were instrumented in such a way that they are Grid-aware in any\nGrid environment and capable to seamlessly use UNICORE and Globus managed resources. The techniques developed in\nGRIP were designed and implemented in a generalized way to ensure that they can be used in other application\ndomains as well.\n\\item A meteorological application, the Relocatable Local Model (RLM), was decomposed in such\na way that the components could execute on the most suitable resources in a Grid, independent of the middleware.\n\\ei\n\nThe results of the GRIP project are important for understanding general interoperability processes between Grid\nmiddleware systems. The experience and knowledge of the GRIP partners allowed to work in many relevant areas\nwithin GGF, like security, architecture, protocols, workflow, production management, and applications, and to\ninfluence the work in GGF.\n\n\n\\subsection{OpenMolGRID -- Open Computing Grid for Molecular Science and Engineering}\n\\label{sec:projects-openmolgrid}\nThe OpenMolGRID\\footnote{funded in part by EC grant IST-2001-37238, duration: September 2002 - February 2005}\nproject \\cite{OpenMolGrid:url} was focused on the development of Grid enabled molecular design and engineering\napplications. {\\em In silico} testing \\cite{Sild:2005:OAW} has become a crucial part in the molecular design\nprocess of new drugs, pesticides, biopolymers, and biomaterials. In a typical design process $O(10^5)$ to\n$O(10^6)$ candidate molecules are generated and their feasibility has to be tested. It is not economical to carry\nout experimental testing on all possible candidates. Therefore, computational screening methods provide a cheap\nand cost effective alternative to reduce the number of candidates. Over the years Quantitative Structure\nActivity\/Property Relationship (QSAR\/QSPR) methods have been shown to be reliable for the prediction of various\nphysical, chemical, and biological activities \\cite{Karelson:2000:MDQ}.\n\nQSPR\/QSAR relies on the observation that molecular compounds with similar structure have similar properties. For\neach specific application a set of molecules is needed for which the target property is known. This requires\nsearching globally distributed information resources for appropriate data. For the purpose of exploring molecular\nsimilarity, descriptors are calculated from the molecular structure. Thousands of molecular descriptors have been\nproposed and are used to characterize molecular structures with respect to different properties. Their calculation\nputs high demands on computer resources and requires high-performance computing.\n\nBased on this complex application the objectives of the OpenMolGRID project were defined as:\n\\bi\n\\item Development of tools for secure and seamless access to distributed information and computational methods\nrelevant to molecular engineering within the UNICORE frame\n\\item Provision of a realistic testbed and reference application in life science\n\\item Development of a toxicity prediction model validated with a large experimental set\n\\item Provision of design principles for next generation molecular engineering systems.\n\\ei\nIn particular this included to use UNICORE to automatize, integrate, and speed-up the drug discovery pipeline.\n\nThe OpenMolGRID project addressed the objectives above by defining abstraction layers for data sources (databases)\nand methods (application software), and integrating all necessary data sources (\\eg ECOTOX \\cite{ecotox:url}) and\nmethods (\\eg 2D\/3D Molecular Structure Conversion and Optimization, Descriptor Calculation, Structure Enumeration)\ninto UNICORE. The project developed application specific user interfaces (plug-ins) and a mechanism to generate a\ncomplete UNICORE Job from an XML workflow specification. This so called Meta-Plug-in takes care of including all\nauxiliary steps like data format transformation and data transfers into the job, distributing data parallel tasks\nover available computational resources, and allocating resources to the tasks. Thereby the molecular design\nprocess was significantly improved as the time to build QSAR\/QSPR models, the probability for mistakes, and the\nvariability of results was reduced. In addition a command line client (CLC) for UNICORE was developed to enable\nthe data warehouse to use Grid resources for its data transformation processes. The CLC offers the generation of\nUNICORE jobs from XML workflow description as well as the job submission, output retrieval, status query, and job\nabortion. The CLC consists of commands, an API, and a queuing component.\n\nBesides the technical achievements of OpenMolGRID and the added value for pharmaceutical companies its results\nwill contribute to the standardization of QSAR models.\n\n\n\\subsection{VIOLA -- Vertically Integrated Optical Testbed for Large Applications}\n\\label{sec:projects-viola}\nThe aim of the VIOLA\\footnote{funded in part by BMBF grant 01AK605F, duration: May 2004 - April 2007} project\n\\cite{VIOLA:url} is to build up a testbed with the latest optical network technology (multi 10 Gigabit Ethernet\nlinks). The goals and objectives of VIOLA are: \\bi\n\\item Testing of new network components and network architectures\n\\item Development and testing of software for dynamic bandwidth management\n\\item Interworking of network technology from different manufacturers\n\\item Development and testing of new applications from the Grid and Virtual Reality (VR) domain\n\\ei\n\nThe performance of the new network technology is evaluated with different scientific applications that need a very\nhigh network performance and network flexibility. UNICORE is used to build up the Grid on top of the hardware\nwithout taking fundamental software modifications. Only an interface to the meta-computer software library\nMetaMPICH \\cite{METAMPICH:url} needs to be integrated into UNICORE. Grid applications from the High Performance\nSupercomputing and Virtual Reality domain are enhanced for an optimized usage of the available bandwidth and the\nprovided Quality of Service classes. In this context a Meta-Scheduler framework is developed, which is able to\nhandle complex workflows and multi-site jobs by coordinating supercomputers and the network connecting them.\n\n\\begin{figure}[htb]\n\\centering\n\\includegraphics[width=\\textwidth]{VIOLA-superscheduler.eps}\n\\caption{The VIOLA Meta-Scheduler architecture.} \\label{fig:VIOLA-supersched}\n\\end{figure}\n\nVIOLA's first generation Meta-Scheduler architecture focuses on the scheduling functionality requiring only\nminimal changes to the UNICORE system. As depicted in \\figref{fig:VIOLA-supersched}, the system comprises the\nAgreement Manager, the Meta-Scheduler itself \\cite{Quecke:2000:MAR}, and a Meta-Scheduling plug-in (which is part\nof the client and not pictured separately). Before submitting a job to a Usite (\\cf \\secref{sec:arch-serverT}),\nthe Meta-Scheduling plug-in and the Meta-Scheduler exchange the data necessary to schedule the resources needed.\nThe Meta-Scheduler is then (acting as an Agreement Consumer in WS--Agreement terms \\cite{GRAAP:url}) contacting\nthe Agreement Manager to request a certain level of service, a request which is translated by the Manager into the\nappropriate resource management system commands. In case of VIOLA's computing resources the targeted resource\nmanagement system is the EASY scheduler. Once all resources are reserved at the requested time the Meta-Scheduler\nnotifies the UNICORE Client via the Meta-Scheduling plug-in to submit the job. This framework will also be used to\nschedule the interconnecting network, but potentially any resource can be scheduled if a respective Agreement\nManager is implemented and the Meta-Scheduling plug-in generates the necessary scheduling information. The\nfollow-on generation of the Meta-Scheduling framework will then be tightly integrated within UNICORE\/GS (\\cf\n\\secref{sec:future-unigrids-GS}).\n\n\n\\subsection{NaReGI -- National Research Grid Initiative}\n\\label{sec:projects-naregi}\nThe Japanese NaReGI project \\cite{NAREGI:url} includes the UNICORE technology as the basic middleware for research\nand development. NaReGI is a collaboration project between industry, academia, and government. The goals and\nobjectives are: \\bi\n\\item Establishment of a national Japanese research Grid infrastructure\n\\item Revitalization of the IT industry through commercialization of Grid middleware and strengthened\ninternational competitiveness\n\\item Dissemination of Grid environments throughout industry\n\\item Trailblazing the standardization of Grid technology\n\\item Cultivation of human resources specializing in IT technology for Grids\n\\ei\n\nSimilar to the GRIP project (\\cf \\secref{sec:projects}) where an interoperability layer between UNICORE and Globus\nToolkit 2 and 3 was developed, the NaReGI project plans to implement such a layer between UNICORE and Condor\n\\cite{CONDOR:url}, called UNICONDORE. This interoperability layer will allow to submit jobs from the UNICORE\nclient to Condor pools and to use Condor commands to submit jobs to UNICORE managed resources.\n\nIn the first phase of the NaReGI testbed UNICORE provides access to about 3000 CPUs in total with approximately 17\nTFlops of peak performance. It is expected to increase the integrated peak performance to 100+ TFlops by the end\nof the project in 2007.\n\n\n\n\\section{UNICORE in Production}\n\\label{sec:production}\nFrom its birth in two German BMBF-funded projects to its extensive use and further development in a variety of EU\nand BMBF research projects, the UNICORE technology ran through an evolutionary process transforming from an\ninitial prototype software to a powerful production Grid middleware.\n\n\\subsection{UNICORE@SourceForge}\n\\label{sec:production-SF}\nSince May 2004, the UNICORE technology with all its components is available as open source software under the BSD\nlicense. It can be downloaded from the SourceForge repository. Besides the core developers of UNICORE (namely\nFujitsu Laboratories of Europe, Intel Germany and the Research Center J\\\"{u}lich), there are numerous contributors\nfrom all over the world, \\eg Norway, Poland, China and Russia. The Web site \\cite{UNICORE-sourceforge:url} offers\na convenient entry point for interested users and developers. In the download section the UNICORE software is\nbundled in different packages, \\eg the client package and individual packages for the different server components\nGateway, NJS, TSI\/IDB, UUDB (\\cf \\secref{sec:arch}), and plug-ins. Until January 2005 more than 2800 downloads of\nUNICORE are counted.\n\nA tracker section linked on the Web site establishes a communication link to the core developer community. The\ncorresponding mailing lists allow users to report bugs, to request new features, and to get informed about bug\nfixes or patches. For the announcement of new software releases a separate mailing list was created. The Grid team\nat the Research Center J\\\"{u}lich is responsible for UNICORE@SourceForge. Its work includes coordinating and driving\nthe development effort, and producing consolidated, stable, and tested releases of the UNICORE software.\n\n\\subsection{Production System on Jump}\n\\label{sec:production-Jump}\nSince July 2004 UNICORE is established as production software to access the supercomputer resources of the John\nvon Neumann-Institute for Computing (NIC) at the Research Center J\\\"{u}lich. These are the 1312-processor IBM p690\ncluster (Jump) \\cite{Jump:url}, the Cray SV1 vector machine, and a new Cray XD1 cluster system. As an alternative\nto the standard SSH login, UNICORE provides an intuitive and easy way for submitting batch jobs to the systems.\nThe academic and industrial users come from all over Germany and from parts of Europe. The applications come from\na broad field of domains, \\eg astrophysics, quantumphysics, medicine, biology, chemistry, and climate research,\njust to name the largest user communities. A dedicated, pre-configured UNICORE client with all required\ncertificates and accessible Vsites is available for download. This alleviates the installation and configuration\nprocess significantly. Furthermore, an online installation guide including a certificate assistant, an user\nmanual, and example jobs help users getting started.\n\nTo provide the NIC-users with adequate certificates and to ease the process of requesting and receiving a\ncertificate, a certificate authority (CA) was established. User certificate requests are generated in the client\nand have to be send to the CA. Since introduction of UNICORE at NIC, more than 120 active users requested a\nUNICORE user certificate.\n\nA mailing list serves as a direct link of the users to UNICORE developers in the Research Center J\\\"{u}lich. The list\nallows to post problems, bug reports, and feature requests. This input is helpful in enhancing UNICORE with new\nfeatures and services, in solving problems, identifying and correcting bugs, and influences new releases\nof UNICORE available at SourceForge.\n\n\\subsection{DEISA -- Distributed European Infrastructure for Scientific Applications}\n\\label{sec:production-DEISA}\nTraditionally, the provision of high performance computing resources to researchers has traditionally been the\nobjective and mission of national HPC centers.On the one hand, there is an increasing global competition between\nEurope, USA, and Japan with growing demands for compute resources at the highest performance level, and on the\nother hand stagnant or even shrinking budgets. To stay competitive major investments are needed every two years --\nan innovation cycle that even the most prosperous countries have difficulties to fund.\n\nTo advance science in Europe, eight leading European HPC centers devised an innovative strategy to build a\nDistributed European Infrastructure for Scientific Applications (DEISA) \\cite{DEISA:url}. The centers join in\nbuilding and operating a tera-scale supercomputing facility. This becomes possible through deep integration of\nexisting national high-end platforms, tightly coupled by a dedicated network and supported by innovative system\nand grid software. The resulting virtual distributed supercomputer has the capability for natural growth in all\ndimensions without singular procurements at the European level. Advances in network technology and the resulting\nincrease in bandwidth and lower latency virtually shrink the distance between the nodes in the distributed\nsuper-cluster. Furthermore, DEISA can expand horizontally by adding new systems, new architectures, and new\npartners thus increasing the capabilities and attractiveness of the infrastructure in a non-disruptive way.\n\nBy using the UNICORE technology, the four core partners of the projects have coupled their systems using virtually\ndedicated 1 Gbit\/s connections. The DEISA super-cluster currently consists of over 4000 IBM Power 4 processors and\n416 SGI processors with an aggregated peak performance of about 22 teraflops. UNICORE provides the seamless,\nsecure and intuitive access to the super-cluster.\n\nThe Research Center J\\\"{u}lich is one of the DEISA core partners and is responsible for introducing UNICORE as Grid\nmiddleware at all partner sites and for providing support to local UNICORE administrators.\n\nAll DEISA partners have installed the UNICORE server components Gateway, NJS, TSI, and UUDB to access the local\nsupercomputer resources of each site via UNICORE. \\figref{fig:DEISA-architecture} shows the DEISA UNICORE\nconfiguration. For clarity only four sites are shown. At each site, a Gateway exists as an access to the DEISA\ninfrastructure. The NJSs are not only registered to their local Gateway, but to all other Gateways at the partner\nsites as well. Local security measures like firewall configurations need to consider this, by permitting access to\nall DEISA users and NJSs. This fully connected architecture has several advantages. If one Gateway has a high\nload, access to the high performance supercomputers through DEISA is not limited. Due to the fully connected\narchitecture, no single point of failure exists and the flexibility is increased.\n\\begin{figure}[htb]\n\\centering\n\\includegraphics[width=0.95\\textwidth]{DEISA-architecture.eps}\n\\caption{The DEISA architecture.} \\label{fig:DEISA-architecture}\n\\end{figure}\n\nThe DEISA partners operate different supercomputer architectures, which are all accessible through UNICORE.\nInitially all partners with IBM p690 clusters are connected to one large virtual supercomputer. In a second step\nother supercomputers of different variety are connected to DEISA, making the virtual supercomputer heterogeneous.\nUNICORE can handle this, as it is designed to serve such heterogeneous architectures in a seamless, secure, and\nintuitive way.\n\nIn December 2004 a first successful UNICORE demonstration between the four DEISA core sites FZJ (Research Center\nJ\\\"{u}lich, Germany), RZG (Computing Center Garching, Germany), CINECA (Italian Interuniversity Consortium, Italy) and\nIDRIS (Institute for Development and Resources in Intensive Scientific Computing, France) was given. Different\nparts of a distributed astrophysical application were generated and submitted with UNICORE to all four sites.\n\nThe experience and knowledge of the researchers, developers, users, and administrators in working with UNICORE in\nthe DEISA project on a large production platform will be used as useful input for future developments of the\nUNICORE technology. A close synchronization with the UniGrids project (\\cf \\secref{sec:future-unigrids}) is\nforeseen.\n\n\n\n\\section{Future of UNICORE}\n\\label{sec:future}\nThe current UNICORE software implements a vertically integrated Grid architecture providing seamless access to\nvarious resources. Every resource is statically integrated into the UNICORE Grid by providing an interface to the\nappropriate resource manager.\n\nOne of the benefits Web services will bring to Grid computing is the concept of loosely coupled distributed\nservices. Merging the idea of ``everything being a service'' with the achievements of the Grid community led to\nGrid services, enabling a new approach to the design of Grid architectures. The adoption of XML and the drive for\nstandardization of the Open Grid Service Architecture provide the tools to move closer to the promise of\ninteroperable Grids. A demonstrator validated the correspondence of UNICORE's architectural model with the\nOGSA\/OGSI (Open Grid Service Infrastructure \\cite{Tuecke:2003:OGSI}) approach, which encouraged the development of\nan OGSA\/OGSI compliant UNICORE Grid architecture in the GRIP project (\\cf \\secref{sec:projects-grip}).\n\nIn \\cite{Menday:2003:GEU} UNICORE is examined for the evolution of a Grid system towards a service oriented Grid,\nprimarily focussing on architectural concepts and models. Based on the current architecture and the enhancements\nprovided by GRIP, first steps already integrate Web services into UNICORE. This included the provision of OGSI\ncompliant port types parallel to the proprietary ones as well as the design of XML based protocols. This work was\ncontinued in the UniGrids project.\n\nAs mentioned above the development of a Grid middleware is an continuous process of integrating new features,\nservices, and adapting to emerging standards, and UNICORE is no exception. In the following we present new\ndevelopments, some technical details, and report on projects, which enhance the UNICORE technology to serve the\ndemands of the Grid in the future \\cite{Jeffrey:2004:NGG}.\n\n\n\\subsection{UniGrids -- Uniform Interface to Grid Services}\n\\label{sec:future-unigrids}\nThe strength of the UNICORE architecture is well-proven as described above. The rapid definition and adoption of\nOGSA allow the UNICORE development community to re-cast and extend the concepts of UNICORE through the use of Web\nservices technologies. The goal of the UniGrids\\footnote{funded in part by EC grant IST-2002-004279, duration:\nJuly 2004 - June 2006} project \\cite{UniGrids:url} is to lift UNICORE on an architecture of loosely-coupled\ncomponents while keeping its 'end-to-end' nature.\n\nThus, the integration of Web services techniques and UNICORE, which already started in the GRIP project (\\cf\n\\secref{sec:projects-grip}), will continue in the \\mbox{UniGrids} project. Interoperability, through adopting and\ninfluencing standards, form the philosophical foundation for UniGrids. The project aims to transform UNICORE into\na system with interfaces that are compliant with the Web Services Resource Framework (WS-RF) \\cite{WSRF:url} and\nthat interoperate with other WS-RF compliant software components.\n\nSuch an approach offers great advantages both for the ease of development of new components by aggregation of\nservices and through the integration of non-UNICORE components into the standards-based infrastructure.\n\nIn this sense, work is continuing in the following areas: \\bi\n\\item Development of a compliant WS-RF hosting environment used for publishing UNICORE job and file services as\nWeb services.\n\\item Support of dynamic virtual organizations by enhancing the UNICORE security infrastructure to allow different\nusage models such as delegation and collective authorization.\n\\item Development of translation mechanisms, such as resource ontologies, to interoperate with other OGSA compliant\nsystems. Support for Grid economics by developing a Service Level Agreement (SLA) framework and cross-Grid\nbrokering services.\n\\item Development and integration of generic software components for visualization and steering of simulations (VISIT\n\\cite{visit:url}), device monitoring and control, and tools for accessing distributed data and databases.\n\\ei\n\nApplications from the scientific and industrial domain, like biomolecular and computational biology, geophysical\ndepth imaging by oil companies, automotive, risk-management, energy, and aerospace are used to prove the\ndevelopments in UniGrids.\n\nThe development in the UniGrids project will lead to UNICORE\/GS, which follows the architecture of OGSA through\nthe standardization of WS-RF and related work like \\eg the Web Services Notification technology \\cite{WSN:url}.\nThe results will be made available under an open source BSD license.\n\n\\subsubsection{UNICORE\/GS}\n\\label{sec:future-unigrids-GS}\nWeb service technology, and in particular the WS-RF, forms the basis for the UNICORE\/GS software. WS-RF is the\nfollow-on to OGSI, but more in line with mainstream Web services architecture \\cite{WSARCH:url}. Based on this new\ntechnology, UNICORE\/GS will retain its key characteristics of seamlessness, security, and intuitiveness from both\nthe user and administrative perspective, but will be built on a service oriented framework. This means that there\nis a loosening of the coupling between the components of the system. UNICORE\/GS keeps the classical UNICORE\ntopology of Usites, each containing a number of Vsites, but provides a new framework for integrating other\nservices and providing common infrastructure functionality as services. This has the implication that new services\nwill be easily integrated into the UNICORE\/GS environment. Conversely, UNICORE\/GS will be well-prepared to make\nuse of external services.\n\nThe WS-RF technology is used to model core functionalities such as job submissions and file transfers as\nWS--Resources. These services are accessible via web service interfaces and thus establishing the UniGrids atomic\nservices layer. This layer will be realized making extensive use of existing UNICORE server components.\n\nAll services in a Usite are accessible through the UniGrids Gateway that provides a secure entrance into the\nUNICORE\/GS infrastructure. The principal is exactly the same as for classic UNICORE, however, the Gateway now\nroutes messages according to Web Services Addressing (WS--Addressing) \\cite{WSA:url}. Authentication is based on\ntransport level HTTPS security, although the intention is to move to Web Services Security (WS--Security)\n\\cite{WSS:url}. Regarding authorized access to resources, the UNICORE User Database (UUDB) will be available as a\nservice to other services in the Usite, and will form the basis for future work concerning virtual organizations\nand fine-grained authorization schemes.\n\nThe underlying UniGrids atomic services layer will provide an excellent framework to deploy higher-level services\nsuch as co-allocation schedulers, workflow engines, and services for provision and easy access to data-intensive,\nremotely-steerable simulations.\n\n\\subsection{NextGrid -- Architecture for Next Generation Grids}\n\\label{sec:future-nextgrids}\nIn comparison to the UniGrids project which evolves the existing UNICORE Grid system to a service-oriented one,\nthe NextGRID\\footnote{funded in part by EC grant IST-2002-511563, duration: September 2004 - August 2007}\n\\cite{nextgrid:url} project aims for the future: The goal is to provide the foundations for the next generation of\nGrids. NextGRID is not a project based on the UNICORE architecture or Grid system as-is, but institutions and\npeople involved in the UNICORE development from the beginning on contribute expertise and experience to NextGRID.\n\nSince it is obvious that there is no such thing as the one and only next generation Grid, and experts envisage the\nco-existence of multiple Grids with well-defined boundaries and access points, NextGRID is going to define a Grid\narchitecture which can be seen as building blocks for Grids. It does not only provide interoperability by-design\nbetween entities which exist within one instantiation of such an architecture, but it also facilitates the\ninteroperability between different Grids developed according to the NextGRID architecture.\n\nAlthough developing a Grid one generation ahead, NextGRID is not starting from scratch. Properties to incarnate\nand functions to realize future Grids are expertly described in \\cite{Priol:2003:NGG} and \\cite{Jeffrey:2004:NGG}.\nThese reports frame NextGRID's architectural development while the Open Grid Services Architecture is going to\ndefine Grid services and their interactions and does therefore make up a staring point for the conceptualization\nand design of NextGRID. In addition, regarding the underlying technology and architectural model, NextGRID\npropagates the usage of Web Services and the adoption of Service-Oriented Achitecture (SOA) \\cite{Erl:2004:SOA}\nconcepts and models.\n\nNextGRID focuses on security, economic sustainability, privacy\/legacy, scalability and usability. The following\nproperties have the highest priorities when carrying out the following work: \\bi\n\\item Developing an architecture for next generation Grids\n\\item Implementing and testing prototypes aligned with the concepts and design of the NextGRID architecture\n\\item Creating reference applications which make use of the NextGRID prototypes\n\\item Facilitating the transition from scientific- to business-oriented Grids by integrating the means to negotiate\na certain Quality of Service (QoS) level\n\\item Specifying the methods, processes, and services necessary to dynamically operate Grids across multiple\norganizations which comprise heterogeneous resources\n\\ei\n\nSince the ongoing UNICORE development in projects like UniGrids shares resources as well as the technological\nfoundation with NextGRID there is a high chance that the outcome of NextGRID will also represent the next step of\nUNICORE's evolution.\n\n\n\n\\section{Conclusion}\n\\label{sec:conclusion}\nIn this paper we presented the evolution of the UNICORE technology from a Grid software with prototype character\ndeveloped in two German projects to a full-grown, well-tested, widely used and accepted Grid middleware. UNICORE\n-- Uniform Interface to Computing Resources -- provides a {\\em seamless, secure and intuitive} access to\ndistributed Grid resources. Although the UNICORE vision was already coined in 1997, the then stated goals and\nobjectives of hiding the seams of resource usage, incorporating a strong security model, and providing an easy to\nuse graphical user interface for scientists and engineers are still valid today: to achieve these goals and\nobjectives, UNICORE is designed as a vertically integrated Grid middleware providing components at all layers of a\nGrid infrastructure, from a graphical user interface down to the interfaces to target machines.\n\nInitially developed in the German projects UNICORE and UNICORE Plus, UNICORE was soon established as a promising\nGrid middleware in several European projects. In the GRIP project an interoperability layer between UNICORE and\nthe Globus Toolkit 2 and 3 was developed to demonstrate the interoperability of independently developed Grid\nsolutions, allowing to build and to demonstrate inter-Grid applications from the bio-molecular and meteorological\ndomain. In the EUROGRID project, European high performance supercomputing centers joined to extend UNICORE with an\nefficient data transfer, resource brokerage mechanisms, ASP services, application coupling methods, and an\ninteractive access. In addition, a Bio-Grid, Meteo-Grid, CAE-Grid, and HPC-Grid were established to integrate a\nvariety of application domains. The main objective of the OpenMolGRID project is to provide a unified and\nextensible information-rich environment based on UNICORE for solving problems from molecular science and\nengineering. In the VIOLA project a vertically integrated testbed with the latest optical network technology is\nbuilt up. UNICORE is used as the Grid middleware for enabling the development and testing of new applications in\nthe optical networked testbed, which provides advanced bandwidth management and QoS features.\n\nWith these developments UNICORE grew to a software system usable in production Grids. In this context UNICORE is\ndeployed in the large German supercomputing centers to provide access to their resources. At the John von\nNeumann-Institute for Computing, Research Center J\\\"{u}lich, many users submit their batch jobs through UNICORE to the\n1312-processor 8.9 TFlop\/s IBM p690 cluster and the Cray SV1 vector machine. Leading European HPC centers joined\nin the project DEISA to build a distributed European infrastructure for scientific applications based on UNICORE\nto build and operate a distributed multi tera-scale supercomputing facility.\n\nThe future of UNICORE is promising and follows the trend of ``Everything being a Service\" by adapting to Open Grid\nService Architecture (OGSA) standards. In this context, the UniGrids project continues the effort of the GRIP\nproject in integrating the Web Services and UNICORE technology to enhance UNICORE to an architecture of\nloosely-coupled components while keeping its ``end-to-end\" nature. To this end UNICORE\/GS will be developed, which\nmakes UNICORE compliant with the Web Services Resource Framework (WS-RF).\n\nToday the UNICORE software is available as open source under a BSD licence from SourceForge for download. This\nenables the community of core UNICORE developers to grow and makes future development efforts open to the public.\n\n\n\n\\section{Acknowledgments}\n\\label{sec:ack}\nThe work summarized in this paper was done by many people. We gratefully thank them for their past, present, and\nfuture contributions in developing the UNICORE technology. Most of the work described here was supported and\nfunded by BMBF and different programmes of the European Commission under the respective contract numbers mentioned\nabove.\n\n\n\\newcommand{\\noopsort}[1]{}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nLet $X$ be a scheme over an algebraically closed field $k$ of\ncharacteristic $p$, with $p\\, >\\, 0$. Fix a $k$-point $x$ of $X$. Nori\nintroduced the notion of fundamental group scheme $\\pi(X,x)$ in \\cite{N1}\nand further developed it in \\cite{N2}. Since then the\nfundamental group scheme is being\nstudied and in the process has turned into\nan important tool in algebraic geometry of positive\ncharacteristic. In \\cite{N2} Nori proves that\n$\\pi(X,x)$ is trivial for proper rational normal varieties. More generally,\n$\\pi(X,x)\\,=\\, 0$ if $X$ is separably rationally connected \\cite{Bi}.\nZhu proves that a general Fano (proper, smooth, connected with\nample anticanonical bundle) hypersurfaces in projective spaces are\nseparably rationally connected \\cite{Zh}. Therefore, the fundamental\ngroup schemes of general Fano hypersurfaces in a projective space\nare trivial.\n\nIn \\cite{CL} Chambert-Loir proves that every proper rationally\nchain connected normal variety has finite \\'etale\nfundamental group, and its order is coprime to $p$\n(the characteristic of $k$) \\cite{CL2}. This result can also be obtained as a\nconsequence of \\cite[Theorem 1.5]{Ko} and \\cite[Theorem 1.6]{Ko}. Shioda gave an example of a rationally\nconnected variety over a field of characteristic $p\\,\\neq\\, 5$ whose \\'etale\nfundamental group is $\\mathbb{Z}\/5\\mathbb{Z}$ \\cite{Sh}. Examples of rationally chain connected varieties whose local \nfundamental group scheme is not trivial are also known.\nFor example, a supersingular Enriques surface $E$ over an algebraically \nclosed field of characteristic $2$ is unirational (see\n\\cite[Corollary 1.3.1]{CD}), hence it is rationally \nchain connected. It is known that there exists a nontrivial\n$\\alpha_2$--torsor over $E$ (see \\cite[Chapter I, \\S 3]{CD}). \n\nWe prove the following (see Theorem \\ref{teoMAIN} and Remark \\ref{remFANO}):\n\n\\begin{theorem}\nLet $k$ be an algebraically closed field and $X$\na proper normal, rationally chain connected $k$--scheme. Let $x\\,\\in\\, X(k)$ be a point.\nThen the fundamental group scheme $\\pi(X,x)$ is finite.\n\\end{theorem}\n\nThe strategy of the proof is similar to that in \\cite{CL}, adapted to the \nnew setting.\n\n\\section{Preliminaries}\\label{sez:PREM}\n\nWe will write $\\pi(X)$ instead of $\\pi(X,x)$ to simplify the notation. \nHowever all the schemes for which we will compute the fundamental group \nscheme are meant to be pointed and all the morphisms between them take the \nmarked point in the domain space to the marked point in the target space. \nThe same convention will be applied to torsors: we assume \nthey are pointed and morphisms between them take the marked point in the \ndomain space to the marked point in the target space.\n\nLet $k$ be an algebraically closed field of any characteristic.\nA proper variety $X$ over $k$ is said to be rationally chain connected if for every algebraically\nclosed field $\\Omega$ containing $k$, for any two points in $X(\\Omega)$ there is a\nproper and connected curve passing through them such that its normalization is a\ndisjoint union of projective lines. If this union consists of only one projective line\nwe say that $X$ is rationally connected.\n\nLet $X$ be a rationally chain connected variety over $k$.\nWe recall \\cite[Lemma 1]{CL} and we sketch its proof:\n\n\\begin{lemma}\\label{lemCL1}\nLet $k\\,\\subseteq\\, \\Omega$ be a field extension\nwhere $\\Omega$ is algebraically closed. Let $$F_{\\Omega}\\,:\\, \\mathbb{P}_{\\Omega}^1\n\\,\\longrightarrow\\, X_{\\Omega}$$ be a rational curve of $X_{\\Omega}$. Let\n$x_0\\,:=\\,F_{\\Omega}(0)$ and $x_{\\infty}\\,:=\\,F_{\\Omega}(\\infty)$ be points of\n$X_{\\Omega}$ then let $V_0$ and $V_{\\infty}$ be their Zariski closure in $X$.\nThen there exist a normal integral $k$-scheme $T$, a morphism\n$$F\\,:\\, \\mathbb{P}_{T}^1\\,\\longrightarrow\\, X$$ such that the morphisms defined as\n$$F_0(t)\\,:=\\,F(0,t)\\,:\\,T\\,\\longrightarrow\\, X\\ ~ \\text{ and }~\\ F_{\\infty}(t)\\,:=\\,\nF(\\infty,t)\\,:\\,T\\,\\longrightarrow\\, X$$\nare dominant over $V_0$ and $V_{\\infty}$ respectively.\n\\end{lemma}\n\n\\begin{proof}\nThere exists a finitely generated $k$-algebra $k\\,\\subseteq\\, A$ contained\nin $\\Omega$, and there is a morphism $$F_{A}\\,:\\,\n\\mathbb{P}_{A}^1\\,\\longrightarrow\\, X_{A}$$ such that $F_{\\Omega}\\,=\\,F_A\n\\otimes_A\n\\Omega$. We set $T\\,:=\\,{\\rm Spec}(A)$ that we assume to be normal (otherwise we replace it with a finite extension). We now consider the morphism\n$F\\,:\\, \\mathbb{P}_{T}^1\\,\\longrightarrow\\, X$ obtained from $F_{A}$ after composing with the projection $X_A\\,\\longrightarrow\\, X$. In \\cite[Lemma 1]{CL} it has been proved that $F_0$ and $F_{\\infty}$ are\ndominant over $V_0$ and $V_{\\infty}$; we briefly recall this last part for the\nconvenience of the reader: We study $F_{0}$ (it will\nbe the same for $F_{\\infty}$). The\nimage by $F_{0}$ of the generic point of ${\\rm Spec}(A)$ is the generic\npoint of $V_0$. Since $V_0$ is closed in $X$, the inverse image\n$F_{0}^{-1}(V_0)$ is closed in ${\\rm Spec}(A)$ and dense. Thus\n$F_{0}^{-1}(V_0)$ coincides with\n${\\rm Spec}(A)$, and hence the image of $F_{0}$ is contained in $V_0$ and contains\nits generic point. Therefore it contains an open dense subset of $V_0$. \n\\end{proof}\n\n\\section{The main theorem}\n\nThe following lemma is well-known. We include\na short proof of it for the convenience of the reader.\n\n\\begin{lemma}\\label{lemGROUPS}\nLet $G$ be a finite $k$-group scheme and let $G^{\\text{\\'et}}$ and $G^{\\rm loc}$ be\nrespectively the maximal \\'etale quotient and the maximal connected quotient. Then the\nnatural morphism $$\\alpha\\,:\\,G\\,\\longrightarrow\\, G^{\\text{\\'et}}\\times G^{\\rm loc}$$\nis faithfully flat. \n\\end{lemma}\n\n\\begin{proof}\nThe field being perfect the reduced subscheme $G_{\\text{red}}$ is a subgroup\nscheme of $$N^{\\text{loc}}\\,:=\\,\\text{kernel} (G\\to G^{\\text{loc}})\\, ,$$ while the\nconnected component $G^0$ of $G$ is $\\text{kernel}(G\\to \nG^{\\text{\\'et}})$. If $\\alpha$ is not faithfully flat we can factor it as in\nthe following diagram:\n$$\\xymatrix{ & G\\ar@{->>}[ldd]\\ar@{->>}[d]^{q}\\ar@{->>}[rdd] & \\\\ &\nG'\\ar@{->>}[ld]\\ar@{^{(}->}[d]^{j}\\ar@{->>}[rd] & \\\\G^{\\text{\\'et}} &\nG^{\\text{\\'et}}\\times G^{\\text{loc}}\\ar@{->>}[l]\\ar@{->>}[r] & G^{\\text{loc}} }\n$$\nwhere $q\\,:\\,G\\,\\twoheadrightarrow\\, G'$ is faithfully flat and $j\\,:\\,G'\\,\n\\hookrightarrow\\, G^{\\text{\\'et}}\\times G^{\\text{loc}}$ is a closed immersion. Clearly\n$G^{\\text{\\'et}}$ and $G^{\\text{loc}}$ are still the maximal \\'etale\nquotient and the maximal connected quotient of $G'$ respectively. So we can\nassume $\\alpha$ is a closed immersion. Therefore, the lemma is equivalent to the\nassertion that $\\alpha$ is an isomorphism.\n\n{}From \\cite[\\S~6, Ex. 9]{WW} it follows that $G^{\\text{\\'et}}$\nis isomorphic to a subgroup-scheme of $G$ which we identify with\n$G^{\\text{\\'et}}$, so in particular $G^{\\text{\\'et}}\\,\\leq\\, \nG_{\\text{red}}\\,\\leq\\, N^{\\text{loc}}$. Therefore, we have \n$$\\vert G^{\\text{\\'et}}\\vert\\vert G^{\\text{loc}}\\vert\\,=\\,\n\\vert G^{\\text{\\'et}}\\vert\\frac{\\vert G\\vert }{\\vert N^{\\text{loc}}\\vert\n}\\,\\leq\\,\\vert G^{\\text{\\'et}}\\vert\\frac{\\vert G\\vert }{\\vert G^{\\text{\\'et}}\n\\vert } \\,=\\, \\vert G\\vert $$\nwhich implies that $\\alpha$ is an isomorphism.\n\\end{proof}\n\nWe recall that when $X$ is a reduced and connected scheme over a field $k$\nthen the fundamental group scheme can be defined. In this case a finite $G$-torsor $Y\\,\\longrightarrow\\, X$ is called Nori-reduced if the canonical morphism $\\pi(X)\n\\,\\longrightarrow\\, G$ is faithfully flat. \n\n\\begin{lemma}\\label{lemTORS}\nLet $X$ be a connected and reduced scheme over $k$. Let $G$ (respectively,\n$H$) be a finite local (respectively, finite \\'etale) $k$--group\nscheme. Let $$Y\\,\\longrightarrow\\, X$$ and $T\\,\\longrightarrow\\, X$ be a $G$--torsor and\nan $H$--torsor respectively. We assume that both $Y$ and $T$ are Nori-reduced. Then\nthe $H\\times G$--torsor $$T\\times_X Y\\,\\longrightarrow\\, X$$ is also Nori-reduced. \n\\end{lemma}\n\n\\begin{proof}\nIf $\\pi(X)\\,\\longrightarrow\\, H\\times G$ is not faithfully flat then there\nexists a triple $(M\\, , Z\\, ,\\iota)$, where\n\\begin{itemize}\n\\item $M\\,\\hookrightarrow\\, H\\times G$ is a subgroup-scheme,\n\n\\item $Z\\,\\longrightarrow\\, X$ is a $M$--torsor, and\n\n\\item $\\iota\\, :\\, Z\\,\\hookrightarrow\\, T\\times_X Y$ is a reduction of structure\ngroup-scheme, to $M$, of the $H\\times G$--torsor $T\\times_X Y$.\n\\end{itemize}\nLet\n$$T'\\,\\longrightarrow\\, X~ \\ \\text{ and }~\\ Y'\\,\\longrightarrow\\, X$$ be the\n$M^{\\text{\\'et}}$ and $M^{\\text{loc}}$--torsors respectively,\nobtained from the $M$--torsor $Z\\,\\longrightarrow\\, X$ using the projections\nof $M$ to $M^{\\text{\\'et}}$ and $M^{\\text{loc}}$ respectively (the notation\nis as in Lemma \\ref{lemGROUPS}).\nWe have a closed immersion $$Z\\,\\hookrightarrow\\, T'\\times_X Y'$$ induced by\nthe closed immersion $M\\,\\hookrightarrow \\,M^{\\text{\\'et}}\\times M^{\\text{loc}}$.\nThe latter is an isomorphism by Lemma \\ref{lemGROUPS}, so the same is true for\n$Z\\,\\hookrightarrow\\, T'\\times_X Y'$.\n\nThe projection $M\\,\\twoheadrightarrow\\, H$ (respectively, $M\\,\\twoheadrightarrow\n\\, G$) clearly factors through $M^{\\text{\\'et}}$ (respectively, $M^{\\text{loc}}$).\nNote that the projections $$M\\,\\longrightarrow\\, H \\ \\ \n\\text{ and } \\ \\ M\\,\\longrightarrow \\,G$$\nare faithfully flat morphisms because the two torsors $Y$ and $T$ are\nNori-reduced. Consequently, the two homomorphisms $M^{\\text{\\'et}}\\,\\longrightarrow\\,\nH$ and $M^{\\rm loc}\\,\\longrightarrow\\, G$ are isomorphisms. Now using\nLemma \\ref{lemGROUPS} it follows that the inclusion $M\\,\\hookrightarrow\\, H\\times G$\nis an isomorphism. Consequently, the $H\\times G$--torsor $T\\times_X Y\\,\\longrightarrow\\,\nX$ is Nori-reduced.\n\\end{proof}\n\nThe following result was proved in \\cite[Proposition 3.6]{EPS} under the assumption\nthat $X$ is proper.\n\n\\begin{corollary}\\label{corTORS}\nLet $X$ be a connected and reduced scheme over $k$. Let $G$ (respectively, $H$)\nbe a finite local (respectively, finite \\'etale) $k$-group\nscheme. Let $Y\\,\\longrightarrow\\, X$ be a $G$--torsor and\n$T\\,\\longrightarrow\\, X$ an $H$--torsor. We assume that both the torsors are\nNori-reduced. Then the $G$--torsor\n$$T\\times_X Y \\,\\longrightarrow\\,T$$ is also Nori-reduced. In particular, the morphism\n$\\pi^{\\rm loc}(T)\\,\\longrightarrow\\, \\pi^{\\rm loc}(X)$ is faithfully flat.\n\\end{corollary}\n\n\\begin{proof}\nLet us assume that there is a finite local $k$-group\nscheme $G_1\\,\\subset\\,G$, and $G_1$--torsor $U\\,\\longrightarrow\\, T$ and a\nreduction\n$$i\\,:\\,U\\,\\hookrightarrow\\, T\\times_X Y$$ of structure group to $G_1$.\nLet $S$ be any $k$-scheme. For any $x\\,\\in\\, X(S)$ we choose $u_x\\,\\in\\, U(S)$ whose\nimage in $X(S)$ is $x$. We set $(t_x\\, ,y_x)\\,:=\\,i(u_x)$, then $$T(S)\\times_{X(S)}Y(S)\n\\,=\\,\\{(ht_x,gy_x)\\, ,~\\ \\forall ~x\\,\\in\\, X(S)\\, ,~\\ \\forall ~ g\\,\\in\\, G(S)\\, ,\n~\\ \\forall ~ h\\,\\in\\, H(S) \\}$$ so the image\nof $U(S)$ by $i_S$ can be identified with the subset \n$$\\{(ht_x\\, ,gy_x)\\, ,~\\ \\forall ~x\\,\\in\\, X\\, ,~\\ \\forall ~g\\,\\in\\,\nG_1\\, ,~\\ \\forall ~h\\,\\in\\, H \\}\\, ;$$ this gives $U$ the structure of an $H\\times\nG_1$--torsor over $X$, contained in the $H\\times G$--torsor $T\\times_X Y$. This\nimplies that $G_1\\,=\\,G$ by Lemma \\ref{lemTORS}.\n\\end{proof}\n\n\\begin{corollary}\\label{corTORS2}\nLet $X$ be a connected reduced scheme over $k$ and $G$ a finite local $k$-group\nscheme. Let $T\\,\\longrightarrow\\, X$ be a finite \\'etale cover, and let $Y\n\\,\\longrightarrow\\, X$ be a $G$--torsor. If\n$Y\\,\\longrightarrow\\, X$ is Nori-reduced and $T$ is connected, then the $G$--torsor\n$$T\\times_X Y\\,\\longrightarrow\\, T$$ is also Nori-reduced. In particular the homomorphism\n$\\pi^{\\rm loc}(T)\\,\\longrightarrow\\, \\pi^{\\rm loc}(X)$ is faithfully flat.\n\\end{corollary}\n\n\\begin{proof} This follows from Corollary \\ref{corTORS} and the fact that there exist a\nfinite \\'etale $k$--group scheme $H'$ and an $H'$--torsor $T'\\,\\longrightarrow\\, X$\nthat dominates $T\\,\\longrightarrow\\, X$.\n\\end{proof}\n\n\\begin{remark}\\label{remNORI}\nIn \\cite{N2}, Nori proved that if\n$i\\,:\\,U\\,\\longrightarrow\\, Y$ is an open immersion between connected and\nreduced schemes with $Y$ normal, then the morphism $\\pi(U)\\,\\longrightarrow\\,\n\\pi(Y)$ induced by $i$ is faithfully flat (see \\S~II, Proposition 6 and\nits corollaries). Consequently, the homomorphism\n$\\pi^{\\rm loc}(U)\\,\\longrightarrow\\, \\pi^{\\rm loc}(Y)$ induced by $i$ is\nalso faithfully flat. \n\\end{remark}\n\n\\begin{notation}\\label{notFINDEX}\nLet $k$ be a field and $u\\,:\\,M\\,\\longrightarrow\\, G$ a $k$-group scheme\nhomomorphism. We say that $u$ is of finite index if the following\nproperty is satisfied: for any $k$-group scheme $Q$ and any faithfully\nflat morphism of $k$-group schemes $G\\,\\longrightarrow\\, Q$, if the group\nscheme image of $M\\to Q$ is finite then $Q$ is also finite.\n\\end{notation}\n\n\\begin{lemma}\\label{lemCLinsep}\nLet $f\\,:\\,X\\,\\longrightarrow\\, Y$ be a finite purely inseparable morphism between normal\nintegral schemes. Then the homomorphism $$\\pi(X)\\,\\longrightarrow\\,\n\\pi(Y)$$ is of finite index, while $\\pi^{\\rm \\acute{e}t}(X)\\,\\longrightarrow\\,\n\\pi^{\\rm \\acute{e}t}(Y)$ is faithfully flat. So in particular $\\pi^{\\rm loc}(X)\\,\\longrightarrow\\,\n\\pi^{\\rm loc}(Y)$ is of finite index. \n\\end{lemma}\n\n\\begin{proof}\nWe assume that $char(k)\\,=\\,p\\,>\\,0$. We observe that under the above assumptions the morphism\n$f$ is surjective (see \\cite[Ex. 5.3.9]{Li}). Let us first consider the case where $Y\\,=\\,X$ with $f\\,:=\\,F_Y$ being \nthe absolute Frobenius morphism of $Y$. Let $T \\,\\longrightarrow\\, Y$ be the universal \n$\\pi(Y)$--torsor of $Y$ (it is a scheme, as all the transition morphisms\nare affine), where $\\pi(Y)$ is the fundamental group scheme of Nori. We set\n$$T^{(p)}\\,:= \\, T\\times_Y Y$$ via the Frobenius $F_Y$ of $Y$. As\nusual, $F_{T\/Y}\\,:\\, T\\,\\longrightarrow\\, T^{(p)}$ is the \nrelative Frobenius. The relative Frobenius commutes with base change, so if we \npull back over $x\\,:\\,{\\rm Spec}(k) \\,\\longrightarrow\\, Y$ (a fixed closed point) what we obtain is the relative\nFrobenius\n$$ F_{\\pi(Y)\/{\\rm Spec}(k)}\\,:\\, \\pi(Y) \\,\\longrightarrow\\, \\pi(Y)^{(p)}\n\\,\\simeq\\, \\pi(Y)\\, ,$$\nwhere the last isomorphism clearly follows from the fact that $k$ is algebraically closed, whence perfect; thus,\nin particular, $$F_{T\/Y}\\,:\\, T \\,\\longrightarrow\\, T^{(p)}$$\nis the natural morphism from the universal torsor to\nthe pro-finite torsor obtained after pulling back. The same holds for any\ntorsor: so let $P$ be a $Q$-torsor, where $\\pi(Y)\\,\\longrightarrow\\, Q$ is a\nfaithfully flat $k$-group scheme homomorphism; then we have the\nrelative Frobenius\n$$ F_{Q\/{\\rm Spec}(k)}\\,:\\, Q \\,\\longrightarrow\\, Q^{(p)}\\,\\simeq\\, Q\\, ,$$ which\nfactors as $Q\\,\\longrightarrow\\, F\\,\\longrightarrow\\, Q$ (where $Q\\,\n\\longrightarrow\\, F$ is faithfully flat and $F\\,\n\\longrightarrow\\, Q$ is a closed immersion). Since the kernel is finite, if\nwe assume $F$ to be finite then $Q$ is also finite thus\n$F_{\\pi(Y)\/{\\rm Spec}(k)}$ is of finite index.\n\nNow $F_{\\pi(Y)\/{\\rm Spec}(k)}$ is a finite endomorphism and this is sufficient to conclude that it is of finite index. As \\'etale\ntorsors are not \nmodified by the Frobenius, it follows that $ F_{\\pi^{\\rm \\acute{e}t}(Y)\/{\\rm\nSpec}(k)}$ is an isomorphism. What has been \nproved for $f\\,=\\,F_Y$ still holds, of course,\nfor $f\\,=\\,F_Y^m$, the Frobenius iterated $m$ times. So now we consider the general case where $f\n\\,:\\,X\\,\\longrightarrow\\,Y$ is the given purely \ninseparable morphism. Then there exist a positive integer $m$ and a morphism $h\\,:\\, Y\\,\\longrightarrow\\, X$ such that \n$$f\\circ h \\,=\\,F_Y^m \\,:\\, Y \\,\\longrightarrow\\, Y$$\n(the absolute Frobenius morphism iterated $m$ times). We consider the pullback \n$$T_X \\,:=\\, T\\times_Y X$$ and the universal $\\pi(X)$--torsor $P\\,\n\\longrightarrow\\, X$ on $X$. There are natural morphisms\n$P\\,\\longrightarrow\\, T_X$ and $$u\\,:\\,\\pi(X)\\,\\longrightarrow\\, \\pi(Y)\\, .$$\nPulling back further to $h\\,:\\, Y\n\\,\\longrightarrow\\,X$, the following factorization is obtained:\n$$\\xymatrix{\\pi(Y) \\ar@\/_1pc\/[rr]_{F^m_{\\pi(Y)\/{\\rm Spec}(k)}} \\ar[r] & \\pi(X) \\ar[r]^u & \\pi(Y).}$$\n\nThe previous discussion yield the following:\n\n\\begin{itemize}\n \\item if we assume that $\\pi(Y)$ and $\\pi(X)$ are both \\'etale, this implies\nthat $$u\n\\,:\\, \\pi(X)\\,\\longrightarrow\\, \\pi(Y)$$ is faithfully flat;\n \\item otherwise we can only conclude that $u$ is of finite index, which is all\nthat we can expect.\n\\end{itemize}\nThis is enough to conclude the proof.\n\\end{proof}\n\n\\begin{lemma}\\label{lemCL2}\nLet $f\\,:\\,X\\,\\longrightarrow\\, Y$ be a dominant morphism between normal\nintegral schemes. Then the homomorphisms\n$$\\varphi^{\\rm loc}:\\pi^{\\rm loc}(X)\\,\\longrightarrow\\,\n\\pi^{\\rm loc}(Y)\\ \\ \\text{ and } \\ \\ \\varphi^{\\rm \\acute{e}t}:\\pi^{\\rm \\acute{e}t}(X)\\,\n\\longrightarrow\\,\n\\pi^{\\rm \\acute{e}t}(Y)$$ induced by $f$ are of finite index. \n\\end{lemma}\n\n\\begin{proof}\nThis is inspired by \\cite[Lemme 2]{CL} (see also \\cite[Lemme 4.4.17]{De}\nfor the zero characteristic case). \n\nLet $t$ be a closed point of the generic fiber of $f$, and let $T$ denote its Zariski\nclosure in $X$. The morphism $f$ induces a generically finite morphism $f_{\\vert T}\\,\n:\\,T \\longrightarrow\\, Y$: indeed its generic fiber has relative dimension\nzero and it this thus a finite number of points; therefore, there exists an\nopen dense subscheme $U\\,\\subseteq\\, Y$ such that\n$$f'\\,:\\,V\\,\\longrightarrow\\, U\\, ,$$\nwhere $V\\,:=\\,T\\times_Y U$, is a finite morphism. Hence there exist a scheme $W$ and two\nfinite morphisms\n$$e\\,:\\,V\\,\\longrightarrow\\, W\\ \\ \\text{ and }\\ \\ i\\,:\\,W\\,\\longrightarrow\\, V$$ such that $i\\circ e\\,=\\,f'$, where $i$ is purely inseparable\nand $e$ is generically \\'etale. This implies\nthat there exists an open dense subscheme $W'\\,\\subseteq\\, W$ such that\n$$e'\\,:\\,V'\\,\\longrightarrow\\, W'\\, ,$$ where $V':=V\\times_W W'$, is a finite \\'etale cover. In what follows \nwe study the morphism\n$$\\varphi^{\\rm loc}\\,:\\,\\pi^{\\rm loc}(X)\\,\\longrightarrow\\,\n\\pi^{\\rm loc}(Y)\\, ,$$ the (similar) details for \n$\\varphi^{\\rm \\acute{e}t}$ are left to the reader. By Corollary\n\\ref{corTORS2} the morphism $\\pi^{\\rm loc}(V')\\,\\longrightarrow\\,\n\\pi^{\\rm loc}(W')$ induced by $e'$ is faithfully flat while the morphism \n$$\\pi^{\\rm loc}(W)\\,\\longrightarrow\\, \\pi^{\\rm loc}(U)$$\ninduced by $i$ is of finite index by Lemma \\ref{lemCLinsep} and clearly \n$\\pi^{\\rm loc}(W')\\,\\longrightarrow\\, \\pi^{\\rm loc}(W)$ is faithfully flat (see Remark \\ref{remNORI})\nso the composition $$u:\\pi^{\\rm loc}(V')\\,\\longrightarrow\\, \\pi^{\\rm loc}(U)$$ is of finite index. Now\nwe have the diagram of homomorphisms\nof local fundamental group schemes \n$$\\xymatrix{\\pi^{\\rm{loc}}(V')\\ar[r]\\ar[d]_{u} & \\pi^{\\rm{loc}}(X)\\ar[d]^{\\varphi^{\\rm loc}}\n\\\\ \\pi^{\\rm{loc}}(U)\\ar[r]^{v} & \\pi^{\\rm{loc}}(Y). }$$\nNow $u$ is of finite index and the homomorphism $v$ is\nfaithfully flat (see, again, Remark \\ref{remNORI}). Hence $\\varphi^{\\rm loc}$ is finite index.\n\\end{proof}\n\nIn \\cite{MS} Mehta and\nSubramanian proved that $$\\pi(X\\times Y)\\,=\\, \\pi(X)\\times \\pi(Y)$$ for two connected, proper and reduced schemes \n$X$ and $Y$. If one of the two schemes ($X$ or $Y$) is not proper anymore then the previous formula may not hold. \nHowever a weaker result will be sufficient for our purposes; the following proposition can be found in \n\\cite{N2}, Chapter II, Proposition 9, here we suggest a different approach :\n\n\\begin{proposition}\\label{propNEWPROD}\nLet $Y$ be an integral scheme over $k$, then the homomorphism $$\\varphi\\,:\\,\n\\pi(\\mathbb{P}^1\\times Y)\\,\\longrightarrow\\, \\pi(Y)$$ induced by \nthe projection $p_2\\,:\\,\\mathbb{P}^1\\times Y\\,\\longrightarrow\\, Y$ is an\nisomorphism.\n\\end{proposition}\n\n\\begin{proof}\nIt is clear that \n$\\varphi$ is faithfully flat as $p_2$ has a section, so we only need to prove\nthat given a finite $k$--group scheme \n$G$ and a $G$--torsor $T\\,\\longrightarrow\\, \\mathbb{P}^1\\times Y$, there exists a\n$G$--torsor $$T'\\,\\longrightarrow\\,\nU$$ whose pullback to $\\mathbb{P}^1\\times Y$ is the given one. First we briefly recall that the fundamental group scheme of $Y$ at a $k$-point $y$ is the automorphism group scheme of the fiber functor $y^{\\ast}$ on the category of essentially finite vector\nbundles, as described in \\cite{N1}. Let $Rep_k(G)$ \ndenote the category of $k$--linear finite dimensional representations of $G$. Then \nassociated to our $G$--torsor $T\\,\\longrightarrow\\, \\mathbb{P}^1\\times Y$ there is a\nfiber functor \n$$F_T\\,:\\,Rep_k(G)\\,\\longrightarrow\\, \\mathcal{Q}coh(\\mathbb{P}^1\\times Y)$$ by a\nfundamental result in \nTannakian theory (recalled for instance in \\cite[Proposition (2.9)]{N1}). From this \nwe will construct a functor $$F\\,:\\,Rep_k(G)\\,\\longrightarrow\\,\n\\mathcal{Q}coh(Y)\\, .$$ For \nany $G$--module $V$, set $$F(V)\\,=\\,(p_2)_*(F_T(V))\\, .$$ We first observe that $F(V)$ is a \nvector bundle: when restricted to $\\mathbb{P}^1$, clearly $F_T(V)$ is an essentially finite \nvector bundle over the projective line, thus trivial, whence $H^1(\\mathbb{P}^1, \nF_T(V))\\,=\\,0$, and the evaluation homomorphism $$(p_2)^*(p_2)_* (F_T(V))\n\\,\\longrightarrow\\, (F_T(V))$$ is an isomorphism.\nMoreover $F$ is compatible with the operations of taking tensor \nproducts, direct sums and duals. Hence $F$ is a fiber functor and we can \nassociate to it a $G$--torsor $T'\\,\\longrightarrow\\, U$ which is the desired one\nsince its pullback to $\\mathbb{P}^1\\times Y$ is isomorphic to\n$T\\,\\longrightarrow\\, \\mathbb{P}^1\\times Y$.\n\\end{proof}\n\nWe now recall that for any $0\\, \\leq\\, \\,\\nu\\, \\leq\\, \\, \\dim(X)$, \nthere is a point in $X(\\Omega)$, where $\\Omega$ in the\nalgebraic closure of the function field of $X$, whose Zariski closure in $X$\nis of dimension $\\nu$.\n\n\\begin{theorem}\\label{teoMAIN}Let $k$ be an algebraically closed field and $X$\na normal, rationally chain connected $k$--scheme. Then $\\pi^{\\rm loc}(X)$ is finite.\n\\end{theorem}\n\n\\begin{proof}\nSince $X$ is rationally chain connected, there exists a chain of rational curves\nconnecting a rational point $x_0\\,\\in\\, X(k)$ to a generic point $x_m\\,\\in\\, X(\\Omega)$,\nwhere $\\Omega$ is the algebraic closure of the function field of $X$. According to Lemma\n\\ref{lemCL1} there exists a sequence of integral subvarieties $V_0\\, , \\cdots\\, , V_m$\nof $X$ where $V_0\\,=\\,x_0$ and $V_m\\,=\\,X$ and for every integer $i\\,\\in\\,\n\\{0, \\cdots , m-1\\}$ a family of rational curves\n$$\nF^i\\,:\\,\\mathbb{P}^1_k\\times T_i \\,\\longrightarrow\\, X\n$$\nwith $T_i$ normal and projective, such that the morphisms\n$$F^i_0\\,:\\,T_i\\,\\longrightarrow\n\\, X \\ \\ \\text{ and }\\ \\ F^i_{\\infty}\\,:\\, T_i\\,\\longrightarrow\\, X\\, ,$$\ndefined by $F^i_0(t)\\,:=\\,\nF^i(0,t)$ and $F^i_{\\infty}(t)\\,:=\\,F^i(\\infty,t)$, are dominant on $V_i$ and $V_{i+1}$\nrespectively. If $V_i$ is not normal then we can consider an open normal\nsubscheme $V_i'\\,\\subset\\, V_i$ and the pullback \n$$\\xymatrix{T_i'\\ar[r]\\ar[d] & V_i'\\ar[d] \\\\ T_i\\ar[r] & V_i. }$$\nIn a similar way, if $V_{i+1}$ is not normal then we can consider an open normal\nsubscheme $V_{i+1}'\\,\\subset\\, V_{i+1}$ and its pullback, as before, that we will call\n$T_i^{''}$. This will not affect $V_0$ and $V_m$ of course. So this induces the\nfollowing commutative diagram on local group schemes:\n$$\\xymatrix{ & & \\pi^{\\text{loc}}(T_i')\\ar[ld]_{\\alpha}\\ar[rd]^{u}\n & & \\\\ & \\pi^{\\text{loc}}(T_i)\\ar[ld]_{\\beta} & &\n\\pi^{\\text{loc}}(V_i')\\ar[dr]^{v} & \\\\ \\pi^{\\text{loc}}(\\mathbb{P}^1_k\\times\nT_i) \\ar[rrrr]^{\\pi(F^i)} & & & & \\pi^{\\text{loc}}(X) \\\\ &\n\\pi^{\\text{loc}}(T_i)\\ar[lu]^{\\gamma} & & \\pi^{\\text{loc}}(V_{i+1}')\\ar[ru]_{w}\n& \\\\ & & \\pi^{\\text{loc}}(T_i^{''})\\ar[lu]^{\\delta}\\ar[ru]_{z} & & }$$\nWe avoid to put the index $i$ on the morphisms not to make notation too\nheavy. We know that $\\pi^{\\text{loc}}(V_0)\\,=\\,0$, both $u$ and $z$ are of finite index by Lemma \\ref{lemCL2}, \nboth $\\alpha$ and $\\delta$ are faithfully flat by \nRemark \\ref{remNORI} and both $\\beta$ and $\\gamma$ are isomorphisms by Proposition \n\\ref{propNEWPROD}. So at each step we prove that the image of\n$\\pi^{\\text{loc}}(V_{i+1}')$ in $\\pi^{\\text{loc}}(X)$ is finite. The last step\nwill finally prove that $\\pi^{\\text{loc}}(X)$ is finite.\n\\end{proof}\n\n\\begin{lemma}\\label{lemLAST}Let $X$ be a rationally chain connected variety and let $f : Y \\longrightarrow X$ be an \\'etale Galois cover. Then $Y$ is rationally chain connected.\n\\end{lemma}\n\n\\begin{proof}\nFix a point $y$ of $Y$. Let $U_y$ be the subset of $Y$ that is rationally chain connected to the point $y$. Let $\\Omega$ be any algebraically closed field containing $k$, then any morphism $g : \\mathbb{P}^1_{\\Omega}\\longrightarrow X_{\\Omega}$ lifts to a morphism\n$g' : \\mathbb{P}^1_{\\Omega}\\longrightarrow Y_{\\Omega}$ using the homotopy lifting property because $\\mathbb{P}^1_{\\Omega}$ is simply connected. Since $X$ is also rationally chain connected, these two together imply that\n$U_y$ is both open and closed. Hence $U_y = Y$, and $Y$ is rationally chain connected.\n\\end{proof}\n\n\\begin{remark}\\label{remFANO}\nLet notations be as in Theorem \\ref{teoMAIN}, then from \\cite[Th\\'eor\\`eme]{CL} and Lemma \\ref{lemLAST} we also obtain that $\\pi(X)$ is finite. If $char(k)\\,=\\,p\\,>\\,0$, and $X$ is moreover smooth and proper, then $\\vert\\pi(X)^{\\rm \\acute{e}t}\\vert$ is coprime to $p$, as proved in \\cite{CL2}. In particular all this holds when $X$ is a Fano variety since in this case it is rationally chain connected (cf. \\cite{Ca} and \\cite{KMM}). Furthermore when $X$ is a general hypersurface of a projective space then then it is separably\nrationally connected, and by \\cite{Bi} this implies that $\\pi(X)\\,=\\,0$.\n\\end{remark}\n\n\\section*{Acknowledgments}\n\nWe thank Antoine Chambert-Loir for a useful communication. We thank the two\nreferees for comments that helped us in improving the paper. The first-named author would like to thank T.I.F.R. for \nits hospitality and Cinzia Casagrande for useful discussion. The second-named author \nthanks Universit\\'e Lille 1 and Niels Borne for hospitality. He also acknowledges the \nsupport of a J. C. Bose Fellowship.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nLife processes such as cellular signaling, control and regulation\narise from complex interactions and reactions between biomolecules.\nA fundamental challenge of understanding and controlling life processes\nis that they are inherently multiscale \u2013 cellular signaling alone\ninvolves $6$ orders of magnitude in lengthscales ($0.1$ nanometers\nto $100$ micrometers) and $18$ orders of magnitude in timescales\n(femtoseconds to hours). Unfortunately, these scales are tightly coupled\n\u2013 a single-point mutation in a protein can disturb the biochemical\ninteractions such that this results in disease or death of the organism.\nNo single experimental or simulation technique can probe all time-\nand lengthscales at a resolution required to understand such a process\ncomprehensively. \n\nIn computer simulations, this dilemma can be mitigated by multiscale\ntechniques \u2013 different parts of the system are described by a high-resolution\nand a low-resolution model, and these parts are coupled to give rise\nto a hybrid simula\\textcolor{black}{tion. A famous example of such\na multiscale model in biophysical chemistry is the coupling of quantum\nmechanics and molecular mechanics (QM\/MM) \\citep{WarshelLevitt_JMB76_QMMM}.\nHere we lay the foundations for a hybrid simulation technique that\ncouples two scales that are particularly useful to model intracellular\ndynamics: a Markov state model (MSM) of the molecular dynamics (MD)\nscale that describes structural changes of biomolecules and their\ncomplexes, and the reaction-diffusion scale that describes diffusion,\nassociation and dissociation on the lengthscale of a cell. We call\nthis approach MSM\/RD, due to the combination of the simulation models\nchosen at these scales:}\n\\begin{enumerate}\n\\item \\textcolor{black}{MSMs of the molecular scale: MD simulation allows\nus to probe molecular processes at atomic detail, but its usefulness\nhas long been limited by the sampling problem. Recently, the combination\nof hard- and software for high-throughput MD simulations \\citep{ShirtsPande_Science2000_FoldingAtHome,BuchEtAl_JCIM10_GPUgrid,Shaw_Science10_Anton,DoerrEtAl_JCTC16_HTMD}\nwith MSMs \\citep{PrinzEtAl_JCP10_MSM1,BowmanPandeNoe_MSMBook,SarichSchuette_MSMBook13}\nhas enabled the extensive statistical description of p}rotein folding\nand conformation changes \\citep{NoeSchuetteReichWeikl_PNAS09_TPT,Bowman_JCP09_Villin,LindorffLarsenEtAl_Science11_AntonFolding,KohlhoffEtAl_NatChem14_GPCR-MSM},\nas well as the association of proteins with ligands \\citep{BuchFabritiis_PNAS11_Binding,SilvaHuang_PlosCB_LaoBinding,PlattnerNoe_NatComm15_TrypsinPlasticity,deSancho2015identification,kubas2016mechanism}\nand even other proteins \\citep{PlattnerEtAl_NatChem17_BarBar}. Using\nmulti-ensemble Markov models (MEMMs) \\citep{WuMeyRostaNoe_JCP14_dTRAM,RostaHummer_DHAM,WuEtAL_PNAS16_TRAM,MeyWuNoe_xTRAM},\nMSMs can be derived that even capture the kinetics of ultra-rare events\nbeyond the seconds timescale at atomistic resolution \\citep{PaulEtAl_PNAS17_Mdm2PMI,CasasnovasEtAl_JACS17_UnbindingKinetics}.\nMSM approaches can thus model the long-lived states and transition\nrates of molecular detail interactions, but the cost of atomistic\nMD sampling limits them to relatively small biomolecules and complexes. \n\\item Reaction-diffusion (RD) scale: While atomic detail is relevant for\nsome processes that affect the cellular scale, it is neither efficient\nnor insightful to maintain atomic resolution at all times for cellular\nprocesses. We choose particle-based reaction-diffusion (PBRD) dynamics\nkinetics as a reference model for the cellular scale. PBRD simulates\nparticles, representing individual copies of proteins, ligands or\nother metabolites. Particles move in space via diffusion and reactive\nspecies will react with a probability according to their reaction\nrate when being clo\\textcolor{black}{se by. Here, a reaction may represent\nmolecular processes such as binding, dissociation, conformational\nchange, or actual enzymatic reactions. PBRD acknowledges that chemical\nreactions are inherently discrete and stochastic in nature \\citep{qian2010cellular},\nand that diffusion in cells is often not fast enough to justify well-stirred\nreaction kinetics \\citep{erban2009stochastic,fange2010stochastic,takahashi2010spatio}.\nA large number of recent software packages and codes implement some\nform of PBRD \\citep{andrews2004stochastic,BiedermannEtAl_BJ15_ReaddyMM,donev2010first,donev2017efficient,hattne2005stochastic,SchoenebergNoe_PlosOne13_ReaDDy,van2005green,ZonTenWolde_PRL05_GFRD},\nsee also the reviews \\citep{mereghetti2011diffusion,SchoenebergUllrichNoe_BMC14_RDReview}.\nHydrodynamic interactions at this scale could be incorporated by particle-based\ncoupling terms \\citep{ermak1978brownian,geyer2009n}. The effect of\ncrowders and complicated boundaries such as membranes on the particle\ndiffusion can be represented by including interaction forces on the\nRD scale \\citep{SchoenebergNoe_PlosOne13_ReaDDy}.}\n\\end{enumerate}\n\\textcolor{black}{In the limit that the conformational transitions\nof all molecules are fast, the MSM dynamics of each molecule effectively\naverages, and the interaction between the molecules (e.g. association)\noccurs with suitably averaged rates, reducing the problem to PBRD.\nHowever, when the lifetimes of some conformations are long compared\nto the typical time between two molecular interactions, or even the\ntime between successive rebinding events of two molecules, the conformation\ndynamics of molecules described by the MSM part couples with the RD\ndynamics. MSM\/RD opens up the possibility to simulate and analyze\nsuch effects quantitatively. For example, bimolecular binding rates\nfrom MD-derived MSMs can be inaccurate due to periodic boundary effects\nand a short-lived dissociated state in comparison to the MSM lag-time\n\\citep{PlattnerEtAl_NatChem17_BarBar}. MSM\/RD can overcome these\nissues by extending the diffusion domain available lessening the periodic\nboundary effects and increasing the lifetime of the dissociated state. }\n\n\\textcolor{black}{The ultimate aim of MSM\/RD is to produce an efficient\nmultiscale simulation that reproduces the essential statistical behavior\nof a practically unaffordable large-scale MD simulation by employing\nonly statistics obtained from simulations of the constituent biomolecules\nin small solvent boxes. As developing a full theory involving rotational\ndiffusion, three- or more-body interactions, hydrodynamics will be\nhighly complex, we here aim to make a first step towards this goal\nby coupling MSM and RD scales for bimolecular systems without large-scale\nhydrodynamic interactions.}\n\n\\textcolor{black}{We first derive a theory of MSM\/RD for bimolecular\nsystems, as depicted in Fig. \\ref{fig:schemeMSMRD-myoglobin}. When\nthe two molecules are far from each other, they both undergo a diffusion\nprocess. When they come close to each other, molecular interactions,\nmodeled with MD-derived MSMs, need to be taken into account. We further\ndevelop an algorithm to couple the MSM and RD scales for the special\ncase of a protein interacting with a ligand, which is one of the main\nadvances in this paper. This is not a trivial undertaking since one\nneeds to solve two problems: to couple the MSM and RD part in such\na way that the correct macroscopic rates and equilibrium probabilities\nare recovered, and to develop a suitable MSM discretization such that\nthis coupling can be made. We demonstrate the validity of our theory\nand algorithms on a toy model of p}rotein-ligand interaction and on\nbinding of carbon monoxide to myoglobin.\n\n\\begin{figure}\n\\centering \n\n\\includegraphics[width=1\\columnwidth]{figs\/multiscale_scheme_simple}\n\n\\caption{Sketch of the MSM\/RD scheme. When molecules $A$ and\\textcolor{black}{{}\n$B$ are not in close proximity, they diffuse freely. When $A$ and\n$B$ are close, they merge into a complex particle $C$ which itself\ndiffuses and whose internal dynamics are encoded by coupled MSM state\ntransitions. When the molecules transition into a dissociated state,\nthey are again separated into two separately diffusing particles $A$\nand $B$ with initial positions depending on the last MSM state. Note\nthat in the dissociated state, molecules $A$ and $B$ could also\npotentially undergo conformational changes encoded in independent\nMSM state transitions.}}\n\n\\label{fig:schemeMSMRD-myoglobin} \n\\end{figure}\n\nIn related work, \\citep{sbailo2017efficient,vijaykumar2015combining}\nhave coupled MD with a diffusion scheme. The work \\citep{VotapkaAmaro_SEEKR_SPCB17}\nfurther incorporates milestoning theory \\citep{faradjian2004computing}\nto compute the local kinetic information in terms of transitions between\nmilestones via short MD runs. In contrast with their work, we do not\nemploy direct MD simulations at the ``small'' scale, but represent\nthe small scale by an MSM as this allows us to operate on roughly\nthe same timesteps for the small and the large scales. Other works\nhave proposed alternative schemes to couple random walks (MSMs) with\nBrownian diffusion schemes, some examples can be seen in \\citep{del2016discrete,flegg2012two,flegg2015convergence}.\nHowever, these works focus on specific contexts that are not directly\napplicable for coupling MD-derived MSMs with reaction-diffusion schemes.\n\n\\section{MSM\/RD: coupling Markov state models and reaction-diffusion}\n\n\\textcolor{black}{We develop a theoretical description for MSM\/RD.\nThe relevant scenarios for MSM\/RD can be classified by the number\nof interacting particles, or the related reaction order:}\n\\begin{enumerate}\n\\item \\textcolor{black}{First-order reactions: isolated diffusing particles\ncan be modeled by an MSM obtained from MD simulations in a solvent\nbox. The MSM directly translates into a set of unimolecular reactions\nthat can be implemented in standard PBRD software. As long as the\nparticles don't interact, the only effect of different states on the\ndynamics are changes between different diffusion constants\/tensors.}\n\\item \\textcolor{black}{Second-order reactions: interactions between two\nmolecules that can be modeled as bimolecular reactions including protein-ligand\nor protein-protein association ($A+B\\rightarrow C$). As soon as the\ncomplex $C$ has been formed, its dynamics may be described by state\ntransitions of an MSM of the complex.}\n\\item \\textcolor{black}{Higher-order reactions: simultaneous interactions\nbetween more than two molecules. }\n\\end{enumerate}\n\\textcolor{black}{In this work, we will focus on second-order reactions.\nFirst-order reactions are trivial state changes of a particle that\nare occurring as part of the MSM dynamics. Consistent with current\nconventions in PBRD frameworks, we follow the convention of breaking\ndown higher-order reactions to second-order reactions, although in\nSec. \\ref{sec:Conclusion} we suggest possible extensions to treat\nthese explicitly.}\n\n\\textcolor{black}{In order to derive the theory for second-order reactions,\nwe concentrate on the dynamics of two molecules, $A$ and $B$. For\nthe sake of simplicity, we assume the two molecules do not have conformational\nchanges of their own, so they can only diffuse and interact with each\nother. However, it is straightforward to extend MSM\/RD to include\nconformational changes (first-order reactions) coupled with second-order\nreactions.}\n\n\\subsection{The \\emph{ground truth} model with full dynamics}\n\n\\textcolor{black}{\\emph{Ground truth}}\\textcolor{black}{{} is a term\noften used in machine learning that refers to a reference model with\nrespect to which modeling errors are measured. In the present context,\nthe }\\textcolor{black}{\\emph{ground truth}}\\textcolor{black}{{} model\ncontains the two (or more) solute molecules whose interactions will\nbe later approximated by an MSM in a }\\textcolor{black}{\\emph{large-scale}}\\textcolor{black}{{}\nsimulation, i.e. a simulation box that is not truncated after a small\nsolvent boundary as customary for MD simulation. Importantly, there\nis no universally correct ground truth, but this model employs the\nMD simulation setup and dynamical model chosen by the user for the\nmodeling task at hand. This choice includes the MD force field, solvation\nconditions and ion concentration, the protonation state at the pH\nof interest or even constant-pH simulations \\citep{DonniniEtAl_JCTC2011_ConstantPH},\nthe treatment of electrostatics, the thermostat, the integrator and\ntime step, etc.}\n\n\\textcolor{black}{If such a large-scale model were simulated for a\nlong time or with many trajectories, it would give rise to statistical\nproperties of the solute molecules that we want to reproduce, such\nas their equilibrium constants and association rates. However, such\na simulation is in general inefficient or infeasible, and our aim\nis that to reproduce its statistical properties using an MSM\/RD model\nthat is parametrized only using small MD simulations of the constituent\nsolute molecules and complexes.}\n\n\\textcolor{black}{For simplicity, we derive the MSM\/RD theory using\nall-atom explicit solvent MD simulations with a Langevin thermostat\nas the ground truth, as this setup is frequently used for MD simulations.\nHowever, the MSM\/RD results apply more generally, e.g. to different\nchoices of thermostats or integrators, as the MSM limit for long-time\ndescription of the dynamics and the overdamped limit for long-time\nand large-scale description of the solute transport are achieved from\na large family of ground truth models.}\n\n\\textcolor{black}{Langevin dynamics evolve as:}\n\\begin{equation}\nm_{k}\\frac{d^{2}}{dt^{2}}x_{k}(t)=-\\nabla_{k}U(\\mathbf{x}_{t})-\\gamma_{k}\\frac{d}{dt}x_{k}+\\sqrt{2k_{B}T\\gamma_{k}}\\boldsymbol{\\xi}_{k}(t),\\label{eq:Langevin}\n\\end{equation}\nwhere $x_{k}$ represents the three-dimensional position of the $k^{th}$\natom in the system (including the solvent), $\\mathbf{x}_{t}=[x_{1}(t),\\dots,x_{k}(t),\\dots,x_{N}(t)]$,\n$N$ the total number of atoms, $U$ is the potential energy and $-\\nabla_{k}U$\nis the force acting on the $k^{th}$ particle, $m_{k}$ is the $k^{th}$\nparticle mass, $\\gamma_{k}$ is the $k^{th}$ damping coefficient,\nand $\\boldsymbol{\\xi}_{k}(t)$ is a Gaussian random force such that\nthe expectations of its components satisfy $E[\\xi_{k,i}(t)]=0$ (zero\nmean) and $E[\\xi_{k,i}(t)\\xi_{k,j}(s)]=\\delta_{ij}\\delta(t-s)$ (white\nnoise) with $k_{B}T$ being the thermal energy. In simulations, we\nuse finite-time-step approximations of (\\ref{eq:Langevin}) and use\nit to generate stochastic trajectories. For the theoretical analysis,\nit is more useful to look at the ensemble dynamics, i.e., the propagation\nof probability densities in time. For this, we can ask: If we start\nthe dynamical system in phase space point $\\mathbf{y}$ and let it\nrun, with which probability will we find it in a point $\\mathbf{x}$\na time $\\tau$ later? We call this probability the transfer probability\n$p(\\mathbf{y}\\rightarrow\\mathbf{x};\\,\\tau)$, and we will use it to\ndescribe the action of the ground truth dynamics \\citep{SchuetteFischerHuisingaDeuflhard_JCompPhys151_146}.\nThe transfer probability $p(\\mathbf{y}\\rightarrow\\mathbf{x};\\,\\tau)$\nsubsumes the full complexity of the MD model, in\\textcolor{black}{cluding\ninteraction energies of the molecules with each other and external\nfields, and it can be constructed regardless of which thermostat or\nintegrator is used. The propagation of prob}ability densities $\\rho(\\mathbf{x};\\,t)$\nin time is formally described by the propagator $\\mathcal{P}_{\\tau}$:\n\\begin{align}\n\\rho(\\mathbf{x};\\,t+\\tau) & =\\mathcal{P}_{\\tau}\\rho(\\mathbf{x};\\,t)\\nonumber \\\\\n & =\\int p(\\mathbf{y}\\rightarrow\\mathbf{x};\\,\\tau)\\rho(\\mathbf{y};\\,t)\\,\\mathrm{d}\\mathbf{y}\\label{eq:propagation_propagator}\n\\end{align}\nWe want to find an efficient algorithm to approximate these dynamics.\nMore specifically we want to approximate certain aspects of these\ndynamics, such as the long-time behavior.\n\nIt is often useful to consider densities relative to the stationary\ndensity $\\pi(\\mathbf{x})$ given by\n\\[\nu(\\mathbf{x};\\,t)=\\frac{\\rho(\\mathbf{x};\\,t)}{\\pi(\\mathbf{x})},\n\\]\nwhich defines the propagator relative to the stationary density, or\ntransfer operator \\citep{SchuetteFischerHuisingaDeuflhard_JCompPhys151_146}:\n\\begin{align}\nu(\\mathbf{x};\\,t+\\tau) & =\\mathcal{T}_{\\tau}u(\\mathbf{x};\\,t)\\nonumber \\\\\n & =\\int\\frac{\\pi(\\mathbf{y})}{\\pi(\\mathbf{x})}p(\\mathbf{y}\\rightarrow\\mathbf{x};\\,\\tau)u(\\mathbf{y};\\,t)\\,\\mathrm{d}\\mathbf{y}\\nonumber \\\\\n & =\\int p(\\mathbf{x}\\rightarrow\\mathbf{y};\\,\\tau)u(\\mathbf{y};\\,t)\\,\\mathrm{d}\\mathbf{y}\\label{eq:propagation_transfer_operator}\n\\end{align}\nThe third row follows from detailed balance. For reversible systems,\nwhere detailed balance is fulfilled, $\\mathcal{T}_{\\tau}$ is often\ncalled backward propagator, as it appears to evolve densities backward\nin time.\n\nWe will now introduce a scale separation by treating molecules $A$\nand $B$ different when they are close (interacting) and far apart\n(non-interacting). More specifically these scales are defined by the\ndistance between the centers of mass of $A$ and $B$, $r_{AB}$:\n\\begin{enumerate}\n\\item MSM domain: molecules are in the \\emph{interaction} region $I=\\{\\mathbf{x}\\mid r_{AB}(\\mathbf{x})R$.\nWe obtain a reference value of $0.402_{0.400}^{0.404}\\:\\mathrm{ns^{-1}}$\n(Sub- and superscript indicate lower and upper bound of the $95\\%$\nprecentile) and an MSM\/RD simulation value of $k_{\\mathrm{off}}=0.400_{0.398}^{0.402}\\:\\mathrm{ns^{-1}}$.\nWe further compute the logarithm of the equilibrium constant $\\log(K_{\\mathrm{eq}})=\\log(k_{\\mathrm{off}}\/k_{\\mathrm{on}}^{*})$\nfor both models and for the chosen values of concentrations, resulting\nin accurate reproduction of the reference values by the MSM\/RD scheme(Fig.\n\\ref{fig:diff3Dpot}f). Thus we verify that the coupling between the\nMSM domain and the RD domain works consistently in the MSM\/RD simulation\nscheme.}\n\n\\textcolor{black}{Next, we want to ensure that also the dynamics between\nthe states inside the MSM are reproduced to a high accuracy. We compare\nMFPTs between all pairs of states conditioned on not leaving the MSM\ndomain. In the reference simulation this is done by placing the particle\nat position $\\boldsymbol{\\mu}_{i}$ and propagating the system until\nstate $j$ is reached. If the particle leaves the MSM domain before\nreaching state $j$, this trajectory is discarded. For the MSM\/RD\nsimulation, we simply start in state $i$ and propagate until state\n$j$ is hit, while discarding trajectories that leave the MSM domain.\nThis procedure is repeated until $10^{4}$ successful trajectories\nare found for both simulations, w}hich are averaged to obtain the\nMFPTs. The relative errors are calculated with Eq. (\\ref{eqn:relativeError});\nall relative errors are below $9\\%$ (Fig. \\ref{fig:diff3Dpot}b).\nWe further observe that negative errors arise for state pairs that\nare close together and thus have short passage times. For these transitions,\nwe tend to overestimate the MFPT in the MSM\/RD simulation as short\nprocesses are truncated in the MSM estimation. Moreover, we observe\nthat the highest positive errors arise for transitions which are far\napart. These are the hardest to sample since for these transitions\nthere are a very high number of possible long and non-direct transition\ntrajectories, which are less likely to be observed . We chose the\nfour transitions with the highest relative error and compared their\nFPTs distribution histograms (Fig. \\ref{fig:diff3Dpot}c). Even though\nthese transitions have the highest errors, we observe the distributions\nmatch well. Therefore, we verify MSM\/RD scheme also describes the\ninternal dynamics accurately.\n\n\\subsection{Binding of CO to myoglobin\\label{subsec:BindingCO}}\n\nAs an application of the MSM\/RD scheme, we study the binding of carbon\nmonoxide (CO) to myoglobin. Myoglobin is a globular protein which\nis responsible for the transport of oxygen in muscle tissue. The binding\nprocess of CO to myoglobin has recently be\\textcolor{black}{en studied\nby de Sancho et al. \\citep{deSancho2015identification}, whose data\nwe use to parametrize the MSM\/RD scheme. The dataset consist of MD\ntrajectories of $20$ CO molecules and one myoglobin protein for a\ntotal simulation time of $500\\:\\mathrm{ns}$. The MD simulation is\nconfined to a periodic box with edge length of $5\\:\\mathrm{nm}$.\nDespite the fact that only one CO molecule can reside in the binding\npocket, the error of treating $20$ CO molecules as being statistically\nindependent is small within statistical uncertainty (see \\citep{deSancho2015identification}\nfor details). We therefore extract$20$ independent CO trajectories,\neffectively increasing the total simulation time to $10\\:\\mu\\mathrm{s}$.}\n\n\\subsubsection{Parametrization of MSM\/RD scheme}\n\nIn order to parametrize the scheme, all frames are first \\textcolor{black}{aligned\nusing the $C_{\\alpha}$ atoms of the myoglobin as reference. On the\naligned data, we run the density-based spatial clustering of applications\nwith noise algorithm (DBSCAN) \\citep{ester1996density}, which finds\na total of 16 metastable regions\/cores. The positions and size of\nthe cores are shown in Fig. \\ref{fig:Myoglobin}a, where it can be\nobserved that the algorithm correctly identifies regions of high ligand\ndensity, including the myoglobin bound state indicated in red. The\nradius of the spherical cores is the radius at which $80\\:\\%$ of\nthe datapoints that were assigned to the respective state are inside\nthe core. Four states are discarded as they are not part of the largest\nconnected set. As the simulation box had been set up to just contain\nthe protein and a $1\\:\\mathrm{nm}$ solvent layer, we choose the largest\nMSM domain that still fits inside the box $(R=2.5\\:\\mathrm{nm)}$.\nAnalogous to the previous example, we follow Sec. \\ref{sec:MSM\/RD-implementation}\nto estimate an MSM for the close-range dynamics and generate $L_{\\mathrm{entry}}$,\n$L_{\\mathrm{exit},s}$, $L_{\\mathrm{trans},s}$ and $P_{\\mathrm{exit},s}$\nto couple the dynamics in the two domains. }\n\n\\textcolor{black}{We compute the implied timescales for the MSM and\nchoose a lag time of $150\\:\\mathrm{ps}$ where timescales are sufficiently\nconverged (Fig. \\ref{fig:Myoglobin}b). The diffusion constant is\ncomputed using the mean squared displacement (MSD) of the parts of\nthe CO trajectories that are far from the protein, with $D=\\Delta\\text{MSD}(t)\/6\\Delta t$.\nWe find a diffusion constant of $\\ensuremath{D_{\\text{CO}}=2.5\\:\\mathrm{nm}^{2}\\mathrm{ns}^{-1}}$,\nwhich is comparable to the experimental value which is in the range\nof $D_{\\text{CO}}=2.03\\:\\mathrm{nm}^{2}\\mathrm{ns}^{-1}$ (at $20\\:C\\text{\\textdegree}$)\nto $D_{\\text{CO}}=2.43\\:\\mathrm{nm}^{2}\\mathrm{ns}^{-1}$ (at $30\\:C\\text{\\textdegree}$)\n\\citep{wise1968diffusion}.}\n\n\\begin{figure}\n\\centering\n\n(a) \\includegraphics[width=0.8\\columnwidth]{figs\/myoglobin\/myoglobin_centers_pbc_high_contrast}\n\n(b)\\includegraphics[width=0.8\\columnwidth]{figs\/myoglobin\/impliedTimescales_myoglobin_pbc}\n\n(c)\\includegraphics[width=0.8\\columnwidth]{figs\/myoglobin\/reactionRateFit_pbc}\n\n\\caption{\\textcolor{black}{Discretization and results of the CO-myoglobin system.}\\textbf{\\textcolor{black}{{}\n(a)}}\\textcolor{black}{{} Definition of the cores (wire frame spheres)\nwithin the myoglobin. The red sphere indicates the bound state. The\ngray spheres correspond to the states that were not in the connected\nset and therefore discarded. The blue dots are positions of the CO\nmolecules for every 50th frame in the vicinity of the protein. }\\textbf{\\textcolor{black}{b)}}\\textcolor{black}{{}\nImplied timescales of the dynamics of the CO myoglobin system. The\ndatapoints and shaded area denote the sample mean and standard deviation\nof the bootstrapping sample over the trajectories: from the 20 given\ntrajectories we resample 20 with replacement. Over this sample we\nrun our discretization process which returns a sample of timescales.\nThe trajectory-samples which are not ergodic or do not lead to a connected\ncount matrix are considered invalid and discarded. Solid lines are\nfound using the full dataset. }\\textbf{\\textcolor{black}{c)}}\\textcolor{black}{{}\nReaction rate as estimated from multiple simulations at different\nconcentrations.}}\n\n\\label{fig:Myoglobin}\n\\end{figure}\n\n\\subsubsection{Comparison of dynamic properties}\n\n\\textcolor{black}{As in the previous example, we compute the binding\nrate by sampling positions sampled uniformly in the RD domain and\nsimulating the MSM\/RD model until it reaches the bound state. For\neach concentration, 200 trajectories are run to estimate the binding\nrate $k_{\\mathrm{on}}^{*}$. These rates are plotted against the concentration\nand shown in Fig. \\ref{fig:Myoglobin}c. The reaction rate $k_{\\mathrm{on}}=57_{52}^{62}\\:\\mu\\mathrm{M}^{-1}\\mathrm{s}^{-1}$\nis obtained as the slope of the linear fit. For the unbinding rate,\nwe start simulations in the bound state and collect MFPTs for leaving\nthe MSM domain; we find a rate of $k_{\\mathrm{off}}=19.0_{18.8}^{19.2}\\:\\mu\\mathrm{s}^{-1}$.\nThe resulting equilibrium constant $K_{\\mathrm{eq}}=k_{\\mathrm{on}}\/k_{\\mathrm{off}}=3.0_{2.7}^{3.3}\\:\\mathrm{M}^{-1}$\nis similar to $3.6\\,\\mathrm{M}^{-1}$ found by de Sancho et al. \\citep{deSancho2015identification},\nboth of which are close to the experimental value of $2.2\\:\\mathrm{M}^{-1}$\\citep{carver1990analysis}\n(see Tab. \\ref{tab:Myoglobin_kinetics} for comparison). The binding\nrate and unbinding rate found by de Sancho et al. \\citep{deSancho2015identification},\nalthough yielding a similar equilibrium constant, are both nearly\nan order of magnitude faster than the ones obtained with MSM\/RD (Tab.\n\\ref{tab:Myoglobin_kinetics}). The first indication that the present\nrates are an improved estimate is the fact that the kinetics (both\nthe MSM relaxation timescales and $k_{\\mathrm{on}}$) are independent\nof the lag time (Fig. \\ref{fig:Myoglobin}b, c). }\n\n\\textcolor{black}{To validate that the MSM\/RD estimates of $k_{\\mathrm{off}}$\nand $k_{\\mathrm{on}}$ have been estimated without significant bias,\nit must be shown that they are statistically consistent with the ground\ntruth (in this case a sufficiently large and sufficiently long MD\nsimulation). Here, $k_{\\mathrm{off}}$ can be estimated directly by\ncounting the frequency of ligand dissociation events from the binding\npocket in the underlying MD simulations. Since there are not sufficient\nfull dissociation pathways from the bound to the dissociated states\nin the MD data in order to make a statistically relevant comparison,\nwe obtain a more precise estimate by computing the MFPT using an MSM\ndirectly constructed from the original MD data with the same discretization\nas used in the MSM\/RD model. This resulted in a reference estimate\nof $23.4_{11.6}^{46.6}\\:\\mu\\mathrm{s}^{-1}$ (95\\% percentile computed\nwith 1000 bootstrap samples), which is consistent with the MSM\/RD\nestimate (Tab. \\ref{tab:Myoglobin_kinetics}).}\n\n\\textcolor{black}{Unfortunately, this method is not as accurate for\nthe binding rate $k_{\\mathrm{on}}$, which is notoriously difficult\nto estimate from small MD simulation boxes, where the length of trajectory\nsegments in which the ligand stays in the dissociated state without\ntouching the protein or crossing the periodic boundary are short compared\nto lagtimes $\\tau$ used in an MSM approach, resulting in biased estimates\n\\citep{PlattnerEtAl_NatChem17_BarBar}. Therefore, we performed another\nMyoglobin MD simulation in an eightfold larger periodic box (edge\nlength $10\\,\\mathrm{nm}$) with the same CO concentration as in the\nsmall MD simulation (resulting in $160$ CO molecules) for a total\nsimulation time of $405\\:\\mathrm{ns}$. For this data, a direct MSM\nestimate of the binding rate yields $74.7_{29.9}^{130.9}\\:\\mu\\mathrm{M}^{-1}\\mathrm{s}^{-1}$\n(95\\% percentile computed with 1000 bootstrap samples). As a result,\nthe MSM\/RD binding and dissociation rates are consistent with standard\nestimates computed directly from MD simulation, and the MSM\/RD modeling\nerror can be concluded to be statistically insignificant.}\n\n\\textcolor{black}{Given the consistency of the model, we also compare\nthe results to experimental measurements, which is essentially a test\nof the MD model (e.g. force field, thermostat, integrator). These\nare yet a factor 4-5 slower than our estimates ($k_{\\mathrm{on}}=12\\:\\mu\\mathrm{M}^{-1}\\mathrm{s}^{-1}$\nand $k_{\\mathrm{off}}=5.3\\:\\mu\\mathrm{s}^{-1}$ found in \\citep{carver1990analysis}),\nconfirming that the major part of the difference between the estimates\nin \\citep{deSancho2015identification} and theexperimental values\ncould be removed by the fact that MSM\/RD is a significantly more accurate\nmodel of the binding kinetics. }\n\n\\begin{table*}\n\\begin{tabular}{|c|>{\\centering}p{2cm}|>{\\centering}p{3cm}|c|>{\\centering}p{2cm}|>{\\centering}p{2cm}|}\n\\hline \n & MSM\/RD & Reference (approx. ground truth) & MSM in \\textcolor{black}{\\citep{deSancho2015identification}} & Experiment \\textcolor{black}{\\citep{carver1990analysis}} & Unit\\tabularnewline\n\\hline \n\\hline \n$k_{\\mathrm{on}}$ & $57.0_{52.0}^{62.0}$ & $74.7_{27.9}^{130.9}$ & 647 & $12$ & $\\mathrm{M}^{-1}\\mu\\mathrm{s}^{-1}$\\tabularnewline\n\\hline \n$k_{\\mathrm{off}}$ & $19.0_{18.8}^{19.2}$ & $23.4_{11.6}^{46.6}$ & 179 & $5.3$ & $\\mu s^{-1}$\\tabularnewline\n\\hline \n$K_{\\mathrm{eq}}$ & $3.0_{2.7}^{3.3}$ & $3.19_{2.6}^{3.8}$ & $3.6$ & $2.2$ & $\\mathrm{M}^{-1}$\\tabularnewline\n\\hline \n\\end{tabular}\n\n\\caption{\\textcolor{black}{\\label{tab:Myoglobin_kinetics}Rates and equilibrium\nconstants for Myglobin-CO estimated from different methods. The reference\nvalues approximate the ground truth by conducting a standard MSM-based\nMFTP estimate from the MD simulation (for $k_{\\mathrm{on}}$ a larger\nsimulation box was used to allow for a generous definition of the\ndissociated state).}}\n\\end{table*}\n\n\\section{Conclusion\\label{sec:Conclusion}}\n\nWe introduced and developed the MSM\/RD scheme, which couples MD-derived\nMSMs with RD simulations. We showed an implementation for protein-ligand\nsystems and applied it to two simple systems. The main advantage of\nthe algorithm is that it can simulate large time- and lengthscales\nwhile conserving molecular resolution and computational efficiency.\nThis is achieved by extracting the characteristic features of the\ndynamics fro\\textcolor{black}{m several short MD simulations into\nan MSM, which can produce new data with great accuracy and at a much\nfaster rate than the original MD simulations. This is a clear advantage\nin comparison to previous works, like \\citep{vijaykumar2015combining,VijaykumarEtAl_Arxiv16_AnisotropicMultiscaleGFRD},\nsince it does not require running MD simulations every time two particles\nare close to each other. It can further yield more accurate binding\nrates than traditional MSM methods by extending the diffusion domain\navailable, lessening the periodic boundary effects and increasing\nthe lifetime of the dissociated state. The scheme can be, in principle,\ncoupled to any RD scheme, like over-damped Langevin dynamics, Langevin\ndynamics, GFRD \\citep{van2005green,ZonTenWolde_PRL05_GFRD} and FPKMC\nalgorithm \\citep{donev2010first}, which could yield additional efficiency\nand accuracy or even incorporate long-range hydrodynamic interactions.}\n\n\\textcolor{black}{We first implemented the MSM\/RD scheme for a simple\nligand diffusion model (Sec. \\ref{subsec:3D-diff-pot}), which served\nto verify the scheme. It reproduced the expected dynamics and binding\/unbinding\nrates of the reference simulation. It was also able to generate an\naccurate MSM for the internal dynamics with a relatively small amount\nof data, which hints that it is feasible to extract the characteristic\ndynamics of a computationally feasible amount of MD simulations. Moreover,\nwe implemented the MSM\/RD scheme for the binding of CO to myoglobin\nsystem. After successfully extracting a self-consistent MSM and a\ncoupling scheme, we found that the equilibrium constant is consistent\nwith previous experimental and computational results \\citep{carver1990analysis,deSancho2015identification}.\nWe also showed that the MSM\/RD estimates are consistent with the underlying\nMD simulations \u2013 in particular our estimated association rate is consistent\nwith the association rate estimated from a reference MD simulation\nconducted in a large simulation box that was not used to parametrize\nthe MSM\/RD model. This is a significant improvement over Ref. \\citep{deSancho2015identification},\nwhere tenfold higher rates were estimated.}\n\n\\textcolor{black}{The MSM\/RD theory we introduced provides the framework\nupon which schemes for more complex systems can be constructed. In\nparticular, the next steps are to include association of two macromolecules,\nwhich may require to account for rototranslational diffusion, and\nthe coupling between protein-ligand association and conformational\nchanges. With the addition of these features, biologically relevant\nscenarios can be simulated. For example, if conformational changes\nof the protein are rare events and have different ligand association\n\/ dissociation rates, then the conformational dynamics and the ligand\nbinding dynamics are nontrivially coupled at high ligand concentrations\n\u2013 see \\citep{PlattnerNoe_NatComm15_TrypsinPlasticity} for the example\nof Trypsin and Benzamidine. A biological relevant example is the activation\nof the Calcium sensor Synaptotagmin in neuronal synapses \\citep{Suedhof_Neuron13_Neurotransmission}.\nHere, a locally very high Calcium concentration is created by the\nopening of voltage-gated Calcium channels as a response to an electric\nsignal. Synaptotagmin then binds up to five Calcium ions while going\nthrough different conformations, while the local Calcium concentration\nis reduced by diffusion. If Synaptotagmin successfully binds enough\nCalcium ions and transitions into an active conformation, it can catalyze\nthe fission of neuronal vesicles, which transduces the signal to the\npostsynaptic side. Such scenarios can be simulated with MSM\/RD simulations,\nin which the channels, the Synaptotagmin proteins and the ions are\nresolved as individual particles, and the binding\/dissociation kinetics\nand conformational changes of Synaptotagmin is encoded in an MSM.}\n\n\\textcolor{black}{MSM\/RD could be extended to deal with higher-order\nreactions. The most direct approach is to treat interactions of order\n2, 3, etc., by different MSMs which are then coupled in a regular\nMSM\/RD framework. The question then is how the higher-order MSMs are\nobtained. The brute-force approach would be to simulate the dynamics\nbetween three or more molecules with MD \u2013 e.g. with the help of enhanced\nsampling methods \u2013 and to extract corresponding higher-order MSMs.\nA cheaper, but approximate approach would be to ignore coupling between\ndifferent states and assume that multiple ligands can bind and transition\nbetween binding sites independently, perhaps except for multiple occupation\nof the same binding site. Based on such an assumption, higher-order\nMSMs could be constructed by tensor products of MSMs with one protein\nand one ligand. In practice, conducting }\\textcolor{black}{\\emph{some}}\\textcolor{black}{{}\nbut not all higher-order simulations and combining them to a generative\nmodel via machine learning methods may present a feasible pathway.}\n\n\\textcolor{black}{Finally, when considering protein interactions at\nhigh concentrations, the diffusion dynamics and long-range interactions\nof proteins are expected to be more complicated and involve hydrodynamic\neffects and anomalous diffusion. To include such effects, appropriate\ndynamical schemes should be included in the RD part.}\n\n\\textcolor{black}{In future developments, we will extend the MSM\/RD\nscheme to address these issues; however, it should be acknowledged\nthat some of these extensions come with their own set of challenges\nthat are not trivial to address.}\n\n\\section*{Acknowledgments}\n\nWe gratefully acknowledge support by the Deutsche Forschungsgemeinschaft\n(grants SFB1114, projects C03 and A04), the Einstein Foundation Berlin\n(ECMath grant CH17) and the European research council (ERC starting\ngrant 307494 \\textquotedbl{}pcCell\\textquotedbl{}). David De Sancho\nwas supported by grants CTQ2015-65320- R and RYC-2016- 19590 from\nthe Spanish Ministry of Economy, Industry and Competitiveness (MINECO).\nWe also thank Tim Hempel and Nuria Plattner for helpful discussions\nand software tutorials.\n\n\\section*{Appendix: MSM\/RD scheme for Sec. \\ref{sec:MSM\/RD-implementation}\\label{sec:Appendix:MSM\/RDscheme}}\n\n\\textcolor{black}{Based on the estimated quantities defined in the\nSec. \\ref{sec:MSM\/RD-implementation}, we introduce an implementation\nof the MSM\/RD algorithm from Sec. \\ref{subsec:MSM\/RD-coupled}.}\n\n\\texttt{\\textcolor{blue}{\\noindent}}\\texttt{\\textcolor{black}{Input: Initial\nmode (RD or MSM), initial condition (coordinates $\\mathbf{c}_{0}$\nor state $s_{0}$, respectively) and $t=0$:}}\n\n\\texttt{\\textcolor{black}{While $t\\leq t_{\\mathrm{final}}:$}}\n\\begin{enumerate}\n\\item \\texttt{\\textcolor{black}{If in RD mode:}}\n\\begin{enumerate}\n\\item \\texttt{\\textcolor{black}{Propagate $\\mathbf{c}_{t}\\rightarrow\\mathbf{c}_{t+\\tau_{\\mathrm{RD}}}$\nby diffusion }}\n\\item \\texttt{\\textcolor{black}{Update time $t\\mathrel{{+}{=}}\\tau_{\\mathrm{RD}}$}}\n\\item \\texttt{\\textcolor{black}{If $r_{AB}(\\mathbf{c}_{t})1-\\text{ }\\!\\!\\epsilon\\!\\!\\text{ }} \\\\\n\\end{matrix} \\right.,\n\\end{equation}\nwhere $\\epsilon$ is the allowable deviation of the algorithm, $f(.)$ is the probability density function of the sample.\n\nIn other words, the goal of asymmetric Gaussian filtering is to filter out as few signals as possible on the premise of tolerating errors. Since the problem is non-convex and requires extensive computation, this optimization problem is only used in the offline phase. In the online phase, we directly utilize the offline phase's solution results to reduce the solution time. \n\n\\section{I-WKNN Algorithm}\n\\subsection{Offline stage of the Algorithm}\n\nAssuming that there are S values of RSSI, they are obtained from sampling the ${{n}^{{th}}}$ AP at the $m$ reference point. They are denoted as ${{\\overrightarrow{r}}_{m,n}}$. In the offline stage, if the signal is lost seriously or fluctuates violently, the data of the source will be removed.\nWhen the signal cannot be measured, it will be replaced by a smaller value $\\text{RSSI}_{\\min}$. If the signal has not received data, the value will be zero after subtracting this value. The proportion of unreceived data can describe the degree of signal loss, that is to say, the ratio of 0-norm value to vector length in step (B). The elimination of the signal is replaced by $\\text{RSSI}_{\\min}$, which is equivalent to the data value when the signal is not received. The normalized variance in step (C) is used to describe the fluctuation. Asymmetric Gaussian filtering in step (D) eliminates partial signals that deviate entirely from the expected value. Then the AP selection algorithm in the offline stage is:\n{\n\\begin{algorithm}[h]\n \\caption{AP selection in offline phase}\n \n \\begin{algorithmic}\n \\STATE \\textbf{(A) Initialize:} $m \\leftarrow 1,n \\leftarrow 1$, initialize ${{\\Theta}_{1}}$ and ${{\\Theta }_{2}}$.\n \n \n \\FOR{m = 1 \\textbf{to} M}\n \\FOR{n = 1 \\textbf{to} N}\n \n \\STATE \\textbf{(B) Eliminated part of AP by the signal loss rate:} \n \\IF{$\\left \\|{{\\overrightarrow{\\text{r}}}_{m,n}}+\\text{RSSI}_{\\min} \\right \\|_{0} \\geq S{{\\Theta }_{1}}$}\n \\STATE ${{\\overrightarrow{\\text{RSSI}}}_{m}}(n) \\leftarrow \\text{RSSI}_{\\min}$.\n \\ENDIF\n \n \\STATE \\textbf{(C) Eliminated part of AP by fluctuation:}\n \\IF{$\\left \\|\\text{S}\\cdot {{\\overrightarrow{\\text{r}}}_{m,n}} - \\left \\| {\\overrightarrow{\\text{r}}_{m,n}} \\right \\|_{1}\\right \\|_{2} \\geq {{\\Theta }_{2}} \\left \\|{{\\overrightarrow{\\text{r}}}_{m,n}}\\right \\|_{1}$}\n \\STATE ${{\\overrightarrow{\\text{RSSI}}}_{m}}(n) \\leftarrow \\text{RSSI}_{\\min}$.\n \\ENDIF\n \n \\STATE \\textbf{(D) Perform asymmetric Gaussian filtering:}\n \\IF{${{\\overrightarrow{\\text{RSSI}}}_{m}}(n)\\ne \\text{RSSI}_{\\min}$}\n \\STATE ccording to criterion of asymmetric Gaussian filtering, eliminate the RSSI value which is less than $\\mu -{{g}_{\\inf }}\\sigma $ or larger than $\\mu +{{g}_{\\text{sup}}}\\sigma $ in ${{\\overrightarrow{\\text{r}}}_{m,n}}$, then take the mean of the subsectors as ${{\\overrightarrow{\\text{RSSI}}}_{m}}(n)$.\n \\ENDIF\n \n \\ENDFOR\n \\ENDFOR\n \n\\end{algorithmic}\n\\label{a1}\n\\end{algorithm}\n\\par\nIn step (A), ${{\\Theta}_{1}}$ is the miss rate threshold, and ${{\\Theta }_{2}}$ is the jitter peak average ratio threshold. $\\text{RSSI}_{\\min}$ in step (B) is a minimum preset value. ${{\\left\\| \\centerdot \\right\\|}_{p}}$ is the p-norm of a vector, where $p=0,1,2$.\n}\n\n\\subsection{I-WKNN Algorithm with Its Online stage}\n\nIn the offline stage, the parameter selection of the Gaussian filter is related to the data distribution in the database. To some extent, it requires human intervention. If the filtering effect is not effective, the parameters need to be changed. In the offline stage, each reference point will have a set of parameters of asymmetric Gaussian filtering for each AP. For the sake of saving time, this set of parameters will be directly used in the online stage. \n\n{\n\\begin{algorithm}[h]\n \\caption{AP selection in offline phase}\n \n \\begin{algorithmic}\n \\STATE \\textbf{(A) Initialize:} $n \\leftarrow 1$, initialize the threshold ${{\\Theta }_{1}}$ and ${{\\Theta }_{2}}$.\n \n \\FOR{n = 1 \\textbf{to} N}\n \\STATE \\textbf{(B) Eliminated part of AP by the signal loss rate:} \n \n \\IF{$\\left \\| \\sum\\limits_{\\tau =t-T+1}^{t} {\\overrightarrow{\\text{RSSI}_{\\text{u}}^{\\left( \\tau \\right)}}\\left( n \\right)}-\\text{RSSI}_{\\min} \\right \\|_{0}\\leq T{{\\Theta }_{1}}$,$\\tau \\in [t-T+1,t]$}\n \n \\STATE $\\overrightarrow{\\text{RSSI}_{\\text{u}}^{\\left( t \\right)}}\\left( n \\right)=\\frac{1}{T}\\sum\\limits_{\\tau =t-T+1}^{t}{\\overrightarrow{\\text{RSSI}_{\\text{u}}^{\\left( \\tau \\right)}}\\left( n \\right)}$,\n \n \\ELSE\n \\STATE Abandoned the $n^{th}$ AP information in time $\\tau$.\n \n \\ENDIF\n \\ENDFOR\n \n \\STATE \\textbf{(D) obtain the RSSI of $N$ APs:} \\\\ $\\overrightarrow{\\text{RSSI}_{\\text{u}}^{\\left( t \\right)}} \\leftarrow \\left( \\overrightarrow{\\text{RSSI}_{\\text{u}}^{\\left( t \\right)}}\\left( 1 \\right),\\overrightarrow{\\text{RSSI}_{\\text{u}}^{\\left( t \\right)}}\\left( 2 \\right),\\cdots ,\\overrightarrow{\\text{RSSI}_{\\text{u}}^{\\left( t \\right)}}\\left( N \\right) \\right)$.\n\\end{algorithmic}\n\\label{a2}\n\\end{algorithm}\nThe algorithm assumes that the measurements at $T$ moments before the current moment are still valid. At time $T$, the AP selection algorithm in the online stage is as follows: In step (B), $T$ is the impact time on the measurement results at the time slot $t$, $\\overrightarrow{\\text{RSSI}}_{\\text{u}}^{(\\tau )}\\left( n \\right)$ is the signal strength received by the user at the time moment $\\tau$, and the value range of $\\tau$ is $[t-T+1,t]$. Step (B) filters out part of the signal values according to the signal strength values, filter the remaining signal according to the criterion of asymmetric Gaussian filtering. The ${{n}^{{th}}}$ AP information received by the user at time slot $t$ after asymmetric Gaussian filtering is the mean value of filtered results after $T$ time slots before $t$ time slot.\n\n\n\\par\nIn addition to standard WKNN, the algorithm introduces the AP selection mechanism and asymmetric Gaussian filtering algorithm. The overall flow chart of the I-WKNN algorithm is shown in Figure~\\ref{f2}, containing the offline stage and online stage. \n\\begin{figure}\n \\centering\n \\includegraphics[width=0.8\\linewidth]{f2.png}\n \\caption{Flow Chart for Positioning.}\n \\label{f2}\n\\end{figure}\n}\n\n\n\\section{The Application in Intelligent Stadiums}\n\n\\subsection{Experimental Scene}\n\nIn this article, the intelligent stadium is based on the client\/server (C\/S) architecture, uses JAVA as the server development language, and MySQL database as the fingerprint database. Our server implements three main functions: it can efficiently complete the database storage and socket communication. \n\n\nThe typical attenuation of WiFi signal is caused by the superposition of signal propagation in space. RSSI can present the effect of dynamic distribution, and the dynamic change and the nature of the most common Gaussian distribution are exceptionally similar under the free space. In addition, when pedestrians are excluded, the collection of RSSI presents a Gaussian distribution, as shown in Figure~\\ref{f3}.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=0.8\\textwidth]{f3.png}\n \\caption{The Distribution of RSSI in An Ideal Situation.}\n \\label{f3}\n\\end{figure}\n\nIn an ideal channel, the data obtained by RSSI sampling at the same place at different times is approximately Gaussian. Under multipath channels, the distribution with maximum information entropy is Gaussian distribution. Under the condition of known mean value and variance, the Gaussian distribution model is first introduced. However, in the actual scene, the RSSI of the WiFi signal will fluctuate, superposition, and disappear due to the influence of shielding, personnel movement, and multipath in the sampling process. Thus, instead of presenting as a Gaussian distribution, it is the double-peak situation in the actual sampling.\n\nDatabase and real-time measured RSSI information and geographical location are uploaded and processed by MATLAB software on the computer. We set up 250 points in the test area as clustering points, then numbered the position of each training point from right to left and from top to bottom. Run the WiFi location client program at each training point, measured each address and signal strength 1000 times. The data was processed, and after Gaussian filtering, the remaining records were written to the MySQL database.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=0.8\\linewidth]{f5.jpg}\n \\caption{The Result of Gaussian Filtering for One AP.}\n \\label{f5}\n\\end{figure}\n\nThe result of Gaussian filtering for one AP is shown in Figure~\\ref{f5}. The blue data should be abandoned, and the red data will remain. From the signal distribution of the AP, the fading ratio is significantly higher than the enhancement ratio, and occasionally the signal cannot be detected, which is represented by $-70$~dB in this example.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=0.8\\linewidth]{f6.jpg}\n \\caption{The Proportion of RSSI for One AP.}\n \\label{f6}\n\\end{figure}\n\nMoreover, we show the corresponding histogram of the proportion of RSSI in Figure~\\ref{f6}. It can be seen that through the Gaussian filter, the signal becomes noticeably more concentrated. Moreover, the adaptive Gaussian filter can better describe the current RSSI distribution of WiFi signals.\n\n\\subsection{The Performance of Accuracy}\n\nIn the online stage, we set $K=5$ and placed 10 APs. The device stores information for the last 20 slots. The following two comparison algorithms are given.\n\\begin{itemize}\n \\item WKNN algorithm: No extra processing for fingerprint database, just find five largest RSSI values, and get the gravity center of the corresponding five points in every moment.\n \\item KNN algorithm: A sample belongs to a category if most of the K most similar (that is, closest to each other in the feature space) samples in the feature space belong to that category.\n\\end{itemize}\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=0.8\\linewidth]{f7.jpg}\n \\caption{The CDF Graph of Accuracy for Three Algorithms.}\n \\label{f7}\n\\end{figure}\n\nThe CDF graph is used to observe the accuracy of each positioning since the result of each positioning is equally important and the positioning error of each positioning needs to be known. It can be seen from the Figure~\\ref{f7}, that the algorithm proposed in this paper is superior to the other two. Although the elimination of some APs seems to discard some information, the accuracy is greatly improved. In addition, it can be seen from the figure that the proportion of deviation of the four positioning algorithms below 2m is 95\\% (I-WKNN), 79\\% (WKNN), 39\\% (WKNN), and 46\\% (KNN), respectively. Similarly, the mean positioning deviations were 1.14~m (I-WKNN), 1.42~m (WKNN), and 2.32~m (KNN), respectively.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=0.8\\linewidth]{f8.jpg}\n \\caption{The PDF Graph of Accuracy for Three Algorithms.}\n \\label{f8}\n\\end{figure}\n\nThe corresponding PDF of location error is shown in Figure~\\ref{f8}. The proposed method has the highest probability of location error of about 1~m, and the maximum error will not exceed 3~m. The error of WKNN is slightly larger than that of our method. The performance of KNN is the worst, with a maximum error of almost 6~m.\n\n\\subsection{The Performance of Time Delay}\n\nTo reduce the influence of various factors such as device heterogeneity and time uncertainty on the positioning accuracy, we used a single device to run the two algorithms and completed 40 positioning. The overall time-consuming and average time-consuming of the experiment are shown in Figure~\\ref{f9}. Because positioning speed is related to hardware and software performance, this chart is for reference only.\n\nUnlike the positioning accuracy, the time delay only needs to be lower than an acceptable value, and the samples with a significant delay should be paid special attention. So the bar chart shows the average delay, the best 20\\% experiment, and the worst 20\\% experiment, rather than every sample.\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=0.8\\linewidth]{f9.png}\n \\caption{The Comparison of Average Time Delay for Three Algorithms.}\n \\label{f9}\n\\end{figure}\n\nAs can be seen from the figure, the KNN method, which has a significant delay, is entirely unsuitable for the rapidly changing scene such as an intelligent stadium. For the best cases, the delay of the I-WKNN algorithm has the smallest time delay. In terms of average delay, I-WKNN has obvious improvement over traditional WKNN. For the worst 20 percent, I-WKNN is still a small advantage over WKNN.\n\n\\section{Conclusion}\n\nAn improved WKNN algorithm is proposed in this paper, called I-WKNN. The improved AP selection algorithm and asymmetric Gaussian filter algorithm optimize the offline and online stage of fingerprint location. In the experiment, the triangulation algorithm, the traditional WKNN algorithm, is used to compare the proposed one. The accuracy of I-WKNN has obvious advantages compared with the other three algorithms. Its average deviation is 1.14~m, and the proportion of the deviation lower than 2~m is 95\\%. I-WKNN algorithm is only worse than triangulation in the time delay, thus ranks second, with an average delay of 326~ms and a maximum delay of 432~ms, which can meet the requirements of rapid positioning of the stadium. It can be seen that in complex environments, it has high precision and fast positioning speed, which will make it suitable for the scene of the stadium.\n\n\n\n\n\\input{ccs.bbl}\n\\bibliographystyle{IEEEtran}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzncav b/data_all_eng_slimpj/shuffled/split2/finalzzncav new file mode 100644 index 0000000000000000000000000000000000000000..e53be8c0d7555bdc431e20cab26c8c4d0672aeef --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzncav @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\\label{sec:intro}\n\nSensors are employed by unmanned autonomous vehicles to navigate through their surroundings, with a substantial dependence on vision-based sensors like RGB cameras. As these sensors are impacted by bad weather conditions, perception pipelines require considerable training on diverse data to increase the robustness on downstream tasks. One specific scenario which causes distortion of images is adverse weather conditions, like heavy snowfall, haze and dust tornados. In these critical situations, weather corruptions can hinder the object detectability and pose a serious threat to navigation and reliability. Thus, there is a need for efficient denoising, deraining and restoration techniques.\n\nHowever, denoising techniques are often evaluated using Image similarity metrics like PSNR, SSIM \\cite{a37} and not by their effectiveness in achieving results for the targeted application. It is possible that the output image of these methods has high image quality but is contextually irrelevant for the object detection task. In this work we evaluate the effectiveness of restoration techniques for denoising images with the intention of better object detection. By introducing a contrastive approach towards restoration evaluation, a method for guiding the training of GANs and restoration progress is proposed. Additionally, attention maps are leveraged for understanding why these techniques assist object detection and why certain classes are easily recognized or ignored.\n\nPrimary contributions of this work are :\n\\begin{enumerate}\n \\item Exploring two new data-based generative adversarial network techniques for denoising weather corrupted images. \n \\item Proposing a novel contrastive approach using a weighted loss for evaluating the training progress and post-training performance of the highlighted restoration techniques. \n \\item Explaining the training progress of the proposed generative denoising methods using attention maps and validating the results using object detection evaluation metrics.\n \\item Evaluating the optimal noise level of the trained Restormer denoising methods (color and grayscale) using attention maps and validating the results using object detection evaluation metrics.\n\\end{enumerate}\n\n\\begin{figure*}\n\\begin{center}\n\\includegraphics[width=\\textwidth,height=5in]{images\/image1.png}\n\\end{center}\n \\caption{RestoreX-AI: Proposed contrastive approach for evaluating restoration models.}\n\\label{fig1}\n\\end{figure*}\n\n\n\n\n\n\\section{Related Work}\n\nThe rapid development and deployment of autonomous vehicles have exposed several critical challenges in computer vision, one of which being object detection robust to weather corruptions. At the foundation of this challenge is the classic vision task of object detection, the progress of which we summarize in the following sub-section. Ultimately, when these modules are integrated in autonomous driving systems, their suggestively \"black-box\" nature, triggers the concerns of industry professionals, car-makers and users alike. Without human-tailored explanations for the vehicle's behavior, even the most advanced systems fail to benefit from widespread adoption due to concerns about safety and reliability. Especially in adverse weather, when even human drivers exercise extra caution, these robust models need to provide irrefutable explanations for the detections made at every state. We discuss the progress of previous works in these 3 directions which set the background of our work in restoration, detection and explainability. \n\n\n\n\n\\subsection{Multi-weather corruption and restoration}\n\nThe thermal variations accompanying weather change can adversely impact the optical, electronic, and mechanical components used in capturing visual data, thus harming the performance of visual recognition systems \\cite{a4}. Frigid temperatures, snowfall or dense fog, for example, can cause condensation on the lens, further blurring the view and obscuring the object boundaries; rain streaks on car windows can generate glares or act as a double lens \\cite{a36}. For an autonomous car, it is critical and essential to overcome the effects of weather conditions to ensure reliability. Number of approaches for this have been reported. For example, one study \\cite{a5} looked at the performance of gated cameras, while another \\cite{a6} expanded the research to include stereo, gated, and thermal cameras, as well as Radar and LiDAR scanners, and found considerable increases in car recognition in varying levels of fog, and other adverse weather conditions. The use of unique methodologies like domain adaptation to transform the weather conditions while keeping objects of interest intact. For example, \\cite{a7} investigates the consequences of synthetic weather images on road segmentation and traffic object detection, whereas \\cite{a8} shows that using synthetic time-of-day (night imagery) improves localization, and \\cite{a9} proposes a de-raining model to improve semantic segmentation. For their efficiency in image restoration and effectiveness in removing weather corruptions, image restoration employing restomers \\cite{a10} and image denoising algorithms \\cite{a11} are becoming increasingly popular.\n\n\n\\subsection{Object Detection}\n\nThe introduction of fast detectors like SSD, Faster RCNN, and YOLO transformed the face of object detection \\cite{a22,a23,a24}. Taking 2D detectors forward, 3D detection expanded with Stereo RCNN \\cite{a25}, AVOD \\cite{a26}, MVLidarNet \\cite{a27} and MVF algorithm \\cite{a28} bringing new perspectives to the task of object detection. Specific tasks like multiscale object detection \\cite{a29}, pedestrian detection in crowds \\cite{a30} and detection under adverse weather \\cite{a31} have also been solved using ensemble methods and data augmentation.\n\n\n\n\\subsection{Explainability in Object Detection}\n\nThe early rise of deployable machine learning technologies was accompanied by criticism of the \"black-box\"-like nature of ML models. Particularly in sensitive applications like medical diagnosis, self-driving cars and algorithmic test checking, there is a need for thorough explainability in models to ensure the trust and safety of users. To meet these requirements, many techniques were proposed for explaining models \\cite{a12,a13,a14,a15,a16}. In \\cite{a17}, explainability is demonstrated by studying what each of its neurons has learned to detect. \\cite{a18} focused on individual predictions, using the technique of heatmaps to highlight important pixels. Some works also interpret classifiers by identifying representative training examples \\cite{a19,a20}. \\cite{a21} introduced a new perspective to this challenge by making CNN-based models more transparent by producing visual explanations. As newer machine learning systems are rapidly adopted, the demand for explainable models which incorporate diverse approaches is gaining attention of the community. \n\n\n\\section{Methodology}\n\nThis work proposes a contrastive approach (loss) for monitoring the training progress of restoration models in the context of object detection. To put forward a diverse range of training samples, new restoration techniques using GANs have also been proposed. First, the restoration models were trained on different tasks and tested on the DAWN dataset \\cite{a35}. As the models trained, their progress was monitored using the contrastive approach and simultaneously attention maps were generations to support the detection task. Finally, the OD performance of all the methods is compared using standard evaluation metrics (class AP and mAP). The 4 restoration techniques experimented with in this study are:\n\n\\begin{enumerate}\n \\item Weather-NightGAN:Conditional GANs trained on night-to-day task for multi-weather corruption restoration.\n \\item Weather-RainGAN:Conditional GANs trained on rain-to-clear task for multi-weather corruption restoration.\n \\item Restormer (Gaussian color denoising) for multi-weather corruption restoration.\n \\item Restormer (Gaussian grayscale denoising) for multi-weather corruption restoration.\n\\end{enumerate}\n\n\n\n\\subsection{Conditional Generative Adversarial Networks}\n\nGANs or Generative Adversarial networks are generative models that learn mapping between noisy z and output image y, G : z \u2192 y. Conditional GANs learn a mapping from observed image x and random noise vector z, to y, G : {x, z} \u2192 y \\cite{a1}. The generator G is trained to produce images similar to the \"real\" images, as compared by an adversarially trained discriminator, D, which is used for detecting the \"fakes\". The final objective of the conditional GAN can be expressed as: \n\n\\begin{equation*}\n G^{*} = arg min_{G} max_{D} \\mathcal{L}_{cGAN} (G, D) + \\lambda \\mathcal{L}_{L1} (G) \n\\end{equation*}\n\nwhere G tries to minimize this objective against an adversarial D that tries to maximize it, i.e. $G^{*} = arg min_{G} max_{D} L_{cGAN} (G, D).$\n\n\nIn this study, we propose 2 new use-cases of the conditional GAN for restoration purposes. In the first case, the GAN is trained on night-to-day images and tested for multi-weather corruption tasks. The intuition behind this is the similarity in corruptions of night and bad weather images, like poor lighting and condensation on lens. In the second use-case, the GAN is trained on only rain-to-clear images (synthetically generated) and tested for multi-weather corruption tasks. The intuition behind this is the similarity in corruptions of rain and bad weather images, like snow and rain streaks. There is an additional challenge which notes if the single-weather trained conditional GAN can adapt to multi-weather corruptions. \n\n\n\n\\subsection{Restormer}\n\nThe restormer is a highly efficient transformer that was proposed for denoising tasks in image restoration \\cite{a2}. It consists of a multi-Dconv head transposed attention (MDTA) and a gated-Dconv feed-forward network (GDFN). These proposed architectural changes gave it the ability to capture long-range pixel interactions, while still remaining applicable to large images. It is both computationally efficient, and has the capacity to handle high-resolution images, a feature critical for a task like adverse weather object detection. In this work, we study the effects of trained noise levels (15, 25 and 50) on the denoising performance of color and grayscale Restormers on the DAWN dataset. The goal is to study how the noise level affects the model performance in OD task and which level is optimal for generating explainable detections.\n\n\\subsection{Grad-CAM}\n\nGradient-weighted Class Activation Mapping (Grad-CAM) is a technique that produces visual explanations for the purpose of making CNN-based models more transparent \\cite{a3}. For getting the class discriminative localization map Grad-CAM ${L_{Grad-CAM}^c}$ $\\epsilon R ^ {u * v}$ of width u and height v for any class c , first comes the computation of gradient of the score for class c, $y^c$ (before the softmax), according to feature maps $A^k$ of a convolutional layer, i.e. $\\frac{\\partial{\\mathbf{y^{c}}}}{\\partial A_{}^k}$ . The neuron importance weights $a_c^k$ are attained by global-average-pooling these gradients flowing back. The 'importance' of feature map k for a target class c is captured by this weight $a_c^k$. A weighted combination of forward activation maps is performed, and followed by a ReLU to obtain,\nThis results in a coarse heat-map of the same size as the convolutional feature maps. Grad-CAM is used for the purpose of explaining object detection in the restored images of different techniques compared in this study. We additionally use the Grad-CAM model's detection probability in calculating the contrastive metric for monitoring training progress.\n\n\n\\subsection{Proposed Approach: RestoreX-AI}\n\nDue to the instability of training of GANs, over-or-under training does not always lead to the perfect solution images for object detection. Parallelly, tuning on high noise levels does not always provide the best images from the Restormer model. Even after producing good images by standard metrics (PSNR, SSIM), their applicability for the OD task remains uncertain, which brings the need for a new evaluation standard. We propose using a weighted sum of the explainability results (detection probability of class provided by the Grad-CAM model) and the similarity of the predicted and actual label to define this new standard. This weighted sum is calculated for every stage of training (one stage can be a user-defined set of epochs), and then used to monitor the progress of the model. We introduce this new parameter for assessing the quality of restoration which is calculated using equation 1. \n\n\\begin{equation}\n \\Delta \\phi = \\Delta ( \\Sigma (S(p,a)*d)\/N )\n \\end{equation}\n \n Here S is the similarity of labels that be measured either by grouping the objects (cars, race cars and taxis have similarity 1, person, groom have 1 and so on), or by strict parameters (cars and race cars have similarity 0). p and a are the predicted and actual labels of the object under detection. N is the number of training samples generated in that stage, which are used to evaluate the current progress of that model. Refer to Appendix \\ref{appendix:a} Table \\ref{tab2} to view the measure of similarity of objects grouped together for this study. The explanation probability is d or the value returned by the Grad-CAM model, which is its prediction of what is present in the image. The quality of restoration between stages can be denoted as $ \\Delta \\phi $, or the difference between qualities at consecutive stages.\n\n\n\\subsection{Datasets}\n\nFor training the Weather-RainGAN and Weather-NightGAN, corresponding images of the same scene in rain-clear and night-day conditions were required. For the Weather-RainGAN, we used Rain 100L \\cite{a32}, which is a synthesized data of rain streaks with corresponding rain-free images. For the Weather-NightGAN we used Transient Attributes dataset \\cite{a34}, which used a high-level image editing method which allows a user to adjust the attributes of a scene, e.g. change a scene to be \"night\" or \"day\". The final testing of all restoration methods required a multi-weather dataset with high-resolution images, for which the DAWN dataset was selected. The DAWN dataset is a large vehicle detection dataset which has captured images of driving scenes in adverse weather conditions \\cite{a35}. It consists of 1000 images from real-traffic scenes as seen in multiple adverse weather conditions including fog, snow, rain, and sandstorms. The images have been annotated with 2D annotations(boxes) with 6 object classes namely car, bus, truck, motorcycle, person and bicycle.\n\n\n\\begin{figure*}\n\\begin{center}\n\\includegraphics[width=\\textwidth,height=8.5in]{images\/image2.png}\n\\end{center}\n \\caption{Foggy weather condition: Grad-CAM Attention Maps for (a) Restormer Grayscale Denoising (b) Restormer Colour Denoising (c) Weather-RainGAN and (d) Weather-NightGAN.}\n\\label{fig2}\n\\end{figure*}\n\n\\begin{figure*}\n\\begin{center}\n\\includegraphics[width=\\textwidth,height=8.5in]{images\/image3.png}\n\\end{center}\n \\caption{Snowfall weather condition: Grad-CAM Attention Maps for (a) Restormer Grayscale Denoising (b) Restormer Colour Denoising (c) Weather-RainGAN and (d) Weather-NightGAN.}\n\\label{fig3}\n\\end{figure*}\n\n\n\\begin{table*}[h]\n \\centering\n\\begin{tabular}{llllll} \\hline \n\\textbf{Image Restoration Technique} & \\textbf{\\begin{tabular}[c]{@{}l@{}}Class 1 AP\\\\ {[}car{]}\\end{tabular}} & \\textbf{\\begin{tabular}[c]{@{}l@{}}Class 2 AP\\\\ {[}bus{]}\\end{tabular}} & \\textbf{\\begin{tabular}[c]{@{}l@{}}Class 3 AP\\\\ {[}person{]}\\end{tabular}} & \\textbf{\\begin{tabular}[c]{@{}l@{}}Class 4 AP\\\\ {[}motorcycle{]}\\end{tabular}} & \\textbf{mAP} \\\\ \\hline \n\\multicolumn{6}{l}{\\textbf{Gaussian Gray Denoising Restormer}} \\\\\\hline \nNoise 15 & 1 & 5 & 22 & 0 & 6 \\\\\nNoise 25 & 1 & 9 & 23 & 0 & 6 \\\\\nNoise 50 & 1 & 0 & 29 & 0 & 6 \\\\ \\hline \n\\multicolumn{6}{l}{\\textbf{Gaussian Color Denoising Restormer}} \\\\ \\hline \nNoise 15 & 1 & 3 & 21 & 0 & 5 \\\\\nNoise 25 & 1 & 5 & 22 & 0 & 6 \\\\\nNoise 50 & 1 & 11 & 24 & 0 & 7 \\\\ \\hline \n\\multicolumn{6}{l}{\\textbf{Weather-RainGAN}} \\\\\\hline \nStage 1 & 1 & 0 & 0 & 0 & 0 \\\\\nStage 2 & 1 & 17 & 0 & 0 & 4 \\\\\nStage 3 & 1 & 0 & 0 & 0 & 0 \\\\\nStage 4 & 1 & 0 & 0 & 76 & 15 \\\\\nStage 5 & 1 & 0 & 0 & 0 & 0 \\\\ \\hline \n\\multicolumn{6}{l}{\\textbf{Weather-NightGAN}} \\\\\\hline\nStage 1 & 11 & 0 & 0 & 0 & 2 \\\\\nStage 2 & 1 & 0 & 0 & 0 & 0 \\\\\nStage 3 & 1 & 0 & 0 & 0 & 0 \\\\\nStage 4 & 17 & 0 & 0 & 0 & 3 \\\\\nStage 5 & 48 & 0 & 0 & 0 & 10 \\\\ \\hline \n\\end{tabular}\n\\caption{Object detection performance of all 4 restoration methods as measured at the designed stages.}\n\\label{tab1}\n\\end{table*}\n\n\n\n\n\\section{Experiments and Results}\n\n\\subsection{Restormer-based Grayscale Denoising}\n\nSection 1 of Table \\ref{tab1} shows the object detection scores of Gaussian grayscale image denoising using the Restormer model. To compare the clarity and detection scores, models trained on different noise levels 15, 25 and 50 are included in testing. The purpose of this experimentation is to identify which noise level is optimal for the object detection task, additionally verified by the explainability setup shown in Figures \\ref{fig2} and \\ref{fig3}. The overall mAP of object detection remains constant on all noise levels, however individual class scores are observed to fluctuate. The AP for bus and person class increases from noise 15 to 25 by 4 AP and 1AP respectively. However the bus AP drops to 0 and person AP boosts to 29 when noise is set to 50. The increase in noise level would correspond to smoother images produced by restormer, however, it is observed that the bus AP drops when noise level is set to 50. This is very interesting to note and also observable in Figure \\ref{fig2}, where the attention maps shift from the bus to the car as the noise level increases.\n\n\\subsection{Restormer-based Colour Denoising}\n\n\nSection 2 of Table \\ref{tab1} shows the object detection scores of Gaussian color image denoising using the Restormer model. To compare the clarity and detection scores, models trained on different noise levels 15, 25 and 50 are included in testing. The purpose of this experimentation is to identify which noise level is optimal for the object detection task, additionally verified by the explainability setup. The overall mAP of object detection increases steadily by 1 with all increasing noise levels, and individual class scores are observed to increase as well. The AP for bus and person class increases from noise 15 to 25 by 2 AP and 1AP respectively. The bus AP further increases to 11 and person AP boosts to 24 when noise is set to 50. The increase in noise level is improving the image quality and object detection. As visible in Figure \\ref{fig3}, the attention maps on the people are focusing in the region of interest as the noise level increases.\n\n\n\\subsection{Weather-RainGAN}\n\nThe utilization of Pix2Pix GAN \\cite{a1} for deraining purposes in mapped rain-clear images is proposed, and validated using our proposed technique. The dataset used for this purpose was Rain 100L \\cite{a32}, which is a synthesized data of rain streaks with corresponding rain-free images. The images in Rain 100 L are originally from BSD 200 dataset \\cite{a33}. The GAN model is trained with the rainy images as source and clear images as target, expecting this technique to produce denoised images on test images of the DAWN dataset. The intuition behind this idea was the inherent similarity between weather corruptions and synthetic rain streaks, with the goal of the GAN learning to work similarly on the 2 tasks. The training epochs are divided into stages(1,2,3,4,5) to monitor the training progress of the GANs and produce results of OD and explainability at each stage. As expected, the GANs are producing highly unstable behavior with increasing and 0 AP at most of the stages. However, the sudden boost in AP at specific epochs inspired the formulation of our proposed weighted explainability measure. It can be seen in Section 3 of Table \\ref{tab1}, that the bus AP is 17 at Stage 4, but 0 at all other stages and the motorcycle AP is 76 at Stage 8 and 0 at other Stages. The car AP however, remains constant at 1 and overall mAP increases over time due to the individual class performance boosts. As these results are vague, we take a closer look at Figures \\ref{fig2} and \\ref{fig3}, to determine possible causes for this object detection performance. It can be observed that the GAN is actually performing quite well in denoising weather conditions like fog and snow, and the attention maps are converging towards the relevant objects as the training progresses. But while the object detection results are very disjoint, the attention maps progress continuously and show improvement. The effectiveness of this GAN can be observed as a deraining solution as the image not only gets clearer, but the object detection and attention maps get more precise over training as well. \n\n\n\n\n\n\n\n\\subsection{Weather-NightGAN}\n\nA new use-case of the PixtoPix GAN \\cite{a1} is proposed, trained on a different use-case for deraining purposes in mapped rain-clear images, and validated our proposed technique using the experimental procedure. The dataset used for this purpose was Transient Attributes dataset \\cite{a34}, which used a high-level image editing method which allows a user to adjust the attributes of a scene, e.g. change a scene to be \"night\" or \"day\". The GAN model is trained with the night images as source and day images as target, expecting this technique to produce denoised images on test images of the DAWN dataset. The intuition behind this idea was the inherent similarity between weather corruptions and dark night images. As expected, the GANs are producing highly unstable behavior with increasing and 0 AP at most of the stages as shown in Table \\ref{tab1}. This technique actually worked out only for 1 class (car) and boosted its AP from 11 to 48 over the training period. The remaining classes had a constant 0 AP. By taking a closer look at Figures \\ref{fig2} and \\ref{fig3}, possible causes for this object detection performance can be determined. The GAN is actually performing quite randomly in denoising weather conditions like fog and snow using night training images, but the attention maps are still converging towards the relevant objects as the training progresses. This is exactly aligned with the initial analysis and intuition for the discovery i.e. cases in which object detection is clear to a CV detector, although imperceptible to the human eye. While the object detection results are very disjoint, the attention maps progress continuously and show improvement. The effectiveness of this GAN can be observed in denoising weather conditions for cars, but not for other classes. The purpose of our methodology is not just to observe the restoration progress in a positive light, but to also stop training in case the GAN goes too far. As can be seen in this particular case, the GAN distorts the images after Stage 2, which may confuse detectors when tested against it. In Stage 4, the GAN produces images which are heavily distorted but oddly easier for car detection than even the original image. \n\nThe attention maps and results for all four restoration techniques and four weather conditions: fog, rain, snow and dust tornado have been displayed in Appendix \\ref{appendix:a}.\n\n\\section{Conclusion}\n\n\nThe goal of our work was to study the relationship between object detection performance, image clarity, explainability and training time in the context of novel versus established restoration techniques. The scope of this study covered 4 different restoration techniques, aiming to denoise images corrupted by over 6 different weather conditions presented in the DAWN dataset, namely fog, snow storm, haze, dust tornadoes, rainfall and mist. All 4 techniques worked differently, with the Restormer-based methods providing stable all-rounded results, while the GANs provided class-specific boosts in performance. The overall rise in mAP before and after applying the techniques was 0\\%, 40\\%, 275\\%, 400\\% respectively. Contrary to popular beliefs, greater denoising does not always guarantee better object detection results as observed in this paper. And conversely, poor denoising does not always guarantee worse object detection results. Particularly for specific tasks like bus and car detection, it can be seen that newer approaches like our proposed method (Weather-RainGAN and Weather-NightGAN) can boost the detector's performance with its resultant images. We present conditional GANs cases that perform superbly on diverse weather conditions ranging from dust tornadoes to snowfall, in spite of having trained on limited single weather conditions.\n\nWe present 2 very interesting observations obtained through this study:\n\\begin{enumerate}\n \\item GANs can generate images complex to the human eye, but comparatively interpretable for vision models post processing. This opens the possibility of exploring the capabilities of vision models beyond the scope of human vision and also warding off potential attacks which can cripple modern detectors. \n \\item Restoration and denoising methods which produce clearer images (as measured using standard image quality metrics like PSNR), may in fact present a greater challenge for machine perception in object detection.\n \n\\end{enumerate}\nUnderstanding how differently humans and detectors perceive information in the same image demands greater exploration of explainability. We would like to open the discussion for countless possibilities arising from these disparate perspectives, E.g. if a model sees a pedestrian on a rainy road which a human cannot see, or conversely a pedestrian visible to a passenger's eye which a model cannot capture. While using denoising techniques are a popular choice for tackling corruptions, it must also be acknowledged that not all techniques are suitable for all use-cases as seen for car detection. Certain classes may respond better to denoising depending on their physical characteristics as perceived by the detectors. Going ahead with building robust, explainable models, this problem must be studied from multiple perspectives in the future. We hope to inspire a new line of research in this direction which deals with the complexity of the task of weather corruptions and can solve it using diverse generalizable solutions. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n{\\small\n\\bibliographystyle{ieee_fullname}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{APPENDIX}\n\n\n\n\n\n\\bibliographystyle{ieeetr}\n\n\\section{Introduction}\n\nA very popular research discipline in control theory is the extension\nof control methodologies originally developed for systems that are\ndescribed by ordinary differential equations (ODEs) to systems governed\nby partial differential equations (PDEs); however, with regard to\nstability investigations this extension is accompanied by a significant\nrise of complexity, see e.g. \\cite{Luo1998} for a comprehensive framework\nfor the stability analysis of infinite-dimensional systems. Therefore,\na lot of research effort has been invested in this topic, where for\nexample the stability analysis of mechanical systems with certain\nboundary conditions has been addressed. For instance, in \\cite{Miletic2015}\nthe stability of an Euler-Bernoulli beam subjected to nonlinear damping\nand a nonlinear spring at the tip is analysed, whereas \\cite{Stuerzer2016}\nis concerned with the stability behaviour of a gantry crane with heavy\nchain and payload. Furthermore, the proof of stability of a Lyapunov-based\ncontrol law as well as a Lyapunov-based observer design for an in-domain\nactuated Euler-Bernoulli beam has been presented in \\cite{Henikl2012}.\n\nA well-known methodology, that has also been extended to the infinite-dimensional\nscenario, is the combination of the port-Hamiltonian (pH) system representation\nwith energy-based control. In this regard, in particular a pH-system\nrepresentation based on an underlying jet-bundle structure, see e.g.\n\\cite{Ennsbrunner2005,Schoeberl2012,Schoeberl2014a}, as well as a\nformulation exploiting Stokes-Dirac structures, see e.g. \\cite{Schaft2002,Gorrec2005},\nhave turned out to be especially suitable. For a detailed comparison\nof these approaches, where the main difference is the choice of the\nvariables, the interested reader is referred to \\cite{Schoeberl2013b}\nor \\cite{Malzer2020}. In fact, with respect to boundary-control systems,\na lot of literature is available, see e.g. \\cite{Schoeberl2011,Rams2017a}\nand \\cite{Macchelli2004,Macchelli2017}, where boundary controllers\nbased on the well-known energy-Casimir method are designed within\nthe jet-bundle and the Stokes-Dirac approach, respectively. Moreover,\nrecently the pH-system description has also been exploited with regard\nto the observer design, see e.g. \\cite{Toledo2019}, where a pH-based\nobserver-design procedure for boundary-control systems has been developed\nwithin the Stokes-Dirac scenario. In light of the observer design,\nof course stability investigations play an important role, since it\nmust be ensured that the observer error tends to zero.\n\nIn \\cite{Malzer2020}, a control-design procedure based on the energy-Casimir\nmethod together with an observer design exploiting the pH-system representation\nhas been presented within the jet-bundle framework as well as within\nthe Stokes-Dirac scenario for infinite-dimensional systems with in-domain\nactuation. Furthermore, the design procedures have been demonstrated\nand compared by means of an in-domain actuated vibrating string; however,\nthe investigation regarding the asymptotic stability of the observer\nerror -- which is of course essential -- has only been sketched.\nTherefore, the aim of this paper is to carry out the stability investigation\nof the observer error of this system in detail. To this end, first\nof all, in Section \\ref{sec:Observer_Design} we summarise the observer\ndesign that exploits the pH-system representation based on a jet-bundle\nstructure, while in Section \\ref{sec:Vibrating_String} the observer\ndesign is explicitly demonstrated for an in-domain actuated vibrating\nstring. Thus, the main contribution of this paper is to verify the\nasymptotic stability of the observer error, where i) it is necessary\nto investigate the well-posedness, see Subsection \\ref{subsec:Wellposedness},\nand ii) to apply LaSalle's invariance principle for infinite-dimensional\nsystems, see Subsection \\ref{subsec:LaSalle}.\n\n\\section{Observer Design based on a Port-Hamiltonian Framework\\label{sec:Observer_Design}}\n\nWith respect to the observer design, see \\cite[Sec. V]{Malzer2020},\nwe intend to exploit a pH-system description for infinite-dimensional\nsystems with $1$-dimensional spatial domain, which is equipped with\nthe spatial coordinate $z\\in[0,L]$. The system representation is\nbased on an underlying jet-bundle structure, and therefore, first\nof all we introduce the bundle $\\pi:\\mathcal{E}\\rightarrow\\mathcal{B}$,\nwhere the total manifold $\\mathcal{E}$ is equipped with the coordinates\n$(z,x^{\\alpha})$, with $x^{\\alpha}$, $\\alpha=1,\\ldots,n$, denoting\nthe dependent variables, while the base manifold $\\mathcal{B}$ possesses\nthe independent (spatial) coordinate $(z)$ solely. Next, we consider\nthe so-called vertical tangent bundle $\\nu_{\\mathcal{E}}:\\mathcal{V}(\\mathcal{E})\\rightarrow\\mathcal{E}$,\nequipped with the coordinates $(z,x^{\\alpha},\\dot{x}^{\\alpha})$,\nwhich is a subbundle of the tangent bundle $\\tau_{\\mathcal{E}}:\\mathcal{T}(\\mathcal{E})\\rightarrow\\mathcal{E}$,\npossessing the coordinates $(z,x^{\\alpha},\\dot{z},\\dot{x}^{\\alpha})$\ntogether with the fibre bases $\\partial_{z}=\\partial\/\\partial z$\nand $\\partial_{\\alpha}=\\partial\/\\partial x^{\\alpha}$. Thus, a vertical\nvector field $v=\\mathcal{E}\\rightarrow\\mathcal{V}(\\mathcal{E})$,\nin local coordinates given as $v=v^{\\alpha}\\partial_{\\alpha}$ with\n$v^{\\alpha}\\in C^{\\infty}(\\mathcal{E})$, i.e. $v^{\\alpha}$ is a\nsmooth function on $\\mathcal{E}$, is defined as a section. A further\nimportant differential-geometric object is the so-called co-tangent\nbundle $\\tau_{\\mathcal{E}}^{*}:\\mathcal{T}^{*}(\\mathcal{E})\\rightarrow\\mathcal{E}$,\npossessing the coordinates $(z,x^{\\alpha},\\dot{z},\\dot{x}_{\\alpha})$\ntogether with the fibre bases $\\mathrm{d}z$ and $\\mathrm{d}x^{\\alpha}$,\nwhich allows to introduce a one-form $w:\\mathcal{E}\\rightarrow\\mathcal{T}^{*}(\\mathcal{E})$\nas a section that can locally be given as $w=\\breve{w}\\mathrm{d}z+w_{\\alpha}\\mathrm{d}x^{\\alpha}$\nwith $\\breve{w},w_{\\alpha}\\in C^{\\infty}(\\mathcal{E})$. With respect\nto the pH-system representation, we are interested in densities $\\mathfrak{H}=\\mathcal{H}\\mathrm{d}z$\nwith $\\mathcal{H}\\in C^{\\infty}(\\mathcal{J}^{1}(\\mathcal{E}))$, where\nthese densities can be formed by sections of certain pullback bundles,\nwhose use is omitted here for ease of presentation. That is, $\\mathcal{H}$\nis a smooth function on the first jet manifold $\\mathcal{J}^{1}(\\mathcal{E})$,\nwhich is equipped with the coordinates $(z,x^{\\alpha},x_{z}^{\\alpha})$,\nwhere the $1$st-order jet variable $x_{z}^{\\alpha}$ corresponds\nto the derivative of $x^{\\alpha}$ with respect to $z$. Moreover,\nthe first prolongation of a vertical vector field reads as $j^{1}(v)=v^{\\alpha}\\partial_{\\alpha}+d_{z}(v^{\\alpha})\\partial_{\\alpha}^{z}$,\nwith $\\partial_{\\alpha}^{z}=\\partial\/\\partial x_{z}^{\\alpha}$, where\nwe exploit the total derivative $d_{z}=\\partial_{z}+x_{z}^{\\alpha}\\partial_{\\alpha}+x_{zz}^{\\alpha}\\partial_{\\alpha}^{z}+\\ldots$.\n\nHaving discussed this essential preliminaries, we are able to introduce\nthe pH-system representation including inputs and outputs on the spatial\ndomain as\\begin{subequations}\\label{eq:pH_sys_jetbundle}\n\\begin{align}\n\\dot{x} & =(\\mathcal{J}-\\mathcal{R})(\\delta\\mathfrak{H})+u\\rfloor\\mathcal{G}\\,,\\label{eq:pH_sys_dynamics}\\\\\ny & =\\mathcal{G}^{*}\\rfloor\\delta\\mathfrak{H}\\,,\n\\end{align}\n\\end{subequations}see e.g. \\cite{Ennsbrunner2005,Schoeberl2008a,Schoeberl2014},\nwhere $\\rfloor$ denotes the so-called Hook operator allowing for\nthe natural contraction between tensor fields. In (\\ref{eq:pH_sys_jetbundle}),\nthe variational derivative $\\delta\\mathfrak{H}=\\delta_{\\alpha}\\mathcal{H}\\mathrm{d}x^{\\alpha}\\wedge\\mathrm{d}z$,\nwith $\\wedge$ denoting the exterior (wedge) product, locally reads\nas $\\delta_{\\alpha}\\mathcal{H}=\\partial_{\\alpha}\\mathcal{H}-d_{z}(\\partial_{\\alpha}^{z}\\mathcal{H})$.\nFurthermore, the linear operators $\\mathcal{J},\\mathcal{R}:\\mathcal{T}^{*}(\\mathcal{E})\\wedge\\mathcal{T}^{*}(\\mathcal{B})\\rightarrow\\mathcal{V}(\\mathcal{E})$\ndescribe the internal power flow and the dissipation effects of the\nsystem, respectively. The coefficients $\\mathcal{J}^{\\alpha\\beta}$\nof the interconnection tensor $\\mathcal{J}$ meet $\\mathcal{J}^{\\alpha\\beta}=-\\mathcal{J}^{\\beta\\alpha}\\in C^{\\infty}(\\mathcal{J}^{2}(\\mathcal{E}))$,\nwhile we have $\\mathcal{R}^{\\alpha\\beta}=\\mathcal{R}^{\\beta\\alpha}\\in C^{\\infty}(\\mathcal{J}^{2}(\\mathcal{E}))$\nand $[\\mathcal{R}^{\\alpha\\beta}]\\geq0$ for the coefficient matrix\nof the symmetric and positive semi-definite dissipation mapping $\\mathcal{R}$.\nWith respect to the dual input and output bundles $\\rho:\\mathcal{U}\\rightarrow\\mathcal{J}^{2}(\\mathcal{E})$\nand $\\varrho:\\mathcal{Y}\\rightarrow\\mathcal{J}^{2}(\\mathcal{E})$,\nwe have the input map and its adjoint output map $\\mathcal{G}:\\mathcal{U}\\rightarrow\\mathcal{V}(\\mathcal{E})$\nand $\\mathcal{G}^{*}:\\mathcal{T}^{*}(\\mathcal{E})\\wedge\\mathcal{T}^{*}(\\mathcal{B})\\rightarrow\\mathcal{Y}$,\nrespectively, and thus, the relation $(u\\rfloor\\mathcal{G})\\rfloor\\delta\\mathfrak{H}=u\\rfloor(\\mathcal{G}^{*}\\rfloor\\delta\\mathfrak{H})=u\\rfloor y$\nholds, see \\cite[Sec. 4]{Ennsbrunner2005} or \\cite[Sec. 3]{Schoeberl2008a}.\nTo be able to determine the formal change of the Hamiltonian functional\n$\\mathscr{H}=\\int_{0}^{L}\\mathcal{H}\\mathrm{d}z$ along solutions\nof (\\ref{eq:pH_sys_dynamics}), we make use of the Lie-derivative\n$\\mathrm{L}_{j^{1}(v)}$, where we set $v=\\dot{x}$ with (\\ref{eq:pH_sys_dynamics}),\nsee \\cite[Sec. IV-A]{Schoeberl2011}, and thus, we obtain\n\\begin{equation}\n\\dot{\\mathscr{H}}=-\\int_{0}^{L}\\mathcal{R}(\\delta\\mathfrak{H})\\rfloor\\delta\\mathfrak{H}+\\int_{0}^{L}u\\rfloor y+\\left.(\\dot{x}\\rfloor\\delta^{\\partial}\\mathfrak{H})\\right|_{0}^{L}\\,\\label{eq:H_p}\n\\end{equation}\nby means of integration by parts and Stoke's theorem. If $\\mathscr{H}$\ncorresponds to the total energy of the system, then (\\ref{eq:H_p})\nstates a power-balance relation, where the first expression describes\nthe energy that is dissipated for example due to damping effects.\nMoreover, the expression $\\int_{0}^{L}u\\rfloor y$ denotes a collocation\nterm distributed over (a part of) the spatial domain. The last term\ncorresponds to collocation restricted to the boundary, which is indicated\nby $(\\cdot)|_{0}^{L}$, where the boundary operator locally reads\nas $\\delta_{\\alpha}^{\\partial}\\mathcal{H}=\\partial_{\\alpha}^{z}\\mathcal{H}$.\nNote that here we consider systems with trivial boundary conditions,\nimplying that the boundary ports $(\\dot{x}^{\\alpha}\\delta_{\\alpha}^{\\partial}\\mathcal{H})|_{0}^{L}$\nvanish.\n\nNext, the intention is to exploit the pH-formulation with respect\nto the observer design. In particular, the copy of the plant (\\ref{eq:pH_sys_dynamics})\nis extended by an error-injection term, and thus, by means of the\nobserver-energy density $\\hat{\\mathcal{H}}$, the observer system\nis locally given by\\begin{subequations}\\label{eq:observer_JB}\n\\begin{align}\n\\dot{\\hat{x}}^{\\hat{\\alpha}} & =(\\mathcal{J}^{\\hat{\\alpha}\\hat{\\beta}}-\\mathcal{R}^{\\hat{\\alpha}\\hat{\\beta}})\\delta_{\\hat{\\beta}}\\hat{\\mathcal{H}}+\\mathcal{G}_{\\xi}^{\\hat{\\alpha}}u^{\\xi}+\\mathcal{K}_{\\eta}^{\\hat{\\alpha}}u_{o}^{\\eta}\\,,\\label{eq:observer_JB_dynamics}\\\\\n\\hat{y}_{\\xi} & =\\mathcal{G}_{\\xi}^{\\hat{\\alpha}}\\delta_{\\hat{\\alpha}}\\hat{\\mathcal{H}}\\,,\\label{eq:observer_JB_output_densities}\n\\end{align}\n\\end{subequations}with $\\hat{\\alpha},\\hat{\\beta}=1,\\ldots,n$ and\n$\\xi,\\eta=1,\\ldots,m$, where we use Einstein's convention on sums.\nIn (\\ref{eq:observer_JB_dynamics}), we have the additional input\n$u_{o}^{\\eta}=\\delta^{\\eta\\xi}(\\bar{y}_{\\xi}-\\hat{\\bar{y}}_{\\xi})$\n-- with the Kronecker-Delta symbol meeting $\\delta^{\\xi\\eta}=1$\nfor $\\xi=\\eta$ and $\\delta^{\\xi\\eta}=0$ for $\\xi\\neq\\eta$ --,\nwhere $\\bar{y}_{\\xi}$ corresponds to the integrated output density\nof the plant according to $\\bar{y}_{\\xi}=\\int_{0}^{L}y_{\\xi}\\mathrm{d}z$,\nwhich is assumed to be available as measurement quantity, while $\\hat{\\bar{y}}_{\\xi}$\nrepresents the copy of the integrated plant-output according to $\\hat{\\bar{y}}_{\\xi}=\\int_{0}^{L}\\hat{y}_{\\xi}\\mathrm{d}z$\nwith (\\ref{eq:observer_JB_output_densities}). The aim is to design\nthe observer gain $\\mathcal{K}_{\\eta}^{\\hat{\\alpha}}$ such that the\nobserver error $\\tilde{x}=x-\\hat{x}$ tends to $0$, where it is beneficial\nto reformulate the observer-error dynamics $\\dot{\\tilde{x}}=\\dot{x}-\\dot{\\hat{x}}$\nas pH-system according to\\begin{subequations}\\label{eq:observer_error_system_JB}\n\\begin{align}\n\\dot{\\tilde{x}}^{\\tilde{\\alpha}} & =(\\mathcal{J}^{\\tilde{\\alpha}\\tilde{\\beta}}-\\mathcal{R}^{\\tilde{\\alpha}\\tilde{\\beta}})\\delta_{\\tilde{\\beta}}\\tilde{\\mathcal{H}}-\\mathcal{K}_{\\xi}^{\\tilde{\\alpha}}u_{o}^{\\xi}\\,,\\\\\n\\tilde{y}_{\\xi} & =-\\mathcal{K}_{\\xi}^{\\tilde{\\alpha}}\\delta_{\\tilde{\\alpha}}\\tilde{\\mathcal{H}}\\,.\\label{eq:observer_error_output_JB}\n\\end{align}\n\\end{subequations}with (\\ref{eq:observer_error_output_JB}) denoting\nthe collocated output density. If we investigate the formal change\nof the error-Hamiltonian $\\tilde{\\mathscr{H}}=\\int_{0}^{L}\\tilde{\\mathcal{H}}\\mathrm{d}z$,\nwhich follows to\n\\begin{multline*}\n\\dot{\\tilde{\\mathscr{H}}}=-\\int_{0}^{L}\\delta_{\\tilde{\\alpha}}(\\tilde{\\mathcal{H}})\\mathcal{R}^{\\tilde{\\alpha}\\tilde{\\beta}}\\delta_{\\tilde{\\beta}}(\\tilde{\\mathcal{H}})\\mathrm{d}z+\\ldots\\\\\n-\\int_{0}^{L}\\delta_{\\tilde{\\alpha}}(\\tilde{\\mathcal{H}})\\mathcal{K}_{\\xi}^{\\tilde{\\alpha}}\\delta^{\\xi\\eta}(\\bar{y}_{\\eta}-\\hat{\\bar{y}}_{\\eta})\\mathrm{d}z\\,,\n\\end{multline*}\nwe find that by means of a proper choice for the components $\\mathcal{K}_{\\xi}^{\\hat{\\alpha}}$\nwe are able to render $\\dot{\\tilde{\\mathscr{H}}}\\leq0$ . Hence, the\ntotal energy of the observer error $\\tilde{\\mathscr{H}}$ is an appropriate\ncandidate for a Lyapunov functional and therefore serves as basis\nwith respect to the stability analysis. Next, the observer-design\nprocedure is demonstrated by an example.\n\n\\section{Observer Design for an In-Domain Actuated Vibrating String\\label{sec:Vibrating_String}}\n\nIn this chapter, we design an infinite-dimensional observer for an\nin-domain actuated vibrating string by exploiting energy considerations.\nThe governing equation of motion of the system under consideration\nreads as\\begin{subequations}\n\\begin{equation}\n\\rho\\frac{\\partial^{2}w}{\\partial t^{2}}=T\\frac{\\partial^{2}w}{\\partial z^{2}}+f(z,t)\\,,\\label{eq:vib_String_eom}\n\\end{equation}\nwhere $w$ describes the vertical deflection of the string, $\\rho$\nthe mass density and $T$ Young's modulus. Regarding the boundary\nconditions, we have that the string is clamped at $z=0$ and free\nat $z=L$, i.e.\n\\begin{align}\nw(0,t) & =0\\,,\\quad T\\frac{\\partial w}{\\partial z}(L,t)=0\\,.\\label{eq:BC_VS}\n\\end{align}\n\\end{subequations}In (\\ref{eq:vib_String_eom}), the distributed\nforce $f(z,t)=g(z)u(t)$ is generated by an actuator behaving like\na piezoelectric patch, where the applied voltage $u(t)$ serves as\nmanipulated variable. The spatially dependent function $g(z)=h(z-L_{p_{1}})-h(z-L_{p_{2}})$,\nwhere $h(\\cdot)$ denotes the Heaviside function, describes the placement\nof the actuator between $z=L_{p_{1}}$ and $z=L_{p_{2}}$. In fact,\nthe force-distribution on the domain $L_{p_{1}}\\leq z\\leq L_{p_{2}}$\nis supposed to be constant and is scaled by $u(t)$.\n\nFirst, the intention is to find a pH-system representation that can\nbe exploited for the observer design. To this end, we introduce the\nunderlying bundle structure based on $\\pi:(z,w,p)\\rightarrow(z)$\ntogether with the generalised momenta $p=\\rho\\dot{w}$, and thus,\n(\\ref{eq:vib_String_eom}) can be rewritten as\n\\begin{equation}\n\\dot{p}=Tw_{zz}+g(z)u\\,.\\label{eq:VS_JB}\n\\end{equation}\nIf we use the Hamiltonian density $\\mathcal{H}=\\frac{1}{2\\rho}p^{2}+\\frac{1}{2}T(w_{z})^{2}\\in\\mathcal{J}^{1}(\\mathcal{E})$,\nwe obtain the appropriate pH-system formulation\\begin{subequations}\\label{eq:pH_formulation_VS}\n\\begin{align}\n\\left[\\begin{array}{c}\n\\dot{w}\\\\\n\\dot{p}\n\\end{array}\\right] & =\\left[\\begin{array}{cc}\n0 & 1\\\\\n-1 & 0\n\\end{array}\\right]\\left[\\begin{array}{c}\n\\delta_{w}\\mathcal{H}\\\\\n\\delta_{p}\\mathcal{H}\n\\end{array}\\right]+\\left[\\begin{array}{c}\n0\\\\\ng(z)\n\\end{array}\\right]u\\,,\\\\\ny & =\\left[\\begin{array}{cc}\n0 & g(z)\\end{array}\\right]\\left[\\begin{array}{c}\n\\delta_{w}\\mathcal{H}\\\\\n\\delta_{p}\\mathcal{H}\n\\end{array}\\right]=g(z)\\frac{p}{\\rho}\\,.\\label{eq:pH_form_VS_output_density}\n\\end{align}\n\\end{subequations}By taking the boundary conditions (\\ref{eq:BC_VS})\ninto account, one finds that the formal change of the Hamiltonian\nfunctional $\\mathscr{H}$ follows to $\\dot{\\mathscr{H}}=\\int_{0}^{L}g(z)\\frac{p}{\\rho}u\\mathrm{d}z$,\ni.e. we have a distributed port that can be used for control purposes.\nIn fact, for the system under consideration, in \\cite{Malzer2020}\na dynamic controller based on the energy-Casimir method has been designed.\nHowever, with regard to this control methodology, it should be mentioned\nthat it yields unsatisfactory results for uncertain initial conditions,\nsee e.g. \\cite{Rams2017b} where this problem is briefly discussed\nfor a boundary-control system. Therefore, in the following we intend\nto design an infinite-dimensional observer in order to overcome this\nobstacle.\n\nConcerning the observer design, it is assumed that the spatial integration\nof the distributed output density (\\ref{eq:pH_form_VS_output_density})\naccording to $\\bar{y}=\\int_{0}^{L}g(z)\\frac{p}{\\rho}\\mathrm{d}z$,\nwhich can be interpreted as the current through the actuator, is available\nas measurement quantity. Thus, if we use the observer density $\\hat{\\mathcal{H}}=\\frac{1}{2\\rho}\\hat{p}^{2}+\\frac{1}{2}T(\\hat{w}_{z})^{2}$\nand the copy of the plant output according to $\\hat{\\bar{y}}=\\int_{0}^{L}g(z)\\frac{\\hat{p}}{\\rho}\\mathrm{d}z$,\nwe are able to introduce an observer for the in-domain actuated vibrating\nstring in the form\n\\begin{align*}\n\\left[\\begin{array}{c}\n\\dot{\\hat{w}}\\\\\n\\dot{\\hat{p}}\n\\end{array}\\right]\\! & =\\!\\left[\\!\\begin{array}{cc}\n0\\! & \\!1\\\\\n-1\\! & \\!0\n\\end{array}\\!\\right]\\!\\left[\\begin{array}{c}\n\\delta_{\\hat{w}}\\hat{\\mathcal{H}}\\\\\n\\delta_{\\hat{p}}\\hat{\\mathcal{H}}\n\\end{array}\\right]\\!+\\!\\left[\\begin{array}{c}\n0\\\\\ng(z)\n\\end{array}\\right]\\!u\\!+\\!\\left[\\begin{array}{c}\nk_{1}\\\\\nk_{2}\n\\end{array}\\right]\\!(\\bar{y}-\\hat{\\bar{y}}),\n\\end{align*}\nwhere the governing equations are restricted to the boundary conditions\n$\\hat{w}(0)=0$ and $T\\hat{w}_{z}(L)=0$. Next, by means of the error\ncoordinates $\\tilde{w}=w-\\hat{w}$, $\\tilde{p}=p-\\hat{p}$, the observer-error\ndynamics can be deduced to\\begin{subequations}\\label{eq:observer_error_dynamics_VS}\n\\begin{align}\n\\dot{\\tilde{w}} & =\\dot{w}-\\dot{\\hat{w}}=\\frac{1}{\\rho}\\tilde{p}-k_{1}(\\bar{y}-\\hat{\\bar{y}})\\,,\\label{eq:observer_error_dyn_w}\\\\\n\\dot{\\tilde{p}} & =\\dot{p}-\\dot{\\hat{p}}=T\\tilde{w}_{zz}-k_{2}(\\bar{y}-\\hat{\\bar{y}})\\,,\\label{eq:observer_error_dyn_p}\n\\end{align}\nwhere the boundary conditions\n\\begin{align}\n\\tilde{w}(0) & =0\\,,\\quad T\\tilde{w}_{z}(L)=0\\label{eq:BC_observer_error_left}\n\\end{align}\n\\end{subequations}hold. With respect to the determination of $k_{1}$\nand $k_{2}$, it is beneficial to reformulate (\\ref{eq:observer_error_dyn_w})\nand (\\ref{eq:observer_error_dyn_p}) as the pH-system\\begin{subequations}\\label{eq:observer_error_VS_JB-1}\n\\begin{align}\n\\left[\\begin{array}{c}\n\\dot{\\tilde{w}}\\\\\n\\dot{\\tilde{p}}\n\\end{array}\\right]\\! & =\\!\\left[\\begin{array}{cc}\n0 & 1\\\\\n-1 & 0\n\\end{array}\\right]\\!\\left[\\begin{array}{c}\n\\delta_{\\tilde{w}}\\tilde{\\mathcal{H}}\\\\\n\\delta_{\\tilde{p}}\\tilde{\\mathcal{H}}\n\\end{array}\\right]\\!-\\!\\left[\\begin{array}{c}\nk_{1}\\\\\nk_{2}\n\\end{array}\\right]\\!(\\bar{y}-\\hat{\\bar{y}}),\\\\\n\\tilde{y} & \\!=\\!-\\!\\left[\\begin{array}{cc}\nk_{1} & k_{2}\\end{array}\\right]\\!\\left[\\begin{array}{c}\n\\delta_{\\tilde{w}}\\tilde{\\mathcal{H}}\\\\\n\\delta_{\\tilde{p}}\\tilde{\\mathcal{H}}\n\\end{array}\\right]\\!=\\!k_{1}T\\tilde{w}_{zz}\\!-\\!k_{2}\\frac{\\tilde{p}}{\\rho},\\label{eq:pH_observer_error_output}\n\\end{align}\n\\end{subequations}where the energy density of the observer error\nreads as $\\tilde{\\mathcal{H}}=\\frac{1}{2\\rho}\\tilde{p}^{2}+\\frac{1}{2}T(\\tilde{w}_{z})^{2}$\nand (\\ref{eq:pH_observer_error_output}) states the corresponding\noutput density. If we investigate the formal change of the error-Hamiltonian\nfunctional $\\tilde{\\mathscr{H}}$, which can be deduced to\n\\begin{align}\n\\dot{\\tilde{\\mathscr{H}}} & =\\int_{0}^{L}(T\\tilde{w}_{zz}k_{1}(\\bar{y}-\\hat{\\bar{y}})-\\frac{\\tilde{p}}{\\rho}k_{2}(\\bar{y}-\\hat{\\bar{y}}))\\mathrm{d}z\\,,\\label{eq:H_tilde_p}\n\\end{align}\nand take into account that $(\\bar{y}-\\hat{\\bar{y}})=\\int_{0}^{L}g(z)\\frac{1}{\\rho}\\tilde{p}\\mathrm{d}z$,\nwe find that the choice $k_{1}=0$ and $k_{2}=kg(z)$ with $k>0$,\nyields\n\\begin{equation}\n\\dot{\\tilde{\\mathscr{H}}}(\\tilde{w},\\tilde{p})=-k(\\bar{y}-\\hat{\\bar{y}})^{2}\\leq0\\,.\\label{eq:H_tilde_p_fin}\n\\end{equation}\nHowever, the fact that $\\tilde{\\mathscr{H}}>0$ and $\\dot{\\tilde{\\mathscr{H}}}\\leq0$\nhold is not sufficient for the convergence of the observer, and therefore,\nin the following, detailed stability investigations are carried out\nto verify that the observer error is asymptotically stable.\n\n\\section{Observer Convergence\\label{sec:Stability_Analysis}}\n\nIn this section, based on functional analysis the convergence of the\nobserver error is proven in two steps. First, we address the well-posedness\nof the observer-error system making heavy use of the well-known Lumer-Phillips\ntheorem, see e.g. \\cite{Liu1999}. Afterwards, LaSalle's invariance\nprinciple for infinite-dimensional systems is applied to show the\nasymptotic stability of the observer error, where beforehand it is\nnecessary to verify the precompactness of the solution trajectories.\n\n\\subsection{Well-posedness of the Observer-Error System\\label{subsec:Wellposedness}}\n\nNow, a careful investigation of the well-posedness of the observer-error\nsystem is carried out. To this end, we reformulate (\\ref{eq:observer_error_dynamics_VS})\nas an abstract Cauchy problem and show that the operator under consideration\ngenerates a $C_{0}$-semigroup of contractions.\n\nFirst, we define the state vector $\\chi=\\left[\\chi^{1},\\chi^{2}\\right]^{T}=\\left[\\tilde{w},\\tilde{p}\\right]^{T}$\ntogether with the state space $\\mathcal{X}=H_{C}^{1}(0,L)\\times L^{2}(0,L)$,\nwhere $H_{C}^{1}(0,L)=\\{\\chi^{1}\\in H^{1}(0,L)|\\chi^{1}(0)=0\\}$,\nwith $H^{l}(0,L)$ denoting a Sobolev space of functions whose derivatives\nup to order $l$ are square integrable, see \\cite{Adams2003} for\na detailed introduction of Sobolev spaces. Thus, the state space $\\mathcal{X}$\nis equipped with the standard norm\n\\begin{equation}\n\\left\\Vert \\chi\\right\\Vert _{n}^{2}=\\left\\langle \\tilde{w},\\tilde{w}\\right\\rangle _{L^{2}}+\\left\\langle \\tilde{w}_{z},\\tilde{w}_{z}\\right\\rangle _{L^{2}}+\\left\\langle \\tilde{p},\\tilde{p}\\right\\rangle _{L^{2}}\\,.\\label{eq:standard_norm}\n\\end{equation}\nNext, to be able to rewrite the observer-error dynamics as an abstract\nCauchy problem of the form $\\dot{\\chi}(t)=\\mathcal{A}\\chi(t)$ with\n$\\chi(0)=\\chi_{0}$, we introduce the linear operator $\\mathcal{A}:\\mathcal{D}(\\mathcal{A})\\subset\\mathcal{X}\\rightarrow\\mathcal{X}$\naccording to\n\\[\n\\mathcal{A}:\\left[\\begin{array}{c}\n\\tilde{w}\\\\\n\\tilde{p}\n\\end{array}\\right]\\rightarrow\\left[\\begin{array}{c}\n\\frac{1}{\\rho}\\tilde{p}\\\\\nT\\tilde{w}_{zz}-kg(z)\\int_{0}^{L}g(z)\\frac{1}{\\rho}\\tilde{p}\\mathrm{d}z\n\\end{array}\\right]\\,,\n\\]\nwhere the (dense) domain of $\\mathcal{A}$ is defined as\n\\begin{multline}\n\\mathcal{D}(\\mathcal{A}):=\\{\\chi\\in\\mathcal{X}|\\tilde{w}\\in(H^{2}(0,L)\\cap H_{C}^{1}(0,L)),\\\\\n\\tilde{p}\\in H_{C}^{1}(0,L),T\\tilde{w}_{z}(L)=0\\}\\,.\\label{eq:domain_A}\n\\end{multline}\nThus, the intention is to investigate the operator $\\mathcal{A}$\nregarding some properties such that a variant of the well-known Lumer-Phillips\ntheorem \\cite[Thm. 1.2.4]{Liu1999} can be applied. With respect to\nthis forthcoming investigations, it is beneficial to introduce\n\\begin{equation}\n\\left\\Vert \\chi\\right\\Vert _{\\mathcal{\\mathcal{X}}}^{2}=T\\left\\langle \\tilde{w}_{z},\\tilde{w}_{z}\\right\\rangle _{L^{2}}+\\frac{1}{\\rho}\\left\\langle \\tilde{p},\\tilde{p}\\right\\rangle _{L^{2}}\\,,\\label{eq:energy_norm}\n\\end{equation}\nwhich is called energy norm due to the equivalence $\\tilde{\\mathscr{H}}=\\frac{1}{2}\\left\\Vert \\chi\\right\\Vert _{\\mathcal{\\mathcal{X}}}^{2}$.\nBecause $\\tilde{w}(0)=0$ and further $\\tilde{w}(z)=\\int_{0}^{z}\\tilde{w}_{z}\\mathrm{d}y_{1}$\nholds, we find constants $c_{1},c_{2}$, which have to meet $00$, such\nthat $c_{1}\\left\\Vert \\chi\\right\\Vert _{n}^{2}\\leq\\left\\Vert \\chi\\right\\Vert _{\\mathcal{X}}^{2}\\leq c_{2}\\left\\Vert \\chi\\right\\Vert _{n}^{2}$\nis fulfilled, and hence, the energy norm (\\ref{eq:energy_norm}) is\nequivalent to the standard norm (\\ref{eq:standard_norm}). Similar\nto the proof of Lemma 2.2 in \\cite{Stuerzer2016}, where they exploit\nthe dense inclusion $H^{2}(0,L)\\subset H^{1}(0,L)$ and modify the\nboundary values of $\\tilde{w}$ and its derivatives in a proper manner,\nit can be shown that the domain $\\mathcal{D}(\\mathcal{A})$ given\nin (\\ref{eq:domain_A}) is dense in $\\mathcal{X}$. Thus, according\nto \\cite[Def. 1.1.1]{Liu1999}, -- since we have the equivalence\n$\\tilde{\\mathscr{H}}=\\frac{1}{2}\\left\\Vert \\chi\\right\\Vert _{\\mathcal{\\mathcal{X}}}^{2}$\n-- the relation (\\ref{eq:H_tilde_p_fin}) implies that $\\mathcal{A}$\nis dissipative.\n\nIn the following, we show that the inverse operator $\\mathcal{A}^{-1}$\nexists and is bounded, i.e. for every $\\bar{\\chi}=\\left[f,h\\right]^{T}\\in\\mathcal{X}$\nand $\\chi=\\left[\\tilde{w},\\tilde{p}\\right]^{T}\\in\\mathcal{D}(\\mathcal{A})$,\nwe can uniquely solve\n\\begin{equation}\n\\mathcal{A}\\!\\left[\\!\\begin{array}{c}\n\\tilde{w}\\\\\n\\tilde{p}\n\\end{array}\\!\\right]\\!=\\!\\left[\\begin{array}{c}\n\\frac{1}{\\rho}\\tilde{p}\\\\\nT\\tilde{w}_{zz}-kg(z)\\int_{0}^{L}g(z)\\frac{1}{\\rho}\\tilde{p}\\mathrm{d}z\n\\end{array}\\right]\\!=\\!\\left[\\begin{array}{c}\nf\\\\\nh\n\\end{array}\\right],\\label{eq:Calc_A-1}\n\\end{equation}\nand prove that $\\mathcal{A}^{-1}$ maps bounded sets in $\\mathcal{X}$\ninto bounded sets in $\\mathcal{K}:=(H^{2}(0,L)\\cap H_{C}^{1}(0,L))\\times H_{C}^{1}(0,L)$.\nFrom the $1$st line of (\\ref{eq:Calc_A-1}) it follows that $\\tilde{p}=\\rho f\\in H_{C}^{1}(0,L)$.\nMoreover, an integration of the $2$nd line of (\\ref{eq:Calc_A-1})\nyields\n\\begin{multline}\n\\tilde{w}_{z}(z)=-\\frac{1}{T}(\\int_{z}^{L}h(y_{2})\\mathrm{d}y_{2}+\\ldots\\\\\n+\\int_{z}^{L}kg(y_{2})\\int_{0}^{L}g(y_{1})f(y_{1})\\mathrm{d}y_{1}\\mathrm{d}y_{2})\\label{eq:w_z_calc}\n\\end{multline}\nas $\\tilde{w}_{z}(L)=0$ holds. If we further integrate (\\ref{eq:w_z_calc}),\nwe obtain\n\\begin{multline}\n\\tilde{w}(z)=-\\frac{1}{T}(\\int_{0}^{z}\\int_{y_{3}}^{L}h(y_{2})\\mathrm{d}y_{2}\\mathrm{d}y_{3}+\\ldots\\\\\n+\\int_{0}^{z}\\int_{y_{3}}^{L}kg(y_{2})\\int_{0}^{L}g(y_{1})f(y_{1})\\mathrm{d}y_{1}\\mathrm{d}y_{2}\\mathrm{d}y_{3})\\label{eq:w_calc}\n\\end{multline}\nas $\\tilde{w}(0)=0$, and thus, $\\tilde{w}(z)$ is uniquely defined\nby $\\bar{\\chi}$. Since we have shown that the inverse operator $\\mathcal{A}^{-1}$\nexists, it remains to investigate the boundedness. To this end, it\nis verified that the norm of $\\chi=\\mathcal{A}^{-1}\\bar{\\chi}$ in\n$\\mathcal{K}$ is bounded by $\\left\\Vert \\bar{\\chi}\\right\\Vert _{\\mathcal{X}}$.\nFirst, we state an inequality that is often used in the sequel; in\nfact, for a -- basically arbitrary -- function $f$, by means of\nthe Cauchy-Schwarz inequality we find the important relation\n\\begin{equation}\n(\\int_{0}^{L}f\\mathrm{d}z)^{2}\\leq C\\int_{0}^{L}\\left|f\\right|^{2}\\mathrm{d}z\\,,\\label{eq:inequalitiy_square}\n\\end{equation}\nwhere it should be mentioned that here and in the following $C$\ndenotes positive, not necessarily equal constants. Next, we investigate\nthe norm $\\left\\Vert \\tilde{w}_{z}\\right\\Vert _{L^{2}}$. Therefore,\nwe substitute (\\ref{eq:w_z_calc}) in $\\left\\Vert \\tilde{w}_{z}\\right\\Vert _{L^{2}}=(\\int_{0}^{L}\\left|\\tilde{w}_{z}\\right|^{2}\\mathrm{d}z)^{1\/2}$\nand apply the Triangle inequality, which yields\n\\begin{multline*}\n\\left\\Vert \\tilde{w}_{z}\\right\\Vert _{L^{2}}\\leq(\\int_{0}^{L}\\frac{1}{T^{2}}(\\int_{z}^{L}h(y_{2})\\mathrm{d}y_{2})^{2}\\mathrm{d}z)^{\\frac{1}{2}}+\\\\\n(\\int_{0}^{L}\\frac{1}{T^{2}}(\\int_{z}^{L}kg(y_{2})\\int_{0}^{L}g(y_{1})f(y_{1})\\mathrm{d}y_{1}\\mathrm{d}y_{2})^{2}\\mathrm{d}z)^{\\frac{1}{2}}.\n\\end{multline*}\n Thus, by means of (\\ref{eq:inequalitiy_square}) and due to the fact\nthat $\\int_{z}^{L}h^{2}\\mathrm{d}y_{2}\\leq\\int_{0}^{L}h^{2}\\mathrm{d}z=\\left\\Vert h\\right\\Vert _{L_{2}}^{2}$\nholds, we obtain\n\\begin{multline}\n\\left\\Vert \\tilde{w}_{z}\\right\\Vert _{L^{2}}\\leq C\\left\\Vert h\\right\\Vert _{L_{2}}\\frac{1}{T}L^{\\frac{1}{2}}+\\\\\nC(\\int_{0}^{L}\\frac{1}{T^{2}}\\int_{z}^{L}k^{2}g^{2}(y_{2})(\\int_{0}^{L}g(y_{1})f(y_{1})\\mathrm{d}y_{1})^{2}\\mathrm{d}y_{2}\\mathrm{d}z)^{\\frac{1}{2}}.\\label{eq:ineq_w_z_L_2}\n\\end{multline}\nNext, we apply the Cauchy-Schwarz inequality to the second term of\nthe right-hand side in (\\ref{eq:ineq_w_z_L_2}), which enables us\nto find the estimate $\\left\\Vert \\tilde{w}_{z}\\right\\Vert _{L^{2}}\\leq C(\\left\\Vert f\\right\\Vert _{H^{1}}+\\left\\Vert h\\right\\Vert _{L^{2}})$.\nSimilarly, by means of the $2$nd line of (\\ref{eq:Calc_A-1}) we\nare able to deduce $\\left\\Vert \\tilde{w}_{zz}\\right\\Vert _{L^{2}}\\leq C(\\left\\Vert f\\right\\Vert _{H^{1}}+\\left\\Vert h\\right\\Vert _{L^{2}})$.\nMoreover, if we substitute (\\ref{eq:w_calc}) in $\\left\\Vert \\tilde{w}\\right\\Vert _{L^{2}}=(\\int_{0}^{L}\\left|\\tilde{w}\\right|^{2}\\mathrm{d}z)^{1\/2}$,\nwe find $\\left\\Vert \\tilde{w}\\right\\Vert _{L^{2}}\\leq C(\\left\\Vert f\\right\\Vert _{H^{1}}+\\left\\Vert h\\right\\Vert _{L^{2}})$,\nand hence, we have $\\left\\Vert \\tilde{w}\\right\\Vert _{H^{2}}\\leq C(\\left\\Vert f\\right\\Vert _{H^{1}}+\\left\\Vert h\\right\\Vert _{L^{2}})$.\nSince from the first line in (\\ref{eq:Calc_A-1}) we immediately get\n$\\left\\Vert \\tilde{p}\\right\\Vert _{H^{1}}=\\rho\\left\\Vert f\\right\\Vert _{H^{1}}$,\nwe can state the important estimate\n\\[\n\\left\\Vert \\tilde{w}\\right\\Vert _{H^{2}}+\\left\\Vert \\tilde{p}\\right\\Vert _{H^{1}}\\leq C(\\left\\Vert f\\right\\Vert _{H^{1}}+\\left\\Vert h\\right\\Vert _{L^{2}})\\,,\n\\]\nwhich shows that $\\mathcal{A}^{-1}$ maps bounded sets in $\\mathcal{X}$\ninto bounded sets in $\\mathcal{K}$. \n\nThe boundedness of $\\mathcal{A}^{-1}$ implies that $\\lambda=0$ cannot\nbe an eigenvalue of $\\mathcal{A}$, and hence, it follows that $0\\in\\rho(\\mathcal{A})$,\nthe resolvent set of $\\mathcal{A}$. Furthermore, since $\\mathcal{D}(\\mathcal{A})$\nis dense in $\\mathcal{X}$ and $\\mathcal{A}$ is dissipative, all\nrequirements for the variant of the Lumer-Phillips theorem according\nto \\cite[Thm. 1.2.4]{Liu1999} are met, and therefore, we are able\nto show that $\\mathcal{A}$ is the infinitesimal generator of a $C_{0}$-semigroup\nof contractions on $\\mathcal{X}$. That is, the norm $\\left\\Vert \\chi(t)\\right\\Vert _{\\mathcal{X}}$\nremains bounded for $t\\rightarrow\\infty$; however, with respect to\nthe observer error it is necessary that it tends to $0$, which is\nshown in the following subsection.\n\n\\subsection{Asymptotic Stability of the Observer-Error System\\label{subsec:LaSalle}}\n\nNow, the objective is to apply LaSalle's invariance principle in order\nto prove the asymptotic stability of the observer error, where the\nproof follows the intention of \\cite[Sec. 3]{Guo2011}. However, the\napplicability of LaSalle's invariance principle according to \\cite[Thm. 3.64]{Luo1998}\nrequires the precompactness of the solution trajectories, which is\nnot ensured in the infinite-dimensional scenario. Since in the previous\nsection we have shown that $\\mathcal{A}^{-1}$ is bounded, by means\nof the Sobolev embedding theorem, it follows that $\\mathcal{A}^{-1}$\nis compact (see proof of Lemma 2.4 in \\cite{Stuerzer2016} or \\cite[p. 201]{Luo1998}),\nwhich further implies the precompactness of the trajectories, see\n\\cite[Rem. 4.2]{Miletic2015}. \n\nIn light of LaSalle's invariance principle, we investigate the set\n$\\mathcal{S}=\\{\\chi\\in\\mathcal{X}|\\dot{\\tilde{\\mathscr{H}}}=0\\}$,\nwhere $\\dot{\\tilde{\\mathscr{H}}}(\\tilde{w},\\tilde{p})=-k(\\int_{0}^{L}g(z)\\frac{1}{\\rho}\\tilde{p}\\mathrm{d}z)^{2}=0$\nimplies $\\int_{0}^{L}g(z)\\frac{1}{\\rho}\\tilde{p}\\mathrm{d}z=0$. In\nthe set $\\mathcal{S}$ we have\\begin{subequations}\\label{eq:eom_S}\n\\begin{align}\n\\rho\\tilde{w}_{tt} & =T\\tilde{w}_{zz}\\,,\\label{eq:eom_S_pde}\\\\\n\\tilde{w}(0,t) & =0\\,,\\label{eq:eom_S_BC_left}\\\\\nT\\tilde{w}_{z}(L,t) & =0\\,,\\label{eq:eom_S_BC_right}\n\\end{align}\n\\end{subequations}which is similar to the problem considered in \\cite[Sec. 3]{Guo2011};\nhowever, the restriction describing the set $\\mathcal{S}$, which\nis constrained to the boundary there, is completely different. To\nbe able to show that the only possible solution in $\\mathcal{S}$\nis the trivial one, we need to investigate the general solution of\n(\\ref{eq:eom_S}). To this end, like in \\cite[Sec. 3]{Guo2011}, we\nfirst focus on determining the eigenvalues and eigenfunctions of (\\ref{eq:eom_S}),\ni.e. we consider\n\\begin{equation}\n\\bar{\\mathcal{A}}\\left[\\begin{array}{cc}\n\\phi & \\kappa\\end{array}\\right]^{T}=\\left[\\begin{array}{cc}\n\\frac{\\kappa}{\\rho} & T\\phi_{zz}\\end{array}\\right]=\\lambda\\left[\\begin{array}{cc}\n\\phi & \\kappa\\end{array}\\right]^{T}\\,.\\label{eq:eigen_equation}\n\\end{equation}\nFrom (\\ref{eq:eigen_equation}) we obtain $\\kappa=\\rho\\lambda\\phi$\nand furthermore\\begin{subequations}\\label{eq:eigen_problem}\n\\begin{align}\n\\phi_{zz} & =\\frac{\\lambda^{2}}{\\vartheta^{2}}\\phi\\,,\\\\\n\\phi(0) & =0\\,,\\label{eq:eigen_problem_BC_left}\\\\\n\\phi_{z}(L) & =0\\,,\\label{eq:eigen_problem_BC_right}\n\\end{align}\n\\end{subequations}where $\\vartheta^{2}=\\frac{T}{\\rho}$. To find\nthe solution of (\\ref{eq:eigen_problem}), we have to investigate\nthe three cases $\\lambda^{2}>0$, $\\lambda^{2}=0$ and $\\lambda^{2}<0$\nin the following. For $\\lambda^{2}>0$ and $\\lambda^{2}=0$, we have\nthe ansatz $\\phi(z)=Ae^{\\frac{\\lambda}{\\vartheta}z}+Be^{-\\frac{\\lambda}{\\vartheta}z}$\nand $\\phi(z)=Az+B$, respectively, where by means of the boundary\nconditions (\\ref{eq:eigen_problem_BC_left}) and (\\ref{eq:eigen_problem_BC_right}),\none can easily deduce that for both cases only the trivial solution\n$\\phi(z)=0$ exists. Thus, we focus on the case $\\lambda^{2}<0$,\nand consequently, due to the fact that $\\lambda$ has an imaginary\ncharacter then, as ansatz for the eigenfunctions we have $\\phi(z)=A\\sin(\\frac{\\left|\\lambda\\right|}{\\vartheta}z)+B\\cos(\\frac{\\left|\\lambda\\right|}{\\vartheta}z)$.\nTo fulfil the boundary condition (\\ref{eq:eigen_problem_BC_left}),\n$B=0$ must be valid, and hence, the ansatz simplifies to $\\phi(z)=A\\sin(\\frac{\\left|\\lambda\\right|}{\\vartheta}z)$.\nFurthermore, by means of the boundary condition (\\ref{eq:eigen_problem_BC_right}),\nwe find $\\partial_{z}\\phi(L)=\\frac{\\left|\\lambda\\right|}{\\vartheta}A\\cos(\\frac{\\left|\\lambda\\right|}{\\vartheta}L)=0$,\nwhich exhibits infinitely many non-trivial solutions for\n\\begin{equation}\n\\left|\\lambda_{k}\\right|=(k-\\frac{1}{2})\\frac{\\pi}{L}\\vartheta\\,,\\label{eq:eigenvalues}\n\\end{equation}\nwith $k=1,2,\\ldots$. With regard to the investigation of the set\n$\\mathcal{S}$, the velocity of the vibrating string is of particular\ninterest. Consequently, since we deduced the (imaginary) eigenvalues\n$\\lambda_{k}=\\pm i\\omega_{k}\\vartheta$ with $\\omega_{k}=(k-\\frac{1}{2})\\frac{\\pi}{L}$,\nthe ansatz for the general solution of the velocity can be given according\nto\\begin{subequations}\\label{eq:solution_a_b}\n\\begin{align}\n\\tilde{w}_{t}(z,t) & \\!=\\!\\overset{\\infty}{\\underset{\\mathop{k=1}}{\\mathop{\\sum}}}\\!(a_{k}\\cos(\\omega_{k}\\vartheta t)\\!+\\!b_{k}\\sin(\\omega_{k}\\vartheta t))\\varphi_{k}(z),\\label{eq:solution_w_t}\n\\end{align}\nwhere the coefficients $A_{k}$ are hidden in $a_{k}$ and $b_{k}$,\nand therefore, for the eigenfunctions we use $\\varphi_{k}(z)=\\sin(\\omega_{k}z)$\nhere and in the sequel. Hence, an integration of (\\ref{eq:solution_w_t})\nyields\n\\begin{equation}\n\\tilde{w}(z,t)\\!=\\!\\overset{\\infty}{\\underset{\\mathop{k=1}}{\\mathop{\\sum}}}\\!(a_{k}\\sin(\\omega_{k}\\vartheta t)\\!-\\!b_{k}\\cos(\\omega_{k}\\vartheta t))\\frac{\\varphi_{k}(z)}{\\omega_{k}\\vartheta}.\\label{eq:solution_w}\n\\end{equation}\n\\end{subequations}By means of $\\sin(x)=\\frac{1}{2i}(e^{ix}-e^{-ix})$\nand $\\cos(x)=\\frac{1}{2}(e^{ix}+e^{-ix})$, after a straightforward\ncomputation we can beneficially rewrite (\\ref{eq:solution_a_b}) according\nto\n\\begin{multline*}\n\\left[\\begin{array}{c}\n\\tilde{w}(z,t)\\\\\n\\tilde{w}_{t}(z,t)\n\\end{array}\\right]=\\overset{\\infty}{\\underset{\\mathop{k=1}}{\\mathop{\\sum}}}c_{k}e^{i\\omega_{k}\\vartheta t}\\left[\\begin{array}{c}\n-i\\frac{\\varphi_{k}}{\\omega_{k}\\vartheta}\\\\\n\\phi_{k}\n\\end{array}\\right]+\\ldots\\\\\n\\overset{\\infty}{\\underset{\\mathop{k=1}}{\\mathop{\\sum}}}c_{-k}e^{-i\\omega_{k}\\vartheta t}\\left[\\begin{array}{c}\ni\\frac{\\varphi_{k}}{\\omega_{k}\\vartheta}\\\\\n\\phi_{k}\n\\end{array}\\right]\\,,\n\\end{multline*}\nwhere the coefficients $c_{k}=\\frac{1}{2}\\left(a_{k}-ib_{k}\\right)$\nand $c_{-k}=\\frac{1}{2}\\left(a_{k}+ib_{k}\\right)$ fulfil (see \\cite[Eq. (3.19)]{Guo2011})\n\\begin{equation}\n\\overset{\\infty}{\\underset{\\mathop{k=1}}{\\mathop{\\sum}}}\\left|c_{\\pm k}\\right|^{2}=\\overset{\\infty}{\\underset{\\mathop{k=1}}{\\mathop{\\sum}}}\\left(a_{k}^{2}+b_{k}^{2}\\right)<\\infty\\,,\\label{eq:coefficients_bounded}\n\\end{equation}\nwhich will play an important role later. Thus, we are able to write\n$\\int_{0}^{L}g(z)\\frac{1}{\\rho}\\tilde{p}\\mathrm{d}z=\\int_{L_{p_{1}}}^{L_{p_{2}}}\\tilde{w}_{t}\\mathrm{d}z=0$\nas\n\\begin{equation}\n\\int_{L_{p_{1}}}^{L_{p_{2}}}\\overset{\\infty}{\\underset{\\mathop{k=1}}{\\mathop{\\sum}}}(c_{k}e^{i\\omega_{k}\\vartheta t}+c_{-k}e^{-i\\omega_{k}\\vartheta t})\\sin(\\omega_{k}z)\\mathrm{d}z=0\\,.\\label{eq:condition_S}\n\\end{equation}\n\nNow, we show that the only solution in $\\mathcal{S}$ is the trivial\none, i.e. $c_{\\pm k}=0\\forall k\\geq1$ is valid. Otherwise, if there\nexists a $k_{0}$ with $\\left|c_{k_{0}}\\right|\\neq0$, due to (\\ref{eq:coefficients_bounded})\nwe can find a $K>k_{0}$ such that\\begin{subequations}\\label{eq:bound_inf_coeffs}\n\\begin{align}\n\\left|\\int_{L_{p_{1}}}^{L_{p_{2}}}\\overset{\\infty}{\\underset{\\mathop{k=K}}{\\mathop{\\sum}}}c_{k}\\varphi_{k}\\mathrm{d}z\\right| & <\\left|\\frac{c_{k_{0}}}{4}\\int_{L_{p_{1}}}^{L_{p_{2}}}\\varphi_{k_{0}}\\mathrm{d}z\\right|\\\\\n\\left|\\int_{L_{p_{1}}}^{L_{p_{2}}}\\overset{\\infty}{\\underset{\\mathop{k=K}}{\\mathop{\\sum}}}c_{-k}\\varphi_{k}\\mathrm{d}z\\right| & <\\left|\\frac{c_{k_{0}}}{4}\\int_{L_{p_{1}}}^{L_{p_{2}}}\\varphi_{k_{0}}\\mathrm{d}z\\right|\n\\end{align}\n\\end{subequations}holds, i.e. the sum of the coefficients from $K$\nto $\\infty$ multiplied with their corresponding eigenfunctions can\nbe bounded by $c_{k_{0}}$ and $\\varphi_{k_{0}}$. Here, it is assumed\nthat $\\int_{L_{p_{1}}}^{L_{p_{2}}}\\sin(\\omega_{k_{0}})\\mathrm{d}z\\neq0$,\ni.e. $\\omega_{k_{0}}\\neq\\frac{2\\pi}{L_{p_{2}}-L_{p_{1}}}j$ with $j\\in\\mathbb{N}_{+}$.\nHowever, if we consider the absolute value of the eigenvalues (\\ref{eq:eigenvalues}),\nwe find that this is ensured for a proper choice of the length of\nthe in-domain actuator according to $L_{p_{2}}-L_{p_{1}}\\neq\\frac{4L}{2k-1}$.\nConsequently, because $\\omega_{k}\\neq\\omega_{l}\\forall k\\neq l$,\nfor $t>0$ we can reformulate (\\ref{eq:condition_S}) as\n\\begin{multline}\n-c_{k_{0}}\\int_{L_{p_{1}}}^{L_{p_{2}}}\\varphi_{k_{0}}\\mathrm{d}z=\\int_{L_{p_{1}}}^{L_{p_{2}}}\\{\\overset{K}{\\underset{\\mathop{k=1,k\\neq k_{0}}}{\\mathop{\\sum}}}c_{k}e^{i(\\omega_{k}-\\omega_{k_{0}})\\vartheta t}\\varphi_{k}+\\\\\n+\\overset{\\infty}{\\underset{\\mathop{k=K+1}}{\\mathop{\\sum}}}c_{k}e^{i(\\omega_{k}-\\omega_{k_{0}})\\vartheta t}\\varphi_{k}+\\overset{K}{\\underset{\\mathop{k=1}}{\\mathop{\\sum}}}c_{-k}e^{-i(\\omega_{k}+\\omega_{k_{0}})\\vartheta t}\\varphi_{k}+\\\\\n+\\overset{\\infty}{\\underset{\\mathop{k=K+1}}{\\mathop{\\sum}}}c_{-k}e^{-i(\\omega_{k}+\\omega_{k_{0}})\\vartheta t}\\varphi_{k}\\}\\mathrm{d}z\\,.\\label{eq:condition_S_ref}\n\\end{multline}\nNext, the idea is to integrate (\\ref{eq:condition_S_ref}) with respect\nto the time $t$ and to investigate the absolute value. Hence, we\nfind that the right-hand side of\n\\begin{multline}\n\\left|c_{k_{0}}\\int_{L_{p_{1}}}^{L_{p_{2}}}\\varphi_{k_{0}}\\mathrm{d}z\\right|t\\leq\\\\\n2\\left|\\int_{0}^{t}\\int_{L_{p_{1}}}^{L_{p_{2}}}\\{\\overset{K}{\\underset{\\mathop{k=1,k\\neq k_{0}}}{\\mathop{\\sum}}}c_{k}e^{i(\\omega_{k}-\\omega_{k_{0}})\\vartheta\\tau}\\varphi_{k}\\}\\mathrm{d}z\\mathrm{d}\\tau\\right|+\\\\\n+2\\left|\\int_{0}^{t}\\int_{L_{p_{1}}}^{L_{p_{2}}}\\{\\overset{K}{\\underset{\\mathop{k=1}}{\\mathop{\\sum}}}c_{-k}e^{-i(\\omega_{k}+\\omega_{k_{0}})\\vartheta\\tau}\\varphi_{k}\\}\\mathrm{d}z\\mathrm{d}\\tau\\right|\\,,\\label{eq:inequaltiy_coefficients}\n\\end{multline}\nwhere we used (\\ref{eq:bound_inf_coeffs}) to obtain an estimation\nfor the sums from $k=K+1$ to $k=\\infty$, is bounded for all $t\\geq0$.\nSince for an appropriate choice of the actuator-length it is ensured\nthat the integral on the left-hand side cannot vanish, the only possibility\nthat inequality (\\ref{eq:inequaltiy_coefficients}) holds for $t\\rightarrow\\infty$\nis that $c_{k_{0}}=0$ is valid. Thus, it is shown that the only possible\nsolution in $\\mathcal{S}$ is the trivial one, which finally proves\nthe asymptotic stability of the observer error and therefore justifies\nthe application of the observer developed in \\cite{Malzer2020}. Furthermore,\nin Figure (\\ref{fig:Comp_w_state_obs}), the comparison of the string\ndeflection $w(L,t)$ and the observer state $\\hat{w}(L,t)$ is depicted,\nwhere the tip of the string is moved from $w(L,0)=0$ to $w(L,t_{end})=0.1$\nand the observer state is initialised as $\\hat{w}(L,0)=0.1$.\n\\begin{figure}\n\\input{w_L_Obs.tex}\\caption{\\label{fig:Comp_w_state_obs}Comparison of the string deflection $w(L,t)$\nand the observer state $\\hat{w}(L,t)$.}\n\\end{figure}\n\n\n\\section{Conclusion and Outlook}\n\nIn this paper, the asymptotic stability of an observer error of an\nin-domain actuated vibrating string, where the observer has been developed\nin \\cite{Malzer2020}, was investigated. First, we showed that the\nlinear operator, which describes the observer error as an abstract\nCauchy problem, is the infinitesimal generator of a contraction semigroup.\nSecond, by means of LaSalle's invariance principle the asymptotic\nstability of the observer error was proven. In fact, by choosing the\nlength of the actuator properly, it was shown that the only possible\nsolution for $\\dot{\\tilde{\\mathscr{H}}}=0$ is the trivial one, which\nimplies that the observer error tends to zero. Future-research tasks\nmight deal with the stability analysis of the closed loop obtained\nby the controller design presented in \\cite{Malzer2020}, or even\nwith the stability investigation of the combination of controller\nand observer.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nThe observation of the sky with space-born instruments, equipped with detectors working at those electro-magnetic frequencies that cannot be accessed from the ground, revealed the existence of several classes of high energy radiation sources. With their location in distant galaxies, Active Galactic Nuclei (AGNs, in brief) turned out to be the most powerful non transient sources of such radiation. Although AGNs appear with many different properties, they share an extreme intrinsic luminosity, ranging between $10^{41}\\, {\\rm erg\\, s^{-1}}$ and $10^{46}\\, {\\rm erg\\, s^{-1}}$, comparable to or greater than the energy output of large galaxies, but released from a region that is smaller than 1~pc in radius. To explain this property we assume that large amounts of matter, in the order of some solar masses per year, are conveyed to the nuclear regions of active galaxies, where they are accreted by a Super Massive Black Hole (SMBH). It is now well established that SMBHs with masses between $10^6\\, {\\rm M_\\odot}$ and some $10^9\\, {\\rm M_\\odot}$ reside in the nuclei of every massive galaxy (Ferrarese \\& Merrit, 2000; Shankar, 2009). In addition, we know that accretion of fuel into their gravitational field leads to the conversion of gravitational binding energy into radiation with very high efficiency (Blandford \\& Znajek, 1977; Shields, 1978).\n\nDue to the presence of relativistic plasmas and strong magnetic fields in the vicinity of the black hole's accretion flow, the spectrum of the emitted radiation results from a combination of thermal and non-thermal components that cover several orders of magnitude in frequency, sometimes extending from radio wavelengths all the way up to $\\gamma$-ray energies. The Fermi Large Area Telescope (Fermi-LAT; see Atwood et al., 2009) gave new life to the study of the $\\gamma$-ray sky, producing, after 4 years of scientific observations, a map of $\\gamma$-ray detections with unprecedented resolution and sensitivity in the energy range between 100~MeV and 300~GeV (Acero et al., 2015). Thanks to this result, a large number of $\\gamma$-ray sources can now be associated with lower energy counterparts. In the extra-Galactic environment, it turned out that the most commonly detected objects are AGNs belonging to the blazar class. Blazars are extremely variable, highly polarized, radio-loud sources, dominated by power-law continuum spectra of type $F_\\nu \\propto \\nu^{-\\alpha}$, with a typical radio spectral slope $\\alpha \\leq 0.5$. Their properties are the consequence of the relativistic beaming of the synchrotron radiation produced by a jet that is collimated and accelerated close to our line of sight (Blandford \\& K\\\"onigl, 1979). They are classically separated into BL Lac objects (BLL), whose optical spectra show a nearly featureless power-law continuum, and Flat Spectrum Radio Quasars (FSRQs), which, instead, are characterized by strong emission lines.\n\nIn addition to the blazars that dominate the extra-Galactic $\\gamma$-ray population, other types of AGNs, together with a large number of sources without a firm classification, are detected as well. In this contribution we describe our investigation on the nature of $\\gamma$-ray AGNs of undetermined type, through the observation of their optical spectra. We focus our attention on targets whose spectral energy distributions (SED) are consistent with those of blazars, although they still lack firm spectroscopic classification, and they are therefore called Blazar Candidates of Undetermined type (BCU in 3FGL terminology, Acero et al., 2015). In this report, we discuss the spectral classification of some BCUs, with respect to the associated SEDs, rather than giving a full list of observations. In the following sections, we describe the techniques used in the attempt to identify the low energy counterparts of the $\\gamma$-ray sources, the details of how we collected the optical spectra and their classification criteria. Finally, we draw a sketch of the $\\gamma$-ray emission in the different classes of objects that we observe, compared with the multiple wavelength properties of their SEDs.\n\n\\section{Association of $\\gamma$-ray sources}\nAt the energies of $\\gamma$-ray photons it is not possible to focus radiation through reflecting or refracting optical devices. The Fermi-LAT, instead, measures the production of $e^\\pm$ pairs, through the conversion of photons in the detector (Atwood et al., 2009). The pair properties are used to reconstruct the energy and the direction of the incoming photon. The precision that can be achieved in the measurement depends mainly on the incoming photon incidence angle and on its energy. It can be roughly estimated that, for a photon of energy between 10 GeV and 100 GeV, hitting the detector at normal incidence, the 95\\% containment angle is approximately 0.5$^\\circ$ (Ackermann et al., 2012). Although the detection of multiple photons from a source can improve the performances, the contribution of nearby sources and background noise, combined with less than optimal detection conditions, results in a still significant uncertainty in the localization of the sources. Thus, in general, the task to associate a low energy counterpart to $\\gamma$-ray sources is not trivial.\n\nIn the case of AGNs, the expected broad band emission provides a reliable way to better constrain the source position. Since $\\gamma$-rays from AGNs arise from Inverse Compton (IC) scattering of low energy seed photons by relativistic plasma particles confined in strong magnetic fields, the $\\gamma$-ray production occurs together with synchrotron radiation. If the energy distribution of the plasma particles is extended enough to support significant $\\gamma$-ray emission, we expect that powerful synchrotron radiation is produced at radio and x-ray energies, as well. Taking advantage from the angular resolution of instruments working at these frequencies, which can measure the position of radiation sources down to a few arcseconds, we are able to identify candidate counterparts to $\\gamma$-ray emission by looking for coincident x-ray and radio sources within the $\\gamma$-ray detection uncertainty radius (Ackermann et al., 2011; Gasparrini et al., 2012). Furthermore, the existence of a connection between $\\gamma$-ray emission and lower frequency radiation implies that large flux variations, that characterize AGNs particularly in the high energies, can be observed in different frequencies. Thanks to the monitoring strategy of Fermi-LAT observations, covering the entire sky approximately every 3~hr, the detection of important flaring activity can be matched with follow-up observations that are able to identify the possible correlated variations of the source in other frequencies. Once the source position is determined by either of the techniques down to a few arcseconds, it is possible to search for the optical counterpart and to obtain its spectrum.\n\n\\section{Optical observations}\nThe latest catalog of Fermi-LAT detected AGNs (3LAC, see Ackermann et al., 2015) includes blazars, radio galaxies, steep spectrum radio quasars (SSRQs), Seyfert galaxies and Narrow Line Seyfert 1 galaxies. In addition to the classified sources, however, a number of undetermined type objects, generally called AGNs or BCUs, still exists. Focusing our attention on these unclassified objects, we carried out a spectroscopic study that combines publicly available spectra, like those extracted from the latest data release of the Sloan Digital Sky Survey (SDSS-DR 12; Alam et al., 2015) and of the 6dF Galaxy Redshift Survey (6dFGRS-DR 3; Jones et al., 2004, 2009), together with new observations performed at the 1.22m and the 1.82m telescopes of the Asiago Astrophysical Observatory (Ciroi et al., 2014).\n\nFig. 1 illustrates examples of spectra obtained in this study. The main purpose of this optical spectroscopic analysis is to identify the AGN class for the source associated to the $\\gamma$-ray emission and to determine its redshift, through the identification of known emission or absorption lines. In general, the detection of emission lines ensures the most reliable measurement of the source redshift, because they are easier to detect and they are originated very close to central SMBH. Absorption lines, on the contrary, are produced either in the host galaxy or along the light path towards us. They are much more difficult to detect and can only be seen if the AGN is not overwhelmingly dominant. It follows that the presence of emission lines characterizes AGNs with radiatively efficient accretion activity, therefore surrounded by nuclear and circum-nuclear regions of ionized gas, like quasars and Seyfert galaxies. BLLs, on the contrary, just give raise to a featureless power-law continuum, where some absorption features can show up, if the jet power is relatively low with respect to the host galaxy luminosity, or when some intervening medium happens to lie along the line of sight to the source.\n\n\\begin{table}[t]\n\\caption{List of sources included in this report. The table provides the $\\gamma$-ray source identifier, the associated counterpart name, the counterpart position (J2000.0), the object redshift and its spectroscopic classification.}\n\\begin{footnotesize}\n\\begin{center}\n\\begin{tabular}{llcccr}\n\\hline\n\\hline\nId. & Counterpart name & R. A. & Dec. & {\\it z} & Class \\\\\n\\hline\n3FGL J$0134.5+2638$ & 1RXS J$013427.2+263846$ & $01:34:28.3$ & $+26:38:45.0$ & $\\geq$0.108 & BL Lac \\\\\n3FGL J$0339.2-1738$ & PKS $0336-177$ & $03:39:13.7$ & $-17:36:00.6$ & 0.065 & Elliptical \\\\\n3FGL J$0904.3+4240$ & S4 $0900+42$ & $09:04:17.1$ & $+42:37:59.0$ & 1.342 & FSRQ \\\\\n3FGL J$1031.0+7440$ & S5 $1027+74$ & $10:31:22.0$ & $+74:41:58.3$ & 0.122 & FSRQ \\\\\n3FGL J$1315.4+1130$ & 1RXS J$131531.9+113327$ & $13:15:32.0$ & $+11:33:27.0$ & $\\geq$0.730 & BL Lac \\\\\n3FGL J$1412.0+5249$ & SBS $1410+530$ & $14:11:49.4$ & $+52:49:00.2$ & 0.076 & Elliptical \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\end{footnotesize}\n\\end{table}\n\\section{Results and models}\nA summary of our spectroscopic observations is presented in Table 1. According to the spectra collected in Fig. 1, our targets belong to different families of optical sources. Together with examples of classic high luminosity blazar objects, characterized by prominent emission lines (FSRQs) or dominant power-law featureless spectra (BLLs), we notice associations with some weaker sources, where the spectrum of the host galaxy, with its characteristic stellar continuum and absorption features, plays an increasing role. Looking at the spectra presented here, we note that in the high redshift regime (approximately from $z \\geq 0.15$) we are able to detect $\\gamma$-ray emission only from objects with powerful jets. At smaller distances, however, $\\gamma$-ray sources likely arising from jets of weaker power or misaligned orientation, which do not significantly contribute to the optical spectra, can be identified as well.\n\nIn order to estimate the power of the jets, which are likely producing the observed $\\gamma$-ray signal, and to compare it with the characteristics of the corresponding optical spectra, we reconstructed the SEDs associated to our targets. We used the ASI Space Data Center SED Builder tool to retrieve multiple frequency data points and to interpolate them with jet radiation models.\\footnote{The SED Builder tool is available at \\texttt{http:\/\/tools.asdc.asi.it\/SED\/}} We took into account archival data available from the literature, including in particular observations performed within extensive sky survey programs, to cover as an extended frequency range as possible. The results of this selection are plotted in Fig. 2. The broad band electro-magnetic emission is generally consistent with the classic two-hump blazar SED, very likely connected with jet activity. There are, however, some objects where an IR to optical radiation excess clearly occurs. The presence of such an excess is made particularly evident by comparing the observations with simple Synchrotron Self-Compton (SSC) models, which we used to interpolate the data. Due to the possibility that external radiation fields may give a relevant contribution to the IC effect (a scenario that is referred to as External Compton, or EC, and is very likely more appropriate, especially in the case of FSRQs), the SSC approach might be an oversimplified interpretation of the SEDs. However, the non simultaneous nature of the different data points collected in this work did not allow to consider more advanced models.\n\nTable 2 reports details of the SED models applied to our targets. The residuals were calculated without taking into account the thermal components. The SSC models are able to reproduce most of the sources with acceptable residuals ($\\chi^2_{red} \\leq 1.5$), while the most notable exceptions are probably due to the difficulties that such models have in explaining the high energy IC tail. Intrinsic source variability at the different observation epochs is also expected to affect the scatter. In general, we observe that jets of large scale and power are required to interpolate classic BLL and FSRQ blazars, while weaker targets are also explained by less powerful jets.\n\n\\subsection{Notes on single sources}\n{\\bf 3FGL J0134.5+2638}, observed with the Asiago 1.22m telescope, reveals a power law continuum spectrum with faint absorption lines. Absence of emission lines having equivalent width $EW \\geq 5\\,$\\AA leads to a BLL classification. Its SED shows the characteristic two-hump behavior of blazars and it is appreciably well interpolated by a SSC model. \\\\\n{\\bf 3FGL J0339.2--1738} is associated with an elliptical galaxy, with spectrum available from 6dFGRS. Flux calibration of the spectrum was obtained deriving an average sensitivity curve for the 6dF instrument, based on the flux calibrated spectra of IC 5135 and UGC 842. The resulting spectrum shows the characteristic continuum and absorption lines of an old stellar population, typical of ellipticals. The associated SED is a two-hump distribution with a prominent radiation excess in the optical window. \\\\\n{\\bf 3FGL J0904.3+4240}, detected by the SDSS, shows the highest redshift in this sample, which brings the strong UV emission lines of C~IV~$\\lambda$1549, C~III]~$\\lambda$1909 and Mg~II~$\\lambda$2798 of quasar spectra into the optical domain. The SED is characteristic of blazars, but with a dominant IC component over the Synchrotron part. \\\\\n{\\bf 3FGL J1031.0+7440} was observed in Asiago, with the 1.82m telescope. It shows the prominent emission lines of a Seyfert 1 galaxy, with a full width at half the maximum ${\\rm FWHM}({\\rm H}\\beta) = 2286 \\pm 350\\, {\\rm km\\, s^{-1}}$. In a standard $\\Lambda$CDM cosmology with $H_0 = 70\\, {\\rm km\\, s^{-1}\\, Mpc^{-1}}$, $\\Omega_\\Lambda = 0.7$ and $\\Omega_M = 0.3$, its redshift corresponds to a distance of 569.8~Mpc. From an apparent magnitude $V = 17.2$, we infer an absolute magnitude $M_V = -21.6$, placing this object on the border between faint quasar and bright Seyfert 1 activity. The blazar SED is accompanied by a small radiation excess in the optical domain, suggesting that the jet power and the thermal contribution from the central engine are comparable in this object. \\\\\n{\\bf 3FGL J1315.4+1130}, also detected by the SDSS, is characterized by the BLL power law continuum and by faint absorption lines. Identification of the strongest features as a Ca~II doublet is consistent with the presence of an absorption feature at the predicted wavelength of Mg~I. Fewer data points are available to reconstruct the SED of this source and we do not appreciate any deviation from a two component blazar SED. \\\\\n{\\bf 3FGL J1412.0+5249} is detected by the SDSS and it shows the characteristics of an elliptical galaxy. Its counterpart is actually a giant elliptical located in a galaxy cluster. The associated SED is the most complex of this sample, featuring a strong optical excess, emitted by the bright host galaxy, and a high energy IC component that is hardly reproduced by SSC models. \\\\\n\n\\begin{table}[t]\n\\caption{SSC model parameters. The table columns report, respectively, the 3FGL source name, the electron energy distribution power-law index before break $\\alpha_{el}^{(1)}$, the electron distribution index after break $\\alpha_{el}^{(2)}$, the logarithm of the break energy (in units of $m_e c^2$), the magnetic field $B$ (expressed in Gauss), the Doppler factor $\\delta$, the jet radius expressed in parsec, and the reduced residuals.}\n\\begin{footnotesize}\n\\begin{center}\n\\begin{tabular}{cccccccc}\n\\hline\n\\hline\nId. & $\\alpha_{el}^{(1)}$ & $\\alpha_{el}^{(2)}$ & $\\log E_{break}$ & $B$ & $\\delta$ & $R_{jet}$ & ${\\chi_{red}^2}^{\\rm a}$ \\\\\n\\hline\n3FGL J$0134.5+2638$ & 1.5 & 4.7 & 4.0 & 1.00 & 10 & 0.001 & 1.180 \\\\\n3FGL J$0339.2-1738$ & 1.5 & 5.0 & 4.0 & 0.75 & 15 & 0.001 & 1.097 \\\\\n3FGL J$0904.3+4240$ & 2.3 & 3.6 & 3.5 & 0.05 & 30 & 0.003 & 1.455 \\\\\n3FGL J$1031.0+7440$ & 1.8 & 5.0 & 4.1 & 1.00 & 15 & 0.001 & 1.339 \\\\\n3FGL J$1315.4+1130$ & 1.6 & 4.8 & 4.8 & 0.60 & 20 & 0.002 & 1.732 \\\\\n3FGL J$1412.0+5249$ & 1.5 & 4.7 & 4.0 & 1.00 & 10 & 0.001 & 1.889 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n$^{\\rm a}$ Residuals of SSC models are computed without taking into account the thermal excess data points.\n\\end{footnotesize}\n\\end{table}\n\\section{Conclusions}\nIn this study we presented the optical spectra of six targets that have been associated to $\\gamma$-ray sources of still undetermined type in 3LAC. In some cases, the spectra of this type of objects can still be obtained from large spectroscopic surveys, such as the SDSS, above all, but, in others, we need specifically planned observations. The increase of sensitivity and resolution achieved by the Fermi-LAT at $\\gamma$-ray energies is now providing better opportunities to identify candidate counterparts to extra-Galactic $\\gamma$-ray emission and, therefore, to improve the selection of targets.\n\nWith an improved ability to detect $\\gamma$-rays from faint sources we can now investigate the occurrence of nuclear activity on different power scales. The detection of faint blazar-like activity in low luminosity AGNs or even apparently normal galaxies opens a new window on the demographics of $\\gamma$-ray sources, as well as on the mechanisms that contribute to black hole growth and jet formation. The possibility that such objects may represent an important contribution to the $\\gamma$-ray radiation of undetermined origin deserves further investigation. Searching for radiation from hidden AGNs is a fundamental science case for instruments designed to observe high energy photons and other hints of jet activity, such as light polarization. Therefore, we plan to further investigate the spectroscopic properties of candidate counterparts to $\\gamma$-ray emission, taking possibly into account light polarization studies as well, through an extensive observational campaign designed for middle class telescopes.\n\n\\section*{Acknowledgements}\nThe \\textit{Fermi}-LAT Collaboration acknowledges support for LAT development, operation and data analysis from NASA and DOE (United States), CEA\/Irfu and IN2P3\/CNRS (France), ASI and INFN (Italy), MEXT, KEK, and JAXA (Japan), and the K.A.~Wallenberg Foundation, the Swedish Research Council and the National Space Board (Sweden). Science analysis support in the operations phase from INAF (Italy) and CNES (France) is also gratefully acknowledged.\n\nThis work is based on observations collected at Copernico (or\/and Schmidt) telescope(s) (Asiago, Italy) of the INAF - Osservatorio Astronomico di Padova. Part of this work is based on archival data, software or online services provided by the ASI SCIENCE DATA CENTER (ASDC).\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nPositive feedback is ubiquitous in biochemical networks and can lead to a bifurcation from a monostable to a bistable cellular state \\cite{mitrophanov2008positive, tkavcik2012optimizing, das2009digital, vogel2016dichotomy}. Near the bifurcation point, the bistable state often reflects a choice between two accessible but opposing cell fates. For example, in T cells, the distribution of doubly phosphorylated ERK (ppERK) can be bimodal \\cite{vogel2016dichotomy}. ppERK is a protein that initiates cell proliferation and is implicated in the self\/non-self decision between mounting an immune response or not \\cite{vogel2016dichotomy, altan2005modeling}.\n\nThe bifurcation point is similar to an Ising-type critical point in physical systems such as fluids, magnets, and superconductors, where a disordered state transitions to one of two ordered states at a critical temperature \\cite{goldenfeld1992lectures}. In fact, universality tells us that the two should not just be similar, they should be the same: because they are both bifurcating systems, both types of systems should exhibit the same critical scaling exponents and therefore belong to the same universality class \\cite{goldenfeld1992lectures}. Although this powerful idea has allowed diverse physical phenomena to be united into specific behavioral classes, the application of universality to biological systems is still developing \\cite{mora2011biological, munoz2018colloquium, salman2012universal, brenner2015universal, pal2014non, ridden2015entropy, qian2016framework, hidalgo2014information}.\n\nBiological tools such as flow cytometry, fluorescence microscopy, and RNA sequencing allow reliable experimental estimates of abundance distributions, inspiring researchers to seek to apply insights from statistical physics to biological data. In particular, recent studies have demonstrated that biological systems on many scales, from molecules \\cite{mora2010maximum}, to cells \\cite{kastner2015critical, krotov2014morphogenesis, de2017critical, chen2012scale, aguilar2018critical, wan2018time}, to populations \\cite{bialek2014social, attanasi2014finite, cavagna2017dynamic}, exhibit signatures consistent with physical systems near a critical point. However, some of these studies have come under scrutiny because some of the signatures, particularly scaling laws, can arise far from or independent of a critical point \\cite{schwab2014zipf, touboul2017power, newman2005power}. Part of the problem is that the identification of appropriate scaling variables from data can be ambiguous, and one is often left looking for scaling relationships in an unguided way. \n\nTypical approaches to the interpretation of abundance distributions include fitting to either detailed mechanistic models of the underlying reaction scheme, or to an effective description of the data such as a Gaussian or lognormal mixture model. The former approach is usually difficult to parameterize and difficult to generalize to other systems. The latter approach often suffers from numerical issues (the likelihood is unbounded and the expectation-maximization algorithm can lead to spurious solutions \\cite{biernacki2003choosing}). Moreover, the vicinity of a bifurcation point is precisely where a mixture analysis is most likely to fail. In contrast, mapping to a statistical physics framework is expected to be universal, in the sense that the precise microscopic details of a broad range of biochemical models are unimportant near the bifurcation point, as they are coarse-grained rather than particular reaction parameters.\n\nHere we provide a framework for mapping well-mixed stochastic models of biochemical feedback to the mean-field Ising model and apply it to published data on T cells. This allows us to extract effective thermodynamic quantities from experimental data without needing to fit to a parametric model of the system. This makes the theory applicable to a broad class of biological datasets without worrying about model selection or goodness-of-fit criteria. The theory provides insights on how T cells respond to drugs and reveals distinctions between one type of drug response and another. Furthermore, we find that one of the thermodynamic quantities (the heat capacity) provides a novel way to estimate absolute molecule number from fluorescence level in bifurcating systems. We demonstrate that our results can be extended to cases where feedback is indirect and discuss further extensions, including to spatiotemporal dynamics.\n\n\n\\section{Results}\n\nWe consider a reaction network in a cell where $X$ is the molecular species of interest, and the other species $A$, $B$, $C$, etc.\\ form a chemical bath for $X$ [Fig.\\ \\ref{fig:setup}(a)]. The reactions of interest produce or degrade an $X$ molecule, can involve the bath species, and in principle are reversible. We allow for nonlinear feedback on $X$, meaning that the production of an $X$ molecule in a particular reaction might require a certain number of $X$ molecules as reactants. This leads to an arbitrary number of reactions of the form\n\\begin{equation}\n\\label{eq:rxns}\nj_rX + Y_r^+ \\xrightleftharpoons[k_r^-]{k_r^+} (j_r+1)X + Y_r^-,\n\\end{equation}\nwhere in the $r$th reaction, $j_r$ are stoichiometric integers describing the nonlinearity, $k_r^\\pm$ are the forward ($+$) and backward ($-$) reaction rates, and $Y_r^\\pm$ represent bath species involved as reactants ($+$) or products ($-$). A simple and well-studied special case of Eq.\\ \\ref{eq:rxns} is Schl\\\"ogl's second model \\cite{schlogl1972chemical, dewel1977renormalization, nicolis1980systematic, brachet1981critical, grassberger1982phase, prakash1997dynamics, liu2007quadratic, vellela2009stochastic}, in which $X$ is either produced spontaneously from bath species $A$, or in a trimolecular reaction from two existing $X$ molecules and bath species $B$ (i.e., $R = 2$, $j_1 = 0$, $j_2 = 2$, $Y_1^+ = A$, $Y_2^+ = B$, and $Y_1^- = Y_2^- = \\emptyset$).\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=\\linewidth]{fig1}\n\\caption{Setup and behavior of the model. (a) We consider well-mixed stochastic biochemical networks described by an effective feedback function $f_n$. (b) Feedback produces either one or two stable steady states. (c) The molecule number distribution is peaked around these states or flat at the bifurcation point. (d) Mapping to the Ising model reveals that the effective reduced temperature drives the distribution to the unimodal ($\\theta > 0$) or bimodal ($\\theta < 0$) state (see c), while the effective field $h$ biases the distribution toward high ($h > 0$) or low ($h < 0$) molecule number. Parameters: $H=3$ and $n_c = 100$ in b, c, and d; $h = 0$ in b and c; and $\\theta = 0$ in d (see also Appendix \\ref{app:param}).}\n\\label{fig:setup}\n\\end{figure}\n\nWe assume that molecules are well-mixed and that the numbers of bath molecules are constant. The latter assumption is equivalent to integrating out all species but $X$, such that the feedback on $X$ arises directly from $X$ itself (Eq.\\ \\ref{eq:rxns}). However, in general the feedback will be indirect, with $X$ regulating dynamic species in the bath that in turn regulate $X$ (this is almost certainly the case in the T cells we study here). Therefore, we consider this more general case later in Section \\ref{sec:indirect} and show that the results discussed below remain unchanged.\n\nThe master equation for the probability of observing $n$ molecules of species $X$ according to Eq.\\ \\ref{eq:rxns} is\n\\begin{equation}\n\\label{eq:me}\n\\dot{p}_n = b_{n-1}p_{n-1} + d_{n+1}p_{n+1} - (b_n + d_n)p_n,\n\\end{equation}\nwhere $b_n = \\sum_{r=1}^R J_{rn}^+$ and $d_n = \\sum_{r=1}^R J_{rn}^-$ are the total birth and death propensities, and $J_{rn}^+ = k_r^+ n_r^+ n!\/(n-j_r)!$ and $J_{rn}^- = k_r^- n_r^- n!\/(n-j_r-1)!$ are the forward and backward propensities of each reaction pair. Here $n_r^\\pm$ are the numbers of molecules of the bath species involved in reaction $r$, and the factorials account for the number of ways that $X$ molecules can meet in a reaction. The steady state of Eq.\\ \\ref{eq:me} is \\cite{van1992stochastic, gardiner1985handbook}\n\\begin{equation}\n\\label{eq:pn}\np_n = p_0 \\prod_{j = 1}^n \\frac{b_{j-1}}{d_j} = \\frac{p_0}{n!} \\prod_{j=1}^n f_j,\n\\end{equation}\nwhere $p_0^{-1} = \\sum_{n=0}^\\infty(1\/n!)\\prod_{j=1}^n f_j$ is set by normalization. In the second step of Eq.\\ \\ref{eq:pn} we define an effective birth propensity $f_n \\equiv nb_{n-1}\/d_n$ corresponding to spontaneous death with propensity $n$ [Fig.\\ \\ref{fig:setup}(a)]. In general, $f_n$ is an arbitrary, nonlinear feedback function governed by the reaction network. For the Schl\\\"ogl model, it is $f_n = [aK^2 + s(n-1)(n-2)]\/[(n-1)(n-2)+K^2]$, where we have introduced the dimensionless quantities $a \\equiv k_1^+n_A\/k_1^-$, $s \\equiv k_2^+ n_B\/k_2^-$, and $K^2 \\equiv k_1^-\/k_2^-$. As a ubiquitous example we also consider the Hill function $f_n = a + sn^H\/(n^H+K^H)$ with coefficient $H$. Importantly, the inverse of Eq.\\ \\ref{eq:pn},\n\\begin{equation}\n\\label{eq:fn}\nf_n = \\frac{np_n}{p_{n-1}},\n\\end{equation}\nallows calculation of the feedback function from the distribution \\cite{walczak2009stochastic}, as utilized when analyzing the experimental data later in Section \\ref{sec:immune}.\n\nThe quantity $f_n-n$ determines the dynamic stability: there can be either one or two stable states $n_*$ [Fig.\\ \\ref{fig:setup}(b)], and the transition from a monostable to a bistable regime occurs at a bifurcation point [Fig.\\ \\ref{fig:setup}(c) inset]. These deterministic regimes correspond stochastically to unimodal and bimodal distributions $p_n$, respectively, with maxima at $n_*$, while the bifurcation point corresponds to a distribution that is flat on top [Fig.\\ \\ref{fig:setup}(c)].\n\n\n\\subsection{Ising mapping and scaling exponents}\n\nTo understand the scaling behavior near the bifurcation point, we expand the stability condition $f_{n_*}-n_*=0$ to third order around a point $n_c$ satisfying $f''_{n_c} = 0$. This choice of $n_c$ eliminates the quadratic term in the dynamic forcing $f_n-n$, equivalent to eliminating the cubic term in an effective potential as in Ginzburg--Landau theory \\cite{kopietz2010introduction}. Defining the parameters\n\\begin{equation}\n\\label{eq:cparam}\nm \\equiv \\frac{n_*-n_c}{n_c}, \\quad\nh \\equiv \\frac{2(f_{n_c} - n_c)}{-f'''_{n_c}n_c^3}, \\quad\n\\theta \\equiv \\frac{2(1-f'_{n_c})}{-f'''_{n_c}n_c^2},\n\\end{equation}\nthe expansion $f_{n_c} + f'_{n_c}(n_*-n_c) + f'''_{n_c}(n_*-n_c)^3\/3! - n_* = 0$ becomes $h - \\theta m - m^3\/3 = 0$. This expression is equivalent to the expansion of the Ising mean field equation $m = \\tanh[(m+h)\/(1+\\theta)]$ for small magnetization $m$, where $\\theta = (T-T_c)\/T_c$ is the reduced temperature, and $h$ is the dimensionless magnetic field \\cite{kopietz2010introduction}. Therefore, in our system we interpret $m$ as the order parameter, $\\theta$ as an effective reduced temperature, and $h$ as an effective field. Explicit expressions for $n_c$, $\\theta$, and $h$ in terms of the biochemical parameters and vice versa are given for the Schl\\\"ogl and Hill models in Appendix \\ref{app:param}.\n\nWe see in Fig.\\ \\ref{fig:setup}(c) and (d) that $n_c$ determines where the distribution is centered, that $\\theta$ drives the system to the unimodal ($\\theta > 0$) or bimodal ($\\theta < 0$) state, and that $h$ biases the system to high ($h > 0$) or low ($h < 0$) molecule numbers. Note that unlike in the Ising model, even when $h=0$ an asymmetry persists between the high and low states [see the purple distribution in Fig.\\ \\ref{fig:setup}(c)]. The reason is that in the master equation (Eq.\\ \\ref{eq:me}), unlike in Ginzburg--Landau theory, fluctuations scale with molecule number, such that the high state is wider than the low state.\n\nThe equivalence between our system and the Ising mean-field equation near the critical point (Eq.\\ \\ref{eq:cparam}) implies that our system has the same scaling exponents $\\beta=1\/2$, $\\gamma=1$, and $\\delta=3$ as the Ising universality class in its mean-field limit \\cite{kopietz2010introduction}. For completeness, we verify in Appendix \\ref{app:scalings} that these scalings are indeed obeyed by the Schl\\\"ogl and Hill models.\n\nHowever, Eq.\\ \\ref{eq:cparam} does not explicitly determine the value of the exponent $\\alpha$. The reason is that, unlike $\\beta$, $\\gamma$, and $\\delta$, the exponent $\\alpha$ depends on the entire distribution $p_n$, not just the maxima. Specifically, $\\alpha$ concerns the heat capacity, $C|_{h=0}\\sim |\\theta|^{-\\alpha}$, which depends on the entropy $S$ and thus $p_n$. The equilibrium definition $C = T\\partial_T S$ generalizes to a nonequilibrium system like ours when one uses the Shannon entropy $S = -k_{\\rm B}\\sum_n p_n \\log p_n$ \\cite{mandal2013nonequilibrium}. Since $T = (1+\\theta)T_c$, we have $C = (1+\\theta)\\partial_\\theta S$, or\n\\begin{equation}\n\\label{eq:C}\n\\frac{C}{k_{\\rm B}} = -(1+\\theta)\\sum_{n=0}^\\infty p_n (1+\\log p_n) \\Bigg( \\psi_n - \\sum_{j=0}^\\infty p_j \\psi_j \\Bigg),\n\\end{equation}\nwhere $\\psi_n \\equiv (1\/2)f'''_{n_c}n_c^2\\sum_{j=1}^n(j-n_c)\/f_j$. Eq.\\ \\ref{eq:C} follows from performing the $\\theta$ derivative using the expression in Eq.\\ \\ref{eq:pn}, the expansion below Eq.\\ \\ref{eq:cparam}, and the definition of $\\theta$ (Eq.\\ \\ref{eq:cparam}). We see in Fig.\\ \\ref{fig:heat1}(a) that when $h=0$, $C$ exhibits a minimum at $\\theta^*$. We see in Fig.\\ \\ref{fig:heat1}(b) that $\\theta^*$ vanishes as the system size increases, $n_c\\to\\infty$. This implies that $C|_{h=0}\\sim |\\theta|^0$ to sub-quadratic order in $\\theta$, or $\\alpha = 0$, again consistent with the Ising universality class in its mean-field limit. Interestingly, whereas $C$ is discontinuous in the mean-field Ising model \\cite{kopietz2010introduction} and constant in the van der Waals model of a fluid \\cite{goldenfeld1992lectures}, it is minimized here; nevertheless, in all cases $\\alpha = 0$. Note from Fig.\\ \\ref{fig:heat1}(a) that $C$ is negative near $\\theta = 0$; negative heat capacity is a well-known feature of nonequilibrium steady states \\cite{zia2002getting, boksenbojm2011heat, bisquert2005master}.\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=\\linewidth]{fig2}\n\\caption{(a) Heat capacity (Eq.\\ \\ref{eq:C}) is minimized at the bifurcation point, corresponding to exponent $\\alpha = 0$. (b) The location of the minimum approaches $\\theta^*\\to0$ as $n_c\\to\\infty$, as expected. Parameters: $H=3$, $n_c = 500$, and $h=0$.}\n\\label{fig:heat1}\n\\end{figure}\n\n\n\n\\subsection{Application to immune cell data}\n\\label{sec:immune}\n\nTo demonstrate the utility of our theory, we apply it to published data from T cells \\cite{vogel2016dichotomy}. In these experiments, chemotherapy drugs inhibit the enzymes MEK and SRC in the biochemical networks of the cells. The inhibition results in bimodal (low dose) or unimodal (high dose) distributions of ppERK abundance, which is measured as fluorescence intensity $I$ by flow cytometry. The distributions are shown for a range of drug doses in Fig.\\ \\ref{fig:experiment}(a) and (b) (the insets show distributions of log intensity for clarity). Experimental details are given in the original publication \\cite{vogel2016dichotomy} and are summarized in Appendix \\ref{app:expt}, along with the drugs and dose amounts.\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=\\linewidth]{fig3}\n\\caption{Application of the theory to immune cell data. Upon administration of either (a) MEK or (b) SRC inhibitor, experimental distributions of T cell ppERK fluorescence intensity are unimodal (bimodal) for high (low) doses. Insets show distributions of log intensity for clarity. (c, d) Feedback functions calculated from the experimental distributions correspondingly exhibit either one or two stable states. (e-g) Effective thermodynamic quantities calculated from the data vary with drug dose in distinct ways for each drug. The results in (c-g) corroborate those in \\cite{vogel2016dichotomy}, but with a much simpler framework that has three parameters instead of five and requires no fitting or prior biological knowledge of the system. Error bars: standard error from filter windows $25 \\le W \\le 35$ (see Appendix \\ref{app:analysis}).}\n\\label{fig:experiment}\n\\end{figure}\n\nFirst, we compute the feedback function $f$ from each distribution using Eq.\\ \\ref{eq:fn} (see Appendix \\ref{app:analysis}). Fig.\\ \\ref{fig:experiment}(c) and (d) show the corresponding forcing functions [compare to Fig.\\ \\ref{fig:setup}(b)]. As expected, in each case we see that the forcing function transitions from two stable states to one stable state as the drug is applied.\n\nThen, we compute $I_c$ (the analog of $n_c$ in units of fluorescence intensity), $\\theta$, and $h$ from the feedback function using Eq.\\ \\ref{eq:cparam} (see Appendix \\ref{app:analysis}). These quantities are shown as a function of drug dose in Fig.\\ \\ref{fig:experiment}(e)-(g). We see that the behavior is different depending on whether MEK inhibitor (MEKi) or SRC inhibitor (SRCi) is applied. Specifically, MEKi decreases $I_c$, increases $\\theta$, and decreases $h$; whereas SRCi only decreases $h$, leaving the other quantities unchanged. Thus, the effective thermodynamic quantities can differentiate cellular responses to different perturbations, such as the application of different drugs.\n\nFurthermore, the mapping provides an intuitive interpretation of the drug responses. MEKi causes a transition from a bimodal to a unimodal state in the expected way: by increasing the reduced temperature $\\theta$ from a negative to a positive value [Fig.\\ \\ref{fig:experiment}(f)]. In the process, $I_c$ decreases [Fig.\\ \\ref{fig:experiment}(e)], meaning that the unimodal state is shifted to lower molecule number, near the lower mode of the bimodal state [Fig.\\ \\ref{fig:experiment}(a) inset]. In contrast, SRCi causes a transition from a bimodal to a unimodal state in a different way: by decreasing the field while leaving $\\theta$ and $I_c$ unchanged [Fig.\\ \\ref{fig:experiment}(e)-(g)]. In essence, the distribution remains bimodal and unshifted, except that the field causes the high mode to diminish in weight [Fig.\\ \\ref{fig:experiment}(b) inset]. Interestingly, the mean dose-response curves are similar for the two drugs \\cite{vogel2016dichotomy}, but our mapping elucidates precisely how the transitions are different at the distribution level. Related conclusions were drawn in \\cite{vogel2016dichotomy}, but those conclusions relied on fitting the distributions to a five-parameter Gaussian mixture model, which is expected to fail near the bifurcation point. Here we use only three parameters and no fitting, and we emerge with an intuitive interpretation in terms of thermodynamic quantities.\n\nFinally, we note that for both drugs the effective field is negative at all doses [Fig.\\ \\ref{fig:experiment}(g)]. The reason is that the fluorescence distributions have long tails (which is why they are often easier to visualize in log space); see Fig.\\ \\ref{fig:experiment}(a) and (b). In the theory, a long tail is indistinguishable from a low-molecule-number bias in the peak, which corresponds to $h < 0$. We address the possible origins and implications of the long tails in the Discussion (Section \\ref{sec:discussion}).\n\n\n\\subsection{Estimation of molecule number}\n\nWe now apply the theory to compute the heat capacity from the T cell data.\nSpecifically, we compute $C$ using Eq.\\ \\ref{eq:C} (see Appendix \\ref{app:analysis}) for all drugs and doses used in the experiments \\cite{vogel2016dichotomy} (Appendix \\ref{app:expt}). Unlike the other thermodynamic quantities, $C$ requires a conversion from fluorescence intensity to molecule number because it depends explicitly on the distribution $p_n$ (Eq.\\ \\ref{eq:C}). Therefore we compute $C$ for various values of the conversion factor $I_1$, where $n = I\/I_1$. The results are shown in Fig.\\ \\ref{fig:heat2}. We see that irrespective of $I_1$ over four orders of magnitude, the data closest to $h=0$ (yellow) exhibit a global minimum in $C$ at $\\theta = 0$, as expected from Fig.\\ \\ref{fig:heat1}(a). However, we also see that the depth of the minimum agrees with that of the theory only for the particular choice $I_1 \\approx 0.1$ [Fig.\\ \\ref{fig:heat2}(c)].\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=\\linewidth]{fig4}\n\\caption{Estimation of molecule number by comparing heat capacity between theory and experiments. (a-d) Rough estimate of fluorescence-to-molecule-number conversion factor $I_1$ (see titles) obtained by comparing depths of theory and experimental minima. ``Hill'' refers to the theoretical curve produced by Hill-function feedback as in Eq.~\\ref{eq:Hill}. Different symbols correspond to different drugs. See Appendix \\ref{app:expt} for drugs (shape) and doses (size). (e) More precise estimate obtained from plotting sum of squared errors (SSE) for data within $-\\Delta\\theta \\le \\theta \\le \\Delta\\theta$ and fitting to parabola (see Appendix D for details). Here $\\Delta\\theta = 0.05$. (f) Estimate is insensitive to value of $\\Delta\\theta$. Theory parameters: $H = 4$, $h = 0$, and $n_c = \\bar{I}_c\/I_1$, where $\\bar{I}_c = 730$ is the average value across all experiments.}\n\\label{fig:heat2}\n\\end{figure}\n\nTo obtain a more precise estimate of $I_1$, we plot the sum of squared errors between the data and the theory as a function of $I_1$ in Fig.\\ \\ref{fig:heat2}(e). We focus on the bifurcation region by considering only values of $\\theta$ within $-\\Delta\\theta \\le \\theta \\le \\Delta\\theta$, and we find that our results are not sensitive to the choice of $\\Delta\\theta$ [Fig.\\ \\ref{fig:heat2}(f)]. This procedure (see the details in Appendix \\ref{app:analysis}) results in an estimate of $I_1 = 0.5$ $\\pm$ $0.2$, as seen in Fig.\\ \\ref{fig:heat2}(f). This value of $I_1$ corresponds to $\\bar{n}_*=$ 170,000 $\\pm$ 70,000 ppERK molecules in the high mode averaged across all cases with no inhibitor. It is possible to compare this value with previous measurements on these cells. In two separate experiments, it was estimated that there are approximately 100,000 \\cite{altan2005modeling} and 214,000 \\cite{hukelmann2016cytotoxic} ERK molecules per cell, and that only about 50\\% of these molecules are doubly phosphorylated during T cell receptor activation \\cite{altan2005modeling} (see Appendix \\ref{app:analysis}). These considerations give a range of roughly 50,000$-$107,000 ppERK molecules, which is consistent with our estimate of 170,000 $\\pm$ 70,000. The agreement is especially notable given that T cell protein abundances generally span six orders of magnitude, from tens to tens of millions of molecules per cell \\cite{hukelmann2016cytotoxic}.\n\nWhy does the heat capacity extract the conversion between fluorescence intensity and molecule number? As mentioned above, $\\alpha$ is the only exponent that is a function of $p_n$ instead of just its maxima. This means that the plot of $C$ vs.\\ $\\theta$ contains information not only about means or modes, but also about fluctuations. The notion that fluctuation information is essential for converting from intensity to molecule number can be seen with a simpler example: a Poisson distribution. Here we would have $\\sigma_I^2\/\\bar{I}^2 = \\sigma_n^2\/\\bar{n}^2 = 1\/\\bar{n} = I_1\/\\bar{I}$. From this relation it is clear that information about not only the mean ($\\bar{I}$) but also the fluctuations ($\\sigma_I^2$) in intensity is necessary and sufficient to infer the conversion factor $I_1$. In our case, the heat capacity is extracting similar information, but for a bifurcating system.\n\n\n\\subsection{Generalization to indirect feedback}\n\\label{sec:indirect}\n\nIn the T cells, it is well known that ppERK does not apply feedback to its own activation directly, but rather indirectly via upstream components \\cite{vogel2016dichotomy, shin2009positive, altan2005modeling}. Therefore, we seek to determine the extent to which the above results are sensitive to our assumption in the theory that the feedback is direct. To this end, we construct a minimal extension of the model in Eq.\\ \\ref{eq:rxns} in which the feedback is indirect:\n\\begin{align}\n& \\emptyset \\xrightleftharpoons[k_2]{k_1} X, \\qquad\n2X \\xrightleftharpoons[k_4]{k_3} D, \\nonumber \\\\\n& D \\xrightarrow{k_5} D + A, \\qquad\nA \\xrightarrow{k_6} A + X, \\qquad\nA \\xrightarrow{k_7} \\emptyset, \\nonumber \\\\\n\\label{eq:indirect}\n& D \\xrightarrow{k_8} D + B, \\qquad\nB + X \\xrightarrow{k_9} B, \\qquad\nB \\xrightarrow{k_{10}} \\emptyset.\n\\end{align}\nHere $X$ is produced, is degraded, and reversibly dimerizes (first line); the dimer $D$ produces a species $A$ that produces $X$ and is degraded (second line); and the dimer also produces a species $B$ that degrades $X$ and is degraded (third line). Eq.\\ \\ref{eq:indirect} is an extension of Eq.\\ \\ref{eq:rxns} because there are multiple stochastic variables ($X$, $D$, $A$, and $B$), there are irreversible reactions, and $X$ feeds back on itself indirectly through $D$, $A$, and $B$ instead of directly.\n\nThe deterministic steady state of Eq.\\ \\ref{eq:indirect} is\n\\begin{equation}\n\\label{eq:multidet}\n0 = \\dot{n}\/k_2 = c_0 - n_* + c_2 n_*^2 - c_3 n_*^3,\n\\end{equation}\nwhere $c_0 \\equiv k_1\/k_2$, $c_2 \\equiv k_3k_5k_6\/(k_2k_4k_7)$, $c_3 \\equiv k_3k_8k_9\/(k_2k_4k_{10})$, and the molecule numbers of $D$, $A$, and $B$ have been eliminated in favor of $n_*$ by setting their own time derivatives to zero. Because Eq.\\ \\ref{eq:multidet} is cubic in $n_*$, we see immediately that it has the same form as the expanded Ising mean field equation $h - \\theta m - m^3\/3 = 0$ (see Eq.\\ \\ref{eq:cparam}). Specifically, defining $m = (n_*-n_c)\/n_c$ as in Eq.\\ \\ref{eq:cparam}, the choice $n_c = c_2\/(3c_3)$ eliminates the term quadratic in $m$ and implies $\\theta = 3c_3\/c_2^2 - 1$ and $h = 9c_0c_3^2\/c_2^3 - 3c_3\/c_2^2 +2\/3$. It immediately follows that this model has the same exponents $\\beta=1\/2$, $\\gamma=1$, and $\\delta=3$ as the mean-field Ising universality class.\n\nTo test whether the heat capacity for this model exhibits the same features as that for the direct feedback model in Fig.\\ \\ref{fig:heat1}(a), we compute the steady state marginal distribution $p_n$ using stochastic simulations \\cite{gillespie1977exact} of Eq.\\ \\ref{eq:indirect}. Specifically, we set $k_3\/k_4 = 1\/n_c$ and $k_5\/k_7 = k_8\/k_{10} = 1$ to ensure that the numbers of $D$, $A$, and $B$ molecules, respectively, are on the order of $n_c$. We then set $k_4\/k_2 = k_7\/k_2 = k_{10}\/k_2 = \\rho$, where $\\rho$ is a free parameter that determines whether the degradation timescales of $D$, $A$, and $B$, respectively, are faster ($\\rho > 1$) or slower ($\\rho < 1$) than that of $X$. These conditions, along with the definitions of $n_c$, $\\theta$, and $h$ above, constitute nine equations for nine reaction rates, plus $k_2$ which sets the units of time. Solving these equations yields expressions for the rates in terms of $n_c$, $\\theta$, $h$, and $\\rho$ that we use in the simulations.\n\nFig.\\ \\ref{fig:indirect}(a) shows the heat capacity $C$ as a function of $\\theta$ for $h = 0$, $n_c = 100$, and $\\rho = \\{0.1, 1, 10\\}$, where $C = (1+\\theta)\\partial_\\theta S$ is computed from the entropy $S = -k_{\\rm B}\\sum_n p_n \\log p_n$ by numerical derivative. We see that for all $\\rho$ values, the curves exhibit a minimum at $\\theta = 0$, implying $\\alpha = 0$, and they rise more steeply for negative than for positive $\\theta$ as in Fig.\\ \\ref{fig:heat1}(a).\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=\\linewidth]{fig5}\n\\caption{Verification that indirect feedback does not qualitatively change modeling assumptions or results. (a) $C$ and $\\theta$ calculated from extended model with indirect feedback. (b) $C$ and $\\theta$ inferred assuming the feedback is direct (Eq.\\ \\ref{eq:fn}). Compare with Fig.\\ \\ref{fig:heat1}(a). Parameters: $n_c=100$ and $h=0$.}\n\\label{fig:indirect}\n\\end{figure}\n\nWe then investigate whether Eq.\\ \\ref{eq:rxns} remains valid as a coarse-grained description of the extended model in Eq.\\ \\ref{eq:indirect}. To answer this question, we infer values of $n_c$, $\\theta$, $h$, and $C$ directly from the simulation data $p_n$ using the same protocol as for the experimental data. That is, we compute $f_n$ via Eq.\\ \\ref{eq:fn}, and then compute $\\theta$, $h$, and $C$ from its derivatives at $n_c$ according to Eqs.\\ \\ref{eq:cparam} and \\ref{eq:C}, where $n_c$ satisfies $f''_{n_c} = 0$. As with the experimental data (see Appendix \\ref{app:analysis}), derivatives are calculated using a Savitsky-Golay filter \\cite{savitzky1964smoothing}, although here we apply the filter directly to $f_n$ and perform the analysis directly in $n$ space, not log space.\n\nFig.\\ \\ref{fig:indirect}(b) shows the result of this procedure for the inferred heat capacity $C$ as a function of the inferred $\\theta$. We see that, as with the exact $C$ and $\\theta$ [Fig.\\ \\ref{fig:indirect}(a)], the data exhibit a minimum at $\\theta = 0$ and rise more steeply for negative than for positive $\\theta$. Note that the values of $C$ and $\\theta$ are different in (a) and (b), which is expected because the shape of $p_n$ is not quantitatively the same in the two models of Eqs.\\ \\ref{eq:rxns} and \\ref{eq:indirect}; nonetheless, the shape of the $C$ vs.\\ $\\theta$ curves remains the same. We have checked that the inferred values of $n_c$ and $h$ are distributed around their known values of $100$ and $0$, respectively, and that the shape persists across a range of filter window sizes.\n\nThese results suggest that the main findings above are not sensitive to our assumption that feedback is direct, and therefore that we are justified in using Eq.\\ \\ref{eq:rxns} as a coarse-grained model to analyze the T cell data.\n\n\n\\section{Discussion}\n\\label{sec:discussion}\n\nWe have employed the fact that a feedback-induced bifurcation exhibits the scaling properties of the mean-field Ising universality class to provide a simple prescription for modeling and analyzing biological data. Contrary to existing mixture-model approaches, our method is most valuable near the bifurcation point, which is where biologically significant cell-fate decisions are expected to take place. Our approach provides the effective order parameter, reduced temperature, magnetic field, and heat capacity from experimental distributions without fitting or needing to know the molecular details. By applying the approach to T cell flow cytometry data, we discovered that these quantities discriminate between cellular responses in an intuitive, interpretable way, and that the heat capacity allows estimation of the molecule number from fluorescence intensity for a bifurcating system. By generalizing the theory to include indirect feedback, we demonstrated the capacity to model realistic signaling cascades where indirect feedback is common. Our approach should be applicable to other systems observed to undergo a pitchfork-like bifurcation and the associated unimodal-to-bimodal transition in abundance distributions, but not to systems which have an absorbing or extinction state, as they are expected to fall under a different universality class \\cite{ohtsuki1987nonequilibrium, grassberger1978reggeon}.\n\nThe theory assumes only birth-death reactions and neglects more complex mechanisms such as bursting \\cite{friedman2006linking, mugler2009spectral} or parameter fluctuations \\cite{shahrezaei2008colored, horsthemke1984noise}. These mechanisms are known to produce long tails and may be responsible for the long tails observed in the experimental data [Fig.\\ \\ref{fig:experiment}(a) and (b)]. Cell-to-cell variability (CCV) may also contribute to the long tails, as it is known to be present in T cell populations \\cite{cotari2013cell}. Our theory neglects CCV and instead assumes that the distribution of molecule numbers across the population is the same as that traced out by a single cell over time. Although CCV may play an important role, one generically expects the role of intrinsic fluctuations to be amplified near a critical point, and models that ignore CCV have been shown to be sufficient to explain both the bimodality \\cite{das2009digital} and variance properties \\cite{prill2015noise} of ppERK in T cells. Moreover, the fact that our theory provides an estimate of the molecule number that is consistent with other estimates suggests that intrinsic fluctuations play a large role. Distinguishing between intrinsic fluctuations and long-lived CCV is an important topic for future work.\n\nOur work provides key tools that can be used for a broader exploration of biological systems. The approach is applicable to any experimental dataset that exhibits unimodal and bimodal abundance distributions, and could lead to a unified picture of diverse cell types and environmental perturbations in terms of effective thermodynamic quantities. At the same time, several extensions of our work are natural. For example, the dynamics of the theory could be probed to investigate the consequences of critical slowing down for driven or dynamically perturbed systems with feedback. Alternatively, the theory could be generalized to systems that are not well-mixed, such as intracellular compartments or communicating populations, to investigate space-dependent universal behavior and its biological implications.\n\n\n\n\\section{Data availability}\nData and code for all figures and the MIFlowCyt record are available at\\\\\n\\url{https:\/\/github.com\/AmirErez\/UniversalImmune}.\n\n\n\n\\section*{Acknowledgments}\nThis work was supported by Human Frontier Science Program grant LT000123\/2014 (Amir Erez), National Institutes of Health (NIH) grant R01 GM082938 (A.E.), Simons Foundation grant 376198 (T.A.B.\\ and A.M.), and the Intramural Research Program of the NIH, Center for Cancer Research, National Cancer Institute.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:Introduction}\nThe use of Low Temperature Detectors (LTDs) for sensing \n X-ray and $\\gamma$-ray signals is quite widespread and well \nestablished~\\cite{Ullom-2015}.\nLTDs are also widely used in the field of fundamental physics, especially for \nDouble Beta Decay (DBD), and Dark Matter (DM) searches~\\cite{Pirro-2017}.\nIn these surveys the need for a hybrid detector, in which an energy \nrelease can be measured through different mechanisms, is of primary importance in order \nto distinguish the nature of interacting particles. For instance hybrid detectors can help identify \nand reject events caused by the natural background. With thermal detectors this can be \nachieved using scintillating or luminescent crystals. The simultaneous and \n\\it independent \\rm readout of the heat and the (escaping) light produced by the \ninteraction reveals the nature of the interacting particles thanks to \nthe different scintillation yields of $n$, $\\alpha$ and $\\gamma\/\\beta$ events.\nThis discrimination technique is presently used for DM \nsearches~\\cite{CRESST-2016,Angloher:2017sft,Angloher:2016hbv}, \nDBD searches~\\cite{CUPID-0-2018,Cupid-Mo-2017,Amore-2017}, and it can be \nalso implemented for rare nuclear decays~\\cite{Alfa-1,Alfa-2,Pattavina:2018nhk}.\n\nAt milli-Kelvin temperatures, the light detectors are usually bolometers \nthemselves: a \\it dark \\rm thin crystal absorbs the photons, producing heat (phonons) that is\nmeasured by a suitable thermometer. \nThe main difference among the various Bolometric Light Detector (BLD) instruments currently in use \nis the choice of the thermometer element, e.g. Transition Edge Sensors \n(TES)~\\cite{TES_LD_CRESST}, Neutron Transmutation Doped (NTD) \nthermistors~\\cite{NTD_LD_Lucifer-2013} or Micro Magnetic Calorimeters (MMC)~\\cite{MMC_LD-2015}. \n\nThe work presented here was performed within the CUPID \nframework~\\cite{CUORE-IHE-2014,CUPID-2015}, the future follow up of \nCUORE~\\cite{CUORE-2018} that represents the largest world-wide bolometric experiment to date. \nThe aim was to develop NTD-based BLDs with improved performance in terms of \nsensitivity, time response and simplified packaging for large arrays. \nUsing the tiny Cherenkov light emission of TeO$_2$~\\cite{Tabarelli-2010,Enriched-TeO2-Cherenkov-2017} to \ndecrease by two order of magnitude the $\\alpha$-induced background, requires a BLD with a S\/N ratio \nof the order of $\\sim$5~\\cite{CUPID-2015}: this corresponds to a RMS baseline resolution of the BLD of the \norder of $\\sim$20~eV being the Cherenkov light signal of the order of 100 eV.\nActually one can work towards the optimization of the light collection~\\cite{Casali-2017} and\/or \ntowards the energy resolution of the BLD or -as we made in this work- both.\nAdditionally, in case of $^{100}$Mo-based compounds, beside the same need to suppress the surface \n$\\alpha$-induced background, a fast time response of the BLD ($\\leq$~1~ms) is mandatory to suppress\nthe background induced the pile-up of the 2$\\nu$ DBD~\\cite{2_nu-Pile_up-2012}: also in \nthis case the S\/N ratio will play an important role~\\cite{2_nu-Pile_up-2016}.\n\n\n\nOur work has therefore focused on two aspects of BLD performance: (1) improving the response of \nthe NTD thermometer and (2) increasing the light collection.\nWhile the first aspect is strictly related to a specific technique, the second aspect \nis worthy of additional remarks. The working principle of a BLD is \nirrespective of the sensor: a thin crystal wafer (usually Si or Ge) absorbs \nthe emitted photons and converts them into heat. \nUnlike a conventional bolometric approach, we have to avoid the optical coupling \nbetween crystal and BLD made with optical grease or similar substance since the unavoidable \nheat flow through the optical coupling and the increase of the heat capacity of the system would reduce the \nindependence of the two detectors, eliminating the possibility of particle \ndiscrimination afforded by the different scintillation yields. Therefore the thermal contact between \nthe luminescent crystal and BLD has to be avoided,\nespecially in the case of extremely low scintillation yields. This is true for most \nof the Mo-based compounds~\\cite{Cupid-Mo-2017} and, even more importantly in case of \nCherenkov signals. A 2615~keV $\\gamma$-ray energy release in a CUORE-like TeO$_2$ absorber \nproduces a light signal in the BLD on the order of $\\sim$100~eV~\\cite{Casali-2017}. \nFor this reason the BLD is always facing the scintillating crystal without directly contacting \nit via a coupling medium.\n \nIn the following section it is shown that if the BLD is simply resting on the crystal \nsurface, held in position only by gravity, the thermal coupling between the BLD and \nthe crystal is almost negligible and the leakage of the BLD thermal signal through the scintillating crystal vanishes. \nThis fact can be explained considering the acoustic mismatch described in \nthe diffused mismatch model whereby the heat carriers (phonons) in insulating materials are \nscattered at the interfaces~\\cite{Matsumoto-1977,Swartz-1989}. This approach shows that the \nthermal resistance between two dielectric crystals is strongly dependent on the surface \nstate, on the different phonon characteristics in the two materials (density and Debye \ntemperature), and on the applied force. This latter parameter has a significant effect. \nWhen two solids are placed in contact with each other, the actual contact area can be much smaller \nthan the cross sections involved due to surface irregularities. By rising the applied force \nbetween the materials, a plastic or permanent deformation occurs and the \"real\" contact surface \narea increases. The result of this action is that the thermal conductance of the contact is directly \nproportional to the applied force~\\cite{Barucci-2001,Ventura-2008}.\n\nAlthough such simple stand will clearly not produce a so-called \"optical matching,\" the \nlight collection will be definitively larger due to geometrical factors~\\footnote{For instance if \nthe BLD is held in its own structure, depending on the mounting scheme, there are generally a \nfew mm of distance from the BLD to the scintillating crystal. \nThis increases the chance for photon escape or absorption by the holding structure rather than the BLD.}.\nIn addition, removing the BLD mounting structure decreases the presence of materials and surfaces close\nto the detector which reduces possible radioactive contamination, a fundamental aspect of dealing with rare event searches.\n\n\n\\section{Bolometric Light Detectors}\n\nOur BLDs are usually constituted by electronic grade undoped Ge wafers, coupled with Ge NTD thermistors. \nWe started to develop these detectors coupled with several scintillating DBD \ncrystals~\\cite{PIRRO-2005} and we deeply characterized their operation and \nperformances~\\cite{light-detectors-2013} to finally realize the LUCIFER~\\cite{LUCIFER-2013} experiment, \nwhich has been renamed CUPID-0~\\cite{CUPID-0-detector_2018}.\n\nEach BLD of CUPID-0 (totalling 26 detectors) was made by a double side polished electronic \ngrade undoped Ge wafer (44.5~mm diameter, 0.17~mm thick). The NTD thermistor, with dimension of \n(2.85~$\\times$~2~$\\times$~0.5)~mm$^3$, \nis glued through six small glue dots ($\\sim$~0.5~mm diameter, 0.05~mm height) made with \nAraldit\\textsuperscript{\\textregistered} Rapid glue.\nThe performance of six of these detectors was evaluated in a dedicated test run~\\cite{LUCIFER-2016} and \nthe results are summarized in Tab.~\\ref{tab-cupid-0-LD}.\n\nTo further optimize our BLDs, we produced a set of devices based on the pioneering work of \nCoron et.al.~\\cite{Coron-2004}. For this study we (1) decreased the heat capacity (size) of the \nthermistor, (2) increased the thermal conductance between the thermistor and the Ge wafer, and (3) \ndecreased the thermal conductance to the thermal bath. \nWith respect to the thermistor size, we used thermistors with a dimension of\n(2.85~$\\times$~1~$\\times$ 0.4)~mm$^3$, roughly 2.5 times smaller than the CUPID-0 devices. \nWe also decided to replace the six glue dots with an uniform glue layer, thus increasing the thermal \nconductance between the thermistors and light-absorbing Ge wafer. \n\nIt should be noted that in our experience the use of glue dots \ninstead of a \\it more effective \\rm thin gluing layer is preferred when coupling inherently different \nmaterials (e.g. TeO$_2$ crystals and Ge thermistors). The dot approach reduces the mechanical stresses \ninduced by differential thermal contraction of the materials when cooled. \nIn such cases, and especially when working with larger-sized thermistors, we sometimes observed cracks on \nthe crystal surface after a cooling cycle. This phenomenon is greatly reduced in our case since we glue \nGermanium thermistors to Germanium light absorbers and use smaller thermistors. Even in this \ncase, however, there are some small unavoidable stresses due to misorientation between the thermistor\nand absorber crystallographic planes, but we have found that these effects never led to visible cracks.\n\nWith respect to the mounting (i.e. the conductance to the thermal bath), there are many ways \nto hold the BLD in place. In earlier work we adopted two~\\cite{light-detectors-2013} or \nthree~\\cite{CUPID-0-detector_2018} small PTFE clamps that squeeze the edge of the Ge, keeping \nit fixed in a Cu standalone holder. \nPTFE is a common material also used by other groups working with NTD \nsensors~\\cite{Lumineu-2017} and with MMC detectors~\\cite{MMC_LD-2015}. \nOther clamping schemes and material choices have been demonstrated by the CRESST group. \nThese include bronze clamps and Silicon or CaWO$_4$-based sticks~\\cite{Strauss-2018}. \nThe design used in~\\cite{Coron-2004}, however, is probably the most complex from a construction point \nof view, using several ultra thin superconductive wires to suspend the Ge wafer from a copper frame to \nproduce a negligible thermal link that maximizes the heat flow from the wafer to the NTD. \n\\begin{table}[t]\n\\centering\n\\caption{Mean performance of six CUPID-0-like light detectors~\\cite{LUCIFER-2016}.\n\\textbf{R$_{work}$} refers to the resistance of the NTD Ge thermistor in working conditions, \n\\textbf{Response} refers to the absolute voltage \ndrop (in $\\mu$V) produced by an energy release of 1\\,keV, \\textbf{Baseline RMS} is the resolution\nafter signal filtering~\\cite{Gatti-1986:1,Alduino-2016:045503}. {\\bf$\\tau_{r}$} and {\\bf$\\tau_{d}$} are the \nrise and decay times, computed as the time difference between the 90$\\%$ and 10$\\%$ of the leading\nedge and as the time difference between the 30$\\%$ and 90$\\%$ of the trailing edge,\nrespectively. The Bessel cut-off frequency is 200 Hz (see last remarks of \nSec.~\\ref{sec:results}).}\n\\label{tab-cupid-0-LD}\n\\begin{tabular}{ccccc}\n\\hline\\noalign{\\smallskip}\nR$_{work}$ &Response &Baseline RMS &$\\tau_{r}$ &$\\tau_{d}$ \\\\\n\n[M$\\Omega$] &[$\\mu$V\/keV] &[eV] &[ms] &[ms]\\\\\n\\hline\n 0.87 & 1.36 & 43 & 1.77 & 5.06 \\\\\n\\hline\n\\end{tabular}\n\\end{table} \n\nWe decided to avoid any kind of holding structure whatsoever so we laid the BLD \ndirectly on the crystal, kept in position only by its weight ($\\sim$1.1 g). \nIn this configuration the main thermal link between the BLD and the cryostat is represented by \nthe thin gold NTD thermistor wires ( 2 $\\times$ 15 mm length, 25 $\\mu$m diameter). As mentioned \nabove, the expected thermal conductance to the scintillating crystal is negligible. The crystal \nchosen for this test was a (50.5~$\\times$~50.5~$\\times$~50.5)~mm$^3$ \nTeO$_2$ crystal. The aim was to test the new setup with a light signal on the order \nof few tens of eV. The Ge light-absorbing wafer \nbelongs to the batch used for CUPID-0, which include a 70~nm SiO anti-reflecting coating~\\cite{Mancuso-2014} \nthat was deposited on the side that rests on the TeO$_2$ crystal.\n\n\\section{Experimental details}\n\\label{sec:experimental_details}\nThe TeO$_2$ crystal was mounted in a similar way as described \nin~\\cite{Enriched-TeO2-Cherenkov-2017,Casali-2014} with the only exception that the TeO$_2$ crystal was standing on \nthe reflecting foil and both TeO$_2$ and BLD were not equipped with Si heaters.\nThese heaters were normally glued \non the bolometer to inject pulsed thermal signals for gain stabilization.\nThe TeO$_2$ face supporting the BLD and the opposite one were polished at (nearly) optical level. \nThe remaining four lateral faces were matted in order to increase light collection~\\cite{Casali-2017}. \n\nThe TeO$_2$ crystal is held by four S-shaped PTFE supports that are fixed to Cu columns. \nThe PTFE contracts upon cooling, creating a tensioned support that maintains the crystal \nposition. \n\nIn order to maximize light collection, the crystal is completely surrounded by a plastic \nreflecting sheet (3M Vikuiti\\textsuperscript{TM}), in the same way as \nin~\\cite{Enriched-TeO2-Cherenkov-2017,Casali-2014}. A photograph of the detectors is presented \nin Fig.~\\ref{fig_0-setup}.\n\\begin{figure}[hbt] \n\\centering \n\\includegraphics[width=0.48\\textwidth]{fig_0-setup.pdf}\n\\caption{Photograph of the detectors. The BLD is simply resting on the TeO$_2$ and the four \nPTFE supports (as well as the thermistor glued on the TeO$_2$) do not hold the BLD in any way: they \nsimply avoid the BLD to lean out from the top surface, as a mere translation constraints. \nThe gold wires of both NTDs are then crimped within micro Cu tubes to ensure the electrical contact \nas well as the thermal conductance to the heat sink. The $^{55}$Fe X-ray source is attached \nto the top reflecting cover sheet that encloses the detectors (with a clearance of $\\sim$4~mm from the BLD) \nand can be observed -reflected by the Ge wafer surface- between the two NTDs.}\n\\label{fig_0-setup}\n\\end{figure}\nThe entire setup was enclosed in a Cu box and thermally coupled to the mixing chamber \nof the CUPID R\\&D cryostat, a $^3$He\/$^4$He dilution refrigerator installed \ndeep underground within Hall C of the Laboratori Nazionali del Gran Sasso, Italy. \nTo avoid vibrations reaching the detectors, the box is mechanically decoupled from the \ncryostat by utilizing a two-stage pendulum system~\\cite{Pirro-2006}. \n\n\nThe thermistors of the detectors are biased with a quasi-constant current produced by applying a \nfixed voltage through large (27+27 or 2+2 G$\\Omega$) load resistors~\\cite{Arnaboldi-2002:1808}. \nWhen light is absorbed in the Ge wafer, a thermal pulse is produced which is subsequently \ntransferred to the NTD sensor, changing the resistance of the thermistor. This, in turn, creates a\nvoltage change across the current-biased NTD which is amplified using \nfront end electronics located just outside the cryostat~\\cite{Arnaboldi-2004}. The signals are \nthen filtered by an anti-aliasing 6-pole Bessel filter (with a cutoff frequency of 16~Hz \nfor the TeO$_2$ crystal and 550~Hz for the BLD) and finally fed into a NI PXI-6284 18-bit ADC.\n\nThe sampling rate of the ADC was 1~kHz for the TeO$_2$ crystal and 8 kHz for the BLD. \nThe two independent triggers are software generated such that when a trigger fires, the \ncorresponding waveform is recorded. Moreover, when the trigger of the \nTeO$_2$ crystal fires, the corresponding waveform of the BLD is always \nrecorded, irrespective of its trigger. A detailed description of the DAQ system can \nbe found in~\\cite{DiDomizio:2018ldc}.\nThe amplitude and the shape of the voltage pulses are then determined via off-line analysis. \nThe pulse amplitude of the thermal signals is estimated by the Optimum Filtering (OF) \ntechnique~\\cite{Gatti-1986:1,Alduino-2016:045503}, that maximizes the signal-to-noise ratio \nin a way that improves the energy resolution and lowers the threshold of the detector. \nThe amplitude of the light signal, however, is evaluated from the filtered waveform \nat a fixed time delay with respect to the TeO$_2$ bolometer, as described in detail \nin~\\cite{Piperno-2001:10005}.\\newline\nThe amplitude of the acquired TeO$_2$ heat signals is energy-calibrated using several \n$\\gamma$-ray peaks from a $^{228}$Th source. \nThe BLD, on the contrary, is calibrated thanks to the 5.9~keV and 6.5~keV X-ray \nquanta produced by a $^{55}$Fe X-ray source permanently faced to the detector.\n\n\\section{Data analysis and results}\n\\subsection{BLD performance}\n\\label{sec:results}\nThe crystals were tested at a cryostat base temperature of $\\sim$11~mK. \nIn order to obtain a fast response, we operated the BLD in the so-called \"over-biased\" \nconfiguration whereby \nthe biasing current of the circuit is set much larger than the current that would ensure the highest \nabsolute thermal response~\\cite{NTD_LD_Lucifer-2013}. This choice ensures a small working \nresistance, thus minimizing the effect of the low pass filtering induced by the overall \ncapacity ($\\sim$200 pF) of the front end readout wires. \n\nIn Fig.~\\ref{fig_1-55Fe} we show the $^{55}$Fe calibration spectrum obtained with the BLD.\nThe baseline energy resolution (ie, the absolute sensitivity) of the BLD is given by \nthe width of randomly acquired baselines (noise) after the application of OF. \nAs is typical for this style of detectors, the energy resolution of monochromatic energy \nabsorption events is much worse than the baseline resolution, irrespective \nof the type of sensor~\\cite{NTD_LD_Lucifer-2013,TES_LD_CRESST}. \n\n\\begin{figure}[hbt] \n\\centering \n\\includegraphics[width=0.48\\textwidth]{fig_1-55Fe.pdf}\n\\caption{Energy distribution of the random sampled noise. The width of the distribution \n($\\sigma\\approx$20 eV) represents the baseline energy resolution of our BLD. The right inset shows\nthe $^{55}$Fe calibration spectrum of the BLD. The x-axis units represent the absolute voltage drop \nacross the thermistor. \nThe RMS resolution on the 5.9 keV and 6.5 keV X-ray peaks is 59 eV (see text).}\n\\label{fig_1-55Fe}\n\\end{figure}\nThe noise and signal power spectra of the BLD are presented in Fig.~\\ref{fig_2-NPS}.\n\n\\begin{figure}[hbt] \n\\centering \n\\includegraphics[width=0.48\\textwidth]{fig_2-NPS.pdf}\n\\caption{Noise power spectrum (black line) and signal power spectrum (blue line) of the BLD. \nThe y-axis scale is in absolute values for the noise. The signal spectrum is scaled in \narbitrary units, being the roll-off induced by the Bessel filter the same between noise and signal.\nThe working resistance of the thermistor is 1.47 M$\\Omega$, biased with a current of 3.7 nA \nthorough (2+2) G$\\Omega$ metallic load resistors. The peaks are due to the microphonic \nnoise induced by the vibration of the readout wires.}\n\\label{fig_2-NPS}\n\\end{figure}\nThe bump that can be observed in Fig.~\\ref{fig_2-NPS} at $\\sim$400 Hz arises from a \nresonance that enhances the thermal noise generated within the thermistor. This occurs \nwhen the impedance of the parasitic capacitance of the link becomes smaller than that of the \nthermistor, which is a fed-backed device~\\cite{Arnaboldi-2005}. \nThe bump is found at the border of the bandwidth of the signal and is rejected from the \noptimum filter algorithm.\n\n\nFig.~\\ref{fig_3-rise-decay} shows the corresponding rise and decay times of $^{55}$Fe X-rays absorption events.\n\\begin{figure}[hbt] \n\\centering \n\\includegraphics[width=0.48\\textwidth]{fig_3-rise-decay.pdf}\n\\caption{Rise and decay times distributions corresponding to the $^{55}$Fe X-rays. The \nBessel cut-off frequency of the Front-End is 550~Hz.}\n\\label{fig_3-rise-decay}\n\\end{figure}\nThe measured rise time shown in Fig.~\\ref{fig_3-rise-decay} is most likely slower than the intrinsic \nrise time of the detector since it contains contributions from the Bessel filter (independent from the \nthermistor impedance) and from the capacitance of the readout wires. This last \ncontribution is difficult to measure since it involves the dynamic resistance of the \nthermistor. The contribution of the 550~Hz Bessel filter to the rise time was evaluated \nin~\\cite{NTD_LD_Lucifer-2013} and reported as 0.65~ms. Thus, after applying a quadratic deconvolution, the \n\\it intrinsic \\rm rise time of our BLD should be of the order of 0.5~ms, compatible with the \nexpectation of ~\\cite{Coron-2004}.\nThe overall performance of the BLD is summarized in Tab.~\\ref{tab-new-BLD}.\n\n\\begin{table}\n\\centering\n\\caption{Performances of the BLD of this work, to be compared with the ones of \nTab.~\\ref{tab-cupid-0-LD}.}\n\\label{tab-new-BLD}\n\\begin{tabular}{ccccc}\n\\hline\nR$_{work}$ &Response &Baseline RMS &$\\tau_{r}$ &$\\tau_{d}$ \\\\\n\n[M$\\Omega$] &[$\\mu$V\/keV] &[eV] &[ms] &[ms]\\\\\n\\hline\n1.47 &3.86 &20 & 0.83 &1.63 \\\\\n\\hline\n\\end{tabular}\n\\end{table} \n\\subsection{Heat and Light measurement}\n\\label{scatter-section}\nIn order to evaluate the long-term discriminatory performance of our BLD, we performed a 70 h run that \nincluded two event-generating calibration sources embedded into the setup. A $^{228}$Th source was placed\na few cm away from the TeO$_2$ crystal and \na \\it smeared \\rm $^{238}$U $\\alpha$ source was applied to the inside of the light reflector facing the TeO$_2$. \nThe aim of the $\\alpha$ source was to directly measure the discrimination capability between \n$\\alpha$ and $\\beta\/\\gamma$ in the DBD region of interest of $^{130}$Te. \nThe source was made using 2 $\\mu l$ of a standard calibrated solution (0.1 \\%) of $^{238}$U, and the dried \nsource deposition was covered with a 6 $\\mu m$ aluminized Mylar foil to smear the \n$\\alpha$ energy.\n\nThe light vs heat scatter plot is presented in Fig.~\\ref{fig_4-scatter-plot} and shows an \nunexpected feature.\n\\begin{figure}[hbt] \n\\centering \n\\includegraphics[width=0.48\\textwidth]{fig_4-scatter-plot.pdf}\n\\caption{Light vs heat scatter plot obtained in a 70 h measurement with the TeO$_2$ exposed \nto a $^{228}$Th source and a smeared $^{238}$U $\\alpha$ source. Unfortunately $\\alpha$ \nenergy loss in the Mylar -constituting the smearing medium- results in a tiny, but \nmeasurable, light emission that increases towards lower energies, i.e. at larger energy \nloss in the Mylar. The events above 4~MeV, on the contrary, are due to internal and\/or \nsurface contaminations and their light emission is compatible with zero \n(see text).}\n\\label{fig_4-scatter-plot}\n\\end{figure}\nThe $^{238}$U $\\alpha$-events arising from the smeared source clearly show a tiny light \nemission that increases towards lower energies. This feature can only be ascribed to an \nenergy loss in the Mylar which emits few scintillation photons. To avoid this effect \nwe usually face the aluminized surface of the Mylar towards the crystal so as to reflect the \n(very few) photons that could be produced in this plastic. This time however, \nwe mistakenly mounted the Mylar with the uncoated side towards the detector. This was confirmed after\nsubsequently opening the cryostat and checking.\n\nThe result is shown in Fig.~\\ref{fig_4-scatter-plot}: the amount of Cherenkov \nlight, produced by a 2615~keV $\\gamma$, that is collected with this new set-up is \n(151~$\\pm$~4)~eV, 50 \\% larger with respect to all our previous measurements with \nmassive crystals~\\cite{Casali-2017}, as well as roughly 50 \\% larger with respect to a \nmeasurement recently performed with a NTD-based light detector~\\cite{Lumineu-2017} of the \nsame type (considering the 40 \\% reduced transmission area between BLD and crystal, as \ndeclared in the article).\nThe light distribution of the 74 events belonging to the internal\n$^{210}$Po $\\alpha$ at 5407~keV (5304~keV $\\alpha$ + 103~keV nucleus recoil) shows a \nmean value of (5.8~$\\pm$~3.3)~eV, still compatible with zero (see Sec.~\\ref{sec:thermal_interference}) \nas it should be if the light only arises from the Cherenkov effect. More importantly, the width of \nthe light distribution of $\\alpha$'s is $\\sigma_{\\alpha}$=(22.7~$\\pm$~ 2.7)~eV, fully compatible \nwith the RMS noise of the BLD of Tab.~\\ref{tab-new-BLD}.\nThe light signal induced by the 2615~keV $\\gamma$ -on the contrary- shows a width of \n$\\sigma_{\\gamma\/\\beta}$=(31.5~$\\pm$~4.3)~eV which is \na result of the photostatistics and the light collection. \n\nIn order to evaluate the Discrimination Power (DP) that can be obtained between the \n$\\alpha$ and $\\beta\/\\gamma$ distributions at 2528~keV (the Q$_{\\beta\\beta}$-value of \nthe DBD of $^{130}$Te) we use the same formula and arguments used \nin~\\cite{Enriched-TeO2-Cherenkov-2017,Lumineu-2017}: the DP can be quantified as the \ndifference between the average values of the two distributions normalized to the square \nroot of the quadratic sum of their widths: \n\\begin{equation} \nDP = \\frac{|\\mu_{\\gamma\/\\beta}-\\mu_{\\alpha}|}{\\sqrt{\\sigma^{2}_{\\gamma\/\\beta}+\n\\sigma^{2}_{\\alpha}}}.\n\\label{eq:DP}\n\\end{equation} \nRe-scaling the light signal from 2615 to 2528~keV, we obtain DP=3.6, using one highly likely \nassumption that an $\\alpha$ particle at 2528~keV will show a light signal equal than the same \nparticle at~5304 keV ($^{210}$Po).\nThis DP is the best ever achieved with large mass TeO$_2$ crystals (M $>$ 7 g) and without \nthe need for additional Neganov-Luke \namplification~\\cite{Lumineu-2017,Casali:2015gya,Gironi:2016nae}, or\nmore sophisticated TES sensors~\\cite{Karo-2014} or both~\\cite{Willers-2014}.\n\n\\section{Thermal conductance}\\label{sec:thermal_interference}\nAs stated in Sec.~\\ref{sec:Introduction}, the actual goal of this work was to experimentally \ndemonstrate that the BLD can rest on the scintillating or luminescent crystal without heat sinking to it. \nUsing the results in the previous section we can now calculate a limit on the heat flow through \nthe Ge wafer and the TeO$_2$. If one assumes that a 5407 keV energy release in the TeO$_2$ produces\na mean value BLD signal that only depends on the heat flow (assuming no light emission), then we have \nan upper limit for the ratio of the heat flow through TeO$_2$ and Ge: 5.8~eV\/5407~keV$\\sim$10$^{-6}$.\n\nIn our case, an extremely low heat conductance was determined experimentally using static conditions. We measured \nthe base resistance of the BLD as 223.5 M$\\Omega$ (corresponding to 11.8~mK), keeping the \nTeO$_2$ thermistor unbiased (i.e. no power dissipation in it). We then gave the maximum \n(allowed by our biasing set-up) bias to the TeO$_2$ thermistor, corresponding to \n4.8 nA, and the TeO$_2$ thermistor changed its resistance from 626 M$\\Omega$ \n(bias~$\\rightarrow$~0) to 1.71 M$\\Omega$. The power dissipated on the TeO$_2$ was therefore 40 pW. \nThe base resistance of the BLD decreased to 222.8 M$\\Omega$, which corresponds to a temperature increase of \nonly $\\approx$~4.3 $\\pm$ 0.2 ~$\\mu$K. \nThe same operation was performed with the BLD in working condition, i.e. bias current \nof 3.7 nA and a resistance of 1.47~M$\\Omega$ (corresponding to $\\sim$23~mK), and no variation\nof the baseline of the BLD was registered.\nA further investigation of the thermal conductance between a Ge-BLD and a TeO$_2$ crystal was performed\nby exploiting a small TeO$_2$ crystal ($20~\\times~20~\\times~14$~mm$^{3}$, 34~g mass).\nWe used a standard BLD, i.e., the same thickness and height as in the previous discussion, but with the Ge wafer\nheld with PTFE clamps in a stand-alone Cu mounting~\\cite{NTD_LD_Lucifer-2013}.\nFor this experiment we rested the $20\\times20$~mm$^{2}$ surface of the 34~g crystal \n on the Ge wafer. The NTD thermistor-equipped TeO$_2$ crystal was surrounded with the same reflecting \nfoil and we performed the same measurement described in Sec.~\\ref{scatter-section} with the same \noverall setup.\nThis time a 5304~keV $^{210}$Po decay occurring in the TeO$_2$ created a mean signal in the BLD \nof (317~$\\pm$~29)~eV, definitively not compatible with the result of Sec.~\\ref{scatter-section}.\nThe mean (light) signal registered in coincidence with the 2615~keV $\\gamma$-line of $^{208}$Tl \nwas (336~$\\pm$~5)~eV. \nThe $\\alpha$-induced signal in the BLD, therefore, has to be ascribed to an effective thermal \ntransfer from the TeO$_2$ to the BLD.\nWe can make a very rough estimation of the size of this transfer using the \nresults of the measurement of Sec.~\\ref{scatter-section}. If we assume the heat conductance \nto be linearly proportional to the pressure force between the two mediums, then we may\nsimply compare the weight differences: 1.1 g in the case of the wafer resting onto the TeO$_2$ \ncrystal versus 34~g in this last configuration.\nTheir ratio, i.e. 31, should be, in first approximation, the ratio between the thermal \nconductance in the two setups. Ascribing the $\\alpha$ signal of Sec.~\\ref{scatter-section} \nexclusively to thermal transfer we would expect a thermal transfer signal of \n(180~$\\pm$~90)~eV, which is compatible with the 317~eV observed during this measurement. On the other\nhand, under the same assumption, we can evaluate the 2615-keV induced Cherenkov light signal\nof this crystal as the difference between the observed signal and the re-scaled thermal transfer \nevaluated from the $\\alpha$. In this way we observe that the energy of the Cherenkov light \nemission in this 34~g crystal is (185~$\\pm$~15)~eV.\n\n\\section{Conclusions}\nWe have demonstrated the possibility of mounting BLDs by simply resting them on the surface of the \ncorresponding scintillating crystal. With this new mounting method the light collection can increase up\nto 50\\% with respect to standard setups. We do not observe appreciable heat flow between the\nscintillating crystal and BLD. \nWe also improved the time response of our thermistor-based light detectors, reaching a rise\ntime of 0.8 ms and demonstrating that 0.5 ms is achievable. This time response is necessary \nto remove the background induced by the pile-up of the 2$\\nu$-DBD mode in the case \nof $^{100}$Mo-based crystals. We reached a baseline resolution\nof 20~eV RMS, more than 2 times better than the average value our previous CUPID-0-like detectors. \nThanks to these developments, we definitively demonstrated that standard thermistor-based\nBLDs can be used for CUPID, both to read out the tiny Cherenkov light of TeO$_2$ as well as to\nread out the Mo-based scintillating crystals. \n\nWe do believe that this simplified technique could be applied to any kind of BLD, irrespective\nof the sensor type. The first approximation thermal conductance between crystal and BLD \ndoes not depend upon the energy of the phonons, so we would expect that thermal transfer \nwould be as negligible in TES or MMC devices as it is in our NTDs. \nMore generally this new technique could be also applied in the case of stacked, standard small\nbolometers, provided that the weight does not exceed a\nfew grams. However, since the measured thermal transfer is rather small, the weight of the\nbolometer will not be a significant limiting factor in low energy threshold applications. \n\n\\section{Acknowledgments}\nThis work was performed within the CUPID experiment founded by INFN and supported by\nthe National Science Foundation under Grant NSF-PHY-1614611.\n\nWe thank the CUPID-0 and the CUORE collaborations for the overall support and for sharing their \nDAQ and software.\nWe express our gratitude to LNGS for the generous hospitality and, in particular, to\nthe mechanical workshop personnel including E. Tatananni, A. Rotilio, A. Corsi, and B.\nRomualdi for their continuous and constructive help. We are also grateful to M. Guetti \nfor his invaluable support and expertise in the cryostat facility maintenance. \nWe acknowledge Dr. C. Arnaboldi for his precious \nsupport, even though he has left this field of research many years ago. We are especially \ngrateful to E. Ferri for her kind support in the thermistor wire-bonding.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzokdk b/data_all_eng_slimpj/shuffled/split2/finalzzokdk new file mode 100644 index 0000000000000000000000000000000000000000..a3ec0b01d1a88ace863eb4e1f6f56d8bf7c657b6 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzokdk @@ -0,0 +1,5 @@ +{"text":"\n\\section{Additional Definitions}\n\n\\begin{definition}[Pairwise Independence Hash Functions]\\label{def:pairwise}\nLet $\\mathcal{H}$ be a family of functions from $\\{1,\\ldots, N\\}$ to $\\{1,\\ldots, M\\}$. The family $\\mathcal{H}$ is \\emph{pairwise independent} if for every $x,y \\in \\{1,\\ldots, N\\}$ such that $x \\neq y$ and for every $a,b \\in \\{1,\\ldots, M\\}$ it holds that \n$$\\Pr_{h \\in \\mathcal{H}}[h(x)=a \\wedge h(y)=b]=1\/M^2~.$$\nThat is, if $h$ is chosen uniformly at random from $\\mathcal{H}$, then the random variable $h(x)$ and $h(y)$ are uniformly distributed and pairwise independent. \n\\end{definition}\n\n\\begin{fact}\\label{fc:pairwise}\\cite{TCS-010}\nThere is an explicit family $\\mathcal{H}$ of pairwise independent has functions from $\\{0,1\\}^n \\to \\{0,1\\}^m$ constructed using $O(\\max\\{\nm,n\\})$ bits and computable in $\\operatorname{\\text{{\\rm poly}}}(n,m)$ time.\n\\end{fact}\n\\section{Overview of the Cycle Space Sampling Technique} \\label{sec:cycle_space_overview}\n\nThe cycle space sampling technique allows to detect cuts in a graph using a connection between cuts and cycles in a graph.\nThis beautiful technique was introduced by Pritchard and Thurimella \\cite{pritchard2011fast}, that showed its applicability for distributed algorithms identifying small cuts in a graph. We next give a short overview of the technique, for full details see \\cite{pritchard2011fast}.\n\nThe \\emph{cycle space} of a graph is the family of all subsets of edges $F$ that have even degree at each vertex, any such subset $\\phi \\subseteq E$ is called a \\emph{binary circulation}. The \\emph{cut space} is the family of all induced edge cuts. It is easy to see that if we take a cycle $C$ in a graph and an induced edge cut, then the number of edges of the cycle that cross the cut is even. The cycle space technique extends this observation and shows that the cycle space and cut space are orthogonal vector spaces. Using this, they show the following (see Propositions 2.2 and 2.5 in \\cite{pritchard2011fast}).\n\n\\begin{claim} \\label{claim_cycle} \nLet $\\phi$ be a uniformly random binary circulation and $F \\subseteq E$. Then\n$$Pr[|F \\cap \\phi| \\ is \\ even] = \\left\\{\n \\begin{array}{ll}\n 1,\\ if\\ F\\ is\\ an\\ induced\\ edge\\ cut\\\\\n 1\/2,\\ otherwise\n \\end{array}\n \\right. $$ \n\\end{claim} \n\nHence, sampling a random binary circulation allows to detect if a subset of edges is an induced edge cut with probability $1\/2$. To reduce the failure probability to $1\/2^b$ we can choose $b$ random binary circulations. To use this technique, the authors provide an efficient way to sample a random binary circulation, we describe next. Let $T$ be a spanning tree of the graph. For any non-tree edge $e$, adding $e$ to the graph creates a cycle. These cycles are the \\emph{fundamental cycles}, and it is shown that the \\emph{fundamental cycles} are a basis for the cycle space. Based on this, they show that sampling a random binary circulation can be done by choosing each fundamental cycle with probability $1\/2$, or equivalently choosing each non-tree edge with probability $1\/2$. The binary circulation $\\phi$ sampled has all the non-tree edges sampled, and each tree edge that appears in odd number of sampled cycles. Given the sampled non-tree edges in $\\phi$, the tree edges in $\\phi$ can be identified using a simple scan of the tree, as shown in \\cite{pritchard2011fast}. Choosing $b$ random binary circulations, is equivalent to choosing a $b$-bit random string $\\phi(e)$ for each non-tree edge. For a tree edge $t$, we define $\\phi(t) = \\oplus_{e \\in C_t} \\phi(e)$, where $C_t$ are all non-tree edges $e$ such that $t$ is in the fundamental cycle of $e$. This again can be computed by a simple scan of the tree, and takes $O((n+m)b)$ time if the labels have size $b$. This gives the following.\n\n\\cycle*\n\nTo see this, let $\\phi_1,...,\\phi_b$ be the sampled binary circulations. If $F$ is an induced edge cut, then from Claim \\ref{claim_cycle}, for every sampled circulation $\\phi_i$, we have that $|F \\cap \\phi_i|$ is even, and hence for all $i$, the $i$'th bit of $\\Moplus_{e \\in F} \\phi(e)$ is equal to 0 as needed. Otherwise, for all $i$, the $i$'th bit $\\Moplus_{e \\in F} \\phi(e)$ equals $0$ with probability $1\/2$, hence the probability that the whole vector equals $0$ is $1\/2^b$, as needed.\n\\section{Fault-Tolerant Approximate Distance Labels}\\label{sec:ft-distance}\nGiven integer parameters $f,k \\geq 1$, an $(f,k)$ \\emph{FT approximate distance labeling scheme} assigns labels $\\mathsf{DistLabel}\\xspace: V \\cup E \\to \\{0,1\\}^{q}$ such that given the labels of $s,t$ and a subset $F \\subseteq E$, $|F|\\leq f$, there exists a decoding algorithm that outputs a distance estimate $\\delta_{G \\setminus F}(s,t)$ satisfying:\n$$\\mbox{\\rm dist}_{G\\setminus F}(s,t) \\leq \\delta_{G \\setminus F}(s,t) \\leq k\\cdot\\mbox{\\rm dist}_{G\\setminus F}(s,t)~.$$\n\nWe next show that there is an efficient transformation from any FT connectivity labeling scheme into an FT approximate distance labeling scheme. This transformation increases the label size by a multiplicative factor of $\\widetilde{O}(n^{1\/k})$. This technique was first introduced by \\cite{chechik2012f} in the context of distance sensitivity oracles, and it is based on the notion of tree covers.\n\n\\begin{definition}[Tree Covers]\\label{def:tree-cover}\nLet $G=(V,E)$ be an undirected graph with edge weights $\\omega$, and let $\\rho,k$ be two integers. Define $B_{\\rho}(v)=\\{ u \\in V ~\\mid~ \\mbox{\\rm dist}_G(u,v)\\leq \\rho\\}$. A tree cover $\\mathsf{TC}\\xspace(G, \\omega, \\rho,k)$ is a collection of rooted trees $\\mathcal{T}=\\{T_1,\\ldots, T_\\ell\\}$ with root $r(T)$ for every $T \\in \\mathcal{T}$ such that:\n\\begin{enumerate}[noitemsep]\n\\item For every vertex $v$ there exists a tree $T \\in \\mathcal{T}$ such that $B_{\\rho}(v) \\subseteq T$.\n\\item The radius of each tree $T$ is at most $(2k-1)\\cdot \\rho$.\n\\item Each vertex participates in $(k \\cdot n^{1\/k})$ trees.\n\\end{enumerate}\nLet $|\\mathsf{TC}\\xspace(G, \\omega, \\rho,k)|$ denote the number of trees in the tree cover $\\mathsf{TC}\\xspace(G, \\omega, \\rho,k)$.\n\\end{definition}\n\n\\begin{proposition}\\cite{Peleg:2000}\nFor any $n$-vertex graph $G=(V,E, \\omega)$, and any parameters $\\rho,k$, one can compute tree covers $\\mathsf{TC}\\xspace(G, \\omega, \\rho,k)$ in time $\\widetilde{O}(|E(G)| \\cdot n^{1\/k})$.\n\\end{proposition}\n\n\\begin{lemma}[From Connectivity Labels to Approximate Distance Labels]\\label{lem:reduction}\nLet $G=(V,E, \\omega)$ be a weighted undirected $n$-vertex graph where $\\omega(e)\\in [1,W]$, and let \n$\\mathsf{ConnLabel}\\xspace: V \\cup E \\to \\{0,1\\}^s$ be an $f$-FT connectivity labeling scheme for $G$ with decoding time $t$. Then for every integer $k\\geq 1$, there is an $(f,(8k-2)(|F|+1))$ FT approximate distance labeling scheme $\\mathsf{DistLabel}\\xspace: V \\cup E \\to \\{0,1\\}^{q}$ for $G$, where $q=O(s \\cdot k \\cdot n^{1\/k}\\cdot \\log (nW))$, and with decoding time $\\widetilde{O}(t \\log{(nW)})$.\n\\end{lemma}\n\n\\paragraph{The labeling algorithm.}\nFor every vertex $u$, the label $\\mathsf{DistLabel}\\xspace(u)$ consists of $K=\\log(nW)$ sub-labels of FT connectivity labels in distinct subgraphs of $G$ defined as follows. The $i^{th}$ sub-label addresses all distances that are at most $2^i$ in $G$. Let $H_i$ be set of heavy edges in $G$ of weight at least $2^i$, and define the $i^{th}$ tree-cover by \n\\begin{equation}\\label{eq:TC-i}\n\\mathsf{TC}\\xspace_i=\\mathsf{TC}\\xspace(G\\setminus H_i,\\omega, 2^i,k)~.\n\\end{equation}\nFor each tree $T_{i,j} \\in \\mathsf{TC}\\xspace_i$, the algorithm applies the FT connectivity scheme on the graph $G_{i,j}=G[V(T_{i,j})]$. For every vertex $u$ and $i \\in \\{1,\\ldots, K\\}$, let $i^*(u)$ be an index of a tree in $\\mathsf{TC}\\xspace_i$ that covers the $2^i$-ball of $u$. I.e., $B_{2^i}(v) \\subseteq T_{i,i^*(u)}$. \nThe label of every $u \\in V$ is then given by:\n$$\\mathsf{DistLabel}\\xspace(u)=\\{\\langle \\mathsf{ConnLabel}\\xspace_{G_{i,j},T_{i,j}}(u), i,j \\rangle ~\\mid~ i \\in [1,K], j \\in \\{1,\\ldots, |\\mathsf{TC}\\xspace_i|\\}, u \\in G_{i,j}\\} \\bigcup \\{i^*(u) ~\\mid~ i \\in [1,K]\\}~.$$\n\nSimilarly, the label of each edge $e \\in G$ contains the FT connectivity label of $e$ in each of the instances $(G_{i,j}, T_{i,j})$:\n$$\\mathsf{DistLabel}\\xspace(e)=\\{\\langle \\mathsf{ConnLabel}\\xspace_{G_{i,j},T_{i,j}}(e), i,j \\rangle ~\\mid~ i \\in [1,K], j \\in \\{1,\\ldots, |\\mathsf{TC}\\xspace_i|\\}, e \\in G_{i,j}\\}.$$ \n\nThe time for assigning the labels is the time for constructing the tree cover and computing the indexes $i^{*}(v)$, and the time for assigning the connectivity labels on each one of the trees. The first part requires polynomial time. The second depends on the connectivity labels. For example, using our scheme from Section \\ref{sec:ftconn-sketch} the time complexity of the second part is $\\widetilde{O}(mn^{1\/k})$, as it is linear in the total number of vertices and edges in the trees.\n\n\n\n\\paragraph{The decoding algorithm.}\nConsider the query $\\langle s,t,F\\rangle$. \nThe algorithm has $K$ phases, in each phase $i \\in [1,K]$ the decoding algorithm of the FT connectivity labels is applied on the instance $G_{i,i^*(s)}, T_{i,i^*(s)}$ where $G_{i,i^*(s)}$ contains the $2^i$ ball of $s$ in $G$. \nIf $t \\notin G_{i,i^*(s)}$, the phase $i$ ends and we continue to phase $i+1$. \nOtherwise, the algorithm decides if $s$ and $t$ are connected in $G_{i,i^*(s)} \\setminus F$ in the following manner. \nLet $F_i=F \\cap G_{i,i^*(s)}$, this subset of edges can be obtained from the labels of the $F$ edges. \nSince the labels of $s,t$ and $F_i$ contain the FT connectivity labels in the subgraph $G_{i,i^*(s)}$ and the tree $T_{i,i^*(s)}$, the algorithm can apply the decoding algorithm of the FT connectivity scheme.\nIf $s$ and $t$ are indeed connected in $G_{i,i^*(s)}\\setminus F_i$, the algorithm returns the estimate $\\delta_{G \\setminus F}(s,t)= (4k-1) \\cdot (|F|+1) \\cdot 2^{i}$. Otherwise, it proceeds to the next phase.\n\nOverall, let $i$ be the minimum index in $\\{1,\\ldots, K\\}$ for which $s$ and $t$ are connected in the subgraph $G_{i,i^*(s)} \\setminus F$. Then the decoding algorithm returns the distance estimate $\\delta_{G \\setminus F}(s,t)=(4k-1) \\cdot (|F|+1) \\cdot 2^{i}$. If no such $i$ exists, the decoding algorithm returns $\\delta_{G \\setminus F}(s,t)=\\infty$, which implies that $s$ and $t$ are not connected in $G \\setminus F$. \n\nThe decoding time is $\\widetilde{O}(t \\log{(nW)})$, where $t$ is the decoding time of the connectivity labels, as we use the decoding algorithm of the connectivity labels $K$ times on the graphs $G_{i,i^*(s)}$. To obtain this, we need to make sure that given the labels of $s,t,F$ we can easily find their connectivity label in the graph $G_{i,i^*(s)}$ if exist. This can be easily done if we store the connectivity labels in a sorted order.\n\n\\paragraph{Analysis.}\nWe now analyze the construction, and start by bounding the size of the labels. By the properties of the tree-cover in Def. \\ref{def:tree-cover}, each vertex appears in $O(K \\cdot k \\cdot n^{1\/k})$ subgraphs. Thus, $\\mathsf{DistLabel}\\xspace(u)$consists of $O(K \\cdot k n^{1\/k})$ FT connectivity labels and the label size is bounded by $O(K \\cdot k n^{1\/k} \\cdot s)$ bits, as desired. Next, we show correctness. By the correctness of the FT connectivity labeling scheme, it is sufficient to show the following. Let $P_{s,t,F}$ be an $s$-$t$ shortest path in $G \\setminus F$ of length $(2^{i-1}, 2^{i}]$. By the properties of the tree cover, there is a tree $T_{i,i^*(s)} \\in \\mathsf{TC}\\xspace_i$ that contains all the vertices of the path $P_{s,t,F}$. Therefore, we have that $s$ and $t$ are connected in $G_{i,i^*(s)}\\setminus F$. Since the labels of $s, t$ and $F_i=F \\cap G_{i,i^*(s)}$ contain the FT connectivity labels in $G_{i,i^*(s)}$, we get that the distance estimate returned by the algorithm satisfies that\n$$\\mbox{\\rm dist}_{G \\setminus F}(s,t)\\leq \\delta_{G \\setminus F}(s,t) \\leq (4k-1)(|F|+1) \\cdot 2^i \\leq (8k-2)(|F|+1) \\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)~.$$\nTo see this, let $j \\leq i$ be the first index such that $s$ and $t$ are connected in $G_{j,j^*(s)}\\setminus F$. The algorithm returns the estimate $(4k-1)(|F|+1) \\cdot 2^j \\leq (4k-1)(|F|+1) \\cdot 2^i = (8k-2)(|F|+1) \\cdot 2^{i-1} \\leq (8k-2)(|F|+1) \\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$. To prove the left inequality, we show that if $s$ and $t$ are connected in $G_{j,j^*(s)}\\setminus F$, there is indeed a path between them in $G \\setminus F$ of length at most $\\delta_{G \\setminus F}(s,t) = (4k-1)(|F|+1) \\cdot 2^j$. First, from the tree cover properties, the radius of $T_{j,j^*(s)}$ is at most $(2k-1)2^j$, implying that any two vertices in $T_{j,j^*(s)}$ are at distance at most $(4k -2) \\cdot 2^j$ from each other. Now the graph $T_{j,j^*(s)} \\setminus F$ has at most $|F|+1$ connected components. Since $G_{j,j^*(s)}\\setminus F$ is connected, it implies that there is a path between $s$ and $t$ in $G_{j,j^*(s)}\\setminus F$. This path traverses at most $|F|+1$ different components in $T_{j,j^*(s)} \\setminus F$, and at most $|F|$ edges connecting them, each one of weight at most $2^j$. As the diameter of each component is bounded by $(4k - 2) \\cdot 2^j$, the length of the path is at most $(4k - 2) \\cdot 2^j \\cdot (|F|+1) + 2^j \\cdot |F| \\leq (4k - 1) \\cdot 2^j \\cdot (|F| + 1)$, as needed.\n\n\\remove{\nLet $\\mathcal{T}=\\bigcup_{i=1}^{K}\\mathsf{TC}\\xspace_i$ be the collection of tree covers with $K=O(\\log (nW))$ scales of distances. We call an edge $(u,v)$ a \\emph{tree edge} if it appears on at least one of the trees in $\\mathcal{T}$. \nUsing Lemma \\ref{lem:useful-recovery-edges}, we have the following decoding algorithm, which becomes useful in the context of routing schemes, as described in the next section. \\mtodo{there could be a problem with treating edges globally as tree edges or non-tree edges, I think for each edge $e \\in G_{i,j}$ we need to know its label in this graph (either tree or non-tree label), extended identifiers are also different for different trees because of the ancestry labels.}\n\\begin{lemma}\\label{lem:approx-dist-recovery}\nConsider the $(f,(8k-2)(f+1))$ approximate distance labels $\\mathsf{DistLabel}\\xspace$ obtained by using Lemma \\ref{lem:reduction} with the FT connectivity labeling scheme of Sec. \\ref{sec:ftconn-sketch}. For any triplet $s,t,F \\subseteq E$ and $|F|\\leq f$, let $F_T$ be the tree edges of $F$. Then, given the labels $\\{\\mathsf{DistLabel}\\xspace(w), w \\in \\{s,t\\} \\cup F_T\\}$ and the extended edge identifiers of $F \\setminus F_T$, the decoding algorithm can be modified to return a labeled $s$-$t$ path $\\widehat{P}$ of length $O(f)$ that provides a succinct description of an $s$-$t$ path in $G \\setminus F$, along with indices $i,j$. Each $G$-edge $e$ of $\\widehat{P}$ is augmented with port information and the extended identifier of $e$; and each non-$G$ edge $e'=(u,v)$ corresponds to a $u$-$v$ path in $T_{i,j} \\setminus F$. In addition, the length of the $s$-$t$ path encoded by $\\widehat{P}$ is bounded by $(8k-2)(|F|+1)\\cdot \\mbox{\\rm dist}_{G\\setminus F}(s,t)$. \n\\end{lemma}\n\\begin{proof}\n\n\n\n\\end{proof}\n}\n\n\\section{Introduction}\nDistributed graph representation is concerned with augmenting each vertex (and possibly also edges) with useful and low-space information in order to efficiently address various graph queries in a distributed manner. As the\nvertices and edges of the network may occasionally fail or malfunction, it is desirable\nto make these representations robust against failures. In this paper, we provide new constructions of succinct \\emph{labeled-based distributed data structures} that can handle connectivity, distance queries and routing in the presence of edge failures. \n\nConnectivity labels are short names attached to each vertex in the $n$-vertex input graph $G$, such that given the labels of a pair of vertices $s$ and $t$ (and no any other information), it is possible to deduce if $s$ and $t$ are connected in $G$. The primary complexity measure of the labeling scheme is the label length (maximum length of a label). In general, labels can be viewed as the \\emph{logical} names of the vertices \\cite{kannan1992implicat,peleg2005informative}, as they are considerably more informative than the physical names that usually correspond to arbitrary $O(\\log n)$-bit identifiers. For example, in routing applications the label of the vertex is treated as its ``address\". It is quite immediate to provide connectivity labeling schemes of logarithmic length. Over the years, these labels have served the basis for devising also approximate distance labels, and compact routing schemes, which are arguably the \\emph{grand finale} of the distributed representation schemes. \n\n\nOur goal in this paper is to provide \\emph{fault-tolerant} analogs for the above mentioned schemes, while paying a small overhead in terms of space and other complexity aspects. Several notions of fault-tolerant labeling and routing schemes have been addressed in the literature; starting with the earlier introduction of FT routing schemes by Dolev \\cite{dolev1984new}, to the more recent formulations of forbidden-set labeling and routing schemes by Courcelle et al. \\cite{courcelle2007forbidden,CourcelleT07}. Despite much activity revolving these topics, FT labeling and routing schemes with \\emph{sub-linear} space are currently known only for a limited collection of graph families. We next elaborate more on the state-of-the-art affairs, and our main objectives.\n\n\n\\paragraph{Fault-Tolerant Connectivity and Distance Labeling.} \nFT connectivity labeling schemes, also known in the literature as \\emph{forbidden-set} labeling \\cite{CourcelleT07}, assign labels to the vertices and the edges of the graph such that given the labels of a vertex pair $s,t$, and the labels of the faulty edges $F$, one can determine if $s$ and $t$ are connected in $G \\setminus F$. \n\n\n\n %\n\nSince their introduction, efficient FT labeling schemes have been devised only for a restricted collection of graph families such as graphs with bounded tree-width and planar graphs \\cite{CourcelleT07,AbrahamCGP16}. In the lack of any FT connectivity labeling schemes for general graphs with sub-linear label length (for any $f\\geq 2$ faults\\footnote{While there is no \\emph{explicit} construction of FT labeling for general graphs, for $f=1$, the centralized distance sensitivity oracle of \\cite{khanna2010approximate} might be modified to provide approximate distance labels against a single fault.}), we ask:\n\n\\begin{question}\\label{q:label}\nIs it possible to design FT connectivity labeling scheme resilient to at most $f$ edge faults, for general graphs with label length of $\\operatorname{\\text{{\\rm poly}}}(\\log n)$ bits, or even $\\operatorname{\\text{{\\rm poly}}}(\\log n,f)$ bits? \n\\end{question}\n\nFT connectivity labels are also closely related to \\emph{connectivity sensitivity oracles} \\cite{patrascu2007planning}, which are low-space centralized data-structures that handle efficiently $\\langle s, t, F \\rangle$ connectivity queries using $S(n)$ space. Our main goal is in providing a \\emph{distributed} variant of such constructions, e.g., where each vertex or edge in the graph ``holds\" only $S(n)\/n$ bits of information, such that an $\\langle s,t, F \\rangle$ query can be addressed using only the information stored by $s,t$ and $F$. \n\nAn important step towards designing FT compact routing schemes involves the computation of \\emph{FT approximate distance labels}. In this setting, given the labels of $s,t$ and the faulty edges $F$, it is required to report an approximation for the $s$-$t$ shortest path distance in $G \\setminus F$.\nFT approximate distance labels can be viewed as the distributed analog of $f$-FT \\emph{distance sensitivity oracles} \\cite{khanna2010approximate,WeimannY10}. \nThese are global succinct data-structures that given an $\\langle s,t,F \\rangle$ query report fast an estimate for the approximate $s$-$t$ distance in $G \\setminus F$. Our goal is to provide FT approximate labeling schemes that match the state-of-the-art space vs. stretch tradeoff of the centralized data structures.\n\n\n\n\\paragraph{Fault-Tolerant Routing.} A desirable requirement in most communication networks is to provide efficient routing protocols in the presence of faults. Specifically, an $f$-FT routing protocol is a distributed algorithm that, for any set of at most $f$ faulty edges $F$, allows a vertex $s$ to route a message to a destination vertex $t$ along an approximate $s$-$t$ shortest path in $G \\setminus F$ (without knowing $F$ in advance). The routing scheme consists of two algorithms: (i) a preprocessing algorithm which computes (succinct) routing tables and labels for each vertex in the graph; and (ii) a routing algorithm that given the received message and the routing table of vertex $v$ determines the next-hop (specified as a port number) on the $v$-$t$ (approximate) shortest path in $G \\setminus F$. The efficiency of the scheme is determined by the tradeoff between the \\emph{stretch} (i.e., the ratio between the weighted length of the $s$-$t$ route in $G\\setminus F$ to the corresponding shortest path distance) and the \\emph{space} of the routing tables, labels and messages. \nWhile the stretch vs. space tradeoff of routing schemes is fully understood in the non-faulty setting, the corresponding bounds in the FT setting are still far from optimal.\nSo far, in all the prior schemes, the space of the individual routing tables could be linear in the worst case, even when allowing a large stretch bound. This is in strike contrast to the standard (non-faulty) compact routing schemes, e.g., by Thorup and Zwick \\cite{thorup2001compact}, which provide each vertex a table of $\\widetilde{O}(n^{1\/k})$ bits, while guaranteeing a route stretch of $2k-1$. The current large gap in the quality of FT routing schemes compared to their non-faulty counterparts leads to the following question.\n\n\\begin{question}\\label{q:route}\nIs it possible to design $f$-fault-tolerant compact routing scheme for general graphs with \\emph{sub-linear} table size and with a sub-logarithmic stretch?\n\\end{question}\n\n\n\n\n\\input{result.tex}\n\n\n\n\\section{Introduction}\nDistributed graph representation is concerned with augmenting each vertex (and possibly also edges) with useful and low-space information in order to efficiently address various graph queries in a distributed manner. As the\nvertices and edges of the network may occasionally fail or malfunction, it is desirable\nto make these representations robust against failures. In this paper, we provide new constructions of succinct \\emph{labeled-based distributed data structures} that can handle connectivity, distance queries and routing in the presence of edge failures. \n\nConnectivity labels are short names attached to each vertex in the $n$-vertex input graph $G$, such that given the labels of a pair of vertices $s$ and $t$ (and no any other information), it is possible to deduce if $s$ and $t$ are connected in $G$. The primary complexity measure of the labeling scheme is the label length (maximum length of a label). In general, labels can be viewed as the \\emph{logical} names of the vertices \\cite{kannan1992implicat,peleg2005informative}, as they are considerably more informative than the physical names that usually correspond to arbitrary $O(\\log n)$-bit identifiers. For example, in routing applications the label of the vertex is treated as its ``address\". It is quite immediate to provide connectivity labeling schemes of logarithmic length. Over the years, these labels have served the basis for devising also approximate distance labels, and compact routing schemes, which are arguably the \\emph{grand finale} of the distributed representation schemes. \n\nOur goal in this paper is to provide \\emph{fault-tolerant} analogs for the above mentioned schemes, while paying a small overhead in terms of space and other complexity aspects. Fault-tolerant (FT) connectivity labeling scheme, also known in the literature as \\emph{forbidden-set} labeling \\cite{CourcelleT07}, assigns labels to the vertices and the edges of the graph such that given the labels of a vertex pair $s,t$, and the labels of the faulty edges $F$, one can determine if $s$ and $t$ are connected in $G \\setminus F$. Several notions of fault-tolerant labeling and routing schemes have been addressed in the literature; starting with the earlier introduction of FT routing schemes by Dolev \\cite{dolev1984new}, to the more recent formulations of forbidden-set labeling and routing schemes by Courcelle et al. \\cite{courcelle2007forbidden,CourcelleT07}. Despite much activity revolving these topics, FT labeling and routing schemes with \\emph{sub-linear} space are currently known only for a limited collection of graph families. We next elaborate more on the state-of-the-art affairs, and our main objectives. \\mtodo{the discussion of related work in the next few pages is quite long. We can consider to have a shorter intro, mainly defining the main problems addressed, and have later a detailed related work section.}\n\n\n\n %\n\n\\paragraph{Fault-Tolerant Connectivity Labeling.} FT labels for connectivity were introduced by \\cite{courcelle2007forbidden} \\mtext{under the term \\emph{forbidden-set labeling}. Forbidden set refers to a subset $F$ of at most $f$ edges, such that given the labels of $s,t$ and $F$ one should determine if $s$ and $t$ are connected in $G \\setminus F$. The forbidden edge set can be treated in this context as faulty edges\\footnote{For routing, the forbidden-set scheme is slightly weaker than FT scheme as explained later.}.} \\mtodo{not sure that this is the best place for discussing the forbidden-set name, maybe it can fit better in a related work section.} Since their introduction, efficient FT labeling schemes have been devised only for a restricted collection of graph families. \\textbf{MP: For example, Courcelle et al. \\cite{CourcelleT07} presented a labeling scheme with logarithmic label length for the families of $n$-vertex graphs with bounded clique-width, tree-width and planar graphs. For $n$-vertex graphs with doubling dimension at most $\\alpha$, Abraham et al. \\cite{AbrahamCGP16} designed FT labeling schemes with label length $O((1 + 1\/\\epsilon)^{2\\alpha}\\log n)$ that output $(1+\\epsilon)$ approximation of the shortest path distances under faults.} In the lack of any FT connectivity labeling schemes for general graphs with sub-linear label length (for any $f\\geq 2$ faults\\footnote{While there is no \\emph{explicit} construction of FT labeling for general graphs, for $f=1$, the centralized distance sensitivity oracle of \\cite{khanna2010approximate} might be modified to provide approximate distance labels against a single fault.}), we ask:\n\n\\begin{question}\\label{q:label}\nIs it possible to design FT connectivity labeling scheme resilient to at most $f$ edge faults, for general graphs with label length of $\\operatorname{\\text{{\\rm poly}}}(\\log n)$ bits, or even $\\operatorname{\\text{{\\rm poly}}}(\\log n,f)$ bits? \n\\end{question}\n\n\\mtodo{The next pargarph is a bit long, we can consider shortning it, and move the more detailed discussion to a related work section. Maybe just focus on the centralized data structures here and not on the certificates?} \\mertodo{Yes, I agree, modifying accordingly.} \nFT connectivity labels are also closely related to \\emph{sensitivity connectivity oracles}, which are low-space centralized data-structure that handle efficiently $\\langle s, t, F \\rangle$ connectivity queries. \\textbf{MP: The first construction of these oracles was given by Patrascu and Thorup \\cite{patrascu2007planning} providing an $S(n)=\\widetilde{O}(fn)$ space oracle that answers $\\langle s,t, F \\rangle$ connectivity queries in $\\widetilde{O}(f)$ time. The state-of-the-art bounds of these oracles are given by Duan and Pettie \\cite{DuanConnectivitySODA17}.} \nOur main goal is in providing a \\emph{distributed} variant of such constructions, e.g., where each vertex or edge in the graph ``holds\" only $S(n)\/n$ bits of information, such that an $\\langle s,t, F \\rangle$ query can be addressed using only the information stored by $s,t$ and $F$. \n\n\\paragraph{Fault-Tolerant Approximate Distance Labeling.} An important step towards designing FT compact routing schemes involves the computation of \\emph{FT approximate distance labels}. In this setting, given the labels of $s,t$ and the faulty edges $F$, it is required to report an approximation for the $s$-$t$ shortest path distance in $G \\setminus F$. \\mtodo{Also the part that starts here may fit better in a related work section (in this case, we will probably need to remove also the question from here - seems that one of the reviwers supported removing the questions in any case).} FT approximate distance labels can be viewed as the distributed analog of $f$-FT \\emph{distance sensitivity oracles} \\cite{khanna2010approximate,WeimannY10}. \nThese are global succinct data-structures that given an $\\langle s,t,F \\rangle$ query report fast an estimate for the approximate $s$-$t$ distance in $G \\setminus F$. \\textbf{MP: Chechik et al. \\cite{chechik2012f} presented the first randomized construction resilient to $f$ edge faults. \nSpecifically, for any $n$-vertex weighted graph, stretch parameter $k$, and a fault bound $f$, they provide a data-structure with $O(f k n^{1+1\/k}\\log(nW))$ space, query time of $\\widetilde{O}(|F|)$, and $O(f k)$ stretch, where $W$ is the weight of the heaviest edge in the graph. Their solution is based on an elegant transformation that converts the FT connectivity oracle of \\cite{patrascu2007planning} into an FT approximate distance oracle.} Our goal is to provide FT approximate labeling schemes that match the state-of-the-art space vs. stretch tradeoff provided by the oracles of \\cite{chechik2012f}, we ask:\n\n\\begin{question}\\label{q:dist-label}\nIs it possible to design FT approximate distance labels with space vs. stretch tradeoff that match the state-of-the-art bounds of the \\emph{centralized} sensitive oracles, e.g., of \\cite{chechik2012f}?\n\\end{question}\n\n\\textbf{MP: While the main focus of this paper is in approximate distances, sensitivity oracles that report (possibly near) exact distances under faults have been studied also thoroughly in e.g., \\cite{demetrescu2002oracles,bernstein2008improved,duan2009dual,WeimannY10,GrandoniW12,ChechikCFK17,van2019sensitive}. Since reporting exact distances requires linear label length already in the fault-free setting \\cite{gavoille2004distance}, we focus on the approximate relaxation, where there is still hope to obtain labels of polylogarithmic length.}\n\\mtodo{Same comment for this paragraph. Also, there is a recent paper about FT exact distance labels in planar graphs \\cite{DBLP:journals\/corr\/abs-2102-07154} that we should probably mention, they show that any directed weighted planar\ngraph admits fault-tolerant distance labels of size $O(n^{2\/3})$. There are also some references cited in their paper that maybe we should discuss as well, for example see the paragraph ``Forbidden-set distance labeling schemes'' in page 2 here: https:\/\/arxiv.org\/pdf\/2102.07154.pdf} \\mertodo{I actually preferred not open up the discussion on special graph families, e.g., planar graphs, graphs with bounded dimension, etc. We do mention it for the direct setting of routing or labeling, but I do not think it should be mentioned for oracles for the following reason. For labels, there was no prior work for general graphs, but for oracles we do have such works so no need to add the extra overhead of special graph families.} \n\n\\paragraph{Fault-Tolerant Routing.} A desirable requirement in most communication networks is to provide efficient routing protocols in the presence of faults. Specifically, an $f$-FT routing protocol is a distributed algorithm that, for any set of at most $f$ faulty edges $F$, allows a vertex $s$ to route a message to a destination vertex $t$ along an approximate $s$-$t$ shortest path in $G \\setminus F$ (without knowing $F$ in advance). The routing scheme consists of two algorithms: (i) a preprocessing algorithm which computes (succinct) routing tables and labels for each vertex in the graph; and (ii) a routing algorithm that given the received message and the routing table of vertex $v$ determines the next-hop (specified as a port number) on the $v$-$t$ (approximate) shortest path in $G \\setminus F$. The efficiency of the scheme is determined by the tradeoff between: \n\\begin{enumerate}[noitemsep]\n\\item the \\emph{stretch} of the route, i.e., the ratio between the length of the route to the $s$-$t$ distance in $G \\setminus F$. \n\\item the \\emph{space} of the routing tables, routing labels and messages. \n\\end{enumerate}\nWhile the stretch vs. space tradeoff of routing schemes is fully understood in the non-faulty setting, the corresponding bounds in the FT setting are still far from optimal. \\mtodo{Also the part that starts here can fit better in a related work section. Also, a reviwer suggested to discuss also \\cite{rajan2012space}. It seems that this work appeard after Chechik et al., and focus on the case of a single edge failure. They show (Theorem 1) a routing scheme with routing tables of size $\\widetilde{O}(k \\deg(v)+ n^{1\/k})$ size per vertex, $O(k^2)$ stretch and $O(k+\\log{n})$ size header that handle a failure of one edge.} \\textbf{MP: The first formalization of FT routing schemes was given by the influential works of Dolev \\cite{dolev1984new} and Peleg \\cite{peleg1987fault}. These earlier works presented the first non-trivial solutions for general graphs supporting at most $\\lambda$ faulty edges, where $\\lambda$ is the edge-connectivity of the graph. Their routing labels had linear size, providing $s$-$t$ routes of possibly linear length (even in cases where the surviving $s$-$t$ path is of $O(1)$ length). In competitive FT routing schemes, it is required to provide $s$-$t$ routes of length that competes with the shortest $s$-$t$ path in $G \\setminus F$, even in cases where $G \\setminus F$ is not connected. Competitive FT routing schemes \\cite{peleg2009good} for general graphs were given by Chechik et al. \\cite{ChechikLPR10,chechik2012f} for the special case of $f\\leq 2$ faults. \nSpecifically, for a given stretch parameter $k$, they gave a routing scheme with a total space bound of $\\widetilde{O}(n^{1+1\/k})$ bits, polylogarithmic-size labels and messages, and a routing \\emph{stretch} of $O(k)$. \nThis scheme was extended later on for any $f$ by Chechik \\cite{chechik2011fault}, at the cost of increasing the routing stretch to $O(f^2(f+\\log^2 n)k)$.}\nSo far, in all these prior schemes, the space of the individual routing tables could be linear in the worst case, even when allowing a large stretch bound. This is in strike contrast to the standard (non-faulty) compact routing schemes, e.g., by Thorup and Zwick \\cite{thorup2001compact}, which provide each vertex a table of $\\widetilde{O}(n^{1\/k})$ bits, while guaranteeing a route stretch of $2k-1$. The current large gap in the quality of FT routing schemes compared to their non-faulty counterparts leads to the following question.\n\n\\begin{question}\\label{q:route}\nIs it possible to design $f$-fault-tolerant compact routing scheme for general graphs with \\emph{sub-linear} table size and with a sub-logarithmic stretch?\n\\end{question}\n\n\\textbf{MP: A more relaxed setting of FT routing scheme which has been studied in the literature is given by the \\emph{forbidden set routing schemes}, introduced by Courcelle and Twigg \\cite{CourcelleT07}. In that setting, it is assumed that the routing protocol knows in advance the set of faulty edges $F$. In contrast, in the FT routing setting, the failing edges are a-priori unknown to the routing algorithm, and can only be detected upon arriving one of their endpoints. Forbidden set routing schemes have been devised to the same class of restricted graph families as obtained for the forbidden set labeling setting \\cite{CourcelleT07,AbrahamCGP16,abraham2012fully}.}\n\n\n\n\\input{result.tex}\n\n\n\n\\subsection{Connectivity Labels Based on Graph Sketches}\\label{sec:ftconn-sketch}\nIn this section, we show the following:\n\\begin{theorem}\nFor every undirected $n$-vertex graph $G=(V,E)$, a positive integer $f$, there is a randomized $f$-FT connectivity labels $\\mathsf{ConnLabel}\\xspace_{G}: V \\cup E \\to \\{0,1\\}^{\\ell}$ of length $\\ell=O(\\log^3 n)$ bits. The decoding time of the scheme is $\\widetilde{O}(f)$, and the computation time for assigning the labels is $\\widetilde{O}(m+n)$.\n\\end{theorem}\nIn Section \\ref{sec:label-alg}, we present the labeling algorithm which assigns labels based on the notion of graph sketches. In Section \\ref{sec:dec-alg} we present the decoding algorithm that given the label information determines if $s$ and $t$ are connected in $G \\setminus F$. When the graph $G$ is clear from the context, we may omit it and simply write $\\mathsf{ConnLabel}\\xspace$. \n\n\n\n\\subsubsection{The Labeling Algorithm}\\label{sec:label-alg}\nGiven a connected graph $G$, let $T$ be an arbitrary rooted spanning tree in $G$ that is used throughout this section. In our future applications of this labeling scheme (e.g., routing), both the graph $G$ and the tree $T \\subseteq G$ will be given as input to the labeling algorithm. In the latter case, we denote the output labels by $\\mathsf{ConnLabel}\\xspace_{G,T}$. Throughout, all vertices have unique ids $\\operatorname{ID}(v)$ between $\\{1,\\ldots,n \\}$. \n\n\\paragraph{Extended Edge Identifiers.} In our algorithm it is important to distinguish between an identifier of a single edge to the bitwise XOR of several edges. For this purpose, we define for each edge $e$ an extended edge identifier $\\operatorname{EID}_T(e)$ that allows distinguishing between these cases, and serves as the identifier of the edge.\nThe extended edge identifier $\\operatorname{EID}_T(e)$ consists of a (randomized) unique distinguishing identifier $\\operatorname{UID}(e)$, as well as additional tree related information that facilitates the decoding procedure. The computation of $\\operatorname{UID}(e)$ is based on the notion of $\\epsilon$-\\emph{bias} sets \\cite{naor1993small}. The construction is randomized and guarantees that, w.h.p., the XOR of the $\\operatorname{UID}$ part of each given subset of edges $S \\subseteq E$, for $|S|\\geq 2$, is not a legal $\\operatorname{UID}$ identifier of any edge.\nLet $\\mathsf{XOR}\\xspace(S)$ be the bitwise XOR of the extended identifiers of edges in $S$, i.e., $\\mathsf{XOR}\\xspace(S)=\\oplus_{e \\in S} \\operatorname{EID}_T(e)$. In addition, let $\\mathsf{XOR}\\xspace_U(S)=\\oplus_{e \\in S} \\operatorname{UID}(e)$. Missing proofs are deferred to Appendix \\ref{sec:miss-proof}.\n\n\\begin{lemma}[Modification of Lemma 2.4 in \\cite{GhaffariP16}]\n\\label{cl:epsbias}\nThere is an algorithm that creates a collection $\\mathcal{I}=\\{\\operatorname{UID}(e_1), \\ldots, \\operatorname{UID}(e_{M})\\}$ of $M=\\binom{n}{2}$ random identifiers for all possible edges $(u,v)$, each of $O(\\log n)$-bits using a seed $\\mathcal{S}_{ID}$ of $O(\\log^2 n)$ bits. These identifiers are such that for each subset $E' \\subseteq E$, where $|E'|\\neq 1$, we have $\\Pr[\\mathsf{XOR}\\xspace_U(E') \\in \\mathcal{I}] \\leq 1\/n^{10}$. In addition, given the identifiers $\\operatorname{ID}(u), \\operatorname{ID}(v)$ of the edge $e=(u,v)$ endpoints, and the seed $\\mathcal{S}_{ID}$, one can determine $\\operatorname{UID}(e)$ in $\\widetilde{O}(1)$ time.\n\\end{lemma}\n\\def\\APPENDUNIQUEID{\n\\begin{proof}[Proof of Lemma \\ref{cl:epsbias}]\nThe lemma is proved in \\cite{GhaffariP16}, the only part that is not discussed there is the time to determine $\\operatorname{UID}(e)$ that follows from \\cite{naor1993small}. \nBy Theorem 3.1 of \\cite{naor1993small}, given the seed $\\mathcal{S}_{ID}$ and the edge identifier $e_j=(\\operatorname{ID}(u), \\operatorname{ID}(v))$, determining the $i^{th}$ bit of $\\operatorname{UID}(e_{j})$ can be done in $O(\\log n)$ time. Thus, determining all $O(\\log n)$ bits, takes $O(\\log^2 n)$ time. \n\\end{proof}\n\nFor every vertex $v \\in G$, let $\\mathsf{ANC}\\xspace_T(v)$ be the ancestor label of $v$ computed for the given tree $T$ using Lemma \\ref{anc_labels}. The extended identifier $\\operatorname{EID}_T(e)$ is given by\n\\begin{equation}\\label{eq:extend-ID}\n\\operatorname{EID}_T(e)=[\\operatorname{UID}(e), \\operatorname{ID}(u), \\operatorname{ID}(v), \\mathsf{ANC}\\xspace_T(u), \\mathsf{ANC}\\xspace_T(v)]~.\n\\end{equation}\nThe identifiers of $\\operatorname{ID}(u), \\operatorname{ID}(v)$ are used in order to verify the validity of the unique identifier $\\operatorname{UID}(e)$. \nWhen the tree $T$ is clear from the context, we might omit it and simply write $\\operatorname{EID}(e)$. As we will see, the labeling scheme will store the seed $\\mathcal{S}_{ID}$ as part of the labels of the tree edges. \n\n\n\\paragraph{Fault-Tolerant Labels via Graph Sketches.} \nGraph sketches are a tool to identify outgoing edges. We start by providing an intuition for them. Say that $S$ is a connected component, and that there are $2^j$ edges outgoing from $S$. If we sample all edges in the graph with probability $1\/2^j$, there is a constant probability that exactly one outgoing edge from $S$ is sampled, and our goal is to find it using local information stored at the vertices of $S$. This information is the \\emph{sketch}. \nThe sketch of each vertex stores the bitwise XOR of sampled edges adjacent to it. Now looking at the XOR of all the sketches of vertices of $S$ allows to detect an outgoing edge. This holds as any sampled edge that has both endpoints in $S$ gets cancelled out, and we are left with the XOR of sampled edges outgoing from $S$. If there is exactly one outgoing edge, we find it. To increase the success probability we can repeat the process $O(\\log{n})$ times. We define sets of vertices $E_{i,j}$, where for $i \\in \\{1, \\ldots, c \\log n\\}$, the set $E_{i,j}$ is obtained by sampling each edge with probability $2^{-j}$. Since we repeat the process $O(\\log{n})$ times for each $j$, then w.h.p we can use the sketches to identify outgoing edge from any component. To use this approach in our context, it is crucial to be able to simulate the sampling process using a small random seed. To do this, we follow \\cite{DuanConnectivityArxiv16,DuanConnectivitySODA17} and use pairwise independent hash functions to decide whether to include edges in sampled sets.\nWe choose $L=c\\log n$ \npairwise independent hash functions $h_1, \\ldots, h_{L}:\\{0,1\\}^{\\Theta(\\log n)} \\to \\{0, \\ldots, 2^{\\log m}-1\\}$,\nand for each $i \\in \\{1, \\ldots, L\\}$ and $j \\in [0,\\log m]$, define the edge set \n$$E_{i,j} =\\{ e \\in E ~\\mid~ h_i(e) \\in [0,2^{\\log m-j})\\}~.$$ \nEach of these hash functions can be defined using a random seed of logarithmic length \\cite{TCS-010}. Thus, a \nrandom seed $\\mathcal{S}_h$ of length $O(L \\log n)$ can be used to determine the collection of all these $L$ functions. As observed in \\cite{DuanConnectivityArxiv16,GibbKKT15}, pairwise independence is sufficient to guarantee that for any set $E' \\subset E$ and any $i$, there exists an index $j$, such that with constant probability $\\mathsf{XOR}\\xspace(E' \\cap E_{i,j})$ is the name (extended identifier) of one edge in $E'$, for a proof see Lemma 5.2 in \\cite{GibbKKT15}.\n\\begin{lemma}\\label{lem:hitting-pairwise}\nFor any edge set $E'$ and any $i$, with constant probability there exists a $j$ satisfying that $|E' \\cap E_{i,j}|=1$.\n\\end{lemma}\n\n\nWe also need to be able to tell that a bit string of $\\mathsf{XOR}\\xspace(E' \\cap E_{i,j})$ is a legal edge ID or not. Here we exploit the extended ids. See Appendix \\ref{sec:miss-proof} for a proof.\n\\begin{lemma} \\label{lemma_unique}\nGiven the seed $\\mathcal{S}_{ID}$, one can determine in $\\widetilde{O}(1)$ time if $\\mathsf{XOR}\\xspace(E' \\cap E_{i,j})$ corresponds to a single edge ID in $G$ or not, w.h.p.\n\\end{lemma}\n\\def\\APPENDLEMMUNIQUE{\n\\begin{proof}[Proof of Lemma \\ref{lemma_unique}]\nLet $X=\\mathsf{XOR}\\xspace(E' \\cap E_{i,j})$. Letting $E''=E' \\cap E_{i,j}$, then $X$ can be written as the concatenation of $\\mathsf{XOR}\\xspace_1(E'')$ and $\\mathsf{XOR}\\xspace_2(E'')$, where $\\mathsf{XOR}\\xspace_1(E'')=\\mathsf{XOR}\\xspace_U(E'')$ is the bit-wise XOR of the unique identifiers $\\operatorname{UID}(e)$ for $e \\in E''$ and $\\mathsf{XOR}\\xspace_2(E'')$ is the bit-wise XOR of the remaining information in the extended identifiers of $E''$. We now show how using the seed and $\\mathsf{XOR}\\xspace_2(E'')$, one can test the validity of $\\mathsf{XOR}\\xspace_1(E'')$.\nThe algorithm detects the case that $|E''| \\geq 2$ as follows. First, in the case that $E''$ is a single edge, $\\mathsf{XOR}\\xspace_2(E'')$ should contain legal ids $\\operatorname{ID}(u),\\operatorname{ID}(v)$. If this is not the case, it follows that $|E''| \\neq 1$. If $\\mathsf{XOR}\\xspace_2(E'')$ contains legal ids $\\operatorname{ID}(u),\\operatorname{ID}(v)$, we use them and the seed $\\mathcal{S}_{ID}$ to determine $\\operatorname{UID}(e)$ for $e = (u,v)$, and we check if $\\mathsf{XOR}\\xspace_1(E'')=\\operatorname{ID}_1(e)$. We have two options, either $E'' = \\{e\\}$ is the single edge $e$, in which case $\\mathsf{XOR}\\xspace_U(E'')=\\operatorname{UID}(e) \\in \\mathcal{I}$, and the verification succeeds. Otherwise $|E''| \\geq 2$, in which case, from Lemma \\ref{cl:epsbias}, $\\Pr[\\mathsf{XOR}\\xspace_U(E'') \\in \\mathcal{I}] \\leq 1\/n^{10}$, hence w.h.p $\\mathsf{XOR}\\xspace_U(E'') \\neq \\operatorname{UID}(e) \\in \\mathcal{I}$ and we identify that $|E''| \\geq 2$.\n\\end{proof}\n\n\nFor each vertex $v$ and indices $i,j$, let $E_{i,j}(v)$ be the edges incident to $v$ in $E_{i,j}$. \nThe $i^{th}$ \\emph{basic sketch unit} of each vertex $v$ is then given by:\n\\begin{equation}\n\\label{eq:vsketch}\n\\mathsf{Sketch}\\xspace_{G,i}(v)=[\\mathsf{XOR}\\xspace(E_{i,0}(v)),\\ldots,\\mathsf{XOR}\\xspace(E_{i,\\log m}(v))].\n\\end{equation}\nThe sketch of each vertex $v$ is defined by a concatenation of $L=\\Theta(\\log n)$ basic sketch units: \n$$\\mathsf{Sketch}\\xspace_G(v)=[\\mathsf{Sketch}\\xspace_{G,1}(v),\\mathsf{Sketch}\\xspace_{G,2}(v), \\ldots\\mathsf{Sketch}\\xspace_{G,L}(v)]~.$$ \nFor every subset of vertices $S$, let \n$\\mathsf{Sketch}\\xspace_G(S)=\\oplus_{v \\in S}\\mathsf{Sketch}\\xspace_G(v).$ When the graph $G$ is clear from the context, we may omit it and write $\\mathsf{Sketch}\\xspace_{i}(v)$ and $\\mathsf{Sketch}\\xspace(v)$. \n\nWe are now ready to define the fault-tolerant connectivity labels of vertices and edges. \nThe label of each vertex $u$ is given by:\n\\begin{equation}\\label{eq:conn-vertex}\n\\mathsf{ConnLabel}\\xspace_{G,T}(u)=\\langle \\mathsf{ANC}\\xspace_T(u), \\operatorname{ID}(u) \\rangle~,\n\\end{equation}\nwhere $\\mathsf{ANC}\\xspace_T(u)$ is the ancestry label of $u$ with respect to the tree $T$. \nFor every $u \\in V(T)$, let $T_u$ be the subtree rooted at $u$. The label $\\mathsf{ConnLabel}\\xspace_{G,T}(e)$ of each \\emph{edge} $e=(u,v)$ is given by:\n\\begin{equation*}\n \\mathsf{ConnLabel}\\xspace_{G,T}(e)=\n \\begin{cases}\n \\langle \\operatorname{EID}_T(e), \\mathsf{Sketch}\\xspace(V(T_u)), \\mathsf{Sketch}\\xspace(V(T_v)), \\mathsf{Sketch}\\xspace(V), \\mathcal{S}_{ID}, \\mathcal{S}_h\\rangle ,& \\mbox{~for~} e \\in T \\\\\n \\langle \\operatorname{EID}_T(e) \\rangle,& \\mbox{~Otherwise}.\n \\end{cases}\n\\end{equation*}\n\nWe complete this subsection by bounding the label size and computation time of the labeling algorithm. For proofs see Appendix \\ref{sec:miss-proof}. \n\\begin{claim}\\label{cl:label-length}\nThe label length is $O(\\log^3 n)$ bits.\n\\end{claim}\n\\def\\APPENDLABELCONSISE{\n\\begin{proof}[Proof of Claim \\ref{cl:label-length}]\nThe label size is dominated by the sketching information $\\mathsf{Sketch}\\xspace(V(T_u))$, which is made of a concatenation of the bitwise XOR of $O(\\log n)$ basic sketch units $\\mathsf{Sketch}\\xspace_i(u)$. By Eq. (\\ref{eq:vsketch}), each unit has $O(\\log^2 n)$ bits, and thus overall, the label has $O(\\log^3 n)$ bits.\n\\end{proof}\n\n\nWe show that assigning the labels takes $\\widetilde{O}(m+n)$ time.\n\\begin{claim}\\label{cl:time-conn-labelsketch}\nThe time complexity of the labeling algorithm is $\\widetilde{O}(m+n).$\n\\end{claim}\n\\def\\APPENDCONNLABELSKETCH{\n\\begin{proof}[Proof of Claim \\ref{cl:time-conn-labelsketch}]\nTo compute the labels of vertices we assign ids to vertices in $O(n)$ time, and compute ancestry labels in $O(n)$ time using Lemma \\ref{anc_labels}. To compute the extended identifiers $\\operatorname{EID}_T(e)$, we also choose the random seed $\\mathcal{S}_{ID}$ and compute $\\operatorname{UID}(e)$ using Lemma \\ref{cl:epsbias}, this takes $\\widetilde{O}(1)$ time per edge, and $\\widetilde{O}(m)$ time for all edges. Lastly, we should compute the sketch values $\\mathsf{Sketch}\\xspace(V(T_u))$. For this, first, we choose the random seed $\\mathcal{S}_h$, and compute the values $\\mathsf{Sketch}\\xspace_G(v)$. For this, we should identify for each vertex the adjacent edges in $E_{i,j}$. For each edge we can identify the sets it belongs to in $\\widetilde{O}(1)$ time using Fact \\ref{fc:pairwise}. This allows us computing the sketch values of all vertices in $\\widetilde{O}(m+n)$ time. We can then compute the values $\\mathsf{Sketch}\\xspace(V(T_u))$ by scanning the tree in $\\widetilde{O}(n)$ time. \n\\end{proof}\n\nFinally, the subsequent decoding algorithm will be based on the following useful property of the graph sketches, stored by our labels. \n\\begin{lemma}\\label{lem:sketch-property}\nFor any subset $S$, given one basic sketch unit $\\mathsf{Sketch}\\xspace_i(S)$ and the seed $\\mathcal{S}_{ID}$ one can compute, with constant probability, an outgoing edge $E(S, V \\setminus S)$ if such exists. The complexity is $\\widetilde{O}(1)$ time.\n\\end{lemma}\n\\def\\APPENDSKETCHPROP{\n\\begin{proof}[Proof of Lemma \\ref{lem:sketch-property}]\nThe proof follows from Lemma \\ref{lem:hitting-pairwise}. Note that by definition of the sketch values $\\mathsf{Sketch}\\xspace_i(S)=\\oplus_{v \\in S}\\mathsf{Sketch}\\xspace_i(v)=[\\mathsf{XOR}\\xspace(E_{i,0}(S)),\\ldots,\\mathsf{XOR}\\xspace(E_{i,\\log m}(S))],$ where $E_{i,j}(S)$ are the outgoing edges from $S$ in $E_{i,j}$ (edges that have both endpoints in $S$ are cancelled out by the XOR operation). Let $E'$ be all the outgoing edges from $S$. From Lemma \\ref{lem:hitting-pairwise}, with constant probability there exists a $j$ such that $|E' \\cap E_{i,j}|=1$. In this case, $\\mathsf{XOR}\\xspace(E_{i,j}(S))$ corresponds to an extended id of a single outgoing edge from $S$. We can check if this happens in $\\widetilde{O}(1)$ time using Lemma \\ref{lemma_unique}. \n\\end{proof}\n\n\n\n\n\\subsubsection{The Decoding Algorithm} \\label{sec:dec-alg}\nWe next describe the decoding algorithm where given a triplet $s,t, F \\in V \\times V \\times E^f$ along with their labels, it determines whether $s$ and $t$ are connected in $G\\setminus F$, w.h.p. \nThe decoding algorithm has four key steps: The first step identifies the at most $f+1$ components $\\mathcal{C}_0=\\{C_1,\\ldots, C_\\ell\\}$ of $T \\setminus F$, as well as the components of $s$ and $t$ in $\\mathcal{C}_0$. The second step uses the label information to compute the sketch value $\\mathsf{Sketch}\\xspace(C_i)$ of each component $C_i \\in \\mathcal{C}_0$. The third step modifies this sketch information into $\\mathsf{Sketch}\\xspace_{G \\setminus F}(C_i)$, by subtracting the information related to the faulty edges. The forth and final step uses the sketch information in order to simulate $L=O(\\log n)$ steps of the Boruvka algorithm. At the end of these steps, the decoding algorithm identifies the connected components of both $s$ and $t$ in $G \\setminus F$. In the case where $s$ and $t$ are indeed connected in $G \\setminus F$, the algorithm also outputs a succinct representation of an $s$-$t$ path in $G \\setminus F$. This extra information would be used later on by our compact routing scheme. We next describe these steps in details. \n\n\\paragraph{Step 1: Identification of the connected components $\\mathcal{C}_0$ in $T \\setminus F$.} \nLet $F_T=F \\cap T$ be the faulty tree edges and let $F_{NT}=F \\setminus F_T$ be the faulty non-tree edges. Let $Q=\\{s,t\\} \\cup V(F_T)$. Each component $C_i$ of $T \\setminus F$ will be identified by the maximum vertex ID in $C_i \\cap V(F_T)$. Note that in the case where $F_T=\\emptyset$, $T \\setminus F=T$ and thus $s$ and $t$ are connected iff $s,t \\in V(T)$. From now on, we therefore assume that $F_T \\neq \\emptyset$. \n\nWe next show that although we do not have full information about the tree $T$ and the vertices of each connected component, the ancestry labels of $V(F_T)$ give us enough information to identify the connected components of $T \\setminus F$. Additionally, given an ancestry label of a vertex $u$, we can identify the connected component of $u$. To obtain this, it is helpful to look at the \\emph{component tree} that is obtained by contracting each connected component of $T \\setminus F$ to one vertex, as follows. Let $\\ell = |F_T|+1.$ The component tree $T_C = (\\mathcal{C}_0, E_C)$ is a tree of $\\ell$ vertices representing the connected components in $T \\setminus F$, and $|F_T|=\\ell-1$ edges corresponding to the edges of $F_T$. There is an edge $(C_i,C_j) \\in E_C$ iff there is an edge $(u,v) \\in F_T$ where $u \\in C_i, v \\in C_j$. See Figure \\ref{componentTreePic} for an illustration. \n\n\n\\setlength{\\intextsep}{0pt}\n\\begin{figure}[h]\n\\centering\n\\setlength{\\abovecaptionskip}{-2pt}\n\\setlength{\\belowcaptionskip}{6pt}\n\\includegraphics[scale=0.55]{componentTree.pdf}\n \\caption{Illustration of the component tree where $F=\\{e_1,e_2,e_3,e_4\\}$. Each connected component of $T \\setminus F$ is contracted to one vertex on the right.}\n\\label{componentTreePic}\n\\end{figure}\n\nWe can construct the tree $T_C$ using the ancestry labels of the edges $F_T$. For this, for each edge $e \\in F_T$ we just need to identify the set of edges from $F_T$ above $e$ in $T$. Moreover, for a given vertex $v$, its connected component is exactly determined by the set of edges in $F_T$ above it in $T$, which can again be identified using the ancestry labels of $v \\cup V(F_T)$. In particular, we can identify the connected components of $s$ and $t$. The component tree can be constructed in $O(f^2)$ time by checking for any pair of edges $e,e' \\in F_T$, if $e$ is above $e'$ in the tree. We next show a faster algorithm taking only $\\widetilde{O}(f)$ time by exploiting properties of the ancestry labels. Moreover, we show that the component of each vertex can be identified in $O(\\log{f})$ time.\n\n\\begin{claim} \\label{claim_component_tree}\nThe component tree can be constructed in $O(f \\log{f})$ time. Additionally, given $\\mathsf{ANC}\\xspace_T(v)$, we can identify the connected component of $v$ in $T \\setminus F$ in $O(\\log{f})$ time.\n\\end{claim}\n\n\\begin{proof}\nOur algorithm uses ancestry labels based on DFS from \\cite{kannan1992implicat}. In this scheme, the label of each vertex $v$ is composed of two numbers $(DFS_1(v),DFS_2(v))$ that represent the first and last times a DFS scan of the tree visits $v$. A vertex $u$ is an ancestor of a vertex $v$ iff the interval $(DFS_1(u),DFS_2(u))$ contains the interval $(DFS_1(v),DFS_2(v))$. To build the component tree, we sort the labels of $V(F_T)$, as described next. First, for each component $C \\in T \\setminus F$, we use the highest vertex in the component to represent the component. For the highest component, this is the root $r$. For any other component, we have that the highest vertex of the component, $v$, is in $V(F_T)$. This holds as the edge connecting $v$ to its parent $p(v)$ is necessarily in $F_T$ (otherwise, $v$ is not the highest vertex in its component), see Figure \\ref{componentTreePic} for illustration. Hence, for any edge $(v,p(v)) \\in F_T$, we have that the vertex $v$ represents one component (we can identify which of the vertices is the parent using the ancestry labels). Hence, we have $|F_T|+1$ vertices $v_i$ representing the components $C_i$ of the component tree, and we also know the ancestry labels $(DFS_1(v_i),DFS_2(v_i))$ of all vertices $v_i$, except $r$. For $r$ we can use the label $(1,M)$ where $M$ is a number greater than all values $DFS_2(v_i)$ of other vertices. We next use these labels to determine the structure of the component tree.\nFor this, we create for each vertex $v_i$ two tuples: $(DFS_1(v_i),v_i,1),(DFS_2(v_i),v_i,2)$, and we sort the $2(|F_T|+1)$ tuples according to their first coordinate. This takes $O(f \\log{f})$ time. We next scan the sorted list, and when we reach the tuple $(DFS_1(v_i),v_i,1)$, we identify the parent of $v_i$ in the component tree, as follows. The first tuple is $(1,r,1)$ and $r$ is set to be the root of the component tree. For a vertex $v_i \\neq r$, we identify its parent when we reach $(DFS_1(v_i),v_i,1)$. Let $(DFS_b(u),u,b)$ be the last tuple before $(DFS_1(v_i),v_i,1)$ in the sorted order. If $b=1$, then $u$ is the parent of $v_i$ in the component tree. If $b=2$, let $w$ be the parent of $u$ in the component tree, then $w$ is also the parent of $v$ in the component tree. Additionally, $w$ was already computed as $(DFS_1(u),u,1)$ appears before $(DFS_1(v_i),v_i,1)$. Hence, we can find the parent of $v$ in $O(1)$ time using the tuple before it. Scanning the list takes $O(f)$ time, and after it we know for each component its parent in the component tree, which gives the complete structure of the tree. We next prove the correctness of the algorithm. \n\nWe first discuss the case that $b=1$. Here $(DFS_1(u),u,1)$ is the last tuple before $(DFS_1(v_i),v_i,1)$. This means that $u$ is necessarily an ancestor of $v$, because the entry $(DFS_1(v_i),v_i,1)$ is between the entries $(DFS_1(u),u,1)$ and $(DFS_2(u),u,2)$, and the DFS scan traverses exactly the subtree of $u$ in the time interval $(DFS_1(u),DFS_2(u))$, implying that $v_i$ is a child of $u$. Moreover, this is the closest ancestor to $v_i$ among the vertices $\\{v_1,v_2,...,v_{\\ell}\\} \\setminus \\{v_i\\}$, as the DFS scan traverses the ancestors of $v_i$ from the highest to the lowest. It follows that $u$ represents the closest component $C$ above $v_i$ in the component tree, as needed. \n\nWe next discuss the case that $b=2$. Here $(DFS_2(u),u,2)$ is the last tuple before $(DFS_1(v_i),v_i,1)$. Note that now $u$ is not an ancestor of $v_i$, as the DFS scan finished scanning the subtree of $u$ before reaching $v_i$, but we claim that $u$ and $v_i$ have the same parent in the component tree. For this, we show they have exactly the same ancestors in the set $\\{v_1,v_2,...,v_{\\ell}\\} \\setminus \\{u,v_i\\}.$ For any ancestor $w\\neq u$ of $u$, we have that $DFS_1(w) < DFS_1(u) < DFS_2(u) < DFS_2(w)$. As $(DFS_1(v_i),v_i,1)$ is the first tuple after $(DFS_2(u),u,2)$, it must hold that $DFS_1(w) < DFS_1(v_i) < DFS_2(w)$, implying that $v_i$ is a child of $w$ as needed. Similarly, any ancestor $w \\neq v_i$ of $v_i$ is also an ancestor of $u$, as we have $DFS_1(w) < DFS_2(u) < DFS_1(v_i) < DFS_2(v_i) < DFS_2(w)$.\nHence, the parent of $u$ in the component tree is also the parent of $v_i$ in the component tree, as needed. \n \nLastly, we show that using similar ideas we can also identify the component of a vertex $v$ in $T \\setminus F$. We create for $v$ the tuple, $(DFS_1(v),v,1)$, and use binary search to find the last tuple smaller or equal to it in the sorted list we computed before, denote it by $(DFS_b(u),u,b)$. \nIf $b=1$ then $v$ is in the component of $u$, and else it is in the component of the parent of $u$ (that was computed before). The complexity of the binary search is $O(\\log{f})$, we next prove correctness. \nOne special case is that $v$ is a root of one of the components in the component tree. In this case, the entry $(DFS_b(u),u,b)$ we find is equal to $(DFS_1(v),v,1)$, and $u=v$ is indeed the component of $v$. Otherwise, $v$ is an internal vertex in its component, and the root of the component is the closest ancestor to $v$ in $\\{v_1,...,v_{\\ell}\\}$.\nIf $b=1$, then as shown before, $u$ is the closest ancestor to $v$ in the component tree, as needed. If $b=2$, then as shown before, $u$ is not an ancestor of $v$, but has exactly the same ancestors in the component tree. Hence, the root $w$ of the component above $u$ is the root of the component of $v$, as needed. \n\\end{proof} \n\n\n\\paragraph{Step 2: Computing the sketch values of each component $\\mathcal{C}_0$ in $G$.} \nFor each component $C_j \\in \\mathcal{C}_0$ the algorithm computes $\\mathsf{Sketch}\\xspace_G(C_j)$ using the sketch information of the vertices in $V(F_T)$. The basic observation here is the following. Given $S' \\subset S$ and $\\mathsf{Sketch}\\xspace(S), \\mathsf{Sketch}\\xspace(S')$, it holds that $\\mathsf{Sketch}\\xspace(S \\setminus S')=\\mathsf{Sketch}\\xspace(S) ~\\oplus~ \\mathsf{Sketch}\\xspace(S')$. To compute the sketch values, first, we define for each component a temporary value $\\mathsf{Sketch}\\xspace'_G(C_j)$ as follows. Let $v_j$ be the highest vertex (closest to the root in $T$) in the component $C_j$. For the component of the root $r$, this is $r$. For any other component $C_j$, let $(C_j,p(C_j))$ be the edge connecting $C_j$ to its parent in the component tree. This edge corresponds to an edge $(v_j,p(v_j)) \\in F_T$, where $v$ is the highest vertex in $C_j$. We define $\\mathsf{Sketch}\\xspace'_G(C_j) = \\mathsf{Sketch}\\xspace_G(V(T_{v_j}))$.\nSince $(v_j,p(v_j)) \\in F_T$, the sketch information $\\mathsf{Sketch}\\xspace'_G(C_j)$ can be obtained from the label of the tree edge $(v_j,p(v_j))$. We also know the temporary sketch value of the component of $r$, as $\\mathsf{Sketch}\\xspace_G(V_{r})=\\mathsf{Sketch}\\xspace_G(V)$ is part of the labels of all tree edges (and we assume that $F_T \\neq \\emptyset$). We next use the temporary sketch values to compute the sketch values of components using the following claim.\n\n\\begin{claim}\nLet $C_j$ be a component in $T \\setminus F$. If $C_j$ is a leaf in the component tree, we have $\\mathsf{Sketch}\\xspace_G(C_j) = \\mathsf{Sketch}\\xspace'_G(C_j).$ Otherwise, let $D=\\{D_1,...,D_t\\}$ be the children of $C_j$ in the component tree and let $\\mathsf{Sketch}\\xspace'(D)=\\oplus_{1 \\leq i \\leq t} \\mathsf{Sketch}\\xspace'_G(D_i)$, then $\\mathsf{Sketch}\\xspace_G(C_j) = \\mathsf{Sketch}\\xspace'_G(C_j) \\oplus \\mathsf{Sketch}\\xspace'(D).$ \n\\end{claim}\n\n\\begin{proof}\nIt holds that $\\mathsf{Sketch}\\xspace_G(C_j) = \\oplus_{v \\in C_j} \\mathsf{Sketch}\\xspace_G(v)$. By definition, $\\mathsf{Sketch}\\xspace'_G(C_j)=\\mathsf{Sketch}\\xspace_G(V(T_{v_j})) = \\oplus_{v \\in V(T_{v_j})} \\mathsf{Sketch}\\xspace_G(v)$ is the XOR of sketches of all vertices in the subtree of $v_j$. As $v_j$ is the highest vertex in $C_j$, if $C_j$ is a leaf component in the component tree, then the vertices in $C_j$ are exactly the vertices in $T_{v_j}$, and the claim follows. Otherwise, the vertices in $C_j$ are all vertices in $T_{v_j}$ that are not contained in any component below $C_j$. Hence, to compute the value $\\mathsf{Sketch}\\xspace_G(C_j)$, we should subtract from $\\mathsf{Sketch}\\xspace_G(V(T_{v_j}))$ the sketch values of vertices in components below $C_j$. Let $D_1,\\ldots,D_t$ be the children of $C_j$ in the component tree, and let $u_1,\\ldots,u_t$ be the highest vertices in the components $D_1,\\ldots,D_t$, respectively. Any vertex that is in some component below $C_j$ is in exactly one of the subtrees $T_{u_1},\\ldots,T_{u_t}$. Hence the sketch value of vertices in components below $C_j$ equals $\\oplus_{1 \\leq i \\leq t} \\mathsf{Sketch}\\xspace_G(V(T_{u_i}))= \\oplus_{1 \\leq i \\leq t} \\mathsf{Sketch}\\xspace'_G(D_i)=\\mathsf{Sketch}\\xspace'(D)$. To conclude, we get $\\mathsf{Sketch}\\xspace_G(C_j)=\\mathsf{Sketch}\\xspace_G(V(T_{v_j})) \\oplus \\mathsf{Sketch}\\xspace'(D)=\\mathsf{Sketch}\\xspace'_G(C_j) \\oplus \\mathsf{Sketch}\\xspace'_G(D)$, as needed.\n\\end{proof}\n\nTo conclude, from the values $\\mathsf{Sketch}\\xspace'_G(C_j)$, we can easily compute the values $\\mathsf{Sketch}\\xspace_G(C_j)$. The complexity is $\\widetilde{O}(f)$, as for each component, the sketch $\\mathsf{Sketch}\\xspace'(C_j)$ participates in two computations, and we have at most $O(f)$ components and the sketches have poly-logarithmic size.\n\n\n\\paragraph{Step 3: Computing the sketch values of each component $\\mathcal{C}_0$ in $G \\setminus F$.} \nFor each faulty edge $e \\in F$ (both tree and non-tree edges), our goal is to subtract the sketch information of $e$ from the corresponding components of the endpoint of $e$. The step does not require the label information of the edges, and it would be sufficient to know only the seed $\\mathcal{S}_h$ that determines the sampling of edges into the sketches, and the extended identifier of the failing edges. Since $F_T \\neq \\emptyset$, the algorithm holds the seed $\\mathcal{S}_h$ (from the label of an edge $e \\in F_T$), and it has the extended identifiers of all edges in $F$ as part of their labels. \n\nUsing the extended identifier of the faulty edge $e=(u,v)$, one can determine in $O(\\log{f})$ time the components in $\\mathcal{C}_0$ to which its endpoints belong, from Claim \\ref{claim_component_tree}. Using the identifier $\\operatorname{EID}(e)$ and the seed $\\mathcal{S}_h$, one can determine all the indices of the sketch to which the edge $e$ was sampled in $\\widetilde{O}(1)$ time using Fact \\ref{fc:pairwise}.\nLetting $C_u, C_v$ be the components of $u$ and $v$ in $T \\setminus F$, respectively. If $C_u \\neq C_v$, then the values $\\mathsf{Sketch}\\xspace_G(C_u),\\mathsf{Sketch}\\xspace_G(C_v)$ are updated by XORing them with the matrix that contains the extended identifier $\\operatorname{EID}(e)$ in the relevant positions. The complexity is poly-logarithmic, as the matrix has poly-logarithmic size. In the case that $C_u=C_v$, as $e$ is an internal edge in the component, it is not part of $\\mathsf{Sketch}\\xspace_G(C_u)$, and there is no need to update the value. Overall, doing the computation for all edges in $F$ takes $\\widetilde{O}(f)$ time.\nFrom that point on, all sketches of the components $\\mathcal{C}_0$ can be treated as sketches that have been computed in $G \\setminus F$. \n\n\n\\paragraph{Step 4: Simulating the Boruvka algorithm.} Finally, our goal is to determine the identifiers of the maximal connected components of $s$ and $t$ of $G \\setminus F$. The input to this step is the identifiers of the components $\\mathcal{C}_0=\\{C_{1}, \\ldots, C_k\\}$ in $T \\setminus F$, along with their sketch information in $G \\setminus F$. While the algorithm does not have information on the vertices of each component, it knows the component identifier of each vertex in $Q$. \n\nThe algorithm consists of $L=O(\\log n)$ phases of the Boruvka algorithm. Each phase $i \\in \\{1,\\ldots, L\\}$ will be given as input a partitioning $\\mathcal{C}_i=\\{C_{i,1}, \\ldots, C_{i,k_i}\\}$ of (not necessarily maximal) connected components in $G \\setminus F$.\nThese components are identified by an $O(\\log n)$ bit identifier, where for each vertex in $Q$, the algorithm receives its unique component identifier in $\\mathcal{C}_i$. In addition, the algorithm receives the sketch information of the components $\\mathcal{C}_i$ in $G \\setminus F$. The output of the phase is a partitioning $\\mathcal{C}_{i+1}$, along with their sketch information in $G \\setminus F$ and the identifiers of the components for each vertex in $U$. A component $C_{i,j} \\in \\mathcal{C}_i$ is \\emph{growable} if it has at least one non-faulty outgoing edge to a vertex in $V \\setminus C_{i,j}$. That is, the component is growable if it is strictly contained in some maximal connected component in $G \\setminus F$. Letting $N_i$ denote the number of growable components in $\\mathcal{C}_i$, the output partitioning $\\mathcal{C}_{i+1}$ of the $i^{th}$ step guarantees that $N_{i+1}\\leq N_i \/2$ w.h.p. To obtain outgoings edges from the growable components in $\\mathcal{C}_i$, the algorithm uses the $i^{th}$ basic-unit sketch $\\mathsf{Sketch}\\xspace_i(C_{i,j})$ of each $C_{i,j} \\in \\mathcal{C}_i$. By Lemma \\ref{lem:sketch-property}, from every growable component in $\\mathcal{C}_i$, we get one outgoing edge $e'=(x,y)$ with constant probability. Using the extended edge identifier of $e'$ the algorithm can also detect the component $C_{i,j'}$ to which the second endpoint, say $y$, of $e'$ belongs using Claim \\ref{claim_component_tree}.\nThat label allows us to compute the component of $y$ in the initial partitioning $T \\setminus F$, i.e., the component $C_{0,q}$ of $y$ in $\\mathcal{C}_0$. Thus $y$ belongs to the unique component $C_{i,j'} \\in \\mathcal{C}_i$ that contains \n$C_{0,q}$. \n\n\n\nAs noted in prior works \\cite{ahn2012analyzing,kapron2013dynamic,DuanConnectivityArxiv16}, it is important to use fresh randomness (i.e., independent sketch information) in each of the Boruvka phases. The reason is that the cut query, namely, asking for a cut edge between $S$ and $V \\setminus S$, should not be correlated with the randomness of the sketches. Note that indeed the components of $\\mathcal{C}_i$ are correlated with the randomness of the first $(i-1)$ basic sketch units of the vertices. Thus, in phase $i$ the algorithm uses the $i^{th}$ basic sketch units of the vertices (which are independent of the other sketch units) to determine the outgoing edges of the components in $\\mathcal{C}_i$.\n\n\nThe algorithm then computes the updated sketches of the merged components. This is done by XORing over the sketches of the components in $\\mathcal{C}_i$ that got merged into a single component in \n$\\mathcal{C}_{i+1}$. In expectation, the number of growable components is reduced by factor $2$ in each phase. Thus after $O(\\log n)$ phases, the expected number of growable components is at most $1\/n^5$, and using Markov inequality, we conclude that w.h.p there are no growable components. The final partitioning $\\mathcal{C}_L$ corresponds w.h.p to the maximal connected components in $G \\setminus F$. The pair $s$ and $t$ are connected in $G \\setminus F$ only if the components $C_s,C_t$ of $s,t$ respectively in $T \\setminus F$ are connected in the final component decomposition.\nWe next show that the complexity of the algorithm is $\\widetilde{O}(f)$. This is also the decoding time of the whole algorithm, as all steps take $\\widetilde{O}(f)$ time, as discussed above. \n\n\\begin{claim}\\label{cl:complexity-step-four}\nThe complexity of step 4 is $\\widetilde{O}(f)$.\n\\end{claim}\n\n\\begin{proof\nThe algorithm has $O(\\log{n})$ phases, where in each phase the following is computed. First, given the sketch values of the current components we identify outgoing edges from the components. This takes $\\widetilde{O}(1)$ time per component from Lemma \\ref{lem:sketch-property}, and $\\widetilde{O}(f)$ time for all components, as we have at most $f+1$ components. Next, for each outgoing edge we identify the components it connects using its ancestry labels, this takes $\\widetilde{O}(1)$ time per edge using Claim \\ref{claim_component_tree}. Then, we merge components accordingly and compute the sketch values of the new components by XORing the sketch values of merged components. Overall this takes $\\widetilde{O}(f)$ time, as we have at most $O(f)$ merges. In more detail, we can use a union-find data structure to implement the merges, where every time we merge components we compute the sketch value of the new component. We also maintain for each original component in $T \\setminus F$ its current component in phase $i$, this allows us to learn the current components connected by an outgoing edge $e$. This information can be maintained as follows. Let $C$ be a component in $T \\setminus F$, and assume we know the component $C_{i,j}$ it belongs to at the beginning of phase $i$. After the merges of phase $i$, $C_{i,j}$ joins some component $C_{i+1,j'}$ of phase $i+1$. We can use the find operation to identify the id of the new component. Overall, we have $O(f)$ merges and $O(f)$ find operations to identify for each component $C \\in T \\setminus F$, the corresponding component $C_{i+1,j'}$ it belongs to, hence the complexity is bounded by $\\widetilde{O}(f)$. \n\\end{proof}\n\nFinally, we show that the decoding algorithm can be slightly modified to output a compressed encoding of an $s$-$t$ path in $G \\setminus F$, using $O(f\\log n)$ bits. This encoding is represented by an $s$-$t$ path $\\widehat{P}$ that has two type of edges, appearing in an alternate manner on $\\widehat{P}$: $G$-edges and edges $e'=(u,v)$ such that the $u$-$v$ tree path is intact in $T \\setminus F$. See Figure \\ref{fig:succ-paths}. \n\\begin{lemma}\\label{lem:useful-recovery-edges}\nConsider a triplet $s,t,F$ such that $s$ and $t$ are connected in $G \\setminus F$. \nThe decoding algorithm can also output a set of at most $f$ recovery edges $R$ such $(T \\setminus F) \\cup R$ is a spanning tree. In addition, it outputs a labeled $s$-$t$ path $\\widehat{P}$ of length $O(f)$ that provides a succinct description of the $s$-$t$ path. The edges of $\\widehat{P}$ are labeled by $0$ and $1$, where $0$-labeled edges correspond to $G$-edges and $1$-labeled edges $e=(x,y)$ correspond to $x$-$y$ paths in $T \\setminus F$. \n\\end{lemma}\n\\begin{proof}\nLet $C_s, C_t$ be the components of $s$ and $t$ in the initial partitioning $\\mathcal{C}_0$. In Step $4$ of the decoding algorithm, the Boruvka algorithm is simulated up to the point that $C_s$ and $C_t$ are connected. Therefore, the algorithm has computed a path $P$ that connects the components $C_s$ and $C_t$. Each vertex on that path corresponds to a component in $\\mathcal{C}_0$, and each edge corresponds to an outgoing edge (discovered using the sketch information). Since $\\mathcal{C}_0$ has at most $f+1$ components, $|P|\\leq f+1$. \nEach such edge $e' \\in P$ corresponds to an edge in $G$. Let $e_1=(x_1,y_1),\\ldots, e_k=(x_k,y_k)$ be the $G$-edges corresponding to the edges of $P$ ordered from $C_s$ to $C_t$. Letting $y_0=s$ and $x_{k+1}=t$, we get that \n$y_i$ and $x_{i+1}$ belong to the same component in $\\mathcal{C}_0$, for every $i \\in \\{0,\\ldots, k\\}$. \nThe labeled path is given by $\\widehat{P}=[s, x_1,y_1, x_2, y_2, \\ldots y_k,t]$ where the edges $(y_i,x_{i+1})$ are labeled $1$ and the edges $(x_{i}, y_i)$ are labeled $0$. Each $0$-labeled edge is a real edge in $G$, and each $1$-labeled edge $(x_{i}, y_i)$ corresponds to a tree path $\\pi(x_i, y_i)$ in $T \\setminus F$. \n\\end{proof}\n\n\n\\begin{figure}[h!]\n\\begin{center}\n\\includegraphics[scale=0.40]{suc-path.pdf}\n\\caption{\\sf Shown is a tree $T$ with faulty edges $e_1,\\ldots, e_4$. The $s$-$t$ path in $G \\setminus F$ is represented by the path $\\widehat{P}=[s,v_1]\\circ (v_1,v_2) \\circ [v_2,v_3] \\circ (v_3,r) \\circ [r,v_4] \\circ (v_4, v_5) \\circ [v_5,t]$. The recovery edges $(v_1,v_2), (v_3,r)$ and $(v_4, v_5)$ are shown in dashed lines. \\label{fig:succ-paths}\n}\n\\end{center}\n\\end{figure}\n\\subsection{Connectivity Labels Based on Graph Sketches}\\label{sec:ftconn-sketch}\n\n\\mtodo{I'm not sure if we want to have the statement of the theorem with respect to a tree. Maybe only have a variant with a tree where it's needed (for the routing)}\n\nIn this section, we show the following:\n\\begin{theorem}\nFor every undirected $n$-vertex graph $G=(V,E)$ and a spanning tree $T \\subseteq G$, a positive integer $f$, there is a randomized $f$-FS connectivity labels $\\mathsf{ConnLabel}\\xspace_{G,T}: V \\cup E \\to \\{0,1\\}^{Q}$ of length $Q=O(\\log^3 n)$ bits. \n\\textbf{MP: add later the computation time of the scheme and the decoding time.}\n\\end{theorem}\nIn Section \\ref{sec:label-alg}, we present the labeling algorithm which assigns labels based on the notion of graph sketches. In Section \\ref{sec:dec-alg} we present the decoding algorithm that given the label information determines if $s$ and $t$ are connected in $G \\setminus F$. When the graph $G$ and the spanning tree $T$ are clear from the context, we may omit it and simply write $\\mathsf{ConnLabel}\\xspace$. \n\n\n\n\\subsubsection{The Labeling Algorithm}\\label{sec:label-alg}\nGiven a graph $G$, let $T$ be an arbitrary rooted tree in $G$ that is used throughout this section.\nFor any node $u \\in V(T)$, let $V_u$ be the subset of nodes in the subtree of $T$ rooted at $u$. The algorithm starts by computing ancestry labels $\\mathsf{ANC}\\xspace(u)$ for all nodes $u$ given the tree $T$ using Lemma \\ref{anc_labels}. Additionally, we assign to all vertices unique ids $\\operatorname{ID}(v)$ between $\\{1,...,n \\}.$\n\n\\paragraph{Extended Edge Identifiers.} Each edge $e$ in $G$ is assigned to a unique identifier $ID_T(e)$ that is made of two parts: a \\emph{distinguishing} part $ID_{1,T}(e)$ (that does not depend on $T$) and a \\emph{logical part} $ID_{2,T}(e)$ that depends on $T$; each of $O(\\log n)$ bits. The distinguishing part $ID_{1,T}(e)$ is defined in a way that guarantees that the XOR of several identifiers does not correspond to a legal identifier of any edge $e \\in G$ w.h.p. The logical part $ID_{2,T}(e)$ contains auxiliary information that aids the decoding algorithm. \nThe computation of the distinguishing parts $ID_{1,T}(e)$ is based on the notion of $\\epsilon$-\\emph{bias} sets \\cite{naor1993small}. The construction is randomized and guarantees that, w.h.p., the XOR of the $ID_{1,T}$ part of each given subset of edges $S \\subseteq E$, for $|S|\\geq 2$, is not a legal $ID_{1,T}$ identifier of any edge.\nLet $\\mathsf{XOR}\\xspace(S)$ be the bitwise XOR of the extended identifiers of edges in $S$, i.e., $\\mathsf{XOR}\\xspace(S)=\\oplus_{e \\in S} \\operatorname{ID}_T(e)$. In addition, let $\\mathsf{XOR}\\xspace_1(S)=\\oplus_{e \\in S} \\operatorname{ID}_{1,T}(e)$.\n\n\\begin{lemma}[Modification of Lemma 2.4 in \\cite{GhaffariP16}]\n\\label{cl:epsbias}\nThere is an algorithm that creates a collection $\\mathcal{I}=\\{\\operatorname{ID}_1(e_1), \\ldots, \\operatorname{ID}_1(e_{M})\\}$ of $M=\\binom{n}{2}$ random identifiers for all possible edges $(u,v)$, each of $O(\\log n)$-bits using a seed $\\mathcal{S}_{ID}$ of $O(\\log^2 n)$ bits. These identifiers are such that for each subset $E' \\subseteq E$, where $|E'|\\neq 1$, we have $\\Pr[\\mathsf{XOR}\\xspace_1(E') \\in \\mathcal{I}] \\leq 1\/n^{10}$. In addition, given the identifiers $\\operatorname{ID}(u), \\operatorname{ID}(v)$ of the edge $e=(u,v)$ endpoints, and the seed $\\mathcal{S}_{ID}$, one can determine $\\operatorname{ID}_1(e)$ in $\\widetilde{O}(1)$ time.\n\\end{lemma}\n\\begin{proof}\nThe lemma is proved in \\cite{GhaffariP16}, the only part that is not discussed there is the time to determine $\\operatorname{ID}_1(e)$ that follows from \\cite{naor1993small}. \nBy Theorem 3.1 of \\cite{naor1993small}, given the seed $\\mathcal{S}_{ID}$ and the edge identifier $e_j=(\\operatorname{ID}(u), \\operatorname{ID}(v))$, determining the $i^{th}$ bit of $\\operatorname{ID}_1(e_{j})$ can be done in $O(\\log n)$ time. Thus, determining all $O(\\log n)$ bits, takes $O(\\log^2 n)$ time. \n\\end{proof}\nThe second part of the identifier $\\operatorname{ID}_{2,T}(e)$ is given by $\\operatorname{ID}_{2,T}(e)=[\\operatorname{ID}(u), \\operatorname{ID}(v), \\mathsf{ANC}\\xspace_T(u), \\mathsf{ANC}\\xspace_T(v)]$. \nThe identifiers of $\\operatorname{ID}_T(u), \\operatorname{ID}_T(v)$ are used in order to verify the validity of the first part of the label $\\operatorname{ID}_1(e)$. When the tree $T$ is clear from the context, we might omit it and simply write $\\operatorname{ID}(e)$. \n\n\\paragraph{Graph Sketches.}\nGraph sketches are a powerful tool to identify outgoing edges \\cite{kapron2013dynamic,ahn2012analyzing}. \\mtodo{I can add some references later, maybe in the related work section} We start by illustrating the intuition behind them. For a vertex $v$, let $\\mathsf{BaseSketch}\\xspace(v)$ be the bitwise xor of all IDs of edges adjacent to $v$. If we take a subset of vertices $S$, and define $\\mathsf{BaseSketch}\\xspace(S) = \\oplus_{v \\in S} \\mathsf{BaseSketch}\\xspace(v)$, we can see that all edges that have both endpoints in $S$ are cancelled, and we are left with the xor of outgoing edges from $S$. If there is only one such edge, we get its id. In the case there are more outgoing edges, we can use sampling to identify one outgoing edge. We next formalize this idea and show how to use it in our labels. \n\n\\paragraph{Forbidden-Set Labels via Graph Sketches.} \nThe graph sketches are based on random sub-sampling of the graph edges with logarithmic number of scales, i.e., with probability of $2^{-i}$ for every $i \\in \\{1,\\ldots, m\\}$. For our purposes and similarly to \\cite{DuanConnectivityArxiv16,DuanConnectivitySODA17}, we use pairwise independent hash functions to decide whether to include edges in sampled sets. Choose $L=c\\log n$ \npairwise independent hash functions $h_1, \\ldots, h_{L}:\\{0,1\\}^{2\\log n} \\to \\{0, \\ldots, 2^{\\log m}-1\\}$, and for each $i \\in \\{1, \\ldots, L\\}$ and $j \\in [0,\\log m]$, define the edge set \n$$E_{i,j} =\\{ e \\in E ~\\mid~ h_i(e) \\in [0,2^{\\log m-j})\\}~.$$ \nEach of these hash functions can be defined using a random seed of logarithmic length \\cite{TCS-010}. Thus, a \nrandom seed $\\mathcal{S}_h$ of length $O(L \\log n)$ can be used to determine the collection of all these $L$ functions. As observed in \\cite{DuanConnectivityArxiv16,GibbKKT15}, pairwise independence is sufficient to guarantee that for any set $E' \\subset E$ and any $i$, there exists a $j$ such that with constant probability $\\mathsf{XOR}\\xspace(E' \\cap E_{i,j})$ is the name (extended identifier) of one edge in $E'$, for a proof see Lemma 5.2 in \\cite{GibbKKT15}.\n\\begin{lemma}\\label{lem:hitting-pairwise}\nFor any edge set $E'$ and any $i$, with constant probability there exists a $j$ satisfying that $|E' \\cap E_{i,j}|=1$.\n\\end{lemma}\n\n\nWe also need to be able to tell that a bit string of $\\mathsf{XOR}\\xspace(E' \\cap E_{i,j})$ is a legal edge ID or not.\n\\textbf{MP: all these lemmas and claims can (should) be moved to the subsequent analysis section.}\n\n\\begin{lemma}\nGiven the seed $\\mathcal{S}_{ID}$, one can determine in $\\widetilde{O}(1)$ time if $\\mathsf{XOR}\\xspace(E' \\cap E_{i,j})$ corresponds to a single edge ID in $G$ or not, w.h.p.\n\\end{lemma}\n\\begin{proof}\nLet $X=\\mathsf{XOR}\\xspace(E' \\cap E_{i,j})$. Letting $E''=E' \\cap E_{i,j}$, then $X$ can be written as the concatenation of $\\mathsf{XOR}\\xspace_1(E'')$ and $\\mathsf{XOR}\\xspace_2(E'')$. \nUsing the seed and $\\mathsf{XOR}\\xspace_2(E'')$, one can test the validity of $\\mathsf{XOR}\\xspace_1(E'')$.\nThe algorithm detects the case that $|E''| \\geq 2$ as follows. First, in the case that $E''$ is a single edge, $\\mathsf{XOR}\\xspace_2(E'')$ should contain legal ids $\\operatorname{ID}(u),\\operatorname{ID}(v)$. If this is not the case, it follows that $|E''| \\neq 1$. If $\\mathsf{XOR}\\xspace_2(E'')$ contains legal ids $\\operatorname{ID}(u),\\operatorname{ID}(v)$, we use them and the seed $\\mathcal{S}_{ID}$ to determine $\\operatorname{ID}_1(e)$ for $e = (u,v)$, and we check if $\\mathsf{XOR}\\xspace_1(E'')=\\operatorname{ID}_1(e)$. We have two options, either $E'' = \\{e\\}$ is the single edge $e$, in which case $\\mathsf{XOR}\\xspace_1(E'')=\\operatorname{ID}_1(e) \\in \\mathcal{I}$, and the verification succeeds. Otherwise $|E''| \\geq 2$, in which case, from Lemma \\ref{cl:epsbias}, $\\Pr[\\mathsf{XOR}\\xspace_1(E'') \\in \\mathcal{I}] \\leq 1\/n^{10}$, hence w.h.p $\\mathsf{XOR}\\xspace_1(E'') \\neq \\operatorname{ID}_1(e) \\in \\mathcal{I}$ and we identify that $|E''| \\geq 2$.\n\\end{proof}\nFor each vertex $v$ and indices $i,j$, let $E_{i,j}(v)$ be the edges incident to $v$ in $E_{i,j}$. \nThe $i^{th}$ \\emph{basic sketch unit} of each node $v$ is then given by:\n\\begin{equation}\n\\label{eq:vsketch}\n\\mathsf{Sketch}\\xspace_i(v)=[\\mathsf{XOR}\\xspace(E_{i,0}(v)),\\ldots,\\mathsf{XOR}\\xspace(E_{i,\\log m}(v))].\n\\end{equation}\nThe sketch of each node $v$ is defined by a concatenation of $L=\\Theta(\\log n)$ basic sketch units: \n$$\\mathsf{Sketch}\\xspace(v)=[\\mathsf{Sketch}\\xspace_{1}(v),\\mathsf{Sketch}\\xspace_{2}(v), \\ldots\\mathsf{Sketch}\\xspace_{L}(v)]~.$$ \nFor every subset of vertices $S$, let \n$\\mathsf{Sketch}\\xspace(S)=\\oplus_{v \\in S}\\mathsf{Sketch}\\xspace(v).$ \n\nWe are now ready to define the forbidden set \\mtodo{fault-tolerant?} connectivity labels of vertices and edges. \nThe label of each vertex $u$ is given by:\n$$\\mathsf{ConnLabel}\\xspace_{G,T}(u)=\\langle \\mathsf{Sketch}\\xspace(V(T(u))), \\mathsf{Sketch}\\xspace(V), \\mathsf{ANC}\\xspace_T(u), \\mathcal{S}_{ID}, \\mathcal{S}_h\\rangle,$$ \nwhere $\\mathsf{ANC}\\xspace_T(u)$ is the ancestry label of $u$ with respect to the tree $T$.\nThe label $\\mathsf{ConnLabel}\\xspace_{G,T}(e)$ of each \\emph{edge} $e=(u,v)$ is as given by:\n\\begin{equation*}\n \\mathsf{ConnLabel}\\xspace(e)=\n \\begin{cases}\n \\langle \\mathsf{ConnLabel}\\xspace_{G,T}(u), \\mathsf{ConnLabel}\\xspace_{G,T}(v)\\rangle ,& \\mbox{~for~} e \\in T \\\\\n \\langle ID_T(e) \\rangle,& \\mbox{~Otherwise}.\n \\end{cases}\n\\end{equation*}\n\n\n\\begin{claim}\nThe label length is $O(\\log^3 n)$ bits.\n\\end{claim}\n\\begin{proof}\nThe label size is dominated by the sketching information $\\mathsf{Sketch}\\xspace(V_u)$, which is made of a concatenation of the bitwise XOR of $O(\\log n)$ basic sketch units $\\mathsf{Sketch}\\xspace_i(u)$. By Eq. (\\ref{eq:vsketch}), each unit has $O(\\log^2 n)$ bits, and thus overall, the label has $O(\\log^3 n)$ bits.\n\\end{proof}\n\n\\mtodo{I think maybe now we want something slightly different, that given a basic sketch unit, we can find with constant probability an outgoing edge? Also, should add a proof.}\n\n\\begin{lemma}\\label{lem:sketch-property}\nFor any subset $S$, given $\\mathsf{Sketch}\\xspace(S)$ one can compute, w.h.p., an outgoing edge $E(S, V \\setminus S)$ if such exists. \n\\end{lemma}\n\n\n\\subsubsection{The Decoding Algorithm} \\label{sec:dec-alg}\nWe next describe the decoding algorithm where given every triplet $s,t, F \\in V \\times V \\times E^f$ along with their labels, it determines whether $s$ and $t$ are connected in $G\\setminus F$, w.h.p. For our decoding algorithm it would actually be sufficient to get as input:\n\\begin{enumerate}[noitemsep]\n\\item the connectivity labels of $s,t$, the labels of the faulty tree-edge $F \\cap E(T)$, and in addition,\n\\item the extended identifiers of the faulty non-tree edges $F \\setminus T$ (which are part of the labels)\\footnote{This property will be important later on for obtaining the compact routing schemes.}. \n\\end{enumerate}\nThe decoding algorithm has four key steps: The first step identifies the at most $f+1$ components $\\mathcal{C}_0=\\{C_1,\\ldots, C_\\ell\\}$ of $T \\setminus F$, as well as the components of $s$ and $t$ in $\\mathcal{C}_0$. The second step uses the label information to compute the sketch value $\\mathsf{Sketch}\\xspace(C_i)$ of each component $C_i \\in \\mathcal{C}_0$. The third step modifies this sketch information into $\\mathsf{Sketch}\\xspace_{G \\setminus F}(C_i)$, by subtracting the information related to the faulty edges. The forth and final step uses the sketch information in order to simulate $L=O(\\log n)$ steps of the Boruvka algorithm. At the end of these steps, the decoding algorithm identifies the connected components of both $s$ and $t$ in $G \\setminus F$. In the case where $s$ and $t$ are indeed connected in $G \\setminus F$, the algorithm also outputs a succinct representation of an $s$-$t$ path in $G \\setminus F$. This extra information would be used later on by our compact routing scheme. We next describe these steps in details. \n\n\\paragraph{Step 1: Identification of the connected components $\\mathcal{C}_0$ in $T \\setminus F$.} \nLet $F_T=F \\cap T$ be the faulty tree edges and let $F_{NT}=F \\setminus F_T$ be the faulty non-tree edges. Let $U=\\{s,t\\} \\cup V(F_T)$. Each component $C_i$ of $T \\setminus F$ will be identified by the maximum vertex ID in $C_i \\cap U$. Note that the non-tree faulty edges $F_{NT}$ have no impact on the components of $T \\setminus F$ (thus their labels are indeed not needed for that step).\nWe next show that although we do not have full information about the tree $T$ and the vertices of each connected component, the ancestry labels of $V(F_T)$ give us enough information to identify the connected components of $T \\setminus F$. Additionally, given an ancestry label of a vertex $u$, we can identify the connected component of $u$. To obtain this, it is helpful to look at the \\emph{component tree} that is obtained by contracting each connected component of $T \\setminus F$ to one vertex, as follows. Let $\\ell = |F_T|+1.$ The component tree $T_C = (\\mathcal{C}_0, E_C)$ is a tree of $\\ell$ vertices representing the connected components in $T \\setminus F$, and $|F_T|=\\ell-1$ edges corresponding to the edges of $F_T$. There is an edge $\\{C_i,C_j\\} \\in E_C$ iff there is an edge $\\{u,v\\} \\in F_T$ where $u \\in C_i, v \\in C_j$. See Figure \\ref{componentTreePic} for an illustration. We can construct the tree $T_C$ using the ancestry labels of the edges $F_T$. For this we just need to identify for any edge in $F_T$ the set of edges from $F_T$ above it in $T$. Moreover, for a given vertex $v$, its connected component is exactly determined by the set of edges in $F_T$ above it in $T$, which can again be identified using the ancestry labels of $v \\cup V(F_T)$. In particular, we can identify the connected components of $s$ and $t$. \\mtodo{add the time complexity, we can probably get a faster algorithm if needed.}\n\n\\setlength{\\intextsep}{0pt}\n\\begin{figure}[h]\n\\centering\n\\setlength{\\abovecaptionskip}{-2pt}\n\\setlength{\\belowcaptionskip}{6pt}\n\\includegraphics[scale=0.55]{componentTree.pdf}\n \\caption{Illustration of the component tree where $F=\\{e_1,e_2,e_3,e_4\\}$. Each connected component of $T \\setminus F$ is contracted to one vertex on the right.}\n\\label{componentTreePic}\n\\end{figure}\n \n\n\\paragraph{Step 2: Computing the sketch values of each component $\\mathcal{C}_0$ in $G$.} \nFor each component $C_j \\in \\mathcal{C}_0$ the algorithm computes $\\mathsf{Sketch}\\xspace_G(C_j)$ using the label information of the nodes in $U$. The basic observation here is the following. Given $S' \\subset S$ and $\\mathsf{Sketch}\\xspace(S), \\mathsf{Sketch}\\xspace(S')$, it holds that $\\mathsf{Sketch}\\xspace(S \\setminus S')=\\mathsf{Sketch}\\xspace(S) ~\\mathsf{XOR}\\xspace~ \\mathsf{Sketch}\\xspace(S')$. To compute the sketch values, first, we define for each component a temporary value $\\mathsf{Sketch}\\xspace'_G(C_j)$ as follows. Let $v_j$ be the highest vertex in the component $C_j$. For the component of the root $r$, this is $r$. For any other component $C_j$, let $\\{C_j,p(C_j)\\}$ be the edge connecting $C_j$ to its parent in the component tree. This edge corresponds to an edge $\\{v,p(v)\\} \\in F_T$, where $v$ is the highest vertex in $C_j$. We define $\\mathsf{Sketch}\\xspace'_G(C_j) = \\mathsf{Sketch}\\xspace_G(V_{v_j})$. Note that this value is part of the label of the vertex $v_j$. For any $v_j \\neq r$, we have $v_j \\in V(F_T)$, and we also learn the identity of $v_j$ when constructing the component tree in Step 1, hence we know $\\mathsf{Sketch}\\xspace'_G(C_j)$. We also know the temporary sketch value of the component of $r$, as $\\mathsf{Sketch}\\xspace_G(V_{r})=\\mathsf{Sketch}\\xspace_G(V)$ is part of the labels of all vertices. We next use the temporary sketch values to compute the sketch values of components using the following claim.\n\n\\begin{claim}\nLet $C_j$ be a component in $T \\setminus F$. If $C_j$ is a leaf in the component tree, we have $\\mathsf{Sketch}\\xspace_G(C_j) = \\mathsf{Sketch}\\xspace'_G(C_j).$ Otherwise, let $D=\\{D_1,...,D_t\\}$ be the children of $C_j$ in the component tree and let $\\mathsf{Sketch}\\xspace'(D)=\\oplus_{1 \\leq i \\leq t} \\mathsf{Sketch}\\xspace'_G(D_i)$, then $\\mathsf{Sketch}\\xspace_G(C_j) = \\mathsf{Sketch}\\xspace'_G(C_j) \\oplus \\mathsf{Sketch}\\xspace'(D).$ \n\\end{claim}\n\n\\begin{proof}\nIt holds that $\\mathsf{Sketch}\\xspace_G(C_j) = \\oplus_{v \\in C_j} \\mathsf{Sketch}\\xspace_G(v)$. By definition, $\\mathsf{Sketch}\\xspace'_G(C_j)=\\mathsf{Sketch}\\xspace_G(V_{v_j}) = \\oplus_{v \\in V_{v_j}} \\mathsf{Sketch}\\xspace_G(v)$ is the xor of sketches of all vertices in the subtree of $v_j$. As $v_j$ is the highest vertex in $C_j$, if $C_j$ is a leaf component in the component tree, then the vertices in $C_j$ are exactly the vertices in $V_{v_j}$, and the claim follows. Otherwise, the vertices in $C_j$ are all vertices in $V_{v_j}$ that are not contained in any component below $C_j$. Hence, to compute the value $\\mathsf{Sketch}\\xspace_G(C_j)$, we should subtract from $\\mathsf{Sketch}\\xspace_G(V_{v_j})$ the sketch values of vertices in components below $C_j$. Let $D_1,...,D_t$ be the children of $C_j$ in the component tree, and let $u_1,...,u_t$ be the highest vertices in the components $D_1,...,D_t$. Any vertex that is in some component below $C_j$ is in exactly one of the subtrees $V_{u_1},...,V_{u_t}$. Hence the sketch value of vertices in components below $C_j$ equals $\\oplus_{1 \\leq i \\leq t} \\mathsf{Sketch}\\xspace_G(V_{u_i})= \\oplus_{1 \\leq i \\leq t} \\mathsf{Sketch}\\xspace'_G(D_i)=\\mathsf{Sketch}\\xspace'(D)$. To conclude, we get $\\mathsf{Sketch}\\xspace_G(C_j)=\\mathsf{Sketch}\\xspace_G(V_{v_j}) \\oplus \\mathsf{Sketch}\\xspace'(D)=\\mathsf{Sketch}\\xspace'_G(C_j) \\oplus \\mathsf{Sketch}\\xspace'(D)$, as needed.\n\\end{proof}\n\nTo conclude, from the values $\\mathsf{Sketch}\\xspace'_G(C_j)$, we can easily compute the values $\\mathsf{Sketch}\\xspace_G(C_j)$. The complexity is $\\tilde{O}(f)$, as for each component, the sketch $\\mathsf{Sketch}\\xspace'(C_j)$ participates in two computations, and we have at most $O(f)$ components and the sketches have poly-logarithmic size.\n\n\n\\paragraph{Step 3: Computing the sketch values of each component $\\mathcal{C}_0$ in $G \\setminus F$.} \nFor each faulty edge $e \\in F$ (both tree and non-tree edges), our goal is to subtract the sketch information of $e$ from the corresponding components of the endpoint of $e$. The step does not require the label information of the edges, and it would be sufficient to know only the seed $\\mathcal{S}_h$ that determines the sampling of edges into the sketches, and the extended identifier of the failing edges. \n\nUsing the extended identifier of the faulty edge $e=(u,v)$, one can determine the components in $\\mathcal{C}_0$ to which its endpoints belong from the ancestry labels of $u$ and $v$, as explained in Step 1. Using the identifier $\\operatorname{ID}(e)$ and the seed $\\mathcal{S}_h$, one can determine all the indices of the sketch to which the edge $e$ was sampled. \\mtodo{what is the complexity of this? MP: It should be $\\widetilde{O}(1)$ using Fact \\ref{fc:pairwise}.}\nLetting $C_u, C_v$ be the components of $u$ and $v$ in $T \\setminus F$, respectively. The values $\\mathsf{Sketch}\\xspace_G(C_u),\\mathsf{Sketch}\\xspace_G(C_v)$ are updated by XORing them with the matrix that contains the identifier $\\operatorname{ID}(e)$ in the relevant positions. \\mtext{The complexity is poly-logarithmic, as the matrix has poly-logarithmic size.}\nFrom that point on, all sketches of the components $\\mathcal{C}_0$ can be treated as sketches that have been computed in $G \\setminus F$. \n\n\n\\paragraph{Step 4: Simulating the Boruvka algorithm.} Finally, our goal is to determine the identifiers of the maximal connected components of $s$ and $t$ of $G \\setminus F$. The input to this step is the identifiers of the components $\\mathcal{C}_0=\\{C_{1}, \\ldots, C_k\\}$ in $T \\setminus F$, along with their sketch information in $G \\setminus F$. While the algorithm does not have information on the nodes of each component, it knows the component identifier of each node in $U$. \n\nThe algorithm consists of $L=O(\\log n)$ phases of the Boruvka algorithm. Each phase $i \\in \\{1,\\ldots, L\\}$ will be given as input a partitioning $\\mathcal{C}_i=\\{C_{i,1}, \\ldots, C_{i,k_i}\\}$ of (not necessarily maximal) connected components in $G \\setminus F$.\nThese components are identified by an $O(\\log n)$ bit identifier, where for each vertex in $U$, the algorithm receives its unique component identifier in $\\mathcal{C}_i$. In addition, the algorithm receives the sketch information of the components $\\mathcal{C}_i$ in $G \\setminus F$. The output of the phase is a partitioning $\\mathcal{C}_{i+1}$, along with their sketch information in $G \\setminus F$ and the identifiers of the components for each node in $U$. A component $C_{i,j} \\in \\mathcal{C}_i$ is \\emph{growable} if it has at least one non-faulty outgoing edge to a node in $V \\setminus C_{i,j}$. That is, the component is growable if it is strictly contained in some maximal connected component in $G \\setminus F$. Letting $N_i$ denote the number of growable components in $\\mathcal{C}_i$, the output partitioning $\\mathcal{C}_{i+1}$ of the $i^{th}$ step guarantees that $N_{i+1}\\leq N_i \/2$ w.h.p. To obtain outgoings edges from the growable components in $\\mathcal{C}_i$, the algorithm uses the $i^{th}$ basic-unit sketch $\\mathsf{Sketch}\\xspace_i(C_{i,j})$ of each $C_{i,j} \\in \\mathcal{C}_i$. By Lemma \\ref{lem:sketch-property}, from every growable component in $\\mathcal{C}_i$, we get one outgoing edge $e'=(x,y)$ with constant probability. Using the extended edge identifier of $e'$ the algorithm can also detect the component $C_{i,j'}$ to which the second endpoint, say $y$, of $e'$ belongs. Specifically, this is done using the ancestry label of the detected edge $e'$. That label allows us to compute the component of $y$ in the initial partitioning $T \\setminus F$, i.e., the component $C_{0,q}$ of $y$ in $\\mathcal{C}_0$. Thus $y$ belongs to the unique component $C_{i,j'} \\in \\mathcal{C}_i$ that contains \n$C_{0,q}$. \n\n\n\nAs noted in prior works \\cite{ahn2012analyzing,kapron2013dynamic,DuanConnectivityArxiv16}, it is important to use fresh randomness (i.e., independent sketch information) in each of the Boruvka phases. The reason is that the cut query, namely, asking for a cut edge between $S$ and $V \\setminus S$, should not be correlated with the randomness of the sketches. Note that indeed the components of $\\mathcal{C}_i$ are correlated with the randomness of the first $(i-1)$ basic sketch units of the vertices. Thus, in phase $i$ the algorithm uses the $i^{th}$ basic sketch units of the vertices (which are independent of the other sketch units) to determine the outgoing edges of the components in $\\mathcal{C}_i$.\n\n\nThe algorithm then computes the updated sketches of the merged components. This is done by xoring over the sketches of the components in $\\mathcal{C}_i$ that got merged into a single component in \n$\\mathcal{C}_{i+1}$. In expectation, the number of growable components is reduced by factor $2$ in each phase. Thus after $O(\\log n)$ phases, the expected number of growable components is at most $1\/n^5$, and using Markov inequality, we conclude w.h.p there are no growable components. The final partitioning $\\mathcal{C}_L$ corresponds w.h.p to the maximal connected components in $G \\setminus F$. The pair $s$ and $t$ are connected in $G \\setminus F$ only if the components $C_s,C_t$ of $s,t$ respectively in $T \\setminus F$ are connected in the final component decomposition.\n\\mtodo{add complexity.}\n\nFinally, we show that the decoding algorithm can be slightly modified to output a compressed encoding of an $s$-$t$ path in $G \\setminus F$, using $O(f\\log n)$ bits. This encoding is represented by an $s$-$t$ path $\\widehat{P}$ that has two type of edges, appearing in an alternate manner on $\\widehat{P}$: $G$-edges and edges $e'=(u,v)$ such that the $u$-$v$ tree path is intact in $T \\setminus F$. See Figure \\ref{fig:succ-paths}. \n\\begin{lemma}\\label{lem:useful-recovery-edges}\nConsider a triplet $s,t,F$ such that $s$ and $t$ are connected in $G \\setminus F$. \nThe decoding algorithm can also output a set of at most $f$ recovery edges $Q$ such $(T \\setminus F) \\cup Q$ is a spanning tree. In addition, it outputs a labeled $s$-$t$ path $\\widehat{P}$ of length $O(f)$ that provides a succinct description of the $s$-$t$ path. The edges of $\\widehat{P}$ are labeled by $0$ and $1$, where $0$-labeled edges correspond to $G$-edges and $1$-labeled edges $e=(x,y)$ correspond to $x$-$y$ paths in $T \\setminus F$. \n\\end{lemma}\n\\begin{proof}\nLet $C_s, C_t$ be the components of $s$ and $t$ in the initial partitioning $\\mathcal{C}_0$. In Step $4$ of the decoding algorithm, the Boruvka algorithm is simulated up to the point that $C_s$ and $C_t$ are connected. Therefore, the algorithm has computed a path $P$ that connects the components $C_s$ and $C_t$. Each node on that path corresponds to a component in $\\mathcal{C}_0$, and each edge corresponds to an outgoing edge (discovered using the sketch information). Since $\\mathcal{C}_0$ has at most $f+1$ components, $|P|\\leq f+1$. \nEach such edge $e' \\in P$ corresponds to an edge in $G$. Let $e_1=(x_1,y_1),\\ldots, e_k=(x_k,y_k)$ be the $G$-edges corresponding to the edges of $P$ ordered from $C_s$ to $C_t$. Letting $y_0=s$ and $x_{k+1}=t$, we get that \n$y_i$ and $x_{i+1}$ belong to the same component in $\\mathcal{C}_0$, for every $i \\in \\{0,\\ldots, k\\}$. \nThe labeled path is given by $\\widehat{P}=[s, x_1,y_1, x_2, y_2, \\ldots y_k,t]$ where the edges $(y_i,x_{i+1})$ are labeled $1$ and the edges $(x_{i}, y_i)$ are labeled $0$. Each $0$-labeled edge is a real edge in $G$, and each $1$-labeled edge $(x_{i}, y_i)$ corresponds to a tree path $\\pi(x_i, y_i)$ in $T \\setminus F$. \n\\end{proof}\n\n\n\\begin{figure}[h!]\n\\begin{center}\n\\includegraphics[scale=0.40]{suc-path.pdf}\n\\caption{\\sf Shown is a tree $T$ with faulty edges $e_1,\\ldots, e_4$. The $s$-$t$ path in $G \\setminus F$ is represented by the path $\\widehat{P}=[s,v_1]\\circ (v_1,v_2) \\circ [v_2,v_3] \\circ (v_3,r) \\circ [r,v_4] \\circ (v_4, v_5) \\circ [v_5,t]$. The recovery edges $(v_1,v_2), (v_3,r)$ and $(v_4, v_5)$ are shown in dashed lines. \\label{fig:succ-paths}\n}\n\\end{center}\n\\end{figure}\n\\section{Fault-Tolerant (FT) Connectivity Labels}\n\n\nWe next discuss two labeling schemes for connectivity that are based on two different approaches. The first one uses the \\emph{cycle space sampling} technique to try to find cuts that disconnect $s$ and $t$. The second one uses \\emph{graph sketches} to try to find a path that connects $s$ and $t$. Since the second approach allows to find a path between $s$ and $t$ if exists, it is also useful later for routing. In terms of label size, the first approach gives labels of size $O(f + \\log{n})$, which is near-optimal if the number of failures is $f=O(\\log{n})$. On the other hand, the second scheme gives labels of size $O(\\log^3{n})$, which is better when the number of failures is large.\nWe next discuss the labeling schemes. During this section, we assume that the input graph $G$ is connected. If not, we can add to the label of each vertex and edge the id of their connected component in $G$, and apply the labeling scheme to each one of the connected components separately. \n\n\\subsection{Connectivity Labels Based on Cycle Space Sampling}\n\n\\subsubsection{The Labeling Algorithm}\n\nOur labels are composed of two ingredients, that we review next.\n\n\\paragraph{Cycle Space Labels.}\nThe cycle space sampling technique, introduced in \\cite{pritchard2011fast}, allows to give the edges of a graph short labels that allow to detect cuts in the graph. For a set of vertices $S$, $\\delta(S)$ is the set of edges with exactly one endpoint in $S$. A subset of edges $F$ is called an \\emph{induced edge cut} if $F = \\delta(S)$ for some $S$.\nThe following is shown in \\cite{pritchard2011fast} (see Corollary 2.9). \n\n \\cycle*\n\n\\remove{\n\\begin{restatable}{lemma}{cycle} \\label{cycle_space_lemma}\nThere is an algorithm that assigns the edges of a graph $G=(V,E)$ $b$-bit labels $\\phi(e)$ such that given a subset of edges $F \\subseteq E$, we have:\n$$Pr[\\Moplus_{e \\in F} \\phi(e) = 0] = \\left\\{\n \\begin{array}{ll}\n 1,\\ if\\ F\\ is\\ an\\ induced\\ edge\\ cut\\\\\n 2^{-b},\\ otherwise\n \\end{array}\n \\right. $$ \nWhere $0$ is the all-zero vector. The time complexity for assigning the labels is $O((m+n)b)$.\n\\end{restatable}\n}\n\nFor an overview of the technique, see Appendix \\ref{sec:cycle_space_overview}. \nIn our algorithm, given a subset of edges $F$ of size at most $f$, we want to be able to check for any subset $F' \\subseteq F$ if $F'$ is an induced edge cut. To support all these $2^f$ queries w.h.p we choose $b=f+ c \\log{n}$ for a constant $c$. This guarantees that the probability of error is at most $\\frac{2^f}{2^{f+c\\log{n}}}=\\frac{1}{n^c}$. This will guarantee that given a query $\\langle s,t,F \\rangle$, our algorithm answers correctly w.h.p. We remark that if we increase the size of labels to $O(f \\log{n})$ we can get an algorithm that is correct for \\emph{all} queries w.h.p. \nThe reason is that we can then check for any subset of edges $F$ of size at most $f$ if $F$ is an induced edge cut. As the number of subsets of size at most $f$ is bounded by $O(n^f)$, we get that the labels are correct for all such subsets w.h.p.\n\n\n\\paragraph{Ancestry Labels.} Our second ingredient are ancestry labels for trees.\nTo use them, we first fix a spanning tree $T$ of the graph rooted at $r$. The goal is to assign vertices short labels, such that given the labels of $u$ and $v$, we can infer if $u$ is an ancestor of $v$ in $T$. A simple labeling scheme based on a DFS scan solves the problem with labels of size $2 \\lceil \\log{n} \\rceil$ per vertex \\cite{kannan1992implicat}, the time for assigning the labels is $O(n)$ for the DFS scan of the tree. Labeling schemes with improved label size appear in \\cite{abiteboul2006compact,alstrup2002improved,fraigniaud2010compact,fraigniaud2010optimal}.\n\n\\begin{lemma} \\label{anc_labels}\nFor every tree $T$, there is an algorithm that assigns the vertices $u$ of the tree labels $\\mathsf{ANC}\\xspace_T(u)$ of $O(\\log{n})$ bits, such that given the labels of $u$ and $v$ we can infer if $u$ is an ancestor of $v$ in $T$ in $O(1)$ time. The time for assigning the labels is $O(n)$. \n\\end{lemma}\n\n\\paragraph{The Final Labels.}\nOur final labels contain the following ingredients:\n\\begin{enumerate}\n\\item The label of the edge $e=(u,v)$ is composed of $(\\phi(e),\\mathsf{ANC}\\xspace_T(u),\\mathsf{ANC}\\xspace_T(v),j)$, where $j$ is a bit indicating if $e$ is a tree edge in $T$. In total, the label size is $O(f + \\log{n})$.\n\\item The label of a vertex $v$ is its ancestry label $\\mathsf{ANC}\\xspace_T(v)$ of size $O(\\log{n})$ bits.\n\\end{enumerate}\n\nAs discussed, the time for assigning the labels is $O((m+n)b)=\\widetilde{O}((m+n)f)$, as $b=f+c\\log{n}$.\nWe next explain how we use these labels to check FT connectivity.\n\n\n\\subsubsection{The Decoding Algorithm}\n\nWe next discuss several observations that allow us to check if $s$ and $t$ are disconnected by $F$.\n\n\\begin{claim} \\label{obs_induced}\nThe vertices $s$ and $t$ are disconnected by $F$ if an only if there is an induced edge cut $F' \\subseteq F$ that disconnects $s$ and $t$.\n\\end{claim}\n\n\\begin{proof}\nFirst, if $F' \\subseteq F$ disconnects $s$ and $t$, then clearly $F$ disconnects $s$ and $t$.\nOn the other hand, if $s$ and $t$ are disconnected by $F$, let $F' \\subseteq F$ be a minimal set of edges whose removal disconnects $s$ and $t$. We show that $F'$ is an induced edge cut. Let $V_s$ be the vertices in the connected component of $s$ in $G \\setminus F'$. We show that all edges in $F'$ are between $V_s$ and $V \\setminus V_s$, implying that $F'$ is an induced edge cut. Assume to the contrary that there is an edge $e \\in F'$ with both endpoints in one of the sides, say $V_s$, then $F' \\setminus \\{e\\}$ is still a cut that disconnects $s$ and $t$ (as $V_s$ is still disconnected from the rest of the graph if we add $e$), contradicting the minimality of $F'$. A symmetric argument shows that $e$ cannot have both its endpoints in $V \\setminus V_s$.\n\\end{proof}\n\nWe next show that given an induced edge cut $F'$, there is a simple way to determine the two sides of the cut induced by $F'$ (see Figure \\ref{cutSidesPic} for illustration). For a vertex $v$ and an induced edge cut $F'$, we denote by $n_v(F')$ the number of edges from $F'$ in the path from the root $r$ to $v$ in the spanning tree $T$. We show the following.\n\n\\remove{\n\\begin{claim}\nLet $F'$ be an induced edge cut, and let $T$ be a spanning tree with root $r$. Let $V_0$ be all the vertices $v$ where in the path from $r$ to $v$ there is an even number of edges from $F'$, and let $V_1 = V \\setminus V_0$. Then $(V_0,V_1)$ is the induced edge cut defined by $F'$.\n\\end{claim}\n}\n\n\\begin{claim} \\label{obs_cut_sides}\nLet $F'$ be an induced edge cut. Let $$V_0=\\{v \\in V|\\ n_v(F')\\ is \\ even \\},$$ $$V_1=\\{v \\in V|\\ n_v(F')\\ is \\ odd \\}.$$ Then $(V_0,V_1)$ is the induced edge cut defined by $F'$.\n\\end{claim}\n\n\\begin{proof}\nSince $F'$ is an induced edge cut, the endpoints of every edge in $F'$ are on different sides of the cut. Hence, if we scan the tree $T$ from the root to the leaves, every time we reach an edge from $F'$ we change the side of the cut. It follows that one side of the cut contains all vertices $v$ such that $n_v(F')$ is even, and the other side has all vertices $v$ such that $n_v(F')$ is odd. Hence $V_0,V_1$ are the two sides of the cut.\n\\end{proof}\n\n\\setlength{\\intextsep}{0pt}\n\\begin{figure}[h]\n\\centering\n\\setlength{\\abovecaptionskip}{-2pt}\n\\setlength{\\belowcaptionskip}{6pt}\n\\includegraphics[scale=0.55]{cutSides2.pdf}\n \\caption{Here $F'=\\{e_1,e_2,e_3,e_4\\}$ is an induced edge cut. On the right, you can see the partition into sides in the tree. Every time we reach an edge from $F'$, we change the side of the cut.}\n\\label{cutSidesPic}\n\\end{figure}\n\nFrom Claims \\ref{obs_induced} and \\ref{obs_cut_sides}, we get the following.\n\n\\begin{corollary} \\label{cor_ft_connectivity}\nThe vertices $s$ and $t$ are disconnected by $F$ if an only if there is an induced edge cut $F' \\subseteq F$, such that one of the values $n_s(F'),n_t(F')$ is even and the other is odd. \n\\end{corollary}\n\nThis gives a simple approach to detect if $s$ and $t$ are disconnected by $F$. We go over all subsets $F' \\subseteq F$, for each one of them we first check if $F'$ is an induced edge cut using the cycle space labels. Second, if $F'$ is an induced edge cut, we compute the values $n_s(F'),n_t(F')$, if the number is even for one of them and odd for the second, we deduce that $F'$ disconnects $s$ and $t$. Note that we can use the ancestry labels to compute the values $n_s(F'),n_t(F')$. For example, for computing $n_s(F')$ we should check how many edges in $F'$ are in the tree path between $r$ to $s$. For this, for each tree edge $e=(u,v)$ in $F'$, we check if it is above $s$ in the tree, which happens if and only if both $u$ and $v$ are ancestors of $s$.\nThis simple approach requires time exponential in $|F|$ for going over all subsets of $F$, we next show a faster way to check the same condition.\n\n\\subsubsection{Faster Decoding Algorithm}\n\nWe next show that checking the condition from Corollary \\ref{cor_ft_connectivity} boils down to solving a system of linear equations.\nFirst, note that from Lemma \\ref{cycle_space_lemma}, w.h.p, a set of edges $F' \\subseteq F$ is an induced edge cut iff $\\Moplus_{e \\in F'} \\phi(e) = 0$. Hence, if we want to check if there is a non-empty subset $F' \\subseteq F$ that is an induced edge cut it is equivalent to checking if there exists a binary vector $x=(x_1,...,x_f) \\neq 0$ such that $\\Moplus_{1 \\leq i \\leq f} x_i \\phi(e_i) = 0$, where $\\{e_1,...e_f\\}$ are the edges of $F$. Or equivalently checking if the vectors $\\{\\phi(e)\\}_{e \\in F}$ are linearly dependant. To check the condition from Corollary \\ref{cor_ft_connectivity}, we generalize this idea. \n\nLet $b = O(f + \\log{n})$ be the size of the cycle space labels.\nGiven a triplet $(s,t,F)$, we assign for each edge $e \\in F$, a binary vector $\\phi'(e)$ of length $b+2$, as follows.\n\\begin{enumerate}\n\\item If $e$ is a tree edge which is in the tree path $r-s$ but not in the path $r-t$, then $\\phi'(e)=10\\phi(e)$.\n\\item If $e$ is a tree edge which is in the tree path $r-t$ but not in the path $r-s$, then $\\phi'(e)=01\\phi(e)$.\n\\item In all other cases, $\\phi'(e)=00\\phi(e).$ \n\\end{enumerate}\n\nWe denote by $w_1,w_2$ binary vectors of length $b+2$ such that $w_1=100..0,w_2=010...0$ (all right entries are equal to 0).\nWe show that the condition from Corollary \\ref{cor_ft_connectivity} holds iff there is a binary vector $x=(x_1,...,x_f)$ and $j \\in \\{1,2\\}$ such that $$\\Moplus_{1 \\leq i \\leq f} x_i \\phi'(e_i) = w_j.$$ This holds iff there is a solution to at least one of the linear systems $Ax=w_1,Ax=w_2$, where $A$ is a $(b+2) \\times f$ matrix that has the vectors $\\{\\phi'(e)\\}_{e \\in F}$ as its column vectors, and $x,w_1,w_2$ are column vectors. All operations are modulo 2.\n\n\\begin{lemma}\nWith high probability, the vertices $s$ and $t$ are disconnected by $F$ if an only if there is a binary vector $x=(x_1,...,x_f)$ and $j \\in \\{1,2\\}$ such that $\\Moplus_{1 \\leq i \\leq f} x_i \\phi'(e_i) = w_j.$\n\\end{lemma}\n\n\\begin{proof}\nWe assume for the proof that the cycle space labels are correct, i.e., a set of edges $F' \\subseteq F$ is an induced edge cut iff $\\Moplus_{e \\in F'} \\phi(e) = 0$. This happens w.h.p from Lemma \\ref{cycle_space_lemma} and the choice of $b=O(f + \\log{n})$.\n\nFirst we show that if $s$ and $t$ are disconnected by $F$, the condition of the lemma holds.\nFrom Corollary \\ref{cor_ft_connectivity}, $s$ and $t$ are disconnected by $F$ iff there is an induced edge cut $F' \\subseteq F$, such that one of the values $n_s(F'),n_t(F')$ is even and the other is odd. Denote by $n'_s(F')$ the number of edges from $F'$ in the $r-s$ tree path that are not in the $r-t$ path, and denote by $n'_t(F')$ the number of edges from $F'$ in the $r-t$ tree path that are not in the $r-s$ path. Note that if one of the values $n_s(F'),n_t(F')$ is even and the other is odd, then also one of $n'_s(F'),n'_t(F')$ is even and the other is odd, as if we denote by $y$ the number of edges from $F'$ that are in both $r-s$ and $r-t$, we get that $n'_s(F') = n_s(F') - y, n'_t(F') = n_t(F') - y$. Assume first that $n'_s(F')$ is even and $n'_t(F')$ is odd. Let $x$ be the characteristic vector of $F'$. We show that $\\Moplus_{1 \\leq i \\leq f} x_i \\phi'(e_i) = w_2$. First, as $F'$ is an induced edge cut, we have that $\\Moplus_{e \\in F'} \\phi(e) = 0$. Hence, the $b$ last bits of $\\Moplus_{1 \\leq i \\leq f} x_i \\phi'(e_i)$ are equal to 0 as needed. $F'$ has even number of edges that are in the path $r-s$ and not $r-t$, as the labels $\\phi'(e)$ of all these edges start in $10$, the XOR of the first 2 bits of these edges sums to $00$. $F'$ has odd number of edges that are in the path $r-t$ but not $r-s$. The labels of all these edges start in $01$, as there is an odd number of them, the XOR of the first 2 bits of these edges sums to $01$. All other edges have labels that start in $00$, hence the XOR of their first 2 bits sums to $00$. Overall we get that $\\Moplus_{1 \\leq i \\leq f} x_i \\phi'(e_i)=\\Moplus_{e \\in F'} \\phi'(e)=010...0=w_2$. The case that $n'_s(F')$ is odd and $n'_t(F')$ is even is symmetric and results in the equation $\\Moplus_{1 \\leq i \\leq f} x_i \\phi'(e_i)=100...0=w_1.$\n\nOn the other hand, if we have that $\\Moplus_{1 \\leq i \\leq f} x_i \\phi'(e_i)=w_j$ for a binary vector $x=(x_1,...,x_f)$ and $j \\in \\{1,2\\}$, we can build from it $F'$ that satisfies the condition in Corollary \\ref{cor_ft_connectivity}, as follows. We define $F'$ to be all edges $e_i \\in F$ such that $x_i =1$. Since $\\Moplus_{1 \\leq i \\leq f} x_i \\phi'(e_i)=\\Moplus_{e \\in F'} \\phi'(e)=w_j$, we have that $\\Moplus_{e \\in F'} \\phi(e) = 0$, hence $F'$ is an induced edge cut. Additionally if $w_j=w_2$, it implies that the XOR of the first 2 bits of labels $\\{\\phi'(e)\\}_{e \\in F'}$ are equal to $01$. By the definition of the labels, this can only happen if $n'_s(F')$ is even and $n'_t(F')$ is odd. Similarly, if $w_j=w_1$, then $n'_s(F')$ is odd and $n'_t(F')$ is even. In both cases we get that one of the values $n_s(F'),n_t(F')$ is even and the other is odd, hence $s$ and $t$ are disconnected by $F$ from Corollary \\ref{cor_ft_connectivity}. \n\\end{proof}\n\nTo conclude, the question if $s$ and $t$ are disconnected by $F$ boils down to checking if there is a solution to at least one of the linear systems $Ax=w_1,Ax=w_2$, where $A$ is a $(b+2) \\times f$ matrix, and $b=O(f + \\log{n})$. Note that we can construct the labels $\\phi'(e)$ and hence the matrix $A$ given the labels of $s,t,F$. For this, we need the labels $\\phi(e)$ of edges in $F$, and also to distinguish for each edge in $F$ if it is in the $r-s,r-t$ paths in the tree. The latter can be deduced from the ancestry labels of $s,t,F$ and from the bits indicating which edges in $F$ are tree edges. A tree edge $e=(u,v) \\in F$ is in the $r-s$ path iff both $u$ and $v$ are ancestors of $s$, this can be checked in $O(1)$ time using the ancestry labels of $u,v,s$. Hence we can build the matrix $A$ in $O(fb)$ time. To check if the linear systems have a solution we can use Gaussian elimination, that takes $O(MN^2)$ time for $M \\times N$ matrix, in our case this is $O((f+\\log{n})f^2)$. Alternatively, we can use $O(N^{\\omega})$ algorithms for $N \\times N$ matrices, where $\\omega$ is the exponent of matrix multiplication.\nFor this, we add zero columns to our matrix $A$ to make it a $(b+2) \\times (b+2)$ matrix $A'$ and increase the length of $x$ to $b+2$, the new system $A'x=w_i$ has a solution iff the original system $Ax=w_i$ has a solution. The complexity here is $O((b+2)^{\\omega})=O((f+\\log{n})^{\\omega})$.\nThis gives the following. \n \n\\begin{theorem}\nThere is a randomized $f$-FT connectivity labeling scheme that assigns the edges and vertices of the graph labels of size $O(\\log{n})$ bits per vertex and $O(f + \\log{n})$ bits per edge. The decoding time of the scheme is $\\min\\{O((f+\\log{n})f^2),O((f+\\log{n})^{\\omega})\\}$. The time complexity for assigning the labels is $\\widetilde{O}((m+n)f).$\n\\end{theorem} \n\n\\remove{\n\\begin{theorem}\nWe can assign the edges and vertices of the graph labels of size $O(\\log{n})$ bits per vertex and $O(f + \\log{n})$ bits per edge, such that given the labels of $(s,t,F)$ we can check if $s$ and $t$ are disconnected by $F$ in $O(f^3 \\log{n})$ \\mtodo{check the complexity} time, w.h.p.\n\\end{theorem} \n} \n\n\\section{Missing Proofs}\\label{sec:miss-proof}\n\n\n\\APPENDUNIQUEID\n\n\\APPENDLEMMUNIQUE\n\n\\APPENDLABELCONSISE\n\n\\APPENDSKETCHPROP\n\n\\APPENDCONNLABELSKETCH\n\\section{Preliminaries}\nGiven a graph $G=(V,E)$, and vertex $u \\in V$, let $\\deg(u,G)$ be the degree of $u$ in $G$. \nGiven a tree $T$ and $u, v \\in T$, denote the $u$-$v$ path in $T$ by $\\pi(u,v,T)$. When the tree $T$ is clear from the context, we may omit it and write $\\pi(u,v)$. For a (possibly weighted) subgraph $G' \\subseteq G$ and a vertex pair $s,t \\in V$, let $\\mbox{\\rm dist}_{G'}(s,t)$ denote the length of the $s$-$t$ shortest path in $G'$. \n\n\\paragraph{Fault-Tolerant Labeling Schemes.}\nFor a given graph $G$, let $\\Pi: V\\times V \\times \\mathcal{G} \\to \\mathbb{R}_{\\geq 0}$\nbe a function defined on pairs of vertices and a subgraph $G' \\subset G$, where $\\mathcal{G}$ is the family of all subgraphs of $G$. For an integer parameter $f\\geq 1$, an $f$-\\emph{fault-tolerant labeling scheme} for a function $\\Pi$ and a graph family $\\mathcal{F}$ is a pair of functions $(L_{\\Pi},D_{\\Pi})$. The function $L_{\\Pi}$ is called the \\emph{labeling function}, and $D_{\\Pi}$ is called the \\emph{decoding function}. For every graph $G$ in the family $\\mathcal{F}$, the labeling function $L_{\\Pi}$ associates with each vertex $u \\in V(G)$ and every edge $e \\in E(G)$, a label $L_{\\Pi}(u,G)$ (resp., $L_{\\Pi}(e,G)$). It is then required that given the labels of any triplets $s,t, F \\in V \\times V \\times E^f$, the decoding function $D_{\\Pi}$ computes $\\Pi(s,t, G \\setminus F)$. The primary complexity measure of a labeling scheme is the \\emph{label length}, measured by the length (in bits) of the largest label it assigns to some vertices (or edges) in $G$ over all graphs $G \\in \\mathcal{F}$. An $f$-FT connectivity labeling scheme is required to output YES iff $s$ and $t$ are connected in $G \\setminus F$. In $f$-FT \\emph{approximate distance labeling scheme} it is required to output an estimate for the $s$-$t$ distance in the graph $G \\setminus F$. Formally, an $f$-FT labeling scheme is $q$\\emph{-approximate} if the value $\\delta(s,t,F)$ returned by the decoder algorithm satisfies that $\\mbox{\\rm dist}_{G \\setminus F}(s,t)\\leq \\delta(s,t,F) \\leq q \\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$. Throughout the paper we provide randomized labeling schemes which provide a high probability guarantee of correctness for any fixed triplet $\\langle s,t, F \\rangle$. \n\n\n\\paragraph{Fault-Tolerant Routing Schemes.} In the setting of FT routing scheme, one is given a pair of source $s$ and destination $t$ as well as $F$ edge faults, which are initially unknown to $s$. The routing scheme consists of \\emph{preprocessing} and \\emph{routing} algorithms. The preprocessing algorithm defines labels $L(u)$ to each of the vertices $u$, and a header $H(M)$ to the designated message $M$. In addition, it defines for every vertex $u$ a routing table $R(u)$. The labels and headers are usually required to be short, i.e., of poly-logarithmic bits. \nThe routing procedure determines at each vertex $u$ the port-number on which $u$ should send the messages it receives. The computation of the next-hop is done by considering the header of the message $H(M)$, the label of the source and destination $L(s)$ and $L(t)$ and the routing table $R(u)$. The routing procedure at vertex $u$ might also edit the header of the message $H(M)$. The failing edges are not known in advance and can only be revealed by reaching (throughout the message routing) one of their endpoints. The \\emph{space} of the scheme is determined based on maximal length of message headers, labels and the individual routing tables. The stretch of the scheme is measured by the ratio between the length of the path traversed until the message arrived its destination and the length of the shortest $s$-$t$ path in $G \\setminus F$. In the more relaxed setting of \\emph{forbidden-set routing schemes} the failing edges are given as input to the routing algorithm.\n\n\n\\subsection{Additional Related Work}\n\n\\paragraph{Fault-Tolerant Labeling Schemes.} FT labels for connectivity were introduced by \\cite{courcelle2007forbidden} under the term \\emph{forbidden-set labeling}. Forbidden set refers to a subset $F$ of at most $f$ edges, such that given the labels of $s,t$ and $F$ one should determine if $s$ and $t$ are connected in $G \\setminus F$. The forbidden edge set can be treated in this context as faulty edges\\footnote{For routing, the forbidden-set scheme is slightly weaker than FT scheme as explained later.}.\nPrevious works study FT connectivity labels only in restricted graph families. For example, Courcelle et al. \\cite{CourcelleT07} presented a labeling scheme with logarithmic label length for the families of $n$-vertex graphs with bounded clique-width, tree-width and planar graphs. For $n$-vertex graphs with doubling dimension at most $\\alpha$, Abraham et al. \\cite{AbrahamCGP16} designed FT labeling schemes with label length $O((1 + 1\/\\epsilon)^{2\\alpha}\\log n)$ that output $(1+\\epsilon)$ approximation of the shortest path distances under faults. Recently, \\cite{DBLP:journals\/corr\/abs-2102-07154} studied FT exact distance labels in planar graphs, and show that any directed weighted planar graph admits fault-tolerant distance labels of size $O(n^{2\/3})$.\n\n\\paragraph{Connectivity and Distance Sensitivity Oracles.} \nConnectivity and distance sensitivity oracles are centralized data structures that support connectivity or distance queries in the presence of failures. \nThe first construction of connectivity sensitivity oracles was given by Patrascu and Thorup \\cite{patrascu2007planning} providing an $S(n)=\\widetilde{O}(fn)$ space oracle that answers $\\langle s,t, F \\rangle$ connectivity queries in $\\widetilde{O}(f)$ time. The state-of-the-art bounds of these oracles are given by Duan and Pettie \\cite{DuanConnectivitySODA17}.\nChechik et al. \\cite{chechik2012f} presented the first randomized construction of distance sensitivity oracle resilient to $f$ edge faults. \nSpecifically, for any $n$-vertex weighted graph, stretch parameter $k$, and a fault bound $f$, they provide a data-structure with $O(f k n^{1+1\/k}\\log(nW))$ space, query time of $\\widetilde{O}(|F|)$, and $O(f k)$ stretch, where $W$ is the weight of the heaviest edge in the graph. Their solution is based on an elegant transformation that converts the FT connectivity oracle of \\cite{patrascu2007planning} into an FT approximate distance oracle.\n\nWhile the main focus of this paper is in approximate distances, sensitivity oracles that report (possibly near) exact distances under faults have been studied also thoroughly in e.g., \\cite{demetrescu2002oracles,bernstein2008improved,duan2009dual,WeimannY10,GrandoniW12,ChechikCFK17,van2019sensitive}. Since reporting exact distances requires linear label length already in the fault-free setting \\cite{gavoille2004distance}, we focus on the approximate relaxation, where there is still hope to obtain labels of polylogarithmic length.\n\n\\paragraph{Fault-Tolerant Routing Schemes.}\nThe first formalization of FT routing schemes was given by the influential works of Dolev \\cite{dolev1984new} and Peleg \\cite{peleg1987fault}. These earlier works presented the first non-trivial solutions for general graphs supporting at most $\\lambda$ faulty edges, where $\\lambda$ is the edge-connectivity of the graph. Their routing labels had linear size, providing $s$-$t$ routes of possibly linear length (even in cases where the surviving $s$-$t$ path is of $O(1)$ length). In competitive FT routing schemes, it is required to provide $s$-$t$ routes of length that competes with the shortest $s$-$t$ path in $G \\setminus F$, even in cases where $G \\setminus F$ is not connected. Competitive FT routing schemes \\cite{peleg2009good} for general graphs were given by Chechik et al. \\cite{ChechikLPR10,chechik2012f} for the special case of $f\\leq 2$ faults. \nSpecifically, for a given stretch parameter $k$, they gave a routing scheme with a total space bound of $\\widetilde{O}(n^{1+1\/k})$ bits, polylogarithmic-size labels and messages, and a routing \\emph{stretch} of $O(k)$. \nThis scheme was extended later on for any $f$ by Chechik \\cite{chechik2011fault}, at the cost of increasing the routing stretch to $O(f^2(f+\\log^2 n)k)$. For a single edge failure, \\cite{rajan2012space} showed a routing scheme with routing tables of size $\\widetilde{O}(k \\deg(v)+ n^{1\/k})$ size per vertex, $O(k^2)$ stretch and $O(k+\\log{n})$ size header.\n\n\\paragraph{Forbidden Set Routing.}\nA more relaxed setting of FT routing scheme which has been studied in the literature is given by the \\emph{forbidden set routing schemes}, introduced by Courcelle and Twigg \\cite{CourcelleT07}. In that setting, it is assumed that the routing protocol knows in advance the set of faulty edges $F$. In contrast, in the FT routing setting, the failing edges are a-priori unknown to the routing algorithm, and can only be detected upon arriving one of their endpoints. Forbidden set routing schemes have been devised to the same class of restricted graph families as obtained for the forbidden set labeling setting \\cite{CourcelleT07,AbrahamCGP16,abraham2012fully}.\n\\subsection{Our Results}\nWe provide space-efficient labeling and routing schemes for any $n$-vertex graph. Our schemes are \\emph{randomized} and provide a high probability guarantee\\footnote{As standard, we use the term high-probability to indicate success guarantee of $1-1\/n^c$ for any given constant $c>1$.} for any given triplet $\\langle s, t, F\\rangle$. In other words, the schemes can faithfully support polynomially many queries\\footnote{The same type of guarantee is provided in the centralized sensitivity oracles, e.g., of \\cite{DuanConnectivitySODA17}. Providing a high probability guarantee over all possible triplets is possible upon increasing the space bound by a factor of $f$ (largest number of faults supported).}. \n\nOur first key result presents two independent schemes for FT connectivity labels. These are the first FT connectivity labels for \\emph{general graphs}.\nThese two constructions yield the following theorem, addressing Question \\ref{q:label}: \n\n\\begin{theorem}\\label{thm:conn-labels}[FT Connectivity Labeling Schemes, Informal]\nFor any $n$-vertex graph and a bound $f$ on the number of edge faults, there is a \\emph{randomized} $f$-FT connectivity labeling scheme with label length of $O(\\min\\{f+\\log n, \\log^3 n\\})$ bits. The labels are computed in $\\widetilde{O}(m)$ time, and the decoding algorithm takes $\\operatorname{\\text{{\\rm poly}}}(f,\\log n)$ time. \n\\end{theorem}\n\nBy the tightness of the label length of fault-free connectivity labels,\nour scheme is optimal for $f=O(\\log n)$. Moreover, the label length is nearly-optimal for any $f$. Our actual scheme provides more information then merely a single bit (connected or not connected). Specifically, we augment the connectivity labels with additional information so that the decoding algorithm, given the labels of $s,t$ and $F$, can also output a succinct description of an $s$-$t$ path in $G\\setminus F$ (if such a path exists). This succinct path representation finds applications in the context of our FT routing schemes. \n\nWe next consider the task of reporting also approximate $s$-$t$ distances in $G\\setminus F$ using the labels of $s,t$ and $F$. We employ the reduction of Chechik et al. \\cite{chechik2012f} to convert the FT connectivity labels into FT approximate distance labels, providing nearly the same space vs. stretch tradeoff as in the centralized data-structures of \\cite{chechik2012f}. Specifically, we show:\n\n\\begin{theorem}\\label{thm:dist-labels}[FT Approximate Distance Labeling Schemes]\nFor any $n$-vertex (possibly weighted) graph, a bound $f$ on the number of edge faults, and a stretch parameter $k$, there is a randomized $f$-FT approximate distance labeling scheme with label length of $O(k \\cdot n^{1\/k}\\cdot \\log (nW) \\cdot \\log^3 n)$. Given the labels of $s,t$ and $F$ the scheme returns a distance estimate \n$$\\mbox{\\rm dist}_{G\\setminus F}(s,t)\\leq \\delta(s,t,F) \\leq (8k-2)(|F|+1)\\mbox{\\rm dist}_{G\\setminus F}(s,t)~.$$\n\\end{theorem}\n\nFor the purpose of routing, we exploit the extra information provided by our connectivity labels, in order to output, in addition to the distance estimate $\\delta(s,t,F)$, also a succinct description of the approximate $s$-$t$ \nshortest path in $G \\setminus F$. Our second key result provides FT compact routing schemes, with an almost optimal tradeoff between the space and stretch, for constant number of faults $f$. We answer Question \\ref{q:route} by showing:\n\n\\begin{theorem}\\label{thm:routing}[FT Compact Routing]\nFor every integers $k,f$, there exists an $f$-sensitive compact routing scheme that given a message $M$ at the source vertex $s$ and the routing label of the destination $t$, in the presence of at most $f$ faulty edges $F$ (unknown to $s$) routes $M$ from $s$ to $t$ in a distributed manner over a path of length at most $32k (|F|+1)^2\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$. The routing labels have $\\widetilde{O}(f)$ bits, the table size of each vertex is $\\widetilde{O}(f^3 \\cdot n^{1\/k} \\log(nW))$, the header size (also known as message size) is bounded by $\\widetilde{O}(f^3)$ bits. \n\\end{theorem}\nThis improves over the state-of-the-art construction of Chechik \\cite{chechik2011fault} that \nobtained routing schemes with stretch of $O(f^2(f+\\log^2 n)k)$ and tables of size $O(\\deg(v)n^{1\/k}\\log{(nW)})$ for every vertex $v$. We note that the construction of Chechik \\cite{chechik2011fault} has a bounded global space of $\\widetilde{O}(n^{1+1\/k} \\log{(nW)})$, but the individual tables might have even super-linear space (e.g., when $k=O(1)$ and $\\deg(v)=O(n)$). For the special case of $f=2$, Chechik et al. \\cite{ChechikLPR10,chechik2012f} provide a stretch bound of $O(k)$, and total space of $\\widetilde{O}(n^{1+1\/k} \\log{(nW)})$, where the space of each table is bounded by $O(\\deg(v)n^{1\/k})$, thus super-linear in the worst case. Our scheme provides an improved bound on the individual tables, nearly matching the fault-free constructions for $f=O(1)$. We also show an improved scheme if one only aims to optimize for the global space, rather than optimizing for the largest table size for a vertex. For comparison of our results to prior work see Table \\ref{table_routing}.\n\n\\begin{table}[h!]\n\\centering\n\\begin{tabular}{ |p{4.2cm}|p{4cm}|p{6cm}|p{0.5cm}|}\n \\hline\t\n \\multicolumn{4}{|c|}{Constructions of Fault-Tolerant Routing Schemes}\\\\\n \\hline\n Reference & Stretch & Table Size & $|F|$ \\\\\n \\hline\n Rajan \\cite{rajan2012space} & $O(k^2)$ & $\\widetilde{O}(k \\deg(v)+ n^{1\/k})$ per vertex & 1\\\\\n Chechik et al. \\cite{chechik2012f} & $O(k)$ & $\\widetilde{O}(n^{1+1\/k} \\log(nW))$ total size & 2\\\\\n Chechik \\cite{chechik2011fault} & $O(|F|^2(|F|+\\log^2 n)k)$ & $\\widetilde{O}(n^{1+1\/k} \\log(nW))$ total size & $f$\\\\\n Chechik \\cite{chechik2011fault} & $O(|F|^2(|F|+\\log^2 n)k)$ & $\\widetilde{O}(\\deg(v)n^{1\/k} \\log(nW))$ per vertex & $f$\\\\\n \\textbf{Here} & $O(|F|^2 k)$ & $\\widetilde{O}(f \\cdot n^{1+1\/k} \\log(nW))$ total size & $f$\\\\\n \\textbf{Here} & $O(|F|^2 k)$ & $\\widetilde{O}(f^3 \\cdot n^{1\/k} \\log(nW))$ per vertex & $f$\\\\\n \\hline\n\n\n \\end{tabular}\n \\caption{Comparison between FT routing schemes with a set of failures $F$}\n\\label{table_routing}\n\\end{table}\n\nFinally, we provide a lower bound result on the minimal stretch regardless for the \\emph{space} of the routing scheme, e.g., even if all vertices store all the graph edges. \n\n\\begin{theorem}[Stretch Lower-Bound for FT Routing]\\label{thm:lb-routing}\nAny FT routing randomized scheme resilient to $f$ faults induces an expected stretch of $\\Omega(f)$ regardless of the size of the routing tables and labels. In particular, this holds even if each routing table contains a complete information on the graph. \n\\end{theorem}\n\n\n\\paragraph{Open Problems.} Our work leaves several interesting open ends. One natural direction is to provide labeling and routing schemes resilient to \\emph{vertex} faults. The major challenge in handling vertex failure is that even a single faulty vertex might disconnect the graph into $\\Omega(n)$ disconnected components. Another interesting direction is to derandomize our constructions. Currently there are no deterministic constructions of FT labeling schemes for general graphs. Finally, it will be also important to provide FT distance approximate labeling schemes whose stretch bound is independent in the number of faults $f$. This problem is also open in the corresponding setting of approximate distance sensitive oracles. \n\n\\section{Compact Routing Schemes}\nIn this section, we explain how to use our FT distance labels to provide compact and low stretch routing schemes. This is the first scheme to provide an almost tight tradeoff between the space and the multiplicative stretch, for a constant number of faults $f=O(1)$. Throughout this section, tree routing operations are performed by using the tree routing scheme of Thorup and Zwick \\cite{thorup2001compact}.\n\\begin{fact}\\label{fc:route-trees}[Routing on Trees]\\cite{thorup2001compact}\nFor every $n$-vertex tree $T$, there exists a routing scheme that assigns each vertex $v \\in V(T)$ a label $L_T(v)$ of $(1+o(1))\\log n$ bits. Given the label of a source vertex\nand the label of a destination, it is possible to compute, in constant time, the port number of the edge from the source that heads in the direction of the destination.\n\\end{fact}\n\n\n\nWe slightly modify the connectivity label of the edges and vertices by augmenting them with routing information. \nFirst, we augment the extended identifier of an edge (see Eq. (\\ref{eq:extend-ID})) with port information and tree routing information, by having:\n\\begin{equation}\\label{eq:edge-extended-routing}\n\\operatorname{EID}_T(e)=[\\operatorname{UID}(e), \\operatorname{ID}(u), \\operatorname{ID}(v), \\mathsf{ANC}\\xspace_T(u), \\mathsf{ANC}\\xspace_T(v), \\mbox{\\tt port}(u,v), \\mbox{\\tt port}(v,u), L_T(u), L_T(v)]~,\n\\end{equation}\nwhere $\\mbox{\\tt port}(u,v)$ is the port number of the edge $(u,v)$ for $u$, and the labels $L_T(u), L_T(v)$ are the tree routing labels taken from Fact \\ref{fc:route-trees}. \nWe then slightly modify the connectivity label of Eq. (\\ref{eq:conn-vertex}) to include also the tree label $L_T(u)$from Fact \\ref{fc:route-trees}, by defining \n\\begin{equation}\\label{eq:conn-vertex-label-routing}\n\\mathsf{ConnLabel}\\xspace_{G,T}(u)=\\langle \\mathsf{ANC}\\xspace_T(u), \\operatorname{ID}(u), L_T(u)\\rangle~.\n\\end{equation}\n\nThroughout this section, when applying the connectivity labels from Section \\ref{sec:ftconn-sketch} on a graph $G$ with a spanning tree $T$, we use these modified extended identifiers and labels. This will also be the basis for the application of the distance labels of Section \\ref{sec:ft-distance}. \nSimilarly to the distance labels of Section \\ref{sec:ft-distance}, we will apply the connectivity labels with respect to the different trees of the tree cover as discussed in Section \\ref{sec:ft-distance}. \nLet $T_{i,j} \\in \\mathsf{TC}\\xspace_i$, recall that $G_{i,j}=G[V(T_{i,j})]$ and that $\\mathcal{T}=\\bigcup_{i=1}^K \\mathsf{TC}\\xspace_i$ for $K=O(\\log (nW))$. \n\n\\begin{lemma}\\label{lem:succint_path_routing}\nConsider a triplet $s,t,F$ such that $s,t,F \\in G_{i,j}$. \\\\\nGiven the connectivity labels $\\{\\mathsf{ConnLabel}\\xspace_{G_{i,j},T_{i,j}}(w)\\}_{w \\in F \\cup \\{s,t\\}}$, we can determine w.h.p if $s$ and $t$ are connected in $G_{i,j} \\setminus F$. If they are connected, we can output a labeled $s$-$t$ path $\\widehat{P}$ of length $O(f)$ that provides a succinct description of the $s$-$t$ path in $G_{i,j} \\setminus F$. The edges of $\\widehat{P}$ are labeled by $0$ and $1$, where $0$-labeled edges correspond to $G_{i,j}$-edges and $1$-labeled edges $e=(x,y)$ correspond to $x$-$y$ paths in $T_{i,j} \\setminus F$. For each $G_{i,j}$-edge, the succinct path description has the port information of the edge, and for each $x-y$ path, the description has the tree routing labels $L_{T_{i,j}}(x),L_{T_{i,j}}(y)$.\nThe length of the $s$-$t$ path encoded by $\\widehat{P}$ is bounded by $(4k-1)(|F|+1)\\cdot 2^i$. \n\\end{lemma}\n\n\\begin{proof}\nThe proof follows the proof of Lemma \\ref{lem:useful-recovery-edges}.\nUsing $\\{\\mathsf{ConnLabel}\\xspace_{G_{i,j},T_{i,j}}(w)\\}_{w \\in F \\cup \\{s,t\\}}$, the decoding algorithm of Section \\ref{sec:ftconn-sketch} determines if $s$ and $t$ are connected in $G_{i,j} \\setminus F$. If they are connected, then from Lemma \\ref{lem:useful-recovery-edges}, we get a succinct description of the $s$-$t$ path in $G_{i,j} \\setminus F$. We next show that the algorithm indeed has the relevant port and tree routing information. For this note that all the vertices in the path $\\widehat{P}$ obtained by Lemma \\ref{lem:useful-recovery-edges} are either $s$ and $t$ or endpoints of the $|F|$ recovery edges found in the algorithm. The labels of $s$ and $t$ contain the tree routing information $L_{T_{i,j}}(s)$ and $L_{T_{i,j}}(t)$, and when the algorithm finds a recovery edge, it learns about its extended id $\\operatorname{EID}_{T_{i,j}}(e)$ that has the port information and tree routing information of its endpoints. Any $G_{i,j}$-edge in $\\widehat{P}$ is a recovery edge, hence the algorithm has its port information, and for any $x$-$y$ path in $T_{i,j} \\setminus F$, the algorithm has the tree routing labels $L_{T_{i,j}}(x),L_{T_{i,j}}(y)$, as needed.\nThe stretch analysis follows the stretch analysis in Section \\ref{sec:ft-distance}. It is based on the fact that $\\widehat{P}$ has as most $|F|+1$ subpaths in $T_{i,j} \\setminus F$, each of length at most $(4k-2)2^i$, and at most $|F|$ recovery edges of weight at most $2^i$.\n\\end{proof}\n\n\n\n\n\n\n\n\\subsection{Forbidden Set Routing (Faulty Edges are Known)}\\label{sec:routing-known}\nWe start by describing the routing scheme in the forbidden set setting, where the faulty edges $F$ are known to the source vertex $s$. We show the following.\n\n\\begin{theorem}\\label{thm:routing-known}[Forbidden-Set Routing]\nFor every integers $k,f$, there exists an $f$-sensitive compact routing scheme that given a message $M$ at the source vertex $s$, a label of the destination $t$, and labels of at most $f$ forbidden edges $F$ (known to $s$), routes $M$ from $s$ to $t$ in a distributed manner over a path of length at most $(8k-2)(|F|+1)\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$. The table size of each vertex is bounded by $\\widetilde{O}(n^{1\/k} \\log{(nW)})$. The header size of the messages is bounded by $\\widetilde{O}(f)$ bits. The labels of vertices and edges have size $\\widetilde{O}(n^{1\/k} \\log(nW))$.\n\\end{theorem}\n\n\\begin{proof}\nThe algorithm is based on the distance labels from Section \\ref{sec:ft-distance} using the slightly modified connectivity labels (augmented with port and tree roting information). Recall that the distance labels are based on applying fault-tolerant connectivity labels on different graphs $G_{i,j}$, we use the slightly modified connectivity labels and the corresponding distance labels. \nThe routing table of each vertex $u$ consists of its distance label $\\mathsf{DistLabel}\\xspace(u)$. The label of an edge $e$ is $\\mathsf{DistLabel}\\xspace(e)$. Each distance label has $\\widetilde{O}(n^{1\/k} \\log(n W))$ bits. \n\nIn the routing algorithm, the vertex $s$ is given the label $\\mathsf{DistLabel}\\xspace(t)$, and the labels $\\{\\mathsf{DistLabel}\\xspace(e)\\}_{e \\in F}$, and it needs to route a message to $t$ in the graph $G \\setminus F$. \nRecall that the algorithm from Section \\ref{sec:ft-distance} works in $K$ phases, where in phase $i$ it checks if $s$ and $t$ are connected in the graph $G_{i,i^*(s)} \\setminus F$ that contains the $2^i$-ball around $s$. Let $i$ be the first iteration where $s$ and $t$ are connected in $G_{i,i^*(s)} \\setminus F$ according to the algorithm, and denote $G_i = G_{i,i^*(s)},T_i = T_{i,i^*(s)}$, and let $F_i = F \\cap G_i$. The algorithm can also give a succinct description of an $s$-$t$ path in $G_i \\setminus F_i$ following Lemma \\ref{lem:succint_path_routing}. For this, note that we indeed have all the required information. The distance labels of edges in $F$ in particular contain the labels $\\{\\mathsf{ConnLabel}\\xspace_{G_i,T_i}(e)\\}_{e \\in F_i}$, and we can also tell which edges of $F$ are in $G_i$ from the labels. Also, the labels of $s,t$ contain the information $\\operatorname{ID}_{T_i}(s),\\operatorname{ID}_{T_i}(t)$ if they are both in $T_i$ (otherwise, they are not connected in level $i$).\n\nThe path $\\widehat{P}$ as described in Lemma \\ref{lem:succint_path_routing} is composed of $O(|F|)$ parts, where segment $(x,y)$ in the path corresponds either to an edge in $G_i$ or to a tree path in $T_i \\setminus F$, it also has the relevant port and tree routing information. Our goal is to route a message according to this path. For this we add to the header of the message the description of $\\widehat{P}$, the indexes $(i,i^*(s))$ of the tree we explore and an index $1 \\leq q \\leq 2|F|+1$ that represents the segment of $\\widehat{P}$ we currently explore, initially $q=1$. Overall, the header size is $\\widetilde{O}(f)$. To route a message according to the path, we work as follows. The header specifies the current segment in $\\widehat{P}$. If the current segment corresponds to an edge $(x,y) \\in G$, then $x$ uses the port information to route the message to $y$ and increases the index $q$. Otherwise, the current segment represents a tree path $(x,y) \\in T_i$ and a vertex $u$ in this path uses its routing label in $T_i$ and the routing label of $y$ in $T_i$ (that is part of the header) to route the message towards $y$. When the message reaches $y$, it increases the index $q$. This completes the description of the routing process. The length of the path described is at most $(8k-2)(|F|+1)\\cdot \\mbox{\\rm dist}_{G\\setminus F}(s,t)$, as shown in Section \\ref{sec:ft-distance}. \n\\end{proof}\n\n\n\n\\subsection{Fault-Tolerant Routing (Faulty Edges are Unknown)}\\label{sec:route-unknown}\nWe now consider the more involved setting where the set of failed edges $F$ are unknown to $s$. In this case, an edge $(u,v) \\in F$ is detected only when the message arrives, during the routing procedure, to one of the endpoints of $e$. Note that the routing scheme should, by definition, be prepared to any set of faulty edges $F$. However, the space bound of our scheme is required to be bounded by $\\widetilde{O}(f n^{1+1\/k})$, which is possibly much smaller than the number of graph edges $m$. This in particular implies that we cannot store the FT distance labels of all the graph edges. Nevertheless, we show that it is sufficient to explicitly store the labeling information for the tree edges in $\\mathcal{T}=\\bigcup_{i=1}^K \\mathsf{TC}\\xspace_i$. The required information for the failed non-tree edges would be revealed throughout the process, by applying the decoding algorithm of Lemma \\ref{lem:succint_path_routing}.\nOur routing scheme eventually routes the message along the $s$-$t$ path encoded by the FT distance labels of $s,t$ and $F$. However, since the labels of $F$ are unknown in advance, the routing scheme will detect these edges in a trail and error fashion which induces an extra factor of $f$ in the final multiplicative stretch. This extra $f$ factor is also shown to be essential, in the end of the section.\nWe proceed by describing the routing tables. \n\n\n\n\\paragraph{The routing labels and tables.} For ease of presentation, we first describe a solution with a multiplicative stretch of $O(kf^2)$, and \\emph{global} space of $\\widetilde{O}(f K \\cdot n^{1+1\/k})$, but the individual tables of some of the vertices might be large. We later on improve the space of each table to $\\widetilde{O}(f^3 K \\cdot n^{1\/k})$ bits.\n\nRecall that $\\mathcal{T}=\\bigcup_i^{K} \\mathsf{TC}\\xspace_i$, for $K=O(\\log (nW))$ is a collection of tree covers in all $K=\\lceil \\log (nW) \\rceil$ distance scales, see Eq. (\\ref{eq:TC-i}). For every vertex $v$, let $\\deg_{\\mathcal{T}}(v)=\\sum_{T_{i,j} \\in \\mathcal{T}}\\deg(u,T_{i,j})$ be the sum of degrees of $u$ in the collection of trees $\\mathcal{T}$. Recall that $G_{i,j}=G[V(T_{i,j})]$.\nFor the routing we apply the FT connectivity labels on the graphs $G_{i,j}$, similarly to Section \\ref{sec:ft-distance}. \n\\\\ \\\\\n\\noindent \\textbf{Routing labels.} The routing process uses at most $f'=f+1$ independent applications of randomized FT connectivity labels from Section \n\\ref{sec:ftconn-sketch}, applied on each one of the graphs $G_{i,j}$. \nIn more details, when we apply the labeling scheme on the graph $G_{i,j}$ with spanning tree $T_{i,j}$, we use $f'$ independent random seeds $\\mathcal{S}_h$ to determine the randomness of the sketches. \nHowever, the seed $\\mathcal{S}_{ID}$ used to determine the extended ids of edges in $G_{i,j}$ is fixed in the $f'$ applications, hence the extended identifiers of the edges (see Eq. (\\ref{eq:extend-ID})) are fixed in all the $f'$ applications, and we only use fresh randomness to compute the sketch information using $f'$ independent seeds $\\mathcal{S}^1_h,\\ldots, \\mathcal{S}^{f'}_h$. \nThis process is done independently on each one of the graphs $G_{i,j}$. \n\nDenote the output connectivity labels obtained by the ${\\ell}^{th}$ application of the scheme (using $\\mathcal{S}^\\ell_h$) on the graph $G_{i,j}$ by $\\mathsf{ConnLabel}\\xspace^{\\ell}_{G_{i,j},T_{i,j}}(w)$ for every $w \\in E(G_{i,j})\\cup V(G_{i,j})$. For every edge $e \\in G_{i,j}$, define its $T_{i,j}$ routing label by\n\\begin{equation}\\label{eq:route-edge-label}\n L_{route,i,j}(e)=\n \\begin{cases}\n (\\mathsf{ConnLabel}\\xspace^1_{G_{i,j},T_{i,j}}(e),\\ldots,\\mathsf{ConnLabel}\\xspace^{f'}_{G_{i,j},T_{i,j}}(e)),& \\mbox{~for~} e \\in T_{i,j} \\\\\n \\operatorname{EID}_{T_{i,j}}(e),& e \\in G_{i,j}\\setminus E(T_{i,j})~.\n \\end{cases}\n\\end{equation}\nEvery $L_{route,i,j}(e)$ label has $O(f \\log^3 n)$ bits. \nIn our routing algorithms, the $T_{i,j}$ routing labels of the discovered faulty edges will be added to the header for the message in order to guide the routing process. \nWe now turn to define the routing labels of vertices. Recall that for a vertex $v$ and index $1 \\leq i \\leq K$, we denote by $i^*(v)$ an index such that the $2^i$-ball around $v$ is contained in $G_{i,i^*(v)}$.\nThe routing label $L_{route}(v)$ of $v$ \nFor every \\emph{vertex} $v$, the routing label of $v$ is given by\n\\begin{equation}\\label{eq:Label-route-vertex}\nL_{route}(v) = \\{(i^*(v), \\mathsf{ConnLabel}\\xspace^1_{G_{i,i^*(v)},T_{i,i^*(v)}}(v) | i \\in [1,K]\\}~.\n\\end{equation}\n\n\nNote that by definition, the connectivity labels of the \\emph{vertices} are the same in all $f'$ applications of the labeling algorithm, and therefore it is sufficient to include only one of these copies in the label. The size of the label is $O(K\\log{n})=O(\\log{n} \\log{nW})$.\n\\\\ \\\\\n\\noindent \\textbf{Routing tables.} The routing table $R_{route}(v)$ of a vertex $v$ has the following information for every tree \n$T_{i,j}$ such that $v \\in T_{i,j}$:\n\\begin{equation}\\label{eq:route-table-ij}\nR_{route,i,j}(v)=\\{L_{route,i,j}(e), e \\in E(v,T_{i,j})\\} \\cup \\{\\mathsf{ConnLabel}\\xspace^1_{G_{i,j},T_{i,j}}(v)\\}~,\n\\end{equation}\nwhere $E(v,T_{i,j})$ is the set of edges incident to $v$ in the tree $T_{i,j}$. \nThe final routing table is given by $R_{route}(v)=\\{R_{route,i,j}(v), (i,j) ~\\mid~ T_{i,j} \\in \\mathcal{T}, v\\in T_{i,j}\\}$.\n\nSince the connectivity labels are of size $\\widetilde{O}(f)$, and as each $v$ appears in $\\deg_{\\mathcal{T}}(v)$ trees, the size of the table is $\\widetilde{O}(f \\deg_{\\mathcal{T}}(v)).$ Since the total number of tree edges in $\\mathcal{T}$ is bounded by $\\widetilde{O}(K \\cdot n^{1+1\/k})$, this provides a global space bound of $\\widetilde{O}(fK \\cdot n^{1+1\/k})$ bits.\n\n\n\\paragraph{The routing algorithm.} In the routing algorithm, the source vertex $s$ initially gets the routing label $L_{route}(t)$ (Eq. (\\ref{eq:Label-route-vertex})) of the destination $t$ and its own routing table, $R_{route}(s)$, and its goal is to find the smallest radius graph $G_{i,j}$ such that $s$ and $t$ are connected in $G_{i,j} \\setminus F$, and use it for routing. \nAs the set $F$ is \\emph{not} known in advance, the algorithm works in $K= O(\\log{nW})$ phases, where in phase $i$ it tries to route a message in the graph $G_{i,i^*(t)}$ (which contains the entire $2^i$-radius ball of $t$). If $s$ and $t$ are connected in $G_{i,i^*(t)} \\setminus F$ the algorithm succeeds, and otherwise we proceed to the next phase, corresponding to the distance scale of $2^{i+1}$. \nWe next describe the algorithm for a single phase $i$, we denote $G_i = G_{i,i^*(t)}, T_i = T_{i,i^*(t)}$. Note that $s$ can deduce the index $i^*(t)$ from the routing label of $t$, and it can check if $s \\in T_i$ using its routing table. If $s \\not \\in T_i$, we proceed to the next phase.\n\nIf $s \\in T_i$, the routing procedure for phase $i$ has at most $|F|+1$ iterations. We maintain the following invariant in the beginning of each iteration $\\ell \\in \\{1,\\ldots, |F|+1\\}$: (i) the iteration starts at vertex $s$, (ii) the algorithm has already detected a subset of $\\ell-1$ faulty edges $F_\\ell \\subseteq F$, and (iii) the header contains the labels $\\mathsf{ConnLabel}\\xspace_{G_i,T_i}(e)$ of all the edges $e \\in F_\\ell$. Each iteration $\\ell \\leq |F|+1$ terminates either at the destination vertex $t$, or at the source vertex $s$. In addition, w.h.p., if $s$ and $t$ are connected in $G_i \\setminus F$, iteration $|F|+1$ terminates at $t$. The invariant holds vacuously for iteration $1$.\n\nWe now describe the $\\ell^{th}$ iteration (of the $i^{th}$ phase) of the routing procedure given the invariant. The source vertex $s$ considers the $\\ell^{th}$ copy of the FT connectivity labels, $\\mathsf{ConnLabel}\\xspace^{\\ell}_{G_i,T_i}(e)$ for every $e\\in F_\\ell$. \nUsing the routing labels of the edges, that are part of the header, the routing label $L_{route}(t)$ (of Eq. (\\ref{eq:Label-route-vertex})) and the routing table $R_{route}(s)$, $s$ can apply the decoding algorithm of \nLemma \\ref{lem:succint_path_routing} to determine if $s$ and $t$ are connected in $G_i \\setminus F_\\ell$. \nIf the answer is no, the algorithm proceeds to the next phase $i+1$.\nOtherwise, by applying the decoding algorithm of Lemma \\ref{lem:succint_path_routing}, it computes the succinct path $\\widehat{P}_\\ell$. The path $\\widehat{P}_\\ell$ encodes an $s$-$t$ path in $G_i \\setminus F_\\ell$, that includes the relevant port and tree routing information of its vertices. The header of the message $H_\\ell$ then contains \n$$H_\\ell=\\langle \\widehat{P}_\\ell, i, i^*(t), \\{L_{route, i, i^*(t)}(e)\\}_{e \\in F_{\\ell}}, q \\rangle~,$$ where $q = O(f)$ is an index indicating the current segment of $\\widehat{P}_\\ell$ we explore. Note that the header $H_\\ell$ contains the $f$ copies of connectivity labels of the $F_{\\ell}$ edges, and not only the $\\ell^{th}$ copy.\nThe size of the header is $\\widetilde{O}(f^2)$, as the description of the path has size $\\widetilde{O}(f)$, and additionally we have at most $f$ faulty edges with labels of size $\\widetilde{O}(f)$.\nLet $P_\\ell$ be the $G$-path encoded by the path $\\mathcal{P}_\\ell$. The algorithm then routes the message along $P_\\ell$ in the same manner as in Sec. \\ref{sec:routing-known}. In the case where $P_{\\ell}\\cap F=\\emptyset$, the iteration successfully terminates at the destination vertex $t$. From now on, we consider the case that $P_{\\ell}$ contains at least one faulty edge. \n\nLet $e=(u,v)$ be the first edge (closest to $s$) on the path $P_\\ell$ that belongs to $F$. Since $P_\\ell \\cap F_\\ell=\\emptyset$, it holds that $e \\in F \\setminus F_\\ell$. Without loss of generality, assume that $u$ is closer to $s$ on $P_\\ell$. Thus the faulty edge $e$ is detected upon arriving to the vertex $u$. \nIn the case where $e$ is a \\emph{non-tree edge}, then it must be a $G$-edge on $\\widehat{P}_\\ell$. Since this path has the extended ids $\\operatorname{EID}_{T_i}(e)$ of its $G$-edges, and since the connectivity label of a non-tree edge $e$ is its extended identifier $\\operatorname{EID}_{T_i}(e)$ in all the $f'$ applications of the scheme on $G_i$\\footnote{This is because we use the same random seed $\\mathcal{S}_{ID}$ in all these applications.}, $u$ can add $L_{route,i,i^*(t)}(e)=\\operatorname{EID}_{T_i}(e)$ to the header of the message. Assume now that $e$ is a tree edge in $T_i$. The vertex $u$ then adds the routing label $L_{route,i,i^*(t)}(e)$ to the header of the message, as $e$ is a tree edge adjacent to $u$ it has this information in its routing table. Finally, it marks the header with the sign $R$, indicating that the message should now be routed in the reverse direction, until arriving $s$ again. This completes the description of iteration $\\ell$. It is easy to see that the invariant is maintained. If $s$ and $t$ are connected in $G_i \\setminus F$, after at most $f$ iterations all faulty edges are detected. In the last iteration, the path computed based on the labeling information is free from faulty edges, and the routing is completed (in the same manner as in Sec. \\ref{sec:routing-known}) at the destination $t$. We next bound the multiplicative stretch of the routing.\n\n\n\n\n\\begin{claim}\\label{cl:route-length}\nFix a set of faulty edges $F$, and let $s,t$ be vertices that are connected in $G \\setminus F$. Then, the message is routed from $s$ to $t$ within $32k (|F|+1)^2 \\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$ steps, w.h.p.\n\\end{claim}\n\\begin{proof}\nFirst note that since each iteration and each graph $G_{i}$ uses an independent set of FT connectivity labels, then in each phase and each iteration the decoding algorithm succeeds w.h.p. and outputs an $s$-$t$ path $\\widehat{P}_\\ell$ if exists. \n\nAssume that $\\mbox{\\rm dist}_{G \\setminus F}(s,t) \\in (2^{i-1},2^i]$. Then, $s$ and $t$ are connected in $G_i \\setminus F$, as $T_i = T_{i,i^*(t)}$ contains the $2^i$-ball around $t$. We show that the algorithm terminates at $t$ in phase $i$ or before it, and that in any phase $j \\leq i$, the routing algorithm traverses a path of length at most $2(4k-1)(|F|+1)^2 \\cdot 2^j$.\n\nLet $j \\leq i$. In the $\\ell$'th iteration of phase $j$, the algorithm first checks if $s$ and $t$ are connected in $G_j \\setminus F_{\\ell}$, where $F_{\\ell}$ is the set of currently detected faults. If the answer is no, the algorithm proceeds to the next phase. Otherwise, it tries to route a message from $s$ to $t$ on the path encoded by $\\widehat{P}_{\\ell}$. The length of the path is bounded by $(4k-1)(|F|+1)\\cdot 2^j$ from Lemma \\ref{lem:succint_path_routing}. The algorithm either succeeds, or finds a faulty edge on the way in which case it returns to $s$ by traversing the same path on the reverse direction. Overall, the algorithm traverses a path of length at most $2(4k-1)(|F|+1)\\cdot 2^j$, in this iteration. In all $|F|+1$ iterations of phase $j$, the length of the path explored is at most $2(4k-1)(|F|+1)^2 \\cdot 2^j$. Summing over all iterations $j \\leq i$, the stretch is bounded by $$\\sum_{j=1}^{i} 2(4k-1)(|F|+1)^2 \\cdot 2^j = 2(4k-1)(|F|+1)^2 \\sum_{j=1}^i 2^j \\leq 2^{i+2} (4k-1)(|F|+1)^2 \\leq 32k (|F|+1)^2 \\mbox{\\rm dist}_{G \\setminus F}(s,t).$$ The last inequality uses the fact that $2^{i-1} \\leq \\mbox{\\rm dist}_{G \\setminus F}(s,t).$ \n\nIn the $i$'th phase, since $s$ and $t$ are connected in $G_i \\setminus F$, then for any $F_{\\ell} \\subseteq F$, $s$ and $t$ are connected in $G_i \\setminus F_{\\ell}$, hence the algorithm always finds a path $\\widehat{P}_{\\ell}$. Hence, it either succeeds in routing the message to $t$ in one of the iterations (or one of the previous phases), or learns about all the failures $F$. In the latter case, in iteration $|F|+1$ it learns about a failure-free path $\\widehat{P}_{|F|+1}$, and the routing terminates at $t$. This completes the proof.\n\\end{proof}\nTo conclude, we have the following.\n\n\\begin{theorem}\nFor every integers $k,f$, there exists an $f$-FT compact routing scheme that given a message $M$ at the source vertex $s$ and a label $L_{route}(t)$ of the destination $t$, in the presence of at most $f$ faulty edges $F$ (unknown to $s$) routes $M$ from $s$ to $t$ in a distributed manner over a path of length at most $32k (|F|+1)^2\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$. \nThe global table size is $\\widetilde{O}(f \\cdot n^{1+1\/k} \\log{(nW)})$.\nThe header size of the messages is bounded by $\\widetilde{O}(f^2)$ bits, and the label size of vertices is $O(\\log{(nW)} \\log{n})$. \n\\end{theorem}\n\n\n\n\n\n\\paragraph{Improving the size of the routing tables.} \nSo far, we have described a routing scheme that consumes a total space of $\\widetilde{O}(f\\cdot n^{1+1\/k}\\log (nW))$ bits, and multiplicative stretch\n$32(|F|+1)^2 k$. We now explain the required modifications needed to providing routing tables with $\\widetilde{O}(f^3\\cdot n^{1\/k})$ bits per vertex. The most space consuming information for a vertex $u$ is the connectivity labeling\ninformation of the edges incident to $u$ in each of the trees $T_{i,j} \\in \\mathcal{T}$. As the degree of $u$ in some of the trees might be $\\Theta(n)$, it leads to tables of possible super-linear size. To reduce the space of the individual tables, we apply a load balancing idea which distributes the labeling information incident to \\emph{high}-degree vertices among their neighbors. \n\nInstead of storing the labeling information of $e=(u,v)$ at the routing tables of $u$ and $v$, we define \nfor every tree $T \\in \\mathcal{T}$ and an edge $e=(u,v) \\in T$, a subset $\\Gamma_T(e)$ of vertices that store the connectivity labeling information of $e$ in $T$. We will make sure that the information on some vertex in $\\Gamma_T(e)$ can be easily extracted in the routing procedure upon arriving one of its endpoints. In addition, we will make sure that each vertex stores the information only for a small number of edges in each of its trees. Consider an edge $e=(u,v)$ in a tree $T$, and assume, without loss of generality, that $u$ is the parent of $v$ in the tree $T$. In the case where $\\deg(u,T)\\leq f+1$, we simply let $\\Gamma_T(e)=\\{u,v\\}$. That is, the label of $e$ is stored by both endpoints of $e$ (as before). The interesting case is where $\\deg(u,T)\\geq f+2$, in which case, $u$ might not be able to store the label of $e$, and will be assisted by its other children as follows. Let $Child(u,T)=[v_1,\\ldots, v_\\ell]$ be the lexicographically ordered list of the children of $u$ in $T$. The algorithm partitions $Child(u,T)$ into consecutive blocks of size $f+1$ (the last block might have $2f+1$ vertices). Letting $[v_{q,1}, \\ldots, v_{q,f+1}] \\subseteq Child(u,T)$ be the block containing $v$, define\n$$\\Gamma_T(e)=\\{v_{q,1}, \\ldots, v_{q,f+1}\\}~.$$\nNote that in particular, $v \\in \\Gamma_T(e)$. Thus, the label of $e$ is stored by $v$ and $\\ell \\in [f,2f-1]$ additional children of $u$ in $T$. \n\nWe then modify the tree labels from Fact \\ref{fc:route-trees} to contain the port information of $\\Gamma_T(e)$. \nIn order to do that, we will be using the more relaxed variant of Fact \\ref{fc:route-trees}, we have:\n\\begin{claim}\\label{cl:route-trees-port}\nFor every $n$-vertex tree $T$, there exists a (deterministic) routing scheme that assigns each vertex $v \\in V(T)$ a label $L_T(v)$ of $O(f\\log^2 n)$ bits and table $R_T(v)$ of $O(f\\log n)$ bits. Given the label $L_T(t)$ of the target $t$ \nand the routing table $R_T(u)$, the vertex $u$ can compute in $\\widetilde{O}(f)$ time: (i) the port number of the edge $e=(u,v)$ on its tree path to $t$, and (ii) the port numbers of the neighbors of $u$ in the set $\\Gamma_T(e=(u,v))$. \n\\end{claim}\n\\begin{proof}\nThe proof follows by slightly modifying the simpler scheme of Fact \\ref{fc:route-trees} by \\cite{thorup2001compact}. Specifically, we will be using the routing scheme based on heavy-light tree decomposition. This scheme assigns each vertex $v$ labels of $O(\\log^2 n)$ bits that contain the port information of the at most $O(\\log n)$ light edges on the root to $v$ path in $T$. The vertices are enumerated in DFS ordering, and the label of each vertex contains its DFS range, and the specification of all light edges on its path in $T$ from the root, along with a port information of these edges. The routing table of $v$ stores its DFS range, the port number of the (unique) heavy child of $v$ and also the port to its parent. In our modification, we augment the label of each vertex $u$ with the port information of $\\Gamma_T(e')$ for every light edge $e'$ appearing on the root to $u$ path in $T$. Since there are $O(\\log n)$ such light edges, the total label information is encoded in $O(f\\log^2 n)$ bits. The routing table $R_T(u)$ is augmented with the port information for the set $\\Gamma_T(e'')$, where $e''$ is the (unique) heavy child of $u$. The routing scheme is then exactly as described at \\cite{thorup2001compact}, only that in addition to the port of the next-hop $e=(u,v)$, we also obtain the port information of $\\Gamma_T(e)$. This increases the labels and tables in the scheme of \\cite{thorup2001compact} by a factor of $O(f)$, the claim follows. \n\\end{proof}\n\nSince the modified claim of tree routing defines now both tree routing labels and tables, we employ the following modifications. The extended identifier $\\operatorname{EID}_T(e)$ of an edge $e=(u,v)$ from Eq. (\\ref{eq:edge-extended-routing})\ncontains the modified tree labels and thus has $O(f\\log^2 n)$ bits.\n\nThe \\emph{routing labels} of Eq. (\\ref{eq:route-edge-label}) are defined in the same manner only using the modified extended edge identifiers. The routing label of each edge has $\\widetilde{O}(f^2)$ bits, and routing label of every vertex has $\\widetilde{O}(f)$ bits. \nWe are now ready to describe the more succinct \\emph{routing tables} of each vertex $v$. We modify the definition of Eq. (\\ref{eq:route-table-ij}) by letting:\n\\begin{equation*}\\label{eq:route-table-ij-mod}\nR_{route,i,j}(v)=\\{L_{route,i,j}(e), e \\in \\Gamma_{T_{i,j}}(e)\\} \\cup \\mathsf{ConnLabel}\\xspace^1_{G_{i,j},T_{i,j}}(v) \\cup R_{T_{i,j}}(v)~,\n\\end{equation*}\nthus the routing table $R_{route,i,j}(v)$ is augmented the tree routing tables $R_{T_{i,j}}(v)$ of Claim \\ref{cl:route-trees-port}. In addition, $R_{route}(v)=\\{R_{route,i,j}(v), (i,j) ~\\mid~ T_{i,j} \\in \\mathcal{T}, v\\in T_{i,j}\\}$ as before.\nWe therefore have:\n\\begin{claim}\\label{cl:route-balance-table}\nThe size of each routing table $R_{route}(v)$ is bounded by $\\widetilde{O}(f^3 K n^{1\/k})$ bits.\n\\end{claim}\n\\begin{proof}\nFor every tree $T_{i,j}$ containing $v$, $v$ stores the routing labels for the tree $T_{i,j}$ of all edges in the set $E'(v,T_{i,j})=\\{e \\in T_{i,j} ~\\mid~ v \\in \\Gamma_{T_{i,j}}(e)\\}$. Since each connectivity label of an edge contains the modified tree labels from Fact \\ref{cl:route-trees-port}, it has $\\widetilde{O}(f)$ bits, and as the routing label for $T_{i,j}$ contains $O(f)$ copies of this label, overall each routing label of an edge has $\\widetilde{O}(f^2)$ bits. Observe that $|E'(v,T_{i,j})|=O(f)$ as each vertex stores the label of its parent in the tree, $O(f)$ child edges, and $O(f)$ child edges of its parent in the tree. Since each $v$ participates in $\\widetilde{O}(K n^{1\/k})$ trees, overall its routing table has $\\widetilde{O}(f^3 K n^{1\/k})$ bits, as required. \n\\end{proof}\n\nIt remains to explain the required modifications for the routing procedure over a tree $T_i=T_{i,i^*(t)}$. Upon arriving to a vertex $u$ incident to a faulty \\emph{tree} edge $e=(u,v)$ the procedure is as follows. If $e$ is a non-tree edge or if $u$ stores the connectivity label $\\mathsf{ConnLabel}\\xspace_{G_i,T_i}(e)$\\footnote{This covers the cases where $v$ is either a parent of $u$ or else, it is one of the at most $f+1$ children of $u$ in $T_i$.}, then $u$ adds the routing label of the edge to the header, as before. In the remaining case it must hold that $e$ is the edge incident to $u$ on its tree path to some vertex $y$. By using the tree routing scheme of Claim \\ref{cl:route-trees-port} we have that given the tree routing labels $L_{T_{i}}(u)$ and $L_{T_{i}}(y)$, the vertex $u$ can also obtain the port numbers of its $\\ell \\in [f,2f-1]$ children in $\\Gamma_{T_{i}}(e)$ that store the label $\\mathsf{ConnLabel}\\xspace_{G_i,T_i}(e)$. Since there are at most $f$ edge faults in the network, and $\\Gamma_{T_{i,j}}(e)$ contains information on at least $f+1$ ports of $u$'s neighbors that contain the label of $e$, the vertex $u$ can access a non-faulty neighbor, say $w$, that has the label information of $e$. That vertex can then add the labeling information of $e$ to the header of the message, and the routing algorithm proceeds as before. Since we use the modified tree labels of Claim \\ref{cl:route-trees-port}, each connectivity label has $\\widetilde{O}(f)$ bits, and each routing label of an edge for a tree $T_{i,j}$ has $\\widetilde{O}(f^2)$ bits. Since the header stores the routing labels of $O(f)$ edges, it consists of $\\widetilde{O}(f^3)$ bits. \n\n\nThe stretch is still bounded by $32k (|F|+1)^2\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$, as we next explain. Recall that in the proof of Claim \\ref{cl:route-length}, we bounded the length of the path we explore in one iteration of the algorithm of phase $j$ by $2(4k-1)(|F|+1)2^j.$ In the new scheme, when we discover a faulty edge, the vertex $u$ may send messages to $|F|+1$ neighbors until it finds the label of the edge. This adds at most $2(|F|+1)2^j$ to the stretch, as the weight of edges in the tree of phase $j$ is at most $2^j$, and we may send messages in both directions. This gives that the length of the path we explore in one iteration is now at most $2(4k-1)(|F|+1)2^j+2(|F|+1)2^j=8k(|F|+1)2^j.$ The rest of the analysis proceeds as in the proof of Claim \\ref{cl:route-length}, and gives that the stretch is bounded by $32k (|F|+1)^2\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$ (we get the same bound as in the original proof we bounded $2(4k-1)$ with $8k$ during the analysis).\nWe therefore have:\n\\begin{theorem}\\label{thm:routing-unknown}[Fault-Tolerant Routing]\nFor every integers $k,f$, there exists an $f$-sensitive compact routing scheme that given a message $M$ at the source vertex $s$ and a label $L_{route}(t)$ of the destination $t$, in the presence of at most $f$ faulty edges $F$ (unknown to $s$) routes $M$ from $s$ to $t$ in a distributed manner over a path of length at most $32k (|F|+1)^2\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$. The routing labels have $\\widetilde{O}(f)$ bits, the table size of each vertex is $\\widetilde{O}(f^3 \\cdot n^{1\/k} \\log(nW))$. The header size of the messages is bounded by $\\widetilde{O}(f^3)$ bits. \n\\end{theorem}\n\n\n\\paragraph{Lower Bound.} Finally, we show that the price of not knowing the set of faulty edges $F$ in advance might indeed incur a multiplicative stretch of $\\Omega(f)$. \n\n\\begin{proof}[Proof of Theorem \\ref{thm:lb-routing}]\nConsider a graph that consists of $f+1$ vertex disjoint $s$-$t$ paths, each of length $L=\\Theta(n\/f)$. The last edge of each of the paths, except for one, is faulty. Assume that the non-faulty path is chosen uniformly at random. Since the routing scheme is oblivious to the faulty edges, it can discover a faulty edge only upon sending the message to one of the edge endpoints. The expected length of the routing is given by:\n$$\\frac{L}{f+1} +2L \\cdot \\left(1-\\frac{1}{f+1} \\right)\\cdot \\frac{1}{f} + \\ldots+ \\left(f+1\\right)L\\cdot \\prod_{i=0}^{f-1} \\left(1-\\frac{1}{f+1-i}\\right)=\\Omega(f L)~.$$ \nSince the $s$-$t$ shortest path under these faults is $L$, the proof follows. See Fig. \\ref{fig:LB-stretch} for an illustration.\n\\end{proof}\n\n\n\\begin{figure}[h!]\n\\begin{center}\n\\includegraphics[scale=0.40]{lb.pdf}\n\\caption{\\sf Illustration for a stretch lower bound for any FT routing schemes. The $s$-$t$ pair are connected by $f+1$ vertex disjoint paths of length $L$. Since the faulty-edge is the last edge of the path, the routing requires $\\Omega(L)$ steps to discover a single faulty edge. As the non-faulty path is chosen uniformly at random, in expectation, the routing requires $\\Omega(fL)$ steps. \\label{fig:LB-stretch}\n}\n\\end{center}\n\\end{figure}\n\n\n\\section{Compact Routing Schemes}\nIn this section, we explain how to use our FT-distance labels to provide compact and low stretch routing schemes. This is the first scheme to provide an almost tight tradeoff between the space and the multiplicative stretch, for a constant number of faults $f=O(1)$. Throughout this section, tree routing operations are performed by using the tree routing scheme of Thorup and Zwick \\cite{thorup2001compact}.\n\\begin{fact}\\label{fc:route-trees}[Routing on Trees]\\cite{thorup2001compact}\nFor every $n$-vertex tree $T$, there exists a routing scheme that assigns each vertex $v \\in V(T)$ a label $L_T(v)$ of $(1+o(1))\\log n$ bits. Given the label of a source vertex\nand the label of a destination, it is possible to compute, in constant time, the port number of the edge from the source that heads in the direction of the destination.\n\\end{fact}\n\n\\textbf{MP: check if needed.} \\mtodo{I've added a discussion of the port information later.}\nIn our routing scheme, each vertex $u$ is required to compute the port number of the next-hop towards the target destination. Towards that goal, we modify the FT-distance labels of Sec. \\ref{sec:ft-distance}, by adding the port information to the extended identifier of the edges. That is, the extended identifier of each edge $e=(u,v)$ is augmented with a third field containing the port number of $v$ w.r.t $u$ and vice-versa. To avoid cumbersome notation, we still refer to this port-augmented FT-distance label of $u$ by $\\mathsf{DistLabel}\\xspace(u)$. \n\n\n\\mtodo{Added the part from here. This adds port and tree routing information to the ids, and adapts Lemma \\ref{lem:useful-recovery-edges} (finding succinct path description) to the context of routing.}\n\nWe slightly modify the connectivity label of the edges and vertices by augmenting them with routing information. \nSpecifically, we define the vertex identifier $\\operatorname{ID}_T(u)=[\\operatorname{ID}(u), L_T(u)]$ where $L_T(u)$ is taken from Fact \\ref{fc:route-trees}. We then slightly modify the \nthe connectivity label of Eq. (\\ref{{eq:conn-node}}) by defining \n$$\\mathsf{ConnLabel}\\xspace_{G,T}(u)=\\langle \\mathsf{ANC}\\xspace_T(u), \\operatorname{ID}_T(u) \\rangle~.$$\nIn addition, we also augment the extended identifier of an edge Eq. (\\ref{eq:extend-ID}) with port information and tree routing information, by having:\n$$\\operatorname{EID}(e)=[\\operatorname{UID}(e), \\operatorname{ID}(u), \\operatorname{ID}(v), \\mathsf{ANC}\\xspace_T(u), \\mathsf{ANC}\\xspace_T(v), \\mbox{\\tt port}(u,v), \\mbox{\\tt port}(v,u), L_T(u), L_T(v)]~,$$\nwhere $\\mbox{\\tt port}(u,v)$ is the port number of the edge $(u,v)$ for $u$. \nThroughout this section, when applying the connectivity labels from Section \\ref{sec:ftconn-sketch} on a graph $G$ with spanning tree $T$, we use these labels and extended identifiers to support routing.\nWe later apply the connectivity labels with respect to the different trees of the tree cover as discussed in Section \\ref{sec:ft-distance}. \nLet $T_{i,j} \\in \\mathsf{TC}\\xspace_i$, recall that $G_{i,j}=G[V(T_{i,j})]$ and that $\\mathcal{T}=\\bigcup_{i=1}^K \\mathsf{TC}\\xspace_i$ for $K=O(\\log (nW))$. \n\n\\begin{lemma}\\label{lem:succint_path_routing}\nConsider a triplet $s,t,F$ such that $s,t,F \\in G_{i,j}$. \nGiven $ID_{T_{i,j}}(s),ID_{T_{i,j}}(t)$ and the connectivity labels $\\{\\mathsf{ConnLabel}\\xspace_{G_{i,j},T_{i,j}}(e)\\}_{e \\in F}$, we can determine w.h.p if $s$ and $t$ are connected in $G_{i,j} \\setminus F$. If they are connected, we can output a labeled $s$-$t$ path $\\widehat{P}$ of length $O(f)$ that provides a succinct description of the $s$-$t$ path in $G_{i,j} \\setminus F$. The edges of $\\widehat{P}$ are labeled by $0$ and $1$, where $0$-labeled edges correspond to $G_{i,j}$-edges and $1$-labeled edges $e=(x,y)$ correspond to $x$-$y$ paths in $T_{i,j} \\setminus F$. For each $G_{i,j}$-edge, the succinct path description has the port information of the edge, and for each $x-y$ path, the description has the tree routing labels $L_{T_{i,j}}(x),L_{T_{i,j}}(y)$.\nThe length of the $s$-$t$ path encoded by $\\widehat{P}$ is bounded by $(4k-1)(f+1)\\cdot 2^i$. \n\\end{lemma}\n\n\\mtodo{For the proof I use the fact that the decoding algorithm only uses the ancestry labels of $s,t$, we should probably add some remark on the topic also on the labels section.}\n\n\\begin{proof}\nThe proof follows the proof of Lemma \\ref{lem:useful-recovery-edges}. To see this, first note that the decoding algorithm from Section \\ref{sec:ftconn-sketch} mostly uses the connectivity labels of the faulty edges $F$, and it needs only the labels $\\mathsf{ANC}\\xspace(s),\\mathsf{ANC}\\xspace(t)$ of $s$ and $t$, to detect the connected components of $s$ and $t$ in $T_{i,j} \\setminus F$. \\mtodo{do we want to edit this there? at least have some remark} \nHence, using $ID_{T_{i,j}}(s),ID_{T_{i,j}}(t)$ and the connectivity labels $\\{\\mathsf{ConnLabel}\\xspace_{G_{i,j},T_{i,j}}(e)\\}_{e \\in F}$, we can first check if $s$ and $t$ are connected in $G_{i,j} \\setminus F$ using the decoding algorithm of Section \\ref{sec:ftconn-sketch}. If they are connected, then from Lemma \\ref{lem:useful-recovery-edges}, we get a succinct description of the $s$-$t$ path in $G_{i,j} \\setminus F$. We next show that the algorithm indeed has the relevant port and tree routing information. For this note that all the vertices in the path $\\widehat{P}$ obtained by Lemma \\ref{lem:useful-recovery-edges} are either $s$ and $t$ or endpoints of the $f$ recovery edges found in the algorithm. The ids $ID_{T_{i,j}}(s),ID_{T_{i,j}}(t)$ of $s$ and $t$ have their tree routing information, and when the algorithm finds a recovery edge, it learns about its extended id $ID_{T_{i,j}}(e)$ that has the port information and tree routing information of its endpoints. Any $G_{i,j}$-edge in $\\widehat{P}$ is a recovery edge, hence the algorithm has its port information, and for any $x$-$y$ path in $T_{i,j} \\setminus F$, the algorithm has the tree routing labels $L_{T_{i,j}}(x),L_{T_{i,j}}(y)$, as needed.\nThe stretch analysis follows the stretch analysis in Section \\ref{sec:ft-distance}. It is based on the fact that $\\widehat{P}$ has as most $f+1$ subpaths in $T_{i,j} \\setminus F$, each of length at most $(4k-2)2^i$, and at most $f$ recovery edges of weight at most $2^i$.\n\\end{proof}\n\n\n\n\n\n\n\n\\subsection{Forbidden Set Routing (Faulty Edges are Known)}\\label{sec:routing-known}\nWe start by describing the routing scheme in the forbidden set setting, where the faulty edges $F$ are known to the source vertex $s$. We show the following.\n\n\\begin{theorem}\\label{thm:routing-known}[Forbidden-Set Routing]\nFor every integers $k,f$, there exists an $f$-sensitive compact routing scheme that given a message $M$ at the source vertex $s$, a label of the destination $t$, and labels of at most $f$ forbidden edges $F$ (known to $s$), routes $M$ from $s$ to $t$ in a distributed manner over a path of length at most $(8k-2)(f+1)\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$. The table size of each vertex is bounded by $\\widetilde{O}(n^{1\/k} \\log{(nW)})$. The header size of the messages is bounded by $\\widetilde{O}(f)$ bits. The labels of vertices and edges have size $\\widetilde{O}(n^{1\/k} \\log{(nW)})$.\n\\end{theorem}\n\n\\mtodo{I edited a bit the proof so it's more similar to the next section and uses Lemma \\ref{lem:succint_path_routing}. The routing tables here still use the distance labels. Also, added a bit more details on the routing process.}\n\n\\begin{proof}\nThe algorithm is based on the distance labels from Section \\ref{sec:ft-distance}. Recall that the distance labels are based on applying fault tolerant connectivity labels on different graphs $G_{i,j}$, we use the connectivity labels from Section \\ref{sec:ftconn-sketch}.\nThe routing table of each vertex $u$ consists of its distance label and the tree routing labels from Fact \\ref{fc:route-trees} for every tree $T_{i,j} \\in \\mathcal{T}$ containing $u$. That is,\n$$R_{route}(u)=\\mathsf{DistLabel}\\xspace(u) \\cup \\{ \\langle L_{T_{i,j}}(u), (i,j) \\rangle ~\\mid~ i \\in [1,K], j \\in \\{1,\\ldots, |\\mathsf{TC}\\xspace_{i}|\\}, u \\in T_{i,j}\\}~.$$\nSince each vertex $u$ participates in $O(k n^{1\/k} \\log(n W))$ trees of $\\mathcal{T}$, and since the distance labels are of size $O(k n^{1\/k} \\log(n W) \\cdot \\log^3{n})$, the table $R_{route}(u)$ has $O(k n^{1\/k} \\log(n W) \\cdot \\log^3{n})$ bits.\nThe label of a vertex $u$ is just $R_{route}(u)$, and the label of an edge $e$ is $\\mathsf{DistLabel}\\xspace(e)$. The labels have size $O(k n^{1\/k} \\log(n W) \\cdot \\log^3{n})$. \\mtodo{we can have shorter labels for vertices as in the next section (but then we need to \"open the box\" of the distance labels), as the labels of edges are anyway large I'm not sure if we want to do it in this section.}\n\n\\mtext{\nIn the routing algorithm, the vertex $s$ is given the label $R_{route}(t)$ of $t$, and the labels $\\{\\mathsf{DistLabel}\\xspace(e)\\}_{e \\in F}$, and it needs to route a message to $t$ in the graph $G \\setminus F$. First, from the distance labels of $s,t$ and $F$, $s$ can check using the algorithm from Section \\ref{sec:ft-distance} if $s$ and $t$ are connected in $G \\setminus F$, and if so get a succinct description of a path between them. In more detail, recall that the algorithm from Section \\ref{sec:ft-distance} works in $K$ phases, where in phase $i$ it checks if $s$ and $t$ are connected in the graph $G_{i,i^*(s)} \\setminus F$ that contains the $2^i$-ball around $s$. Let $i$ be the first iteration where $s$ and $t$ are connected in $G_{i,i^*(s)} \\setminus F$ according to the algorithm, and denote $G_i = G_{i,i^*(s)},T_i = T_{i,i^*(s)}$, and let $F_i = F \\cap G_i$. The algorithm can also give a succinct description of an $s$-$t$ path in $G_i \\setminus F_i$ following Lemma \\ref{lem:succint_path_routing}. For this, note that we indeed have all the required information. The distance labels of edges in $F$ in particular contain the labels $\\{\\mathsf{ConnLabel}\\xspace_{G_i,T_i}(e)\\}_{e \\in F_i}$, and we can also tell which edges of $F$ are in $G_i$ from the labels. Also, the labels of $s,t$ contain the information $ID_{T_i}(s),ID_{T_i}(t)$ if they are both in $T_i$ (otherwise, they are not connected in level $i$).\n\nThe path $\\widehat{P}$ as described in Lemma \\ref{lem:succint_path_routing} is composed of $O(f)$ parts, where segment $(x,y)$ in the path corresponds either to an edge in $G_i$ or to a tree path in $T_i \\setminus F$, it also has the relevant port and tree routing information. Our goal is to route a message according to this path. For this we add to the header of the message the description of $\\widehat{P}$, the indexes $(i,i^*(s))$ of the tree we explore and an index $1 \\leq q \\leq 2f+1$ that represents the segment of $\\widehat{P}$ we currently explore, initially $q=1$. Overall, the header size is $\\widetilde{O}(f)$. To route a message according to the path, we work as follows. The header specifies the current segment in $\\widehat{P}$. If the current segment corresponds to an edge $(x,y) \\in G$, then $x$ uses the port information to route the message to $y$ and increases the index $q$. Otherwise, the current segment represents a tree path $(x,y) \\in T_i$ and a vertex $u$ in this path uses its routing label in $T_i$ and the routing label of $y$ in $T_i$ (that is part of the header) to route the message towards $y$. When the message reaches $y$, it increases the index $q$. This completes the description of the routing process. The length of the path described is at most $(8k-2)(f+1)\\cdot \\mbox{\\rm dist}_{G\\setminus F}(s,t)$, as shown in Section \\ref{sec:ft-distance}. } \n\\end{proof}\n\n\n\n\\subsection{Fault Tolerant Routing (Faulty Edges are Unknown)}\\label{sec:route-unknown}\nWe now consider the more involved setting where the set of failed edges $F$ are unknown to $s$. In this case, an edge $(u,v) \\in F$ is detected only when the message arrives, during the routing procedure, to one of the endpoints of $e$. Note that the routing scheme should, by definition, be prepared to any set of faulty edges $F$. However, the space bound of our scheme is required to be bounded by $\\widetilde{O}(f n^{1+1\/k})$, which is possibly much smaller than the number of graph edges $m$. This in particular implies that we cannot store the FT-distance labels of all the graph edges. Nevertheless, we show that it is sufficient to explicitly store the labeling information for the tree edges in $\\mathcal{T}$. The required information for the failed non-tree edges would be revealed throughout the process, by applying the decoding algorithm of Lemma \\ref{lem:succint_path_routing}.\nOur routing scheme eventually routes the message along the $s$-$t$ path encoded by the FT-distance labels of $s,t$ and $F$. However, since the labels of $F$ are unknown in advance, the routing scheme will detect these edges in a trail and error fashion which induces to an extra factor of $f$ in the final multiplicative stretch. This extra $f$ factor is also shown to be essential, in the end of the section.\nWe proceed by describing the routing tables. \n\n\\remove{\n\\paragraph{The routing tables.} For ease of presentation, we first describe a solution with a multiplicative stretch of $O(kf^2)$, and \\emph{global} space of $\\widetilde{O}(f K \\cdot n^{1+1\/k})$, \\mtodo{should it be $n^{1+2\/k}$?} but the individual tables of some of the vertices might be of linear space. \nRecall that $\\mathcal{T}=\\bigcup_i^{K} \\mathsf{TC}\\xspace_i$, for $K=O(\\log (nW))$ is a collection of tree covers in all $K=\\lceil \\log (nW) \\rceil$ distance scales, see Eq. (\\ref{eq:TC-i}). For every vertex $u$, let $\\deg_T(u)=\\sum_{T_{i,j} \\in \\mathcal{T}}\\deg(u,T_{i,j})$ be the sum of degrees of $u$ in the collection of trees $\\mathcal{T}$.\nThe algorithm computes each vertex $u$ a table of $\\widetilde{O}(f \\cdot \\deg_T(u) \\cdot n^{1\/k})$ bits.\nSince the total number of tree edges in $\\mathcal{T}$ is bounded by $\\widetilde{O}(K \\cdot n^{1+1\/k})$, this provides a global space bound of $\\widetilde{O}(fK \\cdot n^{1+2\/k})$ bits. We later on improve the space of each table to $\\widetilde{O}(fK \\cdot n^{2\/k})$ bits. \\mtodo{maybe extra $k,K$ are needed in some places.}\n\nThe routing process has at most $f+1$ phases, and each phase uses an independent set of randomized FT-distance labels. Therefore, the preprocessing algorithm employs $f$ \\mtodo{$f$ or $f+1$?} independent applications of the $(f,(8k-2)(f+1))$ FT-distance labels scheme. Denote the output labels obtained by the $i^{th}$ application by $\\mathsf{DistLabel}\\xspace_i(w)$ for every $w \\in V(G)\\cup E(G)$. Let $E_T=\\bigcup_{T_{i,j} \\in \\mathcal{T}} E(T_{i,j})$ be the collection of tree edges. The \\emph{routing label} \\mtodo{maybe we want to have shorter labels, and have this information only in the routing tables?} of each $w \\in V\\cup E_T$ is given by \n$$L_{route}(w)=\\{\\mathsf{DistLabel}\\xspace_1(w),\\ldots, \\mathsf{DistLabel}\\xspace_{f+1}(w)\\}~.$$ \nThe routing label $L_{route}(e)$ of a non-tree edge $e \\in E \\setminus E_T$ is simply the extended identifier of $e$. For each vertex $u$, the routing table $R_{route}(u)$ consists of the following:\n\\begin{enumerate\n\\item Tree labels $L_{T_{i,j}}(u)$ for every tree $T_{i,j}$ in $\\mathcal{T}$ that contains $u$ (from Fact \\ref{fc:route-trees}). These labels are augmented with their corresponding tree index $(i,j)$.\n\\item The routing labels $L_{route}(u)$ and $\\{L_{route}(e=(u,v)) ~\\mid~ e \\in T_{i,j}, T_{i,j} \\in \\mathcal{T}\\}$.\n\\end{enumerate}\nThe header information initially contains the succinct path information\\footnote{\\textbf{MP: This is not essential, we can always make the header contain the labels of the currently detected faults.}} obtained by the decoding algorithm of the FT-distance labeling when given the labels $L_{route}$ of $s$ and $t$. \\mtodo{do we want to have $f$ paths? Or only one of them that is relevant for the current phase} As we will see, throughout the routing procedure, the header information will be augmented with the labels of the currently detected faulty edges. \n}\n\n\n\\paragraph{The routing tables.} For ease of presentation, we first describe a solution with a multiplicative stretch of $O(kf^2)$, and \\emph{global} space of $\\widetilde{O}(f K \\cdot n^{1+1\/k})$, but the individual tables of some of the vertices might be large. We later on improve the space of each table to $\\widetilde{O}(fK \\cdot n^{1\/k})$ bits.\n\nRecall that $\\mathcal{T}=\\bigcup_i^{K} \\mathsf{TC}\\xspace_i$, for $K=O(\\log (nW))$ is a collection of tree covers in all $K=\\lceil \\log (nW) \\rceil$ distance scales, see Eq. (\\ref{eq:TC-i}). For every vertex $v$, let $\\deg_T(v)=\\sum_{T_{i,j} \\in \\mathcal{T}}\\deg(u,T_{i,j})$ be the sum of degrees of $u$ in the collection of trees $\\mathcal{T}$. Recall that $G_{i,j}=G[V(T_{i,j})]$.\n\\mtext{For the routing we apply the FT-connectivity labels on the graphs $G_{i,j}$, similarly to Section \\ref{sec:ft-distance}.\nThe routing process uses $f$ independent applications of randomized FT-connectivity labels from Section \\ref{sec:ftconn-sketch}, applied on each one of the graphs $G_{i,j}$. \\mtodo{There is some delicate issue here. In my understanding we do want to have the extended ids fixed in different applications, and only change the randomness of sketches, as I try to explain here.} In more detail, when we apply the labeling scheme on the graph $G_{i,j}$ with spanning tree $T_{i,j}$, we use $f$ independent random seeds $\\mathcal{S}_h$ to determine the randomness of the sketches. However, the seed $\\mathcal{S}_{ID}$ used to determine the extended ids of edges in $G_{i,j}$ is fixed in the $f$ applications, hence the ids are fixed and we only use fresh randomness to compute the sketch information. This process is done independently on each one of the graphs $G_{i,j}$. } \nDenote the output labels obtained by the ${\\ell}^{th}$ application of the scheme on the graph $G_{i,j}$ by $\\mathsf{ConnLabel}\\xspace^{\\ell}_{G_{i,j},T_{i,j}}(w)$ for every $w \\in V(G_{i,j})\\cup E(G_{i,j})$, and we denote by $$\\mathsf{ConnLabel}\\xspace_{G_{i,j},T_{i,j}}(w)=(\\mathsf{ConnLabel}\\xspace^1_{G_{i,j},T_{i,j}}(w),...,\\mathsf{ConnLabel}\\xspace^f_{G_{i,j},T_{i,j}}(w)).$$\nSince in each application the labels are of size $O(\\log^3{n})$, the total size of each label is $O(f \\log^3{n})$. \\mtext{Recall that for a non-tree edge $e \\in G_{i,j}$, we have that $\\mathsf{ConnLabel}\\xspace^{\\ell}_{G_{i,j},T_{i,j}}(e)=\\operatorname{ID}_{T_{i,j}}(e)$. This label does not depend on the sketch information, and hence is the same in all $f$ applications, we will exploit this later in the algorithm.}\n\n\\mtodo{I changed a bit the definition of routing tables and labels. The labels currently are of polylog size (no extra $f$ or $n^{1\/k}$).}\n\\mtext{\nThe routing table $R_{route}(v)$ of a vertex $v$ has the following information for any tree $T_{i,j}$ such that $v \\in T_{i,j}$:\n\\begin{enumerate}\n\\item The values $(ID_{T_{i,j}}(v),i,j)$.\n\\item The connectivity labels $\\mathsf{ConnLabel}\\xspace_{G_{i,j},T_{i,j}}(e)$ for any tree edge $e \\in T_{i,j}$ adjacent to $v$. \n\\end{enumerate}\n\nSince the connectivity labels are of size $\\widetilde{O}(f)$, and since $v$ appears in $\\deg_T(v)$ trees, the size of the table is $\\widetilde{O}(f \\deg_T(v)).$ Since the total number of tree edges in $\\mathcal{T}$ is bounded by $\\widetilde{O}(K \\cdot n^{1+1\/k})$, this provides a global space bound of $\\widetilde{O}(fK \\cdot n^{1+1\/k})$ bits.\n\nRecall that for a vertex $v$ and index $1 \\leq i \\leq K$, we denote by $i^*(v)$ an index such that the $2^i$-ball around $v$ is contained in $T_{i,i^*(v)}$.\nThe routing label $L_{route}(v)$ of $v$ has the id of $v$ in the trees $T_{i,i^*(v)}$.\n$$L_{route}(v) = \\{(i^*(v),ID_{T_{i,i^*(v)}}(v)) | i \\in [1,K]\\}$$\n\nThe size of the label is $O(K\\log{n})=O(\\log{n} \\log{nW})$.}\n\n\\mtodo{In the routing algorithm the main difference is that we have log iterations for the different trees, in each tree the algorithm is similar to before.}\n\n\\paragraph{The routing algorithm.} \\mtext{In the routing algorithm, the source vertex $s$ gets the routing label $L_{route}(t)$ of the destination $t$ and its goal is to find a graph $G_{i,j}$ such that $s$ and $t$ are connected in $G_{i,j} \\setminus F$, and use it for routing. As the set $F$ is not known in advance, the algorithm works in $K= O(\\log{nW})$ iterations, where in iteration $i$ it tries to route a message in $G_{i,i^*(t)}$. If $s$ and $t$ are connected in $G_{i,i^*(t)} \\setminus F$ the algorithm succeeds, and otherwise we proceed to the next iteration. \nWe next describe the algorithm for one iteration $i$, we denote $G_i = G_{i,i^*(t)}, T_i = T_{i,i^*(t)}$. Note that $s$ learns the index $i^*(t)$ from the label of $t$, and it can check if $s \\in T_i$ using its routing table. If $s \\not \\in T_i$, we proceed to the next iteration.}\n\nIf $s \\in T_i$, the routing procedure for iteration $i$ has at most $f+1$ phases. Each phase will start at the source vertex $s$. We will maintain the following invariant in the beginning of each phase $\\ell \\in \\{1,\\ldots, f+1\\}$: (i) the algorithm has already detected a subset of $\\ell-1$ faulty edges $F_\\ell \\subseteq F$, and (ii) the header contains the labels $\\mathsf{ConnLabel}\\xspace_{G_i,T_i}(e)$ of all the edges $e \\in F_\\ell$. Each phase $\\ell \\leq f+1$ will terminate either at the destination vertex $t$, or at the source vertex $s$. In addition, w.h.p., if $s$ and $t$ are connected in $G_i \\setminus F$, phase $f+1$ will terminate at $t$. The invariant holds vacuously for phase $1$.\n\nWe now describe the $\\ell^{th}$ phase of the routing procedure given the invariant. The source vertex $s$ considers the $\\ell^{th}$ copy of the FT-connectivity labels, $\\mathsf{ConnLabel}\\xspace^{\\ell}_{G_i,T_i}(e)$ for every $e \\in F_\\ell$. \nUsing the connectivity labels, that are part of the header, and the ids $ID_{T_i}(s),ID_{T_i}(t)$ that are part of the routing table of $s$ and routing label of $t$, the algorithm first checks if $s$ and $t$ are connected in $G_i \\setminus F_\\ell$ using Lemma \\ref{lem:succint_path_routing}. If the answer is no, the algorithm moves to the next iteration $i+1$.\nOtherwise, by applying the decoding algorithm of Lemma \\ref{lem:succint_path_routing}, it computes the succinct path $\\widehat{P}_\\ell$. The path $\\widehat{P}_\\ell$ encodes an $s$-$t$ path in $G_i \\setminus F_\\ell$, that includes the relevant port and tree routing information of its vertices. The header of the message $H_\\ell$ then contains \n$$H_\\ell=\\langle \\widehat{P}_\\ell, i, i^*(t), \\{\\mathsf{ConnLabel}\\xspace_{G_i,T_i}(e)\\}_{e \\in F_{\\ell}}, q \\rangle~,$$ where $q = O(f)$ is an index indicating the current segment of $\\widehat{P}_\\ell$ we explore. \\mertodo{$H_\\ell$ should contain $\\mathsf{ConnLabel}\\xspace^{\\ell}_{G_i,T_i}(e)$, right?}\n\n\n\\mtext{The size of the header is $\\widetilde{O}(f^2)$, as the description of the path has size $\\widetilde{O}(f)$, and additionally we have at most $f$ faulty edges with labels of size $\\widetilde{O}(f)$.} \\mtodo{in some sense we don't really need to have all this $O(f^2)$ information on the header at once because it's enough that $s$ learns in each phase on one of the labels and then stores it locally, but this I guess doesn't work with the formal definition of routing.}\nLet $P_\\ell$ be the $G$-path encoded by the path $\\mathcal{P}_\\ell$. The algorithm then routes the message along $P_\\ell$ in the same manner as in Sec. \\ref{sec:routing-known}. In the case where $P_{\\ell}\\cap F=\\emptyset$, the phase successfully terminates at the destination vertex $t$. From now on, we consider the case that $P_{\\ell}$ contains at least one faulty edge. \n\nLet $e=(u,v)$ be the first edge (closest to $s$) on the path $P_\\ell$ that belongs to $F$. Since $P_\\ell \\cap F_\\ell=\\emptyset$, it holds that $e \\in F \\setminus F_\\ell$. Without loss of generality, assume that $u$ is closer to $s$ on $P_\\ell$. Thus the faulty edge $e$ is detected upon arriving to the vertex $u$. \nIn the case where $e$ is a \\emph{non-tree edge}, then it must be a $G$-edge on $\\widehat{P}_\\ell$. Since this path has the ids $ID_{T_i}(e)$ of its $G$-edges, and since the connectivity label of a non-tree edge $e$ is $ID_{T_i}(e)$ in all the $f$ applications of the scheme on $G_i$, $u$ can add $\\mathsf{ConnLabel}\\xspace_{G_i,T_i}(e)$ to the header of the message. \\mtodo{here we use the fact that the extended ids are the same in all $f$ phases (of the same tree)} Assume now that $e$ is a tree edge in $T_i$. The vertex $u$ then adds the label $\\mathsf{ConnLabel}\\xspace_{G_i,T_i}(e)$ to the header of the message, as $e$ is a tree edge adjacent to $u$ it has this information in its routing table. Finally, it marks the header with the sign $R$, indicating that the message should now be routed in the reverse direction, until arriving $s$ again. This completes the description of phase $\\ell$. It is easy to see that the invariant is maintained. If $s$ and $t$ are connected in $G_i \\setminus F$, after at most $f$ phases all faulty edges are detected. In the last phase, the path computed based on the labeling information is free from faulty edges, and the routing is completed (in the same manner as in Sec. \\ref{sec:routing-known}) at the destination $t$. We next bound the multiplicative stretch of the routing.\n\n\n\n\n\\mtodo{updated the stretch analysis.}\n\n\\mtext{\n\\begin{claim}\\label{cl:route-length}\nFix a set of faulty edges $F$, and let $s,t$ be vertices that are connected in $G \\setminus F$. Then, the message is routed from $s$ to $t$ within $32k (f+1)^2 \\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$ steps, w.h.p.\n\\end{claim}\n\\begin{proof}\nFirst note that since each phase and each graph $G_{i}$ uses an independent set of FT-connectivity labels, then in each iteration and each phase the decoding algorithm succeeds w.h.p. and outputs an $s$-$t$ path $\\widehat{P}_\\ell$ if exists. \n\nAssume that $\\mbox{\\rm dist}_{G \\setminus F}(s,t) \\in (2^{i-1},2^i]$. Then, $s$ and $t$ are connected in $G_i \\setminus F$, as $T_i = T_{i,i^*(t)}$ contains the $2^i$-ball around $t$. We show that the algorithm terminates at $t$ in iteration $i$ or before it, and that in any iteration $j \\leq i$, the routing algorithm traverses a path of length at most $2(4k-1)(f+1)^2 \\cdot 2^j$.\n\nLet $j \\leq i$. In the $\\ell$'th phase of iteration $j$, the algorithm first checks if $s$ and $t$ are connected in $G_j \\setminus F_{\\ell}$, where $F_{\\ell}$ is the set of currently detected faults. If the answer is no, the algorithm proceeds to the next iteration. Otherwise, it tries to route a message from $s$ to $t$ on the path encoded by $\\widehat{P}_{\\ell}$. The length of the path is bounded by $(4k-1)(f+1)\\cdot 2^j$ from Lemma \\ref{lem:succint_path_routing}. The algorithm either succeeds, or finds a faulty edge on the way in which case it returns to $s$ by traversing the same path on the reverse direction. Overall, the algorithm traverses a path of length at most $2(4k-1)(f+1)\\cdot 2^j$, in this phase. In all $f+1$ phases of iteration $j$, the length of the path explored is at most $2(4k-1)(f+1)^2 \\cdot 2^j$. Summing over all iterations $j \\leq i$, the stretch is bounded by $$\\sum_{j=1}^{i} 2(4k-1)(f+1)^2 \\cdot 2^j = 2(4k-1)(f+1)^2 \\sum_{j=1}^i 2^j \\leq 2^{i+2} (4k-1)(f+1)^2 \\leq 32k (f+1)^2 \\mbox{\\rm dist}_{G \\setminus F}(s,t).$$ The last inequality uses the fact that $2^{i-1} \\leq \\mbox{\\rm dist}_{G \\setminus F}(s,t).$ \n\nIn the $i$'th iteration, since $s$ and $t$ are connected in $G_i \\setminus F$, then for any $F_{\\ell} \\subseteq F$, $s$ and $t$ are connected in $G_i \\setminus F_{\\ell}$, hence the algorithm always finds a path $\\widehat{P}_{\\ell}$. Hence, it either succeeds in routing the message to $t$ in one of the phases (or one of the previous iterations), or learns about all the failures $F$. In the latter case, in phase $f+1$ it learns about a failure-free path $\\widehat{P}_{f+1}$, and the routing terminates at $t$. This completes the proof.\n\\end{proof}\n}\n\n\\remove{\n\\begin{claim}\\label{cl:route-length}\nFix a set of faulty edges $F$. Then, the message is routed from $s$ to $t$ within $(16k-4)(f+1) f\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$ steps, w.h.p.\n\\end{claim}\n\\begin{proof}\nFirst note that since each phase and each graph $G_{i}$ uses an independent set of FT-connectivity labels, w.h.p., the decoding algorithm of phase $\\ell$ applied on the label set $\\{\\mathsf{DistLabel}\\xspace(w), w \\in \\{s,t\\} \\cup F_\\ell\\}$ succeeds w.h.p. and outputs an $s$-$t$ path $\\widehat{P}_\\ell$. \n\nLet $P_{\\ell}$ be the $s$-$t$ path in $G \\setminus F_\\ell$ encoded by $\\widehat{P}_\\ell$. By Lemma \\ref{lem:approx-dist-recovery}, the length of $P_\\ell$ is bounded by $(8k-2)(f+1)\\cdot \\mbox{\\rm dist}_{G \\setminus F_\\ell}(s,t)$. Since in phase $\\ell$ the message is routed along a (possibly) partial path $P_\\ell$ and back to $s$, we have $2|P_\\ell|$ routing hops \\mtodo{change to capture weighted graphs?} in that phase. Overall, we have at most $2\\sum_{i=1} |P_\\ell|$ routing hops. Since \n$F_\\ell \\subseteq F$ for every $\\ell$, it holds that $\\mbox{\\rm dist}_{G \\setminus F_\\ell}(s,t)\\leq \\mbox{\\rm dist}_{G \\setminus F}(s,t)$, and consequently $(16k-4)(f+1) f \\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$ routing steps over all. The claim follows. \n\\end{proof}\n}\n\nTo conclude, we have the following.\n\n\\begin{theorem}\nFor every integers $k,f$, there exists an $f$-sensitive compact routing scheme that given a message $M$ the source vertex $s$ and a label $L_{route}(t)$ of the destination $t$, in the presence of at most $f$ faulty edges $F$ (unknown to $s$) routes $M$ from $s$ to $t$ in a distributed manner over a path of length at most $32k (f+1)^2\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$. \nThe global table size is $\\widetilde{O}(f \\cdot n^{1+1\/k} \\log{(nW)})$.\nThe header size of the messages is bounded by $\\widetilde{O}(f^2)$ bits, and the label size of vertices is $O(\\log{(nW)} \\log{n})$. \n\\end{theorem}\n\n\n\n\n\n\\mtodo{I didn't change the next section, as some of the definitions of routing tables\/labels changed, there are probably also changes here.}\n\n\\paragraph{Improving the size of the routing tables.} \nSo far, we have described a routing scheme that consumes a total space of $\\widetilde{O}(f\\cdot n^{1+1\/k}\\log (nW))$ bits, and multiplicative stretch\n$32(f+1)^2 k$. We now explain the required modifications needed to providing routing tables with $\\widetilde{O}(f^2\\cdot n^{1\/k})$ bits per vertex. The most space consuming information for a vertex $u$ is the connectivity labeling\ninformation of the edges incident to $u$ in each of the trees $T_{i,j} \\in \\mathcal{T}$. As the degree of $u$ in some of the trees might be $\\Theta(n)$, it leads to tables of linear size. To reduce the space of the individual tables, we apply a load balancing idea which distributes the labeling information incident to \\emph{high}-degree vertices among their neighbors. \n\nInstead of storing the labeling information of $e=(u,v)$ at the routing tables of $u$ and $v$, we define \nfor every tree $T \\in \\mathcal{T}$ and an edge $e=(u,v) \\in T$, a subset $\\Gamma_T(e)$ of vertices that store the connectivity labeling information of $e$ in $T$. We will make sure that the information on some vertex in $\\Gamma_T(e)$ can be easily extracted in the routing procedure, and that each vertex stores the information for a small number of edges in each tree. Consider an edge $e=(u,v)$ in a tree $T$, and assume, without loss of generality, that $u$ is the parent of $v$ in the tree $T$. In the case where $\\deg(u,T)\\leq f+1$, we simply let $\\Gamma_T(e)=\\{u,v\\}$. That is, the label of $e$ is stored by both endpoints of $e$ (as before). The interesting case is where $\\deg(u,T)\\geq f+2$, in which case, $u$ might not be able to store the label of $e$, and will assist its other children as follows. Let $Child(u,T)=[v_1,\\ldots, v_\\ell]$ be the lexicographically ordered list of the children of $u$ in $T$. The algorithm partitions $Child(u,T)$ into consecutive blocks of size $f+1$ (the last block might have $2f+1$ vertices). Letting $[v_{q,1}, \\ldots, v_{q,f+1}] \\subseteq Child(u,T)$ be the block containing $v$, define\n$$\\Gamma_T(e)=\\{v_{q,1}, \\ldots, v_{q,f+1}\\}~.$$\nNote that in particular, $v \\in \\Gamma_T(e)$. Thus, the label of $e$ is stored by $v$ and $\\ell \\in [f,2f-1]$ additional children on $u$ in $T$. \n\nWe then modify the tree labels from Fact \\ref{fc:route-trees} to contain the port information of $\\Gamma_T(e)$. \nTo do that, we re-define the identifier of an edge $e$ to include the port information of $\\Gamma_T(e)$. Thus, the identifier of an edge has $O(f\\log n)$ bits. Using Fact \\ref{fc:route-trees}, we then have tree labels of $O(f\\log n)$ such that given the labels of $u$ and $t$, the algorithm can compute the port information $\\Gamma_T(e)$ where $e$ is the edge incident to $u$ on the $u$-$t$ tree path in $T$. We have:\n\\begin{claim}\\label{cl:tree-label-port}\n\n\n\\end{claim}\n\nIn addition, we modify the routing tables of each vertex $v$, so that its includes the connectivity labels $\\mathsf{ConnLabel}\\xspace_{G_{i,j},T_{i,j}}(e)$ for any tree edge $e \\in T_{i,j}$ such that $v \\in \\Gamma_{T_{i,j}}(e)$ for every tree $T_{i,j} \\in \\mathcal{T}$. This should be compared with the solution of the previous subsection, where $v$'s table contained the information for all its incident edges in $T_{i,j}$. We therefore have:\n\\begin{claim}\\label{cl:route-balance-table}\nThe size of each routing table $R_{route}(v)$ is bounded by $\\widetilde{O}(f K n^{1\/k})$ bits.\n\\end{claim}\n\\begin{proof}\nFor every tree $T_{i,j}$ containing $v$, $v$ stores that connectivity label of $\\widetilde{O}(f)$ bits of all edges in the set $E(v,T_{i,j})=\\{e \\in T_{i,j} ~\\mid~ v \\Gamma_{T_{i,j}}(e)\\}$. Note that $|E(v,T_{i,j})|\\leq f$ as each vertex stores the label of its parent in the tree, $O(f)$ child edges, and $O(f)$ child edges of its parent in the tree. Since each $v$ participates in $\\widetilde{O}(K n^{1\/k})$ trees, overall its routing table has $\\widetilde{O}(f K n^{1\/k})$ bits, as required. \n\\end{proof}\n\nIt remains to explain the required modifications for the routing procedure. Upon arriving to a vertex $u$ incident to a faulty \\emph{tree} edge $e=(u,v)$ the procedure is as follows. The interesting case is where $e$ is a tree edge in some path $\\pi(x,y,T_{i,j})$ where $(x,y)$ is an edge in the succinct path $\\widehat{P}$ indicated on the header of the message. \nIn this case, the tree routing algorithm applied at $u$ is given the tree labels $L_{T_{i,j}}(u)$ and $L_{T_{i,j}}(y)$ and output the port information of $\\Gamma_{T_{i,j}}(e)$ for the edge $e=(u,v)$ that lies on the tree path $\\pi(u,y, T_{i,j})$. Since there are at most $f$ edge faults, and $\\Gamma_{T_{i,j}}(e)$ contains information on at least $f+1$ ports of $u$'s neighbors that contain the label of $e$, the vertex $u$ can access a non-faulty neighbor, say $w$, that has the label information of $e$. That vertex can then add the labeling information of $e$ to the header of the message, and the routing algorithm proceeds as before. \n\n\n\nWe now bound the size of each routing table. \n\\begin{claim}\nThe routing table of each vertex has $\\widetilde{O}(f^2 n^{2\/k} \\log (nW))$ bits.\n\\end{claim}\n\\begin{proof}\nEach vertex appears in $O(k n^{1\/k} \\log (nW))$ trees in $\\mathcal{T}$. For each such tree, it might store the routing labels of at $O(f)$ edges. Since each label $L_{route}(e)$ has $\\widetilde{O}(f n^{1\/k} \\log (nW))$ bits, over all it stores $\\widetilde{O}(f^2 \\cdot n^{2\/k}\\log (nW))$ bits. \n\\end{proof}\n\n\\mtodo{how is the stretch affected from the process?}\n\nWe have:\n\\begin{theorem}\\label{thm:routing-unknown}[Fault-Tolerant Routing]\nFor every integers $k,f$, there exists an $f$-sensitive compact routing scheme that given a message $M$ at the source vertex $s$ and a destination $t$, in the presence of at most $f$ faulty edges $F$ (unknown to $s$) routes $M$ from $s$ to $t$ in a distributed manner over a path of length at most $32k f^2\\cdot \\mbox{\\rm dist}_{G \\setminus F}(s,t)$. The table size of each vertex is $\\widetilde{O}(f^2 \\cdot n^{1\/k} \\log(nW))$. The header size of the messages is bounded by $\\widetilde{O}(f^2)$ bits. \n\\end{theorem}\n\n\\paragraph{Lower Bound.} Finally, we show that the price of not knowing the set of faulty edges $F$ in advance might indeed incur a multiplicative stretch of $\\Omega(f)$. \n\n\\begin{theorem}[Stretch Lower-Bound for FT-Routing]\\label{thm:lb-routing}\nAny FT-routing randomized scheme resilient to $f$ faults induces an expected stretch of $\\Omega(f)$ regardless of the size of the routing tables and labels. In particular, this holds even if each routing table contains a complete information on the graph. \n\\end{theorem}\n\\begin{proof}\nConsider a graph that consists of $f+1$ vertex disjoint $s$-$t$ paths, each of length $L=\\Theta(n\/f)$. The last edge of each of the paths, except for one, is faulty. Assume that the non-faulty path is chosen uniformly at random. Since the routing scheme is oblivious to the faulty edges, it can discover a faulty edge only upon sending the message to one of the edge endpoints. The expected length of the routing is given by:\n$$\\frac{L}{f+1} +2L \\cdot \\left(1-\\frac{1}{f+1} \\right)\\cdot \\frac{1}{f} + \\ldots+ \\left(f+1\\right)L\\cdot \\prod_{i=0}^{f-1} \\left(1-\\frac{1}{f+1-i}\\right)=\\Omega(f L)~.$$ \nSince the $s$-$t$ shortest path under these faults is $L$, the proof follows. See Fig. \\ref{fig:LB-stretch} for an illustration.\n\\end{proof}\n\n\n\\begin{figure}[h!]\n\\begin{center}\n\\includegraphics[scale=0.40]{lb.pdf}\n\\caption{\\sf Illustration for a stretch lower bound for any FT-routing schemes. The $s$-$t$ pair are connected by $f+1$ vertex disjoint paths. Since the faulty-edge is the last edge of the path, the routing requires $\\Omega(L)$ steps to discover a single faulty edge. As the non-faulty path is chosen uniformly at random, in expectation, the routing requires $\\Omega(fL)$ steps. \\label{fig:LB-stretch}\n}\n\\end{center}\n\\end{figure}\n\n\n\\subsection{Our Techniques} \\label{sec:techniques}\n\nFor our FT labeling schemes, we present two constructions based on different techniques. \nThe first construction uses the \\emph{cycle-space sampling} technique of Pritchard and Thurimella \\cite{pritchard2011fast} to determine if $s$ and $t$ are disconnected by a set of failures $F$. This technique has been applied in the past mainly in the context of computing small cuts in the distributed setting. \nThe second construction uses the tool of \\emph{linear sketches} by Ahn et al. \\cite{ahn2012analyzing} to try to find a path that connects $s$ and $t$ in $G \\setminus F$. This scheme is also useful for routing.\nWe next give an overview of the two approaches, and the applications for routing. Throughout, we assume that the graph $G$ is originally connected, otherwise the scheme can be applied to each connected component of $G$, which can be indicated in the label of the vertex.\n\n\\paragraph{Connectivity Labels Based on Cycle Space Sampling.} The cycle space sampling technique, introduced by Pritchard and Thurimella \\cite{pritchard2011fast}, allows one to detect cuts in a graph by exploiting the interesting connection between cuts and cycles in a graph. This technique was used in \\cite{pritchard2011fast} to design distributed algorithms for identifying small cuts in a graph. In more details, the technique is based on the relation between \\emph{induced edge cuts} and \\emph{binary circulations}, defined as follows. For a subset of vertices $S$, we denote by $\\delta(S)$ the set of edges with exactly one endpoint in $S$. An \\emph{induced edge cut} is a set of edges of the form $\\delta(S)$ for some $S$. A \\emph{binary circulation} is a set of edges in which every vertex has an even degree. For example, a cycle is a binary circulation. Note that if $F$ is an induced edge cut, and $\\phi$ is a cycle, the number of edges in the intersection $|F \\cap \\phi|$ is even, as the cycle crosses the cut even number of times. This is also true for any binary circulation $\\phi$. The cycle space technique extends this observation and shows that if $\\phi$ is a random binary circulation and $F \\subseteq E$, then\n$$Pr[|F \\cap \\phi| \\ is \\ even] = \\left\\{\n \\begin{array}{ll}\n 1,\\ if\\ F\\ is\\ an\\ induced\\ edge\\ cut\\\\\n 1\/2,\\ otherwise\n \\end{array}\n \\right. $$ \nHence, by choosing a \\emph{random} binary circulation, one can detect if a set of edges $F$ is an induced edge cut with probability $1\/2$. To increase the success probability, we can choose $b$ random binary circulations. \nBased on these ideas, \\cite{pritchard2011fast} showed how to assign the edges of the graph $b$-bit labels with the following property. See Appendix \\ref{sec:cycle_space_overview} for an overview.\n\n\\begin{restatable}{lemma}{cycle} \\label{cycle_space_lemma}\nThere is an algorithm that assigns the edges of a graph $G=(V,E)$, $b$-bit labels $\\phi(e)$ such that given a subset of edges $F \\subseteq E$, we have:\n$$Pr[\\Moplus_{e \\in F} \\phi(e) = 0] = \\left\\{\n \\begin{array}{ll}\n 1,\\ if\\ F\\ is\\ an\\ induced\\ edge\\ cut\\\\\n 2^{-b},\\ otherwise\n \\end{array}\n \\right. $$ \nWhere $0$ is the all-zero vector. The time complexity for assigning the labels is $O((m+n)b)$.\n\\end{restatable}\n\n\\noindent\\textbf{The connectivity labels.} We next explain how to use this technique to build FT connectivity labels. Our goal is to assign labels to the vertices and edges of the graph, such that given the labels of two vertices $s,t$ and a set of failures $F$, we can check if $s$ and $t$ are disconnected by $F$. It is easy to show that $s$ and $t$ are disconnected by $F$ iff there is an \\emph{induced edge cut} $F' \\subseteq F$ that disconnects $s$ and $t$. While we can use the cycle space labels to check if a subset of edges $F' \\subseteq F$ is an induced edge cut, this is still not enough to solve FT connectivity. To do so, we should check if an induced edge cut $F'$ \\emph{disconnects} the vertices $s$ and $t$. To check this, we bring to our construction \\emph{ancestry labels} in trees, and show that we can determine if $s$ and $t$ are in the same side of cut (induced by $F'$) based on the ancestry labels of $s,t$ and $F'$. The key observation is that a spanning tree $T$ of the graph is disconnected to at most $|F'|+1$ connected components, upon removing $F'$, where for any $e \\in F'$ both its endpoints reside on two different sides of the induced edge cut defined by $F'$.\nWe can use this to identify which components of $T \\setminus F'$ are on the same side of the induced edge cut. Moreover, we show that the ancestry labels allow us to determine the connected components of $s$ and $t$ in $T \\setminus F'$. A brute-force implementation of this approach leads to a decoding time that is \\emph{exponential} in $|F|$. I.e., the algorithm should check for any subset $F' \\subseteq F$ if $F'$ is an induced edge cut. To overcome it, we show an efficient way to find $F' \\subseteq F$ that disconnects $s$ and $t$ if exists, by translating our problem to a system of linear equations. This results in a decoding time polynomial in $|F|$ and $\\log{n}$. The size of the labels is $O(f+\\log{n})$, to guarantee that the cycle space labels are correct for any $F' \\subseteq F$ w.h.p. \n\n\\paragraph{Connectivity Labels Based on Graph Sketches.} We next provide some flavor of our labels based graph sketches. The length of the labels obtained in this technique is $O(\\log^3{n})$ bits, which is dominated by the sketching information. A \\emph{graph sketch} of a vertex $v$ is a randomized string of $\\widetilde{O}(1)$ bits that compresses $v$'s edges. The linearity of these sketches allows one to infer, given the sketches of subset of vertices $S$, an outgoing cut edge $(S, V \\setminus S)$. Graph sketches have numerous applications in the context of connectivity computation under various computational settings, e.g., \\cite{kapron2013dynamic,kapralov2014spanners,GibbKKT15,DBLP:conf\/podc\/KingKT15,DBLP:conf\/wdag\/MashreghiK18,GhaffariP16,DuanConnectivitySODA17}. More concretely, our sketch-based labels are inspired by the centralized connectivity sensitivity oracles of Duan and Pettie \\cite{DuanConnectivitySODA17}. A common approach for deducing the graph connectivity merely from the sketches of the individual vertices is based on the well-known Boruvka algorithm \\cite{Boruvka}. This algorithm works in $O(\\log n)$ phases, where in each phase, from each growable component an outgoing edge is selected. All these outgoing\nedges are added to the forest, while ignoring cycles. Each such phase reduces the number of\ngrowable components by a $2$ factor, thus within $O(\\log n)$ phases, a maximal forest is computed. Since this algorithm only requires the computation of outgoing edges it can simulated using $O(\\log n)$ independent sketches for each of the vertices. \n\nOur high level approach for determining the $s$-$t$ connectivity in $G \\setminus F$ mimics this above mentioned procedure. For simplicity assume that $G$ is connected and let $T$ be some spanning tree in $G$. Using ancestry labels, one can infer the components of $T \\setminus F$. Moreover, by augmenting the labels with graph sketching information, one can also deduce the sketch of each component in $T \\setminus F$. \nNote however that these sketches are in $G$ and therefore might encode outgoing edges that belong to $F$. To overcome this technicality, our sketching scheme allows us to cancel out the effect of the faulty edges $F$ from the sketching information. Consequently, we obtain the sketches of each $T \\setminus F$ component in the surviving graph $G \\setminus F$. We can then apply the Boruvka's algorithm on the components of $T \\setminus F$, and infer the $s$-$t$ connectivity in $G \\setminus F$. The actual implementation of this labeling scheme is somewhat more delicate. We note that some of these technicalities are for the sake of our later extension of these labels into compact routing schemes.\n\n\n\n\\remove{\n\\mertodo{This text can be omitted now, see if want to move elsewhere. We start by illustrating the underlying intuition for sketch. For a vertex $v$ and a subset of edges $E' \\subseteq E$, let $\\mathsf{Sketch}\\xspace_{E'}(v)$ be the bitwise XOR of all the IDs of $E'$ edges adjacent to $v$. \nFor a subset of vertices $S$, define $\\mathsf{Sketch}\\xspace_{E'}(S) = \\oplus_{v \\in S} \\mathsf{Sketch}\\xspace_{E'}(v)$. The useful property of sketches is that all edges of $E'$ that have both endpoints in $S$ are cancelled, and thus $\\mathsf{Sketch}\\xspace_{E'}(S)$ corresponds to the XOR of the identifiers of the $E'$ edges outgoing from $S$. If there is only one such edge, then\nits ID corresponds to the value of $\\mathsf{Sketch}\\xspace_{E'}(S)$. By combining this idea with a basic sampling trick one can \nidentify one outgoing edge from any subset $S$. But here we should also require special edge IDs in order to distinguish between an illegal ID, obtained by XORing IDs of several edges, and a true ID of a single edge.\nIntuitively, we first define $O(\\log{m})$ sets of edges $E_j$, where $E_j$ is obtained from $E$ by sampling each edge with probability $1\/2^j$. Next, we define $\\mathsf{Sketch}\\xspace(v) = (\\mathsf{Sketch}\\xspace_{E_0}(v),...,\\mathsf{Sketch}\\xspace_{E_{\\log{m}}}(v))$, and $\\mathsf{Sketch}\\xspace(S) = (\\mathsf{Sketch}\\xspace_{E_0}(S),...,\\mathsf{Sketch}\\xspace_{E_{\\log{m}}}(S))$. These $O(\\log^2{n})$-bit sketch units have the property that given $\\mathsf{Sketch}\\xspace(S)$, with constant probability there is a sketch unit that holds the identifier of exactly one outgoing edge of $S$. In our algorithm, we use $\\Theta(\\log{n})$ sketch units (each time with different sampled sets $E_j$) to be able to eventually find outgoing edges w.h.p. \nA crucial point in this regard is to be able to distinguish between sketch units that hold the XOR of at least two edge identifiers vs. units that store the identifier of \\emph{exactly} one edge (i.e., an outing edge). For that purpose we employ the computation of edge identifiers by \\cite{GhaffariP16}, that have the property that the XOR of any two edge identifiers is not a \\emph{legal} edge identifier of a single edge. We also show that identification of the legal edge can be done efficiently, with no global information.\n\n\\noindent\\textbf{FT Connectivity from graph sketches.} We use the graph sketches together with the well-known Boruvka algorithm \\cite{Boruvka} to identify the connected components of the graph $G \\setminus F$. This eventually allows us to check if $s$ and $t$ are connected in $G \\setminus F$ based on their components. We start by describing our general approach, and then explain how to simulate it based only on labels of $s,t$ and $F$. Let $T$ be a spanning tree of the graph $G$. If the edges $F$ are removed from $G$, it breaks the tree $T$ to at most $|F|+1$ connected components. To figure out the connected components in $G \\setminus F$, we apply Boruvka algorithm on the connected components of $T \\setminus F$. In this algorithm, at each iteration we have a set of components, and our goal is to find an outgoing edge from each connected component, and then merge components connected by an edge. To find outgoing edges, we use the sketches of the components. After repeating the process for $O(\\log{n})$ iterations, we find the connected components of $G \\setminus F$ w.h.p. The vertices $s$ and $t$ are connected in $G \\setminus F$ iff they are in the same component. \n\\noindent\\textbf{The connectivity labels.} At a high-level, to simulate the algorithm using only the information provided by the connectivity labels, we include in the labels of vertices and edges ancestry labels in a spanning tree $T$. In addition, for any tree edge $e=\\{u,v\\} \\in T$ we include in its label the sketch information of $T_v$ and $T_u$, as well as the sketch information of $T$, where $T_v,T_u$ are the subtrees of $T$ rooted at $v$ and $u$, respectively. \nWe show that this information allows us to identify the connected components in $T \\setminus F$, and compute the sketch information of each one of the components. Moreover, the ancestry labels give us an efficient way to identify the connected component of any vertex $v$. This is useful both for identifying the connected components of $s$ and $t$, and to find the connected components of any outgoing edge that the algorithm finds. When we merge components, the sketch information of the new component can be obtained by XORing the sketches of the components we merge. One delicate point in the algorithm is that we originally compute sketches in the original graph $G$, where our algorithm works in the graph $G \\setminus F$, and so we need the sketch information in $G \\setminus F$. To obtain this, we cancel the information about edges from $F$ in the sketches by XORing their IDs in the relevant places. This can be done efficiently by using pairwise independence hash functions to generate the sketches. \n}\n}\n\n\n\\paragraph{Applications for Routing Schemes.} The starting point to our routing scheme is given by our (sketch-based) labeling scheme. These labels allows one to deduce also a succinct description of an $s-t$ path in $G \\setminus F$ if exists, by following the component merging procedure of the Boruvka algorithm. This description is composed of $O(f)$ path segments, where each segment $\\{u,v\\}$ either corresponds to an outgoing (non-tree) edge found in the algorithm using the sketch information, or to a tree path between two vertices $u$ and $v$ in the same connected component in $T \\setminus F$. Given the connectivity labels of $s,t$ and $F$, we can find this description, and use it for routing. Routing across an edge $\\{u,v\\}$ just requires sending a message over the edge, while routing on a tree path between $u$ and $v$ can be done using a routing scheme for trees. \nWhile this approach allows to send a message from $s$ to $t$, there is no bound on the length of the path traversed. Additionally, this approach assumes that the set of failures $F$ is known in advance. We next explain how to overcome these issues.\n\\\\\n\\noindent\\textbf{Bounding the stretch.} To route messages on low-stretch paths we use the notion of \\emph{tree covers}, following the approach in \\cite{chechik2012f}. This approach also allows us to translate our connectivity labels to approximate distance labels as we discuss in Section \\ref{sec:ft-distance}. Here, instead of applying our connectivity scheme on just one graph $G$, we apply it on many subgraphs $G_{i,j}$ of $G$ with the following properties. \n\\begin{enumerate}\n\\item Each vertex $v$ is contained in $\\widetilde{O}(n^{1\/k})$ subgraphs.\n\\item For any $1 \\leq i \\leq \\log(nW)$, and any vertex $v$, there is a subgraph $G_{i,i^*(v)}$ that contains all the vertices in the $2^i$-neighborhood of $v$.\n\\item If $v$ and $u$ are connected in the graph $G_{i,i^*(v)} \\setminus F$, then there is a path between them of length at most $O(k|F| 2^i)$ in the graph $G_{i,i^*(v)} \\setminus F$.\\label{prop_path}\n\\end{enumerate}\nBy applying our connectivity scheme on each one of the subgraphs $G_{i,j}$, we can route a message from $s$ to $t$ on a path of stretch $O(k|F|)$. The size of the labels and routing tables of vertices is $\\widetilde{O}(n^{1\/k})$ as each vertex and edge participate in $\\widetilde{O}(n^{1\/k})$ subgraphs.\n\n\\\\\n\\noindent\\textbf{Faulty edges are unknown.} The scheme we described assumes that the routing algorithm knows the labels of $s,t$ and $F$ in advance, we next explain how to avoid this assumption. Our general approach is to work in phases, where in each phase we try to route a message from $s$ to $t$ according to the currently set of known faults. We either succeed, or learn about the label of a new faulty edge $e \\in F$ and try again. The stretch of the scheme increases to $O(k|F|^2)$ because of the $|F|+1$ phases. Direct application of this approach may require large routing tables, as each vertex may need to know the labels of all edges adjacent to it, to be able to learn the labels of faulty edges found in the algorithm. To overcome it we use the following ingredients. \n\nFirst, recall that in our connectivity labeling scheme we use a spanning tree $T$. In the routing scheme, these are the trees of the tree cover. We show that it is enough for each vertex to store labels only of its adjacent \\emph{tree} edges. \nConsequently, the total size of all routing tables can be bounded by $\\widetilde{O}(fn^{1+1\/k})$.\\footnote{The $f$ term in the size comes from the fact we apply the connectivity labels $f+1$ times to support the $|F|+1$ phases.}\nHowever, this alone is not enough to bound the size of individual routing tables of vertices, as the degree of a vertex in a tree may be linear. To overcome this, we show a clever way to load balance the labels' information between $v$ and its children in the tree. This results in tables of size $\\widetilde{O}(f^3 n^{1\/k})$ per vertex, while keeping the same stretch of the scheme. The increase in the total size of tables comes from the fact we now duplicate labels $f+1$ times, to be able to recover them in the presence of $f$ failures. ","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{s_intro}\n\\addtocounter{footnote}{9}\n\n\nPolycyclic aromatic hydrocarbons (PAHs) are large carbon molecules that can be thought of as the transition between the gaseous and solid phases of the interstellar medium (ISM). They are commonly identified as the source of emission for multiple broad emission features in the mid-infrared, including those at 3.3, 6.2, 7.7, 11.3, 12.7~$\\mu$m \\citep{2008tielens}. While these complex molecules can be excited by optical and infrared photons \\citep{2002li}, excitation by ultraviolet photons is more efficient \\citep{2008tielens}. \n\n\nMany authors have sought to calibrate PAH emission as an extragalactic star formation tracer, mainly because it is relatively unaffected by dust extinction and because PAHs emit at shorter wavelengths than hot dust and can therefore be imaged with better angular resolutions. While early analyses with the Infrared Space Observatory \\citep{1996kessler} found some evidence for a relation between PAH emission and other star formation tracers \\citep{2001roussel,2004forster}, later work, including work with the {\\it Spitzer} Space Telescope \\citep{2004werner}, demonstrated that the PAH emission was actually poorly correlated with other star formation tracers. PAH emission appeared suppressed relative to other star formation tracers in star forming regions or overluminous in diffuse regions \\citep{2004boselli,2004helou,2005calzetti,2007prescott,2008bendo}. The ratio of PAH to hot dust continuum emission was also found to decrease as metallicity decreased \\citep{2005engelbracht,2006madden,2007calzetti,2008engelbracht,2008galliano,2008gordon,2013galametz}. The common explanations are either that low metallicity regions contain fewer PAHs or that the PAHs are exposed to harder ultraviolet radiation in low-metallicity environments with less dust attenuation. None the less, globally-integrated PAH emission has been shown to be correlated with other globally-integrated star formation tracers, and methods have been developed for calculating extinction-corrected star formation rates using a combination of H$\\alpha$ and PAH emission in the 8~$\\mu$m {\\it Spitzer} band \\citep{2008zhu,2009kennicutt}, although \\citet{2007calzetti} warns that even global measurements could be affected by metallicity effects.\n\n\nIn contrast, a few authors have found that PAH emission was associated with emission at $>100~\\mu$m that will primarily trace $\\ltsim30$~K large dust grains. One of the first groups to draw attention to this was \\citet{2002haas}, who demonstrated that PAH emission was better correlated with 850~$\\mu$m from cold dust than 15~$\\mu$m emission from hot dust, although this analysis was limited to regions with high infrared surface brightnesses. The ISO-based analysis by \\citet{2004boselli} also implied that PAHs were associated with diffuse dust rather than star forming regions. Later work by \\citet{2006bendo} and \\cite{2008bendo} demonstrated that the PAH emission showed a strong correlation with 160~$\\mu$m emission and that the 8\/160~$\\mu$m surface brightness ratio was dependent upon the 160~$\\mu$m surface brightness. These results combined with the breakdown in the relation between the PAH and hot dust emission implied that the PAHs were primarily associated with dust in the diffuse ISM or in cold molecular clouds near star forming regions and that both the PAHs and the large dust grains were heated by the same radiation field.\n\n\nData from the {\\it Herschel} Space Observatory \\citep{2010pilbratt} can be used to further study the relation between PAHs and large dust grains. The telescope is able to resolve emission at 160 and 250~$\\mu$m on $<18$~arcsec scales, which is a marked improvement in comparison to the 38~arcsec scales that could be resolved by {\\it Spitzer} at 160~$\\mu$m. Moreover, \\citet{2010bendo}, \\citet{2011boquien}, and \\citet{2012bendo} have demonstrated that $\\leq160$ and $\\geq250$~$\\mu$m emission from nearby galaxies may originate from dust heated by different sources. The 70\/160 and 160\/250~$\\mu$m surface brightness ratios were typically correlated with star formation tracers such as ultraviolet, H$\\alpha$, and 24~$\\mu$m emission and peaked in locations with strong star formation, suggesting that the dust seen at $\\leq160$~$\\mu$m is primarily heated locally by star forming regions. Meanwhile, the 250\/350 and 350\/500~$\\mu$m ratios were more strongly correlated with near-infrared emission and generally varied radially in the same way as the emission from the total stellar population (including both young, intermediate-aged, and evolved stars), demosntrating that the dust was primarily heated by the diffuse interstellar radiation field (ISRF) from these stars. PAH emission can be compared to dust emission observed by {\\it Herschel} to determine which of these two dust components are more closely associated with PAH emission, which would ultimately lead to a better understanding of how the PAHs are excited and how they survive in certain environments in the ISM.\n\n\nSo far, the relation between PAH and dust emission has been investigated using {\\it Herschel} data for only two galaxies. \\cite{2014calapa} have shown that 8~$\\mu$m emission from PAHs in M33 is well correlated with 250~$\\mu$m emission. They go on to further demonstrate the 8\/250~$\\mu$m ratio is correlated with the 3.6~$\\mu$m band tracing the total stellar population, implying that the PAHs are excited by the diffuse ISRF. \\citet{2014lu} present an alternative analysis with M81 in which they divide the PAH emission into components heated by two sources: a component heated by star forming regions traced by H$\\alpha$ emission and a component heated by the diffuse ISRF traced by the cold dust emission at 500~$\\mu$m emission. The results show that most ($\\sim85$\\%) of the 8~$\\mu$m emission from diffuse regions is associated with the cold dust emission, while in star forming regions, most ($\\sim60$\\%) of the 8~$\\mu$m emission is excited by young stars.\n\n\nThe goal of this paper, which is a continuation of the work by \\citet{2013jones}, is to further study the relationship between PAH emission at 8~$\\mu$m and far-infrared emission from large dust grains using {\\it Herschel} Space Observatory \\citep{2010pilbratt} far-infrared images of NGC 2403 and M83. These are two of the fourteen nearby galaxies within the Very Nearby Galaxies Survey (VNGS; PI: C. Wilson), a {\\it Herschel}-SPIRE Local Galaxies Guaranteed Time Program. The VNGS was meant to sample galaxies with multiple morphological and active galactic nucleus types, and includes several well-studied galaxies including the Antennae Galaxies, Arp 220, Centaurus A, M51, and NGC 1068. These two specific galaxies were selected because they are non-interacting nearby ($<10$~Mpc) spiral galaxies with an inclination from face on $\\leq~60^{o}$ and major axes $>10$~arcmin\\footnote{M81 is also in the VNGS, but the analysis of PAH emission from that galaxy is covered by \\citet{2014lu}.} The basic properties of these galaxies are given in Table \\ref{t_galaxies}.\n\n\nNGC 2403 is an SAB(s)cd galaxy \\citep{1991devaucouleurs} with no clear bulge and flocculent spiral structure \\citep{1987elmegreen}. Since the brightest star forming regions are found well outside the centre of the galaxy, it is easy to differentiate between effects related to star forming regions and either effects related to the evolved stellar population (which peaks in the centre of the galaxy) or effects tied to galactocentric radius. This has been exploited previously to illustrate how PAH emission is inhibited relative to hot dust emission in star forming regions \\citep{2008bendo} and to differentiate between different heating sources for the dust seen at 70-500~$\\mu$m \\citep{2012bendo}. M83 (NGC 5236) is an SAB(s)c galaxy \\citep{1991devaucouleurs} with a bright starburst nucleus \\citep{1983bohlin} and two strongly defined grand design spiral arms \\citep{1998elmegreen}. Since we can resolve the spiral structure with {\\it Herschel}, we can compare the properties of arm and interarm regions quite effectively. Both galaxies are at similar distances; we can resolve structures of $<400$~pc in the {\\it Herschel} data. Although both of these galaxies are late-type spiral galaxies, they have the potential to yield different information on how PAHs relate to the far-infrared emission from large dust grains.\n\n\nWe focus our analysis on the {\\it Spitzer} 24~$\\mu$m data, which trace emission from very small grains and hot dust heated locally by star forming regions, and {\\it Herschel} 160 and 250~$\\mu$m data, which trace emission from large dust grains. The prior analysis by \\citet{2008bendo} had shown an association between the 8~$\\mu$m and 160~$\\mu$m emission, but as stated above, the 160~$\\mu$m band may contain significant emission from large dust grains heated by star forming regions, while the 250~$\\mu$m band, at least for NGC~2403 and M83, originates more from dust heated by the diffuse ISRF \\citep{2012bendo} and could be better associated with PAH emission if PAHs are destroyed in star forming regions. The next shortest waveband for which we have data for these two galaxies is at 70~$\\mu$m, but the available 70~$\\mu$m data have a lower signal to noise ratio, and the {\\it Spitzer} data are strongly affected by latent image artefacts. Moreover, the 70~$\\mu$m emission may include emission from the same sources as the 24~$\\mu$m band. The available 350 and 500~$\\mu$m data trace the same thermal component of dust seen at 250~$\\mu$m, but because the resolution of those data are coarser compared to the 250~$\\mu$m waveband, using the data would provide no additional benefit. \n\n\nFor this analysis, we use the techniques developed by \\citet{2008bendo} and \\citet{2012bendo} based upon qualitative analyses of surface brightness ratio maps based on images with matching point spread functions (PSFs) and quantitative analyses of the surface brightnesses and surface brightness rations measured in rebinned versions of these images. Section \\ref{s_data} introduces the data and the data preparation steps. We then present the analysis in Section~\\ref{s_analysis_ratios} and then use these results to identify the PAH excitation sources in Section~\\ref{s_pahexcitation}. Following this, we discuss the implications of these results in Section~\\ref{s_discussion} and provide a summary in Section \\ref{s_conclusions}. \n\n\n\\begin{table*}\n\\centering\n\\begin{minipage}{94mm}\n\\caption{Properties of the sample galaxies$^a$.}\n\\label{t_galaxies}\n\\begin{tabular}{@{}lccccc@{}}\n\\hline\nName \t&\n RA & \n Dec &\n Hubble &\n Distance &\n Size of Optical \\\\ \n&\n (J2000) & \n (J2000) &\n Type &\n (Mpc)$^b$ &\n Disc (arcmin) \\\\ \n\\hline\nNGC 2403 & \n 07 36 54.5 &\n +65 35 58 &\n SAB(s)cd &\n 3.2 $\\pm$0.3 &\n $22.0 \\times 12.3$ \\\\\nM83 &\n 13 37 00.3 &\n -29 52 04 &\n SAB(s)c &\n 4.5$\\pm$ 0.2 &\n $12.9 \\times 11.3$ \\\\\n\\hline\n\\end{tabular}\n$^{a}$ Data are taken from \\cite{1991devaucouleurs} unless otherwise specified.\\\\\n$^{b}$ Distances are taken from \\cite{2001freedman}.\\\\ \n\\end{minipage}\n\\end{table*}\n\n\n\\section{Data}\n\\label{s_data} \n\n\nThe 3.6, 4.5, 5.8 and 8.0~$\\mu$m data for NGC~2403 were observed with the Infrared Array Camera \\citep[IRAC; ][]{2004fazio} on {\\it Spitzer} as part of the {\\it Spitzer} Infrared Nearby Galaxies Survey \\citep[SINGS; ][]{2003kennicutt}, and the 3.6-8.0~$\\mu$m images for M83 were observed with IRAC by the Local Volume Legacy (LVL) Survey \\citep{2009dale}. Both groups used similar drizzle techniques to mosaic basic calibrated data to produce final images with 0.75~arcsec pixels. The full-width at half maxima (FWHMs) of the PSFs are listed in Table~\\ref{t_IRAC}. We also applied correction factors that optimise the data for photometry of extended source emission as suggested by the IRAC Instrument Handbook; these correction factors are listed in Table~\\ref{t_IRAC}. The calibration uncertainty of the data is 3\\% \\citep{2013irac}.\n\n\n\\begin{table}\n\\caption{Properties of the IRAC instrument$^a$.}\n\\label{t_IRAC}\n\\begin{center}\n\\begin{tabular}{lcc}\n\\hline\nChannel &\n FWHM &\n Correction Factors$^b$ \\\\\n\\hline\n3.6~$\\mu$m &\n 1.7 &\n 0.91 \\\\\n4.5~$\\mu$m &\n 1.7 &\n 0.94 \\\\\n5.8~$\\mu$m &\n 1.9 &\n 0.66 \\\\\n8.0~$\\mu$m &\n 2.0 &\n 0.74 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n$^a$ These data are given by the IRAC Instrument Handbook \\citep{2013irac}\\footnote{http:\/\/irsa.ipac.caltech.edu\/data\/SPITZER\/docs\/irac\\\\ \/iracinstrumenthandbook\/IRAC\\_Instrument\\_Handbook.pdf}.\\\\\n$^b$ These correction factors are for extended, diffuse emission. \n\\end{table}\n\n\nThe 24~$\\mu$m data were acquired using the Multiband Imaging Photometer for Spitzer \\citep[MIPS; ][]{2004rieke} on {\\it Spitzer} and were reprocessed by \\citet{2012bendomips} using the MIPS Data Analysis Tools \\citep{2005gordon} along with multiple modifications. The final images have pixel scales of 1.5~arcsec, PSF with FWHM of 6~arcsec \\citep{2007engelbracht}, and calibration uncertainties of 4\\% \\citep{2007engelbracht}.\n\n\nThe 160~$\\mu$m data are updated versions of the 160~$\\mu$m data published by \\citet{2012bendo} and \\citet{2012foyle}. The galaxies were observed at 160~$\\mu$m with the Photodetector Array Camera and Spectrometer \\citep[PACS; ][]{2010poglitsch} on {\\it Herschel} in four pairs of orthogonal scans performed at the 20 arcsec s$^{-1}$ rate. The observations of NGC~2403 covered a $40\\times40$ arcmin region, while the observations of M83 covered a $25\\times25$ arcmin region. The data were processed using the {\\it Herschel} Interactive Processing Environment \\citep[\\small{HIPE}; ][]{2010ott} version 11.1. We used the standard data processing pipeline, which includes cosmic ray removal and cross-talk corrections, for the individual data frames. We then remapped the data using {\\small SCANAMORPHOS} version 23 \\citep{2013roussel}, which also removes additional noise in the data and drift in the background signal. We applied a colour correction of $1.01 \\pm\n0.07$, which has a mean value and uncertainty appropriate for emission from a modified blackbody with a temperature between 15 and 40~K and an emissivity function that scales as $\\lambda^{-\\beta}$ where $\\beta$ is between 1 and 2 \\citep{2011muller}\\footnote{http:\/\/herschel.esac.esa.int\/twiki\/pub\/Public\/PacsCalibrationWeb\\\\ \/cc\\_report\\_v1.pdf}. The FWHM of the PSF is $\\sim12$~arcsec \\citep{2012lutz}\\footnote{\n https:\/\/herschel.esac.esa.int\/twiki\/pub\/Public\/PacsCalibrationWeb\\\\ \/bolopsf\\_20.pdf}, and the flux calibration uncertainty is 5\\%\n\\citep{2013altieri}\\footnote{http:\/\/herschel.esac.esa.int\/Docs\/PACS\/pdf\/pacs\\_om.pdf}.\n\n\nThe 250~$\\mu$m images, produced using data from the Spectral and Photometric Imaging REceiver \\citep[SPIRE; ][]{2010griffin} on {\\it Herschel}, are also updated versions of the 250~$\\mu$m images originally published by \\citet{2012bendo} and \\citet{2012foyle}. The observations consisted of one pair of orthogonal scans using the 30 arcsec s$^{-1}$ scan rate and nominal bias voltage settings. The maps cover a $30\\times30$ arcmin region around NGC~2403 and $40\\times40$~arcmin region around M83. The data were reprocessed using HIPE version 12.1 through a pipeline that includes the standard signal jump correction, cosmic ray removal, low pass filter correction, and bolometer time response corrections, but we used the BRIght Galaxy ADaptive Element method \\citep[][Smith et al., in preparation]{2012smith, 2013auld} to remove drift in the background signal and to destripe the data. The final maps were produced using the naive mapmaker in HIPE and have pixel scales of 6~arcsec. The FWHM of the PSF is specified by the SPIRE Handbook \\citep{2014valtchanov}\\footnote{herschel.esac.esa.int\/Docs\/SPIRE\/spire\\_handbook.pdf} as 18.1~arcsec, and the calibration uncertainty is 4\\% \\citep{2013bendo}. To optimise the data for extended source photometry, we multiplied the data by the point source to extended source conversion factor of 91.289 MJy sr$^{-1}$ (Jy beam$^{-1}$)$^{-1}$ \\citep{2014valtchanov} and then applied a colour correction of $0.997 \\pm 0.029$, which should be appropriate for a modified blackbody with a temperature between 10 and 40~K and a $\\beta$ between 1.5 and 2 \\citep{2014valtchanov}.\n\n\nFor a discussion on the spiral density waves in M83 in Section~\\ref{s_m83pah}, we also included 0.23~$\\mu$m data from the Galaxy Evolution Explorer \\citep[GALEX; ][]{2005martin} produced by \\citet[][ see also \\citealt{2011lee}]{2009dale}. The images have pixel scales of 1.5~arcsec, PSF FWHM of $\\sim6$~arcsec \\citep{2005martin}, and calibration uncertainties of $<1$\\% \\citep{2007morrissey}. We applied a foreground extinction correction based on $A_{0.23\\mu\\text{m}}=0.56$ given by \\citet{2011lee} based on the \\citet{1989cardelli} extinction law with $R_V=3.1$.\n\n\nFor measuring quantitative star formation rates (so that we could identify locations that are strongly influenced by star formation using quantitative criteria), we included H$\\alpha$ data for these two galaxies in our analysis. The H$\\alpha$ image for NGC~2403 was originally produced by \\citet{2002boselli} using data from the 1.20~m Newton Telescope at the Observatoire de Haute Provence. The H$\\alpha$ image for M83 was produced by \\citet{2006meurer} using observations from the Cerro Tololo 1.5 Meter Telescope taken as part of the Survey for Ionization in Neutral Gas Galaxies. We applied extinction corrections for dust attenuation within the Milky Way using calculations performed by the NASA\/IPAC Extragalactic Database\\footnote{http:\/\/ned.ipac.caltech.edu\/} based on data from \\citet{1998schlegel}, and we also use data from the literature to correct for [N{\\small II}] emission falling within the wavebands covered by the H$\\alpha$ filters. Details on the data are given in Table~\\ref{t_hadata}.\n\n\\begin{table}\n\\caption{Properties of and corrections for the H$\\alpha$ images}\n\\label{t_hadata}\n\\begin{center}\n\\begin{tabular}{lp{1.8cm}p{1.8cm}}\n\\hline\nGalaxy & NGC~2403 & M83 \\\\ \\hline\nSource & \\citet{2002boselli} & \\citet{2006meurer} \\\\\nPixel Scale (arcsec ~pixel$^{-1}$) & 0.69 & 0.43 \\\\\nPSF FWHM (arcsec) & 3 & 1.6 \\\\\nCalibration uncertainty & 5\\% & 4\\% \\\\\nForground extinction ($A_R$) & 0.87 & 0.144 \\\\ \\relax\n[N{\\small II}] \/ H$\\alpha$ ratio & $0.28 \\pm 0.05^a$ & $0.40 \\pm 0.13^b$\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n$^a$ Both the 6548 and 6583 {\\AA} [N{\\small II}] lines fall within the band covered by the H$\\alpha$ filter used in the NGC~2403 observations. This number represents the ratio of emission from both lines to H$\\alpha$ emission measured in the radial strip data from \\citet{2010moustakas}.\\\\\n$^b$ The H$\\alpha$ filter used in the M83 observations includes emission from only the [N{\\small II}] 6583 {\\AA} line. This ratio is based on the ratio of ony that line to H$\\alpha$ emission and is calculated using data from \\citet{2005boissier}.\n\\end{table}\n\n\n\n\n\n\\subsection{Data preparation}\n\\label{s_data_prep}\n\n\nTo study the relation of PAH emission to far-infrared emission from large dust grains, we perform analyses using maps in which the PSFs have been matched to the PSF of the 250~$\\mu$m data (with a FWHM of 18~arcsec), and we plot data from images with matching PSFs that have been resampled into 18~arcsec bins that represent individual resolution elements within the maps. The data from these bins should be statistically independent. See \\citet{2008bendo} and \\citet{2012bendo} for additional discussion on this topic.\n\n\nIn the first step of preparing the data, foreground stars were identified by eye and removed from the H$\\alpha$, 3.6, 4.5, 5.8, 8 and 24~$\\mu$m data; these were typically sources that appeared unresolved and that had 3.6\/24~$\\mu$m flux density ratios $~\\rlap{$>$}{\\lower 1.0ex\\hbox{$\\sim$}}$10. Next, the data were convolved with kernels from \\citet{2011aniano}\\footnote{Available from http:\/\/www.astro.princeton.edu\/$\\sim$ganiano\/Kernels.html . Note that the kernels are created using circularised versions of instrumental PSFs. In the case of the IRAC data, the circularised PSFs have FWHM ranging from 1.9 to 2.8~arcsec, which is larger than the original PSFs.} to match the PSFs of the H$\\alpha$, 3.6, 4.5, 5.8, 8, 24 and 160~$\\mu$m data to the 18~arcsec PSF of the 250~$\\mu$m data. This was done to preserve the colour variations across the data when it was rebinned, and it eliminated the need to perform additional aperture corrections. The median background was then measured outside of the optical disc of each galaxy in each waveband and subtracted from the data. For the qualitative map-based analyses, the 3.6 and 8 $\\mu$m images were shifted to match the world coordinate systems of the 24, 160 and 250~$\\mu$m maps so that we could create 8\/24, 8\/160, and 8\/250~$\\mu$m surface brightness ratio maps; the pixel size of each ratio map is set to the pixel size of the image for the longer-wavelength data used in the ratios. For the analyses on binned data and for producing the profiles in Section~\\ref{s_m83pah}, the images were all shifted to match the world coordinate system of the 250~$\\mu$m data and then rebinned into 18~arcsec pixels to match the size of the PSF of the 250~$\\mu$m data. The rebinning was done so that the centre of each galaxy was located at the centre of an 18~arcsec bin. \n\n\nThe emission observed in the 4.5-24~$\\mu$m bands contains stellar continuum emission. We remove this stellar emission by subtracting a rescaled version of the IRAC 3.6~$\\mu$m image \\citep[e.g. ]{2004helou, 2010marble, 2014ciesla}. The IRAC 3.6~$\\mu$m band is suitable for this step because it generally contains unobscured stellar emission \\citep{2003lu}. While hot dust emission may produce 3.6~$\\mu$m emission \\citep{2009mentuch, 2010mentuch} and while emission from PAHs at 3.3~$\\mu$m also falls within the IRAC 3.6~$\\mu$m band, the comparison of 3.6~$\\mu$m emission to H-band emission by Bendo et al. (2014, submitted) suggests that, on the spatial scales of our data, local enhancements in hot dust and 3.3~$\\mu$m PAH emission have a very minor effect on the total 3.6~$\\mu$m emission. The continuum subtraction equations derived by \\citet{2004helou} were based on using an earlier version of Starburst99 \\citep{1999leitherer} to simulate the infrared stellar SED of a stellar population with a Salpeter initial mass function \\citep[IMF; ][]{1955salpeter} and two different metallicities. From this analysis, Helou et al. derived mean 3.6\/8 and 3.6\/24~$\\mu$m stellar surface brightness ratios that could be used to rescale the 3.6~$\\mu$m emission and subtract it from the 8, 4.5, 5.8 and 24~$\\mu$m data. We re-derived these values using a newer version of Starburst99 (version 6.0.3) to simulate a solar metallicity stellar population with a Kroupa IMF \\citep{2001kroupa}, which is now becoming more popular to use than the Salpeter IMF. We also examined the differences resulting from using both the Geneva and Padova stellar evolutionary tracks and found that the selection of one set of tracks over the other did not significantly affect the results. From these tests, we derive the following equations to subtract the stellar continuum from the 4.5-24~$\\mu$m data:\n\\begin{equation}\n\\begin{multlined}\nI_{\\nu}(4.5 \\mu \\mbox{m}~\\mbox{(SCS)} ) \\\\\n = I_\\nu(4.5 \\mu \\mbox{m}) - (0.60 \\pm 0.02) I_\\nu(3.6 \\mu \\mbox{m}) \n\\end{multlined}\n\\end{equation}\n\n\n\\begin{equation}\n\\begin{multlined}\nI_{\\nu}(5.8 \\mu \\mbox{m}~\\mbox{(SCS)} ) \\\\\n = I_\\nu(5.8 \\mu \\mbox{m}) - (0.40 \\pm 0.03) I_\\nu(3.6 \\mu \\mbox{m}) \n\\end{multlined}\n\\end{equation}\n\n\n\\begin{equation}\n\\begin{multlined}\nI_{\\nu}(8 \\mu \\mbox{m}~\\mbox{(SCS)} ) \\\\\n = I_\\nu(8 \\mu \\mbox{m}) - (0.246 \\pm 0.015) I_\\nu(3.6 \\mu \\mbox{m}) \n\\end{multlined}\n\\end{equation}\n\n\n\\begin{equation}\n\\begin{multlined}\nI_\\nu(24 \\mu \\mbox{m} ~\\mbox{(SCS)} ) \\\\\n = I_\\nu(24 \\mu \\mbox{m}) - (0.033 \\pm 0.003) I_\\nu(3.6 \\mu \\mbox{m}). \n\\end{multlined}\n\\end{equation}\nIn these equations, \"SCS\" stands for stellar continuum subtracted. The scaling terms are based on calculations performed at time intervals equally spaced in logarithm space between $10^{7}$ and $10^{10}$ yr. The values of the scaling terms are based on the mean of the results from using the Geneva and Padova tracks. The uncertainties are the greater of either the difference in the mean values measured between the results for the two tracks or the larger of the standard deviations measured in the scaling terms derived for the separate tracks. Changing the metallicities to $Z=0.008$ changed the factors by $\\ltsim1\\sigma$. The uncertainties in the coefficients translate to a $\\ltsim1$\\% uncertainty in the corrected 8 and 24~$\\mu$m maps, which is negligible compared to the calibration uncertainties. The 4.5-8.0~$\\mu$m coefficients derived here are typically within $1\\sigma$ of equivalent coefficients derived in other studies \\citep[e.g.][]{2004helou, 2010marble, 2014ciesla}. The coefficients for the 24~$\\mu$m data may disagree with coefficients from other papers by up to 0.012 or $4\\sigma$, although the values derived in these other papers differ among each other by 0.018. However, this correction is so small for the 24~$\\mu$m data (typically $\\sim1$\\% in NGC~2403 and M83) that the relatively high disagreement among the values should not have a major impact on our analysis or on other analyses relying upon this type of stellar continuum subtraction.\n\n\nThe 8~$\\mu$m band still contains continuum emission from very hot grains. In most solar-metallicity galaxies, this continuum emission may constitute $\\sim20$\\% of the total stellar-continuum-subtracted 8~$\\mu$m emission \\citep[e.g.][]{2007smith}, although in locations with very weak PAH emission, such as star-forming regions or metal-poor dwarf galaxies, a much higher percentage of the 8~$\\mu$m emission may be thermal continuum emission \\citep[e.g.][]{2005engelbracht, 2006cannon, 2008engelbracht, 2008gordon}. To remove the excess dust continuum emission, we use the following equation derived in an empirical analysis of photometric and spectroscopic data by \\citet{2010marble}\\footnote{The equation given by \\citet{2010marble} also includes a term that integrates the emission in frequency and converts the data into units of erg s$^{-1}$ cm$^{-2}$. Since we are comparing the PAH emission in the 8~$\\mu$m band to continuum emission in other bands that is measured in Jy arcsec$^{-2}$, it is easier to keep the 8~$\\mu$m data in units of Jy arcsec$^{-2}$, so we do not include the unit conversion term in this equation.}:\n\\begin{equation}\n\\label{e_pahdustsub}\n\\begin{multlined}\nI_{\\nu}(8 \\mu \\mbox{m}~{\\mbox{(PAH)})} = (I_{\\nu}(8 \\mu \\mbox{m}~{\\mbox{(SCS)})}\\\\\n - (0.091 + .314 I_{\\nu}(8 \\mu \\mbox{m})\/I_{\\nu}(24 \\mu \\mbox{m})) \\\\\n \\times (I_{\\nu}(4.5 \\mu \\mbox{m}~{\\mbox{(SCS)})} + I_{\\nu}(5.8 \\mu \\mbox{m}~{\\mbox{(SCS)})})^{0.718} \\\\\n \\times I_{\\nu}(24 \\mu \\mbox{m}~{\\mbox{(SCS)})}^{0.282}) \n\\end{multlined}\n\\end{equation}\nWhen this equation is applied to our data, the 8~$\\mu$m surface brightnesses typically decrease by $15-20$\\%. Based on the analysis from \\citet{2010marble}, the percentage difference between the 8~$\\mu$m PAH fluxes calculated using this equation and the fluxes of the spectral features measured spectroscopically is 6\\%. Throughout the rest of this paper, when we refer to 8~$\\mu$m emission, we are referring to the 8~$\\mu$m emission calculated using Equation~\\ref{e_pahdustsub}.\n\n\nFor the binned analysis, we wanted to illustrate which bins were more strongly influenced by emission from star forming regions and which regions tend to trace emission from dust predominantly heated by evolved stars. To do this, we created specific star formation rate (SSFR) maps. We first applied an intrinsic extinction correction to the H$\\alpha$ intensities (measured in erg cm$^{-2}$ s$^{-1}$ arcsec$^{-2}$) using \n\\begin{equation}\n\\label{e_hacorr}\n\\begin{multlined}\nI(\\mbox{H$\\alpha$ (corrected)}) = I(\\mbox{H$\\alpha$ (observed)})\\\\\n\t+ 2.0 \\times 10^{-25} (12.5~\\mbox{THz})I_{\\nu}(24 \\mu \\mbox{m}) \\left( \\frac{\\mbox{erg cm$^{-2}$ s$^{-1}$}}{\\mbox{Jy}} \\right),\n\\end{multlined}\n\\end{equation}\nwhich is a variant of the correction equation given by \\citet{2009kennicutt}. Since 24~$\\mu$m emission has been shown to be associated with H$\\alpha$ emission and other star formation tracers \\citep[e.g.][]{2005calzetti, 2007calzetti, 2007prescott, 2014bendo}, it is the best band to use when correcting H$\\alpha$ emission for intrinsic dust extinction. The 24~$\\mu$m band may also contain emission from diffuse dust heated by the radiation field from evolved stars \\citep{2009kennicutt}, which we would expect to affect low surface brightness regions in these galaxies, so low star formation rates derived using Equation~\\ref{e_hacorr} should be treated cautiously.\n\n\nAfter converting the corrected H$\\alpha$ intensities to units of erg s$^{-1}$ pc$^{-2}$ (written as $L(\\mbox{H}\\alpha)\/A$, with $A$ representing the area per pixel in pc$^2$), we used \n\\begin{equation}\n\\Sigma(\\mbox{SFR}) = 7.9 \\times 10^{-42}\\left(\\frac{L(\\mbox{H}\\alpha)}{A}\\right)\\left(\\frac{\\mbox{erg s$^{-1}$}}{\\mbox{M$_\\odot$ yr$^{-1}$}}\\right)\n\\end{equation}\nfrom \\citet{1998kennicutt} to calculate star formation rate surface densities $\\Sigma(\\mbox{SFR})$. To produce maps of the total stellar surface mass density $\\Sigma(\\mbox{M$_{\\star}$})$, we used\n\\begin{equation}\n\\label{e_totalstellar}\n\\begin{multlined}\n\\Sigma(\\mbox{M$_{\\star}$})\n =10^{5.65}\n \\left(\\frac{I_{\\nu}(3.6 \\mu \\mbox{m})^{2.85}I_{\\nu}(4.5 \\mu \\mbox{m})^{-1.85}\\Omega}\n {A}\\right)\\\\ \n \\left(\\frac{D}{0.05}\\right)^{2}\n \\left(\\frac{\\mbox{M$_{\\odot}$ arcsec$^{2}$}}\n {\\mbox{Jy Mpc$^2$ pc$^2$}}\\right)\n\\end{multlined}\n\\end{equation}\nbased on the equation from \\citet{2012eskew}. In this equation, $\\Omega$ is the angular area of the bin in the map, and $D$ distance to the source. We then divided $\\Sigma(\\mbox{SFR})$ by $\\Sigma(\\mbox{M$_{\\star}$})$ to calculate the SSFR.\n\n\n\n\n\\section{Analysis of 8\/24, 8\/160, and 8\/250~$\\mu$\\lowercase{m} ratios}\n\\label{s_analysis_ratios}\n\n\n\\subsection{Map-based analysis}\n\\label{s_analysis_maps}\n\n\n\\begin{figure*}\n\\begin{center}\n\\epsfig{file=jonesa_fig_01.ps}\n\\caption{The 21 $\\times$ 18~arcmin images of NGC 2403 used in the analysis. North is up and east is to the left in each image. The 3.6~$\\mu$m maps trace the intermediate-age and older stars. The 8~$\\mu$m image mainly shows the PAH 7.7~$\\mu$m emission feature but may also contain small amounts of emission from hot dust and stellar sources. The 24~$\\mu$m band traces emission from hot (100~K) dust, and the 160 and 250~$\\mu$m trace emission from colder (15-30~K) dust. The FWHM for each image is shown as a green circle in the lower left corner of each panel, and the light blue ellipse in the 3.6~$\\mu$m image outlines the optical disk of the galaxy.}\n\\label{f_ngc2403_maps}\n\\end{center}\n\\end{figure*}\n\n\n\\begin{figure*}\n\\begin{center}\n\\epsfig{file=jonesa_fig_02.ps}\n\\caption{The 20 $\\times$ 20~arcmin images of M83 used in the analysis. See Figure \\ref{f_ngc2403_maps} for additional information on the image format.}\n\\label{f_m83_maps}\n\\end{center}\n\\end{figure*}\n\n\n\\begin{figure}\n\\epsfig{file=jonesa_fig_03.ps}\n\\caption{The 8\/24, 8\/160 and 8\/250~$\\mu$m surface brightness ratio maps for NGC 2403. These images are based on data where the PSFs are matched to the PSF of the 250~$\\mu$m data. The FWHM for the 250~$\\mu$m PSF is shown by the green circle in the lower left corner of each panel. The colour scales in the images have been adjusted to show the structure in the surface brightness ratios; some of the red or purple pixels may be outside the range of values shown in the colour bars. Data not detected at the $5\\sigma$ level in either band is left blank. The 8\/24~$\\mu$m ratio is low in star forming regions where the 24~$\\mu$m emission is brightest. The 8\/160 and 8\/250~$\\mu$m images are similar in that the ratios generally decrease with radius. However, the 8\/160~$\\mu$m ratio shows more structure, while the 8\/250~$\\mu$m map is generally smoother.}\n\\label{f_ngc2403_ratio}\n\\end{figure}\n\n\n\\begin{figure}\n\\epsfig{file=jonesa_fig_04.ps}\n\\caption{The 8\/24, 8\/160 and 8\/250~$\\mu$m surface brightness ratio maps for M83. These images are based on data where the PSFs are matched to the PSF of the 250~$\\mu$m data. The maps are formatted in the same way as the maps in Figure~\\ref{f_ngc2403_ratio}. We see spiral arm structure in all images. The arm structure in the 8\/24~$\\mu$m image is traced by a series of red point-like sources where the ratio decreases in star forming regions. However, the filamentary spiral structures in the 8\/160 and 8\/250~$\\mu$m maps are locations offset from the 160 and 250~$\\mu$m emission in Figure~\\ref{f_m83_maps} where PAH emission is enhanced relative to cold dust emission.}\n\\label{f_m83_ratio}\n\\end{figure}\n\n\\begin{figure}\n\\epsfig{file=jonesa_fig_05a.ps}\n\\epsfig{file=jonesa_fig_05b.ps}\n\\caption{ Maps showing the specific star formation rate (SSFR) for NGC~2403 and M83. The data are formatted the same as the corresponding ratio maps for each galaxy in Figures \\ref{f_ngc2403_ratio} and \\ref{f_m83_ratio}}\n\\label{f_ssfr}\n\\end{figure}\n\nFigures \\ref{f_ngc2403_maps} and \\ref{f_m83_maps} show the 3.6, 8, 24, 160 and 250~$\\mu$m images used in the analysis (before the application of the convolution or rebinning steps described in Section~\\ref{s_data_prep}). Figures \\ref{f_ngc2403_ratio} and \\ref{f_m83_ratio} show the 8\/24, 8\/160 and 8\/250~$\\mu$m surface brightness ratios of the data after the convolution step but before rebinning. The 8, 24, 160, and 250~$\\mu$m images all look very similar, demonstrating that the PAHs, hot dust, and cold dust are found in the same large-scale structures. However, the ratio maps demonstrate how the PAH emission varies with respect to the dust traced by the other infrared bands. For comparison to these figures, we also show maps of the SSFR in Figure \\ref{f_ssfr}.\n\n\nIn both galaxies, we see a decrease in the 8\/24~$\\mu$m ratio in locations where the 24~$\\mu$m emission peaks. If the PAH emission was tracing star formation in the same way as the hot dust emission, we would see little variation across the 8\/24~$\\mu$m ratio maps. The disparity indicates either that the 24~$\\mu$m emission is enhanced in regions with high SSFR, that the PAH emission is inhibited where the hot dust emission peaks in the centres of regions with high SSFR, or that both effects occur within the star forming regions. In NGC 2403, we see the 8\/24~$\\mu$m ratio is higher in the diffuse regions outside the regions with high SSFR, particularly in the southern half of the galaxy. In M83, we see the enhancement of PAHs relative to the 24~$\\mu$m emission in not only the interarm regions but also between high SSFR regions in the spiral arms. The 8\/24~$\\mu$m ratio is also very low in the starburst nucleus of M83, as is also seen by Wu et al. (2014, submitted). \n\n\nThe 8\/160 and 8\/250~$\\mu$m ratio maps for NGC~2403 and M83 present different results for each galaxy. In NGC~2403, the 8\/160 and 8\/250~$\\mu$m ratios peak near the centre and decrease with radius, although the 8\/160~$\\mu$m map looks more noisy than the 8\/250~$\\mu$m map. Instead of seeing the PAH emission decrease relative to the cold dust emission in individual regions with high SSFR, as was the case in the 8\/24~$\\mu$m ratio maps, we see the PAH emission enhanced relative to the 160 and 250~$\\mu$m emission at the location of the infrared-brightest star forming region in the northeast side of the disc. The 8\/160 and 8\/250~$\\mu$m ratios generally do not change significantly near most other star forming regions. With the exception of the infrared-brightest star forming region, the radial gradients in the 8\/160 and 8\/250~$\\mu$m ratios look similar to the radial gradients in the 3.6~$\\mu$m image seen in Figure~\\ref{f_ngc2403_maps}. In NGC~2403, \\citet{2012bendo} found that the 160\/250~$\\mu$m surface brightness ratios were correlated with H$\\alpha$ emission and peaked in locations with strong star formation, while the 250\/350~$\\mu$m ratios were more strongly correlated with near-infrared emission and generally varied radially in the same way as the older stellar populations. These results demonstrated that the 160~$\\mu$m emission is dominated by dust heated locally in star forming regions but the dust seen at 250~$\\mu$m is heated by the diffuse ISRF. The similarity between the 8\/160, 8\/250, 250\/350, and 3.6~$\\mu$m radial gradients suggests that the PAHs are intermixed with the cold large dust grains and that the enhancement of PAH emission relative to the large dust grains depends on the surface brightness of the evolved stellar population. If this is the case, the 8\/160~$\\mu$m map may looks noisy compared to the 8\/250~$\\mu$m map because emission in the 8 and 160~$\\mu$m bands is affected by different stellar populations while emission in the 8 and 250~$\\mu$m bands is affected by mainly the evolved stellar population.\n\n\nThe dust emission for the different wavebands peak in slightly different places in these profiles of the M83's spiral arms. The 250~$\\mu$m emission, which trace most of the dust mass in the spiral arms, peak on the downstream (or inner) side of the spiral arm. The profile of the 24~$\\mu$m emission tends to appear narrower and peaks $0-7$~arcsec further towards the upstream (or outer) side of the spiral arms (although this is small relative to the 18~arcsec resolution of the data used to create these plots). This is consistent with the classical description of star formation within spiral arms \\citep[e.g.]{1969roberts, 1979elmegreen}. First the gas flows into the spiral arms, then the gas is shocked by the spiral density waves and collapses into stars, and finally young stars emerge on the upstream side of the spiral arms. \n\n\nThe 8~$\\mu$m emission peaks slightly further downstream from the 250~$\\mu$m emission, and the profiles of the 8~$\\mu$m emission on the downstream side of the spiral arms is broader than the profiles on the upstream side. This is particularly pronounced for profiles A, C, and F. While the 8\/24~$\\mu$m ratio drops sharply near the star forming regions as expected, the 8\/250~$\\mu$m ratio peak 10-30~arcsec (or $\\sim$200-650 pc) downstream from the dust lane, as is also seen in the countour overlays in Figure~\\ref{f_m83_overlay}. This demonstrates that the PAHs emission is enhanced relative to the cold dust on the downstream side of the spiral arms well outside the dust lanes. The ultraviolet emission also peaks downstream from the 250~$\\mu$m emission in many of these profiles and that the profiles of the ultraviolet emission in B and C look broader on the downstream side. The possible connection of these profiles to the ultraviolet emission and to the 160\/250~$\\mu$m ratios is discussed further in Section~\\ref{s_m83pah}.\n\n\nThe offset enhancements in PAH emission in the arms of M83 could appear because of astrometry problems, but we have checked the astrometry among the images using foreground and background sources outside the optical disc of the galaxy and found no significant offsets greater than $\\sim1$~arcsec in the sources between images. It is also possible that the broader PAH emission could result from issues related to the PSF matching step, but usually these types of artefacts will appear symmetric around bright sources, whereas the enhanced PAH emisson appears asymmetric. It is more likely that the phenomenon is real and has been difficult to detect before because of limitations in the angular resolution of far-infrared data.\n\n\n\\begin{figure}\n\\epsfig{file=jonesa_fig_06.ps}\n\\caption{The 8~$\\mu$m image of M83 (after the PSF has been matched to the PSF of the 250~$\\mu$m data) showing locations where we produced additional plots to illustrate the offset between the 8\/250~$\\mu$m ratio and the dust emission from the spiral arms. The blue boxes show the locations in Figure~\\ref{f_m83_overlay} where we overlay contours of the 8\/250~$\\mu$m and 160\/250~$\\mu$m ratios on the 250~$\\mu$m data. The cyan lines show the locations of the surface brightness profiles plotted in Figure~\\ref{f_m83_line}. The image is formatted in the same way as Figure~\\ref{f_m83_maps}.}\n\\label{f_m83_line_map}\n\\end{figure}\n\n\n\\begin{figure*}\n\\begin{center}\n\\epsfig{file=jonesa_fig_07a.ps,height=7.1cm}\n\\epsfig{file=jonesa_fig_07b.ps,height=7.1cm}\n\\epsfig{file=jonesa_fig_07c.ps,height=7.1cm}\n\\epsfig{file=jonesa_fig_07d.ps,height=7.1cm}\n\\caption{The 250~$\\mu$m images of the two spiral arms in M83 with the 8\/250~$\\mu$m ratio and 160\/250~$\\mu$m ratio overlaid as contours. The contours for the 8\/250~$\\mu$m ratio start at 0.07 and increase upwards in increments of 0.01. The contours for the 160\/250~$\\mu$m ratio start at 2.4 and increase upwards in increments of 0.2. The images are formatted in the same way as Figure~\\ref{f_m83_maps}. The 8\/250~$\\mu$m ratios themselves are discussed in Section~\\ref{s_analysis_maps}, while both the 8\/250 and 160\/250~$\\mu$m ratios are discussed in Section~\\ref{s_m83pah}.}\n\\label{f_m83_overlay}\n\\end{center}\n\\end{figure*}\n\n\n\\begin{figure*}\n\\begin{center}\n\\epsfig{file=jonesa_fig_08.ps}\n\\caption{Profiles of the H$\\alpha$, 0.23, 8, 24, 160, and 250~$\\mu$m surface brightnesses and 8\/24, {8\/250, and 160\/250}~$\\mu$m surface brightness ratios measured in 18~arcsec wide regions along the locations shown in Figure~\\ref{f_m83_line_map}. The x-axis shows the distance from the peak of the 250~$\\mu$m emission; negative numbers are for the upstream side of the arms, while positive materials are for the downstream side. The profiles were measured in 18~arcsec wide regions in images where the PSF had been matched to the PSF of the 250~$\\mu$m data, which has a FWHM of 18~arcsec (or $\\sim400$~pc), but the data are supersampled at 1~arcsec resolutions to produce smooth curves. All surface brightnesses are normalised so that the peak values are 1, and all ratios are normalised so that they range between 0 and 1. The uncertainties in the normalised surface brightnesses are $~\\rlap{$<$}{\\lower 1.0ex\\hbox{$\\sim$}} 1$\\%, and the uncertainties in the normalised ratios are $~\\rlap{$<$}{\\lower 1.0ex\\hbox{$\\sim$}} 5$\\%. The H$\\alpha$ data shown here are corrected for foreground dust attenuation but not corrected for dust extinction within M83.}\n\\label{f_m83_line}\n\\end{center}\n\\end{figure*}\n\n\n\n\n\\subsection{Analysis of binned data}\n\\label{s_binned_analysis}\n\nIn Figures \\ref{f_8-24}-\\ref{f_8-250}, we plot the relations between the PAH emission at 8~$\\mu$m and either the hot dust emission at 24~$\\mu$m or the cold dust emission at 160 and 250~$\\mu$m. Pearson correlation coefficients for these relations are given in Table \\ref{t_correlation}. To first order, the 8~$\\mu$m surface brightness is well correlated with the 24, 160 and 250~$\\mu$m surface brightnesses. However, the 8\/24, 8\/160 and 8\/250~$\\mu$m ratios reveal the presence of both scatter in the relations between the 8~$\\mu$m emission and emission in other bands as well as systematic variations in these relations. \n\n\nThe variations in the 8\/24~$\\mu$m ratio in Figure~\\ref{f_8-24} show that the 8\/24~$\\mu$m ratio decreases in areas where the 24~$\\mu$m emission is strongest in both galaxies. However, the relations between the 8\/24~$\\mu$m ratio and the 24~$\\mu$m emission differ somewhat between the two galaxies. In NGC~2403, we found that we could see different trends in the data when we separated the 18~arcsec binned data into two subsets where the SSFR was either $\\geq1\\times10^{-10}$~yr$^{-1}$ or $<1\\times10^{-10}$~yr$^{-1}$. The data with low SSFR follow a relation in which $\\log (I_\\nu(8\\mu\\mbox{m})\/I_\\nu(24\\mu\\mbox{m}))$ increases slightly from $\\sim 0.0$ to $\\sim 0.2$ as $\\log (I_\\nu(24\\mu\\mbox{m}))$ increases from -6 to -4. The emission from these regions may orginate mainly from locations in the diffuse ISM with relatively soft radiation fields where the PAH emission is well-correlated with emission from hot, diffuse dust heated by the diffuse ISRF. As both the PAHs and the hot, diffuse dust are stochastically heated, the ratio of PAH to hot dust emission is expected to be roughly constant. The slight decrease in the 8\/24~$\\mu$m ratio as the diffuse 24~$\\mu$m surface brightness decreases is potentially a result of an increase in the hardness of the radiation field as the 24~$\\mu$m surface brightness decreases (possibly as a result of changes in metallicity with radius as found by multiple authors \\citep[e.g. ][]{1994zaritsky,2010moustakas}), which could lead to either PAH emission being suppressed or 24~$\\mu$m emission being enhanced. Data points tracing locations with high SSFR fall below the relation between the 8\/24~$\\mu$m ratio and 24~$\\mu$m surface brightness. These locations would be expected to have harder radiation fields that may enhance the 24~$\\mu$m emission or suppress the PAH emission. While we are able to empirically separate data into regions with high and low 8\/24~$\\mu$m ratios using a SSFR cutoff value of $1\\times10^{-10}$~yr$^{-1}$, additional work in modelling the stellar populations, the PAH excitation, and the dust heating is needed to understand the details of how the SSFR of the stellar populations affects the variations in the 8\/24~$\\mu$m ratio within NGC~2403.\n\n\nIn M83, the relationship between the 8\/24~$\\mu$m ratio and 24~$\\mu$m emission is close to linear at $\\log (I_\\nu(24\\mu\\mbox{m}))>-4$, but it flattens at $\\log (I_\\nu(24\\mu\\mbox{m})) < -4$. Some of the lowest 8\/24~$\\mu$m ratios corresponds to regions with high SSFR in the nucleus and spiral arms where, again, the 24~$\\mu$m emission may be strongly enhanced or the PAH emission is suppressed. The relation between 24~$\\mu$m emission and the 8\/24~$\\mu$m ratio at $\\log (I_\\nu(24\\mu\\mbox{m}))>-4$ in M83 is similar to the relation seen in NGC~2403. Unlike NGC~2403, however, we found that we could not readily separate data in the plots of $\\log (I_\\nu(8\\mu\\mbox{m})\/I_\\nu(24\\mu\\mbox{m}))$ versus $\\log (I_\\nu(24\\mu\\mbox{m}))$ for M83 simply by selecting data by SSFR, as some regions with low SSFR have low 8\/24~$\\mu$m ratios. These regions are mostly locations within radii of 1.5~kpc. The exact reason why we see this is unclear, although it is possible that hard ultraviolet photons from the starburst nucleus leak into the diffuse ISM in this region and destroy the PAHs in the diffuse ISM.\n\n\nFigure \\ref{f_8-160} shows good correlations between the 8~$\\mu$m and 160~$\\mu$m surface brightnesses. The plot of the 8\/160~$\\mu$m vs 160~$\\mu$m for M83 shows that the 8\/160~$\\mu$m ratio is close to constant over a range of infrared surface brightnesses that vary by a factor of 100, indicating that the relation between 8 and 160~$\\mu$m emission is very close to a one-to-one relationship. Some scatter is seen in the 8\/160~$\\mu$m ratio at high 160~$\\mu$m surface brightnesses. Some of these data points are for locations around the infrared-bright centre of M83 where the outer regions of the PSF were not matched perfectly in the convolution step, while other data points sample regions along the spiral arms where the enhancement in the 8\/160~$\\mu$m ratio is offset from the 160~$\\mu$m surface brightness as discussed in Section~\\ref{s_analysis_maps}. In NGC 2403, the 8\/160~$\\mu$m ratio increases with 160~$\\mu$m surface brightness, and the relation exhibits more scatter, indicating that the relation of 8~$\\mu$m emission to 160~$\\mu$m emission in NGC~2403 is different from the relation for M83. \n\n\nThe relations between the 8 and 250~$\\mu$m emission in Figure \\ref{f_8-250} are similar to the relations between the 8 to 160~$\\mu$m emission. For both galaxies, the 8\/250~$\\mu$m ratio increases with the 250~$\\mu$m surface brightness, and the correlation coefficients are relatively strong. In NGC~2403, the correlation coefficient between the 8\/250~$\\mu$m ratio and the 250~$\\mu$m surface brightness is 0.83, which is much higher than the correlation coefficient of 0.66 for the relation between the 8\/160~$\\mu$m ratio and the 160~$\\mu$m surface brightness. Given that the square of the Pearson correlation coefficient indicates the fraction of variance in one quantity that depends upon the other quantity, the difference in the correlation coefficients is equivalent to a $\\sim25$\\% difference in being able to describe the variance in the relations. This suggests that the PAHs are more strongly associated with the colder dust seen at 250~$\\mu$m than the warmer dust seen at 160~$\\mu$m. In M83, the relation between the 8\/250~$\\mu$m ratio and 250~$\\mu$m surface brightness is sloped and also shows significant scatter at high surfaces brightnesses in the same way as the relationship between the 8\/160~$\\mu$m ratio and the 8~$\\mu$m emission.\n\n\nBecause M83 is at a distance $\\sim$1.5 further than NGC 2403, the 18~arcsec bins used in this analysis will cover regions with different spatial scales. In Appendix~\\ref{a_binsizecheck}, we examined how the results for the analysis on NGC~2403 would change if we used 27~arcsec bins, which cover approiximately the same spatial scales as the 18~arcsec bins used for the M83 data. We see no noteable difference in the results using the 27~arcsec bins compared to the 18~arcsec bins; most correlation coefficients change by $\\leq0.05$. Hence, adjusting the bin sizes for the two galaxies to similar spatial scales is unimportant. We will therefore use data measured in the smaller bins as it takes full advantage of the capabilities of the {\\it Herschel} data that we are using and as it allows us to illustrate how the relations are still found in smaller structure in NGC 2403.\n\n\n\\begin{table}\n\\caption{Pearson correlation coefficients for the binned data.}\n\\label{t_correlation}\n\\begin{tabular}{p{5.6cm}cc}\n\\hline\n & \n \tNGC &\n\tM83 \n\t\\\\ \n & \n \t2403 &\n\t\\\\ \\hline\n$\\log (I_\\nu(8\\mu\\mbox{m}))$ vs $\\log(I_\\nu(24\\mu\\mbox{m}))$ &\n \t0.96\t&\n\t0.97\t\\\\\n$\\log (I_\\nu(8\\mu\\mbox{m})\/I_\\nu(24\\mu\\mbox{m}))$ vs $\\log(I_\\nu(24\\mu\\mbox{m}))$ &\n\t-0.13\t&\n\t-0.74\t\\\\\n$\\log (I_\\nu(8\\mu\\mbox{m}))$ vs $\\log(I_\\nu(160\\mu\\mbox{m}))$ &\n\t0.98\t&\n\t0.98\t\\\\ \n$\\log (I_\\nu(8\\mu\\mbox{m})\/I_\\nu(160\\mu\\mbox{m}))$ vs $\\log(I_\\nu(160\\mu\\mbox{m}))$ &\t\n\t0.66\t&\n\t0.17\t\\\\\n$\\log (I_\\nu(8\\mu\\mbox{m}))$ vs $\\log(I_\\nu(250\\mu\\mbox{m}))$ &\n\t0.98\t&\t\n\t0.97\t\\\\ \n$\\log (I_\\nu(8\\mu\\mbox{m})\/I_\\nu(250\\mu\\mbox{m}))$ vs $\\log(I_\\nu(250\\mu\\mbox{m}))$ &\n\t0.83\t&\n\t0.44\t\\\\\t\t\n\t\\hline\n\\end{tabular}\t\n\\end{table}\n\n\n\\begin{figure*}\n\\begin{center}\n\\epsfig{file=jonesa_fig_09.ps}\n\\caption{The 8~$\\mu$m surface brightness and the 8\/24~$\\mu$m ratios as a function of 24~$\\mu$m emission for the 18~arcsec binned data for both galaxies. Only data detected at the $5\\sigma$ level are displayed. The best fitting linear functions between the surface brightnesses (weighted by the errors in both quantities) are shown as black lines in the top panels. The blue points are locations with high SSFR, and the red points and error bars are locations which are predominantly heated by the diffuse ISRF; see Section~\\ref{s_data_prep} for more details. In M83 we highlight locations within a 1.5~kpc radius of the centre in black. }\n\\label{f_8-24}\n\\end{center}\n\\end{figure*}\n\n\n\\begin{figure*}\n\\begin{center}\n\\epsfig{file=jonesa_fig_10.ps}\n\\caption{The 8~$\\mu$m surface brightness and the 8\/160~$\\mu$m ratios as a function of 160~$\\mu$m emission for the 18~arcsec binned data for both galaxies. The data are formatted in the same way as in Figure~\\ref{f_8-24}.}\n\\label{f_8-160}\n\\end{center}\n\\end{figure*}\n\n\n\\begin{figure*}\n\\begin{center}\n\\epsfig{file=jonesa_fig_11.ps}\n\\caption{The 8~$\\mu$m surface brightness and the 8\/250~$\\mu$m ratios as a function of 250~$\\mu$m emission for the 18~arcsec binned data for both galaxies. The data are formatted in the same way as in Figure~\\ref{f_8-24}.}\n\\label{f_8-250}\n\\end{center}\n\\end{figure*}\n\n\n\\section{Identification of PAH excitation sources}\n\\label{s_pahexcitation}\n\n\n\\subsection{PAH excitation in NGC 2403}\n\\label{s_ngc2403pah}\n\nWe can conclude from our analysis that the 8~$\\mu$m emission that we observe from NGC~2403 does not originate from PAHs excited locally within the centres of star forming regions, as the relationship between 8 and 24~$\\mu$m emission shows that PAH 8~$\\mu$m emission decreases relative to 24~$\\mu$m emission within regions with high SSFR. This is partly because the 24~$\\mu$m emission is very sensitive to dust heating and increases significantly within star forming regions \\citep[e.g.][]{2001dale, 2002dale}. However, the strong ultraviolet radiation from these massive young stars probably photodissociates the PAHs in the centres of these regions including species that produce features other than the 7.7~$\\mu$m feature, as has been seen spectroscopically in other galactic and extragalactic star forming regions \\citep{2007berne, 2007lebouteiller, 2007povich, 2008gordon}. PAH emission has been observed in the outer regions of photodissociation regions \\citep{2007berne, 2007lebouteiller, 2007povich}; the photons that excite the PAHs in these locations can also heat the very small grains that produce the 24~$\\mu$m emission. In our data, the emission from the inner and outer regions of these regions will be blended. Integrating over the centres of these regions, the 8\/24~$\\mu$m ratio will still appear low comapred to diffuse regions outside these regions because the PAH emission is suppressed in parts of the regions while the 24~$\\mu$m emission is not, a result also obtained by \\citet{2005calzetti}. The dust emitting at 160~$\\mu$m is also heated by light from star forming regions \\citep{2012bendo}. While the 8~$\\mu$m emission is better correlated with the 160~$\\mu$m band than with the 24~$\\mu$m band, the 8\/160~$\\mu$m ratio still shows significant scatter as a function of 160~$\\mu$m surface brightness, possibly because the PAH emission is still inhibited in the regions from which the 160~$\\mu$m emission is originating.\n\n\nHowever, we see a strong correlation between the 8 and 250~$\\mu$m surface brightnesses, and the relation between the 8\/250~$\\mu$m ratio and the 250~$\\mu$m surface brightness shows that the residuals in the relation between the 8 and 250~$\\mu$m are very small, especially compared to the equivalent residuals for the relations between the 8~$\\mu$m emission and emission in either the 24~$\\mu$m or 160~$\\mu$m bands. This is particularly evident when comparing the correlation coefficients for the 8\/24~$\\mu$m ratio versus 24~$\\mu$m emission, the 8\/160~$\\mu$m versus 160~$\\mu$m emission, and the 8\/250~$\\mu$m ratio versus 250~$\\mu$m emission in Table~\\ref{t_correlation}. This indicates that the PAH emission is much more strongly tied to the dust emitting in the 250~$\\mu$m band. \\citet{2012bendo} demonstrated that the dust emitting at $\\geq 250$~$\\mu$m in NGC~2403 was heated mainly by the diffuse ISRF from the total stellar population. This implies that the PAHs in NGC~2403 are also mainly excited by the diffuse ISRF. Moreover, the map of the 8\/250~$\\mu$m ratio in Figure~\\ref{f_ngc2403_ratio} looks very similar to both the 3.6~$\\mu$m map in Figure~\\ref{f_ngc2403_maps} that traces the light from the total stellar population and the 250\/350~$\\mu$m ratio map from \\citet{2012bendo} that shows the variations in the colour temperatures of the large dust grains heated by the ISRF from these stars.\n\n\nTo examine this relation further, we plot the 8\/250~$\\mu$m ratio versus the 3.6~$\\mu$m surface brightness for NGC 2403 in Figure \\ref{f_2403-8-250}. We find a strong correlation between 8\/250~$\\mu$m ratio and the 3.6~$\\mu$m surface brightness; the Pearson correlation coefficient for the relation between these data in logarithmic space is 0.89. This shows that the enhancement of PAH emission relative to cold dust emission scales with the stellar surface brightness, which implies that the PAHs are primarily mixed in with the large dust grains in the diffuse ISM and that the PAHs are predominantly heated by the diffuse ISRF from the total stellar population. \n\n\n\\begin{figure}\n\\begin{center}\n\\epsfig{file=jonesa_fig_12a.ps}\n\\epsfig{file=jonesa_fig_12b.ps}\n\\caption{The 8\/250$~\\mu$m surface brightness ratio plotted as a function of the 3.6~$\\mu$m surface brightness and galactocentric radius for the 18~arcsec binned data for NGC~2403. The data are formatted in the same way as in Figure~\\ref{f_8-24}. The radii are based on using an inclination of $62.9\\deg$ from \\citet{2008deblok}.}\n\\label{f_2403-8-250}\n\\end{center}\n\\end{figure}\n\n\\begin{figure}\n\\begin{center}\n\\epsfig{file=jonesa_fig_13a.ps}\n\\epsfig{file=jonesa_fig_13b.ps}\n\\caption{To examine whether the relation between the 3.6~$\\mu$m emission and the 8\/250~$\\mu$m ratio is related to hot dust or PAH emission in the 3.6~$\\mu$m band, we first show the relation between 3.6 and 8~$\\mu$m emission. The relation in the top panel could be the result of either the 3.6 and 8~$\\mu$m bands tracing emission from similar sources or the 3.6~$\\mu$m band tracing starlight exciting the PAHs seen in the 8~$\\mu$m band. Next, we plot the 4.5\/3.6$~\\mu$m ratio as a function of the 24$~\\mu$m emission in the bottom panel to examine whether the slope of the SED at 3.6-4.5~$\\mu$m is influenced by hot dust emission. The absence of such a relation as well as the relative invariance of the 4.5\/3.6~$\\mu$m ratio implies that the 3.6 and 4.5~$\\mu$m bands are relatively unaffected by non-stellar emission. The data are formatted in the same way as in Figure~\\ref{f_8-24}.}\n\\label{f_2403_stars}\n\\end{center}\n\\end{figure}\n\n\nIt is also possible that, because the correlation coefficient for the relation between the logarithm of the 3.6~$\\mu$m surface brightness and radius is -0.95, the 8\/250~$\\mu$m ratio actually depends on radius rather than 3.6~$\\mu$m surface brightness. Any radial dependence would be expected to be related to metallicity, which decreases with radius, and this could influence the PAHs. However, the results from \\citet{2008engelbracht} and \\citet{2008gordon} indicated that the apparent dependence of PAH emission on metallicity is really the result of changes in the radiation field illuminating the ISM in lower metallicity environments. The radiation field would be harder in low metallicity systems first because of increases in the stellar temperatures of O and B stars \\citep{2004massey, 2005massey, 2007trundle} and second because of decreases of extinction related to a decrease in the gas to dust ratio. This in turn leads to harder interstellar radiation fields in low-metallicity environments that potentially destroy PAHs. To examine this, we plot the 8\/250~$\\mu$m ratio as a function of radius in the bottom panel of Figure~\\ref{f_2403-8-250}. As expected, the 8\/250~$\\mu$m ratio decreases as radius increases. The relation between the logarithm of the 8\/250~$\\mu$m ratio and radius has a Pearson correlation coefficient of -0.82, which has an absolute value that is similar to the value of 0.89 for the relation between the lorarithms of the 8\/250~$\\mu$m ratio and the 3.6~$\\mu$m emission. However, at the resolution of these data, we do see non-axisymmetric substructures in the 8\/250~$\\mu$m image in Figure~\\ref{f_ngc2403_ratio} that correspond to similar substructure in the 3.6~$\\mu$m images, but these structures are mainly visible at radii of $<10$ kpc. When we look at data within this inner 10~kpc region, we get a correlation coefficient of -0.77 for the relation with radius and 0.91 for the relation with 3.6~$\\mu$m surface brightness. This implies that the total stellar surface brightness is more influentual on PAH excitation than any effects related to radius.\n\n\nAlthough it is unlikely, the correlation between the 8\/250~$\\mu$m ratio and 3.6~$\\mu$m emission could be the result of a correlation between emission in the 3.6 and 8~$\\mu$m bands themselves. Both bands may contain thermal continuum dust emission (although the thermal continuum emission should have been removed from the 8~$\\mu$m data when we applied Equation~\\ref{e_pahdustsub}), and the 3.6~$\\mu$m band may also contain emission from the 3.3~$\\mu$m PAH emission feature, although previous work by \\citet{2003lu} indicated that emission at $<5$~$\\mu$m from nearby galaxies is dominated by stellar emission. To investigate this further, we plot the relation between the 8~$\\mu$m PAH emission and 3.6~$\\mu$m stellar surface brightness in Figure~\\ref{f_2403_stars}. The data are well correlated; the correlation coefficient for the relation in logarithm space is 0.94. While this could indicate that the same emission sources are seen at 3.6 and 8~$\\mu$m, it is also possible that the 8~$\\mu$m emission is correlated with 3.6~$\\mu$m emission because the stars seen at 3.6~$\\mu$m excite the PAHs seen at 8~$\\mu$m. Hence, the correlation between 3.6 and 8~$\\mu$m does not necessarily prove anything about the relation between the emission in these bands. \\citet{2010mentuch} illustrated that it was possible to identify the influence of non-stellar emission at near-infrared wavenegths by examining the 4.5\/3.6~$\\mu$m surface brightness ratio. This ratio would be relatively invariant for stellar emission because it traces emission from the Rayleigh-Jeans side of the stellar SED, but if hot dust emission influences the bands, the ratio should increase. We plot the 4.5\/3.6~$\\mu$m ratio versus 24~$\\mu$m emission in the lower panel of Figure \\ref{f_2403_stars}. This relationship is almost flat. Most of the data points have log($I_\\nu$(4.5~$\\mu$m)\/$I_\\nu$(3.6~$\\mu$m)) values that lie within a range of -0.14 to -0.23, which would be consistent with what was observed for evolved stellar populations by \\citet{2010mentuch}. The absence of significant variations in the 4.5\/3.6~$\\mu$m ratio with 24~$\\mu$m implies that the 3.6 and 4.5~$\\mu$m bands are largely uninfluenced by hot dust emission. We do see a few data points with values of log($I_\\nu$(4.5~$\\mu$m)\/$I_\\nu$(3.6~$\\mu$m))$>-0.14$ where the 3.6 and 4.5~$\\mu$m may be more strongly influenced by non-stellar emission, but these data only weakly influence our results. If we exclude these data, the correlation coefficient for the relation between the 3.6~$\\mu$m data and the 8\/250~$\\mu$m data changes by $<0.01$. This shows that the 3.6~$\\mu$m emission in NGC~2403 is largely dominated by the stellar population and is relatively unaffected by hot dust or PAH emission. Therefore, the most likely explanation for the correlation between the 3.6 and 8~$\\mu$m emission as well as the correlation between the 3.6~$\\mu$m emission and the 8\/250~$\\mu$m ratio is that the PAHs are excited by the stellar population seen at 3.6~$\\mu$m.\n\n\n\\subsection{PAH excitation in M83}\n\\label{s_m83pah}\n\n\nThe results from the 8~$\\mu$m to 24~$\\mu$m relationship in M83 are similar to NGC 2403. We see the 8~$\\mu$m PAH emission is low in regions where the 24~$\\mu$m emission peaks. Again, PAHs are probably being destroyed locally in regions with high SSFR. However, we find that 8~$\\mu$m emission is more strongly related to the 160 and 250~$\\mu$m emission. We also see offsets between the 8\/250~$\\mu$m ratios and the dust mass (as traced by the 250~$\\mu$m band). \\citet{2012bendo} and \\citet{2014bendo} also found that the 160\/250~$\\mu$m colours appeared offset relative to the star forming regions in the spiral arms, and \\citet{2012foyle} found a related offset in the dust colour temperatures. This implies that the enhancement in PAH emission relative to cold dust emission is related to the enhancement of the temperature of the dust seen at 160~$\\mu$m. To examine this relationship further, we map the 250~$\\mu$m emission from the spiral arms overlaid with contours showing the 160\/250~$\\mu$m ratio in Figure~\\ref{f_m83_overlay}, and we show profiles of the 160\/250~$\\mu$m ratio across the spiral arms in Figure~\\ref{f_m83_line}. These plots show that the 8\/250 and 160\/250~$\\mu$m ratios trace similar structures offset from the dust mass as well as the H$\\alpha$ and 24~$\\mu$m emission associated with star formation. To check how well the 8\/250 and 160\/250~$\\mu$m ratios are correlated, we plot the two ratios in Figure~\\ref{f_8-250-160-250}. The relation has a Pearson correlation coefficient of 0.65, implying that the excitation of PAH emission and the heating of the dust seen at 160~$\\mu$m are, to some degree, linked.\n\n\nIn spiral density waves, as mentioned before, large quantities of gas and dust are expected where the ISM is shocked on the upsteam sides of the spiral arms, star forming regions would be found immediately downstream of the shocks, and older stars would be expected further downstream \\citep[e.g.][]{1969roberts, 1979elmegreen, 2008tamburro, 2009martinezgarcia, 2011sanchezgil}. This could cause offset enhancement of PAH emission relative to spiral arm dust lanes, as seen in Figure~\\ref{f_m83_line} and as also implied by the relation in Figure~\\ref{f_8-250-160-250}, in two possible ways. \n\n\nOne possible explanation is that the dense dust lanes on the upstream edge of the spiral arms severely attenuate the starlight escaping from the photoionising stars within star forming regions, but light easily escapes across the downstream side of the spiral arms where the dust density is lower. Such a geometrical arrangement of the star forming regions relative to the dust would produce the slight offsets between the 24~$\\mu$m emission (tracing obscured star formation) and H$\\alpha$ emission (tracing unobscured star formation) seen in most of the profiles in Figure~\\ref{f_m83_line} and may also explain the downstream areas with enhanced H$\\alpha$ emission in profiles C and F. If photoionising light is primarily travelling asymmetrically from the star forming regions and if the 160~$\\mu$m band traces dust heated by the light escaping from the star forming regions into the diffuse ISM, the 160~$\\mu$m emission would appear enhanced relative to 250~$\\mu$m emission along the downstream side of the spiral arms. Similarly, PAHs mixed in with the dust emitting at 160~$\\mu$m would be excited by the ultraviolet light escaping from star forming regions and appear enhanced relative to the dust emission in the same locations, although the total PAH emission itself will peak along the spiral arms where the total mass of the PAHs is greater (which is also true for dust emission observed in any single band).\n\n\nThe other possible explanation is that the PAHs and the dust seen at 160~$\\mu$m are locally heated by a young, non-ionising population of stars (stars with ages older than 4 Myr) that have left the dusty star forming regions in the spiral arms. As shown by \\citet{1999leitherer}, such a population would still produce a substantial amount of ultraviolet and blue light that could strongly enhance the PAH emission and the temperature of the large dust grains on the downstream side of the arms, but the radiation from these stars may not include higher energy photons that destroy PAHs. In the profiles in Figure~\\ref{f_m83_line}, the ultraviolet emission either peaks downstream of the dust mass or has a profile on the downstream side that is broader than the dust emission profile. Additionally, the ultraviolet emission appears to stronger relative to the H$\\alpha$ emission in most downstream locations. This provides additional support for the possibility that the 8~$\\mu$m emission observed in M83 originates from PAHs excited locally by young, non-ionising stars, although additional analysis would be needed to confirm this.\n\n\n\\begin{figure}\n\\begin{center}\n\\epsfig{file=jonesa_fig_14.ps}\n\\caption{The 8\/250$~\\mu$m surface brightness ratio plotted as a function of the 160\/250~$\\mu$m surface brightness ratio for the 18~arcsec binned data. The data are formatted in the same way as in Figure~\\ref{f_8-24}.}\n\\label{f_8-250-160-250}\n\\end{center}\n\\end{figure}\n\n\nIt is also worth briefly noting that we do not see variations in the 8\/160 or 8\/250~$\\mu$m ratios in M83 that imply a dependence upon radius and hence a dependence upon the metallicity, which decreases with radius \\citep{1994zaritsky}. The variations in the PAH emission with respect to the cold dust emission are driven mainly by local excitation of the PAHs.\n\n\n\n \n\\section{Discussion}\n\\label{s_discussion}\n\n\nThe results show that the 8\/24~$\\mu$m ratio decreases in many regions with high SSFR, in agreement with previous findings from \\citet{2004helou}, \\citet{2005calzetti}, \\citet{2006bendo}, \\citet{2006madden}, \\citet{2007berne}, \\citet{2007lebouteiller}, \\citet{2007povich}, \\citet{2008bendo}, \\citet{2008gordon}, and \\citet{2014calapa}. Even though our analysis is mainly focused on the 7.7~$\\mu$m PAH feature that falls within IRAC channel 4, the results from \\citet{2007lebouteiller}, \\citet{2007povich}, and \\citet{2008gordon} suggest that other PAH emission features may also decrease relative to hot dust emission within star forming regions.\n\n\nOur results showing the correlation of the 8~$\\mu$m emission from PAHs with the far-infrared emission from large dust grains is largely in agreement with the results from \\citet{2002haas}, \\cite{2008bendo}, and \\citet{2014calapa}. However, the general conclusion from these papers had been that the PAHs are mixed with large grains heated by the diffuse ISRF. While NGC~2403 certainly fits that scenario, M83 does not. Instead, PAH emission in M83 is more strongly associated with large dust grains heated either by light escaping from star forming regions and travelling hundreds of pc away from the spiral arms or locally by young stars that produce substantial non-ionising ultraviolet radiation, which was unexpected. Further study would be needed to determine whether such variations in PAH excitation are seen among other nearby galaxies as well.\n\n\nPAH may be excited by different radiation fields in NGC 2403 and M83 because of the differences in the spiral structure in the two galaxies. Because NGC~2403 is a flocculent spiral galaxy, star formation is expected to be triggered by clouds collapsing in local gravitational instabilities \\citep[e.g.][]{2003tosaki,2010dobbs}. In such a scenario, inflowing dust on scales of tens or hundreds of parsecs may be roughly symmetrically distributed around star forming regions, although more modelling work on cloud collapse in flocculent galaxies is needed to confirm this. If the dust is distributed this way, dust near the centres of these shells would absorb the ultraviolet and blue light from the star forming regions inside, and any PAHs within these central regions would be destroyed. Meanwhile, dust and PAHs in the outer shells would be shielded from the light from the star forming regions and would instead be heated by the diffuse ISRF. Also note that the stars in spiral arm filaments in flocculent spiral galaxies are not expected to exhibit any age gradients like grand-design spiral galaxies \\citep{2010dobbs}. If young, non-photoionising stars contribute significantly to PAH excitation, then the relatively homogeneous distribution of these stars may result in the PAHs appearing enhanced over broad areas rather than appearing enhanced near the spiral filaments. In contrast to NGC~2403, M83 is a grand design spiral galaxy in which cold, dusty gas flows into star forming sites mainly from one side of the spiral arms and star forming regions emerge from the other side \\citep[e.g. ][]{1979elmegreen, 1993garciaburillo, 2008tamburro, 2009egusa, 2013vlahakis}. Hence, dust will preferentially be located upstream of individual star forming regions within M83. As described in Section~\\ref{s_m83pah}, PAHs are destroyed within the centres of these star forming regions, but PAHs in the diffuse ISM could be excited by starlight diffusing out of the optically-thin side of the star forming regions. Additionally, multiple studies \\citep[e.g. ][]{2009martinezgarcia, 2011sanchezgil} have found gradients in the ages of the stellar populations downstream of spiral arms in grand design spiral galaxies. PAH emission could appear enhanced downstream of star forming regions if the PAHs are destroyed in photoionising regions but strongly excited locally in regions with soft ultraviolet emission from young, non-photoionising stars. If these geometrical descriptions for the relation between PAHs and excitation sources is accurate, then we should find that PAHs are excited by the diffuse ISRF in other flocculent late-type spiral galaxies while PAHs are excited in regions offset from star forming regions in other grand design spiral galaxies.\n\n\nSome dust emission models \\citep[e.g. ][]{2007draine} and radiative transfer models \\citep[e.g. ][]{2011popescu} show PAHs as excited by the radiation fields from all stellar populations regardless of the hardness or intensity of the fields\\footnote{The version of the \\citet{2007draine} model typically applied to infrared SEDs is usually based on dust heated by a radiation field with the same spectral shape as the local ISRF as specified by \\cite{1983mathis}. When applying the dust model to data, only the amplitude of the radiation field is treated as a free parameter. However, \\citet{2014draine} includes an example of SED fitting with the \\citet{2007draine} model in which the spectral shape of the illuminating radiation field is also allowed to vary.}. Our results show that this approach is an oversimplification of PAH excitation. New refinements in dust emission and radiative transfer models are needed to replicate how PAHs are excited by radiation fields from different stellar populations within different galaxies and how the PAH\/dust mass ratio may change with variations in the hardness of the illuminating radiation field. For example, \\citet{2013crocker} used stellar population synthesis and simplified models of dust and PAH absorption to predict the contributions of different stellar populations to PAH excitation in NGC 628 and found that $\\sim$40\\% of the PAHs are excited by stars $<10$~Myr in age, $\\sim20$\\% are excited by stars with ages of 10-100~Myr, and the remainder are excited by stars $>$100~Myr in age. It is also apparent that PAH excitation changes across spiral density waves (either because of details in the geometry of the star forming regions or because of variations in the stellar populations on either side of the waves), and it would be appropriate to make improvements to radiative transfer models so that they can replicate these effects.\n\n\nThese results have multiple implications for using PAH emission as a proxy for other quantities. While PAH emission cannot be used on sub-kpc scales to measure accurate star formation rates, groups such as \\citet{2008zhu} and \\citet{2009kennicutt} have suggested using globally-integrated PAH emission to estimate extinction corrections for optical star formation tracers such as H$\\alpha$ emission, thus producing extinction corrected global star formation metrics. When PAHs are excited by star forming regions, globally-integrated PAH emission should more accurately represent the light attenuated by dust in star forming regions and should provide fairly accurate star formation rates. When PAHs are excited by the diffuse ISRF, however, the connection between star formation and PAH emission is less clear, and star formation rates calculated using PAH emission could be less reliable.\n\n\nPrevious results showing a relation between PAH emission and far-infrared emission from large dust grains had implied that PAHs could be used as a proxy of dust mass \\citep[e.g.][]{2008bendo}. In cases where the PAHs are associated with dust heated by the diffuse ISRF, this should still be appropriate, although metallicity-related effects would still need to be taken into account. In cases where the PAHs are heated by diffuse light from star forming regions or from young, non-photoionising stars that have emerged from star forming regions, the PAH emission will still scale approximately with dust mass but will also vary depending upon the radiation field from the young stars. In this situation, using PAH emission to trace dust mass may be less reliable. \n\n\nMultiple authors have identified an empirical relation between either radially-averaged or globally-integrated PAH and CO emission \\citep{2006regan,2010bendoco, 2013tan, 2013vlahakis}, implying that the PAHs are, to some degree, correlated with molecular gas. This would be expected if the PAHs also trace the cold dust that is found associated with the molecular gas. However, the relation between PAH and CO emission breaks down on small spatial scales, including in NGC~2403 (\\citealt{2010bendoco}, but also see \\citealt{2013tan}). In M51, \\citet{2013vlahakis} found an offset between PAH and CO emission in the spiral arms, with the molecular gas associated with the cold dust in the locations where material is entering the spiral arms and the PAH emission appearing enhanced further downstream where it is excited by light from young stars. Our results imply that, in future work, we may be able to measure a similar offset between PAH and CO emission in M83 as well as other grand design spiral galaxies. While PAH emission was already shown to be a poor tracer of molecular gas on sub-kpc scales, the phenomenology of PAHs excitation in spiral density waves causes even more problems with using it as a proxy for molecular gas.\n\n\n\\section{Conclusions}\n\\label{s_conclusions}\n\n\nWe identified different relations between PAH emission and far-infrared emission from large dust grains in the two galaxies we examined. For NGC 2403, we find the 8~$\\mu$m emission is most strongly associated with emission from cold dust at 250~$\\mu$m. In particular, we find that the 8\/250~$\\mu$m ratio shows a very strong dependence upon the 3.6~$\\mu$m emission from the total stellar population, indicating that the PAHs are mixed in with the diffuse dust and heated by the diffuse ISRF from the total stellar population. Star forming regions play a much less significant role in the excitation of the PAHs observed in the 8~$\\mu$m band. In contrast, we see in M83 that the PAH emission is more strongly associated with the 160~$\\mu$m emission from large grains heated by star forming regions as implied by the strong correlation between the 160\/250~$\\mu$m and 8\/250~$\\mu$m ratios. This illustrates that PAHs in M83 are excited either by starlight escaping asymmetrically from star forming region so that locations towards the downstream edges of the spiral arms show enhancement in 8~$\\mu$m emission compared to the dust mass or that the PAHs are excited locally by young, non-photoionising stars that have migrated downstream from the spiral arms. \n\n\nMany dust emission and radiative transfer models currently treat PAHs as though they are excited by all radiation fields of all intensities from all stellar populations within the galaxies to which they are applied, much in the same way that emission from silicate and large carbonaceous dust grains is modelled. The results from just these two galaxies show that this assumption is not universally applicable. These dust models need to be adjusted to account for the observational results showing that PAHs are sometimes excited by the diffuse ISRF from the total stellar population and sometimes excited either by young, non-photoionising stars that have emerged from star forming regions or light escaping from these regions and travelling hundreds of pc away (although the PAH emission may be inhibited within the star forming regions themselves). Additionally, some models rely upon using a single SED shape (such as the SED of the local ISRF) for the radiation field illuminating PAHs and dust. Such models cannot account for the possibility that PAH emission could be inhibited if the radiation fields are excessively hard. To properly characterise the PAH excitation, it is necessary to model the PAHs as being illuminated by radiation fields with different spectral shapes.\n\n\nOur data here show differences between the PAH excitation within two galaxies with similar Hubble types. We should expand the analysis to include galaxies with a wider range of Hubble types, including E, S0, and Sa galaxies where the evolved stellar populations may play a larger role in dust heating and therefore may be more responsible for PAH excitation. We should also examine other grand-design spiral galaxies to determine whether PAH emission from the spiral arms in these galaxies is offset from the star forming regions in the same way that it is in M83. This research will lead to a better understanding of PAH excitation mechanisms as well as the relation of PAHs to star formation and large dust grains. \n\n\n\\section*{Acknowledgments}\n\n\nThis work has ben produced as part of a MSc Thesis for the University of Manchester. SPIRE has been developed by a consortium of institutes led by Cardiff Univ. (UK) and including: Univ. Lethbridge (Canada); NAOC (China); CEA, LAM (France); IFSI, Univ. Padua (Italy); IAC (Spain); Stockholm Observatory (Sweden); Imperial College London, RAL, UCL-MSSL, UKATC, Univ. Sussex (UK); and Caltech, JPL, NHSC, Univ. Colorado (USA). This development has been supported by national funding agencies: CSA (Canada); NAOC (China); CEA, CNES, CNRS (France); ASI (Italy); MCINN (Spain); SNSB (Sweden); STFC, UKSA (UK); and NASA (USA). IDL is a postdoctoral researcher of the FWO-Vlaanderen (Belgium).\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{1. Introduction}\nIn recent years the effect of the spin of electrons on the\ntransport properties of nanostructures have been studied\nintensively, both theoretically and experimentally. In the context\nof spin-based electronics (spintronics) the possibility to control\nelectrical currents by a weak external magnetic field using the\nZeeman and\/or the spin-orbit interaction is one of the main goals.\n\nMagnetic materials and especially half-metals are natural sources of\nspin-polarized electrons for spintronics. Transport of spin-polarized\nelectrons in nanostructures (quantum dots, suspended nanowires,\netc.) in external magnetic field results in new phenomena where spin,\ncharge and mechanical degrees of freedom are strongly inter-related.\nIn this new field of investigations (spintromechanics, see\nRef.~\\onlinecite{pulkin}) the presence of a mechanically ``soft\"\nsubsystem results both in a strong enhancement of spintronic effects\nand in magnetic control of the mechanical subsystem in the\nclassical as well as in the quantum transport regimes.\n\nVibrational effects are known to be important for the transport\nproperties of molecular transistors (see, e.g., the reviews in\nRefs.~\\onlinecite{galperin} and \\onlinecite{krive}). In single-molecule\ntransistors a strong electron-vibron coupling was observed\nin a $C_{60}$-based transistor with nonmagnetic (gold)\nleads \\cite{park}. The measured current-voltage characteristics in\nthis experiment revealed low-energy periodic step-like features.\nThey were interpreted as a signature of vibron-assisted electron\ntunneling via the fullerene molecule. Experimental $I-V$ curves were\ntheoretically explained \\cite{shekhter,flensberg} in the frames of a\nsimple model of a single-level quantum dot strongly coupled to a\nsingle vibrational mode and weakly coupled to the source and drain\nelectrodes.\n\nLater on $C_{60}$-based molecular transistors with magnetic (Ni)\nleads were fabricated \\cite{pasupathy}. In samples where the\ntunneling coupling to the ferromagnetic electrodes were\nrelatively strong ($\\sim$ tens of meV), Kondo-assisted tunneling via the\n$C_{60}$ molecule was observed. These measurements also proved the\npresence of a strong inhomogeneous magnetic field produced by the\nferromagnetic electrodes in the nano-gap between them. In samples with weak\ntunneling couplings the usual Coulomb blockade picture for a single-electron \ntransistor was observed.\n\nIn the present paper we formulate the conditions for the\nappearance of a vibrational instability of a fullerene molecule\nsuspended in the gap between two magnetic leads with opposite\nmagnetization. Electron shuttling of spin-polarized electrons\nproduced by magnetic (exchange) forces was\npredicted in Ref.~\\onlinecite{kul} for the case of 100\\%\npolarization of the leads. In this limit (realized for\nhalf-metals) the electric current is blocked (spin blockade) in\nthe absence of spin-flips induced by, e.g., an external magnetic\nfield. It was shown that in the absence of dissipation in the\nmechanical subsystem such a magnetic field triggers a shuttle\ninstability even for vanishingly small fields \\cite{kul}. In the\npresence of dissipation a threshold magnetic field is determined\nby the rate of dissipation and it is small for a weak dissipation.\n\nOne of\nour aims here is to develop a theory of\nmagnetic shuttling for conditions corresponding to the experimental\nset-up of Ref.~\\onlinecite{pasupathy}, where the electrons in the\nferromagnetic leads were partially polarized ($\\sim 30\\%$). The\nabsence of a spin blockade in this case qualitatively changes the criterion for\nelectron shuttling. We will show that even in the absence of mechanical\ndissipation, a shuttling regime of electron transport occurs in a\nfinite interval of external magnetic field strengths,\n$H_{\\text{min}}0$).\n\nIt is evident from Eq.~(\\ref{10}), which is biquadratic in the magnetic field ($h$),\nthat a shuttle instability occurs in a finite interval\nof magnetic fields, $h_{\\text{min}}H_{\\text{min}}$; compare Fig.~1) vs. spin polarization $\\eta$\nfor different values of the normalized tunneling rate\n$\\Gamma\/\\hbar\\omega$ of majority spin electrons (solid curve:\n$\\Gamma\/\\hbar\\omega=10$; dashed curve: $\\Gamma\/\\hbar\\omega=3$;\nshort-dashed curve: $\\Gamma\/\\hbar\\omega=0.1$).}\n\\end{figure}\n\nThe appearance of an upper and a lower critical magnetic field has a\nsimple physical explanation. When $\\mu H$ is the largest energy\nscale in our problem, $\\mu H\\gg \\Gamma,\\,\\hbar\\omega$, the fast\nprecession of the electron spin of the dot in a perpendicular external magnetic field\nnullifies the average spin and the magnetic shuttle instability\ndisappears. To estimate the upper field one may compare the\ncharacteristic spin precession frequency, $\\mu\nH_{\\text{max}}\/\\hbar$, with the electron tunneling rate,\n$\\Gamma\/\\hbar$, or the frequency of vibrations $\\omega$. That is\n$\\mu H_{\\text{max}}\\sim\\text{max}\\left(\\Gamma, \\hbar\\omega\\right)$.\nThe lower critical field can be readily estimated for\na high degree of spin polarization, $1-\\eta\\ll 1$.\nIn this case we have to compare the average time between spin\nflips, $\\tau_f$, induced by a constant magnetic field $H$ in the\npresence of an electron tunneling coupling $\\Gamma$ with the\ncharacteristic life-time of minority spin electrons on the dot,\n$\\sim \\hbar\/\\gamma$. The spin-flip rate $\\nu_f$ in weak magnetic\nfields $H$ can be estimated by perturbation theory\nwith the result that $\\hbar\\nu_f\\sim(\\mu H)^2\/\\text{max}(\\Gamma,\n\\hbar\\omega)$. Therefore the lower magnetic field is strongly\nsensitive to spin polarization,\n\\begin{equation}\\label{15}\n\\mu H_{\\text{min}}\\sim\n\\sqrt{\\Gamma\\gamma}\\,\\text{max}(\\Gamma,\\hbar\\omega)\\sim\n\\sqrt{1-\\eta}\\,\\text{max}(\\Gamma,\\hbar\\omega),\n\\end{equation}\nand disappears for 100\\% spin-polarized electrons ($\\eta=1)$.\n\nNext we estimate the maximum rate of (exponential) increase,\n$r_m=-\\text{Im}\\{\\Omega (H_{\\text{opt}})\\}$, of the QD oscillation\namplitude in the shuttle regime.\nIn the adiabatic limit, $\\Gamma\\gg \\hbar\\omega$,\none finds that $g\\mu H_{\\text{opt}}\\simeq 0.4\\, \\Gamma$ and that\n\\begin{equation}\\label{16}\nr_m\\simeq C\\frac{\\omega J}{\\Gamma}\\left(\\frac{x_0}{l}\\right)^2,\n\\end{equation}\nwhere $C\\sim 0.1$ is a small numerical factor. In the case\n$\\Gamma\\ll \\hbar\\omega$, which we are interested in here, the\nmaximum rate is realized when $g\\mu H_{\\text{opt}}\\simeq\n\\hbar\\omega$, corresponding to\n\\begin{equation}\\label{17}\nr_m\\simeq\n\\frac{\\Gamma}{\\hbar}\\frac{J}{\\hbar\\omega}\\left(\\frac{x_0}{l}\\right)^2\\,,\n\\end{equation}\nwhere we omit a numerical factor of the order of one.\n\nIn the presence of dissipation in the mechanical subsystem, which\ncan be described by adding a phenomenological friction term\n$\\gamma_d \\dot{x}_c(t)$ to the equation of motion (\\ref{8})\n($\\gamma_d=\\omega\/Q$, where $Q$ is the quality factor), the\nshuttling regime appears when $r_m>\\omega\/Q$. Therefore electron\nshuttling in a $C_{60}$-based molecular transistor with magnetic\nelectrodes could be realized if the quality factor $Q$ of the\nmechanical resonator obeys the inequality\n\\begin{equation}\n\\label{18}\nQ>Q_{\\text{opt}}=\\frac{\\left(\\hbar\\omega\\right)^2}{J\\Gamma}\n\\left(\\frac{l}{x_0}\\right)^2.\n\\end{equation}\n\nFor the experimental setup in Ref.~\\onlinecite{park}, where\nfullerene vibrations were observed, the factor\n$\\left(l\/x_0\\right)^2\\simeq 10^3$ and\n$\\Gamma\\ll\\hbar\\omega\\sim$~5~meV (one can estimate\n$\\Gamma\\sim$~0.1 -- 0.5~meV from the maximal current measured in\nRef.~\\onlinecite{park}). In the $C_{60}$-based transistor with\nmagnetic (Ni) leads $J\\sim\\Gamma\\sim$~10~meV (see\nRef.~\\onlinecite{pasupathy}). From Eq.~(\\ref{18}) one can estimate\nthat the required quality factor is $Q\\geq 10^3-10^4$. However the\noptimal external magnetic field in this case,\n$H_{\\text{opt}}\\simeq$~50~T, is too high. Instead, we therefore\nestimate $Q$ for magnetic fields in the vicinity of the lower\ncritical magnetic field $H\\geq H_{\\text{min}}$ where magnetic\nfields for a very high degree of electron spin polarization ($\\sim\n99\\%$) could be of the order of a few tesla. In this case\n($\\hbar\\omega\\gg \\Gamma$, $1-\\eta\\ll 1$)\n\\begin{equation}\\label{19}\nr(\\eta)\\simeq \\omega\\frac{J \\Gamma\n(1-\\eta)}{\\Gamma^2+4(1-\\eta)(\\hbar\\omega)^2}\\left(\\frac{l}{x_0}\\right)^2.\n\\end{equation}\nAssuming that $\\Gamma\\simeq\\sqrt{1-\\eta}\\,\\hbar\\omega$ we find\n$Q\\sim Q_{\\text{opt}}\/(1-\\eta)$.\n\n\\section{4. Conclusions}\nIn summary we have considered the feasibility of observing magnetically\ndriven single-electron shuttling under\nrealistic conditions\ncorresponding to an already experimentally realized $C_{60}$-based\nsingle-molecule transistor with magnetic leads. The main\nrequirement for magnetic shuttling is the presence of an external\nmagnetic field that induces electron\nspin flips. We have shown that the optimal magnetic field, defined\nas the field that maximizes the rate of increase of the shuttling\namplitude, is determined by the vibration frequency $\\omega$. For\nfullerene-based single-electron transistors this frequency could\nbe in the THz region \\cite{park}\nwith\ncorresponding\nmagnetic fields\nin the region of several tenths of teslas. For magnetic electrodes\nwith a very high degree of spin polarization one needs less strong\n(by an order of magnitude) magnetic fields. However, the quality\nfactor of the corresponding mechanical resonator has to be\nexceptionally high, $Q\\geq 10^5$.\n\n{\\bf Acknowledgements:} Financial support from the Leading Foreign\nResearch Institutes Recruitment Program (2009-00514) of NRF,\nKorea, and the Swedish Research Council (VR) is gratefully\nacknowledged. OI, IK and SK acknowledge financial support from\nNational Academy of Science of Ukraine, Grant No 4\/15--N. IK and\nSK thank the Department of Physics at the University of Gothenburg\nand the Department of Physics and Astronomy at Seoul National\nUniversity for their hospitality.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{Introduction}\n\nComplex systems of a dispersed phase in a solid matrix can behave very differently from one of their components taken alone. Their broad range of properties explains that examples of dispersions such as composites~\\cite{matthews1999composite} or porous media~\\cite{coussy2011mechanics} are widespread in nature and in the industry. In all dispersions, interfacial forces can appear at the boundary between the dispersed phase and the continuous matrix. A coupling of surface tension forces to the bulk elasticity of a solid has been evidenced in soft systems like biological tissues~\\cite{clements1961pulmonary}, or through the deformation of soft substrates like polymers at the contact line with a drop resting on the solid~\\cite{pericet2008effect}. Capillary forces also affect the overall mechanical properties of nanoporous media~\\cite{duan2005size}. For larger pores, because of the hardness of the matrix in usual porous media, the influence of interfacial effects on the overall properties of the saturated material is negligible~\\cite{dormieux2006microporomechanics}. Dispersions in softer materials could allow for observable coupling of interfacial forces to the bulk elasticity of the solid at larger scales than the nanometer. Many dense suspensions~\\cite{coussot2005rheometry} of geological interest, like muds, or with industrial applications, like fresh concrete or emulsions, behave as soft elastic solids below a critical level of stress~\\cite{coussot2012rheophysique}. To study the role of surface tension forces in soft elastic materials, we investigate the elastic behaviour of dispersions of bubbles in concentrated emulsions. Those aerated emulsions, which have applications in the food~\\cite{vanAken2001333} and cosmetic~\\cite{balzer1991alkylpolyglucosides} industry, have been the subject of stability and rheology studies~\\cite{C1SM06537H, PhysRevLett.104.128301, kogan2013mixtures}. However, their overall elastic properties have not yet been studied in detail.\n\nIn dispersions of bubbles in a soft material, coupling between the elasticity of the matrix and capillary effects is expected to occur through bubble deformation. The elastic deformation of the matrix tends to deform the bubbles and surface tension forces will thus act to minimize the area of the bubble by maintaining a spherical shape. The limit case of negligible surface tension forces is a soft porous medium. Theoretical work shows that adding holes in a solid softens it~\\cite{dormieux2006microporomechanics}. In the limit case of predominant surface tension forces compared to the matrix elasticity, a bubble should no longer be deformable and should behave as a rigid inclusion with no shear stiffness. Experimental and theoretical work have shown that rigid beads in a soft solid strengthen the solid~\\cite{mahaut2008yield}. The case of rigid bubbles is similar except for the boundary condition, changed from no-slip for beads to full-slip for bubbles. Theoretical models in the dilute limit predict a strengthening of the dispersion when adding rigid bubbles~\\cite{dormieux2006microporomechanics}. Between those two limit cases, more work is needed to investigate the elastic response of the soft aerated solid. In this work, we restrain to the range of gas volume fraction $\\phi<50\\%$, so that we do not consider foams of those materials, in which the bubbles are deformed by geometrical constraints. We design model systems and appropriate experimental methods that allow us to measure the shear modulus of dispersions of monodisperse bubbles embedded in a medium of chosen elasticity. We compare our experimental results to estimates of the elastic modulus through a micro-mechanical approach. \n\\section*{Experimental aspects}\nThe dispersion matrices we choose are concentrated oil in water emulsions of shear moduli ranging from 100 to 1000Pa. Concentrated emulsions behave as soft elastic solids for stresses well below their yield stress~\\cite{mason1995elasticity}. In the experimental systems, unless otherwise indicated, the radius of the droplets is around 1 to 2$\\mathrm{\\mu m}$ (the poydispersity is around 20\\%), which, at the considered gas volume fractions, should ensure that there is scale separation between the drops and the bubbles, and consequently validate the use of the emulsion as an elastic continuous medium embedding the bubbles~\\cite{goyon2008spatial}. In all the systems, the yield stress of the emulsion is high enough to ensure that no bubble rise occurs at rest or during measurements~\\cite{Dubash2007123}. Most dispersions are prepared by gently mixing the emulsion with a separately produced monodisperse foam. The foams are obtained by blowing nitrogen plus a small amount of perfluorohexane ($\\mathrm{C_{6}F_{14}}$) through a porous glass frit or through needles: we are able to produce nearly monodisperse foams with average bubble radii $R_{b}$ ranging from 40$\\mathrm{\\mu m}$ to 800$\\mathrm{\\mu m}$. Coarsening is strongly reduced by the presence of $\\mathrm{C_{6}F_{14}}$~\\cite{gandolfo1997interbubble}, meaning that the bubble size is stable during measurements. The continuous phase of the foam is the same as the one in the emulsion, ensuring that mixing is easy and does not induce any chemical effect in the dispersions. The mixing with the foam adds a small amount of continuous phase to the emulsion. To ensure that for a series of experiments at different $\\phi$ in a given emulsion, the elastic modulus of the matrix in the dispersions remains the same, we add controlled amounts of pure continuous phase in order to reach the same the oil volume fraction in the emulsion~\\cite{PhysRevLett.104.128301, kogan2013mixtures}. An example of a dispersion of bubbles in an emulsion is shown on figure~\\ref{fig:photo}. The composition of all the tested emulsions is indicated in table~\\ref{tab:recap_systs}, and illustrates the variety of chemical compositions, surface tensions and elastic properties of the matrix that were used to perform the study.\n\\begin{figure}\n\\centering\n\\includegraphics[scale=0.6]{graph1_leg1_2.png}\n\\caption{Microphotograph of a dispersion of monodisperse bubbles (R=200$\\mu$m) in emulsion (2). The emulsion is transparent, allowing for the visualisation of in-depth bubbles that thus do not have the same apparent radius. Inset: close-up of droplets of emulsion (2) at the interface with a bubble.\\label{fig:photo}}\n\\end{figure}\n\nThe shear modulus of the dispersions is measured on a control stress rheometer by imposing small amplitude oscillations at a frequency of typically 1Hz. The oscillatory stress is chosen to be well below the yield stress of the systems, so that the oscillations are performed in the linear elastic regime of each material. At this frequency, the loss modulus of the systems is negligible. The geometry used to perform the rheometrical measurements is chosen according to the bubble size : for $R_{b}\\le 50\\mathrm{\\mu m}$, the material is sheared between parallel plates (radius $R$=25mm, gap $h$=2.5mm). The planes are serrated to prevent slippage of the dispersion~\\cite{coussot2005rheometry}. Dispersions containing bigger bubbles require a larger thickness of sheared material and are studied in Couette-like devices : for $50\\mathrm{\\mu m}< R_{b}< 800\\mathrm{\\mu m}$, we use a vane in cup (exceptionally a serrated bob in cup) geometry (inner radius $R_i$=12.5mm, outer radius $R_o$=18mm), and for $R_{b}\\ge 800\\mathrm{\\mu m}$, we use vane in cup geometries (either $R_i$=12.5mm and $R_o$=25mm or $R_i$=22.5mm and $R_o$=45mm). \n\\begin{center}\n\\begin{table*}\n{\\renewcommand{\\arraystretch}{1.5}\n\\renewcommand{\\tabcolsep}{0.2cm}\n\\begin{tabular}{|l|c|c|c|c|}\n\\hline\n& \\textbf{oil - vol. fraction} & \\textbf{continuous phase} & \\textbf{$\\mathbf{G'(0)}$ (Pa)} & \\textbf{$\\mathbf{\\gamma}$ (mN.$\\mathbf{m^{-1}}$)}\\\\\n\\hline\nemulsion (1a) & silicon (V20) - 75\\% & Forafac$\\textregistered$ ($\\mathrm{{DuPont}^{TM}}$) 4\\% w. in water & 230 &15.5 $\\pm$ 0.1 \\\\\n\\hline\nemulsion (1b) & silicon (V20) - 73\\% & Forafac$\\textregistered$ ($\\mathrm{{DuPont}^{TM}}$) 4\\% w. in water & 163 &15.5 $\\pm$ 0.1 \\\\\n\\hline\nemulsion (2) & silicon (V350) - 79\\% & TTAB 3\\% w. in water\/glycerol 50\/50 w\/w & 650 & 35.5 $\\pm$ 0.1 \\\\\n\\hline\nemulsion (3) & dodecane - 73\\% & SDS 2.7\\% w. in water & 285 & 36 $\\pm$ 1 \\\\\n\\hline\nemulsion (4) & silicon (V350) - 70\\% & TTAB 3\\% w. in water\/glycerol 36\/64 w\/w & 799 & 35 $\\pm$ 1 \\\\\n\\hline\n\\end{tabular}}\n\\caption{Synthetic description of all the emulsions used as matrices in the bubble dispersions: nature and volume fraction of the oil dispersed phase, composition of the aqueous continuous phase (including the surfactant) and relevant physical constants for the determination of the capillary number: elastic modulus of the matrix, and surface tension between air and the continuous phase. The composition given is the one of the matrix actually embedding the bubbles.\\label{tab:recap_systs}}\n\\end{table*}\n\\end{center}\n\\section*{Results}\nWe start by studying the influence of the bubble radius $R_b$. In this aim, we prepare dispersions of bubbles in emulsion (3) (see table~\\ref{tab:recap_systs} for details). In a first series of experiments, we add bubbles of $R_b=(50\\pm10)\\mathrm{\\mu m}$ ($10\\mathrm{\\mu m}$ being the width of the volume-weighed bubble radius distribution) at various gas volume fractions $\\phi$ in the emulsion. Those bubbles are slightly more polydisperse than is generally used for this study, because of the foam production technique. The shear modulus $G'(\\phi)$ of the dispersions is measured to be slightly decreasing with $\\phi$. This result is reported in dimensionless quantities $\\hat{G}(\\phi)=G'(\\phi)\/G'(0)$ as a function of $\\phi$ on figure~\\ref{fig:1}. We then prepare dispersions of larger bubbles in emulsion (3): a series with $R_b=(143\\pm17)\\mathrm{\\mu m}$ and another one with $R_b=(800\\pm40)\\mathrm{\\mu m}$. The results for $\\hat{G}(\\phi)$ are also reported on figure~\\ref{fig:1}. The measurements show that the larger the bubbles, the softer the dispersion. This result can be understood as a manifestation of a simple physical effect, already evidenced in~\\cite{kogan2013mixtures} (see also~\\cite{rust2002effects} and~\\cite{llewellin2002rheology} for the effect of bubble deformation on the viscosity of bubbly Newtonian fluids), that the interfacial energy to volume ratio is lower in larger bubbles, resulting in least bubble resistance to deformation. \n\\begin{figure}\n\\centering\n\\includegraphics[scale=0.29]{graph1.pdf}\n\\caption{Dimensionless elastic modulus $\\hat{G}$ as a function of the gas volume fraction $\\phi$ for dispersions with three different bubble radii $R_b$ in emulsion (3) [see legend]. The full lines are the computed $\\hat{G}_{homog}(\\phi)$ for $Ca=0.23$ (dark blue), $Ca=0.57$ (green) and $Ca=3.2$ (pink); experimentally measured $Ca$: $0.23\\pm0.05$, $0.57\\pm0.08$, $3.2\\pm0.4$. Inset: $\\hat{G}$ as a function of $\\phi$ for dispersions of $R_b\\approx 150\\mathrm{\\mu m}$ in \\textcolor{spinach}{$\\bullet$} emulsion (3) and \\textcolor{orange}{$\\blacklozenge$} emulsion (4). The full lines are the computed $\\hat{G}_{homog}(\\phi)$ for $Ca=0.57$ (green) and $Ca=1.65$ (orange); experimentally measured $Ca$: $0.57\\pm0.08$, $1.65\\pm0.15$.\\label{fig:1} }\n\\end{figure}\nWe now keep the bubble size constant, and vary the elastic modulus of the matrix: we prepare dispersions of $R_b=143\\mathrm{\\mu m}$ bubbles in emulsion (3) and of $R_b=(150\\pm10)\\mathrm{\\mu m}$ bubbles in emulsion (4) (see table~\\ref{tab:recap_systs}). In the two series of experiments, the bubble sizes are close and the surface tension is similar, but $G'(0)$ is almost three times higher in emulsion (4). $\\hat{G}(\\phi)$ is plotted for both systems on the inset in figure~\\ref{fig:1}. As observed on the previous suspensions, $\\hat{G}(\\phi)$ is a decreasing function of $\\phi$, and this decrease is all the stronger as $G'(0)$ is high.\nTo quantify the competition between the matrix elasticity and the bubble's resistance to deformation, we introduce a capillary number \n\\begin{equation}\nCa=\\frac{G'(0)}{2\\gamma\/R_b}\n\\label{eq:Ca}\n\\end{equation} \nwhich compares the shear modulus of the dispersion medium to the interfacial stress scale, the capillary pressure in the bubbles. This capillary number is equally affected by an increase in $R_b$ or a decrease in $G'(0)$. To quantify the relevance of $Ca$ on the overall elastic response of the dispersion at a given $\\phi$, we perform two series of experiments with close $R_b$, but very different capillary pressure because of very different surface tension, and we adjust the elastic modulus in one of the emulsions so that $Ca$ is similar in both systems. The two experimental systems are as follow: the first one is dispersions of $R_b=143\\mathrm{\\mu m}$ of radius bubbles in emulsion (3), which leads to $Ca=0.57\\pm0.08$, and the second one dispersions of (129$\\pm$10)$\\mathrm{\\mu m}$ of radius bubbles in emulsion (1b) for which $Ca=0.70\\pm0.08$. We observe that the measured values of $\\hat{G}(\\phi, Ca)$ are very close, as can be seen on figure~\\ref{fig:2}. The value of $Ca$ unequivocally determines the elastic behaviour of the dispersion at a given $\\phi$.\n\\begin{figure}\n\\centering\n\\includegraphics[scale=0.30]{graph2.pdf}\n\\caption{Effect of a change in the surface tension: dimensionless elastic modulus $\\hat{G}$ as a function of $\\phi$ for dispersions of \\textcolor{spinach}{$\\bullet$} $R_b=143\\mathrm{\\mu m}$ bubbles in emulsion (3) and \\textcolor{red}{$\\bigstar$} $R_b=129\\mathrm{\\mu m}$ bubbles in emulsion (1). The surface tension is much lower in emulsion (1b), but $G'(0)$ has been chosen to get close values for $Ca$ in both systems. This experimentally measured $Ca$ are $0.57\\pm0.08$ and $0.70\\pm0.08$. The full line is the computed $\\hat{G}_{homog}(\\phi, Ca)$ at $Ca=0.63$, which is compatible with both systems, given the uncertainty on the value of $Ca$.\\label{fig:2}}\n\\end{figure}\n\nWe now investigate the limit value of $Ca\\to \\infty$, for which surface tension forces are negligible and the bubbles can be assimilated to holes in the matrix. This is the case in usual porous materials. $\\hat{G}(\\phi, Ca\\to\\infty)$ can then be computed in the dilute limit~\\cite{dormieux2006microporomechanics}: $\\hat{G}(\\phi, Ca\\to\\infty)=1-\\frac{5}{3}\\phi$. To compare this prediction to experimental data, we design a system in which surface tension effects are bound to be poor: we include the biggest bubbles of this study, of radius (1 $\\pm$ 0.1) mm, in emulsion (2), which has a high elastic modulus (see table~\\ref{tab:recap_systs}). Note that for this system the bubbles are injected directly in the emulsion in a tee-junction in a milli-fluidic device. As before, we measure the elastic modulus of the dispersion at various $\\phi$. The experimental data points for the dimensionless modulus $\\hat{G}(\\phi)$ are compared to the dilute limit for dispersions of holes in an elastic medium on figure~\\ref{fig:3}. We observe that $\\hat{G}$ is a decreasing function of $\\phi$. The exact value of $Ca$ in this system is $9.0\\pm1.2$. The theoretical dilute limit for spherical holes in an elastic medium is already a good estimate of $\\hat{G}(\\phi\\to 0, Ca)$at $Ca\\sim10$.\n\\begin{figure}\n\\centering\n\\includegraphics[scale=0.30]{graph3.pdf}\n\\caption{Remarkable values of $Ca$: $\\mathbf{Ca\\to \\infty}$: $\\hat{G}(\\phi)$ for dispersions of \\textcolor{lgtblue}{$\\blacktriangledown$} $R_b$=1mm bubbles in emulsion (2). The full line is the computed $\\hat{G}_{homog}(\\phi, Ca)$ at $Ca=9.0$ (light blue); experimentally measured $Ca$: $9.0\\pm1.2$. $\\mathbf{Ca\\approx 0.25}$: dimensionless elastic modulus $\\hat{G}$ for dispersions of \\textcolor{darkblue}{$\\bullet$} $R_b$=50$\\mu$m bubbles in emulsion (3) and \\textcolor{gray}{$\\bigstar$} $R_b$=41$\\mu$m in emulsion (1a). The full lines are the computed $\\hat{G}_{homog}(\\phi, Ca)$ at $Ca=0.23$ (dark blue) and $Ca=0.30$ (grey); experimentally measured $Ca$: $0.23\\pm0.05$, $0.30\\pm0.05$. The dashed lines are the dilute limits for rigid (top) and fully deformable (bottom) spheres, with a full slip boundary condition.\\label{fig:3} }\n\\end{figure}\n\nThe limit case of $Ca\\to 0$ also leads to a simplification: the bubbles are stiff compared to the matrix and the dispersion is made of rigid spheres with a full slip boundary condition in an elastic medium. The theoretical dilute limit can be computed as $\\hat{G}(\\phi, Ca=0)=1+\\phi$~\\cite{dormieux2006microporomechanics} and is plotted on figure~\\ref{fig:3}. An experimental validation of this limit with our systems may be biased, because increasing the capillary pressure would mean reducing $R_b$, and we might no longer assume scale separation between the bubbles and the oil droplets. We thus choose not to investigate this limit.\nFrom $Ca\\to 0$ to $Ca\\to \\infty$, $\\hat{G}(\\phi, Ca)$ turns from an increasing to a decreasing function of $\\phi$. Between these two extreme values, we have observed on the dispersion of the smallest bubbles in emulsion (3), already discussed above on figure~\\ref{fig:1}, that $G'(\\phi)$ has little variation with $\\phi$ and is comparable to $G'(0)$. The capillary number in this system is $Ca=0.23\\pm0.05$. To further check the peculiarity of this value of $Ca$, we prepare another dispersion of small bubbles $R_b$=(41$\\pm$5)$\\mathrm{\\mu m}$ in emulsion (1a) (see table~\\ref{tab:recap_systs}), with a close capillary number: $Ca=0.30\\pm0.05$. $\\hat{G}(\\phi, Ca)$ for both dispersions of small bubbles is plotted on figure~\\ref{fig:3}. We observe that in both systems, $\\hat{G}(\\phi, Ca)$ exhibit little dependence on the gas volume fraction, and is of order 1. The non-perturbative effect of bubble addition in the matrix can be seen as an experimental validation of previous micro-mechanical calculations~\\cite{palierne1991rheologica, doi:10.1061\/9780784412992.224} which have shown that a spherical bubble of radius $R_b$ and surface tension $\\gamma$ in an elastic medium can be described as an equivalent elastic sphere of radius $R_b$ and no surface tension. Indeed, the deformation of the bubble under a strain $\\epsilon$ leads to an increase in the bubble area that is proportional to $\\epsilon^2$. The stored interfacial energy scales as $\\gamma \\epsilon^2$, which is analogous to an elastic energy. If the equivalent elasticity of the sphere is equal to that of the matrix, the bubbles are non-perturbative and $\\hat{G}(\\phi)= 1$. The equivalent elasticity of a bubble in a matrix $G'(0)$ can be written as a function of $G'(0)$ and $Ca$~\\cite{palierne1991rheologica, doi:10.1061\/9780784412992.224}:\n\\begin{equation}\nG^{eq}=G'(0)\\frac{8}{3+20Ca}\n\\end{equation}\nwith $Ca$ defined in equation~\\ref{eq:Ca}. The expression of $G^{eq}$ shows that $Ca$ introduced above does not actually compare the equivalent elasticity of the bubble to that of the matrix. This explains why the overall elasticity of the dispersion is unperturbed by the presence of the bubbles for a somewhat unnatural value of $Ca$ around 0.2 to 0.3, which can be understood thanks to the computation of $G^{eq}$: $G^{eq}=G'(0)$ for $Ca=1\/4$. Relying on the equivalent elastic sphere model for a bubble, a micro-mechanical approach allows to compute the overall elastic properties of the dispersions at finite $Ca$. The overall elasticity of a composite material made of elastic spheres in a matrix of another elastic material in the semi-dilute limit can be computed as a function of $Ca$ and $\\phi$, in the framework of the Mori-Tanaka scheme~\\cite{doi:10.1061\/9780784412992.224, palierne1991rheologica}: \n\\begin{equation}\n\\hat{G}_{homog}(\\phi, Ca)=1-\\frac{\\phi(4Ca-1)}{1+\\frac{12}{5}Ca-\\frac{2}{5}\\phi(1-4Ca)}\n\\end{equation}\nNote that this expression is compatible with the previously discussed limits of $Ca \\to \\infty$, $Ca \\to 0$ and $Ca=1\/4$. Predictions of the model for $\\hat{G}(\\phi, Ca)$ at the experimentally measured $Ca$ are plotted in full coloured lines on figures~\\ref{fig:1} to~\\ref{fig:3}. A comparison of $\\hat{G}_{homog}(\\phi, Ca)$ to $\\hat{G}(\\phi, Ca)$ for all the systems we used, at all tested gas volume fractions is presented on figure~\\ref{fig:4}. Experimental measurements and computations are generally in good agreement all over the range of systems we investigated. \n\\begin{figure}\n\\centering\n\\includegraphics[scale=0.3]{graph4.pdf}\n\\caption{Consistency between $G'$ and ${G'}_{homog}$ for all tested systems. $\\bigstar$: emulsion (1), $\\blacktriangledown$: emulsion (2), $\\bullet$: emulsion (3), $\\blacklozenge$: emulsion (4).\\label{fig:4} }\n\\end{figure}\n\nAs we have seen that the two dimensionless parameters $Ca$ and $\\phi$ are enough to understand and predict the elasticity of the dispersions, we now plot $\\hat{G}(\\phi, Ca)$ as a function of $Ca$, for 4 values of $\\phi$, on figure~\\ref{fig:5}. As can be noticed on the graphs~\\ref{fig:1} to~\\ref{fig:3}, the achieved values of $\\phi$ are different for all tested systems. To be able to plot $\\hat{G}(\\phi,Ca)$ at a given $\\phi$, we interpolate the experimental data at the exact values of $\\phi$ used for plotting on figure~\\ref{fig:5}. The full lines are computations of $\\hat{G}_{homog}(\\phi, Ca)$. As expected, $\\hat{G}(\\phi, Ca)$ is a decreasing function of $Ca$: higher values of $Ca$ correspond to more deformable bubbles that lower the overall elastic modulus of the dispersions. The non-perturbative effect of the bubbles for $Ca=1\/4$ is evidenced by the crossing of $\\hat{G}_{homog}(\\phi, Ca)$ at 1 for $Ca=0.25$, whatever the gas volume fraction. Below this value, the increase of $\\hat{G}_{homog}(\\phi, Ca)$ is consistent with previously discussed theoretical limits, but could not be investigated with our experimental systems. The series of data points at $Ca=0.23\\pm0.05$ does not fit in the increasing $\\hat{G}_{homog}(\\phi, Ca)$ regime, perhaps because of broader polydispersity: the uncertainty on the value of $Ca$ mainly arises from the width of the bubble radius distribution and the value of $Ca$ for the largest bubbles in the foam is for instance higher than 0.25. A model computing $\\hat{G}_{homog}(\\phi, Ca)$ as a function of the whole measured distribution of radii may better represent the experimental data.\n\\begin{figure}\n\\centering\n\\includegraphics[scale=0.32]{graph5.pdf}\n\\caption{Dimensionless elastic modulus $\\hat{G}$ as a function of $Ca$ for four different values of the gas volume fraction. The dots are interpolated experimental data points, the full lines are $\\hat{G}_{homog}(\\phi, Ca)$. \\label{fig:5} }\n\\end{figure}\n\n\\section*{Conclusions}\nAs a conclusion, we have designed model systems in which precise control of the bubble stiffness and the matrix elasticity allows to experimentally determine the elastic modulus of dispersions of bubbles in a soft matrix. The results show that the addition of bubbles leads to a softening of the dispersion that is finely tuned by the capillary number. Those model systems enable us to compare our experimental results to estimates of the shear modulus through a micro-mechanical approach. Precise control of the capillary number provides experimental data validating the theoretical description of the bubble as an equivalent elastic sphere. The good agreement between theoretical and measured elastic moduli confirms the generality of the study, which demonstrates that $\\phi$ and $Ca$ entirely govern the overall response of the dispersions. More work remains to do in the limit of small capillary numbers ($Ca<1\/4$), for which the predicted regime of increasing $\\hat{G}(\\phi, Ca)$ could not be experimentally investigated. Emulsions may not be the most relevant matrices for that study and dedicated experiments on even softer media could be more appropriate. \n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nUltraluminous X-ray sources (ULXs) are off-nuclear X-ray sources in galaxies with an X-ray luminosity above the Eddington luminosity of a 10 \\msun~black hole, or $\\sim 10^{39}$ erg s$^{-1}$. Several scenarios have been proposed to explain their high luminosities. Geometrical \\citep{king01} or relativistic \\citep{kording02} beaming may allow for the observation of super-Eddington luminosities. In some sources there is evidence for a new state with truly super-Eddington accretion rates \\citep{gladstone09}. Recent investigations into the X-ray luminosity function (XLF) of ULXs suggest that the majority of ULXs are formed by the high-luminosity tail of X-ray binaries (XRBs) and contain stellar mass black holes (BHs) \\citep{swartz11, mineo12}. The best-fitting XLF exhibits a cut-off around $10^{40}$ erg s$^{-1}$, suggesting that this may be the effective upper limit for the luminosity of the most massive objects in the sample.\n\nHowever, \\citet{swartz11} argue that if they extrapolate their best-fitting XLF, based on a complete sample of ULXs within 14.5 Mpc, to larger distances, they can not explain the relatively large number of ULXs with luminosities above $10^{41}$ erg s$^{-1}$~that have been observed. Hence these ULXs may belong to a different class of objects. These sources would have to exceed the Eddington limit by more than a factor 100 if they contained stellar-mass black holes. They may be good candidates to host the predicted but thus far elusive intermediate-mass black holes (IMBHs). These IMBHs may form in the collapse of a dense stellar cluster \\citep{portegieszwart02}, the collapse of population III stars in the early universe \\citep{madau01} or the direct collapse of massive gas clouds \\citep{begelman06}. They may reside in globular clusters \\citep{maccarone07}, but conclusive evidence for their existence there has not yet been found. For a review on IMBHs and their formation mechanisms see \\citet{vandermarel04}. The best candidate for an IMBH to date is the extremely bright source HLX-1 in ESO 243-49, which reaches maximum X-ray luminosities of $\\sim 10^{42}$ erg s$^{-1}$ \\citep{farrell09}. The recent work by \\citet{sutton12} provides more evidence for extreme ULXs as IMBHs.\n\nMost ULX candidates are discovered by searching for off-nuclear X-ray point sources in galaxies (e.g. from the {\\it Chandra}~or XMM-{\\it Newton}~serendipitous source catalogs; see for example \\citealt{walton11b, liu11}). The ULX catalogs compiled in this way are contaminated with objects that also show up as off-nuclear, bright X-ray sources but are not accreting IMBHs or stellar-mass BHs. Background active galactic nuclei (AGN) and quasars are obvious examples, but some X-ray bright supernovae (most likely type IIn, \\citealt{immler03}) and active foreground stars may also contaminate the catalogs. One way to identify these contaminants if the ULX candidate has a bright optical counterpart is to take an optical spectrum. If emission or absorption lines are present the redshift to the source can be measured. In this way we can determine whether the source is associated with the galaxy or is a background or foreground object (compare e.g. \\citealt{gutierrez13}).\n\nIf the X-ray source is associated with the galaxy, optical spectra can give us additional information to classify the object. Some ULXs \\citep{pakull02, kaaret09} are surrounded by bubbles of ionized gas, which can act as calorimeters and as such tell us if the emission is strongly beamed or not. The intensity ratios of the emission lines from these regions provide information on the source of the ionizing radiation, e.g. whether they are shock ionized or X-ray photo-ionized (e.g. \\citealt{abolmasov07}).\n\nWe selected four high-luminosity (L$_X \\geq 10^{40}$ erg s$^{-1}$) ULX candidates from the catalog of \\citet{walton11b} with accurate positions that we measured using archival {\\it Chandra}~observations and optical counterparts that are sufficiently bright for optical spectroscopy. Two of the ULX candidates are situated in elliptical galaxies (NGC~533~and NGC~741). ULX candidates in elliptical galaxies have a higher chance to be background AGN (39\\%, compared to 24\\% for all sources in the catalog of \\citet{walton11b}). On the other hand, IMBHs may form in dense (globular) clusters \\citep{portegieszwart02, miller02} and the optical counterparts to these ULX candidates could well be just that, making them interesting targets for further investigation. AM~0644-741~is a ring galaxy with a ULX candidate situated in between the nucleus and the ring. ESO~306-003~is a spiral galaxy with a ULX in the outer edge of the disk, apparently associated with an extended optical source. We obtained optical spectra of these four sources with the FOcal Reducer and low dispersion Spectrograph (FORS2) mounted on the Very Large Telescope (VLT) \\citep{appenzeller98}. The observations and data reduction steps are described in Section 2; Section 3 contains the results. In Section 4 we discuss our findings.\n\n\\section{Observations and data reduction}\n\\subsection{X-ray observations}\nWe use archival {\\it Chandra}~observations to get exact source positions for the ULX candidates in NGC~533, NGC~741, AM~0644-741~and ESO~306-003. Table \\ref{chantab} lists the details of all observations. \n\n\\begin{table*}\n\\begin{minipage}{135mm}\n\\caption{The \\textit{Chandra} observations of the four ULX candidates.}\\label{chantab}\n \\begin{tabular}{lccccc}\n \\hline\n Galaxy & Observation ID & Exposure time & Source on CCD & Off-axis angle & Observation date \\\\\n & & (kiloseconds)& & (arcmin) & (UT)\\\\\n \\hline\nNGC~533~& 2880 & 38.1 & ACIS S3 & 0.85 & 2002-07-28\\\\\n NGC~741~& 2223 & 30.74 & ACIS S3 & 2.74 & 2001-01-28\\\\\n AM~0644-741~& 3969 & 39.97 & ACIS S3 & 0.57 & 2003-11-17\\\\\n ESO~306-003~& 4994 & 22.75 & ACIS I3 & 6.60 & 2004-03-10\\\\\n \\hline\n \\end{tabular}\n \n\\medskip\nNotes: {\\it Chandra}~observation ID number, exposure time in kiloseconds, CCD on which the source was detected, the off-axis angle of the source in arcminutes and the observation date.\n\\end{minipage}\n\\end{table*}\n\nWe use \\textsc{Ciao} version 4.4 to process the {\\it Chandra}~observations, with the calibration files from \\textsc{caldb} version 4.5.0. We treat the {\\it Chandra}~observations as follows: first we update the event files with ACIS\\_{}PROCESS\\_{}EVENTS, then we use WAVDETECT to find the position of the ULX candidate. Sources within 3 arcmins of one of the ACIS aimpoints have a 90\\% confidence error circle around the absolute position with a radius of 0.6''; this is valid for the ULX candidates in NGC~533, NGC~741~and AM~0644-741. The candidate in ESO~306-003~has 25 counts and was observed at 6.6' off-axis, which means it has a 95\\% confidence error circle with a radius of $\\sim$1.5'' \\citep{hong05}. For the sources in NGC~533, NGC~741~and AM~0644-741~we extract the source counts in a circle with 6 pixel radius (90\\% encircled energy fraction) around the source positions using SPECEXTRACT. For the ULX candidate in ESO~306-003~we use a circle with a radius of 10 pixels to get the same encircled energy fraction, since it was observed at 6.6' off-axis. As background regions we use circles with 80 pixel radius on the same CCD but not containing any sources. We use \\textsc{XSpec} version 12.6.0 to fit an absorbed powerlaw (pegpwrlw) to the data in the 0.3-8 keV range. We then extrapolate to get the 0.2-12 keV flux to compare this with the values reported by \\citet{walton11b}. For consistency we adopt the same model parameters: a photon index of 1.7 and $N_H = 3 \\times 10^{20}$ cm$^{-2}$, and allow only the flux to vary. We find that all \\textit{Chandra} fluxes are consistent with those from XMM-{\\it Newton}~as reported by \\citet{walton11b}. The positions of the X-ray sources and their fluxes are summarized in Table \\ref{chansrc}.\n\n\\begin{table*}\n\\begin{minipage}{90mm}\n\\caption{The positions and unabsorbed 0.2-12 keV X-ray fluxes of the ULX candidates.}\\label{chansrc}\n \\begin{tabular}{ccccc}\n \\hline\n Host galaxy & Right Ascension & Declination & Source flux \\\\\n &&& (erg cm$^{-2}$ s$^{-1}$)\\\\\n \\hline\nNGC~533~& 01:25:33.63 & +01:46:42.6& $2.9 \\pm 0.2 \\times 10^{-14}$ \\\\\n NGC~741~& 01:56:16.14 & +05:38:13.2 & $2.5 \\pm 0.3 \\times 10^{-14}$ \\\\\n AM~0644-741~& 06:43:02.24 & -74:14:11.1 & $3.5 \\pm 0.2 \\times 10^{-14}$ \\\\\n ESO~306-003~& 05:29:07.21 & -39:24:58.4 & $2.4 \\pm 0.4 \\times 10^{-14}$ \\\\\n \\hline\n \\end{tabular}\n \n\\medskip\nNotes: the positions of the ULX candidates in NGC~533, NGC~741~and AM~0644-741~are accurate to within 0.6''(90\\% confidence level), for the source in ESO~306-003~this value is 1.3''. We fit the fluxes assuming an absorbed powerlaw with photon index 1.7 and $N_H = 3 \\times 10^{20}$ cm$^{-2}$ for consistency with the method used by \\citet{walton11b}. \n\\end{minipage}\n\\end{table*}\n\n\\subsection{Optical images and photometry}\nTo find the optical counterparts of the ULX candidates we use archival optical observations of their host galaxies. NGC~533~and NGC~741~were observed as part of the Sloan Digital Sky Survey (SDSS), and we use the SDSS $r'$-band images to identify the optical counterparts to the ULX candidates in these galaxies (Figure \\ref{fig:agn}). There is no photometric information for the source in NGC~533, so we use the aperture photometry tool in \\textsc{GAIA} to estimate the $r'$-band magnitude. SDSS does provide \\textit{u'}, \\textit{g'}, \\textit{r'}, \\textit{i'} and \\textit{z'} magnitudes for the object in NGC~741, but these are incorrect because the source is too close to the edge of the frame. Therefore we also use \\textsc{GAIA} to estimate the $r'$-band magnitude for this source. For both optical counterparts we find that $r' = 21 \\pm 1$.\n\nThe Hubble Space Telescope (HST) archive contains several observations of AM~0644-741~made with the Advanced Camera for Surveys (ACS). We use the V-band (F555W) image with exposure identifier j8my05o2q, observed on 2004-01-16 with an exposure time of 2200 seconds (see Figure \\ref{fig:agn}). We visually compare the position of point sources from the USNO CCD Astrograph Catalog (UCAC) 3 \\citep{zacharias09} with their counterparts in the HST image and find that the astrometric calibration of the image does not need to be improved. The ULX candidate has a counterpart that is in the DAOPHOT source list of this HST image in the Hubble Legacy Archive (HLA\\footnote{http:\/\/hla.stsci.edu}). It has a V-band magnitude of $21.79 \\pm 0.05$.\n\nWe identify the optical counterpart to the ULX candidate in ESO~306-003~in a 480 seconds R-band observation made on 2004-01-25 UT with VLT\/VIMOS that we retrieved from the ESO archive. Its R-band magnitude is approximately 21, with the caveat that this is an extended source in a region with a very high background level due to the galaxy, which means that this measurement is not very accurate. We also obtained a \\textit{g'}-band, 120 seconds exposure of this galaxy in our VLT\/FORS2 run (see Figure \\ref{fig:eso306}), of which we visually inspected the astrometric solution by comparing the positions of bright stars with those in the UCAC 3.\n\n\\subsection{Optical spectroscopy}\nWe obtained VLT\/FORS2 observations of NGC~741~($3 \\times 1800$ s), AM~0644-741~($3 \\times 1800$ s) and ESO~306-003~($2 \\times 2700$ s) on 2011-12-03 UT under programme 088.B-0076A using the GRIS\\_600V grism and a 1\\arcsec~slit width. This configuration covers the wavelength range 4430-7370 \\AA{}~with a dispersion of 0.74 \\AA{}\/pixel, yielding a resolution of 4.25 \\AA{}~for the 1'' slit (measured at 6300 \\AA). This allows us to observe the H$\\alpha$, [N{\\sc II}] complex and the H$\\beta$ and [O{\\sc III}] lines if the sources are located at the same distance as their apparent host galaxies, with high enough resolution to separate them. The night was photometric so we also observed several spectrophotometric standard stars to perform a flux calibration. The seeing varied between 0.7 and 1.1\\arcsec.\nThe spectra of NGC~533~($3 \\times 1500$ s) were made in service mode on 2012-01-16 UT with the GRIS\\_300V+10 grism and a 0.5\\arcsec~slit width, giving a wavelength coverage from 4450-8700 \\AA{}~with a dispersion of 1.68 \\AA{}\/pixel and a spectral resolution of 6.4 \\AA{}~for the 0.5'' slit (measured at 6300 \\AA{}). The seeing varied during the night and we have no observations of spectrophotometric standards.\n\nTo reduce the spectra we use the \\textsc{starlink} software package \\textsc{Figaro} and the \\textsc{Pamela} package developed by Tom Marsh\\footnote{http:\/\/deneb.astro.warwick.ac.uk\/phsaap\/software}. We follow the steps outlined in the \\textsc{Pamela} manual to extract the spectra, using Keith Horne's optimal extraction algorithm \\citep{horne86}. We then use the software package \\textsc{Molly}, also by Tom Marsh\\footnotemark[\\value{footnote}], to perform the wavelength calibration and, for the data taken on 2011-12-03, the flux calibration. We do not correct for telluric absorption. Because we have multiple spectra of each source we average them to get a better signal-to-noise ratio. The two observations of ESO~306-003~were taken under varying seeing conditions. Because of this the continuum level is different in the two spectra, so we normalize these spectra before averaging them.\nWe use \\textsc{Molly}'s MGFIT task to fit Gaussian profiles to the emission lines in the spectra to determine the full width at half maximum (FWHM) of the lines and the redshift to the sources. \n\n\n\n\\section{Results}\n\n\\begin{table*}\n\\begin{minipage}{150mm}\n\\caption{Source properties of the background AGN}\\label{optinfo}\n \\begin{tabular}{lccccc}\n \\hline\n Source name & In galaxy & z & Line & FWHM & Log(F$_X$\/F$_{\\textrm{opt}}$) \\\\\n &&&& km\/s &\\\\\n \\hline\nCXOU J012533.3+014642 & NGC~533~& $1.8549 \\pm 0.0003$ & C\\textsc{IV} & $2300 \\pm 70 $ & $0.0 \\pm 0.5$\\\\\n& & & C\\textsc{III}] & $7800 \\pm 200$ & \\\\\n& & & Mg{\\sc II} & $5700 \\pm 200$ & \\\\\nCXOU J015616.1+053813 & NGC~741~& $0.8786 \\pm 0.0006$ or & Mg{\\sc II} or & $8400 \\pm 200$ & $0.0 \\pm 0.5$\\\\\n& & $1.7535 \\pm 0.0009$ & C{\\sc III}] & & \\\\\nCXO J064302.2-741411 & AM~0644-741~& $1.3993 \\pm 0.0001$ & C{\\sc III}] & $5100 \\pm 70$ & $0.7 \\pm 0.1$\\\\\n & & & Mg{\\sc II} & $4220 \\pm 40$ &\\\\\n \\hline\n \\end{tabular}\n \n\\medskip\nNotes: Lines used for the redshift determination to the quasars, their FWHM in km\/s and the X-ray to optical flux ratio of these sources. The X-ray to optical flux ratios are calculated using the XMM-{\\it Newton}~0.2-12 keV fluxes from \\citet{walton11b} and the \\textit{r'}-band (for NGC~533~and NGC~741) or V-band (for AM~0644-741) optical fluxes.\n\\end{minipage}\n\\end{table*}\n\n\\begin{figure*}\n\\hbox{\n\\includegraphics[width=0.33\\textwidth]{ngc533_sdss}\n\\includegraphics[width=0.33\\textwidth]{ngc741_sdss}\n\\includegraphics[width=0.33\\textwidth]{a0644_hst}\n}\n\\hbox{\n\\includegraphics[width=0.33\\textwidth]{ngc533_avspec}\n\\includegraphics[width=0.33\\textwidth]{ngc741_avspec}\n\\includegraphics[width=0.33\\textwidth]{a0644_avspec}\n}\n\\caption{The finders and FORS2 spectra of the three ULX candidates that are background AGN. The 90\\% confidence error circles around the X-ray positions have a radius of 0.6'', for NGC~533~and NGC~741~we plot a larger circle for visual clarity. \\emph{Left:} The SDSS \\textit{r'}-band image of NGC~533~with a 1.2'' radius circle around the {\\it Chandra}~position of the ULX candidate and the spectrum in which the C\\textsc{IV}, C{\\sc III}] and Mg{\\sc II} emission lines, redshifted by $z=1.85$, are marked. The absorption features at 6200 \\AA{}~are caused by interstellar absorption, and those at 6900 \\AA{}~and 7600 \\AA{}~are telluric in origin. \\emph{Middle:} The SDSS \\textit{r'}-band image of NGC~741~with a 1.2'' radius circle around the {\\it Chandra}~position of the ULX candidate and the spectrum of the optical counterpart. The marked emission line can be either Mg{\\sc II} $\\lambda2798$ line redshifted by $z=0.88$ or C{\\sc III}] at $z = 1.75$. \\emph{Right:} An HST ACS V-band image of AM~0644-741~with the 0.6'' radius error circle around the {\\it Chandra}~position of the ULX candidate, and the spectrum with the Mg{\\sc II} and C{\\sc III}] lines, redshifted by $z=1.40$, marked.}\\label{fig:agn}\n\\end{figure*}\n\n\\subsection{NGC~533}\nNGC~533~is the dominant elliptical galaxy in a group with the same name at $z = 0.0185$ \\citep{smith00}. The ULX candidate is located at 78'' from the center of the galaxy that has a semi-minor axis of 90'' (based on the D25 isophote, \\citealt{nilson73}). The X-ray source has an unresolved optical counterpart that is visible in the image of the SDSS, with \\textit{r'}-band magnitude $\\approx 21$. Figure \\ref{fig:agn} shows the galaxy with the position of the ULX candidate and the FORS2 spectrum of the source. \n\nThree broad emission lines are visible. We identify these as C\\textsc{IV}, C{\\sc III}] and Mg{\\sc II} at $z = 1.8549 \\pm 0.0003$. This proves the ULX candidate to be a background AGN, not associated with NGC~533. The 0.2-12 keV X-ray luminosity calculated for this source by \\citet{walton11b} was $(2 \\pm 1) \\times 10^{40}$ erg s$^{-1}$, assuming a distance to the ULX of 73.8 Mpc. The true distance to this source is 4730 Mpc (using $H_0 = 75$ km\/s\/Mpc for consistency with \\citealt{walton11b}), which gives this AGN an X-ray luminosity of $(7 \\pm 4) \\times 10^{43}$ erg s$^{-1}$~using the flux as measured with XMM-{\\it Newton}.\n\n\n\\subsection{NGC~741}\nNGC~741~is an elliptical galaxy located at $z = 0.0185$ with a (D25) semi-major axis of 92.7'' \\citep{devaucouleurs91}. The ULX candidate is located 78'' West of the center of NGC~741~and has a counterpart that is unresolved in the SDSS image. Its \\textit{r'}-band magnitude is $\\sim 21$. Figure \\ref{fig:agn} shows the SDSS \\textit{r'}-band image of NGC~741~with the position of the ULX candidate and the FORS2 spectrum of the counterpart.\n\nThe spectrum shows one broad emission line, with a FWHM of 147 \\AA{}. We cannot say with certainty which line this is. The most likely options are that it is either the Mg{\\sc II} $\\lambda2798$ line or the C{\\sc III}] $\\lambda1909$ line. In the first case this ULX candidate would be a background AGN at a redshift of $z = 0.8786 \\pm 0.0006$ with an X-ray luminosity of $(1.6 \\pm 0.6) \\times 10^{43}$ erg s$^{-1}$. In the second case it would be at $z = 1.7535 \\pm 0.0009$, with $L_X = (4.2 \\pm 1.5) \\times 10^{43}$ erg s$^{-1}$. In both cases the source is not a ULX but a background AGN, unconnected to NGC~741.\n\n\\subsection{AM~0644-741}\nAM~0644-741~is a ring galaxy at $z = 0.022$ that shows signs of recent interaction with a smaller galaxy \\citep{few82, lauberts89}. The ULX candidate in this galaxy is located in between the core of the galaxy and the ring. A point-like object with a V-band magnitude of 21.8 is visible at the position of the X-ray source in archival HST images (see Figure \\ref{fig:agn}).\n\nThe FORS2 spectrum of the counterpart shows two emission lines that we identify as Mg{\\sc II} and C{\\sc III}] at $z=1.3993 \\pm 0.0001$. This ULX candidate is another background AGN with an 0.2-12 keV luminosity of $(8.1 \\pm 0.8) \\times 10^{43}$ erg s$^{-1}$.\n\n\n\\subsection{ESO~306-003}\nThe spiral galaxy ESO~306-003, at $z \\approx 0.016$ \\citep{dasilva06}, contains a ULX candidate that is located on the edge of the spiral structure (see Figure \\ref{fig:eso306}). An optical source is visible on the edge of the error circle. Visual inspection shows the profile of the counterpart to be more extended than that of point sources in the same image, but because of the high background level and steep gradient it is not possible to perform an acceptable fit to the profile. The full width at half maximum (FWHM) of point sources in this image (provided by the seeing) is 0.8''. At the distance of ESO~306-003~this yields a lower limit to the size of the source of 240 pc. The two spectra that we obtained of this source show slightly different line ratios and continuum levels (for example, the H$\\beta$\/H$\\alpha$ ratio changes by 10\\%). This can be explained by seeing variations if the optical counterpart to this source is extended: then slit losses can cause the small changes in the line ratios if there are intrinsic spatial variations in the line ratios in the extended source.\n\nThe spectrum is similar to that of an H{\\sc II} region, with narrow Hydrogen emission lines and strong forbidden lines. The redshift of the lines equals that of the center of the galaxy, indicating that if the X-rays are associated with this optical source, this is a bona fide ULX with a luminosity of $1.4 \\pm 0.3 \\times 10^{40}$ erg s$^{-1}$~based on the XMM-{\\it Newton}~flux measured by \\citet{walton11b}. The X-ray flux is constant between the XMM-{\\it Newton}~and {\\it Chandra}~observations. The X-ray to optical flux ratio of the source is log(F$_X$\/F$_{\\textrm{opt}}$)$ = 0.3 \\pm 0.5$, based on the XMM-{\\it Newton}~0.2-12 keV flux from \\citet{walton11b} and the \\textit{r'}-band flux. The line ratios, especially the [O{\\sc I}] $\\lambda$6300\/H$\\alpha$ ratio, place the source among the transition objects in the diagnostic diagrams of \\citet{ho08} (see Figure \\ref{fig:dd}). The He{\\sc II} $\\lambda4686$ emission line has been detected in several ULX nebulae \\citep{pakull02, kaaret09}, but we do not detect it here, possibly because the sensitivity of the detector drops off steeply towards the blue end. The 2-$\\sigma$ upper limit for the equivalent width of this line is 1.0 \\AA{}. This corresponds to a flux of $\\sim 10^{-17}$ erg cm$^{-2}$ s$^{-1}$ or a luminosity of $\\sim 5 \\times 10^{36}$ erg s$^{-1}$.\n\n\\begin{figure*}\n\\hbox{\n\\includegraphics[width=0.4\\textwidth]{eso306_vlt_im}\n\\includegraphics[width=0.6\\textwidth]{eso306_avspec}\n}\n\\caption{\\emph{Left:} The FORS2 \\textit{g'}-band acquisition image of ESO~306-003~with the 1.3'' radius (90\\% confidence) error circle around the position of the ULX candidate. \\emph{Right:} The FORS2 spectrum of the candidate optical counterpart to the X-ray source. Several emission lines, redshifted by $z=0.016$, are marked.}\\label{fig:eso306}\n\\end{figure*}\n\n\\begin{figure}\n\\includegraphics[width=0.5\\textwidth]{dds_OI}\n\\caption{[O{\\sc I}] $\\lambda$6300\/H$\\alpha$ versus [O{\\sc III}] $\\lambda$5007\/H$\\beta$ line ratios for H{\\sc II} regions, AGN (LINERs\nand Seyferts) and transition objects (figure adapted from \\citealt{ho08}). The black dot represents the line ratios for the ULX in ESO~306-003.}\\label{fig:dd}\n\\end{figure}\n\n\\section{Discussion}\nWe obtained VLT\/FORS2 spectra of the optical counterparts of four bright ULX candidates with accurate positions obtained by us from archival {\\it Chandra}~observations. Two of these are located in elliptical galaxies NGC~533~and NGC~741. Another candidate is situated in AM~0644-741, a ring galaxy that recently interacted with a small elliptical galaxy, and in the spiral galaxy ESO~306-003. Three of our four targets turn out to be background AGN with X-ray luminosities ranging from 1 to 8 $\\times 10^{43}$ erg s$^{-1}$; one (in ESO~306-003) seems to be a bona fide ULX. \n\nThe fraction of background AGN in our sample is higher than the fraction estimated by \\citet{walton11b} for their catalog. Although this can be due to small number statistics since we only investigate four sources, it is in line with results from other spectroscopic studies of ULX candidates. Optical spectroscopy of a sample of 23 ULX candidates in total yielded 20 background AGN and three foreground stars (\\citealt{gutierrez05, gutierrez06, gutierrez07, gutierrez13}). Another study that targeted 17 ULX candidates from the catalog of \\citet{colbert02} found that 15 were background AGN and the other two objects were foreground stars \\citet{wong08}.\n\nAll these studies mainly target ULX candidates that are relatively isolated and have a bright optical counterpart, a selection effect induced by the relative ease with which spectroscopic observations can be carried out for these sources. Sources located in crowded areas, like the spiral arms of late type galaxies, are more difficult targets for ground based optical spectroscopic observations. This means that spectroscopic studies are aimed at ULX candidates that have a low X-ray to optical flux ratio and that are situated relatively far away from their suspected host galaxies. As the authors of these previous papers also note, these selection criteria introduce a bias towards background AGN.\n\nA possible method to select ULX candidates that are most likely to be real ULXs is to calculate the expected contribution of background sources based on the known density of AGN in X-ray and optical observations (\\citealt{lopez06, sutton12}). Alternatively it may be possible to use the X-ray to optical flux ratios of ULX candidates to select targets for future spectroscopic studies. All our sources have X-ray to optical flux ratios log(F$_X$\/F$_{\\textrm{opt}}$) in the range between -1 and 1, typical for AGN (e.g. \\citealt{barger03}). Most ULXs show values for log(F$_X$\/F$_{\\textrm{opt}}$) ranging from 2 - 3 (\\citealt{tao11, tao12, sutton12}). The low value that we find for the ULX in ESO~306-003~can be explained if we assume that we do not resolve the ULX counterpart but instead observe the optical flux of the entire HII region that it resides in, thus lowering log(F$_X$\/F$_{\\textrm{opt}}$).\n\nHowever, if we were to select candidates for spectroscopy on the basis of their X-ray to optical flux ratios only we run the risk of missing interesting sources. For instance, ULXs may display different values for log(F$_X$\/F$_{\\textrm{opt}}$) when observed in the high and low states, as was shown for M101 ULX-1 and M81 ULS1 (\\citealt{tao11}). For both sources log(F$_X$\/F$_{\\textrm{opt}}$) is between 2 and 3 during the high state, but around 0 during the low state, well inside the range of optical to X-ray flux ratios found for AGN. Therefore other source properties should be taken into account as well, such as galaxy morphology, the distance of the ULX to its apparent host galaxy and the absolute magnitude of its optical counterpart. \nThe source in AM~0644-741{} is a good example of a candidate with such favorable properties: situated in a ring galaxy, which is a strong sign of a recent interaction phase that triggered star formation, often linked to ULXs (e.g. \\citealt{swartz04}), and close to the center of its apparent host galaxy, decreasing the chance that it is a background AGN (\\citealt{wong08}). It has an optical counterpart of such magnitude that it is consistent with being a bright globular cluster if it is at the distance of AM~0644-741{}. Nevertheless our optical spectrum showed it to be a background object.\n\n\\subsection*{The ULX in ESO~306-003}\nThe X-ray source in ESO~306-003~is the only one of the four candidates in our sample that appears to be a bona fide ULX. The extended nature of the source is confirmed by the fact that the emission line spectrum is consistent with that of an HII region. However, the [OI]\/H$\\alpha$ ratio indicates that some of the ionizing flux could come from an X-ray source. Potentially, we have found a ULX embedded in an HII region. \nAnother possibility is that this ULX candidate is a background AGN shining through an HII region in ESO~306-003. The X-ray to optical flux ratio is similar to that of the other AGN in our sample, so we would expect to see a contribution of redshifted emission lines from the AGN in the optical spectrum. The fact that we do not detect this makes this scenario implausible. \n\nWe find a 2-$\\sigma$ upper limit for the flux of a HeII $\\lambda4686$ line of $10^{-17}$ erg cm$^{-2}$ s$^{-1}$. This corresponds to an upper limit to the luminosity in the line of $\\sim 5 \\times 10^{36}$ erg s$^{-1}$. The presence of this line would be a strong indication of ionization by an X-ray source. We can compare this upper limit with the strength of the HeII $\\lambda4686$ line in other ULX nebulae. For Holmberg II X-1, \\citet{pakull02} report a luminosity of $2.5 \\times 10^{36}$ erg s$^{-1}$. \\citet{kaaret09} report a flux for this line from the ULX in NGC 5408 of $3.3 \\times 10^{-16}$ erg cm$^{-2}$ s$^{-1}$, which translates to a luminosity of $9 \\times 10^{35}$ erg s$^{-1}$~at the distance to NGC 5408 (4.8 Mpc, \\citealt{karachentsev02}). Both these ULXs have an X-ray luminosity of $\\sim 10^{40}$ erg s$^{-1}$ -- similar to ESO~306-003~-- and a HeII $\\lambda4686$ to X-ray luminosity ratio of $\\sim 10^{-4}$. If the same is true for ESO~306-003~then we would expect a HeII $\\lambda4686$ flux of a few times $10^{-18}$ erg cm$^{-2}$ s$^{-1}$, which is just below our 2-$\\sigma$ upper limit. New observations of this source with greater sensitivity at the wavelength of the HeII $\\lambda4686$ line are needed to determine if the nebula is X-ray photo-ionized or not.\n\n\\section*{Acknowledgements}\nPGJ and MAPT acknowledge support from the Netherlands Organisation for Scientific Research. GM acknowledges support from the Spanish Plan Nacional de Astronom\\'{\\i}a y Astrof\\'{\\i}sica under grant AYA2010-21490-C02-02. This research is based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 088B-0076A. This research has made use of software provided by the Chandra X-ray Center (CXC) in the application package CIAO and of the software packages Pamela and Molly provided by Tom Marsh.\n\n \\bibliographystyle{mn_new}\n ","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzoquj b/data_all_eng_slimpj/shuffled/split2/finalzzoquj new file mode 100644 index 0000000000000000000000000000000000000000..88953819f5de1d31cac70b9a4a9c1a4150b7aaf4 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzoquj @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction} \\label{sec:int}\n\n\nRecently, since the developments of both hardware and software\nin computer science enable us to simulate complex\nphysical processes numerically, such computer simulations\nbecome more important from industrial viewpoints.\nEspecially \nthe computation of the incompressible multi-phase fluid dynamics\nhas crucial roles in order to evaluate the behavior of several\ndevices and materials in a micro-region,\n{\\it {e.g.}}, ink-jet printers, solved toners and so on.\nIn the evaluation, it is strongly required that\nthe fluid interfaces with multiple junctions are \nstably and naturally computed from these practical reasons.\n\nIn this article, \nin order to handle the fluid interfaces with multiple junctions \nin a three dimensional micro-region, \nwe investigate a surface tension of an incompressible multi-phase \nflow with multiple junctions as a numerical computational method\nunder the assumption that the Reynolds number is not so large.\nIn the investigation, we encounter many interesting mathematical\nobjects and results,\nwhich are associated with\nlow dimensional interface geometry having singularities, and\nwith the infinite dimensional geometry of incompressible fluid dynamics.\nFurther since even in a macroscopic theory,\nwe introduce artificial intermediate regions in the material\ninterfaces among different fluids or among a solid and fluids,\nthe regions give a resolution of the singularities in the interfaces\nto provide extended Euler equations naturally.\nThus even though we consider the multi-phase fluid model as\na computational model,\nwe believe that it must be connected with mathematical nature of \nreal fluid phenomena as their description.\nWe will mention the background, the motivation and the strategy of\nthis study more precisely as follows.\n\nFor a couple of decades, \nin order to represent the physical process with\nthe interfaces of the multi-phase fluids, the computational\nschemes have been studied well.\nThese schemes are mainly classified into two types.\nThe first type is based on the level-set method \\cite{S}\ndiscovered by H-K. Zhao, T. Chan, B. Merriman, S. Osher\nand L. Wang \\cite{ZCMO,ZMOW}.\nThe second one\nis based on the phase-field theory, which was found by\n J. U. Brakbill, D. B. Kothe and C. Zemach \\cite{BKZ},\nand B. Lafaurie, C. Nardone, R. Scardovelli, S. Zaleski, and G. Zanetti\n\\cite{LZZ}.\nThe authors in Reference \\cite{LZZ} called the scheme SURFER.\nFollowing them, there are many studies on the SURFER scheme,\n{\\it{e.g.}}, \\cite[references therein]{AMS,Cab,Jac}.\n\nThe level-set method is a computational method in which we describe a\n(hyper-)surface in terms of zeros of the level-set function, {\\it{i.e.}}, a\nreal function whose value is a signed distance from the surface,\nsuch as $q(x)$ in Section \\ref{sec:two-one}.\nUsing the scheme based upon the level-set method\nin the three dimensional Euclidean space,\nwe can deal well with topology changes,\ngeometrical objects with singularities, {\\it{e.g.}}, cusps,\nthe multiple junctions of materials, and so on.\nHowever in the computation, we need to deal with the\nconstraint conditions even for two-phase fluids\n\\cite{ZCMO,ZMOW}.\nA dynamical problem with constraint conditions\nis basically complicate and sometimes gives difficulties\nto find its solution since the constraint conditions sometimes\ngenerate an ill-posed problem in the optimization. \nIn the numerical computation for incompressible fluid,\nwe must check the consistency between \nthe incompressible condition and the constraint condition.\nThe check generally requires a complicate implementation\nof the algorithm, and increases computational cost.\nIts failure sometimes makes the computation unstable, especially\nwhen we add some other physical conditions.\nSince instability disturbs the evaluation of a complex\nsystem as a model of a real device, it must be avoided.\n\nOn the other hand, \nusing the SURFER scheme \\cite{LZZ}, \nwe can easily compute effects of the surface tension of a two-phase fluid\nin the Navier-Stokes equation.\nThe phase field model is the model that we represent materials in terms\nof supports of smooth functions which roughly correspond to\nthe partition of unity in pure mathematics \\cite[I p.272]{KN}\nas will be mentioned in Sections \\ref{sec:four} and \\ref{sec:five}.\nWe call these functions\n{\\lq\\lq}color functions{\\rq\\rq} or {\\lq\\lq}phase fields{\\rq\\rq}.\nThe phase fields have artificial intermediate regions which\nrepresent their interfaces approximately.\nIn the SURFER scheme \\cite{LZZ},\nthe surface tension is given as a kind of stress force, or volume force\ndue to the intermediate region.\nHence the scheme makes the numerical computations of the surface tension \nstable.\nHowever\nit is not known how to consider a multi-phase ($N$-phase, $N\\ge2$) flow\n in their scheme.\nIn Reference \\cite{BKZ}, the authors propose a method as an\nextension of the SURFER scheme \\cite{LZZ}\n to the contact angle problem\nby imposing a constraint to fix its angle.\nIn this article, we will generalize \n the SURFER scheme to multi-phase flow\nwithout any constraints.\n\nNature must not impose any constraints even at such a triple junction,\nwhich is governed by a physical principle. If it is a \nHamiltonian system, its determination must obey the minimal\nprinciple or the variational principle.\nWe wish to find a theoretical framework\nin which we can consistently\nhandle the incompressible flows with interfaces including \nthe surface tensions and the multiple junctions without any constraints.\nAs the multiple junctions should be treated as singularities in \na mathematical framework which are very difficult to be handled\nin general, \nit is hard to extend\nmathematical approaches for fluid interface problems without\na multiple junction\n\\cite{BG,SZ} to a theory for the problem with multiple junctions.\nOur purpose of this article is to find such a theoretical\nframework which enables us to solve the fluid interface problems with \nmultiple junctions numerically\nas an extension of the SURFER scheme.\n\n\nFor the purpose, we employ the phase field model.\nThe thickness of the actual intermediate region \nin the interface between a solid and a fluid or between\ntwo fluids is of atomic order and \nis basically negligible in the macroscopic theory. \nHowever the difference\nbetween zero and {\\lq\\lq}the limit to zero{\\rq\\rq} \nsometimes brings a crucial difference in physics and\nmathematics; for example, in the Sato hyperfunction theory,\nthe delta function is regarded as a function in the boundary\nof the holomorphic functions \\cite{KKK,II}, {\\it{i.e.}},\n$\\displaystyle{\\delta(x) =\n \\lim_{\\epsilon \\to 0}\\frac{1}{2\\pi \\ii}\n\\left(\\frac{1}{x - \\ii \\epsilon}\n-\\frac{1}{x + \\ii \\epsilon}\\right)}$\n$\\displaystyle{\n\\equiv \\lim_{\\epsilon \\to 0}\\frac{1}{\\pi }\n\\frac{\\epsilon}{x^2 + \\epsilon^2}}$.\nAs mentioned above,\nthe phase field model has the artificial intermediate region which is\ncontrolled by a small parameter $\\epsilon$ \nand appears explicitly even as a macroscopic\ntheory. We regard that it represents the effects coming from\nthe actual intermediate region of materials.\nNamely, we regard that the stress force expression in \nthe SURFER scheme\nis caused by the artificial intermediate region of the phase-fields\nand it represents well the surface effect\ncoming from that of real materials.\n\nIn order to extend the stress force expression of the two-phase\nflow to that of the multi-phase ($N$-phase, $N\\ge2$) flow,\nwe will first reformulate \nthe SURFER scheme in the framework of the variational theory.\nIn Reference \\cite{Jac}, a similar attempt was reported\nbut unfortunately there were not precise derivations. Our\ninvestigations in Section \\ref{sec:four} \nshow that the surface tension expression of\nthe SURFER scheme\nis derived as a momentum conservation in \nNoether's theorem \\cite{BGG,IZ} and its derivation\n requires a generalization of the Laplace equation \\cite{LL} as\nthe Euler-Lagrange equation \\cite{AM,BGG}, which is not trivial\neven for a static case.\n\nIn order to deal with this problem in a dynamics case consistently,\nwe should also consider the Euler equation in the framework\nof the variational principle.\nIt is well-known that the incompressible fluid dynamics is\ngeometrically interpreted as \na variational problem of an infinite\ndimensional Lie group, related to diffeomorphism, due to\nV. I. Arnold \\cite{Ar,AK}, D. Ebin and J. Marsden \\cite{EM}, H. Omori \\cite{O}\nand so on. Following them, there are so many related works\n\\cite{AF,B,K,NHK,Schm,Shk,Shn,V}.\n\nOn the reformulation of the SURFER scheme \\cite{LZZ} for the dynamical case,\nwe introduce an action integral including \nthe kinematic energy of the incompressible fluid and the surface energy.\nThe variational method reproduces the governing equation in\nthe SURFER scheme.\n\nAfter then, we extend the surface energy to that of multi-phase fields \nand add the energy term to the action integral.\nThe variational principle of the action integral\nleads us to a novel expression of the surface tension\nand the extended Euler equation which we require.\nUsing the extended Euler equation,\nwe can deal with the surface tensions of the \nmulti-phase flows, the multiple junctions of the \nof phase fields including singularities, the topology changes and so on.\nWe can also compute a wall effect naturally and a contact\nangle problem.\nThe computation of the governing equation is freed from any\nconstraints, except the incompressible condition.\n\nIn other words, in this article, we completely unify\nthe theory of the multi-phase ($N$-phase, $N\\ge2$) field\nand the theory of the incompressible fluid dynamics of Euler equation\nas an infinite dimensional geometrical problem.\n\n\nContents are as follows:\nSection \\ref{sec:two} is devoted to the preliminaries\n of the theory of surfaces\nin our Euclidean space from a low-dimensional\ndifferential geometrical viewpoint\n\\cite{M,GMO,FW} and Noether's theorem in the \nclassical field theory \\cite{AM,BGG,IZ}.\nSection \\ref{sec:three} reviews the derivation of the Euler equation\nto the incompressible fluid dynamics following the variational method\nfor an infinite-dimensional Lie algebra based upon Reference \\cite{EM}. \nIn Section \\ref{sec:four}, we reformulate the SURFER scheme \\cite{LZZ}.\nThere the Laplace equation for the surface tension \nand the Euler equation in Reference \\cite{LZZ}\nare naturally obtained by the variational method\nin Propositions \\ref{prop:4-4} and \\ref{prop:4-6}.\nSection \\ref{sec:five} is our main section in which we extend\nthe theory in Reference \\cite{LZZ} to that for a multi-phase flow\nand obtain the Euler equation with the surface tension\nof the multi-phase field in Theorem \\ref{th:5-2}.\nThe extended Euler equation for the multi-phase flow\nis derived from the variational\nprinciple of the action integral in Theorem \\ref{th:5-1}.\nAs a special case, we also derive the \nEuler equation to a two-phase field with wall effects\nin Theorem \\ref{th:5-3}.\nIn Section 6, \nusing these methods in the computational fluid dynamics \\cite{Ch,H,HN},\nwe consider numerical computations \nof the contact angle problem of a two-phase field\nbecause the contact angle problem \nfor the two-phase field circumscribed in a wall\nis the simplest non-trivial triple junction problem.\nBy means of our scheme, for given surface tension coefficients, \nwe show two examples of the numerical computations in which\nthe contact angles automatically appeared\nwithout any geometrical constraints\n and any difficulties for the singularities at triple junctions.\nThe computations were very stable.\nPrecisely speaking, as far as we computed, \nthe computations did not collapse for any boundary conditions\nand for any initial conditions.\n\n\n\n\n\\section{Mathematical Preliminaries} \\label{sec:two}\n\n\\subsection{Preliminary of surface theory} \\label{sec:two-one}\n\nIn this subsection, we review the theory of surfaces \nfrom the viewpoint of low-dimensional differential geometry.\nThe interface problems have been also studied for last three decades\nin pure mathematics,\nwhich are considered as a revision of the \nclassical differential geometry \\cite{E} from a modern point of view\n\\cite{ES,FW,GMO,M,T},\n{\\it{ e.g.}}, generalizations of the Weierstrass-Ennpper\ntheory of the minimal surfaces, isothermal surfaces,\nconstant curvature surfaces, constant mean curvature surfaces,\nWillmore surfaces and so on. \nThey are also closely connected with\nthe harmonic map theory \nand the theory of the variational principle \\cite{FW,GMO}.\n\n\nWe consider a smooth surface $S$ embedded in \nthree dimensional Euclidean space $\\EE^3$.\nLet $x = (x^1,x^2,x^3)$ be of the Cartesian coordinate system\nand represent a point in $\\EE^3$, and \nlet the surface $S$ be locally expressed by a local parameter $(s^1,s^2)$.\nWe assume that the surface $S$ is expressed by\nzeros of a real valued smooth function $q$ over $\\EE^3$, {\\it{i.e.}},\n$$\n\tq(x)=0,\n$$\nsuch that in the region whose $|q|$ is sufficiently small\n($|q| < \\epsT$ for a positive number $\\epsT>0$),\n$|dq|$ agrees with the infinitesimal length in the Euclidean space.\nThen $dq$ means the normal co-vector field (one-form), {\\it{ i.e.}},\nfor the tangent vector field\n$e_\\alpha:=\\partial_\\alpha:=\\partial\/\\partial s^\\alpha$\n($\\alpha = 1, 2$) of $S$,\n\\begin{equation}\n\t\\langle\\partial_\\alpha, dq\\rangle=0 \\quad \\mbox{ over }\\quad\n S = \\{ x \\in \\EE^3 \\ | q(x) = 0\\}.\n\\label{eq:ddq}\n\\end{equation} \nHere $\\langle, \\rangle$ means the pointwise pairing \nbetween the cotangent bundle and the tangent bundle of $\\EE^3$.\nThe function $q$\n can be locally regarded as so-called the level-set function \n\\cite{S,ZMOW}.\nWe could redefine the domain of $q$ such that it is restricted to\na tubular neighborhood $T_S$ of $S$,\n$$\n T_S:=\\{x \\in \\EE^3 \\ | \\ |q(x)|<\\epsT \\}.\n$$ \nOver $T_S$, $q$\nagrees with the level-set function of $S$.\nThere we can naturally define a projection map\n$\\pi: T_S \\to S$ and then we can regard $T_S$ as a fiber bundle over\n$S$, which is homeomorphic to the normal bundle $N_{S}\\to S$.\nHowever the level-set function is defined as a signed \ndistance function which is a global function over $\\EE^3$\nas a continuous function \\cite{S} and thus it has no natural\nprojective structure in general;\nfor example,\nthe level-set function $L$ of a sphere with radius $a$ is given by\n$$\nL(x^1,x^2,x^3) = \\sqrt{(x^1)^2 +(x^2)^2 + (x^3)^2} - a,\n$$ \nwhich induces the natural projective (fiber) structure but \nthe origin $(0, 0, 0)$ in the sphere case. \nThe level-set function has no projective structure \nat $(0, 0, 0)$ in this case, and\nwe can not define its differential there.\nIn other words,\nthe level-set function is not a global function over $\\EE^3$ \nas a smooth function in general.\n\n When we use the strategy of the \nfiber bundle and its connection, we restrict ourselves to\nconsider the function $q$ in $T_S$. \nThen the relation (\\ref{eq:ddq}) and the parameter\n$(s_1, s_2)$ are naturally lifted \nto $T_S$ as an inverse image of $\\pi$.\n\nFurther for $e_q:=\\partial_q:=\\partial\/\\partial q$, we have\n$$\n\t\\partial_\\alpha (e_q) = \\sum_\\beta \\Gamma^\\beta_{\\ \\alpha q} e_\\beta\n \\mbox{ over } S.\n$$\nHere \n$(\\Gamma^\\beta_{\\ \\alpha q})$ is the Weingarten map, which is\na kind of a point-wise $2\\times2$-matrix \n$((\\Gamma^\\beta_{\\alpha q})_{\\alpha\\beta})$ \\cite[Chapter VII]{KN}.\nThe eigenvalue of \n$(\\Gamma^\\beta_{\\alpha q})$ is the principal\ncurvature, whereas a half of its trace\n$\\mbox{tr}(\\Gamma^\\beta_{\\alpha q})\/2$ is known as the \nmean curvature and its determinant \n$\\mbox{det}(\\Gamma^\\beta_{\\alpha q})$\nmeans the Gauss curvature \\cite[Chapter VII]{KN}.\n\n\t\n\nNoting the relation,\n$\\langle e_\\beta, d s^\\alpha\\rangle = \\delta_\\beta^\\alpha$ \nfor $\\alpha, \\beta = 1, 2$, \n the twice of the mean curvature, $\\kappa$, is given by,\n$$\n\t\\sum_{\\alpha} \\partial_\\alpha (e_q) d s^\\alpha = \\kappa\n \\quad \\mbox{over}\\quad S.\n$$\nFurther noting the relation $\\partial_q e_q d q =0$, we obtain\n$$\n\t \\sum_{\\alpha} \\partial_\\alpha (e_q) d s^\\alpha \n\t + \\partial_q(e_q) d q = \\kappa\n \\quad \\mbox{over}\\quad S.\n$$\nDue to the flatness of the Euclidean space, we identify $e_q$ with \n$\\nabla q \/|\\nabla q|$ and then we have the following proposition.\n\n\\begin{proposition} \\label{prop:2-1}\nThe following relation holds at a point over $S$,\n$$\n\t\\mathrm{div}\\left( \\frac{\\nabla q }{|\\nabla q|}\\right) = \\kappa.\n$$\n\\end{proposition}\nFor the case $|\\nabla q| = 1$,\nusing the Hodge star operator \\cite{AM,N} and the exterior\nderivative $d$, we also have \nan alternative expression $*d * dq =\\kappa$ over the surface $S$.\nHere the Hodge star operator is $*: \\Lambda^p(T_S) \\to \\Lambda^{3-p}(T_S)$\nand the exterior derivative $d: \\Lambda^p(T_S) \\to \\Lambda^{p+1}(T_S)$\n$(d \\omega = \\sum_{i=1}^3\\partial_i \\omega d x^i)$,\nwhere $\\Lambda^p(T_S)$ is the set of smooth $p$-forms over\n$T_S$ \\cite{N}.\n\nNoting that as the left hand side of formula in Proposition \\ref{prop:2-1}\ncan be lifted to $T_S$,\nthe formula plays an important role in References \\cite{BKZ,LZZ,ZCMO}\nand in this article.\n\n\\subsection{Preliminary of Noether's theorem} \\label{sec:two-two}\n\nIn this subsection,\n we review Noether's theorem in the variational method which\nappears in a computation of the energy-momentum tensor-field\nin the classical field theory \\cite{AM,BGG,IZ}.\n\nLet the set of $\\ell$ smooth real-valued functions\n over $n$-dimensional Euclidean space $\\EE^n$\n be denoted by $\\cC^{\\infty}(\\EE^n)^{\\otimes\\ell}$,\nwhere $n$ is mainly three.\nLet $x = (x^1,x^2,\\ldots,x^n)$ be of the Cartesian coordinate system\nof $\\EE^n$.\nWe consider the functional \n$I: \\cC^{\\infty}(\\EE^n)^{\\otimes\\ell} \\to \\RR$,\n\\begin{equation}\n\tI = \\int_{\\EE^n} d^n x{ \\cF}(\\phi_a(x),\\partial_i \\phi_a(x)),\n\\label{eq:IinEL}\n\\end{equation}\nwhere\n$\\cF$ is a local functional,\n$\\cF: \\cC^{\\infty}(\\EE^n)^{\\otimes\\ell}|_x \\to \\Lambda^n(\\EE^n)|_x$,\n\\begin{equation*}\n\\begin{split}\n\\cF: (\\phi_a)_{a=1,\\ldots,\\ell}|_x \\mapsto &\n{\\cF}(\\phi_a(x),\\partial_i \\phi_a(x))d^n x\n\\equiv{ \\cF}(\\phi_a(x),\\partial_1 \\phi_a(x),\n\\ldots, \\partial_n \\phi_a(x))d^n x \\\\\n&\n\\equiv{ \\cF}(\\phi_1(x), \\ldots, \\phi_\\ell(x), \n\\partial_1 \\phi_1(x), \\ldots, \\partial_n \\phi_\\ell(x))d^n x \\\\\n\\end{split}\n\\end{equation*}\nand $\\partial_i := \\partial\/ \\partial x^i$, $(i=1, \\cdots, n)$.\nThen we obviously have the the following\nproposition.\n\n\\begin{proposition}\\label{prop:A-1} \nFor the functional $I$ in (\\ref{eq:IinEL}) over\n$\\cC^{\\infty}(\\EE^n)^{\\otimes\\ell}$,\nthe Euler-Lagrange equation coming from the variation\nwith respect to $\\phi_a$ of $(\\phi_b)_{b=1, \\ldots, \\ell}\n \\in \\cC^{\\infty}(\\EE^n)^{\\otimes\\ell}$,\n{\\it{i.e.}}, \n\n\t\\frac{\\delta I}{\\delta \\phi_a(x)} = 0\n$,\n is given by\n\\begin{equation}\n\t\\frac{\\delta \\cF}{\\delta \\phi_a(x)}\n\t-\\sum_{i=1}^n\n\\partial_i \\frac{\\delta \\cF}{\\delta \\partial_i \\phi_a(x)} = 0.\n\\label{eq:AppeA1}\n\\end{equation}\n\\end{proposition}\n\nUsing the equation (\\ref{eq:AppeA1}), \nwe consider an effect of a small translation\n $x$ to $x'= x + \\delta x$ on the functional $I$.\nThe following proposition is known as Noether's theorem\nwhich plays crucial roles in this article.\n\n\\begin{proposition}\\label{prop:A-2} \nThe functional derivative $I$ with respect to $\\delta x_i$ is\ngiven by\n\\begin{equation}\n\\frac{\\delta I}{\\delta x^i}\n = \\sum_{j=1}^n\\partial_j\\left[ \\sum_{a=1}^\\ell\n \\frac{\\delta \\cF}{\\delta\\partial_j \\phi_a}\\partial_i \\phi_a \\right]\n - \\partial_i \\left[ { \\cF}\\right].\n\\label{eq:AppeA2}\n\\end{equation}\nIf $I$ is invariant for the translation, \n(\\ref{eq:AppeA2}) gives the conservation of the momentum.\n\\end{proposition}\n\n\n\\begin{proof}\nFor the variation $x'= x + \\delta x$, the scalar function becomes\n\\begin{equation*}\n\t\\phi_a(x') = \\phi_a(x) +\\sum_{i=1}^n \\partial_i \\phi_a(x)\n\t\\delta x^i + O(\\delta x^2).\n\\end{equation*}\nFrom the relations on the Jacobian and each component,\n\\begin{equation*}\n\t\\frac{\\partial x'}{\\partial x} \n = 1 +\\sum_{i=1}^n \\partial_i \\delta x^i + O(\\delta x^2),\n\\quad\n\t\\frac{\\partial x^k}{\\partial x'{}^i}=\\delta^k_i\n\t -\\partial_i \\delta x^k + O(\\delta x^2),\n\\end{equation*}\nwe have\n\\begin{equation*}\n\\begin{split}\n\t\\frac{\\partial \\phi_a(x')}{\\partial x'{}^i}\n &=\\frac{\\partial \\phi_a(x) +\n \\sum_{j=1}^n \\partial_j \\phi_a(x) \\delta x^j}{\\partial x^k}\n \\frac{\\partial x^k}{\\partial x'{}^i} + O(\\delta x^2)\\\\\n &=\\partial_i \\phi_a+\n \\sum_{j=1}^n (\\partial_i\\partial_j \\phi_a) \\delta x^j + O(\\delta x^2).\n\\end{split}\n\\end{equation*}\nThen up to $\\delta x^2$, we obtain \n\\begin{equation*}\n\\begin{split}\n& \\int_{\\EE^n} d^n x' {\\cF}(\\phi_a(x'),\\partial_i' \\phi_a(x'))\n- \\int_{\\EE^n} d^n x {\\cF}(\\phi_a(x),\\partial_i \\phi_a(x))\\\\\n&= \\int_{\\EE^n}\\Bigr[\n\\sum_{i=1}^n\\sum_{a=1}^\\ell \n\\frac{\\delta {\\cF}}{\\delta \\phi_a} \\partial_i \\phi_a(x) \n\\delta x^i +\\sum_{i,j = 1}^n \\sum_{a=1}^\\ell\n\\frac{\\delta \\cF}{\\delta\\partial_j \\phi_a} \n\\partial_i \\partial_j\\phi_a(x) \\delta x^i\n+\\sum_{j=1}^n{\\cF} \\partial_i \\delta x^i\\Bigr] d^n x\\\\\n &=\\int_{\\EE^n}\\left(\n\\sum_{i=1}^n \\partial_i\\left[ \\sum_{j=1}^n \\sum_{a=1}^\\ell\n\\frac{\\delta \\cF}{\\delta\\partial_i \\phi_a}\\partial_i \\phi_a \n -{ \\cF} \\right] \\delta x^i \\right) d^n x.\\\\\n \\end{split}\n\\end{equation*}\nHere we use the Euler-Lagrange equation (\\ref{eq:AppeA1}) and then\nwe have (\\ref{eq:AppeA2}).\nIf we assume that $I$ is invariant for the variation, it vanishes.\n\\qed\n\\end{proof}\n\n\n\n\n\n\n\\section{Variational principle for incompressible fluid dynamics}\n\\label{sec:three}\n\nAs we will derive the governing equation as the variational\nequation of an incompressible multi-phase flow with interfaces\nusing the variational method,\nlet us review the variational theory of the\nincompressible fluid to obtain the Euler equation\nfollowing References \\cite{Ar,AK,EM,K,Ko,MW,NHK}.\n\nLet $\\Omega$ be a smooth domain in $\\EE^3$.\nThe incompressible fluid dynamics can be interpreted\nas a geometrical problem associated with an infinite dimensional Lie group\n\\cite{AK,EM,O}.\nIt is related to the volume-preserving diffeomorphism \ngroup $\\SDiff(\\Omega)$\nas a subgroup of the diffeomorphism group\n$\\Diff(\\Omega)$. The diffeomorphism group $\\Diff(\\Omega)$\nis generated by a smooth coordinate transformation of $\\Omega$.\nThe Lie algebras $\\sdiff(\\Omega) \\equiv T_e\\SDiff(\\Omega)$ of $\\SDiff(\\Omega)$\nand $\\diff(\\Omega) \\equiv T_e\\Diff(\\Omega)$ of $\\Diff(\\Omega)$ are\n the infinite dimensional real vector spaces.\n The $\\sdiff(\\Omega)$ is a linear subspace of $\\diff(\\Omega)$.\n\nFollowing Ebin and Marsden \\cite{EM}, we consider \nthe geometrical meaning of the action integral of\nan incompressible fluid,\n\\begin{equation}\n\t\\int_T dt \\int_{\\Omega} d^3 x \n\\left(\\frac{1}{2}\\rho |u|^2\\right).\n\\label{eq:Eue}\n\\end{equation}\nHere \n$T:=(0, T_0)$ is a subset of the set of real numbers $\\RR$,\n$(x, t)$ is the Cartesian coordinate of the space-time \n$\\Omega\\times T$, $\\rho$ is the density of the fluid\nwhich is constant in this section, and $u=(u^1, u^2, u^3)$ is\nthe velocity field of the fluid.\n\nGeometrically speaking,\na flow obeying the incompressible fluid dynamics is\nconsidered as a section of\na principal bundle\n$\\IFluid(\\Omega\\times T)$ over the absolute time axis $T \\subset \\RR$ \nas its base space,\n\\begin{equation}\n\\begin{CD}\n\t\\SDiff(\\Omega) @>>> \\IFluid(\\Omega\\times T) \\\\\n @. @V{\\varpi}VV\\\\\n @. T.\\\\\n\\end{CD}\n\\label{eq:PBIFluid}\n\\end{equation}\nThe projection $\\varpi$ is induced from the\ntrivial fiber structure $\\varpi_\\Omega: \\Omega \\times T \n\\to T$, $((x, t) \\to t)$. In the classical (non-relativistic) mechanics,\nevery point of space-time has a unique absolute time $t\\in \\RR$,\nwhich is contrast to one in the relativistic theory.\n\nDue to the Weierstrass polynomial approximation theorem \\cite{Y},\n we can locally approximate\na smooth function by a regular function.\nLet the set of smooth functions\nover $\\Omega$ be denoted by $\\cC^\\infty(\\Omega)$\nand the set of the regular real functions \nby $\\cC^\\omega(\\Omega)$\nwhose element can be expressed by the Taylor expansion\nin terms of local coordinates.\n\nThe action of $\\Diff(\\Omega)$ on \n$\\cC^\\omega(\\Omega) \\subset\\cC^\\infty(\\Omega)$\nis given by\n$$\n\t\\ee^{s u^i \\partial_i} f(x) = f(x + s u),\n$$\nfor an element $f \\in \\cC^\\omega(\\Omega)$, and small $s>0$,\nwhere $\\partial_i := \\partial \/ \\partial x^i$ and \nwe use the Einstein convention; when an index $i$ appears twice,\nwe sum over the index $i$.\nThus the action $\\ee^{s u^i \\partial_i}$\nis regarded as an element of $\\Diff(\\Omega)$.\n\nAs a frame bundle of the principal bundle $\\IFluid(\\Omega\\times T)$, we\nconsider a vector bundle $\\Coor(\\Omega\\times T)$ with infinite rank,\n$$\n\\begin{CD}\n\t\\cC^\\infty(\\Omega) @>>> \\Coor(\\Omega\\times T) \\\\\n @. @V{\\varpi'}VV\\\\\n @. T.\\\\\n\\end{CD}\n$$\nSince $\\cC^\\infty(\\Omega)$ is regarded as a non-countably infinite\ndimensional linear space over $\\RR$,\nwe should regard \n$\\Diff(\\Omega)$ and $\\SDiff(\\Omega)$ as subgroups of\nan infinite dimensional general linear group if defined.\n\nMore rigorously, we should\nconsider the ILH space (inverse limit of Hilbert space) \n(or ILB space (inverse limit of Banach space)) introduced in \nReference \\cite{O} by adding a certain\ntopology to (a subspace of) $\\cC^\\infty(\\Omega\\times T)$,\nand then we also should regard $\\Diff$ and $\\SDiff$ \nas an ILH Lie group.\nHowever our purpose is to obtain an extended Euler equation from a more\npractical viewpoint.\nThus we formulate the theory primitively even though\nwe give up to consider a general solution for a general initial\ncondition.\n\nWe consider smooth sections of $\\Coor(\\Omega\\times T)$ and\n$\\IFluid(\\Omega\\times T)$.\nSmooth sections of $\\Coor(\\Omega\\times T)$ can be\nrealized as $\\cC^\\infty(\\Omega \\times T)$.\nIn the meaning of the Weierstrass polynomial approximation theorem \\cite{Y},\nan appropriate topology in $\\cC^\\infty(\\Omega\\times T)$ makes\n$\\cC^\\omega(\\Omega\\times T)$ dense in $\\cC^\\infty(\\Omega\\times T)$\nby restricting the region $\\Omega \\times T$ appropriately.\nUnder the assumption, we also deal with a smooth section of\n$\\IFluid(\\Omega\\times T)$.\n\n\t\nLet us consider a coordinate function \n$(\\gamma^i(x, t))_{i=1,2,3} \\in \\cC^\\omega(\\Omega \\times T)$\nsuch that\n$$\n\t\\frac{d}{d t} \\gamma^i(x, t) = u^i(x, t), \\quad\n\t\\gamma^i(x, t) = x^i \\ \\ \\mbox{at } \\ t \\in T,\n$$\nwhich means \n$$\n\\gamma^i(x, t + \\delta t) = x^i + u^{i}(x,t) \\delta t + O(\\delta t^2),\n$$\nfor a small $\\delta t$.\nHere the addition is given as a Euclidean move in $\\EE^3$.\nAs an inverse function of $\\gamma=\\gamma(u,t)$, \nwe could regard $u$ as a function of $\\gamma$ and $t$,\n$$\nu(x,t) =u(\\gamma(x,t),t).\n$$\nFurther we introduce a small quantity modeled on $\\delta t\\cdot u^i$,\n\\begin{equation}\n\t\\tgamma^i(x,t) := \\gamma^i(x,t) - x^i.\n\\label{eq:tgamma}\n\\end{equation}\nThen a section $g$\nof $\\IFluid(\\Omega\\times T)$ at $t \\in T$ can written by,\n\\begin{equation}\n\tg(t) = \\ee^{\\tgamma^i \\partial_i} \\in \\IFluid(\\Omega\\times T)\\Bigr|_{t}\n \\approx \\SDiff(\\Omega) \\subset \\Diff(\\Omega).\n\\label{eq:etgamma}\n\\end{equation}\nHere we consider $g$ as an element of $\\SDiff(\\Omega)$ and thus\nit satisfies the condition of\nthe volume preserving, which appears as the constraint that the Jacobian,\n$$\n\\frac{\\partial \\gamma}{\\partial x} \n:=\n\\det\\left(\\frac{\\partial \\gamma^i}{\\partial x^j} \\right)\n= (1 + \n\\tr(\\partial_j u^i) \\delta t) + O(\\delta t^2),\n$$\nmust preserve $1$, {\\it{i.e.}}, the well-known condition\nthat $\\tr (\\partial_j u^i) = \\mathrm{div}(u)$ must vanish,\nor $\\displaystyle{\\frac{d}{dt}\\frac{\\partial \\gamma}{\\partial x} = 0}$.\n\nFollowing Reference \\cite{EM}, we reformulate the\naction integral (\\ref{eq:Eue})\nas {\\lq\\lq}the energy functional{\\rq\\rq}\nin the frame work of the harmonic\nmap theory.\nIn the harmonic map theory \\cite{GMO}\nby considering a smooth map $h : M \\to G$\nfor a $n$-smooth base manifold $M$ and its target group manifold $G$,\n{\\lq\\lq}the energy functional{\\rq\\rq} is given by\n\\begin{equation}\n E = \\frac{1}{2}\\int_M \\tr \\left((h^{-1} d h )*(h^{-1} d h )\\right).\n \\label{eq:B-1}\n\\end{equation}\nHere $*$ means the Hodge star operator, which is\nfor $*:TG\\otimes\\Lambda^p(M) \\to TG\\otimes\\Lambda^{n-p}(M)$ where\n$\\Lambda^p(M)$ is the set of the smooth $p$-forms over $M$ \\cite{N},\nand \n$TG\\otimes\\Lambda^p(M)$ is the set of the tangent bundle $TG$ valued\nsmooth $p$-forms over $M$ \\cite{N}.\nThe term {\\lq\\lq}energy functional{\\rq\\rq} in the harmonic map theory\nmeans that it is an invariance of the system and thus it sometimes\ndiffers from an actual energy in physics.\n\n\nSince in (\\ref{eq:PBIFluid}), the base space $T$ is one-dimensional and\nthe target space $\\IFluid(\\Omega\\times T)|_{t}$ at $t \\in T$ is \nthe infinite dimensional space,\n{\\lq\\lq}the energy functional{\\rq\\rq} (\\ref{eq:B-1})\nin the harmonic map theory corresponds to\nthe action integral $\\cS_{\\free} [\\gamma]$ which is defined by\n$$ \n\\cS_{\\free} [\\gamma]=\n\\frac{1}{2} \\int _T \\int_\\Omega \n\\frac{\\partial \\gamma}{\\partial x} \n\\rho d^3 x \\cdot dx^i \\otimes dx^i \\left(\n\t\\left( \\ee^{-\\tgamma^k \\partial_k}\n dt \\frac{d}{dt}\\ee^{\\tgamma^\\ell \\partial_\\ell} \\right)\n\t\\left( \\ee^{-\\tgamma^j \\partial_j}\n \\frac{d}{dt}\\ee^{\\tgamma^n \\partial_n} \\right)\n \\right).\n$$\nHere $d x^i (\\partial_j ) := \n\\langle\\partial_j , d x^i \\rangle\n = \\delta^i_{\\ j}$ is the natural pairing between\n $T \\Omega$ and $T^* \\Omega$.\nThe trace in (\\ref{eq:B-1}) corresponds to the \nintegral over $\\Omega$ with \n$ \\frac{\\partial \\gamma}{\\partial x} \n\\rho d^3 x \\cdot dx^i \\otimes dx^i$.\nThe Hodge $*$ operator acts on the element such as\n$*\\left( \\ee^{-\\tgamma^k \\partial_k}\n dt \\frac{d}{dt}\\ee^{\\tgamma^\\ell \\partial_\\ell} \\right)\n= \\left( \\ee^{-\\tgamma^k \\partial_k}\n \\frac{d}{dt}\\ee^{\\tgamma^\\ell \\partial_\\ell} \\right)$\nas the natural map from\n$\\diff(\\Omega)$ valued 1-form to 0-form.\nFurther we assume that $\\rho$ is a constant function in this section.\nThen the action integral $\\cS_{\\free} [\\gamma]$\nobviously agrees with (\\ref{eq:Eue}).\n\nWe investigate the functional derivative and the\nvariational principle of this $\\cS_\\free[\\gamma]$.\nLet us consider the variation,\n$$\n\t\\gamma^j(x,t') = \\gamma^j(x, t') + \\delta \\gamma^j(x,t'), \\quad\n\\mbox{and} \\quad\n\t\\tgamma^j(x,t') = \\tgamma^j(x, t') + \\delta \\gamma^j(x,t'), \\quad\n$$\nwhere we implicitly assume that $\\delta \\gamma^j$ is proportional\nto the Dirac $\\delta$ function,\n$\\delta(t' - t)$, for some $t$ and $\\delta \\gamma^j$ vanishes at\n$\\partial \\Omega$.\nAs we have concerns only for local effects or differential\nequations, we implicitly assume that\nwe can neglect the boundary effect arising from $\\partial \\Omega$ on\nthe variational equation.\nIf one needs the boundary effect, he would follow the study of\nShkoller \\cite{Shk}.\nFurther one could use the language of the sheaf theory to\ndescribe the local effects \\cite{KKK}.\nAs we are concerned only with differential equation and thus\nour theory is completely local except Section \\ref{sec:VOF}, we could\ndeal with germs of related bundles \\cite{AGLV} as in Reference \\cite{M},\nwhich is also naturally connected with\n a computational method of fluid dynamics \\cite{M2}.\n\nLet us consider the extremal point of the action integral (\\ref{eq:Eue})\nfollowing the variational principle.\nNoting that $\\partial \\gamma \/\\partial x=1$, \nthe above Jacobian becomes\n$$\n\\frac{\\partial (\\gamma + \\delta \\gamma)}{\\partial x} \n=\n\\frac{\\partial \\gamma}{\\partial x} \n(1 + \\partial_k \\delta \\gamma^k) + O((\\delta \\gamma)^2).\n$$\nSince we employ the projection method, we firstly\nconsider a variation\nin $\\diff(\\Omega)$ rather than $\\sdiff(\\Omega)$.\nFor the variation,\nthe action integral $\\cS_{\\free} [\\gamma]$ with (\\ref{eq:etgamma})\nbecomes \n\\begin{equation}\n\\begin{split}\n\\cS_\\free [\\gamma+\\delta \\gamma] -&\n\\cS_\\free [\\gamma] =\\\\\n &-\\int_T dt \\int_\\Omega \n\\frac{\\partial \\gamma}{\\partial x} \nd^3 x \\cdot dx^i \\otimes dx^i \n \\left(\n\t\\delta \\gamma^k\n \\frac{d}{dt}\\left( \\rho\n g^{-1} \\frac{d}{dt} g \\right)\n+ \\delta \\gamma^k \\partial_k \\frac{1}{2}\\rho |u|^2 \n \\right).\\\\\n\\end{split}\n\\nonumber\n\\end{equation}\n\nNow we have the following proposition.\n\n\\begin{proposition} \\label{prop:3-1}\nUsing the above definitions, the variational principle\nin $\\SDiff(\\Omega)$,\n$$\n\\frac{\\delta \\cS_\\free[\\gamma]}{\\delta \\gamma(x,t)}\n \\Bigr|_{\\SDiff(\\Omega)|_t} = 0,\n$$\nis reduced to the Euler equation,\n\\begin{equation}\n \\frac{\\partial}{\\partial t} \\rho u^i + u^j \\partial_j \\rho u^i\n + \\partial_i p = 0,\n\\label{eq:EulerEq}\n\\end{equation}\nwhere $p$ comes from the projection from $T\\Diff(\\Omega)|_{\\SDiff(\\Omega)}\n\\to T\\SDiff(\\Omega)$.\n\\end{proposition}\n\n\\begin{proof}\nBasically we leave the rigorous proof\nand especially the derivation of $p$ to \\cite{AK,EM}.\nThe existence of $p$ was investigated well in \nAppendix of Reference \\cite{EM}\nas the Hodge decomposition \\cite{AM,N}. (See also\n the following Remark \\ref{rmk:3-2}.)\nExcept the derivation of $p$,\nwe use the above relations and the following relations,\n\\begin{equation*}\n\\begin{split}\n \\frac{d}{dt}\n\t\\left(\\rho \\ee^{-\\tgamma^j \\partial_j}\n \\frac{d}{dt}\\ee^{\\tgamma^n \\partial_n} \\right) \n &=\n\t\\frac{d}{dt} \\left(\\rho u^i(\\gamma(t),t) \\partial_i \\right) \\\\\n &= \\left( \\frac{\\partial}{\\partial t}\\rho u^i|_{x=\\gamma}\n + (\\frac{d}{dt}\\tgamma^j) \\partial_j \\rho u^i \\right) \\partial_i \\\\\n &= \\left( \\frac{\\partial}{\\partial t}\\rho u^i\n + u^j \\partial_j\\rho u^i \\right) \\partial_i \\\\\n &=:\\left(\\frac{D}{Dt} \\rho u^i \\right) \\partial_i. \\\\\n\\end{split}\n\\end{equation*}\nThen we obtain the Euler equation.\n\\qed\n\\end{proof}\n\n\\begin{myremark}\\label{rmk:3-2}\n {\\rm{\nThe Euler equation was obtained by the simple variational principle.\nPhysically speaking, the conservation of \nthe momentum in the sense of Noether's theorem \\cite{BGG,IZ} led to\nthe Euler equation.\nHowever, we could introduce the pressure $p_L$ term as the\nLagrange multiplier of the constraint of the volume preserving.\nIn the case, instead of $\\cS_\\free$, we deal with \n$$\n\\cS_{\\free,p} = \\cS_\\free\n + \\int_T dt \\int_\\Omega p_L(x,t) \\frac{\\partial \\gamma}{\\partial x} \n d^3 x.\n$$\nThen noting the term coming from the Jacobian, the relation,\n$$\n\t\\frac{\\delta \\cS_{\\free,p}\n [\\gamma]}{\\delta\\gamma(x,t)}\\Bigr|_{\\SDiff(\\Omega)|_t} = 0,\n$$\nis reduced to the Euler equation,\n$$\n \\frac{\\partial}{\\partial t} \\rho u^i + u^j \\partial_j \\rho u^i\n + \\partial_i (p_L + \\frac{1}{2}\\rho |u|^2) = 0.\n$$\nAs the pressure is determined by the (divergence free) condition of $u$,\nwe renormalize \\cite[(25)]{Ko},\n$$\n\tp := p_L + \\frac{1}{2}\\rho |u|^2.\n$$\nMore rigorous arguments are left to References \\cite{EM,O} \nand physically interpretations are, {\\it {e.g.}}, in References\n\\cite{AF,B,K,NHK,Schm,V}.\n\n\nWe give a comment on \nthe projection from $T\\Diff(\\Omega)|_{\\SDiff(\\Omega)}\n\\to T\\SDiff(\\Omega)$ in (\\ref{eq:EulerEq}),\n which is known as the projection method.\nFirst we note that\nthe divergence free condition $\\mathrm{div} (u) =0$ \nsimplifies the Euler equation (\\ref{eq:EulerEq}),\n$$\n\\rho \\frac{D u}{D t} + \\nabla p = 0, \\quad \n\\frac{\\partial u^i}{\\partial t} + u^j \\partial_j u^i\n+\\frac{1}{\\rho}\\partial_i p=0.\n$$\nAs mention in Section \\ref{sec:VOF}, in the difference equation \nwe have a natural interpretation of the projection method \\cite{Cho}.\nWe, thus, regard $D u \/ Dt$ in $T\\Diff(\\Omega)|_{\\SDiff(\\Omega)}$ as \n$\\lim_{\\delta t \\to 0}\\frac{u(t + \\delta t)-u(t)}{\\delta t}$\nfor $u(t+\\delta t):=u(t+\\delta t,\\gamma(t+\\delta t)) \\in \\diff(\\Omega)$ \nand $u(t):=u(t,\\gamma(t)) \\in \\sdiff(\\Omega)$, {\\it{i.e.}},\n$\\mathrm{div} \\left(u(t)\\right) = 0$ by considering\n$T\\Diff(\\Omega)$ at the unit $e$ of $\\Diff(\\Omega)$\n up to $\\delta x^2$, as we did in (\\ref{eq:tgamma}) and (\\ref{eq:etgamma}).\nIn order to find the deformation \n$u^\\parallel(t + \\delta t)$ in $\\sdiff(\\Omega)$ by a natural projection\nfrom $\\diff(\\Omega)$ to $\\sdiff(\\Omega)$ \\cite[,p.36]{CM},\nwe decompose $u(t + \\delta t)$ into $u^\\parallel(t + \\delta t)$ and\n$u^\\perp(t + \\delta t)$ such that \n$\\partial_i u^{\\perp i}(t + \\delta t) := \\partial_i u^i(t + \\delta t)$.\nThen $u^\\parallel(t + \\delta t):= u(t + \\delta t)-$\n$u^\\perp(t + \\delta t)$ belongs to $\\sdiff(\\Omega)$.\nThus the pressure $p$ is determined by \\cite{CM}\n\\begin{equation}\n\\partial_i u^i(t + \\delta t)\n+ \\delta t\\partial_i \\frac{1}{\\rho}\\partial_i p =0.\\quad\n\\label{eq:ProjM}\n\\end{equation}\nIn other words, since $u^\\parallel(t + \\delta t)\\equiv$\n$u^i(t + \\delta t) + \\delta t \\frac{1}{\\rho}\\partial_i p$ belongs\nto $\\sdiff(\\Omega)$,\nthe deformation of\n$u^{\\parallel i}(t + \\delta t)- u^i(t)$ which gives\n$D u^\\parallel\/D t$ and the Euler equation (\\ref{eq:EulerEq}) \nis the deformation in $\\IFluid(\\Omega \\times T)$.\nAfter taking the continuous limit $\\delta t \\to 0$,\nthe equation for the pressure\n(\\ref{eq:ProjM}) can be written as \\cite{Cho},\n$$\n(\\partial_i u^j) (\\partial_j u^i)\n+\\partial_i\\frac{1}{\\rho}\\partial_i p=0,\n$$\nby noting the relations $[\\partial_t, \\partial_i]=0$\nand $\\mathrm{div}(u(t))=0$, {\\it{i.e.}},\n$\\partial_i u^i(t + \\delta t) = \\partial_i[ u^i(t) $\n$+ \\frac{\\partial}{\\partial t} u^i(t) \\delta t$\n$+ u^j(t)\\partial_j u^i(t)\\delta t] + O(\\delta t^2)$.\nThe Poisson equation with (\\ref{eq:EulerEq})\nguarantees the divergence free condition.\nHence the pressure $p$ in the incompressible fluid\nis determined geometrically.\n}}\n\\end{myremark}\n\n\n\n\n\\section{Reformulation of Surface tension as a minimal surface energy}\n\\label{sec:four}\n\nIn this section we reformulate \nthe SURFER scheme \\cite{LZZ} following the \nvariational principle and the arguments of previous sections.\n\n\n\\subsection{Analytic expression of surface area}\\label{subsec:fourOne}\n\nWe first should note that in general, the higher dimensional \ngeneralized function like the Dirac delta function has some\ndifficulties in its definition \\cite{Y}. \nFor the difficulties,\nin the Sato hyperfunctions theory \\cite{KKK},\nthe sheaf theory and the cohomology theory are\nnecessary to the descriptions of the higher dimensional generalized functions,\nwhich are too abstract to be applied to a problem with an arbitrary\ngeometrical setting.\nEven for the generalized function in the framework of\nSchwartz distribution theory, we should pay attentions on its treatment.\nHowever since the surface $S$ in this article is \na hypersurface and its codimension is one, \nthe situation makes the problems much easier.\n\nWe assume that the smooth surface $S$ is orientable and compact such\n that we could\ndefine its inner side and outer side. In other words, there\nis a three dimensional subspace (a manifold with boundary) $B$ such that\nits boundary $\\partial B$ agrees with $S$ and $B$ \nis equal to the inner side of $S$ with $S$ itself.\nThen we consider a generalized function $\\theta$ over \n$\\Omega \\subset \\EE^3$ such that\nit vanishes over the complement $B^c = \\Omega \\setminus B$\n and is unity for the interior $B^\\circ:=\nB \\setminus \\partial B$;\n$\\theta$ is known as a characteristic function of $B$.\n\nWe consider the global function \n$\\theta(x)$ and its derivative $d\\theta(x)$ in the sense of\nthe generalized function, which is given by\n\\begin{equation*}\nd \\theta(x) = \\sum_{i}\\partial_i \\theta(x) d x^i =\\partial_q \\theta(x) d q.\n\\end{equation*}\nHere we use the notations in Section \\ref{sec:two-one}.\nUsing the nabla symbol $\\nabla \\theta=(\\partial_i \\theta(x) )_{i=1,2,3}$,\n$|\\nabla \\theta| d^3 x$ is interpreted as\n\\begin{equation*}\n\t|\\nabla \\theta|d^3 x=|(* d \\theta)\\wedge dq|. \\quad\n\\end{equation*}\nHere due to the Hodge star operation \n$*: \\Lambda^p(\\Omega) \\to \\Lambda^{3-p}(\\Omega)$,\n$* d \\theta = \\tilde e \\partial_q \\theta d s^1\\wedge d s^2$\nwhere $\\tilde e$ is the Jacobian between the coordinate systems\n $(d s^1, d s^2, d q)$ and $(d x^1, d x^2, d x^3)$.\nThen we have the following proposition;\n\\begin{proposition}\nIf the integral,\n\\begin{equation*}\n\\cA:=\t\\int_{\\Omega} |\\nabla \\theta| d^3 x \\equiv \\int_{\\Omega} \n |(* d \\theta)\\wedge dq|,\n\\end{equation*}\nis finite, $\\cA$ agrees with the area of the surface $S$.\n\\end{proposition}\n\nIt should be noted that due to the codimension of \n$S \\subset \\Omega$,\nwe have used the fact that the Dirac $\\delta$ function \nalong $q \\in T_S$ is the integrable\n function whose integral is the Heaviside function.\nThis fact is a key of this approach.\n\n\\subsection{Quasi-characteristic function for surface area}\n\n\nFor the later convenience, we introduce a support of \na function over $\\Omega$, which \nis denoted by {\\lq\\lq}supp{\\rq\\rq}, {\\it{i.e.}},\nfor a function $g$ over $\\Omega$, its support is defined by\n$$\n\t\\supp(g) =\\overline{\\{x \\in \\Omega \\ | \\ g(x) \\neq 0\\}},\n$$\nwhere {\\lq\\lq}$\\ \\bar{\\ } \\ ${\\rq\\rq}\n means the closure as the topological space $\\Omega$.\n\n\nOne of our purposes is to express the surface $S$ by means of \nnumerical methods, approximately.\nSince it is difficult to deal with the generalized function $\\theta$\nin a discrete system\nlike the structure lattice \\cite{Ch},\nwe introduce a smooth function $\\xi$ over $\\Omega$\nas a quasi-characteristic function\nwhich approximates the function\n$\\theta$\n\\cite{BKZ,LZZ},\n\\begin{equation}\n \\xi(x) =\\left\\{ \n\\begin{matrix} \n 0 & \\mbox{for } x \\in B^c \\bigcap \\{x \\in \\Omega \\ | \\ \n |q(x)| < \\epsX\/2\\}^c, \\\\\n 1 & \\mbox{for } x \\in B \\bigcap \\{x \\in \\Omega\\ | \\\n |q(x)| < \\epsX\/2\\}^c, \\\\\n \\mbox{monotonically increasing in } q(x)& \\ \\mbox{otherwise}. \n \\end{matrix}\n \\right.\n\\label{eq:epsX}\n\\end{equation}\nWe note that along the line of $d q$ for $q \\in (-\\epsX\/2, \\epsX\/2)$,\n$\\xi$ is a monotonically increasing function which interpolates \nbetween $0$ and $1$.\nWe now implicitly assume that $\\epsX$ is much smaller than\n$\\epsT$ defined in Section \\ref{sec:two-one} so that\nsupport of $|\\nabla \\xi|$ is in the tubular neighborhood $T_S$.\nHowever after formulating\nthe theory, we extend the geometrical setting in Section \\ref{sec:two-one}\nto more general ones which include singularities; there\n$\\epsT$ might lose its mathematical meaning but $\\epsX$\nsurvives as a control parameter which governs the system.\nFor example, as in Reference \\cite{LZZ}, \nwe can also deal with a topology change well.\n\nBy letting $\\xi^c (x) := 1 - \\xi(x)$, $\\xi^c$ and $\\xi$ are\nregarded as the partition of unity \\cite[I p.272]{KN}, or\n$$\n\t\\xi(x) + \\xi^c(x) \\equiv 1.\n$$\nWe call these $\\xi$ and $\\xi^c$ {\\lq\\lq}color functions{\\rq\\rq} or \n{\\lq\\lq}phase fields{\\rq\\rq} in the following sections.\nWe have an approximation\nof the area of the surface $S$ by the following proposition.\n\n\\begin{proposition}\nDepending upon $\\epsX$, we define the integral,\n\\begin{equation*}\n \\cA_{\\xi}:=\\int_{\\Omega} |\\nabla \\xi| d^3 x, \n\\end{equation*}\nand then the following inequality holds,\n\\begin{equation*}\n\\quad |\\cA_\\xi -\\cA | < \\epsX \\cdot \\cA.\n\\end{equation*}\n\\end{proposition}\n\nHere we note that\n$\\cA_\\xi$ is regarded as the approximation of the area $\\cA$ of $S$\ncontrolled by $\\epsX$. In other words,\nwe use $\\epsX$ as the parameter which controls the\ndifference between the characteristic function $\\theta$ and \nthe quasi-characteristic function $\\xi$\nin the phase field model \\cite{BKZ,LZZ}.\n\nLet us consider its extremal point following the \nvariational principle in a purely geometrical sense.\n\n\\begin{proposition} \\label{prop:4-3}\nFor sufficiently small $\\epsX$, we have\n\\begin{equation*}\n\\begin{split}\n\t\\frac{\\delta}{\\delta \\xi(x)} \\cA_\\xi \n &=-\\partial_i\\frac{\\partial_i \\xi }{|\\nabla \\xi| }(x)\n \\\\\n &=\\kappa(x),\n\\end{split}\n\\end{equation*}\nwhere $x \\in S$ or $q=0$. \n\\end{proposition}\n\n\\begin{proof}\nNoting the facts that $\\partial \\xi\/\\partial q <0$ at $q=0$ and\n\\begin{equation*}\n\t|\\nabla \\xi| = \\sqrt{\\nabla \\xi\\cdot \\nabla \\xi},\n\\end{equation*}\nProposition \\ref{prop:A-1} and\nthe equality in Proposition \\ref{prop:2-1} show the relation.\n\\qed\n\\end{proof}\n\n\nIn the vicinity of $S$, $q$ in Section \\ref{sec:two-one}\ncould\nbe identified with the level-set function and \nthe authors in References \\cite{ZCMO,ZMOW} also used this relation.\nSince all of geometrical quantities on $S$ are\nlifted to $T_S$ as the inverse image of $\\pi$,\nthe relation in Proposition \\ref{prop:4-3} is also\ndefined over $(\\supp(|\\nabla \\xi|))^\\circ \\subset T_S$\nand we redefine the $\\kappa$ by the relation from here.\n\n\\bigskip\n\\subsection{Statics}\nLet us consider physical problems as we finish the geometrical\nsetting.\nBefore we consider dynamics of the phase field flow,\nwe consider a statical surface problem.\nLet $\\sigma$ be the surface tension coefficient between two fluids\ncorresponding to $\\xi$ and $\\xi^c$.\nNow let us call $\\xi$ and $\\xi^c$ {\\lq\\lq}color functions{\\rq\\rq} or \n{\\lq\\lq}phase fields{\\rq\\rq}. \nMore precisely, we say that a color function with individual\nphysical parameters is a phase field.\nThe surface energy $\\cE:=\\sigma \\cA$ is, then, approximately given by\n\\begin{equation}\n\t\\cE_\\two := \\sigma \\cA_\\xi = \\sigma\\int_{\\Omega} |\\nabla \\xi| d^3 x.\n\\end{equation}\nAs a statical mechanical problem, we consider\nthe variational method of this system following Section \\ref{sec:two-two}.\n\nSince a statical surface phenomenon is caused by the difference\nof the pressure of each material, we now consider a free energy functional\n\\cite{MM},\n\\begin{equation}\n\t\\cF_\\two :=\\int_{\\Omega} \n \\left(\\sigma|\\nabla \\xi| - (p_1 \\xi + p_2 \\xi^c)\\right) d^3 x,\n \\label{eq:ELs0}\n\\end{equation}\nwhere $p_a$ ($a = 1, 2$) is the proper pressure of each material.\n\n\\begin{proposition}\\label{prop:4-4}\nThe variational problem with respect to $\\xi$, $\\delta \\cF_\\two\/\\delta \\xi =0$,\nreproduces the Laplace equation {\\rm{\\cite[Chap.7]{LL}}},\n\\begin{equation}\n (p_1 - p_2) - \\sigma \\kappa(x) = 0, \\quad x\\in \n (\\supp(|\\nabla \\xi|))^\\circ.\n \\label{eq:ELs}\n\\end{equation}\n\\end{proposition}\n\\begin{proof}\nAs in Proposition \\ref{prop:A-1}, direct computations give the relation.\n\\qed\n\\end{proof}\n\nThis proposition implies that the functional $\\cF_\\two$ is natural. The\nsolutions of (\\ref{eq:ELs}) are given by the constant mean curvature\nsurfaces studied in References \\cite{FW,GMO,T}. \n\nFurthermore we also have another static equation,\nwhose relation to the Laplace equation (\\ref{eq:ELs})\nis written in Remark \n\\ref{rmk:4-2}.\n\\begin{proposition}\\label{prop:4-5}\nFor every point $x \\in \\Omega$,\nthe variation principle,\n$\\delta \\cF_\\two\/\\delta x^i=0$, gives\n\\begin{equation}\n \\sigma\\left( \\sum_j \\partial_i\\frac{\\partial_j \\xi \n \\partial_j \\xi }{|\\nabla \\xi| }\n - \\sum_j\\partial_j\\frac{\\partial_j \\xi \\partial_i \\xi }{|\\nabla \\xi| }\n \\right)\n - (p_1 - p_2) \\partial_i \\xi=0,\n\\label{eq:Amini0}\n\\end{equation}\nor\n\\begin{equation}\n\\partial_j \\tau_{ij}(x) - (p_1 - p_2) \\partial_i \\xi(x)=0,\n\\label{eq:Amini}\n\\end{equation}\nwhere\n$$\n\\tau(x) := \\sigma\\left(I - \\frac{\\nabla \\xi}{|\\nabla \\xi|}\n\\otimes\n\\frac{\\nabla \\xi}{|\\nabla \\xi|}\\right)\n|\\nabla \\xi|(x).\n$$\n\\end{proposition}\n\n\\begin{proof}\nWe are, now, concerned with the variation \n$x \\to x + \\delta x$ for every point $x \\in \\Omega$.\nWe apply Proposition \\ref{prop:A-2} to this case, {\\it{i.e.}},\n\\begin{equation*}\n\\begin{split}\n\\frac{\\delta \\cF_\\two}{\\delta x^i}\n&= -\\sigma \\left[\\partial_i |\\nabla \\xi|- \\partial_j\\left(\n \\partial_i \\xi(x) \\cdot\\frac{\\delta}{\\delta\\partial_j \\xi(x)} \n |\\nabla \\xi| \\right) \\right](x)\n + (p_1 - p_2) \\partial_i \\xi(x),\n\\end{split}\n\\end{equation*}\nby using (\\ref{eq:ELs}) as its Euler-Lagrange equation\n(\\ref{eq:AppeA1}). Further for $x\\not\\in (\\supp(|\\nabla \\xi|))^\\circ$,\nits Euler-Lagrange equation (\\ref{eq:AppeA1}) gives a trivial\nrelation, {\\it{i.e.}}, {\\lq\\lq}$0=0${\\rq\\rq}.\nThen we have (\\ref{eq:Amini}).\n\\qed\n\\end{proof}\n\n\n\\begin{myremark}\\label{rmk:4-0}\n{\\rm{\nIt is worthwhile noting that (\\ref{eq:Amini0}) and (\\ref{eq:Amini}) \nare defined over $\\Omega$\nrather than $(\\supp(|\\nabla \\xi|))^\\circ$ because due to the relation,\n$$\n | \\partial_i \\xi | \\le |\\nabla \\xi|,\n$$\neven at the point at which denominators in the first term in (\\ref{eq:Amini0}) \nvanish,\nthe first term is well-defined and vanishes.\n\nHence (\\ref{eq:Amini0}) and (\\ref{eq:Amini}) \ncould be regarded as an extension of the defined region\nof (\\ref{eq:ELs}) to $\\Omega$ and thus \n (\\ref{eq:Amini0}) and (\\ref{eq:Amini}) \nhave the advantage over (\\ref{eq:ELs}).\nThe extension makes the handling of the surface tension much easier.\n}}\n\\end{myremark}\n\n\\begin{myremark}\\label{rmk:4-1}\n{\\rm{\nIn the statical mechanics, there appears a force $\\partial_i \\tau_{ij}$,\nwhich agrees with one in \n(33) and (34) in Reference \\cite{LZZ}\nand (2.11) in Reference \\cite{Jac}. \nWe should note that in Reference \\cite{Jac},\nit was also stated that \n this term is derived from the momentum conservation \nhowever there was not its derivation in detail. \nThe derivation of the above $\\tau$ needs the \nEuler-Lagrange equation (\\ref{eq:AppeA1}), which corresponds to\n the Laplace equation (\\ref{eq:ELs}) in this case,\nwhen we apply Proposition \\ref{prop:A-2} to\nthis system, though these objects did not appear in Reference \\cite{Jac}. \n}}\n\\end{myremark}\n\n\\begin{myremark}\\label{rmk:4-2}\n{\\rm{\nIn this remark, we comment on the identity between \n (\\ref{eq:ELs}) and (\\ref{eq:Amini}).\nComparing these, we have the identity,\n$$\n\t\\partial_i \\tau_{ij} = \\sigma \\partial_j \\xi \\cdot \\kappa,\n$$\nwhich is, of course, obtained from the primitive computations.\nIt implies that (\\ref{eq:Amini}) can be derived from the \nLaplace equation (\\ref{eq:ELs}) with this relation. \nHowever it is worthwhile noting that both come from the \nvariational principle in this article.\n In fact, when we handle multiple junctions,\nwe need a generalization of the Laplace equations over there \nlike (\\ref{eq:ELM3}), which is not easily obtained by taking the primitive\napproach. \nFurther the derivations from the variational principle\nshow their geometrical meaning in the sense of References \\cite{Ar,AM,BGG}.\n}}\n\\end{myremark}\n\\bigskip\n\\subsection{Dynamics}\n\nNow we investigate the dynamics of the two-phase field.\nThere are two different liquids which are expressed by\nphase fields $\\xi$ and $\\xi^c$ respectively.\nWe assume that they obey the incompressible fluid\ndynamics. \nAs in the previous section,\nwe consider the action of the volume-preserving diffeomorphism \ngroup $\\SDiff(\\Omega)$\non the color functions $\\xi$ and $\\xi^c$.\nWe extend the domain of $\\xi$ and $\\xi^c$ to $\\Omega \\times T$\nand they are smooth sections of $\\Coor(\\Omega\\times T)$.\nFor the given $t$, we will regard\n$\\xi$ and $\\xi^c$ as functions of $\\gamma^i$ in the previous section,\n{\\it{i.e.},} $\\xi=\\xi(\\gamma(x,t))$.\nFor example, the density of the fluid is expressed by the relation,\n $$\n\\rho = \\rho_1 \\xi^c + \\rho_2 \\xi\n$$ \nfor constant proper densities $\\rho_1$ and $\\rho_2$ of the individual\nliquids.\nThe density $\\rho$, now, differs from a constant function\nover $\\Omega\\times T$ in general.\n\nWe consider the action integral $\\cS_{\\two}$ \nincluding the surface energy,\n\\begin{equation}\n\t\\cS_{\\two}[\\gamma]\n = \\int_T d t\\int_{\\Omega}\\left( \\frac{1}{2}\\rho |u|^2 -\n \\sigma|\\nabla \\xi| + (p_1\\xi + p_2 \\xi^c) \\right) d^3 x.\n\\label{eq:AI2d}\n\\end{equation}\nThe ratio between $\\rho$ and $\\sigma$\ndetermines the ratio between the contributions\nof the kinematic part and the potential (or surface energy) part\nin the dynamics of the fluid.\nSince the integrand in \n(\\ref{eq:AI2d}) contains no $\\partial \\xi\/\\partial t$ term,\nwe obtain the same terms in the variational calculations\nfrom the second and the third term in (\\ref{eq:AI2d})\nas those in (\\ref{eq:ELs}) and (\\ref{eq:Amini}) in \n the static case even if we regard $n$ as $4$\nand $x^4$ as $t$ in Section \\ref{sec:two-two}.\nBy applying Proposition \\ref{prop:A-1} to this system, we have\nthe following proposition as the Euler-Lagrange equation for $\\xi$.\n\n\\begin{lemma}\nThe function derivative of $\\cS_{\\two}$ with respect to $\\xi$ gives\n\\begin{equation}\n\t\\frac{1}{2}(\\rho_1 - \\rho_2) |u(x,t)|^2\n + (p_1 - p_2) - \\sigma \\kappa (x,t)= 0, \n \\quad x\\in \n (\\supp(|\\nabla \\xi|))^\\circ,\n\\label{eq:EL2d}\n\\end{equation}\nup to the volume preserving condition.\n\\end{lemma}\nThis could be interpreted as a generalization of the \nLaplace equation (\\ref{eq:ELs}) as in the following remark.\n\n\\begin{myremark}{\\rm{\nHere we give some comments on the\ngeneralized Laplace equation (\\ref{eq:EL2d})\nup to the volume preserving condition.\nThis relation (\\ref{eq:EL2d})\ndoes not look invariant for \nGalileo's transformation, $u \\to u + u_0$ for a constant velocity\n$u_0$.\nHowever for the simplest problem of Galileo's boost,\n{\\it{i.e.}}, static state on a system with a constant velocity $u_0$,\nthe equation (\\ref{eq:EL2d}) gives\n\\begin{equation}\n\t\\frac{1}{2}(\\rho_1 - \\rho_2) |u_0|^2\n + (p_1 - p_2) - \\sigma \\kappa (x,t)= 0, \n \\quad x\\in \n (\\supp(|\\nabla \\xi|))^\\circ,\n\\end{equation}\nwhich might differ from the Laplace equation (\\ref{eq:ELs}).\nHowever for the boost, we should transform $p_a$ into\n\\begin{equation}\n\t\\tilde p_a := p_a + \\frac{1}{2} \\rho_a |u_0|^2.\n\\label{eq:GalileoB}\n\\end{equation}\nThen the above equation of $\\tilde p_a$ agrees with the static one\n (\\ref{eq:ELs}). In other words (\\ref{eq:GalileoB}) makes\n our theory invariant for the Gaililio's transformation.\n\nFor a more general case, we should regard $p_a$ as a function over\n$\\Omega \\times T$ rather than a constant number due to\nthe volume preserving condition.\nThese values are contained in the pressure as mentioned in \n(\\ref{eq:p_PLTwo}). \nThe statement {\\lq\\lq}up to the volume preserving condition{\\rq\\rq}\n has the meaning in this sense.\nIn fact, in the numerical computation,\nthese individual pressures $p_a$'s are not so important\nas we see in Remark \\ref{rmk:4-11}.\nDue to the constraint of the incompressible\n(volume-preserving) condition, the pressure $p$ is determined\nas mentioned in Remark \\ref{rmk:3-2}.\nThere are no contradictions with the \nGalileo's transformation and $\\SDiff(\\Omega)$-action.\n}}\n\\end{myremark}\n\n\n\n\n\nWe consider the infinitesimal action of\n$\\SDiff(\\Omega)$ around its identity.\nAs did in Section \\ref{sec:three},\nwe apply the variational method to this\nsystem in order to obtain the Euler equation with the surface tension.\n\n\n\\begin{proposition}\\label{prop:4-6}\nFor every $(x,t) \\in \\Omega \\times T$,\nthe variational principle,\n$\\delta \\cS_\\two\/\\delta \\gamma^i(x,t) = 0$, gives the equation of motion,\nor the Euler equation with the surface tension,\n\\begin{equation}\n\\begin{split}\n \\frac{D \\rho u^i}{D t} +\n \\sigma \\left( \\sum_j \\partial_i\\frac{\\partial_j \\xi \n \\partial_j \\xi }{|\\nabla \\xi| }\n - \\sum_j \\partial_j\\frac{\\partial_j \\xi \\partial_i \\xi }{|\\nabla \\xi| }\n \\right)\n + \\partial_i p = 0.\n\\label{eq:Eulxi}\n\\end{split}\n\\end{equation}\nHere $p$ is also the pressure coming from the effect of the \nvolume-preserving.\n\\end{proposition}\n\n\\begin{proof}\nThe measure $d^3 x$ is regarded as \n$\\displaystyle{\\frac{\\partial \\gamma}{\\partial x} d^3 x}$ with\n$\\displaystyle{\\frac{\\partial \\gamma}{\\partial x} = 1}$.\nNoting $\\displaystyle{\\frac{d}{dt}\\frac{\\partial \\gamma}{\\partial x} = 0}$,\nthe proof in Proposition \\ref{prop:3-1} and Remark \\ref{rmk:3-2}\nprovide the kinematic part with pressure term and \nProposition \\ref{prop:4-5} gives the remainder.\nIn this proof, the total pressure $p$ is defined in Remark \\ref{rmk:4-11}.\n\\qed\n\\end{proof}\n\n\\begin{myremark} \\label{rmk:4-11}\n{\\rm{\nMore rigorous speaking, as we did in Remark \\ref{rmk:3-2},\nwe also renormalize the pressure,\n\\begin{equation}\n\\begin{split}\n\tp &= p_L + \\frac{1}{2}\\rho |u|^2 + p_1 \\xi + p_2 \\xi^c \\\\\n\t&= p_L + \\frac{1}{2}(\\rho_1 - \\rho_2) \\xi |u|^2 \n+ (p_1 - p_2) \\xi + \\frac{1}{2}\\rho_2 |u|^2 + p_2. \n\\end{split}\n\\label{eq:p_PLTwo}\n\\end{equation}\nAs in Section \\ref{sec:two-two}, the third term in \n(\\ref{eq:Eulxi}) includes the effects from $p_a$'s via the\ngeneralized Laplace equation (\\ref{eq:EL2d}) as the Euler-Lagrange\nequation (\\ref{eq:AppeA1}).\n}}\n\\end{myremark}\n\n\\begin{myremark} \\label{rmk:7} {\\rm{\n\\begin{enumerate}\n\\item\nThe equation of motion (\\ref{eq:Eulxi}) is \nthe same as (24) in Reference \\cite{LZZ} basically.\nWe emphasize that it is \nreproduced by the variational principle.\n\\item\nAs in Reference \\cite{LZZ}, in our framework, we can deal with the \ntopology\nchanges and the singularities which are controlled by the parameter\n$\\epsX$. The above dynamics is well-defined as a field\nequation provided that $\\epsX$ is finite. If needs,\none can evaluate\nits extrapolation for vanishing of $\\epsX$.\n\n\\item\nIn general, $\\epsX$ is not constant for the time\ndevelopment. Due to the equation of motion, it changes.\nAt least, in numerical computation, the numerical\ndiffusion makes the intermediate region wider in general. \nHowever even when the time passes \nbut we regard it as a small parameter,\nthe approximation is justified.\n\n\\item\nSince from Remark \\ref{rmk:4-0}, the surface tension is defined over \n$\\Omega$, the Euler equation is defined over $\\Omega$ without any \nassumptions.\n\n\\item\nIt should be noted that the surface force is\nnot difficult to be computed as in Reference \\cite{LZZ} but\nthere sometimes appear so-called parasite current problems in the \ncomputations even though we will not touch the problem \nin this article.\n\n\\end{enumerate}\n}}\n\\end{myremark}\n\n\\section{Multi-phase flow with multiple junctions}\n\\label{sec:five}\n\nIn this section,\nwe extend the SURFER scheme \\cite{LZZ}\nof two-phase flow to multi-phase ($N$-phase, $N\\ge2$) flow.\n\n\\subsection{Geometry of color functions}\n\nIn order to extend the geometry of the color functions in the previous\nsection, we introduce several geometrical tools.\nFirst let us define a geometrical object similar to smooth $d$-manifold \nwith boundary.\nHere we note that $d$-manifold means $d$-dimensional manifold,\nand $d$-manifold with boundary means that \nits interior is a $d$-manifold and its boundary is\na $(d-1)$-dimensional manifold. We distinguish\na smooth (differential) manifold from a topological manifold here.\n\nWhen we consider multi-junctions in $\\EE^3$,\nwe encounter\na geometrical object with smooth {\\lq\\lq}boundaries{\\rq\\rq} whose dimensions\nare two, one and zero \neven though it is regarded as a topological $3$-manifold with boundary.\n\n\\begin{definition} \\label{def:5-1}\nWe say that a path-connected topological $d$-manifold with boundary $V$ is \n{\\it{a path-connected interior smooth $d$-manifold}}\nif $V$ satisfies the followings:\n\\begin{enumerate}\n\n\\item \nThe interior $V^\\circ$ is a path-connected smooth $d$-manifold, and\n\n\\item\n $V$ has finite path-connected subspaces \n$V_\\alpha$, $(\\alpha = 1, \\cdots, \\ell)$\nsuch that\n\\begin{enumerate}\n\\item $V\\setminus V^\\circ$ is decomposed by $V_\\alpha$, \n{\\it{i.e.}},\n$$\nV\\setminus V^\\circ = \\coprod_{\\alpha = 1}^\\ell V_\\alpha,\n$$\n\n\\item Each $V_\\alpha$ is a path-connected \nsmooth $k$-manifold in $\\Omega$ $(k 0$ and approximate \nthe characteristic functions over $B_a$.\n\n\\bigskip\nTo define color functions $\\xi_a(x)$ $(a=0, 1, 2, \\cdots, N-1)$, \n we introduce another geometrical object, \n{\\it{$\\epsilon$-tubular neighborhood}} in $\\EE^3$:\n\\begin{definition} \\label{def:5-3}\nFor a closed subspace $U \\subset \\Omega$ and a positive number $\\epsilon$, \n{\\it{$\\epsilon$-tubular neighborhood $T_{U, \\epsilon}$ of $U$}} is \ndefined by\n$$\n T_{U, \\epsilon} := \\{ x \\in \\Omega \\ |\\ \\dist(x, U) < \\frac{\\epsilon}{2}\\},\n$$\nwhere $\\dist(x, U)$ is the distance between $x$ and $U$ in $\\EE^3$.\n\\end{definition}\n\nWe assume that each $T_{\\partial_\\sing B_a, \\epsilon}$ has a\nfiber structure over $\\partial_\\sing B_a$ as topological manifolds\nas mentioned in Section \\ref{sec:two-one}.\nUsing the $\\epsilon$-tubular neighborhood, we define \n$\\epsX$-controlled color functions.\n\n\\begin{definition} \\label{def:5-4}\nWe say that \n$N$ smooth non-negative functions $\\{ \\xi_a\\}_{a=0, \\cdots, N-1}$ \nover $\\Omega \\subset \\EE^3$ are\n{\\it{$\\epsX$-controlled color functions associated with\na colored decomposition $\\{ B_a\\}_{a=0, \\cdots, N-1}$ of $\\Omega$}},\nif they satisfy the followings:\n\\begin{enumerate}\n\\item $\\xi_a$ belongs to $\\cC^\\infty(\\Omega)$ and for $x \\in \\Omega$,\n$$\n \\sum_{a=0, 1 \\cdots, N-1} \\xi_a(x) \\equiv 1.\n$$\n\\item For every $ M_a := \\supp(\\xi_a)$ and \n $L_a := \\supp(1-\\xi_a)$, $(a=0, 1, \\cdots, N-1)$,\n\\begin{enumerate}\n \\item $B_a \\varsubsetneqq M_a$,\n\n \\item $L_a^c \\varsubsetneqq B_a$,\n\n \\item $(M_a \\setminus L_a^c)^\\circ \\subset T_{\\partial_\\sing B_a, \\epsX}$,\n\n \\item $(M_a \\setminus L_a^c)^\\circ \\supset \\partial_\\sing B_a$.\n\\end{enumerate}\n\n\\item For $x \\in (M_a \\setminus L_a^c)$,\n we define the smooth function $q_a$ by\n$$\n\tq_a(x) = \\left\\{ \\begin{matrix} \n \\dist(x, \\partial_\\sing B_a), & x \\in (M_a \\setminus B_a), \\\\\n -\\dist(x, \\partial_\\sing B_a), & \n \\mbox{ otherwise. }\n \\end{matrix} \\right. \n$$\nThen for the flow $\\exp( - t \\frac{\\partial}{\\partial q_a})$\non $\\cC^\\infty(\\Omega) |_{ (M_a \\setminus L_a^c)}$,\n$\\xi_a$ monotonically increases along $t \\in U \\subset \\RR$ at\n$x \\in (M_a \\setminus L_a^c)$.\n\\end{enumerate}\nWhen $(M_a \\setminus L_a^c)^\\circ = T_{ \\partial_\\sing B_a, \\epsX}$\nfor every $a$,\n $\\{ \\xi_a\\}_{a=0, \\cdots, N-1}$ are called \n{\\it{proper $\\epsX$-controlled color functions associated with\nthe colored decomposition of $\\Omega \\subset \\EE^3$,\n$\\{ B_a\\}_{a=0, \\cdots, N-1}$}} or merely {\\it{proper}}.\n\\end{definition}\n\n\\bigskip\nThe functions $\\xi_a$'s are, geometrically, the partition of unity \n\\cite[I p.272]{KN} \nand a quasi-characteristic function, roughly speaking, which\nis equal to $1$ in the far inner side of $B_a$, vanishes at the \nfar outer side of\n$B_a$ and monotonically behaves in the artificial intermediate region.\nNoting that the flow $\\exp( - t \\frac{\\partial}{\\partial q})$ corresponds\nto the flow from the outer side to the inner side, \n$\\xi_a$ decreases from the inner side to the outer side.\n\\bigskip\n\nFrom here, let us go on to use the notations $B_a$, $M_a$, $L_a$, and\n$\\xi_a$ in Definition \\ref{def:5-4}.\nFurther for later convenience, we employ the following assumptions\nwhich are not essential in our theory but make the arguments simpler. \n\\begin{assumption}\\label{assump:one} {\\rm{\nWe assume the following:\n\\begin{enumerate}\n\\item {\\it{The colored decomposition $\\{ B_a\\}_{a=0, \\cdots, N-1}$ of $\\Omega$\nand $\\epsX$ satisfy the condition\nthat every $L_a^c$ is not the empty set.}}\n\nThis assumption means that the singularities that we consider can be resolved \nby the above procedure. Since $\\epsX$ can be small enough,\nthis assumption does not have crucial effects on our theory.\n\n\\item {\\it{The colored decomposition $\\{ B_a\\}_{a=0, \\cdots, N-1}$ of $\\Omega$\nand $\\epsX$ satisfy the relation,\n$$\n\t\\partial \\Omega \\bigcap\n \\left(\\bigcup_{a \\neq b; a, b \\neq 0} M_a\\bigcap M_b\\right)\n = \\emptyset,\n$$\nand every intersection $B_a \\bigcap B_0$ perpendicularly intersects\nwith $\\partial \\Omega$.}}\n\nThis describes the asymptotic behavior of the materials.\nFor example $M_0$ will be assigned to a wall in Section \\ref{sec:VOF}.\nThis assumption is neither so essential in this\nmodel but makes the arguments easy of the boundary effect. \nAs mentioned in Section \\ref{sec:three},\nwe neglect the boundary effect because we are concerned only with\na local theory or differential equations. \nIf one wishes to remove this assumption, he could consider smaller\nregion $\\Omega'\\subset \\Omega$ after formulates the problems in \n$\\Omega$.\n\n\\item\n{\\it{ The volume of every $B_a$, the \narea of every $\\partial_\\sing B_a$,\nand the length defined over every one-dimensional object\n in $\\partial_\\sing B_a$ \nare finite.}}\n\nAs our theory is basically local, this assumption is not essential, either.\n\\end{enumerate}\n}}\n\\end{assumption}\n\n\nUnder the assumptions,\nwe fix colored decomposition $\\{ B_a\\}_{a=0, \\cdots, N-1}$ and \n $\\epsX$-controlled color functions $\\{ \\xi_a\\}_{a=0, \\cdots, N-1}$.\n\nAs mentioned in the previous section, we have an approximate\ndescription of the area of $\\partial_\\sing B_a$.\n\n\\begin{proposition} \\label{prop:5-1}\nBy letting\nthe area of $\\partial_\\sing B_a$ be $\\cA_a$, the integral\n$$\n \\cA_{\\xi_a}:=\\int_{\\Omega} |\\nabla \\xi_a| d^3 x,\n$$\napproximates $\\cA_a$ by\n$$\n\t|\\cA_{\\xi_a} - \\cA_a | < \\epsX \\cA_a.\n$$\n\\end{proposition}\n\n\n\nHere we notice that \n$M_{ab}:=M_a \\bigcap M_b$ $(a, b = 0, 1, 2, \\cdots, N-1, a\\neq b)$ means\nthe intermediate region whose interior\nis a $3$-manifold.\nSimilarly we define $M_{abc}:=M_a \\bigcap M_b \\bigcap M_c$\n$(a, b, c = 0, 1, 2, \\cdots, N-1; a\\neq b, c; b\\neq c)$ and so on.\nSince the relation, $\\bigcup M_a = \\Omega$, holds,\nwe look on the intersections of $M_a$ as an approximation of\nthe intersections of $B_a$ which is parameterized by $\\epsX$.\nEven though there exist some singular geometrical\nobjects in $\\{B_a\\}$ \\cite{AGLV},\nwe can avoid its difficulties due to finiteness of $\\epsX$.\n(We expect that the\ncomputational result of a physical process \nmight have weak dependence on $\\epsX$\nwhich is small enough.\nMore precisely the actual value is obtained by the extrapolation of \n$\\epsX = 0$ for series results of different $\\epsX$'s\napproaching to $\\epsX = 0$.)\n\n\\subsection{Surface energy}\n\nLet us define the surface energy $\\cE_\\exact^{(N)}$\nby \n$$\n\\cE_\\exact^{(N)} = \\sum_{a > b} \\sigma_{ab} \\Area(B_a \\bigcap B_b),\n$$ \nwhere $\\sigma_{ab}$ is the surface tension coefficient\n$(\\sigma_{ab}>0$, $\\sigma_{ab} = \\sigma_{b a})$ between\nthe materials corresponding to $B_a$ and $B_b$,\nand $\\Area(U)$ is the area of an interior smooth $2$-manifold $U$.\n\nWe have an approximation of the surface energy $\\cE_\\exact^{(N)}$\nby the following proposition.\n\\begin{proposition} \\label{prop:5-2}\nThe free energy,\n\\begin{equation}\n\t\\cE^{(N)} = \n \\sum_{a > b}\n\\sigma_{ab}\\int_\\Omega d^3x\\ \n\\sqrt{|\\nabla \\xi_a(x)| |\\nabla \\xi_b(x)|}\n(\\xi_a(x) + \\xi_b(x)),\n\\label{eq:cEN}\n\\end{equation}\nhas a positive number $M$ such that\n$$\n |\\cE^{(N)} - \\cE_\\exact^{(N)}| < \\epsX M.\n$$\n\\end{proposition}\n\n\n\\begin{proof}\nFor $a\\neq b$, \n$B_a \\bigcap B_b$ consists of the union of some interior smooth\n$2$-manifolds. Their singular-boundary parts\n$\\partial_\\sing (B_a \\bigcap B_b)\n\\equiv \\{V_\\alpha\\}_{\\alpha \\in I_{ab}}$ are\nunion of some smooth $1$-manifolds and\nsmooth $0$-manifolds.\nThus $\\{V_\\alpha\\}_{\\alpha \\in I_{ab}}$ has no \neffect on the surface energy $\\cE_\\exact^{(N)}$\nbecause they are measureless.\n\nOver the subspace,\n\\begin{equation}\nM_{ab}^\\prop := \\{ x \\in M_{ab} \\ | \\ \n\\xi_a(x) + \\xi_b(x) = 1 \\}^\\circ,\n\\label{eq:Mabprop}\n\\end{equation}\nand for a positive number $\\ell$, we have identities, \n\\begin{equation}\n\\begin{split}\n |\\nabla \\xi_a(x)| (\\xi_a(x) + \\xi_b(x))^{\\ell}\n & = |\\nabla \\xi_b(x)| (\\xi_a(x) + \\xi_b(x))^{\\ell}\\\\\n & = \\sqrt{|\\nabla \\xi_a(x)| |\\nabla \\xi_b(x)|}\n(\\xi_a(x) + \\xi_b(x))^{\\ell}.\\\\\n\\end{split}\n\\label{eq:sym}\n\\end{equation}\nThe sum of the integrals over $M_{ab}^\\prop$ dominates $\\cE^{(N)}$\nif $\\epsX$ is sufficiently small.\n\nWe evaluate the remainder.\nFor example, for different $a$, $b$ and $c$, \nthe part in $\\cE^{(N)}$ coming from\n\\begin{equation}\nM_{abc}^\\prop := \\{ x \\in M_{abc} \\ | \\ \n \\xi_a(x) + \\xi_b(x) + \\xi_c(x) = 1 \\}^\\circ\n\\label{eq:Mabcprop}\n\\end{equation}\nis order of $\\epsX^2$. Namely we have\n\\begin{equation*}\n\\begin{split}\n&\\left|\\int_{M_{abc}} d^3x\\ \n\\sqrt{|\\nabla \\xi_a(x)| |\\nabla \\xi_b(x)|}\n(\\xi_a(x) + \\xi_b(x)) \n-\\Length(B_a\\cap B_b \\cap B_c) \\right|\\\\\n&\\qquad\\qquad\\qquad < \\epsX^2 \\Length(B_a\\cap B_b \\cap B_c),\\\\\n\\end{split}\n\\end{equation*}\nwhere $\\Length(C)$ is the length of a curve $C$.\nThus we find a number $M$ satisfying the inequality.\n\\qed\n\\end{proof}\n\n\n\\begin{myremark}\\label{rmk:5-1} {\\rm{\n\\begin{enumerate}\n\\item\n$M$ is bound by\n$$\n\tM \\le \\max(\\sigma_{ab})\\left(\n \\sum_{a b}\n\\sigma_{ab}\\int_\\Omega d^3x\\ \n|\\nabla \\xi_a(x)| (\\xi_a(x) + \\xi_b(x))^\\ell,\n\\end{equation*}\nusing a positive number $\\ell$. In such a way, there are so many \nvariants which, approximately, represent the surface energy \nin terms of $\\xi_a$'s.\n\\end{enumerate}\n}}\n\\end{myremark}\n\n\n\\subsection{Statics}\nLet us consider the statics of the multi-phase fields.\nIn the above arguments in this section,\nwe have given the geometrical objects, first, and\ndefined the functions $\\xi_a$, functional energy\n$\\cE^{(N)}$ and so on. \nIn this subsection on\nthe static mechanics of the multi-fields, \nwe consider the deformation of these geometrical objects\nand determine a configuration whose corresponds to an extremal point\nof the functional, {\\it{i.e.}}, $\\cF_\\mul$ in the following\nproposition. In other words, we derive the Euler-Lagrange equation\nwhich governs the extremal point of the functional\nand characterizes the configuration of $M_a$, $L_a$ and\napproximately $B_a$ for every $a = 0, \\cdots, N-1$.\n\nLet us introduce the proper pressure\n\\begin{equation}\n\tp_P(x) := \\sum p_a \\xi_a(x),\n\\label{eq:proppress}\n\\end{equation}\nwhere $p_a$ is a certain pressure of each material.\n\n\\begin{proposition}\n\\label{prop:5-8}\nThe Euler-Lagrange equation\nof the static free energy integral,\n\\begin{equation*}\n\t\\cF_\\mul= \\int_{\\Omega}\\left( \n\\sum_{a> b}\n\\sigma_{ab} \n\\sqrt{|\\nabla \\xi_a(x)| |\\nabla \\xi_b(x)| }\n(\\xi_a(x) + \\xi_b(x)) - p_P\n \\right) d^3 x,\n\\end{equation*}\n with respect to $\\xi_a$, {\\it{i.e.}},\n $\\delta \\cF_\\mul\/\\delta \\xi_a = 0$, is given as follows:\n\\begin{enumerate}\n\\item\nFor a point $ x \\in {M_{ab}}^\\prop$ of (\\ref{eq:Mabprop}),\n\\begin{equation}\n (p_a - p_b) - \\sigma_{ab}\\kappa_a (x)= 0, \n\\label{eq:ELM2}\n\\end{equation}\nwhere \n$$\n\t\\kappa_a := - \\partial_i \\frac{\\partial_i \\xi_a}{|\\nabla\\xi_a|}.\n$$\n\n\\item\nFor a point $x \\in M_{abc}^\\prop$ of (\\ref{eq:Mabcprop}),\n\\begin{equation}\n (p_a - p_b - p_c) - \\tilde\\kappa_{abc} (x)= 0, \n\\label{eq:ELM3}\n\\end{equation}\nwhere \n\\begin{equation}\n\\begin{split}\n \\tilde\\kappa_{abc} &:= \n\\sigma_{bc}\\sqrt{|\\nabla \\xi_b(x)| |\\nabla \\xi_c(x)| }\n-\\sigma_{ab}\\sqrt{|\\nabla \\xi_a(x)| |\\nabla \\xi_b(x)| }\n-\\sigma_{ac}\\sqrt{|\\nabla \\xi_a(x)| |\\nabla \\xi_c(x)| }\\\\\n& + \\partial_i \n\\left[\\frac{\\partial_i \\xi_a}{\\sqrt{|\\nabla\\xi_a|}^3}.\n\\left(\\sigma_{ab} \\sqrt{|\\nabla\\xi_b|}( \\xi_a + \\xi_b)+\n\\sigma_{ac} \\sqrt{|\\nabla\\xi_c|}( \\xi_a + \\xi_c) \\right) \\right].\n\\end{split}\n\\end{equation}\n\\end{enumerate}\n\\end{proposition}\n\n\n\\begin{proof}\nFor a point $ x \\in {M_{ab}}^\\prop$ of (\\ref{eq:Mabprop}),\nwe have $ \\xi_a(x) + \\xi_b(x) = 1 $, and thus the\nEuler-Lagrange equation\n(\\ref{eq:AppeA1}) leads (\\ref{eq:ELM2}).\n\nSimilarly for a point $ x \\in {M_{abc}}^\\prop$ of (\\ref{eq:Mabcprop}),\nwe have $ \\xi_a(x) + \\xi_b(x) + \\xi_c(x)= 1 $, and thus the\nconcerned terms of the integrand in the energy functional are given by\n\\begin{equation}\n\\begin{split}\n\\cdots &\n+\\sqrt{|\\nabla \\xi_a(x)| |\\nabla \\xi_b(x)| }(\\xi_a + \\xi_b)\n+\\sqrt{|\\nabla \\xi_a(x)| |\\nabla \\xi_c(x)| }(\\xi_a + \\xi_b) \\\\\n&+\\sqrt{|\\nabla \\xi_b(x)| |\\nabla \\xi_c(x)| }(1 - \\xi_a) +\\cdots. \\\\\n\\end{split}\n\\end{equation}\nThe Euler-Lagrange equation \n(\\ref{eq:AppeA1})\ngives (\\ref{eq:ELM3}).\n\\qed\n\\end{proof}\n\n\\begin{myremark} \n {\\rm{\n\\begin{enumerate} \n\n\\item It is noticed that \n(\\ref{eq:ELM2}) agrees with the Laplace equation (\\ref{eq:ELs}) and thus\nwe also reproduce the Laplace equation locally.\n\n\\item (\\ref{eq:ELM3})\ncould be regarded as another generalization of the Laplace equation\nthough $M_{abc}^\\prop$ does not contribute to the surface energy\nwhen $\\epsX$ vanishes and has a negligible effect even for\na finite $\\epsX$ if $\\epsX$ is sufficiently small.\nIndeed, (\\ref{eq:ELM3}) does not appear in the theory of surface tension\n\\cite{LL}. However \n(\\ref{eq:ELM3}) is necessary and plays a role\n to guarantee the stability\nin the numerical computations\nand to preserve the consistency in numerical approach with\nfinite intermediate regions for $\\epsX \\neq 0$.\n\n\\item Similarly we have similar equations\nfor a higher intersection regions.\n\n\\end{enumerate} \n}}\n\\end{myremark} \n\n\nAs a generalization of (\\ref{eq:Amini0})\nwe immediately have the following.\n\n\\begin{proposition} \\label{prop:5-9}\nFor every point $x \\in \\Omega$,\nthe variational principle, $\\delta \\cF_\\mul\/ \\delta x^i = 0$,\ngives \n\\begin{equation}\n\\begin{split}\n & \\partial_i p_P \n -\\sum_{a > b} \\sigma_{a,b}\\Bigr[\n \\partial_i \\left(\n\\sqrt{|\\nabla \\xi_a| |\\nabla \\xi_b|}\n (\\xi_a+\\xi_b) \\right) \\\\\n &\n - \\partial_j\\left(\n \\frac{\\partial_i \\xi_a \\partial_j \\xi_a }\n {\\sqrt{|\\nabla \\xi_a|}^{3}}\n \\sqrt{|\\nabla \\xi_b|}\n(\\xi_a+\\xi_b) \\right) \\Bigr] = 0.\n\\end{split}\n\\label{eq:MSFe}\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nIt is the same as Proposition \\ref{prop:4-5},\nwhich essentially comes from \nProposition \\ref{prop:A-2}.\n\\qed\n\\end{proof}\n\n\\begin{myremark} \n {\\rm{\nIn Proposition \\ref{prop:5-9}, we can apply the equation without \nany classification of geometry like (\\ref{eq:Mabprop}) and (\\ref{eq:Mabcprop}).\nIt is also noted that (\\ref{eq:MSFe}) is globally defined over \n$\\Omega$ as mentioned in Remark \\ref{rmk:4-0}.\n}}\n\\end{myremark} \n\n\n\n\n\n\n\n\n\n\\subsection{Dynamics}\nUsing these equations, let us consider\nthe dynamics of the multi-phase flow.\nWe extend the colored-decomposition of $\\Omega$ and\nthe $\\epsX$-controlled color functions of $\\{\\xi_a\\}_{a=0,\\cdots,N-1}$\nto those of $\\Omega \\times T$ and $\\cC^\\infty(\\Omega \\times T)$\nusing another fiber structure of $\\Coor(\\Omega\\times T)$.\nMathematically speaking, since our space-time is a\ntrivial bundle $\\Omega\\times T$ and has the fiber structure\n$\\Omega\\times (t_a, t_b) \\to \\Omega$ for a small interval $(t_a, t_b)$\ndue to the integrability,\nwe can consider the pull-back of the map $\\xi_a : \\Omega \\to \\RR$.\nIf we consider a global behavior of $\\xi_a$ with respect to time $t$,\nwe should pay more attentions on the Lagrange picture $\\gamma(x,t)$\nand the integrability.\nHowever as our theory is local, we can regard\n $(t_a, t_b)$ as $T$ with an infinitesimal interval.\n\nThus $\\xi_a$ is redefined as $\\xi_a:=\\xi_a(\\gamma(x,t))$\nfor $(x,t) \\in \\Omega \\times T$ and it is denoted by \n$\\xi_a(x,t)$.\nIn the time development of $\\xi_a$, the control parameter $\\epsX$\nis not necessary to be constant. However in this article,\nwe assume that $\\epsX$ is sufficiently small for every $t \\in T$.\n\nLet the density of each $\\xi_a$ be denoted by $\\rho_a$.\nWe have the global density function $\\rho(x,t)$ and pressure\n$p_P(x,t)$ given by\n$$\n\t\\rho(x,t) = \\sum \\rho_a \\xi_a(x,t), \\quad\n\tp_P(x,t) = \\sum p_a \\xi_a(x,t).\n$$\n\nIn contrast to the previous subsection, in this subsection,\nwe investigate an initial problem. In other words,\nevery configuration of the geometrical objects, \n$M_a$, $L_a$ and approximately $B_a$ ($a = 0, \\cdots, N-1$),\nwith divergence free velocity $u$, ($\\mathrm{div}(u)=0$)\ncan be an initial condition to the dynamics of the multi-phase fields.\nThe following equations which we will derive in this subsection\ngovern the deformations of these\ngeometrical objects as their time-development.\nFurther it is noticed that\nin this subsection,\nthe proper pressure $p_P(x,t)$ has no mathematical nor\nphysical meaning because it becomes a part of the total pressure $p$,\nwhich is determined by the divergence free condition $\\mathrm{div}(u) =0$\nas mentioned in Remark \\ref{rmk:3-2}.\n\n\\bigskip\n\nWe have the first theorem;\n\n\\begin{theorem} \\label{th:5-1}\nThe action integral of the multi-phase fields, or\nthe $\\epsX$-controlled color functions $\\xi_a$\n with physical parameters $\\rho_a$, $\\sigma_{ab}$, $p_a$\n$(a, b = 0, 1, \\cdots, N-1)$ defined above, is given by\n\\begin{equation}\n\t\\cS_\\mul= \\int_T d t\\int_{\\Omega}\\left( \\frac{1}{2}\\rho |u|^2 -\n \\sum_{a> b}\n\\sigma_{ab} \n\\sqrt{|\\nabla \\xi_a| |\\nabla \\xi_b| }\n (\\xi_a + \\xi_b)\n + p_P \\right) d^3 x,\n\\label{eq:actionM}\n\\end{equation}\nunder the volume-preserving deformation.\n\\end{theorem}\n\n\\begin{proof}\nThe action integral is additive.\nThe first term exhibits the kinematic energy of the fluids.\nThe second term represents the surface energy up to $\\epsX$ as in\nProposition \\ref{prop:5-2}.\nThe proper pressure $p_P$ in (\\ref{eq:proppress}) leads the Laplace equations.\nWe can regard it as the action integral of \nthe multi-phase fields with these parameters.\n\\qed\n\\end{proof}\n\nThen we have further generalization of (\\ref{eq:EL2d}) as follows:\n\\begin{lemma} \\label{lemma:5-11}\nAssume that every $M_a(t)$, $M_{ab}^\\prop(t)$ and $M_{abc}^\\prop(t)$ deform\nfor the time-development following a certain equation.\nThe Euler-Lagrange equation of the action integral\n with respect to $\\xi_a$, $\\delta \\cS_\\mul\/ \\delta \\xi_a =0$,\nis given, up to the volume preserving condition, as follows:\n\\begin{enumerate}\n\\item\nFor a point $x \\in M_{ab}^\\prop$, we have\n\\begin{equation}\n\t\\frac{1}{2}(\\rho_a - \\rho_b) |u(x,t)|^2\n + (p_a - p_b) - \\sigma_{ab}\\kappa_a (x,t)= 0. \n\\label{eq:ELM2d}\n\\end{equation}\n\\item\nFor a point $x \\in M_{abc}^\\prop$, we have\n\\begin{equation}\n\t\\frac{1}{2}(\\rho_a - \\rho_b - \\rho_c) |u(x,t)|^2\n + (p_a - p_b - p_c) - \\tilde\\kappa_{abc} (x,t)= 0. \n\\label{eq:ELM3d}\n\\end{equation}\n\\end{enumerate}\n\\end{lemma}\n\nSimilarly we have the similar equations for higher intersection regions.\n\\begin{proof}\nIt is the same as proof of Proposition\n\\ref{prop:5-8}.\n\\qed\n\\end{proof}\n\nUsing these equations, we have the second theorem,\nwhich is our main theorem:\n\\begin{theorem} \\label{th:5-2}\nFor every $(x, t) \\in \\Omega \\times T$,\nthe variational principle, $\\delta \\cS_\\mul\/ \\delta \\gamma(x,t) = 0$,\nprovides the equation of motion,\n\\begin{equation}\n\\begin{split}\n \\frac{D \\rho u^i}{D t} +\n & \\partial_i p \n +\\sum_{a> b} \\sigma_{a,b}\\Bigr[\n \\partial_i \\left(\n\\sqrt{|\\nabla \\xi_a| |\\nabla \\xi_b|}\n (\\xi_a+\\xi_b) \\right) \\\\\n &\n - \\partial_j\\left(\n \\frac{\\partial_i \\xi_a(x) \\partial_j \\xi_a(x) }\n {\\sqrt{|\\nabla \\xi_a|}^{3}}\n \\sqrt{|\\nabla \\xi_b|}\n(\\xi_a+\\xi_b) \\right) \\Bigr] = 0.\n\\end{split}\n\\label{eq:EeqM}\n\\end{equation}\nHere $p$ is the pressure coming from the effect of the \nvolume-preserving or incompressible condition, which\nincludes the proper pressure $p_P$ (\\ref{eq:proppress}).\n\\end{theorem}\n\n\n\\begin{proof}\nWe naturally obtain it by using 1) \nProposition \\ref{prop:3-1} and its proof,\n2) Remark \\ref{rmk:3-2},\n3) Lemma \\ref{lemma:5-11}\nand 4) Proposition \\ref{prop:A-2}.\n\\qed\n\\end{proof}\n\nHere we note that\nby expressing the low-dimensional geometry in terms\nof the global smooth functions $\\xi$'s with finite $\\epsX$,\nwe have unified the infinite dimensional geometry\nor the incompressible fluid dynamics\ngoverned by $\\IFluid(\\Omega \\times T)$, and the $\\epsX$-parameterized\nlow dimensional geometry with singularities to obtain \nthe extended Euler equation (\\ref{eq:EeqM}).\nWhen $\\epsX$ approaches to zero, we must consider the \nhyperfunctions \\cite{KKK,II} instead of $\\cC^\\infty(\\Omega \\times T)$,\nbut we conjecture that our results would be justified even under the limit;\nthe unification would have more rigorous meanings.\n\nIt should be noted that\non the unification,\nit is very crucial that we express the low-dimensional geometry in terms\nof the global smooth functions $\\xi$'s as \nthe infinite-dimensional vector spaces.\nThe $\\SDiff(\\Omega)$ naturally acts on $\\xi$'s and thus \nwe could treat the low-dimensional geometry and the incompressible\nfluid dynamics in the framework of the infinite dimensional\nLie group \\cite{AK,EM,O}.\nIt is contrast to the level-set method.\nAs mentioned in Section \\ref{sec:two-one},\nthe level-set function does not belong to $\\cC^\\infty(\\Omega)$\nand thus we can not consider $\\SDiff(\\Omega)$ action and\ntreat it in the framework.\n\n\\begin{myremark}\\label{rmk:11}{\\rm{\n\\begin{enumerate}\n\\item\n(\\ref{eq:EeqM}) is the Euler equation with the surface tension\nto multi-phase fields\nwhich gives the equation of motion of the multi-phase flow even with\nthe multiple junctions.\nAs we will illustrate examples in Section \\ref{sec:VOF}, \nthe dynamics with the triple junction \ncan be solved without any geometrical constraints.\nIt should also noted that for a point in $M_{ab}^\\prop$, \n(\\ref{eq:EeqM}) is reduced to the original Euler equation in \nReference \\cite{LZZ}\nor (\\ref{eq:Eulxi}).\n\n\\item\nThe Euler equation (\\ref{eq:EeqM}) appears as the momentum conservation in\nthe sense of Noether's theorem (Section \\ref{sec:two-two}).\nIt implies that (\\ref{eq:EeqM}) is natural from the geometrical viewpoint\n\\cite{Ar,AK,EM,K,Ko,MW,NHK}.\n\n\\item\nFurther even though we set $\\{\\xi_a(\\cdot, t)\\}$ as \nproper $\\epsX$-controlled\ncolored functions as an initial state,\n their time-development is not guaranteed \nthat $\\{\\xi_a(\\cdot, t)\\}$, $(t>0)$,\nis proper $\\epsX$-controlled.\nIn general $\\epsX$ may become large for the time development,\nat least, numerically due to the numerical diffusion. (See examples in\nSection \\ref{sec:VOF}).\nHowever even for $t>0$,\nwe can find $\\epsX(t)$ such that\n $\\{\\xi_a(\\cdot, t)\\}$ are $\\epsX(t)$-controlled colored functions\nand if $\\epsX(t)$ is sufficiently small, our approximation is\nguaranteed by $\\epsX(t)$.\n\n\\item\nThe surface tension is also defined over $\\Omega \\times T$ and thus\n the Euler equation is defined over $\\Omega \\times T$ without any \nassumptions due to Remark \\ref{rmk:4-0}.\n\n\\item We may set $\\epsX$ depending upon the individual\nintermediate region between these fields by \nletting $\\epsilon_{ab}$ mean that for\n$\\xi_a$ and $\\xi_b$, $a\\neq b$. Then if we recognize $\\epsX$ as \n$\\displaystyle{\\max_{a, b =0}^{N-1}\\epsilon_{ab}}$, \nabove arguments are applicable for the\ncase.\n\n\\item We defined the $\\epsX$-controlled colored functions\nusing the $\\epsT$-tubular neighborhood $T_{U, \\epsT}$ and\nthe colored decomposition of $\\Omega$\nin Definition \\ref{def:5-4} by letting $\\epsT = \\epsX$.\nOn the other hand,\nas in Reference \\cite{LZZ},\nour formulation can describe a topology change well following\nthe Euler equation (\\ref{eq:EeqM}) such as a split of\na bubble into two bubbles in a liquid. \nThe $\\epsX$-controlled colored functions \ncan represents the geometry for such a topology change without any\ndifficulties. However on the topology change,\nthe path-connected region and \nthe $\\epsX$-tubular neighborhood \nlose their mathematical meaning and thus, more rigorously,\nwe should redefine the $\\epsX$-controlled colored functions. \nSince\nthe $\\epsX$-controlled colored functions represent the geometry\nas an analytic geometry, it is not difficult to modify the definitions\nthough it is too abstract.\nIn other words, we should first define the $\\epsX$-controlled colored \nfunctions $\\xi$'s without the base geometry, and \ncharacterize geometrical objects using\nthe functions $\\xi$'s.\nHowever since such a way is too abstract to find these geometrical meanings,\nwe avoided a needless confusion in these definitions and employed\nDefinition \\ref{def:5-4}.\n\n\\end{enumerate}\n}}\n\\end{myremark}\n\n\n\\subsection{Equation of motion of triple-phase flow}\n\nLet us concentrate ourselves on a triple-phase flow problem,\nnoting (\\ref{eq:sym}).\nFrom the symmetry of the triple phase,\nwe introduce {\\lq\\lq}proper{\\rq\\rq} surface tension coefficients,\n$$\n\t\\sigma_0 = \\frac{\\sigma_{01} + \\sigma_{02} - \\sigma_{12}}{2}, \\quad\n\t\\sigma_1 = \\frac{\\sigma_{01} + \\sigma_{12} - \\sigma_{02}}{2}, \\quad\n\t\\sigma_2 = \\frac{\\sigma_{02} + \\sigma_{12} - \\sigma_{01}}{2}, \\quad\n$$\nor $\\sigma_{ab} = \\sigma_a + \\sigma_b$.\nHere it should be noted that\nthe {\\lq\\lq}proper{\\rq\\rq} surface tension coefficient \nis based upon the speciality of the triple-phase and\ndoes not have more physical meaning than above definition.\n\n\\begin{lemma} \\label{lemma:5-2}\nFor different $a, b,$ and $c$, we have the following\napproximation,\n\\begin{equation}\n\\left|\\int_\\Omega\\left( \\sqrt{|\\nabla \\xi_a| |\\nabla \\xi_b|}\n (\\xi_a + \\xi_b)\n\t+ \\sqrt{|\\nabla \\xi_a| |\\nabla \\xi_c|}\n (\\xi_a + \\xi_c)\n\t- |\\nabla \\xi_a|\\right)d^3 x\\right| < \\epsX \\cA_a.\n\\label{eq:Appr3}\n\\end{equation}\n\\end{lemma}\n\nUsing the relation,\nthe free energy (\\ref{eq:cEN}) has a simpler expression up to\n$\\epsX$.\n\\begin{proposition} \\label{prop:5-3}\n By letting\n\\begin{equation*}\n\t\\cE^{(3)}_{\\mathrm{sym}} := \\sigma_{0}\\int_\\Omega d^3x\\ |\\nabla \\xi_0(x)| \n + \\sigma_{1}\\int_\\Omega d^3x\\ |\\nabla \\xi_1(x)| \n + \\sigma_{2}\\int_\\Omega d^3x\\ |\\nabla \\xi_2(x)| ,\n\\end{equation*}\nwe have a certain number $M$ related to area of the surfaces $\\{B_a\\}$\nsuch that\n\\begin{equation*}\n |\\cE^{(3)} - \\cE^{(3)}_{\\mathrm{sym}}| < \\epsX M.\n\\end{equation*}\n\\end{proposition}\n\n\\begin{proof}\nDue to Lemma \\ref{lemma:5-2}, it is obvious.\n\\qed\n\\end{proof}\n\nThe action integral (\\ref{eq:actionM}) also becomes\n\\begin{equation*}\n\t\\cS_\\tri= \\int_T d t\\int_{\\Omega}\\left( \\frac{1}{2}\\rho |u|^2 -\n\t\\sum_a( \\sigma_a |\\nabla\\xi_a| - p_a \\xi_a )\\right) d^3 x.\n\\end{equation*}\nFor a practical reason, we consider a simpler expression by\nspecifying the problem.\n\n\n\\subsection{Two-phase flow and wall with triple-junction}\n\\label{subsec:5-6}\n\nMore specially we consider the case \nthat $\\xi_o$ corresponds to the wall which does not move.\nFor the case, we can neglect the wall part of the equation, because\nit causes a mere energy-shift of $\\cE^{(3)}_{\\mathrm{sym}}$.\nThen the action integral and the Euler equation become simpler.\nWe have the following theorem as a corollary.\n\n\\begin{theorem} \\label{th:5-3}\nThe action integral of two-phase flow with wall is given by\n\\begin{equation*}\n\t\\cS_\\wall= \\int_T d t\\int_{\\Omega}\n \\left( \\frac{1}{2}\\rho |u|^2 -\n\t\\sum_{a=1}^2( \\sigma_a |\\nabla\\xi_a| - p_a \\xi_a )\\right) d^3 x,\n\\end{equation*}\nand the equation of motion is given by\n\\begin{equation}\n \\frac{D \\rho u^i}{D t} \n + \\partial_i p \n -\\partial_j (\\overline \\tau_{ij}) = 0,\n\\label{eq:Eeq3}\n\\end{equation}\nwhere\n\\begin{equation}\n \\overline \\tau = \\sum_{a=1}^2\\sigma_a \n\\left(I - \n\\frac{\\nabla \\xi_a}{|\\nabla \\xi_a|}\n\\otimes\n\\frac{\\nabla \\xi_a}{|\\nabla \\xi_a|}\\right)\n|\\nabla \\xi_a|.\n\\label{eq:overtau}\n\\end{equation}\n\\end{theorem}\n\nPractically this Euler equation (\\ref{eq:Eeq3})\nis more convenient due to the proper surface tension coefficients.\nHowever this quite differs from the original (\\ref{eq:Amini0}) \nand (\\ref{eq:Amini}) in Reference \\cite{LZZ}\nand governs the motion of two-phase flow with a wall completely. \n\n\\begin{myremark}\\label{rmk:5-2}\n{\\rm{\nEquation (\\ref{eq:Eeq3})\nis the Euler equation with the surface tension\nfor two-phase fields with a wall or triple junctions\nin our theoretical framework.\nWe should note that \nunder the approximation (\\ref{eq:Appr3}),\n(\\ref{eq:Eeq3}) is equivalent to (\\ref{eq:EeqM}),\neven though \n(\\ref{eq:Eeq3}) is far simpler than (\\ref{eq:EeqM}).\n\n From Remark \\ref{rmk:4-0},\n it should be noted that\n$\\overline \\tau$ \nand the Euler equation (\\ref{eq:Eeq3})\nare defined over $\\Omega \\times T$. This property\nas a governing equation is very important for the \ncomputations to be stable, which is\nmentioned in Introduction.\nSince the non-trivial part of $\\overline{\\tau}$\nis localized in $\\Omega$ of each $t\\in T$,\n$\\overline \\tau$ vanishes and has no effect on the equation \n in the other area.\n}}\n\\end{myremark}\n\n\n\nWe will show some numerical computational results\nof this case in the following section.\nThere we could also consider\nthe viscous stress forces and the wall shear stress.\n\n\\section{Numerical computations}\n\\label{sec:VOF}\n\nIn this section, we show some numerical\ncomputations of two-phase flow surrounded by a wall obeying the extended Euler\nequation in Theorem \\ref{th:5-3}.\nAs in Theorem \\ref{th:5-3},\n the wall is expressed by the color function $\\xi_0$ and\nhas the intermediate region $(M_0 \\setminus L_0^c)^\\circ$ \nwhere $\\xi_0$ has its value $(0,1)$.\nAs dynamics of the incompressible two-phase flow with a static wall, \nwe numerically\nsolve the equations,\n\\begin{equation}\n\\begin{split}\n &\\mathrm{div}(u) = 0,\n \\\\\n &\\frac{D \\rho u^i}{D t} + \n (\\partial_i p - K_i) = 0 ,\n \\\\\n &\\frac{D \\rho}{D t} = 0.\n\\end{split}\n\\label{eq:NumEq}\n\\end{equation}\nHere for the numerical computations,\nwe assume that the force $K$ consists of\nthe surface tension, the viscous stress forces,\nand the wall shear stress,\n\\begin{equation}\nK_j = \\partial_i \\bar \\tau_{ij} + \\partial_i \\tau_{ij} + \\hat \\tau_j.\n\\label{eq:Ki}\n\\end{equation}\nHere \n$\\bar \\tau$ is given by (\\ref{eq:overtau}),\n$\\tau$ is the viscous tensor,\n$$\n \\tau_{ij} := 2 \\eta \\left(E_{ij} - \\frac{1}{3} \n \\mathrm{div} (u)\\right), \\quad\n E_{ij} := \n \\frac{1}{2}\\left(\\frac{\\partial u^i}{\\partial x_j}\n +\\frac{\\partial u^j}{\\partial x_i}\\right)\n$$\nwith the viscous constant\n$$\n\\eta(x) := \\eta_1 \\xi_1 + \\eta_2 \\xi_2,\n$$\nand $\\hat \\tau_j$ is the wall shear stress which is localized at\nthe intermediate region $(M_0 \\setminus L_0^c)^\\circ$ \nwhere $\\xi_0$ has its value $(0, 1)$.\n\nThe boundary condition of the interface between the fluid $\\xi_a$ \n$(a = 1, 2)$ and the wall $\\xi_0$ is generated dynamically in this\ncase. In other words, in order that\nthe wall shear stress term suppress the slip over the intermediate region\n$(M_0 \\setminus L_0^c)^\\circ$ asymptotically $t \\to \\infty$ due to\ndamping, we let $\\hat \\tau_j$ be proportional to\n$j$-component of $\\partial u^\\parallel \/\\partial q_0$\nfor the parallel velocity $u^\\parallel$ to the wall and relevant to\n$(1 - \\xi_0(x))$, and make $u$ vanish over $L_0$.\nHere $q_0$, $M_0$, and $L_0$ are of Definition \\ref{def:5-4}.\n\nThe viscous force can not be dealt with in the framework of the \nHamiltonian system because it has dissipation.\nHowever from the conventional consideration of the balance of \nthe momentum \\cite[Sec.13]{EM},\nit is not difficult to evaluate it.\nThe viscosity basically makes the numerical computations stable.\n\n\n\n\nIn the numerical computations,\nwe consider the problem in the structure lattice $\\cL$ \nmarked by $a \\ZZ^3$,\nwhere $\\ZZ$ is the set of the integers and $a$ is a positive number.\nThe lattice consists of cells and faces of each cell.\nLet every cell be a cube with sides of the length $a$.\nWe deal with a subspace $\\Omega_\\cL$\nof the lattice as $\\Omega_\\cL :=\\Omega \\cap \\cL \\subset \\EE^3$.\nThe fields $\\xi$'s are defined over the cells as cellwise constant\nfunctions and \nthe velocity field $u$ is defined over faces as\nfacewise constant functions \\cite{Ch};\n$\\xi$ is a constant function in each cell and depends on the\nposition of the cell,\nand similarly the components of the velocity field,\n$u^1$, $u^2$, and $u^3$ are facewise constant functions\ndefined over $x^2x^3$-faces, \n$x^3x^1$-faces, and $x^1x^2$-faces of each cell respectively.\n\nAs we gave a comment in Remark \\ref{rmk:11} 5, we make the parameter\n$\\epsX$ depend on the intermediate region in this section.\nLet $\\epsilon_{12}$ be the parameter\n for the two-phase field or the liquids, and \n$\\epsilon_0:= \\epsilon_{01}\\equiv\\epsilon_{02}$ be\none for the intermediate region \n$(M_0 \\setminus L_0^c)^\\circ$ between liquids and the wall.\n\nAs mentioned in Introduction,\nwe assume that $\\epsilon_{12}$ for the two-phase field\nin our method is given as $\\epsilon_{12} \\ge a$\nso that we could estimate the intermediate effect in our model\n following References \\cite{AMS,BKZ,Cab,Jac,LZZ},\neven though the thickness of the intermediate region among real liquids\nis of atomic order and is basically negligible in the macroscopic theory. \n\nIn the computational fluid dynamics, the VOF (volume of fluid) method\ndiscovered by Hirt and his coauthors \\cite{Ch,HN,H} \nis well-established when we deal with fluid with a wall.\nSince we handled triple-junction problems as in Section\n \\ref{subsec:5-6}, we reformulate our model in the VOF-method.\nIt implies that\nwe identify $1-\\xi_0$ with the so-called $V$-function $V := 1 - \\xi_0$\nin the VOF method \nbecause $V$ in the VOF method\nmeans the volume fraction of the fluid and corresponds to $1-\\xi_0$\nin our formulation.\n\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[scale=0.50,angle=270]{Fig1.ps}\n\\caption{VOF with porous matter expression: \nFor the consistency between the color function method and\nVOF-method, \nwe consider each cell as a fictitious \nporous material whose volume ratio and \nopen fraction are a value in $[0,1]$ without\nimposing any wall shear stress on fictitious\nsurface of the porous parts in each cell.\nThis expression represents purely geometrical effects.\n}\n\\label{fig:VOF}\n\\end{center}\n\\end{figure}\n\nAs the convention in Reference \\cite{H}, $V$ is also defined as\na cellwise constant function.\nIn the following examples, we will set \n$\\epsilon_0$ to be $a$ or the unit cell basically.\nHowever we can also make it $\\epsilon_0 > a$ as for two-phase field.\nIt means that for the case $\\epsilon_0 > a$,\nwe consider each cell as a fictitious \nporous material whose volume ratio $V \\in [0,1]$ \nwithout imposing any wall shear stress on the fictitious\nsurface of the porous parts itself in each cell as in Figure 1.\n(As mentioned above, we set the wall shear stress $\\hat \\tau_j$ from the\nphysical wall $\\xi_0$. The porous parts are purely fictitious.)\nThe region where $V$ is equal to 1 means the region where fluid freely exists\nwhereas\nthe region where $V$ vanishes means the region where existence of fluid \nis prohibited. The region with $V \\in (0,1)$ is the intermediate\nregion $(M_0 \\setminus L_0^c)^\\circ$. \nHere we emphasize that \nthe fictitious porous in each cell brings\npurely geometrical effects to this model.\n\nThen we could go on to\nconsider the problem in consistency between\nVOF-method and $\\xi_0$ function in the phase-field model.\nLet functions $f_1\\equiv f$ and $f_2$\nover $\\supp(V)$ be defined by the relations,\n\\begin{equation*}\n\t\\xi_1 = V f_1, \\quad \\xi_2 = V f_2, \\quad f_1 + f_2 = 1.\n\\end{equation*}\n\nFurther we also modify the open fraction $A$ in the VOF-method,\nwhich is defined over each face. We interpret $A$ as the open area of \nthe fictitious porous material of each face of each cell,\nwhich also has a value in $[0,1]$ as in Figure 1.\nWe also use the open area fraction $A$ of each face of each cell\n\\cite{H,HN}.\nFor a face belonging to the cell whose $V=1$, $A$ is also\nequal to 1.\nFollowing the convention in discretization by Hirt \\cite{H}, \n$A$ is regarded as an operator acting\non the face-valued functions \nlike\n\\begin{equation*}\n\tA \\circ u \\equiv Au = (A_{1} u^1, \n A_{2} u^2,A_{3} u^3),\n\\end{equation*}\n\\begin{equation}\n (Au)^{1} = A_{1} u^1, \\quad\n (Au)^{2} = A_{2} u^2, \\quad\n (Au)^{3} = A_{3} u^3. \\quad\n\\label{eq:Au}\n\\end{equation}\nHere we note that $A_i a^2$ implicitly appearing in\n(\\ref{eq:Au}) can be interpreted as a \ntwo-chain of homological base associated with a face of a cell.\nFor example, for a velocity field $\\mu := u^i(x) d x^i$ defined\nover a cell in the continuous theory \nand a piece of the boundary element of the\ncell $A_1 a^2$, \nthe discretized $u^1$ defined over the face is given by \n$$\n (A u)^1 := \\frac{1}{a^2} \\int_{A_1 a^2} * \\mu = A_1 u^1,\n$$\nwhere $*$ is the Hodge star operator, {\\it{i.e.}},\n$*\\mu := u^1(x) d x^2 d x^3 +\nu^2(x) d x^3 d x^1 +\nu^3(x) d x^1 d x^2$.\nThus the discretization (\\ref{eq:Au}) is very natural even\nfrom the point of view of the modern differential geometry. \n\nHence $\\mathrm{div} (u) \\equiv \\nabla u$ reads\n$\\nabla A u$ as the difference equation in VOF-method \\cite{H}\nand we employ this discretization method.\n\n\nWe give our algorithm to compute (\\ref{eq:NumEq})\nprecisely as follows.\nAs a convention, we specify the quantities with {\\lq\\lq}old{\\rq\\rq}\nand {\\lq\\lq}new{\\rq\\rq} corresponding to the previous states\nand the next states at each time step respectively\nin the computation.\nIn other words, we give the algorithm that we construct the\nnext states using the previous data by regarding the current\nstate as an intermediate state in the time step.\nWe use the project-method \\cite{Cho,Ch}; \n\\begin{equation*}\n\\begin{split}\n \\mbox{I} &: \\frac{\\rho \\tilde\\uu - \\rho \\uu^\\old }{\\Delta t }\n = -(\\uu^\\old\\cdot \\nabla ) \\rho \\uu^\\old ,\n \\nonumber \\\\\n \\mbox{II} &:\\frac{\\uu^\\new-\\tilde\\uu }{\\Delta t }\n = -\\frac{1}{\\rho}( \\nabla p -\\KK),\n \\nonumber \\\\\n \\mbox{III} &: \\nabla \\uu^\\new =0. \\\\\n\\end{split}\n\\end{equation*}\nThe step I is the part of the advection of the \nvelocity $\\uu^\\old$.\nIn the step I, we define an intermediate velocity\n$\\tilde\\uu$ and after then,\nwe compute $\\uu^\\new$ and $p$ in the steps II and III.\n\nThe time-development of $\\rho$ is given by the equation,\n\\begin{equation*}\n f^\\new = f^\\old + \\Delta t \\nabla ((A \\uu^\\old) f^\\old),\n\\end{equation*}\nand \n\\begin{equation*}\n\\rho = V(\\rho_{1} f + \\rho_{2} (1-f))\n\\end{equation*}\nfor the proper densities $\\rho_{a}$ of $\\xi_a$ $(a = 1, 2)$.\n\nEven for the case that we can deal with multi-phase flow with \nlarge density difference, we evaluate its time-development.\nPrecisely speaking, when we evaluate $\\tilde \\uu$,\nfollowing the idea of Rudman \\cite{R} we \nemploy the momentum advection $\\tilde\\uu$ of $\\uu$,\n$$\n\\tilde\\uu:=\n \\frac{1}{\\rho^\\new}[\\rho^\\old\\uu^\\old\n -\\Delta t(\\uu^\\old\\cdot \\nabla ) \\rho^\\old \\uu^\\old].\n$$\nOur derivation of the Euler equation shows that the Rudman's\nmethod is quite natural.\n\nFollowing the conventional notation, the \nguessed-value of the velocity is denoted by $\\uu^*$, which\nis the initial value for the steps in II and III.\nLet us define\n\\begin{equation*}\n\\uu^* := \\tilde\\uu + \n \\Delta t\\frac{1}{\\rho^\\new} \\KK(\\rho^\\old,f^\\old, \\uu^\\old).\n\\end{equation*}\nIn order to evaluate the guessed velocity,\nwe compute the force $\\KK$ from\n(\\ref{eq:Ki}) noting that \n$\\mathrm{div} \\tau$ and $\\mathrm{div} \\overline\\tau$ \nread\n$\\nabla A \\tau$ and $\\nabla A \\overline\\tau$ respectively.\n\nFollowing the SMAC (Simplified-Marker-and-Cell) method \\cite{AH,Cho,Ch}, \nwe numerically determine the new velocity $\\uu^\\new$\nand the pressure $p$ in a certain boundary condition\nusing the preconditioned conjugate gradient method (PCGM):\n\\begin{equation*}\n\\begin{split}\n\\mbox{(IIa) Evaluate } p \\mbox{ using the PCGM}\n&:\\frac{1}{\\Delta t}\\nabla( A\\circ\\uu^*) \n = \\nabla A \\circ \\frac{1}{\\rho^\\new} \\nabla p, \\\\\n\\mbox{(IIb) By using }p \\mbox{ determine } \\uu^\\new\n&: \\uu^\\new = \\uu^* - \\Delta t \\frac{1}{\\rho^\\new} \\nabla p.\\\\\n\\end{split}\n\\end{equation*}\nMore precisely speaking, (III) $\\nabla(A\\circ \\uu^\\new)=0$ means\nthat we numerically solve the Poisson equation,\n$$\n\\nabla\\left(A \\Delta t \\frac{1}{\\rho^\\new} \\nabla p\\right)\n=\\nabla(A\\circ \\uu^*).\n$$\nThen we obtain $\\uu^\\new$, which obviously\nsatisfies (III) $\\nabla(A\\circ \\uu^\\new)=0$,\nwhich is known as the Hodge decomposition method \\cite{AH,Cho,CM} \nas mentioned in Remark \\ref{rmk:3-2}.\n\n\nFollowing the algorithm, we computed the two-phase flow with\na wall and triple junctions.\nWe illustrate two examples of the numerical\nsolutions of the triple junction problems as follows.\n\n\n\n\n\\bigskip \n\n\\subsection{Example 1}\n\\bigskip \n\nHere we show a computation of\na capillary problem, or the meniscus oscillation,\nin Figure 2.\nWe set two liquids in a parallel wall with the physical parameters;\n$\\eta_1 = \\eta_2 = 0.1$[cp],\n$\\rho_1 = \\rho_2 = 1.0$[pg\/$\\um^3$],\n$\\sigma_1 = 3.349$[pg\/$\\mu$sec${}^2$], \n$\\sigma_2 = 46.651$[pg\/$\\mu$sec${}^2$]. \n\n\nWe used $\\cL:=12[\\um] \\times 0.5[\\um]\\times 16 [\\um]$ lattice whose\nunit length $a$ is $0.125[\\um]$. \nThe first liquid exists in the down side and the second liquid does \nin the upper side in the region $10[\\um] \\times 0.5[\\um]\\times 15 [\\um]$\nsurrounded by the wall and the boundaries with the boundary conditions.\nAs the boundary conditions,\nat the upper side from the bottom of the wall by $15[\\um]$,\nwe fix the constant pressure as $100$[KPa] and,\nalong $x^2$-direction, we set the periodic boundary condition.\n\nWe set $\\epsilon_{12} = \\epsilon_0 = 1$ mesh for the intermediate regions,\nat least, as its initial condition.\nEach time interval is 0.001 [$\\mu$sec]. \n\n\nAs the initial state, we start the state that\nthe fluid surface is flat as in Figure 2 (a)\nand the first liquid exists in the box region\n$10[\\um] \\times 0.5[\\um] \\times 7.0 [\\um]$, which\nis not stable.\nDue to the surface tension, it moves and starts\nto oscillate but due to viscosity, the oscillation decays.\nThough we did not impose the contact angle as a geometrical constraint,\nthe dynamics of the contact angle was calculated due to a balance between\nthe kinematic energy and the potential energy or the surface energy.\nThe oscillation converged to the stable shape with the proper contact\nangle, which is given by\n\\begin{equation}\n\\cos \\varphi = \\frac{\\sigma_2 - \\sigma_1} {\\sigma_2 + \\sigma_1}\n \\equiv \\frac{\\sigma_{02} - \\sigma_{01}} {\\sigma_{12}}.\n\\label{eq:sigma_phi}\n\\end{equation}\nThe angle given by $\\sigma$'s are designed as $30$ [degree] whereas it\nin the numerical experiment in Figure 2 is a little bit\nlarger than $30$ [degree],\nthough it is very difficult to determine it precisely.\nHowever since we could tune the parameters $\\sigma$'s so that we obtain\nthe required state, our formulation is very practical.\n\nDue to the numerical diffusions and others, \nthe thickness of the intermediate regions changes\nin the time development and also depends on the positions of\nthe interfaces, even though it is fixed the same at the \ninitial state. However we consider that it is thin enough\nto evaluate the physical system since the contact angle\nis reasonably estimated.\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[scale=0.50,angle=270]{Fig2.ps}\n\\caption{ The meniscus oscillation:\nEach figure shows the time development.}\n\\label{fig:OS}\n\\end{center}\n\\end{figure}\n\n\\subsection{Example 2}\nThis example is on the computations of the contact angles for different \nsurface tension coefficients displayed in Figure 3.\n\nEven in this case, in order to see the difference between\n the designed contact angle and computed one,\nwe go on to handle two-dimensional symmetrical\nproblems though we used three-dimensional computational software. \nIn other words, we set that\n$x^2$-direction is periodic.\n\nSince the contact angle $\\varphi$ in our convention is given by \nthe formula (\\ref{eq:sigma_phi}).\nBy setting $\\sigma$'s\n$$\n\t\\frac{\\sigma_1}{\\sigma_2} =\n \\frac{1- \\cos\\varphi}{1+ \\cos\\varphi},\n$$\nfor given the contact angle $\\varphi$, we computed\nfive triple junction problems without any geometrical constraints;\neach $\\sigma$ is given in the caption in Figure 3.\nThe other physical parameters are\ngiven by $\\eta_1 = \\eta_2 = 0.1$[cp] \nand $\\rho_1 = \\rho_2 = 1.0$[pg\/$\\um^3$].\n\nIn this computation we used a $240\\times 4\\times 112$ lattice whose\nunit length $a$ is $0.125[\\um]$; $\\Omega= 30[\\um] \\times 0.5[\\um] \n\\times 14[\\um]$.\nWe set the flat layer as a wall\nby thickness $3[\\um]$ from the bottom of $\\Omega$ along the $z$-axis. \nAs the boundary conditions,\nat the upper side from the bottom of the wall by $9[\\um]$,\nwe fix the constant pressure as $100$[KPa].\n\nAs the initial state for each computation.\nwe set a semicylinder with radius $5[\\mu m]$\n in the flat wall like Figure 3 (d).\nWe also set $\\epsilon_{12} = \\epsilon_0 = 1$ mesh for the intermediate regions.\nEach time step also corresponds to 0.001 [sec]. \n\nDue to the viscosity, after time passes sufficiently $50[\\mu$sec], the\nstatic solutions were obtained as illustrated in Figure 3, which \nrecover the contact angles under our approximation within\ngood agreements.\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[scale=0.50,angle=270]{Fig3.ps}\n\\caption{ The different contact angles are illustrated due to \nthe different surface energy: By fixing $\\sigma_1 =1.0000$[pg\/$\\mu$sec${}^3$],\n(a): $\\varphi = 30$ [degree], $\\sigma_2 = 13.9282 $[pg\/$\\mu$sec${}^3$],\n(b): $\\varphi = 45$ [degree], $\\sigma_2 = 5.8284 $[pg\/$\\mu$sec${}^3$],\n(c): $\\varphi = 60$ [degree], $\\sigma_2 = 3.0000$[pg\/$\\mu$sec${}^3$], \n(d): $\\varphi = 90$ [degree], $\\sigma_2 = 1.0000$[pg\/$\\mu$sec${}^3$], \nand\n(e): $\\varphi = 120$ [degree], $\\sigma_2 = 0.3333$[pg\/$\\mu$sec${}^3$].} \n\\label{fig:CA}\n\\end{center}\n\\end{figure}\n\n\\bigskip \n\\bigskip \n\\section{Summary}\n\nBy exploring an incompressible fluid with a phase-field\ngeometrically \\cite{Ar,AK,EM,K,Ko,MW,NHK},\nwe reformulated the expression of the surface tension\nfor the two-phase flow found by \nLafaurie, Nardone, Scardovelli, Zaleski and Zanetti\n\\cite{LZZ} as a variational problem.\nWe reproduced the Euler equation of two-phase flow (\\ref{eq:Eulxi})\nfollowing the variational principle of the action integral\n(\\ref{eq:AI2d}) in Proposition \\ref{prop:4-6}.\n \nThe new formulation along the line of the variational principle\nenabled us to extend (\\ref{eq:Eulxi}) to that for the \n multi-phase ($N$-phase, $N\\ge2$) flow.\nBy extending (\\ref{eq:Eulxi}), we obtained the novel Euler equation \n(\\ref{eq:EeqM}) with the surface tension of the multi-phase fields\nin Theorem \\ref{th:5-2} from the action integral of\nTheorem \\ref{th:5-1}\nas the conservation of momentum in the sense of Noether's theorem.\nThe variational principle for the infinite dimensional system\nin the sense of References \\cite{Ar,AK,EM} gives the equation of motion\nof multi-phase flow controlled by the small parameter $\\epsX$\nwithout any geometrical\nconstraints and any difficulties for the singularities at\nmultiple junctions.\n\nFor the static case, we gave governing equations \n(\\ref{eq:ELM2}), (\\ref{eq:ELM3}) and (\\ref{eq:MSFe}) \nwhich generate the\nlocally constant mean curvature surfaces with triple junctions\nby controlling a parameter $\\epsX$ to avoid these singularities.\nAs the solutions of (\\ref{eq:ELs}) has been studied well\nas the constant mean curvature surfaces \nfor last two decades \\cite{ES,FW,GMO,T}, our extended equations \n(\\ref{eq:ELM2}), (\\ref{eq:ELM3}) and (\\ref{eq:MSFe}) \nmight shed new light on treatment of singularities of their extended\nsurfaces, or a set of locally constant mean \ncurvature surfaces.\n (Even though we need an interpretation of our scheme,\nfor example, \nit can be applied to a soap film problem with triple junction.) \nIt implies that our method might give a method of resolutions\nof singularities in the framework of analytic geometry.\n\n\nBy specifying the problem of the multi-phase flow\n to the contact angle problems\nat triple junctions with a static wall,\nwe obtained the simpler Euler equation (\\ref{eq:Eeq3})\nin Theorem \\ref{th:5-3}.\nUsing the VOF method \\cite{H,HN},\nwe showed two examples of the numerical computations\nin Section \\ref{sec:VOF}.\nIn our computational method,\nfor given surface tension coefficients, \nthe contact angle is automatically generated\nby the surface tension\nwithout any geometrical constraints\n and any difficulties for the singularities at\ntriple junctions. The computations were very stable.\nIt means that the computations did not\ncollapse nor behave wildly for every initial and the boundary\nconditions.\n\nIn our theoretical framework,\nwe have unified the infinite dimensional geometry\nor an incompressible fluid dynamics\ngoverned by $\\IFluid(\\Omega \\times T)$, and the $\\epsX$-parameterized\nlow dimensional geometry with singularities given by the multi-phase fields.\nWe obtained all of equations following the same \nvariational principle.\nWe naturally reproduced the Laplace equations, (\\ref{eq:ELs}) and\n(\\ref{eq:ELM2}), and obtained their generalizations\n(\\ref{eq:EL2d}), \n(\\ref{eq:ELM2}), (\\ref{eq:ELM3}),\n(\\ref{eq:ELM3d}) and (\\ref{eq:MSFe}),\nand the Euler equations,\n(\\ref{eq:Eulxi}),\n(\\ref{eq:EeqM}), and (\\ref{eq:Eeq3})\nin Proposition \\ref{prop:4-6} and Theorems \\ref{th:5-2} and \\ref{th:5-3}.\nThese equations are derived\nfrom the same action integrals by choosing the physical parameters. \nIn the sense of References \\cite{Ar,AM,BGG}, it implies that\nwe gave geometrical interpretations of the multi-phase flow.\nEven though the phase-field model has\nthe artificial intermediate regions with unphysical thickness $\\epsX$, \nour theory supplies a model which shows how to evaluate their effects on\nthe surface tension forces, from geometrical viewpoints.\nThe key fact of the model\nis that we express the low-dimensional geometry in terms\nof the infinite-dimensional vector spaces, or \n{\\it{global functions $\\xi$'s}}\nwhich have natural $\\Diff$ and $\\SDiff$ actions.\nThus\nwe can treat them in the framework of infinite dimensional\nLie group \\cite{AK,EM,O} to consider its Euler equation.\nIt is contrast to the level-set method;\nin analytic geometry and algebraic geometry, zeros of a function\nexpresses a geometrical object and thus the level-set method is \nso natural from the point of view. However as mentioned in Section\n\\ref{sec:two-one}, the level-set function cannot be a global functions \nas $\\cC^\\infty(\\Omega)$ and thus it is difficult to handle the method in the\nframework of the infinite dimensional Lie group $\\SDiff(\\Omega)$.\n\nAs our approach gives a resolution of the singularities\nby a parameter $\\epsX$,\nin future we will explore topology changes, geometrical objects\nwith singularities and so on, more concretely in our theoretical framework.\nWhen $\\epsX$ approaches to zero, we need more rigorous arguments in \nterms of hyperfunctions \\cite{KKK}\nbut we conjecture that our results would be correct for the \nvanishing limit of $\\epsX$ because the Heaviside function is expressed by\n$\\displaystyle{\\theta(q) = \\lim_{\\epsX \\to 0} \\frac{1}{\\pi}\\tan^{-1}\n\\left(\\frac{q}{\\epsX}\\right)}$ in the Sato hyperfunction theory,\nwhich could be basically identified with $\\xi(q)$ of the finite $\\epsX$.\nSince an application of the Sato hyperfunction theory to fluid\ndynamics was reported by Imai on vortex layer and so on \\cite{II},\nwe believe that this approach might give another collaboration\nbetween pure mathematics and fluid mechanics.\n\n\\bigskip \n\\bigskip \n\nAcknowledgements:\n\nThis article is written by the authors in memory\nof their colleague, collaborator and leader Dr. Akira Asai\nwho led to develop this project.\nThe authors are also grateful to Mr. Katsuhiro Watanabe\nfor critical discussions and to the anonymous referee for\nhelpful and crucial comments.\n\n\\bigskip \n\\bigskip \n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section*{Acknowledgements}\n\nFinancial support by the DFG is gratefully acknowledged. AUJL acknowledges financial support by the Swiss SNF and the\nNCCR Quantum Science and Technology.\nComputation time on the Cray XE6 system Hermit and\nthe NEC Nehalem cluster Laki at the HLRS, and the bwGRiD cluster\nare gratefully acknowledged.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nMost massive stars are found in binary systems \\citep{Chini2012,Sana2012}.\nWhen the companion is an early type star, the collision of their stellar winds (wind-wind collision: WWC) \nproduces strong shocks and thermalizes gas to tens of millions of degrees Kelvin.\nThis hot gas emits X-rays, which are a good probe of the wind nature and interaction mechanism.\nThe shocks can also accelerate electrons to GeV energies, which produce radio synchrotron emission \\citep[e.g.,][]{Pittard2006}.\nThese non-thermal electrons are also suspected to up-scatter UV emission from the stars through the inverse-Compton process to X-ray (and higher) energies.\n\nEta Carinae \\citep[$d \\sim$2.3~kpc,][]{Smith2006b} is a nearby example of an extremely massive binary system with energetic WWC activity \\citep{Corcoran1997,Damineli1997,Ishibashi1999}.\nThe primary star is suspected to have had an initial mass of $\\gtrsim$100~\\UNITSOLARMASS\\ \\citep[see][]{Davidson1997,Hillier2001}\nand is currently in the poorly understood Luminous Blue Variable (LBV) stage.\nSince a series of eruptions between 1838$-$1890,\nthe two stars have been enshrouded by bipolar ejecta called the Homunculus Nebula (HN),\nbut their highly eccentric orbit ($e\\sim$0.9) with a period of 5.54 years can be measured from periodic variations at various wavelengths \\citep{Corcoran2005,Damineli2008}.\nThe companion star has not been detected directly, but it is believed to be an O supergiant or WN star \\citep{VernerE2005a}.\nThe primary star has a thick slow wind with $v_{wind} \\sim$420~\\UNITVEL\\ and \\Mdot\\ $\\sim$8.5$\\times$10$^{-4}$~\\UNITSOLARMASSYEAR\\ \\citep{Groh2012},\nwhile the secondary star has a thin fast wind with $v_{wind} \\sim$3000~\\UNITVEL\\ and \\Mdot\\ $\\sim$ 10$^{-5}$~\\UNITSOLARMASSYEAR\\ \\citep{Pittard2002}.\n\nThe WWC of $\\eta$~Car\\ produces luminous X-ray emission from hot plasma up to \\KT\\ $\\sim$4~keV, which has been observed mostly in the 2$-$10~keV band.\nThe emission increases inversely-proportional to the stellar separation, as suggested by WWC theory \\citep{Stevens1992}.\nHowever, the X-ray flux suddenly drops to a minimum level \\citep{Corcoran2010} after reaching a maximum brightness.\nDetailed studies \\citep{Hamaguchi2007b,Hamaguchi2014a} revealed two distinct phases during the X-ray minimum ---\nthe deep X-ray minimum, which has the lowest observed flux level and lasts approximately three weeks,\nand the shallow X-ray minimum, where the emission abruptly increases three-fold from the deep minimum level.\nThe deep minimum is probably produced by an eclipse of the WWC apex by the primary stellar body or wind, \nwhile the shallow minimum probably indicates the intrinsic decline of the WWC activity \\citep{Hamaguchi2014a}.\n\n\\input{tab1}\n\nThere have been several observations of $\\eta$~Car\\ in the hard X-ray band above 10~keV, up to $\\sim$100~keV.\n\\citet{Viotti2002,Viotti2004} claimed a detection of extremely hard X-ray emission from $\\eta$~Car\\ with the PDS instrument on \\SAX, \nbut the measured flux was significantly higher than those of later measurements,\nso source confusion in the wide PDS field of view ($\\sim$1.3$^{\\circ}$ FWHM) was suspected.\n\\citet{Leyder2008,Leyder2010} detected a flat power-law ($\\Gamma \\sim$1$-$2) source between $\\sim$20$-$100~keV with \\INTEGRAL\/ISGRI.\nThey constrained the source position to within 1.6\\ARCMIN\\ of $\\eta$~Car.\nSince they found no X-ray source in a \\CHANDRA\\ image consistent with the observed spectrum above 20~keV,\nthey identified the source as $\\eta$~Car.\n\\citet{Sekiguchi2009} analyzed the first two \\SUZAKU\\ \\citep{Mitsuda2007} observations of $\\eta$~Car\\ around apastron in 2005\nand detected X-ray emission between 15$-$40~keV with the HXD\/PIN instrument.\nThey showed that the spectrum below $\\sim$20~keV can be reproduced by \\KT\\ $\\sim$4~keV plasma emission observed below $\\sim$10~keV,\nwhile the spectrum above 10~keV requires a flat power-law of $\\Gamma \\sim$1.4.\nThese papers suggested that the power-law component may originate from the inverse-Compton up-scattering of stellar UV photons \nby non-thermal GeV electrons accelerated at the WWC region.\nOn the other hand, the \\AGILE\\ and \\FERMI\\ $\\gamma$-ray observatories discovered \na relatively stable $\\gamma-$ray source between 0.1$-$100~GeV \\citep{Tavani2009,Abdo2010},\nwhose spectrum may be connected to this extremely hard X-ray source \\citep{Farnier2011,Reitberger2012}.\n\nThe \\SUZAKU\\ observatory monitored $\\eta$~Car\\ 10 times between 2005$-$2011 and throughout one orbital cycle of $\\eta$~Car.\n\\SUZAKU\\ has the lowest background in the 15$-$40 keV band of any X-ray observatory launched before 2012, so that\nit gives the most reliable results on the orbital modulation of extremely hard X-ray emission from $\\eta$~Car.\nIt also has good sensitivity and spectral resolution between 5$-$9~keV, providing detailed profiles of\nthe $K\\alpha$ and $K\\beta$ line complexes of highly ionized Fe and Ni atoms.\nIn this paper, we present the flux and spectral variation of $\\eta$~Car\\ between 5$-$40 keV with orbital phase,\nfit all the spectra with a consistent model, and discuss the nature of the observed emission components.\n\n\\begin{figure*}[t]\n\\begin{center}\n\\includegraphics[width=0.85\\textwidth]{fig1.eps}\n\\caption{{\\it Left}: Composite XIS3 image of $\\eta$~Car\\ between 5$-$10~keV.\nThe solid blue circle at the center and the solid red regions are the source and background regions for the XIS analysis, respectively.\nThe dotted squares show the XIS {\\FOV}s of individual observations.\n{\\it Right}: Approximate HXD\/PIN {\\FOV}s overlaid on a mosaic \\XMM\/MOS image of the Carina nebula between 2$-$7~keV.\nThe inner and outer boxes are boundaries of half and zero photons to the PIN detector from an on-axis source, respectively.\n\\label{fig:obsimg}\n}\n\\end{center}\n\\end{figure*}\n\n\\section{Observations and Analysis}\n\n\\subsection{Observations}\n\n\\begin{figure}[h]\n\\plotone{fig2.eps}\n\\caption{\nXIS0+3 spectrum of $\\eta$~Car\\ during the deep minimum (SUZ$_{090125}$).\nThe best-fit models of the \\CHANDRA\\ deep minimum spectra of the HN ({\\it dash}, Hamaguchi et al. in prep.) and the CCE \\citep[{\\it dash-dot},][]{Hamaguchi2014a}, \nthe best-fit model of the residual by a thermal ({\\tt apec}) model ({\\it dot}),\nand the sum of these models ({\\it solid grey}) are shown for comparison.\n\\label{fig:deepmin_spec}\n}\n\\end{figure}\n\nSince its launch in 2005, \\SUZAKU\\ has observed $\\eta$~Car\\ fourteen times.\nTable~\\ref{tbl:obslogs} summarizes the former ten observations, which were performed before 2011.\nThe first two observations were performed during the performance verification (PV) phase and their earlier result is summarized in \\citet{Sekiguchi2009}.\nThe subsequent 8 observations were obtained through the guest observer program (AO-2, 3, 4, 6, PI: Kenji Hamaguchi).\nIndividual \\SUZAKU\\ observations are designated SUZ, subscripted with the year, month and day of the observation.\n\nDuring these observations, \\SUZAKU\\ ran two types of instruments:\nthe X-ray Imaging Spectrometer \\citep[XIS,][]{Koyama2006} in the focal plane of the thin-foil X-Ray Telescope \\citep[XRT,][]{Serlemitsos2007}\nand the Hard X-ray Detector \\citep[HXD,][]{Takahashi2007,Kokubun2007}.\nThe XIS consists of four X-ray CCD cameras, XIS0$-$3, three of which (XIS0, 2 and 3)\nuse front-illuminated (FI) CCD chips, while one (XIS1) uses a back-illuminated (BI) chip.\nThe FI chips have good hard X-ray sensitivity, covering $\\sim$0.5$-$10~keV,\nwhile the BI chip has good soft X-ray sensitivity down to $\\sim$0.3~keV.\nThe XIS2 was fatally damaged on 2006 Nov 9 by a mirco-meteorite, so this camera was unavailable after the 3rd observation (SUZ$_{070623}$).\nAnother micro-meteorite damaged one eighth of the XIS0 imaging area in 2009,\nwhile multiple micro-meteorites probably produced small holes on optical blocking filters of all the XISs,\nbut these did not significantly degrade the data quality.\nThe XISs initially had good spectral resolution (FWHM $\\sim$150~eV at 5.9~keV)\\footnote{http:\/\/heasarc.gsfc.nasa.gov\/docs\/astroe\/prop\\_tools\/suzaku\\_td\/node10.html}, \nbut the resolution has gradually degraded with age due to radiation damage,\nwith a substantial recovery in 2006 October after initiating the Spaced Charge Injection (SCI) operation\nwith a sacrifice of the effective imaging area.\nThe XRT has a butterfly-shaped point spread function (PSF) with half power diameter (HPD) of $\\sim$2$'$.\nThe effective area decreases as the off-axis angle increases, due to mirror vignetting.\nThe HXD consists of two types of detectors, the PIN with sensitivity between 15$-$70~keV and the GSO between 40$-$600~keV.\nThe GSO did not detect any significant signal above the non-X-ray background (NXB) level, so\nwe only used the PIN detector. The PIN detector has a collimator with a 34\\ARCMIN$\\times$34\\ARCMIN\\ \\FOV,\non the bottom of which are PIN Si diodes.\nThe depletion voltage to the diodes has been reduced gradually to mitigate the increase of detector noise,\nso that the detection efficiency has gradually decreased since launch.\n\nThe \\SUZAKU\\ point source observations have two default pointing positions --- the XIS nominal position,\nwhich puts the main target at the XRT+XIS focus, and the HXD nominal position, which maximizes the\nHXD collimator opening to the target.\nThe HXD nominal position is at 3.5\\ARCMIN\\ off-centered from the XIS nominal position.\nIn the PV observations, $\\eta$~Car\\ was placed at the XIS nominal position partly for instrument calibration.\nIn AO-2, 3 and 4, we placed $\\eta$~Car\\ at the HXD nominal position to maximize the HXD\/PIN sensitivity to the star.\nIn AO-6, we put again $\\eta$~Car\\ at the XIS nominal position \nbecause a failure of a spacecraft gyro began to affect the XIS flux measurement at the HXD nominal position.\nThe satellite roll angles during the AO observations were optimized within the operational constraints\nsuch that contamination from the nearby high energy sources AXP 1E~1048.1$-$5937 and IGR~J10447-6027 in the HXD\/PIN \\FOV\\ are minimized ($\\lesssim$1~\\%, see the right panel of Figure~\\ref{fig:obsimg}).\nOnly the HXD\/PIN observation in SUZ$_{050829}$ included 5\\% of the emission from 1E~1048.1$-$5937.\n\nAll the XIS observations were operated with the normal mode (no window option) \nbecause the count rates of $\\eta$~Car\\ for each XIS are $\\lesssim$7~\\UNITCPS, a factor of 2 below the threshold of significant photon pile-up.\nHowever, the XIS pileup estimator \\citep{Yamada2012} derived small pile-up for relatively high count rate observations\nsuch as SUZ$_{081210}$ ($\\sim$3\\% pileup at the \\PSF\\ core).\nBecause of this artificial effect,\nthe XIS spectra in SUZ$_{080610}$ and SUZ$_{081210}$ significantly flatten above $\\sim$9~keV.\nWe therefore excluded XIS spectra of these observations above 9~keV.\nThe XIS FI data had an anomaly at the first $\\sim$9~ksec of SUZ$_{070623}$, whose interval we did not analyze.\n\n\\begin{figure*}[hdtp]\n\\epsscale{1.5}\n\\plotone{fig3.eps}\n\\caption{{\\it Top}: \nXIS light curve of the WWC X-rays between 5$-$9~keV (triangle), compared to the \\RXTE\\ light curves between 2$-$10 keV \\citep[solid line,][]{Corcoran2010}.\nThe XIS0 and XIS3 count rates are normalized by the detector efficiency at SUZ$_{050829}$ and their count rates are averaged to derive the XIS count rate.\nThe fore- and back-ground emissions are estimated from the deep X-ray minimum data and subtracted, i.e.\\ the count rate during the deep minimum is zero.\n{\\it Bottom}: \nHXD\/PIN light curves between 15$-$25~keV (diamond) and 25$-$40 keV (open circle).\nThe 25-40 keV data points are slightly shifted to the right to show the error bars clearly.\nThe vertical bars show 1$\\sigma$ errors, including the PIN systematic uncertainty of 1.3\\%.\nThe dashed line shows the 25$-$40~keV flux of the \\INTEGRAL\\ point source in \\citet{Leyder2008}.\nIn both panels, black and grey colors show intervals between 2005$-$2010 July and after 2010 August, respectively.\n\\label{fig:obstiming}\n}\n\\end{figure*}\n\n\\input{tab2}\n\n\n\\subsection{Extraction of the WWC Emission Data}\n\nIn this paper, we analyze hard X-ray data above 5~keV to study the highest energy phenomena of $\\eta$~Car.\nFor consistent analysis, we use data only from the XIS0 and XIS3 among the XISs,\nboth of which are FI sensors running through the $\\eta$~Car\\ observations.\nWe used the HEASoft version 6.14 and the CALDB version hxd20110913, xis20130724, and xrt20110630 for the data calibration.\nThe left panel of Figure~\\ref{fig:obsimg} displays a 5$-$10~keV image from all the XIS3 data.\nThe brightest source at the center is $\\eta$~Car, the second brightest source to the west of $\\eta$~Car\\ is the Wolf-Rayet (WR) binary system WR~25,\nand the third to the north is the O4 star HD~93250.\nThe field includes more unresolved faint point sources, but no serendipitously bright X-ray source appeared during the observations in the XIS \\FOV.\n\nIn the XIS analysis,\nwe defined a source region with a 2.5\\ARCMIN\\ radius circle centered at $\\eta$~Car\\ to minimize contamination from WR~25 and HD~93250.\nThe source region includes $\\sim$90\\% of X-ray photons from the star.\nWe extracted the background from an annulus with a 5\\ARCMIN\\ inner radius and a 8\\ARCMIN\\ outer radius centered at $\\eta$~Car,\nexcluding areas within 3\\ARCMIN\\ from WR~25, HD~93250 and the centers of the X-ray clusters in \\citet{Feigelson2011}.\nThe source region includes hard X-ray sources other than the central point source,\nsuch as X-ray reflection at the HN \\citep{Corcoran2004} and multiple young stars \\citep[e.g.,][]{Wolk2011}.\n\n\\citet{Hamaguchi2014a} indicated that the WWC X-ray emission completely disappeared below 10~keV between 2009 Jan 12 and 28.\nThe SUZ$_{090125}$ observation was performed during this interval, so that the XIS spectrum should originate from the surrounding X-ray components.\nThe known hard X-ray components other than the WWC are the stable hot X-ray plasma, possibly in the foreground wind cavity \\citep[the CCE component,][]{Hamaguchi2007b,Hamaguchi2014a},\nwhich accounts for $\\sim$55\\% of the 5$-$10 keV emission, and X-ray reflection at the HN, which accounts for $\\sim$33\\% of the 5$-$10 keV emission (Figure~\\ref{fig:deepmin_spec}).\nThe remaining $\\sim$12\\% probably originates from hard X-ray point sources within the source region.\nThe CCE component, which can be measured only during the X-ray minimum, did not vary more than $\\sim$10\\%\nbetween 2003 and 2009 (Hamaguchi et al. in prep.), suggesting its stability over an orbital cycle.\nThe HN reflection emission is expected to decline by a factor of $\\sim$4 from periastron (around SUZ$_{090125}$) to apastron (Hamaguchi et al. in prep.).\nIn this paper, we assume that the XIS data at SUZ$_{090125}$ represent the contaminating emission in all the XIS spectra.\nHowever, this assumption significantly overestimates contribution of the fluorescent iron $K$ line from the HN to the apastron spectra\nsince the line flux around apastron is comparable to that at SUZ$_{090125}$.\nWe therefore defer the discussion of the fluorescent iron $K$ line emission to a later paper.\n\n\nThe HXD\/PIN data include significant contamination from NXB, point sources,\nGalactic Ridge X-ray Emission (GRXE) and cosmic ray background (CXB).\nThe NXB is estimated from the tuned background model with 1.3\\% systematic uncertainty (1$\\sigma$)\n(JX-ISAS-SUZAKU-MEMO-2007-09\\footnote{ftp:\/\/legacy.gsfc.nasa.gov\/suzaku\/data\/background\/pinnxb\\_ver2.0\\_tuned\/}).\nThe only high-energy point source that could contaminate the HXD\/PIN data is the AXP 1E~1048.1$-$5937.\nHowever, the HXD\/PIN count rate of 1E~1048.1$-$5937 on 2008 Nov.~30 (Obs ID: 403005010)\nthat excludes the NXB and the typical CXB spectrum was only 3.4$\\times$10$^{-3}$~\\UNITCPS (15$-$40~keV),\nwhich is $\\lesssim$1\/5 of the $\\eta$~Car\\ count rate.\nAn extrapolation of the XIS spectrum to the HXD band accounts for only one-fifth of the detected HXD count rate:\nthe rest probably originates from GRXE.\nIn addition, the HXD band flux should not increase by more than a factor of two at any \\SUZAKU\\ observation of $\\eta$~Car, \nconsidering a factor of $\\lesssim$4 variation of this AXP in the soft band since 1996 \\citep{Dib2014} and no strong color variation observed from the AXPs \\citep{Enoto2010}.\nFurthermore, $<$5\\% of this AXP emission contributes to the HXD\/PIN spectra of $\\eta$~Car.\nConsidering all these results, contamination of this AXP, 1E~1048.1$-$5937, of the HXD spectra of $\\eta$~Car\\ should be negligible.\nThe GRXE emission around $\\eta$~Car\\ is estimated at 1.4$\\times$10$^{-11}$~\\UNITFLUX deg$^{-2}$ between 3$-$20~keV \\cite[see section~5.2 in][]{Hamaguchi2007a}.\nThis is consistent with the remaining HXD\/PIN spectrum of 1E~1048.1$-$5937, assuming an absorbed thermal plasma model ({\\tt apec} $\\times$ {\\tt TBabs})\nwith \\KT = 10 keV and \\NH = 5.0$\\times$10$^{22}$~\\UNITNH.\nSince the GRXE emission is not expected to vary strongly in 30\\ARCMIN,\nwe use this spectrum as GRXE contamination in the HXD\/PIN spectra of $\\eta$~Car.\nThe CXB is estimated from the typical CXB emission \\citep{Boldt1987}, which may \nfluctuate by $\\lesssim$30\\% from region to region \\citep{Miyaji1998}.\nThe CXB\/GRXE contributions are estimated at 1.4$\\times$10$^{-2}$\/4.1$\\times$10$^{-3}$~\\UNITCPS\\ [15$-$25~keV] and \n3.7$\\times$10$^{-3}$\/4.8$\\times$10$^{-4}$~\\UNITCPS\\ [25$-$40~keV], while \nthe CCE and HN contribution should be negligible ($\\lesssim$10$^{-3}$~\\UNITCPS\\ [15$-$25~keV], $\\lesssim$10$^{-4}$~\\UNITCPS\\ [25$-$40~keV]).\nThe CXB and GRXE contribution is excluded from the PIN count rates and spectra as background.\nIn the light curve analysis, \nwe assume that the statistical noise errors are Gaussian and assume a systematic uncertainty of 1.3\\% in the HXD\/PIN NXB model.\n\nThe detector effective areas to $\\eta$~Car\\ varied by up to $\\sim$30\\% for the XIS and $\\sim$12\\% for the HXD\/PIN between the observations\nbecause of changes in observing conditions ---\nthe nominal pointing position, the XIS SCI operation and the sensitivity degradation of the PIN sensors.\nThe efficiency variation in the spectral analysis is automatically considered with spectral responses generated with {\\tt xisrmfgen} and {\\tt xisarfgen} in the HEAsoft tools for \nthe XIS and provided by the calibration team through the calibration database\\footnote{http:\/\/heasarc.gsfc.nasa.gov\/docs\/heasarc\/caldb\/suzaku\/} for the HXD.\nIn the light curve analysis, the average efficiency in a given energy band is calculated from the generated spectral response,\nand the count rates of each observation are normalized at the detector efficiency at SUZ$_{050829}$.\n\n\\begin{figure*}[t]\n\\epsscale{1.3}\n\\plotone{fig4a.eps}\n\\caption{XIS and HXD spectra of $\\eta$~Car\\ and the best-fit model in Tables~\\ref{tbl:specfit_variable}, \\ref{tbl:specfit_constant}.\nThe HXD spectra exclude expected contributions from CXB and GRXE, i.e.\\ both the XIS and HXD spectra should originate within 2.5\\ARCMIN\\ of $\\eta$~Car.\nThe red, blue, green and grey lines in each panel represent the WWC thermal component, the power-law component,\nthe stable foreground thermal component (i.e.\\ CCE, HN and surrounding point sources), and their total, respectively.\nEach bottom panel shows the residuals of the $\\chi^{2}$ fit.\n\\label{fig:obsspec}\n}\n\\end{figure*}\n\n\\begin{figure*}[t]\n\\figurenum{4}\n\\plotone{fig4b.eps}\n\\caption{Continued.\n\\label{fig:obsspec1}\n}\n\\end{figure*}\n\n\n\\section{Result}\n\n\\subsection{Light curve}\n\nFor each observation,\nwe produced an XIS light curve between 5$-$9~keV and an HXD\/PIN light curve between 15$-$25~keV with 500~sec time bins.\nThe XIS light curves at SUZ$_{050829}$, SUZ$_{081210}$, SUZ$_{090610}$, SUZ$_{091121}$, and SUZ$_{110724}$\nwith good photon statistics reject a constant model at a confidence limit above 95\\%.\nThese variations are apparently caused by small flux fluctuations on timescales of $\\lesssim$2~ksec and not by a systematic variation on long timescales,\nas seen in the \\XMM\\ observations in 2003 \\citep{Hamaguchi2007b}.\nThe HXD\/PIN light curves at SUZ$_{050829}$, SUZ$_{060203}$, and SUZ$_{070623}$ reject a constant model at above 95\\% confidence, \nbut they did not show any apparent long-term variation, either.\n\nWe measured from each observation the net XIS count rate between 5$-$9~keV \nand the net HXD\/PIN count rates between 15$-$25~keV and 25$-$40~keV (Table~\\ref{tbl:cnt_rate}).\nThe top panel of Figure~\\ref{fig:obstiming} shows the XIS light curve between 5$-$9~keV.\nIn this plot, we subtracted the XIS count rate in SUZ$_{090125}$ (0.05~\\UNITCPS) from \nXIS count rates of all the XIS observations as contamination from surrounding X-ray sources.\nWe also compare this to the \\RXTE\\ light curve after 2005 between 2$-$10~keV \\citep{Corcoran2010}.\nThe amount of contamination of X-ray sources in the \\RXTE\\ \\FOV\\ is also estimated from the \ndeep X-ray minimum observations \\citep[see details in][]{Hamaguchi2014a} and subtracted from the \\RXTE\\ light curve.\nThis means that both light curves should be of the WWC X-ray emission and directly comparable.\nTheir vertical axes are scaled such that the same height gives the same energy flux\nin the typical $\\eta$~Car\\ spectrum (\\KT\\ $\\sim$4.5~keV, \\NH\\ $\\sim$5$\\times$10$^{22}$~\\UNITNH\\ and $Z \\sim$0.8~solar).\nThese two light curves match very well.\n\nThe bottom panel of Figure~\\ref{fig:obstiming} shows the HXD\/PIN light curves between 15$-$25~keV and 25$-$40~keV.\nTheir vertical axes are scaled such that the data points at SUZ$_{050829}$ overlap.\nThe 15$-$25~keV light curve varied similarly to the 5$-$9~keV light curve outside the minimum: it increased gradually toward periastron.\nThis result indicates that the 15$-$25~keV emission has the same origin as the 5$-$10~keV emission, i.e.\\ the WWC X-rays.\nIt, however, varied differently during the minimum.\nIt declined only by a factor of 3 from SUZ$_{081210}$ at SUZ$_{090125}$ when the 5$-$9~keV flux dropped to zero.\nThe minimum observed flux occurred during the next observation (SUZ$_{090215}$) during the shallow minimum phase.\n\nOn the other hand, the 25$-$40~keV light curve did not show any significant variation near the X-ray maximum,\naccepting a constant flux model (reduced $\\chi^{2}=$ 1.11 for d.o.f. = 9).\nWe converted the 22$-$100~keV flux of the \\INTEGRAL\\ source \\citep[][0.15~\\UNITCPS]{Leyder2008} to the HXD\/PIN count rate \nbetween 25$-$40~keV, assuming a $\\Gamma =1.1$ power-law spectrum, and plotted it with a dotted line.\nThis flux level matches quite well all the data points except SUZ$_{050829}$ and SUZ$_{080610}$.\nThe result suggests that the 25$-$40~keV emission does not originate from the WWC thermal plasma, but from \nthe same component as the \\INTEGRAL\\ source.\n\nIn summary, the light curve analysis suggests two major X-ray emission components between 5$-$40~keV:\nstrongly variable emission below $\\sim$25~keV and stable emission above $\\sim$25~keV.\nThe former component probably corresponds to the WWC thermal plasma emission and the latter to the power-law component \n\\citep{Leyder2008,Sekiguchi2009,Leyder2010}.\n\n\\begin{figure*}[t]\n\\epsscale{1.5}\n\\plotone{fig5.eps}\n\\caption{\nXIS spectrum in SUZ$_{081210}$ fit by an absorbed CE plasma model ({\\tt apec}, {\\it top left}), \nan absorbed NEI plasma model ({\\tt nei}, {\\it top right})\nand an absorbed CE + NEI plasma model ({\\tt apec}+{\\tt nei}, {\\it bottom left}),\nand the \\CHANDRA\\ HETG spectrum obtained between 2008 December 8$-$13 overlaying the best-fit {\\tt apec}+{\\tt nei} model for the SUZ$_{081210}$ spectrum ({\\it bottom right}).\nThe \\CHANDRA\\ spectrum above $\\sim$7.75~keV is contaminated by the METG grating data and therefore is not extracted.\nThe model for the \\CHANDRA\\ spectrum includes the CCE component, but not the HN nor the surrounding point source components,\nwhich are outside of the \\CHANDRA\\ event extracting region.\n\\label{fig:xis_spec_issue}\n}\n\\end{figure*}\n\n\\subsection{Spectrum}\n\nFigures~\\ref{fig:obsspec} shows the XIS0+3 and HXD\/PIN spectra of all observations.\nThe XIS spectra varied as in the 2003 orbital cycle \\citep{Hamaguchi2007b};\nthe hard band slope between 7$-$10~keV, which reflects the hottest temperature of the WWC plasma,\ndid not vary significantly through the cycle.\nThe HXD spectra below 25~keV seem to connect smoothly to the XIS hard band.\nThe spectra above 25~keV do not show prominent features within the limited photon statistics.\n\nThe Helium-like iron line complex at $\\sim$6.7~keV distorted strongly toward the low energy side\naround periastron, as seen in the previous cycle \\citep{Hamaguchi2007b}.\nTo show this distortion clearly,\nwe first fit the XIS spectrum in SUZ$_{081210}$ above 5~keV by an absorbed 1$T$ collision equilibrium (CE) plasma model \n({\\tt apec}, {\\it top left} panel of Figure~\\ref{fig:xis_spec_issue}).\nThe spectrum also shows a fluorescent line from cold iron at 6.4 keV, for which we assume a narrow Gaussian line,\nbased on a \\CHANDRA\\ grating observation of $\\eta$~Car\\ around apastron in 2000 \\citep{Corcoran2001a}.\nWe also add another narrow Gaussian line for Fe K$\\beta$ fluorescence at 7.06~keV with the intensity tied to 12.2\\% of the Fe K$\\alpha$ line \\citep{Yamaguchi2014a}.\nThe spectrum also shows an iron $K$ absorption edge at 7.1~keV, for which the column density of cold iron (\\NFE) is varied independently\nfrom the hydrogen column density (\\NH).\nThe best-fit model has a strong excess at $\\sim$6.5~keV, for which the 6.4~keV line is overestimated to compensate --- \nthis result suggests more emission from lowly ionized iron.\nA marginal enhancement between Ni I $K\\alpha$ and Ni XXVII $K\\alpha$ lines also support the presence of lowly ionized nickel.\n\nHowever, the {\\tt nei} model, a non-equilibrium ionization (NEI) plasma model in {\\tt xspec} (NEIVERS version 3.0),\ndoes not reproduce the Hydrogen-like iron line at 6.9 keV in the spectrum ({\\it top right} panel of Figure~\\ref{fig:xis_spec_issue}).\nThis model still does not reproduce the excess at $\\sim$6.5~keV.\nThe {\\tt pshock} model, which considers plasma distribution in different ionization timescales in the plane parallel shock, gives a similar result.\nThus, there has to be a significant amount of CE plasma emission to reproduce the Hydrogen-like lines.\n\nWe, therefore, fit this spectrum by an absorbed {\\tt apec} plus {\\tt nei} model as a testbed.\nThe plasma temperatures of the {\\tt apec} and {\\tt nei} components cannot be independently determined and therefore are tied.\nTheir elemental abundances are also tied together.\nThe best-fit model reproduces the XIS spectrum well ({\\it bottom left} panel of Figure~\\ref{fig:xis_spec_issue}).\nThe excess at $\\sim$6.5~keV totally disappears, while the Hydrogen-lines are reproduced well.\nThis best-fit model also reproduces well the \\CHANDRA\\ HETG grating spectrum obtained quasi simultaneously\nto SUZ$_{081210}$ between 2009 Dec 8$-$13\n({\\it bottom right} panel of Figure~\\ref{fig:xis_spec_issue}, Observation ID: 10831, 8930, 10827, Total exposure: 74.6~ksec, PI: Corcoran, M.~F.).\nWith a factor of $\\sim$5 better spectral resolution than the XISs, the NEI component is clearly seen as a red wing of the Helium-like Fe $K\\alpha$ line.\nThe residual at the blue side of the Helium-like Fe $K\\alpha$ line, which cannot be resolved with the CCD resolution,\ncan be reproduced by a Doppler broadening of $\\Delta v\\sim$800~\\UNITVEL.\n\nWe therefore use this model for all the spectra but SUZ$_{090125}$, which does not show WWC emission below 10~keV.\nThe spectra outside of the X-ray minimum and maximum (SUZ$_{050829}$, SUZ$_{060203}$, SUZ$_{070623}$, SUZ$_{091121}$ and SUZ$_{110724}$) \ndo not show clear distortions in the Fe $K\\alpha$ line but small excesses at $\\sim$6.5~keV, so that we tie their ionization parameters.\nThe elemental abundances of all the emission components are tied together and the abundance ratios between elements\nabove Helium are fixed at the values of the {\\tt aspl} solar abundance model \\citep{Asplund2009} in the XSPEC modeling.\nThis is a reasonable approach because the hot plasma is heated by the secondary stellar winds and so is expected to \nreflect the elemental abundance of the secondary star, which has an unknown evolutionary status.\nWe also assume the elemental abundance of the absorber to be solar except for iron.\nThe absorbing material should originate from the primary star, which is depleted in hydrogen, carbon and oxygen but rich in nitrogen.\nHowever, these elements do not affect the spectral structure above 5~keV.\nThe other high-$Z$ elements are considered to be solar \\citep{Hillier2001}.\nThe absolute \\NH\\ depends on the hydrogen abundance, but it can be easily adjusted for any abundance models later.\n\nTo this variable WWC model, we add an absorbed power-law model for the extremely hard X-ray component above $\\sim$25~keV.\nThe HXD\/PIN spectra between 25$-$40 keV do not have enough statistics to determine the power-law index and normalization, individually.\nWe therefore fixed the power-law index at 1.4.\nThe result does not change substantially for power law indices in the range 1.0$-$1.8.\nSince the 25$-$40~keV light curve did not show significant flux variations,\nwe tied the normalization of the power-law component between observations.\n\\citet{Leyder2010} constrained the position of this power-law source to within 1.6\\ARCMIN\\ from $\\eta$~Car\\ from the \\INTEGRAL\\ observations.\nSince the XIS source region is within 2.5\\ARCMIN\\ from $\\eta$~Car, the XIS spectra should also include this power-law source.\nA simple extrapolation of this $\\Gamma\\sim$1.0$-$1.8 power-law spectrum should show significant emission below $\\sim$10~keV during the deep X-ray minimum,\nbut neither the XIS spectrum at SUZ$_{090125}$, nor any \\CHANDRA, nor \\XMM\\ spectra during the minimum suggest the presence of this power-law source \\citep{Hamaguchi2014a}.\n\\citet{Leyder2010} did not find any promising candidate of this counterpart other than $\\eta$~Car\\ in a \\CHANDRA\\ image, either.\nThis means that this power-law component is heavily absorbed, at least during the X-ray minimum, and cannot be seen below 10~keV.\nThe absorption to this power-law component cannot be constrained outside the X-ray minimum.\nWe, therefore, assume a constant absorption to this power-law component through the orbital cycle.\n\n\\input{tab3}\n\\input{tab4}\n\nThe 15$-$25~keV flux at SUZ$_{090125}$ is too high for either the thermal component seen in the XIS band \nor the power-law component above 25~keV.\nWe therefore assume the excess as the deeply embedded WWC emission and reproduce it with the model for the WWC thermal component.\nSince the statistics are limited, we fixed the plasma temperature at 4~keV, the typical temperature of the WWC plasma outside of the X-ray minima.\nWe also tied \\NH\\ and \\NFE\\ of SUZ$_{090125}$ because the Fe $K$ absorption edge cannot be measured.\nTo all the spectral models, we add the best-fit model of the XIS spectrum in SUZ$_{090125}$ \nto account for emission from the CCE, the HN and the surrounding point sources.\n\nIn this model fit, the model normalizations for the HXD\/PIN spectra were multiplied by 1.15 for the XIS nominal pointing observations\nand 1.19 for the HXD nominal pointing observations,\naccording to the Suzaku Data Reduction Guide\\footnote{http:\/\/heasarc.gsfc.nasa.gov\/docs\/suzaku\/analysis\/abc\/}.\n\nThe best-fit model reproduced all the spectra very well (reduced $\\chi^2$ =1.08, d.o.f =3437, Tables~\\ref{tbl:specfit_variable}, \\ref{tbl:specfit_constant} and Figure~\\ref{fig:obsspec}).\nThe hottest plasma temperatures of the thermal WWC component are stable at \\KT\\ $\\sim$4~keV outside the minimum\nand the HXD\/PIN spectra showed no signature of hotter plasma.\nThe fit to the SUZ$_{090215}$ spectrum resulted in a low plasma temperature of $\\sim$2~keV, similarly to spectral fits\nto the shallow minimum spectra in 2003 \\citep{Hamaguchi2007b}.\nHowever, \\KT\\ is degenerate with \\NH\\ in fits to strongly absorbed spectra,\nso that this variation may not suggest an actual decline in temperature of the hottest plasma.\nThe elemental abundance of the plasma is close to solar.\nThe \\NH\\ goes down to zero for SUZ$_{060203}$ and SUZ$_{110724}$.\nHowever, the extrapolations of these models significantly overestimate the spectra below 5~keV;\nthe soft band spectra suggest higher \\NH~$\\sim$3$-$5$\\times$10$^{22}$~\\UNITNH.\nThe \\NH\\ probably does not correctly represent the absorption column to the hot plasma \nbecause of lower temperature plasma emission important around 5~keV.\nOn the other hand, \\NFE\\ is determined from the iron edge feature and is therefore a more reliable estimator of the absorption to the hot plasma.\nThe \\NFE\\ increased toward the deep minimum and reached the maximum of $\\approx$8$\\times$10$^{24}$~\\UNITNH\\ at SUZ$_{090125}$.\nThe power-law component also required a very high value of \\NH~$\\sim$2$\\times$10$^{24}$~\\UNITNH.\n\n\\section{Discussion}\n\n\\subsection{Orbital Modulation of the Physical Parameters}\n\n\\SUZAKU\\ sampled a whole orbital cycle of $\\eta$~Car\\ between 2005 and 2011.\nThough the X-ray minimum in 2009 was significantly shorter than those in previous cycles \\citep{Corcoran2010},\nthe latest \\SUZAKU\\ spectrum was very similar to that in the previous cycle, again suggesting a cyclic variation.\n\nThe combined fit to the XIS and HXD\/PIN spectra confirmed that the hottest temperatures of the WWC plasma\nare stable at \\KT~$\\sim$4$-$5~keV through the orbit outside the X-ray minimum (Figure~\\ref{fig:spec_param} {\\it top}).\nThe same conclusion was deduced by \\citet{Ishibashi1999} using the \\RXTE\\ data obtained between 1996 April and 1998 October\nand \\citet{Hamaguchi2007b} using the \\XMM\\ and \\CHANDRA\\ data obtained between 2000 July and 2003 September.\nThe new \\SUZAKU\\ result is important in two ways.\nThe set of observations has a long baseline between 2005 August and 2011 July and samples the whole orbital cycle.\nThe HXD\/PIN provided the best quality measure of the extremely high energy spectra above 15~keV with a smaller \\FOV\\ and\nlower background than the \\RXTE\\ Proportional Counter Array (PCA).\nThe wide band coverage realized with the HXD provided a better measurement\nof the continuum slope in the very high energy range, and therefore increased the sensitivity to the hotter temperature plasma.\nThe \\SUZAKU\\ results show that the plasma temperature does not change prominently outside the minimum,\nin line with predictions by WWC theories.\n\nThe distortion of the Helium-like Fe $K\\alpha$ line was first recognized during the X-ray minimum in 2003\nand discussed as caused by the NEI effect \\citep{Hamaguchi2007b}.\nThe new \\SUZAKU\\ result demonstrates that an NEI plasma with $\\tau~\\sim$0.6$-$1.3$\\times$10$^{11}$~cm$^{-3}$~s$^{-1}$\ncan reproduce this distortion in spectral fits and \nthis NEI plasma component can be present through the orbital cycle, with an increased ratio around periastron.\nThe {\\tt pshock} model, which considers the ionization timescale distribution in a plane parallel shock,\nfails to reproduce the whole He-like Fe $K\\alpha$ line profile.\nThis result suggests that the plasma really has two peaks in the ionization timescale distribution at\n$\\sim$10$^{11}$~cm$^{-3}$~s$^{-1}$ (the NEI component) and above $\\sim$10$^{12}$~cm$^{-3}$~s$^{-1}$ (the CE component).\nThis result probably means that the NEI plasma heats up in $\\sim$1000 (10$^{8}$~\\UNITPPCC$\/n$) sec, where $n$ is the plasma density,\nand then quickly cools down without reaching the thermal equilibrium.\nThe \\EM\\ ratio of the NEI plasma increases from $\\sim$25\\% around apastron to $\\sim$75\\% around periastron (Figure~\\ref{fig:spec_param} {\\it middle}).\nThe ratio is high with a large uncertainty during the shallow minimum (SUZ$_{090215}$), \nbut it is also high after the recovery (SUZ$_{090610}$) as well.\nThis result does not suggest that the ratio is correlated with the X-ray luminosity.\nThe SUZ$_{090610}$ observation is when the WWC apex is wrapped inside the primary wind, and therefore in a high density environment.\nThe NEI plasma may quickly contact the thick cool primary wind and cool down, while the CE plasma may rapidly leave the system in the\npart of the shocked secondary wind which does not come into direct contact with the higher density primary wind.\n\nThe absorption column density of cold iron (\\NFE) increases by a factor of 2$-$4 toward periastron (Figure~\\ref{fig:spec_param} {\\it bottom}).\nThis variation is very similar to the \\NH\\ variation measured from hard band spectra above 5 keV in 2003 \\citep{Hamaguchi2007b}; \nthe absorption to the WWC apex varies periodically as well.\nInterestingly, the lowest \\NFE\\ observed around apastron is still a factor of 2$-$3 higher than absorption to soft X-rays ($\\sim$4$-$5$\\times$10$^{22}$~\\UNITNH),\nthough the WWC apex should be seen through the thin secondary wind.\nSince there is no evidence of iron overabundance in $\\eta$~Car\\ \\citep[e.g.,][]{Hamaguchi2007b,Hillier2001},\nthis perhaps could be explained if the secondary wind piles up over the WWC contact surface.\n\n\\begin{figure*}[t]\n\\plotone{fig6.eps}\n\\caption{Variations of the selected spectral parameters in the best-fit model \n({\\it Top}: plasma temperature, {\\it Middle}: emission measure ratio of the NEI ({\\tt nei}) plasma,\n{\\it Bottom}: absorption column density of cold iron in units of an equivalent hydrogen column density\nin the case of solar abundance matter.\nThe open circle at the bottom panel is of SUZ$_{090125}$, measured from the soft band cut-off.\nThe vertical bars depict 90\\% error ranges.\n\\label{fig:spec_param}\n}\n\\end{figure*}\n\n\\subsection{High 15$-$25~keV Flux during the Deep X-ray Minimum}\n\nThe relatively strong 15$-$25~keV emission at SUZ$_{090125}$ can be reproduced by the WWC emission\nviewed through extremely high photoelectric absorption (\\NH\\ $\\approx$8$\\times$10$^{24}$~\\UNITNH).\nThe \\CHANDRA\\ spectrum obtained at the end of the X-ray eclipse on 2009 Feb 3 also suggested \na very high \\NH\\ of $\\sim$10$^{24}$~\\UNITNH\\ \\citep{Hamaguchi2014a}, \nand the WWC apex should be more embedded at the middle of the X-ray eclipse.\nA peak \\NH\\ of $\\sim$several$\\times$10$^{24}$~\\UNITNH\\ during periastron is also suggested by simulations of WWC X-ray emission from $\\eta$~Car\\ \n\\citep[][Russell et al.\\ in preparation]{Parkin2011}.\nThe large \\NH\\ at SUZ$_{090125}$ is consistent with the picture that \nthe WWC X-ray emission peered through very thick intervening material that totally blocked X-ray emission below 10 keV.\nIf this interpretation is correct,\nthe intervening material would be the inner primary wind, and not the primary stellar body.\n\nIn this interpretation, the WWC activity during the deep X-ray minimum is still strong behind the absorber.\nThe \\EM\\ at SUZ$_{090125}$ is as large as that at the maximum in SUZ$_{081210}$.\nThis is consistent with the WWC theory, in which the luminosity is inversely proportional to the distance between the two stars.\nHowever, the Compton scattering process becomes important at this absorption column.\nSince emission scattered off the line of sight may end up reaching us after another scattering,\nthe amount of attenuation by the Compton scattering also depends on the shape of the surrounding \nintervening material.\nA broad-band spectrum above 10~keV at this phase with good photon statistics is required to correctly \nmeasure the amount of the Compton scattering and hence the intrinsic luminosity.\n\n\n\\subsection{Origin of the Power-law Component}\nThe PIN count rates between 25$-$40~keV did not vary strongly.\nThe $\\gamma$-ray source, 1FGL~J1045.2-5942, detected by the \\FERMI\\ $\\gamma$-ray observatory also only varied by a factor of $\\lesssim$2\nincluding the X-ray minimum, with a possible weak decline after the recovery \\citep{Abdo2010,Farnier2011,Reitberger2012}.\nThis result strengthens the hypothesis \nthat the 25$-$40~keV power-law source is connected to the \\FERMI\\ $\\gamma$-ray source \\citep{Leyder2008,Leyder2010,Abdo2010}.\n\nIn this interpretation, the power-law component originates from emission up-scattered by GeV particles accelerated at the WWC region.\nHowever, our results show that this power-law component does not change significantly around the maximum when the WWC head-on\ncollision is the strongest and around shallow minimum when emission near the WWC apex apparently shuts off \\citep[][and additional references therein]{Hamaguchi2014a}.\nOur result does not suggest that the power-law component does not originate near the WWC stagnation point.\nIt remains to be seen whether the power-law component can be reproduced by models of particle acceleration in the WWC.\n\nTo satisfy low X-ray flux below 10~keV of $\\eta$~Car\\ during the deep X-ray minimum,\nthe power-law source should suffer extremely strong absorption of \\NH~$\\approx$2$\\times$10$^{24}$~\\UNITNH.\nThis high \\NH\\ does not favor the foreground shock region such as the CCE plasma cavity nor the HN lobe,\nwhose extinctions are less than \\NH~$\\sim$5$\\times$10$^{22}$~\\UNITNH.\nOne obvious but less interesting hypothesis is an unrelated neutron star or an active galactic nuclei behind $\\eta$~Car,\nthough the chance of this coincidence is not high.\nA provocative but more interesting hypothesis may be the presence of an active compact object associated with the binary system,\nwhich was once bound to the binary system but ejected by events such as the 1840 eruption. \nThe flat power-law spectrum is similar to those of high-mass X-ray binaries, \nand the luminosity is within the range of systems with wind-fed accretion.\nThe presence of a compact object in the system would require a progenitor with an initial mass greater than the initial mass of \n$\\eta$~Car, i.e. $>$150~\\UNITSOLARMASS. \nThe evolution of such a massive progenitor would probably result in the creation of a black hole.\n\n\\section{Conclusion}\n\nWe analyzed datasets of the 10 \\SUZAKU\\ observations of the super massive star $\\eta$~Car\\ and studied\nthe variation of the extremely hard X-ray emission above 15~keV through the orbital cycle for the first time.\nOur study suggests that the 15$-$25~keV emission originates in the tail of the thermal emission seen below 10~keV,\nwhile the emission above 25~keV is the power-law component observed with \\INTEGRAL.\nThe origin of the power-law component is mysterious.\nThe $K\\alpha$ and $K\\beta$ lines of Fe and Ni ions need emission from both CE and NEI plasmas.\nThe NEI plasma ratio increases toward periastron; this result may suggest an increase of gas density around the WWC apex around periastron.\nIn the summer of 2014, another X-ray observing campaign for the latest periastron passage is being performed with\nmultiple X-ray observatories including \\NUS, which provides focussed imaging up to 80 keV.\nThese observations should provide the best measure of the presence of the deeply embedded X-ray component and the power-law component\nand their spectral properties (e.g., \\NH, \\KT, $\\Gamma$).\nThey should help understand the nature of the WWC emission around periastron and the mysterious power-law source.\n\n\\acknowledgments\n\nThis research has made use of data obtained from the High Energy Astrophysics Science Archive\nResearch Center (HEASARC), provided by NASA's Goddard Space Flight Center.\nThis research has made use of NASA's Astrophysics Data System Bibliographic Services.\nWe appreciate the \\SUZAKU\\ operations team for optimizing the observations, \nand Masahiro Tsujimoto, Keith Arnaud and Adam Foster for suggestions on the XIS data analysis and the appropriate NEI model.\n\nFacilities: \\facility{Suzaku (XIS,HXD)}, \\facility{RXTE (PCA)}, \\facility{CHANDRA (HETG)}\n\n\\input{ms_apjstyle.bbl}\n\n\\end{document}\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Linear $k_\\perp$ factorization is broken}\n\nHeavy nuclei are strongly absorbing targets and bring a new scale into\nthe perturbative QCD (pQCD) description of hard processes \\cite{Mueller}.\nThis has severe consequences for the relations between various \nhard scattering observables. In a regime of small absorption,\nsmall--$x$ processes are adequatly described by the \nlinear $k_\\perp$--factorization, and the pertinent observables\nare linear functionals of a universal unintegrated gluon distribution.\nNot so for the strongly absorbing target, where the linear \n$k_\\perp$--factorization is broken \\cite{DIS_Dijets}, \nand has to be replaced by a new, nonlinear \n$k_\\perp$--factorization \\cite{DIS_Dijets,Nonuniversality}, \nwhere observables are in general\nnonlinear functionals of a properly defined unintegrated glue.\nThe relevant formalism has been worked out for all interesting \nprocesses \\cite{Nonuniversality,Nonlinear} (see \\cite{CGC} \nfor references on related work), but in this \nvery short contribution we concentrate on deep\ninelastic scattering (DIS). Here, in the typical inelastic DIS event \nthe nuclear debris will be left in a state with \nmultiple color excited nucleons after the $q\\bar q$ dipole exchanged\nmany gluons with the target. The partial cross sections for final states \nwith a fixed number of color excited nucleons are the\ntopological cross sections. It is customary to describe \nthem in a language of unitarity cuts through\nmultipomeron exchange diagrams \\cite{AGK}. \nIn our approach \\cite{Cutting_Rules}, color excited \nnucleons in the final state give a clear--cut definition of a cut pomeron.\nTopological cross sections carry useful information on the correlation between \nforward or midrapidity\njet\/dijet production and multiproduction in the nuclear fragmentation region\nas well as on the centrality of a collision.\n\n\\section{Nuclear collective glue and its unitarity cut interpretation}\n\nThe basic ingredient of the nonlinear $k_\\perp$--factorization is\nthe collective nuclear unintegrated glue, which made its first appearance\nin our work on the diffractive breakup of pions into jets $\\pi A \\to \n\\mathrm{jet}_1 \\mathrm{jet}_2 A$ \\cite{NSS}.\nIndeed, in the high energy limit, the nearly back--to--back\njets acquire their large transverse momenta directly from gluons.\nIt is then natural use the diffractive $S$--matrix of a $q \\bar q$--dipole\n$S_A({\\bf{b}},x,{\\bf{r}})$ for defining the nuclear unintegrated glue:\n\\begin{eqnarray}\n\\int {d^2 {\\bf{r}} \\over (2 \\pi)^2} \\, \nS_A({\\bf{b}},x,{\\bf{r}}) \\exp(-i{\\bf{p}} {\\bf{r}}) =\nS_A({\\bf{b}},x,{\\bf{r}} \\to \\infty) \\delta^{(2)}({\\bf{p}}) + \\phi({\\bf{b}},x, {\\bf{p}})\n\\equiv \\Phi({\\bf{b}},x,{\\bf{p}}) \\, .\n\\end{eqnarray}\nNotice that it resums multiple scatterings of a dipole, so that there \nis no straightforward relation to the conventional parton distribution\nwhich corresponds to just two partons in the $t$--channel.\nIt is still meaningful to call it an unintegrated glue \n-- one reason was given above -- another one, besides its role in \nfactorization formulas is its small-$x$ evolution property:\nThe so--defined $\\phi({\\bf{b}},x,{\\bf{p}})$ can be shown \\cite{NS_LPM} \nto obey \\footnote{Strictly speaking\nonly a few iterations of this equation make good sense.} \nthe Balitskii--Kovchegov \\cite{BK} evolution equation. \n\nClose to $x_A \\sim (m_N R_A)^{-1}$, for heavy nuclei, the dipole $S$--matrix is the familiar Glauber--Gribov exponential\n$S_A({\\bf{b}},x_A,{\\bf{r}}) = \\exp[-\\sigma(x_A,{\\bf{r}})T({\\bf{b}})\/2]$; for large dipole sizes it \ncan be expressed as $S_A({\\bf{b}},x_A,{\\bf{r}} \\to \\infty) = \\exp[-\\nu_A(x_A,{\\bf{b}})]$.\nHere the nuclear opacity \n$\\nu_A(x_A,{\\bf{b}}) = {1 \\over 2 } \\sigma_0(x_A) T({\\bf{b}})$, is\ngiven in terms of the dipole cross section for large dipoles \n$\\sigma_0(x) = \\sigma(x_A,{\\bf{r}} \\to \\infty)$.\nIn momentum space, a useful expansion is in terms of multiple convolutions\nof the free--nucleon unintegrated glue\n(we use a notation \n$f(x,{\\bf{p}}) \\propto {\\bf{p}}^{-4} \\partial G(x,{\\bf{p}}^2)\/ \\partial \\log({\\bf{p}}^2)$):\n\\begin{eqnarray}\n\\phi({\\bf{b}},x_A,{\\bf{p}}) = \\sum_{j \\geq 1} w_j \\big(\\nu_A(x_A,{\\bf{b}})\\big)\n f^{(j)}(x_A,{\\bf{p}}) \\, . \n\\label{Nuc_glue}\n\\end{eqnarray}\nHere \n\\begin{equation}\nw_j(x_A,\\nu_A) = {\\nu_A^j(x_A,{\\bf{b}}) \\over j! } \\exp[-\\nu_A(x_A,{\\bf{b}})]\n, \\,\nf^{(j)} (x_A,{\\bf{p}}) = \\displaystyle \\int \\big[ \\prod^j d^2 {\\bf{\\kappa}}_i \nf(x_A,{\\bf{\\kappa}}_i) \\big] \\delta^{(2)}( {\\bf{p}} - \\sum \\kappa_i) \\, . \n\\end{equation}\n\nCuriously, the very same collective nuclear glue is proportional to the\nspectrum of quasielastically scattered quarks:\n\\begin{equation}\n{d\\sigma(qA\\to qX) \\over d^2 {\\bf{b}} d^2 {\\bf{p}}} \n\\propto \\phi({\\bf{b}},x_A,{\\bf{p}}) \\, .\n\\end{equation}\n\nNow, we can state the {\\bf{first unitarity cutting rule}} in momentum space:\nthe $k$--th order term in the expansion (\\ref{Nuc_glue}) \ncorresponds to the topological cross section for the quark--nucleus \nscattering with $k$ color excited nucleons in the final state:\n\\begin{equation}\n{d \\sigma^{(k)} (qA \\to qX) \\over d^2 {\\bf{b}} d^2 {\\bf{p}}} \\propto \nw_k \\big(\\nu_A({\\bf{b}}) \\big) f^{(k)} ({\\bf{p}}) \\, .\n\\end{equation}\nThis simple substitution rule forms at the heart of the cutting\nrules applied to the nonlinear quadratures of \\cite{Nonlinear}.\n\n\\section{Standard AGK vs. QCD}\n\nGiven the close relation between the nuclear unintegrated glue and\nthe Glauber--Gribov scattering theory from color dipoles, one may\nbe tempted to play around with various expansions of the exponential. \nTaking\ninspiration from 1970's hadronic models one may then 'derive' \nexpressions for topological cross sections.\nFor example, the inelastic cross section of the \n$q \\bar{q}$-dipole-nucleus interaction is certainly obtained from:\n\\begin{eqnarray}\n\\Gamma^{inel} ({\\bf{b}}, {\\bf{r}}) &=& 1 - \\exp[- \\sigma({\\bf{r}}) T({\\bf{b}})] \n= \\sum_k \\Gamma^{(k)}({\\bf{b}},{\\bf{r}}) \\, ,\n\\end{eqnarray}\nand $\\Gamma^{(k)}({\\bf{b}},{\\bf{r}}) = \\exp[-\\sigma({\\bf{r}}) T({\\bf{b}})]\n(\\sigma({\\bf{r}}) T({\\bf{b}}))^k\/k!$ is then interpreted as the $k$--cut Pomeron\ntopological cross section. This is entirely incorrect, \nthe reason is that this result neglects the color--coupled channel\nstructure of the intranuclear evolution of the color dipole.\nInterestingly, a simple closed expression can be obtained\nwith full account for color \\cite{Cutting_Rules}:\n\\begin{eqnarray}\n\\Gamma^{(k)}({\\bf{b}},{\\bf{r}}) = \\sigma({\\bf{r}}) T({\\bf{b}}) \\, w_{k-1}(2 \\nu_A({\\bf{b}}) )\n {e^{-2 \\nu_A({\\bf{b}})} \\over \\lambda^k} \\gamma(k,\\lambda) \n\\, ,\n\\nonumber\n\\end{eqnarray}\nwhere $ \\lambda = 2 \\nu_A({\\bf{b}}) - \\sigma({\\bf{r}}) T({\\bf{b}})$, \nand $\\gamma(k,x)$ is an incomplete Gamma--function.\nFor a more quantitative comparison, consult fig 1. We see that\nthe standard Glauber--AGK predicts a strong hierarchy: $k$ cuts \nare suppressed by the $k$--th power of the dipole cross section.\nIn the QCD--cutting rules there is an additional dimensionful parameter,\nthe opacity of a nucleus for large dipoles $\\nu_A$, \nand the distribution over $k$ is substantially broader.\nThis difference will be more dramatic the smaller the dipole and\nreflects itself in the predicted $Q^2$--dependence of \nDIS structure functions with fixed multiplicity of cut Pomerons. More\nfigures, as well as another example for the failure of standard AGK,\ncan be found on the conference website.\n\n\\section{Conclusions}\nTopological cross sections can be obtained from nonlinear $k_\\perp$ \nfactorization formulas by straightforward substitution (cutting) rules.\nFor a correct isolation of topological cross sections a careful\ntreatment of the color coupled channel properties of the color(!) dipole\nintranuclear evolution is mandatory. Don't be misguided by \nsimple formulas derived in a single channel context, or by a too\nliteral analogy between color transparency and the Chudakov--Perkins\nsuppression of multiple ionisation by small size $e^+ e^-$ pairs in QED.\n \n\n\\begin{figure}\n \\includegraphics[height=.4\\textheight, angle=270]{Gl_larger2_proc}\n \\includegraphics[height=.4\\textheight, angle=270]{QCD_larger2_proc}\n \\caption{{\\bf{Left}}: the profile function for $k$ cut Pomerons according to standard Glauber--AGK for a fairly large dipole $r = 0.6$ fm at $x=0.01$ for $A=208$. {\\bf{Right}}: the same for the QCD cutting rules.}\n\\end{figure}\n\n\\begin{theacknowledgments}\n It is a pleasure to thank the organizers for the kind invitation.\nThis work was partially supported by the Polish Ministry for Science\nand Higher Education (MNiSW) under contract 1916\/B\/H03\/2008\/34.\n\\end{theacknowledgments}\n\n\n\n\\bibliographystyle{aipproc} \n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\section{Introduction}\n\\label{sec:introduction}\n\\input{introduction.tex}\n\n\\section{Related Work}\n\\label{sec:relatedwork}\n\\input{relatedwork.tex}\n\n\\section{Methodology}\n\\label{sec:methodology}\n\\input{methodology.tex}\n\n\\section{Experiments}\n\\label{sec:experiments}\n\\input{experiment.tex}\n\n\\section{Conclusion}\\label{conclusion}\n\nWe have presented an easy-to-implement yet effective channel pruning algorithm, \\ie, PEEL, to acquire a desired compact model via reallocating resources from less informative layers to more crucial layers. The intuition of PEEL~is that layers do not contribute equally to the model performance and those having an indispensable influence on the performance should be assigned more resources to magnify their positive effect. We conduct extensive experiments to verify the efficacy of PEEL~on ImageNet dataset with modern CNN architectures, \\ie, ResNet-18, ResNet-50, MobileNetV2, MobileNetV3-small and EfficientNet-B0. Experimental results suggest the effectiveness of PEEL~in uncovering compact yet accurate architectures consistently under various pruning levels, compared with state-of-the-art channel pruning methods. Besides, PEEL~yields stable searching results with small performance variance, and the searching cost is evidently smaller than contemporary channel pruning algorithms (\\eg, DMCP~\\cite{guo2020dmcp}).\n\n{\\small\n\\bibliographystyle{ieee_fullname}\n\n\\subsection{Results}\n\n\\begin{comment}\n\\begin{table\n\\caption{Performance of PEEL~and various channel pruning approaches on ImageNet validation set. Models \\textit{are all} trained with knowledge distillation (KD) for all algorithms, using either the official codes or our reproduced implementation. If the reproduced result with KD is worse than their reported value, we will just use the results reported in their original paper.}\n\\label{result_table}\n\\centering\n\\footnotesize{\n\\begin{tabular}{c|c|c|c}\n\\hline\nBackbone & Method & Top-1 Acc (\\%) & FLOPs \\\\\n\\hline \n\\multirow{10}*{ResNet-18} & Uniform 1.0 $\\times$ & 70.3 & 1.8G \\\\\n\\cline{2-4}\n~ & Uniform 0.85 $\\times$ & 69.0 & 1.33G \\\\\n~ & NR~\\cite{qiao2019neural} & 68.6 & 1.33G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 70.0 & 1.33G \\\\\n~ & \\textbf{PEEL} & \\textbf{70.6} & 1.33G \\\\\n\\cline{2-4}\n~ & Uniform 0.75 $\\times$ & 68.2 & 1.05G \\\\\n~ & NR~\\cite{qiao2019neural} & 68.1 & 1.04G \\\\\n~ & FPGM~\\cite{he2019filter} & 68.4 & 1.04G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 69.4 & 1.04G \\\\\n~ & \\textbf{PEEL} & \\textbf{69.9} & 1.04G \\\\\n\\hline\n\\multirow{18}*{ResNet-50} & Uniform 1.0 $\\times$ & 76.4 & 4.1G \\\\\n\\cline{2-4}\n~ & Uniform 0.85 $\\times$ & 75.8 & 3.0G \\\\\n~ & NR~\\cite{qiao2019neural} & 75.2 & 3.0G \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 76.2 & 3.0G \\\\\n~ & AutoSlim~\\cite{yu2019autoslim} & 76.0 & 3.0G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 76.5 & 3.0G \\\\\n~ & \\textbf{PEEL} & \\textbf{76.9} & 3.0G \\\\\n\\cline{2-4}\n~ & Uniform 0.75 $\\times$ & 74.6 & 2.3G \\\\\n~ & NR~\\cite{qiao2019neural} & 74.3 & 2.4G \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 75.4 & 2.3G \\\\\n~ & FPGM~\\cite{he2019filter} & 75.6 & 2.4G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 76.3 & 2.2G \\\\\n~ & \\textbf{PEEL} & \\textbf{76.8} & 2.2G \\\\\n\\cline{2-4}\n~ & Uniform 0.5 $\\times$ & 72.9 & 1.1G \\\\\n~ & NR~\\cite{qiao2019neural} & 73.1 & 1.1G \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 73.4 & 1.1G \\\\\n~ & AutoSlim~\\cite{yu2019autoslim} & 74.0 & 1.1G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 74.6 & 1.1G \\\\\n~ & \\textbf{PEEL} & \\textbf{75.1} & 1.1G \\\\\n\\hline\n\\multirow{21}*{MobileNetV2} & Uniform 1.0$\\times$ & 72.3 & 300M \\\\\n~ & NR~\\cite{qiao2019neural} & 73.0 & 300M \\\\\n~ & AutoSlim~\\cite{yu2019autoslim} & 74.2 & 300M \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 74.6 & 300M \\\\\n~ & \\textbf{PEEL} & \\textbf{74.8} & 300M \\\\\n\\cline{2-4}\n~ & Uniform 0.75 $\\times$ & 70.1 & 210M \\\\\n~ & NR~\\cite{qiao2019neural} & 70.2 & 211M \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 71.2 & 217M \\\\\n~ & AMC~\\cite{he2018amc} & 70.8 & 211M \\\\\n~ & AutoSlim~\\cite{yu2019autoslim} & 73.0 & 211M \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 73.5 & 211M \\\\\n~ & \\textbf{PEEL} & \\textbf{73.9} & 211M \\\\\n\\cline{2-4}\n~ & Uniform 0.5 $\\times$ & 64.8 & 97M \\\\\n~ & NR~\\cite{qiao2019neural} & 64.2 & 87M \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 63.8 & 87M \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 66.1 & 87M \\\\\n~ & \\textbf{PEEL} & \\textbf{66.6} & 87M \\\\\n\\cline{2-4}\n~ & Uniform 0.35$\\times$ & 60.1 & 59M \\\\\n~ & NR~\\cite{qiao2019neural} & 59.7 & 59M \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 62.7 & 59M \\\\\n~ & \\textbf{PEEL} & \\textbf{62.9} & 59M \\\\\n\\cline{2-4}\n\n\\hline\n\\end{tabular}\n}\n\\end{table}\n\\end{comment}\n\n\n\\begin{table\n\\caption{Performance of PEEL~and various channel pruning approaches with and without knowledge distillation (KD) on ImageNet validation set.}\n\\label{result_table}\n\\centering\n\\footnotesize{\n\\begin{tabular}{c|c|c|c|c}\n\\hline\n\\multirow{2}*{Backbone} & \\multirow{2}*{Method} & \\multicolumn{2}{|c|}{Top-1 Acc (\\%)} & \\multirow{2}*{FLOPs} \\\\\n\\cline{3-4}\n~ & ~ & w\/ KD & w\/o KD & ~ \\\\\n\\hline \n\\multirow{10}*{ResNet-18} & Uniform 1.0 $\\times$ & \\multicolumn{2}{|c|}{70.3} & 1.8G \\\\\n\\cline{2-5}\n~ & Uniform 0.85 $\\times$ & 69.0 & 68.5 & 1.33G \\\\\n~ & NR~\\cite{qiao2019neural} & 68.6 & 68.2 & 1.33G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 70.0 & 69.7 & 1.33G \\\\\n~ & \\textbf{PEEL} & \\textbf{70.6} & 70.1 & 1.33G \\\\\n\\cline{2-5}\n~ & Uniform 0.75 $\\times$ & 68.2 & 67.8 & 1.05G \\\\\n~ & NR~\\cite{qiao2019neural} & 68.1 & 67.9 & 1.04G \\\\\n~ & FPGM~\\cite{he2019filter} & 68.4 & 68.1 & 1.04G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 69.4 & 69.0 & 1.04G \\\\\n~ & \\textbf{PEEL} & \\textbf{69.9} & 69.3 & 1.04G \\\\\n\\hline\n\\multirow{18}*{ResNet-50} & Uniform 1.0 $\\times$ & \\multicolumn{2}{|c|}{76.4} & 4.1G \\\\\n\\cline{2-5}\n~ & Uniform 0.85 $\\times$ & 75.8 & 75.4 & 3.0G \\\\\n~ & NR~\\cite{qiao2019neural} & 75.2 & 74.8 & 3.0G \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 76.2 & 76.0 & 3.0G \\\\\n~ & AutoSlim~\\cite{yu2019autoslim} & 76.0 & 75.8 & 3.0G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 76.5 & 76.4 & 3.0G \\\\\n~ & \\textbf{PEEL} & \\textbf{76.9} & 76.6 & 3.0G \\\\\n\\cline{2-5}\n~ & Uniform 0.75 $\\times$ & 74.6 & 74.1 & 2.3G \\\\\n~ & NR~\\cite{qiao2019neural} & 74.3 & 74.0 & 2.4G \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 75.4 & 75.2 & 2.3G \\\\\n~ & FPGM~\\cite{he2019filter} & 75.6 & 75.5 & 2.4G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 76.3 & 76.2 & 2.2G \\\\\n~ & \\textbf{PEEL} & \\textbf{76.8} & 76.5 & 2.2G \\\\\n\\cline{2-5}\n~ & Uniform 0.5 $\\times$ & 72.9 & 72.7 & 1.1G \\\\\n~ & NR~\\cite{qiao2019neural} & 73.1 & 72.6 & 1.1G \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 73.4 & 73.2 & 1.1G \\\\\n~ & AutoSlim~\\cite{yu2019autoslim} & 74.0 & 73.6 & 1.1G \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 74.6 & 74.4 & 1.1G \\\\\n~ & \\textbf{PEEL} & \\textbf{75.1} & 74.7 & 1.1G \\\\\n\\hline\n\\multirow{21}*{MobileNetV2} & Uniform 1.0$\\times$ & \\multicolumn{2}{|c|}{72.3} & 300M \\\\\n\\cline{2-5}\n~ & NR~\\cite{qiao2019neural} & 73.0 & 72.2 & 300M \\\\\n~ & AutoSlim~\\cite{yu2019autoslim} & 74.2 & 73.1 & 300M \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 74.6 & 73.9 & 300M \\\\\n~ & \\textbf{PEEL} & \\textbf{74.8} & 74.2 & 300M \\\\\n\\cline{2-5}\n~ & Uniform 0.75 $\\times$ & 70.1 & 69.2 & 210M \\\\\n~ & NR~\\cite{qiao2019neural} & 70.2 & 69.4 & 211M \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 71.2 & 70.1 & 217M \\\\\n~ & AMC~\\cite{he2018amc} & 70.8 & 70.0 & 211M \\\\\n~ & AutoSlim~\\cite{yu2019autoslim} & 73.0 & 72.2 & 211M \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 73.5 & 72.4 & 211M \\\\\n~ & \\textbf{PEEL} & \\textbf{73.9} & 73.0 & 211M \\\\\n\\cline{2-5}\n~ & Uniform 0.5 $\\times$ & 64.8 & 64.3 & 97M \\\\\n~ & NR~\\cite{qiao2019neural} & 64.2 & 63.8 & 87M \\\\\n~ & MetaPruning~\\cite{liu2019metapruning} & 63.8 & 63.4 & 87M \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 66.1 & 65.6 & 87M \\\\\n~ & \\textbf{PEEL} & \\textbf{66.6} & 66.0 & 87M \\\\\n\\cline{2-5}\n~ & Uniform 0.35$\\times$ & 60.1 & 59.7 & 59M \\\\\n~ & NR~\\cite{qiao2019neural} & 59.7 & 59.2 & 59M \\\\\n~ & DMCP~\\cite{guo2020dmcp} & 62.7 & 62.4 & 59M \\\\\n~ & \\textbf{PEEL} & \\textbf{62.9} & 62.6 & 59M \\\\\n\\cline{2-5}\n\n\\hline\n\\end{tabular}\n}\n\\end{table}\n\n\n\\noindent\\textbf{Comparisons with state of the arts:}\nThe detailed performance comparison of PEEL~and contemporary channel pruning algorithms is shown in Table~\\ref{result_table}. We also record the floating-point operations (FLOPs) to approximate the forward time of different architectures. From Table.~\\ref{result_table}, we observe that PEEL~achieves 25$\\%$ and 45$\\%$ FLOPs reduction for ResNet-18 and ResNet-50, respectively, with almost no loss of accuracy. \nAs to MobileNetV2, the result is more encouraging as our algorithm can find a strong architecture with \\textbf{2.5\\%} higher in top-1 accuracy than the original model while possessing the same FLOPs. In addition, PEEL~consistently finds better architectures whose performance outperforms those searched by other competitive channel pruning techniques, \\eg, DMCP~\\cite{guo2020dmcp}, NR~\\cite{qiao2019neural} and MetaPruning~\\cite{liu2019metapruning}. For instance, when the backbone model is ResNet-50 and the target FLOPs is 2.2G, the architecture produced by PEEL~is \\textbf{0.5\\%} higher in top-1 accuracy than that found by DMCP. Moreover, the network structure obtained via PEEL~consistently outperforms the uniformly pruned model in all experiments, suggesting the effectiveness of the proposed resource reallocation approach.\n\\begin{comment}\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=1.0\\linewidth]{.\/figure\/FLOPs_acc_res18_50.pdf}\n \\vskip -0.1cm\n \\caption{FLOPs-accuracy spectrum of PEEL, DMCP and uniform pruning on (a) ResNet-18 and (b) ResNet-50.}\n \\centering\n \\vskip -0.2cm\n \\label{fig:flops_acc_res18}\n\\end{figure}\n\n\\noindent\\textbf{A detailed comparison with DMCP:} \nWe compare PEEL~against the recent and representative DMCP~\\cite{guo2020dmcp}.\nFigure~\\ref{fig:flops_acc_res18} shows the accuracy of architectures pruned by PEEL and DMCP under diverse FLOPs constraints on ResNet-18 and ResNet-50. Here, we take the uniform pruning as reference. It is observed that PEEL~can always find better architectures than DMCP in terms of top-1 accuracy. On all FLOPs levels, PEEL~outperforms DMCP by least 0.4\\% in terms of top-1 accuracy. Thanks to the resource reallocation, the top-1 accuracy gap between PEEL~and uniform pruning lies between 1.0\\% and 1.7\\%. \n\\end{comment}\n\n\n\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width=1.0\\linewidth]{.\/arch_comparison_res18.pdf}\n \\vskip -0.1cm\n \\caption{Comparison between structures searched by PEEL~and DMCP on ResNet-18 when $\\#$FLOPs=1.04G. The vertical bar and vertical line denote the mean and variance of the channel numbers in each layer, respectively. Results are averaged over five runs.}\n \\centering\n\n \\label{fig:arch_compare}\n\\end{figure}\n\n\\begin{table}[!t]\n\\caption{Pruning MobileNetV3-small and EfficientNet-B0.}\n\\label{prune_harder_model_table}\n\\centering\n\\small{\n\\begin{tabular}{c|c|c|c}\n\\hline\nBackbone & Method & Top-1 Acc (\\%) & FLOPs \\\\\n\\hline\n\\multirow{2}*{MobileNetV3} & Uniform 1.0 $\\times$ & 67.4 & 56M \\\\\n\\cline{2-4}\n~ & PEEL & 67.2 & 49M \\\\\n\\hline\n\\multirow{2}*{EfficientNet-B0} & Uniform 1.0 $\\times$ & 77.1 & 390M \\\\\n\\cline{2-4}\n~ & PEEL & 77.0 & 346M \\\\\n\\hline\n\\end{tabular}\n}\n\\vspace{-5ex}\n\\end{table}\n\n\\noindent \\textbf{Pruning MobileNetV3-small and EfficientNet-B0:} As shown in Table~\\ref{prune_harder_model_table}, PEEL~saves \\textbf{12.5\\%} FLOPs for MobileNetV3-small and \\textbf{11.3\\%} FLOPs for EfficientNet-B0 without incurring severe performance drops. Note that MobileNetV3-small and EfficientNet-B0 are two extremely lightweight classification models and removing redundancy in these models is nontrivial. The experimental results explicitly showcase the effectiveness and generality of PEEL~on network pruning.\n\nWe also visualize the structures uncovered by PEEL~and DMCP. As depicted in Fig.~\\ref{fig:arch_compare}, both architectures have more channels as the layer goes deeper. This is expected since more channels will be leveraged to compensate for the loss of spatial information incurred by the downsampling operations. Nevertheless, PEEL~tends to place more channels in the early stages while DMCP chooses to put more channels in the later stages. Putting more resources at shallow layers brings more gains as there is more redundancy in the deep layers. Besides, the variance of the number of channels in each layer of PEEL~is much smaller than that of DMCP. For example, the averaged channel variance of DMCP on the last four layers of ResNet-18 is \\textbf{38.8} while the channel variance of PEEL~is \\textbf{21.8}. The gap between the structural variance of DMCP and our algorithm is more evident in ResNet-50 and MobileNetV2. In ResNet-50, the averaged channel variance of PEEL~is almost half of the variance of DMCP for all layers. We provide more details in the supplementary material.\nThe searching instability of DMCP also leads to high performance variance in the searched architectures. The performance of PEEL is much stabler relatively. We summarize the performance of architectures searched by PEEL~and DMCP in Table~\\ref{performance_var_table}. \n\n\n\\begin{table}[t]\n\\caption{Performance variance of searched architectures of PEEL and DMCP on ResNet-50. Results are averaged over five runs.}\n\\vskip 0.1cm\n\\label{performance_var_table}\n\\centering\n\\small{\n\\begin{tabular}{c|c|c|c}\n\\hline\n\\multirow{2}*{Method} & \\multicolumn{3}{c}{$\\#$FLOPs} \\\\\n\\cline{2-4}\n~ & 2.2G & 1.8G & 1.1G \\\\\n\\hline\n\\hline\n\\textbf{PEEL~} & \\textbf{76.7\\% $\\pm$ 0.2\\%} & \\textbf{75.3\\% $\\pm$ 0.3\\%} & \\textbf{75.0\\% $\\pm$ 0.2\\%} \\\\\n\\hline\nDMCP & 75.7\\% $\\pm$ 0.7\\% & 74.2\\% $\\pm$ 0.8\\% & 73.8\\% $\\pm$ 0.8\\% \\\\\n\\hline\n\\end{tabular}\n}\n\\vspace{-3ex}\n\\end{table}\n\n\n\\begin{table}[!t]\n\\caption{Comparison between the training time and memory usage of PEEL~and DMCP on ResNet-50 ($\\#$FLOPs=1.1G). Here, the memory usage is measured on one GPU and the batch size is set as 64 for each GPU.}\n\\vskip 0.1cm\n\\label{train_cost_table}\n\\centering\n\\small{\n\\begin{tabular}{c|c|c}\n\\hline\nAlgorithm & Train Time (H) & Memory usage (G) \\\\\n\\hline\n\\hline\n\\textbf{PEEL~} & \\textbf{37} & \\textbf{4.3} \\\\\n\\hline\nDMCP~\\cite{guo2020dmcp} & 98 & 9.5 \\\\\n\\hline\n\\end{tabular}\n}\n\\vspace{-5ex}\n\\end{table}\n\n\nThe proposed PEEL~not only takes shorter training durations to find and train the desired model, but also consumes less GPU memory during the searching phase. \nHere, we examine the training cost of PEEL and DMCP, \\ie, the total training hours and the GPU memory usage during the searching phase. The original unpruned model is ResNet-50 and the target FLOPs is 1.1 G. We keep all other hyperparameters the same, including batch size (512), GPU types (NVIDIA TITAN X) and number of used GPUs (8). From Table~\\ref{train_cost_table}, the total number of training hours of PEEL~is roughly a third of DMCP. The result is not surprising as DMCP requires training on the original cumbersome model whilst PEEL~merely entails training of a much slimmer model. As to the GPU memory usage, since DMCP needs to collect gradients of several sampled architectures while PEEL~only computes the gradient of one compact architecture, the memory consumption of DMCP is almost twice as that of PEEL~. We also provide a comparison to another representative USNet~\\cite{yu2019universally} in the supplementary material. \n\n\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=1.0\\linewidth]{.\/FLOPs_assign_res18.pdf}\n \\vskip -0.1cm\n \\caption{Where the resources go. Percentage of FLOPs assigned by PEEL~to different groups of (a) ResNet-18 and (b) ResNet-50 under different target FLOPs constraints. X-axis denotes group indices and y-axis represents the target FLOPs. A darker color indicates a larger quantity of assigned resources. Since ResNet-18 and ResNet-50 have only one layer in group 1 and resources assigned to one layer are very limited, we put this layer into group 2 and obtain the above figure.}\n \\centering\n \\vskip -0.4cm\n \\label{fig:flops_assign_res18}\n\\end{figure}\n\n \n\n\\subsection{Ablation study}\n\n\n\\noindent \\textbf{Where the resources go:}\nTo have a deeper understanding on the effect of the resource reallocation module, we visualize the percentage of resources distributed by PEEL~to different groups of layers on ResNet-18 and ResNet-50. As shown in Fig.~\\ref{fig:flops_assign_res18}, layers in the first three groups are given much more resources than the last two groups when the network is slightly trimmed. It is natural as there are more redundant channels in the deep layers in the mild pruning level~\\cite{liu2019metapruning}. As the pruning becomes more aggressive, the percentages of FLOPs assigned to different groups become more even since all layers do not have sufficient channels and call for more resources from the resource pool. And we can observe the same patterns in the resource reallocation of ResNet-18 and ResNet-50 when the FLOPs budget becomes tighter.\n\n\\noindent \\textbf{Effect of $\\lambda$:} The resources available in the resource pool is $(1-\\lambda)M$, thus the pool size is controlled by the hyperparameter $\\lambda$. We select the value of $\\lambda$ from $\\{0.5, 0.6, 0.7, 0.8, 0.9, 1\\}$ and compare the model performance under these settings. As illustrated in Fig.~\\ref{fig:single_multi_round} (a), the performance of the searched model is relatively stable when $\\lambda$ ranges from 0.7 to 0.9. When $\\lambda$ decreases from 0.7, the performance of the final pruned architecture gradually drops. The trend is expected since PEEL~only performs the evaluation of layer importance once on the over-pruned model. The importance evaluation would become inaccurate given a small $\\lambda$, as more resources are assigned to the pool while the over-pruned backbone is too slim. Another phenomenon we observe is that as the pruning becomes more aggressive, the performance of the pruned model is more susceptible to the value of $\\lambda$ (see the green line in Fig.~\\ref{fig:single_multi_round} (a)). We conjecture that the performance drop is caused by the challenging pruning condition. Given very limited FLOPs resources, one has to allocate resources in a very careful manner so that the pruned model can exhibit satisfactory performance.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=1.0\\linewidth]{.\/single_multi_round.pdf}\n \\vskip -0.1cm\n \\caption{One-round v.s. multi-round reallocation. We show the $\\lambda$-accuracy spectrum of (a) one-round and (b) three-round resource reallocation of PEEL~on ResNet-50. Different color denotes different target FLOPs.}\n \\centering\n \\vskip -0.2cm\n \\label{fig:single_multi_round}\n\\end{figure}\n\n\\noindent \\textbf{One-round reallocation v.s. multi-round reallocation:} Recall that we adopt the one-round resource reallocation in our algorithm, \\ie, allocating the resources by just performing a single pass of layer importance estimation. An alternative strategy is to evenly divide the resources into several parts and reallocate these resources successively in multiple rounds. Multi-round reallocation is more expensive as one needs to repeat Step-2 and Step-4 iteratively (see Sec.~\\ref{sec:methodology}), and each iteration involves the fine-tuning of the backbone. As depicted in Fig.~\\ref{fig:single_multi_round}, the performance of the multi-round resource reallocation is more stable than the one-round version when the $\\lambda$ is set 0.6 and 0.5. For instance, when the target FLOPs is 2.2G and $\\lambda$ is set as 0.5, the performance of the one-round reallocation decreases from 76.8\\% to 75.3\\% while the multi-round reallocation can still achieve 76.0\\% in top-1 accuracy. The more stable performance may come from the fact that multi-round resource reallocation reevaluates the importance of each layer in each round, thus allowing a more appropriate resource reallocation.\n\n\\noindent \\textbf{Robustness to the layer importance indicator:} The original PEEL~adopts BN statistics to reflect the importance of each layer. Here, we explore different importance indicators and check whether the proposed PEEL~is sensitive to the chosen indicator. We choose the widely-used filter norm~\\cite{li2017pruning} and reconstruction errors~\\cite{luo2017thinet} as the criteria. Layers with large filter norm or large reconstruction errors are considered as important. However, directly taking filter norm as the evaluating metric is biased since the filter norm of different layers have diverse scales in magnitude~\\cite{chin2020towards}. To correct such bias, we follow~\\cite{chin2020towards} and learn the layer-wise affine transformations for the filter norm. Under this circumstance, filter norm can better reflect the value of each layer. From Table~\\ref{diff_indicator_table}, when pruning ResNet-50 under different FLOPs requirements, architectures searched by PEEL~using BN statistics, filter norm and reconstruction errors achieve similar performance. The results suggest the robustness of PEEL, irrespective of the methods used in evaluating layer importance. \n\n\\begin{table}[t]\n\\caption{Performance of PEEL~with different importance criteria on ResNet-50.}\n\\vskip 0.1cm\n\\label{diff_indicator_table}\n\\centering\n\\small{\n\\begin{tabular}{c|c|c|c}\n\\hline\n\\multirow{2}*{Criterion} & \\multicolumn{3}{c}{$\\#$FLOPs} \\\\\n\\cline{2-4}\n~ & 2.2 G & 1.8 G & 1.1 G \\\\\n\\hline\n\\hline\nBN statistics (Ours) & 76.8\\% & 75.5\\% & 75.1\\% \\\\\n\\hline\nFilter norm~\\cite{li2017pruning} & 76.7\\% & 75.2\\% & 75.2\\% \\\\\n\\hline\nReconstruction errors~\\cite{luo2017thinet} & 76.6\\% & 75.4\\% & 75.0\\% \\\\\n\\hline\n\\end{tabular}\n}\n\\end{table}\n\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=1.0\\linewidth]{.\/different_backbone.pdf}\n \\vskip -0.1cm\n \\caption{Visualization of the architecture found by PEEL~on a different backbone model that has the same number of channels for all layers. The base model is ResNet-18, and target FLOPs is 1.04G.}\n \\centering\n \\vskip -0.4cm\n \\label{fig:diff_backbone}\n\\end{figure}\n\n\\noindent \\textbf{Effect of knowledge distillation:} By comparing the two columns of the Top-1 Acc in Table~\\ref{result_table}, we find that PEEL~consistently yields better performance than baseline pruning approaches with or without knowledge distillation (KD). For instance, when performing pruning on ResNet-18 and target FLOPs is 1.33G, PEEL~with KD is 0.6\\% higher than DMCP with KD. One interesting phenomenon we observe is that KD brings more gains when the backbone model has fewer parameters. For example, KD can bring approximately 0.3\\%, 0.5\\% and 1.0\\% to PEEL~on ResNet-50, ResNet-18 and MobileNetV2, respectively when the pruning is not aggressive. We conjecture that the increased gains are attributed to the deficiency of small models in grasping knowledge in labels by themselves and hence they more eagerly call for the guidance of the original model.\n\n\\noindent \\textbf{Efficacy of PEEL~on a different backbone model:} Recall that we treat the uniformly trimmed model as the backbone and conduct resource reallocation on it. It is natural to wonder whether the resource reallocation would still work if a totally distinct architecture is used. Here, we use an architecture that has the same number of channels across all layers as the new starting point. \nAll configurations are the same as the previous experiments. From Fig.~\\ref{fig:diff_backbone}, we can observe that in this new backbone, groups 1, 2 and 3 are assigned with few parameters while the majority of the resources are distributed to groups 4 and 5. Overall, PEEL~puts more parameters in the deeper layers, and the resulting architecture is \\textbf{8.7\\%} higher in terms of top-1 accuracy in comparison to the original model (62.4\\% v.s. 53.7\\%). The result demonstrates the effectiveness of resource reallocation as well as the insensitivity of our algorithm to the backbone model. \n\n\n\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=1.0\\linewidth]{.\/different_policy.pdf}\n \\vskip -0.1cm\n \\caption{Policy on resource reallocation. Performance of PEEL~with importance-guided, winner-take-all, uniform and random policies on (a) ResNet-18 and (b) ResNet-50, respectively.}\n \\centering\n \\vskip -0.2cm\n \\label{fig:diff_policy}\n\\end{figure}\n\n\n\\noindent \\textbf{Policy on resource reallocation:} Here, we compare our `importance-guided' resource reallocation strategy with other parameter reallocation policies, \\ie, winner-take-all, uniform and random reassignment. The winner-take-all policy determines the most important group according to the computed group significance and puts all resources in that group. \nThe uniform policy adds the same number of channels to all layers while the random policy stochastically selects several layers and increases their channels. Here, we name the original reallocation strategy as importance-guided policy as it distributes resources based upon the estimated layer importance. From Table~\\ref{fig:diff_policy}, our importance-guided reallocation policy evidently outperforms the other three policies on ResNet-18 and ResNet-50. For instance, when the target FLOPs is 1.8G on ResNet-50, the top-1 accuracy of the importance-guided policy is 75.5\\% while the top-1 accuracy of the winner-take-all, uniform and random policy is 74.9\\%, 74.1\\% and 74.0\\%, respectively. The superior performance demonstrates the effectiveness of the importance-guided policy. \n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzqjsh b/data_all_eng_slimpj/shuffled/split2/finalzzqjsh new file mode 100644 index 0000000000000000000000000000000000000000..1c21ea26a1855f70853cb11ecfd268d751896870 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzqjsh @@ -0,0 +1,5 @@ +{"text":"\n\\section{Platform and Evaluation}\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.96\\linewidth]{Images\/tasks_simple_to_complex.png} \\vspace{-3mm}\n\t\\caption[FishGym Tasks]{The control results for benchmark tasks using our trained policies and two-way coupled fluid-structure interaction solver. Top row: cruising in a shallow fluid; second row: cruising in a deep fluid (with buoyancy control); third row: pose control with a U-turn; fourth row: two-fish schooling; bottom row: path following with an arbitrarily specified path.}\n\t\\label{fig:tasksequence} \n\t\\vspace{-15pt}\n\\end{figure}\n\nIn the following, we first describe our setup on simulation and evaluate our fluid simulation platform with our learning algorithms for robot fish swimming in multiple aspects.\n\n\\subsection{Platform setup}\n\\paragraph{Simulation}\nIn most our training tasks, all the fish robots have a density of 1080$kg\/m^3$, and we used local non-inertial fluid-structure interaction solver for simulating robot fish dynamics, with a grid resolution of $100\\times100\\times100$ and a physical time step of 0.004s.\nThe solver costs around 3.5 seconds for simulating one physical second on an NVidia TitanXp GPU with 12G memory.\nFor two-fish schooling, we extended the local domain horizontally to simultaneously include two fishes, with a grid resolution of $150\\times50\\times100$ and the same physical time step, which costs around 4 seconds for simulating one physical second on the same GPU.\n\n\\paragraph{Training}\nThe policy network consists of two layers, each containing 256 units, with an ReLU activation function. \nFor each task, the policy is trained using SAC \\cite{Haarnoja2018}.\nWe train each policy for a total of 2000 simulation rollouts, each of which contains 50 time steps, where a candidate policy executes a new action at each time step. \nWe train the policy network with a batch size of 256.\nThe model parameters are updated for each step, and one gradient step is performed after each rollout.\nWe train all policies on a machine with an NVidia TitanXP GPU, and the training process usually starts to converge after 6 hours (1000 episodes), and have a smooth convergence within 10 hours. \nThe training could be several times faster if we use the most state-of-the-art GPUs, such as NVidia GeForce RTX3090.\n\n\\subsection{Comparison for different types of robot fishes}\nOur platform can support robot fishes designed with different skeleton connectivity and skin shapes.\nFig.~\\ref{fig:different_fish_type_result} shows the training results for three types of robot fishes swimming along an arbitrarily given path.\nThe average distances from the path (around 7 meters long) are as close as 0.03m, 0.05m, 0.08m, respectively, indicating the capability of our platform in supporting a variety of robot fishes.\n\\subsection{Comparison for different simulation models}\nIn the literature, a simple empirical model was proposed as the simulator for robot fish swimming~\\cite{Terzopoulos1994}, which has been used until now~\\cite{grzeszczuk1998neuroanimator,si2014realistic,min2019softcon}.\nIt models the instantaneous force on the surface of the robot due to viscous fluid as:\n\\vspace{-5pt}\n\\begin{equation}\n\tF = -k\\int_{S}(\\mathbf{n} \\cdot \\mathbf{v}) \\mathbf{n} d s ,\n\t\\label{empirical_model_eq}\n\t\\vspace{-5pt}\n\\end{equation}\nwhere $\\mathbf{n}$ is the unit outward normal; $\\mathbf{v}$ is the relative velocity between the surface and the fluid (since there is no fluid simulation, the fluid velocity is assumed to be zero), and $k$ is a constant manually tuned for different robot fishes and the surrounding fluids.\nNote that for a specific system, $k$ can only be determined either by real measurement data or from other physically more accurate simulators.\n\nTo examine the similarity and difference between the empirical model and our physical simulator for robot fish swimming, we conduct two test cases with analyses below.\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.96\\linewidth]{\"Images\/different_robot_fishes\"} \\vspace{-3mm}\n\t\\caption[Fish Gym Tasks]{Different types of robot fishes trained using local policy learning for arbitrary path following. Top: koi robot fish; middle: flatfish robot fish with a different skeleton topology (with branching during modeling); bottom: eel robot fish with a long concatenated skeleton.}\\vspace{-3mm}\n\t\\label{fig:different_fish_type_result}\n\\end{figure}\n\\paragraph{Path following}\nPath following is a primitive task for robot control.\nHere, we compare the similarity and difference of path following using an empirical model and our physical simulator.\nSince there is no clue on how to tune the parameter $k$ in an empirical model, we arbitrarily choose one and learn a policy. \n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.96\\linewidth]{\"Images\/Parameter_need_tune\"} \\vspace{-3mm}\n\t\\caption{Comparison for path following using (left) empirical model with an arbitrarily chosen parameter and (right) our physical simulator. Note that due to improper boundary force, serious drifting happens for empirical model when turning. The numbers indicate the order during path following.} \\vspace{-3mm}\n\t\\label{fig:empirical_failed_path}\n\\end{figure}\n\nFig.~\\ref{fig:empirical_failed_path} (left) shows the control result, indicating serious drifting when turning, while our physical simulator does not require any parameter turning (we directly specify the physical parameter for the fluid as $\\rho$=1000$kg\/m^3$ and $\\nu$=0.00089$m^2\/s$ with a zero-velocity initialization to match that used in the empirical model), leading to a reasonable path following result as shown in Fig.~\\ref{fig:empirical_failed_path} (right).\nNote that the mean path deviation for the empirical model with an arbitrary parameter is as large as 0.5m, while our physical simulator only has a mean path deviation of 0.03m.\nThe empirical model can be much improved if we collect data from our simulator and fit the parameter $k$, leading to a very similar result in a static fluid environment but runs much faster.\nHowever, in some cases where we cannot assume static fluid background, empirical model can completely fail no matter how we fit the parameter $k$, and we demonstrate this case in the following two-fish schooling task.\n\\paragraph{Two-fish schooling}\nFish schooling describes a common phenomenon where fishes tend to swim in a group and one fish follows the other.\nIt has been revealed by scientists that due to the vortex ring generated behind the leader fish, the follower fish tries to utilize the vortex ring to reduce drag and pass through it in order to catch up with the leader fish more efficiently \\cite{Novati2017,Verma2018}.\nWe demonstrate this behavior in Fig.~\\ref{fig:teaser} (bottom row).\nDue to more accurate modeling to capture complex fluid flows, the training can successfully obtain a policy that utilizes the vortex ring, see Fig.~\\ref{fig:teaser} (bottom), while empirical model, on the other hand, fails to learn such a policy due to lack of a real fluid-structure interaction.\n\n\n\n\n\n\n\n\n\n\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.98\\linewidth]{Images\/density_compare} \\vspace{-3mm}\n\t\\caption{Control behaviors of a koi-like robot fish for different fluid densities. Top: $\\rho=1000$; bottom: $\\rho=10$.}\n\t\\label{fig:densitycompare}\n\t\\vspace{-18pt}\n\\end{figure} \n\n\\subsection{Comparison for different fluid settings}\nIn contrast to the empirical model, our simulator can freely set physical parameters, leading to different control behaviors for a trained robot fish.\nHere, we change the fluid density (with a higher density $\\rho=1000$ and a lower density $\\rho=10$, see Fig.~\\ref{fig:densitycompare}) for simulation and train with the same learning parameters to achieve a cruising task.\nIt is observed that the robot fish in a higher density fluid seems to move with less swing amplitude, while the one in a lower density fluid has a larger swing amplitude to generate greater propulsion force, which is consistent with the physical expectations.\n\n\\subsection{Effects of reward weighting}\n\nIn this experiment we investigate effects of different reward weighting. Table~\\ref{energy_table} summarizes the total energy cost by varying weights $w_v$ and $w_e$ in the cruising task, where $w_v$ encourages fish to reach the target position while $w_e$ encourages fish to save its energy.\nA good balance between energy preservation and the time to accomplish the task can be made, e.g., $w_v=1.0$ and $w_e=0.5$.\n\n\\begin{table}[h]\n\\vspace{-3mm}\n\t\\caption{Impact of of weights in the reward} \\vspace{-3mm}\n\t\\label{energy_table}\n\t\\begin{center}\n\t\\begin{tabular}{c|c|c|c}\n\t\\hline\n\t$w_v$ & $w_e$ & Total Energy Cost & Total Time (sec.)\\\\\n\t\\hline\n \t0.00 & 1.00 & 0.0413 & 10.0 \\\\\n \t0.20 & 1.00 & 12.354 & 5.2 \\\\\n \t1.00 & 1.00 & 15.046 & 4.6 \\\\\n \t1.00 & 0.50 & 17.705 & 4.2 \\\\\n \t1.00 & 0.00 & 40.969 & 4.2 \\\\\n\t\\hline\n\t\\end{tabular}\n\t\\end{center}\n\\vspace{-15pt}\n\\end{table}\n\n\n\n\\section{Conclusion}\nIn this paper, we propose a new open-to-use simulation platform for training underwater fish-like robots.\nThe whole platform consists of a new modeling for fish-like underwater robot, a GPU-based non-inertial high-performance fluid-structure interaction solver (as a training environment), and reinforcement learning algorithms with both global and local policy learning.\nFour different benchmark tasks were proposed and trained with our platform, with expected results.\nWe compared and analyzed the new training platform in terms of different results in multiple aspects to evaluate the advantages.\n\nThere are also some limitations.\nFirst, the fish model is a reduced model that may deviate from the real robot design, and is now hence difficult to directly transfer to a real robot once learned.\nSecond, since we use local simulation, the platform is unable to training fish robot with a more complex external environment, e.g., a large vortex.\nFinally, the grid resolution around the fish is not fine enough (otherwise, it will become very slow for training), and the accuracy is not sufficiently high.\nSupporting simulation in more complex fluid environment and developing more efficient training method with higher accuracy deserve our future work.\n\\section{Introduction}\n\\label{sec:intro}\n\nBio-inspired underwater robots often demonstrate strong maneuverability, propulsion efficiency, and deceptive visual appearance.\nThese advantages have motivated a set of academic studies on bio-inspired soft robots and biomimetric fish-like robots in the past years~\\cite{Du2015,Paley2021,Duraisamy2019}.\nIt also opens up some important applications, such as marine education, navigation and rescue, seabed exploration, scientific surveying, etc.~\\cite{Kopman2012,Picardi2020,Berlinger2021,Li2021,Katzschmann2018}.\nHowever, due to lack of sufficient datasets and high physical cost, training their intelligent behaviors in real environments that at least mimic the bionic creatures or even exceed their capabilities in accomplishing complex tasks is still quite challenging.\n\nAlternatively, simulation has been considered as a viable and important tool for acquiring a large number of datasets in different scenarios~\\cite{AvilaBelbutePeres2018,Lee2019,James2019,Bergamin2019}.\nMost of the currently available simulators for robot training mainly target rigid and soft body systems~\\cite{Coumans2015,Lee2018,Todorov2012}.\nExisting simulators for fluid environment are either highly inaccurate (e.g., based on an empirical model~\\cite{Terzopoulos1994}), too restrictive to support different agents or environments~\\cite{Song2017,Song2020,Verma2018}, or expensive to generate a large amount of training data \\cite{Verma2018,Novati2017}.\nThere is currently a dearth of simulation platform which is able to provide a versatile, efficient yet accurate results that could be used for training control policies of underwater robots. \n\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.98\\linewidth]{\"Images\/school_comparision_teaser_2fig.png\"} \\vspace{-3mm}\n\t\\caption{\\textbf{Two-fish schooling behaviors.} With the empirical model (1st row), the follower fish always stays in the same line as the leader fish. However, with our physical simulator (2nd row), the follower fish can utilize the wake vortex ring (blue) and gradually pass through the vortex ring to preserve energy. This motion behavior cannot be acquired through a simple empirical model, highlighting the advantages of the proposed FishGym platform. } \\vspace{-3mm}\n\t\\label{fig:teaser}\n\t\\vspace{-10pt}\n\\end{figure}\nWe propose \\textbf{FishGym}\\footnote{https:\/\/github.com\/fish-gym\/gym-fish}, a high-performance simulation platform targeting the two-way interaction dynamics between fish-like underwater robots and the surrounding fluid environment. The robots are modeled by the skeletons of arbitrary topology with surface skinning, whose motion is driven by the articulated rigid body dynamics~\\cite{Weinstein2006}, while the fluid-structure interaction is achieved using a recently proposed GPU-optimized lattice Boltzmann solver~\\cite{Li2020,Chen2021gpu}, where immersed boundary method~\\cite{li2016immersed,wu2010improved} is employed for efficient two-way coupling.\nTo support simulations in a local fluid domain around the robot to enable training in an infinitely large physical domain, we propose a modification of the original lattice Boltzmann solver that enables simulations in a local frame of reference with acceleration, with higher flexibility in acquiring various training environments. \nThe whole simulation module is then coupled with a reinforcement learning module implemented using PyTorch~\\cite{Raffin2019,Paszke2019}. \n\nTo demonstrate the capability of the proposed simulator, we evaluated existing reinforcement learning algorithm with reward functions and training procedures tailored for underwater robots.\nWe compare the learned control policies with that from the empirical model on several underwater planning and control tasks to assess the feasibility and advantages of our framework. \nAnalyses on the emerged behaviors also indicate consistency with previous studies on fish motion in nature. \nIn summary, we have made the following contributions to training bionic underwater robots: \n\\begin{itemize}\n\\item A GPU-accelerated lattice Boltzmann solver that enables high-performance fluid-structure interaction in a local moving frame of reference to allow robot swimming in an infinitely large domain;\n\\item A high-performance simulation platform to help explore training bionic underwater robots;\n\\item A learning algorithm tailored for bionic under-water robots that is able to acquire natural and efficient control policies for swimming;\n\\item A collection of benchmark tasks for underwater robot to evaluate and compare different learning methods and control policies. \n \n\n\n\n\n\\end{itemize}\n\t \n\n\n\n\n\n\n\n\n\n\\section{FishGym Framework}\nOur physics-based robot learning framework consists of three components:\n1) the robot model for which we focus on fish-like robots, 2) \nthe simulation method for predicting fluid-robot interaction, \nand 3) the robot learning method that leverages our simulation method.\nWe now present their details.\n\n\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.48\\textwidth]{\"Images\/two_fish_skeletons\"}\n\t\\caption[Fish Skeletons]{Illustration of modeled fishes with different skeletons and skins, where the number of joints, the length of each skeleton edge as well as the topology of the skeleton can vary for different types of fishes.}\n\t\\label{fig:fish_model} \n\t\\vspace{-20pt}\n\\end{figure}\n\n\\subsection{Robot model}\nMotivated by the anatomy of the fish structure,\nwe model a fish robot's locomotion by its skeletal structure, that is, bones connected by joints.\nCovering the skeleton are flesh and skin. Provided a skeleton configuration (e.g., with a certain set of joint angles), we use linear blend skinning~\\cite{magnenat1988joint} to determine \nthe fish's surface shape. In our fluid-robot simulation, the fish surface is assumed to be inelastic, and the flesh is treated as a rigid body under the current skeleton configuration. We make this assumption for the sake of computational efficiency.\nBy changing its joint angles, a fish robot can adjust its skeleton pose,\nwhich in turn determines its surface shape.\nThree examples of fish robots with different skeletal structures are shown in Fig.~\\ref{fig:fish_model}.\n\n\n\n\n\nThe bone skeleton is driven by the articulated rigid body dynamics \\cite{Weinstein2006}:\n\\vspace{-8pt}\n\\begin{equation}\n\\mathbf{M}(\\mathbf{q})\\ddot{\\mathbf{q}}+\\mathbf{C}(\\mathbf{q}, \\dot{\\mathbf{q}})=\\boldsymbol{\\tau}_{int}+\\boldsymbol{\\tau}_{ext} ,\n\\vspace{-8pt}\n\\end{equation}\nwhere $\\mathbf{q}, \\dot{\\mathbf{q}}$ and $\\ddot{\\mathbf{q}}$ are respectively the vectors of generalized positions, velocities and accelerations of all joints. $\\mathbf{M}(\\mathbf{q})$ is the mass matrix, and $\\mathbf{C}(\\mathbf{q}, \\dot{\\mathbf{q}})$ accounts for the Coriolis and centrifugal forces;\ndetails of these forces will be described shortly.\n$\\boldsymbol{\\tau}_{ {int }}$ and $\\boldsymbol{\\tau}_{ {ext }}$ are the vectors representing the generalized internal forces (including the spring forces on joints to enable elasticity, damping forces due to velocities, friction forces, as well as the actuation given by the controller) and the generalized external forces (caused by gravity, possible collisions and the surrounding fluids) exerted on the multi-body system.\nWe employ DART \\cite{Lee2018} to solve the above multi-body system, and for each time step, we control the bone shape by applying generalized actuation forces on joints.\nThe skin surface is achieved by employing linear blending method proposed by \\cite{magnenat1988joint}.\n\n\\subsection{GPU-accelerated localized fluid-structure interaction}\nThere exist many simulation methods that may \npredict fluid-robot interaction~\\cite{klingner2006fluid,lv2010novel,dai2005adaptive}. \nThese methods, however, require a fixed simulation domain, inside which the \nunderwater robot moves. \nWhen the simulation domain is large,\nsimulation is costly.\nTo reduce the cost,\nwe assume that the fluid \nfurther away from the robot by a certain distance will not influence the robot motion. \nThereby, we can fix the size of the simulation domain centered around the fish robot and allow the domain to move along with the fish. \nThis setup allows the fish robot to move in an \ninfinite spatial domain while keeping the simulation domain limited. \nBut then, to capture fluid dynamics correctly, we need to simulate fluid-structure interaction in a moving frame of reference.\nBeing able to swim in an infinitely large domain is very important for training fish-like underwater robots; Also crucial is the simulation performance, as\ntraining the learning algorithm will often run fluid simulations\nmany times (see Section~\\ref{sec:fish_learning}).\nWe tackle both problems next.\n\n\\subsubsection{Formulation}\nFluid in a fixed frame of reference is often governed by the following NS equation:\n\\vspace{-6pt}\n\\begin{equation}\n\\frac{\\partial \\mathbf{u}}{\\partial t}+(\\mathbf{u} \\cdot \\nabla) \\mathbf{u}=-\\frac{1}{\\rho} \\nabla p+\\nu \\nabla^{2} \\mathbf{u} + \\mathbf{F},\n\\vspace{-6pt}\n\\end{equation}\nwhere $\\rho$, $\\mathbf{u}$, $p$ and $\\mathbf{F}$ represent the density, velocity, pressure and external force fields, and $\\nu$ is the kinematic viscosity.\nThis equation cannot be used to solve flows in a moving frame of reference around the fish, which should be reformulated in a frame of reference with acceleration.\nAccording to \\cite{Asmuth2016}, by transforming with time-dependent relative translation $\\mathbf{p}$ and rotation $\\mathbf{r}$ (represented as Euler angles) between consecutive frames, the NS equation in an accelerating frame of reference results in an additional virtual force added to the system:\n\\vspace{-6pt}\n\\begin{equation}\n\t\\vspace{-6pt}\n\\mathbf{F}_{ni}=-\\ddot{\\mathbf{p}}-\\ddot{\\mathbf{r}} \\times \\mathbf{x}^{\\prime}-\\dot{\\mathbf{r}} \\times\\left(\\dot{\\mathbf{r}} \\times \\mathbf{x}^{\\prime}\\right)-2 \\dot{\\mathbf{r}} \\times \\mathbf{u}^{\\prime} ,\n\\end{equation}\nwhere all the physical quantities are measured in a moving frame of reference.\nIn case of any immersed solid, e.g., the swimming robot, we apply Neumann boundary condition (i.e., slipping) as an approximation.\n\n\\subsubsection{Simulation}\nTo simulate the above dynamics in an efficient manner, we discard the traditional NS solver; instead, we employ a GPU-optimized LBM solver with immersed boundary (IB) method \\cite{Li-2020} for fluid-structure interaction, whose high efficiency has been demonstrated.\nThe fish-like robot surface is uniformly sampled before simulation, and the external virtual force due to acceleration can be added into the system very easily.\nThe difficulty we need to address is the domain boundary, which in theory should be set according the large-domain simulation.\nHowever, in practice, we do not know the exact values of the domain boundary, and when fish moves with different velocities and accelerations, the flow can go into and outside from any portion of the domain boundary.\nThus, we need a domain boundary treatment which can adapt to this situation, and through a set of experiments, we found that the method described in \\cite{ZhaoLi2002} satisfy this requirement and has been employed in our IB-LB simulation.\nFig.~\\ref{fig:complocalglobal} compares the accuracy between a full global fluid domain simulation (left) where we directly use IB-LB method in \\cite{Li-2020} and the proposed localized fluid-structure interaction (right). \nBoth methods produce nearly identical robot motion trajectories.\n\n\\begin{figure}[t]\n\t\\centering\n\t\\includegraphics[width=0.98\\linewidth]{Images\/comp_local_global} \\vspace{-3mm}\n\t\\caption{Comparison between fluid-structure interaction in a global static fluid domain (left) and the non-inertial counterpart in a moving local fluid domain (right), indicating the closeness of the paths with a known policy.} \\vspace{-5mm}\n\t\\label{fig:complocalglobal}\n\t\\vspace{-3pt}\n\\end{figure}\n\n\\subsection{Reinforcement learning for fish-like robot control}\n\\label{sec:fish_learning}\nDue to complex fish dynamic model, the control of fish-like robot swimming cannot be simply achieved by a model-based controller.\nIn addition, since the control input is usually high-dimensional and cannot be fully decoupled, the PID controller cannot be used either.\nThus, reinforcement learning (RL) is a common choice to train complex control policies for swimming.\nIn this paper, we propose four benchmark tasks that will be trained using RL in order to demonstrate the power of the new simulator together with the learning algorithms.\nThese benchmark tasks are:\n\\begin{itemize}\n\t\\item \\textbf{Cruising.}\n\tThe robot fish tries to swim to reach a given target location that is a distant away from the robot.\n\t\\item \\textbf{Pose control.}\n\tA robot fish tries to control its pose in order to make a U-turn.\n\t\\item \\textbf{Two-fish schooling.}\n\tA robot fish follows a leader fish as closely as possible, where the leader fish is controlled to swim in a straight path.\n\t\\item \\textbf{Path following.}\n\tA robot fish follows a given arbitrary path as closely and efficiently as possible.\n\\end{itemize}\n\nIn RL, an agent learns a policy for a specific task through repeated interaction with the environment.\nGiven a state $\\mathbf{s}_i$, the RL tries to learn a parametric policy $\\pi_{\\boldsymbol{\\theta}}$, which is usually represented by a fully-connected neural network, to produce an action $\\mathbf{a}_i$;\nThe action can be taken by the agent to transit to the next state $\\mathbf{s}_{i+1}$, where a reward $r_i$ is evaluated.\nThe agent iterates transitions until it satisfies one of the exit conditions, e.g., finite time horizon, or success\/failure of a given task.\nA parametric policy $\\pi_{\\boldsymbol{\\theta}}$ is learned by finding the optimal parameters $\\boldsymbol{\\theta}^{*}$ that maximize the expected return:\n\\vspace{-8pt}\n\\begin{equation}\n\t\\vspace{-6pt}\nJ(\\theta)=\\mathbb{E}_{\\tau \\sim p_{\\theta}(\\tau)}\\left[\\sum_{t=0}^{T} \\gamma^{t} r_{t}\\right] ,\n\\end{equation}\nwhere $T$ is the maximum number of control time steps, $\\gamma$ is the discounting factor, and $\\tau$ is the sampled trajectory containing a sequence of states and actions, i.e., $\\tau = (s_0, a_0 , s_1, a_1, ..., s_i, a_i, ..., s_t, a_t)$.\nThe policy learning can be achieved in two different ways.\nDepending on the available resources, we can sample the trajectories from one single task, or from multiple tasks to learn a \\textit{global policy}.\nHowever, if the variety of tasks is large, it is time consuming especially when the simulator is not fast enough.\nOn the other hand, if the tasks can be subdivided into small and simple sub-tasks, we can gather all these sub-tasks together and sample from them to learn a \\textit{local policy}, which is expected to be much more generalizable given a relatively small training set, and could be affordable for limited resources.\nWe adopt both approaches to train different tasks.\n\n\nIn the following, we specify in detail the specific designs on how we train these benchmark tasks.\n\\paragraph{State} \nFor all benchmark tasks, we consider the following state variable:\n\\vspace{-8pt}\n$$\n\\mathbf{s}=(\\mathbf{s}_d, \\mathbf{s}_p, \\mathbf{s}_r, \\mathbf{s}_{task}) ,\n\\vspace{-6pt}\n$$\nwhere $\\mathbf{s}_d = (\\mathbf{q},\\dot{\\mathbf{q}})$ contains the dynamic states, with $\\mathbf{q}$ the vector of generalized joint positions, and $\\dot{\\mathbf{q}}$ is the vector of generalized joint velocities;\n$\\mathbf{s}_p=(\\mathbf{p},\\dot{\\mathbf{p}})$ contains translation, where $\\mathbf{p}$ is the relative translation vector between simulation time steps, and $\\dot{\\mathbf{p}}$ is the relative velocity vector;\n$\\mathbf{s}_r = \\mathbf{r} $ contains rotation (Euler angles);\nand $\\mathbf{s}_{task}$ contains task-specific states. \nIn practice, to reduce input dimension, all state variables are expressed in a local coordinate system. \n\n\\paragraph{Action}\nFor all benchmark tasks, the action can be generally defined as:\n\\vspace{-10pt}\n$$\n\\mathbf{a} = (\\boldsymbol{\\sigma},\\Delta v) ,\n\\vspace{-6pt}\n$$\nwhere $\\boldsymbol{\\sigma}$ is the vector containing the actuation forces applied to the joints, and\n$\\Delta v$ is the change of the bladder's volume inside the robot fish, which controls buoyancy to enable going up and down in a fluid.\n\\paragraph{Reward}\nThe reward for training all benchmark tasks can also be written in a general mathematical form as:\n\\vspace{-4pt}\n$$\nr = w_pr_p + w_vr_v + w_rr_r + w_er_e + w_{task}r_{task} ,\n\\vspace{-4pt}\n$$\nwhere $r_p= \\exp( -\\|\\mathbf{p}\\|_2)$ and $r_v = \\|\\dot{\\mathbf{p}}\\|_2$ drive the robot towards its target position and velocity as fast as possible;\n$r_r= 1-\\|\\mathbf{r}\\|_2$ drives the robot towards its target rotation (pose);\n$r_e =\\|\\mathbf{\\tau}\\|^2$ measures the effort exhausted during the swimming;\n$r_{task}$ is a task-specific reward, which will be specified later;\nand $w_p,w_v,w_r,w_e,w_{task}$ are the corresponding weights for different components in the reward.\nThe weights for each benchmark task are listed in Table.~\\ref{weight_table}.\n\\begin{table}[t]\t\n\t\\caption{Weights used in the reward of each task} \n\t\\vspace{-10pt}\n\t\\label{weight_table}\n\t\\begin{center}\n\t\\begin{tabular}{c|c|c|c|c|c}\n\t\t& $w_v$ & $w_p$ & $w_r$ & $w_e$ & $w_{task}$ \\\\ \\hline\n\t\tCruising & 1 &0 & 0.2 & 0.5 & 0 \\\\\n\t\tPose Control & 0 &0 & 1 & 0 & 0 \\\\\n\t\tTwo Fish Schooling & 0 &1 & 0 & 0.1 & 0 \\\\\n\t\tPath Following & 1 &0 & 0 & 0.5 & 1 \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{center}\n\\vspace{-25pt}\n\\end{table}\nOur proposed four benchmark tasks are trained using either global or local policy learning approaches we described.\n\\paragraph{Global policy learning}\nFor cruising, pose control and two-fish schooling tasks, we use global policy learning with a single input task, meaning that we train robot fish separately for each task, where $r_{task}=0$. Fig.~\\ref{fig:tasksequence} shows the snapshots of the swimming results, where the first two rows show cruising inside a shallow and a deep fluid; the third row shows the pose control for U-turn, and the fourth row shows the two-fish schooling result, which has not been demonstrated in previous works.\n\n\\paragraph{Local policy learning}\n\\begin{figure}[htb]\n\t\\vspace{-6pt}\n\t\\centering\n\t\\includegraphics[width=0.98\\linewidth]{Images\/local_policy.png}\n\t\\vspace{-12pt}\n\t\\caption{Illustration of local policy training for arbitrary path following. Left: a local target is sampled given a random $d$ and $\\theta$; right: when applying learned policy for path following, we always select a local target ahead on the desired path, which changes for each time step.}\n\t\\label{fig:local_policy}\n\t\\vspace{-10pt}\n\\end{figure}\nTraining robot fish following an arbitrarily long path is more difficult, and global policy learning could be resource demanding and time consuming.\nTo make the training easier while also retaining generalizability, we use local policy learning instead.\nIn fact, following an arbitrarily long path can be viewed as following a sequence of short and straight local paths along nearly the tangent direction of the global path given a robot location, greatly simplifying the training process. \nDuring training, we randomly sample local paths (parameterized by $(d,\\theta)$, see Fig.~\\ref{fig:local_policy} (left), where $d$ is the distance to the local path and $\\theta$ is the angle to the target; note that we restrict the robot fish to swim in a horizontal 3D plane) and form a set of trajectories that are representative of the local conditions of a global path; then we can train the local policy once and apply it to any specified path at any time step, similar in idea to \\cite{Peng2017}. \nIn our local policy learning, the task specific state is defined as $s_{task} = \\mathbf{d}$, and the task specific reward $r_{task}$ is:\n\\vspace{-4pt}\n\\begin{equation}\nr_{task} = \\|\\dot{\\mathbf{d}}\\|_2+\\exp( -\\|\\mathbf{d}\\|_2) ,\n\\vspace{-4pt}\n\\end{equation}\nwhich encourages fast and stable convergence to the local path.\nHere, $\\mathbf{d}$ is a vector containing relative distance to the path.\nThe training is initialized randomly by the technique proposed in \\cite{Peng2018}, and on each trial, random initial velocity is enforced on the robot fish and random angles and velocities are set on the joints.\nOnce learned, we apply the policy every some time steps based on the input state and a local target location that is 0.5m ahead on the local path, see Fig.~\\ref{fig:local_policy} (right), and Fig.~\\ref{fig:tasksequence} (bottom) shows a path following result.\n\\section{Related Work}\nWe herein review the relevant work in the literature for both simulation and learning algorithms before we dig into the details of our whole framework.\n\n\\subsection{Simulation environments in robotics}\n\nRobot training can be achieved following OpenAI Gym~\\cite{Brockman2016}, which is an open-source robot learning framework with general definitions, and can be implemented for training a variety of robots with different environments.\nHowever, both the simulator and learning framework should be provided separately.\nAt present, the most commonly used physics simulators in robot are based on rigid-body, soft-body and cloth dynamics~\\cite{Coumans2015,Todorov2012}. \nIn particular, for articulated rigid body systems, DART~\\cite{Lee2018} can be a good choice.\n\nFluid environment was traditionally provided by solving N\\\"avier-Stoke (NS) equation coupled with a rigid body simulator~\\cite{Tan2011}; but efficiency limited their application especially for vortical flows. Recently, a new simulation environment for underwater soft-body creatures appeared relying on the finite-element method and projection dynamics~\\cite{min2019softcon}; however, its choice of empirical formula~\\cite{Terzopoulos1994} on hydrodynamics makes it impossible to create complex flow environment involving vortices and turbulence. The same issue also applies to some marine vehicle simulators~\\cite{cieslak2019stonefish,manhaes2016uuv} based on Fossen model\\cite{fossen2011handbook}.\nVery recently, Gan et al.~\\cite{Gan2020} proposed a fluid environment, but only for limited tasks and accuracy.\n\nThere is lack of versatile and efficient yet accurate fluid simulation environment upon which more general underwater robot training can be performed, and our proposed ``FishGym'' tries to fill the gap by providing highly efficient GPU-based simulator for two-way coupled fluid-structure interaction.\nThere are also some learning frameworks that can be used based on OpenAI Gym, e.g., rllib~\\cite{Liang2018}, Coach~\\cite{Caspi2017} and stable-baselines3~\\cite{Raffin2019}, and we adopted ``stable-baselines3'' for training our fish-like underwater robots. \n\n\n\n\n\\iffalse\n\\subsection{Fish dynamics and actuation}\\label{sec:fish_model_work}\nIn this paper, we focus on the fish-like underwater robots, and we briefly discuss the dynamics and actuation of fishes in order to motivate our later design.\n\n\\paragraph{Fish modeling}\nFish modeling targets the description of fish structure in a proper way for later simulation, and various models have been proposed with different complexity and accuracy.\nOne of the simplest models is to regard fish as a surface mesh with a mass-spring system to approximate the functionality of muscles \\cite{Terzopoulos1994}.\nAnother type of simple fish model is to employ non-deformable rigid\/flexible foil \\cite{Lu2013,Kaya2007,Hover2004,Wang2018}.\nHowever, these simple models cannot produce accurate fish dynamics. \nTo improve accuracy for simulation, articulated rigid body systems involving connected links with joint constraints were considered for underwater creatures \\cite{Tan2011, Liu2015}, but it is inaccurate for fishes.\nA better deformable fish model which is commonly used employs triangle meshes with center-line curvature driven deformation~\\cite{Ming2018,Song2017,Song2020,Novati2017,Verma2018}, but it is less flexible to allow different types of fishes and control behaviors.\nA more accurate physically-based approach is to model fish as a general volumetric, elastically deformable body \\cite{Pan2018}, but it is very costly during fish dynamics simulation.\n\nIn contrast, our fish is modeled by the skeletons of arbitrary topology with surface skinning, whose motion is driven by the articulated rigid body dynamics. This intuitive approach is easy to extend to other skeleton-based bionic robot shape. Secondly, compared to deformable approaches, the utilization of a mature articulated solver can be expected to step into real robotics. Third, our later coupling method which treat the skin as point samples which is physically more accurate and adaptive, compared to surface voxelization\\cite{Tan2011}.\n\nFish dynamics aims to describe the dynamic interaction between fishes and the surrounding fluid.\nLighthill proposed a large-amplitude elongated-body theory \\cite{Lighthill1975}, which first explains mathematically how fish moves in a fluid. \nStarting from that, researchers either used empirical force model \\cite{Terzopoulos1994} or more accurate fluid-structure interaction simulation ~\\cite{Song2017,Song2020,Verma2018,Novati2017} for fish dynamics description. \nWhile empirical model is fast, it is usually inaccurate; in addition, hyper-parameters inside the model are also difficult to tune in order to match the real physical system.\nDirect numerical simulation ~\\cite{dai2012dynamic,song2014three,Novati2017}, on the other hand, is more accurate and reliable, but it is usually very slow, leading to a very long training period. \nWhen fish is immersed inside fluid, the fish surface is often marked as the solid boundary condition for fluid simulation \\cite{Ming2018,Song2017,Song2020,Tan2011, Liu2015}, the fluid forces calculated at the surface are applied to drive the fish motion.\n\nThere are several models could be used for defining a fish's actuation. \nFor example, the torso of the fish is deformed through controlling the contraction ratio of springs in \\cite{Terzopoulos1994}.\nAn alternative way is to manipulate the centerline curvatures using mathematical functions ranging from interpolation \\cite{Song2017}, sinusoidal \\cite{Song2020}, exponential and polynomial \\cite{Ming2018} to weighted sum of cubic splines \\cite{Novati2017,Verma2018}. \nIn \\cite{Tan2011}, a robot is actuated by specifying expected joint poses for a PD controller to output torque signals. \n\\fi \n\n\n\n\\subsection{Fluid-structure interaction}\n\nFluid simulation has been studied for decades.\nTwo different fields have intensively progressed its development.\nIn computational fluid dynamics (CFD), fluid simulation mostly targets accuracy, and a set of fluid solvers are available, from finite difference \\cite{godunov1959finite,Rai-1991,smolarkiewicz1998mpdata}, to finite volume \\cite{eymard2000finite,versteeg2007introduction,pinelli2010immersed}, as well as to finite elements \\cite{wilson1983finite,girault2012finite,elman2014finite}.\nThese algorithms are typically very expensive, which are difficult for training underwater robots.\nIn computer graphics (CG), fluid simulation concerns efficiency more than accuracy, and a large number of more efficient yet less accurate solvers were proposed~\\cite{Stam2001,Kim2005FlowFixer,Becker2007Weakly,Ihmsen2014,Jiang2015Affine,Zehnder2018,Qu2019}.\nHowever, even though GPU acceleration has been used in some of these solvers, efficiency is still not high enough.\nWhen rigid body dynamics is coupled for fluid-structure interaction \\cite{klingner2006fluid,lv2010novel,dai2005adaptive}, the efficiency can be even lower.\n\nIn recent years, lattice Boltzmann method (LBM) has been considered as a very promising alternative to traditional fluid solvers~\\cite{Liu-2012,Daniel-2014,Rosis-2017,Li-2018,Li-2020}, exhibiting excellent efficiency and accuracy (usually an order of magnitude faster than the NS counterpart with comparable accuracy on GPU).\nIts pure local dynamics without solving global equations greatly benefits the highly parallel implementation~\\cite{Li-2018,Li-2020,Chen2021gpu}.\nWhen LBM is coupled with immersed boundary (IB) method~\\cite{Li-2020}, it can be easily used to simulate two-way coupled fluid-structure interaction.\nIn particular, Chen et al.~\\cite{Chen2021gpu} proposed a GPU-optimized implementation of IB-LBM, which provides a super-efficient solver for fluid-structure interaction, making the originally expensive fluid simulation now affordable for robot training purposes.\nOur proposed platform is based on such a solver, with modifications to allow it for simulating fish dynamics in a local moving domain for higher flexibility, which is not supported in any previous works. \n\n\n\n\n\\subsection{Reinforcement learning for robot control}\nReinfocement learning (RL) is a branch of machine learning which aims to train agents using data collected through interaction with the surrounding environment. \nFor real world problems in robotics, model-free RL algorithms are often used~\\cite{Nian2020}. \nThere are two main approaches of model-free RL: policy optimization and Q-learning. \nPolicy optimization algorithms, like PPO~\\cite{Schulman2017a} and A2C\/A3C~\\cite{Mnih2016}, are stable but sample-inefficient. \nQ-learning methods, like DQN~\\cite{Mnih2013} and C51~\\cite{Bellemare2017}, are more sample-efficient but less stable.\nBoth of them have wide applications. \nFor example, PPO was used in multi-robot collision avoidance task~\\cite{long2018towards}, bipedal robot locomotion~\\cite{li2021reinforcement} etc. \nDQN also proves to work well on a certain type of tasks in real robots~\\cite{kato2017autonomous,xin2017application,chen2021non}.\nRecently, SAC~\\cite{Haarnoja2018} emerges to combine the strengths of the above two main approaches and has proved its capability in real robot problems like Dexterous manipulation~\\cite{haarnoja2018soft}, mobile robot navigation~\\cite{de2021soft}, robot arm control~\\cite{wong2021motion}, multi-legged robot~\\cite{haarnoja2018learning}, etc.\nDue to its sample efficiency and wide applications, we adopt SAC in this paper.\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nAlthough the minimal Standard Model (SM) agrees well with \ncurrent electroweak experiments~\\cite{lep_slc_97}, \nit is important to examine consequences of new physics \nmodels beyond the SM at current or future collider experiments. \nOne of the simplest extensions of the SM is to introduce an \nadditional U(1) gauge symmetry, ${\\rm U(1)'}$, \nwhose breaking scale is close to the electroweak scale. \nThe ${\\rm U(1)'}$ symmetry is predicted in a certain class \nof grand unified theories (GUTs) with gauge group whose rank \nis higher than that of the SM. \nIn general, the additional ${\\rm U(1)'}$ gauge boson \n$Z'$ can mix with the hypercharge ${\\rm U(1)}_Y$ gauge boson \nthrough the kinetic term at above the electroweak scale, \nand also it can mix with the SM $Z$ boson after the \nelectroweak symmetry is spontaneously broken. \nThrough those mixings, the $Z'$ boson can affect the electroweak \nobservables at the $Z$-pole and the $W$ boson mass $m_W$. \nBoth the $Z$-$Z'$ mixing and the direct $Z'$ contribution \ncan affect neutral current experiments off the $Z$-pole. \nThe presence of an additional $Z'$ boson can be explored \ndirectly at $p \\bar{p}$ collider experiments. \n\nThe supersymmetric (SUSY) $E_6$ models are the promising candidates \nwhich predict an additional $Z'$ boson at the weak scale (for \na review, see~\\cite{hewett_rizzo}). \nThe gauge group $E_6$ can arise from the perturbative heterotic \nstring theory as a consequence of its compactification. \nIn the $E_6$ models, the SM matter fields in each generation \nare embedded into its fundamental representation ${\\bf 27}$ \nthat also contains several exotic matter fields -- two SM singlets, \na pair of weak doublets and color triplets. \nBecause $E_6$ is a rank-six group, it can have two extra \n${\\rm U(1)}$ factors besides the SM gauge group. \nA superposition of the two extra ${\\rm U(1)}$ groups may \nsurvive as the ${\\rm U(1)'}$ gauge symmetry at the GUT scale. \nThe ${\\rm U(1)'}$ symmetry may break spontaneously at the weak \nscale through the radiative corrections to the mass term of the \nSM singlet scalar field~\\cite{radiative_u1prime}. \n\nIn this paper, we study constraints on the $Z'$ bosons predicted \nin the SUSY $E_6$ models. \nAlthough there are several previous works~\\cite{altarelli,\nlangacker_luo,chm,gherghetta,zprime_lep}, we would like to \nupdate their studies by using the recent results of \nelectroweak experiments, and by allowing for an arbitrary \nkinetic mixing~\\cite{holdom, eta_model,general_zzmixing} \nbetween the $Z'$ boson and the hypercharge $B$ boson. \nIn our study, we use the results of $Z$-pole experiments \nat LEP1 and SLC, and the $m_W$ measurements at Tevatron and LEP2 \nwhich were reported at the summer conferences in \n1997~\\cite{lep_slc_97}. \nWe also study the constraints from low-energy neutral current \n(LENC) experiments: lepton-quark, lepton-lepton scattering \nexperiments and atomic parity violation measurements. \n\nWe find that the lower mass limit of the heavier mass \neigenstate $Z_2$ is obtained as a function of the effective $Z$-$Z'$ \nmixing term $\\zeta$, which is a combination of the mass and kinetic \nmixings. \nIn principle, $\\zeta$ is calculable, together with the gauge \ncoupling $g_E$, once the particle spectrum of the $E_6$ model \nis specified. \nWe show the theoretical prediction for $\\zeta$ and $g_E$ \nin the SUSY $E_6$ models by assuming the minimal particle \ncontent which satisfies the anomaly free condition and the \ngauge coupling unification. \nFor those models, the electroweak data give stringent \nlower mass bound on the $Z_2$ boson. \n\nThis paper is organized as follows. \nIn the next section, we briefly review the additional $Z'$ \nboson in the SUSY $E_6$ models and the generic feature of \n$Z$-$Z'$ mixing in order to fix our notation. \nWe show that the effects of $Z$-$Z'$ mixing and direct \n$Z'$ boson contribution are parametrized \nby the following three terms: \n(i) a tree-level contribution to the $T$ parameter~\\cite{peskin_takeuchi}, \n$T_{\\rm new}$, \n(ii) the effective $Z$-$Z'$ mass mixing angle $\\bar{\\xi}$ \nand \n(iii) a contact term $g_E^2\/c^2_\\chi m_{Z_2}^2$ which appears in the low-energy \nprocesses. \nIn Sec.~3, we collect the latest results of electroweak experiments. \nThere, the theoretical predictions for the electroweak \nobservables are shown together with the SM radiative corrections. \nIn Sec.~4, we show constraints on the $Z'$ bosons from the \nelectroweak data. \nThe presence of non-zero kinetic mixing between the \n${\\rm U(1)}_Y$ and ${\\rm U(1)'}$ gauge bosons \nmodifies the couplings between the $Z'$ boson and the SM \nfermions. \nWe discuss impacts of the kinetic mixing term \non the $\\chi^2$-analysis. \nThe 95\\% CL lower mass limit of the heavier mass eigenstate $Z_2$ \nis given as a function of the effective $Z$-$Z'$ mixing parameter \n$\\zeta$. \nThe $\\zeta$-independent constraints from the low-energy \nexperiments and those from the direct search experiments \nat Tevatron are also discussed. \nIn Sec.~5, \nwe find the theoretical prediction for $\\zeta$ in some \nSUSY $E_6$ models ($\\chi, \\psi, \\eta, \\nu$) \nby assuming the minimal particle content. \nStringent $Z_2$ boson mass bounds are found for most models.\nSec.~6 summarizes our findings. \n\\section{$Z$-$Z'$ mixing in supersymmetric $E_6$ model}\n\\subsection{$Z'$ boson in supersymmetric $E_6$ model}\n\\setcounter{equation}{0} \nSince the rank of $E_6$ is six, \nit has two ${\\rm U(1)}$ factors besides the SM gauge \ngroup which arise from the following decompositions: \n\\begin{eqnarray}\n\t\\begin{array}{rl}\n\tE_6 &\\supset {\\rm SO(10)} \\times {\\rm U(1)}_\\psi \n\\\\\n\t&\\supset {\\rm SU(5)} \\times {\\rm U(1)}_\\chi \\times {\\rm U(1)}_\\psi. \n\t\\end{array}\n\\end{eqnarray}\n\\label{eq:e6_breaking}\n\\hsp{-0.3}\nAn additional $Z'$ boson in the electroweak scale \ncan be parametrized as \na linear combination of the ${\\rm U(1)}_\\psi$ gauge boson \n$Z_\\psi$ and the ${\\rm U(1)}_\\chi$ gauge boson \n$Z_\\chi$ as~\\cite{PDG} \n\\begin{equation}\nZ' = Z_\\chi \\cos \\beta_E + Z_\\psi \\sin \\beta_E. \n\\end{equation}\nIn this paper, we study the following four $Z'$ models \nin some detail: \n\\begin{eqnarray}\n\\begin{array}{|c||c|c|c|c|} \\hline \n~~~\\beta_E~~~ & ~~~0~~~ &~~~\\pi\/2~~~ & \\tan^{-1}(-\\sqrt{5\/3}) \n& \\tan^{-1}(\\sqrt{15}) \\\\ \\hline\n{\\rm model} & \\chi & \\psi & \\eta & \\nu \n\\\\ \\hline \n\\end{array}\n\\end{eqnarray}\nIn the SUSY-$E_6$ models, each generation of the SM quarks \nand leptons is embedded into a {\\bf 27} representation. \nIn Table~1, we show all the matter fields \ncontained in a {\\bf 27} and their classification in SO(10) \nand SU(5). \nThe ${\\rm U(1)'}$ charge assignment on the matter fields \nfor each model is also given in the same table. \nThe normalization of the ${\\rm U(1)'}$ charge follows \nthat of the hypercharge. \n\\esix_contents\nBesides the SM quarks and leptons, there are two SM singlets \n$\\nu^c$ and $S$, a pair of weak doublets $H_u$ and $H_d$, \na pair of color triplets $D$ and $\\overline{D}$ in each generation. \nThe $\\eta$-model arises when $E_6$ breaks into a rank-5 group \ndirectly in a specific compactification of the heterotic \nstring theory~\\cite{eta_ellis}. \nIn the $\\nu$-model, the right-handed neutrinos $\\nu^c$ are \ngauge singlet~\\cite{nu_model} and can have large Majorana \nmasses to realize the see-saw mechanism~\\cite{see-saw}. \n\nThe ${\\rm U(1)'}$ symmetry breaking occurs if the scalar \ncomponent of the SM singlet field develops the vacuum \nexpectation value (VEV). \nIt can be achieved at near the weak scale via radiative \ncorrections to the mass term of the SM singlet scalar \nfield. \nFor example, the terms $SD\\overline{D}$ and $S H_u H_d$ appear \nin the ${\\rm SU(3)}_C \\times {\\rm SU(2)}_L \\times \n{\\rm U(1)}_Y \\times {\\rm U(1)'}$ invariant superpotential. \nIf the Yukawa couplings of the $SD\\overline{D}$ term and\/or \n$S H_u H_d$ term are $O(1)$, the squared mass of the \nscalar component of $S$ can become negative at the weak \nscale through the renormalization group equations (RGEs) \nwith an appropriate boundary condition at the GUT scale. \nRecent studies of the radiative ${\\rm U(1)'}$ symmetry \nbreaking can be found, {\\it e.g.}, in ref.~\\cite{radiative_u1prime}. \n\nSeveral problems may arise in the $E_6$ models from view \nof low-energy phenomenology~\\cite{hewett_rizzo}. \nFor example, the scalar components of extra colored triplets \n$D, \\overline{D}$ in ${\\bf 27}$ could mediate an instant proton \ndecay. \nIt should be forbidden by imposing a certain discrete symmetry \non the general \n${\\rm SU(3)}_C \\times {\\rm SU(2)}_L \\times {\\rm U(1)}_Y \\times\n{\\rm U(1)'}$ invariant superpotential. \nExcept for the $\\nu$-model~\\cite{nu_model}, the large Majorana \nmass of $\\nu^c$ is forbidden by the ${\\rm U(1)'}$ gauge \nsymmetry, and the fine-tuning is needed to make the Dirac neutrino \nmass consistent with the observation. \nFurther discussions can be found in ref.~\\cite{hewett_rizzo}. \nIn the following, we \nassume that these requirements are satisfied by an unknown \nmechanism. \nMoreover we assume that all the super-partners of the SM \nparticles and the exotic matters do not affect the radiative \ncorrections to the electroweak observables significantly, \n{\\it i.e.}, they are assumed to be heavy enough to decouple from \nthe weak boson mass scale. \n\n\\subsection{Phenomenological consequences of $Z$-$Z'$ mixing}\nIf the SM Higgs field carries a non-trivial ${\\rm U(1)'}$ \ncharge, its VEV induces the $Z$-$Z'$ mass mixing. \nOn the other hand, the kinetic mixing between the hypercharge \ngauge boson $B$ and the ${\\rm U(1)'}$ gauge boson $Z'$ \ncan occur through the quantum effects below the GUT scale. \nAfter the electroweak symmetry is broken, the effective \nLagrangian for the neutral gauge bosons in the \n${\\rm SU(2)}_L \\times {\\rm U(1)}_Y \\times {\\rm U(1)'}$ \ntheory is given by~\\cite{eta_model} \n\\begin{eqnarray}\n{\\cal L}_{gauge} \n\t&=& -\\frac{1}{4}Z^{\\mu\\nu}Z_{\\mu\\nu}\n -\\frac{1}{4}Z'^{\\mu\\nu}Z'_{\\mu\\nu} \n\t -\\frac{\\sin \\chi}{2}B^{\\mu\\nu}Z'_{\\mu\\nu}\n\t -\\frac{1}{4}A^{0\\mu\\nu}A^{0}_{\\mu\\nu} \n\\nonumber \\\\ \n\t& & + m^2_{ZZ'} Z^{\\mu}Z'_{\\mu}\n\t +\\frac{1}{2} m^2_Z Z^{\\mu}Z_{\\mu}\n\t +\\frac{1}{2} m^2_{Z'} Z'^\\mu Z'_{\\mu}, \n\\label{eq:l_gauge}\n\\end{eqnarray} \nwhere $F^{\\mu\\nu} (F=Z,Z',A^0,B)$ represents the gauge field strength. \nThe $Z$-$Z'$ mass mixing and the kinetic mixing are characterized \nby $m^2_{ZZ'}$ and $\\sin \\chi$, respectively. \nIn this basis, the interaction Lagrangian for the neutral current \nprocess is given as \n\\begin{eqnarray}\n{\\cal L}_{NC} &=& -\\sum_{f,\\, \\alpha} \\left\\{ \n\t\\; e \\, Q_{f^{}_{\\alpha}} \\overline{f^{}_{\\alpha}}\n\t\\gamma^{\\mu}f^{}_{\\alpha} A^0_{\\mu} +\n\tg^{}_Z \\overline{f^{}_{\\alpha}} \\gamma^{\\mu}\n\t\\left( I^3_{f_L} - Q_{f^{}_{\\alpha}} \\sin^2\\theta_W \\right)\n\tf^{}_{\\alpha} Z^{}_{\\mu} \\right. \\nonumber \\\\ \n\t& & \\left. + g^{}_E Q^{f^{}_{\\alpha}}_E \n\t\\overline{f^{}_{\\alpha}}\\gamma^{\\mu}f^{}_{\\alpha} \n\tZ'_{\\mu} \\right\\}, \n\\label{eq:neutraC}\n\\end{eqnarray}\nwhere $g_Z = g\/\\cos\\theta_W = g_Y\/\\sin\\theta_W$. \nThe ${\\rm U(1)'}$ gauge coupling constant is denoted by $g_E$ \nin the hypercharge normalization. \nThe symbol $f_\\alpha$ denotes the quarks or leptons with \nthe chirality $\\alpha$ ($\\alpha = L$ or $R$). \nThe third component of the weak isospin, the electric charge \nand the ${\\rm U(1)'}$ charge of $f_\\alpha$ are given by \n$I^3_{f_\\alpha}$, $Q_{f_\\alpha}$ and $Q_E^{f_\\alpha}$, respectively. \nThe ${\\rm U(1)'}$ charge of the quarks and leptons listed in Table~1 \nshould be read as \n\\begin{eqnarray}\n\t\\left.\n\t\\begin{array}{l}\nQ_E^Q = Q_E^{u_L} = Q_E^{d_L},~~~Q_E^L = Q_E^{\\nu_L} = Q_E^{e_L}, \n\\\\ \nQ_E^{f^c} = -Q_E^{f_R} ~~~(f=e,u,d),\n\t\\end{array}\n\t\\right \\}. \n\\label{eq:charge_rule}\n\\end{eqnarray}\nThe mass eigenstates $(Z_1,Z_2,A)$ is obtained by \nthe following transformation; \n\\begin{equation}\n\\left( \\begin{array}{c} Z \\\\ Z' \\\\ A^0 \n\\end{array}\\right) \n= \n\\left(\n\t\\begin{array}{ccc}\n\\cos \\xi + \\sin \\xi \\sin \\theta_W \\tan \\chi &\n-\\sin \\xi + \\cos \\xi \\sin\\theta_W \\tan \\chi & 0 \\\\\n\\sin \\xi \/ \\cos \\chi & \\cos \\xi \/ \\cos \\chi & 0 \\\\\n-\\sin\\xi \\cos \\theta_W \\tan \\chi & \n- \\cos \\xi \\cos \\theta_W \\tan \\chi & 1 \n\t\\end{array}\n\\right) \n\\left( \\begin{array}{c} {Z_1} \\\\ \n{Z_2} \\\\ {A} \\end{array}\\right). \n\\end{equation}\nHere the mixing angle $\\xi$ is given by \n\\begin{equation}\n\\tan 2\\xi = \\frac{-2c^{}_{\\chi}(m^2_{ZZ'}+s^{}_W s^{}_{\\chi}\n m^2_Z)}{m^2_{Z'} - (c^2_{\\chi}-s^2_W s^2_{\\chi})m^2_Z+\n 2s^{}_W s^{}_{\\chi} m^2_{ZZ'}}~, \n\\label{eq:angle_xi}\n\\end{equation}\nwith the short-hand notation, \n$c_\\chi = \\cos\\chi$, $s_\\chi = \\sin\\chi$ and $s_W = \\sin\\theta_W$. \nThe physical masses $m_{Z_1}$ and $m_{Z_2}$ ($m_{Z_1} < m_{Z_2}$) \nare given as follows; \n\\begin{subequations}\n\\begin{eqnarray}\nm_{Z_{1}}^2 \n\t &=& m_Z^2 (c_\\xi + s_\\xi s_W t_\\chi)^2 \n\t+ m_{Z'}^2 \\biggl( \\frac{s_\\xi}{c_\\chi} \\biggr)^2\n\t+ 2 m^2_{ZZ'} \\frac{s_\\xi}{c_\\chi} (c_\\xi + s_\\xi s_W t_\\chi), \n\\label{eq:light_Z1}\n\\\\\nm_{Z_{2}}^2 \n\t &=& m_Z^2 (c_\\xi s_W t_\\chi - s_\\xi)^2 \n\t+ m_{Z'}^2 \\biggl( \\frac{c_\\xi}{c_\\chi} \\biggr)^2\n\t+ 2 m^2_{ZZ'} \\frac{c_\\xi}{c_\\chi} (c_\\xi s_W t_\\chi -s_\\xi), \n\\end{eqnarray}\n\\end{subequations}\nwhere $c_\\xi = \\cos\\xi$, $s_\\xi = \\sin\\xi$ and $t_\\chi = \\tan\\chi$. \nThe lighter mass eigenstate $Z_1$ should be identified with \nthe observed $Z$ boson at LEP1 or SLC. \nThe excellent agreement between the current experimental results \nand the SM predictions at the quantum level implies that the \nmixing angle $\\xi$ have to be small. \nIn the limit of small $\\xi$, the interaction Lagrangians \nfor the processes \n$Z_{1,2} \\rightarrow f_\\alpha \\overline{f_\\alpha}$ are expressed as \n\\begin{subequations}\n\\begin{eqnarray}\n{\\cal L}_{Z_1} &=& -\\sum_{f,\\, \\alpha} g^{}_Z \n\t\\overline{f_{\\alpha}} \\gamma^{\\mu} \\left[\n\t\\left( I^{3}_{f_{L}}-Q_{f_{\\alpha}}\\sin^2\\theta_W \n\t\\right) + \\tilde{Q}^{f_{\\alpha}}_E \\bar{\\xi} \\right]\n\tf_{\\alpha} Z_{1 \\mu}, \n\\label{eq:neutral1}\\\\ \n{\\cal L}_{Z_2} &=& -\\sum_{f,\\,\\alpha}\\frac{g^{}_E}{c^{}_{\\chi}}\n\t\\overline{f_{\\alpha}}\\gamma^{\\mu} \\left[ \\tilde{Q}^{f_{\\alpha}}_E\n\t-\\left( I^3_{f_{\\alpha}} - Q_{f_{\\alpha}}\n\t\\sin^2\\theta_W \\right) \\frac{g^{}_Z c^{}_{\\chi}}{g^{}_E}\n\t\\xi \\right]f_{\\alpha} Z_{2 \\mu}, \n\\label{eq:neutral2}\n\\end{eqnarray}\n\\label{eq:neutralboth}\n\\end{subequations}\n\\hsp{-0.3} \nwhere the effective mixing angle $\\bar{\\xi}$ \nin eq.~(\\ref{eq:neutral1}) is given as \n\\begin{equation}\n\\bar{\\xi} = \\frac{g_E}{g_Z\\cos \\chi } \\xi. \n\\end{equation}\nIn eq.~(\\ref{eq:neutralboth}), the effective ${\\rm U(1)'}$ \ncharge $\\tilde{Q}_E^{f_\\alpha}$ is introduced as \na combination of $Q_E^{f_\\alpha}$ and the hypercharge $Y_{f_\\alpha}$: \n\\begin{subequations}\n\\begin{eqnarray}\n\\tilde{Q}^{f_\\alpha}_E &\\equiv& Q^{f_\\alpha}_E + Y_{f_\\alpha} \\delta, \n\\label{eq:effective_charge}\\\\\n\\delta &\\equiv& -\\frac{g^{}_Z}{g^{}_E}s^{}_W s^{}_{\\chi}, \n\\end{eqnarray}\n\\label{eq:u1_charge}\n\\end{subequations}\n\\hsp{-0.3}\nwhere the hypercharge $Y_{f_\\alpha}$ should be read from Table~1 \nin the same manner with $Q_E^{f_\\alpha}$ \n(see, eq.~(\\ref{eq:charge_rule})). \nAs a notable example, one can see from Table~1 \nthat the effective charge $\\tilde{Q}_E^{f_\\alpha}$ of \nthe leptons ($L$ and $e^c$) disappears in the \n$\\eta$-model if $\\delta$ is taken to be $1\/3$~\\cite{eta_model}. \n\nNow, due to the $Z$-$Z'$ mixing, the observed \n$Z$ boson mass $m_{Z_1}$ at LEP1 or SLC is shifted from \nthe SM $Z$ boson mass $m_Z$: \n\\begin{equation}\n\\Delta m^2 \\equiv m_{Z_1}^2 - m_Z^2 \\le 0. \n\\label{eq:mass_shift}\n\\end{equation}\nThe presence of the mass shift affects the \n$T$-parameter~\\cite{peskin_takeuchi} at tree level. \nFollowing the notation of ref.~\\cite{hhkm}, the $T$-parameter \nis expressed in terms of the effective form factors $\\bar{g}_Z^2(0), \n\\bar{g}_W^2(0)$ and the fine structure constant $\\alpha$:\n\\begin{subequations}\n\\begin{eqnarray}\n\\alpha T &\\equiv& 1 - \\frac{\\bar{g}^2_{W}(0)}{m^2_W}\n\t\\frac{m^2_{Z_1}}{\\bar{g}^2_Z(0)} \\\\ \n\t&=&\n\t\\alpha \\left(T_{\\rm SM}^{}+ T_{\\rm new}^{}\\right), \n\\end{eqnarray}\n\\end{subequations}\nwhere \n$T_{\\rm SM}^{}$ and the new physics contribution \n$T_{\\rm new}$ are given by: \n\\begin{subequations}\n\\begin{eqnarray}\n\\alpha T_{\\rm SM} \n\t&=& 1 - \\frac{\\bar{g}^2_{W}(0)}{m^2_W}\n\t\t\\frac{m^2_{Z}}{\\bar{g}^2_Z(0)}, \\\\\n\\alpha T_{\\rm new}\n\t& = & -\\frac{ \\Delta m^2}{m^2_{Z_1}} \\geq 0. \n\\end{eqnarray}\n\\end{subequations}\nIt is worth noting that the sign of $T_{\\rm new}$ is \nalways positive. \nThe effects of the $Z$-$Z'$ mixing in the $Z$-pole experiments \nhave hence been parametrized by the effective mixing angle \n$\\bar{\\xi}$ and the positive parameter $T_{\\rm new}$. \n\nWe note here that we retain \nthe kinetic mixing term $\\delta$ as a part of the effective $Z_1$ \ncoupling $\\tilde{Q}_E^{f_\\alpha}$ in eq.~(\\ref{eq:effective_charge}). \nAs shown in refs.~\\cite{eta_model,general_zzmixing,holdom2}, \nthe kinetic mixing term $\\delta$ can be absorbed into a further \nredefinition of $S$ and $T$. \nSuch re-parametrization may be useful \nif the term $Y_{f_\\alpha} \\delta$ in eq.~(\\ref{eq:effective_charge}) \nis much larger than the $Z'$ charge $Q_E^{f_\\alpha}$. \nIn the $E_6$ models studied in this paper, we find no merit in \nabsorbing the $Y_f \\delta$ term because, the remaining \n$Q_E^{f_\\alpha}$ term is always significant. \nWe therefore adopt $\\tilde{Q}_E^{f_\\alpha}$ as the effective \n$Z_1$ couplings and $T_{\\rm new}$ accounts only for the \nmass shift (\\ref{eq:mass_shift}). \nAll physical consequences such as the bounds on $\\bar{\\xi}$ and \n$m_{Z_2}$ are of course independent of our choice of the \nparametrization. \n\nThe two parameters $T_{\\rm new}$ and $\\bar{\\xi}$ \nare complicated functions of the parameters of the effective \nLagrangian (\\ref{eq:l_gauge}). \nIn the small mixing limit, \nwe find the following useful expressions \n\\begin{subequations}\n\\begin{eqnarray}\n\\bar{\\xi} &=& -\\biggl( \\frac{g_E}{g_Z}\\frac{m_Z}{m_{Z'}} \n\t\\biggr)^2 \\zeta \\biggl[ 1+ O(\\frac{m_Z^2}{m_{Z'}^2})\\biggr], \n\\\\\n\\alpha T_{\\rm new} &=& \\hphantom{-} \\biggl( \\frac{g_E}{g_Z}\n\t\t\\frac{m_Z}{m_{Z'}} \\biggr)^2 \\zeta^2\n\t\\biggl[ 1+ O(\\frac{m_Z^2}{m_{Z'}^2})\\biggr], \n\\end{eqnarray}\n\\label{eq:tnew_xibar}\n\\end{subequations}\n\\hsp{-0.3}\nwhere we introduced an effective mixing parameter $\\zeta$ \n\\begin{equation}\n\\zeta = \\frac{g_Z}{g_E}\\frac{m_{ZZ'}^2}{m_Z^2} - \\delta. \n\\label{eq:zeta}\n\\end{equation}\nThe $Z$-$Z'$ mixing effect disappears at $\\zeta = 0$. \nStringent limits on $m_{Z'}$ and hence on $m_{Z_2}$ \ncan be obtained through the mixing effect if $\\zeta$ is \n$O(1)$. \nWe will show in Sec.~5 that $\\zeta$ is calculable \nonce the particle spectrum of the model is specified. \nThe parameter $\\zeta$ plays an essential role in \nthe analysis of $Z'$ models. \n\nIn the low-energy neutral current processes, effects of \nthe exchange of the heavier mass eigenstate $Z_2$ can be \ndetected. \nIn the small $\\bar{\\xi}$ limit, they constrain the contact \nterm $g_E^2\/c_\\chi^2 m_{Z_2}^2$. \n\\section{Electroweak observables in the $Z'$ model}\n\\setcounter{equation}{0}\nIn this section, we give the theoretical predictions for the \nelectroweak observables which are used in our analysis. \nThe experimental data of the $Z$-pole experiments and the $W$ \nboson mass measurement~\\cite{lep_slc_97} \nare summarized in Table~2. \nThose for the low-energy experiments~\\cite{chm} are listed in \nTable~\\ref{table:low_energy}. \n\\lep_table \nL.E.N.C.\\ _table \n\\subsection{Observables in $Z$-pole experiments}\nThe decay amplitude for the process \n$Z^{}_1 \\rightarrow f_\\alpha \\overline{f_\\alpha}$ is written as\n\\begin{equation}\nT(Z_1 \\rightarrow f_{\\alpha} \\overline{f_{\\alpha}}) \n\t= M^f_{\\alpha}~\\epsilon_{Z_1} \\cdot J_{f_{\\alpha}}, \n\\label{eq:decay_amp}\n\\end{equation}\nwhere $\\epsilon_{Z_1}^\\mu$ is the polarization vector of the \n$Z^{}_1$ boson and \n$J^{\\mu}_{f_{\\alpha}} = \\overline{f_\\alpha}\\gamma^\\mu f_\\alpha$ is \nthe fermion current without the coupling factors. \nThe pseudo-observables of the $Z$-pole experiments are \nexpressed in terms of the real scalar amplitudes $M_\\alpha^f$ \nwith the following normalization~\\cite{lep_slc_97} \n\\begin{equation}\ng^f_{\\alpha} = \\frac{M^f_{\\alpha}}{\\sqrt{4\\sqrt{2}G_Fm^2_{Z_1}}} \n\t\\approx \\frac{M^f_{\\alpha}}{0.74070}. \n\\end{equation}\n\nFollowing our parametrization of the $Z$-$Z'$ mixing in \neq.~(\\ref{eq:neutral1}), the effective coupling $g^f_{\\alpha}$ \nin the $Z'$ models can be expressed as \n\\begin{eqnarray}\ng_\\alpha^f = (g_\\alpha^f)_{\\rm SM} + \\tilde{Q}_E^{f_\\alpha} \\bar{\\xi}. \n\\end{eqnarray}\nThe SM predictions~\\cite{hhkm, hhm} for the effective \ncouplings $(g_\\alpha^f)_{\\rm SM}$ can be parametrized as \n\\begin{subequations}\n\\begin{eqnarray}\n(g^{\\nu}_L)_{\\rm SM} &=& \\makebox[3.3mm]{ } 0.50214 \n\t+ 0.453\\,{\\Delta \\bar{g}^2_Z},\n\\label{eq:amp_nu} \\\\\n(g^e_L)_{\\rm SM} &=& - 0.26941 - 0.244\\,{\\Delta \\bar{g}^2_Z} \n\t+ 1.001\\,{\\Delta \\bar{s}^2}, \n\\label{eq:amp_el}\\\\\n(g^e_R)_{\\rm SM} &=& \\makebox[3.3mm]{ } 0.23201 + 0.208\\,\n\t{\\Delta \\bar{g}^2_Z} + 1.001\\,{\\Delta \\bar{s}^2}, \n\\label{eq:amp_er}\\\\\n(g^u_L)_{\\rm SM} &=& \\makebox[3.3mm]{ } 0.34694 + 0.314\\,\n\t{\\Delta \\bar{g}^2_Z} - 0.668\\,{\\Delta \\bar{s}^2}, \n\\label{eq:amp_ul}\\\\\n(g^u_R)_{\\rm SM} &=& - 0.15466 - 0.139\\,{ \\Delta \\bar{g}^2_Z} \n\t- 0.668 \\,{\\Delta \\bar{s}^2}, \n\\label{eq:amp_ur}\\\\\n(g^d_L)_{\\rm SM} &=& - 0.42451 - 0.383\\,{\\Delta \\bar{g}^2_Z} \n\t+ 0.334\\,{\\Delta \\bar{s}^2}, \n\\label{eq:amp_dl}\\\\\n(g^d_R)_{\\rm SM} &=& \\makebox[3.3mm]{ } 0.07732\n\t+ 0.069\\,{ \\Delta \\bar{g}^2_Z} + 0.334\\,{\\Delta \\bar{s}^2}, \n\\label{eq:amp_dr}\\\\\n(g^b_L)_{\\rm SM} &=& - 0.42109 - 0.383\\,{ \\Delta \\bar{g}^2_Z} \n\t+ 0.334\\,{\\Delta \\bar{s}^2} \n\t+ 0.00043 x_t, \n\\label{eq:amp_bl}\n\\end{eqnarray}\n\\label{eq:amp_sm}\n\\end{subequations}\n\\hsp{-0.3}\nwhere the SM radiative corrections are expressed in terms of \nthe effective couplings $\\Delta \\gzbar$ and $\\Delta \\sbar$~\\cite{hhkm, hhm} \nand the top-quark mass dependence of the $Zb_L^{} b_L^{}$ vertex \ncorrection in $(g^b_L)_{\\rm SM}$ is parametrized by \nthe parameter $x_t^{}$ \n\\begin{equation}\nx_t^{} \\equiv \\frac{m_t - 175~{\\rm GeV}}{10~{\\rm GeV}}. \n\\end{equation}\nThe gauge boson propagator corrections, $\\Delta \\gzbar$ and $\\Delta \\sbar$, \nare defined as the shift in the effective couplings \n$\\bar{g}_Z^2(m_{Z_1}^2)$ and $\\bar{s}^2(m_{Z_1}^2)$~\\cite{hhkm} \nfrom their SM reference values at $m_t^{} = 175~{\\rm GeV}$ and \n$m_H^{} = 100~{\\rm GeV}$. \nThey can be expressed in terms of the $S$ and $T$ parameters as \n\\begin{subequations}\n\\begin{eqnarray}\n\\!\\!\\!\\!\\!\\!\\!\\! \\Delta \\bar{g}^2_Z &=& \\bar{g}_Z^2(m_{Z_1}^2) - 0.55635 \n\t= 0.00412 \\Delta T + 0.00005[1-(100~{\\rm GeV}\/m^{}_H)^2], \\\\ \n\\!\\!\\!\\!\\!\\!\\!\\! \\Delta \\bar{s}^2 &=& \\bar{s}^2(m_{Z_1}^2) - 0.23035 \n\t= 0.00360 \\Delta S - 0.00241 \\Delta T - 0.00023 x_\\alpha^{}, \n\\end{eqnarray}\n\\end{subequations}\nwhere the expansion parameter $x_\\alpha^{}$ is introduced to estimate \nthe uncertainty of the hadronic contribution to the QED coupling \n$1\/\\overline{\\alpha}(m_{Z_1}^2) = 128.75 \\pm 0.09$~\\cite{eidelman}:\n\\begin{equation}\nx_\\alpha^{} \\equiv \\frac{1\/\\overline{\\alpha}(m_{Z_1}^2) - 128.75}{0.09}. \n\\label{eq:xa_qed}\n\\end{equation}\nHere, $\\Delta S,\\Delta T,\\Delta U$ parameters are also measured from their \nSM reference values and they are given as the sum of the SM \nand the new physics contributions \n\\begin{equation}\n\\Delta S = \\Delta S^{}_{\\rm SM}+S^{}_{\\rm new}, \\;\\;\n\\Delta T = \\Delta T^{}_{\\rm SM}+T^{}_{\\rm new}, \\;\\; \n\\Delta U = \\Delta U^{}_{\\rm SM}+U^{}_{\\rm new}. \n\\label{eq:stu_delta}\n\\end{equation}\nThe SM contributions can be parametrized as~\\cite{hhm}\n\\begin{subequations}\n\\begin{eqnarray}\n\\Delta S^{}_{\\rm SM} &=& - 0.007 x^{}_t +0.091 x^{}_H -0.010 x^2_H , \\\\\n\\Delta T^{}_{\\rm SM} &=& (0.130 - 0.003 x^{}_H) x^{}_t +0.003 x^{}_t \n- 0.079 x^{}_H -0.028 x^2_H \\nonumber \\\\ & & +0.0026 x^3_H, \\\\\n\\Delta U^{}_{\\rm SM} &=& 0.022 x^{}_t -0.002x^{}_H, \n\\end{eqnarray}\n\\end{subequations}\nwhere $x_H^{}$ is defined by\n\\begin{eqnarray}\nx_H^{} &\\equiv& \\log (m_H^{}\/100~{\\rm GeV}). \n\\label{eq:higgs_xh}\n\\end{eqnarray}\n\nThe pseudo-observables of the $Z$-pole experiments \nare given by using the above eight effective couplings $g_\\alpha^f$ \nas follows. \nThe partial width of $Z_1$ boson is given by \n\\begin{eqnarray}\n\\Gamma_f &=& \\frac{G_Fm_{Z_1}^{3}}{3\\sqrt{2} \\pi} \\left\\{ \n\t\\left| g^f_L + g^f_R \\right|^2\\frac{C_{fV}}{2} \n\t+ \\left| g^f_L - g^f_R \\right|^2 \\frac{C_{fA}}{2} \\right\\}\n\t\\left( 1+\\frac{3}{4}Q^2_f\\frac{\\bar{\\alpha}(m^2_{Z_1})}{\\pi}\\right)\n \\makebox[10mm][l]{,}\n\\label{eq:partial_width}\n\\end{eqnarray}\nwhere the factors $C_{fV}$ and $C_{fA}$ account for the \nfinite mass corrections and the final state QCD corrections \nfor quarks. \nTheir numerical values are listed in Table~\\ref{tab:cvca}. \nThe $\\alpha_s$-dependence in $C_{qV}, C_{qA}$ \nis parametrized in terms of the parameter $x_s^{}$\n\\begin{equation}\nx_s^{} \\equiv \\frac{\\alpha_s(m_{Z_1})-0.118}{0.003}. \n\\end{equation}\nThe last term proportional to $\\bar{\\alpha}(m^2_{Z_1})\/\\pi$ \nin eq.~(\\ref{eq:partial_width}) accounts for the \nfinal state QED correction. \n\\cvca_tab\nThe total decay width $\\Gamma_{Z_1}$ and the hadronic decay \nwidth $\\Gamma_h$ are given in terms of $\\Gamma_f$: \n\\begin{subequations}\n\\begin{eqnarray}\n\\Gamma_{Z_1} &=& 3\\Gamma_{\\nu} + \\Gamma_e \n\t+ \\Gamma_{\\mu} + \\Gamma_{\\tau} + \\Gamma_h, \n\\label{eq:total_width}\\\\\n\\Gamma_h &=& \\Gamma_u + \\Gamma_c + \\Gamma_d + \\Gamma_s + \\Gamma_b. \n\\label{eq:hadron_width}\n\\end{eqnarray}\n\\end{subequations}\nThe ratios $R_\\ell^{}, R_c^{}, R_b^{}$ and the hadronic peak \ncross section $\\sigma_h^0$ are given by: \n\\begin{equation}\nR_{\\ell} = \\frac{\\Gamma_h}{\\Gamma_e},\\;\nR_c = \\frac{\\Gamma_c}{\\Gamma_h},\\;\nR_b = \\frac{\\Gamma_b}{\\Gamma_h},\\;\n\\sigma^0_h = \\frac{12\\pi}{m^2_{Z_1}}\n\t\\frac{\\Gamma_e\\Gamma_h}{\\Gamma_{Z_1}^2}. \n\\end{equation}\n\nThe left-right asymmetry parameter $A^{f}$ is also \ngiven in terms of the effective couplings $g_\\alpha^f$ as \n\\begin{equation}\nA^f \t= \\frac{(g^{f}_L)^2-(g^{f}_R)^2}{(g^{f}_L)^2+(g^{f}_R)^2}. \n\\end{equation}\nThe forward-backward (FB) asymmetry $A^{0,f}_{FB}$ \nand the left-right (LR) asymmetry $A^{0,f}_{LR}$ \nare then given as follows: \n\\begin{subequations}\n\\begin{eqnarray}\nA^{0,f}_{FB} &=& \\frac{3}{4}A^{e}A^{f}, \\\\\nA^{0,f}_{LR} &=& A^{f}. \n\\end{eqnarray}\n\\end{subequations}\n\n\\subsection{$W$ boson mass}\nThe theoretical prediction of $m^{}_W$\ncan be parametrized as~\\cite{hhkm,hhm}\n\\begin{equation}\nm_W^{}{\\rm (GeV)} =80.402-0.288\\,{\\it \\Delta S}+0.418\\,{\\it \\Delta T} \n+0.337\\,{\\it \\Delta U}+0.012\\,{\\it x_{\\alpha}}, \n\\end{equation}\nby using the same parameters, $\\Delta S, \\Delta T, \\Delta U$ (\\ref{eq:stu_delta}) \nand $x_\\alpha^{}$ (\\ref{eq:xa_qed}). \n\n\\subsection{Observables in low-energy experiments} \n\\setcounter{equation}{0}\nIn this subsection, we show the theoretical predictions for the \nelectroweak observables in the low-energy neutral current \nexperiments (LENC) --- \n(i) polarization asymmetry of the charged lepton scattering off \nnucleus target (\\ref{section_slac}--\\ref{section_mainz}), \n(ii) parity violation in cesium atom (\\ref{section_apv}), \n(iii) inelastic $\\nu_\\mu$-scattering off nucleus target \n(\\ref{section_nq}) and \n(iv) neutrino-electron scattering (\\ref{section_ne}). \nThe experimental data are summarized in Table~\\ref{table:low_energy}. \nTheoretical expressions for the observables of \n(i) and (ii) are conveniently given in terms of \nthe model-independent parameters $C_{1q}, C_{2q}$~\\cite{jekim} \nand $C_{3q}$~\\cite{chm}. \nThe $\\nu_\\mu$-scattering data (iii) and (iv) are expressed \nin terms of the parameters $g_{L\\alpha}^{\\nu_\\mu f}$. \nAll the model-independent parameters can be expressed \ncompactly in terms of the reduced helicity amplitudes \n$M_{\\alpha\\beta}^{f f'}$~\\cite{chm,hhkm} of the \nprocess $f_\\alpha f'_\\beta \\rightarrow f_\\alpha f'_\\beta$: \n\\begin{subequations}\n\\begin{eqnarray}\nC_{1q} &=& \\frac{1}{2 \\sqrt{2} G_F} ( \\hphantom{-} M_{LL}^{\\ell q}\n\t+ M_{LR}^{\\ell q} - M_{RL}^{\\ell q} - M_{RR}^{\\ell q} ), \n\\\\\nC_{2q} &=& \\frac{1}{2 \\sqrt{2} G_F} ( \\hphantom{-} M_{LL}^{\\ell q}\n\t- M_{LR}^{\\ell q} + M_{RL}^{\\ell q} - M_{RR}^{\\ell q} ), \n\\\\\nC_{3q} &=& \\frac{1}{2 \\sqrt{2} G_F} ( \t-M_{LL}^{\\ell q}\n\t+ M_{LR}^{\\ell q} + M_{RL}^{\\ell q} - M_{RR}^{\\ell q} ), \n\\\\\ng_{L\\alpha}^{\\nu_\\mu f} &=& \\frac{1}{2 \\sqrt{2} G_F} \n\t(-M_{L\\alpha}^{\\nu_\\mu f} ). \n\\label{eq:nutrino_amplitude}\n\\end{eqnarray}\n\\end{subequations}\nBelow, we divide these model-independent parameters into two pieces as \n\\begin{subequations}\n\\begin{eqnarray}\nC_{iq} &=& (C_{iq})_{\\rm SM} + \\Delta C_{iq}, \\\\\ng_{L\\alpha}^{\\nu_\\mu f} &=& (g_{L\\alpha}^{\\nu_\\mu f})_{\\rm SM} + \n\t\\Delta g_{L\\alpha}^{\\nu_\\mu f}, \n\\end{eqnarray}\n\\end{subequations}\nwhere the first term in each equation is the SM contribution\nwhich is parametrized conveniently by $\\Delta S$ and $\\Delta T$ in \nref.~\\cite{chm}. \nThe terms $\\Delta C_{iq}$ and $\\Delta g_{L\\alpha}^{\\nu_\\mu f}$ \nrepresent the additional contributions from the $Z$-$Z'$ mixing \nand the $Z_2$ exchange:\n\\begin{subequations}\n\\begin{eqnarray}\n\\Delta C^{}_{1u} &=& \n\t(-0.19s^{}_\\beta-0.15c^{}_\\beta+0.65\\delta )\\bar{\\xi} \n\t-\\frac{g^2_E}{c^2_\\chi}\n\t\\frac{(\\tilde{Q}^L_E-\\tilde{Q}^E_E)\n\t(\\tilde{Q}^Q_E+\\tilde{Q}^U_E)}\t{2\\sqrt{2}G^{}_Fm^2_{Z_2}}, \\\\\t\n\\Delta C^{}_{1d} &=& \n\t(0.36s^{}_\\beta-0.54c^{}_\\beta+0.17\\delta )\\bar{\\xi}\n\t-\\frac{g^2_E}{c^2_\\chi}\n\t\\frac{(\\tilde{Q}^L_E-\\tilde{Q}^E_E)(\\tilde{Q}^Q_E+\\tilde{Q}^D_E)}\n\t{2\\sqrt{2}G^{}_Fm^2_{Z_2}}, \\\\\n\\Delta C^{}_{2u} &=& \n\t(0.02s^{}_\\beta-0.84c^{}_\\beta+1.48\\delta )\\bar{\\xi} \n\t-\\frac{g^2_E}{c^2_\\chi}\\frac{(\\tilde{Q}^L_E+\\tilde{Q}^E_E)\n\t(\\tilde{Q}^Q_E-\\tilde{Q}^U_E)}{2\\sqrt{2}G^{}_Fm^2_{Z_2}}, \\\\\n\\Delta C^{}_{2d} &=&\n\t(0.02s^{}_\\beta+0.84c^{}_\\beta-1.48\\delta )\\bar{\\xi}\n\t-\\frac{g^2_E}{c^2_\\chi}\\frac{(\\tilde{Q}^L_E+\\tilde{Q}^E_E)\n\t(\\tilde{Q}^Q_E-\\tilde{Q}^D_E)}{2\\sqrt{2}G^{}_Fm^2_{Z_2}}, \\\\\n\\Delta C^{}_{3u} &=&\n\t(-0.82c^{}_\\beta+1.00\\delta )\\bar{\\xi}\n\t-\\frac{g^2_E}{c^2_\\chi}\\frac{(\\tilde{Q}^L_E-\\tilde{Q}^E_E)\n\t(\\tilde{Q}^U_E-\\tilde{Q}^Q_E)}{2\\sqrt{2}G^{}_Fm^2_{Z_2}}, \\\\\n\\Delta C^{}_{3d} &=&\n\t(1.06s^{}_\\beta-0.82c^{}_\\beta-1.00\\delta )\\bar{\\xi}\n\t-\\frac{g^2_E}{c^2_\\chi}\\frac{(\\tilde{Q}^L_E-\\tilde{Q}^E_E)\n\t(\\tilde{Q}^D_E-\\tilde{Q}^Q_E)}{2\\sqrt{2}G^{}_Fm^2_{Z_2}}, \\\\\n\\Delta g_{LL}^{\\nu u} &=& \n\t(0.44s^{}_\\beta+0.22c^{}_\\beta-0.18\\delta )\\bar{\\xi}\n\t+\\frac{g^2_E}{c^2_\\chi}\\frac{\\tilde{Q}^L_E \\tilde{Q}^Q_E}\n\t{2\\sqrt{2}G^{}_Fm^2_{Z_2}}, \\\\\n\\Delta g_{LR}^{\\nu u} &=& \n\t(-0.35s^{}_\\beta+0.01c^{}_\\beta+0.82\\delta )\\bar{\\xi}\n\t+\\frac{g^2_E}{c^2_\\chi}\\frac{\\tilde{Q}^L_E \\tilde{Q}^U_E}\n\t{2\\sqrt{2}G^{}_Fm^2_{Z_2}}, \\\\\n\\Delta g_{LL}^{\\nu d} &=& \n\t(0.04s^{}_\\beta-0.72c^{}_\\beta+0.59\\delta )\\bar{\\xi}\n\t+\\frac{g^2_E}{c^2_\\chi}\\frac{\\tilde{Q}^L_E \\tilde{Q}^Q_E}\n\t{2\\sqrt{2}G^{}_Fm^2_{Z_2}}, \\\\\n\\Delta g_{LR}^{\\nu d} &=& \n\t(-0.22s^{}_\\beta-0.52c^{}_\\beta-0.41\\delta )\\bar{\\xi}\n\t+\\frac{g^2_E}{c^2_\\chi}\\frac{\\tilde{Q}^L_E \\tilde{Q}^D_E}\n\t{2\\sqrt{2}G^{}_Fm^2_{Z_2}},\\\\\n\\Delta g_{LL}^{\\nu e} &=& (0.12 s^{}_\\beta + 0.28 c^{}_\\beta \n\t- 0.23 \\delta) \\bar{\\xi} \n\t+\\frac{g^2_E}{c^2_\\chi}\\frac{\\tilde{Q}^L_E \\tilde{Q}^L_E}\n\t{2\\sqrt{2}G^{}_Fm^2_{Z_2}},\n\\\\\n\\Delta g_{LR}^{\\nu e} &=& (-0.14 s^{}_\\beta + 0.49 c^{}_\\beta \n\t- 1.23 \\delta) \\bar{\\xi}\n\t+\\frac{g^2_E}{c^2_\\chi}\\frac{\\tilde{Q}^L_E \\tilde{Q}^e_E}\n\t{2\\sqrt{2}G^{}_Fm^2_{Z_2}}. \n\\end{eqnarray}\n\\label{eq:lenc_extra}\n\\end{subequations}\nwhere $c^{}_\\beta = \\cos\\beta_E$ and $s^{}_\\beta = \\sin\\beta_E$. \n\n\\subsubsection{SLAC $e$D experiment}\n\\label{section_slac}\nThe parity asymmetry in the inelastic scattering of polarized \nelectrons from the deuterium target was measured at SLAC~\\cite{slac}. \nThe experiment constrains the parameters \n$2C_{1u}-C_{1d}$ and $2C_{2u}-C_{2d}$. \nThe most stringent constraint shown in Table~\\ref{table:low_energy} \nis found for the following combination\n\\begin{subequations}\n\\begin{eqnarray}\nA_{\\rm SLAC} &=& 2C_{1u}-C_{1d} +0.206(2C_{2u}-C_{2d}) \\\\\n\t&=& 0.745 - 0.016\\,{\\it \\Delta S} + 0.016\\,{\\it \\Delta T} \n\t\\nonumber \\\\\n\t&&~~~~\n\t+ 2\\Delta C_{1u}- \\Delta C_{1d} \n\t+ 0.206(2\\Delta C_{2u}- \\Delta C_{2d}), \n\\end{eqnarray}\n\\label{eq:slac_sm1}\n\\end{subequations}\nwhere the theoretical prediction~\\cite{chm} is evaluated \nat the mean momentum transfer $\\langle Q^2 \\rangle = $ 1.5 GeV$^2$. \n\n\\subsubsection{CERN $\\mu^\\pm$C experiment}\nThe CERN $\\mu^\\pm$C experiment~\\cite{cern} measured \nthe charge and polarization asymmetry of deep-inelastic \nmuon scattering off the ${}^{12}$C target.\nThe mean momentum transfer of the experiment may be estimated \nat $\\langle Q^2 \\rangle = $ 50 GeV$^2$~\\cite{souder}.\nThe experiment constrains the parameters \n$2C_{2u}-C_{2d}$ and $2C_{3u}-C_{3d}$. \nThe most stringent constraint is found for the following \ncombination~\\cite{chm}\n\\begin{subequations}\n\\begin{eqnarray}\nA_{\\rm CERN} &=& 2C_{3u}-C_{3d}+0.777(2C_{2u}-C_{2d}) \\\\\n\t &=& -1.42-0.016\\,{\\it \\Delta S}+0.0006\\,{\\it \\Delta T} \n\t\\nonumber \\\\\n\t&&~~~+ 2\\Delta C_{3u}- \\Delta C_{3d}\n\t+ 0.777(2\\Delta C_{2u}- \\Delta C_{2d}). \n\\end{eqnarray}\n\\label{eq:cern_sm1}\n\\end{subequations}\n\\subsubsection{Bates $e$C experiment} \nThe polarization asymmetry of the electron elastic scattering\noff the ${}^{12}$C target was measured at Bates \\cite{bates}.\nThe experiment constrains the combination\n\\begin{subequations}\n\\begin{eqnarray}\nA_{\\rm Bates} &=& C_{1u}+C_{1d} \\\\\n\t&=& - 0.1520 - 0.0023\\,{\\it \\Delta S} \n\t+ 0.0004\\,{\\it \\Delta T} \n\t+ \\Delta C_{1u} + \\Delta C_{1d}, \n\\end{eqnarray}\n\\end{subequations}\nwhere the theoretical prediction~\\cite{chm} is evaluated \nat $\\langle Q^2 \\rangle = $ 0.0225 GeV$^2$.\n\\subsubsection{Mainz $e$Be experiment }\n\\label{section_mainz}\nThe polarization asymmetry of electron quasi-elastic scattering\noff the ${}^9$Be target was measured at Mainz \\cite{mainz}.\nThe data shown in Table~\\ref{table:low_energy} is for \nthe combination\n\\begin{subequations}\n\\begin{eqnarray}\nA_{\\rm Mainz} &=& -2.73 C_{1u} + 0.65 C_{1d} - 2.19 C_{2u} \n\t+ 2.03 C_{2d} \n\\\\\n\t&=& -0.876 + 0.043\\Delta S - 0.035\\Delta T\n\t\\nonumber \\\\\n\t&&~~~\n\t-2.73 \\Delta C_{1u} + 0.65 \\Delta C_{1d} \n\t- 2.19 \\Delta C_{2u} + 2.03 \\Delta C_{2d}, \n\\end{eqnarray}\n\\end{subequations}\nwhere the theoretical prediction~\\cite{chm} is evaluated \nat $\\langle Q^2 \\rangle = $ 0.2025 GeV$^2$. \n\\subsubsection{Atomic Parity Violation}\n\\label{section_apv}\nThe experimental results of parity violation in the atom \nare often given in terms of the weak charge $Q^{}_W(A,Z)$\nof nuclei. By using the model-independent parameter $C_{1q}^{}$,\nthe weak charge of a nuclei can be expressed as \n\\begin{equation}\nQ^{}_W(A,Z)=2ZC_{1p}^{}+2(A-Z)C_{1n}^{}. \n\\end{equation}\nBy taking account of the long-distance photonic \ncorrection~\\cite{apv_photonic}, \nwe find $C_{1p}$ and $C_{1n}$ as \n\\begin{subequations}\n\\begin{eqnarray}\nC_{1p} &=& \\hphantom{-} 0.03601 -0.00681 \\Delta S + 0.00477\\,\\Delta T \n\t+ 2\\Delta C_{1u} + \\Delta C_{1d}, \\\\\nC_{1n} &=& -0.49376 - 0.00366\\,{\\it \\Delta T} \n\t+ \\Delta C_{1u} + 2\\Delta C_{1d}. \n\\end{eqnarray}\n\\end{subequations}\nThe data for cesium atom $^{133}_{55}Cs$~\\cite{noecker,wood} \nis given in Table~\\ref{table:low_energy} and \nthe theoretical prediction of the weak charge \nis found to be~\\cite{chm} \n\\begin{equation}\nQ_{W}(^{133}_{55}Cs) = -73.07 -0.749\\,{\\it \\Delta S}\n\t- 0.046\\,{\\it \\Delta T} \n\t+ 376 \\Delta C_{1u} + 422 \\Delta C_{1d}. \n\\end{equation}\n\\subsubsection{Neutrino-quark scattering} \n\\label{section_nq}\nFor the $\\nu_\\mu$-quark scattering, the experimental results \nup to the year 1988 were summarized in ref.~\\cite{fh} in terms \nof the model-independent parameters $g_L^2, g_R^2, \\delta_L^2, \\delta_R^2$. \nThe most stringent constraint on the result in ref.~\\cite{fh} \nis found for the \nfollowing combination: \n\\begin{eqnarray}\nK_{\\rm FH} &=& g_L^2 + 0.879 g_R^2 -0.010 \\delta_L^2 -0.043 \\delta_R^2.\n\\end{eqnarray}\nMore recent CCFR experiment at Tevatron measured the following \ncombination~\\cite{ccfr}\n\\begin{eqnarray}\nK_{\\rm CCFR} &=& 1.7897 g_L^2 + 1.1479 g_R^2 - 0.0916 \\delta_L^2 \n\t- 0.0782 \\delta_R^2. \n\\end{eqnarray}\nThe data are shown in Table~\\ref{table:low_energy} and the \nSM predictions are calculated from our reduced \namplitudes (\\ref{eq:nutrino_amplitude}) as \nfollows~\\cite{chm,hhkm}\n\\begin{eqnarray}\ng_\\alpha^2 = (g_{L\\alpha}^{\\nu_\\mu u})^2 \n\t+ (g_{L\\alpha}^{\\nu_\\mu d})^2, ~~~ \n\\delta_\\alpha^2 = (g_{L\\alpha}^{\\nu_\\mu u})^2 \n\t- (g_{L\\alpha}^{\\nu_\\mu d})^2, \n\\end{eqnarray}\nfor $\\alpha = L$ and $R$, respectively, where \n\\begin{subequations}\n\\begin{eqnarray}\ng_{LL}^{\\nu_\\mu u} &=& \n\t\\hphantom{-} 0.3468 - 0.0023 \\Delta S + 0.0041 \\Delta T, \n\\\\\ng_{LR}^{\\nu_\\mu u} &=& \n\t-0.1549 - 0.0023 \\Delta S + 0.0004 \\Delta T, \n\\\\\ng_{LL}^{\\nu_\\mu d} &=& \n\t-0.4299 + 0.0012 \\Delta S - 0.0039 \\Delta T, \n\\\\\ng_{LR}^{\\nu_\\mu d} &=& \n\t\\hphantom{-} 0.0775 + 0.0012 \\Delta S - 0.0002 \\Delta T.\n\\end{eqnarray}\n\\end{subequations}\nThe above predictions are obtained at the momentum transfer \n$\\langle Q^2 \\rangle = 35~{\\rm GeV}^2$ relevant for the \nCCFR experiment~\\cite{ccfr}. \nThe estimations are found to be valid~\\cite{chm} also \nfor the data of ref.~\\cite{fh}, whose typical scale is \n$\\langle Q^2 \\rangle = 20~{\\rm GeV}^2$. \n\n\\subsubsection{Neutrino-electron scattering} \n\\label{section_ne}\nThe $\\nu_\\mu$-$e$ scattering experiments measure the neutral \ncurrents in a purely leptonic channel. \nThe combined results~\\cite{chm,charm-II} are given in \nTable~\\ref{table:low_energy}. \nThe theoretical predictions \n\\begin{subequations}\n\\begin{eqnarray}\ng_{LL}^{\\nu_\\mu e} &=& -0.273 + 0.0033 \\Delta S - 0.0042 \\Delta T \n\t+ \\Delta g_{LL}^{\\nu_\\mu e}, \n\\\\\ng_{LR}^{\\nu_\\mu e} &=& \\hphantom{-} 0.233 + 0.0033 \\Delta S - 0.0006 \\Delta T \n\t+ \\Delta g_{LR}^{\\nu_\\mu e}, \n\\end{eqnarray}\n\\end{subequations}\nare evaluated at $\\langle Q^2 \\rangle = 2m_e E_\\nu$ \nwith $E_\\nu = 25.7~{\\rm GeV}$ for the CHARM-II \nexperiment~\\cite{charm-II}. \n\\section{Constraints on $Z'$ bosons from electroweak experiments}\n\\setcounter{equation}{0} \nFollowing the parametrization presented in Sec.~3, \nwe can immediately obtain the constraints on $T_{\\rm new}, \\bar{\\xi}$ \nand $g_E^2\/c^2_\\chi m_{Z_2}^2$ from the data listed in Table~2 \nand Table~\\ref{table:low_energy}. \nSetting $S_{\\rm new} = U_{\\rm new} = 0$, we find that \nthe $Z$-pole measurements constrains $T_{\\rm new}$ and $\\bar{\\xi}$ \nwhile $m_W$ data constrains $T_{\\rm new}$. \nThe contact term $g_E^2\/c^2_\\chi m_{Z_2}^2$ is constrained from the LENC data. \nThe number of the free parameters is, therefore, six: \nthe above three parameters and the SM parameters, \n$m_t^{}, \\alpha_s(m_{Z_1})$ and $\\bar{\\alpha}(m_{Z_1}^2)$. \nThroughout our analysis, we use \n\\begin{subequations}\n\\begin{eqnarray}\nm_t^{} &=& 175.6 \\pm 5.5~{\\rm GeV}~\\cite{mt96}, \\\\\n\\alpha_s (m_{Z_1}^{}) &=& 0.118 \\pm 0.003~\\cite{PDG}, \\\\\n1\/\\bar{\\alpha}(m_{Z_1}^2) &=& 128.75 \\pm 0.09~\\cite{eidelman}, \n\\end{eqnarray}\n\\end{subequations}\nas constraints on the SM parameters. \nThe Higgs mass dependence of the results are parametrized \nby $x_H^{}$ (\\ref{eq:higgs_xh}) \nin the range $77~{\\rm GeV} < m_H^{} ~{\\rlap{\\lower 3.5pt\\hbox{$\\mathchar\\sim$}}\\raise 1pt\\hbox{$<$}}\\, 150~{\\rm GeV}$. \nThe lower bound is obtained at the LEP experiment~\\cite{higgs_direct}. \nThe upper bound is the theoretical limit on the lightest Higgs \nboson mass in any supersymmetric models that accommodate \nperturbative unification of the gauge couplings~\\cite{kane}. \nWe first obtain the constraints from the $Z$-pole experiments \nand $W$ boson mass measurement only, and then obtain \nthose by including the LENC experiments. \n\n\\subsection{Constraints from $Z$-pole and $m_W$ data}\nLet us examine first the constraints from the $Z$-pole and \n$m_W$ data by performing the five-parameter fit for $T_{\\rm new}, \n\\bar{\\xi}, m_t^{}, \\alpha_s(m_{Z_1})$ and $\\bar{\\alpha}(m_{Z_1}^2)$. \nThe results for the $\\chi, \\psi, \\eta$ and $\\nu$ models at \n$\\delta = 0$ are summarized as follows: \n\\def(\\roman{enumi}){(\\roman{enumi})}\n\\def\\roman{enumi}{\\roman{enumi}}\n\\begin{enumerate}\n\\item $\\chi$-model ($\\delta = 0$)\n\\begin{eqnarray}\n\\begin{array}{l}\n\t\\left.\n\t\\begin{array}{lcl}\n\tT_{\\rm new} &=& -0.040 + 0.15x_H^{} \\pm 0.12 \\\\\n\t\\bar{\\xi} &=& \\hphantom{-} 0.00017 - 0.00005x_H^{} \\pm 0.00046\n\t\\end{array}\n\t\\right \\} \\rho_{\\rm corr} = 0.28, \\\\\n\\chi^2_{\\rm min}\/({\\rm d.o.f.}) = (16.5 + 0.7 x_H^{})\/(12), \n\\end{array}\n\\label{eq:const_chi}\n\\end{eqnarray}\n\\item $\\psi$-model ($\\delta = 0$)\n\\begin{eqnarray}\n\\begin{array}{l}\n\t\\left.\n\t\\begin{array}{lcl}\n\tT_{\\rm new} &=& -0.043 + 0.16x_H^{} \\pm 0.11 \\\\\n\t\\bar{\\xi} &=& \\hphantom{-} 0.00019 + 0.00012x_H^{} \\pm 0.00050\n\t\\end{array}\n\t\\right \\} \\rho_{\\rm corr} = 0.20, \\\\\n\\chi^2_{\\rm min}\/({\\rm d.o.f.}) = (16.5 + 0.4 x_H^{})\/(12), \n\\end{array}\n\\label{eq:const_psi}\n\\end{eqnarray}\n\\item $\\eta$-model ($\\delta = 0$)\n\\begin{eqnarray}\n\\begin{array}{l}\n\t\\left.\n\t\\begin{array}{lcl}\n\tT_{\\rm new} &=& -0.053 + 0.14x_H^{} \\pm 0.11 \\\\\n\t\\bar{\\xi} &=& -0.00014 - 0.00062x_H^{} \\pm 0.00108 \n\t\\end{array}\n\t\\right \\} \\rho_{\\rm corr} = 0.09, \\\\\n\\chi^2_{\\rm min}\/({\\rm d.o.f.}) = (16.6 + 0.4 x_H^{})\/(12), \n\\end{array}\n\\label{eq:const_eta}\n\\end{eqnarray}\n\\item $\\nu$-model ($\\delta = 0$)\n\\begin{eqnarray}\n\\begin{array}{l}\n\t\\left.\n\t\\begin{array}{lcl}\n\tT_{\\rm new} &=& -0.042 + 0.15x_H^{} \\pm 0.11 \\\\\n\t\\bar{\\xi} &=& \\hphantom{-} 0.00016 + 0.00007x_H^{} \\pm 0.00042 \n\t\\end{array}\n\t\\right \\} \\rho_{\\rm corr} = 0.23, \\\\\n\\chi^2_{\\rm min}\/({\\rm d.o.f.}) = (16.5 + 0.5 x_H^{})\/(12). \n\\end{array}\n\\label{eq:const_nu}\n\\end{eqnarray}\n\\end{enumerate}\nIn the above four $Z'$ models, the results for $T_{\\rm new}$ \nand $\\bar{\\xi}$ are consistent with zero for $x_H^{} = 0$. \nMoreover, the best fits of $T_{\\rm new}$ in all the $Z'$ models \nare in the unphysical \nregion, $T_{\\rm new} < 0$. \nThe parameter $T_{\\rm new}$ could be positive for the large $x_H^{}$: \nFor example, $x_H^{} = 0.41$ ($m_H^{} = 150~{\\rm GeV}$) makes $T_{\\rm new}$ \nin all the four $Z'$ models positive. \nThe allowed range of the effective mixing angle $\\bar{\\xi}$ is \norder of $10^{-3}$ for the $\\eta$-model and $10^{-4}$ for \nthe other three models in 1-$\\sigma$ level. \nThe $x_H^{}$-dependence of $\\bar{\\xi}$ in the $\\eta$-model is \nlarger than the other three models. \nFor comparison, we show the result for the leptophobic \n$\\eta$-model ($\\delta=1\/3$) \n\\begin{enumerate}\n\\addtocounter{enumi}{4}\n\\item leptophobic $\\eta$-model ($\\delta = 1\/3$)\n\\begin{eqnarray}\n\\begin{array}{l}\n\t\\left.\n\t\\begin{array}{lcl}\n\tT_{\\rm new} &=& -0.049 + 0.15x_H^{} \\pm 0.11 \\\\\n\t\\bar{\\xi} &=& \\hphantom{-} 0.00269 + 0.00026x_H^{} \\pm 0.00309 \n\t\\end{array}\n\t\\right \\} \\rho_{\\rm corr} = 0.03, \\\\\n\\chi^2_{\\rm min}\/({\\rm d.o.f.}) = (15.9 + 0.5 x_H^{})\/(12).\n\\end{array}\n\\label{eq:zpole_leptophobic}\n\\end{eqnarray}\n\\end{enumerate}\nBy comparing the $\\eta$-model with no kinetic mixing ($\\delta = 0$) \nin eq.~(\\ref{eq:const_eta}), we find significantly \nweaker constraint on $\\bar{\\xi}$. \n\\tnew_xi \nIn Fig.~\\ref{allowed_eta}, \nwe show the 1-$\\sigma$ and 90\\% CL allowed region on \nthe $(\\bar{\\xi}, T_{\\rm new})$ plane \nin the $\\eta$-model with $\\delta = 0$ and $1\/3$ for \n$m_H^{} = 100~{\\rm GeV}$. \n\nThe best fit results at $m_H^{} = 100~{\\rm GeV}$ under the \nconstraint $T_{\\rm new} \\geq 0$ are shown in \nTable~2. \nWe can see from Table~2 that \nthere is no noticeable improvement of the fit \nfor the $\\chi,\\psi,\\eta$ and $\\nu$ models at $\\delta = 0$. \nThe $\\chi^2_{\\rm min}$ remains almost the same as that of the SM, \neven though each model has two new free parameters, \n$T_{\\rm new}$ and $\\bar{\\xi}$. \nThe fit slightly improves for the leptophobic $\\eta$-model \n($\\delta = 1\/3$) because of the smaller pull factor \nfor the $R_b$ data. \nThe probability of the fit, 18.7\\% CL, is still less \nthan that of the SM, 26.2\\% CL, because the $\\chi^2_{\\rm min}$ \nreduces only 0.8 despite two additional free parameters. \n\nWe explore the whole range of the parameters, $\\beta_E$ and $\\delta$. \nIn Fig.~\\ref{chisq_distribution}, we show the improvement in \n$\\chi^2_{\\rm min}$ over the SM value, $\\chi^2_{\\rm min}({\\rm SM})\n= 16.9$ (see Table~2): \n\\begin{eqnarray}\n\\Delta \\chi^2 \\equiv \\chi_{\\rm min}^2(\\beta_E, \\delta) \n\t- \\chi^2_{\\rm min}({\\rm SM}), \n\\end{eqnarray}\nwhere $\\chi^2_{\\rm min}(\\beta_E, \\delta)$ is evaluated \nat the specific value of $\\beta_E$ and $\\delta$ for \n$m_H^{} = 100~{\\rm GeV}$.\n\\fig_beta_delta \nAs we seen from Fig.~\\ref{chisq_distribution}, the $\\chi^2_{\\rm min}$ \ndepends very mildly in the whole range of the $\\beta_E$ and $\\delta$ \nplane, except near the leptophobic $\\eta$-model \n($\\beta_E = \\tan^{-1}(\\sqrt{5\/3})$ and $\\delta = 1\/3$)~\\cite{eta_model}. \nEven for the best choice of $\\beta_E$ and $\\delta$, the improvement in \n$\\chi^2_{\\rm min}$ is only 1.5 over the SM. \nBecause each model has two additional parameters $T_{\\rm new}$ and \n$\\bar{\\xi}$, we can conclude that no $Z'$ model in this framework \nimproves the fit over the SM. \nThe ``$\\times$'' marks plotted in Fig.~\\ref{chisq_distribution} show \nthe specific models which we will discuss in the next section. \n\n\\subsection{Constraints from $Z$-pole + $m_W$ + LENC data}\nNext we find constraints on the contact term $g_E^2\/c^2_\\chi m_{Z_2}^2$ \nby including the low-energy data in addition to the \n$Z$-pole and $m_W$ data.\nBecause $T_{\\rm new}$ and $\\bar{\\xi}$ are already constrained \nseverely by the $Z$-pole and $m_W$ data, only the contact \nterms proportional to $g_E^2\/c_\\chi^2 m_{Z_2}^2$ \ncontribute to \nthe low-energy observables, except for the special case \nof the leptophobic $\\eta$-model ($\\delta = 1\/3$). \n\nWe summarize the results of the six-parameter \nfit for the $\\psi, \\chi, \\eta$ and $\\nu$ models:\n\\begin{flushleft}\n(i) $\\chi$-model\n\\begin{subequations}\n\\begin{eqnarray}\n& & \\!\\!\\!\\!\\left. \\!\\!\n\\begin{array}{r c l c l c l }\nT_{\\rm new} &\\!\\!=\\!\\!&\\!\\! -0.063\\!\\!&\\!\\!+\\!\\!&\\!\\! 0.14 x^{}_H\\!\\!\n&\\!\\!\\pm\\!\\!&\\!\\!0.11\\!\\!\\!\\! \\\\\n\\bar{\\xi} &\\!\\!=\\!\\!&\\!\\! -0.00005\\!\\!&\\!\\!-\\!\\!&\\!\\!0.00006x^{}_H\\!\\!\n&\\!\\!\\pm\\!\\!&\\!\\! 0.00044\\!\\!\\! \\\\\ng^2_E\/c^2_{\\chi}m^2_{Z_2} &\\!\\!=\\!\\!& \n\\makebox[3.3mm]{}\\!\\!0.26\\!\\!&\\!\\!+\\!\\!&\n\\!\\!0.01 x^{}_H\\!\\! &\\!\\!\\pm\\!\\!&\\!\\!0.21\\!\\!\n\\end{array} \\right\\} \n\\rho_{\\rm corr} \\!=\\! \\left(\n\\begin{array}{rrr}\n\\!\\!1.00\\!&\\!0.25\\!& \\!0.09\\!\\! \\\\\n &\\!1.00\\!& \\!0.15\\! \\\\\n & & \\!1.00\\!\n\\end{array} \\right)\\!\\!,\\makebox[6mm]{ } \n \\\\ \\!\\!\\!\\!\\!&&\\chi^2_{\\rm min}\/ ({\\rm d.o.f.}) \n= (19.9 + 0.9 x^{}_H)\/(20), \n\\end{eqnarray}\n\\end{subequations}\n\\end{flushleft}\n\\begin{flushleft}\n(ii) $\\psi$-model\n\\begin{subequations}\n\\begin{eqnarray}\n& & \\!\\!\\!\\!\\left. \\!\\!\n\\begin{array}{r c l c l c l }\nT_{\\rm new} &\\!\\!=\\!\\!&\\!\\! -0.065 \\!\\! &\n\\!\\!+\\!\\!&\\!\\! 0.15 x^{}_H\\!\\!&\\!\\!\\pm\\!\\!&\\!\\!0.11\\!\\! \\\\\n\\bar{\\xi} &\\!\\!=\\!\\!&\\!\\! -0.00014\\!\\! &\n\\!\\!+\\!\\!&\\!\\! 0.00012x^{}_H\\!\\!&\\!\\!\\pm\\!\\!&\\!\\!0.00050\\!\\!\\! \\\\\ng^2_E\/c^2_{\\chi}m^2_{Z_2} &\\!\\!=\\!\\!&\\!\\! \\makebox[3.3mm]{}1.66\\!\\!&\n\\!\\!+\\!\\!&\\!\\!0.19 x^{}_H\\!\\!&\\!\\!\\pm\\!\\!&\\!\\! 2.90\\!\\!\n\\end{array} \\right\\} \n\\rho^{}_{\\rm corr} \\!=\\! \\left( \n\\begin{array}{rrr}\n\\!\\!1.00\\! & \\!0.19\\! & \\!0.07\\!\\! \\\\\n & \\!1.00\\! & \\!0.03\\! \\\\\n & & \\!1.00\\! \n\\end{array} \\right)\\!\\!,\\makebox[6mm]{ } \n \\\\ \\!\\!\\!\\!\\!&&\\chi^2_{\\rm min}\/ ({\\rm d.o.f.}) \n\t= (21.1+0.8 x^{}_H)\/(20), \n\\end{eqnarray}\n\\end{subequations}\n\\end{flushleft}\n\\begin{flushleft}\n(iii) $\\eta$-model\n\\begin{subequations}\n\\begin{eqnarray}\n\\hsp{-2.5}& & \\!\\!\\!\\!\\left. \\!\\!\n\\begin{array}{r c l c l c l }\nT_{\\rm new}\\! &\\!\\!\\!=\\!\\!&\\!\\! -0.074\\!\\! &\\!\\!+\\!\\!&\n\\!\\! 0.14 x^{}_H\\!\\!&\\!\\!\\pm\\!\\!&\\!\\!0.11\\!\\!\\! \\\\\n\\bar{\\xi}\\! &\\!\\!\\!=\\!\\!&\\!\\! -0.00038\\!\\! &\\!\\!-\\!\\!& \n\\!\\!0.00063x^{}_H\\!\\!&\\!\\!\\pm\\!\\!&\\!\\!0.00106\\!\\!\\!\\! \\\\\ng^2_E\/c^2_{\\chi}m^2_{Z_2}\\! &\\!\\!=\\!\\!&\\!\\!-0.62\\!\\!&\\!\\!+\\!\\!&\n\\!\\!0.08 x^{}_H\\!\\!&\\!\\!\\pm\\!\\!&\\!\\!0.87\\!\\!\\!\n\\end{array} \\right\\}\\! \n\\rho^{}_{\\rm corr} \\!=\\! \\left( \n\\begin{array}{rrr}\n\\!\\!1.00\\! & \\!0.06\\! & \\!-0.05\\!\\! \\\\\n & \\!\\!1.00\\! & \\!-0.22\\!\\! \\\\\n & & \\!1.00\\! \n\\end{array} \\right)\\!\\!,\\makebox[6mm]{ } \n \\\\ \\!\\!\\!\\!\\!&&\\chi^2_{\\rm min}\/ ({\\rm d.o.f.}) \n\t= (20.8+0.5 x^{}_H)\/(20), \n\\end{eqnarray}\n\\end{subequations}\n\\end{flushleft}\n\\begin{flushleft}\n(iv) $\\nu$-model\n\\begin{subequations}\n\\begin{eqnarray}\n\\hsp{-1.5}& & \\!\\!\\!\\!\\left. \\!\\!\n\\begin{array}{r c l c l c l }\nT_{\\rm new} &\\!\\!=\\!\\!&\\!\\! -0.061\\!\\! &\\!\\!+\\!\\!&\n\\!\\! 0.15 x^{}_H\\!\\!&\\!\\!\\pm\\!\\!&\\!\\!0.11\\!\\! \\\\\n\\bar{\\xi} &\\!\\!=\\!\\!&\\!\\! \\makebox[3.3mm]{} 0.00010\\!\\! &\\!\\!+\\!\\!&\n\\!\\! 0.00006x^{}_H\\!\\!&\\!\\!\\pm\\!\\!&\\!\\!0.00041\\!\\!\\!\\! \\\\\ng^2_E\/c^2_{\\chi}m^2_{Z_2}\\! &\\!\\!=\\!\\!&\\!\\!-0.65\\!\\!&\\!\\!+\\!\\!&\n\\!\\!0.04 x^{}_H\\!\\!&\\!\\!\\pm\\!\\!&\\!\\! 0.54\\!\\!\n\\end{array} \\right\\} \n\\rho^{}_{\\rm corr} \\!=\\! \\left( \n\\begin{array}{rrr}\n\\!\\!1.00\\! & \\!0.21\\! & \\!0.07\\! \\\\\n & \\!1.00\\! & \\!0.03\\! \\\\\n & & \\!1.00\\!\n\\end{array} \\right)\\!\\!,\\makebox[6mm]{ } \n \\\\ \\!\\!\\!\\!\\!&&\\chi^2_{\\rm min}\/ ({\\rm d.o.f.}) \n\t= (20.1+0.8 x^{}_H)\/(20).\n\\end{eqnarray}\n\\end{subequations}\n\\end{flushleft}\nThe contact term $g_E^2\/c^2_\\chi m_{Z_2}^2$ in the $\\psi$ and $\\eta$ models \nis consistent with zero in 1-$\\sigma$ level. \nBoth the best fit and the 1-$\\sigma$ error of \nthe parameters $T_{\\rm new}$ and $\\bar{\\xi}$ in all the \n$Z'$ models are slightly affected \nby including the LENC data: The best fit value of $T_{\\rm new}$ \nin all the $Z'$ models cannot be positive \neven for the $m_H^{} = 150~{\\rm GeV}$ $(x_H^{} = 0.41)$. \nSince the leptophobic $\\eta$-model does not have the contact term, \nthe low-energy data constrain the same parameters $T_{\\rm new}$ \nand $\\bar{\\xi}$. \nAfter taking into account both the high-energy and low-energy \ndata, we find \n\\begin{enumerate}\n\\addtocounter{enumi}{4}\n\\item leptophobic $\\eta$ model ($\\delta = 1\/3$)\n\\begin{eqnarray}\n\\begin{array}{l}\n\\left.\n\\begin{array}{lrl}\nT_{\\rm new} &=& -0.074 + 0.148 x_H^{} \\pm 0.110\\\\ \n\\bar{\\xi} &=& \\hphantom{-} 0.00157 + 0.00019 x_H^{} \\pm 0.00279\n\\end{array}\n\t\\right \\}\\rho_{\\rm corr} = 0.02, \\\\\n\\chi^2_{\\rm min}\/({\\rm d.o.f.}) \n\t= (21.2 + 1.0 x_H^{})\/(21). \n\\end{array}\n\\end{eqnarray}\n\\end{enumerate}\nThe allowed range of $\\bar{\\xi}$ is slightly severe as \ncompared to eq.~(\\ref{eq:zpole_leptophobic}). \n\nThe best fit results for $m_H^{} = 100~{\\rm GeV}$ under the \ncondition $T_{\\rm new} \\geq 0$ are shown in Table~\\ref{table:low_energy}. \nIt is noticed that the best fit values for the weak charge of \ncesium atom $^{133}_{55}Cs$ in the $\\chi, \\eta$ and $\\nu$ models \nare quite close to the experimental data. \nThese models lead to $\\Delta \\chi^2 = -1.8$ ($\\chi$), \n$-0.8$ $(\\eta)$ and $-1.6$ $(\\nu)$. \nNo other noticeable point is found in the table. \n\\mass_lenc \n\nThe above constraints on $g_E^2\/c^2_\\chi m_{Z_2}^2$ from the LENC data give \nthe lower mass bound of the heavier mass eigenstate $Z_2$ \nin the $Z'$ models except for the leptophobic $\\eta$-model. \nIn Fig.~\\ref{mass_distribution}, the contour plot of \nthe 95\\% CL lower mass limit of $Z_2$ boson from the \nLENC experiments \nare shown on the $(\\beta_E, \\delta)$ plane by setting \n$g_E = g_Y$ and $m_H^{} = 100~{\\rm GeV}$ \nunder the condition $m_{Z_2} \\geq 0$. \nIn practice, we obtain the 95\\% CL lower limit of \nthe $Z_2$ boson mass $m_{95}$ in the following way: \n\\begin{eqnarray}\n0.05 =\n \\frac{\n\\int^\\infty_{m_{95}} dm_{Z_2} P(m_{Z_2})}\n{\\int^\\infty_{0} dm_{Z_2} P(m_{Z_2})}, \n\\end{eqnarray}\nwhere we assume that the probability density function \n$P(m_{Z_2})$ is proportional to ${\\rm exp}(-\\chi^2(m_{Z_2})\/2)$. \n\nWe can read off from Fig.~\\ref{mass_distribution} that \nthe lower mass bound of the $Z_2$ boson in the $\\psi$ model \nat $\\delta = 0$ is much weaker than those of the other $Z'$ \nmodels. \nIt has been pointed out that the most stringent \nconstraint on the contact term is the APV measurement \nof cesium atom~\\cite{chm}. \nSince all the SM matter fields in the $\\psi$ model \nhave the same ${\\rm U(1)'}$ charge (see Table~1), \nthe couplings of contact interactions are Parity conserving, \nwhich makes constraint from the APV measurement useless. \nWe also find in Fig.~\\ref{mass_distribution} that the lower mass \nbound of the $Z_2$ boson disappears near the leptophobic $\\eta$-model \n($\\beta_E = \\tan^{-1}(\\sqrt{5\/3})$ and $\\delta = 1\/3$)~\\cite{eta_model}. \n\nWe summarize the 95\\% CL lower bound on $m_{Z_2}$ \nfor the $\\chi,\\psi,\\eta$ and $\\nu$ models ($\\delta = 0$) \nin Table~\\ref{mzelenc}. \nFor comparison, we also show the lower bound of $m_{Z_2}$ \nin the previous study~\\cite{cvetic_langacker_review} \nin the same table. \nThe bounds on the $Z_\\chi$ and $Z_\\nu$ masses \nare more severely constrained \nas compared to ref.~\\cite{cvetic_langacker_review}. \nAlthough we used the latest electroweak data, our result \nfor the $Z_\\psi$ boson mass is somewhat weaker than that of \nref.~\\cite{cvetic_langacker_review}. \nIn the analysis of ref.~\\cite{cvetic_langacker_review}, \nthe $e^+e^- \\rightarrow \\mu^+\\mu^-, \n\\tau^+\\tau^-$ data below the $Z$ pole \nare also used besides the $Z$-pole, $m_W$ and the LENC data. \nAs we mentioned before, the lower mass bound of the $Z'$ boson \nis obtained from the LENC data, not from the $Z$-pole data. \nBecause the APV measurement which is most stringent constraint \nin the LENC data does not well constrain the $\\psi$ model, \nit is expected that the $e^+ e^-$ annihilation data below \nthe $Z$-pole play an important role to obtain the bound of \n$Z_\\psi$ boson mass. \n\\bound_lenc \n\nOur results in Table~\\ref{mzelenc} are also slightly weaker than \nthose in ref.~\\cite{chm}. \nThe results in ref.~\\cite{chm} have been obtained \nwithout including the $Z$-$Z'$ mixing effects and by setting \n$m_t^{} = 175~{\\rm GeV}, m_H^{} = 100~{\\rm GeV}, x_\\alpha^{} = 0$ \nand $T_{\\rm new} = 0$. \n\n\\subsection{Lower mass bound of $Z_2$ boson}\nWe have found that the $Z$-pole, $m_W$ and the LENC data \nconstrain ($T_{\\rm new}, \\bar{\\xi}$), $T_{\\rm new}$ and $g_E^2\/c^2_\\chi m_{Z_2}^2$, \nrespectively. \nWe can see from eq.~(\\ref{eq:zeta}) that, for a given $\\zeta$, \nconstraints on $T_{\\rm new}, \\bar{\\xi}$ and $g_E^2\/c^2_\\chi m_{Z_2}^2$ can be \ninterpreted as the bound on $m_{Z_2}$. \nWe show the 95\\% CL lower mass bound of the $Z_2$ boson \nfor $m_H^{} = 100~{\\rm GeV}$ \nin four $Z'$ models as a function of $\\zeta$. \nThe bound is again found under the condition $m_{Z_2} \\geq 0$. \nResults are shown in Fig.~\\ref{mass_95cl}.(a) $\\sim$ \n\\ref{mass_95cl}.(d) for the $\\chi,\\psi,\\eta,\\nu$ models, respectively. \nThe lower bound from the $Z$-pole and $m_W$ data, and \nthat from the LENC data are separately plotted in the same figure. \nIn order to see the $g_E$-dependence of the $m_{Z_2}$ bound \nexplicitly, we show the lower mass bound for the combination \n$m_{Z_2}g_Y\/g_E$. \nWe can read off from Fig.~\\ref{mass_95cl} that the bound on \n$m_{Z_2}g_Y\/g_E$ is approximately independent of $g_E$ for \n$g_E\/g_Y = 0.5 \\sim 2.0$ in each model. \n\\zprime_mass \nAs we expected from the formulae for \n$T_{\\rm new}$ and $\\bar{\\xi}$ in the small \nmixing limit (eq.~(\\ref{eq:tnew_xibar})), \nthe $Z_2$ mass is unbounded from the $Z$-pole data at \n$\\zeta = 0$. \nFor models with very small $\\zeta$, the lower bound on \n$m_{Z_2}$, therefore, comes from the LENC experiments \nand the direct search experiment at Tevatron. \nFor comparison, we plot the 95\\% CL lower bound on \n$m_{Z_2}$ obtained from the direct search experiment~\\cite{direct_search} \nin Fig.~\\ref{mass_95cl}. \nIn the direct search experiment, the $Z'$ decays into the exotic \nparticles, {\\it e.g.}, the decays into the light right-handed neutrinos \nwhich are expected for some models, are not taken into account. \nWe summarize the 95\\% CL lower bound on $m_{Z_2}$ \nfor the $\\chi,\\psi,\\eta$ and $\\nu$ models ($\\delta = 0$) \nobtained from the low-energy data and \nthe direct search experiment~\\cite{direct_search} \nin Table~\\ref{mzelenc}. \n\nThe lower bound of $m_{Z_2}$ is affected by the Higgs boson mass \nthrough the $T$ parameter. \nAs we seen from eqs.~(\\ref{eq:const_chi}) $\\sim$ (\\ref{eq:const_nu}), \n$T_{\\rm new}$ tends to be in the physical region ($T_{\\rm new} \\geq 0$) \nfor large $m_H^{}$ $(x_H^{})$. \nThen, we find that the large Higgs boson mass decreases the lower \nbound of $m_{Z_2}$. \nFor $\\zeta = 1$, \nthe lower $m_{Z_2}$ bound in the $\\chi,\\psi,\\nu$ ($\\eta$) models \nfor $m_H^{} = 150~{\\rm GeV}$ is weaker than that for $m_H^{} = 100~{\\rm GeV}$ \nabout 7\\% (11\\%). \nOn the other hand, the Higgs boson with $m_H^{} = 80~{\\rm GeV}$ \nmakes the lower $m_{Z_2}$ bound in all the $Z'$ models \nsevere about 5\\% as compared to the case for $m_H^{} = 100~{\\rm GeV}$. \nBecause $T_{\\rm new}$ and $\\bar{\\xi}$ are proportional to \n$\\zeta^2$ and $\\zeta$, respectively (see eq.~(\\ref{eq:tnew_xibar})), \nand it is unbounded at $|\\zeta| \\simeq 0$, \nthe lower bound of $m_{Z_2}$ may be independent of $m_H^{}$ \nin the small $|\\zeta|$ region. \nThe $m_H^{}$-dependence of the lower mass bound obtained \nfrom the LENC data is safely negligible. \n\n\nIt should be noted that, at $\\zeta = 0$, \nonly the leptophobic $\\eta$-model ($\\delta = 1\/3$) is \nnot constrained from both the $Z$-pole and the low-energy \ndata. \nThe precise analysis and discussion for the lower mass bound \nof the $Z_2$ boson in the leptophobic $\\eta$-model can be \nfound in ref.~\\cite{uch}. \nIt is shown in ref.~\\cite{uch} that \nthe $\\zeta$-dependence of the lower mass bound is slightly \nmilder than that of the $\\eta$-model with $\\delta = 0$ \nin Fig.~\\ref{mass_95cl}.(c). \n\nIt has been discussed that the presence of $Z_2$ boson \nwhose mass is much heavier than the SM $Z$ boson mass, \nsay 1 TeV, may lead to a find-tuning problem to stabilize \nthe electroweak scale against the ${\\rm U(1)'}$ scale~\\cite{drees}. \nThe $Z_2$ boson with $m_{Z_2} \\leq 1~{\\rm TeV}$ for $g_E = g_Y$ \nis allowed by the electroweak data only if $\\zeta$ satisfies \nthe following condition: \n\\begin{eqnarray} \n\\begin{array}{ll}\n-0.6 ~{\\rlap{\\lower 3.5pt\\hbox{$\\mathchar\\sim$}}\\raise 1pt\\hbox{$<$}}\\, \\zeta ~{\\rlap{\\lower 3.5pt\\hbox{$\\mathchar\\sim$}}\\raise 1pt\\hbox{$<$}}\\, +0.3 & ~~{\\rm for~the}~\\chi,\\psi,\\nu~{\\rm models}, \n\\\\\n-0.7 ~{\\rlap{\\lower 3.5pt\\hbox{$\\mathchar\\sim$}}\\raise 1pt\\hbox{$<$}}\\, \\zeta ~{\\rlap{\\lower 3.5pt\\hbox{$\\mathchar\\sim$}}\\raise 1pt\\hbox{$<$}}\\, +0.6 & ~~{\\rm for~the}~\\eta~{\\rm model}. \n\\end{array}\n\\label{eq:zeta_condition}\n\\end{eqnarray}\nIn principle, the parameter $\\zeta$ is calculable, together \nwith the gauge coupling $g_E$, once the particle spectrum \nof the $E_6$ model is specified. \nIn the next section, we calculate the $\\zeta$ parameter in \nseveral $E_6$ $Z'$ models. \n\n\n\\section{Light $Z'$ boson in minimal SUSY $E_6$-models}\n\\setcounter{equation}{0}\nIt is known that the gauge couplings are not unified \nin the $E_6$ models with three generations of {\\bf 27}. \nIn order to guarantee the gauge coupling unification, \na pair of weak-doublets, $H'$ and $\\overline{H'}$, \nshould be added into \nthe particle spectrum at the electroweak scale~\\cite{dienes}. \nThey could be taken from ${\\bf 27} + {\\bf \\overline{27}}$ \nor the adjoint representation {\\bf 78}. \nThe ${\\rm U(1)'}$ charges of the additional weak doublets \nshould have the same magnitude and opposite sign, $a$ and $-a$, \nto cancel the ${\\rm U(1)'}$ anomaly. \nIn addition, a pair of the complete SU(5) multiplet such as \n${\\bf 5 + \\overline{5}}$ can be added without spoiling the unification \nof the gauge couplings~\\cite{eta_model, dienes}. \n\nThe minimal $E_6$ model which have three generations of {\\bf 27} \nand a pair ${\\bf 2} + {\\bf \\overline{2}}$ depends in principle on the \nthree cases; $H'$ has the same quantum number as $L$ or $H_d$ of \n{\\bf 27}, or $\\overline{H_u}$ of ${\\bf\\overline{27}}$. \nAll three cases will be studied below. \n\\e6_extra \nThe hypercharge and ${\\rm U(1)'}$ charge of \nthe extra weak doublets for the $\\chi,\\psi,\\eta,\\nu$ models \nare listed in Table~\\ref{table:extra_higgs}. \nFor comparison, we also show those in the model of Babu \n{\\it et al.~}~\\cite{eta_model}, where two pairs of \n${\\bf 2 + \\overline{2}}$ from {\\bf 78} and a pair of ${\\bf 3 + \\overline{3}}$ \nfrom ${\\bf 27 + \\overline{27}}$ are introduced \nto achieve the quasi-leptophobity at the weak scale. \n\nLet us recall the definition of $\\zeta$; \n\\begin{equation}\n\\zeta = \\frac{g_Z}{g_E}\\frac{m_{ZZ'}^2}{m_Z^2} - \\delta. \n\\label{eq:zetadef}\n\\end{equation}\nIn the minimal model, \nthe following eight scalar-doublets can develop VEV to \ngive the mass terms $m_Z^2$ and $m_{ZZ'}^2$ in eq.~(\\ref{eq:l_gauge}): \nthree generations of $H_u, H_d$, \nand an extra pair, $H'$ and $\\overline{H'}$. \nThen, $m_Z^2$ and $m_{ZZ'}^2$ are written in terms of \ntheir VEVs as \n\\begin{subequations}\n\\begin{eqnarray}\nm_Z^2 &=& \\frac{1}{2} g_Z^2\\biggl[ \n\t \\sum_{i=1}^3 \\biggl\\{ \n\t\\langle H_u^i \\rangle^2 + \\langle H_d^i \\rangle^2 \n\t\\biggr\\} + \\langle H' \\rangle^2 \n\t+ \\langle \\overline{H'} \\rangle^2 \\biggr], \n\\\\\nm_{ZZ'}^2 &=& \\! g_Z g_E \\biggl[ \n\t\\sum_{i=1}^{3} \\biggl\\{ \n\t-Q_E^{H_u} \\langle H_u^i \\rangle^2 \n\t+ Q_E^{H_d} \\langle H_d^i \\rangle^2 \n\t\\biggr \\}\n\t+ Q_E^{H'} \\langle H' \\rangle^2 \n\t- Q_E^{\\overline{H'}} \\langle \\overline{H'} \\rangle^2 \n\t\\biggr]\\!, \n\\end{eqnarray}\n\\end{subequations}\nwhere $i$ is the generation index. \nThe third component of the weak isospin $I_3$ for the Higgs \nfields are\n\\begin{equation}\nI_3(H_d) = I_3(H') = -I_3(H_u) = -I_3(\\overline{H'}) = 1\/2. \n\\end{equation}\nTaking account of the ${\\rm U(1)'}$ charges of \nthe extra Higgs doublets, $Q_E^{H'} = - Q_E^{\\overline{H'}}$, \nwe find from eq.~(\\ref{eq:zetadef}) \n\\begin{eqnarray}\n\\zeta &=& 2 \\frac{\\displaystyle{ \\sum_{i=1}^3 }\\biggl\\{ \n\t-Q_E^{H_u} \\langle H_u^i \\rangle^2 \n\t+Q_E^{H_d} \\langle H_d^i \\rangle^2 \n\t\\biggr \\}\n\t+ Q_E^{H'} \\biggl( \\langle H' \\rangle^2 \n\t+ \\langle \\overline{H'}\\rangle^2 \\biggr) }\n\t{\\displaystyle{ \\sum_{i=1}^3 }\\biggl\\{ \n\t\\langle H_u^i \\rangle^2 + \\langle H_d^i \\rangle^2 \n\t\\biggr\\}\n\t+ \\langle H'\\rangle^2 + \\langle \\overline{H'} \\rangle^2 } \n\t- \\delta. \n\\label{eq:zeta_final}\n\\end{eqnarray}\nWe note here that the observed $\\mu$-decay constant \nleads to the following sum rule \n\\begin{eqnarray}\nv_u^2 + v_d^2 + v_{H'}^2 + v_{\\overline{H'}}^2 \\equiv \nv^2 = \\frac{1}{\\sqrt{2}G_F} \\approx (246~{\\rm GeV})^2, \n\\end{eqnarray}\nwhere\n\\begin{eqnarray}\n\\left.\n\\begin{array}{rr}\n\t\\displaystyle{\\sum_{i=1}^3\\langle H_u^i \\rangle^2 = \\frac{v_u^2}{2},}\n\t&\n\t\\displaystyle{\\sum_{i=1}^3\\langle H_d^i \\rangle^2 = \\frac{v_d^2}{2}, }\n\t\\\\\n\t\\displaystyle{\\langle H' \\rangle^2 = \\frac{v_{H'}^2}{2}, }\n\t&\n\t\\displaystyle{\\langle \\overline{H'} \\rangle^2 = \\frac{v_{\\overline{H'}}^2}{2}}. \n\\end{array}\n\\right.\n\\end{eqnarray}\nBy further introducing the notation\n\\begin{subequations}\n\\begin{eqnarray}\n\\tan \\beta &=& \\frac{v_u}{v_d}, \n\\\\\nx^2 &=& \\frac{v_{H'}^2 + v_{\\overline{H'}}^2}{v^2}, \n\\end{eqnarray}\n\\end{subequations}\nwe can express eq.~(\\ref{eq:zeta_final}) as \n\\begin{eqnarray}\n\\zeta &=& 2 \\biggl\\{\n\t-Q_E^{H_u}(1-x^2)\\sin^2\\beta +Q_E^{H_d}(1-x^2)\\cos^2\\beta \n\t+Q_E^{H'}x^2\n\t\\biggr\\} \n\t- \\delta. \n\\end{eqnarray}\nBecause $H'$ and $\\overline{H'}$ are taken from {\\bf 27} + \n{$\\bf\\overline{27}$}, the ${\\rm U(1)'}$ charge of \n$H'$, $Q_E^{H'}$, is identified with that of $L$, \n$H_d$ or $\\overline{H_u}$. \n\nAmong all the models, only in the $\\chi$-model one can have \nsmaller number of matter particles. \nIn the $\\chi$-model, three generations of the matter fields \n{\\bf 16} and a pair of Higgs doublets make the model \nanomaly free. \nIn this case, $\\zeta$ is found to be independent of $\\tan\\beta$: \n\\begin{subequations}\n\\begin{eqnarray}\n\\zeta &=& 2 \\frac{ Q_E^{H_d} - Q_E^{H_u} \\tan^2 \\beta }\n\t{1 + \\tan^2 \\beta}\n\t- \\delta \n\\\\\n\t&=& 2 Q_E^{H_d} - \\delta. \n\\end{eqnarray}\n\\end{subequations}\n\n\\coeff_rge\nLet us now examine the kinetic mixing parameter $\\delta$ in each model. \nThe boundary condition of $\\delta$ at the GUT scale is $\\delta = 0$. \nThe non-zero kinetic mixing term can arise at low-energy \nscale through the following RGEs: \n\\begin{subequations}\n\\begin{eqnarray}\n\\frac{d}{dt} \\alpha_i &=& \\frac{1}{2\\pi}b_i \\alpha_i^2, \n\\label{eq:rgea}\n\\\\\n\\frac{d}{dt} \\alpha_4 &=& \\frac{1}{2\\pi}\n( b_E + 2 b_{1E} \\delta + b_1 \\delta^2 ) \\alpha_4^2 ,\n\\label{eq:rgeb}\\\\\n\\frac{d}{dt} \\delta &=& \\frac{1}{2\\pi}\n( b_{1E} + b_1 \\delta ) \\alpha_1, \n\\label{eq:rgec}\n\\end{eqnarray}\n\\label{eq:rge}\n\\end{subequations}\nwhere $i=1,2,3$ and $t=\\ln \\mu$. \nWe define $\\alpha_1$ and $\\alpha_4$ as \n\\begin{eqnarray}\n\\alpha_1 \\equiv \\frac{5}{3}\\frac{g_Y^2}{4\\pi}, \n~~~~~~~\n\\alpha_4 \\equiv \\frac{5}{3}\\frac{g_E^2}{4\\pi}. \n\\end{eqnarray}\nThe coefficients of the $\\beta$-functions for $\\alpha_1, \n\\alpha_4$ and $\\delta$ are: \n\\begin{eqnarray}\nb_1 = \\frac{3}{5} {\\rm Tr} (Y^2), \n~~~~\nb_E = \\frac{3}{5} {\\rm Tr} (Q_E^2), \n~~~~\nb_{1E} = \\frac{3}{5} {\\rm Tr} (Y Q_E). \n\\end{eqnarray}\nFrom eq.~(\\ref{eq:rgec}), we can clearly see that the non-zero $\\delta$ \nis generated at the weak scale if $b_{1E} \\neq 0$ holds. \nIn Table~\\ref{table:coeff_rge}, we list $b_1, b_E$ and $b_{1E}$ \nin the minimal $\\chi,\\psi,\\eta,\\nu$ models and the $\\eta_{\\rm BKM}$ \nmodel~\\cite{eta_model}. \nAs explained above, the $\\chi(16)$ model has three generations \nof {\\bf 16}, and the $\\chi(27)$ model has three generations of \n{\\bf 27}. \nWe can see from Table~\\ref{table:coeff_rge} that the \nmagnitudes of the differences $b_1 - b_2$ and $b_2 - b_3$ are \ncommon among all the models including the minimal supersymmetric \nSM. \nThis guarantees the gauge coupling unification \nat $\\mu = m_{GUT} \\simeq 10^{16}~{\\rm GeV}$. \n\n\\ge_value \nIt is straightforward to obtain $g_E(m_{Z_1})$ and \n$\\delta(m_{Z_1})$ for each model. \nThe analytical solutions of eqs.~(\\ref{eq:rgea})$\\sim$\n(\\ref{eq:rgec}) are as follows:\n\\begin{subequations}\n\\begin{eqnarray}\n\\frac{1}{\\alpha_i(m_{Z_1})} &=& \\frac{1}{\\alpha_{GUT}} \n\t+ \\frac{1}{2\\pi}b_i \\ln \\frac{m_{GUT}}{m_{Z_1}}, \n\\\\\n\\delta(m_{Z_1}) &=& -\\frac{b_{1E}}{b_1}\\biggl( \n\t1 - \\frac{\\alpha_1(m_{Z_1})}{\\alpha_{GUT}} \\biggr), \n\\\\\n\\frac{1}{\\alpha_4(m_{Z_1})} &=& \\frac{1}{\\alpha_{GUT}} \n\t+ \\biggl\\{ \\frac{b_E}{b_1} - \n\t\\biggl(\\frac{b_{1E}}{b_1} \\biggr)^2 \n\t\\biggr \\} \n\t \\biggl\\{ \\frac{1}{\\alpha_1(m_{Z_1})} - \\frac{1}{\\alpha_{GUT}}\n\t\\biggr \\} \n\\nonumber \\\\\n\t&& \n\t- \\biggl( \\frac{b_{1E}}{b_1} \\biggr)^2 \n\t\\frac{ \\alpha_1(m_{Z_1}) - \\alpha_{GUT}}{\\alpha_{GUT}^2}, \n\\end{eqnarray}\n\\end{subequations}\nwhere $\\alpha_{GUT}$ denotes the unified gauge coupling at \n$\\mu = m_{GUT}$. \nIn our calculation, \n$\\alpha_3(m_{Z_1}) = 0.118$ and \n$\\alpha(m_{Z_1}) = e^2(m_{Z_1})\/4\\pi = 1\/128$ \nare used as example. \nThese numbers give $g_Y(m_{Z_1}) = 0.357$. \nWe summarize the predictions for $g_E$ \nand $\\delta$ at $\\mu = m_{Z_1}$ in the \nminimal $E_6$ models and the $\\eta_{\\rm BKM}$ model \nin Table~\\ref{table:ge_delta}. \nIn all the minimal models, the ratio $g_E\/g_Y$ is approximately \nunity and $|\\delta|$ is smaller than about 0.07. \nOn the other hand, the $\\eta_{\\rm BKM}$ model \npredicts \n\\tnb_zeta\n$g_E\/g_Y \\sim 0.86$ and $\\delta \\sim 0.29$, which is close \nto the leptophobic-$\\eta$ model at $\\delta = 1\/3$. \nIn Figs.~\\ref{chisq_distribution} and \\ref{mass_distribution}, \nwe show the predictions of all the models by ``$\\times$'' symbol.\n\nNext, we estimate the parameter $\\zeta$ for several \nsets of $\\tan\\beta$ and $x$. \nIn Table~\\ref{table:zetasummary}, we show the predictions for \n$\\zeta$ in the minimal $\\chi,\\psi,\\eta,\\nu$ models and the \n$\\eta_{\\rm BKM}$ model. \nThe results are shown for $\\tan\\beta = 2$ and $30$, and \n$x^2 = 0$ and $0.5$. \nWe find from the table that the parameter $\\zeta$ is \nin the range $|\\zeta| ~{\\rlap{\\lower 3.5pt\\hbox{$\\mathchar\\sim$}}\\raise 1pt\\hbox{$<$}}\\, 1.35$ for all the models \nexcept for the $\\eta_{\\rm BKM}$ model, where the \npredicted $\\zeta$ lies between $-2.0$ and $-1.2$. \nIt is shown in Fig.~\\ref{mass_95cl}\nthat $m_{Z_2}g_Y\/g_E$ is approximately \nindependent of $g_E\/g_Y$. \nActually, we find in Table~\\ref{table:ge_delta} and \nTable~\\ref{table:zetasummary} that \nthe predicted $|\\delta|$ is smaller than about 0.1 \nand $g_E\/g_Y$ is quite close to unity in all the minimal models. \nWe can, therefore, read off from Fig.~\\ref{mass_95cl} \nthe lower bound of $m_{Z_2}$ in the minimal models at $g_E = g_Y$. \nIn Table~\\ref{table:mass95_zeta}, we summarize the 95\\% CL lower \n$m_{Z_2}$ bound for the minimal $\\chi,\\psi,\\eta,\\nu$ models and \nthe $\\eta_{\\rm BKM}$ model which correspond to the predicted $\\zeta$ \nin Table~\\ref{table:zetasummary}. \n\\masszeta\nMost of the lower mass bounds in Table~\\ref{table:mass95_zeta} \nexceed 1 TeV. \nThe $Z_2$ boson with $m_{Z_2} \\sim O(1~{\\rm TeV})$ should be \nexplored at the future collider such as LHC. \nThe discovery limit of the $Z'$ boson in the $E_6$ models at LHC \nis expected as~\\cite{cvetic_bound}\n\\begin{eqnarray}\n\\begin{array}{cccc}\\hline \n\\chi & \\psi & \\eta & \\nu \\\\ \\hline \n3040 & 2910 & 2980 & *** \\\\ \\hline \n\\end{array}\n\\end{eqnarray}\nAll the lower bounds of $m_{Z_2}$ listed in \nTable~\\ref{table:mass95_zeta} are smaller than 2 TeV \nand they are, therefore, in the detectable range of LHC. \nBut, it should be noticed that most of them \n($1~{\\rm TeV} ~{\\rlap{\\lower 3.5pt\\hbox{$\\mathchar\\sim$}}\\raise 1pt\\hbox{$<$}}\\, m_{Z_2}$) may require the fine-tuning to stabilize \nthe electroweak scale against the ${\\rm U(1)'}$ scale~\\cite{drees}. \n\nThe lower bound of the $Z_2$ boson mass in the $\\eta_{\\rm BKM}$ \nmodel for the predicted $\\zeta$ can be read off from Fig.~2 in \nref.~\\cite{uch}. \nBecause somewhat large $\\zeta$ is predicted in the $\\eta_{\\rm BKM}$ \nmodel, $1 ~{\\rlap{\\lower 3.5pt\\hbox{$\\mathchar\\sim$}}\\raise 1pt\\hbox{$<$}}\\, |\\zeta|$, the lower mass bound is also large \nas compared to the minimal models. \n\n\\section{Summary}\nWe have studied constraints on $Z'$ bosons in the SUSY \n$E_6$ models. \nFour $Z'$ models --- the $\\chi,\\psi,\\eta$ and $\\nu$ models \nare studied in detail. \nThe presence of the $Z'$ boson affects the electroweak processes \nthrough the effective $Z$-$Z'$ mass mixing angle $\\bar{\\xi}$, \na tree level contribution $T_{\\rm new}$ which is a positive \ndefinite quantity, and the contact term $g_E^2\/c^2_\\chi m_{Z_2}^2$. \nThe $Z$-pole, $m_W$ and LENC data constrain ($T_{\\rm new}, \n\\bar{\\xi}$), $T_{\\rm new}$ and $g_E^2\/c^2_\\chi m_{Z_2}^2$, respectively. \nThe convenient parametrization of the electroweak \nobservables in the SM and the $Z'$ models are presented. \nFrom the updated electroweak data, we find that the $Z'$ models \nnever give the significant improvement of the $\\chi^2$-fit \neven if the kinetic mixing is taken into accounted. \nThe 95\\% CL lower mass bound of the heavier mass eigenstate $Z_2$ \nis given as a function of the effective $Z$-$Z'$ mixing \nparameter $\\zeta$. \nThe approximate scaling low is found for the $g_E\/g_Y$-dependence \nof the lower limit of $m_{Z_2}$. \nBy assuming the minimal particle content of the $E_6$ model, \nwe have found the theoretical predictions for $\\zeta$. \nWe have shown that the $E_6$ models \nwith minimal particle content which is consistent with \nthe gauge coupling unification predict the non-zero kinetic mixing \nterm $\\delta$ and the effective mixing parameter $\\zeta$ of \norder one. \nThe present electroweak experiments lead to the lower \nmass bound of order 1 TeV or larger for those models. \n\n\\section*{Acknowledgment}\nThis work is supported in part by Grant-in-Aid for Scientific \nResearch from the Ministry of Education, Science and Culture of Japan.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{s1}\n\nIn this paper, we will study the loop quantum cosmology (LQC)\n\\cite{aa-rev,mbrev} of the Bianchi type II model. These models are\nof special interest to the issue of singularity resolution\nbecause of the intuition derived from the body of results related to\nthe Belinksii, Khalatnikov, Lifshitz (BKL) conjecture\n\\cite{bkl1,bkl2} on the nature of generic, spacelike singularities\nin general relativity (see, e.g., \\cite{bb}). Specifically, as the\nsystem enters the Planck regime, dynamics at any fixed spatial point\nis expected to be well described by the Bianchi I evolution.\nHowever, there are transitions in which the parameters\ncharacterizing the specific Bianchi I space-time change and the\ndynamics of these transitions mimics the Bianchi II time evolution.\nIn a recent paper \\cite{awe2}, we studied the Bianchi I model in the\ncontext of LQC. In this paper we will extend that analysis to the\nBianchi II model. We will follow the same general approach and use\nthe same notation, emphasizing only those points at which the\npresent analysis differs from that of \\cite{awe2}.\n\nBianchi I and II models are special cases of type A Bianchi models\nwhich were analyzed already in the early days of LQC (see in\nparticular \\cite{mb-hom, bdv}). However, as is often the case with\npioneering early works, these papers overlooked some important\nconceptual and technical issues. At the classical level,\ndifficulties faced by the Hamiltonian (and Lagrangian) frameworks in\nnon-compact, homogeneous space-times went unnoticed. In these cases,\nto avoid infinities, it is necessary to introduce an elementary cell\nand restrict all integrals to it \\cite{as,abl}. The Hamiltonian\nframeworks in the early works did not carry out this step. Rather,\nthey were constructed simply by dropping an infinite volume integral\n(a procedure that introduces subtle inconsistencies). In the quantum\ntheory, the kinematical quantum states were assumed to be periodic\n---rather than almost-periodic--- in the connection, and the quantum\nHamiltonian constraint was constructed using a ``pre-$\\mu_o$''\nscheme. Developments over the intervening years have shown that\nthese strategies have severe limitations (see, e.g.,\n\\cite{aps3,acs,cs1,aa-badhonef,cs2}). In this paper, they will be\novercome using ideas and techniques that have been introduced in the\nisotropic and Bianchi I models in these intervening years. Thus, as\nin \\cite{awe2} the classical Hamiltonian framework will be based on\na fiducial cell, quantum kinematics will be constructed using almost\nperiodic functions of connections and quantum dynamics will use the\n``$\\bar\\mu$ scheme.'' Nonetheless, the space-time description of\nBianchi II models in \\cite{mb-hom, bdv}, tailored to LQC, will\nprovide the point of departure of our analysis.\n\nNew elements required in this extension from the Bianchi I model\ncan be summarized as follows. Recall first that the spatially\nhomogeneous slices $M$ in Bianchi models are isomorphic to\n3-dimensional group manifolds. The Bianchi I group is the\n3-dimensional group of translations. Hence the the three Killing\nvectors ${}^o\\xi^a_i$ on $M$ ---the left invariant vector fields on\nthe group manifold--- commute and coincide with the right\ninvariant vector fields ${}^o\\!e^a_i$ which constitute the fiducial\northonormal triads on $M$. In LQC one mimics the strategy used in\nLQG and spin foams and defines the curvature operator in terms of\nholonomies around plaquettes whose edges are tangential to these\nvector fields. The Bianchi II group, on the other hand, is\ngenerated by the two translations and the rotation on a null\n2-plane. Now the Killing vectors ${}^o\\xi^a_i$ no longer commute and\nneither do the fiducial triads ${}^o\\!e^a_i$. Therefore we have to\nfollow another strategy to build the elementary plaquettes.\nHowever, this situation was already encountered in the\nk=$1$, isotropic models \\cite{warsaw,apsv}. There, the desired\nplaquettes can be obtained by alternating between the integral\ncurves of right and left invariant vector fields which do commute.\nHowever, in the isotropic case, the gravitational connection is\ngiven by $A_a^i = c\\,\\, {}^o\\!\\omega_a^i$, where ${}^o\\!\\omega_a^i$ are the covectors\ndual to ${}^o\\!e^a_i$ and the holonomies around these plaquettes turned\nout to be almost periodic functions of the connection component\n$c$ \\cite{warsaw,apsv}. By contrast, in the Bianchi II model we\nhave three connection components $c^i$ because of the presence of\nanisotropies, and, unfortunately, the holonomies around our\nplaquettes are no longer almost periodic functions of $c^i$. (This\nis also the case in more complicated Bianchi models.) Since the\nstandard kinematical Hilbert space of LQC consists of almost\nperiodic functions of $c^i$, these holonomy operators are not\nwell-defined on this Hilbert space. Thus, the strategy \\cite{abl}\nused so far in LQC to define the curvature operator is no longer\nviable.\n\nOne could simply enlarge the kinematical Hilbert space to\naccommodate the new holonomy functions of connections. But then the\nproblem quickly becomes as complicated as full LQG. To solve the\nproblem within the standard, symmetry reduced kinematical framework\nof LQC, one needs to generalize the strategy to define the curvature\noperator. Of course, the generalization must be such that, when\napplied to all previous models, it is compatible with the procedure\nof computing holonomies around suitable plaquettes used there. We\nwill carry out this task by suitably modifying ideas that have\nalready appeared in the literature. This generalization will enable\none to incorporate \\emph{all} class A Bianchi models in the LQC\nframework.\n\nOnce this step is taken, one can readily construct the quantum\nHamiltonian constraint and the physical Hilbert space, following\nsteps that were introduced in the analysis \\cite{awe2} of the\nBianchi I model. However, because Bianchi II space-times have\nspatial curvature, the spin connection compatible with the\northonormal triad is now non-trivial. It leads to two new terms in\nthe Hamiltonian constraint that did not appear in the Bianchi I\nHamiltonian. We will analyze these new terms in some detail. In\nspite of these differences, the big bang singularity is resolved in\nthe same precise sense as in the Bianchi I model \\cite{awe2}: If a\nquantum state is initially supported only on classically\nnon-singular configurations, it continues to be supported on\nnon-singular configurations throughout its evolution.\n\nThe paper is organized as follows. Section \\ref{s2} summarizes the\nclassical Hamiltonian theory describing Bianchi II models. Section\n\\ref{s3} discusses the quantum theory. We first define a non-local\nconnection operator $\\hat{A}_a^i$ and use it to obtain the\nHamiltonian constraint. We then show that the singularity is\nresolved and the Bianchi I quantum dynamics is recovered in the\nappropriate limit. In Section \\ref{s4}, we introduce effective\nequations for the model (with the same caveats as in the Bianchi I\ncase \\cite{awe2})\nFinally, in section V we summarize our results and discuss the new\nelements that appear in the Bianchi II model. In Appendix A we\nimprove on the discussion of discrete symmetries presented in\n\\cite{awe2}. The results on the Bianchi I model obtained in\n\\cite{awe2} carry over without any change. But the change of\nviewpoint is important to the LQC treatment of the Bianchi II model\nand more general situations.\n\n\n\\section{Classical Theory}\n\\label{s2}\n\nThis section is divided into two parts. In the first we recall the\nstructure of Bianchi II space-times and in the second we summarize\nthe phase space formulation, adapted to LQC.\n\n\\subsection{Diagonal Bianchi II Space-times}\n\\label{s2.1}\n\nBecause the issue of discrete symmetries is subtle in background\nindependent contexts, and because it plays a conceptually important\nrole in the quantum theory of Bianchi II models, we will begin with\na brief summary of how various fields are defined\n\\cite{alrev,aa-dis}. This stream-lined discussion brings out the\nassumptions which are often only implicit, making the discussion of\ndiscrete symmetries clearer.\n\nIn the Hamiltonian framework underlying loop quantum gravity (LQG),\none fixes an \\emph{oriented} 3-manifold $M$ and a 3-dimensional\n`internal' vector space $I$ equipped with a positive definite metric\n$q_{ij}$. The internal indices $i,j,k,\\ldots$ are then freely\nlowered and raised by $q_{ij}$ and its inverse. A spatial triad\n$e^a_i$ is an isomorphism from $I$ to tangent space at each point of\n$M$ which associates a vector field $v^a:= e^a_i v^i$ on $M$ to each\nvector $v^i$ in $I$.%\n\\footnote{Thus, in LQG one begins with non-degenerate triads and\nmetrics, passes to the Hamiltonian framework and then, at the end,\nextends the framework to allow degenerate geometries.}\nThe dual co-triads are denoted by $\\omega_a^i$. Given a triad, we\nacquire a positive definite metric $q_{ab}:= q_{ij} \\omega_a^i\n\\omega_b^j$ on $M$. The metric $q_{ab}$ in turn singles out a 3-form\n$\\epsilon_{abc}$ on $M$ which has \\emph{positive orientation} and\nsatisfies $ \\epsilon_{abc}\\epsilon_{def}\\, q^{ad} q^{be}q^{cf}= 3!$.\nOne can then define a 3-form $\\epsilon_{ijk}$ on $I$ via\n$\\epsilon_{ijk} = \\epsilon_{abc} e^a_i e^b_j e^c_k$. Note that\n$\\epsilon_{ijk}$ is automatically compatible with $q_{ij}$, i.e.,\n$\\epsilon_{ijk}\\epsilon_{lmn}\\, q^{il} q^{jm} q^{kn}= 3!$. If a\ntriad $\\bar{e}^a_i$ is obtained by flipping an odd number of the\nvectors in the triad $e^a_i$, then $\\bar{e}^a_i$ and $e^a_i$ have\nopposite orientations and the fields they define satisfy\n$\\bar{q}_{ab} = {q}_{ab},\\, \\bar\\epsilon_{abc} = \\epsilon_{abc}$ but\n$\\bar\\epsilon_{ijk} = - \\epsilon_{ijk}$. Had we fixed\n$\\epsilon_{ijk}$ once and for all on $I$, then $\\epsilon_{abc}$\nwould have flipped sign under this operation and volume integrals on\n$M$ computed with the unbarred and barred triads would have had\nopposite signs. With our conventions, these volume integrals will\nnot change and the parity flips will be symmetries of the symplectic\nstructure and the Hamiltonian constraint.\n\nThe triad also determines an unique spin connection $\\Gamma_a^i$ via\n\\begin{equation} \\label{sc} D_{[a} \\omega_{b]}^i\\, \\equiv \\,\n\\partial_{[a}\\omega_{b]}^i + \\epsilon^{i}{}_{jk} \\Gamma_{[a}^j\n\\omega_{b]}^k \\, =\\, 0\\, .\\end{equation}\nThe gravitational configuration variable $A_a^i$ is then given by\n$A_a^i = \\Gamma_a^i + \\gamma K_a^i$ where $K_{ab} := K_a^i\n\\omega_{bi}$ is the extrinsic curvature of $M$ and $\\gamma$ is the\nBarbero-Immirzi parameter, representing a quantization ambiguity.\n(The numerical value of $\\gamma$ is fixed by the black hole entropy\ncalculation.) The momenta $E^a_i$ carry, as usual, density weight 1\nand are given by: $E^a_i = \\sqrt{q} e^a_i$. The fundamental Poisson\nbracket is:\n\\begin{equation} \\{A_a^i(x), \\, E^b_j(y)\\} = 8\\pi G\\gamma\\,\\, \\delta_a^b\\,\n\\delta^i_j\\, \\delta^3(x,y)\\, .\\end{equation}\n\nIn Bianchi models \\cite{taub,bianchi,atu}, one restricts oneself to\nthose phase space variables admitting a 3-dimensional group of\nsymmetries which act simply and transitively on $M$. Thus, the\n3-metrics $q_{ab}$ under consideration admit a 3-parameter group of\nisometries and $M$ is diffeomorphic to a 3-dimensional Lie group\n$G$. (However, there is no canonical diffeomorphism, so that there\nis no preferred point on $M$ corresponding to the identity element\nof $G$.) To avoid a proliferation of spaces and types of indices, it\nis convenient to identify the internal space $I$ and the Lie-algebra\n$\\mathcal{L} G$ of $G$ via a fixed isomorphism. Then, there is a natural\nisomorphism ${}^o\\xi^a_i$ between $\\mathcal{L} G \\equiv I$ and Killing vector\nfields on $M$: for each internal vector $v^i$, ${}^o\\xi^a_i v^i$ is a\nKilling field on $M$. For brevity we will refer to ${}^o\\xi^a_i$ as\n(left invariant) vector fields on $M$. There is a canonical triad\n${}^o\\!e^a_i$\n---the right invariant vector fields--- which is Lie dragged by the\n${}^o\\xi^a_i$. This triad and the dual co-triad ${}^o\\!\\omega_a^i$ satisfy:\n\\begin{eqnarray} [ {}^o\\xi_i,\\, {}^o\\!e_j ] &=&0, \\quad\\quad [{}^o\\!e_i,\\, {}^o\\!e_j] =\n- {}^o C_{ij}^k\\, {}^o\\!e_k,\\nonumber\\\\\n\\mathcal{L}_{{}^o\\xi_i}\\,( {}^o\\!\\omega^j) &=&0, \\quad\\quad {\\rm d}\\,{}^o\\!\\omega^k = \\frac{1}{2}\\,\n{}^o C_{ij}^k {}^o\\!\\omega^i\\wedge{}^o\\!\\omega^j,\\end{eqnarray}\nwhere ${}^o C_{ij}^k$ denotes the structure constants of $\\mathcal{L} G$. It is\nconvenient to use the fixed fields ${}^o\\!e^a_i$ and ${}^o\\!\\omega_a^i$ as\n\\emph{fiducial} triads and co-triads.\n\nIn the case when $G$ is the Bianchi II group, we have ${}^o C_{ik}^k =0$\nas in all class A Bianchi models and, furthermore, the symmetric\ntensor $k^{kl}:={}^o C_{ij}^k \\, \\epsilon^{ijl}$ has signature +,0,0.\nTherefore, we can fix, once and for all an orthonormal basis\n${}^o b_1^i, {}^o b_2^i, {}^o b_3^i$ in $I$ such that the only non-zero\ncomponents of ${}^o C_{ij}^k$ are\n\\begin{equation} {}^o C_{23}^1 = - {}^o C_{32}^1 = \\tilde{\\alpha}\\, ,\\end{equation}\nwhere $\\tilde{\\alpha}$ is a non-zero real number.%\n\\footnote{Without loss of generality $\\tilde{\\alpha}$ can be chosen to be 1. We\nkeep it general because we will rescale it later (see Eq.\n(\\ref{tilde})) and because we want to be able to pass to the Bianchi\nI case by taking the limit $\\tilde{\\alpha}\\to0$.}\nWe will assume that this basis is so oriented that\n\\begin{equation} \\label{ve1} \\epsilon_{123}\\, :=\\, \\epsilon_{ijk} \\, {}^o b^i_1\\,\n{}^o b^j_2\\, {}^o b^k_3\\, \\, =\\, \\varepsilon\\end{equation}\nwhere $\\varepsilon = \\pm 1$ depending on whether the frame $e^a_i$ (which\ndetermines the sign of $\\epsilon_{ijk}$) is right or left handed.\nThroughout this paper we will set ${}^o\\xi^a_1 = {}^o\\xi^a_i {}^o b^i_1,\\,\n{}^o\\!e^a_1 = {}^o\\!e^a_i{}^o b^i_1,\\, {}^o\\!\\omega_a^1 = {}^o\\!\\omega_a^i{}^o b^1_i$, etc.\n\nThe form of the components of ${}^o C^k_{ij}$ in this basis implies that\n$M$ admits global coordinates $x,y,z$ such that the Bianchi II\nKilling vectors have the fixed form\n\\begin{equation} {}^o\\xi_1^a = \\left(\\frac{\\partial}{\\partial x}\\right)^a, \\qquad\n {}^o\\xi^a_2 = \\left(\\frac{\\partial}{\\partial y}\\right)^a, \\qquad\n {}^o\\xi^a_3 = \\tilde{\\alpha} y\\left(\\frac{\\partial} {\\partial x}\\right)^a+\n \\left(\\frac{\\partial}{\\partial z}\\right)^a. \\end{equation}\nThese expressions bring out the fact that, if we were to attempt to\ncompactify the spatial slices to pass to a $\\mathbb{T}^3$ topology\n---as one can in the Bianchi I model--- we will no longer have\nglobally well-defined Killing fields. Thus, in the Bianchi II model,\nwe are forced to deal with the subtleties associated with\nnon-compactness of the spatially homogeneous slices.\n\nIn the $x,y,z$ chart, the right invariant triad is given by\n\\begin{equation} {}^o\\!e^a_1 = \\left(\\frac{\\partial}{\\partial x}\\right)^a, \\qquad {}^o\\!e^a_2\n= \\tilde{\\alpha} z \\left(\\frac{\\partial}{\\partial\nx}\\right)^a+\\left(\\frac{\\partial}{\\partial y} \\right)^a, \\qquad {}^o\\!e^a_3\n= \\left(\\frac{\\partial}{\\partial z}\\right)^a, \\end{equation}\nand the dual co-triad by\n\\begin{equation} {}^o\\!\\omega_a^1=({\\rm d} x)_a-\\tilde{\\alpha} z({\\rm d} y)_a, \\qquad{}^o\\!\\omega_a^2=({\\rm d} y)_a,\n\\qquad{}^o\\!\\omega_a^3=(dz)_a. \\end{equation}\nThey determine a fiducial 3-metric ${}^o\\!q_{ab}:= q_{ij}{}^o\\!\\omega_a^i{}^o\\!\\omega_b^j$\nwith Bianchi II symmetries:\n\\begin{equation} {}^o\\!q_{ab} {\\rm d} x^a {\\rm d} x^b = ({\\rm d} x-\\tilde{\\alpha} z\\:{\\rm d} y)^2\\,+\\,{\\rm d}\ny^2\\,+\\, {\\rm d} z^2. \\end{equation}\n\nIn the diagonal models, the physical triads $e^a_i$ are related to\nthe fiducial ones by%\n\\footnote{There is no sum if repeated indices are both covariant or\ncontravariant. As usual, the Einstein summation convention holds if a\ncovariant index is contracted with a contravariant index.}\n\\begin{equation} \\label{edef} \\omega_a^i = a^i(\\tau){}^o\\!\\omega_a^i, \\qquad \\mathrm{and}\n\\qquad a_i(\\tau) e^a_i = {}^o\\!e^a_i\n \\end{equation}\nwhere the $a_i$ are the three directional scale factors. Since the\nphysical spatial metric is given by $q_{ab} =\n\\omega_a^i\\omega^{}_{bi}$, the space-time metric can be expressed as\n\\begin{equation} \\label{metric} {\\rm d} s^2= -N {\\rm d}\\tau^2 + a_1(\\tau)^2\\:({\\rm d} x-\\tilde{\\alpha}\nz\\:{\\rm d} y)^2+a_2(\\tau)^2\\:{\\rm d} y^2+a_3(\\tau)^2\\:{\\rm d} z^2 \\end{equation}\nwhere $N$ is the lapse function adapted to the time coordinate\n$\\tau$.\n\nFor later use, let us calculate the spin connection (\\ref{sc})\ndetermined by triads $e^a_i$. From the definition of $\\Gamma_a^i$ it\nfollows that\n\\begin{equation} \\Gamma_a^i =\n-\\epsilon^{ijk}\\,e^b_j\\,\\left(\\partial_{[a}\\omega_{b]k}+\\frac{1}{2}e^c_k\n\\omega^l_a\\partial_{[c}\\omega_{b]l}\\right)\\, . \\end{equation}\nUsing (\\ref{ve1}), the components of $\\Gamma_a^i$ in the internal\nbasis ${}^o b^i_1, {}^o b^i_2, {}^o b^i_3$ can be expressed as\n\\begin{equation} \\Gamma_a^1 = \\frac{\\tilde{\\alpha}\\varepsilon a_1^2}{2a_2a_3}\\:{}^o\\!\\omega_a^1; \\qquad \\Gamma_a^2\n=-\\frac{\\tilde{\\alpha}\\varepsilon a_1}{2a_3}\\:{}^o\\!\\omega_a^2; \\qquad \\Gamma_a^3 = -\\frac{\\tilde{\\alpha}\\varepsilon\na_1}{2a_2}\\:{}^o\\!\\omega_a^3. \\end{equation}\n\nBefore studying the dynamics of the model, let us examine the action\nof internal parity transformation $\\Pi_k$ which flips the $k$th\ntriad vector and leaves the orthogonal vectors alone. (For details\nsee Appendix and \\cite{aa-dis}). Under the parity transformation\n$\\Pi_1$, for example, we have: $e^a_1\\,\\to \\, -e^a_1,\\, e^a_2\\, \\to\ne^a_2,\\, e^a_ 3\\, \\to \\, e^a_3$ and $a_1\\to -a_1,\\, a_2\\to a_2, a_3\n\\to a_3$ whence $\\Gamma_a^1 \\to -\\Gamma^a_1,\\, \\Gamma_a^2 \\to\n\\Gamma^a_2,\\, \\Gamma_a^3 \\to \\Gamma^a_3$. Thus, both $e^a_i$ and\n$\\Gamma_a^i$ are \\emph{proper} internal vectors. $\\varepsilon$ on the other\nhand is a pseudo internal scalar, $\\varepsilon \\to -\\varepsilon$ under every\n$\\Pi_k$. Note that the fiducial quantities carrying a label $o$ do\nnot change under this transformation; it affects only the physical\nquantities.\n\n\\subsection{The Bianchi II Phase space}\n\\label{s2.2}\n\nAs is usual in LQC, we will now use the fiducial triads and\nco-triads to introduce a convenient parametrization of the phase\nspace variables, $E^a_i, A_a^i$. Because we have restricted\nourselves to the diagonal model and these fields are symmetric under\nthe Bianchi II group, from each equivalence class of gauge related\nphase space variables we can choose a pair of the form\n\\begin{equation} \\label{var} E^a_i = \\tilde{p}_i\\sqrt{|{}^o\\!q|}\\,{}^o\\!e^a_i \\qquad \\mathrm{and}\n\\qquad A_a^i = \\tilde{c}^i \\,{}^o\\!\\omega_a^i, \\end{equation}\nwhere, as spelled out in footnote 3, there is no sum over $i$. Thus,\na point in the phase space is now coordinatized by six real numbers\n$\\tilde{p}_i,\\tilde{c}^i$. One would now like to use the symplectic structure in\nfull general relativity to induce a symplectic structure on our\nsix-dimensional phase space. However, because of spatial homogeneity\nand the ${\\mathbb{R}}^3$ spatial topology, the integrals defining the\nsymplectic structure, the Hamiltonian (and the action) all diverge.\nTherefore we have to introduce a fiducial cell $\\mathcal{V}$ and\nrestrict integrals to it \\cite{as,abl}. We will take the fiducial\ncell to be rectangular with edges along the coordinate axes and\nlengths of $L_1, L_2$ and $L_3$ with respect to the \\emph{fiducial}\nmetric ${}^o\\!q_{ab}$. It then follows that the volume of the fiducial\ncell with respect to ${}^o\\!q_{ab}$ is $V_o=L_1L_2L_3$. Then the non-zero\nPoisson brackets are given by:\n\\begin{equation} \\label{pb1} \\{\\tilde{c}^i,\\, \\tilde{p}_j\\} \\, = \\, \\frac{8\\pi G \\gamma}{V_o}\\,\n\\delta^i_j \\end{equation}\nwhere $\\gamma$ is the Barbero-Immirzi parameter. As in the Bianchi I\ncase, we have a 1-parameter ambiguity in the symplectic structure\nbecause of the explicit dependence on $V_o$ and we have to make sure\nthat the final physical results are either independent of $V_o$ or\nremain well-defined as we remove the `regulator' and take the limit\n$V_o \\to \\infty$.\n\nIt is convenient to rescale variables to absorb this dependence in\nthe phase space coordinates (as was done in the treatment of Bianchi\nI model in \\cite{awe2}). Let us set\n\\begin{equation} p_1= L_2L_3\\tilde{p}_1, \\qquad p_2=L_3L_1\\tilde{p}_2, \\qquad p_3= L_1L_2\\tilde{p}_3,\n\\end{equation}\n\\begin{equation} \\label{tilde} c_1=L_1\\tilde{c}_1, \\qquad c_2=L_2\\tilde{c}_2, \\qquad\nc_3=L_3\\tilde{c}_3 \\qquad \\mathrm{and} \\qquad \\alpha =\n\\frac{L_2L_3}{L_1}\\tilde{\\alpha}\\, , \\end{equation}\nwhere the last rescaling has been introduced to absorb factors of\n$L_i$ which would otherwise unnecessarily obscure the expression of\nthe Hamiltonian constraint. The Poisson brackets between these new\nphase space coordinates is given by%\n\\begin{equation} \\label{pb2}\\{c^i,\\, p_j\\} \\, = \\, 8\\pi G \\gamma \\,\\delta^i_j \\,\n. \\end{equation}\nThese variables have direct physical interpretation. For example,\n$p_1$ is the (oriented) area of the 2-3 face of the elementary cell\nwith respect to the \\emph{physical} metric $q_{ab}$ and $h^{(1)} =\n\\exp c_1\\tau_1$ is the holonomy of the physical connection $A_a^i$\nalong the first edge of the elementary cell.\n\n\nOur choice (\\ref{var}) of physical triads and connections has fixed\nthe internal gauge as well as the diffeomorphism freedom.\nFurthermore, it is easy to explicitly verify that, thanks to\n(\\ref{var}), the Gauss and the diffeomorphism constraints are\nautomatically satisfied. Thus, as in \\cite{awe2}, we are left just\nwith the Hamiltonian constraint\n\\begin{equation} \\label{Hgen} \\mathcal{C}_H = \\int_\\mathcal{V}\n\\Big[\\frac{NE^a_iE^b_j}{16\\pi G\\sqrt{|q|}}\n\\big(\\epsilon^{ij}{}_kF_{ab}{}^k-2(1+\\gamma^2)K_{[a}^iK_{b]}^j \\Big)\n+ N \\mathcal{H}_{{\\rm matt}}\\big]\\, {\\rm d}^3x\\, , \\end{equation}\nwhere\n\\begin{equation} F_{ab}{}^k=2\\partial_{[a}A_{b]}^k+\\epsilon_{ij}{}^kA_a^iA_b^j \\end{equation}\nis the curvature of $A_a^i$ and $\\mathcal{H}_{\\rm matt}$ is the matter\nHamiltonian density. As in \\cite{awe2}, our matter field will\nconsist only of a massless scalar field $T$ which will later serve\nas a relational time variable a la Liebniz. (Additional matter\nfields can be incorporated in a straightforward manner, modulo\npossible intricacies of essential self-adjointness.) Thus,\n\\begin{equation} \\mathcal{H}_{{\\rm matt}} = \\frac{1}{2}\\frac{p_T^2}{\\sqrt{|q|}}. \\end{equation}\n\nSince we want to use the massless scalar field as relational time,\nit is convenient to use a harmonic-time gauge, i.e., assume that the\ntime coordinate $\\tau$ in (\\ref{metric}) satisfies $\\Box \\tau=0$.\nThe corresponding lapse function is $N=\\sqrt{|p_1p_2p_3|}$. With\nthis choice, the Hamiltonian constraint simplifies considerably.\nNote first that the basic canonical variables can be expanded as\n\\begin{equation} E^a_i = \\frac{p_i}{V_o}L_i\\sqrt{|{}^o\\!q|}{}^o\\!e^a_i \\qquad {\\rm and} \\qquad\nA_a^i = \\frac{c^i}{L^i}{}^o\\!\\omega_a^i, \\end{equation}\nand the extrinsic curvature is given by\n $$\\qquad K_a^i = \\gamma^{-1} (A_a^i-\\Gamma_a^i).$$\nNext, using $p_1 = ({\\rm sgn}a_1)\\, |a_2a_3|\\,L_2L_3$ etc, the\ncomponents of the spin connection become:\n\\begin{equation} \\Gamma_a^1 = \\frac{\\alpha\\varepsilon p_2p_3}{2p_1^2}\\frac{{}^o\\!\\omega_a^1}{L_1}, \\qquad\n\\Gamma_a^2= -\\frac{\\alpha\\varepsilon p_3}{2p_1}\\frac{{}^o\\!\\omega_a^2}{L_2}, \\qquad\n\\Gamma_a^3=-\\frac{\\alpha\\varepsilon p_2}{2p_1} \\frac{{}^o\\!\\omega_a^3}{L_3} \\, .\\end{equation}\nCollecting terms, the Hamiltonian constraint (\\ref{Hgen}) becomes\n\\begin{align} \\label{Hcl} \\mathcal{C}_H&=-\\frac{1}{8\\pi G\\gamma^2}\n\\Big[p_1p_2c_1c_2+p_2p_3 c_2c_3+p_3p_1c_3c_1+\\alpha\\varepsilon p_2p_3c_1\n\\nonumber \\\\\n&\\qquad\\qquad-(1+ \\gamma^2)\\,\\big(\\frac{\\alpha p_2p_3}{2p_1}\\big)^2\\Big]\n + \\frac{1}{2}p_T^2 \\\\\n& = \\mathcal{C}_H^{\\rm (BI)} - \\frac{1}{8\\pi G\\gamma^2}\\Big[\\alpha\\varepsilon\np_2p_3c_1-(1+ \\gamma^2)\\,\\big(\\frac{\\alpha p_2p_3}{2p_1}\\big)^2\\Big],\n\\end{align}\nwhere $\\mathcal{C}_H^{\\rm (BI)}$ is the Hamiltonian constraint\n(including the matter term) for Bianchi I space-times which has\nalready been studied in \\cite{awe2}. Note that this constraint is\nrecovered in the limit $\\alpha\\to 0$, as it must be.\n\n\nKnowing the form of the Hamiltonian constraint, it is now possible to derive\nthe time evolution of any classical observable $\\mathcal{O}$ by taking its\nPoisson bracket with $\\mathcal{C}_H$:\n\\begin{equation} \\dot{\\mathcal{O}} = \\{\\mathcal{O},\\mathcal{C}_H\\}\\, , \\end{equation}\nwhere the `dot' stands for derivative with respect to harmonic time\n$\\tau$. This gives\n\\begin{equation} \\label{ceom1} \\dot{p_1}=\\gamma^{-1}(p_1p_2c_2+p_1p_3c_3+\\alpha\\varepsilon p_2p_3), \\end{equation}\n\\begin{equation} \\dot{p_2}=\\gamma^{-1}(p_2p_1c_1+p_2p_3c_3), \\end{equation}\n\\begin{equation} \\dot{p_3}=\\gamma^{-1}(p_3p_1c_1+p_3p_2c_2), \\end{equation}\n\\begin{equation}\n\\dot{c_1}=-\\frac{1}{\\gamma}\\Big(p_2c_1c_2+p_3c_1c_3+\\frac{1}{2p_1}(1+\\gamma^2)\n\\big(\\frac{\\alpha p_2p_3}{p_1}\\big)^2\\Big), \\end{equation}\n\\begin{equation} \\dot{c_2}=-\\frac{1}{\\gamma}\\Big(p_1c_2c_1+p_3c_2c_3+\\alpha\\varepsilon\np_3c_1-\\frac{1}{2p_2} (1+\\gamma^2)\\big(\\frac{\\alpha\np_2p_3}{p_1}\\big)^2\\Big), \\end{equation}\n\\begin{equation} \\label{ceom2}\n\\dot{c_3}=-\\frac{1}{\\gamma}\\Big(p_1c_3c_1+p_2c_3c_2+\\alpha\\varepsilon p_2\nc_1-\\frac{1}{2p_3}(1+\\gamma^2)\\big(\\frac{\\alpha p_2p_3}{p_1}\\big)^2\\Big).\n\\end{equation}\nAny initial data satisfying the Hamiltonian constraint can be\nevolved by using the six equations above. It is straightforward to\nextend these results if there are additional matter fields.\n\nFinally, let us consider the parity transformation $\\Pi_k$ which\nflips the $k$th \\emph{physical} triad vector $e^a_k$. (As noted\nbefore, this transformation does not act on any of the fiducial\nquantities which carry a label $o$.) Under this map, we have:\n$q_{ab} \\to q_{ab}, \\, \\epsilon_{abc} \\to \\epsilon_{abc}\\,$ but\n$\\epsilon_{ijk} \\to -\\epsilon_{ijk}, \\, \\varepsilon \\to -\\varepsilon$. The canonical\nvariables $c^i, p_i$ transform as proper internal vectors and\nco-vectors: For example\n\\begin{equation} \\Pi_1(c_1,c_2,c_3) \\rightarrow (-c_1, c_2, c_3) \\qquad {\\rm and}\n\\qquad \\Pi_1(p_1,p_2,p_3) \\rightarrow (-p_1, p_2, p_3)\\, . \\end{equation}\nConsequently, both the symplectic structure and the Hamiltonian\nconstraint are left invariant under any of the parity maps $\\Pi_k$.\n\nThis Hamiltonian description will serve as the point of departure\nfor loop quantization in the next section.\n\n\n\\section{Quantum Theory}\n\\label{s3}\n\nThis section is divided into three parts. In the first, we discuss\nthe kinematics of the model, in the second we define an operator\ncorresponding to the connection $A_a^i$ using holonomies and in the\nthird we introduce the Hamiltonian constraint operator and describe\nits action on states.\n\n\n\\subsection{LQC Kinematics}\n\nThe kinematics for the LQC of Bianchi II models is almost identical\nto that for Bianchi I models. Therefore, in the sub-section we\nclosely follow \\cite{awe2}.\n\nLet us begin by specifying the elementary functions on the\nclassical phase space which will have unambiguous analogs in the\nquantum theory. As in the Bianchi I model, the elementary\nvariables are the momenta $p_i$ and holonomies of the\ngravitational connection $A_a^i$ along the integral curves of the\nright invariant vector fields ${}^o\\!e^a_i$. Let $\\tau_i$ be a basis of\nthe Lie algebra of SU(2), satisfying $\\tau_i \\tau_j =\n\\frac{1}{2}\\epsilon_{ij}{}^k \\tau_k- \\frac{1}{4} \\delta_{ij}\\mathbb{I}$\nwhere $\\mathbb{I}$ is the unit $2\\times2$ matrix. Consider an edge\nof length $\\ell L_k$ with respect to the fiducial metric\n${}^o\\!q_{ab}$, parallel to ${}^o\\!e^a_k$. The holonomy $h_k^{(\\ell)}$ along\nit is given by\n\\begin{equation} \\label{hol} h_k^{(\\ell)}(c_1,c_2,c_3) = \\exp\\left(\\ell\nc_k\\tau_k\\right) = \\cos\\frac{\\ell c_k}{2} \\mathbb{I} + 2\\sin\\frac{\\ell\nc_k}{2}\\tau_k. \\end{equation}\n(Note that $\\ell$ depends of the fiducial cell but not on the\nfiducial metric.) This family of holonomies is completely\ndetermined by the almost periodic functions $\\exp(i\\ell c_k)$ of\nthe connection. These almost periodic functions will be our\nelementary configuration variables which will be promoted\nunambiguously to operators in the quantum theory.\n\nIt is simplest to use the $p$-representation to specify the\ngravitational sector $\\mathcal{H}_{\\rm kin}^{\\rm grav}$ of the kinematic Hilbert space. The\northonormal basis states $|p_1,p_2,p_3\\rangle$ are eigenstates of\nquantum geometry. For example, in the state $|p_1,p_2,p_3\\rangle$\nthe face $S_{23}$ of the fiducial cell $\\mathcal{V}$ (given by $x$\n={\\rm const}) has area $|p_1|$.\nThe basis is orthonormal in the sense\n\\begin{equation} \\langle p_1,p_2,p_3|p_1',p_2',p_3'\\rangle = \\delta_{p_1^{}p_1'}\n\\delta_{p_2^{}p_2'}\\delta_{p_3^{}p_3'}\\, , \\end{equation}\nwhere the right side features Kronecker symbols rather than the\nDirac delta distributions. Hence kinematical states can consist only\nof \\emph{countable} linear combinations\n\\begin{equation} |\\Psi\\rangle \\,=\\,\n\\sum_{p_1,p_2,p_3}\\Psi(p_1,p_2,p_3)|p_1,p_2,p_3\\rangle\\ \\end{equation}\nof these basis states for which the norm\n\\begin{equation} \\label{norm} ||\\Psi ||^2\\, =\\, \\sum_{p_1,p_2,p_3}\\,\n|\\Psi(p_1,p_2,p_3)|^2 \\end{equation}\nis finite. Because the right side features a sum over a countable\nnumber of points on ${\\mathbb{R}}^3$, rather than a 3-dimensional integral,\nLQC kinematics are inequivalent to those of the Schr\\\"odinger\napproach used in Wheeler-DeWitt quantum cosmology.\n\nNext, recall that on the classical phase space the three reflections\n$\\Pi_i:\\,\\,e^a_i\\,\\to\\, -e^a_i$ are large gauge transformations\nunder which physics does not change (since both the metric and the\nextrinsic curvature are left invariant). These large gauge\ntransformations have a natural induced action, denoted by\n$\\hat\\Pi_i$, on the space of wave functions $\\Psi(p_1,p_2,p_3)$. For\nexample,\n\\begin{equation} \\hat\\Pi_1\\Psi(p_1,p_2,p_3)=\\Psi(-p_1,p_2,p_3). \\end{equation}\nSince $\\hat\\Pi_i^2$ is the identity, for each $i$, the group of\nthese large gauge transformations is simply $\\mathbb{Z}_2$. As in Yang-Mills\ntheory, physical states belong to its irreducible representation.\nFor definiteness, as in the isotropic and Bianchi I models, we will\nwork with the symmetric representation. It then follows that\n$\\mathcal{H}_{\\mathrm{kin}}^{\\mathrm{grav}}$ is spanned by wave\nfunctions $\\Psi(p_1,p_2,p_3)$ which satisfy\n\\begin{equation} \\label{parity} \\Psi(p_1,p_2,p_3)=\\Psi(|p_1|,|p_2|,|p_3|) \\end{equation}\nand have a finite norm (\\ref{norm}).\n\nThe action of the elementary operators on\n$\\mathcal{H}_{\\mathrm{kin}}^{\\mathrm{grav}}$ is as follows: the\nmomenta act by multiplication whereas the almost periodic\nfunctions in $c_i$ shift the $i$th argument. For example,\n\\begin{equation} [\\hat p_1 \\Psi](p_1,p_2,p_3) = p_1\\, \\Psi(p_1,p_2,p_3) \\,\\quad\n\\mathrm{and} \\,\\quad \\Big[\\widehat{\\exp(i\\ell c_1)}\\Psi\\Big](p_1,\np_2, p_3) = \\Psi(p_1-8\\pi\\gamma G\\hbar \\ell, p_2, p_3)\\, . \\end{equation}\nThe expressions for $\\hat p_2, \\widehat{\\exp(i\\ell c_2)}, \\hat\np_3$ and $\\widehat{\\exp(i\\ell c_3)}$ are analogous. Finally, we\nneed to define the operator $\\hat{\\varepsilon}$ since $\\varepsilon$ features in\nthe expression of the Hamiltonian constraint. In the classical\ntheory, $\\varepsilon$ is unambiguously defined on non-degenerate triads,\ni.e., when $p_1p_2p_3 \\not= 0$. In quantum theory, wave functions\ncan have support also on degenerate configurations. We will extend\nthe definition to degenerate triads using the basis\n$|p_1,p_2,p_3\\rangle$:\n\\begin{equation} \\label{ve2} \\hat{\\varepsilon}\\,|p_1,p_2,p_3\\rangle := \\left\\{\n\\rlap{\\raise2ex\\hbox{\\,\\,$\\quad|p_1,p_2,p_3 \\rangle$ if $p_1p_2p_3\n\\ge 0$,}}{\\lower2ex\\hbox{\\,\\,$ -\\,|p_1,p_2,p_3 \\rangle$ if\n$p_1p_2p_3<0$.}} \\right. \\end{equation}\nFinally, the full kinematical Hilbert space\n$\\mathcal{H}_{\\mathrm{kin}}$ will be the tensor product\n$\\mathcal{H}_{\\mathrm{kin}}=\\mathcal{H}_{\\mathrm{kin}}^\n{\\mathrm{grav}}\\otimes\\mathcal{H}_{\\mathrm{kin}}^{\\mathrm{matt}}$,\nwhere $\\mathcal{H}_{\\mathrm{kin}}^{\\mathrm{matt}}=L^2({\\mathbb{R}},dT)$ is\nthe matter kinematical Hilbert space for the homogeneous scalar\nfield. On $\\mathcal{H}_ {\\mathrm{kin}}^{\\mathrm{matt}}$, $\\hat T$\nwill act by multiplication and $\\hat p_T:=-i\\hbar \\mathrm{d}_T$\nwill act by differentiation. As in isotropic and Bianchi I models,\nour final results would remain unaffected if we use a ``polymer\nrepresentation'' also for the scalar field.\n\n\n\\subsection{The connection operator $\\hat{A}_a^i$}\n\\label{s3.2}\n\nTo define the quantum Hamiltonian constraint, we cannot directly use\nthe symmetry reduced classical constraint (\\ref{Hcl}) because it\ncontains connection components $c_k$ themselves and in LQC only\nalmost periodic functions of $c_k$ have unambiguous operator\nanalogs. Indeed, in all LQC models considered so far\n\\cite{abl,aps3,warsaw,apsv,kv1,ls,bp,awe2}, we were led to return to\nthe expression (\\ref{Hgen}) in the full theory and mimic the\nprocedure used in LQG \\cite{tt}. More precisely, the key strategy\nwas to follow full LQG (and spin foams) and define a ``field\nstrength operator'' using holonomies around suitable closed loops.\nIn the Bianchi I model, these closed loops were formed by following\nintegral curves of right invariant vector fields (which are also\nleft invariant). As mentioned in section \\ref{s2}, in the Bianchi II\nmodel the right invariant vector fields define the fiducial triads\n${}^o\\!e^a_i$, the left invariant vector fields, the Killing fields\n${}^o\\xi^i$. Neither constitutes a commuting set, whence their integral\ncurves cannot be used to form closed loops. However, as in the k=1\ncase \\cite{warsaw,apsv}, one can hope to exploit the fact that the\nright invariant vector fields do commute with the left invariant\nones and construct the closed loops by alternately following right\nand left invariant vector fields. But, as mentioned in section\n\\ref{s1}, a new problem arises: unlike in the k=1 (or Bianchi I)\nmodel the resulting holonomies are no longer almost periodic\nfunctions of $c_k$, whence the Hilbert space $\\mathcal{H}_{\\rm kin}^{\\rm\ngrav}$ does not support these holonomy operators. For completeness\nwe will first show this fact explicitly and then introduce a new\navenue to bypass this difficulty.\n\nThe problematic curvature component turns out to be $F_{yz}{}^1$. To\nconstruct the corresponding operator, following the strategy used in\nthe k=1 case \\cite{warsaw,apsv}, we will construct a closed loop\n$\\Box_{yz}$ as follows. In the coordinates $(x,y,z)$,\\,\\, i) Move\nfrom $(0,0,0)$ to $(0,\\bar\\mu_2L_2,0)$ following $\\xi^a_2$;\\,\\, ii)\nthen move from $(0,\\bar\\mu_2L_2,0)$ to\n$(0,\\bar\\mu_2L_2,\\bar\\mu_3L_3)$ following ${}^o\\!e^a_3$;\\,\\, iii) then\nmove from $(0,\\bar\\mu_2L_2,\\bar\\mu_3L_3)$ to $(0,0,\\bar\\mu_3L_3)$\nfollowing $-\\xi^a_2$;\\,\\, and, finally, iv) move from\n$(0,0,\\bar\\mu_3L_3)$ to $(0,0,0)$ following $-{}^o\\!e^a_3$. The\nparameters $\\bar\\mu_i$ which determine the `lengths' of these edges\ncan be fixed by the semi-heuristic correspondence between LQC and\nLQG exactly as in the Bianchi I model \\cite{awe2} because the\ngeometric considerations used in that analysis continue to hold\nwithout any modification in all Bianchi models with $\\mathbb{R}^3$ spatial\ntopology:\n\\begin{equation} \\label{mubar} \\bar\\mu_1 =\n\\sqrt\\frac{|p_1|\\Delta\\,\\ell_{\\mathrm{Pl}}^2}{|p_2p_3|}, \\qquad \\bar\\mu_2 =\n\\sqrt\\frac{|p_2|\\Delta\\,\\ell_{\\mathrm{Pl}}^2}{|p_1p_3|}, \\qquad \\bar\\mu_3 =\n\\sqrt\\frac{|p_3| \\Delta\\,\\ell_{\\mathrm{Pl}}^2}{|p_1p_2|} \\end{equation}\nwhere $\\Delta\\,\\ell_{\\mathrm{Pl}}^2 = 4\\sqrt{3}\\pi\\gamma\\,\\ell_{\\mathrm{Pl}}^2$ is the `area gap'.\nThe holonomy around this closed loop $\\Box_{yz}$ is given by\n\\begin{equation} {h}_{\\Box_{yz}} = \\frac{2}{c\\,\\,\\bar\\mu_2\\bar\\mu_3L_2L_3}\\cos\\left(\n\\frac{\\bar\\mu_2c_2}{2}\\right)\\sin\\left(\\frac{\\bar\\mu_2 c}{2}\\right)\n\\Big(c_2\\sin(\\bar\\mu_3c_3)+\n\\alpha\\bar\\mu_3c_1\\cos(\\bar\\mu_3c_3)\\Big) \\end{equation}\nwhere\n\\begin{equation} \\label{c12} c = \\sqrt{\\alpha^2\\bar\\mu_3^2c_1^2+c_2^2}. \\end{equation}\nIf we were to shrink the loop so that the area it encloses goes to\nzero, we do indeed recover the classical expression of $F_{yz}{}^1$.\nHowever, because of presence of the term $c$, if $\\alpha\\not=0$ the\nright side fails to be almost periodic in $c_1$ and $c_2$. Hence\nthis holonomy operator fails to exist on $\\mathcal{H}_{\\rm kin}$. It is clear\nfrom the expression (\\ref{c12}) of $c$ that the problem is\nindependent of the specific way $\\bar\\mu_i$ are fixed.\n\nWe will bypass this difficulty by mimicking another strategy used in\nfull LQG \\cite{tt}: We will use holonomies along segments parallel\nto ${}^o\\!e^a_i$ to define an operator corresponding to the connection\nitself. This is a natural strategy because holonomies along these\nsegments suffice to separate the Bianchi II connections (\\ref{var}).\nLet us set $A_a := A_a^k\\tau_k$. Then we have the identity:\n\\begin{equation} \\label{classA} A_a = \\lim_{\\ell_k \\to 0}\\, \\sum_k\n\\,\\frac{1}{2\\ell_kL_k}\\,\\, \\Big(h_k^{(\\ell_k)} -\n(h_k^{(\\ell_k)})^{-1}\\Big) \\end{equation}\nwhere $h_k^{(\\ell_k)}$ is given by (\\ref{hol}). In the expressions\nof physically interesting operators such as the Hamiltonian\nconstraint of full LQG, one often replaces $A_a$ with the (analog of\nthe) right side of (\\ref{classA}). But because of the specific forms\nof these operators, the limit trivializes on diffeomorphism\ninvariant states of LQG. In LQC, we have gauge fixed the system and\ntherefore cannot appeal to diffeomorphism invariance. Indeed, while\nthe holonomies are well-defined for each non-zero $\\ell_k$, the\nlimit fails to exist on $\\mathcal{H}_{\\rm kin}^{\\rm grav}$. A natural\nstrategy is to shrink $\\ell_k$ to a judiciously chosen non-zero\nvalue. But what would this value be? In the case of plaquettes, we\ncould use the interplay between LQG and LQC directly because the\nargument $p_i$ of LQC quantum states refers to \\emph{quantum} areas\nof faces of the elementary cell $\\mathcal{V}$ \\cite{awe2}. For edges\nwe do not have such direct guidance. There is, nonetheless a natural\nprinciple one can adopt: Normalize $\\ell_k$ such that the numerical\ncoefficient in front of the curvature operator constructed from the\nresulting connection agrees with that in the expression of the\ncurvature operator constructed from holonomies around closed loops,\nin all cases where the second construction is available. We will use\nthis strategy. Let us apply it to the Bianchi I model where\n$F_{ab}{}^k = \\epsilon_{ij}{}^k\\, A_a^i A_b^j$. Using holonomies\naround closed loops one obtains the field strength operator\n\\begin{equation} \\hat{F}_{ab}{}^k = \\epsilon_{ij}{}^k\\,\n\\big(\\frac{\\sin\\bar{\\mu}c}{\\bar{\\mu}L}\\, {}^o\\!\\omega_a\\big)^i\\,\n\\big(\\frac{\\sin\\bar{\\mu}c}{\\bar{\\mu}L}\\, {}^o\\!\\omega_b\\big)^j \\end{equation}\nwhere\n\\begin{equation} \\big(\\frac{\\sin\\bar{\\mu}c}{\\bar{\\mu}L}\\, {}^o\\!\\omega_a\\big)^i =\n\\big(\\frac{\\sin\\bar{\\mu_i}c_i}{\\bar{\\mu_i}L_i}\\, {}^o\\!\\omega_a^i\\big) \\quad\\quad\n\\hbox{\\rm (no sum over i)} \\nonumber \\end{equation}\n(see Eqs (3.12) and (3.13) in \\cite{awe2}). Therefore, our strategy\nyields $\\ell_k = 2\\bar\\mu_k$, that is,\n\\begin{equation} \\label{Aop} \\hat{A}_a^k =\n\\frac{\\widehat{\\sin(\\bar\\mu^kc^k)}}{\\bar\\mu^kL_k}\\,\\,{}^o\\!\\omega_a^k, \\end{equation}\nwhere there is no sum over $k$. Note that the principle stated above\nleads us unambiguously to the factor $2$ in $\\ell_k = 2\\bar\\mu_k$;\nwithout recourse to a systematic strategy, one may have naively set\n$\\ell_k =\\bar\\mu_k$.\n\nIf we compare the expression (\\ref{Aop}) of the connection operator\nwith the expression (\\ref{var}) of the classical connection, we have\neffectively defined an operator $\\hat{c}$ via\n\\begin{equation} \\hat{c}_k = \\frac{\\widehat{\\sin(\\bar\\mu^kc^k)}}{\\bar\\mu^k} \\end{equation}\nwhere there is again no sum over $k$. In the literature such a\nquantization of $c$ is often called ``polymerization.'' Our approach\nis an improvement over such strategies in two respects. First, we\ndid not just make the substitution $c \\rightarrow \\sin \\ell c\/\\ell$\nby hand; a priori one could have used another substitution such as\n$c \\rightarrow \\tan \\ell c\/\\ell$. Rather, as in full LQG, we used\nthe strategy of expressing the connection in term of holonomies,\n`the elementary variables'. But this still leaves open the question\nof what $\\ell$ one should use. We determined this by requiring that\nthe overall normalization of $\\hat{F}_{ab}{}^k$ constructed from\n$\\hat{A}_a^i = c^i (L^i)^{-1}\\,{}^o\\!\\omega_a^i$ should agree with that of\n$\\hat{F}_{ab}{}^k$ constructed from holonomies around appropriate\nclosed loops, when the second construction is possible. Therefore,\nour construction is a bona-fide generalization of the previous\nconstructions used successfully in LQC.\n\nThis strategy has some applications beyond the Bianchi II model\nstudied in this paper. First, the k=$-1$ isotropic case has been\nstudied in detail in \\cite{kv1,ls}. The analysis uses the $\\bar\\mu$\nscheme, carries out a numerical simulation using exact LQC equations\nand shows that the effective equations of the ``embedding approach\"\n\\cite{jw,vt} (discussed in section \\ref{s4}) provide an excellent\napproximation to the quantum evolution. While this is an essentially\nexhaustive treatment, as \\cite{kv1,ls} itself points out, the\ntreatment has a conceptual limitation in that it builds holonomies\naround the closed loops using the extrinsic curvature $K_a^i$\n---rather than $A_a^i$--- as a ``connection''. This limitation can\nbe overcome in a straightforward fashion using our current strategy.\nMore importantly, this strategy is applicable to all class A Bianchi\nmodels, including type IX. Thus, it opens the door to the LQC\ntreatment of all these models in one go.\n\n\n\\subsection{The quantum Hamiltonian constraint}\n\\label{s3.3}\n\nWith the connection operator at hand, one can construct the\nHamiltonian constraint operator starting either from the general LQG\nexpression (\\ref{Hgen}) or the symmetry reduced expression\n(\\ref{Hcl}). We will begin by a small change in the representation\nof kinematical states which will facilitate this task.\n\n\\subsubsection{A more convenient representation}\n\\label{s3.3.1}\n\nIgnoring factor ordering ambiguities for the moment, the\nconstraint operator $\\hat{\\mathcal{C}}_H$ is given by\n\\begin{align} \\label{qHam1} \\hat{\\mathcal{C}}_H = -\\frac{1}{8\\pi\nG\\gamma^2\\Delta\\ell_{\\mathrm{Pl}}^2}&\\Big[p_1 p_2|p_3|\\sin\\bar\\mu_1c_1\n\\sin\\bar\\mu_2c_2+|p_1|p_2p_3\\sin\\bar\\mu_2c_2\\sin\\bar\\mu_3c_3 \\nonumber \\\\\n&+p_1|p_2|p_3\\sin\\bar\\mu_3c_3\\sin\\bar\\mu_1c_1\\Big]-\n\\frac{1}{8\\pi G\\gamma^2}\\Big[\\alpha\\hat{\\varepsilon}p_2p_3\\sqrt\\frac{|p_2p_3|}{|p_1|\\Delta\n\\ell_{\\mathrm{Pl}}^2}\\sin\\bar\\mu_1c_1\\nonumber \\\\ & -(1+\\gamma^2)\\left(\\frac{\\alpha\np_2p_3}{2p_1}\\right)^2\\Big]+\\frac{1}{2}\\hat{p}_T^2 \\end{align}\nwhere for simplicity of notation here and in what follows we have\ndropped the hats on the $p_i$ and $\\sin\\bar\\mu_ic_i$ operators.\nRecall that, classically, the Bianchi II symmetry group reduces to\nthe Bianchi I symmetry group if we set $\\alpha=0$. If one sets\n$\\alpha=0$ in (\\ref{qHam1}), the last two terms disappear and the\noperator $\\hat{\\mathcal{C}}_H$ reduces to that of the Bianchi I\nmodel \\cite{awe2} showing explicitly that our construction is a\nnatural generalization of the strategy used there.\n\nTo obtain the action of operators corresponding to terms of the form\n$\\sin\\bar\\mu_ic_i$ we use the same strategy as in \\cite{awe2}. As\nshown there, it is simplest to introduce dimensionless variables\n\\begin{equation}\n\\lambda_i=\\frac{\\mathrm{sgn}(p_i)\\sqrt{|p_i|}}{(4\\pi\\gamma\\sqrt\\Delta\\ell_{\\mathrm{Pl}}^3)^{1\/3}}\\,\n. \\end{equation}\nThen the kets $|\\lambda_1,\\lambda_2,\\lambda_3\\rangle$ constitute an orthonormal\nbasis in which the operators $p_k$ are diagonal\n\\begin{equation} p_k|\\lambda_1,\\lambda_2,\\lambda_3\\rangle\\, =\\,\n[\\mathrm{sgn}(\\lambda_k)(4\\pi\\gamma\\sqrt\\Delta\\ell_{\\mathrm{Pl}}^3)^{2\/3}\n\\lambda_k^2]\\,\\,|\\lambda_1,\\lambda_2,\\lambda_3\\rangle\\, . \\end{equation}\nQuantum states will now be represented by functions\n$\\Psi(\\lambda_1,\\lambda_2,\\lambda_3)$. The operator $e^{i\\bar\\mu_1c_1}$ acts on\nthem as follows\n\\begin{align} \\big[e^{i\\bar\\mu_1c_1}\\,\\Psi\\big] (\\lambda_1,\\lambda_2,\\lambda_3)\n&= \\Psi(\\lambda_1- \\frac{1}{|\\lambda_2\\lambda_3|},\\lambda_2,\\lambda_3) \\nonumber \\\\\n&= \\Psi(\\frac{v-2\\mathrm{sgn}(\\lambda_2\\lambda_3)}{v}\\cdot\\, \\lambda_1,\\lambda_2,\\lambda_3),\n\\end{align}\nwhere we have introduced the variable $v=2\\lambda_1\\lambda_2\\lambda_3$ which is\nproportional to the volume of the fiducial cell:\n\\begin{equation} \\hat{V}\\,\\Psi(\\lambda_1,\\lambda_2,\\lambda_3)\\, =\\,\n[2\\pi\\gamma\\sqrt\\Delta\\,|v|\\,\\ell_{\\mathrm{Pl}}^3]\\, \\Psi(\\lambda_1, \\lambda_2,\\lambda_3). \\end{equation}\n(The $e^{i\\bar\\mu_1c_1}$ operator is well-defined in spite of the\nappearance of $|\\lambda_2\\lambda_3|$ in the denominator; see \\cite{awe2}.)\nThe operators $e^{i\\bar\\mu_2 c_2}$ and $e^{i\\bar\\mu_3c_3}$ have\nanalogous action.\n\nWe are now ready to write the Hamiltonian constraint explicitly in\nthe $\\lambda_i$-representation. As noted above, the three terms in the\nfirst square bracket on the right hand side of Eq. (\\ref{qHam1})\nconstitute the gravitational part of $\\hat{\\mathcal{C}}_H$ for the\nLQC of Bianchi I model%\n\\footnote{There are some minor changes in the action of these three\nterms since $\\gamma$ is no longer treated as a pseudoscalar (see\nAppendix \\ref{a1}), but these do not affect the discussion.}\nand have been discussed in \\cite{awe2}. In the next two\nsub-sections we will now discuss the last two terms, which are\nspecific to the Bianchi II model.\n\n\\subsubsection{The Fourth term in $\\hat{\\mathcal{C}}_H$}\n\\label{s3.3.2}\n\nUsing a symmetric factor ordering, the fourth term becomes\n\\begin{equation} \\label{hc4} \\hat{\\mathcal{C}}_H^{(4)} = -\\frac{\\alpha\np_2p_3\\sqrt{|p_2p_3|}} {16\\pi\nG\\gamma^2\\sqrt\\Delta\\ell_{\\mathrm{Pl}}}\\,\\,\\widehat{|p_1|^{-1\/4}}\\,(\\hat{\\varepsilon}\\,\n\\sin\\bar\\mu_1c_1+\\sin\\bar\\mu_1c_1\\,\\hat{\\varepsilon})\\,\\widehat{|p_1|^{-1\/4}}\n\\, . \\end{equation}\n(Note that $p_2$ and $p_3$ commute with the other terms in\n$\\hat{\\mathcal{C}}_H^{(4)}$). The operator $p_1$ is self-adjoint on\n$\\mathcal{H}_{\\rm kin}^{\\rm grav}$ whence any measurable function of $p_1$ is\nalso a well-defined self-adjoint operator. However, since kets\n$|\\lambda_1=0, \\lambda_2,\\lambda_3\\rangle$ are normalizable in $\\mathcal{H}_{\\rm kin}^{\\rm\ngrav}$, the naive inverse powers of $\\hat{p}_1$ fail to be densely\ndefined and cannot be self-adjoint. To define inverse powers, as is\nusual in LQG, we will use a variation on the Thiemann inverse triad\nidentities \\cite{tt}. Classically, we have the identity\n\\begin{equation} \\label{class} |p_1|^{-1\/4} = -\\frac{i\\,\\mathrm{sgn}(p_1)}{2\\pi G\\gamma}\n\\sqrt\\frac{|p_2p_3|}{\\Delta\\ell_{\\mathrm{Pl}}^2}\\,\\,\ne^{-i\\bar\\mu_1c_1}\\,\\{e^{i\\bar\\mu_1c_1},|p_1|^{1\/4}\\}\\, . \\end{equation}\nwhich holds for any choice of $\\bar\\mu_1$. Since it is most natural\nto use the same $\\bar\\mu_1$ that featured in the definition of the\nconnection operator, we will make this choice. Eq (\\ref{class})\nsuggests a natural quantization strategy for $|p_1|^{-1\/4}$. Using\nit and the parity considerations, we are led to the following factor\nordering:%\n\\footnote{In the classical theory, $(L_2L_3)^{1\/4}\\,|p_1|^{-1\/4}$ is\nindependent of the choice of the elementary cell. As pointed out in\n\\cite{kv1} the inverse triad operators, by contrast, depend on the\nchoice of the cell. However, one can verify that as we remove the\nregulator, i.e., take the limit $\\mathcal{V} \\to \\mathbb{R}^3$, as in the\nclassical theory, the limiting\n$(L_2L_3)^{1\/4}\\,\\widehat{|p_1|^{-1\/4}}$ has a well defined limit.}\n\\begin{equation} \\widehat{|p_1|^{-1\/4}} = - \\frac{i\\,\\mathrm{sgn}(p_1)}{2\\pi\nG\\gamma}\\sqrt\\frac{|p_2p_3|}{\\Delta\\ell_{\\mathrm{Pl}}^2}\\,\\,e^{-i\\bar\\mu_1c_1\/2}\\,\\,\n\\frac{1}{i\\hbar}[e^{i\\bar\\mu_1c_1},|p_1|^{1\/4}]\\,\\,\ne^{-i\\bar\\mu_1c_1\/2}\\, , \\end{equation}\nwhere, as is common in LQC, $\\mathrm{sgn}(p_1)$ is defined as\n\\begin{equation} \\mathrm{sgn}(p_1) = \\left\\{\\rlap{\\rlap{\\raise4ex\\hbox{\\,\\,$+1$ if $p_1>0$,}}\n{\\raise0ex\\hbox{\\,\\,$0$ if $p_1=0$,}}}\n{\\lower4ex\\hbox{\\,\\,$-1$ if $p_1<0$.}} \\right. \\end{equation}\n\nAt first it may seem surprising that the expression of\n$\\widehat{|p_1|^{-1\/4}}$ involves operators other than ${p_1}$. It\nis therefore important to verify that it has the standard desirable\nproperties. First, as one would hope, it is indeed diagonal in the\neigenbasis of the operators $\\hat{p}_k$:\n\\begin{equation} \\label{inv} \\widehat{|p_1|^{-1\/4}}\\, |\\lambda_1,\\lambda_2,\\lambda_3\\rangle =\n\\frac{\\sqrt2 \\mathrm{sgn}(\\lambda_1)\\,\\sqrt{|\\lambda_2\\lambda_3|}}\n{(4\\pi\\gamma\\sqrt\\Delta\\ell_{\\mathrm{Pl}}^3)^{1\/6}}\n\\left(\\sqrt{|v+\\mathrm{sgn}(\\lambda_2\\lambda_3)|}-\\sqrt{|v-\\mathrm{sgn}(\\lambda_2\\lambda_3)|}\\right)\\,\n|\\lambda_1,\\lambda_2,\\lambda_3\\rangle. \\end{equation}\nSecond, on eigenkets with large volume, the eigenvalue is indeed\nwell-approximated by $p_1^{-1\/4}$, whence on semi-classical states\nit behaves as the inverse of $\\hat{p}^{1\/4}$, just as one would\nhope. Thus, (\\ref{inv}) is a viable candidate for\n$\\widehat{|p_1|^{-1\/4}}$. But there are interesting\nnon-trivialities in the Planck regime. In particular, although\ncounter-intuitive, as is common in LQC the operator annihilates\nstates $|\\lambda_1,\\lambda_2,\\lambda_3\\rangle$ with $v = 2\\lambda_1\\lambda_2\\lambda_3 =0$\n\nFinally, note that the operator $\\hat{\\varepsilon}$ appearing in the\nexpression (\\ref{hc4}) of $\\hat{\\mathcal{C}}_H^{(4)}$ either\noperates immediately before or after $\\widehat{|p_1|^{-1\/4}}$. Since\n$\\widehat{|p_1|^{-1\/4}}$ annihilates all zero volume states and\n$\\hat{\\varepsilon}$ acts on such states as the identity operator, we only\nneed to consider the action of $\\hat{\\varepsilon}$ on states with nonzero\nvolume. In this case, $\\hat{\\varepsilon}$ acts as $\\mathrm{sgn}(v)$. Therefore the\naction of $\\hat{\\mathcal{C}}_H^{(4)}$ can be written as:\n\\begin{align}\n\\Big[\\hat{\\mathcal{C}}_H^{(4)}\\,\\Psi\\Big](\\lambda_1,\\lambda_2,\\lambda_3) =&\n-\\frac{i\\alpha\\pi\\sqrt\\Delta\\hbar\\ell_{\\mathrm{Pl}}^2}{(4\\pi\\gamma\\sqrt\\Delta)^{1\/3}}\\,\\,\n\\mathrm{sgn}(v)\\,\\, (\\lambda_2\\lambda_3)^4\\nonumber\\\\\n\\left(\\sqrt{|v+\\mathrm{sgn}(\\lambda_2\\lambda_3)|}-\\sqrt{|v-\\mathrm{sgn}(\\lambda_2\\lambda_3)|} \\right)\n& \\quad \\Big[\\Phi^+(\\lambda_1,\\lambda_2,\\lambda_3) -\\Phi^-(\\lambda_1,\\lambda_2,\\lambda_3)\\Big]\n\\label{c4}\n\\end{align}\nwhere\n\\begin{align} \\Phi^\\pm(\\lambda_1,\\lambda_2,\\lambda_3) =& \\Big(\\sqrt{\\left|v\\pm2\\mathrm{sgn}(\\lambda_2\n\\lambda_3)+\\mathrm{sgn}(\\lambda_2\\lambda_3))\\right|}\n-\\sqrt{\\left|v\\pm2\\mathrm{sgn}(\\lambda_2\\lambda_3)-\\mathrm{sgn}(\\lambda_2\\lambda_3)\\right|}\\, \\Big)\n\\nonumber \\\\ & \\quad\\: \\times\n\\big(\\mathrm{sgn}(v)+\\mathrm{sgn}(v\\pm2 \\mathrm{sgn}(\\lambda_2\\lambda_3))\\big)\\,\\,\n\\Psi(\\frac{v\\pm2\\mathrm{sgn}(\\lambda_2\\lambda_3)}{v}\\lambda_1,\\lambda_2,\\lambda_3).\\label{phi} \\end{align}\n\nRecall that in the classical theory the singularity corresponds\nprecisely to the phase space points at which the volume vanishes.\nTherefore, as in the Bianchi I model, states with support only on\npoints with $v=0$ will be called `singular' and those which vanish\nat points with $v=0$ will be called regular. The total Hilbert space\n$\\mathcal{H}_{\\rm kin}^{\\rm grav}$ is naturally decomposed as a direct sum $\\mathcal{H}_{\\rm kin}^{\\rm grav} = \\mathcal{H}^{\\rm\ngrav}_{\\rm sing}\\oplus \\mathcal{H}^{\\rm grav}_{\\rm reg}$ of singular and\nregular sub-spaces. We will conclude this discussion by examining\nthe action of $\\hat{\\mathcal{C}}_H^{(4)}$ on these sub-spaces. Note\nfirst that in the action (\\ref{hc4}) of $\\hat{\\mathcal{C}}_H^{(4)}$,\nthe state is first acted upon by the operator\n$\\widehat{|p_1|^{-1\/4}}$. Since this operator annihilates states\n$|\\lambda_1 \\lambda_2,\\lambda_3\\rangle$ with $v = 2\\lambda_1\\lambda_2\\lambda_3 =0$, singular\nstates are simply annihilated by $\\hat{\\mathcal{C}}_H^{(4)}$. In\nparticular this implies that the singular sub-space is mapped to\nitself under this action. It is clear from (\\ref{phi}) that if\n$\\Psi$ is regular, i.e. vanishes on all points with $v =0$,\n$\\Phi^\\pm$ also vanish at these points. Thus the regular sub-space\nis also preserved by this action. This fact will be used in the\ndiscussion of singularity resolution in section \\ref{s3.3.4}.\n\n\n\\emph{Remark:}\\, Our definition of the operator\n$\\widehat{|p|^{-1\/4}}$ is not unique; as is common with non-trivial\nfunctions of elementary variables, there are factor ordering\nambiguities. For example, for $00$. We write\\[\nf_{0,m}^{(p)}=q^{-m}+\\sum_{n=0}^{\\infty}a_{0}^{(p)}(m,n)q^{n}\\]\n so that for $n\\geq0$, the symbol $a_{0}^{(p)}(m,n)$ denotes the coefficient\nof $q^{n}$ in the $m^{th}$ basis element of level $p$. Note that\nthe function $f_{0, m}^{(p)}$ corresponds to Ahlgren's $j_m^{(p)}$.\n\nFor an example of some of these functions, consider the case $p=2$:\n\\begin{align*}\nf_{0,1}^{(2)}(\\tau) & =\\psi^{(2)}(\\tau)\\\\\n & =q^{-1}-24+276q-2048q^{2}+11202q^{3}-49152q^{4}+\\ldots\\\\\nf_{0,2}^{(2)}(\\tau) & =\\psi^{(2)}(\\tau)^{2}+48\\psi^{(2)}(\\tau)\\\\\n & =q^{-2}-24-4096q+98580q^{2}-1228800q^{3}+10745856q^{4}+\\ldots\\\\\nf_{0,3}^{(2)}(\\tau) & =\\psi^{(2)}(\\tau)^{3}+72\\psi^{(2)}(\\tau)^{2}+900\\psi^{(2)}(\\tau)\\\\\n & =q^{-3}-96+33606q-1843200q^{2}+43434816q^{3}-648216576q^{4}+\\ldots\\end{align*}\nThe function $f_{0,m}^{(p)}$ is a level $p$ modular function that\nvanishes at $0$ (if $m\\neq0$) and has a pole of order $m$ at\n$\\infty$. The conditions at the cusps determine this function\nuniquely; if two such functions exist, their difference is a\nholomorphic modular function, which must be a constant. Since both\nfunctions vanish at 0, this constant must be $0$.\n\nThe functions comprising these bases for $p=2,3,5,7$ have\ndivisibility properties which bear a striking resemblance to the\ndivisibility properties of $j(\\tau)$; in many cases they are\nidentical. As an example of some of the divisibility properties we\nencounter with this basis, we experimentally examine the $2$-adic\nvaluation of the even indexed coefficients of $f_{0,m}^{(2)}(\\tau)$\nfor $m=1,3,5,7$ in Table \\ref{tab:2-Adic-Table}. As the data in the\ntable suggest, the $2$-divisibility which $j(\\tau)$ exhibits gives\nus a lower bound on the $2$-divisibility of the odd-indexed $p=2$\nbasis elements.\n\n\n\\begin{table}[h]\n\\noindent \\begin{centering}\n\\label{Flo:2-Adic-Float}\n\\par\\end{centering}\n\\noindent \\begin{centering}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\n\\multicolumn{1}{|c}{} & & \\multicolumn{1}{c}{$a_{0}^{(2)}(m,2)$} & \\multicolumn{1}{c}{$a_{0}^{(2)}(m,4)$} & \\multicolumn{1}{c}{$a_{0}^{(2)}(m,6)$} & \\multicolumn{1}{c}{$a_{0}^{(2)}(m,8)$} & \\multicolumn{1}{c}{$a_{0}^{(2)}(m,10)$} & $a_{0}^{(2)}(m,12)$\\tabularnewline\n\\hline\n & $1$ & $11$ & $14$ & $13$ & $17$ & $12$ & $16$\\tabularnewline\n\\cline{2-8}\n & $3$ & $13$ & $16$ & $15$ & $19$ & $14$ & $18$\\tabularnewline\n\\cline{2-8}\n$m$ & $5$ & $12$ & $15$ & $14$ & $18$ & $13$ & $17$\\tabularnewline\n\\cline{2-8}\n & $7$ & $14$ & $17$ & $16$ & $20$ & $15$ & $19$\\tabularnewline\n\\cline{2-8}\n & min & $11$ & $14$ & $13$ & $17$ & $12$ & $16$\\tabularnewline\n\\hline\n\\multicolumn{8}{|c|}{}\\tabularnewline\n\\hline\n$j(\\tau)$ & & $11$ & $14$ & $13$ & $17$ & $12$ & $16$\\tabularnewline\n\\hline\n\\end{tabular}\n\\par\\end{centering}\n\\noindent \\centering{}\\caption{\\label{tab:2-Adic-Table}$2$-adic\nvaluation of $a_{0}^{(2)}(m,n)$ compared to corresponding\ncoefficients in $j(\\tau)$}\n\\end{table}\n\nNote that these functions form a basis for $M_0^\\infty(p)$, the\nspace of modular forms of weight 0 and level $p$ with poles allowed\nonly at the cusp at $\\infty$. A full basis for the space $M_0^!(p)$\nof weakly holomorphic modular forms of weight $0$ and level $p$ is\ngenerated by the $f_{0, m}^{(p)}(\\tau)$ and the functions\n$(\\phi^{(p)}(\\tau))^n$ for integers $n\\geq 1$.\n\nRecall that the concluding remarks of Lehner's second paper\n\\cite{Lehner:2} state that the coefficients of certain level $p$\nmodular functions having a pole of order less than $p$ at $\\infty$\nhave the same $p$-divisibility properties as the coefficients $c(n)$\nof $j(\\tau)$. More precisely, we have the following theorem.\n\\begin{thm}[Lehner]\n\\label{thm:Lehner-Main} Let $p\\in\\{2,3,5,7\\}$ and let $f(\\tau)$ be a\nmodular function on $\\Gamma_{0}(p)$ having a pole at $\\infty$ of\norder $ \\alpha$,\n\\[\n\\begin{array}{rll}\na_{0}^{(2)}(2^{\\alpha}m',2^{\\beta}n)\\equiv0 & \\pmod{2^{3(\\beta-\\alpha)+8}} & \\text{if }p=2\\\\\na_{0}^{(3)}(3^{\\alpha}m',3^{\\beta}n)\\equiv0 & \\pmod{3^{2(\\beta-\\alpha)+3}} & \\text{if }p=3\\\\\na_{0}^{(5)}(5^{\\alpha}m',5^{\\beta}n)\\equiv0 & \\pmod{5^{(\\beta-\\alpha)+1}} & \\text{if }p=5\\\\\na_{0}^{(7)}(7^{\\alpha}m',7^{\\beta}n)\\equiv0 & \\pmod{7^{(\\beta-\\alpha)}} & \\text{if }p=7.\\end{array}\\]\n\\end{thm}\n\nNote that for basis elements $f_{0,m}^{(p)}$ with $(m,p)=1$, these\ndivisibility properties match those in Theorem\n\\ref{thm:Lehner-Main}; in fact, Lehner's proof is easily extended to\nprove the congruences in these cases. For basis elements with\n$m=p^{\\alpha}m'$ and $\\alpha\\ge1$, the divisibility is ``shifted.''\nThis shifting occurs in the $(\\beta-\\alpha)$ factor in the exponent\nof the modulus.\n\nFor the coefficients $a_0^{(p)}(p^\\alpha m', p^\\beta n)$ with\n$\\alpha > \\beta$, computations suggest that similar congruences\nshould hold. Additionally, it appears that powers of the function\n$\\phi^{(p)}(\\tau)$ have Fourier coefficients with slightly weaker\ndivisibility properties, despite the fact that their Fourier\ncoefficients at $0$ are not integral. It would be interesting to\nmore fully understand these congruences.\n\n\\section{Preliminary Lemmas and Definitions}\n\nIn this section, we provide the necessary definitions and background\nfor the proof of the main theorem.\n\nFor a prime $p$ we define the level $p$ Hecke operator $U_{p}$ by\\[\nU_{p}f(\\tau)=\\frac{1}{p}\\sum_{\\ell=0}^{p-1}f\\left(\\frac{\\tau+\\ell}{p}\\right),\\]\nusing the notation $U_{p}^{n}f=U_{p}U_{p}\\cdots U_{p}f$ for repeated\napplication of $U_{p}$. When $f$ has the Fourier expansion\n$f(\\tau)=\\sum_{n=n_{0}}^{\\infty}a(n)q^{n}$, this operator takes the\nform\\[ U_{p}f(\\tau)=\\sum_{n=n_{0}}^{\\infty}a(pn)q^{n},\\] essentially\n{}``pulling out'' all of the coefficients of $f$ whose index is\ndivisible by $p$. This operator preserves modularity:\nif $f$ is a level $p$ modular function, then $U_{p}f$ is also a level\n$p$ modular function.\n\nFor the primes $p=2,3,5,7$ the topological genus of\n$\\Gamma_{0}(p)\\backslash\\mathcal{H}$ is zero, so the field of level\n$p$ modular functions is generated by a single modular function\ncalled a Hauptmodul. For the primes in consideration, one such\nfunction is $\\psi^{(p)}(\\tau)$. Note that the modular function\n$\\phi^{(p)}(\\tau)=\\psi^{(p)}(\\tau)^{-1}=q+O(q^{2})$ is also a\nHauptmodul.\n\nFurther, for these primes, the fundamental domain for\n$\\Gamma_{0}(p)$ has precisely two cusps, which may be taken to be at\n$\\infty$ and at $0$. Hence, we are most concerned with the behavior\nof these functions at $\\infty$ and at $0$. To switch between cusps,\nwe make the substitution $\\tau\\mapsto-1\/p\\tau$. The following lemma\ngives relations for $\\psi^{(p)}(\\tau)$ and $\\phi^{(p)}(\\tau)$ at\n$0$, and makes clear that powers of $\\phi^{(p)}$ do not satisfy\nLehner's integrality condition.\n\\begin{lem}\n\\label{lem:Psi-At-0}The functions $\\psi^{(p)}(\\tau)$ and $\\phi^{(p)}(\\tau)$\nsatisfy the relations \\begin{align}\n\\psi^{(p)}(-1\/p\\tau) & =p^{\\lambda\/2}\\phi^{(p)}(\\tau)\\label{eq:Psi-At-0}\\\\\n\\phi^{(p)}(-1\/p\\tau) & =p^{-\\lambda\/2}\\psi^{(p)}(\\tau)\\label{eq:Phi-At-0}\\end{align}\n\\end{lem}\n\\begin{proof}\nThe functional equation for $\\eta(\\tau)$ is\n$\\eta(-1\/\\tau)=\\sqrt{-i\\tau}\\eta(\\tau)$. Using this, we find that \\[\n\\psi^{(p)}\\left(\\frac{-1}{p\\tau}\\right) =\n\\left(\\frac{\\eta(-1\/p\\tau)} {\\eta(-1\/\\tau)}\\right)^{\\lambda} =\n\\left(\\frac{\\sqrt{-ip\\tau}\n \\eta(p\\tau)}{\\sqrt{-i\\tau} \\eta(\\tau)}\\right)^{\\lambda} = (\\sqrt{p})^{\\lambda}\n\\left(\\frac{\\eta(p\\tau)}{\\eta(\\tau)}\\right)^{\\lambda} =\np^{\\lambda\/2}\\phi^{(p)}(\\tau).\\] The second statement follows after\nreplacing $\\tau$ by $-1\/p\\tau$ in the first statement.\n\\end{proof}\nWe next state a well-known lemma which gives a formula for\ndetermining the behavior of a modular function at $0$ after $U_{p}$\nhas been applied. A proof can be found in \\cite{Apostol:modular}.\n\\begin{lem}\n\\label{lem:Main-Up-Formula}Let $p$ be prime and let $f(\\tau)$ be\na level $p$ modular function. Then\\begin{equation}\np(U_{p}f)(-1\/p\\tau)=p(U_{p}f)(p\\tau)+f(-1\/p^{2}\\tau)-f(\\tau).\\label{eq:Main-Up-Formula}\\end{equation}\n\n\\end{lem}\nLehner's original papers included the following lemma and its proof, which gives\ntwo important equations. The first gives a formula for $U_{p}\\phi^{(p)}$\nas a polynomial with integral coefficients in $\\phi^{(p)}$; the second\ngives an algebraic relation which is satisfied by $\\phi^{(p)}(\\tau\/p)$.\n\\begin{lem}\n\\label{lem:Modular-Eq-Phi}Let $p\\in\\{2,3,5,7\\}$. Then there exist\nintegers $b_{j}^{(p)}$ such that\n\n\\[\n\\begin{array}{cc}\n\\text{(a)} & U_{p}\\phi^{(p)}(\\tau)=p\\sum\\limits _{j=1}^{p}b_{j}^{(p)}\\phi^{(p)}(\\tau)^{j}.\\end{array}\\]\n\n\nFurther, let $h^{(p)}(\\tau)=p^{\\lambda\/2}\\phi^{(p)}(\\tau\/p).$ Then\\[\n\\begin{array}{cc}\n\\text{(b)} & \\big(h^{(p)}(\\tau)\\big)^{p}+\\sum\\limits _{j=1}^{p}(-1)^{j}g_{j}(\\tau)\\big(h^{(p)}(\\tau)\\big)^{p-j}=0\\end{array}\\]\n\n\nwhere $g_{j}(\\tau)=(-1)^{j+1}p^{\\lambda\/2+2}\\sum\\limits _{\\ell=j}^{p}b_{\\ell}\\phi^{(p)}(\\tau)^{\\ell-j+1}$.\\end{lem}\n\\begin{proof}\n\n(a) Since $\\phi$ vanishes at $\\infty$, $U_{p}\\phi$ also vanishes\nat $\\infty$; we will now consider its behavior at $0$. Using (\\ref{eq:Main-Up-Formula})\nand replacing $\\tau$ by $p\\tau$ in (\\ref{eq:Psi-At-0}) we obtain\\begin{align*}\nU_{p}\\phi(-1\/p\\tau) & =U_{p}\\phi(p\\tau)+p^{-1}\\phi(-1\/p^{2}\\tau)-p^{-1}\\phi(\\tau)\\\\\n & =U_{p}\\phi(p\\tau)+p^{-1}\\psi(p\\tau)-p^{-1}\\phi(\\tau)\\\\\n & =O(q^{p})+p^{-\\lambda\/2-1}q^{-p}+O(1)-p^{-1}q+O(q^{2})\\\\\np^{\\lambda\/2+1}U_{p}\\phi(-1\/p\\tau) & =q^{-p}+O(1)\\end{align*}\n\n\nThe right side of this equation is a level $p$ modular function with\ninteger coefficients, so we may write it as a polynomial in\n$\\psi(\\tau)$ with integer coefficients. The polynomial will not have\na constant term since the left side vanishes at $0$. Therefore,\\[\np^{\\lambda\/2+1}U_{p}\\phi(-1\/p\\tau)=\\sum_{j=1}^{p}c_{j}\\psi(\\tau){}^{j}.\\]\n\n\nNow, replacing $\\tau$ by $-1\/p\\tau$, we find\\[\np^{\\lambda\/2+1}U_{p}\\phi(\\tau)=\\sum_{j=1}^{p}c_{j}p^{\\lambda j\/2}\\phi(\\tau){}^{j}.\\]\n\n\nAfter cancelling the $p^{\\lambda\/2+1}$, we find that\n$U_{p}\\phi(\\tau)=\\sum\\limits _{j=1}^{p}c_{j}'\\phi(\\tau)^{j}$ and we\ncompute the coefficients $c_{j}'$ (the authors used\n\\noun{mathematica}). The computation is finite, and we find that\neach coefficient $c_{j}'$ has a factor of $p$, so the coefficients\n$b_{j}^{(p)}$ are integral. A complete table of values of the\n$b_{j}^{(p)}$ is found in Table \\ref{tab:b_j-Table}.\n\n\\begin{table}[h]\n\\noindent \\begin{centering}\n\\label{Flo:b_j-Float}\n\\par\\end{centering}\n\n\\noindent \\begin{centering}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\cline{3-6}\n\\multicolumn{1}{c}{} & & \\multicolumn{4}{c|}{$p$}\\tabularnewline\n\\cline{3-6}\n\\multicolumn{1}{c}{} & & 2 & 3 & 5 & 7\\tabularnewline\n\\hline\n & 1 & $3\\cdot2^{2}$ & $10\\cdot3^{1}$ & $63\\cdot5^{0}$ & $82\\cdot7^{0}$\\tabularnewline\n\\cline{2-6}\n & 2 & $2^{10}$ & $4\\cdot3^{6}$ & $52\\cdot5^{3}$ & $176\\cdot7^{2}$\\tabularnewline\n\\cline{2-6}\n & 3 & & $3^{10}$ & $63\\cdot5^{5}$ & $845\\cdot7^{3}$\\tabularnewline\n\\cline{2-6}\n$j$ & 4 & & & $6\\cdot5^{8}$ & $272\\cdot7^{5}$\\tabularnewline\n\\cline{2-6}\n & 5 & & & $5^{10}$ & $46\\cdot7^{7}$\\tabularnewline\n\\cline{2-6}\n & 6 & & & & $4\\cdot7^{9}$\\tabularnewline\n\\cline{2-6}\n & 7 & & & & $7^{10}$\\tabularnewline\n\\hline\n\\end{tabular}\n\\par\\end{centering}\n\n\\noindent \\centering{}\\caption{\\label{tab:b_j-Table}Values of $b_{j}^{(p)}$}\n\n\\end{table}\n\n\n(b) We again apply (\\ref{eq:Main-Up-Formula}) to $\\phi(\\tau)$, this\ntime using what we know from $(a)$.\\[\npU_{p}\\phi(-1\/p\\tau)=pU_{p}\\phi(p\\tau)+\\phi(-1\/p^{2}\\tau)-\\phi(\\tau)\\]\n\\[\np^{2}\\sum_{j=1}^{p}b_{j}\\phi(-1\/p\\tau)^{j}=p^{2}\\sum_{j=1}^{p}b_{j}\\phi(p\\tau)^{j}+\\phi(-1\/p^{2}\\tau)-\\phi(\\tau).\\]\nWe now use Lemma \\ref{lem:Psi-At-0} with the knowledge that $\\psi(\\tau)=\\phi(\\tau)^{-1}$\nto obtain\n\\[p^{2}\\sum_{j=1}^{p}b_{j}p^{-\\lambda j\/2}\\phi(\\tau)^{-j}-p^{2}\\sum_{j=1}^{p}b_{j}\\phi(p\\tau)^{j}+\\phi(\\tau)-p^{-\\lambda\/2}\\phi(p\\tau)^{-1}=0.\\]\nAfter replacing $\\tau$ by $\\tau\/p$ and multiplying by $p^{\\lambda\/2}$,\nthis becomes\\begin{equation}\np^{\\lambda\/2+2}\\sum_{j=1}^{p}b_{j}\\big(h(\\tau)^{-j}-\\phi(\\tau)^{j}\\big)+h(\\tau)-\\phi(\\tau)^{-1}=0.\\label{eq:Intermediate-Modular-Eq-Phi}\\end{equation}\n\n\nWe now divide by $h^{-1}-\\phi$. Note two facts: \\[\nh^{-j}-\\phi^{j}=(h^{-1}-\\phi)\\sum_{\\ell=0}^{j-1}h^{-\\ell}\\phi^{j-\\ell-1}\\]\n\\[\n\\frac{h-\\phi^{-1}}{h^{-1}-\\phi}=\\frac{h(h\\phi-1)}{\\phi(1-h\\phi)}=-\\frac{h}{\\phi}.\\]\nSo (\\ref{eq:Intermediate-Modular-Eq-Phi}) becomes\\[\np^{\\lambda\/2+2}\\sum_{j=1}^{p}b_{j}\\sum_{\\ell=0}^{j-1}h^{-\\ell}\\phi^{j-\\ell-1}-\\phi^{-1}h=0\\]\nwhich, after multiplying by $\\phi h^{p-1}$, becomes\\[\np^{\\lambda\/2+2}\\sum_{j=1}^{p}b_{j}\\sum_{\\ell=0}^{j-1}h^{p-\\ell-1}\\phi^{j-\\ell}-h^{p}=0.\\]\nWe now change the order of summation and rearrange terms to obtain\nthe desired formula:\\[\nh(\\tau)^{p}=\\sum_{j=1}^{p}\\big(p^{\\lambda\/2+2}\\sum_{\\ell=j}^{p}b_{\\ell}\\phi(\\tau)^{\\ell-j+1}\\big)h(\\tau){}^{p-j}.\\]\n\n\\end{proof}\nThe next lemma states that when you apply $U_{p}$ to a certain type\nof polynomial in $\\phi_{p}$, you get a similar polynomial back which\nhas picked up a power of $p$. The details of this lemma are found in\nboth \\cite{Lehner:1} and \\cite{Lehner:2}, scattered throughout the\nproofs of the main theorems. For our purposes, it will be more\nuseful in the following form.\n\\begin{lem}\n\\label{lem:Phi-Polynomials}Let $p\\in\\{2,3,5,7\\}$ and let $R^{(p)}$\ndenote the set of polynomials in $\\phi^{(p)}$ of the form\\[\nd_{1}\\phi^{(p)}(\\tau)+\\sum_{n=2}^{N}d_{n}p^{\\gamma}\\phi^{(p)}(\\tau)^{n}\\]\n\\[\n\\begin{array}{ll}\n\\text{where }\\gamma= & \\begin{cases}\n8(n-1) & \\text{if }p=2\\\\\n4(n-1) & \\text{if }p=3\\\\\nn & \\text{if }p=5\\\\\nn & \\text{if }p=7.\\end{cases}\\end{array}\\] Then $U_{p}$ maps\n$R^{(p)}$ to $p^{\\delta}R^{(p)}$ where $\\delta=3,2,1,1$ for\n$p=2,3,5,7$, respectively. That is, applying $U_{p}$ to a polynomial\nof the above form yields a polynomial of the same form with an extra\nfactor of $p^{\\delta}$.\\end{lem}\n\\begin{proof}\nConsider the function\\[\nd_{1}U_{p}\\phi(\\tau)+\\sum_{n=2}^{r}d_{n}p^{\\gamma}U_{p}\\phi(\\tau)^{n}.\\]\nFor the first term, Lemma \\ref{lem:Modular-Eq-Phi}(a) shows that\n$U_{p}\\phi(\\tau)\\in p^{\\delta}R_{p}$ since, by inspection, the $b_{j}^{(p)}$\nintegers are divisible by sufficiently high powers of $p$.\n\nFor the remaining terms, we will prove\\begin{equation}\np^{\\gamma}U_{p}\\phi^{n}=p^{\\delta}r\\label{eq:Up-Phi^t}\\end{equation}\nwhere $r\\in R_{p}$. The result will immediately follow.\n\nBy the definition of $U_{p}$ we have\\begin{equation}\nU_{p}\\phi^{n}=p^{-1}\\sum_{\\ell=0}^{p-1}\\phi\\left(\\frac{\\tau+\\ell}{p}\\right)^{n}=p^{-1-\\lambda t\/2}\\sum_{\\ell=0}^{p-1}h_{\\ell}(\\tau)^{n}\\label{eq:Up-Def-W-Sum}\\end{equation}\nwhere $h_{\\ell}(\\tau)=p^{\\lambda\/2}\\phi\\left(\\frac{\\tau+\\ell}{p}\\right)$\nis related to $h$ from Lemma \\ref{lem:Modular-Eq-Phi}(b). Let $S_{n}$\nbe the sum of the $n^{th}$ powers of the $h_{\\ell}$ so that \\[\nS_{n}=\\sum_{\\ell=0}^{p-1}h_{\\ell}^{n}.\\]\n\n\nDefine the polynomial\n$F(x)=\\sum_{j=0}^{p}(-1)^{j}g_{j}(\\tau)x^{p-j}$ where the\n$g_{j}(\\tau)$ are as in Lemma \\ref{lem:Modular-Eq-Phi}. In the same\nlemma, if we replace $\\tau$ with $\\tau+\\ell$, the $g_{j}(\\tau)$ are\nunaffected since $\\phi(\\tau+1)=\\phi(\\tau)$. Therefore, that lemma\ntells us that the $p$ roots of the polynomial $F(x)$ are precisely\nthe $h_{\\ell}$. Using Newton's formula for the $n^{th}$ power sum of\nthe roots of a polynomial, we obtain\\begin{equation}\nS_{n}=\\sum_{\\ell=0}^{p-1}h_{\\ell}^{n}=\\sum_{j=1}^{n}(-1)^{j+1}g_{j}S_{n-j}\\label{eq:Newtons-Formula}\\end{equation}\nwhere $g_{j}=0$ for $j>p$ and $S_{0}=n$.\n\nWe now proceed case-by-case. The $p=2$ case illustrates the method,\nso we will only include the intermediate steps in the $p=3,5,7$ cases.\n\\begin{caseenv}\n\\item $p=2$. Then, using (\\ref{eq:Up-Def-W-Sum}), equation (\\ref{eq:Up-Phi^t}) is\nequivalent to\\[\n2^{8(n-1)}\\big(2^{-1-12n}S_{n}\\big)=2^{3}r,\\text{ or}\\]\n\\begin{equation}\nS_{n}=2^{4n+12}r.\\label{eq:St-Powers-of-2}\\end{equation}\nWe now use (\\ref{eq:Newtons-Formula}) to calculate $S_{1}$ and $S_{2}$:\\[\nS_{1}=g(1)\\]\n\\[\nS_{2}=g_{1}S_{1}-2g_{2}=g_{1}^{2}-2g_{2}.\\]\nFrom Lemma \\ref{lem:Modular-Eq-Phi} we can compute the values of\nthe $g_{j}$. Using the $b_{j}$ values from the table in that lemma,\nwe have\\[\ng_{1}=2^{14}(b_{1}\\phi_{2}+b_{2}\\phi_{2}^{2})=2^{16}(3\\phi_{2}+2^{8}\\phi_{2}^{2})\\]\n\\[\ng_{2}=-2^{14}b_{2}\\phi_{2}=-2^{24}\\phi_{2}.\\]\nWe can now see that \\[\nS_{1}=g_{1}=2^{16}(3\\phi_{2}+2^{8}\\phi_{2}^{2})\\]\n\\[\nS_{2}=2^{32}(3\\phi_{2}+2^{8}\\phi_{2}^{2})^{2}+2^{25}\\phi_{2}=2^{20}(2^{5}\\phi_{2}+2^{12}3^{2}\\phi_{2}^{2}+2^{21}3\\phi_{2}^{3}+2^{28}\\phi_{2}^{4}).\\]\nThus (\\ref{eq:St-Powers-of-2}) is satisfied for $n=1,2$. We proceed\nby induction. Assume (\\ref{eq:St-Powers-of-2}) is satisfied for all\nintegers $ \\alpha$,\n\\[\n\\begin{array}{rll}\na_{0}^{(2)}(2^{\\alpha}m',2^{\\beta}n)\\equiv0 & \\pmod{2^{3(\\beta-\\alpha)+8}} & \\text{if }p=2\\\\\na_{0}^{(3)}(3^{\\alpha}m',3^{\\beta}n)\\equiv0 & \\pmod{3^{2(\\beta-\\alpha)+3}} & \\text{if }p=3\\\\\na_{0}^{(5)}(5^{\\alpha}m',5^{\\beta}n)\\equiv0 & \\pmod{5^{(\\beta-\\alpha)+1}} & \\text{if }p=5\\\\\na_{0}^{(7)}(7^{\\alpha}m',7^{\\beta}n)\\equiv0 & \\pmod{7^{(\\beta-\\alpha)}} & \\text{if }p=7.\\end{array}\\]\n\\end{thm*}\nThe proof is in three cases. The first illustrates the method for\nthe simplest basis elements, namely those with $(m,p)=1$. The second\ndemonstrates the ``shifting'' property at its first occurence,\n$f_{0,p}^{(p)}$. The third is the general case; it builds\ninductively upon the methods of the first two cases.\n\n\n\\subsection{Case 1: $(m,p)=1$}\n\\begin{proof}\nThis proof is almost identical to Lehner's proof of Theorem 3 in\n\\cite{Lehner:2}, however it applies not only to functions which\nhave poles of order bounded by $p$, but to all basis elements with\n$(m,p)=1$. For ease of notation, let $f(\\tau)=f_{0,m}^{(p)}(\\tau)$.\n\nWe will demonstrate the method with $m=1$, then generalize it to\nall $m$ relatively prime to $p$. First, we will write $U_{p}f(\\tau)$\nas a polynomial in $\\phi(\\tau)$ with integral coefficients, all of\nwhich are divisible by the desired power of $p$. Since $U_{p}$ isolates\nthe coefficients whose index is divisible by $p$, we will have proven\nthe theorem for $\\beta=1$. We will then apply $U_{p}$ repeatedly\nto the polynomial, showing that the result is always another polynomial\nin $\\phi$ with integral coefficients, all of which are divisible\nby the desired higher power of $p$.\n\nConsider the level $p$ modular function $g(\\tau)=pU_{p}f(\\tau)+p^{\\lambda\/2}\\phi(\\tau)$.\nNotice that $g(\\tau)$ is holomorphic at $\\infty$ since both $U_{p}f(\\tau)$\nand $\\phi(\\tau)$ are holomorphic there. The $q$-expansion at $0$\nfor $g(\\tau)$ is given by\\[\ng(-1\/p\\tau)=p(U_{p}f)(-1\/p\\tau)+p^{\\lambda\/2}\\phi(-1\/p\\tau)\\]\nwhich, by Lemmas \\ref{lem:Psi-At-0} and \\ref{lem:Main-Up-Formula} becomes\\[\ng(-1\/p\\tau)=p(U_{p}f)(p\\tau)+f(-1\/p^{2}\\tau)-f(\\tau)+\\psi(\\tau).\\]\nWhen we notice that $f(\\tau)=\\psi(\\tau)$ in this $m=1$ case, we\nobtain\\begin{align*}\ng(-1\/p\\tau) & =p(U_{p}f)(p\\tau)+\\psi(-1\/p^{2}\\tau)-\\psi(\\tau)+\\psi(\\tau)\\\\\n & =p(U_{p}f)(p\\tau)+p^{\\lambda\/2}\\phi(p\\tau),\\end{align*}\nwhich is holomorphic at $\\infty$. Hence, $g(\\tau)$ is a holomorphic\nmodular function on $\\Gamma_{0}(p)$, so it must be constant. Therefore,\n\\begin{equation}\nU_{p}f(\\tau)=c_{0}-p^{\\lambda\/2-1}\\phi(\\tau)\\label{eq:Up-f1=00003DPhi}\\end{equation}\nfor some constant $c_{0}$. The proof is complete for $\\beta=1$.\n\nNote: the prime $13$, having genus zero, would work in this construction;\nhowever, in that case $\\lambda=\\frac{24}{13-1}=2$, so $13^{\\lambda\/2-1}=1$,\nand we gain no new information.\n\nWe now iterate the above process to prove the theorem for $\\beta>1$.\nNotice that \\[\nU_{p}(U_{p}f(\\tau))=c^{(p)}-p^{\\lambda\/2-1}U_{p}\\phi(\\tau).\\] We\nknow from Lemma \\ref{lem:Modular-Eq-Phi} that $U_{p}\\phi$ is a\npolynomial in $\\phi$; in fact, by inspection of the $b_{j}^{(p)}$\nvalues we see that we may write\\begin{align*}\nU_{2}\\phi^{(2)}(\\tau) & =2^{3}\\big(d_{1}^{(2)}\\phi^{(2)}(\\tau)+\\sum_{n=2}^{2}d_{n}^{(2)}2^{8(n-1)}\\phi^{(2)}(\\tau)^{n}\\big)\\\\\nU_{3}\\phi^{(3)}(\\tau) & =3^{2}\\big(d_{1}^{(3)}\\phi^{(3)}(\\tau)+\\sum_{n=2}^{3}d_{n}^{(3)}3^{4(n-1)}\\phi^{(3)}(\\tau)^{n}\\big)\\\\\nU_{5}\\phi^{(5)}(\\tau) & =5\\big(d_{1}^{(5)}\\phi^{(5)}(\\tau)+\\sum_{n=2}^{5}d_{n}^{(5)}5^{n}\\phi^{(5)}(\\tau)^{n}\\big)\\\\\nU_{7}\\phi^{(7)}(\\tau) &\n=7\\big(d_{1}^{(7)}\\phi^{(7)}(\\tau)+\\sum_{n=2}^{7}d_{n}^{(7)}7^{n}\\phi^{(7)}(\\tau)^{n}\\big)\\end{align*}\nfor some integers $d_{n}^{(p)}$. This shows that the second $U_{p}$\niteration is divisible by the correct power of $p$. Further, it\ngives us a polynomial of a suitable form to iterate the process\nusing Lemma \\ref{lem:Phi-Polynomials}. In each of the polynomials\nabove, notice that $U_{p}\\phi(\\tau)=p^{\\delta}r$ for some $r\\in\nR^{(p)}$. Using Lemma \\ref{lem:Phi-Polynomials}, we find that\\[\nU_{p}(U_{p}\\phi)(\\tau)=p^{2\\delta}r'\\] for some $r'\\in R^{(p)}$, and\nfurther\\[ U_{p}^{\\beta}\\phi(\\tau)=p^{\\beta\\delta}r^{(\\beta)}\\] for\nsome $r_{\\beta}\\in R^{(p)}$. This completes the proof for $m=1$.\n\nNow, if $(m,p)=1$, then $U_{p}f(\\tau)$ is holomorphic at $\\infty$,\njust as it was with $m=1$. Moving to the cusp at $0$ we find\nthat $(U_{p}f)(-1\/p\\tau)$ can be written as a polynomial in $\\psi(\\tau)$\nwhich appears as a polynomial in $\\phi(\\tau)$ when we return to $\\infty$.\nSimilar to (\\ref{eq:Up-f1=00003DPhi}), we obain the equality\\[\nU_{p}f(\\tau)=c_{0}+\\sum_{i=1}^{M}p^{\\lambda i\/2-1}c_{i}\\phi(\\tau)^{i}\\]\nfor some $c_{i}\\in\\mathbb{Z}$ and $M\\in\\mathbb{Z}^{+}$. The only\ndifference between this equation and (\\ref{eq:Up-f1=00003DPhi}) is\nthat in this more general case, we find that $U_{p}f$ is a higher\ndegree polynomial in $\\phi$. This formula can easily be iterated\nas before to obtain the desired result.\n\\end{proof}\n\n\\subsection{Case 2: $m=p$}\n\\begin{proof}\nAgain, for ease of notation, denote $f_{0,p}^{(p)}(\\tau)$ by\n$f(\\tau)$. For the $m=p$ case, we will proceed as before; however,\nwe will find that $U_{p}f(\\tau)$ has poles at both $\\infty$ and $0$,\nand that $U_{p}f(\\tau)$ does not possess any interesting\ndivisibility properties, but $U_{p}^{2}f(\\tau)$ does. This property\nwill manifest itself as the {}``shifting'' previously mentioned.\n\nNotice first that $U_{p}f(\\tau)=q^{-1}+O(1)$ has a simple pole at\n$\\infty$. Therefore, we shall deal primarily with the function $U_{p}f(\\tau)-\\psi(\\tau)$,\nwhich is holomorphic at $\\infty$. We can use Lemmas \\ref{lem:Psi-At-0}\nand \\ref{lem:Main-Up-Formula} to view this function at $0$:\n\n\\begin{align*}\np(U_{p}f)(-1\/p\\tau)-p\\psi(-1\/p\\tau) & = p(U_{p}f)(p\\tau)+f(-1\/p^{2}\\tau)-f(\\tau)-p^{\\lambda\/2+1}\\phi(\\tau) \\\\\n & =pq^{-p}+O(1)+O(1)-q^{-p}+O(1)+O(q)\\\\\n & =c_{0}+\\sum_{i=1}^{p}c_{i}\\psi(\\tau)^{i}\n\\end{align*}\nfor some integers $c_{i}$. Replacing $\\tau$ by $-1\/p\\tau$, we obtain\n\\begin{equation}\n(U_{p}f)(\\tau)=\\frac{c_{0}}{p}+\\psi(\\tau)+\\sum_{i=1}^{p}c_{i}p^{\\lambda\ni\/2-1}\\phi(\\tau)^{i}.\\label{eq:Case-2-Upf}\n\\end{equation}\n\n\nThe $\\psi(\\tau)$ term in the equation makes any attempt at\n$p$-divisibility fail; for example, computation shows that the\n$7^{th}$ coefficient of $\\psi^{(2)}(\\tau)$ is odd. However,\n$\\psi(\\tau)$ satisfies Lehner's divisibility properties, so $U_{p}f$\ninherits its $p$-divisibility from $\\psi(\\tau)$. So the function\\[\nU_{p}^{2}f(\\tau)=c_{0}+U_{p}\\psi(\\tau)+\\sum_{i=1}^{p}c_{i}p^{\\lambda\ni\/2-1}U_{p}\\phi(\\tau)^{i}\\] has the same $p$-divisibility as\n$f_{0,1}^{(p)}$; hence, the shift.\n\\end{proof}\n\n\\subsection{Case 3: $m=p^{\\alpha}m'$}\n\\begin{proof}\nWe prove this case using induction on $\\alpha$. Case 1 showed that the theorem is true for\nall $m'$ relatively prime to $p$, so the $\\alpha=0$ base case is complete. Assume Theorem\n\\ref{thm:Andersen-Main}\nholds for all $m$ of the form $m=p^{\\ell}m'$ with $\\ell<\\alpha$.\nWe will show it holds for $m=p^{\\alpha}m'$.\nTo simplify notation,\nlet $f_{\\alpha}(\\tau)=f_{0,p^{\\alpha}m'}^{(p)}(\\tau)$.\n\nSince $f_{\\alpha}(\\tau)=q^{-p^{\\alpha}m'}+O(1)$, we find that\n$U_{p}f_{\\alpha}(\\tau)=q^{-p^{\\alpha-1}m'}+O(1)$ has a pole of order\n$p^{\\alpha-1}m'$ at $\\infty$. So we focus our attention on\n$U_{p}f_{\\alpha}(\\tau)-f_{\\alpha-1}(\\tau)$, which is holomorphic at\n$\\infty$. Using (\\ref{eq:Main-Up-Formula}) we examine this function\nat $0$:\\begin{align*}\np(U_{p}f_{\\alpha})\\left(\\frac{-1}{p\\tau}\\right) -\npf_{\\alpha-1}\\left(\\frac{-1}{p\\tau}\\right) &\n=p(U_{p}f_{\\alpha})(p\\tau)+f_{\\alpha}\\left(\\frac{-1}{p^{2}\\tau}\\right)\n-\nf_{\\alpha}(\\tau)-pf_{\\alpha-1}\\left(\\frac{-1}{p\\tau}\\right)\\\\\n & =pq^{-p^{\\alpha}m'}+O(1)+O(1)-q^{-p^{\\alpha}m'}-O(1)-O(1)\\\\\n & =(p-1)q^{-p^{\\alpha}m'}+O(1).\\end{align*}\n\n\nAs before, we write this function as a polynomial in $\\psi(\\tau)$\nwith integral coefficients $c_{i}$:\\[\np(U_{p}f_{\\alpha})(-1\/p\\tau)-pf_{\\alpha-1}(-1\/p\\tau)=c_{0}+\\sum_{i=1}^{p^{\\alpha}m'}c_{i}\\psi(\\tau)^{i},\\]\nwhich, after switching back to the $q$-expansion at $\\infty$, becomes\\begin{equation}\nU_{p}f_{\\alpha}(\\tau)=\\frac{c_{0}}{p}+f_{\\alpha-1}(\\tau)+\\frac{1}{p}\\sum_{i=1}^{p^{\\alpha}m'}c_{i}p^{\\lambda i\/2}\\phi(\\tau)^{i}.\\label{eq:Case-3-Upf}\\end{equation}\n\n\nNotice that (\\ref{eq:Case-3-Upf}) looks very similar to\n(\\ref{eq:Case-2-Upf}), where $\\psi(\\tau)$ is replaced by\n$f_{\\alpha-1}(\\tau)$, so $U_{p}f_{\\alpha}(\\tau)$ inherits whatever\ndivisibility properties $f_{\\alpha-1}(\\tau)$ has. Our inductive\nhypothesis states that $f_{\\alpha-1}(\\tau)$ exhibits Lehner's\ndivisibility properties only after $U_{p}$ is applied $\\alpha-1$\ntimes. Therefore, applying $U_{p}$ to (\\ref{eq:Case-3-Upf})\n$\\alpha-1$ times, we obtain\\[\nU_{p}^{\\alpha}f_{\\alpha}(\\tau)=\\frac{c_{0}}{p}+U_{p}^{\\alpha-1}f_{\\alpha-1}(\\tau)\n+\\frac{1}{p}\\sum_{i=1}^{p^{\\alpha}m'}c_{i}p^{\\lambda\ni\/2}U_{p}^{\\alpha-1}\\phi(\\tau)^{i}\\] showing that\n$U_{p}^{\\alpha}f_{\\alpha}(\\tau)$ exhibits Lehner's divisibility\nproperties.\n\\end{proof}\n\\bibliographystyle{amsplain}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction\\label{Introduction}}\n\nThe radar head echo is a signal that reflects from the plasma surrounding the\nfast-descending meteoroid and is doppler-shifted by approximately the\nmeteoroid velocity. Only a small volume of the dense plasma sufficiently close\nto the meteoroid contributes to the corresponding radar wave reflection.\nQuantitative knowledge of the spatial structure of the near-meteoroid plasma\nis crucial for accurate modeling the head echo radar reflections\n\\citep{Bronshten:Physics83,Ceplecha:Meteor1998,Close:NewMethod2005,Campbell-Brown:Meteoroid2007}.\n\nIn the companion paper \\citep{Dimant:Formation1_17}, hereinafter referred to as\nPaper~1, we developed a first-principle kinetic theory of the plasma formed\naround a small meteoroid as it moves through the atmosphere at hypersonic\nspeeds. Using a number of easily justified assumptions, we obtained\napproximate analytic expressions describing velocity distributions of meteoric\nions and neutrals. In this paper, we calculate the spatial structure of the\nplasma density that follows from the kinetic theory developed in Paper~1. This\ncalculation demonstrates that this spatial structure differs dramatically from\na simple Gaussian or exponential distribution currently employed for modeling\nradar wave scattering from the meteor plasma \\citep{Close:NewMethod2005,Marshall:FDTD15}. \nThis research does not describe the distribution of\nplasma or neutrals in the meteoroid tail where particles lag well behind the\nmeteoroid after having collided more than once.\n\nSimple analysis of individual collisions between particles indicates that\nheavy meteoric particles in the near-meteor sheath consist predominantly of\nthe `primary' and `secondary' particles. By a primary particle we mean an\nablated meteoroid particle that moves freely with a ballistic trajectory until\nit collides with an atmospheric molecule. These primary particles are\npredominantly neutral. A secondary particle is a former primary particle that\nexperienced exactly one collision, either scattering or ionizing. Most of the\nnear-meteoroid ions responsible for head echoes belong to the group of\nsecondary particles. The vast majority of ions that experienced multiple\ncollisions since the original ablation lag behind the fast-moving meteoroid\nand form a long-lived extended column of plasma visible to radars through\nspecular or non-specular echoes.\n\nGiven the velocity distributions developed in Paper 1 as a function of spatial\ncoordinates, one can integrate over velocity variables to find the\ncorresponding particle density. However, the complexity of these analytic\nexpressions makes this non-trivial. This paper makes an additional simplifying\nassumption about the collision model (the isotropic differential cross-section\nof ionization) and then integrates over the velocities to obtain the meteor\ngas and plasma density as a function of distance from the meteoroid.\n\nThe paper is organized as follows.\nSection~\\ref{Summary of the ion distribution function} summarizes the results\nof Paper~1 on the ion distribution function.\nSection~\\ref{Plasma density calculations} performs the calculations of the\nnear-meteoroid plasma density. Section~\\ref{Discussion} discusses implications\nof our theory and some caveats. Section~\\ref{Conclusions} lists the major\nunderlying assumptions and discusses the paper results.\n\n\\section{Summary of the ion distribution\nfunction\\label{Summary of the ion distribution function}}\n\nPaper~1 does all our calculations in the rest frame of a meteoroid moving\nthrough the atmosphere with the local velocity $-\\vec{U}$, so that in this\nframe the impinging atmospheric particles move with the opposite velocity,\n$\\vec{U}$. We define the coordinate system with the major axis passing through\nthe meteoroid center and parallel to $\\vec{U}$. Due to the axial symmetry\nabout $\\vec{U}$, we characterize the real space by two spherical coordinates:\nthe radial distance from the meteoroid center, $R$, and the polar angle,\n$\\theta$, measured from the major axis ($\\theta=0$ corresponds to the major\nsemi-axis behind the meteoroid, while $\\theta=\\pi$ corresponds to the opposite\nsemi-axis in front of it). Figure~\\ref{Fig:Cartoon_reproduced}, reproduced\nfrom Paper~1, explains all relevant notations.\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=30pc]{Cartoon_5_20_2015.pdf}\n\\caption{Nomenclature of spatial coordinates and velocity variables. The\nspatial variables $R=|\\vec{R}|$, $\\theta$, $\\varphi$ denote the radius and two\nangles of the spherical coordinate system with the origin at the meteoroid\ncenter and the major axis anti-parallel to the meteoroid velocity (shown on\nthe left). All other variables pertain to the particle velocity space:\n$V=|\\vec{V}|$ is the particle speed, $\\vartheta$ is the polar angle of\n$\\vec{V}$ with respect to the local axis parallel to $\\vec{U}$, $\\Phi$ is the\naxial angle measured from the common $\\vec{U}$-$\\vec{R}$ plane; $\\Theta$ is\nthe polar angle of $\\vec{V}$ with respect to the local radial distance\n$\\vec{R}$.}\n\\label{Fig:Cartoon_reproduced}\n\\end{figure}\n\n\nThe velocity distribution of secondary ions, $f^{(2)}$, is expressed as a\nfunction of three velocity variables that are invariants of the ion\ncollisionless motion. These variables include the ion speed, $V$, the cosine\nof the angle between the ion velocity vector $\\vec{V}$ and $\\vec{U}$,\n$\\mu=\\cos\\vartheta$, and a normalized angular momentum variable, $R_{0}$,\nwhich to the minimum distance between the ion trajectory and the meteoroid\ncenter, $R_{0}=R\\sin\\Theta$, where $\\Theta$ is the polar angle of $\\vec{V}$\nwith respect to the local radius vector $\\vec{R}$. The entire set of\nvelocity-space variables also includes a discrete variable $\\sigma_{R}$ which\ntakes two values, $\\pm1$, depending on the sign of the particle radial\nvelocity,%\n\\begin{equation}\nV_{R}=\\frac{dR}{dt}\\equiv V\\cos\\Theta=\\sigma_{R}\\sqrt{1-\\frac{R_{0}^{2}}%\n{R^{2}}}\\ V. \\label{V_r_snova}%\n\\end{equation}\nThe value of $\\sigma_{R}=+1$ corresponds to the outgoing particles, $V_{R}>0$,\nwhile $\\sigma_{R}=-1$ corresponds to the incoming particles, $V_{R}<0$. At any\nlocation, the entire distribution function is given by a sum of the two\ncorresponding functions,%\n\\begin{equation}\nf^{(2)}(V,\\mu,R_{0};R,\\theta)=\\sum_{\\sigma_{R}=\\pm1}f_{\\sigma_{R}}^{(2)}%\n(V,\\mu,R_{0};R,\\theta). \\label{sum_f}%\n\\end{equation}\nThe functions $f_{\\sigma_{R}}^{(2)}$ are non-zero provided $\\mu=\\cos\n\\vartheta>0$; otherwise $f_{\\sigma_{R}}^{(2)}=0$,%\n\\begin{align}\n& \\left. f_{\\sigma_{R}}^{(2)}(V,R,\\theta)\\right\\vert _{\\mu>0}=L_{\\sigma_{R}%\n}\\ \\delta\\left( V-\\frac{2m_{\\beta}\\mu U}{m+m_{\\beta}}\\right) ,\\nonumber\\\\\nL_{\\sigma_{R}} & =\\frac{G_{\\mathrm{ion}}(U,1-2\\mu^{2})n_{0}n_{\\mathrm{A}}%\n}{\\sqrt{3}\\ \\mu U^{2}}\\left( 1+\\frac{m}{m_{\\beta}}\\right) ^{3}%\nI(R,R_{0}),\\label{f^(2)_via_I}\\\\\n& \\left. f_{\\sigma_{R}}^{\\left( 2\\right) }(V,R,\\theta)\\right\\vert _{\\mu\n<0}=0.\\nonumber\n\\end{align}\nThe quantities $n_{0}$ and $n_{\\mathrm{A}}$ are the densities of the ablated\nparticles at the meteoroid surface and of the atmospheric particles at a given\naltitude, respectively. The quantity $G_{\\mathrm{ion}}(U,1-2\\mu^{2})$\noriginates from the differential cross-section of ionizing collisions,\n$G_{\\mathrm{ion}}$, expressed as a function of the relative speed between the\ntwo colliding particles, $u=\\left\\vert \\vec{u}\\right\\vert $, and the cosine of\nthe scattering angle, $\\Theta_{\\mathrm{sc}}$. In this paper, we simplify our\ntreatment by assuming $G_{\\mathrm{ion}}$ to be a function of only $u\\approx\nU$. The corresponding angular dependence in the relevant energy range is\ngenerally unknown, but the assumption of isotropic $G_{\\mathrm{ion}}(U)$ is reasonable.\n\nThe condition $\\mu>0$ is fulfilled if either\n\\begin{subequations}\n\\label{sgn}%\n\\begin{align}\n& \\sigma_{R}=\\mathrm{sgn}(\\cos\\theta)\\qquad\\text{and}\\qquad03r_{\\mathrm{M}}$, the well-convergent\nintegral $J_{a}^{b}$, taken as a function of its integration limits, $b>a\\geq\nR_{0}$, is given by%\n\\begin{equation}\nJ_{a}^{b}\\approx r_{\\mathrm{M}}^{2}\\int_{a}^{b}\\left[ 1+\\left(\n\\frac{R^{\\prime}}{\\lambda_{T}^{(1)}}\\right) ^{2\/3}\\right] \\exp\\left[\n-\\ \\frac{3}{2}\\left( \\frac{R^{\\prime}}{\\lambda_{T}^{(1)}}\\right)\n^{2\/3}\\right] \\frac{dR^{\\prime}}{R^{\\prime}\\sqrt{\\left( R^{\\prime}\\right)\n^{2}-R_{0}^{2}}}. \\label{J_a^b_R>3epsilon}%\n\\end{equation}\nHere $\\lambda_{T}^{(1)}$ is the mean free path of the primary (ablated)\nparticles,%\n\\begin{equation}\n\\lambda_{T}^{(1)}=\\frac{V_{T}}{\\nu_{T}^{(1)}},\\qquad V_{T}=\\left( \\frac\n{T_{M}}{m_{\\mathrm{M}}}\\right) ^{1\/2},\\qquad\\nu_{T}^{(1)}\\approx2\\pi\nn_{\\mathrm{A}}U\\int_{-1}^{1}G^{\\left( 1\\right) }(U,\\Lambda)d\\Lambda,\n\\label{lambda_T^(1)}%\n\\end{equation}\nwhere $T_{M}$ and $m_{\\mathrm{M}}$ are the temperature and mass of the primary\nmeteor particles. The quantity $G^{\\left( 1\\right) }(U,\\Lambda)$ includes\nall collisions that result in scattering of the primary neutral particles. The\nexpression for $G^{\\left( 1\\right) }$, as that for $G_{\\mathrm{ion}}$, takes\ninto account that $V_{T}\\ll U$, so that the collision frequency $\\nu_{T}%\n^{(1)}$ depends only on the meteoroid speed, $U$, and hence is the same for\nall particles. This reduces $\\lambda_{T}^{(1)}$ to a constant value which\nbecomes the characteristic length-scale of the near-meteoroid plasma.\n\nThe general integral $J_{a}^{b}$ cannot be calculated exactly, but the\nparticular integral $J_{R_{0}}^{\\infty}$ has an almost perfect analytic\napproximation,%\n\\begin{equation}\nJ_{R_{0}}^{\\infty}\\approx\\frac{\\pi r_{\\mathrm{M}}^{2}}{2R_{0}}\\sqrt{1+\\frac\n{2}{\\pi}\\left( \\frac{R_{0}}{\\lambda_{T}^{(1)}}\\right) ^{2\/3}}\\ \\exp\\left[\n-\\ \\frac{3}{2}\\left( \\frac{R_{0}}{\\lambda_{T}^{(1)}}\\right) ^{2\/3}\\right] ,\n\\label{J_R_0_interpo}%\n\\end{equation}\naccurate within $1\\%$ for all $R_{0}$. As we demonstrate below, depending on\nthe specific calculation, it may become beneficial to use either the exact\noriginal integral expression for $J_{a}^{b}$ given by\nequation~(\\ref{J_a^b_R>3epsilon}) or (only for $J_{R_{0}}^{\\infty}$) its\napproximation given by equation~(\\ref{J_R_0_interpo}).\n\nFor local calculations of the ion density it is more convenient to pass from\nthe invariant velocity variables $V,R_{0},\\mu$ to local variables\n$V,R_{0},\\Phi$, where $\\Theta$ and $\\Phi$ are the polar and axial angles of\nthe ion velocity about the direction of the local radius-vector $\\vec{R}$, as\ndepicted by Figure~\\ref{Fig:Cartoon_reproduced}.\n\n\\section{Plasma density calculations\\label{Plasma density calculations}}\n\nRadar head echo is determined by the spatial distribution of the electron\ndensity around the meteoroid. The near-meteoroid plasma is quasi-neutral, so\nthat the electron density almost equals that of ions, $n_{e}\\approx n_{i}=n$.\nWe calculate the spatial distribution of the ion density based on the\ndistribution function explained in\nsection~\\ref{Summary of the ion distribution function}.\n\nThe ion density can be easily calculated in the far region of $R\\gg\\lambda\n_{T}^{(1)}$. Albeit less simple, but $n^{(2)}(R,\\theta)$ can also be\nexplicitly calculated in the opposite limit of $R\\ll\\lambda_{T}^{(1)}$. In the\nentire space of arbitrary $R$, we were unable to find a unified purely\nalgebraic expression for $n^{(2)}(R,\\theta)$. However, we have reduced the\ngeneral 3D velocity-space integral to a much simpler expression for $n^{(2)}$\nin terms of normalized variables and parameters, as explained below. This\nuniversal expression involves only treatable analytical functions and two 1D\nintegral functions suitable for simple numerical integration and tabulation.\nThe resultant universal expression for $n^{(2)}$ makes the future analysis and\ncomputer modeling of the radar signal much easier.\n\n\\subsection{Preliminary remarks}\n\nAt a given location determined by $R$ and $\\theta$, the ion density is given\nby $n^{\\left( 2\\right) }=\\sum_{\\sigma_{R}=\\pm1}\\int f_{\\sigma_{R}}%\n^{(2)}V^{2}dVd\\Omega$, where $d\\Omega=d\\left( \\cos\\Theta\\right) d\\Phi$\ndenotes the elementary volume of the local solid angle. Choosing instead of\n$\\Theta$ the new variable $R_{0}=R\\sin\\Theta$, we obtain%\n\\begin{equation}\nn^{\\left( 2\\right) }=\\frac{1}{R}\\sum_{\\sigma_{R}=\\pm1}\\int_{0}^{R}%\n\\frac{R_{0}dR_{0}}{\\sqrt{R^{2}-R_{0}^{2}}}\\int_{0}^{2\\pi}d\\Phi\\int_{0}%\n^{\\infty}f_{\\sigma_{R}}^{(2)}V^{2}dV. \\label{n^(2)_snova}%\n\\end{equation}\n\n\nFirst, we integrate over $V$ to eliminate the $\\delta$-function in equation\n(\\ref{f^(2)_via_I}),%\n\\begin{equation}\n\\int_{0}^{\\infty}f_{\\sigma_{R}}^{(2)}V^{2}dV=\\frac{4\\mu G_{\\mathrm{ion}}%\nn_{0}n_{\\mathrm{A}}}{\\sqrt{3}}\\left( 1+\\frac{m}{m_{\\beta}}\\right)\nI(R,R_{0}). \\label{yields}%\n\\end{equation}\nAs a result of this simple integration, the previously singular factor $\\mu$\nin the expression for $L_{\\sigma_{R}}$ has moved from its denominator to the\nnumerator, reducing dramatically the relative contribution of the\n`small-angle' ($\\Theta_{\\mathrm{sc}}=1-2\\mu^{2}\\approx1$, $\\mu\\ll1$)\nionization where some assumptions of our general theory are invalid\n\\citep{Dimant:Formation1_17}. Since we assumed above isotropic $G_{\\mathrm{ion}%\n}=G_{\\mathrm{ion}}(U)$, the only $\\Phi$-dependent quantity in the right-hand\nside (RHS) of equation (\\ref{yields}) is $\\mu$ in the numerator. This variable\nis expressed in terms of $R_{0}$ and $\\Phi$ as%\n\\begin{equation}\n\\mu=\\sigma_{R}\\sqrt{1-\\frac{R_{0}^{2}}{R^{2}}}\\cos\\theta+\\frac{R_{0}\\sin\n\\theta}{R}\\ \\cos\\Phi. \\label{mumumu}%\n\\end{equation}\nThe function $I(R,R_{0})$, along with the corresponding integral expressions\nfor $J_{a}^{b}$, are described by equations~(\\ref{III}) to\n(\\ref{J_R_0_interpo}).\n\n\\subsection{Long-distance asymptotics, $R\\gg\\lambda_{T}^{(1)}$, behind the\nmeteoroid\\label{Long-distance asymptotics}}\n\nWe start by calculating the ion density at the simplest limit of long radial\ndistances, $R\\gg\\lambda_{T}^{(1)}$, behind the meteoroid. Ignoring\nexponentially small densities (as explained below), we will consider only the\nspace behind the meteoroid, $\\cos\\theta>0$. Outgoing particles within the\ndominant beam-like (along $\\vec{R}$) velocity distribution make the major\ncontribution to $n^{\\left( 2\\right) }$. In equations (\\ref{n^(2)_snova}) and\n(\\ref{yields}), setting $R_{0}\\ll R$, $\\vartheta\\approx\\theta$, $\\mu\n\\approx\\cos\\theta$, while neglecting the exponentially small quantity\n$J_{R}^{\\infty}$ in equation~(\\ref{III}), we obtain $I(R,R_{0})\\approx\n2J_{R_{0}}^{\\infty}$. This allows us to easily integrate the RHS of\nequation~(\\ref{n^(2)_snova}) over $\\Phi$. In the exact integral expression\ngiven by (\\ref{J_a^b_R>3epsilon}), the primary contribution to $J_{R_{0}%\n}^{\\infty}$ arises from components of $f_{+}^{(2)}$ near the meteoroid,\n$R^{\\prime}\\lesssim\\lambda_{T}^{(1)}\\ll R$. This allows us to extend the upper\nintegration limit to infinity. This yields%\n\\begin{align}\n& \\left. n^{\\left( 2\\right) }\\right\\vert _{R\\gg\\lambda_{T}^{(1)}}%\n\\approx\\frac{16\\pi r_{\\mathrm{M}}^{2}G_{\\mathrm{ion}}n_{0}n_{\\mathrm{A}}%\n\\cos\\theta}{R^{2}\\sqrt{3}}\\left( 1+\\frac{m}{m_{\\beta}}\\right) \\nonumber\\\\\n& \\times\\int_{0}^{\\infty}R_{0}dR_{0}\\int_{R_{0}}^{\\infty}\\left[ 1+\\left(\n\\frac{R^{\\prime}}{\\lambda_{T}^{(1)}}\\right) ^{2\/3}\\right] \\exp\\left[\n-\\ \\frac{3}{2}\\left( \\frac{R^{\\prime}}{\\lambda_{T}^{(1)}}\\right)\n^{2\/3}\\right] \\frac{dR^{\\prime}}{R^{\\prime}\\sqrt{\\left( R^{\\prime}\\right)\n^{2}-R_{0}^{2}}}. \\label{n^(2)_long_start}%\n\\end{align}\nChanging here the order of integration with the corresponding adjustment of\nthe integration limits, $\\int_{0}^{\\infty}dR_{0}\\int_{R_{0}}^{\\infty}\\left(\n\\cdots\\right) dR^{\\prime}=\\int_{0}^{\\infty}dR^{\\prime}\\int_{0}^{R^{\\prime}%\n}\\left( \\cdots\\right) dR_{0}$, and using the simple identities\n\\begin{align*}\n\\int_{0}^{R^{\\prime}}\\frac{R_{0}dR_{0}}{\\sqrt{\\left( R^{\\prime}\\right)\n^{2}-R_{0}^{2}}} & =R^{\\prime},\\\\\n\\int_{0}^{\\infty}\\left[ 1+\\left( \\frac{R^{\\prime}}{\\lambda_{T}^{(1)}%\n}\\right) ^{2\/3}\\right] \\exp\\left[ -\\ \\frac{3}{2}\\left( \\frac{R^{\\prime}%\n}{\\lambda_{T}^{(1)}}\\right) ^{2\/3}\\right] dR^{\\prime} & =\\sqrt{\\frac{2\\pi\n}{3}}\\ \\lambda_{T}^{(1)},\n\\end{align*}\nwe obtain for $\\cos\\theta>0$:\n\\begin{equation}\n\\left. n^{\\left( 2\\right) }\\right\\vert _{R\\gg\\lambda_{T}^{(1)}}\\approx\n\\frac{kr_{\\mathrm{M}}^{2}\\lambda_{T}^{(1)}G_{\\mathrm{ion}}n_{0}n_{\\mathrm{A}}%\n}{R^{2}}\\left( 1+\\frac{m}{m_{\\beta}}\\right) \\cos\\theta, \\label{n_R>>lambda}%\n\\end{equation}\nwhere $k=16\\sqrt{2}\\ \\pi^{\\frac{3}{2}}\/3$. Equation~(\\ref{n_R>>lambda}) shows\nthat at $R\\gg\\lambda_{T}^{(1)}$ the density of the major ion population fall\noff as $(\\cos\\theta)\/R^{2}$.\n\nIf, however, instead of using the exact integral expression for $J_{R_{0}%\n}^{\\infty}$ given by equation (\\ref{J_a^b_R>3epsilon}), we employed its\napproximation given by equation~(\\ref{J_R_0_interpo}) then, applying the\nidentity\n\\begin{equation}\n\\int_{0}^{\\infty}\\sqrt{\\left( 1+\\frac{4x}{3\\pi}\\right) x}\\ \\exp\\left(\n-\\ x\\right) dx=\\frac{\\sqrt{3\\pi}}{4}\\exp\\left( \\frac{3\\pi}{8}\\right)\nK_{1}\\left( \\frac{3\\pi}{8}\\right) , \\label{K_1}%\n\\end{equation}\ndeduced from \\citep[][equation~3.372]{Gradshteyn:Table94}, with the modified\nBessel function of the second kind $K_{\\nu}(x)$, we would obtain\nequation~(\\ref{n_R>>lambda}) with $k$ replaced by a formally different\ncoefficient, $k_{1}=(2^{\\frac{3}{2}}\\pi^{\\frac{5}{2}}\/\\sqrt{3})\\exp\\left(\n3\\pi\/8\\right) K_{1}\\left( 3\\pi\/8\\right) $. However, the numerical values of\n$k\\approx42$ and $k_{1}\\approx41.74$ are so close to each other that can be\nconsidered as essentially the same. This confirms that the approximate\nexpression $J_{R_{0}}^{\\infty}$ given by equation~(\\ref{J_R_0_interpo}) is\nreasonably accurate and can be successfully used in other occasions, as done below.\n\n\\subsection{General distances \\label{Moderate distances}}\n\nFor all but largest meteors the radar head echo is formed within moderate\nradial distances of $R\\sim\\lambda_{T}^{(1)}$, the most difficult domain to\ntreat analytically. Below, we reduce the general expression for $n^{\\left(\n2\\right) }$ to a simpler form, more suitable for a further analytic or\nnumerical treatment, and then obtain the explicit spatial distribution of\n$n^{\\left( 2\\right) }$ for $R\\ll\\lambda_{T}^{(1)}$, the limit opposite to\nthat considered in section~\\ref{Long-distance asymptotics}. After that, we\nwill discuss the general case, using numerical integrations.\n\n\\subsubsection{Reduction of the general ion density \\label{Reduction}}\n\nUnder assumption of the isotropic differential cross-section, $G_{\\mathrm{ion}%\n}(U)$, equation~(\\ref{yields}) involves $\\mu$ only as a linear multiplier. For\nthe further analysis, equation~(\\ref{n^(2)_snova}) with the integration over\n$\\Phi$ is no longer convenient. More advantageous is integrating over $\\mu$,\nwhere $\\mu$ is given by equation~(\\ref{mumumu}). Introducing a dimensionless\nvariable%\n\\begin{equation}\n\\xi_{0}=\\frac{R_{0}}{R}=\\sin\\Theta\\leq1 \\label{xi_0}%\n\\end{equation}\nand changing variables $R_{0},\\Phi$ to $\\xi_{0},\\mu$, we arrive at\n\\begin{subequations}\n\\label{n^(2),I_muha}%\n\\begin{align}\n& n^{\\left( 2\\right) }=2\\times\\frac{4n_{0}n_{\\mathrm{A}}}{\\sqrt{3}}\\left(\n1+\\frac{m}{m_{\\beta}}\\right) G_{\\mathrm{ion}}M\\label{n^(2)_prome_2}\\\\\n& M\\equiv\\sum_{\\sigma_{R}=\\pm1}\\int_{0}^{1}\\frac{I(R,\\xi_{0}R)\\xi_{0}d\\xi\n_{0}}{\\sqrt{1-\\xi_{0}^{2}}}\\ I_{\\mu}(\\xi_{0}),\\label{M}\\\\\nI_{\\mu}(\\xi_{0}) & =\\int\\frac{\\mathrm{H}\\left( \\mu\\right) \\mu d\\mu}%\n{\\sqrt{\\xi_{0}^{2}\\sin^{2}\\theta-\\left( \\mu-\\sigma_{R}\\sqrt{1-\\xi_{0}^{2}%\n}\\cos\\theta\\right) ^{2}}}, \\label{I_muha}%\n\\end{align}\nwhere the function $I(R,R_{0})$ is given by equation~(\\ref{III}) and\n$\\mathrm{H}\\left( x\\right) $ is the Heaviside step-function ($\\mathrm{H}%\n\\left( x\\right) =1$ for $x\\geq0$ and $\\mathrm{H}\\left( x\\right) =0$ for\n$x<0$). The latter takes into account the fact that the distribution function\nof secondary particles is non-zero only for positive $\\mu$, as discussed in\nPaper~1. The integration in $I_{\\mu}(\\xi_{0})$ is performed over the entire\n$\\mu$-range where the expression under the square root is non-negative. The\nfactor `$2$' in front of the RHS of equation~(\\ref{n^(2)_prome_2}) takes into\naccount the fact that each value of $\\mu$ corresponds to two symmetric values\nof $\\Phi$ with the same $\\cos\\Phi$ but opposite $\\sin\\Phi$. Introducing%\n\\end{subequations}\n\\begin{equation}\n\\mu_{1}=\\sigma_{R}\\sqrt{1-\\xi_{0}^{2}}\\cos\\theta-\\xi_{0}\\sin\\theta,\\qquad\n\\mu_{2}=\\sigma_{R}\\sqrt{1-\\xi_{0}^{2}}\\cos\\theta+\\xi_{0}\\sin\\theta,\n\\label{mu_1,2}%\n\\end{equation}\nand eliminating the step-function, we can rewrite $I_{\\mu}(\\xi_{0})$ in\nequation~(\\ref{I_muha}) as%\n\\begin{equation}\nI_{\\mu}(\\xi_{0})=\\int_{\\max(\\mu_{1},0)}^{\\max(\\mu_{2},0)}\\frac{\\mu d\\mu}%\n{\\sqrt{\\left( \\mu_{2}-\\mu\\right) \\left( \\mu-\\mu_{1}\\right) }},\n\\label{I_mu}%\n\\end{equation}\nwhere the integration limits take into account that in general case $\\mu\n_{1,2}$ can be negative. For all signs of $\\cos\\theta$, the relations between\n$\\mu_{1,2}$ and $0$ are listed in this table:%\n\\begin{equation}%\n\\begin{tabular}\n[c]{|l|l|l|}\\hline\n& $\\xi_{0}<\\left\\vert \\cos\\theta\\right\\vert $ & $\\xi_{0}>\\left\\vert \\cos\n\\theta\\right\\vert $\\\\\\hline\n$\\sigma_{R}\\cos\\theta<0$ & $\\mu_{1}<\\mu_{2}<0$ & $\\mu_{1}<0<\\mu_{2}$\\\\\\hline\n$\\sigma_{R}\\cos\\theta>0$ & $\\mu_{2}>\\mu_{1}>0$ & $\\mu_{1}<0<\\mu_{2}$\\\\\\hline\n\\end{tabular}\n\\ \\ \\ \\ \\ . \\label{Table:mu>>}%\n\\end{equation}\nAll this yields for $I_{\\mu}$, defined by equation~(\\ref{I_muha}), a\npiece-wise expression:%\n\\begin{equation}\nI_{\\mu}(\\xi_{0})=\\left\\{\n\\begin{array}\n[c]{ccc}%\n\\int_{\\mu_{1}}^{\\mu_{2}}\\frac{\\mu d\\mu}{\\sqrt{\\left( \\mu_{2}-\\mu\\right)\n\\left( \\mu-\\mu_{1}\\right) }} & \\text{if} & \\xi_{0}<\\left\\vert \\cos\n\\theta\\right\\vert \\text{ and }\\sigma_{R}\\cos\\theta>0,\\\\\n\\int_{0}^{\\mu_{2}}\\frac{\\mu d\\mu}{\\sqrt{\\left( \\mu_{2}-\\mu\\right) \\left(\n\\mu-\\mu_{1}\\right) }} & \\text{if} & \\xi_{0}>\\left\\vert \\cos\\theta\\right\\vert\n\\text{ for all }\\sigma_{R}\\cos\\theta,\\\\\n0 & \\text{if} & \\xi_{0}<\\left\\vert \\cos\\theta\\right\\vert \\text{ and }%\n\\sigma_{R}\\cos\\theta<0.\n\\end{array}\n\\right. . \\label{I_mu_proma}%\n\\end{equation}\nUsing the definitions given by equation~(\\ref{mu_1,2}), we obtain:%\n\\begin{equation}\n\\int_{\\mu_{1}}^{\\mu_{2}}\\frac{\\mu d\\mu}{\\sqrt{\\left( \\mu_{2}-\\mu\\right)\n\\left( \\mu-\\mu_{1}\\right) }}=\\pi\\sigma_{R}\\sqrt{1-\\xi_{0}^{2}}\\cos\\theta,\n\\label{Int_mu_full_again}%\n\\end{equation}\nand (for $\\xi_{0}>\\left\\vert \\cos\\theta\\right\\vert $):%\n\\begin{align}\n& \\int_{0}^{\\mu_{2}}\\frac{\\mu d\\mu}{\\sqrt{\\left( \\mu_{2}-\\mu\\right) \\left(\n\\mu-\\mu_{1}\\right) }}\\nonumber\\\\\n& =\\sigma_{R}\\left( \\frac{\\pi}{2}+\\arcsin\\frac{\\sigma_{R}\\sqrt{1-\\xi_{0}%\n^{2}}\\cos\\theta}{\\xi_{0}\\sin\\theta}\\right) \\sqrt{1-\\xi_{0}^{2}}\\cos\n\\theta+\\sqrt{\\xi_{0}^{2}-\\cos^{2}\\theta}. \\label{Int_mu_partial_again}%\n\\end{align}\nRecalling equation~(\\ref{III}), for the quantity $M$, defined by\nequation~(\\ref{M}), we obtain%\n\\begin{align}\nM & =\\int_{0}^{1}\\frac{\\left( 2J_{R_{0}}^{\\infty}-J_{R}^{\\infty}\\right)\n_{\\sigma_{R>0}}\\xi_{0}d\\xi_{0}}{\\sqrt{1-\\xi_{0}^{2}}}\\int_{\\max(\\mu_{1}%\n,0)}^{\\max(\\mu_{2},0)}\\frac{\\mu d\\mu}{\\sqrt{\\left( \\mu_{2}-\\mu\\right)\n\\left( \\mu-\\mu_{1}\\right) _{\\sigma_{R=+1}}}}\\label{K}\\\\\n& +\\int_{0}^{1}\\frac{\\left( J_{R}^{\\infty}\\right) _{\\sigma_{R<0}}\\xi\n_{0}d\\xi_{0}}{\\sqrt{1-\\xi_{0}^{2}}}\\int_{\\max(\\mu_{1},0)}^{\\max(\\mu_{2}%\n,0)}\\frac{\\mu d\\mu}{\\sqrt{\\left( \\mu_{2}-\\mu\\right) \\left( \\mu-\\mu\n_{1}\\right) _{\\sigma_{R=-1}}}}\\nonumber\n\\end{align}\nRegrouping the terms, and using equations~(\\ref{I_mu_proma}%\n)--(\\ref{Int_mu_partial_again}), for all $\\theta$ we obtain%\n\\begin{align}\n& M=\\pi\\left\\vert \\cos\\theta\\right\\vert \\int_{0}^{\\left\\vert \\cos\n\\theta\\right\\vert }J_{R_{0}}^{\\infty}\\xi_{0}d\\xi_{0}+\\left( \\pi\\cos\n\\theta\\right) \\int_{0}^{1}J_{R_{0}}^{R}\\xi_{0}d\\xi_{0}\\nonumber\\\\\n& +2\\int_{\\left\\vert \\cos\\theta\\right\\vert }^{1}J_{R_{0}}^{\\infty}\\xi\n_{0}\\sqrt{\\frac{\\xi_{0}^{2}-\\cos^{2}\\theta}{1-\\xi_{0}^{2}}}\\ d\\xi\n_{0}\\label{K_snova}\\\\\n& +2\\left\\vert \\cos\\theta\\right\\vert \\int_{\\left\\vert \\cos\\theta\\right\\vert\n}^{1}J_{R_{0}}^{\\infty}\\xi_{0}\\arcsin\\frac{\\sqrt{1-\\xi_{0}^{2}}\\left\\vert\n\\cos\\theta\\right\\vert }{\\xi_{0}\\sin\\theta}\\ d\\xi_{0}\\ ,\\nonumber\n\\end{align}\nwhere $J_{R_{0}}^{R}=J_{R_{0}}^{\\infty}-J_{R}^{\\infty}$. All terms in the RHS\nof equation~(\\ref{K_snova}) are symmetric with respect to the sign of\n$(\\cos\\theta)$, except the second term which is antisymmetric. This term is\nresponsible for the entire asymmetry between the locations in front of the\nmeteoroid ($\\cos\\theta<0$) and behind it ($\\cos\\theta>0$).\n\nTo simplify further, we introduce other variables and parameters,%\n\\begin{equation}\n\\eta=\\frac{R^{\\prime}}{R},\\qquad\\beta=\\left( \\frac{R}{\\lambda_{T}^{(1)}%\n}\\right) ^{\\frac{2}{3}},\\qquad q=\\frac{r_{\\mathrm{M}}^{2}}{R},\n\\label{beta_snova}%\n\\end{equation}\nwhere $R^{\\prime}$ is the integration variable in $J_{a}^{b}$, defined by\nequation~(\\ref{J_a^b_R>3epsilon}). With use of these dimensionless quantities,\nequation~(\\ref{J_a^b_R>3epsilon}) yields\n\\begin{subequations}\n\\label{J_R_0^infty,1}%\n\\begin{align}\nJ_{R_{0}}^{\\infty} & =q\\int_{\\xi_{0}}^{\\infty}\\left( 1+\\beta\\eta^{\\frac\n{2}{3}}\\right) \\exp\\left( -\\ \\frac{3\\beta\\eta^{\\frac{2}{3}}}{2}\\right)\n\\frac{d\\eta}{\\eta\\sqrt{\\eta^{2}-\\xi_{0}^{2}}},\\label{J_R_0^infty}\\\\\nJ_{R_{0}}^{R} & =q\\int_{\\xi_{0}}^{1}\\left( 1+\\beta\\eta^{\\frac{2}{3}%\n}\\right) \\exp\\left( -\\ \\frac{3\\beta\\eta^{\\frac{2}{3}}}{2}\\right)\n\\frac{d\\eta}{\\eta\\sqrt{\\eta^{2}-\\xi_{0}^{2}}}. \\label{J_R_0^1}%\n\\end{align}\nFor some calculations, we will also need approximate\nequation~(\\ref{J_R_0_interpo}) for $J_{R_{0}}^{\\infty}$,%\n\\end{subequations}\n\\begin{equation}\nJ_{R_{0}}^{\\infty}\\approx\\frac{\\pi q}{2\\xi_{0}}\\sqrt{1+\\frac{2}{\\pi}\\ \\beta\n\\xi_{0}^{\\frac{2}{3}}}\\exp\\left( -\\ \\frac{3}{2}\\ \\beta\\xi_{0}^{\\frac{2}{3}%\n}\\right) . \\label{J_R_)_approx_dimensionless}%\n\\end{equation}\nFor $J_{R_{0}}^{R}$ we need no approximations, as will become clear soon.\n\nBefore proceeding, we check that the long-distance limit of $R\\gg\\lambda\n_{T}^{(1)}$ (i.e., $\\beta\\gg1$) for the above equations provides a smooth\ntransition to the range of long distances considered in\nsection~\\ref{Long-distance asymptotics}. Beyond a narrow vicinity around\n$\\theta=\\pi\/2$, given by $\\left\\vert \\cos\\theta\\right\\vert \\lesssim\n\\beta^{-3\/2}$, we can easily see that in the RHS of equation~(\\ref{K_snova})\nthe third and fourth terms are exponentially small. Neglecting them and\nextending the same accuracy to the upper integration limit in the first and\nsecond terms, $J_{R_{0}}^{R}\\approx J_{R_{0}}^{\\infty}$, we obtain\n\\begin{equation}\n\\left. M\\right\\vert _{\\beta\\gg1}\\approx\\left\\{\n\\begin{array}\n[c]{ccc}%\n2\\pi\\left( \\cos\\theta\\right) \\int_{0}^{\\infty}J_{R_{0}}^{\\infty}\\xi_{0}%\nd\\xi_{0} & \\text{if} & \\cos\\theta>0,\\\\\n& & \\\\\n0 & \\text{if} & \\cos\\theta<0.\n\\end{array}\n\\right. \\label{K_beta>>1}%\n\\end{equation}\nUsing for $J_{R_{0}}^{\\infty}$ the exact equation~(\\ref{J_R_0^infty}) and\napplying for the double integration the same approach as in\nsection~\\ref{Long-distance asymptotics}, we obtain\n\\[\n\\left. M\\right\\vert _{\\beta\\gg1}\\approx\\frac{2\\pi q\\cos\\theta}{\\beta\n^{\\frac{3}{2}}}\\sqrt{\\frac{2\\pi}{3}}.\n\\]\nReturning from the temporary dimensionless parameters $\\beta,q$ to the\noriginal coordinate $R$ and inserting the corresponding $M$ to\nequation~(\\ref{n^(2)_prome_2}), for $\\cos\\theta>0$ we recreate\nequation~(\\ref{n_R>>lambda}).\n\n\\subsubsection{Short distances, $R\\ll\\lambda_{T}^{(1)}$%\n\\label{Very short distances}}\n\nNow we consider the short-distance limit of $R\\ll\\lambda_{T}^{(1)}$ ($\\beta\n\\ll1$) which is opposite to that discussed in\nsection~\\ref{Long-distance asymptotics}. For not too large integration\nvariables $R^{\\prime}$, $\\eta=R^{\\prime}\/R\\ll\\beta^{-3\/2}$, all factors with\n$\\beta\\eta^{\\frac{2}{3}}$ in equation~(\\ref{J_R_0^infty,1}) can be neglected.\nSince this range of $R^{\\prime}$ makes the dominant contribution to all\nintegrals, we extend this approximation to the entire range of $\\eta$, so\nthat\n\\begin{subequations}\n\\label{J_R,R_0_reduced}%\n\\begin{align}\nJ_{R_{0}}^{\\infty} & \\approx q\\int_{\\xi_{0}}^{\\infty}\\frac{d\\eta}{\\eta\n\\sqrt{\\eta^{2}-\\xi_{0}^{2}}}=\\frac{\\pi q}{2\\xi_{0}},\\label{J_R_0_reduced}\\\\\nJ_{R_{0}}^{R} & \\approx q\\int_{\\xi_{0}}^{1}\\frac{d\\eta}{\\eta\\sqrt{\\eta\n^{2}-\\xi_{0}^{2}}}=\\frac{q}{\\xi_{0}}\\arccos\\xi_{0}.\\label{J_R_reduced}%\n\\end{align}\nIn this limit, the first two terms in the RHS\\ of equation~(\\ref{K_snova}) can\nbe easily integrated, yielding $\\pi q[\\pi(\\cos^{2}\\theta)\/2+\\cos\\theta]$. The\ntwo remaining terms can be expressed in terms of the complete elliptic\nintegrals of the 1st and 2nd kind,%\n\\end{subequations}\n\\begin{equation}\n\\mathrm{K}\\left( k\\right) =\\int_{0}^{1}\\frac{dt}{\\sqrt{\\left(\n1-t^{2}\\right) \\left( 1-k^{2}t^{2}\\right) }},\\qquad\\mathrm{E}\\left(\nk\\right) =\\int_{0}^{1}\\sqrt{\\frac{1-k^{2}t^{2}}{1-t^{2}}}%\n\\ dt,\\label{F,E_complete}%\n\\end{equation}\nrespectively, where $0\\leq k<1$. Indeed, for constant $\\xi_{0}J_{R_{0}%\n}^{\\infty}$, as in equation~(\\ref{J_R_0_reduced}), the third term in\nequation~(\\ref{K_snova}) becomes proportional to%\n\\[\nI_{1}=\\int_{\\left\\vert \\cos\\theta\\right\\vert }^{1}\\sqrt{\\frac{\\xi_{0}^{2}%\n-\\cos^{2}\\theta}{1-\\xi_{0}^{2}}}\\ d\\xi_{0}.\n\\]\nThis integral already resembles an elliptic integral, but reducing $I_{1}$ to\nthose with the real arguments requires additional efforts. Substituting\n$\\xi_{0}=\\sqrt{1-z^{2}\\sin^{2}\\theta}$, we reduce $I_{1}$ to $\\mathrm{E}%\n\\left( \\sin\\theta\\right) -\\left( \\cos^{2}\\theta\\right) \\mathrm{K}\\left(\n\\sin\\theta\\right) $. In a similar way, we can also calculate the fourth term\nin equation~(\\ref{K_snova}). With constant $J_{R_{0}}^{\\infty}\\xi_{0}$, this\nterm becomes proportional to%\n\\[\nI_{2}=\\int_{\\left\\vert \\cos\\theta\\right\\vert }^{1}\\arcsin\\frac{\\sqrt{1-\\xi\n_{0}^{2}}\\left\\vert \\cos\\theta\\right\\vert }{\\xi_{0}\\sin\\theta}\\ d\\xi_{0}.\n\\]\nIntegration of the corresponding indefinite integral by parts gives%\n\\begin{align*}\n& \\int\\arcsin\\frac{\\sqrt{1-\\xi_{0}^{2}}\\left\\vert \\cos\\theta\\right\\vert }%\n{\\xi_{0}\\sin\\theta}\\ d\\xi_{0}\\\\\n& =\\xi_{0}\\arcsin\\frac{\\sqrt{1-\\xi_{0}^{2}}\\left\\vert \\cos\\theta\\right\\vert\n}{\\xi_{0}\\sin\\theta}+\\left\\vert \\cos\\theta\\right\\vert \\int\\frac{d\\xi_{0}%\n}{\\sqrt{\\left( 1-\\xi_{0}^{2}\\right) \\left( \\xi_{0}^{2}-\\cos^{2}%\n\\theta\\right) }}.\n\\end{align*}\nMaking\\ the same substitution for the remaining integral in the RHS as done\nfor $I_{1}$ and evaluating everything over the proper integration limits, we\nobtain $I_{2}=(\\mathrm{K}\\left( \\sin\\theta\\right) -\\pi\/2)|\\cos\\theta|$. When\nadding all terms in the RHS of equation~(\\ref{K_snova}), the $\\mathrm{K}%\n\\left( \\sin\\theta\\right) $-terms in $I_{1,2}$ cancel and equation~(\\ref{M})\nreduces to a simple expression: $M\\approx\\pi q[\\cos\\theta+\\mathrm{E}\\left(\n\\sin\\theta\\right) ]$. As a result, the ion density in the short-distance\nlimit reduces to%\n\\begin{equation}\n\\left. n^{\\left( 2\\right) }\\right\\vert _{R\\ll\\lambda_{T}^{(1)}}\\approx\n\\frac{8\\pi r_{\\mathrm{M}}^{2}G_{\\mathrm{ion}}n_{0}n_{\\mathrm{A}}}{\\sqrt{3}%\n\\,R}\\left( 1+\\frac{m}{m_{\\beta}}\\right) \\left[ \\cos\\theta+\\mathrm{E}\\left(\n\\sin\\theta\\right) \\right] .\\label{n_R<>lambda}) shows that the $1\/R^{2}$-dependency of\n$n^{\\left( 2\\right) }|_{R\\gg\\lambda_{T}^{(1)}}$ transforms to the\n$1\/R$-dependency for $n^{\\left( 2\\right) }|_{R\\ll\\lambda_{T}^{(1)}}$. The\nangular $\\theta$-dependency also changes significantly. While in the\nlong-distance limit of $R\\gg\\lambda_{T}^{(1)}$ ions occupy almost entirely the\nhalf-space behind the meteoroid ($0\\leq\\theta<\\pi\/2$), in the short-distance\nlimit of $R\\ll\\lambda_{T}^{(1)}$ ions show a noticeable presence in front of\nthe meteoroid ($\\pi\/2\\leq\\theta\\leq\\pi$) as well. Red dashed curves in\nFigure~\\ref{Fig:M_versus_theta} show the corresponding angular dependencies\nnormalized to their maximum values at $\\theta=0$.\n\n\\subsubsection{Arbitrary distances \\label{General case}}\n\nThe case of moderate distances $R\\sim\\lambda_{T}^{(1)}$ is covered by general\nequations (\\ref{n^(2),I_muha}) and (\\ref{K_snova}) with $J_{R_{0}}^{\\infty}$\nand $J_{R_{0}}^{R}$ expressed in the original integral form by\nequation~(\\ref{J_R_0^infty,1}), or in an approximate, but explicit, form for\n$J_{R_{0}}^{\\infty}$ by equation~(\\ref{J_R_)_approx_dimensionless}). Unlike\n$J_{R_{0}}^{\\infty}$, the integral $J_{R_{0}}^{R}$ is involved only in the\nsecond term in the RHS of equation~(\\ref{K_snova}). As we show below, this\nterm can be calculated exactly by using the integral form of\nequation~(\\ref{J_R_0^1}). Below we obtain the explicit analytic expressions\nfor the first and second terms in the RHS of equation~(\\ref{K_snova}). Being\nunable to obtain a general analytic approximation for the two last integral\nterms, we will integrate them numerically.\n\n\\paragraph{First term in\\ equation~(\\ref{K_snova}).}\n\nUsing~equation~(\\ref{J_R_0^infty}), for the integral in the first term of the\nexpression for $M$ in~(\\ref{K_snova}), we have%\n\\begin{align}\n& Q_{1}\\equiv\\frac{1}{q}\\int_{0}^{\\left\\vert \\cos\\theta\\right\\vert }J_{R_{0}%\n}^{\\infty}\\xi_{0}d\\xi_{0}\\nonumber\\\\\n& =\\int_{0}^{\\left\\vert \\cos\\theta\\right\\vert }\\left[ \\int_{\\xi_{0}}%\n^{\\infty}\\left( 1+\\beta\\eta^{\\frac{2}{3}}\\right) \\exp\\left( -\\ \\frac\n{3\\beta\\eta^{\\frac{2}{3}}}{2}\\right) \\frac{d\\eta}{\\eta}\\right] \\frac{\\xi\n_{0}d\\xi_{0}}{\\sqrt{\\eta^{2}-\\xi_{0}^{2}}}, \\label{Q_1}%\n\\end{align}\nwhere the dimensionless variables $\\eta$, $\\beta$, and $q$ are defined by\nequation~(\\ref{beta_snova}). Changing the order of integration, we obtain%\n\\begin{align*}\n& Q_{1}=\\int_{0}^{\\left\\vert \\cos\\theta\\right\\vert }\\left( 1+\\beta\n\\eta^{\\frac{2}{3}}\\right) \\exp\\left( -\\ \\frac{3\\beta\\eta^{\\frac{2}{3}}}%\n{2}\\right) \\frac{d\\eta}{\\eta}\\ \\int_{0}^{\\eta}\\frac{\\xi_{0}d\\xi_{0}}%\n{\\sqrt{\\eta^{2}-\\xi_{0}^{2}}}\\\\\n& +\\int_{\\left\\vert \\cos\\theta\\right\\vert }^{\\infty}\\left( 1+\\beta\n\\eta^{\\frac{2}{3}}\\right) \\exp\\left( -\\ \\frac{3\\beta\\eta^{\\frac{2}{3}}}%\n{2}\\right) \\frac{d\\eta}{\\eta}\\ \\int_{0}^{\\left\\vert \\cos\\theta\\right\\vert\n}\\frac{\\xi_{0}d\\xi_{0}}{\\sqrt{\\eta^{2}-\\xi_{0}^{2}}}.\n\\end{align*}\nThese integrations yield%\n\\begin{align}\n& Q_{1}=\\sqrt{\\frac{2\\pi}{3\\beta^{3}}}\\operatorname{erf}\\left( \\sqrt\n{\\frac{3\\beta}{2}}\\left\\vert \\cos\\theta\\right\\vert ^{\\frac{1}{3}}\\right)\n+J_{1}\\nonumber\\\\\n& -\\left( \\left\\vert \\cos\\theta\\right\\vert ^{\\frac{2}{3}}+\\frac{2}{\\beta\n}\\right) \\left\\vert \\cos\\theta\\right\\vert ^{\\frac{1}{3}}\\exp\\left(\n-\\ \\frac{3\\beta\\left\\vert \\cos\\theta\\right\\vert ^{\\frac{2}{3}}}{2}\\right) ,\n\\label{First_term}%\n\\end{align}\nwhere $\\operatorname{erf}(x)=(2\/\\sqrt{\\pi})\\int_{0}^{x}e^{-x^{2}}dx$ is the\nstandard error-function and%\n\\begin{equation}\nJ_{1}=\\int_{\\left\\vert \\cos\\theta\\right\\vert }^{\\infty}\\left( 1+\\beta\n\\eta^{\\frac{2}{3}}\\right) \\exp\\left( -\\ \\frac{3\\beta\\eta^{\\frac{2}{3}}}%\n{2}\\right) \\left( 1-\\sqrt{1-\\frac{\\left\\vert \\cos\\theta\\right\\vert ^{2}%\n}{\\eta^{2}}}\\right) d\\eta. \\label{J_1_snova}%\n\\end{equation}\nThe integral $J_{1}$ cannot be taken exactly, but directly below we obtain its\napproximate value. However, even without doing this, one can easily verify\nthat equations~(\\ref{First_term}) and (\\ref{J_1_snova}) provide both the\ncorrect limit of short distances, $\\lim_{\\beta\\rightarrow0}Q_{1}=\\pi\\left\\vert\n\\cos\\theta\\right\\vert \/2$, and the large-distance asymptotics of $\\beta\\gg1$,\n$Q_{1}\\approx(2\\pi\/3)^{1\/2}\\beta^{-3\/2}$.\n\nNow we find an approximate expression for $J_{1}$ by constructing a proper\nanalytic interpolation between two limiting cases. For small $\\beta$, we have%\n\\begin{equation}\n\\left. J_{1}\\right\\vert _{\\beta\\rightarrow0}=\\left( \\frac{\\pi}{2}-1\\right)\n\\left\\vert \\cos\\theta\\right\\vert . \\label{J_1_beta->0}%\n\\end{equation}\nIn the opposite limit of large $\\beta$, the major contribution to the integral\n$J_{1}$ is made in the small vicinity of the lower integration limit. This\nyields the following asymptotics,%\n\\begin{equation}\nJ_{1}\\approx\\left( 1-\\sqrt{\\frac{\\pi}{2\\beta\\left\\vert \\cos\\theta\\right\\vert\n^{\\frac{2}{3}}}}\\right) \\exp\\left( -\\ \\frac{3\\beta\\left\\vert \\cos\n\\theta\\right\\vert ^{\\frac{2}{3}}}{2}\\right) \\left\\vert \\cos\\theta\\right\\vert\n. \\label{J_1_beta>>1}%\n\\end{equation}\nInterpolating between equations ~(\\ref{J_1_beta->0}) and (\\ref{J_1_beta>>1})\nas%\n\\begin{equation}\nJ_{1}\\approx\\left( 1-\\frac{\\left( 4-\\pi\\right) \\sqrt{2\\pi}}{2\\sqrt\n{2\\pi+\\left( 4-\\pi\\right) ^{2}\\beta\\left\\vert \\cos\\theta\\right\\vert\n^{\\frac{2}{3}}}}\\right) \\exp\\left( -\\ \\frac{3\\beta\\left\\vert \\cos\n\\theta\\right\\vert ^{\\frac{2}{3}}}{2}\\right) \\left\\vert \\cos\\theta\\right\\vert\n, \\label{J_1_approxy}%\n\\end{equation}\nwe obtain a reasonably good approximation for $J_{1}$, valid in the entire\nrange of $\\beta$. Even in the worst case of $\\beta\\left\\vert \\cos\n\\theta\\right\\vert ^{\\frac{2}{3}}\\sim6$, the mismatch between the actual\nintegral value and this approximation is only $\\simeq6\\%$.\n\nCombining equations~(\\ref{First_term}) with (\\ref{J_1_approxy}), for the\ndouble integral $Q_{1}$ defined by equation~(\\ref{Q_1}), to a good accuracy we\nobtain%\n\\begin{align}\n& Q_{1}\\approx\\sqrt{\\frac{2\\pi}{3\\beta^{3}}}\\ \\operatorname{erf}\\left(\n\\sqrt{\\frac{3\\beta}{2}}\\left\\vert \\cos\\theta\\right\\vert ^{\\frac{1}{3}}\\right)\n\\nonumber\\\\\n& -\\left[ \\frac{\\left( 4-\\pi\\right) \\sqrt{2\\pi}\\left\\vert \\cos\n\\theta\\right\\vert }{2\\sqrt{2\\pi+\\beta\\left( 4-\\pi\\right) ^{2}\\left\\vert\n\\cos\\theta\\right\\vert ^{\\frac{2}{3}}}}+\\frac{2\\left\\vert \\cos\\theta\\right\\vert\n^{\\frac{1}{3}}}{\\beta}\\right] \\exp\\left( -\\ \\frac{3\\beta\\left\\vert\n\\cos\\theta\\right\\vert ^{\\frac{2}{3}}}{2}\\right) . \\label{First_term_final}%\n\\end{align}\n\n\n\\paragraph{Second term in\\ equation~(\\ref{K_snova}).}\n\nNow we calculate the integral%\n\\begin{align}\n& Q_{2}\\equiv\\frac{1}{q}\\int_{0}^{1}J_{R_{0}}^{R}\\xi_{0}d\\xi_{0}\\nonumber\\\\\n& =\\int_{0}^{\\left\\vert \\cos\\theta\\right\\vert }\\left[ \\int_{\\xi_{0}}%\n^{1}\\left( 1+\\beta\\eta^{\\frac{2}{3}}\\right) \\exp\\left( -\\ \\frac{3\\beta\n\\eta^{\\frac{2}{3}}}{2}\\right) \\frac{d\\eta}{\\eta}\\right] \\frac{\\xi_{0}%\nd\\xi_{0}}{\\sqrt{\\eta^{2}-\\xi_{0}^{2}}}. \\label{Q_2}%\n\\end{align}\nUnlike $Q_{1}$, this integral can be calculated exactly. Indeed, changing the\norder of integration, we obtain%\n\\begin{align}\n& Q_{2}=\\int_{0}^{1}\\left( 1+\\beta\\eta^{\\frac{2}{3}}\\right) \\exp\\left(\n-\\ \\frac{3\\beta\\eta^{\\frac{2}{3}}}{2}\\right) \\frac{d\\eta}{\\eta}\\ \\int%\n_{0}^{\\eta}\\frac{\\xi_{0}d\\xi_{0}}{\\sqrt{\\eta^{2}-\\xi_{0}^{2}}}\\nonumber\\\\\n& =\\int_{0}^{1}\\left( 1+\\beta\\eta^{\\frac{2}{3}}\\right) \\exp\\left(\n-\\ \\frac{3\\beta\\eta^{\\frac{2}{3}}}{2}\\right) d\\eta\\nonumber\\\\\n& =\\sqrt{\\frac{2\\pi}{3\\beta^{3}}}\\ \\operatorname{erf}\\left( \\sqrt\n{\\frac{3\\beta}{2}}\\right) -\\left( 1+\\frac{2}{\\beta}\\right) \\exp\\left(\n-\\ \\frac{3\\beta}{2}\\right) . \\label{Second_term_final}%\n\\end{align}\n\n\n\\paragraph{Density along the major axis.}\n\nNow we consider two particular positions along the major axis: strictly behind\nthe meteoroid ($\\theta=0$) and strictly in front of it ($\\theta=\\pi$). In both\nthese positions, we have $\\left\\vert \\cos0\\right\\vert =1$, so that the third\nand fourth terms in the RHS\\ of equation~(\\ref{K_snova}) become zero. The\ncombination of the two first terms there is given by%\n\\begin{equation}\n\\pi\\left\\vert \\cos\\theta\\right\\vert \\int_{0}^{\\left\\vert \\cos\\theta\\right\\vert\n}J_{R_{0}}^{\\infty}\\xi_{0}d\\xi_{0}+\\pi\\left( \\cos\\theta\\right) \\int_{0}%\n^{1}J_{R_{0}}^{R}\\xi_{0}d\\xi_{0}=\\pi q\\left( \\left\\vert \\cos\\theta\\right\\vert\nQ_{1}+\\left( \\cos\\theta\\right) Q_{2}\\right) \\label{combination}%\n\\end{equation}\nAs a result, at the major axis behind the meteoroid we obtain\n\\begin{align}\n& \\left. n^{\\left( 2\\right) }\\right\\vert _{\\theta=0}=\\frac{8\\pi\nr_{\\mathrm{M}}^{2}n_{0}n_{\\mathrm{A}}}{R\\sqrt{3}}\\left( 1+\\frac{m}{m_{\\beta}%\n}\\right) G_{\\mathrm{ion}}\\left\\{ 2\\sqrt{\\frac{2\\pi}{3}}\\frac{\\lambda\n_{T}^{(1)}}{R}\\operatorname{erf}\\left[ \\sqrt{\\frac{3}{2}}\\left( \\frac\n{R}{\\lambda_{T}^{(1)}}\\right) ^{\\frac{2}{3}}\\right] \\right. \\nonumber\\\\\n& \\left. -\\left[ \\frac{\\left( 4-\\pi\\right) \\sqrt{2\\pi}}{2\\sqrt\n{2\\pi+\\left( 4-\\pi\\right) ^{2}(R\/\\lambda_{T}^{(1)})^{\\frac{2}{3}}}%\n}+1+4\\left( \\frac{\\lambda_{T}^{(1)}}{R}\\right) ^{\\frac{2}{3}}\\right]\n\\exp\\left[ -\\ \\frac{3}{2}\\left( \\frac{R}{\\lambda_{T}^{(1)}}\\right)\n^{\\frac{2}{3}}\\right] \\right\\} . \\label{n^(2)_theta=0}%\n\\end{align}\nSimilarly, at the major axis in front of of the meteoroid we obtain\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=30pc]{M_versus_theta.pdf}\n\\caption{Density versus the polar angle $\\theta$ for different distances\n(black solid curves from the top to the bottom: $R\/\\lambda_{T}^{(1)}%\n=0.1;0.3;1;3;10$). Red dashed curves show asymptotic solutions given by\nequation~(\\ref{K_beta>>1}) (the top curve) and by equation~(\\ref{n_R<0$, the radial dependence of the density\ngradually changes from $n^{\\left( 2\\right) }\\varpropto1\/R$ for $R\\ll\n\\lambda_{T}^{(1)}$, as described by equation~(\\ref{n_R<>lambda}). This change in the power-law radial\ndependence of $n^{\\left( 2\\right) }$ occurs for the following reason. The\nsource for the secondary ions are the primary neutral particles, whose density\nfalls off near the meteoroid roughly as $1\/R^{2}$. At a given location $R$,\nthe number of ions moving in a certain direction is determined by the total\ncollisional ionization over the preceding segment of the straight-line\ntrajectory aligned with that direction. For $R\\ll\\lambda_{T}^{(1)}$, the total\nintegration over the entire ionization path acquires an additional factor\n$\\varpropto R$ which gradually transforms $1\/R^{2}$ to $1\/R$. On the other\nhand, for $R\\gg\\lambda_{T}^{(1)}$ only localized ionization within $R^{\\prime\n}\\lesssim\\lambda_{T}^{(1)}$ plays a role, resulting in `saturation' of the\nprevious additional factor $R$ at a constant value $\\sim\\lambda_{T}^{(1)}$.\nThis leaves the $\\varpropto1\/R^{2}$ dependence of $n^{\\left( 2\\right) }$\nessentially untouched. This transition works only for locations behind the\nmeteoroid, $\\cos\\theta>0$, because almost all freshly born ions have\nvelocities $\\vec{V}$ with positive $\\mu=\\cos\\vartheta$, where $\\vartheta$ is\nthe angle between $\\vec{V}$ and $-\\vec{U}$, as shown in\nFigure~\\ref{Fig:Cartoon_reproduced}. Regardless of how far away from the\nmeteoroid this $R,\\theta$-point is located, all preceding straight-line\ntrajectory segments with $\\mu>0$ always cross the near-meteoroid volume\n$R^{\\prime}\\lesssim\\lambda_{T}^{(1)}$,\n\nA significantly different situation takes place in front of the meteoroid,\n$\\cos\\theta<0$. At $R\\ll\\lambda_{T}^{(1)}$, straight-line trajectory segments\nwith $\\mu>0$ also cross a part of the near-meteoroid volume $R^{\\prime\n}\\lesssim\\lambda_{T}^{(1)}$. That is why here $\\left. n^{\\left( 2\\right)\n}\\right\\vert _{\\theta=\\pi}$ is quite noticeable, although a few times smaller\nthan $\\left. n^{\\left( 2\\right) }\\right\\vert _{\\theta=0}$. On the other\nhand, at larger distances, $R\\gtrsim\\lambda_{T}^{(1)}$, there are almost no\npreceding trajectory segments with positive $\\mu$ that would cross the near\nregion of $R^{\\prime}\\lesssim\\lambda_{T}^{(1)}$. These trajectories cross the\nregions where the number of the primary particles is itself exponentially\nsmall, so that $\\left. n^{\\left( 2\\right) }\\right\\vert _{\\theta=\\pi\n}\\varpropto\\exp[-\\ (3\/2)(R\/\\lambda_{T}^{(1)})^{2\/3}]$. This exponentially\ndecreasing density remains much less than that behind the meteoroid where\n$n^{\\left( 2\\right) }$ decreases largely by a power law.\n\n\\paragraph{General case.\\label{General case_again}}\n\nFor the general case of $R\\sim\\lambda_{T}^{(1)}$ with $\\cos\\theta\\neq\\pm1$, we\nwere unable to find acceptable analytic approximations for the two last\n(integral) terms in the RHS\\ of equation~(\\ref{K_snova}). Therefore, we will\nintegrate those 1D integrals numerically.\n\nNow we summarize the entire expression for $n^{\\left( 2\\right) }$ by\ncombining equation~(\\ref{n^(2)_prome_2}) with (\\ref{K_snova}), where in the\nfirst two terms the integrals $\\int_{0}^{\\left\\vert \\cos\\theta\\right\\vert\n}J_{R_{0}}^{\\infty}\\xi_{0}d\\xi_{0}=qQ_{1}$ and $\\int_{0}^{1}J_{R_{0}}^{R}%\n\\xi_{0}d\\xi_{0}=qQ_{2}$ were calculated with $Q_{1,2}$ explicitly given by\nequations~(\\ref{First_term_final}) and (\\ref{Second_term_final}) (as it was\ndone above when calculating the ion density at the major axis). For the two\nremaining integral terms in equation~(\\ref{K_snova}) we use the approximation\nfor $J_{R_{0}}^{\\infty}$ given by equation~(\\ref{J_R_)_approx_dimensionless}).\nThis gives%\n\\begin{align}\nn^{\\left( 2\\right) } & =\\frac{8\\pi r_{\\mathrm{M}}^{2}n_{0}n_{\\mathrm{A}}%\n}{\\sqrt{3}R}\\left( 1+\\frac{m}{m_{\\beta}}\\right) G_{\\mathrm{ion}%\n}(U)\\nonumber\\\\\n& \\times\\left[ f_{1}\\left( R,\\cos\\theta\\right) \\left\\vert \\cos\n\\theta\\right\\vert +f_{2}\\left( R\\right) \\cos\\theta+f_{3}\\left( R,\\cos\n\\theta\\right) \\right] , \\label{n^(2)_general}%\n\\end{align}\nwhere%\n\\begin{align}\n& f_{1}\\left( R,\\cos\\theta\\right) =\\frac{\\lambda_{T}^{(1)}}{R}\\sqrt\n{\\frac{2\\pi}{3}}\\operatorname{erf}\\left[ \\sqrt{\\frac{3}{2}}\\left( \\frac\n{R}{\\lambda_{T}^{(1)}}\\right) ^{\\frac{1}{3}}\\left\\vert \\cos\\theta\\right\\vert\n^{\\frac{1}{3}}\\right] \\nonumber\\\\\n& -\\left[ \\frac{\\left( 4-\\pi\\right) \\left\\vert \\cos\\theta\\right\\vert\n}{2\\sqrt{1+\\left. \\left( 4-\\pi\\right) ^{2}(R\/\\lambda_{T}^{(1)})^{\\frac\n{2}{3}}\\left\\vert \\cos\\theta\\right\\vert ^{\\frac{2}{3}}\\right\/ (2\\pi)}%\n}+2\\left( \\frac{\\lambda_{T}^{(1)}}{R}\\right) ^{\\frac{2}{3}}\\left\\vert\n\\cos\\theta\\right\\vert ^{\\frac{1}{3}}\\right] \\nonumber\\\\\n& \\times\\exp\\left[ -\\ \\frac{3\\left\\vert \\cos\\theta\\right\\vert ^{\\frac{2}{3}%\n}}{2}\\left( \\frac{R}{\\lambda_{T}^{(1)}}\\right) ^{\\frac{2}{3}}\\right] ,\n\\label{f_1}%\n\\end{align}\n\n\\begin{align}\nf_{2}\\left( R\\right) & =\\frac{\\lambda_{T}^{(1)}}{R}\\sqrt{\\frac{2\\pi}{3}%\n}\\operatorname{erf}\\left[ \\sqrt{\\frac{3}{2}}\\left( \\frac{R}{\\lambda\n_{T}^{(1)}}\\right) ^{\\frac{1}{3}}\\right] \\nonumber\\\\\n& -\\left[ 1+2\\left( \\frac{\\lambda_{T}^{(1)}}{R}\\right) ^{\\frac{2}{3}%\n}\\right] \\exp\\left[ -\\ \\frac{3}{2}\\left( \\frac{R}{\\lambda_{T}^{(1)}%\n}\\right) ^{\\frac{2}{3}}\\right] , \\label{f_2}%\n\\end{align}\n\n\\begin{align}\nf_{3}(R,\\cos\\theta) & =\\int_{\\left\\vert \\cos\\theta\\right\\vert }^{1}%\n\\sqrt{1+\\frac{2\\xi_{0}^{\\frac{2}{3}}}{\\pi}\\left( \\frac{R}{\\lambda_{T}^{(1)}%\n}\\right) ^{\\frac{2}{3}}}\\exp\\left[ -\\ \\frac{3\\xi_{0}^{\\frac{2}{3}}}%\n{2}\\left( \\frac{R}{\\lambda_{T}^{(1)}}\\right) ^{\\frac{2}{3}}\\right]\n\\nonumber\\\\\n& \\times\\sqrt{\\frac{\\xi_{0}^{2}-\\cos^{2}\\theta}{1-\\xi_{0}^{2}}}\\ d\\xi\n_{0}\\nonumber\\\\\n& +\\left\\vert \\cos\\theta\\right\\vert \\int_{\\left\\vert \\cos\\theta\\right\\vert\n}^{1}\\sqrt{1+\\frac{2\\xi_{0}^{\\frac{2}{3}}}{\\pi}\\left( \\frac{R}{\\lambda\n_{T}^{(1)}}\\right) ^{\\frac{2}{3}}}\\exp\\left[ -\\ \\frac{3\\xi_{0}^{\\frac{2}{3}%\n}}{2}\\left( \\frac{R}{\\lambda_{T}^{(1)}}\\right) ^{\\frac{2}{3}}\\right]\n\\nonumber\\\\\n& \\times\\arcsin\\frac{\\sqrt{1-\\xi_{0}^{2}}\\left\\vert \\cos\\theta\\right\\vert\n}{\\xi_{0}\\sqrt{1-\\cos^{2}\\theta}}\\ d\\xi_{0}. \\label{f_3}%\n\\end{align}\nFor the isotropic differential cross-section the mean free path defined\nequation by equation~(\\ref{lambda_T^(1)}) reduces to\n\\begin{equation}\n\\lambda_{T}^{(1)}=\\frac{1}{4\\pi Un_{\\mathrm{A}}G(U)}\\left( \\frac\n{T_{\\mathrm{M}}}{m_{\\mathrm{M}}}\\right) ^{1\/2}. \\label{lambda_new}%\n\\end{equation}\n\n\nFigures~\\ref{Fig:M_versus_theta}, \\ref{Fig:Density_vs_Radius_logarithm}, and\n\\ref{Fig:3Ddensity}\\ illustrate the general $\\theta,R$-dependences of\n$n^{\\left( 2\\right) }$.\n\nAs might be expected, in Figure~\\ref{Fig:M_versus_theta} the normalized curves\nwith intermediate values of $R\/\\lambda_{T}^{(1)}$ smoothly and uniformly fill\nthe gap between the two asymptotic solutions corresponding to the long,\n$R\\gg\\lambda_{T}^{(1)}$, and short, $R\\ll\\lambda_{T}^{(1)}$, distances, as\ndescribed by equations~(\\ref{n_R>>lambda}) and (\\ref{n_R<